def test_classes(self):
"""
Test to ensure that approriate classes are added to the networkx object
"""
G = nx.Graph()
G.add_node('separate node 1', classes='class1')
G.add_node('separate node 2', classes='class2')
G.add_edge('separate node 1', 'separate node 2')
graph = Graph()
graph.add_graph_from_networkx(G)
expected_nodes = [
Node(classes='class1', data={'id': 'separate node 1'},
position={}),
Node(classes='class2', data={'id': 'separate node 2'}, position={})
]
expected_edges = [
Edge(data={
'source': 'separate node 1',
'target': 'separate node 2'
},
position={})
]
compare_nodes(expected_nodes, graph.nodes)
compare_edges(expected_edges, graph.edges)
def test_custom_node(self):
class custom_node:
def __init__(self, name):
self.name = name
def __str__(self):
return "Node: " + str(self.name)
n1 = custom_node("node 1")
n2 = custom_node("node 2")
G = nx.Graph()
G.add_node(n1)
G.add_node(n2)
G.add_edge(n1, n2)
graph = Graph()
graph.add_graph_from_networkx(G)
expected_nodes = [
Node(classes='', data={'id': 'Node: node 1'}, position={}),
Node(classes='', data={'id': 'Node: node 2'}, position={})
]
expected_edges = [
Edge(data={
'source': 'Node: node 1',
'target': 'Node: node 2'
},
classes='',
position={})
]
compare_edges(expected_nodes, graph.nodes)
compare_edges(expected_edges, graph.edges)
def test_subclassed_node(self):
class CustomNode(Node):
def __init__(self, name, classes='', custom_variable=1234):
super().__init__()
self.data['id'] = name
self.classes = classes
self.custom_variable = custom_variable
n1 = CustomNode("node 1", classes='class1')
n2 = CustomNode("node 2", classes='class2')
G = nx.Graph()
G.add_node(n1)
G.add_node(n2)
G.add_edge(n1, n2)
graph = Graph()
graph.add_graph_from_networkx(G)
expected_nodes = [n1, n2]
expected_edges = [
Edge(data={
'source': 'node 1',
'target': 'node 2'
},
classes='',
position={})
]
for expected, actual in zip(expected_nodes, graph.nodes):
assert expected is actual
compare_edges(expected_edges, graph.edges)
def test_directed(self):
"""
Check that the ' directed ' class is added appropriately
"""
G = nx.Graph()
G.add_node('separate node 1')
G.add_node('separate node 2')
G.add_edge('separate node 1', 'separate node 2')
G.add_edge('separate node 2', 'separate node 1')
graph = Graph()
graph.add_graph_from_networkx(G, directed=True)
expected_nodes = [
Node(classes='', data={'id': 'separate node 1'}, position={}),
Node(classes='', data={'id': 'separate node 2'}, position={})
]
expected_edges = [
Edge(data={
'source': 'separate node 1',
'target': 'separate node 2'
},
classes=' directed ',
position={}),
Edge(data={
'source': 'separate node 2',
'target': 'separate node 1'
},
classes=' directed ',
position={})
]
compare_nodes(expected_nodes, graph.nodes)
compare_edges(expected_edges, graph.edges)
def test_subclassed_node(self):
class CustomNode(Node):
def __init__(self, name, classes="", custom_variable=1234):
super().__init__()
self.data["id"] = name
self.classes = classes
self.custom_variable = custom_variable
n1 = CustomNode("node 1", classes="class1")
n2 = CustomNode("node 2", classes="class2")
G = nx.Graph()
G.add_node(n1)
G.add_node(n2)
G.add_edge(n1, n2)
graph = Graph()
graph.add_graph_from_networkx(G)
expected_nodes = [n1, n2]
expected_edges = [
Edge(data={
"source": "node 1",
"target": "node 2"
}, classes="")
]
for expected, actual in zip(expected_nodes, graph.nodes):
assert expected is actual
compare_edges(expected_edges, graph.edges)
def test_directed(self):
"""
Check that the ' directed ' class is added appropriately
"""
G = nx.Graph()
G.add_node("separate node 1")
G.add_node("separate node 2")
G.add_edge("separate node 1", "separate node 2")
G.add_edge("separate node 2", "separate node 1")
graph = Graph()
graph.add_graph_from_networkx(G, directed=True)
expected_nodes = [
Node(classes="", data={"id": "separate node 1"}, position={}),
Node(classes="", data={"id": "separate node 2"}, position={}),
]
expected_edges = [
Edge(
data={
"source": "separate node 1",
"target": "separate node 2"
},
classes=" directed ",
),
Edge(
data={
"source": "separate node 2",
"target": "separate node 1"
},
classes=" directed ",
),
]
compare_nodes(expected_nodes, graph.nodes)
compare_edges(expected_edges, graph.edges)
def test_hybridgraphs(self):
'''
Test graphs with both directed and undirected edges.
