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_mask(param_fname):
global num_pass
global num_fail
param = dtk.Param(param_fname)
query_data_folder = param.get_string("query_data_folder")
cosmology_name = param.get_string("cosmology_name")
cluster_size_max = param.get_bool("cluster_size_max")
cluster_size = param.get_int("cluster_size")
cluster_start = param.get_int("cluster_start")
random_size_max = param.get_bool("random_size_max")
random_size = param.get_int("random_size")
random_start = param.get_int("random_start")
query_galaxy_only = param.get_bool("query_galaxy_only")
r200_factor = param.get_float("r200_factor")
if "richness_mass_author" in param:
richness_mass_author = param.get_string("richness_mass_author")
else:
richness_mass_author = "Rykoff"
print "Richness mass author: ", richness_mass_author
if "clusters_query" in param:
clusters_query = param.get_bool("clusters_query")
else:
clusters_query = True
if "randoms_query" in param:
randoms_query = param.get_bool("randoms_query")
else:
randoms_query = True
if "spider_clusters" in param:
spider_clusters = param.get_bool("spider_clusters")
else:
spider_clusters = False
if "spider_mean" in param:
spider_mean = param.get_bool("spider_mean")
else:
spider_mean = False
if "spider_bcg_center" in param:
spider_bcg_center = param.get_bool("spider_bcg_center")
else:
spider_bcg_center = False
if "spider_mass_from_richness" in param:
spider_mass_from_richness = param.get_bool("spider_mass_from_richness")
else:
spider_mass_from_richness = False
dtk.ensure_dir(query_data_folder)
background.set_cosmology(cosmology_name)
num_pass = 0
num_fail = 0
if clusters_query:
print "Querying redmapper clusters..."
if not spider_clusters:
cluster_cat = load_redmapper_cluster_fits(
"redmapper_dr8_public_v6.3_catalog.fits")
spider_rad = None
else:
cluster_cat = load_spider_fits(
"/media/luna1/dkorytov/data/spider_xray/catCluster-SPIDERS_RASS_CLUS-v2.0.fits"
)
if spider_bcg_center:
print "spider bcg center"
spider_bcg_center = load_spiders_bcg_fits(
"./SpidersXclusterBCGs-v2.0.fits")
cluster_cat = combine_spiders_bcg(cluster_cat,
spider_bcg_center)
cluster_cat['ra'] = cluster_cat['ra_bcg']
cluster_cat['dec'] = cluster_cat['dec_bcg']
if spider_mass_from_richness:
spider_rad = None # use the default richness -> mass conversion.
print "spider mass from richness"
else:
spider_rad = cluster_cat['r200c_deg']
if (cluster_size_max):
cluster_size = cluster_cat['ra'].size
query_sdss_mask(query_data_folder,
cluster_cat['ra'],
cluster_cat['dec'],
cluster_cat['z'],
cluster_cat['lambda'],
"gal",
cluster_size,
r200_factor=r200_factor,
start=cluster_start,
plot=False,
save_data=True,
spider_rad=spider_rad,
richness_mass_author=richness_mass_author)
if randoms_query:
print "Querying random fields..."
