wuRx_packet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3202ec29",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import scipy.io\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import ticker, cm\n",
    "from matplotlib.colors import LogNorm\n",
    "from matplotlib.ticker import (AutoMinorLocator, MultipleLocator)\n",
    "import importlib\n",
    "import random\n",
    "from scipy import signal\n",
    "from scipy.fft import fft, fftfreq, fftshift\n",
    "from scipy.stats import chi2, ncx2, bernoulli, binom, ncf, norm\n",
    "from scipy.signal import firwin, firwin2\n",
    "from scipy import special\n",
    "\n",
    "\n",
    "from wuRx_lib import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "438114f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def floorc (x):\n",
    "    return (np.floor(np.real(x)) +1j*np.floor(np.imag(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c98d20d",
   "metadata": {},
   "source": [
    "# Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d129b2f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-53.75\n"
     ]
    }
   ],
   "source": [
    "ovs = 16\n",
    "sto_test = -54.0   + 4/ovs\n",
    "cfo      = 54.0  + 6/ovs\n",
    "\n",
    "bypassFilter = True\n",
    "print(sto_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19f805cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "Nup    = 10\n",
    "Ndown  = 2.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c664b431",
   "metadata": {},
   "outputs": [],
   "source": [
    "network_id = np.array([10, 25])\n",
    "symbols    = np.array([24,33,27])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6ad63f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-53.625\n",
      "53100.5859375\n"
     ]
    }
   ],
   "source": [
    "fs=125e3 * ovs\n",
    "Ts = 1 /fs\n",
    "B=125e3 \n",
    "nonIdealRandom=False \n",
    "theta=np.pi/6 \n",
    "sfo_ppm =0 \n",
    "\n",
    "signedMag = False\n",
    "\n",
    "SF=             7\n",
    "SF_MAX=         12\n",
    "SF_MIN=         4\n",
    "\n",
    "NSF     = 2**SF\n",
    "halfNSF = int(NSF/2)\n",
    "##########################\n",
    "# Precision parameters\n",
    "##########################\n",
    "# Input number of bits (from ADC)\n",
    "N=            12\n",
    "Q=            11\n",
    "# Offset applied before dechirping\n",
    "offset=       0\n",
    "# Dechirped number of bits \n",
    "Ncore=        12\n",
    "Qcore=        11\n",
    "# Constant number of bits (downchirp and TF)\n",
    "Nc=           12\n",
    "Qc=           11\n",
    "# Output number of bits\n",
    "No=           16\n",
    "Qo=           15\n",
    "\n",
    "add_noise = False\n",
    "fix_noise = True\n",
    "noise_only= False\n",
    "###################\n",
    "noise_std = 0.1\n",
    "A         = 0.5\n",
    "###################\n",
    "\n",
    "\n",
    "snr_test     = np.array([-4])\n",
    "\n",
    "\n",
    "send_downchirp = False\n",
    "\n",
    "init_offset_r = 0\n",
    "init_offset_i = 0\n",
    "\n",
    "delay_input_start = 2\n",
    "sto = (delay_input_start/ovs)+(not bypassFilter)*0.5+sto_test\n",
    "print(sto)\n",
    "print(cfo*B/NSF)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52ae8c51",
   "metadata": {},
   "source": [
    "# Input data Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f096fedd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_packet(Nup=1, Ndown=1,network_id=[],symbols=[],SF=7,fs=125e3,B=125e3,  theta=0, cfo =0, sto =0, A=1):\n",
    "    N = 2**SF\n",
    "    n_os = int(fs//B)\n",
    "    N_os = N*n_os\n",
    "\n",
    "    Ts     = 1/fs\n",
    "\n",
    "    T_symbol     = n_os*2**SF*Ts\n",
    "\n",
    "    polyphase = -int(np.round(sto*n_os))\n",
    "\n",
    "    Nup_sample     = int(np.round(n_os*Nup   *N))\n",
    "    Ndown_sample   = int(np.round(n_os*Ndown *N))\n",
    "\n",
    "    N_symb       = len(symbols)\n",
    "    N_network_id = len(network_id)\n",
    "    N_sample = Nup_sample+Ndown_sample+(N_symb+N_network_id)*N_os\n",
    "    N_sample_padded = N_sample+np.abs(polyphase)\n",
    "\n",
    "    signal = np.zeros((N_sample_padded), dtype =np.complex_)\n",
    "\n",
