def plot_co_x(cox, start, end, size = (20,20), title = '', weighted=False, weight_threshold=10):
""" Plotting function for keyword graphs
Parameters
--------------------
cox: the coword networkx graph; assumes that nodes have attribute 'topic'
start: start year
end: end year
"""
plt.figure(figsize=size)
plt.title(title +' %s - %s'%(start,end), fontsize=18)
if weighted:
elarge=[(u,v) for (u,v,d) in cox.edges(data=True) if d['weight'] >weight_threshold]
esmall=[(u,v) for (u,v,d) in cox.edges(data=True) if d['weight'] <=weight_threshold]
pos=nx.graphviz_layout(cox) # positions for all nodes
nx.draw_networkx_nodes(cox,pos,
node_color= [s*4500 for s in nx.eigenvector_centrality(cox).values()],
node_size = [s*6+20 for s in nx.degree(cox).values()],
alpha=0.7)
# edges
nx.draw_networkx_edges(cox,pos,edgelist=elarge,
width=1, alpha=0.5, edge_color='black') #, edge_cmap=plt.cm.Blues
nx.draw_networkx_edges(cox,pos,edgelist=esmall,
width=0.3,alpha=0.5,edge_color='yellow',style='dotted')
# labels
nx.draw_networkx_labels(cox,pos,font_size=10,font_family='sans-serif')
plt.axis('off')
else:
nx.draw_graphviz(cox, with_labels=True,
alpha = 0.8, width=0.1,
fontsize=9,
node_color = [s*4 for s in nx.eigenvector_centrality(cox).values()],
node_size = [s*6+20 for s in nx.degree(cox).values()])
def displayGraph(self, g, label=False):
axon, sd = axon_dendrites(g)
sizes = node_sizes(g) * 50
if len(sizes) == 0:
print('Empty graph for cell. Make sure proto file has `*asymmetric` on top. I cannot handle symmetric compartmental connections')
return
weights = np.array([g.edge[e[0]][e[1]]['weight'] for e in g.edges()])
pos = nx.graphviz_layout(g, prog='twopi')
xmin, ymin, xmax, ymax = 1e9, 1e9, -1e9, -1e9
for p in list(pos.values()):
if xmin > p[0]:
xmin = p[0]
if xmax < p[0]:
xmax = p[0]
if ymin > p[1]:
ymin = p[1]
if ymax < p[1]:
ymax = p[1]
edge_widths = 10.0 * weights / max(weights)
node_colors = ['k' if x in axon else 'gray' for x in g.nodes()]
lw = [1 if n.endswith('comp_1') else 0 for n in g.nodes()]
self.axes.clear()
try:
nx.draw_graphviz(g, ax=self.axes, prog='twopi', node_color=node_colors, lw=lw)
except (NameError, AttributeError) as e:
nx.draw_spectral(g, ax=self.axes, node_color=node_colors, lw=lw, with_labels=False, )
def main():
if len(sys.argv) < 2:
sys.exit('Usage: %s $json_file' % sys.argv[0])
if not os.path.exists(sys.argv[1]):
sys.exit('ERROR: %s was not found!' % sys.argv[1])
if len(sys.argv) == 2:
G = merge_same_node(parse_saaf_json(sys.argv[1]))
nx.draw_graphviz(G, prog='dot')
plt.axis('off')
plt.savefig("merged_by_networkx.png")
json.dump(json_graph.node_link_data(G),
open('ford3js.json', 'w'),
sort_keys=True,
indent=4)
if len(sys.argv) == 3:
G1 = merge_same_node(parse_saaf_json(sys.argv[1]))
G2 = merge_same_node(parse_saaf_json(sys.argv[2]))
GM = isomorphism.DiGraphMatcher(G2, G1, node_match=op_match)
#GM = isomorphism.DiGraphMatcher(G2, G1)
print GM.is_isomorphic()
print GM.subgraph_is_isomorphic()
print GM.mapping
def salva_grafoNX_imagem(G):
"""
Salva grafos em formato png e dot
"""
nx.draw_graphviz(G)
nx.write_dot(G, 'relatorios/grafo_lei_vs_lei.dot')
P.savefig('relatorios/grafo_lei_vs_lei.png')
def render_community_graph(self, show_single_nodes=True):
added_nodes = set()
graph = nx.Graph()
for edge, _ in self.adjacency_matrix.iteritems():
player_one = self.__players[edge.player_one]
player_two = self.__players[edge.player_two]
added = False
if show_single_nodes:
graph.add_node(edge.player_one)
graph.add_node(edge.player_two)
added = True
if player_one.community == player_two.community:
graph.add_edge(edge.player_one, edge.player_two)
added = True
if added:
added_nodes.add(edge.player_one)
added_nodes.add(edge.player_two)
