Links, litterature and other resources

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KBleivik
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Links, litterature and other resources

Postby KBleivik » Sun Jan 23, 2011 7:01 pm

1. Python

http://www.panda3d.org/

http://www.python.org/

http://docs.python.org/library/decimal.html

http://wiki.python.org/moin/Why%20is%20 ... 20language

http://www.pycon.org/

PyCon 2011: Connecting The Python Community: http://us.pycon.org/2011/home/

http://pypi.python.org/pypi

http://ipython.scipy.org/

http://corepython.com/

http://codenode.org/

Note that NASA uses python. If you search for

nasa python

hits like

"Comparing Python, NumPy, Matlab, Fortran, etc". : https://modelingguru.nasa.gov/docs/DOC-1762 are among the first. Note that the language is not compared with my favourite mathematical software, http://www.wolfram.com/mathematica/ the only package my professor in mathematics used at the research institute. If a scientific problem could not be solved in mathematica, a programming soultion has to be found. A potential solution may then be found by learning Python and how to integrate it with other languages like C++ (see the section on financial modelling in Python below).

2. Django

http://www.djangoproject.com/

https://docs.djangoproject.com/en/dev/r ... s/install/

http://withdjango.com/

3. Server solutions

http://www.modpython.org/

http://www.lighttpd.net/

http://nginx.org/

http://www.cherrypy.org/

http://www.fastcgi.com

http://wsgi.org/

http://code.google.com/p/modwsgi/

http://code.google.com/p/modwsgi/wiki/I ... structions

http://trac.saddi.com/flup

4. Databases

http://www.sqlite.org/

http://www.postgresql.org/

http://www.mysql.com/

http://cx-oracle.sourceforge.net/

5. Financial Modelling in Python

"Fletcher and Gardner have created a comprehensive resource that will be of interest not only to those working in the field of finance, but also to those using numerical methods in other fields such as engineering, physics, and actuarial mathematics. By showing how to combine the high-level elegance, accessibility, and flexibility of Python, with the low-level computational efficiency of C++, in the context of interesting financial modeling problems, they have provided an implementation template which will be useful to others seeking to jointly optimize the use of computational and human resources. They document all the necessary technical details required in order to make external numerical libraries available from within Python, and they contribute a useful library of their own, which will significantly reduce the start-up costs involved in building financial models. This book is a must read for all those with a need to apply numerical methods in the valuation of financial claims."

Source: http://eu.wiley.com/WileyCDA/WileyTitle ... 87847.html

http://www.quantsoft.co.jp/

Even if this book was written in 2009, it is important for various reasons:
  1. The PPF package that implements a minimal library for exploring financial modelling in Python.
  2. Hybrid Python implementations where Python is integrated with C++ (see the C++ sub forum for additional resources).
Python 2.5 is used in the book. I personally have more than one installation of Python, since I don't have time to do the debugging when testing code that is cut and pasted from the internet or from the code that comes with a book, unless I absolutely need the code. If a problem can be solved in Python 2.+ or 3.+ it is Ok for me. So multiple installations of a program is no problem on todays computers with Gb's of storage. If your computer is low, stop all unnecessary processes. The code that is on the cd that follows with the book need compilation with a C++ compiler. The authors write that it is compiled with the Microsoft Visual Studio C++ compiler version 7.1, 8.0 (express edition), 9 (express edition), http://www.mingw.org/ mingw/gcc-3.4.5, mingw/gcc-4.3.0 with Python versions 2.4 and 2.5 and the C++ libraries, Boost http://www.boost.org/ version 1.33.1, 1.34.0 and Blitz++ http://www.oonumerics.org/blitz/ version 0.9. NumPy http://numpy.scipy.org/ from http://www.scipy.org/ are used. In addtion you will learn how to use Python with graphic packages like GnuPlot: http://www.gnuplot.info/ and Tk http://www.tcl.tk/. The C++ extension modules are most conveniently built using the Boost Build system http://www.boost.org/boost-build2/ See http://www.boost.org/doc/libs/1_34_0/to ... index.html for additional information. The book has a standalone chapter on extending Python from C++. So it is highly recommended for those that wan't to do fairly advanced financial modelling in Python and integrate Python with C++. A strong argument for Python like PHP is the productivity pick up. Python is not static typed like C. Type checking takes place at run time, so Python is dynamically typed.

Related:

http://www.activestate.com/activepython ... ic-modules

6. Web data mining in python

There is an excellent book to cover this topic: by Matthew A. Russel https://twitter.com/#!/ptwobrussell

"Mining the Social Web
Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites"

http://shop.oreilly.com/product/0636920010203.do

The python example code for the book is freely available:

https://github.com/ptwobrussell/Mining-the-Social-Web The authors github account: https://github.com/ptwobrussell

The twitter account for the book is: https://twitter.com/#!/SocialWebMining

The facebook account is: http://www.facebook.com/MiningTheSocialWeb

There is an eBook version that is about to be updated

O'Reilly twitter feed said an update to the ebook was taking place.

Source: http://www.facebook.com/MiningTheSocial ... 6499781607

I have the January 2011 paper version that use Python 2.7, but you should be comfortable with later python 2 versions. If you use python 3.+ you may need to do the debugging yoursef. You should also have configured easy_install

http://peak.telecommunity.com/DevCenter/EasyInstall

http://pypi.python.org/pypi/setuptools

so it is easy to install the python packages that you may need through the book. There is an alternative pip, http://www.pip-installer.org/en/latest/index.html

pip is a tool for installing and managing Python packages, such as those found in the Python Package Index. It’s a replacement for easy_install.


See also:
http://pypi.python.org/pypi/virtualenv

Related link: http://www.activestate.com/activepython

7. cURL for python
http://curl.haxx.se/libcurl/python/

8. Google groups.
http://groups.google.com/group/comp.lang.python/topics

http://groups.google.com/group/python-on-a-chip

9. Python editors

http://wiki.python.org/moin/PythonEditors

I have used Python's integrated development environment http://docs.python.org/py3k/library/idle.html . There is important information about IDLE, Tkinter, and Tcl/Tk on Mac OS X here http://www.python.org/getit/mac/tcltk/.

I have alos used the SPE editor version 0.8.4 that can be downloaded here:
http://developer.berlios.de/projects/python/

An older version is available here: http://sourceforge.net/projects/spe/

10. Other links

https://github.com/heynemann/pynq/wiki

http://www.pyglet.org/

http://www.pythonogre.com/

http://www.pygame.org/wiki/PyOgre

http://www.ogre3d.org/tikiwiki/tiki-ind ... Tutorial+1

http://www.ogre3d.org/

http://www.zope.com/

http://zope2.zope.org/

http://www.wowebook.mobi/category/ruby-python/

http://www.kjellbleivik.com/Books/GameD ... php#python

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