def test_graphtool():
"""Test creating a tree from a suffix tree.
"""
_num_bases, sequences, _ids = seqan.readFastaDNA(fasta_file('dm01r.fasta'))
#for s in sequences:
# s.remove('T')
index = seqan.IndexStringDNASetESA(sequences)
suffix = 'ACGTATGC'
predicate = seqan.traverse.suffixpredicate(suffix)
#predicate = seqan.traverse.depthpredicate(4)
builder = seqan.io.graphtool.Builder(index, predicate=predicate)
#pos = GT.radial_tree_layout(builder.graph, builder.graph.vertex(0))
pos = GT.sfdp_layout(builder.graph)
GT.graph_draw(
builder.graph,
pos=pos,
vertex_size=2,
vertex_fill_color="lightgrey",
vertex_text=builder.occurrences,
vertex_pen_width=seqan.io.graphtool.root_vertex_property(builder),
edge_text=seqan.io.graphtool.edge_labels_for_output(builder),
edge_color=seqan.io.graphtool.color_edges_by_first_symbol(builder),
edge_end_marker="none",
edge_pen_width=2,
edge_dash_style=seqan.io.graphtool.dash_non_suffix_edges(
builder, suffix),
#edge_pen_width=builder.edge_lengths,
output="graphtool.png")
return builder
def session_draw_bis_melty(sessions_id, weblog, weblog_columns_dict):
"""
Draw the graph of sessions with sessions_id given in entry
"""
from graph_tool.all import Graph
from graph_tool.all import graph_draw
session = weblog[weblog.session_id == sessions_id]
session = session.rename(index=str,columns = {weblog_columns_dict['requested_page_column']:'requested_page',\
weblog_columns_dict['referrer_page_column']:'referrer_page'})
s_pages = session[['requested_page', 'requested_external']]
s_pages_ref = session[['referrer_page', 'referrer_external']]
s_pages_ref = s_pages_ref.rename(index=str,
columns={
'referrer_page': 'requested_page',
'referrer_external':
'requested_external'
})
s_pages = s_pages.append(s_pages_ref)
s_pages.drop_duplicates(subset='requested_page', inplace=True)
g = Graph()
v = {}
halo = g.new_vertex_property("bool")
for row in s_pages.itertuples():
v[row.requested_page] = g.add_vertex()
if row.requested_external:
halo[v[row.requested_page]] = True
else:
halo[v[row.requested_page]] = False
session.apply(
lambda x: g.add_edge(v[x.referrer_page], v[x.requested_page]), axis=1)
graph_draw(g,
vertex_halo=halo,
output="./_session" + str(sessions_id) + ".png")
return
def visualize(pathFinder, source=False, target=False):
g, weight = buildWeightedGraph(pathFinder)
ecolor = g.new_edge_property("string")
ewidth = g.new_edge_property("double")
ewidth.a = 1
touch_v = g.new_vertex_property("bool")
touch_e = g.new_edge_property("bool")
if not source:
source = pathFinder.source
if not target:
target = pathFinder.target
dist, pred = gt.astar_search(
g,
source,
weight,
VisitorExample(touch_v, touch_e, target),
heuristic=lambda v: pathFinder.jaccard(v, target))
for e in g.edges():
ecolor[e] = "blue" if touch_e[e] else "black"
v = target
while v != source:
p = g.vertex(pred[v])
for e in v.out_edges():
if e.target() == p:
ecolor[e] = "#a40000"
ewidth[e] = 3
v = p
gt.graph_draw(g,
output_size=(600, 600),
vertex_fill_color=touch_v,
edge_color=ecolor,
edge_pen_width=ewidth,
output="astar-demo.pdf")
def test_graphtool():
"""Test creating a tree from a suffix tree.
"""
_num_bases, sequences, _ids = seqan.readFastaDNA(fasta_file('dm01r.fasta'))
#for s in sequences:
# s.remove('T')
index = seqan.IndexStringDNASetESA(sequences)
suffix = 'ACGTATGC'
predicate = seqan.traverse.suffixpredicate(suffix)
#predicate = seqan.traverse.depthpredicate(4)
builder = seqan.io.graphtool.Builder(index, predicate=predicate)
#pos = GT.radial_tree_layout(builder.graph, builder.graph.vertex(0))
pos = GT.sfdp_layout(builder.graph)
GT.graph_draw(
builder.graph,
pos=pos,
vertex_size=2,
vertex_fill_color="lightgrey",
vertex_text=builder.occurrences,
vertex_pen_width=seqan.io.graphtool.root_vertex_property(builder),
edge_text=seqan.io.graphtool.edge_labels_for_output(builder),
edge_color=seqan.io.graphtool.color_edges_by_first_symbol(builder),
edge_end_marker="none",
edge_pen_width=2,
edge_dash_style=seqan.io.graphtool.dash_non_suffix_edges(builder, suffix),
#edge_pen_width=builder.edge_lengths,
output="graphtool.png"
)
return builder
def main():
conn = sqlite3.connect('../../data/testDefinitions.sqlite')
g = gt.Graph()
cursor = conn.cursor()
cursor.execute('select id, testBed from tests;')
tests = {}
ids = []
for entry in cursor.fetchall():
tests[entry[0]] = g.add_vertex()
cursor.execute('select id from testBeds;')
for entry in cursor.fetchall():
ids.append(entry[0])
for id in ids:
cursor.execute("select tests.id from tests where testBed = '{}'".format(id))
all_items = cursor.fetchall()
if len(all_items) > 1:
for i in range(0, len(all_items)-1):
for j in range(i +1, len(all_items)):
g.add_edge(tests[all_items[i][0]], tests[all_items[j][0]])
#g = gt.GraphView(g, vfilt=gt.label_largest_component(g))
kcore = gt.kcore_decomposition(g)
#gt.graph_draw(g, vertex_fill_color=kcore, vertex_text=kcore, output="test-testBed.pdf")
gt.graph_draw(g, vertex_font_size=12, output_size=(800, 600), output="test-testBed.png")
def paint_graph(self, location, graph, communities):
sys.stdout.write('Drawing graph ... ')
sys.stdout.flush()
network = gt.Graph(graph, directed=False)
folder = os.path.abspath(self._output_dir)
r_cols = randomcolor.RandomColor().generate(count=len(communities) + 1)
colors = [
list(int(r_col[1:][i:i + 2], 16) for i in (0, 2, 4))
for r_col in r_cols
]
color = network.new_vertex_property('int')
base_color = colors.pop()
for v in network.vertices():
color[v] = (base_color[0] << 16) + (
base_color[1] << 8) + base_color[2]
for community in communities:
c = colors.pop()
for v in community:
color[v] = (c[0] << 16) + (c[1] << 8) + c[2]
pos = gt.sfdp_layout(network)
gt.graph_draw(network,
pos=pos,
vertex_fill_color=color,
output=os.path.join(
folder,
str(len(communities)) + '_' + location +
'_graph-communities.svg'))
sys.stdout.write('Ok!\n')
sys.stdout.flush()
def main():
args = parse_arguments()
n = args.nodes
m = args.edges
seed_number = args.seed
if seed_number is None:
seed_number = randint(1000)
directed = args.directed
generator = get_graph(args.graph, n, m, directed, seed_number)
g, source, target = generator.generate()
print("Source " + str(source) + " Target " + str(target))
gt.graph_draw(g,
edge_pen_width=g.ep.cap,
output="graph_initial.pdf",
vertex_text=g.vertex_index,
edge_text=g.ep.cap)
solver = get_algorithm(args.algorithm, g)
solution = solver.get_max_flow(source, target)
print("The maximum flow is " + str(solution))
if args.compare:
generator = get_graph(args.graph, n, m, directed, seed_number)
g, source, target = generator.generate()
cap = g.ep.cap
res = gt.push_relabel_max_flow(g, source, target, cap)
res.a = cap.a - res.a # the actual flow
max_flow = sum(res[e] for e in target.in_edges())
print("The maximum flow of graph-tool is " + str(max_flow))
def main():
data = load_test_data("../../data/testDefinitions.sqlite")
lengths = []
g = gt.Graph(directed=False)
vertices = {}
for test in data.tests.values():
vertices[test.id] = g.add_vertex()
for equipment in data.test_equipment.values():
all_items = []
for test in data.tests.values():
if equipment in test.testEquipment:
all_items.append(test)
if len(all_items) > 0:
lengths.append(len(all_items))
if len(all_items) > 1:
for i in range(0, len(all_items)-1):
for j in range(i, len(all_items)-1):
if i != j:
g.add_edge(vertices[all_items[i].id], vertices[all_items[j].id])
pos = gt.arf_layout(g, max_iter=4)
deg = g.degree_property_map("total")
gt.graph_draw(g, pos=pos, vertex_fill_color=deg, vorder=deg, vertex_text=deg, output_size=(800, 600), output="test-testEquipment.png")
plt.hist(lengths)
plt.title("Equipment-Test Histogram")
plt.xlabel("Equipment required by X tests.")
