nealmcb/ariel3_thunderstorm_noise.ipynb

## ariel3_thunderstorm_noise.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parse ariel3_thunderstorm-noise\n",
    "\n",
    "This is a quick Python example of parsing a tiny part of the relatively complicated Ariel3 satellite thunderstorm-noise data.\n",
    "\n",
    "* Inspired by breqm76\n",
    "* File format documentation at\n",
    "https://docs.google.com/document/d/1GioN-8mch3GeruMrqG6qVLklQRDOh1TiARfYwI7BC4k/edit#heading=h.uydog7egy1sz\n",
    "* Parsing the files at https://spdf.sci.gsfc.nasa.gov/pub/data/ariel/ariel3/langmuir_probe/plasma-frequency_electron-temperature_thunderstorm-noise/DATA2_DR002129_DR002129_20080804_091423/\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.signal import find_peaks_cwt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def chunkstring(string, length):\n",
    "    \"Return the string, split up into fixed-width substrings of given length\"\n",
    "    return (string[0+i:length+i] for i in range(0, len(string), length))\n",
    "\n",
    "def groupOf10(data, offset):\n",
    "    \"print the data as 10 sets of three 5-character numbers\"    \n",
    "    segment = data[offset:offset+10*3*5]\n",
    "    print('\\n'.join(chunkstring(' '.join(chunkstring(segment[:30*5],5)), 18)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# File is encoded as EBCDIC (codec cp500)\n",
    "with open('dr002129_f00001.phys.1', 'rt', encoding='cp500') as ariel3_thunderstorm_noise:\n",
    "    d2129 = ariel3_thunderstorm_noise.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -6.1  -9.0 -14.3 \n",
      " -6.2  -9.1 -14.4 \n",
      " -6.5  -9.1 -14.4 \n",
      " -6.8  -9.2 -14.5 \n",
      " -7.0  -9.3 -14.6 \n",
      " -7.2  -9.4 -14.7 \n",
      " -7.4  -9.5 -14.8 \n",
      " -7.6  -9.7 -14.9 \n",
      " -7.8  -9.8 -15.0 \n",
      " -8.0  -9.9 -15.1\n"
     ]
    }
   ],
   "source": [
    "groupOf10(d2129, 0x11F02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -8.1 -10.0 -15.2 \n",
      " -8.2 -10.1 -15.3 \n",
      " -8.3 -10.2 -15.4 \n",
      " -8.4 -10.3 -15.5 \n",
      " -8.4 -10.4 -15.6 \n",
      " -8.5 -10.5 -15.7 \n",
      " -8.5 -10.6 -15.8 \n",
      " -8.6 -10.7 -15.9 \n",
      " -8.8 -10.8 -16.0 \n",
      " -9.0 -10.9 -16.1\n"
     ]
    }
   ],
   "source": [
    "groupOf10(d2129, 0x11FA6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find length of repeating records: peak autocorrelation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def findRecordLen(segment, maxLen=1000):\n",
    "    \"Find record length in given string, via autocorrelation\"\n",
    "\n",
    "    # Turn string into array of byte with zero mean\n",
    "    arr = np.array([float(ord(c)) for c in segment])\n",
    "    arrNorm = arr - np.mean(arr)\n",
    "\n",
    "    (lags, c, line, b) = plt.acorr(arrNorm, maxlags=maxLen)\n",
    "    \n",
    "    return c[maxLen+1:].argmax() + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "segmentLen = 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = d2129"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fileLen = len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3270980\n"
     ]
    }
   ],
   "source": [
    "print(fileLen)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing the file with itself shifted between -1000 and 1000 bytes, the autocorrelation shows that every 164, and especially every 328 bytes there is a strong similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "328"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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ggZPkNm3alMn2oo9gu8uWLat7WwsXLswljfV8r6wf27ZtczfddFPp+dy5c2vO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f62acfb0710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "findRecordLen(data[:segmentLen])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We see the same record length looking at segments all the way thru the file, every 1/10th of the way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "327098 328\n",
      "654196 328\n",
      "981294 328\n",
      "1308392 328\n",
      "1635490 328\n",
      "1962588 328\n",
      "2289686 328\n",
      "2616784 328\n",
      "2943882 328\n"
     ]
    },
    {
     "data": {
      "image/png": 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e3h6XJnu6nObzk1uaOrPdzGWOGzfOWoYe39bWppYtW6YAqGXLlqmOjg7ru7vv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f62acf94a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for offset in range(fileLen // 10, fileLen, fileLen // 10):\n",
    "    print(\"%d %d\" % (offset, findRecordLen(data[offset:offset+segmentLen])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The file length is actually a perfect multiple of that smaller segment length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9972.5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fileLen / 328"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19945.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fileLen / 164"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fileLen % 164"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## There is more variation if we look at a segment 1/10 or 2/10 of the way thru the file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "offset = fileLen // 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "328"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/gNGeJ/XavXt3w//GpQ5eaf3DP/xD3esA8LZ6e92EjaobtV8ePXpU3/72t7Vs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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f62ac912710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "findRecordLen(data[offset:offset+segmentLen])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## But not later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "i=4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "328"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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umR1TZj4BAIAfZV/x6Jd0nXPueufco865fklPSbogLrFzrt8597fOuY3Oucecc5dI2iLp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f62aceff710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "findRecordLen(data[offset*i:offset*i+segmentLen])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Earlier looking and playing around "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fields in each record of section 1, \"5 MHz or 15MHz data records\"\n",
    "\n",
    "F4.0, A2, 3F3.0, F5.2, F7.3, F8.3, F5.1, F4.0, F3.0, F5.2, F4.1, F5.2, 2F6.1, 2(F6.1, F4.1), F61, F5.2, 3F5.0, 3F6.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "192"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# estimate length of records in section 1\n",
    "4 + 2 + 9 + 5 + 7 + 8 + 5 + 4 + 3 + 5 + 4 + 5 + 12 + 2*(6+4) + 61 + 5 + 3*5 + 3*6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "headerLength = 174 # empirically noticed repeat of pairs of records with this chunking\n",
    "header = d2129[:headerLength]\n",
    "\n",
    "offset = headerLength\n",
    "sOneLength = 164\n",
    "M = 424\n",
    "\n",
    "section1 = d2129[offset:offset+(M * sOneLength)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\x00s                 131    4                 MAD                     0                                                                                     0000000000\\x00s  1.  15'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "header"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the first half or so of each record in what seem to be pretty regular repeats in section 1. There seems to be a smaller interval after the odd records than after the even ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['   0 . 3. 2.23.20 -1.357  69.581506.5  7.40.42.8010.9 3.672100.0  20.9 -15.7 0.0 -15.',\n",
       " '   1  2.26.70 -1.577  69.605506.6  7.40.51.8010.9 3.672100.0  20.9 -10.4 0.0 -10.4 0.',\n",
       " '   2 . 3. 2.51.10 -3.116  69.768507.5  7.41.55.5010.8 3.762100.0  20.9 -15.8 0.0 -15.',\n",
       " '   3  2.54.60 -3.336  69.792507.6  7.42. 4.6010.8 3.762100.0  20.9 -10.5 0.0 -10.5 0.',\n",
       " '   4 . 3. 3.19.10 -4.874  69.955508.6  7.43. 8.3010.6 3.792100.0  21.1 -15.9 0.0 -15.',\n",
       " '   5  3.22.60 -5.094  69.979508.7  7.43.17.4010.6 3.792100.0  21.1 -10.6 0.0 -10.6 0.',\n",
       " '   6 . 3. 3.47.00 -6.631  70.144509.7  7.44.21.5010.4 3.812100.0  21.1 -16.0 0.0 -16.',\n",
       " '   7  3.50.50 -6.851  70.167509.9  7.44.30.6010.3 3.812100.0  21.1 -10.7 0.0 -10.7 0.',\n",
       " '   8 . 3. 4.14.90 -8.388  70.334510.9  7.45.35.1010.1 3.762100.0  20.9 -16.1 0.0 -16.',\n",
       " '   9  4.18.40 -8.607  70.358511.1  7.45.44.3010.0 3.762100.0  20.9 -10.8 0.0 -10.8 0.',\n",
       " '  10 . 3. 4.42.80-10.144  70.528512.2  7.46.49.40 9.8 3.672100.0  20.9 -16.2 0.0 -16.',\n",
       " '  11  4.46.30-10.363  70.552512.4  7.46.58.70 9.7 3.672100.0  20.9 -11.0 0.0 -11.0 0.',\n",
       " '  12 . 3. 5.10.70-11.898  70.725513.6  7.48. 4.70 9.4 3.552100.0  20.9 -16.3 0.0 -16.',\n",
       " '  13  5.14.20-12.117  70.749513.8  7.48.14.10 9.4 3.552100.0  20.9 -11.1 0.0 -11.1 0.',\n",
       " '  14 . 3. 5.38.70-13.651  70.926515.0  7.49.20.80 9.2 3.652100.0  20.6 -16.4 0.0 -16.',\n",
       " '  15  5.42.20-13.871  70.951515.2  7.49.30.40 9.1 3.652100.0  20.6 -11.1 0.0 -11.1 0.',\n",
       " '  16 . 3. 6. 6.60-15.403  71.131516.5  7.50.38.00 8.9 3.612100.0  20.9 -16.5 0.0 -16.',\n",
       " '  17  6.10.10-15.622  71.156516.7  7.50.47.60 8.9 3.612100.0  20.9 -11.2 0.0 -11.2 0.',\n",
       " '  18 . 3. 6.34.50-17.154  71.341518.1  7.51.56.40 8.6 3.552100.0  20.9 -16.6 0.0 -16.',\n",
       " '  19  6.38.00-17.373  71.368518.3  7.52. 6.30 8.6 3.552100.0  20.9 -11.3 0.0 -11.3 0.',\n",
       " '  20 . 3. 7. 2.40-18.903  71.558519.7  7.53.16.30 8.4 3.452100.0  20.9 -16.7 0.0 -16.',\n",
       " '  21  7. 5.90-19.121  71.585520.0  7.53.26.40 8.4 3.452100.0  20.9 -11.4 0.0 -11.4 0.',\n",
       " '  22 . 3. 7.30.30-20.650  71.781521.4  7.54.37.90 8.2 3.272100.0  20.9 -16.8 0.0 -16.',\n",
       " '  23  7.33.80-20.868  71.809521.7  7.54.48.10 8.2 3.272100.0  20.9 -11.5 0.0 -11.5 0.',\n",
       " '  24 . 3. 7.58.30-22.395  72.012523.2  7.56. 1.10 8.0 3.192100.0  20.9 -16.8 0.0 -16.',\n",
       " '  25  8. 1.80-22.613  72.041523.4  7.56.11.50 8.0 3.192100.0  20.9 -11.6 0.0 -11.6 0.',\n",
       " '  26 . 3. 8.26.20-24.138  72.251525.0  7.57.26.50 7.8 3.252100.0  20.9  19.6 4.5  18.',\n",
       " '  27  8.29.70-24.356  72.282525.2  7.57.37.20 7.8 3.252100.0  20.9  21.7 4.6  20.9 4.',\n",
       " '  28 . 3. 8.54.10-25.879  72.501526.8  7.58.54.30 7.7 3.222100.0  20.9 -10.0 0.0 -17.',\n",
       " '  29  8.57.60-26.097  72.532527.1  7.59. 5.40 7.7 3.222100.0  20.9  -6.0 0.1  -7.9 0.',\n",
       " '  30 . 3. 9.22.00-27.619  72.761528.7  8. 0.24.60 7.5 3.192100.0  21.1 -17.0 0.0 -17.',\n",
       " '  31  9.25.50-27.836  72.793529.0  8. 0.35.90 7.5 3.192100.0  21.1 -11.8 0.0 -11.8 0.',\n",
