geovista

Cartographic rendering and mesh analytics powered by PyVista 🌍

Science Score: 67.0%

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Low similarity (3.5%) to scientific vocabulary

Keywords

cartography curvilinear earth-science grid mesh python pyvista rectilinear ugrid unstructured vtk

Keywords from Contributors

iris grib meteorology oceanography spaceweather visualisation energy-system finite-element-analysis open-science meshviewer

Last synced: 6 months ago · JSON representation ·

Repository

Cartographic rendering and mesh analytics powered by PyVista 🌍

Basic Info

Host: GitHub
Owner: bjlittle
License: bsd-3-clause
Language: Python
Default Branch: main
Homepage: https://geovista.readthedocs.io/
Size: 8.46 MB

Statistics

Stars: 202
Watchers: 8
Forks: 30
Open Issues: 33
Releases: 13

Topics

cartography curvilinear earth-science grid mesh python pyvista rectilinear ugrid unstructured vtk

Created almost 5 years ago · Last pushed 6 months ago

Metadata Files

Readme Changelog Contributing License Code of conduct Citation Codeowners Security

Cartographic rendering and mesh analytics powered by PyVista

| | | |--------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | ⚙️ CI | | | 💬 Community | | | 📚 Docs | | | 📈 Health | | | ✨ Meta | | | 📦 Package | | | 🧰 Repo | | | 🛡️ Status | | | | |

Rediscover Your Data

WW3 SMC time-series 🎥

GeoVista is built on the shoulders of giants, namely PyVista and VTK, thus allowing it to easily leverage the power of the GPU.

As a result, it offers a paradigm shift in rendering performance and interactive user experience, as demonstrated by this realtime, time-series animation of WAVEWATCH III® third-generation wave model (WAVE-height, WATer depth and Current Hindcasting) data developed at NOAA/NCEP.

The animation shows a time-series of Sea Surface Wave Significant Height data located on the cell faces of a quasi-unstructured Spherical Multi-Cell (SMC) grid.

Bring your data alive with GeoVista! 🚀

Tempted? Keen to know more? Well, let's begin ...

Motivation

The goal of GeoVista is simple; to complement PyVista with a convenient cartographic capability.

In this regard, from a design perspective we aim to keep GeoVista as pure to PyVista as possible i.e., minimise specialisation as far as practically possible in order to maximise native compatibility within the PyVista and VTK ecosystems.

We intend GeoVista to be a cartographic gateway into the powerful world of PyVista, and all that it offers.

GeoVista is intentionally agnostic to packages such as geopandas, iris, xarray et al, which specialise in preparing your spatial data for visualisation. Rather, we delegate that responsibility and choice of tool to you the user, as we want GeoVista to remain as flexible and open-ended as possible to the entire Scientific Python community.

Simply put, "GeoVista is to PyVista", as "Cartopy is to Matplotlib". Well, that's the aspiration.

Installation

GeoVista is available on both conda-forge and PyPI. See our Installation guide for further details.

Conda

GeoVista is available on conda-forge, and can be easily installed with conda:

shell conda install --channel conda-forge geovista

Also see our conda-forge feedstock.

Pip

GeoVista is also available on PyPI:

shell pip install geovista

Checkout out our PyPI Download Stats, if you like that kinda thing.

Pixi 🧚

Alternatively, install GeoVista with pixi:

shell pixi add geovista`

For more information visit our prefix.dev dashboard.

Quick Start

GeoVista comes with various pre-canned resources to help get you started on your visualisation journey.

Resources

GeoVista makes use of various resources, such as rasters, VTK meshes, Natural Earth features, and sample model data.

If you want to download and cache all registered GeoVista resources to make them available offline, simply: shell geovista download --all Alternatively, just leave GeoVista to download resources on-the-fly, as and when she needs them.

To view the list of registered resources, simply: shell geovista download --list

Want to know more? shell geovista download --help

Plotting Examples

Let's explore a sample of various oceanographic and atmospheric model data using GeoVista.

