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_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_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 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 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 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)))
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):
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()
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()
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 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
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
