{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<link rel=\"stylesheet\" type=\"text/css\" href=\"custom.css\">\n",
    "\n",
    "# Activation impacts on fast cloud responses in a coupled aerosol-climate model ...\n",
    "\n",
    "## ... and nifty visualization / analysis tools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "### or - *Why I don't have cool results to show you (yet)*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "**Daniel Rothenberg (darothen@mit.edu)**\n",
    "\n",
    "Joint Program Student Luncheon, July 16, 2015"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "<div id=\"logos\">\n",
    "    <img src=\"media/paoc_logo.png\" />\n",
    "    <img src=\"media/eaps_logo.png\" />\n",
    "    <img src=\"media/mit_logo.png\" />\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img src=\"https://www.python.org/static/community_logos/python-logo-master-v3-TM.png\">\n",
    "<img src=\"https://store.continuum.io/static/img/anaconda_logo_web.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Python for Scientists\n",
    "\n",
    "- Mature (well-documented, efficient, easy-to-use) data analysis packages\n",
    "- Portable ecosystem - any machine, any operating system\n",
    "- Huge userbase\n",
    "- Many cool tools in active development\n",
    "- Supports many different development styles\n",
    "    - scripting (editing files from terminal/shell)\n",
    "    - notebook environment\n",
    "    - full-blown IDEs for software development - [Spyder](https://github.com/spyder-ide/spyder), [PyCharm](https://www.jetbrains.com/pycharm/), etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"media/jakevdp_ecosystem.png\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Python helps create reproducible, verifiable science"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Reproducibility (1) - Version Control"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import subprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current HEAD git commit:  b'3fde6fc'\n"
     ]
    }
   ],
   "source": [
    "def get_git_versioning():\n",
    "    \"\"\" Returns the currently checked out commit shortname. \"\"\"\n",
    "    return subprocess.check_output(\n",
    "        ['git', 'rev-parse', '--short', 'HEAD']\n",
    "    ).strip()\n",
    "\n",
    "print(\"Current HEAD git commit: \", str(get_git_versioning()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "**Fetch *git* commit at any time, attach it as metadata in whatever figure or output file you create**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "But why should you care about this?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"media/marc_aie_tree.png\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mcommit d667821e910c1104ce48bd80fc2dec29be01f612\u001b[m\r\n",
      "Author: Daniel Rothenberg <darothen@mit.edu>\r\n",
      "Date:   Thu Jul 16 09:59:45 2015 -0400\r\n",
      "\r\n",
      "    Unified naming scheme for notebooks\r\n",
      "\r\n",
      "\u001b[33mcommit 383aaa394614be2ee78a6943bc0a720bb742d98d\u001b[m\r\n",
      "Author: Daniel Rothenberg <darothen@mit.edu>\r\n",
      "Date:   Thu Jul 16 09:55:07 2015 -0400\r\n",
      "\r\n",
      "    updated global radiative forcing (PD - PI) change calculations\r\n"
     ]
    }
   ],
   "source": [
    "!git log -2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "pwd = os.getcwd()\n",
    "os.chdir(\"/Users/daniel/workspace/Research/marc_aie\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Oh no, I broke something!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mdiff --git a/marc_aie/convert.py b/marc_aie/convert.py\u001b[m\r\n",
      "\u001b[1mindex ed7d01a..4120eab 100644\u001b[m\r\n",
      "\u001b[1m--- a/marc_aie/convert.py\u001b[m\r\n",
      "\u001b[1m+++ b/marc_aie/convert.py\u001b[m\r\n",
      "\u001b[36m@@ -109,12 +109,12 @@\u001b[m \u001b[mdef create_master(var, data_dict=None, new_fields=[\"PS\", ]):\u001b[m\r\n",
      "     if isinstance(var, str):\u001b[m\r\n",
      "         assert data_dict is not None\u001b[m\r\n",
      "         acts, aers = decompose_multikeys(list(data_dict.keys()))\u001b[m\r\n",
      "\u001b[31m-        new_fields.append(var)\u001b[m\r\n",
      "\u001b[32m+\u001b[m\u001b[32m        new_fields += var\u001b[m\r\n",
      "     elif isinstance(var, Var):\u001b[m\r\n",
      "         data_dict = var.data\u001b[m\r\n",
      "         acts = var.cases['act']\u001b[m\r\n",
      "         aers = var.cases['aer']\u001b[m\r\n",
