def frf(shape, loc=0, scale=1):
"""Generate White Noise and apply FRF."""
shape = shape[1] * 4, shape[0] # (4*times,freqs)
dij = noise(shape, loc=loc, scale=scale)
# dij = n.ones(shape)
# bins = fringe.gen_frbins(inttime)
# frp, bins = fringe.aa_to_fr_profile(aa, ij, len(afreqs)/2, bins=bins)
# timebins, firs = fringe.frp_to_firs(frp, bins, aa.get_freqs(),
# fq0=aa.get_freqs()[len(afreqs)/2])
# _,blconj,_ = zsa.grid2ij(aa.ant_layout)
# if blconj[a.miriad.ij2bl(ij[0],ij[1])]:
# fir = {(ij[0],ij[1],POL):n.conj(firs)}
# else:
# fir = {(ij[0],ij[1],POL):firs}
wij = n.ones(shape, dtype=bool) # XXX flags are all true (times,freqs)
# dij and wij are (times,freqs)
_d, _w, _, _ = fringe.apply_frf(aa,
dij,
wij,
ij[0],
ij[1],
pol=POL,
bins=bins,
firs=fir)
# _d,_w,_,_ = fringe.apply_frf(aa, _d, wij, ij[0] ,ij[1] ,
# pol=POL,bins=bins,firs=fir)
_d = n.transpose(_d)
_d = _d[:, shape[0] / 2. - shape[0] / 4. / 2.:shape[0] / 2. +
shape[0] / 4. / 2.]
return _d
def frf_data(data): #FRF FULL LENGTH DATA
aa2 = a.cal.get_aa(opts.cal, freqs) #All freqs for data instead of subset
bins = fringe.gen_frbins(inttime)
frp, bins = fringe.aa_to_fr_profile(aa2, ij, len(freqs) / 2, bins=bins)
timebins, firs = fringe.frp_to_firs(frp,
bins,
aa2.get_freqs(),
fq0=aa2.get_freqs()[len(freqs) / 2])
_, blconj, _ = zsa.grid2ij(aa2.ant_layout)
if blconj[a.miriad.ij2bl(ij[0], ij[1])]:
fir = {(ij[0], ij[1], POL): n.conj(firs)}
else:
fir = {(ij[0], ij[1], POL): firs}
dij = n.transpose(data)
wij = n.ones(dij.shape, dtype=bool) #XXX flags are all true (times,freqs)
#dij and wij are (times,freqs)
_d, _w, _, _ = fringe.apply_frf(aa2,
dij,
wij,
ij[0],
ij[1],
pol=POL,
bins=bins,
firs=fir)
_d = n.transpose(_d)
return _d
def frf(shape,loc=0,scale=1): #FRF NOISE
shape = shape[1]*2,shape[0] #(2*times,freqs)
dij = noise(shape,loc=loc,scale=scale)
wij = n.ones(shape,dtype=bool) #XXX flags are all true (times,freqs)
_d,_w,_,_ = fringe.apply_frf(aa,dij,wij,ij[0],ij[1],pol=POL,bins=bins,firs=fir)
_d = n.transpose(_d)
_d = _d[:,shape[0]/4:shape[0]/2+shape[0]/4]
return _d
def frf(shape,doublelen=False): #FRF NOISE
shape = shape[1]*2,shape[0] #(2*times,freqs)
dij = oqe.noise(size=shape)
wij = n.ones(shape,dtype=bool) #XXX flags are all true (times,freqs)
#dij and wij are (times,freqs)
_d,_w,_,_ = fringe.apply_frf(aa,dij,wij,ij[0],ij[1],pol=POL,bins=bins,firs=fir)
_d = n.transpose(_d)
if doublelen: _d = _d
else: _d = _d[:,shape[0]/4:shape[0]/2+shape[0]/4]
return _d
def frf(shape,doublelen=False): #FRF NOISE
shape = shape[1]*2,shape[0] #(2*times,freqs)
dij = oqe.noise(size=shape)
wij = n.ones(shape,dtype=bool) #XXX flags are all true (times,freqs)
#dij and wij are (times,freqs)
_d,_w,_,_ = fringe.apply_frf(aa,dij,wij,ij[0],ij[1],pol=POL,bins=bins,firs=fir)
_d = n.transpose(_d)
if doublelen: _d = _d
else: _d = _d[:,shape[0]/4:shape[0]/2+shape[0]/4]
return _d
def frf(shape, loc=0, scale=1): # FRF NOISE
"""Create Fringe Rate Filtered Noise Sim."""
