def apply(trie: Trie, parameters: Optional[Dict[str, Any]] = None) -> Graph:
"""
Represents the trie
Parameters
-----------------
trie
Trie
parameters
Parameters, including:
- Parameters.FORMAT: the format of the visualization
Returns
-----------------
graph
Representation of the trie
"""
if parameters is None:
parameters = {}
image_format = exec_utils.get_param_value(Parameters.FORMAT, parameters, "png")
filename = tempfile.NamedTemporaryFile(suffix='.gv')
viz = Graph("pt", filename=filename.name, engine='dot', graph_attr={'bgcolor': 'transparent'})
viz.attr('node', shape='ellipse', fixedsize='false')
draw_recursive(trie, None, viz)
viz.attr(overlap='false')
viz.attr(splines='false')
viz.attr(rankdir='LR')
viz.format = image_format
return viz
def generateCompDiagram(comp):
tempDirPath = state.get('tempDirPath')
filepath = join(tempDirPath, uuid.uuid4().hex + '.gv')
indexTracker = {}
g = Graph('G', filename=filepath)
g.format = 'png'
with g.subgraph(name='clusterComponent') as c:
for group in comp.groups:
with c.subgraph(name='cluster' + uuid.uuid4().hex) as a:
a.attr(color='blue')
for port in group.ports:
name = port.portType
idx = 0
if not (name in indexTracker):
indexTracker[name] = 1
else:
idx = indexTracker[name]
indexTracker[name] += 1
if (port.direction == 'in'):
a.node_attr.update(color='green', style='filled')
elif (port.direction == 'out'):
a.node_attr.update(color='red', style='filled')
else:
a.node_attr.update(color='orange')
a.node(name + '_' + str(idx), label=name, style='filled')
g.render()
Popen(["xdg-open", filepath + '.png'])
def graph_data(pkg_name):
if not pkg_name:
abort(404)
filepath = os.path.join(cache_dir, pkg_name.lower())
if not os.path.exists(filepath + '.png'):
nodes, edges = reqs_graph(pkg_name)
if not nodes:
return redirect(url_for('static', filename='img/blank.png'))
dot = Graph()
dot.format = 'png'
dot.node('0', nodes[0], fillcolor='#fbb4ae', style='filled', shape='box')
for i, pkg_name in enumerate(nodes[1:]):
dot.node(str(i+1), pkg_name, fillcolor='#ccebc5', style='filled')
dot.edges([
[str(i[0]), str(i[1])]
for i in edges
])
dot.render(filepath)
return send_file(filepath + '.png')
def imagen(gateway):
#nodos redes activas
temporal = sorted(nodos.items())
nodos.clear()
for i in temporal:
nodos[i[0]] = i[1]
#layouts interfaces reportadas en tabla route
temp2 = sorted(layouts.items())
layouts.clear()
for interfaz in temp2:
layouts[interfaz[0]] = interfaz[1]
#FastEth interfaces activas incompletas
temp2 = sorted(FastEth.items())
FastEth.clear()
for fa in temp2:
FastEth[fa[0]] = fa[1]
#Activas interfaces activas completas
name = layouts.keys()
for uno in name:
for i in FastEth[uno]:
fa = i[:i.index(' ')]
for r in layouts[uno]:
if fa in r:
i = i + r[r.index(' '):]
activas[uno].append(i) #
temp2 = sorted(activas.items())
activas.clear()
for fa in temp2:
activas[fa[0]] = fa[1]
dicredes = {}
cad = ''
grafica = Graph('red')
grafica.node('linux mint', shape='rectangle', label='MV' + '\n' + gateway)
grafica.node('switch', shape='rectangle')
grafica.edge('linux mint', 'switch')
for x in nodos:
aux4 = activas[x]
for y in aux4:
cad = cad + y + '\n'
grafica.node(x, label=x + '\n' + cad, shape='rectangle')
cad = ''
redes = nodos[x]
for red in redes:
if red not in dicredes.keys():
dicredes[red] = []
dicredes[red].append(x)
else:
dicredes[red].append(x)
for d in dicredes:
print(d + ':' + str(dicredes[d]))
if d.find('56') >= 0:
for each in dicredes[d]:
grafica.edge('switch', each, label=d)
if len(dicredes[d]) == 2 and d.find('8') == 0:
grafica.edge(dicredes[d][0], dicredes[d][1], label=d)
grafica.format = 'png'
grafica.render('static/red')
def apply(
tree: ProcessTree,
parameters: Optional[Dict[Union[str, Parameters], Any]] = None
) -> graphviz.Graph:
"""
Obtain a Process Tree representation through GraphViz
