peak_detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2048\n"
     ]
    },
    {
     "data": {
      "image/png": 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fK/fkt74l+/fbLzx20KDS90Qt99n27dLwOuQQKW9r1siooo99rPC4+fOBD3+4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6a5996da90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline  \n",
    "\n",
    "# import pyplot, plotting for python\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import numpy as np \n",
    "file=open('Arry_data2.txt','r')\n",
    "list=[]\n",
    "\n",
    "for line in file:\n",
    "    list.append(float(line.replace('\\n','')))\n",
    "signal=list[0:2048]\n",
    "print len(signal)                \n",
    "plt.plot(np.arange(2048),np.array(signal))\n",
    "plt.show()                 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FdWPN7BEzW21mi81sTGL91JMV65GZ/buZbTKzFYm6Pr/3RjhRs8i+uN7M1pvZ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\nZGuKEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6a7f1ba050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#basic filtering \n",
    "#step 1:\n",
    "import scipy.signal as ss\n",
    "\n",
    "Fs=360.0\n",
    "b,a=ss.iirfilter(6,[(60/Fs)-0.001, (60/Fs)+0.001],btype='bandstop')\n",
    "Out=ss.lfilter(b, a, signal)\n",
    "plt.plot(Out)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def runningMeanFast(x, N):\n",
    "    return np.convolve(x, np.ones((N,))/N)[(N-1):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0o1PSF5HUUKUfnZK+iKSGKv3olPRFJDVU6UdXUtI3s1ozm2dm881sQivjzzez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\npC8iUkWU9EVEqoiSvohIFVHSFxGpIv8fV3PvpnkRhxoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6a591e5f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#print b,a\n",
    "Out=runningMeanFast(signal,10)\n",
    "plt.plot(Out)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.781563353247\n",
      "<type 'numpy.ndarray'>\n",
      "[ 168  460  754 1038 1323 1606 1900 2135]\n"
     ]
    },
    {
     "data": {
      "image/png": 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snXApD9EXQog8keinItEfAJSymV+70FDKZngoZTN3JPpCiIpGop9K8KJfqnfk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6a5963a550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  98  390  684  968 1253 1536 1830 2065]\n",
      "[ 138  430  724 1008 1293 1576 1870 2105]\n",
      "[ 168  460  754 1038 1323 1606 1900 2135]\n",
      "[ 198  490  784 1068 1353 1636 1930 2165]\n",
      "[ 308  600  894 1178 1463 1746 2040 2275]\n"
     ]
    }
   ],
   "source": [
    "signal=np.append(np.zeros(100,dtype=np.float),Out)\n",
    "threshold=(np.mean(signal)+np.max(signal))/2\n",
    "print threshold\n",
    "position=np.where(signal>threshold)\n",
    "print type(position[0])\n",
    "#print len(position[0])\n",
    "aaa=np.zeros((len(signal))+10, dtype=np.float)\n",
    "aaa[position[0]]=1\n",
    "cp=position[0][0]\n",
    "i=1\n",
    "for i in np.arange(1,position[0].size):    \n",
    "    diff=abs(cp-position[0][i])    \n",
    "    if diff < 50:\n",
    "        #print cp,position[0][i],diff\n",
    "        aaa[position[0][i]]=0\n",
    "    cp=position[0][i]\n",
    "\n",
    "    \n",
    "position=np.where(aaa==1)\n",
    "print position[0]\n",
    "\n",
    "r=[position[0]-30,position[0]+30]\n",
    "r2=[position[0]-70,position[0]+140]\n",
    "bb=np.zeros((len(signal))+140, dtype=np.float)\n",
    "bb[r[0]]=1\n",
    "bb[r[1]]=1\n",
    "bb[r2[0]]=1\n",
    "bb[r2[1]]=1\n",
    "plt.plot(bb) \n",
    "plt.plot(aaa)\n",
    "plt.plot(signal)\n",
    "plt.show()\n",
    "\n",
    "print r2[0]\n",
    "print r[0]\n",
    "print position[0]\n",
    "print r[1]\n",
    "print r2[1]\n",
    "i=0\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2288\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signal=np.append(signal,np.zeros(140,dtype=np.float))\n",
    "print len(signal)\n",
    "signal = signal.tolist()\n",
    "type(signal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signal[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  161.   453.   747.  1030.  1315.  1599.  1893.  2128.]\n",
      "[  182.   477.   768.  1052.  1337.  1620.  1915.  2148.]\n",
      "[   98.   429.   723.  1007.  1292.  1575.  1867.  2104.]\n",
      "[  272.   565.   854.  1069.  1417.  1709.  1939.  2165.]\n"
     ]
    }
   ],
   "source": [
    "i=0\n",
    "Q_index=np.array([])\n",
    "S_index=np.array([])\n",
    "P_index=np.array([])\n",
    "T_index=np.array([])\n",
    "\n",
    "for i in np.arange(0,position[0].size):\n",
    "    #print r[0][i],position[0][i],signal.index(min(signal[r[0][i]:position[0][i]]))\n",
    "    Q_index=np.append(Q_index,int(r[0][i]+signal[r[0][i]:position[0][i]].index(min(signal[r[0][i]:position[0][i]]))))\n",
    "    S_index=np.append(S_index,int(position[0][i]+signal[position[0][i]:r[1][i]].index(min(signal[position[0][i]:r[1][i]]))))\n",
    "    P_index=np.append(P_index,int(r2[0][i]+signal[r2[0][i]:r[0][i]].index(min(signal[r2[0][i]:r[0][i]]))))\n",
    "    T_index=np.append(T_index,int(r[1][i]+signal[r[1][i]:r2[1][i]].index(min(signal[r[1][i]:r2[1][i]]))))\n",
    "print Q_index\n",
    "print S_index\n",
    "print P_index\n",
    "print T_index\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2288\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "data type not understood",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-229-87471031c688>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msignal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2288\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: data type not understood"
     ]
    }
   ],
   "source": [
    "print len(signal)\n",
    "np.zeros(np.zeros(1,2288))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zznGfMqXl/oIF7gce6L5smfvHPtZ+HI2NYbp169z/+U/30aPdL7+87enff9/9178O68qX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\npC8iUkWU9EVEqoiSvohIFVHSFxGpIv8fV3PvpnkRhxoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6a598f3350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "signal.size\n",
    "plt.plot(Out)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6994661\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print min(signal[1777:1807])\n",
    "signal[1777:1807].index(min(signal[1777:1807]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "position[0].size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7])"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0,position[0].size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'list' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-104-56ba1724483e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mposition\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'list' object is not callable"
     ]
    }
   ],
   "source": [
    "range(1,len(position))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.plot(aaa)    \n",
    "plt.plot(Out)\n",
    "plt.show()"
   ]
  }
 ],
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