def TpkSZ_calc(r, tau, z, sigmadc, richmin, richmax, gamma=4. / 7.):
'''
pkSZ = zeros(len(r))
for i in range(len(r)):
'''
M200min = DESrichtomass(richmin, z) * h
M200max = DESrichtomass(richmax, z) * h
Mmin, Rnew, Cnew = mass_adv.changeMassDefinitionCModel(
M200min, z, '200m', '200c')
Mmax, Rnew, Cnew = mass_adv.changeMassDefinitionCModel(
M200max, z, '200m', '200c')
pkSZ = TpkSZmodel(tau, r, z, sigmadc, Mmin / h, Mmax / h, gamma)
return pkSZ
def mass_to_M200c(M, z, cosmology): #{{{
# converts M200m mass into M200c mass
M200c, R200c, c200c = mass_adv.changeMassDefinitionCModel(
M,
z,
cosmology['mass_def_initial'],
cosmology['mass_def_batt'],
profile=cosmology['halo_profile'],
c_model=cosmology['concentration_model'])
return M200c
def virial_radius_small_masses(self, M, z, massdef): #{{{
if not self.initialized_colossus:
self._initialize_colossus()
_, rvir, _ = mass_adv.changeMassDefinitionCModel(
M,
z,
massdef,
'vir',
profile=self.halo_profile,
c_model=self.small_mass_concentration_model)
return rvir * 1e-3 # Mpc/h
def masstorich(mass, z, massdef='200c'):
'''
Convert mass to richness in DES according to (McClintock 2018)
'''
assert (len(mass) == len(z))
Mnew = np.zeros(len(mass))
for i in range(len(mass)):
Mnew[i], Rnew, Cnew = mass_adv.changeMassDefinitionCModel(
mass[i] * h, z[i], massdef, '200m')
Mnew = Mnew / h
return 40 * ((Mnew / M0) * (1.35 / (1 + z))**G)**(1. / F)
def virial_radius(M, z, numerics, cosmology): #{{{
#if cosmology['mass_def_initial'] != 'vir':
# print 'Initial Mass definition not virial'
M_vir, r_vir, c_vir = mass_adv.changeMassDefinitionCModel(
M,
z,
cosmology['mass_def_initial'],
'vir',
profile=cosmology['halo_profile'],
c_model=cosmology['concentration_model'])
return r_vir * 1e-3
def convert_mass(self, M, z, massdefin, massdefout): #{{{
if massdefin == massdefout:
return M
if not self.initialized_colossus:
self._initialize_colossus()
Mout, _, _ = mass_adv.changeMassDefinitionCModel(
M,
z,
massdefin,
massdefout,
profile=self.halo_profile,
c_model=self.concentration_model)
return Mout
def mass_to_M200m(M, z, cosmology): #{{{
# converts Mvir mass into M200m mass
if cosmology['mass_def_initial'] == '200m':
return M
else:
M200m, R200m, c200m = mass_adv.changeMassDefinitionCModel(
M,
z,
cosmology['mass_def_initial'],
cosmology['mass_def_Tinker'],
profile=cosmology['halo_profile'],
c_model=cosmology['concentration_model'])
return M200m
def masstoradvir(m, z):
'''
Go mass to viral radius using the colossus package
'''
Mnew, Rnew, Cnew = mass_adv.changeMassDefinitionCModel(m * h,
z,
'200c',
'vir',
profile='nfw',
c_model='diemer19')
thetavir = Rnew * 1e-3 / cosmo.angularDiameterDistance(
z) #Angular distance in radians
return thetavir * 3437.75 / h #Return the angular size in arcmin
def richtoradvir(richness, z):
'''
Convert Richness to virial radii from the DES RedMapper in (McClintock 2018)
'''
h = cosmo.Hz(z) / 100
M200m = M0 * ((richness / 40)**F) * (((1 + z) / 1.35)**G) #M_sun
Mnew, Rnew, Cnew = mass_adv.changeMassDefinitionCModel(M200m * h,
z,
'200m',
'vir',
profile='nfw',
c_model='diemer19')
thetavir = Rnew * 1e-3 / cosmo.angularDiameterDistance(
z) #Angular distance in radians
return thetavir * 3437.75 / h #Return the angular size in arcmin
def load_xcs_file(fname):
df = pd.read_csv(fname, sep='|', skiprows=4)
df.drop(columns=['Unnamed: 0', 'Unnamed: 11'], inplace=True)
df = df[df['r_200'] != ' '].reset_index()
# exit()
df['ra'] = df['ra '].map(radec_string_to_float)
df['dec'] = df['dec '].map(radec_string_to_float)
xcs_cat = {}
xcs_cat['z'] = df['redshift'].astype(float).to_numpy()
xcs_cat['ra'] = df['ra'].astype(float).to_numpy()
xcs_cat['dec'] = df['dec'].to_numpy()
xcs_cat['r200c'] = df['r_200'].astype(float)
xcs_cat['r200c_upper'] = (df['r_200'].astype(float) +
df['r_200_pos_err'].astype(float)).to_numpy()
xcs_cat['r200c_lower'] = (df['r_200'].astype(float) +
df['r_200_neg_err'].astype(float)).to_numpy()
xcs_cat['m200c'] = mass_so.R_to_M(xcs_cat['r200c'] * 0.7, xcs_cat['z'],
'200c') # collosus
xcs_cat['m200c_upper'] = mass_so.R_to_M(xcs_cat['r200c_upper'] * 0.7,
xcs_cat['z'], '200c') # collosus
xcs_cat['m200c_lower'] = mass_so.R_to_M(xcs_cat['r200c_lower'] * 0.7,
xcs_cat['z'], '200c') # collosus
m200m = np.zeros_like(xcs_cat['z'])
r200m = np.zeros_like(xcs_cat['z'])
c200m = np.zeros_like(xcs_cat['z'])
for i in range(0, len(m200m)):
m200m[i], r200m[i], c200m[i] = mass_adv.changeMassDefinitionCModel(
xcs_cat['m200c'][i],
xcs_cat['z'][i],
"200c",
"200m",
c_model='child18')
xcs_cat['m200m'] = m200m
xcs_cat['r200m'] = r200m
xcs_cat['c200m'] = c200m
# plt.figure()
# plt.plot(xcs_cat['ra'], xcs_cat['dec'], 'o')
# plt.show()
# exit()
return xcs_cat
def mass_estimate_spider(spider_cat):
r200c_deg = spider_cat['r200c_deg']
rad = r200c_deg * 60
r200 = background.arcmin_to_r200(rad, spider_cat['z']) # kpc physical
mass_old = background.r200c_to_m200c(r200, spider_cat['z']) #m200c (h?)
mass_new = mass_so.R_to_M(r200 * 0.7, spider_cat['z'], '200c') # collosus
mass = mass_so.R_to_M(r200 * 0.7, spider_cat['z'], '200c') # collosus
spider_cat['m200c'] = mass
# plt.figure()
# plt.loglog(mass_new, mass_old, '.')
# plt.figure()
# plt.semilogx(mass_new, mass_new/mass_old, '.')
# plt.show()
m200m_list = []
m200m_richness_list = []
m200c_richness_list = []
for m200c, redshift, richness in zip(mass, spider_cat['z'],
spider_cat['lambda']):
m200m, r200m, c200m = mass_adv.changeMassDefinitionCModel(
m200c, redshift, "200c", "200m", c_model='child18')
m200m_list.append(m200m)
m200m, r200m = background.lambda_to_m200_r200(
richness, redshift, richness_mass_author="Simet_mean")
m200c, r200c = background.lambda_to_m200_r200(
richness, redshift, richness_mass_author="Simet_crit")
m200m_richness_list.append(m200m)
m200c_richness_list.append(m200c)
mass = np.array(m200m_list)
r200m_r200c_ratio = r200m / r200
