| Name | geovista JSON |
| Version |
0.5.3
JSON |
| download |
| home_page | None |
| Summary | Cartographic rendering and mesh analytics powered by PyVista |
| upload_time | 2024-10-21 20:51:56 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| 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. |
| keywords |
cartography
curvilinear
earth-science
grid
mesh
python
pyvista
rectilinear
ugrid
unstructured
vtk
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<h1 align="center">
<a href="https://github.com/bjlittle/geovista#--------">
<img src="https://raw.githubusercontent.com/bjlittle/geovista-media/2024.07.0/media/branding/logo/primary/geovistalogo.svg"
alt="GeoVista"
width="200"></a>
</h1>
<h3 align="center">
Cartographic rendering and mesh analytics powered by <a href="https://docs.pyvista.org/index.html">PyVista</a>
</h3>
| | |
|--------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| ⚙️ CI | [](https://github.com/bjlittle/geovista/actions/workflows/ci-citation.yml) [](https://github.com/bjlittle/geovista/actions/workflows/ci-locks.yml) [](https://github.com/bjlittle/geovista/actions/workflows/ci-manifest.yml) [](https://github.com/bjlittle/geovista/actions/workflows/ci-tests.yml) [](https://github.com/bjlittle/geovista/actions/workflows/ci-wheels.yml) [](https://results.pre-commit.ci/latest/github/bjlittle/geovista/main) |
| 💬 Community | [](https://github.com/bjlittle/geovista/blob/main/CODE_OF_CONDUCT.md) [](https://github.com/bjlittle/geovista/discussions) [](https://twitter.com/geovista_devs) [](https://www.youtube.com/@geovista_devs/videos) [](https://geovista.readthedocs.io/en/latest/reference/about.html#contributors) |
| 📚 Docs | [](https://geovista.readthedocs.io/en/latest/?badge=latest) |
| 📈 Health | [](https://codecov.io/gh/bjlittle/geovista) |
| ✨ Meta | [](https://github.com/astral-sh/ruff) [](https://numpy.org/neps/nep-0029-deprecation_policy.html) [](https://github.com/bjlittle/geovista/blob/main/LICENSE) [](https://anaconda.org/conda-forge/geovista) |
| 📦 Package | [](https://doi.org/10.5281/zenodo.7608302) [](https://anaconda.org/conda-forge/geovista) [](https://pypi.org/project/geovista/) [](https://pypi.org/project/geovista/) |
| 🧰 Repo | [](https://github.com/bjlittle/geovista/commits/main) [](https://github.com/bjlittle/geovista/graphs/contributors) [](https://github.com/bjlittle/geovista/releases) |
| 🛡️ Status | [](https://github.com/bjlittle/geovista/issues/167) |
| | |
## Rediscover Your Data
[🎥 WW3 SMC time-series](https://github.com/bjlittle/geovista/assets/2051656/876d877e-a6fa-42ff-8153-08c41ff8a19e)
GeoVista is built on the shoulders of giants, namely [PyVista](https://docs.pyvista.org/version/stable/) and
[VTK](https://vtk.org/documentation/), 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, **WAT**er depth
and **C**urrent **H**indcasting) data developed at [NOAA](https://www.noaa.gov/)/[NCEP](https://www.weather.gov/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](https://docs.pyvista.org/index.html) 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](https://vtk.org/) 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](https://geopandas.org/en/stable/),
[iris](https://scitools-iris.readthedocs.io/en/latest/?badge=latest), [xarray](https://docs.xarray.dev/en/stable/)
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](https://geovista.readthedocs.io/) is to
[PyVista](https://docs.pyvista.org/)*", as
"*[Cartopy](https://scitools.org.uk/cartopy/docs/latest/) is to
[Matplotlib](https://matplotlib.org/)*". Well, that's the aspiration.
## Installation
GeoVista is available on both [conda-forge](https://anaconda.org/conda-forge/geovista) and [PyPI](https://pypi.org/project/geovista/).
We recommend using [conda](https://docs.conda.io/projects/conda/en/latest/index.html) to install GeoVista 👍
### Conda
GeoVista is available on [conda-forge](https://anaconda.org/conda-forge/geovista), and can be easily installed with
[conda](https://docs.conda.io/projects/conda/en/latest/index.html):
```shell
conda install -c conda-forge geovista
```
For more information see our [conda-forge feedstock](https://github.com/conda-forge/geovista-feedstock) and
[prefix.dev dashboard](https://prefix.dev/channels/conda-forge/packages/geovista).
### Pip
GeoVista is also available on [PyPI](https://pypi.org/project/geovista/):
```shell
pip install geovista
```
Checkout out our [PyPI Download Stats](https://pypistats.org/packages/geovista), if you like that kinda thing.
## 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](https://www.naturalearthdata.com/features/) 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](https://github.com/NOAA-EMC/WW3) (WW3) **unstructured** triangular mesh, with
[10m Natural Earth coastlines](https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-coastline/), a
[1:50m Natural Earth Cross-Blended Hypsometric Tints](https://www.naturalearthdata.com/downloads/50m-raster-data/50m-cross-blend-hypso/)
base layer, and the gorgeous perceptually uniform [cmocean balance](https://matplotlib.org/cmocean/#balance) diverging
colormap.
