TakeshiHMM-Gazebo-NoiseODOM-REALROBOT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.set_autosave_interval(0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Autosave disabled\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.cluster import  MiniBatchKMeans\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from joblib import dump, load\n",
    "\n",
    "%autosave 0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "class HMM (object):\n",
    "             def __init__(self,A,B,PI):\n",
    "                 self.A=A\n",
    "                 self.B=B\n",
    "                 self.PI=PI   \n",
    "def viterbi(obs,Modelo1,PI):\n",
    "    \n",
    "    delta=np.zeros((len(obs)+1,len(Modelo1.A)))\n",
    "    phi=np.zeros((len(obs)+1,len(A)))+666\n",
    "    path =np.zeros(len(obs)+1)\n",
    "    T=len(obs)\n",
    "    Modelo1.PI = PI\n",
    "    delta[0,:]= Modelo1.PI * Modelo1.B[:,obs[0]]\n",
    "    phi[0,:]=666\n",
    "    for t in range(len(obs)):\n",
    "        for j in range(delta.shape[1]):\n",
    "\n",
    "            delta [t+1,j]=np.max(delta[t] * A[:,j]) * B[j,obs[t]]\n",
    "            phi[t+1,j]= np.argmax(delta[t] * A[:,j])\n",
    "    path[T]=int(np.argmax(delta[T,:]))\n",
    "    for i in np.arange(T-1,0,-1):\n",
    "        #print (i,phi[i+1,int(path[i+1])])\n",
    "        path[i]=phi[i+1,int(path[i+1])]\n",
    "    return(path)\n",
    "def cuantizar_xy(xy, cc):\n",
    "    xycuant=cc\n",
    "    out=np.power(xycuant-xy,2).sum(axis=1).argmin()\n",
    "    return out\n",
    "    \n",
    "def path_to_xy(path,ccxy):\n",
    "    estimated= pd.DataFrame(path.astype(int).T)\n",
    "    estimated.columns=[['Path_vit']]\n",
    "    estimated['xcuant'] = estimated['Path_vit'].apply(lambda x: ccxy[x,0])\n",
    "    estimated['ycuant'] = estimated['Path_vit'].apply(lambda x: ccxy[x,1])                                \n",
    "    return (estimated)\n",
    "def quantized(xyth,ccxyth):\n",
    "    xythcuant=np.argmin(np.linalg.norm(xyth-ccxyth,axis=1))\n",
    "    x,y=ccxyth[xythcuant,:2]\n",
    "    return ((x,y),(xythcuant))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Markov_A_2_grafo(A,ccxyth):\n",
    "    dists=np.zeros(A.shape)\n",
    "    for i in range(A.shape[0]):\n",
    "        for j in range (A.shape[1]):\n",
    "            if A[i,j]!=0 :\n",
    "                dists[i,j]= np.linalg.norm(ccxyth[i]-ccxyth[j])    \n",
    "    \n",
    "    \n",
    "    con = np.where(dists==0,np.inf,dists)\n",
    "    graphe2=grafo(ccxyth,con)\n",
    "    return graphe2\n",
    "\n",
    "\n",
    "class node(object):\n",
    "    def __init__(self,x,y):\n",
    "        self.x=x\n",
    "        self.y=y\n",
    "        \n",
    "        \n",
    "class grafo (object):\n",
    "             def __init__(self,nodos,conec):\n",
    "                 self.nodos=nodos\n",
    "                 self.conec=conec        \n",
    "\n",
    "def dijkstra(nodoinicial,nodofinal,graphe):\n",
    "    \n",
    "\n",
    "    numnodos= len(graphe.nodos)\n",
    "    con = graphe.conec\n",
    "    D= np.ones(numnodos)*np.inf\n",
    "    Prv= np.ones(numnodos)*np.inf\n",
    "    V= np.zeros(numnodos)\n",
    "    a = nodoinicial\n",
    "    D[a]=0\n",
    "    Prv[a]=0\n",
    "    Prv[np.where(con[a]!=np.inf)]=a\n",
    "    V[a]=1\n",
    "    Dacc=D[a]\n",
    "    ########\n",
    "    D=np.minimum(D,con[a]+D[a])\n",
    "    cont=0\n",
    "    sucess=False\n",
    "    while(sucess==False):\n",
    "        a = np.argmin(D+np.where (V==1,np.inf, V))\n",
    "        Dacc=D[a]\n",
    "        Prv[np.where(D>(con[a]+Dacc) )]=a\n",
    "        V[a]=1\n",
    "        D=np.minimum(D,con[a]+Dacc)\n",
    "        if (a== nodofinal):\n",
    "            print(\"RUTA CALCULADA \")\n",
    "            sucess=True\n",
    "    rutainv=[]\n",
    "    rutainv.append(nodofinal)\n",
    "    while(rutainv[-1]!=nodoinicial):\n",
    "        prv=Prv[int(rutainv[-1])]\n",
    "        rutainv.append(prv)\n",
    "\n",
    "    ruta=[]\n",
    "    for n in reversed(rutainv):\n",
    "        ruta.append(n)\n",
    "    return(ruta)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ccxyth,ccvk=np.load('ccxyth.npy'),np.load('ccvk.npy')\n",
    "A,B,PI = np.load('A.npy'),np.load('B.npy'),np.load('PI.npy')\n",
