def build_randoms_RR(Nr=1e6, box='md_sub'):
'''
builds Nr number of random points using numpy.random.uniform
in a subvolume and precomputes the RR.
The set of randoms and precomputed RR will be used to compute tpcf
with Landay-Szalay estimator in each subvolume.
Parameters
----------
Nr : (int)
Number of random points
box : (string)
'md_sub' specifies multidark subvolume box (L_box = 200).
'md_all' specifies multidark entire box (L_box = 1000).
'''
if box == 'md_sub':
L = 200 #this is L_md / 5
if Nr < 5e5:
raise ValueError('Too few number of randoms')
elif box == 'md_all':
L = 1000
if Nr < 2.5e6:
raise ValueError('Too few number of randoms')
xmin , ymin , zmin = 0., 0., 0.
xmax , ymax , zmax = L, L, L
num_randoms = Nr
xran = np.random.uniform(xmin, xmax, num_randoms)
yran = np.random.uniform(ymin, ymax, num_randoms)
zran = np.random.uniform(zmin, zmax, num_randoms)
randoms = np.vstack((xran, yran, zran)).T
verbose = False
period = None
num_threads = cpu_count()
rbins = xi_binedges()
rmax = rbins.max()
approx_cellran_size = [rmax, rmax, rmax]
RR = npairs_3d(
randoms, randoms, rbins, period,
verbose, num_threads,
approx_cellran_size, approx_cellran_size)
RR = np.diff(RR)
output_file_random = ''.join([util.obvs_dir(),
'randoms', '.', box, '.dat'])
np.savetxt(output_file_random,
randoms)
output_file_RR = ''.join([util.obvs_dir(),
'RR', '.', box, '.dat'])
np.savetxt(output_file_RR, RR)
return None
def build_randoms_RR(Nr=1e6, box='md_sub'):
'''
builds Nr number of random points using numpy.random.uniform
in a subvolume and precomputes the RR.
The set of randoms and precomputed RR will be used to compute tpcf
with Landay-Szalay estimator in each subvolume.
Parameters
----------
Nr : (int)
Number of random points
box : (string)
'md_sub' specifies multidark subvolume box (L_box = 200).
'md_all' specifies multidark entire box (L_box = 1000).
'''
if box == 'md_sub':
L = 200 #this is L_md / 5
if Nr < 5e5:
raise ValueError('Too few number of randoms')
elif box == 'md_all':
L = 1000
if Nr < 2.5e6:
raise ValueError('Too few number of randoms')
xmin , ymin , zmin = 0., 0., 0.
xmax , ymax , zmax = L, L, L
num_randoms = Nr
xran = np.random.uniform(xmin, xmax, num_randoms)
yran = np.random.uniform(ymin, ymax, num_randoms)
zran = np.random.uniform(zmin, zmax, num_randoms)
randoms = np.vstack((xran, yran, zran)).T
verbose = False
period = None
num_threads = cpu_count()
rbins = xi_binedges()
rmax = rbins.max()
approx_cellran_size = [rmax, rmax, rmax]
RR = npairs_3d(
randoms, randoms, rbins, period,
verbose, num_threads,
approx_cellran_size, approx_cellran_size)
RR = np.diff(RR)
output_file_random = ''.join([util.obvs_dir(),
'randoms', '.', box, '.dat'])
np.savetxt(output_file_random,
randoms)
output_file_RR = ''.join([util.obvs_dir(),
'RR', '.', box, '.dat'])
np.savetxt(output_file_RR, RR)
return None
def build_xi_bins(Mr=21):
''' Build hardcoded r_bin centers for xi and then save to file.
'''
rbins = xi_binedges()
rbin = .5 * (rbins[1:] + rbins[:-1])
output_file = ''.join([util.obvs_dir(),'xir_rbin.Mr', str(Mr),'.dat'])
np.savetxt(output_file, rbin)
return None
def build_xi_bins(Mr=21):
''' Build hardcoded r_bin centers for xi and then save to file.
'''
rbins = xi_binedges()
rbin = .5 * (rbins[1:] + rbins[:-1])
output_file = ''.join([util.obvs_dir(), 'xir_rbin.Mr', str(Mr), '.dat'])
np.savetxt(output_file, rbin)
return None
def data_cov(Mr=21, b_normal=0.25, inference='abc'):
''' Load the entire covariance matrix with sample variance,
Note: this is for MCMC
'''
if inference == 'abc':
inf_str = 'ABC'
elif inference == 'mcmc':
inf_str = 'MCMC'
cov_fn = ''.join([
util.obvs_dir(), inf_str, '.nbar_xi_gmf_cov', '.no_poisson', '.Mr',
str(Mr), '.bnorm',
str(round(b_normal, 2)), '.dat'
])
cov = np.loadtxt(cov_fn)
return cov
def data_file(Mr=21, b_normal=0.25):
''' Data vector file
Parameters
----------
Mr : (int)
Absolute magnitude M_r cutoff. Default is M_r = -21
b_normal : (float)
FoF linking length parameter used for the GMF.
'''
data_file = ''.join([
util.obvs_dir(), 'data_vector', '.Mr',
str(Mr), '.bnorm',
str(round(b_normal, 2)), '.dat'
])
return data_file
def data_file(Mr=21, b_normal=0.25):
''' Data vector file
Parameters
----------
Mr : (int)
Absolute magnitude M_r cutoff. Default is M_r = -21
b_normal : (float)
FoF linking length parameter used for the GMF.
'''
data_file = ''.join([util.obvs_dir(),
'data_vector',
'.Mr', str(Mr),
'.bnorm', str(round(b_normal, 2)),
'.dat'])
return data_file
def data_cov(Mr=21, b_normal=0.25, inference='abc'):
''' Load the entire covariance matrix with sample variance,
Note: this is for MCMC
'''
if inference == 'abc':
inf_str = 'ABC'
elif inference == 'mcmc':
inf_str = 'MCMC'
cov_fn = ''.join([util.obvs_dir(),
inf_str,
'.nbar_xi_gmf_cov',
'.no_poisson',
'.Mr', str(Mr),
'.bnorm', str(round(b_normal, 2)),
'.dat'])
cov = np.loadtxt(cov_fn)
return cov
def data_random(box='md_sub'):
''' Load pregenerated random points
'''
random_file = ''.join([util.obvs_dir(), 'randoms', '.', box, '.dat'])
randoms = np.loadtxt(random_file)
return randoms
def data_xi_bin(Mr=21):
''' Load the r bins of the xi measurements. These are _not_ the bin edges, but
the center of the bins.
'''
xi_bin_file = ''.join([util.obvs_dir(), 'xir_rbin.Mr', str(Mr), '.dat'])
return np.loadtxt(xi_bin_file)
def build_ABC_cov_nbar_xi_gmf(Mr=21, b_normal=0.25):
''' Build covariance matrix used in ABC for the full nbar, xi, gmf data vector
using realisations of galaxy mocks for "data" HOD parameters in the
halos from the multidark simulation. Covariance matrices for different sets of observables
can be extracted from the full covariance matrix by slicing through
the indices.
Notes
-----
* This covariance matrix is the covariance matrix calculated from the *entire* multidark
box. So this does _not_ account for the sample variance, which the MCMC covariance does.
'''
nbars, xir, gmfs = [], [], []
thr = -1. * np.float(Mr)
model = PrebuiltHodModelFactory('zheng07', threshold=thr)
halocat = CachedHaloCatalog(simname='multidark',
redshift=0,
halo_finder='rockstar')
rbins = xi_binedges() # some setting for tpcf calculations
rmax = rbins.max()
approx_cell1_size = [rmax, rmax, rmax]
approx_cellran_size = [rmax, rmax, rmax]
# load randoms and RRs for the ENTIRE MultiDark volume
###randoms = data_random(box='md_all')
###RR = data_RR(box='md_all')
###NR = len(randoms)
for i in xrange(1, 125):
print 'mock#', i
# populate the mock subvolume
model.populate_mock(halocat)
# returning the positions of galaxies
pos = three_dim_pos_bundle(model.mock.galaxy_table, 'x', 'y', 'z')
# calculate nbar
nbars.append(len(pos) / 1000**3.)
