evaluate.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab #Imports matplotlib and a host of other useful modules\n",
    "import pickle\n",
    "import time\n",
    "\n",
    "import sys\n",
    "# sys.path.append(\"./\")\n",
    "import motion_planner as mp\n",
    "from motion_planner import StateP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "04:31:54 INFO:Logging enabled in notebook\n"
     ]
    }
   ],
   "source": [
    "import logging\n",
    "reload(logging)\n",
    "logging.basicConfig(format='%(asctime)s %(levelname)s:%(message)s', level=logging.INFO, datefmt='%I:%M:%S')\n",
    "logging.info('Logging enabled in notebook')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Manual and visual means of sanity checking results\n",
    "- by printing out data in tables that are suitable for inspection\n",
    "- by visualizing low level path data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mp = reload(mp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# List home directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arcs.ipynb                     ne_sample_traj.pickle\r\n",
      "evaluate.ipynb                 ne_sample_unreachables.pickle\r\n",
      "field_drawings.ipynb           ne_stage1.pickle\r\n",
      "load_mp.py                     ne_stage2.pickle\r\n",
      "mem_profiling.py               ne_stage3.pickle\r\n",
      "motion_planner.py              ne_stage4.pickle\r\n",
      "motion_planner.pyc             ne_unreachables.pickle\r\n",
      "mp_deprecated.ipynb            \u001b[34moldcode\u001b[m\u001b[m/\r\n",
      "n_remaining.pickle             readme.md\r\n",
      "n_sample.pickle                requirements.txt\r\n",
      "n_sample_stage2.pickle         results.txt\r\n",
      "n_sample_stage4.pickle         \u001b[34mruns\u001b[m\u001b[m/\r\n",
      "n_sample_traj.pickle           sample.pickle\r\n",
      "n_sample_unreachables.pickle   sample_stage2.pickle\r\n",
      "n_stage1.pickle                sample_stage4.pickle\r\n",
      "n_stage2.pickle                sample_traj.pickle\r\n",
      "n_stage3.pickle                sample_trajectories.ipynb\r\n",
      "n_stage4.pickle                spacefilling_circles.ipynb\r\n",
      "n_unreachables.pickle          stage1.pickle\r\n",
      "ne_remaining.pickle            stage2.pickle\r\n",
      "ne_sample.pickle               stage3.pickle\r\n",
      "ne_sample_stage2.pickle        stage4.pickle\r\n",
      "ne_sample_stage4.pickle        unreachables.pickle\r\n"
     ]
    }
   ],
   "source": [
    "ls '.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NORTH\n",
      "[(0, 20), (1, 20), (-1, 20)]\n",
      "[0, 354, 6, 350, 10, 346, 14]\n",
      "\n",
      "NORTHEAST\n",
      "[(46, -8), (45, -7), (46, -9)]\n",
      "[300, 294, 306, 290, 310, 286, 314]\n"
     ]
    }
   ],
   "source": [
    "mp=reload(mp)\n",
    "print \"NORTH\"\n",
    "print mp.N_XY_GOALS\n",
    "print mp.N_GOAL_HEADINGS\n",
    "print\n",
    "print \"NORTHEAST\"\n",
    "print mp.NE_XY_GOALS\n",
    "print mp.NE_GOAL_HEADINGS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "11:44:26 INFO:Loading config file from stage1.pickle\n",
      "11:44:26 INFO:Loading config file from stage2.pickle\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Num stage1 stages:  2195\n",
      "Num stage2 states:  32494\n",
      "elapsed: 0 sec\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "#   Load stages\n",
    "#\n",
    "\n",
    "# start = time.time()\n",
    "# filepath = '/temp/valkyrierobotics/mp_stages/stage1.pickle'\n",
    "# config = mp.load_config(filepath)\n",
    "# config = mp.load_config(mp.N_STAGE1_FILEPATH)\n",
    "# print \"Num stage1 stages: \",len(config)\n",
    "\n",
    "# config2 = mp.load_config(mp.N_STAGE2_FILEPATH)\n",
    "# print \"Num stage2 states: \",len(config2)\n",
    "\n",
    "# print \"elapsed: %d sec\" % (time.time()-start)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Sanity checks "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "04:52:53 INFO:Loading n_stage1.pickle\n",
      "04:52:53 INFO:Loading pickle file : n_stage1.pickle\n",
      "04:52:54 INFO:Loaded n_stage1.pickle : 110930 rows  (0.0 min)\n",
      "04:52:54 INFO:Calulating trajectories for 110930 states\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculated 10 trajectories. (0.0 min) \n",
      "==================================================================================================\n",
      "state : next(planned) next(simulated)  target (goal)            move          dist             r\n",
      "==================================================================================================\n",
      "(54, 78, 276)  : \n",
      "\t    (1, 20, 0)     (1, 19, 0)     (1, 20, 0)             lh          85.97           59.0\n",
      "(125, 59, 214)  : \n",
      "\t    (0, 20, 0)  (-1, 23, 358)     (0, 20, 0)             lh         172.44           69.0\n",
      "(13, 47, 316)  : \n",
      "\t  (0, 20, 354)   (0, 20, 354)   (0, 20, 354)             lh          30.07           46.0\n",
      "(57, 113, 304)  : \n",
      "\t  (0, 20, 354)   (0, 20, 354)   (0, 20, 354)             lh         111.98          130.0\n",
      "(22, 70, 310)  : \n",
      "\t   (-1, 20, 0)    (-1, 20, 0)    (-1, 20, 0)             lh          56.77           66.0\n",
      "(6, 48, 340)  : \n",
      "\t    (1, 20, 0)     (0, 20, 0)     (1, 20, 0)             lh          28.57           84.5\n",
      "(59, 43, 216)  : \n",
      "\t    (0, 20, 6)     (0, 21, 4)     (0, 20, 6)             lh          84.81           33.0\n",
      "(62, 57, 250)  : \n",
      "\t  (1, 20, 354)   (1, 19, 354)   (1, 20, 354)             lh          81.99           45.5\n",
      "(52, 64, 260)  : \n",
      "\t    (0, 20, 0)   (0, 20, 358)     (0, 20, 0)             lh          77.26           45.0\n",
      "(-1, 107, 6)  : \n",
      "\t (-1, 20, 354)   (0, 20, 354)  (-1, 20, 354)             rh          87.15          420.0\n"
     ]
    }
   ],
   "source": [
    "mp.sanity_check_stage(\"north\", stage=1, max=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "04:52:58 INFO:Loading ne_stage1.pickle\n",
      "04:52:58 INFO:Loading pickle file : ne_stage1.pickle\n",
      "04:52:59 INFO:Loaded ne_stage1.pickle : 60594 rows  (0.0 min)\n",
      "04:52:59 INFO:Calulating trajectories for 60594 states\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculated 10 trajectories. (0.0 min) \n",
      "==================================================================================================\n",
      "state : next(planned) next(simulated)  target (goal)            move          dist             r\n",
      "==================================================================================================\n",
      "(57, 101, 54)  : \n",
      "\t (46, -8, 294)  (46, -8, 294)  (46, -8, 294)             rh         132.13           63.5\n",
      "(6, 72, 112)  : \n",
      "\t (45, -7, 300)  (50, -4, 308)  (45, -7, 300)             rh         127.14           44.5\n",
      "(70, 105, 42)  : \n",
      "\t (46, -8, 294)  (47, -8, 296)  (46, -8, 294)             rh         134.06           72.0\n",
      "(96, 5, 266)  : \n",
      "\t (45, -7, 300)  (44, -6, 300)  (45, -7, 300)             lh          53.12           92.0\n",
      "(107, -2, 252)  : \n",
      "\t (46, -8, 300)  (46, -8, 300)  (46, -8, 300)             lh          63.14           75.0\n",
      "(73, -6, 248)  : \n",
      "\t (46, -8, 300)  (45, -7, 298)  (46, -8, 300)             lh          27.98           31.5\n",
      "(97, -5, 250)  : \n",
      "\t (46, -9, 300)  (46, -9, 300)  (46, -9, 300)             lh          52.84           60.0\n",
      "(88, -26, 198)  : \n",
      "\t (46, -9, 300)  (45, -9, 298)  (46, -9, 300)             lh          51.66           29.5\n",
      "(121, -12, 236)  : \n",
      "\t (46, -9, 300)  (45, -9, 300)  (46, -9, 300)             lh          79.08           71.0\n",
      "(88, -2, 258)  : \n",
      "\t (46, -9, 300)  (45, -8, 300)  (46, -9, 300)             lh          43.51           60.5\n"
     ]
    }
   ],
   "source": [
    "mp.sanity_check_stage(\"northeast\", stage=1, max=10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Path testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "03:35:05 INFO:Loading pickle file : n_stage1.pickle\n",
      "03:35:07 INFO:Loading pickle file : n_sample.pickle\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "110930\n",
      "16601\n"
     ]
    }
   ],
   "source": [
    "# Load stage1 configuration file\n",
    "config = mp.load_config(mp.N_STAGE1_FILEPATH)\n",
    "print len(config)\n",
    "# Load stage2 configuration file\n",
    "# config2 = mp.load_config(mp.NE_STAGE2_FILEPATH)\n",
    "config2 = mp.load_config('n_sample.pickle')\n",
    "# config2 = mp.load_config('runs/Mar6/n_sample.pickle')\n",
    "print len(config2)\n",
    "#del config2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (54, 78, 276) : g1:     (1, 20, 0)  r:  59.0  type: lh  d: 86.0   g2:           None  d2:  None  samples: 5\n",
      " (125, 59, 214) : g1:     (0, 20, 0)  r:  69.0  type: lh  d:172.4   g2:           None  d2:  None  samples: 6\n",
      "  (13, 47, 316) : g1:   (0, 20, 354)  r:  46.0  type: lh  d: 30.1   g2:           None  d2:  None  samples: 12\n",
      " (57, 113, 304) : g1:   (0, 20, 354)  r: 130.0  type: lh  d:112.0   g2:           None  d2:  None  samples: 13\n",
      "   (6, 48, 340) : g1:     (1, 20, 0)  r:  84.5  type: lh  d: 28.6   g2:           None  d2:  None  samples: 77\n",
      "  (22, 70, 310) : g1:    (-1, 20, 0)  r:  66.0  type: lh  d: 56.8   g2:           None  d2:  None  samples: 8\n",
      "  (59, 43, 216) : g1:     (0, 20, 6)  r:  33.0  type: lh  d: 84.8   g2:           None  d2:  None  samples: 4\n",
      "  (62, 57, 250) : g1:   (1, 20, 354)  r:  45.5  type: lh  d: 82.0   g2:           None  d2:  None  samples: 3\n",
      "  (52, 64, 260) : g1:     (0, 20, 0)  r:  45.0  type: lh  d: 77.3   g2:           None  d2:  None  samples: 8\n",
      "   (-1, 107, 6) : g1:  (-1, 20, 354)  r: 420.0  type: rh  d: 87.2   g2:           None  d2:  None  samples: 10\n",
      "  (11, 41, 300) : g1:     (0, 20, 6)  r:  20.5  type: lh  d: 24.7   g2:           None  d2:  None  samples: 11\n",
      "  (55, 40, 222) : g1:     (1, 20, 0)  r:  31.5  type: lh  d: 72.6   g2:           None  d2:  None  samples: 4\n"
     ]
    }
   ],
   "source": [
    "#   Sample\n",
    "max=11\n",
    "ii=0\n",
    "for k,v in config.iteritems():\n",
    "    if ii>max:\n",
    "        break\n",
    "    mp.print_state(k,v)\n",
    "    ii+=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-72, 144)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mp.NE_LOWER_YLIMIT,mp.UPPER_YLIMIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Radius:  50\n",
      "Number of cells having r=50: 1062\n",
      "Plotted 26/1062 \n"
     ]
    },
    {
     "data": {
      "image/png": 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osrLMuOOOLzF37kXMm1eDBQuacPfdbVi4sBvZ2SLmz7/iKEAX6kjV1RsIETBt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\nT08zLl/eiLKyiSgpISgpITh0aIrX9yFEh7i4+Zg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USj0MDw+Xxevv2LEj8Y/7EJ6uhmkb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\nrpgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e771b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display sample of cells having specified turning radius\n",
    "r = 50\n",
    "print \"Radius: \", r\n",
    "cc = dict([(k,v) for k,v in config.items() if v.r() == r])\n",
    "print \"Number of cells having r=%d: %d\" % (r, len(cc))\n",
    "mp.flow_plot_config(cc,xlim=(mp.LOWER_XLIMIT-5,mp.UPPER_XLIMIT),ylim=(-72,mp.UPPER_YLIMIT), max=25)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Radius:  50\n",
      "State:\n",
      "  (94, 51, 208) : g1:    (1, 20, 10)  r:  50.0  type: lh  d:137.2   g2:           None  d2:  None  samples: 3\n",
      "None\n",
      "\n",
      "Location p((94, 51)) with heading 208 deg (3.6 rad) on radius 50 \n",
      "radius:50, heading : 3.6 radians, 208 degrees,  dist: 137.2\n",
      "Center of turning radius = (49.9, 27.5)\n",
      "Plotted: 1000 \n"
     ]
    },
    {
     "data": {
      "image/png": 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DhcvLZ+Chyx8yfRZCKRtpMm2kpitSDpH34UMf+iFOPvm/KVVJ1nk5hqtlzs1zijbb0ypP\nw5tff5PN1rBFNYvw1vVv4bTK04quv/GZoYcXAMC5o+jtfQddXV/A00+vxdEhzsUmGo7Jhtva2hr4\nZ865eU7RMdvTKk/Dvq/tC/z74kgj17jZ97V9+OCMDxZd9z9H8Zg55/6I11//EV5//d5Rf2cp5BoF\ni7mabLi1tbWBft78780v+WYLBJ9rXD3/5ecxr8h9aw4Dw569UFZWgfLyKsyffwtOPvnzo/6+Usk1\nbBZzLfkx3KHO1yy1ZltqfvWrD+CSX76M7iIP5agpB7Z8/L3XIhUQEcydm8ScOV/GpEnTwiuUTOEY\n7jCWbV5WtNlOK5vGZuu5srIp2PJxYFaRvwHdR4EvtvddACEyGaeeejU+9rF9mDfvOjZbmhCTDber\nq2vCn7Fm25oh7/j1b1cM83ulx4LI1QqRvhvY/LgOKHZX3L2Hgb/dLTj//Bcwf/7/xuTJs8b9XaWU\na5gs5mqy4ba1tU3o/Vv3bMW2Z7YVXbfl4i0lezbCRHO1pKzsvRvYtJxbfJvfHjyMr/7i2xP+rlLK\nNUwWcy3JMdypN0xFtjdbsLxpRRMf7FginnzyYzh06L2bw//ra2W48Znit2fcvX53yf4jTGPDMdxB\n5n9vftFmu/rs1Wy2JeT4Ea7IFFRWfhhf/mR6yMuA635QF2Zp5LGSargr71xZ9GY059ecb/am4TQ+\n06cvwpQpc7Fw4T1YunQPTjjhk0gsTxS94c3bvW/330uXaCJMNtzm5uYxv2fTrk1FHzw4s3ymqeeO\naRpPrladeeb3sGzZi5g9+xKISP/y1OoUPjLrIwXbP9b9GDbt2jSu7yqlXMNkMVeTDbe+vn5M23d0\ndwz5tIadl+8MoiQvjDVX60SK7/57GvbgfbmzGPIldyYLno82GqWWa1gs5mqy4S5YsGBM21+aurTo\n8qYVTZwMyTPWXH12x2fvKLr8kh+N/UkQzFWHxVxNNtyx2LpnK15484WC5RfNu4iTZDSkteesxUXz\nLipY/uLBF8c9tEBksuG2t49+zPXy+y8vWDYFU7y8gfhEjSXXUvDgFx7EtPLCK8vGOrTAXHVYzNVk\nw+3s7BzVdmu2rSl6Ctg/X/zPQZfkhdHmWkq+/1ffL7r8c/d9btSfwVx1WMzV2wsfhnpyw/k15/Os\nBBqTlXeuLHqGCy+IoMFK9sKHT9/16aLL2WxprB78woOYLJMLlq+8c2UE1ZBlgTZcEdkrIs+ISIeI\n7BaRVbnls0XkARF5TkSeEhHVS3c27dqE1959rWD5UFcSEY1k44UbC5b9/vDvOYFGYxL0EW4vgNXO\nuUXOucXOueO3xf82gF85584CcDmALSIyabxfkkgMf3bBNx/+ZsGyE6ecyLMSRjBSrqUssTyBk6bm\nPfTsQC2w8xtI3jPykx+Yqw6LuQbdcCX332CrAdwGAM65JwDsB/CJ8X7JtddeO+S6Tbs24Y+9fyxY\nzrMSRjZcrgT87PM/6/vhQC2w9f8AD28Etv4UDT/44bDvY646LOaqMYZ7l4h0isj3RWSWiJwAoNw5\nl/87/ksAxv286erq6iHXbXhoQ8GyhbMWcnJjFIbLlfoeRLnwxIXAs/XAoVP7Fh46Dbfe89Kw72Ou\nOizmGnTDrXPO1QJYDOANAHfmlhc76h3SqlWr0NjYiMbGRmzfvh2NjY3IZt87vSuVSiGdTve/zmQy\nSCQSaNjRgCPuSN/CJwDkHtpw9yV3o6urq+Da6+bm5gE3MW5vby94MF0ikUAmk+l/nU6nkUq9d6Ob\nbDY76vrytba2DjiPkPXZqO/ui+8Gyu8Apt7dt8H7XwFOuw/nrhp4U13mVzr1pdNprFu3DnV1dUgm\ni99C4Di108JEpBrAs865mSLyFoAzjh/lishjABLOuV8Oes+oTgtLp9P41Kc+VbB80sZJ6MXAe5ou\nnLUQTzc8PeH/n1IwVK400Idv/TB++9Rk4Lm/As76KXBK3/mgbkPxv0vMVUcccw3ttDARqRSRmXmL\nLgNw/HKcbQCuzm23FMApAB4e73f19PQULNu0a1NBswX6jm5pdIrlSoXuvvjuviZ74Q39zRYAGnY0\nFN2eueqwmGtgR7gicjqA+9DXxAXACwCudc69LCInAbgLwOnoexr1Nc65XUU+Y9wXPlTcWIHDxw4P\nWMajW9KyZtuaoo9pGuool0pDaEe4zrkXc6eC/alzrtY591nn3Mu5da855z7lnDvLOXdOsWY7EVv3\nbC1otgCPbknPUDes53m5NByTV5rlD3ADwJce+FLBNrOmzOKZCWM0OFca3tKapQXLNj5UeIEEc9Vh\nMVeTDTd/JrCjuwO/f/f3Bdv841/8Y5gleWGkGVYaqPUvWwuWHXaHC+4kxlx1WMzV5M1rstksKioq\nAADnbT4Pj3c/PmB9OcpxZMMRzVK9lJ8rjU7lTZV49+i7A5YtqVmCx9e/t08yVx1xzNXLm9fkh/xE\n9xMF67+14lthluONuO28Flxfd33BssH7JHPVYTFXkw33uK17tsKh8Aid90ygsAy1r3HyjIox2XCP\nXyny1Qe/WrCuprIm7HK8kX8FDo1escmzG3fd2P8zc9VhMVeTDbeqqgoAcOCtAwXrvvvp74ZdjjeO\n50pjU2zy7J1j7/T/zFx1WMzV5KQZ0DeccNlPLhv4fgh6NxRebUakbcoNUwruUte0oonDWyXGy0kz\noPhwwgdmfCCCSoiAurmF99TPH1YgAow23EwmU3Q4oemTfKLDROTfVYnG5uaVNxcsOz6swFx1WMzV\nZMNN3lT8hOe156wNuRK/tLS0RF2CWUNd1bh1z1bmqsRiribHcItd7FA5qRJvX/e2coVEQzv1u6cW\n/OZVU1mDA18r/G2M/OTlGG5HpqNg2XXLr4ugEqL3fGfldwqWvfrOqxFUQnFlsuH2usIzETgbTFEr\nNqRV7B7NVLpsNtwnuBNrGPx4Ehq7siJ/pa74xhURVOI/i/uruYbbsKMBOHngskkY9xPXKU9tbW3U\nJZhXPb3wwYb3/+H+CCrxn8X91VzD3dyxGZgzcNlV514VTTGeWbZsWdQlmFdsHPeN2W9EUIn/LO6v\n5hrukd7C2y7e8plbIqiEqBBPTaThmGu4ZSgDXh/0mgKR/8hpGr+CffL14tvRxFjcX811q170As8O\nek2BaGtri7oEPz3bdwEEBcvi/mrqwoeGHQ249clbByybhEk4uuFoSBUSjYwXQJQury58+MF//KBg\nGSfMKG54AQQNxVTDPXKME2YUf5w4o6GYarj947WPRFuHr5qbm6MuwU+PcK5Bg8X91VTD7fehqAvw\nU319fdQl+In7qwqL+6vNhjs76gL8tGDBgqhL8BP3VxUW91dTDXfw+Y08B5fiivsqFWNqL+gfB9s3\n6DUFor29PeoSvDFg39zHfVWDxf3VVMPtxzNsVHR2dkZdgjcE8t6L3P7a0V14H2caP4v7q82Gu6Tv\nD/6aFqwrr7wy6hK8UTO95r0Xuf11/fb10RTjKYv7q5mO1bCjoWAZb8tIcVXs4ofd3bsjqITixEzD\nLXaV2fpzecRA8cSnP1AxZhrugKvMftH3B68yC1YiwccUBal/yOsXg15TICzur2b2gAFHB+dHV4fP\nrr322qhL8Er/Pnv+oNcUCIv7q5mGWzmp8r0X7x/0mgJRXV34eBgav+mTp/f98P5BrykQFvdXMw13\n8GPQ+Vh0irvknyWHfU2lx0zDTSxPoGlFE+bNnId109bxsegK0ul01CV45fg+e3L3yWha0cR9NmAW\n99fyqAsYi8TyBBLLE0ilUlGX4qWenp6oS/BOYnkCZ3SfgTXL10Rdincs7q+mnvhARBRnXj3xgYjI\nMpMNN5vNRl2Cl5irDuaqw2KuJhtuMsnZXg3MVQdz1WExV5NjuNlsFhUVFeEVViKYqw7mqiOOuXo5\nhhu3kH3BXHUwVx0WczXZcImILDLZcHkerg7mqoO56rCYq8mGW1VVFXUJXmKuOpirDou5mpw0IyKK\nIy8nzYiILDLZcDOZTNQleIm56mCuOizmarLhtrS0RF2Cl5irDuaqw2KuHMMlIgoIx3CJiGKCDZeI\nKCQmG25ra2vUJXiJuepgrjos5mqy4dbW1kZdgpeYqw7mqsNirpw0IyIKCCfNiIhiwmTD7erqiroE\nLzFXHcxVh8VcTTbctra2qEvwEnPVwVx1WMyVY7hERAHhGC4RUUyw4RIRhcRkw21ubo66BC8xVx3M\nVYfFXE023Pr6+qhL8BJz1cFcdVjMlZNmREQB4aQZEVFMmGy47e3tUZfgJeaqg7nqsJiryYbb2dkZ\ndQleYq46mKsOi7lyDJeIKCAcwyUiigk2XCKikJhsuIlEIuoSvMRcdTBXHRZzNTmGm8lkUF1dHV5h\nJYK56mCuOuKY60hjuCYbLhFRHHHSjIgoJkw23HQ6HXUJXmKuOpirDou5htZwRWS+iDwqIs+KyGMi\ncvZ4P6unpyfI0iiHuepgrjos5hraGK6I/CuAO5xzd4nIJQD+3jl33qBtOIZLRGbFYgxXRGYDOBfA\nPQDgnLsPwBwROSOM7yciioOwhhTmAOh2zvXmLXsZwNzxfFg2mw2kKBqIuepgrjos5mpy0iyZTEZd\ngpeYqw7mqsNirmE13H0AakQk//vmou8ot8CqVavQ2NiIxsZGbN++HY2NjQP+NVu0aNGAGcpMJlNw\n1Ulra+uA27d1dXUVPJKjubl5wLPt29vb0draOmCbRCKBTCbT/zqdTiOVSvW/zmazBfWlUimT9TU1\nNcW6vnyW6mtqaop1fUC88xuqvhNOOCEW9aXTaaxbtw51dXUj/iMQ5qTZLwHc6Zy7U0QuBfB3nDQj\nIp+MNGlWHmItVwG4Q0SSAN4E8MUQv5uIKHKhjeE6555zzl3gnPuQc+4859zT4/2s/F8JKDjMVQdz\n1WExV5OTZlVVVVGX4CXmqoO56rCYK29eQ0QUkFhc+EBEREYbbv5pHhQc5qqDueqwmKvJhtvS0hJ1\nCV5irjqYqw6LuXIMl4goIBzDJSKKCTZcIqKQmGy4g6+XpmAwVx3MVYfFXE023Nra2qhL8BJz1cFc\ndVjMlZNmREQB4aQZEVFMmGy4+ffApOAwVx3MVYfFXE023La2tqhL8BJz1cFcdVjMlWO4REQB4Rgu\nEVFMsOESEYXEZMMd/DA5CgZz1cFcdVjM1WTDra+vj7oELzFXHcxVh8VcOWlGRBQQTpoREcWEyYbb\n3t4edQleYq46mKsOi7mabLidnZ1Rl+Al5qqDueqwmCvHcImIAsIxXCKimGDDJSIKicmGm0gkoi7B\nS8xVB3PVYTFXk2O4mUwG1dXV4RVWIpirDuaqI465jjSGa7LhEhHFESfNiIhiwmTDTafTUZfgJeaq\ng7nqsJiryYbb09MTdQleYq46mKsOi7lyDJeIKCAcwyUiigmTDTebzUZdgpeYqw7mqsNiriYbbjKZ\njLoELzFXHcxVh8VcTY7hZrNZVFRUhFdYiWCuOpirjjjm6uUYbtxC9gVz1cFcdVjM1WTDJSKyyGTD\nTaVSUZfgJeaqg7nqsJiryYZbVVUVdQleYq46mKsOi7manDQjIoojLyfNiIgsMtlwM5lM1CV4ibnq\nYK46LOZqsuG2tLREXYKXmKsO5qrDYq4cwyUiCgjHcImIYoINl4goJCYbbmtra9QleIm56mCuOizm\narLh1tbWRl2Cl5irDuaqw2KunDQjIgoIJ82IiGLCZMPt6uqKugQvMVcdzFWHxVxNNty2traoS/AS\nc9XBXHVYzJVjuEREAeEYLhFRTLDhEhGFxGTDbW5ujroELzFXHcxVh8VcTTbc+vr6qEvwEnPVwVx1\nWMyVk2ZERAHhpBkRUUyYbLjt7e1Rl+Al5qqDueqwmKvJhtvZ2Rl1CV5irjqYqw6LuXIMl4goIBzD\nJSKKCTZcIqKQmGy4iUQi6hK8xFx1MFcdFnM1OYabyWRQXV0dXmElgrnqYK464pjrSGO4JhsuEVEc\ncdKMiCgmTDbcdDoddQleYq46mKsOi7mabLg9PT1Rl+Al5qqDueqwmCvHcImIAsIxXCKimDDZcLPZ\nbNQleIm56mCuOizmarLhJpPJqEvwEnPVwVx1WMzV5BhuNptFRUVFeIWVCOaqg7nqiGOuoYzhisgG\nEXlNRHZHn7KDAAAGVklEQVSLSIeI3DVo/XUi8ryI/E5Ebpzo98UtZF8wVx3MVYfFXMsD/Ky7nXON\ngxeKyHIAawB8BEAvgEdF5FHn3AMBfjcRUewFOYYrQyxfDeAu51zWOfdHALcDWDuRL0qlUhN5Ow2B\nuepgrjos5hpkw10lIv8hIr8QkQvzls8F8FLe6725ZeNWVVU1kbfTEJirDuaqw2Kuo5o0E5F/BzB/\n8GIADsAiAEcAvOGcOyYiFwD4FwBLnHP7ROSnAO5xzqVyn/XnAP7eOXdhke/hhQ9EZFYgk2bOuQuc\ncycN+m927s/9zrnXnHPHctv+O4AOAEtyb38ZwAfyPm5ebtmQVq1ahcbGRjQ2NmL79u1obGwccM5d\nKpUacB11JpMpuDdma2vrgIfMdXV1obm5ecA2zc3N6Orq6n/d3t6O1tbWAdskEglkMpn+1+l0esCv\nMtlslvWxPtZXwvWl02msW7cOdXV1I56qFshpYSJyqnNuf+7nMwHsAlDnnHteRD4B4BYA56Fv0uwR\nABucc/+3yOfwfrgRYq46mKuOOOYa1qW9N4nIUyLSAWALgL91zj0PAM65hwGkAPwGwNMA0sWa7Vi0\ntLRMtF4qgrnqYK46LOZq8sIHIqI44s1riIhigg2XiCgkJhvu4JlICgZz1cFcdVjM1WTDra2tjboE\nLzFXHcxVh8VcOWlGRBQQTpoREcWEyYabf3UJBYe56mCuOizmarLhtrW1RV2Cl5irDuaqw2KuHMMl\nIgoIx3CJiGKCDZeIKCQmG+7g27RRMJirDuaqw2KuJhtufX191CV4ibnqYK46LObKSTMiooBw0oyI\nKCZMNtz8R29QcJirDuaqw2KuJhtuZ2dn1CV4ibnqYK46LObKMVwiooBwDJeIKCbYcImIQmKy4Q5+\nRjwFg7nqYK46LOZqcgw3js+j9wFz1cFcdcQx15HGcE02XCKiOOKkGRFRTJhsuOl0OuoSvMRcdTBX\nHRZzNdlwe3p6oi7BS8xVB3PVYTFXjuESEQWEY7hERDFhsuFms9moS/ASc9XBXHVYzNVkw00mk1GX\n4CXmqoO56rCYq8mGu2LFiqhL8BJz1cFcdVjM1WTD3blzZ9QleIm56mCuOizmarLhEhFZVB51AcUc\nPFhwNsUATz/99Ijb0NgxVx3MVUcccx2pnridh3sqgFeiroOIaIJOc87tH7wwbg1XAJwC4FDUtRAR\njdP7ARxwRZprrBouEZHPOGlGRBQSUw1XROaLyKMi8qyIPCYiZ0ddk0UisldEnhGRDhHZLSKrcstn\ni8gDIvKciDwlInVR1xpnItIiIi+KSK+IfDRv+ZA5ishUEdkiIr8TkS4RuSSa6uNrmFwfEpEXcvvs\nbhG5Nm+diVxjeZbCMFoB3OacuysX6J0Azou4Jot6Aax2zu0ZtPzbAH7lnPtzEVkC4F9EZJ5z7lj4\nJZpwL4BmAI8MWj5cjl8FkHXOnSki8wA8JiK/dM79vzALj7mhcnUArnXObS/yHhO5mjnCFZHZAM4F\ncA8AOOfuAzBHRM6ItDCbJPffYKsB3AYAzrknAOwH8IkQ6zLFOfeIc+4ACrMcLsc1eev2AtgJ4LNh\n1GvFMLkCQ/csE7maabgA5gDods715i17GcDciOqx7i4R6RSR74vILBE5AUC5c+61vG1eAvMdk1Hk\nODf3utg6Gllzbr/dKiKn5y03kaulhkvBqXPO1QJYDOAN9A3NAMWPKIji4nPOuQW5ffcRADuiLmis\nLDXcfQBqRCS/5rnoO8qlMXDOvZL78xiAf0BfA/4DgKMiclLepvPAfMdkFDm+BOADQ6yjYeRfSOCc\nuxXAGSLyJ7lFJnI103Cdc68D2A3g8wAgIpcC2OeceyHSwowRkUoRmZm36DIAHbmftwG4OrfdUvRd\nhPJwuBV64V4MneOPAVyVW3c6+sZ274+gRlNEZFL+P2K5SfNM3qSYiVxNXfggImcBuAPALABvAvii\nc+7pSIsyJrcz3oe+f2wFwAvom/l9ObdD3wXgdACHAVzjnNsVWbExJyK3AfgLACejb2jmkHPurOFy\nFJFKALcDWALgKICv5yaAKadYrgBqAewC8D70na3wOoDG42faWMnVVMMlIrLMzJACEZF1bLhERCFh\nwyUiCgkbLhFRSNhwiYhCwoZLRBQSNlwiopCw4RIRhYQNl4goJP8flVFcZXlRshoAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115ffa3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prints a single state (x,y,h) along with an arc indicating its intended path.\n",
