Describe a visualization

All the matplotlib visualizations implemented so far in NDlib extends the abstract class nndlib.viz.mpl.DiffusionViz.DiffusionPlot.

class ndlib.viz.mpl.DiffusionViz.DiffusionPlot(model, trends)

Conversely, visualizations that use the bokeh library, should extend the abstract class nndlib.viz.bokeh.DiffusionViz.DiffusionPlot.

class ndlib.viz.bokeh.DiffusionViz.DiffusionPlot(model, trends)

Here is introduced the pattern for describing novel matplotlib based visualization, bokeh ones following the same rationale.

So far DiffusionPlot implements the visualization logic only for generic trend line plot built upon simulation iterations and model metadata.

Line Plot Definition

As convention a new visualization should be described in a python file named after it, e.g. a MyViz class should be implemented in a file.

DiffusionPlot.__init__(self, model, iteration)

Initialize self. See help(type(self)) for accurate signature.

In oder to effectively describe the visualization the __init__ function of ndlib.viz.bokeh.DiffusionViz.DiffusionPlot must be specified as follows:

from ndlib.viz.mpl.DiffusionViz import DiffusionPlot

class MyViz(DiffusionPlot):

        def __init__(self, model, trends):
                super(self.__class__, self).__init__(model, trends)
                self.ylabel = "#Nodes"
                self.title = "Diffusion Trend"

Data Preparation

Once described the plot metadata it is necessary to prepare the data to be visualized through the plot() method.

To do so, the iteration_series(percentile) method of the base class has to be overridden in MyViz.

DiffusionPlot.iteration_series(self, percentile)

Prepare the data to be visualized

Parameters:percentile – The percentile for the trend variance area
Returns:a dictionary where iteration ids are keys and the associated values are the computed measures

Such method can access the trend data, as returned by ndlib.models.DiffusionModel.DiffusionModel.build_trends(self, iterations) in self.iterations.