Metadata-Version: 2.1
Name: geoviews
Version: 1.6.6
Summary: 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.
Home-page: http://geoviews.org
License: BSD 3-Clause
Description: <img src="/doc/_static/logo_stacked.png" width="200"/><br>
        
        -----------------
        
        **Geographic visualizations for HoloViews.**
        
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        | --- | --- |
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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
        
        You can install GeoViews and its dependencies using conda:
        
        ```
        conda install -c pyviz geoviews
        ```
        
        Alternatively you can also 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`.
        
        ### 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.
        
        
        ## About HoloViz
        
        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](http://holoviz.org) for related packages that you can use with GeoViews and
        [status.pyviz.org](http://status.pyviz.org) for the current status of projects.
        
Platform: Windows
Platform: Mac OS X
Platform: Linux
Classifier: License :: OSI Approved :: BSD License
Classifier: Development Status :: 5 - Production/Stable
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development :: Libraries
Requires-Python: >=2.7
Description-Content-Type: text/markdown
Provides-Extra: recommended
Provides-Extra: examples_extra
Provides-Extra: doc
Provides-Extra: tests
Provides-Extra: all
Provides-Extra: build