    "# Works best for stage1 or stage2 state.\n",
    "r = 50\n",
    "print \"Radius: \", r\n",
    "ss = [(k,v) for k,v in config.items() if v.r() == r][10]\n",
    "print \"State:\"\n",
    "print mp.print_state(ss[0],ss[1])\n",
    "print\n",
    "mp.flow_plot_state(ss,xlim=(mp.LOWER_XLIMIT,mp.UPPER_XLIMIT+10),ylim=(-72,mp.UPPER_YLIMIT),\n",
    "                   stage2=mp.is_stage2(ss[1]),turntype=ss[1].type())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "State: \n",
      "  (94, 51, 208) : g1:    (1, 20, 10)  r:  50.0  type: lh  d:137.2   g2:           None  d2:  None  samples: 3\n"
     ]
    }
   ],
   "source": [
    "key = (94, 51,208)\n",
    "print \"State: \"\n",
    "mp.print_state(key, config[key])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "#     Visual pathing sanity checks\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cells having 50 <= dist <= 55 : 71 \n",
      "(15, 66, 276) StateP(goal1=(2, 54, 350),dist=53.01,type=lh,r=15.0,goal2=(0, 20, 0),dist2=18.92,rot=None,samples=1\n",
      "(8, 70, 28) StateP(goal1=(9, 58, 340),dist=51.29,type=rh,r=15.0,goal2=(0, 20, 354),dist2=12.39,rot=None,samples=4\n",
      "(33, 57, 318) StateP(goal1=(27, 53, 290),dist=51.44,type=rh,r=15.0,goal2=(1, 20, 354),dist2=7.28,rot=None,samples=3\n",
      "(34, 55, 276) StateP(goal1=(32, 55, 284),dist=51.66,type=lh,r=15.0,goal2=(1, 20, 354),dist2=2.0,rot=None,samples=1\n",
      "(24, 54, 244) StateP(goal1=(3, 43, 354),dist=50.41,type=lh,r=15.0,goal2=(1, 20, 354),dist2=27.33,rot=None,samples=3\n",
      "(39, 46, 298) StateP(goal1=(31, 44, 266),dist=50.68,type=rh,r=15.0,goal2=(1, 20, 354),dist2=8.35,rot=None,samples=2\n",
      "(31, 57, 292) StateP(goal1=(29, 56, 284),dist=51.25,type=rh,r=15.0,goal2=(0, 20, 0),dist2=2.23,rot=None,samples=3\n",
      "(15, 66, 32) StateP(goal1=(14, 48, 316),dist=50.74,type=rh,r=15.0,goal2=(1, 20, 354),dist2=19.32,rot=None,samples=2\n",
      "(15, 72, 344) StateP(goal1=(15, 70, 336),dist=54.14,type=rh,r=15.0,goal2=(1, 20, 354),dist2=2.0,rot=None,samples=5\n",
      "(31, 57, 256) StateP(goal1=(18, 54, 310),dist=53.2,type=lh,r=15.0,goal2=(0, 20, 354),dist2=13.82,rot=None,samples=3\n",
      "(31, 43, 218) StateP(goal1=(7, 42, 330),dist=50.89,type=lh,r=15.0,goal2=(0, 20, 354),dist2=27.85,rot=None,samples=3\n",
      "(3, 55, 180) StateP(goal1=(19, 40, 272),dist=54.48,type=rh,r=15.0,goal2=(1, 20, 6),dist2=24.59,rot=None,samples=2\n",
      "(34, 49, 246) StateP(goal1=(21, 49, 298),dist=50.02,type=lh,r=15.0,goal2=(1, 20, 354),dist2=13.44,rot=None,samples=2\n",
      "(20, 68, 288) StateP(goal1=(11, 60, 336),dist=53.77,type=lh,r=15.0,goal2=(0, 20, 354),dist2=12.39,rot=None,samples=2\n",
      "(30, 55, 246) StateP(goal1=(11, 49, 330),dist=52.69,type=lh,r=15.0,goal2=(1, 20, 354),dist2=21.79,rot=None,samples=1\n",
      "(21, 63, 30) StateP(goal1=(16, 43, 302),dist=51.13,type=rh,r=15.0,goal2=(1, 20, 354),dist2=22.72,rot=None,samples=3\n",
      "(19, 67, 278) StateP(goal1=(7, 57, 342),dist=54.05,type=lh,r=15.0,goal2=(0, 20, 354),dist2=16.42,rot=None,samples=2\n",
      "(24, 60, 30) StateP(goal1=(15, 38, 284),dist=51.99,type=rh,r=15.0,goal2=(1, 20, 0),dist2=27.43,rot=None,samples=3\n",
      "(33, 45, 214) StateP(goal1=(8, 44, 334),dist=54.69,type=lh,r=15.0,goal2=(1, 20, 354),dist2=29.58,rot=None,samples=2\n",
      "(24, 60, 250) StateP(goal1=(5, 51, 344),dist=54.63,type=lh,r=15.0,goal2=(0, 20, 354),dist2=23.29,rot=None,samples=3\n",
      "(6, 72, 318) StateP(goal1=(2, 63, 356),dist=53.04,type=lh,r=15.0,goal2=(1, 20, 0),dist2=10.02,rot=None,samples=3\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "71"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##=== Creates goal1s ===\n",
    "\n",
    "# Selects a small sample of 2nd stage states falling within a distance range, and their goal stage\n",
    "d_min = 50\n",
    "d_max = 55\n",
    "c_filtered = dict([(k,v) for k,v in config2.iteritems() if v.goal2() and v.r() and d_min <= v.dist() <= d_max])\n",
    "print \"Number of cells having %d <= dist <= %d : %d \" % (d_min,d_max,len(c_filtered))\n",
    "ii=0\n",
    "max=20\n",
    "for k,v in c_filtered.items():\n",
    "    print k,v\n",
    "    ii+=1\n",
    "    if ii>max:\n",
    "        break\n",
    "goal1s = list(set([v.goal1() for v in c_filtered.itervalues() if v.goal1()]))\n",
    "len(goal1s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotted 11/71 \n"
     ]
    },
    {
     "data": {
      "image/png": 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HxL6IuDYi3h0R0TTNpyPi7oi4sqM/c83wzYdsNE82micbzZON5qGdjR19nZ+MiA+VUr4V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\n/w9S3AH8egy7gQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b40c590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mp.flow_plot_config(c_filtered,xlim=(-5,100),ylim=(-0.5,100),arrowsize=0.75, arrowlength=0.75, max=10, figsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "71\n",
      "Location p((8, 70)) with heading 28 deg (0.5 rad) on radius 15 \n",
      "radius:15, heading : 0.5 radians, 28 degrees,  dist: 51.3\n",
      "Center of turning radius = (-5.2, 63.0)\n",
      "Plotted: 188 \n"
     ]
    },
    {
     "data": {
      "image/png": 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aTCd4sEkPm/TY2ORHoDsjEfkWgF8BuEYptUFEagE8AOB8AEkAtyilnhvk93Fn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\nsgKHERFZgcOIiKzAYUREVvj/WAPy9X9G2xMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11dff11d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#   Plots a single path from the filtered states\n",
    "#   Plots the grid location and heading, and a tangent circle on its current turning radius.\n",
    "#   If stage2=True, tries to explore backwards from the (x,y) of the state.\n",
    "mp = reload(mp)\n",
    "print len(c_filtered)\n",
    "sss = [(k,v) for k,v in c_filtered.iteritems()][1]\n",
    "mp.flow_plot_state(sss,xlim=(-5.5,60),ylim=(-0.5,100), stage2=True, turntype='rh')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mMar6\u001b[m\u001b[m/                \u001b[34mlogs\u001b[m\u001b[m/                stage2.Mar2.pickle\r\n",
      "\u001b[34mbackups\u001b[m\u001b[m/             \u001b[34mmar5\u001b[m\u001b[m/                stage2.pickle.feb28\r\n",
      "\u001b[34mfeb18\u001b[m\u001b[m/               stage1.Mar2.pickle\r\n"
     ]
    }
   ],
   "source": [
    "ls runs/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "03:56:32 INFO:Loading pickle file : n_sample_stage2.pickle\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count(config2):  177120\n",
      "Number of stage2 goals feeding into a stage1: 910/56643\n",
      "Top stage2 goal1 states: \n",
      "  (72, 56, 240) : 156 \n",
      "  (78, 58, 240) : 150 \n",
      "  (84, 35, 206) : 148 \n",
      "  (83, 43, 218) : 148 \n",
      "  (80, 53, 232) : 147 \n",
      "  (78, 50, 230) : 145 \n",
      "  (60, 75, 272) : 145 \n"
     ]
    }
   ],
   "source": [
    "##\n",
    "##  Find high trafficked stage2 goals : requires a rich set of stage2 goals.\n",
    "##\n",
    "##  goal2 is primary goal, goal1 is stage2 goal.\n",
    "mp = reload(mp)\n",
    "#config2 = mp.load_config(mp.N_STAGE2_FILEPATH)\n",
    "config2 = mp.load_config('n_sample_stage2.pickle')\n",
    "print \"Count(config2): \", len(config2)\n",
    "import operator\n",
    "from collections import defaultdict\n",
    "goal1_counts = defaultdict(int)\n",
    "num_stage2=0\n",
    "for k,v in config2.iteritems():\n",
    "    if mp.is_stage2(v) :\n",
    "        num_stage2 += 1\n",
    "        goal1_counts[v.goal1()]+=1\n",
    "print \"Number of stage2 goals feeding into a stage1: %d/%d\" % (len(goal1_counts),num_stage2)\n",
    "top_goal1s = sorted(goal1_counts.items(), key=operator.itemgetter(1), reverse=True)[:10]\n",
    "print \"Top stage2 goal1 states: \"\n",
    "for gg in top_goal1s[:7]:\n",
    "    print \"%15s : %3s \" % (gg[0],gg[1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Goal1:  (78, 58, 240)\n",
      "\n",
      "Number of stage2 paths to this state: 150\n",
      "\n",
      "(83, 56, 260):StateP(goal1=(78, 58, 240),dist=106.93,type=rh,r=15.0,goal2=(1, 20, 354),dist2=5.41,rot=None,samples=1\n",
      "(74, 30, 104):StateP(goal1=(78, 58, 240),dist=138.44,type=lh,r=15.0,goal2=(1, 20, 354),dist2=36.92,rot=None,samples=1\n",
      "(78, 32, 120):StateP(goal1=(78, 58, 240),dist=132.97,type=lh,r=15.0,goal2=(1, 20, 354),dist2=31.45,rot=None,samples=1\n",
      "(83, 53, 212):StateP(goal1=(78, 58, 240),dist=108.65,type=lh,r=15.0,goal2=(1, 20, 354),dist2=7.13,rot=None,samples=1\n",
      "(71, 30, 92):StateP(goal1=(78, 58, 240),dist=140.35,type=lh,r=15.0,goal2=(1, 20, 354),dist2=38.83,rot=None,samples=1\n",
      "Plotted 150/150 \n"
     ]
    },
    {
     "data": {
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4D06JixsEKDdThP0iQhEREZHO9u67FgCWFYVtNzBgwPcZMuS3DlclJ+kgFRER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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11650d0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##\n",
    "##  Plots stage2 fleur-de-lis semicircle paths all feeding into a single Goal1\n",
    "##\n",
    "which = 1\n",
    "goal1 = top_goal1s[which][0]\n",
    "print\n",
    "print \"Goal1: \", goal1\n",
    "print\n",
    "config_matching_goal1 = dict([(k,v) for k,v in config2.iteritems() if v.goal1() and v.goal1() == goal1])\n",
    "print \"Number of stage2 paths to this state: %d\" % len(config_matching_goal1)\n",
    "print\n",
    "mp.flow_plot_config(config_matching_goal1, xlim=(-5,goal1[0]+40), ylim=(-36,goal1[1]+30), \n",
    "                    arrowsize=1, arrowlength=1, max=150,figsize=11,verbose=True, verbosemax=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Goal1:  (80, 53, 232)\n",
      "\n",
      "(80, 53, 232)  :  StateP(goal1=(0, 20, 354),dist=103.43,type=lh,r=49.5,goal2=None,dist2=None,rot=None,samples=7\n",
      "\n",
      "Number of stage2 paths to this state: 147\n",
      "\n",
      "Primary goal : (0, 20, 354)\n",
      "Primary r    : 49.5\n",
      "Primary type : lh\n",
      "Points along main tributary:  152\n",
      "Points in merged set       :  299\n",
      "\n",
      "(64, 27, 64):StateP(goal1=(80, 53, 232),dist=150.55,type=lh,r=15.0,goal2=(0, 20, 354),dist2=47.12,rot=None,samples=1\n",
      "(81, 77, 124):StateP(goal1=(80, 53, 232),dist=131.28,type=rh,r=15.0,goal2=(0, 20, 354),dist2=27.85,rot=None,samples=2\n",
      "(91, -11, 148):StateP(goal1=(0, 20, 354),dist=127.95,type=lh,r=49.5,goal2=None,dist2=None,rot=None,samples=5\n",
      "(103, 70, 20):StateP(goal1=(80, 53, 232),dist=141.35,type=rh,r=15.0,goal2=(0, 20, 354),dist2=37.92,rot=None,samples=2\n",
      "(92, 79, 78):StateP(goal1=(80, 53, 232),dist=141.47,type=rh,r=15.0,goal2=(0, 20, 354),dist2=38.04,rot=None,samples=2\n",
      "Plotted 299/299 \n"
     ]
    },
    {
     "data": {
      "image/png": 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Axfr1v+HWW88AfIt1ZmZ2bZaV6L0MI4zLLx/E2287cbsTiYmpJDOzZ2dNy3Tx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\nL5Szd1Vnsp944qNccMGH7b7evq4/384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cddcTNP+bPSxsACee+BjHHTdKb5FtMIxw4uMn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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c0d4c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##===========================================\n",
    "##  A main tributary and two of its feeders\n",
    "##===========================================\n",
    "##\n",
    "##  Plots stage2 fleur-de-lis semicircle paths all feeding into a single goal1\n",
    "##  AND\n",
    "##  all paths along same radius and turn radius leading to the same primary goal.\n",
    "##\n",
    "which = 4\n",
    "goal1 = top_goal1s[which][0]\n",
    "print\n",
    "print \"Goal1: \", goal1\n",
    "print\n",
    "print goal1,\" : \",config2[goal1]\n",
    "print\n",
    "config_to_goal1 = dict([(k,v) for k,v in config2.iteritems() if v.goal1() and v.goal1() == goal1])\n",
    "print \"Number of stage2 paths to this state: %d\" % len(config_to_goal1)\n",
    "print\n",
    "\n",
    "primary_goal = config2[goal1].goal1()\n",
    "print \"Primary goal :\", primary_goal\n",
    "primary_r = config2[goal1].r()\n",
    "print \"Primary r    :\", primary_r\n",
    "primary_type = config2[goal1].type()\n",
    "print \"Primary type :\", primary_type\n",
    "\n",
    "config_to_goal2 = dict([(k,v) for k,v in config2.iteritems() if v.goal1() == primary_goal \n",
    "                           and v.r() == primary_r and v.type() == primary_type])\n",
    "print \"Points along main tributary: \", len(config_to_goal2)\n",
    "\n",
    "config_to_goal1.update(config_to_goal2)\n",
    "print \"Points in merged set       : \", len(config_to_goal1)\n",
    "print \n",
    "# Plots only the states along the way from goal1 to goal2\n",
    "# mp.flow_plot_config(config_to_goal2, xlim=(0,mp.UPPER_XLIMIT), ylim=(-0.5,mp.UPPER_YLIMIT), \n",
    "#                     arrowsize=1, arrowlength=1, max=150,figsize=11,verbose=True, verbosemax=5)\n",
    "\n",
    "# Plots the merged states: the two feeders and the main tributary to goal\n",
    "mp.flow_plot_config(config_to_goal1, xlim=(-10,goal1[0]+30), ylim=(-72,mp.UPPER_YLIMIT), \n",
    "                    arrowsize=1, arrowlength=1, max=300, figsize=11,verbose=True, verbosemax=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(162, 144)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mp.UPPER_XLIMIT,mp.UPPER_YLIMIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "04:00:42 INFO:Loading pickle file : ne_stage1.pickle\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "131\n",
      "(81, 25, 324):StateP(goal1=(45, -7, 300),dist=48.49,type=rh,r=120.0,goal2=None,dist2=None,rot=None,samples=14\n",
      "(128, 4, 260):StateP(goal1=(46, -9, 300),dist=84.77,type=lh,r=120.0,goal2=None,dist2=None,rot=None,samples=4\n",
      "(104, 103, 2):StateP(goal1=(45, -7, 300),dist=131.27,type=rh,r=120.0,goal2=None,dist2=None,rot=None,samples=2\n",
      "(92, 44, 334):StateP(goal1=(45, -7, 300),dist=70.34,type=rh,r=120.0,goal2=None,dist2=None,rot=None,samples=13\n",
      "(104, 90, 356):StateP(goal1=(45, -7, 300),dist=118.26,type=rh,r=120.0,goal2=None,dist2=None,rot=None,samples=2\n",
      "Plotted 131/131 \n"
     ]
    },
    {
     "data": {
      "image/png": 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EROMaHj6Cm2/+Jn70oy+O+XmPB9i0SeeiiKbB4/GgoaFhrE9xdAAREU2d1VqA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\nh1deuRv9/d+Fqg4DAN588w386U/vhd8/F0By2l1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ClJxYRKPRE5ZmdJ50ZGsX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f8741d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Prints stage1 paths having a specified radius\n",
    "rr=120\n",
    "ne_stage1=mp.load_config('ne_stage1.pickle')\n",
    "ssrr=dict([(k,v) for k,v in ne_stage1.iteritems() if v.r()==rr])\n",
    "print len(ssrr)\n",
    "mp.flow_plot_config(ssrr, xlim=(-10,mp.UPPER_XLIMIT), ylim=(-72,mp.UPPER_YLIMIT), \n",
    "                    arrowsize=1, arrowlength=1, max=300, figsize=11,verbose=True, verbosemax=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:44:01 INFO:Loading config file from ne_stage1.pickle\n",
      "07:44:04 INFO:Loading config file from ne_stage2.pickle\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "213196\n",
      "462771\n"
     ]
    }
   ],
   "source": [
    "#=======================================\n",
    "# Load stage1 configuration file\n",
    "#=======================================\n",
    "mp = reload(mp)\n",
    "config = mp.load_config(mp.NE_STAGE1_FILEPATH)\n",
    "print len(config)\n",
    "#=======================================\n",
    "# Load stage2 configuration sample\n",
    "#=======================================\n",
    "config2 = mp.load_config(mp.NE_STAGE2_FILEPATH)\n",
    "# config2 = mp.load_config(\"sample_stage2.pickle\")\n",
    "print len(config2)\n",
    "#=======================================\n",
    "# Load overall configuration sample\n",
    "#=======================================\n",
    "#config3 = mp.load_config(\"ne_sample.pickle\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#\n",
    "# FOLLOWING SHOULD BE ZERO : no stage2 states should have a dist2 value\n",
    "#\n",
    "assert(len([(k,v) for k,v in config2.iteritems() if v.goal2() and not v.dist2()])==0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cells having 20.0 <= dist <= 20.5 : 181 \n",
      "   (2, 40, 350) : g1:     (0, 20, 0)  r: 116.0  type: lh  d: 20.0   g2:           None  d2:  None  samples: 75\n",
      "  (13, 35, 286) : g1:     (1, 20, 0)  r:  16.5  type: lh  d: 20.5   g2:           None  d2:  None  samples: 1\n",
      "   (7, 39, 330) : g1:   (1, 20, 354)  r:  48.5  type: lh  d: 20.1   g2:           None  d2:  None  samples: 23\n",
      "   (6, 38, 308) : g1:   (-1, 20, 10)  r:  19.0  type: lh  d: 20.2   g2:           None  d2:  None  samples: 6\n",
      "   (3, 40, 350) : g1:     (1, 20, 0)  r: 240.0  type: lh  d: 20.1   g2:           None  d2:  None  samples: 41\n",
      "   (2, 40, 334) : g1:    (0, 20, 10)  r:  33.0  type: lh  d: 20.4   g2:           None  d2:  None  samples: 19\n",
      "   (4, 40, 334) : g1:     (0, 20, 0)  r:  36.5  type: lh  d: 20.5   g2:           None  d2:  None  samples: 27\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Selects a small set of states falling within a distance range.\n",
    "#\n",
    "d_min = 20\n",
    "d_max = 20.5\n",
    "c_filtered = dict([(k,v) for k,v in config2.iteritems() if d_min <= v.dist() <= d_max])\n",
    "print \"Number of cells having %.1f <= dist <= %.1f : %d \" % (d_min,d_max,len(c_filtered))\n",
    "max=7\n",
    "ii=0\n",
    "for k,v in c_filtered.items():\n",
    "    if ii<max:\n",
    "        mp.print_state(k,v)\n",
    "        ii+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotted 100/100 \n"
     ]
    },
    {
     "data": {
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jdN7B/v13cPr0d2lpOax1SWIQruzSKpSLQS92u53s7Ox+zzNcY2fNmjValyBMaLC5u3Bh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\n4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1nXjRXFzqz22IFFRES6hWGIxx57DHfu3AE0dGM9oJR6r/Z4FMAEgG/XXj4H4Csi8pdY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\nEBERkdc42CEiIiKv/T/7AdomHLiGogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11beede50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Plots a sample of first stage paths\n",
    "mp=reload(mp)\n",
    "fdl_config = dict([(k,v) for k,v in config.iteritems()][:100])\n",
    "mp.flow_plot_config(fdl_config,xlim=(mp.LOWER_XLIMIT,mp.UPPER_XLIMIT),\n",
    "                    ylim=(mp.NE_LOWER_YLIMIT,mp.UPPER_YLIMIT),\n",
    "                    arrowsize=1.75, arrowlength=1.5, max=200,figsize=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "04:02:35 INFO:Loading pickle file : n_stage1.pickle\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "110930\n"
     ]
    }
   ],
   "source": [
    "config = mp.load_config(mp.N_STAGE1_FILEPATH)\n",
    "config = mp.load_config(mp.NE_STAGE1_FILEPATH)\n",
    "print len(config)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotted 0/0 \n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cc5ad50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count: 0\n"
     ]
    }
   ],
   "source": [
    "# Points that are within half robot width from the north wall:\n",
    "# This should be empty, indicating that collision avoidance is working properly \n",
    "close_to_wall = dict([(k,v) for k,v in config.iteritems() if (mp.UPPER_YLIMIT-k[1])<mp.ROBOT_WIDTH/2][:100])\n",
    "mp.flow_plot_config(close_to_wall,xlim=(-1.5,mp.UPPER_XLIMIT),\n",
