def cmb_forecast_data(experiment,
map_params,
l,
cl,
cluster=None,
cl_residual=None,
bl=None,
nber_obs=1):
nx, dx, ny, dy = map_params
if cluster is not None:
kappa_map = lensing.NFW(cluster[0], cluster[1], cluster[2],
1100).convergence_map(map_params)
alpha_vec = lensing.deflection_from_convergence(map_params, kappa_map)
sims_arr = []
for i in range(nber_obs):
sim = tools.make_gaussian_realization(map_params, l, cl)
if cluster is not None:
sim = lensing.lens_map(map_params, sim, alpha_vec)
if cl_residual is not None:
residual_noise_map = tools.make_gaussian_realization(
map_params, l, cl_residual)
sim += residual_noise_map
if bl is not None:
sim = tools.convolve(sim, l, np.sqrt(bl), map_params=map_params)
sims_arr.append(sim)
return sims_arr
def forward(self, X):
"""Forward propagate the output of the previous layer.
Parameters
----------
X : array-like, shape = [N, I, Y, X]
input
Returns
-------
y : array-like, shape = [N, J, Y_o, X_o]
output
"""
if np.prod(X.shape[1:]) != self.n_inputs:
raise ValueError("shape = %s, expected %d inputs" %
(X.shape, self.n_inputs))
self.X = X
A = convolve(X,
self.W,
self.B,
stride_y=self.strides[0],
stride_x=self.strides[1])
# Compute output
self.Y = self.g(A)
return self.Y
def cmb_forecast_test_data(map_params,
l,
cl,
cluster=None,
cl_residuals=None,
bl=None,
nber_obs=1):
if cluster is not None:
kappa_map = lensing.NFW(cluster[0], cluster[1], cluster[2],
1100).convergence_map(map_params)
alpha_vec = lensing.deflection_from_convergence(map_params, kappa_map)
sims_arr_so, sims_arr_fyst, sims_arr_s4wide = [], [], []
for i in range(nber_obs):
sim = tools.make_gaussian_realization(map_params, l, cl)
if cluster is not None:
sim = lensing.lens_map(map_params, sim, alpha_vec)
sim_so, sim_fyst, sim_s4wide = np.copy(sim), np.copy(sim), np.copy(sim)
if cl_residuals is not None:
residual_noise_map_so = tools.make_gaussian_realization(
map_params, l, cl_residuals[0])
sim_so += residual_noise_map_so
residual_noise_map_fyst = tools.make_gaussian_realization(
map_params, l, cl_residuals[1])
sim_fyst += residual_noise_map_fyst
residual_noise_map_s4wide = tools.make_gaussian_realization(
map_params, l, cl_residuals[2])
sim_s4wide += residual_noise_map_s4wide
if bl is not None:
sim_so = tools.convolve(sim_so,
l,
np.sqrt(bl),
map_params=map_params)
sim_fyst = tools.convolve(sim_fyst,
l,
np.sqrt(bl),
map_params=map_params)
sim_s4wide = tools.convolve(sim_s4wide,
l,
np.sqrt(bl),
map_params=map_params)
sims_arr_so.append(sim_so)
sims_arr_fyst.append(sim_fyst)
sims_arr_s4wide.append(sim_s4wide)
return sims_arr_so, sims_arr_fyst, sims_arr_s4wide
def get_aligned_cutout(map_params, image, l=None, cl=None, cl_noise=None):
if cl_noise is None:
l = np.arange(10000)
cl = np.ones(max(l) + 1)
cl_noise = np.zeros(max(l) + 1)
_, dx, _, _ = map_params
cutout = tools.central_cutout(map_params, image, 10)
wiener_filter = tools.wiener_filter(l, cl, cl_noise)
filtered_map = tools.convolve(image, l, wiener_filter, map_params)
low_pass_filter = tools.low_pass_filter(l, 2000)
filtered_map = tools.convolve(filtered_map, l, low_pass_filter, map_params)
filtered_cutout = tools.central_cutout(map_params, filtered_map, 6)
_, _, magnitude, angle = tools.gradient(filtered_cutout, dx)
angle, magnitude_weight = np.median(angle), np.median(magnitude)
cutout_aligned = tools.rotate(cutout, angle)
