def detectability(self,**kwargs):
"""
An a priori detectability proxy.
"""
distance_modulus = kwargs.get('distance_modulus')
distance = mod2dist(distance_modulus)
stellar_mass = kwargs.get('stellar_mass')
extension = kwargs.get('extension')
# Normalized to 10^3 Msolar at mod=18
norm = 10**3/mod2dist(18)**2
detect = stellar_mass / distance**2
detect /= norm
def finalizeObjects(self, objects):
objs = numpy.recarray(len(objects),
dtype=[('NAME','S24'),
('TS','f4'),
('GLON','f4'),
('GLAT','f4'),
('RA','f4'),
('DEC','f4'),
('MODULUS','f4'),
('DISTANCE','f4'),
('RICHNESS','f4'),
('MASS','f4'),
('NANNULUS','i4'),
('NINTERIOR','i4'),
])
objs['TS'] = self.values[objects['IDX_MAX'],objects['ZIDX_MAX']]
lon,lat = objects['X_MAX'],objects['Y_MAX']
coordsys = self.config['coords']['coordsys']
if coordsys.lower() == 'gal':
print("GAL coordintes")
objs['GLON'],objs['GLAT'] = lon,lat
objs['RA'],objs['DEC'] = gal2cel(lon,lat)
else:
print("CEL coordintes")
objs['RA'],objs['DEC'] = lon,lat
objs['GLON'],objs['GLAT'] = cel2gal(lon,lat)
modulus = objects['Z_MAX']
objs['MODULUS'] = modulus
objs['DISTANCE'] = mod2dist(modulus)
nside = healpy.npix2nside(len(self.nannulus))
pix = ang2pix(nside,lon,lat)
richness = self.richness[objects['IDX_MAX'],objects['ZIDX_MAX']]
objs['RICHNESS'] = richness
objs['MASS'] = richness * self.stellar[pix]
objs['NANNULUS'] = self.nannulus[pix].astype(int)
objs['NINTERIOR'] = self.ninterior[pix].astype(int)
# Default name formatting
# http://cdsarc.u-strasbg.fr/ftp/pub/iau/
# http://cds.u-strasbg.fr/vizier/Dic/iau-spec.htx
fmt = "J%(hour)02i%(hmin)04.1f%(deg)+03i%(dmin)02i"
for obj,_ra,_dec in zip(objs,objs['RA'],objs['DEC']):
hms = dec2hms(_ra); dms = dec2dms(_dec)
params = dict(hour=hms[0],hmin=hms[1]+hms[2]/60.,
deg=dms[0],dmin=dms[1]+dms[2]/60.)
obj['NAME'] = fmt%params
out = recfuncs.merge_arrays([objs,objects],usemask=False,
asrecarray=True,flatten=True)
return out
def finalizeObjects(self, objects):
objs = numpy.recarray(len(objects),
dtype=[('NAME','S24'),
('TS','f4'),
('GLON','f4'),
('GLAT','f4'),
('RA','f4'),
('DEC','f4'),
('MODULUS','f4'),
('DISTANCE','f4'),
('RICHNESS','f4'),
('MASS','f4'),
('NANNULUS','i4'),
('NINTERIOR','i4'),
])
objs['TS'] = self.values[objects['IDX_MAX'],objects['ZIDX_MAX']]
glon,glat = objects['X_MAX'],objects['Y_MAX']
objs['GLON'],objs['GLAT'] = glon,glat
ra,dec = gal2cel(glon,glat)
objs['RA'],objs['DEC'] = ra,dec
modulus = objects['Z_MAX']
objs['MODULUS'] = modulus
objs['DISTANCE'] = mod2dist(modulus)
#ninterior = ugali.utils.skymap.readSparseHealpixMap(self.roifile,'NINSIDE')
#nannulus = ugali.utils.skymap.readSparseHealpixMap(self.roifile,'NANNULUS')
#stellar = ugali.utils.skymap.readSparseHealpixMap(self.roifile,'STELLAR')
nside = healpy.npix2nside(len(self.nannulus))
pix = ang2pix(nside,glon,glat)
richness = self.richness[objects['IDX_MAX'],objects['ZIDX_MAX']]
objs['RICHNESS'] = richness
objs['MASS'] = richness * self.stellar[pix]
objs['NANNULUS'] = self.nannulus[pix].astype(int)
objs['NINTERIOR'] = self.ninterior[pix].astype(int)
# Default name formatting
# http://cdsarc.u-strasbg.fr/ftp/pub/iau/
# http://cds.u-strasbg.fr/vizier/Dic/iau-spec.htx
fmt = "J%(hour)02i%(hmin)04.1f%(deg)+03i%(dmin)02i"
for obj,_ra,_dec in zip(objs,ra,dec):
hms = dec2hms(_ra); dms = dec2dms(_dec)
params = dict(hour=hms[0],hmin=hms[1]+hms[2]/60.,
deg=dms[0],dmin=dms[1]+dms[2]/60.)
obj['NAME'] = fmt%params
out = recfuncs.merge_arrays([objs,objects],usemask=False,asrecarray=True,flatten=True)
# This is safer than viewing as FITS_rec
return pyfits.new_table(out).data
def finalizeObjects(self, objects):
objs = np.recarray(len(objects),
dtype=[
('NAME', 'S24'),
('TS', 'f4'),
('GLON', 'f4'),
('GLAT', 'f4'),
('RA', 'f4'),
('DEC', 'f4'),
('MODULUS', 'f4'),
('DISTANCE', 'f4'),
('RICHNESS', 'f4'),
('MASS', 'f4'),
('NANNULUS', 'i4'),
('NINTERIOR', 'i4'),
])
objs['TS'] = self.values[objects['IDX_MAX'], objects['ZIDX_MAX']]
lon, lat = objects['X_MAX'], objects['Y_MAX']
coordsys = self.config['coords']['coordsys']
if coordsys.lower() == 'gal':
print("GAL coordintes")
objs['GLON'], objs['GLAT'] = lon, lat
objs['RA'], objs['DEC'] = gal2cel(lon, lat)
else:
print("CEL coordintes")
objs['RA'], objs['DEC'] = lon, lat
objs['GLON'], objs['GLAT'] = cel2gal(lon, lat)
modulus = objects['Z_MAX']
objs['MODULUS'] = modulus
objs['DISTANCE'] = mod2dist(modulus)
nside = healpy.npix2nside(len(self.nannulus))
pix = ang2pix(nside, lon, lat)
richness = self.richness[objects['IDX_MAX'], objects['ZIDX_MAX']]
objs['RICHNESS'] = richness
objs['MASS'] = richness * self.stellar[pix]
objs['NANNULUS'] = self.nannulus[pix].astype(int)
objs['NINTERIOR'] = self.ninterior[pix].astype(int)
objs['NAME'] = ang2iau(objs['RA'], objs['DEC'], coord='cel')
out = recfuncs.merge_arrays([objs, objects],
usemask=False,
asrecarray=True,
flatten=True)
return out
def finalizeObjects(self, objects):
objs = numpy.recarray(len(objects),
dtype=[
('NAME', 'S24'),
('TS', 'f4'),
('GLON', 'f4'),
('GLAT', 'f4'),
('RA', 'f4'),
('DEC', 'f4'),
('MODULUS', 'f4'),
('DISTANCE', 'f4'),
('RICHNESS', 'f4'),
('MASS', 'f4'),
('NANNULUS', 'i4'),
('NINTERIOR', 'i4'),
])
objs['TS'] = self.values[objects['IDX_MAX'], objects['ZIDX_MAX']]
lon, lat = objects['X_MAX'], objects['Y_MAX']
coordsys = self.config['coords']['coordsys']
if coordsys.lower() == 'gal':
print("GAL coordintes")
objs['GLON'], objs['GLAT'] = lon, lat
objs['RA'], objs['DEC'] = gal2cel(lon, lat)
else:
print("CEL coordintes")
objs['RA'], objs['DEC'] = lon, lat
objs['GLON'], objs['GLAT'] = cel2gal(lon, lat)
modulus = objects['Z_MAX']
objs['MODULUS'] = modulus
objs['DISTANCE'] = mod2dist(modulus)
nside = healpy.npix2nside(len(self.nannulus))
pix = ang2pix(nside, lon, lat)
richness = self.richness[objects['IDX_MAX'], objects['ZIDX_MAX']]
objs['RICHNESS'] = richness
objs['MASS'] = richness * self.stellar[pix]
objs['NANNULUS'] = self.nannulus[pix].astype(int)
objs['NINTERIOR'] = self.ninterior[pix].astype(int)
# Default name formatting
# http://cdsarc.u-strasbg.fr/ftp/pub/iau/
# http://cds.u-strasbg.fr/vizier/Dic/iau-spec.htx
fmt = "J%(hour)02i%(hmin)04.1f%(deg)+03i%(dmin)02i"
for obj, _ra, _dec in zip(objs, objs['RA'], objs['DEC']):
hms = dec2hms(_ra)
dms = dec2dms(_dec)
params = dict(hour=hms[0],
hmin=hms[1] + hms[2] / 60.,
deg=dms[0],
dmin=dms[1] + dms[2] / 60.)
obj['NAME'] = fmt % params
out = recfuncs.merge_arrays([objs, objects],
usemask=False,
asrecarray=True,
flatten=True)
return out
def finalizeObjects(self, objects):
objs = numpy.recarray(len(objects),
dtype=[
('NAME', 'S24'),
('TS', 'f4'),
('GLON', 'f4'),
('GLAT', 'f4'),
('RA', 'f4'),
('DEC', 'f4'),
('MODULUS', 'f4'),
('DISTANCE', 'f4'),
('RICHNESS', 'f4'),
('MASS', 'f4'),
('NANNULUS', 'i4'),
('NINTERIOR', 'i4'),
])
objs['TS'] = self.values[objects['IDX_MAX'], objects['ZIDX_MAX']]
glon, glat = objects['X_MAX'], objects['Y_MAX']
objs['GLON'], objs['GLAT'] = glon, glat
ra, dec = gal2cel(glon, glat)
objs['RA'], objs['DEC'] = ra, dec
modulus = objects['Z_MAX']
objs['MODULUS'] = modulus
objs['DISTANCE'] = mod2dist(modulus)
#ninterior = ugali.utils.skymap.readSparseHealpixMap(self.roifile,'NINSIDE')
#nannulus = ugali.utils.skymap.readSparseHealpixMap(self.roifile,'NANNULUS')
#stellar = ugali.utils.skymap.readSparseHealpixMap(self.roifile,'STELLAR')
nside = healpy.npix2nside(len(self.nannulus))
pix = ang2pix(nside, glon, glat)
richness = self.richness[objects['IDX_MAX'], objects['ZIDX_MAX']]
objs['RICHNESS'] = richness
objs['MASS'] = richness * self.stellar[pix]
objs['NANNULUS'] = self.nannulus[pix].astype(int)
objs['NINTERIOR'] = self.ninterior[pix].astype(int)
# Default name formatting
# http://cdsarc.u-strasbg.fr/ftp/pub/iau/
# http://cds.u-strasbg.fr/vizier/Dic/iau-spec.htx
fmt = "J%(hour)02i%(hmin)04.1f%(deg)+03i%(dmin)02i"
for obj, _ra, _dec in zip(objs, ra, dec):
hms = dec2hms(_ra)
dms = dec2dms(_dec)
params = dict(hour=hms[0],
hmin=hms[1] + hms[2] / 60.,
deg=dms[0],
dmin=dms[1] + dms[2] / 60.)
obj['NAME'] = fmt % params
out = recfuncs.merge_arrays([objs, objects],
usemask=False,
asrecarray=True,
flatten=True)
# This is safer than viewing as FITS_rec
return pyfits.new_table(out).data
def distance(self):
""" Convert to physical distance (kpc) """
return mod2dist(self.distance_modulus)
def get_results(self, **kwargs):
kwargs.setdefault('alpha', self.alpha)
kwargs.setdefault('burn', self.nburn * self.nwalkers)
# Calculate best-fit parameters from MCMC chain
logger.debug('Estimating parameters...')
estimate = self.estimate_params(**kwargs)
params = {k: v[0] for k, v in estimate.items()}
results = dict(estimate)
# Extra parameters from the MCMC chain
logger.debug('Estimating auxiliary parameters...')
try:
results['ra'] = self.estimate('ra', **kwargs)
results['dec'] = self.estimate('dec', **kwargs)
except KeyError:
logger.warn("Didn't find 'ra' or 'dec'")
ra, dec = gal2cel(results['lon'][0], results['lat'][0])
results['ra'] = ugali.utils.stats.interval(ra)
results['dec'] = ugali.utils.stats.interval(dec)
ra, dec = results['ra'][0], results['dec'][0]
glon, glat = lon, lat = results['lon'][0], results['lat'][0]
results.update(gal=[float(glon), float(glat)])
results.update(cel=[float(ra), float(dec)])
try:
results['position_angle_cel'] = self.estimate(
'position_angle_cel', **kwargs)
except KeyError:
results['position_angle_cel'] = ugali.utils.stats.interval(np.nan)
# Update the loglike to the best-fit parameters from the chain
logger.debug('Calculating TS...')
ts = 2 * self.loglike.value(**params)
results['ts'] = ugali.utils.stats.interval(ts, np.nan, np.nan)
#lon,lat = estimate['lon'][0],estimate['lat'][0]
#
#results.update(gal=[float(lon),float(lat)])
#ra,dec = gal2cel(lon,lat)
#results.update(cel=[float(ra),float(dec)])
#results['ra'] = ugali.utils.stats.interval(ra,np.nan,np.nan)
#results['dec'] = ugali.utils.stats.interval(dec,np.nan,np.nan)
# Celestial position angle
# Break ambiguity in direction with '% 180.'
pa, pa_err = results['position_angle']
pa_cel = gal2cel_angle(lon, lat, pa) % 180.
pa_cel_err = np.array(pa_err) - pa + pa_cel
results['position_angle_cel'] = ugali.utils.stats.interval(
pa_cel, pa_cel_err[0], pa_cel_err[1])
mod, mod_err = estimate['distance_modulus']
dist = mod2dist(mod)
dist_lo, dist_hi = [mod2dist(mod_err[0]), mod2dist(mod_err[1])]
results['distance'] = ugali.utils.stats.interval(
dist, dist_lo, dist_hi)
dist, dist_err = results['distance']
ext, ext_err = estimate['extension']
ext_sigma = np.nan_to_num(np.array(ext_err) - ext)
results['extension_arcmin'] = ugali.utils.stats.interval(
60 * ext, 60 * ext_err[0], 60 * ext_err[1])
# Radially symmetric extension (correct for ellipticity).
ell, ell_err = estimate['ellipticity']
rext, rext_err = ext * np.sqrt(1 -
ell), np.array(ext_err) * np.sqrt(1 -
ell)
rext_sigma = np.nan_to_num(np.array(rext_err) - rext)
results['extension_radial'] = ugali.utils.stats.interval(
rext, rext_err[0], rext_err[1])
results['extension_radial_arcmin'] = ugali.utils.stats.interval(
60 * rext, 60 * rext_err[0], 60 * rext_err[1])
# Bayes factor for ellipticity
results['ellipticity_bayes_factor'] = self.bayes_factor(
'ellipticity', burn=kwargs['burn'])
# Physical Size (should do this with the posteriors)
# Radially symmetric
dist_sigma = np.nan_to_num(np.array(dist_err) - dist)
size = np.arctan(np.radians(ext)) * dist
size_sigma = size * np.sqrt((ext_sigma / ext)**2 +
(dist_sigma / dist)**2)
size_err = [size - size_sigma[0], size + size_sigma[1]]
results['physical_size'] = ugali.utils.stats.interval(
size, size_err[0], size_err[1])
rsize = np.arctan(np.radians(rext)) * dist
rsize_sigma = rsize * np.sqrt((rext_sigma / rext)**2 +
(dist_sigma / dist)**2)
rsize_err = [rsize - rsize_sigma[0], rsize + rsize_sigma[1]]
results['physical_size_radial'] = ugali.utils.stats.interval(
rsize, rsize_err[0], rsize_err[1])
# Richness
rich, rich_err = estimate['richness']
# Number of observed stars (sum of p-values)
nobs = self.loglike.p.sum()
nobs_lo, nobs_hi = nobs + np.sqrt(nobs) * np.array([-1, 1])
results['nobs'] = ugali.utils.stats.interval(nobs, nobs_lo, nobs_hi)
# Number of predicted stars (pixelization effects?)
npred = self.loglike.f * rich
npred_lo, npred_hi = rich_err[0] * self.loglike.f, rich_err[
1] * self.loglike.f
results['npred'] = ugali.utils.stats.interval(npred, npred_lo,
npred_hi)
# Careful, depends on the isochrone...
stellar_mass = self.source.stellar_mass()
mass = rich * stellar_mass
mass_lo, mass_hi = rich_err[0] * stellar_mass, rich_err[
1] * stellar_mass
results['mass'] = ugali.utils.stats.interval(mass, mass_lo, mass_hi)
stellar_luminosity = self.source.stellar_luminosity()
lum = rich * stellar_luminosity
lum_lo, lum_hi = rich_err[0] * stellar_luminosity, rich_err[
1] * stellar_luminosity
results['luminosity'] = ugali.utils.stats.interval(lum, lum_lo, lum_hi)
Mv = self.source.absolute_magnitude(rich)
Mv_lo = self.source.absolute_magnitude(rich_err[0])
Mv_hi = self.source.absolute_magnitude(rich_err[1])
results['Mv'] = ugali.utils.stats.interval(Mv, Mv_lo, Mv_hi)
# ADW: WARNING this is very fragile.
# Also, this is not quite right, should cut on the CMD available space
kwargs = dict(richness=rich,
mag_bright=16.,
mag_faint=23.,
n_trials=5000,
alpha=self.alpha,
seed=0)
martin = self.config['results'].get('martin')
if martin:
logger.info("Calculating Martin magnitude...")
if martin > 1: kwargs['n_trials'] = martin
Mv_martin = self.source.isochrone.absolute_magnitude_martin(
**kwargs)
results['Mv_martin'] = Mv_martin
else:
logger.warning("Skipping Martin magnitude")
results['Mv_martin'] = np.nan
mu = surfaceBrightness(Mv, size, dist)
results['surface_brightness'] = ugali.utils.stats.interval(
mu, np.nan, np.nan)
try:
results['constellation'] = ugali.utils.projector.ang2const(
lon, lat)[1]
except:
pass
results['iau'] = ugali.utils.projector.ang2iau(lon, lat)
coord = SkyCoord(ra * u.deg, dec * u.deg, distance=dist * u.kpc)
results['ra_sex'] = str(coord.ra.to_string())
results['dec_sex'] = str(coord.dec.to_string())
# Calculate some separations from GC, LMC, SMC
#NED coordinates with de Grisj distance
LMC = SkyCoord(80.8939 * u.deg,
-69.7561 * u.deg,
distance=49.89 * u.kpc)
#NED coordinates with de Grisj distance
SMC = SkyCoord(13.1866 * u.deg,
-72.8286 * u.deg,
distance=61.94 * u.kpc)
# GC from astropy?
GC = SkyCoord(266.4168262 * u.deg,
-29.0077969 * u.deg,
distance=8.0 * u.kpc)
results['d_gc'] = coord.separation_3d(GC).value
results['d_lmc'] = coord.separation_3d(LMC).value
results['d_smc'] = coord.separation_3d(SMC).value
try:
results['feh'] = float(self.source.isochrone.feh)
except:
results['feh'] = np.nan
output = dict()
output['params'] = params
output['results'] = results
return output
def get_results(self,**kwargs):
kwargs.setdefault('alpha',self.alpha)
kwargs.setdefault('burn',self.nburn*self.nwalkers)
# Calculate best-fit parameters from MCMC chain
logger.debug('Estimating parameters...')