<details>
<summary>🗒 click for code</summary>
```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.
plotter = gv.GeoPlotter()
sargs = {"title": f"{sample.name} / {sample.units}"}
plotter.add_mesh(mesh, show_edges=True, scalar_bar_args=sargs)
plotter.add_base_layer(texture=gv.natural_earth_hypsometric())
plotter.add_coastlines()
plotter.add_graticule()
plotter.view_xy(negative=True)
plotter.add_axes()
plotter.show()
```
</details>
<p align="center"><img src="https://raw.githubusercontent.com/bjlittle/geovista-media/2024.07.0/media/readme/ww3-tri.png" style="width: 75%; height: 75%"></p>
#### Finite Volume Community Ocean Model
Now, let's visualise the bathymetry of the
[Plymouth Sound and Tamar River](https://www.google.com/maps/place/Plymouth+Sound/@50.3337382,-4.2215988,12z/data=!4m5!3m4!1s0x486c93516bbce307:0xded7654eaf4f8f83!8m2!3d50.3638359!4d-4.1441365)
from an [FVCOM](https://www.fvcom.org/) **unstructured** mesh, as kindly provided by the
[Plymouth Marine Laboratory](https://pml.ac.uk/) using the lush [cmocean deep](https://matplotlib.org/cmocean/#deep) colormap.
<details>
<summary>🗒 click for code</summary>
```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.
plotter = gv.GeoPlotter()
sargs = {"title": f"{sample.name} / {sample.units}"}
plotter.add_mesh(mesh, cmap="deep", scalar_bar_args=sargs)
plotter.add_axes()
plotter.show()
```
</details>
<p align="center"><img src="https://raw.githubusercontent.com/bjlittle/geovista-media/2024.07.0/media/readme/tamar.png" style="width: 75%; height: 75%"></p>
#### 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](https://proj.org/operations/projections/moll.html) using a
[PROJ](https://proj.org/index.html) string, with
[10m Natural Earth coastlines](https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-coastline/) and a
[1:50m Natural Earth Cross-Blended Hypsometric Tints](https://www.naturalearthdata.com/downloads/50m-raster-data/50m-cross-blend-hypso/)
base layer.
<details>
<summary>🗒 click for code</summary>
```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.
plotter = gv.GeoPlotter(crs="+proj=moll")
sargs = {"title": f"{sample.name} / {sample.units}"}
plotter.add_mesh(mesh, scalar_bar_args=sargs)
plotter.add_base_layer(texture=gv.natural_earth_hypsometric())
plotter.add_coastlines()
plotter.add_graticule()
plotter.add_axes()
plotter.view_xy()
plotter.show()
```
</details>
<p align="center"><img src="https://raw.githubusercontent.com/bjlittle/geovista-media/2024.07.0/media/readme/lam-moll.png" style="width: 75%; height: 75%"></p>
Using the same **unstructured** LAM data, reproject to
[Equidistant Cylindrical](https://proj.org/operations/projections/eqc.html) but this time using a
[Cartopy Plate Carrée CRS](https://scitools.org.uk/cartopy/docs/latest/reference/projections.html#cartopy.crs.PlateCarree),
also with [10m Natural Earth coastlines](https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-coastline/)
and a
[1:50m Natural Earth Cross-Blended Hypsometric Tints](https://www.naturalearthdata.com/downloads/50m-raster-data/50m-cross-blend-hypso/)
base layer.
<details>
<summary>🗒 click for code</summary>
```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.
plotter = gv.GeoPlotter(crs=ccrs.PlateCarree(central_longitude=180))
sargs = {"title": f"{sample.name} / {sample.units}"}
plotter.add_mesh(mesh, scalar_bar_args=sargs)
plotter.add_base_layer(texture=gv.natural_earth_hypsometric())
plotter.add_coastlines()
plotter.add_graticule()
plotter.add_axes()
plotter.view_xy()
plotter.show()
```
</details>
<p align="center"><img src="https://raw.githubusercontent.com/bjlittle/geovista-media/2024.07.0/media/readme/lam-eqc.png" style="width: 75%; height: 75%"></p>
#### LFRic Cube-Sphere
Now render a [Met Office LFRic](https://www.metoffice.gov.uk/research/approach/modelling-systems/lfric) C48 cube-sphere
**unstructured** mesh of Sea Surface Temperature data on a
[Robinson](https://proj.org/operations/projections/robin.html) projection using an ESRI SRID, with
[10m Natural Earth coastlines](https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-coastline/) and a
[cmocean thermal](https://matplotlib.org/cmocean/#thermal) colormap.
