class RoadMap(object):
def __init__(self, mapfile):
self._mapfile = mapfile
self.DIRECTION_index = 6
self.PATHCLASS_index = 20
self.g = Graph()
self.g.edge_properties["length"] = self.g.new_edge_property("double")
self.g.edge_properties["level"] = self.g.new_edge_property("int")
self.g.vertex_properties["pos"] = self.g.new_vertex_property("vector<double>")
self.cross_pos_index = {}
def load(self):
if self._mapfile[-3:] != 'shp':
self.g = load_graph(self._mapfile)
return
try:
sf = shapefile.Reader(self._mapfile)
except Exception as e:
print(str(e))
return False
roads_records = sf.shapeRecords() # 获取路段信息'
for road_record in roads_records:
cross_s_index = self.add_cross(road_record.shape.points[0])
cross_e_index = self.add_cross(road_record.shape.points[-1])
self.add_road_edge(cross_s_index, cross_e_index, road_record)
if int(road_record.record[self.DIRECTION_index]) == 0: # 若路段是双向车道
self.add_road_edge(cross_e_index, cross_s_index, road_record)
return True
def has_edge(self, s_vertex, e_vertex):
if self.g.num_vertices() >= max(s_vertex, e_vertex):
return self.g.edge(s_vertex, e_vertex)
else:
return None
def add_cross(self, cross_pos):
if cross_pos in self.cross_pos_index:
return self.cross_pos_index.get(cross_pos)
else:
cross_index = self.g.add_vertex()
self.g.vp.pos[cross_index] = cross_pos
self.cross_pos_index[cross_pos] = cross_index
return cross_index
def add_road_edge(self, s_vertex, e_vertex, road):
if self.has_edge(s_vertex, e_vertex):
return self.g.edge(s_vertex, e_vertex)
else:
edge = self.g.add_edge(s_vertex, e_vertex)
self.g.ep.level[edge] = int(road.record[self.PATHCLASS_index])
self.g.ep.length[edge] = self.road_length(road)
return edge
@staticmethod
def road_length(road):
length = 0
for sub_road in zip(road.shape.points[:-1], road.shape.points[1:]):
length += distance.euclidean(sub_road[0], sub_road[1])
return length
def compose_graph(uid_pid_pairs):
# set up graph
g = Graph()
g.vp['pid'] = v_pid_p = g.new_vertex_property('string')
g.vp['count'] = v_count_p = g.new_vertex_property('int')
g.ep['count'] = e_count_p = g.new_edge_property('int')
pid_v_map = {}
uid_last_v_map = {}
vv_e_map = {}
for uid, pid in uid_pid_pairs:
# vertex
v = pid_v_map.get(pid)
if v is None:
v = g.add_vertex()
v_pid_p[v] = pid
v_count_p[v] = 0
pid_v_map[pid] = v
v_count_p[v] += 1
# edge
last_v = uid_last_v_map.get(uid)
uid_last_v_map[uid] = v
if last_v is None:
continue
vv = (last_v, v)
e = vv_e_map.get(vv)
if e is None:
e = g.add_edge(*vv)
e_count_p[e] = 0
vv_e_map[vv] = e
e_count_p[e] += 1
# calculate closeness
g.vp['closeness'] = v_closeness_p = g.new_vertex_property('float')
e_inverse_count_p = g.new_edge_property('int')
e_inverse_count_p.a = e_count_p.a.max()-e_count_p.a
debug('e_inverse_count_p.a: {}', e_inverse_count_p.a)
closeness(g, weight=e_inverse_count_p, vprop=v_closeness_p)
debug('v_closeness_p.a : {}', v_closeness_p.a)
v_closeness_p.a = nan_to_num(v_closeness_p.a)
debug('v_closeness_p.a : {}', v_closeness_p.a)
# fillter
g.vp['picked'] = v_picked_p = g.new_vertex_property('bool')
debug('v_count_p.a.mean() : {}', v_count_p.a.mean())
v_picked_p.a = v_count_p.a > v_count_p.a.mean()
debug('v_picked_p.a : {}', v_picked_p.a)
g.set_vertex_filter(v_picked_p)
g.set_vertex_filter(None)
return g
def gen_er(dicProperties):
np.random.seed()
# initialize graph
graphER = Graph()
nNodes = 0
nEdges = 0
rDens = 0.0
if "Nodes" in dicProperties.keys():
nNodes = dicProperties["Nodes"]
graphER.add_vertex(nNodes)
if "Edges" in dicProperties.keys():
nEdges = dicProperties["Edges"]
rDens = nEdges / float(nNodes**2)
dicProperties["Density"] = rDens
else:
rDens = dicProperties["Density"]
nEdges = int(np.floor(rDens*nNodes**2))
dicProperties["Edges"] = nEdges
else:
nEdges = dicProperties["Edges"]
rDens = dicProperties["Density"]
nNodes = int(np.floor(np.sqrt(nEdges/rDens)))
graphER.add_vertex(nNodes)
dicProperties["Nodes"] = nNodes
# generate edges
numTest,numCurrentEdges = 0,0
while numCurrentEdges != nEdges and numTest < n_MAXTESTS:
lstEdges = np.random.randint(0,nNodes,(nEdges-numCurrentEdges,2))
graphER.add_edge_list(lstEdges)
# remove loops and duplicate edges
remove_self_loops(graphER)
remove_parallel_edges(graphER)
numCurrentEdges = graphER.num_edges()
numTest += 1
graphER.reindex_edges()
nEdges = graphER.num_edges()
rDens = nEdges / float(nNodes**2)
# generate types
rInhibFrac = dicProperties["InhibFrac"]
lstTypesGen = np.random.uniform(0,1,nEdges)
lstTypeLimit = np.full(nEdges,rInhibFrac)
lstIsExcitatory = np.greater(lstTypesGen,lstTypeLimit)
nExc = np.count_nonzero(lstIsExcitatory)
epropType = graphER.new_edge_property("int",np.multiply(2,lstIsExcitatory)-np.repeat(1,nEdges)) # excitatory (True) or inhibitory (False)
graphER.edge_properties["type"] = epropType
# and weights
if dicProperties["Weighted"]:
lstWeights = dicGenWeights[dicProperties["Distribution"]](graphER,dicProperties,nEdges,nExc) # generate the weights
epropW = graphER.new_edge_property("double",lstWeights) # crée la propriété pour stocker les poids
graphER.edge_properties["weight"] = epropW
return graphER
def build_minimum_tree(g, root, terminals, edges, directed=True):
"""remove redundant edges from `edges` so that root can reach each node in terminals
"""
# build the tree
t = Graph(directed=directed)
for _ in range(g.num_vertices()):
t.add_vertex()
for (u, v) in edges:
t.add_edge(u, v)
# mask out redundant edges
vis = init_visitor(t, root)
pbfs_search(t, source=root, terminals=list(terminals), visitor=vis)
minimum_edges = {
e
for u in terminals
for e in extract_edges_from_pred(t, root, u, vis.pred)
}
# print(minimum_edges)
efilt = t.new_edge_property('bool')
efilt.a = False
for u, v in minimum_edges:
efilt[u, v] = True
t.set_edge_filter(efilt)
return filter_nodes_by_edges(t, minimum_edges)
def build_closure(g, terminals, debug=False, verbose=False):
terminals = list(terminals)
# build closure
gc = Graph(directed=False)
gc.add_vertex(g.num_vertices())
edges_with_weight = set()
r2pred = {}
for r in terminals:
if debug:
print('root {}'.format(r))
vis = init_visitor(g, r)
pbfs_search(g, source=r, terminals=terminals, visitor=vis)
new_edges = set(get_edges(vis.dist, r, terminals))
if debug:
print('new edges {}'.format(new_edges))
edges_with_weight |= new_edges
r2pred[r] = vis.pred
for u, v, c in edges_with_weight:
gc.add_edge(u, v)
eweight = gc.new_edge_property('int')
weights = np.array([c for _, _, c in edges_with_weight])
eweight.set_2d_array(weights)
vfilt = gc.new_vertex_property('bool')
vfilt.a = False
for v in terminals:
vfilt[v] = True
gc.set_vertex_filter(vfilt)
return gc, eweight, r2pred
def build_minimum_tree(g, root, terminals, edges, directed=True):
"""remove redundant edges from `edges` so that root can reach each node in terminals
"""
# build the tree
t = Graph(directed=directed)
for _ in range(g.num_vertices()):
t.add_vertex()
for (u, v) in edges:
t.add_edge(u, v)
# mask out redundant edges
vis = init_visitor(t, root)
pbfs_search(t, source=root, terminals=list(terminals), visitor=vis)
minimum_edges = {e
for u in terminals
for e in extract_edges_from_pred(t, root, u, vis.pred)}
# print(minimum_edges)
efilt = t.new_edge_property('bool')
efilt.a = False
for u, v in minimum_edges:
efilt[u, v] = True
t.set_edge_filter(efilt)
return filter_nodes_by_edges(t, minimum_edges)
def build_cooc_graph(coDic, posMap):
'''
Converts a dictionary keeping word-word co-occurrences to a graph object where the edge weight
between nodes (words) corresponds to their co-occurrence.
Args:
coOcDic: A dictionary where keys are tuples with 2 elements, (string1, string2) corresponding to the co-occurrence of two words.
The corresponding value is an integer capturing the times the co-occurrence happened (e.g., through out a book).
posMap: A dictionary from word to Part Of Speech.
Returns:
A graph object.
'''
g = Graph(directed=False)
wordToNodeID = dict(
) #maps a word to the ID of the node that it will be stored
eWeight = g.new_edge_property(
"int") #edges have weights capturing number of co-occurrences
words = g.new_vertex_property(
"object"
) #keep for each node the (potentially unicode) corresponding word as an attribute
POS = g.new_vertex_property("string") #keep the Part Of Speech
nodeID = 0
for word1, word2 in coDic.keys(
): #Each key is a (noun, noun) string. It will become an edge
if word1 not in wordToNodeID:
wordToNodeID[word1] = nodeID
v = g.add_vertex()
assert (str(v) == str(nodeID))
words[v] = word1
POS[v] = posMap[word1]
nodeID += 1
if word2 not in wordToNodeID:
wordToNodeID[word2] = nodeID
v = g.add_vertex()
assert (str(v) == str(nodeID))
words[v] = word2
POS[v] = posMap[word2]
nodeID += 1
source = wordToNodeID[word1]
target = wordToNodeID[word2]
e = g.add_edge(source, target)
eWeight[e] = coDic[(word1, word2)]
g.edge_properties["co-occurrence"] = eWeight
g.vertex_properties["word"] = words
g.vertex_properties["partOfSpeach"] = POS
#Encode the POS as a short number
POS_encoded = g.new_vertex_property("short")
posEncoder = part_of_speech_int_map(posToInt=True)
for v in g.vertices():
POS_encoded[v] = posEncoder[POS[v][0]]
g.vertex_properties["partOfSpeach_encoded"] = POS_encoded
return g
def build_closure(g, terminals, p=None, debug=False, verbose=False):
"""build the transitive closure on terminals"""
def get_edges(dist, root, terminals):
"""get adjacent edges to root with weight"""
return {(root, t, dist[t])
for t in terminals if dist[t] != -1 and t != root}
terminals = list(terminals)
gc = Graph(directed=False)
gc.add_vertex(g.num_vertices())
edges_with_weight = set()
r2pred = {} # root to predecessor map (from bfs)
# shortest path to all other nodes
for r in terminals:
if debug:
print('root {}'.format(r))
targets = list(set(terminals) - {r})
dist_map, pred_map = shortest_distance(g,
source=r,
target=targets,
weights=p,
pred_map=True)
dist_map = dict(zip(targets, dist_map))
# print(dist_map)
# print(pred_map)
new_edges = get_edges(dist_map, r, targets)
# if p is None:
# vis = init_visitor(g, r)
# bfs_search(g, source=r, visitor=vis)
# new_edges = set(get_edges(vis.dist, r, terminals))
# else:
# print('weighted graph')
if debug:
print('new edges {}'.format(new_edges))
edges_with_weight |= new_edges
# r2pred[r] = vis.pred
r2pred[r] = pred_map
for u, v, c in edges_with_weight:
gc.add_edge(u, v)
# edge weights
eweight = gc.new_edge_property('int')
weights = np.array([c for _, _, c in edges_with_weight])
eweight.set_2d_array(weights)
vfilt = gc.new_vertex_property('bool')
vfilt.a = False
for v in terminals:
vfilt[v] = True
gc.set_vertex_filter(vfilt)
return gc, eweight, r2pred
def main():
"""
Visualizes the research network of KTH as a graph.
"""
start_time = time()
# Create our undirected graph to return.
g = Graph(directed=False)
# The edge properties measuring collaboration.
e_times = g.new_edge_property("float")
# Grouping value for the verticies, verticies are in the same group if the
# have the same value.
v_groups = g.new_vertex_property("int")
# Color the verticies based on their faculties colors.
v_colors = g.new_vertex_property("vector<double>")
db_path = '/home/_/kth/kexet/db/kex.db'
query = """SELECT *
FROM final
WHERE (
name LIKE '%kth%' and
name LIKE '%;%' and
keywords is not null and
year >= 2013 and
ContentType = 'Refereegranskat' and
PublicationType = 'Artikel i tidskrift'
);"""
rows = load.rows(db_path, query)
for row in rows:
nobjs = parse.names(row['name'].split(';'))
graph.add_relation(g, nobjs, e_times, v_colors, v_groups)
g.edge_properties["times"] = e_times
g.vertex_properties["colors"] = v_colors
g.vertex_properties["groups"] = v_groups
log.info(g.num_vertices())
log.info(g.num_edges())
g.save('a.gt')
log.info('graph saved: a.gt')
log.info("db & parse %ss" % round(time() - start_time, 2))
# start_time = time()
# g = load_graph('a.gt')
# log.info("loading %ss" % round(time() - start_time, 2))
draw.largest(g.copy())
draw.radial_highest(g.copy())
draw.sfdp(g.copy())
draw.grouped_sfdp(g.copy())
draw.min_tree(g.copy())
draw.radial_random(g.copy())
draw.hierarchy(g.copy())
draw.minimize_blockmodel(g.copy())
draw.netscience(g.copy())
draw.fruchterman(g.copy())
def import_carribean_food_web_graph(save=True, export=True):
saveLoadFolder = "Carribean_FoodWeb"
graphFile = saveLoadPath + saveLoadFolder + "/Carribean_Adjacency_Matrix_raw.txt"
g = Graph(directed=False)
edgeWeights = g.new_edge_property("double")
counter = -1
with open(graphFile, "r") as inF:
for line in inF:
if line[0] == "#": # This line is a header. Skip it.
continue
if counter == -1: # First non header line revels all the species/categories.
categories = line.split()
num_nodes = int(len(categories))
print num_nodes
counter += 1
g.add_vertex(num_nodes)
continue
splitted = line.split()
category = splitted[0]
assert (category == categories[counter])
for neighbor, weight in enumerate(splitted[1:]):
if weight != "0":
e = g.add_edge(g.vertex(neighbor),
g.vertex(counter)) # Neighbor eats him.
edgeWeights[e] = float(weight)
counter += 1
taxaToInt = {"D": 0, "A": 1, "I": 2, "F": 3, "R": 4, "B": 5}
troClass = g.new_vertex_property("int")
for i, categ in enumerate(categories):
troClass.a[i] = taxaToInt[categ[0]]
g.vp["trophic_class"] = troClass
g.ep["edge_weight"] = edgeWeights
g = make_simple_graph(g, undirected=True, gcc=True)
graphName = saveLoadFolder
if save:
save_data(
saveLoadPath + saveLoadFolder + "/" + graphName + ".GT.graph", g)
g.save(saveLoadPath + saveLoadFolder + "/" + graphName + ".graph.xml",
fmt="xml")
if export:
from_GT_To_Greach(
g,
saveLoadPath + saveLoadFolder + "/" + graphName + ".greach.graph")
from_GT_To_Snap(
g, saveLoadPath + saveLoadFolder + "/" + graphName + ".snap.graph")
return g
def shortest_path_cover_logn_apx(g: gt.Graph, weight: gt.EdgePropertyMap):
started_with_directed = g.is_directed()
if not g.is_directed():
reversed_edges = np.fliplr(g.get_edges())
g.set_directed(True)
g.add_edge_list(reversed_edges)
weight.a[-reversed_edges.shape[0]:] = weight.a[:reversed_edges.
shape[0]]
if weight.value_type() not in [
"bool", "int", "int16_t", "int32_t", "int64_t"
]:
#min = np.min(weight.a)
#min_second = np.min(weight.a[weight.a > min])
eps = 1 #min_second - min
scaled_weight = (np.ceil(weight.a / eps) *
(g.num_vertices() + 1)).astype(np.int) # ints >= 1
else:
scaled_weight = weight.a * (g.num_vertices() + 1)
summed_edge_weight = np.sum(scaled_weight)
adjusted_weight = g.new_edge_property("long", vals=scaled_weight - 1)
paths = []
covered_vertices = set()
while len(covered_vertices) != g.num_vertices():
curr_paths = shortest_path_visiting_most_nodes(g, adjusted_weight,
covered_vertices,
summed_edge_weight)
for path in curr_paths:
paths.append(path)
#if len(path) <= 2 switch to fast mode and just add single edges/vertices until done.
