def calcDiskMass(data,samples,
locations,effsel,distmods,
type='tribrokenexpflare'):
"""
NAME:
calcDiskMass
PURPOSE:
calculate the local surface density for a set of density profiles
INPUT:
data - the data array
samples - an array [nparam,ndens] of density-profile parameters
locations - locations of the APOGEE effective selection function
effsel - array (nloc,nD) of the effective selection function, includes area of the field
distmods - grid of distance moduli on which the effective selection function is pre-computed
type= ('exp') type of density profile to use
OUTPUT:
local surface density in Msol/pc^2
HISTORY:
2015-04-29 - Written - Bovy (IAS)
"""
# Setup the density function and its initial parameters
densfunc= fitDens._setup_densfunc(type)
# Setup the integration of the effective volume
effsel, Rgrid, phigrid, zgrid= \
fitDens._setup_effvol(locations,effsel,distmods)
out= []
for sample in samples.T:
# Setup the density function, fix the normalization for Rb < R0
if 'tribroken' in type and numpy.exp(sample[3]) < densprofiles._R0:
norm= numpy.exp(-(sample[0]+sample[2])\
*(numpy.exp(sample[3])-densprofiles._R0))
else:
norm= 1.
tdensfunc= lambda x,y,z: densfunc(x,y,z,params=sample)*norm
out.append(calcDiskMass_single(data,tdensfunc,
effsel,Rgrid,phigrid,zgrid))
return numpy.array(out)*12500.
def predict_spacedist(params,
locations,effsel,distmods,
type='exp',
coord='Z'):
"""
NAME:
predict_spacedist
PURPOSE:
predict the spatial distribution
INPUT:
params - parameters of the density profile
locations - locations of the APOGEE effective selection function to consider
effsel - array (nloc,nD) of the effective selection function, includes area of the field
distmods - grid of distance moduli on which the effective selection function is pre-computed
type= ('exp') type of density profile to fit
coord= ('dm', 'X', or 'Z')
OUTPUT:
(R,model(R))
HISTORY:
2015-03-26 - Written - Bovy (IAS)
"""
if coord.lower() == 'x':
# Grid in X
Xs= numpy.linspace(0.,20.,301)
elif coord.lower() == 'z':
# Grid in X
Xs= numpy.linspace(0.,20.,301)
elif coord.lower() == 'dm':
# Grid in X
Xs= numpy.linspace(7.,15.5,301)
# Setup the density function
rdensfunc= _setup_densfunc(type)
densfunc= lambda x,y,z: rdensfunc(x,y,z,params=params)
# Restore the APOGEE selection function (assumed pre-computed)
selectFile= '../savs/selfunc-nospdata.sav'
if os.path.exists(selectFile):
with open(selectFile,'rb') as savefile:
apo= pickle.load(savefile)
# Now compute the necessary coordinate transformations
ds= 10.**(distmods/5-2.)
Rgrid, phigrid, zgrid, Xgrid= [], [], [], []
for loc in locations:
lcen, bcen= apo.glonGlat(loc)
XYZ= bovy_coords.lbd_to_XYZ(lcen*numpy.ones_like(ds),
bcen*numpy.ones_like(ds),
ds,
degree=True)
Rphiz= bovy_coords.XYZ_to_galcencyl(XYZ[:,0],XYZ[:,1],XYZ[:,2],
Xsun=define_rcsample._R0,
Ysun=0.,
Zsun=define_rcsample._Z0)
Rgrid.append(Rphiz[0])
phigrid.append(Rphiz[1])
zgrid.append(Rphiz[2])
Xgrid.append(Rphiz[0]*numpy.cos(Rphiz[1]))
Rgrid= numpy.array(Rgrid)
phigrid= numpy.array(phigrid)
zgrid= numpy.array(zgrid)
Xgrid= numpy.array(Xgrid)
# Now compute rate(R) for each location and combine
effsel*= numpy.tile(ds**3.*(distmods[1]-distmods[0]),(effsel.shape[0],1))
tdens= densfunc(Rgrid,phigrid,zgrid)
rate= tdens*effsel
out= numpy.zeros((len(locations),len(Xs)))
for ii in range(len(locations)):
if coord.lower() == 'x':
# Jacobian
tjac= numpy.fabs((numpy.roll(distmods,-1)-distmods)/\
(numpy.roll(Xgrid[ii],-1)-Xgrid[ii]))
tjac[-1]= tjac[-2]
tXs= Xgrid[ii,rate[ii] > 0.]