    "                    ylim=(mp.UPPER_YLIMIT-mp.ROBOT_WIDTH,mp.UPPER_YLIMIT),\n",
    "                    arrowsize=1.75, arrowlength=1.5, max=50,figsize=11,\n",
    "                    verbose=True,verbosemax=10)\n",
    "print \"Count: %d\" % len(close_to_wall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotted 0/0 \n"
     ]
    },
    {
     "data": {
      "image/png": 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SNK0vQi8U5KV5AACAbiv79k5l0872TUEuRFdXV12PABSKzMMico+ixXGs7e1tVSqVk8vy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\nAAAAJ/4/ZVOGjgJ4WV8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116797650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count: 0\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Draws points that are within half robot width from the east wall:\n",
    "#\n",
    "# Should be empty. \n",
    "#\n",
    "close_to_wall = dict([(k,v) for k,v in config.iteritems() if (mp.UPPER_XLIMIT-k[0])<mp.ROBOT_WIDTH/2][:100])\n",
    "mp.flow_plot_config(close_to_wall,xlim=(-5,mp.UPPER_XLIMIT),\n",
    "                    ylim=(-36,mp.UPPER_YLIMIT),\n",
    "                    arrowsize=1.75, arrowlength=1.5, max=50,figsize=11,\n",
    "                    verbose=True,verbosemax=10)\n",
    "print \"Count: %d\" % len(close_to_wall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(89, 119, 282):StateP(goal1=(0, 20, 354),dist=141.33,type=lh,r=114.0,goal2=None,dist2=None,rot=None,samples=5\n",
      "(20, 122, 344):StateP(goal1=(1, 20, 354),dist=103.88,type=lh,r=600.0,goal2=None,dist2=None,rot=None,samples=3\n",
      "(94, 122, 276):StateP(goal1=(0, 20, 0),dist=151.26,type=lh,r=103.0,goal2=None,dist2=None,rot=None,samples=5\n",
      "(57, 113, 304):StateP(goal1=(0, 20, 354),dist=111.98,type=lh,r=130.0,goal2=None,dist2=None,rot=None,samples=13\n",
      "(1, 120, 358):StateP(goal1=(-1, 20, 0),dist=100.0,type=lh,r=3000.0,goal2=None,dist2=None,rot=None,samples=48\n",
      "Plotted 101/200 \n"
     ]
    },
    {
     "data": {
      "image/png": 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RTHdo0ZazhSzaIgiCIAhCWxiNRtasWcPChQud0o/Vq1eTnJwsafOEs8qrr77KHXfcAWd7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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bcc7e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Points that are within full robot width from the north wall.\n",
    "# These are properly far enough from the wall to avoid collision\n",
    "# and properly cover that space as desired.\n",
    "close_to_wall = dict([(k,v) for k,v in config.iteritems() if (abs(mp.UPPER_YLIMIT-k[1]) < 1.2*mp.ROBOT_WIDTH)][:200])\n",
    "mp.flow_plot_config(close_to_wall,xlim=(-1.5,mp.UPPER_XLIMIT),\n",
    "                    ylim=(mp.UPPER_YLIMIT-1.5*mp.ROBOT_WIDTH,mp.UPPER_YLIMIT),\n",
    "                    arrowsize=1.75, arrowlength=1.5, max=100,figsize=11,\n",
    "                    verbose=True,verbosemax=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check collisions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arcs.ipynb                     ne_sample_traj.pickle\r\n",
      "evaluate.ipynb                 ne_sample_unreachables.pickle\r\n",
      "field_drawings.ipynb           ne_stage1.pickle\r\n",
      "load_mp.py                     ne_stage2.pickle\r\n",
      "mem_profiling.py               ne_stage3.pickle\r\n",
      "motion_planner.py              ne_stage4.pickle\r\n",
      "motion_planner.pyc             ne_unreachables.pickle\r\n",
      "mp_deprecated.ipynb            \u001b[34moldcode\u001b[m\u001b[m/\r\n",
      "n_remaining.pickle             readme.md\r\n",
      "n_sample.pickle                requirements.txt\r\n",
      "n_sample_stage2.pickle         results.txt\r\n",
      "n_sample_stage4.pickle         \u001b[34mruns\u001b[m\u001b[m/\r\n",
      "n_sample_traj.pickle           sample.pickle\r\n",
      "n_sample_unreachables.pickle   sample_stage2.pickle\r\n",
      "n_stage1.pickle                sample_stage4.pickle\r\n",
      "n_stage2.pickle                sample_traj.pickle\r\n",
      "n_stage3.pickle                sample_trajectories.ipynb\r\n",
      "n_stage4.pickle                spacefilling_circles.ipynb\r\n",
      "n_unreachables.pickle          stage1.pickle\r\n",
      "ne_remaining.pickle            stage2.pickle\r\n",
      "ne_sample.pickle               stage3.pickle\r\n",
      "ne_sample_stage2.pickle        stage4.pickle\r\n",
      "ne_sample_stage4.pickle        unreachables.pickle\r\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "04:42:01 INFO:Loading pickle file : ne_sample_stage2.pickle\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28.5 27.5\n",
      "4\n",
      "Plotted 4/4 \n"
     ]
    },
    {
     "data": {
      "image/png": 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XSdm1axdaWloq2V0i0oSOCdDsm9AzZ87g3nvv9eR1q3Vn/k4A+0XkIqSGVX4G4L+lH/tr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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ed529d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "#   Should be nearly empty \n",
    "#\n",
    "# config = mp.load_config(mp.NE_STAGE1_FILEPATH)\n",
    "# cc=config\n",
    "\n",
    "# Can take a minute for full stage2, better to use a sample\n",
    "# config2 = mp.load_config('n_sample_stage2.pickle')\n",
    "config2 = mp.load_config('ne_sample_stage2.pickle')\n",
    "cc=config2\n",
    "\n",
    "collisions=set()\n",
    "print mp.ROBOT_LENGTH, mp.ROBOT_WIDTH\n",
    "tol = mp.ROBOT_LENGTH/2\n",
    "for k,v in cc.iteritems():\n",
    "    if abs(k[0]-mp.UPPER_XLIMIT) < tol+4:  # <== note added buffer\n",
    "        collisions.add((k[0],k[1]))\n",
    "    if abs(k[1]-mp.UPPER_YLIMIT) < tol+5:  # <==\n",
    "        collisions.add((k[0],k[1]))\n",
    "\n",
    "print len(collisions)\n",
    "mp.plot_xy(collisions, max=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size obstacles: 1066   elapsed: 0.001 sec\n",
      "Collisions: 0/110930  (0.000)    elapsed: 0.4 min)\n"
     ]
    }
   ],
   "source": [
    "#  SHOULD GENERATE 0 COLLISIONS\n",
    "mp = reload(mp)\n",
    "start = time.time()\n",
    "print \"Size obstacles: %d   elapsed: %.3f sec\" % (len(mp.xy_obstacles_set()), (time.time()-start)) \n",
    "collisions = mp.check_collisions(config.iterkeys())\n",
    "print \"Collisions: %d/%d  (%.3f)    elapsed: %.1f min)\" % \\\n",
    "            (len(collisions), len(config), float(len(collisions))/len(config), (time.time()-start)/60.0)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  SHOULD BE EMPTY\n",
    "cconfig = dict([(k,{}) for k in collisions])\n",
    "if cconfig:\n",
    "    mp.flow_plot_config(cconfig,xlim=(-1.5,mp.UPPER_XLIMIT),\n",
    "                    ylim=(mp.NE_LOWER_YLIMIT,mp.UPPER_YLIMIT),\n",
    "                    arrowsize=1.75, arrowlength=1.5, max=500,figsize=9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "09:18:00 INFO:Checking collisions\n",
      "09:18:44 WARNING:Collisions: 1460/172959  (0.008)    elapsed: 0.7 min)\n"
     ]
    }
   ],
   "source": [
    "mp = reload(mp)\n",
    "mp.check_collisions_config(config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "09:18:44 INFO:Checking collisions\n",
      "09:19:46 WARNING:Collisions: 4561/247826  (0.018)    elapsed: 1.0 min)\n"
     ]
    }
   ],
   "source": [
    "mp.check_collisions_config(config2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "03:45:26 INFO:Checking collisions\n",
      "03:45:29 WARNING:Collisions: 1127/17316  (0.065)    elapsed: 0.1 min)\n"
     ]
    }
   ],
   "source": [
    "mp.check_collisions_config(config3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "03:54:21 INFO:Loading config file from stage1.pickle\n",
      "03:54:21 INFO:Checking collisions\n",
      "03:54:21 INFO:No collisions\n"
     ]
    }
   ],
   "source": [
    "configg1 = mp.load_config(mp.N_STAGE1_FILEPATH)\n",
    "mp.check_collisions_config(configg1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "03:58:19 INFO:Loading config file from stage2.pickle\n",
      "03:58:19 INFO:Checking collisions\n",
      "03:58:23 INFO:No collisions\n"
     ]
    }
   ],
   "source": [
    "configg2 = mp.load_config(mp.N_STAGE2_FILEPATH)\n",
    "mp.check_collisions_config(configg2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "05:31:57 INFO:Loading pickle file : ne_sample_unreachables.pickle\n"
     ]
    }
   ],
   "source": [
    "unreachables = mp.load_config('ne_sample_unreachables.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotted 501/11519 \n"
     ]
    },
    {
     "data": {
      "image/png": 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2Ymu3KcJcKdUPYIuIrJc3/ToAK7H3zwH8DoDDSqmfAzACYKmyFZPZ2Vls3bq1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\n17mMZrGXjo1mUUp5bhePNxGvbUHb6z2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LlBkYGNjsuzOeNwp+7TgfXhcvAqdPR7sOSSZe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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cfaed50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This draws the xy locations for which there is at least one heading which results in an unreachable state.\n",
    "# The upper wall right wall, and airship are bordered with many unreachable states, as expected. \n",
    "mp.plot_unreachables_xy(unreachables, max=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11 : (17,-22,128) \n",
      "x,y,t: (17,-22,128) \n",
      "Plotted 1001/1256 \n"
     ]
    },
    {
     "data": {
      "image/png": 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0hLkNwA7U29uj4OXK3l6KvkWBwMGhz3wmo4mJK2pvT4ty1PUrnQzpN7B65ZUz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\nAAAU9v+/kN2o9if5XAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1265d8c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\t\t===   COLLIDES   ===\n"
     ]
    }
   ],
   "source": [
    "which=11\n",
    "ii=0\n",
    "for x,y,h in unreachables:\n",
    "    if ii==which:\n",
    "        print ii,\": (%d,%d,%d) \"% (x,y,h)\n",
    "        mp.plot_check_collision((x,y),h,degrees=True)\n",
    "        break\n",
    "    ii+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del unreachables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quantitative evaluations\n",
    "## Compares stage 1 path distance with actual circular arc length\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "04:26:34 INFO:Loading pickle file : n_stage1.pickle\n"
     ]
    }
   ],
   "source": [
    "config = mp.load_config(mp.N_STAGE1_FILEPATH)\n",
    "#config = mp.load_config(mp.NE_STAGE1_FILEPATH)\n",
    "print \"Num rows: \", len(config)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(54, 78, 276) StateP(goal1=(1, 20, 0),dist=85.97,type=lh,r=59.0,goal2=None,dist2=None,rot=None,samples=5\n",
      "(125, 59, 214) StateP(goal1=(0, 20, 0),dist=172.44,type=lh,r=69.0,goal2=None,dist2=None,rot=None,samples=6\n",
      "(13, 47, 316) StateP(goal1=(0, 20, 354),dist=30.07,type=lh,r=46.0,goal2=None,dist2=None,rot=None,samples=12\n",
      "(57, 113, 304) StateP(goal1=(0, 20, 354),dist=111.98,type=lh,r=130.0,goal2=None,dist2=None,rot=None,samples=13\n",
      "(6, 48, 340) StateP(goal1=(1, 20, 0),dist=28.57,type=lh,r=84.5,goal2=None,dist2=None,rot=None,samples=77\n",
      "(22, 70, 310) StateP(goal1=(-1, 20, 0),dist=56.77,type=lh,r=66.0,goal2=None,dist2=None,rot=None,samples=8\n",
      "(59, 43, 216) StateP(goal1=(0, 20, 6),dist=84.81,type=lh,r=33.0,goal2=None,dist2=None,rot=None,samples=4\n",
      "(62, 57, 250) StateP(goal1=(1, 20, 354),dist=81.99,type=lh,r=45.5,goal2=None,dist2=None,rot=None,samples=3\n",
      "(52, 64, 260) StateP(goal1=(0, 20, 0),dist=77.26,type=lh,r=45.0,goal2=None,dist2=None,rot=None,samples=8\n",
      "(-1, 107, 6) StateP(goal1=(-1, 20, 354),dist=87.15,type=rh,r=420.0,goal2=None,dist2=None,rot=None,samples=10\n",
      "(11, 41, 300) StateP(goal1=(0, 20, 6),dist=24.73,type=lh,r=20.5,goal2=None,dist2=None,rot=None,samples=11\n"
     ]
    }
   ],
   "source": [
    "max=10\n",
    "ii=0\n",
    "for k,v in config.iteritems():\n",
    "    ii+=1\n",
    "    print k,v\n",
    "    if ii>max:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def eval_stage1(config, keys=None, max=20, step=0):\n",
    "    \"\"\"\n",
    "    Prints states where arc_dist \n",
    "    \"\"\"\n",
    "    tol=1.0\n",
    "    if not keys:\n",
    "        keys = config.keys()\n",
    "    for k in keys[step*max:(step+1)*max]:\n",
    "        v = config[k]\n",
    "        if v.dist2():\n",
    "            print \"%15s :  ERROR. Found dist2 field in stage1 plan\" % k\n",
    "        distuple = mp.arc_dist((k[0],k[1]),(v.goal1()[0],v.goal1()[1]),v.r(),h=np.radians(k[2]),turntype=v.type())\n",
    "        sumdist = sum(distuple) if distuple != (None,None) else -1\n",
    "        error = -1.0 if sumdist<0 else abs(v.dist()-sumdist)\n",
    "        distr = (-1,-1) if distuple == (None,None) else distuple\n",
    "        diff = \" diff: %3.1f\" % error if error > tol else ''\n",
    "        print \"%15s : r:%.1f  dist: %6.1f  actual: %6.1f  tup: (%5.1f, %5.1f)  goal1: %8s   %s\" % \\\n",
    "                (k, v.r(), v.dist(), sumdist, distr[0], distr[1], v.goal1(), diff)\n",
    "\n",
    "\n",
    "def eval2_stage1(config, mintol=0.5,maxtol=10,error_tol=25):\n",
    "    \"\"\"\n",
    "    Evaluates stage1 paths using the cw_arclength measure that is used by the stage 1 planner\n",
    "    to fill the 'dist' field in the table. \n",
    "\n",
    "    The stored dist value can differ from the recalculated distance due to discretization,\n",
    "    although we expect that this will be small on average.\n",
    "\n",
    "    Checks performed: \n",
    "    - recalculated path distance for (x,y,h) agrees with the distance stored in the table\n",
    "    - the path destination is nearby the intended goal.\n",
    "    \"\"\"\n",
    "    print \"Min tol: %.2f   Max tol: %.2f   Error tol: %.2f\" % (mintol, maxtol, error_tol)\n",
    "    start_time = time.time()\n",
    "    maxprint = 5\n",
    "    tol=1.0\n",
    "    useable=0\n",
    "    discrepant=0\n",
    "    divergent=0\n",
    "    missing=0\n",
    "    logged=0\n",
    "    for k in config.iterkeys():\n",
    "        v = config[k]\n",
    "        if v.dist2():\n",
    "            print \"%15s :  ERROR. Found dist2 field in stage1 plan\" % k\n",
    "        error = -1\n",
    "        if v.dist() and v.goal1():\n",
    "            (gx,gy,gh) = v.goal1()\n",
    "            cw_arcl = mp.cw_arclength((gx,gy), (k[0], k[1],), v.r(), tol=0.75)\n",
    "            error = abs(cw_arcl-v.dist()) if cw_arcl else -1\n",
    "            if logged < maxprint:\n",
    "                print \"error: %.1f\" % error\n",
    "                logged += 1\n",
    "        if error < 0:\n",
    "            missing += 1\n",
    "        if 0 <= error < mintol:\n",
    "            useable += 1\n",
    "        if error > maxtol:\n",
    "            discrepant += 1\n",
    "        if error > error_tol:\n",
    "            distuple = mp.arc_dist((k[0],k[1]),(0,0),v.r(),h=np.radians(k[2]),turntype=v.type(),tol=100)\n",
    "            sumdist = sum(distuple) if distuple != (None,None) else -1\n",
    "            divergent += 1\n",
    "            diff = \" diff: %3.1f\" % error if error > tol else ''\n",
    "            distr = (-1,-1) if distuple == (None,None) else distuple\n",
    "            print \"%15s : r:%.1f  dist: %6.1f  actual: %6.1f  tup: (%5.1f, %5.1f)  goal1: %8s   %s\" % \\\n",
    "                    (k, v.r(), v.dist(), sumdist, distr[0], distr[1], v.goal1(), diff)\n",
    "\n",
    "    print \"Number of states :       %6d\" % len(config)\n",
    "    print \"       missing   :       %6d\" % missing\n",
    "    print \"       divergent :       %6d  (exceed error tol)\" % divergent\n",
    "    print \"       discrepant:       %6d  (exceed max tol)\" % discrepant\n",
    "    print \"            exact:       %6d  (within min tol)\" % useable\n",
    "    print \"Elapsed:  %d seconds \" % (time.time()-start_time)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (54, 78, 276) : r:59.0  dist:   86.0  actual:   86.0  tup: ( 85.8,   0.2)  goal1: (1, 20, 0)   \n",
      " (125, 59, 214) : r:69.0  dist:  172.4  actual:  175.0  tup: (176.2,  -1.2)  goal1: (0, 20, 0)    diff: 2.6\n",
      "  (13, 47, 316) : r:46.0  dist:   30.1  actual:   30.8  tup: ( 30.4,   0.4)  goal1: (0, 20, 354)   \n",
      " (57, 113, 304) : r:130.0  dist:  112.0  actual:  112.8  tup: (112.3,   0.5)  goal1: (0, 20, 354)   \n",
      "   (6, 48, 340) : r:84.5  dist:   28.6  actual:   28.5  tup: ( 28.6,  -0.1)  goal1: (1, 20, 0)   \n",
      "  (22, 70, 310) : r:66.0  dist:   56.8  actual:   56.5  tup: ( 57.0,  -0.6)  goal1: (-1, 20, 0)   \n",
      "  (59, 43, 216) : r:33.0  dist:   84.8  actual:   86.1  tup: ( 86.6,  -0.5)  goal1: (0, 20, 6)    diff: 1.3\n",
      "  (62, 57, 250) : r:45.5  dist:   82.0  actual:   81.9  tup: ( 81.6,   0.3)  goal1: (1, 20, 354)   \n",
      "  (52, 64, 260) : r:45.0  dist:   77.3  actual:   77.4  tup: ( 78.2,  -0.8)  goal1: (0, 20, 0)   \n",
      "   (-1, 107, 6) : r:420.0  dist:   87.2  actual:   87.1  tup: ( 87.2,  -0.1)  goal1: (-1, 20, 354)   \n",
      "  (11, 41, 300) : r:20.5  dist:   24.7  actual:   25.6  tup: ( 24.6,   1.0)  goal1: (0, 20, 6)   \n",
      "  (55, 40, 222) : r:31.5  dist:   72.6  actual:   73.9  tup: ( 74.8,  -0.9)  goal1: (1, 20, 0)    diff: 1.2\n",
      "  (42, 56, 252) : r:32.0  dist:   65.3  actual:   66.4  tup: ( 65.8,   0.6)  goal1: (0, 20, 10)    diff: 1.1\n",
      "   (-3, 72, 14) : r:157.0  dist:   52.3  actual:   51.5  tup: ( 52.5,  -1.0)  goal1: (0, 20, 354)   \n",
      "  (32, 49, 270) : r:32.5  dist:   47.2  actual:   47.2  tup: ( 47.5,  -0.3)  goal1: (0, 20, 354)   \n",
      "  (59, 86, 268) : r:57.0  dist:  100.3  actual:  100.4  tup: (100.6,  -0.3)  goal1: (1, 20, 10)   \n",
      "    (-4, 44, 8) : r:470.0  dist:   24.2  actual:   24.4  tup: ( 24.2,   0.3)  goal1: (-1, 20, 6)   \n",
      "   (6, 41, 334) : r:64.5  dist:   21.7  actual:   21.9  tup: ( 22.0,  -0.1)  goal1: (0, 20, 354)   \n",
      " (110, 60, 220) : r:62.5  dist:  148.9  actual:  152.2  tup: (152.5,  -0.4)  goal1: (0, 20, 0)    diff: 3.3\n",
      "  (65, 19, 184) : r:32.5  dist:   90.7  actual:   96.8  tup: ( 96.6,   0.2)  goal1: (0, 20, 354)    diff: 6.1\n"
     ]
    }
   ],
   "source": [
    "eval_stage1(config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   (3, 42, 344) : r:83.5  dist:   22.1  actual:   22.1  tup: ( 22.3,  -0.2)  goal1: (0, 20, 0)   \n",
      " (104, 20, 186) : r:52.0  dist:  148.9  actual:  153.1  tup: (152.5,   0.6)  goal1: (0, 20, 354)    diff: 4.2\n",
      "  (11, 36, 290) : r:15.0  dist:   20.4  actual:   21.3  tup: ( 20.1,   1.2)  goal1: (0, 20, 0)   \n",
      " (115, 65, 222) : r:66.5  dist:  158.3  actual:  159.8  tup: (160.7,  -0.9)  goal1: (0, 20, 0)    diff: 1.5\n",
      "  (13, 49, 316) : r:43.0  dist:   32.1  actual:   33.1  tup: ( 32.2,   0.9)  goal1: (0, 20, 354)   \n",
      "  (32, 86, 304) : r:71.5  dist:   76.5  actual:   77.3  tup: ( 76.5,   0.8)  goal1: (0, 20, 6)   \n",
      "  (6, 125, 358) : r:1300.0  dist:  105.2  actual:  107.0  tup: (105.1,   1.9)  goal1: (0, 20, 354)    diff: 1.8\n",
      "   (5, 63, 340) : r:99.5  dist:   43.6  actual:   43.2  tup: ( 43.8,  -0.6)  goal1: (0, 20, 6)   \n",
      "  (26, 85, 318) : r:98.5  dist:   71.2  actual:   72.0  tup: ( 71.3,   0.7)  goal1: (0, 20, 0)   \n",
      "  (61, -1, 132) : r:37.5  dist:   77.7  actual:  155.4  tup: (156.3,  -0.9)  goal1: (0, 20, 10)    diff: 77.7\n"
     ]
    }
   ],
   "source": [
    "kk = sorted([k for k,v in config.iteritems() if v.dist()>71.5 and v.goal1() == (0.0, 0.0)])\n",
    "eval_stage1(config, kk, max=10, step=7)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min tol: 2.10   Max tol: 10.00   Error tol: 18.00\n",
      "error: 0.0\n",
      "error: 0.0\n",
      "error: 0.5\n",
      "error: 0.6\n",
      "error: 0.0\n",
      "Number of states :       110930\n",
      "       missing   :          206\n",
      "       divergent :            0  (exceed error tol)\n",
      "       discrepant:         4173  (exceed max tol)\n",
      "            exact:        91793  (within min tol)\n",
      "Elapsed:  1 seconds \n"
     ]
    }
   ],
   "source": [
