ipynb/SPEED/SPEED Performance-Baseline.ipynb

{
 "metadata": {
  "name": "SPEED Performance-Baseline"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "from paraview.simple import *\n",
      "\n",
      "paraview.simple._DisableFirstRenderCameraReset()\n",
      "\n",
      "import pylab as pl\n",
      "import math\n",
      "import numpy as np\n",
      "from scipy.optimize import curve_fit\n",
      "\n",
      "remote_data = True"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.kernel.zmq.pylab.backend_inline].\n",
        "For more information, type 'help(pylab)'.\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "paraview version 4.0.0-RC3-6-gef1468d\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#from paraview.simple import *\n",
      "\n",
      "#paraview.simple._DisableFirstRenderCameraReset()\n",
      "\n",
      "import math\n",
      "import numpy as np\n",
      "from scipy.optimize import curve_fit\n",
      "\n",
      "\n",
      "\n",
      "datum = -150.0 #nose at -150.0m\n",
      "alpha = 0.0 # degrees\n",
      "beta  = 0.0 # degrees\n",
      "reference_area = 1.0e6 # mm^2\n",
      "reference_length = 1.0e3 # mm\n",
      "reference_point = [0.0,0.0,0.0]\n",
      "pressure = 87510.53 # Pa\n",
      "density  = pressure/(287.0*288.15) # kg/m^3\n",
      "gamma = 1.4\n",
      "speed_of_sound = math.sqrt(gamma*pressure/density) # m/s\n",
      "\n",
      "#bl_position = 0.97\n",
      "#face_area = 0.075823\n",
      "\n",
      "\n",
      "def rotate_vector(vec,alpha_degree,beta_degree):\n",
      "    \"\"\"\n",
      "    Rotate vector by alpha and beta based on ESDU definition\n",
      "    \"\"\"\n",
      "    alpha = math.radians(alpha_degree)\n",
      "    beta = math.radians(beta_degree)\n",
      "    rot = [0.0,0.0,0.0]\n",
      "    rot[0] =  math.cos(alpha)*math.cos(beta)*vec[0] + math.sin(beta)*vec[1]  + math.sin(alpha)*math.cos(beta)*vec[2]\n",
      "    rot[1] = -math.cos(alpha)*math.sin(beta)*vec[0] + math.cos(beta)*vec[1]  - math.sin(alpha)*math.sin(beta)*vec[2]\n",
      "    rot[2] = -math.sin(alpha)*               vec[0]                          + math.cos(alpha)*               vec[2]\n",
      "    return rot\n",
      "\n",
      "\n",
      "def sum_and_zone_filter_array(input,array_name,ignore_zone,filter=None):\n",
      "    sum = [0.0,0.0,0.0]\n",
      "    p = input.GetCellData().GetArray(array_name)\n",
      "    z = input.GetCellData().GetArray(\"zone\")\n",
      "    numCells = input.GetNumberOfCells()\n",
      "    for x in range(numCells):\n",
      "        zone = z.GetValue(x)\n",
      "        if zone not in ignore_zone:\n",
      "            v = p.GetTuple(x)\n",
      "            if filter == None or filter.test(input,x):\n",
      "                #print 'Zone: %i'%(zone)\n",
      "                for i in range(0,3):\n",
      "                    sum[i] += v[i]                        \n",
      "    return sum\n",
      "\n",
      "def sum_and_zone_filter(input,array_name,ignore_zone,filter=None):\n",
      "    sum = [0.0,0.0,0.0]\n",
      "    if input.IsA(\"vtkMultiBlockDataSet\"):\n",
      "        iter = input.NewIterator()\n",
      "        iter.UnRegister(None)\n",
      "        iter.InitTraversal()\n",
      "        while not iter.IsDoneWithTraversal():\n",
      "            cur_input = iter.GetCurrentDataObject()\n",
      "            v = sum_and_zone_filter_array(cur_input,array_name,ignore_zone,filter)\n",
      "            for i in range(0,3):\n",
      "                sum[i] += v[i]                        \n",
      "            iter.GoToNextItem();\n",
      "    else:\n",
      "        sum = sum_and_zone_filter_array(input,array_name,ignore_zone,filter)\n",
      "        \n",
      "    return sum\n",
      "\n",
      "class GeomFilterLT:\n",
      "    def __init__(self,val,idx):\n",
      "        #\n",
      "        self.val = val\n",
