Analyze-Results.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "np.set_printoptions(precision=4, suppress=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "h_laser_low = 1000\n",
    "h_laser_med = 1000\n",
    "h_laser_high = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20160825-143207-156\n"
     ]
    }
   ],
   "source": [
    "date = \"20160825-143207-156\"\n",
    "fname = 'results/' + date + '.txt'\n",
    "\n",
    "print date\n",
    "data = np.loadtxt(fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 3)"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pix_dist = data[: , 0].ravel()\n",
    "altitude = data[: , 1].ravel()\n",
    "alt_err = data[: , 2].ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "print data.shape[0]\n",
    "\n",
    "idx_arr = altitude.argsort(axis=0)\n",
    "print idx_arr.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pix_dist = pix_dist[idx_arr]\n",
    "altitude = altitude[idx_arr]\n",
    "alt_err = alt_err[idx_arr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  149.       291.6622    30.3296]\n",
      " [  148.       293.6329    30.538 ]\n",
      " [  136.       319.5417    33.2841]\n",
      " [  121.       359.1543    37.5083]\n",
      " [   85.       511.2667    54.0763]\n",
      " [   60.       724.2944    78.4936]\n",
      " [   42.      1034.7063   117.5249]\n",
      " [   39.      1114.2991   128.33  ]\n",
      " [   36.      1207.1574   141.3968]\n",
      " [   36.      1207.1574   141.3968]\n",
      " [   36.      1207.1574   141.3968]\n",
      " [   36.      1207.1574   141.3968]\n",
      " [   36.      1207.1574   141.3968]\n",
      " [   36.      1207.1574   141.3968]\n",
      " [   35.      1241.6476   146.3817]\n",
      " [   35.      1241.6476   146.3817]\n",
      " [   35.      1241.6476   146.3817]\n",
      " [   34.      1278.1666   151.7399]\n",
      " [   34.      1278.1666   151.7399]\n",
      " [   34.      1278.1666   151.7399]\n",
      " [   33.      1316.899    157.5144]\n",
      " [   33.      1316.899    157.5144]\n",
      " [   33.      1316.899    157.5144]\n",