<details>
<summary>🗒 click for code</summary>
```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.
plotter = gv.GeoPlotter(crs="ESRI:54030")
sargs = {"title": f"{sample.name} / {sample.units}"}
plotter.add_mesh(mesh, cmap="thermal", show_edges=True, scalar_bar_args=sargs)
plotter.add_coastlines()
plotter.view_xy()
plotter.add_axes()
plotter.show()
```
</details>
<p align="center"><img src="https://raw.githubusercontent.com/bjlittle/geovista-media/2024.07.0/media/readme/lfric-robin.png" style="width: 75%; height: 75%"></p>
#### 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](https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-coastline/) and a
[1:50m Natural Earth I](https://www.naturalearthdata.com/downloads/50m-raster-data/50m-natural-earth-1/) base layer.
<details>
<summary>🗒 click for code</summary>
```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.
plotter = gv.GeoPlotter()
sargs = {"title": f"{sample.name} / {sample.units}"}
plotter.add_mesh(mesh, show_edges=True, scalar_bar_args=sargs)
plotter.add_base_layer(texture=gv.natural_earth_1())
plotter.add_coastlines()
plotter.view_xy()
plotter.add_axes()
plotter.show()
```
</details>
<p align="center"><img src="https://raw.githubusercontent.com/bjlittle/geovista-media/2024.07.0/media/readme/nemo-orca.png" style="width: 75%; height: 75%"></p>
#### OISST AVHRR
Now let's render a [NOAA/NCEI Optimum Interpolation SST](https://www.ncei.noaa.gov/products/optimum-interpolation-sst)
(OISST) Advanced Very High Resolution Radiometer (AVHRR) **rectilinear** mesh, with
[10m Natural Earth coastlines](https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-coastline/) and a
[NASA Blue Marble](https://visibleearth.nasa.gov/collection/1484/blue-marble) base layer.
<details>
<summary>🗒 click for code</summary>
```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.
plotter = gv.GeoPlotter()
sargs = {"title": f"{sample.name} / {sample.units}"}
plotter.add_mesh(mesh, scalar_bar_args=sargs)
plotter.add_base_layer(texture=gv.blue_marble())
plotter.add_coastlines()
plotter.view_xz()
plotter.add_axes()
plotter.show()
```
</details>
<p align="center"><img src="https://raw.githubusercontent.com/bjlittle/geovista-media/2024.07.0/media/readme/oisst-avhrr.png" style="width: 75%; height: 75%"></p>
#### DYNAMICO
Finally, to demonstrate support for non-traditional cell geometries i.e., not triangles or quadrilaterals, we plot
the **unstructured** mesh from the [DYNAMICO](https://gitlab.in2p3.fr/ipsl/projets/dynamico/dynamico) project. This
model uses hexagonal and pentagonal cells, and is a new dynamical core for
[LMD-Z](https://www.lmd.ipsl.fr/en/modelisations/lmdz-en/), the atmospheric General Circulation Model (GCM) part of the
[IPSL-CM](https://cmc.ipsl.fr/ipsl-climate-models/) Earth System Model. The render also contains
[10m Natural Earth coastlines](https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-coastline/).
<details>
<summary>🗒 click for code</summary>
```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.
plotter = gv.GeoPlotter()
sargs = {"title": f"{sample.name} / {sample.units}"}
plotter.add_mesh(mesh, scalar_bar_args=sargs)
plotter.add_coastlines()
plotter.add_axes()
plotter.show()
```
</details>
<p align="center"><img src="https://raw.githubusercontent.com/bjlittle/geovista-media/2024.07.0/media/readme/dynamico-icosahedral.png" style="width: 75%; height: 75%"></p>
## Further Examples
<p align="center">
"<em>Please, sir, I want some more</em>", Charles Dickens, Oliver Twist, 1838.
</p>
Certainly, our pleasure! From the command line, simply:
```bash
geovista examples --run all --verbose
```
Want to know more?
```shell
geovista examples --help
```
## Documentation
The [documentation](https://geovista.readthedocs.io/en/latest/) is built by [Sphinx](https://www.sphinx-doc.org/en/master/) and hosted on [Read the Docs](https://docs.readthedocs.io/en/stable/).
## Ecosystem
Whilst you're here, why not hop on over to the [pyvista-xarray](https://github.com/pyvista/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](https://github.com/pydata/xarray) community, and will be building on top of GeoVista 🎉
## Support
Need help? 😢
Why not check out our [existing GitHub issues](https://github.com/bjlittle/geovista/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](https://github.com/bjlittle/geovista/issues/new/choose) instead.
However, if you'd rather have a natter, then head on over to our
[GitHub Discussions](https://github.com/bjlittle/geovista/discussions). That's definitely the place to wax lyrical all
things GeoVista!
## License
GeoVista is distributed under the terms of the [BSD-3-Clause](https://spdx.org/licenses/BSD-3-Clause.html) license.
## Star History
[](https://star-history.com/#bjlittle/geovista&Date)
## [#ShowYourStripes](https://showyourstripes.info/s/globe)
<h4 align="center">
<a href="https://showyourstripes.info/s/globe">
<img src="https://raw.githubusercontent.com/ed-hawkins/show-your-stripes/master/2022/GLOBE---1850-2022-MO.png"
height="50" width="800"
alt="#showyourstripes Global 1850-2022"></a>
</h4>
**Graphics and Lead Scientist**: [Ed Hawkins](http://www.met.reading.ac.uk/~ed/home/index.php), National Centre for Atmospheric Science, University of Reading.