rand_cat = load_redmapper_randoms_fits(
"redmapper_dr8_public_v6.3_randoms.fits")
if (random_size_max):
random_size = rand_cat['ra'].size
query_sdss_mask(query_data_folder,
rand_cat['ra'],
rand_cat['dec'],
rand_cat['z'],
rand_cat['lambda'],
"rnd",
random_size,
r200_factor=r200_factor,
start=random_start,
plot=False,
save_data=True,
richness_mass_author=richness_mass_author)
"/data/a/cpac/dkorytov/data/spiders/catCluster-SPIDERS_RASS_CLUS-v2.0.fits"
)
spider_bcg_cat = load_spiders_bcg_fits(
"/data/a/cpac/dkorytov/data/spiders/SpidersXclusterBCGs-v2.0.fits")
redmapper_cat = load_redmapper_cluster_fits(
"/data/a/cpac/dkorytov/data/redmapper/redmapper_dr8_public_v6.3_catalog.fits.gz"
)
redmapper_des_cat = load_redmapper_cluster_fits(
"/data/a/cpac/dkorytov/data/redmapper/redmapper_sva1_public_v6.3_catalog.fits.gz"
)
combine_spiders_bcg(spider_cat, spider_bcg_cat)
# plt.figure()
# plt.plot(spider_cat['ra_bcg'], spider_cat['dec_bcg'])
# plt.show()
spider_cat['ra'] = spider_cat['ra_bcg']
spider_cat['dec'] = spider_cat['dec_bcg']
# plt.figure()
# plt.plot(redmapper_cat['ra'], redmapper_cat['dec'], '.', alpha=0.3)
# plt.plot(spider_cat['ra'], spider_cat['dec'], '.r')
# spider_cat = xcs_cat
# redmapper_cat = xcs_cat
find_match(redmapper_des_cat, redmapper_cat)
if __name__ == "__main__":
background.set_cosmology('wmap7')
compare_redmapper_spider()
dtk.save_figs("figs/" + __file__ + "/", extension='.pdf')
plt.show()
cluster_use_random_positions = param.get_bool(
"cluster_use_random_positions")
else:
cluster_use_random_positions = False
if "query_random" in param:
query_random = param.get_bool("query_random")
else:
query_random = True
if "query_cluster" in param:
query_cluster = param.get_bool("query_cluster")
else:
query_cluster = True
cosmo = background.set_cosmology(cosmology_name)
dtk.ensure_dir(query_data_folder)
hdulist = pyfits.open("redmapper_dr8_public_v6.3_catalog.fits")
hdurnd = pyfits.open("redmapper_dr8_public_v6.3_randoms.fits")
#header list can be found in http://arxiv.org/pdf/1303.3562v2.pdf
tdata = hdulist[1].data
rdata = hdurnd[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')
def plot_saved_clusters(param_fname):
param = dtk.Param(param_fname)
query_data_file = param.get_string(
'query_data_folder') + 'query_results.hdf5'
mstar_offset = param.get_int('mstar_cut_offset')
query_results = param.get_string("query_data_folder")
query_cluster_all = param.get_bool('query_cluster_all')
query_cluster_num = param.get_int('query_cluster_num')
query_type = param.get_string('query_type')
cosmology_name = param.get_string("cosmology_name")
galaxy_type = param.get_int("galaxy_type")
galaxy_weight = param.get_int("galaxy_weight")
galaxy_color_type = param.get_int("galaxy_color_type")
galaxy_mag_type = param.get_int("galaxy_mag_type")
galaxy_color_lim = param.get_float_list("galaxy_color_lim%d" %
galaxy_color_type)
galaxy_mag_lim = param.get_float_list("galaxy_mag_lim%d" % galaxy_mag_type)
background_folder = param.get_string("background_folder")
background_force = param.get_bool("background_force")
background_all = param.get_bool("background_all")
background_num = param.get_int("background_num")
# z bins for background estimate
background_z_start = param.get_float('background_z_start')
background_z_end = param.get_float('background_z_end')
background_z_num = param.get_float('background_z_num')
# external data such as spt
spt_plot = param.get_bool("spt_plot")
spt_file = param.get_string("spt_file")
#params for the final cluster bining
z_bins_num = param.get_int("z_bins")
z_bins_start = param.get_float("z_bins_start")
z_bins_end = param.get_float("z_bins_end")
mass_bins_num = param.get_int("mass_bins")
mass_bins_start = param.get_float("mass_bins_start")
mass_bins_end = param.get_float("mass_bins_end")
radial_r200_rescale = param.get_bool('radial_r200_rescale')
radial_bin_num = param.get_int('radial_bin_num')
radial_bin_kpc_min = param.get_float('radial_bin_kpc_min')
radial_bin_kpc_max = param.get_float('radial_bin_kpc_max')
radial_bin_r200_min = param.get_float('radial_bin_r200_min')
radial_bin_r200_max = param.get_float('radial_bin_r200_max')
RS_line_plot = param.get_bool('RS_line_plot')
#params for saving the resulting profiles
result_folder = param.get_string("result_folder")
if ('mstar_cut_offset' in param.data.keys()):
mstar_cut_offset = param.get_float('mstar_cut_offset')
else:
mstar_cut_offset = 1.0
if 'background_r200_factor' in param:
background_r200_factor = param.get_float('background_r200_factor')
else:
background_r200_factor = 1.0
if 'richness_mass_author' in param:
richness_mass_author = param.get_string("richness_mass_author")
else:
richness_mass_author = None
if 'convert_m200m_to_m200c' in param:
convert_m200m_to_m200c = param.get_bool('convert_m200m_to_m200c')
else:
convert_m200m_to_m200c = False
if 'convert_m200c_to_m200m' in param:
convert_m200c_to_m200m = param.get_bool('convert_m200c_to_m200m')
else:
convert_m200c_to_m200m = False
#######################
## Processing Params ##
#######################
background_z_bins = np.linspace(background_z_start, background_z_end,
int(background_z_num))
if (galaxy_type == 1):
galaxy_type_name = "all"
elif (galaxy_type == 2):
galaxy_type_name = "red"
elif (galaxy_type == 3):
galaxy_type_name = "non-red"
else:
print("unknwon galaxy type")
raise
if (galaxy_weight == 1):
galaxy_weight_name = "normal"
elif (galaxy_weight == 2):
galaxy_weight_name = "red squence"
elif (galaxy_weight == 3):
galaxy_weight_name = 'red squence rykoff'
else:
print("unknown galaxy weight")
raise
if (galaxy_color_type == 1):
galaxy_color_name = 'g-r'
elif (galaxy_color_type == 2):
galaxy_color_name = 'g-r - RS(z)'
if (galaxy_mag_type == 1):
galaxy_mag_name = 'i'
elif (galaxy_mag_type == 2):
galaxy_mag_name = 'i-m*(z)'
background.set_gal_clr_type(galaxy_color_type, galaxy_color_lim)
background.set_gal_mag_type(galaxy_mag_type, galaxy_mag_lim)
background.set_gal_type_weight(galaxy_type, galaxy_weight)
background.set_cosmology(cosmology_name)
background.set_mstar_cut(mstar_cut_offset)
background.set_mstar_cut(param.get_float('mstar_cut_offset'))
[dataclstr, datagal, data_pass_mask, clstr_num] = background.get_clstr(
query_type,
query_results,
100,
till_end=False,
# query_cluster_num,
# till_end=query_cluster_all,
richness_mass_author=richness_mass_author,
convert_m200c_to_m200m=convert_m200c_to_m200m,
convert_m200m_to_m200c=convert_m200m_to_m200c)
for i in range(0, 400):
print(list(dataclstr[i].keys()))
print(list(datagal[i].keys()))
gal_prop = dataclstr[i]
gal = datagal[i]
rad = float(gal_prop['r200_arcmin']) / 60
dec = float(gal_prop['dec'])
ra = float(gal_prop['ra'])
mass = float(gal_prop['m200'])
z = float(gal_prop['z'])
if dec > 10 or mass < 2e14 or z > 0.35:
continue
plt.figure()
print(ra, dec, rad)
# c1 = plt.Circle((ra, dec), rad, fill=True, color='r', lw=2)
e_r200 = matplotlib.patches.Ellipse((ra, dec),
rad / np.cos(dec / 360) * 2.0,
rad * 2.0,
fill=False,
color='k',
lw=2)
plt.gca().add_artist(e_r200)
# plotting radial bins
for j in range(0, 16):
rad_bin = float(j) / 16 * rad
e_bin = matplotlib.patches.Ellipse(
(ra, dec),
rad_bin / np.cos(dec / 360) * 2.0,
rad_bin * 2.0,
fill=False,
color='k',
lw=1,
ls='--')
plt.gca().add_artist(e_bin)
plt.ylabel('Dec [deg]')
plt.xlabel('Ra [deg]')
plt.plot([], [], 'k', lw=2, label='$R_{200c}$')
plt.plot([], [], 'k', ls='--', label='radial bins')
## Galaxies
# bright saturated satur_center nopetro deblended_as_moving
cut_flgs = 0x0000000000000002 | 0x0000000000040000 | 0x0000080000000000 | 0x0000000000000100 | 0x0000000100000000
flgs = gal['flags_i'] | gal['flags_r'] | gal['flags_g']
slct1 = gal['type'] != 31
slct2 = np.logical_not(flgs & cut_flgs)
slct3 = gal['m_i'] < background.m_cut(z)
slct = slct1 & slct2 & slct3
plt.plot(gal['ra'][slct],
gal['dec'][slct],
'.',
alpha=0.6,
label='galaxies')
plt.text(
0.05,
0.05,
'M$_{{200c}}$=\n{:2.2e}[$h^{{-1}}$M$_{{\odot}}$]'.format(mass),
transform=plt.gca().transAxes,
verticalalignment='bottom',
horizontalalignment='left')