    "    time_upchirp = np.linspace(0,stop = N_os*Ts,num = N_os,endpoint=False)\n",
    "    time_symbols = np.linspace(0,stop = N_sample_padded*Ts,num = N_sample_padded,endpoint=False) \n",
    "    upchirp_ref   = A*np.exp(2*1j*np.pi*B* ( (np.square(time_upchirp))/(2*T_symbol) - 0.5*(time_upchirp) ))\n",
    "\n",
    "    index=0\n",
    "\n",
    "    Nup_sample_count= Nup_sample\n",
    "    while(Nup_sample_count>0):\n",
    "        if(Nup_sample_count >= N_os):\n",
    "            signal[index   : index+N_os]  = upchirp_ref\n",
    "            index+= N_os\n",
    "            Nup_sample_count -= N_os\n",
    "        else : \n",
    "            signal[index   : index+Nup_sample_count]  = upchirp_ref[:Nup_sample_count]\n",
    "            index+= Nup_sample_count\n",
    "            Nup_sample_count = 0\n",
    "\n",
    "    for i, nid in enumerate(network_id):   \n",
    "        signal[index+ i*N_os : index + (i+1)*N_os]       = np.roll(upchirp_ref,-n_os*nid)\n",
    "    index += N_network_id*N_os\n",
    "\n",
    "    Ndown_sample_count= Ndown_sample\n",
    "    downchirp = np.conjugate(upchirp_ref)\n",
    "    while(Ndown_sample_count>0):\n",
    "        if(Ndown_sample_count >= N_os):\n",
    "            signal[index   : index+N_os]  = downchirp\n",
    "            index += N_os\n",
    "            Ndown_sample_count -= N_os\n",
    "        else : \n",
    "            signal[index   : index+Ndown_sample_count]  = downchirp[:Ndown_sample_count]\n",
    "            index += Ndown_sample_count\n",
    "            Ndown_sample_count =0\n",
    "\n",
    "    for i, symb in enumerate(symbols):   \n",
    "        signal[index+ i*N_os : index + (i+1)*N_os]       = np.roll(upchirp_ref,-n_os*symb)\n",
    "\n",
    "    upchirp_cfo_theta = A* signal * np.exp(1j*theta) * np.exp(2*1j*np.pi*B* time_symbols*(cfo)/N)\n",
    "\n",
    "    upchirp_cfo_theta_sto = np.roll(upchirp_cfo_theta, polyphase)\n",
    "    return upchirp_cfo_theta_sto[:N_sample]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fbd0452",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "in_array = generate_packet(Nup=Nup, Ndown=Ndown,network_id=network_id,symbols=symbols,SF=SF,fs=fs,B=B,  theta=theta, cfo =cfo, sto =sto, A=A)\n",
    "\n",
    "t=  np.linspace(start = 0, stop=NSF*Ts, num=NSF, endpoint=False)\n",
    "\n",
    "in_array          = (np.real(in_array)   + init_offset_r/(2**Q)) +1j*  (np.imag(in_array)   + init_offset_i/(2**Q))\n",
    "downchirp = np.conjugate(LoRa_refchirp(SF=SF,fs=B,B=B))\n",
    "\n",
    "if send_downchirp:\n",
    "    in_array = np.conjugate(in_array)\n",
    "    downchirp= np.conjugate(downchirp)\n",
    "\n",
    "plot_in = True  \n",
    "if plot_in:\n",
    "    figsize=(20,4)\n",
    "    plt.figure(figsize=figsize)\n",
    "    plt.plot(np.arange(len(in_array)),np.real(in_array), color = \"orange\", label = \"Real\")\n",
    "    plt.plot(np.arange(len(in_array)),np.imag(in_array), color = \"red\"   , label = \"Imag\")\n",
    "    plt.title(\"Input vector\")\n",
    "    plt.xlabel(\"Time\")\n",
    "    plt.ylabel(\"Amplitude\")\n",
    "\n",
    "in_scaled, to_plot, quantified = quantif_signedMag(in_array,N,Q)\n",
    "in_sca , to_p , quantif        = quantif_twoComplement(in_array,N,Q)\n",
    "\n",
    "format = \"%.\"+str(N//4)+\"x\"\n",
    "if signedMag:\n",
    "    np.savetxt(\"testvec_files/wuRx_inputs.txt\",quantified,fmt=format)\n",
    "    inputs_real_mixed = read_IQ_outputs_interleaved(\"testvec_files/wuRx_inputs.txt\",N)\n",
    "else:\n",
    "    np.savetxt(\"testvec_files/wuRx_inputs.txt\",quantif ,fmt=format)\n",
    "    inputs_real_mixed = read_IQ_outputs_interleaved_2s (\"testvec_files/wuRx_inputs.txt\",N)\n",
    "\n",
    "\n",
    "if plot_in:\n",
    "    figsize=(20,4)\n",
    "    plt.figure(figsize=figsize)\n",
    "    real_part = np.real(inputs_real_mixed)\n",
    "    imag_part = np.imag(inputs_real_mixed)\n",
    "    plt.plot(np.arange(len(real_part)),real_part, color = \"orange\", label = \"Real\", marker=\".\")\n",
    "    plt.plot(np.arange(len(real_part)),imag_part, color = \"red\"   , label = \"Imag\", marker=\".\")\n",
