Astronomical Python : An Introduction to Modern Scientific Programming

Over the past two decades, Python has become the de facto standard language of data science both in industry and astronomy (with the exception of simulations and other extreme scale computing problems). This course text is a full introduction to programming in Python with an explicit focus on astrophysical applications. The book covers the fundamentals of Python, including the native data types and operations, and how the language, interpreter, and operating system work together. Leaning heavily on standard packages used in astronomy, the book covers the installation and basic structure of the language and libraries; script writing, conditional statements, loops, and other code structures that allow for complex outcome management; the creation and use of functions and classes within Python; the creation of packages and the methods for re-using, importing, and otherwise standardizing code; and plotting. Finally, the book contains several higher level chapters that carry students from the beginner stage of programming into the intermediate. Key Features Provides a comprehensive but accessible introduction to astronomy with Python for beginner undergraduate students Includes modern, worked out examples using real astronomical data Includes interactivity, including with various coding examples PRELIMS.pdf Acknowledgements About the Author Imad Pasha CH001.pdf Chapter Introduction 1.1 How to Use This Book? 1.2 Data Availability References CH002.pdf Chapter Essential Unix Skills 2.1 Operating Systems 2.2 Anatomy of the Terminal 2.3 Common UNIX Commands 2.3.1 Printing the Current Directory 2.3.2 Changing Directories 2.3.3 Viewing Files and Directories 2.3.4 Making Directories 2.3.5 Deleting Files and Directories 2.3.6 Moving/Copying Files and Directories 2.3.7 The Wildcard 2.4 Cancelling Commands 2.5 Tab Complete 2.6 Intermediate Shell Commands 2.6.1 Touch/File Creation 2.6.2 Previewing File Contents 2.6.3 Setting Permissions 2.6.4 Piping Outputs 2.6.5 File and Directory Archives 2.7 SSH and Servers 2.7.1 Logging into a Server 2.7.2 Copying Files from a Local Directory to a Server Using SCP 2.7.3 Copying a File from a Server to a Local Directory 2.7.4 Rsync 2.7.5 Screen and Backgrounding Processes 2.8 Profiles 2.8.1 Aliasing 2.8.2 Environment Variables Exercise 2.2: Easy cd 2.9 Summary CH003.pdf Chapter Installing Python and the Astronomy Stack 3.1 Prerequisites 3.1.1 MacOS 3.1.2 Windows 3.2 Python Environments 3.2.1 When to Create an Environment 3.2.2 Creating and Managing Environments 3.2.3 An Environment for this Textbook 3.3 Editors 3.3.1 Terminal Editors 3.3.2 Text Editors 3.3.3 Jupyter Notebooks 3.3.4 IDE (Integrated Development Environment) 3.4 Summary CH004.pdf Chapter Introduction to Python 4.1 Variables 4.1.1 Defining Variables 4.1.2 Copies of Variables 4.2 Importing External Libraries 4.3 Comments 4.4 Data Types 4.4.1 Strings 4.4.2 Collections of Data: Lists and Dictionaries 4.5 Indexing 4.6 Slicing 4.7 Operations 4.8 Reserved Words 4.9 Filtering and Masking 4.9.1 Multiple Conditions 4.10 Conditional Statements 4.10.1 Multiple Simultaneous Conditions 4.10.2 Equality versus Identity 4.11 Loops and Iterators 4.11.1 For-Loops 4.11.2 While-Loops 4.11.3 Continuing Through and Breaking Out of Loops 4.11.4 Generators 4.12 Cancelling Code Execution 4.13 Shell and Shell-like Commands in Python 4.14 Interpreting Error Messages 4.15 Handling Exceptions 4.16 Summary CH005.pdf Outline placeholder Introduction Chapter Visualization with Matplotlib 5.1 Introduction 5.2 A