def plotGraph(self, path):
if self.graph is None:
raise Exception("The graph has not been generated")
values = [self.colorScheme(n) for n in self.graph.nodes()]
nx.draw_spring(self.graph, cmap="jet", node_color=values, node_size=100)
plt.savefig(path)
plt.close()
def draw_networkx_ex():
G = nx.dodecahedral_graph()
nx.draw(G)
plt.show()
nx.draw_networkx(G, pos=nx.spring_layout(G))
limits = plt.axis('off')
plt.show()
nodes = nx.draw_networkx_nodes(G, pos=nx.spring_layout(G))
plt.show()
edges = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
plt.show()
labels = nx.draw_networkx_labels(G, pos=nx.spring_layout(G))
plt.show()
edge_labels = nx.draw_networkx_edge_labels(G, pos=nx.spring_layout(G))
plt.show()
print("Circular layout")
nx.draw_circular(G)
plt.show()
print("Random layout")
nx.draw_random(G)
plt.show()
print("Spectral layout")
nx.draw_spectral(G)
plt.show()
print("Spring layout")
nx.draw_spring(G)
plt.show()
print("Shell layout")
nx.draw_shell(G)
plt.show()
print("Graphviz")
def construct_de_bruijn_velvet(kmers, draw, outfile): #make list of k-1mers for quick edge construction k1mers = [x[:-1] for x in kmers.keys()] k1mers_array = np.array(k1mers) #find overlaps edge_list = [] for kmer in kmers.keys(): matches = np.where(k1mers_array==kmer[1:]) for match in matches[0]: #print match edge_list.append((kmer, kmers.keys()[match])) #make graph G = nx.DiGraph() #add seq_kmers as nodes and overlaps as edges for kmer in kmers.items(): G.add_node(kmer[0], num=kmer[1]) G.add_edges_from(edge_list) # draw the graph if desired if draw == "True": nx.draw_spring(G) plt.show() #output adjacency list format of the graph if desired if outfile != "": nx.write_adjlist(G, outfile) return G
def get_communities(graph):
betweenness = nx.edge_betweenness_centrality(graph)
sorted_betweeness = [x[0] for x in sorted(betweenness.items(), key = lambda x : x[1], reverse = True)]
best_partitions = []
max_modularity = -1.0
graph_copy = graph.copy()
while sorted_betweeness:
communities = [list(x) for x in nx.connected_components(graph_copy)]
partitions = {}
for i in range(len(communities)):
for node in communities[i]:
partitions[node] = i
modularity = community.modularity(partitions, graph_copy)
if modularity > max_modularity:
best_partitions = communities
max_modularity = modularity
elif modularity <= max_modularity:
break;
graph_copy.remove_edge(*sorted_betweeness[0])
del sorted_betweeness[0]
for partition in best_partitions:
print sorted(partition)
val_map = {}
for partition in best_partitions:
value = random.random()
while value in val_map.values():
value = random.random()
for node in partition:
val_map[node] = value
values = [val_map.get(node) for node in graph.nodes()]
nx.draw_spring(graph, node_color = values, node_size = 500, with_labels = True)
plt.savefig(sys.argv[2])
def get_topics_noun_phrases(num_news, draw=False, url='http://cnn.com'):
texts = get_news(url, num_news)
gb = NounPhraseGraphBuilder(text_processing.clean_punctuation_and_stopwords)
gb.load_texts(texts)
G = gb.create_graph()
print "Graph built"
partition = community.best_partition(G)
words_by_part = get_words_by_partition(partition)
print_topics_from_partitions(G, words_by_part, 10)
mod = community.modularity(partition,G)
print("modularity:", mod)
#print_topics_from_partitions(G, words_by_part, 10)
if draw:
values = [partition.get(node) for node in G.nodes()]
nx.draw_spring(G, cmap = plt.get_cmap('jet'), node_color = values, node_size=30, with_labels=False)
plt.show()
topics = get_topics_from_partitions(G, words_by_part, 10)
return G, topics
def print_graph_nx(vertices, edges, name, draw_spring=False, print_dist=False):
G=nx.DiGraph()
labels = []
for v in vertices:
G.add_node(v)
for v in vertices:
for e in edges:
if(len(e)) > 2:
G.add_edge(e[0], e[1], weight=int(e[2]))
else:
G.add_edge(e[0], e[1])
print("Nodes of graph: ")
print(G.nodes())
print("Edges of graph: ")
print(G.edges())
if not draw_spring:
