def build_graph_renderer(graph, nodes, node_colors, node_sizes,
node_scores, node_labels):
graph_renderer = from_networkx(graph, nx.kamada_kawai_layout)
graph_renderer.node_renderer.data_source.data['index'] = nodes
graph_renderer.node_renderer.data_source.data['color'] = node_colors
graph_renderer.node_renderer.data_source.data['size'] = node_sizes
graph_renderer.node_renderer.data_source.data[
'root_score'] = node_scores[0]
graph_renderer.node_renderer.data_source.data[
'usage_score'] = node_scores[1]
graph_renderer.node_renderer.data_source.data['label'] = node_labels
graph_renderer.node_renderer.glyph = Circle(size="size",
fill_color="color")
graph_renderer.node_renderer.selection_glyph = Circle(
size="size", fill_color="color")
graph_renderer.node_renderer.hover_glyph = Circle(size="size",
fill_color="color")
graph_renderer.edge_renderer.glyph = MultiLine(line_color="#CCCCCC",
line_alpha=0.8,
line_width=5)
graph_renderer.edge_renderer.selection_glyph = MultiLine(
line_color=Spectral4[2], line_width=5)
graph_renderer.edge_renderer.hover_glyph = MultiLine(
line_color=Spectral4[1], line_width=5)
graph_renderer.selection_policy = NodesAndLinkedEdges()
graph_renderer.inspection_policy = NodesAndLinkedEdges()
return graph_renderer
def get_graph(self):
start = self.data['Start']
end = self.data['End']
fl = self.data['Index']
fio = self.data['FIO']
node_indices = end.append(pd.Series(pd.Series.unique(self.data['Start'])), ignore_index=True)
#fl = pd.Series.unique(self.data['Index'])
#fl = self.data['Index']
#edges = zip(start, end)
edges =[]
for i in range(len(start)):
edges.append(tuple([start[i], end[i], {'OGRNIP': str(fl[i]) + " - " + str(fio[i])}]))
G = nx.Graph()
G.add_nodes_from(node_indices)
G.add_edges_from(edges)
#print(G.adj.items())
#print(G.edges.data())
ogrns =[]
for item in G.edges.data():
#print(item[2]['OGRNIP'])
ogrns.append(item[2]['OGRNIP'])
tooltips=[
#("index", "$index"),
#("(x,y)", "($x, $y)"),
("1 - ОГРН", "@start"),
("2 - ОГРН", "@end"),
("ФЛ", "@OGRNIP"),
#("Моя подсказка", "@end"),
]
# hover = HoverTool(tooltips=tooltips)
p = figure(plot_width=self.width, plot_height=self.height, x_range=(-3.1, 3.1), y_range=(-3.1, 3.1),
x_axis_type=None, y_axis_type=None, tools=[],
min_border=0, outline_line_color="white")
# p.add_tools(WheelZoomTool())
# p.add_tools(PanTool())
p.add_tools(HoverTool(tooltips=tooltips), TapTool(), BoxSelectTool(), WheelZoomTool(), PanTool())
#graph = from_networkx(G, nx.circular_layout, scale=1, center=(0, 0))
graph = from_networkx(G, nx.spring_layout, scale=2, center=(0, 0))
graph.node_renderer.glyph = Circle(size=5, fill_color="gray")
graph.node_renderer.hover_glyph = Circle(size=5, fill_color="red")
graph.node_renderer.selection_glyph = Circle(size=5, fill_color="green")
graph.edge_renderer.glyph = MultiLine(line_color="#CCCCCC", line_alpha=0.8, line_width=1)
graph.edge_renderer.selection_glyph = MultiLine(line_color="gray", line_width=1)
graph.edge_renderer.hover_glyph = MultiLine(line_color="gray", line_width=1)
graph.selection_policy = NodesAndLinkedEdges()
graph.inspection_policy = EdgesAndLinkedNodes()
# print(graph.node_renderer.data_source.data)
# print(graph.edge_renderer.data_source.data)
graph.edge_renderer.data_source.data['OGRNIP'] = ogrns
#print(graph.edge_renderer.data_source.data)
#print(graph.node_renderer.data_source.data)
p.renderers.append(graph)
return p
def create_graph():
#Find the layout and color choice
if radio_layout.active == 0:
lay = 'WDegreepos'
elif radio_layout.active == 1:
lay = 'bwcentralpos'
else:
lay = 'ccentralpos'
if radio_color.active == 0:
col = 'DegCol'
elif radio_color.active == 1:
col = 'Colbw'
elif radio_color.active == 2:
col = 'friend'
elif radio_color.active == 3:
col = 'reviewcount'
else:
col = 'comprank'
# Create Network view
graph = GraphRenderer()
graph.node_renderer.data_source.data = nodes_df(G, col)
graph.edge_renderer.data_source.data = edges_df(G)
graph.node_renderer.glyph = Circle(size='size',
fill_color='Col',
line_color="black",
line_alpha=0.1,
fill_alpha=1)
graph.edge_renderer.glyph = MultiLine(line_alpha='alpha',
line_width=0.1,
line_color="#d8b7a4")
graph_layout = dict(nx.get_node_attributes(G, lay))
graph.layout_provider = StaticLayoutProvider(graph_layout=graph_layout)
# Glyph properties on selection
graph.selection_policy = NodesAndLinkedEdges()
graph.inspection_policy = EdgesAndLinkedNodes()
graph.node_renderer.selection_glyph = Circle(size=12,
fill_color='#0A5EB6',
line_color="#002217")
graph.edge_renderer.selection_glyph = MultiLine(line_color="#2972BE",
line_width=0.5,
line_alpha=0.4)
# Adding graph to plot
plot = figure(title="Yelp Users Layout",
x_range=(-6.5, 6.5),
y_range=(-6.5, 6.5),
plot_width=525,
plot_height=525,
toolbar_location="above")
plot.outline_line_alpha = 0
plot.xgrid.grid_line_color = None
plot.ygrid.grid_line_color = None
plot.xaxis.visible = False
plot.yaxis.visible = False
plot.renderers.append(graph) # Adding graph
return row(plot)
def create_cit_map(nodes, edges):
G = nx.DiGraph()
G.add_nodes_from(nodes)
G.add_edges_from(edges)
plot = Plot(plot_width=800, plot_height=800, x_range=Range1d(-1.1,1.1), y_range=Range1d(-1.1,1.1))
plot.title.text = ""
graph_renderer = from_networkx(G, nx.kamada_kawai_layout, scale=1)
graph_renderer.node_renderer.glyph = Circle(size=15)