    "Modelo= HMM(A,B,PI)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "data=pd.read_csv('RealROBOT/lecturasconodometria-noisy.txt')\n",
    "#data=data.iloc[:40,:]\n",
    "\n",
    "cols=['x','y','theta','y_noise','x_noise','theta_noise']\n",
    "n_reads=data.shape[1]-6\n",
    "etiquetas=[]\n",
    "for i in range (n_reads):\n",
    "    etiquetas.append('R'+str(i))\n",
    "\n",
    "        \n",
    "for lab in cols:\n",
    "    etiquetas.append(lab)\n",
    "\n",
    "\n",
    "data.columns=etiquetas\n",
    "\n",
    "\"\"\"if (only_nice!=True):\n",
    "    print(\"NOT ONLY NICE\")\n",
    "    data2=pd.read_csv('lecturasconodometria.txt')\n",
    "    data2.columns=etiquetas\n",
    "    data3= pd.concat((data,data2))\n",
    "    data=data3\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "\n",
    "####\n",
    "#index = data[data.y<-1].index\n",
    "#data=data.drop(index)\n",
    "data.describe()\n",
    "\n",
    "\n",
    "\n",
    "#####\n",
    "\n",
    "xyth=data[['x','y','theta']]\n",
    "\n",
    "lecs=data.iloc[:,0:n_reads]\n",
    "\n",
    "\n",
    "data.describe()\n",
    "data.y_noise=data.y_noise*-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f82ad64c1d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax1.scatter(x=data.x_noise,y=data.y_noise, s=.2 ,c='r' )\n",
    "ax1.scatter(x=data.x,y=data.y, s=.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "rot_ang=3.1416/8\n",
    "R= np.ones((2,2))\n",
    "R[0,0]= np.cos(rot_ang)\n",
    "R[0,1]= -np.sin(rot_ang)\n",
    "R[1,0]= np.sin(rot_ang)\n",
    "R[1,1]= np.cos(rot_ang)\n",
    "rotated=[]\n",
    "for xy in data[['x_noise','y_noise']].values:\n",
    "    xy_rotated=np.matmul(R,xy)\n",
    "    rotated.append(xy_rotated)\n",
    "rotated=np.asarray(rotated)\n",
    "data['x_rot']=rotated[:,0]\n",
    "data['y_rot']=rotated[:,1]\n",
    "tx=data.x.iloc[0]-data.x_rot.iloc[0]\n",
    "ty=data.y.iloc[0]-data.y_rot.iloc[0]\n",
    "tx,ty\n",
    "data.x_rot=data.x_rot+tx\n",
    "data.y_rot=data.y_rot+ty\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f82ad5ae080>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0fHVGpQsAgIIKezqQnq/OCF0AABQU04HxInQBAFBQTAfGi9AFAEDB1KcVJU1PB7LnYfQIXQAAFEyraUWmGqNn7ulLtKVSycvlctLDAAAglypTrpHxSQ2uXDLd9N7qMgRjZqPuXpprO5aMAACgYOp7Gc51GcLF9CIAAHnSxXEe6eOKF6ELAIA86eI4j/RxxYvQBQBAnnRxnEeWjIgXPV0AABQUfVzxotIFAECe1KYXK1u3zurXoocrWYQuAADSpotm+Bm3ufNO6eBB6bbbNHL8W1mLK2UIXQAApE0XzfCzbvObvyn192tw1cCsfi16uJLF4qgAAKRNpVINUcPDUn9/8Nts2VI9vWZN8NuhZyyOCgBAVvX3V4NTt7c599xoxoNQML0IAAAQA0IXAABADAhdAACEbT57HyL3CF0AAIRtPnsfIvcIXQAAhK2LQ/GgONh7EQCAsM1n70PkHpUuAACAGBC6AABIAs32hUPoAgAgCRE123NQ6/QidAEAkISImu05qHV60UgPAEASImq256DW6UXoAgAgR/r7TEOrliY9DLTA9CIAAEAMCF0AAAAxIHQBAADEgNAFAAAQA0IXAACdsIgpQkLoAgCgkyCLmBLMEAChCwCAToIsYhrR6vLIF9bpAgCgkyCLmEa0ujzyZc5Kl5kdZmb3m9lDZvaomX2uxTa/bWYTZvZg7evKhusuM7Onal+Xhf0AAABIXD2Y9fcnPRKkWJBK1yuSznb3/Wa2UNIPzOy77j7StN3/dPffa7zAzBZL+hNJJUkuadTMbnf3n4YxeAAAgKyYs9LlVftrZxfWvoIeuvzdkja7+75a0Nos6bx5jRQAgCwLqdm+MuXaPrZXlamgb8VIi0CN9GbWb2YPSnpB1RB1X4vNPmBmD5vZrWZ2XO2yYyX9pGGbZ2uXtfoZa82sbGbliYmJLh4CAAAZEFKz/cj4pNZuKGtkfDKUYSE+gUKXu1fc/VRJyySdaWYnN23yvyUtd/e3qlrNurnbgbj7encvuXtpYGCg25sDAJBu82y2b65sDa5covWXljS4ckn4Y0Skuloywt1flLRVTVOE7j7p7q/Uzt4g6Yza6T2SjmvYdFntMgAAimWezfbNla3+PtPQqqXq77MoRokIBdl7ccDMjqqdPlzSOZKeaNrm6Iaz50t6vHb6e5LONbPXm9nrJZ1buwwAAARAZSs/guy9eLSkm82sX9WQ9k13/7aZXSOp7O63S/q4mZ0v6ZCkfZJ+W5LcfZ+Z/VdJO2v3dY277wv7QQAAkFf1yhayz9zTt/dDqVTycrmc9DAAAAhXpVJtpB8eZk2vHDGzUXcvzbUdhwECACAuHC6o0AhdAADEhcMFFRrHXgQAIC5BjuOI3KLSBQAAEANCFwAAKcEhfvKN0AUAQErsGNurK766UzvG9iY9lJ8L6ZiRIHQBAJAaLklW+16XdOhhj8vQELoAAEiJoVVLdeNlq2cuhpp06GGPy9AQugAASFi9l0vS7OMqJh165nnMSMxG6AIAIGHNB7WegdCTG4QuAEA+Jd0L1YXYD2qdoecmTwhdAIB8SroXqgv1g1rPmFaMUg/PDctazB+hCwCQT0n3QqVZD89Nx6lQdEToAgDkUxy9UD1M0yVaMerhuYl9KjRHCF0AgPyLqoeph2m6rFaMYp8KzRFCFwAg/6Lq7+phmi7pilFjpY0+rXgQugAA+RdVf1cP03SJVIwaKn6NlbasVt2yxtzTl2pLpZKXy+WkhwEAQL5s2VKt+G3apMpZZ2tkfHK60lY/zbRh98xs1N1Lc21HpQsAgKJgj85EEboAACiKhunQjtOLLJ4aCUIXAABB5CyIDK5cor/+rdP18LMv6vTjXz+zqT9DC8tmCaELAFBc3QSpeQaRxPcMbPMY+/tMT/zLy/rCHU9qwz/u/nlTf6VS/brttulpyMQfQ04QugAAxdVNkJpnP1TiewbWHmNl69ZZwenyoRW6+j2/qMuHVszc/gMfqE5F1vbKTPwx5AR7LwIAiqtSqYaM4eHIVq6vTHmyewbWHuP2496itbc8oPWXljS0aumc2zc+J4k/hpQLuvcioQsAgIiFEVp6vY/53J6wFQxLRgAAkKDGPqgwpud6vY+Oi7G26ftiWjFchC4AACLQGFjCOORPpIcNatPblvShivKG6UUAACKQqam5hj6uivVlZ9wpwfQiAAAJ6ubYilEvyTDn/bdZNBXhInQBAJCwqINON/fPlGJ0mF4EACBhUU9FZmqqM4OYXgQAICO6mYoMrGGPxEjuH10jdAEAkDKh9Hhx/MTUIXQBAJAyofR4zfOwRYjOgqQHAAAAZgqlmb2+RyJSg0oXAAAp0+9TGnrmIfX7VNJDQYgIXQAApA39WLlE6AIAIG3ox8oleroAAEgb+rFyiUoXACD9GtacArKK0AUASL8kepxqQa/y6qFojotIkCwcQhcAIP2S6HGqBb2RjXdHc1xEmuULh2MvAgDQSqUibdumyjvfpZFnXgz/uIW1+9fwcLWHC5kV9NiLNNIDANBKrZm9X9LQqqWR3T+Kg+lFAACAGBC6AADZlGAjeigHpEbhELoAANkUYiN6Y4gKEqhCOSA1CofQBQDIpl72aGyqkjWGqCCBKpQDUqNwCF0AgGyqN6LPZ8+/pipZY4giUCEqhC4AQPE0Vcn6+2x6D8WR8ck5l4dgehHzQegCABRPmypZ0DBFNQzzwTpdAADUrF6+WB9fc4JWL1/ccbvGyhgQFJUuAABqdu7epy9veUo7d+9LeijIIUIXAKAYAqzrVZ82XL18MetwIXSELgBAukS16GmAdb3q04Y7d++jUR6hI3QBANIlxEVPZ+hiXS8a5REFQhcAIF16WfS0kxZ7LLZbfb5e8eq0bATQLUIXACBdeln0tEust4U4EboAAIXRXNliGhFxmjN0mdlhZna/mT1kZo+a2edabPNJM3vMzB42sy1m9saG6ypm9mDt6/awHwAAIKWiaojvQXNli2lExClIpesVSWe7+ymSTpV0npkNNm3zT5JK7v5WSbdK+mLDdQfc/dTa1/mhjBoAkH5RNcT3gMoWkjRn6PKq/bWzC2tf3rTNVnf/P7WzI5KWhTpKAED2RNUQ3wMqW0hSoJ4uM+s3swclvSBps7vf12HzKyR9t+H8YWZWNrMRM3tfD2MFAGRJjA3xQBYECl3uXnH3U1WtYJ1pZie32s7MLpZUknRtw8VvdPeSpN+S9Odm9qY2t11bC2fliYmJrh4EAADN2i0HASSlq70X3f1FSVslndd8nZn9mqT/W9L57v5Kw2321L6PS9om6bQ2973e3UvuXhoYGOhmWAAAzLJjbK+u+OpO7Rjbm/RQAEnB9l4cMLOjaqcPl3SOpCeatjlN0t+qGrheaLj89Wb2mtrppZKGJD0W3vABAGjNJcmampCBBAWpdB0taauZPSxpp6o9Xd82s2vMrL434rWSXivp75uWhvglSWUze0jVCtl/c3dCFwAgckOrlurGy1ZraNXS+d1BCpe8QLaZe/o+A5RKJS+Xy0kPAwBQZFu2VJe82LSpukMA0IaZjdb61ztiRXoAAFpJ4ZIXyLYFSQ8AAIBUqi95AYSEShcAIDr0RQHTCF0AgOiEeSigKAJc/T4PHiQcInKELgBAdMLsi4riWI71+7zuutQdJxL5Q08XACA6YfZFtQhwlSnXyPikBlcumd/xFOv3+Y53SKefTtM8IkWlCwCQDS2O5TgyPqmrbt6pG+4dn9/hfur3uWgRx4lE5AhdAIDMGly5ROt+7UR9ectTGhmfTHo4QEdMLwIAMqu/z3TlO1bq5GOP1ODKJUkPB+iIShcAIP067LnY32caWrV0fj1dQIwIXQCAVKlMubaP7Z3ZoxXFnotAzAhdAIBUGRmf1NoN5Zk9WhySBzlATxcAIFUGVy7R+ktLM3u0OCQPcoDQBQBIlXqPFpA3TC8CAADEgNAFAAAQA0IXAKA3URyIGsghQhdSreWu4wDSheUcgEAIXUi1lruOA0gXlnMAAiF0IdVa7joOIF1aHIg6KKrZKBJCF1ItjMN78E8dCEFEfVtUs1EkhC6EIs3Bhn/qQAgi6tsKUs1O8/8XoBuELoQizcGGKUogBPPp2wpQHWtcCLVdsErz/xegG4QuhCLNwSaMKUqg8ObTt9VFdaxTsErz/xegG+aevnJtqVTycrmc9DAAAL2oVKqBa3i4ZVirTLlGxienw9TI+KRWL1+snbv3aXDlEj4oITPMbNTdS3NtR6ULABCN5upY03RjY3WrXpHeuXsfU4nILUIXACAeTdONraYNmUpEnhG6AKCo4j58T1Mzfqt+S3owkWeELgAoqrgP39PDIqpAHhC6AKCoUnT4HtbiQhEQugCgqFJUeWpeMoIQhjwidAEAEtfcQM+CqMgjQhck8amyE54bZE7QBvnm7WJqrG/1N9XcQM9ejMgjQhck8amyE54bZE7QBvnm7WJqrA/yN8VejMgjVqSHpJkrQ/NPbiaeG2TOHCvBt90u6O16HR5/U8iZoCvSE7pyjn9uADIlpuAHhInDAEESU2MA0qltr2Tca4cBMSJ05VxWmlFpVgeKpe0HwhStHQaEjdCVc2lpRp0rVFGRA1Iowr0Z234gTNHaYUDYCF2IxVyhKisVOaBQIpzqS8sHQiBOhC7EYq5QxT9gIIXmmOqjLQDoDqELsUhLqOJNAujCHFN99Qr29ff8SPfsmmj5d8XfHPBzhC7kVqt/9vSOAeEZXLlEH19zgv5s8y6t/bvWf1dt/+ZiWv0eSBPW6UJubR/bq7Ubylp/aUlDq5ZKYt0yIGyVKdeOsb1yafrvrPFvrO3f3JYt1X6xTZuq1TQgw1gcFYVHwEJuJLxgaOPfkqSOf1f1Dzt/c/EZ6uuz9n9/LIKKHGFxVBReWvrIgJ4lvGBo4xRh0D2RXeo8lc/SECggKl3oqP4Jd/Xyxdq5ex9VIyAJWal0NYyzYn1UmlEYVLoQivqn2pu2P52eBnQacFE0CVeFAleNGypyVJqB2Qhd6Kg+VXD50IreFi8NMyhxbDZkXcgfHOJclqHj9CKH8AE6InSho/qn1UUL+nr71BpmUOIfO7Iu5A8OUS2FUg9zBw5W9Lff/5EOHprqvNAxfVpAR/R0IR7sqQT8XMh/D2Htqdt8P/U9Ec8/5Rh9fedPdPV7flG/86439TxeIG9YMgIAopKzDxH1sDU15frILaPTa9vVLz/9+Ndrwz/u1uVDK7RoARMkQDMa6QEgKjnrK6xPT7o0Y+qw3l5w+KJ+/c673kTgAnrEXxAAdCvmvsLpRvlXD3XVgB+0wb7epzW0ail7HAIRInQBQLfiahiv7eU4MjahtRvKuuHmu1R5//sDV9gaG+w7BTCWdwDiQehC8bDOF7KiNo05+OOH9fE1J+i6Z1wjN9zascLWGK4a9zTkYO9A8ghdmC3voSRn/TjIsdo0Zv9ZZ+nKd6zU9Zet1uCF53SssDWGq8YKVselHrqR9/8PQIQIXZgt76GEdb7QShrDRMM0ZqcpwHbVrRl3FdYUYlL/H9L4+wG6ROjCbHkPJSH148S5CjhikOEPG+2qW5FI6v9Dhn8/QB2hq2ACBQVWlQ6EHpmciTBMhBHQO93H6uWL9fE1J2j18sU9DDJgJSmp/w95/zCIQpgzdJnZYWZ2v5k9ZGaPmtnnWmzzGjP7n2Y2Zmb3mdnyhuuurl3+pJm9O9zho1sEhfCE1iODdIgwTITxd9fpPnbu3qcvb3lKO3fvm/8g0z5tyIdB5MCcK9KbmUk6wt33m9lCST+QtM7dRxq2+V1Jb3X3j5jZRZLe7+4fMrOTJH1d0pmSjpF0l6QT3b3jXxcr0oevvrL06uWLtXP3vp4PFwIU1XwOudN4G0nzOmRPp5978NCUbtr+dG8rxie1yv6WLdWwt2lTNVQBGRTaivRetb92dmHtqzmpXSDp5trpWyWtqYW1CyR9w91fcfenJY2pGsAQs/qn5J2797EeT0To8cqvxt9tq4rTXL/7xj6r+Va9OvVqhVLp6u+vBq5t24I3q4fR3M60IQok0EciM+s3swclvSBps7vf17TJsZJ+IknufkjSzyQtaby85tnaZa1+xlozK5tZeWJiortHgTkxFRY9pm7za8fYXl3x1Z3aMba75+lyAAAcC0lEQVS35d9SN7/7KP4WQ7vPbqcYw5iSZNoQBRIodLl7xd1PlbRM0plmdnLYA3H39e5ecvfSwMBA2HdfeKw4HT2CbX5V3DXlrop7y7+lbn73Ufwthnaf3VadqFIBXelq8t/dX5S0VdJ5TVftkXScJJnZAklHSppsvLxmWe0yIHcIttlVmXLdu2tC9+yaaH2YHDP1manfWv9uc/O777bqRJUK6EqQvRcHzOyo2unDJZ0j6YmmzW6XdFnt9IWS7vZqh/7tki6q7d24QtIJku4Pa/AAEIaR8UldtaGstX/XeorwbauW6vpLS3JpRiijjw9AN4JUuo6WtNXMHpa0U9Werm+b2TVmdn5tmxslLTGzMUmflPTHkuTuj0r6pqTHJN0h6WNz7bkIAHEbXLlE119a0vpLWk8R9veZ+vpMH71ldEYoo48PQDfmXDIiCSwZ0aOkdv0GcqzVkg3zWT4CQP6EtmQEMojDZQBd62bZh06XAUA7hK48Yo8ioOs1pEbGJrT2pvs0MsaSNQCiQejKI/YoArqu+A7++GGt/9b/o8EfPxzpsAAUFz1dAPKp295GeiEBzBM9XQAyqV1vVdDlGaa3sz7WnAKQKoSurAnjWGdAysx1bEMp+PIMLOMAIK0IXVnDnonImcqU64Z7x3XVzTt1w73jWr188cxD6tQ+aAy+8aiOh9qpB7dZtweAlCB0ZQ17JiJnRsYn9eUtT+mCU4/VdXft0s7d+2Yuw1D7oNF/z/c7Ls9Qr3DNuj0ApASN9ADi0aZRvT6luHr5Yu3cvW/2QqMBG9xZqLQFdg4AYkEjfQ5wXLcG9LJlWmXKtf22Laq8//2zpsbrC4wuWtDXukIVsME9VwuVhvV6D7Mdgb9BoGeErhSjIbgBvWyZNjI+qbWPTGnkhluZGg8irNd7mO0I/A0CPWN6McWYLmnANEmmzee1XOjXfxpf72kcE5ASTC/mQK6mS3pVsDWU8ja1PJ/XciyV3rROmaXx9Z7GMQEZQ+gCUqiIU8vNQXNw5ZLol37I05RZWgMkgGmELswP/+AjFUvgSJnmoBlLpTfpJVjC/DtKa4DkfwUwjdCF+UnrP/icKOLUciJBM+kpszD/jpIOkO3wvwKYRiM95oemWqB3Rfg7KsJjROHRSI9oJV0hQCblbQeBnhXh76gIjxEIiNAFNKL/JFJF3EEAAOoIXUAj+k8iVcQdBACgjtAFNEprM3ILvUzVJTXNV8QdBACgjtAFNMpQ/0l9qu6Ge8e7Dk+9TvNlMfABQNIIXUBGDa5coo+vOUHX3bWr6/DUapqvmzDUS2ijrwtAUbFkBJBh3R6fsNP228f2au2GstZfWtLQqqWh/tywbouEsfwD0BJLRgAFELRHql7FunfXhK64eae2j+2ddf3UlOsrF58RqMm9l94s+royjB1NgJ4QupB6zdNe9ATNNtdzUp/Se+L5lySXmuPOyPikPnLLqPr7LJowxFIc+ZChHU2ANCJ0YU5Jh5zmhvH6+e1je6fHFXSMUT2WypTrnl0TunfXRFfjCctcfVL1Hq7L3rZCf3DOifqVpmpW5Es5pKlCQgCcvwztaAKkEaErhzq+4Xd4w2l3u6CNz90GjaDbr16+WL9xytH60p1PavvY3umAYNL0uIKOMaom7vr9/sev7tSOsb2xB8PG0NTq9vUpvQd+/FN9ectT2rl734zb16+XNCM8hiZNFZI0BUAAhULoyqGOwaLDG0672wWtgnQbaIJuv3P3Pm18YI+mVJ0WqweEt61aOj2uoGOMqqIzuHKJ1v3aCerrk7zh57QMhrdu7lhlmU8wbOyT6nT7uR7/yPik1v5dWVeFHUzTVCEJKwBSMQPQJfZezKGOe4c17X1Ur4qYpF9ZuUQ7d++b915lYe5J17zdjrG9cimRBuyg4zx4aEo3bX9alw+t0KIFfbNuK0kjt27W4JUXqn/jxmoI6eHn9Tredretvx7eRrN7Z1u2VD/AbNrU9neZmF72MmQPRaBrQfdeJHSlQJK70G8f26srvrpTlSnXf//t1XrHiQOJjyltgi6lEGg73tDyI82/y14CYZrDJJBSLBmRIc3TQXE2YTdPi7UbU5GFOnWZpmm2sDRMsyW900Ws0vy77GUKNU39d0DOUOlKgeaqUjeLVEbx89tdBrTUUBnZ/sZTYn3tAkAaML2YAe2CTf3y1csX99RjBcSiYZqtYn2EdQCFw/RiytSnXQ4empqefmk3hVffE23n7n2xTPEVakoIHc3rtdAwzcZq8wDQHqErJvWAddP2p6eD1ODKJfrKxWdoqraGU7OgvUS9hib6t1DHawEAosP0YkxaTRlK0g33juu6u3bp+stWz7sHptceMPq3UMdrAQC6x/RiQtpVnerTLosW9E0HoxvuHdeXtzyldb92Yk+Ldfa64CdTQjkRwmKds14LLAAKAKEhdIWsm8PRXHfXLn18zQm68h0rewo8hCZIiubwNhwyBwBCw/RiyLpZZZ1pHIQqisU6w7rPJBcSDfNnp3lBVACJYXoxIUGrTlSn0JUg03xRLNYZ1n0mWTEL82dT+QPQA0IXkCbtwlXW3+yTXOU8zJ/Nau0AekDoygjW0iqIduEq62/2SR4yJ8yfneZD/wBIPUJXRrB+UkG0C1e9vNmzByIApAKhKyPaLgvBG2q+RFFJ2bJF+o3fqH5H/vE/AUgtQldGtG28z3qvD+KRwr2UERH+JwCpxZIRWccu7JgLr5Fi4fcNxI4lIxISe8M7jb2YC6+RYuH3DaQWoStkNLwDAIBWCF0h6/U4iAgBjcSIEq8vAPNE6ApZc8M762slIKpGYt5sIdGoDmDeCF0RazXdSBCLWFQLifJmG1xYATWNQTfrC9UCSAyhq42wgtGs6cZKRSO3bi5e31ecb55RNRLzZhtcWAE1jUGXRnUA80ToamPH2F5d8dWdWn/Pj2YFr24C2az1tbZt0+CVF2r9yX3F6vtK45tnt7LyZpuG6lBYATXPQTcNvycAsSJ0teGSDlWm9Kffe1L3PPHCjJDV0x6Kw8Pq37hRQx9YM3uh0zzL85tn2qQh4IYVULMSdOcjDb8nALFicdQ2Dhys6EN/u10P73lZ7zxhiUafeVHrLy1paNVSVaZcI+OTGly5pFjBCdnA4pjZwO8JyA0WR+3Rhn/crYf3vCxJuuepSX1seNX0dGDbQ/IAaZDn6lCe8HsCCofQ1cblQyu0fPHh0+crmiJkAQCAeSN0tXHw0JSmpg5Nn5+aCvf+WTYCAIBimTN0mdlxZrbVzB4zs0fNbF2LbT5lZg/Wvh4xs4qZLa5dt9vMfli7LjNHsf69r4/qxy++On3+Jz/9P6HeP4cLAgCgWIJUug5J+kN3P0nSoKSPmdlJjRu4+7Xufqq7nyrpaknfd/d9DZucVbt+ziaztPit0vEzzh975OG6d9dEoOUjglSxOFxQA3adBwAUwJyhy92fc/cHaqdflvS4pGM73OTDkr4ezvCSs2jhzObWv7h7TFe1qEw1VqzqYWvH2N45q1g04zdg13lkCR8SAMxTVz1dZrZc0mmS7mtz/b+RdJ6k2xoudkl3mtmoma2d3zDjVZlyPfbPL02ff/1hkkz6vbNXzapMNVas6gHMJapY3cjKGl682ULiQwKAeQscuszstaqGqU+4+0ttNvsNSdubphbf7u6nS3qPqlOT72xz/2vNrGxm5YmJiaDDisSOsb36s7t2TZ///w5JUy6ZST/YNaGvbBvTwUPVzvp6xaoy5Xr42Rf1V//hdA2tWkoVqxtZ2XU+7W+2hMJ4ZOVDAoDUCRS6zGyhqoHra+7+rQ6bXqSmqUV331P7/oKkjZLObHVDd1/v7iV3Lw0MDAQZVmRckuvn/VgHDkmnLDtSJx99pK7cUNYX7nhSN21/esZtbtr+tL5wx5N68vmXCVt5lfY327SHwrzIyocEAKkz54r0ZmaSbpa0z90/0WG7IyU9Lek4d//X2mVHSOpz95drpzdLusbd7+j0M5Nekf7goSl99n/9ULeN7tGhKdfAEQv0vT84S4/8889UOTSlJ154WVe8faUWLeibcZubtj+ty4dWzLg8k1gpO5v4vQFAIsJckX5I0iWSzm5YFuLfm9lHzOwjDdu9X9Kd9cBV8wZJPzCzhyTdL+kf5gpcabBz9z7d/k979JunH6t3nbhEE/96SNd+7wl99JZRLVrUr48Or5oVrBYt6NPvvOtN2Q9cEhWTrMpTBYapUgA5xLEXW6hMudbf8yNdd9dT+spvna5dE/t16a8u1wM//mkxjrdIxQRJ27KlGvw3baoGSQBIsaCVrgVxDCZr+vtMv/TvXqeKu6zP9DvvepMkaWjV0oRHFpN6xQRIStr75wBgHghdANKH4A8gh3LQgBS+ypTr8edfUp9JfXmfSgQAALEgdLUwMj6pv7x7TJ885835mFKkKRlx4bUGAG0RuloYXLlEf3PxGfqlo1+X9FDCwd6I4SJYtNfra43nFkCOEbpa6O8z9fWZPnrLaMfjJ2YGTcnhIsS21+trjecWQI6xZEQblSnXyPhkMZaIQHdYUiM6PLcAMijMxVELqX5MRQIXJM2c9srTIqRpw3MLIMcIXUAQTHsBAHpE6AKCoC8um2jMB5AihC4giDROexEo5kaFEkCKELqArCJQzI0KJYAU4TBAQFYRKObG4YQApAiVrigx/YMohTnlyWsVACJH6ApDuzcspn+QFbxWASByhK4wtHvDYvqne1RcksFrFQAiR+gKQ7s3rDTu8ZZ2VFxmiyOI8loFgMgRusLAG1Z4qLjMFkUQpaIIALEjdCEd6iFAIsA2iyKIUlEEgNgRuvImqxUMQkB7UVRS015RzOrrGAA6IHTlTVbDS9pDQN6kfUo8q69jAOjA3D3pMcxSKpW8XC4nPYxsqlSqb1TDw+l9QwXmwusYQIaY2ai7l+bajhXp84YVuJEHvI4B5BDTi0CR0TsFALEhdMWNNzmkCb1TABAbQlfceJPLjiIEZHZgAIDYELrixptcdhQhIKd9L0YAyBFCV1BhVT2SepMrQtUmbARkAECICF1BZb3qkfXxJ4EqEAAgRISuoLJe9cj6+AEAyDjW6Qoq6+sGZX38AABkHJUuIGr00wEAROgCokc/HQBAhC4gevTTAQBU5NB14IC0dm31u8QUUJyK9lyzFyQAQEUNXZWK9J73SNdfL513XvU8U0Dx4blOXtGCLwCkQDFD17Zt0ve/Xz19zz3V80wBxYfnOnkEXwCIXTFD1/Cw9J/+U/X0Zz9bPZ/3KaA0VTby/lw3StPz3ijM4JvWxwgAKVPM0NXfL/2X/yLddZf0J39SjDd/KhvJSOvzHmbwTetjBICUMXdPegyzlEolL5fLSQ8jX+p9a/WqHuJRhOe9CI8RADows1F3L821HSvSFwUr0iejCM97ER4jAISgmNOLcaDPBQAANCB0RYU+FwAA0IDQFRWWRYgPVUUAQAYQuqJSpGURkkZVEQCQAYSuMKWh4pKGMcSNqiIAIAMIXWFKQ8UlDWOIG1VFAEAGELrClIaKSxrGAAAAZmGdrjClYb2iNIwBAADMQqUL2VTE3jUAQKYRupBNRexdAwBkGqEL2UTvWnioGgJALAhdyCb2WAwPVUMAiAWhC4hKVipIVA0BIBaELiAqWakgUTUEgFgQuoCohFVBykrFDADQEaEriCK/6RX5sfcqrApSVipmAICOCF1BFPlNr8iPPS3ouQKAXCB0BTHfN708VIl4w08ePVcAkAuEriDm+6aXhyoRb/gAAIRiztBlZseZ2VYze8zMHjWzdS22GTazn5nZg7Wv/9xw3Xlm9qSZjZnZH4f9AEITRVWKKhEAAKgJUuk6JOkP3f0kSYOSPmZmJ7XY7l53P7X2dY0kmVm/pL+S9B5JJ0n6cJvbJi+KqlQ3VaI8TEUCAIC25gxd7v6cuz9QO/2ypMclHRvw/s+UNObu4+5+UNI3JF0w38FGKumqVB6mIgEAQFtd9XSZ2XJJp0m6r8XVv2pmD5nZd83sl2uXHSvpJw3bPKs2gc3M1ppZ2czKExMT3QwrHEn3LiUd+gAAQKQChy4ze62k2yR9wt1farr6AUlvdPdTJP2FpE3dDsTd17t7yd1LAwMD3d48+5IOfQAAIFKBQpeZLVQ1cH3N3b/VfL27v+Tu+2unvyNpoZktlbRH0nENmy6rXYY0y0J/WRbGCABAgyB7L5qkGyU97u5farPNv6ttJzM7s3a/k5J2SjrBzFaY2SJJF0m6PazBIyJZ6C8La4yENwBATIJUuoYkXSLp7IYlIf69mX3EzD5S2+ZCSY+Y2UOSvizpIq86JOn3JH1P1Qb8b7r7oxE8ju7s3y+de660apW0b1/So0mfLPSXhTXGLARMAEAumLsnPYZZSqWSl8vl6H7AeedJ3/te9fSb3yw98UR0PwvpVqlUA9fwMP10AIB5MbNRdy/NtV0xV6Q/5pjq91/4BWnHjmTHgmTFsQMDU5gAABU1dP3VX0lXXSX9y79IixcnPRrkHVOYAAAVNXQdfri0fn31e9SociALPXIAgMgVM3TFqVOVg0BWDKzBBgAQoSt6naocTDsBAFAYC5IeQO7VqxytMO0EAEBhELqS1CmQAQCAXGF6EQAAIAaELgAAgBgQuhAd9s4EAGAaoatbeQ8SYT4+9s4EAGAaoatbeQ8SYT4+9s4EAGAaoatbeQ8SYT6+qBcFzXvVEQCQK4SuRpWK9O1vS+eeK7373dL+/bO3yfvq4ll6fHmvOgIAcoV1uhrdead0/vmSe/X8Bz8ofec7yY4J7eW96ggAyBUqXY0efLAauI4+ulrp+d3fDX7bxqkupr3ikaWqHACg8Kh0NTrtNGnRIun666XDDuuuglKf6tq0qXq+fpoV5wEAgAhdM51zTnU6cXi4++pJ81RX2NNelUo12M1nbAAAIHFMLzbqZbqq8bZRTHvRNA4AQKYRurKCpnEAADKN6cWsqFfPAABAJlHpAgAAiAGhK01YagIAgNwidKUJzfIAAOQWoStNaJYHACC3CF1pkrYV1pnuBAAgNISuvAojMDHdCQBAaAhdeRVGYGK6EwCA0BC6upWVKbcwAlPapjsBAMgwQle3sjLlRmACACBVCF3dYsoNAADMA6Grk1ZTiVSQAADAPBC6OsnKVCIAAEg9Qlezgwela6+tfmcqEQAAhITQ1ezaa6VPf1pasULav5+pRAAAEApCV7Ndu6rf//mfpXPPTXYsAAAgNwhdzd785ur3Y4+V7rwz2bEAAIDcWJD0AFLnj/5IWrhQWrdOWrQo6dEAAICcIHQ1W7RI+tSnkh4FAADIGaYXAQAAYkDoSqOsHN8RAAAERuhKIxZlBQAgdwhdacSirAAA5A6N9GlUP74jAADIDSpdAAAAMSB0hYXmdwAA0AGhKyw0vwMAgA4IXUHs2yedcIJ0xhnSz37WuqpF8zsAAOiA0BXE294mjY1JDzwgnXNO66pWvfm9vz+pUQIAgBQjdAWxY4e0eHH19HvfS1ULAAB0jSUjgli8WHruOem666oHwmZJBwAA0CVCV1AcCBsAAPSA6UUAAIAYELoAAABiQOgCAACIAaELAAAgBoQuAACAGBC6AAAAYkDoAgAAiMGcocvMjjOzrWb2mJk9ambrWmzzH8zsYTP7oZntMLNTGq7bXbv8QTMrh/0AMqPV8RoBAEBhBKl0HZL0h+5+kqRBSR8zs5Oatnla0rvc/S2S/quk9U3Xn+Xup7p7qecRZ1Wr4zUCAIDCmHNFend/TtJztdMvm9njko6V9FjDNjsabjIiaVnI48w+jtcIAEChddXTZWbLJZ0m6b4Om10h6bsN513SnWY2amZrux1gbtSP19jfn/RIAABAAgIfe9HMXivpNkmfcPeX2mxzlqqh6+0NF7/d3feY2b+VtNnMnnD3e1rcdq2ktZJ0/PHHd/EQAAAA0i9QpcvMFqoauL7m7t9qs81bJd0g6QJ3n6xf7u57at9fkLRR0pmtbu/u69295O6lgYGB7h5F3PbskY48svq9jkZ5AADQQZC9F03SjZIed/cvtdnmeEnfknSJu+9quPwIM/uF+mlJ50p6JIyBJ+bAAWnFCumll6QTT/x5yKJRHgAAdBBkenFI0iWSfmhmD9Yu+4yk4yXJ3f9G0n+WtETSX1czmg7V9lR8g6SNtcsWSPof7n5HqI8gbuvWSa++Wu3NMquGrDVraJQHAAAdmbsnPYZZSqWSl8spXdLrwIFq8PrSl6T77quGLJrjAQAoLDMbDbIsVuBGetQcfri0vrYM2Zo1yY4FAABkBocBAgAAiAGhCwAAIAaELgAAgBgQugAAAGJA6AIAAIgBoQsAACAGhC4AAIAYELoAAABiQOgCAACIAaELAAAgBoQuAACAGBC6AAAAYkDoAgAAiAGhCwAAIAaELgAAgBgQugAAAGJA6AIAAIgBoQsAACAGhC4AAIAYELoAAABiQOgCAACIAaELAAAgBubuSY9hFjObkPRM0uNoY6mkvUkPosB4/pPH7yBZPP/J43eQrDQ+/29094G5Nkpl6EozMyu7eynpcRQVz3/y+B0ki+c/efwOkpXl55/pRQAAgBgQugAAAGJA6Ore+qQHUHA8/8njd5Asnv/k8TtIVmaff3q6AAAAYkClCwAAIAaEroDM7L+b2Qtm9kjSYykiMzvOzLaa2WNm9qiZrUt6TEViZoeZ2f1m9lDt+f9c0mMqKjPrN7N/MrNvJz2WojGz3Wb2QzN70MzKSY+niMzsKDO71cyeMLPHzexXkx5TN5heDMjM3ilpv6QN7n5y0uMpGjM7WtLR7v6Amf2CpFFJ73P3xxIeWiGYmUk6wt33m9lCST+QtM7dRxIeWuGY2ScllSS9zt1/PenxFImZ7ZZUcve0rRFVGGZ2s6R73f0GM1sk6d+4+4tJjysoKl0Bufs9kvYlPY6icvfn3P2B2umXJT0u6dhkR1UcXrW/dnZh7YtPbDEzs2WS3ivphqTHAsTNzI6U9E5JN0qSux/MUuCSCF3IIDNbLuk0SfclO5JiqU1rPSjpBUmb3Z3nP35/LunTkqaSHkhBuaQ7zWzUzNYmPZgCWiFpQtJNtSn2G8zsiKQH1Q1CFzLFzF4r6TZJn3D3l5IeT5G4e8XdT5W0TNKZZsY0e4zM7NclveDuo0mPpcDe7u6nS3qPpI/V2k4QnwWSTpf0FXc/TdK/SvrjZIfUHUIXMqPWS3SbpK+5+7eSHk9R1cr5WyWdl/RYCmZI0vm1vqJvSDrbzG5JdkjF4u57at9fkLRR0pnJjqhwnpX0bEOV/VZVQ1hmELqQCbVG7hslPe7uX0p6PEVjZgNmdlTt9OGSzpH0RLKjKhZ3v9rdl7n7ckkXSbrb3S9OeFiFYWZH1HbiUW1K61xJ7M0eI3d/XtJPzOzNtYvWSMrUzlQLkh5AVpjZ1yUNS1pqZs9K+hN3vzHZURXKkKRLJP2w1lckSZ9x9+8kOKYiOVrSzWbWr+qHtW+6O0sWoEjeIGlj9fOfFkj6H+5+R7JDKqTfl/S12p6L45IuT3g8XWHJCAAAgBgwvQgAABADQhcAAEAMCF0AAAAxIHQBAADEgNAFAAAQA0IXAABADAhdAAAAMSB0AQAAxOD/B7TLGYhFf/wLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "ax1.scatter(x=data.x_rot,y=data.y_rot, s=.4 ,c='r' )\n",
