cmos2014-python-tutorial-Copy1.ipynb

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     "input": [
      "#Necessary imports\n",
      "import os\n",
      "import re\n",
      "from datetime import datetime, timedelta\n",
      "from netCDF4 import Dataset\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
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     "source": [
      "Data handling and visualization using Python\n",
      "=============================================\n",
      "\n",
      "\n",
      "<span id=\"authors\">\n",
      "Oleksandr (Sasha) Huziy and Jonathan Doyle\n",
      "<span>\n",
      "\n",
      "\n",
      "CMOS, June 2014\n",
      "\n",
      "The presentation is available here: http://tinyurl.com/cmos2014\n",
      "\n",
      "<div class=\"logo\">\n",
      "        <a class=\"image\" href=\"http://python.org\"><img src=\"files/images/python-logo.gif\"/></a> \n",
      "        <a class=\"image\" href=\"http://www.cmos.ca/\"><img src=\"files/images/CMOS_logo.jpg\"/></a>\n",
      "        <a class=\"image\" href=\"http://cnrcwp.uqam.ca\"><img src=\"files/images/logo_2.png\"/></a> \n",
      "        <img src=\"files/images/crsng.png\"/> \n",
      "</div>"
     ]
    },
    {
     "cell_type": "markdown",
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     },
     "source": [
      "Introduction and history\n",
      "=====\n",
      "\n",
      "### What is Python??\n",
      "\n",
      "\"Python is a widely used general-purpose, high-level programming language. Its design philosophy emphasizes code readability.\" *[wiki](http://en.wikipedia.org/wiki/Python_(programming_language))*\n",
      "\n",
      "\"Python is powerful... and fast; \n",
      "plays well with others; \n",
      "runs everywhere; \n",
      "is friendly &amp; easy to learn; \n",
      "is Open.\" *[python.org](http://www.python.org/about/)*\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "### What is Python??\n",
      "\n",
      "<center><img src=\"http://imgs.xkcd.com/comics/python.png\" /></center>\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "### What is Python??\n",
      "\n",
      "Readable and interpreted\n",
      "\n",
      "* Not compiled (computer executes in order)\n",
      "* Easy to develop, debug, and share\n",
      " \n",
      "Easy to learn, powerful, and fast\n",
      "\n",
      "* High-level and dynamically typed\n",
      "* Interfaces with compiled languages (e.g., C, C++, Fortran)\n",
      "* Versitile libraries \"under the hood\"\n",
      " \n",
      "Open and free\n",
      "\n",
      "* Community support and development\n",
      "* Skills transfer without overhead (no site lisence)\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
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     },
     "source": [
      "\n",
      "\n",
      "### Example uses for Python \n",
      "\n",
      "* General programming (e.g., clock applet in Ubuntu)\n",
      "* Video games (e.g., [Ubisoft](http://www.ubisoft.com) in Montreal)\n",
      "* Web development (e.g., [Pinterest](http://www.pinterest.com), [Instagram](http://www.instagram.com))\n",
      "* Data analysis (e.g., visulalization, multi dimensional data, statisitics)\n",
      "* Scripting (e.g,. automating system tasks, smart file manipulation)\n",
      "\n",
      "### Talk on Python history by Python developer Guido Van Rossum [here](http://www.youtube.com/watch?v=ugqu10JV7dk).\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
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     },
     "source": [
      "Some things I've done with python: lake level observations\n",
      "====="
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* [View the demo](https://maps.google.com/maps?q=http://people.sca.uqam.ca/~huziy/mon.kml)\n",
      "\n",
      "* [View the source code](https://github.com/guziy/RPN/blob/master/src/google_maps/googlecharts.py)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
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     },
     "source": [
      "More cool examples:\n",
      "===========\n",
      "\n",
      "* [matplotlib](http://matplotlib.org/gallery.html)\n",
      "* [Basemap](http://matplotlib.org/basemap/users/examples.html)\n",
      "* [Cartopy](http://scitools.org.uk/cartopy/docs/latest/gallery.html)\n",
      "* [PyNGL](http://www.pyngl.ucar.edu/Examples/gallery.shtml)\n"
     ]
    },
    {
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     "metadata": {
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       "slide_type": "slide"
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     },
     "source": [
      "Outline part I\n",
      "========\n",
      "\n",
      "* **Python basics**\n",
      "\n",
      "    * IPython and basic python syntax\n",
      "    \n",
      "    * Builtin data types\n",
      "    \n",
      "    * Operations on file system, strings and dates\n",
      "    \n",
      "* **Libraries for scientific computing**\n",
      "\n",
      "    * NumPy/SciPy\n",
      "\n",
      "* **Handling NetCDF4 files**\n",
      "    \n",
      "    * Netcdf4-python"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
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       "slide_type": "slide"
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     },
     "source": [
      "Getting started\n",
      "=========="
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Open a new terminal: `Ctrl-Alt-T` (Ubuntu) or search for 'terminal' in the dash\n",
      "\n",
      "Make a working directory\n",
      "<pre class=\"co\">\n",
      "    $ mkdir python_workshop\n",
      "</pre>\n",
      "\n",
      "Change into new directory\n",
      "\n",
      "<pre class=\"co\">\n",
      "    $ cd python_workshop \n",
      "</pre>\n",
      "\n",
      "First create new python script (for later)\n",
      "\n",
      "<pre>\n",
      "    $ gedit example01.py & \n",
      "</pre>\n",
      "\n",
      "List the files in directory \n",
      "\n",
      "<pre>\n",
      "    $ ls\n",
      "</pre>\n",
      "\n",
      "Launch the ipython terminal\n",
      "\n",
      "<pre>\n",
      "    $ ipython\n",
      "</pre>\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Using python as a calculator\n",
      "========"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Try addition, subtraction, multiplcation, and divison\n",
      "2 + 2\n",
      "3 - 1\n",
      "2 * 2\n",
      "3 / 2;"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Careful with division!\n",
      "# older python versions treat 2 as an integer and 2. as a float\n",
      "3 / 2.;\n"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Try exponentials\n",
      "2 ^ 3 \n",
      "# this is actually an XOR NOT exponential!\n",
      "2 ** 3\n",
      "9 ** .5\n"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
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     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "3.0"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {
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       "slide_type": "subslide"
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     },
     "source": [
      "Scientific calculator\n",
      "================"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "\n",
      "# Create a variable for pi\n",
      "pi = math.pi\n",
      "print pi\n",
      "\n",
      "math.cos(pi)\n",
      "\n",
      "math.exp(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "3.14159265359\n"
       ]
      },
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       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "2.718281828459045"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "Use the 'Tab' auto-complete in iPython to list other ```math``` functions.\n",
      "\n",
      "Note: ```math``` on works on scalars, not arrays (with arrays we will use ```numpy```)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Quick overview: native types\n",
      "=============="
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "source": [
      "Basic data types \n",
      "```\n",
      "    string (str): 'This is a string', \"This too\"\n",
      "    int: 0, 1, 20, 1000\n",
      "    float: 0.0, 1.0, 20., 1000.0000001\n",
      "    bool: True, False\n",
      "```    \n",
      "\n",
      "Data containers\n",
      "```\n",
      "    list: [1,2,4, 5, \"I am a fifth element\"]\n",
      "    tuple: (1, 2, 3)\n",
      "    dictionary (dict): {'key':'value', 'key2':['list','of','values'], ...}\n",
      "```\n",
      "\n",
      "Extended types (requires an `import`)\n",
      "```\n",
      "    datetime: datetime.datetime.now(), datetime.datetime.utcnow()\n",
      "    array: numpy.array([1,2,3,4])\n",
      "```    \n",
      "\n",
      "\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Quick overview: Flow control\n",
      "==============="
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Python uses a four-space indent for loops, case statments, and function definition\n",
      "\n",
      "Loops:\n",
      "\n",
      "```\n",
      "    for item in item_list:\n",
      "        # Do something, for example:\n",
      "        print item\n",
      "     \n",
      "     while condition==True:\n",
      "         # Do something\n",
