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Aggregate Pattern 1 #48

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af192e1
Added a comment to models
ros62 Jul 13, 2021
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Aggregate 1
ros62 Jul 24, 2021
2226539
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ros62 Jul 24, 2021
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ros62 Jul 24, 2021
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Aggregate Pattern 2
ros62 Jul 25, 2021
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1 change: 1 addition & 0 deletions seirsplus/models.py
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import scipy.integrate


# My first commit July 13 2021
########################################################
#@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@#
#@ @#
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234 changes: 186 additions & 48 deletions seirsplus/networks.py
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import matplotlib.pyplot as pyplot


#Aggregate Pattern
class CommunitySetting():

#The class enables to ensure there is a root aggregate to maintain consistency within the dofferent types of network.

def generate_workplace_contact_network(num_cohorts=1, num_nodes_per_cohort=100, num_teams_per_cohort=10,
mean_intracohort_degree=6, pct_contacts_intercohort=0.2,
farz_params={'alpha':5.0, 'gamma':5.0, 'beta':0.5, 'r':1, 'q':0.0, 'phi':10,

def generate_educationalInstitution_contact_network(num_cohorts=1, num_nodes_per_cohort=100, num_teams_per_cohort=10, mean_intracohort_degree=6, pct_contacts_intercohort=0.2, farz_params={'alpha':5.0, 'gamma':5.0, 'beta':0.5, 'r':1, 'q':0.0, 'phi':10,
'b':0, 'epsilon':1e-6, 'directed': False, 'weighted': False},
distancing_scales=[]):
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Generate FARZ networks of intra-cohort contacts:
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

cohortNetworks = []

teams_indices = {}

for i in range(num_cohorts):

numNodes = num_nodes_per_cohort[i] if isinstance(num_nodes_per_cohort, list) else num_nodes_per_cohort
numTeams = num_teams_per_cohort[i] if isinstance(num_teams_per_cohort, list) else num_teams_per_cohort
cohortMeanDegree = mean_intracohort_degree[i] if isinstance(mean_intracohort_degree, list) else mean_intracohort_degree

farz_params.update({'n':numNodes, 'k':numTeams, 'm':cohortMeanDegree})

cohortNetwork, cohortTeamLabels = FARZ.generate(farz_params=farz_params)

cohortNetworks.append(cohortNetwork)

for node, teams in cohortTeamLabels.items():
for team in teams:
try:
teams_indices['c'+str(i)+'-t'+str(team)].append(node)
except KeyError:
teams_indices['c'+str(i)+'-t'+str(team)] = [node]

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Establish inter-cohort contacts:
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

cohortsAdjMatrices = [networkx.adj_matrix(cohortNetwork) for cohortNetwork in cohortNetworks]

eduAdjMatrix = scipy.sparse.block_diag(cohortsAdjMatrices)
eduInsNetwork = networkx.from_scipy_sparse_matrix(eduAdjMatrix)

N = eduInsNetwork.number_of_nodes()

cohorts_indices = {}
cohortStartIdx = -1
cohortFinalIdx = -1
for c, cohortNetwork in enumerate(cohortNetworks):

cohortStartIdx = cohortFinalIdx + 1
cohortFinalIdx = cohortStartIdx + cohortNetwork.number_of_nodes() - 1
cohorts_indices['c'+str(c)] = list(range(cohortStartIdx, cohortFinalIdx))

for team, indices in teams_indices.items():
if('c'+str(c) in team):
teams_indices[team] = [idx+cohortStartIdx for idx in indices]

for i in list(range(cohortNetwork.number_of_nodes())):
i_intraCohortDegree = cohortNetwork.degree[i]
i_interCohortDegree = int( ((1/(1-pct_contacts_intercohort))*i_intraCohortDegree)-i_intraCohortDegree )
# Add intercohort edges:
if(len(cohortNetworks) > 1):
for d in list(range(i_interCohortDegree)):
j = numpy.random.choice(list(range(0, cohortStartIdx))+list(range(cohortFinalIdx+1, N)))
eduInsNetwork.add_edge(i, j)

return eduInsNetwork, cohorts_indices, teams_indices

# Transportation network added to make sure the information is transferred through the system
def generate_publicTransportation_contact_network(num_buses=1, num_nodes_per_bus=100, num_teams_per_bus=10,
mean_intracohort_degree=6, pct_contacts_intercohort=0.2,
farz_params={'alpha':5.0, 'gamma':5.0, 'beta':0.5, 'r':1, 'q':0.0, 'phi':10,
'b':0, 'epsilon':1e-6, 'directed': False, 'weighted': False},
distancing_scales=[]):