nx.draw(G, with_labels=True, node_color='y')
else:
nx.draw_spring(G, with_labels=True, node_color='y')
#nx.draw_networkx_edge_labels(G,labels)
plt.savefig("%s.png" % name) # save as png
#plt.show()
plt.clf()
def draw_graph(graph, showLabels = True):
# extract nodes from graph
nodes = set([n1 for n1, n2 in graph] + [n2 for n1, n2 in graph])
# create network graph
G=nx.Graph()
# add nodes
for node in nodes:
G.add_node(node)
# add edges
for edge in graph:
G.add_edge(edge[0], edge[1])
# draw graph 1 in shell layout
pos = nx.shell_layout(G)
nx.draw(G, pos)
plt.figure()
# draw graph 2 with a random layout
# might need to zoom in to see edges
# labels are now on top of the nodes
nx.draw_random(G)
plt.figure()
nx.draw_spring(G, node_size=100, with_labels=showLabels, font_size=16, edge_color="grey", width=0.5)
# draw graph 3 the standard way and save
plt.savefig("fig1.png")
plt.show()
def lattice_plot(component_list, file_path):
"""
Creates a lattice style plot of all graph components
"""
graph_fig=plt.figure(figsize=(20,10)) # Create figure
# Set the number of rows in the plot based on an odd or
# even number of components
num_components=len(component_list)
if num_components % 2 > 0:
num_cols=(num_components/2)+1
else:
num_cols=num_components/2
# Plot subgraphs, with centrality annotation
plot_count=1
for G in component_list:
# Find actor in each component with highest degree
in_cent=nx.degree(G)
in_cent=[(b,a) for (a,b) in in_cent.items()]
in_cent.sort()
in_cent.reverse()
high_in=in_cent[0][1]
# Plot with annotation
plt.subplot(2,num_cols,plot_count)
nx.draw_spring(G, node_size=35, with_labels=False)
plt.text( 0,-.1,"Highest degree: "+high_in, color="darkgreen")
plot_count+=1
plt.savefig(file_path)
def draw_graph(self, G, node_list=None, edge_colour='k', node_size=15, node_colour='r', graph_type='spring',
back_bone=None, side_chains=None, terminators=None):
# determine nodelist
if node_list is None:
node_list = G.nodes()
# determine labels
labels = {}
for l_atom in G.nodes_iter():
labels[l_atom] = l_atom.symbol
# draw graphs based on graph_type
if graph_type == 'circular':
nx.draw_circular(G, with_labels=True, labels=labels, node_list=node_list, node_size=node_size,
edge_color=edge_colour, node_color=node_colour)
elif graph_type == 'random':
nx.draw_random(G, with_labels=True, labels=labels, node_list=node_list, node_size=node_size,
edge_color=edge_colour, node_color=node_colour)
elif graph_type == 'spectral':
nx.draw_spectral(G, with_labels=True, labels=labels, node_list=node_list, node_size=node_size,
edge_color=edge_colour, node_color=node_colour)
elif graph_type == 'spring':
nx.draw_spring(G, with_labels=True, labels=labels, node_list=node_list, node_size=node_size,
edge_color=edge_colour, node_color=node_colour)
elif graph_type == 'shell':
nx.draw_shell(G, with_labels=True, labels=labels, node_list=node_list, node_size=node_size,
edge_color=edge_colour, node_color=node_colour)
# elif graph_type == 'protein':
# self.draw_protein(G, with_labels=True, labels=labels, node_list=node_list, node_size=node_size,
# edge_color=edge_colour, node_color=node_colour, back_bone, side_chains, terminators)
else:
nx.draw_networkx(G, with_labels=True, labels=labels, node_list=node_list, node_size=node_size,
edge_color=edge_colour, node_color=node_colour)
plt.show()
def randBearGenealogy(self): """show all direct older relatives of a random bear""" G = nx.Graph() bear = self.getRandomBear() self.helpGenes(G, bear.mom) self.helpGenes(G, bear.dad) nx.draw_spring(G) plt.show()
def draw_Graph( G ): nx.draw_spring(G, node_color=[float(G.degree(v)) for v in G], node_size=40, with_labels=False, cmap=plt.cm.Reds, ) plt.show()
def main():
if len(sys.argv) > 1:
id = sys.argv[1]
else:
id = input("Enter user ID or screen_name: ").lstrip()
# Authorization
session = vk.AuthSession(access_token=VkData.TOKEN)
vk_api = vk.API(session, v=VkData.VERSION)
