def __init__(self, datar, randomr, nofz, universe_params=None):
#
# make sure catalogs have the desired format
#
# cosmology
if universe_params is None:
universe_params = universeparams
cosmo = Cosmology(Om0=universe_params['Om0'],
H0=universe_params['H0'],
flat=universe_params['flat'])
data = catit(datar)
randoms = catit(randomr)
data = nb.ArrayCatalog(data)
randoms = nb.ArrayCatalog(randoms)
data['Position'] = SkyToCartesian(data['RA'],
data['DEC'],
data['Z'],
cosmo=cosmo)
randoms['Position'] = SkyToCartesian(randoms['RA'],
randoms['DEC'],
randoms['Z'],
cosmo=cosmo)
#
self.data = data
self.randoms = randoms
self.nofz = nofz
def NeutHalos(mneut, nreal, nzbin):
''' Read halo catalogs generated by Paco
parameters
----------
mneut : float,
total neutrino mass
nreal : int,
realization number
nzbin : int,
integer specifying the redshift of the snapshot.
nzbin = 0 --> z=3
nzbin = 1 --> z=2
nzbin = 2 --> z=1
nzbin = 3 --> z=0.5
nzbin = 4 --> z=0
'''
if mneut == 0.1:
dir = ''.join([UT.dat_dir(), '0.10eV/', str(nreal)])
else:
dir = ''.join([UT.dat_dir(), str(mneut), 'eV/', str(nreal)])
# read in Gadget header
header = RS.read_gadget_header(''.join(
[dir, '/snapdir_',
str(nzbin).zfill(3), '/snap_',
str(nzbin).zfill(3)]))
# get cosmology from header
Omega_b = 0.049 # fixed baryon
cosmo = NBlab.cosmology.Planck15.clone(Omega_cdm=header['Omega_m'] -
Omega_b,
h=header['h'],
Omega_b=Omega_b)
Fof = readfof.FoF_catalog(dir,
nzbin,
long_ids=False,
swap=False,
SFR=False)
group_data = {}
group_data['Length'] = Fof.GroupLen
group_data['Position'] = Fof.GroupPos / 1e3
group_data['Velocity'] = Fof.GroupVel
group_data['Mass'] = Fof.GroupMass * 1e10
# calculate velocity offset
rsd_factor = (1. + header['z']) / (100. * cosmo.efunc(header['z']))
group_data['VelocityOffset'] = group_data['Velocity'] * rsd_factor
#group_data['RSDPosition'] = group_data['Position'] + group_data['VelocityOffset'] * LOS
# save to ArryCatalog for consistency
cat = NBlab.ArrayCatalog(group_data,
BoxSize=np.array([1000., 1000., 1000.]))
cat = NBlab.HaloCatalog(cat, cosmo=cosmo, redshift=header['z'], mdef='vir')
return cat
def NeutParticles(mneut, nreal, nzbin, clobber=False):
''' Read particle catalog generated by Paco and return NBlab.ArrayCatalog
parameters
----------
mneut : float,
total neutrino mass
nreal : int,
realization number
nzbin : int,
integer specifying the redshift of the snapshot.
nzbin = 0 --> z=3
nzbin = 1 --> z=2
nzbin = 2 --> z=1
nzbin = 3 --> z=0.5
nzbin = 4 --> z=0
clobber : bool, optional
if True, reconstructs the BigFile data
'''
dir = ''.join([
UT.dat_dir(),
str(mneut), 'eV/',
str(nreal), '/snapdir_',
str(nzbin).zfill(3), '/'
])
dir_list = [
dir + '/Matter' + sub for sub in ['', '/Position', '/Velocity', '/ID']
]
if (not np.all([os.path.isdir(dd) for dd in dir_list])) or clobber:
f = ''.join([dir, 'snap_', str(nzbin).zfill(3)])
# read in Gadget header
header = RS.read_gadget_header(f)
# read in CDM particles (parttype = 1) and create catalogue
particle_data = {}
particle_data['Position'] = RS.read_block(f, 'POS ',
parttype=1) / 1000. # Mpc/h
particle_data['Velocity'] = RS.read_block(f, 'VEL ', parttype=1)
particle_data['ID'] = RS.read_block(f, 'ID ', parttype=1)
cat = NBlab.ArrayCatalog(particle_data,
BoxSize=np.array([
header['boxsize'], header['boxsize'],
header['boxsize']
]))
#cat['KDDensity'] = KDDensity(cat).density
cat.save(dir + '/Matter', ["Position", "Velocity", "ID"])
else:
cat = NBlab.BigFileCatalog(dir + '/Matter', header='Header')
return cat
def QPMspectra(rsd=False):
''' calculate the powerspectrum and bispectrum of the QPM
catalog.
