def gen_eor_mdl(ks,
Pk,
shape,
fq_axis=1,
fq0=.1,
fq1=.2,
bm_eff=HERA_BM_EFF_150,
bm_nos=HERA_BM_NOS_150,
kpr=.06):
fqs = np.linspace(fq0, fq1, shape[fq_axis]) # GHz
B = fq1 - fq0 # GHz
z = capo.pspec.f2z(np.average(fqs))
scalar = capo.pspec.X2Y(z) * bm_eff * B
mdl = scipy.interpolate.interp1d(ks, Pk, bounds_error=False, fill_value=0)
etas = np.fft.fftfreq(fqs.size, fqs[1] - fqs[0])
kpls = etas * capo.pspec.dk_deta(z)
ks = np.sqrt(kpls**2 + kpr**2)
Pk = mdl(ks)
dspec_amp = np.sqrt(Pk / scalar)
sh = [1] * len(shape)
sh[fq_axis] = -1
dspec_amp.shape = sh
jy2mK_HERA = jy2T(fqs, bm=bm_nos)
jy2mK_HERA.shape = sh
vis = oqe.noise(shape).astype(np.complex128) * dspec_amp
vis_mK = np.fft.fft(vis, axis=fq_axis)
vis_Jy = vis_mK / jy2mK_HERA
return vis_Jy
def test_q_eor_nocov(self):
k1, k2 = ('a', (0, 1), 'I'), ('b', (0, 1), 'I')
ds = oqe.DataSet({k1: self.eor, k2: self.eor})
q = ds.q_hat(k1, k2, use_cov=False)
self.assertTrue(np.all(q > 0))
self.assertAlmostEqual(np.average(q).real, q.shape[0], 0)
n1, n2 = oqe.noise(self.eor.shape), oqe.noise(self.eor.shape)
ds = oqe.DataSet({k1: self.eor + n1, k2: self.eor + n2})
qn = ds.q_hat(k1, k2, use_cov=False)
self.assertFalse(np.all(qn > 0))
self.assertAlmostEqual(np.average(qn).real, qn.shape[0], 0)
self.assertAlmostEqual(np.average(qn).real, np.average(q).real, 0)
ds = oqe.DataSet({k1: n1, k2: n2})
qn = ds.q_hat(k1, k2, use_cov=False)
self.assertFalse(np.all(qn > 0))
self.assertAlmostEqual(np.average(qn).real, 0, 0)
def test_q_eor_nocov(self):
k1,k2 = ('a',(0,1),'I'), ('b',(0,1),'I')
ds = oqe.DataSet({k1:self.eor, k2:self.eor})
q = ds.q_hat(k1,k2,use_cov=False)
self.assertTrue(np.all(q > 0))
self.assertAlmostEqual(np.average(q).real, q.shape[0], 0)
n1,n2 = oqe.noise(self.eor.shape), oqe.noise(self.eor.shape)
ds = oqe.DataSet({k1:self.eor+n1, k2:self.eor+n2})
qn = ds.q_hat(k1,k2,use_cov=False)
self.assertFalse(np.all(qn > 0))
self.assertAlmostEqual(np.average(qn).real, qn.shape[0], 0)
self.assertAlmostEqual(np.average(qn).real, np.average(q).real, 0)
ds = oqe.DataSet({k1:n1, k2:n2})
qn = ds.q_hat(k1,k2,use_cov=False)
self.assertFalse(np.all(qn > 0))
self.assertAlmostEqual(np.average(qn).real, 0, 0)
def setUpClass(self):
NFREQ = 32
NSAMP = 1024
fq = np.exp(1j*np.linspace(0,2*np.pi,NFREQ)); fq.shape = (1,-1)
ts = np.exp(1j*np.linspace(0,2*np.pi,NSAMP+1)[:-1]); ts.shape = (-1,1)
self.sky = fq * ts
self.eor = oqe.noise(size=(NSAMP,NFREQ))
sets = ['even','odd']
bls = [(0,1),(1,2),(2,3),(3,4),(4,5)]
pols = 'I'
self.data = {}
for s in sets:
self.data[s] = {}
for bl in bls:
self.data[s][bl] = {}
for pol in pols:
self.data[s][bl][pol] = self.sky + self.eor + oqe.noise((NSAMP,NFREQ))
def frf(shape,loc=0,scale=1): #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)
_d = _d[:,shape[0]/4:shape[0]/2+shape[0]/4]
return _d
def frf(shape,loc=0,scale=1): #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)
_d = _d[:,shape[0]/4:shape[0]/2+shape[0]/4]
return _d
def setUpClass(self):
NFREQ = 32
NSAMP = 1024
fq = np.exp(1j * np.linspace(0, 2 * np.pi, NFREQ))
fq.shape = (1, -1)
ts = np.exp(1j * np.linspace(0, 2 * np.pi, NSAMP + 1)[:-1])
ts.shape = (-1, 1)
self.sky = fq * ts
self.eor = oqe.noise(size=(NSAMP, NFREQ))
sets = ['even', 'odd']
bls = [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5)]
pols = 'I'
self.data = {}
for s in sets:
self.data[s] = {}
for bl in bls:
self.data[s][bl] = {}
for pol in pols:
self.data[s][bl][pol] = self.sky + self.eor + oqe.noise(
(NSAMP, NFREQ))
def frf(shape,loc=0,scale=1):
shape = shape[1]*2,shape[0] #(2*times,freqs)
dij = noise(shape,loc=loc,scale=scale)
dij = oqe.noise(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
def frf(shape,loc=0,scale=1):
shape = shape[1]*2,shape[0] #(2*times,freqs)
dij = noise(shape,loc=loc,scale=scale)
dij = oqe.noise(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
def gen_eor_mdl(ks, Pk, shape, fq_axis=1, fq0=.1, fq1=.2, bm_eff=HERA_BM_EFF_150, bm_nos=HERA_BM_NOS_150, kpr=.06):
fqs = np.linspace(fq0,fq1,shape[fq_axis]) # GHz
B = fq1 - fq0 # GHz
z = capo.pspec.f2z(np.average(fqs))
scalar = capo.pspec.X2Y(z) * bm_eff * B
mdl = scipy.interpolate.interp1d(ks, Pk, bounds_error=False, fill_value=0)
etas = np.fft.fftfreq(fqs.size, fqs[1]-fqs[0])
kpls = etas * capo.pspec.dk_deta(z)
ks = np.sqrt(kpls**2 + kpr**2)
Pk = mdl(ks)
dspec_amp = np.sqrt(Pk / scalar)
sh = [1] * len(shape); sh[fq_axis] = -1
dspec_amp.shape = sh
jy2mK_HERA = jy2T(fqs, bm=bm_nos); jy2mK_HERA.shape = sh
vis = oqe.noise(shape).astype(np.complex128) * dspec_amp
vis_mK = np.fft.fft(vis, axis=fq_axis)
vis_Jy = vis_mK / jy2mK_HERA
return vis_Jy
#! /usr/bin/env python
import numpy as np, pylab as plt
import capo, aipy
import capo.oqe as oqe
import sys
CH0,NCHAN = 30, 61
NSAMP = 120
for i in xrange(10):
e = oqe.noise(size=(NCHAN,NSAMP))
v = oqe.noise(size=(NCHAN,NSAMP))
r = e + v
k = ('even',(0,1),'I')
k1 = ('e',(0,1),'I')
k2 = ('v',(0,1),'I')
ds = oqe.DataSet(dsets={k:r.T[:-20]})
print i, np.linalg.cond(ds.C(k))
iC_r = ds.iC(k)
ds.set_data({k:e.T}); q_e = ds.q_hat(k,k)
ds.set_data({k:v.T}); q_v = ds.q_hat(k,k)
ds.set_data({k:r.T}); q_r = ds.q_hat(k,k)
F = ds.get_F(k,k)
ds.set_data({k1:e.T, k2:v.T})
ds.set_iC({k1:iC_r, k2:iC_r})
q_ev = ds.q_hat(k1,k2)
(M,W) = ds.get_MW(F)
