def isolate(expanded_manifest, subset):
section = subset["section"]
name = subset["name"]
uid = f"{section}|{name}"
m = create_minimal_manifest(expanded_manifest)
G = convert_to_graph(expanded_manifest, nx.DiGraph())
node = G.nodes.get(uid)
data = deepcopy(node)
del data["section"]
del data["item_name"]
m[node.get("section")][node.get("item_name")] = data
if subset.get("include_dependencies", False):
click.echo("Including dependencies")
for dependency in nx.edge_dfs(G, uid, orientation="original"):
link, dependency_name, direction = dependency
node = G.nodes.get(dependency_name)
data = deepcopy(node)
del data["section"]
del data["item_name"]
m[node.get("section")][node.get("item_name")] = data
if subset.get("include_reverse_dependencies", False):
click.echo("Including reverse dependencies")
for dependency in nx.edge_dfs(G, uid, orientation="reverse"):
dependency_name, link, direction = dependency
node = G.nodes.get(dependency_name)
data = deepcopy(node)
del data["section"]
del data["item_name"]
m[node.get("section")][node.get("item_name")] = data
return m
def sort_instructions(instructions):
'''Finds the correct execution order for the given instructions.
:param instructions: List of conditional relations for instruction steps
pairs.
:type instructions: list(tuple(int))
'''
# compute dependency tree
G = nx.DiGraph()
G.add_edges_from(instructions)
dependencies, deps_copy = {}, {}
for node in G:
values = list(nx.edge_dfs(G, node, orientation='reverse'))
dependencies[node] = set([t[0] for t in values])
deps_copy[node] = set([t[0] for t in values])
# step order computation
order = ''
while len(dependencies) > 0:
next_node = sorted(dependencies.keys(),
key=lambda x: (len(dependencies[x]), x))[0]
for n, children in dependencies.items():
children.discard(next_node)
del dependencies[next_node]
order += next_node
return deps_copy, order
def adjust_edge_perturb_radii(frcs, graph, perturb_factor=2):
"""
Returns a new graph where the 'perturb_radius' has been adjusted to account for
rounding errors. See train_image for parameters and returns.
调整perturb_radius边的权重, 由于舍入误差
为什么要这样调整而不在添加边的时候直接进行四舍五入? 为什么下一次迭代要算上上一次的误差?
如果没有这个调整效果会差多少?
"""
graph = graph.copy()
total_rounding_error = 0
# 直到源结点遍历完所有它能到达的边才停止, n1 n2为每一访问的边的两端的节点
for n1, n2 in nx.edge_dfs(graph):
desired_radius = distance.euclidean(frcs[n1, 1:],
frcs[n2, 1:]) / perturb_factor
upper = int(np.ceil(desired_radius))
lower = int(np.floor(desired_radius))
round_up_error = total_rounding_error + upper - desired_radius
round_down_error = total_rounding_error + lower - desired_radius
if abs(round_up_error) < abs(round_down_error):
graph.edge[n1][n2]['perturb_radius'] = upper
total_rounding_error = round_up_error
else:
graph.edge[n1][n2]['perturb_radius'] = lower
total_rounding_error = round_down_error
return graph
def _build(G, target=None, full=False, outs=False):
import networkx as nx
from dvc.repo.graph import get_pipeline, get_pipelines
if target:
H = get_pipeline(get_pipelines(G), target)
if not full:
descendants = nx.descendants(G, target)
descendants.add(target)
H.remove_nodes_from(set(G.nodes()) - descendants)
else:
H = G
if outs:
G = nx.DiGraph()
for stage in H.nodes:
G.add_nodes_from(stage.outs)
for from_stage, to_stage in nx.edge_dfs(H):
G.add_edges_from([(from_out, to_out)
for from_out in from_stage.outs
for to_out in to_stage.outs])
H = G
def _relabel(node):
from dvc.stage import Stage
return node.addressing if isinstance(node, Stage) else str(node)
return nx.relabel_nodes(H, _relabel, copy=False)
def upstream_edge_info(wg, n):
upstream_edges = list(nx.edge_dfs(wg, n, orientation='reverse'))
# print(upstream_edges)
up_switch_dict = {}
e_file = gv.filepaths["edges"]
for u in upstream_edges:
# edge info:
p = u[0].lstrip()
q = u[1].lstrip()
edge_of_path_name_1 = p + "_to_" + event_intensity(n)
edge_of_path_name_2 = q + "_to_" + p
edge_search_result_1 = db._edge_search(edge_of_path_name_1)
edge_search_result_2 = db._edge_search(edge_of_path_name_2)
try:
with open(e_file, 'r') as f:
csvr = csv.reader(f)
csvr = list(csvr)
for row in csvr:
if row[0].lstrip() == edge_of_path_name_1 or row[0].lstrip(
) == edge_of_path_name_2:
if edge_search_result_1 != 0 or edge_search_result_2 != 0:
if row[1].lstrip() == "switch":
# print("[i] Upstream Switch: {}, Status: {}, Availability: {}".format(row[0], row[4],
# row[13]))
up_switch_dict[row[0]] = row[4]
if row[1].lstrip() == "transformer":
pass
# print(
# " [i] Upstream Transformer: {}, Status: {}, Availability: {}".format(row[0], row[4],
# row[13]))
except:
print("[x] Error in up-stream edge search.")