:param rsd: (default: False)
if True calculate in redshift space. Otherwise, real-space
'''
str_rsd = ''
if rsd: str_rsd = '.rsd'
f_halo = ''.join([dir_dat, 'halo_ascii.dat'])
f_hdf5 = ''.join([dir_dat, 'halo.mlim1e13.Lbox1050.hdf5'])
f_pell = ''.join([
dir_dat, 'pySpec.Plk.halo.mlim1e13.Lbox1050', '.Ngrid360', str_rsd,
'.dat'
])
f_pnkt = ''.join([
dir_dat, 'pySpec.Plk.halo.mlim1e13.Lbox1050', '.Ngrid360', '.nbodykit',
str_rsd, '.dat'
])
f_b123 = ''.join([
dir_dat, 'pySpec.B123.halo.mlim1e13.Lbox1050', '.Ngrid360', '.Nmax40',
'.Ncut3', '.step3', '.pyfftw', str_rsd, '.dat'
])
Lbox = 1050.
kf = 2. * np.pi / Lbox
# 1. read in ascii file
# 2. impose 10^13 halo mass limit
# 3. calculate RSD positions
# 4. write to hdf5 file
if not os.path.isfile(f_hdf5):
mh, x, y, z, vx, vy, vz = np.loadtxt(f_halo,
unpack=True,
skiprows=1,
usecols=[0, 1, 2, 3, 4, 5, 6])
xyz = np.zeros((len(x), 3))
xyz[:, 0] = x
xyz[:, 1] = y
xyz[:, 2] = z
vxyz = np.zeros((len(x), 3))
vxyz[:, 0] = vx
vxyz[:, 1] = vy
vxyz[:, 2] = vz
# RSD along the z axis
xyz_s = pySpec.applyRSD(xyz.T,
vxyz.T,
0.55,
h=0.7,
omega0_m=0.340563,
LOS='z',
Lbox=Lbox)
mlim = (mh > 1e13)
mh = mh[mlim]
xyz = xyz[mlim, :]
vxyz = vxyz[mlim, :]
xyz_s = xyz_s.T[mlim, :]
f = h5py.File(f_hdf5, 'w')
f.create_dataset('xyz', data=xyz)
f.create_dataset('vxyz', data=vxyz)
f.create_dataset('xyz_s', data=xyz_s)
f.create_dataset('mhalo', data=mh)
f.close()
else:
f = h5py.File(f_hdf5, 'r')
xyz = f['xyz'].value
xyz_s = f['xyz_s'].value
vxyz = f['vxyz'].value
mh = f['mhalo'].value
Nhalo = xyz.shape[0]
print('# halos = %i in %.1f box' % (Nhalo, Lbox))
nhalo = float(Nhalo) / Lbox**3
print('number density = %f' % nhalo)
print('1/nbar = %f' % (1. / nhalo))
# calculate powerspectrum
if not os.path.isfile(f_pell):
# calculate powerspectrum monopole
if not rsd:
spec = pySpec.Pk_periodic(xyz.T,
Lbox=Lbox,
Ngrid=360,
silent=False)
else:
spec = pySpec.Pk_periodic(xyz_s.T,
Lbox=Lbox,
Ngrid=360,
silent=False)
k = spec['k']
p0k = spec['p0k']
cnts = spec['counts']
# save to file
hdr = (
'pyspectrum P_l=0(k) calculation. Lbox=%.1f, k_f=%.5e, SN=%.5e' %
(Lbox, kf, 1. / nhalo))
np.savetxt(f_pell,
np.array([k, p0k, cnts]).T,
fmt='%.5e %.5e %.5e',
delimiter='\t',
header=hdr)
else:
k, p0k, cnts = np.loadtxt(f_pell,
skiprows=1,
unpack=True,
usecols=[0, 1, 2])
# calculate P(k) using nbodykit for santiy check
if not os.path.isfile(f_pnkt):
# get cosmology from header
Omega_m = 0.3175
Omega_b = 0.049 # fixed baryon
h = 0.6711
cosmo = NBlab.cosmology.Planck15.clone(Omega_cdm=Omega_m - Omega_b,
h=h,
Omega_b=Omega_b)
halo_data = {}
if not rsd: halo_data['Position'] = xyz
else: halo_data['Position'] = xyz_s
halo_data['Velocity'] = vxyz
halo_data['Mass'] = mh
print("putting it into array catalog")
halos = NBlab.ArrayCatalog(halo_data,
BoxSize=np.array([Lbox, Lbox, Lbox]))
print("putting it into halo catalog")
halos = NBlab.HaloCatalog(halos, cosmo=cosmo, redshift=0., mdef='vir')
print("putting it into mesh")
mesh = halos.to_mesh(window='tsc',
Nmesh=360,
compensated=True,
position='Position')
print("calculating powerspectrum")
r = NBlab.FFTPower(mesh, mode='1d', dk=kf, kmin=kf, poles=[0, 2, 4])
poles = r.poles
plk = {'k': poles['k']}
for ell in [0, 2, 4]:
P = (poles['power_%d' % ell].real)
if ell == 0:
P = P - poles.attrs[
'shotnoise'] # subtract shotnoise from monopole
plk['p%dk' % ell] = P
plk['shotnoise'] = poles.attrs['shotnoise'] # save shot noise term
# header
hdr = 'pyspectrum P_l(k) calculation. k_f = 2pi/%.1f; P_shotnoise %f' % (
Lbox, plk['shotnoise'])
# write to file
np.savetxt(f_pnkt,
np.array([plk['k'], plk['p0k'], plk['p2k'], plk['p4k']]).T,
header=hdr)
else:
_k, _p0k, _p2k, _p4k = np.loadtxt(f_pnkt,
skiprows=1,
unpack=True,
usecols=[0, 1, 2, 3])
plk = {}
plk['k'] = _k
plk['p0k'] = _p0k
plk['p2k'] = _p2k
plk['p4k'] = _p4k
# calculate bispectrum
if not os.path.isfile(f_b123):
# calculate bispectrum
if not rsd:
bispec = pySpec.Bk_periodic(xyz.T,
Lbox=Lbox,
Ngrid=360,
Nmax=40,
Ncut=3,
step=3,
fft='pyfftw',
nthreads=1,
silent=False)
else:
bispec = pySpec.Bk_periodic(xyz_s.T,
Lbox=Lbox,
Ngrid=360,
Nmax=40,
Ncut=3,
step=3,
fft='pyfftw',
nthreads=1,
silent=False)
i_k = bispec['i_k1']
j_k = bispec['i_k2']
l_k = bispec['i_k3']
p0k1 = bispec['p0k1']
p0k2 = bispec['p0k2']
p0k3 = bispec['p0k3']
b123 = bispec['b123']
b123_sn = bispec['b123_sn']
q123 = bispec['q123']
counts = bispec['counts']
# save to file
hdr = 'pyspectrum bispectrum calculation test. k_f = 2pi/%.1f' % Lbox
np.savetxt(f_b123,
np.array([
i_k, j_k, l_k, p0k1, p0k2, p0k3, b123, q123, counts,
b123_sn
]).T,
fmt='%i %i %i %.5e %.5e %.5e %.5e %.5e %.5e %.5e',
delimiter='\t',
header=hdr)
else:
i_k, j_k, l_k, p0k1, p0k2, p0k3, b123, q123, counts, b123_sn = np.loadtxt(
f_b123, skiprows=1, unpack=True, usecols=range(10))
# plot powerspecrtrum shape triangle plot
fig = plt.figure(figsize=(5, 5))
sub = fig.add_subplot(111)
sub.plot(k, p0k, c='k', lw=1, label='pySpectrum')
sub.plot(plk['k'], plk['p0k'], c='C1', lw=1, label='nbodykit')
sub.plot(i_k * kf, p0k1, c='k', lw=1, ls='--', label='bispectrum code')
sub.legend(loc='lower left', fontsize=20)
sub.set_ylabel('$P_0(k)$', fontsize=25)
#sub.set_ylim([1e2, 3e4])
sub.set_yscale('log')
sub.set_xlabel('$k$', fontsize=25)
sub.set_xlim([3e-3, 1.])
sub.set_xscale('log')
fig.savefig(''.join([dir_dat, 'qpm_p0k', str_rsd, '.png']),
bbox_inches='tight')
# plot bispectrum shape triangle plot
nbin = 31
x_bins = np.linspace(0., 1., nbin + 1)
y_bins = np.linspace(0.5, 1., (nbin // 2) + 1)
fig = plt.figure(figsize=(10, 5))
sub = fig.add_subplot(111)
Bgrid = Plots._BorQgrid(
l_k.astype(float) / i_k.astype(float),
j_k.astype(float) / i_k.astype(float), q123, counts, x_bins, y_bins)
bplot = plt.pcolormesh(x_bins,
y_bins,
Bgrid.T,
vmin=0,
vmax=1,
cmap='RdBu')
cbar = plt.colorbar(bplot, orientation='vertical')
sub.set_title(r'$Q(k_1, k_2, k_3)$ QPM halo catalog', fontsize=25)
sub.set_xlabel('$k_3/k_1$', fontsize=25)
sub.set_ylabel('$k_2/k_1$', fontsize=25)
fig.savefig(''.join([dir_dat, 'qpm_Q123_shape', str_rsd, '.png']),
bbox_inches='tight')
fig = plt.figure(figsize=(10, 5))
sub = fig.add_subplot(111)
Bgrid = Plots._BorQgrid(
l_k.astype(float) / i_k.astype(float),
j_k.astype(float) / i_k.astype(float), b123, counts, x_bins, y_bins)
bplot = plt.pcolormesh(x_bins,
y_bins,
Bgrid.T,
norm=LogNorm(vmin=1e6, vmax=1e8),
cmap='RdBu')
cbar = plt.colorbar(bplot, orientation='vertical')
sub.set_title(r'$B(k_1, k_2, k_3)$ QPM halo catalog', fontsize=25)
sub.set_xlabel('$k_3/k_1$', fontsize=25)
sub.set_ylabel('$k_2/k_1$', fontsize=25)
fig.savefig(''.join([dir_dat, 'qpm_B123_shape', str_rsd, '.png']),
bbox_inches='tight')
# plot bispectrum amplitude
fig = plt.figure(figsize=(10, 5))
sub = fig.add_subplot(111)
sub.scatter(range(len(b123)), q123, c='k', s=1)
sub.set_xlabel(r'$k_1 > k_2 > k_3$ triangle index', fontsize=25)
sub.set_xlim([0, len(b123)])
sub.set_ylabel(r'$Q(k_1, k_2, k_3)$', fontsize=25)
sub.set_ylim([0., 1.])
fig.savefig(''.join([dir_dat, 'qpm_Q123', str_rsd, '.png']),
bbox_inches='tight')
# plot bispectrum amplitude
fig = plt.figure(figsize=(10, 5))
sub = fig.add_subplot(111)
sub.scatter(range(len(b123)), b123, c='k', s=1)
sub.set_xlabel(r'$k_1 > k_2 > k_3$ triangle index', fontsize=25)
sub.set_xlim([0, len(b123)])
sub.set_ylabel(r'$B(k_1, k_2, k_3)$', fontsize=25)
sub.set_yscale('log')
fig.savefig(''.join([dir_dat, 'qpm_B123', str_rsd, '.png']),
bbox_inches='tight')
return None
def AEMspectra(rsd=False):
''' calculate the powerspectrum and bispectrum of the Aemulus simulation box
'''
str_rsd = ''
if rsd: str_rsd = '.rsd'
f_halo = ''.join(
[UT.dat_dir(), 'aemulus/aemulus_test002_halos.mlim1e13.hdf5'])
f_hdf5 = ''.join(
[UT.dat_dir(), 'aemulus/aemulus_test002_halos.mlim1e13.hdf5'])
f_pell = ''.join([
UT.dat_dir(), 'aemulus/pySpec.Plk.halo.mlim1e13.Ngrid360', str_rsd,
'.dat'
])
f_pnkt = ''.join([
UT.dat_dir(), 'aemulus/pySpec.Plk.halo.mlim1e13.Ngrid360.nbodykit',
str_rsd, '.dat'
])
f_b123 = ''.join([
UT.dat_dir(),
'aemulus/pySpec.B123.halo.mlim1e13.Ngrid360.Nmax40.Ncut3.step3.pyfftw',
str_rsd, '.dat'
])
Lbox = 1050.
kf = 2. * np.pi / Lbox
if not os.path.isfile(f_hdf5):
f = h5py.File(f_halo, 'r')
xyz = f['xyz'].value
vxyz = f['vxyz'].value
mh = f['mhalo'].value
xyz_s = pySpec.applyRSD(xyz.T,
vxyz.T,
0.55,
h=0.7,
omega0_m=0.340563,
LOS='z',
Lbox=Lbox)
xyz_s = xyz_s.T
f = h5py.File(f_hdf5, 'w')
f.create_dataset('xyz', data=xyz)
f.create_dataset('vxyz', data=vxyz)
f.create_dataset('xyz_s', data=xyz_s)
f.create_dataset('mhalo', data=mh)
f.close()
else:
f = h5py.File(f_hdf5, 'r')
xyz = f['xyz'].value
vxyz = f['vxyz'].value
xyz_s = f['xyz_s'].value
mh = f['mhalo'].value
f.close()
Nhalo = xyz.shape[0]
print('# halos = %i' % Nhalo)
nhalo = float(Nhalo) / Lbox**3
print('number density = %f' % nhalo)
print('1/nbar = %f' % (1. / nhalo))
# calculate powerspectrum
if not os.path.isfile(f_pell):
# calculate FFTs
if not rsd:
delta = pySpec.FFTperiodic(xyz.T,
fft='fortran',
Lbox=Lbox,
Ngrid=360,
silent=False)
else:
delta = pySpec.FFTperiodic(xyz_s.T,
fft='fortran',
Lbox=Lbox,
Ngrid=360,
silent=False)
delta_fft = pySpec.reflect_delta(delta, Ngrid=360)
# calculate powerspectrum monopole
k, p0k, cnts = pySpec.Pk_periodic(delta_fft)
k = k * kf
p0k = p0k / kf**3 - 1. / nhalo
# save to file
hdr = 'pyspectrum P_l=0(k) calculation. k_f = 2pi/1050.'
np.savetxt(f_pell,
np.array([k, p0k, cnts]).T,
fmt='%.5e %.5e %.5e',
delimiter='\t',
header=hdr)
else:
k, p0k, cnts = np.loadtxt(f_pell,
skiprows=1,
unpack=True,
usecols=[0, 1, 2])
# calculate P(k) using nbodykit for santiy check
if not os.path.isfile(f_pnkt):
# get cosmology from header
Omega_m = 0.3175
Omega_b = 0.049 # fixed baryon
h = 0.6711
cosmo = NBlab.cosmology.Planck15.clone(Omega_cdm=Omega_m - Omega_b,
h=h,
Omega_b=Omega_b)
halo_data = {}
if not rsd: halo_data['Position'] = xyz
else: halo_data['Position'] = xyz_s
halo_data['Velocity'] = vxyz
halo_data['Mass'] = mh
print("putting it into array catalog")
halos = NBlab.ArrayCatalog(halo_data,
BoxSize=np.array([Lbox, Lbox, Lbox]))
print("putting it into halo catalog")
halos = NBlab.HaloCatalog(halos, cosmo=cosmo, redshift=0., mdef='vir')
print("putting it into mesh")
mesh = halos.to_mesh(window='tsc',
Nmesh=360,
compensated=True,
position='Position')
print("calculating powerspectrum")
r = NBlab.FFTPower(mesh, mode='1d', dk=kf, kmin=kf, poles=[0, 2, 4])
poles = r.poles
plk = {'k': poles['k']}
for ell in [0, 2, 4]:
P = (poles['power_%d' % ell].real)
if ell == 0:
P = P - poles.attrs[
'shotnoise'] # subtract shotnoise from monopole
plk['p%dk' % ell] = P
plk['shotnoise'] = poles.attrs['shotnoise'] # save shot noise term
# header
hdr = 'pyspectrum P_l(k) calculation. k_f = 2pi/1050; P_shotnoise ' + str(
plk['shotnoise'])
# write to file
np.savetxt(f_pnkt,
np.array([plk['k'], plk['p0k'], plk['p2k'], plk['p4k']]).T,
header=hdr)
else:
_k, _p0k, _p2k, _p4k = np.loadtxt(f_pnkt,
skiprows=1,
unpack=True,
usecols=[0, 1, 2, 3])
plk = {}
plk['k'] = _k
plk['p0k'] = _p0k
plk['p2k'] = _p2k
plk['p4k'] = _p4k
# calculate bispectrum
if not os.path.isfile(f_b123):
if not rsd:
bispec = pySpec.Bk_periodic(xyz.T,
Lbox=Lbox,
Ngrid=360,
Nmax=40,
Ncut=3,