    "ax1.scatter(x=data.x,y=data.y, s=.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "delta_odom= np.linalg.norm(data[['x','y']].values-data[['x_rot','y_rot']].values,axis=1)\n",
    "data['delta_odom'] =delta_odom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f82ad5a64a8>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax1.plot(data.delta_odom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    387.000000\n",
       "mean       0.227404\n",
       "std        0.125001\n",
       "min        0.000000\n",
       "25%        0.129119\n",
       "50%        0.201100\n",
       "75%        0.284560\n",
       "max        0.540431\n",
       "Name: delta_odom, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.delta_odom.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_1,y_1,th_1= 0,0,0\n",
    "delta_mov=[]\n",
    "delta_th=[]\n",
    "for x,y,th in zip (data.x,data.y,data.theta):\n",
    "    \n",
    "    \n",
    "    delta_mov.append(np.array((x,y))-np.array((x_1,y_1)  ))\n",
    "    if (th-th_1)> 6:\n",
    "        delta_th.append(th-th_1-(2*3.1416))\n",
    "    if (th-th_1)<- 6:\n",
    "        delta_th.append(th-th_1+(2*3.1416))\n",
    "    \n",
    "    if ((th-th_1)>- 6 and (th-th_1)< 6  ):\n",
    "        delta_th.append(th-th_1)\n",
    "    x_1,y_1,th_1=x,y,th\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_1,y_1,th_1= 0,0,0\n",
    "delta_mov_rot=[]\n",
    "delta_th_rot=[]\n",
    "for x,y,th in zip (data.x_rot,data.y_rot,data.theta_noise):\n",
    "    \n",
    "    \n",
    "    delta_mov_rot.append(np.array((x,y))-np.array((x_1,y_1)  ))\n",
    "    if (th-th_1)> 6:\n",
    "        delta_th_rot.append(th-th_1-(2*3.1416))\n",
    "    if (th-th_1)<- 6:\n",
    "        delta_th_rot.append(th-th_1+(2*3.1416))\n",
    "    \n",
    "    if ((th-th_1)>- 6 and (th-th_1)< 6  ):\n",
    "        delta_th_rot.append(th-th_1)\n",
    "    x_1,y_1,th_1=x,y,th\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "delta_th[0],delta_th_rot[0]=0,0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>387.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.000105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.014550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.062631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.009719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.009750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.037794</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                0\n",
       "count  387.000000\n",
       "mean    -0.000105\n",
       "std      0.014550\n",
       "min     -0.062631\n",
       "25%     -0.009719\n",
       "50%      0.000809\n",
       "75%      0.009750\n",
       "max      0.037794"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DELTA_TH=pd.DataFrame(np.array(delta_th)-np.array(delta_th_rot))\n",
    "DELTA_TH.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f82ad6deb38>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DELTA_TH.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>387.000000</td>\n",
       "      <td>387.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.000479</td>\n",
       "      <td>-0.000566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.010180</td>\n",
       "      <td>0.023500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.038263</td>\n",
       "      <td>-0.051277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.004185</td>\n",
       "      <td>-0.012215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000477</td>\n",
       "      <td>-0.001206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.005572</td>\n",
       "      <td>0.007145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.040606</td>\n",
