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setting seed #251

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7 changes: 4 additions & 3 deletions deepszsim/dm_halo_dist.py
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Original file line number Diff line number Diff line change
Expand Up @@ -33,9 +33,10 @@ def flatdist_halo(zmin, zmax, m200min_SM, m200max_SM, size, seed=None):
distribution of random uniform redshifts starting at `m500min_SM` ending
at `m500max_SM` with size `size`
'''
_rng = np.random.default_rng(seed=seed)
rng = np.random.default_rng(seed=seed)
rng_M, rng_z = rng.spawn(2)

zdist=_rng.uniform(low=zmin, high=zmax, size=size)
mdist=_rng.uniform(low=m200min_SM, high=m200max_SM, size=size)
zdist=rng_z.uniform(low=zmin, high=zmax, size=size)
mdist=rng_M.uniform(low=m200min_SM, high=m200max_SM, size=size)

return zdist, mdist
5 changes: 3 additions & 2 deletions deepszsim/noise.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@

import numpy as np

def generate_noise_map(image_size, noise_level, pix_size):
def generate_noise_map(image_size, noise_level, pix_size, seed):
"""
Generates a white noise map based on the noise level and beam size.

Expand All @@ -21,7 +21,8 @@ def generate_noise_map(image_size, noise_level, pix_size):
"""

# Create random noise map
random_noise_map = np.random.normal(0, 1, (image_size, image_size))
rng = np.random.default_rng(seed)
random_noise_map = rng.normal(0, 1, (image_size, image_size))

# Scale random noise map by noise level
scaled_noise_map = random_noise_map * noise_level
Expand Down
28 changes: 20 additions & 8 deletions deepszsim/simclusters.py
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Original file line number Diff line number Diff line change
Expand Up @@ -54,6 +54,9 @@ def __init__(self, M200 = None, redshift_z = None, num_halos = None, halo_params
seed = None, tqverb = False
):

self.rng_ = np.random.default_rng(seed)
self.rng, self.rng_ = self.rng_.spawn(2)

if (M200 is not None) and (redshift_z is not None):
self.M200, self.redshift_z = M200, redshift_z
else:
Expand All @@ -67,11 +70,13 @@ def __init__(self, M200 = None, redshift_z = None, num_halos = None, halo_params
print(f"making {num_halos} clusters uniformly sampled from"
f"{halo_params_dict['zmin']}<z<{halo_params_dict['zmax']}, and "
f"{halo_params_dict['m200min_SM']:1.1e}<M200<{halo_params_dict['m200max_SM']:1.1e}")
randint = self.rng.integers(2**32-1)
self.rng, self.rng_ = self.rng_.spawn(2)
self.redshift_z, self.M200 = dm_halo_dist.flatdist_halo(halo_params_dict['zmin'],
halo_params_dict['zmax'],
halo_params_dict['m200min_SM'],
halo_params_dict['m200max_SM'],
int(num_halos), seed = seed)
int(num_halos), seed = randint)

try:
self._size = len(self.M200)
Expand All @@ -94,7 +99,7 @@ def __init__(self, M200 = None, redshift_z = None, num_halos = None, halo_params
self.tqverb = tqverb

self.alpha, self.gamma = alpha, gamma
self.seed, self._rng = seed, np.random.default_rng(seed)
self.rng, self.rng_ = self.rng_.spawn(2)

if R200_Mpc is not None:
self.R200_Mpc = R200_Mpc
Expand All @@ -104,7 +109,7 @@ def __init__(self, M200 = None, redshift_z = None, num_halos = None, halo_params
angsize_density = '500c')[1:3]
for i in range(self._size)]).T

self.id_list = [f"{int(self.M200[i]/1e9):07}_{int(self.redshift_z[i]*100):03}_{self._rng.integers(10**6):06}"
self.id_list = [f"{int(self.M200[i]/1e9):07}_{int(self.redshift_z[i]*100):03}_{self.rng.integers(10**6):06}"
for i in range(self._size)]
self.clusters.update(zip(self.id_list, [{"params": {'M200': self.M200[i],
'redshift_z': self.redshift_z[i],
Expand Down Expand Up @@ -167,22 +172,29 @@ def get_T_maps(self, add_CMB = True, returnval = False):
if add_CMB: self.ps = simtools.get_cls(ns = self.vars['ns'], cosmo = self.vars['cosmo'])
if self.tqverb: print("making convolved T maps" + (" with CMB" if add_CMB else ""))
_centerpix = self.image_size_pixels // 2
self.rng, self.rng_ = self.rng_.spawn(2)
seeds = self.rng.integers(2**32-1, size = self._size)
for i in tqdm(range(self._size), disable = (not self.tqverb)):
dTm, name = dT_maps[i], self.id_list[i]
curdic = self.clusters[name]
curdic['params']['dT_central'] = dTm[_centerpix,_centerpix]
curdic['maps'] = {}
beamsig_map = simtools.convolve_map_with_gaussian_beam(self.pixel_size_arcmin,
self.beam_size_arcmin, dTm)
self.beam_size_arcmin,
dTm)
if add_CMB:
conv_map, cmb_map = simtools.add_cmb_map_and_convolve(dTm, self.ps,
conv_map, cmb_map = simtools.add_cmb_map_and_convolve(dTm,
self.ps,
self.pixel_size_arcmin,
self.beam_size_arcmin)
self.beam_size_arcmin,
seed = int(seeds[i]))
else:
conv_map, cmb_map = beamsig_map, np.zeros_like(beamsig_map)
if not self.vars['noise_level'] == 0:
noise_map = noise.generate_noise_map(self.image_size_pixels, self.vars['noise_level'],
self.pixel_size_arcmin)
noise_map = noise.generate_noise_map(self.image_size_pixels,
self.vars['noise_level'],
self.pixel_size_arcmin,
seed = int(seeds[i]))
else:
noise_map = np.zeros_like(conv_map)
final_map = conv_map + noise_map
Expand Down
11 changes: 8 additions & 3 deletions deepszsim/simtools.py
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Expand Up @@ -59,8 +59,11 @@ def f_sz(freq_ghz, T_CMB_K):

return fsz

def add_cmb_map_and_convolve(dT_map_uK, ps, pix_size_arcmin,
beam_size_fwhp_arcmin):
def add_cmb_map_and_convolve(dT_map_uK,
ps,
pix_size_arcmin,
beam_size_fwhp_arcmin,
seed):
'''
Parameters:
----------
Expand All @@ -83,7 +86,9 @@ def add_cmb_map_and_convolve(dT_map_uK, ps, pix_size_arcmin,
dT_map_uK.shape[1]+2*padding_value)
#print(expanded_shape)
cmb_map = make_cmb_map(shape=expanded_shape,
pix_size_arcmin=pix_size_arcmin, ps=ps)
pix_size_arcmin=pix_size_arcmin,
ps=ps,
seed = seed)
if type(dT_map_uK) is u.Quantity:
cmb_map = cmb_map *u.uK
dT_map_expanded = np.pad(dT_map_uK, (padding_value,padding_value),
Expand Down
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