#Loop over z
for i in range(z_min,len(z_arr)):
print 'z = %.3f'% (z_arr[i])
#density_file = ''.join(glob.glob(density_path+'%.3fn_all.dat'%(z_arr[i])))
#if not density_file:
# density_file = ''.join(glob.glob(density_path+'%.3fn_all.dat'%(z_arr[i-1])))
#print 'Desnity file = %s' % density_file
#dfile = c2t.DensityFile(density_file)
temper_file = ''.join(glob.glob(xfrac_path+'Temper3D_%.3f.bin'%(z_arr[i])))
print 'Temperature file = %s' % temper_file
tfile = c2t.TemperFile(temper_file)
T_mean = tfile.temper.mean()
T_rms = c2t.rootmeansquare(tfile.temper)
T_var = np.mean(pow(tfile.temper-T_mean,2))
#avg_mG = c2t.mass_weighted_mean_xi(ifile.irate,dfile.raw_density)
print 'min T = ',tfile.temper.min(),' and max T = ',tfile.temper.max()
print 'T_CMB = ',2.725*(1+z_arr[i])
print 'z, avg_vG, rms = %.3f %.3f %.4e' % (z_arr[i], T_mean, T_rms)
out.write('%.3f %.4f %.4e\n' % (z_arr[i], T_mean, T_rms))
log_bins = np.logspace(1.,5.,100)
T_hist, T_bin_edges = np.histogram(tfile.temper,bins=log_bins,density=True)
T_bins = np.zeros(len(T_hist))
for j in range(0,len(T_hist)):
T_bins[j] = (T_bin_edges[j+1] - T_bin_edges[j])/2 + T_bin_edges[j]
pdf_out.write('%.4f %.4e %.4e %.4e\n' % (T_bins[j], T_hist[j], T_mean, T_var))
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)
dT_rms = dT_rms_box.mean()
dT_rms_other = c2t.rootmeansquare(dT_box)
dT_pv_mean = dT_pv_box.mean()
dT_pv_rms_box = np.sqrt((dT_pv_box - dT_pv_mean)**2)
dT_pv_rms = dT_pv_rms_box.mean()
print 'z, nu, dT_mean, dT_rms %.3f %.3f %.4f %.4f %.4f' % (z_arr[i], nu[i], dT_mean, dT_rms, dT_rms_other)
out.write('%.3f %.3f %.4f %.4f\n' % (z_arr[i], nu[i], dT_mean, dT_rms))
out1.write('%.3f %.3f %.4f %.4f\n' % (z_arr[i], nu[i], dT_pv_mean, dT_pv_rms))
out.close()
out1.close()
# Loop over z and read in dT. Calculate away!
for i in range(z_min,len(z_arr)):
print 'z = %.3f'% (z_arr[i])
dT_file = ''.join(glob.glob('./dT_boxes/dT_%.3f.cbin' % (z_arr[i])))
print 'dT file = %s' % dT_file
dT_box = c2t.read_cbin(dT_file, bits=64, order='F')
dT_rsd_file = ''.join(glob.glob('./dT_pv_boxes/dT_pv_%.3f.cbin' % (z_arr[i])))
print 'Distorted dT file = %s' % dT_rsd_file
dT_rsd_box = c2t.read_cbin(dT_rsd_file, bits=64, order='F')
dT_mean = dT_box.mean()
#print dT_mean
dT_rsd_mean = dT_rsd_box.mean()
#print dT_mean, dT_rsd_mean
dT_rms = c2t.rootmeansquare(dT_box)
dT_rsd_rms = c2t.rootmeansquare(dT_rsd_box)
dT_skew = c2t.skewness(dT_box)
dT_rsd_skew = c2t.skewness(dT_rsd_box)
dT_kurt = kurtosis(dT_box.flatten())
dT_rsd_kurt = kurtosis(dT_rsd_box.flatten())
#print dT_skew, dT_rsd_kurt
print 'z, nu, dT_mean, dT_rms %.3f %.3f %.4f %.4f %.4f' % (z_arr[i], nu[i], dT_mean, dT_rms, dT_kurt)
#print 'z, nu, dT_mean, dT_rms %.3f %.3f %.4f %.4f' % (z_arr[i], nu[i], dT_rsd_mean, dT_rsd_rms)
# Set the bandwidth and convert to cells
nu_low = c2t.z_to_nu(z_arr[i]) - dnu/2
nu_high = c2t.z_to_nu(z_arr[i]) + dnu/2
z_low = c2t.nu_to_z(nu_high)
z_high = c2t.nu_to_z(nu_low)
n_cells = (c2t.z_to_cdist(z_high) - c2t.z_to_cdist(z_low))/c2t.conv.LB*len(dT_box)