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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This notebook describes the recommended process for development of\n",
      "functions for the `salishsea_tools.nowcast.figures` module,\n",
      "and provides an example of development of such a function."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import division\n",
      "\n",
      "import os\n",
      "\n",
      "import matplotlib.pyplot as plt\n",
      "import netCDF4 as nc\n",
      "\n",
      "from salishsea_tools import nc_tools\n",
      "\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "The Big Goal"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The big goal of this notebook is to define conventions for functions\n",
      "that will live in `salishsea_tools.nowcast.figures` and be called\n",
      "by automation code to produce daily plots showing features of the results\n",
      "of real-time (nowcast and forecast) run of the SalishSeaCast NEMO model.\n",
      "\n",
      "Let's start with a simple example objective:\n",
      "\n",
      "an hourly plot of model sea surface height (ssh) at Pt. Atkinson\n",
      "\n",
      "To get that plot on a daily basis we need to provide a function\n",
      "that takes the hourly `grid_T` results dataset from NEMO\n",
      "and returns a Matplotlib `figure` object."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So,\n",
      "we'll have a function defintion like:\n",
      "```python\n",
      "def ssh_PtAtkinson(grid_T, bathy=None, figsize=(12, 4)):\n",
      "    \"\"\"Return a figure containing a plot of hourly sea surface height at Pt. Atkinson.\n",
      "    \n",
      "    :arg grid_T: Hourly tracer results dataset from NEMO.\n",
      "    :type grid_T: :class:`netCDF4.Dataset`\n",
      "    \n",
      "    :arg bathy: Bathymetry dataset for the SalishSeaCast NEMO model.\n",
      "    :type bathy: :class:`netCDF4.Dataset`\n",
      "    \n",
      "    :arg figsize: Figure size (width, height) in inches.\n",
      "    :type figsize: 2-tuple\n",
      "    \n",
      "    :returns: Matplotlib figure object\n",
      "    \"\"\"\n",
      "    fig, ax = plt.subplots(1, 1, figsize=figsize)\n",
      "    ...\n",
      "    return fig\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "that the automation code can use like:\n",
      "```python\n",
      "from salishsea_tools.nowcast import figures\n",
      "\n",
      "grid_T = nc.Dataset('nowcast/28oct14/SalishSea_1h_20141028_20141028_grid_T.nc')\n",
      "fig = figures.ssh_PtAtkinson(grid_T)\n",
      "\n",
      "# Render to figure object for display on a web page\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Of course,\n",
      "it's not just the automation code that can use the `salishsea_tools.nowcast.figures` functions,\n",
      "you and your colleagues can use them in exactly the same way in your notebooks."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Conventions"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are a bunch of conventions implied in our function definition fragment:\n",
      "```python\n",
      "def ssh_PtAtkinson(grid_T, bathy=None, figsize=(12, 4)):\n",
      "    \"\"\"Return a figure containing a plot of hourly sea surface height at Pt. Atkinson.\n",
      "    \n",
      "    :arg grid_T: Hourly tracer results dataset from NEMO.\n",
      "    :type grid_T: :class:`netCDF4.Dataset`\n",
      "    \n",
      "    :arg bathy: Bathymetry dataset for the SalishSeaCast NEMO model.\n",
      "    :type bathy: :class:`netCDF4.Dataset`\n",
      "    \n",
      "    :arg figsize: Figure size (width, height) in inches.\n",
      "    :type figsize: 2-tuple\n",
      "    \n",
      "    :returns: Matplotlib figure object\n",
      "    \"\"\"\n",
      "    fig, ax = plt.subplots(1, 1, figsize=figsize)\n",
      "    ...\n",
      "    return fig\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* It's probably too early to say anything about a function naming convention,\n",
      "  other than the function names don't need to contain words like `plot`, \n",
      "  `figure`,\n",
      "  `nowcast`,\n",
      "  `realtime`,\n",
      "  or `forecast`\n",
      "  because their package and module context implies those things."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* We'll pass `netCDF4.Dataset` objects (like `grid_T`) into the functions\n",
      "  rather than passing in file names because the chances are good that\n",
      "  a given dataset (like bathymetry) will be used in more than one function.\n",
      "  By making it the caller's job to load the dataset we avoid\n",
      "  having multiple copies of it in memory,\n",
      "  and the repeatedly doing the comparitively slow process of reading the dataset from disk."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Since many of our functions will need to use the model bathymetry\n",
      "  we'll include a slot for it as the first keyword argument to the function\n",
      "  but we'll set its default value to `None` so that we don't have to\n",
      "  deal with it in functions like `ssh_PtAtkinson()` that don't use it."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Including `figsize` as an argument with a default value\n",
