Image

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Title

Image Element

Dependencies

Bokeh

Backends

Bokeh

Matplotlib

Plotly

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

Image represents a regularly sampled 2D grid of an underlying continuous space of intensity values, which will be colormapped on plotting. The grid of intensity values may be specified as a NxM sized array of values along with a bounds, but it may also be defined through explicit and regularly spaced x/y-coordinate arrays of shape M and N respectively. The two most basic supported constructors of an Image therefore include:

Image((X, Y, Z))

where X is a 1D array of shape M, Y is a 1D array of shape N and Z is a 2D array of shape NxM, or equivalently:

Image(Z, bounds=(x0, y0, x1, y1))

where Z is a 2D array of shape NxM defining the intensity values and the bounds define the (left, bottom, right, top) edges of 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.

ls = np.linspace(0, 10, 200)
xx, yy = np.meshgrid(ls, ls)

bounds=(-1,-1,1,1)   # Coordinate system: (left, bottom, right, top)
img = hv.Image(np.sin(xx)*np.cos(yy), bounds=bounds)
img

In recent versions of Bokeh, you can enable image hover to inspect the raw values:

opts.defaults(
   opts.Image(tools=['hover']),
   opts.Points (color='black', marker='x', size=20))

Slicing, sampling, etc. on an Image all operate in this continuous space, whereas the corresponding operations on a Raster work on the raw array coordinates.

img + img[-0.5:0.5, -0.5:0.5]