Research Software Engineering with Python: Building software that makes research possible

Writing and running software is now as much a part of science as telescopes and test tubes, but most researchers are never taught how to do either well. As a result, it takes them longer to accomplish simple tasks than it should, and it is harder for them to share their work with others than it needs to be. This book introduces the concepts, tools, and skills that researchers need to get more done in less time and with less pain. Based on the practical experiences of its authors, who collectively have spent several decades teaching software skills to scientists, it covers everything graduate-level researchers need to automate their workflows, collaborate with colleagues, ensure that their results are trustworthy, and publish what they have built so that others can build on it. The book assumes only a basic knowledge of Python as a starting point, and shows readers how it, the Unix shell, Git, Make, and related tools can give them more time to focus on the research they actually want to do. Research Software Engineering with Python can be used as the main text in a one-semester course or for self-guided study. A running example shows how to organize a small research project step by step; over a hundred exercises give readers a chance to practice these skills themselves, while a glossary defining over two hundred terms will help readers find their way through the terminology. All of the material can be re-used under a Creative Commons license, and all royalties from sales of the book will be donated to The Carpentries, an organization that teaches foundational coding and data science skills to researchers worldwide.

Table of Contents:
Welcome 0.1The Big Picture 0.2 Intended Audience 0.3 What You Will Learn 0.4 Using this Book 0.5 Contributing and Re-Use 0.6 Acknowledgments Getting Started 1.1 Project Structure 1.2 Downloading the Data 1.3 Installing the Software 1.4 Summary 1.5 Exercises 1.6 Key Points The Basics of the Unix Shell 2.1 Exploring Files and Directories 2.2 Moving Around 2.3 Creating New Files and Directories 2.4 Moving Files and Directories 2.5 Copying Files and Directories 2.6 Deleting Files and Directories 2.7 Wildcards 2.8 Reading the Manual 2.9 Summary 2.10 Exercises 2.11 Key Points Building Tools with the Unix Shell 3.1 Combining Commands 3.2 How Pipes Work 3.3 Repeating Commands on Many Files 3.4 Variable Names 3.5 Redoing Things 3.6 Creating New Filenames Automatically 3.7 Summary 3.8 Exercises 3.9 Key Points Going Further with the Unix Shell 4.1 Creating New Commands 4.2 Making Scripts More Versatile 4.3 Turning Interactive Work into a Script 4.4 Finding Things in Files 4.5 Finding Files 4.6 Configuring the Shell 4.7 Summary 4.8 Exercises . 4.9 Key Points Building Command-Line Tools with Python 5.1 Programs and Modules 5.2 Handling Command-Line Options 5.3 Documentation 5.4 Counting Words 5.5 Pipelining 5.6 Positional and Optional Arguments 5.7 Collating Results 5.8 Writing Our Own Modules 5.9 Plotting 5.10 Summary 5.11 Exercises 5.12 Key Points Using Git at the Command Line 6.1 Setting Up 6.2 Creating a New Repository 6.3 Adding Existing Work 6.4 Describing Commits 6.5 Saving and Tracking Changes 6.6 Synchronizing with Other Repositories 6.7 Exploring History 6.8 Restoring Old Versions of Files 6.9 Ignoring Files 6.10 Summary 6.11 Exercises 6.12 Key Points Going Further with Git 7.1 What’s a Branch? 7.2 Creating a Branch 7.3 What Curve Should We Fit? 7.4 Verifying Zipf’s Law 7.5 Merging 7.6 Handling Conflicts 7.7 A Branch-Based Workflow 7.8 Using Other People’s Work 7.9 Pull Requests 7.10 Handling Conflicts in Pull Requests 7.11 Summary 7.12 Exercises 7.13 Key Points Working in Teams 8.1 What is a Project? 8.2 Include Everyone 8.3 Establish a Code of Conduct 8.4 Include a License 8.5 Planning 8.6 Bug Reports 8.7 Labeling Issues 8.8 Prioritizing 8.9 Meetings 8.10 Making Decisions 8.11 Make All This Obvious to Newcomers 8.12 Handling Conflict 8.13 Summary 8.14 Exercises 8.15 Key Points Automating Analyses with Make 9.1 Updating a Single File 9.2 Managing Multiple Files 9.3 Updating Files When Programs Change 9.4 Reducing Repetition in a Makefile 9.5 Automatic Variables 9.6 Generic Rules 9.7 Defining Sets of Files 9.8 Documenting a Makefile 9.9 Automating Entire Analyses 9.10 Summary 9.11 Exercises 9.12 Key Points Configuring Programs 10.1 Configuration File Formats 10.2 Matplotlib Configuration 10.3 The Global Configuration File 10.4 The User Configuration File 10.5 Adding Command-Line Options 10.6 A Job Control File 10.7 Summary 10.8 Exercises 10.9 Key Points Testing Software 11.1 Assertions 11.2 Unit Testing 11.3 Testing Frameworks 11.4 Testing Floating-Point Values 11.5 Integration Testing 11.6 Regression Testing 11.7 Test Coverage 11.8 Continuous Integration 11.9 When to Write Tests 11.10 Summary 11.11 Exercises 11.12 Key Points Handling Errors 12.1 Exceptions 12.2 Writing Useful Error Messages 12.3 Testing Error Handling 12.4 Reporting Errors 12.5 Summary 12.6 Exercises 12.7 Key Points Tracking Provenance 13.1 Data Provenance 13.2 Code Provenance 13.3 Summary 13.4 Exercises 13.5 Key Points Creating Packages with Python 14.1 Creating a Python Package 14.2 Virtual Environments 14.3 Installing a Development Package 14.4 What Installation Does 14.5 Distributing Packages 14.6 Documenting Packages 14.7 Software Journals 14.8 Summary 14.9 Exercises 14.10 Key Points Finale 15.1 Why We Wrote This Book Appendix A Solutions B Learning Objectives C Key Points D Project Tree E Working Remotely F Writing Readable Code G Documenting Programs H YAML I Anaconda J Glossary K References Index