Example #1
0
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
Example #2
0
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
Example #3
0
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
Example #4
0
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