def real_coeffs(order, complex_coeffs):
pos_index = [
ssht.elm2ind(el, m) for el in range(order) for m in range(1, el + 1)
]
neg_index = [
ssht.elm2ind(el, -m) for el in range(order) for m in range(1, el + 1)
]
complex_coeffs[neg_index] = np.conjugate(complex_coeffs[pos_index])
return complex_coeffs
def sYlm(s, l, m, L, rotation=None):
flm = np.zeros(L*L, dtype=complex)
ind = ssht.elm2ind(l, m)
flm[ind] = 1.0
if rotation:
flm = ssht.rotate_flms(flm, rotation[0], rotation[1], rotation[2], L)
f = ssht.inverse(flm, L, Spin=s)
return f
def test_SHT(L, Method, Reality, Spin, nthreads=1):
if Reality:
# Generate random flms (of real signal).
flm = np.zeros((L * L), dtype=complex)
# Impose reality on flms.
for el in range(L):
m = 0
ind = ssht.elm2ind(el, m)
flm[ind] = np.random.randn()
for m in range(1, el + 1):
ind_pm = ssht.elm2ind(el, m)
ind_nm = ssht.elm2ind(el, -m)
flm[ind_pm] = np.random.randn() + 1j * np.random.randn()
flm[ind_nm] = (-1)**m * np.conj(flm[ind_pm])
else:
flm = np.random.randn(L * L) + 1j * np.random.randn(L * L)
t0 = time()
f = ssht.inverse(
flm,
L,
Reality=Reality,
Method=Method,
Spin=Spin,
backend="ducc",
nthreads=nthreads,
)
flm_syn = ssht.forward(
f,
L,
Reality=Reality,
Method=Method,
Spin=Spin,
backend="ducc",
nthreads=nthreads,
)
tducc = time() - t0
t0 = time()
f2 = ssht.inverse(flm, L, Reality=Reality, Method=Method, Spin=Spin)
flm_syn2 = ssht.forward(f2, L, Reality=Reality, Method=Method, Spin=Spin)
tssht = time() - t0
return (_l2error(f, f2) + _l2error(flm_syn, flm_syn2), tssht / tducc)
# % # % Author: Christopher G R Wallis & Jason McEwen (www.christophergrwallis.org & www.jasonmcewen.org) # % # % pyssht python package to perform spin spherical harmonic transforms # Define parameters. L = 64; el = 4 m = 2 gamma = np.pi/2 beta = np.pi/4 alpha = -np.pi/2 # Generate spherical harmonics. flm = np.zeros((L*L),dtype=complex); ind = ssht.elm2ind(el, m); flm[ind] = 1.0 + 1j*0.0; # Compute function on the sphere. f = ssht.inverse(flm, L) # Rotate spherical harmonic flm_rot = ssht.rotate_flms(flm, alpha, beta, gamma, L) # Compute rotated function on the sphere. f_rot = ssht.inverse(flm_rot, L) # Plot
# % function, using simplest interface with default options.
# %
# % Author: Christopher G R Wallis & Jason McEwen (www.christophergrwallis.org & www.jasonmcewen.org)
# %
# % pyssht python package to perform spin spherical harmonic transforms
# Define parameters.
L = 64
# Generate random flms (of real signal).
flm = np.zeros((L * L), dtype=complex)
# Impose reality on flms.
for el in range(L):
m = 0
ind = ssht.elm2ind(el, m)
flm[ind] = np.random.randn()
for m in range(1, el + 1):
ind_pm = ssht.elm2ind(el, m)
ind_nm = ssht.elm2ind(el, -m)
flm[ind_pm] = np.random.randn() + 1j * np.random.randn()
flm[ind_nm] = (-1)**m * np.conj(flm[ind_pm])
# Compute inverse then forward transform.
f = ssht.inverse(flm, L, Reality=True)
flm_syn = ssht.forward(f, L, Reality=True)
# Compute max error in harmonic space.
maxerr = np.abs(flm_syn - flm).max()
print "Max error: ", maxerr
def test_everything():
# Test indexing functions
ind2elm_check = [
pyssht.ind2elm(i) == sshtn.ind2elm(i) for i in range(L * L)
]
assert all(ind2elm_check), "ind2elm functions do not match"
elm2ind_check = [
pyssht.elm2ind(el, m) == sshtn.elm2ind(el, m) for el in range(L)
for m in range(-el, el)
]
assert all(elm2ind_check), "elm2ind functions do not match"
assert pyssht.sample_shape(L, Method="MW") == sshtn.mw_sample_shape(L)
assert pyssht.sample_shape(L, Method="MWSS") == sshtn.mwss_sample_shape(L)
py_theta, py_phi = pyssht.sample_positions(L, Method="MW", Grid=False)
nb_theta, nb_phi = sshtn.mw_sample_positions(L)
assert np.allclose(py_theta, nb_theta)
assert np.allclose(py_phi, nb_phi)
py_theta, py_phi = pyssht.sample_positions(L, Method="MWSS", Grid=False)
nb_theta, nb_phi = sshtn.mwss_sample_positions(L)
assert np.allclose(py_theta, nb_theta)
assert np.allclose(py_phi, nb_phi)
py_ttheta, py_pphi = pyssht.sample_positions(L, Method="MW", Grid=True)
nb_ttheta, nb_pphi = sshtn.mw_sample_grid(L)
assert np.allclose(py_ttheta, nb_ttheta)
assert np.allclose(py_pphi, nb_pphi)
py_ttheta, py_pphi = pyssht.sample_positions(L, Method="MWSS", Grid=True)
nb_ttheta, nb_pphi = sshtn.mwss_sample_grid(L)
assert np.allclose(py_ttheta, nb_ttheta)
assert np.allclose(py_pphi, nb_pphi)