      "```         "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Quick overview: Flow control\n",
      "==============="
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "Case statements:\n",
      "```    \n",
      "    if condition == True:\n",
      "        # Do one thing, for example:\n",
      "        print condition\n",
      "        \n",
      "    elif condition == False:\n",
      "        # Do something else\n",
      "        print 'This condition failed to pass'\n",
      "    \n",
      "    else:\n",
      "        # If neither condition is sadisfied\n",
      "        print 'Warning, condition was not sadisfied!'\n",
      "```        "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Quick overview: Flow control\n",
      "==============="
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Function definition:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def my_func(input_a, input_b):\n",
      "    \"\"\"This is my function documentation\"\"\"\n",
      "    output = input_a + input_b\n",
      "    return output"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "source": [
      "Python basics - code hierarchy\n",
      "==============\n",
      "\n",
      "+ Each python file is a python module, it can contain function and class definitions\n",
      "\n",
      "\n",
      "+ A folder with python files and a file ```__init__.py``` (might be empty file) is called package. Different packages and modules arelinked using ```import ```statement."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
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     },
     "source": [
      "Exercises on basic syntax\n",
      "=====\n",
      "\n",
      "* Create a script `hello.py` in your favourite editor and add a function say_hello with a keyword argument name, which prints \"hello --value of the parameter name--\". \n",
      "    Run it: ``` python hello.py ```\n",
      "\n",
      "* Use the function say_hello in another module. (i.e create file use_hello.py and call the say_hello function from there)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Follow an example\n",
      "=========\n",
      "\n",
      "Data from the IML-4 buoy outside Rimouski provided by the St. Laurence Global Observatory \n",
      "\n",
      "<img src=\"http://slgo.ca/app-bouees/en/images/carte.png\" />\n",
      "\n",
      "Download data [here](http://pascal.sca.uqam.ca/~doyle/python_examples.tar.gz) save it in the working directory `python_workshop` we created earlier.\n",
      "\n",
      "[<img src=\"files/images/slgo.png\" />](http://slgo.ca/en/buoys/data.html)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Follow an example\n",
      "=========\n",
      "\n",
      "Extract the and open the data to prepare for [parsing](http://docs.scipy.org/doc/numpy/reference/generated/numpy.genfromtxt.html).\n",
      "\n",
      "<pre>\n",
      "    $ tar -xzf IML-4_example.tar.gz\n",
      "    \n",
      "    $ gedit IML-4_salinite.txt\n",
      "\n",
      "\n",
      "    IML-4 \u2014 Water salinity \u2014\n",
      "    Date\t Time (UTC)\t Water salinity (PSU)\n",
      "    2013-05-28\t 10:18:22 PM\t 24.7712\n",
      "    2013-05-28\t 10:33:22 PM\t 24.7736\n",
      "    2013-05-28\t 10:48:22 PM\t 24.7924\n",
      "    ...\n",
      "    2013-10-30\t 04:33:22 PM\t 25.7566\n",
      "    2013-10-30\t 04:48:22 PM\t 25.7388\n",
      "</pre>\n",
      "\n",
      "Follow along with the example, the completed examples are available for reference in the `complete_examlpes` directory."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Builtin data containers\n",
      "=============\n",
      "##Python provides the following data structures. Which are actually classes with attributes and methods.\n",
      "\n",
      "* `list` (range, *, +, pop, len, accessing list elements, slices, last element, 5 last elements)\n",
      "  \n",
      "* `tuple` (not mutable, methods)\n",
      "\n",
      "* `dict` (accessing elements, keys, size)\n",
      "\n",
      "* `set` (set theoretical operations, cannot have 2 equal elements)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Builtin data containers (lists)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#lists (you can conactenate and sort in place)\n",
      "the_list = [1,2,3,4,5]; other_list = 5 * [6];\n",
      "print the_list + other_list"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[1, 2, 3, 4, 5, 6, 6, 6, 6, 6]\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Test if a number is inside a list\n",
      "print 19 in the_list, 5 in the_list, (6 in the_list and 6 in other_list)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "False True False\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Builtin data containers (lists)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#square eleaments of a list\n",
      "#list comprehension\n",
      "print [the_el ** 2 for the_el in the_list] "
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[1, 4, 9, 16, 25]\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Generating list or iterable of integers\n",
      "print range(1,20)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Builtin data containers (lists)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "the_list = [3,6,78,9,0,32,13]\n",
      "##There are some utility functions that can be applied to lists\n",
      "the_list.sort(reverse = True)\n",
      "print the_list"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[78, 32, 13, 9, 6, 3, 0]\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#loop through several lists at the same time\n",
      "for el1, el2 in zip(the_list, other_list):\n",
      "    print(el1+el2),"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "84 38 19 15 12\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Builtin data containers (dictionary)\n",
      "====="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#dictionary\n",
      "author_to_books = { \n",
      "\"Stephen King\": \n",
      "    [\"Carrie\",\"On writing\",\"Green Mile\"],\n",
      "\n",
      "\"Richard Feynman\": \n",
      "    [\"Lectures on computation\", \n",
      "     \"The pleasure of finding things out\"]\n",
      "}\n",
      "#add elements to a dictionary\n",
      "author_to_books[\"Andrey Kolmogorov\"] = \\\n",
      "        [\"Foundations Of The Theory Of Prob...\"]"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Builtin data containers (dictionary)\n",
      "====="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#print the list of authors\n",
      "print author_to_books.keys()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['Stephen King', 'Richard Feynman', 'Andrey Kolmogorov']\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Iterate over keys and values\n",
      "for author, book_list in author_to_books.iteritems():\n",
      "    suffix = \"s\" if len(book_list) > 1 else \"\"\n",
      "    print(\"{0} book{1} by {2};\\n\".format(len(book_list), suffix, author)),"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "3 books by Stephen King;\n",
        "2 books by Richard Feynman;\n",
        "1 book by Andrey Kolmogorov;\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Exercises: builtin containers\n",
      "======"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "source": [
      "* Find a sum of squares of all odd integers that are smaller than 100\n",
      "\n",
      "* Check what this statement prints\n",
      "\n",
      "```\n",
      "    print 10 * [18,]\n",
      "```\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Modules and classes to operate on\n",
      "==============\n",
      "\n",
      "\n",
      "* File system (```os, shutil, sys```)\n",
      "\n",
      "    * create folder, list folder contents, check if file or folder exists \n",
      "\n",
      "* Strings (+, *, join, split, regular expressions) \n",
      "\n",
      "* Dates (datetime, timedelta, ) "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "#File system"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os\n",
      "#print current directory\n",
      "print os.getcwd()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/home/san/Downloads/cmos_tutorial_Jon/CMOS-python-tutorial\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "#File system"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#get list of files in the current directory\n",
      "flist = os.listdir(\".\")\n",
      "print flist[:7]"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['test.nc', 'custom.css', 'norm.daily.nao.index.b500101.current.ascii', 'images', 'default_transition.tpl', 'python_workshop', '.ipynb_checkpoints']\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "#File system"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Check if file exists\n",
      "fname = flist[0]\n",
      "print fname,\":\", os.path.isfile(fname), \\\n",
      "      os.path.isdir(fname),  \\\n",
      "      os.path.islink(fname)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "test.nc : True False False\n"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "You might also find useful the following modules: `sys, shutil, path` "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "#Strings"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = \"climate\"\n",
