{
    "info": {
        "author": "Vishnu P Sreenivasan",
        "author_email": "psvishnu.91@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 2.7"
        ],
        "description": "[![Documentation Status](https://readthedocs.org/projects/local-visualizer/badge/?version=latest)](http://local-visualizer.readthedocs.io/en/latest/?badge=latest)\n[![Build Status](https://travis-ci.org/psvishnu91/local_visualizer.svg?branch=master)](https://travis-ci.org/psvishnu91/local_visualizer)\n[![PyPI version](https://badge.fury.io/py/local-visualizer.svg)](https://badge.fury.io/py/local-visualizer)\n\n## LocalVisualizer\n\nSimple python api to visualize the plots in a script.\n\n* Free software: MIT license\n* Documentation: https://local-visualizer.readthedocs.io\n* PyPI: https://pypi.python.org/pypi/local-visualizer/\n\n### Installation\n``` bash\npip install local-visualizer\n```\n\n### Motivation\n* When moving from an IPython notebook to a script, we lose the diagnostics\n    of visualizing pandas as tables and matplotlib plots.\n* :class:`LocalViz` starts a local http server and creates a html file to\n    which pandas tables and matplotlib plots can be sent over.\n* The html file is dynamically updated for long running scripts.\n\n### Usage\n``` python\nimport logging, sys, numpy as np, pandas as pd, matplotlib.pyplot as plt\nimport local_visualizer\n\nplt.style.use('fivethirtyeight')\nlogging.basicConfig(stream=sys.stdout, level=logging.DEBUG)\n\n# Create the local visualizer instance\nlviz = local_visualizer.LocalViz(html_file='lviz_test.html', port=9112)\n# INFO:root:Starting background server at: http://localhost:9112/.\n# INFO:local_visualizer:Click: http://carpediem:9112/lviz_test.html or http://localhost:9112/lviz_test.html\n\n# Create plots which will be streamed to the html file.\nlviz.h3('Matplotlib :o')\nlviz.p(\n    'Wrap your plots in the figure context manager which takes '\n    'in the kwargs of plt.figure and returns a plt.figure object.',\n)\n\nwith lviz.figure(figsize=(10, 8)) as fig:\n    x = np.linspace(-10, 10, 1000)\n    plt.plot(x, np.sin(x))\n    plt.title('Sine test')\n\nlviz.hr()\n\n# Visualize pandas dataframes as tables.\nlviz.h3('Pandas dataframes')\n\ndf = pd.DataFrame({'A': np.linspace(1, 10, 10)})\ndf = pd.concat(\n    [df, pd.DataFrame(np.random.randn(10, 4), columns=list('BCDE'))],\n    axis=1,\n)\nlviz.write(df)\nlviz.close()\n```\n\n### Output\nThis starts a HTTPServer and creates a html file which is dynamically updated\neach time ``lviz`` is called.\n\n![Output image]( https://i.imgur.com/jjwvAX2.png \"The output of the above commands\")\n\n### Support and Requirements\nPython 2.7\n\n### API methods\n1. `p`: paragraph\n2. `br`: line break\n3. `hr`: Horizontal rule with line breaks\n4. `h1`, `h2`, ..., `h6`: Headers\n5. `write`: Directly write text to the html document (or pass in a `pandas.DataFrame`)\n6. `figure`: Context manager which accepts the kwargs of `plt.figure` and returns a `plt.figure` object\n7. `start`: Applicable if `LocalViz` was initialized with `lazy=True`. Starts the server and creates the html file\n8. `close`: Completes the html file\n9. `del_html`: Deletes the html file\n\n### Credits\nThis package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.\n\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\n\n\n=======\nHistory\n=======\n\n0.2.0 (2017-11-06)\n------------------\n\nThe close method no more deletes the html but only makes the html valid.\n\n0.1.0 (2017-11-05)\n------------------\n\n* First release on PyPI.\n\n\n",
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