def inFootprint(self, pixels, nside=None):
"""
Open each valid filename for the set of pixels and determine the set
of subpixels with valid data.
"""
if np.isscalar(pixels): pixels = np.array([pixels])
if nside is None: nside = self.nside_likelihood
inside = np.zeros(len(pixels), dtype='bool')
if not self.nside_catalog:
catalog_pix = [0]
else:
catalog_pix = superpixel(pixels, nside, self.nside_catalog)
catalog_pix = np.intersect1d(catalog_pix, self.catalog_pixels)
for filenames in self.filenames[catalog_pix]:
# ADW: Need to replace with healpix functions...
#logger.debug("Loading %s"%filenames['mask_1'])
#subpix_1,val_1 = ugali.utils.skymap.readSparseHealpixMap(filenames['mask_1'],'MAGLIM',construct_map=False)
_n, subpix_1, val_1 = read_partial_map(filenames['mask_1'],
'MAGLIM',
fullsky=False)
#logger.debug("Loading %s"%filenames['mask_2'])
#subpix_2,val_2 = ugali.utils.skymap.readSparseHealpixMap(filenames['mask_2'],'MAGLIM',construct_map=False)
_n, subpix_2, val_2 = read_partial_map(filenames['mask_2'],
'MAGLIM',
fullsky=False)
subpix = np.intersect1d(subpix_1, subpix_2)
superpix = np.unique(superpixel(subpix, self.nside_pixel, nside))
inside |= np.in1d(pixels, superpix)
return inside
def inFootprint(config, pixels, nside=None):
"""
Open each valid filename for the set of pixels and determine the set
of subpixels with valid data.
Parameters
----------
config : config
Configuration (file or object)
pixels : array or int
List of pixels to create footprint for
nside : int, optional
Healpix nside
Returns
-------
inside : array
Boolean array of whether pixel is in footprint
"""
logger.info("Calculating survey footprint...")
config = Config(config)
nside_catalog = config['coords']['nside_catalog']
nside_likelihood = config['coords']['nside_likelihood']
nside_pixel = config['coords']['nside_pixel']
if np.isscalar(pixels): pixels = np.array([pixels])
if nside is None: nside = nside_likelihood
filenames = config.getFilenames()
catalog_pixels = filenames['pix'].compressed()
inside = np.zeros(len(pixels), dtype=bool)
if not nside_catalog:
catalog_pix = [0]
else:
catalog_pix = superpixel(pixels, nside, nside_catalog)
catalog_pix = np.intersect1d(catalog_pix, catalog_pixels)
fnames = filenames[catalog_pix]
# Load the first mask
logger.debug("Loading %s" % fnames['mask_1'])
_nside, subpix1, val1 = read_partial_map(fnames['mask_1'],
'MAGLIM',
fullsky=False,
multiproc=8)
# Load the second mask
logger.debug("Loading %s" % fnames['mask_2'])
_nside, subpix2, val2 = read_partial_map(fnames['mask_2'],
'MAGLIM',
fullsky=False,
multiproc=8)
# Run the subpixels
subpix = np.intersect1d(subpix1, subpix2)
superpix = np.unique(superpixel(subpix, nside_pixel, nside))
inside |= np.in1d(pixels, superpix)
return inside
def __init__(self, infiles, roi):
"""
Parameters:
-----------
infiles : list of sparse healpix mask files
roi : roi object
Returns:
--------
mask : MaskBand object
"""
self.roi = roi
self.config = self.roi.config
# ADW: It's overkill to make the full map just to slim it
# down, but we don't have a great way to go from map pixels to
# roi pixels.
nside, pixel, maglim = healpix.read_partial_map(infiles,
column='MAGLIM')
self.nside = nside
# Sparse maps of pixels in various ROI regions
self.mask_roi_sparse = maglim[self.roi.pixels]
# Try to get the detection fraction
self.frac_roi_sparse = (self.mask_roi_sparse > 0)
try:
logger.debug("Reading FRACDET...")