for node in self.nodes:
if node.fide_id in added_nodes:
graph.node[node.fide_id]['elo_rank'] = math.floor(node.elo/100) * 100
min_val = self.__min_elo
max_val = 2900
elo_levels = range(min_val, max_val, 100)
color_levels = np.linspace(1, 0, num=len(elo_levels), endpoint=True)
color_value_map = {elo: color for (elo, color) in zip(elo_levels, color_levels)}
color_values = [color_value_map.get(graph.node[n]['elo_rank'], 0.0) for n in graph.nodes()]
nx.draw_graphviz(graph, cmap=pylab.get_cmap('jet'), node_color=color_values,
node_size=100)
def minimalColoring (probMatrix, contigs, cutoff = 0.01): # create a graph based on the probMatrix probG = nx.Graph() for i in range (len(contigs)): contigi = contigs[i] if contigi in excluded_utgs: print "yo" continue probG.add_node(contigi) for j in range (i+1, len(contigs)): contigj = contigs[j] if contigi in excluded_utgs: print "yo" continue if np.isnan(probMatrix[i][j]): continue if probMatrix[i][j] > cutoff: probG.add_edge(contigi, contigj) nx.draw_graphviz(probG) plt.savefig(out_tag + "_color_groups.png") plt.clf() #print probG.nodes() #print probG.edges() #print nx.find_cliques(probG) components = list(nx.connected_components(probG)) #components = list(nx.find_cliques(probG)) return components
def main():
if len(sys.argv) < 2:
sys.exit('Usage: %s $json_file' % sys.argv[0])
if not os.path.exists(sys.argv[1]):
sys.exit('ERROR: %s was not found!' % sys.argv[1])
if len(sys.argv) == 2:
G = merge_same_node(parse_saaf_json(sys.argv[1]))
nx.draw_graphviz(G, prog = 'dot')
plt.axis('off')
plt.savefig("merged_by_networkx.png")
json.dump(
json_graph.node_link_data(G),
open('ford3js.json', 'w'),
sort_keys = True,
indent = 4
)
if len(sys.argv) == 3:
G1 = merge_same_node(parse_saaf_json(sys.argv[1]))
G2 = merge_same_node(parse_saaf_json(sys.argv[2]))
GM = isomorphism.DiGraphMatcher(G2, G1, node_match = op_match)
#GM = isomorphism.DiGraphMatcher(G2, G1)
print GM.is_isomorphic()
print GM.subgraph_is_isomorphic()
print GM.mapping
def render_graph(self, max_edges=None, min_games=1):
added_nodes = set()
graph = nx.Graph()
for i, (edge, num) in enumerate(self.adjacency_matrix.iteritems()):
if max_edges and i > max_edges:
break
if num < min_games:
continue
graph.add_edge(edge.player_one, edge.player_two)
added_nodes.add(edge.player_one)
added_nodes.add(edge.player_two)
for node in self.nodes:
if node.fide_id in added_nodes:
graph.node[node.fide_id]['elo_rank'] = int(math.floor(node.elo/100) * 100)
min_val = self.__min_elo
max_val = 2900
elo_levels = range(min_val, max_val, 100)
color_levels = np.linspace(1, 0, num=len(elo_levels), endpoint=True)
color_value_map = {elo: color for (elo, color) in zip(elo_levels, color_levels)}
color_values = [color_value_map.get(graph.node[n]['elo_rank'], 0.0) for n in graph.nodes()]
nx.draw_graphviz(graph, cmap=pylab.get_cmap('jet'), node_color=color_values,
node_size=100)
def displayGraph(self, g, label=False):
axon, sd = axon_dendrites(g)
sizes = node_sizes(g) * 50
if len(sizes) == 0:
print(
'Empty graph for cell. Make sure proto file has `*asymmetric` on top. I cannot handle symmetric compartmental connections'
)
return
node_colors = ['k' if x in axon else 'gray' for x in g.nodes()]
lw = [1 if n.endswith('comp_1') else 0 for n in g.nodes()]
self.axes.clear()
try:
nx.draw_graphviz(g,
ax=self.axes,
prog='twopi',
node_color=node_colors,
lw=lw)
except (NameError, AttributeError) as e:
nx.draw_spectral(
g,
ax=self.axes,
node_color=node_colors,
lw=lw,
with_labels=False,
)
def draw_all(filename='out.png'):
"""
draw every graph connected and everything yeah
"""
def _make_abbreviation(string):
s = string.split(" ")
return ''.join([word[0] for word in s])
import matplotlib.pyplot as plt
