Science

• builds and organizes knowledge
• tests explanations about the universe
• systematically,
• objectively,
• transparently,
• and reproducibly.

Otherwise it's not science.

Computers

should...

• improve efficiency,
• reduce human error,
• automate the mundane,
• simplify the complex,
• and accelerate research.

But scientists don't use them effectively.

“ Computational science is a special case of scientific research: the work is easily shared via the Internet since the paper, code, and data are digital and those three aspects are all that is required to reproduce the results, given sufficient computation tools. ” - Stodden, 2010.

Getting Started

“ Organized Skepticism. Scientists are critical: All ideas must be tested and are subject to rigorous structured community scrutiny.” - R.K. Merton, 1942

Data Storage

• Good: pencil and paper
• Better: spreadsheet
• Best: standardized file format, database management system

Formats: Evaluated Nuclear Data File (ENDF), Evaluated Nuclear Structure Data File (ENSDF), Hierarchical Data Format (HDF), etc.

Management: C/Python/Fortran APIs, HDF5, SQL, MySQL, sqlite, MongoDB, Hadoop, etc.

Backing Up Files

• Good: hope
• Better: nightly emails
• Best: remote version control (GitHub, BitBucket, Launchpad, GitLab)

Version Control Systems: cvs, svn, hg, git

Managing Changes

• Good: naming convention
• Better: clever naming convention
• Best: local version control

Getting It Done

“ It takes just as much time to write a good paper as it takes to write a bad one. ” - Polterovich, 2014

Analysis

• Good: pencil and calculator
• Better: spreadsheets, matlab, mathematica
• Best: scripting, open source libraries, modern programming language

Hint: Check out GitHub for existing toolkits for analysis in your domain.

Multiple File Cleanup

• Good: manually edit every file
• Better: search and replace in each file
• Best: scripted batch editing

Hint: try a tutorial on BASH, CSH, Python, or Perl.

Excecuting Workflows

• Good: retype a series of commands
• Better: bash script
• Best: build system

Build System Tools: make, autoconf, automake, cmake, docker, etc.

Data Structures

• Good: 100 string variables holding doubles
• Better: lists of lists of doubles
• Best: appropriate powerful data structures

Hint: In FORTAN, learn about arrays. In C++, learn about maps, vectors, deques, queues, etc. In python, the power lies in dictionaries and numpy arrays.

Caveat: Perhaps DataFrames.

API Design

• Good: single block of procedural code
• Better: separate functions
• Best: small, testable functions, grouped into classes, DRY

DRY: Dont Repeat Yourself. Code replication is bug proliferation.

Hint: github.com/audreyr/cookiecutter or github.com/uwescience/shablona

Variable Naming

• Good: d1, d2, d3
• Better: x, y, z
• Best: p.x, p.y, p.z, p=Point(x,y,z)

Hint: Prof. Jenny Bryan on Naming Things

File I/O

• Good: none, hardcoded variables
• Better: plain text input file, line-by-line homemade string parsing
• Best: file parsing library

Tools: python argparse, xml rng, json, etc.

Getting It Right

“ The scientific method’s central motivation is the ubiquity of error—the awareness that mistakes and self-delusion can creep in absolutely anywhere and that the scientist’s effort is primarily expended in recognizing and rooting out error. ” - Donoho, 2009.

Error Detection

• Good: show results to experts
• Better: integration testing
• Best: unit test suite, continuous integration

Error Diagnostics

• Good: re-re-read the code
• Better: print statements
• Best: use a linter, a debugger, and a profiler

Tools: cpplint, pyflakes, gdb, lldb, pdb, idb, valgrind, kernprof, kcachegrind

Error Correction

• Good: fix code
• Better: fix, add an exception
• Best: fix, add an exception, add a test

Hint: katyhuff.github.io/python-testing

Getting It Together

Merging Collaborative Work

• Good: single master copy, waiting
• Better: emails and patches
• Best: remote version control

Peer Review For Code

• Good: separation of concerns
• Better: shared repository
• Best: peer-reviewed pull requests

“ just-in-time review of small code changes is more likely to succeed than large-scale end-of-work reviews. ” - Petre, Wilson 2014

Teamwork

• Good: weekly research meetings, year-long tasks
• Better: daily conversations, month-long goals
• Best: pair programming, issue tracking

Software Handovers

• Good: zip file, theory paper
• Better: comments in code, example input file
• Best: automated documentation, test suite

Books: Clean Code, Working Effectively with Legacy Code

Tools: sphinx, doxygen, gooletest, unitttest, nosetests

Getting It Out There

“ If a piece of scientific software is released in the forest, does it change the field? ”

Plotting

• Good: custom formatting, clickable GUI
• Better: plot format templates (excel, mathematica)
• Best: scripted plotting, matplotlib, gnuplot, etc.

Writing

• Good: stone tablet, microsoft word
• Better: word with track changes, open office
• Best: plain text markup with version control and a makefile

Tools: LaTeX, markdown, restructured text

Distribution Control

• Good: "email to request access"
• Better: license file
• Best: license file, citation file, DOI, forkable repository

Example: github.com/cyclus

Unique Issue in Nuclear Engineering

Export control is serious.

Community Adoption

• Good: none, internal use only
• Better: online repository, developer email online
• Best: issue tracker, user/developer listhost(s), online documentation

Resources

Online Resources

• Software Carpentry
• Version Control: Github, Pro Git book
• Testing: nose, goolgetest
• Documentation: Sphinx, Doxygen
• Python Worker's Party Listhost
• The Hacker Within Listhost

Papers!

Good Books

• Clean Code - Robert C. Martin
• Working Effectively with Legacy Code - Martin Fowler
• Effective Computation in Physics - Huff, Scopatz