plt.ylabel("Frequency")
plt.show()
def layout_and_plot(net, color_pmap, outfile_pre, filename_mod = '.net',
size_pmap = None, reverse_colors = False):
'''
Plot the net, using a predefined layout if it's included as a vector property.
:param net: The network to plot.
:param color_pmap: Property map on `net` to color nodes.
:size_pmap: Property map on `net` to set size of verticies.
:param outfile_pre: Prefix for output filename.
:param filename_mod: Extension to use on the output filename.
'''
# Define a default size
if size_pmap is None:
size_pmap = net.new_vertex_property('float', val = 20)
# If a layout isn't included, calculate it
if 'layout' not in net.vp:
print('Calculating graph layout')
#net.vp['layout'] = gt.fruchterman_reingold_layout(net)
net.vp['layout'] = gt.sfdp_layout(net, verbose = True)
#net.vp['layout'] = gt.radial_tree_layout(net, 0, r=2)
# Set the colormap
if not reverse_colors:
colormap = bwr
else:
colormap = bwr_r
# Plot the graph
gt.graph_draw(net, vertex_fill_color = color_pmap,
vcmap = colormap,
vertex_size = size_pmap,
edge_pen_width = 1,
pos = net.vp['layout'], #pin = True,
fit_view = 1,
output_size = (2000, 2000),
output = outfile_pre + filename_mod + '.png')
return net.vp['layout']
def visualize(pathFinder, source=False, target=False):
g, weight = buildWeightedGraph(pathFinder)
ecolor = g.new_edge_property("string")
ewidth = g.new_edge_property("double")
ewidth.a = 1
touch_v = g.new_vertex_property("bool")
touch_e = g.new_edge_property("bool")
if not source:
source = pathFinder.source
if not target:
target = pathFinder.target
dist, pred = gt.astar_search(g, source, weight,
VisitorExample(touch_v, touch_e, target),
heuristic=lambda v: pathFinder.jaccard(v, target))
for e in g.edges():
ecolor[e] = "blue" if touch_e[e] else "black"
v = target
while v != source:
p = g.vertex(pred[v])
for e in v.out_edges():
if e.target() == p:
ecolor[e] = "#a40000"
ewidth[e] = 3
v = p
gt.graph_draw(g, output_size=(600, 600), vertex_fill_color=touch_v, edge_color=ecolor,
edge_pen_width=ewidth, output="astar-demo.pdf")
def display(self, save=0, outputsize=(500, 500), filename=None):
#gt.graph_draw(self.__Graph, pos=self.__pos, vertex_text=self.__Graph.vertex_index, vertex_fill_color=self.__colors, vertex_shape="pentagon", vertex_font_size=12,
# output_size=self.__outputsize)
#gt.graph_draw(self.__Graph, pos=self.__pos, vertex_fill_color=self.__colors, vertex_shape="hexagon", vertex_font_size=12,
# output_size=self.__outputsize, output="../20steps.png")
if filename is None:
filename = "../contract.png"
else:
filename = "../" + filename + ".png"
if save == 0:
gt.graph_draw(
self.__Graph,
pos=self.__pos,
vertex_fill_color=self.__vertex_colors,
edge_color=self.__edge_colors,
vertex_shape="hexagon",
vertex_font_size=12,
output_size=outputsize,
#vertex_text=self.__Graph.vertex_index
)
else:
gt.graph_draw(
self.__Graph,
pos=self.__pos,
vertex_fill_color=self.__vertex_colors,
edge_color=self.__edge_colors,
vertex_shape="hexagon",
vertex_font_size=12,
#vertex_text=self.__Graph.vertex_index,
output_size=outputsize,
output=filename)
def loop(self):
for m in range(1, self.seq_length+1):
print ("begin step: ", m)
tree_prop = self.update_edge_weights()
tree_cost = self.cost_of_tree(tree_prop)
self.current_tree = gt.GraphView(self.graph, efilt=tree_prop)
# draw the best spanning tree yet found every 125 iterations
if m % 125 == 0:
position = self.graph_class.position
self.graph_class.draw(self.best_tree, position)
self.prev_degree = self.degree.copy()
self.degree = self.current_tree.degree_property_map("total")
vertex_sum = self.sum_of_vertex_weights()
bound = tree_cost - 2*vertex_sum
print("current best bound: ", self.bound)
if m != self.seq_length:
print( "new bound: ", bound)
if bound > self.bound:
self.bound = bound
self.best_tree = self.current_tree
self.step = self.next_step(m)
self.update_vertex_weights()
else:
print self.bound
gt.graph_draw(gt.GraphView(self.graph, efilt=tree_prop), self.graph_class.position)
def paint_graph(path, graph, communities):
if path:
sys.stdout.write('Drawing graph ... ')
sys.stdout.flush()
network = gt.Graph(graph, directed=False)
folder = os.path.abspath(path)
# colors = random.sample(range(100,151), len(communities))
r_cols = randomcolor.RandomColor().generate(count=len(communities) + 1)
colors = [
list(int(r_col[1:][i:i + 2], 16) for i in (0, 2, 4))
for r_col in r_cols
]
# color = graph.new_vertex_property('vector<float>')
color = network.new_vertex_property('int')
base_color = colors.pop()
for v in network.vertices():
color[v] = (base_color[0] << 16) + (
base_color[1] << 8) + base_color[2]
for community in communities:
c = colors.pop()
for v in community:
color[v] = (c[0] << 16) + (c[1] << 8) + c[2]
pos = gt.sfdp_layout(network)
gt.graph_draw(network,
pos=pos,
vertex_fill_color=color,
output=os.path.join(folder, 'graph-communities.svg'))
sys.stdout.write('Ok!\n')
sys.stdout.flush()
def main():
conn = sqlite3.connect('../../data/testDefinitions.sqlite')
g = gt.Graph()
cursor = conn.cursor()
cursor.execute('select id, testBed from tests;')
tests = {}
ids = []
for entry in cursor.fetchall():
tests[entry[0]] = g.add_vertex()
cursor.execute('select id from testBeds;')
for entry in cursor.fetchall():
ids.append(entry[0])
for id in ids:
cursor.execute(
"select tests.id from tests where testBed = '{}'".format(id))
all_items = cursor.fetchall()
if len(all_items) > 1:
for i in range(0, len(all_items) - 1):
for j in range(i + 1, len(all_items)):
g.add_edge(tests[all_items[i][0]], tests[all_items[j][0]])
#g = gt.GraphView(g, vfilt=gt.label_largest_component(g))
kcore = gt.kcore_decomposition(g)
#gt.graph_draw(g, vertex_fill_color=kcore, vertex_text=kcore, output="test-testBed.pdf")
gt.graph_draw(g,
vertex_font_size=12,
output_size=(800, 600),
output="test-testBed.png")
def label_graph(instance, i, use_simplicial):