# calculate xi(r) for the ENTIRE MultiDark volume
# using the natural estimator DD/RR - 1
xi = tpcf(pos,
rbins,
period=model.mock.Lbox,
max_sample_size=int(3e5),
estimator='Natural',
approx_cell1_size=approx_cell1_size)
xir.append(xi)
# calculate gmf
nbar = len(pos) / 1000**3.
b = b_normal * (nbar)**(-1. / 3)
groups = pyfof.friends_of_friends(pos, b)
w = np.array([len(x) for x in groups])
gbins = gmf_bins()
gmf = np.histogram(w, gbins)[0] / (1000.**3.)
gmfs.append(gmf) # GMF
# save nbar variance
nbar_var = np.var(nbars, axis=0, ddof=1)
nbar_file = ''.join([util.obvs_dir(), 'abc_nbar_var.Mr', str(Mr), '.dat'])
np.savetxt(nbar_file, [nbar_var])
# write full covariance matrix of various combinations of the data
# and invert for the likelihood evaluations
# --- covariance for all three ---
fulldatarr = np.hstack(
(np.array(nbars).reshape(len(nbars),
1), np.array(xir), np.array(gmfs)))
fullcov = np.cov(fulldatarr.T)
fullcorr = np.corrcoef(fulldatarr.T)
# and save the covariance matrix
nopoisson_file = ''.join([
util.obvs_dir(), 'ABC.nbar_xi_gmf_cov', '.no_poisson', '.Mr',
str(Mr), '.bnorm',
str(round(b_normal, 2)), '.dat'
])
np.savetxt(nopoisson_file, fullcov)
return None
def build_MCMC_cov_nbar_xi_gmf(Mr=21, b_normal=0.25):
''' Build covariance matrix used in MCMC for the full nbar, xi, gmf data vector
using realisations of galaxy mocks for "data" HOD parameters in the
halos from the other subvolumes (subvolume 1 to subvolume 125) of
the simulation. Covariance matrices for different sets of observables
can be extracted from the full covariance matrix by slicing through
the indices.
'''
nbars = []
xir = []
gmfs = []
thr = -1. * np.float(Mr)
model = PrebuiltHodModelFactory('zheng07', threshold=thr)
halocat = CachedHaloCatalog(simname='multidark',
redshift=0,
halo_finder='rockstar')
###model.new_haloprop_func_dict = {'sim_subvol': util.mk_id_column}
#some settings for tpcf calculations
rbins = xi_binedges()
rmax = rbins.max()
approx_cell1_size = [rmax, rmax, rmax]
approx_cellran_size = [rmax, rmax, rmax]
#load randoms and RRs
randoms = data_random(box='md_sub')
RR = data_RR(box='md_sub')
NR = len(randoms)
for i in xrange(1, 125):
print 'mock#', i
# populate the mock subvolume
###mocksubvol = lambda x: util.mask_func(x, i)
###model.populate_mock(halocat,
### masking_function=mocksubvol,
### enforce_PBC=False)
model.populate_mock(halocat)
# returning the positions of galaxies in the entire volume
pos = three_dim_pos_bundle(model.mock.galaxy_table, 'x', 'y', 'z')
# masking out the galaxies outside the subvolume i
pos = util.mask_galaxy_table(pos, i)
# calculate nbar
print "shape of pos", pos.shape
nbars.append(len(pos) / 200**3.)
# translate the positions of randoms to the new subbox
xi0, yi0, zi0 = util.random_shifter(i)
temp_randoms = randoms.copy()
temp_randoms[:, 0] += xi0
temp_randoms[:, 1] += yi0
temp_randoms[:, 2] += zi0
#calculate xi(r)
xi = tpcf(pos,
rbins,
pos,
randoms=temp_randoms,
period=None,
max_sample_size=int(3e5),
estimator='Natural',
approx_cell1_size=approx_cell1_size,
approx_cellran_size=approx_cellran_size,
RR_precomputed=RR,
NR_precomputed=NR)
xir.append(xi)
# calculate gmf
nbar = len(pos) / 200**3.
b = b_normal * (nbar)**(-1. / 3)
groups = pyfof.friends_of_friends(pos, b)
w = np.array([len(x) for x in groups])
gbins = gmf_bins()
gmf = np.histogram(w, gbins)[0] / 200.**3.