**Data**: Berkeley Earth, NOAA, UK Met Office, MeteoSwiss, DWD, SMHI, UoR, Meteo France & ZAMG.
<p>
<a href="https://showyourstripes.info/s/globe">#ShowYourStripes</a> is distributed under a
<a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>
<a href="https://creativecommons.org/licenses/by/4.0/">
<img src="https://i.creativecommons.org/l/by/4.0/80x15.png" alt="creative-commons-by" style="border-width:0"></a>
</p>
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"download_url": "https://files.pythonhosted.org/packages/16/7b/f61d5aab215868bb641abc3c29737086809c77474a2bc11dc2bdc61c11fd/geovista-0.5.3.tar.gz",
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"description": "<h1 align=\"center\">\n <a href=\"https://github.com/bjlittle/geovista#--------\">\n <img src=\"https://raw.githubusercontent.com/bjlittle/geovista-media/2024.07.0/media/branding/logo/primary/geovistalogo.svg\"\n alt=\"GeoVista\"\n width=\"200\"></a>\n</h1>\n\n<h3 align=\"center\">\n Cartographic rendering and mesh analytics powered by <a href=\"https://docs.pyvista.org/index.html\">PyVista</a>\n</h3>\n\n| | |\n|--------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| \u2699\ufe0f CI | [](https://github.com/bjlittle/geovista/actions/workflows/ci-citation.yml) [](https://github.com/bjlittle/geovista/actions/workflows/ci-locks.yml) [](https://github.com/bjlittle/geovista/actions/workflows/ci-manifest.yml) [](https://github.com/bjlittle/geovista/actions/workflows/ci-tests.yml) [](https://github.com/bjlittle/geovista/actions/workflows/ci-wheels.yml) [](https://results.pre-commit.ci/latest/github/bjlittle/geovista/main) |\n| \ud83d\udcac Community | [](https://github.com/bjlittle/geovista/blob/main/CODE_OF_CONDUCT.md) [](https://github.com/bjlittle/geovista/discussions) [](https://twitter.com/geovista_devs) [](https://www.youtube.com/@geovista_devs/videos) [](https://geovista.readthedocs.io/en/latest/reference/about.html#contributors) |\n| \ud83d\udcda Docs | [](https://geovista.readthedocs.io/en/latest/?badge=latest) |\n| \ud83d\udcc8 Health | [](https://codecov.io/gh/bjlittle/geovista) |\n| \u2728 Meta | [](https://github.com/astral-sh/ruff) [](https://numpy.org/neps/nep-0029-deprecation_policy.html) [](https://github.com/bjlittle/geovista/blob/main/LICENSE) [](https://anaconda.org/conda-forge/geovista) |\n| \ud83d\udce6 Package | [](https://doi.org/10.5281/zenodo.7608302) [](https://anaconda.org/conda-forge/geovista) [](https://pypi.org/project/geovista/) [](https://pypi.org/project/geovista/) |\n| \ud83e\uddf0 Repo | [](https://github.com/bjlittle/geovista/commits/main) [](https://github.com/bjlittle/geovista/graphs/contributors) [](https://github.com/bjlittle/geovista/releases) |\n| \ud83d\udee1\ufe0f Status | [](https://github.com/bjlittle/geovista/issues/167) |\n| | |\n\n## Rediscover Your Data\n\n[\ud83c\udfa5 WW3 SMC time-series](https://github.com/bjlittle/geovista/assets/2051656/876d877e-a6fa-42ff-8153-08c41ff8a19e)\n\nGeoVista is built on the shoulders of giants, namely [PyVista](https://docs.pyvista.org/version/stable/) and\n[VTK](https://vtk.org/documentation/), thus allowing it to easily leverage the power of the GPU.\n\nAs a result, it offers a paradigm shift in rendering performance and interactive user experience, as demonstrated by\nthis realtime, time-series animation of WAVEWATCH III\u00ae third-generation wave model (**WAVE**-height, **WAT**er depth\nand **C**urrent **H**indcasting) data developed at [NOAA](https://www.noaa.gov/)/[NCEP](https://www.weather.gov/ncep/).\n\nThe animation shows a time-series of Sea Surface Wave Significant Height data located on the cell faces of a\nquasi-unstructured Spherical Multi-Cell (SMC) grid.\n\nBring your data alive with GeoVista! \ud83d\ude80\n\nTempted? Keen to know more? Well, let's begin ...\n\n## Motivation\n\nThe goal of GeoVista is simple; to complement [PyVista](https://docs.pyvista.org/index.html) with a convenient\ncartographic capability.\n\nIn this regard, from a design perspective we aim to keep GeoVista as **pure** to PyVista as possible i.e.,\n**minimise specialisation** as far as practically possible in order to **maximise native compatibility** within the\nPyVista and [VTK](https://vtk.org/) ecosystems.\n\nWe intend GeoVista to be a cartographic gateway into the powerful world of PyVista, and all that it offers.