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 plot_graph(self, run_name, condor_path):
# Graph one plots the memory utilization of various steps
plt.figure(1, figsize=(30, 30))
plt.axis("off")
plt.title(run_name + "/" + "Condor Memory Utilization Scheme")
pos = nx.graphviz_layout(self.G)
nx.draw_networkx(self.G, pos, k=10, with_labels=False, node_size=1500, node_color=self.memory_colors())
nx.draw_networkx_labels(self.G, pos, labels=self.memory_labels())
plt.savefig(condor_path + run_name + "/" + "CondorMemoryUtilization.png")
plt.clf()
# Graph two plots the CPU utilization of various steps
plt.figure(2, figsize=(30, 30))
plt.axis("off")
plt.title("Condor CPU Utilization Scheme")
pos = nx.graphviz_layout(self.G)
nx.draw_networkx(self.G, pos, k=10, with_labels=False, node_size=1500, node_color=self.cpu_colors())
nx.draw_networkx_labels(self.G, pos, self.cpu_labels())
plt.savefig(condor_path + run_name + "/" + "CondorCpuUtilization.png")
plt.clf()
# Graph three plots the GPU utilization of various steps
plt.figure(3, figsize=(30, 30))
plt.axis("off")
plt.title("Condor GPU Utilization Scheme")
pos = nx.graphviz_layout(self.G)
nx.draw_networkx(self.G, pos, k=10, with_labels=False, node_size=1500, node_color=self.gpu_colors())
nx.draw_networkx_labels(self.G, pos, self.gpu_labels())
plt.savefig(condor_path + run_name + "/" + "CondorGpuUtilization.png")
plt.clf()
def position_friends(request, engine, width, height, widthoffset=0, auto = False):
print "Positioning friends"
width = width-widthoffset
ratio = float(width)/float(height)
dajax = Dajax()
s = request.session
user = s['fbuser']
print "Got fbuser object"
p = Person.objects.get(id = user.id)
print "AJAX checking if connections_ready = " +str(p.connections_ready)
#Check if the connections are ready to be returned; if not tell client to wait 5s
if (not p.connections_ready):
print "Connections not ready"
dajax.add_data({'time':3000, 'auto':auto}, 'grapher.waitToPosition')
return dajax.json()
nodes = user.get_friend_ids()
print "Got nodes"
links = user.get_friends_links()
print "Got links"
#print links
G_wrapper = GraphWrapper(nodes, links)
G = G_wrapper.G
print "Producing layout"
if (engine=="sfdp"):
layout=nx.graphviz_layout(G, prog='sfdp', args="-Gratio=%f -GK=1.5" % (ratio))
elif (engine=="twopi"):
layout=nx.graphviz_layout(G, prog='twopi', args="-Gratio=%f" % ratio)
else:
#Invalid layout engine?
return dajax.json()
x_coords = [p[0] for p in layout.values()]
y_coords = [p[1] for p in layout.values()]
min_x = min(x_coords)
max_x = max(x_coords)
min_y = min(y_coords)
max_y = max(y_coords)
x_shift = -1 * min_x
y_shift = -1*min_y
x_scale = float(width-20)/float(max_x-min_x)
y_scale = float(height-20)/float(max_y-min_y)
scaled_layout = {}
for node in layout:
x = int((layout[node][0] + x_shift) * x_scale)+10+widthoffset
y = int((layout[node][1] + y_shift) * y_scale)+10
scaled_layout[node] = (x,y)
#print scaled_layout
if auto:
dajax.add_data(scaled_layout, 'grapher.auto_set_positions')
else:
dajax.add_data(scaled_layout, 'grapher.set_positions')
return dajax.json()
def draw_communities(community_graph, filename):
def get_color(value):
value *= 1000
return "#" + format(int(value * 99999999999999), 'x')[:6]
size = float(len(set(community_graph.partition.values())))
g_layout = community_graph.graph.copy()
# print community_graph.partition
# g_layout.remove_edges_from([
# e for e in g_layout.edges_iter()
# if (community_graph.partition[e[0]] != community_graph.partition[e[1]] and random.random()<.997)
# ])
pos = nx.graphviz_layout(g_layout, prog='sfdp')
# pos = nx.spring_layout(g_layout)
# print [(community_graph.partition[n1], community_graph.partition[n2]) for n1,n2 in g_layout.edges_iter()]
count = 0.
for com in set(community_graph.partition.values()):
count = count + 1.
list_nodes = [
nodes for nodes in community_graph.partition.keys()
if community_graph.partition[nodes] == com
]
# print get_color(count/ size)
nx.draw_networkx_nodes(community_graph.graph,
pos,
list_nodes,
node_size=100,
node_color=get_color(count / size))
nx.draw_networkx_edges(community_graph.graph, pos, alpha=0.2)
fig.savefig(filename + '.png')
def draw(self):
if os.path.exists(self.service_dep_file_path):
graph = nx.DiGraph()
service_dep_file_handle = open(self.service_dep_file_path, 'r')
try:
raw_connection_instance = service_dep_file_handle.readline()
while raw_connection_instance:
connection_instance = raw_connection_instance.strip('\n')
sub_str = connection_instance.split(' ')
source_sub_str = sub_str[0]
destination_sub_str = sub_str[1]
localhost = sub_str[0].split(',')
if len(destination_sub_str) == 0:
destination_sub_str = 'remote process'
graph.add_node(source_sub_str)
graph.add_node(destination_sub_str)
graph.add_edge(source_sub_str, destination_sub_str)
graph.add_edge(source_sub_str, localhost[0])
raw_connection_instance = service_dep_file_handle.readline(
)
# Set the attributes of the grpah
# pos=nx.spring_layout(graph,dim=2,iterations=5)
pos = nx.graphviz_layout(graph, prog='dot')
nx.draw(graph, pos, edge_color='b', font_size=15)
plt.show()
finally:
service_dep_file_handle.close()
def draw(self, figsize=None):
"""Draw game tree."""
plt.figure(figsize=figsize)
try:
pos = nx.graphviz_layout(self.tree, prog='dot')
except AttributeError:
pos = nx.nx_pydot.graphviz_layout(self.tree, prog='dot')
# leaf (terminal) nodes
leaf_nodes = [node for node in self.tree.nodes() if self.is_leaf(node)]
nx.draw_networkx_nodes(self.tree, pos, nodelist=leaf_nodes,
enode_shape='s', node_color="k")
# decision nodes
dec_nodes = [node
for node in self.tree.nodes() if not self.is_leaf(node)]
nx.draw_networkx_nodes(
self.tree, pos, nodelist=dec_nodes, node_shape='o',
node_color=[self.PLAYER_COLORS[self.tree.node[node]["player"]]
for node in dec_nodes])
# labelled edges
nx.draw_networkx_edges(self.tree, pos, arrows=False)
nx.draw_networkx_edge_labels(self.tree, pos,
edge_labels=self.edge_labels)
def latLonNxGraph(inGraph, labels=False, neatoLayout=False, showPlot=False):
''' Draw a networkx graph representing a feeder.'''