# If user enters screen_name, we need to get his ID
id = vk_api.users.get(user_ids=id)[0]["id"]
# Friends Graph is dictionary
# Key - friend, graph vertex
# Value - list of mutual friends, adjacent vertices
graph = {}
# Get list of friends for entered ID
friends = get_friends(vk_api, id)
# Fill graph
for friend in friends:
print('Processing', "\tid: ", friend.id, "\tName : ",
friend.first_name, friend.last_name)
# If the profile is not hidden
if not friend.is_closed:
# Get friends of friend
all_friends = get_friends(vk_api, friend.id)
# Find mutual friends
mutual = []
for i in all_friends:
for j in friends:
if i.id == j.id:
mutual.append(j)
# Add value in dictionary
graph[friend] = mutual
else:
graph[friend] = list()
# Graph visualisation
g = nx.from_dict_of_lists(graph)
options = {
'node_color': 'red',
'node_size': 100,
'line_color': 'black',
'with_labels': True,
'font_color': 'black',
'style': 'dotted',
}
nx.draw_spring(g, **options)
plt.show()
def graph_object(data_1, data_2, data_3):
#instantiate a networkx graph object
G = nx.Graph()
head = 1171944902
G.add_node(head)
#converts dictionaries into list format from data points 1,2 and 3
list_data_1 = list(data_1.keys())
G.add_nodes_from(list_data_1)
list_data_2 = []
for element in data_2:
for i in element.keys():
list_data_2.append(i)
list_data_3 = []
for element in data_3:
for i in element.keys():
list_data_3.append(i)
#add edges from my account and my top 5 most popular friends
for i in range(0, len(list_data_1)):
G.add_edges_from([(1171944902, list_data_1[i])])
#add edges between distance 1 friends and distance 2 friends
for j in range(0, len(data_2)):
for k in list(data_2[j].keys()):
G.add_edges_from([(list_data_1[j], k)])
#add edges between distance 2 friends and distance 3 friends
for l in range(0, len(data_3)):
for m in list(data_3[l].keys()):
G.add_edges_from([(list_data_2[l], m)])
#Just generate a little title for my graph plot
plt.title("Daniel Fatade's Social Media Network")
nx.draw_spring(G, node_color='bisque', with_labels=True)
#This saves the graph figure.
#plt.savefig('my_network_graph')
#These functions calculate the diamter of the graph, the number of nodes and edges present in the graph
#and the average distance of the graph
n_edges = G.number_of_edges()
diameter = nx.diameter(G)
average_dist = nx.average_shortest_path_length(G)
n_nodes = G.nodes()
print("This graph contains {0} distinct nodes with {1} edges. ".format(
len(n_nodes), n_edges))
print("The Diameter of this network is: {0} ".format(diameter))
print("The average distance between each node in the Graph is: {0}".format(
average_dist))
def main():
Arestas = np.loadtxt('ha30_dist.txt')
G = nx.from_numpy_matrix(Arestas)
A = kruskal(G)
print(A.edges)
nx.draw_spring(A, with_labels=True)
plt.savefig("grafo_mst.png")
def main():
"""For testing with iPython"""
# nx.draw_spring(g, with_labels=True)
dfs = nx.Graph()
# for k, v in tarjan(draw_network(), "sum1")[0].items():
# for k, v in tarjan(draw_small(), "A")[0].items():
for k, v in tarjan(draw_wiki(), "A")[0].items():
dfs.add_edge(k, v)
nx.draw_spring(dfs, with_labels=True)
def draw_graph_spring(y, clusters='blue', ns=30):
G = nxG(y)
plt.figure()
nx.draw_spring(G,
cmap=plt.get_cmap('jet'),
node_color=clusters,
node_size=30,
with_labels=False)
def draw_graph_labels(labels):
d = array_to_dict(labels)
values = [d.get(node) for node in G.nodes()]
nx.draw_spring(G,
map=plt.get_cmap('jet'),
node_color=values,
node_size=30,
with_labels=False)
plt.show()
def showGraph( graph ) :
# pos = nx.spring_layout(graph, k=1)
# nx.draw_networkx_nodes(graph, pos, node_size=4000, node_color="white")
# nx.draw_networkx_edges(graph, pos, width=3, alpha=0.5, edge_color="black", arrows=True)
# nx.draw_networkx_labels(graph, pos, font_size=12)
# nx.draw_networkx_edge_labels(graph, pos, label_pos=0.6, font_size = 10)
# plt.axis('off')
nx.draw_spring (graph)
plt.show ()
def show(graph):
