ipython_notebooks/Interference Alignment - Min Leakage Algorithm.ipynb

{
 "metadata": {
  "name": "Interference Alignment - Min Leakage Algorithm"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Min Leakage IA Algorithm"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This notebook simulates the Minimum Leakage Interference Alignment algorithm."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Some initialization code"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# xxxxxxxxxx Add the parent folder to the python path. xxxxxxxxxxxxxxxxxxxx\n",
      "import sys\n",
      "import os\n",
      "pyphysim_folder = \"~/cvs_files/pyphysim/\"\n",
      "pyphysim_folder = os.path.expanduser(pyphysim_folder)\n",
      "sys.path.append(pyphysim_folder)\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "# Import the simulation runner\n",
      "from apps.simulate_ia_minleakage import MinLeakageSimulationRunner\n",
      "from util import simulations\n",
      "\n",
      "# We will use clear_output to erase the progressbar after the simulation has finished.\n",
      "from IPython.display import clear_output\n",
      "\n",
      "# We will use pprint to print the simulation parameters\n",
      "from pprint import pprint"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Run the algorithm with 120 iterations"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The configuration of the simulation is in the 'ia_config_file_120.txt' file. You can edit and run the cell below to update the configuration file."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%file ia_config_file_120.txt\n",
      "\n",
      "[Scenario]\n",
      "        SNR = [  0.   5.  10.  15.  20.  25.  30.]\n",
      "        M = 4\n",
      "        modulator = PSK\n",
      "        NSymbs = 100\n",
      "        K = 3\n",
      "        Nr = 2\n",
      "        Nt = 2\n",
      "        Ns = 1\n",
      "[IA Algorithm]\n",
      "        max_iterations = 120\n",
      "[General]\n",
      "        rep_max = 5000\n",
      "        max_bit_errors = 10000\n",
      "        unpacked_parameters = SNR,"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Overwriting ia_config_file_120.txt\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "All we need to do now is creating the runner object, call its \"simulate\" method and save the results to a file."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "runner_120 = MinLeakageSimulationRunner('ia_config_file_120.txt')\n",
      "pprint(runner_120.params.parameters)\n",
      "runner_120.simulate()\n",
      "\n",
      "# Clear the progressbar output after the simulation ends.\n",
      "clear_output()\n",
      "\n",
      "# xxxxxxxxxx Get the parameters xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "K = runner_120.params[\"K\"]\n",
      "Nr = runner_120.params[\"Nr\"]\n",
      "Nt = runner_120.params[\"Nt\"]\n",
      "Ns = runner_120.params[\"Ns\"]\n",
      "modulator_name = runner_120.modulator.name\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "# File name (without extension) for the figure and result files.\n",
      "results_filename_120 = 'ia_min_leakage_results_{0}_{1}x{2}({3})_120'.format(modulator_name,\n",
      "                                                                    Nr,\n",
      "                                                                    Nt,\n",
      "                                                                    Ns)\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "# xxxxxxxxxx Save the simulation results to a file xxxxxxxxxxxxxxxxxxxx\n",
      "runner_120.results.save_to_file('{0}.pickle'.format(results_filename_120))\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "print \"Runned iterations: {0}\".format(runner_120.runned_reps)\n",
      "print \"Elapsed Time: {0}\".format(runner_120.elapsed_time)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Runned iterations: [82, 157, 397, 1146, 3187, 5000, 5000]\n",
        "Elapsed Time: 21m:56s\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Just to be sure, lets create another runner object and repeat the simulation."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "runner_120_another = MinLeakageSimulationRunner('ia_config_file_120.txt')\n",
      "pprint(runner_120_another.params.parameters)\n",
      "runner_120_another.simulate()\n",
      "\n",
      "# Clear the progressbar output after the simulation ends.\n",
      "clear_output()\n",
      "\n",
