def read_maps(nsimu, maps_dir, pol=True):
"""cut high multipoles of input map and degrade it to low resolution
Parameters
----------
nsimu：integer
mumber of samples
qml_nside : integer
nisde of low multipoles maps
mapsA, mapsB: numpy.ndarray
mapsA noise maps of chanel A
mapsB noise maps of chanel B（optional）
Returns
----------
maps:
Return a degrade maps with Nside = qml_nside
"""
print()
print("Read maps from %s"%maps_dir)
maps=[]
for n in np.arange(nsimu):
progress_bar(n, nsimu)
n1=n+1
if pol == True:
map_in = hp.read_map(maps_dir+"%d.fits"%n1,field=(0,1,2),dtype=float,verbose=0)
else:
map_in = hp.read_map(maps_dir+"%d.fits"%n1,field=0,dtype=float,verbose=0)
maps.append(map_in)
maps=np.array(maps)
return maps
def crosBP(self, mapsA, mapsB=()):
"""
Return the unbiased spectra
Parameters
----------
map1 : 1D array
Pixel map number 1
map2 : 2D array
Pixel map number 2
Returns
----------
cl : array or sequence of arrays
Returns cl or a list of cl's (TT, EE, BB, TE, EB, TB)
# Should compute auto-spectra if map2 == None ?
# Define conditions based on the map size
"""
allcl = []
nsimu=min(mapsA.shape[0],mapsB.shape[0]) if mapsB != () else mapsA.shape[0]
if mapsB != ():
for n in np.arange(nsimu):
progress_bar(n, nsimu)
mapA=mapsA[n]
mapB=mapsB[n]
allcl.append(self.estimator.get_spectra(mapA,mapB))
else:
print("crosBP need another map.")
hcl = np.mean(allcl, 0)
return hcl
def autoBP(self, mapsA):
"""
Return the unbiased spectra
Parameters
----------
map1 : 1D array
Pixel map number 1
map2 : 2D array
Pixel map number 2
Returns
----------
cl : array or sequence of arrays
Returns cl or a list of cl's (TT, EE, BB, TE, EB, TB)
# Should compute auto-spectra if map2 == None ?
# Define conditions based on the map size
"""
allcl = []
nsimu = mapsA.shape[0]
for n in np.arange(nsimu):
progress_bar(n, nsimu)
mapA=mapsA[n]
allcl.append(self.estimator.get_spectra(mapA))
hcl = np.mean(allcl, 0)
return hcl
def degrade_maps(nsimu, nside, qml_nside, maps_dir, pol, qml_mask=None):
"""cut high multipoles of input map and degrade it to low resolution
Parameters
----------
nsimu：integer
mumber of samples
qml_nside : integer
nisde of low multipoles maps
mapsA, mapsB: numpy.ndarray
mapsA noise maps of chanel A
mapsB noise maps of chanel B（optional）
Returns
----------
maps:
Return a degrade maps with Nside = qml_nside
"""
print()
print("degrade maps to Nside = %d"%qml_nside)
ALM = hp.Alm
maps=[]
for n in np.arange(nsimu):
progress_bar(n, nsimu)
n1=n+1
#map_out = hp.read_map(maps_dir+"%d.fits"%n1,field=(0,1,2),dtype=float,verbose=0)
map_in = hp.read_map(maps_dir+"%d.fits"%n1,field=(0,1,2),dtype=np.float64,verbose=0)
Slmax_old=3*nside
Slmax_new=3*qml_nside
alm_old=hp.map2alm(map_in,lmax=Slmax_old,pol=pol,use_weights=True,iter=3)
alm_new=np.zeros_like(np.array(alm_old)[:,:ALM.getsize(Slmax_new)])
for l in np.arange(Slmax_new+1) :
for m in np.arange(l+1) :
idx_new = ALM.getidx(Slmax_new, l, m)
idx_old = ALM.getidx(Slmax_old, l, m)
if l<=qml_nside:
alm_new[:,idx_new]=alm_old[:,idx_old]
elif l<=Slmax_new:
alm_new[:,idx_new]=alm_old[:,idx_old]*0.5*(1+np.sin(l*np.pi/2/qml_nside))
map_out = hp.alm2map(alm_new, nside= qml_nside, pixwin=False)
if qml_mask != ():
npix= hp.nside2npix(qml_nside)
if npix == len(qml_mask):
map_out = map_out*qml_mask
else:
print("The nside of qml_mask inconsistent with qml_nside.")
# hp.write_map(output_dir+"/map_out_%d.fits"%n1,map_out,dtype=np.float64)
# hp.mollview(map_out[1], cmap=plt.cm.jet, title='Q map output')
# plt.savefig(output_dir+'/map_out_%d.eps'%n1,bbox_inches='tight',pad_inches=0.1)
maps.append(map_out)
maps=np.array(maps)
return maps