def read_map_bands(fname, read_bands):
if read_bands:
i_map = -1
else:
i_map = ['g', 'r', 'i', 'z', 'y'].index(o.band)
fskb, temp = fm.read_flat_map(fname, i_map=i_map)
fm.compare_infos(fsk, fskb)
if i_map != -1:
temp = [temp]
return temp
def __init__(self,
hdu_list,
i_bin,
fsk,
mask_binary,
masked_fraction,
contaminants=None):
#Read numbers map
self.fsk, nmap = fm.read_flat_map(None, hdu=hdu_list[2 * i_bin])
#Read N(z)
self.nz_data = hdu_list[2 * i_bin + 1].data.copy()
#Make sure other maps are compatible
fm.compare_infos(self.fsk, fsk)
if not self.fsk.is_map_compatible(mask_binary):
raise ValueError("Mask size is incompatible")
if not self.fsk.is_map_compatible(masked_fraction):
raise ValueError("Mask size is incompatible")
if contaminants is not None:
for ic, c in enumerate(contaminants):
if not self.fsk.is_map_compatible(c):
raise ValueError(
"%d-th contaminant template is incompatible" % ic)
#Translate into delta map
self.weight = masked_fraction * mask_binary
goodpix = np.where(mask_binary > 0.1)[0]
ndens = np.sum(nmap * mask_binary) / np.sum(self.weight)
self.ndens_perad = ndens / (np.radians(self.fsk.dx) *
np.radians(self.fsk.dy))
self.delta = np.zeros_like(self.weight)
self.delta[goodpix] = nmap[goodpix] / (ndens *
masked_fraction[goodpix]) - 1
#Reshape contaminants
conts = None
if contaminants is not None:
conts = [[c.reshape([self.fsk.ny, self.fsk.nx])]
for c in contaminants]
#Form NaMaster field
self.field = nmt.NmtFieldFlat(
np.radians(self.fsk.lx),
np.radians(self.fsk.ly),
self.weight.reshape([self.fsk.ny, self.fsk.nx]),
[self.delta.reshape([self.fsk.ny, self.fsk.nx])],
templates=conts)
nbins = len(data_hdus) // 2
os.system('mkdir -p ' + o.prefix_out)
print("Reading mask")
#Create depth-based mask
fsk, mp_depth = fm.read_flat_map(o.prefix_in + "_10s_depth_mean_fluxerr.fits",
2)
mp_depth[np.isnan(mp_depth)] = 0
mp_depth[mp_depth > 40] = 0
msk_depth = np.zeros_like(mp_depth)
msk_depth[mp_depth >= o.depth_cut] = 1
#Read masked fraction
fskb, mskfrac = fm.read_flat_map(o.prefix_in + '_MaskedFraction.fits', i_map=0)
fm.compare_infos(fsk, fskb)
#Create BO-based mask
msk_bo = np.zeros_like(mskfrac)
msk_bo[mskfrac > o.mask_thr] = 1
msk_t = msk_bo * msk_depth * mskfrac
for ibin in range(nbins):
print("Bin %d" % ibin)
data_hdu = data_hdus[2 * ibin]
for j, cm in enumerate(cont_maps):
print(" " + cm)
path_sys = o.prefix_in + '_%s.fits' % (cm)
xsys, xsys_resc, mean_sys, std_sys = check_sys(data_hdu, path_sys,
msk_t, o.nbins_sys)
if len(xsys) > 1: