Visualize bathymetry with pyvista

pip install 'pyvista[all]' pyvista-xarray geovista trame trame-vtk trame-vuetify

import matplotlib.pyplot as plt
import pyvista as pv
import xarray as xr
import numpy as np
import s3fs

fs = s3fs.S3FileSystem(
    anon=False,
    profile="gfts",
    client_kwargs={
        "endpoint_url": "https://s3.gra.perf.cloud.ovh.net",
        "region_name": "gra",
    },
)

Read Gridded Bathymetry Data

• Gridded ba from https://www.gebco.net/data_and_products/gridded_bathymetry_data/

gfts_url = "s3://gfts-reference-data/GEBCO_2023_sub_ice_topo.nc"

dset = xr.open_dataset(fs.open(gfts_url))
dset

<xarray.Dataset> Size: 7GB
Dimensions:    (lon: 86400, lat: 43200)
Coordinates:
  * lon        (lon) float64 691kB -180.0 -180.0 -180.0 ... 180.0 180.0 180.0
  * lat        (lat) float64 346kB -90.0 -89.99 -89.99 ... 89.99 89.99 90.0
Data variables:
    elevation  (lat, lon) int16 7GB ...
Attributes:
    Conventions:  CF-1.6
    title:        The GEBCO_2020 Grid - a continuous terrain model for oceans...
    institution:  On behalf of the General Bathymetric Chart of the Oceans (G...
    source:       The GEBCO_2020 Grid is the latest global bathymetric produc...
    history:      Information on the development of the data set and the sour...
    references:   DOI: 10.5285/836f016a-33be-6ddc-e053-6c86abc0788e
    comment:      The data in the GEBCO_2020 Grid should not be used for navi...
    node_offset:  1.0

xarray.Dataset

• Dimensions:
  • lon: 86400
  • lat: 43200
• Coordinates: (2)
  • lon
    (lon)
    float64
    -180.0 -180.0 ... 180.0 180.0

    standard_name :

    longitude

    long_name :

    longitude

    units :

    degrees_east

    axis :

    X

    sdn_parameter_urn :

    SDN:P01::ALONZZ01

    sdn_parameter_name :

    Longitude east

    sdn_uom_urn :

    SDN:P06::DEGE

    sdn_uom_name :

    Degrees east

    array([-179.997917, -179.99375 , -179.989583, ...,  179.989583,  179.99375 ,
            179.997917])

  • lat
    (lat)
    float64
    -90.0 -89.99 -89.99 ... 89.99 90.0

    standard_name :

    latitude

    long_name :

    latitude

    units :

    degrees_north

    axis :

    Y

    sdn_parameter_urn :

    SDN:P01::ALATZZ01

    sdn_parameter_name :

    Latitude north

    sdn_uom_urn :

    SDN:P06::DEGN

    sdn_uom_name :

    Degrees north

    array([-89.997917, -89.99375 , -89.989583, ...,  89.989583,  89.99375 ,
            89.997917])

• Data variables: (1)
  • elevation
    (lat, lon)
    int16
    ...

    standard_name :

    height_above_reference_ellipsoid

    long_name :

    Elevation relative to sea level

    units :

    m

    sdn_parameter_urn :

    SDN:P01::ALATZZ01

    sdn_parameter_name :

    Sea floor height (above mean sea level) {bathymetric height}

    sdn_uom_urn :

    SDN:P06::ULAA

    sdn_uom_name :

    Metres

    [3732480000 values with dtype=int16]

• Indexes: (2)
  • lon
    PandasIndex

    PandasIndex(Index([-179.99791666666667,          -179.99375, -179.98958333333334,
           -179.98541666666668,          -179.98125, -179.97708333333333,
           -179.97291666666666,          -179.96875, -179.96458333333334,
           -179.96041666666667,
           ...
            179.96041666666667,  179.96458333333334,           179.96875,
            179.97291666666666,  179.97708333333333,           179.98125,
            179.98541666666665,  179.98958333333331,  179.99374999999998,
            179.99791666666667],
          dtype='float64', name='lon', length=86400))

