def main(args): if THIS_WORKER > 1: time.sleep(5) if not os.path.isdir(args.output_dir): os.mkdir(args.output_dir) if args.seed is not None: tf.random.set_seed(args.seed) # Load first stage with open(os.path.join(args.kappa_first_stage_vae, "model_hparams.json"), "r") as f: kappa_vae_hparams = json.load(f) kappa_vae = VAE(**kappa_vae_hparams) ckpt1 = tf.train.Checkpoint(step=tf.Variable(1), net=kappa_vae) checkpoint_manager1 = tf.train.CheckpointManager( ckpt1, args.kappa_first_stage_vae, 1) checkpoint_manager1.checkpoint.restore( checkpoint_manager1.latest_checkpoint).expect_partial() kappa_vae.trainable = False options = tf.io.TFRecordOptions(compression_type=args.compression_type) with tf.io.TFRecordWriter( os.path.join(args.output_dir, f"data_{THIS_WORKER}.tfrecords"), options) as writer: print( f"Started worker {THIS_WORKER} at {datetime.now().strftime('%y-%m-%d_%H-%M-%S')}" ) for _ in range((THIS_WORKER - 1) * args.batch_size, args.len_dataset, N_WORKERS * args.batch_size): kappa = 10**kappa_vae.sample(args.batch_size) # Most important info to records are kappa and kappa fov, the rest are just fill-ins # to match same description as previous TNG tfrecords records = encode_examples(kappa=kappa, einstein_radius_init=[0.] * args.batch_size, einstein_radius=[0.] * args.batch_size, rescalings=[0.] * args.batch_size, z_source=0., z_lens=0., kappa_fov=args.kappa_fov, sigma_crit=0., kappa_ids=[0] * args.batch_size) for record in records: writer.write(record)
def main(args): files = [] files.extend(glob.glob(os.path.join(args.dataset, "*.tfrecords"))) np.random.shuffle(files) # Read concurrently from multiple records files = tf.data.Dataset.from_tensor_slices(files) dataset = files.interleave(lambda x: tf.data.TFRecordDataset( x, compression_type=args.compression_type), block_length=args.block_length, num_parallel_calls=tf.data.AUTOTUNE) if args.type == "cosmos": from censai.data.cosmos import decode_shape, decode_image as decode, preprocess_image as preprocess elif args.type == "kappa": from censai.data.kappa_tng import decode_shape, decode_train as decode from censai.definitions import log_10 as preprocess # Read off global parameters from first example in dataset for pixels in dataset.map(decode_shape): break vars(args).update({"pixels": int(pixels)}) dataset = dataset.map(decode).map(preprocess).shuffle( args.buffer_size).batch(args.batch_size).take(args.n_plots).cache( args.cache) model_list = glob.glob( os.path.join(os.getenv("CENSAI_PATH"), "models", args.model_prefixe + "*")) for model in model_list: if "second_stage" in model: continue with open(os.path.join(model, "model_hparams.json")) as f: vae_hparams = json.load(f) # load weights vae = VAE(**vae_hparams) ckpt1 = tf.train.Checkpoint(net=vae) checkpoint_manager1 = tf.train.CheckpointManager(ckpt1, model, 1) checkpoint_manager1.checkpoint.restore( checkpoint_manager1.latest_checkpoint).expect_partial() vae.trainable = False model_name = os.path.split(model)[-1] for batch, images in enumerate(dataset): y_pred = vae(images) fig = reconstruction_plot(images, y_pred) fig.suptitle(model_name) fig.savefig( os.path.join( os.getenv("CENSAI_PATH"), "results", "vae_reconstruction_" + model_name + "_" + args.output_postfixe + f"_{batch:02d}.png")) fig.clf() y_pred = vae.sample(args.sampling_size) fig = sampling_plot(y_pred) fig.suptitle(model_name) fig.savefig( os.path.join( os.getenv("CENSAI_PATH"), "results", "vae_sampling_" + model_name + "_" + args.output_postfixe + f"_{batch:02d}.png")) fig.clf()
def main(args): files = glob.glob(os.path.join(args.dataset, "*.tfrecords")) files = tf.data.Dataset.from_tensor_slices(files) dataset = files.interleave(lambda x: tf.data.TFRecordDataset( x, compression_type=args.compression_type), block_length=1, num_parallel_calls=tf.data.AUTOTUNE) for physical_params in dataset.map(decode_physical_model_info): break dataset = dataset.map(decode_train) # files = glob.glob(os.path.join(args.source_dataset, "*.tfrecords")) # files = tf.data.Dataset.from_tensor_slices(files) # source_dataset = files.interleave(lambda x: tf.data.TFRecordDataset(x, compression_type=args.compression_type), # block_length=1, num_parallel_calls=tf.data.AUTOTUNE) # source_dataset = source_dataset.map(decode_image).map(preprocess_image).shuffle(10000).batch(args.sample_size) with open(os.path.join(args.kappa_vae, "model_hparams.json"), "r") as f: kappa_vae_hparams = json.load(f) kappa_vae = VAE(**kappa_vae_hparams) ckpt1 = tf.train.Checkpoint(step=tf.Variable(1), net=kappa_vae) checkpoint_manager1 = tf.train.CheckpointManager(ckpt1, args.kappa_vae, 1) checkpoint_manager1.checkpoint.restore( checkpoint_manager1.latest_checkpoint).expect_partial() with open(os.path.join(args.source_vae, "model_hparams.json"), "r") as f: source_vae_hparams = json.load(f) source_vae = VAE(**source_vae_hparams) ckpt2 = tf.train.Checkpoint(step=tf.Variable(1), net=source_vae) checkpoint_manager2 = tf.train.CheckpointManager(ckpt2, args.source_vae, 1) checkpoint_manager2.checkpoint.restore( checkpoint_manager2.latest_checkpoint).expect_partial() phys = PhysicalModel(pixels=physical_params["pixels"].numpy(), kappa_pixels=physical_params["kappa pixels"].numpy(), src_pixels=physical_params["src pixels"].numpy(), image_fov=physical_params["image fov"].numpy(), kappa_fov=physical_params["kappa fov"].numpy(), src_fov=physical_params["source fov"].numpy(), method="fft") # simulate observations kappa = 10**kappa_vae.sample(args.sample_size) source = preprocess_image(source_vae.sample(args.sample_size)) # for source in source_dataset: # break fwhm = tf.random.normal(shape=[args.sample_size], mean=1.5 * phys.image_fov / phys.pixels, stddev=0.5 * phys.image_fov / phys.pixels) # noise_rms = tf.random.normal(shape=[args.sample_size], mean=args.noise_mean, stddev=args.noise_std) psf = phys.psf_models(fwhm, cutout_size=20) y_vae = phys.forward(source, kappa, psf) with h5py.File( os.path.join(os.getenv("CENSAI_PATH"), "results", args.output_name + ".h5"), 'w') as hf: # rank these observations against the dataset with L2 norm for i in tqdm(range(args.sample_size)): distances = [] for y_d, _, _, _, _ in dataset: distances.append( tf.sqrt(tf.reduce_sum( (y_d - y_vae[i][None, ...])