def mass_esitmate_redmapper(redmapper_cat):
richnesses = redmapper_cat['lambda']
zs = redmapper_cat['z']
m200m_list = []
m200c_list = []
for z, richness in zip(zs, richnesses):
m200m, r200 = background.lambda_to_m200_r200(
richness, z, richness_mass_author="Simet_mean")
m200c, r200 = background.lambda_to_m200_r200(
richness, z, richness_mass_author="Simet_crit")
m200m_list.append(m200m)
m200c_list.append(m200c)
redmapper_cat['m200c'] = np.array(m200c_list)
redmapper_cat['m200m'] = np.array(m200m_list)
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 plot_richness_mass_relation():
background.set_cosmology("wmap7")
richness = np.logspace(np.log10(20), np.log10(300), 100)
m200m_rykoff, r200m_rykoff = background.lambda_to_m200_r200(
richness, 0.25, richness_mass_author="Rykoff_mean")
m200m_simet, r200m_simet = background.lambda_to_m200_r200(
richness, 0.25, richness_mass_author="Simet_mean")
m200m_baxter, r200m_baxter = background.lambda_to_m200_r200(
richness, 0.25, richness_mass_author="Baxter_mean")
m200c_rykoff, r200c_rykoff = background.lambda_to_m200_r200(
richness, 0.25, richness_mass_author="Rykoff_crit")
m200c_simet, r200c_simet = background.lambda_to_m200_r200(
richness, 0.25, richness_mass_author="Simet_crit")
m200c_baxter, r200c_baxter = background.lambda_to_m200_r200(
richness, 0.25, richness_mass_author="Baxter_crit")
plt.figure()
plt.plot(richness, m200m_rykoff, '-r')
plt.plot(richness, m200m_simet, '-b')
plt.plot(richness, m200m_baxter, '-g')
plt.plot(richness, m200c_rykoff, '--r')
plt.plot(richness, m200c_simet, '--b')
plt.plot(richness, m200c_baxter, '--g')
plt.plot([], [], 'r', label='rykoff')
plt.plot([], [], 'b', label='simet')
plt.plot([], [], 'g', label='baxter')
plt.plot([], [], '-k', label='M200m')
plt.plot([], [], '--k', label='M200c')
plt.legend(loc='best', framealpha=0.3)
plt.yscale('log')
plt.xscale('log')
plt.xlabel('Richness')
plt.ylabel(r'M$_{200}$')
plt.grid()
plt.figure()
plt.plot(richness, r200m_rykoff, '-r')
plt.plot(richness, r200m_simet, '-b')
plt.plot(richness, r200m_baxter, '-g')
plt.plot(richness, r200c_rykoff, '--r')
plt.plot(richness, r200c_simet, '--b')
plt.plot(richness, r200c_baxter, '--g')
plt.plot([], [], 'r', label='rykoff')
plt.plot([], [], 'b', label='simet')
plt.plot([], [], 'g', label='baxter')
plt.plot([], [], '-k', label='R200m')
plt.plot([], [], '--k', label='R200c')
plt.legend(loc='best', framealpha=0.3)
plt.yscale('log')
plt.xscale('log')
plt.xlabel('Richness')
plt.ylabel(r'R$_{200}$')
plt.grid()
plt.figure()
plt.plot(richness, m200c_rykoff / m200m_rykoff, '-r', label='rykoff')
plt.plot(richness, m200c_simet / m200m_simet, '-b', label='simet')
plt.plot(richness, m200c_baxter / m200m_baxter, '-g', label='baxter')
plt.legend(loc='best', framealpha=0.3)
plt.xscale('log')
plt.xlabel('Richness')
plt.ylabel(r'M200c/M200m')
plt.grid()
plt.figure()
plt.plot(richness, r200c_rykoff / r200m_rykoff, '-r', label='rykoff')
plt.plot(richness, r200c_simet / r200m_simet, '-b', label='simet')
plt.plot(richness, r200c_baxter / r200m_baxter, '-g', label='baxter')
