def friends_of_friends(zbin, gals, tree, mode, cluster_count):
"""
Function that looks for galaxy friends in a given
redshift bin and returns a list of the resulting
Cluster instances.
"""
#loop over galaxies
cluster_list = []
for i in range(len(gals)):
if mode == 'spec':
link_zbin = z_bins[gals[i].bin]
else:
link_zbin = z_bins[zbin]
if bin_check(mode, gal[i], link_zbin):
#friends loop
if gals[i].clt[zbin] == None:
result = new_kdtree.radius_search(tree, np.array((gals[i].ra, gals[i].dec)),
astro.rad2deg(link_zbin.rfriend) * 60)
if len(result) > 0:
for loop_element in range(len(result)):
j = int(result[loop_element, 1])
dist = astro.deg2rad(result[loop_element, 0] / 60.0)
if ((bin_check(mode, gal[j], link_zbin)) & (gals[i].id != gals[j].id) & (gals[j].clt[zbin] == None)):
if friendship(link_zbin, gals[i], gals[j], dist, opts.mode):
if gals[i].clt[zbin] == None:
gals[i].clt[zbin] = len(cluster_list)
gals[j].clt[zbin] = gals[i].clt[zbin]
cluster_list.append(Cluster(cluster_count))
cluster_count += len(cluster_list)
cluster_list[gals[i].clt[zbin]].extend([gals[i].num], [gals[i].id], [gals[i].ra], [gals[i].dec],
[gals[i].z], [gals[i].xpos], [gals[i].ypos], [gals[i].zpos])
cluster_list[gals[i].clt[zbin]].extend([gals[j].num], [gals[j].id], [gals[j].ra], [gals[j].dec],
[gals[j].z], [gals[j].xpos], [gals[j].ypos], [gals[j].zpos])
else:
gals[j].clt[zbin] = gals[i].clt[zbin]
cluster_list[gals[i].clt[zbin]].extend([gals[j].num], [gals[j].id], [gals[j].ra], [gals[j].dec],
[gals[j].z], [gals[j].xpos], [gals[j].ypos], [gals[j].zpos])
#friends-of-friends loop
if gals[i].clt[zbin] != None:
clt_now = cluster_list[gals[i].clt[zbin]]
for k in range(len(clt_now.g_id)):
if(gals[i].id != clt_now.g_id[k]):
result = new_kdtree.radius_search(tree, np.array((clt_now.g_ra[k], clt_now.g_dec[k])),
astro.rad2deg(link_zbin.rfriend) * 60)
if len(result) > 0:
for loop_element in range(len(result)):
l = int(result[loop_element, 1])
dist = astro.deg2rad(result[loop_element, 0] / 60.0)
if ((bin_check(mode, gal[l], link_zbin)) & (clt_now.g_id[k] != gals[l].id)
& (gals[l].clt[zbin] == None)):
if friendship(link_zbin, gals[clt_now.g_num[k]], gals[l], dist, opts.mode):
gals[l].clt[zbin] = gals[i].clt[zbin]
cluster_list[gals[i].clt[zbin]].extend([gals[l].num], [gals[l].id], [gals[l].ra], [gals[l].dec],
[gals[l].z], [gals[l].xpos], [gals[l].ypos], [gals[l].zpos])
return cluster_list
def friends_of_friends(zbin, gals, tree, mode, cluster_count):
"""
Function that looks for galaxy friends in a given
redshift bin and returns a list of the resulting
Cluster instances.
