def partial_profile(backcat_ids,RA0,DEC0,Z,
RIN,ROUT,ndots,h,nboot=100):
cosmo = LambdaCDM(H0=100*h, Om0=0.3, Ode0=0.7)
backcat = S.data.loc[backcat_ids]
ndots = int(ndots)
if 'KiDS' in np.array(backcat.CATNAME)[0]:
mask = (backcat.Z_B > (Z + 0.1))*(backcat.ODDS >= 0.5)*(backcat.Z_B < 0.9)*(backcat.Z_B > 0.2)
else:
mask = (backcat.Z_B > (Z + 0.1))*(backcat.ODDS >= 0.5)*(backcat.Z_B > 0.2)
catdata = backcat[mask]
dl, ds, dls = gentools.compute_lensing_distances(np.array([Z]), catdata.Z_B, precomputed=True)
dl = (dl*0.7)/h
ds = (ds*0.7)/h
dls = (dls*0.7)/h
KPCSCALE = dl*(((1.0/3600.0)*np.pi)/180.0)*1000.0
BETA_array = dls/ds
Dl = dl*1.e6*pc
sigma_c = (((cvel**2.0)/(4.0*np.pi*G*Dl))*(1./BETA_array))*(pc**2/Msun)
rads, theta, test1,test2 = eq2p2(np.deg2rad(catdata.RAJ2000),
np.deg2rad(catdata.DECJ2000),
np.deg2rad(RA0),
np.deg2rad(DEC0))
#Correct polar angle for e1, e2
theta = theta+np.pi/2.
e1 = catdata.e1
e2 = catdata.e2
#get tangential ellipticities
et = (-e1*np.cos(2*theta)-e2*np.sin(2*theta))*sigma_c
#get cross ellipticities
ex = (-e1*np.sin(2*theta)+e2*np.cos(2*theta))*sigma_c
del(e1)
del(e2)
r=np.rad2deg(rads)*3600*KPCSCALE
del(rads)
peso = catdata.weight
peso = peso/(sigma_c**2)
m = catdata.m
Ntot = len(catdata)
del(catdata)
bines = np.logspace(np.log10(RIN),np.log10(ROUT),num=ndots+1)
dig = np.digitize(r,bines)
DSIGMAwsum_T = []
DSIGMAwsum_X = []
WEIGHTsum = []
Mwsum = []
BOOTwsum_T = np.zeros((nboot,ndots))
BOOTwsum_X = np.zeros((nboot,ndots))
BOOTwsum = np.zeros((nboot,ndots))
NGAL = []
for nbin in range(ndots):
mbin = dig == nbin+1
DSIGMAwsum_T = np.append(DSIGMAwsum_T,(et[mbin]*peso[mbin]).sum())
DSIGMAwsum_X = np.append(DSIGMAwsum_X,(ex[mbin]*peso[mbin]).sum())
WEIGHTsum = np.append(WEIGHTsum,(peso[mbin]).sum())
Mwsum = np.append(Mwsum,(m[mbin]*peso[mbin]).sum())
NGAL = np.append(NGAL,mbin.sum())
index = np.arange(mbin.sum())
if mbin.sum() == 0:
continue
else:
with NumpyRNGContext(1):
bootresult = bootstrap(index, nboot)
INDEX=bootresult.astype(int)
BOOTwsum_T[:,nbin] = np.sum(np.array(et[mbin]*peso[mbin])[INDEX],axis=1)
BOOTwsum_X[:,nbin] = np.sum(np.array(ex[mbin]*peso[mbin])[INDEX],axis=1)
BOOTwsum[:,nbin] = np.sum(np.array(peso[mbin])[INDEX],axis=1)
output = {'DSIGMAwsum_T':DSIGMAwsum_T,'DSIGMAwsum_X':DSIGMAwsum_X,
'WEIGHTsum':WEIGHTsum, 'Mwsum':Mwsum,
'BOOTwsum_T':BOOTwsum_T, 'BOOTwsum_X':BOOTwsum_X, 'BOOTwsum':BOOTwsum,
'Ntot':Ntot,'NGAL':NGAL}
return output
def partial_profile(backcat_ids,
RA0,
DEC0,
Z,
pangle,
RIN,
ROUT,
ndots,
nboot=100):
backcat = S.data.loc[backcat_ids]
ndots = int(ndots)
mask = backcat.Z_B > (Z + 0.1)
catdata = backcat[mask]
dl, ds, dls = gentools.compute_lensing_distances(np.array([Z]),
catdata.Z_B,
precomputed=True)
# dl, ds, dls = gentools.compute_lensing_distances(Z, catdata.Z_B, precomputed=True)
KPCSCALE = dl * (((1.0 / 3600.0) * np.pi) / 180.0) * 1000.0
BETA_array = dls / ds
Dl = dl * 1.e6 * pc
sigma_c = (((cvel**2.0) / (4.0 * np.pi * G * Dl)) *
(1. / BETA_array)) * (pc**2 / Msun)
rads, theta, test1, test2 = eq2p2(np.deg2rad(catdata.RAJ2000),
np.deg2rad(catdata.DECJ2000),
np.deg2rad(RA0), np.deg2rad(DEC0))
theta2 = (2. * np.pi - theta) + np.pi / 2.
