Test PCA.ipynb

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     "text": [
      "Finish reading data \n",
      "(375726L, 3L)\n",
      "(231216L, 3L)\n",
      "Finish traing PCAs\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import glob\n",
    "import os\n",
    "import numpy as np\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from utils import DATA_DIR, SESSION_NAME, SESSION_DATA, SESSION_EVENTS, \\\n",
    "    from_str_labels_to_id_labels\n",
    "import xml.etree.ElementTree as ET\n",
    "\n",
    "\n",
    "def read_project_data():\n",
    "    project_data = {}\n",
    "    \n",
    "    data_length = None\n",
    "    \n",
    "    for file_name in glob.glob(os.path.join(DATA_DIR, '*.txt')):\n",
    "        project_name = file_name[file_name.rfind('/') + 1:]\n",
    "        project_name = project_name[:len(project_name)-4]\n",
    "        project_data[project_name] = []\n",
    "\n",
    "\n",
    "        tree = ET.parse(file_name)\n",
    "\n",
    "        doc = tree.getroot()\n",
    "\n",
    "\n",
    "        for session_element in doc.findall('session'):\n",
    "            session_data = {}\n",
    "\n",
    "            session_name = session_element.attrib['name']\n",
    "\n",
    "            session_data[SESSION_NAME] = session_name\n",
    "            session_data[SESSION_DATA] = []\n",
    "            session_data[SESSION_EVENTS] = []\n",
    "\n",
    "            frame_elements = session_element.findall('data/frame')\n",
    "\n",
    "            for frame_element in frame_elements:\n",
    "                object_point_elements = frame_element.findall('o')\n",
    "\n",
    "                point_data = []\n",
    "                for object_point_element in object_point_elements:\n",
    "                    for s in object_point_element.text.split(','):\n",
    "                        point_data.append(float(s))\n",
    "                        \n",
    "                if data_length == None:\n",
    "                    data_length = len(point_data)\n",
    "\n",
    "                session_data[SESSION_DATA].append(point_data)\n",
    "            \n",
    "            '''\n",
    "            Calculate the difference of data points -> gradient feature\n",
    "            Move all points to the same coordinations\n",
    "            '''\n",
    "            #session_data[SESSION_DATA] = [[(session_data[SESSION_DATA][i][t] - session_data[SESSION_DATA][0][0])\\\n",
    "            #     for t in xrange(data_length)]\\\n",
    "            #     for i in xrange(0, len(session_data[SESSION_DATA]))]\n",
    "            \n",
    "            project_data[project_name].append(session_data) \n",
    "            \n",
    "    return data_length, project_data\n",
    "    \n",
    "'''\n",
    "Find planes for projection of data from DATA_DIR \n",
    "'''\n",
    "all_rig_points = []\n",
    "all_object_points = []\n",
    "\n",
    "no_of_samples = 0\n",
    "\n",
    "for file_name in glob.glob(os.path.join(DATA_DIR, '*.txt')):\n",
    "    tree = ET.parse(file_name)\n",
    "    doc = tree.getroot()\n",
    "    for session_element in doc.findall('session'):\n",
    "        frame_elements = session_element.findall('data/frame')\n",
    "\n",
    "        for frame_element in frame_elements:\n",
    "            object_point_elements = frame_element.findall('o')\n",
    "\n",
    "            rig = object_point_elements[0]\n",
    "            o1, o2 = object_point_elements[1], object_point_elements[2]\n",
    "\n",
    "            rig_points = []\n",
    "            for s in rig.text.split(','):\n",
    "                rig_points.append(float(s))\n",
    "            \n",
    "            # Extracted rig points\n",
    "#             extracted_rig_points = []\n",
    "#             extracted_rig_points.append( (intra_rig_data[1] + intra_rig_data[7] ) / 2 )\n",
    "#             extracted_rig_points.append( intra_rig_data[5] )\n",
    "#             extracted_rig_points.append( intra_rig_data[11] )\n",
    "\n",
    "            o1_points = []\n",
    "            for s in o1.text.split(','):\n",
    "                o1_points.append(float(s))\n",
    "\n",
    "            o2_points = []\n",
    "            for s in o2.text.split(','):\n",
    "                o2_points.append(float(s))\n",
    "\n",
    "\n",
    "            all_rig_points.append(rig_points)\n",
    "            all_object_points.append(o1_points)\n",
    "            all_object_points.append(o2_points)\n",
    "            \n",
    "print 'Finish reading data '\n",
    "\n",
    "# (13 * samples, 3)\n",
    "all_rig_array = np.array(all_rig_points).reshape((-1, 3))\n",
    "\n",
    "print (all_rig_array.shape)\n",
    "\n",
    "# (8 * samples, 3)\n",
    "all_object_array = np.array(all_object_points).reshape((-1, 3))\n",
    "\n",
    "print (all_object_array.shape)\n",
    "\n",
    "'''\n",
    "PCA for inter-object relationship\n",
    "This PCA would be an estimation of projecting on the table surface\n",
    "'''\n",
    "inter_object_pca = PCA(n_components=2)\n",
    "all_points = np.concatenate((all_rig_array, all_object_array), axis = 0)\n",
    "\n",
    "# (13 * samples + 8 * samples, 2)\n",
    "inter_object_pca.fit(all_points)\n",
    "\n",
    "'''\n",
    "PCA for intra-object relationship\n",
    "'''\n",
    "intra_object_pca = PCA(n_components=2)\n",
    "\n",
    "intra_object_pca.fit(all_object_array)\n",
    "\n",
    "'''\n",
    "PCA for intra-rig relationship\n",
    "This would be a good \n",
    "'''\n",
    "intra_rig_pca = PCA(n_components=2)\n",
    "\n",
    "intra_rig_pca.fit(all_rig_array)\n",
    "\n",
    "print 'Finish traing PCAs'"
   ]
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   "source": [
    "data_length = None\n",
    "_, project_data = read_project_data()\n",
    "\n",
    "def plot_project(project_name, sessionIndex = 0, labels = ['shoulder_middle', 'right_hand_tip', 'left_hand_tip', 'o1_centroid', 'o2_centroid', \n",
    "             'average_shoulders', 'left_hand_on_rig', 'right_hand_on_rig', \n",
    "             'o1_corner1', 'o1_corner2','o2_corner1','o2_corner2'], figsize = (5, 7), axis = [-1,1.2,-1,1]):\n",
    "    no_of_points = len(labels)\n",
    "    colormaps_fig = plt.figure(figsize = (5, 7))\n",
    "    ax = colormaps_fig.add_subplot(111)\n",
    "\n",
    "    # Draw the plot\n",
    "    colormap = plt.cm.gist_ncar\n",
    "    plt.gca().set_color_cycle([colormap(i) for i in np.linspace(0, 0.9, no_of_points)])\n",
    "    \n",
    "    plt.axis ( axis )\n",
    "\n",
    "    session_data = project_data[project_name][sessionIndex]\n",
    "    print '-----------------------------------'\n",
    "    print project_name\n",
    "    print '-----------------------------------'\n",
    "    point_datas = session_data[SESSION_DATA]\n",
    "\n",
    "    new_session_datas = []\n",
    "\n",
    "    for point_data in point_datas:\n",
    "        new_session_data = []\n",
    "\n",
    "        \"---------------------------------------------\"\n",
    "        # (21, 2)\n",
    "        inter_object_data = inter_object_pca.transform( np.array(point_data).reshape((-1, 3)) )\n",
    "\n",
    "        # Centroid of three objects projected using inter_object_pca\n",
    "        if 'shoulder_middle' in labels:\n",
    "            new_session_data.append( (inter_object_data[1] + inter_object_data[7] ) / 2 )\n",
    "        if 'right_hand_tip' in labels:\n",
    "            new_session_data.append( np.average( inter_object_data[5:6], axis = 0 ) )\n",
    "        if 'left_hand_tip' in labels:\n",
    "            new_session_data.append( np.average( inter_object_data[11:12], axis = 0 ) )\n",
    "        if 'o1_centroid' in labels:\n",
    "            new_session_data.append( np.average( inter_object_data[13:17], axis = 0 ) )\n",
    "        if 'o2_centroid' in labels:\n",
    "            new_session_data.append( np.average( inter_object_data[17:21], axis = 0 ) )\n",
    "\n",
    "        \"---------------------------------------------\"\n",
    "        # (21, 2)\n",
    "        intra_rig_data = intra_rig_pca.transform( np.array(point_data).reshape((-1, 3)) )\n",
    "    \n",
    "        # Average of two shoulders\n",
    "        if 'average_shoulders' in labels:\n",
    "            new_session_data.append( (intra_rig_data[1] + intra_rig_data[7] ) / 2 )\n",
    "\n",
    "        # Hand tip left\n",
    "        if 'left_hand_on_rig' in labels:\n",
    "            new_session_data.append( intra_rig_data[5] )\n",
    "\n",
    "        # Hand tip right\n",
    "        if 'right_hand_on_rig' in labels:\n",
    "            new_session_data.append( intra_rig_data[11] )\n",
    "\n",
    "        \"---------------------------------------------\"\n",
    "        intra_object_data = intra_object_pca.transform( np.array(point_data).reshape((-1, 3)) )\n",
    "\n",
    "        # Corners of objects (for each object, just pick two corners)\n",
    "        if 'o1_corner1' in labels:\n",
    "            new_session_data.append( intra_object_data[13] )\n",
    "        if 'o1_corner2' in labels:\n",
    "            new_session_data.append( intra_object_data[15] )\n",
    "        if 'o2_corner1' in labels:\n",
    "            new_session_data.append( intra_object_data[17] )\n",
    "        if 'o2_corner2' in labels:\n",
    "            new_session_data.append( intra_object_data[19] )\n",
    "\n",
    "        point_data = np.concatenate( new_session_data )\n",
    "\n",
    "        new_session_datas.append( point_data )\n",
    "    \n",
    "    # (sample_size, 16 )\n",
    "    new_session_datas = np.array(new_session_datas)\n",
    "\n",
    "    # 8 different points' trajectories\n",
    "    handles = []\n",
    "    for i in xrange(no_of_points):\n",
    "        x = new_session_datas[:, 2 * i]\n",
    "        y = new_session_datas[:, 2 * i + 1]\n",
    "        \n",
    "        ax.annotate('Start', xy=(x[0], y[0]), xytext=(x[0] + 0.05, y[0] + 0.05),\n",
    "            arrowprops=dict(facecolor='black', shrink=0.05),\n",
    "            )\n",
    "        \n",
    "        h, = plt.plot(x, y, label = labels[i])\n",
    "\n",
    "        handles.append(h)\n",
    "        \n",
    "    plt.legend(handles=handles)\n",
    "    \n",
    "    return new_session_datas"
   ]
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      "['C:\\\\Users\\\\Tuan\\\\git\\\\ecat_learning\\\\data\\\\selfslideto', 'C:\\\\Users\\\\Tuan\\\\git\\\\ecat_learning\\\\data\\\\rollpast', 'C:\\\\Users\\\\Tuan\\\\git\\\\ecat_learning\\\\data\\\\pullto', 'C:\\\\Users\\\\Tuan\\\\git\\\\ecat_learning\\\\data\\\\pullfrom', 'C:\\\\Users\\\\Tuan\\\\git\\\\ecat_learning\\\\data\\\\selfslidepast', 'C:\\\\Users\\\\Tuan\\\\git\\\\ecat_learning\\\\data\\\\selfrollto', 'C:\\\\Users\\\\Tuan\\\\git\\\\ecat_learning\\\\data\\\\selfrollfrom', 'C:\\\\Users\\\\Tuan\\\\git\\\\ecat_learning\\\\data\\\\pullpast', 'C:\\\\Users\\\\Tuan\\\\git\\\\ecat_learning\\\\data\\\\pushpast', 'C:\\\\Users\\\\Tuan\\\\git\\\\ecat_learning\\\\data\\\\selfrollpast', 'C:\\\\Users\\\\Tuan\\\\git\\\\ecat_learning\\\\data\\\\selfslidefrom', 'C:\\\\Users\\\\Tuan\\\\git\\\\ecat_learning\\\\data\\\\pushto', 'C:\\\\Users\\\\Tuan\\\\git\\\\ecat_learning\\\\data\\\\rollfrom', 'C:\\\\Users\\\\Tuan\\\\git\\\\ecat_learning\\\\data\\\\rollto', 'C:\\\\Users\\\\Tuan\\\\git\\\\ecat_learning\\\\data\\\\pushfrom']\n"
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     "text": [
      "-----------------------------------\n",
      "C:\\Users\\Tuan\\git\\ecat_learning\\data\\selfslideto\n",
      "-----------------------------------\n"
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LyMhg3Lhx2Nn9/Osre3t7Tp06ZT339va2fnZycuLChQu1jndl7BaLhcDAwCr3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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3adc358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample = plot_project('C:\\\\Users\\\\Tuan\\\\git\\\\ecat_learning\\\\data\\\\selfslideto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------\n",
      "C:\\Users\\Tuan\\git\\ecat_learning\\data\\rollpast\n",
      "-----------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-0.4730724 , -0.20131727, -0.37011291, ..., -0.34875523,\n",
       "         0.16078643, -0.45150624],\n",
       "       [-0.4730724 , -0.20131727, -0.37011291, ..., -0.34875523,\n",
       "         0.09632309, -0.4426177 ],\n",
       "       [-0.47523015, -0.20290259, -0.37044407, ..., -0.34867455,\n",
       "         0.1396058 , -0.4473518 ],\n",
       "       ..., \n",
       "       [-0.13475977,  0.10842869,  0.15936661, ...,  0.43961693,\n",
       "         0.03493887,  0.42650646],\n",
       "       [-0.13571893,  0.10707927,  0.17172414, ...,  0.47323383,\n",
       "         0.1180138 ,  0.40628838],\n",
       "       [-0.13550538,  0.10650725,  0.17163499, ...,  0.47551398,\n",
       "         0.12301886,  0.40936289]])"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vOzubmJiYCrft37+f4cOHY7H8eveVu7s7hw4dci2HhIS43lutVk6ePHnB851b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\nxjok2QpujW3gLtSARCnCuw386wKEtoVwtnXi6uHE3tBWpmt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2IUQ+pRSvv76Wxx5bxCuv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\nawAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2cc9f358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_project('C:\\\\Users\\\\Tuan\\\\git\\\\ecat_learning\\\\data\\\\rollpast')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------\n",
      "C:\\Users\\Tuan\\git\\ecat_learning\\data\\pullpast\n",
      "-----------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.36882864,  0.19047585,  0.6255906 , ..., -0.19737135,\n",
       "         0.14537679, -0.28391797],\n",
       "       [ 0.36882864,  0.19047585,  0.6255906 , ..., -0.19737135,\n",
       "         0.14537679, -0.28391797],\n",
       "       [ 0.36882864,  0.19047585,  0.6255906 , ..., -0.19737135,\n",
       "         0.14342057, -0.28409025],\n",
       "       ..., \n",
       "       [ 0.3489498 ,  0.49825973,  0.62133589, ..., -0.19795679,\n",
       "         0.14635493, -0.28383192],\n",
       "       [ 0.34873028,  0.4982369 ,  0.6246377 , ..., -0.19824951,\n",
       "         0.14439868, -0.28400411],\n",
       "       [ 0.34820214,  0.49839916,  0.62198345, ..., -0.19824951,\n",
       "         0.14537679, -0.28391797]])"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JjIws99yRI0e47777cHC49PSVk5MTx48ft+0HBATYPru5uXHu3Lmr9nd57BaL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\noLQcDvgKfPdR+O1iOO2PBrYPKQ8F3MYA+lPAZAZ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IiRFu/iaUlMNJv8zvzyK1k36hkLGU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\nTWffigwHDQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18987208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_project('C:\\\\Users\\\\Tuan\\\\git\\\\ecat_learning\\\\data\\\\pullpast', 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------\n",
      "C:\\Users\\Tuan\\git\\ecat_learning\\data\\pullfrom\n",
      "-----------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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IYPny5SxatIiEhATatWtX1kdoypQp9OzZk65d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lSyKffrNUn2rLWuKaW8NMnXkxy4dcuCttCfM93pz5kWSilzknw5yZ+OjTT6tSaP2d9o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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a3b3390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_project('C:\\\\Users\\\\Tuan\\\\git\\\\ecat_learning\\\\data\\\\pullfrom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------\n",
      "C:\\Users\\Tuan\\git\\ecat_learning\\data\\pushto\n",
      "-----------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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RERFpECoYidSTn2sYM6xpJB/9HbtOrjc7joiIiIiIiEi9qWAk0gAC3LryYFAK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\nA8EIAAAAgIFgBAAAAMBAMAIAAABg8B8+jAbodVNX7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16c31550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_project('C:\\\\Users\\\\Tuan\\\\git\\\\ecat_learning\\\\data\\\\pushto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------\n",
      "C:\\Users\\Tuan\\git\\ecat_learning\\data\\pushfrom\n",
      "-----------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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j9e/fv9zxmjZtqk2bNikpKUkBAQGKiIhQSkqKJGnGjBmKiopSp06d1KlTJ0VF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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25320278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_project('C:\\\\Users\\\\Tuan\\\\git\\\\ecat_learning\\\\data\\\\pushfrom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------\n",
