diff --git a/sw/simulate_2d_dec_fails.py b/sw/simulate_2d_dec_fails.py
deleted file mode 100644
index 651373f..0000000
--- a/sw/simulate_2d_dec_fails.py
+++ /dev/null
@@ -1,106 +0,0 @@
-import numpy as np
-import pandas as pd
-import seaborn as sns
-import matplotlib.pyplot as plt
-import signal
-from timeit import default_timer
-
-from utility import codes, noise, misc
-from utility.simulation.simulators import GenericMultithreadedSimulator
-
-# from cpp_modules.cpp_decoders import ProximalDecoder
-from cpp_modules.cpp_decoders import ProximalDecoder_31_5 as ProximalDecoder
-
-
-def task_func(params):
-    signal.signal(signal.SIGINT, signal.SIG_IGN)
-
-    decoder, num_iterations, x_bpsk, SNR, n, k = params
-
-    dec_fails = 0
-
-    for i in range(num_iterations):
-        x = noise.add_awgn(x_bpsk, SNR, n, k)
-        x_hat, num_iter = decoder.decode(x)
-
-        if num_iter == -1:
-            dec_fails += 1
-
-    return dec_fails / num_iterations
-
-
-def simulate(H_file, SNR, num_iterations, omegas, Ks):
-    sim = GenericMultithreadedSimulator()
-
-    # Define fixed simulation params
-
-    H = codes.read_alist_file(f"res/{H_file}")
-    n_min_k, n = H.shape
-    k = n - n_min_k
-
-    x_bpsk = np.zeros(n) + 1
-
-    # Define params different for each task
-
-    params = {}
-    for i, omega in enumerate(omegas):
-        for j, K in enumerate(Ks):
-            decoder = ProximalDecoder(H=H.astype('int32'), K=K.astype('int32'),
-                                      omega=omega)
-            params[f"{i}_{j}"] = (decoder, num_iterations, x_bpsk, SNR, n, k)
-
-    # Set up simulation
-
-    sim.task_params = params
-    sim.task_func = task_func
-
-    sim.start_or_continue()
-
-    return sim.get_current_results()
-
-
-def reformat_data(results, omegas, Ks):
-    data = np.zeros(1600).reshape(40, 40)
-
-    for key, value in results.items():
-        i_w, i_k = key.split('_')
-        data[int(i_w), int(i_k)] = value
-
-    return pd.DataFrame(data, columns=Ks, index=omegas)
-
-
-def main():
-    # Set up simulation params
-
-    sim_name = "w_log_k_lin_zoomed_in"
-
-    # H_file = "96.3.965.alist"
-    # H_file = "204.33.486.alist"
-    # H_file = "408.33.844.alist"
-    H_file = "BCH_31_26.alist"
-    SNR = 3
-
-    num_iterations = 1000
-    omegas = np.logspace(-0.3, -2.82, 40)
-    Ks = np.ceil(np.linspace(10 ** 1.3, 10 ** 2.3, 40)).astype('int32')
-
-    # Run simulation
-
-    start_time = default_timer()
-    results = simulate(H_file, SNR, num_iterations, omegas, Ks)
-    end_time = default_timer()
-
-    print(f"duration: {end_time - start_time}")
-
-    df = reformat_data(results, omegas, Ks)
-
-    df.to_csv(
-        f"sim_results/2d_dec_fails_{sim_name}_{misc.slugify(H_file)}.csv")
-
-    sns.set_theme()
-    sns.heatmap(df)
-    plt.show()
-
-
-if __name__ == "__main__":
-    main()