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PyVista xarray

PyPI codecov MyBinder

xarray DataArray accessors for PyVista to visualize datasets in 3D

🚀 Usage

You must import pvxarray in order to register the DataArray accessor with xarray. After which, a pyvista namespace of accessors will be available.

Try on MyBinder: https://mybinder.org/v2/gh/pyvista/pyvista-xarray/HEAD

The following is an example to visualize a RectilinearGrid with PyVista:

import pvxarray
import xarray as xr

ds = xr.tutorial.load_dataset("air_temperature")
da = ds.air[dict(time=0)]  # Select DataArray for a timestep

# Plot in 3D
da.pyvista.plot(x="lon", y="lat", show_edges=True, cpos='xy')

# Or grab the mesh object for use with PyVista
mesh = da.pyvista.mesh(x="lon", y="lat")

air_temperature

Or you can read VTK meshes directly to xarray:

import xarray as xr

ds = xr.open_dataset("data.vtk", engine="pyvista")
ds["data array"].pyvista.plot(x="x", y="y", z="z")

⬇️ Installation

🐍 Installing with conda

Conda makes managing pyvista-xarray's dependencies across platforms quite easy and this is the recommended method to install:

conda install -c conda-forge pyvista-xarray

🎡 Installing with pip

If you prefer pip, then you can install from PyPI: https://pypi.org/project/pyvista-xarray/

pip install pyvista-xarray

Upstream Work

Many of the examples leverage brand new features in PyVista v0.38.1 and GeoVista which may not yet be released when you're reading this. Here is a list of pull requests needed for some of the examples:

Work that was required and merged:

💭 Feedback

Please share your thoughts and questions on the Discussions board. If you would like to report any bugs or make feature requests, please open an issue.

If filing a bug report, please share a scooby Report:

import pvxarray
print(pvxarray.Report())

🏏 Further Examples

The following are a few simple examples taken from the xarray and rioxarray documentation. There are also more sophisticated examples in the examples/ directory in this repository.

Simple RectilinearGrid

import numpy as np
import pvxarray
import xarray as xr

lon = np.array([-99.83, -99.32])
lat = np.array([42.25, 42.21])
z = np.array([0, 10])
temp = 15 + 8 * np.random.randn(2, 2, 2)

ds = xr.Dataset(
    {
        "temperature": (["z", "x", "y"], temp),
    },
    coords={
        "lon": (["x"], lon),
        "lat": (["y"], lat),
        "z": (["z"], z),
    },
)

mesh = ds.temperature.pyvista.mesh(x="lon", y="lat", z="z")
mesh.plot()

Raster with rioxarray

import pvxarray
import rioxarray
import xarray as xr

da = rioxarray.open_rasterio("TC_NG_SFBay_US_Geo_COG.tif")
da = da.rio.reproject("EPSG:3857")

# Grab the mesh object for use with PyVista
mesh = da.pyvista.mesh(x="x", y="y", component="band")

mesh.plot(scalars="data", cpos='xy', rgb=True)

raster

import pvxarray
import rioxarray

da = rioxarray.open_rasterio("Elevation.tif")
da = da.rio.reproject("EPSG:3857")

# Grab the mesh object for use with PyVista
mesh = da.pyvista.mesh(x="x", y="y")

# Warp top and plot in 3D
mesh.warp_by_scalar().plot()

topo

StructuredGrid

import pvxarray
import pyvista as pv
import xarray as xr

ds = xr.tutorial.open_dataset("ROMS_example.nc", chunks={"ocean_time": 1})

if ds.Vtransform == 1:
    Zo_rho = ds.hc * (ds.s_rho - ds.Cs_r) + ds.Cs_r * ds.h
    z_rho = Zo_rho + ds.zeta * (1 + Zo_rho / ds.h)
elif ds.Vtransform == 2:
    Zo_rho = (ds.hc * ds.s_rho + ds.Cs_r * ds.h) / (ds.hc + ds.h)
    z_rho = ds.zeta + (ds.zeta + ds.h) * Zo_rho

ds.coords["z_rho"] = z_rho.transpose()  # needing transpose seems to be an xarray bug

da = ds.salt[dict(ocean_time=0)]

# Make array ordering consistent
da = da.transpose("s_rho", "xi_rho", "eta_rho", transpose_coords=False)

# Grab StructuredGrid mesh
mesh = da.pyvista.mesh(x="lon_rho", y="lat_rho", z="z_rho")

# Plot in 3D
p = pv.Plotter()
p.add_mesh(mesh, lighting=False, cmap='plasma', clim=[0, 35])
p.view_vector([1, -1, 1])
p.set_scale(zscale=0.001)
p.show()

raster