      "\u001b[31m-        new_fields.append(var.varname)\u001b[m\r\n",
      "\u001b[32m+\u001b[m\u001b[32m        new_fields += var.varname\u001b[m\r\n",
      "     else:\u001b[m\r\n",
      "         raise ValueError(\"`var` must be a Var or a string.\")\u001b[m\r\n",
      " \u001b[m\r\n"
     ]
    }
   ],
   "source": [
    "!git diff ee8a2e9 ebe1ce2 marc_aie/convert.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## But, I can't make my *[insert-super-secret-project]* public!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "<img src=\"media/github_mit.png\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Reproducibility (2) - Environments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Common Problems\n",
    "\n",
    "- Something changed in a version of a toolkit or a package and now I get different answers!\n",
    "- It's worse, my code doesn't even run any more!\n",
    "- Someone forgot to tell me that I need package *xyz:v.a.b.c* but it won't compile on my machine!\n",
    "- It's worse, it has a conflicting dependency with another package that I **really** need!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Solution - Package Managers\n",
    "\n",
    "### [`conda`](http://conda.pydata.org/docs/)\n",
    "\n",
    "- A python package manager with sophisticated environment management (a là `virtualenv`)\n",
    "- Maintain minimal Python installation for a given project\n",
    "- Distribute and automatically build your dependencies\n",
    "- Automatically comes with [Anaconda Python distribution](http//www.continuum.io/anaconda) and [Miniconda](http://conda.pydata.org/miniconda.html)\n",
    "- Social site [binstar](https://binstar.org) for contributing packages\n",
    "\n",
    "### [`Docker`](https://www.docker.com/)\n",
    "\n",
    "- Full-stack software management\n",
    "- Rapidly re-deploy your entire working environment to a new machine (local, supercomputer, distributed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**environment.yml**\n",
    "\n",
    "```yaml\n",
    "name: marc_aie\n",
    "channels:\n",
    "  - unidata\n",
    "  - scitools\n",
    "dependencies:\n",
    "  - cartopy>=0.12\n",
    "  - ipython>=3.2.0\n",
    "  - ipython-notebook\n",
    "  - matplotlib\n",
    "  - netcdf4\n",
    "  - numpy\n",
    "  - python=3.4\n",
    "  - seaborn\n",
    "  - xlrd\n",
    "  - xray>=0.5\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Create environment based on `environment.yml`**\n",
    "\n",
    "```bash\n",
    "cd [my_repo_dir]\n",
    "conda env create\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "**Activate environment**\n",
    "\n",
    "```bash\n",
    "source activate marc_aie\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# conda environments:\r\n",
      "#\r\n",
      "cartopy_py3              /Users/daniel/anaconda/envs/cartopy_py3\r\n",
      "ipython-dev              /Users/daniel/anaconda/envs/ipython-dev\r\n",
      "iris_1_7_2               /Users/daniel/anaconda/envs/iris_1_7_2\r\n",
      "marc_aie              *  /Users/daniel/anaconda/envs/marc_aie\r\n",
      "marc_aie2                /Users/daniel/anaconda/envs/marc_aie2\r\n",
      "pymc3_test               /Users/daniel/anaconda/envs/pymc3_test\r\n",
      "python3                  /Users/daniel/anaconda/envs/python3\r\n",
      "scipy-tutorial           /Users/daniel/anaconda/envs/scipy-tutorial\r\n",
      "vispy_test               /Users/daniel/anaconda/envs/vispy_test\r\n",
      "root                     /Users/daniel/anaconda\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!conda info -e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# packages in environment at /Users/daniel/anaconda/envs/marc_aie:\n",
      "#\n",
      "binstar                   0.11.0                   py34_0    defaults\n",
      "cartopy                   0.13.0               np19py34_0    https://conda.binstar.org/unidata/osx-64/cartopy-0.13.0-np19py34_0.tar.bz2\n",
      "certifi                   14.05.14                 py34_0    defaults\n",
      "clyent                    0.3.4                    py34_0    defaults\n",
      "curl                      7.38.0                        0    http://repo.continuum.io/pkgs/free/osx-64/curl-7.38.0-0.tar.bz2\n",
      "freetype                  2.5.2                         2    defaults\n",
      "geos                      3.4.2                         4    https://conda.binstar.org/unidata/osx-64/geos-3.4.2-4.tar.bz2\n",