    "    plt.plot(np.arange(len(real_part)),np.ones(len(real_part))*np.mean(real_part), color = \"orange\", linestyle =\"dotted\"   , label = \"Real\")\n",
    "    plt.plot(np.arange(len(real_part)),np.ones(len(real_part))*np.mean(imag_part), color = \"red\"   , linestyle =\"dotted\"   , label = \"Imag\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fd9c022",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1-0j)\n",
      "(-0.9996988186962042+0.02454122852291277j)\n"
     ]
    }
   ],
   "source": [
    "TF_scaled, TF_toCore= quantify_twiddle_factor(NFFT=NSF,Ncore=Nc,Qcore=Qc,path_rtl_file=\"./rtl_files/TF_sim.inc\", signedMag=signedMag)\n",
    "quantify_twiddle_factor(NFFT=2**SF_MAX,Ncore=Nc,Qcore=Qc,path_rtl_file=\"./rtl_files/TF_SFs_sim.inc\", signedMag=signedMag)\n",
    "format = \"%.\"+str(Nc//4)+\"x\"\n",
    "np.savetxt(\"testvec_files/twiddleFactor.txt\",TF_toCore,fmt=format)\n",
    "twiddleFactor_f = np.zeros(NSF, dtype =np.complex_)\n",
    "twiddleFactor_f[0     :NSF//2] =  TF_scaled[0:NSF//2]\n",
    "twiddleFactor_f[NSF//2:NSF ]   = -TF_scaled[0:NSF//2]\n",
    "\n",
    "downc_iq_file, downc_core = quantify_downchirp (SF=SF,                      fs=B,B=B,Ncore=Nc,Qcore=Qc,path_rtl_file=\"./rtl_files/downchirp_sim.inc\" , signedMag=signedMag)\n",
    "quantify_downchirps(SF_min=SF_MIN,SF_max=SF_MAX,fs=fs,B=B,Ncore=Nc,Qcore=Qc,path_rtl_file=\"./rtl_files/downchirp_SFs_sim.inc\", signedMag=signedMag )\n",
    "format = \"%.\"+str(Nc//4)+\"x\"\n",
    "np.savetxt(\"testvec_files/downchirp.txt\",downc_core,fmt=format)\n",
    "\n",
    "print(downchirp[0])\n",
    "print(downchirp[1])\n",
    "\n",
    "if send_downchirp:\n",
    "    downc_iq_file = np.conjugate(downc_iq_file)\n",
    "\n",
    "in_array_del          = np.insert(in_array         , np.zeros(delay_input_start, dtype=np.int32), np.zeros(delay_input_start))[:len(in_array)]\n",
    "inputs_real_mixed_del = np.insert(inputs_real_mixed, np.zeros(delay_input_start, dtype=np.int32), np.zeros(delay_input_start))[:len(inputs_real_mixed)]\n",
    "\n",
    "offset_r = 0\n",
    "offset_i = 0\n",
    "\n",
    "inputs_real_mixed_off = (np.real(inputs_real_mixed_del) + offset_r) +1j*  (np.imag(inputs_real_mixed_del) + offset_i)\n",
    "in_array_off          = (np.real(in_array_del)   + offset_r/(2**Q)) +1j*  (np.imag(in_array_del)   + offset_i/(2**Q))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1417a935",
   "metadata": {},
   "source": [
    "## Simulation of FFT results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e942c8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "est_sto = 0#4\n",
    "factorScale = 1\n",
    "\n",
    "#in_array_off_R          = in_array_off         [est_sto::ovs]/factorScale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "025210e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello\n"
     ]
    }
   ],
   "source": [
    "if bypassFilter:\n",
    "    in_array_off_R          = in_array_off[est_sto::ovs]/factorScale\n",
    "    inputs_real_mixed_off_R = inputs_real_mixed_off[est_sto::ovs]/factorScale\n",
    "    print(\"Hello\")\n",
    "else :\n",
    "    in_array_off_ext = np.roll(np.append(in_array_off,np.zeros(len(taps)-1)),est_sto)\n",
    "    in_array_off_filt= scipy.signal.correlate(in_array_off_ext,taps,mode=\"valid\",method='auto')\n",
    "    in_array_off_R   = in_array_off_filt[::ovs]\n",
    "\n",
    "    inputs_real_mixed_off_ext = np.roll(np.append(inputs_real_mixed_off,np.zeros(len(taps_q)-1)),est_sto)\n",
    "    inputs_real_mixed_off_filt= scipy.signal.correlate(inputs_real_mixed_off_ext,taps_q/(2**q_bits),mode=\"valid\",method='direct')\n",
    "    inputs_real_mixed_off_R   = inputs_real_mixed_off_filt[::ovs]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5fad2cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "use_twiddle   = True\n",
    "stoEstBinEval = 1\n",
    "cfoEstBin     = 2\n",
    "Nc_fcfo       = 3\n",
    "\n",
    "correct_fcfo_idx = [Nc_fcfo-1]\n",
    "correct_lsto_idx = [4]\n",
    "correct_fsto_idx = [4,12]\n",
    "net_ID_idx       = [9,10] \n",
    "downchirp_dem_idx= [11,12] \n",
    "estimate_l_idx   = [12]\n",
    "\n",
