{
    "info": {
        "author": "Scott Ernst",
        "author_email": "swernst@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "Intended Audience :: System Administrators",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Scientific/Engineering :: Mathematics",
            "Topic :: Scientific/Engineering :: Visualization"
        ],
        "description": "Cauldron\n========\n\nThe Unnotebook: The dexterity of a notebook and the wisdom of software\nengineering in one data analysis environment\n\n\n.. image:: https://img.shields.io/pypi/v/cauldron-notebook.svg\n   :target: https://pypi.python.org/pypi/cauldron-notebook\n\n.. image:: https://anaconda.org/sernst/cauldron/badges/version.svg\n   :target: https://anaconda.org/sernst/cauldron\n\n.. image:: https://travis-ci.org/sernst/cauldron.svg?branch=master\n   :target: https://travis-ci.org/sernst/cauldron\n\n.. image:: https://api.codacy.com/project/badge/Grade/13a05e35d76141e79fbd6b19e6ad937e\n   :target: https://www.codacy.com/app/sernst/cauldron\n\n.. image:: https://coveralls.io/repos/github/sernst/cauldron/badge.svg\n   :target: https://coveralls.io/github/sernst/cauldron\n\n.. image:: https://codecov.io/gh/sernst/cauldron/branch/master/graph/badge.svg\n   :target: https://codecov.io/gh/sernst/cauldron\n\n.. image:: https://img.shields.io/badge/license-MIT-blue.svg\n   :target: https://raw.githubusercontent.com/sernst/cauldron/master/LICENSE\n\n.. image:: https://badges.gitter.im/gitterHQ/gitter.svg\n   :target: https://gitter.im/cauldron-notebook/Lobby\n\nThe rest of this documentation pertains to the Cauldron CLI. For more\ngeneral information about Cauldron, including downloads for the desktop\napplication, please visit: http://www.unnotebook.com\n\n\n- `Installation`_\n- `Getting Started`_\n- `Example Projects`_\n- `Tutorial: First Project`_\n- `Docker`_\n\n\nInstallation\n------------\n\nThe latest release of Cauldron is available from both PyPi::\n\n    $ pip install cauldron-notebook\n\nand Anaconda::\n\n   $ conda install -c sernst cauldron\n\nIf you want to use the latest developments, you can install directly from the Github\npage instead of from PyPi::\n\n    $ pip install git+https://github.com/sernst/cauldron.git\n\nYou can also install in development mode if you want to manage updates using git\ninstead of pip. To install in that way, clone a local copy of this repository\nto your local machine and, inside a terminal, ``cd`` into your local copy\ndirectory and run the command::\n\n    $ python3 setup.py develop\n\nOr in an Anaconda installation using its develop command::\n\n   $ conda develop .\n\nwhich must be executed in the root project directory of your local copy of\nCauldron.\n\nGetting Started\n---------------\n\nCauldron can be used as either through its Command Line Interface (CLI) or with\nthe Cauldron desktop application. For more information about the desktop\napplication visit http://www.unnotebook.com where you can find the download\nlinks and documentation. The rest of this README describes using Cauldron\ndirectly from the command line.\n\nCauldron is a shell-based program you start from a terminal. For installations\nthat support python script installation you can start Cauldron\nonce the installation is complete with the ``cauldron`` command::\n\n    $ cauldron\n\nor on Windows using the ``cauldron.exe`` command::\n\n    % cauldron.exe\n\nFor installations where the installation of scripts was not permitted, you can\nstart Cauldron from within a Python shell. To do this import cauldron and\nrun the ``cauldron.run_shell()`` function as follows::\n\n    >>> import cauldron\n    >>> cauldron.run_shell()\n\nOnce started, the Cauldron shell provides all of the functionality you need to\nmanage your analysis projects through a collection of commands. To see a list\nof available commands and their basic descriptions use the ``?`` or ``help``\ncommand on the Cauldron prompt::\n\n    <>: ?\n\nor::\n\n    <>: help\n\nFor more detailed information on a specific command use the ``help`` command\nalong with the name of the command you wish to learn more about. For example,\nto get help on the ``open`` command, you would enter::\n\n    <>: help open\n\non the Cauldron prompt.