# nx.draw(graph, with_labels=True, font_weight='bold')
# nx.draw_circular(graph, with_labels=True, font_weight='bold')
# nx.draw_kamada_kawai(graph, with_labels=True, font_weight='bold')
# nx.draw_spectral(graph, with_labels=True, font_weight='bold')
nx.draw_spring(graph, with_labels=True, font_weight='bold')
# nx.draw(graph, with_labels=True, font_weight='bold')
# nx.draw(graph, with_labels=True, font_weight='bold')
plt.show()
def graph(self, plot=False):
import networkx as nx
G = nx.DiGraph()
for n1,n2 in self.probas:
G.add_edge(n1,n2,{'p':self.probas[n1,n2]})
if plot:
nx.draw_spring(G)
else:
return G
def main():
cities = {}
paths = defaultdict(int)
ants = []
#init city data
with open('cities.txt') as cityFile:
for line in cityFile:
line = line.split() # to deal with blank
if line:
cities[ line[0] ] = int(line[1])
numberOfAnts = int(len(cities)*1.5)
numberOfIterations = 10
optimalDistance = 1000000
optimalPath = []
money = 0
for i in range(numberOfIterations):
#init distance data
with open('distances.txt') as distanceFile:
for line in distanceFile:
line = line.split()
if line:
initMatrixAtIndexes(paths,line[0],line[1],int(line[2]),cities)
ALPHA = random.randint(1,1)
BETA = random.randint(1,1)
#EVAPORATION_RATE = random.random()
ants = []
for i in range(numberOfAnts):
a = Ant('INITIAL')
ants.append(a)
for a in ants:
#Tour until stuck or until visited all cities
for i in range(len(cities)):
if not a.hasVisitedAllCities(cities) and not a.isStuck():
evaporatePheromones(paths,ants)
a.moveToNextEdge(paths)
elif not a.hasReturnedHome() and not a.isStuck():
evaporatePheromones(paths,ants)
a.returnToStartingCity(paths)
if (a.getTravelledDistance()<optimalDistance and not a.isStuck()):
optimalPath = a.getPath()
optimalDistance = a.getTravelledDistance()
money = a.getMoney()
g=nx.DiGraph()
for i in range (len(optimalPath)-1):
g.add_weighted_edges_from([(optimalPath[i],optimalPath[i+1],paths[(optimalPath[i],optimalPath[i+1])]['distance'])])
nx.draw_spring(g)
plt.savefig("file.png")
nx.write_dot(g,'file.dot')
print optimalDistance, optimalPath, money
def ego():
'''
This node is deceptively connected because it's from the index
(page 750 of volume2.pdf).
'''
g_9_56_090 = nx.ego_graph(g, '9.56.090')
nx.draw_spring(g_9_56_090)
plt.savefig("9.56.090.png")
print nodes['9.56.090']
def communities(publications: List[Tuple[int, Set[int]]]) -> None:
graph = get_collaboration_graph(publications, 2006, 2016)
partition = community_louvain.best_partition(graph)
values = [partition.get(node) for node in graph.nodes()]
networkx.draw_spring(graph, cmap=matplotlib.pyplot.get_cmap('tab20'), node_size=60, node_color=values, font_size=8, with_labels=True)
matplotlib.pyplot.show()
def test_edge_colormap():
colors = range(barbell.number_of_edges())
nx.draw_spring(
barbell,
edge_color=colors,
width=4,
edge_cmap=plt.cm.Blues,
with_labels=True,
)
def main(args):
if len(args) != 1:
sys.exit("Usage: python ?.py <graphml file>")
net = args[0]
G = networkx.read_graphml(net)
G = networkx.convert_node_labels_to_integers(G)
investment_dataframe = pandas.read_csv('investment.csv', header=None)
investment_narray = numpy.array(investment_dataframe) #np.ndarray()
investment_list = investment_narray.tolist() #list
investment = []
nodes_color = []
nodes_size = []
for i in range(len(investment_list)):
[inv] = investment_list[i]
investment.append(inv)
'''度的节点size大'''
for i in range(len(G)):
d = G.degree()[i]
if d >= 0 and d < 2:
nodes_size.append(20)
elif d >= 2 and d < 4:
nodes_size.append(50)
elif d >= 4 and d < 6:
nodes_size.append(100)
elif d >= 6 and d < 8:
nodes_size.append(160)
elif d >= 8 and d < 10:
nodes_size.append(200)
elif d >= 10:
nodes_size.append(1000)
'''投资大的颜色偏红'''
for i in range(len(G)):
invest = investment[i]
if invest >= 0 and invest < 0.2:
nodes_color.append('#FFFFB9')
elif invest >= 0.2 and invest < 0.4:
nodes_color.append('#FFFF37')
elif invest >= 0.4 and invest < 0.6:
nodes_color.append('#FF8000')
elif invest >= 0.6 and invest < 0.8:
