Error while `groupby` agg must use 2D or 3D coordinates

[ahuang11]

If there is only one unique category, datashader raises and shows a blank map:

  File "/Users/ahuang/miniconda3/envs/hvplot/lib/python3.10/site-packages/holoviews/util/__init__.py", line 1013, in _process
    return self.p.operation.process_element(element, key, **kwargs)
  File "/Users/ahuang/miniconda3/envs/hvplot/lib/python3.10/site-packages/holoviews/core/operation.py", line 194, in process_element
    return self._apply(element, key)
  File "/Users/ahuang/miniconda3/envs/hvplot/lib/python3.10/site-packages/holoviews/core/operation.py", line 141, in _apply
    ret = self._process(element, key)
  File "/Users/ahuang/miniconda3/envs/hvplot/lib/python3.10/site-packages/holoviews/operation/datashader.py", line 1543, in _process
    shaded = shade._process(self, agg, key)
  File "/Users/ahuang/miniconda3/envs/hvplot/lib/python3.10/site-packages/holoviews/operation/datashader.py", line 1354, in _process
    img = tf.shade(array, **shade_opts)
  File "/Users/ahuang/miniconda3/envs/hvplot/lib/python3.10/site-packages/datashader/transfer_functions/__init__.py", line 717, in shade
    raise ValueError("agg must use 2D or 3D coordinates")
ValueError: agg must use 2D or 3D coordinates

This is not ideal for groupby because it shows a blank map instead of just showing the single category.

To reproduce:
Data download: https://coast.noaa.gov/htdata/CMSP/AISDataHandler/2020/index.html

import pandas as pd
import hvplot.pandas
import geoviews as gv
import pandas as pd
import datashader as ds
import colorcet as cc
import holoviews as hv
import holoviews.operation.datashader as hd
from holoviews.util.transform import lon_lat_to_easting_northing

df = pd.read_csv("AIS_2020_01_01.csv", parse_dates=True, index_col="BaseDateTime")
df = df.assign(
    **pd.concat(lon_lat_to_easting_northing(df["LON"], df["LAT"]), axis=1).rename(
        {"LON": "EASTING", "LAT": "NORTHING"}, axis=1
    )
)

df.index = df.index.round("1Min")
df = df.sort_index()
df.index = df.index.astype(str)

vessel_types = pd.read_csv("AIS_categories.csv")

categories = {
    r.num: r.category if r.category in [0, 2, 3, 6, 7, 16, 14, 19, 12, 18] else 21
    for i, r in vessel_types.iterrows()
}


def category_desc(val):
    """Return description for the category with the indicated integer value"""
    return vessel_types[vessel_types.category == val].iloc[0].category_desc


groups = {categories[i]: category_desc(categories[i]) for i in categories.keys()}
colors = cc.glasbey_bw_minc_20_minl_30
color_key = {
    list(groups.keys())[i]: tuple(int(e * 255.0) for e in v)
    for i, v in enumerate(colors[: (len(groups))][::-1])
}
legend = hv.NdOverlay(
    {
        groups[k]: hv.Points([0, 0], label=str(groups[k])).opts(
            color=cc.rgb_to_hex(*v), size=0
        )
        for k, v in color_key.items()
    }
)

df = df.loc[df["VesselType"].isin(color_key)]

pts = hv.Dataset(df, kdims=["EASTING", "NORTHING"], vdims=["VesselType"]).to(
    hv.Points, groupby=["BaseDateTime"]
)
points = hd.dynspread(
    hd.datashade(pts, aggregator=ds.count_cat("VesselType"), color_key=color_key)
)

tiles = (
    hv.element.tiles.ESRI()
    .opts(alpha=0.4, bgcolor="black")
    .opts(responsive=True, min_height=600)
)
labels = hv.element.tiles.CartoDark().opts(alpha=0.7, level="glyph")
display(
    tiles
    * labels
    * points.opts(show_legend=False)
    * legend.opts(xaxis="bare", yaxis="bare", title="")
)

Works fine when it's not on the one category time