diff --git a/sw/simulate_gradient.py b/sw/simulate_gradient.py
new file mode 100644
index 0000000..76b0d9b
--- /dev/null
+++ b/sw/simulate_gradient.py
@@ -0,0 +1,85 @@
+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 tqdm import tqdm
+
+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_204_102 as ProximalDecoder
+
+
+def simulate(H_file, SNR, omega, K, gamma):
+    H = codes.read_alist_file(f"res/{H_file}")
+    n_min_k, n = H.shape
+    k = n - n_min_k
+
+    decoder = ProximalDecoder(H.astype('int32'), K=K, omega=omega, gamma=gamma)
+
+    c = np.zeros(n)
+    x_bpsk = (c + 1)
+
+    avg_grad_values = np.zeros(shape=(K, 2))
+
+    for i in range(1000):
+        x = noise.add_awgn(x_bpsk, SNR, n, k)
+        grad_values = decoder.get_gradient_values(x)
+
+        for j, (val_h, val_l) in enumerate(grad_values):
+            avg_grad_values[j, 0] += val_h
+            avg_grad_values[j, 1] += val_l
+
+    avg_grad_values = avg_grad_values / 1000
+
+    return avg_grad_values
+
+
+def reformat_data(results):
+    return pd.DataFrame({"k": np.arange(0, results.size // 2, 1), "grad_h": results[:, 0], "grad_l": results[:, 1]})
+
+
+def main():
+    # Set up simulation params
+
+    sim_name = "avg_grad_1dB"
+
+    # H_file = "96.3.965.alist"
+    H_file = "204.33.486.alist"
+    # H_file = "204.33.484.alist"
+    # H_file = "204.55.187.alist"
+    # H_file = "408.33.844.alist"
+    # H_file = "BCH_7_4.alist"
+    # H_file = "BCH_31_11.alist"
+    # H_file = "BCH_31_26.alist"
+
+    SNR = 1
+    omega = 0.05
+    K = 100
+    gamma = 0.05
+
+    # Run simulation
+
+    start_time = default_timer()
+    results = simulate(H_file, SNR, omega, K, gamma)
+    end_time = default_timer()
+
+    print(f"duration: {end_time - start_time}")
+
+    df = reformat_data(results)
+
+    df.to_csv(
+        f"sim_results/{sim_name}_{misc.slugify(H_file)}.csv", index=False)
+
+    sns.set_theme()
+    sns.lineplot(data=df, x="k", y="grad_h")
+    sns.lineplot(data=df, x="k", y="grad_l")
+
+    plt.show()
+
+
+if __name__ == "__main__":
+    main()