Parameters
-----------
tree
Process tree
parameters
Possible parameters of the algorithm
Returns
-----------
gviz
GraphViz object
"""
if parameters is None:
parameters = {}
parameters = copy(parameters)
parameters[ROOT_NODE_PARAMETER] = tree
filename = tempfile.NamedTemporaryFile(suffix='.gv')
bgcolor = exec_utils.get_param_value(Parameters.BGCOLOR, parameters,
"transparent")
viz = Graph("pt",
filename=filename.name,
engine='dot',
graph_attr={'bgcolor': bgcolor})
viz.attr('node', shape='ellipse', fixedsize='false')
image_format = exec_utils.get_param_value(Parameters.FORMAT, parameters,
"png")
enable_deepcopy = exec_utils.get_param_value(Parameters.ENABLE_DEEPCOPY,
parameters, False)
if enable_deepcopy:
# since the process tree object needs to be sorted in the visualization, make a deepcopy of it before
# proceeding
tree = deepcopy(tree)
generic.tree_sort(tree)
repr_tree_2(tree, viz, parameters)
viz.attr(overlap='false')
viz.attr(splines='false')
viz.format = image_format
return viz
def generateGraph():
G = Graph(
engine = 'dot',
filename = 'Btrfs-Graph.dot',
name = 'BRTFS-Browser',
comment = 'https://github.com/Zo0MER/BRTFS-Browser.git',
graph_attr = {'rankdir': 'RL',
'charset':'utf-8',
'bgcolor':'#eeeeee',
'labelloc':'t',
'splines':'compound',
'nodesep':'0.7',
'ranksep':'5'
},
node_attr = {'fontsize': '18.0',
'shape':'box'
}
)
#node with title and hyperlink on github
G.node('meta',
label = 'Btrfs-debug-tree \nhttps://github.com/Zo0MER/BRTFS-Browser.git',
href = 'https://github.com/Zo0MER/BRTFS-Browser.git',
fontcolor = '#4d2600',
fontsize = '30.0'
)
first = inode[0]
inode.remove(inode[0])
if (inode):
#link first item ROOT_TREE_DIR INODE_ITEM, INODE_REF with all INODE_ITEM EXTEND_DATA
for pair in inode:
G.edge(''.join([str(x) for x in first]), ''.join([str(x) for x in pair]))
else:
G.node(first)
#save *.dot and others
pathout = enterPath.get()
filenameout = enterFilename.get()
if (filenameout):
filenameout = filenameout + '.gv.dot'
else:
filenameout = "btrfs-graph"
G.filename = filenameout + '.gv.dot'
G.directory = pathout
G.save()
for t in types:
G.format = t
G.render()
def build_graph(from_list, to_list):
dot = Graph(strict=True)
dot.format = 'svg'
for e in from_list:
for p in to_list:
if e == p:
continue
if not '\t' + e in dot.body:
dot.node(e)
if not '\t' + p in dot.body:
dot.node(p)
dot.edge(e, p)
print(dot.source) # doctest: +NORMALIZE_WHITESPACE
dot.render()
def make_graph(self, name: str, description: str) -> None:
g = Graph('World', filename='world.gv', engine='neato')
g.attr(label=description)
g.attr('node', colorscheme="pastel19", style="filled")
for t in self.territories:
assert t.owner
g.node(t.name,
"{} ({:4d})".format(t.name, t.armies),
fillcolor=str(t.owner.index + 1),
pos=t.get_coordinates())
for i in t.connections:
g.edge(t.name, self.territories[i - 1].name)
g.format = 'svg'
g.render('outputs/{}'.format(name), view=False)
if os.path.exists('outputs/{}'.format(name)):
os.remove('outputs/{}'.format(name))
def apply(tree, parameters=None):
"""
Obtain a Process Tree representation through GraphViz
Parameters
-----------
tree
Process tree
parameters
Possible parameters of the algorithm
Returns
-----------
gviz
GraphViz object
"""
if parameters is None:
parameters = {}
filename = tempfile.NamedTemporaryFile(suffix='.gv')
viz = Graph("pt",
filename=filename.name,
engine='dot',
graph_attr={'bgcolor': 'transparent'})
viz.attr('node', shape='ellipse', fixedsize='false')
image_format = exec_utils.get_param_value(Parameters.FORMAT, parameters,
"png")
color_map = exec_utils.get_param_value(Parameters.COLOR_MAP, parameters,
{})
enable_deepcopy = exec_utils.get_param_value(Parameters.ENABLE_DEEPCOPY,
parameters, True)
if enable_deepcopy:
# since the process tree object needs to be sorted in the visualization, make a deepcopy of it before
# proceeding
tree = deepcopy(tree)
