def generateStatistics(graph, startDate, endDate, recordStep):
"""
For a given theta, simulate the epidemic, and then return a number of
relevant statistics.
"""
times = []
removedIndices = []
for t in numpy.arange(startDate, endDate+1, recordStep):
times.append(t)
removedIndices.append(graph.removedIndsAt(t))
V = graph.getVertexList().getVertices()
removedArray = numpy.array([len(x) for x in removedIndices])
maleArray = numpy.array([numpy.sum(V[x, HIVVertices.genderIndex]==HIVVertices.male) for x in removedIndices])
femaleArray = numpy.array([numpy.sum(V[x, HIVVertices.genderIndex]==HIVVertices.female) for x in removedIndices])
heteroArray = numpy.array([numpy.sum(V[x, HIVVertices.orientationIndex]==HIVVertices.hetero) for x in removedIndices])
biArray = numpy.array([numpy.sum(V[x, HIVVertices.orientationIndex]==HIVVertices.bi) for x in removedIndices])
randDetectArray = numpy.array([numpy.sum(V[x, HIVVertices.detectionTypeIndex]==HIVVertices.randomDetect) for x in removedIndices])
conDetectArray = numpy.array([numpy.sum(V[x, HIVVertices.detectionTypeIndex]==HIVVertices.contactTrace) for x in removedIndices])
vertexArray = numpy.c_[removedArray, maleArray, femaleArray, heteroArray, biArray, randDetectArray, conDetectArray]
graphStats = GraphStatistics()
removedGraphStats = graphStats.sequenceScalarStats(graph, removedIndices, slowStats=False)
return times, vertexArray, removedGraphStats
def testSequenceScalarStats(self):
numFeatures = 1
numVertices = 10
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList)
graph.addEdge(0, 2)
graph.addEdge(0, 1)
subgraphIndices = [[0, 1, 3], [0, 1, 2, 3]]
growthStatistics = GraphStatistics()
statsArray = growthStatistics.sequenceScalarStats(graph, subgraphIndices)
self.assertTrue(statsArray[0, 0] == 3.0)
self.assertTrue(statsArray[1, 0] == 4.0)
self.assertTrue(statsArray[0, 1] == 1.0)
self.assertTrue(statsArray[1, 1] == 2.0)
def generateStatistics(graph, times):
"""
For a given theta, simulate the epidemic, and then return a number of
relevant statistics.
"""
contactIndices = []
removedIndices = []
infectedIndices = []
for t in times:
removedIndices.append(graph.removedIndsAt(t))
infectedIndices.append(graph.infectedIndsAt(t))
contactIndices.append(graph.contactIndsAt(t))
infectedIndices = numpy.array(infectedIndices)
V = graph.getVertexList().getVertices()
graphStatsList = []
for inds in [contactIndices, removedIndices]:
numVerticesArray = numpy.array([len(x) for x in inds])
maleArray = numpy.array([numpy.sum(V[x, HIVVertices.genderIndex]==HIVVertices.male) for x in inds])
femaleArray = numpy.array([numpy.sum(V[x, HIVVertices.genderIndex]==HIVVertices.female) for x in inds])
heteroArray = numpy.array([numpy.sum(V[x, HIVVertices.orientationIndex]==HIVVertices.hetero) for x in inds])
biArray = numpy.array([numpy.sum(V[x, HIVVertices.orientationIndex]==HIVVertices.bi) for x in inds])
randDetectArray = numpy.array([numpy.sum(V[x, HIVVertices.detectionTypeIndex]==HIVVertices.randomDetect) for x in inds])
conDetectArray = numpy.array([numpy.sum(V[x, HIVVertices.detectionTypeIndex]==HIVVertices.contactTrace) for x in inds])
vertexArray = numpy.c_[numVerticesArray, maleArray, femaleArray, heteroArray, biArray, randDetectArray, conDetectArray]
graphStatistics = GraphStatistics()
graphStats = graphStatistics.sequenceScalarStats(graph, inds, slowStats=False)
graphStatsList.append(graphStats)
finalRemovedGraph = graph.subgraph(removedIndices[-1])
finalRemovedDegrees = finalRemovedGraph.degreeDistribution()
return vertexArray, infectedIndices, removedIndices, graphStatsList[0], graphStatsList[1], finalRemovedDegrees
