PeriodicSim.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:6143f878549a2d6a8b5e819bcec95525314ec3036ecc128f786c269925d4854d"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os\n",
      "import scipy.integrate\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "from numpy import f2py\n",
      "import copy\n",
      "%matplotlib inline\n",
      "\n",
      "# local modules\n",
      "from periodic import PeriodicProcess, PeriodicScheduler, \\\n",
      "    nested_dict_to_namedtuple, Logger\n",
      "import uorb\n",
      "from sympy_utils import rhs_to_scipy_ode, \\\n",
      "    save_sympy_expr, load_sympy_expr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "if f2py.compile(source=open('pendulum.f90','r').read(), modulename='pendulum',\n",
      "             source_fn='pendulum.f90'):\n",
      "    raise RuntimeError('compile failed, see console')\n",
      "import pendulum"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class PendulumDynamics(PeriodicProcess):\n",
      "\n",
      "    def __init__(self, period, x0, uorb_manager):\n",
      "        super(PendulumDynamics, self).__init__(period)\n",
      "        self.x0 = x0\n",
      "        self.ode = scipy.integrate.ode(\n",
      "            pendulum.compute_f, pendulum.compute_a)\n",
      "        #self.ode.set_integrator('dopri5')\n",
      "        self.uorb_manager = uorb_manager\n",
      "        self.process_noise_power = 1e-5\n",
      "\n",
      "    def initialize(self, t):\n",
      "        super(PendulumDynamics, self).initialize(t)\n",
      "        self.ode.set_initial_value(self.x0, t)\n",
      "        \n",
      "        # publish sim state\n",
      "        self.sim = uorb.Publication(\n",
      "            self.uorb_manager, 'sim_state',\n",
      "            uorb.Topic_sim_state(*((0,)*16)))\n",
      "        self.sim.publish()\n",
      "        \n",
      "        # subscribe to actuators\n",
      "        self.actuators = uorb.Subscription(\n",
      "            self.uorb_manager, 'actuator_outputs')\n",
      "        \n",
      "    def run(self, t):\n",
      "        # update actuator info\n",
      "        self.actuators.update()\n",
      "        \n",
      "        process_noise_stdev = np.sqrt(self.process_noise_power/self.period)\n",
      "        process_noise = process_noise_stdev*np.random.randn()\n",
      "        \n",
      "        ode = self.ode\n",
      "        if t == ode.t:\n",
      "            return\n",
      "        u = [self.actuators.data.output[0] + process_noise]\n",
      "        m = 1\n",
      "        g = 9.8\n",
      "        l = 1\n",
      "        ode.set_f_params(u, m, g, l)\n",
      "        ode.set_jac_params(u, m, g, l)\n",
      "        ode.integrate(t)\n",
      "        if not ode.successful():\n",
      "            raise ValueError('ode integration failed')\n",
      "            \n",
      "        # acceleration\n",
      "        # TODO, should make sympy output this\n",
      "        x = ode.y\n",
      "        y_accel = pendulum.compute_g(t, x, u, m, g, l)\n",
      "\n",
      "        # publish simulation data\n",
      "        self.sim.data.timestamp=int(1e6*t)\n",
      "        self.sim.data.pitch=ode.y[0]\n",
      "        self.sim.data.pitchspeed=ode.y[1]\n",
      "        self.sim.data.xacc=y_accel[0,0]\n",
      "        self.sim.data.yacc=y_accel[1,0]\n",
      "        self.sim.data.zacc=y_accel[2,0]\n",
      "        self.sim.publish()\n",
      "\n",
      "    @property\n",
      "    def x(self):\n",
      "        return self.ode.y"
     ],
     "language": "python",
     "metadata": {
      "code_folding": []
     },
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class Sensor(PeriodicProcess):\n",
      "    \n",
      "    def __init__(self, period, uorb_manager):\n",
      "        super(Sensor, self).__init__(period)\n",
      "        self.uorb_manager = uorb_manager\n",
      "\n",
      "        self.gyro_noise_power = 1e-5**np.ones(3)\n",
      "        self.gyro_bias = np.zeros(3)\n",
      "        self.accel_noise_power = 1e-4*np.ones(3)\n",
      "        self.mag_noise_power = 1e-4*np.ones(3)\n",
      "        \n",
      "    def initialize(self, t):\n",
      "        super(Sensor, self).initialize(t)     \n",
      "        \n",
      "        #initialize topic\n",
      "        timestamp = int(t*1e6)\n",
      "        self.sensor = uorb.Publication(\n",
      "            self.uorb_manager, 'sensor_combined',\n",
      "            uorb.Topic_sensor_combined(*((0,)*42)))\n",
