def generate_sky_model_y_hybrid(baselines,del_bl,num_bl,fits_file):
"""
y is a vector of the visibilities at different baselines
"""
healmap = a.map.Map(fromfits=fits_file)
px_array = n.arange(healmap.npix()) # gets an array of healpix pixel indices
rx,ry,rz = n.array(healmap.px2crd(px_array,ncrd=3)) # finds the topocentric coords for each healpix pixel
phi,theta = n.array(healmap.px2crd(px_array,ncrd=2)) # phi,theta in math coords
print px_array.shape
true_sky = healmap.map.map
beamsig_largebm = 10/(2*n.pi*del_bl*(num_bl-1))
beamsig_smallbm = 10/(2*n.pi*del_bl)
amp_largebm = uf.gaussian(beamsig_largebm,n.zeros_like(theta),phi)
amp_smallbm = uf.gaussian(beamsig_smallbm,n.zeros_like(theta),phi)
#smallbm_inds = (int(n.floor(num_bl/2)),int(n.floor(num_bl/2)))
smallbm_ind = int(n.floor(num_bl/2))*(num_bl+1)+1
dOmega = 4*n.pi/px_array.shape[0]
visibilities = n.zeros(baselines.shape[0],dtype='complex')
print baselines.shape[0]
for kk in range(baselines.shape[0]):
#print kk
bx,by,bz = baselines[kk]
if kk==smallbm_ind: amp = amp_smallbm
else: amp = amp_largebm
Vis = amp*true_sky*n.exp(2j*n.pi*(bx*rx+by*ry+bz*rz))*dOmega
visibilities[kk] = n.sum(Vis)
return visibilities
def generate_sky_model_y(baselines,beamsig,gsm_map=None,gsm_data_file=None):
"""
y is a vector of the visibilities at different baselines
"""
if gsm_data_file!=None:
healmap = a.map.Map(fromfits=gsm_data_file)
elif gsm_map==None:
return None
px_array = n.arange(healmap.npix()) # gets an array of healpix pixel indices
rx,ry,rz = n.array(healmap.px2crd(px_array,ncrd=3)) # finds the topocentric coords for each healpix pixel
phi,theta = n.array(healmap.px2crd(px_array,ncrd=2)) # phi,theta in math coords
print px_array.shape
true_sky = healmap.map.map
amp = uf.gaussian(beamsig,n.zeros_like(theta),phi)
dOmega = 4*n.pi/px_array.shape[0]
visibilities = n.zeros(baselines.shape[0],dtype='complex')
print baselines.shape[0]
for kk in range(baselines.shape[0]):
print kk
bx,by,bz = baselines[kk]
Vis = amp*true_sky*n.exp(2j*n.pi*(bx*rx+by*ry+bz*rz))*dOmega
visibilities[kk] = n.sum(Vis)
return visibilities
def generate_sky_model_y(baselines,
beam_sig,
gsm_map=None,
gsm_data_file=None):
"""
y is a vector of the visibilities at different baselines
"""
if gsm_data_file != None:
healmap = a.map.Map(fromfits=gsm_data_file)
elif gsm_map != None:
healmap = a.map.Map()
healmap.set_map(gsm_map)
else:
return None
px_array = n.arange(
healmap.npix()) # gets an array of healpix pixel indices
rx, ry, rz = n.array(healmap.px2crd(
px_array,
ncrd=3)) # finds the topocentric coords for each healpix pixel
phi, theta = n.array(healmap.px2crd(px_array,
ncrd=2)) # phi,theta in math coords
true_sky = healmap.map.map
amp = uf.gaussian(beam_sig, n.zeros_like(theta), phi)
dOmega = 4 * n.pi / px_array.shape[0]
visibilities = n.zeros(baselines.shape[0], dtype='complex')
for kk in range(baselines.shape[0]):
bx, by, bz = baselines[kk]
Vis = amp * true_sky * n.exp(2j * n.pi *
(bx * rx + by * ry + bz * rz)) * dOmega
visibilities[kk] = n.sum(Vis)
return visibilities
def get_coeffs_lm_fewer_baselines(aa,baselines,l,m,freqs = n.array([.1,]),savefolderpath=None,beamsig=None):
"""
This function calculates the coefficients in front of the lm spherical
harmonic for the antenna array described in the calfile. The coefficients
are determined by the integral of A(l,m)Y(l,m)exp[-i2pi(ul+vm)]dldm.