      "C:\\Users\\Tuan\\git\\ecat_learning\\data\\selfslideto\n",
      "-----------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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GtofjdJ193lPxF8kTtQ6/LYcLemjGTlHxF8kbvy2Hnu3gWz2jTiKZQKN9RPLA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a5bb710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_project('C:\\\\Users\\\\Tuan\\\\git\\\\ecat_learning\\\\data\\\\selfslideto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------\n",
      "C:\\Users\\Tuan\\git\\ecat_learning\\data\\selfslideto\n",
      "-----------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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iIiIiAoCHhwf33Xcfzz33XGOXUi3TNDFNs7HLuGopMBIRERERERFpYr7//nv69euHt7c3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\na9cu9u7dy969e9m1axfz5s2zX3vkyBF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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ba8be48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_project('C:\\\\Users\\\\Tuan\\\\git\\\\ecat_learning\\\\data\\\\selfslideto', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------\n",
      "C:\\Users\\Tuan\\git\\ecat_learning\\data\\selfslideto\n",
      "-----------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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z4dD6OqxdmF441Y0kRr5ZmM9HCg9zbfYdTAhD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\npNKtmA0tr8PEE9NOogq0KG7gvfl7+VH2rZZFkiRJqnv9KYz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ez1XtOs/fy4GnY4wXdTOs\nz9fzQZth1JMQwmnA94GdgJtCCI/HGE8MIYwHLosxnhRjzIcQPgXcCmSBy2OM89PIK/XDvwH/G0L4\nKLAEeD/A1uc6Hbez/bbzZ1QO+FWM8bZ04krb1931OYRwXufrl8YYbw4hzAwhLAI2An+dYmSpz3pz\nngPvAz4RQsgDm4APphZYKlEI4SrgaGCnEMIy4CtAA3g9V+3Y3nmO13NVvyOADwFPhRAe79x2ATAZ\nSr+ehxi9BVmSJEmSJElbVMItaZIkSZIkSaogFkaSJEmSJEkqYmEkSZIkSZKkIhZGkiRJkiRJKmJh\nJEmSJEmSpCIWRpIkSZIkSSpiYSRJkiRJkqQiFkaSJEmSJEkq8v+YiOfzd92KIgAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a63df28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "import sys\n",
    "sys.path.append('C:\\Users\\Tuan\\git\\strands_qsr_lib\\qsr_lib\\src')\n",
    "from qsrlib.qsrlib import QSRlib, QSRlib_Request_Message\n",
    "\n",
    "from qsrlib_io.world_trace import Object_State, World_Trace\n",
    "\n",
    "def pretty_print_world_qsr_trace(which_qsr, qsrlib_response_message):\n",
    "    print(which_qsr, \"request was made at \", str(qsrlib_response_message.req_made_at)\n",
    "          + \" and received at \" + str(qsrlib_response_message.req_received_at)\n",
    "          + \" and computed at \" + str(qsrlib_response_message.req_finished_at))\n",
    "    print(\"---\")\n",
    "    print(\"Response is:\")\n",
    "    for t in qsrlib_response_message.qsrs.get_sorted_timestamps():\n",
    "        foo = str(t) + \": \"\n",
    "        for k, v in zip(qsrlib_response_message.qsrs.trace[t].qsrs.keys(),\n",
    "                        qsrlib_response_message.qsrs.trace[t].qsrs.values()):\n",
    "            foo += str(k) + \":\" + str(v.qsr) + \"; \"\n",
    "        print(foo)\n",
    "        \n",
    "qsrlib = QSRlib()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------\n",
      "C:\\Users\\Tuan\\git\\ecat_learning\\data\\selfslideto\n",
      "-----------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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LyMhg3Lhx2Nn9/Osre3t7Tp06ZT339va2fnZycuLChQu1jndl7BaLhcDAwCr3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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x39ee4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample = plot_project('C:\\\\Users\\\\Tuan\\\\git\\\\ecat_learning\\\\data\\\\selfslideto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0, 1, 0, 1]]\n",
      "[[0, 0, 0, 0]]\n",
      "[[-1, 1, 0, 0]]\n",
      "[[0, 0, 0, 0]]\n",
      "[[-1, 1, 0, 1]]\n",
      "[[0, 0, 0, 0]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, -1, 0, -1]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, 1, 0, 1]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, -1, 0, 0]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, -1, 0, -1]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, 1, 0, 1]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, -1, 0, -1]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, -1, 0, -1]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, 1, 0, 1]]\n",
      "[[-1, 1, 0, 0]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, -1, 0, -1]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, 1, 0, 1]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, -1, 0, -1]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, 1, 0, 0]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, -1, 0, 0]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, 1, 0, 1]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, -1, 0, -1]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, 1, 0, 1]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, 1, 0, 0]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, 1, -1, 0]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, 0, 1, 0]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, -1, 0, 0]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, 1, 0, 1]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, -1, 0, -1]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, 1, 0, 1]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, 1, 0, 1]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, -1, 0, -1]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, 1, 0, 1]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, -1, 0, -1]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, 1, 0, 1]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, 1, 0, 1]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, 0, 0, 1]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, 1, 0, 1]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, -1, 0, -1]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, 1, 0, 0]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, 0, 0, -1]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, -1, -1, -1]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, 0, 1, 0]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, 1, 0, 1]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, -1, 0, -1]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, 0, 1, 0]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, -1, 0, -1]]\n",
      "[[-1, -1, 0, 0]]\n",
      "[[-1, 0, 0, 0]]\n",
      "[[-1, 1, -1, 0]]\n",
      "[[-1, 1, 0, 0]]\n",
      "[[-1, 1, -1, 0]]\n",
      "[[-1, 1, 0, 0]]\n",
      "[[-1, 1, 0, 1]]\n",
      "[[0, 0, 0, 0]]\n",
      "[[0, 0, -1, 0]]\n",
      "[[0, 0, 0, 0]]\n",
      "[[0, 1, 0, 1]]\n",
      "[[0, 0, 0, 0]]\n",
      "[[0, -1, 0, -1]]\n",
      "[[0, 0, 0, 0]]\n",
      "[[0, 0, 1, 0]]\n",
      "[[0, 0, 0, 0]]\n",
      "[[0, -1, 0, -1]]\n"
     ]
    }
   ],
   "source": [
    "qtcc_relations = dict( (u, i) for (i, u) in enumerate( ['-', '0', '+'] ))\n",
    "\n",
    "def qtcc_index ( qtcc_relation ):\n",
    "    return qtcc_relations [qtcc_relation] - 1\n",
    "\n",
    "def turn_qtcc_to_features(keys, qsrlib_response_message):\n",
    "    feature_chain = []\n",
    "    for t in qsrlib_response_message.qsrs.get_sorted_timestamps():\n",
    "        features = []\n",
    "        # print (qsrlib_response_message.qsrs.trace[t].qsrs.keys())\n",
    "        for k in keys:\n",
    "            if k in qsrlib_response_message.qsrs.trace[t].qsrs:\n",
    "                v = qsrlib_response_message.qsrs.trace[t].qsrs[k]\n",
    "\n",
    "                if 'qtccs' in v.qsr:\n",
    "                    fs = v.qsr['qtccs']\n",
    "                    features.append([qtcc_index(f) for f  in fs.split(',')])\n",
    "                \n",
    "        feature_chain.append(features)\n",
    "    \n",
    "    #feature_chain[0] += [0, 0, 0]\n",
    "    \n",
    "    #feature_chain = [ [feature_chain[t + 1][i] - feature_chain[t][i] for i in xrange(len(feature_chain[0])) ]\n",
    "    #              for t in xrange(len(feature_chain) - 1)]\n",
    "    \n",
    "    #feature_chain = [[0 for i in xrange(len(feature_chain[0]))]] +  feature_chain\n",
    "    return feature_chain\n",
    "\n",
    "def print_qtccs(sample):\n",
    "\n",
    "    o1_corner1 = [Object_State(name=\"o1_corner1\", timestamp=i, x=sample[i][10], y=sample[i][11], width=1, length=1) \n",
    "          for i in xrange(sample.shape[0])]\n",
    "\n",
    "    o1_corner2 = [Object_State(name=\"o1_corner2\", timestamp=i, x=sample[i][12], y=sample[i][13], width=1, length=1) \n",
    "          for i in xrange(sample.shape[0])]\n",
    "\n",
    "    o1_centroid = [Object_State(name=\"o1_centroid\", timestamp=i, x=sample[i][6], y=sample[i][7], width=1, length=1) \n",
    "          for i in xrange(sample.shape[0])]\n",
    "\n",
    "    o2_centroid = [Object_State(name=\"o2_centroid\", timestamp=i, x=sample[i][8], y=sample[i][9], width=1, length=1) \n",
    "          for i in xrange(sample.shape[0])]\n",
    "\n",
    "    world = World_Trace()\n",
    "    world.add_object_state_series(o1_corner1)\n",
    "    world.add_object_state_series(o1_corner2)\n",
    "    world.add_object_state_series(o1_centroid)\n",
    "    world.add_object_state_series(o2_centroid)\n",
    "\n",
    "    qsrlib_request_message = QSRlib_Request_Message(which_qsr=['qtccs'], input_data=world, \n",
    "                    dynamic_args = {'qtccs': {'qsrs_for': [ ('o1_centroid', 'o2_centroid')], \n",
    "                                              'quantisation_factor': 0.001, 'angle_quantisation_factor' : np.pi / 5,\n",
    "                                              #'validate': False, 'no_collapse': True\n",
    "                                   }})\n",
    "    # request your QSRs\n",
    "    qsrlib_response_message = qsrlib.request_qsrs(req_msg=qsrlib_request_message)\n",
    "\n",
    "#     pretty_print_world_qsr_trace(['qtccs', \n",
    "#                                   'cardir', 'argd'], qsrlib_response_message)\n",
    "    \n",
    "    for stuff in turn_qtcc_to_features(['o1_centroid,o2_centroid'], qsrlib_response_message):\n",
    "        print stuff\n",
    "    \n",
    "print_qtccs(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------\n",
      "C:\\Users\\Tuan\\git\\ecat_learning\\data\\rollpast\n",
      "-----------------------------------\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'print_qtccs' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-33-4ce755489b91>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      7\u001b[0m                       axis)\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mprint_qtccs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msample2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'print_qtccs' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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B//znP5kwYYL/vmpVMb3mqNQpjr2fR+y50VjiLBz/JB9LkgVvtpeoHnZca1wk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19def978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['shoulder_middle', 'right_hand_tip', 'left_hand_tip', 'o1_centroid', 'o2_centroid', \n",
    "             'average_shoulders', 'left_hand_on_rig', 'right_hand_on_rig', \n",
    "             'o1_corner1', 'o1_corner2','o2_corner1','o2_corner2']\n",
    "figsize = (10, 5)\n",
    "axis = [-0.5,1,-0.5,0.7]\n",
    "sample2 = plot_project('C:\\\\Users\\\\Tuan\\\\git\\\\ecat_learning\\\\data\\\\rollpast', 2, ['average_shoulders', 'left_hand_on_rig', 'right_hand_on_rig'], figsize,\n",
    "                      axis)\n",
    "\n",
    "print_qtccs(sample2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(['cardir', 'mos', 'argd'], 'request was made at ', '2017-04-18 15:37:17.006000 and received at 2017-04-18 15:37:17.006000 and computed at 2017-04-18 15:37:17.020000')\n",
      "---\n",
      "Response is:\n",
      "0.0: o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '3'}; o1_centroid,o2_centroid:{'cardir': 'e', 'argd': '13'}; \n",
      "1.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'n', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'e', 'argd': '13'}; \n",
      "2.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'n', 'argd': '3'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'e', 'argd': '13'}; \n",
      "3.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '3'}; o2_centroid:{'mos': 's'}; o1_centroid,o2_centroid:{'cardir': 'e', 'argd': '13'}; \n",
      "4.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'n', 'argd': '3'}; o2_centroid:{'mos': 's'}; o1_centroid,o2_centroid:{'cardir': 'e', 'argd': '13'}; \n",
      "5.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'n', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'e', 'argd': '13'}; \n",
      "6.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'n', 'argd': '2'}; o2_centroid:{'mos': 's'}; o1_centroid,o2_centroid:{'cardir': 'e', 'argd': '13'}; \n",
      "7.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '3'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'e', 'argd': '13'}; \n",
      "8.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'n', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'e', 'argd': '13'}; \n",
      "9.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'n', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'e', 'argd': '13'}; \n",
      "10.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'n', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'e', 'argd': '13'}; \n",
      "11.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'n', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'e', 'argd': '13'}; \n",
      "12.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'ne', 'argd': '1'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'e', 'argd': '13'}; \n",
      "13.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'ne', 'argd': '1'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'e', 'argd': '13'}; \n",
      "14.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'e', 'argd': '1'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'e', 'argd': '13'}; \n",
      "15.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '5'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'e', 'argd': '12'}; \n",
      "16.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '6'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'e', 'argd': '12'}; \n",
      "17.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'w', 'argd': '6'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'e', 'argd': '12'}; \n",
      "18.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '3'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'e', 'argd': '11'}; \n",
      "19.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'e', 'argd': '11'}; \n",
      "20.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'e', 'argd': '12'}; \n",
      "21.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'w', 'argd': '3'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'e', 'argd': '12'}; \n",
      "22.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'w', 'argd': '3'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'e', 'argd': '12'}; \n",
      "23.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '1'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'e', 'argd': '11'}; \n",
      "24.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'e', 'argd': '1'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'e', 'argd': '11'}; \n",
      "25.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'w', 'argd': '5'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'e', 'argd': '11'}; \n",
      "26.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'w', 'argd': '5'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'e', 'argd': '11'}; \n",
      "27.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '3'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'e', 'argd': '10'}; \n",
      "28.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'se', 'argd': '10'}; \n",
      "29.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'se', 'argd': '10'}; \n",
      "30.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '3'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'se', 'argd': '10'}; \n",
      "31.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'w', 'argd': '3'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'se', 'argd': '10'}; \n",
