def get_coordinate_samples():
return supercubos.LatinSampler(rng=np.random.default_rng(
seed=SAMPLE_SEED)).get_sym_sample(
PARAM_MINS,
PARAM_MAXES,
NUM_EMULATION_SAMPLES,
)
def get_errorcheck_coords():
return supercubos.LatinSampler(rng=np.random.default_rng(
seed=SAMPLE_SEED)).get_sym_sample(
PARAM_MINS,
PARAM_MAXES,
NUM_ERRORCHECK_SAMPLES,
)
def do_estimation(sample_size, num_pcs):
rng = np.random.default_rng(seed=SEED)
lh = supercubos.LatinSampler(rng=rng).get_lh_sample(
PARAM_MINS, PARAM_MAXES, sample_size)
density_model = get_density_model()
shear_model = get_shear_model(density_model.excess_surface_density)
def wrapped_esd_func(params):
a_sz = params[0:1]
con = params[1:2]
alpha = params[2:3]
beta = params[3:4]
gamma = np.array([GAMMA])
return shear_model.stacked_excess_surface_density(
RADII, a_sz, con, alpha, beta, gamma).squeeze()
esds = _pool_map(wrapped_esd_func, lh)
data = RADII[:, None] * esds
emulator = maszcal.emulate.PcaEmulator.create_from_data(
coords=lh,
data=data,
interpolator_class=maszcal.interpolate.GaussianProcessInterpolator,
interpolator_kwargs={'kernel': Matern()},
num_components=num_pcs,
)
return get_emulator_errors(PARAM_MINS, PARAM_MAXES, emulator,
wrapped_esd_func)
def do_estimation(sample_size, num_pcs):
rng = np.random.default_rng(seed=SEED)
lh = supercubos.LatinSampler(rng=rng).get_lh_sample(PARAM_MINS, PARAM_MAXES, sample_size)
density_model = get_density_model()
two_halo_conv = get_two_halo_convergence()
two_halo_emulator = get_2h_emulator(two_halo_conv)
corrected_lensing_func = get_corrected_lensing_func(density_model, two_halo_emulator)
hmf_interp = get_hmf_interp()
conv_model = get_convergence_model(hmf_interp, corrected_lensing_func)
def wrapped_conv_func(params):
a_sz = params[0:1]
a_2h = params[1:2]
con = params[2:3]
alpha = params[3:4]
beta = params[4:5]
gamma = np.array([GAMMA])
conv = conv_model.stacked_convergence(THETAS, a_sz, a_2h, con, alpha, beta, gamma).squeeze()
return THETAS * conv
convs = _pool_map(wrapped_conv_func, lh)
emulator = maszcal.emulate.PcaEmulator.create_from_data(
coords=lh,
data=convs,
interpolator_class=maszcal.interpolate.GaussianProcessInterpolator,
interpolator_kwargs={'kernel': Matern()},
num_components=num_pcs,
)
return get_emulator_errors(PARAM_MINS, PARAM_MAXES, emulator, wrapped_conv_func)
def _get_z_mu_samples(z_lims, mu_lims, num_emulator_samples):
param_mins = np.stack((z_lims, mu_lims))[:, 0]
param_maxes = np.stack((z_lims, mu_lims))[:, 1]
return supercubos.LatinSampler().get_lh_sample(
param_mins=param_mins,
param_maxes=param_maxes,
num_samples=num_emulator_samples,
)
def get_2nd_order_emulator_errors(PARAM_MINS, PARAM_MAXES, emulator, esd_func):
rand = supercubos.LatinSampler().get_rand_sample(
param_mins=PARAM_MINS,
param_maxes=PARAM_MAXES,
num_samples=NUM_ERRORCHECK_SAMPLES,
)
data_check = _pool_map(esd_func, rand)
emulated_data = emulator(rand)
return (emulated_data - data_check) / data_check
nothing = np.array([])
np.savetxt(DIR + filename + '.header.txt', nothing, header=header)
np.save(DIR + filename + '.npy', array)
def generate_chain_filename():
timestamp = datetime.datetime.now().strftime("%Y-%m-%d-%H%M%S")
return DIR + SETUP_SLUG + '-mcmc-chains_' + timestamp + '.h5'
if __name__ == '__main__':
print('Creating emulator...')
lh = supercubos.LatinSampler(rng=np.random.default_rng(
seed=SAMPLE_SEED)).get_lh_sample(PARAM_MINS, PARAM_MAXES,
NUM_EMULATOR_SAMPLES)
density_model = get_density_model()
two_halo_esd = get_two_halo_esd()
esd_emulator = get_esd_emulator(two_halo_esd)
corrected_lensing_func = get_corrected_lensing_func(
density_model, esd_emulator)
shear_model = get_shear_model(corrected_lensing_func)
def wrapped_esd_func(params):
a_sz = params[0:1]
a_2h = params[1:2]
con = params[2:3]
alpha = params[3:4]
beta = params[4:5]
nothing = np.array([])
np.savetxt(DIR + filename + '.header.txt', nothing, header=header)
np.save(DIR + filename + '.npy', array)
def generate_chain_filename():
timestamp = datetime.datetime.now().strftime("%Y-%m-%d-%H%M%S")
return DIR + SETUP_SLUG + '-mcmc-chains_' + timestamp + '.h5'
if __name__ == '__main__':
print('Creating emulator...')
lh = supercubos.LatinSampler().get_sym_sample(PARAM_MINS, PARAM_MAXES,
NUM_EMULATOR_SAMPLES)
density_model = get_density_model()
shear_model = get_shear_model(density_model.excess_surface_density)
def wrapped_esd_func(params):
a_sz = params[0:1]
con = params[1:2]
alpha = params[2:3]
beta = params[3:4]
gamma = np.array([GAMMA])
return shear_model.stacked_excess_surface_density(
SIM_DATA.radii, a_sz, con, alpha, beta, gamma).squeeze()
esds = _pool_map(wrapped_esd_func, lh)
def coordinate_samples():
param_mins = np.array([np.log(1e12), 0.8, 0.05, 1])
param_maxes = np.array([np.log(8e15), 6, 2, 6])
return supercubos.LatinSampler().get_sym_sample(
param_mins, param_maxes, 100)