'''
G = nx.MultiGraph()
G.add_edge(1, 2, weight=15)
G.add_edge(1, 2, weight=15)
G.add_edge(2, 4)
graph = Graph()
graph.add_graph_from_networkx(G)
# a custom directed edge in an undirected networkx multigraph
e = Edge()
e.data['source'] = '1'
e.data['target'] = '4'
graph.add_edge(e, directed=True)
expected_edges = [
Edge(classes=' multiple_edges ',
data={
'source': '1',
'target': '2',
'weight': 15
},
position={}),
Edge(classes=' multiple_edges ',
data={
'source': '1',
'target': '2',
'weight': 15
},
position={}),
Edge(classes=' multiple_edges ',
data={
'source': '2',
'target': '4'
},
position={}),
Edge(classes=' directed ',
data={
'source': '1',
'target': '4'
},
position={})
]
compare_edges(expected_edges, graph.edges)
def test_lonely_nodes(self):
"""
Test to ensure that nodes with no associated edges end up in the graph
"""
G1 = nx.complete_graph(5)
G2 = nx.Graph()
G2.add_node("unconnected_node")
G2 = nx.complete_graph(1)
graph = Graph()
graph.add_graph_from_networkx(G1)
graph.add_graph_from_networkx(G2)
expected_nodes = [
Node(data={"id": "0"}, position={}),
Node(data={"id": "1"}, position={}),
Node(data={"id": "2"}, position={}),
Node(data={"id": "3"}, position={}),
Node(data={"id": "4"}, position={}),
Node(data={"id": "unconnected_node"}, position={}),
]
# remove individual node using node as input
graph.remove_node(graph.nodes[0])
expected_nodes = [
Node(data={"id": "1"}, position={}),
Node(data={"id": "2"}, position={}),
Node(data={"id": "3"}, position={}),
Node(data={"id": "4"}, position={}),
Node(data={"id": "unconnected_node"}, position={}),
]
# remove individual node using index node as input
graph.remove_node_by_id("3")
expected_nodes = [
Node(data={"id": "1"}, position={}),
Node(data={"id": "2"}, position={}),
Node(data={"id": "4"}, position={}),
Node(data={"id": "unconnected_node"}, position={}),
]
# remove all nodes of the graph
graph.clear()
expected_nodes = []
def test_hybridgraphs(self):
"""
Test graphs with both directed and undirected edges.
"""
G = nx.MultiGraph()
G.add_edge(1, 2, weight=15)
G.add_edge(1, 2, weight=15)
G.add_edge(2, 4)
graph = Graph()
graph.add_graph_from_networkx(G)
# a custom directed edge in an undirected networkx multigraph
e = Edge()
e.data["source"] = "1"
e.data["target"] = "4"
graph.add_edge(e, directed=True)
expected_edges = [
Edge(
classes=" multiple_edges ",
data={
"source": "1",
"target": "2",
"weight": 15
},
),
Edge(
classes=" multiple_edges ",
data={
"source": "1",
"target": "2",
"weight": 15
},
),
Edge(classes=" multiple_edges ",
data={
"source": "2",
"target": "4"
}),
Edge(classes=" directed ", data={
"source": "1",
"target": "4"
}),
]
compare_edges(expected_edges, graph.edges)
def test_classes(self):
"""
Test to ensure that approriate classes are added to the networkx object
"""
G = nx.Graph()
G.add_node("separate node 1", classes="class1")
G.add_node("separate node 2", classes="class2")
G.add_edge("separate node 1", "separate node 2")
graph = Graph()
graph.add_graph_from_networkx(G)
expected_nodes = [
Node(classes="class1", data={"id": "separate node 1"}),
Node(classes="class2", data={"id": "separate node 2"}),
]
expected_edges = [
Edge(data={
"source": "separate node 1",
"target": "separate node 2"
})
]
compare_nodes(expected_nodes, graph.nodes)
compare_edges(expected_edges, graph.edges)
def test_lonely_nodes(self):
"""
Test to ensure that nodes with no associated edges end up in the graph
"""
G1 = nx.complete_graph(5)
G2 = nx.Graph()
G2.add_node('unconnected_node')
G2 = nx.complete_graph(1)
graph = Graph()
graph.add_graph_from_networkx(G1)
graph.add_graph_from_networkx(G2)
expected_nodes = [
Node(data={'id': '0'}, position={}),
Node(data={'id': '1'}, position={}),
Node(data={'id': '2'}, position={}),
Node(data={'id': '3'}, position={}),
Node(data={'id': '4'}, position={}),
Node(data={'id': 'unconnected_node'}, position={})
]
expected_edges = [
Edge(data={
'source': '0',
'target': '1'
}, position={}),
Edge(data={
'source': '0',
'target': '2'
}, position={}),
Edge(data={
'source': '0',
'target': '3'
}, position={}),
Edge(data={
'source': '0',
'target': '4'
}, position={}),
Edge(data={
'source': '1',
'target': '2'
}, position={}),
Edge(data={
'source': '1',
'target': '3'
}, position={}),
Edge(data={
'source': '1',
'target': '4'
}, position={}),
Edge(data={
'source': '2',
'target': '3'
}, position={}),
Edge(data={
'source': '2',
'target': '4'
}, position={}),
Edge(data={
'source': '3',
'target': '4'
}, position={})
]
compare_nodes(expected_nodes, graph.nodes)
compare_edges(expected_edges, graph.edges)
def test_multidigraphs(self):
'''
Test to ensure multidigraphs are generated correctly.
