def opt_simple_model_para(graph: nx.DiGraph, alloc_map,
config: Config) -> None:
graph.add_node(PNO_GRAPH_HEAD_ID)
graph.add_edge(PNO_GRAPH_HEAD_ID, 0)
graph.nodes[PNO_GRAPH_HEAD_ID]["node"] = Node(-1, ops.O2P_GRAPH_HEAD, {},
{}, {}, 0)
cuda_devices = get_valid_cuda_devices()
num_cuda = len(cuda_devices)
total_nodes = graph.number_of_nodes()
# HACK
split_len = total_nodes // 5
for gnode in graph.nodes:
node = graph.nodes[gnode]["node"]
if node.device_type == "gpu":
node.device_id = node.node_id // split_len
# HACKY SAFETY
if node.device_id > num_cuda - 1:
node.device_id = num_cuda - 1
return
def _transitive_closure(def_: Definition,
graph: networkx.DiGraph,
result: networkx.DiGraph,
visited: Optional[Set[Definition]] = None):
"""
Returns a joint graph that comprises the transitive closure of all defs that `def_` depends on and the
current graph `result`. `result` is updated.
"""
if def_ in self._transitive_closures.keys():
closure = self._transitive_closures[def_]
# merge closure into result
result.add_edges_from(closure.edges())
return result
predecessors = list(graph.predecessors(def_))
result.add_node(def_)
result.add_edges_from(
list(
map(lambda e: (*e, graph.get_edge_data(*e)),
map(lambda p: (p, def_), predecessors))))
visited = visited or set()
visited.add(def_)
predecessors_to_visit = set(predecessors) - set(visited)
closure = reduce(
lambda acc, def0: _transitive_closure(
def0, graph, acc, visited), predecessors_to_visit, result)
self._transitive_closures[def_] = closure
return closure
def graph(self) -> DiGraph:
"""
Get a graph of the job indicating the inputs to the job.
Returns
-------
DiGraph
The graph showing the connectivity of the jobs.
"""
from networkx import DiGraph
edges = []
for uuid, refs in self.input_references_grouped.items():
properties: list[str] | str = [
ref.attributes_formatted[-1]
.replace("[", "")
.replace("]", "")
.replace(".", "")
for ref in refs
if ref.attributes
]
properties = properties[0] if len(properties) == 1 else properties
properties = properties if len(properties) > 0 else "output"
edges.append((uuid, self.uuid, {"properties": properties}))
graph = DiGraph()
graph.add_node(self.uuid, job=self, label=self.name)
graph.add_edges_from(edges)
return graph
def extend_refinements_graph(
g: networkx.DiGraph,
stmt: Statement,
less_specifics: List[int],
matches_fun: Optional[Callable[[Statement], str]] = None,
) -> networkx.DiGraph:
"""Extend refinements graph with a new statement and its refinements.
Parameters
----------
g :
A refinements graph to be extended.
stmt :
The statement to be added to the refinements graph.
less_specifics :
A list of statement hashes of statements that are refined
by this statement (i.e., are less specific versions of it).
matches_fun :
An optional function to calculate the matches key and hash of a
given statement. Default: None
"""
sh = stmt.get_hash(matches_fun=matches_fun)
g.add_node(sh, stmt=stmt)
for less_spec in less_specifics:
g.add_edge(less_spec, sh)
return g
def get_node_by_uuid_web(self, uuid, json_out=True):
"""
Returns a networkx graph containing matching node.
:param uuid: The node name.
:return: Graph containing node or none.
"""
graph = DiGraph()
node_query = "match (i)-[r:STATE]->(s) where i.name='{}' and r.to>{}" \
" return i, s".format(uuid, str(time.time()))
query_result = self.graph_db.run(node_query)
result = list(query_result)
if result:
records = result[0]
node = dict(records[0])
state_attrs = dict(records[1])
if 'geo' in state_attrs:
state_attrs['geo'] = json.loads(state_attrs['geo'])
state_attributes = self._unique_attribute_names(
IDEN_PROPS, state_attrs, node["type"])
node.update(state_attributes)
graph.add_node(uuid, node)
if json_out:
graph = json.dumps(json_graph.node_link_data(graph))
return graph
return None
def get_node_by_properties_web(self, properties, start=None, timeframe=0):
"""
Return a list of nodes which match the given properties.
:param properties: A tuple with the key, value. Example: (k, v)
:return: A graph of matching nodes
"""
start = start or time.time()
if not properties:
return None
start = int(float(start))
timeframe = int(float(timeframe))
end = start + timeframe
# Build conditional query
conditional_query = ""
property_key = properties[0]
property_value = properties[1]
propery_operator = "="
condition = '(n.{0}{1}"{2}" OR s.{0}{1}"{2}")'.format(
property_key, propery_operator, property_value)
conditional_query += condition
query = 'match (n)-[r:STATE]->(s) where {0} ' \
'AND (r.from <= {1} AND r.to > {2}) return n, s'\
.format(conditional_query, start, end)
graph = DiGraph()
for id_node, state_node in self.graph_db.run(query):
node = dict(id_node)
state_attributes = self._unique_attribute_names(
IDEN_PROPS, dict(state_node), node["type"])
node.update(state_attributes)
graph.add_node(node["name"], node)
graph_json = json.dumps(json_graph.node_link_data(graph))
return graph_json
def build_component_dependency_graph(
pipeline_definition: Dict[str, Any],
component_definitions: Dict[str, Any]) -> DiGraph:
"""
Builds a dependency graph between components. Dependencies are:
- referenced components during component build time (e.g. init params)
- predecessor components in the pipeline that produce the needed input
This enables sorting the components in a working and meaningful order for instantiation using topological sorting.
