def mask_src_weighted_custom(cat_file, ENERGY, NSIDE):
"""Returns the 'bad pixels' defined by the position of a source and a
certain radius away from that point. The radii increase with the
brightness and rescaled by a factor between 1 and 0.3 shaped as the PSF.
cat_file: str
.fits file with the sorce catalog
ENERGY: float
Mean energy of the map to be masked
NSIDE: int
healpix nside parameter
"""
psf_ref_file = os.path.join(GRATOOLS_CONFIG, 'ascii/PSF_UCV_PSF1.txt')
src_cat = pf.open(cat_file)
NPIX = hp.pixelfunc.nside2npix(NSIDE)
CAT = src_cat[1]
BAD_PIX_SRC = []
SOURCES = CAT.data
src_cat.close()
psf_ref = get_psf_ref(psf_ref_file)
psf_en = psf_ref(ENERGY)
psf_min, psf_max = psf_ref.y[5], psf_ref.y[-1]
norm_min, norm_max = 1, 0.3
norm = norm_min + psf_en*((norm_max - norm_min)/(psf_max - psf_min)) -\
psf_min*((norm_max - norm_min)/(psf_max - psf_min))
logger.info('Normalization of radii due to energy: %.3f'%norm)
logger.info('Psf(%.2f)= %.2f'%(ENERGY, psf_en))
FLUX = np.log10(SOURCES.field('eflux1000'))
flux_min, flux_max = min(FLUX), max(FLUX)
rad_min, rad_max = 1, 5.
RADdeg = rad_min + FLUX*((rad_max - rad_min)/(flux_max - flux_min)) -\
flux_min*((rad_max - rad_min)/(flux_max - flux_min))
RADrad = np.radians(RADdeg)
logger.info('Flux-weighted mask for sources activated')
TS = SOURCES.field('ts')
indTS25 = TS > 25.
GLON = SOURCES.field('GLON')[indTS25]
GLAT = SOURCES.field('GLAT')[indTS25]
logger.info('Num Src: %i'%len(TS))
logger.info('Num Src TS>25: %i'%len(TS[indTS25]))
for i, src in enumerate(SOURCES[indTS25]):
x, y, z = hp.rotator.dir2vec(GLON[i],GLAT[i],lonlat=True)
b_pix= hp.pixelfunc.vec2pix(NSIDE, x, y, z)
BAD_PIX_SRC.append(b_pix)
radintpix = hp.query_disc(NSIDE, (x, y, z), RADrad[i]*norm)
BAD_PIX_SRC.extend(radintpix)
return BAD_PIX_SRC
def main():
"""Simple test unit
"""
from GRATools.utils.gWindowFunc import get_psf_ref
psf_ref_file = os.path.join(GRATOOLS_CONFIG, 'ascii/PSF_UCV_PSF1.txt')
psf_ref = get_psf_ref(psf_ref_file)
energy = np.array([
743.73, 1340.69, 2208.67, 3692.56, 6416.93, 11151.34, 18370.95,
30713.33, 53373.66, 92752.78, 152802.88, 255462.38, 443942.75
])
psf_en = psf_ref(energy)
psf_min, psf_max = psf_ref.y[5], psf_ref.y[-1]
norm_min, norm_max = 1, 0.3
norm = norm_min + psf_en*((norm_max - norm_min)/(psf_max - psf_min)) -\
psf_min*((norm_max - norm_min)/(psf_max - psf_min))
plt.title('Normalization Factor')
plt.plot(energy, norm, 'ro--', ms=5, alpha=0.75)
#plt.plot((1e-12, 1e-5), (2, 2), '-', color='silver', linewidth=1.0)
plt.xlabel('Energy [MeV]')
plt.ylabel('Normalization factor')
plt.yscale('log')
plt.xscale('log')
plt.ylim(0.09, 2)
plt.show()
nside = 512
SRC_CATALOG_FILE = os.path.join(FT_DATA_FOLDER, 'catalogs/gll_psc_v16.fit')
bad_pix = mask_src_weighted(SRC_CATALOG_FILE, 10000, nside)
bad_pix += mask_gp(30, nside)
npix = hp.nside2npix(nside)
mask = np.ones(npix)
for bpix in np.unique(bad_pix):
mask[bpix] = 0
fsky = 1 - (len(np.unique(bad_pix)) / float(npix))
title = 'f$_{sky}$ = %.3f' % fsky
