import numpy as np
import pandas as pd
import holoviews as hv
from holoviews import dim, opts
hv.extension('plotly')
Scatter3D plots represents three-dimensional coordinates which are specified as key dimensions (kdims, default kdims are x, y, z). The points can be given a marker, color and size. Additionally a colorbar can be added.
Scatter3D plots are therefore very similar to Points and Scatter types, but have one additional coordinate dimension.
Like other 3D elements the camera angle can be controlled using azimuth, elevation and distance plot options
y,x = np.mgrid[-5:5, -5:5] * 0.1
z=np.sin(x**2+y**2)
hv.Scatter3D((x.flat,y.flat,z.flat)).opts(cmap='fire', color='z', size=5, colorbar=True, height=600, width=600)
You can also provide your data in a container and specify the kdims to use.
data = pd.DataFrame(dict(lat=x.flat, lon=y.flat, height=z.flat))
hv.Scatter3D(data, kdims=["lat", "lon", "height"]).opts(cmap='blues', color='height', size=3, colorbar=True, height=600, width=600, colorbar_opts={'title': 'height (m)'}, marker="diamond")
Just like all regular 2D elements, Scatter3D types can be overlaid and will follow the default color cycle:
(hv.Scatter3D(np.random.randn(100,4), vdims='Size') * hv.Scatter3D(np.random.randn(100,4)+2, vdims='Size')).opts(
opts.Scatter3D(size=(5+dim('Size'))*2, marker='diamond', height=600, width=600)
)
For full documentation and the available style and plot options, use hv.help(hv.Scatter3D).
hv.help(hv.Scatter3D)