edges = np.genfromtxt("res/new_synthetic/" + instance + ".csv",
delimiter=",",
dtype=np.int)[:, :2]
n = np.max(edges - 1)
g = gt.Graph(directed=False)
g.add_vertex(n)
g.add_edge_list(edges)
#print(edges)
#print(np.sort(g.degree_property_map("total").a))
A, B = florians_procedure(g, use_simplicial)
pos = np.where(A)[0]
pos = g.new_vertex_property("bool", vals=A)
simplicial_string = "_simplicial_start"
if not use_simplicial:
simplicial_string = ""
file = open(
"res/new_synthetic/labels/" + instance + "_" + str(i) +
simplicial_string + "_positive.csv", 'w')
writer = csv.writer(file)
writer.writerows(np.where(A)[0].reshape((-1, 1)))
#print(len(np.where(A)[0])/n)
gt.graph_draw(g,
pos=gt.arf_layout(g, max_iter=0),
vertex_fill_color=pos,
output="res/new_synthetic/images/" + instance + "_" +
str(i) + "_" + simplicial_string + ".svg")
def plot_graph(graph, path, edge_weights=None, vertex_weights=None):
graph_draw(graph,
output_size=(1000, 1000),
output=path,
bg_color=[1, 1, 1, 1],
edge_pen_width=edge_weights,
vertex_size=vertex_weights)
def DrawDependencies(self):
gt.graph_draw(self.__g,
vertex_text=self.__vert_name,
vertex_font_size=2,
vertex_shape="double_circle",
vertex_fill_color="#729fcf",
vertex_pen_width=1,
output="{0}.pdf".format("dependency_graph"))
def worker(Graph,current_count):
U = gt.GraphView(Graph,vfilt=lambda v: Graph.vertex_properties['alive'][v])
gt.graph_draw(U, U.vertex_properties['position'],
vertex_shape=U.vertex_properties['shape'],
vertex_fill_color=U.vertex_properties['fillcolor'],
output=frame_path + 'taxi%06d.png'%count,bg_color=(1,1,1,1),output_size=resolution)
def vis_prm(self, name="prm"):
gt.graph_draw(self._g,
vertex_text=self._g.vertex_index,
vertex_font_size=2,
vertex_shape="double_circle",
vertex_fill_color="#729fcf",
vertex_pen_width=1,
output="{0}.pdf".format(name))
def drawMST(mst, outPrefix, isolate_clustering, clustering_name, overwrite):
"""Plot a layout of the minimum spanning tree
Args:
mst (graph_tool.Graph)
A minimum spanning tree
outPrefix (str)
Output prefix for save files
isolate_clustering (dict)
Dictionary of ID: cluster, used for colouring vertices
clustering_name (str)
Name of clustering scheme to be used for colouring
overwrite (bool)
Overwrite existing output files
"""
import graph_tool.all as gt
graph1_file_name = outPrefix + "/" + os.path.basename(
outPrefix) + "_mst_stress_plot.png"
graph2_file_name = outPrefix + "/" + os.path.basename(
outPrefix) + "_mst_cluster_plot.png"
if overwrite or not os.path.isfile(graph1_file_name) or not os.path.isfile(
graph2_file_name):
sys.stderr.write("Drawing MST\n")
pos = gt.sfdp_layout(mst)
if overwrite or not os.path.isfile(graph1_file_name):
deg = mst.degree_property_map("total")
deg.a = 4 * (np.sqrt(deg.a) * 0.5 + 0.4)
ebet = gt.betweenness(mst)[1]
ebet.a /= ebet.a.max() / 50.
eorder = ebet.copy()
eorder.a *= -1
gt.graph_draw(mst,
pos=pos,
vertex_size=gt.prop_to_size(deg, mi=20, ma=50),
vertex_fill_color=deg,
vorder=deg,
edge_color=ebet,
eorder=eorder,
edge_pen_width=ebet,
output=graph1_file_name,
output_size=(3000, 3000))
if overwrite or not os.path.isfile(graph2_file_name):
cluster_fill = {}
for cluster in set(isolate_clustering[clustering_name].values()):
cluster_fill[cluster] = list(np.random.rand(3)) + [0.9]
plot_color = mst.new_vertex_property('vector<double>')
mst.vertex_properties['plot_color'] = plot_color
for v in mst.vertices():
plot_color[v] = cluster_fill[
isolate_clustering[clustering_name][mst.vp.id[v]]]
gt.graph_draw(
mst,
pos=pos,
vertex_fill_color=mst.vertex_properties['plot_color'],
output=graph2_file_name,
output_size=(3000, 3000))
def print_groups(network, groups):
group_vp = network.new_vertex_property("int")
for i, group in enumerate(groups):
for agent in group:
group_vp[network.vertex_index[agent]] = i
network.vp.group = group_vp
pos = gt.sfdp_layout(network)
gt.graph_draw(network, pos=pos, vertex_fill_color=group_vp)
plt.show()
def influencer_network_anlaysis(year=2020):
path_2016 = '/home/crossb/packaged_ci/graphs/2016/'
path_2020 = '/home/crossb/packaged_ci/graphs/2020/'
top_n_influencers = 30
biased_graphs = load_graphs_gt(path_2020)
biased_influencers = top_influencers(top_n_influencers, biased_graphs)
influencer_network = top_influencer_network(biased_graphs, biased_influencers)
#influencer_network.save(os.path.join(WORKING_DIR, 'influencer_network.gt'))
stats = network_characteristics_gt(influencer_network)
print("Influencer network stats")
for stat, value in stats.items():
print("{}: {}".format(stat, value))
most_infl_influencers = top_influencers(10, {'top': influencer_network})
print("Most influential:", most_infl_influencers)
# save influencer network
gt.save('data/2020/influencer_network.gt')
# networkit stats
nk_graph = load_from_graphtool_nk('data/2020/influencer_network.gt')
characteristics = network_characteristics_nk(nk_graph, 10)
for stat, value in characteristics.items():
if "centrality" in stat:
print("{}: {}".format(
stat,
','.join(['(Node: {}: {})'.format(influencer_network.vp.user_id[n], v) for (n, v) in value])))
else:
print("{}: {}".format(stat, value))
# Draw with the vertices as pie charts
vprops = {'pie_fractions': influencer_network.vp.pie_fractions,
'shape': influencer_network.vp.shape,
'text': influencer_network.vp.text,
'text_color': influencer_network.vp.text_color,
'size': influencer_network.vp.size,
'pie_colors': influencer_network.vp.pie_colors,
'text_position': 200,
'font_size': 14,
'text_offset': [0.0, 1.0]
}
eprops = {'color': 'lightgray'}
# r=1000 leads to cool graph
#pos = gt.fruchterman_reingold_layout(influencer_network, r=35.0, circular=True, n_iter=2000)
pos = gt.arf_layout(influencer_network, d=4, max_iter=0)
gt.graph_draw(influencer_network, pos=pos, vprops=vprops,
eprops=eprops, output='top_influencers.svg')
#with cairo.SVGSurface('top_influencers.svg', 1024, 1280) as surface:
# cr = cairo.Context(surface)
# gt.cairo_draw(influencer_network, pos, cr, vprops=vprops, eprops=eprops,
# ecmap=(cm.get_cmap(), 0.2),
# output='top_influencers.svg')
return
def draw_graph_label_id(self,dgraph,path):
dgraph.vertex_properties["show"] = dgraph.new_vertex_property("string")
for v in dgraph.vertices():
if v in self.border_node:
tmpstr = "o"+str(v)+dgraph.vertex_properties["label"][v]
else:
tmpstr = str(v)+dgraph.vertex_properties["label"][v]
dgraph.vertex_properties["show"][v] =tmpstr
gt.graph_draw(dgraph, vertex_text = dgraph.vertex_properties["show"],output_size=(800, 800),output = path)
del dgraph.vertex_properties["show"]
def paint_full_graph(path, graph, name):
if path:
sys.stdout.write('Drawing full graph ... ')
sys.stdout.flush()
network = gt.Graph(graph, directed=False)
gt.graph_draw(network,
output=os.path.join(os.path.abspath(path),
str(name) + '-graph.svg'))
sys.stdout.write('Ok!\n')
sys.stdout.flush()
def draw_similarity_graph(g):
state = minimize_blockmodel_dl(g)
b = state.b
pos = sfdp_layout(g, eweight=g.edge_properties['sim'])
graph_draw(g, pos, output_size=(1000, 1000), vertex_color=[1, 1, 1, 0],
vertex_size=g.vertex_properties['freq'], edge_pen_width=1.2,
vcmap=matplotlib.cm.gist_heat_r, output="word_similarity.png")
state = minimize_blockmodel_dl(g)
graph_draw(g, pos=pos, vertex_fill_color=b, vertex_shape=b, output="blocks_mdl.png")
def draw_graph(self):
g = self.arcgraph
pos = gt.sfdp_layout(self.arcgraph)
gt.graph_draw(g,
pos,
output_size=(10000, 10000),
vertex_fill_color=g.vp.color,
edge_pen_width=10,
output=join('/mnt', 'g', 'LisaDaten', 'Paper2',
'figures', 'arcgraph' + self.name + '.pdf'))
def draw_graph(graph,
value_map=None,
output=None,
show_ids=False,
directed=False,
position=None):
gt_graph = gt.Graph(directed=directed)
node_map = {node: gt_graph.add_vertex() for node in graph}
if not directed:
seen_edges = set()
for node, edges in graph.iteritems():
i = node_map[node]
for e in edges:
j = node_map[e]
if directed:
gt_graph.add_edge(i, j)
else:
if (j, i) not in seen_edges:
gt_graph.add_edge(i, j)
seen_edges.add((i, j))
if position is None:
position = gt.sfdp_layout(gt_graph)
node_label = gt_graph.new_vertex_property("string")
if value_map is not None:
for id, value in value_map.iteritems():
node = node_map[id]
node_label[node] = id
for id in graph:
if id not in value_map:
node = node_map[id]
node_label[node] = id
elif show_ids:
for id in graph:
node = node_map[id]
node_label[node] = id
if show_ids:
gt.graph_draw(gt_graph,
pos=position,
vertex_text=node_label,
output=output)
else:
gt.graph_draw(gt_graph, pos=position, output=output)
return position
def main():
input_fasta = sys.argv[3]
K = int(sys.argv[1])
x = float(sys.argv[2])
ht = khmer.Nodegraph(K, x, 4)
sparse_graph = gt.Graph()
hashes = sparse_graph.new_vertex_property("long long")
for n, record in enumerate(screed.open(input_fasta)):
if n % 1000 == 0:
print('...loaded and tagged {} sequences'.format(n),
file=sys.stderr)
name = record.name
sequence = record.sequence
ht.consume_sequence_and_tag_with_labels(sequence, n)
tags = ht.sweep_tag_neighborhood(sequence, 0)
for i in range(len(tags) - 1):
src = tags[i]
dst = tags[i + 1]
new = False
srcv = gt.find_vertex(sparse_graph, hashes, src)
if not srcv:
srcv = sparse_graph.add_vertex()
hashes[srcv] = src
new = True
else:
srcv = srcv[0]
dstv = gt.find_vertex(sparse_graph, hashes, dst)
if not dstv:
dstv = sparse_graph.add_vertex()
hashes[dstv] = dst
new = True
else:
dstv = dstv[0]
if new:
e = sparse_graph.add_edge(srcv, dstv)
print('Sparse graph has {} nodes, {} edges'.format(
sparse_graph.num_vertices(), sparse_graph.num_edges()))
comp = gt.label_largest_component(sparse_graph, directed=False)
#pos = gt.radial_tree_layout(sparse_graph, sparse_graph.vertex(0))
gt.graph_draw(sparse_graph,
output_size=(5000, 5000),
output=input_fasta + '_sparse.png')
sparse_graph.set_vertex_filter(comp)
gt.graph_draw(sparse_graph,
output_size=(5000, 5000),
output=input_fasta + '_sparse_comp.png')
def draw_graph_heatmap(graph, value_map, output, directed=False, palette=sns.cubehelix_palette(10, start=.5, rot=-.75), position=None):
# for normalize
values = value_map.values()
maxv = max(values)
minv = min(values)
if len(values) != len(graph):
# some graph nodes missing from map.
# make them 0
minv = min(minv, 0)
gt_graph = gt.Graph(directed=directed)
node_map = {node: gt_graph.add_vertex() for node in graph}
if not directed:
seen_edges = set()
for node, edges in graph.iteritems():
i = node_map[node]
for e in edges:
j = node_map[e]
if directed:
gt_graph.add_edge(i, j)
else:
if (j, i) not in seen_edges:
gt_graph.add_edge(i, j)
seen_edges.add((i, j))
node_intensity = gt_graph.new_vertex_property("vector<float>")
node_label = gt_graph.new_vertex_property("string")
for id, value in value_map.iteritems():
node = node_map[id]
node_intensity[node] = find_color(value, maxv, minv, palette)
node_label[node] = id
for id in graph:
if id not in value_map:
node = node_map[id]
node_intensity[node] = find_color(0, maxv, minv, palette)
node_label[node] = id
if position is None:
position = gt.sfdp_layout(gt_graph)
gt.graph_draw(gt_graph, pos=position,
vertex_text=node_label,
vertex_fill_color=node_intensity,
output=output)
return position
def render(g, eprop, nprop, graph_name, pos):
gt.graph_draw(
g,
# vertex_size=nprop,
# edge_pen_width=eprop,
edge_color=eprop,
vertex_color=nprop,
bg_color=(1, 1, 1, 1),
output="{}.png".format(graph_name),
output_size=(900, 800),
pos=pos)
def plot(self):
layout = arf_layout(self.view)
# also arf_layout? not sure how easy is to draw a tree with multiple roots
# if we want to see features there
self.ax.set_axis_off()
graph_draw(self.view, pos=layout, mplfig=self.ax)
logger.debug("Plot done")
plt.savefig(self._get_plot_file())
def collect_info(self, nType, sbm_graph, highest_l, state, draw=False):
"""
Retrieve the partitions, number of groups, and entropies for the hyperlink layer
of the network.
Parameters:
nType: String for type of network (hyperlink, 2-layer-hyperlink-word, 3-layer-hyperlink-word-tag)
"""
partitions = []
num_of_groups = []
entropies = []
block_SBM_partitions = {
} # store dictionary to map nodes to partition.
# Due to NP-hardness of retrieving optimal partitions, repeat process numerous times.
for i in range(len(self.seeds)):
try:
b = state.project_level(highest_l[i]).get_blocks()
# Store dictionary of partitions
if nType == "hSBM":
# Only contains hyperlink SBM.
for node in sbm_graph.vertices():
block_SBM_partitions[sbm_graph.vp.name[node]] = b[node]
else:
# Need to specify to retrieve partitions for document nodes.
for node in sbm_graph.vertices():
if sbm_graph.vp['kind'][node] == 0:
block_SBM_partitions[
sbm_graph.vp.name[node]] = b[node]
# Retrieve the partition from the SBM and store as parameter.
partition = self.hyperlink_graph.vp[
"partition"] = self.hyperlink_graph.new_vp("int")
# Assign partition label to node properties.
for v in self.hyperlink_graph.vertices():
partition[v] = block_SBM_partitions[
self.hyperlink_graph.vp.name[v]]
# Print output of graph
if draw:
# Visualise graph
print(f"{i}: {state}")
gt.graph_draw(
self.hyperlink_graph,
pos=self.hyperlink_graph.vp.pos,
vertex_fill_color=self.hyperlink_graph.vp.partition)
# Add in results.
partitions.append(list(self.hyperlink_graph.vp.partition))
num_of_groups.append(len(set(block_SBM_partitions.values())))
entropies.append(state.entropy())
except (FileNotFoundError) as e:
pass
return (partitions, num_of_groups, entropies)
def saveSimulation(self):
if not os.path.isdir(OUTPUT_DIR):
os.mkdir(OUTPUT_DIR)
if not os.path.isdir(SIMULATIONS_DIR):
os.mkdir(SIMULATIONS_DIR)
simulation_dir = SIMULATIONS_DIR + "s_" + self.simulation_id + "/"
os.mkdir(simulation_dir)
evolution_img_dir = simulation_dir + "evolution_imgs/"
os.mkdir(evolution_img_dir)
self.__exportSimulationOutputAsXML(dir=simulation_dir)
# exports graph structure in xml: key0 and key1 are respectively opinion property and opinion color property
self.original_graph.save(simulation_dir + "graph.xml")
# 0%, 25%, 50%, 75%, 100%
steps = [0]
steps.append(int(self.rounds / 100 * 25))
steps.append(int(self.rounds / 100 * 50))
steps.append(int(self.rounds / 100 * 75))
steps.append(self.rounds)
layout = gt.sfdp_layout(self.original_graph)
gt.graph_draw(self.original_graph,
pos=layout,
vertex_text=self.original_graph.vertex_index,
vertex_text_color=(1, 1, 1, 1),
edge_color=(1, 1, 1, 0.7),
output=simulation_dir + "graph.png",
output_size=(1600, 1600),
adjust_aspect=False,
bg_color=(0.09411764705882353, 0.11372549019607843,
0.15294117647058825, 1))
# prints graph evolution in .png
for step in steps:
opinion = self.evolutionMap[step][0]
opinion_color = self.evolutionMap[step][1]
gt.graph_draw(self.original_graph,
pos=layout,
vertex_color=(1, 1, 1, 0),
vertex_fill_color=opinion_color,
vertex_text=opinion,
vertex_text_color=(1, 1, 1, 1),
edge_color=(1, 1, 1, 0.7),
output=evolution_img_dir + "round-" + str(step) +
".png",
output_size=(1600, 1600),
adjust_aspect=False,
bg_color=(0.09411764705882353, 0.11372549019607843,
0.15294117647058825, 1))
def worker(Graph, current_count):
U = gt.GraphView(Graph,
vfilt=lambda v: Graph.vertex_properties['alive'][v])
gt.graph_draw(U,
U.vertex_properties['position'],
vertex_shape=U.vertex_properties['shape'],
vertex_fill_color=U.vertex_properties['fillcolor'],
output=frame_path + 'taxi%06d.png' % count,
bg_color=(1, 1, 1, 1),
output_size=resolution)
def draw_cluster_save(graph, path=Path.cwd(), resolution=(4000, 4000)):
pos = gt.sfdp_layout(graph, p=1)
if path.is_dir(): path = path.joinpath(datetime.now().isoformat() + '.png')
fill_color = _fill_vertex_color(graph)
gt.graph_draw(graph,
pos=pos,
vertex_size=9,
vertex_fill_color=fill_color,
bg_color=(1, 1, 1, 1),
output_size=resolution,
output=str(path))
return
def draw(self):
res = gt.max_independent_vertex_set(self.graph)
gt.graph_draw(
self.graph,
vertex_pen_width=2,
edge_pen_width=4,
vertex_fill_color=res,
vertex_text=self.graph.vp['name'],
vertex_font_size=10,
vcmap=matplotlib.cm.plasma,
output='../graph_named.png'
)
def show_gt(self):
"""
Display current state of the network without animation.