WAVEWATCH III

First, let's render a WAVEWATCH III (WW3) unstructured triangular mesh, with 10m Natural Earth coastlines, a 1:50m Natural Earth Cross-Blended Hypsometric Tints base layer, and the gorgeous perceptually uniform cmocean balance diverging colormap.

🗒 Click for code ...

```python import geovista as gv from geovista.pantry.data import ww3_global_tri import geovista.theme # 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. p = gv.GeoPlotter() sargs = {"title": f"{sample.name} / {sample.units}"} p.add_mesh(mesh, show_edges=True, scalar_bar_args=sargs) p.add_base_layer(texture=gv.natural_earth_hypsometric()) p.add_coastlines() p.add_graticule() p.view_xy(negative=True) p.add_axes() p.show() ```

WAVEWATCH III Model, Unstructured Triangular Mesh of Sea Surface Wave Significant Height

Finite Volume Community Ocean Model

Now, let's visualise the bathymetry of the Plymouth Sound and Tamar River from an FVCOM unstructured mesh, as kindly provided by the Plymouth Marine Laboratory using the lush cmocean deep colormap.

🗒 Click for code ...

```python import geovista as gv from geovista.pantry.data import fvcom_tamar import geovista.theme # 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. p = gv.GeoPlotter() sargs = {"title": f"{sample.name} / {sample.units}"} p.add_mesh(mesh, cmap="deep", scalar_bar_args=sargs) p.add_axes() p.show() ```

Finite Volume Community Ocean Model, Unstructured Triangular Mesh of Sea Floor Below Geoid

CF UGRID

Local Area Model

Initial projection support is available within GeoVista for Cylindrical and Pseudo-Cylindrical projections. As GeoVista matures and stabilises, we'll aim to complement this capability with other classes of projections, such as Azimuthal and Conic.

In the meantime, let's showcase our basic projection support with some high-resolution unstructured Local Area Model (LAM) data reprojected to Mollweide using a PROJ string, with 10m Natural Earth coastlines and a 1:50m Natural Earth Cross-Blended Hypsometric Tints base layer.

🗒 Click for code ...

```python import geovista as gv from geovista.pantry.data import lam_pacific import geovista.theme # Load the sample data. sample = lam_pacific() # 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 on a mollweide projection using a Proj string. p = gv.GeoPlotter(crs="+proj=moll") sargs = {"title": f"{sample.name} / {sample.units}"} p.add_mesh(mesh, scalar_bar_args=sargs) p.add_base_layer(texture=gv.natural_earth_hypsometric()) p.add_coastlines() p.add_graticule() p.add_axes() p.view_xy() p.show() ```

CF UGRID Local Area Model, Unstructured Quadrilateral Mesh of Air Potential Temperature

Using the same unstructured LAM data, reproject to Equidistant Cylindrical but this time using a Cartopy Plate Carrée CRS, also with 10m Natural Earth coastlines and a 1:50m Natural Earth Cross-Blended Hypsometric Tints base layer.

🗒 Click for code ...

```python import cartopy.crs as ccrs import geovista as gv from geovista.pantry.data import lam_pacific import geovista.theme # Load the sample data. sample = lam_pacific() # 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 on a Plate Carrée projection using a cartopy CRS. p = gv.GeoPlotter(crs=ccrs.PlateCarree(central_longitude=180)) sargs = {"title": f"{sample.name} / {sample.units}"} p.add_mesh(mesh, scalar_bar_args=sargs) p.add_base_layer(texture=gv.natural_earth_hypsometric()) p.add_coastlines() p.add_graticule() p.add_axes() p.view_xy() p.show() ```

CF UGRID Local Area Model, Unstructured Quadrilateral Mesh of Air Potential Temperature in Plate Carrée Projection

LFRic Cube-Sphere

Now render a Met Office LFRic C48 cube-sphere unstructured mesh of Sea Surface Temperature data on a Robinson Projection using an ESRI SRID, with 10m Natural Earth coastlines and a cmocean thermal colormap.