       "      <td>0.058187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                0           1\n",
       "count  387.000000  387.000000\n",
       "mean     0.000479   -0.000566\n",
       "std      0.010180    0.023500\n",
       "min     -0.038263   -0.051277\n",
       "25%     -0.004185   -0.012215\n",
       "50%      0.000477   -0.001206\n",
       "75%      0.005572    0.007145\n",
       "max      0.040606    0.058187"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DELTA_MOV=pd.DataFrame(np.array(delta_mov)-np.array(delta_mov_rot))\n",
    "DELTA_MOV.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f82ad699e48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f82ad764b70>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DELTA_MOV.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "xyth_od=data[['x_rot','y_rot','theta_noise']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "if True:\n",
    "    \n",
    "    K_corpus=16\n",
    "    N=4\n",
    "    \n",
    "    kmedias=MiniBatchKMeans(init='k-means++', n_clusters=K_corpus, batch_size=1000,\n",
    "                      n_init=10, max_no_improvement=10, verbose=0)\n",
    "    kmedias.fit(lecs)\n",
    "    ccvk=kmedias.cluster_centers_\n",
    "    mbk = MiniBatchKMeans(init='k-means++', n_clusters=N, batch_size=1000,\n",
    "                      n_init=10, max_no_improvement=10, verbose=0)\n",
    "    mbk.fit(xyth)\n",
    "    ccxyth=mbk.cluster_centers_\n",
    "aus=[]\n",
    "aus_odom=[]\n",
    "auvks=[]\n",
    "for i,lec,j in zip(xyth.values, lecs.values,xyth_od.values):\n",
    "    _, au= quantized(i , ccxyth)\n",
    "    _, au_od= quantized(i , ccxyth)\n",
    "    aus.append(au)\n",
    "    aus_odom.append(au_od)\n",
    "\n",
    "    auvk= np.power(lec.T-ccvk,2).sum(axis=1,keepdims=True).argmax()\n",
    "    auvks.append(auvk)\n",
    "\n",
    "data['Y']=aus\n",
    "data['Y_odom']=aus_odom\n",
    "data['Vk']=auvks\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f82a95c0e48>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "fig.suptitle('Quantized Read (HMM Observation)', fontsize=16)\n",
    "\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "ax1.scatter(x=data.x_rot,y=data.y_rot,marker='_' ,c=data.Vk, label='Odom' )\n",
    "ax1.scatter(x=data.x,y=data.y ,marker='|', c=data.Vk,label='Real')\n",
    "ax1.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     339\n",
       "15     30\n",
       "10     15\n",
       "8       3\n",
       "Name: Vk, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Vk.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f82ad53dc18>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.Vk.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f82a94e4c88>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "ax1 = fig.add_subplot(111)\n",
    "fig.suptitle('Quantized POSE (HMM State)', fontsize=16)\n",
    "\n",
    "ax1.scatter(x=data.x_rot,y=data.y_rot,marker='*' ,c=data.Y_odom  ,label='ODOM')\n",
    "ax1.scatter(x=data.x,y=data.y , c=data.Y,label='REAL')\n",
    "ax1.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R0</th>\n",
       "      <th>R1</th>\n",
       "      <th>R2</th>\n",
       "      <th>R3</th>\n",
       "      <th>R4</th>\n",
       "      <th>R5</th>\n",
       "      <th>R6</th>\n",
       "      <th>R7</th>\n",
       "      <th>R8</th>\n",
       "      <th>R9</th>\n",
       "      <th>...</th>\n",
       "      <th>y</th>\n",
       "      <th>theta</th>\n",
       "      <th>y_noise</th>\n",
       "      <th>x_noise</th>\n",
       "      <th>theta_noise</th>\n",
       "      <th>x_rot</th>\n",
       "      <th>y_rot</th>\n",
       "      <th>delta_odom</th>\n",
       "      <th>Y</th>\n",
       "      <th>Vk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.837</td>\n",
       "      <td>4.786</td>\n",
       "      <td>3.918</td>\n",
       "      <td>3.519</td>\n",
       "      <td>3.503</td>\n",
       "      <td>3.485</td>\n",
       "      <td>3.591</td>\n",
       "      <td>3.667</td>\n",
       "      <td>3.731</td>\n",
       "      <td>3.731</td>\n",
       "      <td>...</td>\n",
       "      <td>3.841103</td>\n",
       "      <td>-0.456413</td>\n",
       "      <td>-0.089141</td>\n",
       "      <td>0.443164</td>\n",
       "      <td>0.756949</td>\n",
