def rotate_map_inv(Imag,
a1,
a2,
a3,
X=True,
Y=False,
ZYX=False,
deg=False,
nested=False):
npix = np.shape(Imag)[0]
nside = npix2nside(npix)
indices = np.arange(0, npix)
ang_coord = pix2vec(nside, indices, nested)
ang_coord_array = np.vstack((ang_coord[0], ang_coord[1], ang_coord[2]))
eul = np.linalg.inv(
euler_matrix_new(a1, a2, a3, X=X, Y=Y, ZYX=ZYX, deg=deg))
new_coord = np.dot(eul, ang_coord_array)
theta_arr, phi_arr = vec2ang(new_coord.T)
neigh, weigh = get_interp_weights(nside,
theta_arr,
phi=phi_arr,
nest=nested,
lonlat=False)
thr_val = 1e-8
weigh[np.where(np.abs(weigh) < thr_val)] = 0
weigh = weigh / np.sum(weigh, axis=0)
rotIm = np.zeros_like(Imag)
for k in range(neigh.shape[0]):
rotIm = rotIm + weigh[k] * Imag[neigh[k]]
return rotIm
def find_pointon_healpxs(ra_poi, dec_poi, ang_cut, nhpside=512):
NPIX = hppf.nside2npix(nhpside)
# MAX_PIX_DEG = degrees(hppf.max_pixrad(nhpside))
RADEC_POI = [radians(ra_poi), radians(dec_poi)]
VEC_POI = hppf.ang2vec(pi/2.-RADEC_POI[1], RADEC_POI[0])
ar_pix_incl = []
ang_cut_rad = radians(ang_cut)
for ipix in range(NPIX):
vec = hppf.pix2vec(nhpside, ipix)
ang_dist = get_ang_dist_vectors(VEC_POI, vec)
if ang_dist<ang_cut_rad:
ar_pix_incl.append(ipix)
return ar_pix_incl
def rotate_map(Imag,
a1,
a2,
a3,
X=True,
Y=False,
ZYX=False,
deg=False,
nested=False):
# X : rotation a1 around original Z
# rotation a2 around interm X
# rotation a3 around final Z
# DEFAULT, classical mechanics convention
# Y : rotation a1 around original Z
# rotation a2 around interm Y
# rotation a3 around final Z
# quantum mechanics convention (override X)
# ZYX :rotation a1 around original Z
# rotation a2 around interm Y
# rotation a3 around final X
# aeronautics convention (override X)
# * these last three keywords are obviously mutually exclusive *
npix = np.shape(Imag)[0]
nside = npix2nside(npix)
indices = np.arange(0, npix)
ang_coord = pix2vec(nside, indices, nested)
ang_coord_array = np.vstack((ang_coord[0], ang_coord[1], ang_coord[2]))
eul = euler_matrix_new(a1, a2, a3, X=X, Y=Y, ZYX=ZYX, deg=deg)
new_coord = np.dot(eul, ang_coord_array)
theta_arr, phi_arr = vec2ang(new_coord.T)
neigh, weigh = get_interp_weights(nside,
theta_arr,
phi=phi_arr,
nest=nested,
lonlat=False)
thr_val = 1e-8
weigh[np.where(np.abs(weigh) < thr_val)] = 0
weigh = weigh / np.sum(weigh, axis=0)
rotIm = np.zeros_like(Imag)
for k in range(neigh.shape[0]):
rotIm = rotIm + weigh[k] * Imag[neigh[k]]
return rotIm
infoubl = info['ubl'].dot([[4, 0, 0], [0, 15, 0], [0, 0, 0]])
calfile = 'psa6240_v003'
######get array information and accurate ubl vectors
aa = ap.cal.get_aa(calfile, np.array([freq / 1000.]))