plt.axis('off')
plt.tight_layout()
# Layout the graph via GraphViz neato. Handy if there's no lat/lon data.
if neatoLayout:
# HACK: work on a new graph without attributes because
# graphViz tries to read attrs.
cleanG = nx.Graph(inGraph.edges())
# HACK2: might miss nodes without edges without the following.
cleanG.add_nodes_from(inGraph)
pos = nx.graphviz_layout(cleanG, prog='neato')
else:
pos = {n: inGraph.node[n].get('pos', (0, 0)) for n in inGraph}
# Draw all the edges.
for e in inGraph.edges():
eType = inGraph.edge[e[0]][e[1]].get('type', 'underground_line')
ePhases = inGraph.edge[e[0]][e[1]].get('phases', 1)
standArgs = {
'edgelist': [e],
'edge_color': _obToCol(eType),
'width': 2,
'style': {
'parentChild': 'dotted',
'underground_line': 'dashed'
}.get(eType, 'solid')
}
if ePhases == 3:
standArgs.update({'width': 5})
nx.draw_networkx_edges(inGraph, pos, **standArgs)
standArgs.update({'width': 3, 'edge_color': 'white'})
nx.draw_networkx_edges(inGraph, pos, **standArgs)
standArgs.update({'width': 1, 'edge_color': _obToCol(eType)})
nx.draw_networkx_edges(inGraph, pos**standArgs)
if ePhases == 2:
standArgs.update({'width': 3})
nx.draw_networkx_edges(inGraph, pos, **standArgs)
standArgs.update({'width': 1, 'edge_color': 'white'})
nx.draw_networkx_edges(inGraph, pos, **standArgs)
else:
nx.draw_networkx_edges(inGraph, pos, **standArgs)
# Draw nodes and optional labels.
nx.draw_networkx_nodes(inGraph,
pos,
nodelist=pos.keys(),
node_color=[
_obToCol(inGraph.node[n].get(
'type', 'underground_line'))
for n in inGraph
],
linewidths=0,
node_size=40)
if labels:
nx.draw_networkx_labels(inGraph,
pos,
font_color='black',
font_weight='bold',
font_size=0.25)
if showPlot:
plt.show()
def plot_cell_topology(cell, label=False):
g = cell_to_graph(cell, label=label)
axon, sd = axon_dendrites(g)
node_size = node_sizes(g)
weights = np.array([g.edge[e[0]][e[1]]['weight'] for e in g.edges()])
try:
pos = nx.graphviz_layout(g, prog='twopi', root=cell.path + '/comp_1')
except (NameError, AttributeError) as e:
# this is the best networkx can do by itself. Its Furchtman
# Reingold layout ends up with overlapping edges even for a
# tree. igraph does much better.
pos = nx.spectral_layout(g)
nx.draw_networkx_edges(g,
pos,
width=10 * weights / max(weights),
edge_color='gray',
alpha=0.8)
nx.draw_networkx_nodes(
g,
pos,
with_labels=False,
nnode_size=node_size * 500,
node_color=['k' if x in axon else 'gray' for x in g.nodes()],
linewidths=[1 if n.endswith('comp_1') else 0 for n in g.nodes()],
alpha=0.8)
if label:
labels = dict([(n, g.node[n]['label']) for n in g.nodes()])
nx.draw_networkx_labels(g, pos, labels=labels)
plt.title(cell.__class__.__name__)
def print_pdf_graph(file_f, regulon, conn):
pdf = PdfPages(file_f)
edgesLimits = [50, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000]
#CRP = regulon_set['LexA']
for lim in edgesLimits:
print lim
g = buildSimilarityGraph_top_10_v2(conn, lim)
# Here the node is motif, eg 87878787_1, the first 8 digits represent gi
node_color = [1 if node[0:8] in regulon else 0 for node in g]
pos = nx.graphviz_layout(g, prog="neato")
plt.figure(figsize=(10.0, 10.0))
plt.axis("off")
nx.draw(g,
pos,
node_color=node_color,
node_size=20,
alpha=0.8,
with_labels=False,
cmap=plt.cm.jet,
vmax=1.0,
vmin=0.0)
pdf.savefig()
plt.close()
pdf.close()
def draw_adjacency_graph(adjacency_matrix,
node_color=None,
size=10,
layout='graphviz',
prog='neato',
node_size=80,
colormap='autumn'):
graph = nx.from_scipy_sparse_matrix(adjacency_matrix)
plt.figure(figsize=(size, size))
plt.grid(False)
plt.axis('off')
if layout == 'graphviz':
pos = nx.graphviz_layout(graph, prog=prog)
else:
pos = nx.spring_layout(graph)
if len(node_color) == 0:
node_color = 'gray'
nx.draw_networkx_nodes(graph,
pos,
node_color=node_color,
alpha=0.6,
node_size=node_size,
cmap=plt.get_cmap(colormap))
nx.draw_networkx_edges(graph, pos, alpha=0.5)
plt.show()
def plot(self):
if self.pos == None:
self.pos = nx.graphviz_layout(self)
NODE_SIZE = 500
plt.clf()
nx.draw_networkx_nodes(self, pos=self.pos,
nodelist=self.normal,
node_color=NORMAL_COLOR,
node_size=NODE_SIZE)
nx.draw_networkx_nodes(self, pos=self.pos,
nodelist=self.contam,
node_color=CONTAM_COLOR,
node_size=NODE_SIZE)
nx.draw_networkx_nodes(self, pos=self.pos,
nodelist=self.immune,
node_color=IMMUNE_COLOR,
node_size=NODE_SIZE)
nx.draw_networkx_nodes(self, pos=self.pos,
nodelist=self.dead,
node_color=DEAD_COLOR,
node_size=NODE_SIZE)
nx.draw_networkx_edges(self, pos=self.pos,
edgelist=self.nondead_edges(),
width=2,
edge_color='0.2')
nx.draw_networkx_labels(self, pos=self.pos,
font_color='0.95', font_size=11)
plt.gca().get_xaxis().set_visible(False)
plt.gca().get_yaxis().set_visible(False)
plt.draw()
def datatypes(self, pattern='*', save=None):
graph = self.get_graph.datatypes(pattern)
plt.figure(figsize=(8, 8))
pos = nx.graphviz_layout(graph, prog='twopi', args='')
cost = lambda v: float(graph.degree(v)) ** 3 + \
graph.weights[v] ** 2
node_size = [cost(v) for v in graph]
# node_size = [graph.weights[v] ** 2 for v in graph]
# node_color = [float(graph.degree(v)) for v in graph]
node_color = [cost(v) for v in graph]
nx.draw(graph,
pos,
node_size=node_size,
node_color=node_color,
font_size=13,
font_color='green',
font_weight='bold',
with_labels=True)
plt.axis('off')
if save is not None:
plt.savefig(save)
plt.show()
def field_values(self, field_name, save=None):
graph = self.get_graph.field_values(field_name)
plt.figure(figsize=(8, 8))
pos = nx.graphviz_layout(graph, prog='twopi', args='')
cost = lambda v: graph.weights[v]
graph.weights[field_name] = max([cost(v) for v in graph]) / 2.0
costs = norm_costs([cost(v) for v in graph], 10000)
nx.draw(graph,
pos,
node_size=costs,
node_color=costs,
font_size=13,
font_color='black',
font_weight='bold',
with_labels=True)
plt.axis('off')
if save is not None:
plt.savefig(save)
plt.show()
def _draw_rest_resource(self, graph, save=None):
plt.figure(figsize=(8, 8))
pos = nx.graphviz_layout(graph, prog='twopi', args='')
cost = lambda v: float(graph.degree(v)) ** 3 + \
graph.weights[v] ** 2
node_size = [cost(v) for v in graph]
# node_size = [graph.weights[v] ** 2 for v in graph]
# node_color = [float(graph.degree(v)) for v in graph]
node_color = [cost(v) for v in graph]
nx.draw(graph,
pos,
node_size=node_size,
node_color=node_color,
font_size=13,
font_color='green',
font_weight='bold')
plt.axis('off')
if save is not None:
plt.savefig(save)
plt.show()
def prettyPlotter(self, l=None, filename=None):
"""
Metod służy do rysowania grafu reprezentującego sieć, w którym węzły należące do tej samej grupy są oznaczone jednym kolorem.
