experimental-api-load-test.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "rcParams['figure.figsize'] = (16,9)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pyproj"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wgs84 = pyproj.Proj(init='epsg:4326')\n",
      "dstproj = pyproj.Proj(init='epsg:3857') # spherical mercator, aka \"Google maps\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import json\n",
      "req = json.load(open('../tawhiri/test-requests/simple1.json'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(json.dumps(req, indent=2))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{\n",
        "  \"launch\": {\n",
        "    \"altitude\": 50, \n",
        "    \"when\": {\n",
        "      \"low\": 1415333250, \n",
        "      \"type\": \"uniform\", \n",
        "      \"high\": 1415334250\n",
        "    }, \n",
        "    \"longitude\": 52.1, \n",
        "    \"latitude\": 0.05\n",
        "  }, \n",
        "  \"profile\": {\n",
        "    \"type\": \"simple\", \n",
        "    \"parameters\": {\n",
        "      \"ascentRate\": {\n",
        "        \"mu\": 5, \n",
        "        \"type\": \"gaussian\", \n",
        "        \"sigma\": 0.1\n",
        "      }, \n",
        "      \"descentRate\": 5, \n",
        "      \"burstAltitude\": {\n",
        "        \"mu\": 30000, \n",
        "        \"type\": \"gaussian\", \n",
        "        \"sigma\": 200\n",
        "      }\n",
        "    }\n",
        "  }, \n",
        "  \"sampleCount\": 100\n",
        "}\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import requests\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Obviously fix the URL here for your own needs\n",
      "body = json.dumps(req)\n",
      "%timeit resp = requests.post('http://spica.vpn.richwareham.com:5000/api/broken/predict', data=body, headers={'Content-type': 'application/json'})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1 loops, best of 3: 498 ms per loop\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "resp = requests.post('http://spica.vpn.richwareham.com:5000/api/broken/predict',\n",
      "                     data=json.dumps(req), headers={'Content-type': 'application/json'})\n",
      "resp.status_code"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "200"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results = resp.json()['results']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def join_result(res):\n",
      "    return np.vstack(res)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "traces = list(join_result(r) for r in results)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "terminals = []\n",
      "for t in traces:\n",
      "    xs, ys, zs = t[:,1], t[:,2], t[:,3]\n",
      "    xs, ys, zs = pyproj.transform(wgs84, dstproj, xs, ys, zs)\n",
      "    plot(xs[0], ys[0], 'gx')\n",
      "    plot(xs[-1], ys[-1], 'r.')\n",
      "    plot(xs, ys, 'b', alpha=0.1)\n",
      "    terminals.append((xs[-1], ys[-1], zs[-1]))\n",
      "terminals = np.asarray(terminals)\n",
      "axis('equal')\n",
      "grid('on')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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u6oKOjsa+b926YjlhQmP99Hwee3kzv3yZXd6s8ZUkSb3asQO2by/aU6bAsAZ/\n6TdvLpajRvXvuCRJaiVrfCVJGkSeeAJmzSpqfS+4AG69FYYPr79/lJVSzzzT+KRZkqRmssZXkqQh\navPmYtIL8IIX9H3yGnX9b4QkSXlw4qumsd4iX2aXN/PLW6P5bdhQbR9ySN8nsE58D57HXt7ML19m\nlzdrfCVJUq9qJ76HHda6cUiS1E6s8ZUkaRC58UaYO7do33wzvPnNjfWvnOn1J1SS1O6s8ZUkaYha\nv77anjSpdeOQJKmdOPFV01hvkS+zy5v55a3R/FavrranTOnfsagxHnt5M798mV3erPGVJEm9evrp\nanvMmNaNQ5KkdmKNryRJg8hf/AX88IdFe+lSmDGjsf7W+EqScmGNryRJQ9TatcUyAkaPbu1YJElq\nF0581TTWW+TL7PJmfnlrJL+Uqje3Gjeu8Ylv5Syvz/DtHx57eTO/fJld3qzxlSRJB7RnT/U5vp2d\n0NHRWP/KxLf2btCXXVbsZ9u2/hmjJEmt0GuNb0R0Ad8AXgwk4O3AHuBLQAewG7gspXRPRLwa+Adg\nJLAT+PuU0s/L/cwBvgmMBn6SUnpfuX0U8C1gNrAWeEtK6YnyvUuAj5VD+VRK6Vvl9lnATcBkYBHw\ntpTSrr3GbY2vJGlI2bYNpk+HdevguONg8eLGzvpu3Qpjx8IJJ8CDDxbbKmd/hw2DZ57p/zFLktRX\n/V3jey3FRPUE4CXAQ8BngY+nlE4BPlGuA6wG/ltK6SXAJcC3a/bzFeDSlNIxwDER8dpy+6XA2nL7\nPwNXl/+IyeW+TytfV0TExLLP1cDnyz7ry31IkjSkbd9evAC6umD48Mb6r1tXLPd1Q6w9e+D1rz+4\n8UmS1CoHnPiWE82zUkrXA6SUdqeUNgArgcoktAtYXr6/OKX0VLn9QWBMRIyIiMOA8SmlBeV73wIu\nKNvnAzeW7e8CryrbrwHuSCn1pJR6gDuB10VEAOcAt5afu7FmX2pj1lvky+zyZn55ayS/rVufO/Ed\n1mBB0/LlxfK44/b9/k9/Cg8/3Ng+hzKPvbyZX77MLm+tqvGdBayOiBsi4t6I+HpEdAIfAT4fEX8E\n/gn46D76/iWwqLwE+QhgWc17y8ttlMsnoZhYAxsiYgpw+F59lpWfnQz0pJT27GNfkiQNWZs3F2dm\nAQ47rPGJ7xNPFMuTT37u9uHD4aqrivYppxzcGCVJaoXebnvRQVF7+56yhvcaiknumcDlKaXvRcSF\nwPXAqyu/TbDsAAAgAElEQVSdIuLFwD/WbusnDRXtzp07l5kzZwLQ1dXFySefTHd3N1D9S4LrzV2v\naJfxuF7femVbu4zH9cbWK9vaZTyuN7Ze2VbP5zduBCjWp0zpJqKx7/vtb4v+xVnjyvfPY9Ys+MQn\nurniCti6dR7z5rXPf592Xu/u7m6r8bhufq67nvv64sWL6enpAWDp0qU04oA3t4qIQ4Ffp5Rmlesv\np5j4npVSmlBuC4ozsBPL9enAXcDclNKvy22HAT8r64SJiLcCZ6eU3h0RtwFXppTmR0QHsDKlNDUi\nLgK6U0rvKvt8DfgZcAvwNDAtpbQnIs4ErkgpVWqGK2P35laSpCHl9tvhteWv4dVXw4c+1Fj/N70J\nvvc9WLYMjiivpYqAX/wCzjqreqMrf14lSe2g325uVdbrPhkRx5abzgV+ByyJiFeU214JPFx+cRfw\nY+DDlUlvuZ+VwMaIOL2cKL8N+EH59g8pboQF8FcUk2aAO4DzIqIrIiZRnD2+vZzN/hy4sPzcJcD3\n6/nHqrUqf7VRfswub+aXt0byK874FqZNa/y7Hn20WFYeZ3TxxcXyrLMa35c89nJnfvkyu7wNVH71\nPOHvvcB3ImIk8Cjw1xRnXb9UPopoG/CO8rPvAV5EcQfmK8ptr04prQEuo3ic0RiKu0TfVr5/HfDt\niFhC8TijiwBSSusi4pPAPeXnripvcgXwYeCmiPgUcG+5D0mShrS1a6vtqVMb779qVbEcNapY/uQn\nBz8mSZLaQa/P8c2VlzpLkoaaz3wGPvaxoj1/Ppx+emP9R4+GHTuKG2RFPPfS5uXLi2cEz5kDCxf2\n77glSeqL/n6OryRJysDTT1fb48Y13n/HjmIZ+/hfiOnTi+U99zz/PUmS2p0TXzWN9Rb5Mru8mV/e\nGsmvduLb2Xlw37tzZ7E844znbt/XpFj75rGXN/PLl9nlbaDyc+IrSdIgUVvjO3p0H3fyzhOJq+LZ\nOt9f/xri7ePgba9g7tyDHaEkSa1hja8kSYPES18KixbBmDFFTW7l7sz1igBe8X4451r40ZWw8Aq4\nMmDzJPiPd5Pmf3oghi1JUp9Y4ytJ0hCTEmzYULQ7O2HEiD7u6O5riuWZN8L7D4VngH+/mnM6nfRK\nkvLlxFdNY71Fvswub+aXt3rz270btm4t2mPHQkc9Dyzcj3RFgk2HwMRV8NsLYPH/w89+1vf9DVUe\ne3kzv3yZXd5a+RxfSZLU5nbsgC1bivaECTB8eN/3FVcFzASWHQ3TH4D3DwP29MMoJUlqDWt8JUka\nBNauhSOPhG3b4Oyz4a67Gj/rGwG86aXwkkXQA1yT4IJz4eS7gPJMsCRJbcIaX0mShpjt26vP4Z06\nFYb19Rf+2AeK5TXFJDd9798PfnCSJLWYE181jfUW+TK7vJlf3urNb8sW2FNejdzV1fjEt3JjrMs2\n7oAri0nvtdcW29IVybO9feCxlzfzy5fZ5c3n+EqSpP2q1PcCvOAFjff/9/LE7ooVxTICLr/84Mcl\nSVI7sMZXkqRB4O67obu7aH/uc/B3f9dY/zPOgN/8prruT6gkqd1Z4ytJ0hCzcWO1PXVq4/0XLaq2\nf/zjgx+PJEntxImvmsZ6i3yZXd7ML2/15rduXbXd1dX49+zeXW2//vWN99fzeezlzfzyZXZ5s8ZX\nkiTtV09PtT15ct/3UzsBliRpsLDGV5KkQeDKK+Gqq4r2vffCKac01j/KCil/OiVJubDGV5KkIWbN\nmmq7s7Nv+5g2rX/GIklSu3Hiq6ax3iJfZpc388tbvfmtXl1tjxrV+PesWwdPPdV4P+2fx17ezC9f\nZpc3a3wlSdJ+bdhQbY8Z03j/SZP6byySJLUba3wlSRoETj8dFiyA0aNh+fKDu8GVJEk5sMZXkqQh\nJKXqXZ1Hj4aRI1s7HkmS2o0TXzWN9Rb5Mru8mV/e6slvzx7YsqVojxsHw4cP7JhUH4+9vJlfvswu\nb9b4SpKkfdq1C7ZtK9pjxzrxlSRpb9b4SpKUuU2b4NBDYetWOOMM+MUvYMSIVo9KkqSBZY2vJElD\nyPbtsHNn0Z4wAYb56y5J0nP406imsd4iX2aXN/PLWz35bd0Ku3cX7SlTIOr627cGmsde3swvX2aX\nN2t8JUnSPm3eXG1PnOgZX0mS9maNryRJmZs/H848s2h/4hNw1VWtHY8kSc1gja8kSUNI7RnfSZNa\nNw5JktqVE181jfUW+TK7vJlf3urJb9Omanvq1IEbixrjsZc388uX2eXNGl9JkrRPGzZU211drRuH\nJEntyhpfSZIyd+218P73F+358+H001s7HkmSmsEaX0mShpA1a6rt0aNbNw5JktqVE181jfUW+TK7\nvJlf3urJr6en2u7sHLixqDEee3kzv3yZXd6s8ZUkSfu0dm21PWpU68YhSVK7ssZXkqTMvfa1cPvt\nRfvpp72zsyRpaLDGV5KkIaRyqfOoUTBiRGvHIklSO3Liq6ax3iJfZpc388tbb/mlBJs3F+3Ro6Gj\nY+DHpPp47OXN/PJldnmzxleSJD1PSrBpU9EeMwaGD2/teCRJakfW+EqSlLFdu+DII2HVKjj6aPjd\n72DkyFaPSpKkgWeNryRJQ8Tu3bB9e9EeNw6irp9/SZKGFie+ahrrLfJldnkzv7z1lt+OHbBzZ9Ge\nOBGG+cveNjz28mZ++TK7vFnjK0mSnmfnzurEt6vLM76SJO2LNb6SJGXsiSdg5syiffHF8M1vOvmV\nJA0N1vhKkjREbNtWbU+c6KRXkqR9ceKrprHeIl9mlzfzy1tv+dVOfCdNGtixqDEee3kzv3yZXd5a\nVuMbEV0RcWtEPBQRD0bEGRFxWkTcExH3lctTy89OjoifR8SmiPj/9trPnIh4ICKWRMS1NdtHRcTN\n5fb5ETGj5r1LIuLh8nVxzfZZEfGbss9NETGif/5zSJKUl8ozfAEmT27dOCRJame91vhGxI3A3Sml\n6yOiAxgL/AD4h5TS7RHxOuBDKaVzIqITOAU4ETgxpfTemv0sAN6TUloQET8BvphSui0iLis/e1lE\nvAV4Y0rpooiYDNwDzCl3sQiYnVLaEBG3ALemlG6JiK8A96eUvrrXuK3xlSQNej/9Kbz+9UX7xhuL\nOl9JkoaCfqvxjYiJwFkppesBUkq7U0obgJXAxPJjXcDy8v2tKaVfATv22s9hwPiU0oJy07eAC8r2\n+cCNZfu7wKvK9muAO1JKPSmlHuBO4HUREcA5wK3l526s2ZckSUPK5s3V9sSJ+/+cJElDWW+XOs8C\nVkfEDRFxb0R8vTyr+xHg8xHxR+CfgI/u1W/vU61HAMtq1peX2yrvPQnFxBrYEBFTgMP36rOs/Oxk\noCeltGcf+1Ibs94iX2aXN/PLW2/59fRU205824vHXt7ML19ml7dW1fh2ALOBL6eUZgNbKCa51wGX\np5ReCHwAuH5ARvd8XrssSVKNjRur7XHjWjcOSZLaWUcv7y8DlqWU7inXb6WY+J6WUjq3Zts3etnP\ncmB6zfp0qmdzlwMvBFaUNcQTU0prI2I50F3T50jgZ8A6oCsihpVnfaeX+3ieuXPnMrN8uGFXVxcn\nn3wy3d3FLit/SXC9uesV7TIe1+tbr2xrl/G43th6ZVu7jMf1xtYr2/b3/n/917zyU92MGdP68bpe\nXe/u7m6r8bhufq67nvv64sWL6SkvdVq6dCmNqOfmVr8A/ial9HBEXAl0UtThfjCldHdEvAr4x5TS\nqTV95gJz9rq51W+Ay4EFwI957s2tTkopvTsiLgIuqLm51UKKM85B9eZWPeXNrb6bUro5Ir4KLPbm\nVpKkoej974dry2clLFkCRx/d2vFIktQs/XZzq9J7ge9ExP3AS4BPA+8EPhsRi4FPAe+o+fKlwOeB\nuRHxZEQcX751GcWZ4SXAIyml28rt1wFTImIJ8H6K+mFSSuuAT1Lc2XkBcFV5kyuADwMfLPtMKveh\nNlf5q43yY3Z5M7+89Zbf6tXV9qhRAzsWNcZjL2/mly+zy9tA5dfbpc6klO4HTt1r80Lg9P18fuZ+\nti8CTtrH9h3Am/fT5wbghn1sf3x/3y9J0lCyfn21PXp068YhSVI76/VS51x5qbMkaSh42cvgP/+z\nONu7ciVMmtTqEUmS1Bz9famzJElqU5s2FcuODhgxorVjkSSpXTnxVdNYb5Evs8ub+eWtt/y2bCmW\nY8YUk1+1D4+9vJlfvswubwOVnxNfSZIy9cwzsH170R43Dob5qy5J0j5Z4ytJUqa2b4fDDy9ucPWS\nl8DChV7uLEkaOqzxlSRpCNi1C3bsKNrjx3vGV5Kk/fEnUk1jvUW+zC5v5pe3A+W3Ywfs3Fm0x42D\nqOtv3moWj728mV++zC5v1vhKkqTn2LWrqPOF4jFGnvGVJGnfrPGVJClTjz8ORx1VtN/5TvjqV1s7\nHkmSmskaX0mShoCtW6vtrq7WjUOSpHbnxFdNY71Fvswub+aXtwPlt3lztT1p0sCPRY3x2Mub+eXL\n7PJmja8kSXqO2jO+kye3bhySJLU7a3wlScrUj34Ef/7nRfuWW+DCC1s7HkmSmskaX0mShoBNm6rt\niRNbNw5JktqdE181jfUW+TK7vJlf3g6U38aN1faECQM/FjXGYy9v5pcvs8ubNb6SJOk5aie+nZ2t\nG4ckSe3OGl9JkjL18Y/Dpz9dtP/wBzj22NaOR5KkZrLGV5KkIaCnp9oeNap145Akqd058VXTWG+R\nL7PLm/nl7UD5rV5dbY8ePfBjUWM89vJmfvkyu7xZ4ytJkp5j/fpi2dEBI0a0diySJLUza3wlScrU\ny18Ov/oVjBkDq1bB+PGtHpEkSc1jja8kSUNA5Tm+I0d6xleSpANx4qumsd4iX2aXN/PLWz3P8R0z\nBob5i952PPbyZn75Mru8WeMrSZKe9cwzsHNn0R4/3omvJEkHYo2vJEkZ2rEDDjusuMHVS18K8+fD\n8OGtHpUkSc1jja8kSYPczp2wfXvRHjsWoq6ffUmShiYnvmoa6y3yZXZ5M7+87S+/XbuKF8CkSV7q\n3I489vJmfvkyu7xZ4ytJkp61cyfs3l20fYyRJEkHZo2vJEkZeuIJmDmzaH/gA/CFL7R0OJIkNZ01\nvpIkDXJbtlTbkye3bhySJOXAia+axnqLfJld3swvb/vLr3bi29XVnLGoMR57eTO/fJld3qzxlSRJ\nz/KMryRJ9bPGV5KkDP3f/wvnn1+0f/QjeMMbWjseSZKazRpfSZIGuU2bqm0vdZYk6cCc+KpprLfI\nl9nlzfzytr/8aie+48Y1ZyxqjMde3swvX2aXN2t8JUnSszZvrrZHjmzdOCRJyoE1vpIkZehjH4PP\nfKZoP/YYzJrV2vFIktRs1vhKkjTIrV5dbY8Z07pxSJKUAye+ahrrLfJldnkzv7ztL7/aie+IEc0Z\nixrjsZc388uX2eXNGl9JkvSsjRuL5ahRxUuSJO2fNb6SJGXo1FNh4UIYOxbWrIHRo1s9IkmSmssa\nX0mSBrnK44w6O2H48NaORZKkdufEV01jvUW+zC5v5pe3feWXEmzZUrTHj4eo62/dajaPvbyZX77M\nLm/W+EqSJAB2765OfCdOdOIrSVJvrPGVJCkzW7bAtGnF8uyzYd48J7+SpKHHGl9JkgaxnTuLF8Ck\nSU56JUnqjRNfNY31Fvkyu7yZX972ld/OnbBrV9Hu6mrueFQ/j728mV++zC5vLavxjYiuiLg1Ih6K\niAcj4oyIOC0i7omI+8rlqTWf/2hELImI30fEeTXb50TEA+V719ZsHxURN5fb50fEjJr3LomIh8vX\nxTXbZ0XEb8o+N0XEiP75zyFJUvur1PcCTJ3aunFIkpSLXmt8I+JG4O6U0vUR0QGMBX4A/ENK6faI\neB3woZTSORHxJ8C/AKcCRwD/DhyTUkoRsQB4T0ppQUT8BPhiSum2iLgMODGldFlEvAV4Y0rpooiY\nDNwDzCmHsgiYnVLaEBG3ALemlG6JiK8A96eUvrrXuK3xlSQNSr/7HZx4YtH+zGfgox9t7XgkSWqF\nfqvxjYiJwFkppesBUkq7U0obgJXAxPJjXcDysv0XwL+mlHallJYCjwCnR8RhwPiU0oLyc98CLijb\n5wM3lu3vAq8q268B7kgp9aSUeoA7gddFRADnALeWn7uxZl+SJA16lWf4AkyZ0rpxSJKUi94udZ4F\nrI6IGyLi3oj4ekR0Ah8BPh8RfwT+Caj8rflwYFlN/2UUZ3733r683E65fBKKiTWwISKmHGBfk4Ge\nlNKefexLbcx6i3yZXd7ML2/7ym/z5mr7kEOaNxY1xmMvb+aXL7PLW6tqfDuA2cCXU0qzgS0Uk9zr\ngMtTSi8EPgBcPyCjez6vXZYkDXm1E99x41o3DkmSctHRy/vLgGUppXvK9VspJr6npZTOrdn2jbK9\nHDiypv/0ch/Ly/be2yt9XgisKGuIJ6aU1kbEcqC7ps+RwM+AdUBXRAwrz/pOp3qp9XPMnTuXmTNn\nAtDV1cXJJ59Md3exy8pfElxv7npFu4zH9frWK9vaZTyuN7Ze2dYu43G9sfXKttr358+Hyk/k44/P\nY9689hmv69X17u7uthqP6+bnuuu5ry9evJienh4Ali5dSiPqubnVL4C/SSk9HBFXAp0UdbgfTCnd\nHRGvAv4xpXRqzc2tTqN6c6ujy5tb/Qa4HFgA/Jjn3tzqpJTSuyPiIuCCmptbLaQ44xxUb27VU97c\n6rsppZsj4qvAYm9uJUkaKr74RXjf+4r2Qw/B8ce3djySJLVCv93cqvRe4DsRcT/wEuDTwDuBz0bE\nYuBTwDsAUkoPArcADwI/BS6rmX1eRnFmeAnwSErptnL7dcCUiFgCvJ+ifpiU0jrgkxR3dl4AXFXe\n5Argw8AHyz6Tyn2ozVX+aqP8mF3ezC9v+8pv7dpqe/To5o1FjfHYy5v55cvs8jZQ+fV2qTMppfsp\nHk9UayFw+n4+/xngM/vYvgg4aR/bdwBv3s++bgBu2Mf2x/f3/ZIkDXa1E98xY1o3DkmSctHrpc65\n8lJnSdJg9d//O/zrvxbttWth8uTWjkeSpFbo70udJUlSG1mzpliOGgUjR7Z2LJIk5cCJr5rGeot8\nmV3ezC9v+8pv/fpi2dkJHb0WLalVPPbyZn75Mru8DVR+TnwlScrMhg3Fctw4GD68tWORJCkH1vhK\nkpSRlGD6dFixAk48Ee67z7O+kqShyRpfSZIGqV27YMuWoj1hAgzzl1ySpF75c6mmsd4iX2aXN/PL\n29757d4NO3YU7UMOceLbzjz28mZ++TK7vFnjK0mS2LGjOvGdMqW1Y5EkKRfW+EqSlJGVK+Hww4v2\nRz8Kn/lMa8cjSVKrWOMrSdIgVanvBZg2rXXjkCQpJ0581TTWW+TL7PJmfnnbO7/Nm6ttJ77tzWMv\nb+aXL7PLmzW+kiSJjRur7cmTWzcOSZJyYo2vJEkZ+dGP4M//vGgvWgSzZ7d2PJIktYo1vpIkDVJP\nP11tjx3bunFIkpQTJ75qGust8mV2eTO/vO2d37p11faYMc0dixrjsZc388uX2eXNGl9JksTq1dV2\nZ2frxiFJUk6s8ZUkKSMXXwzf/nbR3rABJkxo7XgkSWoVa3wlSRqkVq0qlqNHw4gRrR2LJEm5cOKr\nprHeIl9mlzfzy9ve+a1ZUyzHj4fhw5s/HtXPYy9v5pcvs8ubNb6SJIkNG4rlhAkwzF9xSZLqYo2v\nJEmZSAkOPbR4pNGpp8L8+U5+JUlDlzW+kiQNQrt3w5YtRXvqVCe9kiTVy59MNY31Fvkyu7yZX95q\n89u5E7ZvL9rTprVmPKqfx17ezC9fZpc3a3wlSRritm2DZ54p2ocf3tqxSJKUE2t8JUnKxB//CDNm\nFO0vfAE+8IHWjkeSpFayxleSpEFo8+Zq+4gjWjcOSZJy48RXTWO9Rb7MLm/ml7fa/Navr273Uuf2\n57GXN/PLl9nlzRpfSZKGuI0bq+2urtaNQ5Kk3FjjK0lSJr79bbj44qL92GMwa1ZrxyNJUitZ4ytJ\n0iC0enW13dnZunFIkpQbJ75qGust8mV2eTO/vNXm99RT1e1jxjR/LGqMx17ezC9fZpc3a3wlSRri\nKhPfjg4YMaK1Y5EkKSfW+EqSlInzzoM774QJE2DNGie/kqShzRpfSZIGoaefLpYTJ8Iwf8ElSaqb\nP5tqGust8mV2eTO/vNXmt25dsZw61YlvDjz28mZ++TK7vFnjK0nSEJZS9Tm+U6dC1HVhlyRJAmt8\nJUnKwq5dMHZssbz0UvjGN1o9IkmSWssaX0mSBpkdO4pJL8D06a0diyRJuXHiq6ax3iJfZpc388tb\nJb+tW6vbjjiiNWNRYzz28mZ++TK7vFnjK0nSEFap7wUnvpIkNcoaX0mSMjB/Ppx5ZtFetAhmz27t\neCRJajVrfCVJGmTWrq22J0xo3TgkScqRE181jfUW+TK7vJlf3ir5rVpV3TZuXGvGosZ47OXN/PJl\ndnmzxleSpCGsduI7ZkzrxiFJUo6s8ZUkKQN/+7fw5S/D8OGwaZOTX0mSrPGVJGmQeeKJYjl2LHR0\ntHYskiTlxomvmsZ6i3yZXd7ML2+V/J56qlifNAmG+eudBY+9vJlfvswuby2r8Y2Iroi4NSIeiogH\nI+KMiLgpIu4rX49HxH3lZ0dGxA0R8V8RsTgiXlGznzkR8UBELImIa2u2j4qIm8vt8yNiRs17l0TE\nw+Xr4prtsyLiN2WfmyJiRP/9J5Ekqf2sXl0sp0514itJUqN6rfGNiBuBu1NK10dEBzA2pbSh5v3P\nAT0ppU9FxN8Cs1NKl0bEVOCnKaWXlp9bALwnpbQgIn4CfDGldFtEXAacmFK6LCLeArwxpXRRREwG\n7gHmlF+1qNz3hoi4Bbg1pXRLRHwFuD+l9NW9xm2NryRpUEgJJk+Gnh644AL43vdaPSJJklqv32p8\nI2IicFZK6XqAlNLuvSa9AbwZ+Ndy0wnAz8vPrgZ6IuLUiDgMGJ9SWlB+7lvABWX7fODGsv1d4FVl\n+zXAHSmlnpRSD3An8LryO88Bbi0/d2PNviRJGnR274bNm4v2kUe2diySJOWot4ulZgGry8uX742I\nr0dEZ837ZwGrUkqPluv3A+dHxPCImEVxtnY6cASwrKbf8nIb5fJJKCbWwIaImAIcvlefZeVnJ1Oc\nYd6zj32pjVlvkS+zy5v55W3evHns2FFMfgFmzmzpcNQAj728mV++zC5vA5Vfb/eF7ABmU1yifE9E\nXAN8BPhE+f5bgX+p+fz1FGd9FwJPAP8JPAP01zXHDe1n7ty5zCz/D6Grq4uTTz6Z7u5uoPof1PXm\nrS9evLitxuN6/euLFy9uq/G43ti6+eW9vnjxYtavByjWN22ax7x57TM+11133fV2W69ol/G43th6\nxb7eX7x4MT09PQAsXbqURhywxjciDgV+nVKaVa6/HPhISum/lfW+yyjqblfsp/+vgEuBDcDPUkon\nlNvfCpydUnp3RNwGXJlSml/uc2VKaWpEXAR0p5TeVfb5GvAz4BbgaWBaSmlPRJwJXJFSeu1e322N\nryRpUHjkETjmmKL9q1/Bn/1Za8cjSVI76Lca35TSU8CTEXFsuelc4Hc17YdqJ70RMSYixpbtVwO7\nUkq/TymtBDZGxOllje7bgB+U3X4IXFK2/wq4q2zfAZxX3lV6EvBq4PZyNvtz4MLyc5cA36/nHytJ\nUo7WrKm2p05t3TgkScrVASe+pfcC34mI+4GXAJ8pt7+F6k2tKqYBiyLiQeDvKSa4FZcB3wCWAI+k\nlG4rt18HTImIJcD7KS6lJqW0DvgkxZ2dFwBXlTe5Avgw8MGyz6RyH2pze1++oHyYXd7ML2/z5s17\n9lFGAOPGtW4saozHXt7ML19ml7eByq+3Gl9SSvcDp+5j+1/vY9tS4Pj97GcRcNI+tu+guDP0vvrc\nANywj+2PA6f3MnRJkgaF5cur7bFjWzcOSZJy1etzfHNlja8kabD4+Mfh058u2tu2wejRrR2PJEnt\noN9qfCVJUus9/nixHDcOOnq9VkuSJO3Nia+axnqLfJld3swvb/PmzXv2UucJE2D48NaOR/Xz2Mub\n+eXL7PI2UPk58ZUkqc2tKJ+fcMQREHVd0CVJkmpZ4ytJUpubMgXWrYMLL4Rbbmn1aCRJag/W+EqS\nNEikBBs3Fu3jjmvtWCRJypUTXzWN9Rb5Mru8mV/e7rxzHrt3F+0jj2ztWNQYj728mV++zC5v1vhK\nkjQEbdtWbR91VOvGIUlSzqzxlSSpjT3yCBxzTNF+4AE48cTWjkeSpHZhja8kSYNE5Y7OAF1drRuH\nJEk5c+KrprHeIl9mlzfzy9tdd817tj1hQuvGocZ57OXN/PJldnmzxleSpCFg+/bn1vWuWlVtjxzZ\n/PFIkjQYWOMrSVIbWbEChg2DQw8t1t/9bvjqV+H/b+/eo+wu63uPv7+ZzEwyuTC5EQIJSVCCnCqF\ngCAqNHjHWsV1FKFFQO2iYmvr8qwWtB6xp63WY1uF1XopKuIVLLgQj4ogmtoeAsmBDBIgEgIJhNxI\nYCb3y8w854/nN84mhgC57L2f7fu11l7ze36XnSfzYWd45vf7Pk9XF/T1wciRje2fJEnNwhpfSZIK\nlFK+2ztq1PC+5cvz14kToa2tMf2SJKl0DnxVN9ZblMvsymZ+5ejthaeeeuYjzUuXzgfgqKMgntfv\ntNUs/OyVzfzKZXZls8ZXkqQWt2FDHvjWevrp/HXOnPr3R5KkVmGNryRJTaC/H+69FzZvhle+Mt/1\nTQna22FgAP7+7+GjH210LyVJah4vpMbXKTIkSWoCqy64jK1Lt9I+qhNu/p9weDe7d+dBL3jHV5Kk\nA+Gjzqob6y3KZXZlM7/mN/jHl5B+8H/ovnc+cdd/ER+4FMh3f2E+ADNnNqx72k9+9spmfuUyu7Id\nqvy84ytJUoNtuv9xBrbt4DB2E2PGEF/8ApAnuxoyZUqDOidJUguwxleSpAYaGICVZ17A4B0LmBRP\n03zG5+oAACAASURBVHvCGcz46bWMnNzN/Plw1ln5vI0b85JGkiQpcx1fSZIKsXEjrP34vzJy/Bja\n0y7G3ft/GXHpJQA8/vjweaNHN6iDkiS1AAe+qhvrLcpldmUzv+aVEqxbB5s5jB1zTqCNAbbNOZH0\nxX8DYPlyGKrx7exsWDe1n/zslc38ymV2ZbPGV5KkFtPXB7t3w+aL/5T+vgdYFTNYf/m/MH1CNwAP\nPZTPmzwZRvirakmS9ps1vpIkNcgjj+Q7vmtfcx4n7FjAjfx3zpy4lFOf/BEjRsBpp8HChXDiibB4\ncaN7K0lSc7HGV5KkJjc4CE89BZs2QUdbP3dyMg/xItLVVxPVj/DHHstfX/ayxvVTkqRW4MBXdWO9\nRbnMrmzm15x27IBdu/KSRVs/fRW3tr2NxS+5kLbpR/164LthA8B8B76F8rNXNvMrl9mV7VDl58BX\nkqQG2LkTtm+H/n540d++lzkDS3nd0itpX5ench4czMcAjjuugR2VJKkFWOMrSVIDrFoFS5bAsr/5\nOsff+WW+xznMZiXvOvw/OXrdPWzeDOPH53Pvvhvmzm1sfyVJajYvpMbXWZ0lSWqAbdvyckZP9qzm\nMI5gPFs4nDX0fepfgaHHnLMpUxrUSUmSWoSPOqturLcol9mVzfya065duc73wh1fYCZPMI//5HA2\nMuPydwPw8MNDZ85n3LiGdVMHwM9e2cyvXGZXNtfxlSSpRQwO5ru9GzdCMIIZPEE7u+ljDGuOm0c3\ntQNfGD26YV2VJKklWOMrSVKd7dgBS5fCt78Nf/KZYziMTYykn35GsPGniznutTP50Ifgyiuhqws2\nb4YRPqMlSdIzuI6vJElNLCVYvRoe+MYCttLFODbTTxs76WTapz4EwP3353OnTXPQK0nSgfJHqerG\neotymV3ZzK/5dHbCggWwZm1wF3PZxUh20c4uRrKBPJPV8uX53EmT5jeuozogfvbKZn7lMruyWeMr\nSVKL6OuDBx+EtUwkMYIHeAkjGGAl05n7j58BYO3afO6sWY3rpyRJrcIaX0mS6mzBArjr7X9PrHuC\nE+lhJ6O4mguZObaXDy75EDNmQFtbPvfqq+GP/7ix/ZUkqRm5jq8kSU3sF7+ACb2r6aSXWaziSSbz\nKhbQuX0E7e15qaMhRx/duH5KktQqrPFV3VhvUS6zK5v5NZeU4L77YM3AEbSzm0GCR5nJSmZwz+sv\no60tPwo95LHH5jesrzowfvbKZn7lMruyHar8HPhKklRHu3fDypUwsf8JttHFoxzNEo5nKk8yZcnP\naWvL9b9Duroa11dJklqFNb6SJNVRXx+ccgpc8fA7OZL1QGI8m/gO57Dlwg/zqc+O59//Hd7//nz+\n6tV5SSNJkvRMruMrSVKT2rABtm+HjUxmM2PoYzwBvJz7aBsznhEjhu/4dnTkpY8kSdKBceCrurHe\nolxmVzbzay6LFkF/P9zJy9nKaNYxlYc4lmt4F52dMGIELFmSz504Ee64Y34De6sD4WevbOZXLrMr\nmzW+kiS1gMceg6efhgn08hDHkYDPcgkLOIv29ryM0bJl+dxJkyCe1wNckiRpX6zxlSSpTvr74dJL\n4ctfhut5O+uYynSeYBVTuJr38da/fhUf+1i+07t9O5x1FvzgBzBmTKN7LklS83EdX0mSmtCWLbB4\ncd4+hkcYzXZGkvhdljCKfkaNynd4t2/P50yblu8AS5KkA+Ojzqob6y3KZXZlM7/msX07LF8OX+IS\njmINL+NBprCOK7iMBzpfTmcn7Nw5fP6UKfCLX8xvWH91YPzslc38ymV2ZWtYjW9EdEfEDRHxYEQ8\nEBGviIjrImJx9Xo0IhZX546KiO9ExC+rcy+veZ+TI+K+iFgWEVfW7O+MiOur/XdGxMyaYxdFxEPV\n68Ka/bMj4q7qmusiov3gfUskSTo0VqyAbdvgRO4mMZJprOUI1vN5LqOfLkaNgjVrhs+fODFPdiVJ\nkg7M8/lxeiXwo5TS8cAJwIMppfNSSiellE4CbqxeAOcBpJROAE4G/iQijq6OfQF4X0rpWODYiHhT\ntf99wMZq/2eBTwNExETg48Cp1euKiDisuubTwD9V1zxdvYea3Lx58xrdBe0nsyub+TWPodmaRzAA\nQCKxgYm8nW/Q1gZjx8KvfjV8fne3+ZXM7MpmfuUyu7Idqvz2OfCtBppnpJS+CpBS6k8p9dUcD+Bc\n4DvVrjXAmIhoA8YAu4BNETENGJdSWlid93XgnGr7rcC11faNwGur7TcCt6aUelNKvcBtwNnVn3kW\ncEN13rU17yVJUtPauDHX8P6YN9LHGNYyme/zZtZzOBG/OfAdO9ZZnSVJOhie647vbODJiLgmIu6J\niKsjoqvm+BnAupTScoCU0k+ATeQB8ArgM9Wg9ShgVc11T1T7qL4+Xl3fD/RFxCTgyD2uWVWdOxHo\nTSkN7uW91MSstyiX2ZXN/JrHwACkBE9wJMuYw0pm8RDHcTa3Ar858B09Gv7jP+Y3prM6YH72ymZ+\n5TK7sh2q/J5rVueRwFzgz1JKiyLic8Dl5EeQAc4Hvj10ckRcAIwGppEHqP8ZEbcfxP6+oPWJLr74\nYmbNmgVAd3c3J5544q9vnQ99Q23Xr93T09NU/bH9/Ns9PT1N1R/bL6xtfs3T7uqCwcH5fI2Z/CEb\n2cJ4VrKCBZxCV8C4cXDPPfn8iHmMGwe//GUPI0Y0R/9t27Ztu4T2kGbpj+0X1h6yt+M9PT309vYC\nsGLFCl6Ifa7jGxFHAAtSSrOr9quBy1NKb4mIkeS7sHNTSqur458H7kgpfbNqfwX4MfBfwM+rOmEi\n4nzgzJTSpRFxC/CJlNKd1XuuSSlNiYjzgHkppfdX13wJ+BnwXWA9MDWlNBgRpwNXpJSGaoaH+u46\nvpKkpvKRj8BVV8G2bVs5hQV8jH/kEq5iPXMYPx5+8Qs47zxYujTf7f3hD/NavpIk6Te9kHV8R+zr\nYEppLfB4RMypdr0OuL9m+8GhQW9lKfCaqhNjgFcAS6v32RQRp1U1uu8Gvl9dczNwUbX9DmDoDvGt\nwBuqWaUnAK8HflKNZn8OvLM67yLgpufzl5UkqVF27YJHHoGODhjJLqazjod4CZs7jgTyer2jRkH1\ni2za22HMmAZ2WJKkFrLPgW/lg8C3IuJe8qzOn6z2v4vhSa2GfAnoiIj7gIXAV1NK1RyWfAD4MrAM\neDildEu1/yvApIhYBnyI/Cg1KaWngL8FFlXv9TdVvTDAZcCHq2smVO+hJrfn4wsqh9mVzfyaw/bt\nsH59HuAu4kw+zRWc2/lDpqWnAOjszEsXbd2az29vz/vMr1xmVzbzK5fZle1Q5fdcNb6klO4FXr6X\n/e/Zy76dwAXP8j53Ay97lmvOfZZrrgGu2cv+R4HTnqvvkiQ1i/Xr893cUaOggwHGso2undv4Ia/n\neH5FR0e+K7xrVz6/owO6uuDppxvbb0mSWsE+a3xLZo2vJKmZ3HYbXHZZHsje9PhJvGhgGZvbJnB6\n+i9WDs7kmGPgm9+EV78aBgfhmGPg9tuhmqNRkiTt4aDV+EqSpINjzZr86HJbG3zi5B8w8vBJ/Msf\n3cHKwZlAvhO8bVse9MLwo86SJOnAOfBV3VhvUS6zK5v5NYetW+Gww/L2qGOmw8qVrO+Y8evjo0bB\nk08On9/RkV/mVy6zK5v5lcvsynao8nPgK0nSIdbfnwex3d15gPuSl8DIkbB79/A548bBhg3D7a6u\nfHdYkiQdOAe+qpuhxadVHrMrm/k13tatMGVKHtyOHJmXKRoYyAPiIRMmwNq1w+1Ro/K55lcusyub\n+ZXL7Mp2qPJz4CtJ0iEWkQexEyfCEUfAKafkge/AwPA5U6fC6tXD7bFj8zWSJOnAOfBV3VhvUS6z\nK5v5Nd7YsbBlC0yfDscdBzNmQEqwY8fwOVOnwsaNw+0xY/KjzuZXLrMrm/mVy+zK1rB1fCVJ0oEZ\nMQLGj88zNre351rfgYE8i/OQKVOgr2+43d2dr5MkSQfOdXwlSaqDxYth+fJ8J/fss6G3F97+dhj6\nxfa3vgVXXgkLF+b25ZfDJz+ZH5OWJEm/yXV8JUlqMpMn55rd8eNze887vt3dz3z0eeJEB72SJB0s\nDnxVN9ZblMvsymZ+zeHww3Nt76RJub1nje+YMc9sT56cv5pfucyubOZXLrMrm+v4SpJUsM5OOOyw\n4bu4AwPPHOiOHg27dg23J06sb/8kSWpl1vhKklQnmzbltXrnzMlLF73qVbBiRR4U33EHvOUtsGZN\nPveOO+D00xvaXUmSmtoLqfF1VmdJkupk/Pi8tBHA7t2wfXve7urKjz4Prevb3g4TJjSmj5IktSIf\ndVbdWG9RLrMrm/k1l6ElinbsgJ078/a4cbnd35/btQNf8yuX2ZXN/MpldmWzxleSpBayfftwTe+k\nSXngu3t3bnd05LvAkiTp4LDGV5KkBrjjDjjrrDz4ffOb4f3vh3PPzQPgadPgkUdg1KhG91KSpObl\nOr6SJDW52ju+Rx2V1/QdHMzt0aPzXV9JknRwOPBV3VhvUS6zK5v5Nadt24a3jz46z/g8VOPb3T1c\nC2x+5TK7splfucyubNb4SpLUQvr6hrePOgq2bh2+4ztuXGP6JElSq7LGV5KkBrjqKviLv8jbN90E\n99wD/+t/5fa558L11zeub5IklcAaX0mSmty6dcPbhx8OGzcOt2fMqH9/JElqZQ58VTfWW5TL7Mpm\nfs1pzZrh7QkToLd3uD1nzvC2+ZXL7MpmfuUyu7JZ4ytJUgtZv354u60NNmwYbh97bP37I0lSK7PG\nV5KkBjj1VFi0CMaOhZ4euPDCvLYvwNKlcNxxje2fJEnNzhpfSZKa2MDA8KPN3d0wdeozZ3k+/PDG\n9EuSpFblwFd1Y71FucyubObXfHbvhi1b8vakSXkgvHPn8PExY4a3za9cZlc28yuX2ZXNGl9JklrE\nzp2waVPenjoV1q7Ng1+Ari7o6Ghc3yRJakXW+EqSVGfr1uUli3bvhj/9U3jnO+H88/NMz0ceCU88\n0egeSpLU/KzxlSSpifX15UEv5BmcBweHH3U+8sjG9UuSpFblwFd1Y71FucyubObXfGonsuruzo85\nb9+e27Vr+IL5lczsymZ+5TK7slnjK0lSixiq7x0SMXzH9/jj698fSZJanTW+kiTV2be+BRdckLe/\n9rU8EP7zP8/tm2+GP/iDhnVNkqRiWOMrSVITe/TR4e2urjzZ1ZAXv7j+/ZEkqdU58FXdWG9RLrMr\nm/k1n5Ur89eODli1CpYsGT42adIzzzW/cpld2cyvXGZXNmt8JUlqEWvW5K/jxsGyZfD448PHxoxp\nTJ8kSWpl1vhKklRnp54KixbBSSfB7/8+3HgjPPggjB4NmzdDW1ujeyhJUvOzxleSpCY1MAAbN+bt\ncePyMkZPP53bM2fCCH8yS5J00PnjVXVjvUW5zK5s5tdcdu+GLVvy9qRJedA7tIbvySfnpY1qmV+5\nzK5s5lcusyubNb6SJLWAXbtgx468fcopedA7MJDbZ57ZuH5JktTKrPGVJKmO1q+HWbPygPeLX4Rb\nboGbbsrH7r4b5s5taPckSSqGNb6SJDWpzZth6PeyW7bAhAnDx445pjF9kiSp1TnwVd1Yb1Eusyub\n+TWXoYmsIuC++555bPz43zzf/MpldmUzv3KZXdkOVX4jD8m7SpKkverry4Peri544IHhdXsnTXJG\nZ0mSDhVrfCVJqqPrroNLLskDXRgeAL/2tfDTnza2b5IklcQaX0mSmtS6dTBqFBx3HOzcCU8+mfc7\no7MkSYeOA1/VjfUW5TK7splfc9mwAUaPhtNPh7Fj8+AX4Iwz9n6++ZXL7MpmfuUyu7I1bB3fiOiO\niBsi4sGIeCAiXhER10XE4ur1aEQsrs79o5r9iyNiICJOqI6dHBH3RcSyiLiy5v07I+L6av+dETGz\n5thFEfFQ9bqwZv/siLiruua6iGg/uN8WSZIOjU2bcl1vRwdMnpzretva4CUvaXTPJElqXc9Z4xsR\n1wL/kVL6akSMBMaklPpqjv8j0JtS+rs9rnspcFNK6cVVeyHwZymlhRHxI+CqlNItEfEB4KUppQ9E\nxLuAt6eUzouIicAi4OTqLe8G5qaU+iLiu8ANKaXvRsQXgHtTSl/c48+3xleS1HTe+15YsgQ++EG4\n/Xb48Y9hcBDWrIGRTjkpSdLzdtBqfCPiMOCMlNJXAVJK/XsMegM4F/jOXi7/w6H9ETENGJdSWlgd\n+zpwTrX9VuDaavtG4LXV9huBW1NKvSmlXuA24OzqzzwLuKE679qa95IkqWkNDuY1fMeNy2v4zpyZ\nlzCaPdtBryRJh9JzPeo8G3gyIq6JiHsi4uqI6Ko5fgawLqW0fC/X1g6IjwJW1Rx7oto3dOxxyANr\noC8iJgFH7nHNqurcieQ7zIN7eS81MestymV2ZTO/5rFrF7S35xmdu7vzQHjrVvid33n2a8yvXGZX\nNvMrl9mVrVHr+I4E5pIfUV4UEZ8DLgc+Xh0/H/j2nhdFxGnAtpTSAwezs8ALenb54osvZtasWQB0\nd3dz4oknMm/ePGD4G2q7fu2enp6m6o/t59/u6elpqv7YfmFt82ue9q5d8NRT8+nrg0WL5jF2LOza\nNZ+JEwH2fr352bZt2/YLaw9plv7YfmHtIXs73tPTQ29vLwArVqzghdhnjW9EHAEsSCnNrtqvBi5P\nKb2lqvddRa67Xb3HdZ8l3wn+h6o9DfhZSun4qn0+cGZK6dKIuAX4RErpzuo916SUpkTEecC8lNL7\nq2u+BPwM+C6wHpiaUhqMiNOBK1JKb9qjD9b4SpKaysaN8IUvQH8/rF6dJ7i67Tb4+tfhtNMa3TtJ\nkspy0Gp8U0prgccjYk6163XA/TXbD+5l0DsCeCdwXc37rAE2RcRpVY3uu4HvV4dvBi6qtt8B3F5t\n3wq8oZpVegLweuAn1Wj259WfQXXtTc/nLytJUqONGwdTp+bHnjs68szO06c3uleSJLW2fQ58Kx8E\nvhUR9wInAJ+s9r+LvU9qdSbwWEppxR77PwB8GVgGPJxSuqXa/xVgUkQsAz5EfpSalNJTwN+SZ3Ze\nCPxNNckVwGXAh6trJlTvoSa35+MLKofZlc38mkdXV17Dd84ciIAVK+Doo/MEV8/G/MpldmUzv3KZ\nXdkOVX7POYdkSule4OV72f+eZzl/PvDKvey/G3jZXvbvJE+Etbf3uga4Zi/7HwV8KEySVJT2atX5\n8ePh+ONh+XJ48Yth1KjG9kuSpFb3nOv4lsoaX0lSsxkYgFtuyUsarV2bB76nngrnnJPvAEuSpOfv\nhdT4umqgJEl10taWH21etw6OPRZGjIAXvchBryRJh9rzqfGVDgrrLcpldmUzv+bS2ZlndR49Og96\nJ03a9/nmVy6zK5v5lcvsynao8nPgK0lSHU2alAe9/f0wYULeliRJh5Y1vpIk1dljj+U7vxs3wjHH\nOLmVJEn746Ct4ytJkg6+tjbYsiVPdtXR0ejeSJLU+hz4qm6styiX2ZXN/JrP5Ml5Qqvp0/MEV/ti\nfuUyu7KZX7nMrmwNW8dXkiQdXJ2d+RFnSZJUH9b4SpIkSZKKY42vJEmSJEkVB76qG+stymV2ZTO/\nsplfucyubOZXLrMrm+v4SpIkSZK0H6zxlSRJkiQVxxpfSZIkSZIqDnxVN9ZblMvsymZ+ZTO/cpld\n2cyvXGZXNmt8JUmSJEnaD9b4SpIkSZKKY42vJEmSJEkVB76qG+stymV2ZTO/splfucyubOZXLrMr\nmzW+kiRJkiTtB2t8JUmSJEnFscZXkiRJkqSKA1/VjfUW5TK7splf2cyvXGZXNvMrl9mVzRpfSZIk\nSZL2gzW+kiRJkqTiWOMrSZIkSVLFga/qxnqLcpld2cyvbOZXLrMrm/mVy+zKZo2vJEmSJEn7wRpf\nSZIkSVJxrPGVJEmSJKniwFd1Y71FucyubOZXNvMrl9mVzfzKZXZls8ZXkiRJkqT9YI2vJEmSJKk4\n1vhKkiRJklRx4Ku6sd6iXGZXNvMrm/mVy+zKZn7lMruyWeMrSZIkSdJ+sMZXkiRJklQca3wlSZIk\nSao48FXdWG9RLrMrm/mVzfzKZXZlM79ymV3ZrPGVJEmSJGk/WOMrSZIkSSqONb6SJEmSJFUc+Kpu\nrLcol9mVzfzKZn7lMruymV+5zK5s1vhKkiRJkrQfrPGVJEmSJBXHGl9JkiRJkirPOfCNiO6IuCEi\nHoyIByLiFRFxXUQsrl6PRsTimvNPiIgFEbEkIn4ZER3V/pMj4r6IWBYRV9ac3xkR11f774yImTXH\nLoqIh6rXhTX7Z0fEXdU110VE+8H7luhQsd6iXGZXNvMrm/mVy+zKZn7lMruyNbLG90rgRyml44ET\ngAdTSuellE5KKZ0E3Fi9iIiRwDeAS1JKLwV+D+iv3ucLwPtSSscCx0bEm6r97wM2Vvs/C3y6eq+J\nwMeBU6vXFRFxWHXNp4F/qq55unoPNbmenp5Gd0H7yezKZn5lM79ymV3ZzK9cZle2Q5XfPge+1UDz\njJTSVwFSSv0ppb6a4wGcC3yn2vUG4Jcppfuq859OKQ1GxDRgXEppYXXe14Fzqu23AtdW2zcCr622\n3wjcmlLqTSn1ArcBZ1d/5lnADdV519a8l5pYb29vo7ug/WR2ZTO/splfucyubOZXLrMr26HK77nu\n+M4GnoyIayLinoi4OiK6ao6fAaxLKS2v2scCKSJuiYi7I+Ivq/1HAatqrnui2jd07HHIA2ugLyIm\nAUfucc2q6tyJQG9KaXAv7yVJkiRJ0jM818B3JDAX+HxKaS6wFbi85vj5wLdr2u3Aq4E/rL6+PSJe\nAxys6ZWdprlgK1asaHQXtJ/MrmzmVzbzK5fZlc38ymV2ZTtU+e1zOaOIOAJYkFKaXbVfDVyeUnpL\nVc+7CpibUlpdHX8XcHZK6eKq/TFgB/BN4OdVnTARcT5wZkrp0oi4BfhESunO6j3XpJSmRMR5wLyU\n0vura74E/Az4LrAemFo9Rn06cEVKaahmeKjvDpIlSZIkqYU93+WMRj7Hm6yNiMcjYk5K6SHgdcD9\n1eHXkSe6Wl1zyU+Av4qI0cBu8uRW/1y9z6aIOA1YCLwbuKq65mbgIuBO4B3A7dX+W4FPRkQ3EMDr\ngctSSikifg68E7i+uvam/f0GSJIkSZJa2z7v+AJExO8CXwY6gOXAe1JKfRFxDflu8L/tcf4fAR8h\nP5b8w5TS5dX+k4GvAaPJs0T/ebW/kzwT9EnARuC8lNKK6th7gI9Wb/13KaVrq/2zgevI9b73ABek\nlHbv/7dBkiRJktSqnnPgK0mSJElSyZ7POr7Ss4qID0bEgxGxJCI+XbP/IxGxLCKWRsQbavafHBH3\nVceurNnfGRHXV/vvjIiZNccuioiHqteF9fvbtb6I+B8RMVitmz20z+yaXER8pvrc3RsR36tZ49z8\nWkhEvKnKcVlEXNbo/vy2iogZEfHziLi/+lk39MTaxIi4rfp83FqVZg1dc9A+hzpwEdEWEYsj4gdV\n2+wKERHdEXFD9TPvgYg4zfzKUeVxf/W9/3b1/W5cfiklX77260VeT/k2oL1qT6m+/jeghzzL9yzg\nYYafLlgInFpt/wh4U7X9AfLs4QDvAq6rtieSH7Hvrl7Lge5G/91b4QXMAG4BHgUmml05L/KcByOq\n7X8A/sH8WusFtFX5zary7AGOb3S/fhtfwBHAidX2WOBXwPHA/wb+qtp/2aH4HPo6aBl+GPgWcHPV\nNrtCXsC1wHur7ZHAYeZXxqvK4BGgs2oPzc3UsPy846sDcSnwqVTVV6eUnqz2vw34Tkppd8r12g8D\np0XENGBcSmlhdd7XgXOq7beS/3EDuBF4bbX9RuDWlFJvSqmXPNB+xgze2m//DPzVHvvMrgAppdvS\n8FrmdwHTq23zax2nAg+nlFZU/8ZeR85XdZZSWptS6qm2twAPAkfxzM/OtQx/pg7m51AHKCKmA28m\nz1czNPGp2RWgeprpjJTSVwFSSv0ppT7MrxSbyJMdd0VeuacLWE0D83PgqwNxLHBm9WjB/Ig4pdp/\nJHmpqyGryP+TsOf+J6r9VF8fh/wPG9AXEZP28V46ABHxNmBVSumXexwyu/K8l/zbTzC/VvLrXCp+\n/5tARMwiT8Z5F3lZxXXVoXXA1Gr7YH0OJ6KD4bPAXwKDNfvMrgyzgScj4pqIuCciro6IMZhfEVJK\nTwH/BDxGHvD2ppRuo4H57XM5IykibiM/5rWnvyb/9zMhpfSKiHg5eY3lY+rZPz2758juI8Abak+v\nS6f0vO0jv4+mlIbq1P4a2JVS+nZdO6d6cObJJhMRY8l3FP4ipbQ5YvifzZRSiggzazIR8RZgfUpp\ncUTM29s5ZtfURgJzgT9LKS2KiM8Bl9eeYH7NKyJeBHyI/NhyH/DvEXFB7Tn1zs+Br/YppfT6ZzsW\nEZcC36vOWxR5kqTJ5N/EzKg5dTr5NzVPMPxIZu1+qmNHA6urxyEOSyltjIgngHk118wAfnZAf6nf\nEs+WXUS8lPxb1Hur/3GbDtwdeZ1ts2sS+/rsAUTExeTH92of6zG/1rFnljN45m+8VUcR0U4e9H4j\npXRTtXtdRByRUlpbPYq3vtp/sD6HTx2av81vlVcCb42INwOjgPER8Q3MrhSryE+nLaraN5B/cb/W\n/IpwCnBHSmkjQER8DzidBubno846EDcBrwGIiDlAR0ppA3AzcF5EdERec/lYYGFKaS2wqZqRL4B3\nA9+v3utmcsE7wDuA26vtW4E3VLP6TSBP6vOTOvzdWlZKaUlKaWpKaXZKaTb5H4+51WMnZleAiHgT\n+dG9t6WUdtQcMr/W8f+AYyNiVkR0kCftuLnBffqtVH1mvgI8kFL6XM2h2s/OReSfiUP7D9bnUAcg\npfTRlNKM6mfdecDPUkrvxuyKUH3fH6/+HxPgdcD9wA8wvxIsBV4REaOr7/vrgAdoZH7PZ1Yuson4\noAAAAPZJREFUX7729iLPuvYN4D7gbmBezbGPkovSlwJvrNl/cnX+w8BVNfs7yY9KLwPuBGbVHHtP\ntX8ZcFGj/96t9iLPuDfR7Mp5Vd/PlcDi6vV582u9F3A2eQbhh4GPNLo/v60v4NXk+tCems/cm8gz\nn/8UeIj8i6LummsO2ufQ10HL8fcYntXZ7Ap5Ab8LLALuJT9leJj5lfMiT6J6f/W9v5Y8dmhYfkNT\nREuSJEmS1JJ81FmSJEmS1NIc+EqSJEmSWpoDX0mSJElSS3PgK0mSJElqaQ58JUmSJEktzYGvJEmS\nJKmlOfCVJEmSJLU0B76SJEmSpJb2/wH7f5F6CFVV3AAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff0d6b12b50>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for t in traces:\n",
      "    plot(t[0,0], t[0,3], 'g.')\n",
      "    plot(t[-1,0], t[-1,3], 'r.')\n",
      "    plot(t[:,0], t[:,3], 'b', alpha=0.1)\n",
      "axis('equal')\n",
      "grid('on')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+hPB7kp6X1Jf09NiS6dOSfkPStKQ/iTF+cnT9o5J+K4RwUdJtSR94mD8MAID9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\n3ib86KNLKpXS8Tj+2hhZec0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kwVRKYXV8EvFgkKYTl8v23iVWXwEAe4fwikzy1i+PbKh7\n8VDz7dPvp5ZhH9bU66VjaBYWbCW207Gw2mjY6my7LZ09u73DmqStK6q+wuoBdnFxSTHa6mq1agE1\nBFshLpVsNbjRSJOQsf/xWS8m6l48eas54RUAgB3QblsYbDYt9G1s2PVm00LqwYO2Aru+bi3Ct25Z\nS3G3u/0tw5K9Bx/E5Ge8+mRhD6XjZ76Wy/Zan0zcbNotrcMAgL1CeEUmeeuXRzbUvXio+fa5d1jT\n+noKh/W6hde1NQur585ZeJ2dtVB5+vTOvKdazd5TtWqPKxW79tpry1vagQcDC6p+XM7UlK3CsvKa\nH3zWi4m6F0/eak54BQBgB2xuWtDzY3EaDdvvurEhzc/btbU1C4pHj25d5TxxYmfeU62WzpkdP/M1\nxtQqHKO9bz/rdXxgEyuvAIC9RHhFJnnrl0c21L14qPn28WFNjYaFv1ZLmpmx64uLttfVhzWFkILl\nwsL2D2ty9br9Xj8OJwQLpKdOLd0983W8ndhXYz28xkh4zQs+68VE3YsnbzUnvAIAsM0GAwurzaYF\nWD9mplSy5w4ftuDqw5pWVy04drs2rGmnjLcNVyrpjFfJrvvxOP43+P7Yej0Nm6JtGACwVwivyCRv\n/fLIhroXDzXfHu22BVOf1utnubZatqo6PW0rsM2m7W9dXbWW4k5n5/a7ShZCPUR7i3CpJN24sXy3\nXThGe+3486WSvTdWXvODz3oxUffiyVvNCa8AAGyz8WFNlYoFVV91rdetfXh93V43PqxpONyZScOu\nUklnt1ardlur2Xvzfa/VajoTtt1OAdZXZgmvAIC9QnhFJnnrl0c21L14qPn2aDQs9K2sWFjd3ExH\n48zNWUBcX7cweOSIBUKf/Hv8+M69r3o9tf36XttyWTp5ckmStQZLad+rtwp7u/F4WzH2Nz7rxUTd\niydvNSe8AgCwzTyYNpsWSlstC6+NhnTsWDr/1Y+s8UFK8/N2f6d4ePXVVOcrq95S7MG227Xnp6Ys\nyA4G2nKkDgAAu4nwikzy1i+PbKh78VDzhzccbh3W1OmkVtteTzp0yPa4djrWKry+bqFyMNjZYU1S\n+j0hpPbgEKRXX12+u6+1UrHXjk8Y9qDrARf7H5/1YqLuxZO3mhNeAQDYRp1OGthULls4jdEeT03Z\nV6tlq7CnTtl+1+lpe343wqu3/fpxOX7W6/hZrv7Y24j9LFifmgwAwF4gvCKTvPXLIxvqXjzU/OE1\nmxb6NjZSePUjZqanbarw+LCmtTXbBzsYSCdO7Ox783bg4TCtsJZK0pkzS3fDaoz2XLm8dWBTuWwB\n2wMt9jc+68VE3YsnbzUnvAIAsI3GhzXNzFg4XVhIIbVatQFO/b61EPse0npdOnp0Z9+b77H1gFoq\nbQ2x3W5akfVzZ314k6/MEl4BAHuF8IpM8tYvj2yoe/FQ84fnbcKNhoW+VstWXDc3LZw2m/ZVr9vr\npqZsRfPQoZ0d1iSlPa7DYWoFrlSkq1eX7x6dMxymo3OGwzS0yQdPSQxtygM+68VE3YsnbzUnvAIA\nsE1itGDabr9+WJMfi7OxYYHQj86pVm0188yZnX9/vpd1MEitwL6qGmPa1+qvCcH+llLJjsvZ3Ezh\nFwCA3UZ4RSZ565dHNtS9eKj5w+l2bXWy1UpnuYZgIbZet2vNpoXAkyettfjgwd2ZNCylKcO9ngVU\n98gjS5JSqPXzXEul14dXP1YH+xuf9WKi7sWTt5oTXgEA2CY+rGlzM4VXP2bGhzVtbtrrzp5N+2KH\nQ5s8vBumptLApvG9rz552O97kO317LZet1BOeAUA7BXCKzLJW788sqHuxUPNH06jYcHw9m0Lqisr\ndparD2sqlaxteDCwFmJXr0uLi7vzHut1Wwn2Pa4xSteuLUtK7cB+3Yc0eWtxp2P3Ca/7H5/1YqLu\nxZO3mhNeAQDYJuvrFgDvHda0sWHH4HhLcb1uAdaD5OJimgS806anU9uwtw773tfxtmFp6wCnWi0N\nbyK8AgD2AuEVmeStXx7ZUPfioeYPZ3MznY3a620Ng4cOWbjt9ax1d3PTbrvd3WsZllJg9nZhSXrs\nsaUtK7FSmjjsf4Pvfx0OCa95wGe9mKh78eSt5oRXAAC2Qa9ne1k7HQt6Pqyp17PVznLZwl+zKZ0+\nba3FBw9aENyNYU3OQ6pkq6kxWqD1IU39fjr7VUqBtVKx52IkvAIA9gbhFZnkrV8e2VD34qHmb16r\nZUF1bc3C4MqKBb7h0B77sKZGw47FWV+3UBujhdnd4i3LkgXZUkl65ZXlu6uwvZ7d+vCmXs/u12p2\n30Mu9jc+68VE3YsnbzUnvAIAsA0aDQuFq6u2orqyYuF0bc1ahiULr/1+Gs7kq57+/G4YD68eWP0I\nncEgTRf21dduN+157fftNf79AADsJsIrMslbvzyyoe7FQ83fvI0Nu/VjclotW23d2JCOH08twz74\nqKq69fQAACAASURBVF6322PHdm9Yk5SOypHsvZRK0qOPLt0d2tTtWnD1CcPdbjpSR7LHtA3vf3zW\ni4m6F0/eak54BQBgG6yvW2AdDu3W947GKB0+bM8PBrYau75uIbLTsRbi3VSr2a0fhROCfUkWZH2l\n1acRdzrptZL9Xd5aDADAbiK8IpO89csjG+pePNT8zRkM0qThSsVahQcDC3rT0xYGGw37OnYsnf/a\n7+99eB0OpS99aVnlsr13Pw7HpxH7yqtPHO50WHnNAz7rxUTdiydvNSe8AgDwkFotC3kbGxZW79yx\nVuB+38Li9HQKr488YkF3etpWPHdzWJOU2n+9FdhDqZQGNJVK9t48zHp49ZVYVl4BAHuB8IpM8tYv\nj2yoe/FQ8zen2bSgeueOhdKVFWl+3tqDFxct/DUa9hofzhSjtQ4vLOzue/Xw6ue3StJb37p097nx\ns11DsJVZnzhcqdjf6t+P/YvPejFR9+LJW80JrwAAPKT1dbv1gUzjw5qOHrXg2uulib7T09ZifOzY\n1jNVd0OplN6H73mV7L6f/zoY2HUPqO22PV+v298UAq3DAIDdR3hFJnnrl0c21L14qPmbs75u7bTD\nobXZ9nppwNHhwxb4hkNpZsbuz8zY68+e3Zv3W69b+PTw+tJLy3fbh/v9NMTJh051OvbaWs2CeLlM\neN3v+KwXE3UvnrzVPFN4DSH8uxDCzRDCc2PXfiaEcCWE8Ozo63vGnvvpEMLFEMILIYT3jl1/Zwjh\nudFzvzx2vR5C+N3R9U+HEM5t1x8IAMBOGg4tvLbbtqK5upqGNR04kIY1ra3ZKuzKil3v9XZ/WJOb\nmkorwb6f1e8Ph2k11gNuu51e02ikkAsAwG7KuvL6/0p63z3XoqRfiDG+Y/T1CUkKITwh6YckPTH6\nnl8Nwf81qF+T9KEY4+OSHg8h+M/8kKTbo+u/KOnn3vRfhB2Rt355ZEPdi4eaP7h221YmNzYslN66\nlc5ynZ62oNhq2df4sKZSSTp5cm/e89SUvW8/5/Utb1m6e9/3u8aY2oh9aFO9bt8XI+F1v+OzXkzU\nvXjyVvNM4TXG+P9JWrnPU+E+175P0u/EGHsxxkuSXpL0rhDCSUmzMcZnRq/7TUnvH93/XkkfG93/\nA0nvyfb2AQDYW82mBb6VFQula2s2rGljw26HwzSsaW7OguFwaEF3fn5v3vPUVGoJ9qFN5XIKrcOh\nffl77fftvodyifAKANh9D7vn9V+EED4bQvhoCMHnJZ6SdGXsNVcknb7P9auj6xrdXpakGGNf0loI\n4fBDvjdso7z1yyMb6l481PzB+QCjzU0Ld76ndXPTVlYbDQt8vs90asoenzy5+8OaXL2ehjLVatKL\nLy6rXk+h1cOqB9tOx15bLlu7sb8G+xef9WKi7sWTt5o/THj9NUmPSnpS0nVJP78t7wgAgH3E97sO\nh9Ya3OulgUcLC7YS6y23jYY0O2urtbt9vus43/Mq2T5WD6Z+VI6HV98P64HVV14JrwCAvfCm/5tv\njPFVvx9C+HVJfzx6eFXS+PzEM7IV16uj+/de9+95RNK1EEJF0nyM8c79fu9TTz2l8+fPS5IWFhb0\n5JNP3u3l9v+ywOPtf7y0tDRR74fHu/fYTcr74TGPJ+nxu9+9pLU16dlnl3XlilSvLylG6fLlZa2v\nS6XSktpt6eWXlzU3J62sLOn4celzn1vWo49K0t68///235b1/PO219WnIn/xi8uq1ZY0GNj9el2a\nn19Sv2/TiKempJMnl9TrSc88Y3/PuXN78/55vD2P3aS8Hx7v/OOlpaWJej883vnHfm1S3s/9Hl+4\ncEGrq6uSpEuXLumNhOjTGL6KEMJ5SX8cY3z76PHJGOP10f2fkvQtMcb/cTSw6bclfausHfjPJH1t\njDGGEP5W0k9IekbSf5T0KzHGT4YQnpb09hjjj4UQPiDp/THGD9znPcSs7xcAgJ3Wbkuf+pR0+bJN\nGb5yxb4OH7bVyne/W3ruOemFF6Rv/3bp2jXp675Oun1b+lf/yl63F157TfqzP5Pe/nbppZek69ft\n/V+6ZO///HmbjHz7dmp5/q7vsn29f/M30o/8iLS4KL31rXvz/gEA+RVCUIzxfrOVVMr4A35H0t9I\n+roQwuUQwv8k6edCCJ8LIXxW0rsl/ZQkxRifl/R7kp6X9AlJT48lzqcl/bqki5JeijF+cnT9o5KO\nhBAuSvpJSR9+E38ndpD/VxIUC3UvHmr+YJpNa6FdXbUBTCsrNpRpY8OC6WBgrcK9nj0fgr1+dtZa\nivfK1JS1BA8Gac/reOuwD2UKIZ3zOhymtuJOJ7UdY3/is15M1L148lbzTG3DMcYfvs/lf/cGr/+I\npI/c5/rfSXr7fa53JP1glvcCAMCkWF+327U16dAhG9J0/Lgdl3P0qIVYX7kcDNKxOadPp2FIe8H3\nucaou23DHqxLJQum5bJ9eVj156pVW3EmvAIAdtse/qsT+8l43zyKg7oXDzV/MBsbFuzGVyPLZQuC\n8/MWaodDC62Nhq3KdjrSmTNf/WfvpEpla3h961uXVK2m1VUPqx5sB4MUXisVC+DDYToHFvsPn/Vi\nou7Fk7eaE14BAHiT7tyxIFet2ipsjBYK63ULft2utRb7quzMjE3pfeSRvX3fvqo6GKTjerxFOEb7\n6vW2Pm610tE6GxsWZJk4DADYTYRXZJK3fnlkQ92Lh5pn1+lYMPVQeuOGBbteTzp40ILf5qatuJ4+\nba/1PaUnTuztew/BArcfiXPx4vLdluBSyVZVw2hUhq+4Npt2W6+ns20Jr/sXn/Viou7Fk7eaE14B\nAHgTmk0Lb6urFl5XVmwQ0+qqTeKNMQ10Gh/WdPCgvW6v+Vmv1aq910oltTz7CvJwmFqhu9208tpq\nsfIKANh9hFdkkrd+eWRD3YuHmme3sWG36+sWBNfXLaQ2m9KxY/Z4vPX2wAELfSdP7u2wJjc9be+n\nXpe+7uvsfNp7V149xIZgf0uplMKrRHjdz/isFxN1L5681XwC/vUJAMD+s76ehjW123a/XLaANztr\nz4dgIdFbiydhWJObnrbVVG9l9qNyQrC9sB5aPcT6dOFq1f4O2oYBALuN8IpM8tYvj2yoe/FQ8+xW\nVy20+rCmUimtTJZK9lyzaROGNzdt+vBwKJ07t9fv3NTraT/riy8u3115ley6r756ePX9seWyhdfB\nIJ0Hi/2Hz3oxUffiyVvNCa8AADygft/ahjc3bQ/r+LCmuTkLeZubFl5PnbJbn+Z79Ohev3szPZ1W\nU/0811ottTkPBumonEol7Xmt1+3+cMjKKwBgdxFekUne+uWRDXUvHmqeTbNpwW9tzdqB79yxVuGV\nFenIEXtNu532unr4m5ubjGFNUlp5jVF68skllUoWTn0QU7ttr/OjdHyfa71uf/tgYF/Yn/isFxN1\nL5681ZzwCgDAAxof1nTggIXYqSkLeEePWktxv28rlz5tuNu1YU1+BM1eO3AgtQL7oKYQ7O/wtuda\nLe2F9TDu+2K7XdqGAQC7i/CKTPLWL49sqHvxUPNsfFiTr1B2Ohb6KhVbWV1bs9fV67ZKOztrr5uU\nYU2Svd8YLVz/wz8sS0rBulSyv8mvhZAe+77Ydju1HWP/4bNeTNS9ePJWc8IrAAAPaGUlHTNz5066\nXqulgUadjoXWRsNuh0PpkUf27j3fq1pNk4V9SrJfq1bTWa7VqgVdP9fWr7XbrLwCAHYX4RWZ5K1f\nHtlQ9+Kh5l/dcGjh1VdUb95MZ6bOzlr4azTs6/hxu+7DkBYX9/rdJ94SHKP0jnfYOa+lku5OHfZV\nWR/m5Mfl+OCptbUUfrH/8FkvJupePHmrOeEVAIAH4CuQq6u2b3RlxULrxoaF08HAVl19r2ulYq+f\nn5+cYU1SWkHt9dKeVz/qx4OrB1OfKtxopInDjcbW5wAA2GmEV2SSt355ZEPdi4eaf3Wbmxbu1tYs\nnK6uWlttp2OThldWLPT5kTPT0xZ4T5+enGFN7sABC6+f+9yypHSOa7mcBk55+K5W7W+sVOzv9bNt\naR3en/isFxN1L5681ZzwCgDAA1hfT+ec+t7WatWC3txcmkRcq9nq5NycBcTTp/f2fd+Ph25vE/b9\nrn6EjmRB3J9rt+22Vktn17LyCgDYLYRXZJK3fnlkQ92Lh5p/dXfuWHCrVm2VNUZbjfRhTc2mBcKD\nBy3szc5aADx7dq/f+esdOGDv8du/fWnLsCZfbfU9sb5i7NOFp6ZSkGXldX/is15M1L148lZzwisA\nABnFmCYNz81J165ZW7CvsEoWXBsN6fBhux+jhb1JGtbkpqctWJdKFliHw9TuHEIa0OSDmXq91Fbc\natnrWXkFAOwWwisyyVu/PLKh7sVDzd9Yq2Urjmtr0syMBdm5uRRWu11beZVs5bVctsA3N2evnzTT\n0/b+nnlm+e704Xrd3reHVV99rVTsb/cw3uvZawiv+xOf9WKi7sWTt5oTXgEAyMiHNa2vWxhdX0/7\nRhcXbXiTZCuZ/b69ptWSzpyZvGFNkrUNe/gcPxLHV1+7XWsj9mDb7abW4m7XvtdbiQEA2GmEV2SS\nt355ZEPdi4eav7H1dQuqHtoaDQt6lYodhbO5aa+r122Vcn7eXn/mzN6+769kaspC6rd8y5Lqdd3d\n91qrWUtwq2VBPAS7bTTSObClUvpngf2Hz3oxUffiyVvNCa8AAGTk+11rNRvc5KHOw9zmpoVaH4R0\n4ICFvUmcNCxZyPbV1lrNrvlxObWahVPf4zo9nfbwlstp+nCns7d/AwCgOAivyCRv/fLIhroXDzV/\nY7duWWCbm5OuXrVw2u3aROEYLcg1m7b/9f9v7+6DJU3LOs//rnx/PedUVVe/0t1Vug3ShksjLk3o\niOWICISLuuEO8AcDDuHODu6osRGu4EasRmzsLG7EDjIxg3+s6KAxgzq6Ko6CoEPtjBpNq3RhAwLd\n0kVXddfLqXPqvL/kycx7/7jy7udUUS9P1TmnMvO5v5+IjJP5ZJ6qJ5+L7OKX931f92Dgv9NoSHff\nPd7zvp44Hfiznz2pSiUL4sNhtq+r5GFVyqYKx/1fNzeZNjyt+Kynibqnp2g1J7wCAJDD1paHtdVV\nD68LCx5aV1elo0c96G1uZg2NYvibm/NRy0lUqXh4jV2GJQ+mpZLf4rrXeD8+Hg591HZlJetEDADA\nQSO8IpeizZdHPtQ9PdT8+tbXPbQtLfmIa2zWtLXl4XV5OdtWJgQPrJubPmV4Eps1SR5K63XpVa86\n8dJ5S9lI63Do514u+4hrqZRNm65Ws/DKXq/Th896mqh7eopWc8IrAAA5rKxk28PELXFik6NOJ+s0\nXK36c3Nz/rqHHhrved9M3PYmTiGuVv0mZfu9xv1czTy8Sh7O19ayYAsAwEEjvCKXos2XRz7UPT3U\n/PouX/ZQGps1xcZFjYaHuo2NbHuc7e2sk++kNmuK2m3piSd8n9c4Nbhe92nEg4EH2TgiW6l4IC+X\n/XgMsoy8Th8+62mi7ukpWs0JrwAA5HDpkofSmRnp+ec99K2v+7pXyacPx2ZOw6EfazalI0fGd855\nNJseuqvVK9e9Sh5e46iqWbZFkJmH883N7DgAAAeN8IpcijZfHvlQ9/RQ82vr9XyKbGzWtLiYNWu6\n6y4fdY2NjBoNH53d2prsZk1RoyF9y7f4mtd6PTsmeXgdDLKtccyu7EC8ve3P03F4+vBZTxN1T0/R\nak54BQDgJmKzpuVlH3FdW8umBd91l693LY3+RQ3BXzPpzZqiVsvfWwg+8hpC1m14OPRgGrsNS1eO\nvG5v+6grI68AgDuB8IpcijZfHvlQ9/RQ82tbXvbQtrPjQW111cNbqeRBdWXFw11cBzoz48Hu2LFx\nn/nNNZvS5z9/8qXOw3HqcK3mP7e3s0ZOZh7kQ/Dnh8MrpxZjevBZTxN1T0/Rak54BQDgJpaWfLS1\nXvcpw6WSh7lGw6fPrq56iIude5tNf8199437zG8ujiDHvV5j06ZKxd/bxoa/13LZ3//Wlv9ebOQU\np0wDAHDQCK/IpWjz5ZEPdU8PNb+2S5c8oM3OSqdPZ9vEzM1lU2u3t30dbAyCrZZPKZ509br06KMn\nNBz6/TjKWi77rdfLphLHrsqDgd+vVLJGVZgufNbTRN3TU7SaE14BALiBwcCnBa+s+HTgS5d8X9eV\nFe8kvLXl4TVuMVOrZfu8xsZHkyzu7zoceliNo8r9vofT7e1sW6ByOZs6XS77766t+e/GDssAABwU\nwityKdp8eeRD3dNDzb/R+roH2NVVD62xWdNwKB0+nE0jjlotH6182cvGd863olKRvvKVky+F1SiG\n2jiNuFzORlxXV/VSd+KlpSzUYnrwWU8TdU9P0WpOeAUA4AZWVnx0NW4Ls7bmAbVS8WZNy8v+ulIp\nm1q8tSUdPz7e874VjYaf++59XuOa18EgOzYYZKOx1apfh9hpmXWvAICDRnhFLkWbL498qHt6qPk3\nWlz0sNZsShcuZOs/dzdrGg79+e1tH40sl6V77hn3mef3uted0MaGvwczvdR5WMoCa5waXS57ODfL\npkhLhNdpw2c9TdQ9PUWrOeEVAIAbuHTJt7+ZnZXOnvXR1o0NX//a73uo6/V8FFLygNds+pTiadFu\neyCtVrPR13g/boUTpxD3+x7SJQ+4MbwybRgAcNAIr8ilaPPlkQ91Tw81v9JwKF2+7FOFZ2ak+Xnv\nKLy56Z2ENzY8tA2HHvbqdX/uyJHpaNYUfeELJ19quBT3d63Xs0ZMca1rbOgU936N73cwILxOGz7r\naaLu6SlazQmvAABcx+amh7KVFR9ZXV31cLez4wF1YcFfF4KHuU7Hg91DD433vG9V3J82BH9/cWp0\nterBdHs7WwMbpw3HqdO9HuEVAHBnEF6RS9HmyyMf6p4ean6lpaVsWrCZdx5utz20xc7D5bKHvLjP\n6+am9PDD4z7zW/OGN5x4aWpwrZZNHa7VPKRubvp7LJX8/W5s+IhsfN3WFmtepw2f9TRR9/QUreaE\nVwAArmNx0YNbbNY0GGRTZ0slH4mNTYz6fX9dtSrdffe4z/zWxOZMIXgwN/NgWqn4+9zYyN5npXJl\neI3Px3WwAAAcFMIrcinafHnkQ93TQ82vND/v4XVmRjpzJhttPXzYw2qcMttuZw2N2m2fUjxNnnji\npMz00l6vccpwCB5sY3fhGNrjtkHlctZxmJHX6cJnPU3UPT1FqznhFQCA61haypo1XbyYbYdz5IhP\nIe73/VYu+3Pr6z7qGpseTYsYWOO2OMOhh1XJA2zsMhzXw4b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D+g7sW/SfqfJf07SR8fPabmBb/Jl24dvuoYdS/4TdJHJf2T0f2KpFnqnsZNPvh4TtKDKdackdeE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\nABOP8AoAAAAAmHiEVwAAAADAxPv/AURwMOUVFhNGAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff0d6b12150>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scatter(terminals[:,0], terminals[:,1])\n",
      "axis('equal')\n",
      "grid('on')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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j8yiL0lHg88BqarU7aDS2tHOKkiS11Kx6TlNK3wcuyDnva7p2M/BMzvnmlNIA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\n+T3xxNOUhWl/9e/TnHTSSw7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cOEi/FcDrqurtGdYL3w58/zTPT/t3kHYbgTWT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\nmrn/AzBnJF4IVS7KAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff0d760dd10>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Perform a kernel densisty estimation on landing area\n",
      "from scipy import stats\n",
      "kernel = stats.gaussian_kde(terminals[:,:2].T)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "xmin = terminals[:,0].min()\n",
      "xmax = terminals[:,0].max()\n",
      "ymin = terminals[:,1].min()\n",
      "ymax = terminals[:,1].max()\n",
      "xrng, yrng = xmax - xmin, ymax - ymin\n",
      "xmin -= 0.25 * xrng\n",
      "xmax += 0.25 * xrng\n",
      "ymin -= 0.25 * yrng\n",
      "ymax += 0.25 * yrng\n",
      "\n",
      "X, Y = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]\n",
      "positions = np.vstack([X.ravel(), Y.ravel()])\n",
      "Z = np.reshape(kernel(positions).T, X.shape)\n",
      "\n",
      "#imshow(np.rot90(Z), extent=[xmin, xmax, ymin, ymax], cmap='gist_earth_r')\n",
      "contour(Z, extent=[xmin, xmax, ymin, ymax])\n",
      "scatter(terminals[:,0], terminals[:,1], edgecolor='none', color='k', alpha=0.5)\n",
      "axis('equal')\n",
      "grid('on')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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oCj4FVCRfH3Q38JnlTW6hHn7+FuRrgZYQwm7gD0muGMcYB1ma670nOddfLJv3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\ndCVJkiRJlWKhK0mSJEmqlP8Ptnzlrpv1nWgAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff0d126cdd0>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    }
   ],
   "metadata": {}
  }
 ]
}