elif coord.lower() == 'z':
# Jacobian
tjac= numpy.fabs((numpy.roll(distmods,-1)-distmods)/\
(numpy.roll(zgrid[ii],-1)-zgrid[ii]))
tjac[-1]= tjac[-2]
tXs= zgrid[ii,rate[ii] > 0.]
elif coord.lower() == 'dm':
# Jacobian
tjac= numpy.ones_like(Xs)
tXs= distmods[rate[ii] > 0.]
sindx= numpy.argsort(tXs)
tXs= tXs[sindx]
trate= rate[ii,rate[ii] > 0.][sindx]
tjac= tjac[rate[ii] > 0.][sindx]
ipthis= numpy.log(trate*tjac+10.**-8.)
baseline= numpy.polynomial.Polynomial.fit(tXs,ipthis,4)
ipthis= ipthis/baseline(tXs)
sp= interpolate.InterpolatedUnivariateSpline(tXs,ipthis,k=3)
tindx= (Xs >= numpy.amin(tXs))\
*(Xs <= numpy.amax(tXs))
out[ii,tindx]= (numpy.exp(sp(Xs[tindx])*baseline(Xs[tindx]))-10.**-8.)
out[numpy.isinf(out)]= 0.
return (Xs,out.sum(axis=0))
def calc_normalisation(params,
nbin,
iso_grid,
fehbin=[-0.1, 0.0],
agebin=[1., 3.],
loggcut=[1.8, 3.0],
teffcut=[4000, 5000],
type='brokenexpflare',
verbose=True,
fitIndx=None,
weights='padova',
distance_cut=False,
lowermass=None):
#first get the values necessary from the isochrone grid
#make a mask for giant stars (+ J-K cut)
if teffcut == None:
giants = (iso_grid[:, 3] >= loggcut[0]) & (
iso_grid[:, 3] < loggcut[1]) & (iso_grid[:, 5] > 0.5)
else:
giants = (iso_grid[:, 3] >= loggcut[0]) & (
iso_grid[:, 3] < loggcut[1]) & (iso_grid[:, 5] > 0.5) & (
10**iso_grid[:, 7] >= teffcut[0]) & (10**iso_grid[:, 7] <
teffcut[1])
#make a mask for the age and feh bin
if agebin == None:
bin = (10**iso_grid[:,0] >= 0.)&(10**iso_grid[:,0] < 13.)&\
(Z2FEH(iso_grid[:,1]) >= fehbin[0])&(Z2FEH(iso_grid[:,1]) < fehbin[1])
else:
bin = (10**iso_grid[:,0] >= agebin[0])&(10**iso_grid[:,0] < agebin[1])&\
(Z2FEH(iso_grid[:,1]) >= fehbin[0])&(Z2FEH(iso_grid[:,1]) < fehbin[1])
if lowermass != None:
giants *= iso_grid[:, 2] >= lowermass
bin *= iso_grid[:, 2] >= lowermass
if len(iso_grid[:, 0][bin]) < 1:
fehs = np.unique(Z2FEH(iso_grid[:, 1]))
cfehbin = fehbin[0] + ((fehbin[1] - fehbin[0]) / 2)
feh_offsets = np.fabs(fehs - cfehbin)
ind = np.argmin(feh_offsets)
cfeh = fehs[ind]
bin = (10**iso_grid[:,0] >= agebin[0])&(10**iso_grid[:,0] < agebin[1])&\
(Z2FEH(iso_grid[:,1]) == cfeh)
#find the average giant mass
mass = iso_grid[:, 2]
if weights == 'padova':
weight = iso_grid[:, 6] * (10**iso_grid[:, 0] / iso_grid[:, 1])
if weights == 'basti':
weight = iso_grid[:, 6]
av_mass = np.sum(mass[giants & bin] * weight[giants & bin]) / np.sum(
weight[giants & bin])
#find the ratio between giants and the total stellar pop. for this bin
mass_total = mass[bin]
weight_total = weight[bin]
mass_bin = mass[giants & bin]
weight_bin = weight[giants & bin]
m_ratio = np.sum(mass_bin * weight_bin) / np.sum(mass_total * weight_total)
