def simulate_cascade(g, p, source=None, return_tree=False):
"""
graph_tool version of simulating cascade
return np.ndarray on vertices as the infection time in cascade
uninfected node has dist -1
"""
if source is None:
source = random.choice(np.arange(g.num_vertices(), dtype=int))
gv = sample_graph_by_p(g, p)
times = get_infection_time(gv, source)
if return_tree:
all_edges = set()
for target in np.nonzero(times != -1)[0]:
path = shortest_path(gv, source=source,
target=gv.vertex(target))[0]
edges = set(zip(path[:-1], path[1:]))
all_edges |= edges
tree = Graph(directed=True)
for _ in range(g.num_vertices()):
tree.add_vertex()
for u, v in all_edges:
tree.add_edge(int(u), int(v))
return source, times, tree
else:
return source, times
def mwgm_graph_tool(pairs, sim_mat):
from graph_tool.all import Graph, max_cardinality_matching
if not isinstance(pairs, list):
pairs = list(pairs)
g = Graph()
weight_map = g.new_edge_property("float")
nodes_dict1 = dict()
nodes_dict2 = dict()
edges = list()
for x, y in pairs:
if x not in nodes_dict1.keys():
n1 = g.add_vertex()
nodes_dict1[x] = n1
if y not in nodes_dict2.keys():
n2 = g.add_vertex()
nodes_dict2[y] = n2
n1 = nodes_dict1.get(x)
n2 = nodes_dict2.get(y)
e = g.add_edge(n1, n2)
edges.append(e)
weight_map[g.edge(n1, n2)] = sim_mat[x, y]
print("graph via graph_tool", g)
res = max_cardinality_matching(g,
heuristic=True,
weight=weight_map,
minimize=False)
edge_index = np.where(res.get_array() == 1)[0].tolist()
matched_pairs = set()
for index in edge_index:
matched_pairs.add(pairs[index])
return matched_pairs
def alignment_graph(lengths=[], pairings=[], alignments=[]):
#print('making graph')
g = Graph(directed=False)
seq_index = g.new_vertex_property("int")
time = g.new_vertex_property("int")
#add vertices
g.add_vertex(sum(lengths))
seq_index.a = np.concatenate([np.repeat(i,l) for i,l in enumerate(lengths)])
time.a = np.concatenate([np.arange(l) for l in lengths])
#add edges (alignments)
alignment_index = g.new_edge_property("int")
segment_index = g.new_edge_property("int")
for i,a in enumerate(alignments):
if len(a) > 0:
j, k = pairings[i]
pairs = np.concatenate(a, axis=0)
indicesJ = (np.arange(lengths[j]) + sum(lengths[:j]))[pairs.T[0]]
indicesK = (np.arange(lengths[k]) + sum(lengths[:k]))[pairs.T[1]]
seg_indices = np.concatenate([np.repeat(i, len(a))
for i,a in enumerate(a)])
g.add_edge_list(np.vstack([indicesJ, indicesK,
np.repeat(i, len(pairs)), seg_indices]).T,
eprops=[alignment_index, segment_index])
#g.add_edge_list([(b, a) for (a, b) in g.edges()])
#print('created alignment graph', g)
#g = prune_isolated_vertices(g)
#print('pruned alignment graph', g)
#g = transitive_closure(g)
#graph_draw(g, output_size=(1000, 1000), output="results/casey_jones_bars.pdf")
return g, seq_index, time, alignment_index, segment_index
def build_word_graph(model_fname, limiar=0.2):
"""
Constroi um grafo de walavras ponderado pela similaridade entre elas
de acordo com o modelo.
:param model_fname: Nome do arquivo com o modelo word2vec como foi salvo
:return: objeto grafo
"""
m = Word2Vec.load(model_fname)
g = Graph()
freq = g.new_vertex_property("int")
weight = g.new_edge_property("float")
i = 0
vdict = {}
for w1, w2 in combinations(m.vocab.keys(), 2):
if w1 == '' or w2 == '':
continue
# print(w1,w2)
v1 = g.add_vertex() if w1 not in vdict else vdict[w1]
vdict[w1] = v1
freq[v1] = m.vocab[w1].count
v2 = g.add_vertex() if w2 not in vdict else vdict[w2]
vdict[w2] = v2
freq[v2] = m.vocab[w2].count
sim = m.similarity(w1, w2)
if sim > 0.1:
e = g.add_edge(v1, v2)
weight[e] = sim
if i > 10000:
break
i += 1
g.vertex_properties['freq'] = freq
g.edge_properties['sim'] = weight
return g
class ZonedNetwork:
def __init__(self,
size: Tuple[int] = (10, 10),
field_size: Tuple[int] = (100, 100)):
self.g = Graph(directed=True)
self.n_zones = size[0] * size[1]
self.fwidth = field_size[0]
self.fheight = field_size[1]
self.n_rows = size[0]
self.n_cols = size[1]
self.row_size: float = self.fheight / self.n_rows
self.col_size: float = self.fwidth / self.n_cols
self.g.add_vertex(self.n_zones)
def get_zone(self, coords: Tuple):
r = int(coords[1] / self.row_size)
c = int(coords[0] / self.col_size)
r = min(self.n_rows - 1, r)
c = min(self.n_cols - 1, c)
return self.g.vertex(r * self.n_cols + c)
def add_passes(self, coords_pairs: List[Tuple]):
pairs = [(self.get_zone((x1, y1)), self.get_zone((x2, y2)))
for x1, y1, x2, y2 in coords_pairs]
return self.g.add_edge_list(pairs)
def save(self, file: str):
self.g.save(file, fmt='graphml')
def test_mospp_small():
G = Graph()
G.add_vertex(1)
G.add_vertex(2)
G.add_vertex(3)
G.add_vertex(4)
c1 = G.new_edge_property("int")
c2 = G.new_edge_property("int")
e1 = G.add_edge(1, 3)
e2 = G.add_edge(3, 4)
e3 = G.add_edge(1, 2)
e4 = G.add_edge(2, 4)
e5 = G.add_edge(1, 4)
c1[e1] = 1
c1[e2] = 1
c1[e3] = 0
c1[e4] = 0
c1[e5] = 2
c2[e1] = 1
c2[e2] = 1
c2[e3] = 1
c2[e4] = 1
c2[e5] = 0
assert [[G.vertex_index[r] for r in route]
for route in mospp(G.vertex(1), G.vertex(4), c1, c2)
] == [[1, 4], [1, 2, 4]]
def build_word_graph(model_fname, limiar=0.2):
"""
Constroi um grafo de walavras ponderado pela similaridade entre elas
de acordo com o modelo.
:param model_fname: Nome do arquivo com o modelo word2vec como foi salvo
:return: objeto grafo
"""
m = Word2Vec.load(model_fname)
g = Graph()
freq = g.new_vertex_property("int")
weight = g.new_edge_property("float")
i = 0
vdict = {}
for w1, w2 in combinations(m.vocab.keys(), 2):
if w1 == '' or w2 == '':
continue
# print(w1,w2)
v1 = g.add_vertex() if w1 not in vdict else vdict[w1]
vdict[w1] = v1
freq[v1] = m.vocab[w1].count
v2 = g.add_vertex() if w2 not in vdict else vdict[w2]
vdict[w2] = v2
freq[v2] = m.vocab[w2].count
sim = m.similarity(w1, w2)
if sim > 0.1:
e = g.add_edge(v1, v2)
weight[e] = sim
if i > 10000:
break
i += 1
g.vertex_properties['freq'] = freq
g.edge_properties['sim'] = weight
return g
def simulate_cascade(g, p, source=None, return_tree=False):
"""
graph_tool version of simulating cascade
return np.ndarray on vertices as the infection time in cascade
uninfected node has dist -1
"""
if source is None:
source = random.choice(np.arange(g.num_vertices(), dtype=int))
gv = sample_graph_by_p(g, p)
times = get_infection_time(gv, source)
if return_tree:
all_edges = set()
for target in np.nonzero(times != -1)[0]:
path = shortest_path(gv, source=source, target=gv.vertex(target))[0]
edges = set(zip(path[:-1], path[1:]))
all_edges |= edges
tree = Graph(directed=True)
for _ in range(g.num_vertices()):
tree.add_vertex()
for u, v in all_edges:
tree.add_edge(int(u), int(v))
return source, times, tree
else:
return source, times
def simulate_cascade(g, p, source=None, return_tree=False):
"""
graph_tool version of simulating cascade
return np.ndarray on vertices as the infection time in cascade
uninfected node has dist -1
"""
gv = sample_graph_by_p(g, p)
if source is None:
# consider the largest cc
infected_nodes = np.nonzero(label_largest_component(gv).a)[0]
source = np.random.choice(infected_nodes)
times = get_infection_time(gv, source)
if return_tree:
# get the tree edges
_, pred_map = shortest_distance(gv, source=source, pred_map=True)
edges = [(pred_map[i], i) for i in infected_nodes if i != source]
# create tree
tree = Graph(directed=True)
tree.add_vertex(g.num_vertices())
for u, v in edges:
tree.add_edge(int(u), int(v))
vfilt = tree.new_vertex_property('bool')
vfilt.a = False
for v in set(itertools.chain(*edges)):
vfilt[v] = True
tree.set_vertex_filter(vfilt)
if return_tree:
return source, times, tree
else:
return source, times
def graph_from_matrix(matrix, directed=False):
g = Graph(directed=directed)
g.add_vertex(len(matrix))
weights = g.new_ep("float")
edges = np.nonzero(matrix)
edges = np.append(edges, [matrix[edges]], axis=0)
g.add_edge_list(list(zip(*edges)), eprops=[weights])
#graph_draw(g, output_size=(1000, 1000), output="results/structure.pdf")
return g, weights
class StackGraph(object):
def __init__(self):
self.g = None
def load(self, filename):
# Initialize the graph
self.g = Graph()
