def group_people_js(request, mode="egograph", output="full"):
"""Return Belfast Group network graph data as JSON, for use with
:meth:`group_people`.
Optionally filter
the data by minimum degree, if min_degree is specified as a url parameter.
When generating the node and link data, nodes are annotated with
degree, in degree, out degree, betweenness centrality, and eigenvector
centrality if available. (In/out degree is only available for directed
graphs.)
:param mode: egograph: network information for a one- or two-degree
egograph centered around the Belfast Group; groupsheet-model:
alternate network graph based on the Group sheets themselves
:param output: full: node and link data; adjacency: adjacency matrix,
used for generating chord diagram
"""
if mode == "egograph":
degree = request.GET.get("degree", 1)
extra_opts = {}
try:
degree = int(degree)
# currently only support 1 or 2 degree
degree = max(1, min(degree, 2))
# NOTE: degree 2 graph is large enough that it *must* be filtered
# to be sensible and usable on the webpage;
# by trial & error, I found a minimum degree of 5 to be reasonable
if degree == 2:
extra_opts["min_degree"] = 5
except ValueError:
# if a value is passed that can't be converted to int, fallback to 1
degree = 1
belfast_group = RdfOrganization(network_data().copy(), BELFAST_GROUP_URI)
graph = belfast_group.ego_graph(radius=degree, types=["Person", "Organization"], **extra_opts)
# annotate nodes in graph with degree
# FIXME: not a directional graph; in/out degree not available
graph = annotate_graph(
graph, fields=["degree", "in_degree", "out_degree", "betweenness_centrality", "eigenvector_centrality"]
)
elif mode == "groupsheet-model":
graph = gexf.read_gexf(settings.GEXF_DATA["bg1"])
graph = annotate_graph(
graph, fields=["degree", "betweenness_centrality", "eigenvector_centrality"] #'in_degree', 'out_degree',
)
if output == "full":
data = json_graph.node_link_data(graph)
if output == "adjacency":
# adjacency matrix for generating chord diagram
matrix = nx.convert_matrix.to_numpy_matrix(graph)
# NOTE: this also works, but as of networx 1.9 requires scipy
# matrix = nx.linalg.graphmatrix.adjacency_matrix(graph)
data = matrix.tolist()
return HttpResponse(json.dumps(data), content_type="application/json")
def egograph_js(request, id):
"Egograph information as JSON for a single person."
uri = local_uri(reverse("people:profile", args=[id]), request)
g = rdf_data()
person = RdfPerson(g, rdflib.URIRef(uri))
graph = person.ego_graph(radius=1, types=["Person", "Organization", "Place"])
# annotate nodes in graph with degree
# NOTE: not a directional graph, so in/out degree not available
graph = annotate_graph(
graph, fields=["degree", "in_degree", "out_degree", "betweenness_centrality", "eigenvector_centrality"]
)
data = json_graph.node_link_data(graph)
return HttpResponse(json.dumps(data), content_type="application/json")
def full_js(request, mode):
"""Return full network graph data as JSON. Optionally filter
the data by minimum degree, if min_degree is specified as a url parameter.
When generating the node and link data, nodes are annotated with
degree, in degree, out degree, betweenness centrality, and eigenvector
centrality if available. (In/out degree is only available for directed
graphs.)
:param mode: full - node and link data; adjacency - adjacency matrix,
used to construct a chord diagram
.. deprecated:: 1.0
This network graph is too large to be usable or viewable in a
browser/javascript display environment, and should not be used;
:meth:`group_people` should be used instead.
"""
# optionally filter by minimum degree
min_degree = request.GET.get("min_degree", None)
filter = {}
if min_degree:
filter["min_degree"] = int(min_degree)
graph = _network_graph(**filter)
if mode == "full":
graph = annotate_graph(
graph, fields=["degree", "in_degree", "out_degree", "betweenness_centrality", "eigenvector_centrality"]
)
# standard nodes & links data
data = json_graph.node_link_data(graph)
if mode == "adjacency":
# adjacency matrix for generating chord diagram
matrix = nx.linalg.graphmatrix.adjacency_matrix(graph)
data = matrix.tolist()
return HttpResponse(json.dumps(data), content_type="application/json")