Added PyTorch Geometric! bumped to v0.7.39

Also fixed bug in Wire.Skeleton

src/topologicpy/ANN.py

@@ -471,27 +471,11 @@ def DatasetBySampleName(name):
         ----------
         name : str
             The  name of the dataset. This can be one of ['breast_cancer', 'california_housing', 'digits', 'iris', 'wine']
-
-        trainRatio : float , optional
-            The ratio of the data to use for training and validation vs. the ratio to use for testing. The default is 0.6
-            which means that 60% of the data will be used for training and validation while 40% of the data will be reserved for testing.
-        randomState : int , optional
-            The randomState parameter is used to ensure reproducibility of the results. When you set the randomState parameter to a specific integer value,
-            it controls the shuffling of the data before splitting it into training and testing sets.
-            This means that every time you run your code with the same randomState value and the same dataset, you will get the same split of the data.
-            The default is 42 which is just a randomly picked integer number. Specify None for random sampling.
         
         Returns
         -------
-        dict
-            Returns the following dictionary:
-            XTrain, XTest, yTrain, yTest, inputSize, outputSize
-            XTrain is the list of features used for training
-            XTest is the list of features used for testing
-            yTrain is the list of targets used for training
-            yTest is the list of targets used for testing
-            inputSize is the size (length) of the input
-            outputSize is the size (length) of the output
+        sklearn.utils._bunch.Bunch
+            The created dataset.
         """
         # Load dataset
         if name == 'breast_cancer':

src/topologicpy/Face.py

@@ -521,8 +521,8 @@ def ByThickenedWire(wire, offsetA: float = 1.0, offsetB: float = 0.0, tolerance:
 
         Returns
         -------
-        topologic_core.Cell
-            The created cell.
+        topologic_core.Face
+            The created face.
 
         """
         from topologicpy.Vertex import Vertex
@@ -2170,19 +2170,15 @@ def Squircle(origin = None, radius: float = 0.5, sides: int = 121, a: float = 2.
         origin : topologic_core.Vertex , optional
             The location of the origin of the squircle. The default is None which results in the squircle being placed at (0, 0, 0).
         radius : float , optional
-            The radius of the squircle. The default is 0.5.
+            The desired radius of the squircle. The default is 0.5.
         sides : int , optional
-            The number of sides of the squircle. The default is 121.
+            The desired number of sides of the squircle. The default is 121.
         a : float , optional
             The "a" factor affects the x position of the points to interpolate between a circle and a square.
             A value of 1 will create a circle. Higher values will create a more square-like shape. The default is 2.0.
         b : float , optional
             The "b" factor affects the y position of the points to interpolate between a circle and a square.
             A value of 1 will create a circle. Higher values will create a more square-like shape. The default is 2.0.
-        radius : float , optional
-            The desired radius of the squircle. The default is 0.5.
-        sides : int , optional
-            The desired number of sides for the squircle. The default is 100.
         direction : list , optional
             The vector representing the up direction of the circle. The default is [0, 0, 1].
         placement : str , optional

src/topologicpy/Graph.py

@@ -4686,6 +4686,8 @@ def ExportToAdjacencyMatrixCSV(adjacencyMatrix, path):
 
         Parameters
         ----------
+        adjacencyMatrix: list
+            The input adjacency matrix.
         path : str
             The desired path to the output folder where the graphs, edges, and nodes CSV files will be saved.
 
@@ -4936,7 +4938,6 @@ def ExportToCSV(graph, path, graphLabel, graphFeatures="",
             The desired graph label column header. The default is "label".
         graphFeaturesHeader : str , optional
             The desired graph features column header. The default is "feat".
-        
         edgeLabelKey : str , optional
             The edge label dictionary key saved in each graph edge. The default is "label".
         defaultEdgeLabel : int , optional
@@ -4971,7 +4972,6 @@ def ExportToCSV(graph, path, graphLabel, graphFeatures="",
             This value is ignored if an edgeMaskKey is foud.
         bidirectional : bool , optional
             If set to True, a reversed edge will also be saved for each edge in the graph. Otherwise, it will not. The default is True.
-        
         nodeFeaturesKeys : list , optional
             The list of features keys saved in the dicitonaries of nodes. The default is [].
         nodeLabelKey : str , optional
@@ -7444,7 +7444,7 @@ def PyvisGraph(graph, path, overwrite: bool = True, height: int = 900, backgroun
             The desired default vertex size. The default is 6.
         vertexSizeKey : str , optional
             If not set to None, the vertex size will be derived from the dictionary value set at this key. If set to "degree", the size of the vertex will be determined by its degree (number of neighbors). The default is None.
-        vertexColor : int , optional
+        vertexColor : str , optional
             The desired default vertex color. his can be a named color or a hexadecimal value. The default is 'black'.
         vertexColorKey : str , optional
             If not set to None, the vertex color will be derived from the dictionary value set at this key. The default is None.
@@ -7884,6 +7884,8 @@ def Show(graph, vertexColor="black", vertexSize=6, vertexLabelKey=None, vertexGr
             The dictionary key to use to display the edge label. The default is None.
         edgeGroupKey : str , optional
             The dictionary key to use to display the edge group. The default is None.
+        edgeGroups : list , optional
+            The list of edge groups against which to index the color of the edge. The default is [].
         showEdges : bool , optional
             If set to True the edges will be drawn. Otherwise, they will not be drawn. The default is True.
         showEdgeLegend : bool , optional

src/topologicpy/Grid.py

@@ -307,8 +307,6 @@ def VerticesByParameters(face=None, uRange=[0.0,0.25,0.5,0.75,1.0], vRange=[0.0,
         ----------
         face : topologic_core.Face , optional
             The input face. If set to None, the grid will be created on the XY plane. The default is None.
-        origin : topologic_core.Vertex , optional
-            The origin of the grid vertices. If set to None: if the face is set, the origin will be set to vertex at the face's 0,0 paratmer. If the face is set to None, the origin will be set to (0, 0, 0). The default is None.
         uRange : list , optional
             A list of *u* parameters for the *u* grid lines from the uOrigin. The default is [0.0,0.25,0.5,0.75,1.0].
         vRange : list , optional

src/topologicpy/Helper.py

@@ -163,40 +163,6 @@ def onestep(cur,y,base):
             iterated_list.append(y)
         return iterated_list
 
-    @staticmethod
-    def K_Means(data, k=4, maxIterations=100):
-        import random
-        def euclidean_distance(p, q):
-            return sum((pi - qi) ** 2 for pi, qi in zip(p, q)) ** 0.5
-
-        # Initialize k centroids randomly
-        centroids = random.sample(data, k)
-
-        for _ in range(maxIterations):
-            # Assign each data point to the nearest centroid
-            clusters = [[] for _ in range(k)]
-            for point in data:
-                distances = [euclidean_distance(point, centroid) for centroid in centroids]
-                nearest_centroid_index = distances.index(min(distances))
-                clusters[nearest_centroid_index].append(point)
-
-            # Compute the new centroids as the mean of the points in each cluster
-            new_centroids = []
-            for cluster in clusters:
-                if not cluster:
-                    # If a cluster is empty, keep the previous centroid
-                    new_centroids.append(centroids[clusters.index(cluster)])
-                else:
-                    new_centroids.append([sum(dim) / len(cluster) for dim in zip(*cluster)])
-
-            # Check if the centroids have converged
-            if new_centroids == centroids:
-                break
-
-            centroids = new_centroids
-
-        return {'clusters': clusters, 'centroids': centroids}
-
     @staticmethod
     def MergeByThreshold(listA, threshold=0.0001):
         """

src/topologicpy/Neo4j.py

@@ -276,7 +276,7 @@ def randomVertex(vertices, minDistance):
         return Graph.ByVerticesEdges(vertices,edges)
 
     @staticmethod
-    def AddGraph(neo4jGraph, graph, labelKey=None, relationshipKey=None, bidirectional=True, deleteAll=True, mantissa: int = 6, tolerance: float = 0.0001):
+    def AddGraph(neo4jGraph, graph, labelKey=None, relationshipKey=None, mantissa: int = 6, tolerance: float = 0.0001):
         """
         Adds the input topologic graph to the input neo4j graph
 
@@ -286,8 +286,10 @@ def AddGraph(neo4jGraph, graph, labelKey=None, relationshipKey=None, bidirection
             The input neo4j graph.
         graph : topologic_core.Graph
             The input topologic graph.
-        categoryKey : str
-            The category key in the dictionary under which to look for the category value.
+        labelKey : str , optional
+            The label key in the dictionary under which to look for the label value.
+        relationshipKey: str , optional
+            The relationship key in the dictionary under which to look for the relationship value.
         mantissa : int, optional
             The desired length of the mantissa. The default is 6.
         tolerance : float , optional

src/topologicpy/Polyskel.py

@@ -115,7 +115,11 @@ def intersect(self, other):
             return None  # Rays are parallel and do not intersect
 
         # Calculate the intersection point using vector algebra
-        t = (other.p - self.p).cross(other.v) / self.v.cross(other.v)
+        denom = self.v.cross(other.v)
+        if not denom == 0:
+            t = (other.p - self.p).cross(other.v) / self.v.cross(other.v)
+        else:
+            return None
         if t >= 0:
             return self.p + self.v * t  # Intersection point
         else: