def test_from_networkx_custom_nodes(self):
import networkx as nx
FG = nx.Graph()
FG.add_weighted_edges_from([(1,2,0.125), (1,3,0.75), (2,4,1.2), (3,4,0.375)])
nodes = Dataset([(1, 'A'), (2, 'B'), (3, 'A'), (4, 'B')], 'index', 'some_attribute')
graph = Graph.from_networkx(FG, nx.circular_layout, nodes=nodes)
self.assertEqual(graph.nodes.dimension_values('some_attribute'), np.array(['A', 'B', 'A', 'B']))
def test_from_networkx_dictionary_positions(self):
import networkx as nx
G = nx.Graph()
G.add_nodes_from([1, 2, 3])
positions = nx.circular_layout(G)
graph = Graph.from_networkx(G, positions)
self.assertEqual(graph.nodes.dimension_values(2), np.array([1, 2, 3]))
def test_from_networkx_with_invalid_edge_attrs(self):
import networkx as nx
FG = nx.Graph()
FG.add_weighted_edges_from([(1, 2, []), (1, 3, []), (2, 4, []),
(3, 4, [])])
graph = Graph.from_networkx(FG, nx.circular_layout)
self.assertEqual(graph.vdims, [])
def test_from_networkx_dictionary_positions(self):
import networkx as nx
G = nx.Graph()
G.add_nodes_from([1, 2, 3])
positions = nx.circular_layout(G)
graph = Graph.from_networkx(G, positions)
self.assertEqual(graph.nodes.dimension_values(2), np.array([1, 2, 3]))
def test_from_networkx_with_edge_attrs(self):
import networkx as nx
FG = nx.Graph()
FG.add_weighted_edges_from([(1, 2, 0.125), (1, 3, 0.75), (2, 4, 1.2),
(3, 4, 0.375)])
graph = Graph.from_networkx(FG, nx.circular_layout)
self.assertEqual(graph.dimension_values('weight'),
np.array([0.125, 0.75, 1.2, 0.375]))
def test_from_networkx_only_nodes(self):
try:
import networkx as nx
except:
raise SkipTest('Test requires networkx to be installed')
G = nx.Graph()
G.add_nodes_from([1, 2, 3])
graph = Graph.from_networkx(G, nx.circular_layout)
self.assertEqual(graph.nodes.dimension_values(2), np.array([1, 2, 3]))
def test_from_networkx_custom_nodes(self):
import networkx as nx
FG = nx.Graph()
FG.add_weighted_edges_from([(1, 2, 0.125), (1, 3, 0.75), (2, 4, 1.2),
(3, 4, 0.375)])
nodes = Dataset([(1, 'A'), (2, 'B'), (3, 'A'), (4, 'B')], 'index',
'some_attribute')
graph = Graph.from_networkx(FG, nx.circular_layout, nodes=nodes)
self.assertEqual(graph.nodes.dimension_values('some_attribute'),
np.array(['A', 'B', 'A', 'B']))
def test_from_networkx_with_invalid_node_attrs(self):
import networkx as nx
FG = nx.Graph()
FG.add_node(1, test=[])
FG.add_node(2, test=[])
FG.add_edge(1, 2)
graph = Graph.from_networkx(FG, nx.circular_layout)
self.assertEqual(graph.nodes.vdims, [])
self.assertEqual(graph.nodes.dimension_values(2), np.array([1, 2]))
self.assertEqual(graph.array(), np.array([(1, 2)]))
def test_from_networkx_with_invalid_node_attrs(self):
import networkx as nx
FG = nx.Graph()
FG.add_node(1, test=[])
FG.add_node(2, test=[])
FG.add_edge(1, 2)
graph = Graph.from_networkx(FG, nx.circular_layout)
self.assertEqual(graph.nodes.vdims, [])
self.assertEqual(graph.nodes.dimension_values(2), np.array([1, 2]))
self.assertEqual(graph.array(), np.array([(1, 2)]))
def test_from_networkx_with_edge_attrs(self):
try:
import networkx as nx
except:
raise SkipTest('Test requires networkx to be installed')
FG = nx.Graph()
FG.add_weighted_edges_from([(1, 2, 0.125), (1, 3, 0.75), (2, 4, 1.2),
(3, 4, 0.375)])
graph = Graph.from_networkx(FG, nx.circular_layout)
self.assertEqual(graph.dimension_values('weight'),
np.array([0.125, 0.75, 1.2, 0.375]))
def test_from_networkx_with_node_attrs(self):
import networkx as nx
G = nx.karate_club_graph()