cutout_aligned -= np.median(cutout_aligned)
return cutout_aligned, magnitude_weight
def get_aligned_cutout(map_params,
image,
image_noiseless=None,
cutout_size_am=10,
cutout_size_for_grad_est_am=6,
l_cut=2000,
l=None,
cl=None,
cl_noise=None):
if image_noiseless is None:
image_noiseless = np.copy(image)
if cl_noise is None:
l = np.arange(10001)
cl = np.ones(max(l) + 1)
cl_noise = np.zeros(max(l) + 1)
_, dx, _, _ = map_params
cutout = tools.central_cutout(image_noiseless, cutout_size_am, map_params)
wiener_filter = tools.wiener_filter(cl, cl_noise)
low_pass_filter = tools.low_pass_filter(l, l_cut)
filtered_map = tools.convolve(image, l, wiener_filter * low_pass_filter,
map_params)
filtered_cutout = tools.central_cutout(filtered_map,
cutout_size_for_grad_est_am,
map_params)
angle, magnitude = get_angle_and_magnitude(filtered_cutout, dx)
angle, magnitude_weight = np.median(angle), np.median(magnitude)
cutout_aligned = scipy.ndimage.rotate(np.nan_to_num(cutout),
np.nan_to_num(angle),
reshape=False,
mode='reflect')
cutout_aligned -= np.median(cutout_aligned)
return cutout_aligned, magnitude_weight
def cmb_mock_data(map_params,
l,
cl,
cluster=None,
centroid_shift_value=0,
nber_ch=1,
cluster_corr_cutouts=None,
cl_extragal=None,
bl=None,
nl=None):
nx, dx, ny, dy = map_params
sim = tools.make_gaussian_realization(map_params, l, cl)
if cluster is not None:
M, c, z = cluster
x_shift, y_shift = np.random.normal(
loc=0.0, scale=centroid_shift_value), np.random.normal(
loc=0.0, scale=centroid_shift_value)
centroid_shift = [x_shift, y_shift]
kappa_map = lensing.NFW(M, c, z, 1100).convergence_map(
map_params, centroid_shift=centroid_shift)
alpha_vec = lensing.deflection_from_convergence(map_params, kappa_map)
sim = lensing.lens_map(map_params, sim, alpha_vec)
sims_ch_arr = [np.copy(sim) for k in range(nber_ch)]
if cluster_corr_cutouts is not None:
if np.asarray(cluster_corr_cutouts).ndim is 3:
cluster_corr_cutouts = [cluster_corr_cutouts]
cluster_corr_cutout = cluster_corr_cutouts[0][0]
nx_cutout, ny_cutout = cluster_corr_cutout.shape[
0], cluster_corr_cutout.shape[1]
s, e = int((nx - nx_cutout) / 2), int((ny + ny_cutout) / 2)
rand_sel = random.randint(0, len(cluster_corr_cutouts[0]) - 1)
rand_ang = random.randint(-180, 180)
for j in range(nber_ch):
cluster_corr_cutout = tools.rotate(
cluster_corr_cutouts[j][rand_sel], rand_ang)
sims_ch_arr[j][s:e,
s:e] = sims_ch_arr[j][s:e,
s:e] + cluster_corr_cutout
if cl_extragal is not None:
if isinstance(cl_extragal, list) is False:
cl_extragal = [cl_extragal]
extragal_maps = tools.make_gaussian_realization(
map_params, l, cl_extragal)
for j in range(nber_ch):
sims_ch_arr[j] += extragal_maps[j]
if bl is not None:
if isinstance(bl, list) is False:
bl = [bl]
for j in range(nber_ch):
sims_ch_arr[j] = tools.convolve(sims_ch_arr[j],
l,
np.sqrt(bl[j]),
map_params=map_params)
if nl is not None:
if isinstance(nl, list) is False:
nl = [nl]
for j in range(nber_ch):
noise_map = tools.make_gaussian_realization(map_params, l, nl[j])
sims_ch_arr[j] += noise_map
if nber_ch == 1:
return sims_ch_arr[0]
return sims_ch_arr
def cmb_test_data(nber_maps,
validation_analyis=False,
clus_position_analysis=False,
extragal_bias_analysis=False):
nx, dx, ny, dy = 240, 0.25, 240, 0.25
map_params = [nx, dx, ny, dy]
l, cl = CosmoCalc().cmb_power_spectrum()
l, bl = exp.beam_power_spectrum(1.4)
l, nl = exp.white_noise_power_spectrum(2.0)
