def apply_tilt_map(a, gt, dx, dy, dz):
x, y = geolib.get_xy_ma(a, gt)
tilt = dx * x + dy * y + dz
out = a - tilt
return out, tilt
print "Populating matrices: A=(%ix%i), b=(%i)" % (M,N,M)
xrefa = []
yrefa = []
for k in range(t.size):
#Check source
#If ATM/LVIS, tilt and offset should be 0, increased weight on dhdt
#Trans should have increased weight on dhdt, not tilt
#Not trans - Remove ASP bias
#Mono reduced weight on tilt
#print k
a = testn[k]
#za should already have ref removed
xa,ya,za = malib.get_xyz(a)
xam,yam = geolib.get_xy_ma(a, gt)
xam = xam.compressed()
yam = yam.compressed()
zam = a.compressed()
#Normalize with global offset and scaling
#xan = (xa - xref)/x.ptp()
#yan = (ya - yref)/x.ptp()
#xan = xa
#yan = ya
#Note: want to use local offset for each DEM
#Constraints are set for individual DEMs
#Proper tilt values can be very high if global offset is used
xref = np.mean(xam)
xrefa.append(xref)
xan = xam - xref
yref = np.mean(yam)