iplotter 0.4.3
JavaScript Charting for iPython/Jupyter notebooks
License: MIT
Keywords: ipython,plotly,c3.js,plot,chart,plotly.js,charts.js,chartist.js,jupyter

IPlotter
========

|PyPI version| |PyPI| |PyPI|

JavaScript charting in ipython/jupyter notebooks
------------------------------------------------

.. raw:: html

   <!-- MarkdownTOC -->

-  `Installation <#installation>`__
-  `C3.js <#c3js>`__
-  `plotly.js <#plotlyjs>`__
-  `Chart.js <#chartjs>`__
-  `Chartist.js <#chartistjs>`__
-  `Google Charts <#google-charts>`__
-  `Usage <#usage>`__
-  `Examples <#examples>`__

   -  `C3 Stacked Area Spline Chart <#c3-stacked-area-spline-chart>`__
   -  `plotly.js HeatMap <#plotlyjs-heatmap>`__
   -  `Chart.js Radar Chart <#chartjs-radar-chart>`__
   -  `Chartist.js Bipolar Area
      Chart <#chartistjs-bipolar-area-chart>`__
   -  `Google Charts stacked Column
      Chart <#google-charts-stacked-column-chart>`__

-  `Multple Charts and Mixing
   Libraries <#multple-charts-and-mixing-libraries>`__
-  `Exporting plots to PNG images with
   Selenium <#exporting-plots-to-png-images-with-selenium>`__

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   <!-- /MarkdownTOC -->

iplotter is a simple package for generating interactive charts in
ipython/jupyter notebooks using popular JavaScript Libraries from python
data structures (dictionaries, lists, etc.)

 ## Installation To install the most recent stable release run
``pip install iplotter``.

To install the latest version run
``pip install git+git://github.com/niloch/iplotter.git@master`` or
``git clone https://github.com/niloch/iplotter.git`` followed by
``pip install -e iplotter/``

 ## `C3.js <http://c3js.org/>`__

C3 is a charting library based on d3.js for making interactive and easy
to understand charts, graphs, and plots. Charts have animated
transitions for hiding/displaying data.

 ## `plotly.js <https://plot.ly/javascript/>`__

Plotly.js is a charting library based on d3.js. While plotly provides a
native client in python, it requires the user to create an account and
by default makes all plots public. plotly.js can be used without
creating an account and are rendered locally to keep data private.

 ## `Chart.js <http://www.chartjs.org/>`__

Chart.js provides 6 chart types via HTML5 canvas elements with
tooltips/hover events in very a lightweight library.

 ## `Chartist.js <http://gionkunz.github.io/chartist-js/index.html>`__

Simple and Responsive SVG charts with media queries and animations.

 ## `Google Charts <https://developers.google.com/chart/>`__

Simple and Powerful interactive charts with SVG/VML formats.

 ## Usage

iplotter attempts to maintain a consistent API across JavaScript
Libraries as much as possible, with slight parameter variations. Each
library class supports the following functions: ``render``, ``plot``,
``save``, ``plot_and_save``. The python chart data,layout,options must
be structured according to the JSON equivalent for each library (see
`C3.js <http://c3js.org/>`__,
`plotly.js <https://plot.ly/javascript/>`__,\ `Chart.js <http://www.chartjs.org/>`__,
`Chartist.js <http://gionkunz.github.io/chartist-js/index.html>`__ and
`Google Charts <https://developers.google.com/chart/>`__ for more
examples). Plots can be rendered in ipython notebooks and saved to the
current directory as html files.

 ## Examples

 ### C3 Stacked Area Spline Chart

.. code:: python

    from iplotter import C3Plotter

    plotter = C3Plotter()

    chart = {
        "data": {
            "columns": [
                ['data1', 300, 350, 300, 0, 0, 120],
                ['data2', 130, 100, 140, 200, 150, 50],
                ['data3', 180, 75, 265, 100, 50, 100]
            ],
            "types": {
                "data1": 'area-spline',
                "data2": 'area-spline',
                "data3": 'area-spline'
            },
            "groups": [['data1', 'data2', 'data3']]
        }
    }

    plotter.plot(chart)

.. figure:: https://github.com/niloch/iplotter/blob/master/imgs/plot1.png?raw=true
   :alt: Plot 1

   Plot1

 ### plotly.js HeatMap

.. code:: python

    from iplotter import PlotlyPlotter

    plotter = PlotlyPlotter()

    data = [
        {
            'colorscale': 'YIGnBu',
            'reversescale': True,
            'type': 'heatmap',
            'x': ['class1', 'class2', 'class3'],
            'y': ['class1', 'class2', 'class3'],
            'z': [[0.7,  0.2,  0.1],
                  [0.2,  0.7,  0.1],
                  [0.15,  0.27,  0.56]]
        }
    ]

    layout = {
        "title": 'Title',
        "xaxis": {
            "tickangle": -45
        },
    }

    plotter.plot_and_save(data, layout=layout, w=600, h=600, filename='heatmap1', overwrite=True)