\n\nGeoVista is intentionally agnostic to packages such as [geopandas](https://geopandas.org/en/stable/),\n[iris](https://scitools-iris.readthedocs.io/en/latest/?badge=latest), [xarray](https://docs.xarray.dev/en/stable/)\net al, which specialise in preparing your spatial data for visualisation. Rather, we delegate that responsibility and\nchoice of tool to you the user, as we want GeoVista to remain as flexible and open-ended as possible to the entire\nScientific Python community.\n\nSimply put, \"*[GeoVista](https://geovista.readthedocs.io/) is to\n[PyVista](https://docs.pyvista.org/)*\", as\n\"*[Cartopy](https://scitools.org.uk/cartopy/docs/latest/) is to\n[Matplotlib](https://matplotlib.org/)*\". Well, that's the aspiration.\n\n## Installation\n\nGeoVista is available on both [conda-forge](https://anaconda.org/conda-forge/geovista) and [PyPI](https://pypi.org/project/geovista/).\n\nWe recommend using [conda](https://docs.conda.io/projects/conda/en/latest/index.html) to install GeoVista \ud83d\udc4d\n\n### Conda\n\nGeoVista is available on [conda-forge](https://anaconda.org/conda-forge/geovista), and can be easily installed with\n[conda](https://docs.conda.io/projects/conda/en/latest/index.html):\n```shell\nconda install -c conda-forge geovista\n```\nFor more information see our [conda-forge feedstock](https://github.com/conda-forge/geovista-feedstock) and\n[prefix.dev dashboard](https://prefix.dev/channels/conda-forge/packages/geovista).\n\n### Pip\n\nGeoVista is also available on [PyPI](https://pypi.org/project/geovista/):\n\n```shell\npip install geovista\n```\n\nCheckout out our [PyPI Download Stats](https://pypistats.org/packages/geovista), if you like that kinda thing.\n\n## Quick Start\n\nGeoVista comes with various pre-canned resources to help get you started on your visualisation journey.\n\n### Resources\n\nGeoVista makes use of various resources, such as rasters, VTK meshes,\n[Natural Earth](https://www.naturalearthdata.com/features/) features, and sample model data.\n\nIf you want to download and cache all registered GeoVista resources to make them available offline, simply:\n```shell\ngeovista download --all\n```\nAlternatively, just leave GeoVista to download resources on-the-fly, as and when she needs them.\n\nTo view the list of registered resources, simply:\n```shell\ngeovista download --list\n```\n\nWant to know more?\n```shell\ngeovista download --help\n```\n\n### Plotting Examples\n\nLet's explore a sample of various oceanographic and atmospheric model data using GeoVista.\n\n#### WAVEWATCH III\n\nFirst, let's render a [WAVEWATCH III](https://github.com/NOAA-EMC/WW3) (WW3) **unstructured** triangular mesh, with\n[10m Natural Earth coastlines](https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-coastline/), a\n[1:50m Natural Earth Cross-Blended Hypsometric Tints](https://www.naturalearthdata.com/downloads/50m-raster-data/50m-cross-blend-hypso/)\nbase layer, and the gorgeous perceptually uniform [cmocean balance](https://matplotlib.org/cmocean/#balance) diverging\ncolormap.\n\n<details>\n<summary>\ud83d\uddd2 click for code</summary>\n\n```python\nimport geovista as gv\nfrom geovista.pantry.data import ww3_global_tri\nimport geovista.theme\n\n# Load the sample data.\nsample = ww3_global_tri()\n\n# Create the mesh from the sample data.\nmesh = gv.Transform.from_unstructured(\n sample.lons, sample.lats, connectivity=sample.connectivity, data=sample.data\n)\n\n# Plot the mesh.\nplotter = gv.GeoPlotter()\nsargs = {\"title\": f\"{sample.name} / {sample.units}\"}\nplotter.add_mesh(mesh, show_edges=True, scalar_bar_args=sargs)\nplotter.add_base_layer(texture=gv.natural_earth_hypsometric())\nplotter.add_coastlines()\nplotter.add_graticule()\nplotter.view_xy(negative=True)\nplotter.add_axes()\nplotter.show()\n```\n</details>\n\n<p align=\"center\"><img src=\"https://raw.githubusercontent.com/bjlittle/geovista-media/2024.07.0/media/readme/ww3-tri.png\" style=\"width: 75%; height: 75%\"></p>\n\n#### Finite Volume Community Ocean Model\n\nNow, let's visualise the bathymetry of the\n[Plymouth Sound and Tamar River](https://www.google.com/maps/place/Plymouth+Sound/@50.3337382,-4.2215988,12z/data=!4m5!3m4!1s0x486c93516bbce307:0xded7654eaf4f8f83!8m2!3d50.3638359!4d-4.1441365)\nfrom an [FVCOM](https://www.fvcom.org/) **unstructured** mesh, as kindly provided by the\n[Plymouth Marine Laboratory](https://pml.ac.uk/) using the lush [cmocean deep](https://matplotlib.org/cmocean/#deep) colormap.