01 - Scientific modules and IPython.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scientific modules and IPython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nikolay Koldunov\n",
    "\n",
    "\n",
    "koldunovn@gmail.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is part of [**Python for Geosciences**](https://github.com/koldunovn/python_for_geosciences) notes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pylab as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Core scientific packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When people say that they do their scientific computations in Python it's only half true. Python is a construction set, similar to MITgcm or other models. Without packages it's only a core, that although very powerful, does not seems to be able to do much by itself.\n",
    "\n",
    "There is a set of packages, that almost every scientist would need:\n",
    "\n",
    "<img  height=\"200\" src=\"files/core.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to talk about all exept Sympy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Installation instructions can be found in the README.md file of this repository. Better to use [rendered version from GitHub](https://github.com/koldunovn/python_for_geosciences/blob/master/README.md).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IPython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to be productive you need comfortable environment, and this is what IPython provides. It was started as enhanced python interactive shell, but with time become architecture for interactive computing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jupyter notebook "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the 0.12 release, IPython provides a new rich text web interface - IPython notebook. Here you can combine:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I love Python\n"
     ]
    }
   ],
   "source": [
    "print('I love Python')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Text (Markdown)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IPython [website](http://ipython.org/).\n",
    "\n",
    "List:\n",
    "\n",
    "* [Python on Codeacademy](http://www.codecademy.com/tracks/python)\n",
    "* [Google's Python Class](https://developers.google.com/edu/python/)\n",
    "\n",
    "Code:\n",
    "\n",
    "    print('hello world')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### $\\LaTeX$ equations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\int_0^\\infty e^{-x^2} dx=\\frac{\\sqrt{\\pi}}{2}$$\n",
    "$$\n",
    "F(x,y)=0 ~~\\mbox{and}~~\n",
    "\\left| \\begin{array}{ccc}\n",
    "  F''_{xx} & F''_{xy} &  F'_x \\\\\n",
    "  F''_{yx} & F''_{yy} &  F'_y \\\\\n",
    "  F'_x     & F'_y     & 0 \n",
    "  \\end{array}\\right| = 0\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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9R/jOwHZMGt6D+Doa9CWnp0zH3CNJx9xFihzMzWPaG6t46bMtJDSpy7Srkji/swZ9SenK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110118320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [1,2,3,4,5]\n",
    "plt.plot(x);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Rich media"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/F4rFuIb1Ie4\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x110b48358>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('F4rFuIb1Ie4')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [IPython website](http://ipython.org/)\n",
    "* [Notebook gallery](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to start Jupyter notebook you have to type:\n",
    "\n",
    "    jupyter notebook\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### You can download and run this lectures:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Web version can be accesed from the [github repository](https://github.com/koldunovn/python_for_geosciences)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can download them as .zip file:\n",
    "\n",
    "    wget https://github.com/koldunovn/python_for_geosciences/archive/master.zip\n",
    "\n",
    "Unzip:\n",
    "    \n",
    "    unzip master.zip\n",
    "\n",
    "And run:\n",
    "\n",
    "    cd python_for_geosciences-master/\n",
    "    jupyter notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Main IPyhton features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting help"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use question mark in order to get help. To execute cell you have to press *Shift+Enter*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question mark after a function will open pager with documentation. Double question mark will show you source code of the function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot??"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Press SHIFT+TAB after opening bracket in order to get help for the function (list of arguments, doc string)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sum("
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accessing the underlying operating system"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can access system functions by typing exclamation mark."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/koldunovn/PYHTON/python_for_geosciences\r\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you already have some netCDF file in the directory and *ncdump* is installed, you can for example look at its header."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "netcdf test_netcdf {\r\n",
      "dimensions:\r\n",
      "\tTIME = 1464 ;\r\n",
      "\tLATITUDE = 73 ;\r\n",
      "\tLONGITUDE = 144 ;\r\n",
      "variables:\r\n",
      "\tfloat TIME(TIME) ;\r\n",
      "\t\tTIME:units = \"hours since 1-1-1 00:00:0.0\" ;\r\n",
      "\tfloat LATITUDE(LATITUDE) ;\r\n",
      "\tfloat LONGITUDE(LONGITUDE) ;\r\n",
      "\tfloat New_air(TIME, LATITUDE, LONGITUDE) ;\r\n",
      "\t\tNew_air:missing_value = -9999.f ;\r\n",
      "}\r\n"
     ]
    }
   ],
   "source": [
    "!ncdump -h test_netcdf.nc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example of [cdo](https://code.zmaw.de/projects/cdo) usage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\r\n",
      "cdo nyear: Processed 1 variable over 1464 timesteps ( 0.03s )\r\n"
     ]
    }
   ],
   "source": [
    "!cdo nyear test_netcdf.nc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get information from OS output to the python variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cdo nmon: Processed 1 variable over 1464 timesteps ( 0.03s )', '13']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nmon = !cdo nmon test_netcdf.nc\n",
    "nmon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Return information from Pyhton variable to the SHELL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13\r\n"
     ]
    }
   ],
   "source": [
    "!echo {nmon[1]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Magic functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The magic function system provides a series of functions which allow you to\n",
    "control the behavior of IPython itself, plus a lot of system-type\n",
    "features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create some set of numbers using [range](http://docs.python.org/2/library/functions.html#range) command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And find out how long does it take to run it with *%timeit* magic function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "601 ns ± 63.2 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit list(range(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print all interactive variables (similar to Matlab function):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable       Type      Data/Info\n",
      "----------------------------------\n",
      "YouTubeVideo   type      <class 'IPython.lib.display.YouTubeVideo'>\n",
      "a              str       hello world!\n",
      "nmon           SList     ['cdo nmon: Processed 1 v<...>mesteps ( 0.03s )', '13']\n",
      "plt            module    <module 'matplotlib.pylab<...>ges/matplotlib/pylab.py'>\n",
      "x              list      n=5\n"
     ]
    }
   ],
   "source": [
    "%whos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cell-oriented magic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Receive as argument both the current line where they are declared and the whole body of the cell. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "485 ns ± 24.8 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "range(10)\n",
    "range(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thre are several cell-oriented magic functions that allow you to run code in other languages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My shell is: /bin/bash\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "\n",
    "echo \"My shell is:\" $SHELL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The variable has the value of 1\n"
     ]
    }
   ],
   "source": [
    "%%perl\n",
    "\n",
    "$variable = 1;\n",
    "print \"The variable has the value of $variable\\n\";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can write content of the cell to a file with *%%writefile* (or *%%file* for ipython < 1.0):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting hello.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile hello.py\n",
    "#if you use ipython < 1.0, use %%file comand\n",
    "#%%file \n",
    "a = 'hello world!'\n",
    "print(a)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then run it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello world!\n"
     ]
    }
   ],
   "source": [
    "%run hello.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *%run* magic will run your python script and load all variables into your interactive namespace for further use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable       Type      Data/Info\n",
      "----------------------------------\n",
      "YouTubeVideo   type      <class 'IPython.lib.display.YouTubeVideo'>\n",
      "a              str       hello world!\n",
      "nmon           SList     ['cdo nmon: Processed 1 v<...>mesteps ( 0.03s )', '13']\n",
      "plt            module    <module 'matplotlib.pylab<...>ges/matplotlib/pylab.py'>\n",
      "x              list      n=5\n"
     ]
    }
   ],
   "source": [
    "%whos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to get information about all magic functions type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "%magic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Links:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[The cell magics in IPython](http://nbviewer.ipython.org/urls/raw.github.com/ipython/ipython/1.x/examples/notebooks/Cell%20Magics.ipynb)"
   ]
  }
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