We'd love your help, either as ideas, documentation, or code. If you have a new module or want to add or fix existing code, please do. We try to follow Python's PEP-8 closely. We really want good, numpy-doc style docstrings, usable off-line documentation, and good unit tests. A good contribution includes all of these.
If you have questions, comments, or suggestions for lmfit, please use the Mailing List, https://groups.google.com/group/lmfit-py. This provides an on-line conversation that is archived and can be searched easily.
If you find a bug with the code or documentation, use the Github Issues, https://github.com/lmfit/lmfit-py/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 on github.com would be greatly appreciated.
If you are at all unsure whether to use the mailing list or the Issue tracker, please start a conversation on the mailing list.
Starting the conversation on the mailing list 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.
IPython Notebooks are very useful for showing code snippets and outcomes, and are very good way to demonstrate a question or raise an issue. Please know that we will read your notebook, but will probably not run it off-line. Also, please do not expect that we know much about your problem domain, or will read your notebook in so much detail as to fully understand what you're trying to do without adequate explanation. State the problem, including what you result you think you should have gotten, and definitely include a conclusion.