plt.clf()
this = sys.modules[__name__]
relationals = [getattr(this, i) for i in this.__dict__ if isinstance(getattr(this,i), Relational)]
biggraph = nx.DiGraph()
for r in relationals:
for n in r._nk.nodes():
biggraph.add_edges_from(n._nk.edges())
for n in biggraph.nodes():
if n.external_parents:
for p in n.external_parents:
biggraph.add_edges_from(p._nk.edges())
if n.external_childs:
for c in n.external_childs:
biggraph.add_edges_from(c._nk.edges())
for n in biggraph.nodes():
if "." not in n.name:
n.name = n.name+"."+_make_abbreviation(n.container.name)
nx.draw_graphviz(biggraph,prog='neato',width=1,node_size=300,font_size=4,overlap='scalexy')
plt.savefig(filename)
def minimalColoring(probMatrix, contigs, cutoff=0.01):
# create a graph based on the probMatrix
probG = nx.Graph()
for i in range(len(contigs)):
contigi = contigs[i]
if contigi in excluded_utgs:
print "yo"
continue
probG.add_node(contigi)
for j in range(i + 1, len(contigs)):
contigj = contigs[j]
if contigi in excluded_utgs:
print "yo"
continue
if np.isnan(probMatrix[i][j]):
continue
if probMatrix[i][j] > cutoff:
probG.add_edge(contigi, contigj)
nx.draw_graphviz(probG)
plt.savefig(out_tag + "_color_groups.png")
plt.clf()
#print probG.nodes()
#print probG.edges()
#print nx.find_cliques(probG)
components = list(nx.connected_components(probG))
#components = list(nx.find_cliques(probG))
return components
def graph_draw(graph, with_labels=False, factor=1):
nx.draw_graphviz(graph,
node_size=[(factor * 16 * graph.degree(n))
for n in graph],
node_color=[graph.depth[n] for n in graph],
with_labels=with_labels)
matplotlib.pyplot.show()
def render_graph(self, max_edges=None, min_games=1):
added_nodes = set()
graph = nx.Graph()
for i, (edge, num) in enumerate(self.adjacency_matrix.iteritems()):
if max_edges and i > max_edges:
break
if num < min_games:
continue
graph.add_edge(edge.player_one, edge.player_two)
added_nodes.add(edge.player_one)
added_nodes.add(edge.player_two)
for node in self.nodes:
if node.fide_id in added_nodes:
graph.node[node.fide_id]['elo_rank'] = int(
math.floor(node.elo / 100) * 100)
min_val = self.__min_elo
max_val = 2900
elo_levels = range(min_val, max_val, 100)
color_levels = np.linspace(1, 0, num=len(elo_levels), endpoint=True)
color_value_map = {
elo: color
for (elo, color) in zip(elo_levels, color_levels)
}
color_values = [
color_value_map.get(graph.node[n]['elo_rank'], 0.0)
for n in graph.nodes()
]
nx.draw_graphviz(graph,
cmap=pylab.get_cmap('jet'),
node_color=color_values,
node_size=100)
def map_flows(catalog):
import analysis as trans
fm = trans.FlowMapper()
read_exceptions = {}
for i,fn in enumerate(os.listdir('.\\repository_data\\')):
print i, fn
try:
sys = catalog.read(''.join(['.\\repository_data\\',fn]))
except Exception as e:
read_exceptions[fn] = e
print '\t',e.message
fm.add_system(sys)
if i > 5:
break
graph = fm.transformation_graph()
fm.stats()
nx.draw_graphviz(graph,prog='dot',root='energy')
print nx.to_numpy_matrix(graph) > 0
# pdg = nx.to_pydot(graph)
# pdg.write_png('transform.png')
# nx.graphviz_layout(graph,prog='neato')
# nx.draw_graphviz(graph)
plt.show()
def plot_graph(graph=None, path=None, dist=None, best=None, best_dist=None,
save=False, name=None, title=None):
"""
Plot a TSP graph.
Parameters
----------
graph: An XML TSP graph. If None, use the default graph from parse_xml_graph.
path: An ordered list of node indices. If given, plot the path. Otherwise, plot
the underlying graph.
dist: Distances of the paths in path.
best: Empirical best path.
best_dist: Empirical best path distance.
save: If True, saves the graph as name.png. Otherwise, draws the graph.
name: Graph is saved as name.png
title: Caption of the graph.