theta_ra = theta2
theta_ra[theta2 > 2. * np.pi] = theta2[theta2 > 2. * np.pi] - 2. * np.pi
#Correct polar angle for e1, e2
theta = theta + np.pi / 2.
e1 = catdata.e1
e2 = catdata.e2
#get tangential ellipticities
et = (-e1 * np.cos(2 * theta) - e2 * np.sin(2 * theta)) * sigma_c
#get cross ellipticities
ex = (-e1 * np.sin(2 * theta) + e2 * np.cos(2 * theta)) * sigma_c
del (e1)
del (e2)
r = np.rad2deg(rads) * 3600 * KPCSCALE
del (rads)
peso = catdata.weight
peso = peso / (sigma_c**2)
m = catdata.m
Ntot = len(catdata)
del (catdata)
bines = np.logspace(np.log10(RIN), np.log10(ROUT), num=ndots + 1)
dig = np.digitize(r, bines)
at = theta_ra - pangle
DSIGMAwsum_T = []
DSIGMAwsum_X = []
WEIGHTsum = []
Mwsum = []
BOOTwsum_T = np.zeros((nboot, ndots))
BOOTwsum_X = np.zeros((nboot, ndots))
BOOTwsum = np.zeros((nboot, ndots))
GAMMATcos_wsum = []
GAMMAXsin_wsum = []
WEIGHTcos_sum = []
WEIGHTsin_sum = []
BOOTwsum_Tcos = np.zeros((nboot, ndots))
BOOTwsum_Xsin = np.zeros((nboot, ndots))
BOOTwsum_cos = np.zeros((nboot, ndots))
BOOTwsum_sin = np.zeros((nboot, ndots))
GAMMATcos_wsum_c = []
GAMMAXsin_wsum_c = []
WEIGHTcos_sum_c = []
WEIGHTsin_sum_c = []
BOOTwsum_Tcos_c = np.zeros((nboot, ndots))
BOOTwsum_Xsin_c = np.zeros((nboot, ndots))
BOOTwsum_cos_c = np.zeros((nboot, ndots))
BOOTwsum_sin_c = np.zeros((nboot, ndots))
for nbin in range(ndots):
mbin = dig == nbin + 1
DSIGMAwsum_T = np.append(DSIGMAwsum_T, (et[mbin] * peso[mbin]).sum())
DSIGMAwsum_X = np.append(DSIGMAwsum_X, (ex[mbin] * peso[mbin]).sum())
WEIGHTsum = np.append(WEIGHTsum, (peso[mbin]).sum())
Mwsum = np.append(Mwsum, (m[mbin] * peso[mbin]).sum())
GAMMATcos_wsum = np.append(GAMMATcos_wsum,
(et[mbin] * np.cos(2. * at[mbin]) *
peso[mbin]).sum())
GAMMAXsin_wsum = np.append(GAMMAXsin_wsum,
(ex[mbin] * np.sin(2. * at[mbin]) *
peso[mbin]).sum())
WEIGHTcos_sum = np.append(WEIGHTcos_sum, ((np.cos(2. * at[mbin])**2) *
peso[mbin]).sum())
WEIGHTsin_sum = np.append(WEIGHTsin_sum, ((np.sin(2. * at[mbin])**2) *
peso[mbin]).sum())
GAMMATcos_wsum_c = np.append(GAMMATcos_wsum_c,
(et[mbin] * np.cos(2. * theta_ra[mbin]) *
peso[mbin]).sum())
GAMMAXsin_wsum_c = np.append(GAMMAXsin_wsum_c,
(ex[mbin] * np.sin(2. * theta_ra[mbin]) *
