Nice book on python, numpy, ipython etc...

A very up-to-date (august 2014) electronic book on python named "Introduction to Python for Econometrics, Statistics and Data Analysis" from Kevin Sheppard is available here:
https://www.kevinsheppard.com/images/0/09/Python_introduction.pdf
It's mainly on econometric, but most of the tools described there are also useful for astronomers.

Sending requests to MySQL and receiving the result from python, using PyMySQL

 Modern astrophysics is using every day more big databases. One of the mostly used interface to databases is MySQl (or its recent free fork MariaDB). I present in this lecture a python library to deal with MySQL databases: PyMySQL. In the lecture the examples are using access to 3MdB (https://sites.google.com/site/mexicanmillionmodels/), which requires a password. You can ask for it to me, or adapt the example to connect to other databases.
The lecture is here:
https://github.com/Morisset/Python-lectures-Notebooks/blob/master/Notebooks/Using_PyMySQL.ipynb


An introduction to MySQL can be found here: https://github.com/Morisset/Python-lectures-Notebooks/blob/master/MySQL.pdf

Optimization, calling Fortran

2014 Python Lecture. Part IX

In this latest lecture of this series, I'll present some tools to optimize your code by CPU and memory profiling. It also contains some tips on using the python debugger.
The notebook is there:
https://github.com/Morisset/Python-lectures-Notebooks/blob/master/Notebooks/Optimization.ipynb


I also give some indications on how one can call Fortran routines from within python, to accelerate the execution of some part the the code.
Here are small examples: https://github.com/Morisset/Python-lectures-Notebooks/blob/master/Notebooks/Calling%20Fortran.ipynb

Object Oriented Programing. Objects, classes, etc...

2014 Python Lecture. Part VIII

In this lecture I'll introduce the basic (and some not that basic) concepts of Object Oriented Programing. I'll use an example to show how to:

• use functions to do simple jobs
• but use objects when things start to be more complex
• define classes, objects, attributes, methods, etc...
• use *args and **kwargs in functions calls
• use the class variables
• add functionalities to classes and objects
• use class inheritance
• use attributes properties

The notebook is here:
https://github.com/Morisset/Python-lectures-Notebooks/blob/master/Notebooks/OOP.ipynb

The astropy library

2014 Python Lecture. Part VII

The Astropy Project is a community effort to develop a single core package for Astronomy in Python and foster interoperability between Python astronomy packages. More informations here: http://www.astropy.org/

In this lecture we will see some of the facilities of the astorpy library, including:

• Constants and Units
• Data Table (a very useful one!)
• Time and Dates
• Etc...

The lecture is here:

https://github.com/Morisset/Python-lectures-Notebooks/blob/master/Notebooks/Using_astropy.ipynb

Useful libraries

2014 Python Lecture. Part VI

This lecture will give some insights to the most useful python libraries. It is NOT exhaustive, you have to read the corresponding manual pages to find the best use you can have of them. The list of all python-included libraries is here: https://docs.python.org/2/library/

I mention in this lecture:

• time and datetime
• timeit
• os
• sys
• subprocess
• glob
• re
• urllib2

The lecture is here:

https://github.com/Morisset/Python-lectures-Notebooks/blob/master/Notebooks/Useful_libraries.ipynb

Introduction to Scipy

2014 Python Lecture. Part V

SciPy (pronounced “Sigh Pie”) is a Python-based ecosystem of open-source software for mathematics, science, and engineering. In particular, these are some of the core packages: Numpy, Scipy library, Matplotlib, ipython, Simpy, and Panda.


