Visualisation of time-slice data

Basic concept

The GriddedData has a method plot to visualise the data. It will calculate the data dimension and plot the data accordingly. For example, if it is a 2D data with latitude and longitude, it will plot the data on a map. If it is a 3D data with latitude, longitude and time, it will plot many maps for each time slice. The plotting engine is matplotlib and cartopy.

Module-based plotting design

The plot method is designed to be modular. You can choose different element: contour, pcolormesh (default), colorbar, land outline, gridline etc.

sst = model.get_var("ocn_temp")
sst.plot(colorbar=True, outline=True)

Customisation

I have to admit it is not very smart to do the customisation. Instead of like matplotlib which you can pass all the arguments to the plotting function, I store the parameters in a dictionary to help achieve the modular design. For example, if you want to change the color map of pcolormesh plot, do it like this:

p = sst.to_GriddedDataVis()
p["pcolormesh_kwargs"]["cmap"] = "Spectral_r"
p.plot()

More Examples

Please see the gallery section.