#now compute and sum the rate for this density function
#load the raw selection function
selectFile = '../savs/selfunc-nospdata.sav'
if os.path.exists(selectFile):
with open(selectFile, 'rb') as savefile:
apo = pickle.load(savefile)
#load the effective selection function
if agebin == None:
with open(
'../essf/maps/essf_rgb_green15_modelmh_feh' +
str(round(fehbin[0], 1)) + '.sav', 'rb') as savefile:
locations = pickle.load(savefile)
effsel = pickle.load(savefile)
distmods = pickle.load(savefile)
with open(
'../essf/maps/essf_rgb_marshall06_modelmh_feh' +
str(round(fehbin[0], 1)) + '.sav', 'rb') as savefile:
mlocations = pickle.load(savefile)
meffsel = pickle.load(savefile)
mdistmods = pickle.load(savefile)
if agebin != None:
if agebin[0] < 1.:
with open(
'../essf/maps/essf_rgb_green15_modelmh_feh' +
str(round(fehbin[0], 1)) + '_age' + str(round(1.0, 1)) +
'.sav', 'rb') as savefile:
locations = pickle.load(savefile)
effsel = pickle.load(savefile)
distmods = pickle.load(savefile)
with open(
'../essf/maps/essf_rgb_marshall06_modelmh_feh' +
str(round(fehbin[0], 1)) + '_age' + str(round(1.0, 1)) +
'.sav', 'rb') as savefile:
mlocations = pickle.load(savefile)
meffsel = pickle.load(savefile)
mdistmods = pickle.load(savefile)
if agebin[0] > 0.9:
with open(
'../essf/maps/essf_rgb_green15_modelmh_feh' +
str(round(fehbin[0], 1)) + '_age' +
str(round(agebin[0], 1)) + '.sav', 'rb') as savefile:
locations = pickle.load(savefile)
effsel = pickle.load(savefile)
distmods = pickle.load(savefile)
with open(
'../essf/maps/essf_rgb_marshall06_modelmh_feh' +
str(round(fehbin[0], 1)) + '_age' +
str(round(agebin[0], 1)) + '.sav', 'rb') as savefile:
mlocations = pickle.load(savefile)
meffsel = pickle.load(savefile)
mdistmods = pickle.load(savefile)
# Fill in regions not covered by Marshall map
meffsel[meffsel < -0.5] = effsel[meffsel < -0.5]
if fitIndx is None:
fitIndx = numpy.ones(len(mlocations), dtype='bool') #True-betwDiskIndx
locations, effsel, distmods = np.array(mlocations)[fitIndx], np.array(
meffsel)[fitIndx], mdistmods
#get the density function and set it up to find the normalisation (surfdens=True)
rdensfunc = _setup_densfunc(type)
densfunc = lambda x: rdensfunc(x, None, None, params=params, surfdens=True)
#evaluate surface density at R0 for the density normalisation (always 1. if R_b > R0)
R0 = densprofiles._R0
Rb = np.exp(params[3])
dens_norm = densfunc(densprofiles._R0)
#set up the density function again with surfdens=False for the rate calculation
rdensfunc = _setup_densfunc(type)
densfunc = lambda x, y, z: rdensfunc(
x, y, z, params=params, surfdens=False)
ds = 10.**(distmods / 5. - 2.)
