Displaying xarray data in napari

This example shows how to view xarray datasets in napari, including scale and translation information.

Currently, napari cannot display irregularly-sampled data, so the code assumes that the data indices are regularly spaced. If your indices are irregular, use xarray.Dataset.interp to create a regularly-spaced version before displaying it in napari.

Tags: visualization-advanced, layers, xarray

import numpy as np
import xarray as xr

import napari

# open the xarray global sea surface temperature (40MB) and North America
# air temperature (30MB) datasets
sst = xr.tutorial.open_dataset('ersstv5')
airtemp = xr.tutorial.open_dataset('air_temperature')


def get_scale_translate(dataset, array_name):
    """Get the translate/offset and scale parameters for an xarray dataset.

    This code assumes that the dataset is regularly spaced. You should
    interpolate your data if it is sampled at irregular spaces.

    Parameters
    ----------
    dataset : xr.Dataset
        The dataset containing the array to be displayed.
    array_name : str
        The name of the xarray DataArray within `dataset` to be displayed in
        napari.

    Returns
    -------
    param_dict : dict[str, list[float]]
        The scale and translate parameters computed from the xarray dimension
        indices.
    """
    array = getattr(dataset, array_name)
    if array is None:
        raise ValueError(f'{dataset} has no array with name {array_name}')
    dims = [getattr(dataset, dim) for dim in array.dims]
    translate = [float(d[0]) for d in dims]
    scale = [float(d[1] - d[0]) for d in dims]
    return {'scale': scale, 'translate': translate}


# Show the raw (not resampled) model data
viewer, sst_layer = napari.imshow(
        sst.sst,
        name='sea surface temp',
        **get_scale_translate(sst, 'sst'),
        colormap='magma',
        )
viewer.dims.axis_labels = sst.sst.dims

air_layer = viewer.add_image(
        airtemp.air,
        name='air temp NA',
        **get_scale_translate(airtemp, 'air'),
        colormap='viridis',
        blending='additive',
        contrast_limits=(-23 + 273, 32 + 273),  # data are in degrees Kelvin
        )

# set a time that overlaps both datasets
viewer.dims.set_point(0, np.datetime64('2013-03-10T18:00:00.000000000'))

# latitude goes from -90 (south, down) to 90 (north, up),
# so we make sure that the camera vertical axis points up.
viewer.camera.orientation2d = ('up', 'right')

# fill the frame
viewer.reset_view(margin=0)


if __name__ == '__main__':
    napari.run()