def findThickestPathLateral(self, skelImg, skelDia, xScale, yScale):
print 'create skeleton graph'
skelGraph, _ = self.makeGraphFast(skelImg, skelDia, xScale, yScale)
rootVertex = self.findRootVertexLateral(skelGraph)
epropW = skelGraph.edge_properties["w"]
path = []
#remove all two-connected ones with 0 label
print 'trace path of thickest diameter'
#find thickest path
pathDetect = True
if skelGraph.num_vertices() > 0:
pathDetect = True
while pathDetect == True:
lastVertex = self.findLastRootVertex(skelGraph)
try:
path, _ = gt.shortest_path(skelGraph,
rootVertex,
lastVertex,
weights=epropW,
pred_map=None)
pathDetect = False
except:
raise
if lastVertex <= 0:
pathDetect = False
else:
skelGraph.remove_vertex(lastVertex)
lastVertex = self.findLastRootVertex(skelGraph)
return path, skelGraph
def get_shortest_path(self, start_key, end_key, key_field):
"""Returns the length of and the vertices involved in the shortest path in the network
:param start_geoid: The string value of the starting GeoID for the shortest path analysis
:param end_geoid: The string value of the ending GeoID for the shortest path analysis
:return shortest_distance, vertex_list, edge_list: The length of the shortest path from the field
weight_distance and the list of vertices and edges in the shortest path network
"""
start_vertex, end_vertex = None, None
vertex_geoid = self._graph.vertex_properties[key_field]
vertices = gutil.find_vertex(self._graph, vertex_geoid, start_key)
if (len(vertices) > 0):
start_vertex = vertices[0]
vertices = gutil.find_vertex(self._graph, vertex_geoid, end_key)
if (len(vertices) > 0):
end_vertex = vertices[0]
if (start_vertex is not None) and (end_vertex is not None):
vertex_list, edge_list = gtopo.shortest_path(self._graph, start_vertex,
end_vertex, self._graph.edge_properties["weight_dist"])
shortest_distance = gtopo.shortest_distance(self._graph, start_vertex,
end_vertex, self._graph.edge_properties["weight_dist"])
return shortest_distance, vertex_list, edge_list
else:
return -1, "Could not find start or end vertex"
def get(self, args):
source = int(args["source"])
target = int(args["target"])
vertex_list, edge_list = shortest_path(graph, graph.vertex(source), graph.vertex(target))
print(vertex_list)
ret = [{"id": graph.vertex_index[v], "name": graph.vertex_properties["name"][v], "photo_filename": graph.vertex_properties["photo_filename"][v]} for v in vertex_list]
return ret
def _compute_shortest_path(graph,
source,
target,
distance=None,
exclude_edge=False):
"""Compute the single shortest path from the source to the target.
Parameters
----------
source : str
Source node ID
target : str
Target node ID
exclude_edge : bool, optional
Flag indicating whether the direct edge from the source to
the target should be excluded from the result (if exists).
"""
source_vertex = graph.vertex(_get_vertex_obj(graph, source))
target_vertex = graph.vertex(_get_vertex_obj(graph, target))
path, _ = shortest_path(
graph,
source_vertex,
target_vertex,
weights=graph.edge_properties[distance] if distance else None)
return tuple([graph.vp["@id"][el] for el in path])
def findThickestPathLateral(self,skelImg,skelDia,xScale,yScale):
print 'create skeleton graph'
skelGraph,_=self.makeGraphFast(skelImg,skelDia,xScale,yScale)
rootVertex=self.findRootVertexLateral(skelGraph)
epropW=skelGraph.edge_properties["w"]
path=[]
#remove all two-connected ones with 0 label
print 'trace path of thickest diameter'
#find thickest path
pathDetect=True
if skelGraph.num_vertices() >0:
pathDetect=True
while pathDetect==True:
lastVertex=self.findLastRootVertex(skelGraph)
try:
path,_=gt.shortest_path(skelGraph, rootVertex, lastVertex , weights=epropW, pred_map=None)
pathDetect=False
except:
raise
if lastVertex <=0:
pathDetect=False
else:
skelGraph.remove_vertex(lastVertex)
lastVertex=self.findLastRootVertex(skelGraph)
return path,skelGraph
def simulation__shortest_redistribution(w):
"""
# objective function
args:
w: a vector contains imposed weights for edges
"""
#extract movements & frequencies
movements = [m[0] for m in movements_frequency_autos]
frequencies = np.array([m[1] for m in movements_frequency_autos])
#computing counts based on Ni and frequencies
counts = frequencies * N
#build movements and counts list
movements_counts = list(zip(movements, counts))
# create edge property map
edge_maxspeed = road_network.edge_properties["maxspeed"]
edge_hdistance = road_network.edge_properties["hdistance"]
# create new property map for total weights
edge_weight = road_network.new_edge_property("double")
#build a dictionary to store number cars on edges
edges = list(road_network.edges())
for edge, wi in list(zip(edges, w)):
edge_weight[edge] = max(wi + edge_hdistance[edge],
0) #to avoid negative weights
dict_edge_cars = dict(zip(edges, [0] * len(edges)))
#build a dictionary to store shortest path of movement
#dict_movement_path = dict()
for mc in movements_counts:
m = mc[0]
m_p = (dict_centroids_projected[m[0]], dict_centroids_projected[m[1]])
m_count = mc[1]
#compute nodes and shortest path
nodes, path = shortest_path(road_network,
m_p[0],
m_p[1],
weights=edge_weight,
negative_weights=False)
#dict_movement_path[m] = path
for edge in path:
dict_edge_cars[edge] += m_count
#build a dictionary to store the time of edges
dict_edge_time = dict(list(zip(edges, [0] * len(edges))))
#compute time on edges
t_tot = 0
for edge in edges:
hdistance = edge_hdistance[edge] #haversine distance of the edge
r_edge = dict_edge_cars[edge] / (hdistance * l) #density of edge
if r_edge == 0: continue
maxspeed = edge_maxspeed[edge]
t_edge = compute_edgetime(r_edge, hdistance, maxspeed)
dict_edge_time[edge] = t_edge
t_tot += t_edge * dict_edge_cars[edge]
#dict_movement_time = dict()
#for m in movements:
#path = dict_movement_path[m]
#m_t = sum([dict_edge_time[e] for e in path])
#dict_movement_time[m] = m_t
return t_tot,
def _compute_shortest_path(self, nodes: Tuple[Point, Point]) -> None:
source_node, target_node = nodes
source_node_wkt = source_node.wkt
target_node_wkt = target_node.wkt
if source_node_wkt != target_node_wkt:
source_vertex = self._graph.find_vertex_from_name(source_node_wkt)
target_vertex = self._graph.find_vertex_from_name(target_node_wkt)
self.logger.info(
f"Compute shortest path from {source_vertex} to {target_vertex}"
)
# shortest path computing...
_, path_edges = shortest_path(
self._graph,
source=source_vertex,
target=target_vertex,
weights=self._graph.edge_weights, # weights is based on line length
)
gdf_copy = self._gdf.copy(deep=True)
# # get path by using edge names
osm_roads_features = gdf_copy[
gdf_copy[self._TOPO_FIELD].isin(
[self._graph.edge_names[edge] for edge in path_edges]
)
]
path_geoms = osm_roads_features[self._GEOMETRY_FIELD].to_list()
path_osm_ids = filter(
lambda x: isinstance(x, str),
osm_roads_features[self._ID_OSM_FIELD].to_list(),
)
path_osm_urls = filter(
lambda x: isinstance(x, str),
osm_roads_features[self._OSM_URL_FIELD].to_list(),
)
# reorder linestring
path_found = linemerge(path_geoms)
if not Point(path_found.coords[0]).wkt == source_node_wkt:
# we have to revert the coord order of the 1+ elements
path_found = LineString(path_found.coords[::-1])
self._output_data.append(
{
"source_node": source_node.wkt,
"target_node": target_node.wkt,
"osm_ids": ", ".join(path_osm_ids),
"osm_urls": ", ".join(path_osm_urls),
"geometry": path_found,
}
)
else:
self.logger.info(
f"Path from {source_node_wkt} to {target_node_wkt} are equals: not proceed!"