However, this function only loops over the baselines of the antennas with
respect to the origin.
"""
im = a.img.Img(size=200, res=.5) #make an image of the sky to get sky coords
tx,ty,tz = im.get_top(center=(200,200)) #get coords of the zenith?
valid = n.logical_not(tx.mask)
tx,ty,tz = tx.flatten(),ty.flatten(),tz.flatten()
theta = n.arctan(ty/tx) # using math convention of theta=[0,2pi], phi=[0,pi]
phi = n.arccos(n.sqrt(1-tx*tx-ty*ty))
#n.set_printoptions(threshold='nan')
#print theta,phi
#quit()
#beam response for an antenna pointing at (tx,ty,tz) with a polarization in x direction
#amp = A(theta) in notes
if beamsig==None:
amp = aa[0].bm_response((tx,ty,tz),pol='x')**2
else:
amp = uf.gaussian(beamsig,n.zeros_like(theta),phi)
na = len(aa.ants) # number of antennas
#coefficient array: rows baselines; cols frequencies
coeffs = n.zeros([(na*na-na)/2,len(freqs)],dtype=n.complex)
# compute normalization for spherical harmonics
Ynorm = special.sph_harm(0,0,0,0)
# loop through all baselines
for jj in range(baselines.shape[0]):
bx,by,bz = baselines[jj]
kk=0
for fq in freqs: #loop over frequencies
phs = n.exp(-2j*n.pi*fq * (bx*tx+by*ty+bz*tz)) #fringe pattern
Y = n.array(special.sph_harm(m,l,theta,phi))/Ynorm #using math convention of theta=[0,2pi], phi=[0,pi]
Y.shape = phs.shape = amp.shape = im.uv.shape
amp = n.where(valid, amp, 0)
phs = n.where(valid, phs, 0)
Y = n.where(valid, Y, 0)
# n.set_printoptions(threshold='nan')
# print Y
# quit()
dc_response = n.sum(amp*Y*phs)/n.sum(amp) #this integrates the amplitude * fringe pattern; units mK?
print 'dc = ',dc_response
jy_response = n.real(dc_response * 100 / C.pspec.jy2T(fq)) # jy2T converts flux density in jansky to temp in mK
print '\t',fq, dc_response, jy_response
coeffs[jj,kk] = dc_response
kk+=1
if savefolderpath!=None: n.savez_compressed('{0}{1}_data_l_{1}_m_{2}'.format(savefolderpath,calfile,l,m),baselines=baselines,frequencies=freqs,coeffs=coeffs)
return baselines,freqs,coeffs
# define parameters related to calculation
maxl = 10
_,beam_sig,del_bl,num_bl,fq = sys.argv
beam_sig=float(beam_sig); del_bl=float(del_bl);num_bl=int(num_bl);fq=float(fq)
savekey = 'grid_del_bl_{0:.2f}_num_bl_{1}_beam_sig_{2:.2f}_fq_{3:.2f}'.format(del_bl,num_bl,beam_sig,fq)
im = a.img.Img(size=200, res=.5) #make an image of the sky to get sky coords
tx,ty,tz = im.get_top(center=(200,200)) #get coords of the zenith?
