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

Python (.py)

Notebook (.ipynb)

Extra packages required

This example requires additional packages that are not available in typical napari installations. For this example to work in recommended napari installations, you will need to install additional packages: xarray. See the plugin manager guide for ways to install additional packages from within napari.

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, 'units':('ns', 'degrees', 'degrees')}


# 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
        )

viewer.layers.units = ('hour', 'degrees', 'degrees')
# 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()