######load beam
nsideB = 8
Bl = nsideB * 3 - 1
beam_healpix = np.zeros(12 * nsideB**2, dtype='float32')
if pol[0] != pol[-1]:
raise Exception(
'ERROR: polarization string %s not supported in the code.' % pol)
for i in range(12 * nsideB**2):
beam_healpix[i] = aa[0].bm_response(
sv.rotatez_matrix(-np.pi / 2).dot(hpf.pix2vec(nsideB, i)), pol[0]
)[0] #in paper's bm_response convention, (x,y) = (0,1) points north.
else:
raise Exception('Cant tell if the iinfo is for PAPER or MITEoR')
vs.initial_zenith = np.array([0, lat_degree * np.pi / 180]) #self.zenithequ
beam_heal_equ = np.array(
sv.rotate_healpixmap(beam_healpix, 0, np.pi / 2 - vs.initial_zenith[1],
vs.initial_zenith[0]))
timer = time.time()
if all_ubl:
ubls = infoubl
else:
ubls = [
ubl for ubl in infoubl
######load beam
bnside = 64
beam_freqs = np.arange(110., 195., 5.)
print "Reading calfile %s..."%calfile,
sys.stdout.flush()
######cal file loaded
aa = ap.cal.get_aa(calfile, beam_freqs/1000.)
print "Done. Antenna layout:"
print aa.ant_layout
sys.stdout.flush()
beam_healpix = np.zeros((len(beam_freqs), 2, 12*bnside**2), dtype='float32')
healpixvecs = np.array(hpf.pix2vec(bnside, range(12*bnside**2)))
paper_healpixvecs = (healpixvecs[:, healpixvecs[2]>=0]).transpose().dot(sv.rotatez_matrix(-np.pi/2).transpose())#in paper's bm_response convention, (x,y) = (0,1) points north.
for p, pol in enumerate(['x', 'y']):
for i, paper_angle in enumerate(paper_healpixvecs):
beam_healpix[:, p, i] = (aa[0].bm_response(paper_angle, pol)**2.).flatten()
local_beam_unpol = si.interp1d(beam_freqs, beam_healpix, axis=0)
freq = 110.
for p in range(2):
plt.subplot(2, 1, p+1)
plt.plot(hpf.get_interp_val(local_beam_unpol(freq)[p], np.arange(0, PI/2, .01), 0))
plt.plot(hpf.get_interp_val(local_beam_unpol(freq)[p], np.arange(0, PI/2, .01), PI/2))
plt.show()
raise Exception('PSA point source not yet programmed.')
lat_degree = -(30.+ 43./60. + 17.5/3600.)
infoubl = info['ubl'].dot([[4,0,0],[0,15,0],[0,0,0]])
calfile = 'psa6240_v003'
######get array information and accurate ubl vectors
aa = ap.cal.get_aa(calfile, np.array([freq/1000.]))
######load beam
nsideB = 8
Bl = nsideB*3 - 1
beam_healpix = np.zeros(12*nsideB**2, dtype='float32')
if pol[0] != pol[-1]:
raise Exception('ERROR: polarization string %s not supported in the code.'%pol)
for i in range(12*nsideB**2):
beam_healpix[i] = aa[0].bm_response(sv.rotatez_matrix(-np.pi/2).dot(hpf.pix2vec(nsideB, i)), pol[0])[0]#in paper's bm_response convention, (x,y) = (0,1) points north.
