django-graphos 0.3.41
Django app to provide a JS agnostic way to work with charts.
License: BSD

Graphos
=======

|Build Status|

Graphos is a Django app to normalize data to create beautiful charts. It
provides a JS agnostic way to work with charts and allows seamless and
quick switching between different chart providers.

-  Demo: http://agiliq.com/demo/graphos/.
-  Docs: http://agiliq.com/docs/django-graphos/.

Supported Backends:
-------------------

-  Python Nested lists
-  Django ORM
-  CSV Files
-  MongoDB

Charting API Supported
----------------------

-  `Flot <http://flotcharts.org>`__
-  `Google Charts API <https://developers.google.com/chart/>`__
-  `YUI Charts <http://yuilibrary.com/yui/docs/charts/>`__
-  `Morris.js <http://morrisjs.github.io/morris.js/>`__
-  `Highcharts <http://www.highcharts.com/>`__
-  `Matplotlib <http://matplotlib.org/api/pyplot_api.html>`__

Chart types supported
---------------------

Flot
~~~~

-  Line chart
-  Bar Chart
-  Column chart
-  Pie chart
-  Point Chart

Google Charts
~~~~~~~~~~~~~

-  Line chart
-  Bar chart
-  Column chart
-  Pie chart
-  Area chart
-  Candlestick chart
-  Treemap chart
-  Gauge chart

YUI
~~~

-  Line chart
-  Bar chart
-  Column chart
-  Pie chart
-  Area chart
-  Spline chart
-  Areaspline chart

Morris.js
~~~~~~~~~

-  Line chart
-  Bar chart
-  Donut chart
-  Area chart

Highcharts
~~~~~~~~~~

(You will need to buy a license if you use highcharts for commerical
use)

-  Line Chart
-  Bar Chart
-  Column Chart
-  Pie Chart
-  Area Chart

C3.js
~~~~~

-  Line chart
-  Column chart (You need to rotate the axis of bar chart to render
   column chart)
-  Bar chart
-  Donut chart
-  Pie chart
-  Spline chart

Matplotlib
~~~~~~~~~~

-  LineChart
-  BarChart

With Graphos, switching from google's LineChart to yui LineChart can be
done within minutes. So would be the case in switching from yui
AreaChart to morris AreaChart.

Running demo project locally
----------------------------

-  Clone the project

   git clone https://github.com/agiliq/django-graphos.git

-  Cd to demo directory

   cd django-graphos/demo\_project/

-  Create local settings.

   cp demo\_project/settings/local.py-dist
   demo\_project/settings/local.py

-  Install requirements

   pip install -r requirements.txt

-  Run migrate

   python manage.py migrate

-  Run server

   python manage.py runserver

The installed demo app shows the various suported chart types.

In case you want to use mongo data while charting, you must have mongodb
properly setup and **pymongo** installed. Make sure mongo server is
running.

::

    mongod --dbpath ~/data/db

Mongo setup is optional and is not needed to get running with demo
project.

Overview of Plot generation
---------------------------

Generating a plot requires two things. A DataSource object and a Chart
object.

In your view, you do something like this:

::

    from graphos.sources.simple import SimpleDataSource
    from graphos.renderers.gchart import LineChart

    data =  [
            ['Year', 'Sales', 'Expenses'],
            [2004, 1000, 400],
            [2005, 1170, 460],
            [2006, 660, 1120],
            [2007, 1030, 540]
        ]
    # DataSource object
    data_source = SimpleDataSource(data=data)
    # Chart object
    chart = LineChart(data_source)
    context = {'chart': chart}
    return render(request, 'yourtemplate.html', context)

And then in the template:

::

    {{ chart.as_html }}

In this example we are planning to use Google chart, as is evident from
the import statement in the view, we import gchart.LineChart. So we must
also include the google chart javascript in our template.

::

    <script type="text/javascript" src="https://www.google.com/jsapi"></script>
    <script type="text/javascript">
        google.load("visualization", "1", {packages:["corechart"]});
    </script>

So the template would look like

::

    <script type="text/javascript" src="https://www.google.com/jsapi"></script>
    <script type="text/javascript">
        google.load("visualization", "1", {packages:["corechart"]});
    </script>

    {{ chart.as_html }}

If we want to use yui LineChart instead of google LineChart, our view
would have:

::

    from graphos.renderers.yui import LineChart
    chart = LineChart(data_source)

And our template would inclue yui javascript and it would look like:

::

    <script src="http://yui.yahooapis.com/3.10.0/build/yui/yui-min.js"></script>
    {{ chart.as_html }}

See, how easy it was to switch from gchart to yui. You did not have to
write or change a single line of javascript to switch from gchart to
yui. All that was taken care of by as\_html() of the chart object.

DataSources
-----------

SimpleDataSource
~~~~~~~~~~~~~~~~

This should be used if you want to generate a chart from Python list.

