63 lines
1.5 KiB
Python
63 lines
1.5 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 timeit import default_timer
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from decoders import proximal, maximum_likelihood
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from utility import simulations, codes, visualization
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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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# Read data from files
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data = []
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for file in os.listdir(results_dir):
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if file.endswith(".csv"):
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df = pd.read_csv(os.path.join(results_dir, file))
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df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
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data.append(df)
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# Create and show graphs
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sns.set_theme()
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fig = visualization.show_BER_curves(data)
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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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# H = codes.read_alist_file("res/204.3.486.alist")
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H = codes.read_alist_file("res/204.55.187.alist")
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k = 102
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n = 204
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decoders = [
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proximal.ProximalDecoder(H, gamma=0.01),
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proximal.ProximalDecoder(H, gamma=0.05),
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proximal.ProximalDecoder(H, gamma=0.15)
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]
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SNRs, BERs = simulations.test_decoders(n, k, decoders, target_frame_errors=100, SNRs=np.arange(1, 6, 0.5))
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df = pd.DataFrame({"SNR": SNRs})
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df["BER_prox_0_01"] = BERs[0]
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df["BER_prox_0_05"] = BERs[1]
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df["BER_prox_0_15"] = BERs[2]
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df.to_csv(f"sim_results/ldpc.csv")
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plot_results()
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if __name__ == "__main__":
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main()
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