{
 "metadata": {
  "name": "",
  "signature": "sha256:b0c3d5b1e224252a084a368e1eec8579db0889da6b38e5feeab39603ec211247"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pypot.dynamixel\n",
      "import pypot.robot"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ports = pypot.dynamixel.get_available_ports()\n",
      "print 'Found ports', ports\n",
      "port = ports[0]\n",
      "print 'Using', port"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Found ports ['/dev/ttyUSB0']\n",
        "Using /dev/ttyUSB0\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "io = pypot.dynamixel.DxlIO(port)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ids = io.scan(range(40))\n",
      "print 'Found ids', ids"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Found ids [11, 36, 37]\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "motors = [pypot.dynamixel.motor.DxlMXMotor(id) for id in ids]\n",
      "motors"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "[<DxlMotor name=motor_11 id=11 pos=0.0>,\n",
        " <DxlMotor name=motor_36 id=36 pos=0.0>,\n",
        " <DxlMotor name=motor_37 id=37 pos=0.0>]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c = pypot.dynamixel.controller.BaseDxlController(io, motors)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "robot = pypot.robot.Robot([c])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "robot.start_sync()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for m in robot.motors:\n",
      "    m.compliant = False\n",
      "    m.goal_position = 0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pypot.primitive.utils import Sinus"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s1 = Sinus(robot, 50, robot.motors, amp=30)\n",
      "s2 = Sinus(robot, 50, robot.motors, amp=5, freq=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s1.start()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "\n",
      "import time\n",
      "\n",
      "pos = []\n",
      "\n",
      "start = time.time()\n",
      "while time.time() - start < 5:\n",
      "    pos.append(robot.motors[0].present_position)\n",
      "    time.sleep(0.02)\n",
      "    \n",
      "plot(linspace(0, 5, len(pos)), pos)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "[<matplotlib.lines.Line2D at 0xa39fa0c>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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fBt4Gvl359U3gl0BdI31dGDd9fUaMEA/sv/9bMiM++UQej+Wvjhlj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\noiiKoiiKoiiKojjF/wfcwR0wd8VFWwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x9bd68cc>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s2.start()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import time\n",
