def calculate_offset_from_true_image_positions(self, model_ra, model_dec,
true_img_ra, true_img_dec,
increasing_dec_i,
abcd_ordering_i):
"""Calculates the difference in arcsec between the (inferred or fed-in) image positions known to `H0Posterior` and the provided true image positions
Parameters
----------
true_img_ra : array-like, of length self.n_img
ra of true image positions in TDLMC order
true_img_dec : array-like, of length self.n_img
dec of true image positions in TDLMC order
Returns
-------
array-like
offset in arcsec for each image
"""
if self.requires_reordering:
model_ra = h0_utils.reorder_to_tdlmc(model_ra, increasing_dec_i,
abcd_ordering_i)
model_dec = h0_utils.reorder_to_tdlmc(model_dec, increasing_dec_i,
abcd_ordering_i)
ra_offset = model_ra - true_img_ra
dec_offset = model_dec - true_img_dec
ra_offset = ra_offset.reshape(-1, 1)
dec_offset = dec_offset.reshape(-1, 1)
offset = np.concatenate([ra_offset, dec_offset], axis=1)
return np.linalg.norm(offset, axis=1)
def get_h0_sample_truth(self, random_state):
"""Get MC samples from the H0Posterior
Parameters
----------
sampled_lens_model_raw : dict
sampled lens model parameters, pre-formatting
random_state : np.random.RandomState object
Returns
-------
tuple of floats
the candidate H0 and its weight
"""
#increasing_dec_i = lens_prior_sample['increasing_dec_i']
# Sample from respective predefined priors
h0_candidate = self.sample_H0(random_state)
k_ext = self.sample_kappa_ext_transformed(
random_state
) #self.kappa_ext #self.sample_kappa_ext(random_state) #
# Define cosmology
cosmo = FlatLambdaCDM(H0=h0_candidate, Om0=self.Om0)
# Tool for getting time delays and velocity dispersions
td_cosmo = TDCosmography(
self.z_lens,
self.z_src,
self.kwargs_model,
cosmo_fiducial=cosmo,
kwargs_lens_eqn_solver=self.kwargs_lens_eqn_solver)
# Velocity dispersion
# TODO: separate sampling function if vel_disp is excluded
# Time delays
inferred_td, x_image, y_image = td_cosmo.time_delays(
self.lens_prior_sample['kwargs_lens'],
self.kwargs_image,
kappa_ext=k_ext)
#print(inferred_td, y_image)
if len(inferred_td) > len(self.measured_td_wrt0) + 1:
inferred_td, x_image, y_image = self.chuck_images(
inferred_td, x_image, y_image)
#print("after correct: ", inferred_td, y_image)
if self.lens_prior_sample['requires_reordering']:
increasing_dec_i = np.argsort(y_image)
inferred_td = h0_utils.reorder_to_tdlmc(inferred_td,
increasing_dec_i,
self.abcd_ordering_i)
else:
inferred_td = np.array(inferred_td)
inferred_td_wrt0 = inferred_td[1:] - inferred_td[0]
#print(inferred_td_wrt0, self.measured_td_wrt0)
ll_td = np.sum(
h0_utils.gaussian_ll_pdf(inferred_td_wrt0, self.measured_td_wrt0,
self.measured_td_err))
log_w = ll_td
weight = mp.exp(log_w)
return h0_candidate, weight
def _reorder_measured_td_to_tdlmc(self):
"""Reorder the measured time delays (same for all lens model samples)
Note
----
Unused!
"""
#print(self.measured_td, self.true_img_dec, self.abcd_ordering_i)
reordered_measured_td = h0_utils.reorder_to_tdlmc(
self.measured_td, np.argsort(self.true_img_dec),
self.abcd_ordering_i)
# Measured time in days (offset from the image with the smallest dec)
self.measured_td_wrt0 = reordered_measured_td[
1:] - reordered_measured_td[0]
def test_reorder_to_tdlmc(self):
"""Test if reordering the lenstronomy dec array --> increasing dec --> ABCD ordering of TDLMC is correct
"""
# D
# B
# A
# C
img_array = np.array([0.7, -0.3, 0.8, -1.2])
increasing_dec_i = np.argsort(img_array)
abcd_ordering_i = np.array([1, 2, 0,
3]) # send abcd_ordering[i] to ith index
reordered_actual = h0_utils.reorder_to_tdlmc(img_array,
increasing_dec_i,
abcd_ordering_i)
reordered = np.array([-0.3, 0.7, -1.2, 0.8])
np.testing.assert_array_equal(reordered,
reordered_actual,
err_msg="reorder_to_tdlmc")
def main():
args = parse_args()
test_cfg = TestConfig.from_file(args.test_config_file_path)
train_val_cfg = TrainValConfig.from_file(test_cfg.train_val_config_file_path)
baobab_cfg = get_baobab_config(test_cfg.data.test_dir)
# Set device and default data type
device = torch.device(test_cfg.device_type)
if device.type == 'cuda':
torch.set_default_tensor_type('torch.cuda.FloatTensor')
else:
torch.set_default_tensor_type('torch.FloatTensor')
seed_everything(test_cfg.global_seed)
############
# Data I/O #
############
test_data = XYCosmoData(test_cfg.data.test_dir, data_cfg=train_val_cfg.data)
master_truth = test_data.cosmo_df
master_truth = metadata_utils.add_qphi_columns(master_truth)
master_truth = metadata_utils.add_gamma_psi_ext_columns(master_truth)
if test_cfg.data.lens_indices is None:
if args.lens_indices_path is None:
# Test on all n_test lenses in the test set
n_test = test_cfg.data.n_test
lens_range = range(n_test)
else:
# Test on the lens indices in a text file at the specified path
lens_range = []
with open(args.lens_indices_path, "r") as f:
for line in f:
lens_range.append(int(line.strip()))
n_test = len(lens_range)
print("Performing H0 inference on {:d} specified lenses...".format(n_test))
else:
if args.lens_indices_path is None:
# Test on the lens indices specified in the test config file
lens_range = test_cfg.data.lens_indices
n_test = len(lens_range)
print("Performing H0 inference on {:d} specified lenses...".format(n_test))
else:
raise ValueError("Specific lens indices were specified in both the test config file and the command-line argument.")
batch_size = max(lens_range) + 1
# Output directory into which the H0 histograms and H0 samples will be saved
out_dir = test_cfg.out_dir
if not os.path.exists(out_dir):
os.makedirs(out_dir)
print("Destination folder path: {:s}".format(out_dir))
else:
raise OSError("Destination folder already exists.")
######################
# Load trained state #
######################
# Instantiate loss function, to append to the MCMC objective as the prior
orig_Y_cols = train_val_cfg.data.Y_cols
# Instantiate MCMC parameter penalty function
params_to_remove = ['lens_light_R_sersic'] #'src_light_R_sersic']
mcmc_Y_cols = [col for col in orig_Y_cols if col not in params_to_remove]
mcmc_Y_dim = len(mcmc_Y_cols)
null_spread = True
#init_D_dt = np.random.uniform(0.0, 10000.0, size=(batch_size, n_walkers, 1)) # FIXME: init H0 hardcoded
kwargs_model = dict(lens_model_list=['PEMD', 'SHEAR'],
point_source_model_list=['SOURCE_POSITION'],
source_light_model_list=['SERSIC_ELLIPSE'])
astro_sig = test_cfg.image_position_likelihood.sigma
# Get H0 samples for each system
if not test_cfg.time_delay_likelihood.baobab_time_delays:
if 'abcd_ordering_i' not in master_truth:
raise ValueError("If the time delay measurements were not generated using Baobab, the user must specify the order of image positions in which the time delays are listed, in order of increasing dec.")
kwargs_lens_eq_solver = {'min_distance': 0.05, 'search_window': baobab_cfg.instrument.pixel_scale*baobab_cfg.image.num_pix, 'num_iter_max': 100}
#n_walkers = test_cfg.numerics.mcmc.walkerRatio*(mcmc_Y_dim + 1) # BNN params + H0 times walker ratio
#init_pos = np.tile(master_truth[mcmc_Y_cols].iloc[:batch_size].values[:, np.newaxis, :], [1, n_walkers, 1])
#init_D_dt = np.random.uniform(0.0, 10000.0, size=(batch_size, n_walkers, 1))
#print(init_pos.shape, init_D_dt.shape)
total_progress = tqdm(total=n_test)
# For each lens system...
for i, lens_i in enumerate(lens_range):
# Each lens gets a unique random state for td and vd measurement error realizations.
rs_lens = np.random.RandomState(lens_i)
###########################
# Relevant data and prior #
###########################
data_i = master_truth.iloc[lens_i].copy()
# Init values for the lens model params
init_info = dict(zip(mcmc_Y_cols, data_i[mcmc_Y_cols].values)) # truth params
lcdm = LCDM(z_lens=data_i['z_lens'], z_source=data_i['z_src'], flat=True)
true_img_dec = np.array(literal_eval(data_i['y_image']))
n_img = len(true_img_dec)
true_td = np.array(literal_eval(data_i['true_td']))
measured_td = true_td + rs_lens.randn(*true_td.shape)*test_cfg.error_model.time_delay_error
measured_td_sig = test_cfg.time_delay_likelihood.sigma # np.ones(n_img - 1)*
measured_img_dec = true_img_dec + rs_lens.randn(n_img)*astro_sig
increasing_dec_i = np.argsort(true_img_dec) #np.argsort(measured_img_dec)
measured_td = h0_utils.reorder_to_tdlmc(measured_td, increasing_dec_i, range(n_img)) # need to use measured dec to order
measured_img_dec = h0_utils.reorder_to_tdlmc(measured_img_dec, increasing_dec_i, range(n_img))
measured_td_wrt0 = measured_td[1:] - measured_td[0]
kwargs_data_joint = dict(time_delays_measured=measured_td_wrt0,
time_delays_uncertainties=measured_td_sig,
)
#############################
# Parameter init and bounds #
#############################
lens_kwargs = mcmc_utils.get_lens_kwargs(init_info, null_spread=null_spread)
ps_kwargs = mcmc_utils.get_ps_kwargs_src_plane(init_info, astro_sig, null_spread=null_spread)
src_light_kwargs = mcmc_utils.get_light_kwargs(init_info['src_light_R_sersic'], null_spread=null_spread)
special_kwargs = mcmc_utils.get_special_kwargs(n_img, astro_sig, D_dt_sigma=2000, null_spread=null_spread) # image position offset and time delay distance, aka the "special" parameters
kwargs_params = {'lens_model': lens_kwargs,
'point_source_model': ps_kwargs,
'source_model': src_light_kwargs,
'special': special_kwargs,}
if test_cfg.numerics.solver_type == 'NONE':
solver_type = 'NONE'
else:
solver_type = 'PROFILE_SHEAR' if n_img == 4 else 'CENTER'
#solver_type = 'NONE'
kwargs_constraints = {'num_point_source_list': [n_img],
'Ddt_sampling': True,
'solver_type': solver_type,}
kwargs_likelihood = {'time_delay_likelihood': True,
'sort_images_by_dec': True,
'prior_lens': [],
'prior_special': [],
'check_bounds': True,
'check_matched_source_position': False,
'source_position_tolerance': 0.01,
'source_position_sigma': 0.01,
'source_position_likelihood': False,
'custom_logL_addition': None,
'kwargs_lens_eq_solver': kwargs_lens_eq_solver}
###########################
# MCMC posterior sampling #
###########################
fitting_seq = FittingSequence(kwargs_data_joint, kwargs_model, kwargs_constraints, kwargs_likelihood, kwargs_params, verbose=False, mpi=False)
if i == 0:
param_class = fitting_seq._updateManager.param_class
n_params, param_class_Y_cols = param_class.num_param()
#init_pos = mcmc_utils.reorder_to_param_class(mcmc_Y_cols, param_class_Y_cols, init_pos, init_D_dt)
# MCMC sample from the post-processed BNN posterior jointly with cosmology
lens_i_start_time = time.time()
#test_cfg.numerics.mcmc.update(init_samples=init_pos[lens_i, :, :])
fitting_kwargs_list_mcmc = [['MCMC', test_cfg.numerics.mcmc]]
#with HiddenPrints():
#try:
chain_list_mcmc = fitting_seq.fit_sequence(fitting_kwargs_list_mcmc)
kwargs_result_mcmc = fitting_seq.best_fit()
#except:
# print("lens {:d} skipped".format(lens_i))
# total_progress.update(1)
# continue
lens_i_end_time = time.time()
inference_time = (lens_i_end_time - lens_i_start_time)/60.0 # min
#############################
# Plotting the MCMC samples #
#############################
# sampler_type : 'EMCEE'
# samples_mcmc : np.array of shape `[n_mcmc_eval, n_params]`
# param_mcmc : list of str of length n_params, the parameter names
sampler_type, samples_mcmc, param_mcmc, _ = chain_list_mcmc[0]
new_samples_mcmc = mcmc_utils.postprocess_mcmc_chain(kwargs_result_mcmc, samples_mcmc, kwargs_model, lens_kwargs[2], ps_kwargs[2], src_light_kwargs[2], special_kwargs[2], kwargs_constraints)
# Plot D_dt histogram
D_dt_samples = new_samples_mcmc['D_dt'].values
true_D_dt = lcdm.D_dt(H_0=data_i['H0'], Om0=0.3)
data_i['D_dt'] = true_D_dt
# Export D_dt samples for this lens
lens_inference_dict = dict(
D_dt_samples=D_dt_samples, # kappa_ext=0 for these samples
inference_time=inference_time,
true_D_dt=true_D_dt,
)
lens_inference_dict_save_path = os.path.join(out_dir, 'D_dt_dict_{0:04d}.npy'.format(lens_i))
np.save(lens_inference_dict_save_path, lens_inference_dict)
# Optionally export the MCMC samples
if test_cfg.export.mcmc_samples:
mcmc_samples_path = os.path.join(out_dir, 'mcmc_samples_{0:04d}.csv'.format(lens_i))
new_samples_mcmc.to_csv(mcmc_samples_path, index=None)
# Optionally export the D_dt histogram
if test_cfg.export.D_dt_histogram:
cleaned_D_dt_samples = h0_utils.remove_outliers_from_lognormal(D_dt_samples, 3)
_ = plotting_utils.plot_D_dt_histogram(cleaned_D_dt_samples, lens_i, true_D_dt, save_dir=out_dir)
# Optionally export the plot of MCMC chain
if test_cfg.export.mcmc_chain:
mcmc_chain_path = os.path.join(out_dir, 'mcmc_chain_{0:04d}.png'.format(lens_i))
plotting_utils.plot_mcmc_chain(chain_list_mcmc, mcmc_chain_path)
# Optionally export posterior cornerplot of select lens model parameters with D_dt
if test_cfg.export.mcmc_corner:
mcmc_corner_path = os.path.join(out_dir, 'mcmc_corner_{0:04d}.png'.format(lens_i))
plotting_utils.plot_mcmc_corner(new_samples_mcmc[test_cfg.export.mcmc_cols], data_i[test_cfg.export.mcmc_cols], test_cfg.export.mcmc_col_labels, mcmc_corner_path)
total_progress.update(1)
gc.collect()
total_progress.close()
def get_h0_sample(self, sampled_lens_model_raw, random_state):
"""Get MC samples from the H0Posterior
Parameters
----------
sampled_lens_model_raw : dict
sampled lens model parameters, pre-formatting
random_state : np.random.RandomState object
Returns
-------
tuple of floats
the candidate H0 and its weight
"""
# Samples from the lens posterior are reinterpreted as samples from the lens model prior in the H0 inference stage
lens_prior_sample = self.format_lens_model(sampled_lens_model_raw)
kwargs_lens = lens_prior_sample['kwargs_lens']
#lens_light_R_sersic = lens_prior_sample['lens_light_R_sersic']
#increasing_dec_i = lens_prior_sample['increasing_dec_i']
# Sample from respective predefined priors
h0_candidate = self.sample_H0(random_state)
k_ext = self.sample_kappa_ext(random_state)
# Define cosmology
cosmo = FlatLambdaCDM(H0=h0_candidate, Om0=self.Om0)
# Tool for getting time delays and velocity dispersions
td_cosmo = TDCosmography(
self.z_lens,
self.z_src,
self.kwargs_model,
cosmo_fiducial=cosmo,
kwargs_lens_eqn_solver=self.kwargs_lens_eqn_solver)
# Velocity dispersion
# TODO: separate sampling function if vel_disp is excluded
if self.exclude_vel_disp:
ll_vd = 0.0
else:
aniso_param = self.sample_aniso_param(random_state)
inferred_vd = self.get_velocity_dispersion(
td_cosmo,
kwargs_lens,
None, #FIXME: only analytic
{'aniso_param': aniso_param},
self.kinematics.kwargs_aperture,
self.kinematics.kwargs_psf,
self.kinematics.anisotropy_model,
None,
self.kinematics.kwargs_numerics,
k_ext)
ll_vd = h0_utils.gaussian_ll_pdf(inferred_vd, self.measured_vd,
self.measured_vd_err)
# Time delays
inferred_td, x_image, y_image = td_cosmo.time_delays(
kwargs_lens, lens_prior_sample['kwargs_ps'], kappa_ext=k_ext)
if len(inferred_td) > len(self.measured_td_wrt0) + 1:
inferred_td, x_image, y_image = self.chuck_images(
inferred_td, x_image, y_image)
if lens_prior_sample['requires_reordering']:
increasing_dec_i = np.argsort(y_image)
inferred_td = h0_utils.reorder_to_tdlmc(inferred_td,
increasing_dec_i,
self.abcd_ordering_i)
else:
inferred_td = np.array(inferred_td)
inferred_td_wrt0 = inferred_td[1:] - inferred_td[0]
#print(inferred_td, self.measured_td)
ll_td = np.sum(
h0_utils.gaussian_ll_pdf(inferred_td_wrt0, self.measured_td_wrt0,
self.measured_td_err))
log_w = ll_vd + ll_td
weight = mp.exp(log_w)
return h0_candidate, weight