htjb · htjb · Aug 15, 2023 · Aug 16, 2023 · Aug 16, 2023 · Aug 16, 2023
diff --git a/Figures/figure1.pdf b/Figures/figure1.pdf
diff --git a/benchmark_duplicates/benchmarks_with_resampled_base_test_0.pdf b/benchmark_duplicates/benchmarks_with_resampled_base_test_0.pdf
diff --git a/benchmark_duplicates/benchmarks_with_resampled_base_test_1.pdf b/benchmark_duplicates/benchmarks_with_resampled_base_test_1.pdf
diff --git a/benchmark_duplicates/benchmarks_with_resampled_base_test_2.pdf b/benchmark_duplicates/benchmarks_with_resampled_base_test_2.pdf
diff --git a/benchmark_duplicates/benchmarks_with_resampled_base_test_3.pdf b/benchmark_duplicates/benchmarks_with_resampled_base_test_3.pdf
diff --git a/benchmark_duplicates/benchmarks_with_resampled_base_test_4.pdf b/benchmark_duplicates/benchmarks_with_resampled_base_test_4.pdf
diff --git a/benchmark_duplicates/benchmarks_with_resampled_base_test_5.pdf b/benchmark_duplicates/benchmarks_with_resampled_base_test_5.pdf
diff --git a/benchmark_duplicates/benchmarks_with_resampled_base_test_6.pdf b/benchmark_duplicates/benchmarks_with_resampled_base_test_6.pdf
diff --git a/benchmark_duplicates/benchmarks_with_resampled_base_test_7.pdf b/benchmark_duplicates/benchmarks_with_resampled_base_test_7.pdf
diff --git a/benchmark_duplicates/benchmarks_with_resampled_base_test_8.pdf b/benchmark_duplicates/benchmarks_with_resampled_base_test_8.pdf
diff --git a/benchmark_duplicates/benchmarks_with_resampled_base_test_9.pdf b/benchmark_duplicates/benchmarks_with_resampled_base_test_9.pdf
diff --git a/benchmark_duplicates/cgm_cluster_maf_0.pkl b/benchmark_duplicates/cgm_cluster_maf_0.pkl
diff --git a/benchmark_duplicates/cgm_cluster_maf_1.pkl b/benchmark_duplicates/cgm_cluster_maf_1.pkl
diff --git a/benchmark_duplicates/cgm_cluster_maf_2.pkl b/benchmark_duplicates/cgm_cluster_maf_2.pkl
diff --git a/benchmark_duplicates/cgm_cluster_maf_3.pkl b/benchmark_duplicates/cgm_cluster_maf_3.pkl
diff --git a/benchmark_duplicates/cgm_cluster_maf_4.pkl b/benchmark_duplicates/cgm_cluster_maf_4.pkl
diff --git a/benchmark_duplicates/cgm_cluster_maf_5.pkl b/benchmark_duplicates/cgm_cluster_maf_5.pkl
diff --git a/benchmark_duplicates/cgm_cluster_maf_6.pkl b/benchmark_duplicates/cgm_cluster_maf_6.pkl
diff --git a/benchmark_duplicates/cgm_cluster_maf_7.pkl b/benchmark_duplicates/cgm_cluster_maf_7.pkl
diff --git a/benchmark_duplicates/cgm_cluster_maf_8.pkl b/benchmark_duplicates/cgm_cluster_maf_8.pkl
diff --git a/benchmark_duplicates/cgm_cluster_maf_9.pkl b/benchmark_duplicates/cgm_cluster_maf_9.pkl
diff --git a/benchmark_duplicates/cgm_realnvp_resampled_base_0.pkl b/benchmark_duplicates/cgm_realnvp_resampled_base_0.pkl
diff --git a/benchmark_duplicates/cgm_realnvp_resampled_base_1.pkl b/benchmark_duplicates/cgm_realnvp_resampled_base_1.pkl
diff --git a/benchmark_duplicates/cgm_realnvp_resampled_base_2.pkl b/benchmark_duplicates/cgm_realnvp_resampled_base_2.pkl
diff --git a/benchmark_duplicates/cgm_realnvp_resampled_base_3.pkl b/benchmark_duplicates/cgm_realnvp_resampled_base_3.pkl
diff --git a/benchmark_duplicates/cgm_realnvp_resampled_base_4.pkl b/benchmark_duplicates/cgm_realnvp_resampled_base_4.pkl
diff --git a/benchmark_duplicates/cgm_realnvp_resampled_base_5.pkl b/benchmark_duplicates/cgm_realnvp_resampled_base_5.pkl
diff --git a/benchmark_duplicates/cgm_realnvp_resampled_base_6.pkl b/benchmark_duplicates/cgm_realnvp_resampled_base_6.pkl
diff --git a/benchmark_duplicates/cgm_realnvp_resampled_base_7.pkl b/benchmark_duplicates/cgm_realnvp_resampled_base_7.pkl
diff --git a/benchmark_duplicates/cgm_realnvp_resampled_base_8.pkl b/benchmark_duplicates/cgm_realnvp_resampled_base_8.pkl
diff --git a/benchmark_duplicates/cgm_realnvp_resampled_base_9.pkl b/benchmark_duplicates/cgm_realnvp_resampled_base_9.pkl
diff --git a/benchmark_duplicates/cgm_single_maf_0.pkl b/benchmark_duplicates/cgm_single_maf_0.pkl
diff --git a/benchmark_duplicates/cgm_single_maf_1.pkl b/benchmark_duplicates/cgm_single_maf_1.pkl
diff --git a/benchmark_duplicates/cgm_single_maf_2.pkl b/benchmark_duplicates/cgm_single_maf_2.pkl
diff --git a/benchmark_duplicates/cgm_single_maf_3.pkl b/benchmark_duplicates/cgm_single_maf_3.pkl
diff --git a/benchmark_duplicates/cgm_single_maf_4.pkl b/benchmark_duplicates/cgm_single_maf_4.pkl
diff --git a/benchmark_duplicates/cgm_single_maf_5.pkl b/benchmark_duplicates/cgm_single_maf_5.pkl
diff --git a/benchmark_duplicates/cgm_single_maf_6.pkl b/benchmark_duplicates/cgm_single_maf_6.pkl
