def mk_mock_coords(radeczfile, outfile, simul_cosmo):
if simul_cosmo == "Planck":
Planck13.__init__(100.0, Planck13.Om0)
cosmo = Planck13
elif simul_cosmo == "WMAP":
WMAP5.__init__(100.0, WMAP5.Om0)
cosmo = WMAP5
rad = np.arange(1.0, 67.0, 5.0)
radecz = h5_arr(radeczfile, "radecz")
cart = np.zeros(radecz.shape)
for i, rdz in enumerate(radecz):
ra = Angle(rdz[0], u.deg)
dec = Angle(rdz[1], u.deg)
losd = cosmo.comoving_distance(rdz[2])
dis = Distance(losd, u.Mpc)
coord = ICRSCoordinates(ra, dec, distance=dis)
cart[i, :] = np.array([coord.x, coord.y, coord.z])
arr2h5(cart, outfile, "coords", mode='w')
def mk_mock_coords(radeczfile, outfile, simul_cosmo):
if simul_cosmo == "Planck":
Planck13.__init__(100.0, Planck13.Om0)
cosmo = Planck13
elif simul_cosmo == "WMAP":
WMAP5.__init__(100.0, WMAP5.Om0)
cosmo = WMAP5
rad = np.arange(1.0, 67.0, 5.0)
radecz = h5_arr(radeczfile, "radecz")
cart = np.zeros(radecz.shape)
for i, rdz in enumerate(radecz):
ra = Angle(rdz[0], u.deg)
dec = Angle(rdz[1], u.deg)
losd = cosmo.comoving_distance(rdz[2])
dis = Distance(losd)
coord = ICRSCoordinates(ra, dec, distance=dis)
cart[i, :] = np.array([coord.x.value, coord.y.value, coord.z.value])
np.savetxt(outfile, cart)
def mk_coords(radecfile, outfile, cosmology):
# Set the cosmology with h free
if cosmology == "Planck":
Planck13.__init__(100.0, Planck13.Om0)
cosmo = Planck13
elif cosmology == "WMAP":
WMAP5.__init__(100.0, WMAP5.Om0)
cosmo = WMAP5
f_in = h5.File(radecfile)
radecz = f_in["radecz"]
f_out = h5.File(outfile)
cart = f_out.create_dataset("cart_pts", shape=(radecz.shape[0], 3),
dtype='float64')
for i in range(radecz.shape[0]):
ra = Angle(radecz[i, 0], u.deg)
dec = Angle(radecz[i, 1], u.deg)
losd = cosmo.comoving_distance(radecz[i, 2])
dis = Distance(losd)
coord = ICRSCoordinates(ra, dec, distance=dis)
cart[i, :] = np.array([coord.x, coord.y, coord.z])
f_in.close()
f_out.close()
def mk_coords(radecfile, outfile, cosmology):
# Set the cosmology with h free
if cosmology == "Planck":
Planck13.__init__(100.0, Planck13.Om0)
cosmo = Planck13
elif cosmology == "WMAP":
WMAP5.__init__(100.0, WMAP5.Om0)
cosmo = WMAP5
f_in = h5.File(radecfile)
radecz = f_in["radecz"]
f_out = h5.File(outfile)
cart = f_out.create_dataset("cart_pts",
shape=(radecz.shape[0], 3),
dtype='float64')
for i in range(radecz.shape[0]):
ra = Angle(radecz[i, 0], u.deg)
dec = Angle(radecz[i, 1], u.deg)
losd = cosmo.comoving_distance(radecz[i, 2])
dis = Distance(losd)
coord = ICRSCoordinates(ra, dec, distance=dis)
cart[i, :] = np.array([coord.x, coord.y, coord.z])
f_in.close()
f_out.close()
def generate_tracks(mt, aexp_list, z0_clusters):
"""tracks most massive progenitors for reach halo"""
mt.add_column("is_main_line", "BOOLEAN", table="mergertree", default=0)
properties = [
"x", "y", "z", "vx", "vy", "vz", "M_hc", "r_hc", "num_particles"
]
z0_halos = mt.get_halo_properties(z0_clusters, properties, aexp_list[0])
for cluster in z0_clusters:
print "Finding progenitor line for z0 id: " + str(cluster)
tree = mt.get_full_tree(cluster)
parent = z0_halos[z0_halos["id"] == cluster].squeeze()
progenitor_line = []
for i, aexp in enumerate(aexp_list[1:]):
parent_id = parent["id"]
progenitors = tree[(tree['aexp'] == aexp)
& (tree['parent_id'] == parent_id)]
#this can be pandas-ized
t1 = WMAP5.age(1. / aexp_list[i - 1] - 1.)
t2 = WMAP5.age(1. / aexp_list[i] - 1.)
dt = math.fabs(t1 - t2) * 1.0e9 * 3.15569e7 #convert to seconds
#apply smaller searchbox to exclude far off merger candidates
searchbox = ut.define_searchbox(parent,
dt,
mt.boxsize,
search_distance=1.5)
#identify most massive progenitor
if not progenitors.empty:
main_progenitor = identify_most_massive_progenitor(
progenitors, searchbox=searchbox)
if main_progenitor is None:
break
mt.mark_main_line(main_progenitor['aexp'],
main_progenitor['id'],
parent_id=main_progenitor['parent_id'])
parent = main_progenitor
progenitor_line.append(parent["id"])
else:
break
print "end of tree for cluster " + str(cluster)
print progenitor_line
mt.commit()
def get_inv_efunc(cosmology):
if cosmology == "Planck":
Planck13.__init__(100.0, Planck13.Om0)
cosmo = Planck13
elif cosmology == "WMAP":
WMAP5.__init__(100.0, WMAP5.Om0)
cosmo = WMAP5
return cosmo.inv_efunc
def get_comv(cosmology):
if cosmology == "Planck":
Planck13.__init__(100.0, Planck13.Om0)
cosmo = Planck13
elif cosmology == "WMAP":
WMAP5.__init__(100.0, WMAP5.Om0)
cosmo = WMAP5
return cosmo.comoving_distance
def mk_mock_srch(radecfile, nzdictfile, Nsph, simul_cosmo):
if simul_cosmo == "Planck":
# First make h free
Planck13.__init__(100.0, Planck13.Om0)
cosmo = Planck13
elif simul_cosmo == "WMAP":
WMAP5.__init__(100.0, WMAP5.Om0)
cosmo = WMAP5
comv = cosmo.comoving_distance
radecarr = h5_arr(radecfile, "good_pts")
nzdict = json.load(open(nzdictfile))
Nrands = radecarr.shape[0]
Narrs = Nsph / Nrands
remain = Nsph % Nrands
radecz = np.zeros((Nsph, 3))
for i in range(Narrs):
start = Nrands * i
stop = Nrands * (i + 1)
radecz[start:stop, :2] = radecarr[:, :]
endchunk = Nrands * (Narrs)
radecz[endchunk:, :2] = radecarr[:remain, :]
rad = np.arange(1.0, 67.0, 5.0)
zlo = nzdict["zlo"]
zhi = nzdict["zhi"]
radeczlist = len(rad) * [radecz]
for r_i, r in enumerate(rad):
dis_near = Distance(comv(zlo).value + r, u.Mpc)
dis_far = Distance(comv(zhi).value - r, u.Mpc)
z_a = dis_near.compute_z(cosmology=cosmo)
z_b = dis_far.compute_z(cosmology=cosmo)
randz = (z_a ** 3 + \
(z_b ** 3 - z_a ** 3) * np.random.rand(Nsph)) ** (1. / 3.)
radeczlist[r_i][:, 2] = randz[:]
arr2h5(
radeczlist[r_i], "{0}/{1}/mocks/mock_srch_pts.hdf5".format(
os.path.dirname(radecfile), simul_cosmo),
"radecz_{0}".format(str(r_i * 5 + 1)))
def generate_tracks(mt, aexp_list, z0_clusters) :
"""tracks most massive progenitors for reach halo"""
mt.add_column("is_main_line", "BOOLEAN", table="mergertree", default=0)
properties = ["x", "y", "z", "vx", "vy", "vz", "M_hc", "r_hc", "num_particles"]
z0_halos = mt.get_halo_properties(z0_clusters, properties, aexp_list[0])
for cluster in z0_clusters:
print "Finding progenitor line for z0 id: "+str(cluster)
tree = mt.get_full_tree(cluster)
parent = z0_halos[z0_halos["id"] == cluster].squeeze()
progenitor_line = []
for i,aexp in enumerate(aexp_list[1:]):
parent_id = parent["id"]
progenitors = tree[(tree['aexp'] == aexp) &
(tree['parent_id'] == parent_id)]
#this can be pandas-ized
t1 = WMAP5.age(1./aexp_list[i-1]-1.)
t2 = WMAP5.age(1./aexp_list[i]-1.)
dt = math.fabs(t1 - t2) * 1.0e9 * 3.15569e7 #convert to seconds
#apply smaller searchbox to exclude far off merger candidates
searchbox = ut.define_searchbox(parent, dt, mt.boxsize,
search_distance=1.5)
#identify most massive progenitor
if not progenitors.empty :
main_progenitor = identify_most_massive_progenitor(progenitors,
searchbox=searchbox)
if main_progenitor is None:
break
mt.mark_main_line(main_progenitor['aexp'], main_progenitor['id'],
parent_id = main_progenitor['parent_id'])
parent = main_progenitor
progenitor_line.append(parent["id"])
else:
break
print "end of tree for cluster "+str(cluster)
print progenitor_line
mt.commit()
def mk_mock_srch(radecfile, nzdictfile, Nsph, simul_cosmo):
if simul_cosmo == "Planck":
# First make h free
Planck13.__init__(100.0, Planck13.Om0)
cosmo = Planck13
elif simul_cosmo == "WMAP":
WMAP5.__init__(100.0, WMAP5.Om0)
cosmo = WMAP5
comv = cosmo.comoving_distance
radecarr = h5_arr(radecfile, "good_pts")
nzdict = json.load(open(nzdictfile))
Nrands = radecarr.shape[0]
Narrs = Nsph / Nrands
remain = Nsph % Nrands
radecz = np.zeros((Nsph, 3))
for i in range(Narrs):
start = Nrands * i
stop = Nrands * (i + 1)
radecz[start:stop, :2] = radecarr[:, :]
endchunk = Nrands * (Narrs)
radecz[endchunk:, :2] = radecarr[:remain, :]
rad = np.arange(1.0, 67.0, 5.0)
zlo = nzdict["zlo"]
zhi = nzdict["zhi"]
radeczlist = len(rad) * [radecz]
for r_i, r in enumerate(rad):
dis_near = Distance(comv(zlo) + r, u.Mpc)
dis_far = Distance(comv(zhi) - r, u.Mpc)
z_a = dis_near.compute_z(cosmology=cosmo)
z_b = dis_far.compute_z(cosmology=cosmo)
randz = (z_a ** 3 + \
(z_b ** 3 - z_a ** 3) * np.random.rand(Nsph)) ** (1. / 3.)
radeczlist[r_i][:, 2] = randz[:]
arr2h5(radeczlist[r_i], "{0}/{1}/mocks/mock_srch_pts.hdf5".format(os.path.dirname(radecfile), simul_cosmo), "radecz_{0}".format(str(r_i * 5 + 1)))
def mock_vpf(mock_cart_coords, spheresfile, simul_cosmo, rad):
gals = h5_arr(mock_cart_coords, "coords")
print gals
name = mock_cart_coords.split("/")[-1].split(".")[0]
if simul_cosmo == "Planck":
# First make h free
Planck13.__init__(100.0, Planck13.Om0)
cosmo = Planck13
elif simul_cosmo == "WMAP":
WMAP5.__init__(100.0, WMAP5.Om0)
cosmo = WMAP5
comv = cosmo.comoving_distance
gal_baum = cKDTree(gals)
spheres = h5_arr(spheresfile, "radecz_{0}".format(str(int(rad))))
print spheres
for i, sphere in enumerate(spheres):
rang = Angle(sphere[0], u.deg)
decang = Angle(sphere[1], u.deg)
dis = Distance(comv(sphere[2]), u.Mpc)
coord = ICRSCoordinates(rang, decang, distance=dis)
sph_cen = np.array([coord.x, coord.y, coord.z])
nn = gal_baum.query(sph_cen)
print "rad: ", rad, ", sphere: ", i
f = open(
"{0}/vpf_out/ascii/{1}_{2}.dat".format(
os.path.dirname(spheresfile), name, str(int(rad))), 'a')
if not nn[0] < rad:
f.write("1\n")
else:
f.write("0\n")
f.close()
def load_tmerger_from_file(sim, dir, aexp=1.0):
last_merger = sim.load_last_merger_epochs(dir,aexp)
tmerger = {}
for id in last_merger.keys():
merger_z = (1./float(last_merger[id])-1)
merger_time = WMAP5.age(merger_z)
cosmic_age = WMAP5.age(1./float(aexp)-1)
# merger_time = cosmocalc(merger_z)["zage_Gyr"]
# cosmic_age = cosmocalc(1./float(aexp)-1)["zage_Gyr"]
tmerger[id] = cosmic_age - merger_time
return tmerger
def mock_vpf(mock_cart_coords, spheresfile, simul_cosmo, rad):
gals = h5_arr(mock_cart_coords, "coords")
print gals
name = mock_cart_coords.split("/")[-1].split(".")[0]
if simul_cosmo == "Planck":
# First make h free
Planck13.__init__(100.0, Planck13.Om0)
cosmo = Planck13
elif simul_cosmo == "WMAP":
WMAP5.__init__(100.0, WMAP5.Om0)
cosmo = WMAP5
comv = cosmo.comoving_distance
gal_baum = cKDTree(gals)
spheres = h5_arr(spheresfile, "radecz_{0}".format(str(int(rad))))
print spheres
for i, sphere in enumerate(spheres):
rang = Angle(sphere[0], u.deg)
decang = Angle(sphere[1], u.deg)
dis = Distance(comv(sphere[2]), u.Mpc)
coord = ICRSCoordinates(rang, decang, distance=dis)
sph_cen = np.array([coord.x, coord.y, coord.z])
nn = gal_baum.query(sph_cen)
print "rad: ", rad, ", sphere: ", i
f = open("{0}/vpf_out/ascii/{1}_{2}.dat".format(os.path.dirname(spheresfile), name, str(int(rad))), 'a')
if not nn[0] < rad:
f.write("1\n")
else:
f.write("0\n")
f.close()
def load_tmerger_from_file(sim, dir, aexp=1.0):
last_merger = sim.load_last_merger_epochs(dir,aexp)
tmerger = {}
for id in last_merger.keys():
merger_z = (1./float(last_merger[id])-1)
merger_time = WMAP5.age(merger_z)
cosmic_age = WMAP5.age(1./float(aexp)-1)
