geoviews 1.15.2a0
GeoViews is a Python library that makes it easy to explore and visualize geographical, meteorological, and oceanographic datasets, such as those used in weather, climate, and remote sensing research.
Requires Python: >=3.10

<img src="https://raw.githubusercontent.com/holoviz/geoviews/refs/heads/main/doc/_static/logo_stacked.png" width="200"/><br>

-----------------

**Geographic visualizations for HoloViews.**

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## What is it?

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](https://geoviews.org), and a good
overview is in our [blog post announcement](https://www.continuum.io/blog/developer-blog/introducing-geoviews).

GeoViews is built on the [HoloViews](https://holoviews.org) library for
building flexible visualizations of multidimensional data.  GeoViews
adds a family of geographic plot types based on the
[Cartopy](http://scitools.org.uk/cartopy) library, plotted using
either the [Matplotlib](http://matplotlib.org) or
[Bokeh](https://bokeh.org) 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.

## Installation

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
```

In certain circumstances proj6 issues may prevent installation or
cause issues (particularly with cartopy<=0.17). If you encounter these
issues ensure you also pin proj4::

    conda install proj4<6

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`.)

In the classic Jupyter notebook environment and JupyterLab, first make sure to load the `gv.extension()`. GeoViews objects will then render themselves if they are the last item in a notebook cell. For versions of `jupyterlab>=3.0` the necessary extension is automatically bundled in the `pyviz_comms` package, which must be >=2.0. However note that for version of `jupyterlab<3.0` you must also manually install the JupyterLab extension with:

```bash
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`.

### Additional dependencies

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](http://scitools.org.uk/cartopy/docs/latest/installing.html#requirements),
or else have set up your system to be able to build them.