nodes_color.append('#F75000')
elif invest >= 0.8 and invest < 0.9:
nodes_color.append('#F75000')
elif invest >= 1:
nodes_color.append('#EA0000')
networkx.draw_spring(G,
width=0.5,
with_labels=False,
font_size=10,
node_size=nodes_size,
node_color=nodes_color,
node_width=0)
plt.show() #可以将这个网络图形显示出来
plt.savefig('RELITU.png')
def draw_community(self):
part = community.best_partition(self.graph)
values = [part.get(node) for node in self.graph.nodes()]
nx.draw_spring(self.graph,
cmap=plt.get_cmap('jet'),
node_color=values,
node_size=30,
with_labels=False)
plt.show()
def showGraph(self): """show a graph of all bears that are alive""" G = nx.Graph() for bear in self.bears: mom = bear.mom dad = bear.dad if dad!=None and mom != None: G.add_edges_from([(str(mom),str(mom)+"-"+str(dad)),(str(mom)+str(dad),str(dad)), (str(mom)+str(dad), str(bear))]) nx.draw_spring(G) plt.show()
def graphattempt(): G = nx.Graph() for friend in frndct: for person in frndct[friend]: G.add_edge(friend, person) nx.write_gexf(G, 'networkforge.gexf') pos=nx.spring_layout(G) nx.draw_spring(G, with_labels=True, font_size=20, font_color='black', node_color = "steelblue") plt.show()
def plot_graph_community(graph):
parts = community_louvain.best_partition(graph)
values = [parts.get(node) for node in graph.nodes()]
nx.draw_spring(graph,
cmap=plt.get_cmap('jet'),
node_color=values,
node_size=35,
with_labels=False)
plt.axis('off')
plt.show()
def plot_graph(graph, race_dict):
print(graph.number_of_nodes())
# print(graph.nodes[:5])
print(graph.number_of_edges())
# nx.draw(graph)
nx.draw_spring(graph)
# plt.savefig("graph.png") # save as png
plt.show()
pass
def test_remove_vertices(g):
g_save = g.copy(False)
g_save.remove_node(1)
g_save.remove_node(2)
g_save.remove_node(3)
g_save.remove_node(4)
g_save.remove_node(5)
nx.draw_spring(g_save, with_labels=True)
plt.savefig("Illustrations/my_graph_with_less_vertices.pdf")
plt.close()
def main():
n = 16
m = 25
G = nx.gnm_random_graph(n, m)
# nx.draw(G, with_labels=True, font_weight='bold')
nx.draw_spring(G, with_labels=True, font_weight='bold')
plt.show()
for node in nx.nodes(G):
pass
def bn_ex():
print("BN Example")
fig, ax = newfig(1.0)
G = nx.DiGraph()
for (p, q) in [('A', 'C'), ('B', 'C'), ('C', 'D')]:
G.add_edge(p, q)
nx.draw_spring(G, node_color='w', ax=ax, with_labels=True, font_size=10)
savefig('bn_ex')
def p_create(p):
"""
create : parameter
| equal_var
| empty
"""
print("TREE", p[1])
print('Status: ' + str(use(p[1])))
nx.draw_spring(graph(p[1]), with_labels=True)
plt.savefig('output.png')
def save_graph(graph):
"""
Networkx draw function does not look nice.
Function needs improvements in the future.
"""
nx.draw_spring(graph, with_labels = True)
plt.draw()
#plt.show()
plt.savefig('graph_with_labels.png')
plt.clf()
def get_motif_example(self, motif_length=25):
"""Method that gets an example motif of motif_length."""
motifs = mf.get_motifs(self.project_ids, motif_length, self.num_motifs_to_sample, self.commits_dl)
second_most_common_motif = sorted(motifs, key=motifs.get, reverse=True)[1]
fig, ax = plt.subplots()
nx.draw_spring(second_most_common_motif, node_size=100, ax=ax)
fig.suptitle('Common Git Motif \n Length {}'.format(motif_length), fontsize=20)
fig.savefig('results/motif_example.png')
return fig
def buffer_match_mappings(graph, title='buffer matches'):
labels = nx.get_node_attributes(graph, 'label')
matches_colour = {
i: ("#00f208" if
(graph.nodes[i]['buffer_match'] == True) else "#c0c1c2")
for i in graph.nodes
}
nx.draw_spring(graph,
node_color=list(matches_colour.values()),
labels=labels)
plt.show()
def show_graph( Graph ): edges=[] for k,v in g.items(): for x in v: edges.append((k,x)) G=nx.Graph() G.add_edges_from(edges) nx.draw_spring(G) plt.show()
def transitive_closure_road_accident():
print(
"Znajdź domknięcie przechodnie relacji hiperonimi dla pierwszego znaczenia wyrażenia wypadek drogowy i przedstaw je w postaci grafu skierowanego."