.. figure:: https://github.com/niloch/iplotter/blob/master/imgs/plot3.png?raw=true
   :alt: Plot 3

   Plot3

 ### Chart.js Radar Chart

.. code:: python

    from iplotter import ChartJSPlotter

    plotter = ChartJSPlotter()

    data = {
        "labels": ["Eating", "Drinking", "Sleeping", "Designing", "Coding",
                   "Cycling", "Running"],
        "datasets": [
            {
                "label": "Trace 1",
                "backgroundColor": "rgba(179,181,198,0.2)",
                "borderColor": "rgba(179,181,198,1)",
                "pointBackgroundColor": "rgba(179,181,198,1)",
                "pointBorderColor": "#fff",
                "pointHoverBackgroundColor": "#fff",
                "pointHoverBorderColor": "rgba(179,181,198,1)",
                "data": [65, 59, 30, 81, 56, 55, 40]
            }, {
                "label": "Trace 2",
                "backgroundColor": "rgba(255,99,132,0.2)",
                "borderColor": "rgba(255,99,132,1)",
                "pointBackgroundColor": "rgba(255,99,132,1)",
                "pointBorderColor": "#fff",
                "pointHoverBackgroundColor": "#fff",
                "pointHoverBorderColor": "rgba(255,99,132,1)",
                "data": [28, 48, 40, 19, 96, 27, 100]
            }
        ]
    }

    plotter.plot_and_save(data, 'radar', options=None)

.. figure:: https://github.com/niloch/iplotter/blob/master/imgs/plot4.png?raw=true
   :alt: Plot 4

   Plot4

 ### Chartist.js Bipolar Area Chart

.. code:: python

    from iplotter import ChartistPlotter

    plotter = ChartistPlotter()

    data = {
        "labels": [1, 2, 3, 4, 5, 6, 7, 8],
        "series": [
            [1, 2, 3, 1, -2, 0, 1, 0], [-2, -1, -2, -1, -2.5, -1, -2, -1],
            [0, 0, 0, 1, 2, 2.5, 2, 1], [2.5, 2, 1, 0.5, 1, 0.5, -1, -2.5]
        ]
    }
    options = {
        "high": 4,
        "low": -3,
        "showArea": True,
        "showLine": False,
        "showPoint": False,
        "height": 420,
        "width": 700
    }

    plotter.save(data, chart_type="Line", options=options)

.. figure:: https://github.com/niloch/iplotter/blob/master/imgs/plot6.png?raw=true
   :alt: Plot 6

   Plot6

 ### Google Charts stacked Column Chart

.. code:: python

    from iplotter import GCPlotter

    plotter = GCPlotter()

    data = [
        ['Genre', 'Fantasy & Sci Fi', 'Romance', 'Mystery/Crime', 'General',
         'Western', 'Literature', {"role": 'annotation'}],
        ['2010', 10, 24, 20, 32, 18, 5, ''],
        ['2020', 16, 22, 23, 30, 16, 9, ''],
        ['2030', 28, 19, 29, 30, 12, 13, '']
    ]

    options = {
        "width": 600,
        "height": 400,
        "legend": {"position": 'top', "maxLines": 3},
        "bar": {"groupWidth": '75%'},
        "isStacked": "true",
    }

    plotter.plot(data, chart_type="ColumnChart",chart_package='corechart', options=options)

.. figure:: https://github.com/niloch/iplotter/blob/master/imgs/plot7.png?raw=true
   :alt: Plot 7

   Plot7

 ## Multple Charts and Mixing Libraries

Saving multiple charts to one file or displaying multiple charts in one
iframe can be achieved by concatenating html strings returned by the
render function. The plotter's ``head`` attribute contains the script
tags for loading the necessary JavasScript libraries and ``div_ids``
must be unique. Charts from different libraries can be mixed together.

.. code:: python

    from iplotter import PlotlyPlotter, C3Plotter
    from IPython.display import HTML

    plotly_plotter = PlotlyPlotter()

    c3_plotter = C3Plotter()