"""
# Check input parameters
if save:
assert (name is not None), 'If saving graph, must provide name'
# Initialize graph
if graph is not None:
g = parse_xml_graph(graph)
else:
g = parse_xml_graph()
G = nx.from_numpy_matrix(g)
# Plot path, if applicable
edges = list()
edge_colors = list()
if path is not None:
edges.extend([(path[i], path[i+1]) for i in range(len(path)-1)])
edges.append((path[-1], path[0]))
edge_colors.extend(['r' for i in range(len(path))])
if best is not None:
edges.extend([(best[i], best[i+1]) for i in range(len(best)-1)])
edges.append((best[-1], best[0]))
edge_colors.extend(['b' for i in range(len(best))])
if path is None and best is None:
edges = G.edges()
plt.clf()
fig = plt.figure(figsize=(14, 5.5))
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
nx.draw_graphviz(G, edgelist=edges, edge_color=edge_colors, with_labels=None,
node_color='k', node_size=100, ax=ax1)
ax1.set_title(title)
ax2.plot(np.arange(1, len(dist)+1), dist, color='r', alpha=0.9, label='Best found path')
ax2.hlines(best_dist, 0, len(dist)+1, color='b', label='Best path')
ax2.set_xlim(1, max(len(dist), 2));
ax2.legend()
if not save:
plt.show()
else:
plt.savefig('temp/{}.png'.format(name))
fig.clf()
def drawPlot(self):
a = nx.to_agraph(self.g)
self.g = nx.from_agraph(a)
self.fig = pylab.figure()
nx.draw_graphviz(self.g, prog='neato')
plt.savefig("path.png")
self.image.set_from_file("path.png")
def plot_graph(graph):
"""
\brief Produce de graphviz file related to a DBGraph and show the graph
\param graph A DBGraph instance
"""
import matplotlib.pyplot as plt
draw_graphviz(graph)
plt.show()
def debug_graph( graph, message="" ):
import matplotlib.pyplot as plt
print message
pos = nx.layout.fruchterman_reingold_layout( graph )
nx.draw( graph, pos )
nx.draw_graphviz( graph )
plt.show()
def graph_draw(graph):
nx.draw_graphviz(
graph,
node_size=[16 * graph.degree(n) for n in graph],
node_color=[graph.depth[n] for n in graph],
with_labels=False,
)
matplotlib.pyplot.show()
def s_draw(listdata):
'''Draw the weighted graph associated with the Seifert data 'listdata'.'''
startree = make_graph(listdata)
labels = dict((n, '%s,%s' %(n,a['weight'])) for n,a in
startree.nodes(data=True))
nx.draw_graphviz(startree, labels=labels, node_size=700, width=3,
alpha=0.7)
plt.show()
def update_graph(self):
'''Redraw graph in matplotlib.pyplot.'''
plt.clf() # erase figure
labels = dict((n, '%s,%s' %(n,a['weight'])) \
for n,a in self.graph.nodes(data=True))
nx.draw_graphviz(self.graph, labels=labels, node_size=700, width=3,
alpha=0.7)
plt.show()
def draw_neighbor(G,compound):
tmp = nx.all_neighbors(G,compound)
Gpart = nx.Graph()
for x in tmp:
Gpart.add_edge(x,compound,color=G.edge[x][compound]['color'])
nx.draw(Gpart)
plt.savefig("path_test.png")
nx.draw_graphviz(Gpart)
nx.write_dot(Gpart,'file_test.dot')
def test_lobster(self):
import networkx as nx
import matplotlib.pyplot as plt
#g = nx.random_lobster(15, 0.8, 0.1)
g = nx.barbell_graph(7, 5)
#g = nx.erdos_renyi_graph(15, 0.2)
nx.draw_graphviz(g)
plt.savefig("/tmp/lobster.png")
print distancematrix.matrix_calls(g.edges(), 7)
def printGraph(G, gname): h = networkx.DiGraph() h.add_edges_from( G.edges() ) networkx.draw_graphviz(h) fname = os.path.sep.join([config.Config.imgCacheDir, 'graph_%s.png'%(gname) ] ) plt.savefig(fname) plt.clf() fname = os.path.sep.join([config.Config.cacheDir, 'graph_%s.gexf'%(gname) ] ) networkx.readwrite.gexf.write_gexf( h, fname)
def test():
from ffnet import mlgraph, ffnet
import networkx as NX
import pylab
conec1 = mlgraph((2, 2, 2), biases=False)
net1 = ffnet(conec1)
conec2 = mlgraph((4, 2, 2, 1), biases=True)
net2 = ffnet(conec2)
NX.draw_graphviz(net1.graph, prog='dot')
pylab.show()
def printGraph(G, gname):
h = networkx.DiGraph()
h.add_edges_from(G.edges())
networkx.draw_graphviz(h)
fname = os.path.sep.join([config.imgCacheDir, 'graph_%s.png' % gname])
plt.savefig(fname)
plt.clf()
fname = os.path.sep.join([config.cacheDir, 'graph_%s.gexf' % gname])
networkx.readwrite.gexf.write_gexf(h, fname)
return
def PlotNetwork(G,OutputFile="Graph.dot"):
"""Plot a graph to screen and also save output."""
try:
networkx.draw_graphviz(G)
except:
logger.error("could not plot graph to screen. Check X / Matplotlib settings.")
