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30 lines
1.4 KiB
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Recommended reading and references
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* A good set of lectures is the `Scipy lecture notes <http://scipy-lectures.github.com/>`_.
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* A good starting point to learn about scientific computing with Python and related ideas is
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`Software carpentry <http://software-carpentry.org>`_, which provides nice video/slides `lectures on Python <http://software-carpentry.org/4_0/python>`_
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* To learn the Python language itself the recommended starting point is the standard `python tutorial <http://docs.python.org/tutorial>`_.
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* Python has a large number of libraries, which can be used in combination with TRIQS. For example,
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* The Python's `standard library <http://docs.python.org/library>`_ is already very rich.
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* `Numpy <http://docs.scipy.org/doc/numpy/user>`_ allows to manipulate multidimensionnal arrays (cf also the `tutorial <http://www.scipy.org/Tentative_NumPy_Tutorial>`_).
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* `Scipy <http://www.scipy.org>`_ includes many packages for scientific computing.
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* `Matplotlib <http://matplotlib.sourceforge.net>`_ offers very nice plotting possibilities.
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* `SymPy <http://sympy.org/>`_ provides some formal computing capabilities.
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* `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.
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