      "#reverse (also works for lists)\n",
      "print s[-1::-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "etamilc\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Dynamically changing parts of a string (for left right justification use '>', '<')\n",
      "tpl = \"Formatting floats: {0:.3f};\\nformatting integers: {1:05d};\\nformatting dates: {2: %Y-%m-%d %H:%M}\"\n",
      "print tpl.format(789.3342353554, 32, datetime.now())"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Formatting floats: 789.334;\n",
        "formatting integers: 00032;\n",
        "formatting dates:  2014-06-04 23:22\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "#Strings"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Splitting\n",
      "s = \"This,is,a,sentence\"\n",
      "s_list = s.split(\",\")\n",
      "print s_list"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['This', 'is', 'a', 'sentence']\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "#Strings"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#joining a list of strings\n",
      "list_of_fruits = [\"apple\", \"banana\", \"cherry\"]\n",
      "x =   \"\"\"\n",
      "        I would like to eat \n",
      "        {0}. \n",
      "        And by the way I am a multiline string.\n",
      "      \"\"\"\n",
      "print x.format(\" or \".join(list_of_fruits))"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        I would like to eat \n",
        "        apple or banana or cherry. \n",
        "        And by the way I am a multiline string.\n",
        "      \n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "source": [
      "#Strings: regular expressions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#regular expressions module\n",
      "import re\n",
      "msg = \"Find 192, numbers 278: and -7 and do smth w 89\"\n",
      "groups = re.findall(r\"-?\\d+\", msg)\n",
      "print groups \n",
      "print [float(el) for el in groups] #convert strings to floats"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['192', '278', '-7', '89']\n",
        "[192.0, 278.0, -7.0, 89.0]\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#regular expressions module\n",
      "groups = re.findall(r\"-?\\d+/\\d+|-?\\d+\\.\\d+|-?\\.?\\d+|-?\\d+\\.?\", \"Find 192.28940, -2/3 numbers 278: and -7 and .005 w 89,fh.5 -354.\")\n",
      "print groups"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['192.28940', '-2/3', '278', '-7', '.005', '89', '.5', '-354']\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "# Dates"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#What time is it\n",
      "from datetime import datetime\n",
      "d = datetime.now(); print d"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2014-06-04 23:22:52.084387\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#hours and minutes\n",
      "d.strftime(\"%H:%M\"), d.strftime(\"%Hh%Mmin\")"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "('23:22', '23h22min')"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "# Dates: How long is the CMOS 2014?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "start_date = datetime(2014, 6, 1, 8, 30)\n",
      "end_date = datetime(2014, 6, 5, 17)\n",
      "print end_date - start_date"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "4 days, 8:30:00\n"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#you can mutiply the interval by an integer\n",
      "print (end_date - start_date) * 2"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "8 days, 17:00:00\n"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "You can generate dates for timeseries with arbitrary steps\n",
      "===="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from datetime import timedelta\n",
      "dt = timedelta(days = 5, hours = 6, minutes = 25)\n",
      "d0 = datetime(2005, 4, 23)\n",
      "[str(d0 + i * dt) for i in range(10)]"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "['2005-04-23 00:00:00',\n",
        " '2005-04-28 06:25:00',\n",
        " '2005-05-03 12:50:00',\n",
        " '2005-05-08 19:15:00',\n",
        " '2005-05-14 01:40:00',\n",
        " '2005-05-19 08:05:00',\n",
        " '2005-05-24 14:30:00',\n",
        " '2005-05-29 20:55:00',\n",
        " '2005-06-04 03:20:00',\n",
        " '2005-06-09 09:45:00']"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "You can use calendar module for different convenience functions with dates\n",
      "=====\n",
      "Example: how many days in a given month in 3001?\n",
      "----"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import calendar\n",
      "for month in range(1, 13):\n",
      "    weekday_of_last_day, ndays = calendar.monthrange(3001, month)\n",
      "    print ndays,"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "31 28 31 30 31 30 31 31 30 31 30 31\n"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Exercises: Operations with strings, dates and file system\n",
      "======\n",
      "* Check what this statement prints\n",
      "\n",
      "```\n",
      "    print 5 * \"123\"\n",
      "```\n",
      "\n",
      "\n",
      "* Count the number of files and number of folders in your home or in the current directory.\n",
      "\n",
      "* Get the sum of digits (not numbers) in the sentence below using module `re`. \n",
      "    \n",
      "    **This is a 78 sentence very confusing (1) which might change232 so you need to 346 automate the procedure of extracting the /21e893/ useful information from here10.21?**\n",
      "    \n",
      "    \n",
      "* Figure out on which day of week you were born (see the `calendar` module), you can have fun by determining on which day of week you'll have your next birthday.\n",
      "\n",
      "* Assume you have a timeseries of length 10000. You know that it starts on 2009-01-02 at 18:00 and the time step is 17 hours 25 minutes, what is the date and time corresponding to the last point of the timeseries."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "NumPy\n",
      "========\n",
      "The library for fast manipulations with big arrays that fit into memory."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "#Creating a numpy array from list\n",
      "np_arr = np.asarray([253,1.9,23, 4, 3.5,6,7,86, 18.9, 10, 89, 99])\n",
      "print np_arr\n",
      "print np_arr + 1\n",
      "print np_arr ** 2\n",
      "print np_arr + 5 * np_arr"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 253.     1.9   23.     4.     3.5    6.     7.    86.    18.9   10.    89.\n",
        "   99. ]\n",
        "[ 254.     2.9   24.     5.     4.5    7.     8.    87.    19.9   11.    90.\n",
        "  100. ]\n",
        "[  6.40090000e+04   3.61000000e+00   5.29000000e+02   1.60000000e+01\n",
        "   1.22500000e+01   3.60000000e+01   4.90000000e+01   7.39600000e+03\n",
        "   3.57210000e+02   1.00000000e+02   7.92100000e+03   9.80100000e+03]\n",
        "[ 1518.     11.4   138.     24.     21.     36.     42.    516.    113.4\n",
        "    60.    534.    594. ]\n"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "NumPy\n",
      "========"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Reshape\n",
      "np_arr.shape = (3,4)\n",
      "print np_arr"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 253.     1.9   23.     4. ]\n",
        " [   3.5    6.     7.    86. ]\n",
        " [  18.9   10.    89.    99. ]]\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#sum along a specified dimension, here \n",
      "print np_arr.sum(axis = 1)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 281.9  102.5  216.9]\n"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Numpy\n",
      "====="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#create prefilled arrays\n",
      "the_zeros = np.zeros((3,9))\n",
      "the_ones = np.ones((3,9))\n",
      "\n",
      "print the_zeros\n",
      "print 20 * \"-\"\n",
      "print the_ones"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.]]\n",
        "--------------------\n",
        "[[ 1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
        " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
        " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.]]\n"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Numpy provides many vectorized functions to efficiently operate on arrays\n",
      "====="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print np.sin(np_arr)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 0.99482373  0.94630009 -0.8462204  -0.7568025 ]\n",
        " [-0.35078323 -0.2794155   0.6569866  -0.92345845]\n",
        " [ 0.05042269 -0.54402111  0.86006941 -0.99920683]]\n"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print np.where(np_arr == np_arr.min()) # <--- indices of the minimum value"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(array([0]), array([1]))\n"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "You can mask arrays easily (and the plotting library will recognize your masking)\n",
      "====="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "masked_np_arr = np.ma.masked_where(np_arr > np.percentile(np_arr, 75), np_arr)\n",
      "print masked_np_arr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[-- 1.9 23.0 4.0]\n",
        " [3.5 6.0 7.0 86.0]\n",
        " [18.9 10.0 -- --]]\n"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Numpy: fancy indexing\n",