else:
raise Exception('Cant tell if the iinfo is for PAPER or MITEoR')
vs.initial_zenith = np.array([0, lat_degree*np.pi/180])#self.zenithequ
beam_heal_equ = np.array(sv.rotate_healpixmap(beam_healpix, 0, np.pi/2 - vs.initial_zenith[1], vs.initial_zenith[0]))
timer = time.time()
if all_ubl:
ubls = infoubl
else:
ubls = [ubl for ubl in infoubl if la.norm(ubl) < inclusion_thresh * (nside_target * 299.792458 / freq)]
print "%i UBLs to include"%len(ubls)
if len(tlist)*len(ubls) < 12*nside**2:
for i in range(5):
pretty_fig(4)
peak_rs, peak_heights = kde_peak_heights(ipix, r_bins, h, (0, max_r))
plt.plot(peak_rs, peak_heights, "wo")
r_cut = 1.3 * mode_radius(rs)
plt.plot(peak_rs[peak_rs < r_cut], peak_heights[peak_rs < r_cut], "ro")
plt.xlabel(r"$r_{\rm KDE}$ [Mpc/$h$]")
plt.ylabel(r"$h_{\rm KDE}$")
plt.title(r"KDE Peak Locations")
valid_peaks = peak_heights > 0
N = 2
ipix = np.arange(npix)[valid_peaks]
peak_rs = peak_rs[valid_peaks]
kde_xs, kde_ys, kde_zs = pf.pix2vec(nside, ipix) * peak_rs
c_ijks = penna_coeff(N, kde_xs, kde_ys, kde_zs)
cut_ipix = ipix[peak_rs < r_cut]
cut_peak_rs = peak_rs[peak_rs < r_cut]
cut_kde_xs,cut_kde_ys,cut_kde_zs = pf.pix2vec(nside,cut_ipix)*cut_peak_rs
cut_cijks = penna_coeff(N, cut_kde_xs, cut_kde_ys, cut_kde_zs)
cut_raw_cijks = penna_coeff(N, xs[rs<r_cut], ys[rs<r_cut], zs[rs<r_cut])
raw_cijks = penna_coeff(N, xs, ys, zs)
for n, plane_fname in enumerate([x_plane_fname,
y_plane_fname,
z_plane_fname]):
pretty_fig(n)
sys.exit(0)
#clean
bright_points = {'cyg':{'ra': '19:59:28.3', 'dec': '40:44:02'}, 'cas':{'ra': '23:23:26', 'dec': '58:48:00'}}
pt_source_range = PI / 40
smooth_scale = PI / 30
pt_source_neighborhood_range = [smooth_scale, PI / 9]
bright_pt_mask = np.zeros(Ashape1, dtype=bool)
bright_pt_neighborhood_mask = np.zeros(Ashape1, dtype=bool)
for source in bright_points.keys():
bright_points[source]['body'] = ephem.FixedBody()
bright_points[source]['body']._ra = bright_points[source]['ra']
bright_points[source]['body']._dec = bright_points[source]['dec']
theta = PI / 2 - bright_points[source]['body']._dec
phi = bright_points[source]['body']._ra
pt_coord = np.array([np.sin(theta) * np.cos(phi), np.sin(theta) * np.sin(phi), np.cos(theta)])
sky_vecs = np.array(hpf.pix2vec(nside_start, np.arange(hpf.nside2npix(nside_start)), nest=True))[:, valid_pix_mask]
bright_pt_mask = bright_pt_mask | (la.norm(sky_vecs - pt_coord[:, None], axis=0) < pt_source_range)
bright_pt_neighborhood_mask = bright_pt_neighborhood_mask | (la.norm(sky_vecs - pt_coord[:, None], axis=0) >= pt_source_neighborhood_range[0])
bright_pt_neighborhood_mask = bright_pt_neighborhood_mask | (la.norm(sky_vecs - pt_coord[:, None], axis=0) <= pt_source_neighborhood_range[1])
raw_psf = psf[:, bright_pt_mask]
cleaned_result2 = np.copy(result[bright_pt_mask])
cleaned_accumulate = np.zeros_like(cleaned_result2)
# clean_stop = 10**3.5 * np.median(sizes)#2 * np.min(np.abs(cleaned_result2))
step_size = 0.02
while np.max(np.abs(cleaned_result2)) > np.median(np.abs(cleaned_result2)) * 1.1:
clean_pix = np.argmax(np.abs(cleaned_result2))
cleaned_accumulate[clean_pix] += step_size * cleaned_result2[clean_pix]
cleaned_result2 -= step_size * cleaned_result2[clean_pix] * raw_psf[bright_pt_mask, clean_pix]
cleaned_result2 = result - raw_psf.dot(cleaned_accumulate)
plot_IQU(result, 'Simulated Dirty Map (Log(K))', 1, shape=(2, 1), coord='cg')
pretty_fig(4)
peak_rs, peak_heights = kde_peak_heights(ipix, r_bins, h, (0, max_r))
plt.plot(peak_rs, peak_heights, "wo")
r_cut = 1.3 * mode_radius(rs)
plt.plot(peak_rs[peak_rs < r_cut], peak_heights[peak_rs < r_cut], "ro")