p_e = ds.p_hat(M,q_e)
p_v = ds.p_hat(M,q_v)
p_r = ds.p_hat(M,q_r)
n.savez(outpath, kpl=kpl, scalar=scalar, times=n.array(lsts),
pk_vs_t=pC, err_vs_t=1./cnt, temp_noise_var=var, nocov_vs_t=pI,
afreqs=afreqs,chans=chans,cmd=' '.join(sys.argv))
#XXX Overwriting to new variables
pCv = pC.copy()
pIv = pI
### Loop to calculate pC of (data/noise+eor) and pI of eor ###
print ' Getting pCr and pIe'
if INJECT_SIG > 0.: #Create a fake EoR signal to inject
print ' INJECTING SIMULATED SIGNAL'
eor = (frf((len(chans),timelen),loc=0,scale=1) * INJECT_SIG).T #create FRF-ered noise
if opts.nofrf:
eor = (oqe.noise((len(chans),timelen))*INJECT_SIG).T #not FRF-ed
data_dict_2 = {}
data_dict_eor = {}
for key in data_dict:
data_dict_2[key] = data_dict[key].copy() + eor.copy() #add injected signal to data
data_dict_eor[key] = eor.copy()
#Set data
ds2 = oqe.DataSet() #data + eor
ds2.set_data(dsets=data_dict_2,conj=conj_dict,wgts=flg_dict)
dse = oqe.DataSet() #just eor
dse.set_data(dsets=data_dict_eor,conj=conj_dict,wgts=flg_dict)
#Change C if wanted
if opts.changeC:
if opts.otherbls != None:
#! /usr/bin/env python
import numpy as np, pylab as plt
import capo, aipy
import capo.oqe as oqe
import sys
CH0, NCHAN = 30, 61
NSAMP = 120
for i in xrange(10):
e = oqe.noise(size=(NCHAN, NSAMP))
v = oqe.noise(size=(NCHAN, NSAMP))
r = e + v
k = ('even', (0, 1), 'I')
k1 = ('e', (0, 1), 'I')
k2 = ('v', (0, 1), 'I')
ds = oqe.DataSet(dsets={k: r.T[:-20]})
print i, np.linalg.cond(ds.C(k))
iC_r = ds.iC(k)
ds.set_data({k: e.T})
q_e = ds.q_hat(k, k)
ds.set_data({k: v.T})
q_v = ds.q_hat(k, k)
ds.set_data({k: r.T})
q_r = ds.q_hat(k, k)
F = ds.get_F(k, k)
ds.set_data({k1: e.T, k2: v.T})
ds.set_iC({k1: iC_r, k2: iC_r})
q_ev = ds.q_hat(k1, k2)
(M, W) = ds.get_MW(F)
ij[0], ij[1]
)]: #makes sure FRP will be the same whether bl is a conjugated one or not
if ij[0] < ij[1]:
temp = (ij[1], ij[0])
ij = temp
timelen = data_dict[keys[0]].shape[0]
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])
fir = {(ij[0], ij[1], POL): firs}
if opts.same and opts.frf:
NOISE = frf((len(chans), timelen)) #same noise on all bls
if opts.same and opts.frf == None: NOISE = oqe.noise((len(chans), timelen))
for key in data_dict:
if opts.same: thing = NOISE.T
if opts.diff and opts.frf: thing = frf((len(chans), timelen)).T
if opts.diff and opts.frf == None:
thing = oqe.noise((len(chans), timelen)).T
if blconj[a.miriad.ij2bl(key[1][0], key[1][1])]:
data_dict[key] = n.conj(thing)
else:
data_dict[key] = thing
flg_dict[key] = n.ones_like(data_dict[key])
#Set data
ds.set_data(dsets=data_dict, conj=conj_dict, wgts=flg_dict)
#Get some statistics
