Metadata-Version: 2.1
Name: datashader
Version: 0.7.0
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: <img src="https://github.com/pyviz/datashader/raw/master/doc/_static/logo_stacked.png" data-canonical-src="https://github.com/pyviz/datashader/raw/master/doc/_static/logo_stacked.png" width="200"/><br>
        
        -----------------
        
        # Turn even the largest data into images, accurately
        
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        | --- | --- |
        | Build Status | [![Linux/MacOS Build Status](https://travis-ci.org/pyviz/datashader.svg?branch=master)](https://travis-ci.org/pyviz/datashader) [![Windows Build status](https://img.shields.io/appveyor/ci/pyviz/datashader/master.svg?logo=appveyor)](https://ci.appveyor.com/project/pyviz/datashader/branch/master) |
        | Coverage | [![codecov](https://codecov.io/gh/pyviz/datashader/branch/master/graph/badge.svg)](https://codecov.io/gh/pyviz/datashader) |
        | Latest dev release | [![Github tag](https://img.shields.io/github/tag/pyviz/datashader.svg?label=tag&colorB=11ccbb)](https://github.com/pyviz/datashader/tags) |
        | Latest release | [![Github release](https://img.shields.io/github/release/pyviz/datashader.svg?label=tag&colorB=11ccbb)](https://github.com/pyviz/datashader/releases) [![PyPI version](https://img.shields.io/pypi/v/datashader.svg?colorB=cc77dd)](https://pypi.python.org/pypi/datashader) [![datashader version](https://img.shields.io/conda/v/pyviz/datashader.svg?colorB=4488ff&style=flat)](https://anaconda.org/pyviz/datashader) [![conda-forge version](https://img.shields.io/conda/v/conda-forge/datashader.svg?label=conda%7Cconda-forge&colorB=4488ff)](https://anaconda.org/conda-forge/datashader) [![defaults version](https://img.shields.io/conda/v/anaconda/datashader.svg?label=conda%7Cdefaults&style=flat&colorB=4488ff)](https://anaconda.org/anaconda/datashader) |
        | Docs | [![site](https://img.shields.io/website-up-down-green-red/http/datashader.org.svg)](http://datashader.org) |
        
        
        ## What is it?
        
        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
        
        Datashader supports Python 2.7, 3.5, 3.6 and 3.7 on Linux, Windows, or
        Mac and can be installed with conda:
        
            conda install datashader
        
        or with pip:
        
            pip install datashader
        
        For the best performance, we recommend using conda so that you are sure
        to get numerical libraries optimized for your platform. The lastest
        releases are avalailable on the pyviz channel `conda install -c pyviz
        datashader` and the latest pre-release versions are avalailable on the
        dev-labelled channel `conda install -c pyviz/label/dev datashader`.
        
        ## Fetching Examples
        
        Once you've installed datashader as above you can fetch the examples:
        
            datashader examples
            cd datashader-examples
        
        This will create a new directory called
        <span class="title-ref">datashader-examples</span> with all the data
        needed to run the examples.
        
        To run all the examples you will need some extra dependencies. If you
        installed datashader **within a conda environment**, with that
        environment active run:
        
            conda env update --file environment.yml
        
        Otherwise create a new environment:
        
            conda env create --name datashader --file environment.yml
            conda activate datashader
        
        ## Developer Instructions
        
        1.  Install Python 3
            [miniconda](https://docs.conda.io/en/latest/miniconda.html) or
            [anaconda](https://www.anaconda.com/distribution/), if you don't
            already have it on your system.
        
        2.  Clone the datashader git repository if you do not already have it:
        
                git clone git://github.com/pyviz/datashader.git
        
        3.  Set up a new conda environment with all of the dependencies needed
            to run the examples:
        
                cd datashader
                conda env create --name datashader --file ./examples/environment.yml
                conda activate datashader
        
        4.  Put the datashader directory into the Python path in this
            environment:
        
                pip install -e .
        
        ## 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.
        
        ## Some Examples
        
        ![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)
        
        
        ## About PyViz
        
        Datashader is part of the PyViz initiative for making Python-based visualization tools work well together.
        See [pyviz.org](http://pyviz.org) for related packages that you can use with Datashader and
        [status.pyviz.org](http://status.pyviz.org) for the current status of each PyViz project.
        
Platform: UNKNOWN
Requires-Python: >=2.7
Description-Content-Type: text/markdown
Provides-Extra: doc
Provides-Extra: tests
Provides-Extra: examples_extra
Provides-Extra: examples
Provides-Extra: all