util.tree_sort(tree)
repr_tree(tree, viz, color_map, parameters)
viz.attr(overlap='false')
viz.attr(splines='false')
viz.format = image_format
return viz
def generateModelDiagram(model, labelsForModel):
tempDirPath = state.get('tempDirPath')
filepath = join(tempDirPath, uuid.uuid4().hex + '.gv')
g = Graph('G', filename=filepath)
g.format = 'png'
for i in range(len(model.components)):
comp = model.components[i]
with g.subgraph(name='clusterComponent' + str(i)) as c:
for group in comp.groups:
with c.subgraph(name='cluster' + uuid.uuid4().hex) as a:
a.attr(color='blue')
for port in group.ports:
name = port.portType
portId = port.portId
if (port.direction == 'in'):
a.node_attr.update(color='green', style='filled')
elif (port.direction == 'out'):
a.node_attr.update(color='red', style='filled')
else:
a.node_attr.update(color='orange')
a.node(portId, label=name, style='filled')
c.attr(label=comp.name)
c.attr(fontsize='20')
for edge in model.edges:
portA = edge.getPortAId()
portB = edge.getPortBId()
g.edge(portA, portB)
lStr = None
if (not len(labelsForModel)):
lStr = 'No predicted label available yet.'
elif (len(labelsForModel) == 1):
lStr = 'Predicted label: ' + labelsForModel[0]
else:
lStr = 'Predicted labels: ' + listAsEnumeratedString(labelsForModel)
g.attr(label=lStr)
g.render()
Popen(["xdg-open", filepath + '.png'])
def __setitem__(self, key, value):
if key in self.cache:
self.cache.pop(key)
else:
# Based on the motif create svg
dot= Graph('G', engine="circo",strict=True)
dot.format = 'svg'
g=Graphlet(key)
dot.attr(bgcolor='transparent')
dot.attr('node', shape='circle')
dot.attr('node', color='black')
dot.attr('node', style='filled')
dot.attr('node', fillcolor='#8EC13A')
#dot.attr('node', label='')
for u,v in (g.edge_list()):
dot.edge(str(v),str(u))
dot.render(self.imageDirectory+"/"+str(key))
value=self.imageDirectory+"/"+str(key)+".svg"
self.cache[key] = value
if len(self.cache) > self.maxLength:
self.cache.popitem(last=False)
def main(data_dir: str):
nodes = file_io.read_nodes(data_dir)
edges = file_io.read_edges(data_dir)
groups = file_io.read_communities(data_dir)
graph = Graph()
for node in nodes:
graph.node(node)
graph.edges(edges)
colors = get_colors(len(groups))
for i in range(len(groups)):
group = groups[i]
color = colors[i]
with graph.subgraph(name='cluster-' + str(i)) as subgraph:
subgraph.attr(color=color)
for node in group:
subgraph.node(node, color=color, style='filled')
graph.format = 'png'
graph.render(data_dir + '\\graph.gv')
graph.view()
def graph(data):
dot = Graph(comment='Kanjis', strict=True)
dot.engine = 'neato'
dot.format = 'svg'
dot.attr(rankdir='TB', overlap="false")
dot.attr('node', fontsize='30')
similaredges = defaultdict(set)
for kanji in data.kanjis:
#try:
k = kanji #.decode('utf-8')
label = ("<b>" + kanji + "</b>\n " + data.descriptions[kanji]
) #.decode('utf-8')
shape = "circle"
fcolor = "0 0 " + str(1 - (0.2 + 0.8 * data.ease[kanji]))
color = fcolor
if (kanji in Similarity.dangerzones):
shape = "doublecircle"
color = "red"
node = dot.node(data.descriptions[kanji],
label=kanji,
shape=shape,
color=color,
fontcolor=fcolor,
fillcolor=data.colors[kanji],
style='filled')
# constraint='true'
# color = "black"
# for similar in data.similars[kanji]:
# color = "black"
# if (similar in dangerzones or kanji in dangerzones):
# color = "red"
# if not similaredges[kanji] or not similar in similaredges[kanji]:
# if random.random() < 0.2 and color != "red": # Random dropoff
# color= "lightgrey"
# constraint='false'
# dot.edge(data.descriptions[kanji], data.descriptions[similar], color=color, constraint=constraint)#.decode('utf-8')
# similaredges[kanji].add(similar)
# similaredges[similar].add(kanji)
for similar in data.similars[kanji]:
color = "black"
