def pixelizeDensity(config, nside=None, force=False):
if nside is None:
nside = config['coords']['nside_likelihood']
coordsys = config['coords']['coordsys'].upper()
filenames = config.getFilenames()
infiles = filenames[~filenames['catalog'].mask]
lon_field = config['catalog']['lon_field'].upper()
lat_field = config['catalog']['lat_field'].upper()
for ii,f in enumerate(infiles.data):
infile = f['catalog']
pix = f['pix']
logger.info('(%i/%i) %s'%(ii+1, len(infiles), infile))
outfile = config['data']['density']%pix
if os.path.exists(outfile) and not force:
logger.info("Found %s; skipping..."%outfile)
continue
outdir = mkdir(os.path.dirname(outfile))
pixels, density = stellarDensity(infile,nside,
lon_field=lon_field,lat_field=lat_field)
data = dict(PIXEL=pixels,DENSITY=density)
healpix.write_partial_map(outfile,data,nside=nside,coord=coordsys[0])
def pixelizeDensity(config, nside=None, force=False):
if nside is None:
nside = config['coords']['nside_likelihood']
coordsys = config['coords']['coordsys'].upper()
filenames = config.getFilenames()
infiles = filenames[~filenames['catalog'].mask]
lon_field = config['catalog']['lon_field'].upper()
lat_field = config['catalog']['lat_field'].upper()
for ii, f in enumerate(infiles.data):
infile = f['catalog']
pix = f['pix']
logger.info('(%i/%i) %s' % (ii + 1, len(infiles), infile))
outfile = config['data']['density'] % pix
if os.path.exists(outfile) and not force:
logger.info("Found %s; skipping..." % outfile)
continue
outdir = mkdir(os.path.dirname(outfile))
pixels, density = stellarDensity(infile,
nside,
lon_field=lon_field,
lat_field=lat_field)
data = dict(PIXEL=pixels, DENSITY=density)
healpix.write_partial_map(outfile,
data,
nside=nside,
coord=coordsys[0])
def write(self, outfile):
"""
Save the likelihood results as a sparse HEALPix map.
"""
data = odict()
data['PIXEL'] = self.roi.pixels_target
# Full data output (too large for survey)
if self.config['scan']['full_pdf']:
data['LOG_LIKELIHOOD'] = self.log_likelihood_sparse_array.T
data['RICHNESS'] = self.richness_sparse_array.T
data['RICHNESS_LOWER'] = self.richness_lower_sparse_array.T
data['RICHNESS_UPPER'] = self.richness_upper_sparse_array.T
data['RICHNESS_LIMIT'] = self.richness_upper_limit_sparse_array.T
#data['STELLAR_MASS']=self.stellar_mass_sparse_array.T
data[
'FRACTION_OBSERVABLE'] = self.fraction_observable_sparse_array.T
else:
data['LOG_LIKELIHOOD'] = self.log_likelihood_sparse_array.T
data['RICHNESS'] = self.richness_sparse_array.T
data[
'FRACTION_OBSERVABLE'] = self.fraction_observable_sparse_array.T
# Convert to 32bit float
for k in data.keys()[1:]:
data[k] = data[k].astype('f4', copy=False)
# Stellar mass can be calculated from STELLAR * RICHNESS
header = odict()
header['STELLAR'] = round(self.stellar_mass_conversion, 8)
header['LKDNSIDE'] = self.config['coords']['nside_likelihood']
header['LKDPIX'] = ang2pix(self.config['coords']['nside_likelihood'],
self.roi.lon, self.roi.lat)
header['NROI'] = self.roi.inROI(self.loglike.catalog_roi.lon,
self.loglike.catalog_roi.lat).sum()
header['NANNULUS'] = self.roi.inAnnulus(
self.loglike.catalog_roi.lon, self.loglike.catalog_roi.lat).sum()
header['NINSIDE'] = self.roi.inInterior(
self.loglike.catalog_roi.lon, self.loglike.catalog_roi.lat).sum()
header['NTARGET'] = self.roi.inTarget(
self.loglike.catalog_roi.lon, self.loglike.catalog_roi.lat).sum()
# Flatten if there is only a single distance modulus
# ADW: Is this really what we want to do?
if len(self.distance_modulus_array) == 1:
for key in data:
data[key] = data[key].flatten()
logger.info("Writing %s..." % outfile)
write_partial_map(outfile,
data,
nside=self.config['coords']['nside_pixel'],
header=header,
clobber=True)
fitsio.write(outfile,
dict(DISTANCE_MODULUS=self.distance_modulus_array.astype(
'f4', copy=False)),
extname='DISTANCE_MODULUS',
clobber=False)
def write(self, outfile):
"""
Save the likelihood results as a sparse HEALPix map.