\n\n<details>\n<summary>\ud83d\uddd2 click for code</summary>\n\n```python\nimport geovista as gv\nfrom geovista.pantry.data import fvcom_tamar\nimport geovista.theme\n\n# Load the sample data.\nsample = fvcom_tamar()\n\n# Create the mesh from the sample data.\nmesh = gv.Transform.from_unstructured(\n sample.lons,\n sample.lats,\n connectivity=sample.connectivity,\n data=sample.face,\n name=\"face\",\n)\n\n# Warp the mesh nodes by the bathymetry.\nmesh.point_data[\"node\"] = sample.node\nmesh.compute_normals(cell_normals=False, point_normals=True, inplace=True)\nmesh.warp_by_scalar(scalars=\"node\", inplace=True, factor=2e-5)\n\n# Plot the mesh.\nplotter = gv.GeoPlotter()\nsargs = {\"title\": f\"{sample.name} / {sample.units}\"}\nplotter.add_mesh(mesh, cmap=\"deep\", scalar_bar_args=sargs)\nplotter.add_axes()\nplotter.show()\n```\n</details>\n\n<p align=\"center\"><img src=\"https://raw.githubusercontent.com/bjlittle/geovista-media/2024.07.0/media/readme/tamar.png\" style=\"width: 75%; height: 75%\"></p>\n\n#### CF UGRID\n\n##### Local Area Model\n\nInitial projection support is available within GeoVista for **Cylindrical** and **Pseudo-Cylindrical** projections. As\nGeoVista matures and stabilises, we'll aim to complement this capability with other classes of projections, such as\n**Azimuthal** and **Conic**.\n\nIn the meantime, let's showcase our basic projection support with some high-resolution **unstructured** Local Area Model\n(LAM) data reprojected to [Mollweide](https://proj.org/operations/projections/moll.html) using a\n[PROJ](https://proj.org/index.html) string, with\n[10m Natural Earth coastlines](https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-coastline/) and a\n[1:50m Natural Earth Cross-Blended Hypsometric Tints](https://www.naturalearthdata.com/downloads/50m-raster-data/50m-cross-blend-hypso/)\nbase layer.\n\n<details>\n<summary>\ud83d\uddd2 click for code</summary>\n\n```python\nimport geovista as gv\nfrom geovista.pantry.data import lam_pacific\nimport geovista.theme\n\n# Load the sample data.\nsample = lam_pacific()\n\n# Create the mesh from the sample data.\nmesh = gv.Transform.from_unstructured(\n sample.lons,\n sample.lats,\n connectivity=sample.connectivity,\n data=sample.data,\n)\n\n# Plot the mesh on a mollweide projection using a Proj string.\nplotter = gv.GeoPlotter(crs=\"+proj=moll\")\nsargs = {\"title\": f\"{sample.name} / {sample.units}\"}\nplotter.add_mesh(mesh, scalar_bar_args=sargs)\nplotter.add_base_layer(texture=gv.natural_earth_hypsometric())\nplotter.add_coastlines()\nplotter.add_graticule()\nplotter.add_axes()\nplotter.view_xy()\nplotter.show()\n```\n</details>\n\n<p align=\"center\"><img src=\"https://raw.githubusercontent.com/bjlittle/geovista-media/2024.07.0/media/readme/lam-moll.png\" style=\"width: 75%; height: 75%\"></p>\n\nUsing the same **unstructured** LAM data, reproject to\n[Equidistant Cylindrical](https://proj.org/operations/projections/eqc.html) but this time using a\n[Cartopy Plate Carr\u00e9e CRS](https://scitools.org.uk/cartopy/docs/latest/reference/projections.html#cartopy.crs.PlateCarree),\nalso with [10m Natural Earth coastlines](https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-coastline/)\nand a\n[1:50m Natural Earth Cross-Blended Hypsometric Tints](https://www.naturalearthdata.com/downloads/50m-raster-data/50m-cross-blend-hypso/)\nbase layer.\n\n<details>\n<summary>\ud83d\uddd2 click for code</summary>\n\n```python\nimport cartopy.crs as ccrs\n\nimport geovista as gv\nfrom geovista.pantry.data import lam_pacific\nimport geovista.theme\n\n# Load the sample data.\nsample = lam_pacific()\n\n# Create the mesh from the sample data.\nmesh = gv.Transform.from_unstructured(\n sample.lons,\n sample.lats,\n connectivity=sample.connectivity,\n data=sample.data,\n)\n\n# Plot the mesh on a Plate Carr\u00e9e projection using a cartopy CRS.