**2)).numpy().astype( np.float32)) k_indices = np.argsort(distances)[:args.k] # save results g = hf.create_group(f"sample_{i:02d}") g.create_dataset(name="matched_source", shape=[args.k, phys.src_pixels, phys.src_pixels], dtype=np.float32) g.create_dataset( name="matched_kappa", shape=[args.k, phys.kappa_pixels, phys.kappa_pixels], dtype=np.float32) g.create_dataset(name="matched_obs", shape=[args.k, phys.pixels, phys.pixels], dtype=np.float32) g.create_dataset(name="matched_psf", shape=[args.k, 20, 20], dtype=np.float32) g.create_dataset(name="matched_noise_rms", shape=[args.k], dtype=np.float32) g.create_dataset(name="obs_L2_distance", shape=[args.k], dtype=np.float32) g["vae_source"] = source[i, ..., 0].numpy().astype(np.float32) g["vae_kappa"] = kappa[i, ..., 0].numpy().astype(np.float32) g["vae_obs"] = y_vae[i, ..., 0].numpy().astype(np.float32) g["vae_psf"] = psf[i, ..., 0].numpy().astype(np.float32) for rank, j in enumerate(k_indices): # fetch back the matched observation for y_d, source_d, kappa_d, noise_rms_d, psf_d in dataset.skip( j): break # g["vae_noise_rms"] = noise_rms[i].numpy().astype(np.float32) g["matched_source"][rank] = source_d[..., 0].numpy().astype( np.float32) g["matched_kappa"][rank] = kappa_d[..., 0].numpy().astype( np.float32) g["matched_obs"][rank] = y_d[..., 0].numpy().astype(np.float32) g["matched_noise_rms"][rank] = noise_rms_d.numpy().astype( np.float32) g["matched_psf"][rank] = psf_d[..., 0].numpy().astype(np.float32) g["obs_L2_distance"][rank] = distances[j]
def distributed_strategy(args): psf_pixels = 20 pixels = 128 model = os.path.join(os.getenv('CENSAI_PATH'), "models", args.model) ps_observation = PowerSpectrum(bins=args.observation_coherence_bins, pixels=pixels) ps_source = PowerSpectrum(bins=args.source_coherence_bins, pixels=pixels) ps_kappa = PowerSpectrum(bins=args.kappa_coherence_bins, pixels=pixels) phys = PhysicalModel( pixels=pixels, kappa_pixels=pixels, src_pixels=pixels, image_fov=7.68, kappa_fov=7.68, src_fov=3., method="fft", ) with open(os.path.join(model, "unet_hparams.json")) as f: unet_params = json.load(f) unet_params["kernel_l2_amp"] = args.l2_amp unet = Model(**unet_params) ckpt = tf.train.Checkpoint(net=unet) checkpoint_manager = tf.train.CheckpointManager(ckpt, model, 1) checkpoint_manager.checkpoint.restore( checkpoint_manager.latest_checkpoint).expect_partial() with open(os.path.join(model, "rim_hparams.json")) as f: rim_params = json.load(f) rim_params["source_link"] = "relu" rim = RIM(phys, unet, **rim_params) kvae_path = os.path.join(os.getenv('CENSAI_PATH'), "models", args.kappa_vae) with open(os.path.join(kvae_path, "model_hparams.json"), "r") as f: kappa_vae_hparams = json.load(f) kappa_vae = VAE(**kappa_vae_hparams) ckpt1 = tf.train.Checkpoint(step=tf.Variable(1), net=kappa_vae) checkpoint_manager1 = tf.train.CheckpointManager(ckpt1, kvae_path, 1) checkpoint_manager1.checkpoint.restore( checkpoint_manager1.latest_checkpoint).expect_partial() svae_path = os.path.join(os.getenv('CENSAI_PATH'), "models", args.source_vae) with open(os.path.join(svae_path, "model_hparams.json"), "r") as f: source_vae_hparams = json.load(f) source_vae = VAE(**source_vae_hparams) ckpt2 = tf.train.Checkpoint(step=tf.Variable(1), net=source_vae) checkpoint_manager2 = tf.train.CheckpointManager(ckpt2, svae_path, 1) checkpoint_manager2.checkpoint.restore( checkpoint_manager2.latest_checkpoint).expect_partial() model_name = os.path.split(model)[-1] wk = tf.keras.layers.Lambda(lambda k: tf.sqrt(k) / tf.reduce_sum( tf.sqrt(k), axis=(1, 2, 3), keepdims=True)) with h5py.File( os.path.join( os.getenv("CENSAI_PATH"), "results", args.experiment_name + "_" + model_name + f"_{THIS_WORKER:02d}.h5"), 'w') as hf: data_len = args.size // N_WORKERS hf.create_dataset(name="observation", shape=[data_len, phys.pixels, phys.pixels, 1], dtype=np.float32) hf.create_dataset(name="psf", shape=[data_len, psf_pixels, psf_pixels, 1], dtype=np.float32) hf.create_dataset(name="psf_fwhm", shape=[data_len], dtype=np.float32) hf.create_dataset(name="noise_rms", shape=[data_len], dtype=np.float32) hf.create_dataset( name="source", shape=[data_len, phys.src_pixels, phys.src_pixels, 1], dtype=np.float32) hf.create_dataset( name="kappa", shape=[data_len, phys.kappa_pixels, phys.kappa_pixels, 1], dtype=np.float32) hf.create_dataset(name="observation_pred", shape=[data_len, phys.pixels, phys.pixels, 1], dtype=np.float32) hf.create_dataset(name="observation_pred_reoptimized", shape=[data_len, phys.pixels, phys.pixels, 1], dtype=np.float32) hf.create_dataset( name="source_pred", shape=[data_len, rim.steps, phys.src_pixels, phys.src_pixels, 1], dtype=np.float32) hf.create_dataset( name="source_pred_reoptimized", shape=[data_len, phys.src_pixels, phys.src_pixels, 1], dtype=np.float32) hf.create_dataset(name="kappa_pred", shape=[ data_len, rim.steps, phys.kappa_pixels, phys.kappa_pixels, 1 ], dtype=np.float32) hf.create_dataset( name="kappa_pred_reoptimized", shape=[data_len, phys.kappa_pixels, phys.kappa_pixels, 1], dtype=np.float32) hf.create_dataset(name="chi_squared", shape=[data_len, rim.steps], dtype=np.float32) hf.create_dataset(name="chi_squared_reoptimized", shape=[data_len, rim.steps], dtype=np.float32) hf.create_dataset(name="chi_squared_reoptimized_series", shape=[data_len, rim.steps, args.re_optimize_steps], dtype=np.float32) hf.create_dataset(name="sampled_chi_squared_reoptimized_series", shape=[data_len, args.re_optimize_steps], dtype=np.float32) hf.create_dataset(name="source_optim_mse", shape=[data_len], dtype=np.float32) hf.create_dataset(name="source_optim_mse_series", shape=[data_len, args.re_optimize_steps], dtype=np.float32) hf.create_dataset(name="sampled_source_optim_mse_series", shape=[data_len, args.re_optimize_steps], dtype=np.float32) hf.create_dataset(name="kappa_optim_mse", shape=[data_len], dtype=np.float32) hf.create_dataset(name="kappa_optim_mse_series", shape=[data_len, args.re_optimize_steps], dtype=np.float32) hf.create_dataset(name="sampled_kappa_optim_mse_series", shape=[data_len, args.re_optimize_steps], dtype=np.float32) hf.create_dataset(name="latent_kappa_gt_distance_init", shape=[data_len, kappa_vae.latent_size], dtype=np.float32) hf.create_dataset(name="latent_source_gt_distance_init", shape=[data_len, source_vae.latent_size], dtype=np.float32) hf.create_dataset(name="latent_kappa_gt_distance_end", shape=[data_len, kappa_vae.latent_size], dtype=np.float32) hf.create_dataset(name="latent_source_gt_distance_end", shape=[data_len, source_vae.latent_size], dtype=np.float32) hf.create_dataset(name="source_coherence_spectrum", shape=[data_len, args.source_coherence_bins], dtype=np.float32) hf.create_dataset(name="source_coherence_spectrum_reoptimized", shape=[data_len, args.source_coherence_bins], dtype=np.float32) hf.create_dataset(name="observation_coherence_spectrum", shape=[data_len, args.observation_coherence_bins], dtype=np.float32) hf.create_dataset(name="observation_coherence_spectrum_reoptimized", shape=[data_len, args.observation_coherence_bins], dtype=np.float32) hf.create_dataset(name="kappa_coherence_spectrum", shape=[data_len, args.kappa_coherence_bins], dtype=np.float32) hf.create_dataset(name="kappa_coherence_spectrum_reoptimized", shape=[data_len, args.kappa_coherence_bins], dtype=np.float32) hf.create_dataset(name="observation_frequencies", shape=[args.observation_coherence_bins], dtype=np.float32) hf.create_dataset(name="source_frequencies", shape=[args.source_coherence_bins], dtype=np.float32) hf.create_dataset(name="kappa_frequencies", shape=[args.kappa_coherence_bins], dtype=np.float32) hf.create_dataset(name="kappa_fov", shape=[1], dtype=np.float32) hf.create_dataset(name="source_fov", shape=[1], dtype=np.float32) hf.create_dataset(name="observation_fov", shape=[1], dtype=np.float32) for i in range(data_len): checkpoint_manager.checkpoint.restore( checkpoint_manager.latest_checkpoint).expect_partial( ) # reset model weights # Produce an observation kappa = 10**kappa_vae.sample(1) source = tf.nn.relu(source_vae.sample(1)) source /= tf.reduce_max(source, axis=(1, 2, 3), keepdims=True) noise_rms = 10**tf.random.uniform(shape=[1], minval=-2.5, maxval=-1) fwhm = tf.random.uniform(shape=[1], minval=0.06, maxval=0.3) psf = phys.psf_models(fwhm, cutout_size=psf_pixels) observation = phys.noisy_forward(source, kappa, noise_rms, psf) # RIM predictions for kappa and source source_pred, kappa_pred, chi_squared = rim.predict( observation, noise_rms, psf) observation_pred = phys.forward(source_pred[-1], kappa_pred[-1], psf) source_o = source_pred[-1] kappa_o = kappa_pred[-1] # Latent code of model predictions z_source, _ = source_vae.encoder(source_o) z_kappa, _ = kappa_vae.encoder(log_10(kappa_o)) # Ground truth latent code for oracle metrics z_source_gt, _ = source_vae.encoder(source) z_kappa_gt, _ = kappa_vae.encoder(log_10(kappa)) # Re-optimize weights of the model STEPS = args.re_optimize_steps learning_rate_schedule = tf.keras.optimizers.schedules.ExponentialDecay( initial_learning_rate=args.learning_rate, decay_rate=args.decay_rate, decay_steps=args.decay_steps, staircase=args.staircase) optim = tf.keras.optimizers.RMSprop( learning_rate=learning_rate_schedule) chi_squared_series = tf.TensorArray(DTYPE, size=STEPS) source_mse = tf.TensorArray(DTYPE, size=STEPS) kappa_mse = tf.TensorArray(DTYPE, size=STEPS) sampled_chi_squared_series = tf.TensorArray(DTYPE, size=STEPS) sampled_source_mse = tf.TensorArray(DTYPE, size=STEPS) sampled_kappa_mse = tf.TensorArray(DTYPE, size=STEPS) best = chi_squared source_best = source_pred[-1] kappa_best = kappa_pred[-1] source_mse_best = tf.reduce_mean((source_best - source)**2) kappa_mse_best = tf.reduce_mean((kappa_best - log_10(kappa))**2) # ===================== Optimization ============================== for current_step in tqdm(range(STEPS)): # ===================== VAE SAMPLING ============================== # L1 distance with ground truth in latent space -- this is changed by an user defined value when using real data # z_source_std = tf.abs(z_source - z_source_gt) # z_kappa_std = tf.abs(z_kappa - z_kappa_gt) z_source_std = args.source_vae_ball_size z_kappa_std = args.kappa_vae_ball_size # Sample latent code, then decode and forward z_s = tf.random.normal( shape=[args.sample_size, source_vae.latent_size], mean=z_source, stddev=z_source_std) z_k = tf.random.normal( shape=[args.sample_size, kappa_vae.latent_size], mean=z_kappa, stddev=z_kappa_std) sampled_source = tf.nn.relu(source_vae.decode(z_s)) sampled_source /= tf.reduce_max(sampled_source, axis=(1, 2, 3), keepdims=True) sampled_kappa = kappa_vae.decode(z_k) # output in log_10 space sampled_observation = phys.noisy_forward( sampled_source, 10**sampled_kappa, noise_rms, tf.tile(psf, [args.sample_size, 1, 1, 1])) with tf.GradientTape() as tape: tape.watch(unet.trainable_variables) s, k, chi_sq = rim.call( sampled_observation, noise_rms, tf.tile(psf, [args.sample_size, 1, 1, 1]), outer_tape=tape) _kappa_mse = tf.reduce_sum(wk(10**sampled_kappa) * (k - sampled_kappa)**2, axis=(2, 3, 4)) cost = tf.reduce_mean(_kappa_mse) cost += tf.reduce_mean((s - sampled_source)**2) cost += tf.reduce_sum(rim.unet.losses) # weight decay grads = tape.gradient(cost, unet.trainable_variables) optim.apply_gradients(zip(grads, unet.trainable_variables)) # Record performance on sampled dataset sampled_chi_squared_series = sampled_chi_squared_series.write( index=current_step, value=tf.squeeze(tf.reduce_mean(chi_sq[-1]))) sampled_source_mse = sampled_source_mse.write( index=current_step, value=tf.reduce_mean((s[-1] - sampled_source)**2)) sampled_kappa_mse = sampled_kappa_mse.write( index=current_step, value=tf.reduce_mean((k[-1] - sampled_kappa)**2)) # Record model prediction on data s, k, chi_sq = rim.call(observation, noise_rms, psf) chi_squared_series = chi_squared_series.write( index=current_step, value=tf.squeeze(chi_sq)) source_o = s[-1] kappa_o = k[-1] # oracle metrics, remove when using real data source_mse = source_mse.write(index=current_step, value=tf.reduce_mean( (source_o - source)**2)) kappa_mse = kappa_mse.write(index=current_step, value=tf.reduce_mean( (kappa_o - log_10(kappa))**2)) if abs(chi_sq[-1, 0] - 1) < abs(best[-1, 0] - 1): source_best = tf.nn.relu(source_o) kappa_best = 10**kappa_o best = chi_sq source_mse_best = tf.reduce_mean((source_best - source)**2) kappa_mse_best = tf.reduce_mean( (kappa_best - log_10(kappa))**2) source_o = source_best kappa_o = kappa_best y_pred = phys.forward(source_o, kappa_o, psf) chi_sq_series = tf.transpose(chi_squared_series.stack()) source_mse = source_mse.stack() kappa_mse = kappa_mse.stack() sampled_chi_squared_series = sampled_chi_squared_series.stack() sampled_source_mse = sampled_source_mse.stack() sampled_kappa_mse = sampled_kappa_mse.stack() # Latent code of optimized model predictions z_source_opt, _ = source_vae.encoder(tf.nn.relu(source_o)) z_kappa_opt, _ = kappa_vae.encoder(log_10(kappa_o)) # Compute Power spectrum of converged predictions _ps_observation = ps_observation.cross_correlation_coefficient( observation[..., 0], observation_pred[..., 0]) _ps_observation2 = ps_observation.cross_correlation_coefficient( observation[..., 0], y_pred[..., 0]) _ps_kappa = ps_kappa.cross_correlation_coefficient( log_10(kappa)[..., 0], log_10(kappa_pred[-1])[..., 0]) _ps_kappa2 = ps_kappa.cross_correlation_coefficient( log_10(kappa)[..., 0], log_10(kappa_o[..., 0])) _ps_source = ps_source.cross_correlation_coefficient( source[..., 0], source_pred[-1][..., 0]) _ps_source2 = ps_source.cross_correlation_coefficient( source[..., 0], source_o[..., 0]) # save results hf["observation"][i] = observation.numpy().astype(np.float32) hf["psf"][i] = psf.numpy().astype(np.float32) hf["psf_fwhm"][i] = fwhm.numpy().astype(np.float32) hf["noise_rms"][i] = noise_rms.numpy().astype(np.float32) hf["source"][i] = source.numpy().astype(np.float32) hf["kappa"][i] = kappa.numpy().astype(np.float32) hf["observation_pred"][i] = observation_pred.numpy().astype( np.float32) hf["observation_pred_reoptimized"][i] = y_pred.numpy().astype( np.float32) hf["source_pred"][i] = tf.transpose( source_pred, perm=(1, 0, 2, 3, 4)).numpy().astype(np.float32) hf["source_pred_reoptimized"][i] = source_o.numpy().astype( np.float32) hf["kappa_pred"][i] = tf.transpose( kappa_pred, perm=(1, 0, 2, 3, 4)).numpy().astype(np.float32) hf["kappa_pred_reoptimized"][i] = kappa_o.numpy().astype( np.float32) hf["chi_squared"][i] = tf.squeeze(chi_squared).numpy().astype( np.float32) hf["chi_squared_reoptimized"][i] = tf.squeeze(best).numpy().astype( np.float32) hf["chi_squared_reoptimized_series"][i] = chi_sq_series.numpy( ).astype(np.float32) hf["sampled_chi_squared_reoptimized_series"][ i] = 2 * sampled_chi_squared_series.numpy().astype(np.float32) hf["source_optim_mse"][i] = source_mse_best.numpy().astype( np.float32) hf["source_optim_mse_series"][i] = source_mse.numpy().astype( np.float32) hf["sampled_source_optim_mse_series"][ i] = sampled_source_mse.numpy().astype(np.float32) hf["kappa_optim_mse"][i] = kappa_mse_best.numpy().astype( np.float32) hf["kappa_optim_mse_series"][i] = kappa_mse.numpy().astype( np.float32) hf["sampled_kappa_optim_mse_series"][i] = sampled_kappa_mse.numpy( ).astype(np.float32) hf["latent_source_gt_distance_init"][i] = tf.abs( z_source - z_source_gt).numpy().squeeze().astype(np.float32) hf["latent_kappa_gt_distance_init"][i] = tf.abs( z_kappa - z_kappa_gt).numpy().squeeze().astype(np.float32) hf["latent_source_gt_distance_end"][i] = tf.abs( z_source_opt - z_source_gt).numpy().squeeze().astype( np.float32) hf["latent_kappa_gt_distance_end"][i] = tf.abs( z_kappa_opt - z_kappa_gt).numpy().squeeze().astype(np.float32) hf["observation_coherence_spectrum"][i] = _ps_observation hf["observation_coherence_spectrum_reoptimized"][ i] = _ps_observation2 hf["source_coherence_spectrum"][i] = _ps_source hf["source_coherence_spectrum_reoptimized"][i] = _ps_source2 hf["kappa_coherence_spectrum"][i] = _ps_kappa hf["kappa_coherence_spectrum_reoptimized"][i] = _ps_kappa2 if i == 0: _, f = np.histogram(np.fft.fftfreq(phys.pixels)[:phys.pixels // 2], bins=ps_observation.bins) f = (f[:-1] + f[1:]) / 2 hf["observation_frequencies"][:] = f _, f = np.histogram(np.fft.fftfreq( phys.src_pixels)[:phys.src_pixels // 2], bins=ps_source.bins) f = (f[:-1] + f[1:]) / 2 hf["source_frequencies"][:] = f _, f = np.histogram(np.fft.fftfreq( phys.kappa_pixels)[:phys.kappa_pixels // 2], bins=ps_kappa.bins) f = (f[:-1] + f[1:]) / 2 hf["kappa_frequencies"][:] = f hf["kappa_fov"][0] = phys.kappa_fov hf["source_fov"][0] = phys.src_fov