      "====="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "arr = np.random.randn(3,5)\n",
      "print \"Sum of positive numbers: \", arr[arr > 0].sum()\n",
      "\n",
      "print \"Sum over (-0.1 <= arr <= 0.1): \", \\\n",
      "       arr[(arr >= -0.1) & (arr <= 0.1)].sum()\n",
      "\n",
      "print \"Sum over (-0.1 > arr) or (arr > 0.1): \", \\\n",
      "       arr[(arr < -0.1) | (arr > 0.1)].sum()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Sum of positive numbers:  3.88101109064\n",
        "Sum over (-0.1 <= arr <= 0.1):  0.000498247812646\n",
        "Sum over (-0.1 > arr) or (arr > 0.1):  -2.28308967323\n"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Exercises: Numpy\n",
      "=====\n",
      "\n",
      "* Generate a 10 x 3 array of random numbers. Count the number of positive and negative elements.\n",
      "* Checkout numpy for MATLAB users transition [table](http://wiki.scipy.org/NumPy_for_Matlab_Users)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "SciPy packages\n",
      "=======\n",
      "\n",
      "* `scipy.constants` - Physical and mathematical constants \n",
      "* `scipy.fftpack` - Fourier transform \n",
      "* `scipy.integrate` - Integration routines \n",
      "* `scipy.interpolate` - Interpolation \n",
      "* `scipy.io` - Data input and output\n",
      "* `scipy.linalg` - Linear algebra routines\n",
      "* `scipy.ndimage` - n-dimensional image package\n",
      "* `scipy.optimize` - Optimization\n",
      "* `scipy.signal` - Signal processing \n",
      "* `scipy.sparse` -  Sparse matrices\n",
      "* `scipy.spatial` -  Spatial data structures and algorithms\n",
      "* `scipy.special` -  Any special mathematical functions\n",
      "* `scipy.stats` -  Statistics\n",
      "\n",
      "And more ...\n",
      "\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "SciPy: ttest example\n",
      "======="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy import stats\n",
      "#generating random variable samples for different distributions\n",
      "x1 = stats.norm.rvs(loc = 50, scale = 20, size=100)\n",
      "x2 = stats.norm.rvs(loc=5, scale = 20, size=100)\n",
      "print stats.ttest_ind(x1, x2)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(array(17.987876946231683), 1.6286884817533376e-43)\n"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x1 = stats.norm.rvs(loc = 50, scale = 20, size=100)\n",
      "x2 = stats.norm.rvs(loc=45, scale = 20, size=100)\n",
      "print stats.ttest_ind(x1, x2)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(array(1.8575787825385832), 0.064713710289353077)\n"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "SciPy: integrate a function\n",
      "=====\n",
      "$$\n",
      "\\int\\limits_{-\\infty}^{+\\infty} e^{-x^2}dx \\;=\\; ?\n",
      "$$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy import integrate"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def the_func(x):\n",
      "    return np.exp(-x ** 2)\n",
      "print integrate.quad(the_func, -np.Inf, np.inf)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(1.7724538509055159, 1.4202636780944923e-08)\n"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"Exact Value sqrt(pi) = {0} ...\".format(np.pi ** 0.5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Exact Value sqrt(pi) = 1.77245385091 ...\n"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Netcdf4-python\n",
      "=================\n",
      "##The python module that for reading and writing NetCDF files in python, created by *Jeff Whitaker*. \n",
      "##Requires installation of C libraries:\n",
      "\n",
      "* **NetCDF4**\n",
      "\n",
      "* **HDF5**\n",
      "\n",
      "\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "#Netcd4-python\n",
      "\n",
      "## Below is the example of creating a netcdf file using the **netcdf4-python** library."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from netCDF4 import Dataset\n",
      "file_name = \"test.nc\"\n",
      "if os.path.isfile(file_name):\n",
      "    os.remove(file_name)\n",
      "    \n",
      "#open the file for writing, you can Also specify format=\"NETCDF4_CLASSIC\" or \"NETCDF3_CLASSIC\"\n",
      "#The format is NETCDF4 by default\n",
      "ds = Dataset(file_name, mode=\"w\")"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [],
     "prompt_number": 44
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Netcdf4-python: create dimensions\n",
      "=========="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ds.createDimension(\"x\", 20)\n",
      "ds.createDimension(\"y\", 20)\n",
      "ds.createDimension(\"time\", None)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "<netCDF4.Dimension at 0x3fe45a0>"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Netcdf4-python: create variables\n",
      "=========="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "var1 = ds.createVariable(\"field1\", \"f4\", (\"time\", \"x\", \"y\"))\n",
      "var2 = ds.createVariable(\"field2\", \"f4\", (\"time\", \"x\", \"y\"))\n",
      "\n",
      "#add netcdf attributes\n",
      "var1.units = \"m/s\"\n",
      "var1.long_name = \"zonal wind speed\""
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "#Write data to the file"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#generate random data and tell to the program where it should go\n",
      "data = np.random.randn(10, 20, 20)\n",
      "var1[:] = data\n",
      "var2[:] = 10 * data + 10\n",
      "#actually write data to the disk\n",
      "ds.close();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%bash\n",
      "#check the file header:\n",
      "ncdump -h test.nc"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "netcdf test {\n",
        "dimensions:\n",
        "\tx = 20 ;\n",
        "\ty = 20 ;\n",
        "\ttime = UNLIMITED ; // (10 currently)\n",
        "variables:\n",
        "\tfloat field1(time, x, y) ;\n",
        "\t\tfield1:units = \"m/s\" ;\n",
        "\t\tfield1:long_name = \"zonal wind speed\" ;\n",
        "\tfloat field2(time, x, y) ;\n",
        "}\n"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Netcdf4-python: reading a netcdf file\n",
      "==============\n",
      "\n",
      "Open the netcdf file for reading:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from netCDF4 import Dataset\n",
      "ds = Dataset(\"test.nc\")"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Select variables of interest: no data loading happens at this point\n",
      "======"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#what variables are in the file\n",
      "print ds.variables.keys()\n",
      "\n",
      "#now data is a netcdf4 Variable object, which contain only links to the data\n",
      "data1_var = ds.variables[\"field1\"]\n",
      "data2_var = ds.variables[\"field2\"]\n",
      "#You can query dimensions and shapes of the variables\n",
      "print data1_var.dimensions, data1_var.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[u'field1', u'field2']\n",
        "(u'time', u'x', u'y') (10, 20, 20)\n"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Read data from the netcdf file for corresponding variables and time steps\n",
      "=========="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#now we ask to really read the data into the memory\n",
      "all_data = data1_var[:]\n",
      "#print all_data.shape\n",
      "data1 = data1_var[1,:,:]\n",
      "data2 = data2_var[2,:,:]\n",
      "print data1.shape, all_data.shape, all_data.mean(axis = 0).mean(axis = 0).mean(axis = 0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(20, 20) (10, 20, 20) 0.00891344\n"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Outline part II\n",
      "========\n",
      "\n",
      "* **Plotting libraries**\n",
      "\n",
      "    * Matplotlib\n",
      "\n",
      "    * Basemap\n",
      "    \n",
      "* **Grouping and subsetting temporal data with ```pandas```**    \n",
      "\n",
      "* **Interpolation using ```cKDTree``` (```KDTree```) class**\n",
      "\n",
      "* **Speeding up your code**"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Matplotlib\n",
      "============\n",
      "\n",
      "##The module for creating publication quality plots (mainly 2D), created by *John Hunter*.\n",
      "\n",
      "##[Matplotlib gallery](http://matplotlib.org/gallery.html)\n",
      "\n",
      "\n",
      "An alternative is PyNGL - a wrapper around NCL developed at NCAR.\n",
      "\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Let us draw the data we just saved to the netcdf file.\n",
      "====\n",
      "\n",
      "Do necessary imports and create configuration objects."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "from matplotlib.colors import BoundaryNorm\n",
      "from matplotlib import cm\n",
      "\n",
      "levels = [-30,-10, -3,-1,0,1,3,10,30,40]\n",
      "bn = BoundaryNorm(levels, len(levels) - 1)\n",
      "cmap = cm.get_cmap(\"jet\", len(levels) - 1);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 52
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Matplotlib: Preparations and options\n",
      "======="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#\n",
      "font_size = 20\n",
      "params = {\n",
      "        'xtick.labelsize': font_size,\n",