return up_switch_dict
def amplify_network(self, epochs=5, alpha=1.):
local_g = self.graph.copy()
influence_epochs = {}
for epoch in range(epochs):
for node, influence in local_g.nodes(data='influence'):
if epoch == 0:
influence_epochs[node] = []
influence_epochs[node].append(influence)
egde_traversal = list(nx.edge_dfs(local_g))
influence = nx.get_node_attributes(local_g, 'influence')
weight_absolute = nx.get_edge_attributes(local_g,
'weight_absolute')
for _, (from_node, to_node, _) in enumerate(egde_traversal):
if influence[to_node] == 0:
computed_influence = 0
else:
computed_influence = influence[to_node] + (
influence[from_node] *
(alpha * weight_absolute[(from_node, to_node, 0)]))
if int(copysign(1, influence[to_node])) == 1:
computed_influence = computed_influence if computed_influence > 0 else 0
elif computed_influence > 0:
computed_influence = computed_influence if computed_influence < 0 else 0
influence[to_node] = computed_influence
nx.set_node_attributes(local_g, influence, 'influence')
return epochs, {'influence_epochs': influence_epochs}
def _build_graph(self, target, commands, outs):
import networkx
from dvc.stage import Stage
from dvc.repo.graph import get_pipeline
target_stage = Stage.load(self.repo, target)
G = get_pipeline(self.repo.pipelines, target_stage)
nodes = set()
for stage in networkx.dfs_preorder_nodes(G, target_stage):
if commands:
if stage.cmd is None:
continue
nodes.add(stage.cmd)
elif outs:
for out in stage.outs:
nodes.add(str(out))
else:
nodes.add(stage.relpath)
edges = []
for from_stage, to_stage in networkx.edge_dfs(G, target_stage):
if commands:
if to_stage.cmd is None:
continue
edges.append((from_stage.cmd, to_stage.cmd))
elif outs:
for from_out in from_stage.outs:
for to_out in to_stage.outs:
edges.append((str(from_out), str(to_out)))
else:
edges.append((from_stage.relpath, to_stage.relpath))
return list(nodes), edges, networkx.is_tree(G)
def reachable_subgraph(self):
"""Return a new StmtGraph that only contains the nodes reachable from
Entry.
"""
sub_graph = StmtGraph()
sub_graph.graph.add_edges_from(nx.edge_dfs(self.graph, Entry))
return sub_graph
def depends_on(self, *, include_filters=True, recursive=False, include_fakes=True):
def gen_actions(include_filters=include_filters, include_fakes=include_fakes):
task_strings = self.declaration_line.split(":", 1)[1].split()
task_name_index_tuples = [
(self.bashfile.find_chunk(task_name=s), s) for s in task_strings
]
for i, task_string in task_name_index_tuples:
if task_string.startswith("@"):
if include_filters:
yield TaskFilter(task_string, bashfile=self.bashfile)
elif i is None:
if include_fakes:
yield FakeTaskScript(task_string, bashfile=self.bashfile)
else:
# Otherwise, create the task.
yield TaskScript(chunk_index=i, bashfile=self.bashfile)
actions = [t for t in gen_actions()]
if recursive:
graph = {}
actions = []
edge_view = networkx.edge_dfs(
self.bashfile.graph, self, orientation="original"
)
for parent, child, _ in edge_view:
if parent not in graph:
graph[parent] = [child]
else:
if child not in graph[parent]:
graph[parent].append(child)
for task in graph:
for action in graph[task]:
for dep_action in graph.get(action, []):
actions.append(dep_action)
actions.append(action)
# Remove filters, if requested to do so.
if not include_filters:
_actions = []
for action in actions:
if not isinstance(action, TaskFilter):
_actions.append(action)
actions = _actions
# Remove duplicates from the list.
_actions = []
for action in actions:
if action not in _actions:
_actions.append(action)
actions = _actions
return actions
def createWaysDict(self):
""" Begin with startNode and traverse the graph, collecting the nodes
of each way. When a new way is encountered, start a new list of
nodes. When a new intersection is encountered, pass the list of
ways to the getWay function for processing.