    "downchirp_quantity = 2.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78830e2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def estimate_fcfo(data, maxData,NSF,Nc,cfoEstBin):\n",
    "    cfoEst = 0\n",
    "    for l in range(1,Nc+1):\n",
    "        #print(data[l,(maxData[l])%NSF])\n",
    "        for p in range(-cfoEstBin,cfoEstBin+1): \n",
    "            val   = data[l,(maxData[l]+p)%NSF] * np.conjugate(data[l-1,(maxData[l-1]+p)%NSF])\n",
    "            cfoEst   += val \n",
    "            \n",
    "    return np.angle(cfoEst)/(2*np.pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d3277c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def estimate_fsto(NSF, datam1,data0,datap1, maxIdx ):\n",
    "    M_est = NSF - maxIdx\n",
    "\n",
    "    twiddle  = np.exp(-1j*2*np.pi*M_est/NSF)\n",
    "    twiddleC = np.exp( 1j*2*np.pi*M_est/NSF)\n",
    "    return - np.real( (twiddleC*datap1 - twiddle*datam1) / (2*data0 - twiddleC*datap1 - twiddle*datam1))\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc0698e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def estimate_L(NSF, sup, sdown ):\n",
    "    k = (sup + sdown) %NSF\n",
    "    if k < int(NSF//2):\n",
    "        Lcfo = (k%NSF)//2\n",
    "    else : \n",
    "        Lcfo = ((k-NSF)%NSF)//2\n",
    "\n",
    "    Lsto = (sup - Lcfo)%NSF\n",
    "    return Lcfo, Lsto\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c46db1e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2: Estimated fCFO 0.31\n",
      "3: Estimated fSTO -0.16, poly : 3\n",
      "10: Network IDs: [10. 25.]\n",
      "12: sup  0 \t sdown 108\n",
      "12: LCFO 54 \t  LSTO 74\n",
      "13: Estimated fSTO 0.47, poly : -8\n"
     ]
    }
   ],
   "source": [
    "Nsamples = len(in_array_off)\n",
    "Nconsumed_samples = 0\n",
    "current_index   = est_sto\n",
    "Ntrials_eval = int(np.round(len(in_array_off) /NSF))\n",
    "res_fft        = np.zeros((Ntrials_eval,NSF) , dtype=np.complex_)\n",
    "maxIndex_iter  = np.zeros((Ntrials_eval), dtype = np.int64)\n",
    "stoEval_iter   = np.zeros((Ntrials_eval))\n",
    "\n",
    "sdown = 0\n",
    "sup   = 0\n",
    "\n",
    "network_id_est = np.zeros((len(net_ID_idx)))\n",
    "network_id_index= 0\n",
    "downc_id_est = np.zeros((len(net_ID_idx)))\n",
    "downc_id_index= 0\n",
    "\n",
    "symbols_est = []\n",
    "\n",
    "fcfo_correction=0\n",
    "Lcfo = 0\n",
    "index_trials=0\n",
    "stoDataBufferEval = np.zeros((2*stoEstBinEval+1), dtype=np.complex_)\n",
    "\n",
    "correct_fcfo = False\n",
    "correct_lsto = False\n",
    "correct_fsto = False\n",
    "waiting_nid  = False\n",
    "starting_nid = False\n",
    "get_nid      = False\n",
    "downchirping = False \n",
    "est_symbol   = False\n",
    "finished_upchirp = False\n",
    "first_symbol = False\n",
    "first_downchirp = False\n",
    "\n",
    "current_idx = 0\n",
    "\n",
    "data_in = np.copy(in_array_off)\n",
    "t_cfo = np.linspace(0,stop = NSF*(1/B),num = NSF,endpoint=False) \n",
    "maxVal = 0\n",
    "\n",
    "while(current_index+NSF*ovs <=  Nsamples): \n",
    "    input_samples =  data_in[current_index:current_index+(NSF*ovs):ovs]*np.exp(2j*np.pi*B*(-Lcfo+fcfo_correction)*t_cfo/NSF)\n",
    "    if downchirping :\n",
    "        dechirped_samples     = input_samples*np.conjugate(downchirp)\n",
    "    else : \n",
    "        dechirped_samples     = input_samples*downchirp\n",
    "    dataFFT = fft(dechirped_samples)\n",
    "    res_fft[index_trials,:] = dataFFT\n",
    "    ## Get power spectral density\n",
    "    dataPSD = np.abs(dataFFT) \n",
    "    argMaxData = np.argmax(dataPSD)\n",
    "    maxIndex_iter[index_trials] = argMaxData\n",
    "\n",
    "    if index_trials == Nc_fcfo-1 :\n",
    "        correct_fcfo = True\n",
    "        \n",
    "    if est_symbol:\n",
    "        symbols_est.append(argMaxData)\n",
    "\n",
    "    if not finished_upchirp: \n",
    "        sup = argMaxData\n",
    "        maxVal = dataPSD[argMaxData]\n",
    "    \n",
    "    ############################\n",
    "    # Frac CFO \n",
    "    ############################\n",
    "    if correct_fcfo and (current_idx != index_trials):\n",