      "        'ytick.labelsize': font_size,\n",
      "        'figure.figsize' : (9, 3)\n",
      "}\n",
      "plt.rcParams.update(params) #set back font size to the default value\n",
      "\n",
      "def apply_some_formatting(axes, im):\n",
      "    axes[1].set_yticks([])\n",
      "    #format colorbar axes\n",
      "    cax = axes[2]\n",
      "    cax.set_aspect(20) \n",
      "    cax.set_anchor(\"W\")\n",
      "    cb = plt.colorbar(im2, cax = cax);"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [],
     "prompt_number": 53
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Matplotlib: plotting 2 fields with nonlinear and shared colorbar\n",
      "============"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axes = plt.subplots(nrows=1, ncols=3)\n",
      "im1 = axes[0].contourf(data1.transpose(), \n",
      "                       levels = levels, \n",
      "                       norm = bn, cmap = cmap)\n",
      "im2 = axes[1].contourf(data2.transpose(), \n",
      "                       levels = levels, \n",
      "                       norm = bn, cmap = cmap)\n",
      "apply_some_formatting(axes, im2)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+pb3EQy0ajt0y3URT9GRECLTZxtCDFEV+xfVnxmjcLwbF7/rrr8eyZcu8NsM0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\nOHHF/w9NW3ThKoUWQQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3fe8d90>"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Execises: Matplotlib\n",
      "=============\n",
      "\n",
      "* Reproduce the panel plot given in the example. (Try different [colormaps](http://matplotlib.org/examples/color/colormaps_reference.html))\n",
      "\n",
      "* Plot a timeseries of daily random data. (Show only month names as Jan, Feb, .. along the x-axis, make sure the labels do not overlap)\n",
      "\n",
      "* Plot CRU temperatures using contourf and pcolormesh. The file can be downloaded from [here](http://scaweb.sca.uqam.ca/~huziy/example_data/mean_temp_cru.nc)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Pandas\n",
      "====\n",
      "\n",
      "* ## Initially designed to process and analyse long timeseries\n",
      "\n",
      "\n",
      "* ## Author: *Wes McKinney*\n",
      "\n",
      "* ## Home page: [pandas.pydata.org](http://pandas.pydata.org/) "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Download and analyse teleconnection indexes\n",
      "======\n",
      "For pandas 1D examples we will load, plot and correlate different teleconnection indices ([source](http://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/teleconnections.shtml))."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Set name to link mapping\n",
      "name_to_link = {\n",
      "\"AAO\" : \"ftp://ftp.cpc.ncep.noaa.gov/cwlinks/norm.daily.aao.index.b790101.current.ascii\",\n",
      "\"AO\" : \"ftp://ftp.cpc.ncep.noaa.gov/cwlinks/norm.daily.ao.index.b500101.current.ascii\",\n",
      "\"NAO\" : \"ftp://ftp.cpc.ncep.noaa.gov/cwlinks/norm.daily.nao.index.b500101.current.ascii\",\n",
      "\"PNA\" : \"ftp://ftp.cpc.ncep.noaa.gov/cwlinks/norm.daily.pna.index.b500101.current.ascii\"\n",
      "}"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [],
     "prompt_number": 55
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Define a helper download function\n",
      "======"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def download_link(url, local_path):\n",
      "    if os.path.isfile(local_path):\n",
      "        return\n",
      "    \n",
      "    import urllib2\n",
      "    s = urllib2.urlopen(url)\n",
      "    with open(local_path, \"wb\") as local_file:\n",
      "        local_file.write(s.read())   "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Download data locally if needed\n",
      "for name, link in name_to_link.iteritems():\n",
      "    fname = os.path.basename(link)\n",
      "    if not os.path.isfile(fname):\n",
      "        download_link(link, fname)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Read data into memory\n",
      "import pandas as pd\n",
      "df_list = []\n",
      "for name, link in name_to_link.iteritems():\n",
      "    fname = os.path.basename(link)\n",
      "    dfi = pd.DataFrame.from_csv(fname, sep = r\"\\s+\", index_col=[0,1,2], parse_dates=False)\n",
      "    dfi.columns = [name]\n",
      "    dfi = dfi.select(lambda t: \"*\" not in t[2]) # because some data is like: 2003  4 30*******\n",
      "    dfi.index = dfi.index.map(lambda t: [int(ti) for ti in t])\n",
      "    dfi.index = dfi.index.map(lambda t: datetime(*t))\n",
      "    df_list.append(dfi)\n",
      "df = pd.concat(df_list, axis = 1)\n",
      "df = df.dropna()\n",
      "df.head(3) #The periods "
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>PNA</th>\n",
        "      <th>AAO</th>\n",
        "      <th>NAO</th>\n",
        "      <th>AO</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1979-01-02</th>\n",
        "      <td>-0.319</td>\n",
        "      <td>-0.888</td>\n",
        "      <td>-0.838</td>\n",
        "      <td>-1.623</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1979-01-03</th>\n",
        "      <td>-0.561</td>\n",
        "      <td> 0.255</td>\n",
        "      <td>-0.538</td>\n",
        "      <td>-1.922</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1979-01-04</th>\n",
        "      <td>-0.479</td>\n",
        "      <td> 0.862</td>\n",
        "      <td>-0.225</td>\n",
        "      <td>-1.180</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "              PNA    AAO    NAO     AO\n",
        "1979-01-02 -0.319 -0.888 -0.838 -1.623\n",
        "1979-01-03 -0.561  0.255 -0.538 -1.922\n",
        "1979-01-04 -0.479  0.862 -0.225 -1.180"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Plot the timeseries\n",
      "====== "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.plot();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HsqyWLfQfjnk5U2tPrgXA6ooAvh97YmAFEkcCAA7X1Ulu4QBQxVubZmdj1Srg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j2cy0vooLE0cOlx7MKFC5g2bRrGjh2LAUrR2BwAIUQxqq0SzO7uWN20qeieVJdBa3PhvYnMVeai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\nlJQUWFhYwMTEBMbGx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lZWws7ODhYWFjA0NMT777+v8oCp54VrfALXqNnootlqikwmw8OH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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x45e9a50>"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "On separate panels\n",
      "====== "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "axes = df.plot(subplots=True, figsize=(10,6), yticks=[-4, 0, 4], lw = 0.5, \n",
      "               style = [\"g\", \"r\", \"b\", \"k\"]);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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WA/D3DavJii6/deFyg7kld5GuiLiZcUUKOeKCJe+kihZj0eo2uxsnRGbvm42P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Zn0WbuW2onHAXsF8txmqaVbGZU1qwLZ2dSvgwbZQ8fw+lSvHklLXn+dDKuDQSrHAw+Isvzf/wHt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\ntFRUVFRUVFRU3MT/A2ytI8uvYaNgAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4d71b10>"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "What about correlations\n",
      "====== "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.corr()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>PNA</th>\n",
        "      <th>AAO</th>\n",
        "      <th>NAO</th>\n",
        "      <th>AO</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>PNA</th>\n",
        "      <td> 1.000000</td>\n",
        "      <td>-0.007727</td>\n",
        "      <td> 0.034345</td>\n",
        "      <td>-0.158895</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AAO</th>\n",
        "      <td>-0.007727</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.050351</td>\n",
        "      <td> 0.050095</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NAO</th>\n",
        "      <td> 0.034345</td>\n",
        "      <td> 0.050351</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.552754</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AO</th>\n",
        "      <td>-0.158895</td>\n",
        "      <td> 0.050095</td>\n",
        "      <td> 0.552754</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 61,
       "text": [
        "          PNA       AAO       NAO        AO\n",
        "PNA  1.000000 -0.007727  0.034345 -0.158895\n",
        "AAO -0.007727  1.000000  0.050351  0.050095\n",
        "NAO  0.034345  0.050351  1.000000  0.552754\n",
        "AO  -0.158895  0.050095  0.552754  1.000000"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Plot monthly mean indices\n",
      "====== "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_monthly = df.groupby(by = lambda d: datetime(d.year, d.month, 15)).mean()\n",
      "df_monthly.hist(figsize=(10, 6), sharex = True, sharey = True);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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dwtatWyEIAlatWoWkpCTd/gUFBZg8eTKcnZ0RGxsLT09PbNmyBUePHkV0dDRy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\nwPwfkTpvswk66gwAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4c80590>"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Exercises: Pandas\n",
      "====== \n",
      "\n",
      "* Using the data in the examples calculate monthly climatologies of the indices and plot them. See if the correlations are different for the climatological values.\n",
