From ea7e11fd423a2f7240cf527fc14a1d0138ffa5f0 Mon Sep 17 00:00:00 2001 From: shervinea Date: Wed, 21 Apr 2021 00:24:53 -0700 Subject: [PATCH 01/18] Adjust convention of docstrings and comments --- datasets/make_files/charge.py | 6 +- datasets/make_files/download_pdb.py | 8 +- datasets/make_files/generate_datasets.py | 124 +++++++++---------- datasets/make_files/hydropathy.py | 8 +- datasets/make_files/isoelectric.py | 10 +- enzynet/PDB.py | 70 ++++++----- enzynet/__init__.py | 2 +- enzynet/indicators.py | 28 ++--- enzynet/keras_utils.py | 66 +++++------ enzynet/real_time.py | 34 +++--- enzynet/tools.py | 24 ++-- enzynet/visualization.py | 75 ++++++------ enzynet/volume.py | 144 ++++++++++++----------- scripts/architecture/enzynet_adapted.py | 48 ++++---- scripts/architecture/enzynet_uniform.py | 48 ++++---- 15 files changed, 352 insertions(+), 343 deletions(-) diff --git a/datasets/make_files/charge.py b/datasets/make_files/charge.py index 316cae6..477056c 100755 --- a/datasets/make_files/charge.py +++ b/datasets/make_files/charge.py @@ -1,4 +1,4 @@ -'Storing charge information in a dictionary' +"""Storing charge information in a dictionary.""" # Authors: Afshine Amidi # Shervine Amidi @@ -8,7 +8,7 @@ from enzynet.tools import dict_to_csv -# Initialization +# Initialization. charge = {} # Info from p. 51 of The cell: a molecular approach, by Hausman et al. @@ -33,5 +33,5 @@ charge['TYR'] = 0 charge['VAL'] = 0 -# Save results +# Save results. dict_to_csv(charge, '../charge.csv') diff --git a/datasets/make_files/download_pdb.py b/datasets/make_files/download_pdb.py index a2688cd..c1f7556 100755 --- a/datasets/make_files/download_pdb.py +++ b/datasets/make_files/download_pdb.py @@ -1,4 +1,4 @@ -'Converts list of PDB enzymes in a text file' +"""Converts list of PDB enzymes in a text file.""" # Authors: Afshine Amidi # Shervine Amidi @@ -10,14 +10,14 @@ from enzynet.tools import read_dict -# Read csv files +# Read csv files. df = pa.read_csv('../dataset_all.csv', header=None) -# Only keep PDB IDs +# Only keep PDB IDs. df = df[0]+', ' df = df.tolist() -# Save to text file with one PDB ID per line +# Save to text file with one PDB ID per line. file = open('../download_pdb.txt','w') for i in range(len(df)): file.write(df[i]) diff --git a/datasets/make_files/generate_datasets.py b/datasets/make_files/generate_datasets.py index b6652a1..5b6dc05 100755 --- a/datasets/make_files/generate_datasets.py +++ b/datasets/make_files/generate_datasets.py @@ -1,4 +1,4 @@ -'Generate datasets' +"""Generate datasets.""" # Authors: Afshine Amidi # Shervine Amidi @@ -15,183 +15,183 @@ from enzynet.tools import read_dict, dict_to_csv -# Date of retrieval of raw datasets from rcsb.org: 07-03-2017 +# Date of retrieval of raw datasets from rcsb.org: 07-03-2017. -# Parameters +# Parameters. n_classes = 6 ##------------------------- Data preprocessing -------------------------------## -# Computations +# Computations. for i in range(1, n_classes+1): - # Initialization - errors = [] # List of indexes with error - enzymes = pa.read_table('raw/' + str(i) + '.txt', header=None) # Load PDB IDs + # Initialization. + errors = [] # List of indexes with error. + enzymes = pa.read_table('raw/' + str(i) + '.txt', header=None) # Load PDB IDs. list_enzymes = enzymes.loc[:,0].tolist() - # Look for errors + # Look for errors. for k, enzyme in enumerate(tqdm(list_enzymes)): try: - # Load local enzyme + # Load local enzyme. local_enzyme = PDB_backbone(enzyme) local_enzyme.get_coords() - # Look for problem - if len(local_enzyme.backbone_atoms) == 0: # Empty enzyme + # Look for problem. + if len(local_enzyme.backbone_atoms) == 0: # Empty enzyme. errors += [k] - except: # File does not exist, i.e. deprecated enzyme + except: # File does not exist, i.e. deprecated enzyme. errors += [k] - # Drop errors and save + # Drop errors and save. enzymes = enzymes.drop(enzymes.index[errors]) enzymes.to_csv(str(i) + '.txt', header=None, index=None) ##-------------- Dictionary with all PDBs in dataset_all.csv -----------------## -# Initialization +# Initialization. labels = {} -# Computations +# Computations. for i in range(1, n_classes+1): - # Load PDB IDs for each class + # Load PDB IDs for each class. enzymes = pa.read_table(str(i) + '.txt', header=None) enzymes = enzymes[0].tolist() - # Add entries to dict + # Add entries to dict. for entry in enzymes: - if entry in labels: # PDB_id already in dict + if entry in labels: # PDB_id already in dict. labels[entry] = labels[entry] + [i] - else: # PDB_id not yet in dict + else: # PDB_id not yet in dict. labels[entry] = [i] -# # Save results -# dict_to_csv(labels, '../dataset_all.csv') # DONE 18-03-2017 +# # Save results. +# dict_to_csv(labels, '../dataset_all.csv') # DONE 18-03-2017. ##--------- Dictionary with single-label PDBs in dataset_single.csv ----------## -# Initialization +# Initialization. labels_temp = read_dict('../dataset_all.csv') labels = {} -# Computations +# Computations. for entry in labels_temp: - if len(labels_temp[entry]) == 3: # Single-label ('[i]') - labels[entry] = int(labels_temp[entry][1]) # Convert list to scalar + if len(labels_temp[entry]) == 3: # Single-label ('[i]'). + labels[entry] = int(labels_temp[entry][1]) # Convert list to scalar. -# # Save results -# dict_to_csv(labels, '../dataset_single.csv') # DONE 18-03-2017 +# # Save results. +# dict_to_csv(labels, '../dataset_single.csv') # DONE 18-03-2017. ##---------- Dictionary with multi-label PDBs in dataset_multi.csv -----------## -# Initialization +# Initialization. labels_temp = read_dict('../dataset_all.csv') labels = {} -# Computations +# Computations. for entry in labels_temp: - if len(labels_temp[entry]) > 3: # Multi-label + if len(labels_temp[entry]) > 3: # Multi-label. labels[entry] = labels_temp[entry] -# # Save results -# dict_to_csv(labels, '../dataset_multi.csv') # DONE 18-03-2017 +# # Save results. +# dict_to_csv(labels, '../dataset_multi.csv') # DONE 18-03-2017. ##----- Dict of ligands pointing to associated PDB in ligands_to_pdb.csv -----## -# Initialization +# Initialization. pdb_all = read_dict('../dataset_all.csv') pdb_all = list(pdb_all.keys()) ligands = {} -# Computations +# Computations. for enzyme in tqdm(pdb_all): - # Load local enzyme + # Load local enzyme. local_enzyme = PDB_backbone(enzyme) - # Get ligands + # Get ligands. local_enzyme.get_ligands() - # Update dict + # Update dict. for local_ligand in local_enzyme.ligands: - if local_ligand in ligands: # Already in dict + if local_ligand in ligands: # Already in dict. ligands[local_ligand] += [enzyme] - else: # New entry + else: # New entry. ligands[local_ligand] = [enzyme] -# # Save results +# # Save results. # dict_to_csv(ligands, '../ligands_to_pdb.csv') ##---- Dict of PDBs pointing to associated ligands in pdb_to_ligands.csv -----## -# Initialization +# Initialization. pdb_all = read_dict('../dataset_all.csv') pdb_all = list(pdb_all.keys()) pdb = {} -# Computations +# Computations. for enzyme in tqdm(pdb_all): - # Load local enzyme + # Load local enzyme. local_enzyme = PDB_backbone(enzyme) - # Get ligands + # Get ligands. local_enzyme.get_ligands() - # Add entry to dict + # Add entry to dict. pdb[enzyme] = local_enzyme.ligands -# # Save results +# # Save results. # dict_to_csv(pdb, '../pdb_to_ligands.csv') ##---------- Split single-label dataset in train/validation/test -------------## -# Initialization +# Initialization. labels_temp = read_dict('../dataset_single.csv') pdb_ids = list(labels_temp) partition = {} -# Parameters -x = 0.8 # Split all 80:20 in train/test and train 80:20 in train/validation +# Parameters. +x = 0.8 # Split all 80:20 in train/test and train 80:20 in train/validation. -# Random shuffling +# Random shuffling. indexes = np.arange(len(pdb_ids)) np.random.shuffle(indexes) -# Train +# Train. train_ids = [pdb_ids[i] for i in indexes[0:int(x*x*len(pdb_ids))]] partition['train'] = train_ids -# Validation +# Validation. val_ids = [pdb_ids[i] for i in indexes[int(x*x*len(pdb_ids)):int(x*len(pdb_ids))]] partition['validation'] = val_ids -# Test +# Test. test_ids = [pdb_ids[i] for i in indexes[int(x*len(pdb_ids)):]] partition['test'] = test_ids -# # Save results -# dict_to_csv(partition, '../partition_single.csv') # DONE 18-03-2017 +# # Save results. +# dict_to_csv(partition, '../partition_single.csv') # DONE 18-03-2017. ##------ Create reduced single-label train/validation/test sets --------------## -# Initialization +# Initialization. partition = read_dict('../partition_single.csv') partition_red = {} -# Parameters +# Parameters. factor_reduction = 0.1 -# Train +# Train. exec("partition['train'] = " + partition['train']) indexes = np.arange(len(partition['train'])) np.random.shuffle(indexes) train_ids = [partition['train'][i] for i in indexes[0:int(factor_reduction*len(indexes))]] partition_red['train'] = train_ids -# Validation +# Validation. exec("partition['validation'] = " + partition['validation']) indexes = np.arange(len(partition['validation'])) np.random.shuffle(indexes) val_ids = [partition['validation'][i] for i in indexes[0:int(factor_reduction*len(indexes))]] partition_red['validation'] = val_ids -# Test +# Test. exec("partition['test'] = " + partition['test']) indexes = np.arange(len(partition['test'])) np.random.shuffle(indexes) test_ids = [partition['test'][i] for i in indexes[0:int(factor_reduction*len(indexes))]] partition_red['test'] = test_ids -# # Save results -# dict_to_csv(partition_red, '../partition_single_red.csv') # DONE 18-03-2017 +# # Save results. +# dict_to_csv(partition_red, '../partition_single_red.csv') # DONE 18-03-2017. diff --git a/datasets/make_files/hydropathy.py b/datasets/make_files/hydropathy.py index e6ea27c..ff70e66 100755 --- a/datasets/make_files/hydropathy.py +++ b/datasets/make_files/hydropathy.py @@ -1,4 +1,4 @@ -'Storing hydropathy information in a dictionary' +"""Storing hydropathy information in a dictionary.""" # Authors: Afshine Amidi # Shervine Amidi @@ -8,10 +8,10 @@ from enzynet.tools import dict_to_csv -# Initialization +# Initialization. hydropathy = {} -# Values from J. Kyte et al. paper +# Values from J. Kyte et al. paper. hydropathy['ILE'] = 4.5 hydropathy['VAL'] = 4.2 hydropathy['LEU'] = 3.8 @@ -33,5 +33,5 @@ hydropathy['LYS'] = -3.9 hydropathy['ARG'] = -4.5 -# Save results +# Save results. dict_to_csv(hydropathy, '../hydropathy.csv') diff --git a/datasets/make_files/isoelectric.py b/datasets/make_files/isoelectric.py index 417dee1..58a3324 100755 --- a/datasets/make_files/isoelectric.py +++ b/datasets/make_files/isoelectric.py @@ -1,4 +1,4 @@ -'Storing isoelectric information in a dictionary' +"""Storing isoelectric information in a dictionary.""" # Authors: Afshine Amidi # Shervine Amidi @@ -8,10 +8,10 @@ from enzynet.tools import dict_to_csv -# Initialization +# Initialization. isoelectric = {} -# Info from chapter 24 of Organic Chemistry (5th edition), by Wade +# Info from chapter 24 of Organic Chemistry (5th edition), by Wade. isoelectric['ALA'] = 6.0 isoelectric['ARG'] = 10.8 isoelectric['ASN'] = 5.4 @@ -33,9 +33,9 @@ isoelectric['TYR'] = 5.7 isoelectric['VAL'] = 6.0 -# Center on neutral value +# Center on neutral value. neutral_value = 6.0 isoelectric = {k: v - neutral_value for k, v in isoelectric.items()} -# Save results +# Save results. dict_to_csv(isoelectric, '../isoelectric.csv') diff --git a/enzynet/PDB.py b/enzynet/PDB.py index 2ae5388..cc3b964 100644 --- a/enzynet/PDB.py +++ b/enzynet/PDB.py @@ -1,4 +1,4 @@ -'Get characteristics from PDB files' +"""Get characteristics from PDB files.""" # Authors: Afshine Amidi # Shervine Amidi @@ -25,10 +25,9 @@ PDB_path = os.path.join(current_directory, '../files/PDB/') datasets_path = os.path.join(current_directory, '../datasets/') -class PDB_backbone(object): - """ - Functions aimed at information extraction from PDB files. +class PDB_backbone(object): + """Functions aimed at information extraction from PDB files. Parameters ---------- @@ -38,13 +37,12 @@ class PDB_backbone(object): path : string (optional, default is 'files/PDB') Path where the PDB file is located, or where the PDB file should be downloaded. - """ def __init__(self, pdb_id, path=PDB_path): - 'Initialization' + """Initialization.""" self.pdb_id = pdb_id.upper() fullfilename = os.path.join(path, pdb_id.lower() + '.pdb') - if os.path.isfile(fullfilename): # File in directory + if os.path.isfile(fullfilename): # File in directory. pass else: urllib.request.urlretrieve('http://files.rcsb.org/download/' + @@ -53,59 +51,59 @@ def __init__(self, pdb_id, path=PDB_path): self.structure = PDBParser().get_structure(pdb_id.upper(), fullfilename) def get_coords(self): - 'Gets coordinates of backbone' - # Initialization + """Gets coordinates of backbone.""" + # Initialization. backbone_coords = [] backbone_atoms = [] - # Computations + # Computations. for model in self.structure: for chain in model: for residue in chain: - if is_aa(residue, standard=True): # Check if amino acid + if is_aa(residue, standard=True): # Check if amino acid. for atom in residue: if atom.get_name() in backbone_ids: backbone_coords = backbone_coords + [atom.get_coord().tolist()] backbone_atoms = backbone_atoms + [atom.get_name()] - # Store results + # Store results. self.backbone_coords = np.array(backbone_coords) self.backbone_atoms = backbone_atoms def get_coords_extended(self, p=5): - 'Adds the coordinates from interpolation betweens atoms of the backbone' - # Initialization + """Adds the coordinates from interpolation betweens atoms of the backbone.""" + # Initialization. self.get_coords() C_coords = self.__get_coords_specific_atom(backbone_ids) new_coords = np.zeros((p*(C_coords.shape[0]-1), C_coords.shape[1])) - # Computations + # Computations. for i in range(1, C_coords.shape[0]): for k in range(p, 0, -1): new_coords[p*i-k,:] = ((p-k+1)*C_coords[i-1,:] + k*C_coords[i,:])/(p+1) - # Store results + # Store results. self.backbone_coords_ext = np.concatenate((self.backbone_coords, new_coords), axis=0) def get_weights(self, weights='hydropathy', scaling=True): - 'Gets weights of each position regarding associated amino-acid' - # Initialization + """Gets weights of each position regarding associated amino-acid.""" + # Initialization. backbone_weights = [] backbone_residues = [] - # Select weights + # Select weights. weights = read_dict(os.path.join(datasets_path, weights + '.csv')) - weights = dict([key, float(value)] for key, value in weights.items()) # Convert values to float + weights = dict([key, float(value)] for key, value in weights.items()) # Convert values to float. - # Scaling + # Scaling. if scaling == True: weights = scale_dict(weights) - # Computations + # Computations. for model in self.structure: for chain in model: for residue in chain: - if is_aa(residue, standard=True): # Check if standard amino acid + if is_aa(residue, standard=True): # Check if standard amino acid. local_residue = residue.get_resname() local_weight = weights[local_residue] for atom in residue: @@ -113,28 +111,28 @@ def get_weights(self, weights='hydropathy', scaling=True): backbone_weights = backbone_weights + [local_weight] backbone_residues = backbone_residues + [local_residue] - # Store results + # Store results. self.backbone_weights = backbone_weights self.backbone_residues = backbone_residues def get_weights_extended(self, p, weights='hydropathy', scaling=True): - 'Adds the weights from interpolation between atoms of the backbone' - # Initialization + """Adds the weights from interpolation between atoms of the backbone.""" + # Initialization. self.get_weights(weights=weights, scaling=scaling) C_weights = self.__get_weights_specific_atom(backbone_ids) new_weights = np.zeros((p*(len(C_weights)-1))) - # Computations + # Computations. for i in range(1, len(C_weights)): for k in range(p, 0, -1): new_weights[p*i-k] = ((p-k+1)*C_weights[i-1] + k*C_weights[i])/(p+1) - # Store results + # Store results. self.backbone_weights_ext = np.concatenate((self.backbone_weights, new_weights), axis=0) def __get_coords_specific_atom(self, specific_atoms): - 'Extracts coordinates of a specific atom only' - for atom in specific_atoms: # Tries to accomplish the task on the first that works + """Extracts coordinates of a specific atom only.""" + for atom in specific_atoms: # Tries to accomplish the task on the first that works. try: return np.array([self.backbone_coords[i,:] for i in range(self.backbone_coords.shape[0]) @@ -143,8 +141,8 @@ def __get_coords_specific_atom(self, specific_atoms): pass def __get_weights_specific_atom(self, specific_atoms): - 'Extracts weights of a specific atom only' - for atom in specific_atoms: # Tries to accomplish the task on the first that works + """Extracts weights of a specific atom only.""" + for atom in specific_atoms: # Tries to accomplish the task on the first that works. try: return [self.backbone_weights[i] for i in range(len(self.backbone_weights)) @@ -153,15 +151,15 @@ def __get_weights_specific_atom(self, specific_atoms): pass def get_ligands(self): - 'Stores ligands in a list' - # Initialization + """Stores ligands in a list.""" + # Initialization. ligands = [] - # Find ligands + # Find ligands. for model in self.structure: for chain in model: for residue in chain: ligands += [residue.get_resname()] - # Store unique IDs + # Store unique IDs. self.ligands = list(set(ligands)) diff --git a/enzynet/__init__.py b/enzynet/__init__.py index b0e5d2e..dfad844 100644 --- a/enzynet/__init__.py +++ b/enzynet/__init__.py @@ -1,4 +1,4 @@ -'Imports' +"""Imports.""" # Authors: Afshine Amidi # Shervine Amidi diff --git a/enzynet/indicators.py b/enzynet/indicators.py index f00d89f..cd2b33b 100644 --- a/enzynet/indicators.py +++ b/enzynet/indicators.py @@ -1,4 +1,4 @@ -'Indicators' +"""Indicators.""" # Authors: Afshine Amidi # Shervine Amidi @@ -9,7 +9,7 @@ def unalikeability(L): - 'Returns unalikeability of a set of labels' # p.4 of Kader2007 + """Returns unalikeability of a set of labels.""" # p.4 of Kader2007. if len(L) <= 1: return 0 else: @@ -22,10 +22,9 @@ def unalikeability(L): somme /= len(L)*(len(L)-1) return somme -class Indicators(object): - """ - Indicators used to assess the model +class Indicators(object): + """Indicators used to assess the model. Parameters ---------- @@ -34,17 +33,16 @@ class Indicators(object): y_pred : NumPy array of size (n_samples, 1) Array of predicted labels. - """ def __init__(self, y_true, y_pred): - 'Initialization' + """Initialization.""" self.n_samples = y_true.shape[0] self.n_classes = 6 self.y_true = y_true self.y_pred = y_pred def confusion_matrix(self): - 'Confusion matrix' + """Confusion matrix.""" self.confusion_matrix = np.zeros((self.n_classes, self.n_classes)) for i, j in zip(self.y_true, self.y_pred): self.confusion_matrix[i-1,j-1] += 1 @@ -55,38 +53,38 @@ def confusion_matrix(self): print('Scaled confusion matrix: \n {0}'.format(np.around(self.confusion_matrix_scaled, 3))) def accuracy(self): - 'Accuracy' + """Accuracy.""" self.accuracy = np.trace(self.confusion_matrix)/self.n_samples print('Accuracy: {0:.3f}'.format(self.accuracy)) def precision_per_class(self): - 'Precision per class' + """Precision per class.""" self.precision_per_class = \ np.diag(self.confusion_matrix/np.sum(self.confusion_matrix, axis=0)) print('Precision per class: {0}'.format(np.around(self.precision_per_class, 3))) def recall_per_class(self): - 'Recall per class' + """Recall per class.""" self.recall_per_class = np.diag(self.confusion_matrix_scaled) print('Recall per class: {0}'.format(np.around(self.recall_per_class, 3))) def f1_per_class(self): - 'F1 per class' + """F1 per class.""" self.f1_per_class = \ 2 * self.precision_per_class * self.recall_per_class/(self.precision_per_class + self.recall_per_class) print('F1 per class: {0}'.format(np.around(self.f1_per_class, 3))) def macro_precision(self): - 'Macro precision' + """Macro precision.""" self.macro_precision = np.sum(self.precision_per_class)/self.n_classes print('Macro precision: {0:.3f}'.format(self.macro_precision)) def macro_recall(self): - 'Macro recall' + """Macro recall.""" self.macro_recall = np.sum(self.recall_per_class)/self.n_classes print('Macro recall: {0:.3f}'.format(self.macro_recall)) def macro_f1(self): - 'Macro F1 score' + """Macro F1 score.""" self.macro_f1 = 2 * self.macro_precision * self.macro_recall/(self.macro_precision + self.macro_recall) print('Macro F1: {0:.3f}'.format(self.macro_f1)) diff --git a/enzynet/keras_utils.py b/enzynet/keras_utils.py index 29dbe49..7d4041b 100644 --- a/enzynet/keras_utils.py +++ b/enzynet/keras_utils.py @@ -1,4 +1,4 @@ -'Additional functions for Keras' +"""Additional functions for Keras.""" # Authors: Afshine Amidi # Shervine Amidi @@ -32,9 +32,7 @@ class Voting(VolumeDataGenerator): - """ - Predicts classes of testing enzymes - + """Predicts classes of testing enzymes. Parameters ---------- @@ -81,13 +79,12 @@ class Voting(VolumeDataGenerator): scaling_weights : boolean (optional, default is True) If True, divides all weights by the weight that is maximum in absolute value. - """ def __init__(self, list_enzymes, labels, voting_type='probabilities', augmentation=['None'], v_size=32, directory_precomputed=precomputed_path, directory_pdb=PDB_path, shuffle=True, p=5, max_radius=40, noise_treatment=False, weights=[], scaling_weights=True): - 'Initialization' + """Initialization.""" VolumeDataGenerator.__init__(self, list_enzymes, labels, v_size=v_size, flips=(0, 0, 0), batch_size=1, directory_precomputed=directory_precomputed, directory_pdb=directory_pdb, shuffle=shuffle, p=p, @@ -97,13 +94,13 @@ def __init__(self, list_enzymes, labels, voting_type='probabilities', augmentati self.augmentation = augmentation def predict(self, model): - 'Predicts classes of testing enzymes' - # Initialization + """Predicts classes of testing enzymes.""" + # Initialization. self.y_pred = np.empty((len(self.list_enzymes), len(self.augmentation)), dtype=int) self.y_true = np.array([self.labels[enzyme] for enzyme in self.list_enzymes], dtype=int) self.y_id = np.array(self.list_enzymes) - # Computations + # Computations. for j, augmentation in enumerate(self.augmentation): print('Augmentation: {0}'.format(augmentation)) for i, enzyme in enumerate(tqdm(self.list_enzymes)): @@ -111,7 +108,7 @@ def predict(self, model): self.__vote(model, enzyme, augmentation) def get_assessment(self): - 'Compute several performance indicators' + """Compute several performance indicators.""" for j, augmentation in enumerate(self.augmentation): print('Augmentation: {0}'.format(augmentation)) indicators = Indicators(self.y_true, self.y_pred[:,j]) @@ -119,81 +116,78 @@ def get_assessment(self): getattr(indicators, method)() def __vote(self, model, enzyme, augmentation): - # Initialization + # Initialization. probability = np.zeros((1, 6)) - # Nothing + # Nothing. if augmentation == 'None': - # Store configuration + # Store configuration. self.flips = (0, 0, 0) - # Generate volume + # Generate volume. X = self._VolumeDataGenerator__data_augmentation([enzyme])[0] - # Voting by adding probabilities + # Voting by adding probabilities. if self.voting_type == 'probabilities': probability += model.predict_proba(X, verbose=0) elif self.voting_type == 'classes': probability[0, model.predict_classes(X, verbose=0)[0]] += 1 - # Flips + # Flips. elif augmentation == 'flips': - # Generate all possibilities + # Generate all possibilities. generator_flips = itertools.product(range(2), repeat=3) - # Computations + # Computations. for flip in generator_flips: - # Store configuration + # Store configuration. self.flips = flip - # Generate volume + # Generate volume. X = self._VolumeDataGenerator__data_augmentation([enzyme])[0] - # Voting by adding probabilities + # Voting by adding probabilities. if self.voting_type == 'probabilities': probability += model.predict_proba(X, verbose=0) elif self.voting_type == 'classes': probability[0, model.predict_classes(X, verbose=0)[0]] += 1 - # Weighted flips + # Weighted flips. elif augmentation == 'weighted_flips': - # Generate all possibilities + # Generate all possibilities. generator_flips = itertools.product(range(2), repeat=3) - # Computations + # Computations. for flip in generator_flips: - # Store configuration + # Store configuration. self.flips = flip factor = 1/(sum(flip)+1) - # Generate volume + # Generate volume. X = self._VolumeDataGenerator__data_augmentation([enzyme])[0] - # Voting by adding probabilities + # Voting by adding probabilities. if self.voting_type == 'probabilities': probability += factor * model.predict_proba(X, verbose=0) elif self.voting_type == 'classes': probability[0, model.predict_classes(X, verbose=0)[0]] += factor * 1 - # Predict label + # Predict label. output_label = np.argmax(probability[0,:]) + 1 return output_label class MetricsHistory(Callback): - """ - Tracks accuracy and loss in real-time, and plots it. - + """Tracks accuracy and loss in real-time, and plots it. Parameters ---------- saving_path : string (optional, default is 'test.csv') Full path to output csv file. - """ def __init__(self, saving_path='test.csv'): - # Initialization + # Initialization. self.display = RealTimePlot(max_entries=200) self.saving_path = saving_path self.epochs = [] @@ -203,21 +197,21 @@ def __init__(self, saving_path='test.csv'): self.val_accs = [] def on_epoch_end(self, epoch, logs={}): - # Store + # Store. self.epochs += [epoch] self.losses += [logs.get('loss')] self.val_losses += [logs.get('val_loss')] self.accs += [logs.get('acc')] self.val_accs += [logs.get('val_acc')] - # Add point to plot + # Add point to plot. self.display.add(x=epoch, y_tr=logs.get('acc'), y_val=logs.get('val_acc')) plt.pause(0.001) - # Save to file + # Save to file. dictionary = {'epochs': self.epochs, 'losses': self.losses, 'val_losses': self.val_losses, diff --git a/enzynet/real_time.py b/enzynet/real_time.py index 1ae353e..24cd32a 100644 --- a/enzynet/real_time.py +++ b/enzynet/real_time.py @@ -1,4 +1,4 @@ -'Plot in real-time' +"""Plot in real-time.""" # Authors: Afshine Amidi # Shervine Amidi @@ -13,63 +13,65 @@ start = time.time() -# Adapted from https://gist.github.com/Uberi/283a13b8a71a46fb4dc8 + +# Adapted from https://gist.github.com/Uberi/283a13b8a71a46fb4dc8. class RealTimePlot(object): def __init__(self, max_entries=200, x_label=r'Epochs', y_label=r'Accuracy'): - # TeX friendly + # TeX friendly. plt.rc('text', usetex=True) plt.rc('font', family='serif') - # Store + # Store. self.fig, self.axes = plt.subplots() self.max_entries = max_entries - # x-axis + # x-axis. self.axis_x = deque(maxlen=max_entries) - # Training accuracy + # Training accuracy. self.axis_y_tr = deque(maxlen=max_entries) self.lineplot_tr, = self.axes.plot([], [], "ro-") - # Validation accuracy + # Validation accuracy. self.axis_y_val = deque(maxlen=max_entries) self.lineplot_val, = self.axes.plot([], [], "bo-") - # Autoscale + # Autoscale. self.axes.set_autoscaley_on(True) - # Set label names + # Set label names. self.axes.set_xlabel(x_label) self.axes.set_ylabel(y_label) def add(self, x, y_tr, y_val=None): - # Add new point + # Add new point. self.axis_x.append(x) self.axis_y_tr.append(y_tr) self.lineplot_tr.set_data(self.axis_x, self.axis_y_tr) - if y_val != None: # Validation accuracy is specified + if y_val != None: # Validation accuracy is specified. self.axis_y_val.append(y_val) self.lineplot_val.set_data(self.axis_x, self.axis_y_val) - # Change axis limits + # Change axis limits. self.axes.set_xlim(self.axis_x[0], self.axis_x[-1] + 1e-15) - self.axes.relim(); self.axes.autoscale_view() # Rescale the y-axis + self.axes.relim(); self.axes.autoscale_view() # Rescale the y-axis. def animate(self, figure, callback, interval=50): import matplotlib.animation as animation def wrapper(frame_index): self.add(*callback(frame_index)) - self.axes.relim(); self.axes.autoscale_view() # Rescale the y-axis + self.axes.relim(); self.axes.autoscale_view() # Rescale the y-axis. return self.lineplot animation.FuncAnimation(figure, wrapper, interval=interval) + if __name__ == "__main__": - # Initialization + # Initialization. display = RealTimePlot(max_entries=100) i = 0 - # Update in real-time + # Update in real-time. while True: display.add(time.time() - start, i, i/2) plt.pause(5) diff --git a/enzynet/tools.py b/enzynet/tools.py index 21ffe6a..5a298ab 100644 --- a/enzynet/tools.py +++ b/enzynet/tools.py @@ -1,4 +1,4 @@ -'Additional tools' +"""Additional tools.""" # Authors: Afshine Amidi # Shervine Amidi @@ -11,38 +11,42 @@ csv.field_size_limit(10**7) + def read_dict(path): - 'Reads Python dictionary stored in a csv file' + """Reads Python dictionary stored in a csv file.""" dictionary = {} for key, val in csv.reader(open(path)): dictionary[key] = val return dictionary + def dict_to_csv(dictionary, path): - 'Saves Python dictionary to a csv file' + """Saves Python dictionary to a csv file.""" w = csv.writer(open(path, 'w')) for key, val in dictionary.items(): w.writerow([key, val]) + def scale_dict(dictionary): - 'Scales values of a dictionary' - maxi = max(map(abs, dictionary.values())) # Max in absolute value + """Scales values of a dictionary.""" + maxi = max(map(abs, dictionary.values())) # Max in absolute value. return {k: v/maxi for k, v in dictionary.items()} + def get_class_weights(dictionary, training_enzymes, mode): - 'Gets class weights for Keras' - # Initialization + """Gets class weights for Keras.""" + # Initialization. counter = [0 for i in range(6)] - # Count classes + # Count classes. for enzyme in training_enzymes: counter[int(dictionary[enzyme])-1] += 1 majority = max(counter) - # Make dictionary + # Make dictionary. class_weights = {i: float(majority/count) for i, count in enumerate(counter)} - # Value according to mode + # Value according to mode. if mode == 'unbalanced': for key in class_weights: class_weights[key] = 1 diff --git a/enzynet/visualization.py b/enzynet/visualization.py index b166d60..3959249 100644 --- a/enzynet/visualization.py +++ b/enzynet/visualization.py @@ -1,4 +1,4 @@ -'3D visualization tools' +"""3D visualization tools.""" # Authors: Afshine Amidi # Shervine Amidi @@ -16,86 +16,87 @@ def visualize_pdb(pdb_id, p=5, v_size=32, num=1, weights=None, max_radius=40, noise_treatment=True): - 'Plots PDB in a volume and saves it in a file' - # Get coordinates + """Plots PDB in a volume and saves it in a file.""" + # Get coordinates. pdb = PDB_backbone(pdb_id) pdb.get_coords_extended(p=p) if weights != None: pdb.get_weights_extended(p=p, weights=weights) - # Center to 0 + # Center to 0. coords = coords_center_to_zero(pdb.backbone_coords_ext) - # Adjust size + # Adjust size. coords = adjust_size(coords, v_size, max_radius) if weights == None: - # Convert to volume + # Convert to volume. volume = coords_to_volume(coords, v_size, noise_treatment=noise_treatment) else: - # Converts to volume of weights + # Converts to volume of weights. volume = weights_to_volume(coords, pdb.backbone_weights_ext, v_size, noise_treatment=noise_treatment) - # Plot + # Plot. plot_volume(volume, pdb_id, v_size, num, weights=weights) + # 3D plot, sources: http://stackoverflow.com/a/35978146/4124317 # https://dawes.wordpress.com/2014/06/27/publication-ready-3d-figures-from-matplotlib/ def plot_volume(volume, pdb_id, v_size, num, weights=None): - 'Plots volume in 3D, interpreting the coordinates as voxels' - # Initialization + """Plots volume in 3D, interpreting the coordinates as voxels.""" + # Initialization. plt.rc('text', usetex=True) plt.rc('font', family='serif') fig = plt.figure(figsize=(4,4)) ax = fig.gca(projection='3d') ax.set_aspect('equal') - # Parameters + # Parameters. len_vol = volume.shape[0] - # Set position of the view + # Set position of the view. ax.view_init(elev=20, azim=135) - # Hide tick labels + # Hide tick labels. ax.set_xticklabels([]) ax.set_yticklabels([]) ax.set_zticklabels([]) - # Plot + # Plot. if weights == None: plot_matrix(ax, volume) else: plot_matrix_of_weights(ax, volume) - # Tick at every unit + # Tick at every unit. ax.set_xticks(np.arange(len_vol)) ax.set_yticks(np.arange(len_vol)) ax.set_zticks(np.arange(len_vol)) - # Min and max that can be seen + # Min and max that can be seen. ax.set_xlim(0, len_vol-1) ax.set_ylim(0, len_vol-1) ax.set_zlim(0, len_vol-1) - # Clear grid + # Clear grid. ax.xaxis.pane.set_edgecolor('black') ax.yaxis.pane.set_edgecolor('black') ax.xaxis.pane.fill = False ax.yaxis.pane.fill = False ax.zaxis.pane.fill = False - # Change thickness of grid + # Change thickness of grid. ax.xaxis._axinfo["grid"]['linewidth'] = 0.1 ax.yaxis._axinfo["grid"]['linewidth'] = 0.1 ax.zaxis._axinfo["grid"]['linewidth'] = 0.1 - # Change thickness of ticks + # Change thickness of ticks. ax.xaxis._axinfo["tick"]['linewidth'] = 0.1 ax.yaxis._axinfo["tick"]['linewidth'] = 0.1 ax.zaxis._axinfo["tick"]['linewidth'] = 0.1 - # Change tick placement + # Change tick placement. ax.xaxis._axinfo['tick']['inward_factor'] = 0 ax.xaxis._axinfo['tick']['outward_factor'] = 0.2 ax.yaxis._axinfo['tick']['inward_factor'] = 0 @@ -104,16 +105,17 @@ def plot_volume(volume, pdb_id, v_size, num, weights=None): ax.zaxis._axinfo['tick']['outward_factor'] = 0.2 ax.zaxis._axinfo['tick']['outward_factor'] = 0.2 - # Save + # Save. plt.savefig('../scripts/volume/' + str(pdb_id) + '_' + str(v_size) + '_' + str(weights) + '_' + str(num) + '.pdf') + def cuboid_data(pos, size=(1,1,1)): - 'Gets coordinates of cuboid' - # Gets the (left, outside, bottom) point + """Gets coordinates of cuboid.""" + # Gets the (left, outside, bottom) point. o = [a - b / 2 for a, b in zip(pos, size)] - # Get the length, width, and height + # Get the length, width, and height. l, w, h = size x = [[o[0], o[0] + l, o[0] + l, o[0], o[0]] for i in range(4)] y = [[o[1], o[1], o[1] + w, o[1] + w, o[1]], @@ -127,54 +129,59 @@ def cuboid_data(pos, size=(1,1,1)): return x, y, z + def plot_cube_at(pos=(0,0,0), ax=None): - 'Plots a cube element at position pos' + """Plots a cube element at position pos.""" if ax != None: X, Y, Z = cuboid_data(pos) ax.plot_surface(X, Y, Z, color='g', rstride=1, cstride=1, alpha=1) + def plot_cube_weights_at(pos=(0,0,0), ax=None, color='g'): - 'Plots a cube element at position pos' + """Plots a cube element at position pos.""" if ax != None: X, Y, Z = cuboid_data(pos) ax.plot_surface(X, Y, Z, color=color, rstride=1, cstride=1, alpha=1) + def plot_matrix(ax, matrix): - 'Plots cubes from a volumic matrix' + """Plots cubes from a volumic matrix.""" for i in range(matrix.shape[0]): for j in range(matrix.shape[1]): for k in range(matrix.shape[2]): if matrix[i,j,k] == 1: plot_cube_at(pos=(i-0.5,j-0.5,k-0.5), ax=ax) + def plot_matrix_of_weights(ax, matrix_of_weights): - 'Plots cubes from a volumic matrix' - # Initialization + """Plots cubes from a volumic matrix.""" + # Initialization. min_value = np.amin(matrix_of_weights) max_value = np.amax(matrix_of_weights) n_colors = 101 - # Check if matrix of weights or not + # Check if matrix of weights or not. if min_value == max_value == 1: return plot_matrix(ax, matrix_of_weights) - # Generate colors + # Generate colors. cm = plt.get_cmap('seismic') cgen = [cm(1.*i/n_colors) for i in range(n_colors)] - # Plot cubes + # Plot cubes. for i in range(matrix_of_weights.shape[0]): for j in range(matrix_of_weights.shape[1]): for k in range(matrix_of_weights.shape[2]): if matrix_of_weights[i,j,k] != 0: - # Translate to [0,100] + # Translate to [0,100]. normalized_weight = (matrix_of_weights[i,j,k] - min_value)/ \ (max_value - min_value) normalized_weight = int(100*normalized_weight) - # Plot cube with color + # Plot cube with color. plot_cube_weights_at(pos=(i-0.5,j-0.5,k-0.5), ax=ax, color=cgen[normalized_weight]) + if __name__ == '__main__': visualize_pdb('2Q3Z', p=0, v_size=32, weights=None) diff --git a/enzynet/volume.py b/enzynet/volume.py index f932952..3976a13 100644 --- a/enzynet/volume.py +++ b/enzynet/volume.py @@ -21,15 +21,12 @@ precomputed_path = os.path.join(current_directory, '../files/precomputed/') PDB_path = os.path.join(current_directory, '../files/PDB/') -class VolumeDataGenerator(keras.utils.Sequence): - """ - Generates batches of volumes containing 3D structure of enzymes as well - as their associated class labels on the fly. +class VolumeDataGenerator(keras.utils.Sequence): + """Generates batches of volumes containing 3D enzymes as well as their associated class labels on the fly. To be passed as argument in the fit_generator function of Keras. - Parameters ---------- v_size : int (optional, default is 32) @@ -76,7 +73,6 @@ class VolumeDataGenerator(keras.utils.Sequence): If True, divides all weights by the weight that is maximum in absolute value. - Example ------- >>> from enzynet.volume import VolumeDataGenerator @@ -91,13 +87,12 @@ class VolumeDataGenerator(keras.utils.Sequence): batch_size=32, shuffle=True, p=5, max_radius=40, noise_treatment=False, weights=[], scaling_weights=True) - """ def __init__(self, list_enzymes, labels, v_size=32, flips=(0.2, 0.2, 0.2), batch_size=32, directory_precomputed=precomputed_path, directory_pdb=PDB_path, shuffle=True, p=5, max_radius=40, noise_treatment=False, weights=[], scaling_weights=True): - 'Initialization' + """Initialization.""" self.batch_size = batch_size self.directory_precomputed = directory_precomputed self.directory_pdb = directory_pdb @@ -115,91 +110,91 @@ def __init__(self, list_enzymes, labels, v_size=32, flips=(0.2, 0.2, 0.2), batch self.on_epoch_end() def check_precomputed(self): - 'Checks if all coordinates and weights have been precomputed, and precomputes them otherwise' - # Initialization + """Checks if all coordinates and weights have been precomputed, and precomputes them otherwise.""" + # Initialization. list_enzymes = list(self.labels) counter = 0 - # Loop over all enzymes + # Loop over all enzymes. for pdb_id in tqdm(list_enzymes): - # Find name of paths + # Find name of paths. names = [precomputed_name(pdb_id, self.directory_precomputed, 'coords', self.p)] + \ [precomputed_name(pdb_id, self.directory_precomputed, 'weights', self.p, weight, self.scaling_weights) for weight in self.weights] - # Precomputes all files - if all([os.path.isfile(name) for name in names]): # Check if all already exist + # Precomputes all files. + if all([os.path.isfile(name) for name in names]): # Checks if all already exist. pass - else: # Precomputes files otherwise + else: # Precomputes files otherwise. save_coords_weights(pdb_id, self.weights, self.p, self.scaling_weights, self.directory_pdb, self.directory_precomputed) counter += 1 print("Had to compute files of {0} enzymes.".format(counter)) def on_epoch_end(self): - 'Updates indexes after each epoch' + """Updates indexes after each epoch.""" self.indexes = np.arange(len(self.list_enzymes)) if self.shuffle == True: np.random.shuffle(self.indexes) def __len__(self): - 'Denotes the number of batches per epoch' + """Denotes the number of batches per epoch.""" return int(np.floor(len(self.list_enzymes) / self.batch_size)) def __getitem__(self, index): - 'Generate one batch of data' - # Generate indexes of the batch + """Generate one batch of data.""" + # Generate indexes of the batch. indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size] - # Find list of IDs + # Find list of IDs. list_enzymes_temp = [self.list_enzymes[k] for k in indexes] - # Generate data + # Generate data. X, y = self.__data_augmentation(list_enzymes_temp) return X, y def __data_augmentation(self, list_enzymes_temp): - 'Returns augmented data with batch_size enzymes' # X : (n_samples, v_size, v_size, v_size, n_channels) - # Initialization - X = np.empty((self.batch_size, # n_enzymes - self.v_size, # dimension w.r.t. x - self.v_size, # dimension w.r.t. y - self.v_size, # dimension w.r.t. z - self.n_channels)) # n_channels + """Returns augmented data with batch_size enzymes.""" # X : (n_samples, v_size, v_size, v_size, n_channels). + # Initialization. + X = np.empty((self.batch_size, # n_enzymes. + self.v_size, # dimension w.r.t. x. + self.v_size, # dimension w.r.t. y. + self.v_size, # dimension w.r.t. z. + self.n_channels)) # n_channels. y = np.empty((self.batch_size), dtype=int) - # Computations + # Computations. for i in range(self.batch_size): - # Store class + # Store class. y[i] = self.labels[list_enzymes_temp[i]] - # Load precomputed coordinates + # Load precomputed coordinates. coords = load_coords(list_enzymes_temp[i], self.p, self.directory_precomputed) coords = coords_center_to_zero(coords) coords = adjust_size(coords, v_size=self.v_size, max_radius=self.max_radius) - # Get weights + # Get weights. local_weights = [] for weight in self.weights: - local_weight = load_weights(list_enzymes_temp[i], weight, self.p, - self.scaling_weights, self.directory_precomputed) # Compute extended weights - local_weights += [local_weight] # Store + local_weight = load_weights(list_enzymes_temp[i], weight, self.p, self.scaling_weights, + self.directory_precomputed) # Computes extended weights. + local_weights += [local_weight] # Store. - # PCA + # PCA. coords = PCA(n_components=3).fit_transform(coords) - # Do flip + # Do flip. coords_temp = flip_around_axis(coords, axis=self.flips) if len(self.weights) == 0: - # Convert to volume and store + # Convert to volume and store. X[i, :, :, :, 0] = coords_to_volume(coords_temp, self.v_size, noise_treatment=self.noise_treatment) else: - # Compute to weights of volume and store + # Compute to weights of volume and store. for k in range(self.n_channels): X[i, :, :, :, k] = weights_to_volume(coords_temp, local_weights[k], self.v_size, noise_treatment=self.noise_treatment) @@ -208,104 +203,115 @@ def __data_augmentation(self, list_enzymes_temp): def coords_to_volume(coords, v_size, noise_treatment=False): - 'Converts coordinates to binary voxels' # Input is centered on [0,0,0] + """Converts coordinates to binary voxels.""" # Input is centered on [0,0,0]. return weights_to_volume(coords=coords, weights=1, v_size=v_size, noise_treatment=noise_treatment) + def weights_to_volume(coords, weights, v_size, noise_treatment=False): - 'Converts coordinates to voxels with weights' # Input is centered on [0,0,0] - # Initialization + """Converts coordinates to voxels with weights.""" # Input is centered on [0,0,0]. + # Initialization. volume = np.zeros((v_size, v_size, v_size)) - # Translate center + # Translate center. coords = coords + np.full((coords.shape[0], 3), (v_size-1)/2) - # Round components + # Round components. coords = coords.astype(int) - # Filter rows with values that are out of the grid + # Filter rows with values that are out of the grid. mask = ((coords >= 0) & (coords < v_size)).all(axis=1) - # Convert to volume + # Convert to volume. volume[tuple(coords[mask].T)] = weights[mask] if type(weights) != int else weights - # Remove noise + # Remove noise. if noise_treatment == True: volume = remove_noise(coords, volume) return volume + def coords_center_to_zero(coords): - 'Centering coordinates on [0,0,0]' + """Centering coordinates on [0,0,0].""" barycenter = get_barycenter(coords) return coords - np.full((coords.shape[0], 3), barycenter) + def adjust_size(coords, v_size=32, max_radius=40): return np.multiply((v_size/2-1)/max_radius, coords) + def sparsify(y): - 'Returns labels in binary NumPy array' + """Returns labels in binary NumPy array.""" n_classes = 6 return np.array([[1 if y[i] == j+1 else 0 for j in range(n_classes)] for i in range(y.shape[0])]) + def flip_around_axis(coords, axis=(0.2, 0.2, 0.2)): - 'Flips coordinates randomly w.r.t. each axis with its associated probability' + """Flips coordinates randomly w.r.t. each axis with its associated probability.""" for col in range(3): if np.random.binomial(1, axis[col]): coords[:,col] = np.negative(coords[:,col]) return coords + def get_barycenter(coords): - 'Gets barycenter point of a Nx3 matrix' + """Gets barycenter point of a Nx3 matrix.""" return np.array([np.mean(coords, axis=0)]) + def remove_noise(coords, volume): - 'Removes isolated atoms from voxel structure' - # Parameters + """Removes isolated atoms from voxel structure.""" + # Parameters. v_size = volume.shape[0] - # Computations + # Computations. for i in range(coords.shape[0]): if all(valeur < v_size-1 for valeur in coords[i,:]) and \ - all(valeur > 0 for valeur in coords[i,:]): # Point inside volume + all(valeur > 0 for valeur in coords[i,:]): # Point inside volume. if np.array_equal(volume[coords[i,0]-1:coords[i,0]+2,coords[i,1]-1:coords[i,1]+2,coords[i,2]-1:coords[i,2]+2], - np.pad(np.array([[[1]]]),1,'constant') * volume[tuple(coords[i])]) == True: # Isolated point + np.pad(np.array([[[1]]]),1,'constant') * volume[tuple(coords[i])]) == True: # Isolated point. volume[coords[i,0]-1:coords[i,0]+2, coords[i,1]-1:coords[i,1]+2, coords[i,2]-1:coords[i,2]+2] = np.zeros((3,3,3)) return volume + def precomputed_name(pdb_id, path, type_file, desired_p, weights_name=None, scaling=True): - 'Returns path in string of precomputed file' + """Returns path in string of precomputed file.""" if type_file == 'coords': return os.path.join(path, pdb_id.lower() + '_coords_p' + str(desired_p) + '.npy') elif type_file == 'weights': return os.path.join(path, pdb_id.lower() + '_' + weights_name + '_p' + str(desired_p) + '_scaling' + str(scaling) + '.npy') + def save_coords_weights(pdb_id, list_weights, desired_p, scaling_weights, source_path, dest_path): - 'Computes coordinates and weights and saves them into .npy files' - # Initialize local PDB + """Computes coordinates and weights and saves them into .npy files.""" + # Initialize local PDB. local_PDB = PDB_backbone(pdb_id=pdb_id, path=source_path) - # Coordinates - local_PDB.get_coords_extended(p=desired_p) # Compute - coords = local_PDB.backbone_coords_ext # Store - np.save(precomputed_name(pdb_id, dest_path, 'coords', desired_p), coords) # Save + # Coordinates. + local_PDB.get_coords_extended(p=desired_p) # Compute. + coords = local_PDB.backbone_coords_ext # Store. + np.save(precomputed_name(pdb_id, dest_path, 'coords', desired_p), coords) # Save. - # Weights + # Weights. for weights_name in list_weights: local_PDB.get_weights_extended(desired_p, weights=weights_name, - scaling=scaling_weights) # Compute - weights = local_PDB.backbone_weights_ext # Store + scaling=scaling_weights) # Compute. + weights = local_PDB.backbone_weights_ext # Store. np.save(precomputed_name(pdb_id, dest_path, 'weights', desired_p, weights_name, scaling_weights), - weights) # Save + weights) # Save. + def load_coords(pdb_id, desired_p, source_path): - 'Loads precomputed coordinates' + """Loads precomputed coordinates.""" return np.load(precomputed_name(pdb_id, source_path, 'coords', desired_p)) + def load_weights(pdb_id, weights_name, desired_p, scaling, source_path): - 'Loads precomputed weights' + """Loads precomputed weights.""" return np.load(precomputed_name(pdb_id, source_path, 'weights', desired_p, weights_name, scaling)) diff --git a/scripts/architecture/enzynet_adapted.py b/scripts/architecture/enzynet_adapted.py index af5a2ec..57cde39 100644 --- a/scripts/architecture/enzynet_adapted.py +++ b/scripts/architecture/enzynet_adapted.py @@ -1,4 +1,4 @@ -'32x32x32 Enzynet architecture with adapted weights' +"""32x32x32 Enzynet architecture with adapted weights.""" # Authors: Afshine Amidi # Shervine Amidi @@ -30,13 +30,13 @@ mode_weights = 'balanced' ##----------------------------- Parameters -----------------------------------## -# PDB +# PDB. weights = [] n_classes = 6 n_channels = 1 + len(weights) scaling_weights = True -# Volume +# Volume. p = 0 v_size = 32 flips = (0.2, 0.2, 0.2) @@ -44,24 +44,24 @@ shuffle = True noise_treatment = False -# Model +# Model. batch_size = 32 max_epochs = 200 period_checkpoint = 50 -# Testing +# Testing. voting_type = 'probabilities' augmentation = ['None', 'flips', 'weighted_flips'] -# Miscellenaous +# Miscellaneous. current_file_name = os.path.basename(__file__)[:-3] stddev_conv3d = np.sqrt(2.0/(n_channels)) ##------------------------------ Dataset -------------------------------------## -# Load dictionary of labels +# Load dictionary of labels. dictionary = read_dict('../../datasets/dataset_single.csv') -# Load partitions +# Load partitions. if mode_dataset == 'full': partition = read_dict('../../datasets/partition_single.csv') elif mode_dataset == 'reduced': @@ -70,15 +70,15 @@ exec("partition['validation'] = " + partition['validation']) exec("partition['test'] = " + partition['test']) -# Final computations +# Final computations. partition['train'] = partition['train'] + partition['validation'] partition['validation'] = partition['test'] -# Get class weights +# Get class weights. class_weights = get_class_weights(dictionary, partition['train'], mode=mode_weights) -# Training generator +# Training generator. training_generator = \ VolumeDataGenerator(list_enzymes=partition['train'], labels=dictionary, @@ -92,7 +92,7 @@ weights=weights, scaling_weights=scaling_weights) -# Validation generator +# Validation generator. validation_generator = \ VolumeDataGenerator(list_enzymes=partition['validation'], labels=dictionary, @@ -106,11 +106,11 @@ weights=weights, scaling_weights=scaling_weights) -# Check if data has been precomputed +# Check if data has been precomputed. training_generator.check_precomputed() ##----------------------------- Testing --------------------------------------## -# Voting object +# Voting object. predictions = \ Voting(list_enzymes=partition['test'], labels=dictionary, @@ -124,10 +124,10 @@ scaling_weights=scaling_weights) ##------------------------------ Model ---------------------------------------## -# Create +# Create. model = Sequential() -# Add layers +# Add layers. model.add(Conv3D(filters=32, kernel_size=9, strides=2, @@ -175,21 +175,21 @@ model.add(Activation('softmax')) -# Track accuracy and loss in real-time +# Track accuracy and loss in real-time. history = MetricsHistory(saving_path=current_file_name + '.csv') -# Checkpoints +# Checkpoints. checkpoints = ModelCheckpoint('checkpoints/' + current_file_name + '_{epoch:02d}' + '.hd5f', save_weights_only=True, period=period_checkpoint) if mode_run == 'train': - # Compile + # Compile. model.compile(optimizer=Adam(lr=0.001, decay=0.00016667), loss='categorical_crossentropy', metrics=['accuracy']) - # Train + # Train. model.fit_generator(generator=training_generator, epochs=max_epochs, verbose=1, @@ -201,16 +201,16 @@ max_queue_size=30) if mode_run == 'test': - # Load weights + # Load weights. weights_path = \ 'checkpoints/' + current_file_name + '_{0:02d}'.format(max_epochs) + '.hd5f' model.load_weights(weights_path) -# Predict +# Predict. predictions.predict(model) -# Compute indicators +# Compute indicators. predictions.get_assessment() -# Clear session +# Clear session. K.clear_session() diff --git a/scripts/architecture/enzynet_uniform.py b/scripts/architecture/enzynet_uniform.py index 17bc243..9173792 100644 --- a/scripts/architecture/enzynet_uniform.py +++ b/scripts/architecture/enzynet_uniform.py @@ -1,4 +1,4 @@ -'32x32x32 Enzynet architecture with uniform weights' +"""32x32x32 Enzynet architecture with uniform weights.""" # Authors: Afshine Amidi # Shervine Amidi @@ -30,13 +30,13 @@ mode_weights = 'unbalanced' ##----------------------------- Parameters -----------------------------------## -# PDB +# PDB. weights = [] n_classes = 6 n_channels = 1 + len(weights) scaling_weights = True -# Volume +# Volume. p = 0 v_size = 32 flips = (0.2, 0.2, 0.2) @@ -44,24 +44,24 @@ shuffle = True noise_treatment = False -# Model +# Model. batch_size = 32 max_epochs = 200 period_checkpoint = 50 -# Testing +# Testing. voting_type = 'probabilities' augmentation = ['None', 'flips', 'weighted_flips'] -# Miscellenaous +# Miscellaneous. current_file_name = os.path.basename(__file__)[:-3] stddev_conv3d = np.sqrt(2.0/(n_channels)) ##------------------------------ Dataset -------------------------------------## -# Load dictionary of labels +# Load dictionary of labels. dictionary = read_dict('../../datasets/dataset_single.csv') -# Load partitions +# Load partitions. if mode_dataset == 'full': partition = read_dict('../../datasets/partition_single.csv') elif mode_dataset == 'reduced': @@ -70,15 +70,15 @@ exec("partition['validation'] = " + partition['validation']) exec("partition['test'] = " + partition['test']) -# Final computations +# Final computations. partition['train'] = partition['train'] + partition['validation'] partition['validation'] = partition['test'] -# Get class weights +# Get class weights. class_weights = get_class_weights(dictionary, partition['train'], mode=mode_weights) -# Training generator +# Training generator. training_generator = \ VolumeDataGenerator(list_enzymes=partition['train'], labels=dictionary, @@ -92,7 +92,7 @@ weights=weights, scaling_weights=scaling_weights) -# Validation generator +# Validation generator. validation_generator = \ VolumeDataGenerator(list_enzymes=partition['validation'], labels=dictionary, @@ -106,11 +106,11 @@ weights=weights, scaling_weights=scaling_weights) -# Check if data has been precomputed +# Check if data has been precomputed. training_generator.check_precomputed() ##----------------------------- Testing --------------------------------------## -# Voting object +# Voting object. predictions = \ Voting(list_enzymes=partition['test'], labels=dictionary, @@ -124,10 +124,10 @@ scaling_weights=scaling_weights) ##------------------------------ Model ---------------------------------------## -# Create +# Create. model = Sequential() -# Add layers +# Add layers. model.add(Conv3D(filters=32, kernel_size=9, strides=2, @@ -175,21 +175,21 @@ model.add(Activation('softmax')) -# Track accuracy and loss in real-time +# Track accuracy and loss in real-time. history = MetricsHistory(saving_path=current_file_name + '.csv') -# Checkpoints +# Checkpoints. checkpoints = ModelCheckpoint('checkpoints/' + current_file_name + '_{epoch:02d}' + '.hd5f', save_weights_only=True, period=period_checkpoint) if mode_run == 'train': - # Compile + # Compile. model.compile(optimizer=Adam(lr=0.001, decay=0.00016667), loss='categorical_crossentropy', metrics=['accuracy']) - # Train + # Train. model.fit_generator(generator=training_generator, epochs=max_epochs, verbose=1, @@ -201,16 +201,16 @@ max_queue_size=30) if mode_run == 'test': - # Load weights + # Load weights. weights_path = \ 'checkpoints/' + current_file_name + '_{0:02d}'.format(max_epochs) + '.hd5f' model.load_weights(weights_path) -# Predict +# Predict. predictions.predict(model) -# Compute indicators +# Compute indicators. predictions.get_assessment() -# Clear session +# Clear session. K.clear_session() From 08acc5aa2861948c21e8af2f3f000befde8a18b3 Mon Sep 17 00:00:00 2001 From: shervinea Date: Wed, 21 Apr 2021 21:18:05 -0700 Subject: [PATCH 02/18] Remove unused imports --- datasets/make_files/charge.py | 1 - datasets/make_files/download_pdb.py | 3 --- datasets/make_files/generate_datasets.py | 3 --- datasets/make_files/hydropathy.py | 1 - datasets/make_files/isoelectric.py | 1 - enzynet/PDB.py | 1 - enzynet/keras_utils.py | 1 - enzynet/real_time.py | 4 +--- enzynet/tools.py | 2 -- enzynet/visualization.py | 2 -- enzynet/volume.py | 2 -- scripts/architecture/enzynet_adapted.py | 1 - scripts/architecture/enzynet_uniform.py | 1 - 13 files changed, 1 insertion(+), 22 deletions(-) diff --git a/datasets/make_files/charge.py b/datasets/make_files/charge.py index 477056c..0563e13 100755 --- a/datasets/make_files/charge.py +++ b/datasets/make_files/charge.py @@ -7,7 +7,6 @@ from enzynet.tools import dict_to_csv - # Initialization. charge = {} diff --git a/datasets/make_files/download_pdb.py b/datasets/make_files/download_pdb.py index c1f7556..9cc4503 100755 --- a/datasets/make_files/download_pdb.py +++ b/datasets/make_files/download_pdb.py @@ -6,9 +6,6 @@ # MIT License import pandas as pa -import csv -from enzynet.tools import read_dict - # Read csv files. df = pa.read_csv('../dataset_all.csv', header=None) diff --git a/datasets/make_files/generate_datasets.py b/datasets/make_files/generate_datasets.py index 5b6dc05..89ced43 100755 --- a/datasets/make_files/generate_datasets.py +++ b/datasets/make_files/generate_datasets.py @@ -8,13 +8,10 @@ import numpy as np import pandas as pa -import os - from tqdm import tqdm from enzynet.PDB import PDB_backbone from enzynet.tools import read_dict, dict_to_csv - # Date of retrieval of raw datasets from rcsb.org: 07-03-2017. # Parameters. diff --git a/datasets/make_files/hydropathy.py b/datasets/make_files/hydropathy.py index ff70e66..cd8bdfa 100755 --- a/datasets/make_files/hydropathy.py +++ b/datasets/make_files/hydropathy.py @@ -7,7 +7,6 @@ from enzynet.tools import dict_to_csv - # Initialization. hydropathy = {} diff --git a/datasets/make_files/isoelectric.py b/datasets/make_files/isoelectric.py index 58a3324..b293518 100755 --- a/datasets/make_files/isoelectric.py +++ b/datasets/make_files/isoelectric.py @@ -7,7 +7,6 @@ from enzynet.tools import dict_to_csv - # Initialization. isoelectric = {} diff --git a/enzynet/PDB.py b/enzynet/PDB.py index cc3b964..d756136 100644 --- a/enzynet/PDB.py +++ b/enzynet/PDB.py @@ -16,7 +16,6 @@ from enzynet.tools import read_dict, scale_dict - warnings.filterwarnings("ignore", category=PDBConstructionWarning) backbone_ids = ['C', 'N', 'CA'] diff --git a/enzynet/keras_utils.py b/enzynet/keras_utils.py index 7d4041b..18b2e50 100644 --- a/enzynet/keras_utils.py +++ b/enzynet/keras_utils.py @@ -21,7 +21,6 @@ from tqdm import tqdm - current_directory = os.path.dirname(os.path.abspath(__file__)) precomputed_path = os.path.join(current_directory, '../files/precomputed/') PDB_path = os.path.join(current_directory, '../files/PDB/') diff --git a/enzynet/real_time.py b/enzynet/real_time.py index 24cd32a..5eb4835 100644 --- a/enzynet/real_time.py +++ b/enzynet/real_time.py @@ -4,13 +4,11 @@ # Shervine Amidi # MIT License -import time, random -import math +import time from collections import deque from matplotlib import pyplot as plt - start = time.time() diff --git a/enzynet/tools.py b/enzynet/tools.py index 5a298ab..51e7e99 100644 --- a/enzynet/tools.py +++ b/enzynet/tools.py @@ -6,8 +6,6 @@ # MIT License import csv -import threading - csv.field_size_limit(10**7) diff --git a/enzynet/visualization.py b/enzynet/visualization.py index 3959249..2c7bd08 100644 --- a/enzynet/visualization.py +++ b/enzynet/visualization.py @@ -8,8 +8,6 @@ import numpy as np import matplotlib.pyplot as plt -from mpl_toolkits.mplot3d import Axes3D - from enzynet.PDB import PDB_backbone from enzynet.volume import adjust_size, coords_to_volume, coords_center_to_zero, weights_to_volume diff --git a/enzynet/volume.py b/enzynet/volume.py index 3976a13..6bd81b6 100644 --- a/enzynet/volume.py +++ b/enzynet/volume.py @@ -11,12 +11,10 @@ import numpy as np from tqdm import tqdm -from sklearn.neighbors import KDTree from sklearn.decomposition import PCA from enzynet.PDB import PDB_backbone - current_directory = os.path.dirname(os.path.abspath(__file__)) precomputed_path = os.path.join(current_directory, '../files/precomputed/') PDB_path = os.path.join(current_directory, '../files/PDB/') diff --git a/scripts/architecture/enzynet_adapted.py b/scripts/architecture/enzynet_adapted.py index 57cde39..55bdb00 100644 --- a/scripts/architecture/enzynet_adapted.py +++ b/scripts/architecture/enzynet_adapted.py @@ -24,7 +24,6 @@ from keras import backend as K - mode_run = 'test' mode_dataset = 'full' mode_weights = 'balanced' diff --git a/scripts/architecture/enzynet_uniform.py b/scripts/architecture/enzynet_uniform.py index 9173792..82bef83 100644 --- a/scripts/architecture/enzynet_uniform.py +++ b/scripts/architecture/enzynet_uniform.py @@ -24,7 +24,6 @@ from keras import backend as K - mode_run = 'test' mode_dataset = 'full' mode_weights = 'unbalanced' From 0cb4331c69addef5a7254ea9b6dabf86bfa98d34 Mon Sep 17 00:00:00 2001 From: shervinea Date: Wed, 21 Apr 2021 21:27:08 -0700 Subject: [PATCH 03/18] Rewrite dictionary initializations --- datasets/make_files/charge.py | 45 +++++++++++++++--------------- datasets/make_files/hydropathy.py | 45 +++++++++++++++--------------- datasets/make_files/isoelectric.py | 45 +++++++++++++++--------------- 3 files changed, 66 insertions(+), 69 deletions(-) diff --git a/datasets/make_files/charge.py b/datasets/make_files/charge.py index 0563e13..479f977 100755 --- a/datasets/make_files/charge.py +++ b/datasets/make_files/charge.py @@ -7,30 +7,29 @@ from enzynet.tools import dict_to_csv -# Initialization. -charge = {} - # Info from p. 51 of The cell: a molecular approach, by Hausman et al. -charge['ALA'] = 0 -charge['ARG'] = 1 -charge['ASN'] = 0 -charge['ASP'] = -1 -charge['CYS'] = 0 -charge['GLU'] = -1 -charge['GLN'] = 0 -charge['GLY'] = 0 -charge['HIS'] = 0.1 -charge['ILE'] = 0 -charge['LEU'] = 0 -charge['LYS'] = 1 -charge['MET'] = 0 -charge['PHE'] = 0 -charge['PRO'] = 0 -charge['SER'] = 0 -charge['THR'] = 0 -charge['TRP'] = 0 -charge['TYR'] = 0 -charge['VAL'] = 0 +charge = { + 'ALA': 0, + 'ARG': 1, + 'ASN': 0, + 'ASP': -1, + 'CYS': 0, + 'GLU': -1, + 'GLN': 0, + 'GLY': 0, + 'HIS': 0.1, + 'ILE': 0, + 'LEU': 0, + 'LYS': 1, + 'MET': 0, + 'PHE': 0, + 'PRO': 0, + 'SER': 0, + 'THR': 0, + 'TRP': 0, + 'TYR': 0, + 'VAL': 0, +} # Save results. dict_to_csv(charge, '../charge.csv') diff --git a/datasets/make_files/hydropathy.py b/datasets/make_files/hydropathy.py index cd8bdfa..a489185 100755 --- a/datasets/make_files/hydropathy.py +++ b/datasets/make_files/hydropathy.py @@ -7,30 +7,29 @@ from enzynet.tools import dict_to_csv -# Initialization. -hydropathy = {} - # Values from J. Kyte et al. paper. -hydropathy['ILE'] = 4.5 -hydropathy['VAL'] = 4.2 -hydropathy['LEU'] = 3.8 -hydropathy['PHE'] = 2.8 -hydropathy['CYS'] = 2.5 -hydropathy['MET'] = 1.9 -hydropathy['ALA'] = 1.8 -hydropathy['GLY'] = -0.4 -hydropathy['THR'] = -0.7 -hydropathy['TRP'] = -0.9 -hydropathy['SER'] = -0.8 -hydropathy['TYR'] = -1.3 -hydropathy['PRO'] = -1.6 -hydropathy['HIS'] = -3.2 -hydropathy['GLU'] = -3.5 -hydropathy['GLN'] = -3.5 -hydropathy['ASP'] = -3.5 -hydropathy['ASN'] = -3.5 -hydropathy['LYS'] = -3.9 -hydropathy['ARG'] = -4.5 +hydropathy = { + 'ILE': 4.5, + 'VAL': 4.2, + 'LEU': 3.8, + 'PHE': 2.8, + 'CYS': 2.5, + 'MET': 1.9, + 'ALA': 1.8, + 'GLY': -0.4, + 'THR': -0.7, + 'TRP': -0.9, + 'SER': -0.8, + 'TYR': -1.3, + 'PRO': -1.6, + 'HIS': -3.2, + 'GLU': -3.5, + 'GLN': -3.5, + 'ASP': -3.5, + 'ASN': -3.5, + 'LYS': -3.9, + 'ARG': -4.5, +} # Save results. dict_to_csv(hydropathy, '../hydropathy.csv') diff --git a/datasets/make_files/isoelectric.py b/datasets/make_files/isoelectric.py index b293518..c091a77 100755 --- a/datasets/make_files/isoelectric.py +++ b/datasets/make_files/isoelectric.py @@ -7,30 +7,29 @@ from enzynet.tools import dict_to_csv -# Initialization. -isoelectric = {} - # Info from chapter 24 of Organic Chemistry (5th edition), by Wade. -isoelectric['ALA'] = 6.0 -isoelectric['ARG'] = 10.8 -isoelectric['ASN'] = 5.4 -isoelectric['ASP'] = 2.8 -isoelectric['CYS'] = 5.0 -isoelectric['GLU'] = 3.2 -isoelectric['GLN'] = 5.7 -isoelectric['GLY'] = 6.0 -isoelectric['HIS'] = 7.6 -isoelectric['ILE'] = 6.0 -isoelectric['LEU'] = 6.0 -isoelectric['LYS'] = 9.7 -isoelectric['MET'] = 5.7 -isoelectric['PHE'] = 5.5 -isoelectric['PRO'] = 6.3 -isoelectric['SER'] = 5.7 -isoelectric['THR'] = 5.6 -isoelectric['TRP'] = 5.9 -isoelectric['TYR'] = 5.7 -isoelectric['VAL'] = 6.0 +isoelectric = { + 'ALA': 6.0, + 'ARG': 10.8, + 'ASN': 5.4, + 'ASP': 2.8, + 'CYS': 5.0, + 'GLU': 3.2, + 'GLN': 5.7, + 'GLY': 6.0, + 'HIS': 7.6, + 'ILE': 6.0, + 'LEU': 6.0, + 'LYS': 9.7, + 'MET': 5.7, + 'PHE': 5.5, + 'PRO': 6.3, + 'SER': 5.7, + 'THR': 5.6, + 'TRP': 5.9, + 'TYR': 5.7, + 'VAL': 6.0, +} # Center on neutral value. neutral_value = 6.0 From c4a002e7df862e23921aaff2fce2a69c4eb74233 Mon Sep 17 00:00:00 2001 From: shervinea Date: Thu, 22 Apr 2021 19:47:14 -0700 Subject: [PATCH 04/18] Use CamelCase convention on class name --- datasets/make_files/generate_datasets.py | 8 ++++---- enzynet/PDB.py | 2 +- enzynet/visualization.py | 4 ++-- enzynet/volume.py | 4 ++-- 4 files changed, 9 insertions(+), 9 deletions(-) diff --git a/datasets/make_files/generate_datasets.py b/datasets/make_files/generate_datasets.py index 89ced43..9e2e78c 100755 --- a/datasets/make_files/generate_datasets.py +++ b/datasets/make_files/generate_datasets.py @@ -9,7 +9,7 @@ import pandas as pa from tqdm import tqdm -from enzynet.PDB import PDB_backbone +from enzynet.PDB import PDBBackbone from enzynet.tools import read_dict, dict_to_csv # Date of retrieval of raw datasets from rcsb.org: 07-03-2017. @@ -29,7 +29,7 @@ for k, enzyme in enumerate(tqdm(list_enzymes)): try: # Load local enzyme. - local_enzyme = PDB_backbone(enzyme) + local_enzyme = PDBBackbone(enzyme) local_enzyme.get_coords() # Look for problem. @@ -98,7 +98,7 @@ # Computations. for enzyme in tqdm(pdb_all): # Load local enzyme. - local_enzyme = PDB_backbone(enzyme) + local_enzyme = PDBBackbone(enzyme) # Get ligands. local_enzyme.get_ligands() @@ -122,7 +122,7 @@ # Computations. for enzyme in tqdm(pdb_all): # Load local enzyme. - local_enzyme = PDB_backbone(enzyme) + local_enzyme = PDBBackbone(enzyme) # Get ligands. local_enzyme.get_ligands() diff --git a/enzynet/PDB.py b/enzynet/PDB.py index d756136..6d58597 100644 --- a/enzynet/PDB.py +++ b/enzynet/PDB.py @@ -25,7 +25,7 @@ datasets_path = os.path.join(current_directory, '../datasets/') -class PDB_backbone(object): +class PDBBackbone(object): """Functions aimed at information extraction from PDB files. Parameters diff --git a/enzynet/visualization.py b/enzynet/visualization.py index 2c7bd08..fd67965 100644 --- a/enzynet/visualization.py +++ b/enzynet/visualization.py @@ -8,7 +8,7 @@ import numpy as np import matplotlib.pyplot as plt -from enzynet.PDB import PDB_backbone +from enzynet.PDB import PDBBackbone from enzynet.volume import adjust_size, coords_to_volume, coords_center_to_zero, weights_to_volume @@ -16,7 +16,7 @@ def visualize_pdb(pdb_id, p=5, v_size=32, num=1, weights=None, max_radius=40, noise_treatment=True): """Plots PDB in a volume and saves it in a file.""" # Get coordinates. - pdb = PDB_backbone(pdb_id) + pdb = PDBBackbone(pdb_id) pdb.get_coords_extended(p=p) if