shape = shape[1]*2, shape[0] # (2*times,freqs)
dij = oqe.noise(size=shape)
wij = n.ones(shape, dtype=bool) # XXX flags are all true (times,freqs)
# dij and wij are (times,freqs)
_d, _w, _, _ = fringe.apply_frf(aa, dij, wij, ij[0], ij[1],
pol=POL, bins=bins, firs=fir)
_d = n.transpose(_d)
_d = _d[:, shape[0]/4:shape[0]/2+shape[0]/4]
return _d
def frf_data(data): #FRF FULL LENGTH DATA
aa2 = a.cal.get_aa(opts.cal, freqs) #All freqs for data instead of subset
bins = fringe.gen_frbins(inttime)
frp, bins = fringe.aa_to_fr_profile(aa2, ij, len(freqs)/2, bins=bins)
timebins, firs = fringe.frp_to_firs(frp, bins, aa2.get_freqs(), fq0=aa2.get_freqs()[len(freqs)/2])
_,blconj,_ = zsa.grid2ij(aa2.ant_layout)
if blconj[a.miriad.ij2bl(ij[0],ij[1])]: fir = {(ij[0],ij[1],POL):n.conj(firs)}
else: fir = {(ij[0],ij[1],POL):firs}
dij = n.transpose(data)
wij = n.ones(dij.shape,dtype=bool) #XXX flags are all true (times,freqs)
#dij and wij are (times,freqs)
_d,_w,_,_ = fringe.apply_frf(aa2,dij,wij,ij[0],ij[1],pol=POL,bins=bins,firs=fir)
_d = n.transpose(_d)
return _d
def frf(shape,loc=0,scale=1): #FRF NOISE
shape = shape[1]*2,shape[0] #(2*times,freqs)
dij = noise(shape,loc=loc,scale=scale)
#bins = fringe.gen_frbins(inttime)
#frp, bins = fringe.aa_to_fr_profile(aa, ij, len(afreqs)/2, bins=bins)
#timebins, firs = fringe.frp_to_firs(frp, bins, aa.get_freqs(), fq0=aa.get_freqs()[len(afreqs)/2])
#_,blconj,_ = zsa.grid2ij(aa.ant_layout)
#if blconj[a.miriad.ij2bl(ij[0],ij[1])]: fir = {(ij[0],ij[1],POL):n.conj(firs)}
#else: fir = {(ij[0],ij[1],POL):firs}
wij = n.ones(shape,dtype=bool) #XXX flags are all true (times,freqs)
#dij and wij are (times,freqs)
_d,_w,_,_ = fringe.apply_frf(aa,dij,wij,ij[0],ij[1],pol=POL,bins=bins,firs=fir)
_d = n.transpose(_d)
_d = _d[:,shape[0]/4:shape[0]/2+shape[0]/4]
return _d
def frf(shape, loc=0, scale=1):
"""Generate White Noise and apply FRF."""