plt.legend(loc='best', framealpha=0.3)
plt.xscale('log')
plt.xlabel('Richness')
plt.ylabel(r'R200c/R200m')
plt.grid()
plt.figure()
a = 1.0 / (1.0 + 0.25)
# plt.plot(m200m_simet, r200m_simet, '-b', label='my simet mean')
# plt.plot(m200c_simet, r200c_simet, '--b', label='my simet crit')
plt.plot(m200m_simet, r200m_simet, '-b', label='my simet mean')
plt.plot(m200c_simet, r200c_simet, '--b', label='my simet crit')
nfw_200m = NFWProfile(mdef='200m', redshift=0.25)
nfw_200c = NFWProfile(mdef='200c', redshift=0.25)
plt.plot(m200m_simet,
nfw_200m.halo_mass_to_halo_radius(m200m_simet) * 1000,
'-r',
label='ht simet mean')
plt.plot(m200c_simet,
nfw_200c.halo_mass_to_halo_radius(m200c_simet) * 1000,
'--r',
label='ht simet crit')
plt.plot(m200m_simet,
M_to_R(m200m_simet, 0.25, '200m'),
'-g',
label='cls simet mean')
plt.plot(m200c_simet,
M_to_R(m200c_simet, 0.25, '200c'),
'--g',
label='cls simet crit')
plt.legend(loc='best', framealpha=0.3)
plt.xscale('log')
plt.yscale('log')
plt.xlabel('M200 [Msun]')
plt.ylabel('R200 [proper kpc]')
plt.grid()
plt.show()
#header list can be found in http://arxiv.org/pdf/1303.3562v2.pdf
hdulist = pyfits.open("redmapper_dr8_public_v6.3_catalog.fits")
tdata = hdulist[1].data
# red_ra = tdata.field('ra')
# red_dec= tdata.field('dec')
red_z = tdata.field('z_lambda')
red_lambda = tdata.field('lambda')
# red_pcen=tdata.field('p_cen')
plt.figure()
h, xbins = np.histogram(red_lambda, bins=256)
plt.plot(dtk.bins_avg(xbins), h)
plt.yscale('log')
plt.xscale('log')
plt.xlabel('Richness')
plt.ylabel('count')
m200m_rykoff = background.lambda_to_m200m_Rykoff(red_lambda, 0.25)
m200m_simet = background.lambda_to_m200m_Simet(red_lambda, 0.25)
m200m_baxter = background.lambda_to_m200m_Baxter(red_lambda, 0.25)
m200c_rykoff = background.lambda_to_m200c_Rykoff(red_lambda, 0.25)
m200c_simet = background.lambda_to_m200c_Simet(red_lambda, 0.25)
m200c_baxter = background.lambda_to_m200c_Baxter(red_lambda, 0.25)
xbins = np.logspace(13.5, 15.5, 100)
xbins_avg = dtk.bins_avg(xbins)
plt.figure()
h, _ = np.histogram(m200m_rykoff, bins=xbins)
plt.plot(xbins_avg, h, label='rykoff')
h, _ = np.histogram(m200m_simet, bins=xbins)
plt.plot(xbins_avg, h, label='simet')
h, _ = np.histogram(m200m_baxter, bins=xbins)
plt.plot(xbins_avg, h, label='baxter')
plt.yscale('log')
plt.xscale('log')
plt.ylabel('Counts')
plt.xlabel('M$_{200m}$ [h$^{-1}$ M$_\odot$]')
plt.legend(loc='best', framealpha=1.0)
plt.grid()
plt.figure()
h, _ = np.histogram(m200c_rykoff, bins=xbins)
plt.plot(xbins_avg, h, label='rykoff')
h, _ = np.histogram(m200c_simet, bins=xbins)
plt.plot(xbins_avg, h, label='simet')
h, _ = np.histogram(m200c_baxter, bins=xbins)
plt.plot(xbins_avg, h, label='baxter')
plt.yscale('log')
plt.xscale('log')
plt.ylabel('Counts')
plt.xlabel('M$_{200c}$ [h$^{-1}$ M$_\odot$]')
plt.legend(loc='best', framealpha=1.0)
plt.grid()
plt.show()
def query_sdss_mask(file_loc,
cat_ra,
cat_dec,
cat_z,
cat_lambda,
name,
num,
r200_factor=1.0,
start=0,
plot=False,
save_data=True,
spider_rad=None,
spider_mean=False,
richness_mass_author='Rykoff'):
global num_pass
