def dbt_histogram():
for i in range(len(redshifts)):
print "Doing redshift " + str(redshifts[i]) + "..."
dbt = IO.readbin("map_dbt_"+str('%.3f' % redshifts[i])+'.bin')
data = dbt.flatten()
print "Generating histogram..."
plot_dbt_histogram(data,"Differential Brightness Temperature, Redshift:" +str('%.3f' % redshifts[i]),"loghist_dbt_shortrange_"+str(i+10)+'_'+str('%.3f' % redshifts[i]),"Differential Brightness Temperature(K)")
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"
