本文整理汇总了Python中fnss.topologies.topology.Topology.name方法的典型用法代码示例。如果您正苦于以下问题：Python Topology.name方法的具体用法？Python Topology.name怎么用？Python Topology.name使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类fnss.topologies.topology.Topology的用法示例。
在下文中一共展示了Topology.name方法的11个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: star_topology
# 需要导入模块: from fnss.topologies.topology import Topology [as 别名]
# 或者: from fnss.topologies.topology.Topology import name [as 别名]
def star_topology(n):
"""
Return a star (a.k.a hub-and-spoke) topology of :math:`n+1` nodes
The root (hub) node has id 0 while all leaf (spoke) nodes have id
:math:`(1, n+1)`.
Each node has the attribute type which can either be *root* (for node 0) or
*leaf* for all other nodes
Parameters
----------
n : int
The number of leaf nodes
Returns
-------
topology : A Topology object
"""
if not isinstance(n, int):
raise TypeError('n argument must be of int type')
if n < 1:
raise ValueError('n argument must be a positive integer')
G = Topology(nx.star_graph(n))
G.name = "star_topology(%d)" % (n)
G.graph['type'] = 'star'
G.node[0]['type'] = 'root'
for v in range(1, n + 1):
G.node[v]['type'] = 'leaf'
return G示例2: k_ary_tree_topology
# 需要导入模块: from fnss.topologies.topology import Topology [as 别名]
# 或者: from fnss.topologies.topology.Topology import name [as 别名]
def k_ary_tree_topology(k, h):
"""
Return a balanced k-ary tree topology of with depth h
Each node has two attributes:
* type: which can either be *root*, *intermediate* or *leaf*
* depth:math:`(0, h)` the height of the node in the tree, where 0 is the
root and h are leaves.
Parameters
----------
k : int
The branching factor of the tree
h : int
The height or depth of the tree
Returns
-------
topology : A Topology object
"""
if not isinstance(k, int) or not isinstance(h, int):
raise TypeError('k and h arguments must be of int type')
if k <= 1:
raise ValueError("Invalid k parameter. It should be > 1")
if h < 1:
raise ValueError("Invalid h parameter. It should be >=1")
G = Topology(nx.balanced_tree(k, h))
G.name = "k_ary_tree_topology(%d,%d)" % (k, h)
G.graph['type'] = 'tree'
G.graph['k'] = k
G.graph['h'] = h
G.node[0]['type'] = 'root'
G.node[0]['depth'] = 0
# Iterate through the tree to assign labels to nodes
v = 1
for depth in range(1, h + 1):
for _ in range(k**depth):
G.node[v]['depth'] = depth
if depth == h:
G.node[v]['type'] = 'leaf'
else:
G.node[v]['type'] = 'intermediate'
v += 1
return G示例3: full_mesh_topology
# 需要导入模块: from fnss.topologies.topology import Topology [as 别名]
# 或者: from fnss.topologies.topology.Topology import name [as 别名]
def full_mesh_topology(n):
"""
Return a fully connected mesh topology of n nodes
Parameters
----------
n : int
The number of nodes
Returns
-------
topology : A Topology object
"""
if not isinstance(n, int):
raise TypeError('n argument must be of int type')
if n < 1:
raise ValueError('n argument must be a positive integer')
G = Topology(nx.complete_graph(n))
G.name = "full_mesh_topology(%d)" % (n)
G.graph['type'] = 'full_mesh'
return G示例4: line_topology
# 需要导入模块: from fnss.topologies.topology import Topology [as 别名]
# 或者: from fnss.topologies.topology.Topology import name [as 别名]
def line_topology(n):
"""
Return a line topology of n nodes
Parameters
----------
n : int
The number of nodes
Returns
-------
topology : A Topology object
"""
if not isinstance(n, int):
raise TypeError('n argument must be of int type')
if n < 1:
raise ValueError('n argument must be a positive integer')
G = Topology(nx.path_graph(n))
G.name = "line_topology(%d)" % (n)
G.graph['type'] = 'line'
return G示例5: erdos_renyi_topology
# 需要导入模块: from fnss.topologies.topology import Topology [as 别名]
# 或者: from fnss.topologies.topology.Topology import name [as 别名]
def erdos_renyi_topology(n, p, seed=None, fast=False):
r"""Return a random graph :math:`G_{n,p}` (Erdos-Renyi graph, binomial
graph).
Chooses each of the possible edges with probability p.
