Update bimodal distribution sampling in NBA ROO functions to use floor and ceiling values

function_hold/NBA_functions.py

@@ -117,6 +117,8 @@ def DK_NBA_ROO_Build(projections_file, floor_var, ceiling_var, std_var, distribu
     salary_file = flex_file.copy()
     
     try:    
+        overall_floor_gpu = np_array(overall_file['Floor'])
+        overall_ceiling_gpu = np_array(overall_file['Ceiling'])
         overall_median_gpu = np_array(overall_file['Median'])
         overall_std_gpu = np_array(overall_file['STD'])
         overall_salary_gpu = np_array(overall_file['Salary'])
@@ -154,9 +156,9 @@ def DK_NBA_ROO_Build(projections_file, floor_var, ceiling_var, std_var, distribu
                 # Bimodal distribution - mixture of two normal distributions
                 # First peak centered at 80% of median, second at 120% of median
                 if np_random.random() < 0.5:
-                    result_gpu = np_random.normal(overall_median_gpu * 0.8, overall_std_gpu)
+                    result_gpu = np_random.normal(overall_floor_gpu, overall_std_gpu)
                 else:
-                    result_gpu = np_random.normal(overall_median_gpu * 1.2, overall_std_gpu)
+                    result_gpu = np_random.normal(overall_ceiling_gpu, overall_std_gpu)
             else:
                 raise ValueError("Invalid distribution type. Must be 'normal', 'poisson', or 'bimodal'")
                 
@@ -340,6 +342,8 @@ def FD_NBA_ROO_Build(projections_file, floor_var, ceiling_var, std_var, distribu
     salary_file = flex_file.copy()
     
     try:    
+        overall_floor_gpu = np_array(overall_file['Floor'])
+        overall_ceiling_gpu = np_array(overall_file['Ceiling'])
         overall_median_gpu = np_array(overall_file['Median'])
         overall_std_gpu = np_array(overall_file['STD'])
         overall_salary_gpu = np_array(overall_file['Salary'])
@@ -377,9 +381,9 @@ def FD_NBA_ROO_Build(projections_file, floor_var, ceiling_var, std_var, distribu
                 # Bimodal distribution - mixture of two normal distributions
                 # First peak centered at 80% of median, second at 120% of median
                 if np_random.random() < 0.5:
-                    result_gpu = np_random.normal(overall_median_gpu * 0.8, overall_std_gpu)
+                    result_gpu = np_random.normal(overall_floor_gpu, overall_std_gpu)
                 else:
-                    result_gpu = np_random.normal(overall_median_gpu * 1.2, overall_std_gpu)
+                    result_gpu = np_random.normal(overall_ceiling_gpu, overall_std_gpu)
             else:
                 raise ValueError("Invalid distribution type. Must be 'normal', 'poisson', or 'bimodal'")