      "# xxxxxxxxxx Get the parameters xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "K = runner_120_another.params[\"K\"]\n",
      "Nr = runner_120_another.params[\"Nr\"]\n",
      "Nt = runner_120_another.params[\"Nt\"]\n",
      "Ns = runner_120_another.params[\"Ns\"]\n",
      "modulator_name = runner_120_another.modulator.name\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "# File name (without extension) for the figure and result files.\n",
      "results_filename_120_another = 'ia_min_leakage_results_{0}_{1}x{2}({3})_120_another'.format(modulator_name,\n",
      "                                                                            Nr,\n",
      "                                                                            Nt,\n",
      "                                                                            Ns)\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "# xxxxxxxxxx Save the simulation results to a file xxxxxxxxxxxxxxxxxxxx\n",
      "runner_120_another.results.save_to_file('{0}.pickle'.format(results_filename_120_another))\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "print \"Runned iterations: {0}\".format(runner_120_another.runned_reps)\n",
      "print \"Elapsed Time: {0}\".format(runner_120_another.elapsed_time)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Runned iterations: [79, 160, 432, 1077, 3120, 5000, 5000]\n",
        "Elapsed Time: 21m:43s\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Plot the results"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot the results for the first simulation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results_120 = simulations.SimulationResults.load_from_file('{0}.pickle'.format(\n",
      "    results_filename_120))\n",
      "\n",
      "# Get the BER and SER from the results object\n",
      "ber = results_120.get_result_values_list('ber')\n",
      "ser = results_120.get_result_values_list('ser')\n",
      "ia_iterations = results_120.params['max_iterations']\n",
      "\n",
      "# Get the SNR from the simulation parameters\n",
      "SNR = np.array(results_120.params['SNR'])\n",
      "\n",
      "# Can only plot if we simulated for more then one value of SNR\n",
      "if SNR.size > 1:\n",
      "    fig = figure(figsize=(12,9))\n",
      "    semilogy(SNR, ber, '--g*', label='BER')\n",
      "    semilogy(SNR, ser, '--b*', label='SER')\n",
      "    xlabel('SNR')\n",
      "    ylabel('Error')\n",
      "    title('Min Leakage IA Algorithm ({5} Iterations)\\nK={0}, Nr={1}, Nt={2}, Ns={3}, {4}'.format(K, Nr, Nt, Ns, modulator_name, ia_iterations))\n",
      "    legend()\n",
      "\n",
      "    grid(True, which='both', axis='both')\n",
      "    show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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Ll6ZevXp8++23AHTo0IEOHTpQs2ZNQkJCKF68eKZm48p8nnvuObp168add97JpUuXePPN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\n"
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot the results from the second simulation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results_120_another = simulations.SimulationResults.load_from_file('{0}.pickle'.format(\n",
      "    results_filename_120_another))\n",
      "\n",
      "# Get the BER and SER from the results object\n",
      "ber_another = results_120_another.get_result_values_list('ber')\n",
      "ser_another = results_120_another.get_result_values_list('ser')\n",
      "ia_iterations_another = results_120_another.params['max_iterations']\n",
      "\n",
      "# Get the SNR from the simulation parameters\n",
      "SNR_another = np.array(results_120_another.params['SNR'])\n",
      "\n",
      "# Can only plot if we simulated for more then one value of SNR\n",
      "if SNR_another.size > 1:\n",
      "    fig = figure(figsize=(12,9))\n",
      "    semilogy(SNR_another, ber_another, '--g*', label='BER')\n",
      "    semilogy(SNR_another, ser_another, '--b*', label='SER')\n",
      "    xlabel('SNR')\n",
      "    ylabel('Error')\n",
      "    title('Min Leakage IA Algorithm ({5} Iterations)\\nK={0}, Nr={1}, Nt={2}, Ns={3}, {4}'.format(K, Nr, Nt, Ns, modulator_name, ia_iterations_another))\n",
      "    legend()\n",
      "\n",
      "    grid(True, which='both', axis='both')\n",
