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How to contribute

Thanks a lot for taking the time to read this little guide and contributing to this project Everybody is welcome to contribute to this Geospatial extension. The repository contains a Python based KNIME extension for geospatial analysis. To get started make yourself familiar with the general information on how to implement a Python based node extension for KNIME Analytics Platform that is available here.

Submitting changes

Please send a GitHub Pull Request to KNIME Geospatial Extension with a clear list of what you've done (read more about pull requests). When you send a pull request, please also include an example or testing workflow that demonstrates or tests the new functionality. We can always use more test coverage. Please follow our coding conventions (below) and make sure all of your commits are atomic (one feature per commit).

Coding Style + Formatting: black

Code must be formatted with “The uncompromising code formatter” Black

  • Black defines the code style, and there is no manual formatting involved
  • Black tries to produce small diffs when code changes
  • Black is available as a command-line tool and in many editors: Editor integration

Linting

We use SonarLint for linting.

Setup for KNIME Analytics Platform

This is a short step by step guide on how to setup your development environment to get started with the node development.

  1. Download and install the latest KNIME nightly build

  2. Install KNIME extension: Open the downloaded KNIME Analytics Platform nightly build and click KNIME -> File-> Install KNIME Extensions, Search for 'Python' and select KNIME Python Extension Development (Labs),KNIME Python Integration (Labs) , Disable the “Group items by category” option and search for 'geo' and select KNIME Geospatial Extension. The KNIME Geospatial Extension contains the Java and Python representation of the geometry data types.

  3. Clone this repository

    git clone  https://github.com/spatial-data-lab/knime-geospatial-extension.git

  4. Before proceeding in KNIME Analytics Platform we have to create the Python environment with all required packages e.g. geopandas to do so execute the following command that uses the geospatial_env.yml file in the local repository clone:

    #browse to the folder where the geospatial_env.yml file of the local repository clone is located and then execute
conda env create -f geospatial_env.yml 


conda activate geospatial_env

conda info
# Record the env location path such as D:\ProgramData\Anaconda3\envs\geospatial_env

  5. Make a copy of the config.yml file e.g. config_YOURNAME.yml. Adapt the copied file to point to the knime_extension folder on your local hard drive as described here. The file tells the KNIME Analytics Platform where to search for the Python extension on your local hard drive during development and debugging.

  6. Add the following line to your knime.ini, located in the KNIME nightly root folder, to point to your adjusted config_YOURNAME.yml file e.g. -Dknime.python.extension.config=D:\Software\knime_470\knimespace\geo\config_YOURNAME.yml

  7. Go back to the KNIME Analytics Platform and configure the following KNIME Python settings:

    • KNIME -> File-> Preference> KNIME> Conda Choose anaconda directory
    • KNIME -> File-> Preference> KNIME> Python(Labs) Choose geospatial_env

  8. Reopen the KNIME Analytics Platform and you should see the Geospatial Analytics extension in your KNIME node repository.

  9. If you do not see the extension have a look at the KNIME log file via View -> Open KNIME log.

Testing

Every KNIME node that is part of this extension should be accompanied by a KNIME workflow that tests its functionality and its corner cases.

To write test workflows KNIME provides the KNIME Testing Framework UI extension that contains several helper nodes. For example, the Testflow Configuration node allows you to specify which errors or warnings are expected by a specific node within the test workflow. This is useful to test that you node properly validates input values e.g. that they must not be empty. The Table Difference Checker node allows you to compare the output table of a node with a gold standard table.

For a detailed introduction on how to write good workflow tests and execute them within the KNIME Analytics Platform have a look at this blog post.

Build

When testing locally you do not need to build anything. All you need to do is adjust the config.yml to your local setup e.g. adjust the src path to the location of the knime_extension folder on your hard drive and the conda_env_path path to the path of the local conda environment that contains all dependencies required by the extension e.g. geopandas and add the path to the adjusted config.yml file to your knime.ini file as described above. However if you want to build the extension locally follow the instructions here.