path_vertices = set(path)
for v in path_vertices.difference(covered_vertices):
for w in g.get_in_neighbors(v):
adjusted_weight[g.edge(w, v)] += 1 #.a[list()] -= 1
if adjusted_weight[g.edge(
w, v)] % (g.num_vertices() + 1) != 0:
exit(5)
new_covered = path_vertices.difference(covered_vertices)
covered_vertices = covered_vertices.union(path_vertices)
print(len(new_covered), len(path), len(covered_vertices), path)
if not started_with_directed:
g.set_directed(False)
for e in reversed_edges:
g.remove_edge(g.edge(e[0], e[1]))
return paths
def co_graph_directed():
'''co_graph_directed
'''
g = Graph(directed=True)
g.add_vertex(2)
edges = [(0, 1), (1, 0), (0, 2), (2, 0), (1, 2), (2, 1)]
g.add_edge_list(edges)
o = g.new_vertex_property('int')
o.a = np.array([3, 4, 2])
co = g.new_edge_property('int')
co.a = np.array([2, 2, 1, 1, 2, 2])
return g, o, co
def largest_strongly_connected_component(self, graph):
from graph_tool import Graph
import graph_tool.all as gt
largest_connected_component = Graph(directed=True)
if not self.is_relationship:
edge_prop_time = largest_connected_component.new_edge_property(
"int")
edge_prop_type = largest_connected_component.new_edge_property(
"string")
for edge in tqdm(graph.edges(data=True)):
e = tuple(edge[:2])
largest_connected_component.add_edge(e[0], e[1])
if not self.is_relationship:
edge_prop_time[e] = edge[-1]["time"]
edge_prop_type[e] = edge[-1]["type"]
largest_connected_component_view = gt.label_largest_component(
largest_connected_component)
largest_connected_component = gt.GraphView(
largest_connected_component,
vfilt=largest_connected_component_view)
print(
"Total nodes {0} in largest strongly connected component.".format(
largest_connected_component.num_vertices()))
print(
"Total edges {0} in largest strongly connected component.".format(
largest_connected_component.num_edges()))
with open(self.output, "w+") as output_file:
for edge in tqdm(largest_connected_component.edges()):
if not self.is_relationship:
output_file.write("{0} {1} {2} {3}\n".format(
edge.source(), edge.target(), edge_prop_time[edge],
edge_prop_type[edge]))
else:
output_file.write("{0} {1}\n".format(
edge.source(), edge.target()))
def transform_nx_gt(g, rescale=False):
from graph_tool import Graph
from limic.util import haversine_distance
h = Graph(directed=False)
h.vp.id = h.new_vertex_property("int64_t")
if rescale:
h.vp.lat = h.new_vertex_property("int32_t")
h.vp.long = h.new_vertex_property("int32_t")
h.ep.weight = h.new_edge_property("int32_t")
else:
h.vp.lat = h.new_vertex_property("double")
h.vp.long = h.new_vertex_property("double")
h.ep.weight = h.new_edge_property("float")
h.ep.type = h.new_edge_property("int32_t")
h.gp.rescaled = h.new_graph_property("bool")
h.gp.rescaled = rescale
pos2vertex = {}
intersection_id = 0
for n in g.nodes():
v = h.add_vertex()
pos2vertex[n[1], n[2]] = v
if n[0] < 0:
intersection_id -= 1
h.vp.id[v] = intersection_id
else:
h.vp.id[v] = n[0]
h.vp.lat[v] = int(n[1] * 10000000) if rescale else n[1]
h.vp.long[v] = int(n[2] * 10000000) if rescale else n[2]
for n, m, d in g.edges.data(data=True):
w = d['weight']
air = d['type']
e = h.add_edge(pos2vertex[n[1], n[2]], pos2vertex[m[1], m[2]])
h.ep.type[e] = air
if rescale and air < 0:
w = haversine_distance(longx=n[2],
latx=n[1],
longy=m[2],
laty=m[1])
h.ep.weight[e] = int(w * 1000) if rescale else w
return h
def graph_from_dataframes(vertex_df, edge_df):
'''Re-creates a Graph object with PropertyMaps taken
from the vertex_df and edge_df DataFrames
Paramters:
==========
verex_df: a DataFrame with an index named 'vertex_index'
edge_df: a DataFrame with a multi-index named ('source', 'target')
Returns:
========
graph: a grah-tool Graph with PropertyMaps copied
from the columns of the input DataFrames
'''
graph = Graph(directed=True)
vertex_index = vertex_df.index.get_level_values(level='vertex_index')
vertices = graph.add_vertex(n=vertex_index.shape[0])
for col in vertex_df.columns:
in_type = vertex_df[col].dtype.name
try:
dtype = ALIASES[in_type]
except KeyError:
log.info('Data type {} not supported'.format(in_type))
continue
prop = graph.new_vertex_property(dtype)
prop.fa = vertex_df[col]
graph.vertex_properties[col] = prop
src = edge_df.index.names.index('source')
trgt = edge_df.index.names.index('target')
### TODO: use the list edge creation
for tup in edge_df.index:
source, target = tup[src], tup[trgt]
try:
edge = graph.add_edge(source, target)
except ValueError:
log.info('Invalid vertex in (source: {}, target: {})'.format(
source, target))
for col in edge_df.columns:
in_type = edge_df[col].dtype.name
try:
dtype = ALIASES[in_type]
except KeyError:
log.info('Data type {} not supported'.format(in_type))
continue
prop = graph.new_edge_property(dtype)
prop.fa = edge_df[col]
graph.edge_properties[col] = prop
return graph
def graph_from_dataframes(vertex_df, edge_df):
'''Re-creates a Graph object with PropertyMaps taken
from the vertex_df and edge_df DataFrames
Paramters:
==========
verex_df: a DataFrame with an index named 'vertex_index'
edge_df: a DataFrame with a multi-index named ('source', 'target')
Returns:
========
graph: a grah-tool Graph with PropertyMaps copied
from the columns of the input DataFrames
'''
graph = Graph(directed=True)
vertex_index = vertex_df.index.get_level_values(level='vertex_index')
vertices = graph.add_vertex(n=vertex_index.shape[0])
for col in vertex_df.columns:
in_type = vertex_df[col].dtype.name
try:
dtype = ALIASES[in_type]
except KeyError:
log.info('Data type {} not supported'.format(in_type))
continue
prop = graph.new_vertex_property(dtype)
prop.fa = vertex_df[col]
graph.vertex_properties[col] = prop
src = edge_df.index.names.index('source')
trgt = edge_df.index.names.index('target')
### TODO: use the list edge creation
for tup in edge_df.index:
source, target = tup[src], tup[trgt]
try:
edge = graph.add_edge(source, target)
except ValueError:
log.info('Invalid vertex in (source: {}, target: {})'.format(source, target))
for col in edge_df.columns:
in_type = edge_df[col].dtype.name
try:
dtype = ALIASES[in_type]
except KeyError:
log.info('Data type {} not supported'.format(in_type))
continue
prop = graph.new_edge_property(dtype)
prop.fa = edge_df[col]
graph.edge_properties[col] = prop
return graph
def basic_graph():
""""""
G = Graph()
v0 = G.add_vertex()
v1 = G.add_vertex()
v2 = G.add_vertex()
v3 = G.add_vertex()
v4 = G.add_vertex()
v5 = G.add_vertex()
v6 = G.add_vertex()
v7 = G.add_vertex()
e0 = G.add_edge(v0, v1)
e1 = G.add_edge(v0, v2)
e2 = G.add_edge(v0, v3)
e3 = G.add_edge(v0, v4)
e4 = G.add_edge(v5, v4)
e5 = G.add_edge(v6, v4)
e6 = G.add_edge(v4, v7)
prop_v = G.new_vertex_property('string')
prop_e = G.new_edge_property('string')
G.vertex_properties['name'] = prop_v
G.edge_properties['c0'] = prop_e
prop_v[v0] = '/John'
prop_v[v1] = '*****@*****.**'
prop_v[v2] = '*****@*****.**'
prop_v[v3] = '/Researcher'
prop_v[v4] = '/Rome'
prop_v[v5] = '/Giacomo'
prop_v[v6] = '/Piero'
prop_v[v7] = '"Roma"@it'
prop_e[e0] = 'foaf:mbox'
prop_e[e1] = 'foaf:mbox'
prop_e[e2] = 'rdf:type'
prop_e[e3] = 'ex:birthPlace'
prop_e[e4] = 'ex:areaOfWork'
prop_e[e5] = 'ex:areaOfWork'
prop_e[e6] = 'foaf:name'
return G
def load_graph(infile):
inmatrix = np.loadtxt(infile, dtype=np.dtype('uint32'), delimiter=" ")
numv = np.amax(inmatrix[:,0:2])
#print numv, inmatrix[:,0:2]
g = Graph(directed=False)
edge_weights = g.new_edge_property("double")
g.edge_properties["weights"] = edge_weights
vlist = list(g.add_vertex(numv))
for i in inmatrix:
edge = g.add_edge(vlist[i[0]-1], vlist[i[1]-1]) # need to convert from 1-based index in file to 0-based
edge_weights[edge] = i[2]
remove_parallel_edges(g)
return g
def g():
g = Graph(directed=True)
g.add_vertex(4)
g.add_edge_list([(0, 1), (1, 0), (1, 3), (3, 1), (0, 2), (2, 0), (2, 3),
(3, 2)])
weights = g.new_edge_property('float')
weights[g.edge(0, 1)] = 0.9
weights[g.edge(1, 0)] = 0.7
weights[g.edge(1, 3)] = 0.8
weights[g.edge(3, 1)] = 0.2
weights[g.edge(2, 3)] = 0.4
weights[g.edge(3, 2)] = 0.3
weights[g.edge(0, 2)] = 0.1
weights[g.edge(2, 0)] = 0.4
g.edge_properties['weights'] = weights
return g
def build_closure(g, terminals, debug=False, verbose=False):
"""build the transitive closure on terminals"""
def get_edges(dist, root, terminals):
"""get adjacent edges to root with weight"""
return ((root, t, dist[t]) for t in terminals
if dist[t] != -1 and t != root)
terminals = list(terminals)
gc = Graph(directed=False)
gc.add_vertex(g.num_vertices())
edges_with_weight = set()
r2pred = {} # root to predecessor map (from bfs)
# bfs to all other nodes
for r in terminals:
if debug:
print('root {}'.format(r))
vis = init_visitor(g, r)
bfs_search(g, source=r, visitor=vis)
new_edges = set(get_edges(vis.dist, r, terminals))
if debug:
print('new edges {}'.format(new_edges))
edges_with_weight |= new_edges
r2pred[r] = vis.pred
for u, v, c in edges_with_weight:
gc.add_edge(u, v)
# edge weights
eweight = gc.new_edge_property('int')
weights = np.array([c for _, _, c in edges_with_weight])
eweight.set_2d_array(weights)
#
vfilt = gc.new_vertex_property('bool')
vfilt.a = False
for v in terminals:
vfilt[v] = True
gc.set_vertex_filter(vfilt)
return gc, eweight, r2pred
def user_network(storage, track, session):
g = Graph()
users = defaultdict(g.add_vertex)
g.graph_properties["track"] = g.new_graph_property("string", track)
g.graph_properties["session"] = g.new_graph_property("string", session)
g.edge_properties["created_at"] = g.new_edge_property("int64_t")
for tweet in storage:
tweeter_id = tweet["user__id_str"]
origin_id = tweet["retweeted_status__user__id_str"]
created_at = arrow.get(tweet["created_at"], DATE_FORMAT).timestamp
if origin_id:
edge = g.add_edge(users[tweeter_id], users[origin_id])
g.edge_properties["created_at"][edge] = created_at
return g
def graph_from_dataframes(vertex_df, edge_df):
'''Re-creates a Graph object with PropertyMaps taken
from the vertex_df and edge_df DataFrames
Paramters:
==========
verex_df: a DataFrame with an index named 'vertex_index'
edge_df: a DataFrame with a multi-index named ('source', 'target')
Returns:
========
graph: a grah-tool Graph with PropertyMaps copied
from the columns of the input DataFrames
'''
graph = Graph(directed=True)
vertex_index = vertex_df.index.get_level_values(level='vertex_index')
vertices = graph.add_vertex(n=vertex_index.shape[0])
for col in vertex_df.columns:
dtype = ALIASES[vertex_df[col].dtype.name]
prop = graph.new_vertex_property(dtype)
prop.a = vertex_df[col]
graph.vertex_properties[col] = prop
src = edge_df.index.names.index('source')
trgt = edge_df.index.names.index('target')
### TODO: use the list edge creation
for tup in edge_df.index:
source, target = tup[src], tup[trgt]
edge = graph.add_edge(source, target)
for col in edge_df.columns:
dtype = ALIASES[edge_df[col].dtype.name]
prop = graph.new_edge_property(dtype)
prop.a = edge_df[col]
graph.edge_properties[col] = prop
return graph
def build_closure(g, terminals,
debug=False,
verbose=False):
terminals = list(terminals)
# build closure
gc = Graph(directed=False)
for _ in range(g.num_vertices()):
gc.add_vertex()
edges_with_weight = set()
r2pred = {}
for r in terminals:
if debug:
print('root {}'.format(r))
vis = init_visitor(g, r)
pbfs_search(g, source=r, terminals=terminals, visitor=vis)
new_edges = set(get_edges(vis.dist, r, terminals))
if debug:
print('new edges {}'.format(new_edges))
edges_with_weight |= new_edges
r2pred[r] = vis.pred
for u, v, c in edges_with_weight:
gc.add_edge(u, v)
eweight = gc.new_edge_property('int')
weights = np.array([c for _, _, c in edges_with_weight])
eweight.set_2d_array(weights)
vfilt = gc.new_vertex_property('bool')
vfilt.a = False
for v in terminals:
vfilt[v] = True
gc.set_vertex_filter(vfilt)
return gc, eweight, r2pred
def makeGraph(self, img, dia, xScale, yScale):
print 'Building Graph Data Structure'
start = time.time()
G = Graph(directed=False)
vprop = G.new_vertex_property('object')
eprop = G.new_edge_property('object')
epropW = G.new_edge_property("int32_t")
avgScale = (xScale + yScale) / 2
test = np.where(img == True)
ss = np.shape(test)
cccc = 0
percentOld = 0.0
print str(np.round(percentOld, 1)) + '%'
for (i, j) in zip(test[1], test[0]):
cccc += 1
percent = (float(cccc) / float(ss[1])) * 100
if percentOld + 10 < percent:
print str(np.round(percent, 1)) + '%'
percentOld = percent
nodeNumber1 = (float(i) * yScale, float(j) * xScale)
if gu.find_vertex(
G, vprop, {
'imgIdx': (j, i),
'coord': nodeNumber1,
'nrOfPaths': 0,
'diameter': float(dia[j][i]) * avgScale
}):
v1 = gu.find_vertex(
G, vprop, {
'imgIdx': (j, i),
'coord': nodeNumber1,
'nrOfPaths': 0,
'diameter': float(dia[j][i]) * avgScale
})[0]
else:
v1 = G.add_vertex()
vprop[G.vertex(v1)] = {
'imgIdx': (j, i),
'coord': nodeNumber1,
'nrOfPaths': 0,
'diameter': float(dia[j][i]) * avgScale
}
try:
if img[j, i + 1] == True:
nodeNumber2 = (float(i + 1) * yScale, float(j) * xScale)
if gu.find_vertex(
G, vprop, {
'imgIdx': (j, i + 1),
'coord': nodeNumber2,
'nrOfPaths': 0,
'diameter': float(dia[j][i + 1]) * avgScale
}):
v2 = gu.find_vertex(
G, vprop, {
'imgIdx': (j, i + 1),
'coord': nodeNumber2,
'nrOfPaths': 0,
'diameter': float(dia[j][i + 1]) * avgScale
})[0]
if gu.find_edge(
G, eprop, {
'coord1':
vprop[v2]['coord'],
'coord2':
vprop[v1]['coord'],
'weight': ((vprop[v1]['diameter'] +
vprop[v2]['diameter']) / 2)**4,
'RTP':
False
}):
pass
else:
e = G.add_edge(v1, v2)
epropW[e] = (((vprop[v1]['diameter'] +
vprop[v2]['diameter']) / 2) /
avgScale)**4
eprop[e] = {
'coord1':
vprop[v1]['coord'],
'coord2':
vprop[v2]['coord'],
'weight': ((vprop[v1]['diameter'] +
vprop[v2]['diameter']) / 2)**4,
'RTP':
False
}
else:
v2 = G.add_vertex()
vprop[G.vertex(v2)] = {
'imgIdx': (j, i + 1),
'coord': nodeNumber2,
'nrOfPaths': 0,
'diameter': float(dia[j][i + 1]) * avgScale
}
e = G.add_edge(v1, v2)
epropW[e] = (
((vprop[v1]['diameter'] + vprop[v2]['diameter']) /
2) / avgScale)**4
eprop[e] = {
'coord1':
vprop[v1]['coord'],
'coord2':
vprop[v2]['coord'],
'weight':
((vprop[v1]['diameter'] + vprop[v2]['diameter']) /
2)**4,
'RTP':
False
}
except:
pass
try:
if img[j, i - 1] == True:
nodeNumber2 = (float(i - 1) * yScale, float(j) * xScale)
if gu.find_vertex(
G, vprop, {
'imgIdx': (j, i - 1),
'coord': nodeNumber2,
'nrOfPaths': 0,
'diameter': float(dia[j][i - 1]) * avgScale
}):
v2 = gu.find_vertex(
G, vprop, {
'imgIdx': (j, i - 1),
'coord': nodeNumber2,
'nrOfPaths': 0,
'diameter': float(dia[j][i - 1]) * avgScale
})[0]
if gu.find_edge(
G, eprop, {
'coord1':
vprop[v2]['coord'],
'coord2':
vprop[v1]['coord'],
'weight': ((vprop[v1]['diameter'] +
vprop[v2]['diameter']) / 2)**4,
'RTP':
False
}):
pass
else:
e = G.add_edge(v1, v2)
epropW[e] = (((vprop[v1]['diameter'] +
vprop[v2]['diameter']) / 2) /
avgScale)**4
eprop[e] = {
'coord1':
vprop[v1]['coord'],
'coord2':
vprop[v2]['coord'],
'weight': ((vprop[v1]['diameter'] +
vprop[v2]['diameter']) / 2)**4,
'RTP':
False
}
else:
v2 = G.add_vertex()
vprop[G.vertex(v2)] = {
'imgIdx': (j, i - 1),
'coord': nodeNumber2,
'nrOfPaths': 0,
'diameter': float(dia[j][i - 1]) * avgScale
}
e = G.add_edge(v1, v2)
epropW[e] = (
((vprop[v1]['diameter'] + vprop[v2]['diameter']) /
2) / avgScale)**4
eprop[e] = {
'coord1':
vprop[v1]['coord'],
'coord2':
vprop[v2]['coord'],
'weight':
((vprop[v1]['diameter'] + vprop[v2]['diameter']) /
2)**4,
'RTP':
False
}
except:
pass
try:
if img[j + 1, i] == True:
nodeNumber2 = (float(i) * yScale, float(j + 1) * xScale)
if gu.find_vertex(
G, vprop, {
'imgIdx': (j + 1, i),
'coord': nodeNumber2,
'nrOfPaths': 0,
'diameter': float(dia[j + 1][i]) * avgScale
}):
v2 = gu.find_vertex(
G, vprop, {
'imgIdx': (j + 1, i),
'coord': nodeNumber2,
'nrOfPaths': 0,
'diameter': float(dia[j + 1][i]) * avgScale
})[0]
if gu.find_edge(
G, eprop, {
'coord1':
vprop[v2]['coord'],
'coord2':
vprop[v1]['coord'],
'weight': ((vprop[v1]['diameter'] +
vprop[v2]['diameter']) / 2)**4,
'RTP':
False
}):
pass
else:
e = G.add_edge(v1, v2)
epropW[e] = (((vprop[v1]['diameter'] +
vprop[v2]['diameter']) / 2) /
avgScale)**4
eprop[e] = {
'coord1':
vprop[v1]['coord'],
'coord2':
vprop[v2]['coord'],
'weight': ((vprop[v1]['diameter'] +
vprop[v2]['diameter']) / 2)**4,
'RTP':
False
}
else:
v2 = G.add_vertex()
vprop[G.vertex(v2)] = {
'imgIdx': (j + 1, i),
'coord': nodeNumber2,
'nrOfPaths': 0,
'diameter': float(dia[j + 1][i]) * avgScale
}
e = G.add_edge(v1, v2)
epropW[e] = (
((vprop[v1]['diameter'] + vprop[v2]['diameter']) /
2) / avgScale)**4
eprop[e] = {
'coord1':
vprop[v1]['coord'],
'coord2':
vprop[v2]['coord'],
'weight':
((vprop[v1]['diameter'] + vprop[v2]['diameter']) /
2)**4,
'RTP':
False
}
except:
pass
try:
if img[j - 1, i] == True:
nodeNumber2 = (float(i) * yScale, float(j - 1) * xScale)
if gu.find_vertex(
G, vprop, {
'imgIdx': (j - 1, i),
'coord': nodeNumber2,
'nrOfPaths': 0,
'diameter': float(dia[j - 1][i]) * avgScale
}):
v2 = gu.find_vertex(
G, vprop, {
'imgIdx': (j - 1, i),
'coord': nodeNumber2,
'nrOfPaths': 0,
'diameter': float(dia[j - 1][i]) * avgScale
})[0]
if gu.find_edge(
G, eprop, {
'coord1':
vprop[v2]['coord'],
'coord2':
vprop[v1]['coord'],
'weight': ((vprop[v1]['diameter'] +
vprop[v2]['diameter']) / 2)**4,
'RTP':
False
}):
pass
else:
e = G.add_edge(v1, v2)
epropW[e] = (((vprop[v1]['diameter'] +
vprop[v2]['diameter']) / 2) /
avgScale)**4
eprop[e] = {
'coord1':
vprop[v1]['coord'],
'coord2':
vprop[v2]['coord'],
'weight': ((vprop[v1]['diameter'] +
vprop[v2]['diameter']) / 2)**4,
'RTP':
False
}
else:
v2 = G.add_vertex()
vprop[G.vertex(v2)] = {
'imgIdx': (j - 1, i),
'coord': nodeNumber2,
'nrOfPaths': 0,
'diameter': float(dia[j - 1][i]) * avgScale
}
e = G.add_edge(v1, v2)
epropW[e] = (
((vprop[v1]['diameter'] + vprop[v2]['diameter']) /
2) / avgScale)**4
eprop[e] = {
'coord1':
vprop[v1]['coord'],
'coord2':
vprop[v2]['coord'],
'weight':
((vprop[v1]['diameter'] + vprop[v2]['diameter']) /
2)**4,
'RTP':
False
}
except:
pass
#
print '100.0%'
print 'selecting largest connected component'
G.edge_properties["ep"] = eprop
G.edge_properties["w"] = epropW
G.vertex_properties["vp"] = vprop
l = gt.label_largest_component(G)
print(l.a)
u = gt.GraphView(G, vfilt=l)
print '# vertices'
print(u.num_vertices())
print(G.num_vertices())
print '# edges'
print(u.num_edges())
print 'building graph finished in: ' + str(time.time() - start) + 's'
return u
class ob_viz(QWidget):
def __init__(self, bg_color):
QWidget.__init__(self)
self.background_color = bg_color
self.c = 0
# K = 0.5
# how many iterations the realignment is supposed to take
self.step = 15
self.rwr_c = 0
# dumper([qt_coords])
dumper(['obv viz init'])
# self.show()
# with open("/tmp/eaf3.csv", "a") as fo:
# wr = csv.writer(fo)
# wr.writerow([self.c, "runs4"])
# dumper([self.c, "runs4"])
# self.node_names [g_id[i] for i in g.vertices()]
def init2(self, emacs_var_dict):
self.emacs_var_dict = emacs_var_dict
self.link_str = self.emacs_var_dict['links']
self.g = Graph()
self.label_ep = self.g.new_edge_property("string")
self.links = self.link_str.split(";")
link_tpls = [i.split(" -- ") for i in self.links]
dumper([str(i) for i in link_tpls])
self.g_id = self.g.add_edge_list(link_tpls,
hashed=True,
string_vals=True,
eprops=[self.label_ep])
self.adj = np.array([(int(i.source()), int(i.target()))
for i in self.g.edges()])
self.node_names = [self.g_id[i] for i in self.g.vertices()]
self.vd = {}
for i in self.g.vertices():
self.vd[self.g_id[i]] = int(i)
# self.pos_vp = sfdp_layout(self.g, K=0.5)
self.pos_vp = fruchterman_reingold_layout(self.g)
self.base_pos_ar = self.pos_vp.get_2d_array((0, 1)).T
self.qt_coords = self.nolz_pos_ar(self.base_pos_ar)
dumper([str(self.qt_coords)])
# dumper([link_str])
def update_graph(self, emacs_var_dict):
"""set new links and nodes"""
new_link_str = emacs_var_dict['links']
new_links = new_link_str.split(";")
new_link_tpls = [i.split(" -- ") for i in new_links]
links_to_add = list(set(new_links) - set(self.links))
links_to_del = list(set(self.links) - set(new_links))
# setting new stuff
self.links = new_links
new_nodes = []
for tpl in new_link_tpls:
new_nodes.append(tpl[0])
new_nodes.append(tpl[1])
new_nodes_unique = list(set(new_nodes))
nodes_to_del = list(set(self.node_names) - set(new_nodes_unique))
nodes_to_add = list(set(new_nodes_unique) - set(self.node_names))
dumper([
"nodes_to_add: ", nodes_to_add, "nodes_to_del: ", nodes_to_del,
"links_to_add: ", links_to_add, "links_to_del: ", links_to_del
])
# first add nodes + index them, but not there yet (first links)
for n in nodes_to_add:
dumper(['adding node'])
v = self.g.add_vertex()
# how to new nodes pos to parents? separate loop afterwards
self.vd[n] = int(v)
self.g_id[v] = n
del_node_ids = [self.vd[i] for i in nodes_to_del]
self.g.remove_vertex(del_node_ids)
# have to reindex after deletion
self.vd = {}
for i in self.g.vertices():
self.vd[self.g_id[i]] = int(i)
dumper(['node deleted'])
# nodes_to_del_id =
# dumper(['old nodes deleted, add new links'])
for l in links_to_add:
tpl = l.split(" -- ")
n0, n1 = tpl[0], tpl[1]
self.g.add_edge(self.vd[n0], self.vd[n1])
# dumper(['new links added, delete old links'])
for l in links_to_del:
tpl = l.split(" -- ")
n0 = tpl[0]
n1 = tpl[1]
dumper([list(self.vd.keys())])
# only remove edge when neither of nodes removed
if n0 in self.vd.keys() and n1 in self.vd.keys():
self.g.remove_edge(self.g.edge(self.vd[n0], self.vd[n1]))
# dumper(['graph modifications done'])
# set positions of new nodes to parent nodes
for n in nodes_to_add:
v = self.g.vertex(self.vd[n])
v_prnt = list(v.all_neighbors())[0]
self.pos_vp[v] = self.pos_vp[v_prnt]
# dumper(['node positions adjusted'])
self.adj = np.array([(int(i.source()), int(i.target()))
for i in self.g.edges()])
self.node_names = [self.g_id[i] for i in self.g.vertices()]
# dumper(['storage objects updated'])
# dumper(["nbr_edges new: ", str(len([i for i in self.g.edges()]))])
# dumper(['nodes_to_add'] + nodes_to_add)
# seems to work
dumper(['to here'])
self.recalculate_layout()
dumper(['to here2'])
def recalculate_layout(self):
"""calculate new change_array, set rwr_c counter"""
dumper(['recalculating starting'])
self.base_pos_ar = self.pos_vp.get_2d_array((0, 1)).T
# set_dict = {'p': 2, 'max_level': 20, 'adaptive_cooling': False,
# 'gamma': 1, 'theta': 1, 'cooling_step': 0.3, 'C': 0.6, 'mu_p': 1.2}
# self.goal_vp = sfdp_layout(self.g, K=0.5, pos=self.pos_vp, **set_dict)
self.goal_vp = fruchterman_reingold_layout(self.g, pos=self.pos_vp)
goal_ar = self.goal_vp.get_2d_array([0, 1]).T
self.chng_ar = (goal_ar - self.base_pos_ar) / self.step
self.rwr_c = self.step
dumper(["base_pos_ar: ", self.base_pos_ar])
dumper(["goal_ar: ", goal_ar])
dumper(["chng_ar: ", self.chng_ar])
dumper(['recalculating done'])
def redraw_layout(self):
"""actually do the drawing, run multiple (step (rwr_c)) times"""
self.cur_pos_ar = np.round(
self.base_pos_ar + self.chng_ar * (self.step - self.rwr_c), 3)
self.qt_coords = self.nolz_pos_ar(self.cur_pos_ar)
self.rwr_c -= 1
self.update()
# dumper(['redrawing'])
# def draw_arrow(qp, p1x, p1y, p2x, p2y):
def draw_arrow(self, qp, p1x, p1y, p2x, p2y, node_width):
"""draw arrow from p1 to rad units before p2"""
# get arrow angle, counterclockwise from center -> east line
# dumper(['painting time'])
angle = degrees(atan2((p1y - p2y), (p1x - p2x)))
# calculate attach point
arw_goal_x = p2x + node_width * cos(radians(angle))
arw_goal_y = p2y + node_width * sin(radians(angle))
# calculate start point: idk how trig works but does
start_px = p1x - node_width * cos(radians(angle))
start_py = p1y - node_width * sin(radians(angle))
# arrow stuff: +/- 30 deg
ar1 = angle + 25
ar2 = angle - 25
arw_len = 10
# need to focus on vector from p2 to p1
ar1_x = arw_goal_x + arw_len * cos(radians(ar1))
ar1_y = arw_goal_y + arw_len * sin(radians(ar1))
ar2_x = arw_goal_x + arw_len * cos(radians(ar2))
ar2_y = arw_goal_y + arw_len * sin(radians(ar2))
# qp.drawLine(p1x, p1y, p2x, p2y)
# qp.drawLine(p1x, p1y, arw_goal_x, arw_goal_y)
qp.drawLine(start_px, start_py, arw_goal_x, arw_goal_y)
qp.drawLine(ar1_x, ar1_y, arw_goal_x, arw_goal_y)
qp.drawLine(ar2_x, ar2_y, arw_goal_x, arw_goal_y)
def paintEvent(self, event):
# dumper(['start painting'])
node_width = 10
qp = QPainter(self)
edges = [(self.qt_coords[i[0]], self.qt_coords[i[1]])
for i in self.adj]
# dumper([str(i) for i in edges])
qp.setPen(QPen(Qt.green, 2, Qt.SolidLine))
# [qp.drawLine(e[0][0], e[0][1], e[1][0], e[1][1]) for e in edges]
[
self.draw_arrow(qp, e[0][0], e[0][1], e[1][0], e[1][1],
(node_width / 2) + 5) for e in edges
]
qp.setPen(QColor(168, 34, 3))
# qp.setPen(Qt.green)
qp.setFont(QFont('Decorative', 10))
[
qp.drawText(t[0][0] + node_width, t[0][1], t[1])
for t in zip(self.qt_coords, self.node_names)
]
# dumper(['done painting'])
qp.setPen(QPen(Qt.black, 3, Qt.SolidLine))
# qp.setBrush(QBrush(Qt.green, Qt.SolidPattern))
dumper(['painting nodes'])
for i in zip(self.qt_coords, self.node_names):
if self.emacs_var_dict['cur_node'] == i[1]:
qp.setPen(QPen(Qt.black, 4, Qt.SolidLine))
qp.drawEllipse(i[0][0] - (node_width / 2),
i[0][1] - (node_width / 2), node_width,
node_width)
qp.setPen(QPen(Qt.black, 3, Qt.SolidLine))
else:
qp.drawEllipse(i[0][0] - (node_width / 2),
i[0][1] - (node_width / 2), node_width,
node_width)
# qp.drawEllipse(self.c, self.c, 7, 7)
# qp.end()
def nolz_pos_ar(self, pos_ar_org):
"""normalize pos ar to window limits"""
# pos_ar_org = goal_ar
size = self.size()
limits = [[20, size.width() - 50], [20, size.height() - 20]]
x_max = max(pos_ar_org[:, 0])
x_min = min(pos_ar_org[:, 0])
y_max = max(pos_ar_org[:, 1])
y_min = min(pos_ar_org[:, 1])
# need linear maping function again
pos_ar2 = pos_ar_org
pos_ar2[:, 0] = (((pos_ar2[:, 0] - x_min) / (x_max - x_min)) *
(limits[0][1] - limits[0][0])) + limits[0][0]
pos_ar2[:, 1] = (((pos_ar2[:, 1] - y_min) / (y_max - y_min)) *
(limits[1][1] - limits[1][0])) + limits[1][0]
return (pos_ar2)
for request_count in range(number_requests):
request = []
temp_source = randint(0,13)
request.append(temp_source)
while(1):
temp_target = randint(0, 13)
if(not(temp_target == temp_source)):
request.append(temp_target)
break
if not(request in all_requests):
all_requests.append(request)
#print("Number of requests are " + str(len(all_requests)))
## Defining the graph properties ##
graph_weight = g.new_edge_property("float")
g.ep.weight = graph_weight
graph_pred_tree = g.new_vertex_property("int")
pred_tree = graph_pred_tree
edges_logger = {}
for e in g.edges():
flags_of_edges = []
# Temporary flag to ensure that alternative path is not on the primary path itself
flags_of_edges.append(1)
# Flags to see which channels are currently in use
for i in range(number_frequency_bands):
flags_of_edges.append(1)
# Flags to keep record of the extent of the usage of a particular channel in a link
def build_truncated_closure(g, cand_source, terminals, infection_times,
k=-1,
debug=False,
verbose=False,
**kawrgs):
"""
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=-1) # k=-1 here because root connects to all other nodes
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
# every temrinal should connetct to at least one earlier terminal
# in this way, connectivity is ensured
for root in terminals_iter:
if root == cand_source:
continue
# connect from some earlier node to root
# if it's earliest, can only connect to peers
early_terminals = [t for t in terminals
if infection_times[t] < infection_times[root]]
same_time_terminals = [t for t in terminals
if infection_times[t] == infection_times[root] if t != root]
late_time_terminals = [t for t in terminals
if infection_times[t] > infection_times[root]]
if debug:
print('root: {}'.format(root))
print('early_terminals: {}'.format(early_terminals))
print('same_time_terminals: {}'.format(same_time_terminals))
print('late_time_terminals: {}'.format(late_time_terminals))
if infection_times[root] == infection_times[terminals].min():
targets = early_terminals + same_time_terminals
else:
targets = early_terminals
targets = list(set(targets) - {cand_source}) # no one can connect to cand_source
if debug:
print('targets: {}'.format(targets))
vis = init_visitor(g, root)
cpbfs_search(g, source=root, visitor=vis,
terminals=targets,
forbidden_nodes=late_time_terminals,
count_threshold=k)
r2pred[root] = vis.pred
for root, v, c in get_edges(vis.dist, root, early_terminals):
if debug:
print('edge ({}, {})'.format(v, root))
edges[(v, root)] = c # from earlier node to root
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())))
return gc, eweight, r2pred
class BaseGraph(object):
"""
Class representing a graph. We do not use pure graph_tool.Graph for we want
to be able to easily change this library. Neither we use inheritance
as graph_tool has inconvenient licence.
