Quick Start#
Now that you’ve installed geovista, let’s take a
quick tour to see some of the features and capabilities on offer.
Resources#
For convenience, geovista comes with numerous pre-canned resources to
help get you started on your visualization journey e.g., rasters, VTK
meshes, Natural Earth Features, and various Earth Science sample data.
geovista will automatically download versioned resources on-the-fly,
as and when she needs them. However, if you want to download and cache all
geovista resources to make them available offline, simply perform the
following on the command line:
geovista download --all
To view the manifest of registered resources:
geovsita download --list
Note
Want to know more?
geovista download --help
Examples#
Note
The Interactive Scene vtk.js backend does not support rendering text or points as spheres.
Let’s explore some atmospheric and oceanographic model data using
geovista, which makes it easy to visualize rectilinear,
curvilinear, and unstructured meshes.
OISST AVHRR#
This example renders a 2-D data array with 1-D X and Y rectilinear coordinates as a mesh of quadrilateral cells in 3-D with coastlines.
The data source is a NOAA/NCEI Optimum Interpolation SST (OISST) Advanced
Very High Resolution Radiometer (AVHRR)
rectilinear grid containing 1,036,800
quadrilateral cells and 1,038,961 points.
The mesh is created from the bounds of 1-D geographic longitude and
latitude coordinates using the from_1d()
method. Each X and Y coordinate has 2 coordinate bounds describing
a quadrilateral cell.
A 2-D array of Sea Surface Temperature data located on the mesh cells are rendered using the perceptually uniform cmocean balance diverging colormap, along with 10m Natural Earth coastlines. Note that, the land cells are masked.
import geovista as gv
from geovista.pantry.data import oisst_avhrr_sst
# Load sample data.
sample = oisst_avhrr_sst()
# Create the mesh from the sample data.
mesh = gv.Transform.from_1d(
sample.lons,
sample.lats,
data=sample.data
)
# Plot the mesh with coastlines.
plotter = gv.GeoPlotter()
sargs = {"title": f"{sample.name} / {sample.units}"}
plotter.add_mesh(
mesh,
cmap="balance",
scalar_bar_args=sargs
)
plotter.add_coastlines(color="white")
plotter.camera.zoom(1.2)
plotter.show()
NEMO ORCA2#
This example renders a 2-D data array with 2-D X and Y curvilinear coordinates as a mesh of quadilateral cells in 3-D. A threshold is applied to remove cells with masked data. Coastlines and a base layer are also added before the results are transformed to a flat 2-D surface with a Plate Carrée projection.
The data source is a Nucleus for European Modelling of the Ocean (NEMO)
ORCA2 global ocean tripolar curvilinear grid
containing 26,640 quadrilateral cells and 106,560
points.
As the grid is curvilinear, it is created from the bounds of 2-D geographic
longitude and latitude coordinates using the
from_2d() method. Each X and Y
coordinate has 4 coordinate bounds describing a quadrilateral cell.
As ORCA2 is an ocean model, we use a threshold to remove 10,209 nan
land mask cells , and texture map
a base layer underneath with a 1:50m Natural Earth I
raster.
A perceptually uniform cmocean thermal colormap is used to render the Sea Water Potential Temperature data located on the grid cells, which is then complemented with 10m Natural Earth coastlines.
Finally, a cartopy CRS is used to transform the actors in the scene to the Equidistant Cylindrical (Plate Carrée`) conformal cylindrical projection.