      "hdf5                      1.8.14                        0    defaults\n",
      "ipython                   3.2.1                    py34_0    defaults\n",
      "ipython-notebook          3.2.1                    py34_0    defaults\n",
      "jinja2                    2.7.3                    py34_1    defaults\n",
      "jpeg                      8d                            2    https://conda.binstar.org/r/osx-64/jpeg-8d-2.tar.bz2\n",
      "jsonschema                2.4.0                    py34_0    defaults\n",
      "libnetcdf                 4.3.2                         1    defaults\n",
      "libpng                    1.6.17                        0    defaults\n",
      "libsodium                 0.4.5                         0    http://repo.continuum.io/pkgs/free/osx-64/libsodium-0.4.5-0.tar.bz2\n",
      "libtiff                   4.0.2                         1    http://repo.continuum.io/pkgs/free/osx-64/libtiff-4.0.2-1.tar.bz2\n",
      "markupsafe                0.23                     py34_0    defaults\n",
      "matplotlib                1.4.3                np19py34_3    defaults\n",
      "mistune                   0.6                      py34_0    defaults\n",
      "mock                      1.0.1                    py34_0    defaults\n",
      "netcdf4                   1.1.8                np19py34_0    defaults\n",
      "nose                      1.3.7                    py34_0    defaults\n",
      "numpy                     1.9.2                    py34_0    defaults\n",
      "openssl                   1.0.1k                        1    http://repo.continuum.io/pkgs/free/osx-64/openssl-1.0.1k-1.tar.bz2\n",
      "owslib                    0.9.0                    py34_0    https://conda.binstar.org/unidata/osx-64/owslib-0.9.0-py34_0.tar.bz2\n",
      "pandas                    0.16.2               np19py34_0    defaults\n",
      "pillow                    2.9.0                    py34_0    defaults\n",
      "pip                       7.1.0                    py34_0    defaults\n",
      "proj4                     4.9.1                         1    https://conda.binstar.org/unidata/osx-64/proj4-4.9.1-1.tar.bz2\n",
      "ptyprocess                0.4                      py34_0    defaults\n",
      "pyepsg                    0.2.0                    py34_0    https://conda.binstar.org/unidata/osx-64/pyepsg-0.2.0-py34_0.tar.bz2\n",
      "pygments                  2.0.2                    py34_0    defaults\n",
      "pyparsing                 2.0.3                    py34_0    defaults\n",
      "pyqt                      4.11.3                   py34_0    defaults\n",
      "pyshp                     1.2.1                    py34_0    https://conda.binstar.org/unidata/osx-64/pyshp-1.2.1-py34_0.tar.bz2\n",
      "python                    3.4.3                         0    defaults\n",
      "python-dateutil           2.4.2                    py34_0    defaults\n",
      "python.app                1.2                      py34_4    defaults\n",
      "pytz                      2015.4                   py34_0    defaults\n",
      "pyyaml                    3.11                     py34_1    defaults\n",
      "pyzmq                     14.7.0                   py34_0    defaults\n",
      "qgrid                     0.1.1                     <pip>\n",
      "qt                        4.8.6                         3    defaults\n",
      "readline                  6.2                           2    <unknown>\n",
      "requests                  2.7.0                    py34_0    defaults\n",
      "scipy                     0.15.1               np19py34_0    defaults\n",
      "seaborn                   0.6.0                np19py34_0    defaults\n",
      "setuptools                18.0.1                   py34_0    defaults\n",
      "shapely                   1.5.8                np19py34_2    https://conda.binstar.org/unidata/osx-64/shapely-1.5.8-np19py34_2.tar.bz2\n",
      "sip                       4.16.5                   py34_0    defaults\n",
      "six                       1.9.0                    py34_0    defaults\n",
      "sqlite                    3.8.4.1                       1    defaults\n",
      "terminado                 0.5                      py34_0    defaults\n",
      "tk                        8.5.18                        0    defaults\n",
      "tornado                   4.2                      py34_0    defaults\n",