      "    show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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9yUWxYsWKFStWrFhx7mJHFBAQQMmSJfHy8sLHx4cuXbrwxx9/AOm9oIeHB15e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\na5FCcPrCaUoVK4VnMU+jUxEREZECpOZ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08ucv68iJI6ZbCrphw6TWraWPPzbdCQD8FcM1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\nJ/8fPR9Bk+dFgUsAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Run the algorithm with 120 iterations, but in parallel"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First we need to create a \"view\" of the engines. Note that you need to start the engines before calling the code below."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.parallel import Client\n",
      "cl = Client()\n",
      "dview = cl.direct_view()\n",
      "\n",
      "# Add the folder containing PyPhysim to the python path in all the\n",
      "# engines\n",
      "dview.execute('import sys')\n",
      "dview.execute('sys.path.append(\"{0}\")'.format(pyphysim_folder))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<AsyncResult: execute>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "runner_120_parallel = MinLeakageSimulationRunner('ia_config_file_120.txt')\n",
      "pprint(runner_120_parallel.params.parameters)\n",
      "runner_120_parallel.simulate_in_parallel(dview)\n",
      "\n",
      "# xxxxxxxxxx Get the parameters xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "K = runner_120_parallel.params[\"K\"]\n",
      "Nr = runner_120_parallel.params[\"Nr\"]\n",
      "Nt = runner_120_parallel.params[\"Nt\"]\n",
      "Ns = runner_120_parallel.params[\"Ns\"]\n",
      "modulator_name = runner_120_parallel.modulator.name\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "# File name (without extension) for the figure and result files.\n",
      "results_filename_120_parallel = 'ia_min_leakage_results_{0}_{1}x{2}({3})_120_parallel'.format(modulator_name,\n",
      "                                                                            Nr,\n",
      "                                                                            Nt,\n",
      "                                                                            Ns)\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "# xxxxxxxxxx Save the simulation results to a file xxxxxxxxxxxxxxxxxxxx\n",
      "runner_120_parallel.results.save_to_file('{0}.pickle'.format(results_filename_120_parallel))\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "print \"Runned iterations: {0}\".format(runner_120_parallel.runned_reps)\n",
      "print \"Elapsed Time: {0}\".format(runner_120_parallel.elapsed_time)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'K': 3,\n",
        " 'M': 4,\n",
        " 'NSymbs': 100,\n",
        " 'Nr': 2,\n",
        " 'Ns': 1,\n",
        " 'Nt': 2,\n",
        " 'SNR': array([  0.,   5.,  10.,  15.,  20.,  25.,  30.]),\n",
        " 'max_bit_errors': 10000,\n",
        " 'max_iterations': 120,\n",
        " 'modulator': 'PSK',\n",
        " 'rep_max': 5000,\n",
        " 'unpacked_parameters': ['SNR']}\n",
        "Runned iterations: [77, 151, 380, 1004, 3202, 5000, 5000]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Elapsed Time: 7m:52s\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Plot the results"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results_120_parallel = simulations.SimulationResults.load_from_file('{0}.pickle'.format(\n",
      "    results_filename_120_parallel))\n",
      "\n",
      "# Get the BER and SER from the results object\n",
      "ber_parallel = results_120_parallel.get_result_values_list('ber')\n",
      "ser_parallel = results_120_parallel.get_result_values_list('ser')\n",
      "ia_iterations_parallel = results_120_parallel.params['max_iterations']\n",
      "\n",
      "# Get the SNR from the simulation parameters\n",
      "SNR_parallel = np.array(results_120_parallel.params['SNR'])\n",
      "\n",
      "# Can only plot if we simulated for more then one value of SNR\n",
      "if SNR_parallel.size > 1:\n",
      "    fig = figure(figsize=(12,9))\n",
      "    semilogy(SNR_parallel, ber_parallel, '--g*', label='BER')\n",
      "    semilogy(SNR_parallel, ser_parallel, '--b*', label='SER')\n",
      "    xlabel('SNR')\n",
      "    ylabel('Error')\n",
      "    title('Min Leakage IA Algorithm ({5} Iterations)\\nK={0}, Nr={1}, Nt={2}, Ns={3}, {4}'.format(K, Nr, Nt, Ns, modulator_name, ia_iterations_parallel))\n",
      "    legend()\n",
      "\n",
      "    grid(True, which='both', axis='both')\n",