style = "invis"
if (similar in Similarity.dangerzones
or kanji in Similarity.dangerzones):
color = "red"
style = ""
if not similaredges[kanji] or not similar in similaredges[
kanji]:
dot.edge(data.descriptions[kanji],
data.descriptions[similar],
color=color,
constraint="true",
style=style) #.decode('utf-8')
similaredges[kanji].add(similar)
similaredges[similar].add(kanji)
for similar in data.semilars[kanji]:
color = "lightgrey"
if (similar in Similarity.dangerzones
or kanji in Similarity.dangerzones):
color = "red"
if not similaredges[kanji] or not similar in similaredges[
kanji]:
if random.random() >= 0.3: # random dropoff
dot.edge(data.descriptions[kanji],
data.descriptions[similar],
color=color,
constraint="false") #.decode('utf-8')
similaredges[kanji].add(similar)
similaredges[similar].add(kanji)
#except Exception:
# print("encoding fial")
return dot
for x in range(0, clusterCounter):
g.subgraph(cCluster[x])
g.subgraph(fieldCluster)
for fA in fieldAu:
#find the color of that author
for cA in colorAu:
if fA[0] == cA[0]:
print(cA)
g.edge(fA[0],fA[1], colorscheme="paired12", color = str(cA[1]), penwidth="2") #create edge
g.body.append('ratio = compress')
g.body.append('size = "9,30"')
g.body.append(' rankdir="LR"')
g.body.append('splines=ortho')
#g.edge_attr.update(style='filled', color='green')
g.format = "svg"
g.render("img/"+filename)
@route('/hello/123')
def index():
return template("img/" + filename + ".svg")
run(host='localhost', port=8080)
cur.close()
conn.close()
else:
print ("No Results Found On DB!!")
def layout(self, layoutType='graph'):
###### need domain information for generating layout using DR
# domain = self.f[:,:-1]
if layoutType == 'graph':
# g = nx.Graph()
g = Graph('ER', engine='neato')
i = 0
index_map = dict()
inverse_index_map = dict()
# for ext in self.seg.keys():
for s in self.graph.keys():
for ext in self.graph[s]:
index_map[ext] = i
inverse_index_map[i] = ext
if ext in self.previousPos.keys():
ppos = self.previousPos[ext]
# print 'previous position is provided for:', ext, ppos[0], ppos[1]
g.node('%d' % ext, pos="%f,%f" % (ppos[0], ppos[1]))
else:
# print "\n\n No previousPos provided for:", ext, '\n\n'
g.node('%d' % ext)
i += 1
for s in self.graph.keys():
index_map[s] = i
inverse_index_map[i] = s
if s in self.previousPos.keys():
ppos = self.previousPos[s]
# print 'previous position is provided for:', s, ppos[0], ppos[1]
g.node('%d' % s, pos="%f,%f" % (ppos[0], ppos[1]))
else:
# print "\n\n No previousPos provided for:", ext, '\n\n'
g.node('%d' % s)
i += 1
for s in self.graph.keys():
for ext in self.graph[s]:
g.edge(str(s),
str(ext),
len=str(linalg.norm(self.loc[s] - self.loc[ext])))
# g.add_edge(index_map[s], index_map[ext], weight=linalg.norm(f[s,:-1] - f[ext,:-1]))
self.currentNodeSize = len(index_map)
if self.currentNodeSize == self.previousNodeSize:
return
self.previousNodeSize = self.currentNodeSize
##print "============== Render Spine =============="
g.format = 'plain'
path = g.render('spine')
input = open('spine.plain')
for l in input.readlines():
l = l.split()
# print l
if l[0] == 'node':
self.pos[int(l[1])] = [float(l[2]), float(l[3])]
#give position to all children
#only if l[1] is extrema
#init the position all the point to the extrema pos
## FIXME update children extrema to the parent location
# print("extremaSet = ", self.extremaSet)
#if int(l[1]) in self.extremaSet:
# self.pos.update(dict.fromkeys(self.extremaSet[int(l[1])], [float(l[2]),float(l[3])]))
elif layoutType == 'PCA':
if recomputePCA:
self.pca = decomposition.PCA(n_components=2)
self.pca.fit(domain)
pos2D = self.pca.transform(domain).tolist()
for index, val in enumerate(pos2D):
self.pos[nodeIndicesList[index]] = val
elif layoutType == 'MDS':
mds = manifold.MDS(n_components=2, max_iter=100, n_init=1)
