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  1. README.md +71 -0
  2. configs/0_dropout_emb.yaml +2 -0
  3. configs/10_layers.yaml +2 -0
  4. configs/128_emb_dim.yaml +2 -0
  5. configs/1_mode.yaml +7 -0
  6. configs/2_modes.yaml +7 -0
  7. configs/5_layers.yaml +6 -0
  8. configs/8_modes.yaml +7 -0
  9. configs/IS.yaml +4 -0
  10. configs/IS_LS.yaml +4 -0
  11. configs/LS.yaml +4 -0
  12. configs/SS.yaml +4 -0
  13. configs/ablate_struct.yaml +2 -0
  14. configs/change_connectivity_false.yaml +2 -0
  15. configs/default.yaml +54 -0
  16. configs/esmc_300m.yaml +2 -0
  17. configs/esmc_300m_ablate_struct.yaml +5 -0
  18. configs/esmc_600m.yaml +2 -0
  19. configs/esmc_600m_ablate_struct.yaml +5 -0
  20. configs/only_close.yaml +4 -0
  21. configs/only_random.yaml +3 -0
  22. configs/rand_emb.yaml +2 -0
  23. configs/rand_emb_ablate_struct.yaml +4 -0
  24. configs/shared_layers.yaml +2 -0
  25. eval_list.txt +824 -0
  26. full_list.txt +0 -0
  27. full_test_list.txt +5585 -0
  28. full_train_list.txt +0 -0
  29. full_val_list.txt +5495 -0
  30. petimot/__init__.py +0 -0
  31. petimot/__main__.py +231 -0
  32. petimot/config/__init__.py +0 -0
  33. petimot/config/config.py +107 -0
  34. petimot/data/__init__.py +0 -0
  35. petimot/data/data_set.py +282 -0
  36. petimot/data/embeddings.py +84 -0
  37. petimot/data/pdb_utils.py +157 -0
  38. petimot/eval/eval.py +286 -0
  39. petimot/infer/infer.py +148 -0
  40. petimot/model/__init__.py +0 -0
  41. petimot/model/loss.py +227 -0
  42. petimot/model/neural_net.py +374 -0
  43. petimot/model/optimizer.py +31 -0
  44. petimot/utils/__init__.py +0 -0
  45. petimot/utils/rigid_utils.py +1445 -0
  46. petimot/utils/seeding.py +42 -0
  47. requirements.txt +8 -0
README.md ADDED
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+ # PETIMOT: Protein Motion Inference from Sparse Data
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+
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+ PETIMOT (Protein sEquence and sTructure-based Inference of MOTions) predicts protein conformational changes using SE(3)-equivariant graph neural networks and pre-trained protein language models.
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+
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+ ## Installation
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+
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+ ```bash
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+ # Create and activate conda environment
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+ conda create -n petimot python=3.9
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+ conda activate petimot
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+
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+ # Clone and install
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+ git clone https://github.com/PhyloSofS-Team/PETIMOT.git
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+ cd petimot
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+ pip install -r requirements.txt
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+ ```
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+
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+
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+ ## Usage
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+
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+ ### Reproduce paper results
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+
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+ 1. Download resources from [Figshare](https://figshare.com/s/ab400d852b4669a83b64):
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+ - Download `default_2025-02-07_21-54-02_epoch_33.pt` into the `weights/` directory
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+ - Download and extract `ground_truth.zip` into the `ground_truth/` directory
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+
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+ 2. Run inference and evaluation:
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+ ```bash
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+ python -m petimot infer_and_evaluate \
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+ --model-path weights/default_2025-02-07_21-54-02_epoch_33.pt \
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+ --list-path eval_list.txt \
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+ --ground-truth-path ground_truth/ \
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+ --prediction-path predictions/ \
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+ --evaluation-path evaluation/
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+ ```
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+
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+ ### Compare with baseline methods
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+
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+ 1. Download baseline predictions from [Figshare](https://figshare.com/s/ab400d852b4669a83b64) :
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+ - Download and extract `baseline_predictions.zip` into the `baselines/` directory
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+
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+ 2. Run evaluation:
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+ ```bash
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+ python -m petimot evaluate \
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+ --prediction-path baselines/alphaflow_pdb_distilled/ \
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+ --ground-truth-path ground_truth/ \
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+ --output-path evaluation/
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+ ```
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+
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+ Available baseline predictions:
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+ - AlphaFlow (distilled)
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+ - ESMFlow (distilled)
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+ - Normal Mode Analysis
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+
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+
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+
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+ ### Predict motions for your own PDB files
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+
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+ ```bash
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+ # Single PDB structure
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+ python -m petimot infer \
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+ --model-path weights/default_2025-02-07_21-54-02_epoch_33.pt \
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+ --list-path protein.pdb \
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+ --output-path predictions/
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+
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+ # Multiple structures (provide paths in a text file)
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+ python -m petimot infer \
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+ --model-path weights/default_2025-02-07_21-54-02_epoch_33.pt \
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+ --list-path protein_list.txt \
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+ --output-path predictions/
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+ ```
configs/0_dropout_emb.yaml ADDED
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+ model:
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+ input_embedding_dropout: 0.0
configs/10_layers.yaml ADDED
@@ -0,0 +1,2 @@
 
 
 
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+ model:
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+ num_layers: 10
configs/128_emb_dim.yaml ADDED
@@ -0,0 +1,2 @@
 
 
 
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+ model:
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+ emb_dim: 128
configs/1_mode.yaml ADDED
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+ training:
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+ nsse_weight: 1.0
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+ rmsip_weight: 0.0
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+ ortho_weight: 0.0
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+
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+ model:
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+ num_modes_pred: 1
configs/2_modes.yaml ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
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+ training:
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+ nsse_weight: 1.0
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+ rmsip_weight: 0.0
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+ ortho_weight: 0.0
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+
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+ model:
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+ num_modes_pred: 2
configs/5_layers.yaml ADDED
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+ training:
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+ nsse_weight: 0.5
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+ rmsip_weight: 0.5
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+
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+ model:
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+ num_layers: 5
configs/8_modes.yaml ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
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+ training:
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+ nsse_weight: 1.0
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+ rmsip_weight: 0.0
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+ ortho_weight: 0.0
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+
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+ model:
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+ num_modes_pred: 8
configs/IS.yaml ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
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+ training:
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+ nsse_weight: 0.0
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+ rmsip_weight: 0.0
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+ ortho_weight: 1.0
configs/IS_LS.yaml ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
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+ training:
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+ nsse_weight: 0.5
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+ rmsip_weight: 0.0
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+ ortho_weight: 0.5
configs/LS.yaml ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
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+ training:
2
+ nsse_weight: 1.0
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+ rmsip_weight: 0.0
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+ ortho_weight: 0.0
configs/SS.yaml ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
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+ training:
2
+ nsse_weight: 0.0
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+ rmsip_weight: 1.0
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+ ortho_weight: 0.0
configs/ablate_struct.yaml ADDED
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+ model:
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+ ablate_structure: true
configs/change_connectivity_false.yaml ADDED
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+ data:
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+ change_connectivity: false
configs/default.yaml ADDED
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+ training:
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+ seed: 7
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+ nsse_weight: 0.5
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+ rmsip_weight: 0.5
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+ ortho_weight: 0.0
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+ batch_size: 32
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+ validation_batch_size: 32
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+ nb_epochs: 500
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+ num_workers: 6
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+ num_workers_val: 2
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+ pin_memory: false
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+ optimizer: "adamw"
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+ learning_rate: 0.0005
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+ weight_decay: 0.01
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+ scheduler_factor: 0.2
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+ scheduler_patience: 10
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+ loss_threshold: 0.6
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+ use_amp: true
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+ grad_clip: 10
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+ early_stop_patience: 50
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+ weights_dir: "weights"
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+ debug: false
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+
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+ data:
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+ training_split_path: "full_train_list.txt"
26
+ validation_split_path: "val_list.txt"
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+ ground_truth_dir: "ground_truth"
28
+ embedding_dir: "embeddings"
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+ emb_model: "ProstT5"
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+ noise: 0.0
31
+ k_nearest: 5
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+ l_random: 10
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+ num_modes_gt: 4
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+ rand_emb: false
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+ change_connectivity: true
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+
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+ model:
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+ emb_dim: 256
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+ edge_dim: 329
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+ num_modes_pred: 4
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+ num_layers: 15
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+ shared_layers: false
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+ mlp_num_layers: 1
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+ start_with_zero_v: false
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+ input_embedding_dropout: 0.8
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+ dropout: 0.4
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+ normalize_between_layers: false
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+ center_between_layers: false
49
+ orthogonalize_between_layers: false
50
+ num_basis: 20
51
+ num_backbone_atoms: 4
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+ max_dist: 20.0
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+ sigma: 1.0
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+ ablate_structure: false
configs/esmc_300m.yaml ADDED
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+ data:
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+ emb_model: "esmc_300m"
configs/esmc_300m_ablate_struct.yaml ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ data:
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+ emb_model: "esmc_300m"
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+
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+ model:
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+ ablate_structure: true
configs/esmc_600m.yaml ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ data:
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+ emb_model: "esmc_600m"
configs/esmc_600m_ablate_struct.yaml ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
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+ data:
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+ emb_model: "esmc_600m"
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+
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+ model:
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+ ablate_structure: true
configs/only_close.yaml ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
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+ data:
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+ k_nearest: 15
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+ l_random: 0
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+ change_connectivity: false
configs/only_random.yaml ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ data:
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+ k_nearest: 0
3
+ l_random: 15
configs/rand_emb.yaml ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ data:
2
+ rand_emb: true
configs/rand_emb_ablate_struct.yaml ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ data:
2
+ rand_emb: true
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+ model:
4
+ ablate_structure: true
configs/shared_layers.yaml ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ model:
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+ shared_layers: true
eval_list.txt ADDED
@@ -0,0 +1,824 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ 2N7LC_7SC3A
207
+ 1AL22_6Q0B2
208
+ 5ZGB2_5ZGH2
209
+ 6U0TA_8G3D1X
210
+ 4QYZD_5CD4G
211
+ 3GOYA_3SMJA
212
+ 1U7LA_7TMMO
213
+ 5ZE7A_5ZE7A
214
+ 6P8VA_6P8VE
215
+ 2BNDA_2BNDB
216
+ 3A1JB_3GGRB
217
+ 6Q975_7ACR5
218
+ 1SEIA_1SEIB
219
+ 3B6YA_3RNUA
220
+ 2BX9A_2KO8A
221
+ 3AUXA_3AUYA
222
+ 2A4EA_3LNDD
223
+ 1LAFE_6MLPE
224
+ 8EDUA_8EDUG
225
+ 2BJ1A_2BJ9B
226
+ 2EAYA_3FJPA
227
+ 6TP9A_6TP9G
228
+ 4OCJA_4OCJA
229
+ 2ZJTA_7UGWD
230
+ 1PP7U_1PP8M
231
+ 3J7OB_4V5ZBb
232
+ 7D2SA_7D2SA
233
+ 1XLSE_1XNXA
234
+ 2ZW4A_2ZW4D
235
+ 4Z3WE_4Z3ZF
236
+ 6F1CA_6F39B
237
+ 5T1PA_5T1PE
238
+ 1E7WA_2XOXA
239
+ 4V61BV_4V61BV
240
+ 1G1QA_1G1RB
241
+ 1JM4B_1JM4B
242
+ 2LR6A_3SOOB
243
+ 3I3YA_3IKHC
244
+ 1S5LH_1S5Lh
245
+ 2WEWA_2WEXA
246
+ 6JITA_6JITB
247
+ 1AXBA_6B2ND
248
+ 5SUPA_7LUVM
249
+ 2Q80A_6C56A
250
+ 3SOPA_6UQQD
251
+ 1YZTA_2OT3B
252
+ 2VRGA_2VRGA
253
+ 2LXNA_7D97D
254
+ 1EQFA_7T36A
255
+ 3DHWA_3DHWB
256
+ 5YQQA_5YQQA
257
+ 4V6WAf_7OLDSf
258
+ 7L6KA_7LF7K
259
+ 1X8GA_3F9OA
260
+ 3J9TR_5VOZT
261
+ 6ECBA_6ECDA
262
+ 1J2FA_5JEMB
263
+ 3SW1A_5LUVB
264
+ 3J0LK_4V7HAK
265
+ 1L8QA_2HCBC
266
+ 6ZXBA_6ZXCC
267
+ 7MWYA_7MWZA
268
+ 2NTXA_2NTXA
269
+ 5GUPa_5O31h
270
+ 6JMXA_6JN8A
271
+ 2HDAA_4LE9A
272
+ 2V1SA_2V1TB
273
+ 2OEXA_4JJYB
274
+ 1SAWA_6FOHB
275
+ 6VQ6L_7KHRL
276
+ 3UO1H_6BPEE
277
+ 1CP3A_7XN5A
278
+ 5MYJBP_7NHKP
279
+ 5H1AA_7CUOB
280
+ 2VL0A_6V03C
281
+ 4X8WA_4X8WE
282
+ 1QMYA_2JQGR
283
+ 3EI1B_4A0KD
284
+ 3U5ZB_3U60C
285
+ 2YKRE_4V6OAH
286
+ 1K3WA_1Q39A
287
+ 5WRHA_6JZRA
288
+ 4W6ZA_4W6ZD
289
+ 4L7ZA_4L80A
290
+ 5L1XA_8E15F
291
+ 7DWNA_7DWND
292
+ 4QX6A_5JYFC
293
+ 3NKDA_5VVKA
294
+ 5LI0b_6S13b
295
+ 5DPOA_6K3BB
296
+ 5LI6A_5LI7A
297
+ 7ADSA_7ADSA
298
+ 1DFVA_6SUAA
299
+ 7RGSA_7RGSA
300
+ 1DV4G_4V4GAG
301
+ 1AL21_5KU01
302
+ 1A22A_1Z7CA
303
+ 4TL6A_6X61G
304
+ 7QIHB_7QIHB
305
+ 1L7IH_8HIJH
306
+ 1UDVA_2A2YB
307
+ 1APCA_5AWIA
308
+ 6NJ8A_6NJ8A
309
+ 2H0BA_6PNPA
310
+ 6N5MB_7EALC
311
+ 1F9NA_1F9NA
312
+ 2J28P_2J28P
313
+ 1IJ9A_1VSCA
314
+ 6SPCh_7UNVh
315
+ 3B5HA_7XY8A
316
+ 4ARJA_4ARJB
317
+ 2YX1A_2ZZMA
318
+ 1HQME_8HSRK
319
+ 5BS1A_5BS1A
320
+ 1B62A_7P8VA
321
+ 3T12A_3T1QA
322
+ 4YMKA_4ZYOA
323
+ 7OX1G_7OX6A
324
+ 5MRCEE_8D8KE
325
+ 1FOSF_1FOSH
326
+ 4S04A_4S05B
327
+ 2LW5A_2LW5A
328
+ 7Y97A_7Y9OA
329
+ 1Q05A_4WLSB
330
+ 6II0A_6II0D
331
+ 7A6HG_8ITYG
332
+ 1C7UA_1EGWA
333
+ 1URKA_2I9BC
334
+ 4L8PA_4N3VA
335
+ 7C4PA_7ELKA
336
+ 4UT9H_6OL6H
337
+ 4XA8A_4XA8A
338
+ 3LREA_7RSIC
339
+ 2JBXA_2JBYA
340
+ 3JCRD_7ABGD
341
+ 1H6TA_4AW4C
342
+ 1FJGQ_4V4Jr
343
+ 3B1NA_3B1OB
344
+ 2ETNA_2ETNB
345
+ 1AP4A_3RV5A
346
+ 3DL3A_3DL3E
347
+ 3EZJB_6WARD
348
+ 4NFTA_7WLPA
349
+ 5TVFB_5TVMB
350
+ 5A5TL_7QP65
351
+ 6SGACd_7ANEq
352
+ 1QBZA_8DVDA
353
+ 3GKKA_5IOXA
354
+ 1VK6A_5IW5B
355
+ 1V1HA_1V1IC
356
+ 1EWIA_1EWIA
357
+ 5FVCA_5FVDC
358
+ 1J5AM_4V4RB5
359
+ 2KPWA_3UMNC
360
+ 4V4NBT_5JB3T
361
+ 2RHEA_7WE9K
362
+ 2IYEA_2YJ4A
363
+ 4OIGA_4OIGA
364
+ 2YKRS_4V70AS
365
+ 1UFKA_1UFKA
366
+ 5CZRA_7N86D
367
+ 1GPWA_1GPWC
368
+ 2MR3A_6J2XO
369
+ 3MSYA_4H83F
370
+ 3HZSA_3VMTA
371
+ 4GLRH_8E7MH
372
+ 2WW9L_6OIGY
373
+ 5KK2E_6NJLH
374
+ 7C42A_7C4CA
375
+ 7F0RA_7F0RA
376
+ 4EIYA_5UIGA
377
+ 1C39A_2RL7D
378
+ 1BA2A_1URPA
379
+ 7NDRA_7NDRA
380
+ 1IKUA_1LA3A
381
+ 2OWQA_2OWQB
382
+ 2LN0A_2LN0A
383
+ 8EIZA_8EIZD
384
+ 6YCXF_6YCYE
385
+ 3JCKE_3JCKE
386
+ 1KYWA_1KYWC
387
+ 4AQ5C_4BOG1
388
+ 3JCMP_5ZWOb
389
+ 3X3MA_5EBDA
390
+ 1GAGA_2B4SB
391
+ 4KBLA_4KBLB
392
+ 3QF7A_3THOA
393
+ 4HKRA_4HKRB
394
+ 1WT5A_7L57H
395
+ 3J7Yk_3J7Yk
396
+ 2XNHA_6HM5A
397
+ 4CBVA_4CBVD
398
+ 6W1SE_6W1SE
399
+ 4K3VA_4NNPA
400
+ 4D10H_6R7NH
401
+ 1MWKA_1MWKA
402
+ 8CJZG_8CJZg
403
+ 3WT1A_3WT2B
404
+ 1HQMA_1HQMA
405
+ 5EXIA_5EXKK
406
+ 5KKOA_5KKOB
407
+ 2ZNIA_2ZNIA
408
+ 8ESQE_8ETJE
409
+ 3J7Oc_4V5ZB6
410
+ 8AX4A_8AX4C
411
+ 4N7WA_4N7XA
412
+ 4C8VA_4C8WI
413
+ 1Z3RA_1Z66A
414
+ 5GJQR_6EPER
415
+ 6EZOE_6EZOF
416
+ 2IVSA_6VHGA
417
+ 7PWFP_7PWFP
418
+ 1I84U_7QIOP
419
+ 1IKNA_1LE9A
420
+ 1A3RH_5W23J
421
+ 6Y61A_7B6TB
422
+ 1U4HA_5JRUF
423
+ 2JPHA_2JPHA
424
+ 5TJ5B_5VOYR
425
+ 2AE0X_2PI8D
426
+ 1BA2A_1DBPA
427
+ 1ABEA_9ABPA
428
+ 4REGA_4REGA
429
+ 4C2MD_6H68D
430
+ 4AJ5K_4AJ5P
431
+ 4EJ7A_4FEVE
432
+ 2W6RA_2W6RA
433
+ 8AP6M1_8APEM1
434
+ 1D4M1_1D4M1
435
+ 1XJ5A_6O64H
436
+ 1KELH_4HLZI
437
+ 2CAZB_2P22B
438
+ 4V0KA_4V0MA
439
+ 4KU7A_4KU8A
440
+ 7UQLA_7UQLB
441
+ 1G85A_2HLVA
442
+ 7O4HD_7O4HD
443
+ 5A9RA_5A9RA
444
+ 7EQEA_7EQEA
445
+ 3AJMA_3L8JA
446
+ 5FMGL_6MUVL
447
+ 5VKUg_7ET3g
448
+ 6AJMA_6AJMA
449
+ 3J3VT_3J3WT
450
+ 4HYEA_4ZMRB
451
+ 5EGPA_5EGPA
452
+ 4XVPD_7Q1ED
453
+ 2D3AA_7V4LC
454
+ 1QMVA_5IJTF
455
+ 5W78A_5W7BA
456
+ 7DT0A_7DT0E
457
+ 5GKEA_5GKEA
458
+ 1DYLA_2YEWG
459
+ 1FJGJ_6GZZJ4
460
+ 6IFSA_6IFXA
461
+ 4CI1B_6BN9B
462
+ 1B7EA_1B7EA
463
+ 1ONCA_1ONCA
464
+ 5U07D_6C66D
465
+ 1NKWL_7A0RK
466
+ 3J7PSV_6FECb
467
+ 4V61AE_6ERIBE
468
+ 2V3JA_7D4IRG
469
+ 7ZTWB_7ZU4A
470
+ 1L8LA_6HYYB
471
+ 3B0WA_6FGQA
472
+ 1Y9RA_4UDAA
473
+ 1H2CA_4LDDC
474
+ 7F9KA_7F9KA
475
+ 1IAZA_4TSYD
476
+ 2G76A_6RIHA
477
+ 1IZNA_6F38K
478
+ 5A9Q7_7PEQCG
479
+ 2L7EA_6MIPA
480
+ 6J3Y11_6J3Z16
481
+ 7SBDH_7SD2A
482
+ 6X5ZO_8ENCP
483
+ 1BXIA_1IMQA
484
+ 4ML1A_4MLYB
485
+ 6LTNA_6M66A
486
+ 4YV6A_6W1AA
487
+ 6XGPA_6XGQA
488
+ 1OVZA_1OVZB
489
+ 2ZPMA_3ID3A
490
+ 1XWRA_1ZS4B
491
+ 1AN2A_1R05A
492
+ 3J7774_6T83Wy
493
+ 7FBIN_8HEBE
494
+ 6ANOA_6AOZC
495
+ 2A74C_2QKIC
496
+ 3J1OI_3J1OI
497
+ 6KVNA_6LCTA
498
+ 5GUPb_7DGSc
499
+ 2CMRA_3MA9A
500
+ 1ACAA_2ABDA
501
+ 5OJ8A_7AIEA
502
+ 1FNTD_5WVID
503
+ 1TRJA_4V6IAa
504
+ 6TAQA_6TAQC
505
+ 2V5MA_4X83A
506
+ 7BTWA_7BTWA
507
+ 4FU3A_4Q96A
508
+ 1AO8A_2L28A
509
+ 6VOYA_7PELA
510
+ 3J1ZP_8F6KC
511
+ 4JZBA_4K10D
512
+ 1QOHA_7U6VD
513
+ 3JCTb_7OHPb
514
+ 6S6BA_6S8BC
515
+ 3U1SH_5UXQH
516
+ 1DFBH_3Q6FF
517
+ 7EY0A_7WLZP
518
+ 1A32A_6HTQo
519
+ 2LVWA_2LVWA
520
+ 6DJLB_6DJLC
521
+ 2QF0A_3B8JA
522
+ 2Y9KA_2Y9KB
523
+ 7DN2a_7F2Pi
524
+ 2LE2A_3ZOQC
525
+ 1SW1A_1SW1A
526
+ 1F9QA_4HSVD
527
+ 2WDPA_6DEUA
528
+ 6H9CA_6H9CA
529
+ 4X5MA_4X5MB
530
+ 6LNBB_6PIFF
531
+ 7PKNQ_7XHNQ
532
+ 3VD6C_4HC7A
533
+ 3R90A_6MS4A
534
+ 1AUTC_6M3CG
535
+ 2WV0A_4U0WB
536
+ 6NR85_7WU75
537
+ 7V9FA_7V9IA
538
+ 5CRAA_5CRBA
539
+ 1G0WA_3B6RB
540
+ 1ES7B_3NH7B
541
+ 5D0YA_5D0YB
542
+ 4IHBA_7K6BA
543
+ 7WHRA_7WHTD
544
+ 5O6SA_5O6TC
545
+ 2OQHA_2OQHD
546
+ 1REPC_2Z9OB
547
+ 4GC5A_4GC9A
548
+ 6K7XA_6WDOA
549
+ 1AJ6A_5MMPA
550
+ 6HIVDK_7PUBDK
551
+ 3PILA_3PINB
552
+ 1C1AB_8E14B
553
+ 5USCA_6U60B
554
+ 7SN9A_7SN9i
555
+ 1IARB_6WGLC
556
+ 5V44A_5V46A
557
+ 6KE6RF_7D4IRF
558
+ 6G18y_6ZXDy
559
+ 2VGMA_3IZQ0
560
+ 1A02F_1A02F
561
+ 1DN0B_1DN0B
562
+ 1MF2H_1OTSC
563
+ 3BCHA_4V5ZAb
564
+ 1BVNT_1HOEA
565
+ 2Q81A_2Q81B
566
+ 5GMKP_5MPSK
567
+ 1ARQA_1QTGA
568
+ 5J09A_5J09A
569
+ 4U3MSM_5DATSM
570
+ 6A27A_6BDUA
571
+ 6WMAA_6WMCA
572
+ 2YPLD_3O4LD
573
+ 1JK9B_5U9MB
574
+ 3OETA_3OETB
575
+ 3TGOC_6LYSC
576
+ 3TTKA_3TTMA
577
+ 4ETVA_6MM6F
578
+ 132LA_1E8LA
579
+ 1EDHA_3Q2LB
580
+ 3J22A_4C0UA
581
+ 3JCSH_4V8MBO
582
+ 3J7PSK_6OLZBK
583
+ 7P37A_7P37A
584
+ 7OLCSR_7OLDSR
585
+ 6PQEA_6PRPA
586
+ 2M7KA_5XOPE
587
+ 1MR3F_2K5UA
588
+ 1DCTA_3UBTB
589
+ 1VK1A_5X0EA
590
+ 2CGHA_3L1AB
591
+ 3J9WB0_6HTQW
592
+ 1SLQA_2B4HA
593
+ 1BMQB_1SC1B
594
+ 1JN0A_7Q54R
595
+ 4V4NBF_4V4NBF
596
+ 1PK2A_1PK2A
597
+ 4JDQA_4JDXF
598
+ 1S50A_4QF9C
599
+ 5GJQV_5LN3V
600
+ 7C4SA_7C4SA
601
+ 3J6XL5_4V6IBQ
602
+ 1NY6A_1NY6H
603
+ 3T5NA_3T5QK
604
+ 6OECA_6OECH
605
+ 1O7BT_2N40A
606
+ 2J28E_4V66B1
607
+ 1P34E_6ESIE
608
+ 3KLTA_3TRTB
609
+ 7Y4L53_7Y5E53
610
+ 1EJ3A_1SL8A
611
+ 4V61B5_4V61B5
612
+ 1CKMA_1CKMA
613
+ 1HX5A_1P3HA
614
+ 6VO6A_6VO6C
615
+ 1AP2A_6LHTL
616
+ 5ADXA_6F38I
617
+ 3QILA_3QILC
618
+ 5GREA_6KDFA
619
+ 1SA0E_8ASNE
620
+ 3W6GA_8HLAL
621
+ 4CSFA_4J4YB
622
+ 3JCKH_3JCKH
623
+ 5OEYA_5OEYB
624
+ 2JTCA_2JTCA
625
+ 2VPNA_2VPOB
626
+ 7OODs_7PASs
627
+ 3J9MAS_6NU3AS
628
+ 3J6X55_4V7HBL
629
+ 1ID0A_3CGZC
630
+ 1VVJR4_4V4ZB3
631
+ 8ESQu_8EUPu
632
+ 1Q5HA_7BPDA
633
+ 5HJIA_5HJJA
634
+ 3J2WQ_5VU2Q
635
+ 3GB4A_6VSHA
636
+ 1NKWE_4V4GBH
637
+ 2AQLA_6AGOD
638
+ 2H3HA_3C6QA
639
+ 1G47A_1G47A
640
+ 4MSOA_4MSOA
641
+ 3J7Pq_7A01K2
642
+ 4DZDA_4DZDA
643
+ 5LPUA_7PC7A
644
+ 2POHA_4LINL
645
+ 6HQCA_6QAYA
646
+ 1DMWA_6HPOA
647
+ 6IDEA_6IDEB
648
+ 5COLA_5COLA
649
+ 5LF9A_5LORB
650
+ 3APZA_3AQ0D
651
+ 2R6AA_2VYFB
652
+ 2ZU6B_2ZU6B
653
+ 2ZIXA_2ZIXA
654
+ 7OC4A_7OC6A
655
+ 4EHWA_4EHYA
656
+ 3GLRA_5D7NF
657
+ 1F4TA_5BV5C
658
+ 1OCCH_5Z62H
659
+ 1YNMA_1YNMA
660
+ 6NR86_7WU76
661
+ 1GGGA_8EYZG
662
+ 1PB7A_4KFQB
663
+ 1ZW0A_1ZW0B
664
+ 6WXWA_6WXYB
665
+ 4V8MA2_4V8MA2
666
+ 4YX1A_4YX1A
667
+ 6P7OA_6P7QB
668
+ 3J7ON_4V5ZBm
669
+ 5DWZC_5DWZC
670
+ 4GRCA_4GRCA
671
+ 3FK3A_3RLSA
672
+ 7ET4A_7ET5A
673
+ 1NCJA_4NUPB
674
+ 2OGXA_6RJ4E
675
+ 5Z1GA_7OHVJ
676
+ 1BE4A_7KPFE
677
+ 1I42A_1S3SH
678
+ 3EEBA_7D5YA
679
+ 5MQFQ_7ABFQ
680
+ 6HIVBH_6HIVBH
681
+ 2KJ3A_2KJ3B
682
+ 1STFI_4N6V0
683
+ 5GUPW_5XTDW
684
+ 1U8YA_5CM8B
685
+ 1TYFA_1YG6C
686
+ 5FJ9I_7Z2ZI
687
+ 2NW2A_4DZBA
688
+ 1JDNA_1JDNA
689
+ 2MZWB_2MZWB
690
+ 1FS0G_5T4QG
691
+ 5I5DA_5I5HA
692
+ 1A4YB_1GV7A
693
+ 1L4UA_2IYUA
694
+ 1BCXA_3EXUA
695
+ 3JCTu_6C0Fu
696
+ 6CAJI_7RLOI
697
+ 3J9WAL_7ASPc
698
+ 2ZXYA_3X15D
699
+ 1NJMT_4V4GDW
700
+ 2KIIA_2KIIA
701
+ 1YDEA_5ICSA
702
+ 3DZUA_4NQAA
703
+ 2MM4A_2MM4A
704
+ 1NKSA_1NKSE
705
+ 6ZJ8A_6ZJ8A
706
+ 4BM9A_6GLCA
707
+ 1A7XA_7T64E
708
+ 1ADQA_6F2ZB
709
+ 1W33A_1W3ZB
710
+ 3PV2A_3PV5B
711
+ 2B69A_2B69A
712
+ 3PHGA_4FSHB
713
+ 2WCPA_3MVSA
714
+ 4IUFA_4Y00B
715
+ 6GIQi_8EC0R
716
+ 5IJNG_7WKKk
717
+ 3JCTr_6ELZr
718
+ 4JPHA_5HK5E
719
+ 3FH2A_6CN8A
720
+ 1VFGA_4X0BA
721
+ 2I1AA_4Z2ZA
722
+ 7EWIA_7EWJG
723
+ 1MUMA_1OQFA
724
+ 6B4CA_6B4CD
725
+ 5O5JG_6JMKA
726
+ 5IBLL_6UULD
727
+ 1B4AA_1B4AE
728
+ 6JXNA_6JXNB
729
+ 2D9SA_2JUJA
730
+ 1K9UA_1K9UA
731
+ 1BU2A_2EUFA
732
+ 4Q37A_4Q37E
733
+ 7ANCA_7CXTB
734
+ 4QRZA_4RJZA
735
+ 5MW5A_5MW7A
736
+ 2K1BA_4X3TD
737
+ 4O64A_6ASBI
738
+ 1Y8GA_2HAKA
739
+ 2LHNA_5L2LB
740
+ 2J28D_4V75BD
741
+ 1Z2CB_2BNXB
742
+ 1BCCE_1BE3E
743
+ 6S3DM_6XWIA
744
+ 5B0UA_7SNYA
745
+ 7EYIG_7N5SA
746
+ 4C3BA_6G0YC
747
+ 1PKQA_7WUHL
748
+ 3J0JI_3V6IB
749
+ 5Z6ZA_7DW5A
750
+ 6FDDA_6FDDC
751
+ 4V61AF_4V61AF
752
+ 1B3TA_6NPMA
753
+ 3ZBEA_5CW7M
754
+ 1JQJA_1JQJA
755
+ 1BY4A_6XWGC
756
+ 1IB2A_3GVOA
757
+ 7W2BA_7W2HT
758
+ 3KKBA_4LLCB
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+ 1ADQA_6F2ZB
111
+ 1AFOA_1AFOA
112
+ 1AFOA_1AFOB
113
+ 1AFOA_2KPFA
114
+ 1AFOA_2KPFB
115
+ 1AFOA_7UZ3B
116
+ 1AGQA_1AGQB
117
+ 1AGQA_1AGQD
118
+ 1AGQA_2V5EB
119
+ 1AGQA_3FUBB
120
+ 1AGQA_6Q2NA
121
+ 1AGWA_3CLYA
122
+ 1AGWA_3KXXB
123
+ 1AGWA_3RHXA
124
+ 1AGWA_6P68C
125
+ 1AGWA_6PNXB
126
+ 1AH1A_1AH1A
127
+ 1AH1A_2X44D
128
+ 1AH1A_3BX7C
129
+ 1AH1A_5GGVY
130
+ 1AH1A_6RQMA
131
+ 1AJ6A_3G7EA
132
+ 1AJ6A_5MMPA
133
+ 1AJ6A_5Z4OB
134
+ 1AJ6A_7DQMA
135
+ 1AJ6A_7P2WA
136
+ 1AKEA_1AKEA
137
+ 1AKEA_4AKEA
138
+ 1AKEA_4X8HA
139
+ 1AKEA_4X8MA
140
+ 1AKEA_7APUA
141
+ 1AL21_1AR91
142
+ 1AL21_3JBG1
143
+ 1AL21_5KTZ1
144
+ 1AL21_5KU01
145
+ 1AL21_6P9O1
146
+ 1AL22_1HXS2
147
+ 1AL22_1POV0
148
+ 1AL22_5KU02
149
+ 1AL22_6P9O2
150
+ 1AL22_6Q0B2
151
+ 1ALUA_1IL6A
152
+ 1ALUA_1P9MB
153
+ 1ALUA_2IL6A
154
+ 1ALUA_4CNIC
155
+ 1ALUA_4ZS7A
156
+ 1AN2A_1AN2A
157
+ 1AN2A_1HLOB
158
+ 1AN2A_1R05A
159
+ 1AN2A_5EYOC
160
+ 1AN2A_8OTSN
161
+ 1AO8A_1AO8A
162
+ 1AO8A_1LUDA
163
+ 1AO8A_2HQPA
164
+ 1AO8A_2L28A
165
+ 1AO8A_2LF1A
166
+ 1AONA_1AONA
167
+ 1AONA_1GRUH
168
+ 1AONA_3WVLD
169
+ 1AONA_4AB3A
170
+ 1AONA_4HELM
171
+ 1AP2A_6BSPB
172
+ 1AP2A_6LHTL
173
+ 1AP2A_6UJCF
174
+ 1AP2A_7UZ8N
175
+ 1AP2A_7WP6I
176
+ 1AP4A_1AP4A
177
+ 1AP4A_2L1RA
178
+ 1AP4A_3RV5A
179
+ 1AP4A_3RV5C
180
+ 1AP4A_4GJEA
181
+ 1APCA_1APCA
182
+ 1APCA_1YZAA
183
+ 1APCA_1YZCA
184
+ 1APCA_5AWIA
185
+ 1APCA_5AWIB
186
+ 1ARQA_1ARQA
187
+ 1ARQA_1ARQB
188
+ 1ARQA_1NLAA
189
+ 1ARQA_1PARB
190
+ 1ARQA_1QTGA
191
+ 1AUPA_1AUPA
192
+ 1AUPA_1BGVA
193
+ 1AUPA_1HRDA
194
+ 1AUPA_1HRDC
195
+ 1AUPA_1K89A
196
+ 1AUTC_1AUTC
197
+ 1AUTC_3F6UH
198
+ 1AUTC_6M3CA
199
+ 1AUTC_6M3CC
200
+ 1AUTC_6M3CG
201
+ 1AVGL_1AVGL
202
+ 1AVGL_1BBRJ
203
+ 1AVGL_1ETSL
204
+ 1AVGL_1UCYL
205
+ 1AVGL_1VITL
206
+ 1AXBA_1JWPA
207
+ 1AXBA_4OQIA
208
+ 1AXBA_4RVAA
209
+ 1AXBA_5NPOB
210
+ 1AXBA_6B2ND
211
+ 1AYGA_1AYGA
212
+ 1AYGA_3VYMA
213
+ 1AYGA_5AURG
214
+ 1AYGA_5AUSA
215
+ 1AYGA_5AUSC
216
+ 1AZ6A_1AZ6A
217
+ 1AZ6A_1AZHA
218
+ 1AZ6A_1AZKA
219
+ 1AZ6A_1CBHA
220
+ 1AZ6A_2MWJA
221
+ 1B05A_1B05A
222
+ 1B05A_1RKMA
223
+ 1B05A_3TCFD
224
+ 1B05A_3TCFF
225
+ 1B05A_3TCHA
226
+ 1B0LA_1CB6A
227
+ 1B0LA_1LCFA
228
+ 1B0LA_1LFHA
229
+ 1B0LA_7JRDB
230
+ 1B0LA_7N88B
231
+ 1B1GA_1BOCA
232
+ 1B1GA_1HT9A
233
+ 1B1GA_1HT9B
234
+ 1B1GA_1KCYA
235
+ 1B1GA_1KQVA
236
+ 1B3TA_1B3TA
237
+ 1B3TA_6NPMA
238
+ 1B3TA_6NPPA
239
+ 1B3TA_8DLFC
240
+ 1B3TA_8DLFD
241
+ 1B4AA_1B4AA
242
+ 1B4AA_1B4AC
243
+ 1B4AA_1B4AD
244
+ 1B4AA_1B4AE
245
+ 1B4AA_1B4AF
246
+ 1B62A_1B63A
247
+ 1B62A_5AKBC
248
+ 1B62A_5AKCH
249
+ 1B62A_5AKDD
250
+ 1B62A_7P8VA
251
+ 1B6FA_1B6FA
252
+ 1B6FA_1BTVA
253
+ 1B6FA_4A80A
254
+ 1B6FA_4BKCA
255
+ 1B6FA_6R3CA
256
+ 1B75A_1B75A
257
+ 1B75A_1D6KA
258
+ 1B75A_4V6PBX
259
+ 1B75A_4V71BV
260
+ 1B75A_4V76BV
261
+ 1B7EA_1B7EA
262
+ 1B7EA_1MM8A
263
+ 1B7EA_1MUHA
264
+ 1B7EA_3ECPA
265
+ 1B7EA_4DM0A
266
+ 1BA2A_1BA2A
267
+ 1BA2A_1BA2B
268
+ 1BA2A_1DBPA
269
+ 1BA2A_1DRJA
270
+ 1BA2A_1URPA
271
+ 1BALA_1BALA
272
+ 1BALA_1W4HA
273
+ 1BALA_2BTGA
274
+ 1BALA_2CYUA
275
+ 1BALA_2WXCA
276
+ 1BBHA_1BBHA
277
+ 1BBHA_1BBHB
278
+ 1BBHA_3VRCA
279
+ 1BBHA_5GYRA
280
+ 1BBHA_5GYRJ
281
+ 1BCCA_1BE3A
282
+ 1BCCA_5GUP5
283
+ 1BCCA_5J4ZAA
284
+ 1BCCA_6QBXa3
285
+ 1BCCA_7DGSk
286
+ 1BCCE_1BE3E
287
+ 1BCCE_2BCCE
288
+ 1BCCE_7DGRA0
289
+ 1BCCE_7DGRp
290
+ 1BCCE_7DGSp
291
+ 1BCXA_1BVVA
292
+ 1BCXA_3EXUA
293
+ 1BCXA_3HD8B
294
+ 1BCXA_3VZLC
295
+ 1BCXA_5TVVB
296
+ 1BE4A_1BE4A
297
+ 1BE4A_4ENOA
298
+ 1BE4A_4ENOB
299
+ 1BE4A_7KPFD
300
+ 1BE4A_7KPFE
301
+ 1BFOA_1C5DA
302
+ 1BFOA_1FN4A
303
+ 1BFOA_3B9KC
304
+ 1BFOA_5W5XL
305
+ 1BFOA_7TE4L
306
+ 1BKTA_1BKTA
307
+ 1BKTA_1SCOA
308
+ 1BKTA_2CK4A
309
+ 1BKTA_2K4UA
310
+ 1BKTA_2MLAA
311
+ 1BL9A_1HZUA
312
+ 1BL9A_1HZVA
313
+ 1BL9A_1N15A
314
+ 1BL9A_5GUWM
315
+ 1BL9A_6TPOA
316
+ 1BMPA_1LXIA
317
+ 1BMPA_1M4UL
318
+ 1BMPA_2QCWA
319
+ 1BMPA_2R53A
320
+ 1BMPA_6OMOJ
321
+ 1BMQB_1BMQB
322
+ 1BMQB_1SC1B
323
+ 1BMQB_2FQQB
324
+ 1BMQB_3D6HB
325
+ 1BMQB_6KN0B
326
+ 1BNCA_2GPSA
327
+ 1BNCA_2J9GB
328
+ 1BNCA_3RUPA
329
+ 1BNCA_3RV3A
330
+ 1BNCA_4HR7A
331
+ 1BQLH_1EO8H
332
+ 1BQLH_1Q0XH
333
+ 1BQLH_3VI3F
334
+ 1BQLH_4CADH
335
+ 1BQLH_5NBWH
336
+ 1BQMA_1HVUA
337
+ 1BQMA_1LWEA
338
+ 1BQMA_5HLFA
339
+ 1BQMA_6AOCC
340
+ 1BQMA_7SJXB
341
+ 1BU2A_1BU2A
342
+ 1BU2A_1JOWA
343
+ 1BU2A_1XO2A
344
+ 1BU2A_2EUFA
345
+ 1BU2A_2F2CA
346
+ 1BVHA_1BVHA
347
+ 1BVHA_1C0EA
348
+ 1BVHA_1Z12A
349
+ 1BVHA_3N8IA
350
+ 1BVHA_7KH8B
351
+ 1BVNT_1HOEA
352
+ 1BVNT_1OK0A
353
+ 1BVNT_2AITA
354
+ 1BVNT_3AITA
355
+ 1BVNT_4AITA
356
+ 1BX4A_2I6AA
357
+ 1BX4A_2I6AB
358
+ 1BX4A_4O1LB
359
+ 1BX4A_5KB6A
360
+ 1BX4A_5KB6B
361
+ 1BXIA_1E0HA
362
+ 1BXIA_1EMVA
363
+ 1BXIA_1IMQA
364
+ 1BXIA_2K5XA
365
+ 1BXIA_2VLQA
366
+ 1BY4A_1BY4A
367
+ 1BY4A_1R0NA
368
+ 1BY4A_1RXRA
369
+ 1BY4A_6FBRA
370
+ 1BY4A_6XWGC
371
+ 1C1AB_1C1AB
372
+ 1C1AB_4FW1A
373
+ 1C1AB_7JN3F
374
+ 1C1AB_8E14B
375
+ 1C1AB_8E14F
376
+ 1C39A_1C39A
377
+ 1C39A_2RL7C
378
+ 1C39A_2RL7D
379
+ 1C39A_2RL8A
380
+ 1C39A_3K41B
381
+ 1C5WB_1LMWD
382
+ 1C5WB_1W12U
383
+ 1C5WB_4DVAU
384
+ 1C5WB_4DW2U
385
+ 1C5WB_5WXFU
386
+ 1C7UA_1C7UB
387
+ 1C7UA_1EGWA
388
+ 1C7UA_1EGWB
389
+ 1C7UA_3MU6A
390
+ 1C7UA_6C9LE
391
+ 1C9BA_1C9BA
392
+ 1C9BA_1TFBA
393
+ 1C9BA_2PHGA
394
+ 1C9BA_5WH1A
395
+ 1C9BA_5WH1C
396
+ 1C9KA_1C9KA
397
+ 1C9KA_1C9KB
398
+ 1C9KA_1CBUA
399
+ 1C9KA_1CBUB
400
+ 1C9KA_1CBUC
401
+ 1C9PB_1C9PB
402
+ 1C9PB_1C9TG
403
+ 1C9PB_1C9TH
404
+ 1C9PB_1C9TK
405
+ 1C9PB_1EJAB
406
+ 1CKMA_1CKMA
407
+ 1CKMA_1CKMB
408
+ 1CKMA_1CKNA
409
+ 1CKMA_1CKNB
410
+ 1CKMA_1CKOA
411
+ 1CLQA_1CLQA
412
+ 1CLQA_1IG9A
413
+ 1CLQA_1IH7A
414
+ 1CLQA_3RMBB
415
+ 1CLQA_4KHNA
416
+ 1CODA_1CODA
417
+ 1CODA_1COEA
418
+ 1CODA_1G6MA
419
+ 1CODA_1V6PA
420
+ 1CODA_1V6PB
421
+ 1CP3A_3DEHB
422
+ 1CP3A_4JQYB
423
+ 1CP3A_4PS0A
424
+ 1CP3A_7XN5A
425
+ 1CP3A_7XN6A
426
+ 1CPQA_1CPQA
427
+ 1CPQA_1CPRA
428
+ 1CPQA_1EKYA
429
+ 1CPQA_1NBBB
430
+ 1CPQA_1RCPB
431
+ 1D0XA_1FMWA
432
+ 1D0XA_2AKAA
433
+ 1D0XA_2XELA
434
+ 1D0XA_6Z2SA
435
+ 1D0XA_6Z7UA
436
+ 1D4M1_1D4M1
437
+ 1D4M1_3J2JA
438
+ 1D4M1_8AT5A
439
+ 1D4M1_8AW6A
440
+ 1D4M1_8AXXA
441
+ 1D6MA_1D6MA
442
+ 1D6MA_1I7DA
443
+ 1D6MA_2O19B
444
+ 1D6MA_2O59A
445
+ 1D6MA_2O5CA
446
+ 1DAPA_1DAPA
447
+ 1DAPA_3DAPB
448
+ 1DAPA_5LOAA
449
+ 1DAPA_5LOAB
450
+ 1DAPA_5LOCB
451
+ 1DCTA_1DCTA
452
+ 1DCTA_1DCTB
453
+ 1DCTA_3UBTA
454
+ 1DCTA_3UBTB
455
+ 1DCTA_3UBTY
456
+ 1DFBH_3Q6FB
457
+ 1DFBH_3Q6FF
458
+ 1DFBH_4OD2B
459
+ 1DFBH_7KQ7H
460
+ 1DFBH_8DB4A
461
+ 1DFVA_1NGLA
462
+ 1DFVA_1X89C
463
+ 1DFVA_6SUAA
464
+ 1DFVA_6Z2CC
465
+ 1DFVA_6Z6ZA
466
+ 1DLFL_1MAJA
467
+ 1DLFL_1MAKA
468
+ 1DLFL_2CJUL
469
+ 1DLFL_6AD0L
470
+ 1DLFL_7MHYN
471
+ 1DMWA_1KW0A
472
+ 1DMWA_1LRMA
473
+ 1DMWA_1MMTA
474
+ 1DMWA_2PAHA
475
+ 1DMWA_6HPOA
476
+ 1DN0B_1DN0B
477
+ 1DN0B_1DN0D
478
+ 1DN0B_1QLRB
479
+ 1DN0B_2J6EH
480
+ 1DN0B_2J6EI
481
+ 1DV4G_1FJGG
482
+ 1DV4G_1RSSA
483
+ 1DV4G_4V4GAG
484
+ 1DV4G_4V4Ih
485
+ 1DV4G_5OT7F
486
+ 1DVVA_2EXVA
487
+ 1DVVA_2PACA
488
+ 1DVVA_3X39B
489
+ 1DVVA_5XECC
490
+ 1DVVA_5XEDC
491
+ 1DYLA_2YEWA
492
+ 1DYLA_2YEWD
493
+ 1DYLA_2YEWG
494
+ 1DYLA_5H23A
495
+ 1DYLA_6NK7L
496
+ 1DYTA_1DYTA
497
+ 1DYTA_1QMTA
498
+ 1DYTA_2KB5A
499
+ 1DYTA_2LVZA
500
+ 1DYTA_4A2OB
501
+ 1E4SA_1E4SA
502
+ 1E4SA_1KJ5A
503
+ 1E4SA_2NLCA
504
+ 1E4SA_2NLEB
505
+ 1E4SA_2NLSA
506
+ 1E5LA_1E5LA
507
+ 1E5LA_1E5LB
508
+ 1E5LA_1E5QB
509
+ 1E5LA_1E5QD
510
+ 1E5LA_1FF9A
511
+ 1E79I_1E79I
512
+ 1E79I_2WSSR
513
+ 1E79I_5AREI
514
+ 1E79I_5ARII
515
+ 1E79I_7AJFAI
516
+ 1E7WA_1P33C
517
+ 1E7WA_1W0CG
518
+ 1E7WA_2XOXA
519
+ 1E7WA_2XOXB
520
+ 1E7WA_5L42A
521
+ 1E8CA_1E8CA
522
+ 1E8CA_1E8CB
523
+ 1E8CA_7B53A
524
+ 1E8CA_7B61A
525
+ 1E8CA_7B6OA
526
+ 1EC5A_1JM0A
527
+ 1EC5A_1NVOA
528
+ 1EC5A_1NVOB
529
+ 1EC5A_1U7MA
530
+ 1EC5A_2KIKB
531
+ 1ECWA_1ECWA
532
+ 1ECWA_1ED1A
533
+ 1ECWA_2K4EA
534
+ 1ECWA_2K4HA
535
+ 1ECWA_2K4IA
536
+ 1ED0A_1ED0A
537
+ 1ED0A_1JMNA
538
+ 1ED0A_1JMPA
539
+ 1ED0A_1ORLA
540
+ 1ED0A_2V9BA
541
+ 1EDHA_1FF5A
542
+ 1EDHA_1Q1PA
543
+ 1EDHA_3LNEA
544
+ 1EDHA_3Q2LB
545
+ 1EDHA_4QD2J
546
+ 1EJ3A_1SL8A
547
+ 1EJ3A_1UHJB
548
+ 1EJ3A_1UHKA
549
+ 1EJ3A_5ZABA
550
+ 1EJ3A_7EG3J
551
+ 1EPSA_1G6SA
552
+ 1EPSA_1X8TA
553
+ 1EPSA_7TM4A
554
+ 1EPSA_7TM5A
555
+ 1EPSA_7TM5B
556
+ 1EQFA_6FICT
557
+ 1EQFA_7K0DA
558
+ 1EQFA_7K6FA
559
+ 1EQFA_7L6XA
560
+ 1EQFA_7T36A
561
+ 1ES7B_2K3GA
562
+ 1ES7B_2QJ9D
563
+ 1ES7B_2QJAD
564
+ 1ES7B_3NH7B
565
+ 1ES7B_3QB4B
566
+ 1EUCA_2FP4A
567
+ 1EUCA_4XX0A
568
+ 1EUCA_6WCVA
569
+ 1EUCA_7JKRA
570
+ 1EUCA_7JMKA
571
+ 1EVUA_1F13B
572
+ 1EVUA_1GGUB
573
+ 1EVUA_5MHLA
574
+ 1EVUA_5MHLB
575
+ 1EVUA_5MHOA
576
+ 1EWIA_1EWIA
577
+ 1EWIA_2B29A
578
+ 1EWIA_4IJLA
579
+ 1EWIA_7XUTA
580
+ 1EWIA_7XUWA
581
+ 1EWKA_1EWVA
582
+ 1EWKA_1ISRA
583
+ 1EWKA_1ISSA
584
+ 1EWKA_3KS9A
585
+ 1EWKA_3KS9B
586
+ 1EX6A_1EX6A
587
+ 1EX6A_1EX7A
588
+ 1EX6A_1GKYA
589
+ 1EX6A_4F4JA
590
+ 1EX6A_4F4JB
591
+ 1EXIA_1EXIA
592
+ 1EXIA_3IAOA
593
+ 1EXIA_3Q1MA
594
+ 1EXIA_7CKQG
595
+ 1EXIA_7CKQI
596
+ 1F3MC_1F3MD
597
+ 1F3MC_4O0TA
598
+ 1F3MC_4ZJJD
599
+ 1F3MC_4ZY4B
600
+ 1F3MC_6FD3A
601
+ 1F4TA_1F4TA
602
+ 1F4TA_1IO9A
603
+ 1F4TA_5BV5C
604
+ 1F4TA_5BV5D
605
+ 1F4TA_7UORB
606
+ 1F8PA_1F8PA
607
+ 1F8PA_1RONA
608
+ 1F8PA_1TZ4A
609
+ 1F8PA_7VGXL
610
+ 1F8PA_7YOOL
611
+ 1F9NA_1F9NA
612
+ 1F9NA_1F9NB
613
+ 1F9NA_1F9NC
614
+ 1F9NA_1F9ND
615
+ 1F9NA_1F9NF
616
+ 1F9QA_1PFNA
617
+ 1F9QA_4HSVB
618
+ 1F9QA_4HSVD
619
+ 1F9QA_4R9YA
620
+ 1F9QA_4RAUI
621
+ 1F9XA_1F9XA
622
+ 1F9XA_1G3FA
623
+ 1F9XA_1TFQA
624
+ 1F9XA_3CM2C
625
+ 1F9XA_3G76H
626
+ 1FAIH_2A6JB
627
+ 1FAIH_2F19H
628
+ 1FAIH_4BH8B
629
+ 1FAIH_5MTHA
630
+ 1FAIH_6P8DD
631
+ 1FGSA_1FGSA
632
+ 1FGSA_1JBVA
633
+ 1FGSA_1JBWA
634
+ 1FGSA_2GC5A
635
+ 1FGSA_2GCAA
636
+ 1FJGJ_1FJGJ
637
+ 1FJGJ_4V4GAJ
638
+ 1FJGJ_4V4Jk
639
+ 1FJGJ_6GZXJ3
640
+ 1FJGJ_6GZZJ4
641
+ 1FJGQ_1IBLQ
642
+ 1FJGQ_1VY4AQ
643
+ 1FJGQ_4V4GAQ
644
+ 1FJGQ_4V4Jr
645
+ 1FJGQ_6GZXQ3
646
+ 1FMKA_1KSWA
647
+ 1FMKA_1Y57A
648
+ 1FMKA_2PTKA
649
+ 1FMKA_4K11A
650
+ 1FMKA_6F3FA
651
+ 1FNTD_4NNNC
652
+ 1FNTD_5MPCd
653
+ 1FNTD_5WVID
654
+ 1FNTD_6EF2D
655
+ 1FNTD_7LSXD
656
+ 1FOHA_1FOHA
657
+ 1FOHA_1PN0A
658
+ 1FOHA_1PN0B
659
+ 1FOHA_1PN0C
660
+ 1FOHA_1PN0D
661
+ 1FOSF_1FOSH
662
+ 1FOSF_5T01B
663
+ 1FOSF_5VPBD
664
+ 1FOSF_7UCCJ
665
+ 1FOSF_7UCDJ
666
+ 1FS0G_3OAAO
667
+ 1FS0G_5T4OG
668
+ 1FS0G_5T4QG
669
+ 1FS0G_8DBTG
670
+ 1FS0G_8DBWG
671
+ 1FU5A_1FU5A
672
+ 1FU5A_1FU6A
673
+ 1FU5A_1OO3A
674
+ 1FU5A_1OO4A
675
+ 1FU5A_7RNSA
676
+ 1FX0A_6FKIC
677
+ 1FX0A_6VM1C
678
+ 1FX0A_6VMBA
679
+ 1FX0A_6VMBC
680
+ 1FX0A_6VOOA
681
+ 1G0WA_1QH4A
682
+ 1G0WA_3B6RB
683
+ 1G0WA_3DRBB
684
+ 1G0WA_4Q2RA
685
+ 1G0WA_7TUNA
686
+ 1G1QA_1G1QA
687
+ 1G1QA_1G1RA
688
+ 1G1QA_1G1RB
689
+ 1G1QA_1G1SA
690
+ 1G1QA_1G1SB
691
+ 1G47A_1G47A
692
+ 1G47A_2KBXB
693
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694
+ 1G47A_3IXEB
695
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696
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697
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698
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699
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700
+ 1G57A_7TYEC
701
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702
+ 1G85A_1GT4A
703
+ 1G85A_1GT4B
704
+ 1G85A_1PBOA
705
+ 1G85A_2HLVA
706
+ 1GAGA_1IRKA
707
+ 1GAGA_2AUHA
708
+ 1GAGA_2B4SB
709
+ 1GAGA_3ETAB
710
+ 1GAGA_4IBMA
711
+ 1GAV0_1GAV0
712
+ 1GAV0_1GAV3
713
+ 1GAV0_1GAVD
714
+ 1GAV0_1UNAA
715
+ 1GAV0_1UNAB
716
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717
+ 1GC5A_4B8SA
718
+ 1GC5A_5O5XA
719
+ 1GC5A_5O5YB
720
+ 1GC5A_5O5ZA
721
+ 1GEFA_1GEFA
722
+ 1GEFA_1GEFB
723
+ 1GEFA_1GEFE
724
+ 1GEFA_1IPIA
725
+ 1GEFA_1IPIB
726
+ 1GGGA_1GGGA
727
+ 1GGGA_1GGGB
728
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729
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730
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731
+ 1GL1I_1KIOA
732
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733
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734
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735
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736
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737
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738
+ 1GMEB_2BYUC
739
+ 1GMEB_2BYUD
740
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741
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742
+ 1GPWA_1THFD
743
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744
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745
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746
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747
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748
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749
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750
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751
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752
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753
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754
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755
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756
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757
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758
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759
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760
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761
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762
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763
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764
+ 1H1DA_5FHRB
765
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766
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767
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768
+ 1H2CA_4LDDA
769
+ 1H2CA_4LDDC
770
+ 1H2CA_7K5LA
771
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772
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773
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774
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775
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776
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777
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778
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779
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780
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781
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782
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783
+ 1HQMA_2O5JB
784
+ 1HQMA_4G7HA
785
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786
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787
+ 1HQME_4G7HE
788
+ 1HQME_8HSGK
789
+ 1HQME_8HSLK
790
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791
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792
+ 1HURA_1R8QB
793
+ 1HURA_1U81A
794
+ 1HURA_8D4EZ
795
+ 1HURA_8D9S3
796
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797
+ 1HX5A_1HX5F
798
+ 1HX5A_1P3HA
799
+ 1HX5A_1P3HB
800
+ 1HX5A_1P3HN
801
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802
+ 1HZ5A_1HZ6B
803
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804
+ 1HZ5A_1K51A
805
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806
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807
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808
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809
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810
+ 1I2DA_1M8PC
811
+ 1I42A_1I42A
812
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813
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814
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815
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816
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817
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818
+ 1I7PA_1QX4B
819
+ 1I7PA_1UMKA
820
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821
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822
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823
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824
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825
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826
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827
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828
+ 1I9WA_8D87B
829
+ 1I9WA_8IHPH
830
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831
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832
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833
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834
+ 1IARB_6OELB
835
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836
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837
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838
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839
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840
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841
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842
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843
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844
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845
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846
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847
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848
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849
+ 1ID0A_3CGZB
850
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851
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852
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853
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854
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855
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856
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857
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858
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859
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860
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861
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862
+ 1IKNA_1LE9A
863
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864
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865
+ 1IKNA_5U01D
866
+ 1IKUA_1IKUA
867
+ 1IKUA_1JSAA
868
+ 1IKUA_1LA3A
869
+ 1IKUA_2I94A
870
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871
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872
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873
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874
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875
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876
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877
+ 1IWOA_2C9MA
878
+ 1IWOA_2C9MB
879
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880
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881
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882
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883
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884
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885
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886
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887
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888
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889
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890
+ 1J1DC_6KN8U
891
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892
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893
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894
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895
+ 1J2FA_5JEME
896
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897
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898
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899
+ 1J5AM_4V4RB5
900
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901
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902
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903
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904
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905
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906
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907
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908
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909
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910
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911
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912
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913
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914
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915
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916
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917
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918
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919
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920
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921
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922
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923
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924
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925
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926
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927
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928
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929
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930
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931
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932
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933
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934
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935
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936
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937
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938
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939
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940
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941
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942
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943
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944
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945
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946
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947
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948
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949
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950
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951
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952
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953
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954
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955
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956
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957
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958
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959
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960
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961
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962
+ 1K9UA_1K9UB
963
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964
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965
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966
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967
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968
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969
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970
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971
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972
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973
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974
+ 1KO2A_6BM9B
975
+ 1KO2A_6BM9C
976
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977
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978
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979
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980
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981
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982
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983
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984
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985
+ 1KTZB_4XJJA
986
+ 1KY9B_1KY9B
987
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988
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989
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990
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991
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992
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993
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994
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995
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996
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997
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998
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999
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1000
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1001
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1002
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1003
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1004
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1005
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1006
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1007
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1008
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1009
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1010
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1011
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1012
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1013
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1014
+ 1L8QA_2HCBD
1015
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1016
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1017
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1018
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1019
+ 1LAFE_6MLDA
1020
+ 1LAFE_6MLPE
1021
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1022
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1023
+ 1LDJA_6TTUC
1024
+ 1LDJA_7B5LC
1025
+ 1LDJA_7Z8TC
1026
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1027
+ 1LILA_4QF1L
1028
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1029
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1030
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1031
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1032
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1033
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1034
+ 1LM8C_4JGHC
1035
+ 1LM8C_6R7NQ
1036
+ 1LQ8A_1LQ8E
1037
+ 1LQ8A_1LQ8G
1038
+ 1LQ8A_2HI9A
1039
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1040
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1041
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1042
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1043
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1044
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1045
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1046
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1047
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1048
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1049
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1050
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1051
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1052
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1053
+ 1M3IA_1M3ID
1054
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1055
+ 1M3IA_1PFOA
1056
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1057
+ 1M5Q1_1M5QB
1058
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1059
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1060
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1061
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1062
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1063
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1064
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1065
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1066
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1067
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1068
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1069
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1070
+ 1MFGA_6Q0NA
1071
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1072
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1073
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1074
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1075
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1076
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1077
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1078
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1079
+ 1MKIA_2OSUB
1080
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1081
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1082
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1083
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1084
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1085
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1086
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1087
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1088
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1089
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1090
+ 1MR3F_7UROB
1091
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1092
+ 1MUMA_1OQFA
1093
+ 1MUMA_1XG3A
1094
+ 1MUMA_1XG3B
1095
+ 1MUMA_1XG3C
1096
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1097
+ 1MWKA_2QU4A
1098
+ 1MWKA_2ZHCA
1099
+ 1MWKA_4A62B
1100
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1101
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1102
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1103
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1104
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1105
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1106
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1107
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1108
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1109
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1110
+ 1N69A_6SLRC
1111
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1112
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1113
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1114
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1115
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1116
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1117
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1118
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1119
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1120
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1121
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1122
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1123
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1124
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1125
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1126
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1127
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1128
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1129
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1130
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1131
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1132
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1133
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1134
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1135
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1136
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1137
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1138
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1139
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1140
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1141
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1142
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1143
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1144
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1145
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1146
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1147
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1148
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1149
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1150
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1151
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1152
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1153
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1154
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1155
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1156
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1157
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1158
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1159
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1160
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1161
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1162
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1163
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1164
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1165
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1166
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1167
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1168
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1169
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1170
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1171
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1172
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1173
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1174
+ 1ONCA_7OR6B
1175
+ 1ONCA_7ORDB
1176
+ 1OQEK_1OQEM
1177
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1178
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1179
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1180
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1181
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1182
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1183
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1184
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1185
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1186
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1187
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1188
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1189
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1190
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1191
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1192
+ 1PB7A_4KCCA
1193
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1194
+ 1PB7A_6USUA
1195
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1196
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1197
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1198
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1199
+ 1PJRA_2PJRF
1200
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1201
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1202
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1203
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1204
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1205
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1206
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1207
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1208
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1209
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1210
+ 1PKQA_7WUHL
1211
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1212
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1213
+ 1PP7U_1PP8O
1214
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1215
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1216
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1217
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1218
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1219
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1220
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1221
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1222
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1223
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1224
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1225
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1226
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1227
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1228
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1229
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1230
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1231
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1232
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1233
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1234
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1235
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1236
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1237
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1238
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1239
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1240
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1241
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1242
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1243
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1244
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1245
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1246
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1247
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1248
