Grid traversal
==============

Create a few test cells to the examples on this page:

.. jupyter-execute::

    import numpy as np
    import h3.api.numpy_int as h3
    import pandas as pd
    import pyarrow as pa

    from h3ronpy.pandas.vector import cells_dataframe_to_geodataframe
    from h3ronpy import DEFAULT_CELL_COLUMN_NAME

    cells = np.array(
        [
            h3.geo_to_h3(5.2, -5.2, 7),
            h3.geo_to_h3(5.3, -5.1, 7),
        ],
        dtype=np.uint64,
    )


Grid-disks with :py:func:`h3ronpy.grid_disk`
---------------------------------------------------

.. jupyter-execute::

    from h3ronpy import grid_disk

    cells_dataframe_to_geodataframe(
        pd.DataFrame({
            DEFAULT_CELL_COLUMN_NAME: pa.array(grid_disk(cells, 9, flatten=True)).to_pandas()}
        )
    ).plot()


Grid-disk aggregates with :py:func:`h3ronpy.grid_disk_aggregate_k`
-------------------------------------------------------------------------

This builds ontop of :py:func:`h3ronpy.grid_disk_distances` while directly
performing simple aggregations to avoid returning potentially very large dataframes.

.. jupyter-execute::

    from h3ronpy import grid_disk_aggregate_k

    cells_dataframe_to_geodataframe(
        pa.table(grid_disk_aggregate_k(cells, 9, "min")).to_pandas()
    ).plot(column="k", legend=True, legend_kwds={"label": "k", "orientation": "horizontal"},)


.. jupyter-execute::

    cells_dataframe_to_geodataframe(
        pa.table(grid_disk_aggregate_k(cells, 9, "max")).to_pandas()
    ).plot(column="k", legend=True, legend_kwds={"label": "k", "orientation": "horizontal"},)