Metadata-Version: 2.1
Name: geoviews
Version: 1.5.1
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
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        <h1>
        GeoViews <img src="/doc/_static/geoviews-logo.png" width="50" height="50">
        </h1>
        
        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 [geo.holoviews.org](http://geo.holoviews.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](http://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](http://bokeh.pydata.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
        ```
        
        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
        ```
        
        **Note -- Do not mix conda-forge and defaults.** I.e., do not install
        packages from conda-forge into a GeoViews environment created with
        defaults. If you are using the base environment of mini/anaconda, or
        an environment created without specifying conda-forge before defaults,
        and you then install from conda-forge, you will very likely have
        incompatibilities in underlying, low-level dependencies. These binary
        (ABI) incompatibilities can lead to segfaults because of differences
        in how non-Python packages are built between conda-forge and defaults.
        
        -----
        
        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/v0.15/installing.html#requirements),
        or else have set up your system to be able to build them.
        
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: all
Provides-Extra: build
Provides-Extra: recommended
Provides-Extra: examples_extra
Provides-Extra: tests
Provides-Extra: doc