# merger_time = cosmocalc(merger_z)["zage_Gyr"]
# cosmic_age = cosmocalc(1./float(aexp)-1)["zage_Gyr"]
tmerger[id] = cosmic_age - merger_time
return tmerger
def mock_vpf(mock_cart_coords, spheresfile, simul_cosmo):
gals = h5_arr(mock_cart_coords, "coords")
name = mock_cart_coords.split("/")[-1].split(".")[0]
if simul_cosmo == "Planck":
# First make h free
Planck13.__init__(100.0, Planck13.Om0)
cosmo = Planck13
elif simul_cosmo == "WMAP":
WMAP5.__init__(100.0, WMAP5.Om0)
cosmo = WMAP5
comv = cosmo.comoving_distance
gal_baum = cKDTree(gals)
rad = np.arange(1.0, 67.0, 5.0)
for r_i, r in enumerate(rad):
spheres = h5_arr(spheresfile, "radecz_{0}".format(str(r_i * 5 + 1)))
voids = np.zeros(spheres.shape[0])
for i, sphere in enumerate(spheres):
rang = Angle(sphere[0], u.deg)
decang = Angle(sphere[1], u.deg)
dis = Distance(comv(sphere[2]), u.Mpc)
coord = ICRSCoordinates(rang, decang, distance=dis)
sph_cen = np.array([coord.x.value, coord.y.value, coord.z.value])
nn = gal_baum.query(sph_cen)
print "rad: ", r, ", sphere: ", i
if not nn[0] < r:
voids[i] = 1
arr2h5(voids,
"{0}/vpf_out/{1}.hdf5".format(os.path.dirname(spheresfile), name),
"voids_{0}".format(str(r_i * 5 + 1)))
def get_theta(
z_max
): #set the theta min and theta max for each redshift to a physical range of 0.3Mpc - 2.5Mpc
theta_min = []
theta_max = []
for red in range(0, 25):
d1 = cosmo.comoving_distance([z_max[red]])
thetamin = 0.3 * 180 / (numpy.pi * d1.value)
thetamax = 2.5 * 180 / (numpy.pi * d1.value)
theta_min.append(thetamin)
theta_max.append(thetamax)
return (theta_min, theta_max)
def _initialize_cosmology():
"""Initlized internal __core__ variables storing the trend of redshift vs
comoving distance. Default cosmology is from WMAP5.
----------------------------------------------------------------------------
Args:
None
Returns:
None
"""
# TODO:
# Talk to someone about this. Are globals the best way to do this.
redshift_array = np.linspace(0.0, 10.0, 1000)
comov_array = WMAP5.comoving_distance(redshift_array)
global _comov_dist_to_redshift_spline
_comov_dist_to_redshift_spline = iu_spline(comov_array, redshift_array)
global _initilized_cosmology
_initilized_cosmology = True
return None
def process_nbar(nbarfile, nz_dict_file, cosmology, radeczfile=None):
"""
Parameters
---------
nbarfile : str
the path to and name of the corrected nbar file
nz_dict_file : str
path to and name of the json file with the nbar dict
cosmology : str, "WMAP" or "Planck"
the cosmology to compute shell volumes with
radeczfile : str, "data" or "mock"
the data or mock file to process
"""
# magic number for width around maximum
Q = 0.65
# magic number for shell vol computation
Nfrac = (6769.0358 * np.pi) / 129600
if cosmology == "Planck":
Planck13.__init__(100.0, Planck13.Om0)
cosmo = Planck13
elif cosmology == "WMAP":
WMAP5.__init__(100.0, WMAP5.Om0)
cosmo = WMAP5
comv = cosmo.comoving_distance
nbar_corr = np.loadtxt(nbarfile)
nz_dict = {"tophat height for zrange": Q}
# Cut out the first bit of crap (works for CMASS, dunno about LOWZ)
ind03 = np.abs(nbar_corr[:, 0] - 0.3).argmin()
nbar_corr = nbar_corr[ind03:, :]
zcen = nbar_corr[:, 0]
z_near = nbar_corr[:, 1]
z_far = nbar_corr[:, 2]
corr_gal_counts = nbar_corr[:, 6]
nbar = []
shell_vols = []
for i in range(len(zcen)):
shell_vols.append(Nfrac * calc_shell_vol(comv, z_near[i], z_far[i], zcen[i]))
nbar.append(corr_gal_counts[i] / shell_vols[i])
nbar = np.array(nbar)
shell_vols = np.array(shell_vols)
# Find nbar peak and index
max_nbar = np.max(nbar)
max_i = int(np.where(nbar == max_nbar)[0])
nz_dict["max_nbar_corr"] = max_nbar
nz_dict["nbar_corr_tophat"] = Q * max_nbar
nz_dict["z_nbar_max"] = zcen[max_i]
# get the interval edge indices
L = np.abs(nbar[:max_i] - max_nbar * Q).argmin()
R = max_i + np.abs(nbar[max_i:] - max_nbar * Q).argmin()
nbar = nbar[L:R + 1]
shell_vols = shell_vols[L:R + 1]
nz_dict["zlo"] = zcen[L]
nz_dict["zhi"] = zcen[R]
nz_dict["avg_nbar_corr"] = np.average(nbar)
nz_dict["total_shell_vol"] = np.sum(shell_vols)
if radeczfile:
radecz = h5_arr(radeczfile, "radecz")
# Make the redshift cut in the nbar array with right cosmology
nbar_corr = nbar_corr[(nz_dict["zlo"] <= nbar_corr[:, 0]) * \
(nbar_corr[:, 0] <= nz_dict["zhi"])]
# Get binning those observed galaxies
zbinedges = np.append(nbar_corr[0, 1], nbar_corr[:, 2])
# Find the counts per bin and convert to nbar
H = np.histogram(radecz[:, 2], bins=zbinedges)
hist_nbar = H[0] / shell_vols
if not radeczfile.split('/')[-2] == "mocks_hierarchical":
# save the average downsampled value if it's the data file
nz_dict["avg_nbar_down"] = np.average(hist_nbar)
# The number to downsample to in each bin
# (multiply bin number by the relative fraction determined from
# corrected distribution of nbar)
nz_dict["nbar_data_tophat"] = 0.95 * nz_dict["nbar_corr_tophat"] * (nz_dict["avg_nbar_down"] / nz_dict["avg_nbar_corr"])
factor_arr = nz_dict["nbar_data_tophat"] / hist_nbar
# if we are dealing with a mockfile, then there is an extra factor to make
# the average equal that of the data
elif radeczfile.split('/')[-2] == "mocks_hierarchical":
# have to open the existing json file
jf = open(nz_dict_file)
nz_dict = json.load(jf)
factor_arr = nz_dict["nbar_data_tophat"] / hist_nbar
jf.close()
num_down = np.rint(factor_arr * H[0])
num_down = num_down.astype(int)
# make a mask for the final array for analysis within the redshift limits
finmask = np.array(radecz.shape[0] * [False])
for i, nd in enumerate(num_down):
"""Turn on the right amount of galaxies in each bin."""