# Each node will store a FunctionWrapper() class instance.
self.g.vertex_properties["functions"] = self.g.new_vertex_property("object")
self.g.vertex_properties["display"] = self.g.new_vertex_property("string")
# Each edge will store a [ ..tbd.. ] .
self.g.edge_properties["calls"] = self.g.new_edge_property("object")
# Load the log file and build the graph
i = 0
f = open(filename, "rb")
for line in f:
i += 1
try:
# Skip any informational lines
if "*" in line: continue
# Extract a call stack snapshot
words = line.split()
time = words[0][2:]
depth = words[1][2:]
stack = [FunctionWrapper(instring=item) for item in words[2].split("->")]
# Add the top 2 functions to the graph, if necessary. Format: f1()->f2()
f1, f2 = stack[-2], stack[-1]
v1, v2 = None, None
# Search for the vertices
for v in self.g.vertices():
if self.g.vp.functions[v] == f1: v1 = v
if self.g.vp.functions[v] == f2: v2 = v
if v1 != None and v2 != None: break
# Add new vertices if needed
if v1 == None:
v1 = self.g.add_vertex()
self.g.vp.functions[v1] = f1
self.g.vp.display[v1] = f1.graphDisplayString()
if v2 == None:
v2 = self.g.add_vertex()
self.g.vp.functions[v2] = f2
self.g.vp.display[v2] = f2.graphDisplayString()
# Add the edge if necessary, and then add data to it
if not self.g.edge(v1, v2):
e = self.g.add_edge(v1, v2)
self.g.ep.calls[e] = CallList(v1, v2)
self.g.ep.calls[e].addCall(time, depth)
except Exception as e:
print "Exception on line", i, ":", e
print [str(x) for x in stack]
exit()
def get_incompatible_segments(g, seg_index, out_edges):
incomp_graph = Graph(directed=False)
num_segs = np.max(seg_index.a)+1
incomp_graph.add_vertex(num_segs)
for v in g.get_vertices():
for vs in group_adjacent(sorted(g.get_out_neighbors(v))):
edges = out_edges[v][np.where(np.isin(out_edges[v][:,1], vs))][:,2]
segments = list(np.unique(seg_index.a[edges]))
[incomp_graph.add_edge(s,t)
for i,s in enumerate(segments) for t in segments[i+1:]]
return label_components(incomp_graph)[0].a
def test_graphtool():
g = Graph(directed=True)
g.add_vertex(4)
g.add_edge_list([(0, 1), (1, 2), (2, 3), (3, 0)])
weight = g.new_edge_property('float')
weight[g.edge(0, 1)] = 1
weight[g.edge(1, 2)] = 2
weight[g.edge(2, 3)] = 3
weight[g.edge(3, 0)] = 4
assert set(gt2edges_and_weights(g, weight)) == {
(0, 1, 1), (1, 2, 2), (2, 3, 3), (3, 0, 4)
}
def session_draw_bis_melty(sessions_id, weblog, weblog_columns_dict):
"""
Draw the graph of sessions with sessions_id given in entry
"""
from graph_tool.all import Graph
from graph_tool.all import graph_draw
session = weblog[weblog.session_id == sessions_id]
session = session.rename(index=str,columns = {weblog_columns_dict['requested_page_column']:'requested_page',\
weblog_columns_dict['referrer_page_column']:'referrer_page'})
s_pages = session[['requested_page', 'requested_external']]
s_pages_ref = session[['referrer_page', 'referrer_external']]
s_pages_ref = s_pages_ref.rename(index=str,
columns={
'referrer_page': 'requested_page',
'referrer_external':
'requested_external'
})
s_pages = s_pages.append(s_pages_ref)
s_pages.drop_duplicates(subset='requested_page', inplace=True)
g = Graph()
v = {}
halo = g.new_vertex_property("bool")
for row in s_pages.itertuples():
v[row.requested_page] = g.add_vertex()
if row.requested_external:
halo[v[row.requested_page]] = True
else:
halo[v[row.requested_page]] = False
session.apply(
lambda x: g.add_edge(v[x.referrer_page], v[x.requested_page]), axis=1)
graph_draw(g,
vertex_halo=halo,
output="./_session" + str(sessions_id) + ".png")
return
def load_train(name):
'''
Training file is numbered from 0 to n. Not all nodes in the training file have their own row.
'''
g = Graph()
node_ids = set()
n = -1
for n, (node_id, neighbor_ids) in enumerate(iter_adj_list(name)):
node_ids.add(node_id)
node_ids.update(neighbor_ids)
n += 1
g.add_vertex(len(node_ids))
for i, (node_id, neighbor_ids) in enumerate(iter_adj_list(name)):
print('adding edge for vertex {}/{}'.format(i + 1, n))
for neighbor_id in neighbor_ids:
g.add_edge(node_id, neighbor_id)
return g
def graph_from_pdb(pdb_str):
g = Graph(directed=False)
vertex_types = g.new_vertex_property("string")
g.vertex_properties['type'] = vertex_types
vertices = []
def get_connect_list():
connect_ids_list = [
[int(str_id) for str_id in line.split()[1:]]
for line in pdb_str.splitlines()
if is_pdb_connect_line(line)
]
return reduce(
lambda acc, e: acc + e,
[
[
(i, j)
for (i, j) in map(
lambda i_j: (i_j[0] - 1, i_j[1] - 1),
zip(cycle(connect_ids[0:1]), connect_ids[1:]),
)
if i < j
]
for connect_ids in connect_ids_list
],
[],
)
connects = get_connect_list()
def get_valence(atom_id):
return sum([1 for connect in connects if atom_id in connect])
atom_lines = [
line for line in pdb_str.splitlines()
if is_pdb_atom_line(line)
]
for (atom_id, line) in enumerate(atom_lines):
fields = pdb_fields(line)
v = g.add_vertex()
vertex_types[v] = type_identifier_for(
fields[11].strip().upper(),
get_valence(atom_id),
)
vertices.append(v)
for (i, j) in connects:
g.add_edge(vertices[i], vertices[j])
return g
def clean_up(g, seg_index):
#plot_matrix(np.triu(adjacency_matrix(g)), "results/clean0.png")
#graph_draw(g, output_size=(1000, 1000), output="results/clean_up0.pdf")
seg_combos = get_segment_combos(g, seg_index)
best = sorted(seg_combos.items(), key=lambda c: c[1], reverse=True)#[:200]
#print(best)
best = best[0][0]
#print(best)
#print(edges[:100])
reduced = Graph(directed=False)
reduced.add_vertex(len(g.get_vertices()))
edges = g.get_edges([seg_index])
edges = edges[np.where(np.isin(edges[:,2], best))]
reduced.add_edge_list(edges)
#print(reduced)
#plot_matrix(np.triu(adjacency_matrix(reduced)), "results/cleani2.png")
#graph_draw(reduced, output_size=(1000, 1000), output="results/clean_up1.pdf")
return reduced
def vary_tasks(f):
num_pipelines = 1
num_stages = 1
num_tasks = [1, 10, 100, 1000, 10000, 100000]
for tasks in num_tasks:
print 'starting'
start = time.time()
# Create empty set of pipes which is equivalent to the entire application
set_of_pipes = set()
for pipe in range(num_pipelines):
# Create empty graph for each pipe
Gpipe = Graph()
for stage in range(num_stages):
# Create a set of tasks to be added to each stage
set_of_tasks = frozenset([Kernel() for _ in range(tasks)])
cur_stage = set_of_tasks
# Add current stage to current pipe
Gpipe.add_vertex(cur_stage)
# Add current pipe to set of pipes
set_of_pipes.add(Gpipe)
end = time.time()
f.write('pipes: %s, stages: %s, tasks: %s, time: %s\n' %
(num_pipelines, num_stages, tasks, end - start))
print 'pipes: %s, stages: %s, tasks: %s, time: %s\n' % (
num_pipelines, num_stages, tasks, end - start)
def build_graph(m_codes, m_list):
n_models, n_attributes = m_codes.shape
g = Graph()
v_map = {}
names = g.new_vertex_property("object")
v_atts = g.add_vertex(n_attributes)
v_mods = g.add_vertex(n_models)
v_imps = g.add_vertex(n_attributes)
for v_idx, v in enumerate(v_atts):
v_n = v_name(v_idx, kind="data")
v_map[v_n] = int(v)
names[v] = v_n
for v_idx, v in enumerate(v_mods):
v_n = v_name(v_idx, kind="model")
v_map[v_n] = int(v)
names[v] = v_n
in_edges = ((d, v) for d in m_list[v_idx].desc_ids)
out_edges = ((v, t) for t in m_list[v_idx].targ_ids)
g.add_edge_list(in_edges)
g.add_edge_list(out_edges)
for v_idx, v in enumerate(v_imps):
v_n = v_name(v_idx, kind="imputation")
v_map[v_n] = int(v)
names[v] = v_n
g.vp.names = names
g.v_map = v_map
return g
class __Graph__:
def __init__(self):
self.graph = GT_Graph()
self.cookies = dict()
self.cookierecvr = CookieRecvr(self)
self.cookierecvr.start()
def new_cookie(self, cookie):
self.cookies[cookie['cid']] = self.graph.add_vertex()
logging.info('added cookie {} to graph'.format(cookie['cid']))
for parent in cookie['parents']:
try:
self.graph.add_edge(self.cookies[parent],
self.cookies[cookie['cid']])
logging.info(
'added eddge from cookie {} to graph'.format(parent))
except KeyError:
logging.info('parent not known in graph')
def fasttest(f):
print >> sys.stderr, "Building graph"
nodes, edges, tags = get_graph(f)
print >> sys.stderr, "%i nodes, %i edges" % (len(nodes), len(edges))
from graph_tool.all import Graph
g = Graph()
node_map = {}
print "Adding vertices"
for osm_id in nodes.iterkeys():
node_map[osm_id] = g.add_vertex()
print "Adding edges"
for a, b in edges:
try:
g.add_edge(node_map[a], node_map[b])
except KeyError:
continue
def to_gt(db):
"""Convert db to graph-tool representation"""
from graph_tool.all import Graph
graph = Graph(directed=True)
mapping = dict()
for native in db.query(vertices, get)():
vertex = graph.add_vertex()
mapping[native.uid] = graph.vertex_index[vertex]
for native in db.query(edges, get)():
start = native.start().uid
start = mapping[start]
end = native.end().uid
end = mapping[end]
graph.add_edge(start, end)
return graph
def to_gt(db):
"""Convert db to graph-tool representation"""
from graph_tool.all import Graph
graph = Graph(directed=True)
mapping = dict()
for native in db.query(vertices, get)():
vertex = graph.add_vertex()
mapping[native.uid] = graph.vertex_index[vertex]
for native in db.query(edges, get)():
start = native.start().uid
start = mapping[start]
end = native.end().uid
end = mapping[end]
graph.add_edge(start, end)
return graph
def vytvořím_graph_tool_graf():
from graph_tool.all import Graph
graf = Graph()
u1 = graf.add_vertex()
u2 = graf.add_vertex()
graf.add_edge(u1, u2)
vprop_double = graf.new_vertex_property("double") # Double-precision floating point
vprop_double[graf.vertex(1)] = 3.1416
vprop_vint = graf.new_vertex_property("vector<int>") # Vector of ints
vprop_vint[graf.vertex(0)] = [1, 3, 42, 54]
eprop_dict = graf.new_edge_property("object") # Arbitrary python object. In this case, a dictionary.
