Duplicating the legend example with other data does not seem to work

I am trying to adapt the code from the [legend example notebook](https://github.com/bokeh/datashader/blob/master/examples/legends.ipynb) to another data set. I replaced the data with the 5 Gaussian distributions, updating the appropriate inputs but the legend is entirely black.

Here is the code I ran (in a jupyter notebook):
```
import pandas as pd
import numpy as np

from bokeh.io import output_notebook, show
from bokeh.plotting import Figure
output_notebook()

import datashader as ds
import datashader.transfer_functions as tf

from datashader.colors import Hot
from datashader.bokeh_ext import create_ramp_legend, create_categorical_legend

# create sample dataset
np.random.seed(1)
num=1000000

dists = {cat: pd.DataFrame(dict(x=np.random.normal(x,s,num),
y=np.random.normal(y,s,num),
val=val,cat=cat))
for x,y,s,val,cat in
[(2,2,0.01,10,"d1"), (2,-2,0.1,20,"d2"), (-2,-2,0.5,30,"d3"), (-2,2,1.0,40,"d4"), (0,0,3,50,"d5")]}

df = pd.concat(dists,ignore_index=True)
df["cat"]=df["cat"].astype("category")
df.tail() # show some of the data in an interactive setting

def create_base_plot():

# taxi data is in meters
xmin = df.x.min()
ymin = df.y.min()
xmax = df.x.max()
ymax = df.y.max()

cvs = ds.Canvas(plot_width=900,
plot_height=600,
x_range=(xmin, xmax),
y_range=(ymin, ymax))

agg = cvs.points(df, 'x', 'y')
img = tf.shade(agg, cmap=Hot, how='eq_hist')
fig = Figure(x_range=(xmin, xmax),
y_range=(ymin, ymax),
plot_width=600,
plot_height=600,
tools='')

fig.background_fill_color = 'black'
fig.toolbar_location = None
fig.axis.visible = False
fig.grid.grid_line_alpha = 0
fig.min_border_left = 0
fig.min_border_right = 0
fig.min_border_top = 0
fig.min_border_bottom = 0

fig.image_rgba(image=[img.data],
x=[xmin],
y=[ymin],
dw=[xmax-xmin],
dh=[ymax-ymin])
return fig, (xmin, ymin, xmax, ymax), agg

fig, extent, datashader_agg = create_base_plot()
show(fig)

legend_fig = create_ramp_legend(datashader_agg,
Hot,
how='eq_hist',
width=600)
show(legend_fig)
```
Here is the result:

I noticed the range for my aggregation is much larger than the taxi example, [0, 728852] compared to [0, 1968].
```
>>> datashader_agg.min()

array(0, dtype=int32)
>>> datashader_agg.max()

array(728852, dtype=int32)
```
The increased range should not be responsible for the error but I will look into that.

I am not certain this is a bug or simply an misunderstanding of the example on my part.