def test_graphtool_export_import(self):
# Export to graph tool and reimport to PyGSP directly
# The exported graph is a simple one without an associated Signal
g = graphs.Bunny()
g_gt = g.to_graphtool()
g2 = graphs.Graph.from_graphtool(g_gt)
np.testing.assert_array_equal(g.W.todense(), g2.W.todense())
def dataSource():
'''
source graph
'''
G = graphs.Bunny()
# G.plot()
# plt.show()
return G
def test_networkx_export_import(self):
# Export to networkx and reimport to PyGSP
# Exporting the Bunny graph
g = graphs.Bunny()
g_nx = g.to_networkx()
g2 = graphs.Graph.from_networkx(g_nx)
np.testing.assert_array_equal(g.W.todense(), g2.W.todense())
def test_bunny(self):
graphs.Bunny()
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_Bunny():
G = graphs.Bunny()
def test_Bunny():
G = graphs.Bunny()
needed_attributes_testing(G)