@type l: list
@param l: lista list węzłów podzielonych na grupy
@param filename: nazwa pliku PNG z wynikowym obrazem
"""
nodeList = l
colorList = [0] * self.graph.number_of_nodes()
for nbunch in nodeList:
for n in nbunch:
colorList[self.graph.nodes().index(n)] = nodeList.index(nbunch)
import matplotlib.pyplot as plt
pos = nx.graphviz_layout(self.graph, prog='neato')
plt.figure()
plt.axis('off')
nx.draw(self.graph, pos, node_color=colorList, with_labels=False)
if filename:
import config
filename = config.PLOT_OUT_DIR+filename
plt.savefig(filename)
else:
plt.show()
def image_from_graph(graph, layout_prog='dot', outpath=None):
"""
Create an image from a :class:`NetworkX.DiGraph`.
The standard image is a PNG image, with a standard *tree* look.
The image type is based on the extension of the file specified by
outpath.
:param root_node: The root of the Hierarchy.
:type root_node: GSGroup or GSHierarchy
:param layout_prog: The graphviz layout program to use to layout
the Hierarchy AGraph before drawing.
:type layout_prog: string
"""
if outpath is None:
outpath = "%s_nxgraph.svg" % root_node.get_name()
#
# Commented out here is the code that creates the Image
# by using the graphviz backend of NetworkX.
#
# agraph = nx.to_agraph(graph)
# agraph.graph_attr.update(**graph_wide_settings)
# agraph.node_attr.update(**node_settings)
# agraph.edge_attr.update(**edge_settings)
# agraph.layout(prog=layout_prog)
# agraph.draw('other_'+outpath)
pos=nx.graphviz_layout(graph, prog=layout_prog)
plt.figure(**figure_settings)
nx.draw(graph, pos, node_color=range(graph.number_of_nodes()),
**drawing_settings)
plt.savefig(outpath)
def draw_graph(self, my_list=None):
# pos = nx.spring_layout(self.G, iterations=20)
pos = nx.graphviz_layout(self.G, prog='sfdp', root='downlod_0', args="-Grankdir=LR")
#pos = nx.spring_layout(self.G,fixed = ['r1_c1'], scale=2)
#print(pos)
#pos = self.h_recur(self.G, 'download_0')
#nx.draw(self.G, pos=pos, with_labels=True)
for node in self.G.nodes():
self.G.node[node]['category'] = 'type_A'
if my_list:
for my_nodes2 in my_list:
self.G.node[my_nodes2]['category'] = 'critical_path'
color_map = {'type_A':'y', 'critical_path':'#FFC266'}
## construct a list of colors then pass to node_color
if my_list:
nx.draw(self.G, pos=pos, node_size=200, node_color=[color_map[self.G.node[node]['category']] for node in self.G])
else:
nx.draw(self.G, pos=pos, node_size=200, node_color='y')
node_labels = nx.get_node_attributes(self.G, 'id')
nx.draw_networkx_labels(self.G, pos, labels=node_labels, font_size=8, font_color='r', font_weight='bold',
font_family='sans-serif')
"""edge_labels = nx.get_edge_attributes(self.G, 'weight')
for key, value in edge_labels.items():
edge_labels[key] = round(value, 4)"""
#print('edge lbl', edge_labels)
#nx.draw_networkx_edge_labels(self.G, pos, labels=edge_labels)
plt.show()
def KernelQuickShiftTreeEmbedding(X, knn=10, knn_density=None, k_threshold=0.9, metric='linear', **args):
if knn_density is None:
knn_density = knn
n_instances = X.shape[0]
# extract pairwise similarity matrix with desired kernel
from sklearn import metrics
K = metrics.pairwise.pairwise_kernels(X, metric=metric, **args)
# compute instance density as average pairwise similarity
import numpy as np
density = np.sum(K, 0) / n_instances
# compute list of nearest neighbors
Ka = np.argsort(-K)
# make matrix of densities ordered by nearest neighbor
Kad = density[Ka]
parent_dict = {}
# for all instances determine parent link
for i, row in enumerate(Kad):
i_density = row[0]
# if a densed neighbor cannot be found then assign parent to the instance itself
parent_dict[i] = i
# for all neighbors from the closest to the furthest
for jj, d in enumerate(row):
# proceed until k neighbors have been explored
if jj > knn_density:
break
j = Ka[i, jj]
if jj > 0:
j_density = d
# if the density of the neighbor is higher than the density of the instance assign parent
if j_density > i_density:
parent_dict[i] = j
break
# make a graph with instances as nodes
import networkx as nx
G = nx.Graph()
G.add_nodes_from(range(n_instances))
# add edge between instance and parent
for i in range(n_instances):
j = parent_dict[i]
G.add_edge(i, j, weight=1)
# determine threshold as k-th quantile on pairwise similarity on the knn similarity
knn_similarities = K[Ka[:, knn]]
# vectorized_pairwise_similarity = np.ravel(K)
k_quantile = np.percentile(knn_similarities, k_threshold * 100)
# add edge between instance and k-th nearest neighbor if similarity > threshold
for i in range(n_instances):
# id of k-th nearest neighbor
jd = Ka[i, knn]
# similarity of k-th nearest neighbor
kd = K[i, jd]
if kd > k_quantile:
G.add_edge(i, jd, weight=1)
# use graph layout algorithm to determine coordinates
X_ = nx.graphviz_layout(G, prog='sfdp', args='-Goverlap=scale')
X_2D = []
for i in range(K.shape[0]):
X_2D.append(list(X_[i]))
X_emb = np.array(X_2D)
from sklearn.preprocessing import scale
return scale(X_emb)
def generate_network(G, output_path, network_data):
initial_pos = nx.graphviz_layout(G, prog='neato')
pos = forceatlas2.forceatlas2_networkx_layout(
G,
pos=initial_pos,
niter=100,
gravity=.12,
strongGravityMode=True,
scalingRatio=5.0) # Optionally specify iteration count
probable_reservoirs = []
min_x = min_y = float('inf')
max_x = max_y = float('-inf')
for node in pos:
if (min_x > pos[node][0]) and (G.degree(node) == 1):
min_x = pos[node][0]
probable_reservoirs.append(node)
if (max_x < pos[node][0]) and (G.degree(node) == 1):
max_x = pos[node][0]
probable_reservoirs.append(node)
if (min_y > pos[node][1]) and (G.degree(node) == 1):
min_y = pos[node][1]
probable_reservoirs.append(node)
if (max_y < pos[node][1]) and (G.degree(node) == 1):
max_y = pos[node][1]
probable_reservoirs.append(node)
random_index = randrange(int(len(probable_reservoirs) / 2),
len(probable_reservoirs))
reservoir = probable_reservoirs[random_index]
node_colors = [
'blue' if node == reservoir else 'red' for node in G.nodes()
]
nx.draw(G, pos, with_labels=False, node_color=node_colors, node_size=50)
plt.show()
write_inp_file(G, pos, output_path, network_data, output_path)
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 architecture(self, with_datatypes=True, save=None):
graph = self.get_graph.architecture(with_datatypes)
plt.figure(figsize=(8,8))
