Geographic visualizations for HoloViews.

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GeoViews is a Python library that makes it easy to explore and visualize any data that includes geographic locations. It has particularly powerful support for multidimensional meteorological and oceanographic datasets, such as those used in weather, climate, and remote sensing research, but is useful for almost anything that you would want to plot on a map! You can see lots of example notebooks at geoviews.org, and a good overview is in our blog post announcement.

GeoViews is built on the HoloViews library for building flexible visualizations of multidimensional data. GeoViews adds a family of geographic plot types based on the Cartopy library, plotted using either the Matplotlib or Bokeh packages.

Each of the new GeoElement plot types is a new HoloViews Element that has an associated geographic projection based on cartopy.crs. The GeoElements currently include Feature, WMTS, Tiles, Points, Contours, Image, QuadMesh, TriMesh, RGB, HSV, Labels, Graph, HexTiles, VectorField and Text objects, each of which can easily be overlaid in the same plots. E.g. an object with temperature data can be overlaid with coastline data using an expression like gv.Image(temperature) * gv.Feature(cartopy.feature.COASTLINE). Each GeoElement can also be freely combined in layouts with any other HoloViews Element , making it simple to make even complex multi-figure layouts of overlaid objects.

If you want the latest GeoViews, you will need an up-to-date environment. Updating is never risk-free, but it is a good idea in general and the commands conda list --revisions and conda install --revision N can usually recover from updates gone awry.

conda update --all

You can then install GeoViews and all of its dependencies with the following:

conda install -c pyviz geoviews

Alternatively you can install the geoviews-core package, which only installs the minimal dependencies required to run geoviews:

conda install -c pyviz geoviews-core

Once installed you can copy the examples into the current directory using the geoviews command and run them using the Jupyter notebook:

geoviews examples
cd geoviews-examples
jupyter notebook

(Here geoviews examples is a shorthand for geoviews copy-examples --path geoviews-examples && geoviews fetch-data --path geoviews-examples.)

To work with JupyterLab you will also need the PyViz JupyterLab extension:

conda install -c conda-forge jupyterlab
jupyter labextension install @pyviz/jupyterlab_pyviz

Once you have installed JupyterLab and the extension launch it with:

jupyter-lab

If you want to try out the latest features between releases, you can get the latest dev release by specifying -c pyviz/label/dev in place of -c pyviz.

If you need to install libraries only available from conda-forge, such as Iris (to use data stored in Iris cubes) or xesmf, you should install from conda-forge:

conda create -n env-name -c pyviz -c conda-forge geoviews iris xesmf
conda activate env-name

GeoViews itself is also installable using pip, but to do that you will first need to have installed the dependencies of cartopy, or else have set up your system to be able to build them.

GeoViews is part of the HoloViz ecosystem, which strives to make browser-based data visualization in Python easier to use, easier to learn, and more powerful. See holoviz.org for related packages that you can use with GeoViews and status.holoviz.org for the current status of projects.