.. highlight:: bash

Learn more about Python, ipython and the notebook
=================================================

The `ipython interpreter <http://ipython.org>`_ is basically a more user-friendly
version of the standard Python interpreter with an enhanced interactive shell that makes it
easy to visualize data.  It also provides the `ipython notebook
<http://ipython.org/notebook.html>`_, a browser-based notebook with support for
text, mathematical expressions, inline plots and inline Python scripts. We
really think it is a very powerful tool and recommend that you spend some time
familiarising yourself with ipython and the notebook. Here are some useful 
links to learn about Python and associated libraries.

* A good starting point to learn about scientific computing with Python and
  related ideas is `Software carpentry <http://software-carpentry.org>`_, which
  provides nice video/slides `lectures on Python
  <http://software-carpentry.org/4_0/python>`_

* For an interactive overview, see the well-written set of lectures on 
  `scientific computing with Python using Ipython notebooks 
  <https://github.com/jrjohansson/scientific-python-lectures>`_

* To learn the Python language itself the recommended starting point is the
  standard `Python tutorial <http://docs.python.org/tutorial>`_.

* A more detailed set of lectures is the `Scipy lecture notes
  <http://scipy-lectures.github.com/>`_.   

* Python has a large number of libraries, which can be used in combination with
  TRIQS. For example:

  * The Python `standard library <http://docs.python.org/library>`_ is already
    very rich.

  * `Numpy <http://docs.scipy.org/doc/numpy/user>`_  allows to manipulate
    multidimensional arrays (cf also the `tutorial
    <http://www.scipy.org/Tentative_NumPy_Tutorial>`_).

  * `Scipy <http://www.scipy.org>`_ includes many packages for scientific
    computing.

  * `Matplotlib <http://matplotlib.sourceforge.net>`_ offers very nice 
    plotting tools that can be integrated into your Python scripts.

  * `SymPy <http://sympy.org/>`_ provides symbolic algebra capabilities.