global num_fail
fails = []
print file_loc + name + "_mask.hdf5"
if (save_data):
hfile = h5py.File(file_loc + name + "_mask.hdf5", 'w')
for i in range(start, num):
start = time.time()
#query columns are defined here:
#http://skyserver.sdss.org/dr8/en/help/browser/browser.asp?n=PhotoObjAll&t=U
print "%d/%d " % (i, num),
ra = cat_ra[i]
dec = cat_dec[i]
z = cat_z[i]
richness = cat_lambda[i]
mass, r200 = background.lambda_to_m200_r200(
richness, z, richness_mass_author=richness_mass_author)
if spider_rad is None:
r200_deg = background.r200_to_arcmin(r200, z) / 60.0
rad = background.r200_to_arcmin(r200, z) * r200_factor
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
print "ra", ra, "dec", dec
print "z", z, "l", richness, "mass", mass, "r200", r200,
## Query and save objects around target
rad_deg = rad / 60.0
rad_mask = rad_deg * 3
a = np.cos(np.pi * dec / 180.0)
query_str = "SELECT ra, dec, radius, type,area FROM Mask where ra < %f and ra > %f and dec < %f and dec > %f and type != 4" % (
ra + rad_mask / a, ra - rad_mask / a, dec + rad_mask,
dec - rad_mask)
result = sqlcl.query(query_str)
result.readline()
try:
data_mask = np.genfromtxt(StringIO(result.read()),
names=True,
delimiter=",",
dtype=['f4', 'f4', 'f4', 'i4', 'S500'])
mask_pass = mask_outside_r200(ra, dec, rad_deg, data_mask['area'],
data_mask['type'])
except ValueError as e:
print("failed query, auto fail")
mask_pass = False
if (mask_pass):
num_pass += 1
else:
num_fail += 1
print "\tpass:"******"\tfract total: %.3f " % (
float(num_pass) / float(num_pass + num_fail)),
if (save_data):
hgroup = hfile.create_group(str(i))
dataset = hgroup.require_dataset("mask_pass", (1, 1), 'u1',
mask_pass)
if (mask_pass):
dataset[0, 0] = True
else:
dataset[0, 0] = False
dt_str = h5py.special_dtype(vlen=str)
dataset = hgroup.require_dataset("mask_points",
(data_mask["area"].size, ),
dtype=dt_str)
if (data_mask["area"].size == 1):
dataset[0] = data_mask['area']
else:
for j in range(0, data_mask["area"].size):
dataset[j] = data_mask["area"][j]
if (plot):
plt.figure()
plt.title("pass: "******"cirlce test: dist: ",dist,"ra,dec: ",ras[j],decs[j]
# plt.plot(ras,decs,'-k')
for j in range(0, data_mask['area'].size):
if (data_mask['area'].size == 1):
x, y = area_str_to_lines(str(data_mask['area']))
mask_type = data_mask['type']
else:
x, y = area_str_to_lines(data_mask['area'][j])
mask_type = data_mask['type'][j]
if (mask_type == 0):
plot_type = 'r'
elif (mask_type == 1):
plot_type = 'b'
elif (mask_type == 2):
plot_type = 'g--'
elif (mask_type == 3):
plot_type = 'k:'
plt.plot(x, y, plot_type)
plt.plot([], [], 'r', label='bleeding')
plt.plot([], [], 'b', label='bright star')
plt.plot([], [], 'g--', label='trail')
plt.plot([], [], 'k:', label='quality hole')
plt.legend(loc='best')
plt.xlabel('ra')
plt.ylabel('dec')
plt.xlim([ra - rad_mask, ra + rad_mask])
plt.ylim([dec - rad_mask, dec + rad_mask])
plt.show()
#close the file we have been writing to.
end = time.time()
print " time: %.2f" % float(end - start)
if (save_data):
hfile.close()
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();