introduction_to_course.ipynb

{
 "metadata": {
  "name": "Lecture-0-Scientific-Computing-with-Python"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Introduction to scientific computing with Python\n",
      "------------------------------------------------\n",
      "------------------------------------------------\n",
      "\n",
      "David Schryer (schryer@ut.ee)\n",
      "\n",
      "* https://github.com/schryer    \n",
      "* https://bitbucket.org/schryer\n",
      "\n",
      "All notebooks in this lecture series are available at\n",
      "\n",
      "* https://github.com/schryer/python_course_material"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The role of computing in science\n",
      "--------------------------------\n",
      "--------------------------------\n",
      "\n",
      "Computing has emerged as a very important part of science.  Currently, the vast majority of both experimental and theoretical papers involve some numerical calculations, simulations and/or computer modeling.  Computation can even be viewed as a separate branch of scientific inquiry:\n",
      "\n",
      "<img src=\"https://rawgithub.com/schryer/python_course_material/master/images/theory-experiment-computation.svg\" style=\"width: 400px;\">\n",
      "\n",
      "**However, computation presents new problems in the communication and reproducibility of scientific results.**\n",
      "\n",
      "It is considered unscientific to withhold crucial details in a theoretical proof or experimental method that hinder other scientists from reproducing the results.  However, in current practice it is rare that source code is published along with computed results. Depending on the complexity of this code, the effect on the reader interested in reproducing the results ranges from minor annoyance to being completely blocked in their task. \n",
      "\n",
      "A number of editorials in high-profile journals have called for increased openness in computational sciences. Some prestigious journals, including Science, have even started to demand of authors to provide the source code for simulation software used in publications to readers upon request. \n",
      "\n",
      "The main goal of this course is to empower scientists and engineers with tools that enable one perform scientific calculations and share both the source code and results of those computations in a reproducible form. \n",
      "\n",
      "### References\n",
      "\n",
      " * [Reproducible Research in Computational Science](http://dx.doi.org/10.1126/science.1213847), Roger D. Peng, Science 334, 1226 (2011).\n",
      "\n",
      " * [Shining Light into Black Boxes](http://dx.doi.org/10.1126/science.1218263), A. Morin et al., Science 336, 159-160 (2012).\n",
      " \n",
      " * [The case for open computer programs](http://dx.doi.org/doi:10.1038/nature10836), D.C. Ince, Nature 482, 485 (2012)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Requirements for scientific computing\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "\n",
      "**Replication** and **reproducibility** are two of the cornerstones in the scientific method. With respect to numerical work, complying with these concepts have the following practical implications:\n",
      "\n",
      "* **Replication**: An author of a scientific paper that involves numerical calculations should be able to reproduce the results upon request. \n",
      "\n",
      "* **Reproducibility**: Other scientists should be able to perform the same numerical simulations and get the same results, given the information about the methods used in a publication.\n",
      "\n",
      "\n",
      "To achieve these goals, we need to:\n",
      "\n",
      "* Keep and take note on *exactly* which source code and version that was used to produce data and figures in published papers.\n",
      "\n",
      "* Record information of which version of external software that was used. Keep access to the environment that was used.\n",
      "\n",
      "* Make sure that old codes and notes are backed up and kept for future reference. \n",
      "\n",
      "* Be ready to give additional information about the methods used, and perhaps also the simulation codes, to an interested reader who requests it (even years after the paper was published!).\n",
      "\n",
      "* Ideally codes should be published online, to make it easier for other scientists interested in the codes to access it.\n",
      "### Tools for managing source code\n",
      "\n",
      "Ensuring replicability and reprodicibility of scientific simulations is a *complicated problem*, but there are good tools to help with this:\n",
      "\n",
      "* Revision Control System (RCS) software. \n",
      "    * Good choices include:\n",
      "        * git - http://git-scm.com\n",
      "        * mercurial - http://mercurial.selenic.com. Also known as `hg`.\n",
      "        * subversion - http://subversion.apache.org. Also known as `svn`.\n",
      "\n",
      "* Online repositories for source code. Available as both private and public repositories. \n",
      "    * Some good alternatives are\n",
      "        * Github - http://www.github.com\n",
      "        * Bitbucket - http://www.bitbucket.com\n",
      "        * Privately hosted repositories on the university's or department's servers.\n",
      "\n",
      "#### Note\n",
      "\t\n",
      "Repositories are also excellent for version controlling manuscripts, figures, thesis files, data files, lab logs, etc. Basically for any digital content that must be preserved and is frequently updated. Again, both public and private repositories are readily available. They are also excellent collaboration tools!"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What is Python?\n",
      "---------------\n",
      "---------------\n",
      "\n",
      "[Python](http://www.python.org/) is a modern, general-purpose, object-oriented, high-level programming language.\n",