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Generate FARZ networks of intra-cohort contacts:
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

cohortNetworks = []

teams_indices = {}

for i in range(num_buses):

numNodes = num_nodes_per_bus[i] if isinstance(num_nodes_per_bus, list) else num_nodes_per_bus
numTeams = num_teams_per_bus[i] if isinstance(num_teams_per_bus, list) else num_teams_per_bus
cohortMeanDegree = mean_intracohort_degree[i] if isinstance(mean_intracohort_degree, list) else mean_intracohort_degree

farz_params.update({'n':numNodes, 'k':numTeams, 'm':cohortMeanDegree})

cohortNetwork, cohortTeamLabels = FARZ.generate(farz_params=farz_params)

cohortNetworks.append(cohortNetwork)

for node, teams in cohortTeamLabels.items():
for team in teams:
try:
teams_indices['c'+str(i)+'-t'+str(team)].append(node)
except KeyError:
teams_indices['c'+str(i)+'-t'+str(team)] = [node]

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Establish inter-cohort contacts:
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

busesAdjMatrices = [networkx.adj_matrix(cohortNetwork) for cohortNetwork in cohortNetworks]

transportAdjMatrix = scipy.sparse.block_diag(busesAdjMatrices)
transportNetwork = networkx.from_scipy_sparse_matrix(transportAdjMatrix)

N = transportNetwork.number_of_nodes()

cohorts_indices = {}
cohortStartIdx = -1
cohortFinalIdx = -1
for c, cohortNetwork in enumerate(cohortNetworks):

cohortStartIdx = cohortFinalIdx + 1
cohortFinalIdx = cohortStartIdx + cohortNetwork.number_of_nodes() - 1
cohorts_indices['c'+str(c)] = list(range(cohortStartIdx, cohortFinalIdx))

for team, indices in teams_indices.items():
if('c'+str(c) in team):
teams_indices[team] = [idx+cohortStartIdx for idx in indices]

for i in list(range(cohortNetwork.number_of_nodes())):
i_intraCohortDegree = cohortNetwork.degree[i]
i_interCohortDegree = int( ((1/(1-pct_contacts_intercohort))*i_intraCohortDegree)-i_intraCohortDegree )
# Add intercohort edges:
if(len(cohortNetworks) > 1):
for d in list(range(i_interCohortDegree)):
j = numpy.random.choice(list(range(0, cohortStartIdx))+list(range(cohortFinalIdx+1, N)))
workplaceNetwork.add_edge(i, j)

return transportNetwork, cohorts_indices, teams_indices

def generate_workplace_contact_network(num_cohorts=1, num_nodes_per_cohort=100, num_teams_per_cohort=10,
mean_intracohort_degree=6, pct_contacts_intercohort=0.2,
farz_params={'alpha':5.0, 'gamma':5.0, 'beta':0.5, 'r':1, 'q':0.0, 'phi':10,
'b':0, 'epsilon':1e-6, 'directed': False, 'weighted': False},
distancing_scales=[]):

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Generate FARZ networks of intra-cohort contacts:
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Generate FARZ networks of intra-cohort contacts:
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

cohortNetworks = []
cohortNetworks = []

teams_indices = {}
teams_indices = {}

for i in range(num_cohorts):
for i in range(num_cohorts):

numNodes = num_nodes_per_cohort[i] if isinstance(num_nodes_per_cohort, list) else num_nodes_per_cohort
numTeams = num_teams_per_cohort[i] if isinstance(num_teams_per_cohort, list) else num_teams_per_cohort
cohortMeanDegree = mean_intracohort_degree[i] if isinstance(mean_intracohort_degree, list) else mean_intracohort_degree
numNodes = num_nodes_per_cohort[i] if isinstance(num_nodes_per_cohort, list) else num_nodes_per_cohort
numTeams = num_teams_per_cohort[i] if isinstance(num_teams_per_cohort, list) else num_teams_per_cohort
cohortMeanDegree = mean_intracohort_degree[i] if isinstance(mean_intracohort_degree, list) else mean_intracohort_degree

farz_params.update({'n':numNodes, 'k':numTeams, 'm':cohortMeanDegree})
farz_params.update({'n':numNodes, 'k':numTeams, 'm':cohortMeanDegree})

cohortNetwork, cohortTeamLabels = FARZ.generate(farz_params=farz_params)
cohortNetwork, cohortTeamLabels = FARZ.generate(farz_params=farz_params)

cohortNetworks.append(cohortNetwork)
cohortNetworks.append(cohortNetwork)

for node, teams in cohortTeamLabels.items():
for team in teams:
try:
teams_indices['c'+str(i)+'-t'+str(team)].append(node)
except KeyError:
teams_indices['c'+str(i)+'-t'+str(team)] = [node]
for node, teams in cohortTeamLabels.items():
for team in teams:
try:
teams_indices['c'+str(i)+'-t'+str(team)].append(node)
except KeyError:
teams_indices['c'+str(i)+'-t'+str(team)] = [node]

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Establish inter-cohort contacts:
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Establish inter-cohort contacts:
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

cohortsAdjMatrices = [networkx.adj_matrix(cohortNetwork) for cohortNetwork in cohortNetworks]
cohortsAdjMatrices = [networkx.adj_matrix(cohortNetwork) for cohortNetwork in cohortNetworks]

workplaceAdjMatrix = scipy.sparse.block_diag(cohortsAdjMatrices)
workplaceNetwork = networkx.from_scipy_sparse_matrix(workplaceAdjMatrix)
workplaceAdjMatrix = scipy.sparse.block_diag(cohortsAdjMatrices)
workplaceNetwork = networkx.from_scipy_sparse_matrix(workplaceAdjMatrix)

N = workplaceNetwork.number_of_nodes()
N = workplaceNetwork.number_of_nodes()

cohorts_indices = {}
cohortStartIdx = -1
cohortFinalIdx = -1
for c, cohortNetwork in enumerate(cohortNetworks):
cohorts_indices = {}
cohortStartIdx = -1
cohortFinalIdx = -1
for c, cohortNetwork in enumerate(cohortNetworks):

cohortStartIdx = cohortFinalIdx + 1
cohortFinalIdx = cohortStartIdx + cohortNetwork.number_of_nodes() - 1
cohorts_indices['c'+str(c)] = list(range(cohortStartIdx, cohortFinalIdx))
cohortStartIdx = cohortFinalIdx + 1
cohortFinalIdx = cohortStartIdx + cohortNetwork.number_of_nodes() - 1
cohorts_indices['c'+str(c)] = list(range(cohortStartIdx, cohortFinalIdx))

for team, indices in teams_indices.items():
if('c'+str(c) in team):
teams_indices[team] = [idx+cohortStartIdx for idx in indices]
for team, indices in teams_indices.items():
if('c'+str(c) in team):
teams_indices[team] = [idx+cohortStartIdx for idx in indices]

for i in list(range(cohortNetwork.number_of_nodes())):
i_intraCohortDegree = cohortNetwork.degree[i]
i_interCohortDegree = int( ((1/(1-pct_contacts_intercohort))*i_intraCohortDegree)-i_intraCohortDegree )
# Add intercohort edges:
if(len(cohortNetworks) > 1):
for d in list(range(i_interCohortDegree)):
j = numpy.random.choice(list(range(0, cohortStartIdx))+list(range(cohortFinalIdx+1, N)))
workplaceNetwork.add_edge(i, j)
for i in list(range(cohortNetwork.number_of_nodes())):
i_intraCohortDegree = cohortNetwork.degree[i]
i_interCohortDegree = int( ((1/(1-pct_contacts_intercohort))*i_intraCohortDegree)-i_intraCohortDegree )
# Add intercohort edges:
if(len(cohortNetworks) > 1):
for d in list(range(i_interCohortDegree)):
j = numpy.random.choice(list(range(0, cohortStartIdx))+list(range(cohortFinalIdx+1, N)))
workplaceNetwork.add_edge(i, j)

return workplaceNetwork, cohorts_indices, teams_indices
return workplaceNetwork, cohorts_indices, teams_indices


# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


def generate_demographic_contact_network(N, demographic_data, layer_generator='FARZ', layer_info=None, distancing_scales=[], isolation_groups=[], verbose=False):
def generate_demographic_contact_network(N, demographic_data, layer_generator='FARZ', layer_info=None, distancing_scales=[], isolation_groups=[], verbose=False):

graphs = {}

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