step=3,
fft='pyfftw',
nthreads=1,
silent=False)
else:
bispec = pySpec.Bk_periodic(xyz_s.T,
Lbox=Lbox,
Ngrid=360,
Nmax=40,
Ncut=3,
step=3,
fft='pyfftw',
nthreads=1,
silent=False)
i_k = bispec['i_k1']
j_k = bispec['i_k2']
l_k = bispec['i_k3']
p0k1 = bispec['p0k1']
p0k2 = bispec['p0k2']
p0k3 = bispec['p0k3']
b123 = bispec['b123']
b123_sn = bispec['b123_sn']
q123 = bispec['q123']
counts = bispec['counts']
# save to file
hdr = 'pyspectrum bispectrum calculation test. k_f = 2pi/%.1f' % Lbox
np.savetxt(f_b123,
np.array([
i_k, j_k, l_k, p0k1, p0k2, p0k3, b123, q123, counts,
b123_sn
]).T,
fmt='%i %i %i %.5e %.5e %.5e %.5e %.5e %.5e %.5e',
delimiter='\t',
header=hdr)
else:
i_k, j_k, l_k, p0k1, p0k2, p0k3, b123, q123, counts, b123_sn = np.loadtxt(
f_b123, skiprows=1, unpack=True, usecols=range(10))
# plot powerspecrtrum shape triangle plot
fig = plt.figure(figsize=(5, 5))
sub = fig.add_subplot(111)
sub.plot(k, p0k, c='k', lw=1, label='pySpectrum')
sub.plot(plk['k'], plk['p0k'], c='C1', lw=1, label='nbodykit')
iksort = np.argsort(i_k)
sub.plot(i_k[iksort] * kf,
p0k1[iksort],
c='k',
lw=1,
ls='--',
label='bispectrum code')
sub.legend(loc='lower left', fontsize=20)
sub.set_ylabel('$P_0(k)$', fontsize=25)
#sub.set_ylim([1e2, 3e4])
sub.set_yscale('log')
sub.set_xlabel('$k$', fontsize=25)
sub.set_xlim([3e-3, 1.])
sub.set_xscale('log')
fig.savefig(''.join([UT.dat_dir(), 'aemulus/aemulus_p0k', str_rsd,
'.png']),
bbox_inches='tight')
# plot bispectrum shape triangle plot
nbin = 31
x_bins = np.linspace(0., 1., nbin + 1)
y_bins = np.linspace(0.5, 1., (nbin // 2) + 1)
fig = plt.figure(figsize=(10, 5))
sub = fig.add_subplot(111)
Bgrid = Plots._BorQgrid(
l_k.astype(float) / i_k.astype(float),
j_k.astype(float) / i_k.astype(float), q123, counts, x_bins, y_bins)
bplot = plt.pcolormesh(x_bins,
y_bins,
Bgrid.T,
vmin=0,
vmax=1,
cmap='RdBu')
cbar = plt.colorbar(bplot, orientation='vertical')
sub.set_title(r'$Q(k_1, k_2, k_3)$ QPM halo catalog', fontsize=25)
sub.set_xlabel('$k_3/k_1$', fontsize=25)
sub.set_ylabel('$k_2/k_1$', fontsize=25)
fig.savefig(''.join(
[UT.dat_dir(), 'aemulus/aemulus_Q123_shape', str_rsd, '.png']),
bbox_inches='tight')
fig = plt.figure(figsize=(10, 5))
sub = fig.add_subplot(111)
Bgrid = Plots._BorQgrid(
l_k.astype(float) / i_k.astype(float),
j_k.astype(float) / i_k.astype(float), b123, counts, x_bins, y_bins)
bplot = plt.pcolormesh(x_bins,
y_bins,
Bgrid.T,
norm=LogNorm(vmin=1e6, vmax=1e8),
cmap='RdBu')
cbar = plt.colorbar(bplot, orientation='vertical')
sub.set_title(r'$B(k_1, k_2, k_3)$ QPM halo catalog', fontsize=25)
sub.set_xlabel('$k_3/k_1$', fontsize=25)
sub.set_ylabel('$k_2/k_1$', fontsize=25)
fig.savefig(''.join(
[UT.dat_dir(), 'aemulus/aemulus_B123_shape', str_rsd, '.png']),
bbox_inches='tight')
# plot bispectrum amplitude
fig = plt.figure(figsize=(10, 5))
sub = fig.add_subplot(111)
sub.scatter(range(len(b123)), q123, c='k', s=1)
sub.set_xlabel(r'$k_1 > k_2 > k_3$ triangle index', fontsize=25)
sub.set_xlim([0, len(b123)])
sub.set_ylabel(r'$Q(k_1, k_2, k_3)$', fontsize=25)
sub.set_ylim([0., 1.])