      "        self.idx = idx\n",
      "        \n",
      "    def test(self,input,x):\n",
      "        centre = input.GetCellData().GetArray(\"centre\").GetTuple(x)\n",
      "        if centre[self.idx] < self.val:\n",
      "            return True\n",
      "        else:\n",
      "            return False\n",
      "\n",
      "class GeomFilterGT:\n",
      "    def __init__(self,val,idx):\n",
      "        #\n",
      "        self.val = val\n",
      "        self.idx = idx\n",
      "        \n",
      "    def test(self,input,x):\n",
      "        centre = input.GetCellData().GetArray(\"centre\").GetTuple(x)\n",
      "        if centre[self.idx] >= self.val:\n",
      "            return True\n",
      "        else:\n",
      "            return False\n",
      "\n",
      "def calc_force(file_root,ignore_zone,half_model=False,filter=None):\n",
      "    \n",
      "    wall = PVDReader( FileName=file_root+'_wall.pvd' )\n",
      "\n",
      "    #CellDatatoPointData1 = CellDatatoPointData(Input=wall)\n",
      "    #CellDatatoPointData1.PassCellData = 1\n",
      "    \n",
      "    #wall = CellCenters(Input=wall)\n",
      "    \n",
      "    #sum = MinMax(Input=wall)\n",
      "    #sum.Operation = \"SUM\"\n",
      "    #sum.UpdatePipeline()\n",
      "\n",
      "    wall.UpdatePipeline()\n",
      "    \n",
      "    sum_client = servermanager.Fetch(wall)\n",
      "    \n",
      "    pforce = sum_and_zone_filter(sum_client,\"pressureforce\",ignore_zone,filter)\n",
      "            \n",
      "    #fforce = sum_client.GetCellData().GetArray(\"frictionforce\").GetTuple(0)\n",
      "    #yplus = wall_slice_client.GetCellData().GetArray(\"yplus\").GetValue(0)\n",
      "    \n",
      "    pforce = rotate_vector(pforce,alpha,beta)\n",
      "    #fforce = rotate_vector(fforce,alpha,beta)\n",
      "\n",
      "    if half_model:\n",
      "        for i in range(0,3):\n",
      "            pforce[i] *= 2.0\n",
      "            \n",
      "    return pforce\n",
      "\n",
      "def calc_moment(file_root,ignore_zone,half_model=False):\n",
      "    \n",
      "    wall = PVDReader( FileName=file_root+'_wall.pvd' )\n",
      "\n",
      "    #CellDatatoPointData1 = CellDatatoPointData(Input=wall)\n",
      "    #CellDatatoPointData1.PassCellData = 1\n",
      "    \n",
      "    #sum = MinMax(Input=wall)\n",
      "    #sum.Operation = \"SUM\"\n",
      "    #sum.UpdatePipeline()\n",
      "\n",
      "    sum_client = servermanager.Fetch(wall)    \n",
      "    pmoment = sum_and_zone_filter(sum_client,\"pressuremoment\",ignore_zone)\n",
      "    #fforce = sum_client.GetCellData().GetArray(\"frictionforce\").GetTuple(0)\n",
      "    #yplus = wall_slice_client.GetCellData().GetArray(\"yplus\").GetValue(0)\n",
      "    \n",
      "    pmoment = rotate_vector(pmoment,alpha,beta)\n",
      "    #fforce = rotate_vector(fforce,alpha,beta)\n",
      "\n",
      "    if half_model:\n",
      "        # This is only valid for X-Z plane reflection\n",
      "        pmoment[0] += -pmoment[0]\n",
      "        pmoment[1] +=  pmoment[1]\n",
      "        pmoment[2] += -pmoment[2]\n",
      "    \n",
      "    return list(pmoment)\n",
      "\n",
      "def calc_lift_centre_of_action(force,moment,ref_point):\n",
      "    # longitudinal centre xs0 at zs0\n",
      "    # spanwise centre ys0 at zs0\n",
      "    # residual Mz moment (Mx=My=0) mzs0\n",
      "    \n",
      "    xs0 = ref_point[0] - moment[1]/force[2]\n",
      "    ys0 = ref_point[1] + moment[0]/force[2]\n",
      "    \n",
      "    zs0 = ref_point[2]\n",
      "    mzs0 = moment[2] - force[1]*(xs0-ref_point[0]) + force[0]*(ys0-ref_point[1])\n",
      "    \n",
      "    return (xs0,ys0,zs0),mzs0 \n",
      "    \n",
      "\n",
      "def get_case(case,num_procs):\n",
      "    return 'C13_2013-04-04-half-'+case+'_P'+num_procs+'_OUTPUT/C13_2013-04-04-half-'+case\n",
      "\n",
      "#Connect('localhost')\n",
      "if remote_data:\n",
      "    #print 'Waiting for remote paraview server connection'\n",
      "    ReverseConnect('11111')\n",
      "\n",
      "num_procs = '24'\n",
      "\n",
      "case_list = [('m0p5',0.5,0.0,0.0), \n",
      "             ('m0p8',0.8,0.0,0.0),\n",
      "             ('m1p1',1.1,0.0,0.0),\n",
      "             ('m1p3',1.5,0.0,0.0)]\n",
      "\n",
      "#case_list = [('m0p5',0.5,0.0,0.0)]\n",
      "\n",
      "force = []\n",
      "force_fwd = []\n",
      "force_aft = []\n",
      "moment = []\n",
      "\n",
      "ignore_zones = [34,35,101]\n",
      "#ignore_zones = []\n",
      "\n",
      "for case in case_list:\n",
      "    case_name = case[0]\n",
      "    mach  = case[1]\n",
      "    q     = (0.5*gamma*pressure*mach*mach)\n",
      "    alpha = case[2]\n",
      "    beta  = case[3]\n",
      "    \n",
      "    \n",
      "    #f_aft =  calc_force(get_case(case_name,num_procs),set(ignore_zones),True,GeomFilterGT((16.0+datum)*1000.0,0))\n",
      "    #for i in range(0,3):\n",
      "    #    f_aft[i] /= reference_area\n",
      "    #print f_aft\n",
      "    \n",
      "    #break\n",
      "    #calc_test(get_case(case_name,num_procs),set(ignore_zones),True)\n",
      "    \n",
      "    f = calc_force(get_case(case_name,num_procs),set(ignore_zones),True)\n",
      "    for i in range(0,3):\n",
      "        f[i] /= reference_area\n",
      "    force.append(f)\n",
      "    \n",
      "    m = calc_moment(get_case(case_name,num_procs),set(ignore_zones),True)\n",
      "    for i in range(0,3):\n",
      "        m[i] /= (reference_length*reference_area)\n",
      "    moment.append(m)\n",
      "\n",
      "    p, mz = calc_lift_centre_of_action(f,m,reference_point)\n",
      "    print 'Centre of lift: %f m' % (p[0]-datum)\n",
      "        \n",
      "    # Calc load fwd of aero centre\n",
      "    f_fwd = calc_force(get_case(case_name,num_procs),set(ignore_zones),True,GeomFilterLT(p[0]*1000.0,0))\n",
      "    for i in range(0,3):\n",
      "        f_fwd[i] /= reference_area\n",
      "    force_fwd.append(f_fwd)\n",
      "    print f_fwd\n",
      "    \n",
      "    # Calc load aft of aero centre\n",
      "    f_aft = calc_force(get_case(case_name,num_procs),set(ignore_zones),True,GeomFilterGT(p[0]*1000.0,0))\n",
      "    for i in range(0,3):\n",
      "        f_aft[i] /= reference_area\n",
      "    force_aft.append(f_aft)\n",
      "    print f_aft\n",
      "\n",
      "if remote_data:\n",
      "    #print 'Disconnecting from remote paraview server connection'\n",
      "    Disconnect()\n",
      "\n",
      "x = [0.0,0.5,0.8,1.1,1.3,1.4]\n",
      "y = []\n",
      "y.append(force[0][0])\n",
      "for f in force:\n",
      "    y.append(f[0])\n",
      "y.append(force[-1][0])\n",
      "\n",
      "from scipy.interpolate import interp1d\n",
      "drag_curve = interp1d(x, y, kind='linear')\n",
      "xnew = np.linspace(0.0, 1.4, 20)\n",
      "\n",
      "xlabel('Mach')\n",
      "ylabel('D/q', multialignment='center')\n",
      "title('Drag Profile')\n",
      "pl.plot(x,y,xnew,drag_curve(xnew))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "use composite data append\n",
        "[0.0, 0.0, 0.0]"
       ]
      },
      {
       "ename": "IndexError",
       "evalue": "list index out of range",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-6-62c2ab77995a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    241\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1.1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1.3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1.4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 243\u001b[0;31m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mforce\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    244\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mforce\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    245\u001b[0m     \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mIndexError\u001b[0m: list index out of range"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.interpolate import interp1d\n",