::

    from graphos.sources.simple import SimpleDataSource
    data = SimpleDataSource(data=data)

Data could be:

::

    data = [
           ['Year', 'Sales', 'Expenses', 'Items Sold', 'Net Profit'],
           ['2004', 1000, 400, 100, 600],
           ['2005', 1170, 460, 120, 710],
           ['2006', 660, 1120, 50, -460],
           ['2007', 1030, 540, 100, 490],
           ]

or it could be

::

    data = [
           ['Year', 'Sales', 'Expenses'],
           ['2004', 1000, 400],
           ['2005', 1170, 460],
           ['2006', 660, 1120],
           ['2007', 1030, 540],
           ]

or it could be

::

    data = [
           ['Year', 'Sales', 'Expenses'],
           ['2004', 1000, 400],
           ['2005', 1170, 460],
           ]

You got the idea.

data has to be a list of lists. First row of data tells the headers.
First element of each list elementis the x axis.

This data essentially tells that in year 2004, sales was 1000 units and
expense was 400 units. And in year 2005, sales was 1170 units and
expense was 460 units.

ModelDataSource
~~~~~~~~~~~~~~~

This should be used if you want to generate a chart from a Django
queryset.

::

    from graphos.sources.model import ModelDataSource
    queryset = Account.objects.all()
    data_source = ModelDataSource(queryset,
                                  fields=['year', 'sales'])

This assumes that there is a Django model called Account which has
fields ``year`` and ``sales``. And you plan to plot year on x axis and
sales on y axis.

Or you could say

::

    data_source = ModelDataSource(queryset,
                                  fields=['year', 'sales', 'expenses'])

This would plot the yearly sale and yearly expense

CSVDataSource
~~~~~~~~~~~~~

This should be used if you want to generate a chart from a CSV file.

::

    from graphos.sources.csv_file import CSVDataSource
    csv_file = open("hello.csv")
    data_source = CSVDataSource(csv_file)

MongoDataSource
~~~~~~~~~~~~~~~

TODO

Charts
------

We have following charts

-  Gchart

   -  gchart.LineChart
   -  gchart.BarChart
   -  gchart.ColumnChart
   -  gchart.PieChart
   -  gchart.AreaChart
   -  gchart.TreeMapChart
   -  gchart.CandlestickChart
   -  gchart.GaugeChart

-  Yui

   -  yui.LineChart
   -  yui.BarChart
   -  yui.ColumnChart
   -  yui.PieChart
   -  yui.AreaChart
   -  yui.SplineChart
   -  yui.AreaSplineChart

-  Flot

   -  flot.LineChart
   -  flot.BarChart
   -  flot.ColumnChart
   -  flot.PieChart
   -  flot.PointChart

-  Morris

   -  morris.LineChart
   -  morris.BarChart
   -  morris.AreaChart
   -  morris.DonutChart

-  Highcharts

   -  highcharts.LineChart
   -  highcharts.BarChart
   -  highcharts.ColumnChart
   -  highcharts.PieChart
   -  highcharts.AreaChart

Most of the chart providers support LineChart, BarChart, ColumnChart and
PieChart, and it is very easy to switch from specific chart type of one
provider to other. eg: It is super quick to switch from gchart LineChart
to flot LineChart.

More Examples
-------------

Using SimpleDataSource with gchart LineChart
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

View
^^^^

::

    data =  [
            ['Year', 'Sales', 'Expenses'],
            [2004, 1000, 400],
            [2005, 1170, 460],
            [2006, 660, 1120],
            [2007, 1030, 540]
        ]
    from graphos.sources.simple import SimpleDataSource
    from graphos.renderers.gchart import LineChart
    chart = LineChart(SimpleDataSource(data=data))

Template
^^^^^^^^

::

    <script type="text/javascript" src="https://www.google.com/jsapi"></script>
    <script type="text/javascript">
        google.load("visualization", "1", {packages:["corechart"]});
    </script>

    {{ chart.as_html }}

Using SimpleDataSource with yui LineChart
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

View
^^^^

::

    data =  [
            ['Year', 'Sales', 'Expenses'],
            [2004, 1000, 400],
            [2005, 1170, 460],
            [2006, 660, 1120],
            [2007, 1030, 540]
        ]
    from graphos.sources.simple import SimpleDataSource
    from graphos.renderers.yui import LineChart
    chart = LineChart(SimpleDataSource(data=data))

Template
^^^^^^^^

::

    <script src="http://yui.yahooapis.com/3.10.0/build/yui/yui-min.js"></script>
    {{ chart.as_html }}

Using SimpleDataSource with yui BarChart
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

View
^^^^

::

    data =  [
            ['Year', 'Sales', 'Expenses'],
            [2004, 1000, 400],
            [2005, 1170, 460],
            [2006, 660, 1120],
            [2007, 1030, 540]
        ]
    from graphos.sources.simple import SimpleDataSource
    from graphos.renderers.yui import BarChart
    chart = BarChart(SimpleDataSource(data=data))

Template
^^^^^^^^

::

    <script src="http://yui.yahooapis.com/3.10.0/build/yui/yui-min.js"></script>
    {{ chart.as_html }}

Using SimpleDataSource with gchart BarChart
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

View
^^^^

::

    data =  [
            ['Year', 'Sales', 'Expenses'],
            [2004, 1000, 400],
            [2005, 1170, 460],
            [2006, 660, 1120],
            [2007, 1030, 540]
        ]
    from graphos.sources.simple import SimpleDataSource
    from graphos.renderers.gchart import BarChart
    chart = BarChart(SimpleDataSource(data=data))

Template
^^^^^^^^

::

    <script type="text/javascript" src="https://www.google.com/jsapi"></script>
    <script type="text/javascript">
        google.load("visualization", "1", {packages:["corechart"]});
    </script>
    {{ chart.as_html }}

Options
-------

Your rendered chart is contained in a div.

Setting id of chart containing div
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

You might want to do additional jquery or javascript operations with
your chart containing div. In such case you might want to set an id on
the div. You can do this while instantiating the chart element.

::

    chart = gchart.LineChart(html_id='gchart_div')

Setting width and height of chart containing div
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

You can control the width and height of chart containing div while
instantiating the chart element.

::

    chart = gchart.LineChart(simple_data_source, height=100, width=100)

Chart specific options
~~~~~~~~~~~~~~~~~~~~~~