pos2D = mds.fit_transform(domain).tolist()
for index, val in enumerate(pos2D):
self.pos[nodeIndicesList[index]] = val
elif layoutType == 'tSNE':
tsne = manifold.TSNE(n_components=2, init='pca', random_state=0)
pos2D = tsne.fit_transform(domain).tolist()
for index, val in enumerate(pos2D):
self.pos[nodeIndicesList[index]] = val
else: #PCA is the default
pca = decomposition.PCA(n_components=2)
pos2D = pca.fit_transform(domain).tolist()
for index, val in enumerate(pos2D):
self.pos[nodeIndicesList[index]] = val
#store position
# print self.pos
# print "compare self.pos vs self.previousPos"
# for p in self.previousPos.keys():
# if self.previousPos[p] != self.pos[p]:
# print p, self.previousPos[p], self.pos[p]
self.previousPos = self.pos
#if only layout one point clear the position cache
if len(self.graph.keys()) <= 1:
self.previousPos = dict()
def graph_pedigree(individualProfileDatabase, family, genNodes, descNode,
save_dir):
"""
The pedigree is graphed using graphviz library, interface for DOT engine. Diferenciates by colour the
individuals acording to its classifier status.
Args:
minimum:
individualProfileDatabase: The output of tbe individual_profile function. A dictionary which
contains as entries, the individuals included. Each individual is labeled with the descendants,
siblings, ascendants, partners, sex, classifier and generation variables.
family: The family to being graphed.
genNodes: information required to build nodes each generation. Generated with the gen_stratification function.
descNode: information required to build nodes that link the descendants with the ascendants. Generated with
the gen_stratification function.
save_dir: Directory where to save results
Returns:
Exports pedigree graph, diferenciates by colour the individuals acording to its classifier status.
"""
pedGen = set()
for i in individualProfileDatabase:
pedGen.add(individualProfileDatabase[i]["generation"][0])
dot = Graph(engine='DOT',
comment="ped_",
graph_attr={
'splines': 'ortho',
'concentrate': 'true'
},
strict=True)
for i in range(min(pedGen), max(pedGen) + 1):
with dot.subgraph(name=str(i)) as subs:
subs.attr(rank='same')
for e in genNodes[i]:
#set format
if (len(e) == 3):
if e[1] == 1:
shape = "rect"
else:
shape = "ellipse"
if e[2] == 2:
color = "green"
else:
color = "red"
subs.node(e[0], shape=shape, style='filled', color=color)
else:
nodeName = str(e[0]) + "_" + str(e[1])
subs.node(nodeName, shape="point")
subs.edge(str(e[0]), nodeName)
subs.edge(nodeName, str(e[1]))
with dot.subgraph(name=str(i + 0.5)) as subs:
for e in descNode[i + 0.5]:
if i == (max(pedGen)):
pass
else:
if type(e) == list:
nodeName = str(e[0]) + "_" + str(e[1])
intergenNodeName = nodeName + "_desc"
subs.node(intergenNodeName, shape="point")
subs.edge(nodeName, intergenNodeName)
else:
nodeName = str(e)
intergenNodeName = nodeName + "_desc"
subs.node(intergenNodeName, shape="point")
subs.edge(nodeName, intergenNodeName)
if i == min(pedGen):
pass
else:
for e in genNodes[i]:
if type(e[0]) == str:
e = int(e[0])
ascendants = individualProfileDatabase[e]["ascendants"]
if len(ascendants) == 1:
if len(individualProfileDatabase[ascendants[0]]
["descendants"]) != 1:
intergenNodeName = str(ascendants[0]) + "_desc"
dot.node(intergenNodeName, shape="point")
dot.edge(str(ascendants[0]), intergenNodeName)
dot.edge(intergenNodeName, str(e))
else:
intergenNodeName = str(ascendants[0]) + "_desc"
dot.edge(intergenNodeName, str(e))
elif len(ascendants) == 2:
if (ascendants in genNodes[i - 1]):
intergenNodeName = str(ascendants[0]) + "_" + str(
ascendants[1]) + "_desc"
dot.edge(intergenNodeName, str(e))
elif ([ascendants[1], ascendants[0]]
in genNodes[i - 1]):
intergenNodeName = str(ascendants[1]) + "_" + str(
ascendants[0]) + "_desc"
dot.edge(intergenNodeName, str(e))
else:
print("Unexpected input.")
