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Contributor Guide

Thank you for your interest in improving this project. This project is open-source under the MIT license and welcomes contributions in the form of bug reports, feature requests, and pull requests.

Here is a list of important resources for contributors:

How to report a bug

Report bugs on the Issue Tracker.

When filing an issue, make sure to answer these questions:

  • Which operating system and Python version are you using?
  • Which version of this project are you using?
  • What did you do?
  • What did you expect to see?
  • What did you see instead?

The best way to get your bug fixed is to provide a test case, and/or steps to reproduce the issue.

How to request a feature

Request features on the Issue Tracker.

How to set up your development environment

You need Python 3.11+ and the following tools:

Install the package with development requirements:

$ poetry install --all-extras

You can now run an interactive Python session, or the command-line interface:

$ poetry run denoise --help
$ poetry run kineticmodel --help

How to test the project

Run the full test suite:

$ nox

List the available Nox sessions:

$ nox --list-sessions

You can also run a specific Nox session. For example, invoke the unit test suite like this:

$ nox --session=tests

The full test suite currently runs tests in Python 3.11 and 3.12. You can also run a specific Python version only like this:

$ nox -p=3.12

Unit tests are located in the tests directory, and are written using the pytest testing framework.

How to submit changes

Open a pull request to submit changes to this project.

Your pull request needs to meet the following guidelines for acceptance:

  • The Nox test suite must pass without errors and warnings.
  • Include unit tests. This project maintains 100% code coverage.
  • If your changes add functionality, update the documentation accordingly.

Feel free to submit early, though—we can always iterate on this.

To run linting and code formatting checks before committing your change, you can install pre-commit as a Git hook by running the following command:

$ nox --session=pre-commit -- install

It is recommended to open an issue before starting work on anything. This will allow a chance to talk it over with the owners and validate your approach.

Contributing a new kinetic model implementation to the toolbox

The .py file implementing your kinetic model should be located under the src/dynamicpet/kineticmodel/ directory. kineticmodel.py in this directory implements the abstract base class that all kinetic model implementations should inherit from:

from .kineticmodel import KineticModel

class MyKineticModelImplementation(KineticModel):
    ...

Your kinetic model class needs to implement the following functions:

  1. get_param_names

    This is a class method that returns a list of the names of kinetic model parameters.

    @classmethod
get_param_names(cls) -> list[str]:
    return ["parameter1", "parameter2"]

    In the fit function below, all model parameters generated and stored in the kinetic model class should be listed here.

  2. fit

    This method performs kinetic model fitting (within mask, if provided) and stores estimated parameters using theset_parameter function.

    mask is a 1-D (for TemporalMatrix time activity curves, or TACs) or 3-D (for TemporalImage TACs) NumpyRealNumberArray and should match the dimensions of the TemporalMatrix or TemporalImage provided for the tacs attribute of the kinetic model. For zero values in mask, the time activity curve of the corresponding element of tac will be ignored when fitting the kinetic model. When mask = None, all tac elements will be used.

    This method does not return anything.

    from ..temporalobject.temporalmatrix import TemporalMatrix
from ..typing_utils import NumpyRealNumberArray

def fit(self, mask: NumpyRealNumberArray | None = None) -> None:
    # perform kinetic model fitting here
    tacs: TemporalMatrix = self.tacs.timeseries_in_mask(mask)

    # in an actual implementation, you would estimate parameters
    # in this toy example, we set param1 and param2 to all 1's and 2's
    # (tacs.num_elements is the number of regions or voxels in tacs)
    param1 = np.ones((tacs.num_elements, 1))
    param2 = 2 * np.ones((tacs.num_elements, 1))

    # save the parameters
    self.set_parameter("parameter1", param1, mask)
    self.set_parameter("parameter2", param2, mask)

  3. fitted_tacs

    This method computes fitted time activity curves using the estimated parameters. The output of this method should match the dimensions and type of TACs supplied (TemporalImage or TemporalMatrix).

    This method is optional.

    from ..temporalobject.temporalimage import TemporalImage
from ..temporalobject.temporalimage import image_maker

def fitted_tacs(self) -> TemporalMatrix | TemporalImage:
    """Get fitted TACs based on estimated model parameters."""
    # in an actual implementation, you would calculate fitted values from
    # estimated parameters
    # in this toy example, we just set it to an empty array
    fitted_tacs_dataobj = np.empty_like(self.tacs.dataobj)

    if isinstance(self.tacs, TemporalImage):
        img = image_maker(fitted_tacs_dataobj, self.tacs.img)
        ti = TemporalImage(img, self.tacs.frame_start, self.tacs.frame_duration)
        return ti
    else:
        tm = TemporalMatrix(
            fitted_tacs_dataobj, self.tacs.frame_start, self.tacs.frame_duration
        )
        return tm

You will also need to write tests with sufficient coverage to verify that your implementation runs correctly. For some examples, see tests/test_kineticmodel.py.

Finally, you'll need to edit the kineticmodel function in __main__.py so that your model implementation can be executed using the command line interface.