.. ESO_chile_python_team documentation master file, created by sphinx-quickstart on Mon Aug 13 12:21:43 2018. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. .. _PyCoffee: PyCoffee's ---------- ---------- This page allows you to see the program of the Pycoffee held at ESO chile in Santiago. For each session you can click on the link of material to download it. 26-08-2018: Julien Milli about Using Principal Component Analysis with images ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ * Principal component Analysis is generally used for dimensionality reduction (data compression or multidimensional data visualisation), or to identify driving factors in multidimensional data clouds. In high contrast imaging, this is used to estimate the glare and speckle halo of the central star, in order to reveal underlying astrophysical signal from a planet or a disk. developed a suite of tools in Python to perform a PCA with images and help the interpretation of those principal components, and I propose to explain those in this pycoffee, along with the basic maths behind PCA. 30-08-2018: Alessandro Razza about SExtractor with Python (I) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ * SExtractor remains a valuable tool to build a catalog of objects from an astronomical image. All the main SExtractor features are now built-in a python library (photutils) which is computationally efficient and highly customizable. In this informal talk, I show how to compute a SExtractor-like background of an image and how to extract a list of sources with their properties as a python table. No particular python skills are required, although previous knowledge of SExtrator and/or a basic understanding of python syntax can be of some help. 6-09-2018: Alessandro Razza about SExtractor with Python (II) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ * In this second and last part of the talk on using SExtractor with python, I will focus on the source extraction process and output data visualization.\n 11-09-2018: Ridlo Wibowo on Rebound: An open-source multi-purpose N-body code ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ * I will introduce REBOUND, a software package that can integrate the motion of particles under the influence of gravity. The particles can represent stars, planets, moons, ring, or dust particles. REBOUND is very flexible and can be customized to accurately and efficiently to solve many problems in astrophysics/dynamics. I will present several examples and its features that may be useful for you. It is written in C and has an easy-to-use Python interface. 24-01-2019: Romain: distribute your package! ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ * Distributing codes is becoming a necessity in science. It allows people to reproduce your results and make science more transparent. In that spirit I will present how to package a python module and how to distribute it in the python package repositories (testing and main). I will also talk briefly how to create a propoer documentation with the sphinx package and how to put it online (github and readthedocs). * :download:`pycoffee_240119.tar.gz <./files/pycoffee_240119.tar.gz>` 9-05-2019: Pedro: Bayesian Correlation ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ * BayesCorr is a simple python program that uses pymc to obtain the posterior distribution of the correlation coefficients of a dataset. It allows you to truly answer the question "Are two variables correlated?" in a statistically meaningful way, and providing a distribution of the correlation coefficient (with median values, confidence intervals, and error bars). It is done through python program that requires no manipulation from the user, and no statistical knowledge, just an ASCII file with data pairs. The program is available here and the paper describing it here. The paper related to this is: http://adsabs.harvard.edu/abs/2016OLEB...46..385F