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Contributing Code

We would love your help, either as ideas, documentation, or code. If you have a new algorithm or want to add or fix existing code, please do! We try to follow the Python coding style conventions (i.e., PEP 8) closely. Additionally, we really want comprehensive docstrings that follow PEP 257 using the numpydoc style, usable offline documentation, and good unit tests for the pytest framework. A good contribution includes all of these. To ensure compliance with our coding style, we make use of the pre-commit framework to run several hooks when committing code. Please follow the instructions below if you intend to contribute to the lmfit repository:

  • clone the GitHub repository: git clone https://github.com/lmfit/lmfit-py.git
  • install all (optional) dependencies either using pip or conda: pip -r install requirements-dev.txt or conda install <packages in requirements-dev.txt>
  • initialize pre-commit running pre-commit install in the lmfit directory
  • create a new branch: git checkout -b <awesome_new_feature>
  • start coding
  • make sure the test-suite passes locally: run pytest in the lmfit directory
  • push to your fork: git push origin
  • open a Pull Request on https://github.com/lmfit/lmfit-py/pulls

If you need any additional help, please send a message to the mailing list!

Using the Mailing List versus GitHub Issues

If you have questions, comments, or suggestions for lmfit, please use the mailing list. This provides an online conversation that is archived and can be searched easily.

If you find a bug with the code or documentation, please use GitHub Issues to submit a bug report. If you have an idea for how to solve the problem and are familiar with Python and GitHub, submitting a Pull Request would be greatly appreciated (see above).

If you are at all unsure whether to use the mailing list or open an Issue, please start a conversation on the mailing list.

Starting the conversation with "How do I do this?" or "Why didn't this work?" instead of "This doesn't work" is preferred, and will better help others with similar questions. No posting about fitting data is inappropriate for the mailing list, but many questions are not Issues. We will try our best to engage in all discussions, but we may simply close GitHub Issues that are actually questions.

Providing an Example with GitHub Issues

If you are reporting a bug with GitHub Issues, we do expect a small, complete, working example that illustrates the problem. Yes, this forces you to invest some time in writing a careful example. That is intentional. If you need to read certain data or have code longer than a few pages, use a GitHub Gist and provide a link in the Issue.

Please understand that the point of the example script is to be read. We may not even run your example. Please do not expect that we know much about your problem domain, or that we will read any example in enough detail to fully understand what you are trying to do without adequate explanation. State the problem, including what result you think you should have gotten, and include what you got. If you get a traceback, include the entire thing.

In addition, please include information on your operating system, Python version and installed dependencies. You can paste the code below in your Python shell to get this information:

import sys, lmfit, numpy, scipy, asteval, uncertainties, six
print('Python: {}\n\nlmfit: {}, scipy: {}, numpy: {}, asteval: {}, uncertainties: {}, six: {}'\
      .format(sys.version, lmfit.__version__, scipy.__version__, numpy.__version__, \
      asteval.__version__, uncertainties.__version__, six.__version__))

Using IPython Notebooks to Show Examples

IPython Notebooks are very useful for showing code snippets and outcomes, and are a good way to demonstrate a question or raise an issue. Please see the above about providing examples. The notebook you provide will be read, but will probably not be run.