networkx.write_dot(G, OutputFile)
logger.info("Wrote: %s", OutputFile)
def drawTaxonomyGraph(taxonomy):
G=nx.DiGraph()
for child in taxonomy:
for parent in taxonomy[child]:
G.add_edge(parent, child, {'weight': 1})
graphviz_pos = nx.graphviz_layout(G, prog="dot")
nx.draw_graphviz(G, with_labels=False, node_size=10, font_size = 7, edge_color='g')
nx.draw_networkx_labels(G, pos = graphviz_pos, font_size = 7)
plt.savefig("taxonomy.png")
def PlotNetwork(G,OutputFile="Graph.dot"):
"""Plot a graph to screen and also save output."""
try:
networkx.draw_graphviz(G)
except:
logger.error("could not plot graph to screen. Check X / Matplotlib settings.")
networkx.write_dot(G, OutputFile)
logger.info("Wrote: %s", OutputFile)
def drawGraph(self):
lbls = {}
for node in self._graph.nodes():
lbls[node] = self._graph.node[node]["nName"]
nx.draw_graphviz(self._graph, prog = "twopi", \
with_labels = True, labels = lbls)
path = "{0}.png".format(len(self._hints))
plt.savefig(path)
plt.show()
return path
def transferConditions(dag, l, maxCount=2):
"""
Return a color (for quality of transfer) and a list
of SynonymsList"""
count = 0
lSet = set()
terms_to_remove = set()
for term in l:
lSet.add(getSynList(dag, term))
# Colored graph
for e in lSet:
dag.node[e]["color"] = "blue"
for synList in lSet:
if synList is None:
return "RED", "Error"
for start in lSet:
for a, b in nx.bfs_edges(dag, start, reverse=True):
if b in lSet:
terms_to_remove.add(b)
for e in terms_to_remove:
if e in lSet:
lSet.remove(e)
if len(lSet) == 1:
finalSynList = list(lSet)[0]
# Draw
dag.node[finalSynList]["fontcolor"] = "green"
nx.draw_graphviz(dag)
nx.write_dot(dag, "output2.dot")
os.popen("dot -Tpng output2.dot > output2.png")
return "GREEN", list(lSet)
else:
fca = firstCommonAncestors(dag, lSet)
for e in fca:
dag.node[e]["fontcolor"] = "orange"
# Draw
nx.draw_graphviz(dag)
nx.write_dot(dag, "output2.dot")
os.popen("dot -Tpng output2.dot > output2.png")
if count <= maxCount:
return "ORANGE", fca
else:
return "RED", "Error"
def demo_write_doc():
"""demo_write_doc"""
g = nx.Graph()
g.add_edges_from([(1, 2), (1, 3)])
g.add_node('sparm')
nx.draw(g)
nx.draw_random(g)
nx.draw_circular(g)
nx.draw_spectral(g)
nx.draw_graphviz(g)
nx.write_dot(g, 'g.dot')
def draw_and_save_grapgh(self):
self.create_network()
print '\n\n** Saving graphs in {0} **'.format(self.stat_dir)
plt.figure(0)
plt.subplot(311)
plt.title('Possible interaction')
fig = nx.draw_graphviz(self.graph_low \
, prog='twopi' \
, node_size= 80 \
, font_size = 6 \
, with_labels = True \
, width = 0.5 \
, node_color = 'yellow' \
)
plt.subplot(312)
plt.title('Possible copies')
nx.draw_graphviz(self.graph_med \
, prog='fdp' \
, node_size= 80 \
, font_size = 6 \
, with_labels = True \
, width = 1.0 \
, node_color = 'yellow' \
)
plt.subplot(313)
plt.title('Definite copies')
nx.draw_graphviz(self.graph_high \
, prog='fdp' \
, node_size= 80 \
, font_size = 6 \
, with_labels = True \
, width = 1.9 \
, node_color = 'yellow' \
)
plt.savefig(self.stat_dir+'/assignment.png')
plt.figure(1)
plt.title('Possible interactions')
fig = nx.draw_graphviz(self.graph_low, prog='twopi')
plt.savefig(self.stat_dir+'/low.png')
plt.figure(2)
plt.title('Possible copies')
nx.draw_graphviz(self.graph_med, prog='twopi')
plt.savefig(self.stat_dir+'/medium.png')
plt.figure(3)
plt.title('Definite copies')
nx.draw_graphviz(self.graph_high, prog='twopi')
plt.savefig(self.stat_dir+'/high.png')
def demo_write_doc():
"""demo_write_doc"""
g = nx.Graph()
g.add_edges_from([(1, 2), (1, 3)])
g.add_node('sparm')
nx.draw(g)
nx.draw_random(g)
nx.draw_circular(g)
nx.draw_spectral(g)
nx.draw_graphviz(g)
nx.write_dot(g, 'g.dot')
def show(self):
graph = nx.DiGraph()
for n in self.nodes():
graph.add_node(n)
for n_i, n_j in self.edges():
graph.add_edge(n_i, n_j)
nx.draw_graphviz(graph)
plt.show()
def draw_digraph(self):
print("Draw DiGraph")
self.load_market()
self.df_markets['cur1_account'] = self.df_markets['market']+'_'+self.df_markets['cur1']
self.df_markets['cur2_account'] = self.df_markets['market']+'_'+self.df_markets['cur2']
print(self.df_markets)
set_cur1 = sets.Set(self.df_markets['cur1'].unique())
set_cur2 = sets.Set(self.df_markets['cur2'].unique())
currencies = set_cur1.union(set_cur2)
print(currencies)
set_acc_cur1 = sets.Set(self.df_markets['cur1_account'].unique())
