# Introduction ¶

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.

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  , 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.

With GeoViews, you can now work easily and naturally with large, multidimensional geographic datasets, instantly visualizing any subset or combination of them, while always being able to access the raw data underlying any plot. Here's a simple example:

In [1]:
import holoviews as hv
import geoviews as gv
import geoviews.feature as gf
import xarray as xr
from cartopy import crs

hv.notebook_extension()

In [2]:
%%opts Feature [projection=crs.Geostationary()]
(gf.ocean + gf.land + gf.ocean * gf.land * gf.coastline * gf.borders).cols(3)

Out[2]:

GeoViews is designed to work well with the Iris and xarray libraries for working with multidimensional arrays, such as those stored in netCDF files. GeoViews also accepts data as NumPy arrays and Pandas data frames. In each case, the data can be left stored in its original, native format, wrapped in a HoloViews or GeoViews object that provides instant interactive visualizations.

The following example loads a dataset originally taken from iris-sample-data and quickly builds an interactive tool for exploring how the data changes over time:

In [3]:
%%opts Image [colorbar=True fig_size=200] (cmap='viridis')
ensemble = xr.open_dataset('./sample-data/ensemble.nc')
dataset = gv.Dataset(ensemble, kdims=['longitude', 'latitude', 'time'], crs=crs.PlateCarree())
dataset.to(gv.Image, ['longitude', 'latitude'], ['surface_temperature'], ['time']) * gf.coastline()

Out[3]:

## Installation ¶

You can then install GeoViews and its other dependencies using conda, many users will want iris and/or xarray as well:

conda install -c conda-forge -c ioam holoviews geoviews
# (Optional)
conda install xarray
conda install -c conda-forge iris


You can now switch to your preferred working directory, grab a copy of the notebooks to run locally, and run them using the Jupyter notebook:

cd ~
python -c 'import geoviews; geoviews.examples("geoviews-examples",include_data=True)'
cd geoviews-examples
jupyter notebook


# Support ¶

GeoViews was developed through a collaboration between Continuum Analytics and the Met Office . GeoViews is completely open source , available under a BSD license freely for both commercial and non-commercial use. Please file bug reports and feature requests on our github site .