pos = nx.graphviz_layout(graph, prog='twopi', args='')
# node_size = [(float(graph.degree(v)) * 5)**3 for v in graph]
# node_size = [graph.weights[v] ** 2 for v in graph]
# node_color = [float(graph.degree(v)) for v in graph]
# node_color = [graph.weights[v] ** 2 for v in graph]
cost = lambda v: float(graph.degree(v)) ** 3 + \
graph.weights[v] ** 2
costs = norm_costs([cost(v) for v in graph], 10000)
nx.draw(graph, pos, labels=graph.labels,
node_size=costs, node_color=costs,
font_size=13, font_color='orange', font_weight='bold'
)
plt.axis('off')
if save is not None:
plt.savefig(save)
plt.show()
def plot(self, graph_type):
# pypy doesn't support matplotlib and networkx, :(
import matplotlib.pyplot as plt
import networkx as nx
nx_graph = nx.Graph()
for node in self.get_nodes().values():
nx_graph.add_node(node.get_id(), type=node.get_type())
for node in self.get_nodes().values():
for link in node.get_links():
end_point = link.get_end_points()
neighbor = end_point[0] if node.get_id() != end_point[0] else end_point[1]
nx_graph.add_edge(node.get_id(), neighbor, weight=100)
hosts = [(u) for (u, v) in nx_graph.nodes(data = True) if v["type"] == "host"]
switches = [(u) for (u, v) in nx_graph.nodes(data = True) if v["type"] == "switch"]
pos = nx.graphviz_layout(nx_graph)
# draw graph
nx.draw_networkx_nodes(nx_graph, pos, nodelist=switches, node_size=100, label="x")
nx.draw_networkx_nodes(nx_graph, pos, nodelist=hosts, node_size=50, node_color='b')
nx.draw_networkx_edges(nx_graph, pos)
plt.savefig("graphs/" + graph_type + '.png')
plt.clf()
def export_graph(
graph_in,
base_dir=None,
show=False,
use_execgraph=False,
show_connectinfo=False,
dotfilename="graph.dot",
format="png",
simple_form=True,
):
""" Displays the graph layout of the pipeline
This function requires that pygraphviz and matplotlib are available on
the system.
Parameters
----------
show : boolean
Indicate whether to generate pygraphviz output fromn
networkx. default [False]
use_execgraph : boolean
Indicates whether to use the specification graph or the
execution graph. default [False]
show_connectioninfo : boolean
Indicates whether to show the edge data on the graph. This
makes the graph rather cluttered. default [False]
"""
graph = deepcopy(graph_in)
if use_execgraph:
graph = generate_expanded_graph(graph)
logger.debug("using execgraph")
else:
logger.debug("using input graph")
if base_dir is None:
base_dir = os.getcwd()
if not os.path.exists(base_dir):
os.makedirs(base_dir)
outfname = fname_presuffix(dotfilename, suffix="_detailed.dot", use_ext=False, newpath=base_dir)
logger.info("Creating detailed dot file: %s" % outfname)
_write_detailed_dot(graph, outfname)
cmd = "dot -T%s -O %s" % (format, outfname)
res = CommandLine(cmd, terminal_output="allatonce").run()
if res.runtime.returncode:
logger.warn("dot2png: %s", res.runtime.stderr)
pklgraph = _create_dot_graph(graph, show_connectinfo, simple_form)
outfname = fname_presuffix(dotfilename, suffix=".dot", use_ext=False, newpath=base_dir)
nx.write_dot(pklgraph, outfname)
logger.info("Creating dot file: %s" % outfname)
cmd = "dot -T%s -O %s" % (format, outfname)
res = CommandLine(cmd, terminal_output="allatonce").run()
if res.runtime.returncode:
logger.warn("dot2png: %s", res.runtime.stderr)
if show:
pos = nx.graphviz_layout(pklgraph, prog="dot")
nx.draw(pklgraph, pos)
if show_connectinfo:
nx.draw_networkx_edge_labels(pklgraph, pos)
def __init__(self, pipeline):
self.pipeline = pipeline
self.graph = pipeline.graph
type2color = dict(file = "orange", topic="red", program="blue")
self.colors = [type2color[self.graph.node[node]["type"]] for node in self.graph.nodes()]
self.positions = nx.graphviz_layout(self.graph, prog="dot")
self.labels = dict(zip(self.graph.nodes(), self.graph.nodes()))
def colorSubcomps(self, G, prog="neato", layout=None):
C = nx.connected_component_subgraphs(G)
pos = nx.graphviz_layout(G, prog=prog) if layout == None else layout
for g in C:
c = [random.random()] * nx.number_of_nodes(g) # random color...
nx.draw(g, pos, node_size=40, node_color=c, vmin=0.0, vmax=1.0, with_labels=False)
def make_tree_figure(wanted_seqs, trop_dict, tree_file):
mat_data = get_pairwise_distances(wanted_seqs, tree_file = tree_file)
tree = Phylo.read(open(tree_file), 'newick')
net = Phylo.to_networkx(tree)
node_mapping = {}
clade = 1
for node in net.nodes():
if node.name is None:
node_mapping[node] = 'Clade-%i' % clade
clade += 1
else:
node_mapping[node] = node.name
new_net = networkx.relabel_nodes(net, node_mapping)
colors = []
for node in new_net.nodes():
if node.startswith('Clade'):
colors.append('w')
elif trop_dict[node]:
colors.append('g')
elif not trop_dict[node]:
colors.append('r')
else:
print node
#print colors, len(colors), len(new_net.nodes())
pos = networkx.graphviz_layout(new_net, 'twopi')
networkx.draw_networkx(new_net, pos, with_labels = False, node_color = colors)
def plot_func(play, stats):
generation = stats["generation"]
best = stats["generation_best"]
every = play.config["live_plot"].get("every", 100)
if generation == 0:
plt.figure(figsize=(10, 8))
if (generation % every) == 0:
plt.clf()
# create graph
graph = nx.DiGraph()
traverse_tree(best.root, graph)
labels = dict((n, d["label"]) for n, d in graph.nodes(data=True))
pos = nx.graphviz_layout(graph, prog='dot')
nx.draw(
graph,
pos,
with_labels=True,
labels=labels,
arrows=False,
node_shape=None
)
# plot graph
plt.draw()
plt.pause(0.0001) # very important else plot won't be displayed
def print_pdf_graph(file_f, regulon, conn):
pdf = PdfPages(file_f)
edgesLimits = [50, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000]
#CRP = regulon_set['LexA']
for lim in edgesLimits:
print lim
g = buildSimilarityGraph_top_10_v2(conn, lim)
# Here the node is motif, eg 87878787_1, the first 8 digits represent gi
node_color = [ 1 if node[0:8] in regulon else 0 for node in g ]
pos = nx.graphviz_layout(g, prog="neato")
plt.figure(figsize=(10.0, 10.0))
plt.axis("off")
nx.draw(g,
pos,
node_color = node_color,
node_size = 20,
alpha=0.8,
with_labels=False,
cmap=plt.cm.jet,
vmax=1.0,
vmin=0.0
)
pdf.savefig()
plt.close()
pdf.close()
def as_path_plot(self,as_path, prefix):
'''This method accpets the list of as-path to plot for the
selected prefix as per the GUI. It uses networkx module.
