def jones2celestial_basis(jones, z0_cza=None):
if z0_cza is None:
z0_cza = np.radians(120.7215)
npix = jones.shape[0]
nside = hp.npix2nside(npix)
hpxidx = np.arange(npix)
cza, ra = hp.pix2ang(nside, hpxidx)
z0 = irf.r_hat_cart(z0_cza, 0.)
RotAxis = np.cross(z0, np.array([0, 0, 1.]))
RotAxis /= np.sqrt(np.dot(RotAxis, RotAxis))
RotAngle = np.arccos(np.dot(z0, [0, 0, 1.]))
R_z0 = irf.rotation_matrix(RotAxis, RotAngle)
### New
jones_b = transform_basis(nside, jones, z0_cza, R_z0.T)
rot = [0., -z0_cza, 0.]
jones_out = irf.unitary_rotate_jones(jones_b, rot, multiway=True)
return jones_out
def horizon_mask(jones, z0_cza):
npix = jones.shape[0]
nside = hp.npix2nside(npix)
hpxidx = np.arange(npix)
cza, ra = hp.pix2ang(nside, hpxidx)
if z0_cza == 0.:
tb, pb = cza, ra
else:
z0 = irf.r_hat_cart(z0_cza, 0.)
RotAxis = np.cross(z0, np.array([0,0,1.]))
RotAxis /= np.sqrt(np.dot(RotAxis,RotAxis))
RotAngle = np.arccos(np.dot(z0, [0,0,1.]))
R_z0 = irf.rotation_matrix(RotAxis, RotAngle)
tb, pb = irf.rotate_sphr_coords(R_z0, cza, ra)
hm = np.zeros((npix,2,2))
hm[np.where(tb < np.pi/2.)] = 1.
return hm
def jones2celestial_basis(jones, z0_cza=None):
if z0_cza is None:
z0_cza = np.radians(120.7215)
npix = jones.shape[0]
nside = hp.npix2nside(npix)
hpxidx = np.arange(npix)
cza, ra = hp.pix2ang(nside, hpxidx)
z0 = irf.r_hat_cart(z0_cza, 0.)
RotAxis = np.cross(z0, np.array([0,0,1.]))
RotAxis /= np.sqrt(np.dot(RotAxis,RotAxis))
RotAngle = np.arccos(np.dot(z0, [0,0,1.]))
R_z0 = irf.rotation_matrix(RotAxis, RotAngle)
jones_b = transform_basis(nside, jones, z0_cza, R_z0.T)
rot = [0., -z0_cza, 0.]
jones_out = irf.unitary_rotate_jones(jones_b, rot, multiway=True)
return jones_out
def horizon_mask(jones, z0_cza):
npix = jones.shape[0]
nside = hp.npix2nside(npix)
hpxidx = np.arange(npix)
cza, ra = hp.pix2ang(nside, hpxidx)
if z0_cza == 0.:
tb, pb = cza, ra
else:
z0 = irf.r_hat_cart(z0_cza, 0.)
RotAxis = np.cross(z0, np.array([0, 0, 1.]))
RotAxis /= np.sqrt(np.dot(RotAxis, RotAxis))
RotAngle = np.arccos(np.dot(z0, [0, 0, 1.]))
R_z0 = irf.rotation_matrix(RotAxis, RotAngle)
tb, pb = irf.rotate_sphr_coords(R_z0, cza, ra)
hm = np.zeros((npix, 2, 2))
hm[np.where(tb < np.pi / 2.)] = 1.
return hm
def instrument_setup(z0_cza, freqs):
"""
This is the CST simulation using the efield basis of z' = -x, y' = -y, x' = -z
frequencies are every 10MHz, from 100-200
Each file contains 8 columns which are ordered as:
(Re(xt),Re(xp),Re(yt),Re(yp),Im(xt),Im(xp),Im(yt),Im(yp)).