zbin_ids = np.where(((zbinedges[i] < radecz[:, 2]) * (radecz[:, 2] <= zbinedges[i + 1])) == True)
if zbin_ids[0].shape[0] == 0:
continue
keep = np.random.choice(zbin_ids[0], size=nd, replace=False)
finmask[keep] = True
radecz = radecz[finmask]
if not radeczfile.split('/')[-2] == "mocks_hierarchical":
# and save downsampled array to a hdf5 file
arr2h5(radecz, "{0}/radecz_down.hdf5".format(os.path.dirname(nz_dict_file)), "radecz", mode='w')
elif radeczfile.split('/')[-2] == "mocks_hierarchical":
# save mocks to a hdf5 file
mock_no = radeczfile.split('/')[-1].split('.')[0]
arr2h5(radecz, "{0}/mocks/rdz_down/{1}.hdf5".format(os.path.dirname(nz_dict_file), mock_no), "radecz", mode='w')
if not radeczfile.split('/')[-2] == "mocks_hierarchical":
# don't save the json if we're working on a mock
nf = open(nz_dict_file, 'w')
json.dump(nz_dict, nf, sort_keys=True, indent=4, separators=(',', ':\t'))
nf.close()
def generate_links(mt, aexp_list, z0_clusters, particles_dir) :
"""Generates links from halo particle data"""
if restart:
phalos, last_aexp = mt.restart_mergertree()
print "restarting merger tree code from %0.4f" % last_aexp
particles_file = "%s/halo_particles_a%0.4f.dat" % (particles_dir, last_aexp)
else:
phalos = z0_clusters
z0id_0 = dict((phalo, phalo) for phalo in phalos)
particles_file = "%s/halo_particles_a%0.4f.dat" % (particles_dir, aexp_list[0])
particles_parent = cart_io.read_halo_particles(particles_file, clusters=phalos)
for i0 in range(len(aexp_list)-1):
i1 = i0 + 1
parent_aexp = aexp_list[i0]
child_aexp = aexp_list[i1]
if restart and child_aexp >= last_aexp:
continue
t1 = WMAP5.age(1./parent_aexp-1.)
t2 = WMAP5.age(1./child_aexp-1.)
dt = math.fabs(t1 - t2) * 1.0e9 * 3.15569e7 #convert to seconds
print "Finding matches for %0.4f, %0.4f" % (parent_aexp, child_aexp)
print str(len(phalos))+" halos identified for matching"
#get halo properties from database
properties = ["x","y","z", "vx","vy","vz", "M_hc", "r_hc", "num_particles"]
halos = mt.get_halo_properties(phalos, properties, parent_aexp)
#load child_particles
particles_file = "%s/halo_particles_a%0.4f.dat" % (particles_dir, child_aexp)
particles_child = cart_io.read_halo_particles(particles_file, min_np = 0)
links_data = []
z0id_1 = {}
#loop through parent halos
for phalo_id in phalos:
phalo = halos[halos["id"] == phalo_id].squeeze()
logging.debug( "Checking cluster,", parent_aexp, phalo )
searchbox = ut.define_searchbox(phalo, dt, mt.boxsize,
search_distance=search_distance_multiplier)
chalos = mt.get_halos_within_distance(child_aexp, searchbox, ["x","y","z"],
mass_cut = 0.01*phalo["M_hc"])
prog_found = False
if not chalos.empty:
z0id = z0id_0[phalo_id]
this_parent_particles_set = set(map(itemgetter(0),particles_parent[phalo_id]))
min_joint_particles = min_joint_particles_factor * len(this_parent_particles_set)
#loop through satellites within search box
for i, chalo in chalos.iterrows():
chalo_id = chalo["id"]
#consider redoing with np.intersect1d(parent, child)
shared_particles = this_parent_particles_set.intersection(map(itemgetter(0),particles_child[chalo_id]))
if len(shared_particles) > min_joint_particles:
ratio = float(len(particles_child[chalo_id]))/float(len(particles_parent[phalo_id]))
logging.debug( "Possible progenitor found: ", chalo_id, len(shared_particles), ratio)
distance = ut.distance_between_halos(chalo, phalo, mt.boxsize)
links_data.append((parent_aexp, child_aexp, phalo_id, chalo_id, len(shared_particles),
ratio, distance, z0id, 0 ))
z0id_1[chalo_id] = z0id
prog_found = True
if not prog_found:
logging.debug("No matches found for halo "+str(phalo_id)+" at aexp = "+str(child_aexp))
mt.mark_leaf(parent_aexp, phalo_id)
mt.insert("mergertree", links_data)
mt.commit()
z0id_0 = z0id_1
phalos = z0id_0.keys()
particles_parent = particles_child
def generate_links(mt, aexp_list, z0_clusters, particles_dir):
"""Generates links from halo particle data"""
if restart:
phalos, last_aexp = mt.restart_mergertree()
print "restarting merger tree code from %0.4f" % last_aexp
particles_file = "%s/halo_particles_a%0.4f.dat" % (particles_dir,
last_aexp)
else:
phalos = z0_clusters
z0id_0 = dict((phalo, phalo) for phalo in phalos)
particles_file = "%s/halo_particles_a%0.4f.dat" % (particles_dir,
aexp_list[0])
particles_parent = cart_io.read_halo_particles(particles_file,
clusters=phalos)
for i0 in range(len(aexp_list) - 1):
i1 = i0 + 1
parent_aexp = aexp_list[i0]
child_aexp = aexp_list[i1]
if restart and child_aexp >= last_aexp:
continue
t1 = WMAP5.age(1. / parent_aexp - 1.)
t2 = WMAP5.age(1. / child_aexp - 1.)
dt = math.fabs(t1 - t2) * 1.0e9 * 3.15569e7 #convert to seconds
print "Finding matches for %0.4f, %0.4f" % (parent_aexp, child_aexp)
print str(len(phalos)) + " halos identified for matching"
#get halo properties from database
properties = [
"x", "y", "z", "vx", "vy", "vz", "M_hc", "r_hc", "num_particles"
]
halos = mt.get_halo_properties(phalos, properties, parent_aexp)
#load child_particles
particles_file = "%s/halo_particles_a%0.4f.dat" % (particles_dir,
child_aexp)
particles_child = cart_io.read_halo_particles(particles_file, min_np=0)
links_data = []
z0id_1 = {}
#loop through parent halos
for phalo_id in phalos:
phalo = halos[halos["id"] == phalo_id].squeeze()
logging.debug("Checking cluster,", parent_aexp, phalo)
searchbox = ut.define_searchbox(
phalo,
dt,
mt.boxsize,
search_distance=search_distance_multiplier)
chalos = mt.get_halos_within_distance(child_aexp,
searchbox, ["x", "y", "z"],
mass_cut=0.01 *
phalo["M_hc"])
prog_found = False
if not chalos.empty:
z0id = z0id_0[phalo_id]
this_parent_particles_set = set(
map(itemgetter(0), particles_parent[phalo_id]))
min_joint_particles = min_joint_particles_factor * len(
this_parent_particles_set)
#loop through satellites within search box
for i, chalo in chalos.iterrows():
chalo_id = chalo["id"]
#consider redoing with np.intersect1d(parent, child)
shared_particles = this_parent_particles_set.intersection(
map(itemgetter(0), particles_child[chalo_id]))
if len(shared_particles) > min_joint_particles:
ratio = float(len(particles_child[chalo_id])) / float(
len(particles_parent[phalo_id]))
logging.debug("Possible progenitor found: ", chalo_id,
len(shared_particles), ratio)
distance = ut.distance_between_halos(
chalo, phalo, mt.boxsize)
links_data.append(
(parent_aexp, child_aexp, phalo_id, chalo_id,
len(shared_particles), ratio, distance, z0id, 0))
z0id_1[chalo_id] = z0id
prog_found = True
if not prog_found:
logging.debug("No matches found for halo " + str(phalo_id) +
" at aexp = " + str(child_aexp))
mt.mark_leaf(parent_aexp, phalo_id)
mt.insert("mergertree", links_data)
mt.commit()
z0id_0 = z0id_1
phalos = z0id_0.keys()
particles_parent = particles_child
def vpf(dat_dir, Nsph, simul_cosmo, rad):
# Grab the data coordinates
gals = h5_arr("./dat/out/{0}/{1}/gals_cart_coords.hdf5".
