2nd semestre, 2015

Date

Speaker

Title (click on it to see Repository)

Sep 10

Bruno Dias, Paul Elliot, Jorge Martins

Introduction to Pycoffee

Sep 17

CANCELLED

Holiday: Independence of Chile

Sep 24

Matt Taylor

Intro to matplotlib

Oct 1

CANCELLED

ESO workshop

Oct 8

Antoine Merand

Intro to iPython

Oct 15

Eric Emsellem

PyCoffee in Garching

Oct 22

Fernando Selman

MUSE data with Python

Oct 29

Steve Ertel

Accessing online catalogues with Astropy

Nov 5

Ignacio Toledo

Python for Data Science (Data Analysis)

Nov 12

Elyar Sedaghati

An interactive introduction to python Classes

Nov 19

Julien Milli

Python and ds9 SAOImage

Nov 26

Paul Eigenthaler

Displaying FITS Images with APLpy (the
Astronomical Plotting Library in Python)

Dec 3

Daniel Moser

The Python experience in Sao Paulo

Dec 10

Antoine Merand

Is Python fast enough?

Dec 17

CANCELLED

Science Day @ Vitacura

Dec 24

CANCELLED

Merry Christmas!

Dec 31

CANCELLED

Happy New Year!



Introduction to PyCoffee
Bruno Dias, Paul Elliot, Jorge Martins
Abstract
As we have been discussing, there will be a Python Coffee every Thursday in the library from 11:00 - 11:30am. Julien Milli and Bruno Dias are organising this event. Comments are welcome! We would like to invite anyone interested to come this Thursday Sep 10th. We will present the ideas, format, and "personality" of the Python Coffee. Also, Paul Elliot and Jorge Martins will give a short introduction to Python.
Repository
- Introduction and definitions of the PyCoffee (*.pdf,Bruno)
- Introduction of Python (*.txt, Paul)
- Introduction of iPython notebook (example code, Paul)
- 20150910_comments_Jorge.txt (*.txt, Jorge)
- Summary of statistics from the initial poll.


Introduction to matplotlib
Matt Taylor
Abstract
I’ll highlight a non-exhaustive list of some of the most useful features that I’ve come across in matplotlib which help to make attractive, ‘next generation’ style figures.
Repository
- Slides presentation (*.pdf)


Introduction to iPython
Antoine Merand
Abstract
I will present the interactive console “IPython”. This console has been designed to replace the standard python console for interacting with your scripts, but it can do much more! I plan on making a small demonstration, but I hope also the session to be interactive with questions from the audience. See you there!
Repository
- http://ipython.org/


PyCoffee in Garching
Eric Emsellem
Abstract
Eric will share with us the topic of the most successful Python Coffee in Garching.
Repository
- Notes by Julien Milli (*.txt)


MUSE data with Python
Fernando Selman
Abstract
I will show my experiences extracting data from a MUSE cube.
Repository
-


Accessing online catalogues with Astropy
Steve Ertel
Abstract
Collecting literature data can be a very annoying task if it is for a large sample of targets. So why not automatizing it?! I'll give a quick overview to get you started on accessing online data bases such as SIMBAD, VizieR using some AstroPy libraries.
Repository
- Slides presentation (*.pdf)
- Astroquery (example code)


Python for Data Science (Data Analysis)
Ignacio Toledo
Abstract
As a data analyst in ALMA, one needs to develop skills on data mining, data cleaning, data analysis and visualization. One of the most used programing languages is Python (fighting for the first place with R). I will show and quickly review some python libraries that might help you on any kind of data analysis, including astronomy, and I will provide some ipython notebook examples. Also, I would like to talk about our experience with python.
Repository
- Example code and files.


An interactive introduction to python Classes
Elyar Sedaghati
Abstract
Being an object-oriented programming language, in python classes are a crucial part of writing a program. I present a very simple and basic introduction to python classes, and their various aspects such as objects, methods, inheritance, etc. We will create a couple of vary simple classes, learn about the way a lower level class can inherit properties of a parent class, and how various attributes of a class can be called and utilized. Once done, I will present a python class, called GP (Gaussian Process), that I have been using for non-parametric and therefore non-deterministic model fitting of data.
Repository
- Example codes


Displaying images with Python and interacting with ds9
Julien Milli
Abstract
I will show how to display images in Python and with the common image display software SAOImage DS9. Image visualisation and analysis is a common task in astronomy. Interfacing Python with DS9 gives a lot of flexibility to manipulate images retrieve data or filter an image. I will show my (little) experience of this tool in the context of high-contrast image analysis.
Repository
- Example code


Displaying FITS Images with APLpy (the Astronomical Plotting Library in Python)
Paul Eigenthaler
Abstract
APLpy is a Python module aimed at producing publication-quality plots of astronomical imaging data in FITS format using matplotlib. I will talk about the basic features of APLpy and give examples for the most important APLpy commands.
Repository
- Example code


The Python experience in Sao Paulo
Daniel Moser
Abstract
Description: Our new colleague that just arrived this week has a lot of experience organizing a series of talks on AstroInformatics and also the Python Boot Camp at Universidade de Sao Paulo, Brazil. He will guide an informal chat about his experience. It will be a good opportunity to meet and welcome Daniel.
Repository
- Webpage of AstroInformatics group at IAG/USP
- Webpage of Python Boot Camp at IAG/USP


Is Python fast enough?
Antoine Merand
Abstract
HPC (high performance computing) does not usually rhyme with Python, because Python is much slower in execution than C or Fortran. However typical codes can be vastly accelerated in python. I would like to present the ways you can accelerate execution of Python code. I would use a practical example to show how to accelerate by a factor x200 the execution between plain python and a combination of inline C code and multiprocessing. I will also show some very simple techniques to parallelise the analysis of data cube.
Repository
-