# Image ¶

Title
Image Element
Dependencies
Matplotlib
Backends
Matplotlib
Bokeh
In [1]:
import numpy as np
import holoviews as hv
hv.extension('matplotlib')


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).

In [2]:
ls = np.linspace(0, 10, 200)
xx, yy = np.meshgrid(ls, ls)

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

Out[2]:

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.

In [3]:
img + img[-0.5:0.5, -0.5:0.5]

Out[3]:

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:

In [4]:
%%opts Points (color='black' marker='x' size=20)
closest = img.closest((0.1,0.1))
print('The value at position %s is %s' % (closest, img[0.1, 0.1]))
img * hv.Points([img.closest((0.1,0.1))])

The value at position (0.105, 0.095000000000000001) is 0.129347201702

Out[4]:

We can also easily take cross-sections of the Image by using the sample method or collapse a dimension using the  reduce  method:

In [5]:
img.sample(x=0) + img.reduce(x=np.mean)

Out[5]: