diff --git a/proc/Untitled.ipynb b/proc/Untitled.ipynb
index 97facfc..b01e90b 100644
--- a/proc/Untitled.ipynb
+++ b/proc/Untitled.ipynb
@@ -1,692 +1,757 @@
 {
  "cells": [
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-13T17:16:55.434600Z",
-     "start_time": "2020-02-13T17:16:54.920370Z"
+     "end_time": "2020-02-14T16:59:52.224327Z",
+     "start_time": "2020-02-14T16:59:51.683530Z"
     }
    },
    "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": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-13T17:16:55.437416Z",
-     "start_time": "2020-02-13T17:16:55.435669Z"
+     "end_time": "2020-02-14T16:59:52.231439Z",
+     "start_time": "2020-02-14T16:59:52.225375Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "fileHigh = './highPass.fir'\n",
+    "tapsHigh = list(pd.read_csv(fileHigh,header = None)[0])\n",
+    "fileLow = './lowPass.fir'\n",
+    "tapsLow = list(pd.read_csv(fileLow,header = None)[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-14T16:59:52.236961Z",
+     "start_time": "2020-02-14T16:59:52.233237Z"
     }
    },
    "outputs": [],
    "source": [
     "plt.close('all')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-13T17:16:55.474704Z",
-     "start_time": "2020-02-13T17:16:55.438876Z"
+     "start_time": "2020-02-14T16:59:51.679Z"
     }
    },
    "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": "2020-02-13T17:16:54.916Z"
+     "start_time": "2020-02-14T16:59:51.680Z"
     }
    },
    "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": "2020-02-13T17:16:54.917Z"
+     "start_time": "2020-02-14T16:59:51.682Z"
     }
    },
    "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",
+    "delta = []\n",
+    "for lbl in ['x','y','z'] :\n",
+    "    ls = list(dataAccRw[lbl])\n",
+    "    for i in range(1,len(ls)):\n",
+    "        dif = abs(ls[i]-ls[i-1])\n",
+    "        if dif != 0:\n",
+    "            delta.append(dif)\n",
+    "print(\"Acc Min delta: \", min(delta))\n",
+    "print(\"Acc Max delta: \", max(delta))\n",
+    "print(\"Acc Avg delta: \", np.average(delta))\n",
+    "print()\n",
+    "minDvAcc = [min(delta),min(delta),min(delta)]\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",
+    "delta = []\n",
+    "for lbl in ['x','y','z'] :\n",
+    "    ls = list(dataGyroRw['z'])\n",
+    "    for i in range(1,len(ls)):\n",
+    "        dif = abs(ls[i]-ls[i-1])\n",
+    "        if dif != 0:\n",
+    "            delta.append(dif)\n",
+    "print(\"Gyro Min delta: \", min(delta))\n",
+    "print(\"Gyro Max delta: \", max(delta))\n",
+    "print(\"Gyro Avg delta: \", np.average(delta))\n",
+    "print()\n",
+    "minDvGyro = [min(delta),min(delta),min(delta)]\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)"
+    "minDvTot = minDvAcc\n",
+    "minDvTot.extend(minDvGyro)\n",
+    "print(minDvTot)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-02-13T17:16:54.919Z"
+     "start_time": "2020-02-14T16:59:51.683Z"
     }
    },
    "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])"
+    "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": "2020-02-13T17:16:54.920Z"
+     "start_time": "2020-02-14T16:59:51.685Z"
     }
    },
    "outputs": [],
    "source": [
-    "l = np.array([1,2,3,4,5])\n",
-    "q = [5,7,9]\n",
-    "q.extend(l)\n",
-    "q"
+    "a = eb.inertial.inertial(filtersTaps=[tapsLow,tapsHigh], dv = minDvTot)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-02-13T17:16:54.922Z"
+     "start_time": "2020-02-14T16:59:51.687Z"
     },
     "scrolled": true
    },
    "outputs": [],
    "source": [
     "numWindow = 0\n",
-    "batchWindow = 10\n",
+    "batchWindow = 50\n",
     "state = 'train'\n",
     "windowPass = False\n",
     "\n",
     "for elem in stream.get():\n",
     "    t,v,lbl,dim = elem[0],elem[1],elem[2],dictSigNames[elem[3]]\n",
     "    if state == 'train':\n",
     "        windowPass = a.buildTrain(t,v,lbl,dim)\n",
     "    else:\n",
-    "        res,orig = a.test(t,v,lbl,dim)\n",
+    "        res,orig,lblsTot = a.test(t,v,lbl,dim)\n",
     "        if type(orig) != type(None):\n",
     "            print()\n",
     "            print(\"/*\"*35)\n",
-    "            print(\"Results: {} vs original: {}\".format(res,orig))\n",
+    "            print(\"Results: {}({} --> {}) vs original: {}\".format(res,lblsTot,lblsTot[np.argmax(res)],orig))\n",
     "            print(\"/*\"*35)\n",
     "            print()\n",
     "            state = 'train'\n",
     "    if windowPass:\n",
     "        windowPass = False\n",
     "        numWindow +=1\n",
     "        print(\"Window Number: \",numWindow)\n",
     "        if numWindow % batchWindow == 0:\n",
     "            success = a.fit()\n",
     "#             print(\"/*\"*35)\n",
     "#             print(success)\n",
     "#             print(\"/*\"*35)\n",
     "            if success:\n",
     "                state = 'test'\n",
     "            else:\n",
     "                print(\"Error in fitting !\")\n",
     "        print(\"----------------------------------------------------\")\n",
     "        \n",
     "    "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-02-13T17:16:54.924Z"
+     "start_time": "2020-02-14T16:59:51.687Z"
     }
    },
    "outputs": [],
    "source": [
     "from sklearn.svm import SVC\n",
