{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "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\n",
    "import src.signal_gen as sg\n",
    "import src.tcp_sync as ts\n",
    "import src.dab_tuning_lib as dt\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "reload(sg)\n",
    "reload(du)\n",
    "reload(dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "use_fft=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "if use_fft: t1 = ts.UhdSyncMsg(port=47009, packet_size=4*16384, packet_type=\"f\"*16384)\n",
    "else: t1 = ts.UhdSyncMsg(port=47009, packet_size=4*1, packet_type=\"f\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import src.SendDictTcp as sdt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "sender = sdt.SendDictTcp('127.0.0.1', 1112)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "sender.send({\"txgain\":80})\n",
    "sender.send({\"rxgain\":15})\n",
    "sender.send({\"a1\":0.8})\n",
    "sender.send({\"a2\":0.0})\n",
    "sender.send({\"a3\":0.0})\n",
    "sender.send({\"a4\":0.0})\n",
    "sender.send({\"a5\":0.0})\n",
    "sender.send({\"a6\":0.0})\n",
    "sender.send({\"a7\":0.0})\n",
    "sender.send({\"a8\":0.0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "t1.has_msg()\n",
    "t = time.time()\n",
    "t1.get_msgs(10000)\n",
    "print( time.time() - t)\n",
    "t = time.time()\n",
    "t1.get_msgs(10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "reload(dt)\n",
    "sender.send({\"txgain\":81})\n",
    "sender.send({\"rxgain\":15})\n",
    "sender.send({\"a1\":0.8})\n",
    "sender.send({\"a2\":0.0})\n",
    "sender.send({\"a3\":0.0})\n",
    "sender.send({\"a4\":0.0})\n",
    "sender.send({\"a5\":0.0})\n",
    "sender.send({\"a6\":0.0})\n",
    "sender.send({\"a7\":0.0})\n",
    "sender.send({\"a8\":0.0})\n",
    "t1.has_msg()\n",
    "np.mean(t1.get_msgs(10000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "reload(dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def measure_sholders(verbose = False, raw_data=False, std_max=0.025):\n",
    "    for i in range(20):\n",
    "        try:\n",
    "            if verbose: print(\"%d measurement\" % i)\n",
    "            t1.has_msg()\n",
    "            msgs = t1.get_msgs_fft(20000)\n",
    "            #sholders = [dt.calc_signal_sholder_ratio(np.array(msg), 8000000) for msg in msgs]\n",
    "            #sholders = [dt.calc_max_in_freq_range(np.array(msg), 8000000, 1000000, 1900000, debug=False) for msg in msgs]\n",
    "            #sholders = [dt.calc_mean_in_freq_range(np.array(msg), 8000000, 1000000, 1900000, debug=False) for msg in msgs]\n",
    "            sholders = np.array(msgs)\n",
    "            std = np.std(sholders)\n",
    "            mean = np.mean(sholders)\n",
    "            std_perc = std/np.abs(mean)\n",
    "            if verbose == 2:\n",
    "                print( {\"mean\":mean, \"std\":std, \"std_perc\":std_perc})\n",
    "            if std_perc > std_max:\n",
    "                if verbose: print(\"%.4f std/mean\" % (std_perc))\n",
    "                continue\n",
    "            else:\n",
    "                if raw_data: return sholders\n",
    "                else: return np.mean(sholders)\n",
    "        except Exception as e:\n",
    "            print e\n",
    "    raise Exception(\"Variance of measurement to high\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "a = np.array(measure_sholders(verbose=2, std_max=100, raw_data=True)).flatten()"
   ]
  },
  {
   "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": [
    "b = a[a<0.04]\n",
    "plt.hist(b, bins=100);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from tqdm import tqdm_notebook\n",
    "\n",
    "sender.send({\"txgain\":83})\n",
    "sender.send({\"rxgain\":15})\n",
    "sender.send({\"a1\":0.8})\n",
    "\n",
    "res = []\n",
    "err = []\n",
    "t = time.time()\n",
    "for a2 in tqdm_notebook(np.linspace(-0.5, 0.5, num=10)):\n",
    "    for a3 in tqdm_notebook([0]):\n",
    "        for a4 in tqdm_notebook([0]):\n",
    "#for a2 in tqdm_notebook(np.linspace(-0.5, 0.5, num=21)):\n",
    "#    for a3 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n",
    "#        for a4 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n",
    "            try:\n",