hp.mollview(mask, title=title, coord='G')
plt.show()
rebinning = np.unique(np.int64(np.logspace(0, 3, 31)))
psf_ref_file = os.path.join(GRATOOLS_CONFIG, 'ascii/PSF_UCV_PSF1.txt')
_l_min = [200, 200, 100, 100, 100, 100, 49, 49, 49, 49, 49, 49, 49]
_l_max = [300, 400, 700, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000]
emin, emax, emean = [], [], []
cls_tocompare, clerrs_tocompare = [], []
for f in Cl_FILES:
emin, emax, emean, cls, clerrs = cl_parse(f)
cls_tocompare.append(cls)
clerrs_tocompare.append(clerrs)
from GRATools.utils.gWindowFunc import get_psf_ref
psf_ref = get_psf_ref(psf_ref_file)
ymin, ymax = -1e-15, 1e-15
for i in range(0, len(cls_tocompare[0])):
psf_en = psf_ref(emean[i])
l_max = _l_max[i]#min(500, 1.9*(np.pi/np.radians(psf_en)))
l_min = _l_min[i]#min(60, max(50-i*5,10))
plt.figure(figsize=(10, 7), dpi=80)
for j, f in enumerate(Cl_FILES):
_l = np.arange(1, len(cls_tocompare[j][i]))
_l_rebin, _cls_rebin, _clerrs_rebin = [], [], []
xerrL, xerrR = [], []
for bmin, bmax in zip(rebinning[:-1], rebinning[1:]):
_l_rebin.append(np.sqrt(bmin*bmax))
xerrL.append(abs(np.sqrt(bmin*bmax)-bmin))
xerrR.append(abs(np.sqrt(bmin*bmax)-bmax))
_index = np.where(np.logical_and(_l>=bmin, _l<bmax))
def mkCsi(**kwargs):
"""
"""
get_var_from_file(kwargs['config'])
ncores = kwargs['ncores']
psf_file = data.PSF_REF_FILE
p = multiprocessing.Pool(processes=ncores)
logger.info('Starting Csi analysis...')
in_label = data.IN_LABEL
out_label = data.OUT_LABEL
binning_label = data.BINNING_LABEL
cl_param_file = os.path.join(GRATOOLS_OUT, '%s_%s_parameters.txt' \
%(in_label, binning_label))
from GRATools.utils.gFTools import get_cl_param
_emin, _emax, _emean, _f, _ferr, _cn, _fsky = get_cl_param(cl_param_file)
csi_txt = open(os.path.join(GRATOOLS_OUT, '%s_%s_csi.txt' \
%(out_label, binning_label)), 'w')
psf_ref = get_psf_ref(psf_file)
#psf_ref.plot(show=False)
#plt.xscale('log')
#plt.yscale('log')
#plt.show()
for i, (emin, emax) in enumerate(zip(_emin, _emax)):
logger.info('Considering bin %.2f - %.2f ...'%(emin, emax))
cont_ang = np.radians(psf_ref(_emean[i]))
csi_txt.write('ENERGY\t %.2f %.2f %.2f\n'%(emin, emax, _emean[i]))
flux_map_name = in_label+'_flux_%i-%i.fits'%(emin, emax)
flux_map = hp.read_map(os.path.join(GRATOOLS_OUT_FLUX, flux_map_name))
flux_map = udgrade_as_psf(flux_map, cont_ang)
R = hp.read_map(os.path.join(GRATOOLS_OUT_FLUX, flux_map_name))
R = udgrade_as_psf(R, cont_ang)
fsky = 1.-(len(np.where(flux_map == hp.UNSEEN)[0])/\
float(len(flux_map)))
logger.info('fsky = %f'%fsky)
npix = len(flux_map)
nside = hp.npix2nside(npix)
_unmask = np.where(flux_map != hp.UNSEEN)[0]
npix_unmask = len(_unmask)
Imean = _f[i]
dI = flux_map - Imean
dR = R - Imean
R = permute_unmasked_pix(R)
dR = permute_unmasked_pix(dR)
th_bins = data.TH_BINNING
theta = []
for thmin, thmax in zip(th_bins[:-1], th_bins[1:]):
th_mean = np.sqrt(thmin*thmax)
theta.append(th_mean)
theta = np.array(theta)
logger.info('Computing Csi...')
