104 lines
2.9 KiB
Python
104 lines
2.9 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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import seaborn as sns
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import pandas as pd
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import typing
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from pathlib import Path
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import os
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from itertools import chain
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from decoders import proximal, naive_soft_decision
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from utility import simulations, encoders, codes
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def test_decoders(G, encoder, decoders: typing.List) -> pd.DataFrame:
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k, n = G.shape
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d = np.zeros(k) # All-zeros assumption
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SNRs = np.linspace(1, 8, 8)
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data = pd.DataFrame({"SNR": SNRs})
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for decoder_name in decoders:
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decoder = decoders[decoder_name]
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_, BERs_sd = simulations.test_decoder(encoder=encoder,
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decoder=decoder,
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d=d,
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SNRs=SNRs,
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target_bit_errors=100,
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N_max=30000)
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data[f"BER_{decoder_name}"] = BERs_sd
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return data
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# TODO: Fix spacing between axes and margins
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def plot_results():
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results_dir = "sim_results"
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code_paths = {}
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for file in os.listdir(results_dir):
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if file.endswith(".csv"):
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code_paths[file.replace(".csv", "")] = os.path.join(results_dir, file)
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sns.set_theme()
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fig, axes = plt.subplots(2, len(code_paths) // 2, figsize=(12, 6))
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fig.suptitle("Bit-Error-Rates of various decoders for different codes")
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axes = list(chain.from_iterable(axes))
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for i, code in enumerate(code_paths):
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data = pd.read_csv(code_paths[code])
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column_names = [column for column in data.columns.values.tolist()
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if column.startswith("BER")]
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ax = axes[i]
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for column in column_names:
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sns.lineplot(ax=ax, data=data, x="SNR", y=column, label=column.lstrip("BER_"))
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ax.set_title(code)
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ax.set(yscale="log")
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ax.set_xlabel("SNR")
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ax.set_ylabel("BER")
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ax.set_yticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 10e0])
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ax.legend()
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plt.show()
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def main():
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Path("sim_results").mkdir(parents=True, exist_ok=True)
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used_codes = [
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"Hamming_7_4",
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"Golay_24_12",
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# "BCH_31_16",
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# "BCH_31_21",
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# "BCH_63_16",
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]
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for used_code in used_codes:
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G = codes.Gs[used_code]
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H = codes.get_systematic_H(G)
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R = codes.get_systematic_R(G)
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encoder = encoders.Encoder(G)
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decoders = {
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"naive_soft_decision": naive_soft_decision.SoftDecisionDecoder(G, H, R),
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"proximal_0_01": proximal.ProximalDecoder(H, R, K=100, gamma=0.01),
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"proximal_0_05": proximal.ProximalDecoder(H, R, K=100, gamma=0.05),
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"proximal_0_15": proximal.ProximalDecoder(H, R, K=100, gamma=0.15),
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}
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# data = test_decoders(G, encoder, decoders)
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# data.to_csv(f"sim_results/{used_code}.csv")
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plot_results()
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if __name__ == "__main__":
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main()
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