else:
pass
else:
pass
dot.node("Family ID: " + str(family), shape="box")
dot.format = 'svg'
dot.render(save_dir + 'pedigree_family_' + str(family) + '.gv', view=True)
di = counts.most_common(50)
df = pd.DataFrame(di, columns=["tag", "value"])
df = df.sort_values('value', ascending=False)
plot = df.plot(xticks=df.index, kind='bar', rot=45)
plot.set_xticklabels(df.tag)
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
print(df)
plt.show()
print(mapped[0].keys)
build_graph(mapped[0]["PERSON"], mapped[0]["GPE"])
#build_graph(prsns, prsns)
exit(0)
dot = Graph(strict=True)
dot.format = 'svg'
print(prsns)
for e in prsns:
for p in prsns:
if e == p:
continue
if not '\t' + e in dot.body:
dot.node(e)
if not '\t' + p in dot.body:
dot.node(p)
dot.edge(e, p)
print(dot.source) # doctest: +NORMALIZE_WHITESPACE
def build_network(self):
if not self.jaccard_dict:
return 'No_dict'
# networkxで計算
G = nx.Graph()
# 接点／単語（node）の追加
nodes = set([j for pair in self.jaccard_dict.keys() for j in pair])
G.add_nodes_from(nodes)
# 線（edge）の追加
for pair, coef in self.jaccard_dict.items():
G.add_edge(pair[0], pair[1], weight=coef)
# focusの場合
if self.focus:
try:
keepnodes = list((G[self.focus]).keys())
except KeyError:
return 'No_focus'
else:
keepnodes = list((G[self.focus]).keys())
keepnodes.append(self.focus)
print(f'🟦{keepnodes}')
#ノード削除
rm_nodes = set(nodes) ^ set(keepnodes)
G.remove_nodes_from(rm_nodes)
G.remove_nodes_from(list(isolates(G)))
print('Number of nodes =', G.number_of_nodes())
print('Number of edges =', G.number_of_edges())
# graphviz.pyで描写
g = Graph(engine='neato')
g.attr(overlap='false')
g.attr(size='800,500')
g.attr(outputorder="edgesfirst")
# networkxのpagerankを活用して色やノードサイズを変化させる
pagerank = nx.pagerank(G)
print(pagerank)
cm = plt.get_cmap('rainbow')
for node, rank in pagerank.items():
ncm = cm(rank * 15)
colorhex = mcolors.to_hex(ncm)
if node in self.emojidict.keys():
g.attr('node',
shape='circle',
color=str(colorhex),
style='filled',
fixedsize='True',
height=20,
imagescale='both',
margin=' 0.001,0.001')
emojivalue = self.emojidict[node]
emoji_img = Image.open(
requests.get(emojivalue[1], stream=True).raw)
bytes = io.BytesIO()
emoji_img.save(bytes, format='PNG')
g.node(bytes)
bytes.close()
else:
g.attr('node',
shape='circle',
color=str(colorhex),
style='filled',
fontsize='22',
fontpath='SourceHanSansHW-Bold.otf',
fixedsize='True',
height=str(len(node) / 3.3 + 10 * rank))
g.node(node)
edges = []
for source in G.edges(data=True):
edges.append([source[0], source[1], source[2]['weight']])
print(edges)
for edge in edges:
g.attr('edge', weight=str(min(edge[2] * 10, 30)))
g.edge(edge[0], edge[1])
g.format = 'png'
datastream = io.BytesIO(g.pipe())
datastream.seek(0)
pngimage = discord.File(datastream,
filename=f'discord_network_graph.png')
datastream.close()
return pngimage
def FieldAuthorGraph(filename, authorName, targets, isPrintAuthor):
global search
institution = []
insAu = []
field = []
fieldAu = []
#include author when author is selected
if isPrintAuthor == '1':
#access author's field
query = "SELECT * FROM `fields` WHERE `scholarID` = '" + search +"'"
cur.execute(query)
for row in cur:
repeat = 0
#check when there are repeated field name
for fie in field:
if fie == row[1]:
repeat = 1
if repeat == 0:
field.append(row[1])
#check if repeated field name and author name
repeat = 0
for fA in fieldAu:
if authorName == fA[0] and row[1] == fA[1]:
repeat = 1
if repeat == 0:
fieldAu.append((authorName, row[1]))
#access author's institution
query = "SELECT * FROM `institutions` WHERE `scholarID` = '" + search +"'"
cur.execute(query)
for row in cur:
repeat = 0
#parse the institution name and save in "uni"
parse = row[1].split(", ")
for parsed in parse:
if "University" in parsed:
uni = parsed
break
else:
uni = row[1]
#check when there are repeated institution name
