Conversion utilties for Parquet data using ArcPy.
1 - Clone this repo.
2 - Create an environment with the requirements.
> make env
3 - Explore - If you are more into Python, a good place to start is jupyter lab from the root of the project, and
look in the ./notebooks directory. If GIS is more your schtick, open the project
./arcgis/arcpy-parquet.aprx.
Based on the pattern provided in the
Cookiecutter Data Science template by Driven Data this
template streamlines a number of commands using the make command pattern.
-
make env- Clone the default ArcGIS Pro Conda environment,arcgispro-pyu3, add all the dependencies inenvironment.ymland install the local project package using the commandpython -m pip install -e ./src/src/<project_package>so you can easily test against the package as you are developing it. -
make data- Run./scripts/make_data.py, which should be the data pipeline to create an output dataset. -
make pytpkg- Create a zipped achive of the Python (*.pyt) toolbox located in./arcgis. This uses the script,./scripts/make_pyt_archive.py, to collect the Python toolbox (*.pyt) along with all the supporting dependencies listed inpyproject.tomland*.xmlfiles with the tool documentation, and put into a zipped archive ready for sharing. -
make docserve- Run live MkDocs documentation server to view documentation updates at http://127.0.0.1:8000. -
make docs- Build the documentation using MkDocs from files in./docsrcand save the output in./docs. -
make test- activates the environment created by themake envormake env_cloneand runs all the tests in the./testingdirectory using PyTest.
Bump2Version is preconfigured based on hints from this article on Medium.
If you want to...
- apply a patch,
bumpversion patch - update version with no breaking changes (minor version update),
bumpversion minor - update version with breaking changes (major version update),
bumpversion major - create a release (tagged in version control - Git),
bumpversion --tag release
Project based on the cookiecutter GeoAI project template. This template, in turn, is simply an extension and light modification of the cookiecutter data science project template. #cookiecutterdatascience