weights != None: diff --git a/enzynet/volume.py b/enzynet/volume.py index 6bd81b6..39fd4dd 100644 --- a/enzynet/volume.py +++ b/enzynet/volume.py @@ -13,7 +13,7 @@ from tqdm import tqdm from sklearn.decomposition import PCA -from enzynet.PDB import PDB_backbone +from enzynet.PDB import PDBBackbone current_directory = os.path.dirname(os.path.abspath(__file__)) precomputed_path = os.path.join(current_directory, '../files/precomputed/') @@ -289,7 +289,7 @@ def save_coords_weights(pdb_id, list_weights, desired_p, scaling_weights, source_path, dest_path): """Computes coordinates and weights and saves them into .npy files.""" # Initialize local PDB. - local_PDB = PDB_backbone(pdb_id=pdb_id, path=source_path) + local_PDB = PDBBackbone(pdb_id=pdb_id, path=source_path) # Coordinates. local_PDB.get_coords_extended(p=desired_p) # Compute. From f0d7bd5a44c6423463680c7c788524c8b3e9adf5 Mon Sep 17 00:00:00 2001 From: shervinea Date: Thu, 22 Apr 2021 19:51:08 -0700 Subject: [PATCH 05/18] Write submodel name in lowercase --- datasets/make_files/generate_datasets.py | 2 +- enzynet/{PDB.py => pdb.py} | 0 enzynet/visualization.py | 2 +- enzynet/volume.py | 2 +- 4 files changed, 3 insertions(+), 3 deletions(-) rename enzynet/{PDB.py => pdb.py} (100%) diff --git a/datasets/make_files/generate_datasets.py b/datasets/make_files/generate_datasets.py index 9e2e78c..1234ff7 100755 --- a/datasets/make_files/generate_datasets.py +++ b/datasets/make_files/generate_datasets.py @@ -9,7 +9,7 @@ import pandas as pa from tqdm import tqdm -from enzynet.PDB import PDBBackbone +from enzynet.pdb import PDBBackbone from enzynet.tools import read_dict, dict_to_csv # Date of retrieval of raw datasets from rcsb.org: 07-03-2017. diff --git a/enzynet/PDB.py b/enzynet/pdb.py similarity index 100% rename from enzynet/PDB.py rename to enzynet/pdb.py diff --git a/enzynet/visualization.py b/enzynet/visualization.py index fd67965..849fb3f 100644 --- a/enzynet/visualization.py +++ b/enzynet/visualization.py @@ -8,7 +8,7 @@ import numpy as np import matplotlib.pyplot as plt -from enzynet.PDB import PDBBackbone +from enzynet.pdb import PDBBackbone from enzynet.volume import adjust_size, coords_to_volume, coords_center_to_zero, weights_to_volume diff --git a/enzynet/volume.py b/enzynet/volume.py index 39fd4dd..2161d0d 100644 --- a/enzynet/volume.py +++ b/enzynet/volume.py @@ -13,7 +13,7 @@ from tqdm import tqdm from sklearn.decomposition import PCA -from enzynet.PDB import PDBBackbone +from enzynet.pdb import PDBBackbone current_directory = os.path.dirname(os.path.abspath(__file__)) precomputed_path = os.path.join(current_directory, '../files/precomputed/') From 73990866294a29cca8e7bb3022f9ac49059155b4 Mon Sep 17 00:00:00 2001 From: shervinea Date: Thu, 22 Apr 2021 22:10:13 -0700 Subject: [PATCH 06/18] Only import modules, not functions nor classes --- datasets/make_files/charge.py | 4 +- datasets/make_files/generate_datasets.py | 36 ++-- datasets/make_files/hydropathy.py | 4 +- datasets/make_files/isoelectric.py | 4 +- enzynet/keras_utils.py | 23 ++- enzynet/pdb.py | 20 +-- enzynet/visualization.py | 22 +-- enzynet/volume.py | 11 +- scripts/architecture/enzynet_adapted.py | 218 ++++++++++++----------- scripts/architecture/enzynet_uniform.py | 216 +++++++++++----------- 10 files changed, 283 insertions(+), 275 deletions(-) diff --git a/datasets/make_files/charge.py b/datasets/make_files/charge.py index 479f977..8a9d8e9 100755 --- a/datasets/make_files/charge.py +++ b/datasets/make_files/charge.py @@ -5,7 +5,7 @@ # MIT License -from enzynet.tools import dict_to_csv +from enzynet import tools # Info from p. 51 of The cell: a molecular approach, by Hausman et al. charge = { @@ -32,4 +32,4 @@ } # Save results. -dict_to_csv(charge, '../charge.csv') +tools.dict_to_csv(charge, '../charge.csv') diff --git a/datasets/make_files/generate_datasets.py b/datasets/make_files/generate_datasets.py index 1234ff7..c02ab98 100755 --- a/datasets/make_files/generate_datasets.py +++ b/datasets/make_files/generate_datasets.py @@ -8,9 +8,9 @@ import numpy as np import pandas as pa +from enzynet import pdb +from enzynet import tools from tqdm import tqdm -from enzynet.pdb import PDBBackbone -from enzynet.tools import read_dict, dict_to_csv # Date of retrieval of raw datasets from rcsb.org: 07-03-2017. @@ -29,7 +29,7 @@ for k, enzyme in enumerate(tqdm(list_enzymes)): try: # Load local enzyme. - local_enzyme = PDBBackbone(enzyme) + local_enzyme = pdb.PDBBackbone(enzyme) local_enzyme.get_coords() # Look for problem. @@ -61,11 +61,11 @@ labels[entry] = [i] # # Save results. -# dict_to_csv(labels, '../dataset_all.csv') # DONE 18-03-2017. +# tools.dict_to_csv(labels, '../dataset_all.csv') # DONE 18-03-2017. ##--------- Dictionary with single-label PDBs in dataset_single.csv ----------## # Initialization. -labels_temp = read_dict('../dataset_all.csv') +labels_temp = tools.read_dict('../dataset_all.csv') labels = {} # Computations. @@ -74,11 +74,11 @@ labels[entry] = int(labels_temp[entry][1]) # Convert list to scalar. # # Save results. -# dict_to_csv(labels, '../dataset_single.csv') # DONE 18-03-2017. +# tools.dict_to_csv(labels, '../dataset_single.csv') # DONE 18-03-2017. ##---------- Dictionary with multi-label PDBs in dataset_multi.csv -----------## # Initialization. -labels_temp = read_dict('../dataset_all.csv') +labels_temp = tools.read_dict('../dataset_all.csv') labels = {} # Computations. @@ -87,18 +87,18 @@ labels[entry] = labels_temp[entry] # # Save results. -# dict_to_csv(labels, '../dataset_multi.csv') # DONE 18-03-2017. +# tools.dict_to_csv(labels, '../dataset_multi.csv') # DONE 18-03-2017. ##----- Dict of ligands pointing to associated PDB in ligands_to_pdb.csv -----## # Initialization. -pdb_all = read_dict('../dataset_all.csv') +pdb_all = tools.read_dict('../dataset_all.csv') pdb_all = list(pdb_all.keys()) ligands = {} # Computations. for enzyme in tqdm(pdb_all): # Load local enzyme. - local_enzyme = PDBBackbone(enzyme) + local_enzyme = pdb.PDBBackbone(enzyme) # Get ligands. local_enzyme.get_ligands() @@ -111,18 +111,18 @@ ligands[local_ligand] = [enzyme] # # Save results. -# dict_to_csv(ligands, '../ligands_to_pdb.csv') +# tools.dict_to_csv(ligands, '../ligands_to_pdb.csv') ##---- Dict of PDBs pointing to associated ligands in pdb_to_ligands.csv -----## # Initialization. -pdb_all = read_dict('../dataset_all.csv') +pdb_all = tools.read_dict('../dataset_all.csv') pdb_all = list(pdb_all.keys()) pdb = {} # Computations. for enzyme in tqdm(pdb_all): # Load local enzyme. - local_enzyme = PDBBackbone(enzyme) + local_enzyme = pdb.PDBBackbone(enzyme) # Get ligands. local_enzyme.get_ligands() @@ -131,11 +131,11 @@ pdb[enzyme] = local_enzyme.ligands # # Save results. -# dict_to_csv(pdb, '../pdb_to_ligands.csv') +# tools.dict_to_csv(pdb, '../pdb_to_ligands.csv') ##---------- Split single-label dataset in train/validation/test -------------## # Initialization. -labels_temp = read_dict('../dataset_single.csv') +labels_temp = tools.read_dict('../dataset_single.csv') pdb_ids = list(labels_temp) partition = {} @@ -159,11 +159,11 @@ partition['test'] = test_ids # # Save results. -# dict_to_csv(partition, '../partition_single.csv') # DONE 18-03-2017. +# tools.dict_to_csv(partition, '../partition_single.csv') # DONE 18-03-2017. ##------ Create reduced single-label train/validation/test sets --------------## # Initialization. -partition = read_dict('../partition_single.csv') +partition = tools.read_dict('../partition_single.csv') partition_red = {} # Parameters. @@ -191,4 +191,4 @@ partition_red['test'] = test_ids # # Save results. -# dict_to_csv(partition_red, '../partition_single_red.csv') # DONE 18-03-2017. +# tools.dict_to_csv(partition_red, '../partition_single_red.csv') # DONE 18-03-2017. diff --git a/datasets/make_files/hydropathy.py b/datasets/make_files/hydropathy.py index a489185..d50ea1c 100755 --- a/datasets/make_files/hydropathy.py +++ b/datasets/make_files/hydropathy.py @@ -5,7 +5,7 @@ # MIT License -from enzynet.tools import dict_to_csv +from enzynet import tools # Values from J. Kyte et al. paper. hydropathy = { @@ -32,4 +32,4 @@ } # Save results. -dict_to_csv(hydropathy, '../hydropathy.csv') +tools.dict_to_csv(hydropathy, '../hydropathy.csv') diff --git a/datasets/make_files/isoelectric.py b/datasets/make_files/isoelectric.py index c091a77..1ee7652 100755 --- a/datasets/make_files/isoelectric.py +++ b/datasets/make_files/isoelectric.py @@ -5,7 +5,7 @@ # MIT License -from enzynet.tools import dict_to_csv +from enzynet import tools # Info from chapter 24 of Organic Chemistry (5th edition), by Wade. isoelectric = { @@ -36,4 +36,4 @@ isoelectric = {k: v - neutral_value for k, v in isoelectric.items()} # Save results. -dict_to_csv(isoelectric, '../isoelectric.csv') +tools.dict_to_csv(isoelectric, '../isoelectric.csv') diff --git a/enzynet/keras_utils.py b/enzynet/keras_utils.py index 18b2e50..2bd7352 100644 --- a/enzynet/keras_utils.py +++ b/enzynet/keras_utils.py @@ -10,15 +10,12 @@ import numpy as np +from enzynet import indicators +from enzynet import real_time +from enzynet import tools +from enzynet import volume from keras.callbacks import Callback - -from enzynet.indicators import Indicators -from enzynet.real_time import RealTimePlot -from enzynet.tools import dict_to_csv -from enzynet.volume import VolumeDataGenerator - from matplotlib import pyplot as plt - from tqdm import tqdm current_directory = os.path.dirname(os.path.abspath(__file__)) @@ -30,7 +27,7 @@ 'macro_precision', 'macro_recall', 'macro_f1'] -class Voting(VolumeDataGenerator): +class Voting(volume.VolumeDataGenerator): """Predicts classes of testing enzymes. Parameters @@ -84,7 +81,7 @@ def __init__(self, list_enzymes, labels, voting_type='probabilities', augmentati directory_pdb=PDB_path, shuffle=True, p=5, max_radius=40, noise_treatment=False, weights=[], scaling_weights=True): """Initialization.""" - VolumeDataGenerator.__init__(self, list_enzymes, labels, v_size=v_size, flips=(0, 0, 0), + volume.VolumeDataGenerator.__init__(self, list_enzymes, labels, v_size=v_size, flips=(0, 0, 0), batch_size=1, directory_precomputed=directory_precomputed, directory_pdb=directory_pdb, shuffle=shuffle, p=p, max_radius=max_radius, noise_treatment=noise_treatment, @@ -110,9 +107,9 @@ def get_assessment(self): """Compute several performance indicators.""" for j, augmentation in enumerate(self.augmentation): print('Augmentation: {0}'.format(augmentation)) - indicators = Indicators(self.y_true, self.y_pred[:,j]) + ind = indicators.Indicators(self.y_true, self.y_pred[:,j]) for method in methods: - getattr(indicators, method)() + getattr(ind, method)() def __vote(self, model, enzyme, augmentation): # Initialization. @@ -187,7 +184,7 @@ class MetricsHistory(Callback): """ def __init__(self, saving_path='test.csv'): # Initialization. - self.display = RealTimePlot(max_entries=200) + self.display = real_time.RealTimePlot(max_entries=200) self.saving_path = saving_path self.epochs = [] self.losses = [] @@ -216,4 +213,4 @@ def on_epoch_end(self, epoch, logs={}): 'val_losses': self.val_losses, 'accs': self.accs, 'val_accs': self.val_accs} - dict_to_csv(dictionary, self.saving_path) + tools.dict_to_csv(dictionary, self.saving_path) diff --git a/enzynet/pdb.py b/enzynet/pdb.py index 6d58597..71a17a4 100644 --- a/enzynet/pdb.py +++ b/enzynet/pdb.py @@ -11,12 +11,12 @@ import numpy as np -from Bio.PDB.PDBExceptions import PDBConstructionWarning -from Bio.PDB import * +from Bio.PDB import PDBExceptions +from Bio.PDB import PDBParser +from Bio.PDB import Polypeptide +from enzynet import tools -from enzynet.tools import read_dict, scale_dict - -warnings.filterwarnings("ignore", category=PDBConstructionWarning) +warnings.filterwarnings("ignore", category=PDBExceptions.PDBConstructionWarning) backbone_ids = ['C', 'N', 'CA'] @@ -47,7 +47,7 @@ def __init__(self, pdb_id, path=PDB_path): urllib.request.urlretrieve('http://files.rcsb.org/download/' + pdb_id.upper() + '.pdb', fullfilename) - self.structure = PDBParser().get_structure(pdb_id.upper(), fullfilename) + self.structure = PDBParser.PDBParser().get_structure(pdb_id.upper(), fullfilename) def get_coords(self): """Gets coordinates of backbone.""" @@ -59,7 +59,7 @@ def get_coords(self): for model in self.structure: for chain in model: for residue in chain: - if is_aa(residue, standard=True): # Check if amino acid. + if Polypeptide.is_aa(residue, standard=True): # Check if amino acid. for atom in residue: if atom.get_name() in backbone_ids: backbone_coords = backbone_coords + [atom.get_coord().tolist()] @@ -91,18 +91,18 @@ def get_weights(self, weights='hydropathy', scaling=True): backbone_residues = [] # Select weights. - weights = read_dict(os.path.join(datasets_path, weights + '.csv')) + weights = tools.read_dict(os.path.join(datasets_path, weights + '.csv')) weights = dict([key, float(value)] for key, value in weights.items()) # Convert values to float. # Scaling. if scaling == True: - weights = scale_dict(weights) + weights = tools.scale_dict(weights) # Computations. for model in self.structure: for chain in model: for residue in chain: - if is_aa(residue, standard=True): # Check if standard amino acid. + if Polypeptide.is_aa(residue, standard=True): # Check if standard amino acid. local_residue = residue.get_resname() local_weight = weights[local_residue] for atom in residue: diff --git a/enzynet/visualization.py b/enzynet/visualization.py index 849fb3f..53d94e2 100644 --- a/enzynet/visualization.py +++ b/enzynet/visualization.py @@ -8,35 +8,35 @@ import numpy as np import matplotlib.pyplot as plt -from enzynet.pdb import PDBBackbone -from enzynet.volume import adjust_size, coords_to_volume, coords_center_to_zero, weights_to_volume +from enzynet import pdb +from enzynet import volume def visualize_pdb(pdb_id, p=5, v_size=32, num=1, weights=None, max_radius=40, noise_treatment=True): """Plots PDB in a volume and saves it in a file.""" # Get coordinates. - pdb = PDBBackbone(pdb_id) - pdb.get_coords_extended(p=p) + pdb_backbone = pdb.PDBBackbone(pdb_id) + pdb_backbone.get_coords_extended(p=p) if weights != None: - pdb.get_weights_extended(p=p, weights=weights) + pdb_backbone.get_weights_extended(p=p, weights=weights) # Center to 0. - coords = coords_center_to_zero(pdb.backbone_coords_ext) + coords = volume.coords_center_to_zero(pdb_backbone.backbone_coords_ext) # Adjust size. - coords = adjust_size(coords, v_size, max_radius) + coords = volume.adjust_size(coords, v_size, max_radius) if weights == None: # Convert to volume. - volume = coords_to_volume(coords, v_size, noise_treatment=noise_treatment) + vol = volume.coords_to_volume(coords, v_size, noise_treatment=noise_treatment) else: # Converts to volume of weights. - volume = weights_to_volume(coords, pdb.backbone_weights_ext, v_size, - noise_treatment=noise_treatment) + vol = volume.weights_to_volume(coords, pdb_backbone.backbone_weights_ext, + v_size, noise_treatment=noise_treatment) # Plot. - plot_volume(volume, pdb_id, v_size, num, weights=weights) + plot_volume(vol, pdb_id, v_size, num, weights=weights) # 3D plot, sources: http://stackoverflow.com/a/35978146/4124317 diff --git a/enzynet/volume.py b/enzynet/volume.py index 2161d0d..ff948fa 100644 --- a/enzynet/volume.py +++ b/enzynet/volume.py @@ -1,4 +1,4 @@ -'Volume generation and augmentation' +"""Volume generation and augmentation.""" # Authors: Afshine Amidi # Shervine Amidi @@ -10,10 +10,9 @@ import numpy as np +from enzynet import pdb +from sklearn import decomposition from tqdm import tqdm -from sklearn.decomposition import PCA - -from enzynet.pdb import PDBBackbone current_directory = os.path.dirname(os.path.abspath(__file__)) precomputed_path = os.path.join(current_directory, '../files/precomputed/') @@ -181,7 +180,7 @@ def __data_augmentation(self, list_enzymes_temp): local_weights += [local_weight] # Store. # PCA. - coords = PCA(n_components=3).fit_transform(coords) + coords = decomposition.PCA(n_components=3).fit_transform(coords) # Do flip. coords_temp = flip_around_axis(coords, axis=self.flips) @@ -289,7 +288,7 @@ def save_coords_weights(pdb_id, list_weights, desired_p, scaling_weights, source_path, dest_path): """Computes coordinates and weights and saves them into .npy files.""" # Initialize local PDB. - local_PDB = PDBBackbone(pdb_id=pdb_id, path=source_path) + local_PDB = pdb.PDBBackbone(pdb_id=pdb_id, path=source_path) # Coordinates. local_PDB.get_coords_extended(p=desired_p) # Compute. diff --git a/scripts/architecture/enzynet_adapted.py b/scripts/architecture/enzynet_adapted.py index 55bdb00..06515ec 100644 --- a/scripts/architecture/enzynet_adapted.py +++ b/scripts/architecture/enzynet_adapted.py @@ -5,24 +5,21 @@ # MIT License -import numpy as np - import os.path -from enzynet.keras_utils import MetricsHistory, Voting -from enzynet.tools import read_dict, get_class_weights -from enzynet.volume import VolumeDataGenerator - -from keras.callbacks import ModelCheckpoint -from keras.initializers import RandomNormal -from keras.layers import Dense, Dropout, Flatten, Activation -from keras.layers import Conv3D, MaxPooling3D -from keras.layers.advanced_activations import LeakyReLU -from keras.models import Sequential -from keras.optimizers import Adam -from keras.regularizers import l2 +import numpy as np +from enzynet import keras_utils +from enzynet import tools +from enzynet import volume from keras import backend as K +from keras import callbacks +from keras import initializers +from keras import layers +from keras.layers import advanced_activations +from keras import models +from keras import optimizers +from keras import regularizers mode_run = 'test' mode_dataset = 'full' @@ -58,13 +55,13 @@ ##------------------------------ Dataset -------------------------------------## # Load dictionary of labels. -dictionary = read_dict('../../datasets/dataset_single.csv') +dictionary = tools.read_dict('../../datasets/dataset_single.csv') # Load partitions. if mode_dataset == 'full': - partition = read_dict('../../datasets/partition_single.csv') + partition = tools.read_dict('../../datasets/partition_single.csv') elif mode_dataset == 'reduced': - partition = read_dict('../../datasets/partition_single_red.csv') + partition = tools.read_dict('../../datasets/partition_single_red.csv') exec("partition['train'] = " + partition['train']) exec("partition['validation'] = " + partition['validation']) exec("partition['test'] = " + partition['test']) @@ -74,117 +71,126 @@ partition['validation'] = partition['test'] # Get class weights. -class_weights = get_class_weights(dictionary, partition['train'], - mode=mode_weights) +class_weights = tools.get_class_weights(dictionary, partition['train'], + mode=mode_weights) # Training generator. -training_generator = \ - VolumeDataGenerator(list_enzymes=partition['train'], - labels=dictionary, - v_size=v_size, - flips=flips, - batch_size=batch_size, - shuffle=shuffle, - p=p, - max_radius=max_radius, - noise_treatment=noise_treatment, - weights=weights, - scaling_weights=scaling_weights) +training_generator = volume.VolumeDataGenerator( + list_enzymes=partition['train'], + labels=dictionary, + v_size=v_size, + flips=flips, + batch_size=batch_size, + shuffle=shuffle, + p=p, + max_radius=max_radius, + noise_treatment=noise_treatment, + weights=weights, + scaling_weights=scaling_weights) # Validation generator. -validation_generator = \ - VolumeDataGenerator(list_enzymes=partition['validation'], - labels=dictionary, - v_size=v_size, - flips=(0, 0, 0), # No flip - batch_size=batch_size, - shuffle=False, # Validate with fixed set - p=p, - max_radius=max_radius, - noise_treatment=noise_treatment, - weights=weights, - scaling_weights=scaling_weights) +validation_generator = volume.VolumeDataGenerator( + list_enzymes=partition['validation'], + labels=dictionary, + v_size=v_size, + flips=(0, 0, 0), # No flip. + batch_size=batch_size, + shuffle=False, # Validate with fixed set. + p=p, + max_radius=max_radius, + noise_treatment=noise_treatment, + weights=weights, + scaling_weights=scaling_weights) # Check if data has been precomputed. training_generator.check_precomputed() ##----------------------------- Testing --------------------------------------## # Voting object. -predictions = \ - Voting(list_enzymes=partition['test'], - labels=dictionary, - voting_type=voting_type, - v_size=v_size, - augmentation=augmentation, - p=p, - max_radius=max_radius, - noise_treatment=noise_treatment, - weights=weights, - scaling_weights=scaling_weights) +predictions = keras_utils.Voting( + list_enzymes=partition['test'], + labels=dictionary, + voting_type=voting_type, + v_size=v_size, + augmentation=augmentation, + p=p, + max_radius=max_radius, + noise_treatment=noise_treatment, + weights=weights, + scaling_weights=scaling_weights) ##------------------------------ Model ---------------------------------------## # Create. -model = Sequential() +model = models.Sequential() # Add layers. -model.add(Conv3D(filters=32, - kernel_size=9, - strides=2, - padding='valid', - kernel_initializer=RandomNormal(mean=0.0, stddev=stddev_conv3d * 9**(-3/2)), - bias_initializer='zeros', - kernel_regularizer=l2(0.001), - bias_regularizer=None, - input_shape=(v_size, v_size, v_size, n_channels))) - -model.add(LeakyReLU(alpha=0.1)) - -model.add(Dropout(rate=0.2)) - -model.add(Conv3D(filters=64, - kernel_size=5, - strides=1, - padding='valid', - kernel_initializer=RandomNormal(mean=0.0, stddev=stddev_conv3d * 5**(-3/2)), - bias_initializer='zeros', - kernel_regularizer=l2(0.001), - bias_regularizer=None)) - -model.add(LeakyReLU(alpha=0.1)) - -model.add(MaxPooling3D(pool_size=(2,2,2))) - -model.add(Dropout(rate=0.3)) - -model.add(Flatten()) - -model.add(Dense(units=128, - kernel_initializer=RandomNormal(mean=0.0, stddev=0.01), - bias_initializer='zeros', - kernel_regularizer=l2(0.001), - bias_regularizer=None)) - -model.add(Dropout(rate=0.4)) - -model.add(Dense(units=n_classes, - kernel_initializer=RandomNormal(mean=0.0, stddev=0.01), - bias_initializer='zeros', - kernel_regularizer=l2(0.001), - bias_regularizer=None)) - -model.add(Activation('softmax')) +model.add( + layers.Conv3D( + filters=32, + kernel_size=9, + strides=2, + padding='valid', + kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=stddev_conv3d * 9**(-3/2)), + bias_initializer='zeros', + kernel_regularizer=regularizers.l2(0.001), + bias_regularizer=None, + input_shape=(v_size, v_size, v_size, n_channels))) + +model.add(advanced_activations.LeakyReLU(alpha=0.1)) + +model.add(layers.Dropout(rate=0.2)) + +model.add( + layers.Conv3D( + filters=64, + kernel_size=5, + strides=1, + padding='valid', + kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=stddev_conv3d * 5**(-3/2)), + bias_initializer='zeros', + kernel_regularizer=regularizers.l2(0.001), + bias_regularizer=None)) + +model.add(advanced_activations.LeakyReLU(alpha=0.1)) + +model.add(layers.MaxPooling3D(pool_size=(2,2,2))) + +model.add(layers.Dropout(rate=0.3)) + +model.add(layers.Flatten()) + +model.add( + layers.Dense( + units=128, + kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.01), + bias_initializer='zeros', + kernel_regularizer=regularizers.l2(0.001), + bias_regularizer=None)) + +model.add(layers.Dropout(rate=0.4)) + +model.add( + layers.Dense( + units=n_classes, + kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.01), + bias_initializer='zeros', + kernel_regularizer=regularizers.l2(0.001), + bias_regularizer=None)) + +model.add(layers.Activation('softmax')) # Track accuracy and loss in real-time. -history = MetricsHistory(saving_path=current_file_name + '.csv') +history = keras_utils.MetricsHistory(saving_path=current_file_name + '.csv') # Checkpoints. -checkpoints = ModelCheckpoint('checkpoints/' + current_file_name + '_{epoch:02d}' + '.hd5f', - save_weights_only=True, - period=period_checkpoint) +checkpoints = callbacks.ModelCheckpoint( + 'checkpoints/' + current_file_name + '_{epoch:02d}' + '.hd5f', + save_weights_only=True, + period=period_checkpoint) if mode_run == 'train': # Compile. - model.compile(optimizer=Adam(lr=0.001, decay=0.00016667), + model.compile(optimizer=optimizers.Adam(lr=0.001, decay=0.00016667), loss='categorical_crossentropy', metrics=['accuracy']) diff --git a/scripts/architecture/enzynet_uniform.py b/scripts/architecture/enzynet_uniform.py index 82bef83..beaca96 100644 --- a/scripts/architecture/enzynet_uniform.py +++ b/scripts/architecture/enzynet_uniform.py @@ -5,24 +5,21 @@ # MIT License -import numpy as np - import os.path -from enzynet.keras_utils import MetricsHistory, Voting -from enzynet.tools import read_dict, get_class_weights -from enzynet.volume import VolumeDataGenerator - -from keras.callbacks import ModelCheckpoint -from keras.initializers import RandomNormal -from keras.layers import Dense, Dropout, Flatten, Activation -from keras.layers import Conv3D, MaxPooling3D -from keras.layers.advanced_activations import LeakyReLU -from keras.models import Sequential -from keras.optimizers import Adam -from keras.regularizers import l2 +import numpy as np +from enzynet import keras_utils +from enzynet import tools +from enzynet import volume from keras import backend as K +from keras import callbacks +from keras import initializers +from keras import layers +from keras.layers import advanced_activations +from keras import models +from keras import optimizers +from keras import regularizers mode_run = 'test' mode_dataset = 'full' @@ -58,13 +55,13 @@ ##------------------------------ Dataset -------------------------------------## # Load dictionary of labels. -dictionary = read_dict('../../datasets/dataset_single.csv') +dictionary = tools.read_dict('../../datasets/dataset_single.csv') # Load partitions. if mode_dataset == 'full': - partition = read_dict('../../datasets/partition_single.csv') + partition = tools.read_dict('../../datasets/partition_single.csv') elif mode_dataset == 'reduced': - partition = read_dict('../../datasets/partition_single_red.csv') + partition = tools.read_dict('../../datasets/partition_single_red.csv') exec("partition['train'] = " + partition['train']) exec("partition['validation'] = " + partition['validation']) exec("partition['test'] = " + partition['test']) @@ -74,117 +71,126 @@ partition['validation'] = partition['test'] # Get class weights. -class_weights = get_class_weights(dictionary, partition['train'], +class_weights = tools.get_class_weights(dictionary, partition['train'], mode=mode_weights) # Training generator. -training_generator = \ - VolumeDataGenerator(list_enzymes=partition['train'], - labels=dictionary, - v_size=v_size, - flips=flips, - batch_size=batch_size, - shuffle=shuffle, - p=p, - max_radius=max_radius, - noise_treatment=noise_treatment, - weights=weights, - scaling_weights=scaling_weights) +training_generator = volume.VolumeDataGenerator( + list_enzymes=partition['train'], + labels=dictionary, + v_size=v_size, + flips=flips, + batch_size=batch_size, + shuffle=shuffle, + p=p, + max_radius=max_radius, + noise_treatment=noise_treatment, + weights=weights, + scaling_weights=scaling_weights) # Validation generator. -validation_generator = \ - VolumeDataGenerator(list_enzymes=partition['validation'], - labels=dictionary, - v_size=v_size, - flips=(0, 0, 0), # No flip - batch_size=batch_size, - shuffle=False, # Validate with fixed set - p=p, - max_radius=max_radius, - noise_treatment=noise_treatment, - weights=weights, - scaling_weights=scaling_weights) +validation_generator = volume.VolumeDataGenerator( + list_enzymes=partition['validation'], + labels=dictionary, + v_size=v_size, + flips=(0, 0, 0), # No flip. + batch_size=batch_size, + shuffle=False, # Validate with fixed set. + p=p, + max_radius=max_radius, + noise_treatment=noise_treatment, + weights=weights, + scaling_weights=scaling_weights) # Check if data has been precomputed. training_generator.check_precomputed() ##----------------------------- Testing --------------------------------------## # Voting object. -predictions = \ - Voting(list_enzymes=partition['test'], - labels=dictionary, - voting_type=voting_type, - v_size=v_size, - augmentation=augmentation, - p=p, - max_radius=max_radius, - noise_treatment=noise_treatment, - weights=weights, - scaling_weights=scaling_weights) +predictions = keras_utils.Voting( + list_enzymes=partition['test'], + labels=dictionary, + voting_type=voting_type, + v_size=v_size, + augmentation=augmentation, + p=p, + max_radius=max_radius, + noise_treatment=noise_treatment, + weights=weights, + scaling_weights=scaling_weights) ##------------------------------ Model ---------------------------------------## # Create. -model = Sequential() +model = models.Sequential() # Add layers. -model.add(Conv3D(filters=32, - kernel_size=9, - strides=2, - padding='valid', - kernel_initializer=RandomNormal(mean=0.0, stddev=stddev_conv3d * 9**(-3/2)), - bias_initializer='zeros', - kernel_regularizer=l2(0.001), - bias_regularizer=None, - input_shape=(v_size, v_size, v_size, n_channels))) - -model.add(LeakyReLU(alpha=0.1)) - -model.add(Dropout(rate=0.2)) - -model.add(Conv3D(filters=64, - kernel_size=5, - strides=1, - padding='valid', - kernel_initializer=RandomNormal(mean=0.0, stddev=stddev_conv3d * 5**(-3/2)), - bias_initializer='zeros', - kernel_regularizer=l2(0.001), - bias_regularizer=None)) - -model.add(LeakyReLU(alpha=0.1)) - -model.add(MaxPooling3D(pool_size=(2,2,2))) - -model.add(Dropout(rate=0.3)) - -model.add(Flatten()) - -model.add(Dense(units=128, - kernel_initializer=RandomNormal(mean=0.0, stddev=0.01), - bias_initializer='zeros', - kernel_regularizer=l2(0.001), - bias_regularizer=None)) - -model.add(Dropout(rate=0.4)) - -model.add(Dense(units=n_classes, - kernel_initializer=RandomNormal(mean=0.0, stddev=0.01), - bias_initializer='zeros', - kernel_regularizer=l2(0.001), - bias_regularizer=None)) - -model.add(Activation('softmax')) +model.add( + layers.Conv3D( + filters=32, + kernel_size=9, + strides=2, + padding='valid', + kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=stddev_conv3d * 9**(-3/2)), + bias_initializer='zeros', + kernel_regularizer=regularizers.l2(0.001), + bias_regularizer=None, + input_shape=(v_size, v_size, v_size, n_channels))) + +model.add(advanced_activations.LeakyReLU(alpha=0.1)) + +model.add(layers.Dropout(rate=0.2)) + +model.add( + layers.Conv3D( + filters=64, + kernel_size=5, + strides=1, + padding='valid', + kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=stddev_conv3d * 5**(-3/2)), + bias_initializer='zeros', + kernel_regularizer=regularizers.l2(0.001), + bias_regularizer=None)) + +model.add(advanced_activations.LeakyReLU(alpha=0.1)) + +model.add(layers.MaxPooling3D(pool_size=(2,2,2))) + +model.add(layers.Dropout(rate=0.3)) + +model.add(layers.Flatten()) + +model.add( + layers.Dense( + units=128, + kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.01), + bias_initializer='zeros', + kernel_regularizer=regularizers.l2(0.001), + bias_regularizer=None)) + +model.add(layers.Dropout(rate=0.4)) + +model.add( + layers.Dense( + units=n_classes, + kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.01), + bias_initializer='zeros', + kernel_regularizer=regularizers.l2(0.001), + bias_regularizer=None)) + +model.add(layers.Activation('softmax')) # Track accuracy and loss in real-time. -history = MetricsHistory(saving_path=current_file_name + '.csv') +history = keras_utils.MetricsHistory(saving_path=current_file_name + '.csv') # Checkpoints. -checkpoints = ModelCheckpoint('checkpoints/' + current_file_name + '_{epoch:02d}' + '.hd5f', - save_weights_only=True, - period=period_checkpoint) +checkpoints = callbacks.ModelCheckpoint( + 'checkpoints/' + current_file_name + '_{epoch:02d}' + '.hd5f', + save_weights_only=True, + period=period_checkpoint) if mode_run == 'train': # Compile. - model.compile(optimizer=Adam(lr=0.001, decay=0.00016667), + model.compile(optimizer=optimizers.Adam(lr=0.001, decay=0.00016667), loss='categorical_crossentropy', metrics=['accuracy']) From b53d795b03276c0c11f479576258c77136a5677b Mon Sep 17 00:00:00 2001 From: shervinea Date: Thu, 22 Apr 2021 22:13:12 -0700 Subject: [PATCH 07/18] Rename Pandas alias --- datasets/make_files/download_pdb.py | 4 ++-- datasets/make_files/generate_datasets.py | 6 +++--- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/datasets/make_files/download_pdb.py b/datasets/make_files/download_pdb.py index 9cc4503..24f667e 100755 --- a/datasets/make_files/download_pdb.py +++ b/datasets/make_files/download_pdb.py @@ -5,10 +5,10 @@ # MIT License -import pandas as pa +import pandas as pd # Read csv files. -df = pa.read_csv('../dataset_all.csv', header=None) +df = pd.read_csv('../dataset_all.csv', header=None) # Only keep PDB IDs. df = df[0]+', ' diff --git a/datasets/make_files/generate_datasets.py b/datasets/make_files/generate_datasets.py index c02ab98..7d626b0 100755 --- a/datasets/make_files/generate_datasets.py +++ b/datasets/make_files/generate_datasets.py @@ -6,7 +6,7 @@ # MIT License import numpy as np -import pandas as pa +import pandas as pd from enzynet import pdb from enzynet import tools @@ -22,7 +22,7 @@ for i in range(1, n_classes+1): # Initialization. errors = [] # List of indexes with error. - enzymes = pa.read_table('raw/' + str(i) + '.txt', header=None) # Load PDB IDs. + enzymes = pd.read_table('raw/' + str(i) + '.txt', header=None) # Load PDB IDs. list_enzymes = enzymes.loc[:,0].tolist() # Look for errors. @@ -50,7 +50,7 @@ # Computations. for i in range(1, n_classes+1): # Load PDB IDs for each class. - enzymes = pa.read_table(str(i) + '.txt', header=None) + enzymes = pd.read_table(str(i) + '.txt', header=None) enzymes = enzymes[0].tolist() # Add entries to dict. From f04489e52fc45f5d2f451e0cb2897f866d2ed6e5 Mon Sep 17 00:00:00 2001 From: shervinea Date: Thu, 22 Apr 2021 22:15:18 -0700 Subject: [PATCH 08/18] Use 'is' keyword when recommended --- enzynet/pdb.py | 2 +- enzynet/visualization.py | 4 ++-- enzynet/volume.py | 6 +++--- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/enzynet/pdb.py b/enzynet/pdb.py index 71a17a4..00e85e1 100644 --- a/enzynet/pdb.py +++ b/enzynet/pdb.py @@ -95,7 +95,7 @@ def get_weights(self, weights='hydropathy', scaling=True): weights = dict([key, float(value)] for key, value in weights.items()) # Convert values to float. # Scaling. - if scaling == True: + if scaling is True: weights = tools.scale_dict(weights) # Computations. diff --git a/enzynet/visualization.py b/enzynet/visualization.py index 53d94e2..7e2d958 100644 --- a/enzynet/visualization.py +++ b/enzynet/visualization.py @@ -28,7 +28,7 @@ def visualize_pdb(pdb_id, p=5, v_size=32, num=1, weights=None, # Adjust size. coords = volume.adjust_size(coords, v_size, max_radius) - if weights == None: + if weights is None: # Convert to volume. vol = volume.coords_to_volume(coords, v_size, noise_treatment=noise_treatment) else: @@ -62,7 +62,7 @@ def plot_volume(volume, pdb_id, v_size, num, weights=None): ax.set_zticklabels([]) # Plot. - if weights == None: + if weights is None: plot_matrix(ax, volume) else: plot_matrix_of_weights(ax, volume) diff --git a/enzynet/volume.py b/enzynet/volume.py index ff948fa..b67aef6 100644 --- a/enzynet/volume.py +++ b/enzynet/volume.py @@ -132,7 +132,7 @@ def check_precomputed(self): def on_epoch_end(self): """Updates indexes after each epoch.""" self.indexes = np.arange(len(self.list_enzymes)) - if self.shuffle == True: + if self.shuffle is True: np.random.shuffle(self.indexes) def __len__(self): @@ -222,7 +222,7 @@ def weights_to_volume(coords, weights, v_size, noise_treatment=False): volume[tuple(coords[mask].T)] = weights[mask] if type(weights) != int else weights # Remove noise. - if noise_treatment == True: + if noise_treatment is True: volume = remove_noise(coords, volume) return volume @@ -268,7 +268,7 @@ def remove_noise(coords, volume): if all(valeur < v_size-1 for valeur in coords[i,:]) and \ all(valeur > 0 for valeur in coords[i,:]): # Point inside volume. if np.array_equal(volume[coords[i,0]-1:coords[i,0]+2,coords[i,1]-1:coords[i,1]+2,coords[i,2]-1:coords[i,2]+2], - np.pad(np.array([[[1]]]),1,'constant') * volume[tuple(coords[i])]) == True: # Isolated point. + np.pad(np.array([[[1]]]),1,'constant') * volume[tuple(coords[i])]) is True: # Isolated point. volume[coords[i,0]-1:coords[i,0]+2, coords[i,1]-1:coords[i,1]+2, coords[i,2]-1:coords[i,2]+2] = np.zeros((3,3,3)) From 9d736e030ae5b12dde7c10f5b5328eca8bb6b3a9 Mon Sep 17 00:00:00 2001 From: shervinea Date: Sat, 24 Apr 2021 13:45:06 -0700 Subject: [PATCH 09/18] Add type checking --- enzynet/indicators.py | 20 +++++------ enzynet/keras_utils.py | 46 ++++++++++++++++--------- enzynet/pdb.py | 30 +++++++++++------ enzynet/real_time.py | 15 +++------ enzynet/tools.py | 12 ++++--- enzynet/visualization.py | 46 +++++++++++++++---------- enzynet/volume.py | 73 +++++++++++++++++++++++++++------------- 7 files changed, 152 insertions(+), 90 deletions(-) diff --git a/enzynet/indicators.py b/enzynet/indicators.py index cd2b33b..6dbcebf 100644 --- a/enzynet/indicators.py +++ b/enzynet/indicators.py @@ -8,7 +8,7 @@ import numpy as np -def unalikeability(L): +def unalikeability(L: np.ndarray) -> float: """Returns unalikeability of a set of labels.""" # p.4 of Kader2007. if len(L) <= 1: return 0 @@ -34,14 +34,14 @@ class Indicators(object): y_pred : NumPy array of size (n_samples, 1) Array of predicted labels. """ - def __init__(self, y_true, y_pred): + def __init__(self, y_true: np.ndarray, y_pred: np.ndarray) -> None: """Initialization.""" self.n_samples = y_true.shape[0] self.n_classes = 6 self.y_true = y_true self.y_pred = y_pred - def confusion_matrix(self): + def confusion_matrix(self) -> None: """Confusion matrix.""" self.confusion_matrix = np.zeros((self.n_classes, self.n_classes)) for i, j in zip(self.y_true, self.y_pred): @@ -52,39 +52,39 @@ def confusion_matrix(self): self.confusion_matrix/np.sum(self.confusion_matrix, axis=1)[:, None] print('Scaled confusion matrix: \n {0}'.format(np.around(self.confusion_matrix_scaled, 3))) - def accuracy(self): + def accuracy(self) -> None: """Accuracy.""" self.accuracy = np.trace(self.confusion_matrix)/self.n_samples print('Accuracy: {0:.3f}'.format(self.accuracy)) - def precision_per_class(self): + def precision_per_class(self) -> None: """Precision per class.""" self.precision_per_class = \ np.diag(self.confusion_matrix/np.sum(self.confusion_matrix, axis=0)) print('Precision per class: {0}'.format(np.around(self.precision_per_class, 3))) - def recall_per_class(self): + def recall_per_class(self) -> None: """Recall per class.""" self.recall_per_class = np.diag(self.confusion_matrix_scaled) print('Recall per class: {0}'.format(np.around(self.recall_per_class, 3))) - def f1_per_class(self): + def f1_per_class(self) -> None: """F1 per class.""" self.f1_per_class = \ 2 * self.precision_per_class * self.recall_per_class/(self.precision_per_class + self.recall_per_class) print('F1 per class: {0}'.format(np.around(self.f1_per_class, 3))) - def macro_precision(self): + def macro_precision(self) -> None: """Macro precision.""" self.macro_precision = np.sum(self.precision_per_class)/self.n_classes print('Macro precision: {0:.3f}'.format(self.macro_precision)) - def macro_recall(self): + def macro_recall(self) -> None: """Macro recall.""" self.macro_recall = np.sum(self.recall_per_class)/self.n_classes print('Macro recall: {0:.3f}'.format(self.macro_recall)) - def macro_f1(self): + def macro_f1(self) -> None: """Macro F1 score.""" self.macro_f1 = 2 * self.macro_precision * self.macro_recall/(self.macro_precision + self.macro_recall) print('Macro F1: {0:.3f}'.format(self.macro_f1)) diff --git a/enzynet/keras_utils.py b/enzynet/keras_utils.py index 2bd7352..2dbe1a0 100644 --- a/enzynet/keras_utils.py +++ b/enzynet/keras_utils.py @@ -5,6 +5,8 @@ # MIT License +from typing import Dict, List, Text + import os.path import itertools @@ -14,6 +16,7 @@ from enzynet import real_time from enzynet import tools from enzynet import volume +from keras import models from keras.callbacks import Callback from matplotlib import pyplot as plt from tqdm import tqdm @@ -76,20 +79,33 @@ class Voting(volume.VolumeDataGenerator): If True, divides all weights by the weight that is maximum in absolute value. """ - def __init__(self, list_enzymes, labels, voting_type='probabilities', augmentation=['None'], - v_size=32, directory_precomputed=precomputed_path, - directory_pdb=PDB_path, shuffle=True, p=5, max_radius=40, - noise_treatment=False, weights=[], scaling_weights=True): + def __init__( + self, + list_enzymes: List[Text], + labels: Dict[Text, int], + voting_type: Text = 'probabilities', + augmentation: List[Text] = ['None'], + v_size: int = 32, + directory_precomputed: Text = precomputed_path, + directory_pdb: Text = PDB_path, + shuffle: bool = True, + p: int = 5, + max_radius: float = 40, + noise_treatment: bool = False, + weights: List = [], + scaling_weights: bool = True + ) -> None: """Initialization.""" - volume.VolumeDataGenerator.__init__(self, list_enzymes, labels, v_size=v_size, flips=(0, 0, 0), - batch_size=1, directory_precomputed=directory_precomputed, - directory_pdb=directory_pdb, shuffle=shuffle, p=p, - max_radius=max_radius, noise_treatment=noise_treatment, - weights=weights, scaling_weights=scaling_weights) + volume.VolumeDataGenerator.__init__( + self, list_enzymes, labels, v_size=v_size, flips=(0, 0, 0), + batch_size=1, directory_precomputed=directory_precomputed, + directory_pdb=directory_pdb, shuffle=shuffle, p=p, + max_radius=max_radius, noise_treatment=noise_treatment, + weights=weights, scaling_weights=scaling_weights) self.voting_type = voting_type self.augmentation = augmentation - def predict(self, model): + def predict(self, model: models.Sequential) -> None: """Predicts classes of testing enzymes.""" # Initialization. self.y_pred = np.empty((len(self.list_enzymes), len(self.augmentation)), dtype=int) @@ -103,7 +119,7 @@ def predict(self, model): self.y_pred[i,j] = \ self.__vote(model, enzyme, augmentation) - def get_assessment(self): + def get_assessment(self) -> None: """Compute several performance indicators.""" for j, augmentation in enumerate(self.augmentation): print('Augmentation: {0}'.format(augmentation)) @@ -111,7 +127,8 @@ def get_assessment(self): for method in methods: getattr(ind, method)() - def __vote(self, model, enzyme, augmentation): + def __vote(self, model: models.Sequential, enzyme: Text, + augmentation: Text) -> int: # Initialization. probability = np.zeros((1, 6)) @@ -182,7 +199,7 @@ class MetricsHistory(Callback): saving_path : string (optional, default is 'test.csv') Full path to output csv file. """ - def __init__(self, saving_path='test.csv'): + def __init__(self, saving_path: Text = 'test.csv') -> None: # Initialization. self.display = real_time.RealTimePlot(max_entries=200) self.saving_path = saving_path @@ -192,7 +209,7 @@ def __init__(self, saving_path='test.csv'): self.accs = [] self.val_accs = [] - def on_epoch_end(self, epoch, logs={}): + def on_epoch_end(self, epoch: int, logs: Dict[Text, float] = {}) -> None: # Store. self.epochs += [epoch] self.losses += [logs.get('loss')] @@ -206,7 +223,6 @@ def on_epoch_end(self, epoch, logs={}): y_val=logs.get('val_acc')) plt.pause(0.001) - # Save to file. dictionary = {'epochs': self.epochs, 'losses': self.losses, diff --git a/enzynet/pdb.py b/enzynet/pdb.py index 00e85e1..4df520b 100644 --- a/enzynet/pdb.py +++ b/enzynet/pdb.py @@ -5,6 +5,8 @@ # MIT License +from typing import List, Sequence, Text + import os.path import urllib.request import warnings @@ -37,7 +39,7 @@ class PDBBackbone(object): Path where the PDB file is located, or where the PDB file should be downloaded. """ - def __init__(self, pdb_id, path=PDB_path): + def __init__(self, pdb_id: Text, path: Text = PDB_path) -> None: """Initialization.""" self.pdb_id = pdb_id.upper() fullfilename = os.path.join(path, pdb_id.lower() + '.pdb') @@ -49,7 +51,7 @@ def __init__(self, pdb_id, path=PDB_path): fullfilename) self.structure = PDBParser.PDBParser().get_structure(pdb_id.upper(), fullfilename) - def get_coords(self): + def get_coords(self) -> None: """Gets coordinates of backbone.""" # Initialization. backbone_coords = [] @@ -69,7 +71,7 @@ def get_coords(self): self.backbone_coords = np.array(backbone_coords) self.backbone_atoms = backbone_atoms - def get_coords_extended(self, p=5): + def get_coords_extended(self, p: int = 5) -> None: """Adds the coordinates from interpolation betweens atoms of the backbone.""" # Initialization. self.get_coords() @@ -84,14 +86,15 @@ def get_coords_extended(self, p=5): # Store results. self.backbone_coords_ext = np.concatenate((self.backbone_coords, new_coords), axis=0) - def get_weights(self, weights='hydropathy', scaling=True): + def get_weights(self, weight_type: Text = 'hydropathy', + scaling: bool = True) -> None: """Gets weights of each position regarding associated amino-acid.""" # Initialization. backbone_weights = [] backbone_residues = [] # Select weights. - weights = tools.read_dict(os.path.join(datasets_path, weights + '.csv')) + weights = tools.read_dict(os.path.join(datasets_path, weight_type + '.csv')) weights = dict([key, float(value)] for key, value in weights.items()) # Convert values to float. # Scaling. @@ -114,10 +117,11 @@ def get_weights(self, weights='hydropathy', scaling=True): self.backbone_weights = backbone_weights self.backbone_residues = backbone_residues - def get_weights_extended(self, p, weights='hydropathy', scaling=True): + def get_weights_extended(self, p: int, weight_type: Text = 'hydropathy', + scaling: bool = True) -> None: """Adds the weights from interpolation between atoms of the backbone.""" # Initialization. - self.get_weights(weights=weights, scaling=scaling) + self.get_weights(weight_type=weight_type, scaling=scaling) C_weights = self.__get_weights_specific_atom(backbone_ids) new_weights = np.zeros((p*(len(C_weights)-1))) @@ -129,7 +133,10 @@ def get_weights_extended(self, p, weights='hydropathy', scaling=True): # Store results. self.backbone_weights_ext = np.concatenate((self.backbone_weights, new_weights), axis=0) - def __get_coords_specific_atom(self, specific_atoms): + def __get_coords_specific_atom( + self, + specific_atoms: Sequence[Text] + ) -> np.ndarray: """Extracts coordinates of a specific atom only.""" for atom in specific_atoms: # Tries to accomplish the task on the first that works. try: @@ -139,7 +146,10 @@ def __get_coords_specific_atom(self, specific_atoms): except: pass - def __get_weights_specific_atom(self, specific_atoms): + def __get_weights_specific_atom( + self, + specific_atoms: Sequence[Text] + ) -> List[float]: """Extracts weights of a specific atom only.""" for atom in specific_atoms: # Tries to accomplish the task on the first that works. try: @@ -149,7 +159,7 @@ def __get_weights_specific_atom(self, specific_atoms): except: pass - def get_ligands(self): + def get_ligands(self) -> None: """Stores ligands in a list.""" # Initialization. ligands = [] diff --git a/enzynet/real_time.py b/enzynet/real_time.py index 5eb4835..59399ff 100644 --- a/enzynet/real_time.py +++ b/enzynet/real_time.py @@ -4,6 +4,8 @@ # Shervine Amidi # MIT License +from typing import Optional, Text + import time from collections import deque @@ -14,7 +16,8 @@ # Adapted from https://gist.github.com/Uberi/283a13b8a71a46fb4dc8. class RealTimePlot(object): - def __init__(self, max_entries=200, x_label=r'Epochs', y_label=r'Accuracy'): + def __init__(self, max_entries: int = 200, x_label: Text = r'Epochs', + y_label: Text = r'Accuracy') -> None: # TeX friendly. plt.rc('text', usetex=True) plt.rc('font', family='serif') @@ -41,7 +44,7 @@ def __init__(self, max_entries=200, x_label=r'Epochs', y_label=r'Accuracy'): self.axes.set_xlabel(x_label) self.axes.set_ylabel(y_label) - def add(self, x, y_tr, y_val=None): + def add(self, x: float, y_tr: float, y_val: Optional[float] = None) -> None: # Add new point. self.axis_x.append(x) self.axis_y_tr.append(y_tr) @@ -55,14 +58,6 @@ def add(self, x, y_tr, y_val=None): self.axes.set_xlim(self.axis_x[0], self.axis_x[-1] + 1e-15) self.axes.relim(); self.axes.autoscale_view() # Rescale the y-axis. - def animate(self, figure, callback, interval=50): - import matplotlib.animation as animation - def wrapper(frame_index): - self.add(*callback(frame_index)) - self.axes.relim(); self.axes.autoscale_view() # Rescale the y-axis. - return self.lineplot - animation.FuncAnimation(figure, wrapper, interval=interval) - if __name__ == "__main__": # Initialization. diff --git a/enzynet/tools.py b/enzynet/tools.py index 51e7e99..8341d9e 100644 --- a/enzynet/tools.py +++ b/enzynet/tools.py @@ -5,12 +5,14 @@ # MIT License +from typing import Any, Dict, Iterator, Text + import csv csv.field_size_limit(10**7) -def read_dict(path): +def read_dict(path: Text) -> Dict[Any, Any]: """Reads Python dictionary stored in a csv file.""" dictionary = {} for key, val in csv.reader(open(path)): @@ -18,20 +20,22 @@ def read_dict(path): return dictionary -def dict_to_csv(dictionary, path): +def dict_to_csv(dictionary: Dict[Any, Any], path: Text) -> None: """Saves Python dictionary to a csv file.""" w = csv.writer(open(path, 'w')) for key, val in dictionary.items(): w.writerow([key, val]) -def scale_dict(dictionary): +def scale_dict(dictionary: Dict[Any, float]) -> Dict[Any, float]: """Scales values of a dictionary.""" maxi = max(map(abs, dictionary.values())) # Max in absolute value. return {k: v/maxi for k, v in dictionary.items()} -def get_class_weights(dictionary, training_enzymes, mode): +def get_class_weights(dictionary: Dict[Text, int], + training_enzymes: Iterator[Text], + mode: Text) -> Dict[int, float]: """Gets class weights for Keras.""" # Initialization. counter = [0 for i in range(6)] diff --git a/enzynet/visualization.py b/enzynet/visualization.py index 7e2d958..2049f3c 100644 --- a/enzynet/visualization.py +++ b/enzynet/visualization.py @@ -5,6 +5,8 @@ # MIT License +from typing import List, Optional, Text, Tuple + import numpy as np import matplotlib.pyplot as plt @@ -12,15 +14,16 @@ from enzynet import volume -def visualize_pdb(pdb_id, p=5, v_size=32, num=1, weights=None, - max_radius=40, noise_treatment=True): +def visualize_pdb(pdb_id: Text, p: int = 5, v_size: int = 32, num: int = 1, + weight_type: Optional[Text] = None, max_radius: int = 40, + noise_treatment: bool = True) -> None: """Plots PDB in a volume and saves it in a file.""" # Get coordinates. pdb_backbone = pdb.PDBBackbone(pdb_id) pdb_backbone.get_coords_extended(p=p) - if weights != None: - pdb_backbone.get_weights_extended(p=p, weights=weights) + if weight_type is not None: + pdb_backbone.get_weights_extended(p=p, weight_type=weight_type) # Center to 0. coords = volume.coords_center_to_zero(pdb_backbone.backbone_coords_ext) @@ -28,7 +31,7 @@ def visualize_pdb(pdb_id, p=5, v_size=32, num=1, weights=None, # Adjust size. coords = volume.adjust_size(coords, v_size, max_radius) - if weights is None: + if weight_type is None: # Convert to volume. vol = volume.coords_to_volume(coords, v_size, noise_treatment=noise_treatment) else: @@ -36,12 +39,13 @@ def visualize_pdb(pdb_id, p=5, v_size=32, num=1, weights=None, vol = volume.weights_to_volume(coords, pdb_backbone.backbone_weights_ext, v_size, noise_treatment=noise_treatment) # Plot. - plot_volume(vol, pdb_id, v_size, num, weights=weights) + plot_volume(vol, pdb_id, v_size, num, weight_type=weight_type) # 3D plot, sources: http://stackoverflow.com/a/35978146/4124317 # https://dawes.wordpress.com/2014/06/27/publication-ready-3d-figures-from-matplotlib/ -def plot_volume(volume, pdb_id, v_size, num, weights=None): +def plot_volume(vol: np.ndarray, pdb_id: Text, v_size: int, num: int, + weight_type: Optional[Text] = None): """Plots volume in 3D, interpreting the coordinates as voxels.""" # Initialization. plt.rc('text', usetex=True) @@ -51,7 +55,7 @@ def plot_volume(volume, pdb_id, v_size, num, weights=None): ax.set_aspect('equal') # Parameters. - len_vol = volume.shape[0] + len_vol = vol.shape[0] # Set position of the view. ax.view_init(elev=20, azim=135) @@ -62,10 +66,10 @@ def plot_volume(volume, pdb_id, v_size, num, weights=None): ax.set_zticklabels([]) # Plot. - if weights is None: - plot_matrix(ax, volume) + if weight_type is None: + plot_matrix(ax, vol) else: - plot_matrix_of_weights(ax, volume) + plot_matrix_of_weights(ax, vol) # Tick at every unit. ax.set_xticks(np.arange(len_vol)) @@ -105,10 +109,13 @@ def plot_volume(volume, pdb_id, v_size, num, weights=None): # Save. plt.savefig('../scripts/volume/' + str(pdb_id) + '_' + str(v_size) + '_' + - str(weights) + '_' + str(num) + '.pdf') + str(weight_type) + '_' + str(num) + '.pdf') -def cuboid_data(pos, size=(1,1,1)): +def cuboid_data( + pos: Tuple[float, float, float], + size: Tuple[int, int, int]=(1,1,1) +) -> Tuple[List[List[float]], List[List[float]], List[List[float]]]: """Gets coordinates of cuboid.""" # Gets the (left, outside, bottom) point. o = [a - b / 2 for a, b in zip(pos, size)] @@ -128,21 +135,24 @@ def cuboid_data(pos, size=(1,1,1)): return x, y, z -def plot_cube_at(pos=(0,0,0), ax=None): +def plot_cube_at(pos: Tuple[float, float, float] = (0,0,0), + ax: Optional[plt.gca] = None) -> None: """Plots a cube element at position pos.""" if ax != None: X, Y, Z = cuboid_data(pos) ax.plot_surface(X, Y, Z, color='g', rstride=1, cstride=1, alpha=1) -def plot_cube_weights_at(pos=(0,0,0), ax=None, color='g'): +def plot_cube_weights_at(pos: Tuple[float, float, float] = (0,0,0), + ax: Optional[plt.gca] = None, + color: Text = 'g') -> None: """Plots a cube element at position pos.""" if ax != None: X, Y, Z = cuboid_data(pos) ax.plot_surface(X, Y, Z, color=color, rstride=1, cstride=1, alpha=1) -def plot_matrix(ax, matrix): +def plot_matrix(ax: Optional[plt.gca], matrix: np.ndarray) -> None: """Plots cubes from a volumic matrix.""" for i in range(matrix.shape[0]): for j in range(matrix.shape[1]): @@ -151,7 +161,7 @@ def plot_matrix(ax, matrix): plot_cube_at(pos=(i-0.5,j-0.5,k-0.5), ax=ax) -def plot_matrix_of_weights(ax, matrix_of_weights): +def plot_matrix_of_weights(ax: plt.gca, matrix_of_weights: np.ndarray) -> None: """Plots cubes from a volumic matrix.""" # Initialization. min_value = np.amin(matrix_of_weights) @@ -182,4 +192,4 @@ def plot_matrix_of_weights(ax, matrix_of_weights): if __name__ == '__main__': - visualize_pdb('2Q3Z', p=0, v_size=32, weights=None) + visualize_pdb('2Q3Z', p=0, v_size=32, weight_type=None) diff --git a/enzynet/volume.py b/enzynet/volume.py index b67aef6..ff2ceee 100644 --- a/enzynet/volume.py +++ b/enzynet/volume.py @@ -5,6 +5,8 @@ # MIT License +from typing import Dict, List, Optional, Sequence, Text, Tuple, Union + import keras import os.path @@ -85,10 +87,22 @@ class VolumeDataGenerator(keras.utils.Sequence): max_radius=40, noise_treatment=False, weights=[], scaling_weights=True) """ - def __init__(self, list_enzymes, labels, v_size=32, flips=(0.2, 0.2, 0.2), batch_size=32, - directory_precomputed=precomputed_path, directory_pdb=PDB_path, - shuffle=True, p=5, max_radius=40, noise_treatment=False, - weights=[], scaling_weights=True): + def __init__( + self, + list_enzymes: List[Text], + labels: Dict[Text, int], + v_size: int = 32, + flips: Tuple[float, float, float] = (0.2, 0.2, 0.2), + batch_size: int = 32, + directory_precomputed: Text = precomputed_path, + directory_pdb: Text = PDB_path, + shuffle: bool = True, + p: int = 5, + max_radius: float = 40, + noise_treatment: bool = False, + weights: List[Text] = [], + scaling_weights: bool = True + ) -> None: """Initialization.""" self.batch_size = batch_size self.directory_precomputed = directory_precomputed @@ -106,7 +120,7 @@ def __init__(self, list_enzymes, labels, v_size=32, flips=(0.2, 0.2, 0.2), batch self.weights = weights self.on_epoch_end() - def check_precomputed(self): + def check_precomputed(self) -> None: """Checks if all coordinates and weights have been precomputed, and precomputes them otherwise.""" # Initialization. list_enzymes = list(self.labels) @@ -129,17 +143,17 @@ def check_precomputed(self): counter += 1 print("Had to compute files of {0} enzymes.".format(counter)) - def on_epoch_end(self): + def on_epoch_end(self) -> None: """Updates indexes after each epoch.""" self.indexes = np.arange(len(self.list_enzymes)) if self.shuffle is True: np.random.shuffle(self.indexes) - def __len__(self): + def __len__(self) -> int: """Denotes the number of batches per epoch.""" return int(np.floor(len(self.list_enzymes) / self.batch_size)) - def __getitem__(self, index): + def __getitem__(self, index: int) -> Tuple[np.ndarray, np.ndarray]: """Generate one batch of data.""" # Generate indexes of the batch. indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size] @@ -152,7 +166,10 @@ def __getitem__(self, index): return X, y - def __data_augmentation(self, list_enzymes_temp): + def __data_augmentation( + self, + list_enzymes_temp: List[Text] + ) -> Tuple[np.ndarray, np.ndarray]: """Returns augmented data with batch_size enzymes.""" # X : (n_samples, v_size, v_size, v_size, n_channels). # Initialization. X = np.empty((self.batch_size, # n_enzymes. @@ -199,12 +216,14 @@ def __data_augmentation(self, list_enzymes_temp): return X, sparsify(y) -def coords_to_volume(coords, v_size, noise_treatment=False): +def coords_to_volume(coords: np.ndarray, v_size: int, + noise_treatment: bool = False) -> np.ndarray: """Converts coordinates to binary voxels.""" # Input is centered on [0,0,0]. return weights_to_volume(coords=coords, weights=1, v_size=v_size, noise_treatment=noise_treatment) -def weights_to_volume(coords, weights, v_size, noise_treatment=False): +def weights_to_volume(coords: np.ndarray, weights: Union[Sequence, int], + v_size: int, noise_treatment: bool = False) -> np.ndarray: """Converts coordinates to voxels with weights.""" # Input is centered on [0,0,0]. # Initialization. volume = np.zeros((v_size, v_size, v_size)) @@ -228,24 +247,28 @@ def weights_to_volume(coords, weights, v_size, noise_treatment=False): return volume -def coords_center_to_zero(coords): +def coords_center_to_zero(coords: np.ndarray) -> np.ndarray: """Centering coordinates on [0,0,0].""" barycenter = get_barycenter(coords) return coords - np.full((coords.shape[0], 3), barycenter) -def adjust_size(coords, v_size=32, max_radius=40): +def adjust_size(coords: np.ndarray, v_size: int = 32, + max_radius: float = 40) -> np.ndarray: return np.multiply((v_size/2-1)/max_radius, coords) -def sparsify(y): +def sparsify(y: np.ndarray) -> np.ndarray: """Returns labels in binary NumPy array.""" n_classes = 6 return np.array([[1 if y[i] == j+1 else 0 for j in range(n_classes)] for i in range(y.shape[0])]) -def flip_around_axis(coords, axis=(0.2, 0.2, 0.2)): +def flip_around_axis( + coords: np.ndarray, + axis: Tuple[float, float, float] = (0.2, 0.2, 0.2) +) -> np.ndarray: """Flips coordinates randomly w.r.t. each axis with its associated probability.""" for col in range(3): if np.random.binomial(1, axis[col]): @@ -253,12 +276,12 @@ def flip_around_axis(coords, axis=(0.2, 0.2, 0.2)): return coords -def get_barycenter(coords): +def get_barycenter(coords: np.ndarray) -> np.ndarray: """Gets barycenter point of a Nx3 matrix.""" return np.array([np.mean(coords, axis=0)]) -def remove_noise(coords, volume): +def remove_noise(coords: np.ndarray, volume: np.ndarray) -> np.ndarray: """Removes isolated atoms from voxel structure.""" # Parameters. v_size = volume.shape[0] @@ -276,7 +299,9 @@ def remove_noise(coords, volume): return volume -def precomputed_name(pdb_id, path, type_file, desired_p, weights_name=None, scaling=True): +def precomputed_name(pdb_id: Text, path: Text, type_file: Text, desired_p: int, + weights_name: Optional[Text] = None, + scaling: bool = True) -> Text: """Returns path in string of precomputed file.""" if type_file == 'coords': return os.path.join(path, pdb_id.lower() + '_coords_p' + str(desired_p) + '.npy') @@ -284,8 +309,9 @@ def precomputed_name(pdb_id, path, type_file, desired_p, weights_name=None, scal return os.path.join(path, pdb_id.lower() + '_' + weights_name + '_p' + str(desired_p) + '_scaling' + str(scaling) + '.npy') -def save_coords_weights(pdb_id, list_weights, desired_p, scaling_weights, - source_path, dest_path): +def save_coords_weights(pdb_id: Text, list_weights: List[Text], desired_p: int, + scaling_weights: bool, source_path: Text, + dest_path: Text) -> None: """Computes coordinates and weights and saves them into .npy files.""" # Initialize local PDB. local_PDB = pdb.PDBBackbone(pdb_id=pdb_id, path=source_path) @@ -297,18 +323,19 @@ def save_coords_weights(pdb_id, list_weights, desired_p, scaling_weights, # Weights. for weights_name in list_weights: - local_PDB.get_weights_extended(desired_p, weights=weights_name, + local_PDB.get_weights_extended(desired_p, weight_type=weights_name, scaling=scaling_weights) # Compute. weights = local_PDB.backbone_weights_ext # Store. np.save(precomputed_name(pdb_id, dest_path, 'weights', desired_p, weights_name, scaling_weights), weights) # Save. -def load_coords(pdb_id, desired_p, source_path): +def load_coords(pdb_id: Text, desired_p: int, source_path: Text) -> np.ndarray: """Loads precomputed coordinates.""" return np.load(precomputed_name(pdb_id, source_path, 'coords', desired_p)) -def load_weights(pdb_id, weights_name, desired_p, scaling, source_path): +def load_weights(pdb_id: Text, weights_name: Text, desired_p: int, + scaling: bool, source_path: Text) -> np.ndarray: """Loads precomputed weights.""" return np.load(precomputed_name(pdb_id, source_path, 'weights', desired_p, weights_name, scaling)) From 29d2138f844886f3870be4c9dae76916da3d0558 Mon Sep 17 00:00:00 2001 From: shervinea Date: Mon, 26 Apr 2021 20:18:40 -0700 Subject: [PATCH 10/18] Store amino-acid weights as module-level variables instead of CSVs --- datasets/charge.csv | 20 -------- datasets/hydropathy.csv | 20 -------- datasets/isoelectric.csv | 20 -------- datasets/make_files/charge.py | 35 ------------- datasets/make_files/hydropathy.py | 35 ------------- datasets/make_files/isoelectric.py | 39 -------------- enzynet/keras_utils.py | 4 +- enzynet/pdb.py | 15 ++++-- enzynet/volume.py | 4 +- enzynet/weights.py | 81 ++++++++++++++++++++++++++++++ 10 files changed, 96 insertions(+), 177 deletions(-) delete mode 100755 datasets/charge.csv delete mode 100755 datasets/hydropathy.csv delete mode 100755 datasets/isoelectric.csv delete mode 100755 datasets/make_files/charge.py delete mode 100755 datasets/make_files/hydropathy.py delete mode 100755 datasets/make_files/isoelectric.py create mode 100644 enzynet/weights.py diff --git a/datasets/charge.csv b/datasets/charge.csv deleted file mode 100755 index a442a66..0000000 --- a/datasets/charge.csv +++ /dev/null @@ -1,20 +0,0 @@ -ALA,0 -ARG,1 -ASN,0 -ASP,-1 -CYS,0 -GLU,-1 -GLN,0 -GLY,0 -HIS,0.1 -ILE,0 -LEU,0 -LYS,1 -MET,0 -PHE,0 -PRO,0 -SER,0 -THR,0 -TRP,0 -TYR,0 -VAL,0 diff --git a/datasets/hydropathy.csv b/datasets/hydropathy.csv deleted file mode 100755 index fd7bf26..0000000 --- a/datasets/hydropathy.csv +++ /dev/null @@ -1,20 +0,0 @@ -PHE,2.8 -GLN,-3.5 -MET,1.9 -SER,-0.8 -LYS,-3.9 -GLY,-0.4 -LEU,3.8 -VAL,4.2 -HIS,-3.2 -TRP,-0.9 -ARG,-4.5 -TYR,-1.3 -ASP,-3.5 -ALA,1.8 -ILE,4.5 -CYS,2.5 -GLU,-3.5 -THR,-0.7 -PRO,-1.6 -ASN,-3.5 diff --git a/datasets/isoelectric.csv b/datasets/isoelectric.csv deleted file mode 100755 index 2796cb0..0000000 --- a/datasets/isoelectric.csv +++ /dev/null @@ -1,20 +0,0 @@ -ALA,0.0 -ARG,4.800000000000001 -ASN,-0.5999999999999996 -ASP,-3.2 -CYS,-1.0 -GLU,-2.8 -GLN,-0.2999999999999998 -GLY,0.0 -HIS,1.5999999999999996 -ILE,0.0 -LEU,0.0 -LYS,3.6999999999999993 -MET,-0.2999999999999998 -PHE,-0.5 -PRO,0.2999999999999998 -SER,-0.2999999999999998 -THR,-0.40000000000000036 -TRP,-0.09999999999999964 -TYR,-0.2999999999999998 -VAL,0.0 diff --git a/datasets/make_files/charge.py b/datasets/make_files/charge.py deleted file mode 100755 index 8a9d8e9..0000000 --- a/datasets/make_files/charge.py +++ /dev/null @@ -1,35 +0,0 @@ -"""Storing charge information in a dictionary.""" - -# Authors: Afshine Amidi -# Shervine Amidi - -# MIT License - -from enzynet import tools - -# Info from p. 51 of The cell: a molecular approach, by Hausman et al. -charge = { - 'ALA': 0, - 'ARG': 1, - 'ASN': 0, - 'ASP': -1, - 'CYS': 0, - 'GLU': -1, - 'GLN': 0, - 'GLY': 0, - 'HIS': 0.1, - 'ILE': 0, - 'LEU': 0, - 'LYS': 1, - 'MET': 0, - 'PHE': 0, - 'PRO': 0, - 'SER': 0, - 'THR': 0, - 'TRP': 0, - 'TYR': 0, - 'VAL': 0, -} - -# Save results. -tools.dict_to_csv(charge, '../charge.csv') diff --git a/datasets/make_files/hydropathy.py b/datasets/make_files/hydropathy.py deleted file mode 100755 index d50ea1c..0000000 --- a/datasets/make_files/hydropathy.py +++ /dev/null @@ -1,35 +0,0 @@ -"""Storing hydropathy information in a dictionary.""" - -# Authors: Afshine Amidi -# Shervine Amidi - -# MIT License - -from enzynet import tools - -# Values from J. Kyte et al. paper. -hydropathy = { - 'ILE': 4.5, - 'VAL': 4.2, - 'LEU': 3.8, - 'PHE': 2.8, - 'CYS': 2.5, - 'MET': 1.9, - 'ALA': 1.8, - 'GLY': -0.4, - 'THR': -0.7, - 'TRP': -0.9, - 'SER': -0.8, - 'TYR': -1.3, - 'PRO': -1.6, - 'HIS': -3.2, - 'GLU': -3.5, - 'GLN': -3.5, - 'ASP': -3.5, - 'ASN': -3.5, - 'LYS': -3.9, - 'ARG': -4.5, -} - -# Save results. -tools.dict_to_csv(hydropathy, '../hydropathy.csv') diff --git a/datasets/make_files/isoelectric.py b/datasets/make_files/isoelectric.py deleted file mode 100755 index 1ee7652..0000000 --- a/datasets/make_files/isoelectric.py +++ /dev/null @@ -1,39 +0,0 @@ -"""Storing isoelectric information in a dictionary.""" - -# Authors: Afshine Amidi -# Shervine Amidi - -# MIT License - -from enzynet import tools - -# Info from chapter 24 of Organic Chemistry (5th edition), by Wade. -isoelectric = { - 'ALA': 6.0, - 'ARG': 10.8, - 'ASN': 5.4, - 'ASP': 2.8, - 'CYS': 5.0, - 'GLU': 3.2, - 'GLN': 5.7, - 'GLY': 6.0, - 'HIS': 7.6, - 'ILE': 6.0, - 'LEU': 6.0, - 'LYS': 9.7, - 'MET': 5.7, - 'PHE': 5.5, - 'PRO': 6.3, - 'SER': 5.7, - 'THR': 5.6, - 'TRP': 5.9, - 'TYR': 5.7, - 'VAL': 6.0, -} - -# Center on neutral value. -neutral_value = 6.0 -isoelectric = {k: v - neutral_value for k, v in