Plotting xarray data

xarray is the standard Python library for labelled, multi-dimensional arrays and is widely used for working with netCDF and other gridded climate data. earthkit-plots accepts xarray DataArray and Dataset objects directly, with no conversion step required.

import earthkit.data as ekd

import earthkit.plots as ekp

Loading data and converting to xarray

We start from the same ERA5 netCDF sample used in the other source-type notebooks, then convert to an xarray Dataset with .to_xarray(). This is a common pattern when you want to do pre-processing (slicing, arithmetic, resampling) before plotting.

nc = ekd.from_source("sample", "era5-monthly-mean-2t-199312.nc")
ds = nc.to_xarray()
ds

<xarray.Dataset> Size: 8MB
Dimensions:    (time: 1, latitude: 721, longitude: 1440)
Coordinates:
  * time       (time) datetime64[ns] 8B 1993-12-01
  * latitude   (latitude) float32 3kB 90.0 89.75 89.5 ... -89.5 -89.75 -90.0
  * longitude  (longitude) float32 6kB 0.0 0.25 0.5 0.75 ... 359.2 359.5 359.8
Data variables:
    t2m        (time, latitude, longitude) float64 8MB ...
Attributes:
    Conventions:  CF-1.6
    history:      2024-05-21 15:58:02 GMT by grib_to_netcdf-2.28.1: /opt/ecmw...

xarray.Dataset

• Dimensions:
  • time: 1
  • latitude: 721
  • longitude: 1440
• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    1993-12-01

    long_name :

    time

    array(['1993-12-01T00:00:00.000000000'], dtype='datetime64[ns]')

  • latitude
    (latitude)
    float32
    90.0 89.75 89.5 ... -89.75 -90.0

    units :

    degrees_north

    long_name :

    latitude

    array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ],
          shape=(721,), dtype=float32)

  • longitude
    (longitude)
    float32
    0.0 0.25 0.5 ... 359.2 359.5 359.8

    units :

    degrees_east

    long_name :

    longitude

    array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,
           3.5975e+02], shape=(1440,), dtype=float32)

• Data variables: (1)
  • t2m
    (time, latitude, longitude)
    float64
    ...

    units :

    K

    long_name :

    2 metre temperature

    [1038240 values with dtype=float64]

• Attributes: (2)

  Conventions :

  CF-1.6

  history :

  2024-05-21 15:58:02 GMT by grib_to_netcdf-2.28.1: /opt/ecmwf/mars-client/bin/grib_to_netcdf -S param -o /cache/data0/adaptor.mars.internal-1716307081.965748-31841-4-d3840535-6aca-47b1-9542-be941d44b511.nc /cache/tmp/d3840535-6aca-47b1-9542-be941d44b511-adaptor.mars.internal-1716307081.4149752-31841-2-tmp.grib

Plotting a Dataset

Pass the Dataset directly to any earthkit-plots method. When the Dataset contains a single variable earthkit-plots selects it automatically; when there are multiple variables you will need to select one (see below).

chart = ekp.Map(domain="Europe")
chart.contourf(ds, units="celsius")
chart.legend()
chart.coastlines()
chart.title("{variable_name} – {time:%B %Y}")
chart.show()

Plotting a DataArray

You can also index into the Dataset and pass a single DataArray. This is useful when the Dataset contains multiple variables and you want to be explicit about which one to plot.

chart = ekp.Map(domain="Europe")
chart.contourf(ds["t2m"], units="celsius")
chart.legend()
chart.coastlines()
chart.title("{variable_name} – {time:%B %Y}")
chart.show()

Automatic style selection

plot() reads the variable name and units from the xarray metadata and selects an appropriate style automatically — exactly as it does for GRIB and netCDF sources.

chart = ekp.Map(domain="Europe")
chart.plot(ds, units="celsius")
chart.legend()
chart.coastlines()
chart.title("{variable_name} – {time:%B %Y}")
chart.show()

Format agnosticism

The code above is almost identical to the GRIB and netCDF examples — the only difference is how the data object is created. Once you have any earthkit-data object, xarray Dataset, or xarray DataArray, the plotting calls are the same:

# GRIB
data = ekd.from_source("sample", "era5-monthly-mean-2t-199312.grib")

# netCDF
data = ekd.from_source("sample", "era5-monthly-mean-2t-199312.nc")

# xarray
data = ekd.from_source("sample", "era5-monthly-mean-2t-199312.nc").to_xarray()

# In every case, the plot call is identical:
chart = ekp.Map(domain="Europe")
chart.plot(data, units="celsius")

This format agnosticism is a deliberate design goal of earthkit-plots: your visualisation code should not need to change just because your data arrives in a different format.