## Contributing Code

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.

## Using the Mailing List and Github Issues

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.

## Using IPython Notebooks to show examples

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.