[0,0,1,1,-1],
[0,0,-1,-1,1]]
).T
# A <--> 0, B <--> 1, etc.
region2gen = { 0 : [0,1],
1 : [2],
2 : [3,4]
}
symbols = numpy.matrix( [[0,1,1],
[1,0,0],
[0,1,1]]
)
map_on_regions = DiGraph()
map_on_regions.from_numpy_matrix( symbols )
if 1:
hom_matrix = utils.load_matlab_matrix( 'henon_index.mat', 'hom_matrix' )
region2gen = utils.convert_matlab_gens( 'henon_gens.mat' )
map_on_regions = utils.index_map_to_region_map( hom_matrix, region2gen, shift=-1)
if 0:
hom_matrix = utils.load_matlab_matrix( 'leslie_index.mat', 'hom_matrix' )
region2gen = utils.convert_matlab_gens( 'leslie_gens.mat' )
map_on_regions = utils.index_map_to_region_map( hom_matrix, region2gen, shift=-1)
########################
#
2 : [3],
3 : [4],
4 : [5],
5 : [6,7]
}
adjmatrix = numpy.matrix( [[0,0,1,0,0,0],
[0,0,0,1,0,0],
[0,0,0,0,1,0],
[1,1,0,0,0,0],
[0,0,0,0,0,1],
[1,1,0,0,0,0]]
)
P = DiGraph()
P.from_numpy_matrix( adjmatrix )
G = DiGraph( )
G.from_numpy_matrix( generators )
edgeset = defaultdict( list )
# length-1 walks
k = 1
for s,t in P.edges():
r2g = ( regions[ s ], regions[ t ] )
edge_matrix = generators[ r2g[0],r2g[1] ]
edgeset[k].append( Walk( s, t, set( [(s,t)] ), edge_matrix, k, sparse=False ) )