:param pipeline_definition: the definition of the pipeline (e.g. use get_pipeline_definition() to obtain it)
:param component_definitions: the definition of the pipeline components (e.g. use get_component_definitions() to obtain it)
"""
graph = DiGraph()
for node in pipeline_definition["nodes"]:
node_name = node["name"]
graph.add_node(node_name)
for input in node["inputs"]:
if input in component_definitions:
graph.add_edge(input, node_name)
for component_name, component_definition in component_definitions.items():
params = component_definition.get("params", {})
referenced_components: List[str] = list()
for param_value in params.values():
# Currently we don't do any additional type validation here.
if param_value in component_definitions:
referenced_components.append(param_value)
for referenced_component in referenced_components:
graph.add_edge(referenced_component, component_name)
return graph
def compute_dependent_cohorts(self, objects, deletion):
model_map = defaultdict(list)
n = len(objects)
r = range(n)
indexed_objects = zip(r, objects)
mG = self.model_dependency_graph[deletion]
oG = DiGraph()
for i in r:
oG.add_node(i)
for v0, v1 in mG.edges():
try:
for i0 in range(n):
for i1 in range(n):
if i0 != i1:
if not deletion and self.concrete_path_exists(
objects[i0], objects[i1]):
oG.add_edge(i0, i1)
elif deletion and self.concrete_path_exists(
objects[i1], objects[i0]):
oG.add_edge(i0, i1)
except KeyError:
pass
components = weakly_connected_component_subgraphs(oG)
cohort_indexes = [reversed(topological_sort(g)) for g in components]
cohorts = [[objects[i] for i in cohort_index]
for cohort_index in cohort_indexes]
return cohorts
def find_connected():
for stemmer in [
'Lancaster', 'WordNetLemmatizer', 'PorterStemmer',
'SnowballStemmer'
]:
with open('output_files/window50/%s_dice_filtered.txt' % stemmer, 'r') as sin, \
open('output_files/window50/%s_dice_connected.txt' % stemmer, 'w') as out, \
open('output_files/window50/%s_dice_sconnected.txt' % stemmer, 'w') as out2:
print('building clusters for %s' % stemmer)
g = DiGraph()
g2 = Graph()
nodes = set()
edges = set()
for line in sin:
stemC1C2, dice = line.rstrip().split(' ')
stem, c1, c2 = stemC1C2.split(',')
nodes.add(stem)
nodes.add(c1)
nodes.add(c2)
edges.add((stem, c1, float(dice)))
edges.add((stem, c2, float(dice)))
edges.add((c1, stem, float(dice)))
edges.add((c2, stem, float(dice)))
for n in nodes:
g.add_node(n)
g2.add_node(n)
for stem, term, dice in edges:
g.add_edge(stem, term, weight=dice)
g2.add_edge(stem, term, weight=dice)
for connected in sorted(nx.connected_components(g2),
key=lambda s: list(s)[0]):
out.write('%s\n' % ' '.join(connected))
for connected in sorted(nx.strongly_connected_components(g),
key=lambda s: list(s)[0]):
out2.write('%s\n' % ' '.join(connected))
def build_graph(alternatives, outranking, credibility=False):
"""There are some conventions to follow in this function:
1. labels (i.e. alternatives' ids) are kept in graph's dictionary (see:
graph.graph)
2. aggregated nodes (only numbers, as list) are kept under 'aggr' key in
node's dict (see: graph.nodes(data=True))
3. weights on the edges are kept under 'weight' key in edge's dict -
similarly as with nodes (see: graph.edges(data=True))
"""
graph = DiGraph() # we need directed graph for this
# creating nodes...
for i, alt in enumerate(alternatives):
graph.add_node(i)
graph.graph.update({i: alt})
# creating edges...
for i, alt in enumerate(alternatives):
relations = outranking.get(alt)
if not relations: # if graph is built from intersectionDistillation
continue
for relation in relations.items():
if relation[1] == 1.0:
weight = credibility[alt][relation[0]] if credibility else None
graph.add_edge(i, alternatives.index(relation[0]),
weight=weight)
return graph
def save(self, filename: str):
"""
Save graph to GEXF file.
:param filename: The filename
"""
graph = DiGraph(mode="dynamic")
for doc_id, doc in self.documents.items():
graph.add_node(
doc_id,
label=doc_id[:7],
start=doc[0],
end=doc[1],
cluster=doc[3], # [(value, start, end), ...]
title=doc[4],
viz={"position": {
"x": doc[2][0],
"y": doc[2][1],
"z": 0
}})
# Add edges (customer assignments in dd-CRP)
for linked_doc_id, spells in doc[5].items():
graph.add_edge(
doc_id,
linked_doc_id,
spells=spells # [(start, end), ...]
)
write_gexf(graph, filename)
def add_proposals_and_relationships_to_network(
n: nx.DiGraph, proposals: int, funding_pool: float,
token_supply: float) -> nx.DiGraph:
participant_count = len(n)
for i in range(proposals):
j = participant_count + i
n.add_node(j, type="proposal", conviction=0, status="candidate", age=0)
r_rv = gamma.rvs(3, loc=0.001, scale=10000)
n.nodes[j]['funds_requested'] = r_rv
n.nodes[j]['trigger'] = trigger_threshold(r_rv, funding_pool,
token_supply)
for i in range(participant_count):
n.add_edge(i, j)
rv = np.random.rand()
a_rv = 1 - 4 * (1 - rv) * rv #polarized distribution
n.edges[(i, j)]['affinity'] = a_rv
n.edges[(i, j)]['tokens'] = 0
n.edges[(i, j)]['conviction'] = 0
n.edges[(i, j)]['type'] = 'support'
# Conflict Rate is a potential variable to optimize
# Relative Influence is a potential variable to optimize
n = initial_conflict_network(n, rate=.25)
n = initial_social_network(n, scale=1)
return n
def _abstract_acyclic_region(self,
graph: networkx.DiGraph,
region,
frontier,
dummy_endnode=None,
secondary_graph=None):
in_edges = self._region_in_edges(graph, region, data=True)
out_edges = self._region_out_edges(graph, region, data=True)
nodes_set = set()
for node_ in list(region.graph.nodes()):
nodes_set.add(node_)
if node_ is not dummy_endnode:
graph.remove_node(node_)
graph.add_node(region)
for src, _, data in in_edges:
if src not in nodes_set:
graph.add_edge(src, region, **data)
for _, dst, data in out_edges:
if dst not in nodes_set:
graph.add_edge(region, dst, **data)
if frontier:
for frontier_node in frontier:
if frontier_node is not dummy_endnode:
graph.add_edge(region, frontier_node)
if secondary_graph is not None:
self._abstract_acyclic_region(secondary_graph, region, {})
def test_format_attributes() -> None:
"""
Testing attribute annotation in xgmml.