"""
def __init__(self):
self._g = None
self._node_dict = {}
self._syn_to_vertex_map = None
self._lemma_to_nodes_dict = None
self._lu_on_vertex_dict = None
def use_graph_tool(self):
"""
Returns underlying graph_tool.Graph. It should be avoided at all costs.
"""
return self._g
def get_node_for_synset_id(self, syn_id):
"""
Lazy function to makes the map of synset identifiers to nodes into
the graph. The building of map is made only on the first funcion call.
The first and the next calls of this function will return the built map.
"""
if not self._syn_to_vertex_map:
self._syn_to_vertex_map = {}
for node in self.all_nodes():
if node.synset:
synset_id = node.synset.synset_id
self._syn_to_vertex_map[synset_id] = node
return self._syn_to_vertex_map.get(syn_id, None)
def pickle(self, filename):
self._g.save(filename)
def unpickle(self, filename):
self._g = load_graph(filename)
def init_graph(self, drctd=False):
self._g = Graph(directed=drctd)
def copy_graph_from(self, g):
self._g = g._g.copy()
def set_directed(self, drctd):
self._g.set_directed(drctd)
def is_directed(self):
return self._g.is_directed()
def merge_graphs(self, g1, g2):
self._g = graph_union(g1._g, g2._g, internal_props=True)
# Node operations:
def all_nodes(self):
for node in self._g.vertices():
yield BaseNode(self._g, node)
def create_node_attribute(self, name, kind, value=None):
if not self.has_node_attribute(name):
node_attr = self._g.new_vertex_property(kind, value)
self._g.vertex_properties[name] = node_attr
def create_node_attributes(self, node_attributes_list):
for attr in node_attributes_list:
if not self.has_node_attribute(attr[0]):
node_attr = self._g.new_vertex_property(attr[1])
self._g.vertex_properties[attr[0]] = node_attr
def has_node_attribute(self, name):
""" Checks if a node attribute already exists """
return name in self._g.vertex_properties
def delete_node_attribute(self, name):
""" Delete node attribute """
del self._g.vertex_properties[name]
def add_node(self, name, node_attributes_list=None):
if node_attributes_list is None:
node_attributes_list = []
if name not in self._node_dict:
new_node = self._g.add_vertex()
self._node_dict[name] = BaseNode(self._g, new_node)
for attr in node_attributes_list:
self._g.vertex_properties[attr[0]][new_node] = attr[1]
return self._node_dict[name]
def get_node(self, name):
return self._node_dict[name]
def remove_node(self, name):
self._g.remove_vertex(self._node_dict[name]._node)
del self._node_dict[name]
def nodes_filter(self,
nodes_to_filter_set,
inverted=False,
replace=False,
soft=False):
"""
Filters out nodes from set
Args:
nodes_to_filter_set (Iterable): Nodes which fill be filtered out.
inverted (bool): If True, nodes NOT in set will be filtered out.
Defaults to False.
replace (bool): Replace current filter instead of combining the two.
Defaults to False.
soft (bool): Hide nodes without removing them so they can be restored
with reset_nodes_filter. Defaults to False.
"""
predicate = lambda node: node not in nodes_to_filter_set
self.nodes_filter_conditional(predicate, inverted, replace, soft)
def nodes_filter_conditional(self,
predicate,
inverted=False,
replace=False,
soft=False):
"""
Filters node based on a predicate
Args:
predicate (Callable): Predicate returning False for nodes that should be
filtered out.
inverted (bool): Invert condition. Defaults to False.
replace (bool): Replace current filter instead of combining the two.
Defaults to False.
soft (bool): Hide nodes without removing them so they can be restored
with reset_nodes_filter. Defaults to False.
"""
(old_filter, old_inverted) = self._g.get_vertex_filter()
new_filter = self._g.new_vertex_property("bool")
for node in self.all_nodes():
kept = predicate(node) != inverted
if not replace and old_filter:
old_kept = bool(old_filter[node._node]) != old_inverted
kept = kept and old_kept
new_filter[node._node] = kept
self._g.set_vertex_filter(new_filter, False)
if not soft:
self.apply_nodes_filter()
def apply_nodes_filter(self):
""" Removes nodes that are currently filtered out """
self._g.purge_vertices()
def reset_nodes_filter(self):
""" Clears node filter """
self._g.set_vertex_filter(None)
# Edge operations:
def num_edges(self):
return self._g.num_edges()
def all_edges(self):
for e in self._g.edges():
yield BaseEdge(self._g, e)
def get_edges_between(self, source, target):
"""
Return all edges between source and target. Source and target can be either
BaseNode or integer.
"""
if isinstance(source, BaseNode):
source = source._node
if isinstance(target, BaseNode):
target = target._node
for e in self._g.edge(source, target, all_edges=True):
yield BaseEdge(self._g, e)
def get_edge(self, source, target, add_missing=False):
"""
Return some edge between source and target. Source and target can be either
BaseNode or integer.
"""
if isinstance(source, BaseNode):
source = source._node
if isinstance(target, BaseNode):
target = target._node
e = self._g.edge(source, target, add_missing)
if e is not None:
return BaseEdge(self._g, e)
else:
return None
def create_edge_attribute(self, name, kind, value=None):
if not self.has_edge_attribute(name):
edge_attr = self._g.new_edge_property(kind, value)
self._g.edge_properties[name] = edge_attr
def alias_edge_attribute(self, name, alias):
self._g.edge_properties[alias] = self._g.edge_properties[name]
def create_edge_attributes(self, edge_attributes_list):
for attr in edge_attributes_list:
if not self.has_edge_attribute(attr[0]):
edge_attr = self._g.new_edge_property(attr[1])
self._g.edge_properties[attr[0]] = edge_attr
def has_edge_attribute(self, name):
""" Checks if an edge attribute already existst """
return name in self._g.edge_properties
def delete_edge_attribute(self, name):
""" Delete edge attribute """
del self._g.edge_properties[name]
def add_edge(self, parent, child, edge_attributes_list=None):
if edge_attributes_list is None:
edge_attributes_list = []
new_edge = self._g.add_edge(parent._node, child._node)
for attr in edge_attributes_list:
self._g.edge_properties[attr[0]][new_edge] = attr[1]
return BaseEdge(self._g, new_edge)
def edges_filter(self, edges_to_filter_set):
edge_filter = self._g.new_edge_property("bool")
for e in self.all_edges():
if e in edges_to_filter_set:
edge_filter[e._edge] = False
else:
edge_filter[e._edge] = True
self._g.set_edge_filter(edge_filter)
self._g.purge_edges()
def ungraph_tool(self, thingy, lemma_on_only_synset_node_dict):
"""
Converts given data structure so that it no longer have any graph_tool dependencies.
"""
logger = logging.getLogger(__name__)
if type(thingy) == dict:
return {
self.ungraph_tool(k, lemma_on_only_synset_node_dict):
self.ungraph_tool(thingy[k], lemma_on_only_synset_node_dict)
for k in thingy
}
nodes_to_translate = set()
for vset in lemma_on_only_synset_node_dict.values():
for v in vset:
nodes_to_translate.add(v)
if type(thingy) == gt.PropertyMap:
dct = {}
if thingy.key_type() == 'v':
for node in nodes_to_translate:
dct[node] = thingy[node.use_graph_tool()]
elif thingy.key_type() == 'e':
for edge in self.all_edges():
dct[edge] = thingy[edge.use_graph_tool()]
else:
logger.error('Unknown property type %s', thingy.key_type())
raise NotImplemented
return dct
def generate_lemma_to_nodes_dict_synsets(self):
"""
This method generates a utility dictionary, which maps lemmas to
corresponding node objects. It is expensive in menas of time
needed to generate the dictionary. It should therefore be executed
at the beginning of the runtime and later its results should be reused
as many times as needed without re-executing the function.
"""
lemma_to_nodes_dict = defaultdict(set)
for node in self.all_nodes():
try:
lu_set = node.synset.lu_set
except KeyError:
continue
for lu in lu_set:
lemma = lu.lemma.lower()
lemma_to_nodes_dict[lemma].add(node)
self._lemma_to_nodes_dict = lemma_to_nodes_dict
def generate_lemma_to_nodes_dict_lexical_units(self):
"""
This method generates a utility dictionary, which maps lemmas to
corresponding node objects. It is expensive in menas of time
needed to generate the dictionary. It should therefore be executed
at the beginning of the runtime and later its results should be reused
as many times as needed without re-executing the function.
"""
lemma_to_nodes_dict = defaultdict(set)
for node in self.all_nodes():
try:
lemma = node.lu.lemma.lower()
lemma_to_nodes_dict[lemma].add(node)
except:
continue
self._lemma_to_nodes_dict = lemma_to_nodes_dict
@property
def lemma_to_nodes_dict(self):
return self._lemma_to_nodes_dict
def _make_lu_on_v_dict(self):
"""
Makes dictionary lu on vertex
"""
lu_on_vertex_dict = defaultdict(set)
for node in self.all_nodes():
try:
nl = node.lu
except Exception:
continue
if nl:
lu_on_vertex_dict[node.lu.lu_id] = node
self._lu_on_vertex_dict = lu_on_vertex_dict
def build_truncated_closure(g,
cand_source,
terminals,
infection_times,
k=-1,
debug=False,
verbose=False,
**kawrgs):
"""
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=-1
) # k=-1 here because root connects to all other nodes
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
# every temrinal should connetct to at least one earlier terminal
# in this way, connectivity is ensured
for root in terminals_iter:
if root == cand_source:
continue
# connect from some earlier node to root
# if it's earliest, can only connect to peers
early_terminals = [
t for t in terminals if infection_times[t] < infection_times[root]
]
same_time_terminals = [
t for t in terminals if infection_times[t] == infection_times[root]
if t != root
]
late_time_terminals = [
t for t in terminals if infection_times[t] > infection_times[root]
]
if debug:
print('root: {}'.format(root))
print('early_terminals: {}'.format(early_terminals))
print('same_time_terminals: {}'.format(same_time_terminals))
print('late_time_terminals: {}'.format(late_time_terminals))
if infection_times[root] == infection_times[terminals].min():
targets = early_terminals + same_time_terminals
else:
targets = early_terminals
targets = list(set(targets) -
{cand_source}) # no one can connect to cand_source
if debug:
print('targets: {}'.format(targets))
vis = init_visitor(g, root)
cpbfs_search(g,
source=root,
visitor=vis,
terminals=targets,
forbidden_nodes=late_time_terminals,
count_threshold=k)
r2pred[root] = vis.pred
for root, v, c in get_edges(vis.dist, root, early_terminals):
if debug:
print('edge ({}, {})'.format(v, root))
edges[(v, root)] = c # from earlier node to root
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())))
return gc, eweight, r2pred
def makeGraph(self,img,dia,xScale,yScale):
print 'Building Graph Data Structure'
start=time.time()
G = Graph(directed=False)
vprop=G.new_vertex_property('object')
eprop=G.new_edge_property('object')
epropW=G.new_edge_property("int32_t")
avgScale=(xScale+yScale)/2
test=np.where(img==True)
ss = np.shape(test)
cccc=0
percentOld=0.0
print str(np.round(percentOld,1))+'%'
for (i,j) in zip(test[1],test[0]):
cccc+=1
percent=(float(cccc)/float(ss[1]))*100
if percentOld+10< percent:
print str(np.round(percent,1))+'%'
percentOld=percent
nodeNumber1 = (float(i)*yScale,float(j)*xScale)
if gu.find_vertex(G, vprop, {'imgIdx':(j,i),'coord':nodeNumber1, 'nrOfPaths':0, 'diameter':float(dia[j][i])*avgScale}):
v1=gu.find_vertex(G, vprop, {'imgIdx':(j,i),'coord':nodeNumber1, 'nrOfPaths':0, 'diameter':float(dia[j][i])*avgScale})[0]
else:
v1=G.add_vertex()
vprop[G.vertex(v1)]={'imgIdx':(j,i),'coord':nodeNumber1, 'nrOfPaths':0, 'diameter':float(dia[j][i])*avgScale}
try:
if img[j,i+1] == True:
nodeNumber2 = (float(i+1)*yScale,float(j)*xScale)
if gu.find_vertex(G, vprop, {'imgIdx':(j,i+1),'coord':nodeNumber2, 'nrOfPaths':0, 'diameter':float(dia[j][i+1])*avgScale}):
v2=gu.find_vertex(G, vprop, {'imgIdx':(j,i+1),'coord':nodeNumber2, 'nrOfPaths':0, 'diameter':float(dia[j][i+1])*avgScale})[0]
if gu.find_edge(G, eprop, {'coord1':vprop[v2]['coord'], 'coord2':vprop[v1]['coord'],'weight':((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)**4,'RTP':False}):
pass
else:
e = G.add_edge(v1, v2)
epropW[e]=(((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)/avgScale)**4
eprop[e]={'coord1':vprop[v1]['coord'], 'coord2':vprop[v2]['coord'],'weight':((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)**4,'RTP':False}
else:
v2=G.add_vertex()
vprop[G.vertex(v2)]={'imgIdx':(j,i+1),'coord':nodeNumber2, 'nrOfPaths':0, 'diameter':float(dia[j][i+1])*avgScale}
e = G.add_edge(v1, v2)
epropW[e]=(((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)/avgScale)**4
eprop[e]={'coord1':vprop[v1]['coord'], 'coord2':vprop[v2]['coord'],'weight':((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)**4,'RTP':False}
except:
pass
try:
if img[j,i-1] == True:
nodeNumber2 = (float(i-1)*yScale,float(j)*xScale)
if gu.find_vertex(G, vprop, {'imgIdx':(j,i-1),'coord':nodeNumber2, 'nrOfPaths':0, 'diameter':float(dia[j][i-1])*avgScale}):
v2=gu.find_vertex(G, vprop, {'imgIdx':(j,i-1),'coord':nodeNumber2, 'nrOfPaths':0, 'diameter':float(dia[j][i-1])*avgScale})[0]
if gu.find_edge(G, eprop, {'coord1':vprop[v2]['coord'], 'coord2':vprop[v1]['coord'],'weight':((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)**4,'RTP':False}):
pass
else:
e = G.add_edge(v1, v2)
epropW[e]=(((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)/avgScale)**4
eprop[e]={'coord1':vprop[v1]['coord'], 'coord2':vprop[v2]['coord'],'weight':((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)**4,'RTP':False}
else:
v2=G.add_vertex()
vprop[G.vertex(v2)]={'imgIdx':(j,i-1),'coord':nodeNumber2, 'nrOfPaths':0, 'diameter':float(dia[j][i-1])*avgScale}
e = G.add_edge(v1, v2)
epropW[e]=(((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)/avgScale)**4
eprop[e]={'coord1':vprop[v1]['coord'], 'coord2':vprop[v2]['coord'],'weight':((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)**4,'RTP':False}
except:pass
try:
if img[j + 1,i] == True:
nodeNumber2 = (float(i)*yScale,float(j+1)*xScale)
if gu.find_vertex(G, vprop, {'imgIdx':(j+1,i),'coord':nodeNumber2, 'nrOfPaths':0, 'diameter':float(dia[j+1][i])*avgScale}):
v2=gu.find_vertex(G, vprop, {'imgIdx':(j+1,i),'coord':nodeNumber2, 'nrOfPaths':0, 'diameter':float(dia[j+1][i])*avgScale})[0]
if gu.find_edge(G, eprop, {'coord1':vprop[v2]['coord'], 'coord2':vprop[v1]['coord'],'weight':((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)**4,'RTP':False}):
pass
else:
e = G.add_edge(v1, v2)
epropW[e]=(((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)/avgScale)**4
eprop[e]={'coord1':vprop[v1]['coord'], 'coord2':vprop[v2]['coord'],'weight':((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)**4,'RTP':False}
else:
v2=G.add_vertex()
vprop[G.vertex(v2)]={'imgIdx':(j+1,i),'coord':nodeNumber2, 'nrOfPaths':0, 'diameter':float(dia[j+1][i])*avgScale}
e = G.add_edge(v1, v2)
epropW[e]=(((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)/avgScale)**4
eprop[e]={'coord1':vprop[v1]['coord'], 'coord2':vprop[v2]['coord'],'weight':((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)**4,'RTP':False}
except:pass
try:
if img[j - 1,i] == True:
nodeNumber2 = (float(i)*yScale,float(j-1)*xScale)
if gu.find_vertex(G, vprop, {'imgIdx':(j-1,i),'coord':nodeNumber2, 'nrOfPaths':0, 'diameter':float(dia[j-1][i])*avgScale}):
v2=gu.find_vertex(G, vprop, {'imgIdx':(j-1,i),'coord':nodeNumber2, 'nrOfPaths':0, 'diameter':float(dia[j-1][i])*avgScale})[0]
if gu.find_edge(G, eprop, {'coord1':vprop[v2]['coord'], 'coord2':vprop[v1]['coord'],'weight':((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)**4,'RTP':False}):
pass
else:
e = G.add_edge(v1, v2)
epropW[e]=(((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)/avgScale)**4
eprop[e]={'coord1':vprop[v1]['coord'], 'coord2':vprop[v2]['coord'],'weight':((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)**4,'RTP':False}
else:
v2=G.add_vertex()
vprop[G.vertex(v2)]={'imgIdx':(j-1,i),'coord':nodeNumber2, 'nrOfPaths':0, 'diameter':float(dia[j-1][i])*avgScale}
e = G.add_edge(v1, v2)
epropW[e]=(((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)/avgScale)**4
eprop[e]={'coord1':vprop[v1]['coord'], 'coord2':vprop[v2]['coord'],'weight':((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)**4,'RTP':False}
except: pass
#
print '100.0%'
print 'selecting largest connected component'
G.edge_properties["ep"] = eprop
G.edge_properties["w"] = epropW
G.vertex_properties["vp"] = vprop
l = gt.label_largest_component(G)
print(l.a)
u = gt.GraphView(G, vfilt=l)
print '# vertices'
print(u.num_vertices())
print(G.num_vertices())
print '# edges'
print(u.num_edges())
print 'building graph finished in: '+str(time.time()-start)+'s'
return u
def build_graph(df_list, sens='ST', top=410, min_sens=0.01,
edge_cutoff=0.0):
"""
Initializes and constructs a graph where vertices are the parameters
selected from the first dataframe in 'df_list', subject to the
constraints set by 'sens', 'top', and 'min_sens'. Edges are the second
order sensitivities of the interactions between those vertices,
with sensitivities greater than 'edge_cutoff'.
Parameters
-----------
df_list : list
A list of two dataframes. The first dataframe should be
the first/total order sensitivities collected by the
function data_processing.get_sa_data().
sens : str, optional
A string with the name of the sensitivity that you would
like to use for the vertices ('ST' or 'S1').
top : int, optional
An integer specifying the number of vertices to display (
the top sensitivity values).
min_sens : float, optional
A float with the minimum sensitivity to allow in the graph.
edge_cutoff : float, optional
A float specifying the minimum second order sensitivity to
show as an edge in the graph.
Returns
--------
g : graph-tool object
a graph-tool graph object of the network described above. Each
vertex has properties 'param', 'sensitivity', and 'confidence'
corresponding to the name of the parameter, value of the sensitivity
index, and it's confidence interval. The only edge property is
'second_sens', the second order sensitivity index for the
interaction between the two vertices it connects.