set_acc_cur2 = sets.Set(self.df_markets['cur2_account'].unique())
accounts = set_acc_cur1.union(set_acc_cur2)
#print(accounts)
G = nx.DiGraph()
G.add_nodes_from(currencies)
G.add_nodes_from(accounts)
for i in self.df_markets.index:
market = self.df_markets['market'][i]
cur1 = self.df_markets['cur1'][i]
cur2 = self.df_markets['cur2'][i]
cur1_account = self.df_markets['cur1_account'][i]
cur2_account = self.df_markets['cur2_account'][i]
active = self.df_markets['active'][i]
fullmarketname = self.df_markets['fullmarketname'][i]
#print("{fullmarketname}".format(fullmarketname=fullmarketname))
G.add_edge(cur1_account, cur2_account) # label=market #, label='0'
G.add_edge(cur2_account, cur1_account)
G.add_edge(cur1_account, cur1)
G.add_edge(cur1, cur1_account)
G.add_edge(cur2_account, cur2)
G.add_edge(cur2, cur2_account)
#nx.draw(G)
#nx.draw_random(G)
#nx.draw_circular(G)
#nx.draw_spectral(G)
#plt.savefig("path.png")
#plt.show()
nx.draw_graphviz(G)
nx.write_dot(G,'path.dot')
def display_graph(similarity, languages):
# display network of languages
num_lang = len(languages)
offset = 0.07
dt = [('len', float)]
similarity = similarity.view(dt)
G = nx.from_numpy_matrix(similarity)
G = nx.relabel_nodes(G, dict(zip(range(len(G.nodes())), languages)))
nx.draw_graphviz(G, prog='neato', alpha=0.5, with_labels=True)
plt.show()
def display_graph(similarity, languages):
# display network of languages
num_lang = len(languages)
offset=0.07
dt = [('len',float)]
similarity = similarity.view(dt)
G = nx.from_numpy_matrix(similarity)
G = nx.relabel_nodes(G, dict(zip(range(len(G.nodes())),languages)))
nx.draw_graphviz(G,prog='neato',alpha=0.5,with_labels=True)
plt.show()
def disallowedEdges (probMatrix, contigs, cutoff = 0.001): badGraph = nx.Graph() for i in range(len(contigs)): contigi = contigs[i] for j in range (i+1, len(contigs)): contigj = contigs[j] if probMatrix[i][j] <= cutoff: badGraph.add_edge(contigi, contigj) nx.draw_graphviz(badGraph) plt.savefig(out_tag + "_disallowededges.png") plt.clf() return badGraph
def disallowedEdges(probMatrix, contigs, cutoff=0.001):
badGraph = nx.Graph()
for i in range(len(contigs)):
contigi = contigs[i]
for j in range(i + 1, len(contigs)):
contigj = contigs[j]
if probMatrix[i][j] <= cutoff:
badGraph.add_edge(contigi, contigj)
nx.draw_graphviz(badGraph)
plt.savefig(out_tag + "_disallowededges.png")
plt.clf()
return badGraph
def plot_networkx_graph(graph, show=True):
plt.figure()
objs, labels, colors = zip(*[(d[0], d[1]['label'], d[1]['color'])
for d in graph.nodes_iter(data=True)])
nx.draw_graphviz(graph,
font_size=10,
iteration=200,
node_color=colors,
scale=1,
labels=dict(zip(objs, labels)))
if show:
plt.show()
def display(self, graph: nx.Graph = None):
online = False
if graph == None:
online = True
graph = self
plt.title('discovery') # hard code the title
nx.draw_graphviz(graph) # expand using spectral layout
#nx.draw_networkx_edge_labels(graph, pos=nx.spectral_layout(self)) # draw labels
#nx.draw_networkx_nodes(graph, pos=nx.graphviz_layout(self), nodelist=[self.head], node_color='g')
if online:
self.head.fullGraph.fig.canvas.draw()
plt.show(block=False) # show non blocking
def draw_landscape(self):
for config in range(2**self.number_genes):
self.landscape.add_node(bin(config))
for config in range(2**self.number_genes):
self.set_configuration(config)
next_config = self.change_state()
next_config.reverse()
bla = [str(x) for x in next_config]
bina = '0b' + ''.join(bla)
self.landscape.add_edge(bin(config), bina)
nx.draw_graphviz(self.landscape)
plt.show()
def displayGraph(self, g, label=False):
axon, sd = axon_dendrites(g)
sizes = node_sizes(g) * 50
if len(sizes) == 0:
print('Empty graph for cell. Make sure proto file has `*asymmetric` on top. I cannot handle symmetric compartmental connections')
return
node_colors = ['k' if x in axon else 'gray' for x in g.nodes()]
lw = [1 if n.endswith('comp_1') else 0 for n in g.nodes()]
self.axes.clear()
try:
nx.draw_graphviz(g, ax=self.axes, prog='twopi', node_color=node_colors, lw=lw)
except (NameError, AttributeError) as e:
nx.draw_spectral(g, ax=self.axes, node_color=node_colors, lw=lw, with_labels=False, )
def draw_network_load(topology, result, filename, plotdir):
"""Draw topology with node colors according to stack and node size and link
color according to server/cache hits and link loads.
