def read_desdr2(ra,dec,radius=1.5):
""" Read the reference catalog in a localized region.
Parameters
----------
ra : ra (deg)
dec : dec (deg)
radius : regional radius (deg)
Returns
-------
refcat : reference catalog columns
"""
nside = 32
pixels = ang2disc(nside, ra, dec, radius, inclusive=True)
dirname = '/data/des40.b/data/y6a1/gold/1.1/healpix'
basename = 'y6_gold_1_0_%05d.fits'
filenames = [os.path.join(dirname,basename%p) for p in pixels]
#filenames = [f for f in filenames if os.path.exists(f)]
columns = DESDR2_COLUMNS
refcat = utils.load_infiles(filenames,columns=columns)
refcat = refcat[refcat['WAVG_MAG_PSF_R'] < 21]
mapping = dict(zip(DESDR2_COLUMNS,REFCAT_COLUMNS))
new_names = [mapping[n] for n in refcat.dtype.names]
refcat.dtype.names = new_names
return refcat
def _cache(self, name=None):
pixel_area = hp.nside2pixarea(self.nside, degrees=True)
vec = ang2vec(self.lon, self.lat)
#self.pix = query_disc(self.nside,vec,self.extension)
self.pix = ang2disc(self.nside,
self.lon,
self.lat,
self.extension,
inclusive=True)
self._norm = 1. / (len(self.pix) * pixel_area)
def get_local_catalog(ra, dec, radius, catalog, **kwargs):
""" Get local catalog from disk
Parameters
----------
ra : right ascension of catalog pixel (deg)
dec: declination of catalog pixel (deg)
catalog : catalog name
Returns
-------
cat : object catalog
"""
from ugali.utils.healpix import ang2disc
mapping = {}
if 'gaia' in catalog.lower():
nside = 32
dirname = '/data/des40.b/data/gaia/dr2/healpix'
basename = 'GaiaSource_%05d.fits'
columns = ['SOURCE_ID', 'RA', 'DEC']
mapping.update({'RA': '_RAJ2000', 'DEC': '_DEJ2000'})
elif 'atlas' in catalog.lower():
nside = 32
dirname = '/data/des40.b/data/atlas-refcat2/healpix'
basename = 'atlas-refcat2_%05d.fits'
columns = ['OBJID', 'RA', 'DEC', 'G']
mapping.update({})
elif 'ps1' in catalog.lower():
nside = 32
dirname = '/data/des40.b/data/ps1/dr1/healpix'
basename = 'ps1_dr1_%05d.fits'
columns = ['RA', 'DEC']
elif 'decals' in catalog.lower():
nside = 32
dirname = '/data/des40.b/data/decals/dr8/south_healpix'
basename = 'decals-dr8-sweep_%05d.fits'
columns = ['OBJID', 'RA', 'DEC']
else:
raise Exception('Unrecognized catalog: %s' % catalog)
pixels = ang2disc(nside, ra, dec, radius, inclusive=True)
filenames = [os.path.join(dirname, basename % p) for p in pixels]
filenames = [f for f in filenames if os.path.exists(f)]
cat = load_infiles(filenames, columns=columns)
names = [mapping.get(n, n) for n in cat.dtype.names]
cat.dtype.names = names
return cat
def calc_surface_intensity(self, factor=10):
"""Calculate the surface intensity for each pixel in the interior
region of the ROI. Pixels are adaptively subsampled around the
kernel centroid out to a radius of 'factor * max_pixrad'.
Parameters:
-----------
factor : the radius of the oversample region in units of max_pixrad
Returns:
--------
surface_intensity : the surface intensity at each pixel
"""
# First we calculate the surface intensity at native resolution
pixels = self.roi.pixels_interior
nside_in = self.config['coords']['nside_pixel']
surface_intensity = self.kernel.pdf(pixels.lon, pixels.lat)
# Then we recalculate the surface intensity around the kernel
# centroid at higher resolution
for i in np.arange(1, 5):
# Select pixels within the region of interest
nside_out = 2**i * nside_in
radius = factor * np.degrees(hp.max_pixrad(nside_out))
pix = ang2disc(nside_in,
self.kernel.lon,
self.kernel.lat,
radius,
inclusive=True)
# Select pix within the interior region of the ROI
idx = ugali.utils.healpix.index_pix_in_pixels(pix, pixels)
pix = pix[(idx >= 0)]
idx = idx[(idx >= 0)]
# Reset the surface intensity for the subsampled pixels
subpix = ugali.utils.healpix.ud_grade_ipix(pix, nside_in,
nside_out)
pix_lon, pix_lat = pix2ang(nside_out, subpix)
surface_intensity[idx] = np.mean(self.kernel.pdf(pix_lon, pix_lat),
axis=1)
return surface_intensity
def read_refcat(ra,dec,radius=1.5):
""" Read the reference catalog in a localized region.
Parameters
----------
ra : ra (deg)
dec : dec (deg)
radius : regional radius (deg)
Returns
-------
refcat : reference catalog columns
"""
nside = 32
pixels = ang2disc(nside, ra, dec, radius, inclusive=True)
dirname = '/data/des40.b/data/atlas-refcat2/healpix'
basename = 'atlas-refcat2_%05d.fits'
filenames = [os.path.join(dirname,basename%p) for p in pixels]
columns = REFCAT_COLUMNS
refcat = utils.load_infiles(filenames,columns=columns)
return refcat
def calc_surface_intensity(self, factor=10):
"""Calculate the surface intensity for each pixel in the interior
region of the ROI. Pixels are adaptively subsampled around the
kernel centroid out to a radius of 'factor * max_pixrad'.
Parameters:
-----------
factor : the radius of the oversample region in units of max_pixrad
Returns:
--------
surface_intensity : the surface intensity at each pixel
"""
# First we calculate the surface intensity at native resolution
pixels = self.roi.pixels_interior
nside_in = self.config['coords']['nside_pixel']
surface_intensity = self.kernel.pdf(pixels.lon,pixels.lat)
# Then we recalculate the surface intensity around the kernel
# centroid at higher resolution
for i in np.arange(1,5):
# Select pixels within the region of interest
nside_out = 2**i * nside_in
radius = factor*np.degrees(hp.max_pixrad(nside_out))
pix = ang2disc(nside_in,self.kernel.lon,self.kernel.lat,
radius,inclusive=True)
# Select pix within the interior region of the ROI
idx = ugali.utils.healpix.index_pix_in_pixels(pix,pixels)
pix = pix[(idx >= 0)]; idx = idx[(idx >= 0)]
# Reset the surface intensity for the subsampled pixels
subpix = ugali.utils.healpix.ud_grade_ipix(pix,nside_in,nside_out)
pix_lon,pix_lat = pix2ang(nside_out,subpix)
surface_intensity[idx]=np.mean(self.kernel.pdf(pix_lon,pix_lat),axis=1)
return surface_intensity