def test_dropNodesByDegree(self):
metaknowledge.dropNodesByDegree(self.G, minDegree=20, maxDegree=100)
self.assertEqual(
metaknowledge.graphStats(self.G, sentenceString=True),
"The graph has 385 nodes, 5929 edges, 0 isolates, 11 self loops, a density of 0.0802083 and a transitivity of 0.954487"
)
self.assertTrue(self.G.edge['Mazur P, 1953, MEM ACAD ROY BELG']
['Livens Gh, 1948, P CAMB PHILOS SOC']['weight'] == 1)
def getThresholds(clargs, grph):
thresDict = collections.OrderedDict([
('0', "Continue"),
('1', "Drop isolates"),
('2', "Remove self loops"),
('3', "Remove edges below some weight"),
('4', "Remove edges above some weight"),
('5', "Remove nodes below some degree"),
('6', "Remove nodes above some degree"),
])
print("The network contains {0} nodes and {1} edges, of which {2} are isolated and {3} are self loops.".format(len(grph.nodes()), len(grph.edges()), len(nx.isolates(grph)), len(grph.selfloop_edges())))
thresID = int(inputMenu(thresDict, header = "What type of filtering to you want? "))
if thresID == 0:
return grph
elif thresID == 1:
metaknowledge.dropNodesByDegree(grph, minDegree = 1)
return getThresholds(clargs, grph)
elif thresID == 2:
metaknowledge.dropEdges(grph, dropSelfLoops = True)
return getThresholds(clargs, grph)
elif thresID == 3:
metaknowledge.dropEdges(grph, minWeight = getNum("What is the minumum weight for an edge to be included? "))
return getThresholds(clargs, grph)
elif thresID == 4:
metaknowledge.dropEdges(grph, minWeight = getNum("What is the maximum weight for an edge to be included? "))
return getThresholds(clargs, grph)
elif thresID == 5:
metaknowledge.dropNodesByDegree(grph, minDegree = getNum("What is the minumum degree for an edge to be included? "))
return getThresholds(clargs, grph)
else:
metaknowledge.dropNodesByDegree(grph, minDegree = getNum("What is the maximum degree for an edge to be included? "))
return getThresholds(clargs, grph)
def getThresholds(clargs, grph):
thresDict = collections.OrderedDict([
('0', "Continue"),
('1', "Drop isolates"),
('2', "Remove self loops"),
('3', "Remove edges below some weight"),
('4', "Remove edges above some weight"),
('5', "Remove nodes below some degree"),
('6', "Remove nodes above some degree"),
])
print("The network contains {0} nodes and {1} edges, of which {2} are isolated and {3} are self loops.".format(len(list(grph.nodes())), len(list(grph.edges())), len(list(nx.isolates(grph))), len(list(grph.selfloop_edges()))))
thresID = int(inputMenu(thresDict, header = "What type of filtering to you want? "))
if thresID == 0:
return grph
elif thresID == 1:
metaknowledge.dropNodesByDegree(grph, minDegree = 1)
return getThresholds(clargs, grph)
elif thresID == 2:
metaknowledge.dropEdges(grph, dropSelfLoops = True)
return getThresholds(clargs, grph)
elif thresID == 3:
metaknowledge.dropEdges(grph, minWeight = getNum("What is the minumum weight for an edge to be included? "))
return getThresholds(clargs, grph)
elif thresID == 4:
metaknowledge.dropEdges(grph, minWeight = getNum("What is the maximum weight for an edge to be included? "))
return getThresholds(clargs, grph)
elif thresID == 5:
metaknowledge.dropNodesByDegree(grph, minDegree = getNum("What is the minumum degree for an edge to be included? "))
return getThresholds(clargs, grph)
else:
metaknowledge.dropNodesByDegree(grph, minDegree = getNum("What is the maximum degree for an edge to be included? "))
return getThresholds(clargs, grph)
def test_dropNodesByDegree(self):
metaknowledge.dropNodesByDegree(self.G, minDegree = 20, maxDegree = 100)
self.assertEqual(metaknowledge.graphStats(self.G), "The graph has 384 nodes, 5902 edges, 0 isolates, 11 self loops, a density of 0.08026 and a transitivity of 0.954765")
self.assertTrue(self.G.edge['Mazur P, 1953, MEM ACAD ROY BELG']['Livens Gh, 1948, P CAMB PHILOS SOC']['weight'] == 1)
