def get_inverse_matrix(self, dst, srcs):
'''
Inverse matrix to solve linear equations
'''
src_obj = self.src_obj
dst_obj = self.dst_obj
mat_size = self.mat_size
amat = np.zeros((mat_size, mat_size), 'f8')
src_local_xyzs = src_obj.xyzs[srcs]
r0 = self.get_r0(srcs)
func = lambda r: sqrt(r*r + r0*r0) # multiquadratic
for i in xrange(mat_size):
for j in xrange(mat_size):
r = angle(src_local_xyzs[i], src_local_xyzs[j])
amat[i,j] = func(r)
try:
invmat = np.linalg.inv(amat)
except Exception, e:
print str(e)
print 'dst', dst
print 'srcs', srcs
print 'amat', amat
print 'r0', r0
sys.exit()
def get_r0(self, srcs):
'''
Define r0 with average distance between src points
'''
src_obj = self.src_obj
distances = list()
for src1 in srcs:
for src2 in srcs:
if src1 != src2:
xyz1 = src_obj.xyzs[src1]
xyz2 = src_obj.xyzs[src2]
distances.append( angle(xyz1, xyz2) )
return np.average(distances)
def get_src_address(self, dst):
src_obj = self.src_obj
dst_obj = self.dst_obj
mat_size = self.mat_size
idxs = set( src_obj.get_surround_idxs(*dst_obj.latlons[dst]) )
while len(idxs) < mat_size:
if -1 in idxs: idxs.remove(-1)
for idx in idxs.copy():
nidxs = set( src_obj.get_neighbors(idx) )
if -1 in nidxs: nidxs.remove(-1)
idxs.update(nidxs)
# sort along distance
dst_xyz = dst_obj.xyzs[dst]
sorted_idxs = sorted(idxs, key=lambda idx:angle(dst_xyz, src_obj.xyzs[idx]))
return sorted_idxs[:mat_size] # list