mirror of
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Thomas Kluyver
064562cb0a
- The business model of Anaconda has changed slightly: now it's a free product with 'add-ons' that cost money. - Enthought has largely replaced EPD with Canopy. - I've moved Anaconda before Canopy: this is obviously subjective, but Anaconda includes a lot more for free (Canopy Express excludes many open source packages). Workshops like Software Carpentry now tend to point new users to Anaconda for installation.
144 lines
5.7 KiB
ReStructuredText
144 lines
5.7 KiB
ReStructuredText
=======================
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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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`IPython <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 <http://numba.pydata.org>`_ is an Numpy aware Python compiler
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(just-in-time (JIT) specializing compiler) which compiles annotated Python (and
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Numpy) code to LLVM (Low Level 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 programming, 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-alignment 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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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 itself is free, and
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Continuum sells a number of proprietary add-ons. Free
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licenses for the add-ons are available for academics and researchers.
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Canopy
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------
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`Canopy <https://www.enthought.com/products/canopy/> is another scientific Python
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distribution, produced by `Enthought <https://www.enthought.com/>`. A limited
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'Canopy Express' variant is available for free, and Enthought charge for the
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full distribution. Free licenses are available for academics.
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