The Nodes represent the AS and the edges represent the
AS connectivity.
'''
G = nx.Graph()
l= as_path
for i,a in enumerate(l):
#print a, type(a), len(a)
G.add_node(a[-1])
for i in xrange(len(a)):
if i == len(a)-1:
break
G.add_node(a[i])
G.add_edge(a[i],a[i+1])
# plt.title("draw_networkx")
# plt.savefig('abcd.png',dpi=500, facecolor='w', edgecolor='w',orientation='landscape', papertype=None, format=None,transparent=False, bbox_inches=None, pad_inches=0.8)
plt.title("AS Path for "+prefix)
pos=nx.graphviz_layout(G,prog='dot')
plt.figure(1,figsize=(10,8))
#nx.draw_networkx(G,pos)
nx.draw(G,pos,with_labels=True,arrows=True, font_size = 9,edge_color='r', node_color='b')
plt.savefig(prefix+'_as_path.png',pad_inches = 0.8)
plt.show()
def draw(graph):
pos = nx.graphviz_layout(graph, prog='sfdp', args='')
list_nodes = graph.nodes()
plt.figure(figsize=(20,10))
nx.draw(graph, pos, node_size=20, alpha=0.4, nodelist=list_nodes, node_color="blue", with_labels=False)
plt.savefig('graphNX.png')
plt.show()
def renameSrcDst(orig_g, s, d, layout=None):
'''
currently only undirected "g" are supported.
change the nodes s and d into nodes "Src" and "Dst". of course
edges are preserved.
TODO: make sure that attributes of nodes and their edges are
preserved, too.
return a new g and its updated (if "layout" was specified) layout.
'''
g = orig_g.copy()
if layout:
import copy
g_layout = copy.copy(layout)
pass
else:
g_layout = nx.graphviz_layout(g)
pass
for oldlabel, newlabel in ((s, 'Src'),
(d, 'Dst')):
for neighbor in g.neighbors(oldlabel):
g.add_edge(newlabel, neighbor)
pass
g.remove_node(oldlabel)
g_layout[newlabel]=g_layout[oldlabel]
del g_layout[oldlabel]
pass
return g, g_layout
def erd(self, subset=None, prog='dot'):
"""
plot the schema's entity relationship diagram (ERD).
The layout programs can be 'dot' (default), 'neato', 'fdp', 'sfdp', 'circo', 'twopi'
"""
if not subset:
g = self.graph
else:
g = self.graph.copy()
for i in g.nodes():
if i not in subset:
g.remove_node(i)
def tableList(tier):
return [i for i in g if self.tables[i].tier==tier]
pos=nx.graphviz_layout(g,prog=prog,args='')
plt.figure(figsize=(8,8))
nx.draw_networkx_edges(g, pos, alpha=0.3)
nx.draw_networkx_nodes(g, pos, nodelist=tableList('manual'),
node_color='g', node_size=200, alpha=0.3)
nx.draw_networkx_nodes(g, pos, nodelist=tableList('computed'),
node_color='r', node_size=200, alpha=0.3)
nx.draw_networkx_nodes(g, pos, nodelist=tableList('imported'),
node_color='b', node_size=200, alpha=0.3)
nx.draw_networkx_nodes(g, pos, nodelist=tableList('lookup'),
node_color='gray', node_size=120, alpha=0.3)
nx.draw_networkx_labels(g, pos, nodelist = subset, font_weight='bold', font_size=9)
nx.draw(g,pos,alpha=0,with_labels=False)
plt.show()
def startGame(self):
print "starting game", self.configuration
self.startNode = [loadGame(self.configuration).toString()]
threads = []
for i in range(10):
t = threading.Thread(target=self.ant, args=(i,))
threads.append(t)
t.start()
print "Waiting..."
for t in threads:
t.join()
print "Waiting done"
# nx.draw(self.stateSpace)
myBoard = loadGame(self.configuration).toString()
pos=nx.graphviz_layout(self.stateSpace,prog='neato')
nx.draw(self.stateSpace,pos=pos,node_size=20)
nx.draw_networkx_nodes(self.stateSpace,pos,
nodelist=[myBoard],
node_color='g',
node_size=100)
nx.draw_networkx_nodes(self.stateSpace,pos,
nodelist=list(self.winNodes),
node_color='b',
node_size=100)
# A = nx.to_agraph(self.stateSpace)
# A.layout(prog='neato', color='red')
# A.draw('color.png')
plt.show()
def draw_adjacency_graph(adjacency_matrix,
node_color=None,
size=10,
layout='graphviz',
prog='neato',
node_size=80,
colormap='autumn'):
"""draw_adjacency_graph."""
graph = nx.from_scipy_sparse_matrix(adjacency_matrix)
plt.figure(figsize=(size, size))
plt.grid(False)
plt.axis('off')
if layout == 'graphviz':
pos = nx.graphviz_layout(graph, prog=prog)
else:
pos = nx.spring_layout(graph)
if len(node_color) == 0:
node_color = 'gray'
nx.draw_networkx_nodes(graph, pos,
node_color=node_color,
alpha=0.6,
node_size=node_size,
cmap=plt.get_cmap(colormap))
nx.draw_networkx_edges(graph, pos, alpha=0.5)
plt.show()
def Main():
G = kw2Jd(searchterm)
H = nx.Graph(G)
font = {'fontname': 'Arial',
'color': 'k',
'fontweight': 'bold',
'fontsize': 14}
plt.rcParams['text.usetex'] = False
plt.figure(figsize=(8, 8))
plt.title("PZ", font)
plt.axis('off')
plt.text(0.5, .95, searchterm,
horizontalalignment='center', transform=plt.gca().transAxes)
try:
pos = nx.graphviz_layout(H)
except:
pos = nx.spring_layout(H, iterations=20)
nx.draw_networkx_edges(
H, pos, alpha=0.1, node_size=0, edge_color='w', width=3)
nx.draw_networkx_labels(H, pos, fontsize=12)
nodesize = [salary.get(v, 0) ** 2 for v in H]
colorcoding = [supply.get(v, 10000) / (openings.get(v, 1) + 1) for v in H]
linewidths = [
(abs(trends.get(v, 0)) - trends.get(v, 10000)) / 10 for v in H]
nodes = H.nodes()
nx.draw_networkx_nodes(H, pos, nodelist=nodes, node_size=nodesize, linewidths=linewidths, cmap=plt.cm.Blues, vmin=min(
colorcoding), vmax=100, node_color=colorcoding)
plt.savefig("pz-networkx.png", dpi=75)
plt.show()
def draw(self):
if os.path.exists(self.service_dep_file_path):
graph=nx.DiGraph()
service_dep_file_handle=open(self.service_dep_file_path,'r')
try:
raw_connection_instance=service_dep_file_handle.readline()
while raw_connection_instance:
connection_instance=raw_connection_instance.strip('\n')
sub_str=connection_instance.split(' ')