isoelectric.items()} - -# Save results. -tools.dict_to_csv(isoelectric, '../isoelectric.csv') diff --git a/enzynet/keras_utils.py b/enzynet/keras_utils.py index 2dbe1a0..fbfa72f 100644 --- a/enzynet/keras_utils.py +++ b/enzynet/keras_utils.py @@ -72,8 +72,8 @@ class Voting(volume.VolumeDataGenerator): If True, voxels with no direct neighbor will be deleted. weights : list of strings (optional, default is []) - List of weights (among the values ['hydropathy', 'charge']) to consider - as additional channels. + List of weights (among the values ['charge', 'hydropathy', + 'isoelectric']) to consider as additional channels. scaling_weights : boolean (optional, default is True) If True, divides all weights by the weight that is maximum in absolute diff --git a/enzynet/pdb.py b/enzynet/pdb.py index 4df520b..257a444 100644 --- a/enzynet/pdb.py +++ b/enzynet/pdb.py @@ -17,6 +17,7 @@ from Bio.PDB import PDBParser from Bio.PDB import Polypeptide from enzynet import tools +from enzynet import weights warnings.filterwarnings("ignore", category=PDBExceptions.PDBConstructionWarning) @@ -94,12 +95,18 @@ def get_weights(self, weight_type: Text = 'hydropathy', backbone_residues = [] # Select weights. - weights = tools.read_dict(os.path.join(datasets_path, weight_type + '.csv')) - weights = dict([key, float(value)] for key, value in weights.items()) # Convert values to float. + if weight_type == 'charge': + aa_to_weight = weights.CHARGE + elif weight_type == 'hydropathy': + aa_to_weight = weights.HYDROPATHY + elif weight_type == 'isoelectric': + aa_to_weight = weights.ISOELECTRIC + else: + raise ValueError(f"{weight_type} mapping is not supported") # Scaling. if scaling is True: - weights = tools.scale_dict(weights) + aa_to_weight = tools.scale_dict(aa_to_weight) # Computations. for model in self.structure: @@ -107,7 +114,7 @@ def get_weights(self, weight_type: Text = 'hydropathy', for residue in chain: if Polypeptide.is_aa(residue, standard=True): # Check if standard amino acid. local_residue = residue.get_resname() - local_weight = weights[local_residue] + local_weight = aa_to_weight[local_residue] for atom in residue: if atom.get_name() in backbone_ids: backbone_weights = backbone_weights + [local_weight] diff --git a/enzynet/volume.py b/enzynet/volume.py index ff2ceee..ad5ef20 100644 --- a/enzynet/volume.py +++ b/enzynet/volume.py @@ -65,8 +65,8 @@ class VolumeDataGenerator(keras.utils.Sequence): If True, voxels with no direct neighbor will be deleted. weights : list of strings (optional, default is []) - List of weights (among the values ['hydropathy', 'charge']) to consider - as additional channels. + List of weights (among the values ['charge', 'hydropathy', + 'isoelectric']) to consider as additional channels. scaling_weights : boolean (optional, default is True) If True, divides all weights by the weight that is maximum in absolute diff --git a/enzynet/weights.py b/enzynet/weights.py new file mode 100644 index 0000000..7bc9e3a --- /dev/null +++ b/enzynet/weights.py @@ -0,0 +1,81 @@ +"""Amino-acid to weights mappings.""" + +# Authors: Afshine Amidi +# Shervine Amidi + +# MIT License + +# Info from p. 51 of The cell: a molecular approach, by Hausman et al. +CHARGE = { + 'ALA': 0, + 'ARG': 1, + 'ASN': 0, + 'ASP': -1, + 'CYS': 0, + 'GLU': -1, + 'GLN': 0, + 'GLY': 0, + 'HIS': 0.1, + 'ILE': 0, + 'LEU': 0, + 'LYS': 1, + 'MET': 0, + 'PHE': 0, + 'PRO': 0, + 'SER': 0, + 'THR': 0, + 'TRP': 0, + 'TYR': 0, + 'VAL': 0, +} + +# Values from J. Kyte et al. paper. +HYDROPATHY = { + 'ILE': 4.5, + 'VAL': 4.2, + 'LEU': 3.8, + 'PHE': 2.8, + 'CYS': 2.5, + 'MET': 1.9, + 'ALA': 1.8, + 'GLY': -0.4, + 'THR': -0.7, + 'TRP': -0.9, + 'SER': -0.8, + 'TYR': -1.3, + 'PRO': -1.6, + 'HIS': -3.2, + 'GLU': -3.5, + 'GLN': -3.5, + 'ASP': -3.5, + 'ASN': -3.5, + 'LYS': -3.9, + 'ARG': -4.5, +} + +# Info from chapter 24 of Organic Chemistry (5th edition), by Wade. +_RAW_ISOELECTRIC = { + 'ALA': 6.0, + 'ARG': 10.8, + 'ASN': 5.4, + 'ASP': 2.8, + 'CYS': 5.0, + 'GLU': 3.2, + 'GLN': 5.7, + 'GLY': 6.0, + 'HIS': 7.6, + 'ILE': 6.0, + 'LEU': 6.0, + 'LYS': 9.7, + 'MET': 5.7, + 'PHE': 5.5, + 'PRO': 6.3, + 'SER': 5.7, + 'THR': 5.6, + 'TRP': 5.9, + 'TYR': 5.7, + 'VAL': 6.0, +} +_NEUTRAL_VALUE = 6.0 +ISOELECTRIC = {amino_acid: raw_value - _NEUTRAL_VALUE + for amino_acid, raw_value in _RAW_ISOELECTRIC.items()} From 701dd8cfdd9f2ed149c551bba20459aa91793fd2 Mon Sep 17 00:00:00 2001 From: shervinea Date: Tue, 24 Aug 2021 00:22:20 -0700 Subject: [PATCH 11/18] Convert constants to upper-case --- datasets/make_files/generate_datasets.py | 22 ++-- enzynet/keras_utils.py | 14 +-- enzynet/pdb.py | 18 ++-- enzynet/volume.py | 10 +- scripts/architecture/enzynet_adapted.py | 126 +++++++++++------------ scripts/architecture/enzynet_uniform.py | 126 +++++++++++------------ 6 files changed, 158 insertions(+), 158 deletions(-) diff --git a/datasets/make_files/generate_datasets.py b/datasets/make_files/generate_datasets.py index 7d626b0..a734749 100755 --- a/datasets/make_files/generate_datasets.py +++ b/datasets/make_files/generate_datasets.py @@ -15,11 +15,11 @@ # Date of retrieval of raw datasets from rcsb.org: 07-03-2017. # Parameters. -n_classes = 6 +N_CLASSES = 6 ##------------------------- Data preprocessing -------------------------------## # Computations. -for i in range(1, n_classes+1): +for i in range(1, N_CLASSES+1): # Initialization. errors = [] # List of indexes with error. enzymes = pd.read_table('raw/' + str(i) + '.txt', header=None) # Load PDB IDs. @@ -48,7 +48,7 @@ labels = {} # Computations. -for i in range(1, n_classes+1): +for i in range(1, N_CLASSES+1): # Load PDB IDs for each class. enzymes = pd.read_table(str(i) + '.txt', header=None) enzymes = enzymes[0].tolist() @@ -140,22 +140,22 @@ partition = {} # Parameters. -x = 0.8 # Split all 80:20 in train/test and train 80:20 in train/validation. +X = 0.8 # Split all 80:20 in train/test and train 80:20 in train/validation. # Random shuffling. indexes = np.arange(len(pdb_ids)) np.random.shuffle(indexes) # Train. -train_ids = [pdb_ids[i] for i in indexes[0:int(x*x*len(pdb_ids))]] +train_ids = [pdb_ids[i] for i in indexes[0:int(X*X*len(pdb_ids))]] partition['train'] = train_ids # Validation. -val_ids = [pdb_ids[i] for i in indexes[int(x*x*len(pdb_ids)):int(x*len(pdb_ids))]] +val_ids = [pdb_ids[i] for i in indexes[int(X*X*len(pdb_ids)):int(X*len(pdb_ids))]] partition['validation'] = val_ids # Test. -test_ids = [pdb_ids[i] for i in indexes[int(x*len(pdb_ids)):]] +test_ids = [pdb_ids[i] for i in indexes[int(X*len(pdb_ids)):]] partition['test'] = test_ids # # Save results. @@ -167,27 +167,27 @@ partition_red = {} # Parameters. -factor_reduction = 0.1 +FACTOR_REDUCTION = 0.1 # Train. exec("partition['train'] = " + partition['train']) indexes = np.arange(len(partition['train'])) np.random.shuffle(indexes) -train_ids = [partition['train'][i] for i in indexes[0:int(factor_reduction*len(indexes))]] +train_ids = [partition['train'][i] for i in indexes[0:int(FACTOR_REDUCTION*len(indexes))]] partition_red['train'] = train_ids # Validation. exec("partition['validation'] = " + partition['validation']) indexes = np.arange(len(partition['validation'])) np.random.shuffle(indexes) -val_ids = [partition['validation'][i] for i in indexes[0:int(factor_reduction*len(indexes))]] +val_ids = [partition['validation'][i] for i in indexes[0:int(FACTOR_REDUCTION*len(indexes))]] partition_red['validation'] = val_ids # Test. exec("partition['test'] = " + partition['test']) indexes = np.arange(len(partition['test'])) np.random.shuffle(indexes) -test_ids = [partition['test'][i] for i in indexes[0:int(factor_reduction*len(indexes))]] +test_ids = [partition['test'][i] for i in indexes[0:int(FACTOR_REDUCTION*len(indexes))]] partition_red['test'] = test_ids # # Save results. diff --git a/enzynet/keras_utils.py b/enzynet/keras_utils.py index fbfa72f..a6f9931 100644 --- a/enzynet/keras_utils.py +++ b/enzynet/keras_utils.py @@ -21,11 +21,11 @@ from matplotlib import pyplot as plt from tqdm import tqdm -current_directory = os.path.dirname(os.path.abspath(__file__)) -precomputed_path = os.path.join(current_directory, '../files/precomputed/') -PDB_path = os.path.join(current_directory, '../files/PDB/') +CURRENT_DIRECTORY = os.path.dirname(os.path.abspath(__file__)) +PRECOMPUTED_PATH = os.path.join(CURRENT_DIRECTORY, '../files/precomputed/') +PDB_PATH = os.path.join(CURRENT_DIRECTORY, '../files/PDB/') -methods = ['confusion_matrix', 'accuracy', +METHODS = ['confusion_matrix', 'accuracy', 'precision_per_class', 'recall_per_class', 'f1_per_class', 'macro_precision', 'macro_recall', 'macro_f1'] @@ -86,8 +86,8 @@ def __init__( voting_type: Text = 'probabilities', augmentation: List[Text] = ['None'], v_size: int = 32, - directory_precomputed: Text = precomputed_path, - directory_pdb: Text = PDB_path, + directory_precomputed: Text = PRECOMPUTED_PATH, + directory_pdb: Text = PDB_PATH, shuffle: bool = True, p: int = 5, max_radius: float = 40, @@ -124,7 +124,7 @@ def get_assessment(self) -> None: for j, augmentation in enumerate(self.augmentation): print('Augmentation: {0}'.format(augmentation)) ind = indicators.Indicators(self.y_true, self.y_pred[:,j]) - for method in methods: + for method in METHODS: getattr(ind, method)() def __vote(self, model: models.Sequential, enzyme: Text, diff --git a/enzynet/pdb.py b/enzynet/pdb.py index 257a444..6539c4b 100644 --- a/enzynet/pdb.py +++ b/enzynet/pdb.py @@ -21,11 +21,11 @@ warnings.filterwarnings("ignore", category=PDBExceptions.PDBConstructionWarning) -backbone_ids = ['C', 'N', 'CA'] +BACKBONE_IDS = ['C', 'N', 'CA'] -current_directory = os.path.dirname(os.path.abspath(__file__)) -PDB_path = os.path.join(current_directory, '../files/PDB/') -datasets_path = os.path.join(current_directory, '../datasets/') +CURRENT_DIRECTORY = os.path.dirname(os.path.abspath(__file__)) +PDB_PATH = os.path.join(CURRENT_DIRECTORY, '../files/PDB/') +DATASETS_PATHS = os.path.join(CURRENT_DIRECTORY, '../datasets/') class PDBBackbone(object): @@ -40,7 +40,7 @@ class PDBBackbone(object): Path where the PDB file is located, or where the PDB file should be downloaded. """ - def __init__(self, pdb_id: Text, path: Text = PDB_path) -> None: + def __init__(self, pdb_id: Text, path: Text = PDB_PATH) -> None: """Initialization.""" self.pdb_id = pdb_id.upper() fullfilename = os.path.join(path, pdb_id.lower() + '.pdb') @@ -64,7 +64,7 @@ def get_coords(self) -> None: for residue in chain: if Polypeptide.is_aa(residue, standard=True): # Check if amino acid. for atom in residue: - if atom.get_name() in backbone_ids: + if atom.get_name() in BACKBONE_IDS: backbone_coords = backbone_coords + [atom.get_coord().tolist()] backbone_atoms = backbone_atoms + [atom.get_name()] @@ -76,7 +76,7 @@ def get_coords_extended(self, p: int = 5) -> None: """Adds the coordinates from interpolation betweens atoms of the backbone.""" # Initialization. self.get_coords() - C_coords = self.__get_coords_specific_atom(backbone_ids) + C_coords = self.__get_coords_specific_atom(BACKBONE_IDS) new_coords = np.zeros((p*(C_coords.shape[0]-1), C_coords.shape[1])) # Computations. @@ -116,7 +116,7 @@ def get_weights(self, weight_type: Text = 'hydropathy', local_residue = residue.get_resname() local_weight = aa_to_weight[local_residue] for atom in residue: - if atom.get_name() in backbone_ids: + if atom.get_name() in BACKBONE_IDS: backbone_weights = backbone_weights + [local_weight] backbone_residues = backbone_residues + [local_residue] @@ -129,7 +129,7 @@ def get_weights_extended(self, p: int, weight_type: Text = 'hydropathy', """Adds the weights from interpolation between atoms of the backbone.""" # Initialization. self.get_weights(weight_type=weight_type, scaling=scaling) - C_weights = self.__get_weights_specific_atom(backbone_ids) + C_weights = self.__get_weights_specific_atom(BACKBONE_IDS) new_weights = np.zeros((p*(len(C_weights)-1))) # Computations. diff --git a/enzynet/volume.py b/enzynet/volume.py index ad5ef20..cd96cfb 100644 --- a/enzynet/volume.py +++ b/enzynet/volume.py @@ -16,9 +16,9 @@ from sklearn import decomposition from tqdm import tqdm -current_directory = os.path.dirname(os.path.abspath(__file__)) -precomputed_path = os.path.join(current_directory, '../files/precomputed/') -PDB_path = os.path.join(current_directory, '../files/PDB/') +CURRENT_DIRECTORY = os.path.dirname(os.path.abspath(__file__)) +PRECOMPUTED_PATH = os.path.join(CURRENT_DIRECTORY, '../files/precomputed/') +PDB_PATH = os.path.join(CURRENT_DIRECTORY, '../files/PDB/') class VolumeDataGenerator(keras.utils.Sequence): @@ -94,8 +94,8 @@ def __init__( v_size: int = 32, flips: Tuple[float, float, float] = (0.2, 0.2, 0.2), batch_size: int = 32, - directory_precomputed: Text = precomputed_path, - directory_pdb: Text = PDB_path, + directory_precomputed: Text = PRECOMPUTED_PATH, + directory_pdb: Text = PDB_PATH, shuffle: bool = True, p: int = 5, max_radius: float = 40, diff --git a/scripts/architecture/enzynet_adapted.py b/scripts/architecture/enzynet_adapted.py index 06515ec..0ee099e 100644 --- a/scripts/architecture/enzynet_adapted.py +++ b/scripts/architecture/enzynet_adapted.py @@ -21,46 +21,46 @@ from keras import optimizers from keras import regularizers -mode_run = 'test' -mode_dataset = 'full' -mode_weights = 'balanced' +MODE_RUN = 'test' +MODE_DATASET = 'full' +MODE_WEIGHTS = 'balanced' ##----------------------------- Parameters -----------------------------------## # PDB. -weights = [] -n_classes = 6 -n_channels = 1 + len(weights) -scaling_weights = True +WEIGHTS = [] +N_CLASSES = 6 +N_CHANNELS = 1 + len(WEIGHTS) +SCALING_WEIGHTS = True # Volume. -p = 0 -v_size = 32 -flips = (0.2, 0.2, 0.2) -max_radius = 40 -shuffle = True -noise_treatment = False +P = 0 +V_SIZE = 32 +FLIPS = (0.2, 0.2, 0.2) +MAX_RADIUS = 40 +SHUFFLE = True +NOISE_TREATMENT = False # Model. -batch_size = 32 -max_epochs = 200 -period_checkpoint = 50 +BATCH_SIZE = 32 +MAX_EPOCHS = 200 +PERIOD_CHECKPOINT = 50 # Testing. -voting_type = 'probabilities' -augmentation = ['None', 'flips', 'weighted_flips'] +VOTING_TYPE = 'probabilities' +AUGMENTATION = ['None', 'flips', 'weighted_flips'] # Miscellaneous. -current_file_name = os.path.basename(__file__)[:-3] -stddev_conv3d = np.sqrt(2.0/(n_channels)) +CURRENT_FILE_NAME = os.path.basename(__file__)[:-3] +STDDEV_CONV3D = np.sqrt(2.0/N_CHANNELS) ##------------------------------ Dataset -------------------------------------## # Load dictionary of labels. -dictionary = tools.read_dict('../../datasets/dataset_single.csv') +DICTIONARY = tools.read_dict('../../datasets/dataset_single.csv') # Load partitions. -if mode_dataset == 'full': +if MODE_DATASET == 'full': partition = tools.read_dict('../../datasets/partition_single.csv') -elif mode_dataset == 'reduced': +elif MODE_DATASET == 'reduced': partition = tools.read_dict('../../datasets/partition_single_red.csv') exec("partition['train'] = " + partition['train']) exec("partition['validation'] = " + partition['validation']) @@ -71,36 +71,36 @@ partition['validation'] = partition['test'] # Get class weights. -class_weights = tools.get_class_weights(dictionary, partition['train'], - mode=mode_weights) +class_weights = tools.get_class_weights(DICTIONARY, partition['train'], + mode=MODE_WEIGHTS) # Training generator. training_generator = volume.VolumeDataGenerator( list_enzymes=partition['train'], - labels=dictionary, - v_size=v_size, - flips=flips, - batch_size=batch_size, - shuffle=shuffle, - p=p, - max_radius=max_radius, - noise_treatment=noise_treatment, - weights=weights, - scaling_weights=scaling_weights) + labels=DICTIONARY, + v_size=V_SIZE, + flips=FLIPS, + batch_size=BATCH_SIZE, + shuffle=SHUFFLE, + p=P, + max_radius=MAX_RADIUS, + noise_treatment=NOISE_TREATMENT, + weights=WEIGHTS, + scaling_weights=SCALING_WEIGHTS) # Validation generator. validation_generator = volume.VolumeDataGenerator( list_enzymes=partition['validation'], - labels=dictionary, - v_size=v_size, + labels=DICTIONARY, + v_size=V_SIZE, flips=(0, 0, 0), # No flip. - batch_size=batch_size, + batch_size=BATCH_SIZE, shuffle=False, # Validate with fixed set. - p=p, - max_radius=max_radius, - noise_treatment=noise_treatment, - weights=weights, - scaling_weights=scaling_weights) + p=P, + max_radius=MAX_RADIUS, + noise_treatment=NOISE_TREATMENT, + weights=WEIGHTS, + scaling_weights=SCALING_WEIGHTS) # Check if data has been precomputed. training_generator.check_precomputed() @@ -109,15 +109,15 @@ # Voting object. predictions = keras_utils.Voting( list_enzymes=partition['test'], - labels=dictionary, - voting_type=voting_type, - v_size=v_size, - augmentation=augmentation, - p=p, - max_radius=max_radius, - noise_treatment=noise_treatment, - weights=weights, - scaling_weights=scaling_weights) + labels=DICTIONARY, + voting_type=VOTING_TYPE, + v_size=V_SIZE, + augmentation=AUGMENTATION, + p=P, + max_radius=MAX_RADIUS, + noise_treatment=NOISE_TREATMENT, + weights=WEIGHTS, + scaling_weights=SCALING_WEIGHTS) ##------------------------------ Model ---------------------------------------## # Create. @@ -130,11 +130,11 @@ kernel_size=9, strides=2, padding='valid', - kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=stddev_conv3d * 9**(-3/2)), + kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=STDDEV_CONV3D * 9 ** (-3 / 2)), bias_initializer='zeros', kernel_regularizer=regularizers.l2(0.001), bias_regularizer=None, - input_shape=(v_size, v_size, v_size, n_channels))) + input_shape=(V_SIZE, V_SIZE, V_SIZE, N_CHANNELS))) model.add(advanced_activations.LeakyReLU(alpha=0.1)) @@ -146,7 +146,7 @@ kernel_size=5, strides=1, padding='valid', - kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=stddev_conv3d * 5**(-3/2)), + kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=STDDEV_CONV3D * 5 ** (-3 / 2)), bias_initializer='zeros', kernel_regularizer=regularizers.l2(0.001), bias_regularizer=None)) @@ -171,7 +171,7 @@ model.add( layers.Dense( - units=n_classes, + units=N_CLASSES, kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.01), bias_initializer='zeros', kernel_regularizer=regularizers.l2(0.001), @@ -180,15 +180,15 @@ model.add(layers.Activation('softmax')) # Track accuracy and loss in real-time. -history = keras_utils.MetricsHistory(saving_path=current_file_name + '.csv') +history = keras_utils.MetricsHistory(saving_path=CURRENT_FILE_NAME + '.csv') # Checkpoints. checkpoints = callbacks.ModelCheckpoint( - 'checkpoints/' + current_file_name + '_{epoch:02d}' + '.hd5f', + 'checkpoints/' + CURRENT_FILE_NAME + '_{epoch:02d}' + '.hd5f', save_weights_only=True, - period=period_checkpoint) + period=PERIOD_CHECKPOINT) -if mode_run == 'train': +if MODE_RUN == 'train': # Compile. model.compile(optimizer=optimizers.Adam(lr=0.001, decay=0.00016667), loss='categorical_crossentropy', @@ -196,7 +196,7 @@ # Train. model.fit_generator(generator=training_generator, - epochs=max_epochs, + epochs=MAX_EPOCHS, verbose=1, validation_data=validation_generator, callbacks=[history, checkpoints], @@ -205,10 +205,10 @@ workers=6, max_queue_size=30) -if mode_run == 'test': +if MODE_RUN == 'test': # Load weights. weights_path = \ - 'checkpoints/' + current_file_name + '_{0:02d}'.format(max_epochs) + '.hd5f' + 'checkpoints/' + CURRENT_FILE_NAME + '_{0:02d}'.format(MAX_EPOCHS) + '.hd5f' model.load_weights(weights_path) # Predict. diff --git a/scripts/architecture/enzynet_uniform.py b/scripts/architecture/enzynet_uniform.py index beaca96..f4bdd0e 100644 --- a/scripts/architecture/enzynet_uniform.py +++ b/scripts/architecture/enzynet_uniform.py @@ -21,46 +21,46 @@ from keras import optimizers from keras import regularizers -mode_run = 'test' -mode_dataset = 'full' -mode_weights = 'unbalanced' +MODE_RUN = 'test' +MODE_DATASET = 'full' +MODE_WEIGHTS = 'unbalanced' ##----------------------------- Parameters -----------------------------------## # PDB. -weights = [] -n_classes = 6 -n_channels = 1 + len(weights) -scaling_weights = True +WEIGHTS = [] +N_CLASSES = 6 +N_CHANNELS = 1 + len(WEIGHTS) +SCALING_WEIGHTS = True # Volume. -p = 0 -v_size = 32 -flips = (0.2, 0.2, 0.2) -max_radius = 40 -shuffle = True -noise_treatment = False +P = 0 +V_SIZE = 32 +FLIPS = (0.2, 0.2, 0.2) +MAX_RADIUS = 40 +SHUFFLE = True +NOISE_TREATMENT = False # Model. -batch_size = 32 -max_epochs = 200 -period_checkpoint = 50 +BATCH_SIZE = 32 +MAX_EPOCHS = 200 +PERIOD_CHECKPOINT = 50 # Testing. -voting_type = 'probabilities' -augmentation = ['None', 'flips', 'weighted_flips'] +VOTING_TYPE = 'probabilities' +AUGMENTATION = ['None', 'flips', 'weighted_flips'] # Miscellaneous. -current_file_name = os.path.basename(__file__)[:-3] -stddev_conv3d = np.sqrt(2.0/(n_channels)) +CURRENT_FILE_NAME = os.path.basename(__file__)[:-3] +STDDEV_CONV3D = np.sqrt(2.0/N_CHANNELS) ##------------------------------ Dataset -------------------------------------## # Load dictionary of labels. -dictionary = tools.read_dict('../../datasets/dataset_single.csv') +DICTIONARY = tools.read_dict('../../datasets/dataset_single.csv') # Load partitions. -if mode_dataset == 'full': +if MODE_DATASET == 'full': partition = tools.read_dict('../../datasets/partition_single.csv') -elif mode_dataset == 'reduced': +elif MODE_DATASET == 'reduced': partition = tools.read_dict('../../datasets/partition_single_red.csv') exec("partition['train'] = " + partition['train']) exec("partition['validation'] = " + partition['validation']) @@ -71,36 +71,36 @@ partition['validation'] = partition['test'] # Get class weights. -class_weights = tools.get_class_weights(dictionary, partition['train'], - mode=mode_weights) +class_weights = tools.get_class_weights(DICTIONARY, partition['train'], + mode=MODE_WEIGHTS) # Training generator. training_generator = volume.VolumeDataGenerator( list_enzymes=partition['train'], - labels=dictionary, - v_size=v_size, - flips=flips, - batch_size=batch_size, - shuffle=shuffle, - p=p, - max_radius=max_radius, - noise_treatment=noise_treatment, - weights=weights, - scaling_weights=scaling_weights) + labels=DICTIONARY, + v_size=V_SIZE, + flips=FLIPS, + batch_size=BATCH_SIZE, + shuffle=SHUFFLE, + p=P, + max_radius=MAX_RADIUS, + noise_treatment=NOISE_TREATMENT, + weights=WEIGHTS, + scaling_weights=SCALING_WEIGHTS) # Validation generator. validation_generator = volume.VolumeDataGenerator( list_enzymes=partition['validation'], - labels=dictionary, - v_size=v_size, + labels=DICTIONARY, + v_size=V_SIZE, flips=(0, 0, 0), # No flip. - batch_size=batch_size, + batch_size=BATCH_SIZE, shuffle=False, # Validate with fixed set. - p=p, - max_radius=max_radius, - noise_treatment=noise_treatment, - weights=weights, - scaling_weights=scaling_weights) + p=P, + max_radius=MAX_RADIUS, + noise_treatment=NOISE_TREATMENT, + weights=WEIGHTS, + scaling_weights=SCALING_WEIGHTS) # Check if data has been precomputed. training_generator.check_precomputed() @@ -109,15 +109,15 @@ # Voting object. predictions = keras_utils.Voting( list_enzymes=partition['test'], - labels=dictionary, - voting_type=voting_type, - v_size=v_size, - augmentation=augmentation, - p=p, - max_radius=max_radius, - noise_treatment=noise_treatment, - weights=weights, - scaling_weights=scaling_weights) + labels=DICTIONARY, + voting_type=VOTING_TYPE, + v_size=V_SIZE, + augmentation=AUGMENTATION, + p=P, + max_radius=MAX_RADIUS, + noise_treatment=NOISE_TREATMENT, + weights=WEIGHTS, + scaling_weights=SCALING_WEIGHTS) ##------------------------------ Model ---------------------------------------## # Create. @@ -130,11 +130,11 @@ kernel_size=9, strides=2, padding='valid', - kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=stddev_conv3d * 9**(-3/2)), + kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=STDDEV_CONV3D * 9 ** (-3 / 2)), bias_initializer='zeros', kernel_regularizer=regularizers.l2(0.001), bias_regularizer=None, - input_shape=(v_size, v_size, v_size, n_channels))) + input_shape=(V_SIZE, V_SIZE, V_SIZE, N_CHANNELS))) model.add(advanced_activations.LeakyReLU(alpha=0.1)) @@ -146,7 +146,7 @@ kernel_size=5, strides=1, padding='valid', - kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=stddev_conv3d * 5**(-3/2)), + kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=STDDEV_CONV3D * 5 ** (-3 / 2)), bias_initializer='zeros', kernel_regularizer=regularizers.l2(0.001), bias_regularizer=None)) @@ -171,7 +171,7 @@ model.add( layers.Dense( - units=n_classes, + units=N_CLASSES, kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.01), bias_initializer='zeros', kernel_regularizer=regularizers.l2(0.001), @@ -180,15 +180,15 @@ model.add(layers.Activation('softmax')) # Track accuracy and loss in real-time. -history = keras_utils.MetricsHistory(saving_path=current_file_name + '.csv') +history = keras_utils.MetricsHistory(saving_path=CURRENT_FILE_NAME + '.csv') # Checkpoints. checkpoints = callbacks.ModelCheckpoint( - 'checkpoints/' + current_file_name + '_{epoch:02d}' + '.hd5f', + 'checkpoints/' + CURRENT_FILE_NAME + '_{epoch:02d}' + '.hd5f', save_weights_only=True, - period=period_checkpoint) + period=PERIOD_CHECKPOINT) -if mode_run == 'train': +if MODE_RUN == 'train': # Compile. model.compile(optimizer=optimizers.Adam(lr=0.001, decay=0.00016667), loss='categorical_crossentropy', @@ -196,7 +196,7 @@ # Train. model.fit_generator(generator=training_generator, - epochs=max_epochs, + epochs=MAX_EPOCHS, verbose=1, validation_data=validation_generator, callbacks=[history, checkpoints], @@ -205,10 +205,10 @@ workers=8, max_queue_size=30) -if mode_run == 'test': +if MODE_RUN == 'test': # Load weights. weights_path = \ - 'checkpoints/' + current_file_name + '_{0:02d}'.format(max_epochs) + '.hd5f' + 'checkpoints/' + CURRENT_FILE_NAME + '_{0:02d}'.format(MAX_EPOCHS) + '.hd5f' model.load_weights(weights_path) # Predict. From f48547aff82496daaed05e79e1607071a3bb09d4 Mon Sep 17 00:00:00 2001 From: shervinea Date: Tue, 24 Aug 2021 00:25:37 -0700 Subject: [PATCH 12/18] Ignore files that are not expected to be committed --- .gitignore | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/.gitignore b/.gitignore index 829aec5..3c5fd85 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,19 @@ +# Mac. **/.DS_Store + +# Notebooks. **/.ipynb_checkpoints + +# PDB caching. +*.pdb + +# Code execution. **/__pycache__ **/.pyc +.idea/** + +# Package. *.egg-info + +# Virtual environment. +venv/** \ No newline at end of file From 2b907d88b6269e2df6f80c508ea9057cdafff66a Mon Sep 17 00:00:00 2001 From: shervinea Date: Tue, 24 Aug 2021 00:33:59 -0700 Subject: [PATCH 13/18] Add space after commas --- datasets/make_files/download_pdb.py | 2 +- datasets/make_files/generate_datasets.py | 2 +- enzynet/keras_utils.py | 2 +- enzynet/pdb.py | 2 +- enzynet/visualization.py | 10 +++++----- enzynet/volume.py | 14 +++++++------- scripts/architecture/enzynet_adapted.py | 2 +- scripts/architecture/enzynet_uniform.py | 2 +- 8 files changed, 18 insertions(+), 18 deletions(-) diff --git a/datasets/make_files/download_pdb.py b/datasets/make_files/download_pdb.py index 24f667e..ccbf109 100755 --- a/datasets/make_files/download_pdb.py +++ b/datasets/make_files/download_pdb.py @@ -15,7 +15,7 @@ df = df.tolist() # Save to text file with one PDB ID per line. -file = open('../download_pdb.txt','w') +file = open('../download_pdb.txt', 'w') for i in range(len(df)): file.write(df[i]) file.close() diff --git a/datasets/make_files/generate_datasets.py b/datasets/make_files/generate_datasets.py index a734749..e20ff29 100755 --- a/datasets/make_files/generate_datasets.py +++ b/datasets/make_files/generate_datasets.py @@ -23,7 +23,7 @@ # Initialization. errors = [] # List of indexes with error. enzymes = pd.read_table('raw/' + str(i) + '.txt', header=None) # Load PDB IDs. - list_enzymes = enzymes.loc[:,0].tolist() + list_enzymes = enzymes.loc[:, 0].tolist() # Look for errors. for k, enzyme in enumerate(tqdm(list_enzymes)): diff --git a/enzynet/keras_utils.py b/enzynet/keras_utils.py index a6f9931..8919a04 100644 --- a/enzynet/keras_utils.py +++ b/enzynet/keras_utils.py @@ -123,7 +123,7 @@ def get_assessment(self) -> None: """Compute several performance indicators.""" for j, augmentation in enumerate(self.augmentation): print('Augmentation: {0}'.format(augmentation)) - ind = indicators.Indicators(self.y_true, self.y_pred[:,j]) + ind = indicators.Indicators(self.y_true, self.y_pred[:, j]) for method in METHODS: getattr(ind, method)() diff --git a/enzynet/pdb.py b/enzynet/pdb.py index 6539c4b..564879b 100644 --- a/enzynet/pdb.py +++ b/enzynet/pdb.py @@ -82,7 +82,7 @@ def get_coords_extended(self, p: int = 5) -> None: # Computations. for i in range(1, C_coords.shape[0]): for k in range(p, 0, -1): - new_coords[p*i-k,:] = ((p-k+1)*C_coords[i-1,:] + k*C_coords[i,:])/(p+1) + new_coords[p*i-k, :] = ((p-k+1)*C_coords[i-1, :] + k*C_coords[i, :])/(p+1) # Store results. self.backbone_coords_ext = np.concatenate((self.backbone_coords, new_coords), axis=0) diff --git a/enzynet/visualization.py b/enzynet/visualization.py index 2049f3c..052342c 100644 --- a/enzynet/visualization.py +++ b/enzynet/visualization.py @@ -143,7 +143,7 @@ def plot_cube_at(pos: Tuple[float, float, float] = (0,0,0), ax.plot_surface(X, Y, Z, color='g', rstride=1, cstride=1, alpha=1) -def plot_cube_weights_at(pos: Tuple[float, float, float] = (0,0,0), +def plot_cube_weights_at(pos: Tuple[float, float, float] = (0, 0, 0), ax: Optional[plt.gca] = None, color: Text = 'g') -> None: """Plots a cube element at position pos.""" @@ -157,8 +157,8 @@ def plot_matrix(ax: Optional[plt.gca], matrix: np.ndarray) -> None: for i in range(matrix.shape[0]): for j in range(matrix.shape[1]): for k in range(matrix.shape[2]): - if matrix[i,j,k] == 1: - plot_cube_at(pos=(i-0.5,j-0.5,k-0.5), ax=ax) + if matrix[i, j, k] == 1: + plot_cube_at(pos=(i-0.5, j-0.5, k-0.5), ax=ax) def plot_matrix_of_weights(ax: plt.gca, matrix_of_weights: np.ndarray) -> None: @@ -182,12 +182,12 @@ def plot_matrix_of_weights(ax: plt.gca, matrix_of_weights: np.ndarray) -> None: for k in range(matrix_of_weights.shape[2]): if matrix_of_weights[i,j,k] != 0: # Translate to [0,100]. - normalized_weight = (matrix_of_weights[i,j,k] - min_value)/ \ + normalized_weight = (matrix_of_weights[i, j, k] - min_value)/ \ (max_value - min_value) normalized_weight = int(100*normalized_weight) # Plot cube with color. - plot_cube_weights_at(pos=(i-0.5,j-0.5,k-0.5), ax=ax, + plot_cube_weights_at(pos=(i-0.5, j-0.5, k-0.5), ax=ax, color=cgen[normalized_weight]) diff --git a/enzynet/volume.py b/enzynet/volume.py index cd96cfb..a9516f7 100644 --- a/enzynet/volume.py +++ b/enzynet/volume.py @@ -272,7 +272,7 @@ def flip_around_axis( """Flips coordinates randomly w.r.t. each axis with its associated probability.""" for col in range(3): if np.random.binomial(1, axis[col]): - coords[:,col] = np.negative(coords[:,col]) + coords[:, col] = np.negative(coords[:, col]) return coords @@ -289,12 +289,12 @@ def remove_noise(coords: np.ndarray, volume: np.ndarray) -> np.ndarray: # Computations. for i in range(coords.shape[0]): if all(valeur < v_size-1 for valeur in coords[i,:]) and \ - all(valeur > 0 for valeur in coords[i,:]): # Point inside volume. - if np.array_equal(volume[coords[i,0]-1:coords[i,0]+2,coords[i,1]-1:coords[i,1]+2,coords[i,2]-1:coords[i,2]+2], - np.pad(np.array([[[1]]]),1,'constant') * volume[tuple(coords[i])]) is True: # Isolated point. - volume[coords[i,0]-1:coords[i,0]+2, - coords[i,1]-1:coords[i,1]+2, - coords[i,2]-1:coords[i,2]+2] = np.zeros((3,3,3)) + all(valeur > 0 for valeur in coords[i, :]): # Point inside volume. + if np.array_equal(volume[coords[i, 0]-1:coords[i, 0]+2, coords[i, 1]-1:coords[i, 1]+2, coords[i, 2]-1:coords[i, 2]+2], + np.pad(np.array([[[1]]]), 1, 'constant') * volume[tuple(coords[i])]) is True: # Isolated point. + volume[coords[i, 0]-1:coords[i, 0]+2, + coords[i, 1]-1:coords[i, 1]+2, + coords[i, 2]-1:coords[i, 2]+2] = np.zeros((3, 3, 3)) return volume diff --git a/scripts/architecture/enzynet_adapted.py b/scripts/architecture/enzynet_adapted.py index 0ee099e..40d447d 100644 --- a/scripts/architecture/enzynet_adapted.py +++ b/scripts/architecture/enzynet_adapted.py @@ -153,7 +153,7 @@ model.add(advanced_activations.LeakyReLU(alpha=0.1)) -model.add(layers.MaxPooling3D(pool_size=(2,2,2))) +model.add(layers.MaxPooling3D(pool_size=(2, 2, 2))) model.add(layers.Dropout(rate=0.3)) diff --git a/scripts/architecture/enzynet_uniform.py b/scripts/architecture/enzynet_uniform.py index f4bdd0e..5bd0f33 100644 --- a/scripts/architecture/enzynet_uniform.py +++ b/scripts/architecture/enzynet_uniform.py @@ -153,7 +153,7 @@ model.add(advanced_activations.LeakyReLU(alpha=0.1)) -model.add(layers.MaxPooling3D(pool_size=(2,2,2))) +model.add(layers.MaxPooling3D(pool_size=(2, 2, 2))) model.add(layers.Dropout(rate=0.3)) From 05092b2b5180c5c28f17347282e668571a5ec7a1 Mon Sep 17 00:00:00 2001 From: shervinea Date: Tue, 24 Aug 2021 23:11:50 -0700 Subject: [PATCH 14/18] Fix PDB instantiation issue --- enzynet/pdb.