def test_smooth_coeval():
'''
With this, smooth_coeval_gauss, smooth_coeval_tophat, gauss_kernel and tophat_kernel
are also tested.
'''
smt = t2c.smooth_coeval(data, 9, box_size_mpc=90)
assert smt[4, 4, 4] < 1
def _smt_xH(self, xH_box, idx):
assert idx == self.astro_par[0,idx]
z = self.astro_par[1, idx]
smt_xn = t2c.smooth_coeval(xH_box, z, box_size_mpc=self.user_par['HII_DIM'], max_baseline=2.0, ratio=1.0, nu_axis=2)
mask_xn = smt_xn>0.5
return mask_xn.astype(int)
def _lc_noise_smt_dT(self, lc1, idx):
assert idx == self.astro_par[0,idx]
z = self.astro_par[1, idx]
noise_lc = t2c.noise_lightcone(ncells=lc.brightness_temp.shape[0], zs=lc.lightcone_redshifts, obs_time=self.tobs, save_uvmap=self.uvfile, boxsize=self.user_par['BOX_LEN'])
# calculate uv-coverage
if(self.zipf):
with zipfile.ZipFile(self.path_uvcov) as myzip:
with myzip.open('uv_coverage_%d/uvmap_z%.3f.npy' %(self.user_par['HII_DIM'], z)) as myfile1:
uv = np.load(myfile1)
with myzip.open('uv_coverage_%d/Nantmap_z%.3f.npy' %(self.user_par['HII_DIM'], z)) as myfile2:
Nant = np.load(myfile2)
else:
file_uv = '%suvmap_z%.3f.npy' %(self.path_uvcov, z)
file_Nant = '%sNantmap_z%.3f.npy' %(self.path_uvcov, z)
if(os.path.exists(file_uv) and os.path.exists(file_Nant)):
uv = np.load(file_uv)
Nant = np.load(file_Nant)
else:
#SKA-Low 2016 configuration
uv, Nant = t2c.get_uv_daily_observation(self.user_par['HII_DIM'], z, filename=None,
total_int_time=6.0, int_time=10.0,
boxsize=self.user_par['BOX_LEN'],
declination=-30.0, verbose=False)
np.save(file_uv, uv)
np.save(file_Nant, Nant)
# calculate Noise cube
random.seed(datetime.now())
noise_cube = t2c.noise_cube_coeval(self.user_par['HII_DIM'], z, depth_mhz=None,
obs_time=self.tobs, filename=None, boxsize=self.user_par['BOX_LEN'],
total_int_time=6.0, int_time=10.0, declination=-30.0,
uv_map=uv, N_ant=Nant, fft_wrap=False, verbose=False)
dT3 = t2c.smooth_coeval(dT1+noise_cube, z,
box_size_mpc=self.user_par['HII_DIM'],
max_baseline=2.0, ratio=1.0, nu_axis=2)
return dT3
depth_mhz=None,
obs_time=t,
filename=None,
boxsize=params['BOX_LEN'],
total_int_time=6.0,
int_time=10.0,
declination=-30.0,
uv_map=uv,
N_ant=Nant,
verbose=True,
fft_wrap=False)
dT1 = t2c.subtract_mean_signal(dT, los_axis=2)
dT2 = dT1 + noise_cube
dT3 = t2c.smooth_coeval(dT2,
z,
box_size_mpc=params['HII_DIM'],
max_baseline=2.0,
ratio=1.0,
nu_axis=2)
smt_xn = t2c.smooth_coeval(xH,
z,
box_size_mpc=params['HII_DIM'],
max_baseline=2.0,
ratio=1.0,
nu_axis=2)
mask_xn = smt_xn > 0.5
# calculate error and prediction
X_tta = SegUnet21cmPredict(unet=model, x=dT3, TTA=False)
X_seg = np.round(np.mean(X_tta, axis=0))
# calculate MCC score
import tools21cm as t2c
### Setting the simulation environment
t2c.set_sim_constants(244)
### Reading files
xfrac_filename = '/disk/dawn-1/garrelt/Reionization/C2Ray_WMAP7/244Mpc/244Mpc_f2_0_250/results/xfrac3d_6.418.bin'
dens_filename = '/disk/dawn-1/sgiri/simulations/244Mpc/coarser_densities/nc250/6.418n_all.dat'
xfrac = t2c.read_c2ray_files(xfrac_filename,
file_type='xfrac') # Ionization fraction file
neut = 1 - xfrac # Neutral fraction file
dens = t2c.read_c2ray_files(dens_filename, file_type='dens') # Density file
### Redshift from filename.....It can be manually given also
z = float(xfrac_filename.split('_')[-1].split('.b')[0])
### Making 21-cm coeval cube
dt = t2c.calc_dt(xfrac, dens, z)
### Smoothing neutral field to SKA resolution
smt_dt = t2c.smooth_coeval(dt, z)
### Generating the binary field from 21-cm signal using KMeans
bin_xf_sim = t2c.threshold_kmeans_3cluster(dt, upper_lim=True, n_jobs=5)
bin_nf_sim = 1. - bin_xf_sim # The neutral binary field at Sim-Res
bin_xf_smt = t2c.threshold_kmeans_3cluster(smt_dt, upper_lim=True, n_jobs=5)
bin_nf_smt = 1. - bin_xf_smt # The neutral binary field at Sim-Res
### Setting the simulation environment
t2c.set_sim_constants(244)
### Reading files
xfrac_filename = '/disk/dawn-1/garrelt/Reionization/C2Ray_WMAP7/244Mpc/244Mpc_f2_0_250/results/xfrac3d_6.450.bin'
xfrac = t2c.read_c2ray_files(xfrac_filename,
file_type='xfrac') # Ionization fraction file
neut = 1 - xfrac # Neutral fraction file
### Redshift from filename.....It can be manually given also
z = float(xfrac_filename.split('_')[-1].split('.b')[0])
### Smoothing neutral field to SKA resolution
smt_neut = t2c.smooth_coeval(neut, z)
### Generating the binary fields
xth = 0.5 # The fixed threshold to identify the regions of interest
bin_neut = neut > xth
bin_smt = smt_neut > xth
### Looking at the slices
fig, axes = plt.subplots(nrows=2, ncols=2)
fig.subplots_adjust(left=0.07,
bottom=0.06,
right=0.90,
top=0.96,
wspace=0.01,
hspace=0.15)
im00 = axes[0, 0].imshow(neut[:, :, 125], vmin=0, vmax=1)