🗒 Click for code ...

```python import geovista as gv from geovista.pantry.data import lfric_sst import geovista.theme # Load the sample data. sample = lfric_sst() # 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 on a Robinson projection using an ESRI spatial reference identifier. p = gv.GeoPlotter(crs="ESRI:54030") sargs = {"title": f"{sample.name} / {sample.units}"} p.add_mesh(mesh, cmap="thermal", show_edges=True, scalar_bar_args=sargs) p.add_coastlines() p.view_xy() p.add_axes() p.show() ```

LFRic Model, Unstructured Cube Sphere of Surface Temperature in Robinson Projection

NEMO ORCA2

So far we've demonstrated GeoVista's ability to cope with unstructured data. Now let's plot a curvilinear mesh using Nucleus for European Modelling of the Ocean (NEMO) ORCA2 Sea Water Potential Temperature data, with 10m Natural Earth coastlines and a 1:50m Natural Earth I base layer.

🗒 Click for code ...

```python import geovista as gv from geovista.pantry.data import nemo_orca2 import geovista.theme # 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. p = gv.GeoPlotter() sargs = {"title": f"{sample.name} / {sample.units}"} p.add_mesh(mesh, show_edges=True, scalar_bar_args=sargs) p.add_base_layer(texture=gv.natural_earth_1()) p.add_coastlines() p.view_xy() p.add_axes() p.show() ```

NEMO ORCA2 Model, Curvilinear Quadrilateral Mesh of Sea Water Potential Temperature

OISST AVHRR

Now let's render a NOAA/NCEI Optimum Interpolation SST (OISST) Advanced Very High Resolution Radiometer (AVHRR) rectilinear mesh, with 10m Natural Earth coastlines and a NASA Blue Marble base layer.

🗒 Click for code ...

```python import geovista as gv from geovista.pantry.data import oisst_avhrr_sst import geovista.theme # 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) # Remove cells from the mesh with NaN values. mesh = mesh.threshold() # Plot the mesh. p = gv.GeoPlotter() sargs = {"title": f"{sample.name} / {sample.units}"} p.add_mesh(mesh, scalar_bar_args=sargs) p.add_base_layer(texture=gv.blue_marble()) p.add_coastlines() p.view_xz() p.add_axes() p.show() ```

Optimum Interpolation Sea Surface Temperature Advanced Very High Resolution Radiometer Model, Rectilinear Quadrilateral Mesh

DYNAMICO

Finally, to demonstrate support for non-traditional cell geometries i.e., not triangles or quadrilaterals, we plot the unstructured mesh from the DYNAMICO project. This model uses hexagonal and pentagonal cells, and is a new dynamical core for LMD-Z, the atmospheric General Circulation Model (GCM) part of the IPSL-CM Earth System Model. The render also contains 10m Natural Earth coastlines.

🗒 Click for code ...

```python import geovista as gv from geovista.pantry.data import dynamico import geovista.theme # Load sample data. sample = dynamico() # Create the mesh from the sample data. mesh = gv.Transform.from_unstructured(sample.lons, sample.lats, data=sample.data) # Plot the mesh. p = gv.GeoPlotter() sargs = {"title": f"{sample.name} / {sample.units}"} p.add_mesh(mesh, scalar_bar_args=sargs) p.add_coastlines() p.add_axes() p.show() ```

DYNAMICO Model, Unstructured Hexagonal Mesh of Surface Air Temperature

Further Examples

"Please, sir, I want some more", Charles Dickens, Oliver Twist, 1838.

Certainly, our pleasure! From the command line, simply:

bash geovista examples --run all --verbose

Want to know more? shell geovista examples --help

Candy Store 🍬

We've opened the doors to the Candy Store 🍬.

A community space where you can openly share and promote how you're using GeoVista for your work, research or pleasure!