diff --git a/benchmark_duplicates/cgm_single_maf_7.pkl b/benchmark_duplicates/cgm_single_maf_7.pkl
diff --git a/benchmark_duplicates/cgm_single_maf_8.pkl b/benchmark_duplicates/cgm_single_maf_8.pkl
diff --git a/benchmark_duplicates/cgm_single_maf_9.pkl b/benchmark_duplicates/cgm_single_maf_9.pkl
diff --git a/benchmark_duplicates/rm_cluster_maf_0.pkl b/benchmark_duplicates/rm_cluster_maf_0.pkl
diff --git a/benchmark_duplicates/rm_cluster_maf_1.pkl b/benchmark_duplicates/rm_cluster_maf_1.pkl
diff --git a/benchmark_duplicates/rm_cluster_maf_2.pkl b/benchmark_duplicates/rm_cluster_maf_2.pkl
diff --git a/benchmark_duplicates/rm_cluster_maf_3.pkl b/benchmark_duplicates/rm_cluster_maf_3.pkl
diff --git a/benchmark_duplicates/rm_cluster_maf_4.pkl b/benchmark_duplicates/rm_cluster_maf_4.pkl
diff --git a/benchmark_duplicates/rm_cluster_maf_5.pkl b/benchmark_duplicates/rm_cluster_maf_5.pkl
diff --git a/benchmark_duplicates/rm_cluster_maf_6.pkl b/benchmark_duplicates/rm_cluster_maf_6.pkl
diff --git a/benchmark_duplicates/rm_cluster_maf_7.pkl b/benchmark_duplicates/rm_cluster_maf_7.pkl
diff --git a/benchmark_duplicates/rm_cluster_maf_8.pkl b/benchmark_duplicates/rm_cluster_maf_8.pkl
diff --git a/benchmark_duplicates/rm_cluster_maf_9.pkl b/benchmark_duplicates/rm_cluster_maf_9.pkl
diff --git a/benchmark_duplicates/rm_realnvp_resampled_base_0.pkl b/benchmark_duplicates/rm_realnvp_resampled_base_0.pkl
diff --git a/benchmark_duplicates/rm_realnvp_resampled_base_1.pkl b/benchmark_duplicates/rm_realnvp_resampled_base_1.pkl
diff --git a/benchmark_duplicates/rm_realnvp_resampled_base_2.pkl b/benchmark_duplicates/rm_realnvp_resampled_base_2.pkl
diff --git a/benchmark_duplicates/rm_realnvp_resampled_base_3.pkl b/benchmark_duplicates/rm_realnvp_resampled_base_3.pkl
diff --git a/benchmark_duplicates/rm_realnvp_resampled_base_4.pkl b/benchmark_duplicates/rm_realnvp_resampled_base_4.pkl
diff --git a/benchmark_duplicates/rm_realnvp_resampled_base_5.pkl b/benchmark_duplicates/rm_realnvp_resampled_base_5.pkl
diff --git a/benchmark_duplicates/rm_realnvp_resampled_base_6.pkl b/benchmark_duplicates/rm_realnvp_resampled_base_6.pkl
diff --git a/benchmark_duplicates/rm_realnvp_resampled_base_7.pkl b/benchmark_duplicates/rm_realnvp_resampled_base_7.pkl
diff --git a/benchmark_duplicates/rm_realnvp_resampled_base_8.pkl b/benchmark_duplicates/rm_realnvp_resampled_base_8.pkl
diff --git a/benchmark_duplicates/rm_realnvp_resampled_base_9.pkl b/benchmark_duplicates/rm_realnvp_resampled_base_9.pkl
diff --git a/benchmark_duplicates/rm_single_maf_0.pkl b/benchmark_duplicates/rm_single_maf_0.pkl
diff --git a/benchmark_duplicates/rm_single_maf_1.pkl b/benchmark_duplicates/rm_single_maf_1.pkl
diff --git a/benchmark_duplicates/rm_single_maf_2.pkl b/benchmark_duplicates/rm_single_maf_2.pkl
diff --git a/benchmark_duplicates/rm_single_maf_3.pkl b/benchmark_duplicates/rm_single_maf_3.pkl
diff --git a/benchmark_duplicates/rm_single_maf_4.pkl b/benchmark_duplicates/rm_single_maf_4.pkl
diff --git a/benchmark_duplicates/rm_single_maf_5.pkl b/benchmark_duplicates/rm_single_maf_5.pkl
diff --git a/benchmark_duplicates/rm_single_maf_6.pkl b/benchmark_duplicates/rm_single_maf_6.pkl
diff --git a/benchmark_duplicates/rm_single_maf_7.pkl b/benchmark_duplicates/rm_single_maf_7.pkl
diff --git a/benchmark_duplicates/rm_single_maf_8.pkl b/benchmark_duplicates/rm_single_maf_8.pkl
diff --git a/benchmark_duplicates/rm_single_maf_9.pkl b/benchmark_duplicates/rm_single_maf_9.pkl
diff --git a/benchmark_duplicates/slurm-29410985.out b/benchmark_duplicates/slurm-29410985.out
diff --git a/benchmark_duplicates/tm_cluster_maf_0.pkl b/benchmark_duplicates/tm_cluster_maf_0.pkl
diff --git a/benchmark_duplicates/tm_cluster_maf_1.pkl b/benchmark_duplicates/tm_cluster_maf_1.pkl
diff --git a/benchmark_duplicates/tm_cluster_maf_2.pkl b/benchmark_duplicates/tm_cluster_maf_2.pkl
diff --git a/benchmark_duplicates/tm_cluster_maf_3.pkl b/benchmark_duplicates/tm_cluster_maf_3.pkl
diff --git a/benchmark_duplicates/tm_cluster_maf_4.pkl b/benchmark_duplicates/tm_cluster_maf_4.pkl
diff --git a/benchmark_duplicates/tm_cluster_maf_5.pkl b/benchmark_duplicates/tm_cluster_maf_5.pkl
diff --git a/benchmark_duplicates/tm_cluster_maf_6.pkl b/benchmark_duplicates/tm_cluster_maf_6.pkl
diff --git a/benchmark_duplicates/tm_cluster_maf_7.pkl b/benchmark_duplicates/tm_cluster_maf_7.pkl
diff --git a/benchmark_duplicates/tm_cluster_maf_8.pkl b/benchmark_duplicates/tm_cluster_maf_8.pkl
diff --git a/benchmark_duplicates/tm_cluster_maf_9.pkl b/benchmark_duplicates/tm_cluster_maf_9.pkl