      " [   32.      1358.0521   163.755 ]\n",
      " [   32.      1358.0521   163.755 ]\n",
      " [   31.      1401.8602   170.5195]\n",
      " [   31.      1401.8602   170.5195]\n",
      " [   31.      1401.8602   170.5195]\n",
      " [   29.      1498.5402   185.9019]\n",
      " [   29.      1498.5402   185.9019]\n",
      " [   29.      1498.5402   185.9019]\n",
      " [   29.      1498.5402   185.9019]\n",
      " [   29.      1498.5402   185.9019]\n",
      " [   27.      1609.5432   204.3578]\n",
      " [   27.      1609.5432   204.3578]\n",
      " [   27.      1609.5432   204.3578]\n",
      " [   27.      1609.5432   204.3578]\n",
      " [   27.      1609.5432   204.3578]\n",
      " [   26.      1671.4487   215.0307]\n",
      " [   26.      1671.4487   215.0307]\n",
      " [   26.      1671.4487   215.0307]\n",
      " [   26.      1671.4487   215.0307]\n",
      " [   25.      1738.3066   226.8706]\n",
      " [   25.      1738.3066   226.8706]\n",
      " [   25.      1738.3066   226.8706]\n",
      " [   25.      1738.3066   226.8706]\n",
      " [   25.      1738.3066   226.8706]\n",
      " [   24.      1810.7361   240.0706]\n",
      " [   24.      1810.7361   240.0706]\n",
      " [   24.      1810.7361   240.0706]]\n"
     ]
    }
   ],
   "source": [
    "# Make data sorted by altitude\n",
    "data[:, 0] = pix_dist\n",
    "data[:, 1] = altitude\n",
    "data[:, 2] = alt_err\n",
    "# Leave only lines with alt_error less than 50% of altitude\n",
    "data = data[ data[:, 2] / data[:, 1] < 0.5 ]\n",
    "print data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 3)"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shape after removing items with ratio_err > 50%\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pix_dist = data[: , 0].ravel()\n",
    "altitude = data[: , 1].ravel()\n",