      "32.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'e', 'argd': '4'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'se', 'argd': '11'}; \n",
      "33.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'e', 'argd': '1'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'se', 'argd': '10'}; \n",
      "34.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '3'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'se', 'argd': '9'}; \n",
      "35.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '3'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'se', 'argd': '9'}; \n",
      "36.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'n', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'se', 'argd': '9'}; \n",
      "37.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'se', 'argd': '9'}; \n",
      "38.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'se', 'argd': '10'}; \n",
      "39.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'w', 'argd': '3'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'se', 'argd': '10'}; \n",
      "40.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'w', 'argd': '4'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'se', 'argd': '10'}; \n",
      "41.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'ne', 'argd': '1'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'se', 'argd': '9'}; \n",
      "42.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'n', 'argd': '3'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'se', 'argd': '9'}; \n",
      "43.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'n', 'argd': '3'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'se', 'argd': '9'}; \n",
      "44.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'n', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'se', 'argd': '9'}; \n",
      "45.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'se', 'argd': '9'}; \n",
      "46.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'se', 'argd': '9'}; \n",
      "47.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'se', 'argd': '9'}; \n",
      "48.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'se', 'argd': '9'}; \n",
      "49.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 'se', 'argd': '10'}; \n",
      "50.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 's', 'argd': '10'}; \n",
      "51.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 's', 'argd': '10'}; \n",
      "52.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 's', 'argd': '10'}; \n",
      "53.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 's', 'argd': '10'}; \n",
      "54.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '1'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 's', 'argd': '10'}; \n",
      "55.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'w', 'argd': '1'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 's', 'argd': '11'}; \n",
      "56.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'w', 'argd': '1'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 's', 'argd': '11'}; \n",
      "57.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'w', 'argd': '1'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 's', 'argd': '11'}; \n",
      "58.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'w', 'argd': '1'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 's', 'argd': '11'}; \n",
      "59.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'w', 'argd': '1'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 's', 'argd': '11'}; \n",
      "60.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'w', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 's', 'argd': '12'}; \n",
      "61.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'sw', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 's', 'argd': '12'}; \n",
      "62.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 's', 'argd': '11'}; \n",
      "63.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '3'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 's', 'argd': '12'}; \n",
      "64.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 's', 'argd': '12'}; \n",
      "65.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 's', 'argd': '12'}; \n",
      "66.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'e', 'argd': '2'}; o2_centroid:{'mos': 'm'}; o1_centroid,o2_centroid:{'cardir': 's', 'argd': '12'}; \n",
      "67.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '2'}; o2_centroid:{'mos': 's'}; o1_centroid,o2_centroid:{'cardir': 's', 'argd': '12'}; \n",
      "68.0: o1_centroid:{'mos': 's'}; o1_corner1,o1_corner2:{'cardir': 'nw', 'argd': '2'}; o2_centroid:{'mos': 's'}; o1_centroid,o2_centroid:{'cardir': 's', 'argd': '12'}; \n"
     ]
    }
   ],
   "source": [
    "o1_corner1 = [Object_State(name=\"o1_corner1\", timestamp=i, x=sample2[i][12], y=sample2[i][13], width=0.1, length=0.1) \n",
    "      for i in xrange(sample2.shape[0])]\n",
    "\n",
    "o1_corner2 = [Object_State(name=\"o1_corner2\", timestamp=i, x=sample2[i][14], y=sample2[i][15], width=0.1, length=0.1) \n",
    "      for i in xrange(sample2.shape[0])]\n",
    "\n",
    "o1_centroid = [Object_State(name=\"o1_centroid\", timestamp=i, x=sample2[i][4], y=sample2[i][5], width=0.1, length=0.1) \n",
    "      for i in xrange(sample2.shape[0])]\n",
    "\n",
    "o2_centroid = [Object_State(name=\"o2_centroid\", timestamp=i, x=sample2[i][6], y=sample2[i][7], width=0.1, length=0.1) \n",
    "      for i in xrange(sample2.shape[0])]\n",
    "\n",
    "world = World_Trace()\n",
    "world.add_object_state_series(o1_corner1)\n",
    "world.add_object_state_series(o1_corner2)\n",
    "world.add_object_state_series(o1_centroid)\n",
    "world.add_object_state_series(o2_centroid)\n",
    "\n",
    "qsrlib_request_message = QSRlib_Request_Message(which_qsr=[#'qtccs', \n",
    "                                                           'cardir', 'mos', 'argd'], input_data=world, \n",
    "                dynamic_args = {#'qtccs': {'qsrs_for': [('o1_corner1', 'o1_corner2'), ('o1_centroid', 'o2_centroid')]},\n",
    "                               'cardir': {'qsrs_for': [('o1_corner1', 'o1_corner2'), ('o1_centroid', 'o2_centroid')]},\n",
    "                                'mos' : {'qsrs_for': ['o1_centroid', 'o2_centroid'], 'quantisation_factor': 0.005},\n",
    "                               'argd': {'qsrs_for': [('o1_corner1', 'o1_corner2'), ('o1_centroid', 'o2_centroid')], \n",
    "                                        'qsr_relations_and_values' : dict((\"\" + str(i), i * 1.0 / 20) for i in xrange(20)) }})\n",
    "# request your QSRs\n",
    "qsrlib_response_message = qsrlib.request_qsrs(req_msg=qsrlib_request_message)\n",
    "\n",
    "pretty_print_world_qsr_trace([#'qtccs', \n",
    "                              'cardir', 'mos', 'argd'], qsrlib_response_message)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cdid = dict( (u, i) for (i, u) in enumerate( ['n', 'nw', 'w', 'sw', 's', 'se', 'e', 'ne', 'eq'] ))\n",
    "mosd = dict( (u, i) for (i, u) in enumerate( ['s', 'm'] ))\n",
    "qtcc_relations = dict( (u, i) for (i, u) in enumerate( ['-', '0', '+'] ))\n",
    "\n",
    "def cardir_index ( cardir ):\n",
    "    return cdid [cardir]\n",
    "\n",
    "def mos_index ( mos ):\n",
    "    return mosd [mos]\n",
    "\n",
    "def qtcc_index ( qtcc_relation ):\n",
    "    return qtcc_relations [qtcc_relation] - 1\n",
    "\n",
    "def turn_response_to_features(keys, qsrlib_response_message):\n",
    "    feature_chain = []\n",
    "    for t in qsrlib_response_message.qsrs.get_sorted_timestamps():\n",
    "        features = []\n",
    "        # print (qsrlib_response_message.qsrs.trace[t].qsrs.keys())\n",
    "        for k in keys:\n",
    "            if k in qsrlib_response_message.qsrs.trace[t].qsrs:\n",
    "                v = qsrlib_response_message.qsrs.trace[t].qsrs[k]\n",
    "\n",
    "                if 'cardir' in v.qsr:\n",
    "                    f = v.qsr['cardir']\n",
    "                    features.append(cardir_index(f))\n",
    "                if 'argd' in v.qsr:\n",
    "                    f = int( v.qsr['argd'] )\n",
    "                    features.append(f)\n",
