[Ontology] Fixed doc

examples/cram_plan_tutorial.md

@@ -32,7 +32,7 @@ from pycram.world_concepts.world_object import Object
 import anytree
 import pycram.failures
 import numpy as np
-import pycrap
+from pycrap.ontologies import Milk, Cereal, Robot, Kitchen, Spoon, Apartment, Bowl
 
 np.random.seed(4)
 
@@ -49,9 +49,9 @@ else:
     world = BulletWorld()
     viz_marker_publisher = VizMarkerPublisher()
 
-robot = Object("pr2", pycrap.Robot, "pr2.urdf")
+robot = Object("pr2", Robot, "pr2.urdf")
 robot_desig = ObjectDesignatorDescription(names=['pr2']).resolve()
-apartment = Object("apartment", pycrap.Apartment, "apartment.urdf")
+apartment = Object("apartment", Apartment, "apartment.urdf")
 apartment_desig = ObjectDesignatorDescription(names=['apartment']).resolve()
 table_top_name = "stove" if use_multiverse else "cooktop"
 table_top = apartment.get_link_position(table_top_name)
@@ -89,7 +89,7 @@ def get_n_random_positions(pose_list, n=4, dist=0.5, random=True):
 
 ```python
 from tf.transformations import quaternion_from_euler
-import pycrap
+
 from pycram.costmaps import SemanticCostmap
 from pycram.pose_generator_and_validator import PoseGenerator
 
@@ -103,7 +103,7 @@ poses_list = list(PoseGenerator(edges_cm, number_of_samples=-1))
 poses_list.sort(reverse=True, key=lambda x: np.linalg.norm(x.position_as_list()))
 object_poses = get_n_random_positions(poses_list)
 object_names = ["bowl", "breakfast_cereal", "spoon"]
-object_types = [pycrap.Bowl, pycrap.Cereal, pycrap.Spoon]
+object_types = [Bowl, Cereal, Spoon]
 objects = {}
 object_desig = {}
 for obj_name, obj_type, obj_pose in zip(object_names, object_types, object_poses):
@@ -140,7 +140,6 @@ Finally, we create a plan where the robot parks his arms, walks to the kitchen c
 execute the plan.
 
 ```python
-import pycrap
 from pycram.external_interfaces.ik import IKError
 from pycram.datastructures.enums import Grasp
 
@@ -159,7 +158,7 @@ def plan(obj_desig: ObjectDesignatorDescription.Object, torso=0.2, place=counter
         ParkArmsActionPerformable(Arms.BOTH).perform()
         good_torsos.append(torso)
         picked_up_arm = pose.reachable_arms[0]
-        grasp = Grasp.TOP if issubclass(obj_desig.world_object.obj_type, pycrap.Spoon) else Grasp.FRONT
+        grasp = Grasp.TOP if issubclass(obj_desig.world_object.obj_type, Spoon) else Grasp.FRONT
         PickUpActionPerformable(object_designator=obj_desig, arm=pose.reachable_arms[0], grasp=grasp,
                                 prepose_distance=0.03).perform()
 

examples/improving_actions.md

@@ -75,7 +75,7 @@ session = sqlalchemy.orm.sessionmaker(bind=engine)()
 Now we construct an empty world with just a floating milk, where we can learn about PickUp actions.
 
 ```python
-from pycrap import Robot, Milk
+from pycrap.ontologies import Robot, Milk
 
 world = BulletWorld(WorldMode.DIRECT)
 print(world.prospection_world)
@@ -167,7 +167,7 @@ Next, we put the learned model to the test in a complex environment, where the m
 area.
 
 ```python
-from pycrap import Apartment
+from pycrap.ontologies import Apartment
 kitchen = Object("apartment", Apartment, "apartment.urdf")
 
 milk.set_pose(Pose([0.5, 3.15, 1.04]))

examples/knowledge_source.md

@@ -141,7 +141,7 @@ from pycram.datastructures.enums import WorldMode, ObjectType
 from pycram.knowledge.knowledge_engine import KnowledgeEngine
 from pycram.datastructures.pose import Pose
 from pycram.datastructures.property import ReachableProperty, SpaceIsFreeProperty
-from pycrap import Robot
+from pycrap.ontologies import Robot
 
 world = BulletWorld(WorldMode.GUI)
 pr2 = Object("pr2", Robot, "pr2.urdf")

examples/location_designator.md

@@ -42,7 +42,7 @@ from pycram.worlds.bullet_world import BulletWorld
 from pycram.world_concepts.world_object import Object
 from pycram.datastructures.enums import ObjectType, WorldMode
 from pycram.datastructures.pose import Pose
-import pycrap
+from pycrap.ontologies import Apartment, Robot, Milk
 
 use_multiverse = False
 viz_marker_publisher = None
@@ -57,8 +57,8 @@ else:
     world = BulletWorld()
     viz_marker_publisher = VizMarkerPublisher()
 
-apartment = Object("apartment", pycrap.Apartment, "apartment.urdf")
-pr2 = Object("pr2", pycrap.Robot, "pr2.urdf")
+apartment = Object("apartment", Apartment, "apartment.urdf")
+pr2 = Object("pr2", Robot, "pr2.urdf")
 ```
 
 Next up we will create the location designator description, the {meth}`~pycram.designators.location_designator.CostmapLocation` that we will be using needs a
@@ -91,7 +91,7 @@ PR2 will be set to 0.2 since otherwise the arms of the robot will be too low to
 
 ```python
 pr2.set_joint_position("torso_lift_joint", 0.2)
-milk = Object("milk", pycrap.Milk, "milk.stl", pose=Pose([1.3, 1, 0.9]))
+milk = Object("milk", Milk, "milk.stl", pose=Pose([1.3, 1, 0.9]))
 
 ```
 
@@ -195,7 +195,7 @@ from pycram.designators.location_designator import *
 apartment_desig = BelieveObject(names=["apartment"])
 handle_name = "cabinet10_drawer1_handle" if use_multiverse else "handle_cab10_t"
 handle_desig = ObjectPart(names=[handle_name], part_of=apartment_desig.resolve())
-robot_desig = BelieveObject(types=[pycrap.Robot])
+robot_desig = BelieveObject(types=[Robot])
 
 access_location = AccessingLocation(handle_desig.resolve(), robot_desig.resolve(),
                                     prepose_distance=0.03).resolve()

examples/motion_designator.md

@@ -27,7 +27,7 @@ from pycram.worlds.bullet_world import BulletWorld
 from pycram.world_concepts.world_object import Object
 from pycram.datastructures.enums import ObjectType, WorldMode
 from pycram.datastructures.pose import Pose
-import pycrap
+import pycrap.ontologies as pycrap
 
 world = BulletWorld(WorldMode.DIRECT)
 pr2 = Object("pr2", pycrap.Robot, "pr2.urdf")
@@ -116,14 +116,13 @@ from pycram.designators.motion_designator import DetectingMotion, LookingMotion
 from pycram.process_module import simulated_robot
 from pycram.datastructures.pose import Pose
 from pycram.datastructures.enums import DetectionTechnique, DetectionState
-from pycrap import  Milk
 from pycram.designators.object_designator import BelieveObject
 
 
 with simulated_robot:
     LookingMotion(target=Pose([1.5, 0, 1], [0, 0, 0, 1])).perform()
 
-    motion_description = DetectingMotion(technique=DetectionTechnique.TYPES,state=DetectionState.START, object_designator_description=BelieveObject(types=[Milk]),
+    motion_description = DetectingMotion(technique=DetectionTechnique.TYPES,state=DetectionState.START, object_designator_description=BelieveObject(types=[pycrap.Milk]),
                                region=None)
 
     obj = motion_description.perform()