A new blog on various actuarial stuffs...

The aim of this blog is sharing information of interest in the actuarial world. We consider informative contents as well as implementation tools in financial, life and pension actuarial matters.
Showing posts with label geek. Show all posts
Showing posts with label geek. Show all posts

Wednesday, 20 July 2011

Cloudnumbers: Your Python Application in the Cloud

cloudnumbers.com supports the interpreted, interactive, object-oriented and extensible programming language Python (http://www.python.org/). Python is a user-friendly open source programming language that is used in many areas. It has become really popular since many programmers consider it to be one of the easiest programming languages to study and work with.
cloudnumbers.com combines High-Performance Computing (HPC) and Python in order to provide a fully scalable platform for all your calculations. Combining Python´s ease-of-use with high performance computing means that ideas can more quickly become applications, increasing the productivity of developers, while overcoming any limitations.
HPC support for Python is provided with the following libraries:
  • mpi4py (http://mpi4py.scipy.org/): This module provides the binding of the Message Passing Interface (MPI) standard to the Python programming language. It allows any Python program to exploit multiple processors.
  • pp (http://www.parallelpython.com/): This module provides mechanism for parallel execution of Python code on multicore machines and on computer clusters.

Thursday, 14 July 2011

Fractal (and Quantum) Finance back again: Try out Fraclab

Fraclab is a Matlab-based toolbox for image and signal processing mainly developed and maintained by people at INRIA. It seems to include powerfull fractal modules used in
Astrophysics, Biomedical, Chemical engineering, Fluid dynamics, Food engineering, Geophysics, Internet traffic, Mathematical analysis, Signal and image processing, Statistics and...Finance.
An interesting feature is also that you do not need to have Matlab installed to get it run since there is a standalone packaged version available (that, by the way, installs a run-time Matlab version on your system during the install process).

Fraclab is available for Windows, Mac OS X and Linux.

Wednesday, 6 July 2011

Rattle: A Graphical User Interface for Data Mining using R

In addition to Rstudio the following tool looks promising: Rattle
Rattle (the R Analytical Tool To Learn Easily) presents statistical and visual summaries of data, transforms data into forms that can be readily modelled, builds both unsupervised and supervised models from the data, presents the performance of models graphically, and scores new datasets.

Friday, 1 July 2011

A new R-gui: RStudio

Investigating using R under other platforms than Microsoft Windows, I have been looking for alternative tools to the well-known R-GUI Tinn-R. I have just discovered RStudio that seems to be a well-promising GUI tool. I have tested it under Mac OS X, Ubuntu 11.04... and Windows XP. Works fine (although still missing some functionnalities, such as export of console output  that should be implemented soon).

Thursday, 27 January 2011

Python Web-Hosting with Numpy : virtualising your environment

Many solutions can be found to host a Python-based website. But really few (none?) of them offer a solutions with pre-installed Numpy, Scipy and Matplotlib. A way for circumeventing this seems to rely on the virtualisation of your Python environment with tools such virtualenv. You would have to proceed as follows
  1. Virtualisation of a own Python environment with the website hoster
  2. Installation of the required tools with easy_install. 
This link points to the referred solution (copied hereunder)...that I have not tested ! 

For your information:
  • I didn't know numpy, scipy and friends could be (easily) installed with easy_install
  • This at least requires the gcc tools or the corresponding compiling and building tools to be installed with the server

You can indeed install third party packages to your hosted space. If it's a pure python package, all that's needed is to unpack it to a directory and then add that directory to your PYTHONPATH environment variable or sys.path.
This can be tiring to do often, and won't work easily for compiled modules. If you have shell access to your python host, the excellent virtualenv package allows you to do set up a private python environment with its own libraries.
To set up your virtualenv, you'll do something like this at the shell:
$ virtualenv $HOME/my_python
$ $HOME/my_python/bin/easy_install numpy
You can keep running easy_install for anything else you want to install in your personal python environment.
Now, when you write your python scripts, you will want to use your private python interpreter, if that is possible:
 #!/home/myuser/my_python/bin/python
import numpy
# script here
If your python env cannot be specified (such as if run by mod_wsgi), you will need to add it to the  import path:
import sys
sys.path.insert(0, '/home/myuser/my_python/lib/python2.5/site-packages')
import numpy