      "xlrd                      0.9.3                    py34_0    defaults\n",
      "xray                      0.5.1                np19py34_0    defaults\n",
      "xz                        5.0.5                         0    defaults\n",
      "yaml                      0.1.6                         0    defaults\n",
      "zeromq                    4.0.5                         1    https://conda.binstar.org/r/osx-64/zeromq-4.0.5-1.tar.bz2\n",
      "zlib                      1.2.8                         1    https://conda.binstar.org/r/osx-64/zlib-1.2.8-1.tar.bz2\n"
     ]
    }
   ],
   "source": [
    "!conda list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using binstar api site https://api.anaconda.org\n",
      "Run 'binstar show <USER/PACKAGE>' to get more details:\n",
      "Packages:\n",
      "     Name                      |  Version | Package Types   | Platforms      \n",
      "     ------------------------- |   ------ | --------------- | ---------------\n",
      "     ChrisBarker/cartopy       |          | conda           | osx-64         \n",
      "                                          : None\n",
      "     asmeurer/cartopy          |   0.11.1 | conda           | linux-64       \n",
      "                                          : None\n",
      "     benbovy/cartopy           |   0.11.2 | conda           | linux-64, osx-64\n",
      "                                          : A library providing cartographic tools for python\n",
      "     christophkeller/cartopy   |   0.11.2 | conda           | osx-64         \n",
      "                                          : A library providing cartographic tools for python\n",
      "     jklymak/cartopy           |    1.6.0 | conda           | osx-64         \n",
      "                                          : None\n",
      "     julienchastang/cartopy    |   0.10.0 | conda           | osx-64         \n",
      "                                          : None\n",
      "     krisvanneste/cartopy      |   0.11.2 | conda           | win-64         \n",
      "                                          : None\n",
      "     nbren12/cartopy           |    1.6.0 | conda           | linux-64, osx-64\n",
      "                                          : None\n",
      "     shoyer/cartopy            |   0.11.2 | conda           | osx-64         \n",
      "                                          : A library providing cartographic tools for python\n",
      "     vsheremet/cartopy         |   0.11.2 | conda           | linux-32       \n",
      "                                          : None\n",
      "Found 10 packages\n"
     ]
    }
   ],
   "source": [
    "!binstar search -t conda cartopy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"media/binstar.png\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<a href=\"http://scitools.org.uk/cartopy/docs/latest/\"><img src=\"http://scitools.org.uk/images/cartopy.png\" class=centered/></a>\n",
    "\n",
    "**[Cartopy](http://scitools.org.uk/cartopy/docs/latest/) is a powerful wrapper for matplotlib enabling cartographic/geographic transformations of your data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading TS (Surface temperature (radiative)) [K]\n",
      "from /Users/daniel/Desktop/MARC_AIE/work/arg_min_smax_F2000_TS.nc\n",
      "Loading TS (Surface temperature (radiative)) [K]\n",
      "from /Users/daniel/Desktop/MARC_AIE/work/arg_min_smax_F1850_TS.nc\n",
      "Loading TS (Surface temperature (radiative)) [K]\n",
      "from /Users/daniel/Desktop/MARC_AIE/work/arg_comp_F2000_TS.nc\n",
      "Loading TS (Surface temperature (radiative)) [K]\n",
      "from /Users/daniel/Desktop/MARC_AIE/work/arg_comp_F1850_TS.nc\n",
      "<xray.DataArray 'TS' (lat: 96, lon: 144)>\n",
      "array([[ 218.58482361,  218.5799408 ,  218.60137939, ...,  218.58233643,\n",
      "         218.57707214,  218.59594727],\n",
      "       [ 218.88679504,  218.29620361,  218.22875977, ...,  219.26359558,\n",
      "         219.11993408,  218.63648987],\n",
      "       [ 218.0002594 ,  218.05961609,  217.96510315, ...,  218.80046082,\n",
      "         218.55628967,  218.3144989 ],\n",
      "       ..., \n",
      "       [ 256.84292603,  256.97180176,  257.05990601, ...,  256.39889526,\n",
      "         256.53231812,  256.67669678],\n",
      "       [ 256.02627563,  256.08166504,  256.13250732, ...,  255.80848694,\n",
      "         255.88676453,  255.96191406],\n",
      "       [ 255.69245911,  255.72254944,  255.75584412, ...,  255.62876892,\n",