\nplotter = gv.GeoPlotter(crs=ccrs.PlateCarree(central_longitude=180))\nsargs = {\"title\": f\"{sample.name} / {sample.units}\"}\nplotter.add_mesh(mesh, scalar_bar_args=sargs)\nplotter.add_base_layer(texture=gv.natural_earth_hypsometric())\nplotter.add_coastlines()\nplotter.add_graticule()\nplotter.add_axes()\nplotter.view_xy()\nplotter.show()\n```\n</details>\n\n<p align=\"center\"><img src=\"https://raw.githubusercontent.com/bjlittle/geovista-media/2024.07.0/media/readme/lam-eqc.png\" style=\"width: 75%; height: 75%\"></p>\n\n#### LFRic Cube-Sphere\n\nNow render a [Met Office LFRic](https://www.metoffice.gov.uk/research/approach/modelling-systems/lfric) C48 cube-sphere\n**unstructured** mesh of Sea Surface Temperature data on a\n[Robinson](https://proj.org/operations/projections/robin.html) projection using an ESRI SRID, with\n[10m Natural Earth coastlines](https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-coastline/) and a\n[cmocean thermal](https://matplotlib.org/cmocean/#thermal) colormap.\n\n<details>\n<summary>\ud83d\uddd2 click for code</summary>\n\n```python\nimport geovista as gv\nfrom geovista.pantry.data import lfric_sst\nimport geovista.theme\n\n# Load the sample data.\nsample = lfric_sst()\n\n# Create the mesh from the sample data.\nmesh = gv.Transform.from_unstructured(\n sample.lons,\n sample.lats,\n connectivity=sample.connectivity,\n data=sample.data,\n)\n\n# Plot the mesh on a Robinson projection using an ESRI spatial reference identifier.\nplotter = gv.GeoPlotter(crs=\"ESRI:54030\")\nsargs = {\"title\": f\"{sample.name} / {sample.units}\"}\nplotter.add_mesh(mesh, cmap=\"thermal\", show_edges=True, scalar_bar_args=sargs)\nplotter.add_coastlines()\nplotter.view_xy()\nplotter.add_axes()\nplotter.show()\n```\n</details>\n\n<p align=\"center\"><img src=\"https://raw.githubusercontent.com/bjlittle/geovista-media/2024.07.0/media/readme/lfric-robin.png\" style=\"width: 75%; height: 75%\"></p>\n\n#### NEMO ORCA2\n\nSo far we've demonstrated GeoVista's ability to cope with **unstructured** data. Now let's plot a **curvilinear** mesh\nusing Nucleus for European Modelling of the Ocean (NEMO) ORCA2 Sea Water Potential Temperature data, with\n[10m Natural Earth coastlines](https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-coastline/) and a\n[1:50m Natural Earth I](https://www.naturalearthdata.com/downloads/50m-raster-data/50m-natural-earth-1/) base layer.\n\n<details>\n<summary>\ud83d\uddd2 click for code</summary>\n\n```python\nimport geovista as gv\nfrom geovista.pantry.data import nemo_orca2\nimport geovista.theme\n\n# Load sample data.\nsample = nemo_orca2()\n\n# Create the mesh from the sample data.\nmesh = gv.Transform.from_2d(sample.lons, sample.lats, data=sample.data)\n\n# Remove cells from the mesh with NaN values.\nmesh = mesh.threshold()\n\n# Plot the mesh.\nplotter = gv.GeoPlotter()\nsargs = {\"title\": f\"{sample.name} / {sample.units}\"}\nplotter.add_mesh(mesh, show_edges=True, scalar_bar_args=sargs)\nplotter.add_base_layer(texture=gv.natural_earth_1())\nplotter.add_coastlines()\nplotter.view_xy()\nplotter.add_axes()\nplotter.show()\n```\n</details>\n\n<p align=\"center\"><img src=\"https://raw.githubusercontent.com/bjlittle/geovista-media/2024.07.0/media/readme/nemo-orca.png\" style=\"width: 75%; height: 75%\"></p>\n\n#### OISST AVHRR\n\nNow let's render a [NOAA/NCEI Optimum Interpolation SST](https://www.ncei.noaa.gov/products/optimum-interpolation-sst)\n(OISST) Advanced Very High Resolution Radiometer (AVHRR) **rectilinear** mesh, with\n[10m Natural Earth coastlines](https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-coastline/) and a\n[NASA Blue Marble](https://visibleearth.nasa.gov/collection/1484/blue-marble) base layer.\n\n<details>\n<summary>\ud83d\uddd2 click for code</summary>\n\n```python\nimport geovista as gv\nfrom geovista.pantry.data import oisst_avhrr_sst\nimport geovista.theme\n\n# Load sample data.\nsample = oisst_avhrr_sst()\n\n# Create the mesh from the sample data.\nmesh = gv.Transform.from_1d(sample.lons, sample.lats, data=sample.data)\n\n# Remove cells from the mesh with NaN values.\nmesh = mesh.threshold()\n\n# Plot the mesh.