Input: graph, startNode
Output: dictionary used for creating VISSIM links
"""
waysDict = {}
ways = []
nodes = []
prevAttr = None
currAttr = None
for fromN, toN in nx.edge_dfs(self.G):
currAttr = self.G.edge[fromN][toN]
if currAttr['highway'] not in self.roadTypes:
continue
if self.isIntersection(fromN):
ways.append(nodes)
waysDict.update(self.getWay(ways))
ways = []
nodes = []
elif self.isNewWay(fromN, toN, prevAttr):
ways.append(nodes)
nodes = []
nodes.append(fromN)
nodes.append(toN)
prevAttr = currAttr
if self.isExterior(toN):
ways.append(nodes)
self.getWay(ways)
ways.append(nodes)
waysDict.update(self.getWay(ways))
return waysDict
def _find_iterative_props(self):
r"""
Find and return properties that need to be iterated while running the
algorithm
Parameters
----------
propnames : string or list of strings
The propnames of the properties that are variable throughout
the algorithm.
"""
import networkx as nx
phase = self.project.phases(self.settings['phase'])
physics = self.project.find_physics(phase=phase)
geometries = self.project.geometries().values()
# Combine dependency graphs of phase and all physics/geometries
dg = phase.models.dependency_graph(deep=True)
for g in geometries:
dg = nx.compose(dg, g.models.dependency_graph(deep=True))
for p in physics:
dg = nx.compose(dg, p.models.dependency_graph(deep=True))
base_props = [self.settings["quantity"]] + self.settings["variable_props"]
if base_props is None:
return []
# Find all props downstream that rely on "quantity" (if at all)
dg = nx.DiGraph(nx.edge_dfs(dg, source=base_props))
if len(dg.nodes) == 0:
return []
dg.remove_nodes_from(base_props)
return list(nx.dag.lexicographical_topological_sort(dg))
def compute_arcs_in_order(self):
self.finalArcs = {k: [] for k in self.K}
self.remainingFuel = {t: 0 for t in self.T}
for i in self.sol:
if self.sol[i] >= 0.9 and i[0] == 'x':
x, y, k = i.split(',')
x = x.replace("x[", "")
k = k.replace("]", "")
for q in range(math.ceil(self.sol[i])):
self.finalArcs[k].append((x, y))
if self.sol[i] >= 0.9 and i[0] == 'r':
x, y = i.split('[')
y = y.replace("]", "")
self.remainingFuel[y] = self.sol[i]
# Create a grpah for each robot
G = {k: nx.MultiDiGraph() for k in self.K}
# Add all nodes in the graph for each robot
for k in self.K:
G[k].add_nodes_from(self.N)
G[k].add_edges_from(self.finalArcs[k])
#print("Nodes in G["+k+"]: ", G[k].nodes(data=True))
#print("Edges in G["+k+"]: ", G[k].edges(data=True))
# Now compute the paths in the above graphs
self.arcsInOrder = {k: [] for k in self.K}
tempArcsInOrder = {k: [] for k in self.K}
for k in self.K:
tempArcsInOrder[k] = list(nx.edge_dfs(G[k], source=self.S[0]))
for arc in tempArcsInOrder[k]:
newArc = tuple((arc[0], arc[1]))
self.arcsInOrder[k].append(newArc)
def _build_graph(self, target, commands=False, outs=False):
import networkx
from dvc import dvcfile
from dvc.repo.graph import get_pipeline
from dvc.utils import parse_target
path, name = parse_target(target)
target_stage = dvcfile.Dvcfile(self.repo, path).stages[name]
G = get_pipeline(self.repo.pipelines, target_stage)
nodes = set()
for stage in networkx.dfs_preorder_nodes(G, target_stage):
if commands:
if stage.cmd is None:
continue
nodes.add(stage.cmd)
elif not outs:
nodes.add(stage.addressing)
edges = []
for from_stage, to_stage in networkx.edge_dfs(G, target_stage):
if commands:
if to_stage.cmd is None:
continue
edges.append((from_stage.cmd, to_stage.cmd))
elif not outs:
edges.append((from_stage.addressing, to_stage.addressing))
if outs:
nodes, edges = self._build_output_graph(G, target_stage)
return list(nodes), edges, networkx.is_tree(G)
def dependency_extraction(sentence, stop_words_list=None):
edges=[]
for token in sentence:
for child in token.children:
edges.append(('{0} {1}'.format(token.lemma_, token.i),'{0} {1}'.format(child.lemma_, child.i)))
graph=nx.DiGraph(edges)
final,roots=[],[]
for num in range(2, 5):
for sub_nodes in itertools.combinations(graph.nodes(), num):
subg = graph.subgraph(sub_nodes)
if nx.is_tree(subg):
d = dict(subg.in_degree(subg.nodes()))
tmp_root,two_parents,stopwords=[],False,False
for i,j in d.items():
if j==0: tmp_root.append(i)
if j>1: two_parents == True
if i.split(' ')[0] in stop_words_list: stopwords=True
if not two_parents and len(tmp_root)==1 and not stopwords:
final.append(subg)
roots.append(tmp_root[0])
features=[]
for i in range(len(roots)):
features.append(tree_to_string(list(nx.edge_dfs(final[i])), roots[i]))
return features
def get_operation(self, graph_connector):
passed = []
failed = []
for u, v in edge_dfs(graph_connector):
origin_attributes = graph_connector.nodes(data=True)[u]
opposite_vertices = None
if origin_attributes in self.vertices_under_resource_types:
opposite_vertices = self.vertices_under_connected_resources_types
elif origin_attributes in self.vertices_under_connected_resources_types:
opposite_vertices = self.vertices_under_resource_types
if not opposite_vertices:
continue
destination_attributes = graph_connector.nodes(data=True)[v]
if destination_attributes in opposite_vertices:
if origin_attributes in self.excluded_vertices or destination_attributes in self.excluded_vertices:
failed.extend([origin_attributes, destination_attributes])
else:
passed.extend([origin_attributes, destination_attributes])
continue
destination_block_type = destination_attributes.get(CustomAttributes.BLOCK_TYPE)
if destination_block_type == BlockType.OUTPUT.value:
try:
output_edges = graph_connector.edges(v, data=True)
_, output_destination, _ = next(iter(output_edges))
output_destination = graph_connector.nodes(data=True)[output_destination]
output_destination_type = output_destination.get(CustomAttributes.RESOURCE_TYPE)
if self.is_associated_edge(origin_attributes.get(CustomAttributes.RESOURCE_TYPE), output_destination_type):
passed.extend([origin_attributes, output_destination])
except StopIteration:
continue
failed.extend([v for v in self.vertices_under_resource_types + self.vertices_under_connected_resources_types if v not in passed])
return passed, failed
def make_graph_nodes_consistent(self, seed_kmer_strings=None):
"""
Take a Cortex graph and make all nodes have kmer_strings that are consistent with each
other. If a seed kmer string is provided, then start with that seed kmer.
"""
if self.graph.is_consistent():
return self
if seed_kmer_strings is None:
seed_kmer_strings = []
graph = CortexDiGraph(self.graph)
new_graph = ConsistentCortexDiGraph(graph=self.graph.graph)
seeds = SeedKmerStringIterator.from_all_kmer_strings_and_seeds(self.graph.nodes(),
seed_kmer_strings)
for seed, lexlo_seed in seeds:
new_graph.add_node(seed, kmer=self.graph.node[lexlo_seed])
seeds.remove(lexlo_seed)
for source, sink, key, direction in nx.edge_dfs(graph, lexlo_seed, 'ignore'):
if direction == 'forward':
rc_after_ref_kmer = True
ref, target = source, sink
elif direction == 'reverse':
ref, target = sink, source
rc_after_ref_kmer = False
else:
raise Exception("unknown direction: {}".format(direction))
if ref not in new_graph.node:
ref = revcomp(ref)
rc_after_ref_kmer = not rc_after_ref_kmer
matched_target, _ = revcomp_target_to_match_ref(target, ref, rc_after_ref_kmer)
new_graph.add_node(matched_target, kmer=graph.node[matched_target])
seeds.remove(lexlo(matched_target))
self.graph = new_graph
return self
def createWaysDict(self):
""" Begin with startNode and traverse the graph, collecting the nodes
of each way. When a new way is encountered, start a new list of
nodes. When a new intersection is encountered, pass the list of
ways to the getWay function for processing.