if validation_analyis is True:
sims_clus_2e14, sims_clus_6e14, sims_clus_10e14 = [], [], []
kappa_map_2e14 = lensing.NFW(2e14, 3, 1,
1100).convergence_map(map_params)
kappa_map_6e14 = lensing.NFW(6e14, 3, 1,
1100).convergence_map(map_params)
kappa_map_10e14 = lensing.NFW(10e14, 3, 1,
1100).convergence_map(map_params)
alpha_vec_2e14 = lensing.deflection_from_convergence(
map_params, kappa_map_2e14)
alpha_vec_6e14 = lensing.deflection_from_convergence(
map_params, kappa_map_6e14)
alpha_vec_10e14 = lensing.deflection_from_convergence(
map_params, kappa_map_10e14)
for i in range(nber_maps):
sim = tools.make_gaussian_realization(map_params, l, cl)
sim_clus_2e14 = lensing.lens_map(map_params, sim, alpha_vec_2e14)
sim_clus_6e14 = lensing.lens_map(map_params, sim, alpha_vec_6e14)
sim_clus_10e14 = lensing.lens_map(map_params, sim, alpha_vec_10e14)
sim_clus_2e14 = tools.convolve(sim_clus_2e14,
l,
np.sqrt(bl),
map_params=map_params)
sim_clus_6e14 = tools.convolve(sim_clus_6e14,
l,
np.sqrt(bl),
map_params=map_params)
sim_clus_10e14 = tools.convolve(sim_clus_10e14,
l,
np.sqrt(bl),
map_params=map_params)
noise_map = tools.make_gaussian_realization(map_params, l, nl)
sim_clus_2e14 += noise_map
sim_clus_6e14 += noise_map
sim_clus_10e14 += noise_map
sims_clus_2e14.append(sim_clus_2e14)
sims_clus_6e14.append(sim_clus_6e14)
sims_clus_10e14.append(sim_clus_10e14)
return sims_clus_2e14, sims_clus_6e14, sims_clus_10e14
if clus_position_analysis is True:
sims_baseline, sims_centorid_shift = [], []
kappa_map_6e14_baseline = lensing.NFW(2e14, 3, 1,
1100).convergence_map(map_params)
alpha_vec_6e14_baseline = lensing.deflection_from_convergence(
map_params, kappa_map_6e14_baseline)
for i in range(nber_maps):
x_shift, y_shift = np.random.normal(
loc=0.0, scale=0.5), np.random.normal(loc=0.0, scale=0.5)
centroid_shift = [x_shift, y_shift]
kappa_map_6e14_centroid_shift = lensing.NFW(
6e14, 3, 1, 1100).convergence_map(map_params, centroid_shift)
alpha_vec_6e14_centroid_shift = lensing.deflection_from_convergence(
map_params, kappa_map_6e14_centroid_shift)
sim = tools.make_gaussian_realization(map_params, l, cl)
sim_baseline = lensing.lens_map(map_params, sim,
alpha_vec_6e14_baseline)
sim_centroid_shift = lensing.lens_map(
map_params, sim, alpha_vec_6e14_centroid_shift)
sim_baseline = tools.convolve(sim_baseline,
l,
np.sqrt(bl),
map_params=map_params)
sim_centroid_shift = tools.convolve(sim_centroid_shift,
l,
np.sqrt(bl),
map_params=map_params)
noise_map = tools.make_gaussian_realization(map_params, l, nl)
sim_baseline += noise_map
sim_centroid_shift += noise_map
sims_baseline.append(sim_baseline)
sims_centroid_shift.append(sim_centroid_shift)
return sims_baseline, sims_centroid_shift
if extragal_bias_analysis is True:
sims_baseline, sims_tsz, sims_ksz, sims_tsz_ksz = [], [], [], []
c500 = concentration.concentration(2e14, '500c', 0.7)
M200c, _, c200c = mass_defs.changeMassDefinition(2e14,
c500,
0.7,
'500c',
'200c',
profile='nfw')
kappa_map_M200c = lensing.NFW(M200c, c200c, 0.7,
1100).convergence_map(map_params)
alpha_vec_M200c = lensing.deflection_from_convergence(
map_params, kappa_map_M200c)
fname = '/Volumes/Extreme_SSD/codes/master_thesis/code/data/mdpl2_cutouts_for_tszksz_clus_detection_M1.7e+14to2.3e+14_z0.6to0.8_15320haloes_boxsize20.0am.npz'
cutouts_dic = np.load(fname, allow_pickle=1,
encoding='latin1')['arr_0'].item()
mass_z_key = list(cutouts_dic.keys())[0]
cutouts = cutouts_dic[mass_z_key]
scale_fac = fg.compton_y_to_delta_Tcmb(145, uK=True)
tsz_cutouts, ksz_cutouts = [], []
for kcntr, keyname in enumerate(cutouts):
tsz_cutout = cutouts[keyname]['y'] * scale_fac
tsz_cutouts.append(tsz_cutout)
ksz_cutout = cutouts[keyname]['ksz'] * random.randrange(-1, 2, 2)
ksz_cutouts.append(ksz_cutout)
s, e = int((nx - 40) / 2), int((ny + 40) / 2)
for i in range(nber_maps):
sim = tools.make_gaussian_realization(map_params, l, cl)
sim_M200c = lensing.lens_map(map_params, sim, alpha_vec_M200c)
sim_baseline, sim_tsz, sim_ksz, sim_tsz_ksz = np.copy(
sim_M200c), np.copy(sim_M200c), np.copy(sim_M200c), np.copy(
sim_M200c)
tsz_cutout = tools.rotate(
tsz_cutouts[random.randint(0,
len(tsz_cutouts) - 1)],
random.randint(-180, 180))
ksz_cutout = tools.rotate(
ksz_cutouts[random.randint(0,
len(ksz_cutouts) - 1)],
random.randint(-180, 180))
tsz_ksz_cutout = tsz_cutout + ksz_cutout
sim_tsz[s:e, s:e] = sim_tsz[s:e, s:e] + tsz_cutout
sim_ksz[s:e, s:e] = sim_ksz[s:e, s:e] + ksz_cutout
sim_tsz_ksz[s:e, s:e] = sim_tsz_ksz[s:e, s:e] + tsz_ksz_cutout
sim_baseline = tools.convolve(sim_baseline,
l,
np.sqrt(bl),
map_params=map_params)
sim_tsz = tools.convolve(sim_tsz,
l,
np.sqrt(bl),
map_params=map_params)
sim_ksz = tools.convolve(sim_ksz,
l,
np.sqrt(bl),
map_params=map_params)
sim_tsz_ksz = tools.convolve(sim_tsz_ksz,
l,
np.sqrt(bl),
map_params=map_params)
noise_map = tools.make_gaussian_realization(map_params, l, nl)
sim_baseline += noise_map
sim_tsz += noise_map
sim_ksz += noise_map
sim_tsz_ksz += noise_map
sims_baseline.append(sim_baseline)
sims_tsz.append(sim_tsz)
sims_ksz.append(sim_ksz)
sims_tsz_ksz.append(sim_tsz_ksz)
return sims_baseline, sims_tsz, sims_ksz, sims_tsz_ksz
def models(nber_fit,
map_params,
l,
cl,
mass_int,
z,
centroid_shift_value=0,
bl=None,
cl_noise=None,
cutout_size_am=10,
use_magnitude_weights=True,
use_noise_weights=False,
apply_noise=True,
average=1):
nx, dx, ny, dy = map_params
if cl_noise is None:
cl_noise = np.zeros(max(l) + 1)
mass_int = np.copy(mass_int) * 1e14
models = []
for k in range(average):
cutouts_clus_arr = []
magnitude_weights_clus_arr = []
profile_models_arr = []
for i in range(nber_fit):
sim = sims.cmb_mock_data(map_params, l, cl)
x_shift, y_shift = np.random.normal(
loc=0.0,
scale=centroid_shift_value / (2**0.5)), np.random.normal(
loc=0.0, scale=centroid_shift_value / (2**0.5))
centroid_shift = [x_shift, y_shift]
grid, _ = tools.make_grid(map_params, grid_shift=centroid_shift)
theta = np.hypot(grid[0], grid[1])
total_noise_map = tools.make_gaussian_realization(
l, cl_noise, map_params)
for j in range(len(mass_int)):
c200c = cosmo.concentration_parameter(mass_int[j], z, 0.674)
kappa_map = lensing.NFW_convergence(mass_int[j],
c200c,
z,
1100,
theta,
dim=2)
alpha_vec = lensing.deflection_from_convergence(
kappa_map, map_params)
sim_lensed = lensing.lens_map(sim, alpha_vec, map_params)
sim_lensed_noise = np.copy(sim_lensed)
sim_lensed_noise += total_noise_map
if bl is not None:
sim_lensed = tools.convolve(sim_lensed,
l,
np.sqrt(bl),
map_params=map_params)
sim_lensed_noise = tools.convolve(sim_lensed_noise,
l,
np.sqrt(bl),
map_params=map_params)
if apply_noise is False:
sim_lensed_noise = np.copy(sim_lensed)
cutout_aligned, magnitude_weight = get_aligned_cutout(
map_params,
sim_lensed_noise,