"""
self.set_properties()
vprops = self.vert_prop
eprops = self.edge_prop
gt.graph_draw(self.g,
pos=self.pos,
eprops=eprops,
vprops=vprops,
output_node=(1000, 500))
def initialize_step(self):
tree_prop = gt.min_spanning_tree(self.graph, weights = self.e_weights)
tree = gt.GraphView(self.graph, efilt=tree_prop)
gt.graph_draw(tree, self.graph_class.position)
self.best_tree = tree
tree_cost = 0
for e in tree.edges():
tree_cost += self.e_weights[e]
t = (1/(2.0*self.graph_class.size)) * tree_cost
return t
def main():
input_fasta = sys.argv[3]
K = int(sys.argv[1])
x = float(sys.argv[2])
ht = khmer.new_hashbits(K, x, 4)
sparse_graph = gt.Graph()
hashes = sparse_graph.new_vertex_property("long long")
for n, record in enumerate(screed.open(input_fasta)):
if n % 1000 == 0:
print >>sys.stderr, '...loaded and tagged {} sequences'.format(n)
name = record.name
sequence = record.sequence
ht.consume_sequence_and_tag_with_labels(sequence, n)
tags = ht.sweep_tag_neighborhood(sequence, 0)
for i in xrange(len(tags) - 1):
src = tags[i]
dst = tags[i + 1]
new = False
srcv = gt.find_vertex(sparse_graph, hashes, src)
if not srcv:
srcv = sparse_graph.add_vertex()
hashes[srcv] = src
new = True
else:
srcv = srcv[0]
dstv = gt.find_vertex(sparse_graph, hashes, dst)
if not dstv:
dstv = sparse_graph.add_vertex()
hashes[dstv] = dst
new = True
else:
dstv = dstv[0]
if new:
e = sparse_graph.add_edge(srcv, dstv)
print 'Sparse graph has {} nodes, {} edges'.format(sparse_graph.num_vertices(), sparse_graph.num_edges())
comp = gt.label_largest_component(sparse_graph, directed=False)
#pos = gt.radial_tree_layout(sparse_graph, sparse_graph.vertex(0))
gt.graph_draw(sparse_graph, output_size=(
5000, 5000), output=input_fasta + '_sparse.png')
sparse_graph.set_vertex_filter(comp)
gt.graph_draw(sparse_graph, output_size=(
5000, 5000), output=input_fasta + '_sparse_comp.png')
def show():
g = gt.load_graph(filename)
kwargs = {}
print 'Graph loaded, now drawing to %s' % imagename
if 'blocks' in g.vp:
kwargs['vertex_fill_color'] = g.vertex_properties['blocks']
if 'rank' in g.vp:
kwargs['vertex_size'] = gt.prop_to_size(g.vp['rank'], mi=5, ma=15)
if 'pos' in g.vp:
kwargs['pos'] = g.vp['pos']
gt.graph_draw(g, vertex_shape=g.vp['rc'], output=imagename, **kwargs)
def main(text, keyword):
words, keyword = tokenize(text, keyword)
print u' | '.join(words)
mst, root = create_mst(words, keyword)
weights_str_ep = mst.new_edge_property('string')
weights_ep = mst.ep['weights']
for e in mst.edges():
weights_str_ep[e] = str(weights_ep[e])
gt.graph_draw(mst,
vertex_text=mst.vp['words'],
edge_text=weights_str_ep,
output_size=(1111, 1111),
output='mst.png')
def draw_graph(graph, value_map=None, output=None, show_ids=False, directed=False, position=None):
gt_graph = gt.Graph(directed=directed)
node_map = {node: gt_graph.add_vertex() for node in graph}
if not directed:
seen_edges = set()
for node, edges in graph.iteritems():
i = node_map[node]
for e in edges:
j = node_map[e]
if directed:
gt_graph.add_edge(i, j)
else:
if (j, i) not in seen_edges:
gt_graph.add_edge(i, j)
seen_edges.add((i, j))
if position is None:
position = gt.sfdp_layout(gt_graph)
node_label = gt_graph.new_vertex_property("string")
if value_map is not None:
for id, value in value_map.iteritems():
node = node_map[id]
node_label[node] = id
for id in graph:
if id not in value_map:
node = node_map[id]
node_label[node] = id
elif show_ids:
for id in graph:
node = node_map[id]
node_label[node] = id
if show_ids:
gt.graph_draw(gt_graph, pos=position,
vertex_text=node_label,
output=output)
else:
gt.graph_draw(gt_graph, pos=position, output=output)
return position
def draw2(g, h, t, output, pos=None):
g = gt.GraphView(g, efilt=lambda e: h[e] or t[e])
print('{} {}'.format(output, 2*g.num_edges()/g.num_vertices()))
width = g.new_edge_property('double')
color = g.new_edge_property('vector<double>')
for e in g.edges():
width[e] = 1.6 if h[e] else 0.8
color[e] = (0.1,0.9,0.1,1) if h[e] and t[e] else (0.9,0.1,0.1,1) if t[e] else (0,0,0,1)
gt.graph_draw(g, pos=pos,
vertex_size=8, vertex_color='black', vertex_fill_color='black',
edge_pen_width=1.6, edge_color=color,
output_size=(500, 500), output=output, bg_color=[1,1,1,1])
def build_graph(self):
logging.info('Building graph')
import graph_tool.all as GT
graph = GT.Graph(directed=False)
vertices = dict()
names = graph.new_vertex_property('string')
shapes = graph.new_vertex_property('string')
colours = graph.new_vertex_property('string')
def get_vertex(name, shape, colour):
if name in vertices:
return vertices[name]
else:
vertex = graph.add_vertex()
names[vertex] = name
shapes[vertex] = shape
colours[vertex] = colour
vertices[name] = vertex
return vertex
def get_word_vertex(name):
return get_vertex(name, 'circle', 'blue')
def get_topic_vertex(name):
return get_vertex(name, 'square', 'red')
def get_document_vertex(name):
return get_vertex(name, 'triangle', 'green')
for k in range(self.statistics.num_topics_used):
name = 'TP: %d' % k
topic_vertex = get_topic_vertex(name)
for w in self.statistics.words_for_topic(k):
word_vertex = get_word_vertex('Word: %d' % w)
graph.add_edge(word_vertex, topic_vertex)
for d in self.statistics.documents_for_topic(k):
doc_vertex = get_document_vertex('Doc: %d' % d)
graph.add_edge(topic_vertex, doc_vertex)
pos = GT.sfdp_layout(graph)
GT.graph_draw(
graph,
pos=pos,
vertex_shape=shapes,
vertex_fill_color=colours)
return graph, vertices, shapes, colours
def draw_graph_frame((graph, dir_name, number)):
vertices_filter = graph.new_vertex_property('bool')
for i in xrange(len(vertices_filter.a)):
if i < number:
vertices_filter.a[i] = True
else:
vertices_filter.a[i] = False
graph.set_vertex_filter(vertices_filter)
graph_draw(
graph,
pos=graph.vertex_properties['pos'],
vertex_fill_color=graph.vertex_properties['pagerank'],
vertex_size=5,
output_size=(1024, 1024),
output=dir_name + str(number) + '.png')
def rysuj_wejscie(input_file, output=None, size=(600, 600)):
rozszerzenie = input_file.split('.')[1]
g2 = load_graph(input_file)
g2.vertex_properties['wyswietlany_tekst'] = g2.new_vertex_property('string')
if rozszerzenie == 'dot':
for v in g2.vertices():
g2.vertex_properties['wyswietlany_tekst'][v] = v.__str__() + ': ' + \
g2.vertex_properties['liczba_kolorow'][v]
elif rozszerzenie == 'xml' or 'graphml':
for v in g2.vertices():
g2.vertex_properties['wyswietlany_tekst'][v] = v.__str__() + ': ' + \
str(g2.vertex_properties['liczba_kolorow'][v])
graph_draw(g2, vertex_text=g2.vertex_properties['wyswietlany_tekst']
, bg_color=[255., 255., 255., 1]
, output_size=size
, output=output
)