    "eval2_stage1(config,mintol=2.1, error_tol=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def eval3_stage1(config, mintol=2.0,maxtol=10,error_tol=100,al_tol=50,max=100):\n",
    "    \"\"\"\n",
    "    Evaluates stage1 paths using a different criterion than eval2_stage1,\n",
    "    comparing the stored 'dist' value with an arc length calculated using the arc_dist method.\n",
    "\n",
    "    The arc_dist method is not necessarily more correct; it is more accurate in certain cases, \n",
    "    but can be fooled into measuring the wrong arc path resulting in large disrepancies for certain edge cases.\n",
    "    \"\"\"\n",
    "    start_time = time.time()\n",
    "    print \"Min tol: %.2f   Max tol: %.2f   Error tol: %.2f\" % (mintol, maxtol, error_tol)\n",
    "    maxprint = 5\n",
    "    tol=1.0\n",
    "    useable=0\n",
    "    discrepant=0\n",
    "    divergent=0\n",
    "    missing=0\n",
    "    logged=0\n",
    "    ii=0\n",
    "    for k in config.iterkeys():\n",
    "        v = config[k]\n",
    "        if v.dist2():\n",
    "            print \"%15s :  ERROR. Found dist2 field in stage1 plan\" % k\n",
    "        distuple = mp.arc_dist((k[0],k[1]),(0,0),v.r(),h=np.radians(k[2]),turntype=v.type(),tol=al_tol)\n",
    "        sumdist = sum(distuple) if distuple != (None,None) else -1\n",
    "        error = -1.0 if sumdist<0 else abs(v.dist()-sumdist)\n",
    "        error = (abs(sumdist-v.dist())) if sumdist>=0 else -1\n",
    "        if logged < maxprint:\n",
    "            print \"error: %.1f\" % error\n",
    "            logged += 1\n",
    "        if error < 0:\n",
    "            missing += 1\n",
    "        if 0 <= error < mintol:\n",
    "            useable += 1\n",
    "        elif error > maxtol:\n",
    "            discrepant += 1\n",
    "        elif error > error_tol:\n",
    "            divergent += 1\n",
    "            diff = \"diff: %3.1f\" % error if error > tol else ''\n",
    "            distr = (-1,-1) if distuple == (None,None) else distuple\n",
    "            if ii<max:\n",
    "                print \"%15s : r:%6.1f  dist:%6.1f arc_dist: %7.1f  tup: (%7.1f, %5.1f)  goal1: %12s   %s\" % \\\n",
    "                    (k, v.r(), v.dist(), sumdist, distr[0], distr[1], v.goal1(), diff)\n",
    "                ii+=1\n",
    "\n",
    "\n",
    "    print \"Number of states :       %6d\" % len(config)\n",
    "    print \"       missing   :       %6d\" % missing\n",
    "    print \"       divergent :       %6d\\t(error > %.1f)\" % (divergent, error_tol)\n",
    "    print \"          useable:       %6d\\t(error < %.1f)\" % (discrepant, maxtol)\n",
    "    print \"         accurate:       %6d\\t(error < %.1f)\" % (useable, mintol)\n",
    "    print \"Elapsed:  %d seconds \" % (time.time()-start_time)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min tol: 3.00   Max tol: 10.00   Error tol: 50.00\n",
      "error: 23.1\n",
      "error: 25.4\n",
      "error: 22.3\n",
      "error: 20.1\n",
      "error: 22.5\n",
      "Number of states :       110930\n",
      "       missing   :            0\n",
      "       divergent :            0\t(error > 50.0)\n",
      "          useable:       110930\t(error < 10.0)\n",
      "         accurate:            0\t(error < 3.0)\n",
      "Elapsed:  5 seconds \n"
     ]
    }
   ],
   "source": [
    "eval3_stage1(config,mintol=3,maxtol=10,error_tol=50,max=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize stage 1 paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5466\n",
      "Plotted 201/5466 \n"
     ]
    },
    {
     "data": {
      "image/png": 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JkiVHHMvJeYQTTnhpQPcODx+FEGG8//5sGhrSAaivd/L226cihDjGp4/OYolg\nypSPSEqaPaDP9ZbNDFQu4zFzNr1M08fbtW1OdTWkpOzl9dfjh3Q9g8rKG3j33U+ZN+9l6uszSEra\nxSuvNDJ9+nFD1whFUYbEYPt4TVN4wVt8H3lkPqef/g7XXbfW7+07mqamKj76KJevvz6BdesKmTmz\niUsuOXIfOEVRjM/Uq5PpIaVGR4d3/U+Ppw1NCzvGJ/wjMnIsCQnnkZOzhpycDUydutX3vf5MI1YU\nZfgwTR/vRx/tYcaMZpYvX8C8eatZtmwG77wTTm3ts31+ZunSpX65d1vbt2zceG2P92vr6v5Jff3K\nzi4Q6ZedgwfCX9lCjcplPGbOppdpCu+yZa/jckUAUF+fwdtvn4QQFqzWEX1+Ji8vzy/3bmraSG3t\nMurruwblLGzdOp+vvrqekhJJdbV3gK1rGvFQ8Fe2UKNyGY+Zs+llmsJ7+eXjSEraBXin7RYUrAQ0\n4uPP7PMz06ZN88u9Y2NPJSrqhG5HOtC0MLKz72X2bEFamnf2mdMpA7LGb2/8lS3UqFzGY+Zsepmm\n8J5xxkk8/XQJRUXzWbRoFjk5G4iPPxOLJfArI3knXNyHph26l9WagNN5M/n58PzzX1NUNJ9Vq9pV\nH6+iKOYpvOBdPlJK775smhZJSsqlQN8z2iorK/127+Tki7BavRtXeneP+COa5t0F+cQT3cyZ8wCT\nJg3dGyT+zBZKVC7jMXM2vUxTeCsq4IILNJYvX8i8eav46qschAg/6uBWSUmJ3+4vhIWsrHmAICIi\nh+TkS/F4WmhtraOtrR6A1tY6Wlvr8Hjajn4xP/BntlCichmPmbPpZYr3eNvbD3DzzU/yt7/93Hes\nqOg+5sx5gDffPMgDD0T4ji9cCPPn+7PVh3R0NPH55xeRnX0v8fGnUVl5HS7X00ecl5V1L6NH3x+Y\nRiiKEnBDukiOEGKJEGKrEMIjhDix2/FkIcRrQohNQoj/CiFO7/a9CCHEP4UQXwshKoUQFw+0kf1o\nF9Onr/GthesdXCshJuZkLrooIuAbWHaxWCLJyyslPv40AOLjz+hcT7d7W23Ex08PXCMURQl5A51A\n8TywGHjvsOMPAR9IKc8XQpwEvCyEGCml7AB+CbillDlCiJHAh0KIN6WU3w6y7T4WSxSzZ99Je/uF\nrFs3g4KClYwfv4n09MdwOODhh+fwzjvTuf32+4d0cCsl5Uq2bJlHa2u171hkZC4jRpwzdI1QFCXk\nDOiJV0rI+9EmAAAgAElEQVT5npSymu77rXtdBjzeec4nwC7gjM7vXd7te98AbwEX6W9y7xISzqWg\nYDRz5jxATs4GpPSQlOR9uJ42LZM5cx44ouguXrzY383oQdOsjB69yPe2gxB2xox5eNAL5/RHoLMF\ni8plPGbOptegB9eEEAmAVUpZ1+3wNiCr89dZnV/39j2/ysl51Pfr5OSLfW8VJCScB3g3tZSyA4/H\nu4367NkDW/FLj5SUK31vO0RGjh+yp92hyBYMKpfxmDmbXqZ5q+Hbb9dSU/Ok7+uGhpW8804EmzfP\nY8+e1wB4770RvPNOOJWVcwDIzc0NeLu6nnqBIXvahaHJFgwql/GYOZtegy68Uso9QLsQIqXb4ZHA\n9s5fbwOy+/hery699FLmzp3L3LlzWbVqFXPnzsXtdvu+v2LFCkpLS31f19TUcPvtM9m69dDrCi+/\nfIAvv/SwY8dvqa39B9u3wzPPNAICu93bnMWLF/d4x7CsrOyIeeXFxcU9FnIuLS3tsXmf2+0+Zvs8\nnu+zevUNPZ52ly5dSllZme/rysrKI/5JNlTtc7lcFBcX97iuap9qn2pfz/aVlpb66lJhYSGDoet1\nMiHEVmC2lPK/nV8/BWyTUi4UQpwMvASMlFJ2CCHuA7KllNcJIUYBHwDHdRbsw6+re1lIl+sZvvrq\n+h6bQ0ZHn0Rr6y5aW2u63cPKlCnriY4+kbKyMtNOZzRrNpXLeMyYbahfJ3tcCLEDSAdKhRCbOr91\nN1DQ+fVTwJWdbzQA/A6IFEJUAa8BP+mt6A5WauqVJCSchxDefl1NiyQt7XqOO25Fj92DbbY0oqIm\nArBhwwZ/NyNkmDWbymU8Zs6mlykmUHRpba3no4/G0d7+LaBRUFCDzZZCVdVcqqsfQ0oPmZl3Mnr0\ng31eQ62dqyjKsahdhrux2ZKYMOEfAMTGTsNm83Y7jxq1CJstHSlbSU39YZ+f926Y6RnytXMVRRle\nTLcDRWLiTMaOfZSYmEOPqxZLOCec8DJ79rxGVNTxfX7Wu2Gm9++irrVz1VOvoij+Zqon3i4ZGbcS\nF3dqj2PR0RPJyrqrx7HDR1ILCxmy6cWBdng2s1C5jMfM2fQyVR/vQLlcLhwOR49jTz45iU8+uYGb\nb/6JoZ92e8tmBiqX8Zgxm9pl2M/WrvWu5fud7xwMyv0VRQl9w3Zwra/Fzft77tE+7/E0+b0NiqIo\nPt71C0LjPyAWkI2NjfJoysuldDgOSpDS6fR+fbRzU1MP9Di3vFzKtLQ2Ca/7jlVV3SU//fRc+dZb\nyLfeQr77boJ855042dz8TZ/XTUnZ2682BMPrr78e7CYEhMplPGbM1tjYKAEJxEodtc6QT7wrV4LL\nFQkce+felSuhtjaqx7netxeswF6qq2HlynZqap7g228PTRNsb9+Dx+PGao3v87p1dXH9akMw7N27\nN9hNCAiVy3jMnE0vQ/bxVlTAOee4qK934HTC6tV9v/blPXcn9fUZvnMBZs5sp6bGSlpaO2vWWHE4\nlrFp0y1I6e5sSxipqVeTm/u3fl/XyINxiqL037AdXHvhhV+xZo29X4ubP/30WbzzTkGPcysq4JFH\n5nPFFRM599xLkdLDJ59M5uDB/wKyc02HT4iOzuvzuk8+OYnNm1/g8svHqqKrKMPIsB1cmzo19YjF\nzfsa7DrppKgjzs3Phx/+8AGczv8HgBAaubl/QwgLAFFRE49adAFycjZw111bQ7Lodl9tyUxULuMx\ncza9DFd4PZ5WDh6sxOPxrkL2zTcPsmnTLaxd+z6zZtFjum9HRxMHDnzu++w33zxIZeUN7N37PgcO\n/Jcnn4SGhhI2bryWzz6bTWRkLikpVwKQmXlnr/dvbd1NXd3z1NU9D8CePa9SV/c8zc1bAht8gObN\nmxfsJgSEymU8Zs6ml+G6GrZs+TXbt3u30/F4mhHChpRtvPjiK/z5z4fWyFy4EC6++Ap2734eIcKQ\nsqXz3HbfU21LSxs2m8A7OKlRUFALgMu1jIyMn6Jp9l7ufy/bty/CYomko+MAmhaJlK0kJMxk4sRX\n/P1bopvb7SY8PPzYJxqMymU8Zsw27LoakpIKEcKCx9MMgJStCGHl8stH4nR6/xJJTd3PBRdASspl\nCGFFyhbfueBdQAcENht4iy7ExhZgsyVhsyWRlfXLXosugMMxBxB0dBwAut751UhPvyVQkXUx2w96\nF5XLeMycTS/DFd7Y2FNIS7upx7bpNlsa06dPZPVqQVHRfBYvvpz8fEhO/gHx8d9DiDDfuXFxp3P8\n8c/3KKzetXuL+nX/yMixnQU9rNuxCWrnYEVR+s1whRdg9OiHsFq979BqWgRO5y0IIcjPh6Kih8jO\nfs13bm7uX30LoWtaBOnpt2CzpZKbu5y33vJ2OXg8bpKSLuz3/UeOvB8pPQAIYWPMmMVDtpdaf3Xf\n3sRMVC7jMXM2vQxZeK3WaNraXmT58gV89VUuDsfVvu+lp/8UgL1732XHjj/Q2lpHc/Myli9fwKZN\nE0lM9O54unPnRZSX/4qvv55EXNxphIUl9vv+XU+9b7xxGXPnvsvrr4fe0258fO8TP4xO5TIeM2fT\ny3CDa+B9Y+H88w9QWxtNSkoDr7+eyLhxlWzfvpg9e1bT1laPEHakbKOhoZhbb72Turo4UlMbee01\n75PyjBltuFxhJCfX8a9/fcb3vvf9AbV12bIarrsuGSmtaBo88wz8sO811hVFMZFhN7gGXdOAowGo\nq0tk1SrYs+d1amuX0dZWD4CULWhaBGVll/mm9tbWxvmmDLtc3j7a3btTeO+9gRVdgGXLUpDS24Xh\n8cATT/gjmaIow4EhC693wfJdwKEFy9PTbyM2drpvs0vwvnFwySVpRyxuXlgIaWktgAun06NrwfPr\nr9+PEO0AaBrceOOgY/lV9y2tzUTlMh4zZ9PLkIU3Px/+539up6hovm+NBE2zcsIJr2C1jgC8A112\nexZTpyazaNEsrr32Ad+5+fmwerVg4sSLefbZT3XNPPvBD1zcc8+VTJu2JSS7GZYsWRLsJgSEymU8\nZs6mlyH7eAGee+4OXn01njvuWNijcO7fX0F5+TSkbCMt7SbGj3+MtWu9hfjMM3tmXbtWMGbMH8jM\nnDvgtjY2rqOiYjqZmXcxZsxi33G1S7GimN+wWySnqWkT69Zt4Yc/nER9vYOUlL385S8Pcd55c4iK\nmgCAy/UPKiuvITPzbsLDs/j661sByMq6ByHsxMefTmvrLjZuvAqAhISZhIUlk5v71DFfC6uvX8Wu\nXY/R0vINTU0bEcLOiBHfIzFxFnV1t/pWPVMrlimKeQ228Bpul+HKyuv5f//vLOrrzwOgri6e1att\nFBR84Cu8DsfVREdP4pNPTkQIO2ABJNu3/xbwsG2bd9KEt0tCsmfPGmw2Z7/uv3XrfRw8eGgVHilb\n2LPnNRobP+DNN2/tXOdX7VKsKErfDNfHm5n5CwoKVvsGzJKSdjJ9eikpKVf0OC86eiLJyVfgnRLc\nAXgAQULCbKKiTsDjcbNypfdpX9PCcTpv6tckiDFjHur1eFZWcUjtUrx06dLg3TyAVC7jMXM2vQxX\neJOSZnPSSZEsWnQBRUXzWbToIr7znVOwWCKPONfb93qoK0UIwahR9zJhwjMIoTF2rPe4x9OKw1HU\nr/uPGHH2EU/HQtjIyLiN/Hw6B/IWBr2bIS/v6EtaGpXKZTxmzqaX4fp4AZqavuKjj47H+yRr4ZRT\nviQyclyv51ZVzWXXrseQ0k1MzDSmTPkAgM2b72TXrj/j8biJjS1g8uT3+91Ol+ufVFZeRVdRz8z8\nle9JeO1aQVhYKtOnq1doFMWshuUEisjI8aSnewfMwsOzOXBgA42NZb2em519L5s25bFs2X3s3/87\n3/GRI+/Hak0ChO9a/ZWSchkWS1znVxYyM39BS4uLtrY9AISFJQw4k6Iow4fhCm9T01d8/HEeNTXL\nAHC7t7Bx45VUVhb1ev7nn49g3ryVLF++gCuvnO7bncJiicBi+Q3x8WeQlHTRgNqgaVbGjv0jYWGp\nZGbeRXPz13zwQRrvv5/Y2caNvP22jU8+mUyw/kVRWVkZlPsGmsplPGbOppfhCm9b27ccPPhfPJ79\n3Y5qJCdf3Ov5K1dCfX0KANXVosduwGvX1jBp0lu99g8fS1paEdOnuxgzZhGRkRM63544REoPVmti\n0FYtKykpCcp9A03lMh4zZ9PLkH28VVVzqa5+DI+na0dgK1OmlBMdPfGIc4dqN+BNm26jpuZJ32Lr\nQlg56aRPiYo63v83UxQlqIZlH++oUYuw2dLpmhpsszmJijqh13MPvWlwf0DfNMjKuhMpOwDvWw4p\nKVeooqsoSq8MWXgtlnCOP/6FzgXOrTgc1xz1n/Q5ORu45pr7+rUjsV7h4dkkJc2m67d01Kjf+ufC\niqKYjiELL0BMzCSys+8D2klOvvyI70sp2blzCZs3/8p37IsvLuXLL6+mvLyNmTM9LFiw2LcjsT9k\nZ/8a8JCRcQfh4Rn+uahOixcvPvZJBqRyGY+Zs+llyD7eLlJ20NKyC7s984gn3ra2b3n//WS8M9a6\nT6Kw8n//9y0PPhgNVAK5LFwI8+f7J8P+/eVERIzDao32zwV1qqysJDc3N6htCASVy3jMmG3YLZIz\nEN4Br7/6dhkWIoyUlCtxu5/m/PP3UVsbqxazURRlwIbl4Fp/jRy5ACEORZTSQ3b23eTnw/Llr/VY\nz1dRFGWomLrw2mxJZGffQ1XVKSxbdh+1tTcSGTkegEmTJCef/IDfi66/B+30KivrfSaf0alcxmPm\nbHqZuvAC1NfPpbj4FZYvX8DPfvZHysvbaG2tR8oWqqrwTTdubx/wvxaOUFEBs2ZJFizAr4N2emzY\nsCF4Nw8glct4zJxNL1P38YL36XPBgkNf//SnJfzgBxd2u6cNKdvJyPg5Y8f+3q/38uegnaIooUP1\n8R7DkWvkWnpM75WyFSEsne/g+vteg76koigmZPrC2zVzrWsg7ayzZhAent3jnMjI8cTFnea3e91w\nwxI1aKcoSp9MX3jBO3NtzhzvQJoQGjk5/4sQNp580rv7RHb2fX5bzCYnZwO33VYa9KJbXFwc3AYE\niMplPGbOptewKLyH7xiRkHAOu3b9iPb2uWzefBrJyQNbFvJYIiLG+vV6etxxxx3BbkJAqFzGY+Zs\nepl6cK2mZhmNje/gcj0NQHj4WDyeA0i5lksuycblCic19QCvvRY9qCfU3btfZNOmn2C1xtHcvAmL\nJYbk5CtITr6IxMTzB51DUZTQMux2GR6IbdsewO3e4vva7a4C4D//icflCgegtjZ60LsBSylpa6un\nra0WgI6O/bhcf6W1tUYVXkVRjmDqroauvtzuYmJO4ZJLUnE4DgKlfnn7IClpNlZrXI9jQlgG/Xra\nYJSWlgbt3oGkchmPmbPpZerCm5g4g/j479D1YK9pEWRm/pL8fHjuuSrOOOMJVq1qG/RAmKaFkZ5+\nG5rmfYoWIpz09Nt9s+SCYe/evUG7dyCpXMZj5mx6GaKPt6LCu4VPYeHAuwSamqr4+OPjkLINq3UE\nBQW1aFoYra27WbcuhVNO+ZrIyMEPhrW0VPPBB1lAB1ZrAtOmfYPVGjPo6yqKEnpMP4GiogJmzuzQ\nPQ03MnIsGRlzAXA6bwMEzc1bcbu3AbBr1yPs2vUYjY0fDKqddruTxMQZAIwdu0QVXUVR+hTyg2sl\nJR3U1FgAqK5G10DYyJH3ApCefisu19Ns2nQjQthpbaVzn7R2YmJOZvLkdYNq66hRvyEmZgqpqVcO\n6jr+4Ha7CQ8PD3Yz/E7lMh4zZ9Mr5J94Z8+2DHoarsUSxZgxD2G3O4iP/y5ChCFlC08+CR6PGyHC\ncDpvHnRbo6MnMnKk/yZjDMa8efOC3YSAULmMx8zZ9DJEH++TT06ivPyn3Hjj9X6ZEbZ586/YtesR\n3G43NhtYLNEUFOzGYjHP38pmfcpQuYzHjNlM38cL3mm4l1/+Y79Nwx05cj4WSzQ2GwhhJy3tRlMV\nXcB0P+hdVC7jMXM2vQxReK3WBADa2xvZv78CKT2DWnDcYonC7X6G5csXsmnTcaSn/8TPLVYURelb\nSBfeuroX+OKLy2hv3wPAe+/Fs379ZN5449+cf36T7jcdKirg6qu/z7Jl47nnnn9TWTna/40PshUr\nVgS7CQGhchmPmbPpFdKFd+fOJeze/cJhRy2sXXsytbWRwKE3HQZi5UqoqbEC8ezenTTgzxtBfHx8\nsJsQECqX8Zg5m14hPbjW1PQVH398IlK2+s6JiBiPzVY5qF2CKypgxowWXC47Tqdk9WoR9GUcFUUx\nDlMPrkVGjmfMmN8jhLdzXtPCcTiKyM+HkpJWiorms2LFFwMumvn5sHq1pKhoPv/6139V0VUUZUiF\ndOEFSE//CdXVV7Bs2QK++moCKSmXATB1ahJz5jxAYuLDAEjZ0ec1ehuImzIlnAsueIBRo94OaPuD\nxeVyBbsJAaFyGY+Zs+kV8oX300815s59nOXL72PevNeorByNx9POgQPenUtra/9OWdlo3n03jubm\nb474vHfKcXuPgbgDBz6noeE1XnwRNm++k8rK66mquotQ6nYZrCVLlgS7CQGhchmPmbPpFdJ9vAAL\nFnhYuPDQ3w8LFkjOOWc0ra3e2WxStnd9munT9xAW1rMj//Cdf+fNq+bss9PRtEg8nibAAnQQFpZM\nQYELIUL+7yJFUYLM1H28ALNnaz2mDBcWCkaM+D5g7VZ0ITJywhFFF47c+ffii5MJDx+Dx9PceUaH\nbxKFKrqKogyFkK803p17f+DbJTg/H8aN+wtRUcchRBgAQoSRmDjrKJ+fxc03/43Vq2Hy5DAmTHgG\nISy+c6RsJy3t2iHJoyiKEvKFF+Cccy5izpwHmDTJA4Cm2Zg4cTUWi3fpRSGsjBhxdp+fz8nZwI03\nLve9vRAXN420tB+zerV3cbbo6ElERIwJbIghtnTp0mA3ISBULuMxcza9DFF409NvA6Cqam7nf7/A\nZnMwceIqhLDi8bQSF1cwoGuOHv0wOTnRgEZ6+q0BaHVw5eXlBbsJAaFyGY+Zs+kV0oNrHk87W7bc\nRUPDqzQ3f9V5lkZYWBLTp3s3lqypWUZzcxWjRz/Y41oHDnxGefk0pOxAypbO69uJjBzHSSdtQAhB\nQ8Matmy5h/z8d9XC5Yqi9JupdxlubXWxc+cjQPd3dD3ExU33fZWWVtTrZ8PCEpCytccAnJStWK0J\nvvVyExNnkpg4MwAtVxRF6VtIdzWEh2eQm/u0bxANvBtWxsd/95iftdvTSUv7MULYfceEsDJ69EO+\nrysrK/3b4BBi1mwql/GYOZteIV14ARyOq8nM/JWvgErZQWzsqf36bHb2rzn0tCyIjS0gLm6a7/sl\nJSV+bm3oMGs2lct4zJxNr5Du4+0ipeTLLy+nvv4VAE4//QCaZuvXNTdtuoWamr8BMGnSO77CO5Cd\ni/s6t7fjg9kRWVEUYxhsH68hCi+Ax9NCRcVpaFoE+fnv9PuaLS27KCsbSWzsdPLz1wLe4jhrlqS6\nWhxzdbOuXY5raiw9zu25wpn3OHinJ9fUWHWtmqYoijGYenCtO02zk5+/DiEG1mS7PZ28vLcJD8/w\nHVu5EqqrvQNsx9q52Lt275G7HJeUeHC57D2OS9m1zq/+HZEVRTG/kO/j7U7TwnTt4BsfX0B4eJbv\n68JCSEtrBRbjdHYcdefiwkJISfHugNF9l+NZsw4esfvx4dOT9eyI7C+LFy8O3s0DSOUyHjNn001K\nGTL/AbGAbGxslIG2fn2HvPDCW+T//d/rxzx39erHZVHRvbK8/NCxpqYt8okn8uS99+7rcfyJJ/Lk\njTc+1uNYMGzcuDG4DQgQlct4zJitsbFRAhKIlTpqnWH6eANh7VqBw3EtublPHfW82tr/x8aNV3Hm\nmYd+r/bvX8/69Sfxne+40bRDr6ytXStITr6M448/tM+UGnBTFHMZNn28geJyPU1Cwrm0t+8nLe36\nXrsywsNHAnDgwH9paanBao2nuXkT4H29rb39AAcOVBAWltB5frbvs96BPG+f7xNPqAE3RVEM9FaD\nv0jp4dNPv8uBAxV89tl+jjvOOynD42lm2rRtPfqCd+z4I1u3zsPjcXe1EO+/LvpqfxhStnX+2obD\nMYd//euJHusBL1wI8+f7P9fhysrKmDZt2rFPNBiVy3jMmM306/H6m5RtuN3b6OjYT1WV95jH00xE\nRE6PogtgsznweNq7fxohwhDi8HeINcDiK7pdwsJSKSwEh6MJGNoBtw0bNgzNjYaYymU8Zs6m17B7\n4gXYt+9jKioKfOs4aFo4WVm/ZuTIe3qcJ6WkvHwq+/evB7xLUlqtI+jocCNl10LqFkaM+D5tbQ0c\nOLDe99mwsFSmTduKxRLBmjVLeeGFXdx++/2qm0FRTEA98eoQG3symZl3o2ne3Ys9nlbfJprdCSEY\nP/6vvp0pNC2c9PSfkpHxU98UZiEEY8f+kczMX6JpEZ3H7Iwb9xcsFu/Xo0evY86cB1TRVRQFMEjh\n7W2X4MGcBzBy5HzCw8cAgoiIsURG5vR6XnT0iTgc1yNEGB5PG07nTWRk/AzvlkFWUlKuJCrqOJKT\nf9D5doMgNvZkkpIu8l2jubmq31kVRTG/kC+85eWS888/2GOX4N50TQM+1nldNC2M554rQAgrDsec\no547evRv0bQIEhNnYLc7sdvTSEn5IaAxevSizuvZcDpvATTGjXsCKVupqDiDTz6ZzL596wDvQu47\ndvxhSHYzLi4uDvg9gkHlMh4zZ9Mr5Pt4D99luK+3Ag7fTbg/bw+4XC4SE+1YrXHH3OiypaUaiyUa\nq9Xbrvb2fbS1NRARMcp3TkdHM62ttUREjMTjaWPdulTa27894lpTp24lImLk0Rs3SC6XC4fDEdB7\nBIPKZTxmzGb6Pt7Ddxnu662AwkLv9wFSUvb26+0Bh8NBWNiIfu0ubLc7fUUXwGqN7VF0ASyWCF9B\n1bQwnM7bfP3I4O37TU29JuBFFzDdD3oXlct4zJxNr5AvvF27BHffZbiv81avhqKi+Tz44JkhMZCV\nnn4THs+hV8yEsDBmzO+D2CJFUUJByBdegBNPPNivtwLy8+Hmm1eQk9O/9wZLS0sH1a5jDebZ7ekk\nJJwHCDQtnNGjf4vNltzvzw/GYLOFKpXLeMycTa+QL7xSSkaMOAuAbdt+y+efX0xDw2t9npuScjkA\nVVV3sn79yeza9Wif1967d6/udnnX6fUcczAvM/MXgMRuz8DpvPWwz/d/MHCgBpMtlKlcxmPmbHqF\n9FoNO3c+ypYtd+LxeCcrfPPNQqRsIyrqOBITz+9x7u7dJXz11bW+waydO/8ISOLizuzz+pdffrnu\ntpWUSGpqvH9vHW3t3fj4M8nI+BnJyZehaYd+u73r/PZvTWA9BpMtlKlcxmPmbHqF9BOv1RqLx9Pq\n+1rKFoSwkJhYeMS5Qlhob2/sdqQDTbOTkHBWQNp2wQXufg36dU2wiIvruU9cKK3dqyjK0Arpwutw\nXE1KymU9dgq2WKKIiZlyxLlJSbPIyvpVj7cIPJ5W4uJO6/P6bre7z+8dywkn7GHRolnce+9eXSuO\ndQ0azp37XkBWLBtMtlCmchmPmbPpFdKFF2DcuCew2Rx4VwYTJCZe0OfrX6NGPUhsbIGvUEdH52Gx\nRPV57Xnz5uluV1vbHnJyNjB/vlVX0fR4WsnJ2cC993oC8gbGYLKFMpXLeMycTa+Qn0AB3nVw168/\nGfBw3HHPk5x8YZ87/L78cjOjR1/IyJH/ITt7PqNGLejzfm63m/Dw8D6/fzR7977Np5+eyRlneAa0\nHZHH04YQFvbvL6e8/GROO20/Vmu0rjYczWCyhTKVy3jMmG3Y7DK8a9fjbNv2IKecUslnn0UzY0Yz\nLlfEYTv8eqip0UhLa+Ohhy7hoovm99otMRhVVXeye/fztLXV4vG4iY0twGKJYuTIhUf04/amouK7\nNDa+C3QAkJAwk4iI0WRl/Qq7Pd2vbVUUJTCGzQ4U6ek3k55+M+B90nW5vCt/9dzh19sFUVMTxjff\nlBAT4/92dHQ00tKyk67C6V2HQZCcfEm/Cm9ExOjOwuu1Z88aAGJjC0hNvcL/DVYUJeSEfB9vbwoL\nITXV+wZD9x1+nU7v07vDcbBfbwmsWLHi2CcdJivrV0ccs9szcDiu7dfn09JuOKyP2kJ09JRel6Uc\nDD3ZjEDlMh4zZ9PLkIU3Px9KSpooKprPc89tIj+/a8qwoKhoPn/+c//Wvo2Pjx/wvSMixnS+Q2wB\nvOsvjB37JzQtrF+fj42d2jlYSOfnNSZM+Ee/1osYCD3ZjEDlMh4zZ9PLkIUXwGZLQ0pBXd2hv03z\n82HOnAdITX2iX9c499xzdd07O/vezgE1QWTkhB5r7x6LEAKn8xY0Lbxz54tioqIm6GrH0ejNFupU\nLuMxcza9DNPH2115uWTmzHZcroWsWbOTgwd/xMiRb3D88c8D0N7+LZs23Ybb/Q25uU9hs6X49f6x\nsacQHZ3P/v0fM27cnwf0VgOAw3ENW7feQ3j4KLKz7zn2BxRFMRXDPfFKKXn88b/gcnn/aV9fn8Eb\nb4ylvb2ODRu+6zuvuvov7NmzhpaW6j6v5XK5dLdj3LjHGTv2f4mLmz7gz9rt6eTm/p0TTngZTTt8\n40z/GEy2UKZyGY+Zs+nl18IrhJghhFgvhKgQQvxXCHFN5/FkIcRrQohNncdPH8x9zj57q2+6bVLS\nTgoKVmKxxB22+6/EZksnOjqvz+ssWbJEdxtiYiaTkXGb7s87HFcRHX2i7s8fy2CyhTKVy3jMnE0v\nv77HK4RoAL4jpfxCCJENVAJJwP8C30gp7xdCnAS8DIyUUnYc9vl+7TLc0dHEU0+dxrp1sygoWElO\nzhc4nTfT0PBK56teIISNjIxfMGbMIr/lUxRFgdB7j9cDjOj8dRxQD7QClwJjAKSUnwghdgFnAG/q\nuWgdQe0AACAASURBVInFEkl+vkZOzgMAvv3QUlOv5NNPv4OUbYBHvRerKEpI8ncf7xXAy0KIb4B3\ngDlADGCVUtZ1O28bkNXfi/a2YHhU1PGdv7J0ziA7jbi4aWRkzAU0bLY0oqImDi6NoihKAPit8Aoh\nLMCvgQullCOBs4Bn8D5VD2zYv5vy8jbOPbfBt2D4q6/+jY8/zqO29u+dZ3gASUPDywCMGnU/sbHe\nAnystw2WLl2qt1khz6zZVC7jMXM2vfz5xDsJSJNSvg/eLgVgJ3Ai0CaE6P5O10hge18XuvTSS5k7\ndy5z587ld797jd27fwO4qa6GNWuiWLXqMz76qOtsyZ49Gg8//IY3kGZj8uT3efXVCMrKynzXrKys\nZPHixT3u89///pfKykrf12VlZUf8kBQXF/cYlS0tLe0xE8ftdjN37tweS9+tWLGix3YnLpfriC2u\nly5desz2LV68WHf78vLyQrp9XQbavq5codq+LgNtX15eXki3D/T//q1duzak29ff37/S0lJfXSos\nPHJN8IHw2+BaZ2H9GpgqpawUQowFyvAW5PuBbVLKhUKIk4GX6OfgWkUFnHPOTurrM3A64aWXXLjd\nI5Gyxfe5yMgJnHzyFwN+n1ZRFEWPkNnevbMP90bgOSFEBfAi8BMp5U7gbqBACLEJeAq48vCi25eu\nBcNvuWU5q1fD1KkOsrPv9S14rmkRZGb+UhVdRVEMw6+Da1LKFVLKE6WU+VLKPCnlis7jdVLKc6WU\n46SUE6WU7wz02t0nQmRl/RKrNbHzK42UlIG/vVBRAbfdVtmvTSYDuRtwoHT/J5iZqFzGY+ZseoX0\nzLWDBzfy1ltvUVy8hqeeKua88/awYsWtdHQ0M368ty8nLe16LJbIAV23ogJmzZI8+mjJMXf47e9u\nwqGmpKQk2E0ICJXLeMycTTcpZcj8B8QCsrGxUXZ0tMq3346URUX3S+9qu97/iorulc3N30gppayr\ne0m2tLjkQC1YIHtcc+FC/5yrKMrw0NjYKAEJxEodtS5kn3g1LQyH4xoKClb1mB589tnbCA/PBiA5\n+SJsttQBX9u7nq+3P/xYO/wWFkJy8u5+nasoitIfIb31T3v7fj78cAxffulk3bpCpk//N7Nm3YbD\ncdWg7/X668+yYsVGbr/9/mOu3fv887/g1Vej+nWuoijmFzJvNQSC1RrD+PFPkZPzOXPmPMC4cRtI\nTu7/2rdHk5/vwWbr34LpJ564nzlz+nduqDj8nUezULmMx8zZ9ArpwguQlDSLxMQZANhsTjZtuo3K\nyh9TXi4H9aaB1TqC6dOho+Mgra31eDxtQO9vMFitCQC0tx+gtXU3UnoGlWkozJ49O9hNCAiVy3jM\nnE2vkO5q8Hja2LTpJhoaXqOt7dDsk61bz+Luu1+kri7Wt8twf59GN2+ex65df8Ljae5xPCvr1zQ2\nPsA55+ymvj4ZpxMeeeRukpMfOeLc8eOfJi2taJBpFUUxKlN3NbS11VFb+0yPogvwySc3UFfnndnW\ntctwf9lsyUjZ3uOYEDZiY09m5Uqor0/2Xfftt0874lywEBNjoD4HRVFCTkgXXrs9nQkTnkWIQxtJ\nalokhYWS5OQGYOBvGqSn30pYmHfyxZdfeo/ZbGkkJs6isBDfGxROJ1x11XSE6LlyZlxcwVEXVw8V\n3ee5m4nKZTxmzqZXSBdegJSUixk58kGEsAMgZRsFBeN45pk3KSqaP6BuBgBNszNmzB8QIpyqKu+U\n4+zsexBC6zY9+e+sXg2nnDKC7Ox7fNOThbCRnT0/EDH9bsOGDcFuQkCoXMZj5mx6hXQfb5fycsnT\nT5dw4okPMm7cZ5x++kFaWnby4YejmDbtG997vX2pqICVK73v5Obng5QennnmEt54YyLf+c6bzJnz\nHywWb3Fdu1YQE3MKU6Z8CHh3u1i+fAbvvHMG3/vep1x99StqXQhFGeYG28cb8oW3ogJmzGjG5Yog\nOXk3jzxyL9///nk0N1exZcudCGHFah1BdPRk8vJeP+Ka3s+34HLZfQNxcOiaqan7ePXVSJKS/kB7\n+z62b/duFZSScgU2m4N9+/6HGTMOUlsbjcPRxKuvRhrqtTJFUfwv1Lb+8buSEg8uVwQAu3cnU1Fx\nBw7HcQjhfUKVsp22tt197ta7ciW4XN5uiq6BOCnxXbO2NpYXX6zl7LPvpnvPS13dv7BY4njjjd9R\nWxsNgMsVyapVA+vaUBRFOVzI9/HOnq31GPC67LLRhIePQcpW3zmaFklq6jW9fv7wAbMLLvAeS0n5\nFijG6YRLLkllxIiz6b5RhqaFk5k5l9mzrSQl1fb4vBEcvrC0WahcxmPmbHqFfFcDwJNPTmLdukLf\nlN39+z+lvPyUzk0tvYNep532bZ+rlB3+eYBXXlnEs8/Wc/fd/0N+Phw8+CWffJLne31MCBsFBdWE\nhSXyj39czJtvHm+oKcMulwuHwxHsZvidymU8Zsxm+j5egI0br6G29h+ceeahtq5Z8yQvvuji1FNf\nZNq0bCZO7HvpubffDkPK9h6f37TpVqqrH+txbOPGIurqngW8y03+f/bOPDyqIuv/n7qdTqcTErIv\nnY0thB0Cohhc0HEHg8u44EZwXHAZHR1nAUcU8UV5Z8XxN6+AOjJuw7gSwBnGBUTBCEiIIDuySpos\nkJBAOlvX74+bdNKkA6FJSN9LfZ6Hx9zqc6vqm8fndKWqzjl9+/4NgNWrB3Ls2CYvW4VCcfZi+j1e\ngKSkezh48A12736WqqoC9u4dx7333kNRkWDRokksWNB2omW3u5bg4ERqavZTUbESl+tHIiLOobZW\nL3rscu0BwGZLo1evmRQXv4OUDaSm/qoxlPgodXVlLWwthISkdLpmhUJhXgLa8VZWfsemTTdRXb0D\ngD17ZiJlDYsX30hRkb4fW1qawooV8Vx6aev31649h6qqbz3PhYWXe4X/6gUzewKSgQM/JC7uOnr3\n/gO1tUWEhKSxcmU89fWHPPb5+T0AGDFiXcBHry1dupQrr7yyq6fR4ShdxsPM2vwloA/X3O4qqqu3\no5dwBylr0LTQxgMvPYzY4YDx433faLDb+3hFvelO1wLo9lVVABIhrEREnAtASsrP6dVrJkJY6N49\nu9G+GYulO6GhmR2osnMoLy/v6il0CkqX8TCzNn8J+D3evXv/wK5dv2tRVdjCyJEbWbbsc95/33nC\nA6+amiK++aYXbrersX8r8fETKC7+p+dWhBA2kpLu9uzntqSqagNr1w4H9AM3TbPTo8cM0tJ+2ZGy\nFQqFwTB1khyA1NRfEhNzrSdkOCgogtDQTC655BImTpxB//7723zXZkuiR49nPSG/UrpJS/stDscD\nnv7ATXr673y+363bYGJirqJp1StEMMnJD3SYNoVCcXYS8I5XCEH//vMJCekBCKKiLkMIQVhYfwBK\nShZQV1eOy7XPZy7dlJRHsVr18kDh4SMJC+tPevoUtm0bzKuvPsnhw89gsznaHL9nz5mAQAgb6elP\nnXJhza7C5XJ19RQ6BaXLeJhZm78EvOMFsFhCGTLk3wQHO4iLu5ni4gXs3PlrAHbufIKVK6N4882b\nfVYD1rRgMjPnAXpZeIDvv0/gd7/7L2++eYwHH/z1CZOpN616LRa7oVa7U6dO7eopdApKl/EwszZ/\nCfg93uNxufaTn5+K/p3RXAni7bdfZN68n3uep0+HaS0SidXUOAkOjkcIjenT4ZlnAFxASCvb46mv\nr8LtriY4OM5/cWcYl8tFSEhIV0+jw1G6jIcZtZl+j/d4QkJScDgeRIjmmwxC2MjJseJw6I44MbGm\nVWivzZaIELrcnBzdBkJwOORJw4CDgroZyukCpvsfvQmly3iYWZu/GM7xAvTp8yfs9l40X/Vyc/HF\nI1m8WCM3dxqvvfbuCUN7s7Jg0aJacnOn8a9/bTFMGLBCoTAHhnS8mmbD7f4P//jH02zfPhSLpRvd\nuuneU0rBsWMbvex9Hbqdc044qakz6NnzyzM59TPGggULunoKnYLSZTzMrM1fAjpyrS0KCuCGG5Ip\nKnqKRYsm8fLLcygoqGfcOI2iouksWbKf0NB/MGhQGSUlv2DcOElRkcbcuXo+3iFDjlFT8yPdusG+\nfb+nvr4CqzWWpKRJXS2tw4iMjOzqKXQKSpfxMLM2fzHc4RrQ4nBMZ8qUHykqepXXX28+IZs0aTp3\n