HexTiles does behave strangely when used with hv.Dataset

I prepare a map using

tile_opts = dict(height=plot_height, xaxis=None, yaxis=None, show_grid=False,
                 sizing_mode="scale_both", responsive=True)
map_tiles = gts.CartoLight.opts(style=dict(alpha=1), plot=tile_opts)

Then I do a plot with lon lat given in mercator coors

points = (hv.HexTiles(data[["lon", "lat", "change"]], kdims=["lon", "lat"],
                     vdims=["value"])
          .options(active_tools=['wheel_zoom'],tools=[hover])
          .opts(colorbar=False, alpha=0.3, aggregator=np.mean, gridsize=250))

map_tiles * points

which works perfectly and plots the hexbins beautifully where they are located (Germany). However, as I am using a hv.Dataset (I want a dropdown with an additional key) like this

points_ds = gv.Dataset(data[["lon", "lat", "value", "date"]])
points = (points_ds.to(hv.HexTiles, kdims=["lon", "lat"], vdims=["value"])
          .options(active_tools=['wheel_zoom'], tools=[hover])
          .opts(colorbar=False, alpha=0.3, gridsize=250))

map_tiles * points

the results are pretty strange. The plot does not appear most of times and when I click through the dropdown it’s always on some other point in the map.

[philippjfr]

Appears to have been fixed in #4182

[anderl80]

No worries, thanks for the support and sorry for distracting from the main problem.

I was able do “isolate” (still sorry for the over-engineered example 😃) the problem more. Please try to execute the following.

# generate random data points for GitHub issue
import random
import datetime
import numpy as np
import pandas as pd

muc_coords = {"lat": 48.137154, "lon": 11.576124}

# generate data
def random_date(start, end):
    """Generate a random datetime between `start` and `end`"""
    return start + datetime.timedelta(
        # Get a random amount of seconds between `start` and `end`
        seconds=random.randint(0, int((end - start).total_seconds())),
    )
lon = [random.randint(0,100)/10000+muc_coords["lon"] for _ in range(0,1000)]
lat = [random.randint(0,100)/10000+muc_coords["lat"] for _ in range(0,1000)]
val = [random.randint(0,100) for _ in range(0,1000)]
date = [str(random_date(datetime.datetime(2019,1,1),datetime.datetime(2019,1,10)).date()) for _ in range(0,1000)]
data = pd.DataFrame(list(zip(lon, lat, val, date)), columns=['lon', 'lat', 'change', 'date'])

# [x] check that there's no day without data
data.date.value_counts().sort_index()

import geoviews as gv
gv.extension("bokeh")

hexs = (gv.HexTiles(data, kdims=["lon", "lat"],
                     vdims=["change", "date"])
          .options(active_tools=['wheel_zoom'],tools=["hover"])
          .opts(colorbar=False, alpha=0.3, aggregator=np.mean))
		  
# everything works fine
hexs

# now we try to add a dropdown menu
hexs_ds = gv.Dataset(data)

hexs = (hexs_ds.to(gv.HexTiles, kdims=["lon", "lat"], vdims=["change"])
          .options(active_tools=['wheel_zoom'], tools=["hover"])
          .opts(colorbar=False, alpha=0.3, aggregator=np.mean))
		  
# click through dropdown menu and find shifted data (zoom out)
hexs

# to double-check try points instead; everything works as expected
points = (hexs_ds.to(gv.Points, kdims=["lon", "lat"], vdims=["change"])
          .options(active_tools=['wheel_zoom'], tools=["hover"])
          .opts(colorbar=False))

No 1 gives me

No 2 gives me (already zoomed out because the starting graph was empty)

Exactly the same happens if I use holoviews only instead of geoviews.