source_sub_str=sub_str[0]
destination_sub_str=sub_str[1]
localhost=sub_str[0].split(',')
if len(destination_sub_str)==0:
destination_sub_str='remote process'
graph.add_node(source_sub_str)
graph.add_node(destination_sub_str)
graph.add_edge(source_sub_str,destination_sub_str)
graph.add_edge(source_sub_str,localhost[0])
raw_connection_instance=service_dep_file_handle.readline()
# Set the attributes of the grpah
# pos=nx.spring_layout(graph,dim=2,iterations=5)
pos=nx.graphviz_layout(graph,prog='dot')
nx.draw(graph,pos,edge_color='b',font_size=15)
plt.show()
finally:
service_dep_file_handle.close()
def getRandom():
try:
mydata = request.json
n=mydata["n"]
binary=mydata["binary"]
nested_taxa=mydata["nested_taxa"]
taxa = [str(i+1) for i in range(n)]
offsetId = mydata["offsetId"]
offsetx = mydata["offsetx"]
offsety = mydata["offsety"]
tg = tgr(taxa, binary=binary, nested_taxa=nested_taxa, id_offset=offsetId)
net = tg.next()
#net = pn()
#net._lastlabel = offsetId
#net.from_eNewick(eNewick)
#session['eNewick'] = eNewick
pos = nx.graphviz_layout(net, 'dot')
minx = min([pos[u][0] for u in net.nodes()])
miny = min([pos[u][1] for u in net.nodes()])
offsettoapplyx = minx - offsetx - 50
offsettoapplyy = miny - offsety
nodes = [{'id':u,'label': net.label(u), 'x':pos[u][0]-offsettoapplyx, 'y':-pos[u][1]+offsettoapplyy} for u in net.nodes()]
edges = [{'source':edge[0],'target':edge[1], 'type': 'arrow'} for edge in net.edges()]
dict = {'nodes':nodes,'edges':edges}
return jsonify(response=dict)
except Exception, err:
return jsonify(response={'error':'Some error occurred. Please chech your data. If you think this is a bug, please contact us (see About section)\n Error message: %s' % err})
def get(self, request, name="root", *args, **kwargs):
import networkx as nx
import matplotlib as mpl
mpl.use("Agg")
import matplotlib.pyplot as plt
import nxedges
plt.figure(figsize=(10, 8))
g = nx.DiGraph()
labels = {}
for service in Service.objects.all():
g.add_node(service.id)
if len(service.name) > 8:
labels[service.id] = service.name[:8] + "\n" + service.name[8:]
else:
labels[service.id] = service.name
for tenant in CoarseTenant.objects.all():
if (not tenant.provider_service) or (not tenant.subscriber_service):
continue
g.add_edge(tenant.subscriber_service.id, tenant.provider_service.id)
pos = nx.graphviz_layout(g)
nxedges.xos_draw_networkx_edges(g, pos, arrow_len=30)
nx.draw_networkx_nodes(g, pos, node_size=5000)
nx.draw_networkx_labels(g, pos, labels, font_size=12)
# plt.axis('off')
plt.savefig("/tmp/foo.png")
return HttpResponse(open("/tmp/foo.png", "r").read(), content_type="image/png")
def show_py(nodes, edges, width, height, outfile):
g = nx.Graph()
labels = dict()
colors = list()
sizes = list()
for e in nodes:
g.add_node(e["id"])
g.node[e["id"]]['color'] = e["color"]
g.node[e["id"]]['size'] = e["size"]
labels[e["id"]] = e["label"]
for e in edges:
g.add_edge(e["source"], e["target"])
for n in g.nodes():
colors.append(g.node[n]["color"])
sizes.append(g.node[n]["size"])
print "XXX 0"
plt.figure(figsize=(width,height));
print "XXX 1"
pos = nx.graphviz_layout(g)
print "XXX 2"
gx = nx.draw_networkx(g, pos, labels=labels, node_size=sizes, node_color=colors, font_size=7, font_color="black", edge_color="grey")
#gx = nx.draw_networkx(g, labels=labels, node_size=sizes, node_color=colors, font_size=7, font_color="black", edge_color="grey")
print "XXX 3"
s = plt.savefig(outfile, bbox_inches="tight")
#s = plt.savefig(outfile)
print "XXX 4"
def visualize(tree, custom_alpha=0.5, labels=False):
G = nx.Graph(tree)
pos = nx.graphviz_layout(G, prog='twopi', args='', root='root')
plt.figure(figsize=(10, 10))
nx.draw(G, pos, node_size=0, alpha=custom_alpha, node_color="blue", with_labels=labels)
plt.axis('equal')
plt.show()
def datatypes(self, pattern='*', save=None):
graph = self.get_graph.datatypes(pattern)
plt.figure(figsize=(8,8))
pos = nx.graphviz_layout(graph, prog='twopi', args='')
cost = lambda v: float(graph.degree(v)) ** 3 + \
graph.weights[v] ** 2
node_size = [cost(v) for v in graph]
# node_size = [graph.weights[v] ** 2 for v in graph]
# node_color = [float(graph.degree(v)) for v in graph]
node_color = [cost(v) for v in graph]
nx.draw(graph, pos,
node_size=node_size, node_color=node_color,
font_size=13, font_color='green', font_weight='bold'
)
plt.axis('off')
if save is not None:
plt.savefig(save)
plt.show()
def architecture(self, with_datatypes=True, save=None):
graph = self.get_graph.architecture(with_datatypes)
plt.figure(figsize=(8, 8))
pos = nx.graphviz_layout(graph, prog='twopi', args='')
# node_size = [(float(graph.degree(v)) * 5)**3 for v in graph]
# node_size = [graph.weights[v] ** 2 for v in graph]
# node_color = [float(graph.degree(v)) for v in graph]
# node_color = [graph.weights[v] ** 2 for v in graph]
cost = lambda v: float(graph.degree(v)) ** 3 + \
graph.weights[v] ** 2
costs = norm_costs([cost(v) for v in graph], 10000)
nx.draw(graph,
pos,
labels=graph.labels,
node_size=costs,
node_color=costs,
font_size=13,
font_color='orange',
font_weight='bold',
with_labels=True)
plt.axis('off')
if save is not None:
plt.savefig(save)
plt.show()
def export_graph(graph_in, base_dir=None, show=False, use_execgraph=False,
show_connectinfo=False, dotfilename='graph.dot', format='png',
simple_form=True):
""" Displays the graph layout of the pipeline
This function requires that pygraphviz and matplotlib are available on
the system.