       " '  32 . 3. 9.49.90-29.355  73.033530.7  8. 1.57.80 7.4 3.182100.0  20.9 -17.1 0.0 -17.',\n",
       " '  33  9.53.40-29.572  73.067530.9  8. 2. 9.50 7.4 3.182100.0  20.9 -11.8 0.0 -11.8 0.',\n",
       " '  34 . 3.10.17.90-31.090  73.318532.7  8. 3.34.10 7.3 3.052100.0  20.9 -17.1 0.0 -17.',\n",
       " '  35 10.21.40-31.307  73.354532.9  8. 3.46.20 7.3 3.052100.0  20.9 -11.9 0.0 -11.9 0.',\n",
       " '  36 . 3.10.45.80-32.821  73.617534.7  8. 5.13.90 7.2 2.992100.0  20.9 -17.2 0.0 -17.',\n",
       " '  37 10.49.30-33.037  73.655535.0  8. 5.26.40 7.2 2.992100.0  20.9 -11.9 0.0 -11.9 0.',\n",
       " '  38 . 3.11.13.70-34.550  73.933536.8  8. 6.57.60 7.1 3.052100.0  20.9 -17.2 0.0 -17.',\n",
       " '  39 11.17.20-34.766  73.973537.0  8. 7.10.70 7.1 3.052100.0  20.9 -12.0 0.0 -12.0 0.',\n",
       " '  40 . 3.11.41.60-36.276  74.267538.9  8. 8.45.70 7.0 2.992100.0  20.9 -17.2 0.0 -17.',\n",
       " '  41 11.45.10-36.492  74.309539.1  8. 8.59.30 7.0 2.992100.0  20.9 -12.0 0.0 -12.0 0.',\n",
       " '  42 . 3.12. 9.50-37.998  74.621541.0  8.10.38.60 6.9 2.932100.0  20.9 -17.3 0.0 -17.',\n",
       " '  43 12.13.00-38.213  74.666541.2  8.10.52.80 6.9 2.932100.0  20.9 -12.1 0.0 -12.1 0.',\n",
       " '  44 . 3.12.37.50-39.717  74.997543.1  8.12.36.70 6.9 2.912100.0  21.7 -17.3 0.0 -17.',\n",
       " '  45 12.41.00-39.932  75.044543.4  8.12.51.60 6.9 2.912100.0  21.7 -12.1 0.0 -12.1 0.',\n",
       " '  46 . 3.13. 5.40-41.433  75.398545.3  8.14.40.80 6.8 2.812100.0  20.9 -17.3 0.0 -17.',\n",
       " '  47 13. 8.90-41.647  75.449545.6  8.14.56.50 6.8 2.812100.0  20.9 -12.1 0.0 -12.1 0.',\n",
       " '  48 . 3.13.33.30-43.144  75.826547.5  8.16.51.60 6.7 2.752100.0  21.7 -17.4 0.0 -17.',\n",
       " '  49 13.36.80-43.358  75.880547.8  8.17. 8.10 6.7 2.752100.0  21.7 -12.1 0.0 -12.1 0.',\n",
       " '  50 . 3.14. 1.20-44.851  76.286549.7  8.19. 9.80 6.7 2.882100.0  21.5 -17.4 0.0 -17.',\n",
       " '  51 14. 4.70-45.065  76.344549.9  8.19.27.30 6.7 2.882100.0  21.5 -12.1 0.0 -12.1 0.',\n",
       " '  52 . 3.14.29.10-46.554  76.780551.9  8.21.36.40 6.6 2.882100.0  20.9 -17.4 0.0 -17.',\n",
       " '  53 14.32.60-46.767  76.843552.2  8.21.55.00 6.6 2.882100.0  20.9 -12.2 0.0 -12.2 0.',\n",
       " '  54 . 3.14.57.10-48.252  77.313554.1  8.24.12.20 6.6 2.882100.0  20.9 -17.4 0.0 -17.',\n",
       " '  55 15. 0.60-48.465  77.381554.4  8.24.31.90 6.6 2.882100.0  20.9 -12.2 0.0 -12.2 0.',\n",
       " '  56 . 3.15.25.00-49.945  77.890556.3  8.26.58.60 6.5 3.052100.0  21.1 -17.4 0.0 -17.',\n",
       " '  57 15.28.50-50.156  77.964556.6  8.27.19.80 6.5 3.052100.0  21.1 -12.1 0.0 -12.1 0.',\n",
       " '  58 . 3.15.52.90-51.631  78.517558.5  8.29.57.10 6.5 3.022100.0  21.1  22.7 4.6  22.',\n",
       " '  59 15.56.40-51.842  78.597558.8  8.30.19.70 6.5 3.022100.0  21.1  21.2 4.7  20.4 4.',\n",
       " '  60 . 3.16.20.80-53.312  79.202560.7  8.33. 9.20 6.4 3.112100.0  20.9  -9.7 0.1 -17.',\n",
       " '  61 16.24.30-53.522  79.289561.0  8.33.33.60 6.4 3.112100.0  20.9  -6.7 0.1  -7.4 0.',\n",
       " '  62 . 3.16.48.70-54.985  79.950562.9  8.36.36.80 6.4 3.602100.0  21.1 -17.3 0.0 -17.',\n",
       " '  63 16.52.20-55.193  80.046563.2  8.37. 3.20 6.4 3.602100.0  21.1 -12.1 0.0 -12.1 0.',\n",
       " '  64 . 3.17.16.70-56.649  80.773565.1  8.40.22.20 6.3 3.022100.0  20.9 -17.4 0.0 -17.',\n",
       " '  65 17.20.20-56.857  80.878565.4  8.40.50.90 6.3 3.022100.0  20.9 -12.1 0.0 -12.1 0.',\n",
       " '  66 . 3.17.44.60-58.305  81.682567.3  8.44.28.30 6.3 3.672100.0  21.7 -17.3 0.0 -17.',\n",
       " '  67 17.48.10-58.511  81.798567.6  8.44.59.70 6.3 3.672100.0  21.7 -12.0 0.0 -12.0 0.',\n",
       " '  68 . 3.18.12.50-59.950  82.691569.5  8.48.58.30 6.2 3.482100.0  20.9 -17.3 0.0 -17.',\n",
       " '  69 18.16.00-60.156  82.820569.8  8.49.32.80 6.2 3.482100.0  20.9 -12.0 0.0 -12.0 0.',\n",
       " '  70 . 3.18.40.40-61.583  83.816571.6  8.53.56.20 6.1 3.192100.0  21.7 -17.3 0.0 -17.',\n",
       " '  71 18.43.90-61.787  83.960571.9  8.54.34.30 6.1 3.192100.0  21.7 -12.1 0.0 -12.1 0.',\n",
       " '  72 . 3.19. 8.30-63.203  85.078573.7  8.59.27.00 6.1 3.552100.0  21.7 -17.2 0.0 -17.',\n",
       " '  73 19.11.80-63.405  85.240574.0  9. 0. 9.30 6.1 3.552100.0  21.7 -12.0 0.0 -12.0 0.',\n",
       " '  74 . 3.19.36.30-64.806  86.501575.8  9. 5.36.60 6.0 3.002100.0  21.7 -17.3 0.0 -17.',\n",
       " '  75 19.39.80-65.006  86.684576.1  9. 6.24.00 6.0 3.002100.0  21.7 -12.0 0.0 -12.0 0.',\n",
       " '  76 . 3.20. 4.20-66.391  88.117577.9  9.12.32.40 6.0 2.132100.0  21.7 -17.3 0.0 -17.',\n",
       " '  77 20. 7.70-66.588  88.325578.1  9.13.25.80 6.0 2.132100.0  21.7 -12.1 0.0 -12.1 0.',\n",
       " '  78 . 3.20.32.10-67.952  89.965579.9  9.20.23.70 5.9 2.372100.0  21.7 -10.4 0.0 -10.',\n",
       " '  79 20.35.60-68.146  90.203580.2  9.21.24.30 5.9 2.372100.0  21.7 -12.1 0.1 -12.1 0.',\n",
       " '  80 . 3.21. 0.00-69.485  92.093581.9  9.29.22.20 5.9 3.142100.0  21.7 -17.1 0.0 -17.',\n",
       " '  81 21. 3.50-69.676  92.367582.2  9.30.31.60 5.9 3.142100.0  21.7 -11.9 0.0 -11.9 0.',\n",
       " '  82 . 3.21.27.90-70.984  94.560583.9  9.39.42.40 5.8 3.402100.0  20.9 -17.1 0.0 -17.',\n",
       " '  83 21.31.40-71.170  94.879584.1  9.41. 2.50 5.8 3.402100.0  20.9 -11.8 0.0 -11.8 0.',\n",
       " '  84 . 3.21.55.90-72.440  97.445585.8  9.51.42.70 5.8 3.112100.0  20.9 -17.1 0.0 -17.',\n",
       " '  85 21.59.40-72.621  97.819586.0  9.53.15.80 5.8 3.112100.0  20.9 -11.8 0.0 -11.8 0.',\n",
       " '  86 . 3.22.23.80-73.843 100.843587.6 10. 5.46.00 5.8 3.242100.0  20.9 -17.0 0.0 -17.',\n",
       " '  87 22.27.30-74.016 101.283587.9 10. 7.35.20 5.8 3.242100.0  20.9 -11.8 0.0 -11.8 0.',\n",
       " '  88 . 3.22.51.70-75.177 104.870589.5 10.22.20.40 5.8 3.272100.0  20.9 -16.9 0.0 -16.',\n",
       " '  89 22.55.20-75.341 105.392589.7 10.24.29.30 5.8 3.272100.0  20.9 -11.7 0.0 -11.7 0.',\n",
       " '  90 . 3.23.19.60-76.421 109.663591.2 10.41.58.80 5.8 4.692100.0  20.9  19.7 4.5  18.',\n",
       " '  91 23.23.10-76.574 110.286591.4 10.44.31.70 5.8 4.692100.0  20.9  22.0 4.6  21.3 4.',\n",
       " '  92 . 3.23.47.50-77.550 115.374592.9 11. 5.17.30 5.8 4.592100.0  21.7  -9.7 0.0 -16.',\n",
       " '  93 23.51.00-77.688 116.116593.2 11. 8.18.80 5.8 4.592100.0  21.7  -5.9 0.1  -7.4 0.',\n",
       " '  94 . 3.24.15.50-78.531 122.137594.6 11.32.48.40 5.8 4.252100.0  21.7 -16.7 0.0 -16.',\n",
       " '  95 24.19.00-78.649 123.014594.8 11.36.22.40 5.8 4.252100.0  21.7 -11.3 0.0 -11.3 0.',\n",
       " '  96 . 3.24.43.40-79.323 130.023596.2 12. 4.49.00 5.9 4.012100.0  21.7 -16.6 0.0 -16.',\n",
       " '  97 24.46.90-79.417 131.042596.4 12. 8.57.10 5.9 4.012100.0  21.7 -11.3 0.0 -11.3 0.',\n",
       " '  98 . 3.25.11.30-79.882 138.959597.8 12.41. 1.40 5.9 3.532100.0  21.7 -16.6 0.0 -16.',\n",
       " '  99 25.14.80-79.945 140.106598.0 12.45.40.20 5.9 3.532100.0  21.7 -11.3 0.0 -11.3 0.',\n",
       " ' 100 . 3.25.39.20-80.171 148.657599.3 13.20.16.90 6.0 3.252100.0  21.7 -16.5 0.0 -16.',\n",
       " ' 101 25.42.70-80.199 149.889599.5 13.25.16.10 6.0 3.252100.0  21.7 -11.3 0.0 -11.3 0.',\n",
       " ' 102 . 3.26. 7.10-80.164 158.625600.7 14. 0.37.20 6.1 3.532100.0  21.7 -16.4 0.0 -16.',\n",
       " ' 103 26.10.60-80.154 159.875600.9 14. 5.40.70 6.1 3.532100.0  21.7 -11.2 0.0 -11.2 0.',\n",
       " ' 104 . 3.26.35.10-79.865 168.289602.1 14.39.44.30 6.1 3.482100.0  21.7 -16.3 0.0 -16.',\n",
       " ' 105 26.38.60-79.819 169.484602.3 14.44.34.60 6.1 3.482100.0  21.7 -11.1 0.0 -11.1 0.',\n",
       " ' 106 . 3.27. 3.00-79.297 177.166603.4 15.15.42.70 6.2 3.272100.0  21.7 -16.3 0.0 -16.',\n",
       " ' 107 27. 6.50-79.218 178.249603.6 15.20. 6.30 6.2 3.272100.0  21.7 -11.0 0.0 -11.0 0.',\n",
       " ' 108 . 3.27.30.90-78.500-175.013604.7 15.47.27.70 6.2 3.452100.0  21.7 -16.2 0.4 -16.',\n",
       " ' 109 27.34.40-78.394-174.068604.8 15.51.18.10 6.2 3.452100.0  21.7  -7.6 0.5 -10.9 0.',\n",
       " ' 110 . 3.27.58.80-77.517-168.311605.9 16.14.44.10 6.2 3.402100.0  21.7 -16.1 1.2 -16.',\n",
       " ' 111 28. 2.30-77.389-167.506606.0 16.18. 0.80 6.2 3.402100.0  21.7 -10.8 0.5 -10.8 0.',\n",
       " ' 112 . 3.28.26.70-76.388-162.653607.0 16.37.49.90 6.3 2.412100.0  21.7 -13.4 1.0 -16.',\n",
       " ' 113 28.30.20-76.243-161.976607.1 16.40.36.00 6.3 2.412100.0  21.7 -10.9 1.0  -9.2 0.',\n",
       " ' 114 . 3.28.54.70-75.147-157.904608.1 16.57.17.80 6.3 2.162100.0  21.7 -10.6 1.3 -15.',\n",
       " ' 115 28.58.20-74.989-157.335608.2 16.59.37.60 6.3 2.162100.0  21.7 -10.8 1.0 -10.5 0.',\n",
       " ' 116 . 3.29.22.60-73.820-153.913609.1 17.13.43.50 6.3 2.332100.0  21.7 -13.2 1.1 -15.',\n",
       " ' 117 29.26.10-73.652-153.435609.2 17.15.41.60 6.3 2.332100.0  21.7  -7.4 1.3  -9.0 0.',\n",
       " ' 118 . 3.29.50.50-72.427-150.545610.0 17.27.39.60 6.3 1.912100.0  21.7   4.3 3.1 -15.',\n",
       " ' 119 29.54.00-72.251-150.142610.1 17.29.20.00 6.3 1.912100.0  21.7  -4.4 1.3  -6.7 0.',\n",
       " ' 120 . 3.30.18.40-70.981-147.685610.9 17.39.34.10 6.3 1.942100.0  21.7   6.7 4.2 -15.',\n",
       " ' 121 30.21.90-70.798-147.341611.0 17.41. 0.00 6.3 1.942100.0  21.7 -10.5 0.6 -10.5 0.',\n",
       " ' 122 . 3.30.46.30-69.494-145.236611.7 17.49.49.70 6.3 2.702100.0  21.7  24.6 4.6  23.',\n",
       " ' 123 30.49.80-69.307-144.941611.8 17.51. 3.90 6.3 2.702100.0  21.7  23.1 4.6  22.4 4.',\n",
       " ' 124 . 3.31.14.30-67.975-143.126612.4 17.58.44.10 6.3 2.722100.0  21.7  -3.1 0.5 -15.',\n",
       " ' 125 31.17.80-67.784-142.871612.5 17.59.48.60 6.3 2.722100.0  21.7  -4.0 0.1  -5.5 0.',\n",
       " ' 126 . 3.31.42.20-66.429-141.292613.1 18. 6.32.10 6.2 1.922100.0  21.5  -8.7 0.0  -8.',\n",
       " ' 127 31.45.70-66.235-141.071613.2 18. 7.28.70 6.2 1.922100.0  21.5 -10.2 0.0 -10.2 0.',\n",
       " ' 128 . 3.32.10.10-64.862-139.687613.7 18.13.25.20 6.2 1.732100.0  21.7  -8.7 0.0  -8.',\n",
       " ' 129 32.13.60-64.666-139.493613.8 18.14.15.30 6.2 1.732100.0  21.7 -10.1 0.0 -10.1 0.',\n",
       " ' 130 . 3.32.38.00-63.277-138.273614.2 18.19.32.50 6.1 2.002100.0  21.7 -15.1 0.0 -15.',\n",
       " ' 131 32.41.50-63.079-138.102614.3 18.20.17.10 6.1 2.002100.0  21.7 -10.0 0.0 -10.0 0.',\n",
       " ' 132 . 3.33. 5.90-61.677-137.020614.7 18.25. 1.10 6.1 2.052100.0  21.7 -15.0 0.0 -15.',\n",
       " ' 133 33. 9.40-61.477-136.868614.8 18.25.41.10 6.1 2.052100.0  21.7  -9.9 0.0  -9.9 0.',\n",
       " ' 134 . 3.33.33.90-60.065-135.903615.1 18.29.57.20 6.0 2.002100.0  21.7 -15.0 0.0 -15.',\n",
       " ' 135 33.37.40-59.863-135.767615.2 18.30.33.30 6.0 2.002100.0  21.7  -9.8 0.0  -9.8 0.',\n",
       " ' 136 . 3.34. 1.80-58.443-134.901615.5 18.34.25.60 6.0 2.132100.0  21.7 -14.9 0.0 -14.',\n",
       " ' 137 34. 5.30-58.240-134.779615.5 18.34.58.40 5.9 2.132100.0  21.7  -9.7 0.0  -9.7 0.',\n",
       " ' 138 . 3.34.29.70-56.811-133.997615.8 18.38.30.30 5.9 2.092100.0  21.7 -14.8 0.0 -14.',\n",