eprop_dict[graf.edges().next()] = {"foo": "bar", "gnu": 42}
gprop_bool = graf.new_graph_property("bool") # Boolean
gprop_bool[graf] = True
graf.save('./data/graph_tool.graphml', fmt='xml')
def session_draw_bis(sessions_id, weblog, weblog_columns_dict):
"""
Draw the graph of sessions with sessions_id given in entry
"""
from graph_tool.all import Graph
from graph_tool.all import graph_draw
session = weblog[weblog.session_id == sessions_id]
session = session.rename(index=str,columns = {weblog_columns_dict['requested_page_column']:'requested_page',\
weblog_columns_dict['referrer_page_column']:'referrer_page'})
s_pages = session['requested_page']
s_pages_ref = session['referrer_page']
#s_pages_ref = s_pages_ref.rename(index = str, columns = {'referrer_page':'requested_page'})
s_pages = s_pages.append(s_pages_ref)
s_pages.drop_duplicates(inplace=True)
g = Graph()
v = {}
for page in s_pages.values:
v[page] = g.add_vertex()
session.apply(
lambda x: g.add_edge(v[x.referrer_page], v[x.requested_page]), axis=1)
graph_draw(g, output="../graph_dump/_session" + str(sessions_id) + ".png")
return
class UNISrt(object):
'''
This is the class represents UNIS in local runtime environment (local to the apps).
All UNIS models defined in the periscope/settings.py will be represented as
a corresponding item of the 'resources' list in this class.
At the initialization phase, UNISrt will create an cache of the UNIS db, (and
will maintain it consistent in a best-effort manner).
'''
# should move this methods to utils
def validate_add_defaults(self, data):
if "$schema" not in data:
return None
schema = self._schemas.get(data["$schema"])
validictory.validate(data, schema)
add_defaults(data, schema)
def __init__(self):
logger.info("starting UNIS Network Runtime Environment...")
fconf = get_file_config(nre_settings.CONFIGFILE)
self.conf = deepcopy(nre_settings.STANDALONE_DEFAULTS)
merge_dicts(self.conf, fconf)
self.unis_url = str(self.conf['properties']['configurations']['unis_url'])
self.ms_url = str(self.conf['properties']['configurations']['ms_url'])
self._unis = unis_client.UNISInstance(self.conf)
self.time_origin = int(time())
self._schemas = SchemaCache()
self._resources = self.conf['resources']
self._subunisclient = {}
for resource in self._resources:
setattr(self, resource, {'new': {}, 'existing': {}})
# construct the hierarchical representation of the network
for resource in self._resources:
# only pullRuntime once at the beginning, as pubsub will only update
# them later when resources are modified on the server
self.pullRuntime(self, self._unis, self._unis.get(resource), resource, False)
# construct the graph representation of the network, of which this NRE is in charge
self.g = Graph()
self.nodebook = {}
for key in self.nodes['existing'].keys():
self.nodebook[key] = self.g.add_vertex()
for key, link in self.links['existing'].iteritems():
if hasattr(link, 'src') and hasattr(link, 'dst'):
self.g.add_edge(self.nodebook[link.src.node.selfRef],\
self.nodebook[link.dst.node.selfRef], add_missing=False)
def pullRuntime(self, mainrt, currentclient, data, resource_name, localnew):
'''
this function should convert the input data into Python runtime objects
'''
model = resources_classes[resource_name]
print resource_name
if data and 'redirect' in data and 'instances' in data:
if len(data['instances']) == 0:
return
for instance_url in data['instances']:
# TODO: needs SSL, not figured out yet, pretend it does not exist for now
if instance_url == 'https://dlt.crest.iu.edu:9000' or instance_url == 'http://iu-ps01.crest.osris.org:8888'\
or instance_url == 'http://dev.crest.iu.edu:8888' or instance_url == 'http://unis.crest.iu.edu:8890'\
or instance_url == 'http://monitor.crest.iu.edu:9000' or instance_url == 'http://sc-ps01.osris.org:8888':
continue
if instance_url not in self._subunisclient:
conf_tmp = deepcopy(self.conf)
conf_tmp['properties']['configurations']['unis_url'] = instance_url
conf_tmp['properties']['configurations']['ms_url'] = instance_url # assume ms is the same as unis
self._subunisclient[instance_url] = unis_client.UNISInstance(conf_tmp)
unis_tmp = self._subunisclient[instance_url]
self.pullRuntime(mainrt, unis_tmp, unis_tmp.get(resource_name), resource_name, False)
elif data and isinstance(data, list):
# sorting: in unisrt res dictionaries, a newer record of same index will be saved
data.sort(key=lambda x: x.get('ts', 0), reverse=False)
for v in data:
model(v, mainrt, currentclient, localnew)
threading.Thread(name=resource_name + '@' + currentclient.config['unis_url'],\
target=self.subscribeRuntime, args=(resource_name, self._unis,)).start()
def pushRuntime(self, resource_name):
'''
this function upload specified resource to UNIS
'''
def pushEntry(k, entry):
data = entry.prep_schema()
groups = data['selfRef'].split('/')
unis_str = '/'.join(groups[:3])
if unis_str in self._subunisclient:
uc = self._subunisclient[unis_str]
else:
uc = self._unis
# use attribute "ts" to indicate an object downloaded from UNIS, and
# only UPDATE the values of this kind of objects.
if hasattr(entry, 'ts'):
url = '/' + resource_name + '/' + getattr(entry, 'id')
uc.put(url, data)
else:
url = '/' + resource_name
uc.post(url, data)
while True:
try:
key, value = getattr(self, resource_name)['new'].popitem()
if not isinstance(value, list):
pushEntry(key, value)
else:
for item in value:
pushEntry(key, item)
except KeyError:
return
def subscribeRuntime(self, resource_name, currentclient):
'''
subscribe a channel(resource) to UNIS, and listen for any new updates on that channel
'''
#name = resources_subscription[resource_name]
name = resource_name
model = resources_classes[resource_name]
#url = self.unis_url.replace('http', 'ws', 1)
unis_url = currentclient.config['unis_url']
url = unis_url.replace('http', 'ws', 1)
url = url + '/subscribe/' + name
ws = create_connection(url)
data = ws.recv()
while data:
model(json.loads(data), self, currentclient, False)
data = ws.recv()
ws.close()
def poke_data(self, query):
'''
try to address this issue:
- ms stores lots of data, and may be separated from unis
- this data is accessible via /data url. They shouldn't be kept on runtime environment (too much)
- however, sometimes they may be needed. e.g. HELM schedules traceroute measurement, and needs the
results to schedule following iperf tests
'''
return self._unis.get('/data/' + query)
def post_data(self, data):
'''
same as poke_data, the other way around
'''
#headers = self._def_headers("data")
print data
return self._unis.pc.do_req('post', '/data', data)#, headers)
def main():
options = parse_arguments()
clusters_enabled = options["mapping_location"] is not None
if options["should_merge"] and not clusters_enabled:
raise "You need to provide a mapping to use merged view.`"
# Get the string containing the input matrix form a file/pipe
if options["matrix_location"] is not None:
with open(options["matrix_location"], 'r') as file:
matrix_string = file.read().strip().split("\n")
else:
matrix_string = sys.stdin
# Parse the input matrix string
height, width, matrix = parse_matrix(matrix_string, options["has_size"])
# Get the string containing the mapping if specified
if clusters_enabled:
with open(options["mapping_location"], 'r') as file:
mapping_string = file.read().strip()
mapping = parse_mapping(mapping_string, options["has_size"])
else:
mapping = None
if options["should_merge"]:
height, width, matrix, mapping = merge_clusters(matrix, mapping)
graph = Graph()
graph.add_vertex(height + width)
shape = graph.new_vertex_property("string")
color = graph.new_vertex_property("string")
index = graph.new_vertex_property("string")
for i in range(height):
v = graph.vertex(i)
shape[v] = "square"
color[v] = "red"
index[v] = str(i)
for i in range(width):
v = graph.vertex(height + i)
shape[v] = "circle"
if clusters_enabled:
color[v] = COLORS[mapping[i] % len(COLORS)]
else:
color[v] = COLORS[0]
index[v] = str(i)
for i in range(height):
for j in range(width):
if abs(matrix[i][j]) < EPSILON:
continue
graph.add_edge(graph.vertex(i), graph.vertex(height + j))
graph.set_directed(False)
if clusters_enabled:
groups = graph.new_vertex_property("int")
for i in range(width):
v = graph.vertex(height + i)
groups[v] = mapping[i]
position = sfdp_layout(graph, groups=groups)
else:
position = None
graph_draw(graph,
pos=position,
vertex_text=index,
vertex_shape=shape,
vertex_fill_color=color,
vertex_pen_width=1.2,
vertex_color="black",
edge_pen_width=3.4,
fit_view=True,
bg_color=(255, 255, 255, 1),
output=options["output_file"])
ver_id = pairs_graph.new_vertex_property("int")
for line in f:
spl_line = line.split(' ')
if len(spl_line) == 1:
continue
pos = int(spl_line[0])
neg = int(spl_line[1])
cur_weight = pos + coefficient * neg
w1 = spl_line[2].strip(' \n\uefef')
w2 = spl_line[3].strip(' \n\uefef')
if w1 not in word_dict:
v1 = pairs_graph.add_vertex()
ver_id[v1] = pairs_graph.vertex_index[v1]
word_dict[w1] = ver_id[v1]
ver_names[v1] = w1
else:
v1 = pairs_graph.vertex(word_dict[w1])
if w2 not in word_dict:
v2 = pairs_graph.add_vertex()
ver_id[v2] = pairs_graph.vertex_index[v2]
word_dict[w2] = ver_id[v2]
ver_names[v2] = w2
else:
v2 = pairs_graph.vertex(word_dict[w2])
if cur_weight == 0:
from graph_tool.all import Graph, graph_draw, fruchterman_reingold_layout
import json
network = json.load(open('../FRRNetwork.json', 'r'))
g = Graph(directed=False)
vprop_text = g.new_vertex_property("string")
vprop_color = g.new_vertex_property("int")
vprop_size = g.new_vertex_property("int")
vprop_shape = g.new_vertex_property("string")
name_to_vertex = {}
for switch in network['switches']:
v_switch = g.add_vertex()
name_to_vertex[switch['name']] = v_switch
vprop_text[v_switch] = switch['name']
vprop_color[v_switch] = 1
vprop_size[v_switch] = 50
vprop_shape[v_switch] = "hexagon"
for host in switch['hosts']:
v_host = g.add_vertex()
e_link = g.add_edge(v_switch, v_host)
vprop_text[v_host] = host['name']
vprop_color[v_host] = 100
vprop_size[v_host] = 40
vprop_shape[v_host] = "circle"
name_to_vertex[host['name']] = v_host
for link in network['switch_links']:
v_node1 = name_to_vertex[link['node1']['name']]
v_node2 = name_to_vertex[link['node2']['name']]
ver_id = pairs_graph.new_vertex_property("int")
for line in f:
spl_line = line.split(' ')
if len(spl_line) == 1:
continue
pos = int(spl_line[0])
neg = int(spl_line[1])
cur_weight = pos + coefficient * neg
w1 = spl_line[2].strip(' \n\uefef')
w2 = spl_line[3].strip(' \n\uefef')
if w1 not in word_dict:
v1 = pairs_graph.add_vertex()
ver_id[v1] = pairs_graph.vertex_index[v1]
word_dict[w1] = ver_id[v1]
ver_names[v1] = w1
else:
v1 = pairs_graph.vertex(word_dict[w1])
if w2 not in word_dict:
v2 = pairs_graph.add_vertex()
ver_id[v2] = pairs_graph.vertex_index[v2]
word_dict[w2] = ver_id[v2]
ver_names[v2] = w2
else:
v2 = pairs_graph.vertex(word_dict[w2])
if cur_weight == 0:
def build_closure(g, cand_source, terminals, infection_times, k=-1,
strictly_smaller=True,
debug=False,
verbose=False):
"""
build a clojure graph in which cand_source + terminals are all connected to each other.
the number of neighbors of each node is determined by k
the larger the k, the denser the graph"""
r2pred = {}
edges = {}
terminals = list(terminals)
# from cand_source to terminals
vis = init_visitor(g, cand_source)
cpbfs_search(g, source=cand_source, visitor=vis, terminals=terminals,
forbidden_nodes=terminals,
count_threshold=k)
r2pred[cand_source] = vis.pred
for u, v, c in get_edges(vis.dist, cand_source, terminals):
edges[(u, v)] = c
if debug:
print('cand_source: {}'.format(cand_source))
print('#terminals: {}'.format(len(terminals)))
print('edges from cand_source: {}'.format(edges))
if verbose:
terminals_iter = tqdm(terminals)
print('building closure graph')
else:
terminals_iter = terminals
# from terminal to other terminals
for root in terminals_iter:
if strictly_smaller:
late_terminals = [t for t in terminals
if infection_times[t] > infection_times[root]]
else:
# respect what the paper presents
late_terminals = [t for t in terminals
if infection_times[t] >= infection_times[root]]
late_terminals = set(late_terminals) - {cand_source} # no one can connect to cand_source
if debug:
print('root: {}'.format(root))
print('late_terminals: {}'.format(late_terminals))
vis = init_visitor(g, root)
cpbfs_search(g, source=root, visitor=vis, terminals=list(late_terminals),
forbidden_nodes=list(set(terminals) - set(late_terminals)),
count_threshold=k)
r2pred[root] = vis.pred
for u, v, c in get_edges(vis.dist, root, late_terminals):
if debug:
print('edge ({}, {})'.format(u, v))
edges[(u, v)] = c
if verbose:
print('returning closure graph')
gc = Graph(directed=True)
for _ in range(g.num_vertices()):
gc.add_vertex()
for (u, v) in edges:
gc.add_edge(u, v)
eweight = gc.new_edge_property('int')
eweight.set_2d_array(np.array(list(edges.values())))
# for e, c in edges.items():
# eweight[e] = c
return gc, eweight, r2pred
def build_region_closure(g, root, regions, infection_times, obs_nodes, debug=False):
"""return a closure graph on the the components"""
regions = copy(regions)
root_region = {'nodes': {root}, 'head': root, 'head_time': -float('inf')}
regions[len(regions)] = root_region
gc = Graph(directed=True)
for _ in range(len(regions)):
gc.add_vertex()
# connect each region
gc_edges = []
original_edge_info = {}
for i, j in combinations(regions, 2):
# make group i the one with *later* head
if regions[i]['head_time'] < regions[j]['head_time']:
i, j = j, i
if debug:
print('i, j={}, {}'.format(i, j))
# only need to connect head i to one of the nodes in group j
# where nodes in j have time stamp < head i
# then an edge from region j to region i (because j is earlier)
head_i = regions[i]['head']
def get_pseudo_time(n):
if n == root:
return - float('inf')
else:
return infection_times[n]
targets = [n for n in regions[j]['nodes'] if get_pseudo_time(n) < regions[i]['head_time']]
if debug:
print('head_i: {}'.format(head_i))
print('targets: {}'.format(targets))
print('regions[j]["nodes"]: {}'.format(regions[j]['nodes']))
if len(targets) == 0:
continue
visitor = init_visitor(g, head_i)
forbidden_nodes = list(set(regions[i]['nodes']) | (set(regions[j]['nodes']) - set(targets)))
if debug:
print('forbidden_nodes: {}'.format(forbidden_nodes))
# NOTE: count_threshold = 1
cpbfs_search(g, source=head_i,
terminals=targets,
forbidden_nodes=forbidden_nodes,
visitor=visitor,
count_threshold=1)
reachable_targets = [t for t in targets if visitor.dist[t] > 0]
if debug:
print('reachable_targets: {}'.format(reachable_targets))
if len(reachable_targets) == 0:
# cannot reach there
continue
source = min(reachable_targets, key=visitor.dist.__getitem__)
dist = visitor.dist[source]
assert dist > 0
gc_edges.append(((j, i, dist)))
original_edge_info[(j, i)] = {
'dist': dist,
'pred': visitor.pred,
'original_edge': (source, head_i)
}
for u, v, _ in gc_edges:
gc.add_edge(u, v)
eweight = gc.new_edge_property('int')
for u, v, c in gc_edges:
eweight[gc.edge(gc.vertex(u), gc.vertex(v))] = c
return gc, eweight, original_edge_info
def path_from_income(self):
"""
:return: GeoDataFrame representing path of commuter to work
"""
# Get jobs within range of income of commuter
streets = self.env.streets.copy().reset_index(drop=True)
origins = self.env.origins.copy()
destinations = self.env.destinations.copy()
destinations = destinations[
(destinations['salary_n'] > self.income[0])
& (destinations['salary_n'] < self.income[1])]
# Get shortest path to one random destination
osm_g = Graph(directed=False)
indices = {}
if len(destinations) > 0:
# Add vertices to graph
for i, osm_id in enumerate(
list(streets['from']) + list(streets['to'])):
v = osm_g.add_vertex()
v.index = int(i)
indices[osm_id] = i
# Add edges to graph
for i in list(streets.index):
o_osm = streets.at[i, 'from']
d_osm = streets.at[i, 'to']
osm_g.add_edge(indices[o_osm], indices[d_osm])
# Randomly choose destination
destination = destinations.loc[
np.random.choice(list(destinations.index)), :]
# Randomly choose origin parcel based on block id
origins = origins[
(origins['Landuse'].isin(['MFH', 'MFL', 'SFA', 'SFD', 'MX']))
& (origins['index_block'] == self.block_id)].reset_index(
drop=True)
if len(origins) > 0:
origin = origins.loc[np.random.choice(list(origins.index)), :]
# Get closest street of origin and destination
osm_origin = streets[streets.centroid == nearest_points(
origin['geometry'], streets.centroid.unary_union)[1]]
osm_destination = streets[streets.centroid == nearest_points(
destination['geometry'], streets.centroid.unary_union)[1]]
# Calculate shortest path
def keys(dictA, value):
return list(dictA.keys())[list(
dictA.values()).index(value)]
path = shortest_path(
osm_g, indices[osm_origin['from'].values[0]],
indices[osm_destination['to'].values[0]])[1]
path_gdf = pd.concat([
streets[
(streets['from'] == keys(indices, int(edge.source())))
& (streets['to'] == keys(indices, int(edge.target())))]
for edge in path
])
return path_gdf
else:
return None
else:
return None
def gen_graph((repo, events)):
graph = Graph()
repo_on_graph = graph.new_graph_property('string')
repo_on_graph[graph] = repo
graph.graph_properties['repo_on_graph'] = repo_on_graph
language_on_graph = graph.new_graph_property('string')
language_on_graph[graph] = events[0]['language']
graph.graph_properties['language_on_graph'] = language_on_graph
events_on_vertices = graph.new_vertex_property('object')
graph.vertex_properties['events_on_vertices'] = events_on_vertices
actors_on_vertices = graph.new_vertex_property('string')
graph.vertex_properties['actors_on_vertices'] = actors_on_vertices
weights_on_edges = graph.new_edge_property('long double')
graph.edge_properties['weights_on_edges'] = weights_on_edges
# pre_vertices = []
pre_events_map = {}
pre_vertices_map = {}
# owner_vertex = graph.add_vertex()
# owner = repo.split('/')[0]
# actors_on_vertices[owner_vertex] = owner
# pre_vertices_map[owner] = owner_vertex
events = sorted(events, key=lambda x: x['created_at'])
for event in events:
actor = event['actor']
if actor in pre_events_map:
continue
created_at = event['created_at']
vertex = graph.add_vertex()
events_on_vertices[vertex] = event
actors_on_vertices[vertex] = actor
if 'actor-following' not in event:
continue
following = set(event['actor-following'])
commons = following.intersection(pre_vertices_map.keys())
# pre_vertices.append(vertex)
# if len(commons) == 0:
# edge = graph.add_edge(vertex, owner_vertex)
# weights_on_edges[edge] = 1.0
for pre_actor in commons:
edge = graph.add_edge(vertex, pre_vertices_map[pre_actor])
interval =\
(created_at - pre_events_map[pre_actor]['created_at']).days
weight = 1.0 / fib(interval + 2)
weights_on_edges[edge] = weight
pre_events_map[actor] = event
pre_vertices_map[actor] = vertex
return graph
class Workflow:
def __init__(self, edges, weights):
self.edges = edges
self.graph = Graph()
self.size = len(edges['target'])
self.graph.add_vertex(self.size)
self.weights = weights
# init weights part
self.graph.vp.weights = self.graph.new_vertex_property('int16_t')
for index in range(0, self.size):
self.graph.vp.weights[index] = weights[index]
for source in self.edges['source'].keys():
for target in self.edges['source'][source]:
self._add_edge(source, target)
self.depth_per_node = {x: 0 for x in range(0, self.size)}
self.accum_weights = {x: 0 for x in range(0, self.size)}
self.find_depth()
self.find_accum_weights(self.size - 1)
self.depth = {x: [] for x in set(self.depth_per_node.values())}
for node, depth in self.depth_per_node.items():
self.depth[depth].append(node)
self.routes_t = {}
self.find_routes(self.size - 1, 0, self.routes_t)
self.routes = []
self.transpose_routes(self.size - 1, self.routes_t[self.size - 1])
def _add_edge(self, source, target):
self.graph.add_edge(self.graph.vertex(source),
self.graph.vertex(target))
def show(self, size=1500):
return graph_draw(self.graph,
vertex_text=self.graph.vertex_index,
vertex_font_size=18,
output_size=(size, size),
output="graph.png")
def find_accum_weights(self, actual_node, accum_weight=0):
already_accum_weight = self.accum_weights[actual_node]
self.accum_weights[actual_node] = max(
already_accum_weight, accum_weight + self.weights[actual_node])
for fathers in self.edges['target'][actual_node]:
self.find_accum_weights(fathers, self.accum_weights[actual_node])
def find_depth(self, actual_node=0, actual_depth=0):
self.depth_per_node[actual_node] = max(
self.depth_per_node[actual_node], actual_depth)
for next_node in self.edges['source'][actual_node]:
self.find_depth(next_node, actual_depth + 1)
def find_routes(self, actual_node, weight=0, routes={}):
weight += self.weights[actual_node]
if actual_node != 0:
routes[actual_node] = {}
for fathers in self.edges['target'][actual_node]:
self.find_routes(fathers, weight, routes[actual_node])
else:
routes[actual_node] = weight
def transpose_routes(self, actual_node, routes, path=[]):
if actual_node != 0:
path = path.copy()
path.append(actual_node)
for child in routes.keys():
self.transpose_routes(child, routes[child], path)
else:
self.routes.append({'path': path, 'weight': routes})
def find_cycles(self):
visited = [False for _ in range(0, self.size)]
return self.find_cycles_helper(0, [])
def find_cycles_helper(self, actual_node, rec_list):
if actual_node in rec_list:
print(rec_list, actual_node)
return True
call_this = []
for child in self.edges['source'][actual_node]:
new_rec_list = rec_list.copy()
new_rec_list.append(actual_node)
call_this.append([child, new_rec_list])
return any([self.find_cycles_helper(x[0], x[1]) for x in call_this])
def steiner_tree_mst(g, root, infection_times, source, terminals,
closure_builder=build_closure,
strictly_smaller=True,
return_closure=False,
k=-1,
debug=False,
verbose=True):
gc, eweight, r2pred = closure_builder(g, root, terminals,
infection_times,
strictly_smaller=strictly_smaller,
k=k,
debug=debug,
verbose=verbose)
# get the minimum spanning arborescence
# graph_tool does not provide minimum_spanning_arborescence
if verbose:
print('getting mst')
gx = gt2nx(gc, root, terminals, edge_attrs={'weight': eweight})
try:
nx_tree = nx.minimum_spanning_arborescence(gx, 'weight')
except nx.exception.NetworkXException:
if debug:
print('fail to find mst')
if return_closure:
return None, gc, None
else:
return None
if verbose:
print('returning tree')
mst_tree = Graph(directed=True)
for _ in range(g.num_vertices()):
mst_tree.add_vertex()
for u, v in nx_tree.edges():
mst_tree.add_edge(u, v)
if verbose:
print('extract edges from original graph')
# extract the edges from the original graph
# sort observations by time
# and also topological order
topological_index = {}
for i, e in enumerate(bfs_iterator(mst_tree, source=root)):
topological_index[int(e.target())] = i
sorted_obs = sorted(
set(terminals) - {root},
key=lambda o: (infection_times[o], topological_index[o]))
tree_nodes = {root}
tree_edges = set()
# print('root', root)
for u in sorted_obs:
if u in tree_nodes:
if debug:
print('{} covered already'.format(u))
continue
# print(u)
v, u = map(int, next(mst_tree.vertex(u).in_edges())) # v is ancestor
tree_nodes.add(v)
late_nodes = [n for n in terminals if infection_times[n] > infection_times[u]]
vis = init_visitor(g, u)
# from child to any tree node, including v
cpbfs_search(g, source=u, terminals=list(tree_nodes),
forbidden_nodes=late_nodes,
visitor=vis,
count_threshold=1)
# dist, pred = shortest_distance(g, source=u, pred_map=True)
node_set = {v for v, d in vis.dist.items() if d > 0}
reachable_tree_nodes = node_set.intersection(tree_nodes)