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])
fir = {(ij[0], ij[1], POL): firs}
#If data is replaced by noise
if opts.noise_only:
if opts.same == None and opts.diff == None:
print 'Need to specify if noise is the same on all baselines (--same) or different (--diff)'
sys.exit()
if opts.same and opts.frf:
NOISE = frf((len(chans), timelen)) #same noise on all bls
if opts.same and opts.frf == None:
NOISE = oqe.noise(size=(len(chans), timelen))
for key in data_dict:
if opts.same: thing = NOISE.T
if opts.diff and opts.frf: thing = frf((len(chans), timelen)).T
if opts.diff and opts.frf == None:
thing = oqe.noise(size=(len(chans), timelen)).T
if blconj[a.miriad.ij2bl(key[1][0], key[1][1])]:
data_dict[key] = n.conj(thing)
else:
data_dict[key] = thing
flg_dict[key] = n.ones_like(data_dict[key])
#Set data
ds.set_data(dsets=data_dict, conj=conj_dict, wgts=flg_dict)
#Get some statistics
#bp = mk_dly_profile([0,-10],[-1.,-.1]))
fg = 0
for cnt in xrange(NFG):
fg_ch = 1e3 * (fqs / .150)**np.random.uniform(-2, 0)
fg_ch.shape = (-1, 1)
bm = mk_dly_profile(
[np.random.uniform(-10, 0),
np.random.uniform(-40, 0)],
[np.random.uniform(-1, -2),
np.random.uniform(-.1, -.2)])
bm.shape = (-1, 1)
fg_t = np.sin((cnt + 1) * ts)
fg_t.shape = (1, -1)
fg += bm * fg_ch * fg_t
eor = .01 * oqe.noise(size=(NCHAN, NSAMP))
#f = 0.3 # inject less eor so pspec goes below the eor signal used for computing ratios
f = 1 # inject less eor so pspec goes below the eor signal used for computing ratios
dat = {}
for k in fg:
dat[k] = (fg[k] + f * eor[k])
NSAMP = fg[k].shape[0]
dat_cut = {}
for k in dat:
dat_cut[k] = np.concatenate([dat[k][:54], dat[k][65:]], axis=0)
ds = oqe.DataSet(dsets=dat)
#ds = oqe.DataSet(dsets=dat_cut)
prf_c_final = {.1: 100, .15: 100, .2: 100}
prf_w_final = {.1: 100, .15: 100, .2: 100}
if blconj[aipy.miriad.ij2bl(ij[0], ij[1])]:
fir = {(ij[0], ij[1], POL): np.conj(firs)} #conjugate fir if needed
else:
fir = {(ij[0], ij[1], POL): firs}
qs_e, qs_v, qs_r, qs_ev = [], [], [], []
ps_e, ps_v, ps_r, ps_ev = [], [], [], []
c_nums = []
for sc in 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 i in xrange(NRUN):
v = oqe.noise(size=(NCHAN, NSAMP * NTIMES)) * sc
wij_ = np.zeros_like(v.T)
dij, wij = v.T, np.logical_not(wij_)
v, _w, _, _ = fringe.apply_frf(aa,
dij,
wij,
ij[0],
ij[1],
pol=POL,
bins=bins,
firs=fir)
e = oqe.noise(size=(NCHAN, NSAMP * NTIMES)).T
r = np.copy(e + v)
wij_ = np.zeros_like(r)
def test_noise(self):
n = oqe.noise((1024, 1024))
self.assertEqual(n.shape, (1024, 1024))
self.assertAlmostEqual(np.var(n), 1, 2)
def test_noise(self):
n = oqe.noise((1024,1024))
self.assertEqual(n.shape, (1024,1024))
