Distributing points randomly in 3D space (values between 0-1 on each axis) instead of stacking all points at the origin (#24)

* add readme

* better import

* add image

Author: kolibril13

README.md

@@ -10,14 +10,14 @@ https://github.com/user-attachments/assets/1b8a26d7-ce35-4717-bf73-bc05dbf3912a
 
 ## How to Use
 
-Option 1: Drag and drop your CSV file directly into Blender’s viewport.
+Option 1: Drag and drop your CSV file directly into Blender's viewport.
 
 Option 2: Use the menu:
 File → Import → CSV
 
 ### **Using the Data**
 
-- The imported data will appear in Blender’s **Spreadsheet Editor**.
+- The imported data will appear in Blender's **Spreadsheet Editor**.
 - Use the **Named Attribute** in **Geometry Nodes** to access the imported data.
 
 And the data will show up in the speadsheet:
@@ -65,6 +65,9 @@ uv run -m pytest
 
 ## Version 0.1.8
 
+- Improved default data visualization by distributing points randomly in 3D space (values between 0-1 on each axis) instead of stacking all points at the origin
+![image](https://github.com/user-attachments/assets/0827092c-3bf5-41fa-aeff-f8efb29877df)
+
 - For performance reasons, the number of unique strings is limited to 3000 by default (processing this many strings takes about 7 seconds on a Mac M3)
 - Note: This limit applies to the number of unique strings, not the total count. For example, a CSV with 100,000 strings but only 100 unique values won't be affected by the limit.
 - You can override this limit when using the API directly:

csv_importer/parsers.py

@@ -6,7 +6,7 @@
 
 
 def polars_df_to_bob(df: pl.DataFrame, name: str, string_limit: int = 3000) -> db.BlenderObject:
-    vertices = np.zeros((len(df), 3), dtype=np.float32)
+    vertices = np.random.random((len(df), 3)).astype(np.float32)
     bob = db.create_bob(vertices, name=name)
 
     update_bob_from_polars_df(bob, df, string_limit=string_limit)
@@ -17,7 +17,7 @@ def update_obj_from_csv(obj: bpy.types.Object, csv_file: str, string_limit: int
     bob = db.BlenderObject(obj)
     df = pl.read_csv(csv_file)
     if len(df) != len(bob):
-        bob.new_from_pydata(np.zeros((len(df), 3), dtype=np.float32))
+        bob.new_from_pydata(np.random.random((len(df), 3)).astype(np.float32))
     update_bob_from_polars_df(bob, df, string_limit=string_limit)