)
meaning = d.WNQUERY.lookUpSense('wypadek drogowy', 1, 'n')
dfs(d.WNQUERY, meaning.wnid, 'n', 'hypernym')
diGraph = networkx.DiGraph()
diGraph.add_edges_from(graph)
plt.figure(figsize=(12, 3))
networkx.draw_spring(diGraph, with_labels=True)
plt.show()
def test_draw(self):
# hold(False)
N = self.G
nx.draw_spring(N)
pylab.savefig("test.png")
nx.draw_random(N)
pylab.savefig("test.ps")
nx.draw_circular(N)
pylab.savefig("test.png")
nx.draw_spectral(N)
pylab.savefig("test.png")
def test_draw(self):
# hold(False)
N=self.G
nx.draw_spring(N)
pylab.savefig("test.png")
nx.draw_random(N)
pylab.savefig("test.ps")
nx.draw_circular(N)
pylab.savefig("test.png")
nx.draw_spectral(N)
pylab.savefig("test.png")
def draw_and_show_with_edge_labels(self):
"""
Draws the graph and displays it with node/edge labels displays. This is somewhat experimental and can be buggy
dependent on the number of nodes and how edge labels are displayed.
:return:
"""
nx.draw_spring(self.current_graph, with_labels=True)
nx.draw_networkx_edge_labels(self.current_graph, pos=nx.spring_layout(self.current_graph))
plt.waitforbuttonpress()
return True
def run_mini_pipeline():
atlas = datasets.fetch_atlas_msdl()
atlas_img = atlas['maps']
labels = pd.read_csv(atlas['labels'])['name']
masker = NiftiMapsMasker(maps_img=atlas_img, standardize=True,
memory='/tmp/nilearn', verbose=0)
data = datasets.fetch_adhd(number_subjects)
figures_folder = '../figures/'
count=0
for func_file, confound_file in zip(data.func, data.confounds):
# fit the data to the atlas mask, regress out confounds
time_series = masker.fit_transform(func_file, confounds=confound_file)
correlation = np.corrcoef(time_series.T)
#plotting starts here
plt.figure(figsize=(10, 10))
plt.imshow(correlation, interpolation="nearest")
x_ticks = plt.xticks(range(len(labels)), labels, rotation=90)
y_ticks = plt.yticks(range(len(labels)), labels)
corr_file = figures_folder+'subject_number_' + str(count) + '_correlation.pdf'
plt.savefig(corr_file)
atlas_region_coords = [plotting.find_xyz_cut_coords(img) for img in image.iter_img(atlas_img)]
threshold = 0.6
plotting.plot_connectome(correlation, atlas_region_coords, edge_threshold=threshold)
connectome_file = figures_folder+'subject_number_' + str(count) + '_connectome.pdf'
plt.savefig(connectome_file)
#graph setup
#binarize correlation matrix
correlation[correlation<threshold] = 0
correlation[correlation != 0] = 1
graph = nx.from_numpy_matrix(correlation)
partition=louvain.best_partition(graph)
values = [partition.get(node) for node in graph.nodes()]
plt.figure()
nx.draw_spring(graph, cmap = plt.get_cmap('jet'), node_color = values, node_size=30, with_labels=True)
graph_file = figures_folder+'subject_number_' + str(count) + '_community.pdf'
plt.savefig(graph_file)
count += 1
plt.close('all')
def draw(draw_graph):
options = {
'node_color': 'black',
'node_size': 50,
'line_color': 'grey',
'linewidths': 0,
'width': 0.1,
'with_labels': True
}
nx.draw_spring(draw_graph, **options)
plt.show()
def test_multilayer():
net = Multilayer([2,5,1])
print net.network_inputs
print net.network_outputs
print net.hidden_layers
print net.network.edges()
print net.evaluate_pattern([1.0, 1.0])
import matplotlib.pyplot as plt
import networkx
networkx.draw_spring(net.network)
plt.show()
def plot_giant_component(self):
'''
Plots the whole graph
'''
try:
nx.draw_spring(self.giant_component)
except:
print('Giant component initialized. Run giant_component method')
def draw_graph(G):
"""
A helper function to draw a nx graph with community.