  • lat
    PandasIndex

    PandasIndex(Index([-89.99791666666667,          -89.99375, -89.98958333333333,
           -89.98541666666667,          -89.98125, -89.97708333333334,
           -89.97291666666666,          -89.96875, -89.96458333333334,
           -89.96041666666666,
           ...
            89.96041666666667,  89.96458333333334,           89.96875,
            89.97291666666666,  89.97708333333333,  89.98124999999999,
            89.98541666666665,  89.98958333333334,           89.99375,
            89.99791666666667],
          dtype='float64', name='lat', length=43200))

• Attributes: (8)

  Conventions :

  CF-1.6

  title :

  The GEBCO_2020 Grid - a continuous terrain model for oceans and land at 15 arc-second intervals

  institution :

  On behalf of the General Bathymetric Chart of the Oceans (GEBCO), the data are held at the British Oceanographic Data Centre (BODC).

  source :

  The GEBCO_2020 Grid is the latest global bathymetric product released by the General Bathymetric Chart of the Oceans (GEBCO) and has been developed through the Nippon Foundation-GEBCO Seabed 2030 Project. This is a collaborative project between the Nippon Foundation of Japan and GEBCO. The Seabed 2030 Project aims to bring together all available bathymetric data to produce the definitive map of the world ocean floor and make it available to all.

  history :

  Information on the development of the data set and the source data sets included in the grid can be found in the data set documentation available from https://www.gebco.net

  references :

  DOI: 10.5285/836f016a-33be-6ddc-e053-6c86abc0788e

  comment :

  The data in the GEBCO_2020 Grid should not be used for navigation or any purpose relating to safety at sea.

  node_offset :

  1.0

bbox = {"latitude": [45, 52], "longitude": [-9, 0]}

dset_area = dset.where(
    (dset.lat >= bbox["latitude"][0])
    & (dset.lat <= bbox["latitude"][1])
    & (dset.lon >= bbox["longitude"][0])
    & (dset.lon <= bbox["longitude"][1]),
    drop=True,
)

dset_area

<xarray.Dataset> Size: 15MB
Dimensions:    (lat: 1680, lon: 2160)
Coordinates:
  * lon        (lon) float64 17kB -8.998 -8.994 -8.99 ... -0.00625 -0.002083
  * lat        (lat) float64 13kB 45.0 45.01 45.01 45.01 ... 51.99 51.99 52.0
Data variables:
    elevation  (lat, lon) float32 15MB -4.897e+03 -4.9e+03 ... 143.0 114.0
Attributes:
    Conventions:  CF-1.6
    title:        The GEBCO_2020 Grid - a continuous terrain model for oceans...
    institution:  On behalf of the General Bathymetric Chart of the Oceans (G...
    source:       The GEBCO_2020 Grid is the latest global bathymetric produc...
    history:      Information on the development of the data set and the sour...
    references:   DOI: 10.5285/836f016a-33be-6ddc-e053-6c86abc0788e
    comment:      The data in the GEBCO_2020 Grid should not be used for navi...
    node_offset:  1.0

xarray.Dataset

• Dimensions:
  • lat: 1680
  • lon: 2160
• Coordinates: (2)
  • lon
    (lon)
    float64
    -8.998 -8.994 ... -0.002083

    standard_name :

    longitude

    long_name :

    longitude

    units :

    degrees_east

    axis :

    X

    sdn_parameter_urn :

    SDN:P01::ALONZZ01

    sdn_parameter_name :

    Longitude east

    sdn_uom_urn :

    SDN:P06::DEGE

    sdn_uom_name :

    Degrees east

    array([-8.997917e+00, -8.993750e+00, -8.989583e+00, ..., -1.041667e-02,
           -6.250000e-03, -2.083333e-03])

  • lat
    (lat)
    float64
    45.0 45.01 45.01 ... 51.99 52.0

    standard_name :

    latitude

    long_name :

    latitude

    units :

    degrees_north

    axis :

    Y

    sdn_parameter_urn :

    SDN:P01::ALATZZ01

    sdn_parameter_name :

    Latitude north

    sdn_uom_urn :

    SDN:P06::DEGN

    sdn_uom_name :

    Degrees north

    array([45.002083, 45.00625 , 45.010417, ..., 51.989583, 51.99375 , 51.997917])

• Data variables: (1)
  • elevation
    (lat, lon)
    float32
    -4.897e+03 -4.9e+03 ... 143.0 114.0

    standard_name :

    height_above_reference_ellipsoid

    long_name :

    Elevation relative to sea level

    units :

    m

    sdn_parameter_urn :