    "                if 'mos' in v.qsr:\n",
    "                    f = v.qsr['mos'] \n",
    "                    features.append(mos_index(f))\n",
    "        # Just to separate qtccs at the end of feature vectors\n",
    "        \n",
    "        for k in keys:\n",
    "            if k in qsrlib_response_message.qsrs.trace[t].qsrs:\n",
    "                v = qsrlib_response_message.qsrs.trace[t].qsrs[k]\n",
    "                if 'qtccs' in v.qsr:\n",
    "                    fs = v.qsr['qtccs']\n",
    "                    features += [qtcc_index(f) for f  in fs.split(',')]\n",
    "        \n",
    "        # print features\n",
    "        feature_chain.append(features)\n",
    "    \n",
    "    if len(feature_chain) == 0:\n",
    "        return feature_chain\n",
    "\n",
    "    # The first frame doesn't has dyadic relation\n",
    "    feature_chain[0] += [0, 0, 0, 0, 0, 0, 0]\n",
    "    \n",
    "    diff_feature_chain = [ [feature_chain[t + 1][i] - feature_chain[t][i] \n",
    "                            for i in xrange(len(feature_chain[0]) - 7) ] + \\\n",
    "                          [feature_chain[t][i] for i in xrange(len(feature_chain[0]) - 7, len(feature_chain[0]))]\n",
    "                    for t in xrange(len(feature_chain) - 1)]\n",
    "    \n",
    "    diff_feature_chain = [[0 for i in xrange(len(feature_chain[0]))]] +  diff_feature_chain\n",
    "\n",
    "    # Concatenate features\n",
    "    # feature_chain = [feature_chain[i] + diff_feature_chain[i] for i in xrange(len(feature_chain))]\n",
    "\n",
    "    return diff_feature_chain\n",
    "        \n",
    "\n",
    "def qsr_feature_extractor ( qsrlib, session_data ):\n",
    "    '''\n",
    "    List of features from qsr\n",
    "\n",
    "    25 features\n",
    "\n",
    "    ('body', 'left_hand') - cardir_diff, argd_diff\n",
    "    ('body', 'right_hand') - cardir_diff, argd_diff \n",
    "    ('left_hand', 'o1_centroid') - cardir_diff, argd_diff \n",
    "    ('right_hand', 'o1_centroid') - cardir_diff, argd_diff \n",
    "    ('left_hand', 'o2_centroid') - cardir_diff, argd_diff\n",
    "    ('right_hand', 'o2_centroid') - cardir_diff, argd_diff \n",
    "    ('o1_centroid', 'o2_centroid') - cardir_diff, argd_diff \n",
    "    ('o1_corner1','o1_corner2') - cardir_diff, argd_diff \n",
    "    ('o2_corner1','o2_corner2') - cardir_diff, argd_diff\n",
    "    'body' - mos\n",
    "    'o1_centroid' - mos\n",
    "    'o2_centroid' - mos\n",
    "    ('o1_centroid', 'o2_centroid') - qtccs features\n",
    "    '''\n",
    "\n",
    "    data_length = len(session_data)\n",
    "    # body centroid\n",
    "    body_centroid = [Object_State(name=\"body\", timestamp=i, x=session_data[i][0], y=session_data[i][1], width=0.1, length=0.1) \n",
    "      for i in xrange(data_length)]\n",
    "    \n",
    "    # left hand tip\n",
    "    left_hand = [Object_State(name=\"left_hand\", timestamp=i, x=session_data[i][2], y=session_data[i][3], width=0.1, length=0.1) \n",
    "      for i in xrange(data_length)]\n",
    "    \n",
    "    # right hand tip\n",
    "    right_hand = [Object_State(name=\"right_hand\", timestamp=i, x=session_data[i][4], y=session_data[i][5], width=0.1, length=0.1) \n",
    "      for i in xrange(data_length)]\n",
    "    \n",
    "    # centroid of o1 object\n",
    "    o1_centroid = [Object_State(name=\"o1_centroid\", timestamp=i, x=session_data[i][6], y=session_data[i][7], width=0.1, length=0.1) \n",
    "          for i in xrange(data_length)]\n",
    "    \n",
    "    # centroid of o2 object\n",
    "    o2_centroid = [Object_State(name=\"o2_centroid\", timestamp=i, x=session_data[i][8], y=session_data[i][9], width=0.1, length=0.1) \n",
    "          for i in xrange(data_length)]\n",
    "    \n",
    "    # o1\n",
    "    o1_corner1 = [Object_State(name=\"o1_corner1\", timestamp=i, x=session_data[i][10], y=session_data[i][11], width=0.1, length=0.1) \n",
    "          for i in xrange(data_length)]\n",
    "    \n",
    "    o1_corner2 = [Object_State(name=\"o1_corner2\", timestamp=i, x=session_data[i][12], y=session_data[i][13], width=0.1, length=0.1) \n",
    "          for i in xrange(data_length)]\n",
    "    \n",
    "    # o2\n",
    "    o2_corner1 = [Object_State(name=\"o2_corner1\", timestamp=i, x=session_data[i][14], y=session_data[i][15], width=0.1, length=0.1) \n",
    "          for i in xrange(data_length)]\n",
    "    \n",
    "    o2_corner2 = [Object_State(name=\"o2_corner2\", timestamp=i, x=session_data[i][16], y=session_data[i][17], width=0.1, length=0.1) \n",
    "          for i in xrange(data_length)]\n",
    "\n",
    "    \n",
    "\n",
    "    world = World_Trace()\n",
    "    world.add_object_state_series(body_centroid)\n",
    "    world.add_object_state_series(left_hand)\n",
    "    world.add_object_state_series(right_hand)\n",
    "    world.add_object_state_series(o1_centroid)\n",
    "    world.add_object_state_series(o2_centroid)\n",
    "    world.add_object_state_series(o1_corner1)\n",
    "    world.add_object_state_series(o1_corner2)\n",
    "    world.add_object_state_series(o2_corner1)\n",
    "    world.add_object_state_series(o2_corner2)\n",
    "    \n",
    "    interest_argd_pairs = [('body', 'left_hand'), ('body', 'right_hand'), ('left_hand', 'o1_centroid'), \n",
    "                           ('right_hand', 'o1_centroid'), ('left_hand', 'o2_centroid'), \n",
    "                           ('right_hand', 'o2_centroid'), ('o1_centroid', 'o2_centroid'), \n",
    "                           ('o1_corner1','o1_corner2'), ('o2_corner1','o2_corner2')]\n",
    "    interest_cardir_pairs = interest_argd_pairs\n",
    "    \n",
    "    interest_argd_pair_keys = [u + ',' + v for (u, v) in interest_argd_pairs]\n",
    "    \n",
    "    interest_mos_elements = ['body', 'o1_centroid', 'o2_centroid']\n",
    "    \n",
    "    interest_cctcs_pair_keys = [('o1_centroid', 'o2_centroid')]\n",
    "    \n",
    "    qsrlib_request_message = QSRlib_Request_Message(which_qsr=['cardir', 'mos', 'argd', 'qtccs'], input_data=world, \n",
    "                    dynamic_args = {'cardir': {'qsrs_for': interest_cardir_pairs},\n",
    "                                    'mos' : {'qsrs_for': interest_mos_elements, 'quantisation_factor': 0.005},\n",
    "                                    'argd': {'qsrs_for': interest_argd_pairs, \n",
    "                                            'qsr_relations_and_values' : dict((\"\" + str(i), i * 1.0 / 20) for i in xrange(20)) },\n",
    "                                    'qtccs': {'qsrs_for': interest_cctcs_pair_keys, \n",
    "                                              'quantisation_factor': 0.001, 'angle_quantisation_factor' : np.pi / 5,\n",
    "                                              'validate': False, 'no_collapse': True\n",
    "                                   }})\n",
    "    # request your QSRs\n",
    "    qsrlib_response_message = qsrlib.request_qsrs(req_msg=qsrlib_request_message)\n",
    "\n",
    "    # pretty_print_world_qsr_trace(['cardir', 'mos', 'argd', 'qtccs'], qsrlib_response_message)\n",
    "    \n",
    "    return turn_response_to_features(interest_argd_pair_keys + interest_mos_elements, qsrlib_response_message)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, -1, 0, 0, 0]\n",
      "[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, -1, -1, 0, -1]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, 0, 0]\n",
      "[0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 1, 0, -1, 1, -1, 0]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, -1, 1, 0, 0]\n",
      "[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, -1, 0]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, 0, 1]\n",
      "[0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0]\n",
      "[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "[0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, -7, 0, 0, 0, 0, 0, 1, 0, 1]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, -1]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0]\n"
     ]
    }
   ],
   "source": [
    "feature_chain = qsr_feature_extractor( qsrlib, sample)\n",
    "for feature in feature_chain:\n",
    "    print feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[0, -1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, -1, 0]\n",
      "[0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0]\n",
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      "[0, 0, 1, -1, -7, 0, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, -1, -1, 0]\n",
      "[0, -1, -1, 0, 0, 0, 0, -1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, -1, 0]\n",
      "[0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 1, 1, 0, -1, 0, -1, 0]\n",
      "[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, -1, 0]\n",
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      "[1, -1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, -1, 0]\n",
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      "[0, 1, 0, 0, 0, -1, 0, 0, 0, 0, 0, -1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, -1, 0]\n",
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      "[0, -1, -1, 3, 0, 1, 0, 0, 0, 0, 0, 6, 0, 0, 0, -1, 0, 0, 1, 1, 0, -1, 0, -1, 0]\n",
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      "[0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, -1, 0, -1, 0]\n",
      "[0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, -1, 0]\n",
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      "[0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, -1, 0]\n",
      "[0, 2, 0, 1, 0, -2, 0, 1, 0, 2, 0, -1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, -1, 0]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, -1, 0]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, -1, 0]\n",
      "[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, -3, 0, 0, 1, 1, 0, 1, 0, -1, 0]\n",
      "[0, 0, -2, -5, 0, 0, 0, -8, 0, 1, -1, -1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, -1, 0]\n",
      "[0, 0, -1, 1, 0, 0, 0, -1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0]\n",
      "[0, -2, 0, 0, 0, 2, 0, 0, 0, -1, 0, -1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, -1, 0]\n"
     ]
    }
   ],
   "source": [
    "feature_chain = qsr_feature_extractor( qsrlib, sample2)\n",
    "for feature in feature_chain:\n",
    "    print feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qsr_feature_extractor( qsrlib, sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------\n",
      "C:\\Users\\Tuan\\git\\ecat_learning\\data\\pullpast\n",
      "-----------------------------------\n"
     ]
    },
    {
     "data": {
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VK6ZMmcIDDzwQ7SinFQqFCIVC0Y5x1lgYSZIkSZKkBvXuu+8yaNAg2rZtS0ZGBrm5uQD0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\nKYuBkSRJkiRJkrL8PxtdcwjPbDGbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x172b9d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample3 = plot_project('C:\\\\Users\\\\Tuan\\\\git\\\\ecat_learning\\\\data\\\\pullpast')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[3, 12, 3, 3, 3, 1, 3, 10, 5, 18, 5, 15, 5, 18, 2, 3, 2, 19],\n",
       " [3, 12, 3, 3, 3, 1, 3, 10, 5, 18, 5, 15, 5, 18, 2, 3, 2, 19, 0, 0, 0],\n",
       " [3, 12, 3, 3, 4, 1, 3, 10, 5, 18, 5, 15, 5, 18, 1, 3, 2, 19, 0, 0, 0],\n",
       " [3, 11, 3, 3, 2, 2, 3, 10, 5, 17, 5, 15, 5, 19, 2, 3, 2, 19, 0, 1, 1],\n",
       " [3, 11, 3, 3, 2, 2, 3, 10, 5, 17, 5, 15, 5, 18, 2, 3, 2, 19, 0, 0, 1],\n",
       " [3, 11, 3, 2, 2, 2, 3, 11, 5, 17, 5, 15, 5, 18, 2, 3, 2, 19, 0, 0, 0],\n",
       " [3, 11, 2, 3, 2, 2, 3, 10, 5, 17, 5, 15, 5, 18, 2, 3, 2, 19, 0, 1, 0],\n",
       " [3, 12, 3, 2, 5, 1, 2, 10, 5, 19, 5, 15, 5, 18, 2, 3, 2, 19, 0, 1, 0],\n",
       " [3, 10, 3, 2, 2, 2, 2, 10, 5, 17, 5, 15, 5, 18, 2, 3, 2, 19, 0, 1, 0],\n",
       " [3, 12, 2, 3, 7, 1, 3, 9, 5, 19, 5, 15, 5, 18, 2, 3, 2, 19, 0, 1, 0],\n",
       " [3, 12, 2, 3, 7, 1, 3, 9, 5, 19, 5, 15, 5, 18, 2, 3, 2, 19, 0, 1, 1],\n",
       " [3, 10, 3, 2, 1, 3, 2, 10, 5, 16, 5, 15, 5, 18, 2, 3, 2, 19, 1, 1, 0],\n",
       " [3, 10, 3, 2, 2, 2, 2, 10, 5, 16, 5, 15, 5, 18, 2, 3, 2, 19, 1, 1, 0],\n",
       " [2, 11, 2, 3, 6, 1, 2, 8, 5, 18, 5, 16, 5, 18, 2, 3, 2, 19, 1, 1, 0],\n",
       " [2, 10, 2, 3, 3, 2, 3, 8, 5, 18, 5, 16, 5, 18, 2, 3, 2, 19, 0, 1, 0],\n",
       " [2, 11, 2, 3, 3, 1, 2, 8, 5, 18, 5, 16, 5, 18, 2, 3, 2, 19, 0, 1, 0],\n",
       " [2, 11, 2, 3, 5, 1, 3, 8, 5, 19, 5, 16, 5, 18, 2, 4, 2, 19, 0, 1, 0],\n",
       " [2, 11, 2, 4, 4, 1, 2, 8, 5, 18, 5, 16, 5, 18, 2, 4, 2, 19, 1, 1, 0],\n",
       " [2, 11, 2, 4, 4, 1, 3, 8, 5, 18, 5, 16, 5, 18, 2, 4, 2, 19, 1, 1, 0],\n",
       " [2, 11, 2, 4, 4, 1, 3, 8, 5, 19, 5, 17, 5, 18, 2, 4, 2, 19, 1, 1, 0],\n",
       " [2, 11, 2, 4, 4, 1, 3, 8, 5, 19, 5, 17, 5, 18, 2, 4, 2, 19, 0, 1, 0],\n",
       " [2, 10, 2, 4, 4, 1, 3, 8, 5, 19, 5, 17, 5, 18, 2, 4, 2, 19, 0, 1, 0],\n",
       " [2, 11, 2, 4, 4, 1, 3, 8, 5, 19, 5, 17, 5, 18, 2, 4, 2, 19, 1, 1, 0],\n",
       " [2, 12, 2, 4, 6, 2, 3, 7, 5, 19, 5, 17, 5, 18, 2, 4, 2, 19, 1, 1, 0],\n",
       " [2, 12, 2, 4, 6, 2, 3, 7, 5, 19, 5, 17, 5, 18, 2, 4, 2, 19, 1, 1, 0],\n",
       " [2, 12, 2, 4, 6, 2, 3, 7, 5, 19, 5, 17, 5, 18, 2, 5, 2, 19, 0, 1, 0],\n",
       " [2, 12, 2, 4, 6, 2, 3, 7, 5, 19, 5, 17, 5, 18, 2, 5, 2, 19, 1, 1, 0],\n",
       " [2, 11, 2, 7, 6, 1, 3, 5, 5, 19, 5, 17, 5, 18, 2, 5, 2, 19, 1, 1, 0],\n",
       " [2, 12, 2, 4, 6, 2, 3, 7, 5, 19, 4, 18, 5, 18, 2, 5, 2, 19, 0, 1, 0],\n",
       " [2, 11, 2, 5, 6, 1, 3, 6, 5, 19, 4, 18, 5, 18, 2, 5, 2, 19, 1, 1, 0],\n",
       " [2, 11, 2, 5, 6, 2, 3, 6, 5, 19, 4, 18, 5, 18, 2, 5, 2, 19, 0, 1, 0],\n",
       " [2, 11, 2, 5, 6, 2, 3, 6, 5, 19, 4, 18, 5, 18, 2, 5, 2, 19, 0, 1, 0],\n",
       " [2, 11, 2, 5, 6, 2, 3, 6, 5, 19, 4, 18, 5, 18, 2, 5, 2, 19, 0, 1, 0],\n",
       " [2, 11, 2, 5, 6, 1, 3, 6, 5, 19, 4, 18, 5, 18, 2, 5, 2, 19, 1, 1, 0],\n",
       " [2, 11, 2, 5, 5, 1, 3, 6, 5, 19, 4, 18, 5, 18, 2, 5, 2, 19, 1, 1, 0],\n",
       " [2, 10, 2, 5, 5, 1, 3, 6, 5, 19, 4, 19, 5, 18, 2, 5, 2, 19, 0, 1, 0],\n",
       " [2, 10, 2, 5, 6, 1, 3, 5, 5, 19, 4, 18, 5, 18, 2, 5, 2, 19, 1, 1, 0],\n",
       " [2, 11, 2, 7, 4, 2, 3, 5, 5, 19, 4, 18, 5, 19, 2, 5, 2, 19, 1, 1, 0],\n",
       " [2, 11, 2, 7, 4, 1, 3, 5, 5, 19, 4, 19, 5, 19, 2, 5, 2, 19, 1, 1, 0],\n",
       " [2, 11, 2, 7, 1, 1, 2, 5, 5, 19, 4, 19, 5, 19, 0, 2, 2, 19, 1, 1, 0],\n",
       " [2, 11, 2, 7, 1, 1, 2, 5, 5, 19, 4, 19, 5, 19, 0, 2, 2, 19, 1, 1, 0],\n",
       " [2, 11, 2, 7, 2, 1, 2, 5, 5, 19, 4, 19, 5, 19, 1, 2, 2, 19, 1, 1, 0],\n",
       " [2, 11, 2, 7, 4, 1, 3, 5, 5, 19, 4, 19, 5, 19, 2, 5, 2, 19, 1, 1, 0],\n",
       " [2, 11, 2, 7, 4, 1, 3, 4, 5, 19, 4, 19, 5, 19, 2, 5, 2, 19, 1, 1, 0],\n",
       " [2, 11, 2, 7, 2, 1, 3, 5, 5, 19, 4, 19, 5, 19, 1, 2, 2, 19, 1, 1, 0],\n",
       " [2, 11, 2, 7, 4, 1, 3, 5, 5, 19, 4, 19, 5, 19, 1, 3, 2, 19, 1, 1, 0],\n",
       " [2, 11, 2, 7, 1, 1, 2, 5, 5, 19, 4, 19, 5, 19, 0, 2, 2, 19, 1, 1, 1],\n",
       " [2, 11, 2, 7, 3, 1, 3, 5, 5, 19, 4, 19, 5, 19, 0, 2, 2, 19, 1, 1, 1],\n",
       " [2, 11, 2, 9, 4, 1, 3, 3, 5, 19, 4, 19, 5, 19, 2, 5, 2, 19, 1, 1, 0],\n",
       " [2, 11, 2, 9, 3, 1, 2, 3, 5, 19, 4, 19, 5, 19, 0, 2, 2, 19, 1, 1, 1],\n",
       " [2, 11, 2, 8, 2, 1, 2, 3, 5, 19, 4, 19, 5, 19, 0, 2, 2, 19, 1, 1, 1],\n",
       " [2, 11, 2, 8, 2, 1, 2, 3, 5, 19, 4, 19, 5, 19, 0, 2, 2, 19, 1, 1, 0],\n",
       " [2, 11, 2, 8, 2, 1, 2, 3, 5, 19, 4, 19, 5, 19, 0, 2, 2, 19, 1, 1, 0],\n",
       " [2, 11, 2, 8, 2, 1, 2, 3, 5, 19, 4, 19, 5, 19, 0, 2, 2, 19, 1, 1, 0],\n",
       " [2, 10, 2, 8, 2, 1, 2, 3, 5, 19, 4, 19, 5, 19, 0, 2, 2, 19, 0, 1, 0],\n",
       " [2, 9, 2, 8, 2, 2, 2, 3, 4, 19, 4, 19, 5, 19, 0, 2, 2, 19, 0, 1, 1],\n",
       " [2, 8, 2, 8, 2, 4, 2, 3, 4, 19, 4, 19, 5, 19, 0, 2, 2, 19, 0, 1, 1],\n",
       " [2, 8, 2, 8, 2, 3, 2, 3, 4, 19, 4, 19, 5, 19, 0, 2, 2, 19, 0, 1, 1],\n",
       " [2, 8, 2, 8, 2, 3, 2, 3, 4, 19, 4, 19, 5, 19, 1, 2, 2, 19, 0, 1, 0],\n",
       " [2, 8, 2, 7, 2, 3, 2, 5, 4, 19, 4, 19, 5, 19, 1, 3, 2, 19, 0, 1, 0],\n",
       " [2, 7, 2, 7, 2, 4, 3, 5, 4, 19, 4, 19, 5, 19, 0, 2, 2, 19, 0, 1, 1],\n",
       " [2, 7, 2, 7, 2, 4, 2, 5, 4, 19, 4, 19, 5, 19, 0, 2, 2, 19, 0, 1, 0],\n",
       " [2, 8, 2, 7, 2, 3, 2, 5, 4, 19, 4, 19, 5, 19, 0, 2, 2, 19, 0, 1, 0],\n",
       " [2, 8, 2, 8, 2, 3, 2, 3, 4, 19, 4, 19, 4, 19, 0, 2, 2, 19, 0, 1, 1],\n",
       " [2, 8, 2, 8, 2, 3, 2, 3, 4, 19, 4, 19, 4, 19, 0, 2, 2, 19, 0, 1, 1],\n",
       " [2, 9, 2, 8, 2, 2, 2, 3, 4, 19, 4, 19, 4, 19, 0, 2, 2, 19, 0, 1, 1],\n",
       " [2, 9, 2, 7, 2, 1, 2, 4, 4, 19, 4, 19, 4, 19, 0, 2, 2, 19, 0, 1, 1],\n",
