def test_node_size():
# add size as attribute and fill with random numbers
nodes = G.nodes()
for u in nodes:
G.node[u]["score"] = random()
# also extract list for testing
scores = [G.nodes[u]["score"] for u in nodes]
# add color as property
a = ArcPlot(G, node_size="score")
assert a.node_sizes == scores
# add types as list
a = ArcPlot(G, node_size=scores)
assert a.node_sizes == scores
def test_edge_widths():
# add weight as attribute and fill with random numbers
edges = G.edges()
for u, v in edges:
G[u][v]["weight"] = random()
# also extract list for testing
weights = [G[u][v]["weight"] for u, v in edges]
# add weights as proptery
c = CircosPlot(G, edge_width="weight")
assert c.edge_widths == weights
a = ArcPlot(G, edge_width="weight")
assert a.edge_widths == weights
# add weights as list
c = CircosPlot(G, edge_width=weights)
assert c.edge_widths == weights
a = ArcPlot(G, edge_width=weights)
assert a.edge_widths == weights
def test_edge_color():
# add color as attribute and fill with random numbers
edges = G.edges()
for u, v in edges:
G[u][v]["type"] = "a" if random() < 0.5 else "b"
# also extract list for testing
types = [G[u][v]["type"] for u, v in edges]
# add color as property
c = CircosPlot(G, edge_color="type")
assert corresponding_lists(c.edge_colors, types)
a = ArcPlot(G, edge_color="type")
assert corresponding_lists(a.edge_colors, types)
Record each node's degree centrality score in its node metadata.
Do this by assigning nx.degree_centrality(G_lmc)[n] to G_lmc.node[n]['degree centrality'] in the second for loop.
Visualize this network with an ArcPlot sorting the nodes by degree centrality (you can do this using the keyword argument node_order='degree centrality').
'''
SOLUTION
# Import necessary modules
from nxviz import ArcPlot
import matplotlib.pyplot as plt
# Identify the largest maximal clique: largest_max_clique
largest_max_clique = set(sorted(nx.find_cliques(G), key=lambda x: len(x))[-1])
# Create a subgraph from the largest_max_clique: G_lmc
G_lmc = G.subgraph(largest_max_clique).copy()
# Go out 1 degree of separation
for node in list(G_lmc.nodes()):
G_lmc.add_nodes_from(G.neighbors(node))
G_lmc.add_edges_from(
zip([node] * len(list(G.neighbors(node))), G.neighbors(node)))
# Record each node's degree centrality score
for n in G_lmc.nodes():
G_lmc.node[n]['degree centrality'] = nx.degree_centrality(G_lmc)[n]
# Create the ArcPlot object: a
a = ArcPlot(graph=G_lmc, node_order='degree centrality')
# Draw the ArcPlot to the screen
a.draw()
plt.show()
# Create the CircosPlot object: c c = CircosPlot(T) # Draw c to the screen c.draw() # Display the plot plt.show() #7 # Import necessary modules import matplotlib.pyplot as plt from nxviz import ArcPlot # Create the un-customized ArcPlot object: a a = ArcPlot(T) # Draw a to the screen a.draw() # Display the plot plt.show() # Create the customized ArcPlot object: a2 a2 = ArcPlot(T, node_order='category', node_color='category') # Draw a2 to the screen a2.draw() # Display the plot plt.show()
# Create the CircosPlot object: c
c = CircosPlot(T)
# Draw c to the screen
c.draw()
# Display the plot
plt.show()
# Import necessary modules
import matplotlib.pyplot as plt
from nxviz import ArcPlot
# Create the un-customized ArcPlot object: a
a = ArcPlot(T)
# Draw a to the screen
a.draw()
# Display the plot
plt.show()
# Create the customized ArcPlot object: a2
a2 = ArcPlot(
T,
node_order='category',
node_color='category',
)
# Draw a2 to the screen
def test_arc_plot():
a = ArcPlot(G) # noqa: F841
diff = diff_plots(a, "arc.png", baseline_dir, result_dir)
assert diff is None
# Create the customized MatrixPlot object: h
h = MatrixPlot(graph=largest_ccs, node_grouping='grouping')
print(' Draw the MatrixPlot ')
h.draw()
plt.savefig('MatrixPlot.png')
plt.clf()
# Iterate over all the nodes in G, including the metadata
for n, d in G.nodes(data=True):
# Calculate the degree of each node: G.node[n]['degree']
G.node[n]['degree'] = nx.degree(G, n)
# Create the ArcPlot object: a
a = ArcPlot(G, node_order='degree')
print(' Draw the ArcPlot to the screen ')
a.draw()
plt.savefig('ArcPlot.png')
# Iterate over all the nodes, including the metadata
for n, d in G.nodes(data=True):
# Calculate the degree of each node: G.node[n]['degree']
G.node[n]['degree'] = nx.degree(G, n)
# Create the CircosPlot object: c
c = CircosPlot(G,
node_order='degree',
node_grouping='grouping',
node_labels=True,
node_order='Y',
font_size=32,
node_color='C',
figsize=(8, 8),
font_weight='bold') # This creates a circosplot object Mat.