nside, pixel, frac = healpix.read_partial_map(infiles,
column='FRACDET')
# This clipping might gloss over bugs...
fractype = self.config['mask'].get('fractype', 'binary')
fracmin = self.config['mask'].get('fracmin', 0.5)
if fractype == 'binary':
frac = np.where(frac < fracmin, 0.0, 1.0)
elif fractype == 'full':
frac = np.where(frac < fracmin, 0.0, frac)
elif not fractype:
pass
else:
msg = "Unrecognized fractype: %s" % fractype
logger.warn(msg)
self.frac_roi_sparse = np.clip(frac[self.roi.pixels], 0.0, 1.0)
except ValueError as e:
# No detection fraction present
msg = "No 'FRACDET' column found in masks; assuming FRACDET = 1.0"
logger.info(msg)
# Explicitly zero the maglim of pixels with fracdet < fracmin
self.mask_roi_sparse[self.frac_roi_sparse == 0] = 0.0
def __init__(self, infiles, roi):
"""
Parameters:
-----------
infiles : list of sparse healpix mask files
roi : roi object
Returns:
--------
mask : MaskBand object
"""
self.roi = roi
self.config = self.roi.config
# ADW: It's overkill to make the full map just to slim it
# down, but we don't have a great way to go from map pixels to
# roi pixels.
nside,pixel,maglim = healpix.read_partial_map(infiles,column='MAGLIM')
self.nside = nside
# Sparse maps of pixels in various ROI regions
self.mask_roi_sparse = maglim[self.roi.pixels]
# Try to get the detection fraction
self.frac_roi_sparse = (self.mask_roi_sparse > 0)
try:
logger.debug("Reading FRACDET...")
nside,pixel,frac=healpix.read_partial_map(infiles,column='FRACDET')
# This clipping might gloss over bugs...
fractype = self.config['mask'].get('fractype','binary')
fracmin = self.config['mask'].get('fracmin',0.5)
if fractype == 'binary':
frac = np.where(frac < fracmin, 0.0, 1.0)
elif fractype == 'full':
frac = np.where(frac < fracmin, 0.0, frac)
elif not fractype:
pass
else:
msg = "Unrecognized fractype: %s"%fractype
logger.warn(msg)
self.frac_roi_sparse = np.clip(frac[self.roi.pixels],0.0,1.0)
except ValueError as e:
# No detection fraction present
msg = "No 'FRACDET' column found in masks; assuming FRACDET = 1.0"
logger.info(msg)
# Explicitly zero the maglim of pixels with fracdet < fracmin
self.mask_roi_sparse[self.frac_roi_sparse == 0] = 0.0
def loadROI(self, filename=None):
"""Load the ROI parameter sparse healpix map.
Parameters
----------
filename : input filename for sparse healpix map (default: roifile)
Returns
-------
None : sets attributes: `ninterior`, `nannulus`, `stellar`
"""
if filename is None: filename = self.roifile
self.ninterior = healpix.read_partial_map(filename, 'NINSIDE')[-1]
self.nannulus = healpix.read_partial_map(filename, 'NANNULUS')[-1]
self.stellar = healpix.read_partial_map(filename, 'STELLAR')[-1]
def loadROI(self, filename=None):
if filename is None: filename = self.roifile
self.ninterior = healpix.read_partial_map(filename, 'NINSIDE')[-1]
self.nannulus = healpix.read_partial_map(filename, 'NANNULUS')[-1]
self.stellar = healpix.read_partial_map(filename, 'STELLAR')[-1]
def loadROI(self,filename=None):
if filename is None: filename = self.roifile
self.ninterior = healpix.read_partial_map(filename,'NINSIDE')[-1]
self.nannulus = healpix.read_partial_map(filename,'NANNULUS')[-1]
self.stellar = healpix.read_partial_map(filename,'STELLAR')[-1]