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TPLOTS.py

@@ -4,12 +4,12 @@ r'''############################################################################
 ################################################################################
 #
 #
-#	      Tegridy Plots Python Module (TPLOTS)
-#	      Version 1.0
+#	    Tegridy Plots Python Module (TPLOTS)
+#	    Version 1.0
 #
-#	      Project Los Angeles
+#	    Project Los Angeles
 #
-#	      Tegridy Code 2024
+#	    Tegridy Code 2025
 #
 #       https://github.com/asigalov61/tegridy-tools
 #
@@ -33,20 +33,20 @@ r'''############################################################################
 ################################################################################
 ################################################################################
 #
-# Critical dependencies
+#       Critical dependencies
 #
-# !pip install numpy
-# !pip install scipy
-# !pip install matplotlib
-# !pip install networkx
-# !pip3 install scikit-learn
+#       !pip install numpy==1.24.4
+#       !pip install scipy
+#       !pip install matplotlib
+#       !pip install networkx
+#       !pip3 install scikit-learn
 #
 ################################################################################
 #
-# Future critical dependencies
+#       Future critical dependencies
 #
-# !pip install umap-learn
-# !pip install alphashape
+#       !pip install umap-learn
+#       !pip install alphashape
 #
 ################################################################################
 '''
@@ -1254,6 +1254,113 @@ def plot_parsons_code(parsons_code,
 
   plt.close()
   
+################################################################################
+
+def plot_tokens_embeddings_constellation(tokens_embeddings,
+                                         start_token,
+                                         end_token,
+                                         plot_size=(10, 10),
+                                         labels_size=12,
+                                         show_grid=False,
+                                         save_plot=''):
+    
+    """
+    Plots token embeddings constellation using MST and graph layout
+    without dimensionality reduction.
+    """
+
+    distance_matrix = metrics.pairwise_distances(tokens_embeddings[start_token:end_token], metric='cosine')
+    
+    token_labels = [str(i) for i in range(start_token, end_token)]
+
+    mst = minimum_spanning_tree(distance_matrix).toarray()
+    
+    n = distance_matrix.shape[0]
+    G = nx.Graph()
+    
+    for i in range(n):
+        for j in range(n):
+            if mst[i, j] > 0:
+                weight = 1 / (distance_matrix[i, j] + 1e-8)
+                G.add_edge(i, j, weight=weight)
+    
+    pos = nx.kamada_kawai_layout(G, weight='weight')
+    
+    points = np.array([pos[i] for i in range(n)])
+    
+    plt.figure(figsize=plot_size)
+    plt.scatter(points[:, 0], points[:, 1], color='blue')
+    
+    for i, label in enumerate(token_labels):
+        plt.annotate(label, (points[i, 0], points[i, 1]),
+                     textcoords="offset points",
+                     xytext=(0, 10),
+                     ha='center',
+                     fontsize=labels_size)
+    
+    for i in range(n):
+        for j in range(n):
+            if mst[i, j] > 0:
+                plt.plot([points[i, 0], points[j, 0]],
+                         [points[i, 1], points[j, 1]],
+                         'k--', alpha=0.5)
+    
+    plt.title('Token Embeddings Constellation with MST', fontsize=labels_size)
+    plt.grid(show_grid)
+    
+    if save_plot:
+        plt.savefig(save_plot, bbox_inches="tight")
+        plt.close()
+        
+    else:
+        plt.show()
+        
+    plt.close()
+    
+################################################################################
+
+def find_token_path(tokens_embeddings, 
+                    start_token, 
+                    end_token, 
+                    verbose=False
+                   ):
+    
+    """
+    Finds the path of tokens between start_token and end_token using
+    the Minimum Spanning Tree (MST) derived from the distance matrix.
+    """
+
+    distance_matrix = metrics.pairwise_distances(tokens_embeddings, metric='cosine')
+    
+    token_labels = [str(i) for i in range(len(distance_matrix))]
+    
+    if verbose:
+        print('Total number of tokens:', len(distance_matrix))
+    
+    mst = minimum_spanning_tree(distance_matrix).toarray()
+    
+    n = distance_matrix.shape[0]
+    G = nx.Graph()
+    
+    for i in range(n):
+        for j in range(n):
+            if mst[i, j] > 0:
+                weight = 1 / (distance_matrix[i, j] + 1e-8)
+                G.add_edge(i, j, weight=weight)
+    
+    try:
+        start_idx = token_labels.index(str(start_token))
+        end_idx = token_labels.index(str(end_token))
+        
+    except ValueError:
+        raise ValueError("Start or end token not found in the provided token labels.")
+    
+    path_indices = nx.shortest_path(G, source=start_idx, target=end_idx)
+    
+    token_path = [int(token_labels[idx]) for idx in path_indices]
+    
+    return token_path
+
 ################################################################################
 # [WIP] Future dev functions
 ################################################################################
@@ -1363,7 +1470,53 @@ plt.show()
 '''
 
 ################################################################################
-#
+
+def plot_tree_horizontal(data):
+    
+    """
+    Given data as a list of levels (each level is a tuple or list of 
+    displacements for each branch), this function computes the cumulative 
+    value per branch (starting from 0) and plots each branch
+    with the tree level mapped to the x-axis and the cumulative value mapped 
+    to the y-axis. This gives a left-to-right tree with branches spanning up 
+    (positive) and down (negative).
+    
+    Parameters:
+        data (list of tuple/list): Each element represents a tree level.
+                                   It is assumed every level has the same length.
+    """
+    
+    # Convert data to a NumPy array with shape (n_levels, n_branches)
+    data = np.array(data)
+    n_levels, n_branches = data.shape
+
+    # Compute cumulative sums along each branch.
+    # Each branch starts at 0 at level 0.
+    cum = np.zeros((n_levels + 1, n_branches))
+    for i in range(n_levels):
+        cum[i + 1, :] = cum[i, :] + data[i, :]
+    
+    plt.figure(figsize=(12, 8))
+    
+    # Plot each branch as a line. For branch j:
+    #   - x coordinates are the tree levels (0 to n_levels)
+    #   - y coordinates are the corresponding cumulative values.
+    for j in range(n_branches):
+        x = np.arange(n_levels + 1)
+        y = cum[:, j]
+        plt.plot(x, y, marker='o', label=f'Branch {j}')
+    
+    plt.title("Horizontal Tree Visualization: Branches Spanning Up and Down", fontsize=14)
+    plt.xlabel("Tree Level (Left = Root)")
+    plt.ylabel("Cumulative Value (Up = Positive, Down = Negative)")
+    
+    # Add a horizontal line at y=0 to emphasize the center.
+    plt.axhline(0, color="gray", linestyle="--")
+    
+    #plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
+    plt.tight_layout()
+    plt.show()
+    
+################################################################################
 # This is the end of TPLOTS Python modules
-#
 ################################################################################