      "         255.6466217 ,  255.66511536]], dtype=float32)\n",
      "Coordinates:\n",
      "  * lat      (lat) float64 -90.0 -88.11 -86.21 -84.32 -82.42 -80.53 -78.63 ...\n",
      "  * lon      (lon) float64 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 ...\n"
     ]
    }
   ],
   "source": [
    "import marc_aie as ma\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "\n",
    "v = ma.CESMVar(\"TS\")\n",
    "v.load_datasets()\n",
    "v.apply(lambda ds: ds['TS'].mean(\"time\"))\n",
    "data = v.data['arg_comp', 'F2000']\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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af/aOD98kq7dDUjf7/q0E9sMPvoOvXHyBVm+ZM4NtwNbCV09UNxLUKSmx5fpeeHmBEX8Y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jwIYK09DmwF3mmtfSL5/gPAu6y1o8B54P/g7VCeB65ITnM78BjwJPBOY0wN0JVcnhTH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11181bfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 5))\n",
    "ax = fig.add_subplot(111, projection=ccrs.PlateCarree())\n",
    "gp = ma.global_plot(data, ax=ax, cmap=\"cubehelix_r\",\n",
    "               levels=np.linspace(210, 320, 23))\n",
    "cb = plt.colorbar(orientation='horizontal')\n",
    "_ = plt.title(v.long_name + \" (%s)\" % v.units)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Under the hood\n",
    "\n",
    "<a href=\"http://xray.readthedocs.org/en/stable/\"><img src=\"media/xray.png\" /></a>\n",
    "\n",
    "([link to xray docs](http://xray.readthedocs.org/en/stable/))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## `xray`\n",
    "\n",
    "- Powerful toolkit for accessing and manipulating and wrangling NetCDF / HDF5 data\n",
    "- Maintained by employees from [**THE CLIMATE CORPORATION**](https://www.climate.com/)\n",
    "- Under active development\n",
    "- Cool features:\n",
    "    - lazy evaluation system; doesn't do any numerical work until it *absolutely* needs to\n",
    "    - implements [`dask`](http://dask.pydata.org/en/latest/) out-of-core computation library\n",
    "    - extends pythonic interfaces for arrays and maps\n",
    "    - serialize to/from NetCDF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Simple xray example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading TS (Surface temperature (radiative)) [K]\n",
      "from /Users/daniel/Desktop/MARC_AIE/work/arg_min_smax_F2000_TS.nc\n",
      "Loading TS (Surface temperature (radiative)) [K]\n",
      "from /Users/daniel/Desktop/MARC_AIE/work/arg_min_smax_F1850_TS.nc\n",
      "Loading TS (Surface temperature (radiative)) [K]\n",
      "from /Users/daniel/Desktop/MARC_AIE/work/arg_comp_F2000_TS.nc\n",
      "Loading TS (Surface temperature (radiative)) [K]\n",
      "from /Users/daniel/Desktop/MARC_AIE/work/arg_comp_F1850_TS.nc\n"
     ]
    }
   ],
   "source": [
    "v = ma.CESMVar(\"TS\")\n",
    "v.load_datasets()\n",
    "data = ma.create_master(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xray.Dataset>\n",
      "Dimensions:    (act: 2, aer: 2, lat: 96, lev: 30, lon: 144, nbnd: 2, time: 12)\n",
      "Coordinates:\n",
      "  * act        (act) <U12 'arg_min_smax' 'arg_comp'\n",
      "  * aer        (aer) <U5 'F2000' 'F1850'\n",
      "  * lev        (lev) float64 3.643 7.595 14.36 24.61 38.27 54.6 72.01 87.82 ...\n",
      "  * time       (time) datetime64[ns] 2007-02-01 2007-03-01 2007-04-01 ...\n",
      "  * lat        (lat) float64 -90.0 -88.11 -86.21 -84.32 -82.42 -80.53 -78.63 ...\n",
      "  * lon        (lon) float64 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 ...\n",
      "  * nbnd       (nbnd) int64 0 1\n",
      "Data variables:\n",
      "    hyam       (lev) float64 0.003643 0.007595 0.01436 0.02461 0.03827 ...\n",
      "    hybm       (lev) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n",
      "    P0         float64 1e+05\n",
      "    time_bnds  (time, nbnd) float64 2.555e+03 2.586e+03 2.586e+03 2.614e+03 ...\n",
      "    gw         (lat) float64 0.0001367 0.001093 0.002185 0.003275 0.004361 ...\n",
      "    PS         (act, aer, time, lat, lon) float64 6.871e+04 6.871e+04 ...\n",
      "    TS         (act, aer, time, lat, lon) float64 239.3 239.3 239.3 239.3 ...\n",
      "Attributes:\n",
      "    Conventions: CF-1.0\n",
      "    source: CAM\n",
      "    case: arg_min_smax\n",
      "    title: UNSET\n",
      "    logname: darothen\n",
      "    host: ys4032\n",
      "    Version: $Name$\n",
      "    revision_Id: $Id$\n",
      "    initial_file: /glade/p/cesm/cseg/inputdata/atm/cam/inic/fv/cami_0000-01-01_1.9x2.5_L30_c070703.nc\n",
      "    topography_file: /glade/p/cesm/cseg/inputdata/atm/cam/topo/USGS-gtopo30_1.9x2.5_remap_c050602.nc\n",
      "    history: Mon Jul 13 10:25:41 2015: ncatted -O -a years_omit,global,o,i,5 -a years_offset,global,o,i,0 -a git_commit,global,o,c,2ae4314 -a units,TS,o,c,K -a long_name,TS,o,c,Surface temperature (radiative) /Users/daniel/Desktop/MARC_AIE/work/arg_min_smax_F2000_TS_monthly.nc\n",
      "Mon Jul 13 10:25:41 2015: ncrcat -O -v TS,time_bnds,hyam,hybm,PS,P0,gw /Users/daniel/Desktop/MARC_AIE/F2000/arg_min_smax/arg_min_smax.cam2.h0.0008-01.nc /Users/daniel/Desktop/MARC_AIE/F2000/arg_min_smax/arg_min_smax.cam2.h0.0008-02....\n",
      "    nco_openmp_thread_number: 1\n",
      "    years_omit: 5\n",
      "    years_offset: 0\n",
      "    git_commit: 2ae4314\n"
     ]
    }
   ],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xray.DataArray 'TS' (act: 2, season: 4, lat: 96)>\n",
      "array([[[  4.61950867e-01,   5.59026612e-01,   4.55550123e-01,\n",
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      "           7.37234751e-02,   5.99406913e-02,   7.06977844e-02,\n",
      "          -1.81972362e-02,  -3.24225956e-02,   7.44480557e-03,\n",
      "          -2.04953088e-02,   3.12847561e-03,  -1.23239376e-01,\n",
      "          -1.53865390e-01,  -3.03961648e-01,  -3.12669401e-01,\n",
      "          -3.13649495e-01,  -3.96696868e-01,  -5.00946327e-01,\n",
      "          -5.35320635e-01,  -4.16026080e-01,  -3.54273478e-01,\n",
      "          -3.62772059e-01,  -3.36979548e-01,  -3.66761879e-01,\n",
      "          -4.05141124e-01,  -3.87227588e-01,  -4.37115846e-01,\n",
      "          -6.54908357e-01,  -8.80530534e-01,  -1.05948247e+00,\n",
      "          -6.80778751e-01,  -8.44186147e-02,   1.41250964e-01,\n",
      "           5.03746668e-02,  -3.87550637e-02,  -2.37722962e-01,\n",
      "          -2.69078537e-01,  -2.62595707e-01,  -4.81267858e-01,\n",
      "          -1.08958294e+00,  -1.45534876e+00,  -1.52456806e+00],\n",
      "        [  6.24256240e-01,   3.63184011e-01,  -2.02166875e-01,\n",
      "          -9.64299061e-01,  -1.26213279e+00,  -1.12855530e+00,\n",
      "          -7.12960314e-01,   1.44051799e-02,   6.30684323e-01,\n",
      "           8.64001168e-01,   5.33826439e-01,   4.31410542e-01,\n",
      "           4.03966374e-01,   2.64586131e-01,   9.69322346e-02,\n",
      "           1.87270553e-02,   2.08275406e-03,   2.62083831e-05,\n",
      "           1.12180357e-04,  -6.14166260e-04,   1.50616964e-03,\n",
      "           9.98150861e-03,   1.84795945e-02,   2.34837709e-02,\n",
      "           2.89206328e-02,   2.84249341e-02,   3.49264498e-02,\n",
      "           2.96546088e-02,   1.25238631e-02,  -7.53106011e-03,\n",
      "           6.28753945e-03,   5.83501745e-02,   8.88781371e-02,\n",
      "           7.93674610e-02,   8.06610673e-02,   7.89381663e-02,\n",
      "           7.79892957e-02,   4.29876822e-02,  -1.25796000e-02,\n",
      "          -6.17358596e-02,  -8.90064946e-02,  -5.71065832e-02,\n",
      "          -2.11909259e-02,  -3.59450446e-03,   7.55394830e-03,\n",
      "           1.40951651e-02,  -2.69713225e-04,   1.36660823e-02,\n",
      "           5.55523766e-02,   5.20463166e-02,   4.83277639e-02,\n",
      "           1.05372535e-02,  -9.17844419e-03,   1.38303969e-02,\n",
      "           7.24507085e-02,  -8.03862678e-03,  -3.03936711e-02,\n",
      "          -2.43237813e-02,  -1.45106139e-02,  -3.52922369e-02,\n",
      "          -1.16051992e-01,  -2.25200300e-01,  -3.14513454e-01,\n",
      "          -3.01121076e-01,  -3.23851091e-01,  -2.27176772e-01,\n",
      "          -2.32331453e-01,  -4.26442394e-01,  -4.64662199e-01,\n",
      "          -6.22044952e-01,  -8.83302441e-01,  -8.77390791e-01,\n",
      "          -5.31117545e-01,  -1.99830656e-01,  -4.22695548e-01,\n",
      "          -3.87133952e-01,  -2.89060310e-01,  -2.27182918e-01,\n",
      "          -2.56863594e-01,  -5.84991243e-01,  -8.32977260e-01,\n",
      "          -8.32065547e-01,  -6.16604946e-01,  -3.46965437e-02,\n",
      "           4.82832485e-01,   9.09155598e-01,   1.27009286e+00,\n",
      "           1.67856082e+00,   2.31488648e+00,   3.10503077e+00,\n",
      "           3.76821928e+00,   4.19009996e+00,   4.75526365e+00,\n",
      "           5.61351133e+00,   6.01343310e+00,   5.91123902e+00],\n",
      "        [  9.56740167e-01,   7.68774245e-01,   8.28318419e-01,\n",
      "           7.75613538e-01,   1.19197143e+00,   1.62212397e+00,\n",
      "           1.82044432e+00,   1.84666295e+00,   1.99456307e+00,\n",
      "           2.12301413e+00,   2.23514733e+00,   2.10825115e+00,\n",
      "           1.56003832e+00,   9.53459775e-01,   3.49730845e-01,\n",
      "           4.69974235e-02,  -3.40035756e-02,  -6.76800057e-02,\n",
      "          -4.80296523e-02,  -1.63751532e-02,  -7.15729042e-03,\n",
      "          -4.31767216e-03,  -5.51908988e-03,   1.56678094e-03,\n",
      "           5.69986414e-03,   5.81345735e-03,   9.84997220e-03,\n",
      "           1.73927590e-02,   1.72858768e-02,   7.95759978e-03,\n",
      "          -2.59683397e-02,  -1.48432696e-02,  -1.25723238e-02,\n",
      "          -3.74680272e-02,  -3.07574096e-02,  -4.14282481e-02,\n",
      "          -2.84253580e-02,  -4.16805126e-02,  -6.95266724e-02,\n",
      "          -9.39949177e-02,  -9.91954803e-02,  -7.66963959e-02,\n",
      "          -8.90266984e-02,  -5.71851377e-02,  -3.41161092e-02,\n",
      "           2.32569377e-03,   1.44530402e-02,   1.87437269e-02,\n",
      "           3.62998821e-02,   2.87455806e-02,   1.09800409e-02,\n",
      "          -2.37171738e-02,  -4.04033661e-02,  -5.80814503e-02,\n",
      "          -6.13633615e-02,  -9.54746670e-02,  -1.65553623e-02,\n",
      "           1.54949330e-01,   7.31214594e-02,   5.83347744e-02,\n",
      "           1.57563881e-01,   2.10483057e-01,   1.55524925e-01,\n",
      "          -2.87205731e-02,  -1.42612175e-01,  -2.64423865e-01,\n",
      "          -3.72209690e-01,  -3.58880220e-01,  -3.21994216e-01,\n",
      "          -3.62546214e-01,  -3.41191610e-01,  -3.58398084e-01,\n",
      "          -3.74227029e-01,  -3.69356650e-01,  -3.10893235e-01,\n",
      "          -2.72119028e-01,  -2.24444001e-01,  -2.62521320e-01,\n",
      "          -3.70167697e-01,  -4.52463715e-01,  -5.32103574e-01,\n",
      "          -6.30819992e-01,  -6.53066141e-01,  -4.73506044e-01,\n",
      "          -3.72824174e-01,  -2.70568000e-01,  -2.07967087e-01,\n",
      "          -1.04214032e-01,  -2.24823069e-01,  -6.03847610e-01,\n",
      "          -6.95897102e-01,  -7.24681925e-01,  -6.97719786e-01,\n",
      "          -7.76748375e-01,  -9.61732970e-01,  -1.54122589e+00]]])\n",
      "Coordinates:\n",
      "  * act      (act) <U12 'arg_min_smax' 'arg_comp'\n",
      "  * lat      (lat) float64 -90.0 -88.11 -86.21 -84.32 -82.42 -80.53 -78.63 ...\n",
      "  * season   (season) object 'DJF' 'JJA' 'MAM' 'SON'\n"
     ]
    }
   ],
   "source": [
    "pd_minus_pi = lambda ds: ds.sel(aer='F2000') - ds.sel(aer='F1850')\n",
    "\n",
    "a = (data['TS']\n",
    "        .pipe(pd_minus_pi)\n",
    "        .mean('lon')\n",
    "        .groupby('time.season')\n",
    "        .mean('time'))\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1143fc0b8>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zp/W94OBgYmJiiImJYdy4cTc8f8yYMXzxxRfk5ORYO7GpqsqXX37JuHHjWLNm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wqqsZkNjO4eE/CzfLpH9GBY8kh2FH/NzhJ/DZG0aBtdo22sP1ja5iVdL7fAfKBxGTw7/kVURcWo\ntvwRKBkZsG0b2jffoGRkNPkPMPQHN1Lz8svUvf027l278ObmYs7OJvql/8Fw1vrn56JXVqJ99RVY\nrU2eOwkhxKViVFUy4hz0iLFxsLSWo+V11Li9VDd4z3iVHTJO/T+pDKIqjjO4VyKR1pYfDzYOjCss\nRElPRzmrjoeu6+wqrGZXoROb2cC4HrHYLZ0jem656lY2nPyCv+xZzpLRTwP+u/VXvzzKlmPlDEuP\n6dgGtuCKvVM3G8znDHQAxWZD6dEDKiubDZhTVJXwRx8FwJubS+jMmcSufKvtga5p+Nas8XdTjRiB\n0oa5p0IIcbGcDvdJGfFM75fM9H5JfD8jnlHdoxncJZK+iWH0tEJ8dRnlmoGP9hXxTX5Vi4ub6Dt3\nou/fDzExqKNHN7sD33lGoI/v2XkCHWBgwmB6R2Ww5vhqTjr9U9nG9PZ3wQfqc/UrNtTbQj1V4lXb\nsaPZvpDhw4h4/HEin/0DkU//HsXS9udD+q5dUFSEkp7u7+YXQogOZDKoRFhNJIdbSY+xkZUQRk5G\nCmOzUhhxZDtWVz27i5x8sK+Ikhp/qezTM3e0jRshNBTDpEnNpqcdKqtld6ET+6lAD9TR7ueiKAq3\nXDUHTdf42943ABjSLRqLMXCntkmot0KJjkbp0gUKC9GLiprtt982F9v06e16NqRXVqJt3gwhIajD\nh1/M5gohxEWlREeTMnY41x7aQo/CIzhdHlYfKGH3zkN4V6xAW78eDAYM116LYrM1Obas1s2WExWY\nDQpje3S+QD9tfNcJxIcm8O7Bd6hyVRES4FPbJNTP4/TiKi3drbeXrmn41q71d7uPHCnd7kKIgKdE\nRmKZch0Dyo8zds+XWDwuvvFa+DKhF96svhhuvhklrmk99AaPj3VHytB0uKZrdKfqcj+bUTUxM3MW\nDd4G3sldCcDoXoHbBS+hfh5KUhLExKAfOYJeVfWdzqWfOOEfTNK9u3S7CyE6DSU8HMPUqcSoPibs\nXkesr56TkQl8Etmd6rOWdNZ0nfVHy6nz+OiXGEZSWEgHtfrimdbzJmwmO2/t+ytun7txUZdAnNom\noX4eiqKgnr5bP7U06oXS8/2LJihXXfWd2yWEEJeT4nBgmD4d283/wbiBPciIs+N0eflgXxEf7S9i\na14lxyrq2JZXSXGNi5TwEK6Kb9v6F4HObrZzQ8+bKKsv5bb3ZrGt7H1So6wBObVNQr0NlO7dweHw\nlz+sv/BnKHpBAahqs64qIYToDBSTCcVqRVUUBiRHMKJbNFGhZirrPeSW1LDhaDkHSmsJsxgZmhYV\n8HPR2+O2fncwoeskjlQd4cmNv6FS+Zxat481B46