shape = shape[1]*4, shape[0] # (4*times,freqs)
dij = noise(shape, loc=loc, scale=scale)
# dij = n.ones(shape)
# bins = fringe.gen_frbins(inttime)
# frp, bins = fringe.aa_to_fr_profile(aa, ij, len(afreqs)/2, bins=bins)
# timebins, firs = fringe.frp_to_firs(frp, bins, aa.get_freqs(),
# fq0=aa.get_freqs()[len(afreqs)/2])
# _,blconj,_ = zsa.grid2ij(aa.ant_layout)
# if blconj[a.miriad.ij2bl(ij[0],ij[1])]:
# fir = {(ij[0],ij[1],POL):n.conj(firs)}
# else:
# fir = {(ij[0],ij[1],POL):firs}
wij = n.ones(shape, dtype=bool) # XXX flags are all true (times,freqs)
# dij and wij are (times,freqs)
_d, _w, _, _ = fringe.apply_frf(aa, dij, wij, ij[0], ij[1],
pol=POL, bins=bins, firs=fir)
def frf(shape, loc=0, scale=1): #FRF NOISE
shape = shape[1] * 2, shape[0] #(2*times,freqs)
dij = noise(shape, loc=loc, scale=scale)
#dij = oqe.noise(size=shape)
#bins = fringe.gen_frbins(inttime)
#frp, bins = fringe.aa_to_fr_profile(aa, ij, len(afreqs)/2, bins=bins)
#timebins, firs = fringe.frp_to_firs(frp, bins, aa.get_freqs(), fq0=aa.get_freqs()[len(afreqs)/2])
#_,blconj,_ = zsa.grid2ij(aa.ant_layout)
#if blconj[a.miriad.ij2bl(ij[0],ij[1])]: fir = {(ij[0],ij[1],POL):n.conj(firs)}
#else: fir = {(ij[0],ij[1],POL):firs}
wij = n.ones(shape, dtype=bool) #XXX flags are all true (times,freqs)
#dij and wij are (times,freqs)
_d, _w, _, _ = fringe.apply_frf(aa,
dij,
wij,
ij[0],
ij[1],
pol=POL,
bins=bins,
firs=fir)
_d = n.transpose(_d)
_d = _d[:, shape[0] / 4:shape[0] / 2 + shape[0] / 4]
return _d
qs_e,qs_v,qs_r,qs_ev = [], [], [], []
ps_e,ps_v,ps_r,ps_ev = [], [], [], []
c_nums = []
bar =ProgressBar(maxval=len(v_scale)*NRUN,widgets=['Performing MC:',Bar(),Percentage(),' ',ETA()]).start()
for cnt,sc in enumerate(v_scale):
tmp_qs_e,tmp_qs_v,tmp_qs_r,tmp_qs_ev = [], [], [], []
tmp_ps_e,tmp_ps_v,tmp_ps_r,tmp_ps_ev = [], [], [], []
tmp_c=[]
for run in xrange(NRUN):
e,v,r = {} , {}, {}
#Gen 1 eor for all bls
v_ = oqe.noise(size=(NCHAN,NSAMP*NFOLDS)) * sc
wij_ = np.zeros_like(v_.T)
dij,wij = v_.T , np.logical_not(wij_)
v_full,_w,_,_ = fringe.apply_frf(aa,dij,wij,ij[0],ij[1],pol=POL,bins=bins,firs=fir)
for num,k in enumerate(days):
e[k],v[k],r[k] = {}, {} ,{}
for n1,bl in enumerate(all_bls):
e[k][bl],v[k][bl],r[k][bl] = {}, {} ,{}
e_ = oqe.noise(size=(NCHAN,NSAMP*NFOLDS)).T
wij_ = np.zeros_like(e_)
dij,wij = e_ , np.logical_not(wij_)
e_,_w,_,_ = fringe.apply_frf(aa,dij,wij,ij[0],ij[1],pol=POL,bins=bins,firs=fir)
r_ = np.copy(e_ + v_full)
def fringe_rate_filter(aa, dij, wij, i, j, pol, bins, fir):
""" Apply frf."""
_d, _w, _, _ = fringe.apply_frf(aa, dij, wij, i, j, pol=pol, bins=bins, firs=fir)
return _d
ns = noise(x[k][bls_master[0]].shape) * NOISE
wij = n.transpose(f[days[0]][bls_master[0]],
[1, 0]) #flags (time,freq)
if opts.filter_noise:
if blconj[a.miriad.ij2bl(ij[0], ij[1])]:
fir = {
(ij[0], ij[1], POL): n.conj(firs)
} #conjugate fir if needed
else:
fir = {(ij[0], ij[1], POL): firs}
dij, wij = n.transpose(ns, [1, 0]), n.logical_not(wij)
ns, _w, _, _ = fringe.apply_frf(aa,
dij,
wij,
ij[0],
ij[1],
pol=POL,
bins=bins,
firs=fir)
ns = n.transpose(ns, [1, 0])
