import numpy as np import holoviews as hv from holoviews import opts hv.extension('matplotlib') hv.output(fig='svg', size=200)
provide a representation of the data as a bivariate histogram useful for visualizing the structure in larger datasets. The
are computed by tesselating the x-, y-ranges of the data, storing the number of points falling in each hexagonal bin. By default
simply counts the data in each bin but it also supports weighted aggregations. Currently only linearly spaced bins are supported when using the bokeh backend.
As a simple example we will generate 100,000 normally distributed points and plot them using
np.random.seed(44) hex_tiles = hv.HexTiles(np.random.randn(100000, 2)) hex_tiles.opts(colorbar=True)
Note that the hover shows a
by default, representing the number of points falling within each bin.
It is also possible to provide additional value dimensions and define an
to aggregate by value. If multiple value dimensions are defined the dimension to be colormapped may be defined using the
. Note however that multiple value dimensions are allowed and will be displayed in the hover tooltip.
xs, ys = np.random.randn(2, 1000) hex_with_values = hv.HexTiles((xs, ys, xs*(ys/2.), (xs**2)*ys), vdims=['Values', 'Hover Values']) points = hv.Points(hex_with_values) (hex_with_values * points).opts( opts.HexTiles(aggregator=np.sum), opts.Points(s=3, color='black'))
do not plot bins that do not contain any data but using the
option it is possible to plot bins with zero values as well or alternatively set a higher cutoff. Using this options can result in a more visually appealing plot particularly when overlaid with other elements.
Here we will plot the same distributions as above and overlay a
plot of the same data:
x, y = np.hstack([np.random.randn(2, 10000), np.random.randn(2, 10000)*0.8+2]) hex_two_distributions = hv.HexTiles((x, y)) bivariate = hv.Bivariate((x, y)) (hex_two_distributions * bivariate).opts( opts.Bivariate(linewidth=3, show_legend=False), opts.HexTiles(min_count=0))