#imply the distance cut if distance_cut == True
if distance_cut == True:
distmods = distmods[ds <= 3.]
ds = ds[ds <= 3.]
effsel = effsel[:, :len(ds)]
#Compute the grid of R, phi and Z for each location
Rgrid, phigrid, zgrid = [], [], []
for loc in locations:
lcen, bcen = apo.glonGlat(loc)
XYZ = bovy_coords.lbd_to_XYZ(lcen * numpy.ones_like(ds),
bcen * numpy.ones_like(ds),
ds,
degree=True)
Rphiz = bovy_coords.XYZ_to_galcencyl(XYZ[:, 0],
XYZ[:, 1],
XYZ[:, 2],
Xsun=define_rgbsample._R0,
Zsun=define_rgbsample._Z0)
Rgrid.append(Rphiz[:, 0])
phigrid.append(Rphiz[:, 1])
zgrid.append(Rphiz[:, 2])
Rgrid = numpy.array(Rgrid)
phigrid = numpy.array(phigrid)
zgrid = numpy.array(zgrid)
# Now compute rate(R) for each location and combine
effsel *= numpy.tile(
ds**2. * (distmods[1] - distmods[0]) * (ds * np.log(10) / 5.),
(effsel.shape[0], 1))
tdens = densfunc(Rgrid, phigrid, zgrid) / dens_norm
rate = tdens * effsel
sumrate = np.sum(rate)
#calculate normalisation N(R0)
norm = (nbin / sumrate)
#convert units (Kpc^2 > pc^2, deg > rad etc)
norm *= 1e-6 * (180 / np.pi)**2
#compute mass in bin using values from isochrones
bin_mass = (norm * av_mass) / m_ratio
if verbose == True:
print bin_mass
return bin_mass, norm, m_ratio, (av_mass * 1e-6 *
(180 / np.pi)**2) / (sumrate * m_ratio)
def generate(locations,
type='exp',
sample='lowlow',
extmap='green15',
nls=101,
nmock=1000,
H0=-1.49,
_dmapg15=None,
ncpu=1):
"""
NAME:
generate
PURPOSE:
generate mock data following a given density
INPUT:
locations - locations to be included in the sample
type= ('exp') type of density profile to sample from
sample= ('lowlow') for selecting mock parameters
extmap= ('green15') extinction map to use ('marshall06' and others use Green15 to fill in unobserved regions)
nls= (101) number of longitude bins to use for each field
nmock= (1000) number of mock data points to generate
H0= (-1.49) absolute magnitude (can be array w/ sampling spread)
ncpu= (1) number of cpus to use to compute the probability
OUTPUT:
mockdata recarray with tags 'RC_GALR_H', 'RC_GALPHI_H', 'RC_GALZ_H'
HISTORY:
2015-04-03 - Written - Bovy (IAS)
"""
if isinstance(H0,float): H0= [H0]
# Setup the density function and its initial parameters
rdensfunc= fitDens._setup_densfunc(type)
mockparams= _setup_mockparams_densfunc(type,sample)
densfunc= lambda x,y,z: rdensfunc(x,y,z,params=mockparams)
# Setup the extinction map
global dmap
global dmapg15
if _dmapg15 is None: dmapg15= mwdust.Green15(filter='2MASS H')
else: dmapg15= _dmapg15
if isinstance(extmap,mwdust.DustMap3D.DustMap3D):
dmap= extmap
elif extmap.lower() == 'green15':
dmap= dmapg15
elif extmap.lower() == 'marshall06':
dmap= mwdust.Marshall06(filter='2MASS H')
elif extmap.lower() == 'sale14':
dmap= mwdust.Sale14(filter='2MASS H')
elif extmap.lower() == 'drimmel03':
dmap= mwdust.Drimmel03(filter='2MASS H')
# Use brute-force rejection sampling to make no approximations
# First need to estimate the max probability to use in rejection;
# Loop through all locations and compute sampling probability on grid in
# (l,b,D)
# First restore the APOGEE selection function (assumed pre-computed)
global apo
selectFile= '../savs/selfunc-nospdata.sav'
if os.path.exists(selectFile):
with open(selectFile,'rb') as savefile:
apo= pickle.load(savefile)
# Now compute the necessary coordinate transformations and evaluate the
# maximum probability
distmods= numpy.linspace(7.,15.5,301)
ds= 10.**(distmods/5-2.)