)
def find_shortest_path(source, destination):
print "Finding the shortest path"
vertices, edges=shortest_path(graph, mapping[source], mapping[destination])
print "Printing path"
for station in vertices:
print index_name[graph.vertex_index[station]]
for edge in edges:
print "Line: ", edge_map[graph.edge_index[edge]]
def essence(sequences, form_diagram, v_to_seq, probs):
print(sequences[:4])
probs.a = -np.log(probs.a)
size = form_diagram.num_vertices()
start = form_diagram.vertex(size-2)
end = form_diagram.vertex(size-1)
vs, es = topology.shortest_path(form_diagram, start, end, probs)
vs = np.array([int(v) for v in vs])[1:-1]
print(v_to_seq[vs])
def shortest_paths(self, src_sid=None, dst_sid=None):
nv = GraphData()
if src_sid is None and dst_sid is None:
raise Exception('src_sid or dst_sid must be set')
elif src_sid is None and dst_sid is not None:
dst = self.__resolve_sid_to_id(dst_sid)
if dst is None:
raise Exception('SID not found!')
total = self.dbsession.query(func.count(
EdgeLookup.id)).filter_by(ad_id=self.domain_id).filter(
EdgeLookup.oid != self.domain_sid + '-513').scalar()
q = self.dbsession.query(
EdgeLookup.id).filter_by(ad_id=self.domain_id).filter(
EdgeLookup.oid != self.domain_sid + '-513')
for nodeid in tqdm(windowed_query(q, EdgeLookup.id, 1000),
desc='running',
total=total):
for i, res in enumerate(shortest_path(self.graph, nodeid,
dst)):
if res == []:
continue
if i % 2 == 0:
self.__result_path_add(nv, res)
elif src_sid is not None and dst_sid is not None:
dst = self.__resolve_sid_to_id(dst_sid)
if dst is None:
raise Exception('SID not found!')
src = self.__resolve_sid_to_id(src_sid)
if src is None:
raise Exception('SID not found!')
for i, res in enumerate(shortest_path(self.graph, src, dst)):
if res == []:
continue
if i % 2 == 0:
self.__result_path_add(nv, res)
else:
raise Exception('Not implemented!')
return nv
def getRootTipPaths(self, thickestPath, G):
print('Calculating Root-Tip Paths')
CPVIDX = []
for i in thickestPath:
CPVIDX.append(G.vertex_index[i])
vprop = G.vertex_properties["vp"]
eprop = G.edge_properties["ep"]
epropW = G.edge_properties["w"]
if len(self.__RTP) == 0:
tips = self.getTips(thickestPath, G)
RTP = []
#print '***** TIPS VAR ******'
#print tips
if tips == -1:
print 'ERROR: No tips found'
return -1
try:
tips.remove(G.vertex_index(thickestPath[0]))
except:
pass
percentOld = 0
for idx, i in enumerate(tips):
percent = (float(idx) / float(len(tips))) * 100
if percentOld + 5 < percent:
print(str(np.round(percent, 1)) + '% '),
percentOld = percent
try:
path, edges = gt.shortest_path(G,
thickestPath[0],
G.vertex(i),
weights=epropW,
pred_map=None)
RTPTmp = []
for k in path:
RTPTmp.append(G.vertex_index[k])
split = self.compareTwoOrderedLists(CPVIDX, RTPTmp)
RTP.append(RTPTmp[split:])
for j in reversed(path):
vprop[j]['nrOfPaths'] += 1
for j in edges:
eprop[j]['RTP'] = True
except:
print 'ERROR: in def getRootTipPaths(self,thickestPath,G): no dijkstra path at ' + str(
idx) + ' in tips'
pass
print 'Number of Root-Tip Paths: ' + str(len(RTP))
self.__RTP = RTP
print 'RTP done!'
return RTP, tips
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 getRootTipPaths(self, thickestPath, G):
print ("Calculating Root-Tip Paths")
CPVIDX = []
for i in thickestPath:
CPVIDX.append(G.vertex_index[i])
vprop = G.vertex_properties["vp"]
eprop = G.edge_properties["ep"]
epropW = G.edge_properties["w"]
if len(self.__RTP) == 0:
tips = self.getTips(thickestPath, G)
RTP = []
# print '***** TIPS VAR ******'
# print tips
if tips == -1:
print "ERROR: No tips found"
return -1
try:
tips.remove(G.vertex_index(thickestPath[0]))
except:
pass
percentOld = 0
for idx, i in enumerate(tips):
percent = (float(idx) / float(len(tips))) * 100
if percentOld + 5 < percent:
print (str(np.round(percent, 1)) + "% "),
percentOld = percent
try:
path, edges = gt.shortest_path(G, thickestPath[0], G.vertex(i), weights=epropW, pred_map=None)
RTPTmp = []
for k in path:
RTPTmp.append(G.vertex_index[k])
split = self.compareTwoOrderedLists(CPVIDX, RTPTmp)
RTP.append(RTPTmp[split:])
for j in reversed(path):
vprop[j]["nrOfPaths"] += 1
for j in edges:
eprop[j]["RTP"] = True
except:
print "ERROR: in def getRootTipPaths(self,thickestPath,G): no dijkstra path at " + str(
idx
) + " in tips"
pass
print "Number of Root-Tip Paths: " + str(len(RTP))
self.__RTP = RTP
print "RTP done!"
