HSV#
Download this notebook from GitHub (right-click to download).
- Title
- HSV Element
- Dependencies
- Bokeh
- Backends
- Bokeh
- Matplotlib
import numpy as np
import holoviews as hv
from holoviews import opts
hv.extension('bokeh')
HSV represents a regularly spaced 2D grid of an underlying continuous space of HSV (hue, saturation and value) color space values. The definition of the grid closely matches the semantics of an Image or RGB element. In the simplest case the grid may be specified as an NxMx3 or NxMx4 array of values along with a bounds. An HSV element may also be defined through explicit and regularly spaced x/y-coordinate arrays. The two most basic supported constructors of an HSV element therefore include:
HSV((X, Y, H, S, V))
where X is a 1D array of shape M, Y is a 1D array of shape N and H/S/V are 2D arrays of shape NxM, or equivalently:
HSV(Z, bounds=(x0, y0, x1, y1))
where Z is a 3D array of stacked H/S/V arrays with shape NxMx3/4 and the bounds define the (left, bottom, right, top) edges of the four corners of the grid. Other gridded formats which support declaring of explicit x/y-coordinate arrays such as xarray are also supported. See the Gridded Datasets user guide for all the other accepted data formats.
x,y = np.mgrid[-50:51, -50:51] * 0.1
h = 0.5 + np.sin(0.2*(x**2+y**2)) / 2.0
s = 0.5*np.cos(y*3)+0.5
v = 0.5*np.cos(x*3)+0.5
hsv = hv.HSV(np.dstack([h, s, v]))
hsv
You can see how this is created from the original channels which will be shown as Image elements with a gray colormap:
opts.defaults(opts.Image(cmap='gray'))
Indexing the components of the HSV element:
hsv[..., 'H'].relabel('H') + hsv[..., 'S'].relabel('S') + hsv[..., 'V'].relabel('V')
An HSV Element can also easily be converted to an RGB Element using the rgb property:
print(hsv.rgb)
hsv.rgb[..., 'R'] + hsv.rgb[..., 'G'] + hsv.rgb[..., 'B']
:RGB [x,y] (R,G,B)
For full documentation and the available style and plot options, use hv.help(hv.HSV).
Download this notebook from GitHub (right-click to download).