    "                sender.send({\"a2\":a2})\n",
    "                sender.send({\"a3\":a3})\n",
    "                sender.send({\"a4\":a4})\n",
    "                losses = measure_sholders(verbose=0, std_max=100, raw_data=True)\n",
    "                for i, loss in enumerate(losses):\n",
    "                    res.append({\"a2\":a2, \"a3\":a3, \"a4\":a4, \"loss\":loss, \"run\":i})\n",
    "            except Exception as e:\n",
    "                err.append(e)\n",
    "        df = pd.DataFrame(res)\n",
    "        df.to_csv(\"/tmp/sholder_a2.csv\")\n",
    "print(time.time() - t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "res = []\n",
    "err = []\n",
    "t = time.time()\n",
    "for a2 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n",
    "    for a3 in tqdm_notebook(np.linspace(-0.5, 0.5, num=21)):\n",
    "        for a4 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n",
    "            try:\n",
    "                sender.send({\"a2\":a2})\n",
    "                sender.send({\"a3\":a3})\n",
    "                sender.send({\"a4\":a4})\n",
    "                losses = measure_sholders(raw_data=True, std=0.03)\n",
    "                for i, loss in enumerate(losses):\n",
    "                    res.append({\"a2\":a2, \"a3\":a3, \"a4\":a4, \"loss\":loss, \"run\":i})\n",
    "            except Exception as e:\n",
    "                err.append(e)\n",
    "        df = pd.DataFrame(res)\n",
    "        df.to_csv(\"/tmp/sholder_a3.csv\")\n",
    "print(time.time() - t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "res = []\n",
    "err = []\n",
    "t = time.time()\n",
    "for a2 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n",
    "    for a3 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n",
    "        for a4 in tqdm_notebook(np.linspace(-0.5, 0.5, num=21)):\n",
    "            try:\n",
    "                sender.send({\"a2\":a2})\n",
    "                sender.send({\"a3\":a3})\n",
    "                sender.send({\"a4\":a4})\n",
    "                losses = measure_sholders(raw_data=True, std=0.03)\n",
    "                for i, loss in enumerate(losses):\n",
    "                    res.append({\"a2\":a2, \"a3\":a3, \"a4\":a4, \"loss\":loss, \"run\":i})\n",
    "            except Exception as e:\n",
    "                err.append(e)\n",
    "        df = pd.DataFrame(res)\n",
    "        df.to_csv(\"/tmp/sholder_a4.csv\")\n",
    "print(time.time() - t)"
   ]
  },
  {
   "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": [
    "from tqdm import tqdm_notebook\n",
    "\n",
    "sender.send({\"txgain\":81})\n",
    "sender.send({\"rxgain\":15})\n",
    "sender.send({\"a1\":0.8})\n",
    "\n",
    "res = []\n",
    "err = []\n",
    "t = time.time()\n",
    "for a2 in tqdm_notebook(np.linspace(-0.5, 0.5, num=21)):\n",
    "    for a3 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n",
    "        for a4 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n",
    "            try:\n",
    "                sender.send({\"a2\":a2})\n",
    "                sender.send({\"a3\":a3})\n",
    "                sender.send({\"a4\":a4})\n",
    "                losses = measure_sholders(raw_data=True, std=0.03)\n",
    "                for i, loss in enumerate(losses):\n",
    "                    res.append({\"a2\":a2, \"a3\":a3, \"a4\":a4, \"loss\":loss, \"run\":i})\n",
    "            except Exception as e:\n",
    "                err.append(e)\n",
    "        df = pd.DataFrame(res)\n",
    "        df.to_csv(\"/tmp/sholder_a2_81.csv\")\n",
    "print(time.time() - t)\n",
    "\n",
    "res = []\n",
    "err = []\n",
    "t = time.time()\n",
    "for a2 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n",
    "    for a3 in tqdm_notebook(np.linspace(-0.5, 0.5, num=21)):\n",
    "        for a4 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n",
    "            try:\n",
    "                sender.send({\"a2\":a2})\n",
    "                sender.send({\"a3\":a3})\n",
    "                sender.send({\"a4\":a4})\n",
    "                losses = measure_sholders(raw_data=True, std=0.03)\n",
    "                for i, loss in enumerate(losses):\n",
    "                    res.append({\"a2\":a2, \"a3\":a3, \"a4\":a4, \"loss\":loss, \"run\":i})\n",