    "        fcfo_eval = estimate_fcfo(res_fft, maxIndex_iter,NSF,Nc_fcfo,cfoEstBin)\n",
    "        print(\"%d: Estimated fCFO %.2f\" %(index_trials,fcfo_eval))\n",
    "        fcfo_correction = -fcfo_eval\n",
    "        correct_lsto = True\n",
    "        correct_fsto = True\n",
    "        correct_fcfo = False\n",
    "        current_idx = index_trials\n",
    "    ############################\n",
    "    # Integer STO \n",
    "    ############################\n",
    "    if correct_lsto and (current_idx != index_trials):\n",
    "        current_index+= ovs*(NSF-argMaxData)\n",
    "        correct_lsto = False\n",
    "    ############################\n",
    "    # Frac STO \n",
    "    ############################\n",
    "    lambda_sto_eval = estimate_fsto(NSF,dataFFT[(argMaxData-1)%NSF],dataFFT[(argMaxData)%NSF],dataFFT[(argMaxData+1)%NSF],argMaxData)\n",
    "    polyphase = -int(np.round(lambda_sto_eval*16))\n",
    "    stoEval_iter[index_trials] = polyphase\n",
    "    if correct_fsto and (current_idx != index_trials):\n",
    "        print(\"%d: Estimated fSTO %.2f, poly : %d\" %(index_trials,lambda_sto_eval, polyphase))\n",
    "        current_index+= polyphase\n",
    "        if not est_symbol:\n",
    "            waiting_nid   = True\n",
    "            current_idx = index_trials\n",
    "            correct_fsto  = False\n",
    "        else:\n",
    "            correct_fsto = False\n",
    "    ############################\n",
    "    # Network IDs\n",
    "    ############################\n",
    "    if starting_nid and (current_idx != index_trials) :\n",
    "        if (dataPSD[argMaxData] > 0.5 * maxVal) and (np.abs((sup - argMaxData)) > 1 ) :\n",
    "            get_nid = True\n",
    "        if (get_nid):\n",
    "            network_id_est[network_id_index] = argMaxData\n",
    "            network_id_index+=1\n",
    "        if (network_id_index==2):\n",
    "            starting_nid= False\n",
    "            get_nid=False\n",
    "            downchirping = True\n",
    "            print(\"%d: Network IDs: %s\"%(index_trials,network_id_est))\n",
    "        current_idx = index_trials\n",
    "\n",
    "    elif waiting_nid and (current_idx != index_trials):\n",
    "        starting_nid = True\n",
    "        waiting_nid=False\n",
    "        finished_upchirp = True\n",
    "        current_idx = index_trials\n",
    "\n",
    "    ############################\n",
    "    # Downchirps\n",
    "    ############################\n",
    "        \n",
    "    if downchirping and (current_idx != index_trials):\n",
    "        downc_id_est[downc_id_index] = argMaxData\n",
    "        downc_id_index+=1\n",
    "        sdown = argMaxData\n",
    "        current_idx = index_trials\n",
    "\n",
    "        if first_downchirp:\n",
    "            Lcfo,Lsto = estimate_L(NSF, sup, sdown )\n",
    "            print(\"%d: sup  %d \\t sdown %d\" %(index_trials,sup,sdown))\n",
    "            print(\"%d: LCFO %d \\t  LSTO %d\" %(index_trials,Lcfo,Lsto))\n",
    "            est_symbol=True\n",
    "            downchirping=False\n",
    "            correct_fsto=True\n",
    "            first_symbol =True\n",
    "            current_index+= (int(-Lsto*ovs)+int(0.25*NSF*ovs))%(NSF*ovs)\n",
    "\n",
    "        first_downchirp = True\n",
    "\n",
    "    current_index = current_index +NSF*ovs\n",
    "    index_trials +=1\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3687adc5",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m res_fft_reshaped \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241m.\u001b[39mreshape(res_fft,  newshape\u001b[38;5;241m=\u001b[39m(Ntrials_eval,NSF))\n\u001b[0;32m      2\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m1\u001b[39m, figsize\u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m20\u001b[39m,\u001b[38;5;241m6\u001b[39m))\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(index_trials):\n",
      "\u001b[1;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "res_fft_reshaped = np.reshape(res_fft,  newshape=(Ntrials_eval,NSF))\n",
    "fig, ax = plt.subplots(1,1, figsize= (20,6))\n",
    "for j in range(index_trials):\n",