diri = hp.pixelfunc.pix2ang(nside, _unmask)
veci = hp.rotator.dir2vec(diri)
xyz = np.array([(veci[0][i], veci[1][i], veci[2][i])
for i in range(0, len(veci[0]))])
args = zip(_unmask, xyz, [dI]*npix_unmask, [dR]*npix_unmask,
[nside]*npix_unmask)
#args = zip(_unmask, xyz, [flux_map]*npix_unmask, [R]*npix_unmask,
# [nside]*npix_unmask)
a = np.array(p.map(csi_compute, args))
SUMij_list = a[:, 0]
SUMf_list = a[:, 1]
SUMR_list = a[:, 2]
SUMij_th = []
SUMf_th = []
SUMR_th = []
for i, s in enumerate(SUMij_list[0]):
SUMij_th.append(np.sum(SUMij_list[:, i]))
SUMf_th.append(np.sum(SUMf_list[:, i]))
SUMR_th.append(np.sum(SUMR_list[:, i]))
csi = (np.array(SUMij_th))/np.array(SUMf_th)#-Imean**2
r = (np.array(SUMR_th))/np.array(SUMf_th)#-Imean**2
csi_txt.write('THETA\t%s\n'%str(list(theta)).replace('[',''). \
replace(']','').replace(', ', ' '))
csi_txt.write('CSI\t%s\n'%str(list(csi)).replace('[',''). \
replace(']','').replace(', ', ' '))
csi_txt.write('R\t%s\n'%str(list(r)).replace('[',''). \
replace(']','').replace(', ', ' '))
csi_txt.close()
p.close()
p.join()
logger.info('Created %s'%(os.path.join(GRATOOLS_OUT, '%s_%s_csi.txt' \
%(out_label, binning_label))))
def mask_src_weighted(cat_file, ENERGY, NSIDE):
"""Returns the 'bad pixels' defined by the position of a source and a
certain radius away from that point. The radii increase with the
brightness.
SOURCE_CAT: str
opened fits file with the sorce catalog
NSIDE: int
healpix nside parameter
"""
from GRATools.utils.gWindowFunc import get_psf_ref
psf_ref_file = os.path.join(GRATOOLS_CONFIG, 'ascii/PSF_UCV_PSF1.txt')
src_cat = pf.open(cat_file)
NPIX = hp.pixelfunc.nside2npix(NSIDE)
CAT = src_cat['LAT_Point_Source_Catalog']
CAT_EXTENDED = src_cat['ExtendedSources']
BAD_PIX_SRC = []
SOURCES = CAT.data
EXT_SOURCES = CAT_EXTENDED.data
src_cat.close()
psf_ref = get_psf_ref(psf_ref_file)
psf_en = psf_ref(ENERGY)
psf_min, psf_max = psf_ref.y[5], psf_ref.y[-1]
norm_min, norm_max = 1, 0.3
norm = norm_min + psf_en*((norm_max - norm_min)/(psf_max - psf_min)) -\
psf_min*((norm_max - norm_min)/(psf_max - psf_min))
logger.info('Normalization of radii due to energy: %.3f' % norm)
print 'Psf(%.2f)= %.2f' % (ENERGY, psf_en)
FLUX = SOURCES.field('Flux1000')
flux_min, flux_max = min(FLUX), max(FLUX)
rad_min, rad_max = 2., 5.
RADdeg = rad_min + FLUX*((rad_max - rad_min)/(flux_max - flux_min)) -\
flux_min*((rad_max - rad_min)/(flux_max - flux_min))
RADrad = np.radians(RADdeg)
#*****
#plt.title('Radius($\phi$)')
#plt.plot(FLUX, RADdeg, 'ro', ms=3, alpha=0.75)
#plt.plot((1e-12, 1e-5), (2, 2), '-', color='silver', linewidth=1.0)
#plt.xlabel('$\phi$')
#plt.ylabel('Radius [$\circ$]')
#plt.yscale('log')
#plt.xscale('log')
#plt.ylim(0.7, 6)
#plt.show()
#*****
logger.info('Masking the extended Sources')
logger.info('-> 10deg around CenA and LMC')
logger.info('-> 5deg around the remaining')
for i, src in enumerate(EXT_SOURCES):
NAME = EXT_SOURCES[i][0]
GLON = EXT_SOURCES[i][4]
GLAT = EXT_SOURCES[i][5]
if NAME == 'LMC' or NAME == 'CenA Lobes':
x, y, z = hp.rotator.dir2vec(GLON, GLAT, lonlat=True)
b_pix = hp.pixelfunc.vec2pix(NSIDE, x, y, z)
BAD_PIX_SRC.append(b_pix)
radintpix = hp.query_disc(NSIDE, (x, y, z), np.radians(10) * norm)
BAD_PIX_SRC.extend(radintpix)
else:
x, y, z = hp.rotator.dir2vec(GLON, GLAT, lonlat=True)
b_pix = hp.pixelfunc.vec2pix(NSIDE, x, y, z)
BAD_PIX_SRC.append(b_pix)
radintpix = hp.query_disc(NSIDE, (x, y, z), np.radians(5) * norm)
BAD_PIX_SRC.extend(radintpix)
logger.info('Flux-weighted mask for sources activated')
for i, src in enumerate(SOURCES):
GLON = SOURCES[i][3]
GLAT = SOURCES[i][4]
x, y, z = hp.rotator.dir2vec(GLON, GLAT, lonlat=True)
b_pix = hp.pixelfunc.vec2pix(NSIDE, x, y, z)
BAD_PIX_SRC.append(b_pix)
radintpix = hp.query_disc(NSIDE, (x, y, z), RADrad[i] * norm)
BAD_PIX_SRC.extend(radintpix)
return BAD_PIX_SRC
def mask_src_weighted(cat_src_file, cat_extsrc_file, ENERGY, NSIDE):
"""Returns the 'bad pixels' defined by the position of a source and a
certain radius away from that point. The radii increase with the
brightness and rescaled by a factor between 1 and 0.3 shaped as the PSF.