    "            except Exception as e:\n",
    "                err.append(e)\n",
    "        df = pd.DataFrame(res)\n",
    "        df.to_csv(\"/tmp/sholder_a3_81.csv\")\n",
    "print(time.time() - t)\n",
    "res = []\n",
    "err = []\n",
    "t = time.time()\n",
    "for a2 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n",
    "    for a3 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n",
    "        for a4 in tqdm_notebook(np.linspace(-0.5, 0.5, num=21)):\n",
    "            try:\n",
    "                sender.send({\"a2\":a2})\n",
    "                sender.send({\"a3\":a3})\n",
    "                sender.send({\"a4\":a4})\n",
    "                losses = measure_sholders(raw_data=True, std=0.03)\n",
    "                for i, loss in enumerate(losses):\n",
    "                    res.append({\"a2\":a2, \"a3\":a3, \"a4\":a4, \"loss\":loss, \"run\":i})\n",
    "            except Exception as e:\n",
    "                err.append(e)\n",
    "        df = pd.DataFrame(res)\n",
    "        df.to_csv(\"/tmp/sholder_a4_81.csv\")\n",
    "print(time.time() - t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from tqdm import tqdm_notebook\n",
    "\n",
    "sender.send({\"txgain\":85})\n",
    "sender.send({\"rxgain\":15})\n",
    "sender.send({\"a1\":0.8})\n",
    "\n",
    "res = []\n",
    "err = []\n",
    "t = time.time()\n",
    "for a2 in tqdm_notebook(np.linspace(-0.5, 0.5, num=21)):\n",
    "    for a3 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n",
    "        for a4 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n",
    "            try:\n",
    "                sender.send({\"a2\":a2})\n",
    "                sender.send({\"a3\":a3})\n",
    "                sender.send({\"a4\":a4})\n",
    "                losses = measure_sholders(raw_data=True, std=0.03)\n",
    "                for i, loss in enumerate(losses):\n",
    "                    res.append({\"a2\":a2, \"a3\":a3, \"a4\":a4, \"loss\":loss, \"run\":i})\n",
    "            except Exception as e:\n",
    "                err.append(e)\n",
    "        df = pd.DataFrame(res)\n",
    "        df.to_csv(\"/tmp/sholder_a2_85.csv\")\n",
    "print(time.time() - t)\n",
    "\n",
    "res = []\n",
    "err = []\n",
    "t = time.time()\n",
    "for a2 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n",
    "    for a3 in tqdm_notebook(np.linspace(-0.5, 0.5, num=21)):\n",
    "        for a4 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n",
    "            try:\n",
    "                sender.send({\"a2\":a2})\n",
    "                sender.send({\"a3\":a3})\n",
    "                sender.send({\"a4\":a4})\n",
    "                losses = measure_sholders(raw_data=True, std=0.03)\n",
    "                for i, loss in enumerate(losses):\n",
    "                    res.append({\"a2\":a2, \"a3\":a3, \"a4\":a4, \"loss\":loss, \"run\":i})\n",
    "            except Exception as e:\n",
    "                err.append(e)\n",
    "        df = pd.DataFrame(res)\n",
    "        df.to_csv(\"/tmp/sholder_a3_85.csv\")\n",
    "print(time.time() - t)\n",
    "res = []\n",
    "err = []\n",
    "t = time.time()\n",
    "for a2 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n",
    "    for a3 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n",
    "        for a4 in tqdm_notebook(np.linspace(-0.5, 0.5, num=21)):\n",
    "            try:\n",
    "                sender.send({\"a2\":a2})\n",
    "                sender.send({\"a3\":a3})\n",
    "                sender.send({\"a4\":a4})\n",
    "                losses = measure_sholders(raw_data=True, std=0.03)\n",
    "                for i, loss in enumerate(losses):\n",
    "                    res.append({\"a2\":a2, \"a3\":a3, \"a4\":a4, \"loss\":loss, \"run\":i})\n",
    "            except Exception as e:\n",
    "                err.append(e)\n",
    "        df = pd.DataFrame(res)\n",
    "        df.to_csv(\"/tmp/sholder_a4_85.csv\")\n",
    "print(time.time() - t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "sender.send({\"txgain\":20})\n",
    "sender.send({\"rxgain\":15})\n",
    "sender.send({\"a1\":0.1})"
   ]
  },
  {
   "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": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}