"""
# get the first/total index dataframe and second order dataframe
df = df_list[0]
df2 = df_list[1]
# Make sure sens is ST or S1
if sens not in set(['ST', 'S1']):
raise ValueError('sens must be ST or S1')
# Make sure that there is a second order index dataframe
try:
if not df2:
raise Exception('Missing second order dataframe!')
except:
pass
# slice the dataframes so the resulting graph will only include the top
# 'top' values of 'sens' greater than 'min_sens'.
df = df.sort_values(sens, ascending=False)
df = df.ix[df[sens] > min_sens, :].head(top)
df = df.reset_index()
# initialize a graph
g = Graph()
vprop_sens = g.new_vertex_property('double')
vprop_conf = g.new_vertex_property('double')
vprop_name = g.new_vertex_property('string')
eprop_sens = g.new_edge_property('double')
g.vertex_properties['param'] = vprop_name
g.vertex_properties['sensitivity'] = vprop_sens
g.vertex_properties['confidence'] = vprop_conf
g.edge_properties['second_sens'] = eprop_sens
# keep a list of all the vertices
v_list = []
# Add the vertices to the graph
for i, param in enumerate(df['Parameter']):
v = g.add_vertex()
vprop_sens[v] = df.ix[i, sens]
vprop_conf[v] = 1 + df.ix[i, '%s_conf' % sens] / df.ix[i, sens]
vprop_name[v] = param
v_list.append(v)
# Make two new columns in second order dataframe that point to the vertices
# connected on each row.
df2['vertex1'] = -999
df2['vertex2'] = -999
for vertex in v_list:
param = g.vp.param[vertex]
df2.ix[df2['Parameter_1'] == param, 'vertex1'] = vertex
df2.ix[df2['Parameter_2'] == param, 'vertex2'] = vertex
# Only allow edges for vertices that we've defined
df_edges = df2[(df2['vertex1'] != -999) & (df2['vertex2'] != -999)]
# eliminate edges below a certain cutoff value
pruned = df_edges[df_edges['S2'] > edge_cutoff]
pruned.reset_index(inplace=True)
# Add the edges for the graph
for i, sensitivity in enumerate(pruned['S2']):
v1 = pruned.ix[i, 'vertex1']
v2 = pruned.ix[i, 'vertex2']
e = g.add_edge(v1, v2)
# multiply by a number to make the lines visible on the plot
eprop_sens[e] = sensitivity * 150
# These are ways you can reference properties of vertices or edges
# g.vp.param[g.vertex(77)]
# g.vp.param[v_list[0]]
print ('Created a graph with %s vertices and %s edges.\nVertices are the '
'top %s %s values greater than %s.\nOnly S2 values (edges) '
'greater than %s are included.' %
(g.num_vertices(), g.num_edges(), top, sens, min_sens, edge_cutoff))
return g
def makeGraphFast(self, img, dia, xScale, yScale):
print('Building Graph Data Structure'),
start = time.time()
G = Graph(directed=False)
sumAddVertices = 0
vprop = G.new_vertex_property('object')
eprop = G.new_edge_property('object')
epropW = G.new_edge_property("float")
h, w = np.shape(img)
avgScale = (xScale + yScale) / 2
addedVerticesLine2 = []
vListLine2 = []
percentOld = 0
counter = 0
'''
Sweep over each line in the image except the last line
'''
for idx, i in enumerate(img[:len(img) - 2]):
'''
Get foreground indices in the current line of the image and make vertices
'''
counter += 1
percent = (float(counter) / float(h)) * 100
if percentOld + 10 < percent:
print(str(np.round(percent, 1)) + '% '),
percentOld = percent
line1 = np.where(i == True)
if len(line1[0]) > 0:
line1 = set(line1[0]).difference(set(addedVerticesLine2))
vL = G.add_vertex(len(list(line1)))
if len(line1) > 1:
vList = vListLine2 + list(vL)
else:
vList = vListLine2 + [vL]
line1 = addedVerticesLine2 + list(line1)
for jdx, j in enumerate(line1):
vprop[vList[jdx]] = {
'imgIdx': (j, idx),
'coord': (float(j) * xScale, float(idx) * yScale),
'nrOfPaths': 0,
'diameter': float(dia[idx][j]) * avgScale
}
'''
keep order of the inserted vertices
'''
sumAddVertices += len(line1)
addedVerticesLine2 = []
vListLine2 = []
'''
Connect foreground indices to neighbours in the next line
'''
for v1 in line1:
va = vList[line1.index(v1)]
diagonalLeft = diagonalRight = True
try:
if img[idx][v1 - 1] == True:
diagonalLeft = False
vb = vList[line1.index(v1 - 1)]
e = G.add_edge(va, vb)
eprop[e] = {
'coord1':
vprop[va]['coord'],
'coord2':
vprop[vb]['coord'],
'weight': ((vprop[va]['diameter'] +
vprop[vb]['diameter']) / 2),
'RTP':
False
}
epropW[e] = 2. / (eprop[e]['weight']**2)
except:
print 'Boundary vertex at: ' + str(
[v1, idx - 1]) + ' image size: ' + str([w, h])
pass
try:
if img[idx][v1 + 1] == True:
diagonalRight = False
vb = vList[line1.index(v1 + 1)]
e = G.add_edge(va, vb)
eprop[e] = {
'coord1':
vprop[va]['coord'],
'coord2':
vprop[vb]['coord'],
'weight': ((vprop[va]['diameter'] +
vprop[vb]['diameter']) / 2),
'RTP':
False
}
epropW[e] = 2. / (eprop[e]['weight']**2)
except:
print 'Boundary vertex at: ' + str(
[v1 + 1, idx]) + ' image size: ' + str([w, h])
pass # just if we are out of bounds
try:
if img[idx + 1][v1] == True:
diagonalRight = False
diagonalLeft = False
vNew = G.add_vertex()
vprop[vNew] = {
'imgIdx': (v1, idx + 1),
'coord':
(float(v1) * xScale, float(idx + 1) * yScale),
'nrOfPaths':
0,
'diameter':
float(dia[idx + 1][v1]) * avgScale
}
vListLine2.append(vNew)
e = G.add_edge(vList[line1.index(v1)], vNew)
eprop[e] = {
'coord1':
vprop[va]['coord'],
'coord2':
vprop[vNew]['coord'],
'weight': ((vprop[va]['diameter'] +
vprop[vNew]['diameter']) / 2),
'RTP':
False
}
epropW[e] = 1. / (eprop[e]['weight']**2)
if v1 not in addedVerticesLine2:
addedVerticesLine2.append(v1)
except:
print 'Boundary vertex at: ' + str(
[v1, idx + 1]) + ' image size: ' + str([w, h])
pass
try:
if diagonalRight == True and img[idx + 1][v1 +
1] == True:
vNew = G.add_vertex()
vprop[vNew] = {
'imgIdx': (v1 + 1, idx + 1),
'coord': (float(v1 + 1) * xScale,
float(idx + 1) * yScale),
'nrOfPaths':
0,
'diameter':
float(dia[idx + 1][v1 + 1]) * avgScale
}
vListLine2.append(vNew)
e = G.add_edge(vList[line1.index(v1)], vNew)
eprop[e] = {
'coord1':
vprop[va]['coord'],
'coord2':
vprop[vNew]['coord'],
'weight': ((vprop[va]['diameter'] +
vprop[vNew]['diameter']) / 2),
'RTP':
False
}
epropW[e] = 1.41 / (eprop[e]['weight']**2)
if v1 + 1 not in addedVerticesLine2:
addedVerticesLine2.append(v1 + 1)
except:
print 'Boundary vertex at: ' + str(
[v1 + 1, idx + 1]) + ' image size: ' + str([w, h])
pass
try:
if diagonalLeft == True and img[idx + 1][v1 -
1] == True:
vNew = G.add_vertex()
vprop[vNew] = {
'imgIdx': (v1 - 1, idx + 1),
'coord': (float(v1 - 1) * xScale,
float(idx + 1) * yScale),
'nrOfPaths':
0,
'diameter':
float(dia[idx + 1][v1 - 1]) * avgScale
}
vListLine2.append(vNew)
e = G.add_edge(vList[line1.index(v1)], vNew)
eprop[e] = {
'coord1':
vprop[va]['coord'],
'coord2':
vprop[vNew]['coord'],
'weight': ((vprop[va]['diameter'] +
vprop[vNew]['diameter']) / 2),
'RTP':
False
}
epropW[e] = 1.41 / (eprop[e]['weight']**2)
if v1 - 1 not in addedVerticesLine2:
addedVerticesLine2.append(v1 - 1)
except:
print 'Boundary vertex at: ' + str(
[v1 - 1, idx + 1]) + ' image size: ' + str([w, h])
pass
try:
if img[idx][v1 + 1] == False and img[idx][
v1 - 1] == False and img[idx + 1][
v1] == False and diagonalLeft == False and diagonalRight == False:
print 'tip detected'
if img[idx - 1][v1 - 1] == False and img[idx - 1][
v1 + 1] == False and img[idx -
1][v1] == False:
print 'floating pixel'
except:
pass
print 'done!'
G.edge_properties["ep"] = eprop
G.edge_properties["w"] = epropW
G.vertex_properties["vp"] = vprop
print 'graph build in ' + str(time.time() - start)
l = gt.label_largest_component(G)
u = gt.GraphView(G, vfilt=l)
print '# vertices'
print(u.num_vertices())
print(G.num_vertices())
if u.num_vertices() != G.num_vertices():
self.__fail = float((G.num_vertices() - u.num_vertices())) / float(
G.num_vertices())
return u, u.num_vertices()
class BoardGraphGraphtool(BoardGraphBase):
def __init__(self, number_of_vertices, graph_type):
super().__init__(number_of_vertices, graph_type)
# Graph tool creates directed multigraph by default.
self._graph = Graph()
self._graph.add_vertex(number_of_vertices)
self._graph.vertex_properties["cell"] = self._graph.new_vertex_property(
"object", number_of_vertices * [BoardCell()]
)
self._graph.edge_properties["direction"
] = self._graph.new_edge_property("object")
self._graph.edge_properties["weight"
] = self._graph.new_edge_property("int")
def __getitem__(self, position):
return self._graph.vp.cell[self._graph.vertex(position)]
def __setitem__(self, position, board_cell):
self._graph.vp.cell[self._graph.vertex(position)] = board_cell
def __contains__(self, position):
return position in range(0, self.vertices_count())
def vertices_count(self):
return self._graph.num_vertices()
def edges_count(self):
return self._graph.num_edges()
def has_edge(self, source_vertice, target_vertice, direction):
for e in self._graph.vertex(source_vertice).out_edges():
if (
int(e.target()) == target_vertice and
self._graph.ep.direction[e] == direction
):
return True
return False
def out_edges_count(self, source_vertice, target_vertice):
return len([
1 for e in self._graph.vertex(source_vertice).out_edges()
if int(e.target()) == target_vertice
])
def reconfigure_edges(self, width, height, tessellation):
"""
Uses tessellation object to create all edges in graph.
"""
self._graph.clear_edges()
for source_vertice in self._graph.vertices():
for direction in tessellation.legal_directions:
neighbor_vertice = tessellation.neighbor_position(
int(source_vertice),
direction,
board_width=width,
board_height=height
)
if neighbor_vertice is not None:
e = self._graph.add_edge(
source_vertice, neighbor_vertice, add_missing=False
)
self._graph.ep.direction[e] = direction
# TODO: Faster version?
# def reconfigure_edges(self, width, height, tessellation):
# """
# Uses tessellation object to create all edges in graph.
# """
# self._graph.clear_edges()
# edges_to_add = []
# directions_to_add = dict()
# for source_vertice in self._graph.vertices():
# for direction in tessellation.legal_directions:
# neighbor_vertice = tessellation.neighbor_position(
# int(source_vertice), direction,
# board_width=width, board_height=height
# )
# if neighbor_vertice is not None:
# edge = (int(source_vertice), neighbor_vertice,)
# edges_to_add.append(edge)
# if edge not in directions_to_add:
# directions_to_add[edge] = deque()
# directions_to_add[edge].append(direction)
# self._graph.add_edge_list(edges_to_add) if edges_to_add else None
# for e in edges_to_add:
# e_descriptors = self._graph.edge(
# s = self._graph.vertex(e[0]),
# t = self._graph.vertex(e[1]),
# all_edges = True
# )
# for e_descriptor in e_descriptors:
# if len(directions_to_add[e]) > 0:
# self._graph.ep.direction[e_descriptor] = directions_to_add[e][0]
# directions_to_add[e].popleft()
def calculate_edge_weights(self):
for e in self._graph.edges():
self._graph.ep.weight[e] = self.out_edge_weight(int(e.target()))
def neighbor(self, from_position, direction):
try:
for e in self._graph.vertex(from_position).out_edges():
if self._graph.ep.direction[e] == direction:
return int(e.target())
except ValueError as e:
raise IndexError(e.args)
return None
def wall_neighbors(self, from_position):
return [
int(n) for n in self._graph.vertex(from_position).out_neighbours()
if self[int(n)].is_wall
]
def all_neighbors(self, from_position):
return [
int(n) for n in self._graph.vertex(from_position).out_neighbours()
]
def shortest_path(self, start_position, end_position):
try:
return [
int(v)
for v in shortest_path(
g=self._graph,
source=self._graph.vertex(start_position),
target=self._graph.vertex(end_position),
)[0]
]
except ValueError:
return []
def dijkstra_path(self, start_position, end_position):
try:
self.calculate_edge_weights()
return [
int(v)
for v in shortest_path(
g=self._graph,
source=self._graph.vertex(start_position),
target=self._graph.vertex(end_position),
weights=self._graph.ep.weight,
)[0]
]
except ValueError:
return []
def position_path_to_direction_path(self, position_path):
retv = []
src_vertice_index = 0
for target_vertice in position_path[1:]:
source_vertice = position_path[src_vertice_index]
src_vertice_index += 1
for out_edge in self._graph.vertex(source_vertice).out_edges():
if int(out_edge.target()) == target_vertice:
retv.append(self._graph.ep.direction[out_edge])
return {
'source_position': position_path[0] if position_path else None,
'path': retv
}
class GraphDataset:
"""
Class for managing datasets with graph data
"""
def __init__(self, name, edges, object_ids, weights, hidden_graph=None):
"""
Params:
name (str): unique string to name this dataset (for pickling and
unpickling)
edges (numpy.ndarray): numpy array of shape [num_edges, 2]
containing the indices of nodes in all edges
objects (List[str]): string object ids for all nodes
weights (numpy.ndarray): numpy array of shape [num_edges]
containing edge weights
hidden_graph (GraphDataset): Graph data that should be excluded
but not considered as negative edges. (i.e. train
edges should not be in eval dataset but they shouldn't be
counted as negatives either)
"""
self.name = name
self.edges = edges
self.object_ids = np.asarray(object_ids)
self.weights = weights
self.hidden_graph = hidden_graph
self.graph = Graph(directed=False)
self.graph.add_vertex(len(object_ids))
edge_weights = [[edge[0], edge[1], weight]
for edge, weight in zip(self.edges, self.weights)]
self.weight_property = self.graph.new_edge_property("float")
eprops = [self.weight_property]
self.graph.add_edge_list(edge_weights, eprops=eprops)
self.manifold_nns = None
def gen_neighbor_data(self, verbose=True) -> Dict:
"""
Generates the graph data needed to run the cython iterator
Returns a dict with the neighbor data which will have values
- 'non_empty_vertices' the indices of vertices which have edges
emanating from them
- 'all_graph_neighbors' a list of lists of ints such that the list of
edges emanating from the vertex with index non_empty_vertices[i] is
stored in all_graph_neighbors[i]
- 'all_graph_weights' a list of lists of ints such that
all_graph_weights[i][j] represents the weight of the connection in
all_graph_neighbors[i][j]
- 'N' number of nodes in the graph
Parameters:
verbose (bool): should graph loading be printed out
"""
all_graph_neighbors = []
all_graph_weights = []
non_empty_vertices = []
empty_vertices = []
if verbose:
iterator = tqdm(range(self.n_nodes()),
desc="Generating Neighbor Data",
dynamic_ncols=True)
else:
iterator = range(self.n_nodes())
for i in iterator:
in_edges = self.graph.get_in_edges(i, [self.weight_property])
out_edges = self.graph.get_out_edges(i, [self.weight_property])
if in_edges.size + out_edges.size > 0:
non_empty_vertices.append(i)
if in_edges.size == 0:
all_graph_neighbors.append(out_edges[:,
1].astype(np.int64))
all_graph_weights.append(out_edges[:,
2].astype(np.float32))
elif out_edges.size == 0:
all_graph_neighbors.append(in_edges[:, 1].astype(np.int64))
all_graph_weights.append(in_edges[:, 2].astype(np.float32))
else:
all_graph_neighbors.append(
np.concatenate([in_edges[:, 0],
out_edges[:, 1]]).astype(np.int64))
all_graph_weights.append(
np.concatenate([in_edges[:, 2],
out_edges[:, 2]]).astype(np.float32))
else:
empty_vertices.append(i)
# graph_neighbors = np.concatenate(all_graph_neighbors)
# graph_neighbor_weights = np.concatenate(all_graph_weights)
non_empty_vertices = np.array(non_empty_vertices, dtype=np.int64)
empty_vertices = np.array(empty_vertices, dtype=np.int64)
return {
"all_graph_neighbors": all_graph_neighbors,
"all_graph_weights": all_graph_weights,
"non_empty_vertices": non_empty_vertices,
"empty_vertices": empty_vertices,
"N": self.n_nodes()
}
def add_manifold_nns(self, graph_embedder: GraphEmbedder):
manifold = graph_embedder.get_manifold()
data_points = graph_embedder.retrieve_nodes(self.n_nodes())
self.manifold_nns = ManifoldNNS(data_points, manifold)
def n_nodes(self) -> int:
"""
Returns the number of nodes in the graph
"""
return len(self.object_ids)
def collapse_nodes(self, node_ids):
all_new_edges = []
for node_id in tqdm(node_ids,
desc="Collapsing Nodes",
dynamic_ncols=True):
in_edges = self.graph.get_in_edges(node_id, [self.weight_property])
out_edges = self.graph.get_out_edges(node_id,
[self.weight_property])
neighbors = np.concatenate([out_edges[:, 1:3], in_edges[:, 0:3:2]])
if neighbors.shape[0] > 1:
neighbor_combos = \
neighbors[comb_index(neighbors.shape[0], 2)]
neighbor_combos = \
neighbor_combos.reshape(neighbor_combos.shape[0], 4)
new_edges = np.zeros((neighbor_combos.shape[0], 3))
new_edges[:, :2] += neighbor_combos[:, 0:3:2]
new_edges[:,2] += (neighbor_combos[:,1] + \
neighbor_combos[:,3])/4
all_new_edges.append(new_edges)
self.graph.add_edge_list(np.concatenate(all_new_edges),
eprops=[self.weight_property])
self.object_ids = np.delete(self.object_ids, np.array(node_ids))
self.graph.remove_vertex(node_ids)
edges_weights = self.graph.get_edges(eprops=[self.weight_property])
edges = edges_weights[:, 0:2]
weights = edges_weights[:, 2]
self.edges = edges
self.weights = weights
def get_neighbor_iterator(
self,
graph_sampling_config: GraphSamplingConfig,
data_fraction: float = 1,
) -> Iterator[GraphDataBatch]:
"""
Gets an efficient iterator of edge batches
"""
neighbor_data = load_or_gen(f"GraphDataset.{self.name}",
self.gen_neighbor_data)
if self.hidden_graph is None:
# GraphDataBatchIterator is defined in cython with these arguments.
# noinspection PyArgumentList
iterator = GraphDataBatchIterator(neighbor_data,
graph_sampling_config)
iterator.data_fraction = data_fraction
else:
hidden_neighbor_data = load_or_gen(
f"GraphDataset.{self.hidden_graph.name}",
self.hidden_graph.gen_neighbor_data)
# GraphDataBatchIterator is defined in cython with these arguments.
# noinspection PyArgumentList
iterator = GraphDataBatchIterator(neighbor_data,
graph_sampling_config,
hidden_neighbor_data)
iterator.data_fraction = data_fraction
if self.manifold_nns is not None:
sampling_config = get_config().sampling
_, nns = \
self.manifold_nns.knn_query_all(sampling_config.manifold_nn_k)
all_manifold_neighbors = [
nns[i][1:].astype(np.int64) for i in range(self.n_nodes())
]
iterator.refresh_manifold_nn(all_manifold_neighbors)
return iterator
@classmethod
def make_train_eval_split(cls, name, edges, object_ids, weights):
"""
Returns a tuple of a train eval split of the graph as defined in the
data config.
"""
data_config = get_config().data
np.random.seed(data_config.split_seed)
if data_config.split_by_edges:
# TODO Doesn't save to file in this mode
shuffle_order = np.arange(edges.shape[0])
np.random.shuffle(shuffle_order)
num_eval = floor(edges.shape[0] * data_config.split_size)
eval_indices = shuffle_order[:num_eval]
train_indices = shuffle_order[num_eval:]
train_edges = edges[train_indices]
train_weights = weights[train_indices]
eval_edges = edges[eval_indices]
eval_weights = weights[eval_indices]
else:
shuffle_order = np.arange(len(object_ids))
np.random.shuffle(shuffle_order)
num_eval = floor(len(object_ids) * data_config.split_size)
eval_indices = shuffle_order[:num_eval]
test_set = data_config.generate_test_set
if test_set:
test_indices = shuffle_order[num_eval:2 * num_eval]
train_indices = shuffle_order[2 * num_eval:] if test_set else \
shuffle_order[num_eval:]
train_edges = []
eval_edges = []
train_weights = []
eval_weights = []
if test_set:
test_edges = []
test_weights = []
for edge, weight in zip(edges, weights):
if test_set and (edge[0] in test_indices
or edge[1] in test_indices):
test_edges.append(edge)
test_weights.append(weight)
elif edge[0] in eval_indices or edge[1] in eval_indices:
eval_edges.append(edge)
eval_weights.append(weight)
else:
train_edges.append(edge)
train_weights.append(weight)
if test_set:
save_graph_data(test_edges, test_weights, object_ids,
data_config.test_path)
save_graph_data(train_edges, train_weights, object_ids,
data_config.train_path)
save_graph_data(eval_edges, eval_weights, object_ids,
data_config.eval_path)
train_edges = np.array(train_edges)
eval_edges = np.array(eval_edges)
train_weights = np.array(train_weights)
eval_weights = np.array(eval_weights)
train_data = GraphDataset(f"{name}_train", train_edges, object_ids,
train_weights)
eval_data = GraphDataset(f"{name}_eval",
eval_edges,
object_ids,
eval_weights,
hidden_graph=train_data)
return train_data, eval_data
def gen_fs(dicProperties):
np.random.seed()
graphFS = Graph()
# on définit la fraction des arcs à utiliser la réciprocité
f = dicProperties["Reciprocity"]
rFracRecip = f/(2.0-f)
# on définit toutes les grandeurs de base
rInDeg = dicProperties["InDeg"]
rOutDeg = dicProperties["OutDeg"]
nNodes = 0
nEdges = 0
rDens = 0.0
if "Nodes" in dicProperties.keys():
nNodes = dicProperties["Nodes"]
graphFS.add_vertex(nNodes)
if "Edges" in dicProperties.keys():
nEdges = dicProperties["Edges"]
rDens = nEdges / float(nNodes**2)
dicProperties["Density"] = rDens
else:
rDens = dicProperties["Density"]
nEdges = int(np.floor(rDens*nNodes**2))
dicProperties["Edges"] = nEdges
else:
nEdges = dicProperties["Edges"]
rDens = dicProperties["Density"]
nNodes = int(np.floor(np.sqrt(nEdges/rDens)))
graphFS.add_vertex(nNodes)
dicProperties["Nodes"] = nNodes
# on définit le nombre d'arcs à créer
nArcs = int(np.floor(rDens*nNodes**2)/(1+rFracRecip))
# on définit les paramètres fonctions de probabilité associées F(x) = A x^{-tau}
Ai = nArcs*(rInDeg-1)/(nNodes)
Ao = nArcs*(rOutDeg-1)/(nNodes)
# on définit les moyennes des distributions de pareto 2 = lomax
rMi = 1/(rInDeg-2.)
rMo = 1/(rOutDeg-2.)