Input: graph, startNode
Output: dictionary used for creating VISSIM links
"""
waysDict = {}
ways = []
nodes = []
prevAttr = None
currAttr = None
for fromN, toN in nx.edge_dfs(self.G):
currAttr = self.G.edge[fromN][toN]
if currAttr['highway'] not in self.roadTypes:
continue
if self.isIntersection(fromN):
ways.append(nodes)
waysDict.update(self.getWay(ways))
ways = []
nodes = []
elif self.isNewWay(fromN, toN, prevAttr):
ways.append(nodes)
nodes = []
nodes.append(fromN)
nodes.append(toN)
prevAttr = currAttr
if self.isExterior(toN):
ways.append(nodes)
self.getWay(ways)
ways.append(nodes)
waysDict.update(self.getWay(ways))
return waysDict
def resiliency(analysis='nodal'):
"""
This function helps compute the resiliency metric of the network of a
node, or a network
:param kwargs:
:return:
"""
# 0. what's the event?
event = gv.event
# 1. get the graphs
g1 = gv.graph_collection[0]
g2 = gv.graph_collection[1]
nodes = g2.nodes()
edges = g2.edges()
# 2. for which node and edge are you doing the analysis?
wg = nx.DiGraph() # wg: working graph
wg.add_edges_from(edges)
for n in nodes:
# Finding Downstream Edges
print("Downstream Edges of %s -->" % n)
downstream_edges = list(nx.dfs_edges(wg, n))
resiliency_downstream(g2, downstream_edges, event)
# Finding Upstream Edges
print("Upstream Edges of %s -->" % n)
upstream_edges = list(nx.edge_dfs(wg, n, orientation='reverse'))
resiliency_upstream(g2, upstream_edges, event)
def transitive_closure(G, reflexive=False):
"""Returns transitive closure of a directed graph
The transitive closure of G = (V,E) is a graph G+ = (V,E+) such that
for all v, w in V there is an edge (v, w) in E+ if and only if there
is a path from v to w in G.
Handling of paths from v to v has some flexibility within this definition.
A reflexive transitive closure creates a self-loop for the path
from v to v of length 0. The usual transitive closure creates a
self-loop only if a cycle exists (a path from v to v with length > 0).
We also allow an option for no self-loops.
Parameters
----------
G : NetworkX DiGraph
A directed graph
reflexive : Bool or None, optional (default: False)
Determines when cycles create self-loops in the Transitive Closure.
If True, trivial cycles (length 0) create self-loops. The result
is a reflexive tranistive closure of G.
If False (the default) non-trivial cycles create self-loops.
If None, self-loops are not created.
Returns
-------
NetworkX DiGraph
The transitive closure of `G`
Raises
------
NetworkXNotImplemented
If `G` is not directed
References
----------
.. [1] http://www.ics.uci.edu/~eppstein/PADS/PartialOrder.py
TODO this function applies to all directed graphs and is probably misplaced
here in dag.py
"""
if reflexive is None:
TC = G.copy()
for v in G:
edges = ((v, u) for u in nx.dfs_preorder_nodes(G, v) if v != u)
TC.add_edges_from(edges)
return TC
if reflexive is True:
TC = G.copy()
for v in G:
edges = ((v, u) for u in nx.dfs_preorder_nodes(G, v))
TC.add_edges_from(edges)
return TC
# reflexive is False
TC = G.copy()
for v in G:
edges = ((v, w) for u, w in nx.edge_dfs(G, v))
TC.add_edges_from(edges)
return TC
def getStartNodes(self):
""" Get a list of nodes that do not overlap when traversed.
"""
edgeNodes = self.getExteriorNodes()
for node in edgeNodes:
for prev, n in nx.edge_dfs(self.G, source=node):
if n in edgeNodes:
edgeNodes.remove(n)
return edgeNodes
def getStartNodes(self):
""" Get a list of nodes that do not overlap when traversed.
"""
edgeNodes = self.getExteriorNodes()
for node in edgeNodes:
for prev, n in nx.edge_dfs(self.G, source=node):
if n in edgeNodes:
edgeNodes.remove(n)
return edgeNodes
def createIntersectionDict(self):
""" Use depth-first search to map intersection nodes and lane widths.
Input: graph, startNode
Output: dictionary mapping of intersection nodes
"""
intersections = {}
for fromN, toN in nx.edge_dfs(self.G):
if self.isIntersection(toN):
intersections[toN] = self.getIntersection(toN)
return intersections
def remove_backward_edges(di_graph, root_id=entity):
dac = nx.DiGraph()
dac.add_nodes_from(di_graph.nodes(data=True))
for (n1, n2) in list(nx.edge_dfs(di_graph, root_id)):
if (n1 in nx.descendants(dac, n2) or n1 == n2):
di_graph.remove_edge(n1, n2)
#dac.add_edge(n1, n2, value=di_graph[n1][n2]["value"])
return di_graph
def createIntersectionDict(self):
""" Use depth-first search to map intersection nodes and lane widths.