      "\n",
      "# Calculate total thrust for jet and rocket\n",
      "\n",
      "thrust_factor = 1.0\n",
      "\n",
      "#jet_thrust = 10000.0 # 10KN thrust for ??\n",
      "jet_thrust = 4.44822162*20000.0 # 20000lb thrust\n",
      "jet_end    = 75.0 # s\n",
      "#rocket_thrust = 12000.0 # 12KN for 20 secs\n",
      "rocket_thrust = 4.44822162*26000.0\n",
      "rocket_start  = 20.0\n",
      "rocket_end    = rocket_start + 20.0\n",
      "\n",
      "def get_thrust(time_array):\n",
      "    thrust_array = []\n",
      "    for t in time_array:\n",
      "        thrust = jet_thrust*thrust_factor\n",
      "        if t >= rocket_start and t <= rocket_end:\n",
      "            thrust += rocket_thrust\n",
      "        thrust_array.append(thrust)    \n",
      "    return thrust_array\n",
      "    \n",
      "# Time sample at 1s intervals\n",
      "time_sample = 0.1\n",
      "time_array  = np.linspace(0.0, 100.0, 100.0/time_sample+1)\n",
      "\n",
      "#print time_array\n",
      "\n",
      "# Get thrust profile\n",
      "thrust_array = get_thrust(time_array)\n",
      "thrust_curve = interp1d(time_array, thrust_array, kind='linear')\n",
      "\n",
      "xlabel('Time (s)')\n",
      "ylabel('Thrust (N)', multialignment='center')\n",
      "title('Thrust Profile')\n",
      "pl.plot(time_array,thrust_array,time_array,thrust_curve(time_array))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "[<matplotlib.lines.Line2D at 0x1156fdad0>,\n",
        " <matplotlib.lines.Line2D at 0x1156fdcd0>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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HNTU1eL1e4uLiCA8PJyQkhMrKSgzDoKioyJwLSk1NpbCwEIDNmzeTmJgIQHJy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\nEB0dPSDb4lyvJbzSzJkzx7z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       "text": [
        "<matplotlib.figure.Figure at 0x115780650>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "full_weight = 6500 #kg\n",
      "jet_fuel    = 500 #kg\n",
      "rocket_fuel = 963+181 #kg\n",
      "\n",
      "thrust_off_weight = -1.0\n",
      "\n",
      "def get_weight(time_array):\n",
      "    weight_array = []\n",
      "    current_weight = full_weight\n",
      "    \n",
      "    for t in time_array:\n",
      "        \n",
      "        if t > 0.0 and t <= jet_end:\n",
      "            current_weight -= jet_fuel/jet_end * time_sample\n",
      "        \n",
      "        if t >= rocket_start and t <= rocket_end:\n",
      "            current_weight -= rocket_fuel/(rocket_end-rocket_start) * time_sample\n",
      "            \n",
      "        weight_array.append(current_weight)   \n",
      "        \n",
      "    return weight_array\n",
      "    \n",
      "# Get thrust profile\n",
      "weight_array = get_weight(time_array)\n",
      "weight_curve = interp1d(time_array, weight_array, kind='linear')\n",
      "\n",
      "xlabel('Time (s)')\n",
      "ylabel('Mass (kg)', multialignment='center')\n",
      "title('Mass Profile')\n",
      "pl.plot(time_array,weight_array,time_array,weight_curve(time_array))\n",
      "\n",
      "savefig(\"mass_profile.png\")\n",
      "from IPython.display import FileLink, display \n",
      "display(FileLink('mass_profile.png')) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<a href='files/mass_profile.png' target='_blank'>mass_profile.png</a><br>"
       ],
       "output_type": "display_data",
       "text": [
        "/Users/jamil/Documents/zCFD/ipython/notebooks/mass_profile.png"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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+e+pvakcRQlTCoTcbXj7k9Ge+/fbb21q4qHueHjSDL3IWyfC5QtQT190TeeaZ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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114fa75d0>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "def miles_to_km(miles):\n",
