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    "info": {
        "author": "Steven Silvester",
        "author_email": "steven.silvester@ieee.org",
        "bugtrack_url": null,
        "classifiers": [
            "Framework :: IPython",
            "License :: OSI Approved :: BSD License",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 3",
            "Topic :: System :: Shells"
        ],
        "description": "A Jupyter kernel base class in Python which includes core magic functions (including help, command and file path completion, parallel and distributed processing, downloads, and much more).\n\n.. image:: https://badge.fury.io/py/metakernel.png/\n    :target: http://badge.fury.io/py/metakernel\n\n.. image:: https://coveralls.io/repos/Calysto/metakernel/badge.png?branch=master\n  :target: https://coveralls.io/r/Calysto/metakernel\n\n.. image:: https://travis-ci.org/Calysto/metakernel.svg\n  :target: https://travis-ci.org/Calysto/metakernel\n\n.. image:: https://anaconda.org/conda-forge/metakernel/badges/version.svg\n    :target: https://anaconda.org/conda-forge/metakernel\n\n.. image:: https://anaconda.org/conda-forge/metakernel/badges/downloads.svg\n    :target: https://anaconda.org/conda-forge/metakernel\n\n\nSee Jupyter's docs on `wrapper kernels\n<http://jupyter-client.readthedocs.io/en/stable/wrapperkernels.html>`_.\n\nAdditional magics can be installed within the new kernel package under a `magics` subpackage.\n\n\nFeatures\n-------------\n- Basic set of line and cell magics for all kernels.\n    - Python magic for accessing python interpreter.\n    - Run kernels in parallel.\n    - Shell magics.\n    - Classroom management magics.\n- Tab completion for magics and file paths.\n- Help for magics using ? or Shift+Tab.\n- Plot magic for setting default plot behavior.\n\nKernels based on Metakernel\n---------------------------\n\n- matlab_kernel, https://github.com/Calysto/matlab_kernel\n- octave_kernel, https://github.com/Calysto/octave_kernel\n- calysto_scheme, https://github.com/Calysto/calysto_scheme\n- calysto_processing, https://github.com/Calysto/calysto_processing\n- java9_kernel, https://github.com/Bachmann1234/java9_kernel\n- xonsh_kernel, https://github.com/Calysto/xonsh_kernel\n- calysto_hy, https://github.com/Calysto/calysto_hy\n- gnuplot_kernel, https://github.com/has2k1/gnuplot_kernel\n- spylon_kernel, https://github.com/mariusvniekerk/spylon-kernel\n- wolfram_kernel, https://github.com/mmatera/iwolfram\n- sas_kernel, https://github.com/palmer0914/sas_kernel\n- pysysh_kernel, https://github.com/Jaesin/psysh_kernel\n- calysto_bash, https://github.com/Calysto/calysto_bash\n\n... and many others.\n\nInstallation\n----------------\nYou can install Metakernel through ``pip``:\n\n.. code::bash\n\n pip install metakernel --upgrade\n\nInstalling `metakernel` from the `conda-forge` channel can be achieved by adding `conda-forge` to your channels with:\n\n.. code::bash\n\n conda config --add channels conda-forge\n\nOnce the `conda-forge` channel has been enabled, `metakernel` can be installed with:\n\n.. code::bash\n\n conda install metakernel\n\nIt is possible to list all of the versions of `metakernel` available on your platform with:\n\n.. code::bash\n\n conda search metakernel --channel conda-forge\n\n\nUse MetaKernel Magics in IPython\n--------------------------------\n\nAlthough MetaKernel is a system for building new kernels, you can use a subset of the magics in the IPython kernel.\n\n.. code:: python\n\n from metakernel import register_ipython_magics\n register_ipython_magics()\n\nPut the following in your (or a system-wide) ``ipython_config.py`` file:\n\n.. code:: python\n\n # /etc/ipython/ipython_config.py\n c = get_config()\n startup = [\n    'from metakernel import register_ipython_magics',\n    'register_ipython_magics()',\n ]\n c.InteractiveShellApp.exec_lines = startup\n\nUse MetaKernel Languages in Parallel\n\nTo use a MetaKernel language in parallel, do the following:\n\n1. Make sure that the Python module `ipyparallel` is installed. In the shell, type:\n\n.. code:: bash\n\n  pip install ipyparallel\n\n\n2. To enable the extension in the notebook, in the shell, type:\n\n.. code:: bash\n\n  ipcluster nbextension enable\n\n\n3. To start up a cluster, with 10 nodes, on a local IP address, in the shell, type:\n\n.. code:: bash\n\n  ipcluster start --n=10 --ip=192.168.1.108\n\n\n4. Initialize the code to use the 10 nodes, inside the notebook from a host kernel ``MODULE`` and ``CLASSNAME`` (can be any metakernel kernel):\n\n.. code:: bash\n\n  %parallel MODULE CLASSNAME\n\n\nFor example:\n\n.. code:: bash\n\n  %parallel calysto_scheme CalystoScheme\n\n\n5. Run code in parallel, inside the notebook, type:\n\nExecute a single line, in parallel:\n\n.. code:: bash\n\n  %px (+ 1 1)\n\n\nOr execute the entire cell, in parallel:\n\n.. code:: bash\n\n  %%px\n  (* cluster_rank cluster_rank)\n\n\nResults come back in a Python list (Scheme vector), in ``cluster_rank`` order. (This will be a JSON representation in the future).\n\nTherefore, the above would produce the result:\n\n.. code:: bash\n\n  #10(0 1 4 9 16 25 36 49 64 81)\n\nYou can get the results back in any of the parallel magics (``%px``, ``%%px``, or ``%pmap``) in the host kernel by accessing the variable ``_`` (single underscore), or by using the ``--set_variable VARIABLE`` flag, like so:\n\n.. code:: bash\n\n  %%px --set_variable results\n  (* cluster_rank cluster_rank)\n\n\nThen, in the next cell, you can access ``results``.\n\nNotice that you can use the variable ``cluster_rank`` to partition parts of a problem so that each node is working on something different.\n\nIn the examples above, use ``-e`` to evaluate the code in the host kernel as well. Note that ``cluster_rank`` is not defined on the host machine, and that this assumes the host kernel is the same as the parallel machines.\n\n\nConfiguration\n-------------\n``Metakernel`` subclasses can be configured by the user.  The\nconfiguration file name is determined by the ``app_name`` property of the subclass.\nFor example, in the ``Octave`` kernel, it is ``octave_kernel``.  The user of the kernel can add an ``octave_kernel_config.py`` file to their\n``jupyter`` config path.  The base ``MetaKernel`` class offers ``plot_settings`` as a configurable trait.  Subclasses can define other traits that they wish to make\nconfigurable.\n\nAs an example:\n\n.. code:: bash\n\n    cat ~/.jupyter/octave_kernel_config.py\n    # use Qt as the default backend for plots\n    c.OctaveKernel.plot_settings = dict(backend='qt')\n\n\nDocumentation\n-----------------------\n\nExample notebooks can be viewed here_.\n\nDocumentation is available online_. Magics have interactive help_ (and online).\n\nFor version information, see the Revision History_.\n\n\n.. _here: http://nbviewer.ipython.org/github/Calysto/metakernel/tree/master/examples/\n\n.. _help: https://github.com/Calysto/metakernel/blob/master/metakernel/magics/README.md\n\n.. _online: http://Calysto.github.io/metakernel/\n\n.. _History: https://github.com/Calysto/metakernel/blob/master/HISTORY.rst\n\n\n",
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