       " ' 139 34.33.20-56.606-133.887615.8 18.39. 0.30 5.9 2.092100.0  21.7  -9.6 0.0  -9.6 0.',\n",
       " ' 140 . 3.34.57.60-55.171-133.180616.0 18.42.14.40 5.8 2.092100.0  21.7 -14.7 0.0 -14.',\n",
       " ' 141 35. 1.10-54.966-133.080616.0 18.42.41.90 5.8 2.092100.0  21.7  -9.5 0.0  -9.5 0.',\n",
       " ' 142 . 3.35.25.50-53.525-132.436616.2 18.45.40.80 5.7 2.132100.0  21.7 -14.6 0.0 -14.',\n",
       " ' 143 35.29.00-53.319-132.345616.2 18.46. 6.10 5.7 2.132100.0  21.7  -9.5 0.0  -9.5 0.',\n",
       " ' 144 . 3.35.53.50-51.873-131.757616.3 18.48.51.80 5.7 2.092100.0  21.7 -14.6 0.0 -14.',\n",
       " ' 145 35.57.00-51.667-131.674616.3 18.49.15.30 5.7 2.092100.0  21.7  -9.4 0.0  -9.4 0.',\n",
       " ' 146 . 3.36.21.40-50.216-131.134616.4 18.51.49.20 5.6 2.052100.0  21.7 -14.5 0.0 -14.',\n",
       " ' 147 36.24.90-50.008-131.058616.4 18.52.11.00 5.6 2.052100.0  21.7  -9.3 0.0  -9.3 0.',\n",
       " ' 148 . 3.36.49.30-48.554-130.561616.4 18.54.34.70 5.6 2.052100.0  21.7 -14.4 0.0 -14.',\n",
       " ' 149 36.52.80-48.346-130.490616.4 18.54.55.10 5.6 2.052100.0  21.7  -9.3 0.1  -9.3 0.',\n",
       " ' 150 . 3.37.17.20-46.887-130.032616.3 18.57. 9.60 5.5 2.152100.0  21.7 -14.4 0.0 -14.',\n",
       " ' 151 37.20.70-46.678-129.967616.3 18.57.28.70 5.5 2.152100.0  21.7  -3.8 0.0  -9.2 0.',\n",
       " ' 152 . 3.37.45.10-45.216-129.542616.2 18.59.35.10 5.5 2.172100.0  21.7 -14.3 0.0 -14.',\n",
       " ' 153 37.48.60-45.008-129.482616.2 18.59.53.10 5.5 2.172100.0  21.7  -9.2 0.2  -9.2 0.',\n",
       " ' 154 . 3.38.13.10-43.543-129.086616.1 19. 1.52.40 5.5 2.152100.0  21.7  22.3 4.6  21.',\n",
       " ' 155 38.16.60-43.334-129.030616.0 19. 2. 9.40 5.5 2.152100.0  21.7  24.3 4.6  23.6 4.',\n",
       " ' 156 . 3.38.41.00-41.866-128.661615.9 19. 4. 2.40 5.6 2.172100.0  21.7  -5.8 0.3  -6.',\n",
       " ' 157 38.44.50-41.656-128.609615.8 19. 4.18.40 5.6 2.172100.0  21.7  -2.9 0.3  -4.4 0.',\n",
       " ' 158 . 3.39. 8.90-40.186-128.264615.6 19. 6. 5.60 5.7 2.222100.0  21.7 -14.3 0.0 -14.',\n",
       " ' 159 39.12.40-39.976-128.215615.6 19. 6.20.90 5.7 2.222100.0  21.7  -9.1 0.1  -9.1 0.',\n",
       " ' 160 . 3.39.36.80-38.503-127.891615.3 19. 8. 3.00 5.8 2.222100.0  21.7 -14.2 0.1 -14.',\n",
       " ' 161 39.40.30-38.293-127.845615.3 19. 8.17.50 5.8 2.222100.0  21.7  -7.5 0.5  -9.1 0.',\n",
       " ' 162 . 3.40. 4.70-36.819-127.541615.0 19. 9.54.90 6.0 2.262100.0  21.7 -14.2 0.1 -14.',\n",
       " ' 163 40. 8.20-36.608-127.498614.9 19.10. 8.80 6.0 2.262100.0  21.7  -9.1 0.2  -9.1 0.',\n",
       " ' 164 . 3.40.32.70-35.131-127.210614.6 19.11.42.20 6.3 2.262100.0  22.0 -14.2 0.0 -14.',\n",
       " ' 165 40.36.20-34.920-127.170614.6 19.11.55.40 6.3 2.262100.0  22.0  -9.1 0.3  -9.1 0.',\n",
       " ' 166 . 3.41. 0.60-33.442-126.898614.2 19.13.25.00 6.7 2.262100.0  21.7 -14.3 0.0 -14.',\n",
       " ' 167 41. 4.10-33.231-126.860614.1 19.13.37.80 6.7 2.262100.0  21.7  -9.1 0.3  -9.1 0.',\n",
       " ' 168 . 3.41.28.50-31.750-126.602613.7 19.15. 3.90 7.1 2.292100.0  22.2 -14.3 0.0 -14.',\n",
       " ' 169 41.32.00-31.539-126.566613.7 19.15.16.20 7.2 2.292100.0  22.2  -9.1 0.2  -9.1 0.',\n",
       " ' 170 . 3.41.56.40-30.058-126.321613.2 19.16.39.40 7.7 2.332100.0  21.7 -14.3 0.0 -14.',\n",
       " ' 171 41.59.90-29.846-126.286613.2 19.16.51.20 7.7 2.332100.0  21.7  -0.1 1.0  -7.4 0.',\n",
       " ' 172 . 3.42.24.30-28.362-126.053612.7 19.18.11.60 8.3 2.372100.0  22.0 -14.3 0.0 -14.',\n",
       " ' 173 42.27.80-28.150-126.020612.6 19.18.23.00 8.4 2.372100.0  22.0  -3.8 0.2  -9.2 0.',\n",
       " ' 174 . 3.42.52.30-26.666-125.797612.1 19.19.40.90 9.1 2.522100.0  21.7 -14.4 0.0 -14.',\n",
       " ' 175 42.55.80-26.454-125.766612.1 19.19.52.00 9.2 2.522100.0  21.7  -9.2 0.0  -9.2 0.',\n",
       " ' 176 . 3.43.20.20-24.968-125.552611.5 19.21. 7.70 9.9 2.722100.0  21.7 -14.4 0.0 -14.',\n",
       " ' 177 43.23.70-24.756-125.522611.5 19.21.18.5010.0 2.722100.0  21.7  -9.2 0.0  -9.2 0.',\n",
       " ' 178 . 3.43.48.10-23.268-125.317610.9 19.22.32.1010.7 2.812100.0  21.5 -14.5 0.0 -14.',\n",
       " ' 179 43.51.60-23.056-125.287610.8 19.22.42.6010.8 2.812100.0  21.5  -9.3 0.0  -9.3 0.',\n",
       " ' 180 . 3.44.16.00-21.567-125.090610.2 19.23.54.5011.5 3.082100.0  21.7 -14.5 0.0 -14.',\n",
       " ' 181 44.19.50-21.355-125.062610.2 19.24. 4.7011.6 3.082100.0  21.7  -9.3 0.0  -9.3 0.',\n",
       " ' 182 . 3.44.43.90-19.866-124.871609.6 19.25.14.9012.2 3.432100.0  21.7 -14.6 0.0 -14.',\n",
       " ' 183 44.47.40-19.653-124.844609.5 19.25.24.9012.3 3.432100.0  21.7  -9.3 0.0  -9.3 0.',\n",
       " ' 184 . 3.45.11.90-18.162-124.659608.8 19.26.33.7012.7 3.652100.0  21.7 -14.6 0.0 -14.',\n",
       " ' 185 45.15.40-17.949-124.633608.8 19.26.43.4012.7 3.652100.0  21.7  -9.3 0.0  -9.3 0.',\n",
       " ' 186 . 3.45.39.80-16.457-124.454608.1 19.27.50.9012.9 4.132100.0  21.7  27.1 4.6  26.',\n",
       " ' 187 45.43.30-16.244-124.428608.0 19.28. 0.5012.9 4.132100.0  21.7  24.2 4.7  23.4 4.',\n",
       " ' 188 . 3.46. 7.70-14.752-124.254607.4 19.29. 6.7012.8 4.692100.0  21.7  -7.7 0.0 -14.',\n",
       " ' 189 46.11.20-14.538-124.230607.3 19.29.16.1012.7 4.692100.0  21.7  -3.9 0.1  -4.6 0.',\n",
       " ' 190 . 3.46.35.60-13.046-124.059606.6 19.30.21.4012.3 5.192100.0  21.7 -14.7 0.0 -14.',\n",
       " ' 191 46.39.10-12.832-124.035606.5 19.30.30.7012.3 5.192100.0  21.7  -9.2 0.0  -9.2 0.',\n",
       " ' 192 . 3.47. 3.50-11.338-123.869605.8 19.31.35.1011.7 5.522100.0  22.0 -14.8 0.0 -14.',\n",
       " ' 193 47. 7.00-11.125-123.845605.7 19.31.44.3011.6 5.522100.0  22.0  -9.2 0.0  -9.2 0.',\n",
       " ' 194 . 3.47.31.50 -9.630-123.681605.0 19.32.48.1011.0 5.462100.0  21.7 -14.9 0.0 -14.',\n",
       " ' 195 47.35.00 -9.417-123.657604.9 19.32.57.2010.9 5.462100.0  21.7  -9.3 0.0  -9.3 0.',\n",
       " ' 196 . 3.47.59.40 -7.921-123.496604.1 19.34. 0.3010.3 5.462100.0  21.7 -15.0 0.0 -15.',\n",
       " ' 197 48. 2.90 -7.707-123.473604.0 19.34. 9.3010.3 5.462100.0  21.7  -9.4 0.0  -9.4 0.',\n",
       " ' 198 . 3.48.27.30 -6.211-123.314603.3 19.35.11.8010.0 5.462100.0  21.7 -15.1 0.0 -15.',\n",
       " ' 199 48.30.80 -5.997-123.292603.2 19.35.20.80 9.9 5.462100.0  21.7  -9.5 0.0  -9.5 0.',\n",
       " ' 200 . 3.48.55.20 -4.501-123.134602.4 19.36.23.00 9.8 5.642100.0  21.7 -15.2 0.0 -15.',\n",
       " ' 201 48.58.70 -4.287-123.112602.3 19.36.31.90 9.8 5.642100.0  21.7  -9.6 0.0  -9.6 0.',\n",
       " ' 202 . 3.49.23.10 -2.789-122.956601.5 19.37.33.80 9.9 5.812100.0  21.7 -15.3 0.0 -15.',\n",
       " ' 203 49.26.60 -2.575-122.933601.4 19.37.42.6010.0 5.812100.0  21.7  -9.6 0.0  -9.6 0.',\n",
       " ' 204 . 3.49.51.10 -1.078-122.778600.7 19.38.44.4010.2 6.052100.0  21.7 -15.3 0.1 -15.',\n",
       " ' 205 49.54.60 -0.864-122.755600.5 19.38.53.2010.2 6.052100.0  21.7  -9.6 0.0  -9.6 0.',\n",
       " ' 206 . 3.50.19.00  0.635-122.600599.8 19.39.55.1010.5 6.592100.0  21.7 -15.4 0.0 -15.',\n",
       " ' 207 50.22.50  0.849-122.577599.6 19.40. 3.9010.5 6.592100.0  21.7  -9.5 0.0  -9.5 0.',\n",
       " ' 208 . 3.50.46.90  2.347-122.422598.8 19.41. 5.7010.7 6.662100.0  21.5 -15.5 0.0 -15.',\n",
       " ' 209 50.50.40  2.561-122.399598.7 19.41.14.5010.8 6.662100.0  21.5  -9.6 0.0  -9.6 0.',\n",
       " ' 210 . 3.51.14.80  4.060-122.243597.9 19.42.16.5010.9 6.542100.0  21.7 -15.6 0.0 -15.',\n",
       " ' 211 51.18.30  4.274-122.221597.8 19.42.25.4010.9 6.542100.0  21.7  -9.7 0.0  -9.7 0.',\n",
       " ' 212 . 3.51.42.70  5.774-122.062597.0 19.43.27.8011.0 6.132100.0  21.7 -15.7 0.1 -15.',\n",
       " ' 213 51.46.20  5.988-122.040596.9 19.43.36.7011.0 6.132100.0  21.7 -10.0 0.0 -10.0 0.',\n",
       " ' 214 . 3.52.10.70  7.487-121.881596.1 19.44.39.3011.0 5.782100.0  21.7 -15.9 0.0 -15.',\n",
       " ' 215 52.14.20  7.701-121.858595.9 19.44.48.3011.0 5.782100.0  21.7 -10.2 0.0 -10.2 0.',\n",
       " ' 216 . 3.52.38.60  9.201-121.696595.1 19.45.51.5011.0 5.552100.0  22.0 -16.0 0.0 -16.',\n",
       " ' 217 52.42.10  9.415-121.673595.0 19.46. 0.5011.0 5.552100.0  22.0 -10.4 0.0 -10.4 0.',\n",
       " ' 218 . 3.53. 6.50 10.915-121.508594.2 19.47. 4.5010.9 5.192100.0  21.7  20.5 4.5  19.',\n",
       " ' 219 53.10.00 11.129-121.485594.0 19.47.13.7010.9 5.192100.0  21.7  22.8 4.7  21.9 4.',\n",
       " ' 220 . 3.53.34.40 12.628-121.317593.2 19.48.18.3010.8 5.192100.0  21.7  -9.2 0.0 -16.',\n",
       " ' 221 53.37.90 12.842-121.293593.1 19.48.27.6010.7 5.192100.0  21.7  -5.3 0.1  -6.0 0.',\n",
       " ' 222 . 3.54. 2.30 14.342-121.122592.2 19.49.33.1010.6 5.052100.0  21.7 -16.3 0.0 -16.',\n",
       " ' 223 54. 5.80 14.556-121.097592.1 19.49.42.5010.5 5.052100.0  21.7 -10.8 0.0 -10.8 0.',\n",
       " ' 224 . 3.54.30.30 16.056-120.922591.2 19.50.49.0010.3 4.902100.0  21.7 -16.4 0.0 -16.',\n",
       " ' 225 54.33.80 16.270-120.897591.1 19.50.58.5010.3 4.902100.0  21.7 -11.0 0.0 -11.0 0.',\n",
       " ' 226 . 3.54.58.20 17.769-120.716590.3 19.52. 6.2010.0 4.692100.0  21.7 -16.5 0.0 -16.',\n",
       " ' 227 55. 1.70 17.983-120.691590.1 19.52.15.9010.0 4.692100.0  21.7 -11.1 0.0 -11.1 0.',\n",
       " ' 228 . 3.55.26.10 19.482-120.504589.3 19.53.25.10 9.7 4.482100.0  21.7 -16.6 0.0 -16.',\n",
       " ' 229 55.29.60 19.696-120.477589.1 19.53.35.00 9.7 4.482100.0  21.7 -11.2 0.0 -11.2 0.',\n",
       " ' 230 . 3.55.54.00 21.195-120.285588.3 19.54.45.70 9.4 4.132100.0  21.7 -16.7 0.0 -16.',\n",
       " ' 231 55.57.50 21.409-120.257588.2 19.54.55.80 9.3 4.132100.0  21.7 -11.4 0.0 -11.4 0.',\n",
       " ' 232 . 3.56.21.90 22.907-120.058587.3 19.56. 8.10 9.1 4.012100.0  21.7 -16.8 0.0 -16.',\n",
       " ' 233 56.25.40 23.121-120.029587.2 19.56.18.50 9.0 4.012100.0  21.7 -11.5 0.0 -11.5 0.',\n",
       " ' 234 . 3.56.49.90 24.618-119.822586.3 19.57.32.60 8.7 3.992100.0  21.7 -16.9 0.0 -16.',\n",
       " ' 235 56.53.40 24.832-119.792586.1 19.57.43.30 8.7 3.992100.0  21.7 -11.5 0.0 -11.5 0.',\n",
       " ' 236 . 3.57.17.80 26.329-119.576585.2 19.58.59.50 8.4 4.012100.0  21.7 -16.9 0.0 -16.',\n",
       " ' 237 57.21.30 26.543-119.545585.1 19.59.10.40 8.4 4.012100.0  21.7 -11.6 0.1 -11.6 0.',\n",
       " ' 238 . 3.57.45.70 28.040-119.320584.2 20. 0.29.00 8.1 3.832100.0  21.7 -17.0 0.0 -17.',\n",
       " ' 239 57.49.20 28.254-119.287584.1 20. 0.40.20 8.1 3.832100.0  21.7 -11.7 0.9 -11.7 0.',\n",
       " ' 240 . 3.58.13.60 29.750-119.051583.2 20. 2. 1.50 7.9 3.672100.0  21.7 -17.0 0.0 -17.',\n",
       " ' 241 58.17.10 29.963-119.017583.1 20. 2.13.10 7.8 3.672100.0  21.7 -11.8 0.5 -11.8 0.',\n",
       " ' 242 . 3.58.41.50 31.458-118.769582.2 20. 3.37.00 7.6 3.702100.0  21.7 -17.1 0.0 -17.',\n",
       " ' 243 58.45.00 31.671-118.733582.0 20. 3.49.10 7.6 3.702100.0  21.7 -11.8 0.8 -11.8 0.',\n",
       " ' 244 . 3.59. 9.50 33.165-118.472581.1 20. 5.16.30 7.4 3.552100.0  21.7 -17.1 0.0 -17.',\n",
       " ' 245 59.13.00 33.379-118.434581.0 20. 5.28.80 7.4 3.552100.0  21.7 -11.9 1.1 -11.9 1.',\n",
