find_skewness.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import netCDF4\n",
    "import numpy as np\n",
    "import jdutil\n",
    "from scipy import signal\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "kN1 = 30*0.018e-2 # roughness to be used on GM model\n",
    "kN2 = 30*0.4e-2    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0054\n"
     ]
    }
   ],
   "source": [
    "print (kN1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "advbfn = '/media/taran/DATADRIVE2/Obs_data/data_netcdf/9917advb-cal.nc'# ; % burstfile name\n",
    "advsfn = '/media/taran/DATADRIVE2/Obs_data/data_netcdf/9917advs-cal.nc'#; % statistics filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "nc_advbfn = netCDF4.Dataset(advbfn)\n",
    "nc_advsfn = netCDF4.Dataset(advsfn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "2014-04-30 08:59:16.936987\n",
      "2014-04-30 09:59:15.937019\n",
      "2014-04-30 10:59:15.937006\n",
      "2014-04-30 11:59:15.936992\n",
      "2014-04-30 12:59:15.937019\n",
      "2014-04-30 13:59:15.937006\n",
      "2014-04-30 14:59:15.936992\n",
      "2014-04-30 15:59:15.937019\n",
      "2014-04-30 16:59:15.937006\n",
      "2014-04-30 17:59:15.936992\n",
      "2014-04-30 18:59:15.937019\n",
      "2014-04-30 19:59:15.937006\n",
      "2014-04-30 20:59:15.936992\n",
      "2014-04-30 21:59:15.937019\n",
      "2014-04-30 22:59:15.937006\n",
      "2014-04-30 23:59:15.936992\n",
      "2014-05-01 00:59:15.937019\n",
      "2014-05-01 01:59:15.937006\n",
      "2014-05-01 02:59:15.936992\n",
      "2014-05-01 03:59:15.937019\n",
      "2014-05-01 04:59:15.937006\n",
      "2014-05-01 05:59:15.936992\n",
      "2014-05-01 06:59:15.937019\n",
      "2014-05-01 07:59:15.937006\n",
      "2014-05-01 08:59:15.936992\n",
      "2014-05-01 09:59:15.937019\n",
      "2014-05-01 10:59:15.937006\n",
      "2014-05-01 11:59:15.936992\n",
      "2014-05-01 12:59:15.937019\n",
      "2014-05-01 13:59:15.937006\n",
      "2014-05-01 14:59:15.936992\n",
      "2014-05-01 15:59:15.937019\n",
      "2014-05-01 16:59:14.937011\n",
      "2014-05-01 17:59:14.936998\n",
      "2014-05-01 18:59:14.936984\n",
      "2014-05-01 19:59:14.937011\n",
      "2014-05-01 20:59:14.936998\n",
      "2014-05-01 21:59:14.936984\n",
      "2014-05-01 22:59:14.937011\n",
      "2014-05-01 23:59:14.936998\n",
      "2014-05-02 00:59:14.936984\n",
      "2014-05-02 01:59:14.937011\n",
      "2014-05-02 02:59:14.936998\n",
      "2014-05-02 03:59:14.936984\n",
      "2014-05-02 04:59:14.937011\n",
      "2014-05-02 05:59:14.936998\n",
      "2014-05-02 06:59:14.936984\n",
      "2014-05-02 07:59:14.937011\n",
      "2014-05-02 08:59:14.936998\n",
      "2014-05-02 09:59:14.936984\n",
      "2014-05-02 10:59:14.937011\n",
      "2014-05-02 11:59:14.936998\n",
      "2014-05-02 12:59:14.936984\n",
      "2014-05-02 13:59:14.937011\n",
      "2014-05-02 14:59:14.936998\n",
      "2014-05-02 15:59:14.936984\n",
      "2014-05-02 16:59:14.937011\n",
      "2014-05-02 17:59:14.936998\n",
      "2014-05-02 18:59:14.936984\n",
      "2014-05-02 19:59:14.937011\n",
      "2014-05-02 20:59:14.936998\n",
      "2014-05-02 21:59:14.936984\n",
      "2014-05-02 22:59:14.937011\n",
      "2014-05-02 23:59:14.936998\n",
      "2014-05-03 00:59:14.936984\n",
      "2014-05-03 01:59:14.937011\n",
      "2014-05-03 02:59:14.936998\n",
      "2014-05-03 03:59:14.936984\n",
      "2014-05-03 04:59:14.937011\n",
      "2014-05-03 05:59:14.936998\n",
      "2014-05-03 06:59:14.936984\n",
      "2014-05-03 07:59:14.937011\n",
      "2014-05-03 08:59:14.936998\n",
      "2014-05-03 09:59:14.936984\n",
      "2014-05-03 10:59:14.937011\n",
      "2014-05-03 11:59:14.936998\n",
      "2014-05-03 12:59:14.936984\n",
      "2014-05-03 13:59:14.937011\n",
      "2014-05-03 14:59:14.936998\n",
      "2014-05-03 15:59:14.936984\n",
      "2014-05-03 16:59:14.937011\n",
      "2014-05-03 17:59:14.936998\n",