cat_src_file: str
.fits file with the source catalog
cat_extsrc_file: str
.fits file with the extended sources catalog
ENERGY: float
Mean energy of the map to be masked
NSIDE: int
healpix nside parameter
"""
psf_ref_file = os.path.join(GRATOOLS_CONFIG, 'ascii/PSF_UCV_custom.txt')
src_cat = pf.open(cat_src_file)
extsrc_cat = pf.open(cat_extsrc_file)
NPIX = hp.pixelfunc.nside2npix(NSIDE)
CAT = src_cat['LAT_Point_Source_Catalog']
CAT_EXTENDED = extsrc_cat['ExtendedSources']
BAD_PIX_SRC = []
SOURCES = CAT.data
EXT_SOURCES = CAT_EXTENDED.data
src_cat.close()
psf_ref = get_psf_ref(psf_ref_file)
if '4FGL' in cat_src_file:
FLUX = np.log10(SOURCES.field('Flux_Density'))
if 'psch' in cat_src_file:
FLUX = np.log10(SOURCES.field('Flux'))
else:
FLUX = np.log10(SOURCES.field('Flux1000'))
flux_min, flux_max = min(FLUX), max(FLUX)
rad_min = 2*psf_ref(ENERGY)
rad_max = 5*psf_ref(ENERGY)
RADdeg = rad_min + FLUX*((rad_max - rad_min)/(flux_max - flux_min)) -\
flux_min*((rad_max - rad_min)/(flux_max - flux_min))
RADrad = np.radians(RADdeg)
logger.info('Masking the extended Sources')
logger.info('-> 10xPSF(%.2f GeV) around CenA and LMC'%(ENERGY/1000))
logger.info('-> 5xPSF(%.2f GeV) around the remaining'%(ENERGY/1000))
for i, src in enumerate(EXT_SOURCES):
NAME = EXT_SOURCES[i][0]
GLON = EXT_SOURCES.field('GLON')[i]
GLAT = EXT_SOURCES.field('GLAT')[i]
if 'LMC' in NAME or 'CenA Lobes' in NAME:
rad = 10*psf_ref(ENERGY)
x, y, z = hp.rotator.dir2vec(GLON,GLAT,lonlat=True)
b_pix= hp.pixelfunc.vec2pix(NSIDE, x, y, z)
BAD_PIX_SRC.append(b_pix)
radintpix = hp.query_disc(NSIDE, (x, y, z), np.radians(10))
BAD_PIX_SRC.extend(radintpix)
else:
rad = 5*psf_ref(ENERGY)
x, y, z = hp.rotator.dir2vec(GLON,GLAT,lonlat=True)
b_pix = hp.pixelfunc.vec2pix(NSIDE, x, y, z)
BAD_PIX_SRC.append(b_pix)
radintpix = hp.query_disc(NSIDE, (x, y, z), np.radians(5))
BAD_PIX_SRC.extend(radintpix)
logger.info('Flux-weighted mask for sources activated')
for i, src in enumerate(SOURCES):
GLON = SOURCES.field('GLON')[i]
GLAT = SOURCES.field('GLAT')[i]
x, y, z = hp.rotator.dir2vec(GLON,GLAT,lonlat=True)
b_pix= hp.pixelfunc.vec2pix(NSIDE, x, y, z)
BAD_PIX_SRC.append(b_pix)
radintpix = hp.query_disc(NSIDE, (x, y, z), RADrad[i])
BAD_PIX_SRC.extend(radintpix)
return BAD_PIX_SRC
def mkCsi(**kwargs):
"""
"""
get_var_from_file(kwargs['config'])
ncores = kwargs['ncores']
psf_file = data.PSF_REF_FILE
p = multiprocessing.Pool(processes=ncores)
logger.info('Starting Csi analysis...')