      "\n",
      "* Further reading: usually we want do time selecting, grouping, merging on 3D or 4D fields. It is also possible with pandas (Checkout Panel data container)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "from matplotlib.dates import DateFormatter\n",
      "df_monthclim = df.groupby(by = lambda d: datetime(2001, d.month, 15)).mean()\n",
      "df_monthclim.plot(lw=2)\n",
      "ax = plt.gca()\n",
      "ax.xaxis.set_major_formatter(DateFormatter(\"%b\"))\n",
      "print df_monthclim.corr()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "          PNA       AAO       NAO        AO\n",
        "PNA  1.000000  0.422835  0.826942 -0.392868\n",
        "AAO  0.422835  1.000000  0.153693 -0.560160\n",
        "NAO  0.826942  0.153693  1.000000 -0.272852\n",
        "AO  -0.392868 -0.560160 -0.272852  1.000000\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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f0OlgwwYYOrQkt6FRClTUPubo6EhSUhKhoaFYZHhqN2zYwLZt2/Dz8wPUyYYu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\nOnVicU5tx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       "text": [
        "<matplotlib.figure.Figure at 0x5bf1490>"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "# Basemap\n",
      "## The author is Jeff Whitaker.  \n",
      "## Basemap code is available on [github](https://github.com/matplotlib/basemap).\n",
      "\n",
      "## Example [gallery](http://matplotlib.org/basemap/users/examples.html).\n",
      "\n",
      "PyNGL - can be used as an alternative."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "# The usual workflow with the library:\n",
      "\n",
      "\n",
      "* **Create a basemap object**\n",
      "```\n",
      "basemap = Basemap(projection=\"...\", ...)\n",
      "```\n",
      "\n",
      "\n",
      "* **Draw your field using:** \n",
      "```\n",
      "basemap.contour(), basemap.contourf(), \n",
      "basemap.pcolormesh()\n",
      "```\n",
      "\n",
      "* **It provides utility functions to draw coastlines, meridians and shapefiles (only those that contain lat/lon coordinates), mask ocean points.**\n",
      "\n",
      "* **Drawing the colorbar is as easy as:**\n",
      "```\n",
      "basemap.colorbar(img)\n",
      "```    \n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "# Basemap\n",
      "Can be used for nice map backgrounds"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from mpl_toolkits.basemap import Basemap\n",
      "b = Basemap()\n",
      "fig, (ax1, ax2) = plt.subplots(1,2)\n",
      "\n",
      "b.warpimage(ax = ax1, scale = 0.1); \n",
      "im = b.etopo(ax = ax2, scale = 0.1); "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wJbZ1AlvKc78yYEuoBSquEzZmiOozhTrckWZ+JqqUxAmolSK+8+3HmJus8MCd\n+6hVS5y7ukmnPyQKQ/6n3/08Y5WIlY0et+xt8LZ7D/Cnn3mc7/+Ohzl+9CDf+RO/yZ998J9Sq5Xo\n9EeMkozJ8QaSnFJ5jKeeeZEf+LmPI4QXneCKZYzWYPMcnEEGns51Jvf1V4pveXA//+4n30e1WnnN\noPSNzWs0/EXTfLVppT+1NfEu7vHxT3ycyytd/vL5JmcuLLOxvoyuzDHavE7aXvbB0eR+JYTLt2/g\nEMgg9g+TGl1qYJI2KhpDSK+1UHEVXa4Vwk11g5p1DmNyTD7yxXQCR4YKysXMXiK3QJ6jUOnmHhh5\nuaYPqkVfGCQZP/X+B/hH33wv5TjkZ3754zz5zFlavQFZlmNNiorrfpbk/EAVzlOZOINQmsWxhCtN\nqFUrfMNdkzxwz9287b5bqJVKRKEq9A5b9Kpj6fpVapUxut0OcRyjVEAchdRqVVbWWvz+p77Mr/7x\ns74uzrNPDnydHSjlEKaLUZMAxfJBjc7W2DWT8MGf/nE6/YT2EM6cfIYTL1+g2Vri2K551pstzm8m\nXF+5gtXz6GgeK+s4qQvCyG2//v+kK9gcQUZdXiIZDDjz+T973QKCX7o8ILOgHFgrQRkmLERpxmqa\nkcSKvzndZhhGVKo15tUap5/4Mhf0fdjqBNZZpJQ3BbmC7CrAt1LKL0UUAmkt0o6ollJmK0O+ee84\nInfkDoSKSNMRwkqCALJRi/G1l6C/xMK04dn2OaLKHOubfRbHp4hL9zMcDGmML9JvX6ZSuk6zP8v1\nzc9xfH+ICr6Xsc6fsaAl2AhtYsgjLm+0KIeTnN5YZ/iG96LUkDQPWTNDhrkj6Q3oddtUhgmTtQoL\ni9NELuSUs5T6LR6sKKbyFlONOlrpbUCw1Z5/WzvfPLHY+kuzjCzNkdoxHFqe+dxv0IvG6Rx+C3Fl\nNxO1eZS1/PlnHuXEeo6YPkpanUTEZawLMM5hSFGddcqjJi4qI6OAer1Ot7PB2PRuXKiJ85xacpUH\n9iywr1aiLKXntqwlM4LE5AxxZIHACU1JKxQCTOYDo/I+wmDJncQUUrNAeeATKY1NfdpN6YBg9fPM\nphmt4SpX5AHU1F7iSoVQevFyFErCzBYTGMfNqn7rLIP+KtXqAnk+YmPzHCvZC6TmAndXp5gjJreO\nl/UqSS6IcljqJjxUX+Bsd4BJOgQ2ZToqM+r3aOmAK9FeHjj4PUXKD7YF1OBXufkXgxASKQRbqqTU\n5PSyDZ7p/bP5AAAgAElEQVRb/23OnzxN3Mmo1KY5sPetzE8f5dkrj6KR3HfofVRL49vvNf9q2wq6\nG+sEzNZqBimL1beSVME/2R99/YBg7sEfxaQ9HwDcACFLEIzh8iFSRzgctnMBwhnSQZ+8v4lJh9h0\nyNbSr+JuN2hhuCGoEqpYGaC2xYBCanS5QTw5v51S+IV//lb+83c9QJKMyLMMHYR87ulX+ODvPc2z\nJ69jkIxVI2qViLsPz/KtD93CT/6HTxCXIj7wE+/i0O5JTrx8iY9+5iV+++f+IULC5750hh/4md/n\nJ7//EW7ZN8nvfOyL3HnrXiqxphSFCCl57MlXefL5Kx6R514rYZOBX2HhHLo2jipSJDIIsOmI6brg\n0d/6l0xPTW7Xfuvlb6UStpYmwVcABOGz1MYYzp69wCuvvkir1eXClWsEUYVHn93k/GqGlDBau4oZ\ndoFiqaajEPcVezVspWO2Qo2UXu0vvchTBpViBUYJVaqgotL2/g3OZtsB3itZCorf5tt7UADFMrK4\nqNuWYM4VKxN8zT0QkUzXQ/78g+/n2kaf9//077C2OSBwXUTYABn4gC8UNh/x9jfM8uknLxCLAeUg\nZ5ikENSZnhzju7/t7TTGYkqR5fCeccYqNSYmp3E2p1QqI5wgMx5AlsoxxhiCICDPDVIInnv+ab50\n4kl27bmF2bk9NFubnF221MohV66t8cd/+nGa+STlEH7tZ76Tv/zcF1i6tsqLL3yJldECxDM4kyJd\nF12aIhMRk/os/9X3vYfVZovf+shvYisHsLJGNlzHuSqqNAdonwqJJiBvQ7qMqt2KRfjVEQKkcGhl\nCKTA5R0CMQDRJbULnP3kf3jdAoKPnuqTyq25QKHDwBEZiuW/kGhITMrvnXiV63oPLRswHAic8Vol\nKf0oEsp6gbCSWCUIkFgliTUoAUpJgtAQCEEpNrxzLmW/CSg7yWef+wWO7s24JVzgRPsSb6ns41mj\nUfVvhGABrRUqy3FmSL9/kp45RVx9L4s658nnf4XJyR5pdw97Dj7MLckXETpnaSPm0ux/xrHoIpXO\ndZ7slNlVg9FwQK+1QhrXuK4WWJ+8FSEMowQGfUura8ilQoYl6tkqR2dKzDbqNHLL6euXuFX1uLem\nGatOEAT6bwUCwGvA0pZ+IMsy1tbWeOXlR+k2m2TDPmFZM6hP4fa9jYmFu3BS8Kef+guujd2OXjyE\nzQ15f5U8HAcnSZVDJ0MwBtHvQFBCkiJUgnYprtWmdsub0SVNtRwwOSk4Fo4orfTBGBJhcVFIpJRn\nJQWE5sYyvBCJVD5V5pzDWkfufBpOFsu3pXUo6ZcNj4UlXHKNiyf+Zyp2Gnfsfcj6JPMqxuUeHEol\nUKQIB+cuP87hvW/DupwsywnDCOcM/WGbgXyeLOmw0tvg1OkvcqX9Mof23Uaz12G2NEa57zBRwHnR\nJp5uUHNVnOmSBxHNtTVm1B72Td/L0tJztNMmjco09WgGUQuJ8gp37HknpWAcY0b84Z/9M249+AiR\nnmDvwQeJwur2e8vzHOccYRDQG2zwqS/+IpMzt/C223/QE7BYpPC6ly19iCjSrG6bdXzt5NNah1Ff\nkZou7L1HJr5+QLD3G/8F6aCF1TUQCswAVMkHaiH9jDBLYYvCdXiqE4E1hnzUxw1buCzF2hQfX7bW\nWEdIHSKC0At9VLC9XE2XKzgnwBkacco//uZDvOMtd1Ebn+Wf/5s/4V993wMc2LuIcBnlconhyKu6\nH3vqNN/29js4fXGN7/qpj7JvocG9ty7yk+9/iAtXN/g3H/or/u2PvZNv/dH/DeMC/uJ/+R4W52b5\nwnNnmZ2sMj9ZIctyXjx9FSEcxw7uptkZ8YHf+RytTsILr5wjTwYoZ7DGFI5Koct1dLWxrQL94beF\nTMzt55FvuJ9jRw4g3E3U999hTkCWZbz6yissr67T7vW5vnSVR19Kef5sE5uNSPstlI6xJsWO+v53\nOLB+GSfFhk1C4FeIbG0mhG9/vxoj8J/bIs0yKgj9fbbKKfymRgiLzT3Ak8LdmKJtA4Yb1JvXEvjO\n6JcSCd77jtv4l+9/K+VyiYUHf4Kxxvj2hjye+vdAo16N+fSv/gBRoHnqS0/zsc+c4Mj+mMXpBseO\nvYkzV9ucO3uWd3/zQyzMTRIEod9EBUGapZSiQoBa5E0vXDhPtdYgyxJqlRrlSplS7BHyYDAiNZYo\nDBkNh6RpwrWrlzAmZ235Mv3BgMnpeYyxBFFM7iTf/SM/x+E7HiTvLxOVY1Q+oBY7vvWbHuauY3sK\nZkQwSFNMPuLCxbP8/h99mHPNcUy4HykNQpa9UliVEToqdBR+Iy85ukKoUowJsEkbFYQYFJlssPLk\n61dD8AenriGpgdjaNCooTprtWT5b6RNrUKJMx+asJY6rmeJSO+NSKhm4iFFqMcYRKg0ipxSGRIEg\nChyBVsjQUSvBWNVxvCKobw5waUrSukhm/or745C2gBX5ILb7l+yrlRj028yPNdjsZYxHVf5otUM2\neoamKPOOOz7AxuYVVq48wVPXfpdDC3dwZO8PEGx+ivL0P8bUJmif//fcMj3Gle4Y/eZ5jtZKZL2M\nvmggs5ygK+momGT+TtbrDc70a2zkNVpOIWzhR2XOpOzw8O5xarnfp0A9/2tYsYuJuMzBw7dSiuNt\nRuSr2baQ0DnSNGV1ZZlTJz+PMRlJ1mduepr1XW8jmj3KenOTv7m6QbDnAHm3Q2ezB0FErssIaxFB\ngGlt4sjRKmC0cZ00dyhdJotq6NokulwiUKCkpSQN5ThnX5Rw28QUAZKEHONAIwmEQuSWkQRtHbpY\nXi4LBiAzFoNCaolyEOHT00pYtBNgLKcv/yG3m+usRQOC+Z9GaEPoBGEhwPT9Daxp8cE/fCcDnXFs\n6l5uPfLtZK11Ov1rnF96ionZY+xfuJ2TvROcWz/F5MQ4ozShf+UaUwf2Mez1acQxrSwlHKuQJ5Lc\nDMg767xp8gAzY/Psb0xjs5wXN5e5YDY5UJ3gdNqmNeyA1Ng8Zaxa5fZoijkauHaHV9eukpo6/+Cd\nv8J6+zIlHTNM+wjpEGnGE698mJfXPo/oDSnJGtPTR+n2rnF48W3cd+f3UqlMF6yruyEAdRnWvlZs\nut0f5A1Xrgtf/p4jja8fECw8/K/8w8KSP2ZvNPqWgtEpTcNeoZpeph72GRsrsbh7PytrywQ65loz\noVyZ5HovxAaTtEaSgJTNUYSzDi39LoTWgsUibMZUdUSzk3B4tsSrGzUmawHN1oAky4njgGN7pvje\nd92FVpIkzXjXW45hnGEwHKK1QMmI5WaPTz11gcdPnOf05RbGGh795e9jbmKM80trvPdffZQ4DPgX\n7/8Gju0ZZ6JeoVGvUimXOHvuLCcvbnDfbXsIo5gwDMnTjJX1JmudlN/82LM89eVXMf2W77SVBiou\n4/A7hlVCy7fdU0OGFb73u97BbceOvoYOloivEB7eECQC9AZ9PvaJTxJGFV69mhLplBdOX+ezL6yR\ndjaKPR6kX8tf5PJVGOPyjFHrWgHKwJmseNZr36vfUKfYs0EqnJIoXSKePUBIFycks5URU6UWyo2I\nohK91hq7Dr6JTz6f+zScEMV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jMOxTKVfAWpIkRiMR1uJKhzQzxJlPoh18ZRmpeTTHGghj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gAAAg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OcSj+P4tmtjJkWV2lfnMcr8zELsXwshcM4hRar48d4jpaRpW9qm4d7hPfTORZSW/MLhgrvV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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5515710>"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "# Basemap: display model results in rotated lat/lon projection"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f_name = \"monthly_mean_qc_0.1_1979_05_PR.rpn.nc\"\n",
      "base_url = \"http://scaweb.sca.uqam.ca/~huziy/example_data\"\n",
      "#Fetch the file if it is not there\n",
      "url = os.path.join(base_url, f_name)\n",
      "download_link(url, f_name)\n",
      "\n",
      "#read the file and check what is inside\n",
      "ds_pr = Dataset(f_name)\n",
      "print ds_pr.variables.keys()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[u'rlon', u'rlat', u'level', u'time', u'lon', u'lat', u'rotated_pole', u'preacc']\n"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Read data from the NetCDF file\n",
      "===="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rotpole = ds_pr.variables[\"rotated_pole\"]\n",
      "lon = ds_pr.variables[\"lon\"][:]\n",
      "lat = ds_pr.variables[\"lat\"][:]\n",
      "data = ds_pr.variables[\"preacc\"][:].squeeze()\n",
      "#rotpole.ncattrs(); - returns a list of netcdf attributes of a variable\n",
      "print rotpole.grid_north_pole_latitude, \\\n",
      "      rotpole.grid_north_pole_longitude, \\\n",
