def xy_pos(pos, centre, H0=100.0):
da = cosmo2.d_angdi(centre[2], *const.BASE) * \
cosmo2.d_H(H0)
x = da *astro.deg2rad((centre[0] - pos[0]) * \
np.cos(astro.deg2rad(pos[1])))
y = da * astro.deg2rad(pos[1] - centre[1])
return x, y
def xy_pos(pos, centre, H0 = 100.0):
da = cosmo2.d_angdi(centre[2], *const.BASE) * \
cosmo2.d_H(H0)
x = da *astro.deg2rad((centre[0] - pos[0]) * \
np.cos(astro.deg2rad(pos[1])))
y = da * astro.deg2rad(pos[1] - centre[1])
return x, y
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 xy_centre(data, centre=None, H0=100.0):
if not centre:
centre = (data.ra.median(), data.dec.median(), data.z.median())
da = cosmo2.d_angdi(centre[2], *const.BASE) * \
cosmo2.d_H(H0)
x = da *astro.deg2rad((centre[0] - data.ra) * \
np.cos(astro.deg2rad(data.dec)))
y = da * astro.deg2rad(data.dec - centre[1])
r = np.sqrt(x**2 + y**2)
return data.join(pd.DataFrame({'x': x, 'y': y, 'r': r}))
def __init__(self, data, number):
"""
Function that initialises a Galaxy instance.
"""
self.num = number
self.id = data[opts.id_col - 1]
self.ra = float(data[opts.ra_col - 1])
self.dec = float(data[opts.dec_col - 1])
self.z = float(data[opts.z_col - 1])
self.dz = float(data[opts.dz_col - 1])
self.v = self.z / (1 + self.z)
self.da = ((opts.c / opts.H0) * cosmo.angdidis(self.z, opts.Omega_M, opts.Omega_L))
self.x = math.sin(astro.deg2rad(90 - self.dec)) * math.cos(astro.deg2rad(self.ra)) * self.da
self.y = math.sin(astro.deg2rad(90 - self.dec)) * math.sin(astro.deg2rad(self.ra)) * self.da
self.photoz_bins = []
def xy_centre(data, centre = None, H0 = 100.0):
if not centre:
centre = (data.ra.median(), data.dec.median(),
data.z.median())
da = cosmo2.d_angdi(centre[2], *const.BASE) * \
cosmo2.d_H(H0)
x = da *astro.deg2rad((centre[0] - data.ra) * \
np.cos(astro.deg2rad(data.dec)))
y = da * astro.deg2rad(data.dec - centre[1])
r = np.sqrt(x ** 2 + y ** 2)
return data.join(pd.DataFrame({'x' : x, 'y' : y, 'r' : r}))
def __init__(self, data, number):
"""
Function that initialises a Galaxy instance.
"""
self.num = number
self.id = data[opts.id_col - 1]
self.ra = float(data[opts.ra_col - 1])
self.dec = float(data[opts.dec_col - 1])
self.z = float(data[opts.z_col - 1])
self.dz = float(data[opts.dz_col - 1])
self.v = self.z / (1 + self.z)
self.da = ((opts.c / opts.H0) *
cosmo.angdidis(self.z, opts.Omega_M, opts.Omega_L))
self.x = math.sin(astro.deg2rad(90 - self.dec)) * math.cos(
astro.deg2rad(self.ra)) * self.da
self.y = math.sin(astro.deg2rad(90 - self.dec)) * math.sin(
astro.deg2rad(self.ra)) * self.da
self.photoz_bins = []
def __init__(self, mem):
self.mem = np.array(mem)
self.size = len(self.mem)
self.ra = np.median(self.mem[:, 0])
self.dec = np.median(self.mem[:, 1])
min_ra = min(self.mem[:, 0])
min_dec = max(self.mem[:, 1])
self.radius = astro.deg2rad(astro.projected_distance(self.ra, min_ra, self.dec, min_dec) / 60.0)
def __init__(self, mem):
self.mem = np.array(mem)
self.size = len(self.mem)
self.ra = np.median(self.mem[:, 0])
self.dec = np.median(self.mem[:, 1])
min_ra = min(self.mem[:, 0])
min_dec = max(self.mem[:, 1])
self.radius = astro.deg2rad(
astro.projected_distance(self.ra, min_ra, self.dec, min_dec) /
60.0)
def friendship(zbin, gal1, gal2, mode):
"""
Function that checks if two galaxies are friends in
a given redshift bin.
"""
if mode == 'spec':
rfriend = zbin.link_r / gal1.da
else:
rfriend = zbin.rfriend
dist = astro.deg2rad(astro.projected_distance(gal1.ra, gal2.ra, gal1.dec, gal2.dec) / 60.0)
check1 = (dist < rfriend)
if mode == 'spec':
check2 = (math.fabs(gal1.v - gal2.v) <= zbin.link_z)
check = (check1 & check2)
else:
check = check1
return check
def friendship(zbin, gal1, gal2, mode):
"""
Function that checks if two galaxies are friends in
a given redshift bin.
"""
if mode == 'spec':
rfriend = zbin.link_r / gal1.da
else:
rfriend = zbin.rfriend
dist = astro.deg2rad(
astro.projected_distance(gal1.ra, gal2.ra, gal1.dec, gal2.dec) / 60.0)
check1 = (dist < rfriend)
if mode == 'spec':
check2 = (math.fabs(gal1.v - gal2.v) <= zbin.link_z)
check = (check1 & check2)
else:
check = check1
return check
dtype = 'float') cluster_num = float(sys.argv[3]) sncl = data[8, data[0] == cluster_num] index = cluster[0] == cluster_num # cluster properties racl = np.median(cluster[4, index]) decl = np.median(cluster[5, index]) zcl = np.median(cluster[6, index]) dists = astro.ang_sep([cluster[4, index], cluster[5, index]], [racl, decl]) * 60.0 area = np.pi * np.mean(dists) ** 2 # arcmin^2 # Mpc per arcmin scale da = cosmo2.d_angdi(zcl, omega_m, omega_l) * cosmo2.d_H(h0) mpca = astro.deg2rad(da / 60.0) # coordinates wrt cluster center gx = 60. * (racl - cluster[4, index]) * np.cos(astro.deg2rad(cluster[5, index])) gy = 60. * (cluster[5, index] - decl) # arcmin # adaptive kernel density map gadk = ss.gaussian_kde(np.vstack([gx * mpca, gy * mpca]), bw_method = 'silverman') xedges = np.linspace(-0.6, 0.6, 100) yedges = np.linspace(-1.0, 1.0, 100) xx, yy = np.meshgrid(xedges, yedges) gridpoints = np.array([xx.ravel(), yy.ravel()]) zz = np.reshape(gadk(gridpoints), xx.shape) indices = np.unravel_index(zz.argmax(), zz.shape)
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