      "        \n",
      "        self.sensor.data.gyro1_rad_s = timestamp\n",
      "        self.sensor.data.gyro_rad_s = np.array([0,0,0])\n",
      "        self.sensor.data.accelerometer_timestamp = timestamp\n",
      "        self.sensor.data.accelerometer_m_s2 = np.array([0,0,0])\n",
      "        self.sensor.magnetometer_timestamp = timestamp\n",
      "        self.sensor.magnetometer_ga = np.array([0,0,0])\n",
      "        self.sensor.publish()\n",
      "        \n",
      "        # get sim data\n",
      "        self.sim = uorb.Subscription(\n",
      "            self.uorb_manager, 'sim_state')\n",
      "\n",
      "    def run(self, t):\n",
      "        # get new simulation data\n",
      "        self.sim.update()\n",
      "        \n",
      "        # noise generator\n",
      "        randn = np.random.randn\n",
      "\n",
      "        timestamp = int(t*1e6)\n",
      "        self.sensor.data.timestamp = timestamp\n",
      "        \n",
      "        # gyro\n",
      "        gyro_ideal = np.array([self.sim.data.rollspeed,\n",
      "                     self.sim.data.pitchspeed,\n",
      "                     self.sim.data.yawspeed])\n",
      "        gyro_stddev = np.sqrt(self.gyro_noise_power/self.period)\n",
      "        gyro_noise = gyro_stddev*randn(3)\n",
      "        gyro = gyro_ideal + gyro_noise\n",
      "        \n",
      "        # accelerometer\n",
      "        accel_ideal = np.array([self.sim.data.xacc,self.sim.data.yacc,self.sim.data.zacc])\n",
      "        accel_stddev = np.sqrt(self.accel_noise_power/self.period)\n",
      "        accel_noise = accel_stddev*randn(3)\n",
      "        accel = accel_ideal + accel_noise\n",
      "\n",
      "        # magnetometer\n",
      "        mag_ideal = np.array([0,0,0])\n",
      "        mag_stddev = np.sqrt(self.mag_noise_power/self.period)\n",
      "        mag_noise = mag_stddev*randn(3)\n",
      "        mag = mag_ideal + mag_noise\n",
      "        \n",
      "        # publish\n",
      "        self.sensor.data.gyro1_timestamp = timestamp\n",
      "        self.sensor.data.gyro_rad_s = gyro\n",
      "        self.sensor.data.accelerometer_timestamp = timestamp\n",
      "        self.sensor.data.accelerometer_m_s2 = accel\n",
      "        self.sensor.magnetometer_timestamp = timestamp\n",
      "        self.sensor.magnetometer_ga = mag\n",
      "        self.sensor.publish()"
     ],
     "language": "python",
     "metadata": {
      "code_folding": []
     },
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class Estimator(PeriodicProcess):\n",
      "\n",
      "    def __init__(self, period, uorb_manager):\n",
      "        super(Estimator, self).__init__(period)\n",
      "        self.uorb_manager = uorb_manager\n",
      "        self.xh = np.array([0,0])\n",
      "        self.P0 = np.eye(2)\n",
      "\n",
      "    def initialize(self, t):\n",
      "        super(Estimator, self).initialize(t)\n",
      "        # initialize position publication\n",
      "        self.pos = uorb.Publication(\n",
      "            self.uorb_manager,\n",
      "            'vehicle_global_position',\n",
      "            uorb.Topic_vehicle_global_position(*((0,)*11)))\n",
      "        \n",
      "        # initialize attitude publication\n",
      "        self.att = uorb.Publication(\n",
      "            self.uorb_manager,\n",
      "            'vehicle_attitude',\n",
      "            uorb.Topic_vehicle_attitude(*((0,)*16)))\n",
      "        \n",
      "        # intialize sensor subscription\n",
      "        self.sensor = uorb.Subscription(\n",
      "            self.uorb_manager,\n",
      "            'sensor_combined')\n",
      "\n",
      "        # TODO, this is a hack, we make the estimator\n",
      "        # just give the sim state for now\n",
      "        self.sim = uorb.Subscription(\n",
      "            self.uorb_manager,\n",
      "            'sim_state')\n",
      "        \n",
      "        # initialize timestamps for checking for sensor updates\n",
      "        self.accel_timestamp_last = -1\n",
      "        self.l = 1\n",
      "        self.g = 9.8\n",
      "        \n",
      "    def run(self, t):\n",
      "        # get new sensor data\n",
      "        # TODO use this to estimate\n",
      "        self.sensor.update()\n",
      "        \n",
      "        if self.sensor.data.accelerometer_timestamp != self.accel_timestamp_last:\n",
      "            self.accel_timestamp_last = self.sensor.data.accelerometer_timestamp\n",