      "\n",
      "General characteristics of Python:\n",
      "----------------------------------\n",
      "\n",
      "* **clean and simple language:** Easy-to-read and intuitive code, easy-to-learn minimalistic syntax, maintainability scales well with size of projects\n",
      "* **expressive language:** Fewer lines of code, fewer bugs, easier to maintain.\n",
      "\n",
      "Technical details:\n",
      "------------------\n",
      "\n",
      "* **dynamically typed:** No need to define the type of variables, function arguments or return types.\n",
      "* **automatic memory management:** No need to explicitly allocate and deallocate memory for variables and data arrays. No memory leak bugs. \n",
      "* **interpreted:** No need to compile the code. The Python interpreter reads and executes the python code directly.\n",
      "\n",
      "Advantages:\n",
      "-----------\n",
      "\n",
      "* The main advantage is ease of programming, minimizing the time required to develop, debug and maintain the code.\n",
      "* Well designed language that encourage many good programming practices:\n",
      " * Modular and object-oriented programming, good system for packaging and re-use of code. This often results in more transparent, maintainable and bug-free code.\n",
      " * Documentation tightly integrated with the code.\n",
      "* A large standard library, and a large collection of add-on packages.\n",
      "\n",
      "Disadvantages:\n",
      "--------------\n",
      "\n",
      "* Because Python is an interpreted and dynamically typed programming language, the execution of Python code can be slower compared to compiled statically typed programming languages, such as C and Fortran.  However, Python itself is written in C, so advanced Python developers can always make their code execute with C like efficiency.  \n",
      "* Somewhat decentralized, with different environments, packages, and documentation hosted in different places. This often makes it more difficult to get started."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Python is widely used in scientific computing because:\n",
      "------------------------------------------------------\n",
      "------------------------------------------------------\n",
      "\n",
      "* Large community of users \n",
      "* Easy to find help and documentation\n",
      "* Extensive ecosystem of scientific libraries and environments:\n",
      "   * numpy: http://numpy.scipy.org - Numerical Python\n",
      "   * scipy: http://www.scipy.org -  Scientific Python\n",
      "   * matplotlib: http://www.matplotlib.org - graphics library\n",
      "* Integrated with optimized C and Fortran libraries:\n",
      "   * blas\n",
      "   * altas blas\n",
      "   * lapack\n",
      "   * arpack\n",
      "   * Intel MKL \n",
      "   * ...\n",
      "* Good support for:\n",
      "    * Parallel processing with processes and threads\n",
      "    * Interprocess communication (MPI)\n",
      "    * GPU computing (OpenCL and CUDA)\n",
      "* Suitable for use on high-performance computing clusters\n",
      "* No license costs (easy on the research budget)\n",
      "\n",
      "Allows for rapid development\n",
      "----------------------------\n",
      "\n",
      "<img src=\"https://rawgithub.com/schryer/python_course_material/master/images/optimizing-what.png\" width=\"600\">\n",
      "\n",
      "<img src=\"https://rawgithub.com/schryer/python_course_material/master/images/optimizing-what-2.png\" width=\"600\">"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The scientific Python software stack\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "\n",
      "<img src=\"https://rawgithub.com/schryer/python_course_material/master/images/scientific-python-stack.svg\" width=\"400\">"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Python interpreter\n",
      "------------------\n",
      "------------------\n",
      "\n",
      "The standard way to use the Python programming language is to use the Python interpreter to run python code. The python interpreter is a program that read and execute the python code in files passed to it as arguments. At the command prompt, the command ``python`` is used to invoke the Python interpreter.\n",
      "\n",
      "For example, to run a file ``my-program.py`` that contains python code from the command prompt, use:\n",
      "\n",
      "    $ python my-program.py\n",
      "\n",
      "We can also start the interpreter by simply typing ``python`` at the command line, and interactively type python code into the interpreter. \n",
      "\n",
      "<img src=\"https://rawgithub.com/schryer/python_course_material/master/images/python-screenshot.jpg\" width=\"600\">\n",
      "\n",
      "This interactive shell is often not very convenient due to a number of limitations."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "IPython\n",
      "-------\n",
      "-------\n",
      "\n",
      "IPython is an interactive shell that addresses the limitation of the standard python interpreter, and it is a work-horse for scientific use of python. It provides an interactive prompt to the python interpreter with a greatly improved user-friendliness.\n",
      "\n",
      "<img src=\"https://rawgithub.com/schryer/python_course_material/master/images/ipython-screenshot.jpg\" width=\"600\">\n",
      "\n",
      "Some of the many useful features of IPython include:\n",
      "\n",
      "* Command history, which can be browsed with the up and down arrows on the keyboard.\n",
      "* Tab auto-completion.\n",
      "* In-line editing of code.\n",
      "* Object introspection, and automatic extract of documentation strings from python objects like classes and functions.\n",
      "* Good interaction with operating system shell.\n",
      "* Support for multiple parallel back-end processes, that can run on computing clusters or cloud services like Amazon EE2.\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "IPython notebook\n",
      "----------------\n",
      "\n",
      "[IPython notebook](<http://ipython.org/ipython-doc/dev/interactive/htmlnotebook.html/) is an HTML-based notebook environment for Python, similar to Mathematica or Maple. It is based the IPython shell, but provides a cell-based environment with great interactivity, where calculations can be organized documented in a structured way.\n",
      "\n",
      "<img src=\"https://rawgithub.com/schryer/python_course_material/master/images/ipython-notebook-screenshot.jpg\" width=\"800\">\n",
      "\n",
      "Although using the a web browser as graphical interface, IPython notebooks are usually run locally, from the same computer that run the browser. To start a new IPython notebook session, run the following command:\n",
      "\n",
      "    $ ipython notebook\n",
      "\n",
      "from a directory where you want the notebooks to be stored. This will open a new browser window (or a new tab in an existing window) with an index page where existing notebooks are shown and from which new notebooks can be created."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Spyder\n",
      "------\n",
      "------\n",
      "\n",
      "[Spyder](http://code.google.com/p/spyderlib/) is a MATLAB-like IDE for scientific computing with python. It has the many advantages of a traditional IDE environment, for example that everything from code editing, execution and debugging is carried out in a single environment, and work on different calculations can be organized as projects in the IDE environment.\n",
      "\n",
      "<img src=\"https://rawgithub.com/schryer/python_course_material/master/images/spyder-screenshot.jpg\" width=\"800\">\n",
      "\n",
      "Some advantages of Spyder:\n",
      "\n",
      "* Powerful code editor, with syntax high-lighting, dynamic code introspection and integration with the python debugger.\n",
      "* Variable explorer, IPython command prompt.\n",
      "* Integrated documentation and help."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Versions of Python\n",
      "------------------\n",
      "------------------\n",
      "\n",
      "There are currently two versions of python: Python 2 and Python 3. Python 3 will eventually supercede Python 2, but it is not backward-compatible with Python 2. A lot of existing python code and packages has been written for Python 2, and it is still the most wide-spread version. For these lectures either version will be fine, but it is probably easier to stick with Python 2 for now, because many useful libraries still require Python 2.\n",
      "\n",
      "To see which version of Python you have, run\n",
      "    \n",
      "    $ python --version\n",
      "    Python 2.7.3\n",
      "    $ python3.2 --version\n",
      "    Python 3.2.3\n",
      "\n",
      "Several versions of Python can be installed in parallel, as shown above.\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Installation\n",
      "------------\n",
      "------------\n",
      "\n",
      "Linux\n",
      "-----\n",
      "\n",
      "In Ubuntu Linux, to installing Python and all the requirements run:\n",
      "\n",
      "    $ sudo apt-get install python ipython ipython-notebook\n",
      "    $ sudo apt-get install python-numpy python-scipy python-matplotlib python-sympy\n",
      "    $ sudo apt-get install spyder\n",
      "\n",
      "MacOS X\n",
      "-------\n",
      "\n",
      "*Macports*\n",
      "\n",
      "Python is included by default in Mac OS X, but for our purposes it will be useful to install a new Python environment using [Macports](http://www.macports.org/), because it makes it much easier to install all the required additional packages. Using Macports, we can install what we need with:\n",
      "\n",
      "    $ sudo port install py27-ipython +pyside+notebook+parallel+scientific\n",
      "    $ sudo port install py27-scipy py27-matplotlib py27-sympy\n",
      "    $ sudo port install py27-spyder\n",
      "\n",
      "These will associate the commands `python` and `ipython` with the versions installed via macports (instead of the one that is shipped with Mac OS X), run the following commands:\n",
      "\n",
      "    $ sudo port select python python27\n",
      "    $ sudo port select ipython ipython27\n",
      "\n",
      "*Fink*\n",
      "\n",
      "Or, alternatively, you can use the [Fink](http://www.finkproject.org/) package manager. After installing Fink, use the following command to install python and the packages that we need:\n",
      "\n",
      "    $ sudo fink install python27 ipython-py27 numpy-py27 matplotlib-py27 scipy-py27 sympy-py27\n",
      "    $ sudo fink install spyder-mac-py27\n",
      "\n",
      "Windows\n",
      "-------\n",
      "\n",
      "Windows lacks a good packaging system, so the easiest way to setup a Python environment is to install a pre-packaged distribution. Some good alternatives are:\n",
      "\n",
      " * [Enthought Python Distribution](http://www.enthought.com/products/epd.php). EPD is a commercial product but is available free for academic use.\n",
      " * [Anaconda CE](http://continuum.io/downloads.html). Anaconda Pro is a commercial product, but Anaconda Community Edition is free.\n",
      " * [Python(x,y)](http://code.google.com/p/pythonxy/). Fully open source.\n",
      "\n",
      "\n",
      "\n",
      "#### Note\n",
      "\n",
      "EPD and Anaconda CE are also available for Linux and Max OS X."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Further reading\n",
      "---------------\n",
      "---------------\n",
      "\n",
      " * [Python](http://www.python.org>). The official Python web site.\n",
      " * [Python tutorials](http://docs.python.org/2/tutorial/). The official Python tutorials.\n",
      " * [Think Python](http://www.greenteapress.com/thinkpython/). A free book on Python."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}