     "svm = SVC(kernel='linear', probability = True, decision_function_shape = \"ovr\")\n",
     "vec = [[1,2,3,4],[7,6,8,9],[1,2,90,5],[4,6,78,0]]\n",
     "lbl = [4,4,3,3]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-02-13T17:16:54.926Z"
+     "start_time": "2020-02-14T16:59:51.689Z"
     }
    },
    "outputs": [],
    "source": [
     "a = svm.fit(vec,lbl)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-02-13T17:16:54.928Z"
+     "start_time": "2020-02-14T16:59:51.691Z"
     }
    },
    "outputs": [],
    "source": [
     "# vec.append([1,4,88,2])\n",
     "# lbl.append(0)\n",
     "svm.predict_proba(vec)"
    ]
   },
   {
    "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": {
     "ExecuteTime": {
-     "start_time": "2020-02-13T17:16:54.930Z"
+     "start_time": "2020-02-14T16:59:51.692Z"
     }
    },
    "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": "2020-02-13T17:16:54.932Z"
+     "start_time": "2020-02-14T16:59:51.694Z"
     }
    },
    "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": "2020-02-13T17:16:54.933Z"
+     "start_time": "2020-02-14T16:59:51.695Z"
     }
    },
    "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)))"
    ]
   },
   {
    "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": {},
    "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": {},
    "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": {},
    "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": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-02-13T17:16:54.947Z"
+     "start_time": "2020-02-14T16:59:51.710Z"
     }
    },
    "outputs": [],
    "source": [
     "import scipy.stats as stats"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-02-13T17:16:54.947Z"
+     "start_time": "2020-02-14T16:59:51.711Z"
     }
    },
    "outputs": [],
    "source": [
     "stats.entropy([1/2, 1/2], base=2)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-02-13T17:16:54.949Z"
+     "start_time": "2020-02-14T16:59:51.711Z"
     }
    },
    "outputs": [],
    "source": [
     "a = [7,7,7,7,7,8,9,9,9,9,9,3,3,2,1,1,-4,-3,-9]\n",
     "sum(np.abs(a))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-02-13T17:16:54.950Z"
+     "start_time": "2020-02-14T16:59:51.713Z"
     }
    },
    "outputs": [],
    "source": [
     "a = [7,7,7,7,7,8,9,9,9,9,9,3,3,2,1,1]\n",
     "np.median(a)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-02-13T17:16:54.951Z"
+     "start_time": "2020-02-14T16:59:51.714Z"
     }
    },
    "outputs": [],
    "source": [
     "len(a)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-02-13T17:16:54.952Z"
+     "start_time": "2020-02-14T16:59:51.715Z"
     }
    },
    "outputs": [],
    "source": [
     "sorted(a)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-02-13T17:16:54.954Z"
+     "start_time": "2020-02-14T16:59:51.717Z"
     }
    },
    "outputs": [],
    "source": [
     "b = a[0:11]\n",
     "c = a[11:len(a)]\n",
     "\n",
     "mb = np.median(b)\n",
     "mc = np.median(c)\n",
     "q = "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-02-13T17:16:54.955Z"
+     "start_time": "2020-02-14T16:59:51.718Z"
     }
    },
    "outputs": [],
    "source": [
     "a = [2,3,5,6,8,4,345,567,3]\n",
     "sum([x**2 for x in a])"
    ]
   },
   {
    "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": {},
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2020-02-14T16:59:51.723Z"
+    }
+   },
    "outputs": [],
-   "source": []
+   "source": [
+    "taps = tapsLow\n",
+    "f = 19.5\n",
+    "t = np.arange(0.1,100,0.02)\n",
+    "y = np.sin(2*np.pi*f*t)\n",
+    "delay = (len(taps)-1)//2\n",
+    "filt = []\n",
+    "tFilt =[]\n",
+    "gain = []\n",
+    "\n",
+    "for i in range(delay,len(t)-delay):\n",
+    "    res = np.dot(y[i-delay:i+delay+1],taps)\n",
+    "    filt.append(res)\n",
+    "    tFilt.append(i*0.02)\n",
+    "\n",
+    "filt = np.array(filt)\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.title('filtered')\n",
+    "plt.plot(tFilt,filt)\n",
+    "#plt.figure()\n",
+    "#plt.title('Original')\n",
+    "plt.plot(t,y)"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
   "language_info": {
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   },
   "varInspector": {
    "cols": {
     "lenName": 16,
     "lenType": 16,
     "lenVar": 40
    },
    "kernels_config": {
     "python": {
      "delete_cmd_postfix": "",
      "delete_cmd_prefix": "del ",
      "library": "var_list.py",
      "varRefreshCmd": "print(var_dic_list())"
     },
     "r": {
      "delete_cmd_postfix": ") ",
      "delete_cmd_prefix": "rm(",
      "library": "var_list.r",
      "varRefreshCmd": "cat(var_dic_list()) "
     }
    },
    "types_to_exclude": [
     "module",
     "function",
     "builtin_function_or_method",
     "instance",
     "_Feature"
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    "window_display": false
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  "nbformat": 4,
  "nbformat_minor": 2
 }
diff --git a/proc/highPass.fir b/proc/highPass.fir
new file mode 100644
index 0000000..49acb96
--- /dev/null
+++ b/proc/highPass.fir
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diff --git a/proc/lowPass.fir b/proc/lowPass.fir
new file mode 100644
index 0000000..80ca42c
--- /dev/null
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