image_noiseless=sim_lensed,
cutout_size_am=cutout_size_am,
l=l,
cl=cl,
cl_noise=cl_noise)
if use_magnitude_weights is False:
magnitude_weight = 1
cutouts_clus_arr.append(cutout_aligned * magnitude_weight)
magnitude_weights_clus_arr.append(magnitude_weight)
stack_bg = np.sum(cutouts_clus_arr[0::len(mass_int)], axis=0) / np.sum(
magnitude_weights_clus_arr[0::len(mass_int)])
for i in range(len(mass_int)):
stack_clus = np.sum(
cutouts_clus_arr[i::len(mass_int)], axis=0) / np.sum(
magnitude_weights_clus_arr[i::len(mass_int)])
stack_dipole = stack_clus - stack_bg
profile_model = np.mean(stack_dipole, axis=0)
profile_models_arr.append(profile_model)
models.append(profile_models_arr)
return np.mean(models, axis=0)
def model_profiles(nber_clus_fit,
nber_rand_fit,
map_params,
l,
cl,
mass_int,
c200c,
z,
centroid_shift_value=0,
cl_extragal=None,
bl=None,
cl_noise=None,
use_magnitude_weights=True,
use_noise_weights=False,
apply_noise=True):
nx, dx, ny, dy = map_params
if cl_noise is None:
cl_noise = np.zeros(max(l) + 1)
mass_int = np.copy(mass_int) * 1e14
cutouts_clus_arr = []
magnitude_weights_clus_arr = []
for i in tqdm(range(nber_clus_fit)):
sim = sims.cmb_mock_data(map_params, l, cl)
x_shift, y_shift = np.random.normal(
loc=0.0, scale=centroid_shift_value), np.random.normal(
loc=0.0, scale=centroid_shift_value)
centroid_shift = [x_shift, y_shift]
for j in range(len(mass_int)):
kappa_map = lensing.NFW(mass_int[j], c200c, z,
1100).convergence_map(
map_params,
centroid_shift=centroid_shift)
alpha_vec = lensing.deflection_from_convergence(
map_params, kappa_map)
sim_lensed = lensing.lens_map(map_params, sim, alpha_vec)
sim_lensed_noise = np.copy(sim_lensed)
total_noise_map = tools.make_gaussian_realization(
map_params, l, cl_noise)
sim_lensed_noise += total_noise_map
if bl is not None:
sim_lensed = tools.convolve(sim_lensed,
l,
np.sqrt(bl),
map_params=map_params)
sim_lensed_noise = tools.convolve(sim_lensed_noise,
l,
np.sqrt(bl),
map_params=map_params)
if apply_noise is False:
sim_lensed_noise = np.copy(sim_lensed)
cutout = tools.central_cutout(map_params, sim_lensed, 10)
wiener_filter = tools.wiener_filter(l, cl, cl_noise)
filtered_map = tools.convolve(sim_lensed_noise, l, wiener_filter,
map_params)
low_pass_filter = tools.low_pass_filter(l, 2000)
filtered_map = tools.convolve(filtered_map, l, low_pass_filter,
map_params)
filtered_cutout = tools.central_cutout(map_params, filtered_map, 6)
_, _, magnitude, angle = tools.gradient(filtered_cutout, dx)
angle, magnitude_weight = np.median(angle), np.median(magnitude)
cutout_aligned = tools.rotate(cutout, angle)
cutout_aligned -= np.median(cutout_aligned)
if use_magnitude_weights is False:
magnitude_weight = 1
cutouts_clus_arr.append(cutout_aligned * magnitude_weight)
magnitude_weights_clus_arr.append(magnitude_weight)
cutouts_rand_arr = []
magnitude_weights_rand_arr = []
for i in tqdm(range(nber_rand_fit)):
sim = sims.cmb_mock_data(map_params, l, cl)
sim_noise = np.copy(sim)
total_noise_map = tools.make_gaussian_realization(
map_params, l, cl_noise)
sim_noise += total_noise_map
if bl is not None:
sim = tools.convolve(sim, l, np.sqrt(bl), map_params=map_params)
sim_noise = tools.convolve(sim_noise,
l,
np.sqrt(bl),
map_params=map_params)
if apply_noise is False:
sim_noise = np.copy(sim)
cutout = tools.central_cutout(map_params, sim, 10)
