#!/usr/bin/env python
    import pive.environment as environment
    import pive.inputmanager as inputmanager

	# Assuming you have a testdata.json file with some datapoints
	# in the same directory. Try to create JSON-Objekts as Key/Value
	# pairs or use a JSON formatted String Object. CSV is also
	# supported.
	input_path = 'samples/numerical.json'

	###########################
	### Basic usage of pive ###
	###########################
	# 1)Set up the environment by creating the input manager and
	# passing it to an environment. Optionally, you can omit
	# an output path. Default is 'output/'.
	manager = inputmanager.InputManager(mergedata=False)
	env = environment.Environment(inputmanager=manager)

	# 2) Load your dataset into the environment to get a
	# list of supported visualizations.
	supported = env.load(input_path)

	# 3) Check if your desired chart is in the list and choose
	# it as your visualization object. Alternatively you can
	# print out the list of the supported charts and choose directly
	# from it. The accessors, e.g. CHART_LINE, are environment
	# constants and represent the charts included in pive.
	if environment.CHART_LINE in supported:
	    chart = env.choose(CHART_LINE)

        #You can now edit the charts properties.
	    chart.set_width(900)
	    chart.set_height(500)

        # 4.1) Let the environment render the chart.
        # The visualizuation files will be generated
        # in the output path defined in the environment.
        env.render(chart)

        # 4.2) Optionally you can receive the
        # javascript code and its dataset as json.
        code = env.render_code(chart)