dOmega = get_dOmega(tx,ty)
valid = n.logical_not(tx.mask)
tx,ty,tz = tx.flatten(),ty.flatten(),tz.flatten()
theta = n.arctan(ty/tx) # using math convention of theta=[0,2pi], phi=[0,pi]
phi = n.arccos(n.sqrt(1-tx*tx-ty*ty))
amp = uf.gaussian(beam_sig,n.zeros_like(theta),phi)
baselines = agg.make_pos_array(del_bl,num_bl)
num0,num1 = len(baselines),(maxl+1)*(maxl+1)
print "num baselines = {0}\n num lms = {1}".format(num0,num1)
lms = n.zeros([num1,2])
ii=0
for ll in range(maxl+1):
for mm in range(-ll,ll+1):
lms[ii] = n.array([ll,mm])
ii+=1
matrix = n.zeros([num0,num1],dtype=n.complex)
assignment_matrix = n.arange(n.prod(matrix.shape)).reshape(matrix.shape)
# define parameters related to task-mastering
dOmega = 4 * n.pi / px_array.shape[0]
phi, theta = n.array(healmap.px2crd(px_array, ncrd=2))
#print 'theta max = ',max(theta)
#print 'phi max = ',max(phi)
_, del_bl, num_bl = sys.argv
del_bl = float(del_bl)
num_bl = int(num_bl)
beamsig_largebm = 10 / (2 * n.pi * del_bl * (num_bl - 1)) #1.0
beamsig_smallbm = 10 / (2 * n.pi * del_bl) #0.25
smallbm_inds = (int(n.floor(num_bl / 2)), int(n.floor(num_bl / 2)))
savekey = 'hybrid_del_bl_{0:.2f}_num_bl_{1}'.format(del_bl, num_bl)
amp_largebm = uf.gaussian(beamsig_largebm, n.zeros_like(theta), phi)
amp_smallbm = uf.gaussian(beamsig_smallbm, n.zeros_like(theta), phi)
baselines = agg.make_pos_array(del_bl, num_bl)
# define matrix to be calculated
num = len(baselines)
matrix = n.zeros([num, num], dtype=n.complex)
# define parameters related to task-mastering
numToDo = 50 #XXX num*(num+1)/2
print 'numToDo = ', numToDo
assn_inds = []
for ii in range(num + 1):
for jj in range(ii + 1):
assn_inds.append((ii, jj))
num_sent = 0 # this functions both as a record of how many assignments have
# been sent and as a tag marking which matrix entry was calculated
global Rdata; Rdata = healmap.map.map
global dOmega; dOmega = 4*n.pi/px_array.shape[0]
phi,theta = n.array(healmap.px2crd(px_array,ncrd=2))
#print 'theta max = ',max(theta)
#print 'phi max = ',max(phi)
_,del_bl,num_bl = sys.argv
del_bl=float(del_bl);num_bl=int(num_bl)
beamsig_largebm = 10/(2*n.pi*del_bl*(num_bl-1)) #1.0
beamsig_smallbm = 10/(2*n.pi*del_bl) #0.25
smallbm_inds = (int(n.floor(num_bl/2)),int(n.floor(num_bl/2)))
savekey = 'hybrid_del_bl_{0:.2f}_num_bl_{1}'.format(del_bl,num_bl)
amp_largebm = uf.gaussian(beamsig_largebm,n.zeros_like(theta),phi)
amp_smallbm = uf.gaussian(beamsig_smallbm,n.zeros_like(theta),phi)
baselines = agg.make_pos_array(del_bl,num_bl)
# define matrix to be calculated
num = len(baselines)
matrix = n.zeros([num,num],dtype=n.complex)
# define parameters related to task-mastering
numToDo = 50 #XXX num*(num+1)/2
print 'numToDo = ',numToDo
assn_inds = []
for ii in range(num+1):
for jj in range(ii+1):
assn_inds.append((ii,jj))
num_sent = 0 # this functions both as a record of how many assignments have
# been sent and as a tag marking which matrix entry was calculated
if __name__ == '__main__':
nside = int(sys.argv[1])
npix = 12 * nside * nside
inputMapFname = sys.argv[2]
outputMapFname = sys.argv[3]
beam_sig = 1.57 * 0.15 / 0.12 * 0.4
beamMap = ap.map.Map(nside)
directionVects_xyz = beamMap.map.px2crd(np.array([i for i in range(npix)]))
directionVects_xyz = np.array(directionVects_xyz).T
directionVects_thetas = beamMap.map.px2crd(np.array(
[i for i in range(npix)]),
ncrd=2)[0]
primaryBeam = uf.gaussian(beam_sig, np.zeros(npix), directionVects_thetas)
totalBeamArea = np.sum(primaryBeam)
inputMap = ap.map.Map(fromfits=inputMapFname)
inputASCII = inputMap.map.get_map()
simpleAverage = np.sum(inputASCII) / npix
inputASCII *= primaryBeam
inputMap.map.set_map(inputASCII)
inputMap.to_fits(outputMapFname)
integratedMonopoleResponse = np.sum(inputASCII)
singleDipoleExtraction = integratedMonopoleResponse / totalBeamArea
print "simple extraction gives:", singleDipoleExtraction
print "simple averaging gives:", simpleAverage
def get_coeffs_lm_fewer_baselines(aa,
baselines,
l,
m,
freqs=n.array([
.1,
]),
savefolderpath=None,
beamsig=None):
"""
This function calculates the coefficients in front of the lm spherical
harmonic for the antenna array described in the calfile. The coefficients
are determined by the integral of A(l,m)Y(l,m)exp[-i2pi(ul+vm)]dldm.