    "alt_err = data[: , 2].ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BJwKjhdvlmfnuMn418HbgIeC0zLykbP8lYB3wJOCizDxtimNavLWgDj/SrcZW\nJY8qxFYlj3Z+Pkmqk8oVb51g8daaKvzwWrC0P7YqeVi8SVKhcoP0SpIkaWZZvEmSJNXI/OkERcTz\nKGY9eDQ+M7/YppwkSZI0id0WbxHxj8DzgOuA0RFLE7B4kyRJmmXT6Xn75cw8qu2ZSJIkabd2+7Rp\nRJwF/HVmbpqdlPaMT5u2pgpPCvqEZftjZ3rfzYyZ1q5YSaqztgwVEhEnABcCtwEPAkExGvDzWk20\nneZ68dbqj14VCou6FCx1jm33viVJzWlX8fYj4E+Aaxm7543MHGklyXab68VboyoUC1WIrUoeVYht\n974lSc1ppXibzj1v/52ZF7aYkyRJkmbQdIq3K8tprr5KcdkUcKgQSZKkTphO8fZkiqLtxIY2hwqR\nJEnqgN0Wb5n51tlIRJ0xPDxCf/86YBcrV85jcHAVy5Yt7XBWkiRpMk5M38V2d7P58PAIK1Z8nC1b\n1gILgJ309AywYcOpUxZwVbjxvio36Xcqttmnih2mQ5KqqS1Pm9aNxduY3RUAK1euZf3691IUbqN2\n0tf3Ec49d6Dl/VYttip5+JSnJGm8Voo3J6afw7Zv38VjCzeABdx6666JwiVJUgU0XbxFxGsj4vh2\nJKPZtXjxPGDnuNadLFpkTS9JUlW18it9PPDnEfH1mU5Gs2twcBU9PQOMFXDFPW+Dg6s6lpMkSZqa\n97x1sencNzX6tOn69bvo65ve06ZVuM/Le94kSd2gXdNj7QOcDjwzM38/IpYDz8rMf2091faxeBtT\nhcKiCrFVycPiTZI0XruKt/OBHwJvzsznlMXcv2fm0a2n2j4Wb2OqUFhUIXam992uYTccokOS5p52\nFW8/yMxjI+LKzHxB2XZ1Zj5/D3JtG4u3MVUonKoQ2+59S5LUqnYNFfLziHgyxZRYREQPDXOcSpIk\nafZMZ27TAeBi4BkRsR54EbCqnUlJkiRpYtN62jQiDgZ+GQjg8sz8SbsTa5WXTcdU4ZJlFWLbvW9J\nklo1o/e8RcQxU70xM69o5kCzxeJtTBUKpyrEtnvfkiS1aqaLt8vK1ScBxwJXU/S8PQ/4QWb+yh7k\n2jYWb2O67QlLJ2OXJHWbdj1t+kVgIDOvLV8/B1iTma9vOdM2sngbY2+TJEnV1q6nTZ81WrgBZOZ/\nAkc2m5wkSZL23HSeNr0mIj4DnFu+7gOuaV9KkiRJmsx0et7eClwHnFYum8o2VdTw8AgrV64FBli5\nci3DwyOF9F4CAAAWQ0lEQVSdTkmSJM0QJ6bvMsPDI6xY8XG2bFkLLAB20tMzwIYNp0454bz3vEmS\nNPvacs9bRAxHxNbxS+tpqp36+9c1FG4AC9iyZS39/es6mJUkSZop07nn7diG9ScBbwAWticd7ant\n23cxVriNWsCtt+7qRDqSJGmG7bbnLTPvaFi2Z+bfAL85C7mpBYsXzwN2jmvdyaJF07m9UZIkVd10\nxnlrnGlhHkVP3Lsy8/ntTKxV3vPmPW+SJNVFuwbpvazh5cPAMPDXmXnDNBI6C3g1sCMzn1e2HQSc\nDywFtgGnZOY95bbVwNvK45yWmZeU7ccA6ygu216Umf9zimPO6eINigKuv38d69fvoq9vHoODq6Ys\n3MDiTZKkTmhX8XZYZm4d17YsM4enkdCLgfuAzzYUb2cCd2TmhyPifcBBmXlGRBwFrAdeCCwBvgks\nz8yMiP8A/igzN0bERcBHM/Mbkxxzzhdvo5xhQZKkamvXDAtfmGbb42Tmd4G7xjW/FjinXD8HOLlc\nPwn4XGY+nJnbgM3AcRHxdGC/zNxYxn224T2SJElzyqRPm0bEEcCzgQMi4nUNm/anuHzZqqdl5g6A\nzLwtIp5Wti8GvtcQt71sexi4paH9lrJdkiRpzplqqJBnUdyvdiDwmob2e4Hfn8EcvFgnSZI0TZMW\nb5n5FeArEfErmfm9yeJasCMiDsnMHeUl0dvL9u3AMxrilpRtk7VPak2MXTruLZe5KAGmeRW9mVhJ\nkrQbk9xIPjQ0xNDQ0B7tetIHFiLiT8uHCj7OBL1jmfmeaR0g4lDgq5n53PL1mcCdmXnmJA8sHE9x\nWXQDYw8sXA68B9gIfA34WGZePMnxfGCh5AMLkiRVWysPLEx12fT68s8f7EFC51F0fB0cETcBA8CH\ngM9HxNuAEeAUgMzcFBEXUEx8/xDw7oYq7A957FAhExZukiRJ3W