def split_by_cc(fname:str, targets:str=None, order:str=None, slice=None,
edge_predicate:str=edge_predicate) -> tuple:
"""Return names of targets written"""
if not targets:
name, ext = os.path.splitext(fname)
targets = name + '_{}' + ext
elif not isinstance(targets, str):
raise ValueError("Target should be a filename to write")
elif '{}' not in targets:
raise ValueError("Target should be a filename to write containing '{}'")
graph = graph_from_file(fname, edge_predicate=edge_predicate)
writtens = []
ccs = networkx.connected_components(graph)
if order in {'biggest first', 'smaller last'}:
ccs = sorted(tuple(ccs), key=len, reverse=True)
elif order in {'biggest last', 'smaller first'}:
ccs = sorted(tuple(ccs), key=len)
elif order == 'random':
ccs = list(ccs)
random.shuffle(ccs)
if slice:
try:
if len(slice) != 2 or any(not isinstance(v, int) for v in slice):
raise TypeError # trigger the exception handling
except TypeError: # slice is not iterable
raise ValueError("Slice must be an iterable of two integers")
start, end = slice
ccs = tuple(ccs)[start:end]
for idx, cc_nodes in enumerate(ccs, start=1):
cc = graph.subgraph(cc_nodes)
target = targets.format(idx)
graph_to_file(cc, target)
writtens.append(target)
return tuple(writtens)
def test_graphml():
file = 'data/test.graphml'
one = comparable_graph(graph_from_file(file))
two = comparable_graph(graph_from_standard_file(file))
assert one == two
assert one == frozenset(map(frozenset, ({'1', '2'}, {'1', '3'},
{'2', '4'}, {'3', '4'})))
def test_many_atoms():
file = 'data/many-atoms.lp'
one = comparable_graph(graph_from_file(file))
assert len(one) == 9, one
assert one == frozenset({
frozenset({'a', 'd'}), frozenset({'a', 'e'}),
frozenset({'b', 'd'}), frozenset({'b', 'e'}),
frozenset({'c', 'd'}), frozenset({'c', 'e'}),
frozenset({'d', 'e'}), frozenset({'d', 'f'}),
frozenset({'e', 'f'}),
})
def convert(fname:str, target:str=None, anonymize:bool=False,
normalize:bool=False, edge_predicate:str=edge_predicate,
target_edge_predicate:str=edge_predicate) -> dict:
"""Write in target the very same graph as input, but in
an clean ASP expanded format.
normalize -- avoid special characters in node names.
anonymize -- rename nodes into integers.
target -- file to write. If None or equal to fname, overwrite.
target_edge_predicate -- edge predicate to use in rewritten file.
"""
fname = commons.normalize_filename(fname)
if target: target = commons.normalize_filename(target)
if not target: target = fname
graph = graph_from_file(fname, edge_predicate=edge_predicate)
if anonymize: graph = anonymized(graph)
if normalize: graph = normalized(graph)
graph_to_file(graph, target, edge_predicate=target_edge_predicate)
def randomize(fname: str,
target: str,
iterations: int,
per_cc: bool = False,
edge_predicate: str = edge_predicate):
"""Write in file of given name a randomized version of input graph.
"""
fname = commons.normalize_filename(fname)
target = commons.normalize_filename(target)
graph = graph_from_file(fname, edge_predicate=edge_predicate)
if per_cc:
graphs = (graph.subgraph(nodes).copy()
for nodes in networkx.connected_components(graph))
else:
graphs = [graph]
def run():
for graph in graphs:
print(tuple(graph.edges))
nb_edge = graph.number_of_edges()
total_iterations = iterations * graph.number_of_edges()
try:
yield networkx.algorithms.double_edge_swap(
graph,
nswap=total_iterations,
max_tries=100 * total_iterations)
except networkx.exception.NetworkXError as err:
print(err.args[0])
yield graph
except networkx.exception.NetworkXAlgorithmError:
print(
"Maximum number of swap attempts reached, or graph can't be swapped. Ignored."
