def recipes_to_graph(recipes):
"""
Given a list of recipes, will create a func dep graph for each which it will then compose.
:param recipes: A list of recipe object
:return: A combined graph where the 'starts' attribute for each node has been set
"""
result_graph = nx.DiGraph()
for r in recipes:
# Turn recipe to graph and get top nodes
r_graph = r.to_DiGraph()
top_nodes = get_top_nodes(r_graph)
top_nodes = [x[0] for x in top_nodes
] # Get only names of nodes not attributes
# Iterate over each node in graph
for node in r_graph.nodes(data=True):
work = node[0]
attributes = node[1]
# If node is a top node, it has its starts attribute set to the recipe name
attributes['starts'] = set()
if work in top_nodes:
attributes['starts'].add(r.name)
# If this node is already in the result_graph we store elements from its start attributes
# in the r_graph node, as to retain it after compose.
if work in result_graph:
res_set = result_graph.node[work]['starts']
attributes['starts'].update(res_set)
# Attributes of r_graph take presedence over result_graph's
result_graph = nx.compose(result_graph, r_graph)
return result_graph
def __nested_remixgroup(dg1, remix_group):
print "============= nested remix_group ================ \n"
dg2 = nx.DiGraph()
data = json.loads(remix_group.networkx_data)
dg2.add_nodes_from(data['nodes'])
dg2.add_edges_from(data['edges'])
print "========== MERGED GROUP NODES: " + str(dg1.nodes())
# FIXME: this combines the graphs correctly
# recheck the time-bound concept
dg1 = nx.compose(dg1, dg2)
print dg1.nodes()
return dg1
def __nested_remixgroup(dg1, remix_group):
print "============= nested remix_group ================ \n"
dg2 = nx.DiGraph()
data = json.loads(remix_group.networkx_data)
dg2.add_nodes_from(data['nodes'])
dg2.add_edges_from(data['edges'])
print "========== MERGED GROUP NODES: " + str(dg1.nodes())
# FIXME: this combines the graphs correctly
# recheck the time-bound concept
dg1 = nx.compose(dg1, dg2)
print dg1.nodes()
return dg1
def create_graph():
"""Composes the graphs of the different plugins into a single graph.
"""
# load plugins
mgr = extension.ExtensionManager(
namespace='doconv.converter',
invoke_on_load=True,
propagate_map_exceptions=True,
)
# check that all plugin dependencies are installed
mgr.map_method("check_dependencies")
# create conversion graph based on found plugins
plugin_graphs = mgr.map_method("get_supported_conversions_graph")
logger.debug("Loaded plugins: {0}".format(mgr.names()))
G = nx.DiGraph()
for graph in plugin_graphs:
G = nx.compose(G, graph)
return G