46Q4W8ITM/v7u2qrDnbczuetOG\nhopldL23t1jv7R1blyRJ7dOucd6uyMxjdtdWFRZvY7wUKklStc3oZdOIeCXwKmBxRHysYdP+FMN3\nSJIkaZZNdc/brcAPKQbP/WFD+73AH7czKUmSJE1sOpdN52dmbXravGw6xsumkiRV20xfNr2W0eG/\n4jH7DCBH5yqVJEnS7JnqsumrZy0LSZIkTctuL5s+7g0RLwbelJl/2J6U9oyXTcd42VSSpGqb6UF6\nG3f8AuB3gDcAw8AXm09PkiRJe2qqe94OB95ULj8BzqfoqXvpLOUmSZKkcaaa23QX8P+At2fmj8q2\nrZl52Czm1zQvm47xsqkkSdXWymXTeVNsex3wY+CyiPiHiHgZxZOmkiRJ6pDpjPO2AHgtxeXTXwc+\nC3wpMy9pf3rNs+dtjD1vkiRVW1vmNh13gIMoHlr4H5n5sibzmxUWb2Ms3iRJqra2F291YPE2xuJN\nkqRqs3jD4m1oqFhG13t7i/Xe3rF1SZJUDRZvWLxJkqT6mOmnTSVJklQxFm+SJEk1YvEmSZJUIxZv\nkiRJNWLxJkmSVCMWb5IkSTVi8SZJklQjFm+SJEk1YvEmSZJUIxZvkiRJNWLxJkmSVCMWb5IkSTVi\n8SZJklQjFm+SJEk1YvEmSZJUIxZvkiRJNWLxJkmSVCMWb5IkSTVi8SZJklQjHSveIuKPI+I/I+Ka\niFgfEU+MiIMi4pKIuCEivhERBzTEr46IzRFxfUSc2Km8JUmSOikyc/YPGrEI+C5wRGb+PCLOBy4C\njgLuyMwPR8T7gIMy84yIOApYD7wQWAJ8E1ieEyQfERM1S5IkVU5EkJnRzHs6edl0L2BBRMwHngxs\nB14LnFNuPwc4uVw/CfhcZj6cmduAzcBxs5uuJElS53WkeMvMW4G/Bm6iKNruycxvAodk5o4y5jbg\naeVbFgM3N+xie9kmSZI0p8zvxEEj4kCKXralwD3A5yOiDxh/vdPrn8DQULGMrvf2Fuu9vWPrkiRp\nbuhI8Qb8BrA1M+8EiIgvAb8K7IiIQzJzR0Q8Hbi9jN8OPKPh/UvKtgmtWbPm0fXe3l56a17hNBZp\nEWOFnCRJqpehoSGG9vCHvFMPLBwHnEXxAMKDwNnARuCZwJ2ZeeYkDywcT3G5dANz9IGFCOjijydJ\n0pzSygMLHel5y8zvR8QXgCuBh8o//x7YD7ggIt4GjACnlPGbIuICYFMZ/+6urtAkSZIm0ZGet3ay\n502SJNVF3YYKkSRJUpMs3iRJkmrE4k2SJKlGLN4kSZJqxOJNkiSpRizeJEmSasTiTZIkqUYs3iRJ\nkmrE4k2SJKlGLN5qYnh4hJUr1wIDrFy5luHhkU6nJEmSOsDpsWpgeHiEFSs+zpYta4EFwE56egbY\nsOFUli1b2un0JElSi5weq0v1969rKNwAFrBly1r6+9d1MCtJktQJFm81sH37LsYKt1ELuPXWXZ1I\nR5IkdZDFWw0sXjwP2DmudSeLFnn6JEmaa/z1r4HBwVX09AwwVsAV97wNDq7qWE6SJKkzfGChJoaH\nR+jvX8f69bvo65vH4OAqH1aQJKnmWnlgweKtZiKgiz+eJElzik+bSpIkdTmLN0mSpBqxeJMkSaoR\nizdJkqQasXiTJEmqEYs3SZKkGrF4kyRJqhGLN0mSpBqxeJMkSaoRizdJkqQasXiTJEmqEYs3SZKk\nGrF4kyRJqpHIzE7nMKMiIrvtMw0NFcvoem9vsd7bO7YuSZLqJyLIzGjqPd1W6HRj8SZJkrpTK8Wb\nl00lSZJqxOJNkiSpRizeJEmSaqRjxVtEHBARn4+I6yPiuog4PiIOiohLIuKGiPhGRBzQEL86IjaX\n8Sd2Km9JkqRO6mTP20eBizLzSOD5wH8BZwDfzMxnAZcCqwEi4ijgFOBI4JXAJyOiqZv7JEmSukFH\nireI2B/4tcw8GyAzH87Me4DXAueUYecAJ5frJwGfK+O2AZuB42Y3a0mSpM7rVM/bMuAnEXF2RFwR\nEX8fEfsAh2TmDoDMvA14Whm/GLi54f3byzZJkqQ5pVPF23zgGOATmXkMsJPikun4