for ins in institution:
if ins == uni:
repeat = 1
break
if repeat == 0:
institution.append(uni)
#check if repeated institution name and author name
repeat = 0
for iA in insAu:
if authorName == iA[0] and uni == iA[1]:
repeat = 1
break
if repeat == 0:
insAu.append((authorName, uni))
#access fields and scholars relationship
for element in targets:
query = "SELECT * FROM `fields` WHERE `scholarID` = '" + element[1] +"'"
cur.execute(query)
for row in cur:
repeat = 0
#check when there are repeated field name
for fie in field:
if fie == row[1]:
repeat = 1
if repeat == 0:
field.append(row[1])
#check if repeated field name and author name
repeat = 0
for fA in fieldAu:
if element[0] == fA[0] and row[1] == fA[1]:
repeat = 1
if repeat == 0:
#find author's name
fieldAu.append((element[0], row[1]))
#access institutions and scholars relationship
for element in targets:
query = "SELECT * FROM `institutions` WHERE `scholarID` = '" + element[1] +"'"
cur.execute(query)
for row in cur:
repeat = 0
#parse the institution name and save in "uni"
parse = row[1].split(", ")
for parsed in parse:
if "University" in parsed:
uni = parsed
break
else:
uni = row[1]
#check when there are repeated institution name
for ins in institution:
if ins == uni:
repeat = 1
break
if repeat == 0:
institution.append(uni)
#check if repeated institution name and author name
repeat = 0
for iA in insAu:
if element[0] == iA[0] and uni == iA[1]:
repeat = 1
break
if repeat == 0:
insAu.append((element[0], uni))
g = Graph('G')
colorAu= []
cCluster = []
clusterCounter = 0
colorNum = 1
#construct multiple clusters and construct its nodes
for ins in institution:
cCluster.append(Graph("cluster_" + str(clusterCounter)))
cCluster[clusterCounter].body.append('style=filled')
cCluster[clusterCounter].body.append("color=lightgrey")
cCluster[clusterCounter].body.append('label = "' + ins +'"')
cCluster[clusterCounter].node_attr.update(style="filled")
for iA in insAu:
#special case for the person it is searching for
if ins == iA[1] and iA[0] == authorName:
cCluster[clusterCounter].node(authorName, color = "cyan")
colorAu.append((authorName, "cyan"))
elif ins == iA[1]:
cCluster[clusterCounter].node(iA[0], colorscheme="paired12",color = str(colorNum))
colorAu.append((iA[0], colorNum)) #give each author a color
#control colorNum
colorNum += 1
if colorNum >= 13:
colorNum = 1
clusterCounter += 1
fieldCluster = Graph("cluster_" + str(clusterCounter+1))
fieldCluster.body.append('style=filled')
fieldCluster.body.append("color=gray72")
fieldCluster.body.append('label = "Field of Study"')
fieldCluster.node_attr.update(style="filled")
for fie in field:
for fA in fieldAu:
if fA[0] == authorName and fA[1] == fie:
fieldCluster.node(fie, color="cyan")
else:
fieldCluster.node(fie)
for x in range(0, clusterCounter):
g.subgraph(cCluster[x])
g.subgraph(fieldCluster)
for fA in fieldAu:
#find the color of that author
for cA in colorAu:
if fA[0] == cA[0]:
g.edge(fA[0],fA[1], colorscheme="paired12", color = str(cA[1]), penwidth="2") #create edge
g.body.append('ratio = compress')
g.body.append('size = "8,30"')
g.body.append(' rankdir="LR"')
g.body.append('splines=line')
#g.edge_attr.update(style='filled', color='green')
g.format = "svg"
g.render("img/"+filename)
def reset_dot():
global dot
dot = Graph('MinorC_AST')
dot.filename = 'MinorC_AST'
dot.format = 'png'
def apply(tree, parameters=None):
"""
Obtain a Process Tree representation through GraphViz
Parameters
-----------
tree
Process tree
parameters
Possible parameters of the algorithm
Returns
-----------
gviz
GraphViz object
"""
if parameters is None:
parameters = {}
filename = tempfile.NamedTemporaryFile(suffix='.gv')
viz = Graph("pt",
filename=filename.name,
engine='dot',
graph_attr={'bgcolor': 'transparent'})