self.assertAlmostEqual(np.var(n), 1, 2)
timelen = data_dict[keys[0]].shape[0]
ij = bls_master[0] #ij = (1,4)
if blconj[a.miriad.ij2bl(ij[0],ij[1])]: #makes sure FRP will be the same whether bl is a conjugated one or not
if ij[0] < ij[1]: temp = (ij[1],ij[0]); ij=temp
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])
fir = {(ij[0],ij[1],POL):firs}
#If data is replaced by noise
if opts.noise_only:
if opts.same == None and opts.diff == None:
print 'Need to specify if noise is the same on all baselines (--same) or different (--diff)'
sys.exit()
if opts.same and opts.frf: NOISE = frf((len(chans),timelen)) #same noise on all bls
if opts.same and opts.frf == None: NOISE = oqe.noise(size=(len(chans),timelen))
for key in data_dict:
if opts.same: thing = NOISE.T
if opts.diff and opts.frf: thing = frf((len(chans),timelen)).T
if opts.diff and opts.frf == None: thing = oqe.noise(size=(len(chans),timelen)).T
if blconj[a.miriad.ij2bl(key[1][0],key[1][1])]: data_dict[key] = n.conj(thing)
else: data_dict[key] = thing
flg_dict[key] = n.ones_like(data_dict[key])
#Set data
ds.set_data(dsets=data_dict,conj=conj_dict,wgts=flg_dict)
#Get some statistics
if LST_STATS:
#collect some metadata from the lst binning process
cnt, var = {}, {}
timelen = data_dict[keys[0]].shape[0]
ij = bls_master[0] #ij = (1,4)
if blconj[a.miriad.ij2bl(ij[0],ij[1])]: #makes sure FRP will be the same whether bl is a conjugated one or not
if ij[0] < ij[1]: temp = (ij[1],ij[0]); ij=temp
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])
fir = {(ij[0],ij[1],POL):firs}
#If data is replaced by noise
if opts.noise_only:
if opts.same == None and opts.diff == None:
print 'Need to specify if noise is the same on all baselines (--same) or different (--diff)'
sys.exit()
if opts.same and opts.frf: NOISE = frf((len(chans),timelen)) #same noise on all bls
if opts.same and opts.frf == None: NOISE = oqe.noise(size=(len(chans),timelen))
for key in data_dict:
if opts.same: thing = NOISE.T
if opts.diff and opts.frf: thing = frf((len(chans),timelen)).T
if opts.diff and opts.frf == None: thing = oqe.noise(size=(len(chans),timelen)).T
if blconj[a.miriad.ij2bl(key[1][0],key[1][1])]: data_dict[key] = n.conj(thing)
else: data_dict[key] = thing
flg_dict[key] = n.ones_like(data_dict[key])
#Set data
ds.set_data(dsets=data_dict,conj=conj_dict,wgts=flg_dict)
#Get some statistics
if LST_STATS:
#collect some metadata from the lst binning process
cnt, var = {}, {}
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] = {}, {}, {}
def make_eor(shape): # Create and fringe rate filter noise
"""Generate White Noise and Apply FRF."""
