def __init__(self, elgmap, ranmap, select_fun, sysm, mask, njack=20):
self.nside = hp.get_nside(elgmap)
#delta = np.zeros(elgmap.size)
#randc = ranmap * select_fun
#sf = (elgmap[mask].sum() / randc[mask].sum())
#delta[mask] = elgmap[mask] / randc[mask] / sf - 1
delta = makedelta(elgmap,
ranmap,
mask,
select_fun=select_fun,
is_sys=False)
#
#
#delta2 = np.zeros(elgmap.size)
#sf2 = (sysm[mask]*ranmap[mask]).sum() / ranmap[mask].sum()
#delta2[mask] = sysm[mask] / (sf2 * ranmap[mask]) - 1.0
#delta2[mask] = sysm[mask] / (sf2) - 1.0
delta2 = makedelta(sysm, ranmap, mask, is_sys=True)
#
#
w = ranmap[mask]
theta, phi = hp.pix2ang(self.nside, np.argwhere(mask).flatten())
thetal, phil, wl, deltal, delta2l = split_jackknife(theta,
phi,
w,
delta[mask],
delta2[mask],
njack=njack)
self.theta = thetal
self.phi = phil
self.weight = wl
self.delta = deltal
self.delta2 = delta2l
self.mean1 = np.average(delta[mask], weights=w)
self.mean2 = np.average(delta2[mask], weights=w)
def __init__(self, elgmap, ranmap, sysmap, select_fun, mask):
self.nside = hp.get_nside(elgmap)
# set up the delta
#delta = np.zeros(elgmap.size)
#randc = ranmap * select_fun
#sf = (elgmap[mask].sum() / randc[mask].sum())
#delta[mask] = elgmap[mask] / randc[mask] / sf - 1
delta = makedelta(elgmap,
ranmap,
mask,
select_fun=select_fun,
is_sys=False)
#
# set up the delta
# delta2 = np.zeros(elgmap.size)
# sf2 = (sysmap[mask]*ranmap[mask]).sum() / ranmap[mask].sum()
# delta2[mask] = sysmap[mask] / (sf2) - 1.0
delta2 = makedelta(sysmap, ranmap, mask, is_sys=True)
#
# weight
w = ranmap[mask]
theta, phi = hp.pix2ang(self.nside, np.argwhere(mask).flatten())
self.theta = theta
self.phi = phi
self.weight = w
self.delta = delta[mask]
self.delta2 = delta2[mask]
self.mean1 = np.average(delta[mask], weights=w)
self.mean2 = np.average(delta2[mask], weights=w)
def __init__(self, elgmap, ranmap, select_fun, mask, njack=20):
# set up the delta
self.nside = hp.get_nside(elgmap)
#delta = np.zeros(elgmap.size)
#randc = ranmap * select_fun
#sf = (elgmap[mask].sum() / randc[mask].sum())
#delta[mask] = elgmap[mask] / randc[mask] / sf - 1
delta = makedelta(elgmap,
ranmap,
mask,
select_fun=select_fun,
is_sys=False)
#
# weight
w = ranmap[mask]
theta, phi = hp.pix2ang(self.nside, np.argwhere(mask).flatten())
thetal, phil, wl, deltal, _ = split_jackknife(theta,
phi,
w,
delta[mask],
delta[mask],
njack=njack)
print('input njack %d, output njack %d' % (njack, len(wl)))
self.theta = thetal
self.phi = phil
self.weight = wl
self.delta = deltal
self.mean1 = np.average(delta[mask], weights=w)
def hd5_2_fits(metaname, fitname, hpmask, hpfrac, cols, mask_in=None, res=512):
metadata = pd.read_hdf(metaname)
ngal = metadata['ngal'].values
nran = metadata['nran'].values
if mask_in is None:
maskou = metadata['mask'].values
else:
maskou = metadata['mask'].values & mask_in
features = metadata[cols][maskou].values
hpind = np.argwhere(maskou).flatten()
fracgood = nran / nran[maskou].mean()
label = ut.makedelta(ngal, fracgood, maskou, select_fun=None,
is_sys=False) + 1
label = label[maskou]
fracgood = fracgood[maskou]
# for n in metadata.columns:
# print('%20s : %d'%(n, np.isnan(metadata[metadata['mask']][n]).sum()))
outdata = np.zeros(features.shape[0],
dtype=[('label', 'f8'), ('hpind', 'i8'),
('features', ('f8', features.shape[1])),
('fracgood', 'f8')])
outdata['label'] = label
outdata['hpind'] = hpind
outdata['features'] = features
outdata['fracgood'] = fracgood
#
#
ft.write(fitname, outdata, clobber=True)
print('wrote %s' % fitname)
#
#
mask = np.zeros(12 * res * res, '?')
mask[hpind] = True
hp.write_map(hpmask, mask, overwrite=True, fits_IDL=False)
print('wrote %s' % hpmask)
#
#
frac = np.zeros(12 * res * res)
frac[hpind] = fracgood
hp.write_map(hpfrac, frac, overwrite=True, fits_IDL=False)
print('wrote %s' % hpfrac)
if not os.path.isfile(ns.wmap):
wmap = None
log += '{:35s} : {}\n'.format('Computing Cl w/o wmap', ns.wmap)
else:
wmap = hp.read_map(ns.wmap, verbose=False)
log += '{:35s} : {}\n'.format('Computing Cl with wmap', ns.wmap)
if ns.smooth:
log += '{:35s}\n'.format('Smoothing the wmap')
wmap[~mask] = np.mean(
wmap[mask]
) # fill in empty pixels ?? required for smoothing
sdeg = np.deg2rad(0.25) # smooth with sigma of 1/4 of a deg
wmap = hp.sphtfunc.smoothing(wmap.copy(), sigma=sdeg)
#
# construct delta
delta_ngal = makedelta(galmap, ranmap, mask, select_fun=wmap)
#
# compute C_l
map_i = hp.ma(mask.astype('f8'))
map_i.mask = np.logical_not(mask)
clmask = hp.anafast(map_i.filled(), lmax=ns.lmax)
sf = ((2 * np.arange(clmask.size) + 1) * clmask).sum() / (4. * np.pi)
map_ngal = hp.ma(delta_ngal * ranmap)
map_ngal.mask = np.logical_not(mask)
cl_auto = hp.anafast(map_ngal.filled(), lmax=ns.lmax) / sf
#
if ns.njack == 0:
cl_err = 0.0
else:
cl_err = clerr_jack(delta_ngal,