      "      rotpole.north_pole_grid_longitude"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "37.8787 106.65 176.709\n"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Creating a basemap object for the data\n",
      "======"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lon_0 = rotpole.grid_north_pole_longitude - 180\n",
      "o_lon_p = rotpole.north_pole_grid_longitude\n",
      "o_lat_p = rotpole.grid_north_pole_latitude\n",
      "b = Basemap(projection=\"rotpole\", \n",
      "            lon_0=lon_0, \n",
      "            o_lon_p = o_lon_p, \n",
      "            o_lat_p = o_lat_p,\n",
      "            llcrnrlon = lon[0, 0], \n",
      "            llcrnrlat = lat[0, 0],\n",
      "            urcrnrlon = lon[-1, -1], \n",
      "            urcrnrlat = lat[-1, -1], \n",
      "            resolution=\"l\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 67
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Converting to the projection coordinates and back to geographic coordinates\n",
      "======="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#(lat, lon) -> (x, y)\n",
      "x, y = b(lon, lat)\n",
      "\n",
      "#(x,y) -> (lat, lon)\n",
      "loninv, latinv = b(x, y, inverse=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 68
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Define plotting function to reuse\n",
      "=====\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_data(x, y, data, ax):\n",
      "    im = b.contourf(x, y, data, ax = ax)\n",
      "    b.colorbar(im, ax = ax)\n",
      "    b.drawcoastlines(linewidth=0.5);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 69
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Plot the data\n",
      "====="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, (ax1, ax2) = plt.subplots(1,2)\n",
      "#plot data as is\n",
      "plot_data(x, y, data, ax1)\n",
      "#mask small values\n",
      "to_plot = np.ma.masked_where(data <= 2, data) \n",
      "plot_data(x, y, to_plot, ax2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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gHI64tfRrdrdt25YjMUdSUhJ79uzB29ubxo0bu2y8CnnDjh07HFpzn+slS3rh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eCLyQa6rDpNtder0mxiU/5OCEXs8wEZK35CN+9LKFUKMeofDD/9gLZ3H0bWXsnUbREIvn6IBgNt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\nVFRUVFTyCKpoq6ioqKio5BH+D6wR3Ew/o3YSAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5a4f890>"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Masking oceans is as easy as\n",
      "====="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from mpl_toolkits.basemap import maskoceans\n",
      "lon1 = lon.copy()\n",
      "lon1[lon1 > 180] = lon1[lon1 > 180] - 360\n",
      "data_no_ocean = maskoceans(lon1, lat, data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 71
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Plot masked field\n",
      "====="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1,1)\n",
      "plot_data(x, y, data_no_ocean, ax)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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UU7q+USgU9OvXj+fPn9OmTRtSUlIIDw9nwoQJFvOdlIdpWGYhvZU9yx9NJnnS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\nXQAAAAFJREFUFDLiqZUAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5daa710>"
       ]
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "# Basemap: plotting wind field (read and convert units)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "download_link(\"http://scaweb.sca.uqam.ca/~huziy/example_data/wind.nc\", \"wind.nc\")\n",
      "ds_wind = Dataset(\"wind.nc\")\n",
      "u = ds_wind.variables[\"UU\"][:]\n",
      "v = ds_wind.variables[\"VV\"][:]\n",
      "coef_wind = 0.51444444444 #m/s in one knot\n",
      "ds_wind.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 73
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "# Basemap: plotting wind field"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "fig.set_size_inches(6,8)\n",
      "Q = b.quiver(x[::8, ::8], y[::8, ::8], \n",
      "             u[::8, ::8] * coef_wind, v[::8, ::8] * coef_wind)\n",
      "# make quiver key.\n",
      "qk = plt.quiverkey(Q, 0.35, 0.1, 5, '5 m/s', labelpos='N',\n",
      "                   coordinates = \"figure\", \n",
      "                   fontproperties = dict(weight=\"bold\"))\n",
      "b.drawcoastlines(linewidth = 0.1);\n",
      "plt.savefig(\"wind.png\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VKhWTkpLMDUp+fn6p6YS0tLSH3n77f434+HjKZDKrU3N6vZ4zZswwb5W+adMm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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4c80390>"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "# Basemap: plotting wind field (saved image, the legend is not cropped)\n",
      "<img src=\"files/images/wind.jpeg\"/>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Exercises: Basemap\n",
      "====="
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "* Plot the CRU temperatures ([here](http://scaweb.sca.uqam.ca/~huziy/example_data/mean_temp_cru.nc)) map using basemap.\n",
      "\n",
      "* Plot fields `preacc` and `air` from [here](http://scaweb.sca.uqam.ca/~huziy/example_data/wm201_Arctic_JJA_1990-2008_moyenneDesMoyennes.nc) using basemap. Add meridians and parallels as well as rivers to the map. Use subplots to put the plots side by side.\n",
      "\n",
      "* Plot field `preacc` from [here](http://scaweb.sca.uqam.ca/~huziy/example_data/pm1989010100_00000498p_0.44deg_africa_PR.nc)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "# Basemap: reading and visualizing shape files (Download data)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from urllib2 import urlopen\n",
      "import re, os"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#download the shape files directory\n",
      "local_folder = \"countries\"\n",
      "remote_folder = 'http://scaweb.sca.uqam.ca/~huziy/example_data/countries/'\n",
      "if not os.path.isfile(local_folder+\"/cntry00.shp\"):\n",
      "    urlpath = urlopen(remote_folder)\n",
      "    string = urlpath.read().decode('utf-8')\n",
      "    pattern = re.compile(r'cntry00\\....\"')\n",
      "    filelist = pattern.findall(string)\n",
      "    filelist = [s[:-1] for s in filelist if not s.endswith('zip\"')]\n",
      "    \n",
      "    if not os.path.isdir(local_folder):\n",
      "        os.mkdir(local_folder)\n",
      "    for fname in filelist:\n",
      "        f_path = os.path.join(local_folder, fname)\n",
      "        remote_f_path = os.path.join(remote_folder, fname)\n",
      "        #download selected files\n",
      "        download_link(remote_f_path, f_path)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 76
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "# Basemap: reading and visualizing shape files"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bworld = Basemap()\n",
      "shp_file = os.path.join(local_folder, \"cntry00\")\n",
      "ncountries, _, _, _, linecollection = bworld.readshapefile(shp_file, \"country\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CVS9wOBzk5uYyLwubzebRdXK3rl27QkpKCmvWrMH+/fsxbtw4rF69Gh4eHjxT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\nNHHixHLvuAcOHCADAwNSVFSkOXPmEMMw5O7u3iCKYa6urjRu3Lh6H6e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5XTWFM7rNiNDQ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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x59a8b90>"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Reading country polygons (and attributes) from the shape file, necessary imports \n",
      "====="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import fiona\n",
      "from matplotlib.collections import PatchCollection\n",
      "from shapely.geometry import MultiPolygon, shape\n",
      "from matplotlib.patches import Polygon\n",
      "import shapely\n",
      "import matplotlib.cm as cm"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 78
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Reading country polygons (and attributes) from the shape file, preparations \n",
      "====="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cmap_shape = cm.get_cmap(\"Greens\", 10)\n",
      "bounds = np.arange(0, 1.65, 0.15) * 1e9\n",
      "bn_shape = BoundaryNorm(bounds, len(bounds) - 1)\n",
      "\n",
      "bworld = Basemap()\n",
      "populations = []; patches = []\n",
      "\n",
      "def to_mpl_poly(shp_poly, apatches): \n",
      "    a = np.asarray(shp_poly.exterior)\n",
      "    x, y = bworld(a[:, 0], a[:, 1])\n",
      "    apatches.append(Polygon(zip(x, y)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 79
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Reading country polygons (and attributes) from the shape file, preparations \n",
      "====="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with fiona.open('countries/cntry00.shp', 'r') as inp:\n",
      "    for f in inp:\n",
      "        the_population = f[\"properties\"][\"POP_CNTRY\"]\n",
      "        sh = shape(f['geometry'])\n",
      "        \n",
      "        if isinstance(sh, shapely.geometry.Polygon):\n",
      "            to_mpl_poly(sh, patches)\n",
      "            populations.append(the_population)\n",
      "        elif isinstance(sh, shapely.geometry.MultiPolygon):\n",
