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dft_tools/doc/tour/getting_started/get_started.rst
tayral edd1ff4529 Restructuring documentation.
A first general restructuration of the doc according to the pattern [tour|tutorial|reference].
In the reference part, objects are documented per topic.
In each topic, [definition|c++|python|hdf5] (not yet implemented)
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.. highlight:: bash
Getting started
===============
TRIQS provides a set of python libraries that allows to easily write scripts
for the study of quantum interaction systems. The executable ``ipytriqs`` which
is installed in the :file:`path_to_install_directory/bin` directory is a normal
ipython interpreter (see below for details about ipython) which has access to
the TRIQS modules. You can either start an interactive session with::
$ ipytriqs
or run scripts with::
$ ipytriqs my_script.py
There is also an executable which allows to start an ipython notebook
in your browser, just type::
$ ipytriqs_notebook
Now you can try to run some scripts to get familiar with TRIQS. Why don't you
:ref:`go to our python tour <tour>` and run some of the examples there?
Learn more about python, ipython and the notebook
-------------------------------------------------
The ``ipytriqs`` executable calls an `ipython interpreter
<http://ipython.org>`_. This 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, 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 learning ipython and the notebook. Here are some
useful links to learn python, ipython, scipy.
* To learn the Python language itself the recommended starting point is the
standard `python tutorial <http://docs.python.org/tutorial>`_.
* A good set of lectures is the `Scipy lecture notes
<http://scipy-lectures.github.com/>`_.
* 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>`_
* 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
multidimensionnal 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
possibilities.
* `SymPy <http://sympy.org/>`_ provides some formal computing capabilities.