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Scientific Applications
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*******
Context
*******

Python is frequently used for high-performance scientific applications. It
is widely used in academia and scientific projects because it is easy to write
and performs well.

Due to its high performance nature, scientific computing in Python often
utilizes external libraries, typically written in faster languages (like C, or
FORTRAN for matrix operations). The main libraries used are `NumPy`_, `SciPy`_
and `Matplotlib`_. Going into detail about these libraries is beyond the scope
of the Python guide. However, a comprehensive introduction to the scientific
Python ecosystem can be found in the `Python Scientific Lecture Notes
<http://scipy-lectures.github.com/>`_


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Tools
*****

IPython
-------

`IPython <http://ipython.org/>`_ is an enhanced version of Python interpreter,
which provides features of great interest to scientists. The `inline mode`
allows graphics and plots to be displayed in the terminal (Qt based version).
Moreover, the `notebook` mode supports literate programming and reproducible
science generating a web-based Python notebook. This notebook allows you to
store chunks of Python code along side the results and additional comments
(HTML, LaTeX, Markdown). The notebook can then be shared and exported in various
file formats.


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Libraries
*********

NumPy
-----

`NumPy <http://numpy.scipy.org/>`_ is a low level library written in C (and
FORTRAN) for high level mathematical functions. NumPy cleverly overcomes the
problem of running slower algorithms on Python by using multidimensional arrays
and functions that operate on arrays. Any algorithm can then be expressed as a
function on arrays, allowing the algorithms to be run quickly.

NumPy is part of the SciPy project, and is released as a separate library so
people who only need the basic requirements can use it without installing the
rest of SciPy.

NumPy is compatible with Python versions 2.4 through to 2.7.2 and 3.1+.

Numba
-----

`Numba <http://numba.pydata.org>`_ is a NumPy aware Python compiler
(just-in-time (JIT) specializing compiler) which compiles annotated Python (and
NumPy) code to LLVM (Low Level Virtual Machine) through special decorators.
Briefly, Numba uses a system that compiles Python code with LLVM to code which
can be natively executed at runtime.

SciPy
-----

`SciPy <http://scipy.org/>`_ is a library that uses NumPy for more mathematical
functions. SciPy uses NumPy arrays as the basic data structure, and comes
with modules for various commonly used tasks in scientific programming,
including linear algebra, integration (calculus), ordinary differential equation
solving and signal processing.

Matplotlib
----------

`Matplotlib <http://matplotlib.sourceforge.net/>`_ is a flexible plotting
library for creating interactive 2D and 3D plots that can also be saved as
manuscript-quality figures. The API in many ways reflects that of `MATLAB
<http://www.mathworks.com/products/matlab/>`_, easing transition of MATLAB
users to Python. Many examples, along with the source code to re-create them,
are available in the `matplotlib gallery
<http://matplotlib.sourceforge.net/gallery.html>`_.

Pandas
------

`Pandas <http://pandas.pydata.org/>`_ is data manipulation library
based on Numpy which provides many useful functions for accessing,
indexing, merging and grouping data easily. The main data structure (DataFrame)
is close to what could be found in the R statistical package; that is,
heterogeneous data tables with name indexing, time series operations and
auto-alignment of data.

Rpy2
----

`Rpy2 <http://rpy2.bitbucket.org>`_ is a Python binding for the R
statistical package allowing the execution of R functions from Python and
passing data back and forth between the two environments. Rpy2 is the object
oriented implementation of the `Rpy <http://rpy.sourceforge.net/rpy.html>`_
bindings.

PsychoPy
--------

`PsychoPy <http://www.psychopy.org/>`_ is a library for cognitive scientists
allowing the creation of cognitive psychology and neuroscience experiments.
The library handles presentation of stimuli, scripting of experimental design
and data collection.


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Resources
*********

Installation of scientific Python packages can be troublesome, as many of
these packages are implemented as Python C extensions which need to be compiled.
This section lists various so-called scientific Python distributions which
provide precompiled and easy-to-install collections of scientific Python
packages.

Unofficial Windows Binaries for Python Extension Packages
---------------------------------------------------------

Many people who do scientific computing are on Windows, yet many of the
scientific computing packages are notoriously difficult to build and install on
this platform. `Christoph Gohlke <http://www.lfd.uci.edu/~gohlke/pythonlibs/>`_
however, has compiled a list of Windows binaries for many useful Python
packages.  The list of packages has grown from a mainly scientific Python
resource to a more general list. If you're on Windows, you may want to check it
out.

Anaconda
--------

`Continuum Analytics <http://continuum.io/>`_ offers the `Anaconda
Python Distribution <https://store.continuum.io/cshop/anaconda>`_ which
includes all the common scientific Python packages as well as many packages
related to data analytics and big data. Anaconda itself is free, and
Continuum sells a number of proprietary add-ons. Free licenses for the
add-ons are available for academics and researchers.

Canopy
------

`Canopy <https://www.enthought.com/products/canopy/>`_ is another scientific
Python distribution, produced by `Enthought <https://www.enthought.com/>`_.
A limited 'Canopy Express' variant is available for free, but Enthought
charges for the full distribution. Free licenses are available for academics.