graph = Graph.from_networkx(G, nx.circular_layout)
clubs = np.array([
'Mr. Hi', 'Mr. Hi', 'Mr. Hi', 'Mr. Hi', 'Mr. Hi', 'Mr. Hi',
'Mr. Hi', 'Mr. Hi', 'Mr. Hi', 'Officer', 'Mr. Hi', 'Mr. Hi',
'Mr. Hi', 'Mr. Hi', 'Officer', 'Officer', 'Mr. Hi', 'Mr. Hi',
'Officer', 'Mr. Hi', 'Officer', 'Mr. Hi', 'Officer', 'Officer',
'Officer', 'Officer', 'Officer', 'Officer', 'Officer', 'Officer',
'Officer', 'Officer', 'Officer', 'Officer'])
self.assertEqual(graph.nodes.dimension_values('club'), clubs)
def test_from_networkx_with_node_attrs(self):
import networkx as nx
G = nx.karate_club_graph()
graph = Graph.from_networkx(G, nx.circular_layout)
clubs = np.array([
'Mr. Hi', 'Mr. Hi', 'Mr. Hi', 'Mr. Hi', 'Mr. Hi', 'Mr. Hi',
'Mr. Hi', 'Mr. Hi', 'Mr. Hi', 'Officer', 'Mr. Hi', 'Mr. Hi',
'Mr. Hi', 'Mr. Hi', 'Officer', 'Officer', 'Mr. Hi', 'Mr. Hi',
'Officer', 'Mr. Hi', 'Officer', 'Mr. Hi', 'Officer', 'Officer',
'Officer', 'Officer', 'Officer', 'Officer', 'Officer', 'Officer',
'Officer', 'Officer', 'Officer', 'Officer'
])
self.assertEqual(graph.nodes.dimension_values('club'), clubs)
def test_from_networkx_with_node_attrs(self):
try:
import networkx as nx
except:
raise SkipTest('Test requires networkx to be installed')
G = nx.karate_club_graph()
graph = Graph.from_networkx(G, nx.circular_layout)
clubs = np.array([
'Mr. Hi', 'Mr. Hi', 'Mr. Hi', 'Mr. Hi', 'Mr. Hi', 'Mr. Hi',
'Mr. Hi', 'Mr. Hi', 'Mr. Hi', 'Officer', 'Mr. Hi', 'Mr. Hi',
'Mr. Hi', 'Mr. Hi', 'Officer', 'Officer', 'Mr. Hi', 'Mr. Hi',
'Officer', 'Mr. Hi', 'Officer', 'Mr. Hi', 'Officer', 'Officer',
'Officer', 'Officer', 'Officer', 'Officer', 'Officer', 'Officer',
'Officer', 'Officer', 'Officer', 'Officer'
])
self.assertEqual(graph.nodes.dimension_values('club'), clubs)
def generateGraph():
df = processCSVMatrix(file)
names = df.columns
# submatrix for quicker development
if (len(names) > 150):
df = df.head(150)[names[0:150]]
# set defaults for HoloViews
extension('bokeh')
renderer('bokeh').webgl = True
defaults = dict(width=400, height=400, padding=0.1)
opts.defaults(opts.EdgePaths(**defaults), opts.Graph(**defaults),
opts.Nodes(**defaults))
G = from_pandas_adjacency(df)
graph = Graph.from_networkx(G,
circular_layout).opts(directed=False,
width=600,
height=600,
arrowhead_length=0.0005)
# Make a panel and widgets with param for choosing a layout
return pn.Column(graph)
def test_from_networkx_only_nodes(self):
import networkx as nx
G = nx.Graph()
G.add_nodes_from([1, 2, 3])
graph = Graph.from_networkx(G, nx.circular_layout)
self.assertEqual(graph.nodes.dimension_values(2), np.array([1, 2, 3]))
def make_plot(self):
from graphion.session.handler import get_directed # dependency cycle fix
if get_directed(self.sid):
G = from_pandas_adjacency(df, create_using=DiGraph)
else:
G = from_pandas_adjacency(df, create_using=Graph)
self.nodeCount = number_of_nodes(G)
"""
Create NetworkX graph layout manager
"""
if diagramType == "FORCE":
layout = spring_layout(G,
k=10.42 / sqrt(self.nodeCount),
seed=server.config['SEED'])
elif diagramType == "HIERARCHICAL":
if self.nodeCount > 1:
layout = graphviz_layout(Graph([
(u, v, d) for u, v, d in G.edges(data=True)
]),
prog='dot')
else:
layout = circular_layout(
G
) # graphviz_layout does not work with one node, just display a "circular_layout"
elif diagramType == "RADIAL":
layout = circular_layout(G)
else:
pass
# get node and edge information from graph
nodes, nodes_coordinates = zip(*sorted(layout.items()))
nodes_x, nodes_y = list(zip(*nodes_coordinates))