return RTP, tips
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 parse_travel_path(path, v, joined_stops, g, vmr):
path_nodes = []
path_edges = []
cur_node = v["cur_node"]
for r, p_or_d in path:
if p_or_d == 'p':
o_or_d = r.road_o
else:
o_or_d = r.road_d
nodes, edges = topology.shortest_path(g, vmr[cur_node], vmr[o_or_d])
path_nodes += [
g.vertex_properties['_graphml_vertex_id'][n] for n in nodes
]
path_edges += edges
cur_node = o_or_d
return path_nodes, path_edges
def mockEventsGT(nx_map,gt_map,nx_index,t,n,w,nmid):
events = []
for i in xrange(0,n):
while True:
b = gt_map.vertex( random.randint(0,gt_map.num_vertices()-1) )
e = gt_map.vertex( random.randint(0,gt_map.num_vertices()-1) )
b1 = nx_index[b]
e1 = nx_index[e]
if nx.has_path(nx_map,b1,e1) and b1!=e1:
break
start = random.randint(0,t)
gt_path = map(lambda x: nx_index[x], gt_top.shortest_path(gt_map,b,e,weights=w)[0] )
events.append( te.TrafficEvent(b,e,gt_path, start) )
print i
return events
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 mockEventsGT(nx_map, gt_map, nx_index, t, n, w, nmid):
events = []
for i in xrange(0, n):
while True:
b = gt_map.vertex(random.randint(0, gt_map.num_vertices() - 1))
e = gt_map.vertex(random.randint(0, gt_map.num_vertices() - 1))
b1 = nx_index[b]
e1 = nx_index[e]
if nx.has_path(nx_map, b1, e1) and b1 != e1:
break
start = random.randint(0, t)
gt_path = map(lambda x: nx_index[x],
gt_top.shortest_path(gt_map, b, e, weights=w)[0])
events.append(te.TrafficEvent(b, e, gt_path, start))
print i
return events
def shortest_path(self, from_sid, to_sid, pred_map=None):
"""
Computes the shortest path between stop `from_sid` and stop `to_sid`
"""
from_v = self.get_vertex(from_sid)
to_v = self.get_vertex(to_sid)
path_vs, _ = topology.shortest_path(
self.gtG,
from_v,
to_v,
weights=self.gtG.ep["duration"],
pred_map=pred_map,
)
if not path_vs:
return []
return [self.gtG.vp["stop_id"][v] for v in path_vs]
def findThickestPath(self, skelImg, skelDia, xScale, yScale):
print 'create skeleton graph'
skelGraph, skelSize = self.makeGraphFast(skelImg, skelDia, xScale,
yScale)
rootVertex, _ = self.findRootVertex(skelGraph)
epropW = skelGraph.edge_properties["w"]
maxDia = np.max(skelDia)
try:
diaIdx = int(len(skelDia) * 0.1)
except:
print "Error line 234 in Segmentation.py"
diaIdx = 1
maxDia10 = np.max(skelDia[0:diaIdx])
print 'max Diameter: ' + str(maxDia)
path = []
#remove all two-connected ones with 0 label
print 'trace path of thickest diameter'
#find thickest path
pathDetect = True
if skelGraph.num_vertices() > 0:
pathDetect = True
while pathDetect == True:
lastVertex = self.findLastRootVertex(skelGraph)
try:
path, _ = gt.shortest_path(skelGraph,
rootVertex,
lastVertex,
weights=epropW,
pred_map=None)
pathDetect = False
except:
raise
if lastVertex <= 0:
pathDetect = False
else:
skelGraph.remove_vertex(lastVertex)
lastVertex = self.findLastRootVertex(skelGraph)
return path, skelGraph, maxDia10, skelSize
def findThickestPath(self,skelImg,skelDia,xScale,yScale):
print 'create skeleton graph'
skelGraph,skelSize=self.makeGraphFast(skelImg,skelDia,xScale,yScale)
rootVertex,_=self.findRootVertex(skelGraph)
epropW=skelGraph.edge_properties["w"]
maxDia=np.max(skelDia)
try:
diaIdx=int(len(skelDia)*0.1)
except:
print "Error line 234 in Segmentation.py"
diaIdx=1
maxDia10=np.max(skelDia[0:diaIdx])
print 'max Diameter: '+ str(maxDia)
path=[]
#remove all two-connected ones with 0 label
print 'trace path of thickest diameter'
#find thickest path
pathDetect=True
if skelGraph.num_vertices() >0:
pathDetect=True
while pathDetect==True:
lastVertex=self.findLastRootVertex(skelGraph)
try:
path,_=gt.shortest_path(skelGraph, rootVertex, lastVertex , weights=epropW, pred_map=None)
pathDetect=False
except:
raise
if lastVertex <=0:
pathDetect=False
else:
skelGraph.remove_vertex(lastVertex)
