Metadata-Version: 2.1
Name: datashader
Version: 0.6.8
Summary: Data visualization toolchain based on aggregating into a grid
Home-page: http://datashader.org
Maintainer: Datashader developers
Maintainer-email: dev@datashader.org
License: New BSD
Description: Datashader
        ----------
        
        [![Travis build Status](https://travis-ci.org/pyviz/datashader.svg?branch=master)](https://travis-ci.org/pyviz/datashader)
        [![Windows build status](https://ci.appveyor.com/api/projects/status/uc7atn5y35ay38eb/branch/master?svg=true)](https://ci.appveyor.com/project/pyviz/datashader/branch/master)
        [![Task Status](https://badge.waffle.io/pyviz/datashader.png?label=ready&title=tasks)](https://waffle.io/pyviz/datashader)
        
        
        Datashader is a data rasterization pipeline for automating the process of
        creating meaningful representations of large amounts of data. Datashader
        breaks the creation of images of data into 3 main steps:
        
        1. Projection
        
           Each record is projected into zero or more bins of a nominal plotting grid
           shape, based on a specified glyph.
        
        2. Aggregation
        
           Reductions are computed for each bin, compressing the potentially large
           dataset into a much smaller *aggregate* array.
        
        3. Transformation
        
           These aggregates are then further processed, eventually creating an image.
        
        Using this very general pipeline, many interesting data visualizations can be
        created in a performant and scalable way. Datashader contains tools for easily
        creating these pipelines in a composable manner, using only a few lines of code.
        Datashader can be used on its own, but it is also designed to work as
        a pre-processing stage in a plotting library, allowing that library
        to work with much larger datasets than it would otherwise.
        
        
        ## Installation
        
        The best way to get started with Datashader is install it together
        with our extensive set of examples, following the instructions in the
        [examples README](/examples/README.md).
        
        If all you need is datashader itself, without any of the files used in
        the examples, you can install it via 
        [conda](https://conda.io/docs/install/quick.html) or 
        [pip](https://pip.pypa.io/en/stable/installing/):
        
        
        ```bash
        conda install datashader
        ```
        
        or 
        
        ```
        pip install datashader
        ```
        
        For the best performance, we recommend using conda so that you are
        sure to get numerical libraries optimized for your platform.
        
        If you want the latest unreleased changes (e.g. to edit the source code
        yourself), first install datashader as above, but then clone the source 
        code and tell Python to use the clone instead:
        
        ```bash
        conda remove --force datashader
        git clone https://github.com/pyviz/datashader.git
        cd datashader
        pip install -e .
        ```
        
        To run the test suite, first `conda install pytest` or
        `pip install pytest`, then run `py.test datashader` in your
        datashader source directory.
        
        ## Learning more
        
        After working through the examples, you can find additional resources linked
        from the [datashader documentation](http://datashader.org),
        including API documentation and papers and talks about the approach.
        
        ## Screenshots
        
        ![USA census](examples/assets/images/usa_census.jpg)
        
        ![NYC races](examples/assets/images/nyc_races.jpg)
        
        ![NYC taxi](examples/assets/images/nyc_pickups_vs_dropoffs.jpg)
        
Platform: UNKNOWN
Requires-Python: >=2.7
Description-Content-Type: text/markdown
Provides-Extra: tests
Provides-Extra: examples
Provides-Extra: doc
Provides-Extra: all
Provides-Extra: examples_extra
