h3ronpy
=======
A data science toolkit for the `H3 geospatial grid <https://h3geo.org/>`_.
.. image:: https://img.shields.io/pypi/v/h3ronpy
:alt: PyPI
:target: https://pypi.python.org/pypi/h3ronpy/
.. image:: https://readthedocs.org/projects/h3ronpy/badge/?version=latest
:alt: ReadTheDocs
:target: https://h3ronpy.readthedocs.io/
.. image:: https://zenodo.org/badge/402118389.svg
:alt: DOI
:target: https://zenodo.org/badge/latestdoi/402118389
This library is not a substitute for the official `python h3 library <https://github.com/uber/h3-py>`_ - instead it provides more
high-level functions on top of H3 and integrations into common dataframe libraries.
Documentation is available on `<https://h3ronpy.readthedocs.io/>`_.
Features
--------
* H3 algorithms provided using the performant `h3o <https://github.com/HydroniumLabs/h3o>`_ library.
* Build on `Apache Arrow <https://arrow.apache.org>`_ and `pyarrow <https://arrow.apache.org/docs/python/index.html>`_ for efficient data handling.
* Dedicated APIs for the the `pandas <https://pandas.pydata.org>`_ and `polars <https://www.pola.rs/>`_ dataframe libraries. The `pandas` support includes `geopandas <https://geopandas.org>`_.
* Multi-threaded conversion of raster data to the H3 grid using `numpy arrays <https://numpy.org/>`_.
* Multi-threaded conversion of vector data, including `geopandas` `GeoDataFrames` and any object which supports the python `__geo_interface__` protocol (`shapely`, `geojson`, ...).
Most parts of this library aim to be well-performing. Benchmarking the conversion of 1000 uint64 cell
values to strings using
* a simplistic list comprehension calling ``h3-py`` ``h3_to_string``
* a numpy vectorized (``numpy.vectorize``) variant of ``h3-py`` ``h3_to_string``
* the ``cells_to_string`` function of this library (release build)
leads to the following result on a standard laptop:
.. code-block::
---------------------------------------------------------------------------------------------- benchmark: 3 tests ---------------------------------------------------------------------------------------------
Name (time in us) Min Max Mean StdDev Median IQR Outliers OPS (Kops/s) Rounds Iterations
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_cells_to_string 48.4710 (1.0) 75.5000 (1.0) 52.4252 (1.0) 1.5461 (1.0) 52.0330 (1.0) 0.4890 (1.0) 307;448 19.0748 (1.0) 4090 1
test_h3_to_string_python_list 290.5460 (5.99) 325.8180 (4.32) 297.5644 (5.68) 4.8769 (3.15) 296.1350 (5.69) 8.2420 (16.85) 806;4 3.3606 (0.18) 2863 1
test_h3_to_string_numpy_vectorized 352.9870 (7.28) 393.8450 (5.22) 360.1159 (6.87) 3.7195 (2.41) 359.4820 (6.91) 3.8420 (7.86) 447;131 2.7769 (0.15) 2334 1
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Legend:
Outliers: 1 Standard Deviation from Mean; 1.5 IQR (InterQuartile Range) from 1st Quartile and 3rd Quartile.
OPS: Operations Per Second, computed as 1 / Mean
The benchmark implementation can be found in ``tests/polars/test_benches.py`` and uses `pytest-benchmark <https://pypi.org/project/pytest-benchmark/>`_.
Limitations
-----------
Not all functionalities of the H3 grid are wrapped by this library, the current feature-set was implemented
when there was a need and the time for it. As a opensource library new features can be requested in the form of github issues
or contributed using pull requests.
License
-------
MIT
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