wiener_filter = tools.wiener_filter(l, cl, cl_noise)
filtered_map = tools.convolve(sim_noise, l, wiener_filter, map_params)
low_pass_filter = tools.low_pass_filter(l, 2000)
filtered_map = tools.convolve(filtered_map, l, low_pass_filter,
map_params)
filtered_cutout = tools.central_cutout(map_params, filtered_map, 6)
_, _, magnitude, angle = tools.gradient(filtered_cutout, dx)
angle, magnitude_weight = np.median(angle), np.median(magnitude)
cutout_aligned = tools.rotate(cutout, angle)
cutout_aligned -= np.median(cutout_aligned)
cutouts_rand_arr.append(cutout_aligned)
magnitude_weights_rand_arr.append(magnitude_weight)
if use_magnitude_weights is False:
magnitude_weights_rand_arr = np.ones(nber_rand_fit)
weighted_cutouts = [
cutouts_rand_arr[i] * magnitude_weights_rand_arr[i]
for i in range(nber_rand_fit)
]
stack_bg = np.sum(weighted_cutouts,
axis=0) / np.sum(magnitude_weights_rand_arr)
profile_models_arr = []
for i in tqdm(range(len(mass_int))):
stack_clus = np.sum(cutouts_clus_arr[i::len(mass_int)],
axis=0) / np.sum(
magnitude_weights_clus_arr[i::len(mass_int)])
stack_dipole = stack_clus - stack_bg
profile_model = np.mean(stack_dipole, axis=0)
profile_models_arr.append(profile_model)
return profile_models_arr
def cmb_test_data(map_params,
l,
cl,
cluster=None,
centroid_shift_value=0,
cluster_corr_cutouts=None,
bl=None,
nl=None,
nber_obs=1,
estimator_validation=False,
noise_comparison=False,
clus_positions=False,
foreground_bias=False):
if estimator_validation is True:
sims_clus_arr = []
kappa_maps = [
lensing.NFW(cluster[i][0], cluster[i][1], cluster[i][2],
1100).convergence_map(map_params)
for i in range(len(cluster))
]
alpha_vecs = [
lensing.deflection_from_convergence(map_params, kappa_maps[i])
for i in range(len(cluster))
]
for i in range(nber_obs):
sim = tools.make_gaussian_realization(map_params, l, cl)
sims_lensed = [
lensing.lens_map(map_params, sim, alpha_vecs[i])
for i in range(len(cluster))
]
if bl is not None:
for j in range(len(sims_lensed)):
sims_lensed[j] = tools.convolve(sims_lensed[j],
l,
np.sqrt(bl),
map_params=map_params)
if nl is not None:
noise_map = tools.make_gaussian_realization(map_params, l, nl)
for j in range(len(sims_lensed)):
sims_lensed[j] += noise_map
sims_clus_arr.append(sims_lensed)
sims_mass_sorted = []
for i in range(len(cluster)):
maps_at_mass_i = []
for j in range(nber_obs):
maps_at_mass_i.append(sims_clus_arr[j][i])
sims_mass_sorted.append(maps_at_mass_i)
return sims_mass_sorted
if noise_comparison is True:
sims_noise_arr = []
kappa_map = lensing.NFW(cluster[0], cluster[1], cluster[2],
1100).convergence_map(map_params)
alpha_vec = lensing.deflection_from_convergence(map_params, kappa_map)
for i in range(nber_obs):
sim = tools.make_gaussian_realization(map_params, l, cl)
sim_lensed = lensing.lens_map(map_params, sim, alpha_vec)
if bl is not None:
sim_lensed = tools.convolve(sim_lensed,
l,
np.sqrt(bl),
map_params=map_params)
sims_noise = [np.copy(sim_lensed) for i in range(len(nl))]
noise_maps = [
tools.make_gaussian_realization(map_params, l, nl[i])
for i in range(len(nl))
]
for i in range(len(sims_noise)):
sims_noise[i] += noise_maps[i]
sims_noise_arr.append(sims_noise)
sims_noise_sorted = []
for i in range(len(nl)):
maps_at_noise_i = []
for j in range(nber_obs):
maps_at_noise_i.append(sims_noise_arr[j][i])
sims_noise_sorted.append(maps_at_noise_i)
return sims_noise_sorted
