formatting + name convention

examples/LennardJones/LennardJones.py

@@ -271,7 +271,11 @@
         os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi"
         os.environ["HYDRAGNN_USE_ddstore"] = "1"
 
-    (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders(
+    (
+        train_loader,
+        val_loader,
+        test_loader,
+    ) = hydragnn.preprocess.create_dataloaders(
         trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"]
     )
 

examples/alexandria/train.py

@@ -509,7 +509,11 @@ def get(self, idx):
         os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi"
         os.environ["HYDRAGNN_USE_ddstore"] = "1"
 
-    (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders(
+    (
+        train_loader,
+        val_loader,
+        test_loader,
+    ) = hydragnn.preprocess.create_dataloaders(
         trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"]
     )
 

examples/ani1_x/train.py

@@ -389,7 +389,11 @@ def get(self, idx):
         os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi"
         os.environ["HYDRAGNN_USE_ddstore"] = "1"
 
-    (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders(
+    (
+        train_loader,
+        val_loader,
+        test_loader,
+    ) = hydragnn.preprocess.create_dataloaders(
         trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"]
     )
 

examples/csce/train_gap.py

@@ -383,7 +383,11 @@ def __getitem__(self, idx):
         os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi"
         os.environ["HYDRAGNN_USE_ddstore"] = "1"
 
-    (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders(
+    (
+        train_loader,
+        val_loader,
+        test_loader,
+    ) = hydragnn.preprocess.create_dataloaders(
         trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"]
     )
     comm.Barrier()

examples/dftb_uv_spectrum/train_discrete_uv_spectrum.py

@@ -317,7 +317,11 @@ def get(self, idx):
         os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi"
         os.environ["HYDRAGNN_USE_ddstore"] = "1"
 
-    (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders(
+    (
+        train_loader,
+        val_loader,
+        test_loader,
+    ) = hydragnn.preprocess.create_dataloaders(
         trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"]
     )
 

examples/dftb_uv_spectrum/train_smooth_uv_spectrum.py

@@ -315,7 +315,11 @@ def get(self, idx):
         os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi"
         os.environ["HYDRAGNN_USE_ddstore"] = "1"
 
-    (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders(
+    (
+        train_loader,
+        val_loader,
+        test_loader,
+    ) = hydragnn.preprocess.create_dataloaders(
         trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"]
     )
 

examples/eam/eam.py

@@ -167,7 +167,11 @@ def info(*args, logtype="info", sep=" "):
         % (len(trainset), len(valset), len(testset))
     )
 
-    (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders(
+    (
+        train_loader,
+        val_loader,
+        test_loader,
+    ) = hydragnn.preprocess.create_dataloaders(
         trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"]
     )
     timer.stop()

examples/ising_model/create_configurations.py

@@ -40,7 +40,7 @@ def E_dimensionless(config, L, spin_function, scale_spin):
                 spin[x, y, z] = spin_function(config[x, y, z])
 
     count_pos = 0
-    number_nodes = L ** 3
+    number_nodes = L**3
     positions = np.zeros((number_nodes, 3))
     atomic_features = np.zeros((number_nodes, 5))
     for x in range(L):
@@ -79,15 +79,15 @@ def create_dataset(
 
     count_config = 0
 
-    for num_downs in tqdm(range(0, L ** 3)):
+    for num_downs in tqdm(range(0, L**3)):
 
-        primal_configuration = np.ones((L ** 3,))
+        primal_configuration = np.ones((L**3,))
         for down in range(0, num_downs):
             primal_configuration[down] = -1.0
 
         # If the current composition has a total number of possible configurations above
         # the hard cutoff threshold, a random configurational subset is picked
-        if scipy.special.binom(L ** 3, num_downs) > histogram_cutoff:
+        if scipy.special.binom(L**3, num_downs) > histogram_cutoff:
             for num_config in range(0, histogram_cutoff):
                 config = np.random.permutation(primal_configuration)
                 config = np.reshape(config, (L, L, L))

examples/ising_model/train_ising.py

@@ -86,7 +86,7 @@ def create_dataset_mpi(
         np.random.seed(seed)
 
     count_config = 0
-    rx = list(nsplit(range(0, L ** 3), comm_size))[rank]
+    rx = list(nsplit(range(0, L**3), comm_size))[rank]
     info("rx", rx.start, rx.stop)
     for num_downs in range(rx.start, rx.stop):
         subdir = os.path.join(dir, str(num_downs))
@@ -98,13 +98,13 @@ def create_dataset_mpi(
         prefix = "output_%d_" % num_downs
         subdir = os.path.join(dir, str(num_downs))
 