       " [2, 9, 2, 6, 2, 1, 2, 4, 4, 19, 4, 19, 4, 19, 0, 2, 2, 19, 0, 1, 0],\n",
       " [2, 9, 2, 7, 2, 1, 2, 4, 4, 19, 4, 19, 4, 19, 0, 2, 2, 19, 0, 1, 0],\n",
       " [2, 9, 2, 7, 2, 1, 2, 4, 4, 19, 4, 19, 4, 19, 0, 2, 2, 19, 1, 1, 0],\n",
       " [1, 7, 2, 8, 3, 7, 2, 2, 4, 19, 4, 19, 4, 19, 0, 2, 2, 19, 0, 1, 1],\n",
       " [1, 7, 2, 8, 3, 7, 2, 2, 4, 19, 4, 19, 4, 19, 0, 2, 2, 19, 1, 1, 1],\n",
       " [1, 7, 2, 8, 3, 6, 2, 2, 4, 19, 4, 19, 4, 19, 0, 2, 2, 19, 0, 1, 0],\n",
       " [1, 8, 2, 8, 3, 5, 2, 1, 4, 19, 4, 19, 4, 19, 0, 2, 2, 19, 0, 1, 0],\n",
       " [1, 7, 2, 8, 3, 6, 2, 1, 4, 19, 4, 19, 4, 19, 0, 2, 2, 19, 0, 1, 0],\n",
       " [1, 7, 2, 8, 2, 3, 2, 2, 4, 19, 4, 19, 4, 19, 0, 2, 2, 19, 0, 1, 0],\n",
       " [1, 7, 2, 8, 2, 3, 2, 2, 4, 19, 4, 19, 4, 19, 0, 2, 2, 19, 0, 1, 1],\n",
       " [1, 7, 2, 9, 2, 3, 2, 1, 4, 19, 4, 19, 4, 19, 0, 2, 2, 19, 0, 1, 0],\n",
       " [1, 7, 1, 6, 2, 3, 3, 5, 4, 19, 4, 19, 4, 19, 0, 2, 2, 19, 0, 1, 1],\n",
       " [1, 7, 1, 6, 2, 3, 2, 4, 4, 19, 4, 19, 4, 19, 0, 2, 2, 19, 0, 0, 1],\n",
       " [1, 7, 1, 6, 2, 3, 2, 4, 4, 19, 4, 19, 4, 19, 0, 2, 2, 19, 0, 0, 0],\n",
       " [1, 7, 1, 6, 2, 3, 2, 4, 4, 19, 4, 19, 4, 19, 0, 2, 2, 19, 0, 0, 0],\n",
       " [1, 7, 1, 6, 2, 4, 2, 4, 4, 19, 4, 19, 4, 19, 0, 2, 2, 19, 0, 0, 1],\n",
       " [1, 6, 1, 7, 2, 5, 2, 3, 4, 19, 4, 19, 4, 19, 0, 2, 2, 19, 0, 0, 1],\n",
       " [1, 6, 1, 7, 2, 5, 2, 3, 4, 19, 4, 19, 4, 19, 0, 2, 2, 19, 0, 0, 0],\n",
       " [1, 6, 1, 7, 2, 5, 2, 3, 4, 19, 4, 19, 4, 19, 0, 2, 2, 19, 0, 0, 0],\n",
       " [1, 6, 1, 7, 2, 5, 2, 3, 4, 19, 4, 19, 4, 19, 0, 2, 2, 19, 0, 0, 0],\n",
       " [1, 6, 1, 7, 2, 5, 2, 3, 4, 19, 4, 19, 4, 19, 0, 2, 2, 19, 0, 0, 1]]"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qsr_feature_extractor( qsrlib, sample3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.45102681179626236"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arccos(0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'module' object has no attribute 'PI'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-291-4e3f8aa72eea>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPI\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'module' object has no attribute 'PI'"
     ]
    }
   ],
   "source": [
    "np.PI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "type object 'long' has no attribute 'PI'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-292-f358482e6580>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlong\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPI\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: type object 'long' has no attribute 'PI'"
     ]
    }
   ],
   "source": [
    "long.PI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.141592653589793"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3141592653589793"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.pi/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15707963267948966"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.pi/20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def _test_constraint_side(pos, line, quantisation_factor=0, angle_quantisation_factor=0):\n",
    "        \"\"\"Test for distance and side constraint using the double cross.\n",
    "\n",
    "        To determine if a point moved, it uses a line (from the cross) and checks\n",
    "        the side the point moved to (depending on the orientation of the line)\n",
    "        and how far away it moved. The latter is used for the `quantisation_factor`\n",
    "        and to create 0-states from noisy data.\n",
    "\n",
    "        Example:\n",
    "            * QTCBS: uses the orthogonal line going through the point to compute the distance constraint.\n",
    "            * QTCCS: uses the connecting line to determine the side constraint.\n",
    "\n",
    "        :param pos: Position of the agent, point in space.\n",
    "        :type pos: ?\n",
    "        :param line: Line to use as reference for the movement.\n",
    "        :type line: ?\n",
    "        :param quantisation_factor: Minimum distance a point has to move to be considered a non-0-state. Same unit of measurement as the described points.\n",
    "        :type quantisation_factor: float\n",
    "        :param angle_quantisation_factor: Minimum angle a point has to move to be considered a non-0-state. Unit of measurement is radiant.\n",
    "        :type angle_quantisation_factor: float\n",
    "        :param constraint: Set to \"side\" if checking for the side constraint. The result for the side has to be inverted.\n",
    "        :type constraint: str\n",
    "        :return: QTCS symbol for this movement.\n",
    "        :rtype: ?\n",
    "        \"\"\"\n",
    "        x0 = pos[-1,0]\n",
    "        y0 = pos[-1,1]\n",
    "        x3 = pos[0,0]\n",
    "        y3 = pos[0,1]\n",
    "        \n",
    "        x1 = line[0,0]\n",
    "        y1 = line[0,1]\n",
    "        x2 = line[1,0]\n",
    "        y2 = line[1,1]\n",
    "\n",
    "        test = (x0 - x1) * (y2 - y1) - (x2 - x1) * (y0 - y1)\n",
    "\n",
    "        d = np.abs(\n",
    "            np.linalg.det(\n",
    "                np.append(\n",
    "                    [line[1,0:2]-line[0,0:2]],\n",
    "                    [pos[-1,0:2]-line[0,0:2]]\n",
    "                ).reshape(-1,2)\n",
    "            )\n",
    "        )/np.linalg.norm(line[1,0:2]-line[0,0:2])\n",
    "\n",
    "        if np.abs(d) > quantisation_factor:\n",
    "            angle = np.arccos( ((x0 - x3) * (x2 - x1) + (y0 - y3) * (y2 - y1)) / ( np.linalg.norm((x0 - x3, y0 - y3)) * np.linalg.norm((x2 - x1, y2 - y1)) ) )\n",
    "            \n",
    "        print angle\n",
    "        res = 0\n",
    "        if test > 0 and np.abs(d) > quantisation_factor and angle > angle_quantisation_factor:\n",
    "            res = 1\n",
    "        elif test < 0 and np.abs(d) > quantisation_factor and angle > angle_quantisation_factor:\n",
    "            res = -1\n",
    "\n",
    "        return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "global name 'line' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-50-5b5976693d68>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0m_test_constraint_side\u001b[0m\u001b[1;33m(\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-49-c18574501240>\u001b[0m in \u001b[0;36m_test_constraint_side\u001b[1;34m(x0, y0, x1, y1, x2, y2, quantisation_factor, angle_quantisation_factor)\u001b[0m\n\u001b[0;32m     29\u001b[0m         np.linalg.det(\n\u001b[0;32m     30\u001b[0m             np.append(\n\u001b[1;32m---> 31\u001b[1;33m                 \u001b[1;33m[\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     32\u001b[0m                 \u001b[1;33m[\u001b[0m\u001b[0mpos\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     33\u001b[0m             ).reshape(-1,2)\n",
      "\u001b[1;31mNameError\u001b[0m: global name 'line' is not defined"
     ]
    }
   ],
   "source": [
    "_test_constraint_side( 2, 2, 1, 0, 2, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def _translate( point, trans_vec):\n",
    "    \"\"\"Translate points by trans_vec.\n",
    "\n",
    "    :param point: Can be [x,y] or list of lists [[x,y],[x,y]].\n",
    "    :type point: list or list of lists\n",
    "    :param trans_vec: The translation vector given as [x, y].\n",
    "    :type trans_vec: ?\n",
    "    :return: Translated points.\n",
    "    :rtype: ?\n",
    "    \"\"\"\n",
    "    point_array = np.array(point)\n",
    "    point_array = point_array + np.array(trans_vec)\n",
    "    return point_array\n",
    "\n",
    "pos_k = [ 1, 0, 2, 2]\n",
    "pos_l = [ 2, 1, 2, 1]\n",
    "\n",
    "pos_k = np.array(pos_k).reshape(-1, 2)\n",
    "pos_l = np.array(pos_l).reshape(-1, 2)\n",
    "\n",
    "RL_ext = np.append(\n",
    "            _translate(pos_k[-2], (pos_k[-2]-pos_l[-2])/2),\n",
    "            _translate(pos_l[-2], (pos_l[-2]-pos_k[-2])/2)\n",
    "        ).reshape(-1,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.321750554397\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_test_constraint_side( pos_k, RL_ext, quantisation_factor = 0.01, angle_quantisation_factor = np.pi/20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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