nbs= nls
lnprobs= numpy.empty((len(locations),len(distmods),nbs,nls))
radii= []
lcens, bcens= [], []
lnprobs= multi.parallel_map(lambda x: _calc_lnprob(locations[x],nls,nbs,
ds,distmods,
H0,
densfunc),
range(len(locations)),
numcores=numpy.amin([len(locations),
multiprocessing.cpu_count(),ncpu]))
lnprobs= numpy.array(lnprobs)
for ll, loc in enumerate(locations):
lcen, bcen= apo.glonGlat(loc)
rad= apo.radius(loc)
radii.append(rad) # save for later
lcens.append(lcen[0])
bcens.append(bcen[0])
maxp= (numpy.exp(numpy.nanmax(lnprobs))-10.**-8.)*1.1 # Just to be sure
# Now generate mock data using rejection sampling
nout= 0
arlocations= numpy.array(locations)
arradii= numpy.array(radii)
arlcens= numpy.array(lcens)
arbcens= numpy.array(bcens)
out= numpy.recarray((nmock,),
dtype=[('RC_DIST_H','f8'),
('RC_DM_H','f8'),
('RC_GALR_H','f8'),
('RC_GALPHI_H','f8'),
('RC_GALZ_H','f8')])
while nout < nmock:
nnew= 2*(nmock-nout)
# nnew new locations
locIndx= numpy.floor(numpy.random.uniform(size=nnew)*len(locations)).astype('int')
newlocations= arlocations[locIndx]
# Point within these locations
newds_coord= numpy.random.uniform(size=nnew)
newds= 10.**((newds_coord*(numpy.amax(distmods)-numpy.amin(distmods))\
+numpy.amin(distmods))/5.-2.)
newdls_coord= numpy.random.uniform(size=nnew)
newdls= newdls_coord*2.*arradii[locIndx]\
-arradii[locIndx]
newdbs_coord= numpy.random.uniform(size=nnew)
newdbs= newdbs_coord*2.*arradii[locIndx]\
-arradii[locIndx]
newr2s= newdls**2.+newdbs**2.
keepIndx= newr2s < arradii[locIndx]**2.
newlocations= newlocations[keepIndx]
newds_coord= newds_coord[keepIndx]
newdls_coord= newdls_coord[keepIndx]
newdbs_coord= newdbs_coord[keepIndx]
newds= newds[keepIndx]
newdls= newdls[keepIndx]
newdbs= newdbs[keepIndx]
newls= newdls+arlcens[locIndx][keepIndx]
newbs= newdbs+arbcens[locIndx][keepIndx]
# Reject?
tps= numpy.zeros_like(newds)
for nloc in list(set(newlocations)):
lindx= newlocations == nloc
pindx= arlocations == nloc
coord= numpy.array([newds_coord[lindx]*(len(distmods)-1.),
newdbs_coord[lindx]*(nbs-1.),
newdls_coord[lindx]*(nls-1.)])
tps[lindx]= \
numpy.exp(ndimage.interpolation.map_coordinates(\
lnprobs[pindx][0],
coord,cval=-10.,
order=1))-10.**-8.
