def alm2map(m, a_l, Nside):
theta = get_ring_thetas(Nside)
g = al2gmtheta(m, a_l, theta)
Npix = 12 * Nside**2
map = np.zeros(Npix)
g_m_theta = np.zeros((4 * Nside - 1, lmax + 1), dtype=np.complex)
print g_m_theta.shape, g.shape
# plt.clf()
# plt.plot(g.real)
g_m_theta[:, m] = g
from wavemoth.fastsht import ShtPlan
fake_input = np.zeros(1, dtype=np.cdouble)
plan = ShtPlan(Nside, lmax, lmax, fake_input, map, 'mmajor')
plan.perform_backward_ffts(0, 4 * Nside - 1)
return map
def alm2map(m, a_l, Nside):
theta = get_ring_thetas(Nside)
g = al2gmtheta(m, a_l, theta)
Npix = 12 * Nside**2
map = np.zeros(Npix)
g_m_theta = np.zeros((4 * Nside - 1, lmax + 1), dtype=np.complex)
print g_m_theta.shape, g.shape
# plt.clf()
# plt.plot(g.real)
g_m_theta[:, m] = g
from wavemoth.fastsht import ShtPlan
fake_input = np.zeros(1, dtype=np.cdouble)
plan = ShtPlan(Nside, lmax, lmax, fake_input, map, 'mmajor')
plan.perform_backward_ffts(0, 4 * Nside - 1)
return map
from cmb.maps import pixel_sphere_map
pixel_sphere_map(m[0, :]).plot(title=title)
Nside = 256
lmax = 2 * Nside
nmaps = 1
ring_counts = get_ring_pixel_counts(Nside)
ring_offsets = np.cumsum(np.r_[0, ring_counts])[2 * Nside - 1:]
input = np.zeros(((lmax + 1) * (lmax + 2) // 2, nmaps), dtype=np.complex128)
sht_output = np.zeros((12 * Nside**2, nmaps))
psht_output = np.zeros((12 * Nside**2, nmaps), order='F')
sht_plan = ShtPlan(Nside, lmax, lmax, input, sht_output, 'mmajor')
psht_plan = PshtMmajorHealpix(lmax=lmax, Nside=Nside, nmaps=nmaps)
def hash_array(x):
h = hashlib.sha1()
h.update(x)
return h.hexdigest()
def do_comparison():
psht_plan.alm2map(input, psht_output)
sht_plan.execute()
d = norm(sht_output - psht_output)
f = norm(psht_output)
# print d
lmax = 2 * Nside
mstride = 100
bins = (lmax + 1) // mstride
mstart = (lmax + 1) - mstride * bins
print 'mstart', mstart
nmaps = 1
J = 200
K_with = 200
K_without = 5
input = np.zeros(((lmax + 1) * (lmax + 2) // 2, nmaps), dtype=np.complex128)
output = np.zeros((nmaps, 12 * Nside**2))
plan = ShtPlan(Nside, lmax, lmax, input, output,
'mmajor')
t_with = np.zeros(J)
t_without = np.zeros(J)
# Try with strides
print 'With strides...'
for j in range(J):
if j % 10 == 0: print j
t0 = clock()
plan.perform_legendre_transform(mstart, lmax + 1, mstride, repeat=K_with)
t_with[j] = (clock() - t0) / K_with
# Try without strides
print 'Without strides...'
from wavemoth.fastsht import ShtPlan
from wavemoth.psht import PshtMmajorHealpix
from wavemoth import *
from cPickle import dumps, loads
assert os.environ['OMP_NUM_THREADS'] == '1'
Nside = 128
lmax = 2 * Nside
J = 200
input = np.zeros((lmax + 1)**2, dtype=np.complex128)
output = np.zeros(12*Nside**2)
plan = ShtPlan(Nside, lmax, lmax, input, output, 'mmajor')
from cmb.maps import harmonic_sphere_map
plan.execute(repeat=1)
with benchmark('MC', J, profile=True):
plan.execute(repeat=J)
T = PshtMmajorHealpix(lmax=lmax, Nside=Nside)
T.alm2map(input, output, repeat=1)
with benchmark('healpix', J):
T.alm2map(input, output, repeat=J)
# with benchmark('M', J):
# for j in range(J * mstride):
lmax = 2 * Nside
mstride = 100
bins = (lmax + 1) // mstride
mstart = (lmax + 1) - mstride * bins
print 'mstart', mstart
nmaps = 1
J = 200
K_with = 200
K_without = 5
input = np.zeros(((lmax + 1) * (lmax + 2) // 2, nmaps), dtype=np.complex128)
output = np.zeros((nmaps, 12 * Nside**2))
plan = ShtPlan(Nside, lmax, lmax, input, output, 'mmajor')
t_with = np.zeros(J)
t_without = np.zeros(J)
# Try with strides
print 'With strides...'
for j in range(J):
if j % 10 == 0: print j
t0 = clock()
plan.perform_legendre_transform(mstart, lmax + 1, mstride, repeat=K_with)
t_with[j] = (clock() - t0) / K_with
# Try without strides
print 'Without strides...'
for j in range(J):
