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Contributing#

This guide aims to ease the contribution process for both novice and experienced contributors.

TopoModelX is a community effort, and everyone is welcome to contribute.

Making Changes#

The preferred way to contribute to topomodelx is to fork the upstream repository and submit a “pull request” (PR).

Follow these steps before submitting a PR:

  1. Synchronize your main branch with the upstream main branch:

    $ git checkout main
$ git pull upstream main

  2. Create a feature branch to hold your development changes:

    $ git checkout -b <branch-name>

  3. Make changes. Make sure that you provide appropriate unit-tests and documentation to your code. See next sections of this contributing guide for details.

  4. When you’re done editing, add changed files using git add and then git commit:

    $ git add <modified_files>
$ git commit -m "Add my feature"

    to record your changes. Then push the changes to your fork of TopoNextX with:

    $ git push origin <branch-name>

  5. Follow these instructions to create a pull request from your fork.

  6. Repeat 3. and 4. following the reviewers requests.

Write Tests#

The tests consist of classes appropriately named, located in the test folder, that check the validity of the code.

Test functions should be located in files whose filenames start with test_. For example:

# test_add.py

def add(x, y):
   return x + y

def test_capital_case():
   assert add(4, 5) == 9

Use an assert statement to check that the function under test returns the correct output.

Run Tests#

Install pytest which is the software tools used to run tests:

$ pip install -e .[dev]

Then run the test using:

$ pytest test_add.py

Verify that the code you have added does not break TopoModelX by running all the tests.

$ pytest test/

Write Documentation#

Building the documentation requires installing specific requirements.

$ pip install -e .[doc]

Intro to Docstrings#

A docstring is a well-formatted description of your function/class/module which includes its purpose, usage, and other information.

There are different markdown languages/formats used for docstrings in Python. The most common three are reStructuredText, numpy, and google docstring styles. For topomodelx, we are using the numpy docstring standard. When writing up your docstrings, please review the NumPy docstring guide to understand the role and syntax of each section. Following this syntax is important not only for readability, it is also required for automated parsing for inclusion into our generated API Reference.

You can look at these for any object by printing out the __doc__ attribute. Try this out with the np.array class and the np.mean function to see good examples:

>>> import numpy as np
>>> print(np.mean.__doc__)

The Anatomy of a Docstring#

These are some of the most common elements for functions (and ones we’d like you to add where appropriate):

  1. Summary - a one-line (here <79 char) description of the object

    1. Begins immediately after the first “”” with a capital letter, ends with a period

    2. If describing a function, use a verb with the imperative mood (e.g. Compute vs Computes)

    3. Use a verb which is as specific as possible, but default to Compute when uncertain (as opposed to Calculate or Evaluate, for example)

  2. Description - a more informative multi-line description of the function

    1. Separated from the summary line by a blank line

    2. Begins with a capital letter and ends with period

  3. Parameters - a formatted list of arguments with type information and description

    1. On the first line, state the parameter name, type, and shape when appropriate. The parameter name should be separated from the rest of the line by a : (with a space on either side). If a parameter is optional, write Optional, default: default_value. as a separate line in the description.

    2. On the next line, indent and write a summary of the parameter beginning with a capital letter and ending with a period.

    3. See Docstring Examples.

  4. Returns (esp. for functions) - a formatted list of returned objects type information and description

    1. The syntax here is the same as in the parameters section above.

    2. See Docstring Examples.

If documenting a class, you would also want to include an Attributes section. There are many other optional sections you can include which are very helpful. For example: Raises, See Also, Notes, Examples, References, etc.

N.B. Within Notes, you can:

  • include LaTex code

  • cite references in text using ids placed in References

Docstring Examples#

Here’s a generic docstring template:

def my_method(self, my_param_1, my_param_2="vector"):
   r"""Write a one-line summary for the method.

   Write a description of the method, including "big O"
   (:math:`O\left(g\left(n\right)\right)`) complexities.

   Parameters
   ----------
   my_param_1 : array-like, shape = (..., dim)
      Write a short description of parameter my_param_1.
   my_param_2 : str, {"vector", "matrix"}
      Write a short description of parameter my_param_2.
      Optional, default: "vector".

   Returns
   -------
   my_result : array-like, shape = (..., dim, dim)
      Write a short description of the result returned by the method.

   Notes
   -----
   If relevant, provide equations with (:math:)
   describing computations performed in the method.

   Example
   -------
   Provide code snippets showing how the method is used.
   You can link to scripts of the examples/ directory.

   Reference
   ---------
   If relevant, provide a reference with associated pdf or
   wikipedia page.
   """

And here’s a filled-in example from the Scikit-Learn project, modified to our syntax:

def fit_predict(self, X, y=None, sample_weight=None):
    """Compute cluster centers and predict cluster index for each sample.

    Convenience method; equivalent to calling fit(X) followed by predict(X).

    Parameters
    ----------
    X : {array-like, sparse_matrix} of shape = (..., n_features)
       New data to transform.
    y : Ignored
       Not used, present here for API consistency by convention.
    sample_weight : array-like, shape [...,], optional
       The weights for each observation in X. If None, all observations
       are assigned equal weight (default: None).

    Returns
    -------
    labels : array, shape = (...,)
       Index of the cluster each sample belongs to.
    """
    return self.fit(X, sample_weight=sample_weight).labels_

In general, have the following in mind:

  1. Use built-in Python types. (bool instead of boolean)

  2. Use [ for defining shapes: array-like, shape = (..., dim)

  3. If a shape can vary, use a list-like notation: array-like, shape = (dimension[:axis), n, dimension[axis:]]

  4. For strings with multiple options, use brackets: input: str, {"log", "squared", "multinomial"}

  5. 1D or 2D data can be a subset of {array-like, ndarray, sparse matrix, dataframe}. Note that array-like can also be a list, while ndarray is explicitly only a numpy.ndarray.

  6. Add “See Also” in docstrings for related classes/functions. “See Also” in docstrings should be one line per reference, with a colon and an explanation.

For Class and Module Examples see the scikit-learn _weight_boosting.py module. The class AdaBoost has a great example using the elements we’ve discussed here. Of course, these examples are rather verbose, but they’re good for understanding the components.

When editing reStructuredText (.rst) files, try to keep line length under 80 characters (exceptions include links and tables).

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