Skip to article frontmatterSkip to article content

For users coming from an Xarray background, much of UXarray’s design is familiar. This notebook showcases an example of transitioning a Structured Grid Xarray workflow to UXarray.

Imports

import matplotlib.pyplot as plt
import uxarray as ux
import xarray as xr
fig_size = 400
plot_kwargs = {"backend": "matplotlib", "aspect": 2, "fig_size": fig_size}

Data

It is common practice to resample unstructured grids to a structured representation for many analysis workflows to utilize familiar and reliable tools.

The datasets used in this example are meant to mimic this workflow, with a source Unstructured Grid and a Structured representation of that same grid.

Structured

base_path = "../../meshfiles/"
ds_path = base_path + "outCSne30.structured.nc"
xrds = xr.open_dataset(ds_path)
xrds

Unstructured

base_path = "../../meshfiles/"
grid_filename = base_path + "outCSne30.grid.ug"
data_filename = base_path + "outCSne30.data.nc"
uxds = ux.open_dataset(grid_filename, data_filename)
uxds

Example Workflows

Below are two simple visualization workflows that someone would run into

  • Creating a single plot
  • Creating a pair of plots (two different color maps are used to mimic different data)

Xarray

xrds["psi"].plot(figsize=(12, 5), cmap="inferno")
fig, axs = plt.subplots(nrows=2, figsize=(12, 10))

xrds["psi"].plot(cmap="inferno", ax=axs[0])
xrds["psi"].plot(cmap="cividis", ax=axs[1])

UXarray

uxds["psi"].plot(width=1000, height=500, cmap="inferno")

The default plotting method works great, but we can chose to set exclude_antimeridian=False to include the entire grid and generate a better looking plot.

See also:

To learn more about this type of plotting functionality and supported parameters, please refer to the Polygon Section
uxds["psi"].plot(width=1000, height=500, cmap="inferno", exclude_antimeridian=False)
(
    uxds["psi"].plot(cmap="inferno", exclude_antimeridian=False, **plot_kwargs)
    + uxds["psi"].plot(cmap="cividis", exclude_antimeridian=False, **plot_kwargs)
).opts(fig_size=fig_size).cols(1)

Using hvPlot to conbine UXarray and Xarray Plots

One of the primary drawbacks to UXarray’s use of HoloViews for visualization is that there is no direct way to integrate plots generated with Xarray and UXarray together. This can be alleviated by using the hvPlot library, specifically hvplot.xarray, on Xarray’s data structures.

See also:

To learn more about hvPlot and xarray, please refer to the hvPlot Documentation
import holoviews as hv
import hvplot.xarray

hv.extension("bokeh")

By using xrds.hvplot() as opposed to xrds.plot(), we can create a simple figure showcasing our Structured Grid figure from Xarray and Unstructured Grid figure from UXarray in a single plot.

hv.extension("bokeh")
(
    xrds.hvplot(cmap="inferno", title="Xarray with hvPlot", width=1000, height=500)
    + uxds["psi"].plot(
        cmap="inferno",
        title="UXarray Plot",
        exclude_antimeridian=False,
        width=1000,
        height=500,
    )
).cols(1)
UXarray Visualization
Plotting API
UXarray Visualization
Grid Topology