fig.savefig(''.join(
[UT.dat_dir(), 'aemulus/aemulus_Q123', str_rsd, '.png']),
bbox_inches='tight')
# plot bispectrum amplitude
fig = plt.figure(figsize=(10, 5))
sub = fig.add_subplot(111)
sub.scatter(range(len(b123)), b123, c='k', s=1)
sub.set_xlabel(r'$k_1 > k_2 > k_3$ triangle index', fontsize=25)
sub.set_xlim([0, len(b123)])
sub.set_ylabel(r'$B(k_1, k_2, k_3)$', fontsize=25)
sub.set_yscale('log')
fig.savefig(''.join(
[UT.dat_dir(), 'aemulus/aemulus_B123', str_rsd, '.png']),
bbox_inches='tight')
return None
comment='#')
print "read in data"
data = {}
data['Position'] = da.from_array(np.column_stack(
(gadget_format[0], gadget_format[1], gadget_format[2])),
chunks=(100, 3))
data['Velocity'] = da.from_array(
np.column_stack((np.zeros(len(gadget_format[0])),
np.zeros(len(gadget_format[0])),
np.zeros(len(gadget_format[0])))),
chunks=(100, 3)) #transform.StackColumns
data = nlab.ArrayCatalog(data)
print "stacked data"
# Convert to MeshSource object, BoxSize in Mpc/h
mesh = data.to_mesh(window='tsc',
Nmesh=Nmesh,
BoxSize=BoxSize,
compensated=True,
position='Position')
print "converted data"
# Get void correlation function from the simulation
# Do the Fourier transform
t = nlab.FFTCorr(mesh, mode='1d', Nmesh=Nmesh,
BoxSize=BoxSize) #, dr = 9.0)
def __init__(self, datar, randomr):
data = catit(datar)
random = catit(randomr)
self.data = nb.ArrayCatalog(data)
self.random = nb.ArrayCatalog(random)
mu_og = _mu_og.reshape((180, 20))[:, 10:]
_pkmu_og = _pkmu_og.reshape((180, 20))
_nmodes_og = _nmodes_og.reshape((180, 20))
nmodes_og = _nmodes_og[:, :10][:, ::-1] + _nmodes_og[:, 10:]
pkmu_og = ((_pkmu_og[:, :10] * _nmodes_og[:, :10])[:, ::-1] +
(_pkmu_og[:, 10:] * _nmodes_og[:, 10:])) / nmodes_og
# P(k,mu) from nbodykit
import nbodykit.lab as NBlab
objs = {}
objs['Position'] = xyz.T
objs['Velocity'] = vxyz.T
objs['RSDPosition'] = xyz_s
cat = NBlab.ArrayCatalog(objs, BoxSize=Lbox)
mesh = cat.to_mesh(window='tsc',
Nmesh=360,
BoxSize=Lbox,
compensated=True,
position='RSDPosition')
r = NBlab.FFTPower(mesh,
mode='2d',
dk=kf,
kmin=0.5 * kf,
Nmu=10,
los=[0, 0, 1])
print(r.power)
fig = plt.figure(figsize=(10, 5))
sub = fig.add_subplot(121)
def fastPM(z, str_flag='', mh_lim=15., Lbox=205., Nmax=40, Ncut=3, step=3):
''' calculate the powerspectrum and bispectrum of the fastPM catalog.
'''
dir_fpm = os.path.join(UT.dat_dir(), 'fastpm')
f_halo = ('halocat_FastPM_40step_N250_IC500_B2_z%.2f%s.txt' %
(z, str_flag))
f_mlim = ('halocat_FastPM_40step_N250_IC500_B2_z%.2f%s.mlim%.fe10' %
(z, str_flag, mh_lim))
f_hdf5 = ('%s/%s.hdf5' % (dir_fpm, f_mlim))
f_pell = ('%s/pySpec.Plk.%s.Lbox%.f.Ngrid360.dat' %
(dir_fpm, f_mlim, Lbox))
f_pnkt = ('%s/pySpec.Plk.%s.Lbox%.f.Ngrid360.nbodykit.dat' %
(dir_fpm, f_mlim, Lbox))
f_b123 = ('%s/pySpec.Bk.%s.Lbox%.f.Ngrid360.step%i.Ncut%i.Nmax%i.dat' %
(dir_fpm, f_mlim, Lbox, step, Ncut, Nmax))
kf = 2. * np.pi / Lbox
if not os.path.isfile(f_hdf5):
# read in halo catalog
dat_halo = np.loadtxt(os.path.join(dir_fpm, f_halo),
unpack=True,
usecols=[0, 1, 2, 3, 7, 8, 9])
mh = dat_halo[0]
Nhalo = len(mh)
print('%i halos in %.f Mpc/h box' % (len(mh), Lbox))
print('%f < M_h/10^10Msun < %f' % (mh.min(), mh.max()))
xyz = np.zeros((Nhalo, 3))
xyz[:, 0] = dat_halo[1]
xyz[:, 1] = dat_halo[2]
xyz[:, 2] = dat_halo[3]
print('%f < x < %f' % (xyz[:, 0].min(), xyz[:, 0].max()))
print('%f < y < %f' % (xyz[:, 1].min(), xyz[:, 1].max()))
print('%f < z < %f' % (xyz[:, 2].min(), xyz[:, 2].max()))
vxyz = np.zeros((Nhalo, 3))
vxyz[:, 0] = dat_halo[4]
vxyz[:, 1] = dat_halo[5]
vxyz[:, 2] = dat_halo[6]
mlim = (mh > 15.)