Simple Plot 5.3 Figures and Axes 5.4 Subplots 5.5 Adjusting Marker Properties 5.6 Adjusting Ticks 5.7 Adjusting Fonts and Fontsizes 5.7.1 LaTeX in Labels 5.8 Multiple Subplots 5.9 Subplot Mosaic 5.10 Research Example: Displaying a Best Fit 5.11 Errorbars 5.12 Plotting N-Dimensional Data 5.13 Colorbars 5.14 Summary References CH006.pdf Chapter Numpy 6.1 Introduction 6.2 The Array 6.3 Precision 6.4 Key Library Functions 6.5 Research Example: An Exoplanet Transit 6.6 Summary References CH007.pdf Chapter SciPy 7.1 Introduction 7.2 Numerical Integration 7.3 Optimization 7.4 Statistics 7.4.1 Distributions 7.5 Summary CH008.pdf Chapter Astropy and Associated Packages 8.1 Introduction 8.2 Units and Constants 8.3 Cosmological Calculations 8.4 Coordinates 8.5 Astroquery 8.6 Research Example: Automatic Offsets 8.7 Research Example: Handling Astronomical Images 8.7.1 The World Coordinate System 8.7.2 Image Cutouts 8.7.3 Aperture Photometry 8.7.4 Combining Images 8.8 Summary References CH009.pdf Outline placeholder Introduction Chapter Functions and Functional Programming 9.1 Introduction 9.2 Defining Functions 9.3 Writing Documentation 9.3.1 Formatting Your Documentation: Best Practices 9.4 Checking Function Inputs 9.5 Local Scope and Global Scope 9.5.1 Debugging with Functions 9.6 Chaining Functions Together 9.7 The Concept of Main() 9.8 Keyword (Optional) Arguments Exercise 9.1 9.9 Packing and Unpacking Function Arguments 9.10 Testing Function Outputs: Unit Testing 9.11 Type-Hinting 9.12 Summary CH010.pdf Chapter Classes and Object Oriented Programming 10.1 Introduction 10.2 Defining Classes 10.3 Setters and Getters 10.4 Representation 10.5 Subclasses (and Superclasses) 10.6 Static Methods 10.7 Abstract Base Classes 10.8 Summary CH011.pdf Chapter Data Science with Astronomical Catalogs 11.1 Introduction 11.2 Filetypes and Reading in Data 11.2.1 ASCII (Text Files) 11.2.2 Reading Tabular Data with Astropy 11.2.3 ASDF (Advanced Science Data Format) 11.2.4 HDF5 (Hierarchical Data Format 5) 11.3 Working with Tabular Data in Pandas 11.3.1 Indexing Columns 11.3.2 Indexing Rows with .loc 11.3.3 Filtering Dataframes 11.3.4 Merging Dataframes Exercise 11.1: Joint Sample Exercise 11.2: Merging Tables Exercise 11.3: Initial Cleaning 11.3.5 Saving Dataframes 11.4 Research Example: Analysis with 3DHST 11.4.1 Star-forming Sequence Exercise 11.5: Redshift dependence 11.4.2 UVJ Diagram Exercise 11.7: Size-mass relation 11.5 Summary References CH012.pdf Chapter Vectorization and Runtime Improvements 12.1 Introduction 12.2 Identifying Bottlenecks 12.3 Fast Array Operations with Numpy Exercise 12.1: Distances from Isochrone Exercise 12.2: Membership Probability 12.4 Jax 12.5 Summary References CH013.pdf Chapter Astronomical Inference 13.1 Introduction 13.2 Fitting a Line to Data 13.3 χ2 Fitting 13.4 Bayesian Inference 13.4.1 Bayes Theorem 13.4.2 Why Does This Help with Uncertainties? 13.4.3 Estimating Integrals 13.4.4 Sampling 13.4.5 Simple Monte Carlo 13.4.6 Importance Sampling Exercise 13.1: The Value of π 13.4.7 Metropolis MCMC Exercise 13.2: Write a Sampler 13.4.8 Emcee: The Workhorse Astronomy Sampler 13.4.9 Priors 13.4.10 Likelihood 13.4.11 Quoting Parameters and Uncertainties 13.5 Summary References CH014.pdf Chapter Software Development 14.1 Introduction 14.2 Why (and When) to make a Python Package a Python Package 14.3 Organizing Packages: Modules and Submodules 14.4 Custom Exceptions and Warnings 14.5 Installation and Development 14.6 Github and Version Control 14.7 Summary CH015.pdf Chapter Conclusions and Next Steps 15.1 Concluding Remarks References