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?