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import param
import paramnb

Tooltips

Doc strings supplied when a parameter is declared are displayed as a 'tooltip' when the mouse hovers over the widget label (x in the widget below)



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class TooltipExample(param.Parameterized):
    x = param.Number(default=1,bounds=(0,2),doc="X position")



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paramnb.Widgets(TooltipExample)

Recursive editing

Parameter types such as param.ObjectSelector allow selection of another Parameterized object; paramNB also allows the parameters of the selected object to be edited by clicking the ... button next to any selected Parameterized object:



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class Location(param.Parameterized):
    duration = param.Integer(default=5, bounds=(0,10))

locations = [Location(name="One"),Location(name="Two")]

class Employee(param.Parameterized):
    age = param.Integer(default=30,bounds=(18,100))
    location = param.ObjectSelector(default=locations[0],objects=locations)

class Task(param.Parameterized):
    employee = param.ObjectSelector(objects=[Employee(name="A"),Employee(name="B")])



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paramnb.Widgets(Task)



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Task.employee.location.duration

View parameters

The paramNB library provides an easy way to manipulate parameters on Parameterized using the widgets in the notebook. In addition to controlling input parameters a common usecase for using widgets in the notebook is to dynamically control some visual display output. In addition to all the standard parameters supplied by the param library, paramNB also supplies so called View parameters, which render their output in a widget area. The output parameters may be updated simply by setting the parameter on the class.

In the first simple example we will declare a Parameterized class with a Number parameter called magnitude and an HTML parameter which will let us display some arbitrary HTML. In this case we will simply generate a pandas dataframe with random data within the update method and use the to_html method to convert it to an HTML table. If we define the update method as the callback of the widgets the table will now update whenever the slider is dragged. To ensure that the output is drawn on initialization we set on_init=True.



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import numpy as np
import pandas as pd



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class HTMLExample(param.Parameterized):
    
    magnitude = param.Number(1, bounds=(0, 10))

    output = paramnb.view.HTML()
    
    def update(self, **kwargs):
        self.output = pd.DataFrame(np.random.rand(10,2)*self.magnitude).to_html()

example = HTMLExample(name='HTMLExample')
paramnb.Widgets(example, on_init=True, callback=example.update)

The HTML parameter accepts any arbitrary HTML string but for convenience paramNB also allows supplying a custom renderer function, which converts the view data to HTML. In this case we declare to parameters to control the amplitude and frequency of a sine curve and then declare an HTML parameter which uses a HoloViews MPLRenderer to render the output. Note that we can additionally supply the size of the output as a tuple of (width, height) in pixels, in this case (300, 300).

Additionally we can declare the view_position, which specifies where the viewing widget will be placed in relation to the input widgets:



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import holoviews as hv
renderer = hv.renderer('matplotlib')

class ImageExample(param.Parameterized):

    color = param.Color(default='#000000', precedence=0)

    element = param.ObjectSelector(default=hv.Curve,
                                   objects=[hv.Curve, hv.Scatter, hv.Area],
                                   precedence=0)

    amplitude = param.Number(default=2, bounds=(2, 5))
    
    frequency = param.Number(default=2, bounds=(1, 10))
    
    output = paramnb.view.Image(renderer=lambda x: (renderer(x)[0], (300, 300)))

    def update(self, **kwargs):
        self.output = self.element(self.amplitude*np.sin(np.linspace(0, np.pi*self.frequency)),
                                  vdims=[hv.Dimension('y', range=(-5, 5))])(style=dict(color=self.color))

example = ImageExample(name='HoloViews Example')
paramnb.Widgets(example, callback=example.update, on_init=True, view_position='right')

Finally, the generic View parameter may also be used to display the rich repr of any object, effectively mirroring the output of IPython's display function. If we load the HoloViews extension with the bokeh backend, and subclass the ImageExample, we can also render bokeh plots in this way:



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hv.extension('bokeh')

class ViewExample(ImageExample):
    
    output = paramnb.view.View()
    
example = ViewExample(name='HoloViews+Bokeh Example')
paramnb.Widgets(example, callback=example.update, on_init=True, view_position='right')