3fUsn39exYwZdk/7k08Wc9llCTQfzlkQQqBpoVxwwWHPPrBCoVC0xVl3uAb64VhcXAmAJ5/upZeu\n9+TdjY3dz/nnf0hU1OVcf73dKx/vddeFYbXG0nwjogEQJCTcqZyuQqE4IxjS02RlwT//uYHc3Gnk\n5bkYPjyI6677HTNnjic3dxozZ44jM3MrDsdksrLglVfe8RTGPOecMDIzX0OIYA558t9IkpLu6UpJ\nHY7T6ezqKXQKSpfxMLM2fzGk4wUYM+YiJk6cgcPxNgDh4cO55JKLmThxBhkZhQhhJSbmGgAuv/w6\nJk6cQWbmdgBiY68lOvpq3n9flx8S0pPw8GFdI6STmD17dldPoVNQuoyHmbX5iyH2eI+vEtzE8uUC\nu70vmZlzcbn2EBt7I6tWxeN2HyM+/g6io68gIuJ8QkP7sHy5ICFhIgkJt9PQcISIiNF8801v3O4a\nevf+A6mpv2hzXm2N31a7QqEwN6avQLFuXQNXX32E4uIoT5XglJQPKC3N4+DB+Y3v2ZCyhsTEeykr\nW0xdXRF6/TSN8PCRhISkUVLyLy/bCy4op7j4PXbu/CXnnbe9zQAJX1WKs7L09quuKqe4ONKrXaFQ\nmB/TH67l5WkUF0cBepXgvDzJtm0PeJwu0Jgy0orTOa/R6QJINM1GZeUaj9Ntsg0J6UNQUHccjp9x\n4YXlJ4xK81WluKm9uDiyVbtCoVCcjIB3vOPHC69bCTk5gv7930QI7yvIUVGXEBY22Kvd7a6mV6/n\nvcKLIciz9ztnzpyTjt9ckdi7yrCv6sWBRHu0GRGly3iYWZu/BLzjzcqCmTPHcffdszx/zkdHX06P\nHs95cupqWiixsTkMHrwETQvzvBscnERa2q9ITJyIECGNtsFER18BwNChQ9s1/uuv53luRTRtJzTN\n6/HHvwrIbYb2aDMiSpfxMLM2fwn4PV7QD9EAryq/69ZJXnnln2Rl/ZmMjHWMHLmRsLB+lJevoLDw\nMqSsJzExl379XsPtrmXdutEcPboeKd1ccMFhgoLaDtA4npKSj/j+++tbVRlevlyQljaFXr1mnp5w\nhUJhKExdZbih4RjV1ds9B2KbN0+iunoL1dXzuemm3hQVTSA29hJmzbqFiy/W66BFRl5E795/YMeO\nR4mOvgrQV7mDBy9izZpB2GzJ7Xa69fUVVFWtp7p6GwC7d8+goeEoQUHR2O09ATh48G2CgxOw2/sS\nE3N1J/wWFAqF2Qhox1tYeAVHjuQjhJ6F7ODB1wGNf/9bUlSkt5WWJrJr10cIITzvJSf/nIiIbLp1\nG+Jps9kSGTFiLZpm87Rt2bKFfv36tTn+7t3PsX//n9A0PeS4qcqxfnCnb2nU1haxc+cTWK2JZGfv\n6yDlp8/JtBkVpct4mFmbvwT0Hm98/M0IEeQpTAl6ZrJbbunldeB1ww1RXu8JIYiIOAdN864+bLf3\nwGZL8jwvXLjwhOMnJt6BEBpu91EApHQBFiyWyBZttQhhJSXlEb91dgYn02ZUlC7jYWZt/hLQe7xS\nuikouIjKytVIWQdoxMffyoABb5Gf/yNz5szhnnsmMHp0/06b09at9+N0zvdUOQ4NHUhq6i/Zvv1B\nT/ViTbOTne08pX1jhUJhXEx9j1cIjQED3vFcEdO0YBIS7gBg1KhkJk6cQffunVvPqVev59E0a+P4\n9sZbEncRHJzSOEcbDscDyukqFIp2E9COFyAkJJW+feegV5uwEBV1mdfnpaUfsX//S3z//c1UV+/y\n2YevROi+2ny1W63R9Or1B/T0kRbi4m5GCAsZGbPZvn0Er78+hbKyX3eYXoVCcRYgpQyYf0AEICsq\nKmRL3G633LTpLrlz55NSSinLyv4jCwuvkcuWIZctQ37xRahctkyTxcUfyuNZt07K+PjDEqR0OPTn\ndeukTEyslfCCp63JNi6uzMtWH79efvvtaLlr13RPv99+65axsT+2sg0UXnjhha6eQqegdBkPM2qr\nqKiQgAQipB++LqBvNTQhhKB//+YQ4R9/fJlDh/7teXa7jwHQvfv5rd71FdorJTidVmC8py0rS7ct\nKYn2ss3KAiEsDB/+lVe/ixYJSksdrWwDhfHjx3f1FDoFpct4mFmbvwT8VoMv+vV7BZstmZbTt1oT\nCA5OaGXrK7Q3JwccDgn0IzGx2q8w4EAPGTbr9R2ly3iYWZu/BPSthhNx7Ng2vv12BA0NVQDExt7A\noEHv+7SdP/8ali8/h0ceedazKi0ogBdfnMbYsdX89Ke/99jOmzeM/Pxbefjh3550BTtv3jB27nyP\nW27pE1CrXYVC0bmY+lbDidi6tS8ff7yB7dtHIEQQkZFj2rQNCxuElMKrLSsLRo6cQWzsHwCQUi8F\nlJFRyJ13TmmXI83IKOTXv94VkE43Pz+/q6fQKShdxsPM2vzFkI63oADGjq3jhRd6MG3aF2zbNoTu\n3S9oZedy7eezz/7LAw/8kvnzp3PllWW8+ebtOJ1vUVb2b3bs0O3y83uzYkUIO3f+xvPu5s0T+f77\nm6mvr/Lq89ix7eze/Sy7dz8LwI8//pXdu5+louLrzhPsB4WFhV09hU5B6TIeZtbmL4bcaji+yvAz\nz9Tx9NPWVnYbN97IH/84hNdff9rTlpv7DPfe+wp1dcWA8IqKAytCSKSs97Scd95O7PZenuddu55m\nz57nEMKClHUIYUXKBmJjr2tzq0OhUJiLs3KrIScHEhP1mwwORwM5Oa2dLkBq6i/Jzs7zqj6cnb2I\n1NQnAO24UOTQxrZmp9u9+xgvpwuQnPxQYxhzHUCj89VIS5vSgQoVCoWZMaTjzcqCJUvs5OZO48UX\nf9fmHmv37tlcdNEIZs68trH68LWMGpVKSsqjxMSM9eTzBQvx8bcSFNScy1eIEFJTf9mqz+DgeByO\nB1u8q9G9+xgiIs7pWJEKhcK0GNLxAgwfLpg4cQYxMS8A4HbX4nbXt7Lr3fsPZGZuZeLEGfTtuwWH\nYzJCCPr1+zuvvhoMCDTNSnz8zfTpM9vjUIOCItpM85iePgX97rQe1tyr1/OdovF0mDLFnCtwpct4\nmFmbvxjW8QJkZLwEwIYN1/PVV5Fs2TKxlU1QUATp6dMA0DQrNTX7qKxcz4YNEXTv/gXbt2chpSQy\ncgwJCbcTFjYIEKSkPOZJR3k8TateEAG72n300Ue7egqdgtJlPMyszV8MEbl2PLW1B9m69T4OHVoK\nQFnZQkAihPde76FDn7Jt233U1Oh7vA0NRxsLZd7HL37xZ5zOLBITV/Daa+978vRmZr7Cjh2PkpT0\nsxPOIT19CkeO5NO79wsdL7ADSExM7OopdApKl/EwszZ/MeSK1+XaS1nZIk+qRt3phhAWNtDLrqGh\nEpdrt+cgDNxomo01a+7xVA52OsNYs+Yuzzvh4cPIyvrihJWHQV/1jhjxNeHhIzpKlkKhOEswpOON\niBhJv37/8FrhCmEhNDTTyy4u7nri4m5scRAGbncNt9zSG4fDDSwlKak24MJ9O4KlS5d29RQ6BaXL\neJhZm78Y0vGCXh2ib995Hufrdtdgt/dtZde37zwslm6e58jIixg5sjuLF2tcfPFc5s17PSAjz06X\n8vLyrp5Cp6B0GQ8za/MXQwRQFBTomcNycvDKtZCXB9nZH2G1/hSQRES4WLTI6mUHcOjQJ2zYMBbQ\nyMycQ2Kifgi3fLkgImI0w4d/hZRuhDDs95BCoTiDnG4ARcA73oICuPrqoxw8GIbDAYsX67bXXFOD\n02nD4YC///19jh79nocffooDB4THrqXz3bp1Mk7nq2Rnl1BZuYaqqvX88IOewNxqjcXtruHcc7dh\ns6mDAIVCcWJMXd4dYOFCycGDemCDdz5dm6ctP/9GpLyRAwfwsmvpeDMyZtOr1wtomq1x9Qu1tRAc\nDHV1pWiaHavVu2imkXG5XISEhHT1NDocpct4mFmbvwT839bjxwuf+XQTE482trlb5NjV34mLK2t1\nYKZpNqzWSCwWO6mpv0YIC/PmeT4lJma8V+l3ozN1aufWousqlC7jYWZt/hLwWw0AS5a8zHvvHeDn\nP3+K4cP1w7R169z89a/PcOONCYwb9xDQnGM3OzuPe+9d3+Y4bncda9cOo7x8C8HBbjQthIED3yMm\nZmyn6juTmHWVoXQZDzNqM/0eL+jhwCtW2OjRYzrp6b/D5dqN3d6L5cv1HLtjxkgaGlxomo2tW+/F\n6XyVMWNOrOvo0c2sXTsMKWvRNDsXXFCOpgV3pjyFQmESzorsZE2hu7t3P83KlXF8801vKiq+JjLy\nJwAUFFzMV19FsH//X0hOfhCAjRuv55tvMtm48SaffYaF9adXr1kAxMZer5yuQqE4YwS849279498\n9VV045Ogvv4QINi48TrKyz8DoKJiBVIK9u59nm+/1fMmlJYuorp6W6O9b1auTCA9fRppab9p08ao\nLFiwoKun0CkoXcbDzNr8JeBvNVRXb/NUEW7KCAY0JjJvRgiory9vYdOAEDYiIy9ts++oqGh69pze\nsRMOECIjI7t6Cp2C0mU8zKzNXwJ+j1fKBr7//lYOHVqM2+0CIDg4GYdjMnv2PNciXwMMGLCAzZvv\n8ORm0LQQBg/+mKioS864FoVCYV5Mv8crhIUBA94hKupKNE0/GQ0N7Ut6+pM4HPd62qzWROLjb6Z3\n798jhL5f63bXEB4+ssvmrlAoFL4IeMcLoGlBDBz4LpGRlwHQrdswhBD06TObmJjxAISHZ1FQAK++\n+iiHD08DNOz2PgQFdaOgQK/TVlDg3a/T6TzDSs4cZtWmdBkPM2vzF0M4XtCTmA8a9AEpKU+QkHAn\noFd/6N//DRISJlJa+ivGjnXzzDPw0ENTKS19jKSkyZ6Q42eegXHjvJ3v7Nmzu0TLmcCs2pQu42Fm\nbf4S8Hu87eX4ysPTp8O0aW23KxQKhb+Yfo+3veghw/qXSGJijSdkOCcH4uL0GxBNIccKhULRlZjG\n8WZlweLFgtzcabz22gJPgpysLHj77TXk5k5j0aI6U+beVSgUxsIQjretw7HjycqCiRNnkJb2sVf7\npZdezsSJM0hOfs+rfc6cOX6PFej40mYGlC7jYWZt/hLwARQFBTB2bANFRRbmzm2dZ7eJurpDnqKW\nJSUL2L27P1JCevpT1NTo+SI3b76Nfft+T0PDUUaM+JahQ4f6NZYROF6bWVC6jIeZtflLwDvehQsl\nRUV6rgZfeXYBamtL+PprByAaWwS7d88AJAcOvNwiyk1QVVWAptmxWOyMGjXKq5+8PE46llE4XptZ\nULqMh5m1+UvAbzX4ysd7PFZrNGFhg5CyobFFAm6io6/CZkv2JNlpCifu3v3CFm3N5ORw0rEUCoXi\ndAl4x5uVBS+9NIvc3Glt/ukvhIX+/d/0cqaaZiM5+UEGD16MxRLRoj2UmJhxAGzZsqXVWDNnjiM3\n92lDbzNAa21mQekyHmbW5i8B73gBxo//TeOh2ZI2bcLCBpKa+mtPCLGmhRAVdSU2WyKDBy9uUY24\nlqgoPZ3kwoULW/WTkVHIxInPGtrpgm9tZkDpMh5m1uYvhgmgaEp6npR0L+Xlyxk8eAmhoRleNuvW\n1fK3v73Eeee9wSWXXEqfPn/0fHbgwDy2bZtMUFAEo0cfQgiBL1avHsSxY99z4YXV1NY6CQlJb9PW\nF74qIisUCnNh+mKXR46s5scfX/I8FxW9BkgaGry1FhTAuHEWiooeZ+HCCSxceJQ+fZo/dzju5dix\nLVgs3X060v37X6Kk5F2OHfsegC+/tAMwYMC/iI/3nUz9eFpWRDb6rQiFQtF5BLzj3bXrKQ4f/qRF\ni36AZrf39bJreSOhtDSJTz+F7GzvvlqugI+npOQ9Kiq+PK5Vo1u39l+FycujVUVk5XgVCsXxBPwe\nb2bmqwQHJ9HyOyIoKIqgoHAvu5ZVhhMSKtp1I2HWrFktxpnn2QduIixsCKGhfY9/rU0C6VZES21m\nQukyHmbW5i8B73hDQlIYPvxrgoNjAX1Fa7f3bmWnhwxDbu40XnjhlnatNMePH+/5OTQ0g/T0p1sc\nzoV66re1l6ZbEXff/VyXbzO01GYmlC7jYWZt/hLwjhdg8+Y0Pv54E7t2XQToK9G2kFJQV9e+/J/9\n+vXzek5L+xU2WxogkLKO+PibT3muGRmF3HPPPC+n2xVhyMdrMwtKl/EwszZ/Cfg93oICuOaaWpzO\nKJKS/svzz4+nf/+ftLL79tsaxo0TOJ3TWbLkXsLCJpGWlke/fq8TG9u+v/k1zUr//m+xbt25xMTk\nEBTUvV3vNTQcY9++33u2Kmpq9uJ0zsdu78sPP5zPuHGSAweEOnBTKBSAAVa8Cxc24HTqpXyKioLY\ns2cJCQm3tbJ79dUPPXalpSl88kkqDQ2VLaLZWpOfn9+qLSLiHIYM+S99+vyp3XM8cmQ1u3c/w+7d\neuFMIYLZuvU+Nm7MYeFC3elC84HbmcCXNjOgdBkPM2vzl4B3vOPHW9p1YPXTn8YRG/sjoB9wZWfn\nAZKoqMva7LuwsNBne3T0ZYSEpLV7jpGRFxES0gMpawGQshYhNJKTH2H8eEFCwpGTzr+jaUub0VG6\njIeZtfmLIQIo5s0bxtq19zN58gMn/DP9ww+fIy+vluzsPDIyCune/SKysr7o/IkDRUV/Z/v2h3C7\nqwH95sX55+/DYgljwYKH+c9/onnkEeNHxCkUitMPoDCE422KWhsz5sRzbWhweQIfNC2U3r3/l+Tk\nhzptvi1xu2tYtSqJ+vrDaFoIffrMxuG4D4CVK+Ooqys96fwVCoUxOCtK/+g3DaCiIp+DB9+m0Fh+\nhAAAIABJREFUocHl085iCfEEVrjdx3C5fqS2tsTnrYKOvmmgaTZSU38NCIKDE0lMvJuKilUcPbqJ\nurrSjhlEoVCYgoB2vHv2zOSbb/pRU7MXgPXrL2Hz5jsoKflXK1uncz5r1w6nunpbY4uFfftm8fnn\nn3DVVYe9qgw33ZR45pkprSoPnw4Ox2RCQweQkfESVVXrKSgYzZo1zVffVq1K4fvvT/2Kmj9MmTLl\njIxzplG6jIeZtflLQF8nq6j4murqHZ5nKV2ARlTU5a1sDx9eTlVVy038BkDjq6+uoLg4Cmi+VSAl\njTcgHu3Q0F6rNZJzz90I6FnQLJZuNDRUeT6vrT3A0aMbkNKNEJ37nffoo492av9dhdJlPMyszV8C\nesXbr98rWCyhXm3h4cOx2ZJa2WZkvIjNlkxzFQoIDnZw442xxMWVAc23CporEieSkHCkU24aaFow\nCQl3eoUh63mD3+p0pwuQmJjY6WN0BUqX8TCzNn8JaMcbHJxAv37zEUK/n6tpoSQmTvJpGxQUzqBB\neQihL+KFCCI+/qdkZcG77+4iN3caH31UQVaWd0Xi55+/odNuGiQl3UNT1QtNCyE5+eeEhw/vnMEU\nCoVhCGjHCxAXdz1xcTciRDBut4vY2BvatA0PH0bv3n9CCBtgISZmfGP7CKQUlJZ+REODi5oa/b7v\ngQN7qa8vRUpJdfXuEwZb+EO3blmeg8GgoGh69nyuQ/s/EUuXLj1jY51JlC7jYWZt/hLQe7xN9O37\nfxw+/CkhIb2w2U78Z0ty8kMcOvQfysuX0737aL7+eg/XXRdFcfF0lizZz8yZ2UAQ06Ytx+m8mnXr\nnkPTLqB371X06vV70tKe6LB5CyFwOB7ghx9+Rb9+81ttm3Qm5eXlZ2ysM4nSZTzMrM1fDHGPF6Cm\n5gCaFoLVGn3SfhoajuJ216FpVu655w+8/vrTns9yc6cBVl5//SmvtkmT/kD//m8SF9f2itofGhqO\ncezYNsLDh3VovwqFousIqHu8QohgIcRfhRDbhBCFQoh/NLbHCSH+3dj+nRDiwlPt22ZztMvpAlgs\nYVitkVgsYVx7bXOO3KZQ4quucrYKL3a7XURGjjnVabVjLqHK6SoUCi86eqthFuCWUvYFEELEN7a/\nAHwtpbxaCHEO8KEQoofs6E1VH1x33RQqK3/K8uXDyM7OIzNzB337/pKXX36Zjz5q4KKLPiYjoxC7\nPbPdjt0IuFwuQkJCunoaHY7SZTzMrM1fOmzFK4QIBe4Gnmxqk1IWN/54M/ByY9ta4Efg4o4a+0Ro\nWjC33PIyEyfOICOjELf7GFFRl3LBBQNwuZ4nM3MrQgQREzP2TEznjDF16tSunkKnoHQZDzNr85cO\n2+MVQgwG8oB/ApcBx4DpwHrgRymlvYXtAuDfUsrXj+ujzT3e00FKyRdf6N8xmmbHZkvD4XiIzZsf\nITp6GFVV6xk48APi4q7vsDG7GrOuMpQu42FGbYG0xxsEpAMbpZQjgUfRnXAQLaMa2sFNN93E448/\nzuOPP86iRYt4/PHHcbma8zMsWLDA64qK0+lsFZY4Z84c8vPz+eGH37FqVQJ798I774DbXU119VZ2\n7nyE99+HTZv0aLfy8hXk5+czZ84cr36mTJmC09lc0WLp0qUsWLDA8+xyuU5rfk1s2bKlVW2qWbNm\nsWXLFs/zqcyv6X/0QJ1fE6c6vyZdgTq/Jk51fiEhIQE9P/D/9zd16tSAnl97f39Lly71+KWcnBxO\nh45c8cYATiBYNnYqhFgNTAE+Ano3bT0IIb4BpkgpPz+ujw5f8a5f/xPKy7+gqTqx77kHM2TIv4mK\nurRDxlQoFOYmYFa8Usoy4DPgKgAhRE+gB7AJeBd4oLF9JOAAzkii3IED38NmS6WpUKY+B33V9Hmj\n27fbexMZecmZmM4Zo+W3uplQuoyHmbX5S0dHrj0A/EoI8R3wAXCflLII+C2QLYTYBrwG3H4mbjQA\nWK1RDBv2GRZLGKCvbuPifooQwXTrpofypqdPQ4hT2g0JeCIjI7t6Cp2C0mU8zKzNXwwRQFFQAHl5\nenKbE+VVOJFdRcXXrF8/BnAzePC/2bXrSSorV2O1JnD++fvRtLZv1rV3fIVCcXZwulsNSCkD5h8Q\nAciKigrZxLffumVCQqUEKR0OKdetkz5Zt05Kh8N9Qjun8x2Zn58p6+qOyIqKb+SyZci9e//iu8MW\n/SYl1Z10fIVCcfZQUVEh0TNgRUg/fF3AJ8nJy5McPNgNOHGV3rw8TlrNNyHhVs47bwtBQeFERJxL\njx6rSU5+4CTj69WNTzZ+oNHyJNdMKF3Gw8za/CXgHe/48Vq7qgzrOXb1n+PiStuVY3fOnA/QtOAT\n2uTk6P2dbPxAY/bs2V09hU5B6TIeZtbmL4bY4503bxirVuWctEpvQQG8+OI0srPzuPfe9R02r/fe\n+w1LlthUlWCFQgEE0HWyzuTii89n4sQZJ3V6WVkwZUoZGRnNJYDa+mI5lWKXQ4ZUtmv8U+1XoVCc\nnQS845VSehKab948ibVrh7N9+y/atE9IuAOAgoKLWbUqmQ0bxrWyKSjQC1+2LIDpi7q6w1RVbaSu\nrgSAQ4f+S2npQurrq3za6/3Kk/arUCjObgLa8VZU5LNyZTQbNlwDwMGDb1BVVUBNzb5WttXVO1m1\nKpmCgtGN735Jbe0BpKxvZasfxAHMOeGB2caN17F27VBKSt5rfL6BjRuvx+mc79O+PQd8Z4rjQyfN\ngtJlPMyszV8C2vFqWkhjld6m7YIGNC2M6OirfdrX15e3sJUIEeIzIi0nBxITXcBQHA7Z5oFZbOz1\nXodvbvdRhLC0mUwnJ4d2HQSeCYYOHdp1g3ciSpfxMLM2fwloxxsePozMzFc9xS5BT3ITFfWTVrZ2\ne28GD17kVdUXoHv37Fa2zcUuP+btt9e2uXebnPwAQUFRnmchgoiLuwWbzeHTPisLZs4cx4MPvsXi\nxV0bbDFq1KiuG7wTUbqMh5m1+UtAO16AxMS7SEq6x5NfwWqNw27v6dM2KupSMjL+z+OopawlPPwc\nn7YjRtiYOHEGKSnvtTm2ptno3ftPaFpIY3+S9PQpbdoDZGQU8qtf7VK3HxQKRZsEvOMFqKr6C2+/\n/SLbtw8jOvqKNu0KCmDevJ9RUfECILDbM9osMFlQALNnP8jXX++ivr6C6updPu3i429mz55reP31\naTidkwgLG9jKxu2upaGhGikb2L59KC+/fGuXH6y1TJdnJpQu42FmbX7jT7hbZ/3DZ8hwg4yPPyxB\nyri4g3LZslU+Q/j00N6GxtBet3z//afl/v1/82n78cdvyri4EgkvyNjYfXLu3KFy2TKLrK7e67Pf\nppDlxESXz5DhTZvulMuWIefOzZIxMfslSBkfXya/+mqLz/HPBC+88EKXjd2ZKF3Gw4zazoKQYUFx\nsZ7dqKQknhUrzm/DDoqKdDkHDgg2bnzGZzhwQ0M17777AyUlscBvKC1NYdWqHIKCInzu3ebl4QlZ\ndjptPm8qhIYORAgbK1deS1lZMgDFxdF88EFxa+MzxG9+85suG7szUbqMh5m1+UvAO97x48UphwzH\nxx9q085isXPrrYOOqzy8hPj4WxHC0spev6lw8ITjJybeiZT1jB69sEW/Rdx++3mnoFShUJwtGCJk\n+O23J/HJJ6kdFjIspWT+/Kv54otzyc7Oo2/fzQwd+hmRkRf4tH/jjRv5/POBJxx//fpLKS9fxvbt\nQ1m1KodJk67joouGt0e2QqEwGGdFyPBVV93GxIkzGDSoDCkbqK+v9Bmam5UFDz/8X0/IsGzeO/ZC\nCMF55yUSHKxXHrZYuvm8dtYSKU+cKN3heABNs5ORUcjkyf/scqd7fH0qs6B0GQ8za/OXtrN/Bwhu\nd73nLu3KlbFomp0ffriYqVMXU1RkYe5cPHdm6+rKCQ8fQWXlN6xf/xMqK78lOfkhevX6H09/lZXf\ncuzYViorVzNaD3KjW7cRuN01WCx2r7HLy1ewdm0Vjz32ImVlySxdeow33/ySSy+9HCGav7Oqq3+g\nsvJbmopq2GyplJd/SWhoP4KD4zr5N+Sb8ePHd8m4nY3SZTzMrM1fAnrFW1tbyldfRVJQ0Lwadbur\nWbVqHEVF+n5sU2jugQPzWLkyGqfz7wCUl3/eGGnmHVCxadOtbNmSS3X1TtLS9Ppr5eWfUFq60MtO\nygY2bBjLW2996zkwczpDefPNlRw5strLtqTkA/bt+71ndV1RsYrCwkvZvPmujv2FnAL9+vXrsrE7\nE6XLeJhZm78EtOPVNBs2WzLNYcAAGmPHVhEb+yPQfODVrdtQhAjC7a5uYWshKuoyrz4djocRIggp\nawGQ0oXFEkFsrPepmRAWEhMnkZ29yOsgbsyYdUREnOtlm5h4V+MKuM7TJ1hISpp0ur8ChUJhQgLa\n8QYFhTN06KdYLBGAvseqaXbOP78Hf/3r0+TmTvNsM0REnOsVtaYjWzlJh+M+TyTapk16f+npv/MU\nw2xJWtpU+vZdz8yZ48jNncbzz9/I1VdP9NpmAAgOjic29jqaKxlbCA/PIi7upg76TZw6+fn5XTZ2\nZ6J0GQ8za/OXgHa8ACEhqQwd+l+PQ3W7jxERcT4jR8a0ypHrcPyMhIS7POHFERGjWlWYsFjspKc/\njabZ2bEDhLCSnPygz7FttkQcjslkZBQ2Hu6VEhd3o0/blJTHPA5ZCEHfvvO6tHJxYWHhyY0MiNJl\nPMyszV8C3vEChIePYODABYAgKCgKmy2V8PCRANTXV3L06Bbq6/UbHX37/j/CwgYAEBMz1md/Dsd9\n7NhxDocOPU1V1Wyfq13Qb0WkpU31PPfq9UKr1W4TERHnExLSE9BISrqPbt0G+am2Y7j//vu7dPzO\nQukyHmbW5i+GuMfbxIEDr1BZWUBV1Vqqq7dTX3/Y81li4iT69XsNgJqaIrZte4DevX9PaGhGq34K\nCuCqq45QXByBw+Fm8WKt1f1cl2s/33zTB2jw5PQNCorHbk8nK+srn7XanM432bNnOsOHr8FqjfT/\nF6FQKAKas+IebxMOxz3Y7T2prFzr5XQ1LZTIyIs9zzZbEoMHf+TT6YIeBlxcrDv2Awc0n2HAVmsM\nmmbzSqReX19MTc2Pbc4vMfEOzjtvu3K6CoXihBjK8QKkpDyK3Z5B02Eb6Fe/YmN97736IicHkpJq\ngSk4HG6fYcAWi52UlMc9B3EAQtjo0+fPJ61MHAhMmXLi9JVGRekyHmbW5i+G2mpoorLyW9atG9W4\nGrUQH38LAwa8dUpjrVtXy6xZv+Whh27nootG+LSpqytj1SqH5+pZaOggRo78rksPzdqL0+kkMTGx\nq6fR4ShdxsOM2s6qrYYmwsNHUF4+k/nzn2X79iwcjvtOuY/hw4N54IE/07Pn8jZtrNYYEhMnIYQV\nIaxkZPzVEE4XMN3/6E0oXcbDzNr8xZAr3oICuOaaOpxOK7GxRSxdmsDw4e37DpFSUl6+jJqafWzZ\nkgtAVNTlWK3x9O//RivHWl29i2++6U1k5KUMG/apv9IUCoWJOCtXvHl54HTqocClpUksXtx+GZWV\n31JY+BO2bXuQ1Y2Rv4cPf0J5+XKf9nZ7T4YM+cRzY8IoLF26tKun0CkoXcbDzNr8xZCOtz05ctsi\nPDyLsLAhuN0uqqr0Nk0LweF4oM1thOjonxASkna60z6jlJeXd/UUOgWly3iYWZu/GHKrASAv7498\n+GHFSXP0+qKqagPffju8xVUxjVGjdhMSkurXvBUKxdnFWbnVAHDBBUOYOHEGQ4fWnvK73boNJiXl\nl56rYnrUmXK6CoXizGAIx7tunWyV9Lx79wsBOHDgZQ4efIeKipWn9H55+XReffVptm8f1mauBiPj\ncrm6egqdgtJlPMyszW/8qZDZWf/wWWXYLWNifmysHizlypU75TffDJBffBEmly1DLluGXL7cKvPz\nM31WA123TjZWFNbfX7dO/5eY6JLwmIyLK5Zr1x5rb3FRw/DYY4919RQ6BaXLeJhRm+mrDC9aJCgr\n06tYHjgAixcHc+zYZtzuoy2sNOLifurz/bw8GisKNydN129F2ICZlJTEsWSJ3ee7RmbmzJldPYVO\nQekyHmbW5i8B73j1GwzNVYZvuimF1NQnvEJ5oYH4+Fvb9f611+ptiYnVQMgp34owCiEhISc3MiBK\nl/EwszZ/CXjHm5UF//jHcnJzp7FoUS1ZWdCz53PYbGk0TT84OJmwsIFtvj9z5jgmT37VkzQ9Kwve\ne28fubnTWLiw6pRvRSgUCsXpEPCOF+DKK29h4sQZxMT8gcOHP+PgwbcYMOBdhLAAQY2ld9oO5c3I\nKOS+++Z7OdhRo5JJTZ1BZuYOv+flq9JxoLBgwYKunkKnoHQZDzNr85eArzLscu3j0KGPAdi160mE\nCEbKWrKznfToMYNdu35LfPwtrd6rr69gx44nADcAFRVfsmXLzwgOTiYoKIy6ujK6dYMNG8ZisyUT\nFXW5VzXik1FQAFdffYyDB0O9Kh0HCpGR5kxNqXQZDzNr85eADqCQUrJyZSwNDVWeDGEAFkt3Lryw\nHCkbqK09iM3maNVXRcXXXtWJm9C0cNzuSvTFvrtpZGJjb2DQoPfaPdenn67l2Web00NOnw7TprX7\ndYVCYWBMHUAhhCAtrXUuz27dBjd+bvHpdAG6dz+f8PDzaJm3V9Ps9Oo1E6s1nmanC0IEkZr62CnN\n7Yordrc6tFMoFIr2ENCOFyAt7QmSkx9BCFtji4WIiPPb9a5eI615N8ViicDhuI++ff+GEDYOHdLb\nQ0LSiYhovTo+EX37bmTmzHFMnx542wyg50A1I0qX8TCzNn8JeMcL0Lv3/xIffwtChKBpwXTrNtyn\n3fGHXVFRYxptBZpmp2fPGWhaMLGxNxAefg7vv6+3p6Q8ccp5dqurd5KRUci0aYHndAFmz57d1VPo\nFJQu42Fmbf4S0Hu8LZGygQ0bxnPo0BJGjtxMWFg/r88LCmDcOD1IwuFoXoUePrycwsJLsVrjOf/8\nvZ6yPUePbmLNmiFoWjDZ2cUEBXVr1xyPHFlNXV0Je/f+noqKLxrfjULTAv6cUqFQdBCnu8drGG8h\nhIWBA9+jqupbQkP7tvo8L093utAcoZaVpa96HY4HiYr6iVettLCwAfTu/XuECG6305VS8t1319DQ\nUOk57Fu1Kh6AESO+JTzc90pcoVAoWmKIrYYmLJYQuncfjRCtp52TAw6HfmCWmOjyOuzq2/cl4uKu\nb/VOaupjpKQ81O7xhRCNocnefyVoWlhjQIdCoVCcHEM53hORlQWLF2vk5k7jpZeea9e+65w5c055\nnJSUR5Cy+UaEpoXQq9cLBAfHnnJfnYk/2oyA0mU8zKzNX0zjeEF3vnqE2/Ptsh86dOgpjxEWNoDw\n8OaqxDZbKg7H5FPup7PxR5sRULqMh5m1+YthDtfay8qV8dTVlXDhhcc4dmwLYWGDO/zgq7j4XTZv\nvpNt2wbxww/vcfPNPQLyZoNCoegczprDtZNRVVVISclH1NWVAPDll2GAJCPjbyQnP9ChY8XGXseO\nHecxdeq/KC1N4B//CMy7vAqFIjAxzVbDrl1PsWfPsy1aJEIE063bsDbf2bJli19jaZqVXbs+oLQ0\nAWi+RRFI+Kst0FG6jIeZtfmLaRxv795/8LouBqBpNiIizm3znYULF/o93qWXFgZ0yPDpaAtklC7j\nYWZt/mKqPV6n8022bv1Z4x1bQVzcLQwc+E6HzxPghx+m8tlnH1NUtJ5rr1XbDArF2YSpk+Q00d68\nt4mJdxAffzNC2NA0O3FxN57S+6fCkSOrAzpkWKFQBC4Bf7hWUABjxzZQVGRpV97bvn1fpqJiJS7X\nHqKjrzjl90+ElG727HkOIYIoL/8MgKNHN2OzpRAUFO5fpwqF4qwj4Fe8CxfWU1RkAdp3iGWxhDFk\nyH8YNCiPoKAI8vJo8/1Zs2ad0lzq6krZvfsZdu9+xtO2Zs1gVq2Kp76+4pT66mxOVZtRULqMh5m1\n+UvAO97x44NO+RArNLQvsbFjAT2UOC6uxOf748ePP6W5BAfH0737RUhZ59UeFjYEi8W/e8edxalq\nMwpKl/EwszZ/McTh2rx5w9iw4VkmTcrxa5sgP38/c+bMJTd3LBdffN5pzbG4+F9s2ZKL213dOGcr\nI0du9Jm4R6FQmJOzIoAiI6OQrKynycrK8ev9UaNScLlmEBQ0Fzi9pMwxMTk0VbXQtBDS0p5UTleh\nUJwSAb/V0ERV1XoaGlzU1LTtONu6vVBQAO+8M5dNmxLZs+cFVq8eRFHRa+Tn55/yPCyWEBISbgME\nNlsKaWm/OeU+zgT+aDMCSpfxMLM2fwlox7tz568pKLjY8/zll3a+/jqZY8e2trLVby9InnkGxo1z\ne5zv6tWHuPLKUubOvZepUxfzyScfcuzYZqqrd1BYWOjXvJKS7icoKJr+/d9C06x+9dHZ+Kst0FG6\njIeZtflLwO7xdutmZ9WqROrrD3nZaFoYo0eXYrGEeLVPnw7PPOP9PG0aPPHEOv74x+YE5bm505g0\n6U/07fs3EhPv6kQ1CoXCrJg2gELTrGRmvooQzWHAQgSRkHBbK6cLTYnQ9Z9jY/czbpz+hXLbbUOI\njS3ytGdn5yFlPaGhmZ0vQqFQKHwQ0IdrcXHXERV1OYcP/7fxCpcgKelen7Z6InT48MNKUlPHERNz\nK/Bbhg8PYsGCTbzxxv+Rnf0fMjIKkRLsdnUgplAouoaAXfE2kZk5z1Oi3WZLJjz8nDZts7Lg2WfD\nycgoZNeuKRQXv8vatcPJzNzCxIkzGDrUhRBBWCzhWK1RTJky5UzJOOOYVZvSZTzMrM1fAnaPt+U9\n3gMH5rJt2/306jWLtLRfn7CP+vojbN/+MAcPvoEQNqSsISZmHGVlixvHCAYko0eXUlp6jMTExE5U\n1HU4nU5TalO6jIcZtZ3uHq8hHK+UbkpK3iUq6gqs1qg233e59rB6dX8AT4CDjoZ+97YBAIslggsu\nOOyzaKZCoVCcDNMerrVECI34+FtO6HQBLJZuhIT0wvvLRNDS6QJERo5RTlehUHQZpvI+VmsMI0as\nISbmGoSwAfrWgsUSTpNUTQslJkbP47B06dKummqnY1ZtSpfxMLM2fzGV4wWwWOwMHPge6elPIYQV\nKRtISfm5pzqF2+0iMvJSAMrLy7tyqp2KWbUpXcbDzNr8xRB7vO+8A3PmwP33w4QJ7e+vtHQRmzff\nyYAB7+B21/D99zdgtcaSnX0QIUTHC1AoFGcFpk+S8/bb9dxxB0gZxJdf6m3tdb6xsddywQVlCKHn\n4+3d+49oml05XYVC0aUE/FbD3LlBSKl/P7jdMHfuqb3f5HQBUlMfIzl5sufZ5XJ1yBwDEbNqU7qM\nh5m1+UvAO9777wch6gHQNMl993Vc31OnTu24zgIMs2pTuoyHmbX5iyH2eJ966hYWL76PcePmMmPG\nAhoaXEhZS1DQ6VV9cLlchIS0zvtgBsyqTekyHmbUdlYEUCxf3rwnm5LyS5zOv7Nz52h2784jJ0dV\n+VUoFGcWUx+uVVfvorJytVfb/v1/ZPv2oUydOofSUk67crBCoVCcaQJ6j3fTplvYvPnOVu0rV46n\ntDQJaF/l4bZYsGDB6UwvoDGrNqXLeJhZm78EtONNS/stTfXNWjJ69EJiYw8A4HDIdlUe9kVkZORp\nzC6wMas2pct4mFmbvwT8Hu/330+gtPRDpKxptNIAN7t2XcaKFedz8809ufrqSV01ZYVCcRZi6j1e\ngMzMl1m92skXX1zE6NFLGD5cUFm5jn79ttKz56eNVpOQUrJ+vSAvj3YduBUU0G5bhUKh6EgCfsVb\nUADXXOPC6QwhNraIpUvjiI6ehdt9jMrK1Rw+/Cnx8RP4+uu9PPnkUg4eDMPhOPGBW0EBjBsHBw44\ncTgSTXk4Z8YcqKB0GREzajN9WsiFCxtwOvU7gKWlSSxeHERk5BjKyj7m8GF9xVtc/E+++upyDh4M\nA05+4JaXp9vA7NM6nAtkZs+e3dVT6BSULuNhZm3+YogV7xVX7Ke0NMWzko2Pn8v27ZOB5rnv2HEu\nTz21AqfTRmxsEf/9b9IJV7xXX13JwYPhJ10dKxQKxfGcFQEU8+YNo6DgCe699w6yskBKyY4dj1BU\n9Apud3MceFhYOfPnf8LQoc9x991fY7HY2xxr2bJl/OMfy3jkkWeV01UoFKfEWeF49cg1C2PG1Hva\npHSzadNtlJUtxO12YbXGMXp0MW53LStW2IiNvY5Bgz6kvv4IFku3VhUnjh79njVrBnHBBeUEBXU/\nQwoVCoUZMP0eLzQVqNRL97jdtQCsX6/x7rtvceDAXYAgLGwoAJoWjN3eh9LSj9i69X5Wroxj0aI/\nMX26vsXQREhIL/LyoLp6p9dYBQW0sjUic+bM6eopdApKl/EwszZ/CejrZEeOrKW8/HOk1J3typVx\n1NWVAV8wYcK5OJ02HI7/409/stCvXzZSSioqviIoKBbYgdP5d7ZtG8CTT+ZSUqKHF3/wgZO4uJeo\nqyuhTx9Yv/5SgoMTiY6+kqqq2Vx99VEOHgwzfCjy0KFDu3oKnYLSZTzMrM1fAtrxbt/+IJWV6zzP\ndXWlgMZ//9sLp1OvqXbggMbWrX/jllugqmoD69dfRFO0m5R1rFw5npKS2EZbePfdvYwb9z8ADBgA\nDQ0VVFdXUlc3nLw8Wt2MMKrjHTVqVFdPoVNQuoyHmbX5S0BvNfTu/YdW1SKCgxP46U8dJCbqkWxJ\nSfWekOHQ0H4kJd2LEFaP/ejRS4iN3Q+AwwG33TaM4OBkrz6FsJCS8hg5OXjZ+huKrFAoFCcioB1v\nZORFOBwPo2n6PV4hgklIuIPhwwUff2wjN3caf/rTZM+qVNOsZGbOpU+fP3ucb2bm98ycOY7Jk19l\n8WIYPjyYzMw5CBHM3r36e3Z7BhERI8nKgpkzx/Hgg28ZepsBYMuWLV09hU5B6TIeZtbzNbx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D9kZpbiscd+gDlzvo5Bg2YgLq5fcDvfkuF2uN1TkJpaAWA6srM/RHLyBEyd+nGX57z+ei8aG+cg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f863e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dmin=0\n",
    "dmax=100\n",
    "rh = dict([(k,v) for k, v in config.iteritems() if v.type() == 'rh' and dmin<v.dist()<dmax])\n",
    "print len(rh)\n",
    "#mp.flow_plot_config(rh,xlim=(-1.5,20),ylim=(-0.5,70),arrowsize=1, arrowlength=1, max=10)\n",
    "mp.flow_plot_config(rh,xlim=(-5,50),ylim=(-36,135),arrowsize=1, arrowlength=1, max=200, figsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "04:39:06 INFO:Loading pickle file : n_stage1.pickle\n",
      "04:39:08 INFO:Loading pickle file : n_sample_stage2.pickle\n"
     ]
    },
    {
     "data": {
      "image/png": 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/iMhn0zT9Uq1W+y9F5Ici8nefX9z3vq6Ku7sAZbB+VxKcxJ1ayjFuP1Jr/tD3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ljJFp30PaDXnI7B+QMsXAQAAGJHJ30M9JrTwbECAACpMdFJD4Feczo4VgAAIDUmOukXLlyo/HfS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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11dd75750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "04:39:13 INFO:Plotted 1001/177120 states\n"
     ]
    }
   ],
   "source": [
    "#mp=reload(mp)\n",
    "config = mp.load_config(\"n_stage1.pickle\")\n",
    "config2 = mp.load_config(\"n_sample_stage2.pickle\")\n",
    "mp.state_scatterplot(config2,minx=-1,maxx=mp.UPPER_XLIMIT,miny=-72,maxy=mp.UPPER_YLIMIT,max=1000,verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "04:39:38 INFO:Loading pickle file : n_stage1.pickle\n",
      "04:39:40 INFO:Loading pickle file : ne_stage1.pickle\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotted 100/100 \n"
     ]
    },
    {
     "data": {
