Refactor module links and improve code documentation.

Mapping/circle_fitting/circle_fitting.py

@@ -16,12 +16,33 @@
 
 def circle_fitting(x, y):
     """
-    Circle Fitting with least squared
-        input: point x-y positions
-        output  cxe x center position
-                cye y center position
-                re  radius of circle
-                error: prediction error
+    Fits a circle to a given set of points using a least-squares approach.
+
+    This function calculates the center (x, y) and radius of a circle that best fits
+    the given set of points in a two-dimensional plane. It minimizes the squared
+    errors between the circle and the provided points and returns the calculated
+    center coordinates, radius, and the fitting error.
+
+    Raises
+    ------
+    ValueError
+        If the input lists x and y do not contain the same number of elements.
+
+    Parameters
+    ----------
+    x : list[float]
+        The x-coordinates of the points.
+    y : list[float]
+        The y-coordinates of the points.
+
+    Returns
+    -------
+    tuple[float, float, float, float]
+        A tuple containing:
+        - The x-coordinate of the center of the fitted circle (float).
+        - The y-coordinate of the center of the fitted circle (float).
+        - The radius of the fitted circle (float).
+        - The fitting error as a deviation metric (float).
     """
 
     sumx = sum(x)

Mapping/kmeans_clustering/kmeans_clustering.py

@@ -17,12 +17,37 @@
 
 
 def kmeans_clustering(rx, ry, nc):
+    """
+    Performs k-means clustering on the given dataset, iteratively adjusting cluster centroids
+    until convergence within a defined threshold or reaching the maximum number of
+    iterations.
+
+    The implementation initializes clusters, calculates initial centroids, and refines the
+    clusters through iterative updates to optimize the cost function based on minimum
+    distance between datapoints and centroids.
+
+    Arguments:
+        rx: List[float]
+            The x-coordinates of the dataset points to be clustered.
+        ry: List[float]
+            The y-coordinates of the dataset points to be clustered.
+        nc: int
+            The number of clusters to group the data into.
+
+    Returns:
+        Clusters
+            An instance containing the final cluster assignments and centroids after
+            convergence.
+
+    Raises:
+        None
+
+    """
     clusters = Clusters(rx, ry, nc)
     clusters.calc_centroid()
 
     pre_cost = float("inf")
     for loop in range(MAX_LOOP):
-        print("loop:", loop)
         cost = clusters.update_clusters()
         clusters.calc_centroid()
 

Mapping/raycasting_grid_map/raycasting_grid_map.py → Mapping/ray_casting_grid_map/ray_casting_grid_map.py

@@ -48,7 +48,7 @@ def atan_zero_to_twopi(y, x):
     return angle
 
 
-def precasting(minx, miny, xw, yw, xyreso, yawreso):
+def pre_casting(minx, miny, xw, yw, xyreso, yawreso):
 
     precast = [[] for i in range(int(round((math.pi * 2.0) / yawreso)) + 1)]
 
@@ -81,7 +81,7 @@ def generate_ray_casting_grid_map(ox, oy, xyreso, yawreso):
 
     pmap = [[0.0 for i in range(yw)] for i in range(xw)]
 
-    precast = precasting(minx, miny, xw, yw, xyreso, yawreso)
+    precast = pre_casting(minx, miny, xw, yw, xyreso, yawreso)
 
     for (x, y) in zip(ox, oy):
 

docs/modules/3_mapping/circle_fitting/circle_fitting_main.rst

@@ -11,3 +11,7 @@ The red crosses are observations from a ranging sensor.
 
 The red circle is the estimated object shape using circle fitting.
 
+Code Link
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autofunction:: Mapping.circle_fitting.circle_fitting.circle_fitting

docs/modules/3_mapping/distance_map/distance_map_main.rst

@@ -14,8 +14,8 @@ The algorithm is demonstrated on a simple 2D grid with obstacles:
 
 .. image:: distance_map.png
 
-API
-~~~
+Code Link
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 .. autofunction:: Mapping.DistanceMap.distance_map.compute_sdf
 

docs/modules/3_mapping/gaussian_grid_map/gaussian_grid_map_main.rst

@@ -6,3 +6,9 @@ Gaussian grid map
 This is a 2D Gaussian grid mapping example.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/gaussian_grid_map/animation.gif
+
+Code Link
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autofunction:: Mapping.gaussian_grid_map.gaussian_grid_map.generate_gaussian_grid_map
+

docs/modules/3_mapping/k_means_object_clustering/k_means_object_clustering_main.rst

@@ -4,3 +4,9 @@ k-means object clustering
 This is a 2D object clustering with k-means algorithm.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/kmeans_clustering/animation.gif
+
+Code Link
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autofunction:: Mapping.kmeans_clustering.kmeans_clustering.kmeans_clustering
+

docs/modules/3_mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_main.rst

@@ -196,3 +196,9 @@ Let’s use this flood fill on real data:
 
 .. image:: lidar_to_grid_map_tutorial_14_1.png
 
+Code Link
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autofunction:: Mapping.lidar_to_grid_map.lidar_to_grid_map.main
+
+

docs/modules/3_mapping/normal_vector_estimation/normal_vector_estimation_main.rst

@@ -25,8 +25,8 @@ This is an example of normal vector calculation:
 
 .. figure:: normal_vector_calc.png
 
-API
-=====
+Code Link
+==========
 
 .. autofunction:: Mapping.normal_vector_estimation.normal_vector_estimation.calc_normal_vector
 
@@ -67,8 +67,8 @@ This is an example of RANSAC based normal vector estimation:
 
 .. figure:: ransac_normal_vector_estimation.png
 
-API
-=====
+Code Link
+==========
 
 .. autofunction:: Mapping.normal_vector_estimation.normal_vector_estimation.ransac_normal_vector_estimation
 

docs/modules/3_mapping/point_cloud_sampling/point_cloud_sampling_main.rst

@@ -27,8 +27,8 @@ This method determines which each point is in a grid, and replaces the point
 clouds that are in the same Voxel with their average to reduce the number of
 points.
 
-API
-=====
+Code Link
+==========
 
 .. autofunction:: Mapping.point_cloud_sampling.point_cloud_sampling.voxel_point_sampling
 
@@ -61,8 +61,8 @@ Although this method does not have good performance comparing the Farthest
 distance sample where each point is distributed farther from each other,
 this is suitable for real-time processing because of its fast computation time.
 
-API
-=====
+Code Link
+==========
 
 .. autofunction:: Mapping.point_cloud_sampling.point_cloud_sampling.poisson_disk_sampling
 

docs/modules/3_mapping/ray_casting_grid_map/ray_casting_grid_map_main.rst

@@ -3,4 +3,9 @@ Ray casting grid map
 
 This is a 2D ray casting grid mapping example.
 
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/raycasting_grid_map/animation.gif
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/raycasting_grid_map/animation.gif
+
+Code Link
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autofunction:: Mapping.ray_casting_grid_map.ray_casting_grid_map.generate_ray_casting_grid_map

docs/modules/3_mapping/rectangle_fitting/rectangle_fitting_main.rst

@@ -57,8 +57,8 @@ This evaluation function uses the squreed distances between the edges of the rec
 Calculating the squared error is the same as calculating the variance.
 The smaller this variance, the more it signifies that the points fit within the rectangle.
 
-API
-~~~~~~
+Code Link
+~~~~~~~~~~~
 
 .. autoclass:: Mapping.rectangle_fitting.rectangle_fitting.LShapeFitting
 	:members:

tests/test_raycasting_grid_map.py → tests/test_ray_casting_grid_map.py

@@ -1,5 +1,5 @@
 import conftest  # Add root path to sys.path
-from Mapping.raycasting_grid_map import raycasting_grid_map as m
+from Mapping.ray_casting_grid_map import ray_casting_grid_map as m
 
 
 def test1():