{
 "metadata": {
  "name": "",
  "signature": "sha256:b1e670d4f1834d1a1056ed85a75ab80948233bb9b877d78c59a140f400b1623c"
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Experimenting with reading in PA observations from Scott Tinis."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import datetime\n",
      "import pandas as pd\n",
      "from dateutil import tz"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dateParse(s1,s2,s3,s4):\n",
      "    s=s1+s2+s3+s4\n",
      "    unaware =datetime.datetime.strptime(s, '%Y%m%d%H:%M')\n",
      "    aware = unaware.replace(tzinfo=tz.tzutc())\n",
      "    return  aware"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 97
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "filename = '/data/nsoontie/MEOPAR/analysis/Nancy/tides/PA_observations/ptatkin_rt.dat'\n",
      "\n",
      "obs = pd.read_csv(filename, delimiter=' ',parse_dates=[[0,1,2,3]],header=None,date_parser=dateParse)\n",
      "obs=obs.rename(columns={'0_1_2_3':'time',4:'wlev'})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 98
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print obs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                            time   wlev\n",
        "0      2014-12-02 18:00:00+00:00  3.373\n",
        "1      2014-12-02 18:01:00+00:00  3.382\n",
        "2      2014-12-02 18:02:00+00:00  3.389\n",
        "3      2014-12-02 18:03:00+00:00  3.397\n",
        "4      2014-12-02 18:04:00+00:00  3.406\n",
        "5      2014-12-02 18:05:00+00:00  3.413\n",
        "6      2014-12-02 18:06:00+00:00  3.420\n",
        "7      2014-12-02 18:07:00+00:00  3.427\n",
        "8      2014-12-02 18:08:00+00:00  3.435\n",
        "9      2014-12-02 18:09:00+00:00  3.443\n",
        "10     2014-12-02 18:10:00+00:00  3.449\n",
        "11     2014-12-02 18:11:00+00:00  3.458\n",
        "12     2014-12-02 18:12:00+00:00  3.465\n",
        "13     2014-12-02 18:13:00+00:00  3.471\n",
        "14     2014-12-02 18:14:00+00:00  3.480\n",
        "15     2014-12-02 18:15:00+00:00  3.488\n",
        "16     2014-12-02 18:16:00+00:00  3.496\n",
        "17     2014-12-02 18:17:00+00:00  3.502\n",
        "18     2014-12-02 18:18:00+00:00  3.509\n",
        "19     2014-12-02 18:19:00+00:00  3.517\n",
        "20     2014-12-02 18:20:00+00:00  3.525\n",
        "21     2014-12-02 18:21:00+00:00  3.533\n",
        "22     2014-12-02 18:22:00+00:00  3.541\n",
        "23     2014-12-02 18:23:00+00:00  3.548\n",
        "24     2014-12-02 18:24:00+00:00  3.554\n",
        "25     2014-12-02 18:25:00+00:00  3.561\n",
        "26     2014-12-02 18:26:00+00:00  3.568\n",
        "27     2014-12-02 18:27:00+00:00  3.574\n",
        "28     2014-12-02 18:28:00+00:00  3.580\n",
        "29     2014-12-02 18:29:00+00:00  3.587\n",
        "...                          ...    ...\n",
        "10004  2014-12-09 16:44:00+00:00  5.381\n",
        "10005  2014-12-09 16:45:00+00:00  5.366\n",
        "10006  2014-12-09 16:46:00+00:00  5.349\n",
        "10007  2014-12-09 16:47:00+00:00  5.365\n",
        "10008  2014-12-09 16:48:00+00:00  5.348\n",
        "10009  2014-12-09 16:49:00+00:00  5.333\n",
        "10010  2014-12-09 16:50:00+00:00  5.343\n",
        "10011  2014-12-09 16:51:00+00:00  5.349\n",
        "10012  2014-12-09 16:52:00+00:00  5.337\n",
        "10013  2014-12-09 16:53:00+00:00  5.322\n",
        "10014  2014-12-09 16:54:00+00:00  5.327\n",
        "10015  2014-12-09 16:55:00+00:00  5.347\n",
        "10016  2014-12-09 16:56:00+00:00  5.337\n",
        "10017  2014-12-09 16:57:00+00:00  5.298\n",
        "10018  2014-12-09 16:58:00+00:00  5.287\n",
        "10019  2014-12-09 16:59:00+00:00  5.340\n",
        "10020  2014-12-09 17:00:00+00:00  5.300\n",
        "10021  2014-12-09 17:01:00+00:00  5.251\n",
        "10022  2014-12-09 17:02:00+00:00  5.286\n",
        "10023  2014-12-09 17:03:00+00:00  5.313\n",
        "10024  2014-12-09 17:04:00+00:00  5.293\n",
        "10025  2014-12-09 17:05:00+00:00  5.278\n",
        "10026  2014-12-09 17:06:00+00:00  5.284\n",
        "10027  2014-12-09 17:07:00+00:00  5.254\n",
        "10028  2014-12-09 17:08:00+00:00  5.253\n",
        "10029  2014-12-09 17:09:00+00:00  5.303\n",
        "10030  2014-12-09 17:10:00+00:00  5.254\n",
        "10031  2014-12-09 17:11:00+00:00  5.231\n",
        "10032  2014-12-09 17:12:00+00:00  5.243\n",
        "10033  2014-12-09 17:13:00+00:00  5.254\n",
        "\n",
        "[10034 rows x 2 columns]\n"
       ]
      }
     ],
     "prompt_number": 99
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pylab as plt\n",
      "%matplotlib inline\n",
      "plt.plot(obs.time,obs.wlev)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 102,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7facfb211490>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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nPWbvt3U+7duXTvzSktZG2b+fAhETS7aUHhdwE2wNdugK+GmnRUfgppkPQH4R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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7facfb288550>"
       ]
      }
     ],
     "prompt_number": 102
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}