      "    return miles * 1.609344\n",
      "\n",
      "def km_to_miles(km):\n",
      "    return km / 1.609344\n",
      "\n",
      "def meters_per_second_to_mph(mps):\n",
      "    return mps*2.23693629\n",
      "\n",
      "def mph_to_meters_per_second(mph):\n",
      "    return mph/2.23693629\n",
      "\n",
      "end_of_measured_mile = miles_to_km(5.5)  # km\n",
      "start_of_measured_mile = end_of_measured_mile - miles_to_km(1.0) # km\n",
      "\n",
      "# Calculate acceleration from a combination of drag and thrust\n",
      "def get_force(current_time, current_speed, current_distance):\n",
      "    #\n",
      "    global thrust_off_weight\n",
      "    mach = current_speed/speed_of_sound\n",
      "    q = (0.5*gamma*pressure*mach*mach)\n",
      "    # Thrust\n",
      "    thrust = thrust_curve([current_time])\n",
      "    # Switch off jet at end of measured mile\n",
      "    if current_distance > end_of_measured_mile*1000.0:\n",
      "        thrust = 0.0\n",
      "        \n",
      "        # Save weight\n",
      "        if thrust_off_weight < 0.0:\n",
      "            thrust_off_weight = weight_curve([current_time])\n",
      "            \n",
      "        # Chute 1\n",
      "        if current_speed < mph_to_meters_per_second(579.0):\n",
      "            thrust += -0.9*q\n",
      "        # Chute 2\n",
      "        if current_speed < mph_to_meters_per_second(393.0):\n",
      "            thrust += -1.8*q\n",
      "        # Brakes\n",
      "        if current_speed < mph_to_meters_per_second(100.0):\n",
      "            thrust += -0.2*9.81*thrust_off_weight\n",
      "        \n",
      "    # Aero Drag\n",
      "    drag_factor = 1.0\n",
      "    mach = current_speed/speed_of_sound\n",
      "    mach = min(mach,1.4)\n",
      "    drag = drag_curve([mach])[0]\n",
      "    drag *= q*drag_factor\n",
      "    \n",
      "    #print thrust, drag\n",
      "    \n",
      "    return thrust-drag\n",
      "    \n",
      "def verlet_integrator(r, v, dt, a):\n",
      "\t\"\"\"Return new position and velocity from current values, time step and acceleration.\n",
      "\n",
      "\tParameters:\n",
      "\t   r is a numpy array giving the current position vector\n",
      "\t   v is a numpy array giving the current velocity vector\n",
      "\t   dt is a float value giving the length of the integration time step\n",
      "\t   a is a function which takes x as a parameter and returns the acceleration vector as an array\n",
      "\n",
      "\tWorks with arrays of any dimension as long as they're all the same.\n",
      "\t\"\"\"\n",
      "\t# Deceptively simple (read about Velocity Verlet on wikipedia)\n",
      "\tr_new = r + v*dt + a(r)*dt**2/2\n",
      "\tv_new = v + (a(r) + a(r_new))/2 * dt\n",
      "\treturn (r_new, v_new)\n",
      "\n",
      "def get_performance(time_array):\n",
      "    \n",
      "    acceleration_array = []\n",
      "    speed_array = []\n",
      "    distance_array = []\n",
      "    \n",
      "    current_speed = 0.0\n",
      "    acceleration = 0.0\n",
      "    current_distance = 0.0\n",
      "    \n",
      "    time_at_start = -1.0\n",
      "    time_at_end = -1.0\n",
      "    \n",
      "    run_complete = False\n",
      "    \n",
      "    # Euler integration\n",
      "    for t in time_array:\n",
      "        # Calculate current speed from previous acceleration\n",
      "        current_speed += acceleration*time_sample\n",
      "        \n",
      "        #new_val = verlet_integrator(current_distance,current_speed,time_sample,acceleration)        \n",
      "        #current_speed = new_val[1]\n",
      "        \n",