Parameters
----------
n : int
The number of nodes.
p : float
Probability for edge creation.
seed : int, optional
Seed for random number generator (default=None).
fast : boolean, optional
Uses the algorithm proposed by [3]_, which is faster for small p
References
----------
.. [1] P. Erdos and A. Renyi, On Random Graphs, Publ. Math. 6, 290 (1959).
.. [2] E. N. Gilbert, Random Graphs, Ann. Math. Stat., 30, 1141 (1959).
.. [3] Vladimir Batagelj and Ulrik Brandes,
"Efficient generation of large random networks",
Phys. Rev. E, 71, 036113, 2005.
"""
# validate input parameters
if not isinstance(n, int) or n < 0:
raise ValueError('n must be a positive integer')
if p > 1 or p < 0:
raise ValueError('p must be a value in (0,1)')
if fast:
G = Topology(nx.fast_gnp_random_graph(n, p, seed=seed))
else:
G = Topology(nx.gnp_random_graph(n, p, seed=seed))
G.name = "erdos_renyi_topology(%s, %s)" % (n, p)
G.graph['type'] = 'er'
return G示例6: waxman_1_topology
# 需要导入模块: from fnss.topologies.topology import Topology [as 别名]
# 或者: from fnss.topologies.topology.Topology import name [as 别名]
def waxman_1_topology(n, alpha=0.4, beta=0.1, L=1.0,
distance_unit='Km', seed=None):
r"""
Return a Waxman-1 random topology.
The Waxman-1 random topology models assigns link between nodes with
probability
.. math::
p = \alpha*exp(-d/(\beta*L)).
where the distance *d* is chosen randomly in *[0,L]*.
Parameters
----------
n : int
Number of nodes
alpha : float
Model parameter chosen in *(0,1]* (higher alpha increases link density)
beta : float
Model parameter chosen in *(0,1]* (higher beta increases difference
between density of short and long links)
L : float
Maximum distance between nodes.
seed : int, optional
Seed for random number generator (default=None).
Returns
-------
G : Topology
Notes
-----
Each node of G has the attributes *latitude* and *longitude*. These
attributes are not expressed in degrees but in *distance_unit*.
Each edge of G has the attribute *length*, which is also expressed in
*distance_unit*.
References
----------
.. [1] B. M. Waxman, Routing of multipoint connections.
IEEE J. Select. Areas Commun. 6(9),(1988) 1617-1622.
"""
# validate input parameters
if not isinstance(n, int) or n <= 0:
raise ValueError('n must be a positive integer')
if alpha > 1 or alpha <= 0 or beta > 1 or beta <= 0:
raise ValueError('alpha and beta must be float values in (0,1]')
if L <= 0:
raise ValueError('L must be a positive number')
if seed is not None:
random.seed(seed)
G = Topology(type='waxman_1', distance_unit=distance_unit)
G.name = "waxman_1_topology(%s, %s, %s, %s)" % (n, alpha, beta, L)
G.add_nodes_from(range(n))
nodes = list(G.nodes())
while nodes:
u = nodes.pop()
for v in nodes:
d = L * random.random()
if random.random() < alpha * math.exp(-d / (beta * L)):
G.add_edge(u, v, length=d)
return G示例7: glp_topology
# 需要导入模块: from fnss.topologies.topology import Topology [as 别名]
# 或者: from fnss.topologies.topology.Topology import name [as 别名]
def glp_topology(n, m, m0, p, beta, seed=None):
r"""
Return a random topology using the Generalized Linear Preference (GLP)
preferential attachment model.
It differs from the extended Barabasi-Albert model in that there is link
rewiring and a beta parameter is introduced to fine-tune preferential
attachment.
More precisely, the GLP topology is built as follows. First, a
line topology with *m0* nodes is created. Then, at each step:
with probability *p*, add *m* new links between existing nodes, selected
with probability:
.. math::
\Pi(i) = \frac{deg(i) - \beta 1}{\sum_{v \in V} (deg(v) - \beta)}
with probability :math:`1-p`, add a new node and attach it to m nodes of
the existing topology selected with probability :math:`\Pi(i)`
Repeat the previous step until the topology comprises n nodes in total.
Parameters
----------
n : int
Number of nodes
m : int
Number of edges to attach from a new node to existing nodes
m0 : int
Number of edges initially attached to the network
p : float
The probability that new links are added
beta : float
Parameter to fine-tune preferntial attachment: beta < 1
seed : int, optional
Seed for random number generator (default=None).