ancestor = min(reachable_tree_nodes, key=vis.dist.__getitem__)
edges = extract_edges_from_pred(g, u, ancestor, vis.pred)
edges = {(j, i) for i, j in edges} # need to reverse it
if debug:
print('tree_nodes', tree_nodes)
print('connecting {} to {}'.format(v, u))
print('using ancestor {}'.format(ancestor))
print('adding edges {}'.format(edges))
tree_nodes |= {u for e in edges for u in e}
tree_edges |= edges
t = Graph(directed=True)
for _ in range(g.num_vertices()):
t.add_vertex()
for u, v in tree_edges:
t.add_edge(t.vertex(u), t.vertex(v))
tree_nodes = {u for e in tree_edges for u in e}
vfilt = t.new_vertex_property('bool')
vfilt.a = False
for v in tree_nodes:
vfilt[t.vertex(v)] = True
t.set_vertex_filter(vfilt)
if return_closure:
return t, gc, mst_tree
else:
return t
class Network:
def __init__(self, nodes_info=None, links_info=None, file_name=None):
self.g = Graph()
if nodes_info and links_info:
self.nodes_info = nodes_info
self.links_info = links_info
self.g.vertex_properties["name"] = self.g.new_vertex_property(
'string')
self.g.vertex_properties["id"] = self.g.new_vertex_property(
'int32_t')
self.g.edge_properties["weight"] = self.g.new_edge_property(
'int32_t')
self.create_network()
self.g.vertex_properties["pagerank"] = pagerank(
self.g, weight=self.g.edge_properties["weight"])
self.g.vertex_properties[
"degree_centrality"] = self.degree_centrality()
elif file_name:
self.load_network(file_name)
def create_network(self):
# Add Nodes
for node in self.nodes_info:
self.add_n(node)
# Add Links
for link in self.links_info:
n_loser = 0
n_winner = 0
loser = link['loser']
winner = link['winner']
weight = link['rounds']
for team_id in self.g.vertex_properties.id:
if loser == team_id:
break
n_loser += 1
for team_id in self.g.vertex_properties.id:
if winner == team_id:
break
n_winner += 1
self.add_l(n_loser, n_winner, 16 / weight * 100)
def load_network(self, file_name):
new_file_name = '..' + sep + '..' + sep + 'network-graphs' + sep + file_name
self.g.load(new_file_name, fmt="gt")
def get_normalized_pagerank(self):
max_pgr = 0
for pgr in self.g.vertex_properties.pagerank:
if pgr > max_pgr:
max_pgr = pgr
return [
self.g.vertex_properties.pagerank[v] / max_pgr
for v in self.g.vertices()
]
def add_n(self, node_info):
n = self.g.add_vertex()
self.g.vertex_properties.id[n] = node_info['id']
self.g.vertex_properties.name[n] = node_info['Team_Name']
def add_l(self, loser, winner, weight):
n1 = self.g.vertex(loser)
n2 = self.g.vertex(winner)
l = self.g.add_edge(n1, n2)
self.g.edge_properties.weight[l] = weight
def draw(self, output_file, fmt):
graph_draw(self.g,
vertex_text=self.g.vertex_index,
output=output_file,
fmt=fmt)
def save_network(self, file_name):
try:
new_file_name = '..' + sep + '..' + sep + 'network-graphs' + sep + file_name
self.g.save(new_file_name, fmt="gt")
except:
return False
return True
def vp_pagerank(self):
return self.g.vertex_properties.pagerank
def vp_degree_cent(self):
return self.g.vertex_properties.degree_centrality
def vp_name(self):
return self.g.vertex_properties.name
def vp_id(self):
return self.g.vertex_properties.id
def ep_weight(self):
return self.g.edge_properties.weight
# Calcula as características básicas da rede
def get_basic_info(self):
info = {}
try:
n_vertices = self.g.num_vertices()
n_edges = self.g.num_edges()
density = n_edges / ((n_vertices * (n_vertices - 1)) / 2)
mean_degree = (2 * n_edges) / n_vertices
# Cálculo do coeficiente de clusterização "na mão", usando a média dos
# coeficientes locais calculados pela Graph Tools
local_cc = local_clustering(self.g)
clustering_coef = fsum(
[local_cc[x] for x in self.g.vertices() if local_cc[x] != 0.0])
clustering_coef /= n_vertices
info["Número de times"] = n_vertices
info["Número de confrontos"] = n_edges
info["Densidade"] = density
info["Grau médio"] = mean_degree
info["Coeficiente de Clusterização"] = clustering_coef
except:
info.clear()
return info
def degree_centrality(self):
degree_centrality = self.g.new_vertex_property('float')
for v in self.g.vertices():
degree_centrality[v] = v.in_degree() / (self.g.num_vertices() - 1)
return degree_centrality
# Calcula a distribuição de graus da rede
def degree_distribution(self):
degree_dist = {}
try:
for v in self.g.vertices():
if v.in_degree() not in degree_dist.keys():
degree_dist[v.in_degree()] = 1
else:
degree_dist[v.in_degree()] += 1
for k in degree_dist.keys():
degree_dist[k] /= self.g.num_vertices()
except:
degree_dist.clear()
return degree_dist
class GeneralGraph():
"""
General wrapper for graph-tool or networkx graphs to add edges and nodes
according to constraints
"""
def __init__(self, directed=True, verbose=1):
self.graphtool = GRAPH_TOOL
# Initialize graph
if self.graphtool:
self.graph = Graph(directed=directed)
self.weight = self.graph.new_edge_property("float")
else:
if directed:
print("directed graph")
self.graph = nx.DiGraph()
else:
self.graph = nx.Graph()
# set metaparameter
self.time_logs = {}
self.verbose = verbose
def set_edge_costs(self,
layer_classes=["resistance"],
class_weights=[1],
**kwargs):
"""
Initialize edge cost variables
:param classes: list of cost categories
:param weights: list of weights for cost categories - must be of same
shape as classes (if None, then equal weighting)
"""
class_weights = np.array(class_weights)
# set different costs:
self.cost_classes = layer_classes
if self.graphtool:
self.cost_props = [
self.graph.new_edge_property("float")
for _ in range(len(layer_classes))
]
self.cost_weights = class_weights / np.sum(class_weights)
if self.verbose:
print(self.cost_classes, self.cost_weights)
# save weighted instance for plotting
self.instance = np.sum(
np.moveaxis(self.cost_instance, 0, -1) * self.cost_weights,
axis=2) * self.hard_constraints
def set_shift(self,
start,
dest,
pylon_dist_min=3,
pylon_dist_max=5,
max_angle=np.pi / 2,
**kwargs):
"""
Initialize shift variable by getting the donut values
:param lower, upper: min and max distance of pylons
:param vec: vector of diretion of edges
:param max_angle: Maximum angle of edges to vec
"""
vec = dest - start
if self.verbose:
print("SHIFT:", pylon_dist_min, pylon_dist_max, vec, max_angle)
self.shifts = get_half_donut(pylon_dist_min,
pylon_dist_max,
vec,
angle_max=max_angle)
self.shift_tuples = self.shifts
def set_corridor(self,
dist_surface,
start_inds,
dest_inds,
sample_func="mean",
sample_method="simple",
factor_or_n_edges=1):
# set new corridor
corridor = (dist_surface > 0).astype(int)
self.factor = factor_or_n_edges
self.cost_rest = self.cost_instance * (self.hard_constraints >
0).astype(int) * corridor
# downsample
tic = time.time()
if self.factor > 1:
self.cost_rest = CostUtils.downsample(self.cost_rest,
self.factor,
mode=sample_method,
func=sample_func)
self.time_logs["downsample"] = round(time.time() - tic, 3)
# repeat because edge artifacts
self.cost_rest = self.cost_rest * (self.hard_constraints >
0).astype(int) * corridor
# add start and end TODO ugly
self.cost_rest[:, dest_inds[0],
dest_inds[1]] = self.cost_instance[:, dest_inds[0],
dest_inds[1]]
self.cost_rest[:, start_inds[0],
start_inds[1]] = self.cost_instance[:, start_inds[0],
start_inds[1]]
def add_nodes(self, nodes):
"""
Add vertices to the graph
param nodes: list of node names if networkx, integer if graphtool
"""
tic = time.time()
# add nodes to graph
if self.graphtool:
_ = self.graph.add_vertex(nodes)
self.n_nodes = len(list(self.graph.vertices()))
else:
self.graph.add_nodes_from(np.arange(nodes))
self.n_nodes = len(self.graph.nodes())
# verbose
if self.verbose:
print("Added nodes:", nodes, "in time:", time.time() - tic)
self.time_logs["add_nodes"] = round(time.time() - tic, 3)
def add_edges(self):
tic_function = time.time()
n_edges = 0
# kernels, posneg = ConstraintUtils.get_kernel(self.shifts,
# self.shift_vals)
# edge_array = []
times_edge_list = []
times_add_edges = []
if self.verbose:
print("n_neighbors:", len(self.shift_tuples))
for i in range(len(self.shift_tuples)):
tic_edges = time.time()
# set cost rest if necessary (random graph)
self.set_cost_rest()
# compute shift and weights
out = self._compute_edges(self.shift_tuples[i])
# Error if -1 entries because graph-tool crashes with -1 nodes
if np.any(out[:, :2].flatten() < 0):
print(np.where(out[:, :2] < 0))
raise RuntimeError
n_edges += len(out)
times_edge_list.append(round(time.time() - tic_edges, 3))
# add edges to graph
tic_graph = time.time()
if self.graphtool:
self.graph.add_edge_list(out, eprops=self.cost_props)
else:
nx_edge_list = [(e[0], e[1], {
"weight": np.sum(e[2:] * self.cost_weights)
}) for e in out]
self.graph.add_edges_from(nx_edge_list)
times_add_edges.append(round(time.time() - tic_graph, 3))
# alternative: collect edges here and add alltogether
# edge_array.append(out)
# # alternative: add edges all in one go
# tic_concat = time.time()
# edge_lists_concat = np.concatenate(edge_array, axis=0)
# self.time_logs["concatenate"] = round(time.time() - tic_concat, 3)
# print("time for concatenate:", self.time_logs["concatenate"])
# tic_graph = time.time()
# self.graph.add_edge_list(edge_lists_concat, eprops=[self.weight])
# self.time_logs["add_edges"] = round(
# (time.time() - tic_graph) / len(shifts), 3
# )
self.n_edges = len(list(self.graph.edges()))
self._update_time_logs(times_add_edges, times_edge_list, tic_function)
