R = np.arcsin(r / np.sqrt(np.sum(sph_cen[:] ** 2)))
# Get coordinates of circle centre on unit sphere
crc_cen = radec2xyz(sphere[:2])[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, sphere[:2]))
l = dis / R
bad_vol += 1.5 * (bad_r_deg / R) ** 2 * np.sqrt(1.0 - l ** 2)
badvols[i] = bad_vol
arr2h5(badvols,
"{0}/mock_badvols.hdf5".format(os.path.dirname(spheresfile)),
"badvols_{0}".format(str(r_i * 5 + 1)))
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()
R = np.arcsin(r / np.sqrt(np.sum(sph_cen[:]**2)))
# Get coordinates of circle centre on unit sphere
crc_cen = radec2xyz(sphere[:2])[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, sphere[:2]))
l = dis / R
bad_vol += 1.5 * (bad_r_deg / R)**2 * np.sqrt(1.0 - l**2)
badvols[i] = bad_vol
arr2h5(badvols,
"{0}/mock_badvols.hdf5".format(os.path.dirname(spheresfile)),
"badvols_{0}".format(str(r_i * 5 + 1)))
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()