\nplotter = gv.GeoPlotter()\nsargs = {\"title\": f\"{sample.name} / {sample.units}\"}\nplotter.add_mesh(mesh, scalar_bar_args=sargs)\nplotter.add_base_layer(texture=gv.blue_marble())\nplotter.add_coastlines()\nplotter.view_xz()\nplotter.add_axes()\nplotter.show()\n```\n</details>\n\n<p align=\"center\"><img src=\"https://raw.githubusercontent.com/bjlittle/geovista-media/2024.07.0/media/readme/oisst-avhrr.png\" style=\"width: 75%; height: 75%\"></p>\n\n#### DYNAMICO\n\nFinally, to demonstrate support for non-traditional cell geometries i.e., not triangles or quadrilaterals, we plot\nthe **unstructured** mesh from the [DYNAMICO](https://gitlab.in2p3.fr/ipsl/projets/dynamico/dynamico) project. This\nmodel uses hexagonal and pentagonal cells, and is a new dynamical core for\n[LMD-Z](https://www.lmd.ipsl.fr/en/modelisations/lmdz-en/), the atmospheric General Circulation Model (GCM) part of the\n[IPSL-CM](https://cmc.ipsl.fr/ipsl-climate-models/) Earth System Model. The render also contains\n[10m Natural Earth coastlines](https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-coastline/).\n\n<details>\n<summary>\ud83d\uddd2 click for code</summary>\n\n```python\nimport geovista as gv\nfrom geovista.pantry.data import dynamico\nimport geovista.theme\n\n# Load sample data.\nsample = dynamico()\n\n# Create the mesh from the sample data.\nmesh = gv.Transform.from_unstructured(sample.lons, sample.lats, data=sample.data)\n\n# Plot the mesh.\nplotter = gv.GeoPlotter()\nsargs = {\"title\": f\"{sample.name} / {sample.units}\"}\nplotter.add_mesh(mesh, scalar_bar_args=sargs)\nplotter.add_coastlines()\nplotter.add_axes()\nplotter.show()\n```\n</details>\n\n<p align=\"center\"><img src=\"https://raw.githubusercontent.com/bjlittle/geovista-media/2024.07.0/media/readme/dynamico-icosahedral.png\" style=\"width: 75%; height: 75%\"></p>\n\n## Further Examples\n\n<p align=\"center\">\n\"<em>Please, sir, I want some more</em>\", Charles Dickens, Oliver Twist, 1838.\n</p>\n\nCertainly, our pleasure! From the command line, simply:\n\n```bash\ngeovista examples --run all --verbose\n```\n\nWant to know more?\n```shell\ngeovista examples --help\n```\n\n\n## Documentation\n\nThe [documentation](https://geovista.readthedocs.io/en/latest/) is built by [Sphinx](https://www.sphinx-doc.org/en/master/) and hosted on [Read the Docs](https://docs.readthedocs.io/en/stable/).\n\n\n## Ecosystem\n\nWhilst you're here, why not hop on over to the [pyvista-xarray](https://github.com/pyvista/pyvista-xarray) project and\ncheck it out!\n\nIt's aiming to provide `xarray DataArray accessors for PyVista to visualize datasets in 3D` for the\n[xarray](https://github.com/pydata/xarray) community, and will be building on top of GeoVista \ud83c\udf89\n\n## Support\n\nNeed help? \ud83d\ude22\n\nWhy not check out our [existing GitHub issues](https://github.com/bjlittle/geovista/issues). See something similar?\nWell, give it a \ud83d\udc4d to raise its priority and feel free to chip in on the conversation. Otherwise, don't hesitate to\ncreate a [new GitHub issue](https://github.com/bjlittle/geovista/issues/new/choose) instead.\n\nHowever, if you'd rather have a natter, then head on over to our\n[GitHub Discussions](https://github.com/bjlittle/geovista/discussions). That's definitely the place to wax lyrical all\nthings GeoVista!\n\n\n## License\n\nGeoVista is distributed under the terms of the [BSD-3-Clause](https://spdx.org/licenses/BSD-3-Clause.html) license.\n\n\n## Star History\n\n[](https://star-history.com/#bjlittle/geovista&Date)\n\n\n## [#ShowYourStripes](https://showyourstripes.info/s/globe)\n\n<h4 align=\"center\">\n <a href=\"https://showyourstripes.info/s/globe\">\n <img src=\"https://raw.githubusercontent.com/ed-hawkins/show-your-stripes/master/2022/GLOBE---1850-2022-MO.png\"\n height=\"50\" width=\"800\"\n alt=\"#showyourstripes Global 1850-2022\"></a>\n</h4>\n\n**Graphics and Lead Scientist**: [Ed Hawkins](http://www.met.reading.ac.uk/~ed/home/index.php), National Centre for Atmospheric Science, University of Reading.\n\n**Data**: Berkeley Earth, NOAA, UK Met Office, MeteoSwiss, DWD, SMHI, UoR, Meteo France & ZAMG.\n\n<p>\n<a href=\"https://showyourstripes.info/s/globe\">#ShowYourStripes</a> is distributed under a\n<a href=\"https://creativecommons.org/licenses/by/4.0/\">Creative Commons Attribution 4.0 International License</a>\n<a href=\"https://creativecommons.org/licenses/by/4.0/\">\n <img src=\"https://i.creativecommons.org/l/by/4.0/80x15.png\" alt=\"creative-commons-by\" style=\"border-width:0\"></a>\n</p>\n",
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"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. ",
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