\n\nExample Projects\n----------------\n\nCauldron comes bundled with a few example projects for demonstration purposes.\nTo open one of these projects, use the command::\n\n    <>: open @examples:[EXAMPLE_PROJECT_NAME]\n\nwhere ``[EXAMPLE_PROJECT_NAME]`` is the name of an existing example project.\nThe ``@examples:`` prefix is an alias in Cauldron that resolves to the path\nwhere the example files are stored. You can also create your own aliases,\nwhich will be explained in detail later.\n\nLike all commands in Cauldron, the open command supports tab auto-completion.\nIf you enter the beginning of the command above::\n\n    <>: open @examples:\n\nand hit the tab key with the cursor at the end of the line, Cauldron will give\nyou a list of the example project subdirectories.\n\nA good example to start would be Cauldron's *hello_cauldron*::\n\n    <>: open @examples:hello_cauldron/\n\nOnce this command is run, the hello_cauldron project will be opened and readied\nfor you to run. The Cauldron shell prompt updates to reflect the open project.\nInstead of ``<>:``, which signifies no open project, the prompt should now be\n``<hello_cauldron>:``.\n\nIf you now enter the ``run`` command without any arguments, all steps (cells)\nin the project will run::\n\n    <hello_cauldron>: run\n\nOnce complete, you can view the current state of the notebook display with the\nshow command::\n\n    <hello_cauldron>: show\n\nwhich opens the current project display file in your default browser. When you\nare finished working on a project, you use the close to command::\n\n   <hello_cauldron>: close\n\nThis empties all of the information Cauldron has been storing for your project\nin memory, and takes you back to the initial command prompt where you started::\n\n   <>:\n\nTutorial: First Project\n-----------------------\n\nThis tutorial walks through creating your first project. It mirrors the\n**@example:hello_cauldron** project that comes installed with Cauldron.\n\nCreate New Project\n~~~~~~~~~~~~~~~~~~\n\nTo create your first project run the Cauldron shell command::\n\n    <>: create hello_cauldron @home:\n\nFor more details about the create command, use the Cauldron shell command::\n\n    <>: help create\n\nThe create command takes two arguments:\n\n#. The name of your new project (``hello_cauldron`` in the example above)\n#. The absolute path to the directory where the project will be saved. In the\n   example above, the ``@home:`` argument is a shortcut to Cauldron's default\n   home directory, which is ~/cauldron/.\n\nWhen the example create command above is executed, a *hello_cauldron* project\nwill be created in the directory *~/cauldron/hello_cauldron/*, with the\nscaffolding for the project already written. The create command also\nimmediately opens the new project in the shell.\n\nAdd First Code Step\n~~~~~~~~~~~~~~~~~~~\n\nNow that the project has been created, you need to add some code to it. To\ndo that, use the ``steps add`` command::\n\n    <hello_cauldron>: steps add create_data.py\n\nThis will create a new step called *S01-create_data.py* in your project\ndirectory and add it to the Cauldron project. Notice that the name you gave\nthe step and the one actual step name are different. There's an *S01-* prefix\nadded to the file. This prefix is added automatically by Cauldron to help you\norganize your files. You can disable this feature when you create a project if\nyou really want to manage the names all yourself, but we'll get into that in\nan advanced tutorial.\n\nThe step file you created is ready to be modified. Open the\n*S01-create_data.py* step file in your choice of Python code editor. You'll\nfind the file in the project directory, which is *~/cauldron/hello_cauldron/*.\nAdd the following code to the *S01-create_data.py* file:\n\n.. code-block:: python3\n\n    import numpy as np\n    import pandas as pd\n    import cauldron as cd\n\n    df = pd.DataFrame(\n        np.random.randn(10, 5),\n        columns=['a', 'b', 'c', 'd', 'e']\n    )\n\n    cd.display.header('Random Data Frame:')\n    cd.display.table(df)\n\n    cd.shared.df = df\n\nOnce you've saved that code to the *S01-create_data.py* file, you can run your\nproject using the ``run`` command::\n\n    <hello_cauldron>: run\n\nThen use the ``show`` command to see the results::\n\n    <hello_cauldron>: show\n\nThe project display file will open in your default browser.