 Hello,

with the following code partly taken from http://holoviews.org/user_guide/Large_Data.html

``````import datashader as ds
import numpy as np
import holoviews as hv

from holoviews import opts
from holoviews.operation import decimate

hv.extension('bokeh','matplotlib')

def random_cov():
"""Random covariance for use in generating 2D Gaussian distributions"""
A = np.random.randn(2,2)
return np.dot(A, A.T)

np.random.seed(3)
kdims=['d1','d2']
num_ks=8

def rand_gauss2d(value=0, n=100000):
"""Return a randomly shaped 2D Gaussian distribution with an associated numeric value"""
g = 100*np.random.multivariate_normal(np.random.randn(2), random_cov(), (n,))
return np.hstack((g,value*np.ones((g.shape,1))))

gaussians = {i: hv.Points(rand_gauss2d(i), kdims, "i") for i in range(num_ks)}
``````

If i zoom into the resulting plot, the data points stay really really small.

Using the ds.count function like this

``````dynspread(datashade(hv.NdOverlay(gaussians, kdims='k'), aggregator=ds.by('k', ds.count())))
``````

i the points do get bigger when zooming in.

I kind of understand i guess, that the dynspreading is counting nearby non empty pixels (?), and i guess with ds.any(), those are never non empty maybe?

Is there somehow a way to enable dynspread with ds.any aggregation?