-        primal_configuration = np.ones((L ** 3,))
+        primal_configuration = np.ones((L**3,))
         for down in range(0, num_downs):
             primal_configuration[down] = -1.0
 
         # If the current composition has a total number of possible configurations above
         # the hard cutoff threshold, a random configurational subset is picked
-        if scipy.special.binom(L ** 3, num_downs) > histogram_cutoff:
+        if scipy.special.binom(L**3, num_downs) > histogram_cutoff:
             for num_config in range(0, histogram_cutoff):
                 config = np.random.permutation(primal_configuration)
                 config = np.reshape(config, (L, L, L))
@@ -355,7 +355,11 @@ def info(*args, logtype="info", sep=" "):
         os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi"
         os.environ["HYDRAGNN_USE_ddstore"] = "1"
 
-    (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders(
+    (
+        train_loader,
+        val_loader,
+        test_loader,
+    ) = hydragnn.preprocess.create_dataloaders(
         trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"]
     )
     timer.stop()

examples/lsms/lsms.py

@@ -170,7 +170,11 @@ def info(*args, logtype="info", sep=" "):
         % (len(trainset), len(valset), len(testset))
     )
 
-    (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders(
+    (
+        train_loader,
+        val_loader,
+        test_loader,
+    ) = hydragnn.preprocess.create_dataloaders(
         trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"]
     )
     timer.stop()

examples/md17/md17.py

@@ -16,6 +16,7 @@
 
 import hydragnn
 
+
 # Update each sample prior to loading.
 def md17_pre_transform(data):
     # Set descriptor as element type.
@@ -72,7 +73,11 @@ def md17_pre_filter(data):
 train, val, test = hydragnn.preprocess.split_dataset(
     dataset, config["NeuralNetwork"]["Training"]["perc_train"], False
 )
-(train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders(
+(
+    train_loader,
+    val_loader,
+    test_loader,
+) = hydragnn.preprocess.create_dataloaders(
     train, val, test, config["NeuralNetwork"]["Training"]["batch_size"]
 )
 

examples/mptrj/train.py

@@ -405,7 +405,11 @@ def get(self, idx):
         os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi"
         os.environ["HYDRAGNN_USE_ddstore"] = "1"
 
-    (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders(
+    (
+        train_loader,
+        val_loader,
+        test_loader,
+    ) = hydragnn.preprocess.create_dataloaders(
         trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"]
     )
 

examples/multidataset/train.py

@@ -338,7 +338,11 @@ def info(*args, logtype="info", sep=" "):
         os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi"
         os.environ["HYDRAGNN_USE_ddstore"] = "1"
 
-    (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders(
+    (
+        train_loader,
+        val_loader,
+        test_loader,
+    ) = hydragnn.preprocess.create_dataloaders(
         trainset,
         valset,
         testset,

examples/multidataset_hpo/gfm.py

@@ -357,7 +357,11 @@ def main():
         os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi"
         os.environ["HYDRAGNN_USE_ddstore"] = "1"
 
-    (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders(
+    (
+        train_loader,
+        val_loader,
+        test_loader,
+    ) = hydragnn.preprocess.create_dataloaders(
         trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"]
     )
 

examples/ogb/train_gap.py

@@ -444,7 +444,11 @@ def __getitem__(self, idx):
         % (len(trainset), len(valset), len(testset))
     )
 
-    (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders(
+    (
+        train_loader,
+        val_loader,
+        test_loader,
+    ) = hydragnn.preprocess.create_dataloaders(
         trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"]
     )
 

examples/open_catalyst_2020/train.py

@@ -359,7 +359,11 @@ def get(self, idx):
         os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi"
         os.environ["HYDRAGNN_USE_ddstore"] = "1"
 
-    (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders(
+    (
+        train_loader,
+        val_loader,
+        test_loader,
+    ) = hydragnn.preprocess.create_dataloaders(
         trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"]
     )
 

examples/open_catalyst_2022/train.py

@@ -421,7 +421,11 @@ def get(self, idx):
         os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi"
         os.environ["HYDRAGNN_USE_ddstore"] = "1"
 
-    (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders(
+    (
+        train_loader,
+        val_loader,
+        test_loader,
+    ) = hydragnn.preprocess.create_dataloaders(
         trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"]
     )
 

examples/qm7x/train.py

@@ -450,7 +450,11 @@ def get(self, idx):
         os.environ["HYDRAGNN_AGGR_BACKEND"] = "mpi"
         os.environ["HYDRAGNN_USE_ddstore"] = "1"
 
-    (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders(
+    (
+        train_loader,
+        val_loader,
+        test_loader,
+    ) = hydragnn.preprocess.create_dataloaders(
         trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"]
     )
 

examples/qm9/qm9.json

@@ -8,6 +8,7 @@
             "model_type": "MACE",
             "radius": 7,
 						"basis_emb_size": 8,
+						"equivariance": false,
 						"envelope_exponent": 5,
 						"max_ell": 2,
 						"node_max_ell": 2,

examples/qm9/qm9.py

@@ -16,6 +16,7 @@
 
 import hydragnn
 
+
 # Update each sample prior to loading.
 def qm9_pre_transform(data):
     # Set descriptor as element type.
@@ -63,7 +64,11 @@ def qm9_pre_filter(data):
 train, val, test = hydragnn.preprocess.split_dataset(
     dataset, config["NeuralNetwork"]["Training"]["perc_train"], False
 )
-(train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders(
+(
+    train_loader,
+    val_loader,
+    test_loader,
+) = hydragnn.preprocess.create_dataloaders(
     train, val, test, config["NeuralNetwork"]["Training"]["batch_size"]
 )
 

examples/qm9_hpo/qm9.py

@@ -12,6 +12,7 @@
 import hydragnn
 import argparse
 
+
 # Update each sample prior to loading.
 def qm9_pre_transform(data):
     # Set descriptor as element type.
@@ -85,7 +86,11 @@ def qm9_pre_filter(data):
 train, val, test = hydragnn.preprocess.split_dataset(
     dataset, config["NeuralNetwork"]["Training"]["perc_train"], False
 )
-(train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders(
+(
+    train_loader,
+    val_loader,
+    test_loader,
+) = hydragnn.preprocess.create_dataloaders(
     train, val, test, config["NeuralNetwork"]["Training"]["batch_size"]
 )
 

examples/qm9_hpo/qm9_deephyper_multi.py

@@ -32,6 +32,7 @@
 DEEPHYPER_LOG_DIR = os.environ["DEEPHYPER_LOG_DIR"]
 DEEPHYPER_DB_HOST = os.environ["DEEPHYPER_DB_HOST"]
 
+
 # Update each sample prior to loading.
 def qm9_pre_transform(data):
     # Set descriptor as element type.

examples/qm9_hpo/qm9_optuna.py

@@ -16,6 +16,7 @@
 
 import pandas as pd
 
+
 # Update each sample prior to loading.
 def qm9_pre_transform(data):
     # Set descriptor as element type.
@@ -73,7 +74,11 @@ def objective(trial):
     if model_type not in ["EGNN", "SchNet", "DimeNet"]:
         config["NeuralNetwork"]["Architecture"]["equivariance"] = False
 
-    (train_loader, val_loader, test_loader,) = hydragnn.preprocess.create_dataloaders(
+    (
+        train_loader,
+        val_loader,
+        test_loader,
+    ) = hydragnn.preprocess.create_dataloaders(
         trainset, valset, testset, config["NeuralNetwork"]["Training"]["batch_size"]
     )
 

examples/qm9_hpo/test_deephyper.py

@@ -6,7 +6,7 @@ def run(job):
     if job.parameters["function"] == "linear":
         y = x + b
     elif job.parameters["function"] == "cubic":
-        y = x ** 3 + b
+        y = x**3 + b
 
     # Maximization!
     return y