Metadata-Version: 2.1
Name: scitools-iris
Version: 2.1.0
Summary: A powerful, format-agnostic, and community-driven Python library for analysing and visualising Earth science data
Home-page: http://scitools.org.uk/iris/
Author: UK Met Office
Author-email: scitools-iris-dev@googlegroups.com
License: UNKNOWN
Description: <h1 align="center">
          <a href="https://scitools.org.uk/iris/docs/latest/" style="display: block; margin: 0 auto;">
           <img src="https://raw.githubusercontent.com/SciTools/iris/master/docs/iris/src/_static/logo_banner.png"
                style="max-width: 40%;" alt="Iris"></a><br>
        </h1>
        
        <h4 align="center">
            Iris is a powerful, format-agnostic, and community-driven Python library for
            analysing and visualising Earth science data
        </h4>
        
        <p align="center">
        <!-- https://shields.io/ is a good source of these -->
        <a href="https://anaconda.org/conda-forge/iris">
        <img src="https://img.shields.io/conda/dn/conda-forge/iris.svg"
             alt="conda-forge downloads" /></a>
        <a href="https://github.com/SciTools/iris/releases">
        <img src="https://img.shields.io/github/tag/SciTools/iris.svg"
             alt="Latest version" /></a>
        <a href="https://github.com/SciTools/iris/commits/master">
        <img src="https://img.shields.io/github/commits-since/SciTools/iris/latest.svg"
             alt="Commits since last release" /></a>
        <a href="https://github.com/SciTools/iris/graphs/contributors">
        <img src="https://img.shields.io/github/contributors/SciTools/iris.svg"
             alt="# contributors" /></a>
        <a href="https://travis-ci.org/SciTools/iris/branches">
        <img src="https://api.travis-ci.org/repositories/SciTools/iris.svg?branch=master"
             alt="Travis-CI" /></a>
        <a href="https://zenodo.org/badge/latestdoi/5312648">
        <img src="https://zenodo.org/badge/5312648.svg"
             alt="zenodo" /></a>
        </p>
        <br>
        
        <!-- NOTE: toc auto-generated with https://github.com/frnmst/md-toc:
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        <h1>Table of contents</h1>
        
        [](TOC)
        
        + [Overview](#overview)
        + [Documentation](#documentation)
        + [Installation](#installation)
        + [Copyright and licence](#copyright-and-licence)
        
        [](TOC)
        
        # Overview
        
        Iris implements a data model based on the [CF conventions](http://cfconventions.org/)
        giving you a powerful, format-agnostic interface for working with your data.
        It excels when working with multi-dimensional Earth Science data, where tabular
        representations become unwieldy and inefficient.
        
        [CF Standard names](http://cfconventions.org/standard-names.html),
        [units](https://github.com/SciTools/cf_units), and coordinate metadata
        are built into Iris, giving you a rich and expressive interface for maintaining
        an accurate representation of your data. Its treatment of data and
          associated metadata as first-class objects includes:
        
          * a visualisation interface based on [matplotlib](https://matplotlib.org/) and
            [cartopy](https://scitools.org.uk/cartopy/docs/latest/),
          * unit conversion,
          * subsetting and extraction,
          * merge and concatenate,
          * aggregations and reductions (including min, max, mean and weighted averages),
          * interpolation and regridding (including nearest-neighbor, linear and area-weighted), and
          * operator overloads (``+``, ``-``, ``*``, ``/``, etc.)
        
        A number of file formats are recognised by Iris, including CF-compliant NetCDF, GRIB,
        and PP, and it has a plugin architecture to allow other formats to be added seamlessly.
        
        Building upon [NumPy](http://www.numpy.org/) and [dask](https://dask.pydata.org/en/latest/),
        Iris scales from efficient single-machine workflows right through to multi-core clusters and HPC.
        Interoperability with packages from the wider scientific Python ecosystem comes from Iris'
        use of standard NumPy/dask arrays as its underlying data storage.
        
        
        # Documentation
        
        The documentation for Iris is available at <https://scitools.org.uk/iris/docs/latest>,
        including a user guide, example code, and gallery.
        
        # Installation
        
        The easiest way to install Iris is with [conda](https://conda.io/miniconda.html):
        
            conda install -c conda-forge iris
        
        Detailed instructions, including information on installing from source,
        are available in [INSTALL](INSTALL).
        
        
        # Copyright and licence
        
        Iris may be freely distributed, modified and used commercially under the terms
        of its [GNU LGPLv3 license](COPYING.LESSER).
        
        
        (C) British Crown Copyright 2010 - 2018, Met Office
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
Provides-Extra: test
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