)
yield graph
if per_cc:
graph = networkx.compose_all(run())
else:
graph = next(run())
return graph_to_file(graph, target, edge_predicate=edge_predicate)
def extract_by_node(fname: str,
target: str = None,
nodes: iter = (),
order: int = 1,
edge_predicate: str = edge_predicate):
"""Write in file of given name a subgraph of input one.
"""
fname = commons.normalize_filename(fname)
if target: target = commons.normalize_filename(target)
if not target: target = fname
graph = graph_from_file(fname, edge_predicate=edge_predicate)
nodes = set(nodes)
all_neighbors = networkx.classes.function.all_neighbors
for _ in range(order):
nodes |= set(
itertools.chain.from_iterable(
all_neighbors(graph, node) for node in nodes))
return graph_to_file(graph.subgraph(nodes),
target,
edge_predicate=edge_predicate)
def yield_info(fname: str,
info_motifs: int = 0,
info_ccs: bool = True,
graphics: bool = False,
outdir: str = '.',
special_nodes: bool = False,
heavy_computations: bool = False,
graph_properties: bool = False,
negative_results: bool = True,
edge_predicate: str = edge_predicate) -> dict:
"""Yield (field, value) infos of targets written
info_motifs -- print info about the n first motifs in the graph
info_ccs -- print info about connected components in the graph
"""
outdir = commons.normalize_filename(outdir)
graph = graph_from_file(fname, edge_predicate=edge_predicate)
nb_node, nb_edge = len(graph.nodes), len(graph.edges)
nb_self_loops = sum(1 for _ in graph.selfloop_edges())
def density(nb_node, nb_edge):
try:
return 2 * nb_edge / (nb_node * (nb_node - 1))
except ZeroDivisionError:
import math
return math.nan
yield '#node', nb_node
yield '#edge', nb_edge
if nb_self_loops:
yield '#loop', nb_self_loops
yield '#edge - #loop', nb_edge - nb_self_loops
else:
yield 'no loop', True
yield 'density', density(nb_node, nb_edge)
if info_motifs:
for motif in ():
clyngor.solve()
if info_ccs:
ccs_nodes = tuple(networkx.connected_components(graph))
ccs = tuple(graph.subgraph(cc) for cc in ccs_nodes)
yield '#cc', len(ccs_nodes)
if len(ccs_nodes) > 1:
node_per_cc = tuple(map(len, ccs_nodes))
yield '#node/cc', node_per_cc
yield '#node/cc (prop)', tuple(nb / nb_node for nb in node_per_cc)
yield '#node/cc (mean)', sum(node_per_cc) / len(node_per_cc)
yield 'density/cc', tuple(
density(len(nodes), len(tuple(cc.edges)))
for cc, nodes in zip(ccs, ccs_nodes))
if graphics:
# TODO: degree distribution (lin-lin, log-lin, lin-log, log-log)
# TODO: motif size distribution (if info_motifs > 1)
# TODO: degree function to clustering coefficient
...
if heavy_computations:
# TODO: concept and AOC poset size and ratio.
...
if special_nodes:
# TODO: equivalences
arti_points = tuple(networkx.articulation_points(graph))
yield '#articulation points', len(arti_points)
if arti_points:
yield 'articulation points', arti_points
if graph_properties:
non_implemented = []
for attrname, attr in vars(networkx).items():
if attrname.startswith('is_'):
attrname = attrname[3:]
if getfullargspec(attr).args == ['G']: # only 1 arg
try:
yield attrname, attr(graph) # discard the 'is_'
except networkx.exception.NetworkXNotImplemented as err:
non_implemented.append(attrname)
except networkx.exception.NetworkXError as err:
non_implemented.append(attrname)
properties = ('transitivity', 'average_clustering',
'average_node_connectivity',
'average_shortest_path_length')
for attrname in properties:
try:
yield attrname, getattr(networkx, attrname)(graph)
except networkx.exception.NetworkXError as err:
non_implemented.append(attrname)
if non_implemented and negative_results:
yield 'non implemented', non_implemented
def test_gml():
file = 'data/test.gml'
one = comparable_graph(graph_from_file(file))
two = comparable_graph(graph_from_standard_file(file))
assert one == two