UPDATE: improvement on variable names after code review

Author: DamienGilliard

src/gh/components/DF_joint_segmentator/code.py

@@ -15,23 +15,14 @@
 ABSTOL = Rhino.RhinoDoc.ActiveDoc.ModelAbsoluteTolerance
 
 class DFJointSegmentator(component):
-    # def __init__(self):
-    #     super(DFJointSegmentator, self).__init__()
+    def __init__(self):
+        super(DFJointSegmentator, self).__init__()
     def RunScript(self, 
                   i_clusters: typing.List[Rhino.Geometry.PointCloud], 
                   i_assembly: diffCheck.df_geometries.DFAssembly,
                   i_angle_threshold: float,
                   i_distance_threshold: float):
-        """
-        Amongst clusters, associates the clusters to the individual joints, 
-        creates a reference point cloud for each joint, 
-        and returns the joint segments, the reference point clouds, and the ICP transformations from the first to the second.
-
-        :param i_clusters: The clusters to be associated to the joints.
-        :param i_assembly: The DFAssembly containing the joints we want to segment.
-        :param i_angle_threshold: The angle threshold for the association of the clusters to the joints.
-        :param i_distance_threshold: The distance threshold for the association of the clusters to the joints.
-        """
+
         if i_angle_threshold is None : i_angle_threshold = 0.1
         if i_distance_threshold is None : i_distance_threshold = 0.1
 
@@ -45,44 +36,44 @@ def RunScript(self,
         n_joints = i_assembly.total_number_joints
 
         # prepping the reference meshes
-        joints = [[] for _ in range(n_joints)]
+        df_joints = [[] for _ in range(n_joints)]
         for joint in i_assembly.all_joints:
             for face in joint.faces:
                 face = face.to_mesh()
                 face.Subdivide()
                 face.Faces.ConvertQuadsToTriangles()
-                joints[joint.id].append(df_cvt.cvt_rhmesh_2_dfmesh(face))
+                df_joints[joint.id].append(df_cvt.cvt_rhmesh_2_dfmesh(face))
 
-        joint_clouds = []
+        ref_rh_joint_clouds = []
         transforms = []
-        joint_segments = []
-        df_clouds = [df_cvt.cvt_rhcloud_2_dfcloud(cluster) for cluster in i_clusters]
+        rh_joint_segments = []
+        df_cloud_clusters = [df_cvt.cvt_rhcloud_2_dfcloud(cluster) for cluster in i_clusters]
 
         # for each joint, find the corresponding clusters and merge them, generate a reference point cloud, and register the merged clusters to the reference point cloud
-        for joint in joints:
+        for df_joint in df_joints:
             # create the reference point cloud
-            joint_cloud = diffcheck_bindings.dfb_geometry.DFPointCloud()
+            ref_df_joint_cloud = diffcheck_bindings.dfb_geometry.DFPointCloud()
 
-            for face in joint:
-                face_cloud = face.sample_points_uniformly(1000)
-                joint_cloud.add_points(face_cloud)
+            for face in df_joint:
+                ref_face_cloud = face.sample_points_uniformly(1000)
+                ref_df_joint_cloud.add_points(ref_face_cloud)
 
-            joint_clouds.append(df_cvt.cvt_dfcloud_2_rhcloud(joint_cloud))
+            ref_rh_joint_clouds.append(df_cvt.cvt_dfcloud_2_rhcloud(ref_df_joint_cloud))
 
             # find the corresponding clusters and merge them
-            segment = diffcheck_bindings.dfb_segmentation.DFSegmentation.associate_clusters(joint, df_clouds, i_angle_threshold, i_distance_threshold)
-            diffcheck_bindings.dfb_segmentation.DFSegmentation.clean_unassociated_clusters(df_clouds, [segment], [joint], i_angle_threshold, i_distance_threshold)
+            df_joint_segment = diffcheck_bindings.dfb_segmentation.DFSegmentation.associate_clusters(df_joint, df_cloud_clusters, i_angle_threshold, i_distance_threshold)
+            diffcheck_bindings.dfb_segmentation.DFSegmentation.clean_unassociated_clusters(df_cloud_clusters, [df_joint_segment], [df_joint], i_angle_threshold, i_distance_threshold)
 
             # register the merged clusters to the reference point cloud
-            registration = diffcheck_bindings.dfb_registrations.DFRefinedRegistration.O3DICP(segment, joint_cloud)
+            registration = diffcheck_bindings.dfb_registrations.DFRefinedRegistration.O3DICP(df_joint_segment, ref_df_joint_cloud)
             res = registration.transformation_matrix
             transforms.append(df_cvt.cvt_ndarray_2_rh_transform(res))
-            joint_segments.append(df_cvt.cvt_dfcloud_2_rhcloud(segment))
+            rh_joint_segments.append(df_cvt.cvt_dfcloud_2_rhcloud(df_joint_segment))
 
         o_joint_segments = []
         o_transforms = []
         o_reference_point_clouds = []
-        for  joint_segment, transform, _joint_cloud in zip(joint_segments, transforms, joint_clouds):
+        for  joint_segment, transform, _joint_cloud in zip(rh_joint_segments, transforms, ref_rh_joint_clouds):
             if joint_segment.IsValid:
                 o_joint_segments.append(joint_segment)
                 o_transforms.append(transform)