Showcase your most awesome GeoVista eye candy with undiluted pride and tell us more about your work.

We'd 💚 to hear from you!

Documentation

The documentation is built by Sphinx and hosted on Read the Docs.

Ecosystem

Whilst you're here, why not hop on over to the pyvista-xarray project and check it out!

It's aiming to provide xarray DataArray accessors for PyVista to visualize datasets in 3D for the xarray community, and will be building on top of GeoVista 🎉

Support

Need help? 😢

Why not check out our existing GitHub issues. See something similar? Well, give it a 👍 to raise its priority and feel free to chip in on the conversation. Otherwise, don't hesitate to create a new GitHub issue instead.

However, if you'd rather have a natter, then head on over to our GitHub Discussions. That's definitely the place to wax lyrical all things GeoVista!

License

GeoVista is distributed under the terms of the BSD-3-Clause license.

Star History

Diggin' GeoVista? Then why not show it some 💚 with a GitHub ⭐!

It's a simple click that means the world to us, and helps others discover our community too!

#ShowYourStripes

Graphics and Lead Scientist: Ed Hawkins, National Centre for Atmospheric Science, University of Reading.

Data: Berkeley Earth, NOAA, UK Met Office, MeteoSwiss, DWD, SMHI, UoR, Meteo France & ZAMG.

#ShowYourStripes is distributed under a Creative Commons Attribution 4.0 International License

Owner

Name: Bill Little
Login: bjlittle
Kind: user
Location: Exeter, Devon, England
Company: Met Office

Website: https://www.metoffice.gov.uk/
Twitter: billjameslittle
Repositories: 136
Profile: https://github.com/bjlittle

Soft, fluffy and machine washable.

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Little"
  given-names: "Bill"
  orcid: "https://orcid.org/0000-0002-1345-9465"
title: "geovista"
abstract: "Cartographic rendering and mesh analytics powered by PyVista"
repository-code: "https://github.com/bjlittle/geovista"
license: "BSD-3-Clause"
license-url: "https://spdx.org/licenses/BSD-3-Clause.html"
doi: "10.5281/zenodo.7608302"
type: "software"

Committers

Last synced: 9 months ago

All Time

Total Commits: 1,754
Total Committers: 20
Avg Commits per committer: 87.7
Development Distribution Score (DDS): 0.332

Past Year

Commits: 559
Committers: 13
Avg Commits per committer: 43.0
Development Distribution Score (DDS): 0.533

Top Committers

Name	Email	Commits
Bill Little	b**e@g**m	1,171
geovista-ci[bot]	1****]	185
pre-commit-ci[bot]	6****]	127
dependabot[bot]	4****]	113
allcontributors[bot]	4****]	87
github-actions[bot]	4****]	27
Martin Yeo	4****s	13
Tetsuo Koyama	t**0@g**m	13
Henry Wright	8****t	3
Bane Sullivan	b**n@g**m	3
Elias	1****O	2
stephenworsley	4****y	2
Andrew Coughtrie	2****e	1
Patrick Peglar	p**r@m**k	1
RichardScottOZ	7****Z	1
beroda	1****a	1
bjlittle-pr-auth[bot]	1****]	1
geovista-actions[bot]	1****]	1
tkknight	2****t	1
user27182	8****2	1

Committer Domains (Top 20 + Academic)

metoffice.gov.uk: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 170
Total pull requests: 1,941
Average time to close issues: about 2 months
Average time to close pull requests: about 24 hours
Total issue authors: 24
Total pull request authors: 18
Average comments per issue: 2.45
Average comments per pull request: 1.02
Merged pull requests: 1,691
Bot issues: 81
Bot pull requests: 902

Past Year

Issues: 98
Pull requests: 786
Average time to close issues: 5 days
Average time to close pull requests: about 19 hours
Issue authors: 9
Pull request authors: 13
Average comments per issue: 0.32
Average comments per pull request: 0.87
Merged pull requests: 643
Bot issues: 78
Bot pull requests: 443

View more stats

Top Authors

Issue Authors

github-actions[bot] (76)
bjlittle (38)
tkoyama010 (12)
trexfeathers (9)
banesullivan (5)
dennissergeev (5)
PBrockmann (4)
geovista-ci[bot] (2)
pp-mo (2)
k-a-mendoza (2)
akaszynski (2)
jbusecke (1)
dependabot[bot] (1)
stephenworsley (1)
allcontributors[bot] (1)

Pull Request Authors

bjlittle (946)
geovista-ci[bot] (323)
dependabot[bot] (237)
pre-commit-ci[bot] (177)
allcontributors[bot] (165)
tkoyama010 (29)
trexfeathers (24)
HGWright (7)
banesullivan (6)
pp-mo (6)
stephenworsley (4)
ESadek-MO (4)
user27182 (4)
tkknight (2)
beroda (2)

Top Labels

Issue Labels

new: issue (99) bot (78) type: documentation (72) type: bug (15) new: documentation (10) new: feature (9) type: infrastructure (9) type: tech debt (8) type: enhancement (5) skip changelog (4) status: blocked (3) new: pull request (3) scipy 2023 (3) scipy 2024 (3) type: dependencies (2) good first issue (2) pyvista (2) status: decision required (2) type: performance (2) stale (2) feature: line projection support (2) type: examples (1) type: cli (1) pixi (1) status: stalled (1) breaking-change (1) feature: crs (1) platform: macOS (1) type: gha (1) type: cache (1)

Pull Request Labels

skip changelog (702) type: infrastructure (575) new: pull request (558) bot (558) type: dependencies (429) type: gha (298) type: documentation (293) type: examples (166) type: testing (102) type: bug (51) type: cache (38) pixi (35) type: cli (21) scipy 2024 (18) type: enhancement (17) breaking-change (16) type: tech debt (13) status: blocked (9) type: merge-back (8) scipy 2023 (5) new: feature (4) type: performance (3) feature: line projection support (2) type: nep-29 (2) feature: pyvista-xarray (1) pyvista (1)

Packages

Total packages: 3
Total downloads:
- pypi 1,028 last-month

Total dependent packages: 2
(may contain duplicates)
Total dependent repositories: 3
(may contain duplicates)
Total versions: 29
Total maintainers: 5

proxy.golang.org: github.com/bjlittle/geovista

Documentation: https://pkg.go.dev/github.com/bjlittle/geovista#section-documentation
License: bsd-3-clause
Latest release: v0.5.3
published over 1 year ago

Versions: 13
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 5.4%

Average: 5.6%

Dependent repos count: 5.8%

Last synced: 6 months ago

pypi.org: geovista

Cartographic rendering and mesh analytics powered by PyVista

Documentation: https://geovista.readthedocs.io
License: BSD 3-Clause License Copyright (c) 2021, GeoVista Contributors. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Latest release: 0.5.3
published over 1 year ago

Versions: 14
Dependent Packages: 2
Dependent Repositories: 3
Downloads: 1,028 Last month

Rankings

Dependent packages count: 4.8%

Stargazers count: 7.6%

Average: 8.9%

Dependent repos count: 8.9%

Forks count: 9.6%

Downloads: 13.5%

Maintainers (5)

Bill.Little ppmo martin.yeo stephen.worsley HGWright

Last synced: 6 months ago

conda-forge.org: geovista

GeoVista provides a cartographic gateway into the world of PyVista. It leverages the power of the GPU through VTK to provide a paradigm shift in rendering performance and interactive user experience.

Homepage: https://github.com/bjlittle/geovista
License: BSD-3-Clause
Latest release: 0.1a0
published over 4 years ago

Versions: 2
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent repos count: 34.0%

Stargazers count: 41.7%

Average: 42.6%

Forks count: 43.4%

Dependent packages count: 51.2%

Last synced: 6 months ago

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