diff --git a/sw/main.py b/sw/main.py
index 08079f7..5ea212e 100644
--- a/sw/main.py
+++ b/sw/main.py
@@ -2,14 +2,12 @@ import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sns
 import pandas as pd
-import typing
 from pathlib import Path
 import os
-from itertools import chain
-from timeit import default_timer
+import sys
 
 from decoders import proximal, maximum_likelihood
-from utility import simulations, codes, visualization
+from utility import simulation, codes, visualization
 
 
 # TODO: Fix spacing between axes and margins
@@ -33,34 +31,47 @@ def plot_results():
 
 
 def main():
-    # Path("sim_results").mkdir(parents=True, exist_ok=True)
-    #
-    # # used_code = "Hamming_7_4"
-    # # used_code = "Golay_24_12"
-    # used_code = "BCH_31_16"
-    # # used_code = "BCH_31_21"
-    # # used_code = "BCH_63_16"
-    #
-    # G = codes.Gs[used_code]
-    # H = codes.get_systematic_H(G)
-    #
-    # decoders = [
-    #     maximum_likelihood.MLDecoder(G, H),
-    #     proximal.ProximalDecoder(H, gamma=0.01),
-    #     proximal.ProximalDecoder(H, gamma=0.05),
-    #     proximal.ProximalDecoder(H, gamma=0.15)
-    # ]
-    #
-    # k, n = G.shape
-    # SNRs, BERs = simulations.test_decoders(n, k, decoders, N_max=30000, target_frame_errors=100)
-    #
-    # df = pd.DataFrame({"SNR": SNRs})
-    # df["BER_ML"] = BERs[0]
-    # df["BER_prox_0_01"] = BERs[0]
-    # df["BER_prox_0_05"] = BERs[1]
-    # df["BER_prox_0_15"] = BERs[2]
-    #
-    # df.to_csv(f"sim_results/{used_code}.csv")
+    Path("sim_results").mkdir(parents=True, exist_ok=True)
+
+    sys.setrecursionlimit(10 * sys.getrecursionlimit())
+
+    sim_mgr = simulation.SimulationManager(results_dir="sim_results", save_dir="sim_saves")
+
+    if sim_mgr.unfinished_simulation_present():
+        print("Found unfinished simulation. Picking up where it was left of")
+        sim_mgr.load_unfinished()
+        sim_mgr.start()
+    else:
+        print("No unfinished simulation present. Starting a new one")
+
+        used_code = "Golay_24_12"
+
+        G = codes.Gs[used_code]
+        H = codes.get_systematic_H(G)
+
+        decoders = [
+            # maximum_likelihood.MLDecoder(G, H),
+            proximal.ProximalDecoder(H, gamma=0.01),
+            proximal.ProximalDecoder(H, gamma=0.05),
+            proximal.ProximalDecoder(H, gamma=0.15)
+        ]
+
+        k, n = G.shape
+
+        sim = simulation.Simulator(n=n, k=k, decoders=decoders, SNRs=np.arange(1, 6, 0.5), target_frame_errors=100)
+
+        sim_mgr.set_simulator(sim)
+        sim_mgr.start()
+
+    SNRs, BERs = sim_mgr.get_current_results()
+
+    df = pd.DataFrame({"SNR": SNRs})
+    # df["ML"] = BERs[0]
+    df["prox_0_01"] = BERs[0]
+    df["prox_0_05"] = BERs[1]
+    df["prox_0_15"] = BERs[2]
+
+    df.to_csv(f"sim_results/golay.csv")
 
     plot_results()