Returns:
None
Raises:
AssertionError if an expected attribute type is not within the node attributes.
"""
graph = DiGraph()
graph.add_node('A', str_value='A', int_value=1, float_value=1.0)
format_attribute_system_str = XGMML(graph, "graph").to_string()
expected_attribute_type = ['integer', 'double', 'string']
xmldoc_actual = minidom.parseString(format_attribute_system_str)
actual_node_attribute_types = [
_get_node_att_type(xmldoc_actual.getElementsByTagName('node')[0],
attribute_name=attribute)
for attribute in ['int_value', 'float_value', 'str_value']
]
while expected_attribute_type:
attribute_type = expected_attribute_type.pop()
assert attribute_type in actual_node_attribute_types
def test_graph_schema_verification_throws_error_when_attributes_missing_from_stops():
g = DiGraph()
g.add_node('1', x=1, y=2)
with pytest.raises(ScheduleElementGraphSchemaError) as e:
verify_graph_schema(g)
assert 'missing the following attributes' in str(e.value)
def test_remove_node():
mock_mapp = DiGraph()
mock_mapp.add_node('X')
mock_mapp.add_edges_from([('A', 'B', {'TP': ['X']}),
('B', 'C', {'TP': ['Y']})])
MapGraph.remove_node.im_func(mock_mapp, 'X')
nt.assert_equal(mock_mapp.edges(), [('B', 'C')])
def select_binary_groups(groups, root=desc.root):
def get_bin_group(grps, bin_grps, rt, graph):
level = grps.successors(rt)
if not level:
bin_grps.append(graph)
return bin_grps
elif len(level) == 2:
graph.add_nodes_from(level)
graph.add_edge(rt, level[0], grps.get_edge_data(rt, level[0]))
graph.add_edge(rt, level[1], grps.get_edge_data(rt, level[1]))
if grps.successors(level[0]):
get_bin_group(grps, bin_grps, rt=level[0], graph=graph)
else:
get_bin_group(grps, bin_grps, rt=level[1], graph=graph)
else:
level.sort()
for i in xrange(0, len(level), 2):
g = DiGraph(graph)
g.add_nodes_from([level[i], level[i + 1]])
g.add_edge(rt, level[i], grps.get_edge_data(rt, level[i]))
g.add_edge(rt, level[i + 1], grps.get_edge_data(rt, level[i + 1]))
if grps.successors(level[i]):
get_bin_group(grps, bin_grps, rt=level[i], graph=g)
else:
get_bin_group(grps, bin_grps, rt=level[i + 1], graph=g)
dg = DiGraph()
dg.add_node(root)
bin_groups = []
get_bin_group(groups, bin_grps=bin_groups, rt=root, graph=dg)
return bin_groups
def add_node(self,
hwaddr,
ip=None,
dpid=None,
conn=None,
ports=None,
fdb=None,
t_stamp=0,
latency=0):
if ports is None:
ports = []
if fdb is None:
fdb = {}
### added timestamp and latency variables
#DiGraph.add_node(self, hwaddr, ip=ip, dpid=dpid, conn=conn,
# ports=ports, fdb=fdb, name=hwaddr[12:] )
### added timestamp and latency variables
DiGraph.add_node(self,
hwaddr,
ip=ip,
dpid=dpid,
conn=conn,
ports=ports,
fdb=fdb,
name=hwaddr[12:],
t_stamp=t_stamp,
latency=latency)
# update time stamp
self.time_stamp += 1
def _analyze(rules: List[Rule]) -> List[List[Rule]]:
# build rule dependency graph
occ: Dict[Atom, Set[RuleIndex]] = {}
dep_graph = DiGraph()
for u, rule in enumerate(rules):
dep_graph.add_node(u)
for lit in rule.body:
occ.setdefault(abs(lit), set()).add(u)
for u, rule in enumerate(rules):
atm, = rule.head
for v in occ.get(atm, []):
dep_graph.add_edge(u, v)
sccs = list(strongly_connected_components(dep_graph))
# build scc dependency graph
# (this part only exists because the networkx library does not document the
# order of components; in principle, the tarjan algorithm guarentees a
# topological order)
scc_rule: Dict[RuleIndex, RuleIndex] = {}
scc_graph = DiGraph()
for i, scc in enumerate(sccs):
scc_graph.add_node(i)
for u in scc:
scc_rule[u] = i
for i, scc in enumerate(sccs):
for u in scc:
for v in dep_graph[u]:
j = scc_rule[u]
if i != j:
scc_graph.add_edge(i, j)
return [[rules[j] for j in sccs[i]] for i in topological_sort(scc_graph)]
def duplicate_nodes(graph: nx.DiGraph, nodes: Set[str]) -> Dict:
"""
Replicates nodes of a graph.
:param graph: graph used to replicate and attach new nodes.
:type graph: networkX DiGraph
:param nodes: nodes to be replicated.
:type nodes: Set[str].
:return: the new nodes replicated.
:rtype: Dict[str].
