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   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import time\n",
    "import scipy\n",
    "from scipy import signal\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mpcol\n",
    "import src.dab_util as du"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import src.signal_gen as sg\n",
    "reload(sg)\n",
    "reload(du)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "path_in  = \"./input.dat\"\n",
    "path_out = \"./output.dat\"\n",
    "a_max = 0.95\n",
    "n_steps = 64\n",
    "amps = np.linspace(0.001, a_max, num = n_steps)\n",
    "txgains = (50, 55, 60, 65, 70, 75, 81, 82, 83, 84, 85, 86, 87, 88, 89)\n",
    "rxgains = (50, 40, 40, 25, 25, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20)\n",
    "txgains = (75, 81, 84, 85, 88, 89)\n",
    "rxgains = (20, 20, 20, 20, 20, 20)\n",
    "txgains = (83,)\n",
    "rxgains = (20,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from grc.amam_ampm import amam_ampm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "sg.gen_ramps(path=\"./input.dat\", n_periods=64, pause=8, amplitudes=amps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "top = amam_ampm()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def extract_measurement(a_in, a_out, db, a_max, n_steps, debug = False):\n",
    "    a_in  = du.crop_signal(a_in)\n",
    "    a_out = du.crop_signal(a_out)\n",
    "    \n",
    "    if debug:\n",
    "        plt.plot(np.abs(a_in.real) + 1, color='b');\n",
    "        plt.plot(np.abs(a_out.real), color='g');\n",
    "        plt.show()\n",
    "    \n",
    "    #l = min(a_out.shape[0], a_in.shape[0])\n",
    "    #a_out = a_out[0:l]\n",
    "    #a_in  = a_in[0:l]\n",
    "    \n",
    "    #c = du.lagcorr(np.abs(a_out), np.abs(a_in), 120000)[:,0]\n",
    "    #c = signal.fftconvolve(a_in, a_out) - a_out.shape[0]\n",
    "    #delay = du.fftlag(np.abs(a_in), np.abs(a_out))\n",
    "    delay = du.fftlag(a_in, a_out) #TODO\n",
    "    \n",
    "    #delay = np.argmax(c)\n",
    "    a_out = a_out[delay - 1:]\n",
    "    \n",
    "    l = min(a_out.shape[0], a_in.shape[0])\n",
    "    a_out = a_out[0:l]\n",
    "    a_in  = a_in[0:l]\n",
    "    \n",
    "    if debug:\n",
    "        print (\"delay = \" + str(delay))\n",
    "        plt.plot(np.abs(a_in), color='g');\n",
    "        plt.plot(np.abs(a_out) - 0.5, color='y');\n",
    "        plt.show()\n",
    "    \n",
    "    bins = np.linspace(+0.5/n_steps,a_max + 0.5/n_steps,num=n_steps)\n",
    "    res = []\n",
    "    \n",
    "    a_out_up = scipy.signal.resample(a_out, a_out.shape[0] * 8)\n",
    "    a_in_up = scipy.signal.resample(a_in, a_in.shape[0] * 8)\n",
    "    \n",
    "    a_out_abs = np.abs(a_out_up)\n",
    "    a_in_abs = np.abs(a_in_up)\n",
    "    for ampl_1, ampl_2 in zip(bins, bins[1:]):\n",
    "        res.append(du.get_amp_ratio(ampl_1, ampl_2, a_out_abs, a_in_abs))\n",
    "    del a_out_abs\n",
    "    del a_in_abs\n",
    "    mean_amp, var_amp = zip(*res)\n",
    "    \n",
    "    res = []\n",
    "    for ampl_1, ampl_2 in zip(bins, bins[1:]):\n",
    "        res.append(du.get_phase(ampl_1, ampl_2, a_out_up, a_in_up))\n",
    "    mean_phase, var_phase = zip(*res)\n",
    "    return mean_amp, var_amp, mean_phase, var_phase, db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "reload(du)\n",
    "res = []\n",
    "\n",
    "for txgain, rxgain in zip(txgains, rxgains):\n",
    "    print (txgain, rxgain)\n",
    "    res_tmp = None\n",
    "    for i in range(10):\n",
    "        top.uhd_usrp_sink_0_0.set_gain(txgain)\n",
    "        top.uhd_usrp_source_0.set_gain(rxgain)\n",
    "        \n",
    "        top.file_sink_out.close()\n",
    "        top.blocks_file_source_0.close()\n",
    "        \n",
    "        top.file_sink_out.open(path_out)\n",
    "        top.blocks_file_source_0.open(path_in, False)\n",
    "        top.start()\n",
    "        \n",
    "        time.sleep(1)\n",
    "        \n",
    "        top.stop()\n",
    "        top.wait()\n",
    "        \n",
    "        a_in  = np.fromfile(path_in, dtype=np.complex64)\n",
    "        a_out = np.fromfile(path_out, dtype=np.complex64)\n",
    "        res_tmp = extract_measurement(a_in, a_out, txgain, a_max, n_steps, debug=True)\n",
    "        \n",
    "        def is_finite(r): return np.all([np.all(np.isfinite(c)) for c in r])\n",
    "        #def has_small_jumps(mean_amp): return np.max(np.abs(np.diff(mean_amp))) / np.median(np.abs(np.diff(mean_amp))) < 100\n",
    "        \n",
    "        #TODO\n",
    "        if is_finite(res_tmp): # and 1 + has_small_jumps(res_tmp[0]):\n",
    "            break\n",
    "        else:\n",
    "            print (is_finite(res_tmp))#, has_small_jumps(res_tmp[0]))\n",
    "        \n",
    "    res.append(res_tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
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  },
  {
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   "metadata": {
    "deletable": true,
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   "source": [
    "fig = plt.figure(figsize=(10,10))\n",
    "ax1 = plt.subplot(211)\n",
    "\n",
    "def plot_with_label(x, y, color, label):\n",
    "    ax1.plot(x, y, color=color, label=txgain)\n",
    "    \n",
    "for idx, (txgain, rxgain) in enumerate(zip(*(txgains, rxgains))):\n",
    "    plot_with_label(\n",
    "        x = amps[1:], \n",
    "        y = 10*np.log(res[idx][0])/np.log(10) - rxgain + 102,\n",
    "        color = mpcol.hsv_to_rgb((idx * 0.75 / len(txgains), 0.6, 1)),\n",
    "        label = txgain\n",
    "    )\n",
    "ax1.set_ylabel(\"Gain [dB]\")\n",
    "\n",
    "ax2 = plt.subplot(212)\n",
    "\n",
    "def plot_with_label(x, y, color, label):\n",
    "    ax2.plot(x, y, color=color, label=txgain)\n",
    "    \n",
    "for idx, (txgain, rxgain) in enumerate(zip(*(txgains, rxgains))):\n",
    "    plot_with_label(\n",
    "        x = amps[1:],\n",
    "        y = res[idx][2],\n",
    "        color = mpcol.hsv_to_rgb((idx * 0.75 / len(txgains), 0.6, 1)),\n",
    "        label = txgain\n",
    "    )\n",
    "\n",
    "ax2.set_ylabel(\"Pase [degree]\")\n",
    "ax2.set_xlabel(\"Amplitude\")\n",
    "\n",
    "#legend\n",
    "# Shrink current axis by 20%\n",
    "box = ax1.get_position()\n",
    "ax1.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "box = ax2.get_position()\n",
    "ax2.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "\n",
    "# Put a legend to the right of the current axis\n",
    "ax1.legend(loc='center left', bbox_to_anchor=(1.05, -0.3))\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
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