diff --git a/README.md b/README.md index b9b33802..186b1b3b 100644 --- a/README.md +++ b/README.md @@ -9,6 +9,26 @@ Cartographic rendering and mesh analytics powered by PyVista +

+ 😍 Bring your data to life! 😍 +

+ +[🎥 WW3 SMC time-series](https://github.com/bjlittle/geovista/assets/2051656/876d877e-a6fa-42ff-8153-08c41ff8a19e) + +
What is this? + +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), developed at [NOAA](https://www.noaa.gov/)/[NCEP](https://www.weather.gov/ncep/), +quasi-unstructured Spherical Multi-Cell (SMC) grid data of Sea Surface Wave Significant Height located on cell faces. + +Like what you see? Loads more information is available below, so keep on scrolling! 🚀 + +
+ | | | |--------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| @@ -22,7 +42,6 @@ | 🛡️ Status | [![scitools](https://img.shields.io/badge/scitools-ownership%20pending-yellow)](https://github.com/bjlittle/geovista/issues/167) | | | | - ## Motivation The goal of GeoVista is simple; to complement [PyVista](https://docs.pyvista.org/index.html) with a convenient @@ -40,22 +59,21 @@ et al, which specialise in preparing your spatial data for visualisation. Rather 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. +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 [mamba](https://github.com/mamba-org/mamba) to install GeoVista 👍 +We recommend using [conda](https://docs.conda.io/projects/conda/en/latest/index.html) to install GeoVista 👍 -### Mamba +### Conda GeoVista is available on [conda-forge](https://anaconda.org/conda-forge/geovista), and can be easily installed with -[mamba](https://github.com/mamba-org/mamba): -```shell -mamba install -c conda-forge geovista -``` -or alternatively with [conda](https://docs.conda.io/projects/conda/en/latest/index.html): +[conda](https://docs.conda.io/projects/conda/en/latest/index.html): ```shell conda install -c conda-forge geovista ``` @@ -90,7 +108,7 @@ cd geovista ``` Create the `geovista-dev` conda development environment: ```shell -mamba env create --file requirements/geovista.yml +conda env create --file requirements/geovista.yml ``` Now activate the environment and install the `main` development branch of GeoVista: ```shell @@ -142,7 +160,7 @@ base layer, and the gorgeous perceptually uniform [cmocean balance](https://matp colormap.
-🗒 +🗒 click for code ```python import geovista as gv @@ -170,17 +188,17 @@ plotter.show() ```
-![ww3-tri](https://raw.githubusercontent.com/bjlittle/geovista-media/2023.09.1/media/readme/ww3-tri.png) +

#### 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](http://fvcom.smast.umassd.edu/fvcom/) **unstructured** mesh, as kindly provided by the +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.
-🗒 +🗒 click for code ```python import geovista as gv @@ -213,7 +231,7 @@ plotter.show() ```
-![tamar](https://raw.githubusercontent.com/bjlittle/geovista-media/2023.09.1/media/readme/tamar.png) +

#### CF UGRID @@ -231,7 +249,7 @@ In the meantime, let's showcase our basic projection support with some high-reso base layer.
-🗒 +🗒 click for code ```python import geovista as gv @@ -262,7 +280,7 @@ plotter.show() ```
-![lam-mollweide](https://raw.githubusercontent.com/bjlittle/geovista-media/2023.09.1/media/readme/lam-moll.png) +

Using the same **unstructured** LAM data, reproject to [Equidistant Cylindrical](https://proj.org/operations/projections/eqc.html) but this time using a @@ -273,7 +291,7 @@ and a base layer.
-🗒 +🗒 click for code ```python import cartopy.crs as ccrs @@ -306,7 +324,7 @@ plotter.show() ```
-![lam-mollweide](https://raw.githubusercontent.com/bjlittle/geovista-media/2023.09.1/media/readme/lam-eqc.png) +

#### LFRic Cube-Sphere @@ -317,7 +335,7 @@ Now render a [Met Office LFRic](https://www.metoffice.gov.uk/research/approach/m [cmocean thermal](https://matplotlib.org/cmocean/#thermal) colormap.
-🗒 +🗒 click for code ```python import geovista as gv @@ -346,7 +364,7 @@ plotter.show() ```
-![lam-mollweide](https://raw.githubusercontent.com/bjlittle/geovista-media/2023.09.1/media/readme/lfric-robin.png) +

#### UM ORCA2 @@ -356,7 +374,7 @@ using Met Office Unified Model (UM) ORCA2 Sea Water Potential Temperature data, [1:50m Natural Earth I](https://www.naturalearthdata.com/downloads/50m-raster-data/50m-natural-earth-1/) base layer.
-🗒 +🗒 click for code ```python import geovista as gv @@ -384,7 +402,7 @@ plotter.show() ```
-![um-orca](https://raw.githubusercontent.com/bjlittle/geovista-media/2023.09.1/media/readme/um-orca.png) +

#### OISST AVHRR @@ -394,7 +412,7 @@ Now let's render a [NOAA/NCEI Optimum Interpolation SST](https://www.ncei.noaa.g [NASA Blue Marble](https://visibleearth.nasa.gov/collection/1484/blue-marble) base layer.
-🗒 +🗒 click for code ```python import geovista as gv @@ -422,7 +440,7 @@ plotter.show() ```
-![oisst-avhrr](https://raw.githubusercontent.com/bjlittle/geovista-media/2023.09.1/media/readme/oisst-avhrr.png) +

#### DYNAMICO @@ -434,7 +452,7 @@ model uses hexagonal and pentagonal cells, and is a new dynamical core for [10m Natural Earth coastlines](https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-coastline/).
-🗒 +🗒 click for code ```python import geovista as gv @@ -457,19 +475,7 @@ plotter.show() ```
-![dynamico-icosahedral](https://raw.githubusercontent.com/bjlittle/geovista-media/2023.09.1/media/readme/dynamico-icosahedral.png) - -## Unreal Reels - -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), developed at [NOAA](https://www.noaa.gov/)/[NCEP](https://www.weather.gov/ncep/), -quasi-unstructured Spherical Multi-Cell (SMC) grid data of Sea Surface Wave Significant Height located on cell faces. - -[🎥 WW3 SMC time-series](https://github.com/bjlittle/geovista/assets/2051656/876d877e-a6fa-42ff-8153-08c41ff8a19e) +

## Further Examples