plt.xlabel(r"$r_{\rm KDE}$ [Mpc/$h$]")
plt.ylabel(r"$h_{\rm KDE}$")
plt.title(r"KDE Peak Locations")
valid_peaks = peak_heights > 0
N = 2
ipix = np.arange(npix)[valid_peaks]
peak_rs = peak_rs[valid_peaks]
kde_xs, kde_ys, kde_zs = pf.pix2vec(nside, ipix) * peak_rs
c_ijks = penna_coeff(N, kde_xs, kde_ys, kde_zs)
cut_ipix = ipix[peak_rs < r_cut]
cut_peak_rs = peak_rs[peak_rs < r_cut]
cut_kde_xs, cut_kde_ys, cut_kde_zs = pf.pix2vec(nside,
cut_ipix) * cut_peak_rs
cut_cijks = penna_coeff(N, cut_kde_xs, cut_kde_ys, cut_kde_zs)
cut_raw_cijks = penna_coeff(N, xs[rs < r_cut], ys[rs < r_cut],
zs[rs < r_cut])
raw_cijks = penna_coeff(N, xs, ys, zs)
for n, plane_fname in enumerate(
[x_plane_fname, y_plane_fname, z_plane_fname]):
pretty_fig(n)
vartag = '_lstbineven_avg4'
datadir = '/home/omniscope/data/PAPER/lstbin_fg/even/'
antpairs = None
#cal file
calfile = 'psa6622_v002'
print "Reading calfile %s..."%calfile,
sys.stdout.flush()
aa = ap.cal.get_aa(calfile, beam_freqs/1000.)
print "Done."
sys.stdout.flush()
bnside = 64
beam_healpix = np.zeros((len(beam_freqs), 2, 12*bnside**2), dtype='float32')
healpixvecs = np.array(hpf.pix2vec(bnside, range(12*bnside**2)))
paper_healpixvecs = (healpixvecs[:, healpixvecs[2]>=0]).transpose().dot(sv.rotatez_matrix(-np.pi/2).transpose())#in paper's bm_response convention, (x,y) = (0,1) points north.
for p, pol in enumerate(['x', 'y']):
for i, paper_angle in enumerate(paper_healpixvecs):
beam_healpix[:, p, i] = (aa[0].bm_response(paper_angle, pol)**2.).flatten()
local_beam_unpol = si.interp1d(beam_freqs, beam_healpix, axis=0)
tag = INSTRUMENT + '_%.2fMHz'%freq
data_tag = 'sim'
vartag = '%.2e'%(bw * deltat)
print '#####################################################'
print '###############',tag,'###########'
print '#####################################################'
A_version = 1.0
nf = 1
# data_filename = glob.glob(datadir + tag + '_xx_*_*' + datatag)[0]
bright_points = {'cyg':{'ra': '19:59:28.3', 'dec': '40:44:02'}, 'cas':{'ra': '23:23:26', 'dec': '58:48:00'}}
pt_source_range = PI / 60
smooth_scale = PI / 30
pt_source_neighborhood_range = [smooth_scale, PI / 9]
bright_pt_mask = np.zeros(npix_nonzero, dtype=bool)
bright_pt_neighborhood_mask = np.zeros(npix_nonzero, dtype=bool)
for source in bright_points.keys():
bright_points[source]['body'] = ephem.FixedBody()
bright_points[source]['body']._ra = bright_points[source]['ra']
bright_points[source]['body']._dec = bright_points[source]['dec']
theta = PI / 2 - bright_points[source]['body']._dec
phi = bright_points[source]['body']._ra
pt_coord = np.array([np.sin(theta) * np.cos(phi), np.sin(theta) * np.sin(phi), np.cos(theta)])
sky_vecs = np.array(hpf.pix2vec(nside, np.arange(hpf.nside2npix(nside)), nest=True))[:, total_mask]
bright_pt_mask = bright_pt_mask | (la.norm(sky_vecs - pt_coord[:, None], axis=0) < pt_source_range)
bright_pt_neighborhood_mask = bright_pt_neighborhood_mask | (la.norm(sky_vecs - pt_coord[:, None], axis=0) >= pt_source_neighborhood_range[0])
bright_pt_neighborhood_mask = bright_pt_neighborhood_mask | (la.norm(sky_vecs - pt_coord[:, None], axis=0) <= pt_source_neighborhood_range[1])
AtNiA_sum = np.fromfile(AtNiA_filename, dtype='float64')
AtNiA_sum.shape = (npix_nonzero, npix_nonzero)
raw_psf_name = AtNiAi_filename + '_rawPSF_ptrange%.3f'%pt_source_range
if os.path.isfile(raw_psf_name):
raw_psf = np.fromfile(raw_psf_name, dtype='float64')
raw_psf.shape = (npix_nonzero, np.sum(bright_pt_mask))
else:
print "Computing PSFs...",
sys.stdout.flush()
timer = time.time()
raw_psf = AtNiAi.dot(AtNiA_sum[:, bright_pt_mask])
print "%.1f min."%((time.time() - timer) / 60.)
