nihalpasham

#[derive(Debug)]
#[repr(C)]
/// Constructs a device-tree `node`, given a name and buffer. The buffer must be adequately sized.
pub struct RawNodeConstructor<'a> {
    fdt_begin_node: u32,
    node_name: &'a [u8],
}

impl<'a> RawNodeConstructor<'a> {
    pub fn make_raw_node(buf: &'a mut [u8], name: &'a str) -> Result<Self> {

nihalpasham

real_tokens = []
imag_tokens = []

for i in range(0, len(symbols_real)):
    if symbols_real[i] == 15:
        token = symbols_real[i:i+9]
        checksum = np.sum(token) % 16
        if checksum <= 4 or checksum >= 13 :
            q = (''.join([format(symbol, 'x') for symbol in token[1:7]]))
#             print(q)

nihalpasham

tokens_real = []
tokens_imag = []

for i in range(0, len(symbols_real)-1):
    if symbols_real[i] == 15:
        token = symbols_real[i:i+8]
        tokens_real.append(token)
        
for i in range(0, len(symbols_imag)-1):
    if symbols_imag[i] == 15:

nihalpasham

symbols_real = []
symbols_imag = []

base_freq = 100                     # This is in Hertz
symbol_rate = 23.6
num_tones = 16                      # This particular MFSK modulation contains 16 tones i.e. (500-100/23.6) =~ 16.95  
                                    # But its apparently odd to have an odd number of frequencies
                                    # So, I settled on '16'. Turns out that was right. 
tone_zero = int(round(base_freq/symbol_rate))       # the first or lowest tone in the sequence 

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

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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)

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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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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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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)

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)