formatting with autopep8

.github/workflows/ci.yml

@@ -31,7 +31,7 @@ jobs:
           key: pickle-cache-${{ github.sha }}
           restore-keys: |
             pickle-cache-
-  
+
 
       - name: Install dependencies
         run: |
@@ -49,7 +49,7 @@ jobs:
           if [ ! -s changed_files.txt ]; then
             echo "No Python files changed."
           fi
-          
+
       - name: Run general tests for all changes
         id: general_tests
         continue-on-error: true
@@ -75,7 +75,7 @@ jobs:
           done < changed_files.txt
 
           # Remove the trailing comma
-          GAMES=${GAMES%,} 
+          GAMES=${GAMES%,}
           echo "GAMES=$GAMES" >> $GITHUB_ENV
 
       - name: Download Pickle Files if Not Cached
@@ -99,3 +99,4 @@ jobs:
           echo "One or more critical tests have failed. Failing workflow."
           exit 1
 
+

dataset_generation/ReadMe.md

@@ -1,7 +1,7 @@
 # ODA: An object-centric dataset for Atari
 
 ## Using generate_dataset.py
-`generate_dataset.py`is the main script for generating object-centric datasets for Atari games. The list of supported games can be found in the OCAtari ReadMe. Games which only supports vision mode should use the `generate_dataset_vision.py`script instead. All parameters and settings are identical and can be used with both scripts. 
+`generate_dataset.py`is the main script for generating object-centric datasets for Atari games. The list of supported games can be found in the OCAtari ReadMe. Games which only supports vision mode should use the `generate_dataset_vision.py`script instead. All parameters and settings are identical and can be used with both scripts.
 
 
 ### Requirements

dataset_generation/datasets_on_all.sh

@@ -6,7 +6,7 @@ do
 done
 
 # VISION ONLY
-for game in Alien Asteroids BeamRider ChopperCommand DemonAttack FishingDerby Frostbite MontezumaRevenge Qbert Riverraid RoadRunner 
+for game in Alien Asteroids BeamRider ChopperCommand DemonAttack FishingDerby Frostbite MontezumaRevenge Qbert Riverraid RoadRunner
 # for game in Breakout Pong
 do
     python3 generate_dataset_vision.py -g $game

dataset_generation/generate_dataset.py

@@ -27,8 +27,10 @@
                     help="The frame interval (default 10)")
 # parser.add_argument("-m", "--mode", choices=["vision", "ram"],
 #                     default="ram", help="The frame interval")
-parser.add_argument("-hud", "--hud", action="store_true", default=True, help="Detect HUD")
-parser.add_argument("-dqn", "--dqn", action="store_true", default=True, help="Use DQN agent")
+parser.add_argument("-hud", "--hud", action="store_true",
+                    default=True, help="Detect HUD")
+parser.add_argument("-dqn", "--dqn", action="store_true",
+                    default=True, help="Use DQN agent")
 opts = parser.parse_args()
 
 # Init the environment
@@ -47,7 +49,7 @@
 # Init an empty dataset
 game_nr = 0
 turn_nr = 0
-dataset = {"INDEX": [], #"OBS": [], 
+dataset = {"INDEX": [],  # "OBS": [],
            "RAM": [], "VIS": [], "HUD": []}
 frames = []
 r_objs = []
@@ -68,9 +70,12 @@
     r_objs.append(deepcopy(env.objects))
     v_objs.append(deepcopy(env.objects_v))
     # dataset["OBS"].append(obs.flatten().tolist())
-    dataset["VIS"].append([x for x in sorted(env.objects_v, key=lambda o: str(o))])
-    dataset["RAM"].append([x for x in sorted(env.objects, key=lambda o: str(o)) if x.hud == False])
-    dataset["HUD"].append([x for x in sorted(env.objects, key=lambda o: str(o)) if x.hud == True])
+    dataset["VIS"].append(
+        [x for x in sorted(env.objects_v, key=lambda o: str(o))])
+    dataset["RAM"].append(
+        [x for x in sorted(env.objects, key=lambda o: str(o)) if x.hud == False])
+    dataset["HUD"].append(
+        [x for x in sorted(env.objects, key=lambda o: str(o)) if x.hud == True])
     turn_nr = turn_nr + 1
 
     # if a game is terminated, restart with a new game and update turn and game counter
@@ -127,7 +132,7 @@
 
 df = pd.DataFrame(dataset, columns=['INDEX', 'RAM', 'HUD', 'VIS'])
 makedirs("data/datasets/", exist_ok=True)
-prefix = f"{opts.game}_dqn" if opts.dqn else f"{opts.game}_random" 
+prefix = f"{opts.game}_dqn" if opts.dqn else f"{opts.game}_random"
 df.to_csv(f"data/datasets/{prefix}.csv", index=False)
 pickle.dump(v_objs, open(f"data/datasets/{prefix}_objects_v.pkl", "wb"))
 pickle.dump(r_objs, open(f"data/datasets/{prefix}_objects_r.pkl", "wb"))

dataset_generation/generate_dataset_vision.py

@@ -4,10 +4,10 @@
 import random
 import matplotlib.pyplot as plt
 from os import path
-#sys.path.append(path.dirname(path.dirname(path.abspath(__file__)))) # noqa
+# sys.path.append(path.dirname(path.dirname(path.abspath(__file__)))) # noqa
 from ocatari.core import OCAtari
 from ocatari.vision.utils import mark_bb, make_darker
-#from ocatari.vision.space_invaders import objects_colors
+# from ocatari.vision.space_invaders import objects_colors
 from ocatari.vision.pong import objects_colors
 from ocatari.utils import load_agent, parser, make_deterministic
 from copy import deepcopy
@@ -21,8 +21,10 @@
                     help="The frame interval (default 10)")
 # parser.add_argument("-m", "--mode", choices=["vision", "ram"],
 #                     default="ram", help="The frame interval")
-parser.add_argument("-hud", "--hud", action="store_true", default=True, help="Detect HUD")
-parser.add_argument("-dqn", "--dqn", action="store_true", default=True, help="Use DQN agent")
+parser.add_argument("-hud", "--hud", action="store_true",
+                    default=True, help="Detect HUD")
+parser.add_argument("-dqn", "--dqn", action="store_true",
+                    default=True, help="Use DQN agent")
 
 opts = parser.parse_args()
 
@@ -45,13 +47,14 @@
 
     obs, reward, terminated, truncated, info = env.step(action)
 
-    #if i % 1000 == 0:
+    # if i % 1000 == 0:
     #    print(f"{i} done")
 
     dataset["INDEX"].append(f"{'%0.5d' %(game_nr)}_{'%0.5d' %(turn_nr)}")
     dataset["OBS"].append(obs.flatten().tolist())
     dataset["RAM"].append([])
-    dataset["VIS"].append([x for x in sorted(env.objects, key=lambda o: str(o))])
+    dataset["VIS"].append(
+        [x for x in sorted(env.objects, key=lambda o: str(o))])
     dataset["HUD"].append([])
     turn_nr = turn_nr+1
 
@@ -89,7 +92,6 @@
         """
 env.close()
 
-df = pd.DataFrame(dataset, columns = ['INDEX', 'OBS', 'RAM', 'HUD', 'VIS'])
+df = pd.DataFrame(dataset, columns=['INDEX', 'OBS', 'RAM', 'HUD', 'VIS'])
 df.to_csv(f"/data/datasets_v/{opts.game}.csv", index=False)
 print(f"Finished {opts.game}")
-