django-graphos 0.2

Supported Backends:

• Python Nested lists

• CSV Files

• MongoDB

• Django ORM

Charting API Supported

• Flot

• Google Charts API

• YUI Charts

• Morris.js

• Highcharts

• Matplotlib

Chart types supported

Demo

• 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]
    ]
data_source = SimpleDataSource(data=data)
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 gchart 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 gchart 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.

Examples

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))

from graphos.sources.csv_file import CSVDataSource
from graphos.renderers.yui import LineChart
csv_file = open("hello.csv")
chart = LineChart(CSVDataSource(csv_file))

from graphos.sources.model import ModelDataSource
from someapp.models import Accounts
queryset = Accounts.objects.filter(foo=bar)
chart = LineChart(ModelDataSource(queryset, fields=["year", "sales", "expenses"]))

Installation

pip install django-graphos

Compatibility

Graphos is compatible with Python 2.7 and Python 3.3+

available on pypi

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.

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']))

License

BSD