Each column is a healpix map with resolution nside = 2**8
"""
nu0 = str(int(p.nu_axis[0] / 1e6))
nuf = str(int(p.nu_axis[-1] / 1e6))
band_str = nu0 + "-" + nuf
# restore_name = p.interp_type + "_" + "band_" + band_str + "mhz_nfreq" + str(p.nfreq)+ "_nside" + str(p.nside) + ".npy"
#
# if os.path.exists('jones_save/' + restore_name) == True:
# return np.load('jones_save/' + restore_name)
#
local_jones0_file = 'local_jones0/nside' + str(
p.nside) + '_band' + band_str + '_Jdata.npy'
if os.path.exists(local_jones0_file) == True:
return np.load(local_jones0_file)
fbase = '/data4/paper/zionos/HERA_jones_data/HERA_Jones_healpix_'
# fbase = '/home/zmart/radcos/polskysim/IonRIME/HERA_jones_data/HERA_Jones_healpix_'
nside_in = 2**8
fnames = [fbase + str(int(f / 1e6)) + 'MHz.txt' for f in freqs]
nfreq_nodes = len(freqs)
npix = hp.nside2npix(nside_in)
hpxidx = np.arange(npix)
cza, ra = hp.pix2ang(nside_in, hpxidx)
z0 = irf.r_hat_cart(z0_cza, 0.)
RotAxis = np.cross(z0, np.array([0, 0, 1.]))
RotAxis /= np.sqrt(np.dot(RotAxis, RotAxis))
RotAngle = np.arccos(np.dot(z0, [0, 0, 1.]))
R_z0 = irf.rotation_matrix(RotAxis, RotAngle)
t0, p0 = irf.rotate_sphr_coords(R_z0, cza, ra)
hm = np.zeros(npix)
hm[np.where(cza < (np.pi / 2. + np.pi / 20.)
)] = 1 # Horizon mask; is 0 below the local horizon.
# added some padding. Idea being to allow for some interpolation near the horizon. Questionable.
npix_out = hp.nside2npix(p.nside)
Jdata = np.zeros((nfreq_nodes, npix_out, 2, 2), dtype='complex128')
for i, f in enumerate(fnames):
J_f = np.loadtxt(f) # J_f.shape = (npix_in, 8)
J_f = J_f * np.tile(hm, 8).reshape(8, npix).transpose(
1, 0) # Apply horizon mask
# Could future "rotation" of these zeroed-maps have small errors at the
# edges of the horizon? due to the way healpy interpolates.
# Unlikely to be important.
# Comment update: Yep, it turns out this happens, BUT it is approximately
# power-preserving. The pixels at the edges of the rotated mask are not
# identically 1, but the sum over the mask is maintained to about a part
# in 1e-5
# Perform a scalar rotation of each component so that the instrument's boresight
# is pointed toward (z0_cza, 0), the location of the instrument on the
# earth in the Current-Epoch-RA/Dec coordinate frame.
J_f = irf.rotate_jones(J_f, R_z0, multiway=False)
if p.nside != nside_in:
# Change the map resolution as needed.
#d = lambda m: hp.ud_grade(m, nside=p.nside, power=-2.)
# I think these two ended up being (roughly) the same?
# The apparent normalization problem was really becuase of an freq. interpolation problem.
# irf.harmonic_ud_grade is probably better for increasing resolution, but hp.ud_grade is
# faster because it's just averaging/tiling instead of doing SHT's
d = lambda m: irf.harmonic_ud_grade(m, nside_in, p.nside)
J_f = (np.asarray(map(d, J_f.T))).T
# The inner transpose is so that correct dimension is map()'ed over,
# and then the outer transpose returns the array to its original shape.
J_f = irf.inverse_flatten_jones(
J_f) # Change shape to (nfreq,npix,2,2), complex-valued
J_f = transform_basis(
p.nside, J_f, z0_cza, R_z0
) # right-multiply by the basis transformation matrix from RA/CZA to the Local CST basis.
