Added support for comment counting and comment density. Now ignores *.csv outputs

Author: michaelberrios-ntid

courseProjectCode/.gitignore

@@ -0,0 +1 @@
+Metrics/*.csv

courseProjectCode/Metrics/loc_counter.py

@@ -1,3 +1,4 @@
+import csv
 from pathlib import Path
 
 root_repository = Path(__file__).resolve().parents[2]
@@ -7,6 +8,7 @@
 algorithms = {}
 total_files = 0
 total_loc = 0
+total_comments = 0
 
 # Recursively find all files ending with .py
 for file_path in root_repository.rglob("*.py"):
@@ -22,44 +24,84 @@
     # Extract file name including its relative path to project, this accounts for files under sub-folder in their categories
     file_name = Path(*relative_path.parts[1:])
 
-    # Count of LOC for each file
+    # Count of LOC and Comments for each file
     loc = 0
-    with open(file_path, "r", encoding="utf-8") as f:
-        for line in f:
+    comments = 0
+    
+    with open(file_path, "r", encoding="utf-8") as file:
+        for line in file:
             if line.strip():
                 loc += 1
+                
+            if line.strip().startswith("#"):
+                comments += 1
 
     # Exclude empty Python package initialization files to reduce noise
     if file_path.name == "__init__.py" and loc == 0:
         continue
 
     total_files += 1
     total_loc += loc
+    total_comments += comments
 
     if category not in algorithms:
         algorithms[category] = {}
 
-    algorithms[category][str(file_name)] = loc
+    algorithms[category][str(file_name)] = {
+        "loc": loc,
+        "comments": comments,
+        "comment_density": comments / loc if loc > 0 else 0
+    }
 
-    print("Category:", category)
-    print("File:", file_name)
-    print("LOC:", algorithms[category][str(file_name)])
-    print()
+    # Can enable in the future with command and switch
+    # print("Category:", category)
+    # print("File:", file_name)
+    # print("LOC:", algorithms[category][str(file_name)])
+    # print("Comments:", comments)
+    # print("Comments Density:", algorithms[category][str(file_name)]["comment_density"])
+    # print()
 
 # Output formatting
 col1 = 28
 col2 = 8
 col3 = 12
-dashes = col1 + col2 + col3 + 2
+dashes = col1 + col2 + col3 + col3 + col3 + 3
 
 print("-" * dashes)
-print(f"{'Category':<{col1}} {'Files':>{col2}} {'LOC':>{col3}}")
+print(f"{'Category':<{col1}} {'Files':>{col2}} {'LOC':>{col3}} {'Comments':>{col3}}{'> Density %':>{col3}}")
+print("-" * dashes)
 
 for category, files in algorithms.items():
     file_count = len(files)
-    category_loc = sum(files.values())
 
-    print(f"{category:<{col1}} {file_count:>{col2}} {category_loc:>{col3}}")
+    # Get the values of loc for each file
+    category_loc = sum(metrics['loc'] for metrics in files.values())
+    category_comments = sum(metrics['comments'] for metrics in files.values())
+    category_comment_density = category_comments / category_loc if category_loc > 0 else 0
+    
+    print(f"{category:<{col1}} {file_count:>{col2}} {category_loc:>{col3}} {category_comments:>{col3}}{category_comment_density * 100:>{col3}.2f}")
 
 print("-" * dashes)
-print(f"{len(algorithms):<{col1}} {total_files:>{col2}} {total_loc:>{col3}}")
+
+# Calculate Total Comments Density
+total_comment_density = total_comments / total_loc if total_loc > 0 else 0
+    
+print(f"{f'Total ({len(algorithms)} categories)':<{col1}} {total_files:>{col2}} {total_loc:>{col3}} {total_comments:>{col3}}{total_comment_density * 100:>{col3}.2f}\n")
+
+# Write results to a csv file
+output_file = Path(__file__).resolve().parent / "loc_results.csv"
+
+print("Saving results to file...")
+with open(output_file, "w", encoding="utf-8", newline="") as csv_file:
+    writer = csv.writer(csv_file)
+    
+    # Insert the header
+    writer.writerow(["Category", "File", "LOC", "Comments", "Comments Density %"])
+    
+    # Insert data rows
+    for category, files, in algorithms.items():
+        for file_name, metrics in files.items():
+            writer.writerow([category, file_name, metrics["loc"], metrics["comments"], metrics["comment_density"] * 100])
+            
+print()
+print(f"Results saved to: {output_file.name}")