"""
groups =nx.get_node_attributes(G,'club').values()
color_list = plt.cm.tab10(np.linspace(0, 1, len(groups)))
color_dict = dict(zip(groups, color_list))
nx.draw_spring(G,seed=0, nodelist=G.nodes(),
node_color=[color_dict[x] for x in groups],
alpha=0.8)
def draw_graph(G):
"""
Genera una imagen con formato .png del grafo.<br/>
**:param G:** Recibe un grafo no dirigido de tipo networkx.<br/>
**:return:** Una imagen .png del grafo.<br/>
"""
nx.draw_spring(G, with_labels=True)
labels = nx.get_edge_attributes(G, 'costo')
# nx.draw_networkx_edge_labels(G, nx.spring_layout(G), edge_labels=labels)
plt.savefig(MEDIA_ROOT + '/item_trazabilidad.png')
plt.clf()
def metagraph_to_spring(
dgl_graph
): # konwersja grafu dgl_heterograph i wyświetlenie spring_layout
# link do dokumentacji: https://networkx.org/documentation/stable/reference/generated/networkx.drawing.layout.spring_layout.html
dgl_metagraph = dgl_graph.metagraph()
G = nx.MultiGraph()
G.add_nodes_from(dgl_metagraph.nodes)
G.add_edges_from(dgl_metagraph.edges)
pos = nx.spring_layout(G)
nx.draw_spring(G)
#return nx.draw(G, pos)
plt.show()
def show(W, lamda, f, m):
fm, fn = np.shape(f)
print('fm,fn:', fm, fn)
lamdaIndicies = np.argsort(lamda)
first = 0
second = 0
print(lamdaIndicies[0], lamdaIndicies[1])
for i in range(fm):
if lamda[lamdaIndicies[i]].real > 1e-2: # 找到特征值比较小的2个，但是不为0的！
print(lamda[lamdaIndicies[i]])
first = lamdaIndicies[i]
second = lamdaIndicies[i + 1]
break
print(first, second)
for i in range(m):
nx_G = read_graph() # 利用networkx包读取Graph信息
color = []
vertex = []
vertex.append(i)
for j in range(m):
if j == i:
color.append(0.7)
elif W[i, j] == 0:
color.append(0.1) # 为0或1
else:
color.append(0.9)
vertex.append(j) #顶点j有关的所有边
# print(vertex)
# 通过相关联的点进行加边
for ii in range(m):
for jj in range(m):
if nx_G.has_edge(ii, jj):
if (ii in vertex or jj in vertex):
continue
else:
nx_G.remove_edge(ii, jj)
"""
fig=plt.figure('lowdata')
ax2 = fig.add_subplot(111)
ax2.scatter(f[:,first], f[:,second], c=color, cmap=plt.cm.Spectral)
plt.show()
"""
nx.draw_spring(nx_G, with_labels=True, node_color=color)
return
return
def wypadek_drogowy_hypernym(query: WNQuery):
wnid = query.lookUpSense("wypadek drogowy", 1, "n").wnid
cl = closure(query, wnid, 'n', 'hypernym')
cl = [(query.getSynset(a, 'n').toString(), query.getSynset(b,
'n').toString())
for (a, b) in cl]
graph = nx.DiGraph()
graph.add_edges_from(cl)
plt.figure(figsize=(15, 10))
nx.draw_spring(graph, arrows=True, with_labels=True)
plt.show()
def graphCommunities(filename):
"""
Partitions and colors graphs into communities w/ best_partition.
Displays graph with spring layout (perhaps spectral would be better?) and saves.
Unsuccessful attempts made to separate clusters visually.
"""
# generate graph
graph = makeGraphFromJSON(filename)
name = filename.split(".json")[0]
# find the groups each node belongs to
part = community.best_partition(graph)
# get total number of clusters and set up dictionary
clusters = {}
total = max(part.values()) + 1
for i in range(total):
clusters[i] = []
# sort nodes into clusters
for node, group in part.items():
clusters[group].append(node)
# turn clusters into a separate graph
for c, nodes in clusters.items():
clusters[c] = graph.subgraph(clusters[c])
## Now display clustering of original graph by fixing a point from each
## cluster and letting networkx arrange the rest of nodes
fixed = {}
x = 0
for _, c in clusters.items():
# find node of highest degree centrality within each cluster
deg = nx.degree_centrality(c)
deg = sorted(deg.items(),key=itemgetter(1), reverse=True)[0]
x += 1
#return clusters, total
# graph
fig = plt.figure()
plt.axis("off")
#pos = nx.spring_layout(graph, fixed=fixed.keys(), pos=fixed)
values = [part.get(node) for node in graph.nodes()]
#nx.draw_networkx(graph, pos=pos, cmap = plt.get_cmap('jet'), node_color = values, node_size=30, with_labels=True)
nx.draw_spring(graph, cmap = plt.get_cmap('jet'), node_color = values, node_size=30, with_labels=True)
# save figure
fig.savefig(name + ".png")
def test_draw(self):
N=self.G
nx.draw_spring(N)
plt.savefig("test.ps")
nx.draw_random(N)
plt.savefig("test.ps")
nx.draw_circular(N)
plt.savefig("test.ps")
nx.draw_spectral(N)
plt.savefig("test.ps")