# Note that CZA = pi/2 - Dec! So this is not quite the RA/Dec basis. But the difference
# in the Stoke parameters between the two is only U -> -U
Jdata[i, :, :, :] = J_f
print i
# If the model at the current nside hasn't been generated before, save it for future reuse.
if os.path.exists(local_jones0_file) == False:
np.save(local_jones0_file, Jdata)
return Jdata
def instrument_setup(z0_cza, freqs):
"""
This is the CST simulation using the efield basis of z' = -x, y' = -y, x' = -z
frequencies are every 10MHz, from 100-200
Each file contains 8 columns which are ordered as:
(Re(xt),Re(xp),Re(yt),Re(yp),Im(xt),Im(xp),Im(yt),Im(yp)).
Each column is a healpix map with resolution nside = 2**8
"""
nu0 = str(int(p.nu_axis[0] / 1e6))
nuf = str(int(p.nu_axis[-1] / 1e6))
band_str = nu0 + "-" + nuf
# restore_name = p.interp_type + "_" + "band_" + band_str + "mhz_nfreq" + str(p.nfreq)+ "_nside" + str(p.nside) + ".npy"
#
# if os.path.exists('jones_save/' + restore_name) == True:
# return np.load('jones_save/' + restore_name)
#
local_jones0_file = 'local_jones0/nside' + str(p.nside) + '_band' + band_str + '_Jdata.npy'
if os.path.exists(local_jones0_file) == True:
return np.load(local_jones0_file)
fbase = '/data4/paper/zionos/HERA_jones_data/HERA_Jones_healpix_'
# fbase = '/home/zmart/radcos/polskysim/IonRIME/HERA_jones_data/HERA_Jones_healpix_'
nside_in = 2**8
fnames = [fbase + str(int(f / 1e6)) + 'MHz.txt' for f in freqs]
nfreq_nodes = len(freqs)
npix = hp.nside2npix(nside_in)
hpxidx = np.arange(npix)
cza, ra = hp.pix2ang(nside_in, hpxidx)
z0 = irf.r_hat_cart(z0_cza, 0.)
RotAxis = np.cross(z0, np.array([0,0,1.]))
RotAxis /= np.sqrt(np.dot(RotAxis,RotAxis))
RotAngle = np.arccos(np.dot(z0, [0,0,1.]))
R_z0 = irf.rotation_matrix(RotAxis, RotAngle)
t0, p0 = irf.rotate_sphr_coords(R_z0, cza, ra)
hm = np.zeros(npix)
hm[np.where(cza < (np.pi / 2. + np.pi / 20.))] = 1 # Horizon mask; is 0 below the local horizon.
# added some padding. Idea being to allow for some interpolation near the horizon. Questionable.
npix_out = hp.nside2npix(p.nside)
Jdata = np.zeros((nfreq_nodes,npix_out,2,2),dtype='complex128')
for i,f in enumerate(fnames):
J_f = np.loadtxt(f) # J_f.shape = (npix_in, 8)
J_f = J_f * np.tile(hm, 8).reshape(8, npix).transpose(1,0) # Apply horizon mask
# Could future "rotation" of these zeroed-maps have small errors at the
# edges of the horizon? due to the way healpy interpolates.
# Unlikely to be important.
# Comment update: Yep, it turns out this happens, BUT it is approximately
# power-preserving. The pixels at the edges of the rotated mask are not
# identically 1, but the sum over the mask is maintained to about a part
# in 1e-5
# Perform a scalar rotation of each component so that the instrument's boresight
# is pointed toward (z0_cza, 0), the location of the instrument on the
# earth in the Current-Epoch-RA/Dec coordinate frame.
J_f = irf.rotate_jones(J_f, R_z0, multiway=False)
if p.nside != nside_in:
# Change the map resolution as needed.
#d = lambda m: hp.ud_grade(m, nside=p.nside, power=-2.)
# I think these two ended up being (roughly) the same?
# The apparent normalization problem was really becuase of an freq. interpolation problem.
# irf.harmonic_ud_grade is probably better for increasing resolution, but hp.ud_grade is
# faster because it's just averaging/tiling instead of doing SHT's
d = lambda m: irf.harmonic_ud_grade(m, nside_in, p.nside)
J_f = (np.asarray(map(d, J_f.T))).T
# The inner transpose is so that correct dimension is map()'ed over,
# and then the outer transpose returns the array to its original shape.
J_f = irf.inverse_flatten_jones(J_f) # Change shape to (nfreq,npix,2,2), complex-valued
J_f = transform_basis(p.nside, J_f, z0_cza, R_z0) # right-multiply by the basis transformation matrix from RA/CZA to the Local CST basis.
# Note that CZA = pi/2 - Dec! So this is not quite the RA/Dec basis. But the difference
# in the Stoke parameters between the two is only U -> -U
Jdata[i,:,:,:] = J_f
print i
# If the model at the current nside hasn't been generated before, save it for future reuse.
if os.path.exists(local_jones0_file) == False:
np.save(local_jones0_file, Jdata)
return Jdata
def jones2celestial_basis(jones, z0_cza=None):
if z0_cza is None:
z0_cza = np.radians(120.7215)
npix = jones.shape[0]
nside = hp.npix2nside(npix)
hpxidx = np.arange(npix)
cza, ra = hp.pix2ang(nside, hpxidx)
z0 = irf.r_hat_cart(z0_cza, 0.)
RotAxis = np.cross(z0, np.array([0,0,1.]))
RotAxis /= np.sqrt(np.dot(RotAxis,RotAxis))
RotAngle = np.arccos(np.dot(z0, [0,0,1.]))
R_z0 = irf.rotation_matrix(RotAxis, RotAngle)
R_jones = irf.rotate_jones(jones, R_z0, multiway=True) # beams are now pointed at -31 deg latitude
jones_out = np.zeros((npix, 2,2), dtype=np.complex128)
########
## This next bit is a routine to patch the topological hole by grabbing pixel
## data from a neighborhood of the corrupted pixels.
## It uses the crucial assumption that in the ra/cza basis the dipoles
## are orthogonal at zenith. This means that for the diagonal components,
## the zenith pixels should be a local maximum, while for the off-diagonal
## components the zenith pixels should be a local minimum (in absolute value).
## Using this assumption, we can cover the corrupted pixel(s) in the
## zenith neighborhood by the maximum pixel of the neighborhood
## for the diagonal, and the minimum of the neighborhood for the off-diagonal.
## As long as the function is relatively flat in this neighborhood, this should
## be a good fix
jones_b = transform_basis(nside, R_jones, z0_cza, R_z0)
cf = [np.real,np.imag]
u = [1.,1.j]
z0pix = hp.vec2pix(nside, z0[0],z0[1],z0[2])
if nside < 128:
z0_nhbrs = hp.get_all_neighbours(nside, z0_cza, phi=0.)
else:
z0_nhbrs = neighbors_of_neighbors(nside, z0_cza, phi=0.)
jones_c = np.zeros((npix,2,2,2), dtype=np.float64)
for k in range(2):
jones_c[:,:,:,k] = cf[k](jones_b)
for i in range(2):
for j in range(2):
for k in range(2):
z0_nbhd = jones_c[z0_nhbrs,i,j,k]
if i == j:
fill_val_pix = np.argmax(abs(z0_nbhd))
fill_val = z0_nbhd[fill_val_pix]
else:
fill_val_pix = np.argmin(abs(z0_nbhd))
fill_val = z0_nbhd[fill_val_pix]
jones_c[z0_nhbrs,i,j,k] = fill_val
jones_c[z0pix,i,j,k] = fill_val
jones_out = jones_c[:,:,:,0] + 1j*jones_c[:,:,:,1]
return jones_out