       " ' 246 . 3.59.37.40 34.871-118.157580.1 20. 6.59.60 7.2 3.552100.0  22.2 -17.2 0.6 -17.',\n",
       " ' 247 59.40.90 35.085-118.118579.9 20. 7.12.60 7.2 3.552100.0  22.2  -2.9 1.7  -2.6 1.',\n",
       " ' 248 . 4. 0. 5.30 36.577-117.826579.0 20. 8.47.10 7.0 3.722100.0  22.0 -17.2 0.1 -17.',\n",
       " ' 249  0. 8.80 36.790-117.784578.9 20. 9. 0.70 7.0 3.722100.0  22.0  -4.2 1.1  -3.9 1.',\n",
       " ' 250 . 4. 0.33.20 38.279-117.474577.9 20.10.39.60 6.9 3.722100.0  22.0  23.9 4.6  23.',\n",
       " ' 251  0.36.70 38.492-117.429577.8 20.10.53.80 6.9 3.722100.0  22.0  21.5 4.7  20.7 4.',\n",
       " ' 252 . 4. 1. 1.10 39.981-117.099576.8 20.12.37.40 6.8 3.702100.0  21.7 -10.3 0.5  -9.',\n",
       " ' 253  1. 4.60 40.193-117.052576.7 20.12.52.30 6.8 3.702100.0  21.7  -1.1 1.1  -0.6 1.',\n",
       " ' 254 . 4. 1.29.10 41.680-116.699575.7 20.14.41.30 6.7 3.722100.0  21.7 -17.3 0.2 -17.',\n",
       " ' 255  1.32.60 41.892-116.648575.6 20.14.56.90 6.7 3.722100.0  21.7  -0.9 2.1   0.1 2.',\n",
       " ' 256 . 4. 1.57.00 43.377-116.271574.6 20.16.52.00 6.6 3.672100.0  21.7 -17.3 0.4 -17.',\n",
       " ' 257  2. 0.50 43.590-116.217574.5 20.17. 8.50 6.6 3.672100.0  21.7  -0.0 2.6   2.3 3.',\n",
       " ' 258 . 4. 2.24.90 45.072-115.811573.5 20.19.10.40 6.5 3.632100.0  21.7 -17.3 0.4 -17.',\n",
       " ' 259  2.28.40 45.284-115.752573.4 20.19.27.90 6.5 3.632100.0  21.7   0.9 3.8   5.0 4.',\n",
       " ' 260 . 4. 2.52.80 46.763-115.316572.4 20.21.37.00 6.5 3.702100.0  21.7 -17.3 0.5 -17.',\n",
       " ' 261  2.56.30 46.974-115.253572.3 20.21.55.60 6.5 3.702100.0  21.7  -7.6 1.3  -3.6 1.',\n",
       " ' 262 . 4. 3.20.70 48.452-114.781571.3 20.24.13.20 6.4 3.722100.0  21.7 -17.3 0.4 -17.',\n",
       " ' 263  3.24.20 48.663-114.713571.1 20.24.33.00 6.4 3.722100.0  21.7 -12.0 1.1  -9.1 1.',\n",
       " ' 264 . 4. 3.48.70 50.136-114.201570.1 20.27. 0.30 6.4 3.672100.0  21.7 -17.3 0.2 -17.',\n",
       " ' 265  3.52.20 50.346-114.128570.0 20.27.21.50 6.4 3.672100.0  21.7 -12.0 1.0  -9.1 1.',\n",
       " ' 266 . 4. 4.16.60 51.816-113.571568.9 20.29.59.60 6.4 3.112100.0  21.7 -17.4 0.8 -17.',\n",
       " ' 267  4.20.10 52.026-113.490568.8 20.30.22.40 6.4 3.112100.0  21.7 -12.1 1.0  -4.7 1.',\n",
       " ' 268 . 4. 4.44.50 53.492-112.882567.8 20.33.12.70 6.3 3.302100.0  21.7  -9.9 0.3  -9.',\n",
       " ' 269  4.48.00 53.701-112.795567.6 20.33.37.30 6.3 3.302100.0  21.7  -3.7 1.7   1.2 2.',\n",
       " ' 270 . 4. 5.12.40 55.161-112.128566.6 20.36.41.80 6.3 3.452100.0  21.7 -17.3 0.2 -17.',\n",
       " ' 271  5.15.90 55.370-112.031566.4 20.37. 8.40 6.3 3.452100.0  21.7 -12.1 1.1  -0.4 1.',\n",
       " ' 272 . 4. 5.40.30 56.825-111.296565.4 20.40.29.30 6.3 3.502100.0  21.7 -17.3 0.2 -17.',\n",
       " ' 273  5.43.80 57.032-111.190565.2 20.40.58.30 6.3 3.502100.0  21.7  -5.9 1.2   5.3 4.',\n",
       " ' 274 . 4. 6. 8.30 58.480-110.376564.1 20.44.37.90 6.2 3.272100.0  21.7 -17.3 0.3 -17.',\n",
       " ' 275  6.11.80 58.686-110.259564.0 20.45. 9.60 6.2 3.272100.0  21.7  -3.7 1.7   7.2 4.',\n",
       " ' 276 . 4. 6.36.20 60.127-109.355562.9 20.49.11.00 6.2 3.302100.0  21.7 -17.3 1.3 -17.',\n",
       " ' 277  6.39.70 60.332-109.224562.8 20.49.45.80 6.2 3.302100.0  21.7   2.1 2.5  10.6 4.',\n",
       " ' 278 . 4. 7. 4.10 61.762-108.213561.7 20.54.12.90 6.2 3.482100.0  21.7 -14.7 2.3  -7.',\n",
       " ' 279  7. 7.60 61.967-108.067561.5 20.54.51.50 6.1 3.482100.0  21.7   7.4 4.6  14.8 4.',\n",
       " ' 280 . 4. 7.32.00 63.386-106.930560.4 20.59.48.90 6.1 3.382100.0  21.7 -17.3 0.8 -17.',\n",
       " ' 281  7.35.50 63.589-106.765560.2 21. 0.31.90 6.1 3.382100.0  21.7   4.9 4.6  12.8 4.',\n",
       " ' 282 . 4. 7.59.90 64.994-105.478559.1 21. 6. 5.10 6.0 3.682100.0  21.7  19.4 4.6  18.',\n",
       " ' 283  8. 3.40 65.195-105.292559.0 21. 6.53.40 6.0 3.682100.0  21.7  21.5 4.6  20.8 4.',\n",
       " ' 284 . 4. 8.27.90 66.584-103.827557.8 21.13. 9.30 6.0 3.672100.0  21.7 -10.2 0.1 -17.',\n",
       " ' 285  8.31.40 66.783-103.615557.7 21.14. 3.80 6.0 3.672100.0  21.7  -6.5 0.1  -6.4 0.',\n",
       " ' 286 . 4. 8.55.80 68.153-101.934556.5 21.21.11.60 5.9 3.722100.0  21.7 -17.1 0.0 -17.',\n",
       " ' 287  8.59.30 68.348-101.690556.4 21.22.13.60 5.9 3.722100.0  21.7 -11.8 0.0 -11.8 0.',\n",
       " ' 288 . 4. 9.23.70 69.693 -99.749555.2 21.30.24.00 5.9 3.582100.0  21.7 -17.1 0.0 -17.',\n",
       " ' 289  9.27.20 69.885 -99.467555.0 21.31.35.20 5.9 3.582100.0  21.7 -11.8 0.0 -11.8 0.',\n",
       " ' 290 . 4. 9.51.60 71.199 -97.205553.9 21.41. 2.50 5.8 3.882100.0  21.7 -17.0 0.0 -17.',\n",
       " ' 291  9.55.10 71.386 -96.876553.7 21.42.25.00 5.8 3.882100.0  21.7 -11.7 0.0 -11.7 0.',\n",
       " ' 292 . 4.10.19.50 72.661 -94.221552.5 21.53.26.50 5.8 4.062100.0  21.5 -17.0 0.0 -17.',\n",
       " ' 293 10.23.00 72.842 -93.835552.3 21.55. 2.70 5.7 4.062100.0  21.5 -11.6 0.0 -11.6 0.',\n",
       " ' 294 . 4.10.47.50 74.068 -90.694551.1 22. 8. 0.80 5.7 4.482100.0  21.7 -16.9 0.0 -16.',\n",
       " ' 295 10.51.00 74.242 -90.237551.0 22. 9.54.10 5.7 4.482100.0  21.7 -11.5 0.0 -11.5 0.',\n",
       " ' 296 . 4.11.15.40 75.403 -86.499549.8 22.25.15.60 5.7 4.482100.0  22.0 -16.8 0.0 -16.',\n",
       " ' 297 11.18.90 75.567 -85.954549.6 22.27.29.90 5.7 4.482100.0  22.0 -11.4 0.0 -11.4 0.',\n",
       " ' 298 . 4.11.43.30 76.643 -81.488548.4 22.45.46.30 5.6 4.252100.0  21.7 -16.8 0.0 -16.',\n",
       " ' 299 11.46.80 76.795 -80.836548.2 22.48.26.10 5.6 4.252100.0  21.7 -11.4 0.0 -11.4 0.',\n",
       " ' 300 . 4.12.11.20 77.760 -75.503547.0 23.10.10.50 5.6 4.012100.0  20.9 -16.8 0.0 -16.',\n",
       " ' 301 12.14.70 77.896 -74.726546.8 23.13.20.60 5.6 4.012100.0  20.9 -11.4 0.0 -11.4 0.',\n",
       " ' 302 . 4.12.39.10 78.719 -68.409545.6 23.39. 0.90 5.6 3.902100.0  21.7 -14.1 0.1 -16.',\n",
       " ' 303 12.42.60 78.834 -67.489545.4 23.42.45.20 5.6 3.902100.0  21.7 -11.4 0.0 -11.4 0.',\n",
       " ' 304 . 4.13. 7.10 79.474 -60.155544.1  0.12.29.80 5.6 3.922100.0  21.7  -8.9 0.1 -16.',\n",
       " ' 305 13.10.60 79.562 -59.089544.0  0.16.49.10 5.6 3.922100.0  21.7 -11.3 0.0 -11.3 0.',\n",
       " ' 306 . 4.13.35.00 79.979 -50.860542.7  0.50. 8.50 5.6 3.652100.0  21.7  -9.6 0.3 -16.',\n",
       " ' 307 13.38.50 80.034 -49.669542.5  0.54.57.90 5.6 3.652100.0  21.7 -11.3 0.0 -11.3 0.',\n",
       " ' 308 . 4.14. 2.90 80.197 -40.878541.3  1.30.32.20 5.6 3.772100.0  21.7  -9.5 0.1 -16.',\n",
       " ' 309 14. 6.40 80.216 -39.613541.1  1.35.39.40 5.6 3.772100.0  21.7 -11.2 0.0 -11.2 0.',\n",
       " ' 310 . 4.14.30.80 80.109 -30.764539.8  2.11.27.60 5.7 4.012100.0  21.7 -11.1 0.0 -16.',\n",
       " ' 311 14.34.30 80.089 -29.500539.7  2.16.34.40 5.7 4.012100.0  21.7 -11.0 0.0 -11.0 0.',\n",
       " ' 312 . 4.14.58.70 79.722 -21.113538.4  2.50.31.50 5.7 4.012100.0  21.7 -11.0 0.1 -16.',\n",
       " ' 313 15. 2.20 79.665 -19.925538.2  2.55.20.30 5.7 4.012100.0  21.7 -11.0 0.0 -11.0 0.',\n",
       " ' 314 . 4.15.26.70 79.067 -12.378536.9  3.25.56.00 5.7 4.012100.0  21.7  25.5 4.7  24.',\n",
       " ' 315 15.30.20 78.977 -11.315536.8  3.30.14.40 5.7 4.012100.0  21.7  22.5 4.6  21.8 4.',\n",
       " ' 316 . 4.15.54.60 78.188  -4.773535.5  3.56.49.00 5.7 3.972100.0  21.7  -6.5 0.1 -16.',\n",
       " ' 317 15.58.10 78.071  -3.857535.3  4. 0.32.40 5.7 3.972100.0  21.7  -6.4 0.1  -6.9 0.',\n",
       " ' 318 . 4.16.22.50 77.128   1.687534.0  4.23. 7.40 5.7 3.792100.0  21.7 -16.0 0.0 -16.',\n",
       " ' 319 16.26.00 76.991   2.461533.9  4.26.16.70 5.7 3.792100.0  21.7 -10.7 0.0 -10.7 0.',\n",
       " ' 320 . 4.16.50.40 75.931   7.110532.6  4.45.16.80 5.8 4.012100.0  21.7 -15.9 0.0 -15.',\n",
       " ' 321 16.53.90 75.777   7.758532.4  4.47.55.90 5.8 4.012100.0  21.7 -10.6 0.0 -10.6 0.',\n",
       " ' 322 . 4.17.18.30 74.625  11.648531.1  5. 3.53.90 5.8 3.452100.0  21.7 -15.8 0.0 -15.',\n",
       " ' 323 17.21.80 74.459  12.191531.0  5. 6. 7.60 5.8 3.452100.0  21.7 -10.6 0.0 -10.6 0.',\n",
       " ' 324 . 4.17.46.30 73.238  15.455529.7  5.19.35.60 5.8 3.402100.0  20.9 -15.7 0.0 -15.',\n",
       " ' 325 17.49.80 73.062  15.911529.5  5.21.28.40 5.8 3.402100.0  20.9 -10.5 0.0 -10.5 0.',\n",
       " ' 326 . 4.18.14.20 71.786  18.667528.3  5.32.54.20 5.8 3.482100.0  20.9 -15.6 0.0 -15.',\n",
       " ' 327 18.17.70 71.603  19.052528.1  5.34.30.10 5.8 3.482100.0  20.9 -10.4 0.0 -10.4 0.',\n",
       " ' 328 . 4.18.42.10 70.285  21.396526.8  5.44.17.10 5.9 3.672100.0  20.9 -15.5 0.0 -15.',\n",
       " ' 329 18.45.60 70.096  21.724526.7  5.45.39.20 5.9 3.672100.0  20.9 -10.2 0.0 -10.2 0.',\n",
       " ' 330 . 4.19.10.00 68.744  23.732525.4  5.54. 5.70 5.9 3.772100.0  20.9 -12.6 0.0 -15.',\n",
       " ' 331 19.13.50 68.550  24.013525.3  5.55.16.70 6.0 3.772100.0  20.9 -10.1 0.0 -10.1 0.',\n",
       " ' 332 . 4.19.37.90 67.172  25.749524.0  6. 2.37.70 6.0 3.772100.0  21.1 -12.6 0.2 -15.',\n",
       " ' 333 19.41.40 66.974  25.992523.9  6. 3.39.60 6.0 3.772100.0  21.1 -10.0 0.0 -10.0 0.',\n",
       " ' 334 . 4.20. 5.90 65.573  27.503522.7  6.10. 6.60 6.1 3.922100.0  21.7  -5.6 1.4  -7.',\n",
       " ' 335 20. 9.40 65.372  27.715522.5  6.11. 0.90 6.1 3.922100.0  21.7  -9.9 0.0  -9.9 0.',\n",
       " ' 336 . 4.20.33.80 63.953  29.039521.3  6.16.43.20 6.2 3.862100.0  21.5  -4.5 2.1  -7.',\n",
       " ' 337 20.37.30 63.750  29.225521.1  6.17.31.40 6.2 3.862100.0  21.5  -9.8 0.0  -9.8 0.',\n",
       " ' 338 . 4.21. 1.70 62.316  30.395520.0  6.22.36.50 6.3 3.722100.0  21.7  88.8 3.8   1.',\n",
       " ' 339 21. 5.20 62.111  30.559519.8  6.23.19.40 6.4 3.722100.0  21.7  -9.7 0.1  -9.7 0.',\n",
       " ' 340 . 4.21.29.60 60.664  31.598518.6  6.27.53.20 6.5 3.652100.0  21.7   1.3 4.3   2.',\n",
       " ' 341 21.33.10 60.457  31.744518.5  6.28.31.70 6.5 3.652100.0  21.7  -9.6 0.1  -9.6 0.',\n",
       " ' 342 . 4.21.57.50 58.999  32.672517.4  6.32.38.90 6.6 3.632100.0  21.7   4.8 4.5   6.',\n",
       " ' 343 22. 1.00 58.790  32.803517.2  6.33.13.70 6.7 3.632100.0  21.7  -9.5 0.1  -9.5 0.',\n",
       " ' 344 . 4.22.25.50 57.323  33.636516.1  6.36.58.10 6.8 3.672100.0  21.7   4.5 4.5   5.',\n",
       " ' 345 22.29.00 57.113  33.753515.9  6.37.29.80 6.8 3.672100.0  21.7  -9.4 0.3  -9.4 0.',\n",
       " ' 346 . 4.22.53.40 55.638  34.506514.9  6.40.54.90 7.0 3.722100.0  20.9  21.9 4.5  21.',\n",
       " ' 347 22.56.90 55.427  34.612514.7  6.41.23.80 7.0 3.722100.0  20.9  23.7 4.6  23.3 4.',\n",
       " ' 348 . 4.23.21.30 53.944  35.295513.7  6.44.32.00 7.1 3.722100.0  20.9   9.2 4.5   9.',\n",
       " ' 349 23.24.80 53.732  35.391513.5  6.44.58.60 7.2 3.722100.0  20.9   0.2 0.6  -0.6 0.',\n",
       " ' 350 . 4.23.49.20 52.244  36.013512.5  6.47.52.40 7.3 3.702100.0  21.7   8.3 4.5   8.',\n",
       " ' 351 23.52.70 52.031  36.101512.4  6.48.17.00 7.3 3.702100.0  21.7  -7.6 0.3  -8.9 0.',\n",
       " ' 352 . 4.24.17.10 50.536  36.670511.4  6.50.58.00 7.5 3.672100.0  20.9  10.9 4.5  10.',\n",
       " ' 353 24.20.60 50.323  36.751511.3  6.51.20.80 7.5 3.672100.0  20.9  -2.9 0.5  -3.7 0.',\n",
