The code used in the ipython practical during the "Les Houches School" memobio2015 ( http://memobio2015.u-strasbg.fr/index.php )
contain :
A rapid presentation of the scientific python environment. It presents :
- Python The core language
- iPython a handy interactive shell
- Notebook a very handy graphic user interface running in the WEB browser
- numpy scipy the numerical libraries
- matplotlib the graphing library
Then some basic examples in function analysis or image analysis.
Finally some ideas about open-source, open-science, and the role of programming in modern science.
An example on how this set-up can be used to process a data-set. Example is given here on a FT-ICR data-set.
This notebook contains a introcution to simple ideas which are important in Comressed Sensing approaches.
This goes though:
-
$\ell_2$ norm (classical cartesian norm) vs$\ell_1$ norm - the effect of minimizing the
$\ell_1$ norm on simple problems - some properties of random matrices, in particular how they approximate an orthogonal transform
A toy example using the Orthogonal Maching Pursuit method (from the scikit-learn library) to solve a Compressed Sensing subsampled measurement.