def load_other():
dsets_other = {}
if opts.Clongtime:
dsets_final = {}
for k in dsets:
firstfile = dsets[k][0]
dsets_final[k] = glob.glob(
'/'.join(firstfile.split('/')[:-1]) + '/lst.*' +
firstfile.split('.')[-1]) #full time range
else:
dsets_final = dsets
for k in dsets_final:
dsets_other[k] = []
for file in dsets_final[k]:
if opts.Cfg:
dsets_other[k].append(
'../../lstbin_fg/' + file.split('/')[1] + '/' +
file.split('/')[-1][:-1]) #fg containing data
elif opts.CnoFRF:
dsets_other[k].append(file[:-1]) #gets rid of 'L' on filename
elif opts.otherbls != None:
oldsep = filter(lambda x: 'sep' in x, file.split('/'))[0]
newsep = oldsep.split('p')[0] + 'p' + opts.otherbls[1:-1]
dsets_other[k].append(file.replace(oldsep, newsep))
elif opts.Clongtime != None:
dsets_other[k].append(file)
data_dict_other = {}
flg_dict_other = {}
conj_dict_other = {}
lsts_other, data_other, flgs_other = {}, {}, {}
keys_other = []
print 'Reading in other set of data to estimate C'
for k in days:
lsts_other[k], data_other[k], flgs_other[k] = capo.miriad.read_files(
dsets_other[k], antstr=antstr, polstr=POL, verbose=True)
lsts_other[k] = n.array(lsts_other[k]['lsts'])
for bl in data_other[k]:
d = n.array(
data_other[k][bl][POL])[:,
chans] * jy2T #extract frequency range
flg = n.array(flgs_other[k][bl][POL])[:, chans]
key_other = (k, bl, POL)
keys_other.append(key_other)
data_dict_other[key_other] = d
flg_dict_other[key_other] = n.logical_not(flg)
conj_dict_other[key_other[1]] = conj[key_other[1]]
ds_other = oqe.DataSet()
inds = oqe.lst_align(lsts_other)
data_dict, flg_dict, lsts = oqe.lst_align_data(inds,
dsets=data_dict_other,
wgts=flg_dict_other,
lsts=lsts_other)
ds_other.set_data(dsets=data_dict_other,
conj=conj_dict_other,
wgts=flg_dict_other)
return keys_other, ds_other
def load_other():
dsets_other = {}
if opts.Clongtime:
dsets_final = {}
for k in dsets:
firstfile = dsets[k][0]
dsets_final[k] = glob.glob('/'.join(firstfile.split('/')[:-1])+'/lst.*'+firstfile.split('.')[-1]) #full time range
else: dsets_final = dsets
for k in dsets_final:
dsets_other[k] = []
for file in dsets_final[k]:
if opts.Cfg: dsets_other[k].append('../../lstbin_fg/'+file.split('/')[1] + '/' + file.split('/')[-1][:-1]) #fg containing data
elif opts.CnoFRF: dsets_other[k].append(file[:-1]) #gets rid of 'L' on filename
elif opts.otherbls != None:
oldsep = filter(lambda x: 'sep' in x, file.split('/'))[0]
newsep = oldsep.split('p')[0]+'p'+opts.otherbls[1:-1]
dsets_other[k].append(file.replace(oldsep,newsep))
elif opts.Clongtime != None: dsets_other[k].append(file)
data_dict_other = {}
flg_dict_other = {}
conj_dict_other = {}
lsts_other,data_other,flgs_other = {},{},{}
keys_other = []
print 'Reading in other set of data to estimate C'
for k in days:
lsts_other[k],data_other[k],flgs_other[k] = capo.miriad.read_files(dsets_other[k], antstr=antstr, polstr=POL,verbose=True)
lsts_other[k] = n.array(lsts_other[k]['lsts'])
for bl in data_other[k]:
d = n.array(data_other[k][bl][POL])[:,chans] * jy2T #extract frequency range
flg = n.array(flgs_other[k][bl][POL])[:,chans]
key_other = (k,bl,POL)
keys_other.append(key_other)
data_dict_other[key_other] = d
flg_dict_other[key_other] = n.logical_not(flg)
conj_dict_other[key_other[1]] = conj[key_other[1]]
ds_other = oqe.DataSet()
inds = oqe.lst_align(lsts_other)
data_dict,flg_dict,lsts = oqe.lst_align_data(inds,dsets=data_dict_other,wgts=flg_dict_other,lsts=lsts_other)
ds_other.set_data(dsets=data_dict_other,conj=conj_dict_other,wgts=flg_dict_other)
return keys_other, ds_other
n_ = make_noise(d, stats[k]['cnt'][:, chans], inttime, sdf)
flg = n.array(flgs[k][bl][POL])[:, chans] # extract freq range
key = (k, bl, POL)
data_dict_v[key] = d
data_dict_n[key] = n_
flg_dict[key] = n.logical_not(flg)
conj_dict[key[1]] = conj[bl]
keys = data_dict_v.keys()
bls_master = []
for key in keys: # populate list of baselines
if key[0] == keys[0][0]:
bls_master.append(key[1])
print 'Baselines:', len(bls_master)
# Align dataset
inds = oqe.lst_align(lsts)
data_dict_v, flg_dict, lsts = oqe.lst_align_data(inds, dsets=data_dict_v,
wgts=flg_dict, lsts=lsts)
data_dict_n = oqe.lst_align_data(inds, dsets=data_dict_n)[0]
nlst = data_dict_v[keys[0]].shape[0]
# the lsts given is a dictionary with 'even','odd', etc.
# Save some information
cnt_full = stats[stats.keys()[0]]['cnt'][inds[stats.keys()[0]]]
cnt_full = cnt_full[:, chans]
lsts = lsts[lsts.keys()[0]]
# calculate the effective number of counts used in the data
cnt_eff = 1./n.sqrt(n.ma.masked_invalid(1./cnt_full**2).mean())
# calculate the effective number of baselines given grouping:
N = len(bls_master)
nbls = N
d = n.array(data[k][bl][POL])[:,
chans] * jy2T #extract frequency range
flg = n.array(flgs[k][bl][POL])[:, chans]
key = (k, bl, POL)
data_dict[key] = d
flg_dict[key] = n.logical_not(flg)
conj_dict[key[1]] = conj[bl]
keys = data_dict.keys()
bls_master = []
for key in keys: #populate list of baselines
if key[0] == keys[0][0]: bls_master.append(key[1])
print 'Baselines:', len(bls_master)
#Align and create dataset
ds = oqe.DataSet(lmode=LMODE)
inds = oqe.lst_align(lsts)
data_dict, flg_dict, lsts = oqe.lst_align_data(
inds, dsets=data_dict, wgts=flg_dict, lsts=lsts
) #the lsts given is a dictionary with 'even','odd', etc., but the lsts returned is one array
#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()
#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]: