2014-03-05 16:01:53 +00:00
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Contributions to pyqtgraph are welcome!
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Please use the following guidelines when preparing changes:
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2014-03-06 16:17:14 +00:00
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* The preferred method for submitting changes is by github pull request
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2016-11-11 02:24:47 +00:00
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against the "develop" branch.
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2014-03-05 16:01:53 +00:00
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* Pull requests should include only a focused and related set of changes.
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2016-11-11 02:24:47 +00:00
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Mixed features and unrelated changes may be rejected.
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2014-03-05 16:01:53 +00:00
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* For major changes, it is recommended to discuss your plans on the mailing
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list or in a github issue before putting in too much effort.
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* Along these lines, please note that pyqtgraph.opengl will be deprecated
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soon and replaced with VisPy.
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* Writing proper documentation and unit tests is highly encouraged. PyQtGraph
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uses nose / py.test style testing, so tests should usually be included in a
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tests/ directory adjacent to the relevant code.
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* Documentation is generated with sphinx; please check that docstring changes
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compile correctly.
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* Style guidelines:
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* PyQtGraph prefers PEP8 for most style issues, but this is not enforced
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rigorously as long as the code is clean and readable.
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2014-06-16 18:41:36 +00:00
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* Use `python setup.py style` to see whether your code follows
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the mandatory style guidelines checked by flake8.
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2014-03-05 16:01:53 +00:00
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* Exception 1: All variable names should use camelCase rather than
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underscore_separation. This is done for consistency with Qt
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* Exception 2: Function docstrings use ReStructuredText tables for
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describing arguments:
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```
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============== ========================================================
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**Arguments:**
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argName1 (type) Description of argument
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argName2 (type) Description of argument. Longer descriptions must
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be wrapped within the column guidelines defined by the
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"====" header and footer.
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============== ========================================================
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```
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QObject subclasses that implement new signals should also describe
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these in a similar table.
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2015-07-12 19:00:29 +00:00
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* Setting up a test environment.
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Tests for a module should ideally cover all code in that module,
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i.e., statement coverage should be at 100%.
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To measure the test coverage, install py.test, pytest-cov and pytest-xdist.
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Then run 'py.test --cov -n 4' to run the test suite with coverage on 4 cores.
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