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1249
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1250
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1251
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1252
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1253
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1254
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1255
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1256
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1257
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1258
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1259
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1260
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1261
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1262
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1263
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1264
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1265
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1266
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1267
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1268
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1269
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1270
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1271
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1272
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1273
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1274
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1275
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1276
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1277
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1278
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1279
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1280
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1281
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1282
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1283
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1284
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1285
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1286
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1287
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1288
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1289
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1290
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1291
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1292
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1293
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1294
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1295
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1296
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1297
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1298
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1299
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1300
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1301
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1302
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1303
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1304
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1305
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1306
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1307
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1308
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1309
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1310
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1311
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1312
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1313
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1314
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1315
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1316
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1317
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1318
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1319
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1320
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1321
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1322
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1323
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1324
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1325
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1326
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1327
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1328
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1329
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1330
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1331
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1332
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1333
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1334
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1335
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1336
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1337
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1338
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1339
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1340
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1341
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1342
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1343
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1344
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1345
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1346
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1347
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1348
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1349
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1350
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1351
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1352
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1353
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1354
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1355
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1356
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1357
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1358
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1359
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1360
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1361
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1362
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1363
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1364
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1365
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1366
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1367
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1368
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1369
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1370
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1371
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1372
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1373
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1374
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1375
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1376
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1377
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1378
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1379
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1380
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1381
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1382
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1383
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1384
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1385
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1386
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1387
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1388
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1389
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1390
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1391
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1392
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1393
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1394
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1395
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1396
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1397
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1398
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1399
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1400
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1401
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1402
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1403
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1404
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1405
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1406
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1407
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1408
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1409
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1410
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1411
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1412
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1413
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1414
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1415
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1416
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1417
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1418
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1419
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1420
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1421
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1422
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1423
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1424
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1425
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1426
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1427
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1428
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1429
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1430
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1431
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1432
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1433
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1434
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1435
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1436
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1437
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1438
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1439
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1440
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1441
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1442
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1443
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1444
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1445
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1446
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1447
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1448
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1449
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1450
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1451
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1452
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1453
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1454
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1455
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1456
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1457
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1458
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1459
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1460
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1461
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1462
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1463
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1464
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1465
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1466
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1467
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1468
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1469
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1470
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1471
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1472
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1473
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1474
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1475
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1476
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1477
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1478
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1479
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1480
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1481
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1482
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1483
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1484
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1485
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1486
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1487
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1488
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1489
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1490
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1491
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1492
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1493
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1494
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1495
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1496
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1497
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1498
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1499
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1500
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1501
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1502
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1503
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1504
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1505
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1506
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1507
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1508
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1509
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1510
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1511
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1512
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1513
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1514
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1515
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1516
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1517
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1518
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1519
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1520
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1521
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1522
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1523
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1524
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1525
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1526
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1527
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1528
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1529
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1530
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1531
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1532
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1533
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1534
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1535
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1536
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1537
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1538
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1539
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1540
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1541
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1542
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1543
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1544
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1545
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1546
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1547
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1548
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1549
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1550
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1551
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1552
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1553
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1554
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1555
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1556
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1557
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1558
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1559
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1560
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1561
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1562
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1563
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1564
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1565
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1566
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1567
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1568
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1569
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1570
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1571
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1572
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1573
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1574
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1575
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1576
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1577
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1578
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1579
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1580
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1581
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1582
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1583
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1584
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1585
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1586
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1587
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1588
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1589
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1590
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1591
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1592
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1593
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1594
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1595
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1596
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1597
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1598
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1599
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1600
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1601
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1602
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1603
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1604
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1605
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1606
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1607
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1608
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1609
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1610
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1611
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1612
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1613
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1614
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1615
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1616
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1617
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1618
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1619
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1620
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1621
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1622
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1623
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1624
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1625
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1626
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1627
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1628
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1629
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1630
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1631
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1632
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1633
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1634
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1635
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1636
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1637
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1638
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1639
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1640
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1641
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1642
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1643
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1644
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1645
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1646
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1647
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1648
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1649
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1650
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1651
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1652
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1653
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1654
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1655
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1656
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1657
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1658
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1659
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1660
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1661
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1662
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1663
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1664
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1665
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1666
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1667
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1668
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1669
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1670
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1671
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1672
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1673
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1674
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1675
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1676
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1677
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1678
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1679
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1680
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1681
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1682
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1683
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1684
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1685
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1686
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1687
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1688
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1689
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1690
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1691
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1692
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1693
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1694
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1695
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1696
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1697
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1698
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1699
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1700
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1701
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1702
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1703
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1704
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1705
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1706
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1707
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1708
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1709
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1710
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1711
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1712
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1713
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1714
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1715
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1716
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1717
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1718
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1719
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1720
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1721
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1722
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1723
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1724
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1725
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1726
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1727
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1728
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1729
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1730
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1731
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1732
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1733
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1734
+ 2C9OA_7OLEA
1735
+ 2C9OA_7OLEC
1736
+ 2CAZB_2CAZB
1737
+ 2CAZB_2F66B
1738
+ 2CAZB_2F66E
1739
+ 2CAZB_2F6MB
1740
+ 2CAZB_2P22B
1741
+ 2CCQA_2CCQA
1742
+ 2CCQA_2CM0A
1743
+ 2CCQA_2D5UA
1744
+ 2CCQA_2HPJA
1745
+ 2CCQA_2HPLA
1746
+ 2CGHA_3L1AA
1747
+ 2CGHA_3L1AB
1748
+ 2CGHA_4OP0A
1749
+ 2CGHA_4XTXA
1750
+ 2CGHA_4XU1B
1751
+ 2CMRA_2CMRA
1752
+ 2CMRA_2XRAA
1753
+ 2CMRA_3MA9A
1754
+ 2CMRA_3O3XA
1755
+ 2CMRA_3O43A
1756
+ 2CSBA_2CSBA
1757
+ 2CSBA_2CSBB
1758
+ 2CSBA_2CSDA
1759
+ 2CSBA_2CSDB
1760
+ 2CSBA_4GFJA
1761
+ 2CT9A_2CT9A
1762
+ 2CT9A_2CT9B
1763
+ 2CT9A_2E30A
1764
+ 2CT9A_7DSVC
1765
+ 2CT9A_7X2UC
1766
+ 2D00A_2D00D
1767
+ 2D00A_2D00E
1768
+ 2D00A_3A5CH
1769
+ 2D00A_5GASL
1770
+ 2D00A_7VAMH
1771
+ 2D3AA_2D3AA
1772
+ 2D3AA_4IS4C
1773
+ 2D3AA_7V4LA
1774
+ 2D3AA_7V4LB
1775
+ 2D3AA_7V4LC
1776
+ 2D9SA_2D9SA
1777
+ 2D9SA_2D9SB
1778
+ 2D9SA_2JUJA
1779
+ 2D9SA_2OO9A
1780
+ 2D9SA_2OO9B
1781
+ 2DW4A_2IW5A
1782
+ 2DW4A_6KGQA
1783
+ 2DW4A_6NQMA
1784
+ 2DW4A_6VYPk
1785
+ 2DW4A_6VYPK
1786
+ 2EAYA_2EAYA
1787
+ 2EAYA_3EFRA
1788
+ 2EAYA_3EFRB
1789
+ 2EAYA_3EFSA
1790
+ 2EAYA_3FJPA
1791
+ 2EB7A_2EB7A
1792
+ 2EB7A_2YR2B
1793
+ 2EB7A_3GF2A
1794
+ 2EB7A_3GFIA
1795
+ 2EB7A_3GFIC
1796
+ 2EFCB_2EFCB
1797
+ 2EFCB_2EFDB
1798
+ 2EFCB_2EFDD
1799
+ 2EFCB_2EFEB
1800
+ 2EFCB_4G01B
1801
+ 2ETNA_2ETNB
1802
+ 2ETNA_2EULD
1803
+ 2ETNA_2F23A
1804
+ 2ETNA_3AOIX
1805
+ 2ETNA_4WQTY
1806
+ 2FE8A_5TL7B
1807
+ 2FE8A_6WUUB
1808
+ 2FE8A_6WUUD
1809
+ 2FE8A_7RZCA
1810
+ 2FE8A_7SKRA
1811
+ 2FSFA_2FSHB
1812
+ 2FSFA_2FSIB
1813
+ 2FSFA_2VDAA
1814
+ 2FSFA_6GOXA
1815
+ 2FSFA_6S0Kh
1816
+ 2FTCP_3J7Y1
1817
+ 2FTCP_4CE46
1818
+ 2FTCP_4V196
1819
+ 2FTCP_5AJ4B6
1820
+ 2FTCP_7QH61
1821
+ 2FUG1_2FUGA
1822
+ 2FUG1_2YBB1
1823
+ 2FUG1_6ZIY1
1824
+ 2FUG1_6ZJN1
1825
+ 2FUG1_6ZJY1
1826
+ 2G76A_2G76A
1827
+ 2G76A_5N6CB
1828
+ 2G76A_6PLGC
1829
+ 2G76A_6PLGG
1830
+ 2G76A_6RIHA
1831
+ 2GCFA_2GCFA
1832
+ 2GCFA_2XMWA
1833
+ 2GCFA_4A48A
1834
+ 2GCFA_4A48B
1835
+ 2GCFA_4A4JA
1836
+ 2GICA_2GICA
1837
+ 2GICA_3PTXE
1838
+ 2GICA_6BJYB
1839
+ 2GICA_6BJYC
1840
+ 2GICA_7UMKA
1841
+ 2GTTA_2GTTM
1842
+ 2GTTA_2GTTN
1843
+ 2GTTA_8B8VA
1844
+ 2GTTA_8FFRA
1845
+ 2GTTA_8FWLA
1846
+ 2H01A_2H01A
1847
+ 2H01A_2H66B
1848
+ 2H01A_2I81A
1849
+ 2H01A_4L0UJ
1850
+ 2H01A_4L0WA
1851
+ 2H0BA_2H0BA
1852
+ 2H0BA_2H0BB
1853
+ 2H0BA_2H0BC
1854
+ 2H0BA_6PNPA
1855
+ 2H0BA_6PNQA
1856
+ 2H3HA_2H3HB
1857
+ 2H3HA_2QVCB
1858
+ 2H3HA_2QVCD
1859
+ 2H3HA_3C6QA
1860
+ 2H3HA_3C6QC
1861
+ 2HDAA_4LE9A
1862
+ 2HDAA_4OMMA
1863
+ 2HDAA_4RTUA
1864
+ 2HDAA_4RTXD
1865
+ 2HDAA_7PVXA
1866
+ 2HEPA_2HEPA
1867
+ 2HEPA_2JVDA
1868
+ 2HEPA_3BHPA
1869
+ 2HEPA_3BHPB
1870
+ 2HEPA_3BHPC
1871
+ 2HIGA_2HIGA
1872
+ 2HIGA_6QU3A
1873
+ 2HIGA_6QU3B
1874
+ 2HIGA_6QU5C
1875
+ 2HIGA_6SY7H
1876
+ 2I1AA_2I1AA
1877
+ 2I1AA_2I1AB
1878
+ 2I1AA_2I1AC
1879
+ 2I1AA_4Z2ZA
1880
+ 2I1AA_4Z2ZB
1881
+ 2I4IA_2I4IA
1882
+ 2I4IA_4PXAA
1883
+ 2I4IA_5E7IA
1884
+ 2I4IA_6O5FB
1885
+ 2I4IA_7LIUB
1886
+ 2I80A_2I80A
1887
+ 2I80A_2I87A
1888
+ 2I80A_2I8CB
1889
+ 2I80A_3N8DB
1890
+ 2I80A_7U9KB
1891
+ 2IJZA_2IJZA
1892
+ 2IJZA_2IJZJ
1893
+ 2IJZA_3WT4A
1894
+ 2IJZA_3WT4B
1895
+ 2IJZA_4OIWB
1896
+ 2IOPA_2IOPA
1897
+ 2IOPA_2IOPC
1898
+ 2IOPA_2IOPD
1899
+ 2IOPA_2IOQA
1900
+ 2IOPA_2IOQB
1901
+ 2IS1A_2IS1A
1902
+ 2IS1A_2IS2A
1903
+ 2IS1A_2IS4B
1904
+ 2IS1A_2IS6B
1905
+ 2IS1A_3LFUA
1906
+ 2IVSA_2X2KA
1907
+ 2IVSA_5AMNA
1908
+ 2IVSA_6FEKA
1909
+ 2IVSA_6VHGA
1910
+ 2IVSA_7RUNA
1911
+ 2IYEA_2YJ4A
1912
+ 2IYEA_2YJ4B
1913
+ 2IYEA_2YJ5A
1914
+ 2IYEA_2YJ5B
1915
+ 2IYEA_2YJ6B
1916
+ 2J0JA_2J0JA
1917
+ 2J0JA_2J0KA
1918
+ 2J0JA_2J0KB
1919
+ 2J0JA_6TY3A
1920
+ 2J0JA_6TY4A
1921
+ 2J284_2J284
1922
+ 2J284_4V5BC4
1923
+ 2J284_4V66BX
1924
+ 2J284_4V69B4
1925
+ 2J284_4V72B4
1926
+ 2J28D_2J28D
1927
+ 2J28D_4V4VBB
1928
+ 2J28D_4V4WBB
1929
+ 2J28D_4V66BY
1930
+ 2J28D_4V75BD
1931
+ 2J28E_2J28E
1932
+ 2J28E_4V4VBC
1933
+ 2J28E_4V4WBC
1934
+ 2J28E_4V66B1
1935
+ 2J28E_4V77BE
1936
+ 2J28P_2J28P
1937
+ 2J28P_4V4WBN
1938
+ 2J28P_4V65BI
1939
+ 2J28P_4V6LBQ
1940
+ 2J28P_4V6PBR
1941
+ 2J28U_2J28U
1942
+ 2J28U_4V4VBS
1943
+ 2J28U_4V4WBS
1944
+ 2J28U_4V72BU
1945
+ 2J28U_4V75BU
1946
+ 2J28Y_2J28Y
1947
+ 2J28Y_4V5BAY
1948
+ 2J28Y_4V66BR
1949
+ 2J28Y_4V70BZ
1950
+ 2J28Y_4V75BZ
1951
+ 2J4JA_2J4JA
1952
+ 2J4JA_2J4KA
1953
+ 2J4JA_2J4KD
1954
+ 2J4JA_2J4LA
1955
+ 2J4JA_2J4LC
1956
+ 2J6LA_4X0TA
1957
+ 2J6LA_4X0UA
1958
+ 2J6LA_4X0UC
1959
+ 2J6LA_4ZULG
1960
+ 2J6LA_6O4EH
1961
+ 2JBXA_2JBXA
1962
+ 2JBXA_2JBXB
1963
+ 2JBXA_2JBYA
1964
+ 2JBXA_2O42A
1965
+ 2JBXA_2O42B
1966
+ 2JPHA_2JPHA
1967
+ 2JPHA_2R2OA
1968
+ 2JPHA_2R2OB
1969
+ 2JPHA_2REXA
1970
+ 2JPHA_2REXC
1971
+ 2JRCA_2JRCA
1972
+ 2JRCA_2LGJA
1973
+ 2JRCA_2NAFA
1974
+ 2JRCA_3KK0A
1975
+ 2JRCA_3TCNB
1976
+ 2JSFA_2JSFA
1977
+ 2JSFA_2R29A
1978
+ 2JSFA_3UZVA
1979
+ 2JSFA_6FLAG
1980
+ 2JSFA_6FLBG
1981
+ 2JTCA_2JTCA
1982
+ 2JTCA_4D8BA
1983
+ 2JTCA_4D8EA
1984
+ 2JTCA_6UKDA
1985
+ 2JTCA_6UQDB
1986
+ 2K1BA_2K1BA
1987
+ 2K1BA_4X3KB
1988
+ 2K1BA_4X3SB
1989
+ 2K1BA_4X3TA
1990
+ 2K1BA_4X3TD
1991
+ 2K1WA_2K1WA
1992
+ 2K1WA_2K1XA
1993
+ 2K1WA_3HZ2A
1994
+ 2K1WA_5HT9A
1995
+ 2K1WA_5HT9B
1996
+ 2K28A_2K28A
1997
+ 2K28A_3I8ZA
1998
+ 2K28A_4X3TE
1999
+ 2K28A_5EPLA
2000
+ 2K28A_5EPLB
2001
+ 2K2JA_2K2JA
2002
+ 2K2JA_2W2WE
2003
+ 2K2JA_2W2WF
2004
+ 2K2JA_2W2WL
2005
+ 2K2JA_2W2XC
2006
+ 2KB8A_2KJ7A
2007
+ 2KB8A_5MGQA
2008
+ 2KB8A_7M62A
2009
+ 2KB8A_7M65C
2010
+ 2KB8A_7YL7A
2011
+ 2KCNA_2KCNA
2012
+ 2KCNA_2NB0A
2013
+ 2KCNA_2NBFA
2014
+ 2KCNA_6HAJB
2015
+ 2KCNA_7PGDA
2016
+ 2KHOA_2KHOA
2017
+ 2KHOA_4B9QC
2018
+ 2KHOA_4JN4A
2019
+ 2KHOA_7KRWA
2020
+ 2KHOA_7KZIA
2021
+ 2KIIA_2KIIA
2022
+ 2KIIA_2KILA
2023
+ 2KIIA_4U99B
2024
+ 2KIIA_4U9BA
2025
+ 2KIIA_4U9JA
2026
+ 2KIXA_2KIXA
2027
+ 2KIXA_2KIXB
2028
+ 2KIXA_2KIXC
2029
+ 2KIXA_6PVRA
2030
+ 2KIXA_6PVTA
2031
+ 2KJ3A_2KJ3A
2032
+ 2KJ3A_2KJ3B
2033
+ 2KJ3A_2LBUA
2034
+ 2KJ3A_2RNMD
2035
+ 2KJ3A_2RNME
2036
+ 2KPWA_2KPWA
2037
+ 2KPWA_3UMNA
2038
+ 2KPWA_3UMNC
2039
+ 2KPWA_7DTGA
2040
+ 2KPWA_7DTGD
2041
+ 2KT6A_2KT6A
2042
+ 2KT6A_3L48A
2043
+ 2KT6A_3L48B
2044
+ 2KT6A_3L48C
2045
+ 2KT6A_3L48D
2046
+ 2L7EA_2L7EA
2047
+ 2L7EA_3QRLA
2048
+ 2L7EA_5D7EA
2049
+ 2L7EA_6MIPA
2050
+ 2L7EA_7F4AA
2051
+ 2LE2A_2LE2A
2052
+ 2LE2A_2LE2B
2053
+ 2LE2A_3ZOQB
2054
+ 2LE2A_3ZOQC
2055
+ 2LE2A_4L5NC
2056
+ 2LHNA_2LHNA
2057
+ 2LHNA_5L2LA
2058
+ 2LHNA_5L2LB
2059
+ 2LHNA_5L2LE
2060
+ 2LHNA_5L2LF
2061
+ 2LN0A_2LN0A
2062
+ 2LN0A_3V43A
2063
+ 2LN0A_4LJNA
2064
+ 2LN0A_4LKAA
2065
+ 2LN0A_6LSBA
2066
+ 2LR6A_2LR6A
2067
+ 2LR6A_2LR6B
2068
+ 2LR6A_3SOOA
2069
+ 2LR6A_3SOOB
2070
+ 2LR6A_3SOOC
2071
+ 2LS7A_2LS7A
2072
+ 2LS7A_4IZ5H
2073
+ 2LS7A_4IZAB
2074
+ 2LS7A_6P6BA
2075
+ 2LS7A_6P6CA
2076
+ 2LVWA_2LVWA
2077
+ 2LVWA_2LVWB
2078
+ 2LVWA_5YPPA
2079
+ 2LVWA_5YUMA
2080
+ 2LVWA_6LPIF
2081
+ 2LW5A_2LW5A
2082
+ 2LW5A_5UZ9I
2083
+ 2LW5A_5XLOM
2084
+ 2LW5A_5XLPM
2085
+ 2LW5A_6B46I
2086
+ 2LXNA_2LXNA
2087
+ 2LXNA_7D95A
2088
+ 2LXNA_7D97B
2089
+ 2LXNA_7D97D
2090
+ 2LXNA_7YC6A
2091
+ 2M7KA_2M7KA
2092
+ 2M7KA_2M7MA
2093
+ 2M7KA_2NXQB
2094
+ 2M7KA_3LI6J
2095
+ 2M7KA_5XOPE
2096
+ 2MM4A_2MM4A
2097
+ 2MM4A_5X29A
2098
+ 2MM4A_5X29B
2099
+ 2MM4A_5X29D
2100
+ 2MM4A_5X29E
2101
+ 2MR3A_2MR3A
2102
+ 2MR3A_3JCKF
2103
+ 2MR3A_5MPDO
2104
+ 2MR3A_6J2NO
2105
+ 2MR3A_6J2XO
2106
+ 2MZ7A_6QJMA
2107
+ 2MZ7A_6QJPA
2108
+ 2MZ7A_7QKWD
2109
+ 2MZ7A_7QL2B
2110
+ 2MZ7A_7QL2C
2111
+ 2MZBA_2MZBA
2112
+ 2MZBA_3RSBA
2113
+ 2MZBA_3RSBB
2114
+ 2MZBA_3RSBC
2115
+ 2MZBA_3RSBD
2116
+ 2MZWB_2MZWB
2117
+ 2MZWB_4ADNA
2118
+ 2MZWB_4ADNB
2119
+ 2MZWB_4ADOB
2120
+ 2MZWB_4E4BA
2121
+ 2N2CA_2N3XA
2122
+ 2N2CA_2N4HA
2123
+ 2N2CA_6N3BA
2124
+ 2N2CA_6N3BB
2125
+ 2N2CA_8A6IA
2126
+ 2N7LC_6MV3A
2127
+ 2N7LC_7SC2A
2128
+ 2N7LC_7SC3A
2129
+ 2N7LC_7SUPA
2130
+ 2N7LC_7SXDA
2131
+ 2NTXA_2NTXA
2132
+ 2NTXA_2NTXB
2133
+ 2NTXA_2NTYA
2134
+ 2NTXA_2NTYB
2135
+ 2NTXA_2WBLA
2136
+ 2NW2A_4DZBA
2137
+ 2NW2A_4G8EA
2138
+ 2NW2A_4IIQA
2139
+ 2NW2A_4PJ5G
2140
+ 2NW2A_5L2KD
2141
+ 2OEXA_2OEXA
2142
+ 2OEXA_2OEXB
2143
+ 2OEXA_2OJQA
2144
+ 2OEXA_4JJYA
2145
+ 2OEXA_4JJYB
2146
+ 2OGXA_4NDOA
2147
+ 2OGXA_6GWVD
2148
+ 2OGXA_6RJ4E
2149
+ 2OGXA_6RKDA
2150
+ 2OGXA_6RKEG
2151
+ 2OK5A_2XWJJ
2152
+ 2OK5A_2XWJL
2153
+ 2OK5A_3HS0I
2154
+ 2OK5A_7JTNC
2155
+ 2OK5A_7JTQA
2156
+ 2OQHA_2OQHA
2157
+ 2OQHA_2OQHB
2158
+ 2OQHA_2OQHC
2159
+ 2OQHA_2OQHD
2160
+ 2OQHA_4DYEA
2161
+ 2OSSA_2OSSA
2162
+ 2OSSA_3UW9A
2163
+ 2OSSA_5EGUD
2164
+ 2OSSA_5KU3A
2165
+ 2OSSA_6G0OA
2166
+ 2OWQA_2OWQB
2167
+ 2OWQA_4DOFA
2168
+ 2OWQA_4QC9A
2169
+ 2OWQA_4QCBB
2170
+ 2OWQA_4YIGI
2171
+ 2OX8A_2OX8A
2172
+ 2OX8A_2OX8B
2173
+ 2OX8A_2OX8D
2174
+ 2OX8A_2OX9A
2175
+ 2OX8A_2OX9C
2176
+ 2POHA_2POHF
2177
+ 2POHA_3C9ID
2178
+ 2POHA_4LINA
2179
+ 2POHA_4LINJ
2180
+ 2POHA_4LINL
2181
+ 2PTQA_2PTQB
2182
+ 2PTQA_2PTRB
2183
+ 2PTQA_3GZHA
2184
+ 2PTQA_4NSLA
2185
+ 2PTQA_4NSLD
2186
+ 2Q80A_6C56A
2187
+ 2Q80A_6C57A
2188
+ 2Q80A_6G31A
2189
+ 2Q80A_6G31E
2190
+ 2Q80A_6G31I
2191
+ 2Q81A_2Q81B
2192
+ 2Q81A_2Q81D
2193
+ 2Q81A_3M52A
2194
+ 2Q81A_3M52B
2195
+ 2Q81A_7AZXA
2196
+ 2QF0A_2QF0B
2197
+ 2QF0A_3B8JA
2198
+ 2QF0A_3LGIB
2199
+ 2QF0A_3LGIC
2200
+ 2QF0A_3LH1A
2201
+ 2QFIA_2QFIA
2202
+ 2QFIA_2QFIB
2203
+ 2QFIA_3H90A
2204
+ 2QFIA_3H90B
2205
+ 2QFIA_3H90D
2206
+ 2R5KA_6L31A
2207
+ 2R5KA_6L31B
2208
+ 2R5KA_6L31E
2209
+ 2R5KA_6L31F
2210
+ 2R5KA_7EW5A
2211
+ 2R6AA_2R6EA
2212
+ 2R6AA_2R6EB
2213
+ 2R6AA_2VYFB
2214
+ 2R6AA_4ESVA
2215
+ 2R6AA_4ESVI
2216
+ 2RDO7_3J0EH
2217
+ 2RDO7_4V9ODV
2218
+ 2RDO7_7N2VEF
2219
+ 2RDO7_7PJWx
2220
+ 2RDO7_7PJYx
2221
+ 2RHEA_2RHEA
2222
+ 2RHEA_7T6XB
2223
+ 2RHEA_7WE9J
2224
+ 2RHEA_7WE9K
2225
+ 2RHEA_7WTJL
2226
+ 2RR8A_2RR8A
2227
+ 2RR8A_3I6XA
2228
+ 2RR8A_3I6XD
2229
+ 2RR8A_5L0OA
2230
+ 2RR8A_5L0OB
2231
+ 2V1SA_2V1SB
2232
+ 2V1SA_2V1TB
2233
+ 2V1SA_3AWRA
2234
+ 2V1SA_3AX2G
2235
+ 2V1SA_3AX5C
2236
+ 2V3JA_3OIJA
2237
+ 2V3JA_3OIJB
2238
+ 2V3JA_6LQSRG
2239
+ 2V3JA_6ZQGJG
2240
+ 2V3JA_7D4IRG
2241
+ 2V5MA_2V5RB
2242
+ 2V5MA_2V5SA
2243
+ 2V5MA_4X83A
2244
+ 2V5MA_4X9BA
2245
+ 2V5MA_4X9IB
2246
+ 2V8QB_2V8QB
2247
+ 2V8QB_2V9JB
2248
+ 2V8QB_4REWB
2249
+ 2V8QB_7JHHB
2250
+ 2V8QB_7JIJB
2251
+ 2VBCA_2VBCA
2252
+ 2VBCA_2WHXA
2253
+ 2VBCA_5YVJB
2254
+ 2VBCA_5YVUB
2255
+ 2VBCA_5YW1B
2256
+ 2VGMA_2VGNA
2257
+ 2VGMA_2VGNB
2258
+ 2VGMA_3IZQ0
2259
+ 2VGMA_3J16A
2260
+ 2VGMA_5M1JA1
2261
+ 2VL0A_2YN6A
2262
+ 2VL0A_6HK0J
2263
+ 2VL0A_6V03C
2264
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2270
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2271
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2276
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2277
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2278
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2279
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2280
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2281
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2282
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2283
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2284
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2285
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2286
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2287
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2288
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2289
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2290
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2291
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2292
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2508
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2509
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2510
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2511
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2512
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2513
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2525
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2527
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2528
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2529
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2530
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2531
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2532
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2533
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2534
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2535
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2536
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2537
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2538
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2539
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2540
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2541
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2543
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2544
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2545
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2546
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2547
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2548
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2549
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2550
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2551
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2552
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2553
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2554
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2555
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2556
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2557
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2558
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2559
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2560
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2561
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2562
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2563
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2564
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2565
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2566
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2567
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2568
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2569
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2570
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2571
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2572
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2573
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2575
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2576
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2577
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2578
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2579
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2580
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2581
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2584
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2585
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2586
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2587
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2588
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2589
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2590
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2591
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2592
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2593
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2594
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2595
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2596
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2597
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2598
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2599
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2600
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2601
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2602
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2603
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2604
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2605
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2606
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2607
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2608
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2609
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2610
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2611
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2612
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2613
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2614
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2615
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2616
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2617
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2618
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2619
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2620
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2621
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2622
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2623
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2624
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2625
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2626
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2627
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2628
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2629
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2630
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2631
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2632
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2633
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2634
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2639
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2640
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2641
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2642
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2643
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2644
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2645
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2646
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2647
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2648
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2649
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2655
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2656
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2657
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2658
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2659
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2660
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2661
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2662
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2663
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2664
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2665
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2666
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2667
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2668
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2669
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2670
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2671
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2672
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2673
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2674
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2675
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2676
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2677
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2678
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2679
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2680
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2681
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2682
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2683
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2684
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2685
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2686
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2687
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2688
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2689
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2690
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2691
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2692
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2694
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2695
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2699
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2703
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2704
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2705
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2707
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2708
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2709
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2711
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2712
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2713
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2714
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2715
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2716
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2717
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2718
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2719
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2720
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2721
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2722
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2723
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2730
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2731
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2732
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2733
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2734
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2735
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2736
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2737
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2738
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2739
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2740
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2741
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2742
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2743
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2744
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2745
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2746
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2747
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2748
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2749
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2750
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2751
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2752
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2753
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2754
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2755
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2756
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2757
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2758
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2759
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2760
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2761
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2762
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2763
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2764
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2765
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2766
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2767
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2768
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2769
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2770
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2771
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2772
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2773
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2774
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2775
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2776
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2777
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2778
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2779
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2780
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2781
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2782
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2783
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2784
+ 3J7OB_4V6XCB
2785
+ 3J7OB_6D9JB
2786
+ 3J7Oc_4V5ZB6
2787
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2788
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2789
+ 3J7Oc_7O81Bc
2790
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2791
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2792
+ 3J7Oh_4V6XCh
2793
+ 3J7Oh_6D9Jh
2794
+ 3J7Oh_6HCFh3
2795
+ 3J7Oh_7OYBh1
2796
+ 3J7ON_3J7ON
2797
+ 3J7ON_4V5ZBm
2798
+ 3J7ON_6D9JN
2799
+ 3J7ON_6ZVK12
2800
+ 3J7ON_7A0112
2801
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2802
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2803
+ 3J7Pq_6HCJt3
2804
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2805
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2806
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2807
+ 3J7PSK_5LZSKK
2808
+ 3J7PSK_6GZ3BK
2809
+ 3J7PSK_6OLZBK
2810
+ 3J7PSK_7A01E3
2811
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2812
+ 3J7PSV_4KZXV
2813
+ 3J7PSV_6D9JWW
2814
+ 3J7PSV_6FECb
2815
+ 3J7PSV_7A01D3
2816
+ 3J7Yc_3J7Yc
2817
+ 3J7Yc_4CE4h
2818
+ 3J7Yc_4V1Ah
2819
+ 3J7Yc_7OI6c
2820
+ 3J7Yc_7PD3c
2821
+ 3J7Yk_3J7Yk
2822
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2823
+ 3J7Yk_6I9Rk
2824
+ 3J7Yk_6NU2k
2825
+ 3J7Yk_7OI6k
2826
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2827
+ 3J96A_3J96C
2828
+ 3J96A_3J97F
2829
+ 3J96A_3J99E
2830
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2831
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2832
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2833
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2834
+ 3J9MAF_6NU3AF
2835
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2836
+ 3J9MAS_3JD5b
2837
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2838
+ 3J9MAS_6NU3AS
2839
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2840
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2841
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2842
+ 3J9TR_3J9Ua
2843
+ 3J9TR_5VOZT
2844
+ 3J9TR_6O7Ti
2845
+ 3J9TR_7FDCV
2846
+ 3J9WAL_5MYJAL
2847
+ 3J9WAL_5ND8l
2848
+ 3J9WAL_6S13l
2849
+ 3J9WAL_7ASO3
2850
+ 3J9WAL_7ASPc
2851
+ 3J9WAR_5T7VS9
2852
+ 3J9WAR_6S13r
2853
+ 3J9WAR_7ASO9
2854
+ 3J9WAR_7ASPi
2855
+ 3J9WAR_7P48r
2856
+ 3J9WB0_3J9WB0
2857
+ 3J9WB0_6HTQW
2858
+ 3J9WB0_7AS9b
2859
+ 3J9WB0_7NHN1
2860
+ 3J9WB0_8BUUX
2861
+ 3J9Yu_4V6LAY
2862
+ 3J9Yu_4V6QAX
2863
+ 3J9Yu_4V6VAU
2864
+ 3J9Yu_7PJTu
2865
+ 3J9Yu_8A3LU
2866
+ 3JA7A_3JA7B
2867
+ 3JA7A_6UZCG
2868
+ 3JA7A_6UZCH
2869
+ 3JA7A_6UZCN
2870
+ 3JA7A_6UZCO
2871
+ 3JA84_3JC74
2872
+ 3JA84_5V8F4
2873
+ 3JA84_5XF84
2874
+ 3JA84_6PTOH
2875
+ 3JA84_7PT64
2876
+ 3JAAA_4DL3A
2877
+ 3JAAA_4DL5A
2878
+ 3JAAA_4RNOA
2879
+ 3JAAA_6M7UA
2880
+ 3JAAA_6M7VA
2881
+ 3JC8Ta_3JC8Ta
2882
+ 3JC8Ta_3JC8Tb
2883
+ 3JC8Ta_3JC8Tc
2884
+ 3JC8Ta_3JC8Ti
2885
+ 3JC8Ta_3JC9Ta
2886
+ 3JCKC_3JCKC
2887
+ 3JCKC_3JCOQ
2888
+ 3JCKC_4CR2Q
2889
+ 3JCKC_6FVTQ
2890
+ 3JCKC_6FVWQ
2891
+ 3JCKE_3JCKE
2892
+ 3JCKE_5WVIU
2893
+ 3JCKE_6FVUU
2894
+ 3JCKE_6FVWU
2895
+ 3JCKE_6J30U
2896
+ 3JCKH_3JCKH
2897
+ 3JCKH_4CR3T
2898
+ 3JCKH_5MPBT
2899
+ 3JCKH_5WVIT
2900
+ 3JCKH_6J30T
2901
+ 3JCMP_5GAMh
2902
+ 3JCMP_5GAOl
2903
+ 3JCMP_5ZWOb
2904
+ 3JCMP_6N7XL
2905
+ 3JCMP_7DCOb
2906
+ 3JCRD_5MQFF
2907
+ 3JCRD_5XJCE
2908
+ 3JCRD_6QDVN
2909
+ 3JCRD_7ABGD
2910
+ 3JCRD_8CH6o
2911
+ 3JCSH_3JCSH
2912
+ 3JCSH_4V8MBO
2913
+ 3JCSH_5T2AM
2914
+ 3JCSH_5T5HO
2915
+ 3JCSH_6AZ3H
2916
+ 3JCTb_7OHPb
2917
+ 3JCTb_7OHQb
2918
+ 3JCTb_7OHWb
2919
+ 3JCTb_7UG6b
2920
+ 3JCTb_7Z34b
2921
+ 3JCTr_3JCTr
2922
+ 3JCTr_6ELZr
2923
+ 3JCTr_6YLGr
2924
+ 3JCTr_6YLXr
2925
+ 3JCTr_7OHTr
2926
+ 3JCTu_6C0Fu
2927
+ 3JCTu_6N8Ju
2928
+ 3JCTu_6YLYu
2929
+ 3JCTu_7OH3u
2930
+ 3JCTu_7Z34u
2931
+ 3JTMA_3JTMA
2932
+ 3JTMA_3N7UB
2933
+ 3JTMA_3N7UF
2934
+ 3JTMA_3N7UH
2935
+ 3JTMA_3NAQA
2936
+ 3JUAB_5OAQL
2937
+ 3JUAB_6GE3L
2938
+ 3JUAB_6GEKL
2939
+ 3JUAB_8A8QC
2940
+ 3JUAB_8A8RM
2941
+ 3KHXA_3KHXA
2942
+ 3KHXA_3KHXB
2943
+ 3KHXA_3KHZA
2944
+ 3KHXA_3KHZB
2945
+ 3KHXA_3KI9A
2946
+ 3KKBA_3KKBA
2947
+ 3KKBA_3KKBB
2948
+ 3KKBA_3L34A
2949
+ 3KKBA_4LLCA
2950
+ 3KKBA_4LLCB
2951
+ 3KLTA_3KLTA
2952
+ 3KLTA_3KLTB
2953
+ 3KLTA_3KLTC
2954
+ 3KLTA_3TRTA
2955
+ 3KLTA_3TRTB
2956
+ 3KTGA_3KTHA
2957
+ 3KTGA_7FESJ
2958
+ 3KTGA_7FESL
2959
+ 3KTGA_7FESM
2960
+ 3KTGA_7P80B
2961
+ 3LREA_3LREB
2962
+ 3LREA_5OAMK
2963
+ 3LREA_5OCUK
2964
+ 3LREA_5OGCK
2965
+ 3LREA_7RSIC
2966
+ 3MSYA_3MSYB
2967
+ 3MSYA_3MSYD
2968
+ 3MSYA_3NO1A
2969
+ 3MSYA_4H83D
2970
+ 3MSYA_4H83F
2971
+ 3N9IA_3N9IA
2972
+ 3N9IA_3N9IB
2973
+ 3N9IA_8I1WB
2974
+ 3N9IA_8I1ZA
2975
+ 3N9IA_8I2CB
2976
+ 3NBXX_3NBXX
2977
+ 3NBXX_6Q7LV
2978
+ 3NBXX_6Q7LW
2979
+ 3NBXX_6Q7LX
2980
+ 3NBXX_6Q7LZ
2981
+ 3NKDA_3NKDA
2982
+ 3NKDA_5DQZD
2983
+ 3NKDA_5DS5B
2984
+ 3NKDA_5VVKA
2985
+ 3NKDA_5WFEB
2986
+ 3NXCA_3NXCA
2987
+ 3NXCA_4GFLB
2988
+ 3NXCA_5HBUA
2989
+ 3NXCA_5HBUB
2990
+ 3NXCA_5K58F
2991
+ 3OCEA_3OCEA
2992
+ 3OCEA_3OCEC
2993
+ 3OCEA_3OCED
2994
+ 3OCEA_3OCFA
2995
+ 3OCEA_3OCFB
2996
+ 3OETA_3OETB
2997
+ 3OETA_3OETD
2998
+ 3OETA_3OETE
2999
+ 3OETA_3OETG
3000
+ 3OETA_3OETH
3001
+ 3OLMA_3OLMA
3002
+ 3OLMA_4LCDA
3003
+ 3OLMA_4LCDB
3004
+ 3OLMA_5HPLA
3005
+ 3OLMA_5HPLB
3006
+ 3P54A_3P54A
3007
+ 3P54A_5WSNA
3008
+ 3P54A_5YWPE
3009
+ 3P54A_6A0PA
3010
+ 3P54A_7KVBA
3011
+ 3PCOB_3PCOD
3012
+ 3PCOB_6OZ5D
3013
+ 3PCOB_6P24B
3014
+ 3PCOB_7N8YB
3015
+ 3PCOB_7N8YD
3016
+ 3PHGA_3PHGB
3017
+ 3PHGA_4FOSA
3018
+ 3PHGA_4FQ8B
3019
+ 3PHGA_4FSHA
3020
+ 3PHGA_4FSHB
3021
+ 3PILA_3PILA
3022
+ 3PILA_3PILB
3023
+ 3PILA_3PIMA
3024
+ 3PILA_3PIMB
3025
+ 3PILA_3PINB
3026
+ 3PJZA_3PJZA
3027
+ 3PJZA_6V4JA
3028
+ 3PJZA_6V4JB
3029
+ 3PJZA_6V4KC
3030
+ 3PJZA_6V4LB
3031
+ 3PPCA_3PPCA
3032
+ 3PPCA_3PPCB
3033
+ 3PPCA_3PPHB
3034
+ 3PPCA_4L5ZA
3035
+ 3PPCA_4QQUA
3036
+ 3PV2A_3PV2B
3037
+ 3PV2A_3PV3C
3038
+ 3PV2A_3PV3D
3039
+ 3PV2A_3PV5B
3040
+ 3PV2A_3PV5C
3041
+ 3Q6GH_3Q6GH
3042
+ 3Q6GH_4RFEA
3043
+ 3Q6GH_4RFNB
3044
+ 3Q6GH_5UKNH
3045
+ 3Q6GH_6P60A
3046
+ 3QF7A_3QG5A
3047
+ 3QF7A_3QG5B
3048
+ 3QF7A_3THOA
3049
+ 3QF7A_4W9ME
3050
+ 3QF7A_4W9MI
3051
+ 3QILA_3QILA
3052
+ 3QILA_3QILC
3053
+ 3QILA_3QILD
3054
+ 3QILA_3QILM
3055
+ 3QILA_3QILR
3056
+ 3QV9A_4HYVA
3057
+ 3QV9A_4KCVB
3058
+ 3QV9A_4KRZA
3059
+ 3QV9A_4KRZB
3060
+ 3QV9A_4KS0B
3061
+ 3R74A_3R74A
3062
+ 3R74A_3R74B
3063
+ 3R74A_3R75A
3064
+ 3R74A_3R75B
3065
+ 3R74A_3R76A
3066
+ 3R90A_3R90C
3067
+ 3R90A_3R90E
3068
+ 3R90A_3R90J
3069
+ 3R90A_5VYCk4
3070
+ 3R90A_6MS4A
3071
+ 3RGXA_3RGXA
3072
+ 3RGXA_3RGZA
3073
+ 3RGXA_3RJ0A
3074
+ 3RGXA_4M7EA
3075
+ 3RGXA_4M7EB
3076
+ 3RJRA_3RJRA
3077
+ 3RJRA_5FFOD
3078
+ 3RJRA_5FFOH
3079
+ 3RJRA_5VQFC
3080
+ 3RJRA_5VQFD
3081
+ 3RKIA_3RKIA
3082
+ 3RKIA_3RKIB
3083
+ 3RKIA_5TDGB
3084
+ 3RKIA_5TPNA
3085
+ 3RKIA_5UDCA
3086
+ 3S2KA_3S2KA
3087
+ 3S2KA_3S8VA
3088
+ 3S2KA_5FWWA
3089
+ 3S2KA_6H16A
3090
+ 3S2KA_8DVMA
3091
+ 3SL7A_3SL7A
3092
+ 3SL7A_3SL7B
3093
+ 3SL7A_4GQYA
3094
+ 3SL7A_4GQYB
3095
+ 3SL7A_4GQYC
3096
+ 3SN6R_3SN6R
3097
+ 3SN6R_4LDEA
3098
+ 3SN6R_4QKXA
3099
+ 3SN6R_5JQHB
3100
+ 3SN6R_6CSYA
3101
+ 3SOPA_3SOPB
3102
+ 3SOPA_4Z54A
3103
+ 3SOPA_4Z54B
3104
+ 3SOPA_6UQQC
3105
+ 3SOPA_6UQQD
3106
+ 3SW1A_3SW1A
3107
+ 3SW1A_5J3WB
3108
+ 3SW1A_5J4EC
3109
+ 3SW1A_5LUVB
3110
+ 3SW1A_7R56A
3111
+ 3T12A_3T12A
3112
+ 3T12A_3T1OA
3113
+ 3T12A_3T1QA
3114
+ 3T12A_3T1TB
3115
+ 3T12A_3T1VC
3116
+ 3T5NA_3T5QA
3117
+ 3T5NA_3T5QE
3118
+ 3T5NA_3T5QG
3119
+ 3T5NA_3T5QI
3120
+ 3T5NA_3T5QK
3121
+ 3T6CA_3T6CA
3122
+ 3T6CA_3T6CB
3123
+ 3T6CA_3TW9B
3124
+ 3T6CA_3TW9D
3125
+ 3T6CA_3TWAA
3126
+ 3T8SA_3T8SA
3127
+ 3T8SA_3T8SB
3128
+ 3T8SA_3UJ0A
3129
+ 3T8SA_3UJ0B
3130
+ 3T8SA_3UJ4A
3131
+ 3TGOC_3TGOC
3132
+ 3TGOC_5EKQC
3133
+ 3TGOC_5LJOC
3134
+ 3TGOC_6LYSC
3135
+ 3TGOC_6V05C
3136
+ 3TJ1A_3TJ1A
3137
+ 3TJ1A_5G5LO
3138
+ 3TJ1A_6RQTO
3139
+ 3TJ1A_6RUOO
3140
+ 3TJ1A_6RWEO
3141
+ 3TTKA_3TTKA
3142
+ 3TTKA_3TTKB
3143
+ 3TTKA_3TTKC
3144
+ 3TTKA_3TTMA
3145
+ 3TTKA_3TTMB
3146
+ 3U1SH_3U1SH
3147
+ 3U1SH_5U1FH
3148
+ 3U1SH_5UXQA
3149
+ 3U1SH_5UXQH
3150
+ 3U1SH_5UY3H
3151
+ 3U3IA_3U3IA
3152
+ 3U3IA_4AKLA
3153
+ 3U3IA_4AQFA
3154
+ 3U3IA_4AQGA
3155
+ 3U3IA_6Z0OB
3156
+ 3U5ZB_3U5ZB
3157
+ 3U5ZB_3U5ZE
3158
+ 3U5ZB_3U60C
3159
+ 3U5ZB_3U61B
3160
+ 3U5ZB_3U61E
3161
+ 3UO1H_6BPCE
3162
+ 3UO1H_6BPEB
3163
+ 3UO1H_6BPEE
3164
+ 3UO1H_7KYLH
3165
+ 3UO1H_7KYLX
3166
+ 3V44A_3V44A
3167
+ 3V44A_3V47A
3168
+ 3V44A_3V47B
3169
+ 3V44A_5GY2B
3170
+ 3V44A_6BXCB
3171
+ 3V6TA_4OSKB
3172
+ 3V6TA_4OSLA
3173
+ 3V6TA_4OT3B
3174
+ 3V6TA_6JTQB
3175
+ 3V6TA_6JVZB
3176
+ 3VD6C_3VEKC
3177
+ 3VD6C_4HC7A
3178
+ 3VD6C_4HC7B
3179
+ 3VD6C_4HC9A
3180
+ 3VD6C_4HCAA
3181
+ 3VD8A_5VAWA
3182
+ 3VD8A_5YGSD
3183
+ 3VD8A_6QF7C
3184
+ 3VD8A_8AG0B
3185
+ 3VD8A_8DX4B
3186
+ 3VH1A_3VH1A
3187
+ 3VH1A_3VH2A
3188
+ 3VH1A_4GSKA
3189
+ 3VH1A_4GSKB
3190
+ 3VH1A_4GSLB
3191
+ 3VKWA_3VKWA
3192
+ 3VKWA_3WRXC
3193
+ 3VKWA_3WRXD
3194
+ 3VKWA_3WRYC
3195
+ 3VKWA_3WRYD
3196
+ 3W6GA_5XBQI
3197
+ 3W6GA_6IU1A
3198
+ 3W6GA_8HH0A
3199
+ 3W6GA_8HLAD
3200
+ 3W6GA_8HLAL
3201
+ 3WBIA_3WBIA
3202
+ 3WBIA_3WBKA
3203
+ 3WBIA_3WBKB
3204
+ 3WBIA_6UZ71
3205
+ 3WBIA_6WOO1
3206
+ 3WG9A_3WG9B
3207
+ 3WG9A_3WG9C
3208
+ 3WG9A_3WGGA
3209
+ 3WG9A_3WGIB
3210
+ 3WG9A_3WGIC
3211
+ 3WKLA_3WKLA
3212
+ 3WKLA_3WKMA
3213
+ 3WKLA_6AKQA
3214
+ 3WKLA_6ICFA
3215
+ 3WKLA_7CQCA
3216
+ 3WMEA_3WMFA
3217
+ 3WMEA_6A6MA
3218
+ 3WMEA_7DQVA
3219
+ 3WMEA_7FC9A
3220
+ 3WMEA_7VR5A
3221
+ 3WSXA_3WSXA
3222
+ 3WSXA_3WSYA
3223
+ 3WSXA_3WSZA
3224
+ 3WSXA_7VT0A
3225
+ 3WSXA_7VT0B
3226
+ 3WT1A_3WT1A
3227
+ 3WT1A_3WT1D
3228
+ 3WT1A_3WT2A
3229
+ 3WT1A_3WT2B
3230
+ 3WT1A_5CRWA
3231
+ 3X2RA_3X2RC
3232
+ 3X2RA_4Q79E
3233
+ 3X2RA_4UV2A
3234
+ 3X2RA_4UV2P
3235
+ 3X2RA_6L7CA
3236
+ 3X3MA_3X3MA
3237
+ 3X3MA_3X3NA
3238
+ 3X3MA_4KK7A
3239
+ 3X3MA_5EBCA
3240
+ 3X3MA_5EBDA
3241
+ 3ZBEA_3ZBEA
3242
+ 3ZBEA_5CW7E
3243
+ 3ZBEA_5CW7G
3244
+ 3ZBEA_5CW7M
3245
+ 3ZBEA_5CZEA
3246
+ 3ZYYX_3ZYYX
3247
+ 3ZYYX_3ZYYY
3248
+ 3ZYYX_4C1NI
3249
+ 3ZYYX_4C1NJ
3250
+ 3ZYYX_4C1NK
3251
+ 4AJ5K_4AJ5K
3252
+ 4AJ5K_4AJ5L
3253
+ 4AJ5K_4AJ5M
3254
+ 4AJ5K_4AJ5P
3255
+ 4AJ5K_4AJ5R
3256
+ 4AQ5C_4AQ5C
3257
+ 4AQ5C_4AQ9C
3258
+ 4AQ5C_4BOG1
3259
+ 4AQ5C_7QKOC
3260
+ 4AQ5C_8F6ZB
3261
+ 4ARJA_4ARJA
3262
+ 4ARJA_4ARJB
3263
+ 4ARJA_4EPIA
3264
+ 4ARJA_4EXMC
3265
+ 4ARJA_4EXMD
3266
+ 4BM9A_4K95A
3267
+ 4BM9A_5N2WA
3268
+ 4BM9A_5N38A
3269
+ 4BM9A_6GLCA
3270
+ 4BM9A_6HUEA
3271
+ 4BSRA_4BSSF
3272
+ 4BSRA_4BSUE
3273
+ 4BSRA_4KNGB
3274
+ 4BSRA_4UFRA
3275
+ 4BSRA_4UFRC
3276
+ 4C0OA_4C0PC
3277
+ 4C0OA_4C0PD
3278
+ 4C0OA_4C0QA
3279
+ 4C0OA_6GX9A
3280
+ 4C0OA_6GX9B
3281
+ 4C2MD_5M5WD
3282
+ 4C2MD_6H67D
3283
+ 4C2MD_6H68D
3284
+ 4C2MD_6RQHD
3285
+ 4C2MD_6RQTD
3286
+ 4C2MI_4C2MI
3287
+ 4C2MI_6HLQI
3288
+ 4C2MI_6HLSI
3289
+ 4C2MI_6RRDI
3290
+ 4C2MI_6RUOI
3291
+ 4C3BA_4C3BP
3292
+ 4C3BA_4C3EK
3293
+ 4C3BA_4C3EO
3294
+ 4C3BA_6G0YC
3295
+ 4C3BA_6G0YF
3296
+ 4C4VA_4C4VA
3297
+ 4C4VA_4C4VB
3298
+ 4C4VA_7NREA
3299
+ 4C4VA_7TT5A
3300
+ 4C4VA_7TT6A
3301
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3302
+ 4C8HA_5HOGB
3303
+ 4C8HA_6PTNF
3304
+ 4C8HA_6PTNG
3305
+ 4C8HA_7PMKF
3306
+ 4C8VA_4C8VH
3307
+ 4C8VA_4C8WI
3308
+ 4C8VA_4C99D
3309
+ 4C8VA_4C9EB
3310
+ 4C8VA_4C9EF
3311
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3312
+ 4CBVA_4CBVB
3313
+ 4CBVA_4CBVC
3314
+ 4CBVA_4CBVD
3315
+ 4CBVA_4CBVF
3316
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3317
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3318
+ 4CFEB_5ISOB
3319
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3320
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3321
+ 4CI1B_6BN9B
3322
+ 4CI1B_6BNBB
3323
+ 4CI1B_6H0FB
3324
+ 4CI1B_7U8FA
3325
+ 4CI1B_8D81B
3326
+ 4CKBA_4CKBD
3327
+ 4CKBA_4CKCA
3328
+ 4CKBA_4CKEA
3329
+ 4CKBA_6RFLO
3330
+ 4CKBA_6RIEO
3331
+ 4COOA_4L0DB
3332
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3333
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3334
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3335
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3336
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3337
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3338
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3339
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3340
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3341
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3342
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3343
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3344
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3345
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3346
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3347
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3348
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3349
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3350
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3351
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3352
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3353
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3354
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3355