    "    ax.plot(np.arange(NSF),np.abs(res_fft[j,:]), color = listColor[j%(len(listColor))], marker=\".\")\n",
    "    ax.set_title(\"Output vector\")   \n",
    "    ax.set_xlabel(\"Time\")\n",
    "    ax.set_ylabel(\"Amplitude\")\n",
    "    \n",
    "print(\"##################### : MaxIdx\")\n",
    "print(maxIndex_iter[:index_trials])\n",
    "print(\"##################### : FSTO EVAL\")\n",
    "print(stoEval_iter[:index_trials])\n",
    "print(\"##################### : Network IDs\")\n",
    "print(network_id_est)\n",
    "print(\"##################### : Downchirps\")\n",
    "print(downc_id_est)\n",
    "print(\"##################### : Symbols\")\n",
    "print(symbols_est)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 681,
   "id": "5be64e95",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "testvec_files/post_fir.txt not found.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[681], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m post_fir  \u001b[38;5;241m=\u001b[39m \u001b[43mread_IQ_outputs_interleaved_2s\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtestvec_files/post_fir.txt\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mN\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      2\u001b[0m post_dech \u001b[38;5;241m=\u001b[39m read_IQ_outputs_interleaved_2s (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtestvec_files/post_dech.txt\u001b[39m\u001b[38;5;124m\"\u001b[39m,N)\n\u001b[0;32m      3\u001b[0m post_fft  \u001b[38;5;241m=\u001b[39m read_IQ_outputs_interleaved_2s (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtestvec_files/post_fft.txt\u001b[39m\u001b[38;5;124m\"\u001b[39m,No)\n",
      "File \u001b[1;32md:\\Pol\\Documents\\Research\\Embedded_LoRa\\lora_test\\wuRx_lib.py:178\u001b[0m, in \u001b[0;36mread_IQ_outputs_interleaved_2s\u001b[1;34m(file_path, N)\u001b[0m\n\u001b[0;32m    177\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread_IQ_outputs_interleaved_2s\u001b[39m(file_path,N):  \n\u001b[1;32m--> 178\u001b[0m     outputs_core \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloadtxt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43mdelimiter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mconverters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43ms\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m16\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mastype(np\u001b[38;5;241m.\u001b[39mint32)\n\u001b[0;32m    179\u001b[0m     outputs_core_ext \u001b[38;5;241m=\u001b[39m sign_extend(outputs_core, N)\n\u001b[0;32m    180\u001b[0m     outputs_core_iq \u001b[38;5;241m=\u001b[39moutputs_core_ext[\u001b[38;5;241m0\u001b[39m::\u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39mj\u001b[38;5;241m*\u001b[39m outputs_core_ext[\u001b[38;5;241m1\u001b[39m::\u001b[38;5;241m2\u001b[39m]\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\numpy\\lib\\npyio.py:1318\u001b[0m, in \u001b[0;36mloadtxt\u001b[1;34m(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin, encoding, max_rows, quotechar, like)\u001b[0m\n\u001b[0;32m   1315\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(delimiter, \u001b[38;5;28mbytes\u001b[39m):\n\u001b[0;32m   1316\u001b[0m     delimiter \u001b[38;5;241m=\u001b[39m delimiter\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlatin1\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m-> 1318\u001b[0m arr \u001b[38;5;241m=\u001b[39m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcomment\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcomment\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdelimiter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdelimiter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1319\u001b[0m \u001b[43m            \u001b[49m\u001b[43mconverters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconverters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskiplines\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskiprows\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43musecols\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43musecols\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1320\u001b[0m \u001b[43m            \u001b[49m\u001b[43munpack\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43munpack\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mndmin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mndmin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1321\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmax_rows\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_rows\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquote\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquotechar\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1323\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m arr\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\numpy\\lib\\npyio.py:955\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(fname, delimiter, comment, quote, imaginary_unit, usecols, skiplines, max_rows, converters, ndmin, unpack, dtype, encoding)\u001b[0m\n\u001b[0;32m    953\u001b[0m     fname \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mfspath(fname)\n\u001b[0;32m    954\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fname, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m--> 955\u001b[0m     fh \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_datasource\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrt\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    956\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m encoding \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    957\u001b[0m         encoding \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(fh, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mencoding\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlatin1\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\numpy\\lib\\_datasource.py:193\u001b[0m, in \u001b[0;36mopen\u001b[1;34m(path, mode, destpath, encoding, newline)\u001b[0m\n\u001b[0;32m    156\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    157\u001b[0m \u001b[38;5;124;03mOpen `path` with `mode` and return the file object.\u001b[39;00m\n\u001b[0;32m    158\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    189\u001b[0m \n\u001b[0;32m    190\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    192\u001b[0m ds \u001b[38;5;241m=\u001b[39m DataSource(destpath)\n\u001b[1;32m--> 193\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnewline\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\numpy\\lib\\_datasource.py:533\u001b[0m, in \u001b[0;36mDataSource.open\u001b[1;34m(self, path, mode, encoding, newline)\u001b[0m\n\u001b[0;32m    530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _file_openers[ext](found, mode\u001b[38;5;241m=\u001b[39mmode,\n\u001b[0;32m    531\u001b[0m                               encoding\u001b[38;5;241m=\u001b[39mencoding, newline\u001b[38;5;241m=\u001b[39mnewline)\n\u001b[0;32m    532\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 533\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: testvec_files/post_fir.txt not found."
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "post_fir  = read_IQ_outputs_interleaved_2s (\"testvec_files/post_fir.txt\",N)\n",
    "post_dech = read_IQ_outputs_interleaved_2s (\"testvec_files/post_dech.txt\",N)\n",
    "post_fft  = read_IQ_outputs_interleaved_2s (\"testvec_files/post_fft.txt\",No)\n",
    "\n",
    "N_reg_trials = int(np.round(len(post_fft) /NSF))\n",
    "\n",
    "post_fir    = post_fir  [:NSF*N_reg_trials]\n",
    "post_dech   = post_dech [:NSF*N_reg_trials]\n",
    "post_fft    = post_fft  [:NSF*N_reg_trials]\n",
    "post_fft = bitreverse_reorder(post_fft,NSF)\n",
    "print(np.shape(post_fir))\n",
    "print(np.shape(post_dech))\n",
    "print(np.shape(post_fft))\n",
    "\n",
    "\n",
    "\n",
    "post_fir_reshaped  = np.reshape(post_fir,   newshape=(N_reg_trials,NSF))\n",
    "post_dech_reshaped = np.reshape(post_dech,  newshape=(N_reg_trials,NSF))\n",
    "## Perform FFT with SciPy\n",
    "post_fir_dechirped = post_fir_reshaped*downchirp\n",
    "post_fir_FFT  = fft(post_fir_dechirped,axis=1)\n",
    "post_dech_FFT = fft(post_dech_reshaped,axis=1)\n",
    "post_fir_PSD  = np.abs(post_fir_FFT) \n",
    "post_dech_PSD = np.abs(post_dech_FFT) \n",
    "post_fft_PSD  = np.abs(post_fft) \n",
    "\n",
