import numpy as np
#import sounddevice as sd
import matplotlib.pyplot as plt

def normalize_signal(signal):
    min_amp = np.min(signal)
    normalized_signal = signal - min_amp
    max_amp = np.max(normalized_signal)
    normalized_signal *= 2.0/max_amp
    normalized_signal -= 1
    return normalized_signal

f = 466.16
f_s = 16000
T_0 = 1/f

t = np.arange(0,T_0,1/f_s)
s = 0.5 * np.sin(2*np.pi*f*t)

# Convert signal covariance
var_s = np.cov(s)

# Calculate required noise variance
var_snr_10 = var_s/(10**(10/10))
var_snr_20 = var_s/(10**(20/10))
var_snr_30 = var_s/(10**(30/10))

# Genearate noise
noise_snr_10 = (var_snr_10**0.5) * np.random.randn(len(s))
noise_snr_20 = (var_snr_20**0.5) * np.random.randn(len(s))
noise_snr_30 = (var_snr_30**0.5) * np.random.randn(len(s))

# Add signal and noise
m_snr_10 = s+noise_snr_10
m_snr_20 = s+noise_snr_20
m_snr_30 = s+noise_snr_30

plt.plot(t,s, label="Pure A$\sharp$ Tone")
plt.plot(t,m_snr_10, label="Corrupted A$\sharp$ Tone (SNR=10dB)")
plt.plot(t,m_snr_20, label="Corrupted A$\sharp$ Tone (SNR=20dB)")
plt.plot(t,m_snr_30, label="Corrupted A$\sharp$ Tone (SNR=30dB)")
plt.legend()
plt.title("Pure and Corrupted A$\sharp$ Tones")
plt.xlabel("Time (s)")
plt.show()
#sd.play(normalize_signal(s), samplerate=f_s, blocking=True)