      "    show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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IyEx/clGsWLFixYoVK1acu9gR+fv7U7JkSTw8PPDy8uKhhx7ixIkTgK0XdHNz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\nAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Run the algorithm with 60 iterations in parallel"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%file ia_config_file_60.txt\n",
      "\n",
      "[Scenario]\n",
      "        SNR = [  0.   5.  10.  15.  20.  25.  30.]\n",
      "        M = 4\n",
      "        modulator = PSK\n",
      "        NSymbs = 100\n",
      "        K = 3\n",
      "        Nr = 2\n",
      "        Nt = 2\n",
      "        Ns = 1\n",
      "[IA Algorithm]\n",
      "        max_iterations = 60\n",
      "[General]\n",
      "        rep_max = 5000\n",
      "        max_bit_errors = 10000\n",
      "        unpacked_parameters = SNR,"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Writing ia_config_file_60.txt\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "runner_60_parallel = MinLeakageSimulationRunner('ia_config_file_60.txt')\n",
      "pprint(runner_60_parallel.params.parameters)\n",
      "runner_60_parallel.simulate_in_parallel(dview)\n",
      "\n",
      "# xxxxxxxxxx Get the parameters xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "K = runner_60_parallel.params[\"K\"]\n",
      "Nr = runner_60_parallel.params[\"Nr\"]\n",
      "Nt = runner_60_parallel.params[\"Nt\"]\n",
      "Ns = runner_60_parallel.params[\"Ns\"]\n",
      "modulator_name = runner_60_parallel.modulator.name\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "# File name (without extension) for the figure and result files.\n",
      "results_filename_60_parallel = 'ia_min_leakage_results_{0}_{1}x{2}({3})_60_parallel'.format(modulator_name,\n",
      "                                                                            Nr,\n",
      "                                                                            Nt,\n",
      "                                                                            Ns)\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "# xxxxxxxxxx Save the simulation results to a file xxxxxxxxxxxxxxxxxxxx\n",
      "runner_60_parallel.results.save_to_file('{0}.pickle'.format(results_filename_60_parallel))\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "print \"Runned iterations: {0}\".format(runner_60_parallel.runned_reps)\n",
      "print \"Elapsed Time: {0}\".format(runner_60_parallel.elapsed_time)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'K': 3,\n",
        " 'M': 4,\n",
        " 'NSymbs': 100,\n",
        " 'Nr': 2,\n",
        " 'Ns': 1,\n",
        " 'Nt': 2,\n",
        " 'SNR': array([  0.,   5.,  10.,  15.,  20.,  25.,  30.]),\n",
        " 'max_bit_errors': 10000,\n",
        " 'max_iterations': 60,\n",
        " 'modulator': 'PSK',\n",
        " 'rep_max': 5000,\n",
        " 'unpacked_parameters': ['SNR']}\n",
        "Runned iterations: [73, 149, 391, 1079, 3189, 5000, 5000]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Elapsed Time: 4m:1s\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Plot the results"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results_60_parallel = simulations.SimulationResults.load_from_file('{0}.pickle'.format(\n",
      "    results_filename_60_parallel))\n",
      "\n",
      "# Get the BER and SER from the results object\n",
      "ber_parallel = results_60_parallel.get_result_values_list('ber')\n",
      "ser_parallel = results_60_parallel.get_result_values_list('ser')\n",
      "ia_iterations_parallel = results_60_parallel.params['max_iterations']\n",
      "\n",
      "# Get the SNR from the simulation parameters\n",
      "SNR_parallel = np.array(results_60_parallel.params['SNR'])\n",
      "\n",
      "# Can only plot if we simulated for more then one value of SNR\n",
      "if SNR_parallel.size > 1:\n",
      "    fig = figure(figsize=(12,9))\n",
      "    semilogy(SNR_parallel, ber_parallel, '--g*', label='BER')\n",
      "    semilogy(SNR_parallel, ser_parallel, '--b*', label='SER')\n",
      "    xlabel('SNR')\n",
      "    ylabel('Error')\n",
      "    title('Min Leakage IA Algorithm ({5} Iterations)\\nK={0}, Nr={1}, Nt={2}, Ns={3}, {4}'.format(K, Nr, Nt, Ns, modulator_name, ia_iterations_parallel))\n",
      "    legend()\n",
      "\n",
      "    grid(True, which='both', axis='both')\n",
      "    show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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+3/Dhw0lOTrZ//fzzzw7bJyYmkpyczOLFi5kwYQKrV6++pdeQE2quRURERCRL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\nTkSuQmCC\n"