Parameters
----------
show : boolean
Indicate whether to generate pygraphviz output fromn
networkx. default [False]
use_execgraph : boolean
Indicates whether to use the specification graph or the
execution graph. default [False]
show_connectioninfo : boolean
Indicates whether to show the edge data on the graph. This
makes the graph rather cluttered. default [False]
"""
graph = deepcopy(graph_in)
if use_execgraph:
graph = generate_expanded_graph(graph)
logger.debug('using execgraph')
else:
logger.debug('using input graph')
if base_dir is None:
base_dir = os.getcwd()
if not os.path.exists(base_dir):
os.makedirs(base_dir)
outfname = fname_presuffix(dotfilename,
suffix='_detailed.dot',
use_ext=False,
newpath=base_dir)
logger.info('Creating detailed dot file: %s' % outfname)
_write_detailed_dot(graph, outfname)
cmd = 'dot -T%s -O %s' % (format, outfname)
res = CommandLine(cmd, terminal_output='allatonce').run()
if res.runtime.returncode:
logger.warn('dot2png: %s', res.runtime.stderr)
pklgraph = _create_dot_graph(graph, show_connectinfo, simple_form)
outfname = fname_presuffix(dotfilename,
suffix='.dot',
use_ext=False,
newpath=base_dir)
nx.drawing.nx_pydot.write_dot(pklgraph, outfname)
logger.info('Creating dot file: %s' % outfname)
cmd = 'dot -T%s -O %s' % (format, outfname)
res = CommandLine(cmd, terminal_output='allatonce').run()
if res.runtime.returncode:
logger.warn('dot2png: %s', res.runtime.stderr)
if show:
pos = nx.graphviz_layout(pklgraph, prog='dot')
nx.draw(pklgraph, pos)
if show_connectinfo:
nx.draw_networkx_edge_labels(pklgraph, pos)
def _draw(self):
pos = nx.graphviz_layout(self.G, prog='dot')
nx.draw(self.G,
pos,
node_size=100,
alpha=0.4,
edge_color='r',
font_size=16)
def _set_layout(nx_graph, scores, labels):
positions = _nx.graphviz_layout(nx_graph, prog="neato") # prog options: neato, dot, fdp, sfdp, twopi, circo
centered_positions = _center_positions(positions)
for node in nx_graph.nodes():
nx_graph.node[node]['viz'] = _get_viz_data(node, centered_positions, scores)
label = labels[node] if labels is not None and node in labels else node
nx_graph.node[node]['label'] = " ".join(label.split()[0:2])
nx_graph.node[node]['id'] = label
def _layout_force(self, graph):
two_dimensional_data_matrix = nx.graphviz_layout(
graph, prog=self.layout_prog, args=self.layout_prog_args)
two_dimensional_data_list = [
list(two_dimensional_data_matrix[i]) for i in range(len(graph))
]
embedded_data_matrix = scale(np.array(two_dimensional_data_list))
return embedded_data_matrix
def _graph_embedding(self, graph):
two_dimensional_data_matrix = nx.graphviz_layout(
graph, prog='sfdp', args='-Goverlap=scale')
two_dimensional_data_list = [
list(two_dimensional_data_matrix[i]) for i in range(len(graph))
]
embedded_data_matrix = scale(np.array(two_dimensional_data_list))
return embedded_data_matrix
def do_plot(self, line):
"Plot topology colored by node placement"
# Import networkx if needed
global nx, plt
if not nx:
try:
import networkx as nx
import matplotlib.pyplot as plt
import pygraphviz
assert pygraphviz # silence pyflakes
except:
error('plot requires networkx, matplotlib and pygraphviz - '
'please install them and try again\n')
return
# Make a networkx Graph
g = nx.Graph()
mn = self.mn
servers, hosts, switches = mn.servers, mn.hosts, mn.switches
nodes = hosts + switches
g.add_nodes_from(nodes)
links = [(link.intf1.node, link.intf2.node) for link in self.mn.links]
g.add_edges_from(links)
# Pick some shapes and colors
# shapes = hlen * [ 's' ] + slen * [ 'o' ]
color = dict(zip(servers, self.colorsFor(servers)))
# Plot it!
pos = nx.graphviz_layout(g)
opts = {
'ax': None,
'font_weight': 'bold',
'width': 2,
'edge_color': 'darkblue'
}
hcolors = [color[getattr(h, 'server', 'localhost')] for h in hosts]
scolors = [color[getattr(s, 'server', 'localhost')] for s in switches]
nx.draw_networkx(g,
pos=pos,
nodelist=hosts,
node_size=800,
label='host',
node_color=hcolors,
node_shape='s',
**opts)
nx.draw_networkx(g,
pos=pos,
nodelist=switches,
node_size=1000,
node_color=scolors,
node_shape='o',
**opts)
# Get rid of axes, add title, and show
fig = plt.gcf()
ax = plt.gca()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
fig.canvas.set_window_title('Mininet')
plt.title('Node Placement', fontweight='bold')
plt.show()
def draw_graph(graph):
pos = nx.graphviz_layout(graph, prog='dot')
nx.draw(graph,
pos,
node_color='#A0CBE2',
edge_color='#BB0000',
width=2,
edge_cmap=plt.cm.Blues,
with_labels=True)
def drawRootedDAG(g):
labels=dict((n,d['label']) for n,d in g.nodes(data=True))
labels={k:str(k)+':'+v for (k,v) in labels.items()}
nx.write_dot(g, 'tmp.dot')
pos = nx.graphviz_layout(g, prog='dot')
# same layout using matplotlib with no labels
nx.draw(g, pos, labels=labels)
pylab.show()
def draw_trait_network_for_culture(self, culture, node_list):
trait_subgraph = self.graph.subgraph(node_list)
pos = nx.graphviz_layout(trait_subgraph, prog='dot')
nx.draw(trait_subgraph,
pos,
with_labels=True,
arrows=False,
label=culture)
plt.show()
def show_graph(graph):
m = graph.copy()
pos = nx.graphviz_layout(m)
nx.draw_networkx_edge_labels(m, pos)
nx.draw_networkx_nodes(m, pos)
nx.draw_networkx_labels(m, pos)
nx.write_dot(m, "m.dot")
os.system("dot -Tps m.dot -o m.ps")
nx.draw(m, pos)
def _plot_sub_plot(graph, new_nodes, prog, type_format, axes):
"""
Plot a single candidate graph.
"""
pos = nx.graphviz_layout(graph, prog=prog)
green_nodes = []
yellow_nodes = []
red_nodes = []
blue_nodes = []
for node, values in graph.nodes(data=True):
shape = 'o'
if values['type'] in type_format:
shape = type_format[values['type']]
color = 'g'
alpha = 0.8
if node in new_nodes:
color = 'b'
alpha = 0.2
elif 'rank' in values and values['rank'] > 7:
color = 'r'
elif 'rank' in values and values['rank'] < 7 and values['rank'] > 3:
color = 'y'
nx.draw_networkx_nodes(graph,
pos,
nodelist=[node],
node_color=color,
node_shape=shape,
alpha=alpha,
ax=axes)
if node in new_nodes:
blue_nodes.append(node)
elif 'rank' in values and values['rank'] > 7:
red_nodes.append(node)
elif 'rank' in values and values['rank'] < 7 and values['rank'] > 3:
yellow_nodes.append(node)
else:
green_nodes.append(node)
# draw the edges
dotted_line = []
normal_line = []
for src, trg in graph.edges():
if src in new_nodes and trg not in new_nodes:
dotted_line.append((src, trg))
else:
normal_line.append((src, trg))
nx.draw_networkx_edges(graph,
pos,
edgelist=dotted_line,
style='dotted',
ax=axes)
nx.draw_networkx_edges(graph, pos, edgelist=normal_line, ax=axes)
# draw labels
nx.draw_networkx_labels(graph, pos, ax=axes)
def drawTreeGraph(myGraph):
pos = networkx.spring_layout(myGraph)
try:
from networkx import graphviz_layout
pos = networkx.graphviz_layout(myGraph, prog='twopi', args='')
except:
try:
pos = networkx.graphviz_layout(myGraph, prog='dot', args='')
except:
pos = networkx.spring_layout(myGraph)
print "fall back to spring layout. Nicer graphs might result if either compatible PyGraphviz or Pydot packages were available"
networkx.draw_networkx(myGraph,
pos,
with_labels=True,
arrows=True,
node_color='#667766',
node_size=10,
alpha=0.2)
def simple_undirected(G, fname=''):
ug = nx.Graph(G.edges())
sg = sorted(nx.connected_component_subgraphs(ug), key=len, reverse=True)
pos = nx.graphviz_layout(sg[0])
nx.draw_networkx_edges(sg[0], pos, alpha=0.2)
if fname == '':
plt.show()
else:
plt.savefig(fname)
def plot_celltype_graph(self, filename='celltype_graph.png'):
"""Display the celltype connectivity graph
"""
try:
pos = nx.graphviz_layout(self.__celltype_graph)
except Exception, e:
print e
pos = nx.spring_layout(self.__celltype_graph)
def plot_selected_groups(g, selected_groups, filename=None):
"""
Display the ImageNet DAG and highlight the selected groups
in different colors.
