def map_dbt_hightemp():
for i in range(start, len(redshifts)):
filename = setup_dirs.resultsdir() + "map_dbt_hightemp" + str("%.3f" % redshifts[i]) + ".bin"
temp_filename = setup_dirs.path() + "Temper3D_" + str("%.3f" % redshifts[i]) + ".bin"
xfrac_filename = setup_dirs.path() + "xfrac3d_" + str("%.3f" % redshifts[i]) + ".bin"
if i % 2 == 0:
density_filename = (
"/research/prace/244Mpc_RT/244Mpc_f2_8.2pS_250/coarser_densities/"
+ str("%.3f" % redshifts[i])
+ "n_all.dat"
)
else:
density_filename = (
"/research/prace/244Mpc_RT/244Mpc_f2_8.2pS_250/coarser_densities/"
+ str("%.3f" % redshifts[i - 1])
+ "n_all.dat"
)
tfile = c2t.TemperFile(temp_filename)
xfile = c2t.XfracFile(xfrac_filename).xi
if ss != " ":
abu_he = 0.074
G_grav = 6.674e-11
pc = 3.086e16
Mpc = 1e6 * pc
H0 = 0.7 * 100.0 * 1.0e5 / Mpc
rho_crit_0 = 3.0 * H0 * H0 / (8.0 * np.pi * G_grav)
Omega_B = 0.044
mu = (1.0 - abu_he) + 4.0 * abu_he
m_p = 1.672661e-24
dfile = np.ones(ss ** 3).reshape(ss, ss, ss)
dfile = dfile * rho_crit_0 * Omega_B / (mu * m_p) * (1.0 + redshifts[i]) ** 3
dfile = dfile * 1.23581719037e-35
else:
dfile = c2t.DensityFile(density_filename).cgs_density
dT_box = c2t.calc_dt(xfile, dfile, redshifts[i])
# IO.writemap(dT_box,filename)
IO.writebin(dT_box, filename)
print "Written map to " + filename
def mean_dbt_hightemp():
file = open(setup_dirs.resultsdir()+'mean_dbt_hightemp.dat','w')
for i in range(len(redshifts)):
filename = setup_dirs.resultsdir()+'map_dbt_hightemp'+str('%.3f' % redshifts[i])+'.dat'
temp_filename = setup_dirs.path()+'Temper3D_'+str('%.3f' % redshifts[i]) + '.bin'
xfrac_filename = setup_dirs.path() +'xfrac3d_'+str('%.3f' % redshifts[i]) + '.bin'
if i%2==0:
density_filename='/research/prace/244Mpc_RT/244Mpc_f2_8.2pS_250/coarser_densities/' + str('%.3f' % redshifts[i]) + 'n_all.dat'
else:
density_filename='/research/prace/244Mpc_RT/244Mpc_f2_8.2pS_250/coarser_densities/' + str('%.3f' % redshifts[i-1]) + 'n_all.dat'
tfile = c2t.TemperFile(temp_filename)
xfile = c2t.XfracFile(xfrac_filename).xi
if ss!=' ':
dfile = np.ones(ss**3).reshape(ss,ss,ss)*1.981e-10*(1+redshifts[i])**3
else:
dfile = c2t.DensityFile(density_filename).cgs_density
dT_box = c2t.calc_dt(xfile, dfile, redshifts[i]) #returned in micro kelvin! update plots
file.write(str(np.mean(dT_box))+'\n')
print "Written mean dbt to " + setup_dirs.resultsdir()+'mean_dbt_hightemp.dat'
