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  • 1
    Keywords: Research-Data processing. ; Computer software-Development. ; Electronic books.
    Description / Table of Contents: Based on the practical experiences of its authors, who collectively have spent several decades teaching software skills to scientists, this book covers everything graduate-level researchers need to automate their workflows, collaborate with colleagues, and ensure that their results are trustworthy.
    Type of Medium: Online Resource
    Pages: 1 online resource (531 pages)
    Edition: 1st ed.
    ISBN: 9781000417128
    DDC: 001.420285
    Language: English
    Note: Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Welcome -- 0.1. The 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 -- 1. 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 -- 2. 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 -- 3. 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 -- 4. 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 -- 5. 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 -- 6. 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 -- 7. 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 -- 8. 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 -- 9. 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 -- 10. 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 -- 11. 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 -- 12. 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 -- 13. Tracking Provenance -- 13.1. Data Provenance -- 13.2. Code Provenance -- 13.3. Summary -- 13.4. Exercises -- 13.5. Key Points -- 14. 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 -- 15. Finale -- 15.1. Why We Wrote This Book -- Appendix -- A. Solutions -- B. Learning Objectives -- B.1. Getting Started -- B.2. The Basics of the Unix Shell -- B.3. Building Tools with the Unix Shell -- B.4. Going Further with the Unix Shell -- B.5. Building Command-Line Tools with Python -- B.6. Using Git at the Command Line -- B.7. Going Further with Git -- B.8. Working in Teams -- B.9. Automating Analyses with Make -- B.10. Configuring Programs -- B.11. Testing Software -- B.12. Handling Errors -- B.13. Tracking Provenance -- B.14. Creating Packages with Python -- C. Key Points -- C.1. Getting Started -- C.2. The Basics of the Unix Shell -- C.3. Building Tools with the Unix Shell -- C.4. Going Further with the Unix Shell -- C.5. Building Command-Line Programs in Python -- C.6. Using Git at the Command Line -- C.7. Going Further with Git -- C.8. Working in Teams -- C.9. Automating Analyses with Make -- C.10. Configuring Programs -- C.11. Testing Software -- C.12. Handling Errors -- C.13. Tracking Provenance -- C.14. Creating Packages with Python -- D. Project Tree -- E. Working Remotely -- E.1. Logging In. , E.2. Copying Files -- E.3. Running Commands -- E.4. Creating Keys -- E.5. Dependencies -- F. Writing Readable Code -- F.1. Python Style -- F.2. Order -- F.3. Checking Style -- F.4. Refactoring -- F.5. Code Reviews -- F.6. Python Features -- F.7. Summary -- G. Documenting Programs -- G.1. Writing Good Docstrings -- G.2. Defining Your Audience -- G.3. Creating an FAQ -- H. YAML -- I. Anaconda -- I.1. Package Management with conda -- I.2. Environment Management with conda -- J. Glossary -- K. References -- Index.
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  • 2
    Publication Date: 2022-05-26
    Description: Author Posting. © American Geophysical Union, 2019. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters, 46(9), (2019):4894-4903, doi:10.1029/2019GL082015.
    Description: The largest contributor to the planetary energy imbalance is well‐mixed greenhouse gases (GHGs), which are partially offset by poorly mixed (and thus northern midlatitude dominated) anthropogenic aerosols (AAs). To isolate the effects of GHGs and AAs, we analyze data from the CMIP5 historical (i.e., all natural and anthropogenic forcing) and single forcing (GHG‐only and AA‐only) experiments. Over the duration of the historical experiment (1861–2005) excess heat uptake at the top of the atmosphere and ocean surface occurs almost exclusively in the Southern Hemisphere, with AAs canceling the influence of GHGs in the Northern Hemisphere. This interhemispheric asymmetry in surface heat uptake is eliminated by a northward oceanic transport of excess heat, as there is little hemispheric difference in historical ocean heat storage after accounting for ocean volume. Data from the 1pctCO2 and RCP 8.5 experiments suggests that the future storage of excess heat will be skewed toward the Northern Hemisphere oceans.
    Description: We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. CMIP data can be accessed at the ESGF website (https://esgfnode.llnl.gov/projects/esgfllnl/). For CMIP the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We also thank Paola Petrelli from the ARC Centre of Excellence for Climate Extremes, for her assistance with downloading/managing the CMIP5 data archive at the National Computational Infrastructure.
    Repository Name: Woods Hole Open Access Server
    Type: Article
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