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 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_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 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 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 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 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)))
import sys
from glob import glob
import numpy as np
from dat.h5_funcs import h5_arr
survey_cap = "CMASS/NGC"
cosmo = "WMAP"
spheresfile = "dat/out/{0}/{1}/mocks/mock_srch_pts.hdf5".format(survey_cap, cosmo)
if len(sys.argv) != 2:
print "usage: rdz2ascii.py <mock no.: int>"
digits = len(sys.argv[1])
i = 0
name = ""
while i < (4 - digits):
name += "0"
i += 1
name += sys.argv[1]
mock_rdzs = glob("dat/out/{0}/{1}/mocks/rdz_down/{2}.hdf5".format(survey_cap, cosmo, name))
for i in range(len(mock_rdzs)):
mock_rdz = h5_arr(mock_rdzs[i], "radecz")
np.savetxt("dat/out/{0}/{1}/mocks/rdz_down/ascii/{2}.dat".format(survey_cap, cosmo, name), mock_rdz)
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
z_far = np.arange((0.5 * (z_near[0] + z_near[1])), z_far[-1] + 0.5 * dzf, 0.5 * dzf)
zcen = 0.5 * (z_far + z_near)
zbinedges = np.append(z_near[0], z_far[:])
print "\nthen\n"
print "zcen: ", zcen
print "z_near: ", z_near
print "z_far: ", z_far
print "zbinedges: ", zbinedges
shell_vols = []
for i in range(len(zcen)):
shell_vols.append(Nfrac * calc_shell_vol(comv, z_near[i], z_far[i], zcen[i]))
shell_vols = np.array(shell_vols)
dat_rdz = h5_arr("dat/out/CMASS/NGC/WMAP/radecz_down.hdf5", "radecz")
dat_hist = np.histogram(dat_rdz[:, 2], bins=zbinedges)[0]
dat_nbar = dat_hist / shell_vols
mock_nbars = []
mock_fns = glob("dat/out/CMASS/NGC/WMAP/mocks/rdz_down/*")
for mock in mock_fns:
radecz = h5_arr(mock, "radecz")
H = np.histogram(radecz[:, 2], bins=zbinedges)
mock_nbars.append(H[0] / shell_vols)
zcen = 0.5 * (z_far + z_near)
zbinedges = np.append(z_near[0], z_far[:])
print "\nthen\n"
print "zcen: ", zcen
print "z_near: ", z_near
print "z_far: ", z_far
print "zbinedges: ", zbinedges
shell_vols = []
for i in range(len(zcen)):
shell_vols.append(Nfrac *
calc_shell_vol(comv, z_near[i], z_far[i], zcen[i]))
shell_vols = np.array(shell_vols)
dat_rdz = h5_arr("dat/out/CMASS/NGC/WMAP/radecz_down.hdf5", "radecz")
dat_hist = np.histogram(dat_rdz[:, 2], bins=zbinedges)[0]
dat_nbar = dat_hist / shell_vols
mock_nbars = []
mock_fns = glob("dat/out/CMASS/NGC/WMAP/mocks/rdz_down/*")
for mock in mock_fns:
radecz = h5_arr(mock, "radecz")
H = np.histogram(radecz[:, 2], bins=zbinedges)
mock_nbars.append(H[0] / shell_vols)
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