      "            y = self.sensor.data.accelerometer_m_s2\n",
      "            \n",
      "            theta = self.xh[0]\n",
      "            theta_dot = self.xh[1]\n",
      "            theta_ddot = 1 # TODO\n",
      "            yh = np.array([self.l*theta_ddot + self.g*np.sin(theta), 0, self.g*np.cos(theta)])\n",
      "            yh = []\n",
      "            \n",
      "        # publish new estimated position\n",
      "        # TODO don't cheat using sim state\n",
      "        self.pos.data.timestamp = int(1e6*t)\n",
      "        self.pos.data.lat = self.sim.data.lat\n",
      "        self.pos.data.lon = self.sim.data.lon\n",
      "        self.pos.data.alt = self.sim.data.alt\n",
      "        self.pos.data.vel_n = self.sim.data.vx\n",
      "        self.pos.data.vel_e = self.sim.data.vy\n",
      "        self.pos.data.vel_d = self.sim.data.vz\n",
      "        self.pos.data.yaw = self.sim.data.yaw\n",
      "        self.pos.publish()\n",
      "        \n",
      "        # publish new estimated attitude\n",
      "        self.att.data.timestamp = int(1e6*t)\n",
      "        self.att.data.roll = self.sim.data.roll\n",
      "        self.att.data.pitch = float(self.sim.data.pitch)\n",
      "        self.att.data.yaw = self.sim.data.yaw\n",
      "        self.att.data.rollspeed = self.sensor.data.gyro_rad_s[0]\n",
      "        self.att.data.pitchspeed = self.sensor.data.gyro_rad_s[1]\n",
      "        self.att.data.yawspeed = self.sensor.data.gyro_rad_s[2]\n",
      "        self.att.publish()"
     ],
     "language": "python",
     "metadata": {
      "code_folding": []
     },
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class Controller(PeriodicProcess):\n",
      "\n",
      "    def __init__(self, period, uorb_manager):\n",
      "        super(Controller, self).__init__(period)\n",
      "        self.uorb_manager = uorb_manager\n",
      "        self.actuators = uorb.Publication(\n",
      "            self.uorb_manager, 'actuator_outputs',\n",
      "            uorb.Topic_actuator_outputs(0,\n",
      "                output=np.array([0,0,0,0,0,0,0,0]).astype(float),\n",
      "                noutputs=1))\n",
      "        self.actuators.publish()\n",
      "        \n",
      "    def initialize(self, t):\n",
      "        super(Controller, self).initialize(t)\n",
      "        \n",
      "        # publish actuator topic\n",
      "        self.actuators.data = uorb.Topic_actuator_outputs(int(t*1e6),\n",
      "                output=np.array([0,0,0,0,0,0,0,0]).astype(float))\n",
      "        self.actuators.publish()\n",
      "        \n",
      "        # subscribe to vehicle global position topic\n",
      "        self.pos = uorb.Subscription(\n",
      "            self.uorb_manager, 'vehicle_global_position')\n",
      "        \n",
      "        self.att = uorb.Subscription(\n",
      "            self.uorb_manager, 'vehicle_attitude')\n",
      "        \n",
      "    def run(self, t):\n",
      "        # get new position/attitude estimation\n",
      "        self.pos.update()\n",
      "        self.att.update()\n",
      "        \n",
      "        u = -20*self.att.data.pitch - 6*self.att.data.pitchspeed\n",
      "        \n",
      "        # publish new actuator controls\n",
      "        self.actuators.data.timestamp = int(t*1e6)\n",
      "        self.actuators.data.output = [u,0,0,0,0,0,0,0]\n",
      "        self.actuators.publish()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First we declare the uORB manager and all processes that will be running. Next, we create the scheduler and pass the list or processes to it. Finally, we run the scheudler."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def run_sim():\n",
      "    # constants\n",
      "    tf = 4\n",
      "\n",
      "    # declare uorb manager, by using pointer copying we can avoid\n",
      "    # copying actual data and make it run much faster, however \n",
      "    # we have to becareful doing this with multi-threading\n",
      "    uorb_manager = uorb.Manager(copy_type='pointer')\n",
      "\n",
      "    # declare periodic processes\n",
      "    controller = Controller(0.02, uorb_manager)\n",
      "    estimator = Estimator(0.002, uorb_manager)\n",
      "    dynamics = PendulumDynamics(0.001, [1,0], uorb_manager)\n",
      "    sensor = Sensor(0.001, uorb_manager)\n",
      "    logger = Logger(0.001, tf, \n",
      "        ['sim_state',\n",
      "         'vehicle_global_position',\n",
      "         'vehicle_attitude',\n",
      "         'sensor_combined',\n",
      "         'actuator_outputs'],\n",
      "        uorb_manager)\n",
      "\n",
      "    # declare scheduler\n",
      "    scheduler = PeriodicScheduler(False, 0, tf, 0.001)\n",
      "    scheduler.process_list = [\n",
      "        dynamics, sensor, estimator, controller, logger]  \n",
      "\n",
      "    # run the scheduler\n",
      "    scheduler.run()\n",
      "    return nested_dict_to_namedtuple(logger.log)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can profile the simulation to see what is taking the most time."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stats = %prun -q -r log = run_sim()\n",
      "stats.sort_stats('tottime').print_stats(20);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "          302502 function calls in 0.608 seconds\n",
        "\n",
        "   Ordered by: internal time\n",
        "   List reduced from 134 to 20 due to restriction <20>\n",
        "\n",
        "   ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n",
        "     3999    0.123    0.000    0.125    0.000 _ode.py:730(run)\n",
        "     3999    0.090    0.000    0.162    0.000 <ipython-input-4-5d4f043927be>:33(run)\n",
        "     3999    0.086    0.000    0.129    0.000 __init__.py:111(run)\n",
        "     3999    0.051    0.000    0.209    0.000 <ipython-input-3-ea772f26b4d7>:26(run)\n",
        "    13926    0.047    0.000    0.047    0.000 {numpy.core.multiarray.array}\n",
        "    15996    0.031    0.000    0.031    0.000 {method 'randn' of 'mtrand.RandomState' objects}\n",
        "     1833    0.027    0.000    0.044    0.000 <ipython-input-5-6fb25091c0a5>:39(run)\n",
        "    11865    0.021    0.000    0.027    0.000 _base.py:57(publish)\n",
        "    30205    0.018    0.000    0.056    0.000 _base.py:100(update)\n",
        "    20000    0.018    0.000    0.565    0.000 __init__.py:35(update)\n",
        "    30205    0.013    0.000    0.022    0.000 _base.py:97(updated)\n",
        "    18272    0.012    0.000    0.016    0.000 _base.py:83(copy)\n",
        "        1    0.010    0.010    0.603    0.603 __init__.py:59(run)\n",
        "    30205    0.010    0.000    0.010    0.000 _base.py:79(updated)\n",
        "    35872    0.009    0.000    0.009    0.000 {method 'keys' of 'dict' objects}\n",
        "    11865    0.007    0.000    0.034    0.000 _base.py:116(publish)\n",
        "    30137    0.007    0.000    0.007    0.000 _base.py:42(_copy_type)\n",
        "     3999    0.006    0.000    0.132    0.000 _ode.py:376(integrate)\n",
        "        1    0.004    0.004    0.025    0.025 __init__.py:123(finalize)\n",
        "        7    0.003    0.000    0.005    0.001 collections.py:288(namedtuple)\n",
        "\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If we aren't profiling it is much faster."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "log = run_sim();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 513 ms, sys: 3.6 ms, total: 517 ms\n",
        "Wall time: 515 ms\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It is easy to plot any of the various uorb topics."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(log.log.t, log.sim_state.pitch);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEACAYAAAC08h1NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJEmSJEmSJEmSJP1/WqaqBC4h0pAAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x500a610>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(log.log.t, log.actuator_outputs.output[:,0], 'r');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIiIiIiIiIiIiIiIiIiISGH6f7Bi73VelSCMAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5806050>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(log.log.t, log.vehicle_attitude.pitch);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEACAYAAAC08h1NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIiIiIiIiIiIiIiI/D/uUlvouiaYeAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x53d8c90>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(log.log.t, log.sensor_combined.gyro_rad_s);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEACAYAAABbMHZzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4/AVAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x582a150>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(log.log.t, log.sensor_combined.accelerometer_m_s2);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUkoppZRSSoWn/wchBqzpOUrgoQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5338250>"
       ]
      }
     ],
     "prompt_number": 14
    }
   ],
   "metadata": {}
  }
 ]
}