      "2014-05-03 18:59:14.936984\n",
      "2014-05-03 19:59:14.937011\n",
      "2014-05-03 20:59:14.936998\n",
      "2014-05-03 21:59:14.936984\n",
      "2014-05-03 22:59:14.937011\n",
      "2014-05-03 23:59:14.936998\n",
      "2014-05-04 00:59:14.936984\n",
      "2014-05-04 01:59:13.937016\n",
      "2014-05-04 02:59:13.937003\n",
      "2014-05-04 03:59:13.936990\n",
      "2014-05-04 04:59:13.937016\n",
      "2014-05-04 05:59:13.937003\n",
      "2014-05-04 06:59:13.936990\n",
      "2014-05-04 06:59:13.936990\n"
     ]
    }
   ],
   "source": [
    "time = nc_advsfn.variables['time']\n",
    "time2= nc_advsfn.variables['time2']\n",
    "#jt = time#+time2#/(3600*24*1000)\n",
    "\n",
    "time2=(time2[:])/(3600.0*24.0*1000.0)\n",
    "jt=time+time2\n",
    "#dn1=jd_to_datetime(jt)\n",
    "#dn2=jd_to_date(jt)\n",
    "\n",
    "#dn1=jdutil.jd_to_datetime(jt[:])\n",
    "for i in range(0,len(jt)):\n",
    "    dn2=jdutil.jd_to_datetime(jt[i])\n",
    "    print(dn2) \n",
    "print(dn2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'netCDF4._netCDF4.Variable'>\n",
      "float32 u_1205(time, sample)\n",
      "    name: u\n",
      "    long_name: Eastward Velocity\n",
      "    generic_name: u\n",
      "    FORTRAN_format:  \n",
      "    units: cm/s\n",
      "    sensor_type: Sontek ADV\n",
      "    sensor_depth: 14.567\n",
      "    initial_sensor_height: 0.533\n",
      "    valid_range: [-300.  300.]\n",
      "    _FillValue: 1e+35\n",
      "    minimum: -126.8887939453125\n",
      "    maximum: 132.4994659423828\n",
      "    serial_number: B245H\n",
      "unlimited dimensions: time\n",
      "current shape = (2045, 8400)\n",
      "filling on\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "u=nc_advbfn['u_1205']\n",
    "v=nc_advbfn['v_1206']\n",
    "print (u)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DETREND \n",
    "u_detrend=signal.detrend(u)\n",
    "v_detrend=signal.detrend(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "#BANDPASS FILTER\n",
    "lowcut=4 \n",
    "highcut=20 \n",
    "fs=8  \n",
    "butter_bandpass(lowcut, highcut, fs, order=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'matplotlib.pyplot' has no attribute 'hold'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-77-2d041089ec0f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\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[0;34m'r-'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'on'\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      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu_detrend\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[0;34m'b-'\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[0;31m#plt.plot(Output_dissip,A[:,0],'ro')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'burst time'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib.pyplot' has no attribute 'hold'"
     ]
    },
    {
     "data": {
      "image/png": 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gxx2bxwz8a3pB4ZeB9q1Ijg8xJh0/nYmu8PsCdZh0xsacD++8eeUUfqfdMp97DvjIR5yJpUx4V79rL6FsuFgp7vuyZW62Y10x8kMNzDbb9B7hU7loJp1QneF1jduiq9jwY8wWPmhmsH+9hG4TvrXAj3/c/H3efjtw8snN5/uN3p/+5LyLNFWfFH4Poi4b/pveBMyaVc6G3wmTDlcnq6wC/OAHLkBXjE2/jMKvmp5P+GusoUcwLQupfHk+abavf67/jGNjrjHX0OoYBweFG9AIP1Rn+CQ5/nxc4cf2FDW1WuS1xRc8v+ceV/+oh+dDWiCl7oH7IlxwAXDEEZm5j76Vf/kX4NOf1idOAa6cd9wRWGcdXeHXMQ5TMxLhlyF8jfhCNnwtzEIrJp1HHgFOOimchtRNXbQojqClClrFpEMI+bx3wqTD48Jz4iryw//wh11j/vvfy/epk/DpfWkmHS19iobKXUQ/9KHm6+tQ+Dxv/js74IBsn1Y4O+ssOf2vfa35WF0xn2JAg89+cEOC/w74s1JE3Oef13s//rO0MlO+JiTCL2PDLzMyPz4e/vhbccucORM47jgX2CkGPC9LlpRT5Fo6HHWk18oC6RKkD+t738v2tUlC/jOefbbbnnSSfB+e77pUXFmFTz2D0Psp44cfQ/jaBDEgc1E+5RQ5/ZCg+MpXmj2r6gSVAzWyvqmFwqNojTAhdtC2yhhczUiEX0bhaySvDdqWcXuLgaQQYu2BPO9LllS34bei8EONaN0xdaT7UUheIIuR4p8bIlUffMyhLDm9+qpTif49fHKhPJXxfAnVy1D9izHpSOYvQmhd6MMPbz7Gy54m5d1wQ3E6VRHq6fzhD/n/tZ5NrEknKfweQJmZtjEtuWTS0VDHxKtYwvefpxsmnZANv+pykxqkd7v++tn+scfK+QiZTXy0ovA32wxYeeXsf23QtgrhS3W0DpMOb+CK7NQhwp8xw215ZFDp+TSTS6ug5zvjDLf1vyV/5j9/z7/+dbYfa9KJHYdpIxLhlzHphNQTv64uhT8+7tzrXnqpmkcJt+FTCOJdd+2OSYcPHEr30EJQVIVUvjwM8+67y+cWKfwlS5rVdysKf8EC+fhLL+X/jzUHhMyOIbfMZcsy+7RGXrxhXrbMxcChd1dGjEgEKOWJJu4dfri7989/Xs8azaF3RYPeUiPMTakxQrBqDKOakQi/jEknxvdWmuAybZp8P/8e8+fnu/cA8O//Dhx9NPDe98offewg1/h4pqjGxqp71bTilikNyvLr6jLp3HGH+0ivvTY7Rp4/PGz1Tjtl+0Wks+uubvuOdzg1zr1fgNYIn/C3v7ktkYvvmtqKwt9jj7zCJ0KX6v7OO2dB/7R6dtdd2f6yZS4GjjRpSQtR4KcZMhm++iqw8cbAOee4+E/vfS/wrncVpx2DTTd129VXb86HlBfNOynGpDM2lgi/J1CV8LWWnF4yn3jFryuK77LBBsD22+ePfec7bnvppfmuOHWXy4St5S5/ZQg/xtxRh8IP+d/HDIQDwB//6LbnnJMdI0KKCYDl32Odddx2+eXdh+5H+NQ+/jKgNELB0zSFL00Oov2VVpJt+FJaN9/cfE9AruNA8zuTCG/jjeU8S3MLNIVPLpz0bmkbC2vzUV/5/UkI+d+jnxetfsb44Y+OJsLvCdRt0iEVoH1YocpNi3tL5/D0QjFyFi7Mn8f3YwlfarBaseFLCp+nTcpTw2abZcRdBFKWkleFtsh0GTOZjzoUvj8RTfPwqKLweY+ujkFb/h59M5z2LUjgvQ7peoJUL/xelgR+3+9/H3jjG4Hbbmu+V+w4XoyXDi8b36SWvHR6AHUo/DKEr5G/Bul8Tt507/PPd6tTAc4uvNZawH//dz4dUvijo/n8//nPee8V/96dIvyQH/7998fNwpUGV8nmqy1TVzSTVLOzE/hkp6JyGBsDTjzRzeT0Qe6HIYWvpS8pfE6oWoC/Imjvpih6qu/+C4QJNdTYcnJ/29vclgfd08DTIvPeZz+b/cZ740X50wbSCVpj6JN/Uvg9gLpt+PSSuQ2/FSVJdkaeV2uzj4nMC4ce6lanAlzsFMCFxeV50Ew6O+wg20QlkqnbpFPk1VAVmjcNEEf44+PuvB12cC6BN97YnFeOiy/O9osI/3//1xEOLVTOcdZZzm+d3l1VH27pXY2N6S6asWlxdcsVdxHhh+ZVhOrDJpu4LY0pANkaDXQuD4CnpQ+4mfD82NZbZ3Ve81ryy1oj/KTw+whlTDoa4ddlw5fwiU+47TvfKVcYcinjoPted10c4QP5hSsIZQi/jMK3Vo6l0wnC10w6vqfVgw+6MvnoR7Pjmslpn32y/aJ3Kil7wje+4ba0NrA/KazbCp83lBrh+w1LrILW7ikNYL/+9dn+t7/tSPvcc+X0+TOT6ysR/513Np+vDdpKCn/DDZvP88/xyzwp/B5AHQqfu4gRodSl8KnSTJ2aN+lQ3PH3vrf5Gr5UnEb4RRNm/GPtMOlIroES4V91lTNXtQpaTem3v5Xz4zdqlK+XXsrIQvMr1waCfUjhhAlkC6d8aOqQvxOpvDTCr1Phc+8bTvhLlshmIP8+L78cl38iWr70KV+a8qmn3D6FyzjvPOChh7JzefpFS33GcgAvA9rnaw0AuklHeoddQCL8MoSvKV2qeEB1G76WD6o0vlsXDV5Kvs4PP9x8v7Gx4kHbIrVVRPhPPukCi8WEfg156UgEsO++zlxVBl/9avMxyhe5PwI64Y+PZ7HkH33UuSr65/Nn0I4X4ZvflMuK/O81+y9t3/3ufANUpPB9N9zYCUCawucmFk74vpCQBm1fesk1vnvtFVb4dB13VeYupUT+3/622/7jP2brLABy/PrTTmueyKWZdIoUPj3b8HB5k04i/C6iandTUg9AVhF8gg4FDuP7S5e6af8LFmT3ee654kFbjgsuaE6X9zgkwvc/7jlzMnLUbKzj48Caa7rAYlJDEGMDLYo9UhXcT9xPe489wvceG2ue+AToDUQVwv+3fyv+PaQOKTCZD0mUVFX42qCt5o++dKleLgQq19//Xlf4lA7dn5uQuFcQmWf220++p0T44+PNK2CFCJ9iAvEJZ1zhlzXpdDJAnIdE+HXY8PlAElfk/BzJhFE04WTddV0YAErviivkNCTCp9gjkyfnCZ+bC/xKx/NywQVubOCTn2xOW+vlcIV/4YWuIdh///y1/GOQVFM7PwTKa5ECI/CPk4OrXE3hVx2QW3XV/P++mUASDxx1K3w+BuXnRxvA9RW+ZNIJRRYdH3cmIz5mxRsVfj+aTPfzn8tlohExNxFxaIQvxTfiCj+ZdLqEG28E7r233DV1uGVK6oe/YH6tpuqlxsG/D//oNYXP05wyRVZ5IT98+o2PBUj51PZ/+EO3veqq/LUhAqgrPPJhhzUfo2fS3pv/LPPnN6ehDfhWUfg+/DhCfsPkN0g+6L5FNnwirpjBQ7/+am6ZPglyBS69z9CcBTp2+eXZ/fk1FNBs2bJ8MLuFC5vT0hop35SmlcPXv57/XUovVuHz8pR613XHkVIwsQh/p53yNrwY1DFoK5EWt7vS+euvD/z0p/K9NcLXBjo1wueVaWREVvghwpc+Himf0r4xcRNt6Bwt1O6NNzq3SAkhUt1ll+Zj0sdW1P2+4ormNIp8rGPzpsEnR19E8Lq02256OpIoGRtzKlka+NUwNpZ/FkndAs3kL5EiT4er9dhBWynkhr/SlDQ/QyN88oQiaCYd6gkUEX5VGz6/16c+5WZDhyYe1oCJRfghHHusW6WGo4xJJ+SKyfd9hfTcc84m//GPy/fQPD0kc0Es4fuVrMiGz8GjF/rQzCB8X3OLlGZ7ah/IUUflXUVjXEMJRUSm3S/G00azXceadCgmTww0kw6QDytAxFEUWsEfj4ix4fuCRVP11JujfIVMOpde2pwPDsktUyL86dP1b5A/g5RnHxrhk0lT6j3xa7X7aCYdwO3fdZfrSf7oR+5Y3cEDBYy0/Q69BMl7w/eUkfzDCZo5RjLp+N1wyTyiKXytYeEVUyN8rZKFBm0B4DOfAbbYIhsMk6Dlk5sJKPaMD2lSmqbw/djzPjmPFFTdItKNMen4CpCgEX6swi9ju+Vpnnyy/kzLluXHkKRGjK6dPDk/sBpS+L4jgbTPl3788IeBz3+++TxeJhTj3s8rvy9BIvxp05zXzqRJutuolH6VxXV805c2U7yswqf/Y2YL14zBUviEJUvcuq7G5Ik4FOdGM+lIH4P/gqUup5Ye/4j4car4vsLn9r/RUUfaQObvTNdws4b0sX3968D7319sSw+ZdMbHM1PMIYfkr5UUvkb4vp01ZnB3dNRNxLnuOj3/MYO2t9yS7XMXRB4SV1P4sYRfNDnMT/PUU3VynjLFuWgWDdpSWhQkTLPh+wIkxqRDs2EJkhDS8i41gNLAPv92aN8fiObnfPe7btuqwvfdiLUGKnbQlj+vFOO/6oB/CUxMwg8pqVdfzWaw0lR2QFfw0rHQoK3f8kuEryl8HtZX867gH/j//E/+/M03b85zjMKXnseHRvhSV9Y3DUkKXyNg/wONcd889VTnrsjdUn3E2OEpXC6QJ2byzQd0hR/bu/CJsiifoXQvukhe4tAfqKZnkRwI/P99k45G+Hx9Af/+fDwLyNf3yZPl98jLVfLS4aKHX8/POeYYPS8SQmNORYRfVeF/85v6/dqIiUn4oe6bNk28DOGHBm39fEgDMlp63OaqqWh+jJ+/bFn+XvwaTrJFjWJR+WmDy5Lr3KJF+XC7nPApHU3h+95WMQqflF0RinzvCSutlO1rSryMwn/iiWYTX2hxkKJ5EhK4m+vnPgfcc0/eDRfInkWz4ft1Okbha4utUxr8/vzc9daT66AUalqy4fvKWjqnUwo/hvAvvrjZg64ov23CYBI+rxxFk4p8xPgla+T/4x83p6fdm5uZJILybfi//GX+HB7Mq8qgbazC52XEy/T00932iiuAf/gHOf8hk46PGIUf49YZo/D5cY3wff9zgk8ar73mxjTmzNEbmBjwsuZLIhJI4T/7LPDlLztPJU3hazZ83wQZGqei87Q0fMLn5669tvweJbGimUP59ZKgCg3qhuCPgWhupDEmnS99KT9jWOKXRPgVUWTDBeI8LKoqfG190+uvj09PI3zNpMPNUlztz5rVXpOORvh33FGcrmbSKbpvkTL/05/C10vXaoSvRdTkbqKhOjQ87N4Rja9cfHF5kufg+ZAWU/dt+NxFkrY00K0pfL/+ayYdzWOHp81/o/LxffBDCl8y6WjXhwi/SARS+Gv/m+durYBcv/wJakU9UW3innZ+G9B2wjfG7GeMecAYM88Yc2z4iorgBah5WRBiCF+qjHygJWTD99OQKq2m8HkwNs2ko3np8AFH7s7XDpNO6Pl8lLHh+zNPtfNOOMG52v75z/lBVQ0xJh3+/HzBFe15JS8dej5a7Hry5HKDtj543ZTIghoBavC5n3qMDf9vf3NxZghV/PAPP1wmPxIwfpm1YtIpsuHze0h5lsB7nf71vmnMh9bD98/n/2+zTXF+24S2Er4xZhjAqQD2B7ApgPcYYzYtvqoi/AFSa/XFMvz4H1IaksI/9lj53BjCf+tbm9PTBoH50nwxJh0OHj9fI/zf/a4ewg99cD5a8dLRGtiTT3ZbHsCuCDF++LyXxF0etYU/ivzwaVm9SZPcPSjgWRk3wY03jrfh8wF/n6i0mbavvOI8fT71qfy1MX74tL/ccu4cTRA89VSzwpcI7pFHmq+XFqApa8MP9f6kvPgNZlnC9+/Jz5ugCn97APOstfOttUsB/ALAgW25Ey/cjTd2a8Eut1w+OiKBV6oyJh3eKscM2vJzyCtDW9BcI+AYkw6Hny+J8LXutPQMPrTnjlmJqsygbVGe+HmkHmMVc4wf/k9+ku1rpBc7aLvuum5LCp8IP9amvO22rh6H6iaR+cyZ2THfLdM36VA6s2YBN92UTy/WD5/KZIUVshnB0oD0kiXFsfMJJ5yQ7YfmCfB3KcXE13ptEqT3Qd9P0aCtf21RfdbqjHZ+G9Buwl8bAF85+PHGsfrBC3C11TKVLKkDWhkKiFvjlHAga6vKKnz6MLR7aAQc46Wjnc9NN0U2WR9W0btLAAAgAElEQVQxHg3+eWVMOjEzbcv64VOI3BBibPgc3LSheXdJJh3CEUe4rU/4sR/3Cis0D6AWXSsRTshLRxJFnOj8dKX3/rrXZQpfmhRnTL78RkfDZVAkSnx3SO5Zxe8h5VmCRsBaj4Uj1qSzZEnx7GE/9lQb0G7Cl3zPcl+EMWaOMWauMWbuIi2KXQx80i2aMctdomIIf5NNgFVW0W2+MQpfInxN4dOEJTIDSOdwhb/11tmCKKOjmf2bK3xfsRXF7Yg1N/Bn5RX4QKUTV8aGX5Qn6bzHHms+VpSH2HvzvLYSWoEil5Yl/OWXL/aYIUj2bjpG25Afvp+eJhAkgbPiihnhSwvFDA1l38DUqc2hlCWUUfhrCzqS/x5y5OAeNPx67TvniDXpLFmScY8URkFqeGtGuwn/cQBvYP+vAyD3VNba0621s621s6dPn179Tn5BUxeXk6pkP9Wi9/HK9sADLhbOSSdlxzT1oBGTNM1cU+oU9tUYnfB5fI+xsczWzD8E36QTO8gaS/iaiUO7nru5hWz4/sfuq0MfsR4wMTNtaeLVbrvlTQ/a8/LBYm0SD4UCKEv41DPwydcf1KbfOZH49/DNXqFZwZrCl0x53KQjEf6yZc2E36rCL2PDJ6y5ppyedn2MO/Dvfpftax52gHt++k4lweXH+WoD2k34twCYZYyZaYyZDOAwAJcGrqkGn3QlwidVzD0vNBc8SV3wdTBjTTr0kRUp/Hnz8q27ZhYqmvhBFWl0NMuPZsMH5Ng+0vMUQZoB6edfSjdm0Nb/2CXC54uAGANssEE4z5Su35jyfb6kIS83jfCfeCLb10i0qkln8uT8eyT4pErPVUQ4PuEXKegiE2BI4UsmnaVLs+tWXDFO4Rf9HjNoK9VjHiqDQ/oefIWvvTMe5K9ImIQUfpV4PyXR1uBp1tpRY8zHAFwJYBjAWdbae9pyM1/hSyYdagR4+N8yNnwOzRbsq/aREbel+DjSPWbNyqfNewOxCp8qEv8QuAryP+ALL9SfLbbiaQtTaOYiadBW6wKHFP7YmIubw/NcZnLNyIhufqPnp/DSIYXPUbdJZ8oUd26I8Nuh8GMIn8pk8mRg8WLdpMPnfiy/vCPYUBn4zzwykq/PoR6rlL4WdE8i4NHRcM/SR8ikQ8LszDObr43tpbaAtvvhW2svt9ZuZK3dwFp7Yttu5KsO7oJmDPCDH8jXlVH4HDG2Pa7wKb66H52zKE++J4Nk8tAIn+7LY/uEnolAE5lC0OzYWu+hjFumX/n5vc49t7nM588vp5AmTQoTPplhQjZ8HnsnpPCJcGIbJ43wubsoz/999zUfI7Ri0uHfiUT411zjvN9iFP7yy+t++IRp05p/52NvZd0yCdKkNaA9hO+fv2xZ9t6kNScmAuF3DH7LSirz6qvd9iMfybtEEnzzyTPPNC84ISHWx5c+Mro39z7R7sFJWrNr80khDz2Ud3O0Ngtcxs8bH88+xk0LpkPEEr4WXkBz0ZQIVvtAihT+qac2l/kRR5Qj/BiFTyYd6UPUTFgaiQ4PV1P4FGTMz4Om8DlaMekUKXxpbGr33bPr/MaIzuMKP0T4ktsmJ/yQDd//naARvmbSibHhcyxblpWzRPhSDB1CrCBrAROT8JcuzexqTz+dHZcqGJ89Oz7uXDoPOyxc+CGvCQK9fPIC8MlBIoiiRRQIVJmefdZtyWTEB9H8/I6PZ/bpInLkYxxF0BR+DOFT/qsM2vpp0fkxcwEI3Dzg35uHuH7++TDh87zcdpt+zyo2/ClTMhs+JyvNhs/h38NX3tbm49Nz8B7h0JBeX6x1v2++eebJJpl0li7Ne5+Fwnv4DQ5QrPB9M+LYWHMd2WKL8gq/LOEvXarPteC/SUgKvwS0gVNuyuHkT+BdK6pgF11Uj0kHCC/UIb1kbVajVPn8ikpE5Icmpg+Y1FcROe63n/6bdC8gXwb+CksEyUMp1qTjl7HfC/nJT8orfM1cRulQEDqp+60pfFq9SILmwVIEbtLh1/oquqrC/8pX5Ptyk47fG/IxMpI1oJoN/667sv0lS8IKXxoH8gmf38tX+B/7GHD//fljw8PtJ/xly7JvXlL4RXzAJ4S2CROT8D/1KTmUQQg8dEIZk04R0RTNALVWvlZz85QISorOCOiETx9IEeHHKg2N9MoQb6xJx/94JLNTmZmKIyP6PAi/bKT5IZo5qwgaGRaBm3Q44cWYdFqx4V97LXDKKdm9it7ppElZozA6mm+MyITJTSb0TGXeF11HIJMNlYlP+Kef3jwZb2hIJ3xtEfQqCl8jfBpbfNObZI8yaUW+mjFxljjU/PBDWGGFrHW/6KLseF0Kv4jwx8flD+n22+W0Jb99P588tol/r/HxrCGog/C12aZlvGU0wvfJyE/TX3ijLHzCLLo3Lw9S3JqXzlFH6fesovC5u+2KKwIvvOD+j0mnFRv+icy/YtKkYjfekZFskqA/aLvccq6e8AY6xqQjgRM+mXSmTHE9ypiZ3kNDeiPHl2Yk+HmMVfiaSYecOCZPzkyxHcbEVPj/8R/hBSb8aeY+OkH4RQHeCKFJTH6ESEpPI/yY4F2xhC+polDaRSj6oPwyLpopHAO/ax1770mTHMloBONPiCIYo3uwFIGb4IpMOhJaccvkCDUu3KQzOur26