    SDN:P01::ALATZZ01

    sdn_parameter_name :

    Sea floor height (above mean sea level) {bathymetric height}

    sdn_uom_urn :

    SDN:P06::ULAA

    sdn_uom_name :

    Metres

    array([[-4897., -4900., -4903., ...,    39.,    39.,    37.],
           [-4895., -4901., -4896., ...,    31.,    33.,    32.],
           [-4900., -4903., -4903., ...,    23.,    24.,    24.],
           ...,
           [  340.,   300.,   269., ...,   140.,   132.,   101.],
           [  377.,   319.,   274., ...,   142.,   138.,   107.],
           [  401.,   331.,   284., ...,   144.,   143.,   114.]],
          dtype=float32)

• Indexes: (2)
  • lon
    PandasIndex

    PandasIndex(Index([    -8.997916666666669,     -8.993750000000006,     -8.989583333333343,
                -8.98541666666668,     -8.981249999999989,     -8.977083333333326,
               -8.972916666666663,               -8.96875,     -8.964583333333337,
               -8.960416666666674,
           ...
            -0.039583333333325754,   -0.03541666666666288,               -0.03125,
            -0.027083333333337123,  -0.022916666666674246,   -0.01875000000001137,
            -0.014583333333348492,  -0.010416666666657193,  -0.006249999999994316,
           -0.0020833333333314386],
          dtype='float64', name='lon', length=2160))

  • lat
    PandasIndex

    PandasIndex(Index([ 45.00208333333333, 45.006249999999994,  45.01041666666666,
            45.01458333333332,  45.01875000000001, 45.022916666666674,
            45.02708333333334,           45.03125,  45.03541666666666,
           45.039583333333326,
           ...
           51.960416666666674,  51.96458333333334,           51.96875,
            51.97291666666666, 51.977083333333326,  51.98124999999999,
            51.98541666666665,  51.98958333333334, 51.993750000000006,
            51.99791666666667],
          dtype='float64', name='lat', length=1680))

• Attributes: (8)

  Conventions :

  CF-1.6

  title :

  The GEBCO_2020 Grid - a continuous terrain model for oceans and land at 15 arc-second intervals

  institution :

  On behalf of the General Bathymetric Chart of the Oceans (GEBCO), the data are held at the British Oceanographic Data Centre (BODC).

  source :

  The GEBCO_2020 Grid is the latest global bathymetric product released by the General Bathymetric Chart of the Oceans (GEBCO) and has been developed through the Nippon Foundation-GEBCO Seabed 2030 Project. This is a collaborative project between the Nippon Foundation of Japan and GEBCO. The Seabed 2030 Project aims to bring together all available bathymetric data to produce the definitive map of the world ocean floor and make it available to all.

  history :

  Information on the development of the data set and the source data sets included in the grid can be found in the data set documentation available from https://www.gebco.net

  references :

  DOI: 10.5285/836f016a-33be-6ddc-e053-6c86abc0788e

  comment :

  The data in the GEBCO_2020 Grid should not be used for navigation or any purpose relating to safety at sea.

  node_offset :

  1.0

3D Visualisation with matplotlib

fig = plt.figure()
ax = fig.add_subplot(projection="3d")


# Creating data
x = dset_area.lon.values
y = dset_area.lat.values

Z = dset_area.elevation.values

X, Y = np.meshgrid(x, y)

# Plot the surface.
ax.plot_surface(X, Y, Z, cmap=plt.cm.YlGnBu_r)

# Tweak the limits and add latex math labels.
ax.set_xlabel(r"Longitude")
ax.set_ylabel(r"Latitude")
ax.set_zlabel(r"Elevation (m)")

plt.show()

Interactive visualisation with pyVista

pv.start_xvfb()

dset_area.where(dset_area.elevation < 0).elevation.pyvista.plot(
    x="lon", y="lat", show_edges=False, cpos="xy"
)

Interactive 3D visualisation with pyvista

# Create and plot structured grid
grid = pv.StructuredGrid(X, Y, Z)
plotter = pv.Plotter()
plotter.add_mesh(
    grid,
    scalars=grid.points[:, -1],
    show_edges=False,
    scalar_bar_args={"vertical": False},
)
plotter.set_scale(zscale=0.001)
plotter.camera_position = "xy"
plotter.show()