# on définit les trois listes contenant les degrés sortant/entrant/bidirectionnels associés aux noeuds i in range(nNodes)
lstInDeg = np.random.pareto(rInDeg,nNodes)+1
lstOutDeg = np.random.pareto(rOutDeg,nNodes)+1
lstInDeg = np.floor(np.multiply(Ai/np.mean(lstInDeg), lstInDeg)).astype(int)
lstOutDeg = np.floor(np.multiply(Ao/np.mean(lstOutDeg), lstOutDeg)).astype(int)
# on génère les stubs qui vont être nécessaires et on les compte
nInStubs = int(np.sum(lstInDeg))
nOutStubs = int(np.sum(lstOutDeg))
lstInStubs = np.zeros(np.sum(lstInDeg))
lstOutStubs = np.zeros(np.sum(lstOutDeg))
nStartIn = 0
nStartOut = 0
for vert in range(nNodes):
nInDegVert = lstInDeg[vert]
nOutDegVert = lstOutDeg[vert]
for j in range(np.max([nInDegVert,nOutDegVert])):
if j < nInDegVert:
lstInStubs[nStartIn+j] += vert
if j < nOutDegVert:
lstOutStubs[nStartOut+j] += vert
nStartOut+=nOutDegVert
nStartIn+=nInDegVert
# on vérifie qu'on a à peu près le nombre voulu d'edges
while nInStubs*(1+rFracRecip)/float(nArcs) < 0.95 :
vert = np.random.randint(0,nNodes)
nAddInStubs = int(np.floor(Ai/rMi*(np.random.pareto(rInDeg)+1)))
lstInStubs = np.append(lstInStubs,np.repeat(vert,nAddInStubs)).astype(int)
nInStubs+=nAddInStubs
while nOutStubs*(1+rFracRecip)/float(nArcs) < 0.95 :
nAddOutStubs = int(np.floor(Ao/rMo*(np.random.pareto(rOutDeg)+1)))
lstOutStubs = np.append(lstOutStubs,np.repeat(vert,nAddOutStubs)).astype(int)
nOutStubs+=nAddOutStubs
# on s'assure d'avoir le même nombre de in et out stubs (1.13 is an experimental correction)
nMaxStubs = int(1.13*(2.0*nArcs)/(2*(1+rFracRecip)))
if nInStubs > nMaxStubs and nOutStubs > nMaxStubs:
np.random.shuffle(lstInStubs)
np.random.shuffle(lstOutStubs)
lstOutStubs.resize(nMaxStubs)
lstInStubs.resize(nMaxStubs)
nOutStubs = nInStubs = nMaxStubs
elif nInStubs < nOutStubs:
np.random.shuffle(lstOutStubs)
lstOutStubs.resize(nInStubs)
nOutStubs = nInStubs
else:
np.random.shuffle(lstInStubs)
lstInStubs.resize(nOutStubs)
nInStubs = nOutStubs
# on crée le graphe, les noeuds et les stubs
nRecip = int(np.floor(nInStubs*rFracRecip))
nEdges = nInStubs + nRecip +1
# les stubs réciproques
np.random.shuffle(lstInStubs)
np.random.shuffle(lstOutStubs)
lstInRecip = lstInStubs[0:nRecip]
lstOutRecip = lstOutStubs[0:nRecip]
lstEdges = np.array([np.concatenate((lstOutStubs,lstInRecip)),np.concatenate((lstInStubs,lstOutRecip))]).astype(int)
# add edges
graphFS.add_edge_list(np.transpose(lstEdges))
remove_self_loops(graphFS)
remove_parallel_edges(graphFS)
lstIsolatedVert = find_vertex(graphFS, graphFS.degree_property_map("total"), 0)
graphFS.remove_vertex(lstIsolatedVert)
graphFS.reindex_edges()
nNodes = graphFS.num_vertices()
nEdges = graphFS.num_edges()
rDens = nEdges / float(nNodes**2)
# generate types
rInhibFrac = dicProperties["InhibFrac"]
lstTypesGen = np.random.uniform(0,1,nEdges)
lstTypeLimit = np.full(nEdges,rInhibFrac)
lstIsExcitatory = np.greater(lstTypesGen,lstTypeLimit)
nExc = np.count_nonzero(lstIsExcitatory)
epropType = graphFS.new_edge_property("int",np.multiply(2,lstIsExcitatory)-np.repeat(1,nEdges)) # excitatory (True) or inhibitory (False)
graphFS.edge_properties["type"] = epropType
# and weights
if dicProperties["Weighted"]:
lstWeights = dicGenWeights[dicProperties["Distribution"]](graphFS,dicProperties,nEdges,nExc) # generate the weights
epropW = graphFS.new_edge_property("double",lstWeights) # crée la propriété pour stocker les poids
graphFS.edge_properties["weight"] = epropW
return graphFS
class SegmentationGraph(object):
"""
Class defining the abstract SegmentationGraph object, its attributes and
implements methods common to all derived graph classes.
The constructor requires the following parameters of the underlying
segmentation that will be used to build the graph.
Args:
scale_factor_to_nm (float): pixel size in nanometers for scaling the
graph
scale_x (int): x axis length in pixels of the segmentation
scale_y (int): y axis length in pixels of the segmentation
scale_z (int): z axis length in pixels of the segmentation
"""
def __init__(self, scale_factor_to_nm, scale_x, scale_y, scale_z):
"""
Constructor.
Args:
scale_factor_to_nm (float): pixel size in nanometers for scaling the
graph
scale_x (int): x axis length in pixels of the segmentation
scale_y (int): y axis length in pixels of the segmentation
scale_z (int): z axis length in pixels of the segmentation
Returns:
None
"""
self.graph = Graph(directed=False)
"""graph_tool.Graph: a graph object storing the segmentation graph
topology, geometry and properties.
"""
self.scale_factor_to_nm = scale_factor_to_nm
"""float: pixel size in nanometers for scaling the coordinates and
distances in the graph
"""
self.scale_x = scale_x
"""int: x axis length in pixels of the segmentation"""
self.scale_y = scale_y
"""int: y axis length in pixels of the segmentation"""
self.scale_z = scale_z
"""int: z axis length in pixels of the segmentation"""
# Add "internal property maps" to the graph.
# vertex property for storing the xyz coordinates in nanometers of the
# corresponding vertex:
self.graph.vp.xyz = self.graph.new_vertex_property("vector<float>")
# edge property for storing the distance in nanometers between the
# connected vertices:
self.graph.ep.distance = self.graph.new_edge_property("float")
self.coordinates_to_vertex_index = {}
"""dist: a dictionary mapping the vertex coordinates in nanometers
(x, y, z) to the vertex index.
"""
self.coordinates_pair_connected = {}
"""dict: a dictionary storing pairs of vertex coordinates in nanometers
that are connected by an edge as a key in a tuple form
((x1, y1, z1), (x2, y2, z2)) with value True.
"""
@staticmethod
def distance_between_voxels(voxel1, voxel2):
"""
Calculates and returns the Euclidean distance between two voxels.
Args:
voxel1 (tuple): first voxel coordinates in form of a tuple of
integers of length 3 (x1, y1, z1)
voxel2 (tuple): second voxel coordinates in form of a tuple of
integers of length 3 (x2, y2, z2)
Returns:
the Euclidean distance between two voxels (float)
"""
if (isinstance(voxel1, tuple) and (len(voxel1) == 3) and
isinstance(voxel2, tuple) and (len(voxel2) == 3)):
sum_of_squared_differences = 0
for i in range(3): # for each dimension
sum_of_squared_differences += (voxel1[i] - voxel2[i]) ** 2
return math.sqrt(sum_of_squared_differences)
else:
error_msg = ('Tuples of integers of length 3 required as first and '
'second input.')
raise pexceptions.PySegInputError(
expr='distance_between_voxels (SegmentationGraph)',
msg=error_msg
)
def update_coordinates_to_vertex_index(self):
"""
Updates graph's dictionary coordinates_to_vertex_index.
The dictionary maps the vertex coordinates (x, y, z) scaled in
nanometers to the vertex index. It has to be updated after purging the
graph, because vertices are renumbered, as well as after reading a graph
from a file (e.g. before density calculation).
Returns:
None
"""
self.coordinates_to_vertex_index = {}
for vd in self.graph.vertices():
[x, y, z] = self.graph.vp.xyz[vd]
self.coordinates_to_vertex_index[
(x, y, z)] = self.graph.vertex_index[vd]
def calculate_density(self, mask=None, target_coordinates=None,
verbose=False):
"""
Calculates ribosome density for each membrane graph vertex.
Calculates shortest geodesic distances (d) for each vertex in the graph
to each reachable ribosome center mapped on the membrane given by a
binary mask with coordinates in pixels or an array of coordinates in nm.
Then, calculates a density measure of ribosomes at each vertex or
membrane voxel: D = sum {over all reachable ribosomes} (1 / (d + 1)).
Adds the density as vertex PropertyMap to the graph. Returns an array
with the same shape as the underlying segmentation with the densities
plus 1, in order to distinguish membrane voxels with 0 density from the
background.
Args:
mask (numpy.ndarray, optional): a binary mask of the ribosome
centers as 3D array where indices are coordinates in pixels
(default None)
target_coordinates (numpy.ndarray, optional): the ribosome centers
coordinates in nm as 2D array in format
[[x1, y1, z1], [x2, y2, z2], ...] (default None)
verbose (boolean, optional): if True (default False), some extra
information will be printed out
Returns:
a 3D numpy ndarray with the densities + 1
Note:
One of the first two parameters, mask or target_coordinates, has to
be given.
"""
import ribosome_density as rd
# If a mask is given, find the set of voxels of ribosome centers mapped
# on the membrane, 'target_voxels', and rescale them to nm,
# 'target_coordinates':
if mask is not None:
if mask.shape != (self.scale_x, self.scale_y, self.scale_z):
error_msg = ("Scales of the input 'mask' have to be equal to "
"those set during the generation of the graph.")
raise pexceptions.PySegInputError(
expr='calculate_density (SegmentationGraph)', msg=error_msg
)
# output as a list of tuples [(x1,y1,z1), (x2,y2,z2), ...] in pixels
target_voxels = rd.get_foreground_voxels_from_mask(mask)
# for rescaling have to convert to an ndarray
target_ndarray_voxels = rd.tupel_list_to_ndarray_voxels(
target_voxels
)
# rescale to nm, output an ndarray [[x1,y1,z1], [x2,y2,z2], ...]
target_ndarray_coordinates = (target_ndarray_voxels
* self.scale_factor_to_nm)
# convert to a list of tuples, which are in nm now
target_coordinates = rd.ndarray_voxels_to_tupel_list(
target_ndarray_coordinates
)
# If target_coordinates are given (in nm), convert them from a numpy
# ndarray to a list of tuples:
elif target_coordinates is not None:
target_coordinates = rd.ndarray_voxels_to_tupel_list(
target_coordinates
)
# Exit if the target_voxels list is empty:
if len(target_coordinates) == 0:
error_msg = ("No target voxels were found! Check your input "
"('mask' or 'target_coordinates').")
raise pexceptions.PySegInputError(
expr='calculate_density (SegmentationGraph)', msg=error_msg
)
print '%s target voxels' % len(target_coordinates)
if verbose:
print target_coordinates
# Pre-filter the target coordinates to those existing in the graph
# (should already all be in the graph, but just in case):
target_coordinates_in_graph = []
for target_xyz in target_coordinates:
if target_xyz in self.coordinates_to_vertex_index:
target_coordinates_in_graph.append(target_xyz)
else:
error_msg = ('Target (%s, %s, %s) not inside the membrane!'
% (target_xyz[0], target_xyz[1], target_xyz[2]))
raise pexceptions.PySegInputWarning(
expr='calculate_density (SegmentationGraph)', msg=error_msg
)
print '%s target coordinates in graph' % len(
target_coordinates_in_graph)
if verbose:
print target_coordinates_in_graph
# Get all indices of the target coordinates:
target_vertices_indices = []
for target_xyz in target_coordinates_in_graph:
v_target_index = self.coordinates_to_vertex_index[target_xyz]
target_vertices_indices.append(v_target_index)
# Density calculation
# Add a new vertex property to the graph, density:
self.graph.vp.density = self.graph.new_vertex_property("float")
# Dictionary mapping voxel coordinates (for the volume returned later)
# to a list of density values falling within that voxel:
voxel_to_densities = {}
# For each vertex in the graph:
for v_membrane in self.graph.vertices():
# Get its coordinates:
membrane_xyz = self.graph.vp.xyz[v_membrane]
if verbose:
print ('Membrane vertex (%s, %s, %s)'
% (membrane_xyz[0], membrane_xyz[1], membrane_xyz[2]))
# Get a distance map with all pairs of distances between current
# graph vertex (membrane_xyz) and target vertices (ribosome
# coordinates):
dist_map = shortest_distance(self.graph, source=v_membrane,
target=target_vertices_indices,
weights=self.graph.ep.distance)
# Iterate over all shortest distances from the membrane vertex to
# the target vertices, while calculating the density:
# Initializing: membrane coordinates with no reachable ribosomes
# will have a value of 0, those with reachable ribosomes > 0.
density = 0
# If there is only one target voxel, dist_map is a single value -
# wrap it into a list.
if len(target_coordinates_in_graph) == 1:
dist_map = [dist_map]
for d in dist_map:
if verbose:
print '\tTarget vertex ...'
# if unreachable, the maximum float64 is stored
if d == np.finfo(np.float64).max:
if verbose:
print '\t\tunreachable'
else:
if verbose:
print '\t\td = %s' % d
density += 1 / (d + 1)
# Add the density of the membrane vertex as a property of the
# current vertex in the graph:
self.graph.vp.density[v_membrane] = density
# Calculate the corresponding voxel of the vertex and add the
# density to the list keyed by the voxel in the dictionary:
# Scaling the coordinates back from nm to voxels. (Without round
# float coordinates are truncated to the next lowest integer.)
voxel_x = int(round(membrane_xyz[0] / self.scale_factor_to_nm))
voxel_y = int(round(membrane_xyz[1] / self.scale_factor_to_nm))
voxel_z = int(round(membrane_xyz[2] / self.scale_factor_to_nm))
voxel = (voxel_x, voxel_y, voxel_z)
if voxel in voxel_to_densities:
voxel_to_densities[voxel].append(density)
else:
voxel_to_densities[voxel] = [density]
if verbose:
print '\tdensity = %s' % density
if (self.graph.vertex_index[v_membrane] + 1) % 1000 == 0:
now = datetime.now()
print ('%s membrane vertices processed on: %s-%s-%s %s:%s:%s'
% (self.graph.vertex_index[v_membrane] + 1, now.year,
now.month, now.day, now.hour, now.minute, now.second))
# Initialize an array scaled like the original segmentation, which will
# hold in each membrane voxel the maximal density among the
# corresponding vertex coordinates in the graph plus 1 and 0 in each
# background (non-membrane) voxel:
densities = np.zeros((self.scale_x, self.scale_y, self.scale_z),
dtype=np.float16)
# The densities array membrane voxels are initialized with 1 in order to
# distinguish membrane voxels from the background.
for voxel in voxel_to_densities:
densities[voxel[0], voxel[1], voxel[2]] = 1 + max(
voxel_to_densities[voxel])
if verbose:
print 'densities:\n%s' % densities
return densities
def graph_to_points_and_lines_polys(self, vertices=True, edges=True,
verbose=False):
"""
Generates a VTK PolyData object from the graph with vertices as
vertex-cells (containing 1 point) and edges as line-cells (containing 2
points).
Args:
vertices (boolean, optional): if True (default) vertices are stored
a VTK PolyData object as vertex-cells
edges (boolean, optional): if True (default) edges are stored a VTK
PolyData object as line-cells
verbose (boolean, optional): if True (default False), some extra
information will be printed out
Returns:
- vtk.vtkPolyData with vertex-cells
- vtk.vtkPolyData with edges as line-cells
"""
# Initialization
poly_verts = vtk.vtkPolyData()
poly_lines = vtk.vtkPolyData()
points = vtk.vtkPoints()
vertex_arrays = list()
edge_arrays = list()
# Vertex property arrays
for prop_key in self.graph.vp.keys():
data_type = self.graph.vp[prop_key].value_type()
if (data_type != 'string' and data_type != 'python::object' and
prop_key != 'xyz'):
if verbose:
print '\nvertex property key: %s' % prop_key
print 'value type: %s' % data_type
if data_type[0:6] != 'vector': # scalar
num_components = 1
else: # vector
num_components = len(
self.graph.vp[prop_key][self.graph.vertex(0)]
)
array = TypesConverter().gt_to_vtk(data_type)
array.SetName(prop_key)
if verbose:
print 'number of components: %s' % num_components
array.SetNumberOfComponents(num_components)
vertex_arrays.append(array)
# Edge property arrays
for prop_key in self.graph.ep.keys():
data_type = self.graph.ep[prop_key].value_type()
if data_type != 'string' and data_type != 'python::object':
if verbose:
print '\nedge property key: %s' % prop_key
print 'value type: %s' % data_type
if data_type[0:6] != 'vector': # scalar
num_components = 1
else: # vector (all edge properties so far are scalars)
# num_components = len(
# self.graph.ep[prop_key][self.graph.edge(0, 1)]
# )
num_components = 3
if verbose:
print ('Sorry, not implemented yet, assuming a vector '
'with 3 components.')
array = TypesConverter().gt_to_vtk(data_type)
array.SetName(prop_key)
if verbose:
print 'number of components: %s' % num_components
array.SetNumberOfComponents(num_components)
edge_arrays.append(array)
if verbose:
print '\nvertex arrays length: %s' % len(vertex_arrays)
print 'edge arrays length: %s' % len(edge_arrays)
# Geometry
lut = np.zeros(shape=self.graph.num_vertices(), dtype=np.int)
for i, vd in enumerate(self.graph.vertices()):
[x, y, z] = self.graph.vp.xyz[vd]
points.InsertPoint(i, x, y, z)
lut[self.graph.vertex_index[vd]] = i
if verbose:
print 'number of points: %s' % points.GetNumberOfPoints()
# Topology
# Vertices
verts = vtk.vtkCellArray()
if vertices:
for vd in self.graph.vertices(): # vd = vertex descriptor
verts.InsertNextCell(1)
verts.InsertCellPoint(lut[self.graph.vertex_index[vd]])
for array in vertex_arrays:
prop_key = array.GetName()
n_comp = array.GetNumberOfComponents()
data_type = self.graph.vp[prop_key].value_type()
data_type = TypesConverter().gt_to_numpy(data_type)
array.InsertNextTuple(self.get_vertex_prop_entry(
prop_key, vd, n_comp, data_type))
if verbose:
print 'number of vertex cells: %s' % verts.GetNumberOfCells()
# Edges
lines = vtk.vtkCellArray()
if edges:
for ed in self.graph.edges(): # ed = edge descriptor
lines.InsertNextCell(2)
lines.InsertCellPoint(lut[self.graph.vertex_index[ed.source()]])
lines.InsertCellPoint(lut[self.graph.vertex_index[ed.target()]])
for array in edge_arrays:
prop_key = array.GetName()
n_comp = array.GetNumberOfComponents()
data_type = self.graph.ep[prop_key].value_type()
data_type = TypesConverter().gt_to_numpy(data_type)
array.InsertNextTuple(self.get_edge_prop_entry(
prop_key, ed, n_comp, data_type))
if verbose:
print 'number of line cells: %s' % lines.GetNumberOfCells()
# vtkPolyData construction
poly_verts.SetPoints(points)
poly_lines.SetPoints(points)
if vertices:
poly_verts.SetVerts(verts)
if edges:
poly_lines.SetLines(lines)
for array in vertex_arrays:
poly_verts.GetCellData().AddArray(array)
for array in edge_arrays:
poly_lines.GetCellData().AddArray(array)
return poly_verts, poly_lines
def get_vertex_prop_entry(self, prop_key, vertex_descriptor, n_comp,
data_type):
"""
Gets a property value of a vertex for inserting into a VTK vtkDataArray
object.
This private function is used by the methods
graph_to_points_and_lines_polys and graph_to_triangle_poly (the latter
of the derived class surface_graphs.TriangleGraph).
Args:
prop_key (str): name of the desired vertex property
vertex_descriptor (graph_tool.Vertex): vertex descriptor of the
current vertex
n_comp (int): number of components of the array (length of the
output tuple)
data_type: numpy data type converted from a graph-tool property
value type by TypesConverter().gt_to_numpy
Returns:
a tuple (with length like n_comp) with the property value of the
vertex converted to a numpy data type
"""
prop = list()
if n_comp == 1:
prop.append(data_type(self.graph.vp[prop_key][vertex_descriptor]))
else:
for i in range(n_comp):
prop.append(data_type(
self.graph.vp[prop_key][vertex_descriptor][i]))
return tuple(prop)
def get_edge_prop_entry(self, prop_key, edge_descriptor, n_comp, data_type):
"""
Gets a property value of an edge for inserting into a VTK vtkDataArray
object.
This private function is used by the method
graph_to_points_and_lines_polys.
Args:
prop_key (str): name of the desired vertex property
edge_descriptor (graph_tool.Edge): edge descriptor of the current
edge
n_comp (int): number of components of the array (length of the
output tuple)
data_type: numpy data type converted from a graph-tool property
value type by TypesConverter().gt_to_numpy
Returns:
a tuple (with length like n_comp) with the property value of the
edge converted to a numpy data type
"""
prop = list()
if n_comp == 1:
prop.append(data_type(self.graph.ep[prop_key][edge_descriptor]))
else:
for i in range(n_comp):
prop.append(data_type(
self.graph.ep[prop_key][edge_descriptor][i]))
return tuple(prop)
# * The following SegmentationGraph methods are needed for the normal vector
# voting algorithm. *
def calculate_average_edge_length(self, prop_e=None, value=1):
"""
Calculates the average edge length in the graph.
If a special edge property is specified, includes only the edges where
this property equals the given value. If there are no edges in the
graph, the given property does not exist or there are no edges with the
given property equaling the given value, None is returned.
Args:
prop_e (str, optional): edge property, if specified only edges where
this property equals the given value will be considered
value (int, optional): value of the specified edge property an edge
has to have in order to be considered (default 1)
Returns:
the average edge length in the graph (float) or None
"""
total_edge_length = 0
average_edge_length = None
if prop_e is None:
print "Considering all edges:"
for ed in self.graph.edges():
total_edge_length += self.graph.ep.distance[ed]
if self.graph.num_edges() > 0:
average_edge_length = total_edge_length / self.graph.num_edges()
else:
print "There are no edges in the graph!"
elif prop_e in self.graph.edge_properties:
print ("Considering only edges with property %s equaling value %s "
% (prop_e, value))
num_special_edges = 0
for ed in self.graph.edges():
if self.graph.edge_properties[prop_e][ed] == value:
num_special_edges += 1
total_edge_length += self.graph.ep.distance[ed]
if num_special_edges > 0:
average_edge_length = total_edge_length / num_special_edges
else:
print ("There are no edges with the property %s equaling value "
"%s!" % (prop_e, value))
print "Average length: %s" % average_edge_length
return average_edge_length
def find_geodesic_neighbors(self, v, g_max, verbose=False):
"""
Finds geodesic neighbor vertices of a given vertex v in the graph that
are within a given maximal geodesic distance g_max from it.
Also finds the corresponding geodesic distances. All edges are
considered.
Args:
v (graph_tool.Vertex): the source vertex
g_max: maximal geodesic distance (in nanometers, if the graph was
scaled)
verbose (boolean, optional): if True (default False), some extra
information will be printed out
Returns:
a dictionary mapping a neighbor vertex index to the geodesic
distance from vertex v
"""
dist_v = shortest_distance(self.graph, source=v, target=None,
weights=self.graph.ep.distance,
max_dist=g_max)
dist_v = dist_v.get_array()
neighbor_id_to_dist = dict()
idxs = np.where(dist_v <= g_max)[0]
for idx in idxs:
dist = dist_v[idx]
if dist != 0: # ignore the source vertex itself
neighbor_id_to_dist[idx] = dist
if verbose:
print "%s neighbors" % len(neighbor_id_to_dist)
return neighbor_id_to_dist
def get_vertex_property_array(self, property_name):
"""
Gets a numpy array with all values of a vertex property of the graph,
printing out the number of values, the minimal and the maximal value.