fujekhRQwFg++3dtmw9kQZt6VhMfShS+BKqKHxAV/iUhylT5Ab0Ix+Jz1tFTEzCj+k6hwbPyhB+UcUN2fCrxPjgx3l0RJ4Xn/BpECzGpBNbsS+/vLXrfRQ1nP5voRDYIcQQPs185OKBFJpGMFpjSa6oZRT+0FC+geZ5LttTAMopfA7tXpQeKXzA5ZPbyqke8vpGJF1mkB1oVvjczBWr8NtF+Nw6UJXwY50lWsDEJPzRUX1GHYGHIpDQKwqfTyLj0CpfSOHTc9eh8DfbLO68WBR9UP5v2mBjLGII/6Mfbf6N7NVa+YXqU1nCp3z6Hh6x6XCSlBS+FHTMhyZaKA+k8AFHvCMjGbH6Y0n8uhiS5iiy4cco/KJBWwllCJ8/Z9Fci6GhbBKedL82Y+IQvja4SeBhY4Fwl7huP3wJjz7qBnCKoLlyhSZt+YS/YEF7B23rgGYiAsqNC8TAJzHpmSWfaSJ8rfy0ekP1pS7CjzHp+OdJxH3AAeE0tDxTej7hc2IlIuRp8HND4ETqe+ksWtR+hR8b3E8ifE3ha+WZCL8E/Bfjq2JfAXAVImHx4uL71aHwr7oq/JL5IFAMqOHzldXChfFumbHk2qodvQzaSfi+Tz5BIlUiN638tMaST+aKxfBwls8lS6qZdIoUPhDXuGs9C67wuWqXFD69v/vuCxM+/3Z5/vm9x8fdYC3Nb4kZj4gh/FVWyfZHR/MhEIq+VS6wfML/8pfzeUiEXwNCLbFfuULdO75UoIQ6/PBjEJqsQfjiF91WU/hAfmyjiPBjbavdIHweP0dCLBH6ilOqM5LCLyL84WGdQCn/ZeoDJ4fXXsvnR2qMpN4iP0+q7zEDpzzPUo+Bej2UT1rPF2hecH3KlHwjJoGLFfpueW8HcOU8dSqw1lrh/BOGhsotLem7mPp1RGuYfJPOoYfm85AIvwb4g7b+h1dW4YdQReFXIf9YhT97tttqg7YzZ7q0/JALEqoETwPKr9NaBlTGUkPGsfHGcenxvA4Py+UhlXsR4Y+NOW+U3/+++bcqCt8nuSI/fEBuBCRTA0eRu6WUrqS4ucJfskQetOXXhxQ+H7yk6yXCHxtrblCKEFL4vkvt6Ciw665uf7XVikM98H2u8P1Gxn8O/35txsQjfIrT4Ree/4GOjMgEFfogpTgZdG9pcLWqZwX3v465zvd88NUp+S2HCBOIJ/xXX82nV8VzJBY8DosPXu4xzweETTqaEgvZ8BcsAPbYo/l4HYSvmTcIUo+E+3tL9T1mlSXJjMOJrGjQ1s/n5MlhDzn+jmmfm7eAZieEImy4YZZGEeFPnpw3DfH5FpITiNbjoucbH3f39Ak/KfwawBWgpPB9aC//da+Tzyci4S+TQC9Ks/kSYgfaAF3NaRXWt81LbplaYCsfZRQ+/xDaSfj0fqV78GeNJfyQSWd4OE7hx/baWrXhA2GTjkT4PtlUgVSHuVouGrT1Gxmu8DXw59hii+x+ksKPKU/KQ0jhT5mSJ/yHH9ZjUwH5dyApfLpnDOGfcQbwve/peasJE4/wpUh8EgloJh0tmh298CJ3TvrN99vm94yFVoE0s4nvbslVEk36iVVE8+fH5fHVVztP+HwheZ4PQlWF73vXaF1vn/BjG/FWbfhAuDcl1d1nnsn2q44nSd5BvsLn9Y9/W/43FkP4/Dlp6UL/fVAcqhhXy1jC9xU+X3g9RPjSoC3dm99TI/w11ihnnqqIwSB8yddYM+lobnX0wrXuqDH5D4DA78E/vhC07rtG+FSxJBs+rZpkbbleRgidJHwq73XWKT6vqsInUGPtq2t+XRUzHb2XMuVf1oYfWtdXU/shAo5R+FxgFCl8v/wkSOTpvw9S+L7JRMK99zbnWVoDV/KP/+EP3ZY/H6URS/gxNvx2jn8xTFzC54QsEb6mDJ56Sk7ftx37Cp+rGo3wt9xSTluC1n3XKsYzz7jKSjZZXw1S+dRJ+EuXAg8+mJFPJxR+SM0VxRvn0Hpe3EZdpPAJUq9OQt2DttJ7DM0+9geqAefpEpoLEqPwedrPP5+Vh/++NFMZh0Se0vuQCP/KK/V0OeFLwoAWqeF48EG3lUQXfx88Pb+cY0w6ifBLgmakLrec2+cvTuoqaSYdbXqz36WTIhFKCr8OGz7f1yqG74XkE34VhelDu3cdhB+q8ET4w8PNKp+/m1iFL5EfkJWPpvB9wqdnjs1/GbNKkQ1fKusPfKD52H/9Vz49AtX9mECDGuFzgcPTvuwyXeEbEzZR+r1Tup9fdpT32HEKLvKksTptBqyfLt3PF2J0nN8n1obfisdgCUwcwid/9cmTmxdI5itdcVKWCllzUyNSIeVC6UsvWavQZQhRU3MaYfgfjq+S6lD47SL8GDssrcE7NJSPWzI8nHeniyF8/368TKknp32Y3AURyDcQRaiq8Pn5IcLn9fzTn3Zb3jhKjdzSpfnJRhKkPHAimzQpn/YKK+QJ78AD8+mFTGKSwJEEFcXtoePHHSfHtd922+y6opAP/qAth1R2fhho3hvhDV5S+G0ArRc5ebJb7YovdsJbc6pMmu1vq63k9DWTDrcxSqpGU/shaB+3VjH8tH2FT/ltB+HzCThV041VOL5iuv/+8l46/uAdLzuqK5rCX3552TtDyv93vpPtSw3u+94HnHeePlhX1qTDf//a17LVqAiSEBkfd3WeJu5J0Bo4zaTz8Y/nv4V/+Zd8eiH3Uq6y+YAriS16TlpOkJ/DV7BbvNj9rbtulhblSzL9taLw+bPwiWexhJ8UfknQBKXrr3dTrrmnCe/y00vSFP6pp8rpayadTit8rWIUKXzNfawstAarDsIP2cD5vXwS4/lqlfC1iT6EyZPlxlhqDDmR05iSb6o79FD92f1GJ9SwSe9WIip/f2QE+M//lPMAyIS//PK6iuXvaHi4eMKS1HDxKKyczP3rSeFTeIUTT8wT9iqruD/pm5e+jyLCDyl8ID/+w3s4/jtIg7Y1gAhfemGcrKmyxNjw+UuIUfghG77vn1sETeGHSJfAn6NKlMWie3CCmjOnMyYdnoeiQbGYBs1vYKRxlqIPU5p5KuVfGxgk0DXaYhhFJp1Ywo/xzPHL/5prgNNOk8+VFL5v0vHdMv2B1JBpiseF54LKzwPZ8Pk3L33/EuFLIRN8t0wOqeH0y1vr7SeF3wYUhSDg5MdfvtSqDg8DO++cnUOgRoNMRVRJQgpfs1eWmXwSY9LxwRuoKoSvDXT7eeCxUTql8H1y4WUZE+63SOFzk59mw+d2Yq7qrrsuf65EyjzN0DMXmXSktKVj2nPy/WnT8ufNmJHNTvXzzL2S6Brf1XJkJG/S8MNohwifv0Ou8InM6TlHR+VBYR+8kZK+UUq3iPAlM62f91iTjuSeHVv/W8TgEX5I4Q8NZW5q0mAeociGH2PSCRG3Fr8klvCLGoxQ5Zo6Vfb4kHownBjLEj6loykvCf4H5P/vm960dxwi/CJ/aW5y4Arfd7vVQjP4eaO65QcCK0v4IYWv1UX/On+cRPPD12z4kyblFb6ffsjMKdV3Y+RJhb65SHrfksKX3i3P57e+paermXSkQVvJS+cb32i+95NPNh9rAwaD8CWTznLLyZVjeBiYN8/tSwqfEDtoW3UAt6zC90lcUmVAM9lJ8CspQVL4nJTKEr6kiEKEL9nwi0w62jvWbNsxJh0OToB+3qUylAhfe3Y/D9pEn6Jj2nMW1Sk+idA/V4ulU2TD998JV9GxhG9tNiDL59j4Y24HHdScHvUYNLOrdN9ddsn/VsaG73OBMfq8BEKHIs8OBuFLCn/KlHyXlDA0lEU7lAif3NykQduQDV/7yA4/vDnPZSde+V1RicT8fGrlFSL8GIUvub1p6YW8J/xrfHLhC2RzLw1K20eRwi/y+/bPDZ1fVIZ0DYf/Pnx7b8g0V2bQtig+k//+pfv6Jp0iG75PjGusUfwcUoRPa4GLL3