Hi,
I am trying to replicate the Large data tutorial using datashader and holoviews. Below is the minimal code
import holoviews as hv
import numpy as np
from holoviews import opts
from holoviews.operation.datashader import datashade,
hv.extension('bokeh')
hv.output(backend='bokeh')
def random_walk(n, f=5000):
"""Random walk in a 2D space, smoothed with a filter of length f"""
xs = np.convolve(np.random.normal(0, 0.1, size=n), np.ones(f)/f).cumsum()
ys = np.convolve(np.random.normal(0, 0.1, size=n), np.ones(f)/f).cumsum()
xs += 0.1*np.sin(0.1*np.array(range(n-1+f))) # add wobble on x axis
xs += np.random.normal(0, 0.005, size=n-1+f) # add measurement noise
ys += np.random.normal(0, 0.005, size=n-1+f)
return np.column_stack([xs, ys])
np.random.seed(1)
paths = hv.Path([0.15*random_walk(100000) for i in range(10)],label="Paths")
#No invert_axes option and displays the random walk path with normal zoom behaviour
#datashade(paths).opts(opts.RGB(width=600,height=600,invert_axes=False))
#Zoomed result disappears when invert_axes=True
datashade(paths).opts(opts.RGB(invert_axes=True))
Zoom behaviour when invert_axes=False
Zoom behaviour when invert_axes=True
As you can see the zoom behavior is incorrect when the invert_axes=True
Am I setting the parameter correctly?