AdocsE2SJKnB\n/A4d9xbg5sz8Qfn6XyiKtx0RcUhm7oiIpwO3l9u3A89oeP+Ssm1Ca9aseXS9t7eXXqchkCRJFTA0\nNMTQ6LRJLerYDAsR8W3g9zPzxogYAPYpN92ZmWdGxPuAgzLzjPKBhfXA8RSXSzcAyyeaSsEZFiRJ\nUl20MsNCp3reAN4DrI+IJwBbgbcCewEXRMTbgBGKJ0zJzE0RcQGwCXgIeLcVmiRJmouc21SSJKlD\nnNtUkiSpy1m8SZIk1YjFmyRJUo108oGFOWtoqFhG10dHMuntHVuXJEmaiA8sdFgE1ChdSZI0g3xg\nQZIkqctZvEmSJNWIxZskSVKNWLxJkiTViMWbJElSjVi8SZIk1YjFmyRJUo1YvHXI8PAIK1euBQZY\nuXItw8MjnU5JkiTVgIP0dsDw8AgrVnycLVvWAguAnfT0DLBhw6ksW7a00+lJkqRZ4iC9NdHfv66h\ncANYwJYta+nvX9fBrCRJUh1YvHXA9u27GCvcRi3g1lt3dSIdSZJUIxZvHbB48Txg57jWnSxa5OmQ\nJElTs1rogMHBVfT0DDBWwBX3vA0OrupYTpIkqR58YKFDhodH6O9fx/r1u+jrm8fg4CofVpAkaY5p\n5YEFi7cOi4AapStJkmaQT5tKkiR1OYs3SZKkGrF4kyRJqhGLN0mSpBqxeJMkSaoRizdJkqQasXiT\nJEmqEcd564ChoWIZXe/tLdZ7e8fWJUlS93OQXupRvEmSJIGD9EqSJHU9izdJkqQasXiTJEmqEYs3\nSZKkGrF4kyRJqhGLN0mSpBrpaPEWEfMi4oqIuLB8fVBEXBIRN0TENyLigIbY1RGxOSKuj4gTO5e1\nJElS53S65+00YFPD6zOAb2bms4BLgdUAEXEUcApwJPBK4JMR0dSYKJIkSd2gY8VbRCwBXgV8pqH5\ntcA55fo5wMnl+knA5zLz4czcBmwGjpulVCVJkiqjkz1v/xf4X0DjdAiHZOYOgMy8DXha2b4YuLkh\nbnvZJkmSNKd0pHiLiN8EdmTmVcBUlz+d50qSJKnB/A4d90XASRHxKuDJwH4R8U/AbRFxSGbuiIin\nA7eX8duBZzS8f0nZNqE1a9Y8ut7b20uvs71LkqQKGBoaYmhoaI/20fGJ6SPiBOD0zDwpIj4M3JGZ\nZ0bE+4CDMvOM8oGF9cDxFJdLNwDLJ5qBvlMT0w8NFcvo+mi92Ns7ti5JktSolYnpq1a8LQQuoOhl\nGwFOycy7y7jVwNuBh4DTMvOSSfbXkeLtsTlAh1OQJEk1UMvibaZZvEmSpLpopXjr9DhvkiRJaoLF\nmyRJUo1YvM2g4eERVq5cCwywcuVahodHOp2SJEnqMt7zNkOGh0dYseLjbNmyFlgA7KSnZ4ANG05l\n2bKls56PJEmqPu9566D+/nUNhRvAArZsWUt//7oOZiVJkrqNxdsM2b59F2OF26gF3Hrrrk6kI0mS\nulSnZliohWYG3l28eB6wk8cWcDtZtMj6WJIkzRzveZv2fqceu8173iRJUrMcpJfOFW9QFHD9/etY\nv34XfX3zGBxcZeEmSZImZfFGZ4u3VmIlSdLc5dOmkiRJXc7ibTcceFeSJFWJl02n0OpDCF42lSRJ\n0+E9bxTF28BA8ZkmGtKjmeE/Vq5cy/r172X88B99fR/h3HMHWt6vJEkStFa8deU4b2vWTL6tsZiK\nGCu4JtLMwLsWaZIkaTZ4z9sUxgbebeTAu5IkqXPmZBUy3YcQBgdX0dMzwFgBV9zzNji4albynGuG\npuoGVeV5/urN81dfnru5pyuLt6kKstGHEIp72Yp72las+PiE8cuWLWXDhlPp6/sIMEBf30ecMaGN\n/Aeo3jx/9eb5qy/P3dzTlcXbVAVZf/+6hqdHARawZcta+vvXTbivZcuWlg8nrOXccwcs3CRJUkd1\nZfE2VUHWzEMIkiRJVdOVQ4V0OgdJkqTpmvPjvEmSJHWzLr1sKkmS1J0s3iRJkmqka4q3iHhFRPxX\nRNwYEe/rdD6aWkScFRE7IuKahraDIuKSiLghIr4REQd0MkdNLCKWRMSlEXFdRFwbEe8p2z1/NRAR\ne