worked

algorithms/algorithms_new.py

@@ -871,7 +871,7 @@ def make_gap_lower(districts_dict, all_tracts, num_districts, used_tracts,
     '''
     Use the efficiency gap to construct an optimal district plan using
     the entire population. It looks through districts randomly to find
-    a district under "goal" or stops if has looked at districts 4500 times.
+    a district under "goal" or stops if has looked at districts 1000 times.
 
     Inputs:
         districts_dict: a dictionary of dictionaries containing a tract plans in the state
@@ -890,7 +890,7 @@ def make_gap_lower(districts_dict, all_tracts, num_districts, used_tracts,
     gap = calculate_efficiency_gap_from_dict(districts_dict)
     goal_percent = goal / 100
     print(goal_percent, gap)
-    while abs(gap) > goal_percent and (count - 4500):
+    while abs(gap) > goal_percent and (count - 10000):
         count += 1
         new = False
         check_pop = random.choice(range(1,num_districts+1))
@@ -943,12 +943,13 @@ def make_gap_lower(districts_dict, all_tracts, num_districts, used_tracts,
                     minus_total_population_D_R(districts_dict, check_pop, best_total ,best_dem, best_rep)
 
 
-            gap = calculate_efficiency_gap_from_dict(districts_dict)
             printer = ["lower", check_pop, count]
             for num in districts_dict:
                 printer.append(districts_dict[num]["total_population"])
             printer.append(calculate_efficiency_gap_from_dict(districts_dict))
             print(printer)
+        gap = calculate_efficiency_gap_from_dict(districts_dict)
+
 
     return districts_dict