The document introduces Scipy, Numpy and related tools for scientific computing in Python. It provides links to documentation and tutorials for Scipy and Numpy for numerical operations, Matplotlib for data visualization, and IPython for an interactive coding environment. It also includes short examples and explanations of Numpy arrays, plotting, data analysis workflows, and accessing help documentation.

1. Scipy, Numpy and friends.
Michele Mattioni - mattioni@ebi.ac.uk
CompNeur group.
http://www.ebi.ac.uk/~mattioni/pylab_pres/
or
https://github.com/mattions/pylab_pres
EBI is an Outstation of the European Molecular Biology Laboratory.

2. Scipy, Numpy and friends by Michele Mattioni is licensed under
CC BY-NC-SA 3.0.
Permissions beyond the scope of this license may be available
at https://github.com/mattions.

3. Go here:
http://www.ebi.ac.uk/~mattioni/pylab_pres/
or
https://github.com/mattions/pylab_pres

4. Scipy and Numpy - http://www.scipy.org
Data structure for
mathematical operation
Collections of high level
with speed in mind.
mathematical operations:
- regression
- interpolation
- optimization
- integration

5. Matplotlib - http://matplotlib.sourceforge.net/
- Display and plot you data quickly
- Different kind of plot available

6. Ipython - http://ipython.scipy.org
- Enhanced python console
- Trials and errors
encouraged
- Quick access to the help.

7. Plot of an exponential function

8. Difference between Console and Script
Console Program
- Quick to write - Slower to write
- Messy to maintain - Easier to maintain

9. HELP!
Different ways to access help:
Console: name_of_the _function?
Online: http://docs.scipy.org/doc/
Suggested:
Tutorial http://www.scipy.org/Tentative_NumPy_Tutorial

10. Numpy array

11. Plotting

12. Importing your data and visualize them

13. Fitting the data