Input: graph, startNode
Output: dictionary mapping of intersection nodes
"""
intersections = {}
for fromN, toN in nx.edge_dfs(self.G):
if self.isIntersection(toN):
intersections[toN] = self.getIntersection(toN)
return intersections
def descendants(self, parent):
"""
Returns descendants (children and children of children, etc.) of `parent`
"""
try:
edges = list(
nx.edge_dfs(self._graph, parent, orientation="reverse"))
except nx.exception.NetworkXError:
edges = []
return [edge[0] for edge in edges]
def getPath(G, source=None):
# G: the graph of which the edge is a Relation(targetTable.name, sourceTable.name)
# return the execution path, list of tuple<str, str>
# source is a list of node name(str) from where dfs starts.
if(source == None):
source = findGraphHead(G)
else:
if(source != findGraphHead(G)):
raise ValueError("The input head is not the same as the real head of Graph. "
"head is the node which has no parent. ")
return reversEdgeList(list(nx.edge_dfs(G, source=[source], orientation='original')))
def assign_inverts(G, data_key='ELEVATIONI'):
"""
Assign invert values for each node in the graph, G by
walking up the network and accumulating elevation based on
sewer slope and length
"""
G1 = G.copy()
topo_sorted_nodes = nx.topological_sort(G1, reverse=True)
terminal_nodes = [n for n,d in G1.out_degree_iter() if d == 0]
#Assign starting El
for tn in terminal_nodes:
#set the terminal node invert by measuring the delta between
#it and then nearest upstream node having elevation data
G1.node[tn]['invert'] = 0
#find tn position in the topologically sorted nodes
i = topo_sorted_nodes.index(tn)
while G1.node[tn]['invert'] == 0:
delta = 0
#loop through sorted nodes, starting at the tn position
for n in topo_sorted_nodes[i:]:
#depth first search to nearest node with trusted elevation data
for u,v,direction in nx.edge_dfs(G1, n, 'reverse'):
delta += elevation_change(G1, u, v)
if 'invert_trusted' in G1.node[u]:
G1.node[tn]['invert'] = G1.node[u]['invert_trusted'] - delta
break
if G1.node[tn]['invert'] != 0:
break
for n in topo_sorted_nodes:
for p in G1.predecessors(n):
el_0 = G1.node[n]['invert']
el_2 = el_0 + elevation_change(G1,p,n)
#fill invert values where nodes already have trusted invert vals
if G1.node[n].get('invert_trusted', 0) != 0:
el_0 = G1.node[n]['invert_trusted']
G.node[n]['invert'] = el_0
if G1.node[p].get('invert_trusted', 0) != 0:
el_2 = G1.node[p]['invert_trusted']
G1.node[p]['invert'] = el_2
return G1
def calc_voltages_PU(graph):
"""Calculates the trueVoltagePU for each node on the network, starting from the "service" node
then running down the digraph edges out to the load nodes using an oriented depth-first created
tree of the graph structure. The segment_vdrop_PU function is used to calculate the per unit voltage drop along
each edge, set edge per unit currents, and to assign the trueVoltagePU to each node.
"""
serviceNode = graph.get_service_node()
#Oriented tree structure of digraph starting at serviceNode
graphStructure = nx.edge_dfs(graph, source=serviceNode)
#Calculate voltages starting at source and working towards loads
for i in graphStructure:
segment_vdrop_PU(graph, i[0], i[1])
def find_ordering(labels, order_type, root_node):
edges = []
added_edges = []
for label in labels:
x, name, y = label.split('.')
