def load_binned_ty(mb,
dtype='dr2_smica',
fltr='cinv',
cmask='Lmask',
mtype=0,
ytype='nilc',
bhe=['lens']):
# load filenames
d = prjlib.data_directory()
p = prjlib.init_analysis(dtype=dtype, fltr=fltr, wtype=cmask)
cy = init_compy(p.ids, masktype=mtype, ytype=ytype)
qobj = quad_func.quad(stag=p.stag,
root=d['root'],
ids=p.ids,
qtype='tau',
bhe=bhe,
rlmin=100,
rlmax=2048)
ftxy = init_cross(qobj, cy, p.ids, p.stag)
# optimal binning
yy = savgol_filter((np.loadtxt(cy.fclyy)).T[1], 51, 3)
al = (np.loadtxt(qobj.f['TT'].al)).T[1]
vl = np.sqrt(al * yy) / np.sqrt(qobj.l + 1e-30)
# results
return bn.binned_spec(mb, ftxy.xl, cn=1, opt=True, vl=vl)
def init_quad(snmax, qtypes, **ikwargs):
d = local.data_directory()
kwargs = {\
'n0min':1, \
'n0max':int(snmax/2), \
'mfmin':1, \
'mfmax':snmax, \
'rdmin':1, \
'rdmax':snmax \
}
qlist = {}
qlist['lens'] = ['TT', 'TE', 'EE', 'TB', 'EB']
qlist['ilens'] = ['TB', 'EB', 'BB']
# setup parameters for lensing reconstruction (see cmblensplus/utils/quad_func.py)
return {
qt: quad_func.quad(root=d['root'],
qtype=qt,
qlist=qlist[qt],
**kwargs,
**ikwargs)
for qt in qtypes
}
def init_quad(ids, stag, rlz=[], **kwargs):
d = prjlib.data_directory()
# setup parameters for lensing reconstruction (see cmblensplus/utils/quad_func.py)
qtau = quad_func.quad(rlz=rlz,
stag=stag,
root=d['root'],
ids=ids,
qtype='tau',
**kwargs)
qlen = quad_func.quad(rlz=rlz,
stag=stag,
root=d['root'],
ids=ids,
qtype='lens',
**kwargs)
qsrc = quad_func.quad(rlz=rlz,
stag=stag,
root=d['root'],
ids=ids,
qtype='src',
**kwargs)
qtbh = quad_func.quad(rlz=rlz,
stag=stag,
root=d['root'],
ids=ids,
qtype='tau',
bhe=['lens'],
**kwargs)
qtBH = quad_func.quad(rlz=rlz,
stag=stag,
root=d['root'],
ids=ids,
qtype='tau',
bhe=['lens', 'src'],
**kwargs)
#qtau.fname(d['root'],ids,stag)
#qlen.fname(d['root'],ids,stag)
#qsrc.fname(d['root'],ids,stag)
#qtbh.fname(d['root'],ids,stag)
#qtBH.fname(d['root'],ids,stag)
return qtau, qlen, qsrc, qtbh, qtBH
def load_binned_tt(mb,
dtype='dr2_smica',
fltr='cinv',
cmask='Lmask',
bhe=['lens'],
snmax=100):
# filename
d = prjlib.data_directory()
p = prjlib.init_analysis(snmax=snmax, dtype=dtype, fltr=fltr, wtype=cmask)
qobj = quad_func.quad(stag=p.stag,
root=d['root'],
ids=p.ids,
qtype='tau',
bhe=bhe,
rlmin=100,
rlmax=2048)
# optimal filter
al = (np.loadtxt(qobj.f['TT'].al)).T[1]
vl = al / np.sqrt(qobj.l + 1e-30)
# binned spectra
mtt, __, stt, ott = bn.binned_spec(mb,
qobj.f['TT'].cl,
cn=1,
doreal=True,
opt=True,
vl=vl)
# noise bias
nb = bn.binning((np.loadtxt(qobj.f['TT'].n0bs)).T[1], mb, vl=vl)
rd = np.array([(np.loadtxt(qobj.f['TT'].rdn0[i])).T[1] for i in p.rlz])
rb = bn.binning(rd, mb, vl=vl)
# debias
ott = ott - rb[0] - nb / (qobj.mfsim)
stt = stt - rb[1:, :] - nb / (qobj.mfsim - 1)
# sim mean and std
mtt = mtt - np.mean(rb[1:, :], axis=0) - nb / (qobj.mfsim - 1)