format(dat_dir, simul_cosmo), "cart_pts")
# Get details about the redshift interval being considered
nbar_dict = json.load(open("./dat/out/{0}/{1}/nbar_zrange.json".
format(dat_dir, simul_cosmo)))
zlo = nbar_dict["zlo"]
zhi = nbar_dict["zhi"]
# Get the search points
good_pts = h5_arr("./dat/out/{0}/srch_radec.hdf5".format(dat_dir), "good_pts")
bad_pts = h5_arr("./dat/out/{0}/veto.hdf5".format(dat_dir),
"bad_pts")
# Set angular radius of effective area around bad points
bad_r = np.arccos(1.0 - (np.pi * 9.8544099e-05) / (2 * 180 ** 2))
bad_r_deg = np.rad2deg(bad_r)
# Set the cosmology with h free
# Here the cosmology is based on WMAP (for first MultiDark simulation)
if simul_cosmo == "Planck":
# First make h free
Planck13.__init__(100.0, Planck13.Om0)
cosmo = Planck13
elif simul_cosmo == "WMAP":
WMAP5.__init__(100.0, WMAP5.Om0)
cosmo = WMAP5
comv = cosmo.comoving_distance
# Build the trees
# galaxy tree
gal_baum = cKDTree(gals)
# tree of bad points (angular coordinates on unit sphere)
bad_xyz = radec2xyz(bad_pts)
veto_baum = cKDTree(bad_xyz)
# Initialise final output arrays
# rad = np.arange(1.0, 67.0, 5.0) doing it one radius at a time
# P_0 = np.zeros(rad.shape)
# No. of spheres and norm
# Nsph_arr = Nsph * np.array(4 * [0.01] + 4 * [0.1] + 4 * [1.0])
# norm = 1. / Nsph_arr
# norm = 1. / Nsph
rand_i = 0
for r_i, r in enumerate(rad):
# start the count of successful voids
count = 0
# Custom zrange for sphere size
dis_near = Distance(comv(zlo).value + r, u.Mpc)
dis_far = Distance(comv(zhi).value - r, u.Mpc)
z_a = dis_near.compute_z(cosmology=cosmo)
z_b = dis_far.compute_z(cosmology=cosmo)
for i in range(Nsph): # _arr[r_i]):
# compensate for finite length of mask file
rand_i = rand_i % 999999
radec = good_pts[rand_i, :]
rang = Angle(radec[0], u.deg)
decang = Angle(radec[1], u.deg)
randz = (z_a ** 3 + \
(z_b ** 3 - z_a ** 3) * np.random.rand(1)[0]) ** (1. / 3.)
dis = Distance(comv(randz), u.Mpc)
coord = ICRSCoordinates(rang, decang, distance=dis)
sph_cen = np.array([coord.x.value, coord.y.value, coord.z.value])
nn = gal_baum.query(sph_cen)
print "rad: ", r, ", sphere: ", i
if not nn[0] < r:
# add instance to probability count
count += 1
# record quality of sphere using spline values for intersection
# with bad points
# Get radius of circular projection of sphere
R = np.arcsin(r / np.sqrt(np.sum(sph_cen[:] ** 2)))
# Get coordinates of circle centre on unit sphere
crc_cen = radec2xyz(radec)[0]
# Compute tree search radius from Cosine rule
# (include points extending beyond sphere edge to account for
# finite area around bad points)
l_srch = np.sqrt(2. - 2. * np.cos(R))
# Run search
pierce_l = veto_baum.query_ball_point(crc_cen, l_srch)
bad_vol = 0.
R = np.degrees(R) # need in degrees for bad_vol computation
for pt in pierce_l:
pt_ang = bad_pts[pt]
dis = np.degrees(central_angle(pt_ang, radec))
l = dis / R
bad_vol += 1.5 * (bad_r_deg / R) ** 2 \
* np.sqrt(1.0 - l ** 2)
f_r = open("./dat/out/{0}/{1}/vpf_out/volfrac_{2}.dat".
format(dat_dir, simul_cosmo, r),
'a')
f_r.write("{0}\n".format(bad_vol))
f_r.close()
rand_i += 1
Create a custom cosmology object >>> from astropy.cosmology import FlatLambdaCDM >>> cosmo = FlatLambdaCDM(H0=70, Om0=0.3) >>> cosmo FlatLambdaCDM(H0=70, Om0=0.3, Ode0=0.7) Compute the comoving volume to z=6.5 in cubic Mpc using this cosmology >>> cosmo.comoving_volume(6.5) 2521696198211.6924 Compute the age of the universe in Gyr using the pre-defined WMAP 5-year and WMAP 9-year cosmologies >>> from astropy.cosmology import WMAP5, WMAP9 >>> WMAP5.age(0) 13.723782349795023 >>> WMAP9.age(0) 13.768899510689097 Create a cosmology with a varying `w' >>> from astropy.cosmology import Flatw0waCDM >>> cosmo = Flatw0waCDM(H0=70, Om0=0.3, w0=-1, wa=0.2) Find the separation in proper kpc at z=4 corresponding to 10 arcsec in this cosmology compared to a WMAP9 cosmology >>> cosmo.kpc_proper_per_arcmin(4) * 10 / 60. 68.87214405278925 >>> WMAP9.kpc_proper_per_arcmin(4) * 10 / 60. 71.21374615575363
def calc_age_from_aexp(aexp): return WMAP5.age(1./float(aexp)-1).value
from scipy.spatial import cKDTree
simul_cosmo = "WMAP"
def spherical_cap(h):
return 0.75 * (h ** 2) * (1 - h / 3)
if simul_cosmo == "Planck":
# First make h free
Planck13.__init__(100.0, Planck13.Om0)
cosmo = Planck13
elif simul_cosmo == "WMAP":
WMAP5.__init__(100.0, WMAP5.Om0)
cosmo = WMAP5
Nsph = 10000000
rad = np.arange(5.0, 66.0, 5.0)
As = np.arange(0.0, 1.0, 0.05)
Bs = np.arange(0.0, 1.0, 0.05)
splarr = np.loadtxt("test_dat/edge_splarr.dat")
A, B = np.meshgrid(As, Bs)
inty = interp2d(A[0, :], B[:, 0], splarr)
for r_i, r in enumerate(rad):
bad_pts = np.loadtxt("in/CMASS_DATA/north_block_outside.dat", usecols=(0, 1))
nbar_vals = json.load(
open("out/{0}/{1}/nbar_zrange.json".format(survey_cap, simul_cosmo)))
zlo = nbar_vals["zlo"]
zhi = nbar_vals["zhi"]
bad_r = np.arccos(1.0 - (np.pi * 9.8544099e-05) / (2 * 180**2))
bad_r_deg = np.rad2deg(bad_r)
if simul_cosmo == "Planck":
Planck13.__init__(100.0, Planck13.Om0)
cosmo = Planck13
elif simul_cosmo == "WMAP":
WMAP5.__init__(100.0, WMAP5.Om0)
cosmo = WMAP5
comv = cosmo.comoving_distance
def radec2xyz(radecarr):
radecarr = np.atleast_2d(radecarr)
xyzarr = np.zeros((radecarr.shape[0], 3))
xyzarr[:, 0] = np.cos(np.radians(radecarr[:, 1])) * \
np.cos(np.radians(radecarr[:, 0]))
xyzarr[:, 1] = np.cos(np.radians(radecarr[:, 1])) * \
np.sin(np.radians(radecarr[:, 0]))
xyzarr[:, 2] = np.sin(np.radians(radecarr[:, 1]))
return xyzarr
def process_nbar(nbarfile, nz_dict_file, cosmology, radeczfile=None):
"""
Parameters
---------
nbarfile : str
the path to and name of the corrected nbar file
nz_dict_file : str
path to and name of the json file with the nbar dict
cosmology : str, "WMAP" or "Planck"
the cosmology to compute shell volumes with
radeczfile : str, "data" or "mock"
the data or mock file to process
"""
# magic number for width around maximum
Q = 0.65
# magic number for shell vol computation
Nfrac = (6769.0358 * np.pi) / 129600
if cosmology == "Planck":
Planck13.__init__(100.0, Planck13.Om0)
cosmo = Planck13
elif cosmology == "WMAP":
WMAP5.__init__(100.0, WMAP5.Om0)
cosmo = WMAP5
comv = cosmo.comoving_distance
nbar_corr = np.loadtxt(nbarfile)
nz_dict = {"tophat height for zrange": Q}
# Cut out the first bit of crap (works for CMASS, dunno about LOWZ)
ind03 = np.abs(nbar_corr[:, 0] - 0.3).argmin()
nbar_corr = nbar_corr[ind03:, :]
zcen = nbar_corr[:, 0]
z_near = nbar_corr[:, 1]
z_far = nbar_corr[:, 2]
corr_gal_counts = nbar_corr[:, 6]
nbar = []
shell_vols = []
for i in range(len(zcen)):
shell_vols.append(Nfrac * calc_shell_vol(comv, z_near[i], z_far[i], zcen[i]))
nbar.append(corr_gal_counts[i] / shell_vols[i])
nbar = np.array(nbar)
# Find nbar peak and index
max_nbar = np.max(nbar)
max_i = int(np.where(nbar == max_nbar)[0])
nz_dict["max_nbar_corr"] = max_nbar
nz_dict["nbar_corr_tophat"] = Q * max_nbar
nz_dict["z_nbar_max"] = zcen[max_i]
# get the interval edge indices
L = np.abs(nbar[:max_i] - max_nbar * Q).argmin()
R = max_i + np.abs(nbar[max_i:] - max_nbar * Q).argmin()
nbar = nbar[L:R + 1]
shell_vols = shell_vols[L:R + 1]
nz_dict["zlo"] = zcen[L]
nz_dict["zhi"] = zcen[R]
nz_dict["avg_nbar_corr"] = np.average(nbar)
nz_dict["total_shell_vol"] = np.sum(shell_vols)
if radeczfile:
radecz = h5_arr(radeczfile, "radecz")
# Make the redshift cut in the nbar array with right cosmology
nbar_corr = nbar_corr[(nz_dict["zlo"] <= nbar_corr[:, 0]) * \
(nbar_corr[:, 0] <= nz_dict["zhi"])]
# Get binning those observed galaxies
zbinedges = np.append(nbar_corr[0, 1], nbar_corr[:, 2])
# Find the counts per bin
H = np.histogram(radecz[:, 2], bins=zbinedges)
# The number to downsample to in each bin
# (multiply bin number by the relative fraction determined from
# corrected distribution of nbar)
num_down = np.rint((nz_dict["nbar_corr_tophat"] / nbar[:]) * H[0])
num_down = num_down.astype(int)
# make a mask for the final array for analysis within the redshift limits
finmask = np.array(radecz.shape[0] * [False])
for i, nd in enumerate(num_down):
"""Turn on the right amount of galaxies in each bin."""
zbin_ids = np.where(((zbinedges[i] < radecz[:, 2]) * (radecz[:, 2] <= zbinedges[i + 1])) == True)
if zbin_ids[0].shape[0] == 0:
continue
keep = np.random.choice(zbin_ids[0], size=nd, replace=False)
finmask[keep] = True
radecz = radecz[finmask]
if not radeczfile.split('/')[-2] == "mocks_hierarchical":
# now get nbar for the downsampled data for use in mock processing and simulation
gal_counts = np.histogram(radecz[:, 2], bins=zbinedges)[0]
nbar_down = []
for i in range(len(gal_counts)):
nbar_down.append(gal_counts[i] / shell_vols[i])
nbar_down = np.array(nbar_down)
# save the average downsampled value
nz_dict["avg_nbar_down"] = np.average(nbar_down)
# and save downsampled array to a hdf5 file
arr2h5(radecz, "{0}/radecz_down.hdf5".format(os.path.dirname(nz_dict_file)), "radecz")
# if we are dealing with a mockfile, then there is an extra factor to make
# the average equal that of the data
if radeczfile.split('/')[-2] == "mocks_hierarchical":
# have to open the existing json file
jf = open(nz_dict_file)
nz_dict = json.load(jf)
gal_counts = np.histogram(radecz[:, 2], bins=zbinedges)[0]
nbar_mock = []
for i in range(len(gal_counts)):
nbar_mock.append(gal_counts[i] / shell_vols[i])
nbar_mock = np.array(nbar_mock)
num_down = np.rint((nz_dict["avg_nbar_down"] / np.average(nbar_mock)) * H[0])
num_down = num_down.astype(int)
finmask = np.array(radecz.shape[0] * [False])
for i, nd in enumerate(num_down):
"""Turn on the right amount of galaxies in each bin."""
zbin_ids = np.where(((zbinedges[i] < radecz[:, 2]) * \
(radecz[:, 2] <= zbinedges[i + 1])) == True)
keep = np.random.choice(zbin_ids[0], size=nd, replace=False)
finmask[keep] = True
radecz = radecz[finmask]
# and save to a hdf5 file
mock_no = radeczfile.split('/')[-1].split('.')[0]
arr2h5(radecz, "{0}/mocks/rdz_down/{1}.hdf5".format(os.path.dirname(nz_dict_file), mock_no), "radecz")
jf.close()
if not radeczfile.split('/')[-2] == "mocks_hierarchical":
# don't save the json if we're working on a mock
nf = open(nz_dict_file, 'w')
json.dump(nz_dict, nf, sort_keys=True, indent=4, separators=(',', ':\t'))
nf.close()
if len(expected_matches) > 0:
matches = mt.get_halo_properties(expected_matches, properties, child_aexp)
for match in matches:
print match
print "x distance: ", halo["x"] - match["x"]
print "y distance: ", halo["y"] - match["y"]
print "z distance: ", halo["z"] - match["z"]
shared_particles = list( set(map(itemgetter(0),particles_parent[phalo]))
& set(map(itemgetter(0),particles_child[match["id"]])) )
print "shared particles: ", len(shared_particles )
print "mass ratio: ", float(match["M_hc"])/float(halos["M_hc"])
# print "Checking cluster,", parent_aexp, phalo
#determine time difference between the two epochs in seconds
t1 = WMAP5.age(1./parent_aexp-1.).value
t2 = WMAP5.age(1./child_aexp-1.).value
dt = math.fabs(t1 - t2) * 1.0e9 * 3.15569e7 #convert to seconds
#search cube determined by parent halo velocity + parent halo radius
pos = np.array([halo["x"], halo["y"], halo["z"]])
vel = np.array([halo["vx"], halo["vy"], halo["vz"]])
searchbox = mt.define_searchbox(pos, vel, dt, halo["r_hc"], search_distance = search_distance_multiplier)
print searchbox
chalos = mt.get_halos_within_distance(child_aexp,searchbox, [],
masscut = 0.01*halo["M_hc"])
print sorted(chalos)
#loop through satellites within search box
prog_found = False
# Planck13.comoving_distance(ar_z)) # # plt.plot(ar_z, Planck13.comoving_distance(ar_z) / Planck13.comoving_distance(ar_z)) # plt.plot(ar_z, Planck13.comoving_distance(ar_z + ar_delta_z_planck13 - ar_delta_z_wmap7) / # Planck13.comoving_distance(ar_z)) # plt.show() # print(scipy.misc.derivative(func=Planck13.comoving_distance, x0=2, dx=0.1)) # ar_dcmv_dz_planck13 = np.array([scipy.misc.derivative( # func=lambda x: Planck13.comoving_distance(x).value, x0=z, dx=0.01) for z in ar_z]) # ar_dcmv_dz_wmap7 = np.array([scipy.misc.derivative( # func=lambda x: WMAP7.comoving_distance(x).value, x0=z, dx=0.01) for z in ar_z]) # plt.plot(ar_z, -(ar_dcmv_dz_planck13 - ar_dcmv_dz_wmap7) * ar_delta_z_planck13) # plt.show() del scipy.misc ar_base_cmvd_planck13 = Planck13.comoving_distance(ar_z) ar_true_planck13_cmvd = Planck13.comoving_distance(ar_z + ar_delta_z_planck13) ar_base_cmvd_wmap5 = WMAP5.comoving_distance(ar_z) ar_wmap5_apparent_cmvd = WMAP5.comoving_distance(ar_z + ar_delta_z_planck13) ar_base_cmvd_wmap7 = WMAP7.comoving_distance(ar_z) ar_wmap7_apparent_cmvd = WMAP7.comoving_distance(ar_z + ar_delta_z_planck13) ar_base_cmvd_wmap9 = WMAP9.comoving_distance(ar_z) ar_wmap9_apparent_cmvd = WMAP9.comoving_distance(ar_z + ar_delta_z_planck13) plt.plot(ar_z, ar_true_planck13_cmvd - ar_base_cmvd_planck13) plt.plot(ar_z, ar_wmap5_apparent_cmvd - ar_base_cmvd_wmap5) plt.plot(ar_z, ar_wmap7_apparent_cmvd - ar_base_cmvd_wmap7) plt.plot(ar_z, ar_wmap9_apparent_cmvd - ar_base_cmvd_wmap9) # plt.plot(ar_z, ar_wmap7_apparent_cmvd - ar_true_planck13_cmvd) plt.show()
def box_completeness(Nsph, simul_cosmo):
if simul_cosmo == "Planck":
# First make h free
Planck13.__init__(100.0, Planck13.Om0)
cosmo = Planck13
elif simul_cosmo == "WMAP":
WMAP5.__init__(100.0, WMAP5.Om0)
cosmo = WMAP5
rad = np.arange(5.0, 66.0, 5.0)
As = np.arange(0.0, 1.0, 0.05)
Bs = np.arange(0.0, 1.0, 0.05)
splarr = np.loadtxt("test_dat/edge_splarr.dat")
A, B = np.meshgrid(As, Bs)
inty = interp2d(A[0, :], B[:, 0], splarr)
# this number from survey I think
# should be 2% of "sky" area
# had 138621 for some reason, now used 2% to get
bad_pts = 1000 * np.random.rand(34834737093, 2)
bad_r = 0.0004275 # determined from quick calculation for now
bad_A = np.pi * bad_r ** 2
badbaum = cKDTree(bad_pts)
for r_i, r in enumerate(rad):
spheres = 1000 * np.random.rand(Nsph, 2)
bound_bool = (spheres[:, 0] < r) + (spheres[:, 1] < r) + \
((1000 - spheres[:, 0]) < r) + \
((1000 - spheres[:, 1]) < r)
bad_inds = np.where(bound_bool == True)
badsphs = spheres[bound_bool]
pickle_bool = ((badsphs[:, 0] ** 2 + badsphs[:, 1] ** 2) < r) + \
(((1000 - badsphs[:, 0]) ** 2 + (1000 - badsphs[:, 0]) ** 2)
< r)
pickle_inds = bad_inds[pickle_bool]
for i, sph in enumerate(spheres):
badvol = 0.
pierce_pts = badbaum.query_ball_point(sph, r)
for pt in pierce_pts:
# retrieve coordinates of points within sphere
pt_coord = bad_pts[pt]
# calculate fractional projected distance from centre
dis = np.sqrt((sph[0] - pt_coord[0]) ** 2 + \
(sph[1] - pt_coord[1]) ** 2) / r
# calculate length pierced through sphere
l = 2 * np.sqrt(1 - dis ** 2)
badvol += l * bad_A
# check if sphere at boundary
if i in bad_inds:
if i in pickle_inds:
badvol += inty(sph[0] / r, sph[1] / r)
else:
if sph[0] < r:
badvol += spherical_cap(1 - sph[0] / r)
elif 1000 - sph[0] < r:
badvol += spherical_cap(1 - (1000 - sph[0]) / r)
if sph[1] < r:
badvol += spherical_cap(1 - sph[1] / r)
elif 1000 - sph[1] < r:
badvol += spherical_cap(1 - (1000 - sph[1]) / r)
f = open("test_dat/simul_badvol.dat", 'a')
f.write("{0}\n".format(badvol))
f.close()
def M0_to_kg(M0):
return M0 * 1.98847e30
if action == 'redshiftcalc':
H0 = 70
distance = float(form.getfirst("distance", ""))
redshift = round((distance * H0) / (const.c * 1e-3),
4) # const.c is in meters, converting to km
print(json.dumps({'redshift': redshift}))
elif action == 'distancecalc':
redshift = float(form.getfirst("redshift", ""))
distance = WMAP5.comoving_distance(redshift)
print(json.dumps({'distance': distance}))
elif action == 'orbitalsepcalc':
orbital_p = float(form.getfirst("orbital_p", ""))
total_m = float(form.getfirst("total_m", ""))
orbital_s = s_to_yrs(
np.sqrt((4 * (const.pi**2) * (pc_to_m(orbital_s) / 2)**3) /
(const.G * M0_to_kg(total_m))))
print(json.dumps({'orbital_s': orbital_s}))
elif action == 'orbitalpercalc':
vlos = region['z-velocity']*1.e-5 ##km/s T,P,rho = region['Temperature'], region['Pressure'], region['H_NumberDensity'] #T = [K], P = [dyne/cm^2], rho = [atoms/cm^3] emiss_HI = region['Emission_LyA']*(4.*np.pi*1.63e-11) emiss_OVI = region['Emission_OVI']*(4.*np.pi*1.92e-11) emiss_CIV = region['Emission_CIV']*(4.*np.pi*1.28e-11) nHI,ne= region['HI_NumberDensity'], region['Electron_NumberDensity'] Z = region['Metallicity'] / 0.02 #setting the ra and dec dlum_0 = cosmo.luminosity_distance(z_cos) dlum_0 = dlum_0.to(u.Mpc).value ## this should maybe be using the physical distances... dra = ((region['x']-pos[0])*pf['Mpc'])/dlum_0 ddec = ((region['y']-pos[1])*pf['Mpc'])/dlum_0 ra_0 = ra_los*u.degree.to(u.radian) dec_0 = dec_los*u.degree.to(u.radian) ra = np.zeros(len(x))+ra_0+dra dec = np.zeros(len(y))+dec_0+ddec #now the dlum worries about the z component
def give_proper_kpc(z,angle,unit): if len(angle) == len(unit): out = cosmo.kpc_proper_per_arcmin(z)*(angle[x]*unit[x]).to(u.arcmin) for x in range(len(angle))] else: out = None return out
def calc_age_from_aexp(aexp): return WMAP5.age(1./float(aexp)-1).value
def give_angular_size(z,length,unit): if len(length) == len(unit): out = [cosmo.arcsec_per_kpc_proper(z)*(length[x]*unit[x]).to(u.kpc) for x in range(len(length))] else: out = None return out
def vpf(dat_dir, Nsph, simul_cosmo, rad):
# Grab the data coordinates
gals = h5_arr(
"./dat/out/{0}/{1}/gals_cart_coords.hdf5".format(dat_dir, simul_cosmo),
"cart_pts")
# Get details about the redshift interval being considered
nbar_dict = json.load(
open("./dat/out/{0}/{1}/nbar_zrange.json".format(dat_dir,
simul_cosmo)))
zlo = nbar_dict["zlo"]
zhi = nbar_dict["zhi"]
# Get the search points
good_pts = h5_arr("./dat/out/{0}/srch_radec.hdf5".format(dat_dir),
"good_pts")
bad_pts = h5_arr("./dat/out/{0}/veto.hdf5".format(dat_dir), "bad_pts")
# Set angular radius of effective area around bad points
bad_r = np.arccos(1.0 - (np.pi * 9.8544099e-05) / (2 * 180**2))
bad_r_deg = np.rad2deg(bad_r)
# Set the cosmology with h free
# Here the cosmology is based on WMAP (for first MultiDark simulation)
if simul_cosmo == "Planck":
# First make h free
Planck13.__init__(100.0, Planck13.Om0)
cosmo = Planck13
elif simul_cosmo == "WMAP":
WMAP5.__init__(100.0, WMAP5.Om0)
cosmo = WMAP5
comv = cosmo.comoving_distance
# Build the trees
# galaxy tree
gal_baum = cKDTree(gals)
# tree of bad points (angular coordinates on unit sphere)
bad_xyz = radec2xyz(bad_pts)
veto_baum = cKDTree(bad_xyz)
# Initialise final output arrays
# rad = np.arange(1.0, 67.0, 5.0) doing it one radius at a time
# P_0 = np.zeros(rad.shape)
# No. of spheres and norm
# Nsph_arr = Nsph * np.array(4 * [0.01] + 4 * [0.1] + 4 * [1.0])
# norm = 1. / Nsph_arr
# norm = 1. / Nsph
rand_i = 0
for r_i, r in enumerate(rad):
# start the count of successful voids
count = 0
# Custom zrange for sphere size
dis_near = Distance(comv(zlo).value + r, u.Mpc)
dis_far = Distance(comv(zhi).value - r, u.Mpc)
z_a = dis_near.compute_z(cosmology=cosmo)
z_b = dis_far.compute_z(cosmology=cosmo)
for i in range(Nsph): # _arr[r_i]):
# compensate for finite length of mask file
rand_i = rand_i % 999999
radec = good_pts[rand_i, :]
rang = Angle(radec[0], u.deg)
decang = Angle(radec[1], u.deg)
randz = (z_a ** 3 + \
(z_b ** 3 - z_a ** 3) * np.random.rand(1)[0]) ** (1. / 3.)
dis = Distance(comv(randz), u.Mpc)
coord = ICRSCoordinates(rang, decang, distance=dis)
sph_cen = np.array([coord.x.value, coord.y.value, coord.z.value])
nn = gal_baum.query(sph_cen)
print "rad: ", r, ", sphere: ", i
if not nn[0] < r:
# add instance to probability count
count += 1
# record quality of sphere using spline values for intersection
# with bad points
# Get radius of circular projection of sphere
R = np.arcsin(r / np.sqrt(np.sum(sph_cen[:]**2)))
# Get coordinates of circle centre on unit sphere
crc_cen = radec2xyz(radec)[0]
# Compute tree search radius from Cosine rule
# (include points extending beyond sphere edge to account for
# finite area around bad points)
l_srch = np.sqrt(2. - 2. * np.cos(R))
# Run search
pierce_l = veto_baum.query_ball_point(crc_cen, l_srch)
bad_vol = 0.
R = np.degrees(R) # need in degrees for bad_vol computation
for pt in pierce_l:
pt_ang = bad_pts[pt]
dis = np.degrees(central_angle(pt_ang, radec))
l = dis / R
bad_vol += 1.5 * (bad_r_deg / R) ** 2 \
* np.sqrt(1.0 - l ** 2)
f_r = open(
"./dat/out/{0}/{1}/vpf_out/volfrac_{2}.dat".format(
dat_dir, simul_cosmo, r), 'a')
f_r.write("{0}\n".format(bad_vol))
f_r.close()
rand_i += 1
print match
print "x distance: ", halo["x"] - match["x"]
print "y distance: ", halo["y"] - match["y"]
print "z distance: ", halo["z"] - match["z"]
shared_particles = list(
set(map(itemgetter(0), particles_parent[phalo]))
& set(map(itemgetter(0), particles_child[match["id"]]))
)
print "shared particles: ", len(shared_particles)
print "mass ratio: ", float(match["M_hc"]) / float(
halos["M_hc"])
# print "Checking cluster,", parent_aexp, phalo
#determine time difference between the two epochs in seconds
t1 = WMAP5.age(1. / parent_aexp - 1.).value
t2 = WMAP5.age(1. / child_aexp - 1.).value
dt = math.fabs(t1 - t2) * 1.0e9 * 3.15569e7 #convert to seconds
#search cube determined by parent halo velocity + parent halo radius
pos = np.array([halo["x"], halo["y"], halo["z"]])
vel = np.array([halo["vx"], halo["vy"], halo["vz"]])
searchbox = mt.define_searchbox(
pos,
vel,
dt,
halo["r_hc"],
search_distance=search_distance_multiplier)
print searchbox
chalos = mt.get_halos_within_distance(child_aexp,
