nemo_tendencies.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:a8255c055ef0bea2ca3fe59a0748fbbf7fef565152a20b72f69981a3c9fbc2f6"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%bash\n",
      "rsync huziy@skynet3.sca.uqam.ca:/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3_Superior/EXP00/u2_total_trend.txt .\n",
      "head u2_total_trend.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " total trend               u2=  0.0000000000000E+00\n",
        " total trend               u2=  0.4072381471423E-09\n",
        " total trend               u2= -0.1086312335147E-09\n",
        " total trend               u2= -0.1315227412263E-10\n",
        " total trend               u2=  0.4295987387185E-11\n",
        " total trend               u2=  0.4044980232066E-10\n",
        " total trend               u2=  0.7981051083812E-10\n",
        " total trend               u2=  0.9690540805178E-10\n",
        " total trend               u2=  0.1060944793519E-09\n",
        " total trend               u2=  0.1126933124203E-09\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "du2 = np.loadtxt(\"u2_total_trend.txt\", usecols = [3,])\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "du2.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "(692821,)"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(du2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10530d410>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6x6jh9J4bPyHbg3ar/rpxRLPsznS3ymL9ernvu/Esu40SQ7J9+3a0a9cOvXv3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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1051e90d0>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from datetime import timedelta\n",
      "dt = 700000 * timedelta(minutes = 30)\n",
      "from dateutil.relativedelta import relativedelta\n",
      "rdt = relativedelta(seconds=dt.total_seconds())\n",
      "\n",
      "str(dt)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "'14583 days, 8:00:00'"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rdt.years"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "0"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "shapely.geometry.LineString??"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import shapely\n",
      "print shapely.__version__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1.3.1\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}