\n\nAdd Another Step\n~~~~~~~~~~~~~~~~\n\nNow we'll add another code step to plot each column in our DataFrame. Once\nagain use the steps command::\n\n    <hello_cauldron>: steps add plot_data.py\n\nOpen the *S02-plot_data.py* step file and add the following code:\n\n.. code-block:: python3\n\n    import matplotlib.pyplot as plt\n    import cauldron as cd\n\n    df = cd.shared.df\n\n    for column_name in df.columns:\n        plt.plot(df[column_name])\n\n    plt.title('Random Plot')\n    plt.xlabel('Indexes')\n    plt.ylabel('Values')\n\n    cd.display.pyplot()\n\nWe used matplotlib for this tutorial, but Cauldron also supports Seaborn,\nBokeh, Plotly or any other Python plotting library that can produce an HTML\noutput. There are Cauldron example projects showing how to plot using each of\nthese libraries.\n\nNow run the project again::\n\n    <hello_cauldron>: run\n\nYou'll notice that the shell output looks like::\n\n    === RUNNING ===\n    [S01-create_data.py]: Nothing to update\n    [S02-plot_data.py]: Updated\n\nThe *S01-create_data.py* step was not run because it hasn't been modified since\nthe last time you executed the ``run`` command. Just like other notebooks, the\nresults of running a step (cell) persist until you close the project and do not\nneed to be updated each time. Cauldron watches for changes to your files and\nonly updates steps if the files have been modified, or an early step was\nmodified that may affect their output.\n\nNow you can view the updated project display simply by refreshing your browser.\nHowever, if you already closed the project display browser window, you can show\nit again at any time with the ``show`` command.\n\nAnd that's that. You've successfully created your first Cauldron project. You\ncan close your project with the ``close`` command::\n\n   <hello_cauldron>: close\n\nOr, if you want to exit the Cauldron shell at any time, use the ``exit``\ncommand::\n\n   <>: exit\n\nSee Cauldron's documentation at http://www.unnotebook.com/docs/ for more\ninformation.\n\nDocker\n------\n\nCauldron supports running in docker containers for both local and remote\nworkflows. There are 3 officially supported docker containers available at:\n\nhttps://hub.docker.com/r/swernst/cauldron/tags/\n\nThey are:\n\n- ``standard``: Includes the Python 3.6 distribution in an Ubuntu environment.\n- ``conda``: Includes the full Anaconda distribution of Python 3.6 built upon Anaconda's official docker image.\n- ``miniconda``: Includes the slimmed-down mini Anaconda distribution of Python 3.6 built upon Anaconda's official docker image.\n\nIn all three cases, Cauldron is pre-installed with dependencies and the default\ncommand for each container is to start the Cauldron kernel on the exposed port\n5010. One of these containers can be pulled using the docker pull command::\n\n   $ docker pull swernst/cauldron:latest-standard\n\nIf you do not specify a specific tag, the latest standard image will be used.\nOnce the image has been pulled, you can start a Cauldron kernel::\n\n   $ docker run -d --rm -p 5010:5010 swernst/cauldron:latest-standard\n\nAfter the container starts, you can access the kernel through the exposed 5010\nport. If you are using the desktop application, you can connect to this\ncontainer locally by specifying the local kernel URL, ``http://127.0.0.1:5010``\ninstead of a Python executable path.\n\nThe Cauldron command shell also allows you drive the kernel by connecting to it\nfrom a locally running Cauldron shell. To do this, you use the ``connect``\ncommand::\n\n   <>: connect http://127.0.0.1:5010\n\nOnce connected, all shell commands you issue, e.g. opening a project, will be\nrelayed to the kernel. All project files will be synchronized between the\nlocal environment and the kernel's environment. This means you can interact\nwith a local project exactly like you normally would, but all of the execution\nwill happen in the kernel's environment, not your local one.\n\n\n",
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