       " ' 354 . 4.24.45.10 48.823  37.274510.3  6.53.50.70 7.7 3.692100.0  20.9  13.2 4.5  13.',\n",
       " ' 355 24.48.60 48.609  37.347510.2  6.54.11.90 7.7 3.692100.0  20.9  -9.1 0.4  -9.1 0.',\n",
       " ' 356 . 4.25.13.00 47.106  37.829509.3  6.56.32.00 7.9 3.862100.0  20.9  10.4 4.5  10.',\n",
       " ' 357 25.16.50 46.891  37.897509.2  6.56.51.90 7.9 3.862100.0  20.9  -9.0 0.1  -9.0 0.',\n",
       " ' 358 . 4.25.40.90 45.384  38.343508.3  6.59. 3.20 8.0 3.812100.0  20.9   7.3 4.5   5.',\n",
       " ' 359 25.44.40 45.168  38.406508.2  6.59.21.80 8.0 3.812100.0  20.9  -7.1 0.3  -8.5 0.',\n",
       " ' 360 . 4.26. 8.80 43.657  38.819507.4  7. 1.25.50 8.2 3.792100.0  20.9  11.1 4.5  12.',\n",
       " ' 361 26.12.30 43.441  38.878507.3  7. 1.43.00 8.2 3.792100.0  20.9  -8.9 0.4  -8.9 0.',\n",
       " ' 362 . 4.26.36.70 41.926  39.264506.5  7. 3.40.00 8.3 3.792100.0  20.9   8.1 4.5   8.',\n",
       " ' 363 26.40.20 41.710  39.318506.4  7. 3.56.60 8.3 3.792100.0  20.9  -5.6 1.0  -7.2 0.',\n",
       " ' 364 . 4.27. 4.70 40.192  39.678505.7  7. 5.47.30 8.4 3.752100.0  21.7   0.9 4.2   0.',\n",
       " ' 365 27. 8.20 39.976  39.729505.5  7. 6. 3.00 8.4 3.752100.0  21.7  -3.5 1.0  -5.0 1.',\n",
       " ' 366 . 4.27.32.60 38.456  40.066504.9  7. 7.48.30 8.5 3.772100.0  21.7   2.0 3.9   0.',\n",
       " ' 367 27.36.10 38.239  40.113504.8  7. 8. 3.30 8.5 3.772100.0  21.7  -7.3 0.9  -8.9 0.',\n",
       " ' 368 . 4.28. 0.50 36.716  40.430504.2  7. 9.43.70 8.5 3.912100.0  21.7  -1.4 2.9  -7.',\n",
       " ' 369 28. 4.00 36.498  40.475504.1  7. 9.57.90 8.5 3.912100.0  21.7  -7.2 0.7  -8.5 0.',\n",
       " ' 370 . 4.28.28.40 34.973  40.774503.5  7.11.34.10 8.5 3.922100.0  21.7  -3.5 1.9 -14.',\n",
       " ' 371 28.31.90 34.755  40.816503.4  7.11.47.70 8.5 3.922100.0  21.7  -8.8 0.6  -8.8 0.',\n",
       " ' 372 . 4.28.56.30 33.229  41.098502.9  7.13.19.90 8.5 3.902100.0  21.7  -3.6 1.4 -14.',\n",
       " ' 373 28.59.80 33.011  41.138502.9  7.13.33.00 8.5 3.902100.0  21.7  -8.8 0.6  -8.8 0.',\n",
       " ' 374 . 4.29.24.30 31.482  41.405502.4  7.15. 1.50 8.5 3.922100.0  20.9 -11.4 1.2 -14.',\n",
       " ' 375 29.27.80 31.264  41.443502.3  7.15.14.00 8.5 3.922100.0  20.9  -8.9 0.1  -8.9 0.',\n",
       " ' 376 . 4.29.52.20 29.733  41.697502.0  7.16.39.30 8.4 3.922100.0  20.9   1.4 3.1 -14.',\n",
       " ' 377 29.55.70 29.514  41.733501.9  7.16.51.50 8.4 3.922100.0  20.9  -8.9 0.2  -8.9 0.',\n",
       " ' 378 . 4.30.20.10 27.982  41.975501.6  7.18.14.00 8.4 3.832100.0  20.9  26.9 4.6  26.',\n",
       " ' 379 30.23.60 27.763  42.009501.5  7.18.25.70 8.4 3.832100.0  20.9  24.0 4.7  23.5 4.',\n",
       " ' 380 . 4.30.48.00 26.230  42.240501.3  7.19.45.50 8.5 3.882100.0  21.1   2.7 3.8 -14.',\n",
       " ' 381 30.51.50 26.011  42.272501.2  7.19.56.90 8.5 3.882100.0  21.1  -3.6 0.2  -5.0 0.',\n",
       " ' 382 . 4.31.15.90 24.477  42.493501.0  7.21.14.30 8.6 3.832100.0  21.1   2.4 4.1 -14.',\n",
       " ' 383 31.19.40 24.258  42.525501.0  7.21.25.30 8.6 3.832100.0  21.1  -9.1 0.0  -9.1 0.',\n",
       " ' 384 . 4.31.43.90 22.722  42.737500.9  7.22.40.80 8.8 3.722100.0  20.9   0.6 3.6 -14.',\n",
       " ' 385 31.47.40 22.503  42.768500.8  7.22.51.60 8.8 3.722100.0  20.9  -9.1 0.0  -9.1 0.',\n",
       " ' 386 . 4.32.11.80 20.966  42.972500.8  7.24. 5.20 9.0 3.552100.0  20.9  -0.2 2.9 -14.',\n",
       " ' 387 32.15.30 20.746  43.002500.8  7.24.15.60 9.1 3.552100.0  20.9  -9.2 0.0  -9.2 0.',\n",
       " ' 388 . 4.32.39.70 19.209  43.199500.8  7.25.27.50 9.3 3.402100.0  20.9  -3.2 1.1 -14.',\n",
       " ' 389 32.43.20 18.989  43.227500.8  7.25.37.80 9.4 3.402100.0  20.9  -9.3 0.0  -9.3 0.',\n",
       " ' 390 . 4.33. 7.60 17.451  43.418500.8  7.26.48.00 9.7 3.272100.0  20.9  -7.7 0.3 -14.',\n",
       " ' 391 33.11.10 17.231  43.445500.8  7.26.58.00 9.7 3.272100.0  20.9  -9.4 0.0  -9.4 0.',\n",
       " ' 392 . 4.33.35.50 15.692  43.631501.0  7.28. 7.10 9.9 3.222100.0  20.9 -12.0 0.0 -14.',\n",
       " ' 393 33.39.00 15.472  43.658501.0  7.28.16.90 9.9 3.222100.0  20.9  -9.5 0.0  -9.5 0.',\n",
       " ' 394 . 4.34. 3.50 13.932  43.839501.2  7.29.24.8010.1 3.052100.0  20.9 -14.9 0.0 -14.',\n",
       " ' 395 34. 7.00 13.712  43.864501.2  7.29.34.4010.1 3.052100.0  20.9  -9.7 0.0  -9.7 0.',\n",
       " ' 396 . 4.34.31.40 12.172  44.041501.5  7.30.41.2010.2 2.912100.0  20.9 -15.0 0.0 -15.',\n",
       " ' 397 34.34.90 11.952  44.066501.5  7.30.50.7010.2 2.912100.0  20.9  -9.8 0.0  -9.8 0.',\n",
       " ' 398 . 4.34.59.30 10.412  44.240501.9  7.31.56.8010.2 2.842100.0  20.9 -15.1 0.0 -15.',\n",
       " ' 399 35. 2.80 10.192  44.264501.9  7.32. 6.2010.2 2.842100.0  20.9  -9.9 0.0  -9.9 0.',\n",
       " ' 400 . 4.35.27.20  8.652  44.435502.4  7.33.11.5010.3 2.782100.0  21.7 -15.2 0.0 -15.',\n",
       " ' 401 35.30.70  8.432  44.459502.4  7.33.20.8010.3 2.782100.0  21.7 -10.0 0.0 -10.0 0.',\n",
       " ' 402 . 4.35.55.10  6.892  44.626502.9  7.34.25.4010.4 2.782100.0  20.9 -15.3 0.0 -15.',\n",
       " ' 403 35.58.60  6.671  44.650503.0  7.34.34.7010.4 2.782100.0  20.9 -10.1 0.0 -10.1 0.',\n",
       " ' 404 . 4.36.23.10  5.131  44.816503.5  7.35.38.8010.6 2.782100.0  20.9 -15.4 0.1 -15.',\n",
       " ' 405 36.26.60  4.911  44.839503.6  7.35.48.0010.6 2.782100.0  20.9 -10.2 0.1 -10.2 0.',\n",
       " ' 406 . 4.36.51.00  3.370  45.003504.3  7.36.51.7010.7 2.882100.0  20.9 -15.5 0.0 -15.',\n",
       " ' 407 36.54.50  3.150  45.027504.4  7.37. 0.8010.8 2.882100.0  20.9 -10.3 0.0 -10.3 0.',\n",
       " ' 408 . 4.37.18.90  1.610  45.190505.1  7.38. 4.5010.8 2.962100.0  20.9 -15.6 0.0 -15.',\n",
       " ' 409 37.22.40  1.390  45.214505.2  7.38.13.6010.8 2.962100.0  20.9 -10.4 0.0 -10.4 0.',\n",
       " ' 410 . 4.37.46.80 -0.149  45.376505.9  7.39.17.0010.8 3.022100.0  21.1  20.8 4.5  20.',\n",
       " ' 411 37.50.30 -0.369  45.399506.0  7.39.26.0010.8 3.022100.0  21.1  22.9 4.7  22.0 4.',\n",
       " ' 412 . 4.38.14.70 -1.909  45.561506.9  7.40.29.5010.8 3.072100.0  21.1  -9.7 0.1 -15.',\n",
       " ' 413 38.18.20 -2.129  45.585507.0  7.40.38.6010.7 3.072100.0  21.1  -5.6 0.1  -6.8 0.',\n",
       " ' 414 . 4.38.42.70 -3.667  45.748507.9  7.41.42.2010.6 3.052100.0  20.9 -15.9 0.0 -15.',\n",
       " ' 415 38.46.20 -3.887  45.772508.0  7.41.51.3010.6 3.052100.0  20.9 -10.7 0.0 -10.7 0.',\n",
       " ' 416 . 4.39.10.60 -5.425  45.936509.0  7.42.55.2010.4 2.962100.0  20.9 -16.0 0.0 -16.',\n",
       " ' 417 39.14.10 -5.645  45.959509.1  7.43. 4.3010.4 2.962100.0  20.9 -10.8 0.0 -10.8 0.',\n",
       " ' 418 . 4.39.38.50 -7.182  46.125510.2  7.44. 8.6010.2 2.812100.0  21.7 -16.1 0.0 -16.',\n",
       " ' 419 39.42.00 -7.402  46.149510.3  7.44.17.8010.2 2.812100.0  21.7 -10.9 0.0 -10.9 0.',\n",
       " ' 420 . 4.40. 6.40 -8.939  46.317511.4  7.45.22.50 9.9 2.722100.0  21.7 -16.2 0.0 -16.',\n",
       " ' 421 40. 9.90 -9.158  46.341511.6  7.45.31.80 9.9 2.722100.0  21.7 -11.0 0.0 -11.0 0.',\n",
       " ' 422 . 4.40. 6.40 -8.939  46.317511.4  7.45.22.50 9.9 2.722100.0  21.7  88.888.8  88.',\n",
       " ' 423 40. 9.90 -9.158  46.341511.6  7.45.31.80 9.9 2.722100.0  21.7  88.888.8  88.888.']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[\"%4d %s\" % (i, l[:80]) for i, l in enumerate(list(chunkstring(section1, 164)))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So it's a bit cleaner (but we miss half the records) to look every 328 bytes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['   0 . 3. 2.23.20 -1.357  69.581506.5  7.40.42.8010.9 3.672100.0  20.9 -15.7 0.0 -15.',\n",
       " '   1 . 3. 2.51.10 -3.116  69.768507.5  7.41.55.5010.8 3.762100.0  20.9 -15.8 0.0 -15.',\n",
       " '   2 . 3. 3.19.10 -4.874  69.955508.6  7.43. 8.3010.6 3.792100.0  21.1 -15.9 0.0 -15.',\n",
       " '   3 . 3. 3.47.00 -6.631  70.144509.7  7.44.21.5010.4 3.812100.0  21.1 -16.0 0.0 -16.',\n",
       " '   4 . 3. 4.14.90 -8.388  70.334510.9  7.45.35.1010.1 3.762100.0  20.9 -16.1 0.0 -16.',\n",
       " '   5 . 3. 4.42.80-10.144  70.528512.2  7.46.49.40 9.8 3.672100.0  20.9 -16.2 0.0 -16.',\n",
       " '   6 . 3. 5.10.70-11.898  70.725513.6  7.48. 4.70 9.4 3.552100.0  20.9 -16.3 0.0 -16.',\n",
       " '   7 . 3. 5.38.70-13.651  70.926515.0  7.49.20.80 9.2 3.652100.0  20.6 -16.4 0.0 -16.',\n",
       " '   8 . 3. 6. 6.60-15.403  71.131516.5  7.50.38.00 8.9 3.612100.0  20.9 -16.5 0.0 -16.',\n",
       " '   9 . 3. 6.34.50-17.154  71.341518.1  7.51.56.40 8.6 3.552100.0  20.9 -16.6 0.0 -16.',\n",
       " '  10 . 3. 7. 2.40-18.903  71.558519.7  7.53.16.30 8.4 3.452100.0  20.9 -16.7 0.0 -16.',\n",
       " '  11 . 3. 7.30.30-20.650  71.781521.4  7.54.37.90 8.2 3.272100.0  20.9 -16.8 0.0 -16.',\n",
       " '  12 . 3. 7.58.30-22.395  72.012523.2  7.56. 1.10 8.0 3.192100.0  20.9 -16.8 0.0 -16.',\n",
       " '  13 . 3. 8.26.20-24.138  72.251525.0  7.57.26.50 7.8 3.252100.0  20.9  19.6 4.5  18.',\n",
       " '  14 . 3. 8.54.10-25.879  72.501526.8  7.58.54.30 7.7 3.222100.0  20.9 -10.0 0.0 -17.',\n",
       " '  15 . 3. 9.22.00-27.619  72.761528.7  8. 0.24.60 7.5 3.192100.0  21.1 -17.0 0.0 -17.',\n",
       " '  16 . 3. 9.49.90-29.355  73.033530.7  8. 1.57.80 7.4 3.182100.0  20.9 -17.1 0.0 -17.',\n",
       " '  17 . 3.10.17.90-31.090  73.318532.7  8. 3.34.10 7.3 3.052100.0  20.9 -17.1 0.0 -17.',\n",
       " '  18 . 3.10.45.80-32.821  73.617534.7  8. 5.13.90 7.2 2.992100.0  20.9 -17.2 0.0 -17.',\n",
       " '  19 . 3.11.13.70-34.550  73.933536.8  8. 6.57.60 7.1 3.052100.0  20.9 -17.2 0.0 -17.',\n",
       " '  20 . 3.11.41.60-36.276  74.267538.9  8. 8.45.70 7.0 2.992100.0  20.9 -17.2 0.0 -17.',\n",
       " '  21 . 3.12. 9.50-37.998  74.621541.0  8.10.38.60 6.9 2.932100.0  20.9 -17.3 0.0 -17.',\n",
       " '  22 . 3.12.37.50-39.717  74.997543.1  8.12.36.70 6.9 2.912100.0  21.7 -17.3 0.0 -17.',\n",
       " '  23 . 3.13. 5.40-41.433  75.398545.3  8.14.40.80 6.8 2.812100.0  20.9 -17.3 0.0 -17.',\n",
       " '  24 . 3.13.33.30-43.144  75.826547.5  8.16.51.60 6.7 2.752100.0  21.7 -17.4 0.0 -17.',\n",
       " '  25 . 3.14. 1.20-44.851  76.286549.7  8.19. 9.80 6.7 2.882100.0  21.5 -17.4 0.0 -17.',\n",
       " '  26 . 3.14.29.10-46.554  76.780551.9  8.21.36.40 6.6 2.882100.0  20.9 -17.4 0.0 -17.',\n",
       " '  27 . 3.14.57.10-48.252  77.313554.1  8.24.12.20 6.6 2.882100.0  20.9 -17.4 0.0 -17.',\n",
       " '  28 . 3.15.25.00-49.945  77.890556.3  8.26.58.60 6.5 3.052100.0  21.1 -17.4 0.0 -17.',\n",
       " '  29 . 3.15.52.90-51.631  78.517558.5  8.29.57.10 6.5 3.022100.0  21.1  22.7 4.6  22.',\n",
       " '  30 . 3.16.20.80-53.312  79.202560.7  8.33. 9.20 6.4 3.112100.0  20.9  -9.7 0.1 -17.',\n",
       " '  31 . 3.16.48.70-54.985  79.950562.9  8.36.36.80 6.4 3.602100.0  21.1 -17.3 0.0 -17.',\n",
       " '  32 . 3.17.16.70-56.649  80.773565.1  8.40.22.20 6.3 3.022100.0  20.9 -17.4 0.0 -17.',\n",
       " '  33 . 3.17.44.60-58.305  81.682567.3  8.44.28.30 6.3 3.672100.0  21.7 -17.3 0.0 -17.',\n",
