{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import glob \n",
    "import pandas as pds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'glob' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-9719ee11a4bd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mfolder\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mglob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"./2016*\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0minputsig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfolder\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"/input.txt\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0minputsig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0;34m\"CH{}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputsig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0moutputsig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfolder\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"/output_f0.txt\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0moutputsig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Tstamp\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0;34m\"CH{}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputsig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'glob' is not defined"
     ]
    }
   ],
   "source": [
    "for folder in glob.glob(\"./2016*\"):\n",
    "    inputsig = pds.read_csv(folder+\"/input.txt\",sep=\" \",header=None)\n",
    "    inputsig.columns=[ \"CH{}\".format(i) for i in range(inputsig.shape[1])]\n",
    "    outputsig = pds.read_csv(folder+\"/output_f0.txt\",sep=\" \",header=None)\n",
    "    outputsig.columns=[\"Tstamp\"]+[ \"CH{}\".format(i) for i in range(outputsig.shape[1]-1)]\n",
    "    f, axarr = plt.subplots(1,2,figsize=(14, 6))\n",
    "    (outputsig.filter(regex=\"CH*\")- inputsig*0.8912)[150:].plot(ax=axarr[0])\n",
    "    axarr[0].legend(loc='upper right')\n",
    "    (outputsig.filter(regex=\"CH*\")[:100]/0.8912).plot(ax=axarr[1])\n",
    "    axarr[1].legend(loc='upper right')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABI4AAAH/CAYAAAAxLZZaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8c1def0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "folder=\"./simulation/\"\n",
    "inputsig = pds.read_csv(folder+\"/input.txt\",sep=\" \",header=None)\n",
    "inputsig.columns=[ \"CH{}\".format(i) for i in range(inputsig.shape[1])]\n",
    "outputsig = pds.read_csv(folder+\"/output_f2.txt\",sep=\" \",header=None)\n",
    "outputsig.columns=[\"Tstamp\"]+[ \"CH{}\".format(i) for i in range(outputsig.shape[1]-1)]\n",
    "f, axarr = plt.subplots(1,2,figsize=(14, 6))\n",
    "(outputsig.filter(regex=\"CH*\")- inputsig*0.8912)[150:].plot(ax=axarr[0])\n",
    "axarr[0].legend(loc='upper right')\n",
    "(outputsig.filter(regex=\"CH*\")[:100]/0.8912).plot(ax=axarr[1])\n",
    "axarr[1].legend(loc='upper right')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}