Returns
-------
G : Topology
References
----------
.. [1] T. Bu and D. Towsey "On distinguishing between Internet power law
topology generators", Proceeding od the 21st IEEE INFOCOM conference.
IEEE, volume 2, pages 638-647, 2002.
"""
def calc_pi(G, beta):
"""Calculate GLP Pi function for all nodes of the graph"""
# validate input parameter
if beta >= 1:
raise ValueError('beta must be < 1')
degree = dict(G.degree())
den = float(sum(degree.values()) - (G.number_of_nodes() * beta))
return {node: (degree[node] - beta) / den for node in G.nodes()}
def add_m_links(G, pi):
"""Add m links between existing nodes to the graph"""
n_nodes = G.number_of_nodes()
n_edges = G.number_of_edges()
max_n_edges = (n_nodes * (n_nodes - 1)) / 2
if n_edges + m > max_n_edges: # cannot add m links
add_node(G, pi) # add a new node instead
# return in any case because before doing another operation
# (add node or links) we need to recalculate pi
return
new_links = 0
while new_links < m:
u = random_from_pdf(pi)
v = random_from_pdf(pi)
if u != v and not G.has_edge(u, v):
G.add_edge(u, v)
new_links += 1
def add_node(G, pi):
"""Add one node to the graph and connect it to m existing nodes"""
new_node = G.number_of_nodes()
G.add_node(new_node)
new_links = 0
while new_links < m:
existing_node = random_from_pdf(pi)
if not G.has_edge(new_node, existing_node):
G.add_edge(new_node, existing_node)
new_links += 1
# validate input parameters
if n < 1 or m < 1 or m0 < 1:
raise ValueError('n, m and m0 must be a positive integers')
if beta >= 1:
raise ValueError('beta must be < 1')
if m >= m0:
raise ValueError('m must be <= m0')
if p > 1 or p < 0:
raise ValueError('p must be included between 0 and 1')
if seed is not None:
random.seed(seed)
# step 1: create a graph of m0 nodes connected by n-1 edges
G = Topology(nx.path_graph(m0))
G.graph['type'] = 'glp'
G.name = "glp_topology(%d, %d, %d, %f, %f)" % (n, m, m0, p, beta)
#.........这里部分代码省略.........
示例8: extended_barabasi_albert_topology
# 需要导入模块: from fnss.topologies.topology import Topology [as 别名]
# 或者: from fnss.topologies.topology.Topology import name [as 别名]
def extended_barabasi_albert_topology(n, m, m0, p, q, seed=None):
r"""
Return a random topology using the extended Barabasi-Albert preferential
attachment model.
Differently from the original Barabasi-Albert model, this model takes into
account the presence of local events, such as the addition of new links or
the rewiring of existing links.
More precisely, the Barabasi-Albert topology is built as follows. First, a
topology with *m0* isolated nodes is created. Then, at each step:
with probability *p* add *m* new links between existing nodes, selected
with probability:
.. math::
\Pi(i) = \frac{deg(i) + 1}{\sum_{v \in V} (deg(v) + 1)}
with probability *q* rewire *m* links. Each link to be rewired is selected as
follows: a node i is randomly selected and a link is randomly removed from
it. The node i is then connected to a new node randomly selected with
probability :math:`\Pi(i)`,
with probability :math:`1-p-q` add a new node and attach it to m nodes of
the existing topology selected with probability :math:`\Pi(i)`
Repeat the previous step until the topology comprises n nodes in total.
Parameters
----------
n : int
Number of nodes
m : int
Number of edges to attach from a new node to existing nodes
m0 : int
Number of edges initially attached to the network
p : float
The probability that new links are added
q : float
The probability that existing links are rewired
seed : int, optional
Seed for random number generator (default=None).
Returns
-------
G : Topology
References
----------
.. [1] A. L. Barabasi and R. Albert "Topology of evolving networks: local
events and universality", Physical Review Letters 85(24), 2000.