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/enzynet/pdb.py b/enzynet/pdb.py index 564879b..7f2d5e2 100644 --- a/enzynet/pdb.py +++ b/enzynet/pdb.py @@ -50,7 +50,7 @@ def __init__(self, pdb_id: Text, path: Text = PDB_PATH) -> None: urllib.request.urlretrieve('http://files.rcsb.org/download/' + pdb_id.upper() + '.pdb', fullfilename) - self.structure = PDBParser.PDBParser().get_structure(pdb_id.upper(), fullfilename) + self.structure = PDBParser().get_structure(pdb_id.upper(), fullfilename) def get_coords(self) -> None: """Gets coordinates of backbone.""" From cb456b52f06e7aecfea45af2bf898f49e748624c Mon Sep 17 00:00:00 2001 From: shervinea Date: Tue, 24 Aug 2021 23:15:27 -0700 Subject: [PATCH 15/18] Uniformization of date format --- datasets/make_files/generate_datasets.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/datasets/make_files/generate_datasets.py b/datasets/make_files/generate_datasets.py index e20ff29..dd1dfe9 100755 --- a/datasets/make_files/generate_datasets.py +++ b/datasets/make_files/generate_datasets.py @@ -41,7 +41,7 @@ # Drop errors and save. enzymes = enzymes.drop(enzymes.index[errors]) - enzymes.to_csv(str(i) + '.txt', header=None, index=None) + # enzymes.to_csv(str(i) + '.txt', header=None, index=None) # DONE 2017-03-18. ##-------------- Dictionary with all PDBs in dataset_all.csv -----------------## # Initialization. @@ -61,7 +61,7 @@ labels[entry] = [i] # # Save results. -# tools.dict_to_csv(labels, '../dataset_all.csv') # DONE 18-03-2017. +# tools.dict_to_csv(labels, '../dataset_all.csv') # DONE 2017-03-18. ##--------- Dictionary with single-label PDBs in dataset_single.csv ----------## # Initialization. @@ -74,7 +74,7 @@ labels[entry] = int(labels_temp[entry][1]) # Convert list to scalar. # # Save results. -# tools.dict_to_csv(labels, '../dataset_single.csv') # DONE 18-03-2017. +# tools.dict_to_csv(labels, '../dataset_single.csv') # DONE 2017-03-18. ##---------- Dictionary with multi-label PDBs in dataset_multi.csv -----------## # Initialization. @@ -87,7 +87,7 @@ labels[entry] = labels_temp[entry] # # Save results. -# tools.dict_to_csv(labels, '../dataset_multi.csv') # DONE 18-03-2017. +# tools.dict_to_csv(labels, '../dataset_multi.csv') # DONE 2017-03-18. ##----- Dict of ligands pointing to associated PDB in ligands_to_pdb.csv -----## # Initialization. @@ -111,7 +111,7 @@ ligands[local_ligand] = [enzyme] # # Save results. -# tools.dict_to_csv(ligands, '../ligands_to_pdb.csv') +# tools.dict_to_csv(ligands, '../ligands_to_pdb.csv') # DONE 2017-03-18. ##---- Dict of PDBs pointing to associated ligands in pdb_to_ligands.csv -----## # Initialization. @@ -131,7 +131,7 @@ pdb[enzyme] = local_enzyme.ligands # # Save results. -# tools.dict_to_csv(pdb, '../pdb_to_ligands.csv') +# tools.dict_to_csv(pdb, '../pdb_to_ligands.csv') # DONE 2017-03-18. ##---------- Split single-label dataset in train/validation/test -------------## # Initialization. @@ -159,7 +159,7 @@ partition['test'] = test_ids # # Save results. -# tools.dict_to_csv(partition, '../partition_single.csv') # DONE 18-03-2017. +# tools.dict_to_csv(partition, '../partition_single.csv') # DONE 2017-03-18. ##------ Create reduced single-label train/validation/test sets --------------## # Initialization. @@ -191,4 +191,4 @@ partition_red['test'] = test_ids # # Save results. -# tools.dict_to_csv(partition_red, '../partition_single_red.csv') # DONE 18-03-2017. +# tools.dict_to_csv(partition_red, '../partition_single_red.csv') # DONE 2017-03-18. From 8261e962238e9eda30ce3d44d53c4e5a1e0f4b98 Mon Sep 17 00:00:00 2001 From: shervinea Date: Tue, 24 Aug 2021 23:50:52 -0700 Subject: [PATCH 16/18] Simplify PDB ID generation --- datasets/download_pdb.txt | 2 +- datasets/make_files/download_pdb.py | 18 ++++++++---------- 2 files changed, 9 insertions(+), 11 deletions(-) diff --git a/datasets/download_pdb.txt b/datasets/download_pdb.txt index 7c17ac5..61c1284 100755 --- a/datasets/download_pdb.txt +++ b/datasets/download_pdb.txt @@ -1 +1 @@ -3TK5, 3PHD, 4OBO, 5F9F, 5F38, 3K8E, 4GVZ, 2RC2, 1AF7, 4KLG, 4D9M, 2NQN, 5FNH, 2IDO, 4GIU, 4LW4, 3G65, 3HKJ, 3M07, 1YW8, 4D3F, 4HRA, 2Y2E, 3CG8, 2R2N, 4R8L, 1TWJ, 2FYS, 1ZOG, 5JKS, 1L5H, 3A99, 1GPW, 2PB5, 2VEP, 1GFH, 4NAK, 3E2L, 3LC3, 3RN6, 2ODO, 4N3V, 4LNG, 1OXA, 3OOW, 3L2E, 4C8A, 2NYR, 1YVK, 3LTS, 2VJQ, 3CM7, 3O1B, 2BUH, 3T8I, 3KOW, 1QE3, 4IX0, 5TG5, 5JA0, 3GU7, 5G55, 4TX1, 1Q8R, 5C4I, 1MQ7, 5FGH, 4RQ5, 1CSB, 3LZ6, 2CMN, 1LGP, 3VHO, 3DFY, 4BY3, 5CH8, 1F0Y, 3KT0, 4ONN, 1UT9, 1THQ, 2GP5, 1M1Z, 1OIY, 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3TI1, 2MYT, 3YGS, 2A4V, 4GCW, 3NAL, 2W68, 4M73, 4I8D, 5L8K, 3DKE, 2WB8, 4MLR, 1F3T, 3NE0, 4GBR, 3M8Q, 1L1Y, 5L7H, 2UYR, 3JWM, 1K55, 5LAX, 1YKZ, 4YPA, 3WMX, 1PHP, 5FDI, 1A53, 1VL8, 3EQX, 2QBN, 3Q62, 4W9P, 1DK2, 5FCT, 1QG2, 1CPD, 3SZU, 4WKU, 4MA5, 1N3Z, 5DQ2, 4CTH, 2Z3P, 5LUW, 3WQZ, 3GM8, 5P2G, 1S60, 1CL5, 4QTZ, 1TNH, 4HR5, 2V1S, 1RPI, 3WIQ, 5A4J, 1WB0, 2ZUW, 3IBI, 1OJ9, 4A52, 1L1Q, 3EST, 1J7I, 1BQ4, 2WXO, 4U1A, 1QJG, 3MJZ, 3U8G, 1AJX, 3I8V, 3KBS, 1PJB, 2WIL, 1ZY8, 3CSI, 3E7H, 2FFU, 2HLH, 1CV4, 4JZR, 3DAU, 5XIN, 5AXB, 1EQA, 2BDX, 5B4C, 1S6L, 3B7T, 3S15, 1YJA, 2GTB, 2GNH, 4JSW, 2W1G, 5EUM, 1SPD, 3ZDU, 3A3D, 6ATJ, 1WMW, 4YVC, 2OOG, 4D3A, 4C76, 1XEN, 3CM8, 1ZQF, 4B8L, 5ESY, 1R2Z, 1MVP, 1V9H, 2AYA, 2NLK, 3DXJ, 4RSD, 2JFU, 4IBX, 4ITT, 2GQV, 4ANC, 3OM4, 2ZXF, 4GHM, 1G4I, 1OTW, 1QJ3, 1BP0, 1MKQ, 3IME, 4P3C, 4N8Q, 5DB8, 1LW5, 129L, 3QBE, 2JF2, 3NVW, 4X3C, 5TR7, 1UCG, 3JV7, 3C74, 3CO0, 2A8X, 4CVC, 4GUN, 3A2L, 1P7S, 3AIC, 4E5M, 5L1W, 2G27, 3RNF, 3K5N, 3FWA, 2B5Z, 4BR5, 2J6U, 4Q1G, 3QU2, 1PEY, 3MOS, 2JID, 1TVI, 2FW9, 4ACX, 5AK3, 3TN6, 1KZL, 2R62, 2WHJ, 5F1O, 2Y0D, 3TZO, 3PM8, 1TO2, 4GYZ, 5KXN, 2P3M, 3OU0, 2HUK, 4KX8, 2PJD, 1CNG, 4PIX, 1J6V, 1EUV, 4J06, 3MQR, 1K2S, 1SZE, 4M5W, 5TMB, 2UZD, 1TPH, 5P6Z, 1QRF, 5I67, 5ULV, 5GXU, 1M15, 189L, 1DRF, 2ABE, 3DTC, 5HQ1, 4R4M, 4N2H, 4HTL, 4S35, 3RRF, 1L09, 3QD8, 4RG3, 5CPH, 2JIU, 1LLH, 4GIM, 3MOE, 1JT1, 4X5K, 4EEV, 4D1Z, 2R9L, 1Q97, 3KRX, 3HQC, 4PP7, 3HV5, 1K3T, 1F4L, 1QQQ, 1CM7, 1XU9, 4RCT, 1GMQ, 2GX4, 3FNE, 3H53, 3EJP, 1SD8, 5P14, 5AX9, 3K9W, 4BXF, 1MQF, 3ECD, 2ZZ4, 4O0C, 2XD5, 1XGD, 1DI0, 2O28, 1CVD, 4YJ6, 2GEP, 1M9K, 1L47, 3UOU, 4JSY, 2F91, 3GCU, 1UMG, 5HBF, 1YYQ, 1ESP, 4YLK, 2YU7, 3AR6, 4MS7, 1YB5, 1TLU, 3FY9, 3MNQ, 4LT2, 1RDD, 2HOR, 1EOM, 1J0W, 4J1Y, 3T9F, 1CXP, 4DSH, 4D85, 1O43, 2CC0, 3FG4, 1HXA, 5UPU, 1FUG, 1TPE, 4U6Z, 4Q81, 1DY7, 1KFG, 1E3X, 4HKT, 3HOX, 3WHO, 3E31, 4MFD, 3KNV, 5APN, 3QJ5, 1WZE, 5A0U, 8KME, 4O7P, 1DNU, 1PAD, 4QQN, 5IHX, 2AAZ, 2Q3O, 3HMB, 3DE2, 3PIE, 1JXV, 4LK4, 3V5J, 1W2I, 3WUS, 3R2O, 2DDX, 4WA4, 1M0D, 1Y1J, 2PX2, 2AT2, 3EXE, 5CAB, 4CX0, 3PHI, 4LJN, 1E3I, 4RQK, 1XH5, 1NSO, 2IU2, 3NKX, 1UBJ, 5JX3, 1QNQ, 4ZQN, 1PG5, 3A1D, 4IZD, 1QVA, 3W7T, 4BTY, 4DST, 4YUO, 4X0U, 1A5A, 3THD, 5CT6, 2R2L, 5IVH, 1B8F, 5EY9, 3SPL, 1L83, 3ICU, 3KM3, 7ICN, 3DX5, 3ES9, 3ZGP, 2BI5, 4CGT, 4HVQ, 3NAS, 3VM0, 4FXR, 4Y61, 4HMJ, 4NDZ, 3T40, 4JY1, 1BRG, 3DS6, 4JH7, 1QWS, 4IC7, 4EPH, 3RG6, 4R0X, 3UZU, 2PMJ, 1NUE, 1C9V, 2GRU, 4QDB, 2EJC, 4OLL, 4HDN, 4ZFW, 3ZMP, 1OCN, 1T4T, 1LEH, 3MHT, 1TCX, 3AE0, 3TRO, 1TS9, 3NTZ, 1LL9, 1NWC, 5IR4, 4CIE, 1VHM, 4GN9, 5AR4, 5LOT, 3VF5, 4HAA, 3NTA, 5EH8, 3DR2, 3MY0, 4JNZ, 5JBW, 3ABU, 5FNK, 3HJJ, 2LZ2, 3TAR, 3M3D, 3LCR, 4U94, 3C61, 3GVY, 2XYF, 1MFI, 2KHU, 2Q94, 4QK3, 4O5S, 4LB5, 2GGO, 3PZ3, 2J0A, 2ITY, 1SON, 4CFX, 1JUF, 2O8U, 5A4Q, 2QD3, 5ADS, 4RC8, 4D48, 1XUF, 4K8Z, 5AR3, 5T68, 5TF4, 2GFY, 2OK5, 5LQY, 3KTQ, 2AJC, 5G02, 1XBV, 5FN6, 3W42, 1F08, 1L4F, 3FLZ, 4UDM, 4RWH, 1IQ5, 2JEI, 4I2F, 5FAW, 5LIE, 4ZLE, 1O7W, 3ZM1, 1WWB, 4N20, 3W35, 4A35, 4AU7, 2PUQ, 1QMT, 4ZRK, 3SBB, 1OMO, 4W5H, 2ERK, 3HBL, 1E6I, 4NAJ, 3BNH, 1QAO, 3AWN, 3DAP, 1HNB, 4BP3, 5LIP, 3AS2, 3N5R, 3D7R, 1EKK, 1EXH, 1PT0, 4MHS, 1G9J, 1S73, 4XMD, 2CNC, 2XQK, 4AL1, 4CMW, 2XR9, 4GUE, 1VJI, 5G4B, 4O50, 3WL0, 1ED5, 5IDZ, 4KZ3, 1I0D, 3MH0, 2BEK, 1N7P, 4AOO, 3U18, 4LLS, 2B5W, 3T1V, 1TWZ, 2GML, 4ZWO, 1XGO, 5TRB, 4XLG, 1QBN, 3K6Y, 3L85, 4XG2, 1CPG, 5JES, 1J11, 2YWP, 5HQ8, 4PZ7, 2C2E, 5CHV, 1GE7, 1W2K, 5AOQ, 1CSJ, 4NT8, 3CHF, 3TQ1, 3N4J, 1BGP, 4N0W, 1YKM, 4MPU, 2FKE, 3LWX, 5L86, 2F1D, 1CD2, 2ALY, 1P42, 4PLZ, 4AFS, 3PNS, 1HOX, 1DHT, 4D4M, 3BHM, 2H0Q, 3AEY, 5CWY, 4C9Z, 1L6G, 5FHR, 5E97, 2IWM, 5AMR, 4FYZ, 3UE8, 2V7K, 1PYD, 2MYU, 2ECJ, 2PQG, 1KTT, 2Y4D, 5DHQ, 1GBK, 2R0M, 3UV3, 1WT1, 1KZF, 4R8D, 2EI2, 2FWJ, 5CV6, 2LGL, 1T2V, 2HMF, 3E0O, 1PIO, 1QFN, 3SSB, 1H9C, 4NYZ, 5JK4, 5BP1, 4MZ0, 3OUH, 4JZE, 2A9W, 3HLJ, 1V9A, 3G8V, 1BWD, 3EJM, 5L58, 2GVC, 3S6D, 4XJT, 3S37, 1UXL, 2YPU, 5HPB, 3K9O, 2O0B, 3WIO, 3TNH, 4BK2, 3QE0, 3TOQ, 3EVD, 3KE8, 1SDU, 4Q40, 3NH4, 3QO9, 2DNR, 1CP6, 1K92, 3K91, 2VNN, 3RV9, 4IKO, 2AMD, 2QKK, 2VVM, 1VGW, 4QS7, 4N0O, 1PFV, 5EO3, 2NO7, 1QAJ, 2XAV, 4L2N, 3QCX, 5EAX, 4NHY, 5AMD, 3UOA, 4NP4, 4C62, 1TD1, 1TEZ, 1JLD, 3DOJ, 1RYR, 4YP0, 5DXH, 2E5D, 3GR6, 4MU0, 5FWJ, 1PPJ, 4C0J, 5F9U, 3S8B, 4A8A, 4U17, 5DG8, 4MTW, 5SXJ, 3FYI, 5E4F, 5E8E, 1QLY, 5K0E, 4CQN, 5I46, 3BDR, 5CUO, 3KE1, 2AS1, 4K39, 3EOM, 1R9O, 5HNG, 2OGD, 4QP7, 2AC3, 2AJ8, 2AGE, 2YLS, 4JT4, 1CML, 2GCB, 4QY3, 3CRM, 2GZ3, 2GC7, 2DWJ, 3L8A, 2L3R, 1KOL, 1YJK, 2PSE, 3I5Y, 3EPJ, 4QG9, 5GIO, 2HBR, 3B5W, 2A9U, 4NED, 1L4G, 2G6V, 4AY9, 4V8S, 4KBJ, 3E7W, 1GII, 2VHE, 4IDM, 4XSY, 1WKG, 2DXP, 2J4L, 3KE5, 4O68, 1G4C, 5ETP, 3TMS, 1WH0, 5A8O, 2OWM, 1CT4, 1HTP, 5HDV, 2V98, 2OML, 1NYY, 4ONL, 1G86, 1YGC, 4Z4F, 2L19, 260L, 4ZI6, 3HKP, 3EXB, 2XKJ, 3EA7, 4I8K, 5IQI, 2XNM, 2OFP, 3QCD, 2WPM, 2O07, 4WD0, 2ZPG, 2MPT, 4ONQ, 5F82, 1AP5, 2CIV, 1NGI, 3WBF, 3ZMT, 5BU9, 3I6F, 1W0N, 2RJ7, 3M81, 5BVD, 2W1Z, 4PT5, 7CA2, 3KOZ, 4UVD, 4HZR, 3ZWY, 3SKY, 5JOE, 4QU5, 4KFZ, 5JUO, 1HXH, 4X2Z, 2HDP, 4BNW, 2KR9, 2XT6, 1HQU, 1V7T, 5JJ7, 2I54, 2KAI, 3K3H, 3RKE, 3TC5, 4PG6, 3PZP, 3NAV, 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4PSW, 4IIP, 1PKD, 2LGR, 4X0W, 2E0X, 3ENA, 5H40, 2YZN, 2QVB, 4MZS, 5M9Q, 3SXU, 3EZ5, 2AJF, 5C9B, 1EHK, 1DIY, 4M9D, 5F23, 4JPM, 1JOE, 5AHG, 1DYI, 4GAG, 2JI1, 3GNC, 1Z4K, 2PZB, 2NXO, 4Z7C, 1UDU, 5P0H, 4FP3, 1WO3, 4D3N, 3GRT, 4P2P, 4I4I, 3AI2, 2ZA2, 4IUS, 1FNT, 5KSW, 1NAL, 1LZ5, 2YAW, 3G6W, 2A0B, 3C3A, 3X21, 1CWH, 3FPI, 1TG2, 4ATN, 4TSC, 4Y56, 4H3F, 3PNC, 4JH0, 2WNI, 3T3K, 2BDO, 2PEM, 1JU4, 2R7Q, 1TAR, 3U7S, 4UVL, 4K1J, 4FWU, 3OSL, 3FEF, 4PFK, 5JF3, 3MD0, 4HK9, 3DXV, 3V3H, 3GM3, 2HPZ, 4H2S, 4KC1, 4FS3, 1QWY, 4IXV, 2LQB, 4ZS3, 1TID, 3T9W, 2HWW, 4DRI, 4NLT, 1LMZ, 2YHX, 2V8J, 1VZU, 4ZBC, 1KRQ, 1LTU, 5FJA, 3NPC, 1CGX, 4CAZ, 4O27, 2P47, 2Z6C, 5LJJ, 2J3D, 3GAK, 2FKB, 3QXJ, 5JS3, 2ZZ6, 1UJ0, 5EIF, 3L9P, 2GRC, 3I94, 4JNC, 5DDM, 2D0V, 5CGM, 1DR4, 3HHI, 5KPP, 1NU7, 4EGG, 1T6P, 1ZJY, 2P3E, 2LWD, 3PE9, 2QAK, 4ZA2, 3WKZ, 2AAJ, 1KZJ, 3CI8, 1ZCN, 3H3O, 4H5X, 1H73, 3ONG, 4V87, 3PXS, 1RLW, 5F3G, 1BK4, 3BCJ, 2PUS, 4EW4, 1J0X, 3E7A, 2WYS, 2E9Z, 1I2D, 1BUX, 5F6U, 3SNO, 2EU2, 5FKJ, 1DWA, 3LGP, 3NR1, 2Q9Z, 2HNC, 1SFW, 2I4O, 5JAZ, 1T6M, 3SS3, 2O3E, 1RPM, 1HNI, 1MMB, 4YUZ, 12AS, 4GFG, 1B3O, 4GUX, 1H72, 3OJJ, 1EN5, 5B1E, 2XWL, 3QC9, 3LJZ, 4LHO, 2Z28, 2GBB, 1V77, 5H1V, 3D4P, 4ENV, 1KND, 3CKE, 2FJX, 2HU8, 3Q74, 2W66, 2O7Q, 2BJD, 4WOC, 4QUT, 4RHE, 4YH5, 1CPT, 4PUN, 2PKW, 2AM1, 4KA8, 3TO9, 4A6B, 2AKO, 1HWK, 4HSG, 1ZGB, 1WH1, 4C21, 5JSF, 4KPE, 5ELQ, 1AX3, 5CYH, 2I26, 2ZBH, 1MLI, 3CRA, 1IEW, 1QV9, 1KHP, 4A8L, 1IN8, 2DSC, 4MSG, 3DPG, 3LK9, 2X47, 4NRI, 3RS8, 3V17, 2WIO, 1KRC, 2CMY, 1T38, 1J7L, 1SHH, 1IKJ, 2WX3, 2X2F, 1MP4, 3UWI, 2A8T, 1KZ1, 4IGO, 1U0B, 1B3X, 3AEL, 1LY3, 3BU3, 1L7V, 1U2P, 4X2L, 3IW4, 4X4U, 2EXR, 5EOZ, 3L1U, 4G22, 4AO9, 4LTC, 5CEC, 3V9Z, 2Q01, 2PPD, 5AP6, 5B59, 4WJ4, 4MTI, 2D8J, 2DFF, 3CZA, 4ZDK, 2CHL, 4EWJ, 3HXV, 1WOR, 5JQN, 1Q1L, 1DS7, 2AT8, 2GHE, 2BX4, 4EE0, 4QB8, 2XNN, 2GAA, 4R6C, 3OX1, 5DHU, 2ZKB, 4BIC, 2XNB, 1YFH, 3QGV, 3TNZ, 3IPS, 4PL2, 1K8W, 1WKD, 2WHB, 1WAO, 3LLF, 4KWV, 4NTC, 4CA9, 2JZC, 8ICY, 2F2S, 1SWW, 3RKH, 1XGP, 2NP7, 1BS5, 1VB3, 2EI6, 2XYB, 5JJB, 4PAH, 5HIF, 2YIV, 3SRX, 1YY6, 2I4X, 4S0I, 4EKK, 4A8I, 4V3L, 1NSS, 3PTK, 2LXH, 2YUU, 3S8R, 3C8O, 3WON, 3MI4, 4BPG, 1TJS, 5CE5, 2OXN, 1LWH, 2XUR, 5DAI, 3NDY, 5TPR, 1LBY, 4XJO, 4AS3, 1B6Z, 1MSN, 1WZY, 5F52, 3QK2, 2RD0, 1IOV, 2RK7, 4FLL, 1OM2, 1IE3, 2PZA, 4D1I, 3HYV, 3VR6, 1LQJ, 1DG8, 4PIY, 1EU5, 3MVN, 1NVM, 2E6M, 1ARS, 2H40, 4X84, 3Q3F, 3QYA, 4QTX, 3OAA, 4EV4, 2EYM, 1M2X, 4KH1, 3CV5, 3FNM, 3SBA, 1G4E, 4KYK, 2JFN, 4TYP, 2HI8, 5JPU, 1W34, 4NX8, 1T05, 4EVY, 3VTI, 3PZ5, 2HU4, 2NXH, 5FPQ, 1BA3, 1QSG, 3FU7, 2AOT, 2WXI, 4U53, 2Y7C, 3V50, 3QS9, 2QO1, 1TN8, 3W02, 3NOM, 3C7C, 2C18, 1O2V, 1GK2, 5B2C, 2BBF, 4F85, 2J7G, 4ZIX, 3AKH, 4WSE, 4ZWG, 2Q8D, 1G7J, 1I1Z, 5M4F, 3KVC, 4D8S, 1C0C, 1A31, 1RLV, 4AWO, 1B7B, 5IUI, 4ASI, 1HFU, 3ACC, 4JCT, 1VMI, 4D6D, 1D2K, 2CCG, 1RTV, 1EZV, 1IM8, 3PZ4, 3KEM, 1O8M, 2QLM, 2ZJF, 3KJZ, 2HU5, 4O03, 2LFC, 3UF0, 1XMO, 3QKI, 2HUQ, 1EVJ, 3QSK, 2W2C, 3IZQ, 2ZTT, 1QFE, 2G9G, 5LHE, 5F5E, 3SOX, 3BC3, 4W2F, 4J54, 3FUW, 1IKT, 1J01, 1RZO, 3PPJ, 3KUH, 1M7X, 1W7N, 1QLG, 4PW4, 2JD5, 5HW6, 3VPS, 4QRV, 3E24, 1GTT, 2HL2, 3SO2, 4PP1, 4WLN, 4DEC, 3LNB, 1UJM, 1JXH, 3N1A, 2BED, 4XV9, 6UPJ, 2YVS, 4QBW, 3TEH, 4JS9, 4E4C, 2PLC, 1XBA, 3E4G, 4FOP, 5D1D, 2C6D, 4IE9, 4Q95, 2R78, 5AI5, 3K4V, 5CB1, 4LWQ, 1NFU, 1GWC, 4UU1, 1YV5, 3L31, 2VHD, 1M64, 4QTF, 3N3G, 3WC2, 3RIX, 2Y78, 3UTH, 3ZWQ, 3TR9, 3L0C, 1F7O, 4CLL, 3KJN, 5OZX, 2CNQ, 4TWA, 1O12, 4AI7, 1C1Q, 3QPP, 4U9P, 1NVE, 1G2P, 3EQR, 4ASD, 3CZY, 5D0N, 4Q2B, 1CAO, 1HB2, 4EYW, 2BF4, 2UUF, 1QW8, 4B6R, 3RU9, 4QRI, 2XJK, 1GHB, 5C8W, 4L0N, 3IDV, 3ADP, 1EZA, 1RLA, 2Q0S, 4HNZ, 5EL7, 1HNX, 1LQT, 4HZG, 5ARC, 2CR9, 3F8W, 5CTS, 3ERO, 3JT9, 2G6I, 1J1C, 1DYH, 4EFK, 4DWT, 2OZX, 3EWI, 2HL6, 2F56, 3OVZ, 4XRA, 1FSP, 1FBT, 1FQ7, 2UY2, 3KHS, 4CKV, 4R88, 1WTG, 3I7F, 4L31, 4RQ2, 3KOX, 3U3O, 1ZVF, 1L9N, 2VWJ, 4U7X, 4EM4, 4DA5, 2LJP, 3U5P, 1PC8, 2GVU, 2OAE, 1V2J, 5P06, 4IM9, 4EIV, 5BWX, 2H1X, 3TUC, 3P1E, 1W0X, 5ABT, 3T87, 4MN8, 3ACL, 3NZQ, 3BG0, 3QG2, 4A4C, 1RB3, 4GL2, 2YR4, 1XZD, 3IKE, 2O0W, 5ABW, 3TG0, 4OP5, 5I4M, 1H8M, 2ZLD, 4AUT, 3RMB, 1UR5, 5OZA, 1YYO, 1HJ4, 2AY6, 3KTN, 2YIB, 4F6P, 1U0C, 1L5K, 2BD3, 1XVO, 2AY5, 3R7E, 3LC1, 2AMO, 1I3I, 3U6D, 4JF5, 4KGJ, 5FN4, 2GWL, 2AL1, 1M5W, 2A0I, 2X0J, 2NVL, 3SC6, 1M8H, 1Z02, 1ZD1, 4P68, 4NCS, 3C92, 1HTQ, 1TL1, 4GE7, 4TV1, 1NB7, 4B3K, 2NZ8, 2JEG, 1P5E, 3UXE, 1RC5, 4EK5, 4JWS, 1THE, 3BD7, 1NW6, 3MR5, 1K7I, 4BXO, 1X6U, 2YAB, 1P9P, 3BYX, 3GBD, 3BKZ, 4HX5, 4FKR, 1NUX, 4X2F, 4PNU, 1IEF, 4CF6, 1UPF, 2XRU, 5MEN, 1SZ2, 4Y5F, 4K13, 1UB7, 1WEP, 2PY5, 3SJL, 4O4W, 3L4J, 5C5P, 4IMX, 5AJR, 2OID, 3D8C, 1K4P, 1NJS, 2A5G, 3JWY, 3B5L, 3CMM, 2IMF, 5D3E, 1KY0, 4I8B, 1IQ0, 5A8X, 2BIL, 4L2A, 4D5O, 220L, 5G0B, 5FLM, 4PLB, 4EPE, 5AMG, 3NIB, 4LCO, 1BOI, 4XX6, 1GHE, 1BXC, 5CPA, 5G3Y, 2XPA, 2CW0, 4XPD, 3L4M, 2YBI, 3BII, 3CK2, 3RPV, 4FHV, 2YG3, 3B6K, 1E0U, 4MNL, 4K7V, 4K4C, 4Q32, 4FDN, 5D8Q, 4TXK, 4KIV, 4J9F, 1FKW, 4YUG, 1U2T, 5C5N, 2WV8, 1RYS, 3R75, 2VOM, 1TSD, 4WCU, 2I4R, 3LHU, 3BHY, 2EZU, 4YE3, 2YPD, 2YIJ, 1HW9, 3S7W, 4U9Y, 3MNJ, 1KKJ, 4NL8, 5CAP, 2C8H, 3N2R, 4Q24, 3ZPX, 2CL5, 2IGK, 2J75, 5P3V, 1A0D, 2WNF, 3GBR, 3NN9, 2FMJ, 1SRM, 3OLG, 2QU6, 2PSS, 5T8F, 4HCK, 4ESG, 2WDP, 1JLH, 1J3I, 3LZ5, 1B6K, 4CLO, 1HHQ, 5UMF, 3R89, 5CI2, 2IK2, 3FY2, 4BOQ, 1PQM, 3P17, 3N45, 3NNU, 4D3T, 4WNT, 2BZ8, 4FKP, 3TUI, 2VLO, 3NKT, 3MJT, 4GWA, 2QRK, 1K8O, 4HV4, 2WWH, 3S2A, 5TVG, 5E2P, 3ZGE, 1K56, 4WS6, 4RVK, 3G91, 2XOZ, 4XLS, 4DOF, 1IKA, 5P2D, 4HME, 5AO3, 2XR0, 1SLL, 4XN9, 2WO5, 3M5Z, 3UFU, 4ICL, 1DG9, 4PYK, 1G66, 2BBW, 1M21, 2II4, 3D2M, 4IUM, 4KG5, 4BDC, 1FFB, 2EIV, 1OES, 2RDJ, 5CXH, 4J96, 1FSI, 2M00, 1FO2, 3B04, 1SR2, 3H76, 3BCF, 1AG8, 3N3B, 1DD7, 5A3P, 3GF7, 4MO8, 4YA7, 3I6Z, 2LQI, 4JA7, 5IKP, 2FSS, 4C4I, 5B4U, 4XR7, 3RBB, 1V19, 1BXB, 5T0G, 4BRX, 3O2L, 3PSW, 3ID6, 4LC8, 2X3A, 3KVM, 4BAA, 2GC1, 1PJC, 5HGQ, 5TW7, 4WPI, 3KIJ, 5AH1, 1IM6, 2G4U, 3J16, 4QX5, 3UPQ, 2J5E, 2FYE, 5TX7, 4RDH, 2Q74, 4ZZI, 5DBN, 2VSI, 2ANQ, 2Z54, 1K30, 4ZW6, 2OE2, 4JFA, 2WQY, 2YKM, 4QEH, 4ZX1, 5BOZ, 1GHP, 2EKC, 1GHS, 1XIG, 2JEV, 2NQJ, 4UF6, 2H9E, 4I3Y, 3AON, 2FXZ, 3ERQ, 4A4Q, 4ZJ7, 2BFW, 5C1D, 3N17, 5H2G, 1FS5, 2IVB, 1FEH, 1R6H, 4Q7V, 3MXP, 1WZ2, 3ZSI, 4BFL, 1PV2, 2BVA, 4QFZ, 3V1M, 1EYP, 1KV6, 1FGK, 1KLU, 3KE0, 2BRT, 3PB9, 2YB1, 3Q90, 3WEI, 2EYO, 2QZY, 3J95, 1KKK, 2VO7, 4BBN, 3FBD, 4KS8, 2MRF, 2IO2, 1FWJ, 3Q86, 3FQA, 1HCH, 4MAB, 2FZH, 3F8Y, 3IUI, 5IGQ, 4EGQ, 4CJ4, 5HMS, 5C1E, 4IUG, 1UKW, 5A28, 1P0S, 2ZJZ, 1AE7, 3R6W, 2ZFP, 5BQ1, 1R3C, 3ADZ, 4PBX, 2VZ0, 2BUB, 1HFW, 2QVN, 4UE3, 4MJ2, 3MQK, 5LQJ, 4IEW, 2V3K, 5JKT, 4XM5, 3UOZ, 2LB3, 5DDL, 1FWO, 1IIN, 3CEH, 4NA6, 1DFQ, 3FXO, 2I3U, 2C3L, 3M9N, 2OQB, 1AIW, 3H1W, 5HZG, 5M2F, 1Y5N, 4DSU, 1WA8, 2H30, 1BEP, 3GH6, 1Q27, 1K07, 2P9E, 4EAR, 2WHM, 4WZV, 3ECM, 5P07, 4HJL, 3LCT, 2CYH, 5IEE, 2EK2, 2P16, 2F4N, 5FO8, 4OCJ, 1MEK, 2D3Y, 1DTE, 5GL5, 1O7N, 4M38, 2WP8, 3HV9, 1O41, 1CQH, 5MF6, 1HQ2, 1XC8, 3L5M, 4UPA, 4LGM, 1B8J, 2DBT, 4G3S, 3QSM, 1S1U, 3LCS, 3S82, 1TM7, 4CGL, 4ZA7, 4RHF, 4OH7, 1O8P, 3SM8, 3MF5, 1QZU, 2IWF, 1M0U, 4E51, 3Q00, 4V4E, 3SR4, 5J4C, 1ZL6, 4OKZ, 2YGX, 5DTK, 2HR6, 4TVU, 3WQR, 4XN1, 1SBH, 2W96, 1D2V, 2EL1, 4V1Y, 1ODT, 4QSJ, 4ZYV, 4HM1, 4G2R, 1BIY, 4TO1, 2QIW, 1TOC, 1W2A, 2I3R, 2MZN, 3QG1, 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1TB4, 1TDE, 1TDF, 1TDK, 1TDN, 1TDO, 1TDR, 1TDW, 1TEH, 1TFZ, 1TG2, 1TG5, 1TGU, 1TH2, 1TH3, 1TH4, 1TIK, 1TIW, 1TJ0, 1TJ1, 1TJ2, 1TJC, 1TJO, 1TK1, 1TK6, 1TKL, 1TKO, 1TKP, 1TLB, 1TLL, 1TLT, 1TMO, 1TMX, 1TO4, 1TO5, 1TOH, 1TQ5, 1TQN, 1TQQ, 1TRB, 1TT0, 1TT7, 1TU5, 1TUV, 1TV5, 1TVC, 1TVE, 1TWN, 1TWR, 1TXG, 1TXN, 1TXX, 1TY9, 1TYO, 1TYP, 1TYT, 1TZL, 1U0X, 1U13, 1U28, 1U2D, 1U2G, 1U2J, 1U2K, 1U2L, 1U31, 1U3T, 1U3U, 1U3V, 1U3W, 1U4O, 1U4S, 1U5A, 1U5B, 1U5C, 1U67, 1U6I, 1U6J, 1U6K, 1U70, 1U71, 1U72, 1U74, 1U75, 1U7T, 1U8F, 1UB2, 1UBB, 1UBH, 1UBJ, 1UBK, 1UBL, 1UBM, 1UBO, 1UBR, 1UBT, 1UBU, 1UC7, 1UDY, 1UE8, 1UED, 1UER, 1UES, 1UH5, 1UI7, 1UI8, 1UJM, 1UKW, 1ULI, 1ULJ, 1ULS, 1ULU, 1ULW, 1ULX, 1UM9, 1UMB, 1UMC, 1UMD, 1UMK, 1UMN, 1UNB, 1UNF, 1UO9, 1UOB, 1UOF, 1UOG, 1UP8, 1UPX, 1UR3, 1UR5, 1URM, 1US0, 1US1, 1UUF, 1UUM, 1UUO, 1UUV, 1UUW, 1UVH, 1UXG, 1UXH, 1UXI, 1UXJ, 1UXK, 1UXL, 1UXM, 1UXN, 1UXP, 1UXQ, 1UXR, 1UXT, 1UXU, 1UXV, 1UXY, 1UYU, 1UZL, 1UZM, 1UZN, 1UZR, 1UZW, 1V0H, 1V10, 1V11, 1V16, 1V1M, 1V1R, 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1ZUA, 1ZUM, 1ZUQ, 1ZV2, 1ZVF, 1ZVI, 1ZVL, 1ZW1, 1ZWK, 1ZWL, 1ZX1, 1ZX9, 1ZXB, 1ZXI, 1ZXL, 1ZY8, 1ZYE, 1ZYN, 1ZYP, 1ZYZ, 1ZZ6, 1ZZ7, 1ZZ8, 1ZZ9, 1ZZB, 1ZZC, 1ZZD, 1ZZE, 1ZZH, 1ZZQ, 1ZZR, 1ZZS, 1ZZT, 1ZZU, 2A03, 2A06, 2A1M, 2A1N, 2A1O, 2A1T, 2A1X, 2A1Y, 2A2J, 2A3T, 2A4K, 2A4V, 2A5L, 2A79, 2A7N, 2A7P, 2A7R, 2A85, 2A87, 2A89, 2A8X, 2A92, 2A94, 2A99, 2A9A, 2A9B, 2A9C, 2A9D, 2A9E, 2AA3, 2AAQ, 2ABA, 2ABB, 2ACQ, 2ACR, 2ACS, 2ACU, 2ACZ, 2AD6, 2AD7, 2AD8, 2ADP, 2ADQ, 2AE1, 2AE2, 2AEO, 2AEX, 2AF2, 2AFH, 2AFI, 2AFN, 2AFT, 2AFY, 2AG5, 2AG8, 2AGL, 2AGT, 2AGW, 2AGX, 2AGY, 2AGZ, 2AH0, 2AH1, 2AHK, 2AHL, 2AHR, 2AII, 2AIJ, 2AIK, 2AKJ, 2ALR, 2ALX, 2AMF, 2AMO, 2AMU, 2AN0, 2AN2, 2ANI, 2ANO, 2ANQ, 2ANZ, 2AOP, 2APG, 2APS, 2AQ6, 2AQ8, 2AQD, 2AQH, 2AQI, 2AQJ, 2AQK, 2AQN, 2AQP, 2AQQ, 2AQR, 2AQS, 2AQT, 2AR8, 2ARD, 2AS1, 2AS2, 2AS3, 2AS4, 2AS6, 2ASY, 2AT0, 2AT3, 2AT5, 2AT6, 2AT8, 2ATF, 2ATJ, 2AUV, 2AV8, 2AVF, 2AW5, 2AW9, 2AWP, 2AXQ, 2AXR, 2AXT, 2AYL, 2AYQ, 2AZN, 2AZQ, 2B08, 2B0J, 2B0M, 2B0T, 2B0Z, 2B10, 2B11, 2B12, 2B1X, 2B24, 2B35, 2B36, 2B37, 2B3I, 2B3Z, 2B4G, 2B4Q, 2B4R, 2B4T, 2B5H, 2B5O, 2B5V, 2B5W, 2B5X, 2B5Y, 2B67, 2B6F, 2B76, 2B7R, 2B7S, 2B81, 2B83, 2BBK, 2BC0, 2BC1, 2BCC, 2BCN, 2BCP, 2BD0, 2BDM, 2BEL, 2BEU, 2BEV, 2BEW, 2BF2, 2BF3, 2BF4, 2BF5, 2BF7, 2BFA, 2BFB, 2BFC, 2BFD, 2BFE, 2BFF, 2BFM, 2BFO, 2BFP, 2BGI, 2BGJ, 2BGK, 2BGL, 2BGM, 2BGQ, 2BGS, 2BH9, 2BHJ, 2BHL, 2BHP, 2BHQ, 2BI4, 2BIH, 2BII, 2BJA, 2BJK, 2BJS, 2BJY, 2BK3, 2BK4, 2BK5, 2BK6, 2BKA, 2BKB, 2BKC, 2BKJ, 2BL4, 2BL9, 2BLA, 2BLB, 2BLC, 2BLE, 2BMA, 2BMH, 2BMW, 2BMX, 2BN4, 2BNM, 2BNN, 2BNO, 2BOQ, 2BOY, 2BP1, 2BP7, 2BPI, 2BPO, 2BQ1, 2BRA, 2BRT, 2BRU, 2BRY, 2BS2, 2BS3, 2BS4, 2BSA, 2BSL, 2BT3, 2BU9, 2BUM, 2BUQ, 2BUR, 2BUT, 2BUU, 2BUV, 2BUW, 2BUX, 2BUY, 2BUZ, 2BV0, 2BVF, 2BVG, 2BVH, 2BVJ, 2BW0, 2BW1, 2BW4, 2BW5, 2BWD, 2BWG, 2BWI, 2BX7, 2BXR, 2BXS, 2BXV, 2BYB, 2BZ9, 2BZN, 2BZS, 2C07, 2C0C, 2C0D, 2C0U, 2C10, 2C11, 2C12, 2C1D, 2C1U, 2C1V, 2C29, 2C2F, 2C2J, 2C2S, 2C2T, 2C2U, 2C2X, 2C2Y, 2C3C, 2C3D, 2C3M, 2C3O, 2C3P, 2C3U, 2C3Y, 2C42, 2C4C, 2C64, 2C65, 2C66, 2C67, 2C6H, 2C6Q, 2C6R, 2C70, 2C72, 2C73, 2C75, 2C76, 2C7G, 2C7V, 2C7X, 2C82, 2C8V, 2C91, 2C9S, 2C9U, 2C9V, 2CA0, 2CAG, 2CAH, 2CB2, 2CCA, 2CCD, 2CCP, 2CD2, 2CD7, 2CD8, 2CD9, 2CDA, 2CDB, 2CDC, 2CDH, 2CDU, 2CDY, 2CE4, 2CEI, 2CEP, 2CF5, 2CF6, 2CF7, 2CFC, 2CFD, 2CFG, 2CFI, 2CFK, 2CFL, 2CFW, 2CFY, 2CG0, 2CG1, 2CGN, 2CGO, 2CHI, 2CI0, 2CIB, 2CIG, 2CIH, 2CIK, 2CIV, 2CIW, 2CIX, 2CIY, 2CIZ, 2CJ0, 2CJ1, 2CJ2, 2CKJ, 2CL4, 2CLB, 2CLP, 2CLU, 2CMD, 2CMJ, 2CMV, 2CN6, 2CN7, 2CND, 2CO8, 2COM, 2CP4, 2CPO, 2CPP, 2CQ2, 2CQ8, 2CU0, 2CUA, 2CUK, 2CV4, 2CVO, 2CVQ, 2CVS, 2CVT, 2CVU, 2CVV, 2CVW, 2CVX, 2CVY, 2CVZ, 2CW2, 2CW3, 2CWF, 2CWH, 2CWL, 2CWT, 2CWU, 2CWV, 2CX3, 2CX4, 2CX9, 2CY0, 2CYP, 2CZC, 2D09, 2D0E, 2D0K, 2D0T, 2D0U, 2D0V, 2D1C, 2D1E, 2D1Q, 2D1R, 2D1S, 2D1T, 2D1W, 2D2C, 2D36, 2D37, 2D38, 2D3Q, 2D3T, 2D40, 2D4A, 2D4E, 2D4V, 2D5C, 2D5N, 2D88, 2D8A, 2DAP, 2DBQ, 2DBR, 2DBV, 2DBZ, 2DC1, 2DDH, 2DE5, 2DE6, 2DE7, 2DFD, 2DFV, 2DHF, 2DHT, 2DJI, 2DJL, 2DJX, 2DK9, 2DKE, 2DKH, 2DKI, 2DKK, 2DKN, 2DLD, 2DMR, 2DOR, 2DPG, 2DPH, 2DQ4, 2DRC, 2DTD, 2DTE, 2DTX, 2DU2, 2DU8, 2DUX, 2DUZ, 2DV0, 2DV6, 2DVM, 2DW4, 2DWS, 2DWT, 2DY2, 2DY5, 2DYR, 2DYS, 2DZJ, 2E0C, 2E1M, 2E1Q, 2E2G, 2E2M, 2E2T, 2E2U, 2E2V, 2E37, 2E39, 2E3A, 2E3B, 2E3T, 2E46, 2E47, 2E48, 2E49, 2E4A, 2E4G, 2E5M, 2E5V, 2E68, 2E6A, 2E6D, 2E6F, 2E6R, 2E74, 2E75, 2E76, 2E77, 2E7E, 2E80, 2E81, 2E82, 2E86, 2E9E, 2E9K, 2EBA, 2ECR, 2ECU, 2ED4, 2EER, 2EEZ, 2EFB, 2EGG, 2EGH, 2EHA, 2EHQ, 2EHU, 2EHZ, 2EI0, 2EI1, 2EI2, 2EI3, 2EIB, 2EIC, 2EID, 2EIE, 2EII, 2EIJ, 2EIK, 2EIL, 2EIM, 2EIN, 2EIT, 2EIW, 2EIX, 2EJ6, 2EJD, 2EJL, 2EJR, 2EJV, 2EJW, 2EKG, 2EKL, 2EKP, 2EKQ, 2EP5, 2EP7, 2EQ6, 2EQ7, 2EQ8, 2EQ9, 2EQY, 2ESD, 2ET1, 2ET6, 2ET7, 2ETE, 2EUD, 2EUH, 2EUN, 2EUO, 2EUP, 2EUQ, 2EUR, 2EUS, 2EUT, 2EUU, 2EV9, 2EW2, 2EW8, 2EWM, 2EXR, 2EZ4, 2EZ8, 2EZ9, 2EZT, 2EZU, 2F1K, 2F1O, 2F2K, 2F5V, 2F5Z, 2F6C, 2F7N, 2F8A, 2F9Q, 2FBW, 2FBZ, 2FC1, 2FC2, 2FD8, 2FDF, 2FDG, 2FDH, 2FDI, 2FDJ, 2FDK, 2FDU, 2FDV, 2FDW, 2FDY, 2FE6, 2FER, 2FEU, 2FHA, 2FHS, 2FJA, 2FJB, 2FJD, 2FJE, 2FJS, 2FKZ, 2FL0, 2FLQ, 2FMN, 2FMO, 2FMU, 2FN7, 2FNQ, 2FOI, 2FON, 2FPT, 2FPV, 2FPY, 2FQD, 2FQE, 2FQF, 2FQG, 2FQI, 2FQL, 2FR7, 2FR8, 2FRD, 2FRV, 2FRZ, 2FSS, 2FSV, 2FUB, 2FUG, 2FWE, 2FWF, 2FWG, 2FWH, 2FWL, 2FWM, 2FXI, 2FXL, 2FY6, 2FYN, 2FYU, 2FZ8, 2FZ9, 2FZB, 2FZD, 2FZE, 2FZH, 2FZI, 2FZJ, 2FZM, 2FZN, 2FZV, 2FZW, 2G17, 2G18, 2G19, 2G1M, 2G25, 2G28, 2G37, 2G4O, 2G5C, 2G5W, 2G67, 2G6H, 2G6I, 2G6J, 2G6K, 2G6L, 2G6M, 2G6N, 2G6O, 2G6V, 2G76, 2G82, 2G8S, 2G8Y, 2GA5, 2GAS, 2GB0, 2GB8, 2GBT, 2GBU, 2GBV, 2GC4, 2GC7, 2GCG, 2GCU, 2GD1, 2GDS, 2GDZ, 2GEP, 2GER, 2GEW, 2GF2, 2GF3, 2GF7, 2GFA, 2GGN, 2GGS, 2GH2, 2GH5, 2GHC, 2GHD, 2GHE, 2GHH, 2GHK, 2GJ1, 2GJL, 2GJM, 2GJN, 2GLX, 2GMH, 2GMJ, 2GMY, 2GO1, 2GOJ, 2GOY, 2GP3, 2GP5, 2GPC, 2GPT, 2GQT, 2GQU, 2GQV, 2GQW, 2GQX, 2GR0, 2GR1, 2GR2, 2GR3, 2GR6, 2GR9, 2GRA, 2GRT, 2GS3, 2GSD, 2GSM, 2GSW, 2GT3, 2GUG, 2GV8, 2GVC, 2GYY, 2GZ1, 2GZ2, 2GZ3, 2H01, 2H0V, 2H1S, 2H2Q, 2H30, 2H3X, 2H47, 2H5G, 2H5U, 2H63, 2H6E, 2H6P, 2H7Q, 2H7R, 2H7S, 2H88, 2H89, 2H94, 2H9I, 2HAY, 2HBT, 2HBU, 2HDH, 2HE3, 2HE5, 2HE8, 2HEJ, 2HG2, 2HHZ, 2HI1, 2HI4, 2HI7, 2HI8, 2HIB, 2HIQ, 2HJ3, 2HJ4, 2HJB, 2HJI, 2HK7, 2HK8, 2HK9, 2HKI, 2HKM, 2HKO, 2HKR, 2HLP, 2HM9, 2HMJ, 2HMK, 2HML, 2HMM, 2HMN, 2HMO, 2HPD, 2HPV, 2HQ1, 2HQM, 2HQP, 2HRB, 2HRG, 2HRH, 2HS6, 2HS8, 2HSA, 2HSD, 2HTN, 2HU2, 2HUO, 2HV5, 2HVB, 2HVN, 2HVO, 2HX2, 2HX3, 2HX4, 2HXC, 2HXV, 2HZE, 2HZF, 2I0K, 2I0R, 2I0S, 2I0T, 2I16, 2I17, 2I3A, 2I3G, 2I3Y, 2I5P, 2I99, 2I9P, 2IA8, 2IAA, 2IBA, 2IBN, 2IBZ, 2IC0, 2IC1, 2ICQ, 2ICV, 2ID2, 2IDZ, 2IE0, 2IEA, 2IEB, 2IED, 2IEM, 2IG9, 2IGA, 2IGK, 2IGM, 2IGN, 2IGO, 2IH8, 2IH9, 2IID, 2IJ2, 2IJ3, 2IJ4, 2IJ5, 2IJ7, 2IJC, 2IJY, 2IKC, 2IKG, 2IKH, 2IKI, 2IKJ, 2ILM, 2ILT, 2ILU, 2IMP, 2INE, 2INQ, 2INZ, 2IOD, 2IPA, 2IPF, 2IPG, 2IPI, 2IPS, 2IPW, 2IQ0, 2IQD, 2IQF, 2IRW, 2IS7, 2ISA, 2ISF, 2ISJ, 2ISK, 2ISL, 2ITH, 2IU2, 2IUF, 2IUJ, 2IUK, 2IUP, 2IUQ, 2IUR, 2IUV, 2IUW, 2IV0, 2IV2, 2IVD, 2IVE, 2IVF, 2IVI, 2IVJ, 2IW5, 2IWF, 2IWK, 2IX5, 2IX6, 2IY4, 2IY6, 2IYO, 2IYP, 2IZ0, 2IZ1, 2IZZ, 2J18, 2J19, 2J1M, 2J2F, 2J2M, 2J3H, 2J3I, 2J3J, 2J3K, 2J3N, 2J40, 2J4S, 2J55, 2J56, 2J57, 2J5K, 2J5M, 2J5N, 2J5Q, 2J5R, 2J5W, 2J6I, 2J6L, 2J6X, 2J7A, 2J8T, 2J8Z, 2J9F, 2JAE, 2JB1, 2JB2, 2JB3, 2JB4, 2JB8, 2JBR, 2JBS, 2JBT, 2JBV, 2JCV, 2JCW, 2JCX, 2JCY, 2JD0, 2JD1, 2JD2, 2JFC, 2JGD, 2JHF, 2JHG, 2JI1, 2JI2, 2JI3, 2JIF, 2JIM, 2JIO, 2JIP, 2JIQ, 2JIR, 2JJN, 2JJO, 2JJP, 2JJY, 2JK6, 2JKC, 2JKV, 2JKX, 2JLP, 2JNK, 2JO6, 2JRZ, 2JSY, 2JSZ, 2JTI, 2JU5, 2JXJ, 2JYB, 2JZA, 2K0R, 2K4W, 2K8D, 2K8V, 2K9F, 2KGG, 2KGI, 2KGK, 2KLB, 2KRU, 2KT2, 2KT3, 2KV1, 2KZN, 2L17, 2L18, 2L19, 2L1U, 2L28, 2L3D, 2L8M, 2L90, 2LDB, 2LDX, 2LF1, 2LFC, 2LL6, 2LL7, 2LLN, 2LM1, 2LQD, 2LQO, 2LQQ, 2LU5, 2LV3, 2M56, 2MA5, 2MAD, 2MBR, 2MDA, 2MG5, 2MIN, 2MIQ, 2MNY, 2MNZ, 2MP3, 2MTA, 2MU0, 2MYN, 2MYP, 2MYT, 2MYU, 2N0S, 2N18, 2N5F, 2N8J, 2NAC, 2NAD, 2NAM, 2NAP, 2NIP, 2NLI, 2NLO, 2NM0, 2NNB, 2NNH, 2NNI, 2NNJ, 2NNL, 2NNX, 2NOD, 2NOS, 2NOX, 2NP9, 2NPX, 2NQT, 2NQX, 2NRD, 2NSD, 2NSE, 2NSI, 2NTJ, 2NTN, 2NTV, 2NUK, 2NUM, 2NV6, 2NVB, 2NVC, 2NVD, 2NVE, 2NVF, 2NVG, 2NVK, 2NVL, 2NW7, 2NW8, 2NW9, 2NWF, 2NYA, 2NYB, 2NZ5, 2NZA, 2NZL, 2O01, 2O23, 2O2P, 2O2Q, 2O2R, 2O2S, 2O2Y, 2O3J, 2O48, 2O4C, 2O4D, 2O4U, 2O50, 2O60, 2O7P, 2O7Q, 2O7S, 2O86, 2O8V, 2O9Z, 2OA1, 2OAL, 2OAM, 2OBC, 2OBI, 2OBY, 2OCC, 2ODX, 2OFM, 2OFP, 2OFR, 2OG5, 2OG6, 2OG7, 2OHH, 2OHI, 2OHJ, 2OHX, 2OIP, 2OIZ, 2OJV, 2OJY, 2OK4, 2OK6, 2OK7, 2OK8, 2OL4, 2ONM, 2ONN, 2ONO, 2ONP, 2OO5, 2OOR, 2OOS, 2OOV, 2OP0, 2OP1, 2OPX, 2OQ2, 2OQ6, 2OQE, 2ORO, 2ORP, 2ORQ, 2ORR, 2ORS, 2ORT, 2OS2, 2OT4, 2OT7, 2OUI, 2OUW, 2OX0, 2OXI, 2OYE, 2OYO, 2OYU, 2OZ0, 2OZ1, 2OZL, 2OZN, 2OZP, 2P0M, 2P2S, 2P31, 2P3X, 2P4H, 2P4K, 2P4Q, 2P4T, 2P5B, 2P5N, 2P5Q, 2P5R, 2P68, 2P80, 2P85, 2P91, 2P9C, 2P9E, 2P9G, 2PA3, 2PAH, 2PAL, 2PCB, 2PCC, 2PCD, 2PD3, 2PD4, 2PD5, 2PD6, 2PD9, 2PDA, 2PDB, 2PDC, 2PDF, 2PDG, 2PDH, 2PDI, 2PDJ, 2PDK, 2PDL, 2PDM, 2PDN, 2PDP, 2PDQ, 2PDU, 2PDW, 2PDX, 2PDY, 2PES, 2PEV, 2PF8, 2PFG, 2PFH, 2PFX, 2PG0, 2PG5, 2PG6, 2PG7, 2PG8, 2PGD, 2PH3, 2PHD, 2PHH, 2PHM, 2PK3, 2PKQ, 2PKR, 2PLA, 2PLC, 2PN8, 2PNC, 2PNF, 2POQ, 2PP7, 2PP8, 2PP9, 2PPA, 2PPC, 2PPD, 2PPE, 2PPF, 2PPT, 2PQ4, 2PR2, 2PRH, 2PRL, 2PRM, 2PRR, 2PSD, 2PSE, 2PSF, 2PSH, 2PSJ, 2PT3, 2PTG, 2PU9, 2PUK, 2PUM, 2PUO, 2PV7, 2PVB, 2PVD, 2PVG, 2PVO, 2PW6, 2PWJ, 2PWZ, 2PXJ, 2PZN, 2Q0K, 2Q0L, 2Q1L, 2Q2L, 2Q2Q, 2Q2V, 2Q2W, 2Q32, 2Q3E, 2Q3O, 2Q3R, 2Q45, 2Q49, 2Q4A, 2Q4E, 2Q4S, 2Q4W, 2Q50, 2Q62, 2Q6B, 2Q6C, 2Q6N, 2Q7Q, 2Q7V, 2Q85, 2Q8C, 2Q8D, 2Q8E, 2Q99, 2Q9F, 2Q9G, 2Q9O, 2QA1, 2QA2, 2QAE, 2QBL, 2QBM, 2QBN, 2QBO, 2QCU, 2QE0, 2QFV, 2QFW, 2QFX, 2QFY, 2QG4, 2QHX, 2QIO, 2QJK, 2QJP, 2QJY, 2QK8, 2QKA, 2QKC, 2QKN, 2QMY, 2QMZ, 2QNK, 2QPD, 2QPE, 2QPK, 2QPM, 2QPP, 2QQ5, 2QQR, 2QQS, 2QQT, 2QR2, 2QRB, 2QRJ, 2QRK, 2QRL, 2QT6, 2QT8, 2QTA, 2QTC, 2QTL, 2QTZ, 2QW8, 2QWX, 2QX4, 2QX6, 2QX7, 2QX8, 2QX9, 2QXW, 2QYS, 2QYT, 2QZ9, 2QZZ, 2R00, 2R0M, 2R0N, 2R1R, 2R24, 2R27, 2R2F, 2R2G, 2R37, 2R45, 2R46, 2R4E, 2R4F, 2R4J, 2R5L, 2R5V, 2R6H, 2R6J, 2R96, 2R97, 2R9R, 2R9Z, 2RAA, 2RAB, 2RAF, 2RBE, 2RBT, 2RBU, 2RBV, 2RBW, 2RBX, 2RBY, 2RBZ, 2RC0, 2RC1, 2RC2, 2RC5, 2RC6, 2RCC, 2RCV, 2RCY, 2RDN, 2RDQ, 2RDR, 2RDS, 2RDT, 2RDU, 2RDW, 2RDZ, 2REH, 2REM, 2RF7, 2RFB, 2RFC, 2RFQ, 2RG1, 2RGH, 2RGJ, 2RGO, 2RGZ, 2RH2, 2RH4, 2RH8, 2RHC, 2RHR, 2RII, 2RIR, 2RK1, 2RK2, 2RM5, 2RM6, 2ROM, 2SBL, 2SOD, 2TMD, 2TMG, 2TOH, 2TPR, 2UUQ, 2UVD, 2UVN, 2UW1, 2UW2, 2UWH, 2UX1, 2UXN, 2UXQ, 2UXR, 2UXW, 2UXX, 2UYY, 2UZA, 2UZZ, 2V0A, 2V15, 2V1D, 2V1M, 2V1S, 2V1T, 2V23, 2V27, 2V28, 2V2E, 2V2G, 2V32, 2V3A, 2V3B, 2V3V, 2V41, 2V45, 2V4J, 2V5F, 2V5Z, 2V60, 2V61, 2V65, 2V6B, 2V6F, 2V6G, 2V6M, 2V6O, 2V7P, 2V8T, 2V8U, 2VAO, 2VAU, 2VBB, 2VBD, 2VBP, 2VCF, 2VCK, 2VCL, 2VCM, 2VCN, 2VCS, 2VCY, 2VD7, 2VDC, 2VDD, 2VDE, 2VDG, 2VE1, 2VE3, 2VE4, 2VFR, 2VFS, 2VFT, 2VFU, 2VFV, 2VGR, 2VHD, 2VHV, 2VHW, 2VHX, 2VHY, 2VHZ, 2VIG, 2VIM, 2VKA, 2VKU, 2VL2, 2VL3, 2VL9, 2VLE, 2VLT, 2VLU, 2VLV, 2VM1, 2VM2, 2VN0, 2VNA, 2VNH, 2VNI, 2VNJ, 2VNK, 2VOE, 2VOJ, 2VOU, 2VR0, 2VR6, 2VR7, 2VR8, 2VRL, 2VRM, 2VRO, 2VUP, 2VUX, 2VVL, 2VVM, 2VW4, 2VW6, 2VW7, 2VWG, 2VWH, 2VWP, 2VWQ, 2VXH, 2VXI, 2VXS, 2VYN, 2VYQ, 2VYV, 2VZ0, 2VZ1, 2VZ2, 2VZ3, 2VZ7, 2VZL, 2VZM, 2W09, 2W0A, 2W0B, 2W0H, 2W0Q, 2W0U, 2W0X, 2W1Q, 2W1S, 2W1U, 2W23, 2W2I, 2W3A, 2W3B, 2W3M, 2W3R, 2W3S, 2W3V, 2W3W, 2W4Q, 2W54, 2W55, 2W7W, 2W8N, 2W8O, 2W8P, 2W8Q, 2W8R, 2W8Z, 2W90, 2W98, 2W9G, 2W9H, 2W9R, 2W9S, 2W9T, 2WA3, 2WA4, 2WBA, 2WBI, 2WBO, 2WBP, 2WBQ, 2WD4, 2WD7, 2WD8, 2WDB, 2WDQ, 2WDR, 2WDV, 2WDW, 2WDZ, 2WE7, 2WE8, 2WEK, 2WFC, 2WGH, 2WGQ, 2WGR, 2WGY, 2WH8, 2WHD, 2WHF, 2WHW, 2WI9, 2WIO, 2WJ3, 2WJ4, 2WJ6, 2WKO, 2WLU, 2WM2, 2WM4, 2WM5, 2WME, 2WMZ, 2WO0, 2WO7, 2WOF, 2WOH, 2WOI, 2WOV, 2WOW, 2WOX, 2WP5, 2WP6, 2WP9, 2WPC, 2WPE, 2WPF, 2WPN, 2WQ8, 2WQY, 2WS3, 2WSB, 2WSC, 2WSD, 2WSE, 2WSF, 2WTL, 2WU2, 2WU5, 2WUZ, 2WV2, 2WV8, 2WWN, 2WWO, 2WWR, 2WWU, 2WX2, 2WYT, 2WYU, 2WYV, 2WYW, 2WYZ, 2WZ0, 2WZ5, 2WZ6, 2WZV, 2WZW, 2X06, 2X07, 2X08, 2X0I, 2X0J, 2X0L, 2X0N, 2X0R, 2X0X, 2X1H, 2X22, 2X23, 2X2N, 2X3U, 2X50, 2X58, 2X5J, 2X5K, 2X5L, 2X5W, 2X7H, 2X7Y, 2X80, 2X8C, 2X8G, 2X8H, 2X8L, 2X99, 2X9G, 2X9I, 2X9J, 2X9N, 2X9O, 2X9V, 2XAA, 2XAF, 2XAG, 2XAH, 2XAJ, 2XAK, 2XAP, 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5FF3, 5FF4, 5FGJ, 5FHZ, 5FI8, 5FII, 5FIW, 5FJ2, 5FJ3, 5FJH, 5FJK, 5FLE, 5FLI, 5FLJ, 5FMH, 5FN0, 5FNB, 5FNE, 5FNN, 5FNO, 5FNP, 5FNY, 5FOI, 5FP3, 5FP4, 5FP7, 5FP8, 5FP9, 5FPA, 5FPB, 5FPL, 5FPU, 5FRC, 5FS6, 5FS7, 5FS8, 5FS9, 5FUJ, 5FUK, 5FUN, 5FUP, 5FUQ, 5FV3, 5FVO, 5FVP, 5FVQ, 5FVR, 5FVS, 5FVT, 5FVU, 5FVV, 5FVW, 5FVX, 5FVY, 5FVZ, 5FW0, 5FW4, 5FWE, 5FWJ, 5FWN, 5FX8, 5FXE, 5FXF, 5FXP, 5FXV, 5FXW, 5FXX, 5FXZ, 5FY0, 5FY1, 5FY7, 5FY8, 5FYC, 5FYD, 5FYH, 5FYI, 5FYM, 5FYT, 5FYU, 5FYY, 5FYZ, 5FZ0, 5FZ1, 5FZ3, 5FZ4, 5FZ6, 5FZ7, 5FZ9, 5FZA, 5FZB, 5FZC, 5FZH, 5FZK, 5FZL, 5FZO, 5G0N, 5G0O, 5G0P, 5G0S, 5G0T, 5G0U, 5G0V, 5G0W, 5G2O, 5G5G, 5G5H, 5G65, 5G66, 5G67, 5G68, 5G69, 5G6A, 5G6B, 5G6C, 5G6D, 5G6E, 5G6F, 5G6G, 5G6H, 5G6I, 5G6J, 5G6K, 5G6L, 5G6M, 5G6N, 5G6O, 5G6P, 5G6Q, 5GEP, 5GH0, 5GIR, 5GIS, 5GKN, 5GLS, 5GMO, 5GN8, 5GOU, 5GRE, 5GRF, 5GRH, 5GRI, 5GRL, 5GRT, 5GT2, 5GXG, 5GXU, 5H2Z, 5H59, 5H5J, 5H5Q, 5H5R, 5H5S, 5H8V, 5H8Y, 5H92, 5HA7, 5HDI, 5HF0, 5HF2, 5HFK, 5HHV, 5HHX, 5HHZ, 5HI3, 5HI4, 5HI5, 5HI6, 5HIN, 5HIW, 5HMR, 5HPB, 5HQE, 5HQP, 5HQX, 5HQY, 5HQZ, 5HRU, 5HS4, 5HS6, 5HSA, 5HSR, 5HSU, 5HT4, 5HT5, 5HTO, 5HUI, 5HVB, 5HVE, 5HWS, 5HWZ, 5HXW, 5HYM, 5I0R, 5I0S, 5I0T, 5I0U, 5I5I, 5I5J, 5I5M, 5I68, 5I6K, 5I6L, 5I6M, 5I6N, 5I6O, 5I6P, 5I7E, 5I7F, 5I7S, 5I7V, 5I8S, 5I8T, 5I8W, 5I8Y, 5I8Z, 5I91, 5I93, 5I95, 5I9L, 5I9M, 5I9N, 5IBD, 5IBE, 5IBF, 5IBG, 5IBH, 5IBI, 5IBJ, 5IBO, 5ICD, 5ICM, 5ICS, 5ID2, 5IFL, 5IJZ, 5IK1, 5IKD, 5IKG, 5IKI, 5IKQ, 5IKR, 5IKT, 5IKV, 5ILG, 5ILO, 5IM3, 5IQY, 5IR4, 5IR5, 5ISL, 5IT1, 5IT3, 5ITV, 5ITW, 5IUT, 5IUZ, 5IVB, 5IVC, 5IVE, 5IVF, 5IVJ, 5IVV, 5IVY, 5IW0, 5IWF, 5IXS, 5IXY, 5IY5, 5IZ4, 5J07, 5J0C, 5J0F, 5J0G, 5J32, 5J33, 5J34, 5J5R, 5J6D, 5J78, 5J7I, 5J8S, 5J8W, 5J92, 5J93, 5J9B, 5J9C, 5J9V, 5JA3, 5JAC, 5JAI, 5JAM, 5JAQ, 5JAZ, 5JBI, 5JC1, 5JCG, 5JCT, 5JE8, 5JFO, 5JH5, 5JHX, 5JHY, 5JHZ, 5JKL, 5JKM, 5JLC, 5JMP, 5JMW, 5JNL, 5JO0, 5JPQ, 5JPR, 5JQ2, 5JQR, 5JQU, 5JQV, 5JRO, 5JRY, 5JS6, 5JSC, 5JSF, 5JTD, 5JVY, 5JVZ, 5JW1, 5JW6, 5JWP, 5JY1, 5JY6, 5JYA, 5JYE, 5JYF, 5K02, 5K0Z, 5K10, 5K11, 5K12, 5K1E, 5K4L, 5K4X, 5K4Z, 5K8C, 5K8E, 5K9C, 5K9D, 5KAF, 5KAI, 5KBK, 5KBL, 5KBM, 5KCP, 5KCZ, 5KIA, 5KIR, 5KJ1, 5KJ6, 5KJA, 5KJB, 5KJC, 5KJD, 5KJE, 5KJF, 5KK0, 5KKA, 5KKC, 5KKZ, 5KLI, 5KLV, 5KNB, 5KNC, 5KND, 5KOH, 5KOI, 5KOJ, 5KOW, 5KOX, 5KR7, 5KSW, 5KT0, 5KVH, 5KVI, 5KVV, 5KWF, 5KXJ, 5KYT, 5KYV, 5KZ5, 5L01, 5L02, 5L05, 5L1O, 5L1P, 5L1Q, 5L1R, 5L1S, 5L1T, 5L1U, 5L1V, 5L1W, 5L3B, 5L3C, 5L3D, 5L3E, 5L3F, 5L3G, 5L46, 5L4S, 5L51, 5L53, 5L57, 5L58, 5L6F, 5L6G, 5L71, 5L7T, 5L7W, 5L7Y, 5L86, 5L90, 5L91, 5L92, 5L94, 5L9A, 5L9B, 5L9R, 5L9V, 5LA9, 5LAS, 5LAT, 5LB6, 5LBB, 5LBC, 5LBE, 5LBF, 5LBQ, 5LBT, 5LBU, 5LC1, 5LC5, 5LC8, 5LCX, 5LDG, 5LDH, 5LDW, 5LDX, 5LE0, 5LGE, 5LGN, 5LGT, 5LGU, 5LHG, 5LHH, 5LHI, 5LI6, 5LI7, 5LI8, 5LIE, 5LIK, 5LIU, 5LIV, 5LIW, 5LIX, 5LIY, 5LL1, 5LNK, 5LOQ, 5LPN, 5LSM, 5LUF, 5LW9, 5LWB, 5LXE, 5M0Z, 5M10, 5M2F, 5M6D, 5M98, 5MBX, 5MDH, 5MGB, 5MHS, 5MRL, 5MTP, 5MTQ, 5MTR, 5NSE, 5PAH, 5R1R, 5SUN, 5SVF, 5SVN, 5SVO, 5SW4, 5SW5, 5SW6, 5SWU, 5SWV, 5SX0, 5SX1, 5SX2, 5SX3, 5SX6, 5SX7, 5SXQ, 5SXR, 5SXS, 5SXT, 5SXW, 5SXX, 5SYH, 5SYI, 5SYJ, 5SYK, 5SYL, 5SYU, 5SYV, 5SYW, 5SYX, 5SYY, 5SZG, 5T0L, 5T0V, 5T2K, 5T5I, 5T5M, 5T5Q, 5T6Q, 5T73, 5T8Y, 5TBO, 5TC4, 5TD3, 5TE8, 5TF4, 5TFT, 5TFU, 5TI9, 5TIA, 5TIS, 5TJH, 5TPG, 5TQH, 5TQV, 5TR3, 5TRT, 5TTI, 5TVR, 5TVS, 5TWT, 5TX7, 5TZS, 5U1P, 5U25, 5U26, 5U27, 5U55, 5U57, 5U58, 5U5D, 5U5U, 5U5V, 5U5W, 5U5X, 5U5Y, 5U5Z, 5U60, 5U61, 5U63, 5U8U, 5U8V, 5U8W, 5U9P, 5UC8, 5UC9, 5UCA, 5UG2, 5UGS, 5UGT, 5UGU, 5UJF, 5UJK, 5ULV, 5UME, 5UMH, 5UNN, 5UOG, 5UPU, 5UPV, 5UQF, 5UQG, 5UQH, 5URQ, 5URR, 5USX, 5UTX, 5UUV, 5UWX, 5WQ5, 5WV3, 6ADH, 6ATJ, 6CCP, 6COX, 6CP4, 6CPP, 6DFR, 6GEP, 6ICD, 6LDH, 6NSE, 6PAH, 6R1R, 7ADH, 7ATJ, 7CAT, 7CCP, 7CPP, 7DFR, 7GEP, 7ICD, 7MDH, 7NSE, 7R1R, 8ADH, 8CAT, 8CPP, 8DFR, 8GEP, 8ICD, 8LDH, 8NSE, 9ICD, 9LDB, 9LDT, 9NSE, 10GS, 10MH, 11GS, 12GS, 13GS, 13PK, 14GS, 16GS, 16PK, 17GS, 18GS, 19GS, 1A06, 1A07, 1A08, 1A09, 1A0B, 1A0F, 1A0G, 1A0N, 1A0O, 1A1A, 1A1B, 1A1C, 1A1D, 1A1E, 1A1S, 1A25, 1A26, 1A2K, 1A2N, 1A3A, 1A3G, 1A3W, 1A3X, 1A47, 1A49, 1A4V, 1A59, 1A5T, 1A5U, 1A5V, 1A5W, 1A5X, 1A69, 1A7J, 1A81, 1A8I, 1A95, 1A96, 1A97, 1A98, 1A9O, 1A9P, 1A9Q, 1A9R, 1A9S, 1A9T, 1A9U, 1AAM, 1AAT, 1AAW, 1AB2, 1ABB, 1ABO, 1ABQ, 1ACM, 1AD1, 1AD4, 1AD5, 1AE9, 1AER, 1AF7, 1AFW, 1AGS, 1AGW, 1AH5, 1AHE, 1AHF, 1AHG, 1AHP, 1AHX, 1AHY, 1AIA, 1AIB, 1AIC, 1AIH, 1AIQ, 1AJ0, 1AJ2, 1AJ8, 1AJM, 1AJR, 1AJS, 1AJZ, 1AK2, 1AKA, 1AKB, 1AKC, 1AKE, 1AKM, 1AKY, 1AL6, 1AMA, 1AMQ, 1AMR, 1AMS, 1AMZ, 1AN5, 1ANK, 1AO0, 1AOB, 1AOT, 1AOU, 1APM, 1AQ1, 1AQF, 1AQI, 1AQJ, 1AQU, 1AQV, 1AQW, 1AQX, 1AQY, 1ARG, 1ARH, 1ARI, 1ARO, 1ARS, 