{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import time\n",
    "import src.gen_source as gen_source\n",
    "import src.two_tone_lib as tt\n",
    "import src.dab_tuning_lib as dt\n",
    "\n",
    "import src.tcp_async as tcp_async\n",
    "import src.tcp_sync as tcp_sync\n",
    "\n",
    "#from live_analyse_py import live_analyse_py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    __IPYTHON__\n",
    "    reload(tcp_async)\n",
    "    reload(tcp_sync)\n",
    "    reload(gen_source)\n",
    "    reload(tt)\n",
    "    reload(td)\n",
    "except:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "sync = tcp_sync.UhdSyncMsg(packet_size=4*8192,\n",
    "                           packet_type=\"\".join([\"f\"]*8192))\n",
    "async = tcp_async.UhdAsyncMsg()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "top = live_analyse_py()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "top.start()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "sync.has_msg()\n",
    "async.has_msg()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "top.set_txgain(85)\n",
    "top.set_rxgain(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "tt.gen_two_tone(debug = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "a = np.fromfile(\"./input.dat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def measure(param, debug = False):\n",
    "    #x2, x3, x4, x5, x6, x7, x8 = param\n",
    "    x1, x2, x3, x4, x5, x6, x7, x8 = param + [1]\n",
    "    \n",
    "    repeat = True\n",
    "    while repeat:\n",
    "        #tt.gen_two_tone(debug = True, predist=tt.predist_poly, par=(x2, x3, x4))\n",
    "        \n",
    "        #top.dpd_memless_poly_0.set_a1(0.8)\n",
    "        #top.dpd_memless_poly_0.set_a2(x2)\n",
    "        #top.dpd_memless_poly_0.set_a3(x3)\n",
    "        #top.dpd_memless_poly_0.set_a4(x4)\n",
    "        #top.dpd_memless_poly_0.set_a5(x5)\n",
    "        #top.dpd_memless_poly_0.set_a6(x6)\n",
    "        #top.dpd_memless_poly_0.set_a7(x7)\n",
    "        #top.dpd_memless_poly_0.set_a8(x8)\n",
    "        \n",
    "        top.dpd_lut_0.set_a1(x1)\n",
    "        top.dpd_lut_0.set_a2(x2)\n",
    "        top.dpd_lut_0.set_a3(x3)\n",
    "        top.dpd_lut_0.set_a4(x4)\n",
    "        top.dpd_lut_0.set_a5(x5)\n",
    "        top.dpd_lut_0.set_a6(x6)\n",
    "        top.dpd_lut_0.set_a7(x7)\n",
    "        top.dpd_lut_0.set_a8(1)\n",
    "        \n",
    "        sync.has_msg()\n",
    "        np.array(sync.get_msgs(3))\n",
    "        msgs = np.array(sync.get_msgs(5))\n",
    "        msgs = [np.fft.fftshift(msg) for msg in msgs]\n",
    "        \n",
    "        if async.has_msg():\n",
    "            print (\"repeat due to async message\")\n",
    "            continue\n",
    "            \n",
    "        a = np.array(msgs)\n",
    "        mean_msg = a.mean(axis = 0)\n",
    "        suffix = \"x_2_%.3f_x_3_%.3f_x_4_%.3fx_5_%.3fx_6_%.3fx_7_%.3fx_8_%.3f\" % \\\n",
    "                (x2, x3, x4, x5, x6, x7, x8)\n",
    "        sig_to_noise = tt.analyse_power_spec(mean_msg, debug=debug, debug_path=\"/tmp/out\", suffix=suffix)\n",
    "        #sig_to_noise = dt.calc_signal_sholder_ratio(mean_msg, sampling_rate=8000000, debug=debug, debug_path=\"/tmp/out\", suffix=suffix)\n",
    "        print(sig_to_noise, x2, x3, x4, x5, x6, x7, x8)\n",
    "        repeat = False\n",
    "        \n",
    "        return sig_to_noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def simple_opt(pars, i, d, func):\n",
    "    par = pars[i]\n",
    "    test_pars = []\n",
    "    for x in [-3, -2, -1, 0, 1, 2, 3]:\n",
    "        debug = x == 0\n",
    "        new_par = list(pars)\n",
    "        new_par[i] = par + x * d \n",
    "        test_pars.append([new_par, debug])\n",
    "    res = [func(par_new, debug) for par_new, debug in test_pars]\n",
    "    sel = np.argmax(res)\n",
    "    best_par = zip(*test_pars)[0][sel]\n",
    "    return best_par\n",
    "\n",
    "#pars = [1,1,1]\n",
    "#i_rand = np.random.randint(0, len(pars))\n",
    "#pars = simple_opt(pars, i_rand, 0.01, lambda x:np.sum(x))\n",
    "#pars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#top.blocks_file_source_0.open(\"../dab_normalized_c64.dat\", True)"
   ]
  },
  {
   "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": [
    "reload(dt)\n",
    "top.set_txgain(84)\n",
    "top.set_rxgain(5)\n",
    "\n",
    "pars = np.array(range(1,8))/8.\n",
    "\n",
    "for i in range(10000):\n",
    "    i_rand = np.random.randint(0, len(pars))\n",
    "    pars = simple_opt(pars, i_rand, 0.05, measure)"
   ]
  },
  {
   "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": [
    "sync.stop()\n",
    "async.stop()\n",
    "top.stop()\n",
    "top.wait()"
   ]
  },
  {
   "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": []
  },
  {
   "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": []
  }
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