i = -1
if (x, y) in added_edges:
i = added_edges.index((x, y))
elif (y, x) in added_edges:
i = added_edges.index((y, x))
if i != -1:
edges[i][2]['name'].append(name)
else:
edges.append((x, y, {'name': [name]}))
added_edges.append((x, y))
G = nx.Graph()
G.add_edges_from(edges)
if root_node == 'none':
degrees = nx.degree_centrality(G)
root_node = max(degrees, key=degrees.get)
if order_type == 'bfs':
new_labels = nx.edge_bfs(G, root_node)
if order_type == 'dfs':
new_labels = nx.edge_dfs(G, root_node)
names = nx.get_edge_attributes(G, 'name')
ordered_labels = []
for label in new_labels:
x, y = label
if label in names:
edge_names = names[label]
else:
edge_names = names[(y, x)]
for name in edge_names:
new_label = '.'.join([x, name, y])
if new_label in labels:
ordered_labels.append(new_label)
else:
new_label = '.'.join([y, name, x])
ordered_labels.append(new_label)
return ordered_labels
def right_vector(G):
node_to_index = {n: i for i, n in enumerate(G.nodes())}
M = np.zeros((G.size(), G.order()), int)
for i, e in enumerate(G.edges(data='weight')):
M[i][node_to_index[e[0]]] = 1
w = e[2]
dfs = list(nx.edge_dfs(G, e))
L = [f for f in dfs if G.get_edge_data(*f)['weight'] > w]
if L != []:
M[i][node_to_index[L[0][0]]] = -1
else:
M[i][node_to_index[dfs[-1][1]]] = -1
return M
def solve_out_tips(graph, ending_nodes):
""" Removes unecessary ending nodes in a graph
:Parameters:
graph : (nx.Digraph)
ending_nodes : list of nodes
Returns:
graph : (nx.Digraph)
"""
# Paths identification
paths = {}
common_nodes = []
for end in ending_nodes:
# Find node with several predecessors
common_node = -1
full_path = nx.edge_dfs(graph, end, orientation='reverse')
path = []
for edge in full_path:
if path == []:
path.append(edge[0])
path.append(edge[1])
if len(list(graph.successors(edge[0]))) > 1:
common_node = edge[0]
if common_node not in common_nodes:
common_nodes.append(common_node)
break
paths[end] = (path, common_node, int(path_average_weight(graph, path)))
# Select best paths
for node in common_nodes:
if node == -1:
break
sub_paths = {
i: paths[i]
for i in paths.keys() if common_node == paths[i][1]
}
path = []
weights = []
lengths = []
for d_key in sub_paths:
path.append(sub_paths[d_key][0])
weights.append(sub_paths[d_key][2])
lengths.append(len(sub_paths[d_key][0]) - 1)
# Update graph
graph = select_best_path(graph,
path,
weights,
lengths,
delete_sink_node=True)
return graph
def count_orbits(orbits):
'''Finds the number of direct and indirect orbits from the given list of
orbits.
:param orbits: List of orbit pairs in the form (object at the center of the
orbit, revolving object).
:type orbits: list(tuple(str))
:return: Orbits count.
:rtype: int
'''
G = nx.DiGraph()
G.add_edges_from(orbits)
return G, sum(
[len(list(nx.edge_dfs(G, node, orientation='reverse'))) for node in G])
def connected_subgraph(G, H, start_node, matched_nodes):
h_dfs = list(nx.edge_dfs(H, start_node))
for start in matched_nodes:
h_map = dict.fromkeys(H.nodes(), 0)
h_map[h_dfs[0][0]] = 1
g_map = {start: h_dfs[0][0]}
g_un = set(G.edges())
i = 0
g_stack = []
last_node = h_dfs[0][0]
while i < len(h_dfs):
if last_node != h_dfs[i][0]:
for node in g_map:
if g_map[node] == h_dfs[i][0]:
start = node
edge = next_edge(G, H, start, h_dfs[i], g_un, g_map, h_map)
if edge != False:
try:
g_un.remove(edge)
except:
g_un.remove((edge[1], edge[0]))
start = edge[1]
g_stack.append(edge)
#print(edge)
i += 1
else:
i -= 1
if i != -1:
last_edge = g_stack.pop()
start = last_edge[0]
h_map[g_map[last_edge[0]]] -= 1
res_edge = []
for res_i in range(len(g_stack) - 1, -1, -1):
if g_stack[res_i][0] == start:
res_edge.append(g_stack.pop())
g_un.update(res_edge)
for res in res_edge:
h_map[g_map[res[0]]] -= 1
if i == -1:
#print('No Match')
break
if i == len(h_dfs):
#print("Match_Found")
#print(g_stack)
#print(g_map)
return g_map
#print(g_map," ",g_stack)
return False
def adjust_edge_perturb_radii(frcs,
graph,
perturb_factor=2):
"""Returns a new graph where the 'perturb_radius' has been adjusted to account for
rounding errors. See train_image for parameters and returns.
"""
graph = graph.copy()
total_rounding_error = 0
for n1, n2 in nx.edge_dfs(graph):
desired_radius = distance.euclidean(frcs[n1, 1:], frcs[n2, 1:]) / perturb_factor
upper = int(np.ceil(desired_radius))
lower = int(np.floor(desired_radius))
round_up_error = total_rounding_error + upper - desired_radius
round_down_error = total_rounding_error + lower - desired_radius
if abs(round_up_error) < abs(round_down_error):
graph.edge[n1][n2]['perturb_radius'] = upper
total_rounding_error = round_up_error
else:
graph.edge[n1][n2]['perturb_radius'] = lower
total_rounding_error = round_down_error
return graph
def find_cycle(G, source=None, orientation=None):
"""Returns a cycle found via depth-first traversal.