In this lecture, I will show exemples covering:

• Some useful methods
• nanmean
• constants
• Integrations
• Interpolations
• 2D-interpolations
• data fitting
• multivariate estimation

The lecture is here:

https://github.com/Morisset/Python-lectures-Notebooks/blob/master/Notebooks/intro_Scipy.ipynb

How to make plots, images, 3D, etc, using Matplotlib

2014 Python Lecture. Part IV

This lecture is dedicated to the plotting library matplotlib. The topics are:

• Simple plot
• Controlling colors ans symbols
• Overplot
• Fixing axes limits
• Labels, titles
• Legends
• The object oriented way to use Matplotlib
• Scatter
• log plots
• Multiple plots
• Everything is object
• Error bars
• Sharing axes
• Histograms
• Boxplots
• Ticks, axes and spines
• A plot inside a plot
• Play with all the objects of a plot
• Filled regions
• 2D-histograms
• 2D data sets and images
• Contour
• 3D scatter plots
• Saving plots
• Access and clear the current figure and axe
• What's happen when not in a Notebook? plt.show() and plt.ion() commands

The lecture Notebook is there:
https://github.com/Morisset/Python-lectures-Notebooks/blob/master/Notebooks/intro_Matplotlib.ipynb

Interacting with files: reading writing, ascii and fits

2014 Python lecture. Part III

It's time to play with files containing data! In this lecture, we'll see how to read and write files (ascii and fits).

• Reading a simple ASCII file
• How to treat special rows (comments, header)
• classical way
• using numpy.loadtxt
• using numpy.genfromtxt

• Dealing with missing data
• Data in a fixed size format
• Writing files
• simple method
• Pickle files (python format)
• FITS files

The ipython notebook is there:

https://github.com/Morisset/Python-lectures-Notebooks/blob/master/Notebooks/Interact%20with%20files.ipynb

Introduction to Numpy

2014 Python lecture. Part II

The introduction to Numpy can be seen here:
https://github.com/Morisset/Python-lectures-Notebooks/blob/master/Notebooks/intro_numpy.ipynb

The topics that are presented are:

• The Array class
• create an array
• 1D, 2D 3D arrays
• creating array from scratch
• arrays share memory (views)
• random generator
• timing a command
• slicing arrays
• assignments
• using masks
• the where function
• some operations with arrays
• broadcasting
• calling scripts
• structured arrays and record arrays
• NaN other ANSI values.

Any comments are welcome.

Chris.Morisset a t Gmail.com

Python: Basics

2014 Python lecture. Part I

The introduction to Python I'm giving at IA-UNAM is accessible here:

https://github.com/Morisset/Python-lectures-Notebooks/blob/master/Notebooks/intro_Python.ipynb

I will modify this notebook during the lecture (August 2014), so reload it to have the latest version.

The topics of this first lecture are:

• Using python as a calculator
• assignments
• comments
• types
• complex numbers
• booleans
• printing strings
• strings
• Tuples, lists and dictionaries
• Blocks
• List and dictionary comprehension
• Functions, procedures
• Scripting
• Importing libraries

If you want to have an interactive session with this lecture using the ipython notebook facilities, follow the link above and download the ipynb file (download button at the right top of the web page). Save the file in a directory from where you execute the following (you must have a recent version of ipython installed):
ipython notebook
It should open a new tab in your web browser, with the list of ipynb files in the directory. Click on the one you want, will open a new tab similar to the first one, but this one is executed on YOUR computer, it means you are able to interact with the commands. You can change the commands, and execute a cell by SHIFT-ENTER. You can add comments in new cells, and save the result.

Any comments are welcome.

Brief introduction to Python

2014 Python lecture. Part 0

Back to the Python lecture, I want to share here the very quick introduction I gave before starting to play with python: https://github.com/Morisset/Python-lectures-Notebooks/blob/master/Notebooks/Intro_1.pdf
You may want to install python from Ureka from this site: http://ssb.stsci.edu/ureka/

Using ipython Notebook to teach scientific python

A very good and efficient way to teach python and python related tools, is to use ipython Notebook: http://ipython.org/notebook.html

As example of this use, the following link is a collection of lectures on python, numpy, scipy, matplotlib, use of Fortran from python, etc:
https://github.com/jrjohansson/scientific-python-lectures
Enjoy them.