Create a report using a Jupyter Notebook

Gist - Creating reports using Jupyter Notebooks.ipynb

{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Gist - Creating reports using Jupyter Notebooks\nGordon Bean  \n*brazilbean* on **GitHub**"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Overview\nThis gist demonstrates one way to use Jupyter Notebooks to create HTML reports generated with in-line or dynamic code.\n\nNote: this method relies on the [Python Markdown](https://github.com/ipython-contrib/IPython-notebook-extensions/tree/master/nbextensions/usability/python-markdown) notebook extension."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## The gist\n### Key ideas\n- Use Raw notebook cells to insert open and close HTML comment tags to hide swaths of cells from the final HTML output.\n  - For other formats (e.g. $\\LaTeX$) or custom use-cases, you can create a nbconvert pre-processor that screens cells for special comment tags and excludes them.\n- Use the Python Markdown extension to embed code in Markdown cells. \n\n### Putting it together\n- Compute results first\n- Capture figures using `save_img_tag`\n- Compile report sections in Markdown cells\n- Export the report using nbconvert\n\n### Caveats\n- The Python Markdown extension requires that you execute all of the code in a notebook session before exporting. If you want to embed results in markdown cells using nbconvert on a notebook that hasn't already been rendered (i.e. using the Execution pre-processor), you need to get fancy with the pre-processing (out of scope for this gist)."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Example"
    },
    {
      "metadata": {},
      "cell_type": "raw",
      "source": "<!-- hide code"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Imports\n*NOTE: You can continue to use Markdown cells to structure the hidden sections.*"
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt\nimport numpy as np\nimport io, base64\n\n%matplotlib inline",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Handy functions"
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "def save_img_tag(fig=None, format='png', alt=\"image\", bbox_inches='tight'):\n    if fig is None:\n        fig = plt.gcf()\n    \n    # Save data to BytesIO stream\n    data = io.BytesIO();\n    plt.savefig(data, format=format, bbox_inches=bbox_inches);\n    data.seek(0)\n    \n    # Encode in base64\n    data64 = base64.b64encode(data.read())\n    \n    # Convert to <img> tag with URI\n    uri = 'data:image/png;base64,' + (data64.decode())\n    img_tag = '<img alt=\"{}\" src = \"{}\" />'.format(alt, uri)\n    \n    return img_tag",
      "execution_count": 10,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Compute results"
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "foo = np.random.rand(6,6)\nfoo",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([[ 0.20258051,  0.45594695,  0.59081644,  0.77863322,  0.95562066,\n         0.77541216],\n       [ 0.2313933 ,  0.7773782 ,  0.63143983,  0.22231121,  0.51016535,\n         0.93809008],\n       [ 0.54847662,  0.15736908,  0.61739686,  0.21555361,  0.27859176,\n         0.30264647],\n       [ 0.13091739,  0.51608414,  0.63882124,  0.18896472,  0.23827327,\n         0.48095137],\n       [ 0.35712307,  0.80664108,  0.078699  ,  0.09834737,  0.97956833,\n         0.81227637],\n       [ 0.48393442,  0.95500417,  0.07009516,  0.83570162,  0.41744175,\n         0.84856411]])"
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "plt.imshow(foo, interpolation='none')\nplt.title('An example heatmap')\n\nexample_heatmap = save_img_tag()",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": "<matplotlib.figure.Figure at 0x7bd7588>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "raw",
      "source": "end hide code -->"
    },
    {
      "metadata": {
        "variables": {
          "\"{}x{}\".format(*foo.shape)": "6x6",
          "example_heatmap": "<img alt=\"image\" src = \"data:image/png;base64,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\" />"
        }
      },
      "cell_type": "markdown",
      "source": "In order to create this example plot (who's dimensions are {{\"{}x{}\".format(*foo.shape)}}):\n\n{{example_heatmap}}\n\nI did the computation in hidden cells, saved the results, and displayed those results in this Markdown cells using the Python Markdown Jupyter Notebook extension. "
    },
    {
      "metadata": {},
      "cell_type": "raw",
      "source": "<!-- hide more code"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Convert the notebook to HTML"
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "import tempfile, subprocess\n\ndef nbconvert(notebook, to='html', path_to_python_markdown=None):\n    '''Execute a system call to nbconvert\n    \n    If python-markdown is not on you PYTHONPATH, provide the path to the nbextension as an argument.\n    '''\n    \n    if path_to_python_markdown is None:\n        # Guess\n        path_to_python_markdown = os.path.expandvars(r\"C:\\Users\\%USERNAME%\\.jupyter\\nbextensions\\usability\\python-markdown\")\n    \n    with tempfile.NamedTemporaryFile() as f:\n        tmpf = f.name + '.py'\n    \n    with open(tmpf, 'wt') as tmp:\n        print(\"import sys\", file=tmp)\n        print(\"sys.path.append(r'{}')\".format(path_to_python_markdown), file=tmp)\n        print(\"c=get_config()\", file=tmp)\n        print(\"c.NbConvertApp.notebooks = ['{}']\".format(notebook), file=tmp)\n        if os.path.exists(path_to_python_markdown):\n            print(\"c.Exporter.preprocessors = [ 'pymdpreprocessor.PyMarkdownPreprocessor' ]\", file=tmp)\n        else:\n            print(\"Could not find python-markdown extension - simple markdown formatting will be used.\")\n        \n    cmd = 'jupyter nbconvert --config \"{}\" --to {} --reveal-prefix=\"http://cdn.jsdelivr.net/reveal.js/2.5.0\"'.format(tmpf, to)\n    print(subprocess.check_output(cmd, stderr=subprocess.STDOUT).decode())\n    \n    os.remove(tmpf)\n    ",
      "execution_count": 16,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "import os\n\n# Provide the name of this notebook\nname = 'Gist - Creating reports using Jupyter Notebooks'\n\n# Convert the notebook\n# Be sure to save the notebook before converting to HTML\nnbconvert(name + '.ipynb', to='html')\n\nreport = name + '.html'\nprint (report)",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": "[NbConvertApp] Converting notebook Gist - Creating reports using Jupyter Notebooks.ipynb to html\r\n[NbConvertApp] Writing 280004 bytes to Gist - Creating reports using Jupyter Notebooks.html\r\n\nGist - Creating reports using Jupyter Notebooks.html\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "raw",
      "source": "end hide code -->"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Output\nGitHub uses nbviewer to render the notebook file. Thus, what you see in the gist is an almost-rendered version of the final report. To see the actual output, see [here](https://gist.github.com/brazilbean/832ac5006551d1dea5758909d84c50b9).\n\nTo see the underlying notebook, download the gist and open it with your Jupyter Notebook server."
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "toc": {
      "threshold": 6,
      "number_sections": false,
      "toc_cell": false,
      "toc_window_display": true,
      "toc_section_display": "block",
      "sideBar": true,
      "navigate_menu": true
    },
    "nav_menu": {},
    "language_info": {
      "codemirror_mode": {
        "version": 3,
        "name": "ipython"
      },
      "version": "3.5.1",
      "mimetype": "text/x-python",
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py",
      "name": "python"
    },
    "gist": {
      "id": "1095e0af3ff2b5f37faeb0f63f0fb917",
      "data": {
        "description": "Create a report using a Jupyter Notebook",
        "public": true
      }
    },
    "_draft": {
      "nbviewer_url": "https://gist.github.com/1095e0af3ff2b5f37faeb0f63f0fb917"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}