0fEf0TEleX5GyjbPX81ERHzIuKKiLiwfO25q4mI2BYRV5d//75ftjV9/rqieIuIecDfAi8Hng28\nKSKO6GxW2o2zKc5XozOAb2bms4BLgdWznpWm42HgTzLz2cCvAH9Y/n3z/NVAZj4IvDQzXwAcDbwy\nIo7D81cnpwGbGl577upjF9CbmS/IzOPKtqbPX1cUb8BxwObMHMnMh4DPAa/tcE6aQmZ+F7hrXPNr\ngXPK9XOAk2c1KU1LZt6WmVeV6/cB1wNL8PzVRmbeX67uTTHHdeL5q4WIWAK8CvhMQ7Pnrj6Cx9de\nTZ+/bineFgM3N7y+pWxTvTwtM3dAUSAAT+twPtqNiDiUovfmcuAQz189lJfdrgRuAzZk5kY8f3Xx\nf4H/RVFwj/Lc1UcCGyJiY0T8XtnW9Pmb38YEpT3lODYVFhH7Al8ATsvM+yYYY9HzV1GZuQt4QUTs\nD3wpIp7N48+X569iIuI3gR2ZeVVE9E4R6rmrrhdl5o8j4qnAJRFxAy383euWnrftwDMbXi8p21Qv\nOyLiEICIeDpwe4fz0SQiYj5F4fZPmfmVstnzVzOZ+VNgCHgFnr86eBFwUkRsBf4Z+PWI+CfgNs9d\nPWTmj8s//xv4MsVtX03/3euW4m0j8IsRsTQingi8Ebiwwzlp96JcRl0IrCrX3wJ8ZfwbVBn/CGzK\nzI82tHn+aiAinjL6NFtEPBlYQXHfouev4jLz/Zn5zMw8jOJ37tLM/F3gq3juKi8i9imvWBARC4AT\ngWtp4e9e18ywEBGvAD5KUZCelZkf6nBKmkJEnAf0AgcDO4ABiv8L+TzwDGAEOCUz7+5UjppYRLwI\n+A7FPzpZLu8Hvg9cgOev0iLiuRQ3Rc8rl/Mz8wMRsRDPX21ExAnA6Zl5kueuHiJiGfAlin8z5wPr\nM/NDrZy/rineJEmS5oJuuWwqSZI0J1i8SZIk1YjFmyRJUo1YvEmSJNWIxZskSVKNWLxJkiTViMWb\npBkREbsi4q8aXp8eEf97hvZ9dkS8bib2tZvjvD4iNkXEt9p9rGnksjYifr3Nx3hLOaK7pBqxeJM0\nUx4EXlcOOFkZEbFXE+FvB34vM1/WrnymIyLmZeZAZl7a5kOtAha3+RiSZpjFm6SZ8jDw98CfjN8w\nvucsIu4t/zwhIoYi4ssR8aOI+GBE/E5E/EdEXF2OSD5qRURsjIj/KifoJiLmRcSHy/irIuL3G/b7\nnYj4CnDdBPm8KSKuKZcPlm39wIuBsyLizAne896I+H55nIGy7dgyzydGxIKI+M+IOKo8/rcj4l/L\nfD/ZsJ8VEfHvEfGDiDg/IvYp24cj4kMR8QPg9Y3fWbntLyPiyjKHF0TExRGxOSLesZscl5a9iX9f\n5ndxROwdEb8NHAucGxFXRMTe0zrLkjrO4k3STEngE0BfROw3jdhRzwP+ADgK+F1geWYeD5wFnNoQ\ntzQzXwi8Gvh0OY/x24G7y/jjgD+IiKVl/AuAUzPziMYDR8QvAB+imJ7taOC4iDgpMweBHwC/k5nv\nG/eeFWVex5X7PTYiXpyZP6CYh/ADwJnAZzNzU/m2FwJ/CBxJMffy6yLiYODPgZdl5rHAD3lssfuT\nzDw2My+Y4DvblpkvAL4LnA28DvgVYO1UOZbv/UXg45n5HOAe4Lcz818aPu8xmfngBMeUVEHzO52A\npO6RmfdFxDnAacDPpvm2jZl5O0BEbAEuKduvpSiwRl1QHuNHZdwRFBM7Pzci3lDG7A8sBx4Cvp+Z\nN01wvBcCl2XmneUx1wMvoZgcGiAmeM+JFD1/V5TbF5TH+S4wCGwsP29jsfn9zBwpj/HPFL16D1IU\nqf8WEQE8Afj3hvecP8GxR321/PNaYEFm3g/cHxEPRMT+U+R4MzCcmdeW7/8hcGjDfif6vJIqzOJN\n0kz7KHAFRe/QqIcpe/rLouWJDdsae3x2NbzexWP/jWrsrYvydVD0rm1oTKCctHvnFDk2W7AE8MHM\n/IcJtj0F2LfM9UmMFa3jJ44ezfeSzOyb5DhT5dz4vYz/zuZPlmPZE9kY/0iZp6Sa8rKppJkSAJl5\nF0Uv2dsbtm2juL8K4LUUPU7NekMUeoBlwA3AN4B3R8R8gIhYPnoP2RS+D7wkIhaWDzO8CRjazXu+\nAbwtIhaUx1kUEU8pt32a4lLoeuDDDe85rrzfbB7wPyh66S4HXlR+BiJin4hYPp0PP4XRQnSiHJ86\nLma8eyl6KyXViD1vkmZKY0/TX1Pc7zXa9g/AVyLiSooiY7IepvG9VY1uoii89gPekZk/j4jPUFwC\nvKLs0bsdOHnKJDNvi4gzGCvY/jUz/3Wq42fmhog4AvhecRjuBVZGxCuBn2fm58oi7d8iorfczw+A\nv6W43+zSzPwSQESsAv65fEAgKQq/zRMcOydZf1x6U+VI0TM32fvXUdw/eD/wK973JtVDZE71b4Ik\nqVnlZdvTM/OkTuciqft42VSSJKlG7HmTJEmqEXveJEmSasTiTZIkqUYs3iRJkmrE4k2SJKlGLN4k\nSZJqxOJNkiSpRv5/9XuDJwzTSYwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffccefd12d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.errorbar(range(altitude.shape[0]), altitude, yerr=alt_err, marker='o', linestyle='None')\n",
    "plt.title(date)\n",
    "plt.xlabel('Number of experiment')\n",
    "plt.ylabel('Altitude, m')\n",
    "plt.plot(range(altitude.shape[0]), h_laser_low * np.ones_like(altitude), 'r', label='Laser')\n",
    "plt.plot(range(altitude.shape[0]), h_laser_med * np.ones_like(altitude), 'r')\n",
    "plt.plot(range(altitude.shape[0]), h_laser_high * np.ones_like(altitude), 'r')\n",
    "plt.legend(loc = 'upper left')\n",
    "plt.ylim( (min( altitude.min(), 0.6 * h_laser_low),\n",
    "           max(altitude.max(), 1.1 * h_laser_high)) )\n",
    "plt.savefig('results/' +'plot_' + date + '_' + str(altitude.shape[0]) + '.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  291.6622   293.6329   319.5417   359.1543   511.2667   724.2944\n",
      "  1034.7063  1114.2991  1207.1574  1207.1574  1207.1574  1207.1574\n",
      "  1207.1574  1207.1574  1241.6476  1241.6476  1241.6476  1278.1666\n",
      "  1278.1666  1278.1666  1316.899   1316.899   1316.899   1358.0521\n",
      "  1358.0521  1401.8602  1401.8602  1401.8602  1498.5402  1498.5402\n",
      "  1498.5402  1498.5402  1498.5402  1609.5432  1609.5432  1609.5432\n",
      "  1609.5432  1609.5432  1671.4487  1671.4487  1671.4487  1671.4487\n",
      "  1738.3066  1738.3066  1738.3066  1738.3066  1738.3066  1810.7361\n",
      "  1810.7361  1810.7361]\n"
     ]
    }
   ],
   "source": [
    "print altitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}