graph_renderer.node_renderer.selection_glyph = Circle(size=15)
graph_renderer.node_renderer.hover_glyph = Circle(size=15)
graph_renderer.edge_renderer.glyph = MultiLine(line_color="#CCCCCC", line_alpha=0.8, line_width=5)
graph_renderer.edge_renderer.selection_glyph = MultiLine(line_width=5)
graph_renderer.edge_renderer.hover_glyph = MultiLine(line_width=5)
graph_renderer.selection_policy = NodesAndLinkedEdges()
graph_renderer.inspection_policy = EdgesAndLinkedNodes()
pos = graph_renderer.layout_provider.graph_layout
x,y=zip(*pos.values())
source = ColumnDataSource({'x':x,'y':y,'names': nodes})
labels = LabelSet(x='x', y='y', text='names', source=source, level='glyph', x_offset=5, y_offset=5)
plot.renderers.append(labels)
plot.renderers.append(graph_renderer)
#plot.add_layout(labels)
output_file("interactive_graphs.html")
show(plot)
def graphNetwork(data, tags):
G = nx.from_numpy_matrix(data)
graph_plot = Plot(plot_width=800,
plot_height=800,
x_range=Range1d(-1.1, 1.1),
y_range=Range1d(-1.1, 1.1))
graph_plot.title.text = "Graph.\nTotal Nodes: %i\nTotal Edges: %i" % (
G.number_of_nodes(), G.number_of_edges())
node_hover_tool = HoverTool(tooltips=[("hashtag", "@hashtag")])
graph_plot.add_tools(node_hover_tool, BoxZoomTool(), ResetTool(),
WheelZoomTool(), BoxSelectTool())
graph_renderer = from_networkx(G, nx.spring_layout, scale=1, center=(0, 0))
graph_renderer.node_renderer.glyph = Circle(size=15, fill_color='#277bb6')
graph_renderer.node_renderer.hover_glyph = Circle(size=18,
fill_color='#E84A5F')
graph_renderer.edge_renderer.glyph = MultiLine(line_color="gray",
line_alpha=0.8,
line_width=0.5)
graph_renderer.edge_renderer.hover_glyph = MultiLine(line_color='#e09e8f',
line_width=3)
graph_renderer.inspection_policy = NodesAndLinkedEdges()
graph_renderer.selection_policy = EdgesAndLinkedNodes()
graph_renderer.node_renderer.data_source.data['hashtag'] = tags
graph_plot.renderers.append(graph_renderer)
output_file("interactive_graphs.html")
show(graph_plot)
def NLD_pocessing_graph(g, weights, colors, layout):
graph = from_networkx(g, layout, scale=1, center=(0, 0))
# nodes and egdes attributes
graph.node_renderer.data_source.data['degree'] = list(zip(*g.degree))[1]
graph.edge_renderer.data_source.data['weight'] = weights
graph.edge_renderer.data_source.add(colors, 'color')
graph.node_renderer.glyph = Circle(size='nodesize',
fill_alpha=0.8,
fill_color='royalblue')
graph.node_renderer.selection_glyph = Circle(size=10,
fill_alpha=0.8,
fill_color='red')
graph.node_renderer.hover_glyph = Circle(size=10,
fill_alpha=0.8,
fill_color='yellow')
graph.edge_renderer.glyph = MultiLine(line_width=3,
line_alpha=0.8,
line_color='color')
graph.edge_renderer.selection_glyph = MultiLine(line_width=4,
line_alpha=0.8,
line_color='red')
graph.edge_renderer.hover_glyph = MultiLine(line_width=4,
line_alpha=0.8,
line_color='yellow')
graph.edge_renderer.glyph.line_width = {'field': 'weight'}
graph.selection_policy = NodesAndLinkedEdges()
graph.inspection_policy = NodesAndLinkedEdges()
return graph
def circuit_from(bqm):
G = bqm.to_networkx_graph()
plot = Plot(plot_width=600, plot_height=400,
x_range=Range1d(-0.1, 1.1), y_range=Range1d(-.1, 1.1))
plot.title.text = "Multiplication as a BQM"
plot.add_tools(HoverTool(tooltips=None), TapTool(), BoxSelectTool())
graph_renderer = from_networkx(G, circuit_layout)
circle_size = 25
graph_renderer.node_renderer.glyph = Circle(
size=circle_size, fill_color="#F5F7FB")
graph_renderer.node_renderer.selection_glyph = Circle(size=circle_size, fill_color="#EEA64E")
graph_renderer.node_renderer.hover_glyph = Circle(size=circle_size, fill_color="#FFE86C")
edge_size = 2
graph_renderer.edge_renderer.glyph = MultiLine(line_color="#CCCCCC", line_alpha=0.8, line_width=edge_size)
graph_renderer.edge_renderer.selection_glyph = MultiLine(line_color="#EEA64E", line_width=edge_size)
graph_renderer.edge_renderer.hover_glyph = MultiLine(line_color="#FFE86C", line_width=edge_size)
graph_renderer.selection_policy = NodesAndLinkedEdges()
graph_renderer.inspection_policy = NodesAndLinkedEdges()
plot.renderers.append(graph_renderer)
plot.background_fill_color = "#202239"
add_labels(plot)
show(Row(plot))
def define_edges(self):
start = []
end = []
for key in self.vertices:
# Make a copy of the edges in roder to preserve the integrity of
# the Graph
connected_nodes = self.graph.vertices[str(key)].copy()
# If there are Edges for the current looped Vertex
if len(connected_nodes):
for i in range(len(connected_nodes)):
start.append(key) # Add node
end.append(int(connected_nodes.pop())) # Add related edge
# print(start)
# print(end)
# print(self.graph.vertices[str(key)])
color = ['#111111']*len(start)
self.bokeh_graph.edge_renderer.data_source.data = dict(
start=start, end=end,
)
self.bokeh_graph.edge_renderer.glyph = MultiLine(
line_color="#CCCCCC", line_alpha=0.8, line_width=1)
self.bokeh_graph.edge_renderer.selection_glyph = MultiLine(
line_color=Spectral4[2], line_width=3)
self.bokeh_graph.edge_renderer.hover_glyph = MultiLine(
line_color=Spectral4[1], line_width=3)
print(self.bokeh_graph.edge_renderer.data_source.data.keys())
def create_transactor_figure(G):
plot = Plot(plot_width=800,
plot_height=600,
x_range=Range1d(-1.1, 1.1),
y_range=Range1d(-1.1, 1.1))
plot.title.text = "Transactor Visualization"
plot.add_tools(HoverTool(tooltips=None), TapTool(), BoxSelectTool(),
