function readRisks(infile, modelRisks) {
	while ((getline < infile)) {
		if ($1 ~ /^[ /t]*#/) { continue } #skip comments
		if ($0 ~ /^[ /t]*$/) { continue } #skip blank lines
		setRiskRanges(modelRisks, $1, $2, $3, $4, $6, $7, $8)
	}

	close(infile)
}

#Given a "worst" attribute/value pair weight, interpolate the weight in
#the given direction with each step away from the "worst" causing a drop
#of weight/(2^distance-from-worst)
#eg:
# sced 1, cplx 6 has a weight of 4 and decreases in the positive direction
# for sced and the negative direction for cplx.
# therefore sced 2, cplx 6 has a weight of 2 as it is 1 away from the s1c6
# and 4 / (2^(0 + 1) = 2. For diagonal movement (say sced 2, cplx 5) the distance
# is the sum of both the sced and cplx displacements rather than the square
# root of this distance, so sced 2, cplx 5 has a weight of 1 as
# 4 / (2^(1+1) = 1
#
#ex: sced1, cplx6 -> 4 + -
#        cplx
#    1 2 3 4 5 6
#s  +-----------
#c 1|      1 2 4
#e 2|        1 2
#d 3|          1
function setRiskRanges(modelRisks, attr1, val1, attr2, val2, weight, dir1, dir2,	xoffset,yoffset,x,y,falloffDistance) {

	falloffDistance = ( log(weight) / log(2) ) + 1

	xoffset = 0
	yoffset = 0

	for (x = 0; x <= falloffDistance; x++) {
		for(y = 0; y < falloffDistance - x; y++) {
			modelRisks[attr1,xoffset+val1] = \
				append(attr2 "@" yoffset+val2 "=" weight/(2^(y+x)),\
				       modelRisks[attr1,xoffset+val1])

			modelRisks[attr2,yoffset+val2] = \
				append(attr1 "@" xoffset+val1 "=" weight/(2^(y+x)),\
				       modelRisks[attr2,yoffset+val2])

			if (dir2 == "+")
				yoffset += 1
			else
				yoffset -= 1
		}
		yoffset = 0
		if (dir1 == "+")
			xoffset += 1
		else
			xoffset -= 1
	}
}