if self.verbose:
print("DONE adding", n_edges, "edges:", time.time() - tic_function)
def _update_time_logs(self, times_add_edges, times_edge_list,
tic_function):
self.time_logs["add_edges"] = round(np.mean(times_add_edges), 3)
self.time_logs["add_edges_times"] = times_add_edges
self.time_logs["edge_list"] = round(np.mean(times_edge_list), 3)
self.time_logs["edge_list_times"] = times_edge_list
self.time_logs["add_all_edges"] = round(time.time() - tic_function, 3)
if self.verbose:
print("Done adding edges:", len(list(self.graph.edges())))
def sum_costs(self):
"""
Additive weighting of costs
Take the individual edge costs, compute weighted sum --> self.weight
"""
# add sum of all costs
if not self.graphtool:
return
tic = time.time()
summed_costs_arr = np.zeros(self.cost_props[0].get_array().shape)
for i in range(len(self.cost_props)):
prop = self.cost_props[i].get_array()
summed_costs_arr += prop * self.cost_weights[i]
self.weight.a = summed_costs_arr
self.time_logs["sum_of_costs"] = round(time.time() - tic, 3)
def remove_vertices(self, dist_surface, delete_padding=0):
"""
Remove edges in a certain corridor (or all) to replace them by
a refined surface
@param dist_surface: a surface where each pixel value corresponds to
the distance of the pixel to the shortest path
@param delete_padding: define padding in which part of the corridor to
delete vertices (cannot delete all because then graph unconnected)
"""
tic = time.time()
self.graph.clear_edges()
self.graph.shrink_to_fit()
self.time_logs["remove_edges"] = round(time.time() - tic, 3)
def get_pareto(self,
vary,
source,
dest,
out_path=None,
compare=[0, 1],
plot=1):
"""
Arguments:
vary: how many weights to explore
e.g 3 --> each cost class can have weight 0, 0.5 or 1
source, dest: as always the source and destination vertex
out_path: where to save the pareto figure(s)
compare: indices of cost classes to compare
Returns:
paths: All found paths
pareto: The costs for each combination of weights
"""
tic = time.time()
# initialize lists
pareto = list()
paths = list()
cost_sum = list()
# get the edge costs
cost_arrs = [cost.get_array() for cost in self.cost_props]
# [self.cost_props[comp].get_array() for comp in compare]
# get vary weights between 0 and 1
var_weights = np.around(np.linspace(0, 1, vary), 2)
# construct weights array
if len(compare) == 2:
weights = [[v, 1 - v] for v in var_weights]
elif len(compare) == 3:
weights = list()
for w0 in var_weights:
for w1 in var_weights[var_weights <= 1 - w0]:
weights.append([w0, w1, 1 - w0 - w1])
else:
raise ValueError("argument compare can only have length 2 or 3")
# w_avail: keep weights of non-compare classes, get leftover amount
w_avail = np.sum(np.asarray(self.cost_weights)[compare])
# compute paths for each combination of weights
for j in range(len(weights)):
# option 2: np.zeros(len(cost_arrs)) + non_compare_weight
w = self.cost_weights.copy()
# replace the ones we want to compare
w[compare] = np.array(weights[j]) * w_avail
# weighted sum of edge costs
self.weight.a = np.sum(
[cost_arrs[i] * w[i] for i in range(len(cost_arrs))], axis=0)
# get shortest path
path, path_costs, _ = self.get_shortest_path(source, dest)
# don't take cost_sum bc this is sum of original weighting
pareto.append(np.sum(path_costs, axis=0)[compare])
paths.append(path)
# take overall sum of costs (unweighted) that this w leads to
cost_sum.append(np.sum(path_costs))
# print best weighting
best_weight = np.argmin(cost_sum)
w = self.cost_weights.copy()
w[compare] = np.array(weights[best_weight]) * w_avail
print("Best weights:", w, "with (unweighted) costs:", np.min(cost_sum))
self.time_logs["pareto"] = round(time.time() - tic, 3)
pareto = np.array(pareto)
classes = [self.cost_classes[comp] for comp in compare]
# Plotting
if plot:
if len(compare) == 2:
plot_pareto_scatter_2d(pareto,
weights,
classes,
cost_sum=cost_sum,
out_path=out_path)
elif len(compare) == 3:
# plot_pareto_3d(pareto, weights, classes)
plot_pareto_scatter_3d(pareto,
weights,
classes,
cost_sum=cost_sum,
out_path=out_path)
return paths, weights, cost_sum
def get_shortest_path(self, source, target):
"""
Compute shortest path from source vertex to target vertex
"""
tic = (time.time())
# #if source and target are given as indices:
if self.graphtool:
vertices_path, _ = shortest_path(self.graph,
source,
target,
weights=self.weight,
negative_weights=True)
else:
try:
vertices_path = nx.dijkstra_path(self.graph, source, target)
except nx.exception.NetworkXNoPath:
return []
self.time_logs["shortest_path"] = round(time.time() - tic, 3)
return vertices_path
def save_graph(self, OUT_PATH):
"""
Save the graph in OUT_PATH
"""
if self.graphtool:
for i, cost_class in enumerate(self.cost_classes):
self.graph.edge_properties[cost_class] = self.cost_props[i]
self.graph.edge_properties["weight"] = self.weight
self.graph.save(OUT_PATH + ".xml.gz")
else:
nx.write_weighted_edgelist(self.graph,
OUT_PATH + '.weighted.edgelist')
def load_graph(self, IN_PATH):
"""
Retrieve graph from IN_PATH
"""
if self.graphtool:
self.g_prev = load_graph(IN_PATH + ".xml.gz")
self.weight_prev = self.g_prev.ep.weight
# weight = G2.ep.weight[G2.edge(66, 69)]
else:
self.g_prev = nx.read_edgelist(IN_PATH + '.weighted.edgelist',
nodetype=int,
data=(('weight', float), ))
# -----------------------------------------------------------------------
# INTERFACE
def single_sp(self, **kwargs):
"""
Function for full processing until shortest path
"""
self.start_inds = kwargs["start_inds"]
self.dest_inds = kwargs["dest_inds"]
self.set_shift(self.start_inds, self.dest_inds, **kwargs)
# self.set_corridor(
# np.ones(self.hard_constraints.shape) * 0.5,
# self.start_inds,
# self.dest_inds,
# factor_or_n_edges=1
# )
if self.verbose:
print("1) Initialize shifts and instance (corridor)")
self.set_edge_costs(**kwargs)
# add vertices
self.add_nodes()
if self.verbose:
print("2) Initialize distances to inf and predecessors")
self.add_edges()
if self.verbose:
print("3) Compute source shortest path tree")
print("number of vertices and edges:", self.n_nodes, self.n_edges)
# weighted sum of all costs
self.sum_costs()
source_v, target_v = self.add_start_and_dest(self.start_inds,
self.dest_inds)
# get actual best path
path, path_costs, cost_sum = self.get_shortest_path(source_v, target_v)
if self.verbose:
print("4) shortest path", cost_sum)
return path, path_costs, cost_sum
def gen_graph((repo, events)):
graph = Graph()
repo_on_graph = graph.new_graph_property('string')
repo_on_graph[graph] = repo
graph.graph_properties['repo_on_graph'] = repo_on_graph
language_on_graph = graph.new_graph_property('string')
language_on_graph[graph] = events[0]['language']
graph.graph_properties['language_on_graph'] = language_on_graph
events_on_vertices = graph.new_vertex_property('object')
graph.vertex_properties['events_on_vertices'] = events_on_vertices
actors_on_vertices = graph.new_vertex_property('string')
graph.vertex_properties['actors_on_vertices'] = actors_on_vertices
weights_on_edges = graph.new_edge_property('long double')
graph.edge_properties['weights_on_edges'] = weights_on_edges
# pre_vertices = []
pre_events_map = {}
pre_vertices_map = {}
owner_vertex = graph.add_vertex()
owner = repo.split('/')[0]
dummy_event = {'created_at': events[0]['repo-created_at']}
actors_on_vertices[owner_vertex] = owner
events_on_vertices[owner_vertex] = dummy_event
pre_vertices_map[owner] = owner_vertex
pre_events_map[owner] = dummy_event
events = sorted(events, key=lambda x: x['created_at'])
for event in events:
actor = event['actor']
if actor in pre_events_map:
continue
created_at = event['created_at']
vertex = graph.add_vertex()
events_on_vertices[vertex] = event
actors_on_vertices[vertex] = actor
# if 'actor-following' not in event:
# continue
following = set(event['actor-following'])
commons = following.intersection(pre_vertices_map.keys())
# pre_vertices.append(vertex)
# if len(commons) == 0:
# edge = graph.add_edge(vertex, owner_vertex)
# weights_on_edges[edge] = 1.0
for pre_actor in commons:
interval =\
(created_at - pre_events_map[pre_actor]['created_at']).days
if interval < 0:
continue
edge = graph.add_edge(vertex, pre_vertices_map[pre_actor])
if pre_actor == owner:
weight = 1.0
else:
weight = 1.0 / fib(interval + 2)
weights_on_edges[edge] = weight
pre_events_map[actor] = event
pre_vertices_map[actor] = vertex
return graph
data = client.get_collection_view('[database page link]')
graph = Graph(directed=False)
# Dictionary from entry id to vertex id to keep track of vertices that have been added
entries = {}
vertex_names = graph.new_vertex_property('string')
# For every table entry
for row in data.collection.get_rows():
# If this entry is not in the graph, add it
if row.id not in entries:
entries[row.id] = graph.add_vertex()
vertex_names[entries[row.id]] = row.title
# Add the links of the current entry to the graph
for child in row.children:
if isinstance(child, CollectionRowBlock):
if child.id not in entries:
entries[child.id] = graph.add_vertex()
vertex_names[entries[child.id]] = child.title
graph.add_edge(entries[row.id], entries[child.id])
graph_draw(graph,
vertex_text=vertex_names,
vertex_font_size=8,
class TTC(AbstractMatchingAlgorithm):
"""This class searches for cycles where each student gets his best option.
