HazImp

A natural hazard impact assessment tool.

For the latest documentation, installation guide and user guide, see https://hazimp.readthedocs.io

This branch enables users to develop new functions for HazImp.

Status

Data

All exposure and hazard data under version control is for testing purposes only and should not be considered as realistic. The provenance for this data is unknown.

Contributing code

Generally, development of new functionality and bug fixes happens in the develop branch in HazImp. The master branch is only for significant releases, and maintenance. Even then, any bug-fixes should follow the procedure outlined below to contribute fixes - that is, create a new (temporary) branch, make the fix, then submit teh fix as a pull request. Once the pull request has been approved and merged, the temporary branch can be deleted.

The preferred way to contribute to HazImp is to fork the main repository on GitHub:

1. Fork the project repository: click on the 'Fork' button near the top of the page. This creates a copy of the code under your account on the GitHub server.

2. Clone this copy to your local disk:: $ git clone [email protected]:YourLogin/hazimp.git $ cd hazimp

3. Create a branch to hold your changes::

      $ git checkout -b my-feature
   

   and start making changes. Never work in the master branch! We recommend starting from the develop branch, where we do our development work.

4. Check the code using the unit test suite. This will also run a quick check of the code syntax using pycodestyle and produce a test coverage report using coverage::

     $ pytest --cov
   

   If this passes with no errors, then you can commit the changes.

5. Work on this copy on your computer using Git to do the version control. When you're done editing, do::

      $ git add modified_files
      $ git commit
   

   to record your changes in Git, then push them to GitHub with::

      $ git push -u origin my-feature
   

Finally, go to the web page of your fork of the hazimp repo, and click 'Pull request' to send your changes to the maintainers for review. request. This will send an email to the committers.

(If any of the above seems like magic to you, then look up the Git documentation on the web.)

It is recommended to check that your contribution complies with the following rules before submitting a pull request:

• All public methods should have informative docstrings with sample usage presented as doctests when appropriate.

• When adding additional functionality, provide at least one example script in the examples/ folder. Have a look at other examples for reference. Examples should demonstrate why the new functionality is useful in practice.

• At least one paragraph of narrative documentation with links to references in the literature (with PDF links when possible) and an example.

You can also check for common programming errors with the following tools:

• Code with good unittest coverage. This will also check syntax to ensure PEP8 conformance. Check with::

     $ pip install pytest coverage --user
     $ pytest
  

• No PEP8 warnings, check with::

      $ pip install pep8
      $ pep8 path/to/module.py
  

• AutoPEP8 can help you fix some of the easy redundant errors::

      $ pip install autopep8
      $ autopep8 path/to/pep8.py
  

Issues

A great way to start contributing to HazImp is to pick an item from the list of Issues in the issue tracker. (Well there are none there yet, but we will be putting some up soon!) Resolving these issues allow you to start contributing to the project without much prior knowledge. Your assistance in this area will be greatly appreciated by the more experienced developers as it helps free up their time to concentrate on other issues.

Documentation

We are glad to accept any sort of documentation: function docstrings, reStructuredText documents (like this one), tutorials, etc. reStructuredText documents (will) live in the source code repository under the docs/ directory.

When writing documentation, it is important to keep a good compromise between mathematical and algorithmic details, and give intuition to the reader on what the algorithm does. It is best to always start with a small paragraph with a hand-waving explanation of what the method does to the data and a figure (coming from an example) illustrating it.