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3356
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3357
+ 4D6WA_4D6WB
3358
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3359
+ 4D6WA_5MDMC
3360
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3361
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3362
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3363
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3364
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3365
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3366
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3367
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3368
+ 4DJDC_4DJEC
3369
+ 4DJDC_4DJEE
3370
+ 4DJDC_4DJFE
3371
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3372
+ 4DPGI_4DPGJ
3373
+ 4DPGI_4DPGL
3374
+ 4DPGI_4YCUC
3375
+ 4DPGI_6ILDC
3376
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3377
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3378
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3379
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3380
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3381
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3382
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3383
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3384
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3385
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3386
+ 4EG1A_4EG4A
3387
+ 4EG1A_4EG7B
3388
+ 4EG1A_4EGAA
3389
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3390
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3391
+ 4EHWA_4EHYA
3392
+ 4EHWA_4ITLA
3393
+ 4EHWA_4ITMA
3394
+ 4EHWA_4ITNA
3395
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3396
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3397
+ 4EIYA_5UIGA
3398
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3399
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3400
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3401
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3402
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3403
+ 4EJ7A_4FEUC
3404
+ 4EJ7A_4FEVE
3405
+ 4EJ7A_4GKHC
3406
+ 4ETVA_4ETVA
3407
+ 4ETVA_6MM6D
3408
+ 4ETVA_6MM6F
3409
+ 4ETVA_6MM7C
3410
+ 4ETVA_6MM7E
3411
+ 4F3TA_4KXTA
3412
+ 4F3TA_5VM9A
3413
+ 4F3TA_5WEAA
3414
+ 4F3TA_6MDZA
3415
+ 4F3TA_6MFNA
3416
+ 4F5CA_4F5CA
3417
+ 4F5CA_4F5CB
3418
+ 4F5CA_4FKEA
3419
+ 4F5CA_5LDSB
3420
+ 4F5CA_7VPPA
3421
+ 4FU3A_4FU3B
3422
+ 4FU3A_4HFGA
3423
+ 4FU3A_4HFGB
3424
+ 4FU3A_4Q94A
3425
+ 4FU3A_4Q96A
3426
+ 4FXGA_4FXGA
3427
+ 4FXGA_4FXKA
3428
+ 4FXGA_4XAMB
3429
+ 4FXGA_5JPNA
3430
+ 4FXGA_5JTWA
3431
+ 4FZQA_4FZQA
3432
+ 4FZQA_4FZQB
3433
+ 4FZQA_4FZQC
3434
+ 4FZQA_4FZQE
3435
+ 4FZQA_4FZQF
3436
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3437
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3438
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3439
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3440
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3441
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3442
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3443
+ 4G6GA_5YJWA
3444
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3445
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3446
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3447
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3448
+ 4GC5A_8CSP5
3449
+ 4GC5A_8CSR5
3450
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3451
+ 4GLRH_5IG7J
3452
+ 4GLRH_6UTEI
3453
+ 4GLRH_7CJ2C
3454
+ 4GLRH_7KQKH
3455
+ 4GLRH_8E7MH
3456
+ 4GPKA_4GPKB
3457
+ 4GPKA_4GPKE
3458
+ 4GPKA_4GPKG
3459
+ 4GPKA_5DBKA
3460
+ 4GPKA_5DBKB
3461
+ 4GRCA_4GRCA
3462
+ 4GRCA_6I43A
3463
+ 4GRCA_6Q3DB
3464
+ 4GRCA_6TB8A
3465
+ 4GRCA_6TB8B
3466
+ 4H1SA_6VC9A
3467
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3468
+ 4H1SA_6VCAC
3469
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3470
+ 4H1SA_7JV8A
3471
+ 4HKRA_4HKRA
3472
+ 4HKRA_4HKRB
3473
+ 4HKRA_4HKSA
3474
+ 4HKRA_4HKSB
3475
+ 4HKRA_6BBGA
3476
+ 4HNWA_4HNYA
3477
+ 4HNWA_4HNYC
3478
+ 4HNWA_4XNHA
3479
+ 4HNWA_4Y49G
3480
+ 4HNWA_6HD5t
3481
+ 4HYEA_4HYEA
3482
+ 4HYEA_4HYEB
3483
+ 4HYEA_4ZMRB
3484
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3485
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3486
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3487
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3488
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3489
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3490
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3491
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3492
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3493
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3494
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3495
+ 4IUFA_4Y00D
3496
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3497
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3498
+ 4JDQA_4JDXB
3499
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3500
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3501
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3502
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3503
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3504
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3505
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3506
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3507
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3508
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3509
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3510
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3511
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3512
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3513
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3514
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3515
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3516
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3517
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3518
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3519
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3520
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3521
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3522
+ 4KBLA_4KC9A
3523
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3524
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3525
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3526
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3527
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3528
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3529
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3530
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3531
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3532
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3533
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3534
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3535
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3536
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3537
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3538
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3539
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3540
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3541
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3542
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3543
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3544
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3545
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3546
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3547
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3548
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3549
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3550
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3551
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3552
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3553
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3554
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3555
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3556
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3557
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3558
+ 4LVCA_5M5KD
3559
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3560
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3561
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3562
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3563
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3564
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3565
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3566
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3567
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3568
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3569
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3570
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3571
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3572
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3573
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3574
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3575
+ 4MSOA_4OTLD
3576
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3577
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3578
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3579
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3580
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3581
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3582
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3583
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3584
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3585
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3586
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3587
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3588
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3589
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3590
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3591
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3592
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3593
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3594
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3595
+ 4N7WA_6LH1A
3596
+ 4NFTA_4NFTD
3597
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3598
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3599
+ 4NFTA_7VCLA
3600
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3601
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3602
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3603
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3604
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3605
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3606
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3607
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3608
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3609
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3610
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3611
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3612
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3613
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3614
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3615
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3616
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3617
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3618
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3619
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3620
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3621
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3622
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3623
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3624
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3625
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3626
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3627
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3628
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3629
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3630
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3631
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3632
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3633
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3634
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3635
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3636
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3637
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3638
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3639
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3640
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3641
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3642
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3643
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3644
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3645
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3646
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3647
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3648
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3649
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3650
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3651
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3652
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3653
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3654
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3655
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3656
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3657
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3658
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3659
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3660
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3661
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3662
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3663
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3664
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3665
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3666
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3667
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3668
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3669
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3670
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3671
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3672
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3673
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3674
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3675
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3676
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3677
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3678
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3679
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3680
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3681
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3682
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3683
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3684
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3685
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3686
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3687
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3688
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3689
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3690
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3691
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3692
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3693
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3694
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3695
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3696
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3697
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3698
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3699
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3700
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3701
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3702
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3703
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3704
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3705
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3706
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3707
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3708
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3709
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3710
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3711
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3712
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3713
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3714
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3715
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3716
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3717
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3718
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3719
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3720
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3721
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3722
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3723
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3724
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3725
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3726
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3727
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3728
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3729
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3730
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3731
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3732
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3733
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3734
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3735
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3736
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3737
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3738
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3739
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3740
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3741
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3742
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3743
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3744
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3745
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3746
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3747
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3748
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3749
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3750
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3751
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3752
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3753
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3754
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3755
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3756
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3757
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3758
+ 4V61AE_5MMMe
3759
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3760
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3761
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3762
+ 4V61AF_5MMJf
3763
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3764
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3765
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3766
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3767
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3768
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3769
+ 4V61B2_5X8P1
3770
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3771
+ 4V61B5_4V61B5
3772
+ 4V61B5_5MLC5
3773
+ 4V61B5_5MMI4
3774
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3775
+ 4V61B5_6ERIAd
3776
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3777
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3778
+ 4V61BE_5MMIC
3779
+ 4V61BE_5MMMC
3780
+ 4V61BE_5X8PC
3781
+ 4V61BV_4V61BV
3782
+ 4V61BV_5H1SV
3783
+ 4V61BV_5MLCV
3784
+ 4V61BV_5MMMU
3785
+ 4V61BV_6ERIAT
3786
+ 4V6WAf_4V6WAf
3787
+ 4V6WAf_6XU7Af
3788
+ 4V6WAf_6XU8Af
3789
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3790
+ 4V6WAf_7Z3OSf
3791
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3792
+ 4V81C_4V8RAG
3793
+ 4V81C_5GW5G
3794
+ 4V81C_7YLUG
3795
+ 4V81C_7YLVg
3796
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3797
+ 4V8MA2_5OPTO
3798
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3799
+ 4V8MA2_6AZ1H
3800
+ 4V8MA2_7ASEo
3801
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3802
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3803
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3804
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3805
+ 4V92Bf_7MPIBf
3806
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3807
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3808
+ 4W6ZA_4W6ZD
3809
+ 4W6ZA_7KCBA
3810
+ 4W6ZA_7NTMC
3811
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3812
+ 4WCEV_4WFBV
3813
+ 4WCEV_5TCULB
3814
+ 4WCEV_7ASOd
3815
+ 4WCEV_7TTWK
3816
+ 4WGIA_4WMXA
3817
+ 4WGIA_6DM8A
3818
+ 4WGIA_6DM8D
3819
+ 4WGIA_6DM8G
3820
+ 4WGIA_8G3SA
3821
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3822
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3937
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3959
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3960
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3961
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3962
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3963
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3964
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3965
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3972
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3977
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4061
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4062
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4280
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4299
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4300
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4301
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4302
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4303
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4304
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4305
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4311
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4312
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4313
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4314
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4315
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4316
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4317
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4318
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4319
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4320
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4321
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4322
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4323
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4324
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4325
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4326
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4328
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4330
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4331
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4332
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4333
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4340
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4345
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4346
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4347
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4348
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4349
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4350
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4351
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4352
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4353
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4354
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4355
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4356
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4357
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4358
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4359
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4360
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4361
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4362
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4363
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4364
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4365
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4366
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4367
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4368
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4369
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4370
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4371
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4372
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4373
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4374
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4375
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4376
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4377
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4378
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4379
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4380
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4381
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4382
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4383
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4384
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4385
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4386
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4387
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4388
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4389
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4390
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4391
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4392
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4393
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4394
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4395
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4396
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4397
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4398
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4399
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4400
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4401
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4402
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4403
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4404
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4405
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4406
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4407
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4408
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4409
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4410
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4411
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4412
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4413
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4414
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4415
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4416
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4417
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4418
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4419
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4420
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4421
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4422
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4423
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4424
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4425
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4426
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4427
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4428
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4429
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4430
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4431
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4432
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4433
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4434
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4435
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4436
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4437
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4438
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4439
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4440
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4441
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4442
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4443
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4444
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4445
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4446
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4447
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4448
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4449
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4450
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4451
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4452
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4453
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4454
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4455
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4456
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4457
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4458
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4459
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4460
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4461
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4462
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4463
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4464
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4465
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4466
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4467
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4468
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4469
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4470
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4471
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4472
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4473
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4474
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4475
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4476
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4477
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4478
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4479
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4480
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4481
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4482
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4483
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4484
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4485
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4486
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4487
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4488
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4489
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4490
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4491
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4492
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4493
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4494
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4495
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4496
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4497
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4498
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4499
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4500
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4501
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4502
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4503
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4504
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4505
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4506
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4507
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4508
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4509
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4510
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4511
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4512
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4513
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4514
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4515
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4516
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4517
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4518
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4519
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4520
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4521
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4522
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4523
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4525
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4526
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4527
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4528
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4530
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4532
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4533
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4534
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4535
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4536
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4537
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4538
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4539
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4540
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4541
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4542
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4543
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4544
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4545
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4546
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4551
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4552
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4553
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4554
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4555
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4556
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4557
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4558
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4559
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4560
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4561
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4562
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4563
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4564
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4565
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4566
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4567
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4568
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4569
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4570
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4571
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4572
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4573
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4574
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4575
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4577
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4578
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4579
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4580
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4583
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4586
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4587
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4588
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4589
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4590
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4591
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4592
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4593
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4594
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4595
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4596
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4597
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4598
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4599
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4600
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4601
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4602
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4604
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4605
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4606
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4607
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4608
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4609
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4610
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4611
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4612
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4613
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4614
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4615
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4616
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4618
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4620
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4622
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4623
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4628
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4629
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4630
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4632
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4634
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4635
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4636
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4638
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4639
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4650
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4653
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4654
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4655
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4656
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4657
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4658
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4659
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4660
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4664
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4665
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4666
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4667
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4668
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4669
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4670
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4671
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4672
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4673
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4674
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4675
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4676
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4677
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4678
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4679
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4680
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4681
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4682
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4683
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4684
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4685
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4686
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4687
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4688
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4689
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4690
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4691
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4692
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4693
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4694
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4695
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4696
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4697
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4698
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4699
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4700
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4701
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4702
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4703
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4704
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4705
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4706
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4707
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4708
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4709
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4710
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4711
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4712
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4713
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4714
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4715
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4716
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4717
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4718
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4719
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4720
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4721
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4722
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4723
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4725
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4726
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4727
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4728
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4729
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4730
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4731
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4732
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4733
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4734
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4735
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4736
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4737
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4738
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4739
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4740
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4748
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4750
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4751
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4752
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4753
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4754
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4756
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4757
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4758
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4759
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4760
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4761
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4762
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4763
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4764
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4765
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4766
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4767
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4768
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4769
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4770
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4771
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4772
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4774
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4775
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4777
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4778
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4779
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4780
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4781
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4782
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4783
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4785
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4786
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4787
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4788
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4789
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4790
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4791
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4792
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4793
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4795
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4796
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4797
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4798
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4799
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4800
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4801
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4802
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4803
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4804
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4805
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4806
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4807
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4808
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4809
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4810
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4811
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4813
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4814
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4815
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4817
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4818
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4819
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4820
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4821
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4822
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4823
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4827
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4830
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4833
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4834
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4835
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4836
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4838
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4839
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4840
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4842
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4843
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4844
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4845
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4846
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4847
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4848
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4849
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4850
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4851
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4852
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4853
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4854
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4855
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4856
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4857
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4858
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4859
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4860
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4861
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4862
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4863
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4864
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4865
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4866
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4867
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4868
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4869
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4870
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190
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191
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192
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193
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194
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195
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196
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197
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198
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199
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200
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201
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202
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203
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204
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205
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206
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207
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208
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209
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210
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211
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212
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213
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214
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215
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216
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217
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218
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219
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220
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221
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222
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223
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224
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225
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226
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227
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228
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229
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230
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231
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232
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233
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234
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235
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236
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237
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238
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239
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240
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241
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242
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243
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244
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245
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246
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247
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248
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249
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250
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251
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252
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253
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254
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255
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256
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257
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258
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259
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260
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261
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262
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263
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264
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265
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266
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267
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268
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269
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270
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271
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272
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273
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274
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275
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276
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277
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278
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279
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280
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281
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282
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283
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284
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285
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286
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287
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288
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289
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290
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291
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292
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293
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294
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295
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296
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297
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298
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299
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300
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301
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302
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303
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304
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305
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306
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307
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308
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309
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310
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311
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312
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313
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314
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315
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316
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317
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318
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319
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320
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321
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322
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323
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324
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325
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326
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327
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328
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329
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330
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331
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332
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333
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334
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335
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336
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337
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338
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339
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340
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341
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342
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343
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344
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345
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346
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347
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348
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349
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350
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351
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352
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353
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354
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355
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356
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357
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358
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359
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360
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361
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362
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363
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364
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365
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366
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367
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368
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369
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370
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371
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372
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373
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374
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375
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376
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377
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378
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379
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380
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381
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382
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383
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384
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385
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386
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387
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388
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389
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390
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391
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392
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393
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394
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395
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396
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397
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398
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399
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400
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401
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402
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403
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404
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405
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406
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407
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408
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409
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410
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411
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412
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413
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414
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415
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416
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417
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418
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419
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420
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421
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422
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423
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424
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425
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426
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427
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428
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429
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430
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431
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432
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433
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434
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435
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436
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437
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438
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439
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440
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441
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442
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443
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444
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445
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446
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447