      "image/png": 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TInFMklRdvVVXXdWo3bt/JCnQsWP79NRTb9cbb/y73SEdQrYAM8gWbCiG3okr2aJjEmHp\ndFoVFRW2x0CB+L7U3i7V10vz5vXoiScWj3jc80r1trftVDxeZWlCd5AtwAyyBVtO1ztxiUvZ6urq\nUm1trXSWS7lKCzsScuHKNyByk0y+WXrfseN5SZLnxTVu3OWqrLxelZU3jLooOblAHwTHtHv3T1RV\ndZNKSiaY/i1EBtkCzCBbsCWZTI5YkLhWhi+2bHHGBAiRIAh06NBmjRt3mTyvJKfP8X3p1lv3afv2\nSZo2bUA//GFcc+Y8rp6ed2rSpKWqqfm/Ki2daHhyAADsogwfXpTfI6ylpcX2CLDE8zyNHz8350WJ\nlD1Tsn37JEnSjh1V+vu/b1JPzzsllWj/fl89Pe/S4OA+QxNHC9kCzCBbCAMXy/CuZIvye4RVVdEl\nQO5SKWmo+6dEItD73rdo6JFjCoLD2r//aT311E0aHDztDyeKCtkCzCBbCAMXy/CuZIvyO1BEhhfo\nZ89+RL7/NnleuTyvVJlMWlJG1133HPuiAACcdnIZ3rXOSZSxwSJQhAYH9+rll/9KFRWXavz4KzRu\n3OUaN26OYrEy26MBAFAwdE7Cg45JhOVyDR5wJqWllbr88n/QJZd8WtXVKzRhwgIWJUPIFmAG2UIY\nudA5cSVbdEwibP369bZHAJxEtgAzyBbCyIXOiSvZomMCwLiT908BACBM6JyEBx0TAMb4vrR8eaBt\n2zwlElJHh7R48TFt2tSgadPep2nT3mN7RAAATqBzYg8dEwBGtbVJ27Z5kqS+Pumee+5RX9+9ev31\nNj333Ae1fft3LU8IAMCbXOicRBUdkwhrbm62PQIwquH7p1RXb9M119yjF1/8LwqCYwqCo+rt/WP1\n93/H7pAnIVuAGWQLURDFzokr2cq1Y8LCJIRqampsjwCMKpnMXr7V2Cj99KczdccdG+V5cUnZy0Oz\ni5M/0u7d/2530GHIFmAG2UIUJJNJdXR0qLGxMTKXcbmSrWQyqdbW1lGPo2MCIC8OH35NjzxyiaSM\nYrFxQ/uoLNLs2U0aP3627fEAADgtCvGF0dXVpdraWonyO4BCOHDgWZWWTlVZ2XR5nmd7HAAAzopC\nfGFQfo+w3t5e2yMA52TChIUqL58R2kUJ2QLMIFuIqrAX4l3JFuX3CNuwYYPtEQAnkS3ADLKFqAp7\nId6VbLHBIgAAGIFNUYFTsQljYbDBIgAAkJRdlCxbdkj9/eNObIrKv7mAkeicmEHHpEgEwTHt2/eU\nguCY7VEAACHW1ib194+TlN0UNWSX0gOhEPbOSVTRMYmwpqamnI/t6/uannwyqa6uSvX03KKtW7+i\nAwfcKEoB+TaWbAGuSaWk6uqtkrKbo+bzUnqyBVeErXPiSrbomERYb2+v5s+ff9rHTr4+eOfOB7Vp\nU4Ok42dMShWLlevtb98X2ndGAmw5W7YQbXQnctPe/lU9+OAO/bf/1qglS/L3GkG24JIwdU5cyhYd\nE8f4vlRXF6ivz1N19VatW1enefN6hh0RUyxWpvnzv61p0/7Q2pwAUEjZvxuzlyfRnTizIAi0d++j\n8v0bNH78QmUyB3XVVT/R+PFX2B4NCC06J/lBx8RBbW1SX1/2J1y7ds3SU099RjU1/66ysoSkEpWX\nX6Srr36CRQmAopL9uzH7Md2JM3vuuQ/K92+UJB08+KzS6VeUyRy2PBUQbnRO8oOOSYR1d3ef9v5U\nKvvTQCn76x13vF9TprxLicRHNXXqLbrmmqc1YcKiAk4KRMuZsoVoG/53Y3X1VtXVhedKgDCZMuVm\neV7JiduxWEXeXjPIFlxlu3PiSrZy7ZiwMAmhnp6e096fTGYvUWhsHHmpwqWXflZXXdWheLyqgFMC\n0XOmbCHajv/d+Nd/vV/r1tVp/PjbdejQyxoY+KXt0UJl5swP6+KLPyXPK5ckTZy4eMRC5XyQLbgq\nmUyqo6NDjY2NVi7jciVbyWRSra2tox5HxwQAzhGF6/B55JFLdfjwFknZf3DfdNOg3YFCJggCbdr0\nB9q16we65JL/odmz77I9EhBJbMI4dl1dXaqtrZXomABAfh0vXK9dK91yyxvyfdsTYevWv9eRI68N\n3Tom6ZjC9MO3MPA8TwsW3K+LLvqkpk//oO1xgEg6Xohfu3at6urq5PMCMCrf99XQ0DDqcSxMAOAc\nDC9c79gxRV/9apOCIFBf39f0yiv8FNqGgwefkzTyLXCD4IidYUKspKRC8+b9L02YsMD2KEAkUYgf\nO8rvEbZq1SrbIwBOyme2hheuZ8w4oKVLH9AvfhHTCy98XK+8slbp9G/y9lzIzbx5X9Hll98z1KHI\nXsqVybAwKQRet1BMClmIdyVbbLAYYf39/Tn94QEYm3xny/ezb01bXy/NmPFNPf/8RyRJnleuiy76\nr5o792/z9lzI3cGDL2nTpt/XgQPP6MYbX1c8PsX2SM7jdQvFplCbMLqULTZYBIACCIKMHnlklo4c\n6ZeU/Ts1FhuvG2/coZKSCXaHK1KZzFEdPvwbjRs3x/YoABzHJoyjY4NFACgQz4tpxozbVVExR5Kn\nWGy8MpmD2r79O7ZHK1qxWJxFCYCCoHMyOjomEdbZ2Wl7BMBJJrM1e/ZdWrr0Rd14424tWtSqiy/+\ntKZMebex5wPChNctFDOTnRNXssUGixE2MDBgewTASYXIVjxepQsuWK45c5o0btxlxp8PCANet1DM\nTrcJo+/7amxsPO+3EnYlW8lkUnfdNfo7VtIxAQAAAPKEzsmp6JgAAAAABUbn5FR0TCIsnU7bHgFw\nEtkCzCBbwJvy2TlxJVt0TCJs9erVtkcAnES28s/3pcbG7K8oXmQLeNPpOieSzql34kq26JhEWDqd\nVkVFhe0xAOeQrfzyfamuTurrkxIJqaNDKvLLqIsW2QLO7lx7J65ki45JhLnwDQiEEdnKr7a27KJE\nyv7KZdTFi2wBZ3euvRNXskXHBABgVColJRIZSVJ19VYtX35YQRDo6NHXLU8GAOFicq+TKKBjEmEt\nLS22RwCcRLbyK5mUOjpiWrPmDa1bV6d9+yrU23u7/vM/q7V//zO2x0MBkS3g7M7UOxmNK9lKJpNq\nbW0d9bjSAsyCMaqqqrI9AuAkspV/yaSUTE7RwMD/1lNPvUPbt39Tnlem3bt/ookTr7Q9HgqEbAGj\nSyaTIxYkvu+rra1NqVTqjAuVYssW5XcAwHkJgmN68snrdeDArxQERyRJlZU3asmSX1qeDADCqdg2\nYaT8DgAoCM8rkeeVKgiOyfPikqR9+x7VsWOHLE8GAOFUbJswUn6PsFz+4ACMHdky5+qru3XNNU9p\n5syPKhabqCAY1P79bG5SLMgWMDa5luFdyRbl9whbv3697REAJ+U7W2wuONLEiVfq8su/qhtv3KnF\ni3+uysobbI+EAuF1Cxib05XhT7cBoyvZYoNFADBo5OaCGXV0xNhcEABwTlzvnNAxAQCDRm4uGNOD\nD+6SJA0O7tPg4H6LkwEAosb1zgkdEwAwKLu5YPbj6uqtuuSS39b27Q9o48br9dhjlysIjtkdEAAQ\nGa5vwEjHJMKam5ttjwA4KZ/Zym4umO2YdHbO0Lx5PXruuQ/o0KGXNDj4hl5/vSNvzwWEHa9bwPk5\nU+ekvr5+ROckqnLtmLDBYgjV1NTYHgFwUr6zld1cUJJKtXnzZ/Tqq00KgqMKgqPasuWLqq5ekdfn\nA8KK1y3g/A3fgHF452Tjxo2R75z4vq81a9aMehxnTEJo6dKltkcAnGQqW0GQUV/fP8rzynX8r9V9\n+x7V/v1PG3k+IGx43QLyy7XOCR0TACgQz4vpuuue04IF39LMmf+/KipmS5L27XvC8mQAgChyrXOS\na8eEtwsOod7eXs2fP9/2GIBzCpmto0cHVFo6WZ7nFeT5AJt43QLyz/d9feMb39Dtt98e6cu4juvq\n6lJtba3E2wVHy4YNG2yPADipkNmKx6vkeR6bMKIo8LoF5F8ymdSsWbNGLEpOtwmjS8Z0xsTzvPWS\nUpLeImlxEARPD91/oaR/ljRHUlrSnwVB0DX02DhJ90m6VtIxSf8jCILvn+Hrc8YEgDN8X1q27LD6\n+8s1c+agfvjDUjZhBACckyhvwmhqg8XvSbpR0isn3f9FSY8EQXC5pNsl3e95XsnQY38pKR0EwTxJ\nt0j6B8/zpozxeQEgctrapP7+cknStm2l+va3f6Fjxw7oySeX6rXX/tHydACAKIlyId5I+T0Igl8G\nQdAn6eSLphsk3TN0zBOSXpP0jqHH3jvssVck/UzS743leQEgikZuwrhdCxZ8Ul1dE7V//5N6+eVP\na3DwtD8wAgDgFFEuxBdsg0XP86ZKKg2CYMewu7dIumTo40uGbp/uMZxGU1OT7REAJxU6W8M3Yfzp\nT6frxhuvlCQFwaCCYFCvvvp3BZ0HMIXXLcCM4dk60yaMUeic5LrB4jm9K5fneb+WtCIIgqeHFiav\nBUEwbtjjLZJ+HATBNz3P2ytpXhAE24cea5J0KAiCtaf5unRMxLubAKbYzNbg4F49/PAMZTKHTtwX\ni43TDTdsVTw+1cpMQL7wugWYcbZsRalzYqpjcoogCHZLGvQ8b9qwuy+V9Juhj7coW5Y/3WOn9Z73\nvEcrV67UypUr1d7erpUrVyqdTp94vKWlRZ2dnSdu9/f3a9WqVSO+RnNzs7q7u0/c7u3tPeUnOk1N\nTert7T1xu7u7W83NzSOOWbVq1Yhr4jo7O9XS0nLidjqdzvt8x78BwzrfcczHfFGbb8uWLdbme/zx\nTXroobersnKpSkunDH2dQ3r22W+fOCbs//+Yj/nONN/wfziFcb7hmI/5ojTf/Pnzzzjfd77znRGd\nky984QsFn086+/+/zs5OrVy5Uh/4wAdy6pic9xmTodvfkLQlCIJGz/OulfSgpEuDIDjmed5nJb0l\nCILbPc+7TNIjkhYOLWhO/rqcMQFQFI4efUMHDz6rSZOuUywWtz0OACBiiv6Mied593ie96qkiyR1\nep73wtBDfyXpbUO3vyHpg0EQHBt67EuSxnue95KkHyv7VsKnLErwpuErVQD5E6ZsxeNTNHnyjSxK\n4IQwZQtwydmyFaXOSa4dk9KxfNEgCP70DPfvkHTzGR47KOl9Y3meYtfT06OlS5faHgNwDtkCzCBb\ngBmjZSuZTJ44SzL8DMq9994bqjMovu9rzZo1ox7Hzu8h9LGPfcz2CICTyBZgBtkCzBhLtsK8z4mR\nfUwAAAAAhE+Y9znJdR+Tcyq/m0L5HQAAADg3vu+rvb1d9fX1obmM67iuri7V1tZKJt8uGPl38lu3\nAcgPsgWYQbYAM8aarWQyqTvvvHPEoiSshfjT4YxJCPX39+d0ugvA2JAtwAyyBZhxvtkKy1sKF2yD\nReQff7kDZpAtwAyyBZhxvtkKSyGe8jsAAABQxMJSiM+1/M7CJIQ6OzttjwA4iWxhrHxfamzM/ooz\nI1uAGeebrbBswmhkg0UUxsDAgO0RACeRLYyF70vLlh1Rf3+Z7r1X6uiQQvYmN6FBtgAz8pGtMGzC\nyAaLEfbe977X9giAk8gWxqKtTervL5Mk9fVJIdqrLHTIFmBGvrNlq3NCxwQAgPOQSkmJREaSNH36\nXoVorzIAOCe2Oid0TCIsnU7bHgFwEtnCWCSTUkdHTLfddqc+//laLVq0S2+88R+2xwolsgWYke9s\n2eqc5NoxYWESQqtXr7Y9AuAksoWxWrw40J13Bpo3r0cPP3yhenp+S4cOvWx7rNAhW4AZJrI1fBPG\n452TtWvXqq6uztjihI5JhK1bt872CICTyBbGqrf3I/rNb74oyTtxX2npFHsDhRTZAswwna1CdU7o\nmERYRUWF7REAJ5EtjNXUqbcouygJhu6JqbR0ssWJwolsAWaYzlahOie5dky8IAhGPahQPM+rlLRn\nz549qqystD0OAAB6442f6Ve/Wq5MJq2Skkq9/e28NS4Ad/i+r/b2dtXX1xt96+Curi7V1tZK0uQg\nCPae7hjOmIRQS0uL7REAJ5EtnIspU96pJUseUzx+geLxqac8vnFjUPSbMJItwIxCZGt45+Q4G5sw\nSmywGEpVVVW2RwCcRLZwriZOvFLXXPO0PK9kxP2+L918807t2jWtqDdhJFuAGTayZWITRt/31dDQ\nMOpxnDGcoSLDAAAgAElEQVQJoZtvvtn2CICTyBbOR3n5TJWVTRtxX1ubtGtX9r5i3oSRbAFm2MiW\niUI85XcAAAxLpaQLL9whSUokxCaMACLPRCGeDRYjLJcVJYCxI1vIt2RSevDB7brttjv1wANPKJG4\nX5nMYdtjFRzZAsywkS0TmzCywWKErV+/3vYIgJPIFvLt4MEXNGPGN/XhD39emcy1eu65D2nnzgdt\nj1VwZAsww1a28r0JIxssRtgXvvAF2yMATiJbyLetW7+irVv/7sRtzyvVhAlXWpzIDrIFmBGGbOWj\nc0LHBAAAwy677PMaN26ePC8uSQqCjMaPn295KgDIn3x0TuiYAABgWDxepcWLf654/EJJnsaNm61Y\nLG57LADIm3x0TuiYRFhzc7PtEQAnkS2YUF4+U8nkQyotnazKyhtsj2MF2QLMCEu2zrdzkmvHhA0W\nQ6impsb2CICTyBZMGTdujq6//mWVlk62PYoVZAswI4zZOl3nZLQNGHPtmHhBEORlyHzwPK9S0p49\ne/aosrLS9jgAAAAAhhm+M3wikchpZ3jf97Vs2bLji5PJQRDsPd1xXMoFSPJ9qbEx++txL7+8Rs88\n84c6fPg1e4MBAACEyOk6J5LO2jvJtWPCGZMQ6u3t1fz5vKtLofi+VFcn9fVJicQxtbfHtGSJp0cf\nvUKHDm2W55Xq0kvv1MUXf0qxWLntcXEeyBZgBtkCzIhKtkY7i8IZk5A5cmSXtm37pl5//Yfau/dR\nHTq0WYOD+0577IYNGwo8XXFra8suSiSpr69EX/nKZ/Xzn3s6dOgFSccUBIe1Zcvn9eijc5VOb7E6\nK84P2QLMIFuAGVHJ1mh7ndAxCZktW76oX//6fygWGy9pUJnMEcViFbrxxl0qKRlne7yiNvyMycyZ\nR3X//d2aO3ejXnrpz4eOiMnzSlVWNkPJZJcqKi6xOi8AAECY5OuMCQuTAtm793Ft3HjdiduxWIWm\nTXu/rrjiPnmeZ3EySNnFSXu7VF8vJZNSEBzTL34Rl+Rp6tRbdfHFn1JV1U38WQEAAJyG7/tqb29X\nfX39acvwXV1dqq2tlViY2OH70gMPPKt58z6gefN6TtzveeWaPPntuuqqHysW4x2bw2rv3sdUXn6R\nyssvsj0KAABApPi+r7a2NqVSKSWTyZwWJnRMDPF96eabd+tLX1qo1as7dOjQN7Rw4fckSePGzdNb\n3/p/zrgoaWpqKuSoOIPKyutYlDiGbAFmkC3AjKhm6+RNGB944AE1NDSM+nn8uN6QtjZp586pkqRd\nu2bp8cc/optvPqq5c9dr2rT3qqRkwhk/d8WKFYUaEygqZAswg2wBZkQ1WyeX4e+9996cyu+cMTEk\nlZISiezHiUS2uxCLxTVr1idUVjb9rJ8bhbeFi7pjxw7q9dd/qCA4ZnsUFBDZAswgW4AZUc1WKpVS\nYugfwolEQh/96Ec1Y8aMUT+PMyaGJJNSR8fIQjXs8f3sWaxUKvtnsW3b1/TSS3+uiorZmjt3vS64\nYDnFdgAAgDw4vgnj8DL8wYMHdccdd5z18yi/h1B3d7eWLl1qewxn+L60bFla/f0Vqq7epr/7uz/X\nrFmt8ry4guCoPK9MEyfW6PLL79GkSUtsjwuDyBZgBtkCzHAlW2ywGGE9PT2jH4SctbVJ/f0VkqRd\nu2bq3//9CklSEAwO/XpE+/Y9rq1bv2JtRhQG2QLMIFuAGa5kiw0WgSHDN1BMJLKX2FVWflqvvvq3\n8rwylZdfrEsvvVPTpr1XsViZ7XEBAACcwgaLwDAnb6C4a1e7Xn31y3rLW1ZpypTfoV8CAABg0H33\n3Xe8Y8LCBAAAAEDh0TGJsFWrVtkeAXAS2QLMIFuAGa5ki45JhPX39+f0Xs8AxoZsAWaQLcAMV7JF\nxwQAAABAKHR1dam2tlbiUi4AQJT5vtTYmP0VAOAmFiYh1NnZaXsEwElkK5p8X7r11v1auzb71t8s\nTsKHbAFmuJIt3/fV0NAw6nEsTEJoYGDA9giAk8hWNLW1Sdu3T5SU3Y+ovd3yQDgF2QLMcCVblN8B\nAE7IbpJ6TH19JZo+fb9+/OOJSiZtTwUAyBVvFwwAcEIyKXV0lOi22+7U5z///ymRuF9btqxTEGRs\njwYAyEEymdRdd9016nGcMQmhdDqtiooK22MAziFb0XXw4IvatOkPdODAr+R5ZQqCQb3jHUflefx8\nLQzIFmCGK9nijEmErV692vYIgJPIVnQ988wKHTiwSZIUBEdUUjKRRUmIkC3ADFeyRcckwlxZHQNh\nQ7aia2DgIT399K3KZNKSMiovv0Q33LDF9lgYQrYAM1zJFmdMIsyFb0AgjMhWdFVV1erqqx9XPF4t\nyVM8fqHtkTAM2QLMcCVbuXZMWJgAACJhwoSFuuYaX+PGXa5Jk662PQ4AIEe+72vNmjWjHsfCJIRa\nWlpsjwA4iWxFX3l5Qtddt0lXXNFsexQMQ7YAM1zJVq4dExYmIVRVVWV7BMBJZMsNnldiewSchGwB\nZriSrVQqpRkzZox6HOV3AAAAAEZ1dXWptrZWovwOAAAAIMxYmIRQLtfgARg7sgWYQbYAM1zJlu/7\namhoGPU4FiYhtH79etsjAE4iW4AZZAsww5VsscEiAAAAAOvYYBEAAACAdWywCAAAAMA6NliMsOZm\nNg4DTCBbgBlkCzDDlWyxwWKE1dTU2B4BcBLZAswgW4AZrmSLDRYBAAAAhMJ9992nO+64Q6L8DgAA\nAMAGOiYR1tvba3sEwElkCzCDbAFmuJItOiYRtmHDBtsjAE4iW4AZZAsww5Vs0TEBAAAAEApdXV2q\nra2V6JgAAAAACDMWJgAAAACM8X1fDQ0Nox7HwiSEmpqabI8AOIlsAWaQLcAMV7JF+T3CVqxYYXsE\nwElkCzCDbAFmuJItyu8AAAAAQoENFgEAAABYxQaLEdbd3W17BMBJZAswg2wBZriSLTomEdbT02N7\nhEjbv79HDz88U729d2jv3kcVpssVYRfZAswgW4AZrmSLjgmKhu9LbW1SKiUlk9Ibb/xcTz/9OwqC\nQJ7nqaxspi666BO65JJP2R4VAGDRya8XAAonl44JCxNEmu9Ly5dntG1bTNXVW7VuXZ3mzTv5pwsx\nlZZO1o03vi7P86zMCQCwK/t6EWjbNk+JhNTRweIEKBTf97Vs2bLjl3NRfoeb2tqkbduy38a7ds3S\n44/frmnT3nficc+Lq7r693Tddb0sSgCgiGVfL7KvA319Unu75YGAIkLHJMJWrVple4TQGBjo0n/+\n5ww9++yHtHPngxoc3Dfi8VRKSiSyHycS0sc//gktXPiASkomKx6fpiuv3KArr/xXlZVNszA9woZs\nAWZEIVuplFRdvVVS9vWivt7yQEAOopCtXNAxibD+/v6c/vBcdPL1v7t2dWjTpt9XEAwqFhunTOaw\npkz5LdXUdI74nPb27IvM8dPyR45sVyw2QaWlEy39ThBGxZwtwKRCZOt8+yFBkNHXv75Ezz23Xn/0\nR+/gMi5EgkuvW3RMECm+L9XVZU+xX3jhTv3N37z7lL6I55Vp4sSrtGTJo/I8TvgBQDHIvj4E6usb\nez/k0KFf68UX/6s8r0Svv96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AsSNbgBlRyJbvS8uXZ7R2rVRXx+IE0RCFbOWCjkmErV+/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\nAAAAADjHiQkAAAAA5/4/tBtoPMOISIkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b7bbe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "config = mp.load_config(\"n_stage1.pickle\")\n",
    "config = mp.load_config(\"ne_stage1.pickle\")\n",
    "ccr = dict([(k,v) for k,v in config.iteritems() if v.type()=='rh'][:50])\n",
    "ccl = dict([(k,v) for k,v in config.iteritems() if v.type()=='lh'][:50])\n",
    "ccl.update(ccr)\n",
    "mp.flow_plot_config(ccl,xlim=(-15,mp.UPPER_XLIMIT),ylim=(-72,mp.UPPER_YLIMIT),arrowsize=1, arrowlength=1, max=200,figsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Num of STAGE 1 cells having 20.00 <= dist <= 30.00 : 2233 \n",
      "   (62, 8, 336) : g1:  (46, -8, 294)  r:  32.0  type: rh  d: 23.1   g2:           None  d2:  None  samples: 9\n",
      "   (66, 8, 320) : g1:  (46, -8, 300)  r:  75.0  type: rh  d: 25.7   g2:           None  d2:  None  samples: 54\n",
      "  (66, -1, 268) : g1:  (45, -7, 300)  r:  40.5  type: lh  d: 22.1   g2:           None  d2:  None  samples: 17\n",
      "   (61, 6, 334) : g1:  (46, -8, 294)  r:  30.5  type: rh  d: 20.9   g2:           None  d2:  None  samples: 2\n",
      "  (66, -1, 274) : g1:  (46, -8, 300)  r:  47.5  type: lh  d: 21.4   g2:           None  d2:  None  samples: 22\n",
      "  (65, 14, 336) : g1:  (46, -8, 300)  r:  48.5  type: rh  d: 29.5   g2:           None  d2:  None  samples: 11\n",
      "  (73, -6, 248) : g1:  (46, -8, 300)  r:  31.5  type: lh  d: 28.0   g2:           None  d2:  None  samples: 5\n",
      "  (67, 11, 328) : g1:  (46, -8, 294)  r:  50.0  type: rh  d: 28.7   g2:           None  d2:  None  samples: 27\n",
      "  (60, 14, 350) : g1:  (45, -7, 300)  r:  31.0  type: rh  d: 26.6   g2:           None  d2:  None  samples: 2\n",
      "   (71, 7, 300) : g1:  (46, -8, 300)  r:1700.0  type: rh  d: 29.1   g2:           None  d2:  None  samples: 28\n",
      "   (61, 8, 326) : g1:  (45, -7, 300)  r:  50.5  type: rh  d: 22.1   g2:           None  d2:  None  samples: 23\n",
      "    (55, 12, 6) : g1:  (45, -7, 294)  r:  18.5  type: rh  d: 22.9   g2:           None  d2:  None  samples: 2\n",
      "   (60, 7, 340) : g1:  (46, -8, 294)  r:  26.5  type: rh  d: 21.1   g2:           None  d2:  None  samples: 6\n",
      "   (65, 7, 322) : g1:  (46, -8, 294)  r:  51.5  type: rh  d: 24.4   g2:           None  d2:  None  samples: 37\n",
      "   (65, 5, 300) : g1:  (45, -7, 300)  r:1400.0  type: rh  d: 23.3   g2:           None  d2:  None  samples: 26\n",
      "  (60, 10, 340) : g1:  (46, -8, 300)  r:  33.5  type: rh  d: 23.3   g2:           None  d2:  None  samples: 3\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Selects subset of stage 1 states falling within a dist range\n",
    "#\n",
    "d_min = 20\n",
    "d_max = 30\n",
    "c_1_40 = dict([(k,v) for k,v in config.iteritems() if not v.goal2() and v.dist() and d_min <= v.dist() <= d_max])\n",
    "print \"\\nNum of STAGE 1 cells having %.2f <= dist <= %.2f : %d \" % (d_min,d_max,len(c_1_40))\n",
    "ii=0\n",
    "for k,v in c_1_40.items():\n",
    "    mp.print_state(k,v)\n",
    "    ii+=1\n",
    "    if ii>15:\n",
    "        break\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Where are the remaining unexplored states?\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "04:32:48 INFO:Loading pickle file : n_remaining.pickle\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotted 201/11328 \n"
     ]
    },
    {
     "data": {
      "image/png": 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AQIdTAkMrLP4AAKCDKYGhFQpfmpidna16BKBkcg85yT6dTAnMucuUfYUvTczNzVU9AlAy\nuYecZJ9OpgTm3GXKvsIXAADoAkpgaEUre/7WlzsSAADwXNTr9dMu+hTB8Fy58gcAAB1GEQwnU/jS\nxNjYWNUjACWTe8hJ9ulGimCay5T9Vgtf0l75W1hYaGlTJNA95B5ykn26kSt/zWXKfqtX/tIu/gAA\noJMpguFkCl8AAKBLna4IRgkMZ5N2z9/MzEzVIwAlk3vISfbJYvlW0O3bt8fmzZujKIqqR6pUpuwX\nRRGjo6NNj0u7+Dtw4EDVIwAlk3vISfbJQgnMqTJlX+ELAAAkogQmL4UvAACQjBKYvBS+nMXi4mL0\n9vZWPQZQIrmHnGSfTJTAPEP2V0q752/btm1VjwCUTO4hJ9kns8wlMJmyr/Clife///1VjwCUTO4h\nJ9kns8wlMJmy32rhS9rFn0vAkI/cQ06yT2YjIyMxMDAQEREDAwMxPDxc8UTlyZT9kZGR6O/vb3qc\nwhcAAOhiSmByaKXwJe2Vv507d1Y9AlAyuYecZJ/s6vV63HLLLekWftmyPzg42PSYtIu/vr6+qkcA\nSib3kJPsw0pFUcT4+HhXF8Bky/7c3FzTY9z2CQAAiXgYfPdp9SHvaa/8AQBARpkbQLuVts8mWvnh\nAN1F7iEn2YdTZWkAzZT9Vts+0y7+duzYUfUIQMnkHnKSfThVvV6PPXv2xPj4eFff8pkp+/V6Paam\nppoeZ88fAAAQRVHE9PR0jIyMdO2CsJu18qgHiz8AAEhOCUxnU/gCAAC0RAlMZ1P40sTk5GTVIwAl\nk3vISfahuW4sgcmUfYUvTQwODlY9AlAyuYecZB+a68YSmEzZV/gCAACcFyUwnUPhCwAAcE6UwHQO\nhS9NzM/PVz0CUDK5h5xkH85Np5fAZMq+wpcmdu3aVfUIQMnkHnKSfTg3nV4Ckyn7rRa+uO0TAAA4\nraIoYvfu3TE8POyWzzbXyp6/9eWOBAAAdIp6vb5i0acEpnO58gcAALRECUx7UvjSxMTERNUjACWT\ne8hJ9mH1dFIJTKbsK3xpYsuWLVWPAJRM7iEn2YfV00klMJmyr/AFAABYdUpg2pPCFwAAYFUpgelc\naW/7nJ2drXoEoGRyDznJPqyt5RKY7du3x+bNm6MoiqpHiohc2S+KIkZHR5sel3bxNzc3V/UIQMnk\nHnKSfVhb7VoCkyn7rRa+2PMHAACcM49/qF6rj3qw+AMAAM6LEpjqKXwBAADWnBKYzpB2z9/Y2FjV\nIwAlk3vISfahfO1QApMp+wpfmnjf+95X9QhAyeQecpJ9KF87lMBkyn6rhS9pF3/9/f1VjwCUTO4h\nJ9mH8o2MjMTAwEBERAwMDMTw8HDpM2TK/sjISEufV+ELAACw6pTAlKuVwpe0V/5mZmaqHgEomdxD\nTrIP1ajX63HLLbecsvAriiLGx8dL2QMo+yulXfwdOHCg6hGAksk95CT70B7KLoHJlH2FL03cfPPN\nVY8AlEzuISfZh/ZQdglMpuwrfAEAANpGO5TAdCuFL00sLi5Gb2/vmp4DaC9yDznJPrSPMktgsmVf\n4ctZbNu2reoRgJLJPeQk+9A+TlcCE7E2RTCyv5Irf0Aacg85yT60t+UimH379sXAwEDs2bNnVa4K\nZsp+URQxNDS0vO/Plb9ny/KLADxD7iEn2Yf2tlZFMJmyr/AFAABoe4pgzl+rhS9pF387d+6segSg\nZHIPOck+tLd6vR579uyJ8fHxVbvlMyJX9uv1ekxNTTU9bn0Js7Slvr6+qkcASib3kJPsQ/ur1+un\nLYGZnp6OkZGRc1oQyv5KaQtfAACA9rRWJTDdSuELAADQkdaqBKZbKXxpopUfDtBd5B5ykn3oPKtR\nApMp+wpfmtixY0fVIwAlk3vISfah86xGCUym7Lda+GLPHwAA0BHOtwSmm+3duzc2bdoUcZY9fxZ/\nAABA21MCc2YKXwAAgK6hBObMFL40MTk5WfUIQMnkHnKSfegOz7UEJlP2Fb40MTg4WPUIQMnkHnKS\nfegOz7UEJlP2Fb4AAABdTwnMMQpfAACArqUE5hiFL03Mz89XPQJQMrmHnGQfutfZSmAyZV/hSxO7\ndu2qegSgZHIPOck+dK+zlcBkyn6rhS9u+wQAADpWURSxe/fuGB4eTnnL5zJ7/gAAABI4dOhQXHHF\nFRH2/AEAAFkURRHj4+NRFEXVo5Rmbm6u6TFpF38TExNVjwCUTO4hJ9mHXJYbQLdv3x6bN29OsQAs\niiJGR0ebHpd28bdly5aqRwBKJveQk+xDLmdrAO1W2j6b2LhxY9UjACWTe8hJ9iGXszWAdittnwAA\nQEoZG0BbaftMe+Vvdna26hGAksk95CT7kE+9Xo+3vOUtpyz8MpbAPFvaxV8rbThAd5F7yEn2IaeT\ns9/tJTBrUvhSq9V21Gq1B2q12lKtVnv1Sa9fXavV/qxWq91bq9U+X6vVbjrpexfXarWP1Wq1+2q1\n2nytVvup5/RJ1si73/3uqkcASib3kJPsQ04nZ7/bS2DWqvDlv0TEjRHxN896/bci4o5Go3F9RPxi\nRHysVqutO/69X4mIxUaj8bKI+DsR8aFarXblczwvAADAOen2Epg1LXyp1WoPRMSWRqPx+eNfPxYR\n1zUajW8e/3o2IrY1Go3/UavV7omIX2w0Gn9x/Ht/FBH/rdFo/MFp3lfhCwAAsOq6vQSmlMKXWq32\nvIhYv7zwO+6rEfFdx//9u45/fbrvVWZsbKzqEYCSyT3kJPuQ07OzX6/X45ZbbkldApO28OV973tf\n1SMAJZN7yEn2Iadm2e+mEpg1KXw5nUaj8WhEHKnVatec9PKLI+LB4//+1Yh40Rm+d1o/8zM/E1u3\nbo2tW7fG7t27Y+vWrbG4uHji+zt37oyZmZkTXy8sLKxY2U9OTp5S7Tw/Px8TExMnvu7v74+JiYmY\nn58/8drs7GxMTk6e8j5jY2OnbJ6cmZmJnTt3nvh6cXFxTeaLCPOZz3yrPN/c3Fxbz9fuPz/zma9T\n5zt5H0w7zncy85nPfKs3X39//1nnO1MJTCf9/GZmZmLr1q1x8803t1T4slp7/v4gIr7aaDTGa7Xa\n6yLiTyPixY1G42itVvuNiHhRo9H4xVqt9pKIuCMivuf4ovHZ72vPHwAAsOaWr/zt27cvBgYGYs+e\nPR27F7AoihgaGlpeAK7Onr9arfZ7tVrtaxHxgoiYqdVq9x7/1q9FxBuPf/0HEfHORqNx9Pj3fjsi\nLqnVavdHxJ9FxHtPt/Ar28krdyAHuYecZB9yapb9er0ee/bsifHx8Y5e+EUc+yxTU1NNj1v/XN60\n0Wi85wyvfzMi3nqG7z0RET/7XM5ThgMHDlQ9AlAyuYecZB9yaiX79Xp9xaKvKIqYnp6OkZGRjl4Q\nns453fa5Vtz2CQAAVKVTbwVdk9s+AQAAutWZSmDa3fT0dEuFL2kXfye39wA5yD3kJPuQ07lkf2Rk\nJAYGBiIiYmBgIIaHh1d7rDUxMjJySrPxmaRd/G3btq3qEYCSyT3kJPuQ07lkv1NLYFotfEm7529x\ncTF6e3vX9BxAe5F7yEn2IafVzH4nlMDs3bs3Nm3aFHGWPX9pF38AAADNdEIJjMIXAACA89QJJTAK\nX5rYuXNn1SMAJZN7yEn2IafVyn4nlMAofGmir6+v6hGAksk95CT7kNNqZb8TSmAUvgAAAKyRdiuB\nUfgCAACwytqtBEbhSxOtbIgEuovcQ06yDzmtZfbbrQRG4UsTO3bsqHoEoGRyDznJPuS0ltlvtxKY\nVgtf3PYJAADwHBVFEbt3747h4eGO2fO3vtyRAAAAOl+9Xj/toq/dimBO5sofAADAKqiqCEbhSxOT\nk5NVjwCUTO4hJ9mHnKrIflVFMApfmhgcHKx6BKBkcg85yT7kVEX2qyqCUfgCAABQsqqKYBS+AAAA\nlOh0RTDtUgKT9rbP+fn5qkcASib3kJPsQ07tkv3lEpjt27fH5s2boyiKNTnH6Oho0+PSLv527dpV\n9QhAyeQecpJ9yKldsl9GCUyrhS/2/AEAAKyRMh7/0OqjHiz+AAAA1lAZJTAKXwAAACrWLiUwaff8\nTUxMVD0CUDK5h5xkH3Jq5+yvdgmMwpcmtmzZUvUIQMnkHnKSfcipnbO/2iUwrRa+pF38bdy4seoR\ngJLJPeQk+5BTO2d/ZGQkBgYGIiJiYGAghoeHz/v9+vv7mx6n8AUAAKBkq10C00rhS1te+ZubW/tz\nzM7Orv1JgLYi95CT7ENO7Z79er0et9xyyykLv6IoYnx8fE0eBB/Rpou/0dGINfq8J8yVscIE2orc\nQ06yDzl1WvbPpwSmowtfFhYi1uDB96d497vfvbYnANqO3ENOsg85dVr2z6cEpqMLX/r7I85zzyMA\nAEDHOJ8SmI4ufLnttoNx000KXwAAgDzOpwSmYwtfBgfX/hxjY2NrfxKgrcg95CT7kFMnZv90JTCt\nGmxhEdWWV/7KeNTDwsJCS5dGge4h95CT7ENO3ZL9oihieno6RkZGzroobOXKX9rFHwAAQDtbbgDd\nt29fDAwMxJ49e067ACyKIoaGhpZLXzrrtk8AAIDsWm0A7ei2zzLMzMxUPQJQMrmHnGQfcuqG7Lfa\nANpq22faxd+BAweqHgEomdxDTrIPOXVD9uv1euzZsyfGx8fPeMvn8nFTU1NN38+ePwAAgA5yuhIY\nhS8AAABd5HQlMBHRUuHL+hLnbCuLi4vR29tb9RhAieQecpJ9yKlbs3+6EphGo6Hw5Wy2bdtW9QhA\nyeQecpJ9yKlbs3+6EphWC1/S3vbZrf9LAHBmcg85yT7k1M3ZL4oidu/eHcPDw89pz1/a2z679RcB\nODO5h5xkH3Lq5uzX6/UzNn+eTdrbPgEAALpBURQxOjra9Li0i7+dO3dWPQJQMrmHnGQfcsqU/enp\naYUvZ9PX11f1CEDJ5B5ykn3IKVP2Fb4AAAAk0UrhS9orfwAAAJmkXfy1ck8s0F3kHnKSfcgpU/YV\nvjSxY8eOqkcASib3kJPsQ06Zst9q4Ys9fwAAAB2sKIoYGhpaXgDa8wcAANCN6vV6TE1NNT3O4g8A\nACCBtIu/ycnJqkcASib3kJPsQ06Zsq/wpYnBwcGqRwBKJveQk+xDTpmyr/AFAAAgAYUvAAAACSh8\naWJ+fr7qEYCSyT3kJPuQk+yvlHbxt2vXrqpHAEom95CT7ENOmbLfauGLPX8AAAAdbHx8PLZv3778\npT1/AAAA3WhkZCT6+/ubHmfxBwAA0MEUvjQxMTFR9QhAyeQecpJ9yEn2V0q7+NuyZUvVIwAlk3vI\nSfYhp0zZb7XwJe3ib+PGjVWPAJRM7iEn2YecMmV/enp6+QHvZ5V28QcAANANFL40MTs7W/UIQMnk\nHnKSfcgpU/YVvjQxNzdX9QhAyeQecpJ9yEn2V/KQdwAAgA5WFEUMDQ0t7/vzkHcAAIBupPAFAAAg\nAYUvTYyNjVU9AlAyuYecZB9yypT9Vgtf0u75W1hYaGl1DHQPuYecZB9yypb9vXv3xqZNmyLOsucv\n7eIPAACgGyh8AQAASEDhSxMzMzNVjwCUTO4hJ9mHnDJlX+FLEwcOHKh6BKBkcg85yT7klCn7Cl8A\nAACSaKXwJe2VPwAAgG5QFEWMjo42PS7t4m9xcbHqEYCSyT3kJPuQU6bsK3xpYtu2bVWPAJRM7iEn\n2YecMmW/1cKXtHv+FhcXo7e3d03PAbQXuYecZB9yypZ9e/7OItMvAnCM3ENOsg85Zcv+4OBg02PS\nLv4AAAC6xdzcXNNj0i7+du7cWfUIQMnkHnKSfcgpU/a1fTbR19dX9QhAyeQecpJ9yClT9ltt+0xb\n+AIAANANiqKIoaGh5QWgwhcAAIBuVK/XY2pqqulxaRd/rVwWBbqL3ENOsg85yf5KaRd/O3bsqHoE\noGRyDznJPuSUKfutFr7Y8wcAANDBxsfHY/v27ctf2vMHAADQjUZGRqK/v7/pcRZ/AAAAHUzhSxOT\nk5NVjwCUTO4hJ9mHnGR/pbSLv8HBwapHAEom95CT7ENOmbKv8AUAACABhS8AAAAJKHxpYn5+vuoR\ngJLJPeQk+5BTpuwrfGli165dVY8AlEzuISfZh5xkfyV7/gAAADpYURQxNDQUCwsLEfb8AQAAdKfp\n6enlhd+ViKBhAAAgAElEQVRZWfwBAAB0MIUvTUxMTFQ9AlAyuYecZB9yypR9hS9NbNmypeoRgJLJ\nPeQk+5CT7K+k8AUAAKCDKXwBAABIQOFLE7Ozs1WPAJRM7iEn2YecMmVf4UsTc3NzVY8AlEzuISfZ\nh5wyZb/Vwhd7/gAAADrc3r17Y9OmTRH2/AEAAHSnoihidHS06XEWfwAAAB1M4UsTY2NjVY8AlEzu\nISfZh5wyZb/Vwpe0e/4WFhZa+gEB3UPuISfZh5yyZd+ev7PI9IsAHCP3kJPsQ06yv1LaxR8AAEA3\nUPjSxMzMTNUjACWTe8hJ9iGnTNlX+NLEgQMHqh4BKJncQ06yDzllyr7CFwAAgCQUvgAAABARiRd/\ni4uLVY8AlEzuISfZh5wyZV/hSxPbtm2regSgZHIPOck+5JQp+60WvqTd87e4uBi9vb1reg6gvcg9\n5CT7kFOm7BdFEUNDQ8sLQHv+ni3LLwLwDLmHnGQfcsqU/Xq9HlNTU02PS7v4AwAAyCTt4m/nzp1V\njwCUTO4hJ9mHnDJlX+FLE319fVWPAJRM7iEn2YecMmVf4QsAAEACCl8AAAASUPjSRCuXRYHuIveQ\nk+xDTrK/UtrF344dO6oeASiZ3ENOsg85Zcp+q4Uv9vwBAAB0sPHx8di+ffvyl/b8AQAAdKORkZHo\n7+9vepzFHwAAQAdT+NLE5ORk1SMAJZN7yEn2ISfZXynt4m9wcLDqEYCSyT3kJPuQU6bsK3wBAABI\nQOELAABAAgpfmpifn696BKBkcg85yT7klCn7Cl+a2LVrV9UjACWTe8hJ9iGnbNlvZY+jPX8AAAAd\nbu/evbFp06YIe/4AAAC6U6ttnxZ/AAAAHWx6ejoWFhaaHpd28TcxMVH1CEDJ5B5ykn3IKVP2tX02\nsWXLlqpHAEom95CT7ENOmbKv7bOJjRs3Vj0CUDK5h5xkH3KS/ZXSLv4AAAC6gcKXJmZnZ6seASiZ\n3ENOsg85Zcq+wpcm5ubmqh4BKJncQ06yDzllyn6rhS8e8g4AANDhPOQdAACAiLD4AwAA6GgKX5oY\nGxuregSgZHIPOck+5JQp+60WvqTd87ewsNDSpkige8g95CT7kFOm7BdFEUNDQ8sLwDPu+Uu7+AMA\nAOgWHVv4kqiVFQAAoBRtufgbHY0oirU9x8zMzNqeAGg7cg85yT7klCn7HV34srAQsXv32p7jwIED\na3sCoO3IPeQk+5BTpux3dOFLf//B+MQnNkS9fuz1paWno6fnwkpnAwAAaEetFr605ZW/qak4sfB7\n5JFPxGc+c0Xce+9746mn9lc7GAAAQJup1+sxNTXV9Li2XPwtLT154t+fempfNBqN2L//P8Ts7ItW\nbRG4uLh43u8BdBa5h5xkH3KS/ZXacvH3kz/5aHz4w6+JT3+6Fvfe+w+j0Xjq+D+Hjy8Cvyv27//I\neZ1j27ZtqzMs0DHkHnKSfcgpU/ZbLXxpyz1/EQfjF37hd+Lnf/6fR0QjItZFxNGIiOjp6Y2ent7Y\nuPE/x1VXbT7ncy0uLkZvb+9qjA10CLmHnGQfcsqU/fHx8di+ffvyl5215+95z3so3vjGj8eNNz4S\n11//4ajVeqKn5+JYv74vXvKSfxE33PDQeS38IiLNLwLwDLmHnGQfcsqU/ZGRkejv7296XFsu/n7j\nN26ON7zhhXHBBVfGJZe8PC68sD9e8pJ/Hjfc8FC88IVbY926S6oeEQAAoC10dOHLd3/3vTEw8J6I\niOjruyluuOHBVV/07dy5c9XeC+gMcg85yT7kJPsrteXib926S+J5z/uxNT1HX1/fmr4/0H7kHnKS\nfcgpU/Y7uvBl//67or+/XvU4AAAAba+jC18uueS6qkcAAADoCB1d+FKGhYWFqkcASib3kJPsQ06Z\nst/RhS9l2LFjR9UjACWTe8hJ9iEn2V+pLff8HTx4MDZs2FD1OAAAAG2vKIoYGhpavtrZWXv+AAAA\naM309HRLt7la/AEAAHQwhS9NTE5OVj0CUDK5h5xkH3LKlH2FL00MDg5WPQJQMrmHnGQfcpL9lRS+\nAAAAdDCFLwAAAAkofGlifn6+6hGAksk95CT7kFOm7Ct8aWLXrl1VjwCUTO4hJ9mHnDJlv9XCF3v+\nAAAAOtzevXtj06ZNEfb8AQAAdKeiKGJ0dLTpcRZ/AAAAHUzhSxMTExNVjwCUTO4hJ9mHnDJlX+FL\nE1u2bKl6BKBkcg85yT7klCn7rRa+pF38bdy4seoRgJLJPeQk+5CT7K+UdvEHAADQDRS+NDE7O1v1\nCEDJ5B5ykn3IKVP2Fb40MTc3V/UIQMnkHnKSfcgpU/ZbLXzxkHcAAIAO5yHvAAAACQwODjY9xuIP\nAACgw7Vym2vaxd/Y2FjVIwAlk3vISfYhp0zZb7XtM+2ev4WFhZY2RQLdQ+4hJ9mHnDJlf3x8PLZv\n3778pT1/z5blFwF4htxDTrIPOWXKfqttn2kXfwAAAN2gXq/H1NRU0+PSLv5mZmaqHgEomdxDTrIP\nOcn+SmkXfwcOHKh6BKBkcg85yT7klCn7Cl8AAAASUPgCAACQgMKXJhYXF6seASiZ3ENOsg85Zcq+\nwpcmtm3bVvUIQMnkHnKSfchJ9ldKu+dvcXExent71/QcQHuRe8hJ9iGnTNkviiKGhoZiYWEhwp6/\nlbL8IgDPkHvISfYhp0zZn56eXl74nVXaxR8AAEA3UPjSxM6dO6seASiZ3ENOsg85Zcq+wpcm+vr6\nqh4BKJncQ06yDznJ/kppC18AAAC6gcIXAACABBS+NNHKDwfoLnIPOck+5JQp+wpfmtixY0fVIwAl\nk3vISfYhp0zZb7XwxZ4/AACADrd3797YtGlThD1/AAAA3akoihgdHW16nMUfAABAB1P40sTk5GTV\nIwAlk3vISfYhp0zZV/jSxODgYNUjACWTe8hJ9iGnTNlX+AIAAJCEwhcAAIAup/Clifn5+apHAEom\n95CT7ENOmbKv8KWJXbt2VT0CUDK5h5xkH3LKlP1WC1/s+QMAAOhwHbvnb26u6gkAAAC6S1su/kZH\nI4qi6ikAAADaX0cXviwsROzevbbnmJiYWNsTAG1H7iEn2YecMmW/owtf+vsjhofX9hxbtmxZ2xMA\nbUfuISfZh5wyZb+jC19uu+1g3HSTwhcAAIBWdGzhy+Bg1RMAAAB0l7Zc/JVhdna26hGAksk95CT7\nkFOm7Hd04UsZ5jxPAtKRe8hJ9iGnTNlvtfClLff8ecg7AABAa4qiiKGhoeUFYGft+QMAAKA19Xo9\npqammh5n8QcAAJBA2sXf2NhY1SMAJZN7yEn2IadM2W+18CXtnr+FhYWWHoQIdA+5h5xkH3LKlP3x\n8fHYvn378pf2/D1bll8E4BlyDznJPuSUKfsjIyMtfd60iz8AAIBuoPCliZmZmapHAEom95CT7ENO\nsr9S2sXfgQMHqh4BKJncQ06yDzllyr7CFwAAgAQUvgAAACSg8KWJxcXFqkcASib3kJPsQ06Zsq/w\npYlt27ZVPQJQMrmHnGQfcpL9ldLu+VtcXIze3t41PQfQXuQecpJ9yClT9ouiiKGhoVhYWIiw52+l\nLL8IwDPkHnKSfcgpU/anp6eXF35ntaqLv1qt9je1Wu1LtVqtqNVqd9VqtZ85/vrVtVrtz2q12r21\nWu3ztVrtptU8LwAAQFZVFb4sRcRoo9GoNxqN72s0Gv/l+Ou/FRF3NBqN6yPiFyPiY7Vabd0qn/s5\n2blzZ5WnByog95CT7ENOmbJfVeFL7fg/zzYaEb8XEdFoNO6MiIci4k2rfO7npK+vr8rTAxWQe8hJ\n9iGnbNkfHBxsesyqFr7UarUHImJ5c+FfRMSvRUQjIh5qNBoXn3Tczoj4s0aj8ZFn/f0NEXHwttsO\nxk03ecg7AABAK/bu3RubNm2KKLHw5aZGozEYEd8XEY9ExH86/vrprgae0ehoRFGs8mQAAABdqCiK\nGB0dbXrcqi7+Go3G14//eTQi/k0cWww+GhFHarXaNScd+uKIePBM77Ow8DPx3vduja1bt8bu3btj\n69atpzykcefOnTEzM3PS8QsxNjZ2yntMTk7G7Ozsia/n5+djYmLilL8zMTER8/PzJ16bnZ2NycnJ\nU95nbGzslOacmZmZU+4fXlxcXJP5IsJ85jPfKs936623tvV87f7zM5/5OnW+k9+nHec7mfnMZ77V\nm29hYaGt54s4/5/fzMxMbN26NW6++eaW2j5X7bbPWq12SURc0Gg0Dh7/emtEjDQajTfXarU/iIiv\nNhqN8Vqt9rqI+NOIePHxReLJ77EhIg729x+MT3xiQ9TrqzLaaY2NjcUHPvCBtTsB0HbkHnKSfcgp\nU/Zbfc7fai7+XhIRfxLHribWIuKvI+J9jUbjweNX/f5zRLwkIp6KiPc2Go3bTvMeGyLi4Kc+9UD8\n8A+/eFXmAgAA6Hat7Plbv1onazQaD8SxvX6n+943I+Ktrb7XwMCfxGOP/UhceOF3xEUXXbtaIwIA\nAKS1aou/1fSVr/xKLCzUoq/vh+M1r/nzqscBAABoW5UUvqymnp6L4sorf2TN3v/ZGyuB7if3kJPs\nQ06Zsj89Pd1S4UvbLv6Wlp6Oa65pvno9V608BBHoLnIPOck+5JQp+yMjI9Hf39/0uFV9yPv5Wi58\n2bMn4qqrXh6vf/18078DAACQXRUPeV8ltejv/4WqhwAAAOgabbn4W7/+ijW95TMiTnm4IpCD3ENO\nsg85Zcp+Rxe+3HDDV+Pii1+6pufYtWvXmr4/0H7kHnKSfcgpU/ZbLXxpyz1/Bw8ejA0bNlQ9DgAA\nQNsriiKGhoaWF4CdtucPAACAVtTr9Ziammp6nMUfAABAAmkXfxMTE1WPAJRM7iEn2YecMmW/owtf\nyrBly5aqRwBKJveQk+xDTpmyr/AFAAAgAYUvAAAACSh8aWJ2drbqEYCSyT3kJPuQk+yvlHbxNzc3\nV/UIQMnkHnKSfcgpU/ZbLXyx5w8AAKCDjY+Px/bt25e/tOcPAACgG42MjER/f3/T4yz+AAAAOpjC\nlybGxsaqHgEomdxDTrIPOcn+Smn3/C0sLLR0aRToHnIPOck+5JQp+57z10SWXwTgGXIPOck+5JQp\n+9PT08sLv7NKu/gDAADoBgpfmpiZmal6BKBkcg85yT7klCn7Cl+aOHDgQNUjACWTe8hJ9iEn2V8p\nbeELAABAN1D4AgAAkIDClyYWFxerHgEomdxDTrIPOWXKvsKXJrZt21b1CEDJ5B5ykn3IKVP2Wy18\nacs9f7fddjBuumlt9/wtLi5Gb2/vmp4DaC9yDznJPuSULft79+6NTZs2RXTanr/R0YiiWNtzZPpF\nAI6Re8hJ9iGnTNkviiJGR0ebHteWi7+FhYjdu6ueAgAAoP11dOFLf3/E8PDanmPnzp1rewKg7cg9\n5CT7kFOm7Hd04cvUVES9vrbn6OvrW9sTAG1H7iEn2YecMmW/owtfPOQdAACgdR1b+HLw4F/GE098\nueoxAAAA2l5HF77cfffb4s47vy+Wlp5es3O0siES6C5yDznJPuSUKfsdXfjSaCzFlVe+JXp6Llyz\nc+zYsWPN3htoT3IPOck+5JQp+60WvrTlnr+Pf3xdvOlNc3Hppa+seiQAAIC217F7/q688u9Y+AEA\nAKyitlz8vfjFv171CAAAAB2howtfLr30FWt+jsnJyTU/B9Be5B5ykn3IKVP2O7rwpQyDg4NVjwCU\nTO4hJ9mHnDJlv6MLXzzkHQAAoHUdW/gCAABA61q50pl28Tc/P1/1CEDJ5B5ykn3IKVv25+bmmh6T\ndvG3a9euqkcASib3kJPsQ06Zst9q26c9fwAAAB1sfHw8tm/fvvylPX8AAADdqNW2T4s/AACADlav\n12NqaqrpcWkXfxMTE1WPAJRM7iEn2YecZH+ltIu/LVu2VD0CUDK5h5xkH3LKlP1WC1/SLv42btxY\n9QhAyeQecpJ9yClT9qenp2NhYaHpcWkXfwAAAN1A4UsTs7OzVY8AlEzuISfZh5wyZV/hSxNzc3NV\njwCnVRQR4+PH/mR1yT3kJPuQk+yv5CHv0EaKIuJtb3ssvvGNy+Paa4/Exz++Pur1iKeffji++c0/\njIGB90RPz0VVjwkAQBspiiKGhoaW9/15yDt0gunpiG984/KIiNi/f3186EP/Jo4ceSz+5m9+I+6/\n/x/FV77yaxVPCABAu1H4Ah1oZCRiYODYv19zzbfjB37gI/GZz2yIffs+FBER+/b9u3j00T+vcEIA\nANqNwpcmxsbGqh4BVqjXI/bsObbn75OfvDJ+6Zf+8pTvNxqH44tf/Nk4fPiRiibsbHIPOck+5JQp\n+60WvqTd87ewsNDS6hiebWnpcPyv//VDUautj76+TXH55a+Lyy9/bVx00bFLdkVx7PbNkZFji7nz\n8bd/e0/ceeerI+KZnNZqF8SVV/5YvOpVe6JWq53fCZKRe8hJ9iGnbNnfu3dvbNq0KeIse/7WlztS\n+8j0i8DqqtXWx+HD34onn7w3Dh26PWq1C2Np6fFYv/7KqNXuiJ/5meti//718fu/f+wq3vksAI8c\n+Xb09FwSjcaRaDSORMTRaDQOx8GDt8XS0hOxbt2lq/a5MpB7yEn2IadM2S+KIkZHR5sel3bxB+eq\nVqvFS17ymzE///OxtLQYjcbhiIg4evSJ+G//7Xmxf/+xWO3bF7F79/kt/vr6bopNm/72xNeNxlI0\nGoej0ViKdesuPq/PAQBAd1D40sTMzEzVI6T1la/8Wtx990g8+eRfVz3KObv66p+KCy646pTXrr32\nl+Knf/rquOqqr0fEseKW4eHVPW+t1hM9PRdZ+J0juYecZB9yypR9hS9NHDhwoOoR0nr88c/HI4/s\nib/4i5fH/fdvjSNHDlY90nNWq62LF73oN6Knpzd6enojImLfvg/FxRf/Urz//Ztj69a9533LJ6tP\n7iEn2YecMmW/1cKXtIu/m2++ueoR0rrkkldERC0ajSOxb9+/jzvueGE89NCHop3Kh1rR3//3Yt26\ny+PSSwfje793d2zY8MZYWPiDiIi47LIfaOk9br014s1vPvZnmYriWKNoUZR73qrJPeQk+5CT7K+U\ntu2T6jz00IfiK1/5J7G09MRJr/bEpk1PRk/PhZXNdS6OHj32Ge6444Vx9OihuPfeV8bY2MfjkUde\nENdeeyQ+/vH1Z7z6d+utEe9859FoNNZFT08jPvrRWrzjHRGPPVbEgw/+y3j5y38v1q+/YtVnLoqI\nt7/9cOzff0EMDJx/KQ0AANUqiiKGhoaW9/2dse0z7ZW/xcXFqkdIq7f3pRHxzCMKarUL47Wv/YuO\nW/hFRKxbd0msW3dJXHrp90SjcSQ++9kt8cgjL4iIiP3718fu3Wf+u//2334jGo11ERGxtFSLf/Wv\nPhdPP/2teOCBfxrf+tbOuOeen4hG4+iqzzw9HbF//wUR8UwpTRZyDznJPuSUKfsKX5rYtm1b1SN0\nvaWlw/HVr/5WPPbYXae8fvHFL4mlpSeiVju2AGk0no5LL311FSOumg0bboiIdXHjjbtOFL709z91\n2sKXo0efiC9+8X+LH/7hX45a7UhERPT0HI23ve2Dcfvt18Sjj34iIhpx6NAdcf/9v7Lqs46MxJqW\n0rQzuYecZB9yypT9Vgtf0t72ubi4GL29vWt6juyefvrhuP32qyOiJ573vLfFddf9y7j00u+JpaXD\n8cADt8RVV22Jiy++Lm6//Zq46KLvjBtu+FrVI5+zb3zjj+LLX/4HsbT0eNx332vjvvv+U7zjHa9c\ncTvlE0/cF3ff/fZYXHwwGo2n4lOfGo3PfGZnvOtdET/7s424/fb+OHz4W7H8UPda7YK4/voPx7XX\n/vyJ9zh06C/j6acX4sorf/ScWj+//e1PxR//8T+Or33ttviJn9iQ6pZPuYecZB9yypb9Vh7ynnbx\nRzn27r0ijh49FMceKdmIq6/+6XjpS98fF1/80hPHPPjgRMzM3Br33XdrvOMdr4gXvejPI6IRl176\nPXHhhQNRq9XiG9+4NQ4c+HRs2PCGuPzy18Yll3xP9PS0z2MqH3/8S/GXf/mqqNV64mUv+90YGHj3\nimM+9an/Hn/4h7Nxww3/b7zsZc80rbz5zccyePjwgbj99v5oNJ561t9cF6973efj0ku/JyIi7rrr\nB48/XP6CuPLKH41rrnlHPP/5b48LLrjyrDM+8sgn4+KLvzvuuWdLPPHEF0+cFwCAzmfxR+X+6q9e\nF489dudJr9Tiiit+MOr12068UhQRP/ZjD8Ujj7wgBgYa8c/+2Q/Gddd9LiKORk/PxXHxxd8dS0tP\nxZNP3hc9PZccXxzV4pJLNsZ3fde2+I7v+NmyP9YKjcbR+NznXh79/T8fL37xr6/4flFEvOUt++Ph\nh6+Nq676erz//ZvjZS+bi4iI/v5/GN/xHe+ISy99VRTFD0ajcSTWrbs01q27LNav3xDr1m2Il7/8\n90+Uv+zbNxn337/1RGFOT88lsbS0GK985X+Jq6/+ydPO9+STD8TnPvfSOHan91JERLzqVX8WfX1v\n8sxAAIAOp/CliZ07d1Y9QgqXXfbaU76++OLr4vrrf++U16an40RJyr59tbjnnt+Mnp5j+wGXlp6M\nxx+/O5588t6IqMXS0uPRaByJRuNwPP74F+LQodlSPkcztdq6eMMb7j/twi/i2Gd8+OFrIyLi4Ye/\nM26/feTE9xYWPhyf//zbYv36vnj96+fjDW+4P173urn4vu/7bLz61X8Wr3zlzlNaP6+++qdjaemZ\nq4NLS09ET09v9Pa+5Izz9fZ+1/E9lkvHX1kXd9/99njggX967h/6uMOHD8S+fZNx9OiT5/1ea03u\nISfZh5wyZV/hSxN9fX1Vj5DCZZe9Omq19VGrHbtFc2lp8cTti8tGRo4Vj0Qc+/Od7/yBE8cvu/LK\nH4urrx49URITUYtabV1cd93EWn+EVXHyZ7zqqq/HG984fcr3G40j8eST97f0Xhdc8Py48sofOuW1\nF7zgvXH55WfevFerrYuLL37ZSa8cjXXrLolrr/0HLZ3zTJ544stx552Dce+974nHH7/nvN6rDHIP\nOck+5JQp+60WvqRd/L31rW+teoQUrrjiprj44uvjla/8k7juut+Jp576euzf/x9OOaZeP/asufHx\nY39+//dfFi960a9HT88ztyN++9v/PS67bDAajeUrV41oNA7H3Xd3RlVlvR6xe/fR+IVfuCU++MH3\nxfXXf/FZRyzF3Xe/PY4efbzpez355F/Hk0/+dUT0RK127PEYX/vab8f+/R8569/bsOH1J/69Vrso\nBgf/PC699BXP8ZM849FHZ+LOO78vnnrq69HTc0kcPfq35/xeZZF7yEn2IadM2a/X6zE1NdX0uLSL\nP1rTaDTiyJHHVrx+660Rb37zsT/P5rLLXhU/8ANfiKuuGokXvvAfR09Pb+zZ87vx67/+t1EUpx57\n5MiTcejQ5+LBByfigguujZ6e3rjvvnp88pN/Hffd95p44IGx6Om5KO677zXxyU9+JQ4d+u349rf/\ne3z607W4884nYnw8VrxnO/kf/+PfR1G8OWq1/yeuvPItERFx332D8ZGP3BL33ffqWFz8WszP/2I0\n24e7tPR0LC7+dUQcu2L4vOdtjnXrLo8vf/nvx8zMH53x53D55a87fhX2gmg0/r/41//69ef082o0\nGvHgg78Td9+9+fi+w6WIqLW0cAUAoDppC18WFhZaujSaWaOxFF/+8rtiYeE/xhve8ED09n5XRER8\n9KNPxM//fG8sLfVET0/ERz8a8Y53tPaed911ON7ylm8eL3eJmJr6Qjz00Ifjve/91eNlKA/F+98/\nHK997SVx6NBvx803f09885tXxMBAxIc//B/ioYd+N8bGPn7i73/sY3fGvff+g1Ne27Mn2u7xBbfe\nGvHOdx6JRmN99PQsxW/91u9GX99/PDH3cgnMxo1fiRtvfDh6ei4643s1Go24/fZr4vDhh4+/Uota\n7YK4777XxK/92p+e8efw2GN/FXfddWMcPvxf4+/9vbfE/v09MTDQiD17as/p53X48KPx2c8+PyJq\nsfxIip6ey+LlL/9wW5TvnI3cQ06yDzllyr7ClyZ27NhR9Qile/zx+aZXlZY1GksxP//34xvf+MOo\n1S6Ifft+P770pZ+LT3+6Fr/zO7OxtHTsV2dpKeL3f7/1GXbvvuCkcpeIP/mTR+ITn7jipDKUF8Qd\nd2yJF77wH8dHPvLJ+OY3rzhx7J13/lJ89aufOuXv/8//+f3xta/dccpru3e3Pk9ZJicb0Wgs73vs\niY997Hvjs5/dcmLu5RKYpaXFuOeen4ovfvHnYmnp6dO+V61Wi+c97+1xbPEVcWwB1ogvfvHfnPXn\ncPnlr42bbnosPvvZvxP79/ccP64WH/3osdKcRmMpiuKH4r77/tFZP8v69VfGa15zW1x66eDxeS6I\nRuNwLC21/5W/jLkHZB+yypR9hS9NfOADH6h6hFIdPDgbf/mXr4h77vnxOHLktP9DwAmNxtH40pd+\nLr71raloNBaj0XgqHnzwX8Q3vvHRiIh417uORE/P8hWfo/Gud7U+x4/8yBfiqqu+HhER1157JF7x\nil+OG2/cdeK1q676erz5zXfFF77wk3Hjjbvi2muPRMSxspTh4Yif+qnnn1IOMzwc8RM/cfGK19rN\nu99di57jaevpifgn/+SH4pd+6SdP+dxvfON0NBqN+Pa3Z+Kb3/xYfO1rHzzj+z3/+Zujp+eSE183\nGofj537uhqY/h56eC1aUz2zc+J749Kdrcf/9/0ccOnRH7Nv3ofj2tz99xnPXarXo67spnnjiSxER\n0d//i1Gr9cQFF1zT+g+kItlyDxwj+5BTpuy3WviS9rbPbA4cuC3m5o7tM7vwwmviVa/6eFx22atO\ne+wXvvCOePjhP4lG4/Apr99ww7646KJr4ytfGYuPfOSB+PjH3x1vf/vvxW/+Zms1uo1GI+666wfi\nrgotPBUAACAASURBVLuOxB13/Pjxh50fe9bdffcNxu23j8Qb37g7Xvay/xURETfe+HDcc8/zY/fu\nYwuZ5VsTiyJaeq3d3Hrrsauk73rXsdtkH3/8C/Gxj70z7rjjJ+ONb/xEfPd3f+6U42u1C+P1r783\nentftOK9Dh/+dnz2s1dFT8+F8fznb45vfeuP4+qrb46nn/6jln4OJ/+8XvrSz0ZR/OAp37/wwoF4\n/evvjXXrLj3t3z9y5FB85jNXxMDAe+P66/9tLC0diZ6e9ac9FgCAtdfKQ979t7UkGo2jUauti6Wl\nJ+Kppx6Kv/qr74/rr5+Ma6/9hRXHHj78jeO3h/ZET09vRDRiaempePjh/xoveMH/Hr29L4wf/dEP\nxo/+6J9Go3E4/uIvvhjPe95b4/DhR2Ljxv87arV1p53h4Yf/NP72b++OiI2xtLR04vV1666Il73s\niycWghERmzYtRk/PRVGvr1zEtPpau3nHO07dG3nRRS+KN7/5zfHjP/698fzn/2o88cS9ceedrz7p\nbzTiy19+TwwO/tmK97rggivjFa/4w7jkkuvj8su/L7785ffE/v2T8fjjb45G4z1NZzn153VjvPCF\nvxJf//r/FY3GsVtNDx9+NL7ylf8zHn/838X09LFHVZy80J6c/ON47WsH401vOnY7hYUfAED7S3vl\nb3JyMt797nev6TnayaOP/nncc8+Pr9iX9frX3x8XX3zdiuMbjaOxuPjVeOKJe+PJJ78cjz/+hfjO\n7/yVuPTS6+PQoc9FUbwpGo2nVvy9173unjhy5LE4evRgHD36eFx11Y9HrdYTS0tPxezsS+ILX7jm\nWSUnPxWvf/018eijH4/l8pBa7cK47roPxnd+53vX5GfRropiUxw8uPdZr/bE937vrrjqqs1N//7H\nPvb345d/+Z8/5+Kbo0cfj898pi+O/Z+CIydev+++wbjlls/FwsJFJ94vImLz5kbs21eLq6/+ZszM\nXNP2i+6TZcs9cIzsw//P3nmHR1Wlf/xz78ykF9IrEEoSeggCgYQqqAgEUFcQUXFViq7lt3bExrqi\nrru66roKFiwIyrqUJFhWUVBIQIQhhJYMJEBCJr3Xyczc3x83GTIkQIAAgZzP8/jInHvKe8/cF+47\n55zv2znpTL7fVsGXTvtzfVRU1OU24ZxR0y6UoNP5nEdrK03BFah53sLD/4WTU1irtdWk4D1xdu4J\nTLK75uo6yLZCdCo7d0YhyzoUBRSljujoZDw9R1Jc/A0mk5Ft2+bbiZykpj5BePhtnAz81CTu+fmf\ndLrgLyTkEZycelBXdxyTKZeGhkLM5nLKyn5qU/BnMKyguFj9c5PgS1sCM43GlT59PqW2NhOzuZS8\nvBWYzaVs2zadvDxHW38rV6bg4TGS3FxVaKaw0L/NY3QUrkS/FwgEF47wfYGgc9KZfL+tgi+dduXv\nSuTYsVc5evQFIiM/JjBwzjm1LS3dRGrqRBwdu1FffxyAcePO/7tPSelKfX3Oaa9Lkg4Pj1gGD/4Z\nSZKwWhsoKfme9PRIZs7sRW6uTHAwrFjxNQ4OtwISzs7h+PrOwNv7Rjw9R54x3UFnQU1qr5x2K21z\n9HqYOlUN1C4k5UVm5nNkZ/+NjIy+PPNMEkVFofj6nmD16j34+ExplzEEAoFAIBAIBO1HW1f+RPB3\nBXHkyNNkZ/8NSdIREHAXERH/anOApCgKVVV7cHMbRF7ep6Sn38vQoam4uQ06e+NWOHjwbvLzP0er\n7YLZXNLiuiw7M3x4Ok5OXVtcay42MmhQNUVFiXh5XYuDQ8dXi+zoXIjwTUHBGjIzF9O//5ekpk7C\nbC5qJsSTwN13b0Kn87GNMXWqQv/+eTg6Bp2x39raLGpqDuDjM+UC7kwgEAgEAoFAcCbaIvjSaYO/\nQ4cO0adPn4s6Rntz5MiTZGf/HVCQZSecnHoxcGASzs5h59zX5s0SBkMUubl77MQ8WkOvp4Xox65d\n9axbV82YMT81rtypZ8S2bZtBXNw3TJ58LyEhHW+PdWv3cqXa0973kpY2jeLi5skBZdTtwidxcRlA\ndfW/Wbu2jOjof9Cr1zZGjDiKo2OIXT2TqZCCgq8wGj+kuloV8omLKzrPLcvtx5Xo9wKB4MIRvi8Q\ndE46m++3JfjrtHn+NmzYcLlNOGcUxULT2TirtY6amgOkpk44r77q6r5g0aKNvPiiulVQrwertQGL\npcaunl4PU6aY7erp9RAfr+Xll72ZM2cEBkMURuO9LFq0kU8/fZHFizeSn38Oyf8uEeq9KHb3Amra\ngu3be5Gd/c/LYI+5hT1FRYmkpPSgqir1LG2tLdqeL4qitBCb0elOruhJkppTMDVVw6239uRf/4rn\nqadWkpk5CgeHILt+UlOvIzk5kCNHHrcFfrLsxu7dcZSX26ezuNRciX4vEAguHOH7AkHnpDP5vl6v\nZ+bMmWet12mDv6eeeupym3DOqMHfSZycetCr1+vn1deOHbfbhFeaxEHS0+9h61ZvTKZCQD0n+NZb\nL2A0au3qJSSA0aieQSsqCsVo3ENGxoe2/goL/UhKks7LrouJardqV24u/Otfr1JS8j+Ki5Oor88h\nM/Mpjh//+yW25+TcfvDBSurqssnN/Tf19UfR60dTWdl6VLdhg4LRKNvaJia2Wq3N1NYewWy2/4Go\noeGE7c+Kov4osG3b9FMEe55Gkk7+NWK11lNdnQ5Y7dRgrdYqamvTkaTL+1xciX4vEAguHOH7AkHn\npDP5flsFXzpt8Hdloq766XTBADg59SIn559kZb1wzj1Nm6YKdoD6/5Ej11NQsAZQ2LfvJjZvlkhN\nnUhc3AYCA2tt9eLjW7Y9XVlHo7mNfn6FjBjxJXv33sDBg3NQFBOKYiIrazHHjr12Ce1Rv1Nf3xNE\nR/+d7du7UVLyHQAWS1VjALirRdthwz7A11cV3GmP+S4v/6Uxp6N6XhN0dtclyZGAgLuIi9tgG9fX\n9wS33trDrp5G48Q11/zWon9J0uHn9wc8PIZfmKECgUAgEAgEghZMmzaNwMDAs9brtGf+rkRycz8i\nK+tZGhryaZ62wdf3ZgYM+O8599ck3DFhQjpmc8v0DQMHfouPz6RWRUTaWtbRONXG6uqD7NzZz66O\nJDnQvftzhIU9e8ntOXz4UXJy3mxuDbLszODBm/HwGGYrbTqzaTTuaZf5PnjwLvLzP0en86dr18ex\nWus5evS5ZjU0ODiEYDIdt4nAjBu3i7lzN7boa+fOgVRX77MrkyQHYmIMODl1uzBDBQKBQCAQCASt\nIs78nYHXXrs0qzvtiaKYaWjIo3ngZzAM5t13Z7B798nATa+HJUvOfg4sOhqeeaYcWZ6IopxM7i3L\nTnTr9hw+PpNs9Z5/3j7AaGtZW2mrzRfa56k21tQcANQtrJLkiCy7oigWcnPf5VL9MNJ8mLq6o7Y/\ny7ILsuyM1VqD0fgJJ068T2npzxQXfwPArFnLz3u+T6V79xewWn/khx9ySUk5ytGjz+Hi0oeGhnV8\n8snzGAyDMZmOEx7+L2677WPuuedNxo+/1q6P1ath6NDfSUjoZ7MfJEBD166PdYjA70r0e4FAcOEI\n3xcIOifC91vSaVf+rlT1n/z8Lzl0aC6KYsJgiGLRoo0UF4fg65vD0qVT0Wq78Mwz35CX59KmPGzH\nji0lK2sxsuyK+qIOimJCq/UkNjb/kpzRaq/8dC37VMjNlez6rK/PY9++afTo8Ve8va+nvDyFjIwF\nuLj0w81tIM7Okbi4ROLs3BuNxrl9bvAcbPTweJry8i24ukbh6tqv0ZZIHBz8+fVXVyRJawvUx4yp\nR5Yd2t0WX98cPvpoDV27PmpXtmrVLq67bjoAVqsZWdba2q9eDXPmWFAUDZJkZtmy/cybF4XB8GcK\nC9cwfPghtFr3drH1QrhS/V4gEFwYwvcFgs5JZ/L9tub507ZW2BnoaA+C1Wri+PG/ERg4t9XceE0E\nBNyGk1N39u6dxLZtM+zEN3777U4aGirIy1OVGZuEQM4USAUF3YeLSySKYmlMKK7+38kp7JKJcyQk\nqLa21ea293lS3OWjj77kjTfGU1S0lqoqPWlp8QwYsAEfn0kMG7b3Au/g/Fi/vp7cXEebjYmJ8Pzz\nr7aoZ7FUN57Dk5ut0GpITg6iX781aDTOFBWto2fPv533d9Z8voqKQtmz51H0evuylJRQrrtOrd88\n8ANYtgwURV1BVRQtq1ZFMW8ehIe/SXj4m3QUOprfCwSCS4PwfYGgc9KZfF8IvlxhVFfv4+jR59Dr\nR2MyFZ2xrqfnSIYO1TN+/E78/UsBdcXsgQce45FHlpyT8IqDgz9+frfg7z+TgIDbCAiYQ2DgnXTp\nMro9bqtNTJtGu4qXNPV5Ukwlh6ioV0lODsRg+BOKYkZRTOzbN53i4pZn1i4VMTErz3rf+flfsnWr\nD/n5K5Flx2ZXLJjNZdTWprN37ySys/9Off3x87blQkV8FiwAufFvE1mG+afJ9KEoCvn5q6mrO3be\ntgoEAoFAIBAI7BGCL2dh+/btjBgx4qKOcS4YjSswGB5EURpwcelDdPS2s26TUxQre/bIV6TwSnMq\nKn7jq6/mc+TIV8yaFdluNp86D7m5y8jIWGhXR5J09O//Nb6+09pn0HNgyxYnMjL6tCraoihWsrIW\nk539DxTFSo8efyU7+2+YzaWABll2om/fzzl8+P+orz+BLDsRGfkhAQG3nZctFRW/sX79UvbufYbZ\ns6O45ho10DyXZ2n1ali+XA38Zs9Wy2prj5CT8xY+PtPw9p6I2VzF1q3uSJKO7t1fpFu3x9tt62pb\n6Gh+LxAILg3C9wWCzkln8/22CL502m2fqampHephqKzchdVaB1ipqckgLW0yUVE/nrLaY48kyURH\nt3wpb62so9HQUEpx8Ub8/G5m9+4YwsNh3rzIdh3j1HmQJC2SpENRGmhS0bRa68nMfPqyBH+KUs/g\nwRbmzbMvN5urOHBgJqWlPzfaqsFiqWrc+lmBo2NXBg1K4tCh+zCZ8gALVmst5eVbzzv4M5srMBic\n2LSpBlfX+/Hz60tw8EKio93b/CzNnq3+pygKpaU/cfz4q5SW/gxYkWVnvL0notW6IUmOKEo9x4+/\nRF7eB0RGrsDLa9x52X2udDS/FwgElwbh+wJB50T4fks6bfC3YMGCy22CHRUVKYAVUIMCVYjkAfr0\n+ejyGnaRyM1dRlbWItLT7wMgJOSRSzCqDp3OB2fnCNzcBuPi0gcXl0hcXftfgrFVCgsTyMl5Ay+v\nCQB07/5cizp79oynunpPs/N9FiyWKtzcorFaaxkwYC2Zmc9QWbkTsDTWsVJW9lObbDCbqzhx4h2C\ngu7DwcEPgG++ieHll8ehKFpSU0ehKHczcWIQffqswN//1jbfX0XFbg4cuJX6+pzGwFVBll3o0mWc\nrY5O543JZMRqraOu7iipqdcRFvYcYWHPt3mc86Wj+b1AILg0CN8XCDonncn39Xo9M2fOPGu9Thv8\ndSQURaGm5qBdmbNzL7p0mXCZLLr4aDSujStvagL53Nx3aWgopG/flRdNaCYo6C6Cgu66KH23lerq\nVMrLf6WiYjug5tc7ceLfDByYaNvm6+//B44dO4TFUmVrZzaXM2hQEoqiIEkSHh4jKS39kdrajMYa\nGmpq0rFYas+oUmqx1LF37w1UVCQDEt27P01FxU7+8Y96FGUUoAq2JCb+kWuv/ZKGhjOfPz0VkymX\nurosmqcjsVpr8fSMtX12cAjEZDI2ftIiSTJOTvbJ4gUCgUAgEAgEbUcIvlxBKIoZN7fB+PicVNSI\niUknMPD2y2jVxUWjcaX546coUFr6v8bVoqsXT89YZFnd9gjqKm9l5Y7Gs3wq3bo9xcCBTUI0aiBs\nsZSrnxoD48DAO4iJSbe1CQ19CF/fm2z1W8NqbWDfvngqK3cBkJW1iM2bJXbvHs7Uqe8gSebGMcxM\nnbqc8PB/ERJy/zndn6/vVAYMWGdnu7Nzb7RaT1sdR8eQZi3MREX9j8DAO89pHIFAIBAIBALBSdoq\n+NJpg79FixZdbhNsyLKOIUOSGTgwgQEDEgEwmQovs1UXF43GjabgQJKccHXtz9Chey+p+MflwN19\neOPZThVJ0jFgwPoWCdD37BkLwNixZgYMSKR377da9NUk1hQWtoTevd9kwICv0WicWh1XUSwcODCL\nsrKttsCziejoZF566SvefnsjgwdvYvHiOUyYsAaD4f7G9B/nxr59MwDQaNwBCS+viXbXPTxi0Wq9\n6ddvTeO9jjvnMc6XjuT3AoHg0iF8XyDonHQm34+OjmbNmjVnrddpg79HHrkUZ8zOHR+fKQAcPXrx\nzz9dTjQaVxSlHklyxNd3BkOGbMfRMehym3XR0WrdbVscJcmB3r3/ibf3DXZ1TKYCAMLCXkSSZHx9\np7aa+7G4OAmAkJCHzzpuZuazFBWtQ1HqWlxzcYkAYN68YXz++b+YMGENQ4bsAGDbNv9zuDvYuXMw\nAFFRPxITcxh//9sJCrrXrk737ouIjc3H3/9WBg36AYDs7DfOaZzzpaP6vUAguLgI3xcIOiedzfej\noqLOWqfTBn9tWRa9HEiShMEQxdKl/uj1l9uai4eDQxAgExb2F/r1W3XaFaurkSbxk+DghYSEPNDi\n+ldf3c8nnzxPScnJHwD0eliyBLtn4v33t/F//7eJr7/uctYxvb0n4eo6GNDYlUuSIzqdDwCOjsH0\n778WgN27Y9BounDwYCiPPLKpTc9iYuLbvPvudGpqPsTLawIODn7U16/kjTeuadG+KUm8t/dENBpP\nli/fwbhxZlavPvs4F0JH9XuBQHBxEb4vEHROOpvvp6amnrVOp83z11HR6+HGGyvJz3cnOBiSkjp+\n2obzxWKp61RBXxOVlbspLPwvPXr8BUk6GYxVVe0lI6MvN9xQSlGRv+37N5nymDLFSnFxsK3s0CGY\nM8eComiQZYWVKyVbbr3T8dtvfampMXBSIVTC3/8O+vX7DIDi4u/IylpMVdVuAAyGKBYt2khxcQjB\nwfCf/2QxcGAl7u6DWvT93XeruOOOsba6GzbUcODAbTz66Hu2stM9yytWHOPee0NQFC2yDB9/nMeQ\nIfNoaCjBbC7DYqnAbK5Eo3EjJsZwRkEbgUAgEAgEgs6IXq9n8uTJTaIvp83z12lX/r7//vvLbQLQ\nlMz7RcrLtwGQkAD5+arqY26ummD7aqUzBn4A7u5D6NnzZbvALzv7n/z+exSrVu2lqEjdapmbC+vX\n1/Hee+9RXBxsK0tMhGXLQFHU9larxLvvqmdEa2uzSE+fR0NDGSZTPiUl33P8+Gvs23cLEREfIMsn\n51yWXfDxmWT7nJ//OVVVe2yft22bTnFxiG3cjz5aTVra5Bb3U1LyI19+mWFX9913X+Wnn6Ltypqe\nZav15JnD6uoDvPXWERRF23gNPvxQS3FxEhUVydTUHKC+PgeLpRJHx9Az5r1sCx3F7wUCwaVF+L5A\n0DnpTL4v1D7PQllZ2eU2oRGFY8eWoNePITPzWeLjzQSr7/kEB0N8/JlbC658cnLeITPzSUBi1Kgf\n8PcvASA4WCE0dARxcRvw9c23lcXHw4IFIDd6rySZGT/+Qfbvn0la2hSMxg9JSQkiOTmQfftuJjPz\nBYqK1nL8+GtYrdWNbRyxWmvw9BxtsyMy8gO6dBlrCxDj4jbYbPH1NTJixNeYTLmYzZW2Ng0NxRw4\nMIu4uPX4+p5orJvDDTfk8/DDz7Z4lsvLk/nlFxdOnHiXyko9O3f2Jz5+GbKs7kCQZbj/fh+cnHrS\nXLlUkrT067cKSbqwv7I6jt8LBIJLifB9gaBz0pl8v61qn2LbZwdg82YNYEWWnXB2jsRiSeR//+tK\nfPzF2fKZkXE/Vms9fn4z8fIaf8GrKYLz58SJ9zh8+BFbigtX1yj27NGQkjKNkSPXER6eSnDwg2zZ\nso2UlOlMmJDBlCkL6dJlNKtXw/LlMH8+REXdSUHByjaNOXashezsf1BRsYMBA762u2a1mti//w+U\nlv6A1dpARcWrJCXpGDbsc8LDdyHLTkRFbcLTMxZFUUhLm0pp6Y8oigmDIYrk5GnMmzeX2NhegLqN\nOTER27N86NB95OWtAE6qiPr53UZy8mOsWhXNvffW0LWrj13KD1l2okePV+natXMd2hYIBAKBQCA4\nF3799VfGjBkDZ9j2KZK8dwAkSYOiWLFa66iu3ocsR/CnP23Ax+f6izJeZeUuKit/p6BgNSDh7T0J\nf/9Z+PrOEIHgGdDr1W2506a1T1Cel7cSg+EhTp7Bg9paA+HhdYSHq+fuAgPvwctrHJGRKwgPfxGQ\nSE39Dx4esdx882pmz1YVUnNzR1FQ8AXNk6s3sWnTTBITFxAfv4wJE9ag14+ipmY5mzY9QUPDyXtR\n78+B+Pi1+PrOpaBgFSUlm2hoGAmYG3uT2b49m+3bYeTItTg4fEfzQE5RZIzGj4GXAbXvpv6tVhOF\nhV/Z1XdxGdhYlkn37pOord2AojTQo8cr1NVlYTR+gIvLAEJDHzrtPLb39yIQCAQCgUBwtdJpg7+6\nujqcnC7dmbOcnH/h6TkKd/fBLa5Jks620tEUCFoslS3qtRdBQfOort6P1VoDQFHROoqK1jFwYJIt\n1YTAHr0eJk+uJy/PkeXL20eIR1HMyLIDVmst6hZHxfadNJGX9yk6XWCz3IAKitJAefmvmEy5ODoG\nYbWaOXz4z7Y+mrNp00xefvkLFEVLWtpYQEe3bvtYtMiL4mJs9wIwZYoZo1HL8uUaEhM/x2y+iyee\niKaoyJ+kpHtYunQqAIsXT6CwEPz9J7J0aSx9+qSTkTGARYtWUlwczLffluDi8inBwV9hsZQ1bhO1\nYDIVYbHU2tlXU7MPg2Eoixatpbg4hI0b57F06VT69AkmOHgeFksFYWFLkCQZRbEAkt3WT70eJk0q\np6DAs03fy6X2e4FA0DEQvi8QdE46k+/r9Xpmzpx51nqd9szfM888c8nGqq3N4vDhh9i3bzoWS02L\n65KkpUmCX1FMjBiRib//LRfNHj+/W+xEN0CDt/ckvLwuzkrj1UBCAuTlqaui7SXEExR0N6NGlTZ+\nUpBlF7vrsuyCLOtwdAxt0XbIkO24u1/TWE9L//5r6dr1cdzchiJJLjSdl0tMXNBMTEXD1q0ryc3d\n00KIJSEBjEZtY5lEUpLMtm032MRniopC2bHjdpKTb6Gw0BeAggJPTpz4lbi4ArZsGW0TpSko8GbN\nmkxKS7+loiKFmpp91NQcxGwupPkqZ9N9b9t2o82eoqJQdu36E35+t6LT+dCv32pbHsJDh+5jyxYt\nv/7qQUpKd37/fQjvvfdvCgo87e7lTFxKvxcIBB0H4fsCQeekM/m+EHw5C0uXLr1kYxUUrEGSnGho\nKODIkcdbXPf0HI2PzxT69lWTnBUVbbio9uh03nh5TWj8pAEsyLIrsqy7qONeyUybpgqZQHsL8ahB\nf7duT9Oz52uoQZsGN7dowsPfJS6uiNDQ+xuTvEs4OqrJ3svKNtv14uMzieDg+6mq2oWf33S6dVsM\nQHz8MiRJ3bIpy+r5wGnTaCbEYiE+3r7M1zen1bIHHnicqVPrWszDgQOziYvbQFCQqbHuCWJjT32G\nJdSNBhKnEhe30danr28OMTFfUVm585QfKNRk9JKkw2KppL7+OFVVeoYNW35O38ul9HuBQNBxEL4v\nEHROOpPvX3WCL4pipb4+Gyen7s3KFOrqjuHk1B1JavlS2VHYvr0ndXVZgLrKN3Dgt3h7T2xRr64u\nm+3buwEQF1eKTnf25N3nS0HBGg4cmIWHx0icncPJz/8Mb+8pDBqUdNHGvNL54IPB/P77/SxcuKDd\nzpYdP/43MjOfYvToWurrszEalxMY+EdcXfvZ1Ssq+gaLpZyAgNls396DurqjxMRk4uzcw1YnM/