"""
new_nodes = {}
for node in nodes:
new_node = f"{node}_{uuid4()}"
graph.add_node(new_node, **graph.nodes[node])
nx.set_node_attributes(graph, {new_node: node}, "duplicate_of")
new_nodes[node] = new_node
for node, new_node in new_nodes.items():
for parent, _ in graph.in_edges(node):
if parent in new_nodes:
graph.add_edge(new_nodes[parent], new_node)
else:
graph.add_edge(parent, new_node)
for _, child in graph.out_edges(node):
if child in new_nodes:
graph.add_edge(new_node, new_nodes[child])
else:
graph.add_edge(new_node, child)
return new_nodes
def test_graph_schema_verification_throws_error_when_routes_missing():
g = DiGraph()
g.add_node('1', x=1, y=2, id='1', epsg='epsg:27700')
with pytest.raises(ScheduleElementGraphSchemaError) as e:
verify_graph_schema(g)
assert 'Graph is missing `routes` attribute' in str(e.value)
def main():
# Create an example Boolean circuit.
#
# This circuit has a ∧ at the output and two ∨s at the next layer.
# The third layer has a variable x that appears in the left ∨, a
# variable y that appears in both the left and right ∨s, and a
# negation for the variable z that appears as the sole node in the
# fourth layer.
circuit = DiGraph()
# Layer 0
circuit.add_node(0, label='∧')
# Layer 1
circuit.add_node(1, label='∨')
circuit.add_node(2, label='∨')
circuit.add_edge(0, 1)
circuit.add_edge(0, 2)
# Layer 2
circuit.add_node(3, label='x')
circuit.add_node(4, label='y')
circuit.add_node(5, label='¬')
circuit.add_edge(1, 3)
circuit.add_edge(1, 4)
circuit.add_edge(2, 4)
circuit.add_edge(2, 5)
# Layer 3
circuit.add_node(6, label='z')
circuit.add_edge(5, 6)
# Convert the circuit to an equivalent formula.
formula = circuit_to_formula(circuit)
print(formula_to_string(formula))
def lazy_load_trees(skeleton_ids, node_properties):
""" Return a lazy collection of pairs of (long, DiGraph)
representing (skeleton_id, tree).
The node_properties is a list of strings, each being a name of a column
in the django model of the Treenode table that is not the treenode id, parent_id
or skeleton_id. """
values_list = ('id', 'parent_id', 'skeleton_id')
props = tuple(set(node_properties) - set(values_list))
values_list += props
ts = Treenode.objects.filter(skeleton__in=skeleton_ids) \
.order_by('skeleton') \
.values_list(*values_list)
skid = None
tree = None
for t in ts:
if t[2] != skid:
if tree:
yield (skid, tree)
# Prepare for the next one
skid = t[2]
tree = DiGraph()
fields = {k: v for k, v in izip(props, islice(t, 3, 3 + len(props)))}
tree.add_node(t[0], fields)
if t[1]:
# From child to parent
tree.add_edge(t[0], t[1])
if tree:
yield (skid, tree)
def lazy_load_trees(skeleton_ids, node_properties):
""" Return a lazy collection of pairs of (long, DiGraph)
representing (skeleton_id, tree).
The node_properties is a list of strings, each being a name of a column
in the django model of the Treenode table that is not the treenode id, parent_id
or skeleton_id. """
values_list = ('id', 'parent_id', 'skeleton_id')
props = tuple(set(node_properties) - set(values_list))
values_list += props
ts = Treenode.objects.filter(skeleton__in=skeleton_ids) \
.order_by('skeleton') \
.values_list(*values_list)
skid = None
tree = None
for t in ts:
if t[2] != skid:
if tree:
yield (skid, tree)
# Prepare for the next one
skid = t[2]
tree = DiGraph()
fields = {k: v for k,v in izip(props, islice(t, 3, 3 + len(props)))}
tree.add_node(t[0], fields)
if t[1]:
# From child to parent
tree.add_edge(t[0], t[1])
if tree:
yield (skid, tree)
def test_get_paths_self_edge(self):
expected = [[1]]
graph = DiGraph()
graph.add_node(1)
graph.add_edge(1, 1)
actual = get_paths(graph)
self.assertEqual(expected, actual)
def z_to_rz(gate: Gate) -> Optional[DiGraph]:
if gate._gate != 'Z':
return None
else:
graph = DiGraph()
graph.add_node(Gate('RZ', gate._qubits, 'pi'))
return graph
def main():
# Create an example Boolean circuit.
#
# This circuit has a ∧ at the output and two ∨s at the next layer.
# The third layer has a variable x that appears in the left ∨, a
# variable y that appears in both the left and right ∨s, and a
# negation for the variable z that appears as the sole node in the
# fourth layer.
circuit = DiGraph()
# Layer 0
circuit.add_node(0, label='∧')
# Layer 1
circuit.add_node(1, label='∨')
circuit.add_node(2, label='∨')
circuit.add_edge(0, 1)
circuit.add_edge(0, 2)
# Layer 2
circuit.add_node(3, label='x')
circuit.add_node(4, label='y')
circuit.add_node(5, label='¬')
circuit.add_edge(1, 3)
circuit.add_edge(1, 4)
circuit.add_edge(2, 4)
circuit.add_edge(2, 5)
# Layer 3
circuit.add_node(6, label='z')
circuit.add_edge(5, 6)
# Convert the circuit to an equivalent formula.
formula = circuit_to_formula(circuit)
print(formula_to_string(formula))
def compute_dependent_cohorts(self, objects, deletion):
model_map = defaultdict(list)
n = len(objects)
r = range(n)
indexed_objects = zip(r, objects)
mG = self.model_dependency_graph[deletion]
oG = DiGraph()
for i in r:
oG.add_node(i)
for v0, v1 in mG.edges():
try:
for i0 in range(n):
for i1 in range(n):
if i0 != i1:
if not deletion and self.concrete_path_exists(
objects[i0], objects[i1]):
oG.add_edge(i0, i1)
elif deletion and self.concrete_path_exists(objects[i1], objects[i0]):
oG.add_edge(i0, i1)
except KeyError:
pass
components = weakly_connected_component_subgraphs(oG)
cohort_indexes = [reversed(topological_sort(g)) for g in components]
cohorts = [[objects[i] for i in cohort_index]
for cohort_index in cohort_indexes]
return cohorts
class KTN():
_topology = None
_system = None
network = DiGraph()
def __init__(self, topology, system):
self._topology = topology
self._system = system
def reset(self):
self.network = DiGraph()
def add_transition(self, origin=None, end=None):
if origin not in self.network:
self.network.add_node(origin)
if end in self.network[origin]:
self.network[origin][end]['weight'] += 1
else:
self.network.add_edge(origin, end, weight=1)
def _add_node(self, g: nx.DiGraph, node: Task) -> None: # type: ignore[override]
"""
Recursively add a node and all of its children to a given graph.