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448
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449
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450
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451
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452
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453
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454
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455
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456
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457
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458
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459
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460
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461
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462
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463
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464
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465
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466
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467
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468
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469
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470
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471
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472
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473
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474
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475
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476
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477
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478
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479
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480
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481
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482
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483
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484
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485
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486
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487
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488
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489
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490
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491
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492
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493
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494
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495
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496
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497
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498
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499
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500
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501
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502
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503
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504
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505
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506
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507
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508
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509
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510
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511
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512
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513
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514
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515
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516
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517
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518
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519
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520
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521
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522
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523
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524
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525
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526
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527
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528
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529
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530
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531
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532
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533
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534
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535
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536
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537
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538
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539
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540
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541
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542
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543
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544
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545
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546
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547
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548
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549
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550
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551
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552
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553
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554
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555
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556
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557
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558
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559
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560
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561
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562
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563
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564
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565
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566
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567
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568
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569
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570
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571
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572
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573
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574
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575
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576
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577
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578
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579
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580
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581
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582
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583
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584
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585
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586
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587
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588
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589
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590
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591
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592
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593
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594
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595
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596
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597
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598
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599
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600
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601
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602
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603
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604
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605
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606
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607
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608
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609
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610
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611
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612
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613
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614
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615
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616
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617
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618
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619
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620
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621
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622
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623
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624
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625
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626
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627
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628
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629
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630
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631
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632
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633
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634
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635
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636
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637
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638
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639
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640
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641
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642
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643
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644
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645
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646
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647
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648
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649
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650
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651
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652
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653
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654
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655
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656
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657
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658
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659
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660
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661
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662
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663
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664
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665
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666
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667
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668
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669
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670
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671
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672
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673
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674
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675
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676
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677
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678
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679
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680
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681
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682
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683
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684
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685
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686
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687
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688
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689
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690
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691
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692
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693
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694
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695
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696
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697
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698
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699
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700
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701
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702
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703
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704
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705
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706
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707
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708
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709
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710
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711
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712
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713
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714
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715
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716
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717
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718
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719
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720
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721
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722
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723
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724
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725
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726
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727
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728
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729
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730
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731
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732
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733
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734
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735
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736
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737
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738
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739
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740
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741
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742
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743
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744
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745
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746
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747
+ 1IOMA_1IXEA
748
+ 1IOMA_1IXEB
749
+ 1IOMA_1IXEC
750
+ 1IOMA_1IXED
751
+ 1IQPA_1IQPA
752
+ 1IQPA_1IQPC
753
+ 1IQPA_1IQPD
754
+ 1IQPA_1IQPE
755
+ 1IQPA_1IQPF
756
+ 1IRUG_5LE5T
757
+ 1IRUG_5LN3G
758
+ 1IRUG_5T0IM
759
+ 1IRUG_6EPCG
760
+ 1IRUG_6EPDG
761
+ 1IU3C_1IU3C
762
+ 1IU3C_1IU3F
763
+ 1IU3C_1J3EA
764
+ 1IU3C_1LRRA
765
+ 1IU3C_1LRRD
766
+ 1IWLA_2ZPCA
767
+ 1IWLA_6FHMA
768
+ 1IWLA_6FHMB
769
+ 1IWLA_7ARMA
770
+ 1IWLA_7Z6XA
771
+ 1IZLC_1IZLC
772
+ 1IZLC_1IZLM
773
+ 1IZLC_3WU2C
774
+ 1IZLC_6YP7C
775
+ 1IZLC_7NHQC
776
+ 1IZLD_1IZLD
777
+ 1IZLD_1IZLN
778
+ 1IZLD_1W5CD
779
+ 1IZLD_4PBUd
780
+ 1IZLD_7NHQD
781
+ 1J1HA_1J1HA
782
+ 1J1HA_2CZJA
783
+ 1J1HA_3IYQB
784
+ 1J1HA_3IYRB
785
+ 1J1HA_3IZ4B
786
+ 1J5AK_2ZJPC
787
+ 1J5AK_4V4GBF
788
+ 1J5AK_5DM7C
789
+ 1J5AK_5JVGC
790
+ 1J5AK_7A0RC
791
+ 1J5HA_1J5HA
792
+ 1J5HA_1J5IA
793
+ 1J5HA_1O5PA
794
+ 1J5HA_2G0KA
795
+ 1J5HA_2G0LA
796
+ 1J8BA_1J8BA
797
+ 1J8BA_1PUGA
798
+ 1J8BA_1PUGB
799
+ 1J8BA_1PUGC
800
+ 1J8BA_1PUGD
801
+ 1JGSA_1JGSA
802
+ 1JGSA_3VB2A
803
+ 1JGSA_3VOEA
804
+ 1JGSA_5H3RA
805
+ 1JGSA_5H3RB
806
+ 1JPYA_1JPYX
807
+ 1JPYA_5N92F
808
+ 1JPYA_5NANE
809
+ 1JPYA_5NANF
810
+ 1JPYA_6HGOC
811
+ 1JQHA_1P4OB
812
+ 1JQHA_3D94A
813
+ 1JQHA_3F5PB
814
+ 1JQHA_3LVPD
815
+ 1JQHA_3O23A
816
+ 1JRKA_1JRKA
817
+ 1JRKA_1JRKD
818
+ 1JRKA_1K26A
819
+ 1JRKA_1K26B
820
+ 1JRKA_1K2EB
821
+ 1JS3A_1JS3B
822
+ 1JS3A_1JS6A
823
+ 1JS3A_3RBFB
824
+ 1JS3A_3RBLB
825
+ 1JS3A_3RCHA
826
+ 1JSUC_1JSUC
827
+ 1JSUC_6ATHC
828
+ 1JSUC_6P8EC
829
+ 1JSUC_6P8FC
830
+ 1JSUC_6P8GC
831
+ 1JT0A_1JT0A
832
+ 1JT0A_1JT0D
833
+ 1JT0A_1JT6A
834
+ 1JT0A_1JUMA
835
+ 1JT0A_3BT9B
836
+ 1JWHA_3BQCA
837
+ 1JWHA_4DGLC
838
+ 1JWHA_5M44A
839
+ 1JWHA_5Y9MA
840
+ 1JWHA_6EHUA
841
+ 1K04A_1OW6A
842
+ 1K04A_1PV3A
843
+ 1K04A_3S9OA
844
+ 1K04A_3S9OC
845
+ 1K04A_7W7ZA
846
+ 1K2PA_1K2PA
847
+ 1K2PA_1K2PB
848
+ 1K2PA_3P08B
849
+ 1K2PA_3PIYA
850
+ 1K2PA_5P9GA
851
+ 1K92A_1K92A
852
+ 1K92A_1K97A
853
+ 1K92A_1KP3A
854
+ 1K92A_5US8A
855
+ 1K92A_5US8B
856
+ 1KAMA_1KAMB
857
+ 1KAMA_1KAMC
858
+ 1KAMA_1KAMD
859
+ 1KAMA_1KAQA
860
+ 1KAMA_1KAQB
861
+ 1KBAA_1KBAA
862
+ 1KBAA_1KBAB
863
+ 1KBAA_2NBTA
864
+ 1KBAA_7ULRA
865
+ 1KBAA_7ULRB
866
+ 1KGQA_1KGQA
867
+ 1KGQA_1KGTA
868
+ 1KGQA_3BXYA
869
+ 1KGQA_3GOSA
870
+ 1KGQA_3GOSC
871
+ 1KGYA_1KGYA
872
+ 1KGYA_1NUKA
873
+ 1KGYA_1SHWB
874
+ 1KGYA_2QBXB
875
+ 1KGYA_3ETPA
876
+ 1KILE_1KILE
877
+ 1KILE_3RK3E
878
+ 1KILE_3RL0g
879
+ 1KILE_3RL0i
880
+ 1KILE_5W5DE
881
+ 1KJ0A_1KJ0A
882
+ 1KJ0A_1WO9A
883
+ 1KJ0A_2F91B
884
+ 1KJ0A_2VU8I
885
+ 1KJ0A_2XTTA
886
+ 1KPKA_1KPLD
887
+ 1KPKA_1OTSA
888
+ 1KPKA_7N9WA
889
+ 1KPKA_7N9WB
890
+ 1KPKA_7RO0A
891
+ 1KPSB_1KPSD
892
+ 1KPSB_1Z5SC
893
+ 1KPSB_2GRPB
894
+ 1KPSB_2IO2C
895
+ 1KPSB_2IO3C
896
+ 1L9VA_1L9VA
897
+ 1L9VA_4G0AA
898
+ 1L9VA_4G0AD
899
+ 1L9VA_7PKOC
900
+ 1L9VA_7PKPA
901
+ 1LISA_2LISA
902
+ 1LISA_2LYNA
903
+ 1LISA_5II7A
904
+ 1LISA_5II9B
905
+ 1LISA_5UTGA
906
+ 1LNQA_5BKKA
907
+ 1LNQA_6U68A
908
+ 1LNQA_6U68B
909
+ 1LNQA_6U68C
910
+ 1LNQA_6U68D
911
+ 1LTXR_1LTXR
912
+ 1LTXR_1VG9A
913
+ 1LTXR_1VG9C
914
+ 1LTXR_1VG9E
915
+ 1LTXR_1VG9G
916
+ 1LVAA_1LVAA
917
+ 1LVAA_2PLYA
918
+ 1LVAA_2PLYB
919
+ 1LVAA_2UWMA
920
+ 1LVAA_2UWMB
921
+ 1LWUC_1LWUC
922
+ 1LWUC_1LWUF
923
+ 1LWUC_1LWUI
924
+ 1LWUC_1N73C
925
+ 1LWUC_1N73F
926
+ 1LZWA_1MBUC
927
+ 1LZWA_1R6OC
928
+ 1LZWA_1R6OD
929
+ 1LZWA_2W9RA
930
+ 1LZWA_2WA8C
931
+ 1M08A_1M08A
932
+ 1M08A_1PT3A
933
+ 1M08A_3FBDA
934
+ 1M08A_3FBDD
935
+ 1M08A_7CEIB
936
+ 1M12A_1M12A
937
+ 1M12A_1SN6A
938
+ 1M12A_2GTGA
939
+ 1M12A_2QYPA
940
+ 1M12A_2QYPB
941
+ 1MHPX_1VHPA
942
+ 1MHPX_3ZHKA
943
+ 1MHPX_6OROD
944
+ 1MHPX_8DT8D
945
+ 1MHPX_8DT8H
946
+ 1MI1A_1MI1A
947
+ 1MI1A_1T77A
948
+ 1MI1A_1T77B
949
+ 1MI1A_1T77C
950
+ 1MI1A_1T77D
951
+ 1MIQA_1MIQA
952
+ 1MIQA_1MIQB
953
+ 1MIQA_1QS8A
954
+ 1MIQA_1QS8B
955
+ 1MIQA_2ANLB
956
+ 1MU5A_1MU5A
957
+ 1MU5A_1MX0C
958
+ 1MU5A_1Z5AA
959
+ 1MU5A_2ZBKF
960
+ 1MU5A_2ZBKH
961
+ 1MYOA_1MYOA
962
+ 1MYOA_2KXPC
963
+ 1MYOA_2MYOA
964
+ 1MYOA_3AAAC
965
+ 1MYOA_7DF7A
966
+ 1N0UA_1N0UA
967
+ 1N0UA_1ZM2A
968
+ 1N0UA_2E1RA
969
+ 1N0UA_2P8YT
970
+ 1N0UA_3B82E
971
+ 1N3BA_1N3BB
972
+ 1N3BA_1VHLA
973
+ 1N3BA_1VIYA
974
+ 1N3BA_6ARIA
975
+ 1N3BA_6ARIB
976
+ 1NKW1_3PIO1
977
+ 1NKW1_4V4GB3
978
+ 1NKW1_5DM61
979
+ 1NKW1_5DM71
980
+ 1NKW1_5JVG1
981
+ 1NKWG_2ZJPF
982
+ 1NKWG_2ZJQF
983
+ 1NKWG_4V4GBJ
984
+ 1NKWG_5DM6F
985
+ 1NKWG_5DM7F
986
+ 1NKWR_2D3OR
987
+ 1NKWR_4V4GBU
988
+ 1NKWR_5DM6Q
989
+ 1NKWR_7A0RQ
990
+ 1NKWR_7A0SQ
991
+ 1NO4A_1NO4B
992
+ 1NO4A_1NO4C
993
+ 1NO4A_1NO4D
994
+ 1NO4A_1NOHA
995
+ 1NO4A_1NOHB
996
+ 1NSOA_1NSOA
997
+ 1NSOA_3SQFA
998
+ 1NSOA_6S1WB
999
+ 1NSOA_7BGTA
1000
+ 1NSOA_7BGTB
1001
+ 1NW4A_1NW4A
1002
+ 1NW4A_2B94A
1003
+ 1NW4A_2BSXA
1004
+ 1NW4A_3EMVA
1005
+ 1NW4A_3ENZD
1006
+ 1OCCF_1OCCF
1007
+ 1OCCF_1OCZF
1008
+ 1OCCF_2OCCF
1009
+ 1OCCF_5J4ZBP
1010
+ 1OCCF_5ZCPF
1011
+ 1ODDA_2JPBA
1012
+ 1ODDA_6LXMA
1013
+ 1ODDA_6LXMB
1014
+ 1ODDA_6LXMC
1015
+ 1ODDA_6LXNB
1016
+ 1OHUA_1OHUA
1017
+ 1OHUA_1OHUB
1018
+ 1OHUA_1TY4A
1019
+ 1OHUA_1TY4B
1020
+ 1OHUA_2A5YA
1021
+ 1OJLA_1OJLA
1022
+ 1OJLA_1OJLB
1023
+ 1OJLA_1OJLC
1024
+ 1OJLA_1OJLD
1025
+ 1OJLA_1OJLE
1026
+ 1OMSA_1OMSA
1027
+ 1OMSA_1OMSC
1028
+ 1OMSA_1P9YA
1029
+ 1OMSA_4URDA
1030
+ 1OMSA_7D6Zh
1031
+ 1ONQA_4X6CA
1032
+ 1ONQA_4X6DC
1033
+ 1ONQA_7KOZA
1034
+ 1ONQA_7RYNA
1035
+ 1ONQA_7RYOA
1036
+ 1OPMA_1OPMA
1037
+ 1OPMA_5WJAA
1038
+ 1OPMA_6ALAA
1039
+ 1OPMA_6ALAC
1040
+ 1OPMA_8DSNK
1041
+ 1OWTA_1OWTA
1042
+ 1OWTA_2FJZA
1043
+ 1OWTA_2FK3B
1044
+ 1OWTA_2FKLB
1045
+ 1OWTA_7MRSB
1046
+ 1P0ZA_1P0ZA
1047
+ 1P0ZA_1P0ZE
1048
+ 1P0ZA_2J80A
1049
+ 1P0ZA_2V9AA
1050
+ 1P0ZA_2V9AB
1051
+ 1P8DA_4DK7C
1052
+ 1P8DA_4DK8A
1053
+ 1P8DA_5JY3A
1054
+ 1P8DA_5JY3C
1055
+ 1P8DA_6JIOD
1056
+ 1P9MC_1P9MC
1057
+ 1P9MC_5FUCC
1058
+ 1P9MC_5FUCD
1059
+ 1P9MC_7DC8C
1060
+ 1P9MC_7DC8F
1061
+ 1PA7A_1V5KA
1062
+ 1PA7A_1VKAA
1063
+ 1PA7A_1WYOA
1064
+ 1PA7A_3CO1A
1065
+ 1PA7A_3JAKM
1066
+ 1PGNA_2JKVC
1067
+ 1PGNA_2JKVF
1068
+ 1PGNA_4GWGA
1069
+ 1PGNA_5UQ9B
1070
+ 1PGNA_5UQ9F
1071
+ 1PJWA_1PJWA
1072
+ 1PJWA_6S93B
1073
+ 1PJWA_6S93C
1074
+ 1PJWA_6S94B
1075
+ 1PJWA_6S95A
1076
+ 1PKLA_1PKLD
1077
+ 1PKLA_1PKLG
1078
+ 1PKLA_3E0WA
1079
+ 1PKLA_3HQOA
1080
+ 1PKLA_3HQPB
1081
+ 1PVLA_1PVLA
1082
+ 1PVLA_4Q7GA
1083
+ 1PVLA_6U2SA
1084
+ 1PVLA_6U33A
1085
+ 1PVLA_6U3YA
1086
+ 1Q14A_1Q14A
1087
+ 1Q14A_1Q17A
1088
+ 1Q14A_1Q17C
1089
+ 1Q14A_1SZCA
1090
+ 1Q14A_2OD2A
1091
+ 1Q59A_1Q59A
1092
+ 1Q59A_2WH6A
1093
+ 1Q59A_4OYDC
1094
+ 1Q59A_7P33A
1095
+ 1Q59A_7P33B
1096
+ 1Q8MA_1Q8MA
1097
+ 1Q8MA_1Q8MB
1098
+ 1Q8MA_1Q8MD
1099
+ 1Q8MA_1SMOA
1100
+ 1Q8MA_1SMOB
1101
+ 1Q90B_1VF5A
1102
+ 1Q90B_2D2CA
1103
+ 1Q90B_2E74A
1104
+ 1Q90B_7ZXYA
1105
+ 1Q90B_7ZYVA
1106
+ 1QG3A_3F7PC
1107
+ 1QG3A_3F7PD
1108
+ 1QG3A_3F7QA
1109
+ 1QG3A_3F7QB
1110
+ 1QG3A_3F7RA
1111
+ 1QMGA_1QMGA
1112
+ 1QMGA_1QMGC
1113
+ 1QMGA_3FR7A
1114
+ 1QMGA_3FR7B
1115
+ 1QMGA_3FR8A
1116
+ 1QPCA_1QPCA
1117
+ 1QPCA_2PL0A
1118
+ 1QPCA_3MPMA
1119
+ 1QPCA_4C3FA
1120
+ 1QPCA_6PDJA
1121
+ 1QYMA_1QYMA
1122
+ 1QYMA_1TR4A
1123
+ 1QYMA_4NIKA
1124
+ 1QYMA_5VHQG
1125
+ 1QYMA_5VHRG
1126
+ 1R3BA_1R3BA
1127
+ 1R3BA_5B5VA
1128
+ 1R3BA_5B6BB
1129
+ 1R3BA_5B6BH
1130
+ 1R3BA_5TWFB
1131
+ 1R4MB_1R4MD
1132
+ 1R4MB_1R4ND
1133
+ 1R4MB_1YOVB
1134
+ 1R4MB_3DBLF
1135
+ 1R4MB_3GZND
1136
+ 1R5PA_1VGLA
1137
+ 1R5PA_2QKEB
1138
+ 1R5PA_2QKEC
1139
+ 1R5PA_5JYTA
1140
+ 1R5PA_5JYVB
1141
+ 1RA6A_1RDRA
1142
+ 1RA6A_3OL6A
1143
+ 1RA6A_4K4TA
1144
+ 1RA6A_4K4WA
1145
+ 1RA6A_4NLQA
1146
+ 1RFNB_1RFNB
1147
+ 1RFNB_2WPME
1148
+ 1RFNB_3LC3B
1149
+ 1RFNB_5EGMB
1150
+ 1RFNB_7AHVL
1151
+ 1RH5B_1RHZB
1152
+ 1RH5B_3BO0B
1153
+ 1RH5B_3DKNB
1154
+ 1RH5B_4V4NA7
1155
+ 1RH5B_4V7IAB
1156
+ 1RJVA_1RJVA
1157
+ 1RJVA_1RK9A
1158
+ 1RJVA_1RTP1
1159
+ 1RJVA_2JWWA
1160
+ 1RJVA_3F45A
1161
+ 1RKRA_1RKRB
1162
+ 1RKRA_1RKRC
1163
+ 1RKRA_6L1VA
1164
+ 1RKRA_6L1VC
1165
+ 1RKRA_6L1VE
1166
+ 1RLGA_4BW0B
1167
+ 1RLGA_5G4UG
1168
+ 1RLGA_6HCTC
1169
+ 1RLGA_6HCTD
1170
+ 1RLGA_6HCTG
1171
+ 1RRMA_1RRMB
1172
+ 1RRMA_2BI4A
1173
+ 1RRMA_7QNIA
1174
+ 1RRMA_7QNJA
1175
+ 1RRMA_7R0PB
1176
+ 1RZ4A_1RZ4A
1177
+ 1RZ4A_5A5TK
1178
+ 1RZ4A_7A09K
1179
+ 1RZ4A_7QP63
1180
+ 1RZ4A_7QP73
1181
+ 1S5LB_3WU2B
1182
+ 1S5LB_5KAIB
1183
+ 1S5LB_7EDAB
1184
+ 1S5LB_7NHPB
1185
+ 1S5LB_7NHQB
1186
+ 1S5LI_1S5Li
1187
+ 1S5LI_1S5LI
1188
+ 1S5LI_3A0Bi
1189
+ 1S5LI_3A0HI
1190
+ 1S5LI_3WU2I
1191
+ 1SCMA_1WDCA
1192
+ 1SCMA_3JTDA
1193
+ 1SCMA_3PN7A
1194
+ 1SCMA_3PN7D
1195
+ 1SCMA_3TUYA
1196
+ 1SJ2A_2CCDB
1197
+ 1SJ2A_6ZJIBP1
1198
+ 1SJ2A_7A2IA
1199
+ 1SJ2A_7A7AA
1200
+ 1SJ2A_7A8ZB
1201
+ 1SQEA_1SQEA
1202
+ 1SQEA_1SQEB
1203
+ 1SQEA_2ZDPA
1204
+ 1SQEA_4FNHA
1205
+ 1SQEA_4FNHB
1206
+ 1SR9A_3FIGB
1207
+ 1SR9A_3HPSA
1208
+ 1SR9A_3HPSB
1209
+ 1SR9A_3HPZB
1210
+ 1SR9A_3HQ1A
1211
+ 1ST0A_1ST0A
1212
+ 1ST0A_1VLRA
1213
+ 1ST0A_4QEBB
1214
+ 1ST0A_5OSYA
1215
+ 1ST0A_5OSYB
1216
+ 1SXJC_1SXJC
1217
+ 1SXJC_7STBC
1218
+ 1SXJC_7STEC
1219
+ 1SXJC_7TFLC
1220
+ 1SXJC_7THJC
1221
+ 1T2KA_1T2KB
1222
+ 1T2KA_2O6GE
1223
+ 1T2KA_2O6GG
1224
+ 1T2KA_2PI0C
1225
+ 1T2KA_2PI0D
1226
+ 1T3WA_1T3WA
1227
+ 1T3WA_1T3WB
1228
+ 1T3WA_2HAJA
1229
+ 1T3WA_6CBRA
1230
+ 1T3WA_6CBSA
1231
+ 1TENA_1TENA
1232
+ 1TENA_2RB8A
1233
+ 1TENA_2RBLA
1234
+ 1TENA_2RBLB
1235
+ 1TENA_6BRBD
1236
+ 1TJGH_3IDGB
1237
+ 1TJGH_3LZFH
1238
+ 1TJGH_6URMD
1239
+ 1TJGH_7MMOA
1240
+ 1TJGH_7MU4H
1241
+ 1TW2A_1TW2B
1242
+ 1TW2A_1TW3A
1243
+ 1TW2A_7PGJA
1244
+ 1TW2A_7PHEB
1245
+ 1TW2A_7PHFB
1246
+ 1TZNA_1TZOA
1247
+ 1TZNA_6UJIA
1248
+ 1TZNA_6UZBB
1249
+ 1TZNA_6UZBG
1250
+ 1TZNA_7KXRB
1251
+ 1U4CA_1U4CB
1252
+ 1U4CA_1YFQA
1253
+ 1U4CA_2I3SC
1254
+ 1U4CA_2I3TA
1255
+ 1U4CA_4BL0D
1256
+ 1V0JA_4RPHB
1257
+ 1V0JA_4XGKA
1258
+ 1V0JA_5ER9B
1259
+ 1V0JA_5F3RA
1260
+ 1V0JA_6D9CA
1261
+ 1V76A_1V76A
1262
+ 1V76A_1V76B
1263
+ 1V76A_2KI7A
1264
+ 1V76A_2ZAEA
1265
+ 1V76A_2ZAEC
1266
+ 1V8JA_1V8JA
1267
+ 1V8JA_1V8KA
1268
+ 1V8JA_4UBFA
1269
+ 1V8JA_4UBFD
1270
+ 1V8JA_4Y05A
1271
+ 1VCLA_1VCLA
1272
+ 1VCLA_1VCLB
1273
+ 1VCLA_2Z49A
1274
+ 1VCLA_3W9TC
1275
+ 1VCLA_3W9TG
1276
+ 1VDZA_3I72A
1277
+ 1VDZA_3QIAA
1278
+ 1VDZA_3SDZA
1279
+ 1VDZA_3SE0A
1280
+ 1VDZA_5X09A
1281
+ 1VGYA_1VGYB
1282
+ 1VGYA_4O23B
1283
+ 1VGYA_4PPZA
1284
+ 1VGYA_5UEJA
1285
+ 1VGYA_5UEJB
1286
+ 1VR4A_1VR4A
1287
+ 1VR4A_1VR4C
1288
+ 1VR4A_1VR4D
1289
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1290
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1291
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1292
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1293
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1294
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1295
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1296
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1297
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1298
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1299
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1300
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1301
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1302
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1303
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1304
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1305
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1306
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1307
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1308
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1309
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1310
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1311
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1312
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1313
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1314
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1315
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1316
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1317
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1318
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1319
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1320
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1321
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1322
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1323
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1324
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1325
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1326
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1327
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1328
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1329
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1330
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1331
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1332
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1333
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1334
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1335
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1336
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1337
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1338
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1339
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1340
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1341
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1342
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1343
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1344
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1345
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1346
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1347
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1348
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1349
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1350
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1351
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1352
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1353
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1354
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1355
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1356
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1357
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1358
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1359
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1360
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1361
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1362
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1363
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1364
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1365
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1366
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1367
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1368
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1369
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1370
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1371
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1372
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1373
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1374
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1375
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1376
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1377
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1378
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1379
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1380
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1381
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1382
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1383
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1384
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1385
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1386
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1387
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1388
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1389
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1390
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1391
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1392
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1393
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1394
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1395
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1396
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1397
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1398
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1399
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1400
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1401
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1402
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1403
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1404
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1405
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1406
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1407
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1408
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1409
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1410
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1411
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1412
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1413
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1414
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1415
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1416
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1417
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1418
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1419
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1420
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1421
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1422
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1423
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1424
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1425
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1426
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1427
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1428
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1429
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1430
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1431
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1432
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1433
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1434
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1435
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1436
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1437
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1438
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1439
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1440
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1441
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1442
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1443
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1444
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1445
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1446
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1447
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1448
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1449
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1450
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1451
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1452
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1453
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1454
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1455
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1456
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1457
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1458
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1459
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1460
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1461
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1462
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1463
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1464
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1465
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1466
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1467
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1468
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1469
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1470
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1471
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1472
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1473
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1474
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1475
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1476
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1477
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1478
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1479
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1480
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1481
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1482
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1483
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1484
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1485
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1486
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1487
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1488
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1489
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1490
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1491
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1492
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1493
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1494
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1495
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1496
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1497
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1498
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1499
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1500
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1501
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1502
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1503
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1504
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1505
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1506
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1507
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1508
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1509
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1510
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1511
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1512
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1513
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1514
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1515
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1516
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1517
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1518
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1519
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1520
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1521
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1522
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1523
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1524
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1525
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1526
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1527
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1528
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1529
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1530
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1531
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1532
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1533
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1534
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1535
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1536
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1537
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1538
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1539
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1540
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1541
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1542
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1543
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1544
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1545
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1546
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1547
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1548
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1549
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1550
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1551
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1552
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1553
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1554
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1555
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1556
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1557
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1558
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1559
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1560
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1561
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1562
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1563
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1564
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1565
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1566
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1567
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1568
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1569
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1570
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1571
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1572
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1573
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1574
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1575
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1576
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1577
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1578
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1579
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1580
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1581
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1582
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1583
+ 2D9IA_3FAUB
1584
+ 2D9IA_3FAUC
1585
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1586
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1587
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1588
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1589
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1590
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1591
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1592
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1593
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1594
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1595
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1596
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1597
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1598
+ 2DMWA_2VX8B
1599
+ 2DMWA_2VX8C
1600
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1601
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1602
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1603
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1604
+ 2DNVA_3I91B
1605
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1606
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1607
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1608
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1609
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1610
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1611
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1612
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1613
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1614
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1615
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1616
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1617
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1618
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1619
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1620
+ 2E5QA_6WAUD
1621
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1622
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1623
+ 2EKOA_4QQGE
1624
+ 2EKOA_4QQGF
1625
+ 2EKOA_4QQGG
1626
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1627
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1628
+ 2ELBA_2Z0OA
1629
+ 2ELBA_5C5BA
1630
+ 2ELBA_5C5BC
1631
+ 2EWVA_2EWWA
1632
+ 2EWVA_2GSZB
1633
+ 2EWVA_2GSZC
1634
+ 2EWVA_2GSZD
1635
+ 2EWVA_2GSZE
1636
+ 2F1CX_2IWVA
1637
+ 2F1CX_2IWWB
1638
+ 2F1CX_2JQYA
1639
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1640
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1641
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1642
+ 2F1TA_2F1TC
1643
+ 2F1TA_2F1VA
1644
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1645
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1646
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1647
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1648
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1649
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1650
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1651
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1652
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1653
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1654
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1655
+ 2F8XC_6PY8E
1656
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1657
+ 2FA4A_2FA4B
1658
+ 2FA4A_2HSYA
1659
+ 2FA4A_3PINA
1660
+ 2FA4A_7BVVB
1661
+ 2FMYA_2FMYA
1662
+ 2FMYA_2FMYB
1663
+ 2FMYA_2FMYC
1664
+ 2FMYA_2FMYD
1665
+ 2FMYA_2HKXA
1666
+ 2FPDA_2FPDD
1667
+ 2FPDA_2FPED
1668
+ 2FPDA_7NYKD
1669
+ 2FPDA_7NYLB
1670
+ 2FPDA_7NYMD
1671
+ 2FSSA_2J6ID
1672
+ 2FSSA_6D4BA
1673
+ 2FSSA_6D4BB
1674
+ 2FSSA_8HTYC
1675
+ 2FSSA_8IQ7D
1676
+ 2FTXA_2FTXA
1677
+ 2FTXA_2FV4A
1678
+ 2FTXA_5T6JB
1679
+ 2FTXA_5TCSD
1680
+ 2FTXA_7KDFD
1681
+ 2FXMA_2FXMA
1682
+ 2FXMA_2FXOA
1683
+ 2FXMA_2FXOB
1684
+ 2FXMA_2FXOC
1685
+ 2FXMA_2FXOD
1686
+ 2GC9A_2GC9B
1687
+ 2GC9A_2W2AA
1688
+ 2GC9A_2W2BA
1689
+ 2GC9A_2W2FB
1690
+ 2GC9A_2WSJB
1691
+ 2GESA_2GETA
1692
+ 2GESA_2ZS7A
1693
+ 2GESA_5XLVB
1694
+ 2GESA_5XLWB
1695
+ 2GESA_5XMBC
1696
+ 2GHWB_2GHWB
1697
+ 2GHWB_5WYMA
1698
+ 2GHWB_5WYMC
1699
+ 2GHWB_7AH1A
1700
+ 2GHWB_7ZCEE
1701
+ 2GI7A_5OU7A
1702
+ 2GI7A_5OU7D
1703
+ 2GI7A_7NMUA
1704
+ 2GI7A_7NMUB
1705
+ 2GI7A_7R58A
1706
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1707
+ 2GJ2A_2GJ2B
1708
+ 2GJ2A_2GJIA
1709
+ 2GJ2A_2ZUGA
1710
+ 2GJ2A_2ZUGB
1711
+ 2GTYA_2GTYA
1712
+ 2GTYA_3LKYA
1713
+ 2GTYA_3LL0A
1714
+ 2GTYA_7RIAB
1715
+ 2GTYA_7RIBA
1716
+ 2GX9A_3EE8A
1717
+ 2GX9A_3O9TA
1718
+ 2GX9A_3O9UB
1719
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1720
+ 2GX9A_6NU0A
1721
+ 2H3KA_2H3KA
1722
+ 2H3KA_3OVUB
1723
+ 2H3KA_3S48A
1724
+ 2H3KA_3S48B
1725
+ 2H3KA_4WJGX
1726
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1727
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1728
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1729
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1730
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1731
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1732
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1733
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1734
+ 2H5EA_4V8OAY
1735
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1736
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1737
+ 2HAUA_3S9MC
1738
+ 2HAUA_5DYHA
1739
+ 2HAUA_6D03D
1740
+ 2HAUA_6SOZC
1741
+ 2HDPA_2HDPA
1742
+ 2HDPA_2HDPB
1743
+ 2HDPA_2VJFA
1744
+ 2HDPA_6SQPA
1745
+ 2HDPA_7THLC
1746
+ 2HFDA_2HFDA
1747
+ 2HFDA_2QGVA
1748
+ 2HFDA_2QGVE
1749
+ 2HFDA_2QGVH
1750
+ 2HFDA_2QGVI
1751
+ 2HG0A_2HG0A
1752
+ 2HG0A_2I69A
1753
+ 2HG0A_3IYWA
1754
+ 2HG0A_3IYWC
1755
+ 2HG0A_7KV9A
1756
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1757
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1758
+ 2HJ8A_5TL6C
1759
+ 2HJ8A_6XA9D
1760
+ 2HJ8A_6XA9F
1761
+ 2HLDI_3OE7I
1762
+ 2HLDI_3ZRYI
1763
+ 2HLDI_6CP6I
1764
+ 2HLDI_7TK5I
1765
+ 2HLDI_7TKSI
1766
+ 2HVQA_2HVQA
1767
+ 2HVQA_2HVRA
1768
+ 2HVQA_2HVRB
1769
+ 2HVQA_2HVSA
1770
+ 2HVQA_2HVSB
1771
+ 2I62A_2IIPD
1772
+ 2I62A_5XVKB
1773
+ 2I62A_7EGUA
1774
+ 2I62A_7EHZA
1775
+ 2I62A_7WMCA
1776
+ 2ID5A_2ID5A
1777
+ 2ID5A_2ID5B
1778
+ 2ID5A_2ID5C
1779
+ 2ID5A_2ID5D
1780
+ 2ID5A_4OQTA
1781
+ 2IW3A_2IW3B
1782
+ 2IW3A_2IWHA
1783
+ 2IW3A_2IX3A
1784
+ 2IW3A_2IX8A
1785
+ 2IW3A_7B7DEF
1786
+ 2IYFA_2IYFB
1787
+ 2IYFA_4M7PA
1788
+ 2IYFA_4M83B
1789
+ 2IYFA_7XX4A
1790
+ 2IYFA_7XX4B
1791
+ 2J28I_2J28I
1792
+ 2J28I_4V70BI
1793
+ 2J28I_4V74BI
1794
+ 2J28I_4V7ABI
1795
+ 2J28I_6O9KI
1796
+ 2JJNA_2JJNA
1797
+ 2JJNA_2JJOA
1798
+ 2JJNA_2JJPA
1799
+ 2JJNA_2WIOA
1800
+ 2JJNA_2XFHA
1801
+ 2JJSC_2JJTC
1802
+ 2JJSC_2VSCC
1803
+ 2JJSC_2VSCD
1804
+ 2JJSC_7MYZD
1805
+ 2JJSC_7XJFC
1806
+ 2JK4A_2K4TA
1807
+ 2JK4A_5JDPA
1808
+ 2JK4A_5XDOA
1809
+ 2JK4A_6TIRA
1810
+ 2JK4A_7QI2A
1811
+ 2JLNA_2JLNA
1812
+ 2JLNA_2X79A
1813
+ 2JLNA_4D1AA
1814
+ 2JLNA_4D1BA
1815
+ 2JLNA_4D1DA
1816
+ 2JX0A_2JX0A
1817
+ 2JX0A_6IUHA
1818
+ 2JX0A_6IUIB
1819
+ 2JX0A_6JMUA
1820
+ 2JX0A_6JMUB
1821
+ 2K07A_2K07A
1822
+ 2K07A_2Z6OA
1823
+ 2K07A_2Z6PA
1824
+ 2K07A_7NW1A
1825
+ 2K07A_7OVCA
1826
+ 2K0AA_2K0AA
1827
+ 2K0AA_5GM6J
1828
+ 2K0AA_5NRLS
1829
+ 2K0AA_5ZWM5
1830
+ 2K0AA_7DCO5
1831
+ 2KCAA_2KCAA
1832
+ 2KCAA_5A20E
1833
+ 2KCAA_5A20F
1834
+ 2KCAA_5A21E
1835
+ 2KCAA_7Z4W1
1836
+ 2KGKA_2KGKA
1837
+ 2KGKA_3FL8A
1838
+ 2KGKA_3FL9C
1839
+ 2KGKA_3SA2A
1840
+ 2KGKA_8SSXA
1841
+ 2KIHA_2L0JC
1842
+ 2KIHA_2L0JD
1843
+ 2KIHA_2N70B
1844
+ 2KIHA_2RLFA
1845
+ 2KIHA_6OUGH
1846
+ 2KL3A_2KL3A
1847
+ 2KL3A_3ILMA
1848
+ 2KL3A_3ILMB
1849
+ 2KL3A_3ILMC
1850
+ 2KL3A_3ILMD
1851
+ 2KRTA_2KRTA
1852
+ 2KRTA_3JVCA
1853
+ 2KRTA_3JVCB
1854
+ 2KRTA_3JVCC
1855
+ 2KRTA_3K63A
1856
+ 2KS9A_2KSAA
1857
+ 2KS9A_7P00R
1858
+ 2KS9A_7P02R
1859
+ 2KS9A_7RMGR
1860
+ 2KS9A_7RMHR
1861
+ 2KXFA_2KXFA
1862
+ 2KXFA_2KXHA
1863
+ 2KXFA_5KW1B
1864
+ 2KXFA_5KWQB
1865
+ 2KXFA_7Q8AB
1866
+ 2LZGA_2LZGA
1867
+ 2LZGA_2MPSA
1868
+ 2LZGA_2RUHA
1869
+ 2LZGA_4HBMB
1870
+ 2LZGA_4UE1B
1871
+ 2M5RA_2M5RA
1872
+ 2M5RA_5ZWSA
1873
+ 2M5RA_5ZWTB
1874
+ 2M5RA_6SGAFc
1875
+ 2M5RA_7AM2CC
1876
+ 2MOEA_2MOEA
1877
+ 2MOEA_3VXVA
1878
+ 2MOEA_3VXXA
1879
+ 2MOEA_3VYQD
1880
+ 2MOEA_4LG7A
1881
+ 2MUQA_2MUQA
1882
+ 2MUQA_2MURA
1883
+ 2MUQA_3WWQC
1884
+ 2MUQA_3WWQF
1885
+ 2MUQA_3WWQI
1886
+ 2MY1A_2MY1A
1887
+ 2MY1A_5GM6T
1888
+ 2MY1A_5LJ3L
1889
+ 2MY1A_5MPSL
1890
+ 2MY1A_5WSGT
1891
+ 2MYIA_2MYIA
1892
+ 2MYIA_4JG3A
1893
+ 2MYIA_6O1LR
1894
+ 2MYIA_6O1MS
1895
+ 2MYIA_8BVJA
1896
+ 2MZEA_2MZEA
1897
+ 2MZEA_2MZHA
1898
+ 2MZEA_2MZIA
1899
+ 2MZEA_5UE2A
1900
+ 2MZEA_5UE5A
1901
+ 2N4PA_2N4PA
1902
+ 2N4PA_5X4FA
1903
+ 2N4PA_6B1GA
1904
+ 2N4PA_6B1GB
1905
+ 2N4PA_6T4BC
1906
+ 2N5ZA_2N5ZA
1907
+ 2N5ZA_4OW1A
1908
+ 2N5ZA_4OW1E
1909
+ 2N5ZA_4OW1S
1910
+ 2N5ZA_4OW1T
1911
+ 2NBMA_2NBMA
1912
+ 2NBMA_2NBNA
1913
+ 2NBMA_2QZTA
1914
+ 2NBMA_2QZTB
1915
+ 2NBMA_3BDQA
1916
+ 2NNWA_3NMUA
1917
+ 2NNWA_4BY9C
1918
+ 2NNWA_4BY9F
1919
+ 2NNWA_4BY9I
1920
+ 2NNWA_4BY9L
1921
+ 2NO8A_2NO8A
1922
+ 2NO8A_2WPTA
1923
+ 2NO8A_3U43A
1924
+ 2NO8A_6EREC
1925
+ 2NO8A_6ERED
1926
+ 2NOQG_2NOQG
1927
+ 2NOQG_4V7RDA
1928
+ 2NOQG_6OIGz
1929
+ 2NOQG_6XIQL1
1930
+ 2NOQG_8BQXBT
1931
+ 2NOQH_2NOQH
1932
+ 2NOQH_3J0Qk
1933
+ 2NOQH_3J6X51
1934
+ 2NOQH_4V7HBJ
1935
+ 2NOQH_7MPJAJ
1936
+ 2NXWA_2Q5JB
1937
+ 2NXWA_2Q5LA
1938
+ 2NXWA_2Q5LB
1939
+ 2NXWA_2Q5OB
1940
+ 2NXWA_2Q5QB
1941
+ 2NYTA_2NYTA
1942
+ 2NYTA_2NYTB
1943
+ 2NYTA_2NYTC
1944
+ 2NYTA_2NYTD
1945
+ 2NYTA_2RPZA
1946
+ 2O013_2O013
1947
+ 2O013_2WSC3
1948
+ 2O013_2WSE3
1949
+ 2O013_2WSF3
1950
+ 2O013_3LW53
1951
+ 2O01L_2O01L
1952
+ 2O01L_2WSFL
1953
+ 2O01L_3LW5L
1954
+ 2O01L_6YEZL
1955
+ 2O01L_6ZXSL
1956
+ 2O66A_2O66A
1957
+ 2O66A_2O66B
1958
+ 2O66A_2O67B
1959
+ 2O66A_2RD5C
1960
+ 2O66A_2RD5D
1961
+ 2O7KA_2O7KA
1962
+ 2O7KA_2O85A
1963
+ 2O7KA_2O89A
1964
+ 2O7KA_3DIEA
1965
+ 2O7KA_3DIEB
1966
+ 2O8BB_2O8BB
1967
+ 2O8BB_2O8DB
1968
+ 2O8BB_2O8EB
1969
+ 2O8BB_2O8FB
1970
+ 2O8BB_8AG6B
1971
+ 2OAYA_2OAYA
1972
+ 2OAYA_5DU3A
1973
+ 2OAYA_5DU3B
1974
+ 2OAYA_5DUQB
1975
+ 2OAYA_7AKVA
1976
+ 2OGWA_2OSVA
1977
+ 2OGWA_2PRSB
1978
+ 2OGWA_2XH8A
1979
+ 2OGWA_4BBPA
1980
+ 2OGWA_7RCJF
1981
+ 2OIVA_5JP1A
1982
+ 2OIVA_5JP3A
1983
+ 2OIVA_5JP3C
1984
+ 2OIVA_5JP3E
1985
+ 2OIVA_5JP3G
1986
+ 2OLUA_2OLVA
1987
+ 2OLUA_2OLVB
1988
+ 2OLUA_3DWKA
1989
+ 2OLUA_3DWKB
1990
+ 2OLUA_3DWKC
1991
+ 2OZ6A_7FEWB
1992
+ 2OZ6A_7FF0B
1993
+ 2OZ6A_7FF9E
1994
+ 2OZ6A_7FF9F
1995
+ 2OZ6A_7FF9G
1996
+ 2P4VA_2P4VA
1997
+ 2P4VA_2P4VE
1998
+ 2P4VA_6RI7F
1999
+ 2P4VA_6RI7G
2000
+ 2P4VA_6RINF
2001
+ 2PA8D_2PA8D
2002
+ 2PA8D_2WAQD
2003
+ 2PA8D_2WB1D
2004
+ 2PA8D_3HKZD
2005
+ 2PA8D_4V8SAS
2006
+ 2PM6A_2PM6A
2007
+ 2PM6A_2PM7C
2008
+ 2PM6A_3MZLB
2009
+ 2PM6A_3MZLF
2010
+ 2PM6A_3MZLH
2011
+ 2PMZP_2WAQP
2012
+ 2PMZP_3HKZP
2013
+ 2PMZP_4AYBP
2014
+ 2PMZP_7OQ4P
2015
+ 2PMZP_7OQYP
2016
+ 2PROA_2PROA
2017
+ 2PROA_2PROB
2018
+ 2PROA_3PROC
2019
+ 2PROA_3PROD
2020
+ 2PROA_4PROD
2021
+ 2Q3EA_2Q3EA
2022
+ 2Q3EA_3ITKE
2023
+ 2Q3EA_3ITKF
2024
+ 2Q3EA_3TF5A
2025
+ 2Q3EA_4EDFC
2026
+ 2QL2A_2YPAB
2027
+ 2QL2A_6MGNA
2028
+ 2QL2A_6OD3G
2029
+ 2QL2A_6OD3H
2030
+ 2QL2A_6OD3I
2031
+ 2QM4A_2R9AA
2032
+ 2QM4A_7NFCQ
2033
+ 2QM4A_7NFCR
2034
+ 2QM4A_7NFEF
2035
+ 2QM4A_7NFEG
2036
+ 2QTSA_2QTSA
2037
+ 2QTSA_2QTSB
2038
+ 2QTSA_2QTSC
2039
+ 2QTSA_6L6IA
2040
+ 2QTSA_7CFSA
2041
+ 2QUOA_2QUOA
2042
+ 2QUOA_3X29B
2043
+ 2QUOA_7KP4B
2044
+ 2QUOA_7TDMB
2045
+ 2QUOA_7TDNB
2046
+ 2R4RA_2R4RA
2047
+ 2R4RA_3KJ6A
2048
+ 2R4RA_6KR8A
2049
+ 2R4RA_6NI3R
2050
+ 2R4RA_7BZ2R
2051
+ 2R84A_2R85A
2052
+ 2R84A_2R85B
2053
+ 2R84A_2R86A
2054
+ 2R84A_2R87A
2055
+ 2R84A_2R87C
2056
+ 2R8VA_2R8VA
2057
+ 2R8VA_3D2MA
2058
+ 2R8VA_3D2PA
2059
+ 2R8VA_3D2PB
2060
+ 2R8VA_4I49A
2061
+ 2RCNA_2RCNA
2062
+ 2RCNA_2YKRW
2063
+ 2RCNA_4A2IV
2064
+ 2RCNA_5NO2Z
2065
+ 2RCNA_5UZ4Z
2066
+ 2RFJA_2RFJA
2067
+ 2RFJA_2RFJB
2068
+ 2RFJA_2WP2A
2069
+ 2RFJA_5VBRB
2070
+ 2RFJA_8CZAA
2071
+ 2RFTB_2RFTB
2072
+ 2RFTB_4FQKD
2073
+ 2RFTB_4FQMJ
2074
+ 2RFTB_4NKJA
2075
+ 2RFTB_6CNVB
2076
+ 2RVJA_2RVJA
2077
+ 2RVJA_7VKJA
2078
+ 2RVJA_7VKJF
2079
+ 2RVJA_7VKJG
2080
+ 2RVJA_7VKJH
2081
+ 2UY6B_2UY6B
2082
+ 2UY6B_2UY6C
2083
+ 2UY6B_2UY7B
2084
+ 2UY6B_2UY7H
2085
+ 2UY6B_5FLUA
2086
+ 2UZXB_2UZYB
2087
+ 2UZXB_2UZYD
2088
+ 2UZXB_6GCUD
2089
+ 2UZXB_7MO7B
2090
+ 2UZXB_7MO7E
2091
+ 2V6XA_2V6XA
2092
+ 2V6XA_4NIQA
2093
+ 2V6XA_5FVKA
2094
+ 2V6XA_5FVKB
2095
+ 2V6XA_5FVLA
2096
+ 2V7FA_2V7FA
2097
+ 2V7FA_4V4NBU
2098
+ 2V7FA_4V6UAU
2099
+ 2V7FA_6SW9U
2100
+ 2V7FA_6TMFV
2101
+ 2VDCG_2VDCH
2102
+ 2VDCG_6S6SG
2103
+ 2VDCG_6S6SH
2104
+ 2VDCG_6S6TE
2105
+ 2VDCG_6S6TG
2106
+ 2VJEB_2VJED
2107
+ 2VJEB_2VJFB
2108
+ 2VJEB_2VJFD
2109
+ 2VJEB_5MNJH
2110
+ 2VJEB_7MLAA
2111
+ 2VSMA_3D11A
2112
+ 2VSMA_7TXZA
2113
+ 2VSMA_7TXZB
2114
+ 2VSMA_7TY0C
2115
+ 2VSMA_7TY0D
2116
+ 2VTVA_2VTVA
2117
+ 2VTVA_2X76A
2118
+ 2VTVA_4BRSA
2119
+ 2VTVA_4BVLB
2120
+ 2VTVA_5MLXA
2121
+ 2VXQH_5UBZH
2122
+ 2VXQH_6GLXC
2123
+ 2VXQH_6NIPA
2124
+ 2VXQH_6NIPH
2125
+ 2VXQH_6R2SB
2126
+ 2WAQQ_2WAQQ
2127
+ 2WAQQ_3HKZY
2128
+ 2WAQQ_3HKZZ
2129
+ 2WAQQ_4AYBQ
2130
+ 2WAQQ_4V8SBQ
2131
+ 2WG5A_2WG5C
2132
+ 2WG5A_2WG5G
2133
+ 2WG5A_2WG5L
2134
+ 2WG5A_2WG6A
2135
+ 2WG5A_2WG6H
2136
+ 2WKBA_2WKBA
2137
+ 2WKBA_2WKBE
2138
+ 2WKBA_2WKBF
2139
+ 2WKBA_3GACA
2140
+ 2WKBA_3GADF
2141
+ 2WPDJ_3U2FM
2142
+ 2WPDJ_5LQXN
2143
+ 2WPDJ_6WTDK
2144
+ 2WPDJ_6WTDL
2145
+ 2WPDJ_6WTDO
2146
+ 2WQMA_2WQNA
2147
+ 2WQMA_5DE2B
2148
+ 2WQMA_6GT1A
2149
+ 2WQMA_6S76C
2150
+ 2WQMA_6S76D
2151
+ 2WVRA_2ZXXA
2152
+ 2WVRA_2ZXXB
2153
+ 2WVRA_2ZXXD
2154
+ 2WVRA_2ZXXE
2155
+ 2WVRA_4BRYA
2156
+ 2WW9B_2WW9B
2157
+ 2WW9B_2WWAB
2158
+ 2WW9B_6N3QC
2159
+ 2WW9B_7AFTC
2160
+ 2WW9B_7KB5C
2161
+ 2WWBB_2WWBB
2162
+ 2WWBB_3J7Q2
2163
+ 2WWBB_4CG6B
2164
+ 2WWBB_8B6Cq
2165
+ 2WWBB_8B6LC
2166
+ 2WXWA_2WXWA
2167
+ 2WXWA_2X0BB
2168
+ 2WXWA_2X0BF
2169
+ 2WXWA_6I3FA
2170
+ 2WXWA_6I3IA
2171
+ 2X2ZA_2X2ZA
2172
+ 2X2ZA_2X2ZD
2173
+ 2X2ZA_2Y8RA
2174
+ 2X2ZA_2Y8SD
2175
+ 2X2ZA_2Y8TD
2176
+ 2X4IA_2X4IA
2177
+ 2X4IA_2X4IC
2178
+ 2X4IA_2X4ID
2179
+ 2X4IA_6SCFA
2180
+ 2X4IA_6SCFF
2181
+ 2X4MA_2X4MA
2182
+ 2X4MA_2X4MC
2183
+ 2X4MA_2X4MD
2184
+ 2X4MA_2X55A
2185
+ 2X4MA_4DCBA
2186
+ 2X5SA_2X5SA
2187
+ 2X5SA_2X5ZB
2188
+ 2X5SA_2X60A
2189
+ 2X5SA_2X60B
2190
+ 2X5SA_2X65A
2191
+ 2XFBB_2XFBB
2192
+ 2XFBB_2XFBE
2193
+ 2XFBB_2XFBG
2194
+ 2XFBB_2XFBI
2195
+ 2XFBB_5VU2D
2196
+ 2XIKA_3GGFA
2197
+ 2XIKA_3ZHPC
2198
+ 2XIKA_4FZAB
2199
+ 2XIKA_4QMQA
2200
+ 2XIKA_4QO9B
2201
+ 2XJYB_2XJYB
2202
+ 2XJYB_2XJZI
2203
+ 2XJYB_2XJZJ
2204
+ 2XJYB_2XJZM
2205
+ 2XJYB_2YPAD
2206
+ 2XNDJ_6TT71
2207
+ 2XNDJ_7AJFAM
2208
+ 2XNDJ_7AJFAO
2209
+ 2XNDJ_7AJFAP
2210
+ 2XNDJ_7AJFAQ
2211
+ 2XS2A_2XS2A
2212
+ 2XS2A_2XS5A
2213
+ 2XS2A_2XS5B
2214
+ 2XS2A_2XS7A
2215
+ 2XS2A_2XSFA
2216
+ 2XSAA_2XSAA
2217
+ 2XSAA_2XSBA
2218
+ 2XSAA_7KHSB
2219
+ 2XSAA_7KHSC
2220
+ 2XSAA_7KHSD
2221
+ 2XT3A_2XT3A
2222
+ 2XT3A_4A14A
2223
+ 2XT3A_6MLQC
2224
+ 2XT3A_6MLRC
2225
+ 2XT3A_7RX0C
2226
+ 2XTBA_2XTBA
2227
+ 2XTBA_3OTXB
2228
+ 2XTBA_4N08A
2229
+ 2XTBA_4N09A
2230
+ 2XTBA_4N09B
2231
+ 2XZBA_2YN9A
2232
+ 2XZBA_4UX2A
2233
+ 2XZBA_5YLUA
2234
+ 2XZBA_5YLVA
2235
+ 2XZBA_6JXJA
2236
+ 2Y2ZA_2Y2ZA
2237
+ 2Y2ZA_2Y30A
2238
+ 2Y2ZA_2Y31A
2239
+ 2Y2ZA_3ZQLA
2240
+ 2Y2ZA_3ZQLB
2241
+ 2Y3VA_2Y3VA
2242
+ 2Y3VA_2Y3VB
2243
+ 2Y3VA_2Y3VC
2244
+ 2Y3VA_2Y3VD
2245
+ 2Y3VA_2Y3WA
2246
+ 2Y4TA_2Y4TA
2247
+ 2Y4TA_2Y4TC
2248
+ 2Y4TA_2Y4UA
2249
+ 2Y4TA_3IEGA
2250
+ 2Y4TA_3IEGB
2251
+ 2YKOA_2YKOB
2252
+ 2YKOA_2YKOC
2253
+ 2YKOA_2YKQA
2254
+ 2YKOA_2YKQB
2255
+ 2YKOA_2YKQC
2256
+ 2YKRB_2YKRB
2257
+ 2YKRB_4V6PAE
2258
+ 2YKRB_4V77AB
2259
+ 2YKRB_4V79AB
2260
+ 2YKRB_6VWMa
2261
+ 2YKRC_2YKRC
2262
+ 2YKRC_4V6LAG
2263
+ 2YKRC_4V74AC
2264
+ 2YKRC_4V78AC
2265
+ 2YKRC_4V7IBC
2266
+ 2YKRK_2YKRK
2267
+ 2YKRK_4V72AK
2268
+ 2YKRK_4V77AK
2269
+ 2YKRK_4V78AK
2270
+ 2YKRK_4V9OHK
2271
+ 2YKRL_2YKRL
2272
+ 2YKRL_4V65AD
2273
+ 2YKRL_4V66AD
2274
+ 2YKRL_4V70AL
2275
+ 2YKRL_4V71AL
2276
+ 2YKRP_2YKRP
2277
+ 2YKRP_4V65AI
2278
+ 2YKRP_4V6OAS
2279
+ 2YKRP_4V73AP
2280
+ 2YKRP_4V76AP
2281
+ 2YKRT_2YKRT
2282
+ 2YKRT_4V6LAX
2283
+ 2YKRT_4V77AT
2284
+ 2YKRT_4V78AT
2285
+ 2YKRT_4V79AT
2286
+ 2YM9A_2YM9A
2287
+ 2YM9A_2YM9B
2288
+ 2YM9A_3O00A
2289
+ 2YM9A_3O01A
2290
+ 2YM9A_3O01B
2291
+ 2YNAA_2YNAA
2292
+ 2YNAA_4WMFC
2293
+ 2YNAA_4YOIA
2294
+ 2YNAA_6VH3B
2295
+ 2YNAA_7D3CA
2296
+ 2YQ2A_2YQ2A
2297
+ 2YQ2A_2YQ2B
2298
+ 2YQ2A_2YQ3B
2299
+ 2YQ2A_4JNTA
2300
+ 2YQ2A_4JNTB
2301
+ 2YT4A_2YT4A
2302
+ 2YT4A_6V5BB
2303
+ 2YT4A_6V5BC
2304
+ 2YT4A_6V5CB
2305
+ 2YT4A_6V5CC
2306
+ 2YULA_2YULA
2307
+ 2YULA_3F27D
2308
+ 2YULA_4Y60C
2309
+ 2YULA_6L6YD
2310
+ 2YULA_6L6YF
2311
+ 2Z1EA_2Z1EA
2312
+ 2Z1EA_2Z1FA
2313
+ 2Z1EA_3VYSC
2314
+ 2Z1EA_3VYTC
2315
+ 2Z1EA_3WJPA
2316
+ 2ZIUB_2ZIUB
2317
+ 2ZIUB_2ZIWB
2318
+ 2ZIUB_2ZIXB
2319
+ 2ZIUB_4P0PB
2320
+ 2ZIUB_4P0QB
2321
+ 2ZNLA_2ZNLA
2322
+ 2ZNLA_5IF2A
2323
+ 2ZNLA_5IF5A
2324
+ 2ZNLA_6QX8A
2325
+ 2ZNLA_7NISA
2326
+ 2ZYKA_2ZYKA
2327
+ 2ZYKA_2ZYKB
2328
+ 2ZYKA_2ZYMA
2329
+ 2ZYKA_2ZYNA
2330
+ 2ZYKA_2ZYOA
2331
+ 3A1JA_3A1JA
2332
+ 3A1JA_3G65A
2333
+ 3A1JA_3GGRA
2334
+ 3A1JA_7Z6HA
2335
+ 3A1JA_8GNNA
2336
+ 3AFEA_3AFEA
2337
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2338
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2339
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2340
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2341
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2342
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2343
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2344
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2345
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2346
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2347
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2348
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2349
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2350
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2351
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2352
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2353
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2354
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2355
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2356
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2357
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2358
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2359
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2360
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2361
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2362
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2363
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2364
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2365
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2366
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2367
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2368
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2369
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2370
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2371
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2372
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2373
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2374
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2375
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2376
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2377
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2378
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2379
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2380
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2381
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2382
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2383
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2384
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2385
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2386
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2387
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2388
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2389
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2390
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2391
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2392
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2393
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2394
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2395
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2396
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2397
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2398
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2399
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2400
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2401
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2402
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2403
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2404
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2405
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2406
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2407
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2408
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2409
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2410
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2411
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2412
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2413
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2414
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2415
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2416
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2417
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2418
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2419
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2420
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2421
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2422
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2423
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2424
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2425
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2426
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2427
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2428
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2429
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2430
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2431
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2432
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2433
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2434
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2435
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2436
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2437
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2438
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2439
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2440
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2441
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2442
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2443
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2444
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2445
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2446
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2447
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2448
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2449
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2450
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2451
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2452
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2453
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2454
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2455
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2456
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2457
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2458
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2459
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2460
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2461
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2462
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2463
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2464
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2465
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2466
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2467
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2468
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2469
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2470
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2471
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2472
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2473
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2474
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2475
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2476
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2477
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2478
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2479
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2480
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2481
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2482
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2483
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2484
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2485
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2486
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2487
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2488
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2489
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2490
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2491
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2492
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2493
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2494
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2495
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2496
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2497
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2498
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2499
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2500
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2501
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2502
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2503
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2504
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2505
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2506
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2507
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2508
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2509
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2510
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2511
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2512
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2513
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2514
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2515
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2516
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2517
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2518
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2519
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2520
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2521
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2522
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2523
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2524
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2525
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2526
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2527
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2528
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2529
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2530
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2531
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2532
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2533
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2534
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2535
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2536
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2537
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2538
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2539
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2540
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2541
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2542
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2543
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2544
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2545
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2546
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2547
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2548
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2549
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2550
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2551
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2552
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2553
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2554
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2555
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2556
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2557
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2558
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2559
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2560
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2561
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2562
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2563
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2564
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2565
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2566
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2567
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2568
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2569
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2570
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2571
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2572
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2573
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2574
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2575
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2576
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2577
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2578
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2579
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2580
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2581
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2582
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2583
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2584
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2585
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2586
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2587
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2588
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2589
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2590
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2591
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2592
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2593
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2594
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2595
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2596
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2597
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2598
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2599
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2600
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2601
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2602
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2603
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2604
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2605
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2606
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2607
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2608
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2609
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2610
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2611
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2612
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2613
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2614
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2615
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2616
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2617
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2618
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2619
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2620
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2621
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2622
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2623
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2624
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2625
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2626
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2627
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2628
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2629
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2630
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2631
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2632
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2633
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2634
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2635
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2636
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2637
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2638
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2639
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2640
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2641
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2642
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2643
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2644
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2645
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2646
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2647
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2648
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2649
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2650
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2651
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2652
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2653
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2654
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2655
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2656
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2657
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2658
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2659
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2660
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2661
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2662
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2663
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2664
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2665
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2666
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2667
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2668
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2669
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2670
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2671
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2672
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2673
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2674
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2675
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2676
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2677
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2678
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2679
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2680
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2681
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2682
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2683
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2684
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2685
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2686
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2687
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2688
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2689
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2690
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2691
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2692
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2693
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2694
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2695
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2696
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2697
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2698
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2699
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2700
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2701
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2702
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2703
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2704
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2705
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2706
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2707
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2708
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2709
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2710
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2711
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2712
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2713
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2714
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2715
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2716
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2717
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2718
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2719
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2720
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2721
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2722
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2723
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2724
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2725
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2726
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2727
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2728
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2729
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2730
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2731
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2732
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2733
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2734
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2735
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2736
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2737
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2738
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2739
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2740
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2741
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2742
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2743
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2744
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2745
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2746
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2747
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2748
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2749
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2750
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2751
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2752
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2753
+ 3LYFA_4H5PB
2754
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2755
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2756
+ 3M8OH_3M8OH
2757
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2758
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2759
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2760
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2761
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2762
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2763
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2764
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2765
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2766
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2767
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2768
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2769
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2770
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2771
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2772
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2773
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2774
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2775
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2776
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2777
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2778
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2779
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2780
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2781
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2782
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2783
+ 3MTNB_5ZD0A
2784
+ 3MTNB_6OB1B
2785
+ 3MTNB_7M3QB
2786
+ 3MV7D_3MV7D
2787
+ 3MV7D_5KS9E
2788
+ 3MV7D_5KSAC
2789
+ 3MV7D_6XCOD
2790
+ 3MV7D_8ES9A
2791
+ 3N0XA_3N0XA
2792
+ 3N0XA_3NNDA
2793
+ 3N0XA_3NNDB
2794
+ 3N0XA_3NNDC
2795
+ 3N0XA_3NNDD
2796
+ 3NTCH_3NTCH
2797
+ 3NTCH_4YGVA
2798
+ 3NTCH_6ANII
2799
+ 3NTCH_6T3JH
2800
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2801
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2802
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2803
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2804
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2805
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2806
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2807
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2808
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2809
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2810
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2811
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2812
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2813
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2814
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2815
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2816
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2817
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2818
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2819
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2820
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2821
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2822
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2823
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2824
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2825
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2826
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2827
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2828
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2829
+ 3OSYA_5GSOA
2830
+ 3OSYA_5GSWE
2831
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2832
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2833
+ 3P7IA_3QUJB
2834
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2835
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2836
+ 3PBLA_3PBLA
2837
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2838
+ 3PBLA_6C38A
2839
+ 3PBLA_6CM4A
2840
+ 3PBLA_6LUQA
2841
+ 3PNTB_3PNTB
2842
+ 3PNTB_3QB2B
2843
+ 3PNTB_3QB2C
2844
+ 3PNTB_4KT6B
2845
+ 3PNTB_4KT6D
2846
+ 3PXGA_3PXGA
2847
+ 3PXGA_3PXGB
2848
+ 3PXGA_3PXGC
2849
+ 3PXGA_3PXGD
2850
+ 3PXGA_3PXGF
2851
+ 3Q0XA_6ZZ8B
2852
+ 3Q0XA_6ZZ8D
2853
+ 3Q0XA_6ZZCA
2854
+ 3Q0XA_6ZZCB
2855
+ 3Q0XA_6ZZCC
2856
+ 3Q5DA_3Q5DA
2857
+ 3Q5DA_3Q5EA
2858
+ 3Q5DA_3QNUA
2859
+ 3Q5DA_6B9EA
2860
+ 3Q5DA_6XJNA
2861
+ 3QLEA_3QLEA
2862
+ 3QLEA_4QQFA
2863
+ 3QLEA_4QQFC
2864
+ 3QLEA_4QQFD
2865
+ 3QLEA_4QQFF
2866
+ 3QS2A_3QS2A
2867
+ 3QS2A_3QS2B
2868
+ 3QS2A_3QS3B
2869
+ 3QS2A_3QS3E
2870
+ 3QS2A_3QS3H
2871
+ 3R23A_3R23A
2872
+ 3R23A_3R23B
2873
+ 3R23A_3R5XA
2874
+ 3R23A_3R5XB
2875
+ 3R23A_3R5XD
2876
+ 3R7WB_3R7WB
2877
+ 3R7WB_3R7WD
2878
+ 3R7WB_4ARZB
2879
+ 3R7WB_6JWPB
2880
+ 3R7WB_6JWPG
2881
+ 3R8JA_3R8JA
2882
+ 3R8JA_3R8JB
2883
+ 3R8JA_3R8KA
2884
+ 3R8JA_3R8KB
2885
+ 3R8JA_4AYZA
2886
+ 3RFUA_3RFUA
2887
+ 3RFUA_3RFUC
2888
+ 3RFUA_4BBJA
2889
+ 3RFUA_4BEVA
2890
+ 3RFUA_4BYGA
2891
+ 3RJQA_4I53B
2892
+ 3RJQA_4LSVG
2893
+ 3RJQA_5F6JE
2894
+ 3RJQA_5TE7G
2895
+ 3RJQA_6XCJG
2896
+ 3RKOA_3RKOA
2897
+ 3RKOA_3RKOE
2898
+ 3RKOA_7NYRA
2899
+ 3RKOA_7P64A
2900
+ 3RKOA_7Z80A
2901
+ 3RONA_3RONA
2902
+ 3RONA_3RONB
2903
+ 3RONA_6T14A
2904
+ 3RONA_6T1AA
2905
+ 3RONA_6T1CA
2906
+ 3RUKA_5IRQB
2907
+ 3RUKA_6CHIC
2908
+ 3RUKA_6CIRD
2909
+ 3RUKA_6WR1A
2910
+ 3RUKA_6WR1B
2911
+ 3SBYA_3SBYA
2912
+ 3SBYA_3SBZA
2913
+ 3SBYA_3SC0A
2914
+ 3SBYA_3SOMP
2915
+ 3SBYA_5UOSA
2916
+ 3SDEB_3SDEB
2917
+ 3SDEB_5IFMG
2918
+ 3SDEB_5IFMJ
2919
+ 3SDEB_6WMZD
2920
+ 3SDEB_7LRQB
2921
+ 3SIAA_3SIAA
2922
+ 3SIAA_3SIBA
2923
+ 3SIAA_3SJSA
2924
+ 3SIAA_4Q04A
2925
+ 3SIAA_4Q04B
2926
+ 3SWKA_3SWKA
2927
+ 3SWKA_3SWKB
2928
+ 3SWKA_5WHFD
2929
+ 3SWKA_5WHFE
2930
+ 3SWKA_5WHFH
2931
+ 3SYKA_3SYKA
2932
+ 3SYKA_3SYKB
2933
+ 3SYKA_3SYLB
2934
+ 3SYKA_3ZUHA
2935
+ 3SYKA_3ZUHC
2936
+ 3TESA_3TESA
2937
+ 3TESA_4LPTC
2938
+ 3TESA_4LPXA
2939
+ 3TESA_4LPYA
2940
+ 3TESA_4M6AJ
2941
+ 3TFKC_3TFKC
2942
+ 3TFKC_3VXMD
2943
+ 3TFKC_4Z7WE
2944
+ 3TFKC_6VM8D
2945
+ 3TFKC_8GVIA
2946
+ 3TGXA_3TGXC
2947
+ 3TGXA_4NZDA
2948
+ 3TGXA_4NZDB
2949
+ 3TGXA_4NZDC
2950
+ 3TGXA_7KQ7B
2951
+ 3TNXA_3TNXA
2952
+ 3TNXA_3USVA
2953
+ 3TNXA_3USVC
2954
+ 3TNXA_4QRVA
2955
+ 3TNXA_4QRVB
2956
+ 3TV0A_5ADXU
2957
+ 3TV0A_5AFUU
2958
+ 3TV0A_6F1TU
2959
+ 3TV0A_6ZNLU
2960
+ 3TV0A_7Z8FU
2961
+ 3TV3H_3TV3H
2962
+ 3TV3H_3TWCH
2963
+ 3TV3H_3TYGH
2964
+ 3TV3H_5C7KA
2965
+ 3TV3H_5JS9A
2966
+ 3U2SC_3U2SC
2967
+ 3U2SC_3U2SG
2968
+ 3U2SC_4DQOC
2969
+ 3U2SC_4YWGG
2970
+ 3U2SC_4YWGQ
2971
+ 3UF8A_4G50B
2972
+ 3UF8A_4GGQA
2973
+ 3UF8A_5KLXB
2974
+ 3UF8A_5V8TB
2975
+ 3UF8A_6O49A
2976
+ 3UTMA_3UTMA
2977
+ 3UTMA_5HKPA
2978
+ 3UTMA_6CF6B
2979
+ 3UTMA_6URQA
2980
+ 3UTMA_6URQB
2981
+ 3UX4A_3UX4A
2982
+ 3UX4A_3UX4C
2983
+ 3UX4A_6NSJA
2984
+ 3UX4A_6NSJC
2985
+ 3UX4A_6NSKA
2986
+ 3VBFA_3ZFGA
2987
+ 3VBFA_4AEDA
2988
+ 3VBFA_6DIZA
2989
+ 3VBFA_6LQDA
2990
+ 3VBFA_8C6DA
2991
+ 3VGYD_3VGYD
2992
+ 3VGYD_3VH7B
2993
+ 3VGYD_3VH7D
2994
+ 3VGYD_3VH7F
2995
+ 3VGYD_3W19D
2996
+ 3VVNA_3VVOA
2997
+ 3VVNA_3VVPA
2998
+ 3VVNA_3VVPB
2999
+ 3VVNA_4MLBC
3000
+ 3VVNA_6FHZA
3001
+ 3WBPA_3WBPA
3002
+ 3WBPA_3WBQB
3003
+ 3WBPA_3WBRB
3004
+ 3WBPA_4ZESB
3005
+ 3WBPA_4ZETA
3006
+ 3WDKA_3WDKA
3007
+ 3WDKA_3WDKD
3008
+ 3WDKA_3WDLC
3009
+ 3WDKA_3WDMD
3010
+ 3WDKA_4MB0D
3011
+ 3WGOA_3WGOB
3012
+ 3WGOA_3WGQA
3013
+ 3WGOA_3WGYA
3014
+ 3WGOA_3WGYB
3015
+ 3WGOA_3WGZA
3016
+ 3WJKA_5ZE6A
3017
+ 3WJKA_5ZE6B
3018
+ 3WJKA_5ZE6D
3019
+ 3WJKA_5ZLFB
3020
+ 3WJKA_5ZLFD
3021
+ 3WSVA_3WSVB
3022
+ 3WSVA_3WSVC
3023
+ 3WSVA_3WSVD
3024
+ 3WSVA_3WSWA
3025
+ 3WSVA_3WSWD
3026
+ 3WWSA_3WWSA
3027
+ 3WWSA_4HKDD
3028
+ 3WWSA_4L0NA
3029
+ 3WWSA_4L0NE
3030
+ 3WWSA_4L0NI
3031
+ 3X0XA_3X0YA
3032
+ 3X0XA_3X0YH
3033
+ 3X0XA_4DOYA
3034
+ 3X0XA_4DOYB
3035
+ 3X0XA_4DOYH
3036
+ 3ZFHA_4DQWA
3037
+ 3ZFHA_4DQWB
3038
+ 3ZFHA_6GJVE
3039
+ 3ZFHA_6GJVF
3040
+ 3ZFHA_7PJIA
3041
+ 3ZGHA_3ZGIA
3042
+ 3ZGHA_3ZGIB
3043
+ 3ZGHA_5JUIA
3044
+ 3ZGHA_5JUIB
3045
+ 3ZGHA_5JUIC
3046
+ 3ZGQA_3ZGQA
3047
+ 3ZGQA_4HOQA
3048
+ 3ZGQA_4HORA
3049
+ 3ZGQA_4HOTA
3050
+ 3ZGQA_4J0UA
3051
+ 3ZLAA_4IJSA
3052
+ 3ZLAA_4IJSB
3053
+ 3ZLAA_4IJSC
3054
+ 3ZLAA_4IJSD
3055
+ 3ZLAA_7AOYA
3056
+ 3ZLDA_3ZLDA
3057
+ 3ZLDA_3ZLEA
3058
+ 3ZLDA_3ZLED
3059
+ 3ZLDA_3ZLEE
3060
+ 3ZLDA_3ZLEF
3061
+ 3ZZFA_3ZZFA
3062
+ 3ZZFA_3ZZFD
3063
+ 3ZZFA_3ZZGA
3064
+ 3ZZFA_3ZZGD
3065
+ 3ZZFA_3ZZHC
3066
+ 4A1NA_5T5CA
3067
+ 4A1NA_5ZKJA
3068
+ 4A1NA_6IIDA
3069
+ 4A1NA_7R6TD
3070
+ 4A1NA_7R6VI
3071
+ 4AFNA_4AFNB
3072
+ 4AFNA_4AG3C
3073
+ 4AFNA_4BNVD
3074
+ 4AFNA_4BNWC
3075
+ 4AFNA_4BO4B
3076
+ 4AKFA_4AKFA
3077
+ 4AKFA_4KYIA
3078
+ 4AKFA_4KYIC
3079
+ 4AKFA_4KYIE
3080
+ 4AKFA_4KYIG
3081
+ 4AVPA_4CO8A
3082
+ 4AVPA_4UNOA
3083
+ 4AVPA_4UUVA
3084
+ 4AVPA_4UUVD
3085
+ 4AVPA_5ILVA
3086
+ 4B8WA_4B8WA
3087
+ 4B8WA_4BKPC
3088
+ 4B8WA_4BKPD
3089
+ 4B8WA_4BL5C
3090
+ 4B8WA_4E5YC
3091
+ 4B99A_4B99A
3092
+ 4B99A_4IC7D
3093
+ 4B99A_4IC8A
3094
+ 4B99A_4IC8B
3095
+ 4B99A_4ZSGA
3096
+ 4BB9A_4BB9A
3097
+ 4BB9A_4BBAA
3098
+ 4BB9A_4LC9A
3099
+ 4BB9A_4LY9B
3100
+ 4BB9A_4OLHA
3101
+ 4BJMA_4BJMD
3102
+ 4BJMA_4BJNA
3103
+ 4BJMA_4BJNC
3104
+ 4BJMA_4BJNF
3105
+ 4BJMA_4BJNG
3106
+ 4BL8A_4BL8A
3107
+ 4BL8A_4BL9C
3108
+ 4BL8A_4BLAA
3109
+ 4BL8A_4BLAD
3110
+ 4BL8A_4BLDA
3111
+ 4BSOA_4BSOA
3112
+ 4BSOA_4BSPA
3113
+ 4BSOA_4BSRD
3114
+ 4BSOA_4CDKH
3115
+ 4BSOA_4LI2B
3116
+ 4BTGA_4BTGA
3117
+ 4BTGA_4BTGB
3118
+ 4BTGA_4BTQA
3119
+ 4BTGA_4BTQB
3120
+ 4BTGA_6HY0B
3121
+ 4C03A_4C04A
3122
+ 4C03A_4C06A
3123
+ 4C03A_5E8RA
3124
+ 4C03A_6SQ4B
3125
+ 4C03A_6W6DA
3126
+ 4C1QA_4C1QA
3127
+ 4C1QA_4IJDA
3128
+ 4C1QA_4IJDB
3129
+ 4C1QA_6NM4A
3130
+ 4C1QA_6NM4B
3131
+ 4C2CA_4C2CA
3132
+ 4C2CA_4C2DA
3133
+ 4C2CA_4C2DB
3134
+ 4C2CA_4C2HA
3135
+ 4C2CA_4C2HB
3136
+ 4C48C_5NC5F
3137
+ 4C48C_5NC5G
3138
+ 4C48C_5NG5N
3139
+ 4C48C_5NG5O
3140
+ 4C48C_6SGSG
3141
+ 4COFA_4COFC
3142
+ 4COFA_6D6TA
3143
+ 4COFA_6D6UA
3144
+ 4COFA_6D6UC
3145
+ 4COFA_6DW0E
3146
+ 4CSU9_4CSU9
3147
+ 4CSU9_5M04A
3148
+ 4CSU9_7BL29P1
3149
+ 4CSU9_7BL49
3150
+ 4CSU9_7BL59
3151
+ 4CT8A_4CT8A
3152
+ 4CT8A_4CT9B
3153
+ 4CT8A_4CTAA
3154
+ 4CT8A_4UOCB
3155
+ 4CT8A_4UUXA
3156
+ 4CVNE_4CVNE
3157
+ 4CVNE_4CW7D
3158
+ 4CVNE_4V4NBM
3159
+ 4CVNE_4V6UAM
3160
+ 4CVNE_7ZAGM
3161
+ 4CXIA_4CXIA
3162
+ 4CXIA_5DAFA
3163
+ 4CXIA_5NLBA
3164
+ 4CXIA_6W66C
3165
+ 4CXIA_6WCQC
3166
+ 4CYIA_4CYIF
3167
+ 4CYIA_4CYIG
3168
+ 4CYIA_4CYJA
3169
+ 4CYIA_4CYJB
3170
+ 4CYIA_4CYJD
3171
+ 4CZTA_4CZTA
3172
+ 4CZTA_4CZTB
3173
+ 4CZTA_4CZTD
3174
+ 4CZTA_4CZUB
3175
+ 4CZTA_4CZUD
3176
+ 4D0PA_4D10D
3177
+ 4D0PA_4WSNT
3178
+ 4D0PA_6R7FD
3179
+ 4D0PA_6R7HD
3180
+ 4D0PA_6R7ND
3181
+ 4D10A_4D10A
3182
+ 4D10A_6R6HA
3183
+ 4D10A_6R7HA
3184
+ 4D10A_6R7IA
3185
+ 4D10A_6R7NA
3186
+ 4D10F_4WSNF
3187
+ 4D10F_6R6HF
3188
+ 4D10F_6R7FF
3189
+ 4D10F_6R7HF
3190
+ 4D10F_6R7IF
3191
+ 4D8JA_4D8JA
3192
+ 4D8JA_4D8JC
3193
+ 4D8JA_4D8JG
3194
+ 4D8JA_4D8JH
3195
+ 4D8JA_4D8JL
3196
+ 4DAJA_4DAJC
3197
+ 4DAJA_4U14A
3198
+ 4DAJA_4U15B
3199
+ 4DAJA_5ZHPA
3200
+ 4DAJA_5ZHPB
3201
+ 4DB6A_4DB6A
3202
+ 4DB6A_4DB8D
3203
+ 4DB6A_4DB9A
3204
+ 4DB6A_4DBAC
3205
+ 4DB6A_5MFGD
3206
+ 4DX8A_4DX9e
3207
+ 4DX8A_4DX9o
3208
+ 4DX8A_4DX9O
3209
+ 4DX8A_4DX9q
3210
+ 4DX8A_4DX9w
3211
+ 4E29A_4E2HC
3212
+ 4E29A_4ZM1A
3213
+ 4E29A_4ZM5C
3214
+ 4E29A_5NBZA
3215
+ 4E29A_5NBZB
3216
+ 4E7XA_4E7XA
3217
+ 4E7XA_4E7XC
3218
+ 4E7XA_4E8FB
3219
+ 4E7XA_4UD4B
3220
+ 4E7XA_4UD5B
3221
+ 4EGYA_4EGYA
3222
+ 4EGYA_4EGZA
3223
+ 4EGYA_4EGZB
3224
+ 4EGYA_5D4RA
3225
+ 4EGYA_5D4SB
3226
+ 4EPLA_4EPLA
3227
+ 4EPLA_5ECHA
3228
+ 4EPLA_5ECND
3229
+ 4EPLA_5ECPD
3230
+ 4EPLA_5GZZA
3231
+ 4F0FA_4F0FA
3232
+ 4F0FA_4F1TA
3233
+ 4F0FA_4YZMA
3234
+ 4F0FA_4YZMB
3235
+ 4F0FA_4YZNA
3236
+ 4F15A_4F15A
3237
+ 4F15A_4F15D
3238
+ 4F15A_4F15J
3239
+ 4F15A_4LVHA
3240
+ 4F15A_8DIMA
3241
+ 4F2ME_4F2ME
3242
+ 4F2ME_4F2MF
3243
+ 4F2ME_4F5CE
3244
+ 4F2ME_4F5CF
3245
+ 4F2ME_7U0LB
3246
+ 4F57H_4F57H
3247
+ 4F57H_4F58I
3248
+ 4F57H_4F58K
3249
+ 4F57H_8D6ZE
3250
+ 4F57H_8D6ZG
3251
+ 4F7BA_4F7BA
3252
+ 4F7BA_4F7BB
3253
+ 4F7BA_4TVZB
3254
+ 4F7BA_5UPHA
3255
+ 4F7BA_5UPHB
3256
+ 4FR4A_4FR4A
3257
+ 4FR4A_4FR4B
3258
+ 4FR4A_4FR4D
3259
+ 4FR4A_4FR4E
3260
+ 4FR4A_4FR4F
3261
+ 4GEIA_4GEIA
3262
+ 4GEIA_4GEJC
3263
+ 4GEIA_4GEJD
3264
+ 4GEIA_4GEJG
3265
+ 4GEIA_4GFXA
3266
+ 4GLRI_4GLRI
3267
+ 4GLRI_4GLRK
3268
+ 4GLRI_6NMVL
3269
+ 4GLRI_7KPGL
3270
+ 4GLRI_7KQKL
3271
+ 4I5IA_4I5IB
3272
+ 4I5IA_4IF6A
3273
+ 4I5IA_4IG9C
3274
+ 4I5IA_4IG9E
3275
+ 4I5IA_4KXQA
3276
+ 4I8OA_4I8OA
3277
+ 4I8OA_6Y2PA
3278
+ 4I8OA_6Y2PB
3279
+ 4I8OA_6Y2QB
3280
+ 4I8OA_6Y2RA
3281
+ 4ICQA_4ICQB
3282
+ 4ICQA_4ICRA
3283
+ 4ICQA_4ICRB
3284
+ 4ICQA_4ICSA
3285
+ 4ICQA_4ICSB
3286
+ 4IFTA_4IFTA
3287
+ 4IFTA_4IG4A
3288
+ 4IFTA_4IG4B
3289
+ 4IFTA_4KN8A
3290
+ 4IFTA_4O8CB
3291
+ 4IPEA_5TVUA
3292
+ 4IPEA_6XG6A
3293
+ 4IPEA_6XG6B
3294
+ 4IPEA_7KCMA
3295
+ 4IPEA_7KLUA
3296
+ 4IRPA_4IRPA
3297
+ 4IRPA_4IRPB
3298
+ 4IRPA_4IRQA
3299
+ 4IRPA_4IRQC
3300
+ 4IRPA_4IRQD
3301
+ 4IY0A_4IY2A
3302
+ 4IY0A_4IY3C
3303
+ 4IY0A_4IYSA
3304
+ 4IY0A_5MMZA
3305
+ 4IY0A_6RS2C
3306
+ 4J2NA_4J2NA
3307
+ 4J2NA_4J2NB
3308
+ 4J2NA_4J2NC
3309
+ 4J2NA_4J2ND
3310
+ 4J2NA_4J2NE
3311
+ 4J4RA_4J4RA
3312
+ 4J4RA_4J4SA
3313
+ 4J4RA_4J4SB
3314
+ 4J4RA_4J4SD
3315
+ 4J4RA_4J4UB
3316
+ 4JXYA_4JXYA
3317
+ 4JXYA_4JYXA
3318
+ 4JXYA_4JYXC
3319
+ 4JXYA_4JYXD
3320
+ 4JXYA_4JYXE
3321
+ 4K46A_4K46A
3322
+ 4K46A_4NP6A
3323
+ 4K46A_4NP6B
3324
+ 4K46A_4NP6C
3325
+ 4K46A_4NP6D
3326
+ 4K5YA_4K5YA
3327
+ 4K5YA_4K5YB
3328
+ 4K5YA_4Z9GA
3329
+ 4K5YA_4Z9GB
3330
+ 4K5YA_4Z9GC
3331
+ 4KT0K_4L6V0
3332
+ 4KT0K_4L6Vk
3333
+ 4KT0K_5OY0K
3334
+ 4KT0K_6UZV8
3335
+ 4KT0K_7O1VK
3336
+ 4L0EA_4L0EA
3337
+ 4L0EA_4L0FA
3338
+ 4L0EA_4PWVA
3339
+ 4L0EA_4PXHA
3340
+ 4L0EA_4PXHC
3341
+ 4L3TA_4NGEA
3342
+ 4L3TA_4RPUA
3343
+ 4L3TA_6XOTA
3344
+ 4L3TA_6XOUA
3345
+ 4L3TA_6XOWA
3346
+ 4L5GA_4L5GA
3347
+ 4L5GA_4L5GB
3348
+ 4L5GA_4XAXB
3349
+ 4L5GA_4XLRN
3350
+ 4L5GA_4XLSN
3351
+ 4LGDA_4LGDA
3352
+ 4LGDA_4LGDB
3353
+ 4LGDA_4LGDC
3354
+ 4LGDA_4LGDD
3355
+ 4LGDA_6AO5A
3356
+ 4MTQA_4MTQB
3357
+ 4MTQA_4MTSA
3358
+ 4MTQA_4MTSB
3359
+ 4MTQA_4MTTA
3360
+ 4MTQA_4MTTB
3361
+ 4MVCA_4MVCA
3362
+ 4MVCA_4MVCB
3363
+ 4MVCA_4MVDB
3364
+ 4MVCA_4MVDC
3365
+ 4MVCA_4MVDF
3366
+ 4N0TA_4N0TA
3367
+ 4N0TA_5TF6A
3368
+ 4N0TA_5TF6C
3369
+ 4N0TA_5VSUA
3370
+ 4N0TA_6ASOA
3371
+ 4N44A_4N44A
3372
+ 4N44A_4N44B
3373
+ 4N44A_4N45A
3374
+ 4N44A_4N45B
3375
+ 4N44A_4WYRB
3376
+ 4N9F1_4N9FM
3377
+ 4N9F1_4N9FS
3378
+ 4N9F1_6NILC
3379
+ 4N9F1_8CX1G
3380
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3381
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3382
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3383
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3384
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3385
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3386
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3387
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3388
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3389
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3390
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3391
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3392
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3393
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3394
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3395
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3396
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3397
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3398
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3399
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3400
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3401
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3402
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3403
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3404
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3405
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3406
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3407
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3408
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3409
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3410
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3411
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3412
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3413
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3414
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3415
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3416
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3417
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3418
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3419
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3420
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3421
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3422
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3423
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3424
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3425
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3426
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3427
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3428
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3429
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3430
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3431
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3432
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3433
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3434
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3435
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3436
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3437
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3438
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3439
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3440
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3441
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3442
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3443
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3444
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3445
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3446
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3447
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3448
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3449
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3450
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3451
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3452
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3453
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3454
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3455
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3456
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3457
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3458
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3459
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3460
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3461
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3462
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3463
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3464
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3465
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3466
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3467
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3468
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3469
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3470
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3471
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3472
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3473
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3474
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3475
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3476
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3477
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3478
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3479
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3480
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3481
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3482
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3483
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3484
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3485
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3486
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3487
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3488
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3489
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3490
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3491
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3492
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3493
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3494
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3495
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3496
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3497
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3498
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3499
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3500
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3501
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3502
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3503
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3504
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3505
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3506
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3507
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3508
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3509
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3510
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3511
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3512
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3513
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3514
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3515
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3516
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3517
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3518
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3519
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3520
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3521
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3522
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3523
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3524
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3525
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3526
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3527
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3528
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3529
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3530
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3531
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3532
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3533
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3534
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3535
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3536
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3537
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3538
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3539
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3540
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3541
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3542
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3543
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3544
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3545
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3546
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3547
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3548
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3549
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3550
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3551
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3552
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3553
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3554
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3555
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3556
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3557
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3558
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3559
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3560
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3561
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3562
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3563
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3564
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3565
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3566
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3567
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3568
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3569
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3570
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3571
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3572
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3573
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3574
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3575
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3576
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3577
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3578
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3579
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3580
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3581
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3582
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3583
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3584
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3585
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3586
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3587
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3588
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3589
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3590
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3591
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3592
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3593
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3594
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3595
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3596
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3597
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3598
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3599
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3600
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3601
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3602
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3603
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3604
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3605
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3606
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3607
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3608
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3609
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3610
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3611
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3612
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3613
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3614
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3615
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3616
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3617
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3618
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3619
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3620
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3621
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3622
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3623
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3624
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3625
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3626
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3627
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3628
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3629
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3630
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3631
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3632
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3633
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3634
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3635
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3636
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3637
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3638
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3639
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3640
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3641
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3642
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3643
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3644
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3645
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3646
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3647
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3648
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3649
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3650
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3651
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3652
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3653
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3654
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3655
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3656
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3657
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3658
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3659
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3660
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3661
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3662
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3663
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3664
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3665
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3666
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3667
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3668
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3669
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3670
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3671
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3672
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3673
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3674
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3675
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3676
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3677
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3678
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3679
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3680
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3681
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3682
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3683
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3684
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3685
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3686
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3687
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3688
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3689
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3690
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3691
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3692
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3693
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3694
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3695
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3696
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3697
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3698
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3699
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3700
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3701
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3702
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3703
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3704
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3705
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3706
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3707
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3708
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3709
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3710
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3711
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3712
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3713
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3714
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3715
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3716
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3717
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3718
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3719
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3720
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3721
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3722
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3723
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3724
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3725
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3726
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3727
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3728
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3729
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3730
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3731
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3732
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3733
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3734
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3735
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3736
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3737
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3738
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3739
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3740
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3741
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3742
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3743
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3744
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3745
+ 5CBNA_5CBOK
3746
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3747
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3748
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3749
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3750
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3751
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3752
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3753
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3754
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3755
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3756
+ 5DHVB_5DHVB
3757
+ 5DHVB_5DHZL
3758
+ 5DHVB_6CF2B
3759
+ 5DHVB_6OSHL
3760
+ 5DHVB_6OSVL
3761
+ 5DO7B_5DO7B
3762
+ 5DO7B_5DO7D
3763
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3764
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3765
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3766
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3767
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3768
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3769
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3770
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3771
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3772
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3773
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3774
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3775
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3776
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3777
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3778
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3779
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3780
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3781
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3782
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3783
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3784
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3785
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3786
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3787
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3788
+ 5ELUA_5OHLE
3789
+ 5ELUA_5OHMD
3790
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3791
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3792
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3793
+ 5ERPA_7A7Dc
3794
+ 5ERPA_7A7Dd
3795
+ 5ERPA_7A7Df
3796
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3797
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3798
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3799
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3800
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3801
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3802
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3803
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3804
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3805
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3806
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3807
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3808
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3809
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3810
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3811
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3812
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3813
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3814
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3815
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3816
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3817
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3818
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3819
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3820
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3821
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3822
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3823
+ 5FLME_6XREE
3824
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3825
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3826
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3827
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3828
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3829
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3830
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3831
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3832
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3833
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3834
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3835
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3836
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3837
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3838
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3839
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3840
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3841
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3842
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3843
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3844
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3845
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3846
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3847
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3848
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3849
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3850
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3851
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3852
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3853
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3854
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3855
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3856
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3857
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3858
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3859
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3860
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3861
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3862
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3863
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3864
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3865
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3866
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3867
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3868
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3869
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3870
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3871
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3872
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3873
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3874
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3875
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3876
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3877
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3878
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3879
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3880
+ 5GPNh_7R4GM
3881
+ 5GPNV_5GPNV
3882
+ 5GPNV_7DGQA4
3883
+ 5GPNV_7DGRA3
3884
+ 5GPNV_7DGSA3
3885
+ 5GPNV_7DGSt
3886
+ 5GPYA_5GPYA
3887
+ 5GPYA_5IY8Q
3888
+ 5GPYA_7EG9U
3889
+ 5GPYA_7EGCU
3890
+ 5GPYA_7NVUW
3891
+ 5GUDA_5GUDB
3892
+ 5GUDA_5GUDD
3893
+ 5GUDA_5IJZA
3894
+ 5GUDA_5IJZJ
3895
+ 5GUDA_5IJZL
3896
+ 5GUPc_5GUPc
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5459
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5460
+ 8AP6D_8APHd
5461
+ 8B40A_8B40A
5462
+ 8B40A_8B40D
5463
+ 8B40A_8B42E
5464
+ 8B40A_8DS3F
5465
+ 8B40A_8F79F
5466
+ 8D3LA_8D3LA
5467
+ 8D3LA_8D3MB
5468
+ 8D3LA_8D3MD
5469
+ 8D3LA_8D3QB
5470
+ 8D3LA_8D3QC
5471
+ 8DTPA_8DTPA
5472
+ 8DTPA_8DTPF
5473
+ 8DTPA_8DUEA
5474
+ 8DTPA_8DUOF
5475
+ 8DTPA_8G0ZD
5476
+ 8EC2A_8EC2B
5477
+ 8EC2A_8EC2G
5478
+ 8EC2A_8ECJA
5479
+ 8EC2A_8ECJB
5480
+ 8EC2A_8ECJG
5481
+ 8EMCA_8EMCD
5482
+ 8EMCA_8EMHB
5483
+ 8EMCA_8EMHC
5484
+ 8EMCA_8EMHG
5485
+ 8EMCA_8EMHJ
5486
+ 8ESQG_8ESQG
5487
+ 8ESQG_8ETGG
5488
+ 8ESQG_8ETJG
5489
+ 8ESQG_8EUGG
5490
+ 8ESQG_8EUIG
5491
+ 8FXHA_8FXHA
5492
+ 8FXHA_8FXHB
5493
+ 8FXHA_8FXHC
5494
+ 8FXHA_8FXIB
5495
+ 8FXHA_8FXIC
petimot/__init__.py ADDED
File without changes
petimot/__main__.py ADDED
@@ -0,0 +1,231 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import typer
2
+
3
+ app = typer.Typer()
4
+
5
+
6
+ @app.command(name="infer")
7
+ def run_infer(
8
+ model_path: str = typer.Option(
9
+ "",
10
+ "--model-path",
11
+ "-m",
12
+ help="Path to model checkpoint",
13
+ ),
14
+ config_file: str = typer.Option(
15
+ "configs/default.yaml",
16
+ "--config-file",
17
+ "-c",
18
+ help="Path to configuration file",
19
+ ),
20
+ list_path: str = typer.Option(
21
+ "",
22
+ "--list-path",
23
+ "-l",
24
+ help="Path to file containing input files or sample indices, or direct path to a PDB/PT file",
25
+ ),
26
+ output_path: str = typer.Option(
27
+ "predictions",
28
+ "--output-path",
29
+ "-o",
30
+ help="Output directory path",
31
+ ),
32
+ ):
33
+ """
34
+ Run inference using a pretrained model. The input can be:
35
+ 1. A direct path to a .pdb or .pt file
36
+ 2. A text file containing paths or sample names
37
+ """
38
+ from petimot.infer.infer import infer
39
+
40
+ if list_path.endswith((".pdb", ".pt")):
41
+ input_list = [list_path]
42
+ else:
43
+ try:
44
+ with open(list_path, "r") as f:
45
+ input_list = [line.strip() for line in f if line.strip()]
46
+ except Exception as e:
47
+ raise typer.BadParameter(f"Error reading list file: {e}")
48
+
49
+ if not input_list:
50
+ raise typer.BadParameter("No input files specified")
51
+
52
+ infer(
53
+ model_path=model_path,
54
+ config_file=config_file,
55
+ input_list=input_list,
56
+ output_path=output_path,
57
+ )
58
+
59
+
60
+ @app.command(name="evaluate")
61
+ def run_evaluate(
62
+ prediction_path: str = typer.Option(
63
+ "",
64
+ "--prediction-path",
65
+ "-p",
66
+ help="Path to the directory containing predicted modes.",
67
+ ),
68
+ sample_ids_file: str = typer.Option(
69
+ "eval_list.txt",
70
+ "--list-path",
71
+ "-l",
72
+ help="Path to a text file containing sample IDs (one per line).",
73
+ ),
74
+ ground_truth_path: str = typer.Option(
75
+ "ground_truth",
76
+ "--ground-truth-path",
77
+ "-g",
78
+ help="Path to the directory containing ground truth .pt files.",
79
+ ),
80
+ output_path: str = typer.Option(
81
+ "evaluation",
82
+ "--output-path",
83
+ "-o",
84
+ help="Path to the directory where evaluation results will be saved.",
85
+ ),
86
+ num_modes_pred: int = typer.Option(
87
+ 4,
88
+ "--num-modes-pred",
89
+ "-np",
90
+ help="Number of predicted modes.",
91
+ ),
92
+ num_modes_gt: int = typer.Option(
93
+ 4,
94
+ "--num-modes-gt",
95
+ "-ng",
96
+ help="Number of ground truth modes.",
97
+ ),
98
+ device: str = typer.Option(
99
+ "cuda",
100
+ "--device",
101
+ "-d",
102
+ help="Device to use for evaluation, e.g., 'cuda' or 'cpu'.",
103
+ ),
104
+ ):
105
+ """
106
+ Evaluate predicted modes against ground truth data stored in .pt files.
107
+ """
108
+ from petimot.eval.eval import evaluate
109
+
110
+ sample_ids = None
111
+ if sample_ids_file:
112
+ try:
113
+ with open(sample_ids_file, "r") as f:
114
+ sample_ids = [line.strip() for line in f if line.strip()]
115
+ print(f"Loaded {len(sample_ids)} sample IDs from {sample_ids_file}")
116
+ except Exception as e:
117
+ raise typer.BadParameter(f"Error reading sample IDs file: {e}")
118
+
119
+ evaluate(
120
+ prediction_path=prediction_path,
121
+ ground_truth_path=ground_truth_path,
122
+ output_path=output_path,
123
+ sample_ids=sample_ids,
124
+ num_modes_pred=num_modes_pred,
125
+ num_modes_gt=num_modes_gt,
126
+ device=device,
127
+ )
128
+
129
+
130
+ @app.command(name="infer_and_evaluate")
131
+ def run_infer_and_evaluate(
132
+ model_path: str = typer.Option(
133
+ "",
134
+ "--model-path",
135
+ "-m",
136
+ help="Path to model checkpoint",
137
+ ),
138
+ config_file: str = typer.Option(
139
+ "configs/default.yaml",
140
+ "--config-file",
141
+ "-c",
142
+ help="Path to configuration file",
143
+ ),
144
+ list_path: str = typer.Option(
145
+ "eval_list.txt",
146
+ "--list-path",
147
+ "-l",
148
+ help="Path to file containing input files or sample indices",
149
+ ),
150
+ ground_truth_path: str = typer.Option(
151
+ "ground_truth",
152
+ "--ground-truth-path",
153
+ "-g",
154
+ help="Path to the directory containing ground truth .pt files.",
155
+ ),
156
+ prediction_path: str = typer.Option(
157
+ "predictions",
158
+ "--prediction-path",
159
+ "-p",
160
+ help="Directory path for saving predictions",
161
+ ),
162
+ evaluation_path: str = typer.Option(
163
+ "evaluation",
164
+ "--evaluation-path",
165
+ "-e",
166
+ help="Directory path for saving evaluation results",
167
+ ),
168
+ num_modes_pred: int = typer.Option(
169
+ 4,
170
+ "--num-modes-pred",
171
+ "-np",
172
+ help="Number of predicted modes.",
173
+ ),
174
+ num_modes_gt: int = typer.Option(
175
+ 4,
176
+ "--num-modes-gt",
177
+ "-ng",
178
+ help="Number of ground truth modes.",
179
+ ),
180
+ device: str = typer.Option(
181
+ "cuda",
182
+ "--device",
183
+ "-d",
184
+ help="Device to use for inference and evaluation.",
185
+ ),
186
+ ):
187
+ """
188
+ Run inference using a pretrained model and immediately evaluate the results.
189
+ This combined command streamlines the workflow by performing both inference
190
+ and evaluation in one step, while keeping predictions and evaluation results
191
+ in separate directories.
192
+ """
193
+ from petimot.infer.infer import infer
194
+ from petimot.eval.eval import evaluate
195
+ import os
196
+
197
+ with open(list_path, "r") as f:
198
+ input_list = [line.strip() for line in f if line.strip()]
199
+
200
+ print(f"Starting inference phase...")
201
+ infer(
202
+ model_path=model_path,
203
+ config_file=config_file,
204
+ input_list=input_list,
205
+ output_path=prediction_path,
206
+ )
207
+
208
+ model_name = os.path.splitext(os.path.basename(model_path))[0]
209
+ model_prediction_path = os.path.join(prediction_path, model_name)
210
+
211
+ print(f"\nStarting evaluation phase...")
212
+
213
+ results = evaluate(
214
+ prediction_path=model_prediction_path,
215
+ ground_truth_path=ground_truth_path,
216
+ output_path=evaluation_path,
217
+ sample_ids=input_list,
218
+ num_modes_pred=num_modes_pred,
219
+ num_modes_gt=num_modes_gt,
220
+ device=device,
221
+ )
222
+
223
+ print("\nInference and evaluation complete!")
224
+ print(f"Predictions saved to: {model_prediction_path}")
225
+ print(f"Evaluation results saved to: {evaluation_path}")
226
+
227
+ return results
228
+
229
+
230
+ if __name__ == "__main__":
231
+ app()
petimot/config/__init__.py ADDED
File without changes
petimot/config/config.py ADDED
@@ -0,0 +1,107 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from dataclasses import dataclass, field
2
+ import torch
3
+ import yaml
4
+
5
+
6
+ @dataclass
7
+ class ModelConfig:
8
+ # Architecture parameters
9
+ emb_dim: int = 256
10
+ edge_dim: int = 329
11
+ num_modes_pred: int = 4
12
+ num_layers: int = 15
13
+ shared_layers: bool = False
14
+ mlp_num_layers: int = 1
15
+ start_with_zero_v: bool = False
16
+
17
+ # Regularization
18
+ input_embedding_dropout: float = 0.8
19
+ dropout: float = 0.4
20
+ normalize_between_layers: bool = False
21
+ center_between_layers: bool = False
22
+ orthogonalize_between_layers: bool = False
23
+
24
+ # Geometric features
25
+ num_basis: int = 20
26
+ num_backbone_atoms: int = 4
27
+ max_dist: float = 20.0
28
+ sigma: float = 1.0
29
+
30
+ # Ablation
31
+ ablate_structure: bool = False
32
+
33
+ def __post_init__(self):
34
+ expected_edge_dim = 9 + (self.num_backbone_atoms**2 * self.num_basis)
35
+ if self.edge_dim != expected_edge_dim:
36
+ raise ValueError(
37
+ f"edge_dim must be 9 + (num_backbone_atoms² * num_basis) = "
38
+ f"9 + ({self.num_backbone_atoms}² * {self.num_basis}) = {expected_edge_dim}. "
39
+ f"Got {self.edge_dim}"
40
+ )
41
+
42
+
43
+ @dataclass
44
+ class DataConfig:
45
+ training_split_path: str = "full_train_list.txt"
46
+ validation_split_path: str = "val_list.txt"
47
+ ground_truth_dir: str = "ground_truth"
48
+ embedding_dir: str = "embeddings"
49
+ emb_model: str = "ProstT5"
50
+ noise: float = 0.0
51
+ k_nearest: int = 5
52
+ l_random: int = 10
53
+ num_modes_gt: int = 4
54
+ rand_emb: bool = False
55
+ change_connectivity: bool = True
56
+
57
+
58
+ @dataclass
59
+ class TrainingConfig:
60
+ seed: int = 7
61
+ nsse_weight: float = 0.5 # LS loss
62
+ rmsip_weight: float = 0.5 # SS loss
63
+ ortho_weight: float = 0.0 # IS loss
64
+ batch_size: int = 32
65
+ validation_batch_size: int = 32
66
+ nb_epochs: int = 500
67
+ device: str = "cuda" if torch.cuda.is_available() else "cpu"
68
+ optimizer: str = "adamw"
69
+ learning_rate: float = 5e-4
70
+ weight_decay: float = 0.01
71
+ grad_clip: float = 10
72
+ use_amp: bool = True
73
+ scheduler_factor: float = 0.2
74
+ scheduler_patience: int = 10
75
+ early_stop_patience: int = 50
76
+ loss_threshold: float = 0.6
77
+ num_workers: int = 6
78
+ num_workers_val: int = 2
79
+ pin_memory: bool = False
80
+ weights_dir: str = "weights"
81
+ debug: bool = False
82
+
83
+
84
+ @dataclass
85
+ class Config:
86
+ model: ModelConfig = field(default_factory=ModelConfig)
87
+ data: DataConfig = field(default_factory=DataConfig)
88
+ training: TrainingConfig = field(default_factory=TrainingConfig)
89
+
90
+ @classmethod
91
+ def from_file(cls, path: str) -> "Config":
92
+ with open(path, "r") as f:
93
+ config_dict = yaml.safe_load(f) or {}
94
+ return cls(
95
+ model=ModelConfig(**config_dict.get("model", {})),
96
+ data=DataConfig(**config_dict.get("data", {})),
97
+ training=TrainingConfig(**config_dict.get("training", {})),
98
+ )
99
+
100
+ def save(self, path: str) -> None:
101
+ config_dict = {
102
+ "model": self.model.__dict__,
103
+ "data": self.data.__dict__,
104
+ "training": self.training.__dict__,
105
+ }
106
+ with open(path, "w") as f:
107
+ yaml.dump(config_dict, f, sort_keys=False)
petimot/data/__init__.py ADDED
File without changes
petimot/data/data_set.py ADDED
@@ -0,0 +1,282 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ from torch_geometric.data import Data, Dataset
3
+ from abc import ABC, abstractmethod
4
+ from typing import Tuple, List, Optional, Union
5
+ from pathlib import Path
6
+ import logging
7
+ from .embeddings import EmbeddingManager
8
+ from .pdb_utils import load_backbone_coordinates
9
+ import warnings
10
+
11
+ logger = logging.getLogger(__name__)
12
+ logging.basicConfig(level=logging.INFO)
13
+
14
+
15
+ def generate_edges_no_self(num_nodes: int) -> Tuple[torch.Tensor, torch.Tensor]:
16
+ i, j = torch.meshgrid(
17
+ torch.arange(num_nodes), torch.arange(num_nodes), indexing="ij"
18
+ )
19
+ mask = i != j
20
+ return torch.vstack((i[mask], j[mask]))
21
+
22
+
23
+ def build_knn_edges(
24
+ dist_matrix: torch.Tensor, k: int
25
+ ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
26
+ num_nodes = dist_matrix.size(0)
27
+ _, nearest_neighbors = torch.topk(dist_matrix, k=k + 1, largest=False)
28
+ nearest_neighbors = nearest_neighbors[:, 1:]
29
+ k = nearest_neighbors.size(1)
30
+ row_indices = (
31
+ torch.arange(num_nodes, device=dist_matrix.device)[:, None]
32
+ .expand(-1, k)
33
+ .flatten()
34
+ )
35
+ col_indices = nearest_neighbors.flatten()
36
+ mask = torch.ones(
37
+ (num_nodes, num_nodes), dtype=torch.bool, device=dist_matrix.device
38
+ )
39
+ mask[torch.arange(num_nodes), torch.arange(num_nodes)] = False
40
+ mask.scatter_(1, nearest_neighbors, False)
41
+
42
+ return row_indices, col_indices, mask
43
+
44
+
45
+ def build_random_edges(
46
+ num_nodes: int, mask: torch.Tensor, l: int
47
+ ) -> tuple[torch.Tensor, torch.Tensor]:
48
+ rand_probs = torch.rand((num_nodes, num_nodes), device=mask.device).masked_fill(
49
+ ~mask, -float("inf")
50
+ )
51
+ _, random_indices = torch.topk(rand_probs, l, dim=1)
52
+ row_indices = (
53
+ torch.arange(num_nodes, device=mask.device)[:, None].expand(-1, l).flatten()
54
+ )
55
+ col_indices = random_indices.flatten()
56
+
57
+ return row_indices, col_indices
58
+
59
+
60
+ def build_connectivity(
61
+ seq_len: int,
62
+ dist_matrix: torch.Tensor,
63
+ k_nearest: int,
64
+ l_random: int,
65
+ num_layers: int,
66
+ change_connectivity: bool,
67
+ ) -> tuple[torch.Tensor, torch.Tensor]:
68
+
69
+ if k_nearest + l_random >= seq_len - 1:
70
+ row_index, col_index_fixed = generate_edges_no_self(seq_len)
71
+ if change_connectivity:
72
+ col_index = col_index_fixed.unsqueeze(0).repeat(num_layers, 1)
73
+ else:
74
+ col_index = col_index_fixed
75
+ return row_index, col_index
76
+
77
+ if k_nearest > 0:
78
+ row_index_knn, col_index_knn, mask = build_knn_edges(dist_matrix, k_nearest)
79
+ else:
80
+
81
+ mask = torch.ones((seq_len, seq_len), dtype=torch.bool)
82
+ mask.fill_diagonal_(False)
83
+ row_index_knn = torch.empty(0, dtype=torch.long)
84
+ col_index_knn = torch.empty(0, dtype=torch.long)
85
+
86
+ if not change_connectivity:
87
+ if l_random > 0:
88
+ row_index_rand, col_index_rand = build_random_edges(seq_len, mask, l_random)
89
+ else:
90
+ row_index_rand = torch.empty(0, dtype=torch.long)
91
+ col_index_rand = torch.empty(0, dtype=torch.long)
92
+ row_index_total = torch.cat([row_index_knn, row_index_rand], dim=0)
93
+ col_index_total = torch.cat([col_index_knn, col_index_rand], dim=0)
94
+ return row_index_total, col_index_total
95
+ else:
96
+ fixed_row = row_index_knn
97
+ fixed_col = col_index_knn
98
+ col_indices_layers = []
99
+ for _ in range(num_layers):
100
+ if l_random > 0:
101
+ row_index_rand_layer, col_index_rand_layer = build_random_edges(
102
+ seq_len, mask, l_random
103
+ )
104
+ else:
105
+ row_index_rand_layer = torch.empty(0, dtype=torch.long)
106
+ col_index_rand_layer = torch.empty(0, dtype=torch.long)
107
+ combined_col = torch.cat([fixed_col, col_index_rand_layer], dim=0)
108
+ col_indices_layers.append(combined_col)
109
+
110
+ if l_random > 0:
111
+ row_index_rand = row_index_rand_layer
112
+ else:
113
+ row_index_rand = torch.empty(0, dtype=torch.long)
114
+ total_row_index = torch.cat([fixed_row, row_index_rand], dim=0)
115
+ col_index = torch.stack(col_indices_layers, dim=0)
116
+ return total_row_index, col_index
117
+
118
+
119
+ class BaseDataset(Dataset, ABC):
120
+ def __init__(
121
+ self,
122
+ ground_truth_dir: Union[str, Path],
123
+ embedding_dir: Union[str, Path],
124
+ emb_model: str,
125
+ device: str,
126
+ k_nearest: int,
127
+ l_random: int,
128
+ num_layers: int,
129
+ change_connectivity: bool,
130
+ ):
131
+ super().__init__()
132
+ self.ground_truth_dir = Path(ground_truth_dir)
133
+ self.embedding_dir = Path(embedding_dir)
134
+ self.emb_model = emb_model.lower()
135
+ self.device = device
136
+ self.k_nearest = k_nearest
137
+ self.l_random = l_random
138
+ self.num_layers = num_layers
139
+ self.change_connectivity = change_connectivity
140
+
141
+ def _build_connectivity(
142
+ self, ca_coords: torch.Tensor
143
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
144
+ dist_matrix = torch.cdist(ca_coords, ca_coords)
145
+ return build_connectivity(
146
+ seq_len=ca_coords.size(0),
147
+ dist_matrix=dist_matrix,
148
+ k_nearest=self.k_nearest,
149
+ l_random=self.l_random,
150
+ num_layers=self.num_layers,
151
+ change_connectivity=self.change_connectivity,
152
+ )
153
+
154
+ def _load_embedding(self, name: str, seq_len: int) -> torch.Tensor:
155
+ emb_path = self.embedding_dir / f"{name}_{self.emb_model}.pt"
156
+ if not emb_path.exists():
157
+ raise FileNotFoundError(f"Embedding {emb_path} not found")
158
+
159
+ emb = torch.load(emb_path, map_location="cpu", weights_only=True)
160
+ if emb.size(0) == seq_len + 2: # Handle BOS/EOS tokens
161
+ emb = emb[1:-1]
162
+ elif emb.size(0) != seq_len:
163
+ raise ValueError(f"Embedding size mismatch: {emb.size(0)} vs {seq_len}")
164
+
165
+ return emb
166
+
167
+ def _compute_embeddings(self, names: List[str], sequences: List[str]) -> None:
168
+ """Shared logic for computing missing embeddings."""
169
+ if not names:
170
+ return
171
+
172
+ logger.info(f"Computing embeddings for {len(names)} samples")
173
+ EmbeddingManager(
174
+ embedding_dir=self.embedding_dir,
175
+ emb_model=self.emb_model,
176
+ device=self.device,
177
+ ).get_or_compute_embeddings(names, sequences)
178
+
179
+ @abstractmethod
180
+ def __getitem__(self, idx: int) -> Data:
181
+ pass
182
+
183
+ @abstractmethod
184
+ def __len__(self) -> int:
185
+ pass
186
+
187
+
188
+ class InferenceDataset(BaseDataset):
189
+ def __init__(
190
+ self,
191
+ entries: List[str],
192
+ ground_truth_dir: Union[str, Path],
193
+ embedding_dir: Union[str, Path],
194
+ emb_model: str,
195
+ device: str,
196
+ k_nearest: int,
197
+ l_random: int,
198
+ num_layers: int,
199
+ change_connectivity: bool,
200
+ ):
201
+ super().__init__(
202
+ ground_truth_dir=ground_truth_dir,
203
+ embedding_dir=embedding_dir,
204
+ emb_model=emb_model,
205
+ device=device,
206
+ k_nearest=k_nearest,
207
+ l_random=l_random,
208
+ num_layers=num_layers,
209
+ change_connectivity=change_connectivity,
210
+ )
211
+ self.file_paths = self._resolve_entries(entries)
212
+ self._compute_missing_embeddings()
213
+
214
+ def _resolve_entries(self, entries: List[str]) -> List[Path]:
215
+ resolved = []
216
+ for entry in entries:
217
+ path = Path(entry)
218
+ if path.exists():
219
+ resolved.append(path.resolve())
220
+ continue
221
+
222
+ for ext in [".pt"]:
223
+ candidate = self.ground_truth_dir / f"{entry}{ext}"
224
+ if candidate.exists():
225
+ resolved.append(candidate)
226
+ break
227
+ else:
228
+ logger.warning(f"Couldn't resolve entry: {entry}")
229
+
230
+ if not resolved:
231
+ raise ValueError("No valid input files found")
232
+ return resolved
233
+
234
+ def _compute_missing_embeddings(self) -> None:
235
+ valid_names, sequences = [], []
236
+ for path in self.file_paths:
237
+ emb_path = self.embedding_dir / f"{path.stem}_{self.emb_model}.pt"
238
+ if emb_path.exists():
239
+ continue
240
+
241
+ try:
242
+ sequences.append(self._extract_sequence(path))
243
+ valid_names.append(path.stem)
244
+ except Exception as e:
245
+ logger.error(f"Failed processing {path.name}: {e}")
246
+
247
+ self._compute_embeddings(valid_names, sequences)
248
+
249
+ def _extract_sequence(self, path: Path) -> str:
250
+ if path.suffix == ".pt":
251
+ return torch.load(path, map_location="cpu", weights_only=True).get(
252
+ "seq", ""
253
+ )
254
+ elif path.suffix == ".pdb":
255
+ seq = load_backbone_coordinates(str(path), allow_hetatm=True)["seq"]
256
+ return seq
257
+ raise ValueError(f"Unsupported file type: {path.suffix}")
258
+
259
+ def __len__(self) -> int:
260
+ return len(self.file_paths)
261
+
262
+ def __getitem__(self, idx: int) -> Data:
263
+ path = self.file_paths[idx]
264
+
265
+ if path.suffix == ".pt":
266
+ data = torch.load(path, map_location="cpu", weights_only=True)
267
+ bb, coverage = data["bb"], data.get("coverage", torch.ones(len(data["bb"])))
268
+ else:
269
+ bb = load_backbone_coordinates(str(path), allow_hetatm=True)["bb"]
270
+ coverage = torch.ones(len(bb))
271
+
272
+ row_idx, col_idx = self._build_connectivity(bb[:, 1])
273
+
274
+ return Data(
275
+ x=self._load_embedding(path.stem, bb.size(0)),
276
+ row_index=row_idx,
277
+ col_index=col_idx,
278
+ bb=bb,
279
+ coverage=coverage,
280
+ name=path.stem,
281
+ path=str(path),
282
+ )
petimot/data/embeddings.py ADDED
@@ -0,0 +1,84 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from pathlib import Path
2
+ import torch
3
+ import re
4
+ from transformers import T5Tokenizer, T5EncoderModel
5
+ from typing import List, Union
6
+
7
+
8
+ class EmbeddingManager:
9
+ def __init__(
10
+ self,
11
+ embedding_dir: Path,
12
+ emb_model: str,
13
+ device: Union[str, torch.device] = "cuda",
14
+ ):
15
+ emb_model = emb_model.lower()
16
+ valid_models = ["prostt5", "esmc_300m", "esmc_600m"]
17
+ if emb_model not in valid_models:
18
+ raise ValueError(
19
+ f"Unsupported model: {emb_model}. Choose from {valid_models}."
20
+ )
21
+
22
+ self.embedding_dir = Path(embedding_dir)
23
+ self.embedding_dir.mkdir(exist_ok=True)
24
+ self.emb_model = emb_model
25
+
26
+ if isinstance(device, str):
27
+ self.device = torch.device(device)
28
+ else:
29
+ self.device = device
30
+
31
+ if emb_model == "prostt5":
32
+ self.tokenizer = T5Tokenizer.from_pretrained(
33
+ "Rostlab/ProstT5", do_lower_case=False, legacy=True
34
+ )
35
+ self.model = T5EncoderModel.from_pretrained("Rostlab/ProstT5").to(
36
+ self.device
37
+ )
38
+
39
+ if self.device.type == "cuda":
40
+ self.model = self.model.half()
41
+ else:
42
+ self.model = self.model.float()
43
+
44
+ elif emb_model.startswith("esmc"):
45
+ from esm.models.esmc import ESMC
46
+ from esm.sdk.api import ESMProtein, LogitsConfig
47
+
48
+ self.model = ESMC.from_pretrained(emb_model).to(self.device)
49
+ self.model = self.model.float()
50
+ self.ESMProtein = ESMProtein
51
+ self.LogitsConfig = LogitsConfig
52
+ self.tokenizer = None
53
+
54
+ self.model.eval()
55
+
56
+ def get_or_compute_embeddings(self, names: List[str], sequences: List[str]) -> None:
57
+ for name, seq in zip(names, sequences):
58
+ emb_path = self.embedding_dir / f"{name}_{self.emb_model}.pt"
59
+ if not emb_path.exists():
60
+ emb = self._compute_embedding(seq)
61
+ torch.save(emb.cpu(), emb_path)
62
+
63
+ @torch.no_grad()
64
+ def _compute_embedding(self, sequence: str) -> torch.Tensor:
65
+ sequence = re.sub(r"[UZOB]", "X", sequence)
66
+
67
+ if self.emb_model == "prostt5":
68
+ sequence = "<AA2fold> " + " ".join(list(sequence))
69
+ ids = self.tokenizer.encode_plus(
70
+ sequence, add_special_tokens=True, return_tensors="pt"
71
+ ).to(self.device)
72
+ outputs = self.model(ids.input_ids, attention_mask=ids.attention_mask)
73
+ embeddings = outputs.last_hidden_state.squeeze(0)[1:-1].float()
74
+
75
+ elif self.emb_model.startswith("esmc"):
76
+ protein = self.ESMProtein(sequence=sequence)
77
+ protein_tensor = self.model.encode(protein)
78
+ logits_output = self.model.logits(
79
+ protein_tensor, self.LogitsConfig(sequence=True, return_embeddings=True)
80
+ )
81
+
82
+ embeddings = logits_output.embeddings.squeeze(0)
83
+
84
+ return embeddings
petimot/data/pdb_utils.py ADDED
@@ -0,0 +1,157 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from typing import Dict, Tuple, List
2
+ import torch
3
+ from pathlib import Path
4
+ import os
5
+
6
+
7
+ def load_backbone_coordinates(
8
+ pdb_path: str,
9
+ allow_hetatm: bool = False,
10
+ ) -> Tuple[torch.Tensor, ...]:
11
+
12
+ THREE_TO_ONE = {
13
+ "ALA": "A",
14
+ "ARG": "R",
15
+ "ASN": "N",
16
+ "ASP": "D",
17
+ "CYS": "C",
18
+ "GLN": "Q",
19
+ "GLU": "E",
20
+ "GLY": "G",
21
+ "HIS": "H",
22
+ "ILE": "I",
23
+ "LEU": "L",
24
+ "LYS": "K",
25
+ "MET": "M",
26
+ "PHE": "F",
27
+ "PRO": "P",
28
+ "SER": "S",
29
+ "THR": "T",
30
+ "TRP": "W",
31
+ "TYR": "Y",
32
+ "VAL": "V",
33
+ "SEC": "U",
34
+ "PYL": "O",
35
+ "UNK": "X",
36
+ # Non-standard and modified amino acids
37
+ "ABA": "A",
38
+ "ALY": "K",
39
+ "BFD": "D",
40
+ "CAF": "C",
41
+ "CAS": "C",
42
+ "CGU": "E",
43
+ "CME": "C",
44
+ "CSD": "C",
45
+ "CSO": "C",
46
+ "CSS": "C",
47
+ "CSX": "C",
48
+ "CXM": "M",
49
+ "DAL": "A",
50
+ "DCY": "C",
51
+ "DHA": "S",
52
+ "DLE": "L",
53
+ "DSN": "S",
54
+ "FME": "M",
55
+ "HIC": "H",
56
+ "HYP": "P",
57
+ "IAS": "D",
58
+ "KCX": "K",
59
+ "LLP": "K",
60
+ "M3L": "K",
61
+ "MDO": "A",
62
+ "MEN": "N",
63
+ "MEQ": "Q",
64
+ "MHO": "M",
65
+ "MLE": "L",
66
+ "MLY": "K",
67
+ "MLZ": "K",
68
+ "MSE": "M",
69
+ "MVA": "V",
70
+ "NEP": "H",
71
+ "NLE": "L",
72
+ "OCS": "C",
73
+ "PCA": "E",
74
+ "PHD": "D",
75
+ "PTR": "Y",
76
+ "SAR": "G",
77
+ "SCH": "C",
78
+ "SCY": "C",
79
+ "SEP": "S",
80
+ "SMC": "C",
81
+ "SME": "M",
82
+ "SNC": "C",
83
+ "TPO": "T",
84
+ "TYS": "Y",
85
+ "YCM": "C",
86
+ }
87
+
88
+ residues, types, numbers = [], [], []
89
+ current = {"coords": [], "type": None, "num": None}
90
+
91
+ try:
92
+ with open(pdb_path, "r") as f:
93
+ for line in f:
94
+ if not (
95
+ line.startswith("ATOM")
96
+ or (allow_hetatm and line.startswith("HETATM"))
97
+ ):
98
+ continue
99
+
100
+ atom = line[12:16].strip()
101
+ res_type = line[17:20].strip()
102
+
103
+ if res_type == "HOH" or atom not in ["N", "CA", "C", "O"]:
104
+ continue
105
+
106
+ coords = torch.tensor(
107
+ [float(line[30:38]), float(line[38:46]), float(line[46:54])]
108
+ )
109
+
110
+ current["coords"].append(coords)
111
+
112
+ if not current["type"]:
113
+ current["type"] = res_type
114
+ current["num"] = int(line[22:26])
115
+
116
+ if len(current["coords"]) == 4:
117
+ residues.append(torch.stack(current["coords"]))
118
+ types.append(current["type"])
119
+ numbers.append(current["num"])
120
+ current = {"coords": [], "type": None, "num": None}
121
+
122
+ except FileNotFoundError:
123
+ raise FileNotFoundError(f"PDB file not found: {pdb_path}")
124
+ except Exception as e:
125
+ raise ValueError(f"Error parsing PDB file: {e}")
126
+
127
+ if not residues:
128
+ raise ValueError("No valid backbone atoms found")
129
+
130
+ backbone = torch.stack(residues)
131
+
132
+ output_path = f"extracted_{Path(pdb_path).name}"
133
+ if not os.path.exists(output_path):
134
+ with open(output_path, "w") as f:
135
+ atom_num = 1
136
+ res_num = 1
137
+ for res_idx, residue in enumerate(residues):
138
+ res_type = types[res_idx]
139
+ for atom_idx, (atom_name, coords) in enumerate(
140
+ zip(["N", "CA", "C", "O"], residue)
141
+ ):
142
+ # Standard PDB format
143
+ f.write(
144
+ f"ATOM {atom_num:5d} {atom_name:<3s} {res_type:3s} A{res_num:4d} "
145
+ f"{coords[0]:8.3f}{coords[1]:8.3f}{coords[2]:8.3f}"
146
+ f" 1.00 0.00 {atom_name[0]:>2s}\n"
147
+ )
148
+ atom_num += 1
149
+ res_num += 1
150
+ f.write("END\n")
151
+
152
+ outputs = {"bb": backbone}
153
+ seq = "".join(THREE_TO_ONE.get(t, "X") for t in types)
154
+ outputs["seq"] = seq
155
+ outputs["residue_types"] = types
156
+ outputs["residue_numbers"] = numbers
157
+ return outputs
petimot/eval/eval.py ADDED
@@ -0,0 +1,286 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ import numpy as np
3
+ import os
4
+ import json
5
+ from pathlib import Path
6
+ from typing import Dict, List, Optional, Tuple
7
+ from tqdm import tqdm
8
+ from petimot.model.loss import (
9
+ compute_NSSE_matrix,
10
+ compute_rmsip_sq,
11
+ select_minimum_indices,
12
+ )
13
+
14
+
15
+ def compute_magnitude_error_matrix(
16
+ eigvects: torch.Tensor, coverage: torch.Tensor, modes_pred: torch.Tensor
17
+ ) -> torch.Tensor:
18
+ N, nmode_gt, _ = eigvects.shape
19
+
20
+ modes_pred = modes_pred - modes_pred.mean(dim=0, keepdim=True)
21
+
22
+ eigvects = eigvects - eigvects.mean(dim=0, keepdim=True)
23
+
24
+ gt_magnitudes = torch.norm(eigvects, dim=2)[:, :, None] # Shape: (N, nmode_gt,1)
25
+ pred_magnitudes = torch.norm(modes_pred, dim=2)[
26
+ :, None, :
27
+ ] # Shape: (N, 1, nmode_pred)
28
+
29
+ coverage = coverage[:, None, None]
30
+ sqrt_cov = torch.sqrt(coverage) # Shape: (N, 1, 1)
31
+
32
+ numerator = torch.sum(sqrt_cov * gt_magnitudes * pred_magnitudes, dim=0)
33
+
34
+ denominator = torch.sum(coverage * pred_magnitudes.pow(2), dim=0) + 1e-8
35
+ c_optimal = numerator / denominator
36
+
37
+ pred_magnitudes = (
38
+ sqrt_cov * pred_magnitudes * c_optimal[None, :, :]
39
+ ) # Shape: (N, 1, nmode_pred)
40
+
41
+ sum_squared_error_matrix = (
42
+ torch.sum((gt_magnitudes - pred_magnitudes) ** 2, dim=0) / N
43
+ )
44
+
45
+ return sum_squared_error_matrix
46
+
47
+
48
+ def compute_optimal_assignment_metrics(
49
+ matrix: torch.Tensor, maximize: bool = False
50
+ ) -> float:
51
+
52
+ cost_matrix = -matrix if maximize else matrix
53
+
54
+ # Get optimal indices using Hungarian algorithm
55
+ indices = select_minimum_indices(cost_matrix)
56
+
57
+ optimal_cost = matrix[indices[:, 0], indices[:, 1]].mean().item()
58
+
59
+ return optimal_cost
60
+
61
+
62
+ def load_ground_truth(
63
+ file_path: str, num_modes_gt: int, device: str
64
+ ) -> Optional[Tuple[torch.Tensor, torch.Tensor]]:
65
+ try:
66
+ data = torch.load(file_path, map_location=device)
67
+ eigvects = data["eigvects"]
68
+ seq_length = int(len(eigvects) / 3)
69
+
70
+ eigvects = eigvects[:, :num_modes_gt]
71
+ eigvects *= seq_length**0.5
72
+ eigvects = eigvects.reshape(-1, 3, num_modes_gt).permute(0, 2, 1)
73
+
74
+ coverage = data.get("coverage", torch.ones(eigvects.shape[0], device=device))
75
+ if not isinstance(coverage, torch.Tensor):
76
+ coverage = torch.tensor(coverage, device=device)
77
+
78
+ return eigvects, coverage
79
+
80
+ except Exception as e:
81
+ print(f"Error loading ground truth: {e}")
82
+ return None
83
+
84
+
85
+ def load_predictions(
86
+ base_path: str, base_name: str, num_modes: int, device: str
87
+ ) -> Optional[torch.Tensor]:
88
+ try:
89
+ modes = []
90
+ for k in range(num_modes):
91
+ pred_file = os.path.join(base_path, f"{base_name}_mode_{k}.txt")
92
+ modes.append(np.loadtxt(pred_file))
93
+ return torch.tensor(np.stack(modes, axis=1), device=device, dtype=torch.float32)
94
+ except Exception as e:
95
+ print(f"Error loading predictions for {base_name}: {e}")
96
+ return None
97
+
98
+
99
+ def save_matrix(
100
+ output_path: str,
101
+ base_name: str,
102
+ matrix: torch.Tensor,
103
+ num_modes_gt: int,
104
+ metric_name: str,
105
+ ):
106
+ matrix_path = os.path.join(output_path, f"{base_name}_{metric_name}_matrix.csv")
107
+ with open(matrix_path, "w") as f:
108
+ f.write("mode_name," + ",".join(f"gt_{j}" for j in range(num_modes_gt)) + "\n")
109
+
110
+ matrix_cpu = matrix.cpu()
111
+ for i in range(len(matrix_cpu)):
112
+ row = f"pred_{i}," + ",".join(f"{val:.6f}" for val in matrix_cpu[i])
113
+ f.write(f"{row}\n")
114
+
115
+
116
+ def save_sample_metrics(output_path: str, base_name: str, metrics: Dict) -> None:
117
+ metrics_path = os.path.join(output_path, f"{base_name}_metrics.json")
118
+ with open(metrics_path, "w") as f:
119
+ json.dump(metrics, f, indent=2)
120
+
121
+
122
+ def evaluate(
123
+ prediction_path: str,
124
+ ground_truth_path: str,
125
+ output_path: str,
126
+ sample_ids: List[str],
127
+ num_modes_pred: int = 4,
128
+ num_modes_gt: int = 4,
129
+ device: str = "cuda",
130
+ success_threshold: float = 0.6,
131
+ ) -> Dict:
132
+
133
+ if device == "cuda" and not torch.cuda.is_available():
134
+ device = "cpu"
135
+
136
+ prediction_subdir = os.path.basename(prediction_path.rstrip("/"))
137
+ output_path = os.path.join(output_path, prediction_subdir)
138
+
139
+ os.makedirs(output_path, exist_ok=True)
140
+ if not sample_ids:
141
+ all_files = os.listdir(prediction_path)
142
+ mode0_files = [f for f in all_files if f.endswith("_mode_0.txt")]
143
+ if not mode0_files:
144
+ raise ValueError(
145
+ f"No files ending with '_mode_0.txt' found in {prediction_path}"
146
+ )
147
+
148
+ sample_ids = [Path(f).stem.rsplit("_mode", 1)[0] for f in mode0_files]
149
+
150
+ sample_ids = [
151
+ Path(sample_id).stem.rsplit("_mode", 1)[0] for sample_id in sample_ids
152
+ ]
153
+
154
+ min_losses = []
155
+ min_magnitude_errors = []
156
+ rmsip_sq_scores = []
157
+
158
+ optimal_losses = []
159
+ optimal_magnitudes = []
160
+ stats = {"total": 0, "success": 0}
161
+
162
+ missing_files = []
163
+ for sample_id in sample_ids:
164
+ mode0_file = os.path.join(prediction_path, f"{sample_id}_mode_0.txt")
165
+ gt_file = os.path.join(ground_truth_path, f"{sample_id}.pt")
166
+
167
+ if not os.path.exists(mode0_file):
168
+ missing_files.append(f"Missing prediction file: {mode0_file}")
169
+ if not os.path.exists(gt_file):
170
+ missing_files.append(f"Missing ground truth file: {gt_file}")
171
+
172
+ if missing_files:
173
+ print("Warning: Some files are missing:")
174
+ for msg in missing_files:
175
+ print(f" {msg}")
176
+ print("Proceeding with available files...")
