Visualisation of time-slice data
=====================================


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


.. code-block:: python

    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:

.. code-block:: python

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



More Examples
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Please see the gallery section.