def smoothed_lightcone_dbt(id="", ht=""):
infile = open(setup_dirs.resultsdir() + "dbt_lightcone" + ht + ".bin", "rb")
outfile = setup_dirs.resultsdir() + "dbt_lightcone_smooth" + ht + ".bin"
zfile = open(setup_dirs.resultsdir() + "dbt_lightcone_redshifts.bin", "rb")
dT_box = np.load(infile)
log = open("stats.dat", "w")
zs = np.load(zfile)
dT_box3 = np.zeros((len(dT_box[:, 1, 1]) / 2, len(dT_box[1, :, 1]) / 2, len(dT_box[1, 1, :])))
dT_box2 = np.zeros((len(dT_box[:, 1, 1]) / 2, len(dT_box[1, :, 1]) / 2, len(dT_box[1, 1, :])))
for z in range(len(dT_box[1, 1, :]) - 1, -1, -1):
wl = 0.21 * (1 + zs[z])
c = 299792.458
omm = 0.27
oml = 0.73
omr = 0.0
omk = 1.0 - omm - oml - omr
H0 = 70.0
def integrand(x, omm, oml, omr, omk):
return 1.0 / np.sqrt(omr * ((1 + x) ** 4) + omm * ((1 + x) ** 3) + oml + omk * ((1 + x) ** 2))
def dc(z, omm, oml, omr, omk):
return quad(integrand, 0, z, args=(omm, oml, omr, omk))[0] # /(1.0+z)
vec_dc = np.vectorize(dc)
bw_r = wl / (2.0e3) # radians
bw = bw_r * 3437.74677 # arcminutesi
log.write("Wavelength of 21-cm from redshift " + str(zs[z]) + " is " + str(wl) + "m\n")
log.write("At redshift: " + str(zs[z]) + " smoothing with a " + str(bw) + " arc minute beam.\n")
rc = bw_r * c / H0 * vec_dc(zs[z], omm, oml, omr, omk) # comoving Mpc
Hz = H0 * np.sqrt(omr * (1 + zs[z]) ** 4 + omm * (1 + zs[z]) ** 3 + oml + omk * (1 + zs[z]) ** 2)
log.write("rc = " + str(rc) + "\n")
# dnu=nu0*Hz*rc/(c*(1+zs[z])**2)
dz = rc * Hz / c
log.write("$\Delta$ z = " + str(dz) + "\n")
ncs = rc * 250.0 * 0.7 / 244.0
log.write(str(ncs) + " cells in the z direction\n")
log.write("\n")
# rc_h=rc/0.7 # comoving Mpc/h
# print "This corresponds to "+str(rc_h)+"Mpc/h on the sky"
dT_box2[:, :, z] = c2t.beam_convolve(dT_box[:, :, z], zs[z], 244.0, beam_w=bw)
if z > ncs and z + ncs < zs[len(zs) - 1]:
for x in range(len(dT_box2)):
for y in range(len(dT_box2)):
dT_box3[x, y, z] = np.mean(dT_box2[x, y, z - ncs / 2.0 : z + ncs / 2.0])
else:
print "..."
dT_box3[:, :, z] = dT_box2[:, :, z]
IO.writebin(dT_box3[:, :, z], setup_dirs.resultsdir() + "smoothed_map_dbt_" + ht + str("%.3f" % zs[z]) + ".bin")
IO.writebin(dT_box3, outfile)
print "Written map to " + outfile
def plot_smoothed_dbt():
mini = -457
maxi=83
# print mini,maxi
for i in range(start,len(redshifts)):
print "Doing redshift " + str(redshifts[i])+"..."
dbt = np.load("../generate_data/"+setup_dirs.resultsdir()+"map_dbt_"+str('%.3f' % redshifts[i])+".bin")
dbt = c2t.beam_convolve(dbt[len(dbt[:,0,0])/2,:,:],redshifts[i],244.,beam_w=3.)