vtt = np.std(stt, axis=0)
# subtract average of sim
ott = ott - mtt
return mtt, vtt, stt, ott
def __init__(self, config, ver=''):
#//// get parameters ////#
conf = config['DEFAULT']
self.nside = conf.getint('nside', 4096) #Nside for fullsky cmb map
self.npix = 12 * self.nside**2
self.lmin = conf.getint('lmin', 0)
self.lmax = conf.getint('lmax', 3000)
self.olmin = 1
self.olmax = self.lmax
self.ol = [self.olmin, self.olmax]
self.bn = conf.getint('bn', 30)
self.binspc = conf.get('binspc', '')
self.snmin = conf.getint('snmin', 0)
self.snmax = conf.getint('snmax', 100)
self.stype = conf.get('stype', 'lcmb')
self.PSA = conf.get('PSA', 's14&15_deep56')
self.ascale = conf.getint('ascale', 1)
self.doreal = conf.getboolean('doreal', False)
self.dodust = conf.getboolean('dodust', False)
self.dearot = conf.getboolean('dearot', False)
self.lcut = conf.getint('lcut', 100)
self.ver = conf.get('ver', ver)
# reconstruction
self.quad = quad_func.quad(config['QUADREC'])
#//// Error check ////#
#dearot
if self.dearot:
if not self.doreal:
sys.exit('derot abs angle: doreal should be True')
if self.stype != 'lcmb':
sys.exit('derot abs angle: stype should be lcmb')
#//// derived parameters ////#
# total number of real + sim
self.snum = self.snmax - self.snmin + 1
self.psa = self.PSA.replace('&', '+')
#mtype
if '0p' in self.stype or self.stype == 'dust':
self.mlist = ['E', 'B']
else:
self.mlist = ['T', 'E', 'B']
#rlz num
if '0p' in self.stype:
self.quad.n0min = 1
self.quad.n0max = 50
self.quad.rdmin = 1
self.quad.rdmax = 100
self.quad.mfmin = 1
self.quad.mfmax = 100
self.quad.n0sim = 50
self.quad.rdsim = 100
self.quad.mfsim = 100
#specific version
if self.ver == 'v1':
self.quad.n0min = 1
self.quad.n0max = 100
self.quad.rdmin = 1
self.quad.rdmax = 200
self.quad.mfmin = 1
self.quad.mfmax = 200
self.quad.n0sim = 100
self.quad.rdsim = 200
self.quad.mfsim = 200
if self.ver == 'v2':
self.quad.n0min = 1
self.quad.n0max = 250
self.quad.rdmin = 1
self.quad.rdmax = 500
self.quad.mfmin = 201
self.quad.mfmax = 500
self.quad.n0sim = 250
self.quad.rdsim = 500
self.quad.mfsim = 300
#doreal
if self.stype in ['dust']:
self.doreal = False
#noreal
if self.stype in ['absrot', 'relrot']:
self.doreal = False
self.snmin = 1
# directory
self.Dir = '/global/homes/t/toshiyan/Work/Ongoing/ACT/data/curvedsky/'
# tag
self.stag = self.stype + '_' + self.psa + '_ns' + str(
self.nside) + '_lc' + str(self.lcut) + '_a' + str(
self.ascale) + 'deg'
self.ids = [str(i).zfill(5) for i in range(501)]
if self.doreal: self.ids[0] = 'real'
if self.dearot: self.ids[0] = self.ids[0] + '_dearot'
# alpha reconstruction
quad_func.quad.fname(self.quad, self.Dir, self.ids, self.stag)
# for v1 rec files
if self.ver != '':
q = 'EB'
for i in range(501):
self.quad.f[q].cl[i] = self.quad.f[q].cl[i].replace(
'.dat', '_' + self.ver + '.dat')