Metadata-Version: 1.1
Name: conda
Version: 4.5.0
Summary: OS-agnostic, system-level binary package manager.
Home-page: https://github.com/conda/conda
Author: Anaconda, Inc.
Author-email: conda@continuum.io
License: BSD-3-Clause
Description: .. NOTE: This file serves both as the README on GitHub and the index.html for
           conda.pydata.org. If you update this file, be sure to cd to the web
           directory and run ``make html; make live``
        
        .. image:: https://s3.amazonaws.com/conda-dev/conda_logo.svg
           :alt: Conda Logo
        
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            :alt: Windows tests (Appveyor)
        
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        Conda is a cross-platform, language-agnostic binary package manager. It is the
        package manager used by `Anaconda
        <http://docs.continuum.io/anaconda/index.html>`_ installations, but it may be
        used for other systems as well.  Conda makes environments first-class
        citizens, making it easy to create independent environments even for C
        libraries. Conda is written entirely in Python, and is BSD licensed open
        source.
        
        Conda is enhanced by organizations, tools, and repositories created and managed by
        the amazing members of the conda community.  Some of them can be found
        `here <https://github.com/conda/conda/wiki/Conda-Community>`_.
        
        
        Installation
        ------------
        
        Conda is a part of the `Anaconda distribution <https://store.continuum.io/cshop/anaconda/>`_.  You can also download a
        minimal installation that only includes conda and its dependencies, called
        `Miniconda <http://conda.pydata.org/miniconda.html>`_.
        
        
        Getting Started
        ---------------
        
        If you install Anaconda, you will already have hundreds of packages
        installed.  You can see what packages are installed by running
        
        .. code-block:: bash
        
           $ conda list
        
        to see all the packages that are available, use
        
        .. code-block:: bash
        
           $ conda search
        
        and to install a package, use
        
        .. code-block:: bash
        
           $ conda install <package-name>
        
        
        The real power of conda comes from its ability to manage environments. In
        conda, an environment can be thought of as a completely separate installation.
        Conda installs packages into environments efficiently using `hard links
        <http://en.wikipedia.org/wiki/Hard_links>`_ by default when it is possible, so
        environments are space efficient, and take seconds to create.
        
        The default environment, which ``conda`` itself is installed into is called
        ``base``.  To create another environment, use the ``conda create``
        command. For instance, to create an environment with the IPython notebook and
        NumPy 1.6, which is older than the version that comes with Anaconda by
        default, you would run
        
        .. code-block:: bash
        
           $ conda create -n numpy16 ipython-notebook numpy=1.6
        
        This creates an environment called ``numpy16`` with the latest version of
        the IPython notebook, NumPy 1.6, and their dependencies.
        
        We can now activate this environment, use
        
        .. code-block:: bash
        
           # On Linux and Mac OS X
           $ source activate numpy16
        
           # On Windows
           > activate numpy16
        
        This puts the bin directory of the ``numpy16`` environment in the front of the
        ``PATH``, and sets it as the default environment for all subsequent conda commands.
        
        To go back to the base environment, use
        
        .. code-block:: bash
        
           # On Linux and Mac OS X
           $ source deactivate
        
           # On Windows
           > deactivate
        
        
        Building Your Own Packages
        --------------------------
        
        You can easily build your own packages for conda, and upload them
        to `anaconda.org <https://anaconda.org>`_, a free service for hosting
        packages for conda, as well as other package managers.
        To build a package, create a recipe.
        See http://github.com/conda/conda-recipes for many example recipes, and
        http://docs.continuum.io/conda/build.html for documentation on how to build
        recipes.
        
        To upload to anaconda.org, create an account.  Then, install the
        anaconda-client and login
        
        .. code-block:: bash
        
           $ conda install anaconda-client
           $ anaconda login
        
        Then, after you build your recipe
        
        .. code-block:: bash
        
           $ conda build <recipe-dir>
        
        you will be prompted to upload to anaconda.org.
        
        To add your anaconda.org channel, or the channel of others to conda so
        that ``conda install`` will find and install their packages, run
        
        .. code-block:: bash
        
           $ conda config --add channels https://conda.anaconda.org/username
        
        (replacing ``username`` with the user name of the person whose channel you want
        to add).
        
        Getting Help
        ------------
        
        The documentation for conda is at http://conda.pydata.org/docs/. You can
        subscribe to the `conda mailing list
        <https://groups.google.com/a/continuum.io/forum/#!forum/conda>`_.  The source
        code and issue tracker for conda are on `GitHub <https://github.com/conda/conda>`_.
        
        Contributing
        ------------
        
        Contributions to conda are welcome. Just fork the GitHub repository and send a
        pull request.
        
        To develop on conda, the easiest way is to use a development build. This can be
        accomplished as follows:
        
        * clone the conda git repository to a computer with conda already installed
        * navigate to the root directory of the git clone
        * run ``$CONDA/bin/python setup.py develop`` where ``$CONDA`` is the path to your
          miniconda installation
        
        Note building a development file requires git to be installed.
        
        To undo this, run ``$CONDA/bin/python setup.py develop -u``.  Note that if you
        used a python other than ``$CONDA/bin/python`` to install, you may have to manually
        delete the conda executable.  For example, on OS X, if you use a homebrew python
        located at ``/usr/local/bin/python``, then you'll need to ``rm /usr/local/bin/conda``
        so that ``which -a conda`` lists first your miniconda installation.
        
        If you are worried about breaking your conda installation, you can install a
        separate instance of `Miniconda <http://conda.pydata.org/miniconda.html>`_ and
        work off it. This is also the only way to test conda in both Python 2 and
        Python 3, as conda can only be installed into a base environment.
        
        To run the tests, set up a testing environment by running
        
        * ``$CONDA/bin/python -m pip install -r utils/requirements-test.txt``.
        * ``$CONDA/bin/python utils/setup-testing.py develop``
        
        and then running ``py.test`` in the conda directory.  You can also run tests using the
        Makefile by running ``make unit``, ``make smoketest`` (a single integration test), or
        ``make integration``.  The tests are also run by various CI systems when you make a
        pull request.
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