       " '  34 . 3.18.12.50-59.950  82.691569.5  8.48.58.30 6.2 3.482100.0  20.9 -17.3 0.0 -17.',\n",
       " '  35 . 3.18.40.40-61.583  83.816571.6  8.53.56.20 6.1 3.192100.0  21.7 -17.3 0.0 -17.',\n",
       " '  36 . 3.19. 8.30-63.203  85.078573.7  8.59.27.00 6.1 3.552100.0  21.7 -17.2 0.0 -17.',\n",
       " '  37 . 3.19.36.30-64.806  86.501575.8  9. 5.36.60 6.0 3.002100.0  21.7 -17.3 0.0 -17.',\n",
       " '  38 . 3.20. 4.20-66.391  88.117577.9  9.12.32.40 6.0 2.132100.0  21.7 -17.3 0.0 -17.',\n",
       " '  39 . 3.20.32.10-67.952  89.965579.9  9.20.23.70 5.9 2.372100.0  21.7 -10.4 0.0 -10.',\n",
       " '  40 . 3.21. 0.00-69.485  92.093581.9  9.29.22.20 5.9 3.142100.0  21.7 -17.1 0.0 -17.',\n",
       " '  41 . 3.21.27.90-70.984  94.560583.9  9.39.42.40 5.8 3.402100.0  20.9 -17.1 0.0 -17.',\n",
       " '  42 . 3.21.55.90-72.440  97.445585.8  9.51.42.70 5.8 3.112100.0  20.9 -17.1 0.0 -17.',\n",
       " '  43 . 3.22.23.80-73.843 100.843587.6 10. 5.46.00 5.8 3.242100.0  20.9 -17.0 0.0 -17.',\n",
       " '  44 . 3.22.51.70-75.177 104.870589.5 10.22.20.40 5.8 3.272100.0  20.9 -16.9 0.0 -16.',\n",
       " '  45 . 3.23.19.60-76.421 109.663591.2 10.41.58.80 5.8 4.692100.0  20.9  19.7 4.5  18.',\n",
       " '  46 . 3.23.47.50-77.550 115.374592.9 11. 5.17.30 5.8 4.592100.0  21.7  -9.7 0.0 -16.',\n",
       " '  47 . 3.24.15.50-78.531 122.137594.6 11.32.48.40 5.8 4.252100.0  21.7 -16.7 0.0 -16.',\n",
       " '  48 . 3.24.43.40-79.323 130.023596.2 12. 4.49.00 5.9 4.012100.0  21.7 -16.6 0.0 -16.',\n",
       " '  49 . 3.25.11.30-79.882 138.959597.8 12.41. 1.40 5.9 3.532100.0  21.7 -16.6 0.0 -16.',\n",
       " '  50 . 3.25.39.20-80.171 148.657599.3 13.20.16.90 6.0 3.252100.0  21.7 -16.5 0.0 -16.',\n",
       " '  51 . 3.26. 7.10-80.164 158.625600.7 14. 0.37.20 6.1 3.532100.0  21.7 -16.4 0.0 -16.',\n",
       " '  52 . 3.26.35.10-79.865 168.289602.1 14.39.44.30 6.1 3.482100.0  21.7 -16.3 0.0 -16.',\n",
       " '  53 . 3.27. 3.00-79.297 177.166603.4 15.15.42.70 6.2 3.272100.0  21.7 -16.3 0.0 -16.',\n",
       " '  54 . 3.27.30.90-78.500-175.013604.7 15.47.27.70 6.2 3.452100.0  21.7 -16.2 0.4 -16.',\n",
       " '  55 . 3.27.58.80-77.517-168.311605.9 16.14.44.10 6.2 3.402100.0  21.7 -16.1 1.2 -16.',\n",
       " '  56 . 3.28.26.70-76.388-162.653607.0 16.37.49.90 6.3 2.412100.0  21.7 -13.4 1.0 -16.',\n",
       " '  57 . 3.28.54.70-75.147-157.904608.1 16.57.17.80 6.3 2.162100.0  21.7 -10.6 1.3 -15.',\n",
       " '  58 . 3.29.22.60-73.820-153.913609.1 17.13.43.50 6.3 2.332100.0  21.7 -13.2 1.1 -15.',\n",
       " '  59 . 3.29.50.50-72.427-150.545610.0 17.27.39.60 6.3 1.912100.0  21.7   4.3 3.1 -15.',\n",
       " '  60 . 3.30.18.40-70.981-147.685610.9 17.39.34.10 6.3 1.942100.0  21.7   6.7 4.2 -15.',\n",
       " '  61 . 3.30.46.30-69.494-145.236611.7 17.49.49.70 6.3 2.702100.0  21.7  24.6 4.6  23.',\n",
       " '  62 . 3.31.14.30-67.975-143.126612.4 17.58.44.10 6.3 2.722100.0  21.7  -3.1 0.5 -15.',\n",
       " '  63 . 3.31.42.20-66.429-141.292613.1 18. 6.32.10 6.2 1.922100.0  21.5  -8.7 0.0  -8.',\n",
       " '  64 . 3.32.10.10-64.862-139.687613.7 18.13.25.20 6.2 1.732100.0  21.7  -8.7 0.0  -8.',\n",
       " '  65 . 3.32.38.00-63.277-138.273614.2 18.19.32.50 6.1 2.002100.0  21.7 -15.1 0.0 -15.',\n",
       " '  66 . 3.33. 5.90-61.677-137.020614.7 18.25. 1.10 6.1 2.052100.0  21.7 -15.0 0.0 -15.',\n",
       " '  67 . 3.33.33.90-60.065-135.903615.1 18.29.57.20 6.0 2.002100.0  21.7 -15.0 0.0 -15.',\n",
       " '  68 . 3.34. 1.80-58.443-134.901615.5 18.34.25.60 6.0 2.132100.0  21.7 -14.9 0.0 -14.',\n",
       " '  69 . 3.34.29.70-56.811-133.997615.8 18.38.30.30 5.9 2.092100.0  21.7 -14.8 0.0 -14.',\n",
       " '  70 . 3.34.57.60-55.171-133.180616.0 18.42.14.40 5.8 2.092100.0  21.7 -14.7 0.0 -14.',\n",
       " '  71 . 3.35.25.50-53.525-132.436616.2 18.45.40.80 5.7 2.132100.0  21.7 -14.6 0.0 -14.',\n",
       " '  72 . 3.35.53.50-51.873-131.757616.3 18.48.51.80 5.7 2.092100.0  21.7 -14.6 0.0 -14.',\n",
       " '  73 . 3.36.21.40-50.216-131.134616.4 18.51.49.20 5.6 2.052100.0  21.7 -14.5 0.0 -14.',\n",
       " '  74 . 3.36.49.30-48.554-130.561616.4 18.54.34.70 5.6 2.052100.0  21.7 -14.4 0.0 -14.',\n",
       " '  75 . 3.37.17.20-46.887-130.032616.3 18.57. 9.60 5.5 2.152100.0  21.7 -14.4 0.0 -14.',\n",
       " '  76 . 3.37.45.10-45.216-129.542616.2 18.59.35.10 5.5 2.172100.0  21.7 -14.3 0.0 -14.',\n",
       " '  77 . 3.38.13.10-43.543-129.086616.1 19. 1.52.40 5.5 2.152100.0  21.7  22.3 4.6  21.',\n",
       " '  78 . 3.38.41.00-41.866-128.661615.9 19. 4. 2.40 5.6 2.172100.0  21.7  -5.8 0.3  -6.',\n",
       " '  79 . 3.39. 8.90-40.186-128.264615.6 19. 6. 5.60 5.7 2.222100.0  21.7 -14.3 0.0 -14.',\n",
       " '  80 . 3.39.36.80-38.503-127.891615.3 19. 8. 3.00 5.8 2.222100.0  21.7 -14.2 0.1 -14.',\n",
       " '  81 . 3.40. 4.70-36.819-127.541615.0 19. 9.54.90 6.0 2.262100.0  21.7 -14.2 0.1 -14.',\n",
       " '  82 . 3.40.32.70-35.131-127.210614.6 19.11.42.20 6.3 2.262100.0  22.0 -14.2 0.0 -14.',\n",
       " '  83 . 3.41. 0.60-33.442-126.898614.2 19.13.25.00 6.7 2.262100.0  21.7 -14.3 0.0 -14.',\n",
       " '  84 . 3.41.28.50-31.750-126.602613.7 19.15. 3.90 7.1 2.292100.0  22.2 -14.3 0.0 -14.',\n",
       " '  85 . 3.41.56.40-30.058-126.321613.2 19.16.39.40 7.7 2.332100.0  21.7 -14.3 0.0 -14.',\n",
       " '  86 . 3.42.24.30-28.362-126.053612.7 19.18.11.60 8.3 2.372100.0  22.0 -14.3 0.0 -14.',\n",
       " '  87 . 3.42.52.30-26.666-125.797612.1 19.19.40.90 9.1 2.522100.0  21.7 -14.4 0.0 -14.',\n",
       " '  88 . 3.43.20.20-24.968-125.552611.5 19.21. 7.70 9.9 2.722100.0  21.7 -14.4 0.0 -14.',\n",
       " '  89 . 3.43.48.10-23.268-125.317610.9 19.22.32.1010.7 2.812100.0  21.5 -14.5 0.0 -14.',\n",
       " '  90 . 3.44.16.00-21.567-125.090610.2 19.23.54.5011.5 3.082100.0  21.7 -14.5 0.0 -14.',\n",
       " '  91 . 3.44.43.90-19.866-124.871609.6 19.25.14.9012.2 3.432100.0  21.7 -14.6 0.0 -14.',\n",
       " '  92 . 3.45.11.90-18.162-124.659608.8 19.26.33.7012.7 3.652100.0  21.7 -14.6 0.0 -14.',\n",
       " '  93 . 3.45.39.80-16.457-124.454608.1 19.27.50.9012.9 4.132100.0  21.7  27.1 4.6  26.',\n",
       " '  94 . 3.46. 7.70-14.752-124.254607.4 19.29. 6.7012.8 4.692100.0  21.7  -7.7 0.0 -14.',\n",
       " '  95 . 3.46.35.60-13.046-124.059606.6 19.30.21.4012.3 5.192100.0  21.7 -14.7 0.0 -14.',\n",
       " '  96 . 3.47. 3.50-11.338-123.869605.8 19.31.35.1011.7 5.522100.0  22.0 -14.8 0.0 -14.',\n",
       " '  97 . 3.47.31.50 -9.630-123.681605.0 19.32.48.1011.0 5.462100.0  21.7 -14.9 0.0 -14.',\n",
       " '  98 . 3.47.59.40 -7.921-123.496604.1 19.34. 0.3010.3 5.462100.0  21.7 -15.0 0.0 -15.',\n",
       " '  99 . 3.48.27.30 -6.211-123.314603.3 19.35.11.8010.0 5.462100.0  21.7 -15.1 0.0 -15.',\n",
       " ' 100 . 3.48.55.20 -4.501-123.134602.4 19.36.23.00 9.8 5.642100.0  21.7 -15.2 0.0 -15.',\n",
       " ' 101 . 3.49.23.10 -2.789-122.956601.5 19.37.33.80 9.9 5.812100.0  21.7 -15.3 0.0 -15.',\n",
       " ' 102 . 3.49.51.10 -1.078-122.778600.7 19.38.44.4010.2 6.052100.0  21.7 -15.3 0.1 -15.',\n",
       " ' 103 . 3.50.19.00  0.635-122.600599.8 19.39.55.1010.5 6.592100.0  21.7 -15.4 0.0 -15.',\n",
       " ' 104 . 3.50.46.90  2.347-122.422598.8 19.41. 5.7010.7 6.662100.0  21.5 -15.5 0.0 -15.',\n",
       " ' 105 . 3.51.14.80  4.060-122.243597.9 19.42.16.5010.9 6.542100.0  21.7 -15.6 0.0 -15.',\n",
       " ' 106 . 3.51.42.70  5.774-122.062597.0 19.43.27.8011.0 6.132100.0  21.7 -15.7 0.1 -15.',\n",
       " ' 107 . 3.52.10.70  7.487-121.881596.1 19.44.39.3011.0 5.782100.0  21.7 -15.9 0.0 -15.',\n",
       " ' 108 . 3.52.38.60  9.201-121.696595.1 19.45.51.5011.0 5.552100.0  22.0 -16.0 0.0 -16.',\n",
       " ' 109 . 3.53. 6.50 10.915-121.508594.2 19.47. 4.5010.9 5.192100.0  21.7  20.5 4.5  19.',\n",
       " ' 110 . 3.53.34.40 12.628-121.317593.2 19.48.18.3010.8 5.192100.0  21.7  -9.2 0.0 -16.',\n",
       " ' 111 . 3.54. 2.30 14.342-121.122592.2 19.49.33.1010.6 5.052100.0  21.7 -16.3 0.0 -16.',\n",
       " ' 112 . 3.54.30.30 16.056-120.922591.2 19.50.49.0010.3 4.902100.0  21.7 -16.4 0.0 -16.',\n",
       " ' 113 . 3.54.58.20 17.769-120.716590.3 19.52. 6.2010.0 4.692100.0  21.7 -16.5 0.0 -16.',\n",
       " ' 114 . 3.55.26.10 19.482-120.504589.3 19.53.25.10 9.7 4.482100.0  21.7 -16.6 0.0 -16.',\n",
       " ' 115 . 3.55.54.00 21.195-120.285588.3 19.54.45.70 9.4 4.132100.0  21.7 -16.7 0.0 -16.',\n",
       " ' 116 . 3.56.21.90 22.907-120.058587.3 19.56. 8.10 9.1 4.012100.0  21.7 -16.8 0.0 -16.',\n",
       " ' 117 . 3.56.49.90 24.618-119.822586.3 19.57.32.60 8.7 3.992100.0  21.7 -16.9 0.0 -16.',\n",
       " ' 118 . 3.57.17.80 26.329-119.576585.2 19.58.59.50 8.4 4.012100.0  21.7 -16.9 0.0 -16.',\n",
       " ' 119 . 3.57.45.70 28.040-119.320584.2 20. 0.29.00 8.1 3.832100.0  21.7 -17.0 0.0 -17.',\n",
       " ' 120 . 3.58.13.60 29.750-119.051583.2 20. 2. 1.50 7.9 3.672100.0  21.7 -17.0 0.0 -17.',\n",
       " ' 121 . 3.58.41.50 31.458-118.769582.2 20. 3.37.00 7.6 3.702100.0  21.7 -17.1 0.0 -17.',\n",
       " ' 122 . 3.59. 9.50 33.165-118.472581.1 20. 5.16.30 7.4 3.552100.0  21.7 -17.1 0.0 -17.',\n",
       " ' 123 . 3.59.37.40 34.871-118.157580.1 20. 6.59.60 7.2 3.552100.0  22.2 -17.2 0.6 -17.',\n",
       " ' 124 . 4. 0. 5.30 36.577-117.826579.0 20. 8.47.10 7.0 3.722100.0  22.0 -17.2 0.1 -17.',\n",
       " ' 125 . 4. 0.33.20 38.279-117.474577.9 20.10.39.60 6.9 3.722100.0  22.0  23.9 4.6  23.',\n",
       " ' 126 . 4. 1. 1.10 39.981-117.099576.8 20.12.37.40 6.8 3.702100.0  21.7 -10.3 0.5  -9.',\n",
       " ' 127 . 4. 1.29.10 41.680-116.699575.7 20.14.41.30 6.7 3.722100.0  21.7 -17.3 0.2 -17.',\n",
       " ' 128 . 4. 1.57.00 43.377-116.271574.6 20.16.52.00 6.6 3.672100.0  21.7 -17.3 0.4 -17.',\n",
       " ' 129 . 4. 2.24.90 45.072-115.811573.5 20.19.10.40 6.5 3.632100.0  21.7 -17.3 0.4 -17.',\n",
       " ' 130 . 4. 2.52.80 46.763-115.316572.4 20.21.37.00 6.5 3.702100.0  21.7 -17.3 0.5 -17.',\n",
       " ' 131 . 4. 3.20.70 48.452-114.781571.3 20.24.13.20 6.4 3.722100.0  21.7 -17.3 0.4 -17.',\n",
       " ' 132 . 4. 3.48.70 50.136-114.201570.1 20.27. 0.30 6.4 3.672100.0  21.7 -17.3 0.2 -17.',\n",
       " ' 133 . 4. 4.16.60 51.816-113.571568.9 20.29.59.60 6.4 3.112100.0  21.7 -17.4 0.8 -17.',\n",
       " ' 134 . 4. 4.44.50 53.492-112.882567.8 20.33.12.70 6.3 3.302100.0  21.7  -9.9 0.3  -9.',\n",
       " ' 135 . 4. 5.12.40 55.161-112.128566.6 20.36.41.80 6.3 3.452100.0  21.7 -17.3 0.2 -17.',\n",
       " ' 136 . 4. 5.40.30 56.825-111.296565.4 20.40.29.30 6.3 3.502100.0  21.7 -17.3 0.2 -17.',\n",
       " ' 137 . 4. 6. 8.30 58.480-110.376564.1 20.44.37.90 6.2 3.272100.0  21.7 -17.3 0.3 -17.',\n",
       " ' 138 . 4. 6.36.20 60.127-109.355562.9 20.49.11.00 6.2 3.302100.0  21.7 -17.3 1.3 -17.',\n",
       " ' 139 . 4. 7. 4.10 61.762-108.213561.7 20.54.12.90 6.2 3.482100.0  21.7 -14.7 2.3  -7.',\n",
       " ' 140 . 4. 7.32.00 63.386-106.930560.4 20.59.48.90 6.1 3.382100.0  21.7 -17.3 0.8 -17.',\n",