"""
def calc_pi(G):
"""Calculate extended-BA Pi function for all nodes of the graph"""
degree = dict(G.degree())
den = float(sum(degree.values()) + G.number_of_nodes())
return {node: (degree[node] + 1) / den for node in G.nodes()}
# input parameters
if n < 1 or m < 1 or m0 < 1:
raise ValueError('n, m and m0 must be a positive integer')
if m >= m0:
raise ValueError('m must be <= m0')
if n < m0:
raise ValueError('n must be > m0')
if p > 1 or p < 0:
raise ValueError('p must be included between 0 and 1')
if q > 1 or q < 0:
raise ValueError('q must be included between 0 and 1')
if p + q > 1:
raise ValueError('p + q must be <= 1')
if seed is not None:
random.seed(seed)
G = Topology(type='extended_ba')
G.name = "ext_ba_topology(%d, %d, %d, %f, %f)" % (n, m, m0, p, q)
# Step 1: Add m0 isolated nodes
G.add_nodes_from(range(m0))
while G.number_of_nodes() < n:
pi = calc_pi(G)
r = random.random()
if r <= p:
# add m new links with probability p
n_nodes = G.number_of_nodes()
n_edges = G.number_of_edges()
max_n_edges = (n_nodes * (n_nodes - 1)) / 2
if n_edges + m > max_n_edges: # cannot add m links
continue # rewire or add nodes
new_links = 0
while new_links < m:
u = random_from_pdf(pi)
v = random_from_pdf(pi)
if u is not v and not G.has_edge(u, v):
G.add_edge(u, v)
new_links += 1
elif r > p and r <= p + q:
# rewire m links with probability q
rewired_links = 0
while rewired_links < m:
i = random.choice(list(G.nodes())) # pick up node randomly (uniform)
#.........这里部分代码省略.........
示例9: barabasi_albert_topology
# 需要导入模块: from fnss.topologies.topology import Topology [as 别名]
# 或者: from fnss.topologies.topology.Topology import name [as 别名]
def barabasi_albert_topology(n, m, m0, seed=None):
r"""
Return a random topology using Barabasi-Albert preferential attachment
model.
A topology of n nodes is grown by attaching new nodes each with m links
that are preferentially attached to existing nodes with high degree.
More precisely, the Barabasi-Albert topology is built as follows. First, a
line topology with m0 nodes is created. Then at each step, one node is
added and connected to m existing nodes. These nodes are selected randomly
with probability
.. math::
\Pi(i) = \frac{deg(i)}{sum_{v \in V} deg V}.
Where i is the selected node and V is the set of nodes of the graph.
Parameters
----------
n : int
Number of nodes
m : int
Number of edges to attach from a new node to existing nodes
m0 : int
Number of nodes initially attached to the network
seed : int, optional
Seed for random number generator (default=None).
Returns
-------
G : Topology
Notes
-----
The initialization is a graph with with m nodes connected by :math:`m -1`
edges.
It does not use the Barabasi-Albert method provided by NetworkX because it
does not allow to specify *m0* parameter.
There are no disconnected subgraphs in the topology.
References
----------
.. [1] A. L. Barabasi and R. Albert "Emergence of scaling in
random networks", Science 286, pp 509-512, 1999.
"""
def calc_pi(G):
"""Calculate BA Pi function for all nodes of the graph"""
degree = dict(G.degree())
den = float(sum(degree.values()))
return {node: degree[node] / den for node in G.nodes()}
# input parameters
if n < 1 or m < 1 or m0 < 1:
raise ValueError('n, m and m0 must be positive integers')
if m >= m0:
raise ValueError('m must be <= m0')
if n < m0:
raise ValueError('n must be > m0')
if seed is not None:
random.seed(seed)
# Step 1: Add m0 nodes. These nodes are interconnected together
# because otherwise they will end up isolated at the end
G = Topology(nx.path_graph(m0))
G.name = "ba_topology(%d,%d,%d)" % (n, m, m0)
G.graph['type'] = 'ba'
# Step 2: Add one node and connect it with m links
while G.number_of_nodes() < n:
pi = calc_pi(G)
u = G.number_of_nodes()
G.add_node(u)
new_links = 0
while new_links < m:
v = random_from_pdf(pi)
if not G.has_edge(u, v):
G.add_edge(u, v)
new_links += 1
return G示例10: waxman_2_topology
# 需要导入模块: from fnss.topologies.topology import Topology [as 别名]
# 或者: from fnss.topologies.topology.Topology import name [as 别名]
def waxman_2_topology(n, alpha=0.4, beta=0.1, domain=(0, 0, 1, 1),
distance_unit='Km', seed=None):
r"""Return a Waxman-2 random topology.
The Waxman-2 random topology models place n nodes uniformly at random
in a rectangular domain. Two nodes u, v are connected with a link
with probability
.. math::
p = \alpha*exp(-d/(\beta*L)).
where the distance *d* is the Euclidean distance between the nodes u and v.
and *L* is the maximum distance between all nodes in the graph.
Parameters
----------
n : int
Number of nodes
alpha : float
Model parameter chosen in *(0,1]* (higher alpha increases link density)
beta : float
Model parameter chosen in *(0,1]* (higher beta increases difference
between density of short and long links)
domain : tuple of numbers, optional
Domain size (xmin, ymin, xmax, ymax)
seed : int, optional
Seed for random number generator (default=None).
Returns
-------
G : Topology
Notes
-----
Each edge of G has the attribute *length*
References
----------
.. [1] B. M. Waxman, Routing of multipoint connections.
IEEE J. Select. Areas Commun. 6(9),(1988) 1617-1622.
"""
# validate input parameters
if not isinstance(n, int) or n <= 0:
raise ValueError('n must be a positive integer')
if alpha > 1 or alpha <= 0 or beta > 1 or beta <= 0:
raise ValueError('alpha and beta must be float values in (0,1]')
if not isinstance(domain, tuple) or len(domain) != 4:
raise ValueError('domain must be a tuple of 4 number')
(xmin, ymin, xmax, ymax) = domain
if xmin > xmax:
raise ValueError('In domain, xmin cannot be greater than xmax')
if ymin > ymax:
raise ValueError('In domain, ymin cannot be greater than ymax')
if seed is not None:
random.seed(seed)
G = Topology(type='waxman_2', distance_unit=distance_unit)
G.name = "waxman_2_topology(%s, %s, %s)" % (n, alpha, beta)
G.add_nodes_from(range(n))
for v in G.nodes():
G.node[v]['latitude'] = (ymin + (ymax - ymin)) * random.random()
G.node[v]['longitude'] = (xmin + (xmax - xmin)) * random.random()
l = {}
nodes = list(G.nodes())
while nodes:
u = nodes.pop()
for v in nodes:
x_u = G.node[u]['longitude']
x_v = G.node[v]['longitude']
y_u = G.node[u]['latitude']
y_v = G.node[v]['latitude']
l[(u, v)] = math.sqrt((x_u - x_v) ** 2 + (y_u - y_v) ** 2)
L = max(l.values())
for (u, v), d in l.items():
if random.random() < alpha * math.exp(-d / (beta * L)):
G.add_edge(u, v, length=d)
return G示例11: dumbbell_topology
# 需要导入模块: from fnss.topologies.topology import Topology [as 别名]
# 或者: from fnss.topologies.topology.Topology import name [as 别名]
def dumbbell_topology(m1, m2):
"""
Return a dumbbell topology consisting of two star topologies
connected by a path.
More precisely, two star graphs :math:`K_{m1}` form the left and right
bells, and are connected by a path :math:`P_{m2}`.
The :math:`2*m1+m2` nodes are numbered as follows.
* :math:`0,...,m1-1` for the left barbell,
* :math:`m1,...,m1+m2-1` for the path,
* :math:`m1+m2,...,2*m1+m2-1` for the right barbell.
The 3 subgraphs are joined via the edges :math:`(m1-1,m1)` and
:math:`(m1+m2-1,m1+m2)`. If m2 = 0, this is merely two star topologies
joined together.
Please notice that this dumbbell topology is different from the barbell
graph generated by networkx's barbell_graph function. That barbell graph
consists of two complete graphs connected by a path. This consists of two
stars whose roots are connected by a path. This dumbbell topology is
particularly useful for simulating transport layer protocols.
All nodes and edges of this topology have an attribute *type* which can be
either *right bell*, *core* or *left_bell*
Parameters
----------
m1 : int
The number of nodes in each bell
m2 : int
The number of nodes in the path
Returns
-------
topology : A Topology object
"""
if not isinstance(m1, int) or not isinstance(m2, int):
raise TypeError('m1 and m2 arguments must be of int type')
if m1 < 2:
raise ValueError("Invalid graph description, m1 should be >= 2")
if m2 < 1:
raise ValueError("Invalid graph description, m2 should be >= 1")
G = Topology(type='dumbbell')
G.name = "dumbbell_topology(%d,%d)" % (m1, m2)
# left bell
G.add_node(m1)
for v in range(m1):
G.add_node(v, type='left_bell')
G.add_edge(v, m1, type='left_bell')
# right bell
for v in range(m1):
G.add_node(v + m1 + m2, type='right_bell')
G.add_edge(v + m1 + m2, m1 + m2 - 1, type='right_bell')
# connecting path
for v in range(m1, m1 + m2 - 1):
G.node[v]['type'] = 'core'
G.add_edge(v, v + 1, type='core')
G.node[m1 + m2 - 1]['type'] = 'core'
return G