This function is used when creating the nx.DiGraph representation of the pipeline to add all the tasks to the
graph. As additional information, a "fillcolor" attribute is set according to the task's status
(see :py:attr:`~Pipeline.colormap`). This is used when creating a PNG image of the pipeline via
:py:meth:`~DAG.plot_graph`.
Args:
g: Graph to add the node to
node: Node to add
Returns:
"""
child: Task
g.add_node(
node, style="filled", fillcolor=self.colormap[node.status], label=node.name
)
for child in node.children: # type: ignore
if not g.has_node(child):
self._add_node(g, child)
g.add_edge(node, child)
def _merge_nodes(self,
graph: networkx.DiGraph,
node_a,
node_b,
force_multinode=False): # pylint:disable=no-self-use
in_edges = list(graph.in_edges(node_a, data=True))
out_edges = list(graph.out_edges(node_b, data=True))
if not force_multinode and len(in_edges) <= 1 and len(out_edges) <= 1:
# it forms a region by itself :-)
new_node = None
else:
new_node = MultiNode([node_a, node_b])
graph.remove_node(node_a)
graph.remove_node(node_b)
if new_node is not None:
graph.add_node(new_node)
for src, _, data in in_edges:
if src is node_b:
src = new_node
graph.add_edge(src, new_node, **data)
for _, dst, data in out_edges:
if dst is node_a:
dst = new_node
graph.add_edge(new_node, dst, **data)
assert not node_a in graph
assert not node_b in graph
def _dict_to_graph(data: Dict[str, Any]) -> Tuple[DiGraph, Dict[str, int]]:
"""Convert dictionary representation of the graph to a directed graph.
:param data: graph as a dictionary
:return: directed graph
"""
graph = DiGraph()
node2id = {}
for node, properties in data['node_list'].items():
node2id[node] = properties['id']
graph.add_node(int(properties['id']),
name=node,
isTarget=bool(properties['isTarget']))
for node, adj in data['adj_list'].items():
source = int(node)
increases = adj.get('increases', [])
decreases = adj.get('decreases', [])
for n in increases:
graph.add_edge(source, n, polarity=1)
for n in decreases:
graph.add_edge(source, n, polarity=-1)
return graph, node2id
def dump(nodes, weighted_edges, white, black, flatten_colours,
flatten_weights):
graph = DiGraph()
for node in nodes:
graph.add_node(node.id, label=node.name)
(min_weight, max_weight) = log_range('Weight',
map(lambda we: we[0], weighted_edges))
if flatten_colours or flatten_weights:
flattened_weights = flatten(
dict((weight, weight) for (weight, edge) in weighted_edges))
for (weight, edge) in weighted_edges:
colour = flattened_weights[weight] if flatten_colours else (
weight - min_weight) / (max_weight - min_weight)
weight = flattened_weights[weight] if flatten_weights else weight
graph.add_edge(edge.from_.id,
edge.to_.id,
weight=weight,
viz=rgb(colour, white, black))
#write_graphml(graph, 'uykfe.graphml')
write_gexf(graph, 'uykfe.gexf')
# fix namespace bug
with open('uykfe.gexf') as input:
xml = input.read()
xml = xml.replace('xmlns:viz="http://www.gexf.net/1.1draft/viz" ', '', 1)
with open('uykfe.gexf', 'w') as output:
xml = output.write(xml)
def generate(self):
g = DiGraph()
g.add_node("start",
serverport=self.tgen_port,
loglevel="info",
heartbeat="1 minute")
return g
def src_snk(pathway: nx.DiGraph,labels: pd.DataFrame,verbose=True) -> nx.DiGraph:
"""
:pathway graph
:labels dataframe of labels
:returns graph with source and sink added
"""
pathway.add_node('SRC')
pathway.add_node('SNK')
#get source nodes
try:
sources = list(labels[labels['Node type'] == 'source']['#Node'])
except:
sources = list(labels[labels['node_symbol'] == 'receptor']['#node'])
if verbose:
print('sources: {}'.format(sources))
for s in sources:
pathway.add_edge('SRC',s)
#get sink nodes
try:
sinks = list(labels[labels['Node type'] == 'target']['#Node'])
except:
sinks = list(labels[labels['node_symbol'] == 'tf']['#node'])
if verbose:
print('sinks: {}'.format(sinks))
for s in sinks:
pathway.add_edge(s,'SNK')
return pathway
def addCell(self, cell, gateName=None):
mod = self.__nl.mods[self.__nl.topMod]
yaml = self.__nl.yaml
self.__cells.add(cell)
name = gateName if gateName else mod.cells[cell].submodname
if "clocks" in yaml[name]:
self.__flops.add(cell)
digraph.add_node(self, cell)
def vytvořím_networkx_graf():
from networkx import DiGraph, write_graphml
graf = DiGraph()
graf.add_node(1, time='5pm')
graf.add_node(2)
graf.add_edge(1,2, weight=25)
write_graphml(graf, './data/networkx.graphml')
def get_connection_graph(names, connections):
G = DiGraph()
# add names to check if it is connected
for n in names:
G.add_node(n)
for c in connections:
dp1 = c.dp1
dp2 = c.dp2
G.add_edge(dp1, dp2)
return G
def getStageGraph(self, phase):
graph = DiGraph()
for stage in self.structureInfo[phase][consts.STAGES_KEY].keys():
graph.add_node(stage)
graph.node[stage]["name"] = str(stage)
for edge in self.structureInfo[phase][consts.STAGE_TO_STAGE_KEY]:
graph.add_edge(edge[consts.SRC_KEY], edge[consts.DEST_KEY])
return graph