# Generate random flms (of complex signal).
np.random.seed(89834)
flm = np.random.randn(L * L) + 1j * np.random.randn(L * L)
# Zero harmonic coefficients with el<|spin|.
ind_min = np.abs(s)**2
flm[0:ind_min] = 0.0 + 1j * 0.0
# MW inverse complex transform
f_py_mw = pyssht.inverse(flm, L, Spin=s, Method="MW")
f_nb_mw = np.empty(sshtn.mw_sample_shape(L), dtype=np.complex128)
sshtn.mw_inverse_sov_sym(flm, L, s, f_nb_mw)
assert np.allclose(f_py_mw, f_nb_mw)
# MW forward complex transform, recovering input
rec_flm_py_mw = pyssht.forward(f_py_mw, L, Spin=s, Method="MW")
rec_flm_nb_mw = np.empty(L * L, dtype=np.complex128)
sshtn.mw_forward_sov_conv_sym(f_nb_mw, L, s, rec_flm_nb_mw)
assert np.allclose(rec_flm_py_mw, rec_flm_nb_mw)
assert np.allclose(rec_flm_nb_mw, flm)
# MW forward real transform
f_re = np.random.randn(*sshtn.mw_sample_shape(L))
flm_py_re_mw = pyssht.forward(f_re, L, Spin=0, Method="MW", Reality=True)
flm_nb_re_mw = np.empty(L * L, dtype=np.complex128)
sshtn.mw_forward_sov_conv_sym_real(f_re, L, flm_nb_re_mw)
assert np.allclose(flm_py_re_mw, flm_nb_re_mw)
# MW inverse real transform
rec_f_re_py = pyssht.inverse(flm_py_re_mw,
L,
Spin=0,
Method="MW",
Reality=True)
rec_f_re_nb = np.empty(sshtn.mw_sample_shape(L), dtype=np.float64)
sshtn.mw_inverse_sov_sym_real(flm_nb_re_mw, L, rec_f_re_nb)
assert np.allclose(rec_f_re_py, rec_f_re_nb)
# Note that rec_f_re_{py,nb} != f_re since f_re is not band-limited at L
# MWSS invserse complex transform
f_py_mwss = pyssht.inverse(flm, L, Spin=s, Method="MWSS", Reality=False)
f_nb_mwss = np.empty(sshtn.mwss_sample_shape(L), dtype=np.complex128)
sshtn.mw_inverse_sov_sym_ss(flm, L, s, f_nb_mwss)
assert np.allclose(f_py_mwss, f_nb_mwss)
# MWSS forward complex transform
rec_flm_py_mwss = pyssht.forward(f_py_mwss,
L,
Spin=s,
Method="MWSS",
Reality=False)
rec_flm_nb_mwss = np.empty(L * L, dtype=np.complex128)
sshtn.mw_forward_sov_conv_sym_ss(f_nb_mwss, L, s, rec_flm_nb_mwss)
assert np.allclose(rec_flm_py_mwss, rec_flm_nb_mwss)
assert np.allclose(rec_flm_nb_mwss, flm)
# MWSS forward real transform
f_re2 = np.random.randn(*sshtn.mwss_sample_shape(L))
flm_py_re_mwss = pyssht.forward(f_re2,
L,
Spin=0,
Method="MWSS",
Reality=True)
flm_nb_re_mwss = np.empty(L * L, dtype=np.complex128)
sshtn.mw_forward_sov_conv_sym_ss_real(f_re2, L, flm_nb_re_mwss)
assert np.allclose(flm_py_re_mwss, flm_nb_re_mwss)
# MWSS inverse real transform
rec_f_re_py_mwss = pyssht.inverse(flm_py_re_mwss,
L,
Spin=0,
Method="MWSS",
Reality=True)
rec_f_re_nb_mwss = np.empty(sshtn.mwss_sample_shape(L), dtype=np.float64)
sshtn.mw_inverse_sov_sym_ss_real(flm_nb_re_mwss, L, rec_f_re_nb_mwss)
assert np.allclose(rec_f_re_py_mwss, rec_f_re_nb_mwss)
assert np.allclose(pyssht.generate_dl(np.pi / 2, 10),
sshtn.generate_dl(np.pi / 2, 10))