Parameters
----------
g : networkx.DiGraph
selected_groups : list of ('node', list of leaf nodes) tuples
filename : string, optional
If given, write plot out to filename.
"""
fig = plt.figure(figsize=(12, 12))
gray = (0.85, 0.85, 0.85, 1)
graydark = (0.5, 0.5, 0.5, 1)
ns = 30
pos = nx.graphviz_layout(g, prog='sfdp')
nx.draw_networkx_nodes(g,
pos,
with_labels=False,
node_size=ns,
node_color=gray,
linewidth=None).set_edgecolor(graydark)
root = [n for n in g.nodes() if g.predecessors(n) == []]
nx.draw_networkx_nodes(g,
pos,
nodelist=root,
with_labels=False,
node_size=ns * 2,
node_color=gray,
linewidth=None).set_edgecolor(graydark)
nx.draw_networkx_edges(g,
pos,
arrows=False,
edge_color=[gray] * len(g.edges()))
cmap = plt.get_cmap('Accent')
colors = np.linspace(0, 1, len(selected_groups))
darken = lambda c, r: (c[0] * r, c[1] * r, c[2] * r, c[3])
for i, group in enumerate(selected_groups):
nx.draw_networkx_nodes(g,
pos,
node_size=ns,
nodelist=group[-1],
node_color=cmap(colors[i])).set_edgecolor(
darken(cmap(colors[i]), 0.75))
plt.axis('equal')
plt.axis('off')
if filename is not None:
plt.savefig(filename, dpi=300, facecolor='none')
return fig
def draw_graph(thisGraph, graphTitle, MAIN_DIRECTORY, edgeWeight=None):
'''
Purpose:: Utility function to draw graph in the hierachial format
Input:: thisGraph: a Networkx directed graph representing a subgraph of connected cloud elements
graphTitle: a string representing the graph title
MAIN_DIRECTORY: a string representing the path to the main directory where the data generated is saved
edgeWeight: (optional) a list of integers representing the edge weights in the graph
Returns:: None
Generates:: A plot of the thisGraph
'''
imgFilename = MAIN_DIRECTORY + '/images/' + graphTitle + '.gif'
fig = plt.figure(facecolor='white', figsize=(16, 12))
edge95 = [(u, v) for (u, v, d) in thisGraph.edges(data=True)
if d['weight'] == edgeWeight[0]]
edge90 = [(u, v) for (u, v, d) in thisGraph.edges(data=True)
if d['weight'] == edgeWeight[1]]
edgeOverlap = [(u, v) for (u, v, d) in thisGraph.edges(data=True)
if d['weight'] == edgeWeight[2]]
nx.write_dot(thisGraph, 'test.dot')
plt.title(graphTitle)
pos = nx.graphviz_layout(thisGraph, prog='dot')
# Draw graph in parts
# nodes
nx.draw_networkx_nodes(thisGraph, pos, with_labels=True, arrows=False)
# edges
nx.draw_networkx_edges(thisGraph,
pos,
edgelist=edge95,
alpha=0.5,
arrows=False)
nx.draw_networkx_edges(thisGraph,
pos,
edgelist=edge90,
edge_color='b',
style='dashed',
arrows=False)
nx.draw_networkx_edges(thisGraph,
pos,
edgelist=edgeOverlap,
edge_color='y',
style='dashed',
arrows=False)
# labels
nx.draw_networkx_labels(thisGraph, pos, arrows=False)
plt.axis('off')
plt.savefig(imgFilename, facecolor=fig.get_facecolor(), transparent=True)
# Do some clean up...and ensure that we are in the right dir
os.chdir((MAIN_DIRECTORY + '/'))
subprocess.call('rm test.dot', shell=True)
def draw_graph(graph,
node_labels=None,
labels=None,
graph_layout='neato',
node_size=400,
node_color='blue',
node_alpha=0.3,
node_text_size=12,
edge_color='red',
edge_alpha=0.3,
edge_tickness=1,
edge_text_pos=0.3,
text_font='sans-serif'):
# create networkx graph
G = nx.Graph()
# add edges
for edge in graph:
G.add_edge(edge[0], edge[1])
# these are different layouts for the network you may try
# shell seems to work best
if graph_layout == 'spring':
graph_pos = nx.spring_layout(G)
else:
graph_pos = nx.graphviz_layout(G, prog='neato')
# draw graph
nx.draw_networkx_nodes(G,
graph_pos,
node_size=node_size,
alpha=node_alpha,
node_color=node_color)
nx.draw_networkx_edges(G,
graph_pos,
width=edge_tickness,
alpha=edge_alpha,
edge_color=edge_color)
nx.draw_networkx_labels(G,
graph_pos,
labels=node_labels,
font_size=node_text_size,
font_family=text_font)
if labels is None:
labels = range(len(graph))
edge_labels = dict(zip(graph, labels))
nx.draw_networkx_edge_labels(G,
graph_pos,
edge_labels=edge_labels,
label_pos=edge_text_pos)
# show graph
plt.show()
def draw_graph(ctx, dataset, root, gp):
"""Draw a directed graph of forwarding"""
import networkx as nx
import matplotlib.pyplot as plt
from graphviz import Digraph
def edges_to_adj_map(edges):
from collections import defaultdict
map = defaultdict(list)
for vertex, neighbour in edges:
map[vertex].append(neighbour)
return dict(map)
def visit_children(G, adj_map, root, add_edge):
stack = [root]
while stack:
node = stack.pop()
neighbours = adj_map.get(node, [])
stack.extend(neighbours)
for neighbour in neighbours:
add_edge(G, node, neighbour)
if gp == "nx":
G = nx.DiGraph()
add_edge = lambda G, a, b: G.add_edge(a, b)
elif gp == "gv":
G = Digraph()
add_edge = lambda G, a, b: G.edge(a, b)
else:
raise Exception
if dataset == "desired":
for vertex, neighbours in forwarders.items():
for neighbour in neighbours:
if root is None or vertex == root:
add_edge(G, vertex, neighbour)
elif dataset == "current":
existing_forwarders = get_existing_forwarders(ctx.obj[FORWARDERS_HTML])
if root is None:
for vertex, neighbour in existing_forwarders:
add_edge(G, vertex, neighbour)
else:
adj_map = edges_to_adj_map(existing_forwarders)
visit_children(G, adj_map, root, add_edge)
if gp == "nx":
# pos = nx.spring_layout(G, k=0.2) # positions for all nodes
pos = nx.graphviz_layout(G)
nx.draw_networkx_nodes(G, pos, node_size=200)
nx.draw_networkx_edges(G, pos, width=0.5, alpha=1)
nx.draw_networkx_labels(G, pos, font_size=10, font_family='sans-serif')
plt.axis('off')
plt.show()
elif gp == "gv":
G.render("graph.gv", view=True)