def subgroup_identification(sample, mode,
a_logrank=desc.a_logrank,
cov_at_level=desc.cov_at_level,
min_sub_size=desc.min_sub_size):
subgroups = DiGraph()
cov_used = {key: val for key in sample.keys() for val in [False]}
subgroups.add_node(desc.root)
global global_counter
global_counter = 1
sub_ident(sample, subgroups, cov_used, a_logrank, cov_at_level, min_sub_size, mode,
parent=desc.root, recurs_level=1)
return subgroups
def createDiGraphCopy(self, geneTuples):
copyGraph = DiGraph()
for node in self:
if len(self.getGenesByNode(node).intersection(geneTuples)) > 0 or len(self.getPropagatedGenesByNode(node).intersection(geneTuples)) > 0:
copyGraph.add_node(node, {'gene':set(self.getGenesByNode(node).intersection(geneTuples)), 'propGene':set(self.getPropagatedGenesByNode(node).intersection(geneTuples)), 'pmid': set(self.getPubMedByNode(node)), 'mergeGene': set(), 'mergePMID': set(), 'mergeCount': 0, 'infoLoss': 0})
for i in copyGraph:
for edge in self.edge[i]:
if edge in copyGraph:
copyGraph.add_edge(i, edge, self.edge[i][edge])
return copyGraph
def build_allele_graph(self, num_alleles=None):
"""
Method that builds segregation graph from the given pedigree. See the
constructor for descriptions of parameters.
Returns
-------
G : networkx.DiGraph
NetworkX DiGraph representation of the segregation network defined
by the given pedigree and allele assignments. Nodes of the graph
are named using the following conventions:
allele node = id_parent_locus
ex.: 1_0_0 -> paternal allele of locus 0 for ID = 1
ex.: 2_1_3 -> maternal allele of locus 3 for ID = 2
segregation node = S_parent_child_locus
ex.: S_1_6_1 -> segrgation indicator of parent ID = 1
to child ID = 6 for locus 1
"""
num_alleles = num_alleles if num_alleles else self.len
G = DiGraph()
# for each allele
for i in range(num_alleles):
# for each k = parent, v = [children, sex]
for k,v in self.ped.pedigree.items():
#for k in self.ped.ped_graph.nodes():
# make parent nodes, one for each allele, at locus i
G.add_node(self.mk_nd(k, 1, i))
G.add_node(self.mk_nd(k, 0, i))
# for each k = parent, v = [children, sex]
for k,v in self.ped.pedigree.items():
# for each child of k
for c in v['cs']:
# determine if parent is male or female
type = 0 if v['sex'] == 'm' else 1
# add edges from parent alleles to children alleles
G.add_edge(self.mk_nd(k, 1, i), self.mk_nd(c, type, i))
G.add_edge(self.mk_nd(k, 0, i), self.mk_nd(c, type, i))
# add other allele of child, just in case it's not in the graph
G.add_node(self.mk_nd(c, int(not type), i))
# add segregation node edge
G.add_edge(self.mk_nd('S', k, c, i), self.mk_nd(c, type, i))
# if applicable, add edge between segregation indicators
if i > 0:
G.add_edge(self.mk_nd('S', k, c, i - 1), self.mk_nd('S', k, c, i))
# for UAI: vars are indexed by topological sort
self.vars = topological_sort(G)
self.var_idxs = dict([(v,i) for i,v in enumerate(self.vars)])
self.allele_graph = G
return G
def __init__(self, debug=0):
digraph.__init__(self)
self.__debug = debug
self.__inputs = set()
self.__outputs = set()
self.__cells = set()
self.__flops = set()
self.__virtual = dict() # maps name --> gate
self.__pins = dict()
self.__flopsIn = dict()
# create input, output nodes
digraph.add_node(self, "__INPUTS__")
digraph.add_node(self, "__OUTPUTS__")
def _rename_nodes(self, graph):
new_graph = DiGraph()
# print len(self._node_names)
for node in graph.nodes():
# Add all the nodes with the names given in the data set
new_node_name = self._node_names[node]
new_graph.add_node(new_node_name)
for edge in graph.edges():
# Add all the with the names given in the data set
new_source_node = self._node_names[ edge[0] ]
new_destination_node = self._node_names[ edge[1] ]
new_graph.add_edge(new_source_node, new_destination_node)
new_graph.name = self._network_name
return new_graph
def load_cliques(self,cliques): self.cliques=cliques for k in range(len(cliques)): DiGraph.add_node(self,k,keywords=cliques[k]) for i in range(len(cliques)): for j in range(i): a=set(cliques[i]) b=set(cliques[j]) common_words=len(a.intersection(b)) dis_i2j=common_words/len(a) dis_j2i=common_words/len(b) if dis_i2j>0: DiGraph.add_edge(self,i,j,weight=dis_i2j) if dis_j2i>0: DiGraph.add_edge(self,j,i,weight=dis_j2i)
def add_node(self, hwaddr, ip=None, dpid=None, conn=None, ports=None,
fdb=None, t_stamp=0, latency=0 ):
if ports is None:
ports = []
if fdb is None:
fdb = {}
### added timestamp and latency variables
#DiGraph.add_node(self, hwaddr, ip=ip, dpid=dpid, conn=conn,
# ports=ports, fdb=fdb, name=hwaddr[12:] )
### added timestamp and latency variables
DiGraph.add_node(self, hwaddr, ip=ip, dpid=dpid, conn=conn,
ports=ports, fdb=fdb, name=hwaddr[12:], t_stamp=t_stamp,
latency=latency )
# update time stamp
self.time_stamp += 1
def get_tasks(do_tasks, dep_graph):
"""Given a list of tasks to perform and a dependency graph, return the tasks
that must be performed, in the correct order"""
