Image#
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- Title
- Image Element
- Dependencies
- Plotly
- Backends
- Bokeh
- Matplotlib
- Plotly
import numpy as np
import holoviews as hv
hv.extension('plotly')
Like Raster, a HoloViews Image allows you to view 2D arrays using an arbitrary color map. Unlike Raster, an Image is associated with a 2D coordinate system in continuous space, which is appropriate for values sampled from some underlying continuous distribution (as in a photograph or other measurements from locations in real space).
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
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]
Notice how, because our declared coordinate system is continuous, we can slice with any floating-point value we choose. The appropriate range of the samples in the input numpy array will always be displayed, whether or not there are samples at those specific floating-point values. This also allows us to index by a floating value, since the Image is defined as a continuous space it will snap to the closest coordinate, to inspect the closest coordinate we can use the closest method:
closest = img.closest((0.1,0.1))
points = hv.Points([closest])
print('The value at position %s is %s' % (closest, img[0.1, 0.1]))
img * points.opts(color='black', marker='cross', size=10)
The value at position (np.float64(0.105), np.float64(0.095)) is 0.1293472017023185
We can also easily take cross-sections of the Image by using the sample method or collapse a dimension using the reduce method:
img.sample(x=0) + img.reduce(x=np.mean)
For full documentation and the available style and plot options, use hv.help(hv.Image).
Download this notebook from GitHub (right-click to download).