Different chart providers give different options to customise the chart.

Google chart api allows setting title for the rendered chart, see
`Gchart
documentation <https://developers.google.com/chart/interactive/docs/quick_start>`__,
using ``title`` attribute. You can accomplish this by adding a keyword
argument called ``options`` while instantiating the chart element.

::

    chart = gchart.LineChart(simple_data_source, height=100, width=100, options={'title': 'Sales growth'})

Google pie chart allows making the chart as 3 dimensional. You can
accomplish this by using keyword argument ``options``.

::

    pie_chart = gchart.PieChart(simple_data_source, options={'is3D': True})

Morris.js allows options like lineWidth, smooth etc. You can find more
`here <http://morrisjs.github.io/morris.js/lines.html#lines>`__. You can
accomplish this by using ``options``.

::

    chart = morris.LineChart(simple_data_source, options={'lineWidth': 50, 'smooth': False})

Installation
------------

pip install django-graphos

Compatibility
-------------

Graphos is compatible with Python 2.7 and Python 3.3+

`available on pypi <https://pypi.python.org/pypi/django-graphos/>`__

Handling non serializable fields
--------------------------------

You need to override get\_data() of existing DataSource and convert
datetime field into something which could be serialized.

Assuming you are using a Python list as data, then you need to do:

::

    from graphos.sources.simple import SimpleDataSource
    class MySimpleDataSource(SimpleDataSource):
        def get_data(self):
            data = super(MySimpleDataSource, self).get_data()
            header = data[0]
            data_without_header = data[1:]
            for row in data_without_header:
                # Assuming first column contains datetime
                row[0] = row[0].year
            data_without_header.insert(0, header)
            return data_without_header

And data has

::

    d1 = datetime(2015, 7, 8, 1, 1)
    d2 = datetime(2016, 7, 8, 3, 1)

    data1 = [
             ['Year', 'Sales', 'Expenses', 'Items Sold', 'Net Profit'],
             [d1, 1000, 400, 100, 600],
             [d2, 1170, 460, 120, 310],
     ]

    chart = flot.LineChart(MySimpleDataSource(data=data1))

If you are planning to use queryset with ModelDataSource, then you would
create following class

::

    from graphos.sources.model import ModelDataSource
    class MyModelDataSource(ModelDataSource):
        def get_data(self):
            data = super(MyModelDataSource, self).get_data()
            header = data[0]
            data_without_header = data[1:]
            for row in data_without_header:
                # Assuming second column contains datetime
                row[1] = row[1].year
            data_without_header.insert(0, header)
            return data_without_header

And you would use this class like:

::

    queryset = Account.objects.all()
    chart = flot.LineChart(MyModelDataSource(queryset=queryset, fields=['sales', 'datetime_field','expenses']))

Creating new DataSource
-----------------------

A DataSource is a class which has these three methods.

::

    get_data
    get_header
    get_first_column

``get_header`` is used by a ``Renderer`` to create the labels.
``get_first_column`` is used to set the x axis labels ``get_data`` is
used to get the data for display. It should always return a nested list.
Eg:

::

    [
        ['Year', 'Sales', 'Expenses'],
        [2004, 1000, 400],
        [2005, 1170, 460],
        [2006, 660, 1120],
        [2007, 1030, 540]
    ]

If you create a class extending ``SimpleDataSource``, and implement
``get_data``. You get ``get_header`` and ``get_first_column`` for free.

Creating new Renderer
---------------------

A renderer is a class which takes a ``DataSource`` and can convert it to
the html to display.

The only required method on a ``Renderer`` is ``as_html``. This will
convert the data to a format which can display the chart.

Generally you will convert the data to json and pass it to the template
which you return.

License
-------

BSD

.. |Build Status| image:: https://travis-ci.org/agiliq/django-graphos.png
   :target: https://travis-ci.org/agiliq/django-graphos