import matplotlib.pyplot as plt

import numpy as np

import scipy.signal as signal

import scipy.io.wavfile as wav

from scipy.fftpack import fft, fftfreq, fftshift

import os



wav_path = "SDRSharp_20201118_173512Z_403100kHz_IQ.wav"

fs, data = wav.read(wav_path, mmap=True)

data_iq = data[:, 0] + 1j * data[:, 1]



plt.specgram(data_iq, NFFT=1024, Fs=fs)

plt.title(os.path.basename(wav_path))

plt.xlabel("Time")

plt.ylabel("Frequency")

plt.show() 

sig_freq_hz = 100000

sig_new_center_hz = 30000

sig_bandwith_hz = 10000

freq = sig_freq_hz - sig_new_center_hz

fc = np.exp(-1.0j * 2.0 * np.pi * freq / fs * np.arange(len(data_iq)))

y = data_iq * fc



def butter_bandpass(lowcut, highcut, fs, order=5):

    nyq = 0.5 * fs

    low = lowcut / nyq

    high = highcut / nyq

    b, a = signal.butter(order, [low, high], btype='band')

    return b, a



def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):

    b, a = butter_bandpass(lowcut, highcut, fs, order=order)

    y = signal.lfilter(b, a, data)

    return y



real = y.real

real_filteded = butter_bandpass_filter(real, sig_new_center_hz - sig_bandwith_hz, sig_new_center_hz + sig_bandwith_hz, fs, order=4)

resample = 4

if resample > 1: real_filteded = real_filteded[::resample]



wav.write(wav_path.replace(".wav", "-converted.wav"), fs//resample, real_filteded.astype(np.int16))

print("WAV saved")