peso[mbin]).sum())
WEIGHTcos_sum_c = np.append(WEIGHTcos_sum_c,
((np.cos(2. * theta_ra[mbin])**2) *
peso[mbin]).sum())
WEIGHTsin_sum_c = np.append(WEIGHTsin_sum_c,
((np.sin(2. * theta_ra[mbin])**2) *
peso[mbin]).sum())
index = np.arange(mbin.sum())
if mbin.sum() == 0:
continue
else:
with NumpyRNGContext(1):
bootresult = bootstrap(index, nboot)
INDEX = bootresult.astype(int)
BOOTwsum_T[:,
nbin] = np.sum(np.array(et[mbin] * peso[mbin])[INDEX],
axis=1)
BOOTwsum_X[:,
nbin] = np.sum(np.array(ex[mbin] * peso[mbin])[INDEX],
axis=1)
BOOTwsum[:, nbin] = np.sum(np.array(peso[mbin])[INDEX], axis=1)
BOOTwsum_Tcos[:, nbin] = np.sum(np.array(
et[mbin] * np.cos(2. * at[mbin]) * peso[mbin])[INDEX],
axis=1)
BOOTwsum_Xsin[:, nbin] = np.sum(np.array(
ex[mbin] * np.sin(2. * at[mbin]) * peso[mbin])[INDEX],
axis=1)
BOOTwsum_cos[:, nbin] = np.sum(np.array(
peso[mbin] * (np.cos(2. * at[mbin])**2))[INDEX],
axis=1)
BOOTwsum_sin[:, nbin] = np.sum(np.array(
peso[mbin] * (np.sin(2. * at[mbin])**2))[INDEX],
axis=1)
BOOTwsum_Tcos_c[:, nbin] = np.sum(np.array(
et[mbin] * np.cos(2. * theta_ra[mbin]) * peso[mbin])[INDEX],
axis=1)
BOOTwsum_Xsin_c[:, nbin] = np.sum(np.array(
ex[mbin] * np.sin(2. * theta_ra[mbin]) * peso[mbin])[INDEX],
axis=1)
BOOTwsum_cos_c[:, nbin] = np.sum(np.array(
peso[mbin] * (np.cos(2. * theta_ra[mbin])**2))[INDEX],
axis=1)
BOOTwsum_sin_c[:, nbin] = np.sum(np.array(
peso[mbin] * (np.sin(2. * theta_ra[mbin])**2))[INDEX],
axis=1)
output = {
'DSIGMAwsum_T': DSIGMAwsum_T,
'DSIGMAwsum_X': DSIGMAwsum_X,
'WEIGHTsum': WEIGHTsum,
'Mwsum': Mwsum,
'BOOTwsum_T': BOOTwsum_T,
'BOOTwsum_X': BOOTwsum_X,
'BOOTwsum': BOOTwsum,
'GAMMATcos_wsum': GAMMATcos_wsum,
'GAMMAXsin_wsum': GAMMAXsin_wsum,
'WEIGHTcos_sum': WEIGHTcos_sum,
'WEIGHTsin_sum': WEIGHTsin_sum,
'GAMMATcos_wsum_c': GAMMATcos_wsum_c,
'GAMMAXsin_wsum_c': GAMMAXsin_wsum_c,
'WEIGHTcos_sum_c': WEIGHTcos_sum_c,
'WEIGHTsin_sum_c': WEIGHTsin_sum_c,
'BOOTwsum_Tcos': BOOTwsum_Tcos,
'BOOTwsum_Xsin': BOOTwsum_Xsin,
'BOOTwsum_cos': BOOTwsum_cos,
'BOOTwsum_sin': BOOTwsum_sin,
'BOOTwsum_Tcos_c': BOOTwsum_Tcos,
'BOOTwsum_Xsin_c': BOOTwsum_Xsin,
'BOOTwsum_cos_c': BOOTwsum_cos,
'BOOTwsum_sin_c': BOOTwsum_sin,
'Ntot': Ntot
}
return output