if conj[bl]: ns = n.conj(ns)
noise_array[k][bl] = n.transpose(ns.T * jy2T, [1, 0])
for k in days:
for bl in x[k]:
if opts.noise_only: x[k][bl] = n.copy(noise_array[k][bl])
else: x[k][bl] += noise_array[k][bl]
if PLOT and True:
fig, axes = p.subplots(nrows=2, ncols=1)
p.subplot(211)
#get filter which is baseline dependent
m_bl = a.miriad.ij2bl(bl[0],bl[1]) #miriad bl
sep = bl2sep[m_bl]
i,j = bl
#fir = firs[sep]
#if blconj[m_bl]: fir = n.conj(fir)
#print map(int, a.miriad.bl2ij(m_bl)), sep, blconj[m_bl]
print bl
for pol in data[bl].keys():
if blconj[m_bl]: fir = {(i,j,pol):n.conj(firs[sep])} #conjugate fir if needed
else: fir = {(i,j,pol):firs[sep]}
if not _d[bl].has_key(pol): _d[bl][pol], _w[bl][pol] = {}, {}
#_d[bl][pol] = n.zeros_like(data[bl][pol])
#_w[bl][pol] = n.zeros_like(data[bl][pol])
dij,wij = data[bl][pol],n.logical_not(flags[bl][pol])
_d[bl][pol],_w[bl][pol],_,_ = fringe.apply_frf(aa,dij,wij,i,j,pol=pol,bins=bins,firs=fir)
""" #stuff below is now in fringe.apply_frf
for ch in xrange(data[bl][pol].shape[1]):
#flg = n.logical_not(flags[bl][pol][:,ch])
#_d[bl][pol][:,ch] = n.convolve(flags[bl][pol][:,ch]*data[bl][pol][:,ch], n.conj(firs[ch,:]), mode='same')
_d[bl][pol][:,ch] = n.convolve(flg*data[bl][pol][:,ch], n.conj(fir[ch,:]), mode='same')
#_d[bl][pol][:,ch] = n.convolve(flg*data[bl][pol][:,ch], firs[ch,:], mode='same')
#_w[bl][pol][:,ch] = n.convolve(flags[bl][pol][:,ch], n.abs(n.conj(firs[ch,:])), mode='same')
_w[bl][pol][:,ch] = n.convolve(flg, n.abs(n.conj(fir[ch,:])), mode='same')
#_w[bl][pol][:,ch] = n.convolve(flg, n.abs(firs[ch,:]), mode='same')
#_d[bl][pol][:,ch] = n.where(flags[bl][pol][:,ch]>0, _d[bl][pol][:,ch]/_w[bl][pol][:,ch], 1)
_d[bl][pol][:,ch] = n.where(flg>0, _d[bl][pol][:,ch]/_w[bl][pol][:,ch], 1)
"""
def mfunc(uv, p, d, f):
uvw,t,(i,j) = p
for bl in all_bls:
d = noise((nchan,ntimes)).T * NOISE * jy2T
flg = n.zeros_like(d)
if conj[bl]: d = n.conj(d) #conjugate if necessary
x[k][bl] = n.transpose(d, [1,0]) #swap time and freq axes
f[k][bl] = n.transpose(flg, [1,0])
#eor = x.pop('eor'); days = x.keys() #make up for putting eor in list above
bls_master = x.values()[0].keys()
nbls = len(bls_master)
print 'Baselines:', nbls
if INJECT_SIG > 0.: #Create a fake EoR signal to inject
print 'INJECTING SIMULATED SIGNAL'
eor1 = noise((nchan,ntimes)).T * INJECT_SIG * jy2T
wij_ = n.zeros_like(eor1)
dij,wij = eor1 , n.logical_not(wij_)
_d,_w,_,_ = fringe.apply_frf(aa,dij,wij,ij[0],ij[1],pol=POL,bins=bins,firs=fir)
### OLD CODE TO FRF ###
#for cnt,ch in enumerate(chans):
# eor1[cnt] = n.convolve(eor1[cnt], n.conj(firs[cnt]), mode='same') #conjugate firs!!!
eor2 = n.transpose(_d, [1,0])
# eor2 = clip_array(eor2,x[k][bls_master[0]].shape)
eor = n.copy(eor2)
# eor1 = clip_array(eor1,x[k][bls_master[0]].shape)
for k in days:
for bl in x[k]:
_x = x[k][bl].copy() + eor.copy() #add injected signal to data
wij = n.logical_not(f[k][bl].T)
_x_,_,_,_ =fringe.apply_frf(aa,_x.T,wij,ij[0],ij[1],pol=POL,bins=bins,firs=fir)
_x_ = n.transpose(_x_,[1,0])
x[k][bl] = clip_array(_x_,ntimes,axis=1)