diff --git a/benchmark_duplicates/tm_realnvp_resampled_base_0.pkl b/benchmark_duplicates/tm_realnvp_resampled_base_0.pkl
diff --git a/benchmark_duplicates/tm_realnvp_resampled_base_1.pkl b/benchmark_duplicates/tm_realnvp_resampled_base_1.pkl
diff --git a/benchmark_duplicates/tm_realnvp_resampled_base_2.pkl b/benchmark_duplicates/tm_realnvp_resampled_base_2.pkl
diff --git a/benchmark_duplicates/tm_realnvp_resampled_base_3.pkl b/benchmark_duplicates/tm_realnvp_resampled_base_3.pkl
diff --git a/benchmark_duplicates/tm_realnvp_resampled_base_4.pkl b/benchmark_duplicates/tm_realnvp_resampled_base_4.pkl
diff --git a/benchmark_duplicates/tm_realnvp_resampled_base_5.pkl b/benchmark_duplicates/tm_realnvp_resampled_base_5.pkl
diff --git a/benchmark_duplicates/tm_realnvp_resampled_base_6.pkl b/benchmark_duplicates/tm_realnvp_resampled_base_6.pkl
diff --git a/benchmark_duplicates/tm_realnvp_resampled_base_7.pkl b/benchmark_duplicates/tm_realnvp_resampled_base_7.pkl
diff --git a/benchmark_duplicates/tm_realnvp_resampled_base_8.pkl b/benchmark_duplicates/tm_realnvp_resampled_base_8.pkl
diff --git a/benchmark_duplicates/tm_realnvp_resampled_base_9.pkl b/benchmark_duplicates/tm_realnvp_resampled_base_9.pkl
diff --git a/benchmark_duplicates/tm_single_maf_0.pkl b/benchmark_duplicates/tm_single_maf_0.pkl
diff --git a/benchmark_duplicates/tm_single_maf_1.pkl b/benchmark_duplicates/tm_single_maf_1.pkl
diff --git a/benchmark_duplicates/tm_single_maf_2.pkl b/benchmark_duplicates/tm_single_maf_2.pkl
diff --git a/benchmark_duplicates/tm_single_maf_3.pkl b/benchmark_duplicates/tm_single_maf_3.pkl
diff --git a/benchmark_duplicates/tm_single_maf_4.pkl b/benchmark_duplicates/tm_single_maf_4.pkl
diff --git a/benchmark_duplicates/tm_single_maf_5.pkl b/benchmark_duplicates/tm_single_maf_5.pkl
diff --git a/benchmark_duplicates/tm_single_maf_6.pkl b/benchmark_duplicates/tm_single_maf_6.pkl
diff --git a/benchmark_duplicates/tm_single_maf_7.pkl b/benchmark_duplicates/tm_single_maf_7.pkl
diff --git a/benchmark_duplicates/tm_single_maf_8.pkl b/benchmark_duplicates/tm_single_maf_8.pkl
diff --git a/benchmark_duplicates/tm_single_maf_9.pkl b/benchmark_duplicates/tm_single_maf_9.pkl
diff --git a/benchmarks.py b/benchmarks.py
diff --git a/clustering.py b/clustering.py
diff --git a/clustering/clustering_0.pdf b/clustering/clustering_0.pdf
diff --git a/clustering/clustering_1.pdf b/clustering/clustering_1.pdf
diff --git a/clustering/clustering_2.pdf b/clustering/clustering_2.pdf
diff --git a/clustering/clustering_3.pdf b/clustering/clustering_3.pdf
diff --git a/clustering/clustering_4.pdf b/clustering/clustering_4.pdf
diff --git a/clustering/clustering_5.pdf b/clustering/clustering_5.pdf
diff --git a/clustering/clustering_6.pdf b/clustering/clustering_6.pdf
diff --git a/clustering/clustering_7.pdf b/clustering/clustering_7.pdf
diff --git a/clustering/clustering_8.pdf b/clustering/clustering_8.pdf
diff --git a/clustering/clustering_9.pdf b/clustering/clustering_9.pdf
diff --git a/clustering/rm_agglomerative_clustering_maf_0.pkl b/clustering/rm_agglomerative_clustering_maf_0.pkl
diff --git a/clustering/rm_agglomerative_clustering_maf_1.pkl b/clustering/rm_agglomerative_clustering_maf_1.pkl
diff --git a/clustering/rm_agglomerative_clustering_maf_2.pkl b/clustering/rm_agglomerative_clustering_maf_2.pkl
diff --git a/clustering/rm_agglomerative_clustering_maf_3.pkl b/clustering/rm_agglomerative_clustering_maf_3.pkl
diff --git a/clustering/rm_agglomerative_clustering_maf_4.pkl b/clustering/rm_agglomerative_clustering_maf_4.pkl
diff --git a/clustering/rm_agglomerative_clustering_maf_5.pkl b/clustering/rm_agglomerative_clustering_maf_5.pkl
diff --git a/clustering/rm_agglomerative_clustering_maf_6.pkl b/clustering/rm_agglomerative_clustering_maf_6.pkl
diff --git a/clustering/rm_agglomerative_clustering_maf_7.pkl b/clustering/rm_agglomerative_clustering_maf_7.pkl
diff --git a/clustering/rm_agglomerative_clustering_maf_8.pkl b/clustering/rm_agglomerative_clustering_maf_8.pkl
diff --git a/clustering/rm_agglomerative_clustering_maf_9.pkl b/clustering/rm_agglomerative_clustering_maf_9.pkl
diff --git a/clustering/rm_birch_clustering_maf_0.pkl b/clustering/rm_birch_clustering_maf_0.pkl
diff --git a/clustering/rm_birch_clustering_maf_1.pkl b/clustering/rm_birch_clustering_maf_1.pkl
diff --git a/clustering/rm_birch_clustering_maf_2.pkl b/clustering/rm_birch_clustering_maf_2.pkl
diff --git a/clustering/rm_birch_clustering_maf_3.pkl b/clustering/rm_birch_clustering_maf_3.pkl