plt.text(0.95,
0.05,
'z={:.2f}'.format(z),
transform=plt.gca().transAxes,
verticalalignment='bottom',
horizontalalignment='right')
plt.legend(framealpha=0.0, labelspacing=0)
plt.axis('equal')
pyplot_zoom_out(top=0.2, right=-0.5, left=0.3)
plt.tight_layout()
dtk.save_figs('figs/' + __file__ + '/', '.pdf')
plt.close('all')
def query(param_fname):
param = dtk.Param(param_fname)
query_data_folder = param.get_string("query_data_folder")
cluster_size_max = param.get_bool("cluster_size_max")
cluster_size = param.get_int("cluster_size")
cluster_start = param.get_int("cluster_start")
random_size_max = param.get_bool("random_size_max")
random_size = param.get_int("random_size")
random_start = param.get_int("random_start")
query_galaxy_only = param.get_bool("query_galaxy_only")
r200_factor = param.get_float("r200_factor")
cosmology_name = param.get_string("cosmology_name")
if "richness_mass_author" in param:
richness_mass_author = param.get_string("richness_mass_author")
else:
richness_mass_author = "Rykoff_crit"
if "mass_type" in param:
mass_type = param.get_string("mass_type")
assert (mass_type in ['critical', 'crit', 'mean']), "Mass type ("+mass_type+") not understood"
else:
mass_type = "critical"
if "cluster_use_random_positions" in param:
cluster_use_random_positions = param.get_bool("cluster_use_random_positions")
else:
cluster_use_random_positions = False
if "query_random" in param:
query_random = param.get_bool("query_random")
else:
query_random = True
if "query_cluster" in param:
query_cluster = param.get_bool("query_cluster")
else:
query_cluster = True
if "spider_clusters" in param:
spider_clusters = param.get_bool("spider_clusters")
else:
spider_clusters = False
if "spider_mean" in param:
spider_mean = param.get_bool("spider_mean")
else:
spider_mean = False
if "spider_bcg_center" in param:
spider_bcg_center = param.get_bool("spider_bcg_center")
else:
spider_bcg_center = False
if "spider_mass_from_richness" in param:
spider_mass_from_richness = param.get_bool("spider_mass_from_richness")
else:
spider_mass_from_richness = False
cosmo = background.set_cosmology(cosmology_name)
dtk.ensure_dir(query_data_folder)
if query_random:
print("Querying random fields...")
rand_cat = load_redmapper_randoms_fits("redmapper_dr8_public_v6.3_randoms.fits")
if(random_size_max):
random_size = cat['ra'].size
query_sdss_culster(query_data_folder, rand_cat['ra'],
rand_cat['dec'], rand_cat['z'],
rand_cat['lambda'], "rnd", random_size,
start=random_start, plot=False,
query_galaxy_only=query_galaxy_only,
r200_factor=r200_factor,
richness_mass_author=richness_mass_author)
else:
print ("Not quering random fields...")
if query_cluster:
print( "Querying redmapper clusters...")
print(spider_clusters)
if not spider_clusters:
cluster_cat = load_redmapper_cluster_fits("redmapper_dr8_public_v6.3_catalog.fits")
spider_rad = None
else:
cluster_cat = load_spider_fits("/media/luna1/dkorytov/data/spider_xray/catCluster-SPIDERS_RASS_CLUS-v2.0.fits")
if spider_bcg_center:
spider_bcg_center = load_spiders_bcg_fits("./SpidersXclusterBCGs-v2.0.fits")
cluster_cat = combine_spiders_bcg(cluster_cat, spider_bcg_center)
cluster_cat['ra'] = cluster_cat['ra_bcg']
cluster_cat['dec'] = cluster_cat['dec_bcg']
if spider_mass_from_richness:
spider_rad = None # use the default richness -> mass conversion.
print("spider mass from richness")
else:
spider_rad = cluster_cat['r200c_deg']
if(cluster_size_max):
cluster_size = cluster_cat['ra'].size
if(cluster_use_random_positions ):
raise
cluster_cat['ra'][:cluster_size] = rand_cat['ra'][:cluster_size]
cluster_cat['dec'][:cluster_size] = rand_cat['dec'][:cluster_size]
# query_cat(cat,
query_sdss_culster(query_data_folder, cluster_cat['ra'],
cluster_cat['dec'], cluster_cat['z'],
cluster_cat['lambda'], "gal", cluster_size,
start=cluster_start, plot=False,
spider_rad=spider_rad,
spider_mean=spider_mean,
r200_factor=r200_factor,
richness_mass_author=richness_mass_author,
)
else:
print("Not querying redmapper clusters...")