nx.draw_spring(N.to_directed())
plt.savefig("test.ps")
os.unlink('test.ps')
def fun_save_graph(self,graph,file_name,str_time):
# prints the graph you hand it as a .png file in the directory specified by file_name
import matplotlib
import matplotlib.pyplot as plt
import networkx as nx
plt.figure()
nx.draw_spring(graph,node_color='w',with_labels=True,node_size=1000,arrows=True)
plt.savefig("out/" + str_time + "/%(pHolder_file_name)s" %dict(pHolder_file_name = file_name))
print('Saved graph '+"out/" + str_time + "/%(file_name)s" %dict(file_name = file_name))
def plot_induced_subgraphs(self):
plt.figure(1)
partition = self.find_partition()[1]
communities = [partition[v] for v in partition]
newGraph=self.G
for community in communities:
nx.subgraph(newGraph, [key for key in partition if partition[key]==community])
node_color=[float(partition[v]) for v in partition]
labels = {}
for node in newGraph.nodes():
labels[node]= newGraph.node[node].get('name', '')
nx.draw_spring(newGraph,node_color=node_color, labels=labels)
plt.show()
plt.savefig("C:\\Users\\Heschoon\\Dropbox\\ULB\\Current trends of artificial intelligence\\Trends_project\\graphs\\graph_induced.pdf")
def draw_spring_communities(self, full_names='False', k=None, iterations=50, scale=1.0):
partition = self.find_partition()[1]
#colors = ['red', 'blue', 'green', 'cyan', 'magenta', 'yellow']
#node_color=[colors[partition[v]] for v in partition]
node_color=[float(partition[v])*2.0 for v in partition]
if not(full_names):
labels = self.compute_labels()
else:
labels = {}
for node in self.G.nodes():
labels[node]= self.G.node[node].get('name', '')
nx.draw_spring(self.G,node_color=node_color, labels=labels, k=k, iterations=iterations, scale=scale, cmap=plt.cm.hsv)
plt.show()
plt.savefig("C:\\Users\\Heschoon\\Dropbox\\ULB\\Current trends of artificial intelligence\\Trends_project\\graphs\\graph_spring.pdf")
def run_and_trace(self, name, workload, old=False, sync_period=0,
step_size=1, ignore_remaining=False, show_graph=False,
staleness=0):
"""
Run and produce a log of the simulation for each timestep
Convert an old format workload to new format if old=TRUE
Dump the metrics, workload, and (if old-format) the converted
new-format workload to JSON as files
"""
filename = 'logs/' + name
dir = os.path.dirname(filename)
try:
os.stat(dir)
except:
os.mkdir(dir)
f = open(filename + '.workload', 'w')
print >>f, json.dumps(workload,sort_keys=True, indent=4)
f.close()
if (old):
# log the converted old_to_new network graph
workload = old_to_new(workload)
f = open(filename + '.newworkload', 'w')
print >>f, json.dumps(workload,sort_keys=True, indent=4)
f.close()
metrics = self.run(workload, sync_period, step_size,
ignore_remaining, show_graph=show_graph,
staleness=staleness)
else:
metrics = self.run(workload, sync_period, step_size,
ignore_remaining, show_graph=show_graph,
staleness=staleness)
f = open(filename + '.metrics', 'w')
print >>f, json.dumps(metrics, sort_keys=True, indent=4)
f.close()
if show_graph:
# log the network graph if not already drawn
try:
os.stat(filename + ".pdf")
except:
nx.draw_spring(self.graph)
plt.savefig(filename + ".pdf")
plt.close()
return metrics
def main():
# Create a NetworkX graph object
gmail_graph=graphFromCSV("../email_analysis/email_graph.csv")
# Draw entire graph
full_graph=plt.figure(figsize=(10,10))
nx.draw_spring(gmail_graph, arrows=False, node_size=50, with_labels=False)
plt.savefig("../../../images/graphs/full_graph.png")
# Draw [email protected]'s Ego graph
ego_plot(gmail_graph, "*****@*****.**", "../../../images/graphs/ann9enigma_ego.png")
# Create a lattice plot of all weakly connected components
gmail_components=nx.weakly_connected_component_subgraphs(gmail_graph)
lattice_plot(gmail_components, "../../../images/graphs/gmail_subgraphs.png")
def draw_graph(D):
D = D.to_undirected()
plot.figure(figsize=(16,16))
nx.draw_spring(D,
node_color=[float(D.degree(v)) for v in D],
#edge_color=[np.log(D[e1][e2]["weight"]+1) for e1,e2 in D.edges()],
width=3,
edge_cmap=plot.cm.Reds,
with_labels=True,
cmap=plot.cm.Reds,
node_size=1000,
font_size=8,
iterations=100000)
plot.title("Spring Network Layout of %s" % D.name)
plot.savefig("results/network_graphs/%s.png" % D.name)
plot.close()