if clus_positions is True:
sims_clus_baseline, sims_clus_centroid_shift = [], []
kappa_map_baseline = lensing.NFW(cluster[0], cluster[1], cluster[2],
1100).convergence_map(map_params)
alpha_vec_baseline = lensing.deflection_from_convergence(
map_params, kappa_map_baseline)
for i in range(nber_obs):
x_shift, y_shift = np.random.normal(
loc=0.0, scale=centroid_shift_value), np.random.normal(
loc=0.0, scale=centroid_shift_value)
centroid_shift = [x_shift, y_shift]
kappa_map_centroid_shift = lensing.NFW(
cluster[0], cluster[1], cluster[2],
1100).convergence_map(map_params, centroid_shift)
alpha_vec_centroid_shift = lensing.deflection_from_convergence(
map_params, kappa_map_centroid_shift)
sim = tools.make_gaussian_realization(map_params, l, cl)
sim_lensed_baseline = lensing.lens_map(map_params, sim,
alpha_vec_baseline)
sim_lensed_centroid_shift = lensing.lens_map(
map_params, sim, alpha_vec_centroid_shift)
if bl is not None:
sim_lensed_baseline = tools.convolve(sim_lensed_baseline,
l,
np.sqrt(bl),
map_params=map_params)
sim_lensed_centroid_shift = tools.convolve(
sim_lensed_centroid_shift,
l,
np.sqrt(bl),
map_params=map_params)
if nl is not None:
noise_map = tools.make_gaussian_realization(map_params, l, nl)
sim_lensed_baseline += noise_map
sim_lensed_centroid_shift += noise_map
sims_clus_baseline.append(sim_lensed_baseline)
sims_clus_centroid_shift.append(sim_lensed_centroid_shift)
return sims_clus_baseline, sims_clus_centroid_shift
if foreground_bias is True:
fname = '/Volumes/Extreme_SSD/codes/master_thesis/code/data/mdpl2_cutouts_for_tszksz_clus_detection_M1.7e+14to2.3e+14_z0.6to0.8_15320haloes_boxsize10.0am_dx0.5am.npz'
cutouts_dic = np.load(fname, allow_pickle=1,
encoding='latin1')['arr_0'].item()
mass_z_key = list(cutouts_dic.keys())[0]
cutouts = cutouts_dic[mass_z_key]
scale_fac = fg.compton_y_to_delta_Tcmb(150, uK=True)
tsz_cutouts, ksz_cutouts = [], []
for kcntr, keyname in enumerate(cutouts):
tsz_cutout = cutouts[keyname]['y'] * scale_fac
tsz_cutouts.append(tsz_cutout)
ksz_cutout = cutouts[keyname]['ksz'] * random.randrange(-1, 2, 2)
ksz_cutouts.append(ksz_cutout)
nx, dx, ny, dy = map_params
cluster_corr_cutout = ksz_cutouts[0]
nx_cutout, ny_cutout = cluster_corr_cutout.shape[
0], cluster_corr_cutout.shape[1]
s, e = int((nx - nx_cutout) / 2), int((ny + ny_cutout) / 2)
sims_clus_baseline, sims_clus_tsz, sims_clus_ksz, sims_clus_tsz_ksz = [], [], [], []
kappa_map = lensing.NFW(cluster[0], cluster[1], cluster[2],
1100).convergence_map(map_params)
alpha_vec = lensing.deflection_from_convergence(map_params, kappa_map)
for i in range(nber_obs):
sim = tools.make_gaussian_realization(map_params, l, cl)
sim_lensed = lensing.lens_map(map_params, sim, alpha_vec)
sim_lensed_baseline, sim_lensed_tsz, sim_lensed_ksz, sim_lensed_tsz_ksz = np.copy(
sim_lensed), np.copy(sim_lensed), np.copy(sim_lensed), np.copy(
sim_lensed)
tsz_cutout = tools.rotate(
tsz_cutouts[random.randint(0,
len(tsz_cutouts) - 1)],
random.randint(-180, 180))
ksz_cutout = tools.rotate(
ksz_cutouts[random.randint(0,
len(ksz_cutouts) - 1)],
random.randint(-180, 180))
tsz_ksz_cutout = tsz_cutout + ksz_cutout
sim_lensed_tsz[s:e, s:e] = sim_lensed_tsz[s:e, s:e] + tsz_cutout
sim_lensed_ksz[s:e, s:e] = sim_lensed_ksz[s:e, s:e] + ksz_cutout
sim_lensed_tsz_ksz[s:e,
s:e] = sim_lensed_tsz_ksz[s:e,
s:e] + tsz_ksz_cutout
if bl is not None:
sim_lensed_baseline = tools.convolve(sim_lensed_baseline,
l,
np.sqrt(bl),
map_params=map_params)
sim_lensed_tsz = tools.convolve(sim_lensed_tsz,
l,
np.sqrt(bl),
map_params=map_params)
sim_lensed_ksz = tools.convolve(sim_lensed_ksz,
l,
np.sqrt(bl),
map_params=map_params)
sim_lensed_tsz_ksz = tools.convolve(sim_lensed_tsz_ksz,
l,
np.sqrt(bl),
map_params=map_params)
if nl is not None:
noise_map = tools.make_gaussian_realization(map_params, l, nl)
sim_lensed_baseline += noise_map
sim_lensed_tsz += noise_map
sim_lensed_ksz += noise_map
sim_lensed_tsz_ksz += noise_map
sims_clus_baseline.append(sim_lensed_baseline)
sims_clus_tsz.append(sim_lensed_tsz)
sims_clus_ksz.append(sim_lensed_ksz)
sims_clus_tsz_ksz.append(sim_lensed_tsz_ksz)
return sims_clus_baseline, sims_clus_tsz, sims_clus_ksz, sims_clus_tsz_ksz
def cmb_mock_data(map_params,
l,
cl,
cluster=None,
centroid_shift_value=0,
nber_ch=1,
cluster_corr_cutouts=None,
cl_extragal=None,
bl=None,
nl=None,
nber_obs=1):
nx, dx, ny, dy = map_params
sims = []
for i in range(nber_obs):
sim = tools.make_gaussian_realization(l, cl, map_params)
if cluster is not None:
M, c, z = cluster
x_shift, y_shift = np.random.normal(
loc=0.0,
scale=centroid_shift_value / (2**0.5)), np.random.normal(
loc=0.0, scale=centroid_shift_value / (2**0.5))
centroid_shift = [x_shift, y_shift]
grid, _ = tools.make_grid(map_params, grid_shift=centroid_shift)
theta = np.hypot(grid[0], grid[1])
kappa_map = lensing.NFW_convergence(M, c, z, 1100, theta, dim=2)
alpha_vec = lensing.deflection_from_convergence(
kappa_map, map_params)
sim = lensing.lens_map(sim, alpha_vec, map_params)
sims_ch_arr = [np.copy(sim) for k in range(nber_ch)]
if cluster_corr_cutouts is not None:
if np.asarray(cluster_corr_cutouts).ndim == 3:
cluster_corr_cutouts = [cluster_corr_cutouts]
cluster_corr_cutout = cluster_corr_cutouts[0][0]
nx_cutout, ny_cutout = cluster_corr_cutout.shape[
0], cluster_corr_cutout.shape[1]
s, e = int((nx - nx_cutout) / 2), int((ny + ny_cutout) / 2)
rand_sel = random.randint(0, len(cluster_corr_cutouts[0]) - 1)
rand_ang = random.randint(-180, 180)
for j in range(nber_ch):
cluster_corr_cutout = scipy.ndimage.rotate(
np.nan_to_num(cluster_corr_cutouts[j][rand_sel]),
np.nan_to_num(rand_ang),
reshape=False,
mode='reflect')
sims_ch_arr[j][s:e,
s:e] = sims_ch_arr[j][s:e,
s:e] + cluster_corr_cutout
if cl_extragal is not None:
if isinstance(cl_extragal, list) is False:
cl_extragal = [cl_extragal]
for j in range(nber_ch):
extragal_noise_map = tools.make_gaussian_realization(
l, cl_extragal[j], map_params, random_seed=0)
sims_ch_arr[j] += extragal_noise_map
if bl is not None:
if isinstance(bl, list) is False:
bl = [bl]
for j in range(nber_ch):
sims_ch_arr[j] = tools.convolve(sims_ch_arr[j],
l,
np.sqrt(bl[j]),
mapparams=map_params)
if nl is not None:
if isinstance(nl, list) is False:
nl = [nl]
for j in range(nber_ch):
noise_map = tools.make_gaussian_realization(
l, nl[j], map_params)
sims_ch_arr[j] += noise_map
sims.append(sims_ch_arr)
if nber_ch == 1 and nber_obs == 1:
return sims[0][0]
if nber_ch == 1:
sims_one_freq = []
for i in range(len(sims)):
sims_one_freq.append(sims[i][0])
return sims_one_freq
if nber_obs == 1:
return sims[0]
sims_freq_sorted = []
for i in range(nber_ch):
maps_at_freq_i = []
for j in range(nber_obs):
maps_at_freq_i.append(sims[j][i])
sims_freq_sorted.append(maps_at_freq_i)
return sims_freq_sorted