      "        if current_speed < 0.0 or run_complete: # Stop the car\n",
      "            current_speed = 0.0\n",
      "            acceleration = 0.0\n",
      "            run_complete = True\n",
      "        else:\n",
      "            #current_speed = max(0.0,current_speed)\n",
      "            current_distance += current_speed*time_sample\n",
      "            #current_distance = new_val[0]\n",
      "            # a = F/m\n",
      "            if thrust_off_weight > 0.0:\n",
      "                weight = thrust_off_weight\n",
      "            else:\n",
      "                weight = weight_curve([t])\n",
      "            \n",
      "            acceleration = get_force(t,current_speed, current_distance)/weight\n",
      "        \n",
      "        if current_distance >= start_of_measured_mile*1000.0 and time_at_start < 0.0:\n",
      "            time_at_start = t\n",
      "            \n",
      "        if current_distance >= end_of_measured_mile*1000.0 and time_at_end < 0.0:\n",
      "            time_at_end = t\n",
      "             \n",
      "        acceleration_array.append(acceleration/9.81)\n",
      "        speed_array.append(meters_per_second_to_mph(current_speed))\n",
      "        distance_array.append(km_to_miles(current_distance/1000.0))\n",
      "    \n",
      "    return acceleration_array, speed_array, distance_array, time_at_start, time_at_end\n",
      "\n",
      "acceleration, speed, distance, timer_start, timer_end  = get_performance(time_array)\n",
      "\n",
      "#print timer_start, timer_end\n",
      "print 'Average speed: %f mph'%(1.0/(timer_end-timer_start)*3600.0)\n",
      "\n",
      "#pl.plot(time_array,speed)        \n",
      "#pl.plot(distance,speed)        \n",
      "#pl.plot(time_array,acceleration)        \n",
      "\n",
      "timer_start = [timer_start,timer_start]\n",
      "timer_end = [timer_end, timer_end]\n",
      "start_timing_line = [km_to_miles(start_of_measured_mile),km_to_miles(start_of_measured_mile)]\n",
      "end_timing_line = [km_to_miles(end_of_measured_mile),km_to_miles(end_of_measured_mile)]\n",
      "\n",
      "speed_timing_line = [0.0,1400]\n",
      "\n",
      "fig = figure(figsize=(25, 10),dpi=100, facecolor='w', edgecolor='k')\n",
      "            \n",
      "ax = fig.add_subplot(1,3,1)\n",
      "ax.grid(True)\n",
      "xlabel('time (s)')\n",
      "ylabel('speed (mph)', multialignment='center')\n",
      "title('Speed - Time')\n",
      "ax.plot(time_array, speed, color='r', label='speed (mph)')\n",
      "ax.plot(timer_start,speed_timing_line,color='b')\n",
      "ax.plot(timer_end,speed_timing_line,color='b')\n",
      "\n",
      "ax = fig.add_subplot(1,3,2)\n",
      "ax.grid(True)\n",
      "xlabel('distance (miles)')\n",
      "ylabel('speed (mph)', multialignment='center')\n",
      "title('Speed - Distance')\n",
      "ax.plot(distance, speed, color='r', label='speed (mph)')\n",
      "ax.plot(start_timing_line,speed_timing_line,color='b')\n",
      "ax.plot(end_timing_line,speed_timing_line,color='b')\n",
      "\n",
      "ax = fig.add_subplot(1,3,3)\n",
      "ax.grid(True)\n",
      "xlabel('time (s)')\n",
      "ylabel('acceleration (g)', multialignment='center')\n",
      "title('Acceleration - Time')\n",
      "ax.plot(time_array, acceleration, color='r', label='acceleration (g)')\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Average speed: 1058.823529 mph\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "[<matplotlib.lines.Line2D at 0x1226dbb50>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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FVDBV3zUpGFOmwJ13wk03Vf9nGjWChg1h/vychFSQdUwg6xiGdZTikZZjLyd5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\nkyY52xdc4FxMvnRpu3EBcL38z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       "text": [
        "<matplotlib.figure.Figure at 0x122895410>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}