b7Dz3UfA1h+nQ9Pf7eQw2N/y7LKnxu0uHXDw3JDXMatC0JIvybb27+TfPSkYhveNi5ldE5BKo0PC1e6R9/PGzDl/a32SYfL5vQqg1fI3xe6Yt8+mMVPlehWtwXDTw93+Wu6Br//m9+c/a/H/hNM+log7bchq8p/M9+Nvu/aGaudO8ihS8RvlZ/Qr0H6TyttxlL+L6veazC9/O1yirAO96h51lynIhZf3eTTeTj0rPSMVpQHXDfiSaGqip8v9c/NJSfLyGl30a0jfCNMV8wxjxhjLm98fe2dt0LQEb4223X/Juk8DWb+9AQ8PrXu31OCkT01mbHedeXYl/76YW60Rq5lg2eBiAXlrfovkUqByin8DkpFZmsJPKUPhD+EZ13HvCFLzSn45MLH2D0Z0prA/NVFf7wsO6l4xNnWZNOGcIPeZhJ52m9zTIKnxNXjMIfHs7mtvzkJ81pSg38m97kvL9uu60cEZ5ySj4N/158XsLIiBOH556bnffyy7rJp6oNn3/n9BtvZAgUd7/NaLfC/19r7VaNv8vDp7eAIpMOb1FDhM8VCSd8UvivvJK3x/GKUMVLJ4bwY2baAsD558vn+Qpfq7DSORx+5aV9XtEJ/ruQ7kXncK8O/tzbbZcPDUDn+h9fSK36+Y/10tHWN5CeU+tJSNf7v/vdf57PMgp/6tTmY5qqL+o1xhI+J0eeNl9idGgIWHNNR7b/9E/ZOTQRTVtV7PvfB7beOm/DD4G+0aVLgbvuyqdHafAy3m47YL31svMWL5bFC53v7/vlxs28Re+UlwMhxrusBkwck87SpRmp0Mw6wqqrZvvSTFtf7dDL5pWRXPGeeCL/oVIar3+9XBFCH2wM4YdMOsa4j0NyZwOabfhlFL70LP5zSITvk48UZEyy4YfGKySTDv+/aCyDP1NI4Re550lL22mDw9/6Vja1n87zu/uUnl++1uquvPy5igYEtefk1/vX+YO2EmEW2fCPOUZ2YOC47LLs2qI8hwj/kUeyfQp8KPU4AOCFF4rzxesWL6t3vEMXDlJeQiadGTOa793vJp0GPmaMudMYc5YxJhCwo0Vwhe93bbk9meKYj4/LxMcJhL8E7v7FXybtb7dd+0w6oZm24+POLqrZa/0GLcaGX9Rb8clCInxf0fOxEyk9SpO/K8nW6RO+H4yriPClLrd/TsjTyCf8IoU/POzGg/bbL3/MJwM/rZgxIK0HdsMNwN13y+e1asPnaRTZ8CdNCnulECh0sf8c0v4VV7jt+97ntjNm5MXdE0/I91i0yG1/+tPmRpZj3XXzzzRzptvfYgs5T34aNH8nZNLZcUe3/6lPyc/ZRrRE+MaYa4wxdwt/BwI4DcAGALYCsBCA4HwKGGPmGGPmGmPmLqIXUwWc8MnF6aijmtcXJcLXbO4aIXIClhR+kYmIUIfC52mccII8SO3ft6yXDn8uSSHGKHzNpKMRPu3zXopmovDLq4jwpcVoikw63N4rgZufeJoSgUhqUiJ8TeH7jRn/Pnj+ObnuuGN+olMVhe8TvnSdny4//9vflu8j4W9/az6mNcb77utU+v77u//9OuYHaSNwl0dNnQNuXWj+Psl7joeRKEqDu/QWeelMmuTq1//7f9m1/TDxylq7l7V2c+HvEmvt3621Y9bacQBnABBGKgBr7enW2tnW2tnTJZeqWIyOZhVzwQK3XXddYLfd8pWGWle/S0rgHyS/jjxB9tknP2grqf0yNnzNXv7SS/J1/Ny11pIHqf3zOPzBNgl//WuxSccnsKqEzwdtad/3gvLh29D9/PhErTWympdOjMKP+fj5b34efMKnWDDS+Xx/4cLmtIFmcuGIIXz/uhjCNybznPFNKB/6UPYeQgpfgmaGA5wIkMoJ0MekqKd4+OF674pAQotWiKNzpfLw6+ctt2TnFyl8/9k6iLaZdIwxa7J/DwJwt3ZuLeCET6D/12RZoVCwXMX6Jg+J7DbYwI3iX3FFvtJISj2G8EMKn0+/1gi/qMLw+/IBLE44ReRWRPial06RSYeTu3RMGjeR4KtJPz+++57WyFZV+H5jUUT4knCQbPgEauS5zZznjZsVQ+QunRfr8eMTnLQ/NJSVtWTSofLT6qjUe5PyI9Ut2uf2+6J7UV5mzgwT/lVXOVPLGms405ExTonHNPLUW+F1RLLh+8/RQbTzjl8zxtxljLkTwO4A/q2N98rb5AlUyJJ5hKtzX8HQy+JhlUdGnPrk12kKX/tYuGqXzBkc2iSboq44Bz/v9tvz10gN3Z57ytdLJBOj8P0p7yGTjmS/luCbVPxrjj4aeOc7gfe/X7+fT/i8yx9S+P79iwhEKy/Nvi2VET/njW+Uj8cqfK1ehgZtNfs1J3w/Dn2I8H/0I7eV3BFD5KqlqZlFKC+aqZJjs82Ar3/dnfumN7ln3HBDvdHj2Gcft/3DHwZP4Vtr32et3cJau6W19p3W2oXhq1oALXfGIanGH//YbS+8ULfh//d/u30+g5Ofwz9YyYavqSo+wMSvCxG+5rkSS/hUEf188vtec00+fghdLzVSMTb8G2/M54fHMJLSk3oAEkZGmnsPfLLVqqsCl1ySzbjUzAO80ea+2DEKXxrDaYXwTzrJbWmQUBtY5O58UjmFjlW14WvnUhlNmtS8glTIpHPQQcDVVwP/+q/FeS6jjLk3npQXfi1PY8cdwxMFJaI2BrjgAuCmm9z/ZPadOzeO8CeYwu8sJMKXFD55BTzzjKx0jXGzZn1oE2bK2PClKeMa4YdMQTwfEvh5fCCU59MnTpr9qJmZJALTTDoHH5y/NmTD1+yyUh78c/7yl+bzihpfXgbGZIOAPE9F95fmYcSadKT395nPOHMhNVJ0PMbFlJ4hlAegug1fyrO1WS9u0iQ5pIGUNs/zXnvF90qkY/7MWu174I2PVFdvuCEcy0ZrhN797mwiVdE7CdWFDmHiEP74uK7w+cvlq9VLtuwVV2yOWsjT4tfF2PAlmzU/rhE+775z74wqhM9XHOL59Mlt5kxnF331VbkyllH4fmNCCkoiYJ6n0Ecgddu5twNBM9fRb/yD3Hzz7BxN4dNgv+9yWNaGL/1uTGYu1NKi8/xn4QiRZx0Kn+/zQVsfsYO2oUaqSBlril6DHxqiDDSFL51z5JFJ4bcVCxa4Fa7I//iAA9xWIlLuuiWRAgDsvXfzdRLhx9jwNXtsSOHPnp3t82BRIcL/939vvu9qq+WvkRo6wrrr5sPb8jg1ZWz4Zb10KM0qUQOpB+f31Py8hhptKd8+fNNeaBDQzwOHZDsv+l+7tsherj1/mUFb6Vxf4fsI2fC1e/vXSERJ+aFxA80d088Lv7YstJ4iB/3/hjf0rA2/4tP3GGhREholLxrE2mKLbJ9IqChQEkGy4Zf1w5detkb4/FyuQEOEf/LJ7g9wNlJa55dfX0T4/n0kRRJD+JrHlDZoS2V6zjnAL3+Zj3p5220uyJwGeg7uoSMRcRnC9xU+D4dNMVtoiT3/PgSJ9HioXI3gfZPOf/xHc3ju4WFX30dGgNVXb34+/zn9fBTVo5hBW074RSGtQyq2aP4Cvx/PA7lZ0ru68MLi0AtUd7bZJhOFvlNBCFI90gh/fDzOSyeZdFqE39WSPgAawJw+XQ7rC2Q2Xb6guUR8vg23DOGHFD53W+MKpowNcK+9XFzvstEyedpS/v1KK1VmHryK30tS+NzEsuKKwCGHuOn5hK23bs6ftdlHzt0Y/byWVfia/ZyrWR7el/f2AEfOPI9+HiQPH/83Pw8nnQR8/vP5c4eHnbsxt52XUfghG75GTPx4txS+H5rcf4c+DjwQePhh571F58VE3wzlSTPpcI9BP//JpFMj/AKUKsGMGS62ydlny2YGwBHOI48Af/xj8X14l16z4WsfTojwuX1SC54WW2H4oPXwcN67QkMZwpcUzx57ALfemv0veeFIE1iOO07PkwZJlUv2cOlZfDVLvSNaBIfAXRCPP745TbrfSScBX/mK26fBcr93GLLVxyi/2Hev9XCq+uHzxijGhl+F8EP2curR//nPxWlzrL9+Pr2yhK8JPukcifBD9bFDmBiE7xdmkcIfGXFuU/vvnyddH+uuqytgSR1qyllTdNKkIz+fBJ+wpfSKoMXiqWrS8QcPJcIfHs6bYYrcMlv9CIoUvjajlptV+PG3vz2fNg0I894E731JPYJPfhK4777M1NKqwpcg1RmJxGIUfhnC7/SgrVRHiPCrgJNyGWhiQUtba9SL3n8HMDEIn+CTBf9/zpxmn9/QgFtRrBna0rWnny6bEbQuXMiGz49V8cPn6IbC13pbkkknRvUWQXoOLSyDlD/+jET4NNB9zjluFjcnfI04CVOm5F0GtUYs1oYvgfbDpFYAABiwSURBVOdh443l9Hha/jV8349nE6vwfZPOlVc2u8jWpfD5MVqvQgozHEJVwg/l009b4gIgXHfajIkxaEvQbGuAi6/tQ3sp/u/acf/DkHoWWovOCTU0SBw72KbBbzDKED6/hzTYWKTwOaTeT2iFplhQ2tzOXaTw+UIY/j15LBWejkb4MeSsDdqGTDpFpMTzcPnlbnERPyy4fw+tTj38cP4aLmT8c4sIn0/wi1X40rcasuEfcIBbJ+ETnyhOW0IdCl97Nl42Prf4403S9R3AxCJ8vwBDiyaEFH7oOv/DKEP4vEKEFH7MAFoReFovvljcFffvU5fClxpXyYZf5SMwpvldS+nElKnk8cMxaZJMnEV17YYb5ONaGcWUAc/z+us3LxQjnacRvlR2MQ0FEb4U/6idg7bDw80rocWiToWvmXTGxnRPwTJOF23AxDDpaDb8ThA+vbSPfSz7XSN8iTw1wtfsvq1WGAoPDRQTPq0pwM8PET6HRmaSWSE0oQhws1HLLANX1aSjET5XaEXLA0qghTlCeWzVhi9Ba4Q5SUuEH6q31jpTFyC7ZdZF+K2Y+oru14rC99OS0va/laTw2wDfpNMuwueVkPZXWy1bgEF7qWVs+CFTUJV80zUxJh1p5SfJTDI0lOWff0RlTDoxCv+rX9XzKqFI4fN1if28hhS+b9Kh2O88QJ0P38sqNKBXJ+FrwqEoKqnfEGmKu8gPv5VBW6kXUTQJrQyo3LiQiUGMwue84/doqT4lhV8jtNHz0PllC14jPqkbF2PS4ec/+2zzKkB1En5ZG34o8BRvsHgQM43M2mHDlyDVAWkxa/+eGpHeeafbPvgg8Npr2XE/RK8EHpiLD+aGbPhFiH33muDgz7nTTsVpSHVu0qRiP3zp2lD+pGvuv99teSDDVkAeVk8+We66GIUvrYdcNGjbBYU/MQhfU0whhc/Vyuc/7yLfxUAjPolIQ4Tvd59XXrl50lLdCp8WiIn5ULnS1QZtqdxoBqqUt5ANv11dd57XGMInciZzhY8773SNMoFPztPAlzjk+QmZdPh9CB/+sHytBmmMCcgTT9EsZiDfc+aEX2TSoTrGw4JICA3a0uIwdeGOO9z2178ud51UL/1jH/iAW2Xv+OPjTDpJ4VeEby+M/Rheftlth4aAL37RRb7z8eij+Wn+PH2fqKRBrBiFD7j47Zdfnr/POee42Dh1Ej6fJRoz01Zby5efJy2zSM+98sr5a6QKrz1fK5DMItz0xH+XvHQ0sXD//cBGG2X/0yQrP/QBx777FueRQGVGMWKk5f9OPz3fwwhBqy9lypl/V9wUGaPwf/vb4rRDCp8C/1EI4lZRVVBI5eXzzIorAqed5uq8Zj2I6U22EROD8P2p1oSQwv/JT9y2qBK88Y3ZBBqCRHzcnKEN6kn2O9qefXY+TC8AHHaYm/lZJ+Hz9U6LPlSqjBLhxwyyUtk/9phTqhLhS++nLoUvNfp8XCJUptpzrbNOfhZ0aIUuQI+17ueRyrqo3g4Nxd2Tny/dj78HvrqaBEnhc4HTCnGFCH/yZFce24srpJbHu97lthtsUO66GJMOh2/S0RT+v/6r+/Y7hIlB+HztSaC+AR4N/D60SPp//VfehkegvMyYIX98MUqrTi8djhiTTozCJ/DeA+F1r8srHl7hSR1fc42cXisoMuksWRIuU+1jPuig8l46oRnbBDKRhIRKGcR4e224YXEaWgwiTWhxfPCDxWmHCL9uUNoUgC0WMYO20vlFJh0A+OY3s9XZOoCJSfiE2A+nauS8oSHZzup/ZEuWuIXB66jcdQ76FEU5pJmmb31r8/004uDp8cVe+DXS8774Yv02/CKFv2BB/j584JXO0Wzz/gcb6+m15ppurVTpWgLV4zImmxBiBm1DeecKn97r6Gi2ZkPR4DsPrx3KX2x+WsFGG7n0tXkLGsoqfL+nGOum2mZMDLdMX2mceCKwcGGziSR0fSxIyYcG3egYkQg/XrUCtGrS4SDPEwlrreXiway/PvDd77pjWgCoXXcFrrvOlfv73ueOa2YwvmTiuuu6MZIZM/Lp1QGpbCRlbi1w5pnZ8VVXBc4/H9hjDzld35xC6YTMLJI9XjPpbLKJ8waidR1agabwtSU0i9LgdRkAfvMb4PrrZbUc24Ntd2/cx9Sp5b93QP52WzXpdAETS+FT4c6a5RYS5kv7SXjve91W88jQQJVeq6y8kv/1r/LxomNF0BRbCGecka2dSvAHiX1ssokceM1X+J/7nNvfd9/mpQ0J5AZ3ySXZsW98w225V1LdXjocWhA5/yM8+GBglVXyx6inoyn8EGnG5JGWmPzCF1wv5LLLyqdZdA++7z+fhOOPB37xi6yOLluWbyjWWENebQzIvqkQwXXBNbESYgZtOWJNOh3GxFD4mkknhJ/9zG3L+vhqdl5JAZ91VtaVr0PhVzXpfOQjzcdCqzv50Gz4e+6ZPc9558U3oLTw9ZIl8YuYx0L6GPmMV/4hzpzZHE/GB8089lGF8KmXwwf6AWC33eq13wP6oG2Msv7yl92Wu1aWrTNV/PB7EWVNTzGhFbqAPintAPxAV7E4+mi33XLLctdJIQG0iJazZmX7vWbS0dS4hhgvnaEhmfyk56TZjrzBbacf/vXXZ/v8+Je+VD79t77V9WykwegQqCzqtNWH7gWECf/II+U0+DrQrdRXCf1C+GUVfjLptBHStOUY7LCD22rqTYOk8CdPlhX+Jz/ZfB3QG4TPP+Qi7LSTi4QoKfyyefja17J9qSfQTj98PsGI57vIh55w2GFuS+an6693DcXixe7/UA9BwjrrlL+mLLR3NTzswgzznt/pp8s9jCrvhHpvE8Wk06rC5wvodBF9UtoBUGGWHQCqagqSJnhNmybPRNVm1pF54ZZbqt3b368CPr5QhD/+0cU6l1TzY4+Vuyf33uH+6Z3w0uENjN9Yh3DEEc6Gvd56+eNEbFXQiQHLIrfMp592YzshUB1+5zvj70vvOfQ+eZ4ks2OvoKyXjjawm0w6NaCqwn/0Ubd94IFy10nhgPnkCWmpPT9/117rtjSOUPbe/v2roMwEHkAm5RdeKJcGtwFzjx1CO/3waZm7tdYqHrTVIJ237bZuW8egbTugmXHKzrSdPx8499z4a2LFFP/9jDPqH8OoC2X98H1OSiadGlFVqZ9/vttKoQGKIE013247uQJok5SqVuw6TTplSUoi0ZAnlA+ef8kttJ0K/5BDgO98xw1CcpNP2ciJHK3kt9Muia2Y4mbOdD2y2DxXIfxeRlmF71/XI374fVLaAVQ16VT94Djh8/g5RBxShEl+nX9OGbTy0fooqzYkG/4uu8RdS70prvApdtFaa2XH2umHPzQEHHOMs9n7Aeuqgp6Hj03EotNkV1Xhc1CdCY17TTTCL6vwNZNOPyt8Y8yhxph7jDHjxpjZ3m/HGWPmGWMeMMYo0aNqQmzs7bogmXQmTcqiRvJ1PTXCp4lJ3IunzL39tKugbKMjKfxYN73rrnNbHpaWTEo89EJdqjf0gfLfWxk