{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Corriger une position en fonction du couple mesuré sur un moteur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from poppy.creatures import Poppy4dofArmMini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mini_dof = Poppy4dofArmMini(simulator='vrep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pypot.primitive import Primitive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Graph_primitive(Primitive):\n",
    "    def __init__(self,robot,motors_name):\n",
    "        self.robot = robot\n",
    "        Primitive.__init__(self, robot)\n",
    "        self.fake_motors={}\n",
    "        for name in motors_name:\n",
    "            self.fake_motors[name] = getattr(self.robot, name)  \n",
    "        self.position={}\n",
    "        self.load={}\n",
    "        self.speed={}\n",
    "        \n",
    "    def setup(self):\n",
    "        for m in self.fake_motors.keys():\n",
    "            self.position[m] = []\n",
    "            self.speed[m] = []\n",
    "            self.load[m] = []\n",
    "        self.python_time=[]\n",
    "        self.pypot_time=[]\n",
    "    \n",
    "    def run(self):\n",
    "        t0 = time.time()\n",
    "        while not self.should_stop():\n",
    "            for m in self.fake_motors.keys():\n",
    "                self.position[m].append(self.fake_motors[m].present_position)\n",
    "                self.load[m].append(self.fake_motors[m].present_load)\n",
    "                self.speed[m].append(self.fake_motors[m].present_speed)\n",
    "            self.python_time.append(time.time()-t0)\n",
    "            self.pypot_time.append(self.elapsed_time)\n",
    "            time.sleep(0.02)\n",
    "            \n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "graph = Graph_primitive(mini_dof,['m3',])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "graph.start()\n",
    "mini_dof.m2.goto_position(90,2,wait=True)\n",
    "mini_dof.m2.goto_position(-90,4,wait=True)\n",
    "mini_dof.m2.goto_position(90,4,wait=True)\n",
    "mini_dof.m2.goto_position(0,2,wait=True)\n",
    "graph.stop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x82680f0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x81491f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(1)\n",
    "plot(graph.pypot_time,graph.load['m3'])\n",
    "xlabel('elapsed time seconds')\n",
    "ylabel('load')\n",
    "title ('Record load function of elapsed time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Filter_PID:\n",
    "    def __init__(self,nb_record=10,goal=0):\n",
    "        self.nb_record = nb_record\n",
    "        self.goal = goal\n",
    "        self.record_pos=[0 for i in range(nb_record)]\n",
    "        self.filter_load=[[0,0] for i in range(nb_record*10)]\n",
    "        \n",
    "    def add(self,l,t):\n",
    "        self.record_pos.append(l-self.goal)\n",
    "        del self.record_pos[0]\n",
    "        self.filter_load.append([t,sum(self.record_pos)/len(self.record_pos)])\n",
    "        del self.filter_load[0]\n",
    "    \n",
    "    def integrate(self,nb_values=10):\n",
    "        x=[i[0] for i in self.filter_load]\n",
    "        y=[i[1] for i in self.filter_load]\n",
    "        return np.trapz(y[-nb_values-1:-1],x[-nb_values-1:-1])\n",
    "       \n",
    "    def derivative(self):\n",
    "        if self.filter_load[-1][0] != 0:\n",
    "            return ((self.filter_load[-1][1]+self.filter_load[-2][1])/2-(self.filter_load[-10][1]+self.filter_load[-9][1])/2)/((self.filter_load[-1][0]+self.filter_load[-2][0])/2-(self.filter_load[-10][0]+self.filter_load[-9][0])/2)\n",
    "        else :\n",
    "            return 0\n",
    "    \n",
    "    def last(self):\n",
    "        return self.filter_load[-1][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Load_primitive(Primitive):\n",
    "    def __init__(self,robot,motors_name):\n",
    "        self.robot = robot\n",
    "        Primitive.__init__(self, robot)  \n",
    "        self.fake_motors = getattr(self.robot, motors_name) \n",
    "    \n",
    "    def setup(self):\n",
    "        self.load=Filter_PID(30)\n",
    "        self.filter_load=[]\n",
    "        self.python_time=[]\n",
    "        self.pypot_time=[]\n",
    "        \n",
    "    \n",
    "    def run(self):\n",
    "        t0 = time.time()\n",
    "        while not self.should_stop():\n",
    "            self.load.add(self.fake_motors.present_load,self.elapsed_time)\n",
    "            self.filter_load.append(self.load.last())\n",
    "            self.python_time.append(time.time()-t0)\n",
    "            self.pypot_time.append(self.elapsed_time)\n",
    "            time.sleep(0.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "load = Load_primitive(mini_dof,'m3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "load.start()\n",
    "mini_dof.m2.goto_position(90,2,wait=True)\n",
    "mini_dof.m2.goto_position(-90,4,wait=True)\n",
    "mini_dof.m2.goto_position(90,4,wait=True)\n",
    "mini_dof.m2.goto_position(0,2,wait=True)\n",
    "load.stop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x8252870>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "NMDiiNYAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x80d51d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(1)\n",
    "plot(load.pypot_time,load.filter_load)\n",
    "xlabel('elapsed time seconds')\n",
    "ylabel('Filter load')\n",
    "title ('Record filter load function of elapsed time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "load.start()\n",
    "mini_dof.m2.goto_position(90,2,wait=True)\n",
    "time.sleep(2)\n",
    "mini_dof.m3.goto_position(-90,2,wait=True)\n",
    "time.sleep(2)\n",
    "load.stop()\n",
    "mini_dof.m2.goto_position(0,2)\n",
    "mini_dof.m3.goto_position(0,2,wait=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x855a3d0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "RU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x84df150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(1)\n",
    "plot(load.pypot_time,load.filter_load)\n",
    "xlabel('elapsed time seconds')\n",
    "ylabel('Filter load')\n",
    "title ('Record filter load function of elapsed time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Load_primitive_correction(Primitive):\n",
    "    def __init__(self,robot,motors_name):\n",
    "        self.robot = robot\n",
    "        Primitive.__init__(self, robot)  \n",
    "        self.fake_motors = getattr(self.robot, motors_name) \n",
    "        self.P=4\n",
    "        self.I=1\n",
    "        self.D=1\n",
    "        \n",
    "    \n",
    "    def setup(self):\n",
    "        self.load=Filter_PID(40)\n",
    "        self.filter_load=[]\n",
    "        self.python_time=[]\n",
    "        self.pypot_time=[]\n",
    "        self.P_record=[]\n",
    "        self.I_record=[]\n",
    "        self.D_record=[]\n",
    "        self.correction=[]\n",
    "        self.angle=self.fake_motors.present_position\n",
    "        self.angle_record=[]\n",
    "    \n",
    "    \n",
    "    \n",
    "    def run(self):\n",
    "        t0 = time.time()\n",
    "        while not self.should_stop():\n",
    "            # ajout du oresent load à l'objet Filter PID \n",
    "            self.load.add(self.fake_motors.present_load,self.elapsed_time)\n",
    "            #ajout de la moyenne des load calculer par Filter PID\n",
    "            self.filter_load.append(self.load.last())\n",
    "            # ajout du temps\n",
    "            self.python_time.append(time.time()-t0)\n",
    "            self.pypot_time.append(self.elapsed_time)\n",
    "            \n",
    "            # calcul de la correction en fonction de l'écart avec l'objectif\n",
    "            P = self.P*self.load.last()\n",
    "            I = self.I*self.load.integrate(30)\n",
    "            D = self.D*self.load.derivative()\n",
    "            \n",
    "            self.P_record.append(P)\n",
    "            self.I_record.append(I)\n",
    "            self.D_record.append(D)\n",
    "            \n",
    "            correction_value = P + D + I\n",
    "            self.correction.append(correction_value)\n",
    "            self.angle_record.append(self.angle)\n",
    "            \n",
    "            self.angle = self.fake_motors.present_position - correction_value\n",
    "            self.fake_motors.goal_position = self.angle\n",
    "            \n",
    "            \n",
    "            \n",
    "            # pause déterminant la fréquance de la boucle\n",
    "            t1 = self.elapsed_time\n",
    "            while self.elapsed_time-t1<0.02:\n",
    "                time.sleep(0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "load = Load_primitive_correction(mini_dof,'m3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "load.start()\n",
    "mini_dof.m2.goto_position(90,4,wait=True)\n",
    "\n",
    "\n",
    "mini_dof.m2.goto_position(0,4,wait=True)\n",
    "time.sleep(1)\n",
    "\n",
    "load.stop()\n",
    "mini_dof.m3.goto_position(0,2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x9c4fdb0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x995de70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9c357f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(1)\n",
    "plot(load.pypot_time,load.P_record,'b-')\n",
    "plot(load.pypot_time,load.I_record,'r-')\n",
    "plot(load.pypot_time,load.D_record,'g-')\n",
    "plot(load.pypot_time,load.correction,'c-')\n",
    "twinx()\n",
    "plot(load.pypot_time,load.angle_record,'b-')\n",
    "xlabel('elapsed time seconds')\n",
    "ylabel('Correction')\n",
    "title ('Record filter load function of elapsed time')\n",
    "\n",
    "figure(2)\n",
    "plot(load.pypot_time,load.correction,'c-')\n",
    "plot(load.pypot_time,load.filter_load,'g-')\n",
    "xlabel('elapsed time seconds')\n",
    "ylabel('Filter load')\n",
    "title ('Record filter load function of elapsed time')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mini_dof.reset_simulation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}