diff --git a/clustering/rm_birch_clustering_maf_4.pkl b/clustering/rm_birch_clustering_maf_4.pkl
diff --git a/clustering/rm_birch_clustering_maf_5.pkl b/clustering/rm_birch_clustering_maf_5.pkl
diff --git a/clustering/rm_birch_clustering_maf_6.pkl b/clustering/rm_birch_clustering_maf_6.pkl
diff --git a/clustering/rm_birch_clustering_maf_7.pkl b/clustering/rm_birch_clustering_maf_7.pkl
diff --git a/clustering/rm_birch_clustering_maf_8.pkl b/clustering/rm_birch_clustering_maf_8.pkl
diff --git a/clustering/rm_birch_clustering_maf_9.pkl b/clustering/rm_birch_clustering_maf_9.pkl
diff --git a/clustering/rm_kmeans_maf_0.pkl b/clustering/rm_kmeans_maf_0.pkl
diff --git a/clustering/rm_kmeans_maf_1.pkl b/clustering/rm_kmeans_maf_1.pkl
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diff --git a/clustering/rm_kmeans_maf_9.pkl b/clustering/rm_kmeans_maf_9.pkl
diff --git a/clustering/rm_mean_shift_maf_0.pkl b/clustering/rm_mean_shift_maf_0.pkl
diff --git a/clustering/rm_mean_shift_maf_1.pkl b/clustering/rm_mean_shift_maf_1.pkl
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diff --git a/clustering/rm_mean_shift_maf_9.pkl b/clustering/rm_mean_shift_maf_9.pkl
diff --git a/clustering/rm_minibatch_kmeans_maf_0.pkl b/clustering/rm_minibatch_kmeans_maf_0.pkl
diff --git a/clustering/rm_minibatch_kmeans_maf_1.pkl b/clustering/rm_minibatch_kmeans_maf_1.pkl
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diff --git a/clustering/rm_minibatch_kmeans_maf_9.pkl b/clustering/rm_minibatch_kmeans_maf_9.pkl
diff --git a/clustering/rm_realnvp_resampled_base_0.pkl b/clustering/rm_realnvp_resampled_base_0.pkl
diff --git a/clustering/rm_single_maf_0.pkl b/clustering/rm_single_maf_0.pkl
diff --git a/clustering/rm_single_maf_1.pkl b/clustering/rm_single_maf_1.pkl
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diff --git a/clustering/rm_single_maf_9.pkl b/clustering/rm_single_maf_9.pkl
diff --git a/clustering/rm_spectral_clustering_maf_0.pkl b/clustering/rm_spectral_clustering_maf_0.pkl
diff --git a/clustering/rm_spectral_clustering_maf_1.pkl b/clustering/rm_spectral_clustering_maf_1.pkl
diff --git a/clustering/rm_spectral_clustering_maf_2.pkl b/clustering/rm_spectral_clustering_maf_2.pkl
diff --git a/clustering/rm_spectral_clustering_maf_3.pkl b/clustering/rm_spectral_clustering_maf_3.pkl
diff --git a/clustering/rm_spectral_clustering_maf_4.pkl b/clustering/rm_spectral_clustering_maf_4.pkl
diff --git a/clustering/rm_spectral_clustering_maf_5.pkl b/clustering/rm_spectral_clustering_maf_5.pkl
diff --git a/clustering/rm_spectral_clustering_maf_6.pkl b/clustering/rm_spectral_clustering_maf_6.pkl
diff --git a/clustering/rm_spectral_clustering_maf_7.pkl b/clustering/rm_spectral_clustering_maf_7.pkl
diff --git a/clustering/rm_spectral_clustering_maf_8.pkl b/clustering/rm_spectral_clustering_maf_8.pkl
diff --git a/clustering/rm_spectral_clustering_maf_9.pkl b/clustering/rm_spectral_clustering_maf_9.pkl
diff --git a/clustering/slurm-29848070.out b/clustering/slurm-29848070.out
@@ -0,0 +1,51 @@
+Loading rhel8/default-icl
+  Loading requirement: dot rhel8/slurm singularity/current rhel8/global
+    cuda/11.4 vgl/3.1/64 intel-oneapi-compilers/2022.1.0/gcc/b6zld2mz
+    intel-oneapi-mpi/2021.6.0/intel/guxuvcpm
+Changed directory to /rds/user/htjb2/hpc-work/piecewise-normalizing-flows-reviews.
+
+JobID: 29848070
+======
+Time: Tue 17 Oct 12:09:52 BST 2023
+Running on master node: cpu-q-88
+Current directory: /rds/user/htjb2/hpc-work/piecewise-normalizing-flows-reviews
+
+Nodes allocated:
+================
+cpu-q-88
+
+numtasks=76, numnodes=1, mpi_tasks_per_node=76 (OMP_NUM_THREADS=1)
+
+Executing command:
+==================
+python clustering.py 
+
+2023-10-17 12:09:58.630128: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+2023-10-17 12:09:58.840275: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2023-10-17 12:10:01.003094: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+2023-10-17 12:10:07.092746: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:268] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
+2023-10-17 12:10:08.231180: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xc5e0710 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
+2023-10-17 12:10:08.231363: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
+2023-10-17 12:10:08.322517: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:255] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
+2023-10-17 12:10:08.800507: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.
