def __init__(self,
n_nodes,
ux,
spectral_profile,
type_graph="erdos",
type_noise="gaussian",
save_history=True,
seed=None,
**kargs):
####### The algorithm asks the user a spectral_profile for the Graph filter that will be applied
### to white noise so the output signal is stationary with respect to the graph structure.
#### This section initialize the structure of the graph signal
### Input
# n_nodes=number of nodes
# ux= mean of the signal
# spectral_profile= a function generating the power spectral density of the sygnal.
# type_graph=available graph model (erdos,euclidean,barabasi_albert)
# type_noise = distribution of the noise (gaussian,uniform) that will be used to ge
self.n_nodes = n_nodes
self.type_graph = type_graph
self.type_noise = type_noise
self.ux = ux
self.spectral_profile = spectral_profile
self.seed = seed
if type_graph == "erdos":
self.G = graphs.ErdosRenyi(self.n_nodes,
kargs['p'],
seed=self.seed)
self.G.set_coordinates()
if type_graph == "barabasi_albert":
self.G = graphs.BarabasiAlbert(self.n_nodes,
m=kargs['m'],
m0=kargs['m'],
seed=self.seed)
self.G.set_coordinates()
if type_graph == "Minnesota":
self.G = graphs.Minnesota()
self.generate_fourier()
self.generate_filter()
# import numpy as np # from scipy import sparse # # from pygsp import utils # from pygsp.graphs import Graph import pygsp.graphs as graphs import matplotlib.pyplot as plt G = graphs.Minnesota() fig, axes = plt.subplots(1, 2) _ = axes[0].spy(G.W, markersize=0.5) _ = G.plot(ax=axes[1]) plt.show()
def test_minnesota(self):
graphs.Minnesota()
def real(N, graph_name, connected=True):
r"""
A convenience method for loading toy graphs that have been collected from the internet.
Parameters:
----------
N : int
The number of nodes.
graph_name : a string
Use to select which graph is returned. Choices include
* airfoil
Graph from airflow simulation
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.50.9217&rep=rep1&type=pdf
http://networkrepository.com/airfoil1.php
* yeast
Network of protein-to-protein interactions in budding yeast.
http://networkrepository.com/bio-yeast.php
* minnesota
Minnesota road network.
I am using the version provided by the PyGSP software package (initially taken from the MatlabBGL library.)
* bunny
The Stanford bunny is a computer graphics 3D test model developed by Greg Turk and Marc Levoy in 1994 at Stanford University
I am using the version provided by the PyGSP software package.
connected : Boolean
Set to True if only the giant component is to be returned.
"""
directory = os.path.dirname(graph_utils.__file__) + '/data/'
tries = 0
while True:
tries = tries + 1
if graph_name == 'airfoil':
G = graphs.Airfoil()
G = graphs.Graph(W=G.W[0:N, 0:N], coords=G.coords[0:N, :])
elif graph_name == 'yeast':
file = directory + 'bio-yeast.npy'
W = np.load(file)
G = graphs.Graph(W=W[0:N, 0:N])
elif graph_name == 'minnesota':
G = graphs.Minnesota()
W = G.W.astype(np.float)
G = graphs.Graph(W=W[0:N, 0:N], coords=G.coords[0:N, :])
elif graph_name == 'bunny':
G = graphs.Bunny()
W = G.W.astype(np.float)
G = graphs.Graph(W=W[0:N, 0:N], coords=G.coords[0:N, :])
if connected == False or G.is_connected(): break
if tries > 1:
print(
'WARNING: disconnected graph.. trying to use the giant component'
)
G, _ = graph_utils.get_giant_component(G)
break
return G
def real(N, graph_name, connected=True):
r"""
A convenience method for loading toy graphs that have been collected from the internet.
Parameters:
----------
N : int
The number of nodes. Set N=-1 to return the entire graph.
graph_name : a string
Use to select which graph is returned. Choices include
* airfoil
Graph from airflow simulation
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.50.9217&rep=rep1&type=pdf
http://networkrepository.com/airfoil1.php
* yeast
Network of protein-to-protein interactions in budding yeast.
http://networkrepository.com/bio-yeast.php
* minnesota
Minnesota road network.
I am using the version provided by the PyGSP software package (initially taken from the MatlabBGL library.)
* bunny
The Stanford bunny is a computer graphics 3D test model developed by Greg Turk and Marc Levoy in 1994 at Stanford University
I am using the version provided by the PyGSP software package.
connected : Boolean
Set to True if only the giant component is to be returned.
"""
directory = os.path.join(
os.path.dirname(os.path.dirname(graph_utils.__file__)), "data"
)
tries = 0
while True:
tries = tries + 1
if graph_name == "airfoil":
G = graphs.Airfoil()
G = graphs.Graph(W=G.W[0:N, 0:N], coords=G.coords[0:N, :])
elif graph_name == "yeast":
W = download_yeast()
G = graphs.Graph(W=W[0:N, 0:N])
elif graph_name == "minnesota":
G = graphs.Minnesota()
W = G.W.astype(np.float)
G = graphs.Graph(W=W[0:N, 0:N], coords=G.coords[0:N, :])
elif graph_name == "bunny":
G = graphs.Bunny()
W = G.W.astype(np.float)
G = graphs.Graph(W=W[0:N, 0:N], coords=G.coords[0:N, :])
if connected == False or G.is_connected():
break
if tries > 1:
print("WARNING: Disconnected graph. Using the giant component.")
G, _ = graph_utils.get_giant_component(G)
break
if not hasattr(G, 'coords'):
try:
import networkx as nx
graph = nx.from_scipy_sparse_matrix(G.W)
pos = nx.nx_agraph.graphviz_layout(graph, prog='neato')
G.set_coordinates(np.array(list(pos.values())))
except ImportError:
G.set_coordinates()
return G
def test_Minnesota():
G = graphs.Minnesota()
def test_Minnesota():
G = graphs.Minnesota()
needed_attributes_testing(G)
