import matplotlib.pyplot as plt
import numpy as np


signal = list(map(float, open('./data/signal.txt', 'r').readlines()))

import numpy as np
import matplotlib.pyplot as plt

# 读取并处理信号数据
with open('./data/signal_filtered.txt', 'r') as file:
    signal_filtered = [complex(line.strip().replace(' ', '')) for line in file]

# 获取实部和虚部
signal_filtered_real = [z.real for z in signal_filtered]
signal_filtered_imag = [z.imag for z in signal_filtered]

# 绘制复平面
plt.figure(figsize=(8, 8))

# 原始信号复平面展示
plt.scatter(signal_filtered_real, signal_filtered_imag, color='b', label='Filtered Signal')
plt.axhline(0, color='grey', lw=0.5)
plt.axvline(0, color='grey', lw=0.5)
plt.grid(True, which='both', linestyle='--', lw=0.5)
plt.xlabel('Real Part')
plt.ylabel('Imaginary Part')
plt.title('Filtered Signal on Complex Plane')
plt.legend()

# 保存图表
plt.savefig('./img/signal_filtered_complex_plane.png')
plt.clf()





signal_compressed = open('./data/signal_compressed.txt', 'r').readlines()
signal_compressed = map(lambda x: x.replace(' ', '').replace('\n', ''), signal_compressed)
signal_compressed = list(map(complex, signal_compressed))

plt.plot(np.abs(signal_compressed), label='abs',mfc='w',color='b')
plt.legend()
plt.savefig('./img/signal_compressed.png')

plt.clf()






signal_reduced = open('./data/signal_reduced.txt', 'r').readlines()
signal_reduced = map(lambda x: x.replace(' ', '').replace('\n', ''), signal_reduced)
signal_reduced = list(map(complex, signal_reduced))

plt.plot(np.abs(signal_reduced), label='abs',mfc='w',color='b')
plt.legend()
plt.savefig('./img/signal_reduced.png')

plt.clf()







hilbert_coefficients = open('./data/hilbert_coefficients.txt', 'r').readlines()
hilbert_coefficients = map(lambda x: x.replace(' ', '').replace('\n', ''), hilbert_coefficients)
hilbert_coefficients = list(map(complex, hilbert_coefficients))

plt.xlim(0, 10)
plt.ylim(-1.2, 1.2)
plt.plot(np.real(hilbert_coefficients), label='real', marker='x', mfc='w',color='r')
plt.plot(np.imag(hilbert_coefficients), label='imag', marker='x', mfc='w',color='b')
plt.legend()
plt.savefig('./img/hilbert_coefficients.png')

plt.clf()









reference_signal = open('./data/reference_signal.txt', 'r').readlines()
reference_signal = map(lambda x: x.replace(' ', '').replace('\n', ''), reference_signal)
reference_signal = list(map(complex, reference_signal))

plt.ylim(-1.2, 1.2)
plt.plot(np.real(reference_signal), label='real' ,mfc='w',color='b')
plt.legend()
plt.savefig('./img/reference_signal.png')

plt.clf()