estimate = self.estimate_params(**kwargs)
params = {k:v[0] for k,v in estimate.items()}
results = dict(estimate)
# Extra parameters from the MCMC chain
logger.debug('Estimating auxiliary parameters...')
try:
results['ra'] = self.estimate('ra',**kwargs)
results['dec'] = self.estimate('dec',**kwargs)
except KeyError:
logger.warn("Didn't find 'ra' or 'dec'")
ra,dec = gal2cel(results['lon'][0],results['lat'][0])
results['ra'] = ugali.utils.stats.interval(ra)
results['dec'] = ugali.utils.stats.interval(dec)
ra,dec = results['ra'][0],results['dec'][0]
glon,glat = lon,lat = results['lon'][0],results['lat'][0]
results.update(gal=[float(glon),float(glat)])
results.update(cel=[float(ra),float(dec)])
try:
results['position_angle_cel'] = self.estimate('position_angle_cel',**kwargs)
except KeyError:
results['position_angle_cel'] = ugali.utils.stats.interval(np.nan)
# Update the loglike to the best-fit parameters from the chain
logger.debug('Calculating TS...')
ts = 2*self.loglike.value(**params)
results['ts'] = ugali.utils.stats.interval(ts,np.nan,np.nan)
#lon,lat = estimate['lon'][0],estimate['lat'][0]
#
#results.update(gal=[float(lon),float(lat)])
#ra,dec = gal2cel(lon,lat)
#results.update(cel=[float(ra),float(dec)])
#results['ra'] = ugali.utils.stats.interval(ra,np.nan,np.nan)
#results['dec'] = ugali.utils.stats.interval(dec,np.nan,np.nan)
# Celestial position angle
# Break ambiguity in direction with '% 180.'
pa,pa_err = results['position_angle']
pa_cel = gal2cel_angle(lon,lat,pa) % 180.
pa_cel_err = np.array(pa_err) - pa + pa_cel
results['position_angle_cel'] = ugali.utils.stats.interval(pa_cel,pa_cel_err[0],pa_cel_err[1])
mod,mod_err = estimate['distance_modulus']
dist = mod2dist(mod)
dist_lo,dist_hi = [mod2dist(mod_err[0]),mod2dist(mod_err[1])]
results['distance'] = ugali.utils.stats.interval(dist,dist_lo,dist_hi)
dist,dist_err = results['distance']
ext,ext_err = estimate['extension']
ext_sigma = np.nan_to_num(np.array(ext_err) - ext)
results['extension_arcmin'] = ugali.utils.stats.interval(60*ext,60*ext_err[0],60*ext_err[1])
# Radially symmetric extension (correct for ellipticity).
ell,ell_err = estimate['ellipticity']
rext,rext_err = ext*np.sqrt(1-ell),np.array(ext_err)*np.sqrt(1-ell)
rext_sigma = np.nan_to_num(np.array(rext_err) - rext)
results['extension_radial'] = ugali.utils.stats.interval(rext,rext_err[0],rext_err[1])
results['extension_radial_arcmin'] = ugali.utils.stats.interval(60*rext,60*rext_err[0],60*rext_err[1])
# Bayes factor for ellipticity
results['ellipticity_bayes_factor'] = self.bayes_factor('ellipticity',burn=kwargs['burn'])
# Physical Size (should do this with the posteriors)
# Radially symmetric
dist_sigma = np.nan_to_num(np.array(dist_err) - dist)
size = np.arctan(np.radians(ext)) * dist
size_sigma = size * np.sqrt((ext_sigma/ext)**2 + (dist_sigma/dist)**2)
size_err = [size-size_sigma[0],size+size_sigma[1]]
results['physical_size'] = ugali.utils.stats.interval(size,size_err[0],size_err[1])
rsize = np.arctan(np.radians(rext)) * dist
rsize_sigma = rsize * np.sqrt((rext_sigma/rext)**2 + (dist_sigma/dist)**2)
rsize_err = [rsize-rsize_sigma[0],rsize+rsize_sigma[1]]
results['physical_size_radial'] = ugali.utils.stats.interval(rsize,rsize_err[0],rsize_err[1])
# Richness
rich,rich_err = estimate['richness']
# Number of observed stars (sum of p-values)
nobs = self.loglike.p.sum()
nobs_lo,nobs_hi = nobs + np.sqrt(nobs)*np.array([-1,1])
results['nobs'] = ugali.utils.stats.interval(nobs,nobs_lo,nobs_hi)
# Number of predicted stars (pixelization effects?)
npred = self.loglike.f*rich
npred_lo,npred_hi = rich_err[0]*self.loglike.f,rich_err[1]*self.loglike.f
results['npred'] = ugali.utils.stats.interval(npred,npred_lo,npred_hi)
# Careful, depends on the isochrone...
stellar_mass = self.source.stellar_mass()
mass = rich*stellar_mass
mass_lo,mass_hi = rich_err[0]*stellar_mass,rich_err[1]*stellar_mass
results['mass'] = ugali.utils.stats.interval(mass,mass_lo,mass_hi)
stellar_luminosity = self.source.stellar_luminosity()
lum = rich*stellar_luminosity
lum_lo,lum_hi = rich_err[0]*stellar_luminosity,rich_err[1]*stellar_luminosity
results['luminosity'] = ugali.utils.stats.interval(lum,lum_lo,lum_hi)
# Absolute magnitude only calculated for DES isochrones with g,r
try:
Mv = self.source.absolute_magnitude(rich)
Mv_lo = self.source.absolute_magnitude(rich_err[0])
Mv_hi = self.source.absolute_magnitude(rich_err[1])
results['Mv'] = ugali.utils.stats.interval(Mv,Mv_lo,Mv_hi)
except ValueError as e:
logger.warning("Skipping absolute magnitude")
logger.warn(str(e))
results['Mv'] = np.nan
# ADW: WARNING this is very fragile.
# Also, this is not quite right, should cut on the CMD available space
kwargs = dict(richness=rich,mag_bright=16., mag_faint=23.,
n_trials=5000,alpha=self.alpha, seed=0)
martin = self.config['results'].get('martin')
if martin:
logger.info("Calculating Martin magnitude...")
if martin > 1: kwargs['n_trials'] = martin
Mv_martin = self.source.isochrone.absolute_magnitude_martin(**kwargs)
results['Mv_martin'] = Mv_martin
else:
logger.warning("Skipping Martin magnitude")
results['Mv_martin'] = np.nan
mu = surfaceBrightness(Mv, size, dist)
results['surface_brightness'] = ugali.utils.stats.interval(mu,np.nan,np.nan)
try:
results['constellation'] = ang2const(lon,lat,self.coordsys)[1]
except:
pass
results['iau'] = ugali.utils.projector.ang2iau(lon,lat)
coord = SkyCoord(ra*u.deg,dec*u.deg,distance=dist*u.kpc)
results['ra_sex'] = str(coord.ra.to_string())
results['dec_sex'] = str(coord.dec.to_string())
# Calculate some separations from GC, LMC, SMC
#NED coordinates with de Grisj distance
LMC = SkyCoord(80.8939*u.deg,-69.7561*u.deg,distance=49.89*u.kpc)
#NED coordinates with de Grisj distance
SMC = SkyCoord(13.1866*u.deg,-72.8286*u.deg,distance=61.94*u.kpc)
# GC from astropy?
GC = SkyCoord(266.4168262*u.deg,-29.0077969*u.deg,distance=8.0*u.kpc)
results['d_gc'] = coord.separation_3d(GC).value
results['d_lmc'] = coord.separation_3d(LMC).value
results['d_smc'] = coord.separation_3d(SMC).value
try:
results['feh'] = float(self.source.isochrone.feh)
except:
results['feh'] = np.nan
output = dict()
output['params'] = params
output['results'] = results
return output
def distance(self):
""" Convert to physical distance (kpc) """
return mod2dist(self.distance_modulus)