add debugging notebook and vstack

csv_importer/parsers.py

@@ -9,9 +9,51 @@ def polars_df_to_bob(df: pl.DataFrame, name: str) -> db.BlenderObject:
 
     for col in df.columns:
         col_dtype = df[col].dtype
-        if col_dtype in [pl.Utf8]:
+        if col_dtype in [pl.Utf8]:  # skip strings
             continue
-        data = df[col].to_numpy()
+        data = np.vstack(df[col].to_numpy())
         bob.store_named_attribute(data, col)
 
     return bob
+
+    """
+    Converts a Polars DataFrame into a Blender object (bob) and stores its columns as named attributes.
+
+    `np.vstack` is used to ensure that 3D vectors are properly porcessed.
+    
+    Example:
+
+        import polars as pl
+        import numpy as np
+        import databpy as db
+
+        # Create a DataFrame
+        df = pl.DataFrame({
+            "Star": [
+                [58.2136, 91.8819, 0.0],
+                [58.1961, 92.215, 0.0]
+            ],
+            "Is_Visible": [True, False],
+            "Intensity": [10, 20],
+        })
+
+        #  convert "Star" column to a NumPy array (won't work in databpy)
+        df["Star"].to_numpy()
+        # Output:
+        # array([array([58.2136, 91.8819,  0.    ]),
+        #        array([58.1961, 92.215 ,  0.    ])], dtype=object)
+
+        # Use np.vstack to stack the arrays vertically (this will work in databpy)
+        np.vstack(df["Star"].to_numpy())
+        # Output:
+        # array([[58.2136, 91.8819,  0.    ],
+        #        [58.1961, 92.215 ,  0.    ]])
+
+        vertices = np.zeros((len(df), 3), dtype=np.float32)
+        bob = db.create_bob(vertices, name="DataWithVector")
+
+        for col in df.columns:
+            data = np.vstack(df[col].to_numpy())
+            bob.store_named_attribute(data, col)
+
+    """