rad *= r200m_r200c_ratio
r200 *= r200m_r200c_ratio
spider_cat['m200m'] = np.array(m200m_list)
spider_cat['rad'] = rad
spider_cat['m200m_lambda'] = np.array(m200m_richness_list)
spider_cat['m200c_lambda'] = np.array(m200c_richness_list)
def check_halo_conversion(sod_location, time_step, redshift):
a = 1.0/(1.0 + redshift)
cat = load_halo_cat(sod_location.replace("${step}", str(time_step)), h_scaling)
M200c_col, R200c_col, c200c_col = mass_adv.changeMassDefinitionCModel(cat['sod_halo_mass']/0.7, redshift,
"200c","200c", c_model='child18')#cat['sod_cdelta']
plt.figure()
plt.plot(cat['sod_halo_mass']/0.7, M200c_col, '.', alpha=0.3)
plt.yscale('log')
plt.xscale('log')
plt.title('mass')
plot_1to1()
plt.figure()
plt.plot(cat['sod_halo_radius'], R200c_col/1000.0/0.7*a, '.', alpha=0.3)
plt.yscale('log')
plt.xscale('log')
plt.title('radius')
plot_1to1()
plt.figure()
plt.plot(cat['sod_halo_radius'], (R200c_col/1000.0/0.7*a)/cat['sod_halo_radius'], '.', alpha=0.3)
plt.xscale('log')
plt.show()
def convert_halo_m200c_to_m200m( sod_location, sod_output, time_step, redshift , h_scaling, write_catalog=False):
a = 1.0/(1.0+redshift)
# rho_mean = get_rho_mean_over_rho_crit(redshift)
# This catalog has h=0.7
cat = load_halo_cat(sod_location.replace("${step}", str(time_step)), h_scaling)
M200m_col, R200m_col, c200m_col = mass_adv.changeMassDefinitionCModel(cat['sod_halo_mass']/0.7, redshift,
"200c","200m", c_model='child18')#cat['sod_cdelta']
M200c_col, R200c_col, c200c_col = mass_adv.changeMassDefinitionCModel(cat['sod_halo_mass']/0.7, redshift,
"200c","200c", c_model='child18')#cat['sod_cdelta']
R_200m = R200m_col/1000.0/0.7
M_200m = M200m_col*0.7
R_200c = R200c_col/1000.0/0.7
M_200c = M200c_col*0.7
cat['sod_halo_radius_r200m'] = R_200m*a
cat['sod_halo_mass_m200m'] = M_200m
cat['sod_halo_cdelta_200m'] = c200m_col
cat['sod_halo_radius_r200c'] = R_200c*a
cat['sod_halo_mass_m200c'] = M_200c
cat['sod_halo_cdelta_200c'] = c200c_col
if( write_catalog):
print("writing catalog")
write_halo_cat(sod_output.replace("${step}", str(time_step)), cat, h_scaling)
exit()
else:
print("plotting tests")
h, xbins, ybins = np.histogram2d(np.log10(cat['sod_halo_mass']), np.log10(M_200c), bins = 250)
plt.figure()
plt.pcolor(xbins, ybins, h.T, cmap = 'Blues', norm = clr.LogNorm())
plt.plot([np.min(xbins), np.max(xbins)], [np.min(xbins), np.max(xbins)], '--k')
plt.ylabel('Col M200c')
plt.xlabel('SOD M200c')
print(np.mean(R_200c*a/cat['sod_halo_radius']), np.mean(cat['sod_halo_radius']/R_200c/a), a)
h, xbins, ybins = np.histogram2d(cat['sod_halo_radius'],R_200c*a, bins = 250)
plt.figure()
plt.pcolor(xbins, ybins, h.T, cmap = 'Blues', norm = clr.LogNorm())
plt.plot([np.min(xbins), np.max(xbins)], [np.min(xbins), np.max(xbins)], '--k')
plt.ylabel('Col R200c')
plt.xlabel('SOD R200c')
plt.show()
h, xbins, ybins = np.histogram2d(np.log10(M_200c), R_200m/R_200c, bins = 250)
plt.figure()
plt.pcolor(xbins, ybins, h.T, cmap = 'Blues', norm = clr.LogNorm())