d/+DLqPM+6LiMFFVF7dsXbcMG9L17UbKz230O\n3e2G0lKIiwvIBQyEEKK9ukRY6RJhxavpVNS5Ka11U9XgoU9CGCZDcN0zOsxh/GbUk5TUFfO/B95m\n+VdbqC4bxJ82r2NiZreObl6j4PrULyGlVy8AtLwLW3ZPLyoCXUdJTLyYzRJCiA5nVBVi7RYy4x0M\nTYvC0YkHxp1PbGgcd/T/MR/MXsaI3hZ+NHBERzepieD95C8yxWKBmBgoKkL3eNp9t60XFPjPI6Eu\nhBCdns1s4fU53+voZjQjd+rtoCQlgaa1OGf9fPSCAlAUlDZWnRNCCCHaS0K9HZTkZODbUextpXu9\nUFwM0dGyvKoQQohLRkK9HZSEBFCU9od6cTFomv9OXwghhLhEJNTbQTGbITYWSkqarbPeqtPz0+V5\nuhBCiEtIQr2dGp+rFxa2+ZjGQXLyPF0IIcQlJKHeTu19rq77fP7u96golJDOXy5RCCFE4Gr3lDan\n08mCBQuora3F4/GwcOFCrr766kvRtoCkxMeDqrb9uXpJCXi90vUuhBDikmt3qL/66qsMGzaM2bNn\nc+TIER588EHefvvtS9G2gKSYTBAX55+v7nKddx11mZ8uhBDicml3qM+dOxfzqWlZXq8Xy3lCLRgp\nSUnohYXoBQUoXbu2+loJdSGEEJdLq6G+cuVKli9f3mTbkiVLyMrKoqSkhJ/97Gc88sgjl7SBgUhJ\nTkbfts3fBd9KqOunB9SFh6OEhl6+BgohhLgitRrqM2bMYMaMGc2279+/nwcffJCf//zn5OTknPdN\nXnjhBZYuXXrhrQwwSlwcGAznf65eVgYej9ylCyGEuCza3f1+8OBB7r//fp577jl69+7dpmPuu+8+\n7rvvvibb8vLyGD9+fHvfPiAoRiNKfDx6fj56Q8M5R7Xrx/xL8kmoCyGEuBzaHerPPPMMHo+H3/zm\nNwCEhYWxbNmyi96wQKckJ/tDPT/fv976WfSyMrSvv4aQEJTU1A5ooRBCiCtNu0P9xRdfvBTt6HRO\nl3zVT56Es0Jd93rxrV4NmoY6ZozMTxdCCHFZSPGZCxUbCxYL+t69aN98g67rjbu0jRuhogKlTx/U\ntLQObKQQQogriYT6BVIMBgyTJoHVivbll2hr1qB7vWjHjqHv3g1RUahDh3Z0M4UQQlxB2t39Lr6l\nJCRg+MEP8K1ahZ6bi6+iApxOMBgwjBuHYpSPVwghxOUjd+rfkWKzYbj+epTevf0lYRsaUIcMQYmO\n7uimCSGEuMLIreRFoBiNqKNHoyckQG0tSlZWRzdJCCHEFUhC/SJRFAUlI6OjmyGEEOIKJt3vQggh\nRJCQUBdCCCGChIS6EEIIESQk1IUQQoggIaEuhBBCBAkJdSGEECJISKgLIYQQQUJCXQghhAgSEupC\nCCFEkJBQF0IIIYKEhLoQQggRJCTUhRBCiCAhoS6EEEIECQl1IYQQIkhIqAshhBBBQkJdCCGECBIS\n6kIIIUSQkFAXQgghgoSEuhBCCBEkJNSFEEKIICGhLoQQQgQJCXUhhBAiSEioCyGEEEFCQl0IIYQI\nEhLqQgghRJCQUBdCCCGChIS6EEIIESQk1IUQQoggIaEuhBBCBAkJdSGEECJISKgLIYQQQUJCXQgh\nhAgSEupCCCFEkJBQF0IIIYKEhLoQQggRJCTUhRBCiCAhoS6EEEIEiQsO9UOHDpGTk4Pb7b6Y7RFC\nCCHEBbqgUK+pqeHJJ5/EYrFc7PYIIYQQ4gK1O9R1Xeexxx5j/vz5EupCCCFEADG2tnPlypUsX768\nybakpCQmT55MRkbGJW2YEEIIIdpH0XVdb88BEydOJD4+HoAdO3bQv39/Xn/99VaPeeGFF1i6dGmL\n+1avXk1KSkp7miCEEEKIFrQ71M80btw4PvzwQ8xmc7uP9Xq9FBYWkpCQgNHYaoeBEEIIIdrgO6Wp\noigX/sZGo9yhCyGEEBfRd7pTF0IIIUTgkOIzQgghRJCQUBdCCCGChIS6EEIIESQk1IUQQoggIaEu\nhBBCBImAnyB+ej67EEIIcSW5kDouAR/qhYWFjB8/vqObIYQQQlxWF1JxNeBDPSEhAfD/caLzGT9+\nvFy7TkyuX+cl165zGz9+fGP+tUfAh/rprgepPtd5ybXr3OT6dV5y7Tq3CymhLgPlhBBCiCAhoS6E\nEEIECQl1IYQQIkgYFi9evLijG9EWQ4YM6egmiAsk165zk+vXecm169wu5PrJKm1CCCFEkJDudyGE\nECJISKgLIYQQQUJCXQghhAgSEupCCCFEkJBQF0IIIYKEhLoQQggRJAKy9vvHH3/Mhx9+yH/9138B\nsH37dn77299iMBgYPnw49957LwBLly5l7dq1GAwGHn74Yfr169eRzRan6LrOqFGj6Nq1KwDZ2dk8\n8MAD57yOIvBomsbixYvJzc3FZDLxxBNPkJqa2tHNEq248cYbsdvtAHTp0oV58+axcOFCVFWlZ8+e\nLFq0CEVROriV4kw7duzg6aef5vXXX+fYsWMtXq+33nqLFStWYDQaufvuuxkzZkzrJ9UDzOOPP65P\nmjRJnz9/fuO2adOm6cePH9d1XdfvvPNOfc+ePfquXbv02bNn67qu6/n5+fpNN93UIe0VzR09elSf\nN29es+0tXUcRmD766CN94cKFuq7r+vbt2/W77767g1skWtPQ0KDfcMMNTbbNmzdP37x5s67ruv7Y\nY4/pH3/8cUc0TZzDSy+9pE+ZMkWfOXOmrustX6/i4mJ9ypQputvt1p1Opz5lyhTd5XK1et6A637P\nzs5m8eLF6Kdq4tTU1OB2u+nSpQsAI0aMYMOGDWzbto3hw4cDkJiYiM/no6KiosPaLb61e/duiouL\nmT17NnfddRdHjhw553UUgWnbtm2MHDkSgP79+7Nr164ObpFozb59+6ivr+f2229nzpw5bN++nT17\n9jBo0CAARo0aJd+3AJOWlsbSpUsbs66l67Vz506ys7MxmUzY7XbS0tLYv39/q+ftsO73lStXsnz5\n8ibblixZwuTJk9m0aVPjtpqamsYuJQCbzcaJEyewWCxEREQ02V5TU0NkZOSlb7xo1NJ1XLRoEfPm\nzePaa69l69atLFiwgGXLlrV4HUVgOvt7ZzAY0DQNVQ24+wABWK1Wbr/9dmbMmMHRo0e54447muwP\nDQ3F6XR2UOtESyZOnEheXl7jv+tnFHe12Ww4nU5qampwOBxNttfU1LR63g4L9RkzZjBjxozzvs5u\nt1NbW9v47zU1NYSFhWEymZpsr62tbfLHi8ujpevY0NCAwWAAYODAgRQXF2Oz2Vq8jiIwnf29k0AP\nbF27diUtLa3xnyMiIti7d2/j/traWvm+Bbgzv1+n//949vewLdcx4L+ldrsdk8nEiRMn0HWd9evX\nk5OTQ3Z2NuvWrUPXdfLz89E0rcmdu+g4y5Yt47XXXgP83YJJSUnnvI4iMGVnZ/P5558D/oGqvXv3\n7uAWida8/fbb/O53vwOgqKiI2tpahg8fzubNmwH4/PPP5fsW4DIzM5tdr379+rFlyxbcbjdOp5ND\nhw7Rs2fPVs8TkKPfFUVpMkrzV7/6FQ899BA+n48RI0Y0jnLPyclh5syZaJrGokWLOqq54ix33XUX\nCxYsYO3atRiNRpYsWQKc+zqKwDNhwgTWr1/PzTffDNB4DUVgmj59Or/4xS+45ZZbAP/1ioiI4NFH\nH8Xj8ZCens6kSZM6uJWiJaezbuHChc2ul6IozJ49m1mzZqFpGvPnz8dsNrd+Pl2XVdqEEEKIYBDw\n3e9CCCGEaBsJdSGEECJISKgLIYQQQUJCXQghhAgSEupCCCFEkJBQF0IIIYKEhLoQQggRJP4PMAy8\nFU/nKzwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114559e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style='ticks')\n",
    "\n",
    "with sns.color_palette('Paired'):\n",
    "\n",
    "    for seas in a.season:\n",
    "        for act in a.act:\n",
    "            d = a.sel(season=seas, act=act)\n",
    "            plt.plot(d.lat, d, label=\"%s - %s\" %\n",
    "                     (act.values, seas.values))\n",
    "            \n",
    "sns.despine(offset=10)\n",
    "plt.legend(loc='best')"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}