The cycle is a list of edges indicating the cyclic path.
Orientation of directed edges is controlled by `orientation`.
Parameters
----------
G : graph
A directed/undirected graph/multigraph.
source : node, list of nodes
The node from which the traversal begins. If None, then a source
is chosen arbitrarily and repeatedly until all edges from each node in
the graph are searched.
orientation : None | 'original' | 'reverse' | 'ignore' (default: None)
For directed graphs and directed multigraphs, edge traversals need not
respect the original orientation of the edges.
When set to 'reverse' every edge is traversed in the reverse direction.
When set to 'ignore', every edge is treated as undirected.
When set to 'original', every edge is treated as directed.
In all three cases, the yielded edge tuples add a last entry to
indicate the direction in which that edge was traversed.
If orientation is None, the yielded edge has no direction indicated.
The direction is respected, but not reported.
Returns
-------
edges : directed edges
A list of directed edges indicating the path taken for the loop.
If no cycle is found, then an exception is raised.
For graphs, an edge is of the form `(u, v)` where `u` and `v`
are the tail and head of the edge as determined by the traversal.
For multigraphs, an edge is of the form `(u, v, key)`, where `key` is
the key of the edge. When the graph is directed, then `u` and `v`
are always in the order of the actual directed edge.
If orientation is not None then the edge tuple is extended to include
the direction of traversal ('forward' or 'reverse') on that edge.
Raises
------
NetworkXNoCycle
If no cycle was found.
Examples
--------
In this example, we construct a DAG and find, in the first call, that there
are no directed cycles, and so an exception is raised. In the second call,
we ignore edge orientations and find that there is an undirected cycle.
Note that the second call finds a directed cycle while effectively
traversing an undirected graph, and so, we found an "undirected cycle".
This means that this DAG structure does not form a directed tree (which
is also known as a polytree).
>>> import networkx as nx
>>> G = nx.DiGraph([(0, 1), (0, 2), (1, 2)])
>>> try:
... nx.find_cycle(G, orientation='original')
... except:
... pass
...
>>> list(nx.find_cycle(G, orientation='ignore'))
[(0, 1, 'forward'), (1, 2, 'forward'), (0, 2, 'reverse')]
"""
if not G.is_directed() or orientation in (None, 'original'):
def tailhead(edge):
return edge[:2]
elif orientation == 'reverse':
def tailhead(edge):
return edge[1], edge[0]
elif orientation == 'ignore':
def tailhead(edge):
if edge[-1] == 'reverse':
return edge[1], edge[0]
return edge[:2]
explored = set()
cycle = []
final_node = None
for start_node in G.nbunch_iter(source):
if start_node in explored:
# No loop is possible.
continue
edges = []
# All nodes seen in this iteration of edge_dfs
seen = {start_node}
# Nodes in active path.
active_nodes = {start_node}
previous_head = None
for edge in nx.edge_dfs(G, start_node, orientation):
# Determine if this edge is a continuation of the active path.
tail, head = tailhead(edge)
if head in explored:
# Then we've already explored it. No loop is possible.
continue
if previous_head is not None and tail != previous_head:
# This edge results from backtracking.
# Pop until we get a node whose head equals the current tail.
# So for example, we might have:
# (0, 1), (1, 2), (2, 3), (1, 4)
# which must become:
# (0, 1), (1, 4)
while True:
try:
popped_edge = edges.pop()
except IndexError:
edges = []
active_nodes = {tail}
break
else:
popped_head = tailhead(popped_edge)[1]
active_nodes.remove(popped_head)
if edges:
last_head = tailhead(edges[-1])[1]
if tail == last_head:
break
edges.append(edge)
if head in active_nodes:
# We have a loop!
cycle.extend(edges)
final_node = head
break
else:
seen.add(head)
active_nodes.add(head)
previous_head = head
if cycle:
break
else:
explored.update(seen)
else:
assert(len(cycle) == 0)
raise nx.exception.NetworkXNoCycle('No cycle found.')
# We now have a list of edges which ends on a cycle.
# So we need to remove from the beginning edges that are not relevant.
for i, edge in enumerate(cycle):
tail, head = tailhead(edge)
if tail == final_node:
break
return cycle[i:]