This takes a list of students, a list of schools and a ruleset
(which is used to calculate priorities).
This works by generating a directed graph, where each student points
at at his best option, and each school points at the student (or students)
with the highest priority.
"""
EDGE_WIDTH_SIZE_FACTOR = 700
"""Size factor (in the image) of each edge that is not part of the main cycle."""
EDGE_WIDTH_CYCLE_SIZE = 10
"""Size factor (in the image) of each edge that takes part of the main cycle."""
def __init__(self,
generate_images=False,
images_folder="TTC_images",
use_longest_cycle=True):
"""Initializes the algorithm.
:param generate_images: If the process generates images or not.
:type generate_images: bool
:param images_folder: Where images are saved.
:type images_folder: str
:param use_longest_cycle: If the algorithm applies the longest cycle available, or the first one encountered.
:type use_longest_cycle: bool
"""
self.generate_images = generate_images
self.images_folder = images_folder
self.use_longest_cycle = use_longest_cycle
self.__graph = None
self.__vertices_by_school_id = None
self.__vertices_by_student_id = None
self.__students_by_id = None
self.__schools_by_id = None
self.__entity_id = None
self.__entity_type = None
def reset_variables(self):
"""Resets all variables."""
self.__graph = Graph()
self.__vertices_by_school_id = {}
self.__vertices_by_student_id = {}
self.__students_by_id = {}
self.__schools_by_id = {}
self.__entity_id = self.__graph.new_vertex_property("int")
self.__graph.vertex_properties["entity_id"] = self.__entity_id
self.__entity_type = self.__graph.new_vertex_property("string")
self.__graph.vertex_properties["entity_type"] = self.__entity_type
def run(self, students, schools, ruleset):
"""Runs the algorithm.
First it creates the graph, then it lists all the cycles available,
after that it selects one cycle, and applies it. Finally, it starts
the process again.
:param students: List of students.
:type students: list
:param schools: List of school.
:type schools: list
:param ruleset: Set of rules used.
:type ruleset: Ruleset
"""
self.reset_variables()
can_improve = True
iteration_counter = 1
while can_improve:
self.structure_graph(students, schools)
cycles = [c for c in all_circuits(self.__graph, unique=True)]
# print("CYCLES", cycles, "iteration", iteration_counter)
cycle_edges = []
if cycles:
for cycle in cycles: # ToDo: Possible optimisation: apply all disjoint cycles at once
for current_v_index in range(len(cycle)):
next_v_index = (current_v_index + 1) % len(cycle)
from_v = self.__graph.vertex(cycle[current_v_index])
target_v = self.__graph.vertex(cycle[next_v_index])
edge = self.__graph.edge(from_v, target_v)
cycle_edges.append(edge)
if self.__entity_type[from_v] == "st":
sel_student = self.__students_by_id[
self.__entity_id[from_v]]
sel_school = self.__schools_by_id[
self.__entity_id[target_v]]
sel_student.assigned_school = sel_school
sel_school.assignation.append(sel_student)
# vertex_school_target_id = self.__entity_id[target_v]
# vertex_school_target = self.__schools_by_id[vertex_school_target_id]
# print("CYCLE: Student", sel_student.id, "School", sel_school.id)
# print("VVV: School {} -> School {} (Student {}) ".format(self.__entity_id[from_v], self.__entity_id[target_v], self.__entity_id[self.__graph.edge(from_v, target_v)]))
if self.generate_images:
self.generate_image(cycle_edges,
iteration_n=iteration_counter)
else:
can_improve = False
self.__graph.clear()
iteration_counter += 1
def structure_graph(self, students, schools):
"""Creates a graph where students points to schools, and schools points to students.
In the graph, each student points at at his best option, and each school points
at the student (or students) with the highest priority.
:param students: List of students.
:type students: list
:param schools:
:type schools: list
"""
if not self.__students_by_id and not self.__schools_by_id:
for student in students:
self.__students_by_id[student.id] = student
for school in schools:
self.__schools_by_id[school.id] = school
for school in schools:
setattr(school, 'preferences',
StudentQueue(school, preference_mode=True))
remaining_students = [
student for student in students if not student.assigned_school
]
for student in remaining_students:
for pref_school in student.preferences:
pref_school.preferences.append(student)
for student in remaining_students:
v_source_student = self.create_vertex_student(student)
pref_school = next(
(school for school in student.preferences if
len(school.assignation.get_all_students()) < school.capacity),
None)
if pref_school:
v_target_school = self.create_vertex_school(pref_school)
self.create_edge(v_source_student, v_target_school)
for school in schools:
if len(school.assignation.get_all_students()) < school.capacity:
v_source_school = self.create_vertex_school(school)
pref_student = next(
iter(school.preferences.get_all_students()), None)
if pref_student:
v_target_student = self.create_vertex_student(pref_student)
self.create_edge(v_source_school, v_target_student)
# graph_draw(self.__graph,
# vertex_text=self.__entity_id, vertex_shape="circle",
# output_size=(1000, 1000), bg_color=[1., 1., 1., 1], output="graph.png")
def create_vertex_student(self, student):
"""Defines a new student as a vertex in the graph (if it did not existed before)."""
if student.id in self.__vertices_by_student_id:
vertex = self.__vertices_by_student_id[student.id]
else:
vertex = self.__graph.add_vertex()
self.__vertices_by_student_id[student.id] = vertex
self.__entity_id[vertex] = student.id
self.__entity_type[
vertex] = "st" # ToDo: There may be other ways to do this.
return vertex
def create_vertex_school(self, school):
"""Defines a new school as a vertex in the graph (if it did not existed before)."""
if school.id in self.__vertices_by_school_id:
vertex = self.__vertices_by_school_id[school.id]
else:
vertex = self.__graph.add_vertex()
self.__vertices_by_school_id[school.id] = vertex
self.__entity_id[vertex] = school.id
self.__entity_type[vertex] = "sc"
return vertex
def create_edge(self, source_v, target_v):
"""Creates a directed edge between two vertices."""
self.__graph.add_edge(source_v, target_v)
def generate_image(self, cycle_edges, iteration_n=0):
"""Generates an image of a graph.
:param cycle_edges: Edges which are part of the main cycle (they will be highlighted in red).
:type cycle_edges: list
:param iteration_n: Number of iteration of the algorithm (this is added in the filename of the image).
:type iteration_n: int
.. DANGER::
This is an experimental feature.
"""
edge_color = self.__graph.new_edge_property("vector<float>")
edge_width = self.__graph.new_edge_property("int")
for edge in self.__graph.edges():
if edge in cycle_edges:
edge_color[edge] = [1., 0.2, 0.2, 0.999]
edge_width[edge] = 7
else:
edge_color[edge] = [0., 0., 0., 0.3]
edge_width[edge] = 4
vertex_shape = self.__graph.new_vertex_property("string")
vertex_size = self.__graph.new_vertex_property("int")
for vertex in self.__graph.vertices():
if self.__entity_type[vertex] == "st":
vertex_shape[vertex] = "circle"
vertex_size[vertex] = 1
else:
vertex_shape[vertex] = "double_circle"
vertex_size[vertex] = 100
# pos = sfdp_layout(self.__graph, C=10, p=5, theta=2, gamma=1)
pos = arf_layout(self.__graph, d=0.2, a=3)
graph_draw(
self.__graph,
pos=pos,
vertex_text=self.__entity_id,
vertex_font_size=
1, # ToDo: Move image related code outside the class.
vertex_fill_color=[0.97, 0.97, 0.97, 1],
vertex_color=[0.05, 0.05, 0.05, 0.95],
vertex_shape=vertex_shape,
edge_color=edge_color,
edge_pen_width=edge_width,
output_size=(1000, 1000),
bg_color=[1., 1., 1., 1],
output=self.generate_filename(iteration_n))
def generate_filename(self, iteration_n): # ToDo: Move this to utils
"""Returns a filename (which is used to generate the images)."""
filename = "Graph (iteration {})".format(
iteration_n) if iteration_n > 0 else "Graph"
output_file = gen_filepath(self.images_folder,
filename=filename,
extension="png")
return output_file