plt.ylabel('R_200m/R_200c')
plt.xlabel('M_200c')
h, xbins, ybins = np.histogram2d(np.log10(M_200c), M_200m/M_200c, bins = 250)
plt.figure()
plt.pcolor(xbins, ybins, h.T, cmap = 'Blues', norm = clr.LogNorm())
plt.ylabel('M_200m/M_200c')
plt.xlabel('M_200c')
h, xbins = np.histogram(cat['sod_halo_cdelta'], bins = 100)
plt.figure()
plt.plot(dtk.bins_avg(xbins), h, '-x')
M200m_col, R200m_col, c200m_col = mass_adv.changeMassDefinitionCModel(cat['sod_halo_mass']/0.7, redshift,
"200c","200m", c_model='child18')#cat['sod_cdelta']
plt.figure()
h, xbins,ybins = np.histogram2d(np.log10(M_200m), np.log10(M200m_col), bins =256)
plt.pcolor(xbins, ybins, h.T, cmap='Blues', norm=clr.LogNorm())
plt.plot([np.min(xbins), np.max(xbins)], [np.min(xbins), np.max(xbins)], '--k')
plt.xlabel("my m200m")
plt.ylabel('colossus m200m')
plt.figure()
h, xbins, ybins = np.histogram2d(np.log10(M_200c), M_200c/M_200m, bins=100)
plt.pcolor(xbins, ybins, h.T, cmap='Blues', norm=clr.LogNorm())
xbins_cen = dtk.bins_avg(xbins)
median = dtk.binned_median(np.log10(M_200c), M_200c/M_200m, xbins)
plt.plot(xbins_cen, median, '-k', label='median')
plt.legend(loc='best', framealpha=0.0)
plt.xlabel("M200c")
plt.ylabel("M200c/M200m")
plt.figure()
h, xbins, ybins = np.histogram2d(np.log10(M_200m), M_200c/M_200m, bins=100)
plt.pcolor(xbins, ybins, h.T, cmap='Blues', norm=clr.LogNorm())
xbins_cen = dtk.bins_avg(xbins)
median = dtk.binned_median(np.log10(M_200m), M_200c/M_200m, xbins)
plt.plot(xbins_cen, median, '-k', label='median')
plt.legend(loc='best', framealpha=0.0)
plt.xlabel("M200m")
plt.ylabel("M200c/M200m")
plt.figure()
M200m_col_lin, R200m_col_lin, c200m_col_lin = mass_adv.changeMassDefinitionCModel(np.logspace(12,15,100), redshift,
"200c", "200m", c_model='child18')
h, xbins, ybins = np.histogram2d(np.log10(M_200m), np.log10(R_200m), bins=100)
plt.pcolor(xbins, ybins, h.T, cmap='Blues', norm=clr.LogNorm())
plt.plot(np.log10(M200m_col_lin), np.log10(R200m_col_lin), '--k', label='Colossus')
plt.xlabel('M200m')
plt.ylabel('R200m')
plt.show()
def query_sdss_culster(file_loc, cat_ra, cat_dec, cat_z, cat_lambda,
name, num, start=0, plot=False,
spider_rad=None, spider_mean=False,
query_galaxy_only=True, r200_factor=None,
richness_mass_author=None):
fails = []
if(query_galaxy_only):
query_table = "Galaxy"
else:
query_table = "PhotoObj"
print("querying...")
hfile = h5py.File(file_loc+"query_results.hdf5",mode='a')
for i in range(start,num):
#try:
start = time.time()
print("%d/%d"%(i+1,num))
keys = hfile.keys()
if("%s%d"%(name,i) in keys and "%s_prop%d"%(name,i) in keys):
continue;
if "%s%d"%(name,i) in keys:
del hfile["%s%d"%(name,i)]
if "%s_prop%d"%(name,i) in keys:
del hfile["%s_prop%d"%(name,i)]
#query columns are defined here:
#http://skyserver.sdss.org/dr8/en/help/browser/browser.asp?n=PhotoObjAll&t=U
ra = cat_ra[i]
dec = cat_dec[i]
z = cat_z[i]
richness = cat_lambda[i]