WheelZoomTool(), PanTool())
graph_renderer = from_networkx(G, nx.spring_layout, scale=1, center=(0, 0))
graph_renderer.node_renderer.glyph = Circle(size=4,
fill_color=Spectral4[0])
graph_renderer.node_renderer.selection_glyph = Circle(
size=4, fill_color=Spectral4[2])
graph_renderer.node_renderer.hover_glyph = Circle(size=4,
fill_color=Spectral4[1])
graph_renderer.edge_renderer.glyph = MultiLine(line_color="#CCCCCC",
line_alpha=0.8,
line_width=3)
graph_renderer.edge_renderer.selection_glyph = MultiLine(
line_color=Spectral4[2], line_width=3)
graph_renderer.edge_renderer.hover_glyph = MultiLine(
line_color=Spectral4[1], line_width=3)
graph_renderer.selection_policy = NodesAndLinkedEdges()
graph_renderer.inspection_policy = EdgesAndLinkedNodes()
plot.renderers.append(graph_renderer)
return plot
def plot_subgraph( onto_graph:nx.Graph, node_subset:Union[List[str],Set[str]],
in_IPython:bool=False):
"""
"""
G = onto_graph.subgraph(node_subset)
# Show with Bokeh
plot = Plot(plot_width=1600, plot_height=800,
x_range=Range1d(-1.1, 1.1), y_range=Range1d(-1.1, 1.1))
plot.title.text = "Graph Interaction Demonstration"
node_hover_tool = HoverTool(tooltips=[("index", "@index"), ("club", "@club")])
plot.add_tools(node_hover_tool, BoxZoomTool(), ResetTool(), TapTool(), BoxSelectTool(), PanTool())
graph_renderer = from_networkx(G, nx.kamada_kawai_layout, scale=1, center=(0, 0))
graph_renderer.node_renderer.glyph = Circle(size=15, fill_color=Spectral4[0])
graph_renderer.node_renderer.selection_glyph = Circle(size=15, fill_color=Spectral4[2])
graph_renderer.node_renderer.hover_glyph = Circle(size=15, fill_color=Spectral4[1])
graph_renderer.edge_renderer.glyph = MultiLine(line_alpha=0.8, line_width=1)
graph_renderer.edge_renderer.selection_glyph = MultiLine(line_color=Spectral4[2], line_width=5)
#graph_renderer.edge_renderer.hover_glyph = MultiLine(line_color=Spectral4[1], line_width=5)
graph_renderer.selection_policy = NodesAndLinkedEdges()
#graph_renderer.inspection_policy = EdgesAndLinkedNodes()
plot.renderers.append(graph_renderer)
#output_file("interactive_graphs.html")
if in_IPython: output_notebook()
show(plot)
def test_html(name):
# Use a breakpoint in the code line below to debug your script.
print(f'Hi, {name}')
G = nx.karate_club_graph()
plot = Plot(plot_width=400, plot_height=400,
x_range=Range1d(-1.1, 1.1), y_range=Range1d(-1.1, 1.1))
plot.title.text = "Graph Interaction Demonstration"
plot.add_tools(HoverTool(tooltips=None), TapTool(), BoxSelectTool())
graph_renderer = from_networkx(G, nx.circular_layout, scale=1, center=(0, 0))
graph_renderer.node_renderer.glyph = Circle(size=15, fill_color=Spectral4[0])
graph_renderer.node_renderer.selection_glyph = Circle(size=15, fill_color=Spectral4[2])
graph_renderer.node_renderer.hover_glyph = Circle(size=15, fill_color=Spectral4[1])
graph_renderer.edge_renderer.glyph = MultiLine(line_color="#CCCCCC", line_alpha=0.8, line_width=5)
graph_renderer.edge_renderer.selection_glyph = MultiLine(line_color=Spectral4[2], line_width=5)
graph_renderer.edge_renderer.hover_glyph = MultiLine(line_color=Spectral4[1], line_width=5)
graph_renderer.selection_policy = NodesAndLinkedEdges()
graph_renderer.inspection_policy = EdgesAndLinkedNodes()
plot.renderers.append(graph_renderer)
output_file("interactive_graphs.html")
show(plot)
def create_graph(layout_func,
inspection_policy=None,
selection_policy=None,
**kwargs):
plot = Plot(width=400,
height=400,
x_range=Range1d(-1.1, 1.1),
y_range=Range1d(-1.1, 1.1))
graph_renderer = from_networkx(G, layout_func, **kwargs)
graph_renderer.node_renderer.glyph = Circle(size=15,
fill_color=Spectral4[0])
graph_renderer.node_renderer.selection_glyph = Circle(
size=15, fill_color=Spectral4[2])
graph_renderer.node_renderer.hover_glyph = Circle(size=15,
fill_color=Spectral4[1])
graph_renderer.edge_renderer.glyph = MultiLine(line_color="#CCCCCC",
line_alpha=0.8,
line_width=5)
graph_renderer.edge_renderer.selection_glyph = MultiLine(
line_color=Spectral4[2], line_width=5)
graph_renderer.edge_renderer.hover_glyph = MultiLine(
line_color=Spectral4[1], line_width=5)
if inspection_policy is not None:
graph_renderer.inspection_policy = inspection_policy
if selection_policy is not None:
graph_renderer.selection_policy = selection_policy
plot.renderers.append(graph_renderer)
return plot
def NLD_FD_pocessing_graph(g, weights, colors):
degree_sequence = sorted([d for n, d in g.degree()], reverse=True)
magicnumber = (g.number_of_nodes() / degree_sequence[0]**2)
r = 2.2 / magicnumber
my_points = nx.fruchterman_reingold_layout(g, k=r, iterations=100)
graph_fd = from_networkx(g, my_points)
#posxy=nx.fruchterman_reingold_layout(g)
#for i in range(len(posxy()):
# posxy
#radii = np.random.random(size=N) * 1.5
my_colors = []
for key, value in my_points.items():
x = value[0] + 1.0 # x = -1 .. +1, so move to 0 ... 2
y = value[1] + 1.0 # y = -1 .. +1, so move to 0 ... 2
my_colors.append("#%02x%02x%02x" %
(int(50 + 100 * x), int(30 + 100 * y), 150))
# nodes and egdes attributes
graph_fd.node_renderer.data_source.data['degree'] = list(zip(*g.degree))[1]
graph_fd.node_renderer.data_source.data['degree2'] = [
sqrt(x + 6) for x in graph_fd.node_renderer.data_source.data['degree']
]
graph_fd.node_renderer.data_source.data['nodessize'] = [
