diff --git a/proc/Untitled.ipynb b/proc/Untitled.ipynb
index 834e958..6f43266 100644
--- a/proc/Untitled.ipynb
+++ b/proc/Untitled.ipynb
@@ -1,361 +1,387 @@
 {
  "cells": [
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-02-10T16:11:37.736576Z",
      "start_time": "2020-02-10T16:11:37.221872Z"
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    },
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "import os\n",
     "import pandas as pd \n",
     "import sys\n",
     "import matplotlib.pyplot as plt\n",
     "import eventBased as eb\n",
     "\n",
     "dataFolder = '../data'\n",
     "experiment = 4\n",
     "sub = 72\n",
     "dataRwLb = os.path.join(dataFolder, \"dataLabeld\")\n",
     "dataEBLb = os.path.join(dataFolder, \"dataEB/sub\"+str(sub))\n",
     "baseFreq = 50\n",
     "dT = 1/baseFreq\n",
     "\n",
     "start = 10000\n",
     "stop = int(start+baseFreq*2.56)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-11T13:37:09.940977Z",
+     "start_time": "2020-02-11T13:37:09.935897Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = [1,2,3,4]\n",
+    "sum(a)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-02-10T16:11:37.739191Z",
      "start_time": "2020-02-10T16:11:37.737565Z"
     }
    },
    "outputs": [],
    "source": [
     "plt.close('all')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-02-10T16:11:37.777411Z",
      "start_time": "2020-02-10T16:11:37.740526Z"
     }
    },
    "outputs": [],
    "source": [
     "%matplotlib auto\n",
     "np.set_printoptions(precision=3)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## load data "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "start_time": "2020-02-10T16:11:37.216Z"
     }
    },
    "outputs": [],
    "source": [
     "def getExpNo(s):\n",
     "    idx = 0\n",
     "    for i in range(len(s)):\n",
     "        idx = i\n",
     "        if s[i].isalpha():\n",
     "            break\n",
     "    return int(s[0:idx])\n",
     "def pik(ls, s):\n",
     "    for e in ls:\n",
     "        if s in e:\n",
     "            return e\n",
     "def generateFreqScale(dt,N, correctCoeff = None):\n",
     "    if correctCoeff:\n",
     "        dt *= correctCoeff\n",
     "    fs = np.arange(-1/(2*dt),1/(2*dt)-dT/10,1/(dt*N))\n",
     "    return fs \n",
     "\n",
     "pathToRwFiles = sorted([os.path.join(dataRwLb,x) for x in os.listdir(dataRwLb) if getExpNo(x) == experiment])\n",
     "pathToEBFiles = sorted([os.path.join(dataEBLb,x) for x in os.listdir(dataEBLb) if getExpNo(x) == experiment])\n",
     "\n",
     "#Original data ---------------------------------------\n",
     "dataAccRw = pd.read_csv(pik(pathToRwFiles, \"acc\"))\n",
     "dataGyroRw = pd.read_csv(pik(pathToRwFiles, \"gyro\"))\n",
     "\n",
     "#EB data ---------------------------------------\n",
     "dataAccX = pd.read_csv(pik(pathToEBFiles, \"accx\"), header = None)\n",
     "dataAccY = pd.read_csv(pik(pathToEBFiles, \"accy\"), header = None)\n",
     "dataAccZ = pd.read_csv(pik(pathToEBFiles, \"accz\"), header = None)\n",
     "dataGyroX = pd.read_csv(pik(pathToEBFiles, \"gyrox\"), header = None)\n",
     "dataGyroY = pd.read_csv(pik(pathToEBFiles, \"gyroy\"), header = None)\n",
     "dataGyroZ = pd.read_csv(pik(pathToEBFiles, \"gyroz\"), header = None)\n",
     "\n",
     "#Some infos:\n",
     "lengthT = len(dataAccRw)/baseFreq\n",
     "print(\"Length of the signal, in seconds: {}s\".format(lengthT))\n",
     "print(\"--------------------------------------------------------------------\")\n",
     "avgFreqAccX = len(dataAccX)/lengthT\n",
     "print(\"Average frequency for the x accelerometer: {}Hz\".format(avgFreqAccX))\n",
     "avgFreqAccY = len(dataAccY)/lengthT\n",
     "print(\"Average frequency for the y accelerometer: {}Hz\".format(avgFreqAccY))\n",
     "avgFreqAccZ = len(dataAccZ)/lengthT\n",
     "print(\"Average frequency for the z accelerometer: {}Hz\".format(avgFreqAccZ))\n",
     "print(\"--------------------------------------------------------------------\")\n",
     "avgFreqGyroX = len(dataGyroX)/lengthT\n",
     "print(\"Average frequency for the x gyroscope: {}Hz\".format(avgFreqGyroX))\n",
     "avgFreqGyroY = len(dataGyroY)/lengthT\n",