1ART, 1ASA, 1ASB, 1ASC, 1ASD, 1ASE, 1ASF, 1ASG, 1ASL, 1ASM, 1ASN, 1AT1, 1ATP, 1AUE, 1AV6, 1AVZ, 1AW9, 1AWJ, 1AWO, 1AWW, 1AWX, 1AX3, 1AXD, 1AXR, 1AXW, 1AY0, 1AY4, 1AY5, 1AY8, 1AYA, 1AYB, 1AYC, 1AYD, 1AYZ, 1AZG, 1AZY, 1B02, 1B0X, 1B38, 1B39, 1B3N, 1B3Q, 1B42, 1B48, 1B49, 1B4D, 1B4F, 1B4P, 1B4S, 1B4X, 1B55, 1B5D, 1B5E, 1B5O, 1B5P, 1B5S, 1B6B, 1B6C, 1B6T, 1B7B, 1B8H, 1B8N, 1B8O, 1B8X, 1B92, 1B99, 1B9D, 1B9F, 1BAK, 1BAL, 1BAY, 1BBL, 1BBZ, 1BC5, 1BCP, 1BD3, 1BD4, 1BDF, 1BDG, 1BDJ, 1BDU, 1BDY, 1BE4, 1BG0, 1BG3, 1BG5, 1BGT, 1BGU, 1BGX, 1BHF, 1BHH, 1BHI, 1BHJ, 1BHL, 1BHN, 1BI5, 1BI7, 1BI8, 1BID, 1BIF, 1BJ4, 1BJG, 1BJN, 1BJO, 1BJP, 1BJW, 1BKG, 1BKL, 1BKM, 1BKO, 1BKP, 1BKX, 1BKY, 1BL6, 1BL7, 1BLE, 1BLJ, 1BLK, 1BLX, 1BMK, 1BMT, 1BNO, 1BNP, 1BO1, 1BO5, 1BO6, 1BO7, 1BO8, 1BOB, 1BOH, 1BOI, 1BOO, 1BOT, 1BP0, 1BP6, 1BPB, 1BPD, 1BPE, 1BPJ, 1BPV, 1BPX, 1BPY, 1BPZ, 1BQ1, 1BQ2, 1BQ6, 1BQA, 1BQD, 1BQM, 1BQN, 1BRW, 1BS0, 1BSF, 1BSP, 1BT4, 1BTE, 1BTK, 1BU1, 1BU6, 1BUH, 1BUX, 1BW0, 1BWD, 1BWF, 1BWN, 1BX3, 1BX4, 1BX6, 1BX9, 1BXD, 1BXI, 1BYE, 1BYG, 1BYN, 1BYU, 1BZY, 1C0M, 1C0N, 1C0T, 1C0U, 1C1A, 1C1B, 1C1C, 1C1Y, 1C2P, 1C2T, 1C2Y, 1C3E, 1C3J, 1C3Q, 1C3X, 1C41, 1C44, 1C4T, 1C4Z, 1C50, 1C72, 1C8K, 1C8L, 1C9C, 1C9Y, 1CB0, 1CBF, 1CBK, 1CDD, 1CDE, 1CDG, 1CDK, 1CDL, 1CDM, 1CEZ, 1CF4, 1CG6, 1CGK, 1CGT, 1CGU, 1CGV, 1CGW, 1CGX, 1CGY, 1CGZ, 1CHW, 1CI7, 1CIA, 1CIU, 1CJ0, 1CJA, 1CJB, 1CJM, 1CJW, 1CKE, 1CKI, 1CKJ, 1CKK, 1CKM, 1CKN, 1CKO, 1CKP, 1CLA, 1CLQ, 1CM0, 1CM1, 1CM2, 1CM3, 1CM4, 1CM5, 1CM8, 1CMK, 1CML, 1COO, 1COZ, 1CQ6, 1CQ7, 1CQ8, 1CR0, 1CR1, 1CR2, 1CR4, 1CRK, 1CS1, 1CSC, 1CSH, 1CSI, 1CSJ, 1CSK, 1CSN, 1CSR, 1CSS, 1CSY, 1CSZ, 1CTP, 1CTS, 1CVS, 1CWD, 1CWE, 1CWY, 1CXE, 1CXF, 1CXH, 1CXI, 1CXK, 1CXL, 1CXZ, 1CYG, 1CZ1, 1CZA, 1CZC, 1CZE, 1D01, 1D06, 1D09, 1D0E, 1D0Q, 1D0S, 1D0V, 1D1U, 1D2C, 1D2G, 1D2H, 1D2Z, 1D3C, 1D5A, 1D6F, 1D6H, 1D6I, 1D6J, 1D6N, 1D6S, 1D7F, 1D8C, 1D8D, 1D8E, 1D8Y, 1D9D, 1D9E, 1D9F, 1DAA, 1DAW, 1DAY, 1DBR, 1DCE, 1DCF, 1DCJ, 1DCT, 1DCZ, 1DD2, 1DD8, 1DD9, 1DDE, 1DDM, 1DDU, 1DED, 1DEK, 1DEL, 1DFO, 1DGK, 1DGM, 1DHS, 1DI0, 1DI6, 1DI7, 1DI8, 1DI9, 1DIK, 1DJ9, 1DJE, 1DJS, 1DK2, 1DK3, 1DKR, 1DKU, 1DL5, 1DLG, 1DLO, 1DLU, 1DLV, 1DM2, 1DM3, 1DMA, 1DML, 1DMZ, 1DNA, 1DOQ, 1DP2, 1DP6, 1DP8, 1DP9, 1DPB, 1DPC, 1DPD, 1DPI, 1DQN, 1DQP, 1DQY, 1DQZ, 1DRM, 1DS5, 1DSY, 1DTU, 1DTY, 1DU2, 1DUG, 1DUS, 1DUV, 1DVR, 1DXH, 1DY3, 1DZ7, 1DZF, 1E0A, 1E0C, 1E0H, 1E0J, 1E0K, 1E0N, 1E0O, 1E0T, 1E0U, 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2ESR, 2ETK, 2ETM, 2ETR, 2ETZ, 2EU0, 2EU8, 2EU9, 2EUF, 2EVA, 2EVB, 2EVN, 2EWA, 2EWG, 2EWS, 2EX0, 2EX1, 2EX3, 2EX4, 2EXC, 2EXE, 2EXM, 2EZA, 2EZB, 2EZC, 2EZW, 2F02, 2F0Y, 2F17, 2F1F, 2F2C, 2F2S, 2F2T, 2F2U, 2F3I, 2F3M, 2F3P, 2F3Q, 2F3R, 2F3S, 2F3T, 2F3U, 2F48, 2F49, 2F4J, 2F4N, 2F4W, 2F57, 2F59, 2F5I, 2F62, 2F63, 2F64, 2F65, 2F67, 2F69, 2F6U, 2F6X, 2F7E, 2F7K, 2F7M, 2F7X, 2F7Z, 2F86, 2F89, 2F8C, 2F8E, 2F8F, 2F8J, 2F8V, 2F8Z, 2F92, 2F94, 2F9G, 2F9K, 2F9T, 2F9W, 2FA2, 2FAP, 2FB8, 2FBM, 2FCA, 2FCI, 2FDB, 2FEM, 2FEO, 2FEP, 2FET, 2FEW, 2FF5, 2FFC, 2FFR, 2FFU, 2FFV, 2FG6, 2FG7, 2FGC, 2FGI, 2FH9, 2FHE, 2FHJ, 2FHK, 2FIQ, 2FJV, 2FJW, 2FJX, 2FK7, 2FK8, 2FK9, 2FLL, 2FLN, 2FLP, 2FM4, 2FM7, 2FMP, 2FMQ, 2FMS, 2FMT, 2FN6, 2FNI, 2FNU, 2FO0, 2FOR, 2FPO, 2FR0, 2FR1, 2FRX, 2FS5, 2FSL, 2FSM, 2FSO, 2FSP, 2FST, 2FSU, 2FT0, 2FTK, 2FTN, 2FTO, 2FTQ, 2FTZ, 2FUH, 2FUM, 2FV7, 2FVD, 2FVJ, 2FVP, 2FVQ, 2FVR, 2FVS, 2FW2, 2FW3, 2FXF, 2FXV, 2FY2, 2FY3, 2FY4, 2FY5, 2FYC, 2FYD, 2FYF, 2FYO, 2FYS, 2FYT, 2FZC, 2FZG, 2FZK, 2G01, 2G03, 2G0B, 2G0Q, 2G15, 2G1H, 2G1J, 2G1K, 2G1P, 2G1T, 2G2D, 2G2F, 2G2H, 2G2I, 2G2O, 2G2Y, 2G2Z, 2G35, 2G3A, 2G3T, 2G4C, 2G50, 2G6W, 2G70, 2G71, 2G72, 2G7H, 2G7M, 2G86, 2G89, 2G8A, 2G8D, 2G8N, 2G8X, 2G95, 2G96, 2G97, 2G9Q, 2G9R, 2G9U, 2G9V, 2G9X, 2G9Z, 2GA8, 2GA9, 2GAA, 2GAF, 2GAK, 2GAM, 2GAQ, 2GAR, 2GB4, 2GBL, 2GC8, 2GCD, 2GDO, 2GDR, 2GDU, 2GDV, 2GE9, 2GEB, 2GEJ, 2GEK, 2GES, 2GET, 2GEU, 2GEV, 2GFC, 2GFS, 2GFV, 2GFW, 2GFX, 2GFY, 2GG4, 2GG6, 2GGA, 2GGD, 2GGO, 2GGQ, 2GHG, 2GHL, 2GHM, 2GHO, 2GHQ, 2GHR, 2GHT, 2GIQ, 2GIR, 2GIV, 2GJ3, 2GJ4, 2GK9, 2GKS, 2GL0, 2GLR, 2GLU, 2GM7, 2GM8, 2GM9, 2GMI, 2GMX, 2GNF, 2GNG, 2GNH, 2GNI, 2GNJ, 2GNL, 2GO0, 2GOO, 2GP6, 2GPA, 2GPB, 2GPH, 2GPN, 2GQ2, 2GQ3, 2GQD, 2GQG, 2GQH, 2GRJ, 2GS2, 2GS6, 2GS7, 2GSF, 2GSQ, 2GSR, 2GSS, 2GST, 2GTD, 2GTK, 2GTM, 2GTN, 2GTR, 2GTU, 2GU8, 2GUI, 2GV9, 2GVG, 2GVJ, 2GVL, 2GVQ, 2GWH, 2GWL, 2GWM, 2GWS, 2GY5, 2GY7, 2GYK, 2GYO, 2GZE, 2GZF, 2GZG, 2GZI, 2GZJ, 2H00, 2H06, 2H07, 2H08, 2H11, 2H12, 2H1F, 2H1H, 2H1R, 2H1Y, 2H21, 2H23, 2H29, 2H2A, 2H2E, 2H2J, 2H2W, 2H34, 2H3B, 2H3D, 2H3E, 2H3G, 2H3P, 2H3U, 2H3W, 2H4T, 2H62, 2H64, 2H6D, 2H6F, 2H6G, 2H6H, 2H6I, 2H7D, 2H7E, 2H7O, 2H7V, 2H8A, 2H8D, 2H8F, 2H8H, 2H8K, 2H8O, 2H92, 2H96, 2H9R, 2H9V, 2HA8, 2HAI, 2HAJ, 2HAK, 2HBH, 2HC4, 2HCD, 2HCK, 2HCN, 2HCR, 2HCS, 2HDA, 2HDB, 2HDK, 2HDX, 2HEL, 2HEN, 2HF1, 2HFP, 2HFS, 2HFU, 2HFZ, 2HG4, 2HG8, 2HGW, 2HGX, 2HH9, 2HHC, 2HHF, 2HHP, 2HHQ, 2HHS, 2HHT, 2HHU, 2HHV, 2HHW, 2HHX, 2HID, 2HIG, 2HIW, 2HJ9, 2HJE, 2HK5, 2HLE, 2HLH, 2HLQ, 2HLR, 2HLZ, 2HMA, 2HMF, 2HMI, 2HMY, 2HN9, 2HND, 2HNH, 2HNL, 2HNY, 2HNZ, 2HOE, 2HOG, 2HPI, 2HPM, 2HPR, 2HQA, 2HQQ, 2HR1, 2HR7, 2HR8, 2HRD, 2HRO, 2HSE, 2HSW, 2HTE, 2HTF, 2HTM, 2HUF, 2HUI, 2HUQ, 2HUR, 2HUT, 2HUU, 2HUV, 2HUX, 2HUZ, 2HV9, 2HVD, 2HVE, 2HVH, 2HVI, 2HW0, 2HW1, 2HW2, 2HW3, 2HW6, 2HW7, 2HWG, 2HWH, 2HWI, 2HWN, 2HWO, 2HWP, 2HWT, 2HWU, 2HXL, 2HXQ, 2HY0, 2HY5, 2HY7, 2HY8, 2HYB, 2HYY, 2HZ0, 2HZ4, 2HZI, 2HZN, 2I00, 2I0E, 2I0F, 2I0H, 2I0V, 2I0Y, 2I19, 2I1L, 2I1M, 2I1R, 2I1V, 2I1W, 2I29, 2I2A, 2I2B, 2I2C, 2I2D, 2I2F, 2I2X, 2I3S, 2I40, 2I4T, 2I5B, 2I5E, 2I5J, 2I5K, 2I5O, 2I5T, 2I62, 2I6A, 2I6B, 2I6D, 2I6L, 2I6U, 2I79, 2I7C, 2I7N, 2I7P, 2I7Q, 2I8A, 2I94, 2I9G, 2I9K, 2IA5, 2IA6, 2IAJ, 2IB7, 2IB8, 2IB9, 2IBK, 2IBP, 2IBS, 2IBT, 2IBU, 2IBW, 2IBY, 2IC3, 2IC7, 2ICX, 2ICY, 2IDJ, 2IDK, 2IDO, 2IDX, 2IE8, 2IEG, 2IEI, 2IEJ, 2IEP, 2IEW, 2IF2, 2IF8, 2IFC, 2IFG, 2IFT, 2IG7, 2IGB, 2IGQ, 2IH2, 2IH4, 2IH5, 2IHJ, 2IHK, 2IHM, 2IHT, 2IHU, 2IHV, 2IHW, 2IHZ, 2II3, 2II4, 2II5, 2II6, 2IIB, 2IIK, 2IIM, 2IIP, 2IIQ, 2IIR, 2IJ9, 2IJA, 2IJD, 2IJF, 2IJM, 2IJN, 2IKF, 2IL3, 2IL4, 2ILL, 2ILV, 2ILY, 2ILZ, 2IM0, 2IM1, 2IM2, 2IM3, 2IMI, 2IMK, 2IMW, 2IN6, 2IO6, 2IP2, 2IPO, 2IPX, 2IQ5, 2IRU, 2IRX, 2IRY, 2ISC, 2ISO, 2ISP, 2ISQ, 2IT2, 2IT3, 2ITM, 2ITN, 2ITO, 2ITP, 2ITQ, 2ITT, 2ITU, 2ITV, 2ITW, 2ITX, 2ITY, 2ITZ, 2IU0, 2IU3, 2IU4, 2IU6, 2IU8, 2IU9, 2IUA, 2IUH, 2IV7, 2IVN, 2IVO, 2IVP, 2IVS, 2IVT, 2IVU, 2IVV, 2IW1, 2IW6, 2IW8, 2IW9, 2IWA, 2IWI, 2IWL, 2IWY, 2IWZ, 2IX4, 2IYA, 2IYF, 2IYQ, 2IYR, 2IYS, 2IYT, 2IYU, 2IYV, 2IYW, 2IYX, 2IYY, 2IYZ, 2IZR, 2IZS, 2IZT, 2IZU, 2IZX, 2IZY, 2J0A, 2J0B, 2J0F, 2J0I, 2J0J, 2J0K, 2J0L, 2J0M, 2J0W, 2J0X, 2J1O, 2J1P, 2J1Q, 2J2I, 2J2S, 2J41, 2J48, 2J4J, 2J4K, 2J4L, 2J4Z, 2J50, 2J51, 2J5E, 2J5F, 2J5T, 2J5V, 2J6A, 2J6H, 2J6M, 2J6S, 2J6T, 2J6U, 2J7T, 2J80, 2J87, 2J8A, 2J8H, 2J8M, 2J8N, 2J8O, 2J8R, 2J90, 2J9H, 2J9M, 2J9R, 2JA1, 2JA5, 2JA6, 2JA7, 2JA8, 2JAM, 2JAN, 2JAQ, 2JAS, 2JAT, 2JAV, 2JBH, 2JBM, 2JBO, 2JBP, 2JBZ, 2JC3, 2JC6, 2JCA, 2JCJ, 2JCK, 2JCL, 2JCO, 2JCS, 2JD5, 2JDO, 2JDR, 2JDS, 2JDT, 2JDV, 2JDW, 2JDX, 2JED, 2JEF, 2JEG, 2JEI, 2JEJ, 2JEL, 2JEO, 2JEV, 2JF2, 2JF3, 2JFB, 2JFD, 2JFK, 2JFL, 2JFM, 2JG1, 2JG2, 2JG3, 2JG5, 2JGR, 2JGT, 2JGU, 2JGV, 2JGZ, 2JI5, 2JII, 2JIT, 2JIU, 2JIV, 2JJ4, 2JJ8, 2JJE, 2JJF, 2JJG, 2JJH, 2JJM, 2JJQ, 2JJX, 2JK8, 2JKK, 2JKM, 2JKO, 2JKQ, 2JKY, 2JKZ, 2JL9, 2JLA, 2JLC, 2JLD, 2JLF, 2JLG, 2JO8, 2JQI, 2JQJ, 2JQL, 2JQX, 2JRH, 2JTQ, 2JTR, 2JTS, 2JU0, 2JU1, 2JU2, 2JV0, 2JVI, 2JVJ, 2JVK, 2JVN, 2JW5, 2JWA, 2JYI, 2JYQ, 2JZB, 2JZC, 2JZH, 2JZN, 2JZO, 2K0F, 2K1K, 2K1L, 2K2N, 2K3G, 2K5X, 2K79, 2K7A, 2K7V, 2K8F, 2K9Y, 2KA4, 2KA6, 2KBX, 2KCE, 2KCW, 2KDC, 2KEQ, 2KFN, 2KFZ, 2KGT, 2KHU, 2KHW, 2KI5, 2KIO, 2KIT, 2KJE, 2KJH, 2KK1, 2KK6, 2KKF, 2KKJ, 2KL2, 2KLI, 2KLY, 2KO1, 2KOI, 2KQF, 2KR5, 2KR9, 2KS1, 2KS6, 2KSD, 2KSE, 2KSF, 2KSO, 2KTF, 2KTQ, 2KTY, 2KU7, 2KUD, 2KUE, 2KUF, 2KUI, 2KUL, 2KUP, 2KUQ, 2KWF, 2KWU, 2KWV, 2KX7, 2KX9, 2KXE, 2KYL, 2KYM, 2KYR, 2KYU, 2KZM, 2KZZ, 2L05, 2L0F, 2L0G, 2L0I, 2L14, 2L2P, 2L2Q, 2L2T, 2L30, 2L31, 2L4K, 2L5H, 2L5S, 2L6F, 2L6G, 2L6H, 2L6W, 2L7C, 2L7L, 2L7P, 2L84, 2L85, 2L9U, 2L9Z, 2LAH, 2LAV, 2LCJ, 2LCR, 2LCS, 2LCT, 2LCX, 2LD6, 2LE0, 2LE3, 2LFR, 2LFS, 2LGC, 2LGZ, 2LJR, 2LK4, 2LMC, 2LMJ, 2LN0, 2LO6, 2LP4, 2LP5, 2LQH, 2LQI, 2LRK, 2LRL, 2LRU, 2LSG, 2LSI, 2LSJ, 2LSK, 2LSY, 2LTO, 2LTU, 2LUL, 2LV6, 2LV9, 2LVW, 2LW8, 2LWW, 2LXE, 2LXP, 2LXS, 2LXT, 2LY7, 2LZL, 2LZN, 2M0B, 2M20, 2M2I, 2M2T, 2M2U, 2M2V, 2M2W, 2M59, 2M76, 2M9X, 2MAX, 2MBB, 2MC1, 2MC6, 2MDC, 2MDG, 2MDI, 2MDJ, 2MET, 2MEU, 2MFQ, 2MFR, 2MH0, 2ML8, 2MOW, 2MP0, 2MPK, 2MQI, 2MRJ, 2MRK, 2MSE, 2MSR, 2MT6, 2MTN, 2MTZ, 2MV7, 2MWC, 2MX1, 2MZB, 2MZD, 2N19, 2N1A, 2N1G, 2N2A, 2N56, 2N58, 2N5S, 2N5T, 2N6D, 2N73, 2N7Z, 2N83, 2N8A, 2N90, 2NAA, 2NCK, 2NCZ, 2ND1, 2NLX, 2NMT, 2NNW, 2NO0, 2NO1, 2NO3, 2NO6, 2NO7, 2NO9, 2NOA, 2NOM, 2NP6, 2NP7, 2NP8, 2NPA, 2NPO, 2NPQ, 2NPT, 2NPU, 2NQ5, 2NQK, 2NQM, 2NQN, 2NQO, 2NQQ, 2NQR, 2NQS, 2NQU, 2NQV, 2NQZ, 2NRB, 2NRC, 2NRH, 2NRO, 2NRP, 2NRS, 2NRU, 2NRY, 2NSO, 2NT8, 2NTO, 2NV7, 2NVQ, 2NVT, 2NVX, 2NVY, 2NVZ, 2NWR, 2NWS, 2NX1, 2NX3, 2NXC, 2NXE, 2NXG, 2NXH, 2NXI, 2NXJ, 2NXN, 2NYG, 2NYU, 2NZ8, 2NZI, 2NZT, 2NZU, 2NZV, 2NZW, 2NZX, 2NZY, 2O05, 2O06, 2O07, 2O0B, 2O0D, 2O0E, 2O0L, 2O0R, 2O0U, 2O0X, 2O0Z, 2O15, 2O18, 2O1B, 2O1P, 2O1S, 2O1X, 2O25, 2O26, 2O28, 2O2K, 2O2U, 2O2V, 2O3A, 2O3P, 2O5D, 2O5G, 2O5I, 2O5J, 2O5K, 2O63, 2O64, 2O65, 2O6H, 2O6L, 2O88, 2O8J, 2O8R, 2O8Y, 2O9B, 2O9C, 2O9I, 2OA7, 2OAC, 2OAD, 2OAT, 2OB1, 2OB2, 2OB4, 2OBF, 2OBJ, 2OBV, 2OBX, 2OC4, 2OC9, 2OCP, 2OCX, 2ODB, 2ODT, 2OEC, 2OEF, 2OEG, 2OEN, 2OF2, 2OF4, 2OFF, 2OFU, 2OFV, 2OFW, 2OFX, 2OG8, 2OGA, 2OGD, 2OGE, 2OGQ, 2OGV, 2OGY, 2OH0, 2OH1, 2OH2, 2OH4, 2OI2, 2OI3, 2OI4, 2OI5, 2OI6, 2OI7, 2OIB, 2OIC, 2OID, 2OIQ, 2OJ2, 2OJ9, 2OJF, 2OJG, 2OJI, 2OJJ, 2OJW, 2OJX, 2OK1, 2OKC, 2OKO, 2OKR, 2OLC, 2OLS, 2OLU, 2OLV, 2OMD, 2OMK, 2ON6, 2ONB, 2ONL, 2ONY, 2ONZ, 2OO3, 2OO7, 2OO8, 2OOC, 2OOX, 2OOY, 2OPB, 2OPM, 2OPN, 2OPP, 2OPQ, 2OPR, 2OPS, 2OQ1, 2OQ3, 2OQB, 2OQO, 2ORA, 2ORD, 2ORE, 2ORI, 2ORV, 2ORW, 2OSB, 2OSC, 2OTC, 2OU2, 2OU7, 2OV2, 2OV8, 2OVB, 2OVF, 2OW3, 2OWB, 2OWC, 2OWF, 2OWG, 2OWH, 2OWJ, 2OWK, 2OWU, 2OWV, 2OWW, 2OWX, 2OXD, 2OXF, 2OXO, 2OXX, 2OXY, 2OYN, 2OYQ, 2OYR, 2OZA, 2OZG, 2OZM, 2OZO, 2OZS, 2OZU, 2P02, 2P04, 2P08, 2P0C, 2P0E, 2P0U, 2P0W, 2P1C, 2P1D, 2P1J, 2P1R, 2P1Z, 2P2G, 2P2H, 2P2I, 2P2O, 2P2V, 2P2W, 2P2X, 2P33, 2P35, 2P3G, 2P3L, 2P3M, 2P3O, 2P3Q, 2P3S, 2P40, 2P41, 2P4I, 2P4S, 2P54, 2P55, 2P56, 2P5C, 2P5F, 2P5G, 2P5I, 2P5O, 2P5X, 2P66, 2P6D, 2P6E, 2P6F, 2P6G, 2P6I, 2P6K, 2P6L, 2P8U, 2P9D, 2P9Q, 2P9T, 2PA4, 2PA8, 2PAA, 2PAW, 2PAX, 2PB0, 2PB1, 2PB2, 2PB4, 2PB5, 2PB6, 2PBE, 2PBF, 2PBO, 2PBR, 2PC1, 2PC6, 2PC8, 2PCA, 2PCG, 2PCH, 2PCI, 2PCK, 2PCM, 2PDD, 2PDE, 2PDO, 2PE0, 2PE1, 2PE2, 2PEG, 2PEY, 2PF0, 2PFD, 2PFF, 2PFK, 2PFL, 2PFN, 2PFO, 2PFP, 2PFQ, 2PFR, 2PGA, 2PGG, 2PGT, 2PGV, 2PGX, 2PGY, 2PH5, 2PHK, 2PI4, 2PI5, 2PJD, 2PJY, 2PK9, 2PKF, 2PKK, 2PKM, 2PKN, 2PKW, 2PL0, 2PL5, 2PLD, 2PLE, 2PLR, 2PLW, 2PMI, 2PMT, 2PMZ, 2PN7, 2PNA, 2PNB, 2PNG, 2PNR, 2PO4, 2POC, 2POL, 2POT, 2PPB, 2PPH, 2PPQ, 2PQ6, 2PQ8, 2PQ9, 2PQB, 2PQC, 2PQD, 2PQF, 2PQT, 2PR1, 2PR8, 2PRB, 2PRG, 2PRI, 2PRJ, 2PRY, 2PRZ, 2PS1, 2PSQ, 2PSS, 2PT5, 2PT6, 2PT9, 2PTK, 2PU8, 2PUI, 2PUL, 2PUN, 2PUP, 2PUS, 2PUT, 2PUU, 2PUV, 2PUW, 2PVF, 2PVH, 2PVJ, 2PVK, 2PVL, 2PVM, 2PVN, 2PVQ, 2PVR, 2PVY, 2PWL, 2PWP, 2PWU, 2PWV, 2PWY, 2PX2, 2PX4, 2PX5, 2PX6, 2PX7, 2PX8, 2PXA, 2PXC, 2PXI, 2PY3, 2PY5, 2PY6, 2PYD, 2PYI, 2PYJ, 2PYL, 2PYW, 2PZ5, 2PZI, 2PZP, 2PZR, 2PZS, 2PZY, 2Q0B, 2Q0C, 2Q0D, 2Q0E, 2Q0F, 2Q0G, 2Q0N, 2Q2R, 2Q3B, 2Q3C, 2Q3D, 2Q3M, 2Q3Z, 2Q41, 2Q44, 2Q4J, 2Q4V, 2Q4Y, 2Q53, 2Q5F, 2Q5R, 2Q5T, 2Q5W, 2Q66, 2Q6I, 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2CC0, 2CC1, 2CCL, 2CCQ, 2CCR, 2CDR, 2CDS, 2CE3, 2CE7, 2CEA, 2CEJ, 2CEK, 2CEL, 2CEM, 2CEN, 2CEQ, 2CER, 2CES, 2CET, 2CEV, 2CF4, 2CF8, 2CF9, 2CFR, 2CFS, 2CFT, 2CFV, 2CGA, 2CGI, 2CHA, 2CHM, 2CHN, 2CHO, 2CI1, 2CI3, 2CI4, 2CI5, 2CI6, 2CI7, 2CIJ, 2CIO, 2CIP, 2CIT, 2CJE, 2CJI, 2CJL, 2CJP, 2CJX, 2CJY, 2CJZ, 2CK3, 2CKA, 2CKC, 2CKG, 2CKH, 2CKI, 2CKM, 2CKR, 2CKS, 2CL2, 2CLT, 2CLY, 2CM0, 2CM1, 2CM2, 2CM3, 2CM7, 2CM8, 2CMA, 2CMB, 2CMC, 2CMF, 2CML, 2CMU, 2CMY, 2CN0, 2CN1, 2CNC, 2CNE, 2CNF, 2CNG, 2CNH, 2CNI, 2CNK, 2CNL, 2CNN, 2CNO, 2COV, 2CPR, 2CPT, 2CPU, 2CPW, 2CQA, 2CS1, 2CS5, 2CTB, 2CTC, 2CU7, 2CUT, 2CUU, 2CV3, 2CVR, 2CWI, 2CWJ, 2CWR, 2CXV, 2CY7, 2CY9, 2CYD, 2CYE, 2CYG, 2CZH, 2CZI, 2CZK, 2CZN, 2CZQ, 2CZV, 2D00, 2D05, 2D07, 2D0A, 2D0B, 2D0C, 2D0D, 2D0F, 2D0G, 2D0H, 2D1G, 2D1I, 2D1J, 2D1K, 2D1N, 2D1O, 2D1Z, 2D20, 2D22, 2D23, 2D24, 2D26, 2D2D, 2D2G, 2D2H, 2D2J, 2D2O, 2D2V, 2D30, 2D3K, 2D3L, 2D3N, 2D3Y, 2D43, 2D44, 2D49, 2D4G, 2D4H, 2D4I, 2D4J, 2D4K, 2D4L, 2D4M, 2D4N, 2D5J, 2D5L, 2D5R, 2D5U, 2D6B, 2D73, 2D7D, 2D7E, 2D7G, 2D7H, 2D7U, 2D80, 2D81, 2D8L, 2D8W, 2D91, 2D97, 2D98, 2D9I, 2D9W, 2DAG, 2DAK, 2DAT, 2DB2, 2DB3, 2DB4, 2DBO, 2DBT, 2DC6, 2DC7, 2DC8, 2DC9, 2DCA, 2DCB, 2DCC, 2DCD, 2DCE, 2DCF, 2DCH, 2DCJ, 2DCK, 2DCM, 2DCY, 2DCZ, 2DD4, 2DD5, 2DDF, 2DDG, 2DDK, 2DDR, 2DDS, 2DDT, 2DDU, 2DDX, 2DDY, 2DE2, 2DE3, 2DE4, 2DE8, 2DE9, 2DEA, 2DEF, 2DEM, 2DEO, 2DEP, 2DEW, 2DEX, 2DEY, 2DF5, 2DFB, 2DFC, 2DFE, 2DFF, 2DFH, 2DFI, 2DFJ, 2DFP, 2DFX, 2DGA, 2DGZ, 2DHC, 2DHD, 2DHE, 2DHR, 2DI4, 2DIE, 2DJF, 2DJG, 2DJH, 2DJM, 2DJU, 2DKF, 2DKO, 2DKQ, 2DKV, 2DL6, 2DLE, 2DLH, 2DLW, 2DM9, 2DMA, 2DN7, 2DNJ, 2DNR, 2DO2, 2DOH, 2DOI, 2DOJ, 2DOK, 2DOO, 2DOS, 2DP4, 2DP6, 2DP8, 2DPX, 2DPY, 2DPZ, 2DQ6, 2DQA, 2DQB, 2DQC, 2DQD, 2DQE, 2DQF, 2DQG, 2DQH, 2DQI, 2DQJ, 2DQK, 2DQM, 2DQS, 2DQV, 2DQX, 2DQY, 2DQZ, 2DR0, 2DRO, 2DRQ, 2DRR, 2DRS, 2DS9, 2DSB, 2DSC, 2DSD, 2DSF, 2DSK, 2DSN, 2DUA, 2DUC, 2DUJ, 2DUK, 2DVC, 2DVN, 2DVO, 2DVP, 2DW0, 2DW1, 2DW2, 2DW5, 2DWA, 2DWH, 2DWI, 2DWJ, 2DWL, 2DWM, 2DWN, 2DX0, 2DXB, 2DXC, 2DXL, 2DXN, 2DXP, 2DXR, 2DXY, 2DY7, 2DY8, 2DYN, 2DYU, 2DYV, 2DYX, 2DZY, 2DZZ, 2E00, 2E01, 2E02, 2E03, 2E0J, 2E0L, 2E0M, 2E0O, 2E0P, 2E0S, 2E0T, 2E11, 2E1B, 2E1E, 2E1F, 2E1S, 2E1W, 2E25, 2E26, 2E29, 2E2D, 2E2K, 2E2L, 2E33, 2E3J, 2E3W, 2E3X, 2E3Z, 2E40, 2E4L, 2E4T, 2E52, 2E5K, 2E5T, 2E5U, 2E5X, 2E5Y, 2E67, 2E69, 2E6B, 2E6C, 2E6E, 2E6G, 2E6H, 2E6L, 2E6M, 2E7V, 2E7Y, 2E85, 2E8I, 2E8O, 2E8Y, 2E8Z, 2E9B, 2E9L, 2E9M, 2EA0, 2EA1, 2EA2, 2EA3, 2EA4, 2EAB, 2EAC, 2EAD, 2EAE, 2EAR, 2EAT, 2EAU, 2EB0, 2EB1, 2EBJ, 2EBN, 2EBS, 2EC9, 2ECF, 2EDA, 2EDC, 2EDX, 2EDY, 2EE1, 2EE4, 2EE5, 2EEE, 2EEK, 2EEP, 2EEX, 2EF1, 2EF4, 2EF5, 2EG6, 2EG7, 2EG8, 2EG9, 2EGI, 2EGJ, 2EGR, 2EHG, 2EI6, 2EI7, 2EI8, 2EIP, 2EIV, 2EIZ, 2EJ1, 2EKS, 2ENB, 2END, 2ENG, 2ENU, 2ENW, 2ENX, 2EO0, 2EO7, 2EO8, 2EOA, 2EOB, 2EPB, 2EPE, 2EPK, 2EPL, 2EPM, 2EPN, 2EPO, 2EQD, 2EQE, 2EQF, 2EQG, 2EQI, 2EQL, 2EQS, 2ER0, 2ER6, 2ER7, 2ER9, 2ERH, 2ERO, 2ERP, 2ERQ, 2ERV, 2ES4, 2ESB, 2EST, 2ETF, 2ETJ, 2ETL, 2EUG, 2EVC, 2EVM, 2EVO, 2EW5, 2EW6, 2EW7, 2EW9, 2EWB, 2EWO, 2EWY, 2EX2, 2EX5, 2EX6, 2EX8, 2EX9, 2EXA, 2EXB, 2EXH, 2EXI, 2EXJ, 2EXK, 2EXO, 2EXZ, 2EY1, 2EY2, 2EY5, 2EY6, 2EYF, 2EYH, 2EYJ, 2EYL, 2EYM, 2EYO, 2EYP, 2EYQ, 2EZ6, 2EZH, 2EZI, 2EZK, 2EZL, 2EZV, 2F03, 2F0D, 2F0E, 2F0F, 2F0G, 2F0H, 2F0I, 2F0J, 2F0K, 2F0L, 2F0M, 2F0N, 2F0O, 2F0P, 2F0Q, 2F0S, 2F0T, 2F0U, 2F0V, 2F0W, 2F0X, 2F0Z, 2F10, 2F11, 2F12, 2F13, 2F16, 2F18, 2F1A, 2F1B, 2F1G, 2F1W, 2F1X, 2F1Y, 2F1Z, 2F24, 2F25, 2F26, 2F27, 2F28, 2F29, 2F2G, 2F2H, 2F2N, 2F2Q, 2F30, 2F32, 2F3B, 2F3C, 2F3D, 2F3E, 2F3F, 2F3K, 2F3V, 2F3W, 2F43, 2F47, 2F4A, 2F4G, 2F4M, 2F4O, 2F4Y, 2F55, 2F56, 2F5M, 2F5N, 2F5O, 2F5P, 2F5Q, 2F5S, 2F5W, 2F61, 2F6B, 2F6D, 2F6E, 2F6F, 2F6I, 2F6T, 2F6V, 2F6W, 2F6Y, 2F6Z, 2F70, 2F71, 2F73, 2F7D, 2F7O, 2F7P, 2F7Q, 2F7R, 2F7T, 2F7V, 2F80, 2F81, 2F83, 2F8G, 2F8H, 2F8Q, 2F91, 2F96, 2F9B, 2F9L, 2F9M, 2F9N, 2F9O, 2F9P, 2F9R, 2F9Z, 2FA7, 2FAK, 2FBA, 2FBB, 2FBD, 2FBL, 2FBP, 2FBT, 2FBV, 2FBX, 2FBY, 2FC0, 2FCC, 2FD6, 2FDA, 2FDD, 2FDE, 2FDP, 2FE1, 2FE4, 2FE8, 2FEA, 2FEB, 2FEK, 2FEQ, 2FES, 2FF1, 2FF2, 2FFQ, 2FFY, 2FFZ, 2FGE, 2FGL, 2FGN, 2FGU, 2FGV, 2FGZ, 2FH6, 2FH7, 2FH8, 2FHB, 2FHC, 2FHF, 2FHG, 2FHH, 2FHI, 2FHM, 2FHR, 2FHX, 2FHY, 2FHZ, 2FI3, 2FI4, 2FI5, 2FIE, 2FIR, 2FIT, 2FIV, 2FIX, 2FJ0, 2FJL, 2FJM, 2FJN, 2FJP, 2FJU, 2FK6, 2FKA, 2FKB, 2FKC, 2FKH, 2FL3, 2FLB, 2FLC, 2FLK, 2FLO, 2FLR, 2FLT, 2FLW, 2FLZ, 2FM0, 2FM5, 2FM6, 2FMB, 2FMF, 2FMH, 2FMI, 2FMJ, 2FMK, 2FNS, 2FNT, 2FNX, 2FO5, 2FO9, 2FOA, 2FOB, 2FOC, 2FOD, 2FOE, 2FOF, 2FOG, 2FOH, 2FOJ, 2FOK, 2FOM, 2FOO, 2FOP, 2FOZ, 2FP0, 2FP3, 2FP7, 2FPQ, 2FPR, 2FPS, 2FPU, 2FPW, 2FPX, 2FPZ, 2FQ9, 2FQQ, 2FR5, 2FR6, 2FRA, 2FRQ, 2FS2, 2FS4, 2FS8, 2FS9, 2FT2, 2FTD, 2FTL, 2FTM, 2FTW, 2FTY, 2FU6, 2FU7, 2FU8, 2FU9, 2FUD, 2FUF, 2FUN, 2FUZ, 2FV0, 2FV1, 2FV5, 2FV9, 2FVH, 2FVK, 2FVM, 2FVV, 2FVZ, 2FWW, 2FX4, 2FX5, 2FX6, 2FXD, 2FXE, 2FXR, 2FXY, 2FXZ, 2FYE, 2FYH, 2FYM, 2FYV, 2FZS, 2FZZ, 2G00, 2G06, 2G07, 2G08, 2G09, 2G0A, 2G0C, 2G1A, 2G1N, 2G1O, 2G1R, 2G1S, 2G1Y, 2G20, 2G21, 2G22, 2G24, 2G26, 2G27, 2G2N, 2G2P, 2G2U, 2G2W, 2G3F, 2G3I, 2G3J, 2G3M, 2G3N, 2G43, 2G45, 2G47, 2G48, 2G49, 2G4D, 2G4F, 2G4K, 2G4P, 2G4Q, 2G4T, 2G4U, 2G4V, 2G4W, 2G4X, 2G4Z, 2G51, 2G52, 2G54, 2G55, 2G56, 2G58, 2G59, 2G5D, 2G5N, 2G5P, 2G5T, 2G5V, 2G5X, 2G63, 2G69, 2G6D, 2G6G, 2G6P, 2G6Z, 2G7C, 2G7E, 2G7F, 2G7K, 2G7N, 2G7P, 2G7Q, 2G7R, 2G7Y, 2G80, 2G81, 2G8E, 2G8F, 2G8H, 2G8I, 2G8J, 2G8K, 2G8Q, 2G8R, 2G8T, 2G8U, 2G8V, 2G8W, 2G93, 2G94, 2G9D, 2G9F, 2G9G, 2G9N, 2G9O, 2G9Y, 2GA2, 2GA3, 2GA4, 2GA7, 2GAC, 2GAW, 2GB5, 2GBC, 2GBF, 2GBG, 2GBI, 2GBZ, 2GCF, 2GCH, 2GCT, 2GD4, 2GDD, 2GDE, 2GDN, 2GDT, 2GE5, 2GEF, 2GEZ, 2GFH, 2GFI, 2GFJ, 2GFK, 2GFO, 2GFQ, 2GFT, 2GG0, 2GG2, 2GG3, 2GG5, 2GG7, 2GG8, 2GG9, 2GGB, 2GGC, 2GGK, 2GGL, 2GGP, 2GH4, 2GH6, 2GHU, 2GI4, 2GIE, 2GIG, 2GIH, 2GII, 2GIJ, 2GJ8, 2GJ9, 2GJA, 2GJK, 2GJP, 2GJR, 2GJT, 2GJX, 2GK1, 2GK6, 2GK7, 2GKL, 2GKO, 2GKV, 2GL9, 2GLF, 2GLJ, 2GLQ, 2GMC, 2GMD, 2GMK, 2GMN, 2GMT, 2GMW, 2GNS, 2GO3, 2GO4, 2GOK, 2GP0, 2GP9, 2GPL, 2GPZ, 2GQ0, 2GQ1, 2GQC, 2GRC, 2GRE, 2GSE, 2GSI, 2GSN, 2GSO, 2GSP, 2GSU, 2GT2, 2GT4, 2GT7, 2GT8, 2GTB, 2GTQ, 2GTX, 2GU0, 2GU1, 2GU2, 2GU4, 2GU5, 2GU6, 2GU7, 2GUW, 2GUY, 2GV0, 2GV1, 2GV6, 2GV7, 2GVH, 2GVN, 2GVU, 2GVV, 2GVW, 2GVX, 2GVY, 2GWF, 2GWN, 2GWO, 2GX4, 2GX6, 2GXA, 2GXB, 2GYU, 2GYV, 2GYW, 2GZ5, 2GZ7, 2GZ8, 2GZ9, 2GZA, 2GZD, 2GZH, 2H02, 2H03, 2H04, 2H0E, 2H0F, 2H0J, 2H0Q, 2H0R, 2H0T, 2H0Y, 2H10, 2H1E, 2H1I, 2H1J, 2H1L, 2H1N, 2H1U, 2H1X, 2H2D, 2H2F, 2H2G, 2H2H, 2H2I, 2H2N, 2H2U, 2H2Z, 2H40, 2H42, 2H44, 2H48, 2H4C, 2H4F, 2H4G, 2H4H, 2H4I, 2H4J, 2H4K, 2H4V, 2H4W, 2H4Y, 2H51, 2H54, 2H56, 2H59, 2H5C, 2H5D, 2H5I, 2H5J, 2H5L, 2H5S, 2H5X, 2H5Z, 2H60, 2H65, 2H6A, 2H6J, 2H6M, 2H6S, 2H6T, 2H6U, 2H7C, 2H7J, 2H8C, 2H8E, 2H8G, 2H8N, 2H9E, 2H9H, 2H9J, 2H9K, 2H9T, 2H9Y, 2HA0, 2HA2, 2HA3, 2HA4, 2HA5, 2HA6, 2HA7, 2HAD, 2HAH, 2HAL, 2HAW, 2HB1, 2HB2, 2HB3, 2HB4, 2HB5, 2HB6, 2HB9, 2HBJ, 2HBK, 2HBL, 2HBM, 2HBQ, 2HBR, 2HBY, 2HBZ, 2HC0, 2HC1, 2HC2, 2HC8, 2HC9, 2HCA, 2HCM, 2HCT, 2HD0, 2HD1, 2HD4, 2HD5, 2HDO, 2HDQ, 2HDR, 2HDS, 2HDU, 2HEA, 2HEB, 2HEC, 2HED, 2HEE, 2HEF, 2HEG, 2HEZ, 2HF0, 2HF2, 2HF7, 2HGT, 2HH5, 2HHA, 2HHL, 2HHM, 2HHN, 2HI0, 2HIH, 2HIM, 2HIS, 2HIZ, 2HJH, 2HJP, 2HJV, 2HKP, 2HKY, 2HL6, 2HL8, 2HL9, 2HLC, 2HLD, 2HLN, 2HLT, 2HLU, 2HM1, 2HM7, 2HNP, 2HNQ, 2HNT, 2HOB, 2HOH, 2HOQ, 2HOW, 2HP5, 2HP6, 2HP9, 2HPA, 2HPB, 2HPJ, 2HPL, 2HPO, 2HPP, 2HPQ, 2HPT, 2HPZ, 2HQU, 2HR6, 2HRM, 2HRQ, 2HRR, 2HRV, 2HRW, 2HS1, 2HS2, 2HS7, 2HS9, 2HSO, 2HSP, 2HSX, 2HSZ, 2HT1, 2HT5, 2HT7, 2HT8, 2HTQ, 2HTR, 2HTU, 2HTV, 2HTW, 2HTX, 2HTY, 2HU0, 2HU1, 2HU3, 2HU4, 2HU5, 2HU6, 2HU7, 2HU8, 2HUB, 2HUC, 2HUK, 2HUL, 2HUM, 2HVL, 2HVM, 2HVV, 2HVW, 2HVX, 2HW4, 2HWK, 2HWL, 2HWW, 2HWX, 2HX5, 2HX6, 2HXB, 2HXD, 2HXM, 2HXP, 2HXY, 2HXZ, 2HY1, 2HY3, 2HYD, 2HYI, 2HYK, 2HYO, 2HYP, 2HZP, 2HZY, 2I03, 2I0U, 2I0X, 2I1Y, 2I25, 2I26, 2I33, 2I34, 2I3B, 2I3C, 2I3E, 2I3P, 2I3Q, 2I3R, 2I3U, 2I3Z, 2I42, 2I47, 2I4D, 2I4E, 2I4G, 2I4H, 2I4I, 2I4Q, 2I4R, 2I4U, 2I4V, 2I4W, 2I4X, 2I50, 2I5D, 2I5I, 2I5S, 2I5W, 2I5X, 2I65, 2I66, 2I67, 2I6J, 2I6M, 2I6O, 2I6P, 2I6Q, 2I6S, 2I6Z, 2I72, 2I74, 2I75, 2I78, 2I7A, 2I7D, 2I7T, 2I7V, 2I8E, 2I8L, 2I9A, 2I9B, 2I9U, 2IAE, 2IAO, 2IAP, 2IAQ, 2IAR, 2IAS, 2IAT, 2IAU, 2IAV, 2IAW, 2IAX, 2IBI, 2IC8, 2ICS, 2ICU, 2ID0, 2ID4, 2ID8, 2IDW, 2IE3, 2IE4, 2IEH, 2IEN, 2IEO, 2IER, 2IES, 2IEY, 2IEZ, 2IF0, 2IFD, 2IFF, 2IFR, 2IFV, 2IFW, 2IGC, 2IGI, 2IGL, 2IGX, 2IGY, 2IHL, 2IHN, 2IHP, 2IHS, 2IHY, 2IIT, 2IIV, 2IJO, 2IJZ, 2IK0, 2IK1, 2IK2, 2IK4, 2IK6, 2IK7, 2IK9, 2IKO, 2IKQ, 2IKU, 2IL2, 2ILN, 2ILP, 2ILX, 2IMA, 2IMB, 2IMC, 2IMG, 2IMO, 2IMR, 2IMZ, 2IN0, 2IN2, 2IN8, 2IN9, 2IO0, 2IO1, 2IO2, 2IO3, 2IO7, 2IO8, 2IO9, 2IOA, 2IOB, 2IOC, 2IOF, 2IOH, 2IOT, 2IPB, 2IPH, 2IPP, 2IQ1, 2IQ6, 2IQ7, 2IQG, 2IRV, 2IRZ, 2IS0, 2IS1, 2IS2, 2IS3, 2IS4, 2IS6, 2ISD, 2ISI, 2IUC, 2IUL, 2IUX, 2IUZ, 2IVH, 2IVK, 2IVY, 2IVZ, 2IW0, 2IW2, 2IW4, 2IWM, 2IX0, 2IX1, 2IX9, 2IXA, 2IXB, 2IXD, 2IXS, 2IXU, 2IXV, 2IY0, 2IY1, 2IYC, 2IYD, 2IYE, 2IYK, 2IZO, 2J0E, 2J0Q, 2J0S, 2J0T, 2J0U, 2J13, 2J16, 2J17, 2J1A, 2J1E, 2J1L, 2J25, 2J2C, 2J2U, 2J30, 2J31, 2J32, 2J33, 2J34, 2J38, 2J3D, 2J3E, 2J3Q, 2J47, 2J4C, 2J4E, 2J4F, 2J4G, 2J4H, 2J4I, 2J4Q, 2J4U, 2J5G, 2J5S, 2J62, 2J65, 2J68, 2J69, 2J6Y, 2J6Z, 2J70, 2J71, 2J72, 2J73, 2J74, 2J75, 2J77, 2J78, 2J79, 2J7B, 2J7C, 2J7D, 2J7E, 2J7F, 2J7G, 2J7H, 2J7K, 2J7M, 2J7V, 2J83, 2J88, 2J8F, 2J8G, 2J8J, 2J8L, 2J8X, 2J8Y, 2J92, 2J94, 2J95, 2J9A, 2J9N, 2J9O, 2J9P, 2JA9, 2JAI, 2JAJ, 2JAL, 2JAO, 2JAR, 2JAU, 2JAW, 2JAY, 2JAZ, 2JB0, 2JBF, 2JBG, 2JBJ, 2JBK, 2JBU, 2JBW, 2JC4, 2JC5, 2JC7, 2JC9, 2JCM, 2JDI, 2JDL, 2JE6, 2JE8, 2JEA, 2JEB, 2JEM, 2JEN, 2JEP, 2JEQ, 2JER, 2JET, 2JEW, 2JEY, 2JEZ, 2JF0, 2JF4, 2JF6, 2JF7, 2JFE, 2JG0, 2JG4, 2JG6, 2JGA, 2JGE, 2JGF, 2JGI, 2JGJ, 2JGK, 2JGL, 2JGM, 2JGN, 2JH0, 2JH2, 2JH5, 2JH6, 2JHJ, 2JHN, 2JHQ, 2JIC, 2JID, 2JIE, 2JIH, 2JIW, 2JIZ, 2JJ1, 2JJ2, 2JJB, 2JJD, 2JJI, 2JJJ, 2JJK, 2JJR, 2JK0, 2JKA, 2JKB, 2JKE, 2JKH, 2JKP, 2JLQ, 2JLR, 2JLS, 2JLU, 2JLV, 2JLW, 2JLX, 2JLY, 2JLZ, 2JM1, 2JMU, 2JMX, 2JNP, 2JO0, 2JOA, 2JOG, 2JON, 2JPN, 2JQ9, 2JQH, 2JQK, 2JRC, 2JRI, 2JSD, 2JT2, 2JT5, 2JT6, 2JTC, 2JU4, 2JVB, 2JXR, 2JXY, 2JYS, 2JYW, 2JZI, 2K11, 2K1G, 2K1R, 2K2G, 2K2J, 2K2Q, 2K31, 2K3R, 2K3V, 2K3W, 2K4P, 2K4R, 2K51, 2K6H, 2K72, 2K7J, 2K7K, 2K7L, 2K7Z, 2K88, 2K9C, 2KAF, 2KAI, 2KAU, 2KB5, 2KB6, 2KBE, 2KBF, 2KBO, 2KBW, 2KC8, 2KC9, 2KDT, 2KDV, 2KDW, 2KE3, 2KEM, 2KF3, 2KF4, 2KF5, 2KF6, 2KHS, 2KHX, 2KHZ, 2KI7, 2KIE, 2KIG, 2KIJ, 2KKH, 2KKN, 2KLH, 2KLQ, 2KLZ, 2KMN, 2KMU, 2KMV, 2KMX, 2KN7, 2KNE, 2KNF, 2KNO, 2KOU, 2KOY, 2KP7, 2KPN, 2KQ3, 2KQ8, 2KR6, 2KRJ, 2KSG, 2KSV, 2KU1, 2KU2, 2KV2, 2KVR, 2KX3, 2KYT, 2KZR, 2L0R, 2L0S, 2L2D, 2L2K, 2L37, 2L3C, 2L3H, 2L3J, 2L47, 2L4Z, 2L54, 2L5U, 2L67, 2L68, 2L6K, 2L6M, 2L75, 2L77, 2L78, 2L79, 2L80, 2L8A, 2L8B, 2L8L, 2L8R, 2L95, 2LAA, 2LAB, 2LBC, 2LBM, 2LBS, 2LC9, 2LCB, 2LCQ, 2LD1, 2LDI, 2LE8, 2LEN, 2LEP, 2LFJ, 2LGD, 2LGJ, 2LGR, 2LHI, 2LHM, 2LIP, 2LIZ, 2LJH, 2LJP, 2LKK, 2LKT, 2LKV, 2LLZ, 2LNA, 2LNC, 2LOB, 2LOX, 2LOZ, 2LPR, 2LQB, 2LQJ, 2LRO, 2LRP, 2LRR, 2LRS, 2LS6, 2LT5, 2LTJ, 2LUO, 2LUP, 2LUQ, 2LVA, 2LVX, 2LVZ, 2LWD, 2LWE, 2LYM, 2LYO, 2LYZ, 2LZ2, 2LZH, 2LZM, 2LZT, 2M00, 2M0A, 2M0Z, 2M10, 2M17, 2M2F, 2M3D, 2M45, 2M5C, 2M5D, 2M65, 2M73, 2M75, 2M7E, 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2PHB, 2PHI, 2PHO, 2PIY, 2PIZ, 2PJ0, 2PJ1, 2PJ2, 2PJ3, 2PJ4, 2PJ5, 2PJ6, 2PJ7, 2PJ8, 2PJ9, 2PJA, 2PJB, 2PJC, 2PJF, 2PJG, 2PJH, 2PJI, 2PJO, 2PJR, 2PJT, 2PK0, 2PK4, 2PKA, 2PKC, 2PKS, 2PKY, 2PL3, 2PL9, 2PLL, 2PLM, 2PLQ, 2PLX, 2PM8, 2PMC, 2PMJ, 2PMS, 2PMW, 2PNM, 2PNQ, 2PNS, 2PNZ, 2PO0, 2PO1, 2PO2, 2POF, 2POJ, 2POO, 2PPL, 2PQ2, 2PQ5, 2PQE, 2PQG, 2PQI, 2PQJ, 2PQK, 2PQX, 2PQY, 2PQZ, 2PR3, 2PRD, 2PRE, 2PRK, 2PRO, 2PRQ, 2PSG, 2PSX, 2PSY, 2PTC, 2PTH, 2PTN, 2PU2, 2PU3, 2PU4, 2PU5, 2PU7, 2PUH, 2PUJ, 2PUQ, 2PUX, 2PUZ, 2PV9, 2PVA, 2PVI, 2PVS, 2PVT, 2PVV, 2PVW, 2PW3, 2PW5, 2PW7, 2PW8, 2PWA, 2PWB, 2PWC, 2PWR, 2PWS, 2PWX, 2PX1, 2PY1, 2PY4, 2PYC, 2PYK, 2PYM, 2PYN, 2PYU, 2PYZ, 2PZD, 2PZH, 2PZT, 2PZU, 2PZW, 2Q05, 2Q09, 2Q0H, 2Q0M, 2Q0Q, 2Q0S, 2Q0Z, 2Q11, 2Q15, 2Q16, 2Q1J, 2Q1P, 2Q1Z, 2Q2B, 2Q3N, 2Q3U, 2Q42, 2Q43, 2Q47, 2Q4D, 2Q4G, 2Q4O, 2Q4P, 2Q4T, 2Q4X, 2Q4Z, 2Q51, 2Q5E, 2Q5Z, 2Q63, 2Q64, 2Q6D, 2Q6F, 2Q6G, 2Q6T, 2Q73, 2Q74, 2Q7T, 2Q7U, 2Q8J, 2Q8M, 2Q8W, 2Q8X, 2Q92, 2Q93, 2Q94, 2Q95, 2Q96, 2Q9D, 2Q9E, 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1ZO8, 1ZOB, 1ZOD, 1ZOT, 1ZPD, 1ZSA, 1ZSB, 1ZSC, 1ZTB, 2A0N, 2A0S, 2A4A, 2A5V, 2A6L, 2A6N, 2A8C, 2A8D, 2AAG, 2AAJ, 2AAL, 2ABE, 2ABK, 2ABW, 2AC2, 2AC4, 2AE0, 2AE8, 2AEK, 2AEL, 2AET, 2AF7, 2AG0, 2AG1, 2AHC, 2AHJ, 2AKM, 2AKZ, 2AL1, 2AL2, 2ALD, 2AMT, 2AQW, 2ATS, 2AW1, 2AX2, 2B3X, 2B3Y, 2B69, 2BDR, 2BLL, 2BRP, 2BRV, 2BRW, 2BSP, 2BT4, 2BW7, 2C0A, 2C13, 2C14, 2C15, 2C16, 2C18, 2C19, 2C1H, 2C21, 2C2B, 2C2G, 2C2I, 2C31, 2C3F, 2C3Z, 2C44, 2C45, 2C4V, 2C4W, 2C57, 2C5Q, 2C8J, 2CA2, 2CAB, 2CBA, 2CBB, 2CBC, 2CBD, 2CBE, 2CJF, 2CLE, 2CLF, 2CLH, 2CLI, 2CLK, 2CLL, 2CLM, 2CLO, 2COL, 2CV8, 2CW6, 2CWS, 2CWX, 2CXE, 2CYZ, 2CZ0, 2CZ1, 2CZ5, 2CZ6, 2CZ7, 2CZD, 2CZE, 2CZF, 2CZL, 2D0Q, 2D1F, 2D2X, 2D5K, 2D69, 2DGD, 2DGK, 2DGL, 2DGM, 2DH5, 2DH6, 2DHN, 2DHQ, 2DJ5, 2DJ6, 2DKB, 2DP5, 2DPP, 2DUB, 2DVT, 2DVU, 2DVX, 2DW6, 2DW7, 2DXA, 2DZP, 2DZS, 2DZT, 2DZU, 2DZV, 2DZW, 2DZX, 2E09, 2E0I, 2E22, 2E24, 2E4O, 2E5L, 2E6Y, 2E7Z, 2E9F, 2EB4, 2EB5, 2EB6, 2EBB, 2EEO, 2EGZ, 2EHH, 2EJA, 2EKC, 2EPH, 2ESF, 2EU2, 2EU3, 2EV1, 2EV2, 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3DTV, 3DU0, 3DV7, 3DVB, 3DVC, 3DVD, 3DX5, 3DZ1, 3DZ2, 3DZ3, 3DZ4, 3DZ5, 3DZ6, 3DZ7, 3E04, 3E1V, 3E1W, 3E24, 3E28, 3E2A, 3E2W, 3E2X, 3E31, 3E3F, 3E3G, 3E3I, 3E96, 3EA1, 3EA2, 3EA3, 3EB2, 3ED0, 3EFI, 3EFT, 3EIS, 3EKA, 3EKG, 3EKL, 3EKZ, 3ELC, 3ELF, 3ELP, 3ENL, 3EOL, 3EOO, 3EOR, 3EP3, 3EP4, 3EP5, 3EP6, 3EP7, 3EP8, 3EP9, 3EPA, 3EPB, 3EPM, 3EPN, 3EPO, 3ERN, 3ES7, 3ES8, 3ESJ, 3EWU, 3EWW, 3EWX, 3EWY, 3EWZ, 3EX1, 3EX2, 3EX3, 3EX4, 3EX6, 3EXR, 3EXS, 3EXT, 3EYX, 3F0D, 3F0E, 3F0F, 3F0G, 3F0N, 3F0O, 3F0P, 3F2F, 3F2G, 3F2H, 3F4W, 3F4X, 3F6B, 3F6E, 3F6M, 3F7B, 3F7U, 3F8E, 3F9T, 3FA3, 3FA4, 3FBA, 3FDD, 3FE4, 3FEM, 3FFP, 3FKK, 3FKR, 3FLU, 3FN8, 3FPI, 3FSJ, 3FUA, 3FVM, 3FVZ, 3FW0, 3FW3, 3FY4, 3FYY, 3FZ6, 3FZ7, 3FZ8, 3FZN, 3G0S, 3G18, 3G1A, 3G1D, 3G1F, 3G1H, 3G1S, 3G1V, 3G1X, 3G1Y, 3G22, 3G24, 3G3D, 3G3M, 3G4D, 3G4F, 3G82, 3GAK, 3GAY, 3GB6, 3GC2, 3GD6, 3GDK, 3GDL, 3GDM, 3GDN, 3GDP, 3GDR, 3GDT, 3GF3, 3GF7, 3GHZ, 3GLM, 3GMA, 3GNE, 3GOQ, 3GTD, 3GVQ, 3GVR, 3GVV, 3GVW, 3GW0, 3GW3, 3GWQ, 3GZ0, 3GZC, 3GZD, 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3TQI, 3TQO, 3TQT, 3TS1, 3TSY, 3TU1, 3TUG, 3TUP, 3TUT, 3TUX, 3TV1, 3TV5, 3TVU, 3TVW, 3TW3, 3TW6, 3TW7, 3TY5, 3TY8, 3TY9, 3TZ3, 3TZE, 3TZL, 3U02, 3U16, 3U17, 3U54, 3U55, 3U5M, 3U5N, 3U5O, 3U5P, 3U9R, 3U9S, 3U9T, 3UAG, 3UFG, 3UFX, 3UGJ, 3UGQ, 3UGT, 3UH0, 3UIN, 3UIO, 3UIP, 3UJN, 3UK2, 3UMM, 3UOW, 3UQ8, 3UV9, 3UW4, 3UW5, 3UY4, 3V3B, 3V3L, 3V4Z, 3VA7, 3VBB, 3VBG, 3VEN, 3VEO, 3VER, 3VES, 3VET, 3VEW, 3VEX, 3VEZ, 3VF2, 3VF4, 3VGJ, 3VGO, 3VIU, 3VK6, 3VMM, 3VNN, 3VOT, 3VPB, 3VPC, 3VPD, 3VQF, 3VQG, 3VQV, 3VQW, 3VQX, 3VQY, 3VRN, 3VRQ, 3VU8, 3VZV, 3W3S, 3W5O, 3W69, 3WDK, 3WDL, 3WDM, 3WE5, 3WNZ, 3WO0, 3WO1, 3WQY, 3WQZ, 3WV4, 3WV5, 3WVN, 3ZGZ, 3ZIU, 3ZJT, 3ZJU, 3ZJV, 3ZL8, 3ZM5, 3ZM6, 3ZO0, 3ZTG, 3ZVY, 3ZVZ, 3ZXI, 3ZXR, 3ZXV, 4A0C, 4A0K, 4A0L, 4A91, 4ACF, 4AH6, 4AQ7, 4ARC, 4ARI, 4AS1, 4ASI, 4AYC, 4B0T, 4B3N, 4B7Y, 4B86, 4B8E, 4BAX, 4BBN, 4BE8, 4BJR, 4BL7, 4BM9, 4BPF, 4BPG, 4BPH, 4BUB, 4BUC, 4BVX, 4BVY, 4BW9, 4BWA, 4C12, 4C13, 4C5A, 4C5B, 4C5C, 4C84, 4C85, 4C86, 4C8A, 4C8C, 4C8F, 4C8P, 4C8T, 4C8U, 4C99, 4C9A, 4C9E, 4C9R, 4C9U, 4C9Z, 4CA1, 4CC5, 4CC6, 4CDJ, 4CDK, 4CFG, 4CH3, 4CH4, 4CH5, 4CH6, 4CQN, 4CS2, 4CS3, 4CS4, 4CVK, 4CVL, 4CVM, 4CY3, 4D79, 4D7A, 4DBG, 4DDH, 4DDK, 4DDM, 4DE5, 4DG8, 4DG9, 4DIJ, 4DLP, 4DPG, 4DQ2, 4DQV, 4DTD, 4DTF, 4DTH, 4DTL, 4DWQ, 4DWR, 4E1C, 4E1D, 4E1F, 4E4T, 4E51, 4EAT, 4EB9, 4EBR, 4EC4, 4EEQ, 4EF6, 4EFB, 4EFE, 4EFK, 4EG0, 4EG1, 4EG3, 4EG4, 4EG5, 4EG6, 4EG7, 4EG8, 4EGA, 4EGJ, 4EGQ, 4EO4, 4EPL, 4EPM, 4EQ4, 4EQ5, 4EQL, 4ERE, 4ERF, 4EWV, 4EX5, 4F52, 4FB8, 4FE2, 4FGR, 4FO9, 4FU0, 4FUQ, 4FUT, 4FZJ, 4G2R, 4G3O, 4G5F, 4G5Y, 4G6Z, 4G84, 4G85, 4GA0, 4GA1, 4GA2, 4GAO, 4GB0, 4GBA, 4GHU, 4GIZ, 4GLW, 4GLX, 4GPL, 4GQK, 4GRI, 4GSK, 4GXQ, 4GXR, 4GY5, 4H02, 4H1Z, 4H2S, 4H2T, 4H2U, 4H2V, 4H2W, 4H2X, 4H2Y, 4H3S, 4HA8, 4HBM, 4HFZ, 4HG7, 4HJR, 4HJX, 4HK4, 4HL7, 4HNT, 4HNU, 4HNV, 4HPP, 4HPW, 4HQ6, 4HR7, 4HTO, 4HV4, 4HVC, 4HWO, 4HWP, 4HWR, 4HWS, 4HWT, 4HY0, 4HY4, 4HY5, 4I1F, 4I1H, 4I7B, 4I7C, 4I7D, 4I9Y, 4IC2, 4IC3, 4IFI, 4IGK, 4II3, 4IOJ, 4IOK, 4IOL, 4IOM, 4IPF, 4IS4, 4ISJ, 4ISZ, 4IT0, 4IWX, 4IWY, 4IZ6, 4IZO, 4J15, 4J3E, 4J3Y, 4J44, 4J45, 4J46, 4J47, 4J48, 4J74, 4J75, 4J76, 4J7D, 4J7E, 4JFA, 4JIM, 4JJK, 4JJZ, 4JKI, 4JLU, 4JRG, 4JSC, 4JUE, 4JUY, 4JV7, 4JV9, 4JVE, 4JVR, 4JWR, 4JX4, 4JX5, 4JX6, 4JXX, 4JXZ, 4JYZ, 4K47, 4K48, 4K7D, 4K7S, 4K7U, 4K7W, 4K86, 4K87, 4K88, 4K95, 4KBL, 4KBQ, 4KC9, 4KJU, 4KJV, 4KMN, 4KMP, 4KNG, 4KQE, 4KR2, 4KR3, 4L1K, 4L1M, 4L39, 4L6E, 4L6W, 4L78, 4L87, 4LCD, 4LG8, 4LGC, 4LGE, 4LGU, 4LGY, 4LH6, 4LH7, 4LJO, 4LJP, 4LJQ, 4LNF, 4LNI, 4LNK, 4LNN, 4LNO, 4LNS, 4LOC, 4LQW, 4LTB, 4LUN, 4LWT, 4LWU, 4LWV, 4M0G, 4M1N, 4M3L, 4M6V, 4M9D, 4M9U, 4MA0, 4MA5, 4MAM, 4MB0, 4MB2, 4MDN, 4MDQ, 4ME6, 4MFD, 4MFE, 4MGH, 4MIM, 4MQ6, 4MTI, 4MTZ, 4MU7, 4MUE, 4MUF, 4MUG, 4MUH, 4MUI, 4MUJ, 4MUK, 4MUL, 4MUN, 4MV1, 4MV3, 4MV4, 4MV6, 4MV7, 4MV8, 4MV9, 4MVT, 4MVW, 4MVX, 4MVY, 4MW0, 4MW1, 4MW2, 4MW4, 4MW5, 4MW6, 4MW7, 4MW9, 4MWB, 4MWC, 4MWD, 4MWE, 4MZY, 4N0H, 4N0I, 4N7F, 4N7H, 4NAW, 4NCX, 4ND6, 4ND7, 4NDA, 4NKH, 4NNJ, 4NQJ, 4NX2, 4NYE, 4NZP, 4O2D, 4O2W, 4O7L, 4O7N, 4O7R, 4O7S, 4O7T, 4O7V, 4O7W, 4O7Y, 4O7Z, 4O81, 4O82, 4O83, 4O84, 4O86, 4O89, 4O8J, 4OAS, 4OBA, 4OBY, 4OCC, 4ODE, 4ODF, 4OFB, 4OGN, 4OGT, 4OGV, 4OJM, 4OLF, 4OP0, 4OQ3, 4OUD, 4OWF, 4OXC, 4OYJ, 4P09, 4P22, 4P3N, 4P3O, 4P3P, 4P5O, 4P71, 4P72, 4P73, 4P74, 4P75, 4PBR, 4PBS, 4PBT, 4PG3, 4PHC, 4PPE, 4PUF, 4PW5, 4PW6, 4PW7, 4PY2, 4PZP, 4Q0G, 4Q15, 4Q16, 4Q2T, 4Q2X, 4Q2Y, 4Q6G, 4Q93, 4QBT, 4QDI, 4QEI, 4QF5, 4QO4, 4QOC, 4QQD, 4QRD, 4QRE, 4QSH, 4QSK, 4QSL, 4QT6, 4R1L, 4R1M, 4R3Z, 4R7E, 4R7G, 4RDH, 4RDI, 4RDX, 4RJ9, 4RKG, 4RKH, 4RMF, 4ROF, 4RQE, 4RQF, 4RR6, 4RR7, 4RR8, 4RR9, 4RRA, 4RRB, 4RRC, 4RRD, 4RRF, 4RRG, 4RRH, 4RRI, 4RRJ, 4RRK, 4RRL, 4RRM, 4RRQ, 4RRR, 4RVN, 4RVO, 4RVZ, 4RZQ, 4S02, 4S03, 4S0I, 4S0J, 4S0K, 4S0L, 4S0R, 4S17, 4TQD, 4TQF, 4TS1, 4TSE, 4TTV, 4TVA, 4TVR, 4TWA, 4U4A, 4U5Y, 4U7J, 4UAG, 4UCO, 4UCR, 4UCS, 4UCT, 4UCU, 4UCV, 4UD7, 4UE1, 4UFS, 4UFZ, 4UMN, 4UP7, 4UP8, 4UP9, 4UPA, 4W5V, 4WBD, 4WD3, 4WF2, 4WI1, 4WIM, 4WIN, 4WIO, 4WJ3, 4WJ4, 4WOP, 4WT2, 4WVS, 4WVT, 4WVU, 4WYO, 4WZ0, 4WZ2, 4WZ8, 4X5O, 4XEM, 4XEO, 4XFD, 4XFJ, 4XI1, 4XI6, 4XI7, 4XIB, 4XKZ, 4XOM, 4XOO, 4XOQ, 4XTU, 4XTV, 4XTW, 4XTX, 4XTY, 4XTZ, 4XU0, 4XU1, 4XU2, 4XU3, 4XX0, 4XXB, 4XYC, 4Y07, 4Y18, 4Y1L, 4Y2G, 4YAB, 4YAD, 4YAT, 4YAX, 4YBM, 4YBS, 4YBT, 4YC9, 4YCU, 4YCV, 4YCW, 4YDQ, 4YE6, 4YE8, 4YE9, 4YED, 4YLR, 4YLS, 4YP0, 4YPF, 4YRC, 4YRE, 4YRF, 4YRG, 4YRI, 4YRJ, 4YRK, 4YRL, 4YRM, 4YRN, 4YRO, 4YRP, 4YRQ, 4YRR, 4YRS, 4YRT, 4YUB, 4YYE, 4Z30, 4Z31, 4Z8M, 4ZAJ, 4ZB4, 4ZDI, 4ZDJ, 4ZDK, 4ZFI, 4ZGK, 4ZIB, 4ZIY, 4ZLC, 4ZLD, 4ZQI, 4ZQL, 4ZT2, 4ZT3, 4ZT4, 4ZT5, 4ZT6, 4ZT7, 4ZTD, 4ZYA, 4ZYC, 4ZYF, 4ZYI, 4ZYN, 5A1N, 5A34, 5A4J, 5A5E, 5A5F, 5A5G, 5A5H, 5AGH, 5AGI, 5AGJ, 5AGR, 5AGS, 5AGT, 5AH5, 5AVM, 5AXI, 5B0N, 5B0T, 5BMU, 5BNZ, 5BPF, 5BPH, 5BSM, 5BSR, 5BST, 5BSU, 5BSV, 5BSW, 5BUQ, 5BUR, 5BUS, 5C0K, 5C0L, 5C1O, 5C1P, 5C1Z, 5C23, 5C3H, 5C3K, 5C5A, 5C5H, 5C7A, 5C7B, 5C7C, 5C7D, 5C7J, 5C7M, 5C83, 5C84, 5C91, 5C9V, 5CAE, 5CAW, 5CLL, 5CLQ, 5CQ2, 5CS0, 5CS4, 5CSA, 5CSK, 5CSL, 5CTB, 5CTC, 5CTE, 5D2M, 5D8D, 5DIN, 5DK4, 5DKA, 5DM3, 5DMX, 5DOT, 5DOU, 5DWS, 5DZD, 5E3I, 5E6M, 5EIU, 5EKD, 5ELN, 5ELO, 5EPL, 5EWU, 5EY9, 5F23, 5F5W, 5F6D, 5F6E, 5F6U, 5F6V, 5F6W, 5F6X, 5F6Y, 5F7T, 5FEY, 5FG0, 5FG1, 5FLG, 5FLL, 5FM0, 5FO4, 5FOC, 5FOD, 5FOF, 5FOG, 5FOL, 5FOM, 5FON, 5FPO, 5FPR, 5FQ2, 5GMK, 5GRO, 5GTD, 5H1T, 5H1U, 5H1V, 5H9M, 5HG0, 5HGQ, 5HKW, 5HKX, 5HKY, 5HKZ, 5HL0, 5HM3, 5HMH, 5HMI, 5HMK, 5HPK, 5HPL, 5HPS, 5HQG, 5HUH, 5HUJ, 5HVQ, 5I33, 5I34, 5I6E, 5I6I, 5IAY, 5IE0, 5IE2, 5IE3, 5IEA, 5IFI, 5IFU, 5IGQ, 5IHX, 5IJX, 5IM8, 5J2U, 5J3X, 5J58, 5J59, 5J5A, 5JLD, 5JNE, 5JQW, 5JRH, 5JW7, 5K0S, 5K0T, 5K1P, 5K1X, 5K3Q, 5K85, 5K8F, 5KDR, 5KEI, 5KHA, 5KKN, 5KNN, 5KOD, 5KWV, 5L9W, 5LAV, 5LAW, 5LAY, 5LAZ, 5LDF, 5LJN, 5LN2, 5LP8, 5SXP, 5T5S, 5T76, 5TEV, 5TEW, 5TGT, 5TR5, 5TRB, 5TRC, 5TRF, 5TV5, 5TV6, 5TV8, 5TVA, 5TW7, 5UCM, 5UCR, 5URB, 5US8 \ No newline at end of file diff --git a/datasets/make_files/download_pdb.py b/datasets/make_files/download_pdb.py index ccbf109..ba6492d 100755 --- a/datasets/make_files/download_pdb.py +++ b/datasets/make_files/download_pdb.py @@ -7,15 +7,13 @@ import pandas as pd -# Read csv files. -df = pd.read_csv('../dataset_all.csv', header=None) -# Only keep PDB IDs. -df = df[0]+', ' -df = df.tolist() +def create_download_file() -> None: + """Creates the RCSB download file containing all relevant PDB IDs.""" + df = pd.read_csv('../dataset_all.csv', header=None) + with open('../download_pdb.txt', 'w') as file: + file.write(", ".join(df[0].tolist())) -# Save to text file with one PDB ID per line. -file = open('../download_pdb.txt', 'w') -for i in range(len(df)): - file.write(df[i]) -file.close() + +if __name__ == "__main__": + create_download_file() From fc031a5a69f807dce4a3dd2735bb06b0f9e135d3 Mon Sep 17 00:00:00 2001 From: shervinea Date: Wed, 25 Aug 2021 23:49:18 -0700 Subject: [PATCH 17/18] Make dataset generation more readable --- datasets/make_files/generate_datasets.py | 329 +++++++++++------------ requirements.txt | 2 + 2 files changed, 164 insertions(+), 167 deletions(-) diff --git a/datasets/make_files/generate_datasets.py b/datasets/make_files/generate_datasets.py index dd1dfe9..7d9ed0e 100755 --- a/datasets/make_files/generate_datasets.py +++ b/datasets/make_files/generate_datasets.py @@ -5,6 +5,12 @@ # MIT License +from absl import app +from absl import flags +from typing import Any, Dict, List, Text + +import more_itertools + import numpy as np import pandas as pd @@ -12,183 +18,172 @@ from