# Xray Spiders have their own r200c, so we don't need to compute it.
if spider_rad is None:
mass, r200 = background.lambda_to_m200_r200(richness,z, richness_mass_author=richness_mass_author)
rad = background.r200_to_arcmin(r200, z)
else:
r200c_deg = spider_rad[i]
rad = r200c_deg * 60
r200 = background.arcmin_to_r200(rad, z)
mass = background.r200c_to_m200c(r200, z)
if spider_mean:
m200m, r200m, c200m =mass_adv.changeMassDefinitionCModel(mass, z,
"200c", "200m",
c_model='child18')
mass = m200m
r200m_r200c_ratio = r200m/r200
rad *= r200m_r200c_ratio
r200 *= r200m_r200c_ratio
hgroup = hfile.create_group("%s_prop%d"%(name,i))
hgroup.create_dataset("ra",data=ra)
hgroup.create_dataset("dec",data=dec)
hgroup.create_dataset("z",data=z)
hgroup.create_dataset("mass",data=mass)
hgroup.create_dataset("rad",data=rad)
hgroup.create_dataset("r200",data=r200)
hgroup.create_dataset("richness", data = richness)
## Query and save objects around target
query_str = "select p.ra, p.dec, p.type,p.insidemask,p.flags_g,p.flags_i,p.flags_r,p.cModelMag_u, p.cModelMagErr_u,p.cModelMag_g, p.cModelMagErr_g,p.cModelMag_r, p.cModelMagErr_r,p.cModelMag_i, p.cModelMagErr_i,p.cModelMag_z, p.cModelMagErr_z from "+query_table+" p join dbo.fGetNearbyObjEq(%f,%f,%f) r on p.ObjID = r.ObjID"%(ra, dec, r200_factor*rad)
result = sqlcl.query(query_str).read()
# datagal = np.genfromtxt(StringIO(result),names=True,delimiter=', ',dtype=['f8','f8','i2','i1','i8','i8','i8', 'f4','f4', 'f4','f4', 'f4','f4', 'f4','f4', 'f4','f4'])
try:
datagal = np.genfromtxt(StringIO(result),names=True,skip_header=1,delimiter=',',dtype=['f8','f8','i2','i1','i8','i8','i8', 'f4','f4', 'f4','f4', 'f4','f4', 'f4','f4', 'f4','f4'])
except ValueError as e:
print(query_str)
continue
hgroup = hfile.create_group("%s%d"%(name,i))
hgroup.create_dataset("ra",data=datagal['ra'])
hgroup.create_dataset("dec",data=datagal['dec'])
hgroup.create_dataset("type",data=datagal['type'])
hgroup.create_dataset("insidemask",data=datagal['insidemask'])
hgroup.create_dataset("flags_g",data=datagal['flags_g'])
hgroup.create_dataset("flags_i",data=datagal['flags_i'])
hgroup.create_dataset("flags_r",data=datagal['flags_r'])
hgroup.create_dataset("mag_u",data=datagal['cModelMag_u'])
hgroup.create_dataset("mag_err_u",data=datagal['cModelMagErr_u'])
hgroup.create_dataset("mag_g",data=datagal['cModelMag_g'])
hgroup.create_dataset("mag_err_g",data=datagal['cModelMagErr_g'])
hgroup.create_dataset("mag_r",data=datagal['cModelMag_r'])
hgroup.create_dataset("mag_err_r",data=datagal['cModelMagErr_r'])
hgroup.create_dataset("mag_i",data=datagal['cModelMag_i'])
hgroup.create_dataset("mag_err_i",data=datagal['cModelMagErr_i'])
hgroup.create_dataset("mag_z",data=datagal['cModelMag_z'])
hgroup.create_dataset("mag_err_z",data=datagal['cModelMagErr_z'])
end = time.time()
print(" time: %.2f"%float(end-start))
if(plot):
plt.figure()
legends = ["uknown","cosmic_ray","defect","galaxy","ghost","knownobj","star","trail","sky","notatype"]
slct1 = datagal['insidemask']==0
for i in range(0,10):
slct = (datagal["type"] == i) & slct1
plt.plot(datagal['ra'][slct],datagal['dec'][slct],'x',label=legends[i])
plt.legend(loc='best')
plt.xlabel('ra')
plt.ylabel('dec')
plt.show()
# except ValueError as ie:
# print ie
# fails.append(i)
# print "Failure"
np.save(file_loc+"fail_indexs.npy",fails)
hfile.close();
def m500c_to_m200c(m500c, z):
cosmology.setCosmology('WMAP7')
M200m_col, R200m_col, c200m_col = mass_adv.changeMassDefinitionCModel(
m500c, z, "500c", "200c", c_model='child18') #cat['sod_cdelta']
return M200c_col
def m200c_to_m200m(m200c, z):
#TODO More Accurate translation
cosmology.setCosmology('WMAP7')
M200m_col, R200m_col, c200m_col = mass_adv.changeMassDefinitionCModel(
m200c, z, "200c", "200m", c_model='child18') #cat['sod_cdelta']
return M200c_col