     "print(\"Average frequency for the y gyroscope: {}Hz\".format(avgFreqGyroY))\n",
     "avgFreqGyroZ = len(dataGyroZ)/lengthT\n",
     "print(\"Average frequency for the z gyroscope: {}Hz\".format(avgFreqGyroZ))\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "start_time": "2020-02-10T16:11:37.218Z"
     }
    },
    "outputs": [],
    "source": [
     "accx = [list(dataAccX[0]),list(dataAccX[1]),list(dataAccX[2])]\n",
     "accy = [list(dataAccY[0]),list(dataAccY[1]),list(dataAccY[2])]\n",
     "accz = [list(dataAccZ[0]),list(dataAccZ[1]),list(dataAccZ[2])]\n",
     "\n",
     "gyrox = [list(dataGyroX[0]),list(dataGyroX[1]),list(dataGyroX[2])]\n",
     "gyroy = [list(dataGyroY[0]),list(dataGyroY[1]),list(dataGyroY[2])]\n",
     "gyroz = [list(dataGyroZ[0]),list(dataGyroZ[1]),list(dataGyroZ[2])]\n",
     "\n",
     "dictSigNames = {'accx': 0,'accy': 1,'accz': 2,'gyrox': 3,'gyroy': 4,'gyroz': 5}\n",
     "\n",
     "stream =  eb.VectorStreamer()\n",
     "stream.load(dictSigNames.keys(),accx,accy,accz,gyrox,gyroy,gyroz)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "start_time": "2020-02-10T16:11:37.219Z"
     }
    },
    "outputs": [],
    "source": [
     "f1 = [1,1,1,1]\n",
     "f2 = [1,2,3,4,5,6]\n",
     "f3 = [-1,-2,-3,-2,-1,0,1,2,3,2,1,0,-1,-2,-3,-2,-1,0]\n",
     "a = eb.inertial.inertial(filtersTaps=[f1,f2,f3])"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "start_time": "2020-02-10T16:11:37.221Z"
     }
    },
    "outputs": [],
    "source": [
     "for elem in stream.get():\n",
     "    a.buildTrain(elem[0],elem[1],elem[2],dictSigNames[elem[3]])"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
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    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
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    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
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    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "start_time": "2020-02-10T16:11:37.226Z"
     }
    },
    "outputs": [],
    "source": [
     "eb = signalDescriptor(dataAccX[0],dataAccX[1])\n",
     "rw = signalDescriptor(np.arange(len(dataAccRw['x'])),dataAccRw['x'])"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "start_time": "2020-02-10T16:11:37.227Z"
     }
    },
    "outputs": [],
    "source": [
     "fEB = eb.nudft(startT = start, endT = stop)\n",
     "fRW = rw.nudft(startT = start, endT = stop-1)\n",
     "print(\"Finished nuDft\")\n",
     "fEBres = eb.resampledDFT(startT = start, endT = stop)\n",
     "fRWres = rw.resampledDFT(startT = start, endT = stop)\n",
     "print(\"Finished reDft\")\n",
     "f = np.fft.fft(dataAccRw['x'][start:stop])\n",
     "print(\"Finished numpy fft\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "start_time": "2020-02-10T16:11:37.229Z"
     }
    },
    "outputs": [],
    "source": [
     "fRWScale = generateFreqScale(dT,len(f))\n",
     "fEBScale = generateFreqScale(dT,len(fEB),len(f)/len(fEB))\n",
     "#--------------------------------------\n",
     "plt.figure()\n",
     "plt.title(\"data Raw\")\n",
     "plt.plot(dataAccRw['x'][start:stop])\n",
     "\n",
     "plt.figure()\n",
     "plt.title(\"data EB sampled\")\n",
     "t,v = eb.findInterval(start,stop)\n",
     "plt.plot(t,v)\n",
     "#--------------------------------------\n",
     "plt.figure()\n",
     "plt.title(\"Non uniform DFT on Event Based\")\n",
     "plt.plot(fEBScale,np.abs(eb.shift(fEB)))\n",
     "\n",
     "plt.figure()\n",
     "plt.title(\"Non uniform DFT on Raw\")\n",
     "plt.plot(fRWScale,np.abs(rw.shift(fRW)))\n",
     "#---------------------------------------\n",
     "plt.figure()\n",
     "plt.title(\"uniform DFT on re-sampled Event Based\")\n",
     "plt.plot(fRWScale,np.abs(eb.shift(fEBres)))\n",
     "\n",
     "plt.figure()\n",
     "plt.title(\"uniform DFT on raw signal\")\n",
     "plt.plot(fRWScale,np.abs(rw.shift(fRWres)))\n",
     "#---------------------------------------\n",
     "plt.figure()\n",
     "plt.title(\"Numpy FFT on the original signal\")\n",
     "plt.plot(fRWScale,np.abs(rw.shift(f)))"
    ]
   }
  ],
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