       " ' 141 . 4. 7.59.90 64.994-105.478559.1 21. 6. 5.10 6.0 3.682100.0  21.7  19.4 4.6  18.',\n",
       " ' 142 . 4. 8.27.90 66.584-103.827557.8 21.13. 9.30 6.0 3.672100.0  21.7 -10.2 0.1 -17.',\n",
       " ' 143 . 4. 8.55.80 68.153-101.934556.5 21.21.11.60 5.9 3.722100.0  21.7 -17.1 0.0 -17.',\n",
       " ' 144 . 4. 9.23.70 69.693 -99.749555.2 21.30.24.00 5.9 3.582100.0  21.7 -17.1 0.0 -17.',\n",
       " ' 145 . 4. 9.51.60 71.199 -97.205553.9 21.41. 2.50 5.8 3.882100.0  21.7 -17.0 0.0 -17.',\n",
       " ' 146 . 4.10.19.50 72.661 -94.221552.5 21.53.26.50 5.8 4.062100.0  21.5 -17.0 0.0 -17.',\n",
       " ' 147 . 4.10.47.50 74.068 -90.694551.1 22. 8. 0.80 5.7 4.482100.0  21.7 -16.9 0.0 -16.',\n",
       " ' 148 . 4.11.15.40 75.403 -86.499549.8 22.25.15.60 5.7 4.482100.0  22.0 -16.8 0.0 -16.',\n",
       " ' 149 . 4.11.43.30 76.643 -81.488548.4 22.45.46.30 5.6 4.252100.0  21.7 -16.8 0.0 -16.',\n",
       " ' 150 . 4.12.11.20 77.760 -75.503547.0 23.10.10.50 5.6 4.012100.0  20.9 -16.8 0.0 -16.',\n",
       " ' 151 . 4.12.39.10 78.719 -68.409545.6 23.39. 0.90 5.6 3.902100.0  21.7 -14.1 0.1 -16.',\n",
       " ' 152 . 4.13. 7.10 79.474 -60.155544.1  0.12.29.80 5.6 3.922100.0  21.7  -8.9 0.1 -16.',\n",
       " ' 153 . 4.13.35.00 79.979 -50.860542.7  0.50. 8.50 5.6 3.652100.0  21.7  -9.6 0.3 -16.',\n",
       " ' 154 . 4.14. 2.90 80.197 -40.878541.3  1.30.32.20 5.6 3.772100.0  21.7  -9.5 0.1 -16.',\n",
       " ' 155 . 4.14.30.80 80.109 -30.764539.8  2.11.27.60 5.7 4.012100.0  21.7 -11.1 0.0 -16.',\n",
       " ' 156 . 4.14.58.70 79.722 -21.113538.4  2.50.31.50 5.7 4.012100.0  21.7 -11.0 0.1 -16.',\n",
       " ' 157 . 4.15.26.70 79.067 -12.378536.9  3.25.56.00 5.7 4.012100.0  21.7  25.5 4.7  24.',\n",
       " ' 158 . 4.15.54.60 78.188  -4.773535.5  3.56.49.00 5.7 3.972100.0  21.7  -6.5 0.1 -16.',\n",
       " ' 159 . 4.16.22.50 77.128   1.687534.0  4.23. 7.40 5.7 3.792100.0  21.7 -16.0 0.0 -16.',\n",
       " ' 160 . 4.16.50.40 75.931   7.110532.6  4.45.16.80 5.8 4.012100.0  21.7 -15.9 0.0 -15.',\n",
       " ' 161 . 4.17.18.30 74.625  11.648531.1  5. 3.53.90 5.8 3.452100.0  21.7 -15.8 0.0 -15.',\n",
       " ' 162 . 4.17.46.30 73.238  15.455529.7  5.19.35.60 5.8 3.402100.0  20.9 -15.7 0.0 -15.',\n",
       " ' 163 . 4.18.14.20 71.786  18.667528.3  5.32.54.20 5.8 3.482100.0  20.9 -15.6 0.0 -15.',\n",
       " ' 164 . 4.18.42.10 70.285  21.396526.8  5.44.17.10 5.9 3.672100.0  20.9 -15.5 0.0 -15.',\n",
       " ' 165 . 4.19.10.00 68.744  23.732525.4  5.54. 5.70 5.9 3.772100.0  20.9 -12.6 0.0 -15.',\n",
       " ' 166 . 4.19.37.90 67.172  25.749524.0  6. 2.37.70 6.0 3.772100.0  21.1 -12.6 0.2 -15.',\n",
       " ' 167 . 4.20. 5.90 65.573  27.503522.7  6.10. 6.60 6.1 3.922100.0  21.7  -5.6 1.4  -7.',\n",
       " ' 168 . 4.20.33.80 63.953  29.039521.3  6.16.43.20 6.2 3.862100.0  21.5  -4.5 2.1  -7.',\n",
       " ' 169 . 4.21. 1.70 62.316  30.395520.0  6.22.36.50 6.3 3.722100.0  21.7  88.8 3.8   1.',\n",
       " ' 170 . 4.21.29.60 60.664  31.598518.6  6.27.53.20 6.5 3.652100.0  21.7   1.3 4.3   2.',\n",
       " ' 171 . 4.21.57.50 58.999  32.672517.4  6.32.38.90 6.6 3.632100.0  21.7   4.8 4.5   6.',\n",
       " ' 172 . 4.22.25.50 57.323  33.636516.1  6.36.58.10 6.8 3.672100.0  21.7   4.5 4.5   5.',\n",
       " ' 173 . 4.22.53.40 55.638  34.506514.9  6.40.54.90 7.0 3.722100.0  20.9  21.9 4.5  21.',\n",
       " ' 174 . 4.23.21.30 53.944  35.295513.7  6.44.32.00 7.1 3.722100.0  20.9   9.2 4.5   9.',\n",
       " ' 175 . 4.23.49.20 52.244  36.013512.5  6.47.52.40 7.3 3.702100.0  21.7   8.3 4.5   8.',\n",
       " ' 176 . 4.24.17.10 50.536  36.670511.4  6.50.58.00 7.5 3.672100.0  20.9  10.9 4.5  10.',\n",
       " ' 177 . 4.24.45.10 48.823  37.274510.3  6.53.50.70 7.7 3.692100.0  20.9  13.2 4.5  13.',\n",
       " ' 178 . 4.25.13.00 47.106  37.829509.3  6.56.32.00 7.9 3.862100.0  20.9  10.4 4.5  10.',\n",
       " ' 179 . 4.25.40.90 45.384  38.343508.3  6.59. 3.20 8.0 3.812100.0  20.9   7.3 4.5   5.',\n",
       " ' 180 . 4.26. 8.80 43.657  38.819507.4  7. 1.25.50 8.2 3.792100.0  20.9  11.1 4.5  12.',\n",
       " ' 181 . 4.26.36.70 41.926  39.264506.5  7. 3.40.00 8.3 3.792100.0  20.9   8.1 4.5   8.',\n",
       " ' 182 . 4.27. 4.70 40.192  39.678505.7  7. 5.47.30 8.4 3.752100.0  21.7   0.9 4.2   0.',\n",
       " ' 183 . 4.27.32.60 38.456  40.066504.9  7. 7.48.30 8.5 3.772100.0  21.7   2.0 3.9   0.',\n",
       " ' 184 . 4.28. 0.50 36.716  40.430504.2  7. 9.43.70 8.5 3.912100.0  21.7  -1.4 2.9  -7.',\n",
       " ' 185 . 4.28.28.40 34.973  40.774503.5  7.11.34.10 8.5 3.922100.0  21.7  -3.5 1.9 -14.',\n",
       " ' 186 . 4.28.56.30 33.229  41.098502.9  7.13.19.90 8.5 3.902100.0  21.7  -3.6 1.4 -14.',\n",
       " ' 187 . 4.29.24.30 31.482  41.405502.4  7.15. 1.50 8.5 3.922100.0  20.9 -11.4 1.2 -14.',\n",
       " ' 188 . 4.29.52.20 29.733  41.697502.0  7.16.39.30 8.4 3.922100.0  20.9   1.4 3.1 -14.',\n",
       " ' 189 . 4.30.20.10 27.982  41.975501.6  7.18.14.00 8.4 3.832100.0  20.9  26.9 4.6  26.',\n",
       " ' 190 . 4.30.48.00 26.230  42.240501.3  7.19.45.50 8.5 3.882100.0  21.1   2.7 3.8 -14.',\n",
       " ' 191 . 4.31.15.90 24.477  42.493501.0  7.21.14.30 8.6 3.832100.0  21.1   2.4 4.1 -14.',\n",
       " ' 192 . 4.31.43.90 22.722  42.737500.9  7.22.40.80 8.8 3.722100.0  20.9   0.6 3.6 -14.',\n",
       " ' 193 . 4.32.11.80 20.966  42.972500.8  7.24. 5.20 9.0 3.552100.0  20.9  -0.2 2.9 -14.',\n",
       " ' 194 . 4.32.39.70 19.209  43.199500.8  7.25.27.50 9.3 3.402100.0  20.9  -3.2 1.1 -14.',\n",
       " ' 195 . 4.33. 7.60 17.451  43.418500.8  7.26.48.00 9.7 3.272100.0  20.9  -7.7 0.3 -14.',\n",
       " ' 196 . 4.33.35.50 15.692  43.631501.0  7.28. 7.10 9.9 3.222100.0  20.9 -12.0 0.0 -14.',\n",
       " ' 197 . 4.34. 3.50 13.932  43.839501.2  7.29.24.8010.1 3.052100.0  20.9 -14.9 0.0 -14.',\n",
       " ' 198 . 4.34.31.40 12.172  44.041501.5  7.30.41.2010.2 2.912100.0  20.9 -15.0 0.0 -15.',\n",
       " ' 199 . 4.34.59.30 10.412  44.240501.9  7.31.56.8010.2 2.842100.0  20.9 -15.1 0.0 -15.',\n",
       " ' 200 . 4.35.27.20  8.652  44.435502.4  7.33.11.5010.3 2.782100.0  21.7 -15.2 0.0 -15.',\n",
       " ' 201 . 4.35.55.10  6.892  44.626502.9  7.34.25.4010.4 2.782100.0  20.9 -15.3 0.0 -15.',\n",
       " ' 202 . 4.36.23.10  5.131  44.816503.5  7.35.38.8010.6 2.782100.0  20.9 -15.4 0.1 -15.',\n",
       " ' 203 . 4.36.51.00  3.370  45.003504.3  7.36.51.7010.7 2.882100.0  20.9 -15.5 0.0 -15.',\n",
       " ' 204 . 4.37.18.90  1.610  45.190505.1  7.38. 4.5010.8 2.962100.0  20.9 -15.6 0.0 -15.',\n",
       " ' 205 . 4.37.46.80 -0.149  45.376505.9  7.39.17.0010.8 3.022100.0  21.1  20.8 4.5  20.',\n",
       " ' 206 . 4.38.14.70 -1.909  45.561506.9  7.40.29.5010.8 3.072100.0  21.1  -9.7 0.1 -15.',\n",
       " ' 207 . 4.38.42.70 -3.667  45.748507.9  7.41.42.2010.6 3.052100.0  20.9 -15.9 0.0 -15.',\n",
       " ' 208 . 4.39.10.60 -5.425  45.936509.0  7.42.55.2010.4 2.962100.0  20.9 -16.0 0.0 -16.',\n",
       " ' 209 . 4.39.38.50 -7.182  46.125510.2  7.44. 8.6010.2 2.812100.0  21.7 -16.1 0.0 -16.',\n",
       " ' 210 . 4.40. 6.40 -8.939  46.317511.4  7.45.22.50 9.9 2.722100.0  21.7 -16.2 0.0 -16.',\n",
       " ' 211 . 4.40. 6.40 -8.939  46.317511.4  7.45.22.50 9.9 2.722100.0  21.7  88.888.8  88.']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[\"%4d %s\" % (i, l[:80]) for i, l in enumerate(list(chunkstring(section1, 328)))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now older random poking around...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gap = '. 0. 0. 0.00  0.000   0.000  0.0  0. 0. 0.00 0.0 0.00   0.0   0.0   0.0 0.0   0.0 0.0   0.0 0.00   0.   0.   0.  0.00  0.00  0.00                       00\\x00s  25                                                                                                                                                            00\\x00s    2.2  -71.4                                                                                                                                                  00\\x00s     2   212                                                                                                                                                    00\\x00s '"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "649"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(gap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "offset = headerLength + M * sOneLength + 677\n",
    "sTwoLength = 10 * 3 * 5\n",
    "section2 = d2129[offset:offset+(M * sTwoLength)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -9.3 -11.5 -16.8 \n",
      " -9.5 -11.6 -16.8 \n",
      " -9.5 -11.7 -16.9 \n",
      " -9.5 -11.7 -17.0 \n",
      " -9.6 -11.8 -17.0 \n",
      " -9.7 -11.8 -17.1 \n",
      " -9.9 -11.9 -17.1 \n",
      " -9.9 -11.9 -17.2 \n",
      " -9.9 -12.0 -17.2 \n",
      "            00\u0000s-\n"
     ]
    }
   ],
   "source": [
    "groupOf10(section2, sTwoLength)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.0- 12.0- 17.2- \n",
      "10.1- 12.1- 17.3- \n",
      "10.1- 12.1- 17.3- \n",
      "10.2- 12.1- 17.3- \n",
      "10.3- 12.1- 17.4- \n",
      "10.2- 12.1- 17.4- \n",
      "10.2- 12.2- 17.4- \n",
      "10.2- 12.2- 17.4- \n",
      "10.1- 12.1- 17.4- \n",
      "10.1- 12.2- 17.4 \n"
     ]
    }
   ],
   "source": [
    "groupOf10(section2[sTwoLength:], sTwoLength)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "164"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4r2afnWRycmniclnfs37MWDDr9e3b9yUunzat26R9h2XaufMlVbFcZXjwwd9Q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\nvCF4AAAAb/4/c8zmyyRh588AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f62ac737860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "findRecordLen(data[M * sOneLength + 677:M * sOneLength + 677+sTwoLength*10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'8 -7.9-10.6-15.9 -7.9-10.7-16.0 -8.1-10.8-16.1 -8.3-11.0-16.2 -8.6-11.1-16.3 -8.6-11.1-16.4 -8.7-11.2-16.5 -8.8-11.3-16.6          00\\x00s -9.0-11.4-16.7 -9.3-11.5-16.8 -9.5-11.6-16.8 -9.5-11.7-16.9 -9.5-11.7-17.0 -9.6-11.8-17.0 -9.7-11.8-17.1 -9.9-11.9-17.1 -9.9-11.9-17.2 -9.9-12.0-17.2          00\\x00s-10.0-12.0-17.2-10.1-12.1-17.3-10.1-12.1-17.3-10.2-12.1-17.3-10.3-12.1-17.4-10.2-12.1-17.4-10.2-12.2-17.4-10.2-12.2-17.4-10.1-12.1-17.4-10.1-12.2-17.4          00\\x00s-10.1-12.1-17.4 -9.5-12.1-17.3-10.1-12.1-17.4 -9.4-12.0-17.3 -9.6-12.0-17.3 -9.9-12.1-17.3 -9.5-12.0-17.2-10.0-12.0-17.3-10.5-12.1-17.3-10.4-12.1-17.2          00\\x00s -9.8-11.9-17.1 -9.5-11.8-17.1 -9.7-11.8-17.1 -9.6-11.8-17.0 -9.5-11.7-16.9 -6.1-11.3-16.8 -6.6-11.3-16.7 -7.7-11.3-16.7 -8.2-11.3-16.6 -8.9-11.3-16.6          00\\x00s -9.1-11.3-16.5 -8.7-11.2-16.4 -8.7-11.1-16.3 -8.8-11.0-16.3 -8.5-10.9-16.2 -8.5-10.8-16.1 -9.2-10.9-16.0 -9.2-10.8-15.9 -9.0-10.7-15.8 -9.1-10.6-15.8          00\\x00s -9.0-10.5-15.7 -8.5-10.3-15.5 -8.4-10.2-15.4 -8.7-10.2-15.3 -8.7-10.1-15.3 -8.5-10.0-15.1 -8.3 -9.9-15.0 -8.3 -9.8-15.0 -8.1 -9.7-14.9 -8.1 -9.6-14.8          00\\x00s -8.0 -9.5-14.7 -7.9 -9.5-14.6 -7.8 -9.4-14.6 -7.8 -9.3-14.5 -7.7 -9.3-14.4 -7.6 -9.2-14.4 -7.6 -9.2-14.3 -7.6 -9.1-14.3 -7.5 -9.1-14.3 -7.5 -9.1-14.3          00\\x00s -7.5 -9.1-14.2 -7.4 -9.1-14.2 -7.5 -9.1-14.2 -7.5 -9.1-14.3 -7.5 -9.1-14.3 -7.5 -9.1-14.3 -7.5 -9.2-14.3 -7.5 -9.2-14.4 -7.4 -9.2-14.4 -7.4 -9.3-14.5          00\\x00s -7.2 -9.3-14.5 -7.0 -9.3-14.6 -6.8 -9.3-14.6 -6.0 -9'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[headerLength + M * sOneLength + 677:headerLength + M * sOneLength + 677+sTwoLength*10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d = np.array([float(ord(c)) for c in d2129])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "segmentraw = d[headerLength:10000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "segment = segmentraw - np.mean(segmentraw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ac = np.correlate(segment, segment, mode='same')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9826"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9826"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(segment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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K278w5H5Pxg7gHRFxYUScHBE7geXARwAi4rKIKKfmVwDPjYjt9enfDlwI/Enm\ncs4pfX19bNq0qeunSI499ti205x55pmTGrd48WI2bdrEpk2b6Ovr66pcxfacf37trOaZz3zmhGmO\nOeaYtstpNk239XT66bX7ks8//3zOPPPMST+tk0NRz0Vd9dLznve8Sc+7fv36LLGvfmdf/epXN1xO\nr2JffMf7+/s544yWr/SZditWrMgW+5NPPrmj6RrVf69iX9VJ7FvFt9l3pZl169YBszP2c1nWJCOl\n9DHgEmAztTTvDOCclNK365Mso5Z0FNPfB7wWeGV9+vcCv5lSatbzMav19fU13QlaHbCLBrPRwbZY\n7vbt23nve8c/BVxM32y+TrTaMVuNW7JkCdu3b2f79u309fXR19fHli1bGjY81XHF9mzatKlp+TtJ\nkFasWDHphKA83znnnAMwqYYzt6Kei7oq9CL2v/EbvzHpeQcHB7PFvhOdfD8aqSYnxXf8yiuvZM2a\nNZNaZi45Yl+0Q+97X2eX0hqto1exn4xG+3uRLLRTxP7pT386AKeeeirQPvadnPA0s2HDhlnXpuSW\n/Y2fKaUrUkonpZR+LqV0akrpH0vjLkwpvboy/T+klNbVp39+SunK3GXMpVWS0eqAXTSY3TYa1cam\nVSJTVZyNdLoDtVt2X18fW7dubdrYNBvXjWpjsmTJkp4kGdMxXzvTGfteb8N0xL5q8eLFk9qOXiUn\nvdTtsqcS+27P9tuZ7tgXPXvFiQE8mSy0U8T+6KOP7mqdk/3OAGzcuNEkQ7NHo8bmpJNO6nj6bhqQ\n4mykvAOVd9zq8nvdOHWjaEzLZej0DKHZgajVQWrx4sXAxPpotrxeHLSXL18+Ydj555/fdBunEvsN\nGzawZcuWjg9us+nyEdTKv3r16rblajW+2YG5WewbHWh6lXg0WvZUYt+qXMcccwxbtmwZ207orJd1\nOlXLU76sV/TutLuE1Cr2zXqC+vr6WL9+fUdlnIl6mStMMmZI8aUsvvzr16+fsKM0amxaXTudSobd\nSLU8K1asYMOGDT1dRytF47JhwwZWrVo1VlfLly9ny5Yt4w7EnZ4hNGtsWvWCFG9BbdWQleO3YcOG\njuqpHPtNmzaNa+gbxbLV5ZtuY19uuJcsWcLWrVvHLaNaF+Xtm84ko9GZKowv/1RjD82Tsk5jv2nT\nJlasWNF2PdWyNIp9I1OJfXV8uXzHHnssW7duHdvOYnz5+1uO/VQuSXZr8eLFbNmyZcL6JnNZb7JJ\nxuDgYMtMpWk7AAAQ60lEQVTltor9bEvGZ4pJRmZF41HNdIsdv/giDg4Ojl2bLHauVatWdXxQ37Bh\nQ9uGqlCss9UZy7nnnsuqVavG3Zi1ZMkSNm7c2HB7ciga/o0bN7JmzZqxulixYgVbt24d27Fb6aZe\nmsWqkerBoRy/cj1Vl9eoESp6kYqGvkiqcsa+VU/Hueeey+bNm8fFvvr9zN2AFt/N4kz1t37rt4Ba\nT96WLVt45zvf2XYZ3dRLEaNOYl+NTzV+5bqZjbFvF7ti/ypvX/m7PZ2xL+/nReyf//znt5z/2c9+\nNtB5G5Ur9iYcNSYZmRVfvmZnG0VCUT5LKXauNWvWjDtYtbJx48axdbXbaaoHmnPPPXfCWdIb3/hG\n1qxZM+HGrOLmrW7O2rp10kkn8cpXvnJCY1JsX/XfsmqDW66XdtrFqqzauMDEG9sKzXoIGsW+KPNk\nY99ONV7dxL6wZMmSsUa218lmtYevvE6o9eRt3bqVF77whRPmnUrsixh1Evtm8Sni36xHarbFvihH\n1ZIlS+jr62tahlWrVjXcP6eqiP073vGOcXXTbeyLyynNYlmth1yxb7XOhST3D6SpjSKhABr+wmDx\nmNcjjzzCM57xjJbdwtUEoNMu9De+8Y1jyy12kmY7S3Hz1ujoKFu2bOG0007r+bv6Tz75ZG655Zax\ndbTrCi/fj9FpgwtPvkOhmL/dL14WXfdAyxvboHEsq8qxb6SIfSc/VFbEvrhnp9ODfzn27ZSTqOp3\nrVdWrFjBli1bOOOMMzqKfdlkYl8c7Nv1BrSLfTF869atjIy0f2nibIh9UY5iGRs2bGj6G0DV2N9y\nyy0dbWc3itifeeaZvPWtb207/VT3+05i32n7203sFxqTjFmueMyr02nLCUCx83TzMqrVq1ezZcsW\nVq9e3dG6cu5U5YN2s/Fbtmzp6o7txYsXjzWmGzZs4MUvfvHY/M0Sg2I9q1ev5uyzz57MpkxKEfvR\n0VF+8Rd/sW2CWY59uaep09gXZ6irVq1quY5CjviXD76tHiMsGv/i/51oFPsips22Y6HEvvzkR7sE\nOpd2iVe5LFPZ74vkol3si3V12v6qMS+XzGHF2Uej4eWbuZp11RVnPOVMPtcjhjm0KmurmziLrs6i\nUWu3rTNdJ92sv9PYN7JmzRpuueWWWfd+iEaKxr94P0MnjH1NccCdyrpmUqtyNrtHpZPYN7uEpamx\nJ2MOa3f20U7RPdmu12IumsyrxIszpFWrVs36xmYyZ/JljRLM+cLYt1YccJtdGpmLyr0bnVyqbDTv\ndPdWLRQmGXPcVLoxu+2enOmGt5NytLsBq93yO+mqnw2m2o07HxPMIvaTuU9oIcW+WEan+9Jc2e+7\nuR+q2bzqPS+XZNbooNfNY2e90OnNbc3Mlm7UTspR7TIum4lX+k53rKuaxb6bywVz7UAzW2Jf1ewy\nRc71Ndvvu9mXZrreplqO2bDfL+RLMSYZmTVq+DZu3Dh2g2X5S5erQS8OKN3cgT0f5Xilb7uYVR8x\nnO4DzVRjP18ONDPxOufqgab6/pSyHPu++33NdO73xfBqvXea1M9HXi6ZIY266HrdbTdbzkJnm17W\nS7OYlddR7r4tNzbNGqhelcvYTzQdddzq/oBW8/Rq3zf2jU3Xfj/ZyzbzlUnGPOa1xsamo15aNTad\nNFC9XL+eNB11PJnY95Kxb8x6mRleLlmAFsL1Qc/mGjP2C9dCiD1M7eZv9Z49GQtQp0+VdGK2dvHb\nbdlYL2M/Wxn7xnod+17tr71OCov4+/bN2cEkQ1NiF//CNlsPNMqvV/ur+/38ZpKxgMxUQ97XN7WX\nB2n28kCjVtq9qj4nE9fZwSRjAZmphrwXLw+a6voXemOzUBNMYz+zdVC8qn4mmLjODiYZmvdsbBZ2\ngmnsrQPNHJ8ukSRJWZhkSJKkLEwyJElSFiYZkiQpC5MMSZKUhUmGJEnKwiRDkiRlYZIhSZKyyJpk\nRMQxEfE/I+JQ/XNNRCxqMf2REfHHEXFHRDwSEQ9ExNURsXBf1ydJ0hyVuydjGFgNvAY4G1gDXNNi\n+qfXp9kGnAKcC7wA+HTeYkqSpF7L9lrxiDiZWmLxSymlPfVh7wS+FBErUkr7q/OklH5Qn6e8nN8E\nvhIRJ6SUvp2rvJIkqbdy9mScBhwqEgyAlNJXgMPA6V0s5xggAYd6WzxJkpRTziRjGfDdBsO/Wx/X\nVkT8G+Ay4KMppUd6WDZJkpRZ10lGRGyJiCdafB6PiP6pFiwijgT+GgjgP091eZIkaXpN5p6My6nd\n0NnKfcBLgOMajDsOeLDVzPUE4+PAc4FXd9KLMTg4yKJF4x9cGRgYYGBgoN2skiTNe8PDwwwPjz98\nHz58OOs6u04yUkrfB77fbrqI+BKwKCLWlW78fCnwLOC2FvMVCcZJwJkppYc7KdfOnTvp759yB4ok\nSfNSoxPvkZER1q5dm22d2e7JSCndDXwBuDIiXhoRLwOGgM+WnyyJiLsj4vX1/x8JfALoB9YDR0XE\n0vrnqFxllSRJvZf7PRkDwP+llmx8HrgduKAyzQqguM7xbOB1wAn1ab8DjNb/PS1zWSVJUg9le08G\nQErpMBOTiuo0R5T+/y3giBaTS5KkOcLfLpEkSVmYZEiSpCyyXi7Rk/r6+ti0adPY/yVJ85dtfo1J\nxjTp6+tj+/btM10MzQAbm4XL2C9ctvk1kVKa6TJMSf3tonv37t3rezIkSepC6T0Za1NKI71evvdk\nSJKkLEwyJElSFiYZkiQpC5MMSZKUhUmGJEnKwiRDkiRlYZIhSZKyMMmQJElZmGRIkqQsTDIkSVIW\nJhmSJCkLkwxJkpSFSYYkScrCJEOSJGVhkiFJkrIwyZAkSVmYZEiSpCxMMiRJUhYmGZIkKQuTDEmS\nlIVJhiRJysIkQ5IkZWGSsYANDw/PdBHmHOtscqy37llnk2O9zS5Zk4yIOCYi/mdEHKp/romIRV3M\nf0VEPBER785ZzoXKnbF71tnkWG/ds84mx3qbXXL3ZAwDq4HXAGcDa4BrOpkxIs4FXgo8kK10kiQp\nmyNzLTgiTqaWWPxSSmlPfdg7gS9FxIqU0v4W8z4b+GB9/s/lKqMkSconZ0/GacChIsEASCl9BTgM\nnN5spogIar0dH0gp7ctYPkmSlFG2ngxgGfDdBsO/Wx/XzO8CP0kp/XmH63kawL595iPdOnz4MCMj\nIzNdjDnFOpsc66171tnkWG/dKR07n5ZlBSmlrj7AFuCJFp/HgX7g94C7G8x/D/A7TZa9FhgFlpWG\nfRN4d4vyvBVIfvz48ePHj59Jf97abT7QyWcyPRmXU7uhs5X7gJcAxzUYdxzwYJP5zgCWAPfXrpoA\ncASwIyIuSSmd1GCeLwDn19f5WJtySZKkJz0NeB61Y2nPRb03oPcLrt34eSfw0tKNny8FbgNObnTj\nZ0QcC/RVBt9A7R6Nq1rdLCpJkmaXbPdkpJTujogvAFdGxLuAAHYBny0nCxFxN7XLJ59OKT0MPFxe\nTkT8FHjQBEOSpLkl93syBoD/S60b5vPA7cAFlWlWAK1e0JWnq0WSJGWV7XKJJEla2PztEkmSlIVJ\nhiRJymLOJRkR8R8i4ssR8eOIOBARf1MZvzwiPhsRj9THfzAijqxM8+KIuKW+jPsjYvP0bsX0i4in\nRsTt9R+cW10ZZ52VRMRzI+IvIuLe+vbuj4itEXFUZTrrrY2IuLhej49GxO6IOGOmyzRTIuL3IuL/\nRMQPIuKhiLguIl7QYLqtEfFA/Ttzc0S8qDL+qRFxef0790hEfLr+UwzzXkT8br0N21EZbp1VRMTx\n9R8oPRgRP4qIkYg4pTJN/nrL8fKNXB/gjcD3gHcCz6d20+h5pfFPoXaj6d9S+2G2VwPfBj5YmuaZ\n1F74dS2wEngDtVedD8709mWuuz8F/he1l6Wtts5a1tXZwP8Afpna8+Ovo/Zulw9Yb13V41uAfwUu\nBF4I7AR+CJww02Wbofr4HPAf69+FFwOfpfZ+n58rTfM71J6wez3wImrvJHoAOLo0zUeAfwHOpPY+\nor8Dvkr9Hrv5+gFOBe6tb+sO66xlXR1D7UWWf0HtJZfPqW/7idNdbzNeGV1U2hHA/cB/ajHNOcBP\ngaWlYW8Bfgw8o/73RcD3gSMrlX3/TG9jxro7h9o7S06m9lbW1ZVx1ln7OnwP8M/WW1d19mXgzyvD\n7gL+cKbLNhs+wOL6/nhGadh3gPeU/n5q/UDwzvrfz6KWuL2pNE0f8DPgrJnepox19Qxqb4t+NXAz\n45MM62xiff0RcGubaaal3ubS5ZJ+4HiAerfPdyLicxGxqjTNy4CvpZQeKg37ArU3mq0tTXNrSuln\nlWmOj4jn5iv+zIiIpcAQsB54tMEk1llnjqGWMBSstxbql5bWAjdWRt1Aix9IXGCOofaI/vcBIuJE\nar/rNFZnKaWfALfyZJ2to/Z+o/I0o8DXmN/1+iFq71i6qTzQOmvqV4E9EfGx+qW5kYh4RzFyOutt\nLiUZJ1F7odcW4L8C/4Fa1nVLRBxTn2YZUG70SSkdAn7Ckz/KNmGa+t9B6x9um6uuAj6cUvpqk/HW\nWRsR8Xzgv1DrOixYb60tptb72Gj75/u2d2on8A8ppbvqfy+jlnS0qrOl1H5A8nCLaeaViPh1YA21\n38Oqss4aO4laT+o9wGuotV1/FhH/sT5+2uptxpOMiNhSv5Gn2efxiOgvlfUPUkqfqh80L6RWUW+e\nsQ2YAZ3WWUS8m1o34x8Xs85gsWdcF9+18jzHA9cDf51SumpmSq75JiI+BKyi9sJCNRERJ1C7n+z8\nlNJPZ7o8c8hTgL0ppc0ppX9KKV0JXAm8a7oLkvOn3jvV6Q+uPav+/7HfpU0p/SQi7qV2UwvUbs77\npfKM9V6Op1K7Aa+YZmll+UupJSvNfrhttumkzr4FbAZOA/41Ylx+sSciPppSupCFU2fQ+XcNGEsw\nbgL+MaW0sTLdQqq3yThI7SbjRts/37e9pYi4nNrNxC+vdz8XHqR2IlCto/LfDwJPjYhFlTPMpdR+\nF2q+WUvtRzNH4slG7AjgFRHxX6jdZ2adTTRK6VhZtw84r/7/6fuuzfQNKl3cyPJMavcUXFgadlS9\nIt5R//vfU7sZ77jSNNWb8d5F7QmVeX8zHnACtbuGi8+vUGv43wAcb521rLtnU+tqvJYGd1Jbbx3V\nYaMbP+9kAd/4Cfw5tRvYT2oyvtnNeEUb1+pmvF+Z6e3LUF9HV9qwFwH/B7gaWGmdNa23j1K58ZPa\n5bkvTvd3bcYro8uK20ntcZqzgBdQezxnFFhUH/8U4J+o3Vy2htojiP8C/GlpGc+qV+5HqXVXngsc\nAi6Z6e2bhvp7LhOfLrHOJtbT8cD+ep0cTy1zX8r4J0mst/b1+P8Aj1G7rHlyff/9AbB8pss2Q/Xx\n4Xoj/vLydwp4Wmma36Z2I+gbgH8L/BW1R6OPriznW9SetDiF2mOFe5mnj2M2qMfq0yXW2cQ6WldP\nEH6P2use3krt8fFfn+56m/HK6LLijgA+QC2xOETtTv2VlWlOAD4DPAIcqDdsR1WmWQXcQu2s8wHg\nfTO9bdNUf8+l8p4M66xhPb2tXk/lzxPA49Zb13X5LmrvNngU2A38u5ku0wzWxRMNvlePAxdUpvv9\n+nflx/UD6osq448CPlj/zj0CfAp49kxv3zTW402UkgzrrGk9vRa4o14ndwJvbzBN9nrzB9IkSVIW\nM/50iSRJmp9MMiRJUhYmGZIkKQuTDEmSlIVJhiRJysIkQ5IkZWGSIUmSsjDJkCRJWZhkSJKkLEwy\nJElSFiYZkiQpi/8fyiXi0yNKk9YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f62ac6bf940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(lags, c, line, b) = plt.acorr(segment, maxlags=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "peak at 328"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "indexes = find_peaks_cwt(c, np.arange(1, 550))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[159, 173, 329, 500, 671, 828, 841, 860, 863, 867]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 54166.11764706,   5834.81577088, -10615.02935789, ...,\n",
       "        34634.98710504, -10615.02935789,   5834.81577088])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "center = ac.argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   7057.91179464,   98418.20329019,  110062.09340166,\n",
       "         38561.95929167,   54536.94282875])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ac[center+320:center+325]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-61695.80966463621"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ac[322]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "327"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ac[center+1:].argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f62ac72f518>]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wwQcwezZssknauREpn5tugo8+gnfegW23TTs3Uu2am5tpbm7O+qylJV7B0/EqHQStBHwd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\nq6T/AbW1Io1wTRQyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f62ac5cacc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9826"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(segment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# times out... (lags, c, line, b) = plt.acorr(d - np.mean(d), maxlags=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.00663063,  0.09246006,  0.10339904,  0.03622745,  0.05123533,\n",
       "        0.11427109,  0.03218082, -0.11386569,  0.93707748, -0.11588184])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[500+320:500+330]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "327"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[500 + 1:].argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-500, -499, -498, ...,  498,  499,  500])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1001"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00080294224440384282"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[10]"
   ]
  }
 ],
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