Skip to content

Refactor/scenarios/scientific #246

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
kennethreitz merged 11 commits into from
Mar 6, 2013
Merged

Refactor/scenarios/scientific #246

Changes from all commits
Commits
Show all changes
11 commits
Select commit Hold shift + click to select a range
7915805
fix trailing whitespace
esc Feb 21, 2013
70c7f81
add matplotlib to sentence about packages
esc Feb 21, 2013
9ecebf5
move the Matplotlib section up
esc Feb 21, 2013
39e0b38
fix capitalization
esc Feb 21, 2013
b7755bc
limit line length to 80
esc Feb 21, 2013
fadc692
insert whitespace after headings for source coherence
esc Feb 21, 2013
46d48e8
grammar and wording tweaks for the scipy description
esc Feb 21, 2013
1539432
collect all Python distributions under 'Resources'
esc Feb 21, 2013
d73f340
fix name of the EPD
esc Feb 21, 2013
852f29d
add Anaconda
esc Feb 21, 2013
9cbaf20
mention the Python Scientific Lecture Notes
esc Feb 21, 2013
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
70 changes: 46 additions & 24 deletions docs/scenarios/scientific.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -10,15 +10,19 @@ is widely used in academia and scientific projects because it is easy to write,
and it performs really well.

Due to its high performance nature, scientific computing in python often refers
to external libraries, typically written in faster languages (like C, or FORTRAN
for matrix operations). The main libraries used are `NumPy`_ and
`SciPy`_.
to 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/>`_

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
Expand All @@ -33,42 +37,60 @@ NumPy is compatible with Python versions 2.4 through to 2.7.2 and 3.1+.

SciPy
-----
`SciPy <http://scipy.org/>`_ is a library that uses Numpy for more mathematical
function. SciPy uses NumPy arrays as its basic data structure. SciPy comes with
modules for various commonly used tasks in scientific programing like linear
algebra, integration (calculus), ordinary differential equation solvers and
signal processing.

Enthought
---------

Installing NumPy and SciPy can be a daunting task. Which is why the
`Enthought Python distribution <http://enthought.com/>`_ was created. With
Enthought, scientific python has never been easier (one click to install about
100 scientific python packages). The Enthought Python Distribution comes in two
variants: a free version `EPD Free <http://enthought.com/products/epd_free.php>`_
and a paid version with various `pricing options.
<http://enthought.com/products/epd_sublevels.php>`_
`SciPy <http://scipy.org/>`_ is a library that uses Numpy for more mathematical
functions. SciPy uses NumPy arrays as the basic data structure. SciPy comes
with modules for various commonly used tasks in scientific programing, for
example: linear algebra, integration (calculus), ordinary differential equation
solvers and signal processing.

Matplotlib
----------

`matplotlib <http://matplotlib.sourceforge.net/>`_ is a flexible plotting
`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, can be browsed at the `matplotlib gallery <http://matplotlib.sourceforge.net/gallery.html>`_.
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,
can be browsed at the `matplotlib gallery
<http://matplotlib.sourceforge.net/gallery.html>`_.

Resources
:::::::::

Installation of scientific Python packages can be troublesome. Many of these
packages are implemented as Python C extensions which need to be compiled.
This section lists various so-called 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. And yet many of the
scientific computing packages are notoriously difficult to build and install.
`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. It might be a good idea to check it out if you're on Windows.

For a quick introduction to scientific python:
Enthought Python Distribution (EPD)
-----------------------------------

Installing NumPy and SciPy can be a daunting task. Which is why the
`Enthought Python distribution <http://enthought.com/>`_ was created. With
Enthought, scientific python has never been easier (one click to install about
100 scientific python packages). The Enthought Python Distribution comes in two
variants: a free version `EPD Free <http://enthought.com/products/epd_free.php>`_
and a paid version with various `pricing options.
<http://enthought.com/products/epd_sublevels.php>`_

Anaconda
--------

http://scipy-lectures.github.com
`Continuum Analytics <http://continuum.io/>`_ offers the `Anaconda
Python Distribution <https://store.continuum.io/cshop/anaconda>`_ which
includes all the common scientific python packages and additionally many
packages related to data analytics and big data. Anaconda comes in two
flavours, a paid for version and a completely free and open source community
edition, Anaconda CE, which contains a slightly reduced feature set. Free
licences for the paid-for version are available for academics and researchers.