fixes in relative imports

examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/__init__.py

@@ -0,0 +1 @@
+import gym

examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_alternating_legs_env_randomizer.py

@@ -4,6 +4,12 @@ The randomization include swing_offset, extension_offset of all legs that mimics
 bent legs, desired_pitch from user input, battery voltage and motor damping.
 """
 
+import os,  inspect
+currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+parentdir = os.path.dirname(os.path.dirname(currentdir))
+parentdir = os.path.dirname(os.path.dirname(parentdir))
+os.sys.path.insert(0,parentdir)
+
 import numpy as np
 import tensorflow as tf
 from pybullet_envs.minitaur.envs import env_randomizer_base

examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer.py

@@ -1,6 +1,12 @@
 """Randomize the minitaur_gym_env when reset() is called."""
 import random
 
+import os,  inspect
+currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+parentdir = os.path.dirname(os.path.dirname(currentdir))
+parentdir = os.path.dirname(os.path.dirname(parentdir))
+os.sys.path.insert(0,parentdir)
+
 import numpy as np
 from pybullet_envs.minitaur.envs import env_randomizer_base
 

examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer_from_config.py

@@ -7,6 +7,12 @@ from __future__ import print_function
 import functools
 import random
 
+import os,  inspect
+currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+parentdir = os.path.dirname(os.path.dirname(currentdir))
+parentdir = os.path.dirname(os.path.dirname(parentdir))
+os.sys.path.insert(0,parentdir)
+
 import numpy as np
 import tensorflow as tf
 from pybullet_envs.minitaur.envs import env_randomizer_base

examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_push_randomizer.py

@@ -4,6 +4,12 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import os,  inspect
+currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+parentdir = os.path.dirname(os.path.dirname(currentdir))
+parentdir = os.path.dirname(os.path.dirname(parentdir))
+os.sys.path.insert(0,parentdir)
+
 import math
 import numpy as np
 from pybullet_envs.minitaur.envs import env_randomizer_base

examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_terrain_randomizer.py

@@ -4,6 +4,12 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import os,  inspect
+currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+parentdir = os.path.dirname(os.path.dirname(currentdir))
+parentdir = os.path.dirname(os.path.dirname(parentdir))
+os.sys.path.insert(0,parentdir)
+
 import itertools
 import math
 import enum

examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur.py

@@ -1,13 +1,19 @@
 """This file implements the functionalities of a minitaur using pybullet.
 
 """
+
+import os,  inspect
+currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+parentdir = os.path.dirname(os.path.dirname(currentdir))
+os.sys.path.insert(0,parentdir)
+
 import collections
 import copy
 import math
 import re
 
 import numpy as np
-import motor
+from pybullet_envs.minitaur.envs import motor
 
 INIT_POSITION = [0, 0, .2]
 INIT_RACK_POSITION = [0, 0, 1]

examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_alternating_legs_env.py

@@ -3,9 +3,14 @@
 """
 import math
 
+import os,  inspect
+currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+parentdir = os.path.dirname(os.path.dirname(currentdir))
+os.sys.path.insert(0,parentdir)
+
 from gym import spaces
 import numpy as np
-import minitaur_gym_env
+from pybullet_envs.minitaur.envs import minitaur_gym_env
 
 INIT_EXTENSION_POS = 2.6
 INIT_SWING_POS = 0.0

examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_alternating_legs_env_example.py

@@ -3,12 +3,17 @@
 """
 import time
 
+import os,  inspect
+currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+parentdir = os.path.dirname(os.path.dirname(currentdir))
+os.sys.path.insert(0,parentdir)
+
 import os
 import numpy as np
 import tensorflow as tf
-import minitaur_alternating_legs_env
+from pybullet_envs.minitaur.envs import minitaur_alternating_legs_env
-import minitaur_gym_env
+from pybullet_envs.minitaur.envs import minitaur_gym_env
-from env_randomizers import minitaur_alternating_legs_env_randomizer as randomizer_lib
+from pybullet_envs.minitaur.envs.env_randomizers import minitaur_alternating_legs_env_randomizer as randomizer_lib
 
 #FLAGS = flags.FLAGS
 #flags.DEFINE_string("log_path", None, "The directory to write the log file.")

examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_ball_gym_env.py

@@ -4,9 +4,14 @@
 import math
 import random
 
+import os,  inspect
+currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+parentdir = os.path.dirname(os.path.dirname(currentdir))
+os.sys.path.insert(0,parentdir)
+
 from gym import spaces
 import numpy as np
-import minitaur_gym_env
+from pybullet_envs.minitaur.envs import minitaur_gym_env
 import pybullet_data
 
 GOAL_DISTANCE_THRESHOLD = 0.8

examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_derpy.py

@@ -6,8 +6,13 @@ It is the result of first pass system identification for the derpy robot. The
 """
 import math
 
+import os,  inspect
+currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+parentdir = os.path.dirname(os.path.dirname(currentdir))
+os.sys.path.insert(0,parentdir)
+
 import numpy as np
-import minitaur
+from pybullet_envs.minitaur.envs  import minitaur
 
 KNEE_CONSTRAINT_POINT_LONG = [0, 0.0055, 0.088]
 KNEE_CONSTRAINT_POINT_SHORT = [0, 0.0055, 0.100]

examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_gym_env.py

@@ -4,19 +4,24 @@
 import math
 import time
 
+import os,  inspect
+currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+parentdir = os.path.dirname(os.path.dirname(currentdir))
+os.sys.path.insert(0,parentdir)
+
 import gym
 from gym import spaces
 from gym.utils import seeding
 import numpy as np
 import pybullet
-import bullet_client
+from pybullet_envs.minitaur.envs import bullet_client
 import pybullet_data
-import minitaur
+from pybullet_envs.minitaur.envs import minitaur
-import minitaur_derpy
+from pybullet_envs.minitaur.envs import minitaur_derpy
-import minitaur_logging
+from pybullet_envs.minitaur.envs import minitaur_logging
-import minitaur_logging_pb2
+from pybullet_envs.minitaur.envs import minitaur_logging_pb2
-import minitaur_rainbow_dash
+from pybullet_envs.minitaur.envs import minitaur_rainbow_dash
-import motor
+from pybullet_envs.minitaur.envs import motor
 from pkg_resources import parse_version
 
 NUM_MOTORS = 8

examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_gym_env_example.py

@@ -5,10 +5,15 @@
 import csv
 import math
 
+import os,  inspect
+currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+parentdir = os.path.dirname(os.path.dirname(currentdir))
+os.sys.path.insert(0,parentdir)
+
 import argparse
 import numpy as np
 import tensorflow as tf
-import minitaur_gym_env
+from pybullet_envs.minitaur.envs import minitaur_gym_env
 import time
 
 

examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_logging.py

@@ -10,12 +10,17 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import os,  inspect
+currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+parentdir = os.path.dirname(os.path.dirname(currentdir))
+os.sys.path.insert(0,parentdir)
+
 import datetime
 import os
 import time
 
 import tensorflow as tf
-import minitaur_logging_pb2
+from pybullet_envs.minitaur.envs import minitaur_logging_pb2
 
 NUM_MOTORS = 8
 

examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_logging_pb2.py

@@ -2,6 +2,12 @@
 # source: minitaur_logging.proto
 
 import sys
+
+import os,  inspect
+currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+parentdir = os.path.dirname(os.path.dirname(currentdir))
+os.sys.path.insert(0,parentdir)
+
 _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
 from google.protobuf import descriptor as _descriptor
 from google.protobuf import message as _message
@@ -13,8 +19,8 @@ from google.protobuf import descriptor_pb2
 _sym_db = _symbol_database.Default()
 
 
-import timestamp_pb2 as timestamp__pb2
+from pybullet_envs.minitaur.envs import timestamp_pb2 as timestamp__pb2
-import vector_pb2 as vector__pb2
+from pybullet_envs.minitaur.envs import vector_pb2 as vector__pb2
 
 
 DESCRIPTOR = _descriptor.FileDescriptor(

examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_rainbow_dash.py

@@ -3,10 +3,16 @@
 It is the result of first pass system identification for the rainbow dash robot.
 
 """
+
+import os,  inspect
+currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+parentdir = os.path.dirname(os.path.dirname(currentdir))
+os.sys.path.insert(0,parentdir)
+
 import math
 
 import numpy as np
-import minitaur
+from pybullet_envs.minitaur.envs import minitaur
 
 KNEE_CONSTRAINT_POINT_LONG = [0, 0.0045, 0.088]
 KNEE_CONSTRAINT_POINT_SHORT = [0, 0.0045, 0.100]

examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_reactive_env.py

@@ -1,11 +1,17 @@
 """This file implements the gym environment of minitaur alternating legs.
 
 """
+
+import os,  inspect
+currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+parentdir = os.path.dirname(os.path.dirname(currentdir))
+os.sys.path.insert(0,parentdir)
+
 import collections
 import math
 from gym import spaces
 import numpy as np
-import minitaur_gym_env
+from pybullet_envs.minitaur.envs import minitaur_gym_env
 
 INIT_EXTENSION_POS = 2.0
 INIT_SWING_POS = 0.0