       "      <td>5.170155</td>\n",
       "      <td>3.841103</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.087</td>\n",
       "      <td>2.077</td>\n",
       "      <td>2.074</td>\n",
       "      <td>2.070</td>\n",
       "      <td>2.049</td>\n",
       "      <td>2.037</td>\n",
       "      <td>2.030</td>\n",
       "      <td>2.016</td>\n",
       "      <td>2.010</td>\n",
       "      <td>2.009</td>\n",
       "      <td>...</td>\n",
       "      <td>3.827890</td>\n",
       "      <td>-0.249890</td>\n",
       "      <td>-0.123866</td>\n",
       "      <td>0.483270</td>\n",
       "      <td>0.982732</td>\n",
       "      <td>5.220498</td>\n",
       "      <td>3.824369</td>\n",
       "      <td>0.011454</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.872</td>\n",
       "      <td>2.872</td>\n",
       "      <td>2.872</td>\n",
       "      <td>2.867</td>\n",
       "      <td>2.860</td>\n",
       "      <td>2.808</td>\n",
       "      <td>2.831</td>\n",
       "      <td>2.829</td>\n",
       "      <td>2.779</td>\n",
       "      <td>2.779</td>\n",
       "      <td>...</td>\n",
       "      <td>3.822501</td>\n",
       "      <td>-0.047217</td>\n",
       "      <td>-0.184033</td>\n",
       "      <td>0.610695</td>\n",
       "      <td>1.199857</td>\n",
       "      <td>5.361248</td>\n",
       "      <td>3.817546</td>\n",
       "      <td>0.023623</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.038</td>\n",
       "      <td>1.040</td>\n",
       "      <td>1.045</td>\n",
       "      <td>1.039</td>\n",
       "      <td>1.038</td>\n",
       "      <td>1.043</td>\n",
       "      <td>1.049</td>\n",
       "      <td>1.036</td>\n",
       "      <td>1.039</td>\n",
       "      <td>1.042</td>\n",
       "      <td>...</td>\n",
       "      <td>3.824503</td>\n",
       "      <td>0.020102</td>\n",
       "      <td>-0.315272</td>\n",
       "      <td>1.004013</td>\n",
       "      <td>1.277146</td>\n",
       "      <td>5.774849</td>\n",
       "      <td>3.846814</td>\n",
       "      <td>0.037892</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.996</td>\n",
       "      <td>1.009</td>\n",
       "      <td>1.009</td>\n",
       "      <td>0.993</td>\n",
       "      <td>0.991</td>\n",
       "      <td>0.994</td>\n",
       "      <td>1.005</td>\n",
       "      <td>1.016</td>\n",
       "      <td>1.017</td>\n",
       "      <td>1.004</td>\n",
       "      <td>...</td>\n",
       "      <td>3.822814</td>\n",
       "      <td>0.100004</td>\n",
       "      <td>-0.391900</td>\n",
       "      <td>1.238858</td>\n",
       "      <td>1.350533</td>\n",
       "      <td>6.021141</td>\n",
       "      <td>3.865890</td>\n",
       "      <td>0.052226</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 974 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      R0     R1     R2     R3     R4     R5     R6     R7     R8     R9  ...  \\\n",
       "0  4.837  4.786  3.918  3.519  3.503  3.485  3.591  3.667  3.731  3.731  ...   \n",
       "1  2.087  2.077  2.074  2.070  2.049  2.037  2.030  2.016  2.010  2.009  ...   \n",
       "2  2.872  2.872  2.872  2.867  2.860  2.808  2.831  2.829  2.779  2.779  ...   \n",
       "3  1.038  1.040  1.045  1.039  1.038  1.043  1.049  1.036  1.039  1.042  ...   \n",
       "4  0.996  1.009  1.009  0.993  0.991  0.994  1.005  1.016  1.017  1.004  ...   \n",
       "\n",
       "          y     theta   y_noise   x_noise  theta_noise     x_rot     y_rot  \\\n",
       "0  3.841103 -0.456413 -0.089141  0.443164     0.756949  5.170155  3.841103   \n",
       "1  3.827890 -0.249890 -0.123866  0.483270     0.982732  5.220498  3.824369   \n",
       "2  3.822501 -0.047217 -0.184033  0.610695     1.199857  5.361248  3.817546   \n",
       "3  3.824503  0.020102 -0.315272  1.004013     1.277146  5.774849  3.846814   \n",
       "4  3.822814  0.100004 -0.391900  1.238858     1.350533  6.021141  3.865890   \n",
       "\n",
       "   delta_odom  Y  Vk  \n",
       "0    0.000000  1   0  \n",
       "1    0.011454  1   0  \n",
       "2    0.023623  1   0  \n",
       "3    0.037892  1   0  \n",
       "4    0.052226  1   0  \n",
       "\n",
       "[5 rows x 974 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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