def supplement(self):
""" Add some supplemental columns """
from ugali.utils.projector import gal2cel, gal2cel_angle
kwargs = dict(usemask=False, asrecarray=True)
out = copy.deepcopy(self)
if ('lon' in out.names) and ('lat' in out.names):
# Ignore entries that are all zero
zeros = np.all(self.ndarray == 0, axis=1)
ra, dec = gal2cel(out.lon, out.lat)
ra[zeros] = 0
dec[zeros] = 0
out = recfuncs.append_fields(out, ['ra', 'dec'], [ra, dec],
**kwargs).view(Samples)
if 'position_angle' in out.names:
pa = gal2cel_angle(out.lon, out.lat, out.position_angle)
pa = pa - 180. * (pa > 180.)
pa[zeros] = 0
out = recfuncs.append_fields(out, ['position_angle_cel'], [pa],
**kwargs).view(Samples)
return out
def supplement(self, coordsys='gal'):
""" Add some supplemental columns """
from ugali.utils.projector import gal2cel, gal2cel_angle
from ugali.utils.projector import cel2gal, cel2gal_angle
coordsys = coordsys.lower()
kwargs = dict(usemask=False, asrecarray=True)
out = copy.deepcopy(self)
if ('lon' in out.names) and ('lat' in out.names):
# Ignore entries that are all zero
zeros = np.all(self.ndarray == 0, axis=1)
if coordsys == 'gal':
ra, dec = gal2cel(out.lon, out.lat)
glon, glat = out.lon, out.lat
else:
ra, dec = out.lon, out.lat
glon, glat = cel2gal(out.lon, out.lat)
ra[zeros] = 0
dec[zeros] = 0
glon[zeros] = 0
glat[zeros] = 0
names = ['ra', 'dec', 'glon', 'glat']
arrs = [ra, dec, glon, glat]
out = mlab.rec_append_fields(out, names, arrs).view(Samples)
#out = recfuncs.append_fields(out,names,arrs,**kwargs).view(Samples)
if 'position_angle' in out.names:
if coordsys == 'gal':
pa_gal = out.position_angle
pa_cel = gal2cel_angle(out.lon, out.lat,
out.position_angle)
pa_cel = pa_cel - 180. * (pa_cel > 180.)
else:
pa_gal = cel2gal_angle(out.lon, out.lat,
out.position_angle)
pa_cel = out.position_angle
pa_gal = pa_gal - 180. * (pa_gal > 180.)
pa_gal[zeros] = 0
pa_cel[zeros] = 0
names = ['position_angle_gal', 'position_angle_cel']
arrs = [pa_gal, pa_cel]
out = recfuncs.append_fields(out, names, arrs,
**kwargs).view(Samples)
return out
def supplement(self,coordsys='gal'):
""" Add some supplemental columns """
from ugali.utils.projector import gal2cel, gal2cel_angle
from ugali.utils.projector import cel2gal, cel2gal_angle
coordsys = coordsys.lower()
kwargs = dict(usemask=False, asrecarray=True)
out = copy.deepcopy(self)
if ('lon' in out.names) and ('lat' in out.names):
# Ignore entries that are all zero
zeros = np.all(self.ndarray==0,axis=1)
if coordsys == 'gal':
ra,dec = gal2cel(out.lon,out.lat)
glon,glat = out.lon,out.lat
else:
ra,dec = out.lon,out.lat
glon,glat = cel2gal(out.lon,out.lat)
ra[zeros] = 0; dec[zeros] = 0
glon[zeros] = 0; glat[zeros] = 0
names = ['ra','dec','glon','glat']
arrs = [ra,dec,glon,glat]
out = mlab.rec_append_fields(out,names,arrs).view(Samples)
#out = recfuncs.append_fields(out,names,arrs,**kwargs).view(Samples)
if 'position_angle' in out.names:
if coordsys == 'gal':
pa_gal = out.position_angle
pa_cel = gal2cel_angle(out.lon,out.lat,out.position_angle)
pa_cel = pa_cel - 180.*(pa_cel > 180.)
else:
pa_gal = cel2gal_angle(out.lon,out.lat,out.position_angle)
pa_cel = out.position_angle
pa_gal = pa_gal - 180.*(pa_gal > 180.)
pa_gal[zeros] = 0; pa_cel[zeros] = 0
names = ['position_angle_gal','position_angle_cel']
arrs = [pa_gal,pa_cel]
out = recfuncs.append_fields(out,names,arrs,**kwargs).view(Samples)
return out
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