DOC: Remove redundant sphinx directives duplicating type annotations

AutoscoperM/AutoscoperM.py

@@ -1004,7 +1004,6 @@ def extractSubVolumeForVRG(
         :param cameraDebugMode: Whether or not to keep the extracted volume in the scene, defaults to False
 
         :return: tuple containing the extracted volume and the bounds of the volume
-        :rtype: tuple[vtk.vtkImageData, list[float]]
         """
         mergedSegmentationNode = SubVolumeExtraction.mergeSegments(volumeNode, segmentationNode)
         newVolumeNode = SubVolumeExtraction.extractSubVolume(

AutoscoperM/AutoscoperMLib/RadiographGeneration.py

@@ -74,22 +74,12 @@ def generateNCameras(
     Generate N cameras
 
     :param N: Number of cameras to generate
-    :type N: int
-
     :param bounds: Bounds of the volume
-    :type bounds: list[int]
-
     :param offset: Offset from the volume. Defaults to 100.
-    :type offset: int
-
     :param imageSize: Image size. Defaults to [512,512].
-    :type imageSize: list[int]
-
     :param camDebugMode: Whether or not to show the cameras in the scene. Defaults to False.
-    :type camDebugMode: bool
 
     :return: List of cameras
-    :rtype: list[Camera]
     """
     # find the center of the bounds
     center = [(bounds[0] + bounds[1]) / 2, (bounds[2] + bounds[3]) / 2, (bounds[4] + bounds[5]) / 2]
@@ -165,17 +155,12 @@ def generateCamerasFromMarkups(
     Generate cameras from a markups fiducial node
 
     :param fiduaicalNode: Markups fiducial node
-    :type fiduaicalNode: slicer.vtkMRMLMarkupsFiducialNode
     :param volumeBounds: Bounds of the volume
-    :type volumeBounds: list[int]
     :param clippingRange: Clipping range
-    :type clippingRange: tuple[int]
     :param viewAngle: View angle
-    :type viewAngle: int
     :param imageSize: Image size. Defaults to [512,512].
-    :type imageSize: list[int]
     :param cameraDebug: Whether or not to show the cameras in the scene. Defaults to False.
-    :type cameraDebug: bool
+
     :return: List of cameras
     """
     center = [
@@ -210,16 +195,10 @@ def optimizeCameras(
     Optimize the cameras by finding the N cameras with the best data intensity density.
 
     :param cameras: Cameras
-    :type cameras: list[Camera]
-
     :param cameraDir: Camera directory
-    :type cameraDir: str
-
     :param nOptimizedCameras: Number of optimized cameras to find
-    :type nOptimizedCameras: int
 
     :return: Optimized cameras
-    :rtype: list[Camera]
     """
     import glob
     import os

TrackingEvaluation/TrackingEvaluationLib/data.py

@@ -11,10 +11,8 @@ def loadTraAsSequence(data: np.ndarray) -> list[vtk.vtkMatrix4x4]:
     Converts the tracking data to a list of vtkMatrix4x4.
 
     :param data: The tracking data.
-    :type data: np.ndarray
 
     :return: The tracking data as a sequence.
-    :rtype: list[vtk.vtkMatrix4x4]
     """
     _, cols = data.shape
 
@@ -50,10 +48,8 @@ def tmpTFMLoader(
     Loads a tfm file and returns the matrix.
 
     :param fileName: The filename of the tfm file.
-    :type fileName: str
 
     :return: The matrix from the tfm file.
-    :rtype: vtk.vtkMatrix4x4
     """
     with open(fileName) as f:
         lines = f.readlines()
@@ -73,9 +69,7 @@ class ModelData:
     Class to store the model data.
 
     :param modelFileName: The filename of the model.
-    :type modelFileName: str
     :param groundTruthSequenceData: The ground truth sequence data.
-    :type groundTruthSequenceData: np.ndarray
     """
 
     def __init__(self, modelFileName: str, groundTruthSequenceData: np.ndarray):
@@ -140,7 +134,6 @@ def loadUserTrackingSequence(self, userSequence: list[vtk.vtkMatrix4x4]):
         Load the user tracking sequence.
 
         :param userSequence: The user tracking sequence.
-        :type userSequence: list[vtk.vtkMatrix4x4]
         """
         self.userSequence = userSequence
 
@@ -149,7 +142,6 @@ def updateTransform(self, index: int):
         Update the transform of the model node.
 
         :param index: The index of the transform.
-        :type index: int
         """
         if self.userSequence is None:
             slicer.util.errorDisplay("No user tracking data!")
@@ -168,11 +160,8 @@ def evaluateError(self, index: int, translationTol: float = 1.0, degreeTol: floa
         in the correct position.
 
         :param index: The index of the transform.
-        :type index: int
         :param translationTol: The translation tolerance in mm.
-        :type translationTol: float
         :param degreeTol: The rotation tolerance in degrees.
-        :type degreeTol: float
         """
         if self.userSequence is None:
             slicer.util.errorDisplay("No user tracking data!")
@@ -241,7 +230,6 @@ def setColor(self, rgb: tuple[float, float, float] = (1.0, 1.0, 1.0)):
         Update the color of the model node.
 
         :param rgb: The RGB color to set.
-        :type rgb: Tuple[float, float, float]
         """
         self.userDisplayNode.SetColor(rgb[0], rgb[1], rgb[2])
 
@@ -250,7 +238,6 @@ def setGroundTruthVisible(self, visible: bool):
         Hide or show the ground truth model node.
 
         :param visible: whether the ground truth model node is visible or not.
-        :type visible: bool
         """
         self.groundTruthDisplayNode.SetVisibility(visible)
 
@@ -260,9 +247,7 @@ class Scene:
     Class to store the scene data.
 
     :param sampleDataType: The sample data type.
-    :type sampleDataType: str
     :param userSequenceFileName: The filename of the user sequence.
-    :type userSequenceFileName: str
     """
 
     def __init__(self, sampleDataType: str, userSequenceFileName: str):
@@ -312,10 +297,8 @@ def _parseUserSequence(self, userSequenceFileName: str) -> list[int]:
         Internal function to parse the user sequence.
 
         :param userSequenceFileName: The filename of the user sequence.
-        :type userSequenceFileName: str
 
         :return: List of indices that correspond to any models with Nan tracking values.
-        :rtype: list[int]
         """
 
         if not os.path.exists(userSequenceFileName):
@@ -355,11 +338,8 @@ def updateTransforms(self, index: int, translationTol: float = 1.0, degreeTol: f
         Update the transforms of the models and evaluate the error compared to the ground truth.
 
         :param index: The index of the transform.
-        :type index: int
         :param translationTol: The translation tolerance.
-        :type translationTol: float
         :param degreeTol: The degree tolerance.
-        :type degreeTol: float
         """
         self.currentFrame = index
         for i, model in enumerate(self.models):  # Probably want to make this multithreaded instead of sequential.