Contributing¶

Even if you’re not in a position to contribute code to Pipecat, there are many ways you can help the project out:

Send the names of serial ports that work with a device on your platform, so we can document it.
Let us know if one of our existing devices works with some other model or brand of hardware, so we can document that.
Send us a file containing sample output from your device.
Tell us if our code doesn’t work with your device.
Spread the word!

Getting Started¶

If you haven’t already, you’ll want to get familiar with the Pipecat repository at http://github.com/shead-custom-design/pipecat ... there, you’ll find the Pipecat sources, issue tracker, and wiki.

Next, you’ll need to install Pipecat’s Dependencies. Then, you’ll be ready to get Pipecat’s source code and use setuptools to install it. To do this, you’ll almost certainly want to use “develop mode”. Develop mode is a a feature provided by setuptools that links the Pipecat source code into the install directory instead of copying it ... that way you can edit the source code in your git sandbox, and you don’t have to re-install it to test your changes:

$ git clone https://github.com/sandialabs/pipecat.git
$ cd pipecat
$ python setup.py develop

Versioning¶

Pipecat version numbers follow the Semantic Versioning standard.

Coding Style¶

The Pipecat source code follows the PEP-8 Style Guide for Python Code.

Running Regression Tests¶

To run the Pipecat test suite, simply run regression.py from the top-level source directory:

$ cd pipecat
$ python regression.py

The tests will run, providing feedback on successes / failures.

Test Coverage¶

When you run the test suite with regression.py, it also automatically generates code coverage statistics. To see the coverage results, open .cover/index.html in a web browser.

Building the Documentation¶

To build the documentation, run:

$ cd pipecat
$ python docs/setup.py

Note that significant subsets of the documentation are written using Jupyter notebooks, so the docs/setup.py script requires Jupyter to convert the notebooks into restructured text files for inclusion with the rest of the documentation.

Once the documentation is built, you can view it by opening docs/_build/html/index.html in a web browser.