Python for Astronomers
Konkoly Observatory
Introduction
This website collects the educational material for the Python for Astronomers course compiled and held by Attila Bódi. The course is primarily intended for students and researchers in any carrier stage in the field of astronomy. The lectures provide detailed, step-by-step guides to solving a variaty of astronomical/astrophysical problems in Python. The topics are listed below.
Planned agenda
Introduction to different python versions, jupyter, google colab
Installing/updating packages
Absolute basics of python (syntax, modules, data types)
Loading and writing files
Plotting
Functions, lambda function
Tables, Pandas data structures and data analysis
Advanced data formats (pickle, hdf5, fits)
Units, constants, uncertainties
Linear algebra (integration, equation systems)
Data manipulation 1 (interpolation, filtering (smoothing), bad values)
Data manipulation 2 (binning, sigma clipping, de-trending)
Optimization (fitting, minimizing functions, brute force, bootstrap)
Time series analysis (Fourier-, Lomb-Scargle-, time-frequency analysis)
Ground- and space-based photometry
Bayesian inference with Markov Chain Monte Carlo
Introduction to Gaussian Process regression
Parallel execution
Introduction to Machine Learning 1 (classification: K-Nearest Neighbors, Decision Trees, Naive Bayes, Support Vector Machines, Logistic Regression)
Introduction to Machine Learning 2 (neural networks, deep learning and other magic words; scikit-learn, tensorflow, keras)
Version-control: git, github
Slides
Contact:
Attila Bódi: bodi.attila (at) csfk.org
© Attila Bódi 2020-23