      "            for sh1 in sh.geoms:\n",
      "                to_mpl_poly(sh1, patches)\n",
      "                populations.append(the_population)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 80
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Coloring the countries based on the attribute value\n",
      "========="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from matplotlib.ticker import ScalarFormatter\n",
      "sf = ScalarFormatter(useMathText=True)\n",
      "fig = plt.figure(); ax = fig.add_subplot(111)\n",
      "pcol = PatchCollection(patches, cmap = cmap_shape, norm = bn_shape)\n",
      "pcol.set_array(np.array(populations))\n",
      "ax.add_collection(pcol)\n",
      "cb = bworld.colorbar(pcol, ticks = bounds, format = sf)\n",
      "cb.ax.yaxis.get_offset_text().set_position((-2,0))\n",
      "bworld.drawcoastlines(ax = ax, linewidth = 0);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HxjNt/Ayio2J4nRHjhzJzwXT+2L6H8cMm6ZzzjPlTGTlheKF/l/+LZGVlMWPi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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xb8fe710>"
       ]
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Exercise: shape files\n",
      "======\n",
      "\n",
      "* There is a [script](http://scaweb.sca.uqam.ca/~huziy/example_data/fiona_test.py) which is supposed to plot world population but it contains a bug. Copy it to your working directory and try to fix the bug. Try launching it and see if the produced image is correct."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "cKDTree\n",
      "======\n",
      "\n",
      "* Is a class defined in ```scipy.spatial``` package.  \n",
      "\n",
      "* Alternatives: ```pyresample``` and ```basemap.interp```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "cKDTree - workflow\n",
      "======\n",
      "\n",
      "##1. Convert all lat/lon coordinates to Cartesian coordinates\n",
      "<pre class=\"co\">\n",
      "    (xs, ys, zs) #correspond to coordinates of the source grid\n",
      "    (xt, yt, zt) #coordinates of the target grid\n",
      "    #All x,y,z - are flattened 1d arrays  \n",
      "</pre>\n",
      "\n",
      "##2. Create a cKDTree object representing source grid\n",
      "<pre class=\"co\">\n",
      "    tree = cKDTree(data = zip(xs, ys, zs))\n",
      "</pre>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "cKDTree - workflow\n",
      "======\n",
      "## 3. Query indices of the nearest target cells and distances to the corresponding source cells\n",
      "\n",
      "<pre class=\"co\">\n",
      "dists, inds = tree.query(zip(xt, yt, zt), k = 1)\n",
      "#k = 1, means find 1 nearest point\n",
      "</pre>\n",
      "\n",
      "## 4. Get interpolated data (and reshape it to 2D)\n",
      "\n",
      "<pre class=\"co\">\n",
      "data_target = data_source[inds].reshape(lon_target.shape)\n",
      "</pre>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "cKDTree\n",
      "======\n",
      "\n",
      "* For an example usage of ```cKDTree```, please read my post at [earthpy.org](http://earthpy.org/interpolation_between_grids_with_ckdtree.html) \n",
      "\n",
      "\n",
      "* There is a folowup to that post about ```pyresample``` [here](http://earthpy.org/interpolation_between_grids_with_pyresample.html)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "cKDTree: exercise\n",
      "======\n",
      "\n",
      "* Interpolate CRU temperatures to the model grid, use inverse distance weighting for intepolation from 20 nearest points.\n",
      "\n",
      "    $$\n",
      "    T_t = \\frac{\\sum_{i=1}^{20}w_i T_{s,i}}{\\sum_{i=1}^{20}w_i}\n",
      "    $$ \n",
      "\n",
      "    where $w_i = 1/d_i^2$, $d_i$ - is the distance between corresponding source and target grid points.\n",
      "    Grid longitudes and latitudes are defined in this [file](http://scaweb.sca.uqam.ca/~huziy/example_data/wind.nc). The observation temperature field is [here](http://scaweb.sca.uqam.ca/~huziy/example_data/mean_temp_cru.nc)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Speeding up your code\n",
      "=====\n",
      "\n",
      "* Avoid loops and try to make use of Numpy/Scipy/Pandas functionality as much as possible\n",
      "* If you have a lot of computations the speedup can be achieved by delegating the computations to the NumExpr module.\n",
      "* You can use Cython to compile libraries to get C-like speed.\n",
      "* Use multiprocessing module if the tasks are fairly independent and cannot be solved using the above methods"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Multiprocessing example\n",
      "===="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from multiprocessing import Pool\n",
      "\n",
      "def func(x):\n",
      "    return x ** 2\n",
      "\n",
      "p1 = Pool(processes=1)\n",
      "p2 = Pool(processes=2)\n",
      "\n",
      "nums = np.arange(100000)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [],
     "prompt_number": 82
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Tests of performance gain by using 2 processes instead of 1\n",
      "----"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%timeit \n",
      "\n",
      "p1.map(func, nums)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1 loops, best of 3: 1.03 s per loop\n"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%timeit \n",
      "\n",
      "p2.map(func, nums)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1 loops, best of 3: 938 ms per loop\n"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Squaring example using Numpy\n",
      "==="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%timeit\n",
      "sq = nums ** 2"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10000 loops, best of 3: 128 \u00b5s per loop\n"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Squaring example using NumExpr\n",
      "==="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numexpr as ne\n",
      "print ne.detect_number_of_cores()\n",
      "print ne.evaluate(\"nums ** 2\")[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2\n",
        "[ 0  1  4  9 16]\n"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%timeit\n",
      "ne.evaluate(\"nums ** 2\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1000 loops, best of 3: 153 \u00b5s per loop\n"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ne.set_num_threads(1)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 88,
       "text": [
        "2"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%timeit\n",
      "ne.evaluate(\"nums**2\")"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10000 loops, best of 3: 168 \u00b5s per loop\n"
       ]
      }
     ],
     "prompt_number": 89
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Squaring example using cythonized function\n",
      "==="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext cythonmagic"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 90
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%cython\n",
      "def square_list(the_list):\n",
      "    cdef list result = []\n",
      "    cdef int i\n",
      "    app_meth = result.append\n",
      "    for i in the_list:\n",
      "        app_meth(i ** 2)\n",
      "    return result"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%timeit\n",
      "square_list(nums)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%timeit \n",
      "[i ** 2 for i in nums]"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Resources\n",
      "=========\n",
      "\n",
      "* Python: [http://www.python.org/](http://www.python.org/)\n",
      "* IPython: [http://ipython.org/](http://ipython.org/)\n",
      "* NumPy: [http://www.numpy.org/](http://www.numpy.org/) - there is a page NumPy for MATLAB users, might be useful for those familiar with MATLAB.\n",
      "* SciPy: [http://www.scipy.org/](http://www.scipy.org/)\n",
      "* [Scipy lectures](http://scipy-lectures.github.io/)\n",
      "* Scientific python lectures: [http://scipy-lectures.github.io/](http://scipy-lectures.github.io/)\n",
      "* Matplotlib: [http://matplotlib.org/](http://matplotlib.org/)\n",
      "* Scitools (iris and cartopy): [http://scitools.org.uk/index.html](http://scitools.org.uk/index.html)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Resources\n",
      "=========\n",
      "\n",
      "* NetCDF4: [http://netcdf4-python.googlecode.com/svn/trunk/docs/netCDF4-module.html](http://netcdf4-python.googlecode.com/svn/trunk/docs/netCDF4-module.html)\n",
      "* Basemap: [http://matplotlib.org/basemap/](http://matplotlib.org/basemap/)\n",
      "* [Notebook on shape files](http://nbviewer.ipython.org/github/mqlaql/geospatial-data/blob/master/Geospatial-Data-with-Python.ipynb)\n",
      "* My favourite python IDE: [Pycharm](http://www.jetbrains.com/pycharm/), you might also checkout many other like Eclipse, Netbeans, Spyder...\n",
      "* On creating presentations using IPython and a lot of other things: [Damian Avilla's blog](http://www.damian.oquanta.info/index.html)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#from IPython.display import HTML\n",
      "\n",
      "#s = open(\"custom.css\").read()\n",
      "#s = \"<style>\" + s + \"</style>\"\n",
      "#HTML(s)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}