#IO.readmap("dbt_"+str('%.3f' % redshifts[i]))
# print np.min(dbt)
plot(dbt,i,"$\delta T_b$","smoothed_dbt",mini,maxi,'seismic')
print "Complete"
def smoothed_lightcone_dbt2(id="", ht=""):
print "hello"
infile = open(setup_dirs.resultsdir() + "dbt_lightcone" + ht + ".bin", "rb")
outfile = setup_dirs.resultsdir() + "dbt_lightcone_smooth" + ht + ".bin"
zfile = open(setup_dirs.resultsdir() + "dbt_lightcone_redshifts.bin", "rb")
dT_box = np.load(infile)
log = open("stats.dat", "w")
zs = np.load(zfile)
dT_box3 = np.zeros((len(dT_box[:, 1, 1]) / 2, len(dT_box[1, :, 1]) / 2, len(dT_box[1, 1, :])))
dT_box2 = np.zeros((len(dT_box[:, 1, 1]) / 2, len(dT_box[1, :, 1]) / 2, len(dT_box[1, 1, :])))
for z in range(len(dT_box[1, 1, :])):
wl = 0.21 * (1 + zs[z])
c = 299792.458
omm = 0.27
oml = 0.73
omr = 0.0
omk = 1.0 - omm - oml - omr
H0 = 70.0
print 1
def integrand(x, omm, oml, omr, omk):
return 1.0 / np.sqrt(omr * ((1 + x) ** 4) + omm * ((1 + x) ** 3) + oml + omk * ((1 + x) ** 2))
def dc(z, omm, oml, omr, omk):
return quad(integrand, 0, z, args=(omm, oml, omr, omk))[0] # /(1.0+z)
vec_dc = np.vectorize(dc)
print 2
bw_r = wl / (2.0e3) # radians
bw = bw_r * 3437.74677 # arcminutesi
stats.write("Wavelength of 21-cm from redshift " + str(zs[z]) + " is " + str(wl) + "m")
stats.write("At redshift: " + str(zs[z]) + " smoothing with a " + str(bw_r) + " radian beam")
stats.write("At redshift: " + str(zs[z]) + " smoothing with a " + str(bw) + " arc minute beam")
stats.write("\n")
print 3
dT_box2[:, :, z] = c2t.beam_convolve(dT_box[:, :, z], zs[z], 244.0, beam_w=bw)
print 4
dT_box3 = lightcone_smooth.frequency_direction_same(dT_box2, zs[0], 244.0, 2)
print 5
IO.writebin(dT_box3[:, :, z], setup_dirs.resultsdir() + "smoothed_map_dbt_" + ht + str("%.3f" % zs[z]) + ".bin")
IO.writebin(dT_box3, outfile)
print "Written map to " + outfile
dTconv_rsd_var = 0.
count = 0
width = int(width)
# Add up the dT slices in the bandwidth and find mean, rms, skewness
while (k < len(dT_box)-width-1):
#print 'k=',k
dT_slice = frac*dT_box[k,:,:]
dT_rsd_slice = frac*dT_rsd_box[k,:,:]
for j in range(1,width+1):
dT_slice += dT_box[k+j,:,:]
dT_rsd_slice += dT_rsd_box[k+j,:,:]
dT_slice /= n_cells
dT_rsd_slice /= n_cells
#print 'slices', dT_slice, dT_rsd_slice
dTconv_slice = c2t.beam_convolve(dT_slice, z_arr[i], c2t.conv.LB, beam_w=beam)
dTconv_rsd_slice = c2t.beam_convolve(dT_rsd_slice, z_arr[i], c2t.conv.LB, beam_w=beam)
#print 'shapes conv', dTconv_slice, dTconv_rsd_slice
if k == 0:
dT_PDF_box = dTconv_slice
dT_rsd_PDF_box = dTconv_rsd_slice
elif k > 0:
dT_PDF_box = np.dstack((dT_PDF_box,dTconv_slice))
dT_rsd_PDF_box = np.dstack((dT_rsd_PDF_box,dTconv_rsd_slice))
#print 'shapes ', k, dT_PDF_box.shape, dT_rsd_PDF_box.shape
dTconv_mean += dTconv_slice.mean()
dTconv_rsd_mean += dTconv_rsd_slice.mean()
dTconv_var += np.mean(pow(dTconv_slice-dTconv_mean,2))
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
pl.figure()
pl.subplot(221)
pl.imshow(n_hi[128,:,:])
pl.colorbar()
pl.title('$n_{HI}$')