enzynet import tools from tqdm import tqdm +FLAGS = flags.FLAGS +np.random.seed(0) # For reproducibility. + +flags.DEFINE_bool('save', False, 'Whether to save generation results.') + # Date of retrieval of raw datasets from rcsb.org: 07-03-2017. # Parameters. -N_CLASSES = 6 - -##------------------------- Data preprocessing -------------------------------## -# Computations. -for i in range(1, N_CLASSES+1): - # Initialization. - errors = [] # List of indexes with error. - enzymes = pd.read_table('raw/' + str(i) + '.txt', header=None) # Load PDB IDs. - list_enzymes = enzymes.loc[:, 0].tolist() - - # Look for errors. - for k, enzyme in enumerate(tqdm(list_enzymes)): - try: - # Load local enzyme. - local_enzyme = pdb.PDBBackbone(enzyme) - local_enzyme.get_coords() - - # Look for problem. - if len(local_enzyme.backbone_atoms) == 0: # Empty enzyme. - errors += [k] +_ELEMENT_DELIMITER = ', ' +_STRINGS_TO_IGNORE = ['\'', '"', '[', ']'] - except: # File does not exist, i.e. deprecated enzyme. - errors += [k] - - # Drop errors and save. - enzymes = enzymes.drop(enzymes.index[errors]) - # enzymes.to_csv(str(i) + '.txt', header=None, index=None) # DONE 2017-03-18. - -##-------------- Dictionary with all PDBs in dataset_all.csv -----------------## -# Initialization. -labels = {} - -# Computations. -for i in range(1, N_CLASSES+1): - # Load PDB IDs for each class. - enzymes = pd.read_table(str(i) + '.txt', header=None) - enzymes = enzymes[0].tolist() - - # Add entries to dict. - for entry in enzymes: - if entry in labels: # PDB_id already in dict. - labels[entry] = labels[entry] + [i] - else: # PDB_id not yet in dict. - labels[entry] = [i] - -# # Save results. -# tools.dict_to_csv(labels, '../dataset_all.csv') # DONE 2017-03-18. - -##--------- Dictionary with single-label PDBs in dataset_single.csv ----------## -# Initialization. -labels_temp = tools.read_dict('../dataset_all.csv') -labels = {} - -# Computations. -for entry in labels_temp: - if len(labels_temp[entry]) == 3: # Single-label ('[i]'). - labels[entry] = int(labels_temp[entry][1]) # Convert list to scalar. - -# # Save results. -# tools.dict_to_csv(labels, '../dataset_single.csv') # DONE 2017-03-18. - -##---------- Dictionary with multi-label PDBs in dataset_multi.csv -----------## -# Initialization. -labels_temp = tools.read_dict('../dataset_all.csv') -labels = {} - -# Computations. -for entry in labels_temp: - if len(labels_temp[entry]) > 3: # Multi-label. - labels[entry] = labels_temp[entry] - -# # Save results. -# tools.dict_to_csv(labels, '../dataset_multi.csv') # DONE 2017-03-18. - -##----- Dict of ligands pointing to associated PDB in ligands_to_pdb.csv -----## -# Initialization. -pdb_all = tools.read_dict('../dataset_all.csv') -pdb_all = list(pdb_all.keys()) -ligands = {} - -# Computations. -for enzyme in tqdm(pdb_all): - # Load local enzyme. - local_enzyme = pdb.PDBBackbone(enzyme) - - # Get ligands. - local_enzyme.get_ligands() - - # Update dict. - for local_ligand in local_enzyme.ligands: - if local_ligand in ligands: # Already in dict. - ligands[local_ligand] += [enzyme] - else: # New entry. - ligands[local_ligand] = [enzyme] - -# # Save results. -# tools.dict_to_csv(ligands, '../ligands_to_pdb.csv') # DONE 2017-03-18. - -##---- Dict of PDBs pointing to associated ligands in pdb_to_ligands.csv -----## -# Initialization. -pdb_all = tools.read_dict('../dataset_all.csv') -pdb_all = list(pdb_all.keys()) -pdb = {} - -# Computations. -for enzyme in tqdm(pdb_all): - # Load local enzyme. - local_enzyme = pdb.PDBBackbone(enzyme) - - # Get ligands. - local_enzyme.get_ligands() - - # Add entry to dict. - pdb[enzyme] = local_enzyme.ligands - -# # Save results. -# tools.dict_to_csv(pdb, '../pdb_to_ligands.csv') # DONE 2017-03-18. - -##---------- Split single-label dataset in train/validation/test -------------## -# Initialization. -labels_temp = tools.read_dict('../dataset_single.csv') -pdb_ids = list(labels_temp) -partition = {} +N_CLASSES = 6 +RATIO_TRAIN = 0.8 # Split all 80:20 in train/test and train 80:20 in train/validation. +FACTOR_REDUCTION = 0.1 -# Parameters. -X = 0.8 # Split all 80:20 in train/test and train 80:20 in train/validation. -# Random shuffling. -indexes = np.arange(len(pdb_ids)) -np.random.shuffle(indexes) +def find_correct_ids(save: bool = False) -> None: + """Finds PDB IDs that contain valid coordinates.""" + # Computations. + for pdb_class in range(1, N_CLASSES+1): + # Initialization. + errors = [] + enzymes = pd.read_table('raw/' + str(pdb_class) + '.txt', header=None) + list_enzymes = enzymes.loc[:, 0].tolist() -# Train. -train_ids = [pdb_ids[i] for i in indexes[0:int(X*X*len(pdb_ids))]] -partition['train'] = train_ids + # Look for errors. + for k, enzyme in enumerate(tqdm(list_enzymes)): + try: + # Load local enzyme. + local_enzyme = pdb.PDBBackbone(enzyme) + local_enzyme.get_coords() -# Validation. -val_ids = [pdb_ids[i] for i in indexes[int(X*X*len(pdb_ids)):int(X*len(pdb_ids))]] -partition['validation'] = val_ids + # Look for problem. + if len(local_enzyme.backbone_atoms) == 0: # Empty enzyme. + errors += [k] -# Test. -test_ids = [pdb_ids[i] for i in indexes[int(X*len(pdb_ids)):]] -partition['test'] = test_ids + except: # File does not exist, i.e. deprecated enzyme. + errors += [k] -# # Save results. -# tools.dict_to_csv(partition, '../partition_single.csv') # DONE 2017-03-18. + # Drop errors and save. + enzymes = enzymes.drop(enzymes.index[errors]) -##------ Create reduced single-label train/validation/test sets --------------## -# Initialization. -partition = tools.read_dict('../partition_single.csv') -partition_red = {} + if save: # DONE 2017-03-18. + enzymes.to_csv(str(pdb_class) + '.txt', header=None, index=None) -# Parameters. -FACTOR_REDUCTION = 0.1 -# Train. -exec("partition['train'] = " + partition['train']) -indexes = np.arange(len(partition['train'])) -np.random.shuffle(indexes) -train_ids = [partition['train'][i] for i in indexes[0:int(FACTOR_REDUCTION*len(indexes))]] -partition_red['train'] = train_ids - -# Validation. -exec("partition['validation'] = " + partition['validation']) -indexes = np.arange(len(partition['validation'])) -np.random.shuffle(indexes) -val_ids = [partition['validation'][i] for i in indexes[0:int(FACTOR_REDUCTION*len(indexes))]] -partition_red['validation'] = val_ids - -# Test. -exec("partition['test'] = " + partition['test']) -indexes = np.arange(len(partition['test'])) -np.random.shuffle(indexes) -test_ids = [partition['test'][i] for i in indexes[0:int(FACTOR_REDUCTION*len(indexes))]] -partition_red['test'] = test_ids - -# # Save results. -# tools.dict_to_csv(partition_red, '../partition_single_red.csv') # DONE 2017-03-18. +def create_pdb_to_class_mapping(save: bool = False) -> None: + """Creates dictionary linking PDB IDs to their corresponding classes.""" + # Initialization. + labels_all = {} + labels_single = {} + labels_multi = {} + + # Compute labels of all enzymes. + for pdb_class in range(1, N_CLASSES+1): + # Load PDB IDs for each class. + enzymes = pd.read_table(str(pdb_class) + '.txt', header=None) + enzymes = enzymes[0].tolist() + + # Add entries to dict. + for pdb_id in enzymes: + if pdb_id in labels_all: # PDB_id already in dict. + labels_all[pdb_id] += [pdb_class] + else: # PDB_id not yet in dict. + labels_all[pdb_id] = [pdb_class] + + # Create single- and multi-only datasets. + for pdb_id, pdb_classes in labels_all.items(): + if len(pdb_classes) == 1: + labels_single[pdb_id] = more_itertools.one(pdb_classes) + else: + labels_multi[pdb_id] = pdb_classes + + if save: # DONE 2017-03-18. + tools.dict_to_csv(labels_all, '../dataset_all.csv') + tools.dict_to_csv(labels_single, '../dataset_single.csv') + tools.dict_to_csv(labels_multi, '../dataset_multi.csv') + + +def create_pdb_ligand_mappings(save: bool = False) -> None: + """Creates mappings linking PDBs to ligands.""" + # Initialization. + ligands = {} + pdbs = {} + + # Computations. + pdb_ids = list(tools.read_dict('../dataset_all.csv')) + for pdb_id in tqdm(pdb_ids): + # Get ligands. + local_enzyme = pdb.PDBBackbone(pdb_id) + local_enzyme.get_ligands() + + # PDB ID -> ligands mapping. + pdbs[pdb_id] = local_enzyme.ligands + + # Ligand -> PDB IDs mapping. + for ligand_id in local_enzyme.ligands: + if ligand_id in ligands: # Already in dict. + ligands[ligand_id] += [pdb_id] + else: # New entry. + ligands[ligand_id] = [pdb_id] + + if save: # DONE 2017-03-18. + tools.dict_to_csv(pdbs, '../pdb_to_ligands.csv') + tools.dict_to_csv(ligands, '../ligands_to_pdb.csv') + + +def split_dataset_into_train_val_test(save: bool = False) -> None: + """Split single-label into training/validation/testing sets.""" + # Random index shuffling. + pdb_ids = list(tools.read_dict('../dataset_single.csv')) + indexes = np.arange(len(pdb_ids)) + np.random.shuffle(indexes) + + # Train: ratio**2, val: ratio*(1-ratio), test: 1-ratio. + # With ratio = 0.8, train/val/test have proportion 64% / 16% / 20%. + train_ids = [pdb_ids[i] for i in indexes[0:int(RATIO_TRAIN**2 * len(pdb_ids))]] + val_ids = [pdb_ids[i] for i in indexes[int(RATIO_TRAIN**2 * len(pdb_ids)):int(RATIO_TRAIN * len(pdb_ids))]] + test_ids = [pdb_ids[i] for i in indexes[int(RATIO_TRAIN * len(pdb_ids)):]] + + partition = { + 'train': train_ids, + 'validation': val_ids, + 'test': test_ids, + } + + if save: # DONE 2017-03-18. + tools.dict_to_csv(partition, '../partition_single.csv') + + +def _create_reduced_set(list_ids: List[Text]) -> List[Text]: + """Returns a subset of proportion FACTOR_REDUCTION from the initial set.""" + indices = np.arange(len(list_ids)) + np.random.shuffle(indices) + return [list_ids[i] for i in indices[:int(FACTOR_REDUCTION * len(indices))]] + + +def _format_dict_values_to_lists_of_strings( + raw_dict: Dict[Any, Text]) -> Dict[Any, List[Text]]: + """Turns string values of dictionary into lists of strings.""" + out_dict = {} + for key, value in raw_dict.items(): + for string_to_ignore in _STRINGS_TO_IGNORE: + value = value.replace(string_to_ignore, '') + out_dict[key] = value.split(_ELEMENT_DELIMITER) + return out_dict + + +def create_reduced_sets(save: bool = False) -> None: + """Create reduced single-label train/validation/test sets.""" + # Create reduced sets. + partition = _format_dict_values_to_lists_of_strings( + tools.read_dict('../partition_single.csv')) + partition_red = {key: _create_reduced_set(key) for key in partition} + + if save: # DONE 2017-03-18. + tools.dict_to_csv(partition_red, '../partition_single_red.csv') + + +def main(_): + find_correct_ids(FLAGS.save) + create_pdb_to_class_mapping(FLAGS.save) + create_pdb_ligand_mappings(FLAGS.save) + split_dataset_into_train_val_test(FLAGS.save) + create_reduced_sets(FLAGS.save) + + +if __name__ == '__main__': + app.run(main) \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index f5b4da2..62e9d69 100755 --- a/requirements.txt +++ b/requirements.txt @@ -12,4 +12,6 @@ matplotlib biopython # Miscellaneous +absl-py +more_itertools tqdm From cd06ad174464d0d6c7ebfe116b05bec9cb8eef7b Mon Sep 17 00:00:00 2001 From: shervinea Date: Mon, 6 Sep 2021 23:50:19 -0700 Subject: [PATCH 18/18] Add flags to training/testing + concatenate scripts into a single one --- scripts/architecture/enzynet_adapted.py | 221 ------------------- scripts/architecture/enzynet_uniform.py | 221 ------------------- scripts/architecture/run_enzynet.py | 281 ++++++++++++++++++++++++ 3 files changed, 281 insertions(+), 442 deletions(-) delete mode 100644 scripts/architecture/enzynet_adapted.py delete mode 100644 scripts/architecture/enzynet_uniform.py create mode 100644 scripts/architecture/run_enzynet.py diff --git a/scripts/architecture/enzynet_adapted.py b/scripts/architecture/enzynet_adapted.py deleted file mode 100644 index 40d447d..0000000 --- a/scripts/architecture/enzynet_adapted.py +++ /dev/null @@ -1,221 +0,0 @@ -"""32x32x32 Enzynet architecture with adapted weights.""" - -# Authors: Afshine Amidi -# Shervine Amidi - -# MIT License - -import os.path - -import numpy as np - -from enzynet import keras_utils -from enzynet import tools -from enzynet import volume -from keras import backend as K -from keras import callbacks -from keras import initializers -from keras import layers -from keras.layers import advanced_activations -from keras import models -from keras import optimizers -from keras import regularizers - -MODE_RUN = 'test' -MODE_DATASET = 'full' -MODE_WEIGHTS = 'balanced' - -##----------------------------- Parameters -----------------------------------## -# PDB. -WEIGHTS = [] -N_CLASSES = 6 -N_CHANNELS = 1 + len(WEIGHTS) -SCALING_WEIGHTS = True - -# Volume. -P = 0 -V_SIZE = 32 -FLIPS = (0.2, 0.2, 0.2) -MAX_RADIUS = 40 -SHUFFLE = True -NOISE_TREATMENT = False - -# Model. -BATCH_SIZE = 32 -MAX_EPOCHS = 200 -PERIOD_CHECKPOINT = 50 - -# Testing. -VOTING_TYPE = 'probabilities' -AUGMENTATION = ['None', 'flips', 'weighted_flips'] - -# Miscellaneous. -CURRENT_FILE_NAME = os.path.basename(__file__)[:-3] -STDDEV_CONV3D = np.sqrt(2.0/N_CHANNELS) - -##------------------------------ Dataset -------------------------------------## -# Load dictionary of labels. -DICTIONARY = tools.read_dict('../../datasets/dataset_single.csv') - -# Load partitions. -if MODE_DATASET == 'full': - partition = tools.read_dict('../../datasets/partition_single.csv') -elif MODE_DATASET == 'reduced': - partition = tools.read_dict('../../datasets/partition_single_red.csv') -exec("partition['train'] = " + partition['train']) -exec("partition['validation'] = " + partition['validation']) -exec("partition['test'] = " + partition['test']) - -# Final computations. -partition['train'] = partition['train'] + partition['validation'] -partition['validation'] = partition['test'] - -# Get class weights. -class_weights = tools.get_class_weights(DICTIONARY, partition['train'], - mode=MODE_WEIGHTS) - -# Training generator. -training_generator = volume.VolumeDataGenerator( - list_enzymes=partition['train'], - labels=DICTIONARY, - v_size=V_SIZE, - flips=FLIPS, - batch_size=BATCH_SIZE, - shuffle=SHUFFLE, - p=P, - max_radius=MAX_RADIUS, - noise_treatment=NOISE_TREATMENT, - weights=WEIGHTS, - scaling_weights=SCALING_WEIGHTS) - -# Validation generator. -validation_generator = volume.VolumeDataGenerator( - list_enzymes=partition['validation'], - labels=DICTIONARY, - v_size=V_SIZE, - flips=(0, 0, 0), # No flip. - batch_size=BATCH_SIZE, - shuffle=False, # Validate with fixed set. - p=P, - max_radius=MAX_RADIUS, - noise_treatment=NOISE_TREATMENT, - weights=WEIGHTS, - scaling_weights=SCALING_WEIGHTS) - -# Check if data has been precomputed. -training_generator.check_precomputed() - -##----------------------------- Testing --------------------------------------## -# Voting object. -predictions = keras_utils.Voting( - list_enzymes=partition['test'], - labels=DICTIONARY, - voting_type=VOTING_TYPE, - v_size=V_SIZE, - augmentation=AUGMENTATION, - p=P, - max_radius=MAX_RADIUS, - noise_treatment=NOISE_TREATMENT, - weights=WEIGHTS, - scaling_weights=SCALING_WEIGHTS) - -##------------------------------ Model ---------------------------------------## -# Create. -model = models.Sequential() - -# Add layers. -model.add( - layers.Conv3D( - filters=32, - kernel_size=9, - strides=2, - padding='valid', - kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=STDDEV_CONV3D * 9 ** (-3 / 2)), - bias_initializer='zeros', - kernel_regularizer=regularizers.l2(0.001), - bias_regularizer=None, - input_shape=(V_SIZE, V_SIZE, V_SIZE, N_CHANNELS))) - -model.add(advanced_activations.LeakyReLU(alpha=0.1)) - -model.add(layers.Dropout(rate=0.2)) - -model.add( - layers.Conv3D( - filters=64, - kernel_size=5, - strides=1, - padding='valid', - kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=STDDEV_CONV3D * 5 ** (-3 / 2)), - bias_initializer='zeros', - kernel_regularizer=regularizers.l2(0.001), - bias_regularizer=None)) - -model.add(advanced_activations.LeakyReLU(alpha=0.1)) - -model.add(layers.MaxPooling3D(pool_size=(2, 2, 2))) - -model.add(layers.Dropout(rate=0.3)) - -model.add(layers.Flatten()) - -model.add( - layers.Dense( - units=128, - kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.01), - bias_initializer='zeros', - kernel_regularizer=regularizers.l2(0.001), - bias_regularizer=None)) - -model.add(layers.Dropout(rate=0.4)) - -model.add( - layers.Dense( - units=N_CLASSES, - kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.01), - bias_initializer='zeros', - kernel_regularizer=regularizers.l2(0.001), - bias_regularizer=None)) - -model.add(layers.Activation('softmax')) - -# Track accuracy and loss in real-time. -history = keras_utils.MetricsHistory(saving_path=CURRENT_FILE_NAME + '.csv') - -# Checkpoints. -checkpoints = callbacks.ModelCheckpoint( - 'checkpoints/' + CURRENT_FILE_NAME + '_{epoch:02d}' + '.hd5f', - save_weights_only=True, - period=PERIOD_CHECKPOINT) - -if MODE_RUN == 'train': - # Compile. - model.compile(optimizer=optimizers.Adam(lr=0.001, decay=0.00016667), - loss='categorical_crossentropy', - metrics=['accuracy']) - - # Train. - model.fit_generator(generator=training_generator, - epochs=MAX_EPOCHS, - verbose=1, - validation_data=validation_generator, - callbacks=[history, checkpoints], - class_weight=class_weights, - use_multiprocessing=True, - workers=6, - max_queue_size=30) - -if MODE_RUN == 'test': - # Load weights. - weights_path = \ - 'checkpoints/' + CURRENT_FILE_NAME + '_{0:02d}'.format(MAX_EPOCHS) + '.hd5f' - model.load_weights(weights_path) - -# Predict. -predictions.predict(model) - -# Compute indicators. -predictions.get_assessment() - -# Clear session. -K.clear_session() diff --git a/scripts/architecture/enzynet_uniform.py b/scripts/architecture/enzynet_uniform.py deleted file mode 100644 index 5bd0f33..0000000 --- a/scripts/architecture/enzynet_uniform.py +++ /dev/null @@ -1,221 +0,0 @@ -"""32x32x32 Enzynet architecture with uniform weights.""" - -# Authors: Afshine Amidi -# Shervine Amidi - -# MIT License - -import os.path - -import numpy as np - -from enzynet import keras_utils -from enzynet import tools -from enzynet import volume -from keras import backend as K -from keras import callbacks -from keras import initializers -from keras import layers -from keras.layers import advanced_activations -from keras import models -from keras import optimizers -from keras import regularizers - -MODE_RUN = 'test' -MODE_DATASET = 'full' -MODE_WEIGHTS = 'unbalanced' - -##----------------------------- Parameters -----------------------------------## -# PDB. -WEIGHTS = [] -N_CLASSES = 6 -N_CHANNELS = 1 + len(WEIGHTS) -SCALING_WEIGHTS = True - -# Volume. -P = 0 -V_SIZE = 32 -FLIPS = (0.2, 0.2, 0.2) -MAX_RADIUS = 40 -SHUFFLE = True -NOISE_TREATMENT = False - -# Model. -BATCH_SIZE = 32 -MAX_EPOCHS = 200 -PERIOD_CHECKPOINT = 50 - -# Testing. -VOTING_TYPE = 'probabilities' -AUGMENTATION = ['None', 'flips', 'weighted_flips'] - -# Miscellaneous. -CURRENT_FILE_NAME = os.path.basename(__file__)[:-3] -STDDEV_CONV3D = np.sqrt(2.0/N_CHANNELS) - -##------------------------------ Dataset -------------------------------------## -# Load dictionary of labels. -DICTIONARY = tools.read_dict('../../datasets/dataset_single.csv') - -# Load partitions. -if MODE_DATASET == 'full': - partition = tools.read_dict('../../datasets/partition_single.csv') -elif MODE_DATASET == 'reduced': - partition = tools.read_dict('../../datasets/partition_single_red.csv') -exec("partition['train'] = " + partition['train']) -exec("partition['validation'] = " + partition['validation']) -exec("partition['test'] = " + partition['test']) - -# Final computations. -partition['train'] = partition['train'] + partition['validation'] -partition['validation'] = partition['test'] - -# Get class weights. -class_weights = tools.get_class_weights(DICTIONARY, partition['train'], - mode=MODE_WEIGHTS) - -# Training generator. -training_generator = volume.VolumeDataGenerator( - list_enzymes=partition['train'], - labels=DICTIONARY, - v_size=V_SIZE, - flips=FLIPS, - batch_size=BATCH_SIZE, - shuffle=SHUFFLE, - p=P, - max_radius=MAX_RADIUS, - noise_treatment=NOISE_TREATMENT, - weights=WEIGHTS, - scaling_weights=SCALING_WEIGHTS) - -# Validation generator. -validation_generator = volume.VolumeDataGenerator( - list_enzymes=partition['validation'], - labels=DICTIONARY, - v_size=V_SIZE, - flips=(0, 0, 0), # No flip. - batch_size=BATCH_SIZE, - shuffle=False, # Validate with fixed set. - p=P, - max_radius=MAX_RADIUS, - noise_treatment=NOISE_TREATMENT, - weights=WEIGHTS, - scaling_weights=SCALING_WEIGHTS) - -# Check if data has been precomputed. -training_generator.check_precomputed() - -##----------------------------- Testing --------------------------------------## -# Voting object. -predictions = keras_utils.Voting( - list_enzymes=partition['test'], - labels=DICTIONARY, - voting_type=VOTING_TYPE, - v_size=V_SIZE, - augmentation=AUGMENTATION, - p=P, - max_radius=MAX_RADIUS, - noise_treatment=NOISE_TREATMENT, - weights=WEIGHTS, - scaling_weights=SCALING_WEIGHTS) - -##------------------------------ Model ---------------------------------------## -# Create. -model = models.Sequential() - -# Add layers. -model.add( - layers.Conv3D( - filters=32, - kernel_size=9, - strides=2, - padding='valid', - kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=STDDEV_CONV3D * 9 ** (-3 / 2)), - bias_initializer='zeros', - kernel_regularizer=regularizers.l2(0.001), - bias_regularizer=None, - input_shape=(V_SIZE, V_SIZE, V_SIZE, N_CHANNELS))) - -model.add(advanced_activations.LeakyReLU(alpha=0.1)) - -model.add(layers.Dropout(rate=0.2)) - -model.add( - layers.Conv3D( - filters=64, - kernel_size=5, - strides=1, - padding='valid', - kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=STDDEV_CONV3D * 5 ** (-3 / 2)), - bias_initializer='zeros', - kernel_regularizer=regularizers.l2(0.001), - bias_regularizer=None)) - -model.add(advanced_activations.LeakyReLU(alpha=0.1)) - -model.add(layers.MaxPooling3D(pool_size=(2, 2, 2))) - -model.add(layers.Dropout(rate=0.3)) - -model.add(layers.Flatten()) - -model.add( - layers.Dense( - units=128, - kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.01), - bias_initializer='zeros', - kernel_regularizer=regularizers.l2(0.001), - bias_regularizer=None)) - -model.add(layers.Dropout(rate=0.4)) - -model.add( - layers.Dense( - units=N_CLASSES, - kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.01), - bias_initializer='zeros', - kernel_regularizer=regularizers.l2(0.001), - bias_regularizer=None)) - -model.add(layers.Activation('softmax')) - -# Track accuracy and loss in real-time. -history = keras_utils.MetricsHistory(saving_path=CURRENT_FILE_NAME + '.csv') - -# Checkpoints. -checkpoints = callbacks.ModelCheckpoint( - 'checkpoints/' + CURRENT_FILE_NAME + '_{epoch:02d}' + '.hd5f', - save_weights_only=True, - period=PERIOD_CHECKPOINT) - -if MODE_RUN == 'train': - # Compile. - model.compile(optimizer=optimizers.Adam(lr=0.001, decay=0.00016667), - loss='categorical_crossentropy', - metrics=['accuracy']) - - # Train. - model.fit_generator(generator=training_generator, - epochs=MAX_EPOCHS, - verbose=1, - validation_data=validation_generator, - callbacks=[history, checkpoints], - class_weight=class_weights, - use_multiprocessing=True, - workers=8, - max_queue_size=30) - -if MODE_RUN == 'test': - # Load weights. - weights_path = \ - 'checkpoints/' + CURRENT_FILE_NAME + '_{0:02d}'.format(MAX_EPOCHS) + '.hd5f' - model.load_weights(weights_path) - -# Predict. -predictions.predict(model) - -# Compute indicators. -predictions.get_assessment() - -# Clear session. -K.clear_session() diff --git a/scripts/architecture/run_enzynet.py b/scripts/architecture/run_enzynet.py new file mode 100644 index 0000000..6c7f861 --- /dev/null +++ b/scripts/architecture/run_enzynet.py @@ -0,0 +1,281 @@ +"""Run the EnzyNet architecture.""" + +# Authors: Afshine Amidi +# Shervine Amidi + +# MIT License + +from absl import app +from absl import flags + +import numpy as np + +from enzynet import keras_utils +from enzynet import tools +from enzynet import volume +from keras import backend as K +from keras import callbacks +from keras import initializers +from keras import layers +from keras.layers import advanced_activations +from keras import models +from keras import optimizers +from keras import regularizers + +FLAGS = flags.FLAGS + +# Main parameters. +flags.DEFINE_enum('mode_dataset', default='full', + enum_values=['full', 'reduced'], + help='Version of the dataset to use. "full" represents the ' + 'entire dataset whereas "reduced" denotes just a 10% fixed ' + 'random subset.') +flags.DEFINE_enum('mode_run', default='test', enum_values=['train', 'test'], + help='Whether to run the training or testing portion of the ' + 'code.') + +# Volume parameters. +flags.DEFINE_list('weights', default=[], help='Weights to be used along with ' + 'coordinates to characterize PDB volumes. Each weight type ' + 'specified here adds an additional channel.') +flags.DEFINE_bool('scaling_weights', default=True, help='If set to True, ' + 'weight values are scaled to have a maximum magnitude of 1. ' + 'Note: this parameter is ignored if no weights are ' + 'specified.') +flags.DEFINE_integer('p', lower_bound=0, default=0, help='Number of ' + 'interpolated points between two consecutive represented ' + 'atoms. This parameter is used for finer grid ' + 'representations in order to draw lines between ' + 'consecutive points.') +flags.DEFINE_integer('v_size', lower_bound=1, default=32, help='Size of each ' + 'dimension of the grid where enzymes are represented. The ' + 'paper used a value of 32 but higher values such as 64 or ' + '96 (cf. Figure 2 of the paper) could prove to carry ' + 'finer, more useful structural information. In this case, ' + 'please note that the architecture would also likely need ' + 'an upgrade in order to see performance gains.') +flags.DEFINE_float('max_radius', lower_bound=0.1, default=40, help='Maximum ' + 'enzyme radius (in angstroms) that entirely fits in the ' + 'volume. A higher value means more information is included ' + 'in the grid at a coarser resolution. Dataset statistics ' + 'are provided in Figure 4 of the paper.') +flags.DEFINE_bool('shuffle', default=True, help='Whether to shuffle the order ' + 'of dataset exploration in-between training epochs.') +flags.DEFINE_bool('noise_treatment', default=False, help='Whether to remove ' + 'isolated atoms from the enzyme volume. This is makes the ' + 'structure visually more coherent at fine grid sizes (> 64) ' + 'but is an almost no-op at lower resoltions (e.g. 32).') +flags.DEFINE_float('flip_probability', lower_bound=0.0, upper_bound=1.0, + default=0.2, help='Probability of flipping the enzyme with ' + 'respect to any axis. Used as an augmentation technique ' + 'during training.') + +# Training parameters. +flags.DEFINE_integer('batch_size', lower_bound=1, default=32, help='Training ' + 'and validation batch size.') +flags.DEFINE_integer('max_epochs', lower_bound=1, default=200, help='Number of ' + 'training epochs.') +flags.DEFINE_enum('mode_weights', default='unbalanced', + enum_values=['unbalanced', 'balanced'], + help='Characterization of the strategy for mistake ' + 'penalization in the calculation of the loss.') +flags.DEFINE_integer('period_checkpoint', lower_bound=1, default=50, + help='Epoch frequency at which model weights are saved ' + 'during the training process.') + +# Testing parameters. +flags.DEFINE_enum('voting_type', default='probabilities', + enum_values=['classes', 'probabilities'], + help='Determines how voting decisions are merged. ' + '"probabilities" is used when the predicted class is the ' + 'argmax of the sum of probabilities, "classes" when the ' + 'argmax operates on the sum of predicted classes.') +flags.DEFINE_list('augmentation', default=['None', 'flips', 'weighted_flips'], + help='Denotes the augmentation techniques used at testing ' + 'time.') + +##------------------------------ Constants -----------------------------------## +# PDB. +N_CLASSES = 6 +N_CHANNELS = 1 + len(FLAGS.weights) + +# Miscellaneous. +LOSS_TYPE_TO_NAME = { + 'unbalanced': 'enzynet_uniform', + 'balanced': 'enzynet_adapted', +} +STDDEV_CONV3D = np.sqrt(2.0/N_CHANNELS) + + +def main(_): + ##---------------------------- Dataset -----------------------------------## + # Load dictionary of labels. + DICTIONARY = tools.read_dict('../../datasets/dataset_single.csv') + + # Load partitions. + if FLAGS.mode_dataset == 'full': + partition = tools.read_dict('../../datasets/partition_single.csv') + elif FLAGS.mode_dataset == 'reduced': + partition = tools.read_dict('../../datasets/partition_single_red.csv') + exec("partition['train'] = " + partition['train']) + exec("partition['validation'] = " + partition['validation']) + exec("partition['test'] = " + partition['test']) + + # Final computations. + partition['train'] = partition['train'] + partition['validation'] + partition['validation'] = partition['test'] + + # Get class weights and run type. + class_weights = tools.get_class_weights(DICTIONARY, partition['train'], + mode=FLAGS.mode_weights) + run_type = LOSS_TYPE_TO_NAME[FLAGS.mode_weights] + + # Training generator. + training_generator = volume.VolumeDataGenerator( + list_enzymes=partition['train'], + labels=DICTIONARY, + v_size=FLAGS.v_size, + flips=(FLAGS.flip_probability,) * 3, + batch_size=FLAGS.batch_size, + shuffle=FLAGS.shuffle, + p=FLAGS.p, + max_radius=FLAGS.max_radius, + noise_treatment=FLAGS.noise_treatment, + weights=FLAGS.weights, + scaling_weights=FLAGS.scaling_weights) + + # Validation generator. + validation_generator = volume.VolumeDataGenerator( + list_enzymes=partition['validation'], + labels=DICTIONARY, + v_size=FLAGS.v_size, + flips=(0,) * 3, # No flip. + batch_size=FLAGS.batch_size, + shuffle=False, # Validate with fixed set. + p=FLAGS.p, + max_radius=FLAGS.max_radius, + noise_treatment=FLAGS.noise_treatment, + weights=FLAGS.weights, + scaling_weights=FLAGS.scaling_weights) + + # Check if data has been precomputed. + training_generator.check_precomputed() + + ##--------------------------- Testing ------------------------------------## + # Voting object. + predictions = keras_utils.Voting( + list_enzymes=partition['test'], + labels=DICTIONARY, + voting_type=FLAGS.voting_type, + v_size=FLAGS.v_size, + augmentation=FLAGS.augmentation, + p=FLAGS.p, + max_radius=FLAGS.max_radius, + noise_treatment=FLAGS.noise_treatment, + weights=FLAGS.weights, + scaling_weights=FLAGS.scaling_weights) + + ##---------------------------- Model -------------------------------------## + # Create. + model = models.Sequential() + + # Add layers. + model.add( + layers.Conv3D( + filters=32, + kernel_size=9, + strides=2, + padding='valid', + kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=STDDEV_CONV3D * 9 ** (-3 / 2)), + bias_initializer='zeros', + kernel_regularizer=regularizers.l2(0.001), + bias_regularizer=None, + input_shape=(FLAGS.v_size, FLAGS.v_size, FLAGS.v_size, N_CHANNELS))) + + model.add(advanced_activations.LeakyReLU(alpha=0.1)) + + model.add(layers.Dropout(rate=0.2)) + + model.add( + layers.Conv3D( + filters=64, + kernel_size=5, + strides=1, + padding='valid', + kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=STDDEV_CONV3D * 5 ** (-3 / 2)), + bias_initializer='zeros', + kernel_regularizer=regularizers.l2(0.001), + bias_regularizer=None)) + + model.add(advanced_activations.LeakyReLU(alpha=0.1)) + + model.add(layers.MaxPooling3D(pool_size=(2, 2, 2))) + + model.add(layers.Dropout(rate=0.3)) + + model.add(layers.Flatten()) + + model.add( + layers.Dense( + units=128, + kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.01), + bias_initializer='zeros', + kernel_regularizer=regularizers.l2(0.001), + bias_regularizer=None)) + + model.add(layers.Dropout(rate=0.4)) + + model.add( + layers.Dense( + units=N_CLASSES, + kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.01), + bias_initializer='zeros', + kernel_regularizer=regularizers.l2(0.001), + bias_regularizer=None)) + + model.add(layers.Activation('softmax')) + + # Track accuracy and loss in real-time. + history = keras_utils.MetricsHistory(saving_path=run_type + '.csv') + + # Checkpoints. + checkpoints = callbacks.ModelCheckpoint( + 'checkpoints/' + run_type + '_{epoch:02d}' + '.hd5f', + save_weights_only=True, + period=FLAGS.period_checkpoint) + + if FLAGS.mode_run == 'train': + # Compile. + model.compile(optimizer=optimizers.Adam(lr=0.001, decay=0.00016667), + loss='categorical_crossentropy', + metrics=['accuracy']) + + # Train. + model.fit_generator(generator=training_generator, + epochs=FLAGS.max_epochs, + verbose=1, + validation_data=validation_generator, + callbacks=[history, checkpoints], + class_weight=class_weights, + use_multiprocessing=True, + workers=8, + max_queue_size=30) + + if FLAGS.mode_run == 'test': + # Load weights. + weights_path = \ + 'checkpoints/' + run_type + '_{0:02d}'.format(FLAGS.max_epochs) + '.hd5f' + model.load_weights(weights_path) + + # Predict. + predictions.predict(model) + + # Compute indicators. + predictions.get_assessment() + + # Clear session. + K.clear_session() + + +if __name__ == '__main__': + app.run(main)