Args:
property_name (str): vertex property name
Returns:
an array (numpy.ndarray) with all values of the vertex property
"""
if (isinstance(property_name, str) and
property_name in self.graph.vertex_properties):
values = self.graph.vertex_properties[property_name].get_array()
print '%s "%s" values' % (len(values), property_name)
print 'min = %s, max = %s' % (min(values), max(values))
return values
else:
error_msg = ('The input "%s" is not a str object or is not found '
'in vertex properties of the graph.' % property_name)
raise pexceptions.PySegInputError(
expr='get_vertex_property_array (SegmentationGraph)',
msg=error_msg)
def makeGraphFast(self,img,dia,xScale,yScale):
print('Building Graph Data Structure'),
start=time.time()
G = Graph(directed=False)
sumAddVertices=0
vprop=G.new_vertex_property('object')
eprop=G.new_edge_property('object')
epropW=G.new_edge_property("float")
h, w = np.shape(img)
if xScale>0 and yScale>0: avgScale=(xScale+yScale)/2
else:
avgScale=1.
xScale=1.
yScale=1.
addedVerticesLine2=[]
vListLine2=[]
percentOld=0
counter=0
'''
Sweep over each line in the image except the last line
'''
for idx,i in enumerate(img[:len(img)-2]):
'''
Get foreground indices in the current line of the image and make vertices
'''
counter+=1
percent=(float(counter)/float(h))*100
if percentOld+10< percent:
print (str(np.round(percent,1))+'% '),
percentOld=percent
line1=np.where(i==True)
if len(line1[0])>0:
line1=set(line1[0]).difference(set(addedVerticesLine2))
vL=G.add_vertex(len(list(line1)))
if len(line1)>1 :
vList=vListLine2+list(vL)
else: vList=vListLine2+[vL]
line1=addedVerticesLine2+list(line1)
for jdx,j in enumerate(line1):
vprop[vList[jdx]]={'imgIdx':(j,idx),'coord': (float(j)*xScale,float(idx)*yScale), 'nrOfPaths':0, 'diameter':float(dia[idx][j])*avgScale}
'''
keep order of the inserted vertices
'''
sumAddVertices+=len(line1)
addedVerticesLine2=[]
vListLine2=[]
'''
Connect foreground indices to neighbours in the next line
'''
for v1 in line1:
va=vList[line1.index(v1)]
diagonalLeft = diagonalRight = True
try:
if img[idx][v1-1]==True:
diagonalLeft=False
vb=vList[line1.index(v1-1)]
e=G.add_edge(va,vb)
eprop[e]={'coord1':vprop[va]['coord'], 'coord2':vprop[vb]['coord'],'weight':((vprop[va]['diameter']+vprop[vb]['diameter'])/2),'RTP':False}
epropW[e]=2./(eprop[e]['weight']**2)
except:
print 'Boundary vertex at: '+str([v1,idx-1])+' image size: '+ str([w,h])
pass
try:
if img[idx][v1+1]==True:
diagonalRight=False
vb=vList[line1.index(v1+1)]
e=G.add_edge(va,vb)
eprop[e]={'coord1':vprop[va]['coord'], 'coord2':vprop[vb]['coord'],'weight':((vprop[va]['diameter']+vprop[vb]['diameter'])/2),'RTP':False}
epropW[e]=2./(eprop[e]['weight']**2)
except:
print 'Boundary vertex at: '+str([v1+1,idx])+' image size: '+ str([w,h])
pass # just if we are out of bounds
try:
if img[idx+1][v1]==True:
diagonalRight=False
diagonalLeft=False
vNew=G.add_vertex()
vprop[vNew]={'imgIdx':(v1,idx+1),'coord': (float(v1)*xScale,float(idx+1)*yScale), 'nrOfPaths':0, 'diameter':float(dia[idx+1][v1])*avgScale}
vListLine2.append(vNew)
e=G.add_edge(vList[line1.index(v1)],vNew)
eprop[e]={'coord1':vprop[va]['coord'], 'coord2':vprop[vNew]['coord'],'weight':((vprop[va]['diameter']+vprop[vNew]['diameter'])/2),'RTP':False}
epropW[e]=1./(eprop[e]['weight']**2)
if v1 not in addedVerticesLine2: addedVerticesLine2.append(v1)
except:
print 'Boundary vertex at: '+str([v1,idx+1])+' image size: '+ str([w,h])
pass
try:
if diagonalRight == True and img[idx+1][v1+1]==True:
vNew=G.add_vertex()
vprop[vNew]={'imgIdx':(v1+1,idx+1),'coord': (float(v1+1)*xScale,float(idx+1)*yScale), 'nrOfPaths':0, 'diameter':float(dia[idx+1][v1+1])*avgScale}
vListLine2.append(vNew)
e=G.add_edge(vList[line1.index(v1)],vNew)
eprop[e]={'coord1':vprop[va]['coord'], 'coord2':vprop[vNew]['coord'],'weight':((vprop[va]['diameter']+vprop[vNew]['diameter'])/2),'RTP':False}
epropW[e]=1.41/(eprop[e]['weight']**2)
if v1+1 not in addedVerticesLine2: addedVerticesLine2.append(v1+1)
except:
print 'Boundary vertex at: '+str([v1+1,idx+1])+' image size: '+ str([w,h])
pass
try:
if diagonalLeft == True and img[idx+1][v1-1]==True:
vNew=G.add_vertex()
vprop[vNew]={'imgIdx':(v1-1,idx+1),'coord': (float(v1-1)*xScale,float(idx+1)*yScale), 'nrOfPaths':0, 'diameter':float(dia[idx+1][v1-1])*avgScale}
vListLine2.append(vNew)
e=G.add_edge(vList[line1.index(v1)],vNew)
eprop[e]={'coord1':vprop[va]['coord'], 'coord2':vprop[vNew]['coord'],'weight':((vprop[va]['diameter']+vprop[vNew]['diameter'])/2),'RTP':False}
epropW[e]=1.41/(eprop[e]['weight']**2)
if v1-1 not in addedVerticesLine2: addedVerticesLine2.append(v1-1)
except:
print 'Boundary vertex at: '+str([v1-1,idx+1])+' image size: '+ str([w,h])
pass
try:
if img[idx][v1+1]==False and img[idx][v1-1]==False and img[idx+1][v1]==False and diagonalLeft==False and diagonalRight==False:
print 'tip detected'
if img[idx-1][v1-1]==False and img[idx-1][v1+1]==False and img[idx-1][v1]==False:
print 'floating pixel'
except:
pass
print'done!'
G.edge_properties["ep"] = eprop
G.edge_properties["w"] = epropW
G.vertex_properties["vp"] = vprop
print 'graph build in '+str(time.time()-start)
l = gt.label_largest_component(G)
u = gt.GraphView(G, vfilt=l)
print '# vertices'
print(u.num_vertices())
print(G.num_vertices())
if u.num_vertices()!=G.num_vertices(): self.__fail=float((G.num_vertices()-u.num_vertices()))/float(G.num_vertices())
return u,u.num_vertices()
def main():
conn = serial_interface.connect()
cur_track = track.init_tracka()
g = Graph()
g.add_vertex(len(cur_track))
for (vi, node) in enumerate(cur_track):
node.i = vi
n_title = g.new_vertex_property("string")
n_color = g.new_vertex_property("string")
n_pos = g.new_vertex_property("vector<double>")
e_title = g.new_edge_property("string")
e_dist = g.new_edge_property("double")
for node in cur_track:
v = g.vertex(node.i)
n_title[v] = node.name
if node.typ == track.NODE_EXIT:
# Invert points to match our ASCII display.
n_pos[v] = (-node.reverse.coord_x, -node.reverse.coord_y)
else:
n_pos[v] = (-node.coord_x, -node.coord_y)
e = g.add_edge(g.vertex(node.i), g.vertex(node.reverse.i))
if node.typ == track.NODE_SENSOR: n_color[v] = "blue"
elif node.typ == track.NODE_BRANCH: n_color[v] = "orange"
elif node.typ == track.NODE_MERGE: n_color[v] = "yellow"
elif node.typ == track.NODE_ENTER: n_color[v] = "green"
elif node.typ == track.NODE_EXIT: n_color[v] = "red"
else: n_color[v] = "white"
for edge in node.edge:
if edge.src is None: continue
e = g.add_edge(g.vertex(edge.src.i), g.vertex(edge.dest.i))
e_dist[e] = edge.dist
e_title[e] = "%.2f" % (edge.dist)
win = graph_tool.draw.GraphWindow(g,
n_pos, (640, 480),
edge_text=e_title,
vertex_fill_color=n_color,
vertex_text=n_title)
win.show_all()
def destroy_callback(*args, **kwargs):
win.destroy()
Gtk.main_quit()
def set_switch(sw, d):
for node in cur_track:
if node.typ == track.NODE_BRANCH and node.num == sw:
node.switch_direction = d
return
print "WARN: Could not find switch %d" % sw
class Train():
num = -1
vel = 0.
speed = 0.
edge = cur_track[0].edge[0]
edge_dist = 0
SPEEDX = 1.
def __init__(self, num):
self.num = num
def update(self):
# Super shitty deacceleration model
self.vel = self.vel + (0.018 / self.SPEEDX) * (self.speed -
self.vel)
self.edge_dist += self.vel
while True:
e = self.e()
if self.edge_dist < e_dist[e]: break
if self.edge.dest.typ == track.NODE_SENSOR:
conn.set_sensor_tripped(self.edge.dest.num)
self.edge = self.edge.dest.edge[
self.edge.dest.switch_direction]
self.edge_dist -= e_dist[e]
def draw(self, n_pos, da, cr):
e = self.e()
start, end = np.array(n_pos[e.source()]), np.array(
n_pos[e.target()])
alpha = self.edge_dist / e_dist[e]
pos = start + alpha * (end - start)
dp = win.graph.pos_to_device(pos) # dp: device position
cr.rectangle(dp[0] - 10, dp[1] - 10, 20, 20)
cr.set_source_rgb(102. / 256, 102. / 256, 102. / 256)
cr.fill()
cr.move_to(dp[0] - 10, dp[1] + 10 - 12. / 2)
cr.set_source_rgb(1., 1., 1.)
cr.set_font_size(12)
cr.show_text("%d" % self.num)
cr.fill()
def e(self):
return g.edge(self.edge.src.i, self.edge.dest.i)
def set_speed(self, speed):
self.speed = speed / self.SPEEDX
def toggle_reverse(self):
self.edge = self.edge.reverse
self.edge_dist = e_dist[self.e()] - self.edge_dist
def find_train(train_number):
for train in trains:
if train.num == train_number:
return train
train = Train(train_number)
trains.append(train)
return train
trains = [Train(12)]
startup_time = time.time()
accumulated_error = [0.]
last_time = [time.time()]
last_sensor_poll = [0]
FPS = 30.
def my_draw(da, cr):
(typ, a1, a2) = conn.next_cmd()
if typ is None: pass
elif typ == 'set_speed': find_train(a1).set_speed(a2)
elif typ == 'toggle_reverse': find_train(a1).toggle_reverse()
elif typ == 'switch': set_switch(a1, a2)
elif typ == 'sensor':
last_sensor_poll[0] = round(
(time.time() - startup_time) * 1000) / 1000
else:
print "Ignoring command %s" % typ
cur_time = time.time()
dt = cur_time - last_time[0] + accumulated_error[0]
num_steps = int(dt * FPS)
accumulated_error[0] = dt - num_steps / FPS
for train in trains:
for _ in range(0, num_steps):
train.update()
train.draw(n_pos, da, cr)
cr.move_to(10., 10.)
cr.set_source_rgb(0., 0., 0.)
cr.set_font_size(12)
cr.show_text("Last polled at %.3f" % last_sensor_poll[0])
da.queue_draw()
last_time[0] = cur_time
win.connect("delete_event", destroy_callback)
win.graph.connect("draw", my_draw)
Gtk.main()
class SegmentationGraph(object):
"""
Class defining the abstract SegmentationGraph object, its attributes and
implements methods common to all derived graph classes.
The constructor requires the following parameters of the underlying
segmentation that will be used to build the graph.
"""
def __init__(self):
"""
Constructor of the abstract SegmentationGraph object.
Returns:
None
"""
self.graph = Graph(directed=False)
"""graph_tool.Graph: a graph object storing the segmentation graph
topology, geometry and properties (initially empty).
"""
# Add "internal property maps" to the graph.
# vertex property for storing the xyz coordinates of the corresponding
# vertex:
self.graph.vp.xyz = self.graph.new_vertex_property("vector<float>")
# edge property for storing the distance between the connected vertices:
self.graph.ep.distance = self.graph.new_edge_property("float")
self.coordinates_to_vertex_index = {}
"""dict: a dictionary mapping the vertex coordinates (x, y, z) to the
vertex index.
"""
self.coordinates_pair_connected = set()
"""set: a set storing pairs of vertex coordinates that are
connected by an edge in a tuple form ((x1, y1, z1), (x2, y2, z2)).
"""
@staticmethod
def distance_between_voxels(voxel1, voxel2):
"""
Calculates and returns the Euclidean distance between two voxels.
Args:
voxel1 (tuple): first voxel coordinates in form of a tuple of
floats of length 3 (x1, y1, z1)
voxel2 (tuple): second voxel coordinates in form of a tuple of
floats of length 3 (x2, y2, z2)
Returns:
the Euclidean distance between two voxels (float)
"""
if (isinstance(voxel1, tuple) and (len(voxel1) == 3)
and isinstance(voxel2, tuple) and (len(voxel2) == 3)):
sum_of_squared_differences = 0
for i in range(3): # for each dimension
sum_of_squared_differences += (voxel1[i] - voxel2[i])**2
return math.sqrt(sum_of_squared_differences)
else:
raise pexceptions.PySegInputError(
expr='distance_between_voxels (SegmentationGraph)',
msg=('Tuples of integers of length 3 required as first and '
'second input.'))
def update_coordinates_to_vertex_index(self):
"""
Updates graph's dictionary coordinates_to_vertex_index.
The dictionary maps the vertex coordinates (x, y, z) to the vertex
index. It has to be updated after purging the graph, because vertices
are renumbered, as well as after reading a graph from a file (e.g.
before density calculation).
Returns:
None
"""
self.coordinates_to_vertex_index = {}
for vd in self.graph.vertices():
[x, y, z] = self.graph.vp.xyz[vd]
self.coordinates_to_vertex_index[(x, y,
z)] = self.graph.vertex_index[vd]
def calculate_density(self,
size,
scale,
mask=None,
target_coordinates=None,
verbose=False):
"""
Calculates ribosome density for each membrane graph vertex.
Calculates shortest geodesic distances (d) for each vertex in the graph
to each reachable ribosome center mapped on the membrane given by a
binary mask with coordinates in pixels or an array of coordinates in
given units.
Then, calculates a density measure of ribosomes at each vertex or
membrane voxel: D = sum {over all reachable ribosomes} (1 / (d + 1)).
Adds the density as vertex PropertyMap to the graph. Returns an array
with the same shape as the underlying segmentation with the densities
plus 1, in order to distinguish membrane voxels with 0 density from the
background.
Args:
size (tuple): size in voxels (X, Y, Z) of the original segmentation
scale (tuple): pixel size (X, Y, Z) in given units of the original
segmentation
mask (numpy.ndarray, optional): a binary mask of the ribosome
centers as 3D array where indices are coordinates in pixels
(default None)
target_coordinates (numpy.ndarray, optional): the ribosome centers
coordinates in given units as 2D array in format
[[x1, y1, z1], [x2, y2, z2], ...] (default None)
verbose (boolean, optional): if True (default False), some extra
information will be printed out
Returns:
a 3D numpy ndarray with the densities + 1
Note:
One of the two parameters, mask or target_coordinates, has to be
given.
"""
from . import ribosome_density as rd
# If a mask is given, find the set of voxels of ribosome centers mapped
# on the membrane, 'target_voxels', and rescale them to units,
# 'target_coordinates':
if mask is not None:
if mask.shape != size:
raise pexceptions.PySegInputError(
expr='calculate_density (SegmentationGraph)',
msg=("Size of the input 'mask' have to be equal to those "
"set during the generation of the graph."))
# output as a list of tuples [(x1,y1,z1), (x2,y2,z2), ...] in pixels
target_voxels = rd.get_foreground_voxels_from_mask(mask)
# for rescaling have to convert to an ndarray
target_ndarray_voxels = rd.tupel_list_to_ndarray_voxels(
target_voxels)
# rescale to units, output an ndarray [[x1,y1,z1], [x2,y2,z2], ...]
target_ndarray_coordinates = (target_ndarray_voxels *
np.asarray(scale))
# convert to a list of tuples, which are in units now
target_coordinates = rd.ndarray_voxels_to_tupel_list(
target_ndarray_coordinates)
# If target_coordinates are given (in units), convert them from a numpy
# ndarray to a list of tuples:
elif target_coordinates is not None:
target_coordinates = rd.ndarray_voxels_to_tupel_list(
target_coordinates)
# Exit if the target_voxels list is empty:
if len(target_coordinates) == 0:
raise pexceptions.PySegInputError(
expr='calculate_density (SegmentationGraph)',
msg="No target voxels were found! Check your input ('mask' or "
"'target_coordinates').")
print('{} target voxels'.format(len(target_coordinates)))
if verbose:
print(target_coordinates)
# Pre-filter the target coordinates to those existing in the graph
# (should already all be in the graph, but just in case):
target_coordinates_in_graph = []
for target_xyz in target_coordinates:
if target_xyz in self.coordinates_to_vertex_index:
target_coordinates_in_graph.append(target_xyz)
else:
raise pexceptions.PySegInputWarning(
expr='calculate_density (SegmentationGraph)',
msg=('Target ({}, {}, {}) not inside the membrane!'.format(
target_xyz[0], target_xyz[1], target_xyz[2])))
print('{} target coordinates in graph'.format(
len(target_coordinates_in_graph)))
if verbose:
print(target_coordinates_in_graph)
# Get all indices of the target coordinates:
target_vertices_indices = []
for target_xyz in target_coordinates_in_graph:
v_target_index = self.coordinates_to_vertex_index[target_xyz]
target_vertices_indices.append(v_target_index)
# Density calculation
# Add a new vertex property to the graph, density:
self.graph.vp.density = self.graph.new_vertex_property("float")
# Dictionary mapping voxel coordinates (for the volume returned later)
# to a list of density values falling within that voxel:
voxel_to_densities = {}
# For each vertex in the graph:
for v_membrane in self.graph.vertices():
# Get its coordinates:
membrane_xyz = self.graph.vp.xyz[v_membrane]
if verbose:
print('Membrane vertex ({}, {}, {})'.format(
membrane_xyz[0], membrane_xyz[1], membrane_xyz[2]))
# Get a distance map with all pairs of distances between current
# graph vertex (membrane_xyz) and target vertices (ribosome
# coordinates):
dist_map = shortest_distance(self.graph,
source=v_membrane,
target=target_vertices_indices,
weights=self.graph.ep.distance)
# Iterate over all shortest distances from the membrane vertex to
# the target vertices, while calculating the density:
# Initializing: membrane coordinates with no reachable ribosomes
# will have a value of 0, those with reachable ribosomes > 0.
density = 0
# If there is only one target voxel, dist_map is a single value -
# wrap it into a list.
if len(target_coordinates_in_graph) == 1:
dist_map = [dist_map]
for d in dist_map:
if verbose:
print('\tTarget vertex ...')
# if unreachable, the maximum float64 is stored
if d == np.finfo(np.float64).max:
if verbose:
print('\t\tunreachable')
else:
if verbose:
print('\t\td = {}'.format(d))
density += 1 / (d + 1)
# Add the density of the membrane vertex as a property of the
# current vertex in the graph:
self.graph.vp.density[v_membrane] = density
# Calculate the corresponding voxel of the vertex and add the
# density to the list keyed by the voxel in the dictionary:
# Scaling the coordinates back from units to voxels. (Without round
# float coordinates are truncated to the next lowest integer.)
voxel_x = int(round(membrane_xyz[0] / scale[0]))
voxel_y = int(round(membrane_xyz[1] / scale[1]))
voxel_z = int(round(membrane_xyz[2] / scale[2]))
voxel = (voxel_x, voxel_y, voxel_z)
if voxel in voxel_to_densities:
voxel_to_densities[voxel].append(density)
else:
voxel_to_densities[voxel] = [density]
if verbose:
print('\tdensity = {}'.format(density))
if (self.graph.vertex_index[v_membrane] + 1) % 1000 == 0:
now = datetime.now()
print('{} membrane vertices processed on: {}-{}-{} {}:{}:{}'.
format(self.graph.vertex_index[v_membrane] + 1, now.year,
now.month, now.day, now.hour, now.minute,
now.second))
# Initialize an array scaled like the original segmentation, which will
# hold in each membrane voxel the maximal density among the
# corresponding vertex coordinates in the graph plus 1 and 0 in each
# background (non-membrane) voxel:
densities = np.zeros(size, dtype=np.float16)
# The densities array membrane voxels are initialized with 1 in order to
# distinguish membrane voxels from the background.
for voxel in voxel_to_densities:
densities[voxel[0], voxel[1],
voxel[2]] = 1 + max(voxel_to_densities[voxel])
if verbose:
print('densities:\n{}'.format(densities))
return densities
def graph_to_points_and_lines_polys(self,
vertices=True,
edges=True,
verbose=False):
"""
Generates a VTK PolyData object from the graph with vertices as
vertex-cells (containing 1 point) and edges as line-cells (containing 2
points).
Args:
vertices (boolean, optional): if True (default) vertices are stored
a VTK PolyData object as vertex-cells
edges (boolean, optional): if True (default) edges are stored a VTK
PolyData object as line-cells
verbose (boolean, optional): if True (default False), some extra
information will be printed out
Returns:
- vtk.vtkPolyData with vertex-cells
- vtk.vtkPolyData with edges as line-cells
"""
# Initialization
poly_verts = vtk.vtkPolyData()
poly_lines = vtk.vtkPolyData()
points = vtk.vtkPoints()
vertex_arrays = list()
edge_arrays = list()
# Vertex property arrays
for prop_key in list(self.graph.vp.keys()):
data_type = self.graph.vp[prop_key].value_type()
if (data_type != 'string' and data_type != 'python::object'
and prop_key != 'xyz'):
if verbose:
print('\nvertex property key: {}'.format(prop_key))
print('value type: {}'.format(data_type))
if data_type[0:6] != 'vector': # scalar
num_components = 1
else: # vector
num_components = len(
self.graph.vp[prop_key][self.graph.vertex(0)])
array = TypesConverter().gt_to_vtk(data_type)
array.SetName(prop_key)
if verbose:
print('number of components: {}'.format(num_components))
array.SetNumberOfComponents(num_components)
vertex_arrays.append(array)
# Edge property arrays
for prop_key in list(self.graph.ep.keys()):
data_type = self.graph.ep[prop_key].value_type()
if data_type != 'string' and data_type != 'python::object':
if verbose:
print('\nedge property key: {}'.format(prop_key))
print('value type: {}'.format(data_type))
if data_type[0:6] != 'vector': # scalar
num_components = 1
else: # vector (all edge properties so far are scalars)
# num_components = len(
# self.graph.ep[prop_key][self.graph.edge(0, 1)])
num_components = 3
if verbose:
print('Sorry, not implemented yet, assuming a vector '
'with 3 components.')
array = TypesConverter().gt_to_vtk(data_type)
array.SetName(prop_key)
if verbose:
print('number of components: {}'.format(num_components))
array.SetNumberOfComponents(num_components)
edge_arrays.append(array)
if verbose:
print('\nvertex arrays length: {}'.format(len(vertex_arrays)))
print('edge arrays length: {}'.format(len(edge_arrays)))
# Geometry
lut = np.zeros(shape=self.graph.num_vertices(), dtype=np.int)
for i, vd in enumerate(self.graph.vertices()):
[x, y, z] = self.graph.vp.xyz[vd]
points.InsertPoint(i, x, y, z)
lut[self.graph.vertex_index[vd]] = i
if verbose:
print('number of points: {}'.format(points.GetNumberOfPoints()))
# Topology
# Vertices
verts = vtk.vtkCellArray()
if vertices:
for vd in self.graph.vertices(): # vd = vertex descriptor
verts.InsertNextCell(1)
verts.InsertCellPoint(lut[self.graph.vertex_index[vd]])
for array in vertex_arrays:
prop_key = array.GetName()
n_comp = array.GetNumberOfComponents()
data_type = self.graph.vp[prop_key].value_type()
data_type = TypesConverter().gt_to_numpy(data_type)
array.InsertNextTuple(
self.get_vertex_prop_entry(prop_key, vd, n_comp,
data_type))
if verbose:
print('number of vertex cells: {}'.format(
verts.GetNumberOfCells()))
# Edges
lines = vtk.vtkCellArray()
if edges:
for ed in self.graph.edges(): # ed = edge descriptor
lines.InsertNextCell(2)
lines.InsertCellPoint(
lut[self.graph.vertex_index[ed.source()]])
lines.InsertCellPoint(
lut[self.graph.vertex_index[ed.target()]])
for array in edge_arrays:
prop_key = array.GetName()
n_comp = array.GetNumberOfComponents()
data_type = self.graph.ep[prop_key].value_type()
data_type = TypesConverter().gt_to_numpy(data_type)
array.InsertNextTuple(
self.get_edge_prop_entry(prop_key, ed, n_comp,
data_type))
if verbose:
print('number of line cells: {}'.format(
lines.GetNumberOfCells()))
# vtkPolyData construction
poly_verts.SetPoints(points)
poly_lines.SetPoints(points)
if vertices:
poly_verts.SetVerts(verts)
if edges:
poly_lines.SetLines(lines)
for array in vertex_arrays:
poly_verts.GetCellData().AddArray(array)
for array in edge_arrays:
poly_lines.GetCellData().AddArray(array)
return poly_verts, poly_lines
def get_vertex_prop_entry(self, prop_key, vertex_descriptor, n_comp,
data_type):
"""
Gets a property value of a vertex for inserting into a VTK vtkDataArray
object.
