I'm trying to visualize categorical spatial data using Datashader and Holoviews, similarly to https://anaconda.org/jbednar/census-hv-dask/notebook. However, when I try to assign different colors to categories, I always end up with same (presumably default) colors (An example of the output image.)
Here is the code I'm running in Jupyter notebook. Could anyone advise me on how to make the custom color map work? Or at least run the code to see if you end up with colors matching the legend or not. Thank you!
from sklearn.datasets.samples_generator import make_blobs
from matplotlib import pyplot
import pandas as pd
import holoviews as hv
import geoviews as gv
import datashader as ds
from cartopy import crs
from matplotlib.cm import get_cmap
from holoviews.operation.datashader import datashade, aggregate
hv.notebook_extension('bokeh', width=95)
# Generating blob data:
X, y = make_blobs(n_samples=5000000, centers=5, n_features=2)
df = pd.DataFrame(dict(x=X[:,0], y=X[:,1], label=y))
# Plotting the blobs using datashader and holoviews:
%opts Overlay [width=800 height=455 xaxis=None yaxis=None show_grid=False]
%opts Shape (fill_color=None line_width=1.5) [apply_ranges=False]
%opts Points [apply_ranges=False] WMTS (alpha=0.5) NdOverlay [tools=['tap']]
color_key = {0:'red', 1:'blue', 2:'green', 3:'yellow', 4:'black'}
labels = {0:'red', 1:'blue', 2:'green', 3:'yellow', 4:'black'}
color_points = hv.NdOverlay({labels[k]: gv.Points([0,0], crs=crs.PlateCarree(),
label=labels[k])(style=dict(color=v))
for k, v in color_key.items()})
dataset = gv.Dataset(df, kdims=['x', 'y'], vdims=['label'])
shaded = datashade(hv.Points(dataset), cmap=color_key, aggregator=ds.count_cat('label'))
shaded * color_points