x * 115 / (sqrt(g.number_of_nodes() + (np.mean(degree_sequence))))
for x in graph_fd.node_renderer.data_source.data['degree2']
]
graph_fd.node_renderer.data_source.data['my_fill_color'] = my_colors
graph_fd.edge_renderer.data_source.data['weight'] = weights
graph_fd.edge_renderer.data_source.add(colors, 'color')
graph_fd.node_renderer.glyph = Circle(size="nodesize",
fill_color='my_fill_color',
fill_alpha=0.85)
graph_fd.node_renderer.selection_glyph = Circle(size=15,
fill_alpha=0.8,
fill_color='red')
graph_fd.node_renderer.hover_glyph = Circle(size=15,
fill_alpha=0.8,
fill_color='yellow')
graph_fd.edge_renderer.glyph = MultiLine(line_width=2.5,
line_alpha=0.8,
line_color='color')
graph_fd.edge_renderer.selection_glyph = MultiLine(line_width=2.5,
line_alpha=0.8,
line_color='red')
graph_fd.edge_renderer.hover_glyph = MultiLine(line_width=2.5,
line_alpha=0.8,
line_color='yellow')
graph_fd.edge_renderer.glyph.line_width = {'field': 'weight'}
graph_fd.selection_policy = NodesAndLinkedEdges()
graph_fd.inspection_policy = NodesAndLinkedEdges()
return graph_fd
def graphNetwork(adjm, data):
G = nx.from_numpy_matrix(adjm)
data['degree'] = list(dict(G.degree).values())
subgraphs = getSubgraphs(G)
subgraphs = data.join(subgraphs.set_index('nodes'))
subgraphs['subsets'] = adjm.sum(axis=0)
graph_plot = Plot(plot_width=800,
plot_height=800,
x_range=Range1d(-1.1, 1.1),
y_range=Range1d(-1.1, 1.1))
graph_plot.title.text = "Graph.\nTotal Nodes: %i\nTotal Edges: %i\t Total subgraphs:%i" % (
G.number_of_nodes(), G.number_of_edges(),
len(subgraphs.subgraph.unique()))
node_hover_tool = HoverTool(
tooltips=[("hashtag",
"@hashtag"), ("freq", "@frequency"), (
'degree',
'@degree'), ('subsets',
'@subsets'), ('subgraph',
'@subgraph'), ('ix', '@ix')])
graph_plot.add_tools(node_hover_tool, BoxZoomTool(), ResetTool(),
WheelZoomTool())
graph_renderer = from_networkx(G, nx.spring_layout, scale=1, center=(0, 0))
graph_renderer.node_renderer.glyph = Circle(size=18, fill_color='#277bb6')
graph_renderer.node_renderer.hover_glyph = Circle(size=18,
fill_color='#E84A5F')
graph_renderer.node_renderer.glyph.properties_with_values()
graph_renderer.edge_renderer.glyph = MultiLine(line_color="gray",
line_alpha=0.7,
line_width=0.3)
graph_renderer.edge_renderer.hover_glyph = MultiLine(line_color='#e09e8f',
line_width=3)
graph_renderer.node_renderer.data_source.data[
'hashtag'] = subgraphs.ht.values
graph_renderer.node_renderer.data_source.data[
'frequency'] = subgraphs.frq.values
graph_renderer.node_renderer.data_source.data[
'degree'] = subgraphs.degree.values
graph_renderer.node_renderer.data_source.data[
'subgraph'] = subgraphs.subgraph.values
graph_renderer.node_renderer.data_source.data['ix'] = list(subgraphs.index)
graph_renderer.node_renderer.data_source.data[
'subsets'] = subgraphs.subsets.values
graph_renderer.inspection_policy = NodesAndLinkedEdges()
graph_renderer.selection_policy = NodesAndLinkedEdges()
graph_plot.toolbar.active_inspect = [node_hover_tool]
graph_plot.renderers.append(graph_renderer)
#output_file(path+topic+"_graph_N"+str(nodeThresh)+'E'+str(edgeThresh)+".html")
show(graph_plot)
subgraphs = subgraphs.sort_values(by='subgraph')
return subgraphs
def make_plottable(colors=None, sizes=None, tweets=None):
global NETWORK
if not NETWORK:
NETWORK = reload_json(USER_GRAPH_FNAME,
nx.DiGraph,
transform=nx.node_link_graph)
graph = from_networkx(NETWORK, graphviz_layout, prog="sfdp")
if not colors:
node_color = Spectral4[0]
else:
node_color = "color"
graph.node_renderer.data_source.add(colors, "color")
if not sizes:
node_size = 8
else:
node_size = "size"
graph.node_renderer.data_source.add(sizes, "size")
if tweets:
graph.node_renderer.data_source.add(tweets, "desc")
edge_width = 0.3
select_edge_width = 2
node_alpha = 0.95
edge_alpha = 0.3
edge_color = "#666666"
select_color = Spectral4[2]
hover_color = Spectral4[1]
graph.node_renderer.glyph = Circle(
size=node_size,
fill_color=node_color,
fill_alpha=node_alpha,
line_color=node_color,
line_alpha=node_alpha,
)
graph.node_renderer.selection_glyph = Circle(size=node_size,
fill_color=select_color,
line_color=select_color)
graph.node_renderer.hover_glyph = Circle(size=node_size,
fill_color=hover_color,
line_color=hover_color)
graph.edge_renderer.glyph = MultiLine(line_color=edge_color,
line_alpha=edge_alpha,
line_width=edge_width)
graph.edge_renderer.selection_glyph = MultiLine(
line_color=select_color, line_width=select_edge_width)
graph.edge_renderer.hover_glyph = MultiLine(line_color=hover_color,
line_width=select_edge_width)
graph.selection_policy = NodesAndLinkedEdges()
graph.inspection_policy = NodesAndLinkedEdges()
return graph
def drawgraph(self, ds, filterMin, filterMax, layout):
G = nx.Graph() # create an empty graph with no nodes and no edges
# nodes = []
# for i in range(len(ds.getNodes())):
# nodes.append([])
# # print(ds.getNodes()[i].getName())
# for j in range(len(ds.getNodes())):
# nodes[i].append(ds.getNodes()[i].getLinks()[j][1])
# # print(" " + str(ds.getNodes()[i].getLinks()[j][1]))
# ds.toMinSpanTree()
nodes = ds.getDoubleList(filterMin, filterMax, False)
x = filterMin
adj = np.array(nodes)
G = nx.from_numpy_matrix(adj)
for i in range(len(G.node)):
G.node[i]['name'] = ds.getNames()[i]
#pos = nx.drawing.layout.circular_layout(G, 1, None, 2)
#nx.draw_networkx(G, pos, with_labels=True)
# pt.show()
#plt.show()
