# Hsv¶

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- Title
- HSV Element
- Dependencies
- Matplotlib
- Backends
- Matplotlib
- Bokeh

```
import numpy as np
import holoviews as hv
from holoviews import opts
hv.extension('matplotlib')
```

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 and in the simplest case the grid may be specified as a `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 array 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:

```
opts.defaults(opts.Image(cmap='gray'))
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']
```

For full documentation and the available style and plot options, use `hv.help(hv.HSV).`

Download this notebook from GitHub (right-click to download).