def test_subvol_gmf(Mr):
"""
"""
thr = -1.0 * np.float(Mr)
model = PrebuiltHodModelFactory("zheng07", threshold=thr, halocat="multidark", redshift=0.0)
model.new_haloprop_func_dict = {"sim_subvol": util.mk_id_column}
datsubvol = lambda x: util.mask_func(x, 0)
model.populate_mock(simname="multidark", masking_function=datsubvol, enforce_PBC=False)
# compute group richness
# These are wrong because they assume periodicity
# rich = richness(model.mock.compute_fof_group_ids())
# gmf = GMF(rich) #GMF
# print gmf
# print GMF(rich , counts = True)
galaxy_sample = model.mock.galaxy_table
x = galaxy_sample["x"]
y = galaxy_sample["y"]
z = galaxy_sample["z"]
vz = galaxy_sample["vz"]
pos = three_dim_pos_bundle(model.mock.galaxy_table, "x", "y", "z", velocity=vz, velocity_distortion_dimension="z")
b_para, b_perp = 0.2, 0.2
groups = FoFGroups(pos, b_perp, b_para, period=None, Lbox=200, num_threads="max")
gids = groups.group_ids
rich = richness(gids)
gmf = GMF(rich)
print gmf
print GMF(rich, counts=True)
return None
def test_GMFbinning(Mr):
""" Tests for the GMF binning scheme in order to make it sensible.
"""
gids_saved = "gids_saved.p"
if not os.path.isfile(gids_saved):
thr = -1.0 * np.float(Mr)
model = PrebuiltHodModelFactory("zheng07", threshold=thr, halocat="multidark", redshift=0.0)
model.new_haloprop_func_dict = {"sim_subvol": util.mk_id_column}
datsubvol = lambda x: util.mask_func(x, 0)
model.populate_mock(simname="multidark", masking_function=datsubvol, enforce_PBC=False)
galaxy_sample = model.mock.galaxy_table
x = galaxy_sample["x"]
y = galaxy_sample["y"]
z = galaxy_sample["z"]
vz = galaxy_sample["vz"]
pos = three_dim_pos_bundle(
model.mock.galaxy_table, "x", "y", "z", velocity=vz, velocity_distortion_dimension="z"
)
b_para, b_perp = 0.2, 0.2
groups = FoFGroups(pos, b_perp, b_para, period=None, Lbox=200, num_threads="max")
pickle.dump(groups, open(gids_saved, "wb"))
else:
groups = pickle.load(open(gids_saved, "rb"))
gids = groups.group_ids
rich = richness(gids)
# print "rich=" , rich
rbins = np.logspace(np.log10(3.0), np.log10(20), 10)
rbins = np.array([1, 2.0, 3.0, 4.0, 5.0, 6.0, 7, 9, 11, 14, 17, 20])
gmf = GMF(rich, counts=False, bins=rbins)
gmf_counts = GMF(rich, counts=True, bins=rbins)
print rbins
print gmf
print gmf_counts
fig = plt.figure(1)
sub = fig.add_subplot(111)
sub.plot(0.5 * (rbins[:-1] + rbins[1:]), gmf)
sub.set_xscale("log")
sub.set_yscale("log")
plt.show()
# print gmf
# print GMF(rich , counts = True)
return None
def test_subvol_gmf(Mr):
'''
'''
thr = -1. * np.float(Mr)
model = PrebuiltHodModelFactory('zheng07',
threshold=thr,
halocat='multidark',
redshift=0.)