dij = oqe.noise(size=shape)
return dij
if blconj[aipy.miriad.ij2bl(ij[0],ij[1])]: fir = {(ij[0],ij[1],POL):np.conj(firs)} #conjugate fir if needed
else: fir = {(ij[0],ij[1],POL):firs}
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)
timebins, firs = fringe.frp_to_firs(frp, bins, aa.get_freqs(), fq0=aa.get_freqs()[len(afreqs)/2])#, fr_width_scale=1.3, maxfr=1.3e-3)
if blconj[aipy.miriad.ij2bl(ij[0],ij[1])]: fir = {(ij[0],ij[1],POL):np.conj(firs)} #conjugate fir if needed
else: fir = {(ij[0],ij[1],POL):firs}
qs_e,qs_v,qs_r,qs_ev = [], [], [], []
ps_e,ps_v,ps_r,ps_ev = [], [], [], []
c_nums = []
for sc in 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 i in xrange(NRUN):
v = oqe.noise(size=(NCHAN,NSAMP*NTIMES)) * sc
wij_ = np.zeros_like(v.T)
dij,wij = v.T , np.logical_not(wij_)
v,_w,_,_ = fringe.apply_frf(aa,dij,wij,ij[0],ij[1],pol=POL,bins=bins,firs=fir)
e = oqe.noise(size=(NCHAN,NSAMP*NTIMES)).T
r = np.copy(e + v)
wij_ = np.zeros_like(r)
dij,wij = r , np.logical_not(wij_)
r,_w,_,_ = fringe.apply_frf(aa,dij,wij,ij[0],ij[1],pol=POL,bins=bins,firs=fir)
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)
def mk_dly_profile(dbs, slopes):
prf = 0.
for db,slope in zip(dbs,slopes):
prf += 10**((db + slope * np.abs(dly))/10.)
phs = np.random.uniform(0,2*np.pi,size=NCHAN)
phs[0] = 0; phs[1:NCHAN/2+1] = phs[NCHAN/2+1:]
return np.fft.fft(prf*np.exp(1j*phs))
#bp = mk_dly_profile([0,-10],[-1.,-.1]))
fg = 0
for cnt in xrange(NFG):
fg_ch = 1e3 * (fqs/.150)**np.random.uniform(-2,0); fg_ch.shape = (-1,1)
bm = mk_dly_profile([np.random.uniform(-10,0),np.random.uniform(-40,0)],[np.random.uniform(-1,-2),np.random.uniform(-.1,-.2)]); bm.shape = (-1,1)
fg_t = np.sin((cnt+1)*ts); fg_t.shape = (1,-1)
fg += bm * fg_ch * fg_t
eor = .01*oqe.noise(size=(NCHAN,NSAMP))
f = 0.3 # inject less eor so pspec goes below the eor signal used for computing ratios
#f = 1 # inject less eor so pspec goes below the eor signal used for computing ratios
dat = {}
for k in fg: dat[k] = (fg[k] + f*eor[k])
NSAMP = fg[k].shape[0]
dat_cut = {}
for k in dat: dat_cut[k] = np.concatenate([dat[k][:54],dat[k][65:]], axis=0)
ds = oqe.DataSet(dsets=dat)
#ds = oqe.DataSet(dsets=dat_cut)
prf_c_final = {.1:100, .15:100, .2:100}
prf_w_final = {.1:100, .15:100, .2:100}
for boot in xrange(1):
print boot
'(--same) or different (--diff)')
sys.exit()
# Prep FRF Stuff
ij = bls_master[0] # ij = (1,4)
if blconj[a.miriad.ij2bl(ij[0], ij[1])]:
# makes sure FRP will be the same whether bl is a conjugated one or not
if ij[0] < ij[1]:
temp = (ij[1], ij[0])
ij = temp
timelen = data_dict[keys[0]].shape[0]
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])
fir = {(ij[0],ij[1],POL):firs}
if opts.same and opts.frf: NOISE = frf((len(chans),timelen)) #same noise on all bls
if opts.same and opts.frf == None: NOISE = oqe.noise((len(chans),timelen))
for key in data_dict:
if opts.same: thing = NOISE.T
if opts.diff and opts.frf: thing = frf((len(chans),timelen)).T
if opts.diff and opts.frf == None: thing = oqe.noise((len(chans),timelen)).T
if blconj[a.miriad.ij2bl(key[1][0],key[1][1])]: data_dict[key] = n.conj(thing)
else: data_dict[key] = thing
flg_dict[key] = n.ones_like(data_dict[key])
# Set data
ds.set_data(dsets=data_dict, conj=conj_dict, wgts=flg_dict)
# Get some statistics
if LST_STATS:
# collect some metadata from the lst binning process
cnt, var = {}, {}