    "fig, ax = plt.subplots(1,1, figsize= (20,6))\n",
    "\n",
    "for j in range(N_reg_trials,0,-1):\n",
    "    ax.scatter(np.arange(NSF),post_fir_PSD[j-1,:] , color = listColor[j%(len(listColor))], marker=\".\")\n",
    "    ax.scatter(np.arange(NSF),post_dech_PSD[j-1,:], color = listColor[j%(len(listColor))], marker=\"*\")\n",
    "    ax.plot(np.arange(NSF),post_fft_PSD[j-1,:]/np.max(post_fft_PSD)*np.max(post_dech_PSD), color = listColor[j%(len(listColor))])\n",
    "    ax.set_title(\"Output vector\")   \n",
    "    ax.set_xlabel(\"Time\")\n",
    "    ax.set_ylabel(\"Amplitude\")\n",
    " \n",
    "ax.set_title(\"Output vector\")   \n",
    "ax.set_xlabel(\"Time\")\n",
    "ax.set_ylabel(\"Amplitude\")\n",
    "\n",
    "\n",
    "maxIndex_post = np.argmax(post_dech_PSD, axis=1)\n",
    "maxIndex_fft = np.argmax(post_fft_PSD, axis=1)\n",
    "print(np.max(post_fft_PSD,axis=1))\n",
    "print(maxIndex_fft)\n",
    "print(maxIndex_post)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ca73207",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3679 ===> 2 \n",
      "1146 ===> 1 \n",
      "-2504 ===> -1 \n",
      "10435 ===> 5 \n",
      "-3501 ===> -2 \n",
      "68 ===> 0 \n",
      "-6027 ===> -3 \n",
      "-12143 ===> -6 \n"
     ]
    }
   ],
   "source": [
    "stoEstBin = 1\n",
    "\n",
    "iter = N_reg_trials\n",
    "\n",
    "stoDataBuffer      = np.zeros((2*stoEstBin+1), dtype=np.complex_)\n",
    "\n",
    "for it in range(iter):\n",
    "    M_est = NSF - maxIndex_post[it]\n",
    "    for ip, p in enumerate(range(-stoEstBin,stoEstBin+1)): \n",
    "        stoDataBuffer[ip]      = floorc(post_fft[it,(maxIndex_fft[it]+p)%NSF]*4)\n",
    "\n",
    "    twiddle  = np.exp(-1j*2*np.pi*M_est/NSF)\n",
    "    twiddleC = np.exp( 1j*2*np.pi*M_est/NSF)\n",
    "    lambda_sto      = - np.real( (twiddleC*stoDataBuffer[2] - twiddle*stoDataBuffer[0]) / (2*stoDataBuffer[1] - twiddleC*stoDataBuffer[2] - twiddle*stoDataBuffer[0]))\n",
    "    num   = stoDataBuffer[2] - stoDataBuffer[0]\n",
    "    denum = stoDataBuffer[1] - stoDataBuffer[0] - stoDataBuffer[2]\n",
    "\n",
    "    print(\"%d ===> %d \"%(lambda_sto*(2**15),np.round(ovs*lambda_sto)))\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cc81cad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2521 ===> 1 \n",
      "2572 ===> 1 \n",
      "-2506 ===> -1 \n",
      "-2506 ===> -1 \n",
      "10433 ===> 5 \n",
      "-7285 ===> -4 \n",
      "-6098 ===> -3 \n",
      "-8750 ===> -4 \n"
     ]
    }
   ],
   "source": [
    "stoEstBin = 1\n",
    "\n",
    "iter = N_reg_trials\n",
    "\n",
    "stoDataBuffer      = np.zeros((2*stoEstBin+1), dtype=np.complex_)\n",
    "\n",
    "for it in range(iter):\n",
    "    M_est = 0 #NSF - maxIndex_post[Nc]\n",
    "    for ip, p in enumerate(range(-stoEstBin,stoEstBin+1)): \n",
    "        stoDataBuffer[ip]      = post_dech_FFT[it,(maxIndex_post[it]+p)%NSF]\n",
    "\n",
    "    twiddle  = np.exp(-1j*2*np.pi*M_est/NSF)\n",
    "    twiddleC = np.exp( 1j*2*np.pi*M_est/NSF)\n",
    "    lambda_sto      = - np.real( (twiddleC*stoDataBuffer[2] - twiddle*stoDataBuffer[0]) / (2*stoDataBuffer[1] - twiddleC*stoDataBuffer[2] - twiddle*stoDataBuffer[0]))\n",
    "    #print(lambda_sto*(2**15))\n",
    "    print(\"%d ===> %d \"%(lambda_sto*(2**15),np.round(ovs*lambda_sto)))\n",
    "\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aeec8cb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.4542889543786341\n",
      "0.03207743359121746\n"
     ]
    }
   ],
   "source": [
    "Nc = 3\n",
    "cfoEstBin = 2\n",
    "\n",
    "iter = min(2,Nsymbols-((Nc-1)))\n",
    "\n",
    "for it in range(iter):\n",
    "    cfoEst       = 0\n",
    "    cfoEst_float = 0\n",
    "    cfoEst_q31 = 0\n",
    "    for l in range(1,Nc):\n",
    "        for p in range(-cfoEstBin,cfoEstBin+1): \n",
    "            val_q31   = floorc(((post_fft[it+l,(maxIndex_fft[it+l]+p)%NSF]/(2**1)) * np.conjugate((post_fft[it+l-1,(maxIndex_fft[it+l-1]+p)%NSF])/(2**1)) ) )\n",
    "            cfoEst_q31   += val_q31 \n",
    "\n",
    "\n",
    "    print(np.angle(cfoEst_q31)*16/(2*np.pi) )\n"
   ]
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