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Nicolas Louveton
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147 lines
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=======================
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Scientific Applications
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=======================
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Context
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:::::::
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Python is frequently used for high-performance scientific applications. Python
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is widely used in academia and scientific projects because it is easy to write,
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and it performs really well.
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Due to its high performance nature, scientific computing in python often refers
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to external libraries, typically written in faster languages (like C, or
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FORTRAN for matrix operations). The main libraries used are `NumPy`_, `SciPy`_
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and `Matplotlib`_. Going into detail about these libraries is beyond the scope
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of the Python guide. However, a comprehensive introduction to the scientific
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Python ecosystem can be found in the `Python Scientific Lecture Notes
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<http://scipy-lectures.github.com/>`_
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Tools
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IPython
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-------
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`IPytthon <http://ipython.org/>`_ is an enhanced version of Python interpreter.
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The features it provides are of great interest for the scientists. The `inline mode`
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allow graphics and plots to be displayed in the terminal (Qt based version).
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Moreover the `notebook` mode supports literate programming and reproducible science
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generating a web-based python notebook. This notebook allowing to store chunk of
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Python code along side to the results and additional comments (HTML, LaTeX, Markdown).
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The notebook could be shared and exported in various file formats.
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Libraries
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NumPy
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-----
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`NumPy <http://numpy.scipy.org/>`_ is a low level library written in C (and
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FORTRAN) for high level mathematical functions. NumPy cleverly overcomes the
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problem of running slower algorithms on Python by using multidimensional arrays
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and functions that operate on arrays. Any algorithm can then be expressed as a
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function on arrays, allowing the algorithms to be run quickly.
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NumPy is part of the SciPy project, and is released as a separate library so
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people who only need the basic requirements can just use NumPy.
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NumPy is compatible with Python versions 2.4 through to 2.7.2 and 3.1+.
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Numba
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-----
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Numba is an Numpy aware Python compiler (just-in-time (JIT) specializing
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compiler) which compiles annotated Python (and Numpy) code to LLVM (Low Level
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Virtual Machine) (through special decorators).
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Briefly, Numba using system that compiles Python code with LLVM to code which
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can be natively executed at runtime.
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SciPy
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-----
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`SciPy <http://scipy.org/>`_ is a library that uses Numpy for more mathematical
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functions. SciPy uses NumPy arrays as the basic data structure. SciPy comes
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with modules for various commonly used tasks in scientific programing, for
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example: linear algebra, integration (calculus), ordinary differential equation
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solvers and signal processing.
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Matplotlib
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----------
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`Matplotlib <http://matplotlib.sourceforge.net/>`_ is a flexible plotting
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library for creating interactive 2D and 3D plots that can also be saved as
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manuscript-quality figures. The API in many ways reflects that of `MATLAB
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<http://www.mathworks.com/products/matlab/>`_, easing transition of MATLAB
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users to Python. Many examples, along with the source code to re-create them,
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can be browsed at the `matplotlib gallery
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<http://matplotlib.sourceforge.net/gallery.html>`_.
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Pandas
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------
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`Pandas <http://pandas.pydata.org/>`_ is data manipulation library
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based on Numpy and which provides many useful functions for accessing,
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indexing, merging and grouping data easily. The main data structure (DataFrame)
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is close to what could be found in the R statistical package, that is
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an heterogeneous data tables with name indexing, time series operations
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and auto-alignement of data.
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Rpy2
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----
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`Rpy2 <http://rpy.sourceforge.net/rpy2.html>`_ is a Python binding for the R
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statistical package allowing to execute R functions from Python and passing
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data back and forth the two environments. Rpy2 is the object oriented
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implementation of the binding based on `Rpy <http://rpy.sourceforge.net/rpy.html>`_.
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PsychoPy
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--------
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`PsychoPy <http://www.psychopy.org/>`_ is a library for cognitive scientists
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allowing to create cognitive psychology and neuroscience experiments. The library
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handles both presentation of stimuli, scripting of experimental design and
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data collection.
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Resources
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Installation of scientific Python packages can be troublesome. Many of these
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packages are implemented as Python C extensions which need to be compiled.
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This section lists various so-called scientific Python distributions which
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provide precompiled and easy-to-install collections of scientific Python
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packages.
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Unofficial Windows Binaries for Python Extension Packages
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---------------------------------------------------------
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Many people who do scientific computing are on Windows. And yet many of the
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scientific computing packages are notoriously difficult to build and install.
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`Christoph Gohlke <http://www.lfd.uci.edu/~gohlke/pythonlibs/>`_ however, has
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compiled a list of Windows binaries for many useful Python packages. The list
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of packages has grown from a mainly scientific python resource to a more
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general list. It might be a good idea to check it out if you're on Windows.
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Enthought Python Distribution (EPD)
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-----------------------------------
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Installing NumPy and SciPy can be a daunting task. Which is why the
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`Enthought Python distribution <http://enthought.com/>`_ was created. With
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Enthought, scientific python has never been easier (one click to install about
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100 scientific python packages). The Enthought Python Distribution comes in two
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variants: a free version `EPD Free <http://enthought.com/products/epd_free.php>`_
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and a paid version with various `pricing options.
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<http://enthought.com/products/epd_sublevels.php>`_
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Anaconda
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--------
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`Continuum Analytics <http://continuum.io/>`_ offers the `Anaconda
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Python Distribution <https://store.continuum.io/cshop/anaconda>`_ which
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includes all the common scientific python packages and additionally many
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packages related to data analytics and big data. Anaconda comes in two
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flavors, a paid for version and a completely free and open source community
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edition, Anaconda CE, which contains a slightly reduced feature set. Free
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licenses for the paid-for version are available for academics and researchers.
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