05 Working with Gridded Data ¶
Many datasets in science and engineering consist of n-dimensional data. Gridded datasets usually represent observations of some continuous variable across multiple dimensions---a monochrome image representing luminance values across a 2D surface, volumetric 3D data, an RGB image sequence over time, or any other multi-dimensional parameter space. This type of data is particularly common in research areas that make use of spatial imaging or modeling, such as climatology, biology, and astronomy, but can also be used to represent any arbitrary data that varies over multiple dimensions.
For gridded data, we'll use xarray, a convenient way of working with and representing labelled n-dimensional arrays, like pandas for labelled n-D arrays, along with our other usual libraries:
import numpy as np import holoviews as hv from holoviews import opts import xarray as xr hv.extension('bokeh')