mirror of
https://github.com/triqs/dft_tools
synced 2024-12-27 06:43:40 +01:00
30 lines
1.4 KiB
ReStructuredText
30 lines
1.4 KiB
ReStructuredText
|
|
Recommended reading and references
|
|
----------------------------------------------------------
|
|
|
|
* 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>`_
|
|
|
|
* To learn the Python language itself the recommended starting point is the standard `python tutorial <http://docs.python.org/tutorial>`_.
|
|
|
|
* Python has a large number of libraries, which can be used in combination with TRIQS. For example,
|
|
|
|
* The Python's `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.
|
|
|
|
* `Cython <http://cython.org/>`_ is a quick way to build simple `C` modules in computationally intense case. For more complex situations we use boost.python tools.
|
|
|
|
|
|
|
|
|
|
|