def smoothed_coeval_box(ht=""):
log2 = open("coeval_stats.dat", "w")
for z in range(len(redshifts)):
infile = "map_dbt_" + ht + str("%0.3f" % redshifts[z]) + ".bin"
dT_box = IO.readbin(infile)
dT_box3 = np.zeros((len(dT_box[:, 1, 1]) / 2, len(dT_box[1, :, 1]) / 2, len(dT_box[1, 1, :])))
dT_box2 = np.zeros((len(dT_box[:, 1, 1]) / 2, len(dT_box[1, :, 1]) / 2, len(dT_box[1, 1, :])))
wl = 0.21 * (1 + redshifts[z])
c = 299792.458
omm = 0.27
oml = 0.73
omr = 0.0
omk = 1.0 - omm - oml - omr
H0 = 70.0
def integrand(x, omm, oml, omr, omk):
return 1.0 / np.sqrt(omr * ((1 + x) ** 4) + omm * ((1 + x) ** 3) + oml + omk * ((1 + x) ** 2))
def dc(z, omm, oml, omr, omk):
return quad(integrand, 0, z, args=(omm, oml, omr, omk))[0] # /(1.0+z)
vec_dc = np.vectorize(dc)
bw_r = wl / (2.0e3) # radians
bw = bw_r * 3437.74677 # arcminutesi
log2.write("Wavelength of 21-cm from redshift " + str(redshifts[z]) + " is " + str(wl) + "m\n")
log2.write("At redshift: " + str(redshifts[z]) + " smoothing with a " + str(bw) + " arc minute beam.\n")
rc = bw_r * c / H0 * vec_dc(redshifts[z], omm, oml, omr, omk) # comoving Mpc
Hz = H0 * np.sqrt(
omr * (1 + redshifts[z]) ** 4 + omm * (1 + redshifts[z]) ** 3 + oml + omk * (1 + redshifts[z]) ** 2
)
log2.write("rc = " + str(rc) + "\n")
dz = rc * Hz / c
log2.write("$\Delta$ z = " + str(dz) + "\n")
ncs = rc * 250.0 * 0.7 / 244.0
log2.write(str(ncs) + " cells in the z direction\n")
log2.write("\n")
for k in range(len(dT_box2[1, 1, :])):
dT_box2[:, :, k] = c2t.beam_convolve(dT_box[:, :, k], redshifts[z], 244.0, beam_w=bw)
for i in range(0): # len(dT_box2[:,1,1])):
for j in range(0): # len(dT_box2[1,:,1])):
for k in range(0): # len(dT_box2[1,1,:])):
L = len(dT_box2[1, 1, :])
if k - ncs / 2 > 0 and k + ncs / 2 < L:
dT_box3[i, j, k] = np.mean(dT_box2[i, j, k - ncs / 2.0 : k + ncs / 2.0])
if k + ncs / 2.0 - k + ncs / 2.0 != ncs:
print "Wrong smoothing width for tophat function!!"
elif (k - ncs) / 2 < 0:
if k + ncs / 2.0 + k - ncs / 2 != ncs:
print "Wrong smoothing width for tophat function!!"
dT_box3[i, j, k] = (
np.sum(dT_box2[i, j, 0 : (k + ncs / 2.0)]) + np.sum(dT_box2[i, j, L - ncs / 2 + k : L])
) / ncs
elif (k + ncs) / 2 > 10:
if L - k + ncs / 2 + L - k + ncs / 2 != ncs:
print "Wrong smoothing width for tophat function!!"
dT_box3[i, j, k] = (
np.sum(dT_box2[i, j, k - ncs / 2 : L]) + np.sum(dT_box2[i, j, L - k + ncs / 2 : L])
) / ncs
IO.writebin(
dT_box2, setup_dirs.resultsdir() + "smooth_coevalmap_dbt_" + ht + str("%.3f" % redshifts[z]) + ".bin"
)
print np.mean(dT_box2)
print "Written map"
#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('$n_{HI}$')
pl.subplot(222)
pl.imshow(dT_slice)
pl.colorbar()
pl.title('dT')
pl.subplot(223)
#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('$n_{HI}$')
pl.subplot(222)
pl.imshow(dT_slice)
pl.colorbar()
pl.title('dT')
pl.subplot(223)
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
pl.figure()
pl.subplot(221)
pl.imshow(n_hi[128, :, :])
pl.colorbar()
pl.title('$n_{HI}$')