xfrac_file = ''.join(glob.glob(xfrac_path+'xfrac3d_%.3f.bin'%(z_arr[i])))
print 'Ionized fraction file = %s' % xfrac_file
xfile = c2t.XfracFile(xfrac_file)
vel_file = ''.join(glob.glob(density_path+'%.3fv_all.dat'%(z_arr[i])))
if not vel_file:
vel_file = ''.join(glob.glob(density_path+'%.3fv_all.dat'%(z_arr[i-1])))
print 'Velocity file = %s' % vel_file
vfile = c2t.VelocityFile(vel_file)
kms = vfile.get_kms_from_density(dfile)
mesh = (xfile.mesh_x, xfile.mesh_y, xfile.mesh_z)
print 'mesh = ', mesh
dT_box = c2t.calc_dt(xfile, dfile, xfile.z)
dT_file = './dT_boxes/dT_%.3f.cbin' % (z_arr[i])
print 'dT_box file = %s' % dT_file
#rho = dfile.cgs_density
#print 'rho_crit*OmegaB',c2t.const.rho_crit_0*c2t.const.OmegaB#*(1+z_arr[i])**3
#print 'rho.mean()',rho.mean()
c2t.save_cbin(dT_file, dT_box, bits=64, order='F')
dT_pv_box = c2t.get_distorted_dt(dT_box, kms, xfile.z, num_particles=40)
dT_pv_file = './dT_pv_boxes/dT_pv_%.3f.cbin' % (z_arr[i])
print 'dT_pv_box file = %s' % dT_pv_file
c2t.save_cbin(dT_pv_file, dT_pv_box, bits=64, order='F')
dT_mean = dT_box.mean()
dT_rms_box = np.sqrt((dT_box - dT_mean)**2)
print 'The mass-averaged mean ionized fraction is:', c2t.mass_weighted_mean_xi(xfile.xi, dfile.raw_density)
#Read a velocity data file and store it as a VelocityFile object
vfile = c2t.VelocityFile(velocity_filename)
#Since the velocity data is actually momentum, we need the density to convert it to km/s
kms = vfile.get_kms_from_density(dfile)
print 'Gas velocity at cell (100,100,100) is ', kms[:,100,100,100], 'km/s'
#Calculate neutral hydrogen number density
n_hi = dfile.cgs_density*xfile.xi/c2t.m_p
#Calculate differential brightness temperature
#The calc_dt method can also take names of files so you don't have to load the
#files yourself in advance.
dT = c2t.calc_dt(xfile, dfile, z=xfile.z)
#Get a slice through the center
dT_slice = dT[128,:,:]
#Convolve with a Gaussian beam, assuming a 2 km maximum baseline
dT_slice_conv = c2t.beam_convolve(dT_slice, xfile.z, fov_mpc=c2t.conv.LB, \
max_baseline=2000.)
#c2raytools comes with a few simple plotting functions to quickly
#visualize data. For example, to plot a slice through the ionization
#fraction data, you can simply do:
c2t.plot_slice(xfile)
#If you want more control over the plotting, you have to use, e.g.,
#We are using the 114/h Mpc simulation box, so set all the proper conversion factors
c2t.set_sim_constants(boxsize_cMpc = 114.)