def draw_graph(graph):
vertices_names = graph.new_vertex_property('string')
for vertex in graph.vertices():
vertices_names[vertex] = \
graph.vertex_properties['actors_on_vertices'][vertex] + \
' ' + str(graph.vertex_properties['pagerank'][vertex])
pos = sfdp_layout(graph, eweight=graph.edge_properties['weights_on_edges'])
graph_draw(
graph,
pos=pos,
vertex_fill_color=graph.vertex_properties['pagerank'],
vertex_text=vertices_names,
vertex_font_size=5,
output_size=(1024, 1024),
output='pagerank/' +
graph.graph_properties['repo_on_graph'].replace('/', '%') + '.pdf')
def rysuj_graf_wejsciowy(g, output=None, size=(600, 600), bez_napisow=False):
gx = Graph(g)
gx.vertex_properties['wyswietlany_tekst'] = gx.new_vertex_property('string')
for v in gx.vertices():
gx.vertex_properties['wyswietlany_tekst'][v] = v.__str__() + ': ' + \
str(gx.vertex_properties['liczba_kolorow'][v])
if bez_napisow:
graph_draw(gx
# , vertex_text=gx.vertex_properties['wyswietlany_tekst']
, bg_color=[255., 255., 255., 1]
, output_size=size
, output=output
)
else:
graph_draw(gx
, vertex_text=gx.vertex_properties['wyswietlany_tekst']
, bg_color=[255., 255., 255., 1]
, output_size=size
, output=output
)
def test1():
gt.seed_rng(42)
seed(42)
NUMBER_OF_NODES = 20
points = random((NUMBER_OF_NODES, 2))
points[0] = [0, 0]
points[1] = [1, 1]
g, pos = gt.triangulation(points, type="delaunay")
g.set_directed(True)
edges = list(g.edges())
# reciprocate edges
for e in edges:
g.add_edge(e.target(), e.source())
# The capacity will be defined as the inverse euclidean distance
cap = g.new_edge_property("double")
for e in g.edges():
cap[e] = min(1.0 / norm(pos[e.target()].a - pos[e.source()].a), 10)
g.edge_properties["cap"] = cap
g.vertex_properties["pos"] = pos
g.save("flow-example.xml.gz")
gt.graph_draw(g, pos=pos, edge_pen_width=gt.prop_to_size(cap, mi=0, ma=3, power=1),
output="flow-example.pdf")
def draw_radial_graph(self,
output_path,
vertex_text=None,
vertex_fill_color=[0.640625, 0, 0, 0.9],
bg_color=(.75, .75, .75, 1),
output_size=(1000, 1000),
vcmap=matplotlib.cm.summer,
**kwargs):
"""
Draws the structureGraph as a radial graph to output_path
:param output_path: path to save the image of the graph to
:return:
"""
pos = gt.radial_tree_layout(self.structureGraph, self.structureGraph.vertex(0))
gt.graph_draw(self.structureGraph,
pos=pos,
output=output_path,
bg_color=bg_color,
vertex_text=vertex_text,
output_size=output_size,
vertex_fill_color=vertex_fill_color,
vcmap=vcmap,
**kwargs)
def sfdp_draw(graph, output="lattice_3d.pdf"):
'''
Deprecated
'''
output = os.path.join('saved_graph/pdf', output)
sfdp_pos = gt.graph_draw(graph,
pos=gt.sfdp_layout(graph,
cooling_step=0.95,
epsilon=1e-3,
multilevel=True),
output_size=(300,300),
output=output)
# print 'graph view saved to %s' %output
return sfdp_pos
def render(g, file_name, layout):
# Perform Layout
pos = get_pos(g, algorithm=layout)
generate_color_map(g)
vertex_color = g.new_vertex_property('vector<double>', val=[0, 0, 0, 0])
vertex_size = g.new_vertex_property('double', val=30)
vertex_font_size = g.new_vertex_property('int', val=10)
vertex_font_family = g.new_vertex_property('string', 'helvatica')
vertex_text_position = g.new_vertex_property('double', val=(math.pi*(2.0/8.0)))
vertex_text_color = g.new_vertex_property('vector<double>', val=[0.3, 0.3, 0.3, 1.0])
# Edge properties
edge_color = g.new_edge_property('vector<double>', val=[0.179, 0.203,
0.210, 0.9])
edge_pen_width = g.new_edge_property('double', val=6)
edge_end_marker = g.new_edge_property('string', val='none')
gt.graph_draw(g, pos=pos,
vertex_color=vertex_color,
vertex_fill_color=g.vertex_properties['fill_color'],
vertex_shape=g.vertex_properties['shape'],
# vertex_text=g.vertex_properties['label'],
vertex_font_size=vertex_font_size,
vertex_font_family=vertex_font_family,
vertex_text_position=vertex_text_position,
vertex_text_color=vertex_text_color,
vertex_size=vertex_size,
edge_color=edge_color,
edge_pen_width=edge_pen_width,
edge_end_marker=edge_end_marker,
output_size=(1600, 1600), output=file_name)
return file_name
def pseudo3d_draw(graph, rtz, output="lattice_3d.pdf",
z_angle=0.12, theta_rot=0.1,
RGB=(0.8, 0.1, 0.), **kwargs):
'''
Deprecated
'''
rhos, thetas, zeds = rtz
thetas += theta_rot
output = os.path.join('saved_graph/pdf', output)
red, green, blue = RGB
pseudo_x = graph.new_vertex_property('float')
pseudo_y = graph.new_vertex_property('float')
vertex_red = graph.new_vertex_property('float')
vertex_green = graph.new_vertex_property('float')
vertex_blue = graph.new_vertex_property('float')
vertex_alpha = graph.new_vertex_property('float')
pseudo_x.a = zeds * np.cos(z_angle)\
- rhos * np.cos(thetas) * np.sin(z_angle)
pseudo_y.a = rhos * np.sin(thetas)
depth = rhos * np.cos(thetas)
normed_depth = (depth - depth.min()) / (depth.max() - depth.min())
vertex_alpha.a = normed_depth * 0.7 + 0.3
vertex_red.a = np.ones(rhos.shape, dtype=np.float) * red
vertex_green.a = np.ones(rhos.shape, dtype=np.float) * green
vertex_blue.a = np.ones(rhos.shape, dtype=np.float) * blue
rgba = [vertex_red, vertex_green, vertex_blue, vertex_alpha]
pseudo3d_color = gt.group_vector_property(rgba,
value_type='float')
xy = [pseudo_x, pseudo_y]
pseudo3d_pos = gt.group_vector_property(xy, value_type='float')
pmap = gt.graph_draw(graph, pseudo3d_pos,
vertex_fill_color=pseudo3d_color,
vertex_color=pseudo3d_color,
edge_pen_width=2.,
output=output, **kwargs)
del pmap
return pseudo3d_pos
new = False
srcv = gt.find_vertex(sparse_graph, hashes, src)
if not srcv:
srcv = sparse_graph.add_vertex()
hashes[srcv] = src
new = True
else:
srcv = srcv[0]
dstv = gt.find_vertex(sparse_graph, hashes, dst)
if not dstv:
dstv = sparse_graph.add_vertex()
hashes[dstv] = dst
new = True
else:
dstv = dstv[0]
if new:
e = sparse_graph.add_edge(srcv, dstv)
print 'Sparse graph has {} nodes, {} edges'.format(sparse_graph.num_vertices(), sparse_graph.num_edges())
comp = gt.label_largest_component(sparse_graph, directed=False)
#pos = gt.radial_tree_layout(sparse_graph, sparse_graph.vertex(0))
gt.graph_draw(sparse_graph, output_size=(
5000, 5000), output=input_fasta + '_sparse.png')
sparse_graph.set_vertex_filter(comp)
gt.graph_draw(sparse_graph, output_size=(
5000, 5000), output=input_fasta + '_sparse_comp.png')
def plot_window():
gt.graph_draw(G, G.vertex_properties['position'],
vertex_shape=G.vertex_properties['shape'],
vertex_fill_color=G.vertex_properties['fillcolor'],
output=base_path + './frames/taxi%06d.png')
def plot(
self,
source_list,
sink_list,
max_depth,
max_paths
):
print 'WARNING: This is actually a plotting function!!!'