gmfs.append(gmf)
# save nbar variance
nbar_var = np.var(nbars, axis=0, ddof=1)
nbar_file = ''.join([util.obvs_dir(), 'nbar_var.Mr', str(Mr), '.dat'])
np.savetxt(nbar_file, [nbar_var])
# write full covariance matrix of various combinations of the data
# and invert for the likelihood evaluations
# --- covariance for all three ---
fulldatarr = np.hstack(
(np.array(nbars).reshape(len(nbars),
1), np.array(xir), np.array(gmfs)))
fullcov = np.cov(fulldatarr.T)
fullcorr = np.corrcoef(fulldatarr.T)
# and save the covariance matrix
nopoisson_file = ''.join([
util.obvs_dir(), 'MCMC.nbar_xi_gmf_cov', '.no_poisson', '.Mr',
str(Mr), '.bnorm',
str(round(b_normal, 2)), '.dat'
])
np.savetxt(nopoisson_file, fullcov)
return None
def build_MCMC_cov_nbar_xi_gmf(Mr=21, b_normal=0.25):
''' Build covariance matrix used in MCMC for the full nbar, xi, gmf data vector
using realisations of galaxy mocks for "data" HOD parameters in the
halos from the other subvolumes (subvolume 1 to subvolume 125) of
the simulation. Covariance matrices for different sets of observables
can be extracted from the full covariance matrix by slicing through
the indices.
'''
nbars = []
xir = []
gmfs = []
thr = -1. * np.float(Mr)
model = PrebuiltHodModelFactory('zheng07', threshold=thr)
halocat = CachedHaloCatalog(simname = 'multidark', redshift = 0, halo_finder = 'rockstar')
###model.new_haloprop_func_dict = {'sim_subvol': util.mk_id_column}
#some settings for tpcf calculations
rbins = xi_binedges()
rmax = rbins.max()
approx_cell1_size = [rmax , rmax , rmax]
approx_cellran_size = [rmax , rmax , rmax]
#load randoms and RRs
randoms = data_random(box='md_sub')
RR = data_RR(box='md_sub')
NR = len(randoms)
for i in xrange(1,125):
print 'mock#', i
# populate the mock subvolume
###mocksubvol = lambda x: util.mask_func(x, i)
###model.populate_mock(halocat,
### masking_function=mocksubvol,
### enforce_PBC=False)
model.populate_mock(halocat)
# returning the positions of galaxies in the entire volume
pos = three_dim_pos_bundle(model.mock.galaxy_table, 'x', 'y', 'z')
# masking out the galaxies outside the subvolume i
pos = util.mask_galaxy_table(pos , i)
# calculate nbar
print "shape of pos" , pos.shape
nbars.append(len(pos) / 200**3.)
# translate the positions of randoms to the new subbox
xi0 , yi0 , zi0 = util.random_shifter(i)
temp_randoms = randoms.copy()
temp_randoms[:,0] += xi0
temp_randoms[:,1] += yi0
temp_randoms[:,2] += zi0
#calculate xi(r)
xi=tpcf(
pos, rbins, pos,
randoms=temp_randoms, period=None,
max_sample_size=int(3e5), estimator='Natural',
approx_cell1_size=approx_cell1_size,
approx_cellran_size=approx_cellran_size,
RR_precomputed = RR,
NR_precomputed = NR)
xir.append(xi)
# calculate gmf
nbar = len(pos) / 200**3.
b = b_normal * (nbar)**(-1./3)
groups = pyfof.friends_of_friends(pos , b)
w = np.array([len(x) for x in groups])
gbins = gmf_bins()
gmf = np.histogram(w , gbins)[0] / 200.**3.
gmfs.append(gmf)
# save nbar variance
nbar_var = np.var(nbars, axis=0, ddof=1)
nbar_file = ''.join([util.obvs_dir(), 'nbar_var.Mr', str(Mr), '.dat'])
np.savetxt(nbar_file, [nbar_var])
# write full covariance matrix of various combinations of the data
# and invert for the likelihood evaluations
# --- covariance for all three ---
fulldatarr = np.hstack((np.array(nbars).reshape(len(nbars), 1),
np.array(xir),
np.array(gmfs)))
fullcov = np.cov(fulldatarr.T)
fullcorr = np.corrcoef(fulldatarr.T)
# and save the covariance matrix
nopoisson_file = ''.join([util.obvs_dir(),
'MCMC.nbar_xi_gmf_cov', '.no_poisson', '.Mr', str(Mr), '.bnorm', str(round(b_normal,2)), '.dat'])
np.savetxt(nopoisson_file, fullcov)
return None
def data_RR(box='md_sub'):
''' Load precomputed RR pairs
'''
RR_file = ''.join([util.obvs_dir(), 'RR', '.', box, '.dat'])
RR = np.loadtxt(RR_file)
return RR
def data_random(box='md_sub'):
''' Load pregenerated random points
'''
random_file = ''.join([util.obvs_dir(), 'randoms', '.', box, '.dat'])
randoms = np.loadtxt(random_file)
return randoms
def data_RR(box='md_sub'):
''' Load precomputed RR pairs
'''
RR_file = ''.join([util.obvs_dir(), 'RR', '.', box, '.dat'])
RR = np.loadtxt(RR_file)
return RR
def data_xi_bin(Mr=21):
''' Load the r bins of the xi measurements. These are _not_ the bin edges, but
the center of the bins.
'''
xi_bin_file = ''.join([util.obvs_dir(),'xir_rbin.Mr', str(Mr),'.dat'])
return np.loadtxt(xi_bin_file)
def build_ABC_cov_nbar_xi_gmf(Mr=21, b_normal=0.25):
''' Build covariance matrix used in ABC for the full nbar, xi, gmf data vector
using realisations of galaxy mocks for "data" HOD parameters in the
halos from the multidark simulation. Covariance matrices for different sets of observables
can be extracted from the full covariance matrix by slicing through
the indices.
Notes
-----
* This covariance matrix is the covariance matrix calculated from the *entire* multidark
box. So this does _not_ account for the sample variance, which the MCMC covariance does.
'''
nbars, xir, gmfs = [], [], []
thr = -1. * np.float(Mr)
model = PrebuiltHodModelFactory('zheng07', threshold=thr)
halocat = CachedHaloCatalog(simname = 'multidark', redshift = 0, halo_finder = 'rockstar')
rbins = xi_binedges() # some setting for tpcf calculations
rmax = rbins.max()
approx_cell1_size = [rmax , rmax , rmax]
approx_cellran_size = [rmax , rmax , rmax]
# load randoms and RRs for the ENTIRE MultiDark volume
###randoms = data_random(box='md_all')
###RR = data_RR(box='md_all')
###NR = len(randoms)
for i in xrange(1,125):
print 'mock#', i
# populate the mock subvolume
model.populate_mock(halocat)
# returning the positions of galaxies
pos = three_dim_pos_bundle(model.mock.galaxy_table, 'x', 'y', 'z')
# calculate nbar
nbars.append(len(pos) / 1000**3.)
# calculate xi(r) for the ENTIRE MultiDark volume
# using the natural estimator DD/RR - 1
xi = tpcf(
pos, rbins, period=model.mock.Lbox,
max_sample_size=int(3e5), estimator='Natural',
approx_cell1_size=approx_cell1_size)
xir.append(xi)
# calculate gmf
nbar = len(pos) / 1000**3.
b = b_normal * (nbar)**(-1./3)
groups = pyfof.friends_of_friends(pos , b)
w = np.array([len(x) for x in groups])
gbins = gmf_bins()
gmf = np.histogram(w , gbins)[0] / (1000.**3.)
gmfs.append(gmf) # GMF
# save nbar variance
nbar_var = np.var(nbars, axis=0, ddof=1)
nbar_file = ''.join([util.obvs_dir(), 'abc_nbar_var.Mr', str(Mr), '.dat'])
np.savetxt(nbar_file, [nbar_var])
# write full covariance matrix of various combinations of the data
# and invert for the likelihood evaluations
# --- covariance for all three ---
fulldatarr = np.hstack((np.array(nbars).reshape(len(nbars), 1),
np.array(xir),
np.array(gmfs)))
fullcov = np.cov(fulldatarr.T)
fullcorr = np.corrcoef(fulldatarr.T)
# and save the covariance matrix
nopoisson_file = ''.join([util.obvs_dir(),
'ABC.nbar_xi_gmf_cov', '.no_poisson', '.Mr', str(Mr), '.bnorm', str(round(b_normal, 2)), '.dat'])
np.savetxt(nopoisson_file, fullcov)
return None