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Run the algorithm with only 5 iterations"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%file ia_config_file_5.txt\n",
      "\n",
      "[Scenario]\n",
      "        SNR = [  0.   5.  10.  15.  20.  25.  30.]\n",
      "        M = 4\n",
      "        modulator = PSK\n",
      "        NSymbs = 100\n",
      "        K = 3\n",
      "        Nr = 2\n",
      "        Nt = 2\n",
      "        Ns = 1\n",
      "[IA Algorithm]\n",
      "        max_iterations = 5\n",
      "[General]\n",
      "        rep_max = 5000\n",
      "        max_bit_errors = 10000\n",
      "        unpacked_parameters = SNR,"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Writing ia_config_file_5.txt\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "runner_5_parallel = MinLeakageSimulationRunner('ia_config_file_5.txt')\n",
      "pprint(runner_5_parallel.params.parameters)\n",
      "runner_5_parallel.simulate_in_parallel(dview)\n",
      "\n",
      "# xxxxxxxxxx Get the parameters xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "K = runner_5_parallel.params[\"K\"]\n",
      "Nr = runner_5_parallel.params[\"Nr\"]\n",
      "Nt = runner_5_parallel.params[\"Nt\"]\n",
      "Ns = runner_5_parallel.params[\"Ns\"]\n",
      "modulator_name = runner_5_parallel.modulator.name\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "# File name (without extension) for the figure and result files.\n",
      "results_filename_5_parallel = 'ia_min_leakage_results_{0}_{1}x{2}({3})_5_parallel'.format(modulator_name,\n",
      "                                                                            Nr,\n",
      "                                                                            Nt,\n",
      "                                                                            Ns)\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "# xxxxxxxxxx Save the simulation results to a file xxxxxxxxxxxxxxxxxxxx\n",
      "runner_5_parallel.results.save_to_file('{0}.pickle'.format(results_filename_5_parallel))\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "print \"Runned iterations: {0}\".format(runner_5_parallel.runned_reps)\n",
      "print \"Elapsed Time: {0}\".format(runner_5_parallel.elapsed_time)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'K': 3,\n",
        " 'M': 4,\n",
        " 'NSymbs': 100,\n",
        " 'Nr': 2,\n",
        " 'Ns': 1,\n",
        " 'Nt': 2,\n",
        " 'SNR': array([  0.,   5.,  10.,  15.,  20.,  25.,  30.]),\n",
        " 'max_bit_errors': 10000,\n",
        " 'max_iterations': 5,\n",
        " 'modulator': 'PSK',\n",
        " 'rep_max': 5000,\n",
        " 'unpacked_parameters': ['SNR']}\n",
        "Runned iterations: [74, 139, 274, 549, 853, 893, 937]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Elapsed Time: 5.51s\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Plot the results"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results_5_parallel = simulations.SimulationResults.load_from_file('{0}.pickle'.format(\n",
      "    results_filename_5_parallel))\n",
      "\n",
      "# Get the BER and SER from the results object\n",
      "ber_parallel = results_5_parallel.get_result_values_list('ber')\n",
      "ser_parallel = results_5_parallel.get_result_values_list('ser')\n",
      "ia_iterations_parallel = results_5_parallel.params['max_iterations']\n",
      "\n",
      "# Get the SNR from the simulation parameters\n",
      "SNR_parallel = np.array(results_5_parallel.params['SNR'])\n",
      "\n",
      "# Can only plot if we simulated for more then one value of SNR\n",
      "if SNR_parallel.size > 1:\n",
      "    fig = figure(figsize=(12,9))\n",
      "    semilogy(SNR_parallel, ber_parallel, '--g*', label='BER')\n",
      "    semilogy(SNR_parallel, ser_parallel, '--b*', label='SER')\n",
      "    xlabel('SNR')\n",
      "    ylabel('Error')\n",
      "    title('Min Leakage IA Algorithm ({5} Iterations)\\nK={0}, Nr={1}, Nt={2}, Ns={3}, {4}'.format(K, Nr, Nt, Ns, modulator_name, ia_iterations_parallel))\n",
      "    legend()\n",
      "\n",
      "    grid(True, which='both', axis='both')\n",
      "    show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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1HqXRztFXbTs6crRei3xNOizpT0kPSRV+rKB3nnxH7Vu3l8PhyFS2wMBAnTlz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\nxkNCSt8fEOCq6fRp6ejRkjPvPXpIo0cPU716DTR16sflL+J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      }
     ],
     "prompt_number": 15
    }
   ],
   "metadata": {}
  }
 ]
}