102 lines
2.3 KiB
Python
102 lines
2.3 KiB
Python
import numpy as np
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import pandas as pd
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import seaborn as sns
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import matplotlib.pyplot as plt
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import signal
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from timeit import default_timer
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from utility import codes, noise, misc
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from utility.simulation.simulators import GenericMultithreadedSimulator
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from cpp_modules.cpp_decoders import ProximalDecoder
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def task_func(params):
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signal.signal(signal.SIGINT, signal.SIG_IGN)
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decoder, num_iterations, x_bpsk, SNR, n, k = params
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dec_fails = 0
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for i in range(num_iterations):
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x = noise.add_awgn(x_bpsk, SNR, n, k)
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x_hat, num_iter = decoder.decode(x)
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if x_hat is None:
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dec_fails += 1
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return dec_fails / num_iterations
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def simulate(H_file, SNR, num_iterations, omegas, Ks):
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sim = GenericMultithreadedSimulator()
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# Define fixed simulation params
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H = codes.read_alist_file(f"res/{H_file}")
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n_min_k, n = H.shape
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k = n - n_min_k
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x_bpsk = np.zeros(n) + 1
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# Define params different for each task
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params = {}
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for i, omega in enumerate(omegas):
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for j, K in enumerate(Ks):
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decoder = ProximalDecoder(H=H.astype('int32'), K=K.astype('int32'),
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omega=omega)
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params[f"{i}_{j}"] = (decoder, num_iterations, x_bpsk, SNR, n, k)
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# Set up simulation
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sim.task_params = params
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sim.task_func = task_func
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sim.start_or_continue()
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return sim.get_current_results()
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def reformat_data(results, omegas, Ks):
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data = np.zeros(1600).reshape(40, 40)
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for key, value in results.items():
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i_w, i_k = key.split('_')
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data[int(i_w), int(i_k)] = value
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return pd.DataFrame(data, columns=Ks, index=omegas)
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def main():
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# Set up simulation params
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sim_name = "w_log_k_lin_zoomed_in"
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H_file = "96.3.965.alist"
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SNR = 3
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num_iterations = 1000
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omegas = np.logspace(-0.3, -2.82, 40)
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Ks = np.ceil(np.linspace(10 ** 1.3, 10 ** 2.3, 40)).astype('int32')
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# Run simulation
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start_time = default_timer()
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results = simulate(H_file, SNR, num_iterations, omegas, Ks)
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end_time = default_timer()
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print(f"duration: {end_time - start_time}")
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df = reformat_data(results, omegas, Ks)
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df.to_csv(
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f"sim_results/2d_dec_fails_{sim_name}_{misc.slugify(H_file)}.csv")
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sns.set_theme()
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sns.heatmap(df)
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plt.show()
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if __name__ == "__main__":
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main()
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