Nodes are colored according to COLORMAP. Edge are colored on a blue-red
scale where blue means min link load and red means max link load.
Sources and caches have variable size proportional to their hit ratios.
Parameters
----------
topology : Topology
The topology to draw
result : Tree
The tree representing the specific experiment result from which metric
are read
plotdir : string
The directory onto which draw plots
filename : string
The name of the image file to save
"""
stack = stack_map(topology)
node_color = [COLORMAP[stack[v]] for v in topology.nodes_iter()]
node_min = 50
node_max = 600
hits = result["CACHE_HIT_RATIO"]["PER_NODE_CACHE_HIT_RATIO"].copy()
hits.update(result["CACHE_HIT_RATIO"]["PER_NODE_SERVER_HIT_RATIO"])
hits = np.array(
[hits[v] if v in hits else 0 for v in topology.nodes_iter()])
min_hits = np.min(hits)
max_hits = np.max(hits)
hits = node_min + (node_max - node_min) * (hits - min_hits) / (max_hits -
min_hits)
link_load = result["LINK_LOAD"]["PER_LINK_INTERNAL"].copy()
link_load.update(result["LINK_LOAD"]["PER_LINK_EXTERNAL"])
link_load = [
link_load[e] if e in link_load else 0 for e in topology.edges()
]
plt.figure()
nx.draw_graphviz(
topology,
node_color=node_color,
node_size=hits,
width=2.0,
edge_color=link_load,
edge_cmap=mpl.colors.LinearSegmentedColormap.from_list(
"bluered", ["blue", "red"]),
with_labels=False,
)
plt.savefig(os.path.join(plotdir, filename), bbox_inches="tight")
def draw_neighbor(G, compound):
tmp = G.neighbors(str(compound))
Gpart = nx.Graph()
print tmp
for x in tmp:
# print x
print G.edge[x][compound]["weight"]
Gpart.add_edge(x, compound, color=G.edge[x][compound]["color"], weight=G.edge[x][compound]["weight"])
nx.draw(Gpart)
name = "path_%s.png" % str(compound)
plt.savefig(name)
nx.draw_graphviz(Gpart)
name1 = "file_%s.dot" % str(compound)
nx.write_dot(Gpart, name1)
def visualize_rooted_tree(t, prog='dot'):
'''
Visualize tree graph using graphviz dot.
Parameters
----------
t : networkx.DiGraph
The tree to visualize.
Returns
-------
None
'''
nx.draw_graphviz(t, prog=prog, font_size=6, node_shape='s',
edge_color='gray', node_size=500, node_color='c')
def create_graphviz_files(inm, tps, path):
"""Write master data to .dot file for use in GVEdit
Args:
inm (Graph): Graph structure to be written to file
tps (Graph): Graph structure to be written to file
path (String): Path to save the created .dot files
Returns:
float: Void
"""
nx.draw_graphviz(inm)
nx.write(inm, path + "inm/inm.dot")
nx.draw_graphviz(tps)
nx.write(tps, path + "tps/tps.dot")
def draw_graph(self, subgraph=None):
"""Draw the DAG representing the topology of the SemiNFG.
:arg subgraph: (Optional) node names of subset of nodes to include in
drawing. Default is self.nodes. If not specified, all nodes are
graphed.
:type subgraph: set or list
.. note::
This method uses the :py:mod:`matplotlib.pyplot` and
:py:mod:`networkx` packages.
