Visualising data on unstructured grids
In this example, we will explore different methods of visualising unstructured grid data.
[1]:
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.
[2]:
# 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.
[3]:
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.
[4]:
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()
/Users/mavj/ek-plots-tests/earthkit-plots/src/earthkit/plots/metadata/labels.py:121: UserWarning: No key "variable_name" found in layer metadata.
warnings.warn(f'No key "{attr}" found in layer metadata.')
/Users/mavj/ek-plots-tests/earthkit-plots/src/earthkit/plots/metadata/labels.py:121: UserWarning: No key "units" found in layer metadata.
warnings.warn(f'No key "{attr}" found in layer metadata.')
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).
[5]:
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()
/Users/mavj/ek-plots-tests/earthkit-plots/src/earthkit/plots/components/subplots.py:421: UserWarning: The 'interpolation_method' argument is only valid for unstructured data.
warnings.warn(
/Users/mavj/ek-plots-tests/earthkit-plots/src/earthkit/plots/metadata/labels.py:121: UserWarning: No key "variable_name" found in layer metadata.
warnings.warn(f'No key "{attr}" found in layer metadata.')
/Users/mavj/ek-plots-tests/earthkit-plots/src/earthkit/plots/metadata/labels.py:121: UserWarning: No key "units" found in layer metadata.
warnings.warn(f'No key "{attr}" found in layer metadata.')
Alternatively, you could use the tricontourf method to draw contour regions given a provided triangulation method (Delauney triangulation by default).
[6]:
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()
/Users/mavj/ek-plots-tests/earthkit-plots/src/earthkit/plots/metadata/labels.py:121: UserWarning: No key "variable_name" found in layer metadata.
warnings.warn(f'No key "{attr}" found in layer metadata.')
/Users/mavj/ek-plots-tests/earthkit-plots/src/earthkit/plots/metadata/labels.py:121: UserWarning: No key "units" found in layer metadata.
warnings.warn(f'No key "{attr}" found in layer metadata.')
