Visualising data on unstructured grids

In this example, we will explore different methods of visualising unstructured grid data.

import earthkit.plots

import numpy as np
import cartopy.crs as ccrs
from scipy.interpolate import griddata

First, let’s generate a sample set of random (unstructured) points.

# Example data (unstructured points)
points = np.random.rand(500, 2) * 10  # 500 random points
values = np.sin(points[:, 0]) + np.cos(points[:, 1])  # Some sample data

x = points[:, 0]
y = points[:, 1]

Next, let’s generate a diverging colour palette for visualising this data.

style = earthkit.plots.styles.Style(
    levels=np.arange(-2, 2.1, 0.25),
    colors="Spectral_r",
)

The most straightforward way to visualise this data is as a scatter plot, which simply shows the unstructured points, coloured according to their value.

chart = earthkit.plots.Map()

chart.scatter(x=x, y=y, z=values, style=style)
chart.legend()

chart.coastlines()
chart.gridlines()

chart.title("Scatter plot")

chart.show()

It’s also possible to plot this data with the contourf method, like any regular gridded data. Under the hood, earthkit will interpolate your unstructured data onto a regular grid in your target coordinate reference system, using the interpolation method of your choice (linear (default), nearest neighbour or cubic).

chart = earthkit.plots.Map()

chart.contourf(x=x, y=y, z=values, style=style, interpolation_method="linear")
chart.legend()

chart.scatter(x=points[:, 0], y=points[:, 1], color="none", marker=".")

chart.coastlines()
chart.gridlines()

chart.title("Linear interpolation")

chart.show()

Alternatively, you could use the tricontourf method to draw contour regions given a provided triangulation method (Delauney triangulation by default).

chart = earthkit.plots.Map()

chart.tricontourf(x=x, y=y, z=values, style=style)
chart.legend()

chart.scatter(x=points[:, 0], y=points[:, 1], color="none", marker=".")

chart.coastlines()
chart.gridlines()

chart.title("Delaunay triangulation")

chart.show()