177
+
178
+ for base_name in tqdm(sample_ids, desc="Evaluating samples"):
179
+ gt_data = load_ground_truth(
180
+ os.path.join(ground_truth_path, f"{base_name}.pt"), num_modes_gt, device
181
+ )
182
+ if gt_data is None:
183
+ continue
184
+
185
+ modes_pred = load_predictions(
186
+ prediction_path, base_name, num_modes_pred, device
187
+ )
188
+ if modes_pred is None:
189
+ continue
190
+
191
+ eigvects, coverage = gt_data
192
+ loss_matrix = compute_NSSE_matrix(eigvects, coverage, modes_pred).T
193
+ magnitude_error_matrix = compute_magnitude_error_matrix(
194
+ eigvects, coverage, modes_pred
195
+ ).T
196
+
197
+ rmsip_sq = compute_rmsip_sq(eigvects, coverage, modes_pred).item()
198
+
199
+ optimal_loss = compute_optimal_assignment_metrics(loss_matrix, maximize=False)
200
+
201
+ optimal_magnitude = compute_optimal_assignment_metrics(
202
+ magnitude_error_matrix, maximize=False
203
+ )
204
+
205
+ stats["total"] += 1
206
+ min_loss = torch.min(loss_matrix).item()
207
+ min_magnitude_error = torch.min(magnitude_error_matrix).item()
208
+
209
+ stats["success"] += int(min_loss < success_threshold)
210
+ min_losses.append(min_loss)
211
+ min_magnitude_errors.append(min_magnitude_error)
212
+ rmsip_sq_scores.append(rmsip_sq)
213
+
214
+ optimal_losses.append(optimal_loss)
215
+ optimal_magnitudes.append(optimal_magnitude)
216
+
217
+ sample_metrics = {
218
+ "nsse_metrics": {"min_loss": min_loss, "optimal_assignment": optimal_loss},
219
+ "magnitude_metrics": {
220
+ "min_error": min_magnitude_error,
221
+ "optimal_assignment": optimal_magnitude,
222
+ },
223
+ "rmsip_sq": rmsip_sq,
224
+ "success": min_loss < success_threshold,
225
+ }
226
+ save_sample_metrics(output_path, base_name, sample_metrics)
227
+
228
+ save_matrix(output_path, base_name, loss_matrix, num_modes_gt, "loss")
229
+ save_matrix(
230
+ output_path,
231
+ base_name,
232
+ magnitude_error_matrix,
233
+ num_modes_gt,
234
+ "magnitude_error",
235
+ )
236
+
237
+ results = {
238
+ "total_samples": stats["total"],
239
+ "success_rate": stats["success"] / stats["total"] if stats["total"] > 0 else 0,
240
+ "nsse_metrics": {
241
+ "mean_min_loss": float(np.mean(min_losses)) if min_losses else 0,
242
+ "std_min_loss": float(np.std(min_losses)) if min_losses else 0,
243
+ "optimal_assignment_mean": (
244
+ float(np.mean(optimal_losses)) if optimal_losses else 0
245
+ ),
246
+ "optimal_assignment_std": (
247
+ float(np.std(optimal_losses)) if optimal_losses else 0
248
+ ),
249
+ },
250
+ "magnitude_metrics": {
251
+ "mean_min_error": (
252
+ float(np.mean(min_magnitude_errors)) if min_magnitude_errors else 0
253
+ ),
254
+ "std_min_error": (
255
+ float(np.std(min_magnitude_errors)) if min_magnitude_errors else 0
256
+ ),
257
+ "optimal_assignment_mean": (
258
+ float(np.mean(optimal_magnitudes)) if optimal_magnitudes else 0
259
+ ),
260
+ "optimal_assignment_std": (
261
+ float(np.std(optimal_magnitudes)) if optimal_magnitudes else 0
262
+ ),
263
+ },
264
+ "rmsip_sq_metrics": {
265
+ "mean_rmsip_sq": float(np.mean(rmsip_sq_scores)) if rmsip_sq_scores else 0,
266
+ "std_rmsip_sq": float(np.std(rmsip_sq_scores)) if rmsip_sq_scores else 0,
267
+ },
268
+ }
269
+
270
+ with open(os.path.join(output_path, "evaluation_results.json"), "w") as f:
271
+ json.dump(results, f, indent=2)
272
+
273
+ print(f"\nEvaluation complete. Results saved to {output_path}")
274
+ print(f"Success rate: {results['success_rate']:.2%}")
275
+ print(f"NSSE metrics:")
276
+ print(
277
+ f" Min loss (mean ± std): {results['nsse_metrics']['mean_min_loss']:.4f} ± {results['nsse_metrics']['std_min_loss']:.4f}"
278
+ )
279
+ print(
280
+ f" Optimal assignment (mean ± std): {results['nsse_metrics']['optimal_assignment_mean']:.4f} ± {results['nsse_metrics']['optimal_assignment_std']:.4f}"
281
+ )
282
+ print(
283
+ f"Mean rmsip_sq: {results['rmsip_sq_metrics']['mean_rmsip_sq']:.4f} ± {results['rmsip_sq_metrics']['std_rmsip_sq']:.4f}"
284
+ )
285
+
286
+ return results
petimot/infer/infer.py ADDED
@@ -0,0 +1,148 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ import logging
3
+ import random
4
+ import numpy as np
5
+ from pathlib import Path
6
+ from typing import List
7
+ from tqdm import tqdm
8
+
9
+ from torch_geometric.loader import DataLoader
10
+
11
+ from petimot.model.neural_net import ProteinMotionMPNN
12
+ from petimot.config.config import Config
13
+ from petimot.data.data_set import InferenceDataset
14
+ from petimot.utils.seeding import set_seed
15
+
16
+ logger = logging.getLogger(__name__)
17
+ logging.basicConfig(level=logging.INFO)
18
+
19
+ EMBEDDING_DIM_MAP = {"prostt5": 1024, "esmc_300m": 960, "esmc_600m": 1152}
20
+
21
+
22
+ def setup_model(model_path: str, config: Config, device: str) -> ProteinMotionMPNN:
23
+
24
+ model = ProteinMotionMPNN(
25
+ input_dim=EMBEDDING_DIM_MAP[config.data.emb_model.lower()],
26
+ emb_dim=config.model.emb_dim,
27
+ edge_dim=config.model.edge_dim,
28
+ num_modes_pred=config.model.num_modes_pred,
29
+ num_layers=config.model.num_layers,
30
+ shared_layers=config.model.shared_layers,
31
+ mlp_num_layers=config.model.mlp_num_layers,
32
+ input_embedding_dropout=0.0,
33
+ dropout=0.0,
34
+ num_basis=config.model.num_basis,
35
+ max_dist=config.model.max_dist,
36
+ sigma=config.model.sigma,
37
+ change_connectivity=config.data.change_connectivity,
38
+ normalize_between_layers=config.model.normalize_between_layers,
39
+ center_between_layers=config.model.center_between_layers,
40
+ orthogonalize_between_layers=config.model.orthogonalize_between_layers,
41
+ start_with_zero_v=config.model.start_with_zero_v,
42
+ ablate_structure=config.model.ablate_structure,
43
+ ).to(device)
44
+
45
+ try:
46
+ state_dict = torch.load(model_path, map_location=device, weights_only=True)
47
+ model.load_state_dict(state_dict)
48
+ model.eval()
49
+ logger.info(f"Successfully loaded model weights from {model_path}")
50
+ except Exception as e:
51
+ error_msg = f"Failed to load model weights from {model_path}: {e}"
52
+ logger.error(error_msg)
53
+ raise RuntimeError(error_msg)
54
+
55
+ return model
56
+
57
+
58
+ def save_predictions(output_dir: Path, name: str, modes_pred: torch.Tensor) -> None:
59
+
60
+ modes_pred = modes_pred - modes_pred.mean(dim=0, keepdim=True)
61
+ modes_pred_np = modes_pred.cpu().numpy()
62
+ num_modes = modes_pred_np.shape[1]
63
+
64
+ for k in range(num_modes):
65
+ out_path = output_dir / f"{name}_mode_{k}.txt"
66
+ try:
67
+ np.savetxt(out_path, modes_pred_np[:, k, :], fmt="%.6f")
68
+ logger.debug(f"Saved mode {k} predictions to {out_path}")
69
+ except Exception as e:
70
+ logger.error(f"Failed to save prediction to {out_path}: {e}")
71
+
72
+
73
+ def infer(
74
+ model_path: str,
75
+ config_file: str,
76
+ input_list: List[str],
77
+ output_path: str,
78
+ device: str = "cuda",
79
+ ) -> None:
80
+
81
+ if device == "cuda" and not torch.cuda.is_available():
82
+ logger.warning("CUDA is not available. Falling back to CPU.")
83
+ device = "cpu"
84
+
85
+ output_dir = Path(output_path)
86
+ output_dir.mkdir(parents=True, exist_ok=True)
87
+
88
+ model_stem = Path(model_path).stem
89
+ output_subdir = output_dir / model_stem
90
+ output_subdir.mkdir(parents=True, exist_ok=True)
91
+
92
+ config = Config.from_file(config_file)
93
+ set_seed(config.training.seed, deterministic_algorithms=True)
94
+
95
+ model = setup_model(model_path, config, device)
96
+
97
+ dataset = InferenceDataset(
98
+ entries=input_list,
99
+ ground_truth_dir=config.data.ground_truth_dir,
100
+ embedding_dir=config.data.embedding_dir,
101
+ emb_model=config.data.emb_model,
102
+ device=device,
103
+ k_nearest=config.data.k_nearest,
104
+ l_random=config.data.l_random,
105
+ num_layers=config.model.num_layers,
106
+ change_connectivity=config.data.change_connectivity,
107
+ )
108
+
109
+ def seed_worker(worker_id):
110
+ worker_seed = torch.initial_seed() % 2**32
111
+ np.random.seed(worker_seed)
112
+ random.seed(worker_seed)
113
+
114
+ g = torch.Generator()
115
+ g.manual_seed(config.training.seed)
116
+
117
+ dataloader = DataLoader(
118
+ dataset,
119
+ batch_size=config.training.validation_batch_size,
120
+ num_workers=config.training.num_workers_val,
121
+ worker_init_fn=seed_worker,
122
+ generator=g,
123
+ shuffle=False,
124
+ )
125
+
126
+ logger.info(f"Starting inference on {len(dataset)} protein(s)")
127
+
128
+ with torch.no_grad():
129
+ for batch in tqdm(dataloader, desc="Processing proteins"):
130
+ try:
131
+ batch = batch.to(device)
132
+
133
+ _, modes_pred = model(batch) # (N, emb_dim), (N, num_modes, 3)
134
+
135
+ ptr = batch.ptr # shape [num_graphs + 1]
136
+ for graph_idx, (start_idx, end_idx) in enumerate(
137
+ zip(ptr[:-1], ptr[1:])
138
+ ):
139
+ name = batch.name[graph_idx]
140
+ modes_pred_graph = modes_pred[start_idx:end_idx]
141
+
142
+ save_predictions(output_subdir, name, modes_pred_graph)
143
+
144
+ except Exception as e:
145
+ logger.error(f"Error processing batch: {e}")
146
+ continue
147
+
148
+ logger.info(f"Inference complete. Results saved to {output_subdir}")
petimot/model/__init__.py ADDED
File without changes
petimot/model/loss.py ADDED
@@ -0,0 +1,227 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ from torch_geometric.data import Batch
3
+ from scipy.optimize import linear_sum_assignment
4
+ import sys
5
+
6
+
7
+ def compute_NSSE_matrix(
8
+ eigvects: torch.Tensor,
9
+ coverage: torch.Tensor,
10
+ modes_pred: torch.Tensor,
11
+ ) -> torch.Tensor:
12
+ N, _, _ = eigvects.shape
13
+
14
+ modes_pred = modes_pred - modes_pred.mean(dim=0, keepdim=True)
15
+
16
+ # Expand tensors for broadcasting
17
+ coverage = coverage.view(N, 1, 1, 1)
18
+ sqrt_cov = torch.sqrt(coverage) # (N, 1, 1, 1)
19
+ eigvects_expanded = eigvects.unsqueeze(2) # (N, gt, 1, 3)
20
+ modes_pred_expanded = modes_pred.unsqueeze(1) # (N, 1, pred, 3)
21
+
22
+ # Optimal scaling calculation
23
+ numerator = torch.sum(
24
+ sqrt_cov * eigvects_expanded * modes_pred_expanded, dim=(0, -1)
25
+ )
26
+ denominator = torch.sum(coverage * modes_pred_expanded.pow(2), dim=(0, -1)) + 1e-8
27
+ c_optimal = numerator / denominator
28
+
29
+ modes_adjusted = (
30
+ sqrt_cov * modes_pred_expanded * c_optimal.unsqueeze(0).unsqueeze(-1)
31
+ )
32
+
33
+ loss_matrix = (
34
+ torch.sum((eigvects_expanded - modes_adjusted).pow(2), dim=(0, -1)) / N
35
+ ) # nb_gt, nb_pred
36
+
37
+ return loss_matrix
38
+
39
+
40
+ def select_minimum_indices(cost_matrix: torch.Tensor) -> torch.Tensor:
41
+
42
+ cost_np = cost_matrix.detach().cpu().numpy()
43
+
44
+ # Get optimal indices
45
+ row_idx, col_idx = linear_sum_assignment(cost_np)
46
+
47
+ indices = torch.stack(
48
+ [
49
+ torch.tensor(row_idx, device=cost_matrix.device),
50
+ torch.tensor(col_idx, device=cost_matrix.device),
51
+ ],
52
+ dim=1,
53
+ )
54
+
55
+ return indices
56
+
57
+
58
+ def elementwise_loss(
59
+ eigvects: torch.Tensor, coverage: torch.Tensor, modes_pred: torch.Tensor
60
+ ) -> torch.Tensor:
61
+ loss_matrix = compute_NSSE_matrix(eigvects, coverage, modes_pred)
62
+ matched_indices = select_minimum_indices(loss_matrix)
63
+ return loss_matrix[matched_indices[:, 0], matched_indices[:, 1]] # LS loss
64
+
65
+
66
+ def compute_nsse_loss(batch: Batch, modes_pred: torch.Tensor) -> torch.Tensor:
67
+
68
+ eigvects, coverage, ptr = batch.eigvects, batch.coverage, batch.ptr
69
+ losses = []
70
+
71
+ for i in range(len(ptr) - 1):
72
+ start, end = ptr[i], ptr[i + 1]
73
+ losses.append(
74
+ elementwise_loss(
75
+ eigvects[start:end],
76
+ coverage[start:end],
77
+ modes_pred[start:end],
78
+ )
79
+ )
80
+
81
+ return torch.stack(losses)
82
+
83
+
84
+ def orthogonalize_modes(modes: torch.Tensor) -> torch.Tensor:
85
+ N, K, D = modes.shape # [number of residues, number of modes, 3]
86
+
87
+ modes_reshaped = modes.transpose(0, 1).reshape(K, -1) # shape [K, N*D]
88
+
89
+ Q, _ = torch.linalg.qr(modes_reshaped.T) # Q: [N*D, K], so that Q^T Q = I
90
+
91
+ modes_ortho = Q.reshape(N, D, K).transpose(1, 2) # [N, K, D]
92
+ return modes_ortho # this is Q, with Q^T Q = I
93
+
94
+
95
+ def compute_rmsip_sq(
96
+ eigvects: torch.Tensor, coverage: torch.Tensor, modes_pred: torch.Tensor
97
+ ) -> float:
98
+
99
+ eigvects = eigvects - eigvects.mean(dim=0, keepdim=True)
100
+ eigvects = eigvects / torch.norm(eigvects, dim=(0, 2), keepdim=True)
101
+
102
+ modes_pred = modes_pred - modes_pred.mean(dim=0, keepdim=True)
103
+
104
+ n_modes = min(eigvects.shape[1], modes_pred.shape[1])
105
+
106
+ sqrt_cov = torch.sqrt(coverage)[:, None, None] # shape [N, 1, 1]
107
+ weighted_modes_pred = modes_pred * sqrt_cov # shape [N, K, D]
108
+
109
+ weighted_modes_pred_ortho = orthogonalize_modes(weighted_modes_pred)[:, :n_modes, :]
110
+
111
+ eigvects = eigvects[:, :n_modes, :]
112
+
113
+ inner_products = torch.einsum("nkd,nld->kl", eigvects, weighted_modes_pred_ortho)
114
+
115
+ rmsip_sq = torch.sum(inner_products**2) / n_modes
116
+
117
+ return rmsip_sq
118
+
119
+
120
+ def compute_rmsip(
121
+ eigvects: torch.Tensor, coverage: torch.Tensor, modes_pred: torch.Tensor
122
+ ) -> float:
123
+ return torch.sqrt(compute_rmsip_sq(eigvects, coverage, modes_pred)).item()
124
+
125
+
126
+ def compute_rmsip_loss_sample(
127
+ eigvects: torch.Tensor, coverage: torch.Tensor, modes_pred: torch.Tensor
128
+ ) -> torch.Tensor:
129
+
130
+ rmsip_sq = compute_rmsip_sq(eigvects, coverage, modes_pred)
131
+
132
+ loss = 1.0 - rmsip_sq # SS loss
133
+
134
+ return loss
135
+
136
+
137
+ def compute_rmsip_loss(batch: Batch, modes_pred: torch.Tensor) -> torch.Tensor:
138
+
139
+ eigvects, coverage, ptr = batch.eigvects, batch.coverage, batch.ptr
140
+ losses = []
141
+
142
+ for i in range(len(ptr) - 1):
143
+ start, end = ptr[i], ptr[i + 1]
144
+ loss = compute_rmsip_loss_sample(
145
+ eigvects[start:end], coverage[start:end], modes_pred[start:end]
146
+ )
147
+ losses.append(loss)
148
+
149
+ return torch.stack(losses)
150
+
151
+
152
+ def compute_rmsip_sq_without_ortho(
153
+ eigvects: torch.Tensor, coverage: torch.Tensor, modes_pred: torch.Tensor
154
+ ) -> float:
155
+
156
+ eigvects = eigvects - eigvects.mean(dim=0, keepdim=True)
157
+ eigvects = eigvects / torch.norm(eigvects, dim=(0, 2), keepdim=True)
158
+
159
+ modes_pred = modes_pred - modes_pred.mean(dim=0, keepdim=True)
160
+
161
+ n_modes = min(eigvects.shape[1], modes_pred.shape[1])
162
+
163
+ sqrt_cov = torch.sqrt(coverage)[:, None, None] # shape [N, 1, 1]
164
+ weighted_modes_pred = modes_pred * sqrt_cov # shape [N, K, D]
165
+ weighted_modes_pred = weighted_modes_pred[:, :n_modes, :]
166
+ weighted_modes_pred = weighted_modes_pred / torch.norm(
167
+ weighted_modes_pred, dim=(0, 2), keepdim=True
168
+ )
169
+
170
+ eigvects = eigvects[:, :n_modes, :]
171
+
172
+ inner_products = torch.einsum("nkd,nld->kl", eigvects, weighted_modes_pred)
173
+
174
+ rmsip_sq = torch.sum(inner_products**2) / (n_modes * n_modes)
175
+
176
+ return rmsip_sq
177
+
178
+
179
+ def compute_self_cosine_loss(
180
+ modes_pred: torch.Tensor, coverage: torch.Tensor
181
+ ) -> torch.Tensor:
182
+
183
+ modes_pred = modes_pred - modes_pred.mean(dim=0, keepdim=True)
184
+
185
+ coverage = coverage[:, None, None] # [N, 1, 1]
186
+
187
+ weighted_modes = modes_pred * torch.sqrt(coverage) # [N, K, 3]
188
+
189
+ norms = torch.norm(weighted_modes, dim=(0, 2), keepdim=True) # [1, K, 1]
190
+ normalized_modes = weighted_modes / norms # [N, K, 3]
191
+
192
+ cosine_matrix = torch.einsum("nid,njd->ij", normalized_modes, normalized_modes)
193
+
194
+ # Mask out diagonal elements (we don't want to include self-similarity)
195
+ mask = ~torch.eye(cosine_matrix.shape[0], dtype=bool, device=cosine_matrix.device)
196
+
197
+ n_modes = cosine_matrix.shape[0]
198
+ cosine_loss = torch.sum(cosine_matrix[mask] ** 2) / (n_modes * n_modes)
199
+
200
+ return cosine_loss
201
+
202
+
203
+ def compute_combined_loss_sample(
204
+ eigvects: torch.Tensor, coverage: torch.Tensor, modes_pred: torch.Tensor
205
+ ) -> torch.Tensor:
206
+ rmsip_sq = compute_rmsip_sq_without_ortho(eigvects, coverage, modes_pred)
207
+ rmsip_loss = 1.0 - rmsip_sq
208
+
209
+ cosine_loss = compute_self_cosine_loss(modes_pred, coverage)
210
+
211
+ combined_loss = rmsip_loss + cosine_loss # IS loss
212
+
213
+ return combined_loss
214
+
215
+
216
+ def compute_ortho_loss(batch: Batch, modes_pred: torch.Tensor) -> torch.Tensor:
217
+ eigvects, coverage, ptr = batch.eigvects, batch.coverage, batch.ptr
218
+ losses = []
219
+
220
+ for i in range(len(ptr) - 1):
221
+ start, end = ptr[i], ptr[i + 1]
222
+ loss = compute_combined_loss_sample(
223
+ eigvects[start:end], coverage[start:end], modes_pred[start:end]
224
+ )
225
+ losses.append(loss)
226
+
227
+ return torch.stack(losses)
petimot/model/neural_net.py ADDED
@@ -0,0 +1,374 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ import torch.nn as nn
3
+ from torch_geometric.nn import MessagePassing
4
+ from typing import Tuple
5
+ from petimot.utils.rigid_utils import Rigid
6
+
7
+
8
+ class MLP(nn.Module):
9
+ def __init__(
10
+ self,
11
+ input_dim: int,
12
+ output_dim: int,
13
+ hidden_dim: int,
14
+ num_layers: int,
15
+ dropout: float,
16
+ ):
17
+ super(MLP, self).__init__()
18
+
19
+ if num_layers <= 0:
20
+ self.network = nn.Linear(input_dim, output_dim)
21
+ else:
22
+ layers = [nn.Linear(input_dim, hidden_dim), nn.GELU(), nn.Dropout(dropout)]
23
+
24
+ for _ in range(num_layers - 1):
25
+ layers.append(nn.Linear(hidden_dim, hidden_dim))
26
+ layers.append(nn.GELU())
27
+ layers.append(nn.Dropout(dropout))
28
+
29
+ layers.append(nn.Linear(hidden_dim, output_dim))
30
+
31
+ self.network = nn.Sequential(*layers)
32
+
33
+ def forward(self, x):
34
+ return self.network(x)
35
+
36
+
37
+ class MPNNLayer(MessagePassing):
38
+
39
+ def __init__(
40
+ self,
41
+ emb_dim: int,
42
+ edge_dim: int,
43
+ num_modes_pred: int,
44
+ mlp_num_layers: int,
45
+ dropout: float,
46
+ normalize_between_layers: bool,
47
+ center_between_layers: bool,
48
+ orthogonalize_between_layers: bool,
49
+ ablate_structure: bool,
50
+ ):
51
+ # We use `flow="target_to_source"`,
52
+ # meaning the first row of edge_index are target nodes
53
+ # and the second row are source nodes.
54
+ super().__init__(aggr="mean", flow="target_to_source")
55
+ self.normalize_between_layers = normalize_between_layers
56
+ self.center_between_layers = center_between_layers
57
+ self.orthogonalize_between_layers = orthogonalize_between_layers
58
+ self.ablate_structure = ablate_structure
59
+
60
+ if self.ablate_structure:
61
+ message_input_dim = 2 * emb_dim + 3 * (num_modes_pred * 3)
62
+ else:
63
+ message_input_dim = 2 * emb_dim + edge_dim + 3 * (num_modes_pred * 3)
64
+ # Message MLP => outputs new node embeddings (emb_dim)
65
+ self.message_mlp = MLP(
66
+ input_dim=message_input_dim,
67
+ output_dim=emb_dim,
68
+ hidden_dim=emb_dim,
69
+ num_layers=mlp_num_layers,
70
+ dropout=dropout,
71
+ )
72
+
73
+ # A linear layer to update v from [x, v]. (No bias if you prefer.)
74
+ self.vect_mlp = nn.Linear(
75
+ emb_dim + num_modes_pred * 3, num_modes_pred * 3, bias=False
76
+ )
77
+
78
+ self.num_modes_pred = num_modes_pred
79
+ self.norm_x = nn.LayerNorm(emb_dim)
80
+
81
+ def forward(
82
+ self,
83
+ x: torch.Tensor,
84
+ v: torch.Tensor,
85
+ edge_index: torch.Tensor,
86
+ edge_rots: Rigid,
87
+ edge_attr: torch.Tensor,
88
+ ptr: torch.Tensor,
89
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
90
+
91
+ x_update = self.propagate(
92
+ edge_index=edge_index,
93
+ x=x,
94
+ v=v,
95
+ edge_rots=edge_rots,
96
+ edge_attr=edge_attr,
97
+ )
98
+
99
+ x = x + x_update
100
+ x = self.norm_x(x)
101
+
102
+ v_update = self.vect_mlp(torch.cat([x, v], dim=-1))
103
+ v = v + v_update
104
+
105
+ if (
106
+ self.normalize_between_layers
107
+ or self.center_between_layers
108
+ or self.orthogonalize_between_layers
109
+ ):
110
+ v = self.normalize_vectors(
111
+ v,
112
+ ptr,
113
+ orthogonalize=self.orthogonalize_between_layers,
114
+ normalize=self.normalize_between_layers,
115
+ center=self.center_between_layers,
116
+ )
117
+
118
+ return x, v
119
+
120
+ def message(
121
+ self,
122
+ x_i: torch.Tensor,
123
+ x_j: torch.Tensor,
124
+ v_i: torch.Tensor,
125
+ v_j: torch.Tensor,
126
+ edge_rots: Rigid,
127
+ edge_attr: torch.Tensor,
128
+ ) -> torch.Tensor:
129
+
130
+ vj = v_j.reshape(-1, self.num_modes_pred, 3)
131
+ vj_rotated = edge_rots.apply_batch(vj)
132
+ vj_rotated = vj_rotated.view(vj_rotated.shape[0], -1)
133
+
134
+ s1 = v_i - vj_rotated
135
+
136
+ if self.ablate_structure:
137
+ message = self.message_mlp(
138
+ torch.cat([x_i, x_j, v_i, vj_rotated, s1], dim=-1)
139
+ )
140
+ else:
141
+ message = self.message_mlp(
142
+ torch.cat([x_i, x_j, v_i, vj_rotated, s1, edge_attr], dim=-1)
143
+ )
144
+
145
+ return message
146
+
147
+ def normalize_vectors(
148
+ self,
149
+ v: torch.Tensor,
150
+ ptr: torch.Tensor,
151
+ orthogonalize: bool,
152
+ normalize: bool,
153
+ center: bool,
154
+ ) -> torch.Tensor:
155
+
156
+ v_norm = torch.empty_like(v)
157
+ v_reshaped = v.view(
158
+ v.shape[0], self.num_modes_pred, 3
159
+ ) # [N, num_modes_pred, 3]
160
+
161
+ for i in range(len(ptr) - 1):
162
+ start, end = ptr[i], ptr[i + 1]
163
+ n_residues = end - start
164
+ v_graph = v_reshaped[start:end]
165
+
166
+ if center:
167
+ v_graph = v_graph - v_graph.mean(dim=0, keepdim=True)
168
+
169
+ if orthogonalize and not normalize:
170
+ norms_input = torch.norm(v_graph, dim=(0, 2))
171
+
172
+ if orthogonalize:
173
+ v_flat = v_graph.reshape(-1, self.num_modes_pred)
174
+
175
+ q, _ = torch.linalg.qr(v_flat)
176
+
177
+ if normalize:
178
+ # Scale Q to maintain magnitude proportional to sqrt(N)
179
+ scale = n_residues.to(v.device).sqrt()
180
+ q = q * scale
181
+ else:
182
+ # Restore original norms
183
+ current_norms = torch.norm(q, dim=0)
184
+ scale_factors = norms_input / current_norms
185
+ q = q * scale_factors.unsqueeze(0)
186
+
187
+ q_reshaped = q.reshape(n_residues, -1)
188
+ v_norm[start:end] = q_reshaped
189
+
190
+ else:
191
+ if normalize:
192
+ # Just normalize without orthogonalization
193
+ scale = n_residues.to(v.device).sqrt()
194
+ current_norms = torch.norm(v_graph, dim=(0, 2))
195
+ scale_factors = scale / current_norms
196
+ v_graph = v_graph * scale_factors.unsqueeze(0).unsqueeze(-1)
197
+ v_norm[start:end] = v_graph.reshape(n_residues, -1)
198
+ else:
199
+ # Neither normalize nor orthogonalize, just return (possibly centered) vectors
200
+ v_norm[start:end] = v_graph.reshape(n_residues, -1)
201
+
202
+ return v_norm
203
+
204
+
205
+ class ProteinMotionMPNN(nn.Module):
206
+
207
+ def __init__(
208
+ self,
209
+ input_dim: int,
210
+ emb_dim: int,
211
+ edge_dim: int,
212
+ num_modes_pred: int,
213
+ num_layers: int,
214
+ shared_layers: bool,
215
+ mlp_num_layers: int,
216
+ input_embedding_dropout: float,
217
+ dropout: float,
218
+ num_basis: int,
219
+ max_dist: float,
220
+ sigma: float,
221
+ change_connectivity: bool,
222
+ normalize_between_layers: bool,
223
+ center_between_layers: bool,
224
+ orthogonalize_between_layers: bool,
225
+ start_with_zero_v: bool,
226
+ ablate_structure: bool,
227
+ ):
228
+ super().__init__()
229
+
230
+ self.num_modes_pred = num_modes_pred
231
+ self.num_layers = num_layers
232
+ self.shared_layers = shared_layers
233
+ self.edge_dim = edge_dim
234
+
235
+ self.input_proj = nn.Linear(input_dim, emb_dim)
236
+ self.input_norm = nn.LayerNorm(input_dim)
237
+ self.input_dropout = nn.Dropout(input_embedding_dropout)
238
+
239
+ self.num_basis = num_basis
240
+ self.max_dist = max_dist
241
+ self.sigma = sigma
242
+
243
+ self.change_connectivity = change_connectivity
244
+ self.start_with_zero_v = start_with_zero_v
245
+
246
+ if shared_layers:
247
+ self.mpnn = MPNNLayer(
248
+ emb_dim=emb_dim,
249
+ edge_dim=edge_dim,
250
+ num_modes_pred=num_modes_pred,
251
+ mlp_num_layers=mlp_num_layers,
252
+ dropout=dropout,
253
+ normalize_between_layers=normalize_between_layers,
254
+ center_between_layers=center_between_layers,
255
+ orthogonalize_between_layers=orthogonalize_between_layers,
256
+ ablate_structure=ablate_structure,
257
+ )
258
+ else:
259
+ self.mpnn_layers = nn.ModuleList()
260
+ for _ in range(num_layers):
261
+ layer = MPNNLayer(
262
+ emb_dim=emb_dim,
263
+ edge_dim=edge_dim,
264
+ num_modes_pred=num_modes_pred,
265
+ mlp_num_layers=mlp_num_layers,
266
+ dropout=dropout,
267
+ normalize_between_layers=normalize_between_layers,
268
+ center_between_layers=center_between_layers,
269
+ orthogonalize_between_layers=orthogonalize_between_layers,
270
+ ablate_structure=ablate_structure,
271
+ )
272
+ self.mpnn_layers.append(layer)
273
+
274
+ def compute_radial_basis_fullbb(
275
+ self, atoms_i: torch.Tensor, atoms_j: torch.Tensor
276
+ ) -> torch.Tensor:
277
+
278
+ # Pairwise distances among the sets of atoms
279
+ # e.g. [E, 3, 3] for each edge => cdist => [E, 3, 3]
280
+ distances = torch.cdist(atoms_i, atoms_j) # shape [E, X, X]
281
+
282
+ # Expand distances along a new dimension for the radial basis
283
+ # => shape [E, X, X, num_basis]
284
+ mu = torch.linspace(0, self.max_dist, self.num_basis, device=distances.device)
285
+ radial = torch.exp(-((distances.unsqueeze(-1) - mu) ** 2) / (2 * self.sigma**2))
286
+
287
+ # Flatten the X*X dimension
288
+ # => shape [E, X*X*num_basis]
289
+ radial = radial.view(distances.size(0), -1)
290
+ return radial
291
+
292
+ def compute_edge_features_from_index(
293
+ self, rigids, row_index: torch.Tensor, col_index: torch.Tensor, bb: torch.Tensor
294
+ ) -> Tuple:
295
+
296
+ T_i = rigids[row_index]
297
+ T_j = rigids[col_index]
298
+ T_i_inv = T_i.invert()
299
+ composed_ij = T_i_inv.compose(T_j)
300
+ edge_rots = composed_ij.get_rots()
301
+ edge_quats = edge_rots.get_quats()
302
+ edge_trans = composed_ij.get_trans()
303
+ edge_log_dist = torch.log(torch.norm(edge_trans, dim=-1) + 1e-8)
304
+ chain_distance = torch.log(torch.abs(row_index - col_index) + 1)
305
+ atoms_i = bb[row_index]
306
+ atoms_j = bb[col_index]
307
+ radial_features = self.compute_radial_basis_fullbb(atoms_i, atoms_j)
308
+ edge_attr = torch.cat(
309
+ [
310
+ edge_quats,
311
+ edge_trans,
312
+ chain_distance.unsqueeze(-1),
313
+ edge_log_dist.unsqueeze(-1),
314
+ radial_features,
315
+ ],
316
+ dim=-1,
317
+ )
318
+ return edge_rots, edge_attr
319
+
320
+ def forward(self, data) -> Tuple[torch.Tensor, torch.Tensor]:
321
+
322
+ emb = self.input_norm(data.x)
323
+ emb = self.input_dropout(emb)
324
+
325
+ emb = self.input_proj(emb)
326
+
327
+ if self.start_with_zero_v:
328
+ v = torch.zeros(emb.shape[0], self.num_modes_pred * 3, device=emb.device)
329
+ else:
330
+ v = torch.rand(emb.shape[0], self.num_modes_pred * 3, device=emb.device)
331
+
332
+ row_index, col_index, ptr = data.row_index, data.col_index, data.ptr
333
+
334
+ rigids = Rigid.make_transform_from_reference(
335
+ data.bb[:, 0, :], data.bb[:, 1, :], data.bb[:, 2, :]
336
+ )
337
+
338
+ if self.change_connectivity:
339
+
340
+ for layer_idx in range(self.num_layers):
341
+ current_edge_index = torch.stack(
342
+ [row_index, col_index[layer_idx]], dim=0
343
+ )
344
+
345
+ edge_rots, edge_attr = self.compute_edge_features_from_index(
346
+ rigids, row_index, col_index[layer_idx], data.bb
347
+ )
348
+
349
+ if self.shared_layers:
350
+ emb, v = self.mpnn(
351
+ emb, v, current_edge_index, edge_rots, edge_attr, ptr
352
+ )
353
+ else:
354
+ emb, v = self.mpnn_layers[layer_idx](
355
+ emb, v, current_edge_index, edge_rots, edge_attr, ptr
356
+ )
357
+ else:
358
+ edge_index = torch.stack([row_index, col_index], dim=0)
359
+ edge_rots, edge_attr = self.compute_edge_features_from_index(
360
+ rigids, row_index, col_index, data.bb
361
+ )
362
+
363
+ if self.shared_layers:
364
+ for _ in range(self.num_layers):
365
+ emb, v = self.mpnn(emb, v, edge_index, edge_rots, edge_attr, ptr)
366
+ else:
367
+ for layer in self.mpnn_layers:
368
+ emb, v = layer(emb, v, edge_index, edge_rots, edge_attr, ptr)
369
+
370
+ # Convert local vectors v to global coordinates
371
+ v_reshaped = v.view(v.shape[0], self.num_modes_pred, 3)
372
+ v_global = rigids.get_rots().apply_batch(v_reshaped)
373
+
374
+ return emb, v_global
petimot/model/optimizer.py ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+
3
+
4
+ def get_optimizer(parameters, optimizer_name, learning_rate, **kwargs):
5
+
6
+ optimizer_name = optimizer_name.lower()
7
+ optimizer_map = {
8
+ "adam": torch.optim.Adam,
9
+ "sgd": torch.optim.SGD,
10
+ "adagrad": torch.optim.Adagrad,
11
+ "adadelta": torch.optim.Adadelta,
12
+ "rmsprop": torch.optim.RMSprop,
13
+ "adamw": torch.optim.AdamW,
14
+ }
15
+
16
+ if optimizer_name not in optimizer_map:
17
+ raise ValueError(
18
+ f"Invalid optimizer name: {optimizer_name}. "
19
+ f"Valid options: {list(optimizer_map.keys())}"
20
+ )
21
+
22
+ defaults = {
23
+ "adam": {"weight_decay": 0.0, "amsgrad": False},
24
+ "adamw": {"weight_decay": 0.01},
25
+ "sgd": {"momentum": 0.9, "nesterov": True},
26
+ "rmsprop": {"alpha": 0.99, "momentum": 0.0},
27
+ }.get(optimizer_name, {})
28
+
29
+ final_params = {**defaults, **kwargs}
30
+
31
+ return optimizer_map[optimizer_name](parameters, lr=learning_rate, **final_params)
petimot/utils/__init__.py ADDED
File without changes
petimot/utils/rigid_utils.py ADDED
@@ -0,0 +1,1445 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2021 AlQuraishi Laboratory
2
+ # Copyright 2021 DeepMind Technologies Limited
3
+ #
4
+ # Licensed under the Apache License, Version 2.0 (the "License");
5
+ # you may not use this file except in compliance with the License.
6
+ # You may obtain a copy of the License at
7
+ #
8
+ # http://www.apache.org/licenses/LICENSE-2.0
9
+ #
10
+ # Unless required by applicable law or agreed to in writing, software
11
+ # distributed under the License is distributed on an "AS IS" BASIS,
12
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ # See the License for the specific language governing permissions and
14
+ # limitations under the License.
15
+
16
+ # Modifications to original file:
17
+ # - `rot_vec_mul_batch`: Applies a batch of rotations to a batch of vectors.
18
+ # - `apply_batch`: Applies rotations to a batch of 3D coordinates.
19
+ # - `invert_apply_batch`: Applies the inverse of the batch rotation to coordinates.
20
+
21
+ from __future__ import annotations
22
+ from functools import lru_cache
23
+ from typing import Tuple, Any, Sequence, Callable, Optional
24
+
25
+ import numpy as np
26
+ import torch
27
+
28
+
29
+ def rot_vec_mul_batch(r: torch.Tensor, t: torch.Tensor) -> torch.Tensor:
30
+ """
31
+ Applies a batch of rotations to a batch of vectors. Supports t with shape [Batch, N, 3].
32
+
33
+ Args:
34
+ r: [Batch, 3, 3] rotation matrices
35
+ t: [Batch, N, 3] coordinate tensors
36
+ Returns:
37
+ [Batch, N, 3] rotated coordinates
38
+ """
39
+ x, y, z = torch.unbind(t, dim=-1) # x, y, z have shape [Batch, N]
40
+
41
+ return torch.stack(
42
+ [
43
+ r[..., 0, 0].unsqueeze(-1) * x
44
+ + r[..., 0, 1].unsqueeze(-1) * y
45
+ + r[..., 0, 2].unsqueeze(-1) * z,
46
+ r[..., 1, 0].unsqueeze(-1) * x
47
+ + r[..., 1, 1].unsqueeze(-1) * y
48
+ + r[..., 1, 2].unsqueeze(-1) * z,
49
+ r[..., 2, 0].unsqueeze(-1) * x
50
+ + r[..., 2, 1].unsqueeze(-1) * y
51
+ + r[..., 2, 2].unsqueeze(-1) * z,
52
+ ],
53
+ dim=-1,
54
+ )
55
+
56
+
57
+ def rot_matmul(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
58
+ """
59
+ Performs matrix multiplication of two rotation matrix tensors. Written
60
+ out by hand to avoid AMP downcasting.
61
+
62
+ Args:
63
+ a: [*, 3, 3] left multiplicand
64
+ b: [*, 3, 3] right multiplicand
65
+ Returns:
66
+ The product ab
67
+ """
68
+
69
+ def row_mul(i):
70
+ return torch.stack(
71
+ [
72
+ a[..., i, 0] * b[..., 0, 0]
73
+ + a[..., i, 1] * b[..., 1, 0]
74
+ + a[..., i, 2] * b[..., 2, 0],
75
+ a[..., i, 0] * b[..., 0, 1]
76
+ + a[..., i, 1] * b[..., 1, 1]
77
+ + a[..., i, 2] * b[..., 2, 1],
78
+ a[..., i, 0] * b[..., 0, 2]
79
+ + a[..., i, 1] * b[..., 1, 2]
80
+ + a[..., i, 2] * b[..., 2, 2],
81
+ ],
82
+ dim=-1,
83
+ )
84
+
85
+ return torch.stack(
86
+ [
87
+ row_mul(0),
88
+ row_mul(1),
89
+ row_mul(2),
90
+ ],
91
+ dim=-2,
92
+ )
93
+
94
+
95
+ def rot_vec_mul(r: torch.Tensor, t: torch.Tensor) -> torch.Tensor:
96
+ """
97
+ Applies a rotation to a vector. Written out by hand to avoid transfer
98
+ to avoid AMP downcasting.
99
+
100
+ Args:
101
+ r: [*, 3, 3] rotation matrices
102
+ t: [*, 3] coordinate tensors
103
+ Returns:
104
+ [*, 3] rotated coordinates
105
+ """
106
+ x, y, z = torch.unbind(t, dim=-1)
107
+ return torch.stack(
108
+ [
109
+ r[..., 0, 0] * x + r[..., 0, 1] * y + r[..., 0, 2] * z,
110
+ r[..., 1, 0] * x + r[..., 1, 1] * y + r[..., 1, 2] * z,
111
+ r[..., 2, 0] * x + r[..., 2, 1] * y + r[..., 2, 2] * z,
112
+ ],
113
+ dim=-1,
114
+ )
115
+
116
+
117
+ @lru_cache(maxsize=None)
118
+ def identity_rot_mats(
119
+ batch_dims: Tuple[int],
120
+ dtype: Optional[torch.dtype] = None,
121
+ device: Optional[torch.device] = None,
122
+ requires_grad: bool = True,
123
+ ) -> torch.Tensor:
124
+ rots = torch.eye(3, dtype=dtype, device=device, requires_grad=requires_grad)
125
+ rots = rots.view(*((1,) * len(batch_dims)), 3, 3)
126
+ rots = rots.expand(*batch_dims, -1, -1)
127
+ rots = rots.contiguous()
128
+
129
+ return rots
130
+
131
+
132
+ @lru_cache(maxsize=None)
133
+ def identity_trans(
134
+ batch_dims: Tuple[int],
135
+ dtype: Optional[torch.dtype] = None,
136
+ device: Optional[torch.device] = None,
137
+ requires_grad: bool = True,
138
+ ) -> torch.Tensor:
139
+ trans = torch.zeros(
140
+ (*batch_dims, 3), dtype=dtype, device=device, requires_grad=requires_grad
141
+ )
142
+ return trans
143
+
144
+
145
+ @lru_cache(maxsize=None)
146
+ def identity_quats(
147
+ batch_dims: Tuple[int],
148
+ dtype: Optional[torch.dtype] = None,
149
+ device: Optional[torch.device] = None,
150
+ requires_grad: bool = True,
151
+ ) -> torch.Tensor:
152
+ quat = torch.zeros(
153
+ (*batch_dims, 4), dtype=dtype, device=device, requires_grad=requires_grad
154
+ )
155
+
156
+ with torch.no_grad():
157
+ quat[..., 0] = 1
158
+
159
+ return quat
160
+
161
+
162
+ _quat_elements = ["a", "b", "c", "d"]
163
+ _qtr_keys = [l1 + l2 for l1 in _quat_elements for l2 in _quat_elements]
164
+ _qtr_ind_dict = {key: ind for ind, key in enumerate(_qtr_keys)}
165
+
166
+
167
+ def _to_mat(pairs):
168
+ mat = np.zeros((4, 4))
169
+ for pair in pairs:
170
+ key, value = pair
171
+ ind = _qtr_ind_dict[key]
172
+ mat[ind // 4][ind % 4] = value
173
+
174
+ return mat
175
+
176
+
177
+ _QTR_MAT = np.zeros((4, 4, 3, 3))
178
+ _QTR_MAT[..., 0, 0] = _to_mat([("aa", 1), ("bb", 1), ("cc", -1), ("dd", -1)])
179
+ _QTR_MAT[..., 0, 1] = _to_mat([("bc", 2), ("ad", -2)])
180
+ _QTR_MAT[..., 0, 2] = _to_mat([("bd", 2), ("ac", 2)])
181
+ _QTR_MAT[..., 1, 0] = _to_mat([("bc", 2), ("ad", 2)])
182
+ _QTR_MAT[..., 1, 1] = _to_mat([("aa", 1), ("bb", -1), ("cc", 1), ("dd", -1)])
183
+ _QTR_MAT[..., 1, 2] = _to_mat([("cd", 2), ("ab", -2)])
184
+ _QTR_MAT[..., 2, 0] = _to_mat([("bd", 2), ("ac", -2)])
185
+ _QTR_MAT[..., 2, 1] = _to_mat([("cd", 2), ("ab", 2)])
186
+ _QTR_MAT[..., 2, 2] = _to_mat([("aa", 1), ("bb", -1), ("cc", -1), ("dd", 1)])
187
+
188
+
189
+ def quat_to_rot(quat: torch.Tensor) -> torch.Tensor:
190
+ """
191
+ Converts a quaternion to a rotation matrix.
192
+
193
+ Args:
194
+ quat: [*, 4] quaternions
195
+ Returns:
196
+ [*, 3, 3] rotation matrices
197
+ """
198
+ # [*, 4, 4]
199
+ quat = quat[..., None] * quat[..., None, :]
200
+
201
+ # [4, 4, 3, 3]
202
+ mat = _get_quat("_QTR_MAT", dtype=quat.dtype, device=quat.device)
203
+
204
+ # [*, 4, 4, 3, 3]
205
+ shaped_qtr_mat = mat.view((1,) * len(quat.shape[:-2]) + mat.shape)
206
+ quat = quat[..., None, None] * shaped_qtr_mat
207
+
208
+ # [*, 3, 3]
209
+ return torch.sum(quat, dim=(-3, -4))
210
+
211
+
212
+ def rot_to_quat(
213
+ rot: torch.Tensor,
214
+ ):
215
+ if rot.shape[-2:] != (3, 3):
216
+ raise ValueError("Input rotation is incorrectly shaped")
217
+
218
+ rot = [[rot[..., i, j] for j in range(3)] for i in range(3)]
219
+ [[xx, xy, xz], [yx, yy, yz], [zx, zy, zz]] = rot
220
+
221
+ k = [
222
+ [
223
+ xx + yy + zz,
224
+ zy - yz,
225
+ xz - zx,
226
+ yx - xy,
227
+ ],
228
+ [
229
+ zy - yz,
230
+ xx - yy - zz,
231
+ xy + yx,
232
+ xz + zx,
233
+ ],
234
+ [
235
+ xz - zx,
236
+ xy + yx,
237
+ yy - xx - zz,
238
+ yz + zy,
239
+ ],
240
+ [
241
+ yx - xy,
242
+ xz + zx,
243
+ yz + zy,
244
+ zz - xx - yy,
245
+ ],
246
+ ]
247
+
248
+ k = (1.0 / 3.0) * torch.stack([torch.stack(t, dim=-1) for t in k], dim=-2)
249
+
250
+ _, vectors = torch.linalg.eigh(k)
251
+ return vectors[..., -1]
252
+
253
+
254
+ _QUAT_MULTIPLY = np.zeros((4, 4, 4))
255
+ _QUAT_MULTIPLY[:, :, 0] = [[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, -1]]
256
+
257
+ _QUAT_MULTIPLY[:, :, 1] = [[0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 0, 1], [0, 0, -1, 0]]
258
+
259
+ _QUAT_MULTIPLY[:, :, 2] = [[0, 0, 1, 0], [0, 0, 0, -1], [1, 0, 0, 0], [0, 1, 0, 0]]
260
+
261
+ _QUAT_MULTIPLY[:, :, 3] = [[0, 0, 0, 1], [0, 0, 1, 0], [0, -1, 0, 0], [1, 0, 0, 0]]
262
+
263
+ _QUAT_MULTIPLY_BY_VEC = _QUAT_MULTIPLY[:, 1:, :]
264
+
265
+ _CACHED_QUATS = {
266
+ "_QTR_MAT": _QTR_MAT,
267
+ "_QUAT_MULTIPLY": _QUAT_MULTIPLY,
268
+ "_QUAT_MULTIPLY_BY_VEC": _QUAT_MULTIPLY_BY_VEC,
269
+ }
270
+
271
+
272
+ @lru_cache(maxsize=None)
273
+ def _get_quat(quat_key, dtype, device):
274
+ return torch.tensor(_CACHED_QUATS[quat_key], dtype=dtype, device=device)
275
+
276
+
277
+ def quat_multiply(quat1, quat2):
278
+ """Multiply a quaternion by another quaternion."""
