add_tools ( HoverTool ( tooltips = None ), TapTool (), BoxSelectTool ()) graph_renderer = from_networkx ( G, nx. text = "Graph Interaction Demonstration" plot. Import networkx as nx from bokeh.models import ( BoxSelectTool, Circle, HoverTool, MultiLine, NodesAndLinkedEdges, Plot, Range1d, TapTool ) from bokeh.palettes import Spectral4 from otting import from_networkx, show G = nx. append ( bezier ( sy, ey, 0, steps )) graph. append ( bezier ( sx, ex, 0, steps )) ys. layout_provider = StaticLayoutProvider ( graph_layout = graph_layout ) # draw quadratic bezier paths def bezier ( start, end, control, steps ): return xs, ys =, sx, sy = graph_layout steps = for node_index in node_indices : ex, ey = graph_layout xs.
#Bokeh python zip
data = dict ( start = * N, end = node_indices ) # create a static layout circ = x = y = graph_layout = dict ( zip ( node_indices, zip ( x, y ))) graph. glyph = Ellipse ( height = 0.1, width = 0.2, fill_color = "color" ) graph. Import math from bokeh.models import Ellipse, GraphRenderer, StaticLayoutProvider from bokeh.palettes import Spectral8 from otting import figure, show N = 8 node_indices = list ( range ( N )) plot = figure ( title = "Graph Layout Demonstration", x_range = ( - 1.1, 1.1 ), y_range = ( - 1.1, 1.1 ), tools = "", toolbar_location = None ) graph = GraphRenderer () graph. The following codes snippet uses this provider model to produce a data = dict ( start = * N, end = node_indices )īokeh comes with a built-in LayoutProvider model that includesĪ dictionary of (x,y) coordinates for nodes. data = dict ( index = node_indices, fill_color = Spectral8 ) # add the rest of the assigned values to the data source graph. glyph = Ellipse ( height = 0.1, width = 0.2, fill_color = "fill_color" ) # assign a palette to ``fill_color`` and add it to the data source graph. Import math from otting import figure from bokeh.models import GraphRenderer, Ellipse from bokeh.palettes import Spectral8 # list the nodes and initialize a plot N = 8 node_indices = list ( range ( N )) plot = figure ( title = "Graph layout demonstration", x_range = ( - 1.1, 1.1 ), y_range = ( - 1.1, 1.1 ), tools = "", toolbar_location = None ) graph = GraphRenderer () # replace the node glyph with an ellipse # set its height, width, and fill_color graph. Glyph styling or make data available for callbacks or hover tooltips.Īssigns scalar values to the height and width attributes of the Ellipse,Īssigns a palette to the fill_color attribute of the Ellipse,Īnd adds the assigned values to the node data source. You can add extra meta-data to these sources to enable vectorized Indices for the start and end of the edges. The ColumnDataSource of the edge sub-renderer must have a "index" column with the unique indices of the nodes. The ColumnDataSource of the node sub-renderer must have an Observe the following requirements for the data sources belonging Of edges through the edge_renderer property. You can similarly modify the style properties The default Circle node glyph with any instance of the XYGlyph such as The node_renderer property of the GraphRenderer. This lets you customize nodes by modifying The GraphRenderer model maintains separate sub- GlyphRenderersįor graph nodes and edges.
#Bokeh python code
These types of callbacks require a Bokeh server to be running such that the Python code can be executed.īoth types of callbacks can be used with widgets, although an easier-to-use widget toolkit built on top of Bokeh, called Panel, is recommended for sophisticated widget and dashboard creation.Bokeh lets you create network graph visualizations and configure through the execution of arbitrary Python code. Python callbacks allow for transformations of any and all plot features, data sources, etc. Javascript callback are used to provide the interactivity in the previous example.
plots can still be output to stand alone HTML and embedded in web sites backed by standard web servers. These allow for fast updating of the plot display while maintaining the "stand alone" nature of the figure, i.e. $x$/$y$-axis scaling, by writing Javascript code that is executed on set interactions, e.g. Javascript callbacks allow for transformations of the plot's data sources and other features, e.g. With Bokeh, you can make sophisticated interactive visualizations with callbacks.
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