image_format = exec_utils.get_param_value(Parameters.FORMAT, parameters,
"png")
color_map = exec_utils.get_param_value(Parameters.COLOR_MAP, parameters,
{})
node_color = get_color(tree, color_map)
enable_deepcopy = exec_utils.get_param_value(Parameters.ENABLE_DEEPCOPY,
parameters, True)
if enable_deepcopy:
# since the process tree object needs to be sorted in the visualization, make a deepcopy of it before
# proceeding
tree = deepcopy(tree)
util.tree_sort(tree)
# add first operator
if tree.operator:
viz.attr('node', shape='ellipse', fixedsize='false', fontsize="14")
op_node_identifier = str(uuid.uuid4())
viz.node(op_node_identifier,
operators_mapping[str(tree.operator)],
color=node_color,
fontcolor=node_color)
viz = repr_tree(tree, viz, op_node_identifier, 0, color_map,
parameters)
else:
viz.attr('node', shape='ellipse', fixedsize='false', fontsize="16")
this_trans_id = str(uuid.uuid4())
if tree.label is None:
viz.node(this_trans_id,
"tau",
style='filled',
fillcolor='black',
shape='point',
width="0.075")
else:
viz.node(this_trans_id,
str(tree),
color=node_color,
fontcolor=node_color)
viz.attr(overlap='false')
viz.attr(fontsize='16')
viz.attr(splines='false')
viz.format = image_format
return viz
def CoAuGraph(filename, targets):
coAuthorR = []
coAuthorRLinks = []
institution = []
insAu = []
#access other source author and coauthors relationship
for element in targets:
query = "SELECT * FROM `connections` WHERE `sourceScholarID` = '" + element[1] +"' AND ("
idx = 0
for target in targets:
if idx == 0:
query += "targetScholarID = '" + target[1] +"'"
else:
query += " OR targetScholarID = '" + target[1] +"'"
idx += 1
query += ")"
cur.execute(query)
for row in cur:
#change id into name
for target in targets:
if target[1] == row[0]:
rowZero = target[0]
if target[1] == row[1]:
rowOne = target[0]
#avoid repeated edges
repeat = 0
for coA in coAuthorR:
if (coA[0] == rowOne and coA[1] == rowZero) or (coA[0] == rowZero and coA[1] == rowOne):
repeat = 1
#if not repeated then insert the relationship
if repeat == 0:
coAuthorR.append((rowZero, rowOne))
#access institutions and scholars relationship
for target in targets:
query = "SELECT * FROM `institutions` WHERE `scholarID` = '" + target[1] +"'"
cur.execute(query)
for row in cur:
repeat = 0
#parse the institution name and save in "uni"
parse = row[1].split(", ")
for parsed in parse:
if "University" in parsed:
uni = parsed
break
else:
uni = row[1]
#check when there are repeated institution name
for ins in institution:
if ins == uni:
repeat = 1
break
if repeat == 0:
institution.append(uni)
#check if repeated institution name and author name
repeat = 0
for iA in insAu:
if target[0] == iA[0] and uni == iA[1]:
repeat = 1
break
if repeat == 0:
insAu.append((target[0], uni))
g = Graph('G')
cCluster = []
clusterCounter = 0
#construct multiple clusters and construct its nodes
for ins in institution:
cCluster.append(Graph("cluster_" + str(clusterCounter)))
cCluster[clusterCounter].body.append('style=filled')
cCluster[clusterCounter].body.append("color=lightgrey")
cCluster[clusterCounter].body.append('label = "' + ins +'"')
cCluster[clusterCounter].node_attr.update(style="filled")
for iA in insAu:
if ins == iA[1]:
cCluster[clusterCounter].node(iA[0], fontsize = "13")
clusterCounter += 1
aCluster = Graph("cluster_"+ str(clusterCounter+1))
aCluster.body.append('style=filled')
aCluster.body.append("color=orange")
aCluster.body.append('label = "Author"')
aCluster.node(search)
aCluster.node_attr.update(style="filled")
for num in range(0,clusterCounter):
g.subgraph(cCluster[num])
for cAR in coAuthorR:
g.edge(cAR[0], cAR[1], penwidth="1.7e")
g.body.append('ratio = compress')
g.body.append('size = "13,30"')
g.body.append(' rankdir="BT"')
g.body.append('splines=line')
#g.body.append('nodesep="0.3"')
g.format = "svg"
g.render("img/"+filename)
