def test_cholesky(self):
self.noise_inv.shape = (self.size, self.size)
# Do a normal cholesky and compare.
lapack_chol = linalg.cholesky(self.noise_inv)
_c.call_cholesky(self.noise_inv)
for ii in range(self.size):
self.assertTrue(
sp.allclose(self.noise_inv[ii, ii:], lapack_chol[ii, ii:]))
def test_cholesky(self):
self.noise_inv.shape = (self.size, self.size)
# Do a normal cholesky and compare.
lapack_chol = linalg.cholesky(self.noise_inv)
_c.call_cholesky(self.noise_inv)
for ii in range(self.size):
self.assertTrue(sp.allclose(self.noise_inv[ii,ii:],
lapack_chol[ii,ii:]))
def solve(noise_inv_filename, noise_inv, dirty_map,
return_noise_diag=False, feedback=0):
"""Solve for the clean map.
Matrix and vector passed in as algebra objects with no block diagonality.
The system is solved using a GPU accelerated cholesky decomposition.
Optionally, the diagonal on the noise matrix is returned.
"""
# Import the cython stuff locally so some clean maps can be made on any
# machine.
import _cholesky as _c
# Put into the 2D matrix shape.
expanded = noise_inv.view()
side_size = noise_inv.shape[0] * noise_inv.shape[1] * noise_inv.shape[2]
expanded.shape = (side_size,) * 2
# Allowcate memory for the cholesky and copy the upper triangular data.
# Instead of copying, open the matrix in copy on write mode.
if feedback > 1:
print "Copying matrix."
time_before = time.time() / 60.
# OLD WAY.
# tri_copy = _c.up_tri_copy(expanded)
# NEW WAY.
tri_copy = up_tri_copy_from_file(noise_inv_filename)
time_after = time.time() / 60.
if feedback > 1:
print "\nMatrix copying time: %.2f minutes." % (time_after-time_before)
#tri_copy = expanded
# Get the diagonal of the giant noise inverse.
if feedback > 1:
print "Getting noise inverse diagonal."
time_before = time.time() / 60.
tri_copy.shape = side_size*side_size
noise_inv_diag = tri_copy[::side_size+1]
tri_copy.shape = (side_size,side_size)
noise_inv_diag.shape = dirty_map.shape
noise_inv_diag = algebra.as_alg_like(noise_inv_diag, dirty_map)
time_after = time.time() / 60.
if feedback > 1:
print "\nNoise inverse diagonal gotten in: %.2f minutes." \
% (time_after-time_before)
# Cholesky decompose it.
if feedback > 1:
print "Cholesky decomposition."
time_before = time.time() / 60.
_c.call_cholesky(tri_copy)
time_after = time.time() / 60.
if feedback > 1:
print "\nCholesky decomposition time: %.2f minutes." \
% (time_after-time_before)
# Solve for the clean map.
flat_map = dirty_map.view()
flat_map.shape = (flat_map.size,)
if feedback > 1:
print "Solving for clean map."
time_before = time.time() / 60.
clean_map = _c.cho_solve(tri_copy, flat_map)
time_after = time.time() / 60.
if feedback > 1:
print "\nClean map solving time: %.2f minutes." % (time_after-time_before)
# Reshape and cast as a map.
clean_map.shape = dirty_map.shape
clean_map = algebra.as_alg_like(clean_map, dirty_map)
if not return_noise_diag:
return clean_map, noise_inv_diag, tri_copy
else:
if feedback > 1:
print "Getting noise diagonal."
noise_diag = sp.empty(side_size, dtype=float)
time_before = time.time() / 60.
_c.inv_diag_from_chol(tri_copy, noise_diag)
time_after = time.time() / 60.
if feedback > 1:
print "\nNoise diagonal gotten in: %.2f minutes." \
% (time_after-time_before)
noise_diag.shape = dirty_map.shape
noise_diag = algebra.as_alg_like(noise_diag, dirty_map)
return clean_map, noise_diag, noise_inv_diag, tri_copy
def solve(noise_inv_filename,
noise_inv,
dirty_map,
return_noise_diag=False,
feedback=0):
"""Solve for the clean map.
Matrix and vector passed in as algebra objects with no block diagonality.
The system is solved using a GPU accelerated cholesky decomposition.
Optionally, the diagonal on the noise matrix is returned.
"""
# Import the cython stuff locally so some clean maps can be made on any
# machine.
import _cholesky as _c
# Put into the 2D matrix shape.
expanded = noise_inv.view()
side_size = noise_inv.shape[0] * noise_inv.shape[1] * noise_inv.shape[2]
expanded.shape = (side_size, ) * 2
# Allowcate memory for the cholesky and copy the upper triangular data.
# Instead of copying, open the matrix in copy on write mode.
if feedback > 1:
print "Copying matrix."
time_before = time.time() / 60.
# OLD WAY.
# tri_copy = _c.up_tri_copy(expanded)
# NEW WAY.
tri_copy = up_tri_copy_from_file(noise_inv_filename)
time_after = time.time() / 60.
if feedback > 1:
print "\nMatrix copying time: %.2f minutes." % (time_after -
time_before)
#tri_copy = expanded
# Get the diagonal of the giant noise inverse.
if feedback > 1:
print "Getting noise inverse diagonal."
time_before = time.time() / 60.
tri_copy.shape = side_size * side_size
noise_inv_diag = tri_copy[::side_size + 1]
tri_copy.shape = (side_size, side_size)
noise_inv_diag.shape = dirty_map.shape
noise_inv_diag = algebra.as_alg_like(noise_inv_diag, dirty_map)
time_after = time.time() / 60.
if feedback > 1:
print "\nNoise inverse diagonal gotten in: %.2f minutes." \
% (time_after-time_before)
# Cholesky decompose it.
if feedback > 1:
print "Cholesky decomposition."
time_before = time.time() / 60.
_c.call_cholesky(tri_copy)
time_after = time.time() / 60.
if feedback > 1:
print "\nCholesky decomposition time: %.2f minutes." \
% (time_after-time_before)
# Solve for the clean map.
flat_map = dirty_map.view()
flat_map.shape = (flat_map.size, )
if feedback > 1:
print "Solving for clean map."
time_before = time.time() / 60.
clean_map = _c.cho_solve(tri_copy, flat_map)
time_after = time.time() / 60.
if feedback > 1:
print "\nClean map solving time: %.2f minutes." % (time_after -
time_before)
# Reshape and cast as a map.
clean_map.shape = dirty_map.shape
clean_map = algebra.as_alg_like(clean_map, dirty_map)
if not return_noise_diag:
return clean_map, noise_inv_diag, tri_copy
else:
if feedback > 1:
print "Getting noise diagonal."
noise_diag = sp.empty(side_size, dtype=float)
time_before = time.time() / 60.
_c.inv_diag_from_chol(tri_copy, noise_diag)
time_after = time.time() / 60.
if feedback > 1:
print "\nNoise diagonal gotten in: %.2f minutes." \
% (time_after-time_before)
noise_diag.shape = dirty_map.shape
noise_diag = algebra.as_alg_like(noise_diag, dirty_map)
return clean_map, noise_diag, noise_inv_diag, tri_copy
