Advanced track bootcamp

Bootcamp to cover more advanced topics in scientific Python.


We are going to start up using a typical development workflow in Python, using the example of making a package for ordinary least squares fitting.

To make things clear to our future selves, and other collaborators, we will document what we are doing as we go.


  • General pattern of work: explore; write tests; write code; have code reviewed and merged; repeat.

  • Dividing into groups;

  • Revision on git, github, pull requests.

  • Editors;

  • Getting help:

    • Python docs at;
    • IPython tab completion, something?, something??;
    • np.lookfor;
    • your search engine (sigh).

If you haven’t read this already, read

Information on installing via pip on Debian / Ubuntu at :


  1. Installing Python 3, pip;
    • install Python 3 / pip as necessary.
  2. Working with forks and remotes; making feature branches; making pull requests;
    • forking the repo; cloning the repo; adding the remotes; make feature branch edit-readme; edit the README, make a pull request.
  3. Introduction to Restructured Text. ReST in Sphinx;
    • make feature branch rst-readme; edit README.rst; install docutils; check rendering with; add; commit. Check rendering on github. Do pull request (PR).
  4. Starting from origin/master;
    • get merged changes from github; make new feature branch add-requirements starting at the merged state.
  5. Introduction to virtualenvs and virtualenvwrapper;
    • install virtualenvwrapper; create ols virtualenv.
  6. Pip installs, requirements files:
    • install numpy; feature branch (FB) add-requirements; specify in requirements file, add requirements.txt file, commit, do PR.
  7. The Python path; Python packages;
    • create package structure for ols package. Show you can import ols. Add some explanatory text in init file. Import ols again to show docstring. Change into another directory and try import. Fix. Make FB package-structure; add and commit; do PR.
  8. The .gitignore file;
    • add .gitignore file to FB add-gitignore; Commit, PR.
  9. The file; installing in pip develop mode:
    • create for ols, pip install in develop mode. Show that you can now import from anywhere. Make FB add-setup. Commit; do PR.
  10. The explore phase. Reminder about ordinary least squares fitting for model $Y = X B + E$. See GLM intro;
    • in IPython, recreate psychopathy $Y$, clamminess $vec{x}$. Create $X$. Fit model and find coefficients $B$.
  11. Test first development; pytest; comparing arrays; typical imports such as import numpy as np:
    • FB add-test-fit. Install pytest with pip. Add to requirements. Make a test for a new function testing that that you can reconstruct parameters from the GLM intro. Run the test and make sure it fails. Write the function fit in ols/ Commit, do PR.
  12. Floating point, almost equal
    • FB close-tests. Test that results are close to expected. Do PR.
  13. Numpy and almost equal;
    • FB np-testing; use np.testing to do the almost equal test. Document your testing procedure in the README.
  14. Importing from the top level:
    • FB: top-level-imports. How do you import fit directly from ols? As in from ols import fit? Do PR.
  15. Remember the README:
    • FB document-testing. Document the testing procedure so someone else can reproduce it.
  16. Check collaboration / replication.
    • Start in a new directory; make a new virtualenv, and follow the instructions in the README exactly, to run the tests. Fix any problems; do a PR.
  17. Testing edge cases; reminder on reshape and introduction to newaxis:
    • what happens if you pass a list into the fit function? Or a 1D array? FB extend-tests; Make test cases. Run the tests. Fix. What happens if the design is rank deficient? Test. Fix if necessary. Do PR.
  18. Dealing with errors. Try / except / finally.
    • use try / except to test / confirm that error arises from passing design and data of different number of rows.
  19. More advanced pytests:
    • see if you can find a neater way to test for errors being raised using pytest.
  20. Rethinking the design as objects. The Model and Results objects. Thinking about the design.
    • write tests for the Model and Results objects. Run tests and fail. Write Model and results objects.
  21. Loading text files with numpy. Working out which directory your test is running in.
    • I will add a random X and Y to the repository, and save the expected coefficients out as another text file. Make a feature branch; in the tests, load the X and Y, fit the model, test the coefficients against the result I got. Do a PR.
  22. Testing against other packages:
    • Load text files from R. Find model coefficients using lm. Save from R session. Make test from saved R results. Clue: R command for writing a simple text file of numeric values is write.table(var, row.names=FALSE, col.names=FALSE, file='filename.txt').
  23. Decorators and properties.
    • write tests for residuals as a property. Fix tests.