{
 "metadata": {
  "name": "",
  "signature": "sha256:4861804527546582a7f655966fdd5e1f2179c02e819506b864496a86d71e857a"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Setup"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#import sys\n",
      "#sys.path.append('C:\\Users\\u0098668\\Documents\\Modelica\\ModelicaRes')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from modelicares.exps.simulators import dymosim, fmi\n",
      "from modelicares import SimResList, SimResSequence\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "L = [100, 50, 20, 15] # Inductances"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Dymosim"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with dymosim(results_dir='ChuaCircuit/results') as simulator:\n",
      "    simulator.run('ChuaCircuit/dymosim', StopTime=1)\n",
      "    for l in L:   \n",
      "        simulator.continue_run(1, {'L.L': l})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "FMI"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with fmi(results_dir='ChuaCircuit/results-fmu') as simulator:\n",
      "    #simulator.run('ChuaCircuit/CSCVode.fmu', StopTime=1)\n",
      "    simulator.run('ChuaCircuit/ChuaCircuit.fmu', StopTime=1)\n",
      "    for l in L:   \n",
      "        simulator.continue_run(1, {'L.L': l})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Simulation interval    : 0.0 - 1 seconds.\n",
        "Elapsed simulation time: 0.036441 seconds.\n",
        "Simulation interval    : 1.0 - 2.0 seconds."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Elapsed simulation time: 0.036377 seconds.\n",
        "Simulation interval    : 2.0 - 3.0 seconds."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Elapsed simulation time: 0.034834 seconds.\n",
        "Simulation interval    : 3.0 - 4.0 seconds."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Elapsed simulation time: 0.033969 seconds.\n",
        "Simulation interval    : 4.0 - 5.0 seconds."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Elapsed simulation time: 0.034474 seconds.\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Comparison"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sequences = SimResList([SimResSequence('ChuaCircuit/results/1/dsres*'), \n",
      "                        SimResSequence('ChuaCircuit/results-fmu/1/dsres*')])\n",
      "sequences.label = [\"Dymosim\", \"FMI\"]\n",
      "sequences.plot('L.i', title=\"Effect of varying inductance during simulation,\\nevaluated using Dymosim and FMI\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZuuyECRNgaWkJMzMzdOjQAT169JBYr6+vL2JjY2FgYAAPD49yMXz77bfo0qULOnXqhE6d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\nHmGBf/vvYSFGm2EvjtfX17Ny5UocHBwGg6K4uJi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       "text": [
        "<matplotlib.figure.Figure at 0x7f7ecf8ede50>"
       ]
      }
     ],
     "prompt_number": 6
    }
   ],
   "metadata": {}
  }
 ]
}