Nhalo = np.sum(mlim)
print('%i halos in %.f Mpc/h box with Mh > %f' % (Nhalo, Lbox, mh_lim))
mh = mh[mlim]
xyz = xyz[mlim, :]
vxyz = vxyz[mlim, :]
f = h5py.File(f_hdf5, 'w')
f.create_dataset('xyz', data=xyz)
f.create_dataset('vxyz', data=vxyz)
f.create_dataset('mhalo', data=mh)
f.close()
else:
f = h5py.File(f_hdf5, 'r')
xyz = f['xyz'].value
vxyz = f['vxyz'].value
mh = f['mhalo'].value
Nhalo = xyz.shape[0]
print('%i halos in %.f Mpc/h box with Mh > %f' %
(len(mh), Lbox, mh_lim))
nhalo = float(Nhalo) / Lbox**3
print('number density = %f' % nhalo)
print('1/nbar = %f' % (1. / nhalo))
# calculate powerspectrum
if not os.path.isfile(f_pell):
delta = pySpec.FFTperiodic(xyz.T,
fft='fortran',
Lbox=Lbox,
Ngrid=360,
silent=False)
delta_fft = pySpec.reflect_delta(delta, Ngrid=360)
# calculate powerspectrum monopole
k, p0k, cnts = pySpec.Pk_periodic(delta_fft)
k *= kf
p0k = p0k / (kf**3) - 1. / nhalo
# save to file
hdr = ('pySpectrum P_l=0(k). Nhalo=%i, Lbox=%.f, k_f=%.5e, SN=%.5e' %
(Nhalo, Lbox, kf, 1. / nhalo))
hdr += '\n k, p0k, counts'
np.savetxt(f_pell,
np.array([k, p0k, cnts]).T,
fmt='%.5e %.5e %.5e',
delimiter='\t',
header=hdr)
else:
k, p0k, cnts = np.loadtxt(f_pell,
skiprows=1,
unpack=True,
usecols=[0, 1, 2])
# calculate P(k) using nbodykit for santiy check
if not os.path.isfile(f_pnkt):
cosmo = NBlab.cosmology.Planck15
halo_data = {}
halo_data['Position'] = xyz
halo_data['Velocity'] = vxyz
halo_data['Mass'] = mh
print("putting it into array catalog")
halos = NBlab.ArrayCatalog(halo_data,
BoxSize=np.array([Lbox, Lbox, Lbox]))
print("putting it into halo catalog")
halos = NBlab.HaloCatalog(halos, cosmo=cosmo, redshift=z, mdef='vir')
print("putting it into mesh")
mesh = halos.to_mesh(window='tsc',
Nmesh=360,
compensated=True,
position='Position')
print("calculating powerspectrum")
r = NBlab.FFTPower(mesh, mode='1d', dk=kf, kmin=kf, poles=[0, 2, 4])
poles = r.poles
plk = {'k': poles['k']}
for ell in [0, 2, 4]:
P = (poles['power_%d' % ell].real)
if ell == 0:
P = P - poles.attrs[
'shotnoise'] # subtract shotnoise from monopole
plk['p%dk' % ell] = P
plk['shotnoise'] = poles.attrs['shotnoise'] # save shot noise term
# header
hdr = ('pySpectrum P_l(k). Nhalo=%i, Lbox=%.f, k_f=%.5e, SN=%.5e' %
(Nhalo, Lbox, kf, plk['shotnoise']))
hdr += '\n k, p0k, p2k, p4k'
# save to file
np.savetxt(f_pnkt,
np.array([plk['k'], plk['p0k'], plk['p2k'], plk['p4k']]).T,
header=hdr)
else:
_k, _p0k, _p2k, _p4k = np.loadtxt(f_pnkt,
skiprows=1,
unpack=True,
usecols=[0, 1, 2, 3])
plk = {}
plk['k'] = _k
plk['p0k'] = _p0k
plk['p2k'] = _p2k
plk['p4k'] = _p4k
# calculate bispectrum
if not os.path.isfile(f_b123):
# calculate bispectrum
bispec = pySpec.Bk_periodic(xyz.T,
Lbox=Lbox,
Ngrid=360,
Nmax=40,
Ncut=3,
step=3,
fft='pyfftw',
nthreads=1,
silent=False)
i_k = bispec['i_k1']
j_k = bispec['i_k2']
l_k = bispec['i_k3']
p0k1 = bispec['p0k1']
p0k2 = bispec['p0k2']
p0k3 = bispec['p0k3']
b123 = bispec['b123']
b123_sn = bispec['b123_sn']
q123 = bispec['q123']
counts = bispec['counts']
# save to file
hdr = 'pyspectrum bispectrum calculation test. k_f = 2pi/%.1f' % Lbox
hdr += '\n i_k1, i_k2, i_k3, p0k1, p0k2, p0k3, bk, qk, counts, bk_shotnoise'
np.savetxt(f_b123,
np.array([
i_k, j_k, l_k, p0k1, p0k2, p0k3, b123, q123, counts,
b123_sn
]).T,
fmt='%i %i %i %.5e %.5e %.5e %.5e %.5e %.5e %.5e',
delimiter='\t',
header=hdr)
else:
i_k, j_k, l_k, p0k1, p0k2, p0k3, b123, q123, counts, b123_sn = np.loadtxt(
f_b123, skiprows=1, unpack=True, usecols=range(10))
# plot powerspecrtrum shape triangle plot
fig = plt.figure(figsize=(5, 5))
sub = fig.add_subplot(111)
sub.plot(k, p0k, c='k', lw=1, label='pySpectrum')
sub.plot(plk['k'], plk['p0k'], c='C1', lw=1, label='nbodykit')
iksort = np.argsort(i_k)
sub.plot(i_k[iksort] * kf,
p0k1[iksort],
c='k',
lw=1,
ls='--',
label='bispectrum code')
sub.legend(loc='lower left', fontsize=20)
sub.set_ylabel('$P_0(k)$', fontsize=25)
sub.set_ylim([1e0, 1e4])
sub.set_yscale('log')
sub.set_xlabel('$k$', fontsize=25)
sub.set_xlim([1e-2, 10.])