lastVertex=self.findLastRootVertex(skelGraph)
return path,skelGraph,maxDia10,skelSize
def path_exists(g: gt.Graph, source, target):
vp, _ = gt_top.shortest_path(g, source, target)
return len(vp) != 0
def path_from_income(self):
"""
:return: GeoDataFrame representing path of commuter to work
"""
# Get jobs within range of income of commuter
streets = self.env.streets.copy().reset_index(drop=True)
origins = self.env.origins.copy()
destinations = self.env.destinations.copy()
destinations = destinations[
(destinations['salary_n'] > self.income[0])
& (destinations['salary_n'] < self.income[1])]
# Get shortest path to one random destination
osm_g = Graph(directed=False)
indices = {}
if len(destinations) > 0:
# Add vertices to graph
for i, osm_id in enumerate(
list(streets['from']) + list(streets['to'])):
v = osm_g.add_vertex()
v.index = int(i)
indices[osm_id] = i
# Add edges to graph
for i in list(streets.index):
o_osm = streets.at[i, 'from']
d_osm = streets.at[i, 'to']
osm_g.add_edge(indices[o_osm], indices[d_osm])
# Randomly choose destination
destination = destinations.loc[
np.random.choice(list(destinations.index)), :]
# Randomly choose origin parcel based on block id
origins = origins[
(origins['Landuse'].isin(['MFH', 'MFL', 'SFA', 'SFD', 'MX']))
& (origins['index_block'] == self.block_id)].reset_index(
drop=True)
if len(origins) > 0:
origin = origins.loc[np.random.choice(list(origins.index)), :]
# Get closest street of origin and destination
osm_origin = streets[streets.centroid == nearest_points(
origin['geometry'], streets.centroid.unary_union)[1]]
osm_destination = streets[streets.centroid == nearest_points(
destination['geometry'], streets.centroid.unary_union)[1]]
# Calculate shortest path
def keys(dictA, value):
return list(dictA.keys())[list(
dictA.values()).index(value)]
path = shortest_path(
osm_g, indices[osm_origin['from'].values[0]],
indices[osm_destination['to'].values[0]])[1]
path_gdf = pd.concat([
streets[
(streets['from'] == keys(indices, int(edge.source())))
& (streets['to'] == keys(indices, int(edge.target())))]
for edge in path
])
return path_gdf
else:
return None
else:
return None
def shortest_path(g,
source,
target,
directed=None,
weights=None,
combine_weights="mean"):
'''
Returns a shortest path between `source`and `target`.
The algorithms returns an empty list if there is no path between the nodes.
Parameters
----------
g : :class:`~nngt.Graph`
Graph to analyze.
source : int
Node from which the path starts.
target : int
Node where the path ends.
directed : bool, optional (default: ``g.is_directed()``)
Whether the edges should be considered as directed or not
(automatically set to False if `g` is undirected).
weights : str or array, optional (default: binary)
Whether to use weighted edges to compute the distances. By default,
all edges are considered to have distance 1.
combine_weights : str, optional (default: 'mean')
How to combine the weights of reciprocal edges if the graph is directed
but `directed` is set to False. It can be:
* "sum": the sum of the edge attribute values will be used for the new
edge.
* "mean": the mean of the edge attribute values will be used for the
new edge.
* "min": the minimum of the edge attribute values will be used for the
new edge.
* "max": the maximum of the edge attribute values will be used for the
new edge.
Returns
-------
path : array of ints
Order of the nodes making up the path from `source` to `target`.
References
----------
.. [gt-sp] :gtdoc:`topology.shortest_path`
'''
# source == target case
if source == target:
return [source]
# non-trivial cases
g, graph, w = _get_gt_graph(g,
directed,
weights,
combine_weights,
return_all=True)
w = _get_gt_weights(g, w)
path, _ = gtt.shortest_path(graph, source, target, weights=w)
return [int(v) for v in path]