    plotly_chart = [{
        "type": 'choropleth',
        "locationmode": 'USA-states',
        "locations": ["AL", "AK", "AZ", "AR", "CA", "CO", "CT", "DE", "FL", "GA",
                      "HI", "ID", "IL", "IN", "IA", "KS", "KY", "LA", "ME", "MD",
                      "MA", "MI", "MN", "MS", "MO", "MT", "NE", "NV", "NH", "NJ",
                      "NM", "NY", "NC", "ND", "OH", "OK", "OR", "PA", "RI", "SC",
                      "SD", "TN", "TX", "UT", "VT", "VA", "WA", "WV", "WI", "WY"],
        "z": [1390.63, 13.31, 1463.17, 3586.02, 16472.88, 1851.33, 259.62, 282.19,
              3764.09, 2860.84, 401.84, 2078.89, 8709.48, 5050.23, 11273.76,
              4589.01, 1889.15, 1914.23, 278.37, 692.75, 248.65, 3164.16, 7192.33,
              2170.8, 3933.42, 1718, 7114.13, 139.89, 73.06, 500.4, 751.58, 1488.9,
              3806.05, 3761.96, 3979.79, 1646.41, 1794.57, 1969.87, 31.59, 929.93,
              3770.19, 1535.13, 6648.22, 453.39, 180.14, 1146.48, 3894.81, 138.89,
              3090.23, 349.69],
        "text":
        ["Alabama", "Alaska", "Arizona", "Arkansas", " California", "Colorado",
         "Connecticut", "Delaware", "Florida", "Georgia", "Hawaii", "Idaho",
         "Illinois", "Indiana", "Iowa", "Kansas", "Kentucky", "Louisiana", "Maine",
         "Maryland", "Massachusetts", "Michigan", "Minnesota", "Mississippi",
         "Missouri", "Montana", "Nebraska", "Nevada", "New Hampshire",
         "New Jersey", "New Mexico", "New York", "North Carolina", "North Dakota",
         "Ohio", "Oklahoma", "Oregon", "Pennsylvania", "Rhode Island",
         "South Carolina", "South Dakota", "Tennessee", "Texas", "Utah", "Vermont",
         "Virginia", "Washington", "West Virginia", "Wisconsin", "Wyoming"],
        "zmin": 0,
        "zmax": 17000,
        "colorscale": [
            [0, 'rgb(242,240,247)'], [0.2, 'rgb(218,218,235)'],
            [0.4, 'rgb(188,189,220)'], [0.6, 'rgb(158,154,200)'],
            [0.8, 'rgb(117,107,177)'], [1, 'rgb(84,39,143)']
        ],
        "colorbar": {
            "title": 'Millions USD',
            "thickness": 0.2
        },
        "marker": {
            "line": {
                "color": 'rgb(255,255,255)',
                "width": 2
            }
        }
    }]

    plotly_layout = {
        "title": '2011 US Agriculture Exports by State',
        "geo": {
            "scope": 'usa',
            "showlakes": True,
            "lakecolor": 'rgb(255,255,255)'
        }
    }

    c3_chart = {
        "data": {
            "columns": [
                ['data1', 300, 350, 300, 0, 0, 120],
                ['data2', 130, 100, 140, 200, 150, 50],
                ['data3', 180, 75, 265, 100, 50, 100]
            ],
            "type": "pie",
        }
    }

    # plotter.head will return the html string containing script tags for loading the plotly.js/C3.js libraries
    multiple_plot_html = plotly_plotter.head + c3_plotter.head

    multiple_plot_html += c3_plotter.render(data=c3_chart, div_id="chart_1")

    multiple_plot_html += plotly_plotter.render(
        data=plotly_chart, layout=plotly_layout, div_id="chart_2")

    # display multiple plots in iframe
    HTML(c3_plotter.iframe.format(source=multiple_plot_html, w=600, h=900))
    # Write multiple plots to file
    with open("multiple_plots.html", 'w') as outfile:
        outfile.write(multiple_plot_html)

.. figure:: https://github.com/niloch/iplotter/blob/master/imgs/plot5.png?raw=true
   :alt: Plot 5

   Plot5

 ## Exporting plots to PNG images with
`Selenium <http://www.seleniumhq.org/>`__

Saved interactive HTML plots can be converted to static png images
programatically for inclusion in other documents via a Selenium helper
class. The user will need to download a compatible webdriver and include
it in their PATH. The expected default is the `Chrome
webdriver <https://sites.google.com/a/chromium.org/chromedriver/>`__

Using the context manager syntax is recommended as in
``with VirtualBrowser() as browser`` to ensure the browswer session is
properly released. However it can be used as a normal object by calling
``browser().quit()`` to end the session.

.. code:: python

    from iplotter import C3Plotter, ChartJSPlotter, VirtualBrowser

    plotter1 = C3Plotter()
    plotter2 = ChartJSPlotter()

    ####  specify data for charts here...

    plotter1.save(data1, filename="chart1")  # save first plot to chart1.html
    plotter2.save(data2, filename="chart2")  # save second plot to chart2.html

    charts = ["chart1", "chart2"]

    with VirtualBrowser() as browser:
        for chart in charts:
            browser.save_as_png(
                filename=chart, width=300,
                height=200)  # save html chart to filename + '.png'

.. |PyPI version| image:: https://badge.fury.io/py/iplotter.svg
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