+2023-10-17 12:10:09.222538: W tensorflow/compiler/tf2xla/kernels/random_ops.cc:57] Warning: Using tf.random.uniform with XLA compilation will ignore seeds; consider using tf.random.stateless_uniform instead if reproducible behavior is desired. random_uniform/RandomUniform
+WARNING:tensorflow:5 out of the last 5 calls to <function MAF.log_prob at 0x14bc798403a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
+WARNING:tensorflow:6 out of the last 6 calls to <function MAF.log_prob at 0x14bc798769d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
+WARNING:tensorflow:5 out of the last 6 calls to <function clusterMAF.__call__ at 0x14bc7861b280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
+WARNING:tensorflow:6 out of the last 7 calls to <function clusterMAF.__call__ at 0x14bc900443a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
+/rds/user/htjb2/hpc-work/piecewise-normalizing-flows-reviews/myenv/lib/python3.8/site-packages/margarine/clustered.py:174: UserWarning: The number of clusters is 20. This is the maximum number of clusters that can be used. If you require more clusters, please specify the 'cluster_number' kwarg. margarine will continue with 20 clusters.
+  warnings.warn("The number of clusters is 20. " +
+WARNING:tensorflow:5 out of the last 5 calls to <function MAF.__call__ at 0x14bc56997670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
+WARNING:tensorflow:5 out of the last 5 calls to <function MAF.sample at 0x14bc544d33a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
+WARNING:tensorflow:6 out of the last 6 calls to <function MAF.__call__ at 0x14bbe53c7ee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
+WARNING:tensorflow:6 out of the last 6 calls to <function MAF.sample at 0x14bbe7508700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
+/rds/user/htjb2/hpc-work/piecewise-normalizing-flows-reviews/myenv/lib/python3.8/site-packages/margarine/clustered.py:174: UserWarning: The number of clusters is 20. This is the maximum number of clusters that can be used. If you require more clusters, please specify the 'cluster_number' kwarg. margarine will continue with 20 clusters.
+  warnings.warn("The number of clusters is 20. " +
+maf 0.04031182 0.028188227102607325
+kmeans 0.036751173 0.014605213892256943
+minbatchkmeans 0.036751173 0.014605213892256943
+mean_shift 0.039975084 0.016644943875773675
+spectral_clustering 0.80715007 0.07794943311681986
+agglomerative_clustering 0.4231082 0.06230989730486836
+birch 0.035159063 0.014266189726573182
diff --git a/datasets/gas.py b/datasets/gas.py
@@ -0,0 +1,73 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+import datasets
+
+
+class GAS:
+
+    class Data:
+
+        def __init__(self, data):
+
+            self.x = data.astype(np.float32)
+            self.N = self.x.shape[0]
+
+    def __init__(self):
+
+        file = 'raw_physical_data/gas/ethylene_CO.pickle'
+        trn, val, tst = load_data_and_clean_and_split(file)
+
+        self.trn = self.Data(trn)
+        self.val = self.Data(val)
+        self.tst = self.Data(tst)
+
+        self.n_dims = self.trn.x.shape[1]
+
+
+def load_data(file):
+
+    data = pd.read_pickle(file)
+    # data = pd.read_pickle(file).sample(frac=0.25)
+    # data.to_pickle(file)
+    data.drop("Meth", axis=1, inplace=True)
+    data.drop("Eth", axis=1, inplace=True)
+    data.drop("Time", axis=1, inplace=True)
+    return data
+
+
+def get_correlation_numbers(data):
+    C = data.corr()
+    A = C > 0.98
+    B = A.values.sum(axis=1)
+    return B
+
+
+def load_data_and_clean(file):
+
+    data = load_data(file)
+    B = get_correlation_numbers(data)
+
+    while np.any(B > 1):
+        col_to_remove = np.where(B > 1)[0][0]
+        col_name = data.columns[col_to_remove]
+        data.drop(col_name, axis=1, inplace=True)
+        B = get_correlation_numbers(data)
+    # print(data.corr())
+    data = (data-data.mean())/data.std()
+
+    return data
+
+
+def load_data_and_clean_and_split(file):
+
+    data = load_data_and_clean(file).values
+    N_test = int(0.1*data.shape[0])
+    data_test = data[-N_test:]
+    data_train = data[0:-N_test]
+    N_validate = int(0.1*data_train.shape[0])
+    data_validate = data_train[-N_validate:]
+    data_train = data_train[0:-N_validate]
+
+    return data_train, data_validate, data_test
diff --git a/figure1.py b/figure1.py
@@ -2,6 +2,7 @@
 from anesthetic import MCMCSamples
 import matplotlib.pyplot as plt
 from margarine.maf import MAF
+from margarine.clustered import clusterMAF
 
 # generate a set of multi-modal samples
 nsamples = 5000
@@ -17,32 +18,32 @@
 fig, axes = plt.subplots(1, 3, figsize=(6.3, 3))
 
 # plot original samples
-axes[0].hist2d(data[:, 0], data[:, 1], bins=80)
+axes[0].hist2d(data[:, 0], data[:, 1], bins=80, cmap='Blues')
 
 # try and load the example maf only flow else generate and plot samples
 try:
     bij = MAF.load("figure1_normal_maf.pkl")
 except:
     bij = MAF(samples[names].values, 
-              samples.get_weights().astype('float64'))
+              weights=samples.get_weights().astype('float64'))
     bij.train(10000, early_stop=True)
     bij.save("figure1_normal_maf.pkl")
 bij_samples = bij.sample(nsamples)
-axes[1].hist2d(bij_samples[:, 0], bij_samples[:, 1], bins=80)
+axes[1].hist2d(bij_samples[:, 0], bij_samples[:, 1], bins=80, cmap='Blues')
 
 # try and load the example cluster maf only flow else generate and plot samples
 try:
-    bij = MAF.load("figure1_cluster.pkl")
+    bij = clusterMAF.load("figure1_cluster.pkl")
 except:
-    bij = MAF(samples[names].values, 
-              samples.get_weights().astype('float64'), clustering=True)
+    bij = clusterMAF(samples[names].values, 
+              weights=samples.get_weights().astype('float64'), clustering=True)
     bij.train(10000, early_stop=True)
     bij.save("figure1_cluster.pkl")
 bij_samples = bij.sample(nsamples)
-axes[2].hist2d(bij_samples[:, 0], bij_samples[:, 1], bins=80)
+axes[2].hist2d(bij_samples[:, 0], bij_samples[:, 1], bins=80, cmap='Blues')
 