#XXX: Is it important that if a task has "foo" before "bar" as a dep,
# that foo executes before bar? Why? ATM this may not happen.
#Each task that the user has specified gets its own execution graph
task_graphs = []
for task in do_tasks:
exgraph = DiGraph()
exgraph.add_node(task)
_get_deps(task, exgraph, dep_graph)
task_graphs.append(exgraph)
return flatten(reversed(topological_sort(g)) for g in task_graphs)
def dump(nodes, weighted_edges, white, black, flatten_colours, flatten_weights):
graph = DiGraph()
for node in nodes:
graph.add_node(node.id, label=node.name)
(min_weight, max_weight) = log_range('Weight', map(lambda we: we[0], weighted_edges))
if flatten_colours or flatten_weights:
flattened_weights = flatten(dict((weight, weight) for (weight, edge) in weighted_edges))
for (weight, edge) in weighted_edges:
colour = flattened_weights[weight] if flatten_colours else (weight - min_weight) / (max_weight - min_weight)
weight = flattened_weights[weight] if flatten_weights else weight
graph.add_edge(edge.from_.id, edge.to_.id,
weight=weight,
viz=rgb(colour, white, black))
#write_graphml(graph, 'uykfe.graphml')
write_gexf(graph, 'uykfe.gexf')
# fix namespace bug
with open('uykfe.gexf') as input:
xml = input.read()
xml = xml.replace('xmlns:viz="http://www.gexf.net/1.1draft/viz" ', '', 1)
with open('uykfe.gexf', 'w') as output:
xml = output.write(xml)
def build_graph(alternatives, outranking, cut_threshold):
# There are some conventions to follow here:
# 1. labels (i.e. alternatives' ids) are kept in graph's dictionary (see: graph.graph)
# 2. aggregated nodes (only numbers, as list) are kept under 'aggr' key in node's
# dict (see: graph.nodes(data=True))
# 3. weights on the edges are kept under 'weight' key in edge's dict - similarly as
# with nodes (see: graph.edges(data=True))
graph = DiGraph()
# creating nodes...
for i, alternative in enumerate(alternatives):
graph.add_node(i)
graph.graph.update({i: alternative})
# creating edges...
for i, alternative in enumerate(alternatives):
relations = outranking.get(alternative)
if not relations: # if graph is built from intersectionDistillation
continue
for relation in relations.items():
if relation[1] >= cut_threshold:
graph.add_edge(i, alternatives.index(relation[0]), weight=relation[1])
return graph
def make_dependency_graph(tasks):
dep_graph = DiGraph()
#build the dependency graph in two steps. First add the nodes
for task in tasks:
assert not dep_graph.has_node(task), "Cannot add duplicate task: %s" % task
dep_graph.add_node(task)
#then add the edges.
taskdeps = [(name, task.__pub_dependencies__)
for name, task in tasks.iteritems()
if hasattr(task, "__pub_dependencies__")]
for task, deps in taskdeps:
for dep in deps:
dep_graph.add_edge(task, dep)
cycles = list(simple_cycles(dep_graph))
if cycles:
raise DependencyCycle("Cycle in the dependency graph: %s %s" % (dep_graph, cycles))
return dep_graph
def resolve_versions(self, dependencies):
# type: (ProjectIdentifier, Revision) -> [ProjectIdentifier, Tag]
"""Given an array of project identifier/version pairs work out the build order"""
graph = DiGraph()
versions_for_identifier = dict(dependencies)
for identifier, version in dependencies:
parent = Node(identifier, version)
graph.add_node(parent)
assert isinstance(version, Revision)
dependencies_for_node = self._dependencies_for_node(Node(identifier, version))
for dependency, _ in dependencies_for_node:
version = versions_for_identifier[dependency]
child = Node(dependency, version)
graph.add_edge(parent, child)
build_order = topological_sort(graph, reverse=True)
return build_order
def _get_category_graph(skos_categories_path):
print("Computing category graph...", end=" ")
category_labels = _get_labels(skos_categories_path)
g = DiGraph()
with open(skos_categories_path) as f:
for chunks in csv.reader(f, delimiter=" "):
if chunks[1] == BROADER:
source, target = chunks[0], chunks[2]
source, target = (
source[source.rfind(":") + 1:-1].decode("utf-8"),
target[target.rfind(":") + 1:-1].decode("utf-8")
)
# The original relation is "broader", but for our purposes
# "wider" is more appropriate.
g.add_edge(category_labels[target], category_labels[source])
elif chunks[1] == TYPE:
source = chunks[0]
source = source[source.rfind(":") + 1:-1].decode("utf-8")
g.add_node(category_labels[source])
print("done")
return g
def spanning_tree(tree, preserve):
""" Return a new DiGraph with the spanning tree including the desired nodes.