num_source_nodes = len(source_list)
num_sink_nodes = len(sink_list)
# super_source_vertex = g.add_vertex()
# super_sink_vertex = g.add_vertex()
super_source_vertex = 'super_source_vertex'
super_sink_vertex = 'super_sink_vertex'
edge_list = zip([super_source_vertex] * num_source_nodes, source_list)
for e in edge_list:
self.add_edge(*e)
edge_list = zip(sink_list, [super_sink_vertex] * num_sink_nodes)
for e in edge_list:
self.add_edge(*e)
g = self.G
pos = gt.arf_layout(g, max_iter=0)
gt.graph_draw(g, pos=pos, vertex_text=self.G.vertex_index)
time.sleep(1000)
exit()
gt.graph_draw(self.G, vertex_text=self.G.vertex_index)
time.sleep(1000)
# print edge_list
# Add edges:
# \forall sink \in sink_list. sink -> super sink node
edge_list = zip(sink_list, [dummy_super_sink_node] * num_sink_nodes)
H.add_edges_from(edge_list)
# print edge_list
# print '='*80
# TODO: WHY?
# Switching this on with def path_gen(), results in empty path and no further results!!
# #xplanation required!
# for path in nx.all_simple_paths(H, dummy_super_source_node, dummy_super_sink_node):
# print path
# print '='*80
# TODO: how to do this with lambda?
# Also, is this indeed correct?
def path_gen():
for i in nx.all_simple_paths(H, dummy_super_source_node,
dummy_super_sink_node):
# Remove the first (super source)
# and the last element (super sink)
yield i[1:-1]
# return lambda: [yield i[1:-1] for i in nx.all_simple_paths(H,
# dummy_super_source_node, dummy_super_sink_node)]
return path_gen()
#! /usr/bin/env python # -*- coding: utf-8 -*- """ @author: lockheed Information and Electronics Engineering Huazhong University of science and technology E-mail:[email protected] Created on: 3/13/14 9:06 PM Copyright (C) lockheedphoenix """ import graph_tool.all as gt """ 20140320 nig g = gt.load_graph('BA_networks.xml.gz') pos = gt.arf_layout(g) g.vertex_properties['pos'] = pos g.save('BA_networks_2.xml.gz') """ g = gt.lattice([40, 40], True) pos = gt.sfdp_layout(g, cooling_step=0.99, epsilon=1e-3) g.vertex_properties['pos'] = pos g.save('3Dlattice.xml.gz') gt.graph_draw(g, pos)
def draw(self, filename, engine='graphviz'):
"""Draws the TS. The filename extension determines the format.
[engine] Rendering engine used to draw the TS. Valid values:
cairo, graphviz, astg (for draw_astg)
If [filename] is None and engine is 'cairo', then the interactive
window is used"""
if engine == 'graphviz':
vprops = {'label':self.vp_state_name}
eprops = {'label':self.ep_edge_label}
if 'frequency' in self.g.vertex_properties:
# vp_width = self.g.new_vertex_property("float")
all_traces = self.vp_state_frequency[self.get_state(self.get_initial_state())]
# for v in self.g.vertices():
# vp_width[v] = 3.0*self.vp_state_frequency[v]/all_traces
# vprops['width'] = vp_width
vsize=(self.vp_state_frequency, 2.0/all_traces)
else:
vsize=0.105
if 'frequency' in self.g.edge_properties:
all_traces = self.vp_state_frequency[self.get_state(self.get_initial_state())]
penwidth=(self.ep_edge_frequency, 10.0/all_traces)
else:
penwidth=1.0
print vprops
if self.g.num_vertices() < 150:
layout = 'dot'
overlap = False
else:
layout = None
overlap = 'Prism'
gt.graphviz_draw(self.g, ratio='auto', vprops=vprops, splines=True,
eprops=eprops, sep=1.0, overlap=overlap, vsize=vsize,
penwidth=penwidth, layout=layout, output=filename)
elif engine=='cairo': #use cairo
pos = gt.sfdp_layout(self.g)
vprops = {'text':self.vp_state_name}
if 'frequency' in self.g.vertex_properties:
vp_width = self.g.new_vertex_property("float")
all_traces = self.vp_state_frequency[self.get_state(self.get_initial_state())]
#use numpy array access
# vp_widht.a = int(max(100.0*self.vp_state_frequency.a/all_traces)
for v in self.g.vertices():
vp_width[v] = int(100.0*self.vp_state_frequency[v]/all_traces)
vprops['size'] = vp_width
gt.graph_draw(self.g, pos=pos, vprops=vprops, output=filename)
elif engine=='astg':
if filename.endswith('.ps'):
format = '-Fps'
elif filename.endswith('.gif'):
format = '-Fgif'
elif filename.endswith('.dot'):
format = '-Fdot'
elif filename.endswith('.png'):
format = '-Fpng'
elif filename.endswith('.svg'):
format = '-Fsvg'
else:
raise TypeError, 'Unsupported output for draw_astg'
#check if file can be forwarded as input_filename
if self.filename and not self.modified_since_last_write:
input_filename = self.filename
else:
# or create tmp file with save
tmpfile = tempfile.NamedTemporaryFile(mode='w', delete=False)
print "Saving TS to temporary file '{0}'".format( tmpfile.name )
self.save(tmpfile)
tmpfile.close()
input_filename = tmpfile.name
params = ['draw_astg', '-sg', '-noinfo', format, input_filename]
output = subprocess.check_output( params )
with open(filename,'w+b') as f:
f.write(output)
# call fastlayout function
start = time.clock()
pos = fl.layout_fr_omp_simd( nodes*dimension, edgelist, max_it, temp )
pos = np.reshape(pos, (nodes, dimension))
pos_prop = g.new_vertex_property('vector<double>')
for v in g.vertices():
i = g.vertex_index[v]
pos_prop[v] = pos[i]#[::-1]
fastlayout = time.clock()
fastlayout = fastlayout - start
# draw the graph in graph_tool
gt.graph_draw(g, pos=pos_prop, output="output/fr-fastlayout.pdf")
# call fastlayout function with new data structure
start = time.clock()
pos = fl.layout_fr_stride_omp_simd( nodes*dimension, edgelist, max_it, temp )
pos = zip(pos[0:nodes], pos[nodes:2*nodes])
pos_prop2 = g.new_vertex_property('vector<double>')
for v in g.vertices():
i = g.vertex_index[v]
pos_prop2[v] = pos[i]#[::-1]
fastlayout_strided = time.clock()
fastlayout_strided = fastlayout - start
grv= self.graph.vertex_properties["rv"][gv]
given_rvs.append(grv)
cpt_dims.append(len(grv.values))
self.graph.add_edge(gv, v)
elif node.nodeName == "TABLE":
cpt_dims.append(len(rv.values))
table_str= node.childNodes[0].data
table_vals_str= table_str.split()
n= len(table_vals_str)
assert(n == numpy.prod(cpt_dims))
rv.cpt= numpy.zeros(n)
for ix in range(n):
rv.cpt[ix]= float(table_vals_str[ix])
rv.cpt.resize(tuple(cpt_dims))
if (not rv.validateCPT()):
raise ValueError('Invalid CPT for' + for_node_name)
rv.prettyPrintCPT(self.graph)
#
if __name__== '__main__':
"""Unit testing
"""
dom = xd.parse("alarm.xmlbif")
bn= JBN()
bn.load(dom)
# visualize the BN graph
gt.graph_draw(bn.graph, size=(15,15), output="bn.pdf",
vprops={"label":bn.graph.vertex_properties["rv"]}, layout="dot", vcolor="white", overlap=False, splines=True,
ratio="compress", penwidth=2.0)