XYZ= bovy_coords.lbd_to_XYZ(newls,newbs,newds,degree=True)
Rphiz= bovy_coords.XYZ_to_galcencyl(XYZ[:,0],XYZ[:,1],XYZ[:,2],
Xsun=define_rcsample._R0,
Ysun=0.,
Zsun=define_rcsample._Z0)
testp= numpy.random.uniform(size=len(newds))*maxp
keepIndx= tps > testp
if numpy.sum(keepIndx) > nmock-nout:
rangeIndx= numpy.zeros(len(keepIndx),dtype='int')
rangeIndx[keepIndx]= numpy.arange(numpy.sum(keepIndx))
keepIndx*= (rangeIndx < nmock-nout)
out['RC_DIST_H'][nout:nout+numpy.sum(keepIndx)]= newds[keepIndx]
out['RC_DM_H'][nout:nout+numpy.sum(keepIndx)]= newds_coord[keepIndx]*(numpy.amax(distmods)-numpy.amin(distmods))\
+numpy.amin(distmods)
out['RC_GALR_H'][nout:nout+numpy.sum(keepIndx)]= Rphiz[0][keepIndx]
out['RC_GALPHI_H'][nout:nout+numpy.sum(keepIndx)]= Rphiz[1][keepIndx]
out['RC_GALZ_H'][nout:nout+numpy.sum(keepIndx)]= Rphiz[2][keepIndx]
nout= nout+numpy.sum(keepIndx)
return (out,lnprobs)
def generate(locations,
type='exp',
sample='lowlow',
extmap='green15',
nls=101,
nmock=1000,
H0=-1.49,
_dmapg15=None,
ncpu=1):
"""
NAME:
generate
PURPOSE:
generate mock data following a given density
INPUT:
locations - locations to be included in the sample
type= ('exp') type of density profile to sample from
sample= ('lowlow') for selecting mock parameters
extmap= ('green15') extinction map to use ('marshall06' and others use Green15 to fill in unobserved regions)
nls= (101) number of longitude bins to use for each field
nmock= (1000) number of mock data points to generate
H0= (-1.49) absolute magnitude (can be array w/ sampling spread)
ncpu= (1) number of cpus to use to compute the probability
OUTPUT:
mockdata recarray with tags 'RC_GALR_H', 'RC_GALPHI_H', 'RC_GALZ_H'
HISTORY:
2015-04-03 - Written - Bovy (IAS)
"""
if isinstance(H0, float): H0 = [H0]
# Setup the density function and its initial parameters
rdensfunc = fitDens._setup_densfunc(type)
mockparams = _setup_mockparams_densfunc(type, sample)
densfunc = lambda x, y, z: rdensfunc(x, y, z, params=mockparams)
# Setup the extinction map
global dmap
global dmapg15
if _dmapg15 is None: dmapg15 = mwdust.Green15(filter='2MASS H')
else: dmapg15 = _dmapg15
if isinstance(extmap, mwdust.DustMap3D.DustMap3D):
dmap = extmap
elif extmap.lower() == 'green15':
dmap = dmapg15
elif extmap.lower() == 'marshall06':
dmap = mwdust.Marshall06(filter='2MASS H')
elif extmap.lower() == 'sale14':
dmap = mwdust.Sale14(filter='2MASS H')
elif extmap.lower() == 'drimmel03':
dmap = mwdust.Drimmel03(filter='2MASS H')
# Use brute-force rejection sampling to make no approximations
# First need to estimate the max probability to use in rejection;
# Loop through all locations and compute sampling probability on grid in
# (l,b,D)
# First restore the APOGEE selection function (assumed pre-computed)
global apo
selectFile = '../savs/selfunc-nospdata.sav'
if os.path.exists(selectFile):
with open(selectFile, 'rb') as savefile:
apo = pickle.load(savefile)
# Now compute the necessary coordinate transformations and evaluate the
# maximum probability
distmods = numpy.linspace(7., 15.5, 301)
ds = 10.**(distmods / 5 - 2.)
