{
 "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",
    "\n",
    "import src.tcp_async as tcp_async\n",
    "import src.tcp_sync as tcp_sync\n",
    "import src.dab_util as du\n",
    "import src.dab_tuning_lib as dt\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(du)\n",
    "    reload(dt)\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": [
    "top.set_txgain(86)\n",
    "top.set_rxgain(10)"
   ]
  },
  {
   "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": [
    "sync.has_msg()\n",
    "async.has_msg()"
   ]
  },
  {
   "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": [
    "msgs = sync.get_msgs(1)\n",
    "msgs = [np.fft.fftshift(msg) for msg in msgs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def measure(param):\n",
    "    n_avg = 20\n",
    "    x2, x3, x4, x5, x6, x7, x8 = param\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(1)\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",
    "        sync.has_msg()\n",
    "        np.array(sync.get_msgs(0.8))\n",
    "        msgs = np.array(sync.get_msgs(n_avg))\n",
    "        scores = np.zeros(n_avg)\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=True, debug_path=\"/tmp/out\", suffix=suffix)\n",
    "        for i in range(n_avg):\n",
    "            if i == 0:\n",
    "                scores[i] = dt.calc_signal_sholder_ratio(msgs[0], sampling_rate=8000000, debug=True, debug_path=\"/tmp/out\", suffix=suffix)\n",
    "            else:\n",
    "                scores[i] = dt.calc_signal_sholder_ratio(msgs[0], sampling_rate=8000000)\n",
    "                \n",
    "        score = np.mean(scores)\n",
    "        print(score, x2, x3, x4, x5, x6, x7, x8)\n",
    "        repeat = False\n",
    "        \n",
    "        return score"
   ]
  },
  {
   "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 [-1, 0, 1]:\n",
    "        new_par = list(pars)\n",
    "        new_par[i] = par + x * d \n",
    "        test_pars.append(new_par)\n",
    "    res = [func(par_new) for par_new in test_pars]\n",
    "    sel = np.argmax(res)\n",
    "    best_par = test_pars[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.set_txgain(86)\n",
    "top.set_rxgain(5)\n",
    "\n",
    "pars = np.zeros(7)\n",
    "\n",
    "for i in range(10000):\n",
    "    i_rand = np.random.randint(0, len(pars))\n",
    "    pars = simple_opt(pars, i_rand, 0.005, measure)\n"
   ]
  },
  {
   "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": [
    "top.set_txgain(85)\n",
    "\n",
    "params = []\n",
    "for x2 in np.linspace(-0.1, 0.1, num = 11):\n",
    "    for x3 in np.linspace(-0.1, 0.1, num = 11):\n",
    "        for x4 in np.linspace(-0.1, 0.1, num = 11):\n",
    "            params.append((x2, x3, x4))\n",
    "            \n",
    "t_start = time.time()\n",
    "for idx, param in enumerate(params):\n",
    "    measure(param)\n",
    "    time_per_element = (time.time() - t_start) / (idx + 1)\n",
    "    print (\"Time per Element \" + str(time_per_element) +\n",
    "           \", total: \" + str(time_per_element * len(params)),\n",
    "           \", left: \" + str(time_per_element * (len(params) - 1 - idx))\n",
    "          )"
   ]
  },
  {
   "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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