HoloViews is designed to work with scientific and engineering data, which is often in the form of discrete samples from an underlying continuous system. Imaging data is one clear example: measurements taken at a regular interval over a grid covering a two-dimensional area. Although the measurements are discrete, they approximate a continuous distribution, and HoloViews provides extensive support for working naturally with data of this type.
In this user guide we will show the support provided by HoloViews for working with two-dimensional regularly sampled grid data like images, and then in subsequent sections discuss how HoloViews supports one-dimensional, higher-dimensional, and irregularly sampled data with continuous coordinates.
import numpy as np import holoviews as hv from holoviews import opts hv.extension('bokeh') np.set_printoptions(precision=2, linewidth=80) opts.defaults(opts.HeatMap(cmap='fire'), opts.Layout(shared_axes=False))