def edge_lines_to_graph(edge_lines):
probe_graph = dict([(i_site,set([])) for i_site in xrange(N_SITES)])
site_names = [site.name for site in PROBE_SITES]
for line in edge_lines:
src,targ = line.split()
add_edge(probe_graph,site_names.index(src),site_names.index(targ))
return probe_graph
def edge_lines_to_graph(edge_lines):
probe_graph = dict([(i_site, set([])) for i_site in xrange(N_SITES)])
site_names = [site.name for site in PROBE_SITES]
for line in edge_lines:
src, targ = line.split()
add_edge(probe_graph, site_names.index(src), site_names.index(targ))
return probe_graph
def path_graph(Locs):
"""
Returns a line graph for a set of one-dimensional locations.
Parameters
-------
Locs : ndarray, or anything that gets cast into ndarray upon np.array(Locs)
Returns
------
out : graph, i.e. dictionary key -> set(keys)
"""
Locs = np.array(Locs).flatten()
if len(Locs)==1:
return complete_graph(1)
else:
G = {}
SortInds = np.argsort(Locs)
for src,targ in zip(SortInds[1:],SortInds[:-1]):
add_edge(G,src,targ)
return G
def path_graph(Locs):
"""
Returns a line graph for a set of one-dimensional locations.
Parameters
-------
Locs : ndarray, or anything that gets cast into ndarray upon np.array(Locs)
Returns
------
out : graph, i.e. dictionary key -> set(keys)
"""
Locs = np.array(Locs).flatten()
if len(Locs) == 1:
return complete_graph(1)
else:
G = {}
SortInds = np.argsort(Locs)
for src, targ in zip(SortInds[1:], SortInds[:-1]):
add_edge(G, src, targ)
return G