"""
G = nx.DiGraph()
if not subgraph:
nodelist = self.node_dict.values()
else:
nodelist = []
for name in subgraph:
nodelist.append(self.node_dict[name])
nodeset = set(nodelist)
for n in nodeset:
for child in nodeset.intersection(self.edges[n.name]):
G.add_edge(n.name, child.name)
pos = nx.spring_layout(G, iterations=100)
# nx.draw_networkx(G, pos)
fig = nx.draw_graphviz(G, prog='dot')
plt.show()
return fig
def drawTableGraph(self, DG=None, outputFnamePrefix=None):
"""
"""
sys.stderr.write(
"Drawing the reference graph of all tables in the graph ...\n")
import pylab, numpy
import matplotlib as mpl
counter = 0
for unDirectedSubG in nx.connected_component_subgraphs(
DG.to_undirected()):
#connected_component_subgraphs() can't work on directed graph.
#nx.draw_circular(DG,with_labels=False, alpha=0.5)
pylab.clf()
#get rid of all cushion around the figure
axe_pvalue = pylab.axes(
[0, 0, 1.0, 1.0],
frameon=False) #left gap, bottom gap, width, height
pylab.figure(axe_pvalue.figure.number)
#pylab.axis("off")
#pylab.figure(figsize=(100, 60))
layout = 'dot'
"""
pos = nx.graphviz_layout(DG, prog=layout)
nx.draw_networkx_edges(DG, pos, alpha=0.9, width=0.8)
nx.draw_networkx_nodes(DG, pos, alpha=0.9, width=0, linewidths=0.5, cmap=mpl.cm.jet, vmin=0, vmax=1.0)
"""
subG = DG.subgraph(unDirectedSubG.nodes())
#check toplogical sortable
sys.stderr.write("is component %s a DAG? %s.\n" %
(counter, nx.is_directed_acyclic_graph(subG)))
nx.draw_graphviz(subG,
prog=layout,
with_labels=True,
node_size=10,
font_size=1,
width=0.2,
linewidths=0.2,
alpha=0.5)
pylab.savefig('%s_graphviz_%s_component_%s.png' %
(outputFnamePrefix, layout, counter),
dpi=700)
counter += 1
sys.stderr.write(".\n")
def draw_stack_deployment(topology, filename, plotdir):
"""Draw a topology with different node colors according to stack
Parameters
----------
topology : Topology
The topology to draw
plotdir : string
The directory onto which draw plots
filename : string
The name of the image file to save
"""
stack = stack_map(topology)
node_color = [COLORMAP[stack[v]] for v in topology.nodes_iter()]
plt.figure()
nx.draw_graphviz(topology, node_color=node_color, with_labels=False)
plt.savefig(os.path.join(plotdir, filename), bbox_inches="tight")
def draw_graph(self, docName):
examples = inspec_data_reader()
docText = [text for filename, text, gold_labels in examples if filename == docName][0]
words = self.tokenize(docText) # Note: tuples if using a synFilter
cooccurrence_dict, G = self.create_graph(words)
G = G.to_undirected()
labels = nx.draw_graphviz(G, with_labels=True, edge_color='0.5', width=0.5, font_color='blue', node_color='0.5', node_size=800)
plt.show()
def drawGraph(sg):
protSet = set()
for ind, node in enumerate(sg.nodes()):
if node.startswith("R_"):
protSet.add(ind)
sizes = []
colors = []
for i in xrange(0,len(sg)):
if i in protSet:
sizes.append(200)
colors.append('r')
else:
sizes.append(50)
colors.append('b')
nx.draw_graphviz(sg, node_size = sizes, with_labels = False, node_color = colors)
def centrality_drawGraph(G, CentralMeasure):
color_values = [CentralMeasure[node] for node in G.nodes()]
size_values = [1000 * CentralMeasure[node] + 100 for node in G.nodes()]
nx.draw_graphviz(G,
cmap=plt.get_cmap('Blues'),
node_color=color_values,
vmin=0.0,
vmax=1.0,
edge_color='b',
with_labels=True,
node_size=size_values)
##nx.draw( G , """pos=nx.circular_layout(G), """ cmap = plt.get_cmap('YlGn') , node_color=values ,
## vmin = 0.0 , vmax = 1.0 , edge_color='b', with_labels = True , node_size=[v * 10000 for v in d.values()] )
plt.show()
def draw(self):
r""" Draw the system
Draw the system with graphviz.
Examples
--------
>>> import pylab as p
>>> motor = Component('M', 1e-4, 3e-2)
>>> powers = [Component('P{}'.format(i), 1e-6, 2e-4) for i in (0,1)]
>>> S = System()
>>> S['E'] = [powers[0], powers[1]]
>>> S[powers[0]] = S[powers[1]] = [motor]
>>> S[motor] = 'S'
>>> S.draw()
>>> p.show()
"""
nx.draw_graphviz(self._graph)
