Refactor projection logic in sim_contest function to improve clarity and performance. Updated handling of the first column using 'vec_cpt_projection_map' and adjusted the application of 'vec_projection_map' for remaining columns, ensuring consistent simulation calculations.

app.py

@@ -214,9 +214,10 @@ def sim_contest(Sim_size, seed_frame, maps_dict, sharp_split, Contest_Size):
         sample_arrays1 = np.c_[
             fp_random,
             np.sum(np.random.normal(
-                loc=np.where(np.arange(fp_random.shape[1]) == 0,  # Check if it's the first column
-                    vec_cpt_projection_map(fp_random[:, :-7]),         # Use cpt_projection_map for first column
-                    vec_projection_map(fp_random[:, :-7])),        # Use original for other columns
+                loc=np.concatenate([
+                    vec_cpt_projection_map(fp_random[:, 0:1]),  # Apply cpt_projection_map to first column
+                    vec_projection_map(fp_random[:, 1:-7])  # Apply original projection to remaining columns
+                ], axis=1),
                 scale=vec_stdev_map(fp_random[:, :-7])),
             axis=1)
         ]