elif 'PSA' in infofile:
lat_degree = -(30.+ 43./60. + 17.5/3600.)
infoubl = info['ubl'].dot([[4,0,0],[0,15,0],[0,0,0]])
calfile = 'psa6240_v003'
######get array information and accurate ubl vectors
aa = ap.cal.get_aa(calfile, np.array([freq/1000.]))
######load beam
nsideB = 8
Bl = nsideB*3 - 1
beam_healpix = np.zeros(12*nsideB**2, dtype='float32')
if pol[0] != pol[-1]:
raise Exception('ERROR: polarization string %s not supported in the code.'%pol)
for i in range(12*nsideB**2):
beam_healpix[i] = aa[0].bm_response(sv.rotatez_matrix(-np.pi/2).dot(hpf.pix2vec(nsideB, i)), pol[0])[0]#in paper's bm_response convention, (x,y) = (0,1) points north.
else:
raise Exception('Cant tell if the iinfo is for PAPER or MITEoR')
vs.initial_zenith = np.array([0, lat_degree*np.pi/180])#self.zenithequ
beam_heal_equ = np.array(sv.rotate_healpixmap(beam_healpix, 0, np.pi/2 - vs.initial_zenith[1], vs.initial_zenith[0]))
timer = time.time()
if all_ubl:
ubls = infoubl
else:
ubls = [ubl for ubl in infoubl if la.norm(ubl) < inclusion_thresh * (nside_target * 299.792458 / freq)]
print "%i UBLs to include"%len(ubls)
if len(tlist)*len(ubls) < 12*nside**2:
for i in range(5):
#####get ubl information########
#calibrator = omni.RedundantCalibrator_PAPER(aa)
#ubls = calibrator.compute_redundantinfo()
ubls = np.array([[0,0,0]])
unit_ubls = np.round(ubls)
######construct visibility_simulatorplt.imshow()a
vs = sv.Visibility_Simulator()
vs.initial_zenith = np.array([initial_lst, aa.lat])
######load beam
nsideB = 32
Bl = nsideB*3 - 1
beam_healpix = np.zeros((len(freqs),12*nsideB**2), dtype='float32')
healpixvecs = np.array(hpf.pix2vec(nsideB, range(12*nsideB**2)))
for i, paper_angle in enumerate((healpixvecs[:, healpixvecs[2]>=0]).transpose().dot(sv.rotatez_matrix(-np.pi/2).transpose())):#in paper's bm_response convention, (x,y) = (0,1) points north.
beam_healpix[:, i] = (aa[0].bm_response(paper_angle, pol[0]) * aa[0].bm_response(paper_angle, pol[1])).flatten()
#hpv.mollview(beam_healpix[0], title='PAPER_beam')
#plt.show()
#exit()
######load GSM weights
gsm_weights = np.loadtxt(datadir + '/components.dat')
gsm_weight0_f = si.interp1d(np.log(gsm_weights[:,0]), np.log(gsm_weights[:,1]), kind = 'linear', axis = 0)
gsm_weights_f = si.interp1d(gsm_weights[:,0], gsm_weights[:,2:], kind = 'linear', axis = 0)
#for i in range(4):
#plt.plot(np.log(gsm_weights[:,0]), [gsm_weights_f(np.log(freq))[i]/gsm_weights_f(np.log(45))[i] for freq in gsm_weights[:,0]])
#plt.show()