'''
G = nx.MultiDiGraph()
G.add_edge(1, 2, weight=15)
G.add_edge(1, 2, weight=15)
G.add_edge(1, 2, weight=1)
G.add_edge(2, 1, weight=10)
G.add_edge(2, 4)
graph = Graph()
graph.add_graph_from_networkx(G)
expected_edges = [
Edge(classes=' directed multiple_edges ',
data={
'source': '1',
'target': '2',
'weight': 15
},
position={}),
Edge(classes=' directed multiple_edges ',
data={
'source': '1',
'target': '2',
'weight': 15
},
position={}),
Edge(classes=' directed multiple_edges ',
data={
'source': '1',
'target': '2',
'weight': 1
},
position={}),
Edge(classes=' directed multiple_edges ',
data={
'source': '2',
'target': '1',
'weight': 10
},
position={}),
Edge(classes=' directed multiple_edges ',
data={
'source': '2',
'target': '4'
},
position={})
]
expected_nodes = [
Node(data={'id': '1'}, position={}),
Node(data={'id': '2'}, position={}),
Node(data={'id': '4'}, position={})
]
compare_edges(expected_edges, graph.edges)
compare_nodes(expected_nodes, graph.nodes)
graph = Graph()
# override default behavior
graph.add_graph_from_networkx(G, multiple_edges=False, directed=False)
expected_edges = [
Edge(data={
'source': '1',
'target': '2',
'weight': 15
},
position={}),
Edge(data={
'source': '2',
'target': '4'
}, position={})
]
compare_edges(expected_edges, graph.edges)
def test_lonely_nodes(self):
"""
Test to ensure that nodes with no associated edges end up in the graph
"""
G1 = nx.complete_graph(5)
G2 = nx.Graph()
G2.add_node("unconnected_node")
G2 = nx.complete_graph(1)
graph = Graph()
graph.add_graph_from_networkx(G1)
graph.add_graph_from_networkx(G2)
expected_nodes = [
Node(data={"id": "0"}, position={}),
Node(data={"id": "1"}, position={}),
Node(data={"id": "2"}, position={}),
Node(data={"id": "3"}, position={}),
Node(data={"id": "4"}, position={}),
Node(data={"id": "unconnected_node"}, position={}),
]
expected_edges = [
Edge(data={
"source": "0",
"target": "1"
}),
Edge(data={
"source": "0",
"target": "2"
}),
Edge(data={
"source": "0",
"target": "3"
}),
Edge(data={
"source": "0",
"target": "4"
}),
Edge(data={
"source": "1",
"target": "2"
}),
Edge(data={
"source": "1",
"target": "3"
}),
Edge(data={
"source": "1",
"target": "4"
}),
Edge(data={
"source": "2",
"target": "3"
}),
Edge(data={
"source": "2",
"target": "4"
}),
Edge(data={
"source": "3",
"target": "4"
}),
]
compare_nodes(expected_nodes, graph.nodes)
compare_edges(expected_edges, graph.edges)
def test_multidigraphs(self):
"""
Test to ensure multidigraphs are generated correctly.
"""
G = nx.MultiDiGraph()
G.add_edge(1, 2, weight=15)
G.add_edge(1, 2, weight=15)
G.add_edge(1, 2, weight=1)
G.add_edge(2, 1, weight=10)
G.add_edge(2, 4)
graph = Graph()
graph.add_graph_from_networkx(G)
expected_edges = [
Edge(
classes=" directed multiple_edges ",
data={
"source": "1",
"target": "2",
"weight": 15
},
),
Edge(
classes=" directed multiple_edges ",
data={
"source": "1",
"target": "2",
"weight": 15
},
),
Edge(
classes=" directed multiple_edges ",
data={
"source": "1",
"target": "2",
"weight": 1
},
),
Edge(
classes=" directed multiple_edges ",
data={
"source": "2",
"target": "1",
"weight": 10
},
),
Edge(
classes=" directed multiple_edges ",
data={
"source": "2",
"target": "4"
},
),
]
expected_nodes = [
Node(data={"id": "1"}, position={}),
Node(data={"id": "2"}, position={}),
Node(data={"id": "4"}, position={}),
]
compare_edges(expected_edges, graph.edges)
compare_nodes(expected_nodes, graph.nodes)
graph = Graph()
# override default behavior
graph.add_graph_from_networkx(G, multiple_edges=False, directed=False)
expected_edges = [
Edge(data={
"source": "1",
"target": "2",
"weight": 15
}),
Edge(data={
"source": "2",
"target": "4"
}),
]
compare_edges(expected_edges, graph.edges)