 # formatting
-title = ['Target.', 'MAF\nGaussian Base\ne.g. Papamakarios et al. 2017', 
+title = ['Target', 'MAF\nGaussian Base\ne.g. Papamakarios et al. 2017', 
          'Piecewise MAF\nGaussian Base\nThis work']
 for i in range(len(axes)):
     axes[i].set_xticks([])

diff --git a/figure1_cluster.pkl b/figure1_cluster.pkl
diff --git a/figure1_normal_maf.pkl b/figure1_normal_maf.pkl
diff --git a/figure2.pdf b/figure2.pdf
diff --git a/figure2.py b/figure2.py
@@ -3,9 +3,23 @@
 from target_dists_stimper import CircularGaussianMixture
 from sklearn.metrics import silhouette_score
 from sklearn.cluster import KMeans
+from matplotlib import rc
+import matplotlib as mpl
+
+# figure formatting
+mpl.rcParams['axes.prop_cycle'] = mpl.cycler('color',
+    ['ff7f00', '984ea3', '999999', '377eb8', 
+     '4daf4a','f781bf', 'a65628', 'e41a1c', 'dede00'])
+mpl.rcParams['text.usetex'] = True
+#mpl.rcParams['text.latex.preamble'] = [
+#    r'\usepackage{amsmath}',
+#    r'\usepackage{amssymb}']
+rc('font', family='serif')
+rc('font', serif='cm')
+rc('savefig', pad_inches=0.05)
 