279
+ mat = _get_quat("_QUAT_MULTIPLY", dtype=quat1.dtype, device=quat1.device)
280
+ reshaped_mat = mat.view((1,) * len(quat1.shape[:-1]) + mat.shape)
281
+ return torch.sum(
282
+ reshaped_mat * quat1[..., :, None, None] * quat2[..., None, :, None],
283
+ dim=(-3, -2),
284
+ )
285
+
286
+
287
+ def quat_multiply_by_vec(quat, vec):
288
+ """Multiply a quaternion by a pure-vector quaternion."""
289
+ mat = _get_quat("_QUAT_MULTIPLY_BY_VEC", dtype=quat.dtype, device=quat.device)
290
+ reshaped_mat = mat.view((1,) * len(quat.shape[:-1]) + mat.shape)
291
+ return torch.sum(
292
+ reshaped_mat * quat[..., :, None, None] * vec[..., None, :, None], dim=(-3, -2)
293
+ )
294
+
295
+
296
+ def invert_rot_mat(rot_mat: torch.Tensor):
297
+ return rot_mat.transpose(-1, -2)
298
+
299
+
300
+ def invert_quat(quat: torch.Tensor):
301
+ quat_prime = quat.clone()
302
+ quat_prime[..., 1:] *= -1
303
+ inv = quat_prime / torch.sum(quat**2, dim=-1, keepdim=True)
304
+ return inv
305
+
306
+
307
+ class Rotation:
308
+ """
309
+ A 3D rotation. Depending on how the object is initialized, the
310
+ rotation is represented by either a rotation matrix or a
311
+ quaternion, though both formats are made available by helper functions.
312
+ To simplify gradient computation, the underlying format of the
313
+ rotation cannot be changed in-place. Like Rigid, the class is designed
314
+ to mimic the behavior of a torch Tensor, almost as if each Rotation
315
+ object were a tensor of rotations, in one format or another.
316
+ """
317
+
318
+ def __init__(
319
+ self,
320
+ rot_mats: Optional[torch.Tensor] = None,
321
+ quats: Optional[torch.Tensor] = None,
322
+ normalize_quats: bool = True,
323
+ ):
324
+ """
325
+ Args:
326
+ rot_mats:
327
+ A [*, 3, 3] rotation matrix tensor. Mutually exclusive with
328
+ quats
329
+ quats:
330
+ A [*, 4] quaternion. Mutually exclusive with rot_mats. If
331
+ normalize_quats is not True, must be a unit quaternion
332
+ normalize_quats:
333
+ If quats is specified, whether to normalize quats
334
+ """
335
+ if (rot_mats is None and quats is None) or (
336
+ rot_mats is not None and quats is not None
337
+ ):
338
+ raise ValueError("Exactly one input argument must be specified")
339
+
340
+ if (rot_mats is not None and rot_mats.shape[-2:] != (3, 3)) or (
341
+ quats is not None and quats.shape[-1] != 4
342
+ ):
343
+ raise ValueError("Incorrectly shaped rotation matrix or quaternion")
344
+
345
+ # Force full-precision
346
+ if quats is not None:
347
+ quats = quats.to(dtype=torch.float32)
348
+ if rot_mats is not None:
349
+ rot_mats = rot_mats.to(dtype=torch.float32)
350
+
351
+ if quats is not None and normalize_quats:
352
+ quats = quats / torch.linalg.norm(quats, dim=-1, keepdim=True)
353
+
354
+ self._rot_mats = rot_mats
355
+ self._quats = quats
356
+
357
+ @staticmethod
358
+ def identity(
359
+ shape,
360
+ dtype: Optional[torch.dtype] = None,
361
+ device: Optional[torch.device] = None,
362
+ requires_grad: bool = True,
363
+ fmt: str = "quat",
364
+ ) -> Rotation:
365
+ """
366
+ Returns an identity Rotation.
367
+
368
+ Args:
369
+ shape:
370
+ The "shape" of the resulting Rotation object. See documentation
371
+ for the shape property
372
+ dtype:
373
+ The torch dtype for the rotation
374
+ device:
375
+ The torch device for the new rotation
376
+ requires_grad:
377
+ Whether the underlying tensors in the new rotation object
378
+ should require gradient computation
379
+ fmt:
380
+ One of "quat" or "rot_mat". Determines the underlying format
381
+ of the new object's rotation
382
+ Returns:
383
+ A new identity rotation
384
+ """
385
+ if fmt == "rot_mat":
386
+ rot_mats = identity_rot_mats(
387
+ shape,
388
+ dtype,
389
+ device,
390
+ requires_grad,
391
+ )
392
+ return Rotation(rot_mats=rot_mats, quats=None)
393
+ elif fmt == "quat":
394
+ quats = identity_quats(shape, dtype, device, requires_grad)
395
+ return Rotation(rot_mats=None, quats=quats, normalize_quats=False)
396
+ else:
397
+ raise ValueError(f"Invalid format: f{fmt}")
398
+
399
+ # Magic methods
400
+
401
+ def __getitem__(self, index: Any) -> Rotation:
402
+ """
403
+ Allows torch-style indexing over the virtual shape of the rotation
404
+ object. See documentation for the shape property.
405
+
406
+ Args:
407
+ index:
408
+ A torch index. E.g. (1, 3, 2), or (slice(None,))
409
+ Returns:
410
+ The indexed rotation
411
+ """
412
+ if type(index) != tuple:
413
+ index = (index,)
414
+
415
+ if self._rot_mats is not None:
416
+ rot_mats = self._rot_mats[index + (slice(None), slice(None))]
417
+ return Rotation(rot_mats=rot_mats)
418
+ elif self._quats is not None:
419
+ quats = self._quats[index + (slice(None),)]
420
+ return Rotation(quats=quats, normalize_quats=False)
421
+ else:
422
+ raise ValueError("Both rotations are None")
423
+
424
+ def __mul__(
425
+ self,
426
+ right: torch.Tensor,
427
+ ) -> Rotation:
428
+ """
429
+ Pointwise left multiplication of the rotation with a tensor. Can be
430
+ used to e.g. mask the Rotation.
431
+
432
+ Args:
433
+ right:
434
+ The tensor multiplicand
435
+ Returns:
436
+ The product
437
+ """
438
+ if not (isinstance(right, torch.Tensor)):
439
+ raise TypeError("The other multiplicand must be a Tensor")
440
+
441
+ if self._rot_mats is not None:
442
+ rot_mats = self._rot_mats * right[..., None, None]
443
+ return Rotation(rot_mats=rot_mats, quats=None)
444
+ elif self._quats is not None:
445
+ quats = self._quats * right[..., None]
446
+ return Rotation(rot_mats=None, quats=quats, normalize_quats=False)
447
+ else:
448
+ raise ValueError("Both rotations are None")
449
+
450
+ def __rmul__(
451
+ self,
452
+ left: torch.Tensor,
453
+ ) -> Rotation:
454
+ """
455
+ Reverse pointwise multiplication of the rotation with a tensor.
456
+
457
+ Args:
458
+ left:
459
+ The left multiplicand
460
+ Returns:
461
+ The product
462
+ """
463
+ return self.__mul__(left)
464
+
465
+ # Properties
466
+
467
+ @property
468
+ def shape(self) -> torch.Size:
469
+ """
470
+ Returns the virtual shape of the rotation object. This shape is
471
+ defined as the batch dimensions of the underlying rotation matrix
472
+ or quaternion. If the Rotation was initialized with a [10, 3, 3]
473
+ rotation matrix tensor, for example, the resulting shape would be
474
+ [10].
475
+
476
+ Returns:
477
+ The virtual shape of the rotation object
478
+ """
479
+ s = None
480
+ if self._quats is not None:
481
+ s = self._quats.shape[:-1]
482
+ else:
483
+ s = self._rot_mats.shape[:-2]
484
+
485
+ return s
486
+
487
+ @property
488
+ def dtype(self) -> torch.dtype:
489
+ """
490
+ Returns the dtype of the underlying rotation.
491
+
492
+ Returns:
493
+ The dtype of the underlying rotation
494
+ """
495
+ if self._rot_mats is not None:
496
+ return self._rot_mats.dtype
497
+ elif self._quats is not None:
498
+ return self._quats.dtype
499
+ else:
500
+ raise ValueError("Both rotations are None")
501
+
502
+ @property
503
+ def device(self) -> torch.device:
504
+ """
505
+ The device of the underlying rotation
506
+
507
+ Returns:
508
+ The device of the underlying rotation
509
+ """
510
+ if self._rot_mats is not None:
511
+ return self._rot_mats.device
512
+ elif self._quats is not None:
513
+ return self._quats.device
514
+ else:
515
+ raise ValueError("Both rotations are None")
516
+
517
+ @property
518
+ def requires_grad(self) -> bool:
519
+ """
520
+ Returns the requires_grad property of the underlying rotation
521
+
522
+ Returns:
523
+ The requires_grad property of the underlying tensor
524
+ """
525
+ if self._rot_mats is not None:
526
+ return self._rot_mats.requires_grad
527
+ elif self._quats is not None:
528
+ return self._quats.requires_grad
529
+ else:
530
+ raise ValueError("Both rotations are None")
531
+
532
+ def get_rot_mats(self) -> torch.Tensor:
533
+ """
534
+ Returns the underlying rotation as a rotation matrix tensor.
535
+
536
+ Returns:
537
+ The rotation as a rotation matrix tensor
538
+ """
539
+ rot_mats = self._rot_mats
540
+ if rot_mats is None:
541
+ if self._quats is None:
542
+ raise ValueError("Both rotations are None")
543
+ else:
544
+ rot_mats = quat_to_rot(self._quats)
545
+
546
+ return rot_mats
547
+
548
+ def get_quats(self) -> torch.Tensor:
549
+ """
550
+ Returns the underlying rotation as a quaternion tensor.
551
+
552
+ Depending on whether the Rotation was initialized with a
553
+ quaternion, this function may call torch.linalg.eigh.
554
+
555
+ Returns:
556
+ The rotation as a quaternion tensor.
557
+ """
558
+ quats = self._quats
559
+ if quats is None:
560
+ if self._rot_mats is None:
561
+ raise ValueError("Both rotations are None")
562
+ else:
563
+ quats = rot_to_quat(self._rot_mats)
564
+
565
+ return quats
566
+
567
+ def get_cur_rot(self) -> torch.Tensor:
568
+ """
569
+ Return the underlying rotation in its current form
570
+
571
+ Returns:
572
+ The stored rotation
573
+ """
574
+ if self._rot_mats is not None:
575
+ return self._rot_mats
576
+ elif self._quats is not None:
577
+ return self._quats
578
+ else:
579
+ raise ValueError("Both rotations are None")
580
+
581
+ # Rotation functions
582
+
583
+ def compose_q_update_vec(
584
+ self, q_update_vec: torch.Tensor, normalize_quats: bool = True
585
+ ) -> Rotation:
586
+ """
587
+ Returns a new quaternion Rotation after updating the current
588
+ object's underlying rotation with a quaternion update, formatted
589
+ as a [*, 3] tensor whose final three columns represent x, y, z such
590
+ that (1, x, y, z) is the desired (not necessarily unit) quaternion
591
+ update.
592
+
593
+ Args:
594
+ q_update_vec:
595
+ A [*, 3] quaternion update tensor
596
+ normalize_quats:
597
+ Whether to normalize the output quaternion
598
+ Returns:
599
+ An updated Rotation
600
+ """
601
+ quats = self.get_quats()
602
+ new_quats = quats + quat_multiply_by_vec(quats, q_update_vec)
603
+ return Rotation(
604
+ rot_mats=None,
605
+ quats=new_quats,
606
+ normalize_quats=normalize_quats,
607
+ )
608
+
609
+ def compose_r(self, r: Rotation) -> Rotation:
610
+ """
611
+ Compose the rotation matrices of the current Rotation object with
612
+ those of another.
613
+
614
+ Args:
615
+ r:
616
+ An update rotation object
617
+ Returns:
618
+ An updated rotation object
619
+ """
620
+ r1 = self.get_rot_mats()
621
+ r2 = r.get_rot_mats()
622
+ new_rot_mats = rot_matmul(r1, r2)
623
+ return Rotation(rot_mats=new_rot_mats, quats=None)
624
+
625
+ def compose_q(self, r: Rotation, normalize_quats: bool = True) -> Rotation:
626
+ """
627
+ Compose the quaternions of the current Rotation object with those
628
+ of another.
629
+
630
+ Depending on whether either Rotation was initialized with
631
+ quaternions, this function may call torch.linalg.eigh.
632
+
633
+ Args:
634
+ r:
635
+ An update rotation object
636
+ Returns:
637
+ An updated rotation object
638
+ """
639
+ q1 = self.get_quats()
640
+ q2 = r.get_quats()
641
+ new_quats = quat_multiply(q1, q2)
642
+ return Rotation(rot_mats=None, quats=new_quats, normalize_quats=normalize_quats)
643
+
644
+ def apply(self, pts: torch.Tensor) -> torch.Tensor:
645
+ """
646
+ Apply the current Rotation as a rotation matrix to a set of 3D
647
+ coordinates.
648
+
649
+ Args:
650
+ pts:
651
+ A [*, 3] set of points
652
+ Returns:
653
+ [*, 3] rotated points
654
+ """
655
+ rot_mats = self.get_rot_mats()
656
+ return rot_vec_mul(rot_mats, pts)
657
+
658
+ def apply_batch(self, pts: torch.Tensor) -> torch.Tensor:
659
+ """
660
+ Apply the current Rotation as a rotation matrix to a batch of 3D
661
+ coordinates.
662
+
663
+ Args:
664
+ pts:
665
+ A [Batch, N, 3] set of points
666
+ Returns:
667
+ [Batch, N, 3] rotated points
668
+ """
669
+ rot_mats = self.get_rot_mats() # Assume shape [Batch, 3, 3]
670
+ return rot_vec_mul_batch(rot_mats, pts)
671
+
672
+ def invert_apply(self, pts: torch.Tensor) -> torch.Tensor:
673
+ """
674
+ The inverse of the apply() method.
675
+
676
+ Args:
677
+ pts:
678
+ A [*, 3] set of points
679
+ Returns:
680
+ [*, 3] inverse-rotated points
681
+ """
682
+ rot_mats = self.get_rot_mats()
683
+ inv_rot_mats = invert_rot_mat(rot_mats)
684
+ return rot_vec_mul(inv_rot_mats, pts)
685
+
686
+ def invert_apply_batch(self, pts: torch.Tensor) -> torch.Tensor:
687
+ """
688
+ The inverse of the apply_batch() method, applying the inverse rotation
689
+ to a batch of 3D coordinates.
690
+
691
+ Args:
692
+ pts:
693
+ A [Batch, N, 3] set of points
694
+ Returns:
695
+ [Batch, N, 3] inverse-rotated points
696
+ """
697
+ rot_mats = self.get_rot_mats() # Assume shape [Batch, 3, 3]
698
+ inv_rot_mats = rot_mats.transpose(
699
+ -1, -2
700
+ ) # Transpose to get the inverse rotation matrices
701
+ return rot_vec_mul_batch(inv_rot_mats, pts)
702
+
703
+ def invert(self) -> Rotation:
704
+ """
705
+ Returns the inverse of the current Rotation.
706
+
707
+ Returns:
708
+ The inverse of the current Rotation
709
+ """
710
+ if self._rot_mats is not None:
711
+ return Rotation(rot_mats=invert_rot_mat(self._rot_mats), quats=None)
712
+ elif self._quats is not None:
713
+ return Rotation(
714
+ rot_mats=None,
715
+ quats=invert_quat(self._quats),
716
+ normalize_quats=False,
717
+ )
718
+ else:
719
+ raise ValueError("Both rotations are None")
720
+
721
+ # "Tensor" stuff
722
+
723
+ def unsqueeze(
724
+ self,
725
+ dim: int,
726
+ ) -> Rigid:
727
+ """
728
+ Analogous to torch.unsqueeze. The dimension is relative to the
729
+ shape of the Rotation object.
730
+
731
+ Args:
732
+ dim: A positive or negative dimension index.
733
+ Returns:
734
+ The unsqueezed Rotation.
735
+ """
736
+ if dim >= len(self.shape):
737
+ raise ValueError("Invalid dimension")
738
+
739
+ if self._rot_mats is not None:
740
+ rot_mats = self._rot_mats.unsqueeze(dim if dim >= 0 else dim - 2)
741
+ return Rotation(rot_mats=rot_mats, quats=None)
742
+ elif self._quats is not None:
743
+ quats = self._quats.unsqueeze(dim if dim >= 0 else dim - 1)
744
+ return Rotation(rot_mats=None, quats=quats, normalize_quats=False)
745
+ else:
746
+ raise ValueError("Both rotations are None")
747
+
748
+ @staticmethod
749
+ def cat(
750
+ rs: Sequence[Rotation],
751
+ dim: int,
752
+ ) -> Rigid:
753
+ """
754
+ Concatenates rotations along one of the batch dimensions. Analogous
755
+ to torch.cat().
756
+
757
+ Note that the output of this operation is always a rotation matrix,
758
+ regardless of the format of input rotations.
759
+
760
+ Args:
761
+ rs:
762
+ A list of rotation objects
763
+ dim:
764
+ The dimension along which the rotations should be
765
+ concatenated
766
+ Returns:
767
+ A concatenated Rotation object in rotation matrix format
768
+ """
769
+ rot_mats = [r.get_rot_mats() for r in rs]
770
+ rot_mats = torch.cat(rot_mats, dim=dim if dim >= 0 else dim - 2)
771
+
772
+ return Rotation(rot_mats=rot_mats, quats=None)
773
+
774
+ def map_tensor_fn(self, fn: Callable[torch.Tensor, torch.Tensor]) -> Rotation:
775
+ """
776
+ Apply a Tensor -> Tensor function to underlying rotation tensors,
777
+ mapping over the rotation dimension(s). Can be used e.g. to sum out
778
+ a one-hot batch dimension.
779
+
780
+ Args:
781
+ fn:
782
+ A Tensor -> Tensor function to be mapped over the Rotation
783
+ Returns:
784
+ The transformed Rotation object
785
+ """
786
+ if self._rot_mats is not None:
787
+ rot_mats = self._rot_mats.view(self._rot_mats.shape[:-2] + (9,))
788
+ rot_mats = torch.stack(
789
+ list(map(fn, torch.unbind(rot_mats, dim=-1))), dim=-1
790
+ )
791
+ rot_mats = rot_mats.view(rot_mats.shape[:-1] + (3, 3))
792
+ return Rotation(rot_mats=rot_mats, quats=None)
793
+ elif self._quats is not None:
794
+ quats = torch.stack(
795
+ list(map(fn, torch.unbind(self._quats, dim=-1))), dim=-1
796
+ )
797
+ return Rotation(rot_mats=None, quats=quats, normalize_quats=False)
798
+ else:
799
+ raise ValueError("Both rotations are None")
800
+
801
+ def cuda(self) -> Rotation:
802
+ """
803
+ Analogous to the cuda() method of torch Tensors
804
+
805
+ Returns:
806
+ A copy of the Rotation in CUDA memory
807
+ """
808
+ if self._rot_mats is not None:
809
+ return Rotation(rot_mats=self._rot_mats.cuda(), quats=None)
810
+ elif self._quats is not None:
811
+ return Rotation(
812
+ rot_mats=None, quats=self._quats.cuda(), normalize_quats=False
813
+ )
814
+ else:
815
+ raise ValueError("Both rotations are None")
816
+
817
+ def to(
818
+ self, device: Optional[torch.device], dtype: Optional[torch.dtype]
819
+ ) -> Rotation:
820
+ """
821
+ Analogous to the to() method of torch Tensors
822
+
823
+ Args:
824
+ device:
825
+ A torch device
826
+ dtype:
827
+ A torch dtype
828
+ Returns:
829
+ A copy of the Rotation using the new device and dtype
830
+ """
831
+ if self._rot_mats is not None:
832
+ return Rotation(
833
+ rot_mats=self._rot_mats.to(device=device, dtype=dtype),
834
+ quats=None,
835
+ )
836
+ elif self._quats is not None:
837
+ return Rotation(
838
+ rot_mats=None,
839
+ quats=self._quats.to(device=device, dtype=dtype),
840
+ normalize_quats=False,
841
+ )
842
+ else:
843
+ raise ValueError("Both rotations are None")
844
+
845
+ def detach(self) -> Rotation:
846
+ """
847
+ Returns a copy of the Rotation whose underlying Tensor has been
848
+ detached from its torch graph.
849
+
850
+ Returns:
851
+ A copy of the Rotation whose underlying Tensor has been detached
852
+ from its torch graph
853
+ """
854
+ if self._rot_mats is not None:
855
+ return Rotation(rot_mats=self._rot_mats.detach(), quats=None)
856
+ elif self._quats is not None:
857
+ return Rotation(
858
+ rot_mats=None,
859
+ quats=self._quats.detach(),
860
+ normalize_quats=False,
861
+ )
862
+ else:
863
+ raise ValueError("Both rotations are None")
864
+
865
+
866
+ class Rigid:
867
+ """
868
+ A class representing a rigid transformation. Little more than a wrapper
869
+ around two objects: a Rotation object and a [*, 3] translation
870
+ Designed to behave approximately like a single torch tensor with the
871
+ shape of the shared batch dimensions of its component parts.
872
+ """
873
+
874
+ def __init__(
875
+ self,
876
+ rots: Optional[Rotation],
877
+ trans: Optional[torch.Tensor],
878
+ ):
879
+ """
880
+ Args:
881
+ rots: A [*, 3, 3] rotation tensor
882
+ trans: A corresponding [*, 3] translation tensor
883
+ """
884
+ # (we need device, dtype, etc. from at least one input)
885
+
886
+ batch_dims, dtype, device, requires_grad = None, None, None, None
887
+ if trans is not None:
888
+ batch_dims = trans.shape[:-1]
889
+ dtype = trans.dtype
890
+ device = trans.device
891
+ requires_grad = trans.requires_grad
892
+ elif rots is not None:
893
+ batch_dims = rots.shape
894
+ dtype = rots.dtype
895
+ device = rots.device
896
+ requires_grad = rots.requires_grad
897
+ else:
898
+ raise ValueError("At least one input argument must be specified")
899
+
900
+ if rots is None:
901
+ rots = Rotation.identity(
902
+ batch_dims,
903
+ dtype,
904
+ device,
905
+ requires_grad,
906
+ )
907
+ elif trans is None:
908
+ trans = identity_trans(
909
+ batch_dims,
910
+ dtype,
911
+ device,
912
+ requires_grad,
913
+ )
914
+
915
+ if (rots.shape != trans.shape[:-1]) or (rots.device != trans.device):
916
+ raise ValueError("Rots and trans incompatible")
917
+
918
+ # Force full precision. Happens to the rotations automatically.
919
+ trans = trans.to(dtype=torch.float32)
920
+
921
+ self._rots = rots
922
+ self._trans = trans
923
+
924
+ @staticmethod
925
+ def identity(
926
+ shape: Tuple[int],
927
+ dtype: Optional[torch.dtype] = None,
928
+ device: Optional[torch.device] = None,
929
+ requires_grad: bool = True,
930
+ fmt: str = "quat",
931
+ ) -> Rigid:
932
+ """
933
+ Constructs an identity transformation.
934
+
935
+ Args:
936
+ shape:
937
+ The desired shape
938
+ dtype:
939
+ The dtype of both internal tensors
940
+ device:
941
+ The device of both internal tensors
942
+ requires_grad:
943
+ Whether grad should be enabled for the internal tensors
944
+ Returns:
945
+ The identity transformation
946
+ """
947
+ return Rigid(
948
+ Rotation.identity(shape, dtype, device, requires_grad, fmt=fmt),
949
+ identity_trans(shape, dtype, device, requires_grad),
950
+ )
951
+
952
+ def __getitem__(
953
+ self,
954
+ index: Any,
955
+ ) -> Rigid:
956
+ """
957
+ Indexes the affine transformation with PyTorch-style indices.
958
+ The index is applied to the shared dimensions of both the rotation
959
+ and the translation.
960
+
961
+ E.g.::
962
+
963
+ r = Rotation(rot_mats=torch.rand(10, 10, 3, 3), quats=None)
964
+ t = Rigid(r, torch.rand(10, 10, 3))
965
+ indexed = t[3, 4:6]
966
+ assert(indexed.shape == (2,))
967
+ assert(indexed.get_rots().shape == (2,))
968
+ assert(indexed.get_trans().shape == (2, 3))
969
+
970
+ Args:
971
+ index: A standard torch tensor index. E.g. 8, (10, None, 3),
972
+ or (3, slice(0, 1, None))
973
+ Returns:
974
+ The indexed tensor
975
+ """
976
+ if type(index) != tuple:
977
+ index = (index,)
978
+
979
+ return Rigid(
980
+ self._rots[index],
981
+ self._trans[index + (slice(None),)],
982
+ )
983
+
984
+ def __mul__(
985
+ self,
986
+ right: torch.Tensor,
987
+ ) -> Rigid:
988
+ """
989
+ Pointwise left multiplication of the transformation with a tensor.
990
+ Can be used to e.g. mask the Rigid.
991
+
992
+ Args:
993
+ right:
994
+ The tensor multiplicand
995
+ Returns:
996
+ The product
997
+ """
998
+ if not (isinstance(right, torch.Tensor)):
999
+ raise TypeError("The other multiplicand must be a Tensor")
1000
+
1001
+ new_rots = self._rots * right
1002
+ new_trans = self._trans * right[..., None]
1003
+
1004
+ return Rigid(new_rots, new_trans)
1005
+
1006
+ def __rmul__(
1007
+ self,
1008
+ left: torch.Tensor,
1009
+ ) -> Rigid:
1010
+ """
1011
+ Reverse pointwise multiplication of the transformation with a
1012
+ tensor.
1013
+
1014
+ Args:
1015
+ left:
1016
+ The left multiplicand
1017
+ Returns:
1018
+ The product
1019
+ """
1020
+ return self.__mul__(left)
1021
+
1022
+ @property
1023
+ def shape(self) -> torch.Size:
1024
+ """
1025
+ Returns the shape of the shared dimensions of the rotation and
1026
+ the translation.
1027
+
1028
+ Returns:
1029
+ The shape of the transformation
1030
+ """
1031
+ s = self._trans.shape[:-1]
1032
+ return s
1033
+
1034
+ @property
1035
+ def device(self) -> torch.device:
1036
+ """
1037
+ Returns the device on which the Rigid's tensors are located.
1038
+
1039
+ Returns:
1040
+ The device on which the Rigid's tensors are located
1041
+ """
1042
+ return self._trans.device
1043
+
1044
+ @property
1045
+ def dtype(self) -> torch.dtype:
1046
+ """
1047
+ Returns the dtype of the Rigid tensors.
1048
+
1049
+ Returns:
1050
+ The dtype of the Rigid tensors
1051
+ """
1052
+ return self._rots.dtype
1053
+
1054
+ def get_rots(self) -> Rotation:
1055
+ """
1056
+ Getter for the rotation.
1057
+
1058
+ Returns:
1059
+ The rotation object
1060
+ """
1061
+ return self._rots
1062
+
1063
+ def get_trans(self) -> torch.Tensor:
1064
+ """
1065
+ Getter for the translation.
1066
+
1067
+ Returns:
1068
+ The stored translation
1069
+ """
1070
+ return self._trans
1071
+
1072
+ def compose_q_update_vec(
1073
+ self,
1074
+ q_update_vec: torch.Tensor,
1075
+ ) -> Rigid:
1076
+ """
1077
+ Composes the transformation with a quaternion update vector of
1078
+ shape [*, 6], where the final 6 columns represent the x, y, and
1079
+ z values of a quaternion of form (1, x, y, z) followed by a 3D
1080
+ translation.
1081
+
1082
+ Args:
1083
+ q_vec: The quaternion update vector.
1084
+ Returns:
1085
+ The composed transformation.
1086
+ """
1087
+ q_vec, t_vec = q_update_vec[..., :3], q_update_vec[..., 3:]
1088
+ new_rots = self._rots.compose_q_update_vec(q_vec)
1089
+
1090
+ trans_update = self._rots.apply(t_vec)
1091
+ new_translation = self._trans + trans_update
1092
+
1093
+ return Rigid(new_rots, new_translation)
1094
+
1095
+ def compose(
1096
+ self,
1097
+ r: Rigid,
1098
+ ) -> Rigid:
1099
+ """
1100
+ Composes the current rigid object with another.
1101
+
1102
+ Args:
1103
+ r:
1104
+ Another Rigid object
1105
+ Returns:
1106
+ The composition of the two transformations
1107
+ """
1108
+ new_rot = self._rots.compose_r(r._rots)
1109
+ new_trans = self._rots.apply(r._trans) + self._trans
1110
+ return Rigid(new_rot, new_trans)
1111
+
1112
+ def apply(
1113
+ self,
1114
+ pts: torch.Tensor,
1115
+ ) -> torch.Tensor:
1116
+ """
1117
+ Applies the transformation to a coordinate tensor.
1118
+
1119
+ Args:
1120
+ pts: A [*, 3] coordinate tensor.
1121
+ Returns:
1122
+ The transformed points.
1123
+ """
1124
+ rotated = self._rots.apply(pts)
1125
+ return rotated + self._trans
1126
+
1127
+ def invert_apply(self, pts: torch.Tensor) -> torch.Tensor:
1128
+ """
1129
+ Applies the inverse of the transformation to a coordinate tensor.
1130
+
1131
+ Args:
1132
+ pts: A [*, 3] coordinate tensor
1133
+ Returns:
1134
+ The transformed points.
1135
+ """
1136
+ pts = pts - self._trans
1137
+ return self._rots.invert_apply(pts)
1138
+
1139
+ def invert(self) -> Rigid:
1140
+ """
1141
+ Inverts the transformation.
1142
+
1143
+ Returns:
1144
+ The inverse transformation.
1145
+ """
1146
+ rot_inv = self._rots.invert()
1147
+ trn_inv = rot_inv.apply(self._trans)
1148
+
1149
+ return Rigid(rot_inv, -1 * trn_inv)
1150
+
1151
+ def map_tensor_fn(self, fn: Callable[torch.Tensor, torch.Tensor]) -> Rigid:
1152
+ """
1153
+ Apply a Tensor -> Tensor function to underlying translation and
1154
+ rotation tensors, mapping over the translation/rotation dimensions
1155
+ respectively.
1156
+
1157
+ Args:
1158
+ fn:
1159
+ A Tensor -> Tensor function to be mapped over the Rigid
1160
+ Returns:
1161
+ The transformed Rigid object
1162
+ """
1163
+ new_rots = self._rots.map_tensor_fn(fn)
1164
+ new_trans = torch.stack(
1165
+ list(map(fn, torch.unbind(self._trans, dim=-1))), dim=-1
1166
+ )
1167
+
1168
+ return Rigid(new_rots, new_trans)
1169
+
1170
+ def to_tensor_4x4(self) -> torch.Tensor:
1171
+ """
1172
+ Converts a transformation to a homogenous transformation tensor.
1173
+
1174
+ Returns:
1175
+ A [*, 4, 4] homogenous transformation tensor
1176
+ """
1177
+ tensor = self._trans.new_zeros((*self.shape, 4, 4))
1178
+ tensor[..., :3, :3] = self._rots.get_rot_mats()
1179
+ tensor[..., :3, 3] = self._trans
1180
+ tensor[..., 3, 3] = 1
1181
+ return tensor
1182
+
1183
+ @staticmethod
1184
+ def from_tensor_4x4(t: torch.Tensor) -> Rigid:
1185
+ """
1186
+ Constructs a transformation from a homogenous transformation
1187
+ tensor.
1188
+
1189
+ Args:
1190
+ t: [*, 4, 4] homogenous transformation tensor
1191
+ Returns:
1192
+ T object with shape [*]
1193
+ """
1194
+ if t.shape[-2:] != (4, 4):
1195
+ raise ValueError("Incorrectly shaped input tensor")
1196
+
1197
+ rots = Rotation(rot_mats=t[..., :3, :3], quats=None)
1198
+ trans = t[..., :3, 3]
1199
+
1200
+ return Rigid(rots, trans)
1201
+
1202
+ def to_tensor_7(self) -> torch.Tensor:
1203
+ """
1204
+ Converts a transformation to a tensor with 7 final columns, four
1205
+ for the quaternion followed by three for the translation.
1206
+
1207
+ Returns:
1208
+ A [*, 7] tensor representation of the transformation
1209
+ """
1210
+ tensor = self._trans.new_zeros((*self.shape, 7))
1211
+ tensor[..., :4] = self._rots.get_quats()
1212
+ tensor[..., 4:] = self._trans
1213
+
1214
+ return tensor
1215
+
1216
+ @staticmethod
1217
+ def from_tensor_7(
1218
+ t: torch.Tensor,
1219
+ normalize_quats: bool = False,
1220
+ ) -> Rigid:
1221
+ if t.shape[-1] != 7:
1222
+ raise ValueError("Incorrectly shaped input tensor")
1223
+
1224
+ quats, trans = t[..., :4], t[..., 4:]
1225
+
1226
+ rots = Rotation(rot_mats=None, quats=quats, normalize_quats=normalize_quats)
1227
+
1228
+ return Rigid(rots, trans)
1229
+
1230
+ @staticmethod
1231
+ def from_3_points(
1232
+ p_neg_x_axis: torch.Tensor,
1233
+ origin: torch.Tensor,
1234
+ p_xy_plane: torch.Tensor,
1235
+ eps: float = 1e-8,
1236
+ ) -> Rigid:
1237
+ """
1238
+ Implements algorithm 21. Constructs transformations from sets of 3
1239
+ points using the Gram-Schmidt algorithm.
1240
+
1241
+ Args:
1242
+ p_neg_x_axis: [*, 3] coordinates
1243
+ origin: [*, 3] coordinates used as frame origins
1244
+ p_xy_plane: [*, 3] coordinates
1245
+ eps: Small epsilon value
1246
+ Returns:
1247
+ A transformation object of shape [*]
1248
+ """
1249
+ p_neg_x_axis = torch.unbind(p_neg_x_axis, dim=-1)
1250
+ origin = torch.unbind(origin, dim=-1)
1251
+ p_xy_plane = torch.unbind(p_xy_plane, dim=-1)
1252
+
1253
+ e0 = [c1 - c2 for c1, c2 in zip(origin, p_neg_x_axis)]
1254
+ e1 = [c1 - c2 for c1, c2 in zip(p_xy_plane, origin)]
1255
+
1256
+ denom = torch.sqrt(sum((c * c for c in e0)) + eps)
1257
+ e0 = [c / denom for c in e0]
1258
+ dot = sum((c1 * c2 for c1, c2 in zip(e0, e1)))
1259
+ e1 = [c2 - c1 * dot for c1, c2 in zip(e0, e1)]
1260
+ denom = torch.sqrt(sum((c * c for c in e1)) + eps)
1261
+ e1 = [c / denom for c in e1]
1262
+ e2 = [
1263
+ e0[1] * e1[2] - e0[2] * e1[1],
1264
+ e0[2] * e1[0] - e0[0] * e1[2],
1265
+ e0[0] * e1[1] - e0[1] * e1[0],
1266
+ ]
1267
+
1268
+ rots = torch.stack([c for tup in zip(e0, e1, e2) for c in tup], dim=-1)
1269
+ rots = rots.reshape(rots.shape[:-1] + (3, 3))
1270
+
1271
+ rot_obj = Rotation(rot_mats=rots, quats=None)
1272
+
1273
+ return Rigid(rot_obj, torch.stack(origin, dim=-1))
1274
+
1275
+ def unsqueeze(
1276
+ self,
1277
+ dim: int,
1278
+ ) -> Rigid:
1279
+ """
1280
+ Analogous to torch.unsqueeze. The dimension is relative to the
1281
+ shared dimensions of the rotation/translation.
1282
+
1283
+ Args:
1284
+ dim: A positive or negative dimension index.
1285
+ Returns:
1286
+ The unsqueezed transformation.
1287
+ """
1288
+ if dim >= len(self.shape):
1289
+ raise ValueError("Invalid dimension")
1290
+ rots = self._rots.unsqueeze(dim)
1291
+ trans = self._trans.unsqueeze(dim if dim >= 0 else dim - 1)
1292
+
1293
+ return Rigid(rots, trans)
1294
+
1295
+ @staticmethod
1296
+ def cat(
1297
+ ts: Sequence[Rigid],
1298
+ dim: int,
1299
+ ) -> Rigid:
1300
+ """
1301
+ Concatenates transformations along a new dimension.
1302
+
1303
+ Args:
1304
+ ts:
1305
+ A list of T objects
1306
+ dim:
1307
+ The dimension along which the transformations should be
1308
+ concatenated
1309
+ Returns:
1310
+ A concatenated transformation object
1311
+ """
1312
+ rots = Rotation.cat([t._rots for t in ts], dim)
1313
+ trans = torch.cat([t._trans for t in ts], dim=dim if dim >= 0 else dim - 1)
1314
+
1315
+ return Rigid(rots, trans)
1316
+
1317
+ def apply_rot_fn(self, fn: Callable[Rotation, Rotation]) -> Rigid:
1318
+ """
1319
+ Applies a Rotation -> Rotation function to the stored rotation
1320
+ object.
1321
+
1322
+ Args:
1323
+ fn: A function of type Rotation -> Rotation
1324
+ Returns:
1325
+ A transformation object with a transformed rotation.
1326
+ """
1327
+ return Rigid(fn(self._rots), self._trans)
1328
+
1329
+ def apply_trans_fn(self, fn: Callable[torch.Tensor, torch.Tensor]) -> Rigid:
1330
+ """
1331
+ Applies a Tensor -> Tensor function to the stored translation.
1332
+
1333
+ Args:
1334
+ fn:
1335
+ A function of type Tensor -> Tensor to be applied to the
1336
+ translation
1337
+ Returns:
1338
+ A transformation object with a transformed translation.
1339
+ """
1340
+ return Rigid(self._rots, fn(self._trans))
1341
+
1342
+ def scale_translation(self, trans_scale_factor: float) -> Rigid:
1343
+ """
1344
+ Scales the translation by a constant factor.
1345
+
1346
+ Args:
1347
+ trans_scale_factor:
1348
+ The constant factor
1349
+ Returns:
1350
+ A transformation object with a scaled translation.
1351
+ """
1352
+ fn = lambda t: t * trans_scale_factor
1353
+ return self.apply_trans_fn(fn)
1354
+
1355
+ def stop_rot_gradient(self) -> Rigid:
1356
+ """
1357
+ Detaches the underlying rotation object
1358
+
1359
+ Returns:
1360
+ A transformation object with detached rotations
1361
+ """
1362
+ fn = lambda r: r.detach()
1363
+ return self.apply_rot_fn(fn)
1364
+
1365
+ @staticmethod
1366
+ def make_transform_from_reference(n_xyz, ca_xyz, c_xyz, eps=1e-20):
1367
+ """
1368
+ Returns a transformation object from reference coordinates.
1369
+
1370
+ Note that this method does not take care of symmetries. If you
1371
+ provide the atom positions in the non-standard way, the N atom will
1372
+ end up not at [-0.527250, 1.359329, 0.0] but instead at
1373
+ [-0.527250, -1.359329, 0.0]. You need to take care of such cases in
1374
+ your code.
1375
+
1376
+ Args:
1377
+ n_xyz: A [*, 3] tensor of nitrogen xyz coordinates.
1378
+ ca_xyz: A [*, 3] tensor of carbon alpha xyz coordinates.
1379
+ c_xyz: A [*, 3] tensor of carbon xyz coordinates.
1380
+ Returns:
1381
+ A transformation object. After applying the translation and
1382
+ rotation to the reference backbone, the coordinates will
1383
+ approximately equal to the input coordinates.
1384
+ """
1385
+ translation = -1 * ca_xyz
1386
+ n_xyz = n_xyz + translation
1387
+ c_xyz = c_xyz + translation
1388
+
1389
+ c_x, c_y, c_z = [c_xyz[..., i] for i in range(3)]
1390
+ norm = torch.sqrt(eps + c_x**2 + c_y**2)
1391
+ sin_c1 = -c_y / norm
1392
+ cos_c1 = c_x / norm
1393
+ zeros = sin_c1.new_zeros(sin_c1.shape)
1394
+ ones = sin_c1.new_ones(sin_c1.shape)
1395
+
1396
+ c1_rots = sin_c1.new_zeros((*sin_c1.shape, 3, 3))
1397
+ c1_rots[..., 0, 0] = cos_c1
1398
+ c1_rots[..., 0, 1] = -1 * sin_c1
1399
+ c1_rots[..., 1, 0] = sin_c1
1400
+ c1_rots[..., 1, 1] = cos_c1
1401
+ c1_rots[..., 2, 2] = 1
1402
+
1403
+ norm = torch.sqrt(eps + c_x**2 + c_y**2 + c_z**2)
1404
+ sin_c2 = c_z / norm
1405
+ cos_c2 = torch.sqrt(c_x**2 + c_y**2) / norm
1406
+
1407
+ c2_rots = sin_c2.new_zeros((*sin_c2.shape, 3, 3))
1408
+ c2_rots[..., 0, 0] = cos_c2
1409
+ c2_rots[..., 0, 2] = sin_c2
1410
+ c2_rots[..., 1, 1] = 1
1411
+ c2_rots[..., 2, 0] = -1 * sin_c2
1412
+ c2_rots[..., 2, 2] = cos_c2
1413
+
1414
+ c_rots = rot_matmul(c2_rots, c1_rots)
1415
+ n_xyz = rot_vec_mul(c_rots, n_xyz)
1416
+
1417
+ _, n_y, n_z = [n_xyz[..., i] for i in range(3)]
1418
+ norm = torch.sqrt(eps + n_y**2 + n_z**2)
1419
+ sin_n = -n_z / norm
1420
+ cos_n = n_y / norm
1421
+
1422
+ n_rots = sin_c2.new_zeros((*sin_c2.shape, 3, 3))
1423
+ n_rots[..., 0, 0] = 1
1424
+ n_rots[..., 1, 1] = cos_n
1425
+ n_rots[..., 1, 2] = -1 * sin_n
1426
+ n_rots[..., 2, 1] = sin_n
1427
+ n_rots[..., 2, 2] = cos_n
1428
+
1429
+ rots = rot_matmul(n_rots, c_rots)
1430
+
1431
+ rots = rots.transpose(-1, -2)
1432
+ translation = -1 * translation
1433
+
1434
+ rot_obj = Rotation(rot_mats=rots, quats=None)
1435
+
1436
+ return Rigid(rot_obj, translation)
1437
+
1438
+ def cuda(self) -> Rigid:
1439
+ """
1440
+ Moves the transformation object to GPU memory
1441
+
1442
+ Returns:
1443
+ A version of the transformation on GPU
1444
+ """
1445
+ return Rigid(self._rots.cuda(), self._trans.cuda())
petimot/utils/seeding.py ADDED
@@ -0,0 +1,42 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import random
2
+ import numpy as np
3
+ import torch
4
+ import torch_geometric
5
+ import os
6
+
7
+
8
+ def set_seed(seed: int, deterministic_algorithms: bool = False):
9
+ """
10
+ Set seeds for reproducibility and optionally enable deterministic algorithms.
11
+
12
+ Args:
13
+ seed: Integer seed for reproducibility
14
+ deterministic_algorithms: If True, enables deterministic CUDA algorithms
15
+ and disables cuDNN benchmarking. This will impact performance.
16
+ """
17
+ # Basic seeding
18
+ random.seed(seed)
19
+ np.random.seed(seed)
20
+ torch.manual_seed(seed)
21
+ if torch.cuda.is_available():
22
+ torch.cuda.manual_seed_all(seed)
23
+
24
+ # PyG specific seeding
25
+ torch_geometric.seed_everything(seed)
26
+
27
+ if deterministic_algorithms:
28
+ # These settings impact performance but ensure reproducibility
29
+ torch.backends.cudnn.deterministic = True
30
+ torch.backends.cudnn.benchmark = False
31
+ os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
32
+ torch.use_deterministic_algorithms(True)
33
+ else:
34
+ # Better performance settings
35
+ torch.backends.cudnn.deterministic = False
36
+ torch.backends.cudnn.benchmark = True
37
+ if "CUBLAS_WORKSPACE_CONFIG" in os.environ:
38
+ del os.environ["CUBLAS_WORKSPACE_CONFIG"]
39
+ torch.use_deterministic_algorithms(False)
40
+
41
+ print(f"Set seed to {seed}")
42
+ print(f"Deterministic mode: {deterministic_algorithms}")
requirements.txt ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ torch>=2.0.0
2
+ torch_geometric>=2.0.0
3
+ wandb>=0.19.0
4
+ transformers==4.48.3
5
+ sentencepiece==0.2.0
6
+ tqdm>=4.65.0
7
+ scipy>=1.13.0
8
+ typer==0.15.1