Mat.draw()
plt.savefig('./figures/Cerco_credit_transaction.png')
plt.show()
## Arcplot
from nxviz import ArcPlot
Mat = ArcPlot(G,
node_labels=True,
font_size=25,
node_order='Y',
node_color='C',
figsize=(8, 8))
Mat.draw()
plt.savefig('./figures/Arc_credit_transaction.png')
plt.show()
# Centrality Measures
## Degree Centrality
pos = node_pos
draw_graph(G, pos, nx.in_degree_centrality(G), 'In-degree Centrality',
'./figures/indegree_credit_transaction.png')
draw_graph(G, pos, nx.out_degree_centrality(G), 'Out-degree Centrality',
'./figures/outdegree_credit_transaction.png')
# Draw c to the screen c.draw() # Display the plot plt.show() # Visualizing using Arc plots # Following on what you've learned about the nxviz API, now try making an ArcPlot of the network. Two keyword arguments that you will try here are node_order='keyX' and node_color='keyX', in which you specify a key in the node metadata dictionary to color and order the nodes by. # matplotlib.pyplot has been imported for you as plt. # Import necessary modules import matplotlib.pyplot as plt from nxviz import ArcPlot # Create the un-customized ArcPlot object: a a = ArcPlot(T) # Draw a to the screen a.draw() # Display the plot plt.show() # Create the customized ArcPlot object: a2 a2 = ArcPlot(node_order='category',node_color='category',graph = T) # Draw a2 to the screen a2.draw() # Display the plot plt.show()
# Draw c to the screen c.draw() # Display the plot plt.show() -------------------------------------------------- # Exercise_7 # Import necessary modules import matplotlib.pyplot as plt from nxviz import ArcPlot # Create the un-customized ArcPlot object: a a = ArcPlot(T) # Draw a to the screen a.draw() # Display the plot plt.show() # Create the customized ArcPlot object: a2 a2 = ArcPlot(T, node_order="category", node_color="category") # Draw a2 to the screen a2.draw() # Display the plot plt.show()
def test_arc_plot():
a = ArcPlot(G) # noqa: F841
def test_answer(mf_counts):
assert mf_counts['female'] == 17
assert mf_counts['male'] == 12
test_answer(mf_count)
from nxviz import MatrixPlot
m = MatrixPlot(g)
m.draw()
plt.show()
from nxviz import ArcPlot
a = ArcPlot(g)
a.draw()
from nxviz import CircosPlot
c = CircosPlot(g)
c.draw()
plt.show()
# plt.savefig('images/seventh.png', dpi=300)
#%% hiveplot
#%%
#%%
DG = nx.DiGraph() # make a directed graph (digraph)
DG.add_nodes_from(["S", "A", "B", "C", "D", "E", "T"]) # add nodes
from nxviz import ArcPlot
from nxviz import CircosPlot
# plt.style.use('ggplot')
from itertools import combinations
T = nx.erdos_renyi_graph(n=45, p=0.8, seed=456)
c = CircosPlot(T)
# Draw c to the screen
c.draw()
plt.pause(2)
plt.clf()
# Create the un-customized ArcPlot object: a
a = ArcPlot(T)
# Draw a to the screen
a.draw()
# Display the plot
plt.pause(2)
plt.close()
def nodes_with_m_nbrs(G, m):
"""
Returns all nodes in graph G that have m neighbors.
"""
nodes = set()
def test_arc_plot():
a = ArcPlot(G)