Note
Basic projection support is available within geovista for
Cylindrical and Pseudo-Cylindrical projections. As geovista
matures, we’ll aim to enrich that capability and complement it with
other classes of projections, such as Azimuthal and Conic.
import cartopy.crs as ccrs
import geovista as gv
from geovista.pantry.data import nemo_orca2
# Load sample data.
sample = nemo_orca2()
# Create the mesh from the sample data.
mesh = gv.Transform.from_2d(
sample.lons,
sample.lats,
data=sample.data
)
# Remove cells from the mesh with NaN values.
mesh = mesh.threshold()
# Plot the mesh on a Plate Carrée projection using a cartopy CRS.
plotter = gv.GeoPlotter(crs=ccrs.PlateCarree())
sargs = {"title": f"{sample.name} / {sample.units}"}
plotter.add_mesh(
mesh,
cmap="thermal",
scalar_bar_args=sargs
)
plotter.add_base_layer(texture=gv.natural_earth_1())
plotter.add_coastlines(color="white")
plotter.view_xy()
plotter.camera.zoom(1.4)
plotter.show()
WAVEWATCH III#
geovista provides rich support for easily constructing a
mesh surface from various unstructured Earth Science model
data.
To demonstrate this we create a WAVEWATCH III (WW3) unstructured
triangular mesh from 1-D X and Y unstructured coordinates and 2-D
connectivity using the
from_unstructured() method.
The sample.connectivity, with shape (30559, 3), is used to index into
the 16,160 1-D geographical longitude and latitude points to create a
mesh containing 30,559 triangular cells.
A 1-D array of Sea Surface Wave Significant Height data is located on the mesh nodes, which is then interpolated across the mesh cells and rendered with the perceptually uniform cmocean balance divergent colormap.
As the WAVEWATCH III contains no land based cells, the 1:50m Natural Earth Cross-Blended Hypsometric Tints texture mapped base layer is visible underneath without the need to threshold the mesh.
Finally, the render is decorated with 10m Natural Earth coastlines.
import geovista as gv
from geovista.pantry.data import ww3_global_tri
# Load the sample data.
sample = ww3_global_tri()
# Create the mesh from the sample data.
mesh = gv.Transform.from_unstructured(
sample.lons,
sample.lats,
connectivity=sample.connectivity,
data=sample.data,
)
# Plot the mesh.
plotter = gv.GeoPlotter()
sargs = {"title": f"{sample.name} / {sample.units}"}
plotter.add_mesh(
mesh,
cmap="balance",
scalar_bar_args=sargs
)
plotter.add_coastlines(color="white")
plotter.add_base_layer(texture=gv.natural_earth_hypsometric())
plotter.view_xy(negative=True)
plotter.camera.zoom(1.2)
plotter.show()
Finite Volume Community Ocean Model#
This final example showcases how PyVista can take visualization of Earth Science data to the next dimension, quite literally.
We use Finite Volume Community Ocean Model (FVCOM) data to create an extruded mesh of the Plymouth Sound and Tamar River bathymetry in Cornwall, England.
First the from_unstructured() method is used
to create a triangular mesh from 1-D X and Y unstructured coordinates and
2-D connectivity.
A 1-D array of Sea Floor Depth Below Geoid data is added to the mesh cells, but also the mesh points, which are then used to displace the mesh points by a proportionally scaled amount in the direction of the mesh surface normals.
This displacement or warping of the mesh reveals the relief of the river and sea floor bathymetry, which we are then free to explore interactively.
import geovista as gv
from geovista.pantry.data import fvcom_tamar
# Load the sample data.
sample = fvcom_tamar()
# Create the mesh from the sample data.
mesh = gv.Transform.from_unstructured(
sample.lons,
sample.lats,
connectivity=sample.connectivity,
data=sample.face,
name="face",
)
# Warp the mesh nodes by the bathymetry.
mesh.point_data["node"] = sample.node
mesh.compute_normals(cell_normals=False, point_normals=True, inplace=True)
mesh.warp_by_scalar(scalars="node", inplace=True, factor=2e-5)
# Plot the mesh.
plotter = gv.GeoPlotter()
sargs = {"title": f"{sample.name} / {sample.units}"}
plotter.add_mesh(
mesh,
cmap="deep",
scalar_bar_args=sargs
)
plotter.view_poi()
plotter.show()
And Finally …#
Hopefully this whirlwind tour of geovista has whet your appetite for much
more!
If so, then let’s explore the next steps on your
geovista journey.