gistfile1.txt

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 "metadata": {
  "name": "",
  "signature": "sha256:9e4150b049ad8b9ee7600a8fe76567ad97f185a1fd5cf7f86f63e60c83795bf7"
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 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Salish Sea NEMO Model Daily Nowcast Figures"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Set-up"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import division\n",
      "\n",
      "import datetime\n",
      "from glob import glob\n",
      "import os\n",
      "\n",
      "from IPython.core.display import HTML\n",
      "import netCDF4 as nc\n",
      "\n",
      "from salishsea_tools.nowcast import figures\n",
      "\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def results_dataset(period, grid, results_dir):\n",
      "    \"\"\"Return the results dataset for period (e.g. 1h or 1d)\n",
      "    and grid (e.g. grid_T, grid_U) from results_dir.\n",
      "    \"\"\"\n",
      "    filename_pattern = 'SalishSea_{period}_*_{grid}.nc'\n",
      "    filepaths = glob(os.path.join(results_dir, filename_pattern.format(period=period, grid=grid)))\n",
      "    return nc.Dataset(filepaths[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "run_date = datetime.date.today()\n",
      "run_date = datetime.date(2014, 11, 17)\n",
      "# Results dataset location\n",
      "results_home = '/data/dlatorne/MEOPAR/SalishSea/nowcast/'\n",
      "results_dir = os.path.join(results_home, run_date.strftime('%d%b%y').lower())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Load the results datasets:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "grid_T_hr = results_dataset('1h', 'grid_T', results_dir)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Display the figures:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "HTML('<h2>{:%d%b%y} Figures</h2>'.format(run_date))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<h2>17Nov14 Figures</h2>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<IPython.core.display.HTML at 0x7f11f9eabf50>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = figures.ssh_PtAtkinson(grid_T_hr)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f12105cd510>"
       ]
      }
     ],
     "prompt_number": 6
    }
   ],
   "metadata": {}
  }
 ]
}