"""
data = odict()
data['PIXEL']=self.roi.pixels_target
# Full data output (too large for survey)
if self.config['scan']['full_pdf']:
data['LOG_LIKELIHOOD']=self.log_likelihood_sparse_array.T
data['RICHNESS']=self.richness_sparse_array.T
data['RICHNESS_LOWER']=self.richness_lower_sparse_array.T
data['RICHNESS_UPPER']=self.richness_upper_sparse_array.T
data['RICHNESS_LIMIT']=self.richness_upper_limit_sparse_array.T
#data['STELLAR_MASS']=self.stellar_mass_sparse_array.T
data['FRACTION_OBSERVABLE']=self.fraction_observable_sparse_array.T
else:
data['LOG_LIKELIHOOD']=self.log_likelihood_sparse_array.T
data['RICHNESS']=self.richness_sparse_array.T
data['FRACTION_OBSERVABLE']=self.fraction_observable_sparse_array.T
# Convert to 32bit float
for k in list(data.keys())[1:]:
data[k] = data[k].astype('f4',copy=False)
# Stellar mass can be calculated from STELLAR * RICHNESS
header = odict()
header['STELLAR']=round(self.stellar_mass_conversion,8)
header['LKDNSIDE']=self.config['coords']['nside_likelihood']
header['LKDPIX']=ang2pix(self.config['coords']['nside_likelihood'],
self.roi.lon,self.roi.lat)
header['NROI']=self.roi.inROI(self.loglike.catalog_roi.lon,
self.loglike.catalog_roi.lat).sum()
header['NANNULUS']=self.roi.inAnnulus(self.loglike.catalog_roi.lon,
self.loglike.catalog_roi.lat).sum()
header['NINSIDE']=self.roi.inInterior(self.loglike.catalog_roi.lon,
self.loglike.catalog_roi.lat).sum()
header['NTARGET']=self.roi.inTarget(self.loglike.catalog_roi.lon,
self.loglike.catalog_roi.lat).sum()
# Flatten if there is only a single distance modulus
# ADW: Is this really what we want to do?
if len(self.distance_modulus_array) == 1:
for key in data:
data[key] = data[key].flatten()
logger.info("Writing %s..."%outfile)
write_partial_map(outfile,data,
nside=self.config['coords']['nside_pixel'],
header=header,
clobber=True
)
fitsio.write(outfile,
dict(DISTANCE_MODULUS=self.distance_modulus_array.astype('f4',copy=False)),
extname='DISTANCE_MODULUS',
clobber=False)
def writeLabels(self,filename=None):
if filename is None: filename = self.labelfile
# ADW: Is it necessary to convert labels?
data_dict = {'PIXEL':self.pixels,
'LABEL':self.labels.astype(float,copy=False)}
logger.info("Writing %s..."%filename)
healpix.write_partial_map(filename,data_dict,self.nside)
fitsio.write(filename,
{'DISTANCE_MODULUS':self.distances.astype('f4',copy=False)},
extname='DISTANCE_MODULUS',
clobber=False)
def writeLabels(self, filename=None):
if filename is None: filename = self.labelfile
# ADW: Is it necessary to convert labels?
data_dict = {
'PIXEL': self.pixels,
'LABEL': self.labels.astype(float, copy=False)
}
logger.info("Writing %s..." % filename)
healpix.write_partial_map(filename, data_dict, self.nside)
fitsio.write(
filename,
{'DISTANCE_MODULUS': self.distances.astype('f4', copy=False)},
extname='DISTANCE_MODULUS',
clobber=False)
def writeLabels(self, filename=None):
"""Write the labeled array to a file.
Parameters
----------
filename : output filename (default: labelfile)
Returns
-------
None
"""
if filename is None: filename = self.labelfile
data_dict = {'PIXEL': self.pixels, 'LABEL': self.labels}
healpix.write_partial_map(filename, data_dict, self.nside)
fitsio.write(
filename,
{'DISTANCE_MODULUS': self.distances.astype('f4', copy=False)},
extname='DISTANCE_MODULUS',
clobber=False)