#Read density
dfile = c2t.DensityFile(density_filename)
#Read velocity data file and get the actual velocity
vfile = c2t.VelocityFile(velocity_filename)
kms = vfile.get_kms_from_density(dfile)
#To speed things up, we will assume that the IGM is completely neutral, so instead
#of reading an ionization fraction file, we will just make an array of zeros
xi = np.zeros_like(dfile.cgs_density)
#Calculate the dT
dT_realspace = c2t.calc_dt(xi, dfile, z=dfile.z)
#Make the redshift-space volume
dT_redshiftspace = c2t.get_distorted_dt(dT_realspace, kms, \
dfile.z, los_axis=0, num_particles=20)
#Calculate spherically-averaged power spectra,
#using 20 logarithmically spaced k bins, from 1e-1 to 10
kbins = 10**np.linspace(-1, 1, 20)
ps_dist, k = c2t.power_spectrum_1d(dT_redshiftspace, kbins)
ps_nodist, k = c2t.power_spectrum_1d(dT_realspace, kbins)
#Plot ratio. On large scales, this should be close to 1.83
pl.semilogx(k, ps_dist/ps_nodist)
pl.xlabel('$k \; \mathrm{[Mpc^{-1}]}$')
pl.ylabel('$P_k^{\mathrm{PV}}/P_k^{\mathrm{NoPV}}$')
print 'The volume-averaged mean ionized fraction is: ', xfile.xi.mean()
print 'The mass-averaged mean ionized fraction is:', c2t.mass_weighted_mean_xi(
xfile.xi, dfile.raw_density)
#Read a velocity data file
vfile = c2t.VelocityFile(velocity_filename)
#Since the velocity data is actually momentum, we need the density to convert it to km/s
kms = vfile.get_kms_from_density(dfile)
print 'Gas velocity at cell (100,100,100) is ', kms[:, 100, 100, 100], 'km/s'
#Calculate neutral hydrogen number density
n_hi = dfile.cgs_density * xfile.xi / c2t.m_p
#Calculate differential brightness temperature
dT = c2t.calc_dt(xfile, dfile)
#Get a slice through the center
dT_slice = dT[128, :, :]
#Convolve with a Gaussian beam, assuming a 2 km maximum baseline
dT_slice_conv = c2t.beam_convolve(dT_slice, xfile.z, c2t.boxsize, \
max_baseline=2000.)
#Plot some stuff
pl.figure()
pl.subplot(221)
pl.imshow(n_hi[128, :, :])
pl.colorbar()
pl.title('$n_{HI}$')
print 'The volume-averaged mean ionized fraction is: ', xfile.xi.mean()
print 'The mass-averaged mean ionized fraction is:', c2t.mass_weighted_mean_xi(xfile.xi, dfile.raw_density)
#Read a velocity data file
vfile = c2t.VelocityFile(velocity_filename)
#Since the velocity data is actually momentum, we need the density to convert it to km/s
kms = vfile.get_kms_from_density(dfile)
print 'Gas velocity at cell (100,100,100) is ', kms[:,100,100,100], 'km/s'
#Calculate neutral hydrogen number density
n_hi = dfile.cgs_density*xfile.xi/c2t.m_p
#Calculate differential brightness temperature
dT = c2t.calc_dt(xfile, dfile)
#Get a slice through the center
dT_slice = dT[128,:,:]
#Convolve with a Gaussian beam, assuming a 2 km maximum baseline
dT_slice_conv = c2t.beam_convolve(dT_slice, xfile.z, c2t.boxsize, \
max_baseline=2000.)
#Plot some stuff
pl.figure()
pl.subplot(221)
pl.imshow(n_hi[128,:,:])
pl.colorbar()
pl.title('$n_{HI}$')
#We are using the 114/h Mpc simulation box, so set all the proper conversion factors
c2t.set_sim_constants(boxsize_cMpc=114.)
#Read density
dfile = c2t.DensityFile(density_filename)
#Read velocity data file and get the actual velocity
vfile = c2t.VelocityFile(velocity_filename)
kms = vfile.get_kms_from_density(dfile)
#To speed things up, we will assume that the IGM is completely neutral, so instead
#of reading an ionization fraction file, we will just make an array of zeros
xi = np.zeros_like(dfile.cgs_density)
#Calculate the dT
dT_realspace = c2t.calc_dt(xi, dfile, z=dfile.z)
#Make the redshift-space volume
dT_redshiftspace = c2t.get_distorted_dt(dT_realspace, kms, \
dfile.z, los_axis=0, num_particles=20)
#Calculate spherically-averaged power spectra,
#using 20 logarithmically spaced k bins, from 1e-1 to 10
kbins = 10**np.linspace(-1, 1, 20)
ps_dist, k = c2t.power_spectrum_1d(dT_redshiftspace, kbins)
ps_nodist, k = c2t.power_spectrum_1d(dT_realspace, kbins)
#Plot ratio. On large scales, this should be close to 1.83
pl.semilogx(k, ps_dist / ps_nodist)
pl.xlabel('$k \; \mathrm{[Mpc^{-1}]}$')
pl.ylabel('$P_k^{\mathrm{PV}}/P_k^{\mathrm{NoPV}}$')
xvs_ = 10**np.linspace(np.log10(3.44e-10),np.log10(0.20),10) #xvs = ph_count_info[[np.abs(ph_count_info[:,-2]-x).argmin() for x in xvs_],-2] zs_ = ph_count_info[[np.abs(ph_count_info[:,-2]-x).argmin() for x in xvs_], 0] dens_zs = owntools.get_zs_list(dens_dir, file_type='/*n_all.dat') zs = dens_zs[[np.abs(dens_zs-i).argmin() for i in zs_]] xvs = ph_count_info[[np.abs(ph_count_info[:,0]-i).argmin() for i in zs],-2] z = 9.026 #8.636 gg_xf = c2t.XfracFile(grizzly_dir+str(z)+'xhiifrac.dat').xi #cc_xf = owntools.coeval_xfrac(xfrac_dir, z) cube_d = owntools.coeval_dens(dens_dir, z) cube_21 = c2t.calc_dt(gg_xf, cube_d, z); cube_21 -= cube_21.mean() cube_m = cube_d/cube_d.mean(dtype=np.float64) - 1. #owntools.coeval_overdens(dens_dir, z) P_dd, ks_m = c2t.power_spectrum_1d(cube_m, kbins=100, box_dims=c2t.conv.LB) P_21, ks_x = c2t.power_spectrum_1d(cube_21, kbins=100, box_dims=c2t.conv.LB) f_dd, k_dd = squeezed_bispectrum._integrated_bispectrum_normalized_cross(cube_m, cube_m, Ncuts=Ncuts) f_21d, k_xd = squeezed_bispectrum._integrated_bispectrum_normalized_cross(cube_21, cube_m, Ncuts=Ncuts) f_xx, k_xx = squeezed_bispectrum._integrated_bispectrum_normalized_cross(cube_x-cube_x.mean(), cube_x-cube_x.mean(), Ncuts=Ncuts) f_x_ = squeezed_bispectrum._integrated_bispectrum_normalized_cross1(1-cube_x, cube_m, Ncuts=Ncuts) ks = k_dd.copy() ### Zeta calculation sources = np.loadtxt(parent_dir+'sources/'+str(z)+'-coarser_sources.dat', skiprows=1) M_min = sources[np.nonzero(sources[:,-2]),-2].min()*c2t.conv.M_grid*c2t.const.solar_masses_per_gram #solarunit
xfile.xi, dfile.raw_density)
#Read a velocity data file and store it as a VelocityFile object
vfile = c2t.VelocityFile(velocity_filename)
#Since the velocity data is actually momentum, we need the density to convert it to km/s
kms = vfile.get_kms_from_density(dfile)
print 'Gas velocity at cell (100,100,100) is ', kms[:, 100, 100, 100], 'km/s'
#Calculate neutral hydrogen number density
n_hi = dfile.cgs_density * xfile.xi / c2t.m_p
#Calculate differential brightness temperature
#The calc_dt method can also take names of files so you don't have to load the
#files yourself in advance.
dT = c2t.calc_dt(xfile, dfile, z=xfile.z)
#Get a slice through the center
dT_slice = dT[128, :, :]
#Convolve with a Gaussian beam, assuming a 2 km maximum baseline
dT_slice_conv = c2t.beam_convolve(dT_slice, xfile.z, fov_mpc=c2t.conv.LB, \
max_baseline=2000.)
#c2raytools comes with a few simple plotting functions to quickly
#visualize data. For example, to plot a slice through the ionization
#fraction data, you can simply do:
c2t.plot_slice(xfile)
#If you want more control over the plotting, you have to use, e.g.,
#matplotlib directly