plot = Plot(plot_width=500, plot_height=500,
x_range=Range1d(-1.1, 1.1), y_range=Range1d(-1.1, 1.1))
node_hover_tool = HoverTool(tooltips=[("Name of this node", "@name")])
# plot.add_tools(node_hover_tool, BoxZoomTool(), ResetTool())
plot.add_tools(node_hover_tool, TapTool(), BoxSelectTool(), BoxZoomTool(), UndoTool(), RedoTool(), SaveTool(),
ResetTool())
plot.toolbar_location = 'left'
if layout == 0:
graph_renderer = from_networkx(G, nx.circular_layout, scale=1, center=(0, 0))
elif layout == 1:
graph_renderer = from_networkx(G, nx.spring_layout, scale=1, center=(0, 0))
else:
graph_renderer = from_networkx(G, nx.random_layout)
graph_renderer.node_renderer.glyph = Circle(size=15, fill_color=Spectral4[0])
graph_renderer.node_renderer.selection_glyph = Circle(size=15, fill_color=Spectral4[2])
graph_renderer.node_renderer.hover_glyph = Circle(size=15, fill_color=Spectral4[1])
graph_renderer.edge_renderer.glyph = MultiLine(line_color="#CCCCCC", line_alpha=0.8, line_width=5)
graph_renderer.edge_renderer.selection_glyph = MultiLine(line_color=Spectral4[2], line_width=5)
graph_renderer.edge_renderer.hover_glyph = MultiLine(line_color=Spectral4[1], line_width=5)
graph_renderer.selection_policy = NodesAndLinkedEdges()
#graph_renderer.inspection_policy = EdgesAndLinkedNodes()
plot.renderers.append(graph_renderer)
output_file("interactive_graphs.html")
return plot
def NLD_random_processing_graph(g, weights, colors, layout):
degree_sequence = sorted([d for n, d in g.degree()], reverse=True)
magicnumber = (g.number_of_nodes() / degree_sequence[0]**2)
r = 2.2 / magicnumber
my_points = nx.random_layout(g)
graph = from_networkx(g, layout)
my_colors = []
for key, value in my_points.items():
x = value[0] + 1.0 # x = -1 .. +1, so move to 0 ... 2
y = value[1] + 1.0 # y = -1 .. +1, so move to 0 ... 2
my_colors.append("#%02x%02x%02x" %
(int(50 + 100 * x), int(30 + 100 * y), 150))
# nodes and egdes attributes
graph.node_renderer.data_source.data['degree'] = list(zip(*g.degree))[1]
graph.node_renderer.data_source.data['degree2'] = [
sqrt(x + 6) for x in graph.node_renderer.data_source.data['degree']
]
graph.node_renderer.data_source.data['nodessize'] = [
x * 115 / (sqrt(g.number_of_nodes() + (np.mean(degree_sequence))))
for x in graph.node_renderer.data_source.data['degree2']
]
graph.node_renderer.data_source.data['my_fill_color'] = my_colors
graph.edge_renderer.data_source.data['weight'] = weights
graph.edge_renderer.data_source.add(colors, 'color')
graph.node_renderer.glyph = Circle(size='nodesize',
fill_alpha=0.85,
fill_color='my_fill_color')
graph.node_renderer.selection_glyph = Circle(size=10,
fill_alpha=0.8,
fill_color='red')
graph.node_renderer.hover_glyph = Circle(size=10,
fill_alpha=0.8,
fill_color='yellow')
graph.edge_renderer.glyph = MultiLine(line_width=3,
line_alpha=0.8,
line_color='color')
graph.edge_renderer.selection_glyph = MultiLine(line_width=4,
line_alpha=0.8,
line_color='red')
graph.edge_renderer.hover_glyph = MultiLine(line_width=4,
line_alpha=0.8,
line_color='yellow')
graph.edge_renderer.glyph.line_width = {'field': 'weight'}
graph.selection_policy = NodesAndLinkedEdges()
graph.inspection_policy = NodesAndLinkedEdges()
return graph
def plot_bokeh_network_i(G,
id,
layout=nx.circular_layout,
output="testing.html"):
plot = Plot(plot_width=400,
plot_height=400,
x_range=Range1d(-1.1, 1.1),
y_range=Range1d(-1.1, 1.1))
plot.title.text = "Graph Interaction Demonstration"
plot.add_tools(HoverTool(tooltips=None), TapTool(), BoxSelectTool(),
WheelZoomTool())
graph_renderer = from_networkx(G, layout, scale=1, center=(0, 0))
graph_renderer.node_renderer.glyph = Circle(size=15,
fill_color=Spectral4[0])
graph_renderer.node_renderer.selection_glyph = Circle(
size=15, fill_color=Spectral4[2])
graph_renderer.node_renderer.hover_glyph = Circle(size=15,
fill_color=Spectral4[1])
graph_renderer.edge_renderer.glyph = MultiLine(line_color="#CCCCCC",
line_alpha=0.8,
line_width=5)
graph_renderer.edge_renderer.selection_glyph = MultiLine(
line_color=Spectral4[2], line_width=5)
graph_renderer.edge_renderer.hover_glyph = MultiLine(
line_color=Spectral4[1], line_width=5)
graph_renderer.selection_policy = NodesAndLinkedEdges()
graph_renderer.inspection_policy = EdgesAndLinkedNodes()
x, y = zip(*graph_renderer.layout_provider.graph_layout.values())
node_labels = nx.get_node_attributes(G, 'hostname')
source = ColumnDataSource({
'x':
x,
'y':
y,
'label': [node_labels[id[i]] for i in range(len(x))]
})
labels = LabelSet(x='x',
y='y',
text='label',
source=source,
background_fill_color='white')
plot.renderers.append(graph_renderer)
output_file(output)
save(plot)
def create_figure(G, ntx):
plot = Plot(plot_width=800,
plot_height=600,
x_range=Range1d(-1.1, 1.1),
y_range=Range1d(-1.1, 1.1))
#plot.title.text = "BTC Block Visualization"
citation = Label(x=0, y=-20, x_units='screen', y_units='screen',
text='This block contains '+str(ntx)+\
' transactions between '+str(len(G.nodes()))+\
' addresses. The most active address transacted with '+str(max(list(dict(G.degree()).values())))+\
' addresses.',
render_mode='css',
border_line_color='red', border_line_alpha=1.0,
background_fill_color='white', background_fill_alpha=1.0)
#citation = Label(x=40, y=0, x_units='screen', y_units='screen',
# text='There were '+str(ntx)+' transactions total. The most active transactor was involved in '+str(max(list(dict(G.degree()).values())))+' transactions.',
# render_mode='css',
# border_line_color='black', border_line_alpha=1.0,
# background_fill_color='white', background_fill_alpha=1.0)
plot.add_layout(citation)
plot.add_tools(HoverTool(tooltips=None), TapTool(), BoxSelectTool(),
WheelZoomTool(), PanTool())
graph_renderer = from_networkx(G,
nx.circular_layout,
scale=1,
center=(0, 0))
graph_renderer.node_renderer.glyph = Circle(size=4,
fill_color=Spectral4[0])
graph_renderer.node_renderer.selection_glyph = Circle(
size=4, fill_color=Spectral4[2])
graph_renderer.node_renderer.hover_glyph = Circle(size=4,
fill_color=Spectral4[1])
graph_renderer.edge_renderer.glyph = MultiLine(line_color="#CCCCCC",
line_alpha=0.8,
line_width=3)
graph_renderer.edge_renderer.selection_glyph = MultiLine(
line_color=Spectral4[2], line_width=3)
graph_renderer.edge_renderer.hover_glyph = MultiLine(
line_color=Spectral4[1], line_width=3)
graph_renderer.selection_policy = NodesAndLinkedEdges()
graph_renderer.inspection_policy = EdgesAndLinkedNodes()
plot.renderers.append(graph_renderer)
return plot
def visual():
'''
visualze whole graph
'''
nx = networkx.Graph()
color_movie = Spectral4[0]
color_actor = Spectral4[3]
for aid in g.all_actors:
inf = g.all_actors[aid]['age']
nx.add_node(aid, type = 'Actor', item_name = aid, info = "Age: " + str(inf), color = color_actor, size = 10)
for mid in g.all_movies:
# if g.all_movies[mid]['actors'] == []:
# continue
inf = g.all_movies[mid]['box_office']
nx.add_node(mid, type = 'Movie', item_name = mid,info = "Box Office: " + str(inf), color = color_movie, size = 15)
for mid in g.all_movies:
movie = g.all_movies[mid]
for aid in movie['actors']:
if aid not in g.all_actors:
continue
nx.add_edge(mid, aid)
for aid in g.all_actors:
for mid in g.all_actors[aid]['movies']:
if mid not in g.all_movies:
continue
nx.add_edge(aid, mid)
p = Plot(plot_width=1200, plot_height=1200,
x_range=Range1d(-3, 3), y_range=Range1d(-3, 3))
p.title.text = "Visualization of the graph"
p.add_tools(HoverTool(tooltips=[("Name", "@item_name"), ("Info", "@info")]), TapTool(), BoxSelectTool())
graph_renderer = from_networkx(nx, networkx.spring_layout, scale=7, center=(0, 0))
graph_renderer.node_renderer.glyph = Circle(size='size', fill_color='color')
graph_renderer.node_renderer.selection_glyph = Circle(size='size', fill_color=Spectral4[2])
graph_renderer.node_renderer.hover_glyph = Circle(size='size', fill_color=Spectral4[1])
graph_renderer.edge_renderer.glyph = MultiLine(line_color="#CCCCCC", line_alpha=0.8, line_width=1)
graph_renderer.edge_renderer.selection_glyph = MultiLine(line_color='#440154', line_width=1)
graph_renderer.edge_renderer.hover_glyph = MultiLine(line_color=Spectral4[1], line_width=1)
graph_renderer.selection_policy = NodesAndLinkedEdges()
graph_renderer.inspection_policy = NodesOnly()
p.renderers.append(graph_renderer)
script, div = components(p)
return render_template("visualization.html", script=script, div=div)
def visualize_self_attention_scores(tokens, scores, filename="/notebooks/embedding/self-attention.png",
use_notebook=False):
mean_prob = np.mean(scores)
weighted_edges = []
for idx_1, token_prob_dist_1 in enumerate(scores):
for idx_2, el in enumerate(token_prob_dist_1):
if idx_1 == idx_2 or el < mean_prob:
weighted_edges.append((tokens[idx_1], tokens[idx_2], 0))
else:
weighted_edges.append((tokens[idx_1], tokens[idx_2], el))
max_prob = np.max([el[2] for el in weighted_edges])
weighted_edges = [(el[0], el[1], (el[2] - mean_prob) / (max_prob - mean_prob)) for el in weighted_edges]
G = nx.Graph()
G.add_nodes_from([el for el in tokens])
G.add_weighted_edges_from(weighted_edges)
plot = Plot(plot_width=500, plot_height=500,
x_range=Range1d(-1.1, 1.1), y_range=Range1d(-1.1, 1.1))
plot.add_tools(HoverTool(tooltips=None), TapTool(), BoxSelectTool())
graph_renderer = from_networkx(G, nx.circular_layout, scale=1, center=(0, 0))
graph_renderer.node_renderer.data_source.data['colors'] = Spectral8[:len(tokens)]
graph_renderer.node_renderer.glyph = Circle(size=15, line_color=None, fill_color="colors")
graph_renderer.node_renderer.selection_glyph = Circle(size=15, fill_color="colors")
graph_renderer.node_renderer.hover_glyph = Circle(size=15, fill_color="grey")
graph_renderer.edge_renderer.data_source.data["line_width"] = [G.get_edge_data(a, b)['weight'] * 3 for a, b in
G.edges()]
graph_renderer.edge_renderer.glyph = MultiLine(line_color="#CCCCCC", line_width={'field': 'line_width'})
graph_renderer.edge_renderer.selection_glyph = MultiLine(line_color="grey", line_width=5)
graph_renderer.edge_renderer.hover_glyph = MultiLine(line_color="grey", line_width=5)
graph_renderer.selection_policy = NodesAndLinkedEdges()
graph_renderer.inspection_policy = EdgesAndLinkedNodes()
plot.renderers.append(graph_renderer)
x, y = zip(*graph_renderer.layout_provider.graph_layout.values())
data = {'x': list(x), 'y': list(y), 'connectionNames': tokens}
source = ColumnDataSource(data)
labels = LabelSet(x='x', y='y', text='connectionNames', source=source, text_align='center')
plot.renderers.append(labels)
plot.add_tools(SaveTool())
if use_notebook:
output_notebook()
show(plot)
else:
export_png(plot, filename)
print("save @ " + filename)
def get_bokeh_plot(self):
"""Get a nice Bokeh plot of the network."""
