black formatting

gflownet/policy/mlp.py

@@ -62,7 +62,7 @@ def make_mlp(self, activation):
     def parse_config(self, config):
         if config is None:
             config = OmegaConf.create()
-            config.type = "mlp"  
+            config.type = "mlp"
         self.checkpoint = config.get("checkpoint", None)
         self.shared_weights = config.get("shared_weights", False)
         self.n_hid = config.get("n_hid", 128)

scripts/crystal/eval_crystalgflownet.py

@@ -1,6 +1,7 @@
 """
 Computes evaluation metrics and plots from a pre-trained GFlowNet model.
 """
+
 import pickle
 import shutil
 import sys

scripts/crystal/eval_gflownet.py

@@ -229,30 +229,30 @@ def main(args):
         env.proxy.is_bandgap = False
 
         # Test
-#         samples = [env.readable2state(readable) for readable in gflownet.buffer.test["samples"]]
-#         energies = env.proxy(env.states2proxy(samples))
-#         df = pd.DataFrame(
-#             {
-#                 "readable": gflownet.buffer.test["samples"],
-#                 "energies": energies.tolist(),
-#             }
-#         )
-#         df.to_csv(output_dir / f"val.csv")
-#         dct = {"x": samples, "energy": energies.tolist()}
-#         pickle.dump(dct, open(output_dir / f"val.pkl", "wb"))
-# 
-#         # Train
-#         samples = [env.readable2state(readable) for readable in gflownet.buffer.train["samples"]]
-#         energies = env.proxy(env.states2proxy(samples))
-#         df = pd.DataFrame(
-#             {
-#                 "readable": gflownet.buffer.train["samples"],
-#                 "energies": energies.tolist(),
-#             }
-#         )
-#         df.to_csv(output_dir / f"train.csv")
-#         dct = {"x": samples, "energy": energies.tolist()}
-#         pickle.dump(dct, open(output_dir / f"train.pkl", "wb"))
+    #         samples = [env.readable2state(readable) for readable in gflownet.buffer.test["samples"]]
+    #         energies = env.proxy(env.states2proxy(samples))
+    #         df = pd.DataFrame(
+    #             {
+    #                 "readable": gflownet.buffer.test["samples"],
+    #                 "energies": energies.tolist(),
+    #             }
+    #         )
+    #         df.to_csv(output_dir / f"val.csv")
+    #         dct = {"x": samples, "energy": energies.tolist()}
+    #         pickle.dump(dct, open(output_dir / f"val.pkl", "wb"))
+    #
+    #         # Train
+    #         samples = [env.readable2state(readable) for readable in gflownet.buffer.train["samples"]]
+    #         energies = env.proxy(env.states2proxy(samples))
+    #         df = pd.DataFrame(
+    #             {
+    #                 "readable": gflownet.buffer.train["samples"],
+    #                 "energies": energies.tolist(),
+    #             }
+    #         )
+    #         df.to_csv(output_dir / f"train.csv")
+    #         dct = {"x": samples, "energy": energies.tolist()}
+    #         pickle.dump(dct, open(output_dir / f"train.pkl", "wb"))
 
     if args.n_samples > 0 and args.n_samples <= 1e5 and not args.random_only:
         print(

scripts/crystal/sample_uniform_with_rewards.py

@@ -3,6 +3,7 @@
 should be run with the same config as main.py, e.g. 
 python sample_uniform_with_rewards.py +experiments=crystals/albatross_sg_first logger.do.online=False user=sasha
 """
+
 import pickle
 import sys
 

scripts/pyxtal/compatibility_sg_n_atoms.py

@@ -3,6 +3,7 @@
 combinations spanned by the N_SYMMETRY_GROUPS, N_SPECIES and MAX_N_ATOMS. The results
 are printed to stdout.
 """
+
 import itertools
 import time
 

scripts/pyxtal/get_n_compatible_for_sg.py

@@ -3,6 +3,7 @@
 spanned by the --max_n_atoms and --max_n_species. The results are written to a file in
 --output_dir.
 """
+
 import itertools
 import time
 from argparse import ArgumentParser

scripts/pyxtal/pyxtal_vs_pymatgen.py

@@ -2,6 +2,7 @@
 A simple script to determine which space group symbols are different in pyxtal and
 pymatgen.
 """
+
 from argparse import ArgumentParser
 
 from pymatgen.symmetry.groups import (