nihalpasham

code = [0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 
        0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 
        0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 
        1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 
        0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 
        1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 
        1, 1, 1, 1, 1, 1, 1]

degree = 7
# print(u)

nihalpasham

import numpy as np
import matplotlib.pyplot as plt 
from matplotlib.pyplot import step, xlim, ylim, show

a = np.array([-1, -1, -1, -1, 1, -1, 1, -1, 1, 1, -1, -1, -1, 1, -1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, 1, -1, 1, -1, 1, -1, 1, -1, -1, -1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, -1, 1, 1, 1, -1, 1, -1, 1, 1, 1, -1, -1, 1, 1, 1, -1, -1, -1, 1, 1, -1, 1, 1, -1, -1, 1, 1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, 1, 1, 1, 1, 1, -1, 1, -1, -1, 1, 1, -1, 1, -1, -1, -1, 1, -1, 1, 1, 1, 1, -1, 1, 1, -1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1])

b = np.tile(a, 1)
x = np.arange(0, 127)

fig, (ax1) = plt.subplots(1, figsize=(15,4))

nihalpasham

import numpy as np
import matplotlib.pyplot as plt 
from matplotlib.pyplot import step, xlim, ylim, show

k = 16
a = np.array([-1, -1, -1, -1, 1, -1, 1, -1, 1, 1, -1, -1, -1, 1, -1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, 1, -1, 1, -1, 1, -1, 1, -1, -1, -1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, -1, 1, 1, 1, -1, 1, -1, 1, 1, 1, -1, -1, 1, 1, 1, -1, -1, -1, 1, 1, -1, 1, 1, -1, -1, 1, 1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, 1, 1, 1, 1, 1, -1, 1, -1, -1, 1, 1, -1, 1, -1, -1, -1, 1, -1, 1, 1, 1, 1, -1, 1, 1, -1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1])

# code-sequence
b = np.tile(a, 1)
x = np.arange(0, 127)

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number_of_code_samples = len(us1)
print(len(us1))

correlation_magnitude_buffer = []
squared_correlation_magnitude_buffer = []

# so that we dont overshoot when sliding the code signal forward
range_ = (int(number_of_complex_samples/number_of_code_samples)-1)*number_of_code_samples

nihalpasham

import numpy as np
import os
from scipy.io import wavfile
import matplotlib.pyplot as plt 

file_path = "/Users/Nil/devspace/Github_repos/alltheFSKs/iPhone_MFSK_Gpay_samples/iPhone_stereo_r_48khz_raw_c4755658_frequency_translated_lowpass_filtered.wav"

fs, data = wavfile.read(file_path)

# print(fs)

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h3 = sorted((v,i) for (v,i) in enumerate(squared_correlation_magnitude_buffer) if (i > 125) and ( v < 4000))
h4 = max([i for v,i in h3])

print(h3)
print(h4)

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i = 3844

peaks = []

while True:
    
    v = max(normalized_correlation_magnitude_buffer[i-200 : i+200])

    s, = np.where(np.isclose(normalized_correlation_magnitude_buffer, v, rtol = 1e-09)) 

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x = np.linspace(0, 42.33, 2032)
y = np.sin(2 * np.pi * ((4+0)/42.33) * x)

print(len(x))
print(len(y))

z = np.multiply(y, us1)

mot = np.multiply(z, us1)

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f = signal.resample(mot, 512)

N = 512
# sample spacing
T = 1.0 / 12000.0
yf = fft(f)
xf = fftfreq(N, T)
xf = fftshift(xf)
yplot = fftshift(yf)

nihalpasham

from scipy.fftpack import fft, fftfreq, fftshift
from scipy import signal
from scipy.signal import butter,filtfilt


k = 38389                               # one of the peaks chosen randomly 
sample_rate = 48000
nyq_rate = sample_rate / 2.0
order = 9