plot = Plot(
plot_width=400,
plot_height=400,
x_range=Range1d(-1.1, 1.1),
y_range=Range1d(-1.1, 1.1),
)
plot.title.text = "Agents network"
plot.add_tools(
HoverTool(tooltips=[("AgentID", "@index")]),
TapTool(),
)
graph_renderer = from_networkx(
self.graph,
nx.spring_layout,
scale=1,
center=(0, 0),
)
graph_renderer.node_renderer.glyph = Circle(
size=15,
fill_color=Spectral4[0],
)
graph_renderer.node_renderer.selection_glyph = Circle(
size=15,
fill_color=Spectral4[2],
)
graph_renderer.node_renderer.hover_glyph = Circle(
size=15,
fill_color=Spectral4[1],
)
graph_renderer.edge_renderer.glyph = MultiLine(
line_color="#CCCCCC",
line_alpha=0.8,
line_width=3,
)
graph_renderer.edge_renderer.selection_glyph = MultiLine(
line_color=Spectral4[2],
line_width=3,
)
graph_renderer.selection_policy = NodesAndLinkedEdges()
graph_renderer.inspection_policy = NodesOnly()
plot.renderers.append(graph_renderer)
return plot
def network_total_following(self, user_dict, n_neighbors=5):
graph = nx.Graph()
self.add_nodes(graph, user_dict)
self.add_edges(graph, user_dict)
for node in list(graph.nodes()):
if len(list(graph.neighbors(node))) < n_neighbors:
graph.remove_node(node)
plot = Plot(
plot_width=1140,
plot_height=720,
x_range=Range1d(-1.1,1.1),
y_range=Range1d(-1.1,1.1))
plot.add_tools(
HoverTool(tooltips=[('User','@name'),
('Neighbors', '@neighbors')],
line_policy='nearest'),
TapTool(),
BoxSelectTool(),
PanTool(),
WheelZoomTool(),
ResetTool())
graph_render = from_networkx(graph, nx.spring_layout, scale=2.5, center=(0,0))
graph_render.node_renderer.data_source.data['name'] = list(graph.nodes())
list_edges = self.get_list_of_edges(graph)
graph_render.node_renderer.data_source.data['neighbors'] = list_edges
mapper = linear_cmap('neighbors', palette=Plasma11, low=min(list_edges), high=max(list_edges))
graph_render.node_renderer.glyph = Circle(
size=10,
fill_color=mapper)
graph_render.node_renderer.hover_glyph = Circle(
size=200,
fill_color=mapper)
graph_render.node_renderer.selection_glyph = Circle(
size=200,
fill_color=mapper)
graph_render.edge_renderer.glyph = MultiLine(
line_color=Greys4[2],
line_alpha=0.3,
line_width=1)
graph_render.edge_renderer.hover_glyph = MultiLine(
line_color=Greys4[1],
line_width=2)
graph_render.edge_renderer.selection_glyph = MultiLine(
line_color=Greys4[0],
line_width=3)
graph_render.selection_policy = NodesAndLinkedEdges()
graph_render.inspection_policy = NodesAndLinkedEdges()
plot.renderers.append(graph_render)
show(plot)
def plot_create(self, doc):
plot = figure(title="Belief Graph")
plot.axis.visible = False
node_labels = nx.get_node_attributes(self.bel_graph, 'id')
node_labels = tuple([label for label in node_labels.values()])
ncolor = ['#2b83ba' for name in node_labels]
nsize = [15 for name in node_labels]
graph = from_networkx(self.bel_graph,
nx.spring_layout,
scale=1,
center=(0, 0))
graph.node_renderer.glyph = Circle(size='nsize', fill_color='ncolor')
graph.node_renderer.selection_glyph = Circle(size='nsize',
fill_color='#054872')
graph.node_renderer.hover_glyph = Circle(size='nsize',
fill_color='#abdda4')
graph.name = "bel_graph"
graph.node_renderer.data_source.data.update(
dict(id=node_labels, ncolor=ncolor, nsize=nsize))
graph.edge_renderer.glyph = MultiLine(line_color="#CCCCCC",
line_alpha=0.8,
line_width=5)
graph.edge_renderer.selection_glyph = MultiLine(line_color='#054872',
line_width=5)
graph.edge_renderer.hover_glyph = MultiLine(line_color='#abdda4',
line_width=5)
graph.selection_policy = NodesAndLinkedEdges()
# graph.inspection_policy = EdgesAndLinkedNodes()
plot.renderers.append(graph)
x, y = zip(*graph.layout_provider.graph_layout.values())
source_graph = ColumnDataSource(data=dict(x=x, y=y, id=node_labels))
labels = LabelSet(x='x', y='y', text='id', source=source_graph)
plot.renderers.append(labels)
# add tools to the plot
plot.add_tools(HoverTool(names=["bel_graph"]), TapTool())
doc.add_root(plot)
def plotGraph(graph, title=None, graphSavePath=None, networkPath=None):
for k, adjacencies in zip(list(graph.nodes.keys()), graph.adjacency()):
graph.nodes[k]["degree"] = len(adjacencies[1])
if networkPath is not None:
for city in graph.nodes():
graph.nodes[city]["code"] = graph.networkPath.stationList.getCode(
city)
plot_height = 700
plot = Plot(plot_width=900,
plot_height=plot_height,
x_range=Range1d(-1.1, 1.1),
y_range=Range1d(-1.1, 1.1))
plot.title.text = title
node_hover_tool = HoverTool(
tooltips=[("Miasto",
"@index"), ("Kod miasta",
"@code"), ("Stopien wierzcholka", "@degree")])
plot.add_tools(WheelZoomTool(), PanTool(), SaveTool(), node_hover_tool,
BoxZoomTool(), ResetTool()) # , TapTool())
graph_renderer = from_networkx(graph,
nx.spring_layout,
scale=1,
center=(0, 0))
graph_renderer.node_renderer.glyph = Circle(size=12, fill_color="yellow")
graph_renderer.node_renderer.selection_glyph = Circle(size=15,
fill_color="blue")
graph_renderer.node_renderer.hover_glyph = Circle(size=15,
fill_color="red")
graph_renderer.edge_renderer.glyph = MultiLine(line_color="green",
line_alpha=0.3,
line_width=1)
graph_renderer.edge_renderer.selection_glyph = MultiLine(line_color="blue",
line_width=1.2)
graph_renderer.edge_renderer.hover_glyph = MultiLine(line_color="red",
line_width=1.2)
plot.renderers.append(graph_renderer)
columnCities = ColumnDataSource(data=dict(city=list(graph.nodes.keys())))
columnsNetwork = [
TableColumn(field="city", title="Miasto"),
]
table = DataTable(source=columnCities,
columns=columnsNetwork,
width=155,
height=plot_height)
layout = row(plot, table)
if graphSavePath is not None:
output_file(graphSavePath, title)
show(layout)
def modify_doc(doc):
plot = Plot(height=400,
width=400,
x_range=Range1d(0, 3),
y_range=Range1d(0, 3),
min_border=0)
renderer = plot.add_glyph(source, MultiLine(xs='xs', ys='ys'))
tool = PolyEditTool(renderers=[renderer])
psource = ColumnDataSource(dict(x=[], y=[]))
prenderer = plot.add_glyph(psource, Circle(x='x', y='y', size=10))
tool.vertex_renderer = prenderer
plot.add_tools(tool)
plot.toolbar.active_multi = tool
plot.toolbar_sticky = False
div = Div(text='False')
def cb(attr, old, new):
try:
if cds_data_almost_equal(new, expected):
div.text = 'True'
except ValueError:
return
source.on_change('data', cb)
code = RECORD("matches", "div.text")
plot.add_tools(
CustomAction(callback=CustomJS(args=dict(div=div), code=code)))
doc.add_root(column(plot, div))
def test_graphrenderer_check_malformed_graph_source_no_edge_start_or_end():
edge_source = ColumnDataSource()
edge_renderer = GlyphRenderer(data_source=edge_source, glyph=MultiLine())
renderer = GraphRenderer(edge_renderer=edge_renderer)
check = renderer._check_malformed_graph_source()
assert check != []
def bokeh(G, node_color=None, node_label=None, node_size=100, node_size_scale=[25,200], alpha=0.8, font_size=18, cmap='Set1', width=40, height=30, pos=None, filename=None, title=None, methodtype='default', verbose=3):
import networkx as nx
from bokeh.io import show, output_file
from bokeh.models import Plot, Range1d, MultiLine, Circle, HoverTool, BoxZoomTool, ResetTool
from bokeh.models.graphs import from_networkx
from bokeh.palettes import Spectral4
SAME_CLUB_COLOR, DIFFERENT_CLUB_COLOR = "black", "red"
edge_attrs = {}
for start_node, end_node, _ in G.edges(data=True):
edge_color = SAME_CLUB_COLOR if G.nodes[start_node]["club"] == G.nodes[end_node]["club"] else DIFFERENT_CLUB_COLOR
edge_attrs[(start_node, end_node)] = edge_color
nx.set_edge_attributes(G, edge_attrs, "edge_color")
# Show with Bokeh
plot = Plot(plot_width=400, plot_height=400,
x_range=Range1d(-1.1, 1.1), y_range=Range1d(-1.1, 1.1))
plot.title.text = "Graph Interaction Demonstration"
node_hover_tool = HoverTool(tooltips=[("index", "@index"), ("club", "@club")])
plot.add_tools(node_hover_tool, BoxZoomTool(), ResetTool())
graph_renderer = from_networkx(G, nx.spring_layout, scale=1, center=(0, 0))
graph_renderer.node_renderer.glyph = Circle(size=15, fill_color=Spectral4[0])
graph_renderer.edge_renderer.glyph = MultiLine(line_color="edge_color", line_alpha=0.8, line_width=1)
plot.renderers.append(graph_renderer)
output_file("interactive_graphs.html")
show(plot)
def get_graph_renderer(S, line_dash):
# we need a consistent ordering of the edges
edgelist = S.edges()
nodelist = S.nodes()
# get the colors assigned to each edge based on friendly/hostile
sign_edge_color = ['#87DACD' if S[u][v]['sign'] == 1 else '#FC9291' for u, v in edgelist]
# get the colors assigned to each node by coloring
try:
coloring_node_color = ['#4378F8' if nodelist[v]['color'] else '#FFE897' for v in nodelist]
except KeyError:
coloring_node_color = ['#FFFFFF' for __ in nodelist]
graph_renderer = from_networkx(S, layout_wrapper)
circle_size = 10
graph_renderer.node_renderer.data_source.add(coloring_node_color, 'color')
graph_renderer.node_renderer.glyph = Circle(size=circle_size, fill_color='color')
edge_size = 2
graph_renderer.edge_renderer.data_source.add(sign_edge_color, 'color')
try:
graph_renderer.edge_renderer.data_source.add([S[u][v]['event_year'] for u, v in edgelist], 'event_year')
graph_renderer.edge_renderer.data_source.add(
[S[u][v]['event_description'] for u, v in edgelist], 'event_description')
plot.add_tools(HoverTool(tooltips=[("Year", "@event_year"), ("Description", "@event_description")],
line_policy="interp"))
except KeyError:
pass
graph_renderer.edge_renderer.glyph = MultiLine(line_color='color', line_dash=line_dash)
graph_renderer.inspection_policy = EdgesAndLinkedNodes()
return graph_renderer
def plotNetXBokeh(src_abv,src_belw,q_csid):
max_XNodeVal = max(src_belw['xs_node'])
max_YNodeVal = max(src_belw['ys_node'])
buffer = 0.1
plot = figure(sizing_mode='scale_width',x_range=(-max_XNodeVal-buffer, max_XNodeVal+buffer),y_range=(-max_YNodeVal-buffer, max_YNodeVal+buffer))
plot.outline_line_color = None
source_belw = ColumnDataSource(src_belw)
source_abv = ColumnDataSource(src_abv)
glyph = MultiLine(xs="xs_line", ys="ys_line", line_color='lcolour', line_width='weight',line_alpha='alpha')
plot.add_glyph(source_belw, glyph)
plot.add_glyph(source_abv, glyph) #add the above threshold lines last so it overlays the below lines
plot.circle('xs_node', 'ys_node', source=source_belw, size='size', color='colour',line_color='#020c15',line_width=0.5,name='showhover')
plot.circle('xs_node', 'ys_node', source=source_abv, size='size', color='colour',line_color='#020c15',line_width=0.5,name='showhover')
dictHoverLabels = {'CSID':'csid','Name':'cname','EucliDist':'euclidist'}
node_hover_tool = customHoverTool(dictHoverLabels,400)
# node_hover_tool = hover = HoverTool(names=['showhover'], tooltips="""
# <div style="word-wrap: break-word; width: 400px;">
# <p><span style="font-size: 5; font-weight: bold;">CSID: @csid</span><p>
# <p><span style="font-size: 5; font-weight: bold;">Name: @cname</span><p>
# <p><span style="font-size: 5; font-weight: bold;">EucliDist: @euclidist</span><p>
# </div>
# """)
plot.add_tools(node_hover_tool)
plot.toolbar.logo = None
plot.min_border = 0
plot.axis.visible = False
plot.toolbar.autohide = True
plot.grid.visible = False
return plot