# calculate centrality
centrality = degree_centrality(G)
_, nodeCentralities = zip(*sorted(centrality.items()))
if self.nodeCount > 1:
# get degree information
if is_directed(G):
inDegreeSize = dict(G.in_degree)
inDegree = inDegreeSize.copy()
outDegreeSize = dict(G.out_degree)
outDegree = outDegreeSize.copy()
totalDegreeSize = {}
for n in nodes:
totalDegreeSize[n] = inDegreeSize[n] + outDegreeSize[n]
totalDegree = totalDegreeSize.copy()
else:
inDegreeSize = dict(G.degree)
inDegree = inDegreeSize.copy()
outDegreeSize = inDegreeSize.copy()
outDegree = inDegreeSize.copy()
totalDegreeSize = inDegreeSize.copy()
totalDegree = inDegreeSize.copy()
# get weight information
if is_directed(G):
inWeightSize = dict(G.in_degree(weight='weight'))
inWeight = inWeightSize.copy()
outWeightSize = dict(G.out_degree(weight='weight'))
outWeight = outWeightSize.copy()
totalWeightSize = {}
for n in nodes:
totalWeightSize[n] = inWeightSize[n] + outWeightSize[n]
totalWeight = totalWeightSize.copy()
else:
inWeightSize = dict(G.degree(weight='weight'))
inWeight = inWeightSize.copy()
outWeightSize = inWeightSize.copy()
outWeight = inWeightSize.copy()
totalWeightSize = inWeightSize.copy()
totalWeight = inWeightSize.copy()
# Creating a scale to ensure that the node sizes don't go bananas
minNodeSize = 0.1 # minNodeSize * maxNodeSize = minimum node size
maxIn = -maxsize - 1
minIn = maxsize
maxOut = -maxsize - 1
minOut = maxsize
maxTot = -maxsize - 1
minTot = maxsize
maxInw = -maxsize - 1
minInw = maxsize
maxOutw = -maxsize - 1
minOutw = maxsize
maxTotw = -maxsize - 1
minTotw = maxsize
for n in nodes:
ind = inDegreeSize[n]
outd = outDegreeSize[n]
totd = totalDegreeSize[n]
inw = inWeightSize[n]
outw = outWeightSize[n]
totw = totalWeightSize[n]
if ind > maxIn:
maxIn = ind
elif ind < minIn:
minIn = ind
if outd > maxOut:
maxOut = outd
elif outd < minOut:
minOut = outd
if totd > maxTot:
maxTot = totd
elif totd < minTot:
minTot = totd
if inw > maxInw:
maxInw = inw
elif inw < minInw:
minInw = inw
if outw > maxOutw:
maxOutw = outw
elif outw < minOutw:
minOutw = outw
if totw > maxTotw:
maxTotw = totw
elif totw < minTotw:
minTotw = totw
if maxIn == minIn:
sameInDegree = True
else:
sameInDegree = False
for n in nodes:
result = (inDegreeSize[n] - minIn) / maxIn
if result < minNodeSize:
inDegreeSize[n] = minNodeSize
else:
inDegreeSize[n] = result
if maxOut == minOut:
sameOutDegree = True
else:
sameOutDegree = False
for n in nodes:
result = (outDegreeSize[n] - minOut) / maxOut
if result < minNodeSize:
outDegreeSize[n] = minNodeSize
else:
outDegreeSize[n] = result
if maxTot == minTot:
sameTotalDegree = True
else:
sameTotalDegree = False
for n in nodes:
result = (totalDegreeSize[n] - minTot) / maxTot
if result < minNodeSize:
totalDegreeSize[n] = minNodeSize
else:
totalDegreeSize[n] = result
if maxInw == minInw:
sameInWeight = True
else:
sameInWeight = False
for n in nodes:
result = (inWeightSize[n] - minInw) / maxInw
if result < minNodeSize:
inWeightSize[n] = minNodeSize
else:
inWeightSize[n] = result
if maxOutw == minOutw:
sameOutWeight = True
else:
sameOutWeight = False
for n in nodes:
result = (outWeightSize[n] - minOutw) / maxOutw
if result < minNodeSize:
outWeightSize[n] = minNodeSize
else:
outWeightSize[n] = result
if maxTotw == minTotw:
sameTotalWeight = True
else:
sameTotalWeight = False
for n in nodes:
result = (totalWeightSize[n] - minTotw) / maxTotw
if result < minNodeSize:
totalWeightSize[n] = minNodeSize
else:
totalWeightSize[n] = result
# Making a dictionary for all attributes, and ensuring none of the values go crazy.
attributes = {}
maxNodeSize = 30
for n in nodes:
outd = outDegreeSize[n]
totd = totalDegreeSize[n]
inw = inWeightSize[n]
outw = outWeightSize[n]
totw = totalWeightSize[n]
if sameInDegree:
ind = 1
else:
ind = inDegreeSize[n]
if sameOutDegree:
outd = 1
else:
outd = outDegreeSize[n]
if sameTotalDegree:
totd = 1
else:
totd = totalDegreeSize[n]
if sameInWeight:
inw = 1
else:
inw = inWeightSize[n]
if sameOutWeight:
outw = 1
else:
outw = outWeightSize[n]
if sameTotalWeight:
totw = 1
else:
totw = totalWeightSize[n]
attributes[n] = {
'indegreesize': ind * maxNodeSize,
'outdegreesize': outd * maxNodeSize,
'totaldegreesize': totd * maxNodeSize,
'inweightsize': inw * maxNodeSize,
'outweightsize': outw * maxNodeSize,
'totalweightsize': totw * maxNodeSize,
'indegree': inDegree[n],
'outdegree': outDegree[n],
'totaldegree': totalDegree[n],
'inweight': inWeight[n],
'outweight': outWeight[n],
'totalweight': totalWeight[n],
'count': 0
}
set_node_attributes(G, attributes)
plot = HVGraph.from_networkx(G, layout).opts(
directed=get_directed(self.sid), arrowhead_length=0.01)
# disabling displaying all node info on hovering over the node
tooltips = [('Index', '@index'), ('In-Degree', '@indegree'),
('Out-Degree', '@outdegree'),
('Total Degree', '@totaldegree'),
('In Edge Weight', '@inweight'),
('Out Edge-Weight', '@outweight'),
('Total Edge-Weight', '@totalweight')]
hover = HoverTool(tooltips=tooltips)
else:
attributes = {}
for n in nodes:
attributes[n] = {
'indegreesize': 1,
'outdegreesize': 1,
'totaldegreesize': 1,
'inweightsize': 1,
'outweightsize': 1,
'totalweightsize': 1,
'indegree': 0,
'outdegree': 0,
'totaldegree': 0,
'inweight': 0,
'outweight': 0,
'totalweight': 0,
'count': 0
}
set_node_attributes(G, attributes)
plot = HVGraph.from_networkx(G, layout).opts(
directed=get_directed(self.sid), arrowhead_length=0.01)
tooltips = [('Index', '@index'), ('In-Degree', '@indegree'),
('Out-Degree', '@outdegree'),
('Total Degree', '@totaldegree'),
('In Edge Weight', '@inweight'),
('Out Edge-Weight', '@outweight'),
('Total Edge-Weight', '@totalweight')]
hover = HoverTool(tooltips=tooltips)
# Make custom dictionary with color palettes
for c in self.colorList:
if c == 'cividis':
self.colorMap[c] = Cividis256
elif c == 'viridis':
self.colorMap[c] = Viridis256
elif c == 'inferno':
self.colorMap[c] = Inferno256
else:
self.colorMap[c] = palette[c]
if max(nodeCentralities) > 0:
if datashaded and self.nodeCount > 1:
plot = bundle_graph(plot)
points = plot.nodes
points.opts(cmap=self.colorMap[self.color_palette],
color=self.node_color,
size=self.node_size,
tools=['box_select', 'lasso_select', 'tap', hover],
active_tools=['wheel_zoom'],
toolbar='above',
show_legend=False,
width=self.size,
height=self.size)
plot.opts(node_size=0,
node_color=None,
node_line_width=0,
node_hover_fill_color='green')
return plot, points
def test_from_networkx_only_nodes(self):
import networkx as nx
G = nx.Graph()
G.add_nodes_from([1, 2, 3])
graph = Graph.from_networkx(G, nx.circular_layout)
self.assertEqual(graph.nodes.dimension_values(2), np.array([1, 2, 3]))
def test_from_networkx_with_invalid_edge_attrs(self):
import networkx as nx
FG = nx.Graph()
FG.add_weighted_edges_from([(1,2,[]), (1,3,[]), (2,4,[]), (3,4,[])])
graph = Graph.from_networkx(FG, nx.circular_layout)
self.assertEqual(graph.vdims, [])
def test_from_networkx_with_edge_attrs(self):
import networkx as nx
FG = nx.Graph()
FG.add_weighted_edges_from([(1,2,0.125), (1,3,0.75), (2,4,1.2), (3,4,0.375)])
graph = Graph.from_networkx(FG, nx.circular_layout)
self.assertEqual(graph.dimension_values('weight'), np.array([0.125, 0.75, 1.2, 0.375]))