preserve: the set of nodes that delimit the spanning tree. """
spanning = DiGraph()
preserve = set(preserve) # duplicate, will be altered
if 1 == len(preserve):
spanning.add_node(iter(preserve).next())
return spanning
if len(tree.successors(find_root(tree))) > 1:
tree = tree.copy()
# First end node found
endNode = (node for node in tree if not next(tree.successors_iter(node), None)).next()
reroot(tree, endNode)
n_seen = 0
# Start from shortest sequence
for seq in sorted(partition(tree), key=len):
path = []
for node in seq:
if node in preserve:
path.append(node)
if node not in spanning:
n_seen += 1
if len(preserve) == n_seen:
break
elif path:
path.append(node)
if path:
spanning.add_path(path)
if seq[-1] == path[-1]:
preserve.add(path[-1])
if len(preserve) == n_seen:
break
return spanning
def example_invalid_subtrees():
is1 = DiGraph()
is1.add_node('NP-2')
is1.add_edges_from([('D', 'the'), ('N-2', 'apple')])
is2 = DiGraph()
is2.add_node('D')
is3 = DiGraph()
is3.add_nodes_from(['D', 'N-2'])
is4 = DiGraph()
is4.add_edges_from([('NP-2', 'D'), ('D', 'the')])
is5 = DiGraph()
is5.add_nodes_from(['the'])
is6 = DiGraph()
is6.add_nodes_from(['the', 'apple'])
is7 = DiGraph()
is7.add_edges_from([('the', 'apple')])
return [is1, is2, is3, is4, is5, is6, is7]
class PipelineFramework(object):
"""A PipelineFramework is a set Tasks that may have data dependencies."""
def __init__(self, tasks_reqs):
"""Construct a PipelineFramework based on the given Tasks and their requirements.
A PipelineFramework is the structure of the pipeline, it contains no patient data.
:param tasks_reqs: the Tasks and their requirements
:type tasks_reqs: iterable of tuples, each with a Task and its list of required UIDs
:raises: ValueError
"""
self.dag = DiGraph()
task_dict = {}
for task, _ in tasks_reqs:
if task_dict.get(task._uid) is not None:
raise ValueError("Pipeline contains duplicate Task {}".format(task._uid))
self.dag.add_node(task, done=False)
task_dict[task._uid] = task
for task, reqs in tasks_reqs:
for req_uid in reqs:
uid = task_dict.get(req_uid)
if uid is None:
raise KeyError("Unknown UID {} set as requirement for {}".format(req_uid, task._uid))
self.dag.add_edge(uid, task)
if not is_directed_acyclic_graph(self.dag):
raise ValueError("Pipeline contains a cycle.")
def __len__(self):
"""Determine the length of the Pipeline.
:returns: the number of Tasks in this Pipeline
:rtype: {int}
"""
return self.dag.__len__()
def compute_dependent_cohorts(self, objects, deletion):
n = len(objects)
r = list(range(n))
oG = DiGraph()
for i in r:
oG.add_node(i)
try:
for i0 in range(n):
for i1 in range(n):
if i0 != i1:
if deletion:
path_args = (objects[i1], objects[i0])
else:
path_args = (objects[i0], objects[i1])
is_connected, edge_type = self.concrete_path_exists(*path_args)
if is_connected:
try:
edge_type = oG[i1][i0]["type"]
if edge_type == PROXY_EDGE:
oG.remove_edge(i1, i0)
oG.add_edge(i0, i1, type=edge_type)
except KeyError:
oG.add_edge(i0, i1, type=edge_type)
except KeyError:
pass
components = weakly_connected_component_subgraphs(oG)
cohort_indexes = [reversed(list(topological_sort(g))) for g in components]
cohorts = [
[objects[i] for i in cohort_index] for cohort_index in cohort_indexes
]
return cohorts
def lazy_load_trees(skeleton_ids, node_properties):
""" Return a lazy collection of pairs of (long, DiGraph)
representing (skeleton_id, tree).
The node_properties is a list of strings, each being a name of a column
in the django model of the Treenode table that is not the treenode id, parent_id
or skeleton_id. """
values_list = ("id", "parent_id", "skeleton_id")
props = tuple(set(node_properties) - set(values_list))
values_list += props
ts = Treenode.objects.filter(skeleton__in=skeleton_ids).order_by("skeleton").values_list(*values_list)
skid = None
tree = None
for t in ts:
if t[2] != skid:
if tree:
yield (skid, tree)
# Prepare for the next one
skid = t[2]
tree = DiGraph()
props = {k: v for k, v in izip(props, islice(t, 3, 3 + len(props)))}
# Hack: why doesn't django parse well the location?
loc = props.get("location")
if loc:
props["location"] = Double3D(*(imap(float, loc[1:-1].split(","))))
tree.add_node(t[0], props)
if t[1]:
# From child to parent
tree.add_edge(t[0], t[1])
if tree:
yield (skid, tree)
class NetworkXKeyedGraph(AbstractKeyedGraph):
def __init__(self):
self.__nx_graph = DiGraph()
self.__nodes = {}
def source(self):
return self.__nx_graph
def add_node(self, key, **kw):
if self.__nodes.has_key(key):
node = self.__nodes[key]
else:
node = NetworkXNode(key, self.__nx_graph)
self.__nodes[key] = node
self.__nx_graph.add_node(node, **kw)
return node
def find_node_by_key(self, key):
return self.__nodes.get(key, None)
def iterator(self):
class Iterator(AbstractGraphIterator):
def __init__(self, nx_graph):
self.__iterator = nx_graph.nodes_iter()
def next(self):
return self.__iterator.next()
return Iterator(self.__nx_graph)
def size(self):
return len(self.__nx_graph)
def __repr__(self):
return str(dict(id=hash(self), size=self.size()))
def generate_tgen_filetransfer_clients(servers):
# webclients
G = DiGraph()
G.add_node("start", socksproxy="localhost:9000", serverport="8888", peers=servers)
G.add_node("transfer", type="get", protocol="tcp", size="320 KiB")
G.add_node("pause", time="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60")
G.add_edge("start", "transfer")
G.add_edge("transfer", "pause")
G.add_edge("pause", "start")
write_graphml(G, "conf/tgen.torwebclient.graphml.xml")
# bulkclients
G = DiGraph()
G.add_node("start", socksproxy="localhost:9000", serverport="8888", peers=servers)
G.add_node("transfer", type="get", protocol="tcp", size="5 MiB")
G.add_edge("start", "transfer")
G.add_edge("transfer", "start")
write_graphml(G, "conf/tgen.torbulkclient.graphml.xml")