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Deleted simulate_2d_dec_fails.py

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Andreas Tsouchlos 2022-12-05 16:44:58 +01:00
parent f8503e4f0a
commit c1145bcbb1
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sw/simulate_2d_dec_fails.py
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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()