function()
{
#Program input.routine.1()
#
#Does all preparation required prior to running the project() program. Can
#be revised to implement different assumptions. This routine (".1") generally
#keeps things as simple as possible, assuming most forces and parameters
#constant over time.
#
#   #argument parameter stuff
#
#
#  #initialize arrays, specifying dimensions (all must be >= 2)
#
	initialize(aa = 60, dd = 20, tt = 50)	#
#
#  #forces of mortality from non-HIV-related causes
#
	mu.female <- compute.mu(-0.15, 1)	#                   #lists containing mu and
	mu.male <- compute.mu(-0.05, 1)	#   #                   #  mu.b for single year
	mu.f <<- array(data = mu.female$mu, dim = dim(mu.f))	#for female initial pop
	mu.m <<- array(data = mu.male$mu, dim = dim(mu.f))	#   #for male initial pop
	mu.fb <- rep(mu.female$mu.b, times = tt)	#           #for female births during period
	mu.mb <- rep(mu.male$mu.b, times = tt)	#   #           #for male births during period
	names(mu.fb) <- names(mu.mb) <- 0:(tt - 1)	#           #assign names
	mu.fb <<- mu.fb	#                                       #write global variable
	mu.mb <<- mu.mb	#                                       #write global variable
#
#  #force of becoming sexually active (male paramter values are for equal area under curve)
#
	upsilon.f <<- array(data = compute.upsilon(v.scale = 0.006053, gamma = 6.46732, peak = 9), dim = dim(upsilon.f)
		)	#
	upsilon.m <<- array(data = compute.upsilon(v.scale = 0.000223, gamma = 6.46732, peak = 10.5), dim = dim(
		upsilon.m))	#
#
#  #force of becoming sexually inactive
#
	tau.f <<- array(data = compute.tau(begin.age = 30, slope = 0.0004), dim = dim(tau.f))	#
	tau.m <<- array(data = compute.tau(begin.age = 40, slope = 0.0003), dim = dim(tau.f))	#
#
#  #age pattern phi for iota, kappa and theta (force of new infection parameters)
#
	phi.f <- compute.phi(v.scale = 0.00015173, gamma = 3.8724, peak = 13.669)	#
	phi.m <- compute.phi(v.scale = 3.4485e-005, gamma = 3.8125, peak = 23.285)	#
#
	phi.f <<- phi.f	#
	phi.m <<- phi.m	#
#
#
#   #iota, kappa and theta
#
	iota.f <<- array(data = 0.004 * phi.f, dim = dim(iota.f))	#
	iota.m <<- array(data = 0.004 * phi.m, dim = dim(iota.m))	#
#
	kappa.f <<- 0.1 * phi.f	#
	kappa.m <<- 0.1 * phi.m	#
#
	level.f <- compute.theta.time.trend(initial.level = 0.3, start.change = 10, stop.change = 20)	#
	level.m <- compute.theta.time.trend(initial.level = 0.3, start.change = 10, stop.change = 20)	#
#
	theta.f <<- phi.f %o% level.f	#
	theta.m <<- phi.m %o% level.m	#
#
#  #forces of mortality from HIV-related causes
#
	alpha <<- array(data = get.alpha()[1:aa, 1:dd], dim = dim(alpha))	#
#
#  #fertility rates and sex ratio at birth
# 
	beta.y <<- array(data = compute.beta(), dim = dim(beta.y))	#
	beta.z <<- array(data = compute.beta(), dim = dim(beta.z))	#
	srb <- 1.05	#
	sigma.f <<- 1/(1 + srb)	#
	sigma.m <<- srb/(1 + srb)	#
#
#  #compute initial population distribution
#
	growth.rate <- 0.03
	stable.ad.f <- compute.stable.ad(r = growth.rate, mu = mu.f[, 1])	#
	stable.ad.m <- compute.stable.ad(r = growth.rate, mu = mu.m[, 1])	#
#
	p.active.f <- compute.proportions.active(mu.f[, 1], upsilon.f[, 1], tau.f[, 1])	#
	p.active.m <- compute.proportions.active(mu.m[, 1], upsilon.m[, 1], tau.m[, 1])	#
#
#
	xf1 <- stable.ad.f * (1 - p.active.f)	#
	yf1 <- stable.ad.f * p.active.f	#
	xm1 <- stable.ad.m * (1 - p.active.m)	#
	ym1 <- stable.ad.m * p.active.m	#
#
	factor <- 100000/sum(xf1 + yf1 + xm1 + ym1)	#
#
	xf[, 1] <<- xf1 * factor	#
	yf[, 1] <<- yf1 * factor	#
	xm[, 1] <<- xm1 * factor	#
	ym[, 1] <<- ym1 * factor	#
#
#  #wrap up
#
	write.ascii(input.routine.1)
	"All done"
}