"""
#loop over galaxies
cluster_list = []
for i in range(len(gals)):
if mode == 'spec':
link_zbin = z_bins[gals[i].bin]
else:
link_zbin = z_bins[zbin]
if bin_check(mode, gal[i], link_zbin):
#friends loop
if gals[i].clt[zbin] == None:
result = new_kdtree.radius_search(
tree, np.array((gals[i].ra, gals[i].dec)),
astro.rad2deg(link_zbin.rfriend) * 60)
if len(result) > 0:
for loop_element in range(len(result)):
j = int(result[loop_element, 1])
dist = astro.deg2rad(result[loop_element, 0] / 60.0)
if ((bin_check(mode, gal[j], link_zbin)) &
(gals[i].id != gals[j].id) &
(gals[j].clt[zbin] == None)):
if friendship(link_zbin, gals[i], gals[j], dist,
opts.mode):
if gals[i].clt[zbin] == None:
gals[i].clt[zbin] = len(cluster_list)
gals[j].clt[zbin] = gals[i].clt[zbin]
cluster_list.append(Cluster(cluster_count))
cluster_count += len(cluster_list)
cluster_list[gals[i].clt[zbin]].extend(
[gals[i].num], [gals[i].id],
[gals[i].ra], [gals[i].dec],
[gals[i].z], [gals[i].xpos],
[gals[i].ypos], [gals[i].zpos])
cluster_list[gals[i].clt[zbin]].extend(
[gals[j].num], [gals[j].id],
[gals[j].ra], [gals[j].dec],
[gals[j].z], [gals[j].xpos],
[gals[j].ypos], [gals[j].zpos])
else:
gals[j].clt[zbin] = gals[i].clt[zbin]
cluster_list[gals[i].clt[zbin]].extend(
[gals[j].num], [gals[j].id],
[gals[j].ra], [gals[j].dec],
[gals[j].z], [gals[j].xpos],
[gals[j].ypos], [gals[j].zpos])
#friends-of-friends loop
if gals[i].clt[zbin] != None:
clt_now = cluster_list[gals[i].clt[zbin]]
for k in range(len(clt_now.g_id)):
if (gals[i].id != clt_now.g_id[k]):
result = new_kdtree.radius_search(
tree, np.array(
(clt_now.g_ra[k], clt_now.g_dec[k])),
astro.rad2deg(link_zbin.rfriend) * 60)
if len(result) > 0:
for loop_element in range(len(result)):
l = int(result[loop_element, 1])
dist = astro.deg2rad(result[loop_element, 0] /
60.0)
if ((bin_check(mode, gal[l], link_zbin)) &
(clt_now.g_id[k] != gals[l].id)
& (gals[l].clt[zbin] == None)):
if friendship(link_zbin,
gals[clt_now.g_num[k]],
gals[l], dist, opts.mode):
gals[l].clt[zbin] = gals[i].clt[zbin]
cluster_list[gals[i].clt[zbin]].extend(
[gals[l].num], [gals[l].id],
[gals[l].ra], [gals[l].dec],
[gals[l].z], [gals[l].xpos],
[gals[l].ypos], [gals[l].zpos])
return cluster_list
u_dict = dict(zip(*u_id))
# DEFINE CONSTANTS
H0 = 100 #km/s/Mpc
# PRINT HALO FILE INFO
for i in range(len(u_id[0])):
x = (u_id[0][i] == data[opts.columns[0] - 1])
id = data[opts.columns[0] - 1, x][0]
central = data[opts.columns[2] - 1, x][0]
ra = np.median(np.array(data[opts.columns[3] - 1, x], dtype = 'f'))
min_ra = np.min(np.array(data[opts.columns[3] - 1, x], dtype = 'f'))
max_ra = np.max(np.array(data[opts.columns[3] - 1, x], dtype = 'f'))
dec = np.median(np.array(data[opts.columns[4] - 1, x], dtype = 'f'))
min_dec = np.min(np.array(data[opts.columns[4] - 1, x], dtype = 'f'))
max_dec = np.max(np.array(data[opts.columns[4] - 1, x], dtype = 'f'))
z = np.median(np.array(data[opts.columns[5] - 1, x], dtype = 'f'))
rich = u_dict[id]
mass = np.array(data[opts.columns[1] - 1, x][0], dtype = 'float')
r200_val = r200(mass, H0, 0.0, *const.BASE) #Mpc
da = cosmo2.d_angdi(z, *const.BASE) * cosmo2.d_H(H0) #Mpc
r200_val_arcmin = astro.rad2deg(r200_val / da) * 60.0 #arcmin
if rich >= opts.mem_limit:
print id, central, ra, dec, z, rich, mass, r200_val_arcmin, min_ra, max_ra, min_dec, max_dec
u_dict = dict(zip(*u_id))
# DEFINE CONSTANTS
H0 = 100 #km/s/Mpc
# PRINT HALO FILE INFO
for i in range(len(u_id[0])):
x = (u_id[0][i] == data[opts.columns[0] - 1])
id = data[opts.columns[0] - 1, x][0]
central = data[opts.columns[2] - 1, x][0]
ra = np.median(np.array(data[opts.columns[3] - 1, x], dtype='f'))
min_ra = np.min(np.array(data[opts.columns[3] - 1, x], dtype='f'))
max_ra = np.max(np.array(data[opts.columns[3] - 1, x], dtype='f'))
dec = np.median(np.array(data[opts.columns[4] - 1, x], dtype='f'))
min_dec = np.min(np.array(data[opts.columns[4] - 1, x], dtype='f'))
max_dec = np.max(np.array(data[opts.columns[4] - 1, x], dtype='f'))
z = np.median(np.array(data[opts.columns[5] - 1, x], dtype='f'))
rich = u_dict[id]
mass = np.array(data[opts.columns[1] - 1, x][0], dtype='float')
r200_val = r200(mass, H0, 0.0, *const.BASE) #Mpc
da = cosmo2.d_angdi(z, *const.BASE) * cosmo2.d_H(H0) #Mpc
r200_val_arcmin = astro.rad2deg(r200_val / da) * 60.0 #arcmin
if rich >= opts.mem_limit:
print id, central, ra, dec, z, rich, mass, r200_val_arcmin, min_ra, max_ra, min_dec, max_dec