This function is used by the methods graph_to_points_and_lines_polys and
graph_to_triangle_poly (the latter of the derived classes PointGraph and
TriangleGraph (in surface_graphs).
Args:
prop_key (str): name of the desired vertex property
vertex_descriptor (graph_tool.Vertex): vertex descriptor of the
current vertex
n_comp (int): number of components of the array (length of the
output tuple)
data_type: numpy data type converted from a graph-tool property
value type by TypesConverter().gt_to_numpy
Returns:
a tuple (with length like n_comp) with the property value of the
vertex converted to a numpy data type
"""
prop = list()
if n_comp == 1:
prop.append(data_type(self.graph.vp[prop_key][vertex_descriptor]))
else:
for i in range(n_comp):
prop.append(
data_type(self.graph.vp[prop_key][vertex_descriptor][i]))
return tuple(prop)
def get_edge_prop_entry(self, prop_key, edge_descriptor, n_comp,
data_type):
"""
Gets a property value of an edge for inserting into a VTK vtkDataArray
object.
This private function is used by the method
graph_to_points_and_lines_polys.
Args:
prop_key (str): name of the desired vertex property
edge_descriptor (graph_tool.Edge): edge descriptor of the current
edge
n_comp (int): number of components of the array (length of the
output tuple)
data_type: numpy data type converted from a graph-tool property
value type by TypesConverter().gt_to_numpy
Returns:
a tuple (with length like n_comp) with the property value of the
edge converted to a numpy data type
"""
prop = list()
if n_comp == 1:
prop.append(data_type(self.graph.ep[prop_key][edge_descriptor]))
else:
for i in range(n_comp):
prop.append(
data_type(self.graph.ep[prop_key][edge_descriptor][i]))
return tuple(prop)
# * The following SegmentationGraph methods are needed for the normal vector
# voting algorithm. *
def calculate_average_edge_length(self,
prop_e=None,
value=1,
verbose=False):
"""
Calculates the average edge length in the graph.
If a special edge property is specified, includes only the edges where
this property equals the given value. If there are no edges in the
graph, the given property does not exist or there are no edges with the
given property equaling the given value, None is returned.
Args:
prop_e (str, optional): edge property, if specified only edges where
this property equals the given value will be considered
value (int, optional): value of the specified edge property an edge
has to have in order to be considered (default 1)
verbose (boolean, optional): if True (default False), some extra
information will be printed out
Returns:
the average edge length in the graph (float) or None
"""
total_edge_length = 0
average_edge_length = None
if prop_e is None:
if verbose:
print("Considering all edges:")
if self.graph.num_edges() > 0:
if verbose:
print("{} edges".format(self.graph.num_edges()))
average_edge_length = np.mean(self.graph.ep.distance.a)
else:
print("There are no edges in the graph!")
elif prop_e in self.graph.edge_properties:
if verbose:
print("Considering only edges with property {} equaling value "
"{}!".format(prop_e, value))
num_special_edges = 0
for ed in self.graph.edges():
if self.graph.edge_properties[prop_e][ed] == value:
num_special_edges += 1
total_edge_length += self.graph.ep.distance[ed]
if num_special_edges > 0:
if verbose:
print("{} such edges".format(num_special_edges))
average_edge_length = total_edge_length / num_special_edges
else:
print("There are no edges with the property {} equaling value "
"{}!".format(prop_e, value))
if verbose:
print("Average length: {}".format(average_edge_length))
return average_edge_length
def find_geodesic_neighbors(self,
v,
g_max,
full_dist_map=None,
only_surface=False,
verbose=False):
"""
Finds geodesic neighbor vertices of a given vertex v in the graph that
are within a given maximal geodesic distance g_max from it.
Also finds the corresponding geodesic distances. All edges are
considered. The distances are calculated with Dijkstra's algorithm.
Args:
v (graph_tool.Vertex): the source vertex
g_max: maximal geodesic distance (in the units of the graph)
full_dist_map (graph_tool.PropertyMap, optional): the full distance
map for the whole graph; if None, a local distance map is
calculated for each vertex (default)
only_surface (boolean, optional): if True (default False), only
neighbors classified as surface patch (class 1) are considered
verbose (boolean, optional): if True (default False), some extra
information will be printed out
Returns:
a dictionary mapping a neighbor vertex index to the geodesic
distance from vertex v
"""
if full_dist_map is not None:
dist_v = full_dist_map[v].get_array()
else:
dist_v = shortest_distance(self.graph,
source=v,
target=None,
weights=self.graph.ep.distance,
max_dist=g_max)
dist_v = dist_v.get_array()
# numpy array of distances from v to all vertices, in vertex index order
vertex = self.graph.vertex
orientation_class = self.graph.vp.orientation_class
neighbor_id_to_dist = dict()
idxs = np.where(dist_v <= g_max)[0] # others are INF
for idx in idxs:
dist = dist_v[idx]
if dist != 0: # ignore the source vertex itself
v_i = vertex(idx)
if (not only_surface) or orientation_class[v_i] == 1:
neighbor_id_to_dist[idx] = dist
if verbose:
print("{} neighbors".format(len(neighbor_id_to_dist)))
return neighbor_id_to_dist
def find_geodesic_neighbors_exact(self,
o,
g_max,
only_surface=False,
verbose=False,
debug=False):
"""
Finds geodesic neighbor vertices of the origin vertex o in the graph
that are within a given maximal geodesic distance g_max from it.
Also finds the corresponding geodesic distances. All edges and faces are
considered. The distances are calculated with Sun's and Abidi's
algorithm, a simplification of Kimmels' and Sethian's fast marching
algorithm.
Args:
o (graph_tool.Vertex): the source vertex
g_max: maximal geodesic distance (in the units of the graph)
only_surface (boolean, optional): if True (default False), only
neighbors classified as surface patch (class 1) are considered
verbose (boolean, optional): if True (default False), some extra
information will be printed out
debug (boolean, optional): if True (default False), some more extra
information will be printed out
Returns:
a dictionary mapping a neighbor vertex index to the geodesic
distance from vertex o
"""
# Shortcuts
xyz = self.graph.vp.xyz
vertex = self.graph.vertex
orientation_class = self.graph.vp.orientation_class
distance_between_voxels = self.distance_between_voxels
calculate_geodesic_distance = self._calculate_geodesic_distance
insert_geo_dist_vertex_id = self._insert_geo_dist_vertex_id
# Initialization
geo_dist_heap = [] # heap has the smallest geodesic distance first
# dictionaries to keep track which geodesic distance belongs to which
# vertex or vertices and vice versa
geo_dist_to_vertex_ids = {}
vertex_id_to_geo_dist = {}
neighbor_id_to_dist = {} # output dictionary
# Tag the center point (o) as Alive:
self.graph.vp.tag = self.graph.new_vertex_property("string")
tag = self.graph.vp.tag # shortcut
tag[o] = "Alive"
if debug:
print("Vertex o={}: Alive".format(int(o)))
vertex_id_to_geo_dist[int(o)] = 0 # need it for geo. dist. calculation
xyz_o = tuple(xyz[o])
for n in o.all_neighbours():
# Tag all neighboring points of the center point (n) as Close
tag[n] = "Close"
# Geodesic distance in this case = Euclidean between o and n
xyz_n = tuple(xyz[n])
on = distance_between_voxels(xyz_o, xyz_n)
if debug:
print("Vertex n={}: Close with distance {}".format(int(n), on))
heappush(geo_dist_heap, on)
insert_geo_dist_vertex_id(geo_dist_to_vertex_ids, on, int(n))
vertex_id_to_geo_dist[int(n)] = on
# Repeat while the smallest distance is <= g_max
while len(geo_dist_heap) >= 1 and geo_dist_heap[0] <= g_max:
if debug:
print("\n{} distances in heap, first={}".format(
len(geo_dist_heap), geo_dist_heap[0]))
# 1. Change the tag of the point in Close with the smallest
# geodesic distance (a) from Close to Alive
smallest_geo_dist = heappop(geo_dist_heap)
closest_vertices_ids = geo_dist_to_vertex_ids[smallest_geo_dist]
a = vertex(closest_vertices_ids[0])
if len(closest_vertices_ids) > 1: # move the first one (a) to the
# back, so it's not taken again next time
closest_vertices_ids.pop(0)
closest_vertices_ids.append(int(a))
tag[a] = "Alive"
# only proceed if a is a surface patch:
if only_surface and orientation_class[a] != 1:
continue
neighbor_id_to_dist[int(a)] = smallest_geo_dist # add a to output
if debug:
print("Vertex a={}: Alive".format(int(a)))
neighbors_a = set(a.all_neighbours()) # actually don't have
# duplicates, but like this can use fast sets' intersection method
for c in neighbors_a:
# 2. Tag all neighboring points (c) of this point as Close,
# but skip those which are Alive already
if tag[c] == "Alive":
if debug:
print("Skipping Alive neighbor {}".format(int(c)))
continue
tag[c] = "Close"
if debug:
print("Vertex c={}: Close".format(int(c)))
# 3. Recompute the geodesic distance of these neighboring
# points, using only values of points that are Alive, and renew
# it only if the recomputed result is smaller
# Find Alive point b, belonging to the same triangle as a and c:
# iterate over an intersection of the neighbors of a and c
neighbors_c = set(c.all_neighbours())
common_neighbors_a_c = neighbors_a.intersection(neighbors_c)
for b in common_neighbors_a_c:
# check if b is tagged Alive
if tag[b] == "Alive":
if debug:
print("\tUsing vertex b={}".format(int(b)))
new_geo_dist_c = calculate_geodesic_distance(
a,
b,
xyz[c].a,
vertex_id_to_geo_dist,
verbose=verbose)
if int(c) not in vertex_id_to_geo_dist: # add c
if debug:
print("\tadding new distance {}".format(
new_geo_dist_c))
vertex_id_to_geo_dist[int(c)] = new_geo_dist_c
heappush(geo_dist_heap, new_geo_dist_c)
insert_geo_dist_vertex_id(geo_dist_to_vertex_ids,
new_geo_dist_c, int(c))
else:
old_geo_dist_c = vertex_id_to_geo_dist[int(c)]
if new_geo_dist_c < old_geo_dist_c: # update c
if debug:
print(
"\tupdating distance {} to {}".format(
old_geo_dist_c, new_geo_dist_c))
vertex_id_to_geo_dist[int(c)] = new_geo_dist_c
if old_geo_dist_c in geo_dist_heap:
# check because it is sometimes not there
geo_dist_heap.remove(old_geo_dist_c)
heappush(geo_dist_heap, new_geo_dist_c)
old_geo_dist_vertex_ids = geo_dist_to_vertex_ids[
old_geo_dist_c]
if len(old_geo_dist_vertex_ids) == 1:
del geo_dist_to_vertex_ids[old_geo_dist_c]
else:
old_geo_dist_vertex_ids.remove(int(c))
insert_geo_dist_vertex_id(
geo_dist_to_vertex_ids, new_geo_dist_c,
int(c))
elif debug:
print("\tkeeping the old distance={}, because "
"it's <= the new={}".format(
old_geo_dist_c, new_geo_dist_c))
# if debug:
# print(geo_dist_heap)
# print(geo_dist_to_vertex_ids)
# print(vertex_id_to_geo_dist)
# print(neighbor_id_to_dist)
break # one Alive b is expected, stop iteration
else:
if debug:
print("\tNo common neighbors of a and c are Alive")
del self.graph.vertex_properties["tag"]
if debug:
print("Vertex o={} has {} geodesic neighbors".format(
int(o), len(neighbor_id_to_dist)))
if verbose:
print("{} neighbors".format(len(neighbor_id_to_dist)))
return neighbor_id_to_dist
def _calculate_geodesic_distance(self,
a,
b,
xyz_c,
vertex_id_to_geo_dist,
verbose=False):
geo_dist_a = vertex_id_to_geo_dist[int(a)]
geo_dist_b = vertex_id_to_geo_dist[int(b)]
xyz_a = self.graph.vp.xyz[a].a
xyz_b = self.graph.vp.xyz[b].a
ab = euclidean_distance(xyz_a, xyz_b)
ac = euclidean_distance(xyz_a, xyz_c)
bc = euclidean_distance(xyz_b, xyz_c)
# maybe faster to use linalg.euclidean_distance directly on np.ndarrays
alpha = nice_acos((ab**2 + ac**2 - bc**2) / (2 * ab * ac))
beta = nice_acos((ab**2 + bc**2 - ac**2) / (2 * ab * bc))
if alpha < (math.pi / 2) and beta < (math.pi / 2):
if verbose:
print("\ttriangle abc is acute")
theta = nice_acos((geo_dist_a**2 + ab**2 - geo_dist_b**2) /
(2 * ab * geo_dist_a))
geo_dist_c = math.sqrt(ac**2 + geo_dist_a**2 - 2 * ac *
geo_dist_a * math.cos(alpha + theta))
else:
if verbose:
print("\ttriangle abc is obtuse")
geo_dist_c = min(geo_dist_a + ac, geo_dist_b + bc)
return geo_dist_c
@staticmethod
def _insert_geo_dist_vertex_id(geo_dist_to_vertices, geo_dist, vertex_ind):
if geo_dist in geo_dist_to_vertices:
geo_dist_to_vertices[geo_dist].append(vertex_ind)
else:
geo_dist_to_vertices[geo_dist] = [vertex_ind]
def get_vertex_property_array(self, property_name):
"""
Gets a numpy array with all values of a vertex property of the graph,
printing out the number of values, the minimal and the maximal value.
Args:
property_name (str): vertex property name
Returns:
an array (numpy.ndarray) with all values of the vertex property
"""
if (isinstance(property_name, str)
and property_name in self.graph.vertex_properties):
values = np.array(
self.graph.vertex_properties[property_name].get_array())
print('{} "{}" values'.format(len(values), property_name))
print('min = {}, max = {}, mean = {}'.format(
min(values), max(values), np.mean(values)))
return values
else:
raise pexceptions.PySegInputError(
expr='get_vertex_property_array (SegmentationGraph)',
msg=('The input "{}" is not a str object or is not found in '
'vertex properties of the graph.'.format(property_name)))
e2015 = child_graph.add_edge(child_graph.vertex_index[20], child_graph.vertex_index[15])
e2115 = child_graph.add_edge(child_graph.vertex_index[21], child_graph.vertex_index[15])
e1716 = child_graph.add_edge(child_graph.vertex_index[17], child_graph.vertex_index[16])
e2116 = child_graph.add_edge(child_graph.vertex_index[21], child_graph.vertex_index[16])
e2216 = child_graph.add_edge(child_graph.vertex_index[22], child_graph.vertex_index[16])
e2317 = child_graph.add_edge(child_graph.vertex_index[23], child_graph.vertex_index[17])
e1918 = child_graph.add_edge(child_graph.vertex_index[19], child_graph.vertex_index[18])
e2019 = child_graph.add_edge(child_graph.vertex_index[20], child_graph.vertex_index[19])
e2120 = child_graph.add_edge(child_graph.vertex_index[21], child_graph.vertex_index[20])
e2221 = child_graph.add_edge(child_graph.vertex_index[22], child_graph.vertex_index[21])
e2322 = child_graph.add_edge(child_graph.vertex_index[23], child_graph.vertex_index[22])
## Property definition
graph_name = child_graph.new_graph_property("string")
layer_capacities = child_graph.new_edge_property("int")
layer_res_capacity = child_graph.new_edge_property("int")
layer_flow = child_graph.new_edge_property("int")
alternate_path = child_graph.new_edge_property("int")
flag_path = child_graph.new_edge_property("int")
## Property Assignment
child_graph.gp.layer_name = graph_name
child_graph.ep.edge_capacity = layer_capacities
child_graph.ep.residual_capacity = layer_res_capacity
child_graph.ep.edge_flow = layer_flow
child_graph.ep.shared_path = alternate_path
child_graph.ep.path_flag = flag_path
## Setting the name of the graph
child_graph.gp.layer_name = "Layer_" + str(i)
def draw_for(self, phase: Phase, model: GameModel):
self.log.debug('drawing phase flow {0}'.format(phase.name))
TMPUTILS.clear_container(self.main_panel)
top = Gtk.HBox()
name = Gtk.Label(phase.name)
top.pack_start(name, True, True, 0)
refresh_button = Gtk.Button('Refresh')
refresh_button.connect('clicked', lambda w: self.draw_for(phase, model))
top.pack_start(refresh_button, True, True, 0)
self.main_panel.pack_start(top, False, False, 0)
#TMPUTILS.start_rule_color = TMPUTILS.rule_color
#start = phase.rules[0]
#start = Rule('Początek {0}'.format(phase.name))
start = Rule('Start {0}'.format(phase.name))
start.next = phase.rules
phase.rules = [start]
rules_set = phase.all_rules_set()
phase.rules = start.next
graph = Graph()
graph.vp.name = graph.new_vertex_property('string')
graph.vp.fullname = graph.new_vertex_property('string')
graph.vp.color = graph.new_vertex_property('string')
graph.vp.shape = graph.new_vertex_property('string')
graph.vp.rotation = graph.new_vertex_property('float')
graph.vp.text_pos = graph.new_vertex_property('float')
graph.vp.text_rotation = graph.new_vertex_property('float')
graph.ep.text = graph.new_edge_property('string')
graph.ep.text_color = graph.new_edge_property('string')
rule_vertex = {}
self.vertex_rule = {}
for rule in rules_set:
vertex = graph.add_vertex()
rule_vertex[rule] = vertex
self.vertex_rule[vertex] = rule
graph.vp.name[vertex] = rule.verticle_name()
graph.vp.fullname[vertex] = rule.name
if rule is start:
color = self.config.start_rule_color()
elif issubclass(rule.__class__, ChangePhase):
color = TMPUTILS.end_rule_color(rule, model)
elif len([r for k, v in rule.rules_dict().items() for r in v]) == 0:
color = self.config.wrong_rule_color()
else:
color = TMPUTILS.rule_color()
graph.vp.color[vertex] = color
#graph.vp.shape[vertex] = 'square' if issubclass(rule.__class__, If) else 'circle'
graph.vp.shape[vertex] = self.config.rule_shape(rule)
#graph.vp.rotation[vertex] = pi / 4 if issubclass(rule.__class__, If) else 0
graph.vp.rotation[vertex] = self.config.rule_rotation(rule)
graph.vp.text_pos[vertex] = 0
#graph.vp.text_rotation[vertex] = - pi / 4 if issubclass(rule.__class__, If) else 0
graph.vp.text_rotation[vertex] = self.config.rule_text_rotation(rule)
for rule in rules_set:
for next_text, next_rule_list in rule.rules_dict().items():
for next_rule in next_rule_list:
edge = graph.add_edge(rule_vertex[rule], rule_vertex[next_rule])
#as_polish = {'No': "Nie", 'Yes': "Tak"}
#graph.ep.text[edge] = as_polish[next_text] if next_text in as_polish else next_text
graph.ep.text[edge] = next_text
graph.ep.text_color[edge] = TMPUTILS.text_color(next_text)
pos = sfdp_layout(graph)
vprops = {
'text': graph.vp.name,
'fill_color': graph.vp.color,
'shape': graph.vp.shape,
'rotation': graph.vp.rotation,
'text_position': graph.vp.text_pos,
'text_rotation': graph.vp.text_rotation
}
eprops = {
'text': graph.ep.text,
'text_color': graph.ep.text_color
}
self.graph_widget = GraphWidget(graph, pos, display_props=[graph.vp.fullname], vprops=vprops, eprops=eprops,
vertex_size=50)
#jest cos takiego jak GraphWidget.key_press_callback ale u mnie nie dziala...
self.graph_widget.connect('button-release-event', self.on_vertex_clicked)
self.main_panel.pack_start(self.graph_widget, True, True, 0)
self.show_all()
def contract_graph_by_nodes(g, nodes, weights=None):
"""
contract graph by nodes (only for undirected)
note: the supernode is node 0 in the new graph
Params:
----------
g: Graph, undirected
weights: edge_property_map
nodes: list of ints
Returns:
----------
- Graph: a contracted graph where `nodes` are merged into a supernode
- edge_property_map: new weight
"""
if len(nodes) == 1:
return g, weights
nodes = set(nodes)
# print('nodes:', nodes)
# re-align the nodes
# `v \in nodes` are considered node 0
# get the old node to new node mapping
o2n_map = {}
c = 1
for v in g.vertices():
v = int(v)
if v not in nodes:
o2n_map[v] = c
c += 1
else:
o2n_map[v] = 0
# print('o2n_map:', o2n_map)
# calculate new edges and new weights
e2w = defaultdict(float)
for e in g.edges():
u, v = map(int, [e.source(), e.target()])
nu, nv = sorted([o2n_map[u], o2n_map[v]])
if weights:
e2w[(nu, nv)] += weights[g.edge(u, v)]
else:
e2w[(nu, nv)] += 1
# print('e2w:', e2w)
# create the new graph
new_g = Graph(directed=False)
# for _ in range(g.num_vertices() - len(nodes) + 1):
# new_g.add_vertex()
edges = []
for u, v in e2w:
e = new_g.add_edge(u, v)
edges.append(e)
new_weights = new_g.new_edge_property('float')
for e, w in zip(edges, e2w.values()):
new_weights[e] = w
return new_g, new_weights
def main():
conn = serial_interface.connect()
cur_track = track.init_tracka()
g = Graph()
g.add_vertex(len(cur_track))
for (vi, node) in enumerate(cur_track): node.i = vi
n_title = g.new_vertex_property("string")
n_color = g.new_vertex_property("string")
n_pos = g.new_vertex_property("vector<double>")
e_title = g.new_edge_property("string")
e_dist = g.new_edge_property("double")
for node in cur_track:
v = g.vertex(node.i)
n_title[v] = node.name
if node.typ == track.NODE_EXIT:
# Invert points to match our ASCII display.
n_pos[v] = (-node.reverse.coord_x, -node.reverse.coord_y)
else:
n_pos[v] = (-node.coord_x, -node.coord_y)
e = g.add_edge(g.vertex(node.i), g.vertex(node.reverse.i))
if node.typ == track.NODE_SENSOR: n_color[v] = "blue"
elif node.typ == track.NODE_BRANCH: n_color[v] = "orange"
elif node.typ == track.NODE_MERGE: n_color[v] = "yellow"
elif node.typ == track.NODE_ENTER: n_color[v] = "green"
elif node.typ == track.NODE_EXIT: n_color[v] = "red"
else: n_color[v] = "white"
for edge in node.edge:
if edge.src is None: continue
e = g.add_edge(g.vertex(edge.src.i), g.vertex(edge.dest.i))
e_dist[e] = edge.dist
e_title[e] = "%.2f" % (edge.dist)
win = graph_tool.draw.GraphWindow(g, n_pos, (640, 480), edge_text=e_title, vertex_fill_color=n_color, vertex_text=n_title)
win.show_all()
def destroy_callback(*args, **kwargs):
win.destroy()
Gtk.main_quit()
def set_switch(sw, d):
for node in cur_track:
if node.typ == track.NODE_BRANCH and node.num == sw:
node.switch_direction = d
return
print "WARN: Could not find switch %d" % sw
class Train():
num = -1
vel = 0.
