| Description |
1 online resource (528 pages) |
| Contents |
Welcome0.1The Big Picture0.2 Intended Audience0.3 What You Will Learn0.4 Using this Book0.5 Contributing and Re-Use0.6 AcknowledgmentsGetting Started1.1 Project Structure1.2 Downloading the Data1.3 Installing the Software1.4 Summary1.5 Exercises1.6 Key PointsThe Basics of the Unix Shell2.1 Exploring Files and Directories2.2 Moving Around2.3 Creating New Files and Directories2.4 Moving Files and Directories2.5 Copying Files and Directories2.6 Deleting Files and Directories2.7 Wildcards2.8 Reading the Manual2.9 Summary2.10 Exercises2.11 Key PointsBuilding Tools with the Unix Shell3.1 Combining Commands3.2 How Pipes Work3.3 Repeating Commands on Many Files3.4 Variable Names3.5 Redoing Things3.6 Creating New Filenames Automatically3.7 Summary3.8 Exercises3.9 Key PointsGoing Further with the Unix Shell4.1 Creating New Commands4.2 Making Scripts More Versatile4.3 Turning Interactive Work into a Script4.4 Finding Things in Files4.5 Finding Files4.6 Configuring the Shell4.7 Summary4.8 Exercises .4.9 Key PointsBuilding Command-Line Tools with Python5.1 Programs and Modules5.2 Handling Command-Line Options5.3 Documentation5.4 Counting Words5.5 Pipelining5.6 Positional and Optional Arguments5.7 Collating Results5.8 Writing Our Own Modules5.9 Plotting5.10 Summary5.11 Exercises5.12 Key PointsUsing Git at the Command Line6.1 Setting Up6.2 Creating a New Repository6.3 Adding Existing Work6.4 Describing Commits6.5 Saving and Tracking Changes6.6 Synchronizing with Other Repositories6.7 Exploring History6.8 Restoring Old Versions of Files6.9 Ignoring Files6.10 Summary6.11 Exercises6.12 Key PointsGoing Further with Git7.1 What's a Branch?7.2 Creating a Branch7.3 What Curve Should We Fit?7.4 Verifying Zipf's Law7.5 Merging7.6 Handling Conflicts7.7 A Branch-Based Workflow7.8 Using Other People's Work7.9 Pull Requests7.10 Handling Conflicts in Pull Requests7.11 Summary7.12 Exercises7.13 Key PointsWorking in Teams8.1 What is a Project?8.2 Include Everyone8.3 Establish a Code of Conduct8.4 Include a License8.5 Planning8.6 Bug Reports8.7 Labeling Issues8.8 Prioritizing8.9 Meetings8.10 Making Decisions8.11 Make All This Obvious to Newcomers8.12 Handling Conflict8.13 Summary8.14 Exercises8.15 Key PointsAutomating Analyses with Make9.1 Updating a Single File9.2 Managing Multiple Files9.3 Updating Files When Programs Change9.4 Reducing Repetition in a Makefile9.5 Automatic Variables9.6 Generic Rules9.7 Defining Sets of Files9.8 Documenting a Makefile9.9 Automating Entire Analyses9.10 Summary9.11 Exercises9.12 Key PointsConfiguring Programs10.1 Configuration File Formats10.2 Matplotlib Configuration10.3 The Global Configuration File10.4 The User Configuration File10.5 Adding Command-Line Options10.6 A Job Control File10.7 Summary10.8 Exercises10.9 Key PointsTesting Software11.1 Assertions11.2 Unit Testing11.3 Testing Frameworks11.4 Testing Floating-Point Values11.5 Integration Testing11.6 Regression Testing11.7 Test Coverage11.8 Continuous Integration11.9 When to Write Tests11.10 Summary11.11 Exercises11.12 Key PointsHandling Errors12.1 Exceptions12.2 Writing Useful Error Messages12.3 Testing Error Handling12.4 Reporting Errors12.5 Summary12.6 Exercises12.7 Key PointsTracking Provenance13.1 Data Provenance13.2 Code Provenance13.3 Summary13.4 Exercises13.5 Key PointsCreating Packages with Python14.1 Creating a Python Package14.2 Virtual Environments14.3 Installing a Development Package14.4 What Installation Does14.5 Distributing Packages14.6 Documenting Packages14.7 Software Journals14.8 Summary14.9 Exercises14.10 Key PointsFinale15.1 Why We Wrote This BookAppendixA SolutionsB Learning ObjectivesC Key PointsD Project TreeE Working RemotelyF Writing Readable CodeG Documenting ProgramsH YAMLI AnacondaJ GlossaryK ReferencesIndex |
| Summary |
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 |
| Notes |
Dr. Damien B. Irving is post-doctoral researcher in climate science at the University of New South Wales living in Hobart, Tasmania. With a strong interest in data science education and open/reproducible research, Damien is involved in The Carpentries community as an instructor, lesson author and Regional Coordinator for Australia, is an Associate Editor with the Journal of Open Research Software, and is currently the Global Coordinator for the Research Bazaar, a worldwide festival promoting the digital literacy emerging at the center of modern research. Dr. Kate L. Hertweck is a scientist and educator who endeavors to uphold core values like diversity/equity/inclusion, accessibility of information, and learning over knowing. They currently lead training and community efforts to support biomedical researchers at Fred Hutchinson Cancer Research Center in Seattle, Washington. Kate is an instructor and trainer for the Carpentries and has also participated in that group's lesson development/maintenance and community governance. Dr. Luke Johnston is a diabetes epidemiologist working at the Steno Diabetes Center Aarhus in Denmark. He is passionate about educating researchers on modern computing tools and skills, having taught many Carpentry workshops as well as creating and instructing several intensive courses teaching computing skills and analytic reproducibility to diabetes researchers. When he isn't teaching or doing research, he is building software tools to automate common research workflows and tasks. Dr. Joel Ostblom is a post-doctoral teaching fellow in the Master's of Data Science program at the University of British Columbia in Vancouver, B.C. He has co-created or led the development of several courses and workshops at the University of Toronto and the University of British Columbia. Joel cares deeply about spreading data literacy and excitement over programmatic data analysis, which is reflected in his contributions to open source projects and data science learning resources. Dr. Charlotte Wickham is a data scientist and educator, who teaches in the Statistics Department at Oregon State University, as well as operating her own consulting and training business. She loves to help people build their data super powers in the R programming language. She currently lives in Corvallis, Oregon, but originally hails from New Zealand. Dr. Greg Wilsonis a programmer and educator based in Toronto, Ontario, and was the co-founder and first Executive Director of Software Carpentry. A member of the Python Software Foundation, Greg has written or edited over a dozen books and received ACM SIGSOFT's Influential Educator Award in 2020 |
|
Vendor-supplied metadata |
| Subject |
Research -- Data processing.
|
|
Computer software -- Development.
|
|
Python (Computer program language)
|
|
COMPUTERS -- Computer Graphics -- Game Programming & Design.
|
|
COMPUTERS -- Programming -- General.
|
|
COMPUTERS -- Programming Languages -- General.
|
|
Computer software -- Development.
|
|
Python (Computer program language)
|
|
Research -- Data processing.
|
| Form |
Electronic book
|
| Author |
Hertweck, Kate
|
|
Johnston, Luke
|
|
Ostblom, Joel
|
|
Wickham, Charlotte
|
|
Wilson, Greg
|
| ISBN |
9781003143482 |
|
1003143482 |
|
9781000417128 |
|
1000417123 |
|
9781000417159 |
|
1000417158 |
|