      "  allows the caller to adjust the size of the figure to their needs.\n",
      "  Please choose an aspect ratio for `figsize` that is appropriate to\n",
      "  the information in the figure;\n",
      "  it serves as a hint for other users."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Include a docstring in the function and use the Sphinx `:arg ...:`, \n",
      "  `:type ...:`,\n",
      "  and `:returns:` markup\n",
      "  (see the Sphinx [Info Field Lists](http://sphinx-doc.org/domains.html#info-field-lists) documentation)\n",
      "  so that the nice looking documentation like \n",
      "  [this](http://salishsea-meopar-tools.readthedocs.org/en/latest/SalishSeaTools/salishsea-tools.html#module-viz_tools)\n",
      "  will be generated automatically."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Use the Matplotlib object interface,\n",
      "  starting with a call to `plt.subplots()` to create `figure` and `axes` instances.\n",
      "  Most of your plotting commands will be `axes` method calls\n",
      "  (e.g. `ax.plot(...)`),\n",
      "  with a few `figure` method calls,\n",
      "  and rare `plt` method calls.\n",
      "  That minimized global state and side-effects that may interfere with other plot functions."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Return the `figure` object so that the automation code can render it appropriately\n",
      "  for inclusion in a web page.\n",
      "  If you,\n",
      "  or someone else,\n",
      "  calls your function in a notebook it will render automatically,\n",
      "  in the notebook if `%matplotlib inline` has been set,\n",
      "  or in a separate window otherwise."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Developing a New `nowcast.figures` Function"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The recommended workflow to develop a new `nowcast.figures` function is:\n",
      "\n",
      "1. Create the function in a notebook cell.\n",
      "\n",
      "2. Create 1 or more notebook cell(s) containing code\n",
      "to prepare the inputs for the function,\n",
      "and call the function;\n",
      "i.e. to drive the function.\n",
      "\n",
      "3. Iteratively test and edit the cells in steps 1 and 2\n",
      "until the function produces the plot that you want.\n",
      "\n",
      "4. Export the function to `nowcast.figures`.\n",
      "\n",
      "5. Use your favourite code editor to do any necessary clean-up in `nowcast.figures`.\n",
      "\n",
      "6. Commit and push your `nowcast.figures` changes."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Function Definition"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#%%writefile -a figures.py\n",
      "\n",
      "def ssh_PtAtkinson(grid_T, bathy=None, figsize=(12, 4)):\n",
      "    \"\"\"Return a figure containing a plot of hourly sea surface height at Pt. Atkinson.\n",
      "    \n",
      "    :arg grid_T: Hourly tracer results dataset from NEMO.\n",
      "    :type grid_T: :class:`netCDF4.Dataset`\n",
      "    \n",
      "    :arg bathy: Bathymetry dataset for the SalishSeaCast NEMO model.\n",
      "    :type bathy: :class:`netCDF4.Dataset`\n",
      "    \n",
      "    :arg figsize: Figure size (width, height) in inches.\n",
      "    :type figsize: 2-tuple\n",
      "    \n",
      "    :returns: Matplotlib figure object\n",
      "    \"\"\"\n",
      "    fig, ax = plt.subplots(1, 1, figsize=figsize)\n",
      "    ssh = grid_T.variables['sossheig']\n",
      "    results_date = nc_tools.timestamp(grid_T, 0).format('YYYY-MM-DD')\n",
      "    ax.plot(ssh[:, 468, 328], 'o')\n",
      "    ax.set_xlim(0, 23)\n",
      "    ax.set_xlabel('UTC Hour on {}'.format(results_date))\n",
      "    ax.set_ylabel('{label} [{units}]'.format(label=ssh.long_name.title(), units=ssh.units))\n",
      "    ax.grid()\n",
      "    ax.set_title('Pt. Atkinson Hourly Sea Surface Height on {}'.format(results_date))\n",
      "    return fig\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Function Driver"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "grid_T = nc.Dataset(\n",
      "    os.path.expanduser('~/MEOPAR/downloaded_results/SalishSea/nowcast/30oct14/SalishSea_1h_20141030_20141030_grid_T.nc'))\n",
      "\n",
      "fig = ssh_PtAtkinson(grid_T)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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MDa0rKCIzMzMz6wQ1i+rS1HYRMRgRH6j8al+InWPz5urdMps2zWpzJJY3959Z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\nzKxJ/x/4VPeoFEzyMwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f874ec8ac10>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = fig.axes[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax.set_axis_bgcolor('#2b3e50')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig.set_facecolor('#2b3e50')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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Bzmh1K/xiUns6yX2M1BzMITLCSc4jMz7ZPqy6RfXG3EuIkgV6UYOuc7T6tGjV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\nUSyoZodAJwDziBrFHCIjMI/MYXpP9db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       "prompt_number": 15,
       "text": [
        "<matplotlib.figure.Figure at 0x7f877a1f2fd0>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}