      "\n",
      "pos = []\n",
      "\n",
      "start = time.time()\n",
      "while time.time() - start < 5:\n",
      "    pos.append(robot.motors[0].present_position)\n",
      "    time.sleep(0.02)\n",
      "    \n",
      "plot(linspace(0, 5, len(pos)), pos)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "[<matplotlib.lines.Line2D at 0xa4fe50c>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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26lSd7uiixo3hxRflDRyUrbEKC2VBlSZ1c2Ix2Yxj1izZQzQIpk+XudWa1M26\n7z6ZePH11/4fWxN7EmIxuPFG+PvfTUci9HQ7GJo0kXbJDzwgI3jTpk2T+evKrObNYfx4M035NLEn\n6bzzYPHiYCxG0MQeHKNGSbuBDz4wHYksrLNpO78wu/xyKCqSMzo/aWJPUqNG0kHxv/7LbByOI4ld\nR2bBkJYm091MtxtwHJg9W0oxyrxGjeA3v5HtKv2kF0/rYcMGaf6zeLG5rnlFRTJKXLHCzPHVoXbv\nlja/06aZ26WouBiGD5eNNVQw7NwJPXrAxx/LjJlU6cVTjxx9NPzyl2ZHZ1qGCZ7GjeGKK8y2G5g1\nC0480dzx1aGOPFI2OXnoIf+OqYm9nm65RRao7Nlj5via2IPpuuvghRfMzWufNUvLMEF0443wySf+\nnUlpYq+nzEw45hhZ+em3eH1dE3vwZGXB8cfLblYmaGIPpmbNYPRoePttf46niT0FpjY6LiuTGRhZ\nWf4fW9Xt+uvhb3/z/7j79knjuhNO8P/Yqm5jxsCkSf4cSxN7Ci66SDor+t1mYPZsqaMGYZWjOtTo\n0VBaKhfX/VRUJAvWWrb097gqMSNGyGKlrVu9P1Yqif0iYDGwHzi+2u/uAYqBpcCZKRwj0Nq3l9Pu\nTz7x97jxxK6CKT1dzub++U9/j6tlmGBr0kSmJ3/4offHSiWxLwTOBz6vdnsOcEnl95HAEykeJ9BG\nj5aNLvykiT34Ro3y77Q7ThN78I0Z40++SCXhLgVqWn95LvAqsBdYBZQAhtvOe2f0aHjvPdnwwg/x\nBShaRw22wYNljcHatf4dUxN78I0aJSP2vXu9PY4XI+lOQNVtKVYDGR4cJxCys2Wf1Nmz/TneqlUy\nL9bUwiiVmPR0+NnP5EPfD7t2wZIlsjGMCq6MDFms9OWX3h6nrq3xJgMdarj9t0AyJ5o1LjEdN27c\nTz/n5+eTn5+fxFMGx+jRctrtx3ZYWoYJj3POkWmPfmyfV1goq12POsr7Y6nUxMu3iaa7goICCpLc\n+9KNeRVTgduBuZX/fXfl94crv38I3A9Ub14Z2pYC1X3xhWy4MH++98e65x444gi4/37vj6VSs3q1\njKA3bJBeMl76618luT/1lLfHUambNw8uvljaP9SHny0Fqh7kHWAs0AjoDvQEZrp0nEA6+WTZHs2P\nVWWFhdC3r/fHUanr3BlatJASide0vh4effvKZiirVnl3jFQS+/lAGTAIeA+INywtAl6r/P4B8CsO\nU4qxRYMKWtHoAAALOklEQVQGsk3au+96f6zCQq2jhsnQodIUzGua2MMjLU0atXm5aj2VxP4m0AU4\nEqnDn1Xldw8B2cCxwEcpHCM04nV2L23cCNu3Q7du3h5HuWfoUGn/4KVt2+RssXdvb4+j3DNiRHAT\nu6pi+HC50u1lU7DCQujTR1echsmQIbJbvZeXk+bMkddFerp3x1DuGjECpkyRfYu9oIndJS1bytTH\nuXPrvm99aRkmfLKy5NS7vhfKEqFlmPDJyJCV615NuNDE7qIhQ7w97Z4/Xy+chk0s5n09dc4cnQIb\nRvn53uULTewu8rqeOn++jtjDyOt66rx50L+/d8+vvOHlQFC3xnPR+vWyZd6mTTJTxk3bt0O7drBl\ni+zUo8JjwwZZPLRpEzSsa0lgkn78UXb02rpVa+xhU991Dro1ns/at5c32aJF7j/3/PmQm6tJPYyO\nPlo2ZvFip/oFC+R1oUk9fLxc56CJ3WUnnwwzZrj/vHPmaOOvMBs2DJJcFZ4QLcOEm1flW03sLhs0\nyJvErj1iwu2UU+Crr9x/Xk3s4TZ4sLQkcZsmdpd5NWLXxB5up5wC06e7P59dE3u4nXyy7KrkNr14\n6rJ9+6SN77ffync3bNsGHTrA999rLTXMMjPho49kE3Q37N0rNdpNm7SrY1hVVEDr1rLOoV27xB6j\nF08NaNhQRtYzXWx7tmgRHHecJvWwO/VUGbW7pahIPiw0qYdXWpq0+3Z71K6J3QODBrn7Bl60CPLy\n3Hs+ZUa8HOMWLcPYYdAg96+/aGL3wODB7nb0W7hQGzzZwIvErgvWws+LCRea2D0weLCUYtxqCKYj\ndjvk5UFZmSwyc8P8+Tpit8FJJ8kah3373HtOTeweaNFCVhq6tSBl0SIdsdugYUNp1uXG6KyiQhO7\nLdq0ga5d3W0IpondI6ed5s7Cgw0b5JNcN6+2g1vlmJUrZQDRpk3qz6XMc3uhkiZ2jwwdKn24UxWv\nr2sPdju4ldj1wqldNLGHxJAh8gZOtZG+lmHsMmiQO/VUTex2GTJEVqBWVLjzfJrYPdK2rTQFS7XB\nz9y5OvPBJq1aQZcuciaWCk3sdsnIcLchmCZ2D7nRXkA3UbCPG+UYTez2cbMco4ndQ6nOT92+HVas\n0FKMbU49VfbHra9162QqbZcu7sWkzNPEHhKprigrLIScHGjUyL2YlHmpjtjj0xz1grpdhg6VhY1u\ntNDSxO6hvDz45htp3lUf2tHRTj17ys5Ha9bU7/FahrFTjx6S1FeuTP25UknsFwGLgf3A8VVuzwR2\nAvMqv55I4Rihlp4um2PUtxyjm2vYKRZLbdQ+d64mdhvFYu6VY1JJ7AuB84GawigB+ld+/SqFY4Te\nGWfAJ5/U77GzZ2tit9XQofXfUWnGDCnzKfu4tf4llcS+FFieegh2q+8O9Zs2SV+RPn3cj0mZN3x4\n/T7wV6+G3buhe3f3Y1LmDRsmr4tU6+xe1di7I2WYAmCwR8cIhQEDpM6+fn1yj/viCzldd3tXexUM\nffpIM7Bvv03ucV9/LaN1vXBqp1695N922bLUnqeutDEZ6FDD7b8FJh3mMWuBLsAWpPb+FpALbKt+\nx3Hjxv30c35+Pvn5+XXFGzoNG0J+PkyZAr/4ReKPmzZNTsuUndLSZNQ+eTJcfXXij9MyjN1isQNn\n+cceK7cVFBRQkGTdzo3P/anA7cDcJH9v5dZ4NXniCRlpvfBC4o8ZMAAef1xaACs7PfecbJU3YULi\njxkyBMaPl1N2ZacJE+CVV+Cdd2r+fSJb47mV2O8A5lT+d1tktL4f6IFcXO0NVJ/0F5nEvmqVbH/1\n3XfQoEHd99+2Tbo5bt4MjRt7Hp4yZM0aKcmsW5fYtoe7d0s3x+++g2bNvI9PmbFxo0yJ3bCh5jUs\nXu95ej5QBgwC3gM+qLz9NKAQqbG/DlzPoUk9UjIzpW9MovugTpsm89c1qdstI0Mugia6CvWrryA3\nV5O67dq1kzJMKtMeU0nsbyK19COROvxZlbe/gYzQ+wMnIEk/8kaPhkmHuypRzeTJUmdT9hszRl8X\n6lBjxhy+FJMIXXnqk1Gj9A2sDjV6NLz9dmLT2yZPlguuyn7xxF7farUmdp+cdJLUzOuaxvTdd7B2\nrS5Miop+/aShV11tfMvLYelS6Riq7JebKzNk6tveWRO7Txo0gIsvhldfrf1+n3wCp5+e2EVWFX6x\nGFxxBTz1VO33mzJFZkjpdZdoiMXgvPPgjTfq93hN7D669FKZxlTb6dWkSTBypH8xKfOuuUZeFzt2\nHP4+r78OF1zgX0zKvLFjYeLE+pVjNLH7aOBA2Spv1qyaf79tm8xr1jdwtHTtKiWWiRNr/r2+LqJp\n4ECZ4lpYmPxjNbH7KBaD226D3/ym5k/ht9+WBSi683z03HwzPPJIzXteTpokZZjWrf2PS5kTi8mo\n/ZVXkn+sJnaf3XAD/PADvPTSob97+eXk2g4oe4wYAU2bwptvHnz7vn2ycnnsWDNxKbOuvRaefz75\nDXs0sfusQQN48km4/XYoKZHb9uyBm26ShlDnnms2PmVGLAa/+518xRvGOQ7ccQc0aSLXZ1T09Ogh\nrScuvBAWLEj8cdo70IATT5R+HyNHSgfHyZOlsdP06fImVtE0erT04B84UGZGzZgBzZtLfV27fEbX\nOefAo4/K3g7nnJPYY0w2/4xMr5iaOI4sQCgvlznuOTmmI1JBMXWq9Bfq2VM2vtYWvQpkxD5nDlx1\nlT9NwOor0oldKaXqw+smYEoppQJIE7tSSllGE7tSSllGE7tSSllGE7tSSllGE7tSSllGE7tSSllG\nE7tSSllGE7tSSllGE7tSSllGE7tSSlkmlcT+CLAEKAT+BbSo8rt7gGJgKXBmCsdQSimVpFQS+8dA\nLtAXWI4kc4Ac4JLK7yOBJ1I8jvUKCgpMhxAY+rc4QP8WB+jfIjmpJNzJQHwjr6+BzpU/nwu8CuwF\nVgElwMAUjmM9fdEeoH+LA/RvcYD+LZLj1kj6KuD9yp87Aaur/G41kOHScZRSStWhrn1ZJgMdarj9\nt8Ckyp/vBfYAtW25qo3XlVLKJ6lutHEFcC1wBrCr8ra7K78/XPn9Q+B+pFxTVQmQleLxlVIqakqB\nbK+efCSwGGhb7fYcYD7QCOheGYRu7qWUUiFQDHwDzKv8eqLK736LjMiXAj/zPzSllFJKKaVUSkYi\no/li4C7DsZj0LLAeWGg6kADoAkxFynuLgJvNhmPUEcg1qflAEfBHs+EEQgOkMjCprjtabhWwAPlb\nzDQbysEaIGWaTCAdefEeZzIgg4YA/dHEDjL7ql/lz02BZUT3dQFwVOX3hsAMYLDBWILgNuBl4B3T\ngRi2Emhd151MrAgdiCT2VcgipgnIoqYomgZsMR1EQKxDPuQBfkTaVXQyF45xOyq/N0IGQ+UGYzGt\nM3A28DQ6EQMS+BuYSOwZQFmV/9YFTKq6TORMpvoU2ShJQz7o1iMlqiKz4Rj1OHAnB1a6R5kDfALM\nRqaa18hEYtfFSqo2TYF/Av8PGblHVQVSmuoMDAXyjUZjzihgA1JT1tE6nIoMes4C/h0p5x7CRGJf\ng1woi+vCwS0IVHSlA28ALwFvGY4lKLYC7wEnmg7EkFOAMUht+VVgGPCi0YjM+q7y+0bgTQLUh6sh\nsmgpE6kfRvniKcjfQS+eymjsReS0O+raAi0rfz4S+BxZ3R11pxHtWTFHAc0qf24CfEnA2qKfhcx6\nKOFAu98oehVYC+xGrjtcaTYcowYj5Yf5HFj0NtJoRObkAXORv8UCpL6sJLFHeVZMd+Q1MR+ZEhzl\n3KmUUkoppZRSSimllFJKKaWUUkoppZRSSimllFJKKaWU8sr/AY/1js4swYFCAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x9bb920c>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s1.stop()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import time\n",
      "\n",
      "pos = []\n",
      "\n",
      "start = time.time()\n",
      "while time.time() - start < 5:\n",
      "    pos.append(robot.motors[0].present_position)\n",
      "    time.sleep(0.02)\n",
      "    \n",
      "plot(linspace(0, 5, len(pos)), pos)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "[<matplotlib.lines.Line2D at 0xa54d9ac>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9G7zznbBlS/Efi0WBh7ZQytxKrRJLlsCePeW+o6yFYqsCxMZy+cwZ6Osr9vlm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\nkJCQkJCQkJCQkJCQkBAe/x/Cuc9Mu0bHBgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xa5d560c>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s2.stop()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import time\n",
      "\n",
      "pos = []\n",
      "\n",
      "s1.start()\n",
      "s2.start()\n",
      "start = time.time()\n",
      "while time.time() - start < 10:\n",
      "    pos.append(robot.motors[0].present_position)\n",
      "    time.sleep(0.02)\n",
      "    \n",
      "    if (3 < time.time() - start < 9):\n",
      "        s2.pause()\n",
      "    else:\n",
      "        s2.resume()\n",
      "    \n",
      "plot(linspace(0, 10, len(pos)), pos)\n",
      "\n",
      "s1.stop()\n",
      "s2.stop()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SyTiysUmEi8JmS+Rdu/kHBIuiXMaqyB8+7J7Ig+TArlkTfbuuePKVleIH2yjA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\nyv8vV+bPhw9+ED70IXN9ipr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       "text": [
        "<matplotlib.figure.Figure at 0x9bd61ac>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}