NZ\nsrNfp2lrKEBMTBbLlqWwZk0cDz3Ujdmz1fyKP//8E//5TzZDh77HTTf9BV/faej1sHr1XsLD7+KO\nO9bi7NyT3bstvPPOEmJjE7jzzgS2b+9OXt59/PxzNDfcUMCECZPQ60fi4zMDs3kdiYkwZMgy3NwW\ntrjX7t3/yrFjz9qVSZIDYWF/4X//W01y8jRiYxMID08FJDQad0aNKrFthc7IWIjR+EGLfl1dy/n2\nW4+LJpAkEAgEAoFAcCXQFsGXDhv8ubk5U1Wlp7z8V0pKvqeiIgWLpYrIyE+wWMopKfmBioqtmM1l\n9O+/Dj+/GZfb/FaprT3Cjh0RNBe5AJmQkEcICVmAi0ukrbS09CdSUydgMESxbdt0brghmzFjhuPr\nOx1Hx6A2j9kWAQyLpY7CwjX4+t6MVuvGkSNPkJ39d3Jz78ZgWNGpxDPONF+KolBfn42jYwhbtmjp\n3v1ZevR4qd3G/uCDwWzbNp1HHlnS5vlWFCtbtmjYtGkmv/76JQsWSMyaZSY5OZDvv59gE3e5996B\nhIWdDLZqatLJyXmLvLzPAKvtrOGwYftxde1rq7d5s/pDiofHKDZsiGTDhtttYjEAJtM6Vq3SExf3\nLeHhOwEYO9aKXm/m/fc/ZNiwZY0BnBaDoT/btk0nLm5DYxl88cUTJCYuJD5+Offdt4Ha2kOt3udP\nP93F5s3vcu21jzJuXMugDyAg4A4CAu7C1bUfDg7BZ/0RSFGsmExGamuzUJT6ZivgAoFAIBAIBFc2\nV2zwV1pazP79kZjNFUiSpvElFdQtYzKy7NhMGENDbOwJHBwCzmmsr776ilmzZrWj9a1z7NgrZGU9\ni33wByDj7BxBTMxBW4miWFixYjxPPrma4uIQfH1zWLp0BoMGVRET0/oL8qmoAhgVFBR4EBx8bsIk\nGzcuY+7cqRQXh5xz2ysVVciljrw8p1bvOS/vMw4dmkvT9lgnp574+Eyla9cncHJqeRbvXMe+7roT\n5zXfK1Yc5957g1EULbKs8P77u8nOfpO//vUTFEWLJJlZvHgOjz56P15e4wDYu3cyJSXf0VwUB2Pa\nYAAAIABJREFURqv1IS6uEEmSMJnyycl5i+PHXwHsxWKa+uvWLZ1nnvmWoqKgxudzKuHhqZSWvsT8\n+fdSVBSEv38pSUkOHDhwK4899oHtWX733df54QdHPvywaXurwn33PcWcOa83uzMJSdKxc+c/efLJ\nebaxX3nlDR57bCE7doTT0FBgNxeS5ISi1CHLznh6jmbQoO/sgsC6uhwOHpxDXV0m332Xy/jxViRJ\ni6IojBpVglbbedLKCASdlUv1b75AIOhYdCbf1+v1TJ48uenc32mDvw555k+SZDw8RgKSLfCTJEdc\nXQcgSTq7wM/P7w/nHPgBdOly8bZUNsfdPQYfn2m4ug5pcS0i4l27z5KkYc+ep+3EL5KTp9Kjx1/a\nPF5CAhQUqC+z5ypM8vvvC1oIgVztqEIuqgpUa/fs4tK3UZBHFSqpq8vkxIm3KS//9YLGtVob+Prr\nvPOe708/7dZMyEVi2TIrCQn32MoURct33z1lt006PPxdHByCOHnuTsLHZ7ItUDIaP7EFfmAvFqMo\nWpKS5rNt23SKitRVaPX5nAbIbNjQYCsvKPDi229d2b59tt2z/P33ASQmLrQb/8cf/8bAgd/j7h4D\ngFbbhf79vyYpacEp9/IksuxEQ0NRi7lQlLrGeajHZMrl1B9aTCYj5eW/UF+fg6tr0zUNQUH3iMBP\nIOgkXKp/8wUCQceiM/n+FS74ItGz52soSj1NL4qKYiYy8hPbix6AJEl06/bEeY1www03tIehZ8Xb\n+1oGDlxHZOSyU65YcXcf1qL+rFnd8fU9AajiF7Gx6/H2ntTm8VSRDsXWfvJkNVAuLd2M2Vzehrbq\nn9tX1KTjcjYhF3f3oWi1PrbPkqTDwyMOf/9z/xWptjaLgwfvZMeOSH75xYWwsCm27zo4WDmn+V6w\nAGRZabTJzI03/v0UcRcLjz46xO5MoJNT98bgSEGSHJFlJ3x8Tg4aGvow3t6TkWU1GG7enySZmTp1\nOXFxCfj5FQJNz+e3eHrGMW/eHXbPzpgxWxky5G27Z3nEiDXMnXvc7j7mz4eSko1UVu4ANFgs9ezb\nN4PRo29Hli2N96LWq6010PT3gSQ5IkmnSjdb6dv3CyRJY1fq4TEMPz9V+nj4cBrby/To8XLbJ1wg\nEFzRXKp/8wUCQceiM/n+FS34snv3Q5SXvwOALDtjtdbh6TnKttqi0XhisVTg5jaYoUN3X0aL246i\nKGzZIgMy/ft/zf79NwMwenQNGo2zXb1PP53Eli0xzJjhjqfnkwDExRWh0/m01nUL9HpYt66Krl1H\nER6eSlDQAozGZXTrtpiePf961raJiXQq8QxVyGU+Cxc+0Oo9Hz78OCdOvIOimJBlZ4YPT29U3zw3\nioq+Yd8++zyKBkMUev3jzJt3xznNt6IovPjiPSQmzuWPfyxl4ED1edq0aSZJSfOZOvVDFi/+m52d\nTWf5oqJ+5tixv1BWtoW4uEJ0Om9bHbO5GoPhAQoKvkJR6pv1t9x25i839x5++CGE2NgExo+/jl69\n/oYkSbZnZ8qUOurre2Ey5WIwRNmEXO65ZxsajSuvvKLmF5w/HxYtUn+YSE0df8odakhL+5q1a2cw\nfz7Mng2FhevZv/8mHB270bPnKxw8+EfA1GJuBg36Dm/vk3/ZHzhwOwUFqxs/SciyI2FhS+jW7cm2\nT7hAIBAIBAJBB6ctZ/465MrfP/+pvqSOGVPPkSN7+fOft7B2rbqlbNSoMoYO3YWzcy/Cwtq+HfJU\n2rIs2p5IksQXXzzBbbcZ+PDDm4iJOQzAwoUvEBZm5ZVXTtYLC3sBRZEoKlpPRMT7AHzyyQSee64S\nvd6+X70eliyhRblG40Z4uNrWaFRXHUtKvj+rndHR8PzznSfwAwgPT+Whhzad9p4DAmbTtFoWEfH+\neQV+AL6+k+nT5zMk6WRKjfDwVGbOvJPycom0tBnU1Bxm9WoYNw5Wrz59X7W1BsaP/5w33hhvC/wA\nJkxYw5tvTmTixLVkZT0HqM/G/PnvYDBEMWDAery8xmG1fs+mTRXs23cy8NPr4eGHE9i6NQ0npzBA\nPROo5g08SXW1HmdnVY00J+fvHD78f2zf3oPIyHSefx68vP6K2Vxiu7+5c5dyzTUutjyGkybBXXeZ\n6N//dTZvlloEfpLkQM+er/DQQzP4+Wc18FPnL55Bg34gJuYIDg4ByHLzv74kPDxGAbB37yQeeugD\nunc3MW/ekxQUrMbHZzrR0SmUlChotV6Ehj5y5i9LIBBcVVzqf/MFAkHHQPh+Szrkyh+U8fTTBxg4\nMIY77lBQFA2SZGblSg23394+KR0WLVrEK6+8YldWX59HaekPyLIjkuSALDsiyw44Ona1JZo+HYqi\nkJ4+D6u1Dll2pK7uKN7ek9HpvPH3n8XSpWaef96dpm1rS5eq4y1ZEkCT+MWDDy5j8mQH7rnnNvLy\nXBoFNW7C0zOWBx54soUwiCruUk5BgaetHODGG6vIz3fD1zePpUvjCQ//vXF+dYweXYksO7bLHF7J\n1NZmkpo6AVl2oabmAADBwQ8QGDgXD4/hdnUVRWH79m64ukYxcGDiBacVKS7eyL59N6Mo6qpVaOij\n5OS8AdiLrMgyrFx5MvhpjtH4EQbDI1it1WcYSUNt7cfcffc0Cgq64OdXyHvvvYWimHj44aUYjVqC\ngxWSktT7Ofnc5LB06TSOHw/n5ZdX2fyvSfBl0aKNzQSJphIRcRCwEhGxnLq6LI4dWwpYkCQtkuQA\nKFittcTEZHLoUA+bIFFT++uvv5Xa2iPk5X2KJGno2fM1unb98xnnMCPjIXJz/03T+T5V9KWe2Fgj\nTz+9jTffvIkmv3rhhRO8+KIqzvPIIzfz0kvP4OExtM3fl0AguPJp7d98gUBw9dOZfL+tgi/aS2tW\nW5H44ouBeHtvRlGuBVTRhw8+gNtvb58RWnsQ8vJWkJX1DLLsStOLo6I0oNV6ERtrPONLv9VaQ1HR\neszmYltZefmvKEoDBsOfePfdfcBJcYnly0FRmu/LlfjPf66jsvIz8vLUFRJVUONGQNdCGCQ6uknc\nxdNWnpBgBrTk57s1tg8kJWWKLfgDmcrKXXh6xp7jbF2NSNTVHbUryc19D0nStgj+JElixIijKIql\nXfJJ+vhMYfDgn0hNvR5HxxB69/4HvXv/A4BFi443E3JRn5PWgr/S0k2nBH46JEnG3X0oNTWHGp9D\nC199dYSCAvWwc2GhH0lJMuCG0ahtvGeJhASF7Ow3yc9/FDgpNKTXj0NR1PNzTYIvUVG/nCJINK0x\nhYPM8eOvUVubjotLf9zdh+Hk1BWt1gsHhyCcnXvj7NzDTpCoqCgUo3EP3btDWdkW8vI+pVevfxAa\n+tBZ51CjccXJqRsWSxUNDUVERn5Ily5j0Ol8WbVqCM1FZT79NJQXX1Q/vfXW2rZ8RQKB4Cqjs7z8\nCQQCezqT71/hgi8KN9zwF+Ljl9lELZpEHy4mISEP4uAQgtVag9Va1fhyLRMQMOesL/0ajSt9+35K\n8yltSrRttdYSH/8+zSX2589XRTtOohAfv4zx43/Hz08NIFVBjQRiY5OaiZJYbcIgzQVafH1zCA4e\nyvDhn9mJbIwbl9q4zVBGUUyUlW05z9m5unB27kGXLtfalcmyC927P9dqfUnSIMsO7Ta+p2ccw4al\nMXjwz3blEyc+0Uy0RX1OKit3UVWVZlevtPSnRrsccXAIplev14mLK2DIkK2MGlXEqFFlREcnM3Fi\nuu3Z8fXNIS7ue26+2ceuLDg4mpiYzwgIqLSVxcYmEB+/3E7wZf58mbi4Dfj65jfWO0FsbEKjRVZq\na9MBqK3NIDT0QXr0+Au5ucvIzHyyUbzp9KJCXbqMZfToqjYFfgC9er3KiBFZ9Ov3FQDHj7+KojSw\nZYuW+Ph/c6qvCQQCgUAgEFzNXNGCL3fd9TCvvNKP4OAFrF59UhyitRWQ9qaiYgd6/SgUxWwr02g8\nGTnyGFqt5xnb5ud/xcGD6vmw5kiSI4pSz6+/lvHxx542oQvATvxizpzXycx80k4kIzw8FVl24fjx\nyWza1Jc5c0YyceKNtr6bRDZuvLGM6movAAyGaJKTpxIb+z3h4b813QVgITj4fiIi/n2h03RVUFz8\nTeP2y3pk2YmePV8nNPTBy2aP2VzO1q1dSE1dw/r1t9qe+e3be1JXl4WX10TCwl7CYqkkLW0aTk5h\n9OixFD+/6UhSy99xiou/IS1tSrPnaSNDhuhwcupBcnIGKSnTGDlyHeHhqXTt+jgpKVls2tSP2NgE\nIiIOoigmtm59ioSE2dxww1Jeeukr9u27iZSULHJyfmPYsA9xd/+zbfvqSSQcHAIZNmw/u3YNo67u\nCJKkJShoHr16vc7eva6tigrV15+gqCgBb+8pODt3a9OcpaXFU1ychPqji7oFdPhwA2+91dtOVEYg\nEAgEAoHgaueKTfL+1lvRzJu3FmfnsFbr6fXqlsdp085fmGTZsmUssF96s5GVtYTs7FcBkGVX21ZO\nT88xKMqPJCbqWoxdU5POzp1RthWO1vvN5NNPe7BgQeuBrNVq4rffIu22I2o07lgslY1JqS34+99G\nv36rWu1fTSj/HE056VQ5fG2z7YEOhIb+H717v3ZaGzsTimIhJSUUkykPZ+dwhg07gCxf2p3QFksN\naWlTcXMbQmnp/6iuTmPsWItdMLdv360UFX0NyEiSBgeHYOrrj3P48DCSk+OZOrWBa6+9Fk/P0ezZ\nI7N2bRnduo1r3I4JeXn38d13ocTFJXHrrX9nz56JgLmFLZKks61WNzF2rBmTKY+UlFD69Pkcq7We\njRvf4fffH2LhwnsJCPiIjIx5gMKmTTNJTFxAfPwyJkxYi5fXeNLSfPjxxwji4jYQEZGOTudNv36r\n6NJlLADV1YcoKlpLfv4q29nLsLAXCAt74axzV119iJ07B2AwDGDbtunExW1g2rSnCQi47bRtzuT3\nAoHg6kX4vkDQOelsvt+W4K9DnvlbsuQLwsLWMW1aS9EHvR6mTLFiNMosXw6JiQ0MGaJrpZczExUV\nddprYWHPUlycRF3dEUaOPIYkObJv3zR27Mhl0aICiotDWL78pPAKgMlUgEbjgqI4oigmrNY6uz5V\nIY+uKAr82pgf/NQAMCfnLerqjtmVWSyVqFs21Zf1kpLvUBSlxTZUs7mKo0efp3mCa0WpPyUYNZGX\n9wE9e758yYOcjogkaQgN/TOZmU8RGfnRZZkTRbFQVvYz5eXbbCtoRUXr8fGJR5bV59rTM5aSko1Y\nrbUoipX6+mMYDFEsWrSW4uIQEhNPsHRpPB4eI3jwwRcoKgrA1zeJ1atTcXeP4eGHJQoLffjuu8fw\n9X0Tb2+Zlr/5SC0CPwCjcQWKov6YcOjQnRgMQ2yCL0lJkJR0L127ZvLJJ0d4+eWVKIqWvXvHIMvz\nCA+v4c9/fouiIn82bpzH0qVTCQ9P4+jRlxg8eCzp6fdjNC5r/GGjwWZHXd0JcnLeOqsi57FjL2Ew\nDGbRog0UF4ewceM84Gbuumssjo5BrbY5k98LBIKrF+H7AkHnpDP5vl6vZ+bMmWet1yEjgJKSEH7/\n/T769v0/Cgq+QqNxQaNxRaNx49//vg2j8WFAFTl5552XeOCBBAID76Jr10fbPMaIESNOe02SNAwe\n/BOK0oBG40pZ2VZKS38gI2N3q8IrAF26jGbUKFXe3mpt4JdfnGgeiCUmLrAT8njnneMMH/4eJpMR\nk8mIg0MQXbs+Rk3NASor9dTWGrBaaxpbn+zHYqmkunofbm4D7WzWat2IjPyIqqo9VFcfoLb2SOMK\notmWWkBRGrBYqigp+QZf32ltnqurmZCQh/D2vrHFfF4qNBq3xuDn5NbJ/ftvoUePV+ne/SlATTR/\n6lbi7dvnNBNeCSE5eVpjepCAxrJQvvhiOYryG4WFSwDIz3cjMdHK3LlKY/7M2sbVPnOL/pvIyJiH\nJJ38a2Lbtql2PvD2288zd+5SEhM32Z5vRdHyyy8rkGUoKsJmT3LyNMaOHU2PHi8B0KXLOIzG5S2C\nzry8D3FwCCIk5KFWt7MC1NXlUFDwJcnJz9sJ0KSmPo5O53va+T6T3wsEgqsX4fsCQeekM/n+FS34\nEhgIN93kDig0NBRRV5dJdXUaFRUpxMR8bCdWERubQHV1KqWlm9rVBq3W3Zb8uqxsC4qiEBFxL0FB\n6kt6c7GKU6mtNTR7adUCMvHxH9iJZ0yY8AzZ2a+Tn/8ppaX/o7T0R1xd+9OnzwqGDdvD6NFVjBhx\ngl693sLb+8bG1BPOKIpCScl3rY4bGHgXvXu/QVTUd4wYYWDsWBMjR+YxaND/CA39M87OEShKA2Vl\nv7TnVF3RaDTOly3wA1VJVKPxaPbZAWfn3gQEzLGVublFY7XW2+UHnDHD2U7YJzZ2PXFx61r4xuzZ\nfQkOVgO74GCF+HgdPXr8lYEDk4iLKyU2toCePV9Hpwuw6x90yLILwcEPcFI5k0bBF/sxAgPv5vHH\nR7YQqhkzZvMpNiZQV5dFZuaTpKXdREDArFPGBDWfopa+fVeeNvAD0Ol8CQq6j5kzwwgMrG+8P7j3\n3pm2FVOBQCAQCASCzsIVLfjyyy/ljB7tQWHhOvbvv7nZdVWsYu9eZztBFEnSERdXeFZBluYcOnSI\nPn36tKnunj0TKCv7CZDIzBzF0aMJ3HRTl9OeNywo+A8HDsykKV1EE5s2zSQpaT7Tp69l/PiPmm3J\nlAgOXkBExHuntcFsrqCg4Etyct4mJORPhITc3ybbT0XdnuqBRuN0Xu0F7c/27b0bRVGccHcfwqBB\n37R4lnftGoEs6+jT5zN27OgJgMEwmJSUGTbRFrWsSdwliVmz3sfDY7hNFOhUgZXmKIqFgwfvoqBA\nPU/q7T2FPn0+xsHBn7y8VRw6dDIYbRrj+utzmD37Y1v5c8/NIilpAU8+eS2zZ0NBwdds2PAqycmT\niY1NIjxc31hTBiRcXPpRU2OvYipJjgQE3EmfPh+0ef7acn9NnIvfCwSCqwfh+wJB56Sz+f4Ve+av\nuHgjmzerCf1UEYmFxMe/z4QJa6itzSA8HNvLLoCimNm9ezRRUd+f9qzPqWzYsKFND4OiKFRW7mz6\nRM+eKfTvP5yBA3+ned6+5nh5XUtNzUf8979ljBjxReNLr0K3bulERf1CSMi2xq12MgbDQJtYRW6u\nRNeuj1Ne/iqJiRo7URmt1oPg4PkEB1+Ybr2Dg/8FtRe0PzqdH3V1R/D1jadv35WtppQYMiQFdVVM\nJjo6Bb1+JFptIJLk2qKuokiAlYyMhVxzzS6io6U2CCPJuLldQ2npT7i49KW6eh81Nek4OPjj5BRi\nVzMi4hDh4WlIkozZ/GazQFU9S6goCqWlP5GT8wbh4bsID99l1/6LLx6z+fScOWrwJ8tOWK11aLVd\n6N37jbZNXCPR0W0Xfmqr3wsEgqsL4fsCQedE+H5LOuTKn7f3AV59dTbwIQsXDsFqlZEkM4sXz2Hu\n3FByclp7OZQYOjS13bfw1dQY+O23Ppw8d6emTIiI+IDg4PtabaOK0pgxGrX4+uawdOlUAJtQRutl\nJ1i6dIpdWXCwvaiM4OokL28ltbWHCQt7/oxbHZuTlPQud9894wzPUw5Ll97E9OlP4O9/5sO/BsP/\nkZ//OWZzWeNZQFUdNjo6BU/PESiKgtlchtH4ITk5bxIS8ghmcymK0kCvXq9jNpfy8ccG7r9/KIqi\ntfnqhAlrWoz1xRdP8OGHr9G0Kr5oUSqLFikUFydy9OgLhIY+jpNTKA4Ogfj7zzqneRQIBAKBQCDo\nrOj1eiZPntx07u/KWvkrKQnBaNzDTz+p4iigikhs3foVS5aY0On8yMo6NXmXgkbj1u62VFfvp7ng\nilbbhcjID/H2vv60bdavb8BoVM8dNQldKIpkJ0zRsiyElJSbcHcfeVpRGcGlpb4+j927Y7Baa1AU\nK4piBaxIkoYhQ7bj4hLRLuMEBt5xzm127foTxWoGkjM8YzcSE/PpWYO/iortmM3lgNUW+Lm6DsDT\nUz0kLUkSOp0X3bo9QbduT7Rof+TI47z33p12gi9JSfNZvPh1Dh26l7KyTTT9aJKY2PwMocSnnwZz\n/fWByLIzsuxKbu57WK116HQ++Pnd2uZgWCAQCAQCgaAzc8ULvsTHw4IFIMvqyqQsW5g/H2TZgaCg\ne5FllxbtNJqWW+AuFF/faYSHL0N9eQWzuQQ/vxloNPbjZ2f/A71+DNu2BRIaOryF0EVc3DenlCUR\nF5dgJ54xcuQ6Bgx4Fn9/9a3+TKIygvZFURRqa4+Ql7eS9PQF7NgRQUpKCFZrDQ0NRZjNJVgsZVgs\nFVgslWg07pfV3mnT1OcDwM+viFmzIomL20BgoJpiJDhYYeHCB+jb97NW2xuNH7Nv383U159g4MBE\nmnJDAsiyM926nT4zutH4Cfv2/QGrtZ6amnTy8j4hPn6ZnaDRLbd8h5NTNwID5+LqGsXw4QeIiyvm\n3nvLOXkOVuGWWzY25qKswWqtxmqtRpI0REYuF4GfQCAQCAQCQRu5KgRfAF5//Z+sWjWQqVOXc/fd\nPQkKugcXl3A2b5Ya2zjahFNGj65uEZSdjtdee42nnnqqTXVzct4iM3MRVmstANHRyXh6jrSrs2vX\nMCord9H0YntSeCOBiIhD+PhM5ujR6/jvf41cd102kybdjpfXRD7/fDo//zy4mXiNIxkZfThy5Gtm\nzeotVv0uETt2RFBbe6Rx22MN6veoITLyYzIy5tmlYgAYM6bhsudKPFXoZPNmCYMhCqNxz1nFT9LT\n52M0fogkOZySCxK0Wi9iY/NaPXuoKFaSk4NpaCjC1bUf1dXqmb2wsJf4+ONDJCb+kfj4Fcybdw1d\nu7bM0wnwyiuwfLnC3Ln5PPRQKmlpk2zXJMkBH5+pDBjw3/OYkbNzLn4vEAiuHoTvCwSdk87m+1es\n4Etzbroph2HDHgUUsrO1ZGe/RlbWBLZseZ4//CGIqKg6jhx5HLAiy85t7nf69OltrltcnGQL/AyG\nKD77bDMLFvQmJsbPVmfQoB/Ys2cMNTUZKEo94eGpNlEaD49x9OnzGSaTMyEhpfTt64u3t5oPMCzs\nB+bOTUSn88PDYzpeXuOJjh6Ju3sPTsnjLmhEr4eEBOwEcS4ERVHw9r6B3Nzltm2PKhYKC7+ka9en\nyM5+jYyMvjZxHtDh7T0Zs3kdiYkO7WbL6Wjtnqur95OTs5mKiqFADF27Po7B8AMWiwloGbg1x2CI\n5r//fYHYWFUp1MEhkPLyV/nqq0xuvTW41cAPoLR0EwcOdGPbtgXExW0gPBzGjDEhSRqmT49lwoRJ\nKIoVP79XT2v3pElQW1tLSEg8aWm/2/Uvy85ERCy7kKk6I+fi9wKB4OpB+L5A0DkRvt+SDrny1yT4\n0lzRswmDIaqZIIqVpCSZ7t1/oKzsZ3r2XNruNlmtZrZu9cBqrbUb29+/lO++87J74Teby9Hrx1JT\ncwiwIMuuWCzlrdh9UsiluvogWq0njo7B7W771YheD5MnN5CXp2t3QZycnHc5cuTPLZKOQ8vn7m9/\nu4uamn2XRJxHFRBSz5EGBtazcaMDkiQxaVI5BQWe+Pqe4OOP1xESMp/rry88qz0//LCR2bMHn1Ys\nJjhYISmpdYXQNWse4YEHnmzW9mauv/5mund/mtrao+zc2RcXlz4MHapn924rU6aYyMtzstkDcOON\n1eTnu+Lrm8Nnn/3EjTfehdH4Cenpf6Rfv//g7/+H9p9EgUAgEAgEgquYtgq+dMhDNSUlIWzffhve\n3jfi6NjN7tq2bdObCaLIJCaCt/d1FyXwA6iry7St+jUfu6DAi8RE+7parSfR0b/g6joARTEzcGAi\n48YpjB1r5uDBZS2EXABcXfuKwO8c+O9/S8nLU8V0ms9jWzCbq2hoKDvt9dDQPzFgwAZk2QnQ0KPH\nK4wbpzBixFFSU1+0e+6OHFlJTs7OVr/T9iYhAZuAUF6eI++88wJvvfUCBQVqioWiohDWrSth2bJP\nzmpPXt5nrFr1ewvxIXu/klptazIV8s03XnZtU1KmUFS0nrKyZGprDxMVtYW+fVdTXp7C228vIS/P\nyc6ehATIz3e1td+58y4AAgPvYuTInHYN/KxWEyZTQbv1JxAIBAKBQNBRueIFXx588GkGDfqGkJCH\n7K7FxW3A1zcfuDBBlO3bt7epnotLBNHR2xgx4njj2DlnHFur9WDw4M30778BT89YACRJw513xtgE\nOoSQy/kzcOCis34HrVFdvZ+dOwfw++9RZ6zn43MjQ4bsoEuXsfj6zgDUM3BjxvzcYtwZM3SX5Dtt\nLu4SHGxh9uzBTJqUe4pY0FqGDn0fX9/c09pjNK4gPf0+YmPX2bVVBYk2EBxsOeO9FBcnEhe33q7t\nPffcxKBB35KWNpm0tCnodF5kZCxAr48lLm4DQUFmuz7t7+XkOJIk4+gY0nLQU8jJeYesrCVYrfVn\nrbt//0x27OhNScn3trK2+r1AILi6EL4vEHROOpPvXzWCLxZLNQbDIxQUfInVWgdYyMu7j4yMD84q\nanEmli1bxoIFC85Yx2o1UVb2M4WF6ykqWkdDQwEGwyB27ryb++//v3Me+1SBDsG5s3mzxOHDw8nN\n3dHmeVQVPO9DUUxotd6MGlXU5vGqqtJIS5uCyVRARkZ/jh79hj/8IcA27qX6Tk8dx2pt4KOPYkhO\nnmoTC9JoPDEYBrNr1/3cd98sO3tycz8kI2MhTaqeTYJEt9wSRFxcJKmpE6itXcHOnXef9l7q6nLZ\nvr0bBsMAkpOnExu7gXnz9nDkyJPk5LzTuF22KYC8n4iIf7c6PxcyZ7t2jaCy8jccHUOIjFyBt/dE\nTKYCdDo/pFMOye7ZM4Gysp+QJB1duz5Jjx5LWL78w7P6vUAguPpoy7/5AoHg6qOz+X5bBF86ZPBX\nXl6Oh4eH3bWamgz27BmPyWTE3382/fp9YXf9dCIgRUUbMJvLCQy865ztycv7jEOH5qJ04tR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xf3DQwRhXKXgXKl0QqODhzYqu7uh3T8/sr+zLz88uezPudvf/vIhEuTWltXjvp7Dx3aruMFP/1z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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1164c9050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rem = mp.load_config('n_remaining.pickle')\n",
    "mp.flow_plot_xyt(rem, max=200, xlim=(-5,mp.UPPER_XLIMIT), ylim=(-75,mp.UPPER_YLIMIT),\n",
    "                 arrowsize=1, arrowlength=1, figsize=18, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "04:32:56 INFO:Loading pickle file : ne_remaining.pickle\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotted 201/15138 \n"
     ]
    },
    {
     "data": {
      "image/png": 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xzkVhO1f+0i7+AAAAOtlyA+iRI0diYGAgDh06tOoCsCiKGBoaWi596a7bPgEA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hjs2aPg+edbsHjxbfjYx27GxInfRHr6tJi3wdwTiYnZJxITsz+QsIUvpH0sd6G+XnvtN7jvvk/B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\nnU7IshzXdYmMiBkgIiIi0q9Yz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KWlm/Hkk+koKgqd8buc0aOvx/XXV2Ds2Ls48SOKgZay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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1188e6290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rem = mp.load_config('ne_remaining.pickle')\n",
    "mp.flow_plot_xyt(rem, max=200, xlim=(-5,mp.UPPER_XLIMIT), ylim=(-75,mp.UPPER_YLIMIT),\n",
    "                 arrowsize=1, arrowlength=1, figsize=18, verbose=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Trajectory data analysis\n",
    "Examines state trajectory data.\n",
    "\n",
    "If you wish to also display the simulated graphical path for each leg of the trajectory, use path_plot_xyt() instead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "02:38:38 INFO:Loading pickle file : n_sample_traj.pickle\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(18, 87, 218)\n",
      "[((18, 87, 354), (18, 87, 138), (1, 20, 354), 'qt', 0.1, None), ((17, 83, 338), (21, 89, 122), (1, 20, 354), 'rh', 4.13, 15.0), ((1, 20, 354), (70, 130, 138), (1, 20, 354), 'lh', 65.2, 230.0)]\n",
      "\n",
      "Sample:\n",
      "\n",
      "  (54, 78, 276) : g1:     (1, 20, 0)  r:  59.0  type: lh  d: 86.0   g2:           None  d2:  None  samples: 5\n",
      " (125, 59, 214) : g1:     (0, 20, 0)  r:  69.0  type: lh  d:172.4   g2:           None  d2:  None  samples: 6\n",
      "  (13, 47, 316) : g1:   (0, 20, 354)  r:  46.0  type: lh  d: 30.1   g2:           None  d2:  None  samples: 12\n",
      " (57, 113, 304) : g1:   (0, 20, 354)  r: 130.0  type: lh  d:112.0   g2:           None  d2:  None  samples: 13\n",
      "   (6, 48, 340) : g1:     (1, 20, 0)  r:  84.5  type: lh  d: 28.6   g2:           None  d2:  None  samples: 77\n",
      "  (22, 70, 310) : g1:    (-1, 20, 0)  r:  66.0  type: lh  d: 56.8   g2:           None  d2:  None  samples: 8\n",
      "  (59, 43, 216) : g1:     (0, 20, 6)  r:  33.0  type: lh  d: 84.8   g2:           None  d2:  None  samples: 4\n",
      "  (62, 57, 250) : g1:   (1, 20, 354)  r:  45.5  type: lh  d: 82.0   g2:           None  d2:  None  samples: 3\n",
      "  (52, 64, 260) : g1:     (0, 20, 0)  r:  45.0  type: lh  d: 77.3   g2:           None  d2:  None  samples: 8\n",
      "   (-1, 107, 6) : g1:  (-1, 20, 354)  r: 420.0  type: rh  d: 87.2   g2:           None  d2:  None  samples: 10\n",
      "  (11, 41, 300) : g1:     (0, 20, 6)  r:  20.5  type: lh  d: 24.7   g2:           None  d2:  None  samples: 11\n",
      "  (55, 40, 222) : g1:     (1, 20, 0)  r:  31.5  type: lh  d: 72.6   g2:           None  d2:  None  samples: 4\n"
     ]
    }
   ],
   "source": [
    "trajs = mp.load_config('n_sample_traj.pickle')\n",
    "print next(trajs.iterkeys())\n",
    "print next(trajs.itervalues())\n",
    "print\n",
    "#   Sample\n",
    "print \"Sample:\\n\"\n",
    "max=11\n",
    "ii=0\n",
    "for k,v in config.iteritems():\n",
    "    if ii>max:\n",
    "        break\n",
    "    mp.print_state(k,v)\n",
    "    ii+=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:09:28 INFO:Loading pickle file : ne_sample_traj.pickle\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of trajectories loaded:  20000\n"
     ]
    }
   ],
   "source": [
    "#trajs = mp.load_config('n_sample_traj.pickle')\n",
    "trajs = mp.load_config('ne_sample_traj.pickle')\n",
    "print \"Number of trajectories loaded: \", len(trajs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of trajectories length 4:  78\n",
      "Key #4:    (140, -36, 16)\n",
      "(140, -36, 16)  ==>\n",
      "\t\n",
      "\t((112, 58, 16), (112, 58, 16), (0, 20, 354), '|', -98.08, None)\n",
      "\t((112, 58, 314), (112, 58, 116), (0, 20, 354), 'qt', 0.1, None)\n",
      "\t((92, 57, 230), (131, 52, 32), (0, 20, 354), 'rh', 21.92, 15.0)\n",
      "\t((0, 20, 354), (229, 56, 152), (0, 20, 354), 'lh', 119.08, 56.5)\n",
      "\n",
      "Waypoints: [(140, -36, 16), (112, 58, 16), (112, 58, 314), (92, 57, 230), (0, 20, 354)]\n",
      "\n",
      "Trajectory:\n",
      "{(140, -36, 16): [((112, 58, 16), (112, 58, 16), (0, 20, 354), '|', -98.08, None), ((112, 58, 314), (112, 58, 116), (0, 20, 354), 'qt', 0.1, None), ((92, 57, 230), (131, 52, 32), (0, 20, 354), 'rh', 21.92, 15.0), ((0, 20, 354), (229, 56, 152), (0, 20, 354), 'lh', 119.08, 56.5)]}\n",
      "\n",
      "\n",
      "State :  (140, -36, 16)\n",
      "State :  (112, 58, 16)\n",
      "State :  (112, 58, 314)\n",
      "State :  (92, 57, 230)\n",
      "State :  (0, 20, 354)\n",
      "Plotted 5/5 \n"
     ]
    },
    {
     "data": {
      "image/png": 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rf79z504cO3bsqn1w1c9LJpMYHR2t+XjlfW5ERES1rHc/OE4oISKiwFpvmwAnlHioXi+N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\nCSEkABmLxeRME5sUzczMyFgsVt4jSMU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      "text/plain": [
       "<matplotlib.figure.Figure at 0x116397890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#    For the paths belonging to a selected trajectory, \n",
    "#    - draws the waypoints \n",
    "#    - prints the trajectory data.\n",
    "#\n",
    "#   Does NOT draw the connecting paths, only the (x,y,h) location + heading.\n",
    "#   (To display the simulated graphical path for each leg of the trajectory, see path_plot_xyt)\n",
    "\n",
    "which = 4\n",
    "k4s = [k for k in trajs if len(trajs[k])==4]\n",
    "print \"Number of trajectories length 4: \", len(k4s)\n",
    "key = k4s[which]\n",
    "print \"Key #%d:   \" % which, key\n",
    "trajectories = dict([(k,v) for k,v in trajs.iteritems() if k==key])\n",
    "print key,\" ==>\\n\\t\"\n",
    "print \"\\t\",\"\\n\\t\".join([str(x) for x in trajectories[key]])\n",
    "waypoints = [key] + [x[0] for x in trajectories[key]]\n",
    "print \"\\nWaypoints:\",waypoints\n",
    "trajectory = [key] + trajectories[key]\n",
    "print \"\\nTrajectory:\"\n",
    "print trajectories\n",
    "print\n",
    "print\n",
    "xlim,ylim=mp.UPPER_XLIMIT,mp.UPPER_YLIMIT\n",
    "#xlim,ylim=25,40\n",
    "mp.flow_plot_xyt(waypoints, xlim=(-5,xlim+5), ylim=(-72,ylim), \n",
    "                 max=50, arrowsize=3, arrowlength=3, figsize=8, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "12:05:03 INFO:Loading pickle file from ne_sample.pickle\n",
      "12:05:03 INFO:Loading pickle file from ne_sample_unreachables.pickle\n",
      "12:05:11 INFO:Number of bins: 36456 \n",
      "12:05:11 INFO:max reached\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plotted 10001\n"
     ]
    },
    {
     "data": {
      "image/png": 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AxBK9AAAAxBK9AAAAxBK9AAAAxBK9AAAAxBK9AAAAxBK9AAAAxBK9AAAAxBK9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120f76e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mp = reload(mp)\n",
    "configsample = mp.load_config('ne_sample.pickle')\n",
    "unreachables = mp.load_config('ne_sample_unreachables.pickle')\n",
    "mp.binplot_unexplored('northeast', configsample, unreachables, max=10000)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}