nbs = nls
lnprobs = numpy.empty((len(locations), len(distmods), nbs, nls))
radii = []
lcens, bcens = [], []
lnprobs = multi.parallel_map(lambda x: _calc_lnprob(
locations[x], nls, nbs, ds, distmods, H0, densfunc),
range(len(locations)),
numcores=numpy.amin([
len(locations),
multiprocessing.cpu_count(), ncpu
]))
lnprobs = numpy.array(lnprobs)
for ll, loc in enumerate(locations):
lcen, bcen = apo.glonGlat(loc)
rad = apo.radius(loc)
radii.append(rad) # save for later
lcens.append(lcen[0])
bcens.append(bcen[0])
maxp = (numpy.exp(numpy.nanmax(lnprobs)) -
10.**-8.) * 1.1 # Just to be sure
# Now generate mock data using rejection sampling
nout = 0
arlocations = numpy.array(locations)
arradii = numpy.array(radii)
arlcens = numpy.array(lcens)
arbcens = numpy.array(bcens)
out = numpy.recarray((nmock, ),
dtype=[('RC_DIST_H', 'f8'), ('RC_DM_H', 'f8'),
('RC_GALR_H', 'f8'), ('RC_GALPHI_H', 'f8'),
('RC_GALZ_H', 'f8')])
while nout < nmock:
nnew = 2 * (nmock - nout)
# nnew new locations
locIndx = numpy.floor(
numpy.random.uniform(size=nnew) * len(locations)).astype('int')
newlocations = arlocations[locIndx]
# Point within these locations
newds_coord = numpy.random.uniform(size=nnew)
newds= 10.**((newds_coord*(numpy.amax(distmods)-numpy.amin(distmods))\
+numpy.amin(distmods))/5.-2.)
newdls_coord = numpy.random.uniform(size=nnew)
newdls= newdls_coord*2.*arradii[locIndx]\
-arradii[locIndx]
newdbs_coord = numpy.random.uniform(size=nnew)
newdbs= newdbs_coord*2.*arradii[locIndx]\
-arradii[locIndx]
newr2s = newdls**2. + newdbs**2.
keepIndx = newr2s < arradii[locIndx]**2.
newlocations = newlocations[keepIndx]
newds_coord = newds_coord[keepIndx]
newdls_coord = newdls_coord[keepIndx]
newdbs_coord = newdbs_coord[keepIndx]
newds = newds[keepIndx]
newdls = newdls[keepIndx]
newdbs = newdbs[keepIndx]
newls = newdls + arlcens[locIndx][keepIndx]
newbs = newdbs + arbcens[locIndx][keepIndx]
# Reject?
tps = numpy.zeros_like(newds)
for nloc in list(set(newlocations)):
lindx = newlocations == nloc
pindx = arlocations == nloc
coord = numpy.array([
newds_coord[lindx] * (len(distmods) - 1.),
newdbs_coord[lindx] * (nbs - 1.),
newdls_coord[lindx] * (nls - 1.)
])
tps[lindx]= \
numpy.exp(ndimage.interpolation.map_coordinates(\
lnprobs[pindx][0],
coord,cval=-10.,
order=1))-10.**-8.
XYZ = bovy_coords.lbd_to_XYZ(newls, newbs, newds, degree=True)
Rphiz = bovy_coords.XYZ_to_galcencyl(XYZ[:, 0],
XYZ[:, 1],
XYZ[:, 2],
Xsun=define_rcsample._R0,
Ysun=0.,
Zsun=define_rcsample._Z0)
testp = numpy.random.uniform(size=len(newds)) * maxp
keepIndx = tps > testp
if numpy.sum(keepIndx) > nmock - nout:
rangeIndx = numpy.zeros(len(keepIndx), dtype='int')
rangeIndx[keepIndx] = numpy.arange(numpy.sum(keepIndx))
keepIndx *= (rangeIndx < nmock - nout)
out['RC_DIST_H'][nout:nout + numpy.sum(keepIndx)] = newds[keepIndx]
out['RC_DM_H'][nout:nout+numpy.sum(keepIndx)]= newds_coord[keepIndx]*(numpy.amax(distmods)-numpy.amin(distmods))\
+numpy.amin(distmods)
out['RC_GALR_H'][nout:nout + numpy.sum(keepIndx)] = Rphiz[0][keepIndx]
out['RC_GALPHI_H'][nout:nout +
numpy.sum(keepIndx)] = Rphiz[1][keepIndx]
out['RC_GALZ_H'][nout:nout + numpy.sum(keepIndx)] = Rphiz[2][keepIndx]
nout = nout + numpy.sum(keepIndx)
return (out, lnprobs)