However, this function only loops over the baselines of the antennas with
respect to the origin.
"""
im = a.img.Img(size=200,
res=.5) #make an image of the sky to get sky coords
tx, ty, tz = im.get_top(center=(200, 200)) #get coords of the zenith?
valid = n.logical_not(tx.mask)
tx, ty, tz = tx.flatten(), ty.flatten(), tz.flatten()
theta = n.arctan(ty /
tx) # using math convention of theta=[0,2pi], phi=[0,pi]
phi = n.arccos(n.sqrt(1 - tx * tx - ty * ty))
#n.set_printoptions(threshold='nan')
#print theta,phi
#quit()
#beam response for an antenna pointing at (tx,ty,tz) with a polarization in x direction
#amp = A(theta) in notes
if beamsig == None:
amp = aa[0].bm_response((tx, ty, tz), pol='x')**2
else:
amp = uf.gaussian(beamsig, n.zeros_like(theta), phi)
na = len(aa.ants) # number of antennas
#coefficient array: rows baselines; cols frequencies
coeffs = n.zeros([(na * na - na) / 2, len(freqs)], dtype=n.complex)
# compute normalization for spherical harmonics
Ynorm = special.sph_harm(0, 0, 0, 0)
# loop through all baselines
for jj in range(baselines.shape[0]):
bx, by, bz = baselines[jj]
kk = 0
for fq in freqs: #loop over frequencies
phs = n.exp(-2j * n.pi * fq *
(bx * tx + by * ty + bz * tz)) #fringe pattern
Y = n.array(
special.sph_harm(m, l, theta, phi)
) / Ynorm #using math convention of theta=[0,2pi], phi=[0,pi]
Y.shape = phs.shape = amp.shape = im.uv.shape
amp = n.where(valid, amp, 0)
phs = n.where(valid, phs, 0)
Y = n.where(valid, Y, 0)
# n.set_printoptions(threshold='nan')
# print Y
# quit()
dc_response = n.sum(amp * Y * phs) / n.sum(
amp
) #this integrates the amplitude * fringe pattern; units mK?
print 'dc = ', dc_response
jy_response = n.real(
dc_response * 100 / C.pspec.jy2T(fq)
) # jy2T converts flux density in jansky to temp in mK
print '\t', fq, dc_response, jy_response
coeffs[jj, kk] = dc_response
kk += 1
if savefolderpath != None:
n.savez_compressed('{0}{1}_data_l_{1}_m_{2}'.format(
savefolderpath, calfile, l, m),
baselines=baselines,
frequencies=freqs,
coeffs=coeffs)
return baselines, freqs, coeffs
import basic_amp_aa_grid_gauss as agg
if __name__=='__main__':
nside = int(sys.argv[1])
npix = 12 * nside * nside
inputMapFname = sys.argv[2]
outputMapFname = sys.argv[3]
beam_sig = 1.57 * 0.15 / 0.12 * 0.4
beamMap = ap.map.Map(nside)
directionVects_xyz = beamMap.map.px2crd(np.array([i for i in range(npix)]))
directionVects_xyz = np.array(directionVects_xyz).T
directionVects_thetas = beamMap.map.px2crd(np.array([i for i in range(npix)]),ncrd=2)[0]
primaryBeam = uf.gaussian(beam_sig,np.zeros(npix),directionVects_thetas)
totalBeamArea = np.sum(primaryBeam)
inputMap = ap.map.Map(fromfits=inputMapFname)
inputASCII = inputMap.map.get_map()
simpleAverage = np.sum(inputASCII) / npix
inputASCII *= primaryBeam
inputMap.map.set_map(inputASCII)
inputMap.to_fits(outputMapFname)
integratedMonopoleResponse = np.sum(inputASCII)
singleDipoleExtraction = integratedMonopoleResponse / totalBeamArea
print "simple extraction gives:",singleDipoleExtraction
print "simple averaging gives:",simpleAverage