speed = 0.
edge = cur_track[0].edge[0]
edge_dist = 0
SPEEDX = 1.
def __init__(self, num):
self.num = num
def update(self):
# Super shitty deacceleration model
self.vel = self.vel + (0.018/self.SPEEDX)*(self.speed - self.vel)
self.edge_dist += self.vel
while True:
e = self.e()
if self.edge_dist < e_dist[e]: break
if self.edge.dest.typ == track.NODE_SENSOR:
conn.set_sensor_tripped(self.edge.dest.num)
self.edge = self.edge.dest.edge[self.edge.dest.switch_direction]
self.edge_dist -= e_dist[e]
def draw(self, n_pos, da, cr):
e = self.e()
start, end = np.array(n_pos[e.source()]), np.array(n_pos[e.target()])
alpha = self.edge_dist / e_dist[e]
pos = start + alpha*(end - start)
dp = win.graph.pos_to_device(pos) # dp: device position
cr.rectangle(dp[0]-10, dp[1]-10, 20, 20)
cr.set_source_rgb(102. / 256, 102. / 256, 102. / 256)
cr.fill()
cr.move_to(dp[0]-10, dp[1] + 10 - 12./2)
cr.set_source_rgb(1., 1., 1.)
cr.set_font_size(12)
cr.show_text("%d" % self.num)
cr.fill()
def e(self): return g.edge(self.edge.src.i, self.edge.dest.i)
def set_speed(self, speed): self.speed = speed/self.SPEEDX
def toggle_reverse(self):
self.edge = self.edge.reverse
self.edge_dist = e_dist[self.e()] - self.edge_dist
def find_train(train_number):
for train in trains:
if train.num == train_number:
return train
train = Train(train_number)
trains.append(train)
return train
trains = [Train(12)]
startup_time = time.time()
accumulated_error = [0.]
last_time = [time.time()]
last_sensor_poll = [0]
FPS = 30.
def my_draw(da, cr):
(typ, a1, a2) = conn.next_cmd()
if typ is None: pass
elif typ == 'set_speed': find_train(a1).set_speed(a2)
elif typ == 'toggle_reverse': find_train(a1).toggle_reverse()
elif typ == 'switch': set_switch(a1, a2)
elif typ == 'sensor': last_sensor_poll[0] = round((time.time() - startup_time) * 1000)/1000
else: print "Ignoring command %s" % typ
cur_time = time.time()
dt = cur_time - last_time[0] + accumulated_error[0]
num_steps = int(dt*FPS)
accumulated_error[0] = dt - num_steps/FPS
for train in trains:
for _ in range(0, num_steps): train.update()
train.draw(n_pos, da, cr)
cr.move_to(10., 10.)
cr.set_source_rgb(0., 0., 0.)
cr.set_font_size(12)
cr.show_text("Last polled at %.3f" % last_sensor_poll[0])
da.queue_draw()
last_time[0] = cur_time
win.connect("delete_event", destroy_callback)
win.graph.connect("draw", my_draw)
Gtk.main()
def cumulative_cooccurrence_graph(steps, sequences, directed=False):
'''cumulative_cooccurrence_graph
Creates a cumulative cooccurrence graph.
Parameters
----------
steps : :obj:`iter` of :obj:`int` or :obj:`str`
A series that contains sequential labels for the nested groups.
sequences : :obj:`iter` of :obj:`iter` of :obj:`int`
Nested iterable of integers representing vertices in the graph. Number
of nested iterables should be equal to `len(steps)`.
directed : :obj:`bool`
Currently has no effect. In future this will determine whether to build
a bi-directional cooccurrence graph.
Returns
-------
g : :obj:`graph_tool.Graph`
A graph. Vertices are elements. Edges link terms that have cooccurred
at least once in the series.
o_props : :obj:`dict`
Property maps with vertex occurrence values at each step.
o_cumsum_props : :obj:`dict`
Property maps with cumulative vertex cooccurrence values at each step.
co_props : :obj:`dict`
Property maps with edge cooccurrnce values at each step.
co_cumsum_props : :obj:`dict`
Property maps with cumulative edge cooccurrence values at each step.
'''
g = Graph(directed=directed)
o_total = Counter(chain(*chain(*sequences)))
n_vertices = len(o_total)
g.add_vertex(n_vertices)
o_max = dict_to_vertex_prop(g, o_total, 'int')
co_total = cooccurrence_counts(chain(*sequences))
edge_list = ((c[0], c[1], count) for c, count in co_total.items())
co_max = g.new_edge_property('int')
g.add_edge_list(edge_list, eprops=[co_max])
edges = g.get_edges()
edge_indices = dict(zip([(e[0], e[1]) for e in edges], edges[:, 2]))
o_props = {}
co_props = {}
o_cumsum_props = {}
co_cumsum_props = {}
for i, (step, seq) in enumerate(zip(steps[:-1], sequences[:-1])):
logging.info(f'Calculating cooccurrences at step {step}')
o_step = Counter(chain(*seq))
o_props[step] = dict_to_vertex_prop(g, o_step, 'int')
combos = (combinations(sorted(ids), 2) for ids in seq)
co_step = Counter(chain(*combos))
co_props[step] = dict_to_edge_prop(g, co_step, 'int', edge_indices)
o_cumsum = g.new_vertex_property('int')
co_cumsum = g.new_edge_property('int')
if i == 0:
o_cumsum.a = o_cumsum.a + o_props[step].a
co_cumsum.a = co_cumsum.a + co_props[step].a
else:
o_cumsum.a = o_cumsum_props[steps[i - 1]].a + o_props[step].a
co_cumsum.a = co_cumsum_props[steps[i - 1]].a + co_props[step].a
o_cumsum_props[step] = o_cumsum
co_cumsum_props[step] = co_cumsum
# fill in the last step without needing to count occurrences
# or cooccurrences
step_max = steps[-1]
o = g.new_vertex_property('int')
co = g.new_edge_property('int')
o.a = o_max.a - o_cumsum.a
co.a = co_max.a - co_cumsum.a
o_props[step_max] = o
co_props[step_max] = co
o_cumsum_props[step_max] = o_max
co_cumsum_props[step_max] = co_max
steps_prop = g.new_graph_property('vector<int>')
steps_prop.set_value(steps)
g.gp['steps'] = steps_prop
return g, o_props, o_cumsum_props, co_props, co_cumsum_props
def build_graph(df_list, sens='ST', top=410, min_sens=0.01, edge_cutoff=0.0):
"""
Initializes and constructs a graph where vertices are the parameters
selected from the first dataframe in 'df_list', subject to the
constraints set by 'sens', 'top', and 'min_sens'. Edges are the second
order sensitivities of the interactions between those vertices,
with sensitivities greater than 'edge_cutoff'.
Parameters
-----------
df_list : list
A list of two dataframes. The first dataframe should be
the first/total order sensitivities collected by the
function data_processing.get_sa_data().
sens : str, optional
A string with the name of the sensitivity that you would
like to use for the vertices ('ST' or 'S1').
top : int, optional
An integer specifying the number of vertices to display (
the top sensitivity values).
min_sens : float, optional
A float with the minimum sensitivity to allow in the graph.
edge_cutoff : float, optional
A float specifying the minimum second order sensitivity to
show as an edge in the graph.
Returns
--------
g : graph-tool object
a graph-tool graph object of the network described above. Each
vertex has properties 'param', 'sensitivity', and 'confidence'
corresponding to the name of the parameter, value of the sensitivity
index, and it's confidence interval. The only edge property is
'second_sens', the second order sensitivity index for the
interaction between the two vertices it connects.
"""
# get the first/total index dataframe and second order dataframe
df = df_list[0]
df2 = df_list[1]
# Make sure sens is ST or S1
if sens not in set(['ST', 'S1']):
raise ValueError('sens must be ST or S1')
# Make sure that there is a second order index dataframe
try:
if not df2:
raise Exception('Missing second order dataframe!')
except:
pass
# slice the dataframes so the resulting graph will only include the top
# 'top' values of 'sens' greater than 'min_sens'.
df = df.sort_values(sens, ascending=False)
df = df.ix[df[sens] > min_sens, :].head(top)
df = df.reset_index()
# initialize a graph
g = Graph()
vprop_sens = g.new_vertex_property('double')
vprop_conf = g.new_vertex_property('double')
vprop_name = g.new_vertex_property('string')
eprop_sens = g.new_edge_property('double')
g.vertex_properties['param'] = vprop_name
g.vertex_properties['sensitivity'] = vprop_sens
g.vertex_properties['confidence'] = vprop_conf
g.edge_properties['second_sens'] = eprop_sens
# keep a list of all the vertices
v_list = []
# Add the vertices to the graph
for i, param in enumerate(df['Parameter']):
v = g.add_vertex()
vprop_sens[v] = df.ix[i, sens]
vprop_conf[v] = 1 + df.ix[i, '%s_conf' % sens] / df.ix[i, sens]
vprop_name[v] = param
v_list.append(v)
# Make two new columns in second order dataframe that point to the vertices
# connected on each row.
df2['vertex1'] = -999
df2['vertex2'] = -999
for vertex in v_list:
param = g.vp.param[vertex]
df2.ix[df2['Parameter_1'] == param, 'vertex1'] = vertex
df2.ix[df2['Parameter_2'] == param, 'vertex2'] = vertex
# Only allow edges for vertices that we've defined
df_edges = df2[(df2['vertex1'] != -999) & (df2['vertex2'] != -999)]
# eliminate edges below a certain cutoff value
pruned = df_edges[df_edges['S2'] > edge_cutoff]
pruned.reset_index(inplace=True)
# Add the edges for the graph
for i, sensitivity in enumerate(pruned['S2']):
v1 = pruned.ix[i, 'vertex1']
v2 = pruned.ix[i, 'vertex2']
e = g.add_edge(v1, v2)
# multiply by a number to make the lines visible on the plot
eprop_sens[e] = sensitivity * 150
# These are ways you can reference properties of vertices or edges
# g.vp.param[g.vertex(77)]
# g.vp.param[v_list[0]]
print('Created a graph with %s vertices and %s edges.\nVertices are the '
'top %s %s values greater than %s.\nOnly S2 values (edges) '
'greater than %s are included.' %
(g.num_vertices(), g.num_edges(), top, sens, min_sens, edge_cutoff))
return g
def build_closure_with_order(g,
cand_source,
terminals,
infection_times,
k=-1,
strictly_smaller=True,
return_r2pred=False,
debug=False,
verbose=False):
"""
build transitive closure with infection order constraint
g: gt.Graph(directed=False)
cand_source: int
terminals: list of int
infection_times: dict int -> float
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
note that vertex ids are preserved (without re-mapping to consecutive integers)
return:
gt.Graph(directed=True)
"""
if return_r2pred:
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)
if return_r2pred:
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)
if return_r2pred:
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)
gc.add_vertex(g.num_vertices())
vfilt = gc.new_vertex_property('bool')
vfilt.a = False
for (u, v) in edges:
gc.add_edge(u, v)
vfilt[u] = vfilt[v] = True
eweight = gc.new_edge_property('int')
eweight.set_2d_array(np.array(list(edges.values())))
gc.set_vertex_filter(vfilt)
rets = (gc, eweight)
if return_r2pred:
rets += (r2pred, )
return rets
class SkeletonData(object):
"""
class to store and process skeleton data, like generated from starlab mean curvature skeleton
"""
def __init__(self, fname=None, mesh_name=None, filter_sb=False):
"""
@param filter_sb: if filter out Short Branch
"""
if fname != None:
self.skel_name = fname
self.read_skel_file(fname)
self._filter_short_branch(filter=filter_sb, short=10)
self._parse_data()
self.mesh_name = mesh_name
self.vert_radius = None
def calc_skel_properties(self):
"""
calc all properties needed for matching
"""
self.calc_node_centricity()
self.calc_skel_radius()
self.calc_path_length_ratio()
self.calc_path_radius_ratio()
self.normalize_skeleton()
def read_skel_file(self, fname, dim=3):
if fname == None:
print 'please input skeleton file name'
sys.exit(0)
elif os.path.isfile(fname):
self.verts_init = []
self.edges_init = []
with open(fname) as sf:
for line in sf:
line = line.strip('\n')
line = line.split(' ')
if line[0] == '#':
continue
elif line[0] == 'v':
self.verts_init.append([x for x in line[1:(dim+1)]])
#### attention!! verts of edge start from 1 in files ####
elif line[0] == 'e':
self.edges_init.append([int(x)-1 for x in line[1:3]])
else:
print 'not support this format'
sys.exit(0)
else:
print 'no such flie', fname
sys.exit(0)
def _filter_short_branch(self, filter=False, short=30):
"""
filter out very short branches: do this maybe not right for some models, for models with flat part, it is right
I will test how this effect the final matching results
need to delete nodes, switch with the last one then delete last
"""
if filter == False:
self.verts = self.verts_init
self.edges = self.edges_init
else:
init_graph = Graph(directed=False)
init_graph.add_vertex(len(self.verts_init))
for edge in self.edges_init:
init_graph.add_edge(init_graph.vertex(edge[0]), init_graph.vertex(edge[1]))
terminal_node = []
for v in init_graph.vertices():
if v.out_degree() == 1:
terminal_node.append(v)
visitor = DepthVisitor()
short_nodes = []
for tn in terminal_node:
search.dfs_search(init_graph, tn, visitor)
tmp_node = visitor.get_short_branch(min_length=short)
visitor.reset()
for n in tmp_node:
short_nodes.append(n)
## get edges on the short paths
short_nodes = list(set(short_nodes))
short_edges = []
temp_verts = self.verts_init[:]
v_num = len(self.verts_init)
if len(short_nodes):
for v in reversed(sorted(short_nodes)):
for ve in init_graph.vertex(v).out_edges():
short_edges.append(ve)
## delete edges first, then vertex
short_edges = list(set(short_edges))
for e in short_edges:
init_graph.remove_edge(e)
print 'deleting vertex',
for v in reversed(sorted(short_nodes)):
print v,
temp_verts[int(v)] = temp_verts[v_num-1]
init_graph.remove_vertex(v, fast=True)
v_num -= 1
print '\ndeleting related edges' # already done above, just info user
else:
print 'no short branches'
######## new vertices and edges ########
self.verts = temp_verts[:v_num]
self.edges = []
for e in init_graph.edges():
self.edges.append([int(e.source()), int(e.target())])
def create_virtual_node(self):
"""
I am planning use this function to make virtual nodes for those feature nodes
"""
pass
def _parse_data(self):
"""
extract interal points(degree>2) and endpoints(degree=1)
extract segments
"""
if self.verts == None or self.edges == None:
print 'please first call read_skel_file function'
else:
self.verts = np.array(self.verts, dtype=np.float)
self.edges = np.array(self.edges, dtype=np.int)
terminal_index = []
junction_index = []
self.skel_graph = Graph(directed=False)
self.skel_graph.add_vertex(len(self.verts))
for edge in self.edges :
self.skel_graph.add_edge(self.skel_graph.vertex(edge[0]), self.skel_graph.vertex(edge[1]))
for v in self.skel_graph.vertices():
if v.out_degree() == 2 :
continue
elif v.out_degree() == 1 :
terminal_index.append(int(v))
elif v.out_degree() > 2 :
junction_index.append(int(v))
self.terminal = self.verts[terminal_index]
self.junction = self.verts[junction_index]
self.terminal_index = terminal_index
self.junction_index = junction_index
self.feature_node_index = junction_index + terminal_index
self.feature_node = self.verts[self.feature_node_index]
"""
edge_vert_index = self.edges.flatten()
print 'edge vertex index dtype', edge_vert_index.dtype
if 0 in edge_vert_index:
print 'vertex start from 0'
else:
print 'vertex start from 1'
print 'skeleton vertex num', self.skel_graph.num_vertices()
print 'skeleton edge num', self.skel_graph.num_edges()
"""
def _calc_edge_length(self):
"""
calc edge length and make it edge property map in graph-tool
"""
vec = self.verts[self.edges[:,0]] - self.verts[self.edges[:,1]]
edge_length = np.sqrt(np.sum(vec**2, axis=-1))
self.edge_length_map = self.skel_graph.new_edge_property("double")
self.edge_length_map.a = edge_length
def calc_node_centricity(self):
"""
calc node centricity of feature nodes(terminal and junction nodes)
T1 in Oscar's EG 2010 paper
"""
self._calc_edge_length()
node_centricity = []
for n_idx in self.feature_node_index:
dist = topology.shortest_distance(self.skel_graph, source=self.skel_graph.vertex(n_idx), weights=self.edge_length_map)
node_centricity.append(dist.a.mean())
node_centricity = np.array(node_centricity)
self.node_centricity = node_centricity / np.max(node_centricity)
def calc_skel_radius(self, mesh_name=None, dim=3):
"""
calc nearest mesh vertex of skeleton vertex
"""
if mesh_name != None:
self.mesh_name = mesh_name
if self.mesh_name == None:
print 'please set mesh_name before calc_skel_radius'
elif os.path.isfile(self.mesh_name):
mesh = om.TriMesh()
assert om.read_mesh(mesh, self.mesh_name)
mesh_vertices = np.zeros((mesh.n_vertices(), dim), dtype=float)
for n, vh in enumerate(mesh.vertices()):
for i in xrange(3):
mesh_vertices[n, i] = mesh.point(vh)[i]
nbrs = NearestNeighbors(n_neighbors=1, algorithm='ball_tree').fit(mesh_vertices)
self.vert_radius, indices = nbrs.kneighbors(self.verts)
else:
print 'cannot find mesh file', self.mesh_name
sys.exit(0)
def calc_path_radius(self, start, end):
"""
utile function for other function
calc skeleton **mean** vertex radius along some segment
"""
if self.vert_radius == None:
print 'please call calc_skel_radius function first'
return None
elif start in self.feature_node_index and end in self.feature_node_index:
v_list, e_list = topology.shortest_path(self.skel_graph, self.skel_graph.vertex(start), self.skel_graph.vertex(end), weights=self.edge_length_map)
v_idx_list = []
for v in v_list:
v_idx_list.append(int(v))
v_radius = self.vert_radius[v_idx_list]
return v_radius.mean()
else:
print 'input vertex index is not feature node index'
return None
def calc_path_length_ratio(self):
"""
for each feature node pair segment, calculate path length ratio
normalized, to make it scale invariant
"""
path_length = np.zeros((len(self.feature_node_index), len(self.feature_node_index)), dtype=float)
for i, n_idx in enumerate(self.feature_node_index):
for j, m_idx in enumerate(self.feature_node_index[i+1:], start=i+1):
length = topology.shortest_distance(self.skel_graph, self.skel_graph.vertex(n_idx), self.skel_graph.vertex(m_idx), weights=self.edge_length_map)
if length != None :
path_length[i,j] = path_length[j,i] = length
else:
print 'compute path length ratio error'
return None
### extract path length from each feature node to junction nodes ###
### Careful!! path_length MUST start from junction node
self.path_to_junction = path_length[:,:len(self.junction_index)]
self.path_length_ratio = path_length / path_length.max()
return self.path_length_ratio
def calc_path_radius_ratio(self):
"""
for each feature node pair segment, calculate path radius ratio
normalized, to make it scale invariant
"""
path_radius = np.zeros((len(self.feature_node_index), len(self.feature_node_index)), dtype=float)
for i, n_idx in enumerate(self.feature_node_index):
for j, m_idx in enumerate(self.feature_node_index[i+1:], start=i+1):
radius = self.calc_path_radius(n_idx, m_idx)
if radius != None :
path_radius[i, j] = path_radius[j, i] = radius
else:
print 'comptue path radius error'
return None
self.path_radius_ratio = path_radius / path_radius.max()
return self.path_radius_ratio
def normalize_skeleton(self):
"""
calc the pose-normalized skeleton to distinguish symmetric nodes
using multidimensional scaling method(MDS)
"""
v_num = len(self.verts)
geodesic_dist = np.zeros((v_num, v_num))
geodesic_dist_map = topology.shortest_distance(self.skel_graph, weights=self.edge_length_map)
for i in xrange(v_num):
geodesic_dist[i] = geodesic_dist_map[self.skel_graph.vertex(i)].a
mds = manifold.MDS(n_components=3, max_iter=500, eps=1e-10, dissimilarity="precomputed", n_jobs=-2, n_init=1)
verts_mean = self.verts - self.verts.mean(axis=0)
normalized_verts = mds.fit(geodesic_dist, init=verts_mean).embedding_
#scale = np.sqrt((verts_mean ** 2).sum()) / np.sqrt((normalized_verts ** 2).sum())
#normalized_verts *= scale
self.normalized_verts = normalized_verts
self.normalized_feature_verts = normalized_verts[self.feature_node_index]
return self.normalized_verts
def write_file(self, file_path='./'):
"""
maybe need to save file after filter
same as starlab mean curvature skeleton
"""
file_name = os.path.basename(self.skel_name)
full_name = file_path + file_name
v_num = len(self.verts)
e_num = len(self.edges)
first_line = '# D:3 ' + 'NV:' + str(v_num) + ' NE:' + str(e_num) + '\n'
with open(full_name, 'w') as f:
f.write(first_line)
for v in self.verts:
line = 'v ' + str(v[0]) + ' ' + str(v[1]) + ' ' + str(v[2]) + '\n'
f.write(line)
for e in self.edges:
line = 'e ' + str(e[0]+1) + ' ' + str(e[1]+1) + '\n'
f.write(line)
class MapGraph(object):
def __init__(self):
self.g = Graph()
self.dvertex_index = dict()
self.vertex_label = self.g.new_vertex_property("string")
self.g.vertex_properties["label"] = self.vertex_label
self.edge_weight = self.g.new_edge_property("int")
self.g.edge_properties["weight"] = self.edge_weight
def has_vertex(self, label):
"""返回index"""
return self.dvertex_index.get(label)
def has_edge(self, s_label, e_label):
s_vertex = self.dvertex_index.get(s_label)
e_vertex = self.dvertex_index.get(e_label)
if s_vertex and e_vertex:
return self.g.edge(s_vertex, e_vertex)
else:
return None
def add_edge(self, s_label, e_label):
if self.has_edge(s_label, e_label):
return self.g.edge(s_label, e_label)
s_vertex = self.add_vertex(s_label)
e_vertex = self.add_vertex(e_label)
return self.g.add_edge(s_vertex, e_vertex)
def add_vertex(self, label):
"""如果点存在则直接返回节点索引号"""
if self.dvertex_index.get(label):
return self.dvertex_index.get(label)
v = self.g.add_vertex()
self.vertex_label[v] = label
self.dvertex_index[label] = v
return v
def add_edge_weight(self, s_label, e_label, weight):
if self.has_edge(s_label, e_label):
self.edge_weight[self.g.edge(s_label, e_label)] += weight
else:
edge = self.add_edge(s_label, e_label)
self.edge_weight[edge] = weight
@classmethod
def networkx_to_graph_tool(cls, nx_g):
gt_g = MapGraph()
for e in nx_g.edges():
gt_g.add_edge_weight(e[0], e[1], nx_g[e[0]][e[1]]["weight"])
return gt_g
def all_paths(self, s_label, e_label):
if self.has_vertex(s_label) and self.has_vertex(e_label):
time_s = time.time()
s_vertex = self.dvertex_index.get(s_label)
e_vertex = self.dvertex_index.get(e_label)
for path in all_paths(
self.g,
s_vertex,
e_vertex,
cutoff=shortest_distance(self.g, s_vertex, e_vertex) *
1.5):
if time.time() - time_s > 60 * 10:
break
yield path