model.new_haloprop_func_dict = {'sim_subvol': util.mk_id_column}
datsubvol = lambda x: util.mask_func(x, 0)
model.populate_mock(simname='multidark',
masking_function=datsubvol,
enforce_PBC=False)
#compute group richness
# These are wrong because they assume periodicity
#rich = richness(model.mock.compute_fof_group_ids())
#gmf = GMF(rich) #GMF
#print gmf
#print GMF(rich , counts = True)
galaxy_sample = model.mock.galaxy_table
x = galaxy_sample['x']
y = galaxy_sample['y']
z = galaxy_sample['z']
vz = galaxy_sample['vz']
pos = three_dim_pos_bundle(model.mock.galaxy_table,
'x',
'y',
'z',
velocity=vz,
velocity_distortion_dimension="z")
b_para, b_perp = 0.2, 0.2
groups = FoFGroups(pos,
b_perp,
b_para,
period=None,
Lbox=200,
num_threads='max')
gids = groups.group_ids
rich = richness(gids)
gmf = GMF(rich)
print gmf
print GMF(rich, counts=True)
return None
def build_xi_nbar_gmf(Mr=21):
'''
Build "data" xi, nbar, GMF values and write to file
'''
model = PrebuiltHodModelFactory('zheng07', threshold = -1.0*np.float(Mr))
model.populate_mock(halocat = halocat , enforce_PBC = False) # population mock realization
pos = three_dim_pos_bundle(model.mock.galaxy_table, 'x', 'y', 'z')
# write xi
rbins = hardcoded_xi_bins()
rmax = rbins.max()
approx_cell1_size = [rmax, rmax, rmax]
approx_cell2_size = approx_cell1_size
approx_cellran_size = [rmax, rmax, rmax]
period = np.array([Lbox , Lbox , Lbox])
data_xir = tpcf(
sample1, rbins, sample2 = sample2,
randoms=randoms, period = period,
max_sample_size=int(1e4), estimator='Landy-Szalay',
approx_cell1_size=approx_cell1_size,
approx_cellran_size=approx_cellran_size,
RR_precomputed = RR,
NR_precomputed = NR1)
output_file = ''.join([util.dat_dir(), 'xir.Mr', str(Mr), '.dat'])
np.savetxt(output_file, data_xir)
# write nbar values
nbar = model.mock.number_density
output_file = ''.join([util.dat_dir(), 'nbar.Mr', str(Mr), '.dat'])
np.savetxt(output_file, [nbar])
# write GMF
rich = richness(model.mock.compute_fof_group_ids())
gmf = GMF(rich) # GMF
output_file = ''.join([util.dat_dir(), 'gmf.Mr', str(Mr), '.dat'])
np.savetxt(output_file, gmf)
return None
def test_GMFbinning(Mr):
''' Tests for the GMF binning scheme in order to make it sensible.
'''
gids_saved = 'gids_saved.p'
if not os.path.isfile(gids_saved):
thr = -1. * np.float(Mr)
model = PrebuiltHodModelFactory('zheng07',
threshold=thr,
halocat='multidark',
redshift=0.)
model.new_haloprop_func_dict = {'sim_subvol': util.mk_id_column}
datsubvol = lambda x: util.mask_func(x, 0)
model.populate_mock(simname='multidark',
masking_function=datsubvol,
enforce_PBC=False)
galaxy_sample = model.mock.galaxy_table
x = galaxy_sample['x']
y = galaxy_sample['y']
z = galaxy_sample['z']
vz = galaxy_sample['vz']
pos = three_dim_pos_bundle(model.mock.galaxy_table,
'x',
'y',
'z',
velocity=vz,
velocity_distortion_dimension="z")
b_para, b_perp = 0.2, 0.2
groups = FoFGroups(pos,
b_perp,
b_para,
period=None,
Lbox=200,
num_threads='max')
pickle.dump(groups, open(gids_saved, 'wb'))
else:
groups = pickle.load(open(gids_saved, 'rb'))
gids = groups.group_ids
rich = richness(gids)
#print "rich=" , rich
rbins = np.logspace(np.log10(3.), np.log10(20), 10)
rbins = np.array([1, 2., 3., 4., 5., 6., 7, 9, 11, 14, 17, 20])
gmf = GMF(rich, counts=False, bins=rbins)
gmf_counts = GMF(rich, counts=True, bins=rbins)
print rbins
print gmf
print gmf_counts
fig = plt.figure(1)
sub = fig.add_subplot(111)
sub.plot(0.5 * (rbins[:-1] + rbins[1:]), gmf)
sub.set_xscale('log')
sub.set_yscale('log')
plt.show()
#print gmf
#print GMF(rich , counts = True)
return None