sub.set_xscale('log')
fig.savefig(f_pell.replace('.dat', '.png'), bbox_inches='tight')
# plot bispectrum shape triangle plot
nbin = 31
x_bins = np.linspace(0., 1., nbin + 1)
y_bins = np.linspace(0.5, 1., (nbin // 2) + 1)
fig = plt.figure(figsize=(10, 5))
sub = fig.add_subplot(111)
Bgrid = Plots._BorQgrid(
l_k.astype(float) / i_k.astype(float),
j_k.astype(float) / i_k.astype(float), q123, counts, x_bins, y_bins)
bplot = plt.pcolormesh(x_bins,
y_bins,
Bgrid.T,
vmin=0,
vmax=1,
cmap='RdBu')
cbar = plt.colorbar(bplot, orientation='vertical')
sub.set_title(r'$Q(k_1, k_2, k_3)$ FastPM halo catalog', fontsize=25)
sub.set_xlabel('$k_3/k_1$', fontsize=25)
sub.set_ylabel('$k_2/k_1$', fontsize=25)
fig.savefig(f_b123.replace('.dat', '.Qk_shape.png'), bbox_inches='tight')
fig = plt.figure(figsize=(10, 5))
sub = fig.add_subplot(111)
Bgrid = Plots._BorQgrid(
l_k.astype(float) / i_k.astype(float),
j_k.astype(float) / i_k.astype(float), b123, counts, x_bins, y_bins)
bplot = plt.pcolormesh(x_bins,
y_bins,
Bgrid.T,
norm=LogNorm(vmin=1e6, vmax=1e8),
cmap='RdBu')
cbar = plt.colorbar(bplot, orientation='vertical')
sub.set_title(r'$B(k_1, k_2, k_3)$ FastPM halo catalog', fontsize=25)
sub.set_xlabel('$k_3/k_1$', fontsize=25)
sub.set_ylabel('$k_2/k_1$', fontsize=25)
fig.savefig(f_b123.replace('.dat', '.Bk_shape.png'), bbox_inches='tight')
# plot bispectrum amplitude
fig = plt.figure(figsize=(10, 5))
sub = fig.add_subplot(111)
sub.scatter(range(len(b123)), q123, c='k', s=1)
sub.set_xlabel(r'$k_1 > k_2 > k_3$ triangle index', fontsize=25)
sub.set_xlim([0, len(b123)])
sub.set_ylabel(r'$Q(k_1, k_2, k_3)$', fontsize=25)
sub.set_ylim([0., 1.])
fig.savefig(f_b123.replace('.dat', '.Qk.png'), bbox_inches='tight')
fig = plt.figure(figsize=(10, 5))
sub = fig.add_subplot(111)
sub.scatter(range(len(b123)), b123, c='k', s=1)
sub.set_xlabel(r'$k_1 > k_2 > k_3$ triangle index', fontsize=25)
sub.set_xlim([0, len(b123)])
sub.set_ylabel(r'$B(k_1, k_2, k_3)$', fontsize=25)
sub.set_yscale('log')
fig.savefig(f_b123.replace('.dat', '.Bk.png'), bbox_inches='tight')
return None
def read_BigFile(filename):
FILE = bf.BigFile(filename)
n = FILE.size
pos = FILE.read('Position', 0, n)
return nb.ArrayCatalog(pos)
def Halos(halo_folder,
z=0.5,
Om=None,
Ob=None,
h=None,
ns=None,
s8=None,
Mnu=0.):
''' read in Quijote halo catalog given the folder and snapshot # and store it as
a nbodykit HaloCatalog object. The HaloCatalog object is convenient for
populating with galaxies and etc.
Parameters
----------
halo_folder : string
directory that contains the halo catalogs e.g. on tiger it'd be
something like: /projects/QUIJOTE/Halos/latin_hypercube/HR_0/
Return
------
cat : nbodykit.lab.HaloCatalog
Quijote halo catalog
'''
# if snapshot folder is not specified
# then all values have to be specified in kwargs
assert all([tt is not None for tt in [Om, Ol, z, h, Hz]])
# redshift snapshot
assert z in quijote_zsnap_dict.keys(
), 'snapshots are available at z=0, 0.5, 1, 2, 3'
snapnum = quijote_zsnap_dict[z]
# this cosmology is not used for anything. but it's included for nbodykit
cosmo = NBlab.cosmology.Planck15.clone(h=h,
Omega0_b=Ob,
Omega0_cdm=Om - Ob,
m_ncdm=Mnu,
n_s=ns)
Ol = 1. - Om
Hz = 100.0 * np.sqrt(Om * (1. + z)**3 + Ol) # km/s/(Mpc/h)
# read FOF catalog (~90.6 ms)
Fof = readfof.FoF_catalog(halo_folder,
snapnum,
read_IDs=False,
long_ids=False,
swap=False,
SFR=False)
group_data = {}
group_data['Length'] = Fof.GroupLen
group_data['Position'] = Fof.GroupPos / 1e3
group_data['Velocity'] = Fof.GroupVel
group_data['Mass'] = Fof.GroupMass * 1e10
# calculate velocity offset
rsd_factor = (1. + z) / Hz
group_data['VelocityOffset'] = group_data['Velocity'] * rsd_factor
# save to ArryCatalog for consistency
cat = NBlab.ArrayCatalog(group_data,
BoxSize=np.array([1000., 1000., 1000.]))
cat = NBlab.HaloCatalog(cat, cosmo=cosmo, redshift=z, mdef='vir')
cat.attrs['Om'] = Om
cat.attrs['Ob'] = Ob
cat.attrs['Ol'] = Ol
cat.attrs['h'] = h
cat.attrs['ns'] = ns
cat.attrs['s8'] = s8
cat.attrs['Hz'] = Hz # km/s/(Mpc/h)
cat.attrs['rsd_factor'] = rsd_factor
return cat
