From 27c36855298273290a3e7c3658a249442055cb0c Mon Sep 17 00:00:00 2001
From: Andreas Tsouchlos <an.tsouchlos@gmail.com>
Date: Mon, 28 Nov 2022 16:01:11 +0100
Subject: [PATCH] Added simulate_2d_dec_fails.py

---
 sw/simulate_2d_dec_fails.py | 101 ++++++++++++++++++++++++++++++++++++
 1 file changed, 101 insertions(+)
 create mode 100644 sw/simulate_2d_dec_fails.py

diff --git a/sw/simulate_2d_dec_fails.py b/sw/simulate_2d_dec_fails.py
new file mode 100644
index 0000000..e237a8a
--- /dev/null
+++ b/sw/simulate_2d_dec_fails.py
@@ -0,0 +1,101 @@
+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
+
+
+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 x_hat is None:
+            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"
+    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()