in_label = data.IN_LABEL
out_label = data.OUT_LABEL
binning_label = data.BINNING_LABEL
cl_param_file = os.path.join(GRATOOLS_OUT, '%s_%s_parameters.txt' \
%(in_label, binning_label))
from GRATools.utils.gFTools import get_cl_param
_emin, _emax, _emean, _f, _ferr, _cn, _fsky = get_cl_param(cl_param_file)
csi_txt = open(os.path.join(GRATOOLS_OUT, '%s_%s_csi.txt' \
%(out_label, binning_label)), 'w')
psf_ref = get_psf_ref(psf_file)
#psf_ref.plot(show=False)
#plt.xscale('log')
#plt.yscale('log')
#plt.show()
for i, (emin, emax) in enumerate(zip(_emin, _emax)):
logger.info('Considering bin %.2f - %.2f ...' % (emin, emax))
cont_ang = np.radians(psf_ref(_emean[i]))
csi_txt.write('ENERGY\t %.2f %.2f %.2f\n' % (emin, emax, _emean[i]))
flux_map_name = in_label + '_flux_%i-%i.fits' % (emin, emax)
flux_map = hp.read_map(os.path.join(GRATOOLS_OUT_FLUX, flux_map_name))
flux_map = udgrade_as_psf(flux_map, cont_ang)
R = hp.read_map(os.path.join(GRATOOLS_OUT_FLUX, flux_map_name))
R = udgrade_as_psf(R, cont_ang)
fsky = 1.-(len(np.where(flux_map == hp.UNSEEN)[0])/\
float(len(flux_map)))
logger.info('fsky = %f' % fsky)
npix = len(flux_map)
nside = hp.npix2nside(npix)
_unmask = np.where(flux_map != hp.UNSEEN)[0]
npix_unmask = len(_unmask)
Imean = _f[i]
dI = flux_map - Imean
dR = R - Imean
R = permute_unmasked_pix(R)
dR = permute_unmasked_pix(dR)
th_bins = data.TH_BINNING
theta = []
for thmin, thmax in zip(th_bins[:-1], th_bins[1:]):
th_mean = np.sqrt(thmin * thmax)
theta.append(th_mean)
theta = np.array(theta)
logger.info('Computing Csi...')
diri = hp.pixelfunc.pix2ang(nside, _unmask)
veci = hp.rotator.dir2vec(diri)
xyz = np.array([(veci[0][i], veci[1][i], veci[2][i])
for i in range(0, len(veci[0]))])
args = zip(_unmask, xyz, [dI] * npix_unmask, [dR] * npix_unmask,
[nside] * npix_unmask)
#args = zip(_unmask, xyz, [flux_map]*npix_unmask, [R]*npix_unmask,
# [nside]*npix_unmask)
a = np.array(p.map(csi_compute, args))
SUMij_list = a[:, 0]
SUMf_list = a[:, 1]
SUMR_list = a[:, 2]
SUMij_th = []
SUMf_th = []
SUMR_th = []
for i, s in enumerate(SUMij_list[0]):
SUMij_th.append(np.sum(SUMij_list[:, i]))
SUMf_th.append(np.sum(SUMf_list[:, i]))
SUMR_th.append(np.sum(SUMR_list[:, i]))
csi = (np.array(SUMij_th)) / np.array(SUMf_th) #-Imean**2
r = (np.array(SUMR_th)) / np.array(SUMf_th) #-Imean**2
csi_txt.write('THETA\t%s\n'%str(list(theta)).replace('[',''). \
replace(']','').replace(', ', ' '))
csi_txt.write('CSI\t%s\n'%str(list(csi)).replace('[',''). \
replace(']','').replace(', ', ' '))
csi_txt.write('R\t%s\n'%str(list(r)).replace('[',''). \
replace(']','').replace(', ', ' '))
csi_txt.close()
p.close()
p.join()
logger.info('Created %s'%(os.path.join(GRATOOLS_OUT, '%s_%s_csi.txt' \
%(out_label, binning_label))))