 # generate example samples
-nsample = 10000
+nsample = 1000
 cgm = CircularGaussianMixture()
 s = cgm.sample(nsample).numpy()
 
@@ -24,7 +38,16 @@
 print(cluster_number)
 
 # plot a few examples of the clustering and the corresponding score.
-fig, axes = plt.subplots(2, 3, figsize=(6.3, 4))
+fig = plt.figure(constrained_layout=True, figsize=(6.3, 4))
+gs = fig.add_gridspec(2, 5, width_ratios=[1, 1, 1, 0.8, 0.8])
+
+axes = []
+for i in range(2):
+    ax = []
+    for j in range(3):
+        ax.append(fig.add_subplot(gs[i, j]))
+    axes.append(ax)
+axes = np.array(axes)
 
 cmap = 'inferno'
 
@@ -36,18 +59,24 @@
 
 axes[1, 2].scatter(s[:, 0], s[:, 1], c=labels[10], s=1, cmap=cmap)
 
-axes[0, 0].set_title(r'$k=2, s=$' + '{:.3f}'.format(-losses[0]))
-axes[0, 1].set_title(r'$k=4, s=$' + '{:.3f}'.format(-losses[2]))
-axes[0, 2].set_title(r'$k=6, s=$' + '{:.3f}'.format(-losses[4]))
-axes[1, 0].set_title(r'$k=8, s=$' + '{:.3f}'.format(-losses[6]))
-axes[1, 1].set_title(r'$k=10, s=$' + '{:.3f}'.format(-losses[8]))
-axes[1, 2].set_title(r'$k=12, s=$' + '{:.3f}'.format(-losses[10]))
+axes[0, 0].set_title(r'$k=2$,' +'\n' + r'$s=$' + '{:.3f}'.format(-losses[0]), fontsize=10)
+axes[0, 1].set_title(r'$k=4$,' +'\n' + r'$s=$' + '{:.3f}'.format(-losses[2]), fontsize=10)
+axes[0, 2].set_title(r'$k=6$,' +'\n' + r'$s=$' + '{:.3f}'.format(-losses[4]), fontsize=10)
+axes[1, 0].set_title(r'$k=8$,' +'\n' + r'$s=$' + '{:.3f}'.format(-losses[6]), fontsize=10)
+axes[1, 1].set_title(r'$k=10$,' +'\n' + r'$s=$' + '{:.3f}'.format(-losses[8]), fontsize=10)
+axes[1, 2].set_title(r'$k=12$,' +'\n' + r'$s=$' + '{:.3f}'.format(-losses[10]), fontsize=10)
 
 for i in range(2):
     for j in range(3):
         axes[i, j].set_xticks([])
         axes[i, j].set_yticks([])
 
-plt.tight_layout()
+ax = fig.add_subplot(gs[:, 3:])
+ax.plot(ks, -losses, 'o-', c='k')
+ax.set_xlabel('Number of clusters')
+ax.set_ylabel('Silhouette score')
+ax.set_xticks(ks[::2])
+
+#plt.tight_layout()
 plt.savefig('figure2.pdf', dpi=300)
 plt.show()