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Made main.py work with the new simulation functions

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This commit is contained in:
Andreas Tsouchlos 2022-11-10 12:19:01 +01:00
parent f791a9a086
commit 2fae3ba3be
2 changed files with 22 additions and 45 deletions
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65
sw/main.py
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@ -12,32 +12,6 @@ from decoders import proximal, maximum_likelihood
from utility import simulations, codes
def test_decoders(G, decoders: typing.List) -> pd.DataFrame:
k, n = G.shape
x = np.zeros(n) # All-zeros assumption
SNRs = np.linspace(1, 7, 7)
data = pd.DataFrame({"SNR": SNRs})
start_time = default_timer()
for decoder_name in decoders:
decoder = decoders[decoder_name]
_, BERs_sd = simulations.test_decoder(x,
decoder=decoder,
SNRs=SNRs,
target_frame_errors=100,
N_max=50000)
data[f"BER_{decoder_name}"] = BERs_sd
stop_time = default_timer()
print(f"Elapsed time: {stop_time-start_time:.2f}s")
return data
# TODO: Fix spacing between axes and margins
def plot_results():
results_dir = "sim_results"
@ -78,27 +52,32 @@ def plot_results():
def main():
Path("sim_results").mkdir(parents=True, exist_ok=True)
used_codes = [
"Hamming_7_4",
"Golay_24_12",
# "BCH_31_16",
# "BCH_31_21",
# "BCH_63_16",
# used_code = "Hamming_7_4"
# used_code = "Golay_24_12"
used_code = "BCH_31_16"
# used_code = "BCH_31_21"
# used_code = "BCH_63_16"
G = codes.Gs[used_code]
H = codes.get_systematic_H(G)
decoders = [
maximum_likelihood.MLDecoder(G, H),
proximal.ProximalDecoder(H, gamma=0.01),
proximal.ProximalDecoder(H, gamma=0.05),
proximal.ProximalDecoder(H, gamma=0.15)
]
for used_code in used_codes:
G = codes.Gs[used_code]
H = codes.get_systematic_H(G)
k, n = G.shape
SNRs, BERs = simulations.test_decoders(n, k, decoders, N_max=30000, target_frame_errors=100)
decoders = {
"ML": maximum_likelihood.MLDecoder(G, H),
"proximal_0_01": proximal.ProximalDecoder(H, gamma=0.01),
"proximal_0_05": proximal.ProximalDecoder(H, gamma=0.05),
"proximal_0_15": proximal.ProximalDecoder(H, gamma=0.15),
}
df = pd.DataFrame({"SNR": SNRs})
df["BER_ML"] = BERs[0]
df["BER_prox_0_01"] = BERs[0]
df["BER_prox_0_05"] = BERs[1]
df["BER_prox_0_15"] = BERs[2]
data = test_decoders(G, decoders)
data.to_csv(f"sim_results/{used_code}.csv")
df.to_csv(f"sim_results/{used_code}.csv")
plot_results()

2
sw/utility/noise.py
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@ -12,8 +12,6 @@ def get_noise_variance_from_SNR(SNR: float, n: int, k: int) -> float:
:param k: Length of a dataword of the used code
:return: Variance of the noise
"""
# noise_amp = (1 / np.sqrt(SNR_linear)) * signal_amp
SNR_linear = 10 ** (SNR / 10)
variance = 1 / (2 * (k/n) * SNR_linear)