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unittest.jl
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unittest.jl
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using DataArrays, DataFrames
using Match
using ParallelDataTransfer
using QuadGK
using Parameters #module
using Distributions
using StatsBase
using Base.Profile
include("parameters.jl")
include("PopStruct.jl")
include("functions.jl")
sigma1 = 0.4
ef1 = 0.4
P=InfluenzaParameters(
precaution_factorS = sigma1,
precaution_factorV = 0.0,
VaccineEfficacy = ef1,
GeneralCoverage = 1,
Prob_transmission = 0.079,
sim_time = 365,
grid_size_human = 10000
)
humans = Array{Human}(P.grid_size_human)
setup_human(humans)
setup_demographic(humans,P)
vaccination(humans,P)
Number_in_age_group = zeros(Int64,15)
Age_group_Matrix = Matrix{Int64}(15,P.grid_size_human)
for i = 1:P.grid_size_human
Age_group_Matrix[humans[i].contact_group,(Number_in_age_group[humans[i].contact_group]+1)] = humans[i].index
Number_in_age_group[humans[i].contact_group] += 1
end
########################
f = open(string("VaccineEffectiveness/VaccineEffec","$ef1",".dat"),"w")
for j = 1:15
A = find(x -> x.vaccinationStatus == 1 && x.contact_group == j,humans)
soma = 0.0
for k = A
soma += humans[k].vaccineEfficacy
end
soma = soma/length(A)
println(f,"$j $soma")
end
close(f)
end
#################
######erasing the age groups
for i=1:P.grid_size_human
humans[i].contact_group = 1
end
function test_humanage()
ag = map(x -> x.age, humans)
plot(x = ag, Geom.histogram)
a = find(x -> x.gender == FEMALE, humans)
ag = zeros(Int64, length(a))
for i=1:length(a)
ag[i] = humans[a[i]].agegroup
end
ag = map(x -> x.agegroup, humans)
# ag = map(x -> x.age, humans)
plot(x = ag, Geom.histogram)
find(x -> x.age >= 15, humans)
find(x -> x.age >= 15 && x.gender == MALE, humans)
find(x -> x.age >= 15 && x.gender == FEMALE, humans)
end
#### Testing LogNormal
d = LogNormal(1,sqrt(0.4356))
d1 = Vector{Float64}(3000)
for i = 1:length(d1)
d1[i] = min(15,rand(d))
end
writedlm("testeLogNormal.dat",d1)
##Contact distribution
X = Matrix{Float64}(15,15)
X[1,:] = ContactMatrix[1,:]
for i = 2:15
for j = 1:15
X[i,j] = ContactMatrix[i,j]-ContactMatrix[i-1,j]
end
end
writedlm("MatrixContact2.dat",X)
########
NB = N_Binomial()
ContactMatrix = ContactMatrixFunc()
ContactMatrix2 = ContactMatrixFunc2()
for i=1:P.grid_size_human
humans[i].daily_contacts = rand(NB[humans[i].contact_group])
end
@profile finding_contact(humans,1,ContactMatrix)
## to do
## if there is a protection level, the
function myfunc()
A = rand(200, 200, 400)
maximum(A)
end
#############################################################
#### Testing Frailty indexx, vaccine coverage etc
FrIndex = Matrix{Float64}(length(humans),2)
for i = 1:length(humans)
rd = rand()
MaxFra,MinFra = FrailtyIndex(humans[i])
FrIndex[i,2] = rd*(MaxFra-MinFra)+MinFra
FrIndex[i,1] = humans[i].age
end
writedlm("Frailty.dat",FrIndex)
for i = 1:length(humans)
FrIndex[i,2] = humans[i].vaccineEfficacy
FrIndex[i,1] = humans[i].age
end
writedlm("VaccineEffic.dat",FrIndex)
for i = 1:length(humans)
FrIndex[i,2] = humans[i].Coverage
FrIndex[i,1] = humans[i].age
end
writedlm("VaccineCov.dat",FrIndex)
find(x -> x.age <= 64 && x.age >= 50, humans)
find(x -> x.age <= 64 && x.age >= 50 && x.vaccinationStatus == 1, humans)
###############################################################33
######################### Testing number of daily contacts
TestMatrix = zeros(Int64,15,15)
NB = N_Binomial()
ContactMatrix = ContactMatrixFunc()
for t = 1:P.sim_time
for i=1:P.grid_size_human
humans[i].daily_contacts = rand(NB[humans[i].contact_group])
for j=1:humans[i].daily_contacts
r =finding_contact2(humans,i,ContactMatrix,Age_group_Matrix,Number_in_age_group)# rand(1:P.grid_size_human)#
TestMatrix[humans[i].contact_group,humans[r].contact_group]+=1
end
end
end
TestMatrix2 = zeros(Float64,15,15)
for i = 1:15
TestMatrix2[:,i] = TestMatrix[:,i]/Number_in_age_group[i]
end
TestMatrix2 = TestMatrix2/P.sim_time
writedlm("DailyContactsP10000.dat",TestMatrix2)