refactor(visualize_dataset_distribution.py, utils.py): move get_all_categories to imported file

Author: MaoSong2022

scripts/visualize_dataset_distribution.py

@@ -4,7 +4,24 @@
 import numpy as np
 import csv
 
-from vrdu import utils
+
+def get_all_categories():
+    """
+    Retrieves all categories from the "category_count.csv" file.
+
+    Returns:
+        categories (list): A list of all categories.
+
+    Reference:
+        https://arxiv.org/category_taxonomy
+    """
+    categories = []
+    with open("scripts/category_count.csv", "r") as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            categories.append(row["categories"])
+
+    return categories
 
 
 def visualize_distribution(dict1, dict2):
@@ -50,7 +67,7 @@ def visualize_distribution(dict1, dict2):
 
 
 def analyze_raw_data(path):
-    all_categories = utils.get_all_categories()
+    all_categories = get_all_categories()
 
     data = defaultdict(int)
     for category in all_categories:

vrdu/utils.py

@@ -221,24 +221,6 @@ def convert_eps_image_to_pdf_image(eps_image_path: str, pdf_image_path: str):
     subprocess.run(["epspdf", eps_image_path, pdf_image_path])
 
 
-def get_all_categories():
-    """
-    Retrieves all categories from the "category_count.csv" file.
-
-    Returns:
-        categories (list): A list of all categories.
-
-    Reference:
-        https://arxiv.org/category_taxonomy
-    """
-    categories = []
-    with open("scripts/category_count.csv", "r") as f:
-        reader = csv.DictReader(f)
-        for row in reader:
-            categories.append(row["categories"])
-
-    return categories
-
 
 def extract_macro_definitions(tex_file) -> List[str]:
     """