8HRpy5eivlxyDflL4hNmz3TyBUPyXiUb4ZRX+BDXp3A3g3QCu5weNMZsCOAzAZgD2A/BdY0z7+jJVTTrHHZffxoIr/Ouuc+6dQ0PZ4sp8UoumqiiIWVllWKdJx/cDD0Ei5dgKfMwxzdfS/vh4FgaXIkW2ipB5rReIph8VvjEuLMGaaxafd/nlwHvek8Wfj8lTL6PsTFvCRDLpWGvvs9ZKI54HAviFtXaJtXYBgHkAagp3J6CqSecf/9F5blAQr1hwt8xddgFOPdX9T2EFeHc3RDJl7eh1eOnQrNE3vKHcdaHY5UUgWz/3lCG30H/+Z+C229z+vHnl8qQhVuF3c5Cw02RXB+HHYuedgZ//PPyMvdDwxqCsH77fKEwQha9hbQDcX+/xxrEmGGPmGGPmGmPmLlq0qNrdWjHpVKn40sQrDRrhd3PQdvPN3bYs4bTycZLpZ9my7Ni0aa4cPv7xLMiWFsOmLJLCb4Zm0uomeiUfIfB80joaRWGl+9WkY4y5xhhzt/B3YNFlwjGR4ay1p1trZ1trZ0+fPj0233lUNelURSiEboxbJp1TlnTrsOHPmeO2b397uetaUcWUby3sApVDXYpbMj+VDS3QbnSqvq66ahYHntBl08L/oV8In5cXzZ0pWtyFZo/3WGiFYHNjrd2rQrqPA+D2gnUACOECa0K3CL8VhV8VdRD+VltVI1Z+vz/8IbyaEQcpfG3c4Kij3B+F3G0VoZmRvUA0nWp0JCXaC88P9E4+QuDvaptt3BoORx2ln+9zUr8o/Iq4FMBhxpgpxpiZAGYBKOnsXgL77+/84L/whbbdIgfNpCOtANSLhF8VvPLuvHO5mOLSgiocH/2oS5fi8LcKiUx7zYbfTbLrFYXfCz2tGHDPu5iFX7bbLn9O1cmhNaNVt8yDjDGPA9gBwK+NMVcCgLX2HgDnAbgXwBUAjrHWVghCHYlp09zkqU02adstctBMOu95j9vyNUw1t8yqFb2bdthWPs5dd3Xb2DUKWkVS+MVIhF8OXGjFBGukkBt+OXf5eVvqX1hrLwJwkfLbiQBObCX9noVm0tlhh2bFqBF0VZs1r0Bl4/hr+Id/iDuvFV/57bd3A7ad6tL2ssKvO25QFfRCgwd0nQCjwb3pyqh1f9C2y5gY8fA7DWnilYa6VSVPg/z+W8HSpfH5apUkO2m/lBonqcHtptJNCr93Gp4Q+ATDGJOO5pbZ5QauT0q7x/Dcc25bdtBWqiBVZ7vG3j+ESZPiP/66Ps5DD83WImgXpMZJIv9ufIC9oPB7hfD7ReFXJXw6h8a7YlYaayOSwq8CWgOVrwSkQVP4fJZpGXTzQ63LDHLeea3nJYRYP3w676c/zaJpdgrdJLteUdb9QvjcpBNjw/cJ/+ij2y9yIpAIvxUsXBg+J2RSKItufqj98nECYT98/3da37gTSAo/g7SGRC+ChxKPseF3Or5XJHorN/2GmFg0IS+dVkw6nUaPVd5CxIZW6KZJJyn8rP7zhXF6EXvvne1XMen0CHrkrU9ghEw6ifDbg5BJp5sqOyn8DLTqWdl4Vp0GX8GrzBravVLODSSTTruhmXFOOcXZBffZp3p6nUYvTFaKBXW7//jH7Jik8AfVRNYrRDRpUn/UJ8CN3e2wA3Dwwe7/Iq+zHjXpJMJvN7SPer31shW3yqAXBm37AS++2HysaNC2k+gFhd9P77JX8OEPu+2MGcD99+fXZPbRoyadRPjtRt0f1qAq0jrQKzZ8aoy6qWx7jIj6CuedB/z6127ZxxB6rJxTM99utIPwH34YuOeeetONQT8RPpHpRhtlx3pF4RPqWuylCpLCr47VVwc++MHic3rUpNNbuekXfOc78efWTShDQ85ffNNN6023DPrB5kphJyiIFdA7Cp+QbPgTFz0SLM1HIvwq2HDD+HNDiz2XRTLpxIEIX1swphfs6InwJy7uv99te6ycE+FXQZmXWHYJwxB6oYvYTwqfe1L0mpdO0aBfu9EL9Wgig9Zofuyx4vM6jDRoWwVEeDGLdfiNwx13ACusUP3ePaYYehYS4feaDZ+WmuwGUj3qDMqGTmkzEuFXAU28qBJ/f8stW7t3UmZxCM2GpHLspspOM20nPnqsYU1vvQok9dgpDKrNuSxmzXLbbbfNjkn5r3uMpQySDX/io8tLGvpIhF8F0oBgp9ALyqwfbPj77APceSfwoQ9lx3i+6R3ysLedRiL8iYutt3bbNdbobj489Fbz0y947TW3LTIHXHcdcOON9d+7FxR+PxA+AGyxRf5/KXJmN33hu4leEA4TGddeC5x7LrDjjt3OSQ6J8Ktg2TK3pXUrJeyyi/urG938UIkcb721e3loBZzw58932wce6E5euo1E+O3FSisBRx7Z7Vw0IRF+FRx6qFPvJ5zQ+Xt3syt+yy1ue++93ctDXXj22W7nICGh40jNfBUstxxw2mnAaqt1/t7dVGZ1LZo+6Hj727udg4QBRVL4/YZE+NXBTTrrrNO9fJx/PvDMM927f8LAIin8fkM3CX/VVbt37zqw0krZPs2hiJk8VzeWWw5Ye+3O3zdh4JEIv9/QC4T/+c93Lw+tgK9LSkiDlwkDhFTb+w3dHLQl7ySJOPsV/eJimpBQA5INv9/QTUX68Y8DN9zQk+5mpdFvcwoSEmpAS+xhjDnUGHOPMWbcGDObHV/PGPOqMeb2xt/3Ws9qAoDuEv6aawLXXw9Mn969PCQkJFRGqwr/bgDvBvB94beHrbVbtZh+QkJ7QKax5Zfvbj4SEjqIlgjfWnsfAJh+CqrV7+hmsK+JhHXWcRPnDj+82zlJSOgY2mkfmGmM+Ysx5jpjzFvbeJ/BQgp6VR5vfnPzMWOAz37WLReZkDAgCCp8Y8w1AKSQb8dbay9RLlsI4I3W2meMMdsCuNgYs5m19gUh/TkA5gDAG7vhE50w8XHzzf0/aSwhoQYECd9au1fZRK21SwAsaezfaox5GMBGAOYK554O4HQAmD17dnKZ0LD22sATT3Q7F/2JyZPrX2oyIaEP0Ra3TGPMdACLrbVjxpj1AcwCML8d9xoY/OUvifATWsdVVwFPPtntXCR0CS0RvjHmIADfBjAdwK+NMbdba/cFsAuALxljRgGMATjKWru45dwOMqZPT+6QCa1j7727nYOELqJVL52LAFwkHL8AwAWtpJ2QkJCQUC9SaIWEhISEAUEi/ISEhIQBQSL8hISEhAFBIvyEhISEAUEi/ISEhIQBQSL8hISEhAFBIvyEhISEAYGxPbQAhDFmEYBHW0hiNQBP15SdiYhUPsVI5RNGKqNidKt81rXWBmdm9hThtwpjzFxr7ezwmYOJVD7FSOUTRiqjYvR6+SSTTkJCQsKAIBF+QkJCwoBgohH+6d3OQI8jlU8xUvmEkcqoGD1dPhPKhp+QkJCQoGOiKfyEhISEBAUTgvCNMfsZYx4wxswzxhzb7fx0CsaYNxhjfm+Muc8Yc48x5l8bx1c1xlxtjHmosV2lcdwYY05plNOdxphtWFpHNM5/yBhzRLeeqR0wxgw31lf+VeP/mcaYmxrPeq4xZnLj+JTG//Mav6/H0jiucfwBY8y+3XmS9sAYs7Ix5nxjzP2NurRDqkN5GGP+rfGN3W2MOccYs1xf1iNrbV//ARgG8DCA9QFMBnAHgE27na8OPfuaALZp7E8F8CCATQF8DcCxjePHAvhqY/9tAH4DwAB4C4CbGsdXhVuRbFUAqzT2V+n289VYTp8E8HMAv2r8fx6Awxr73wPwz439owF8r7F/GIBzG/ubNurVFAAzG/VtuNvPVWP5nA3gI439yQBWTnUoVz5rA1gAYHlWfz7Qj/VoIij87QHMs9bOt9YuBfALAAd2OU8dgbV2obX2tsb+iwDug6ucB8J9xGhs39XYPxDAj63DnwGsbIxZE8C+AK621i621j4L4GoA+3XwUdoGY8w6AN4O4MzG/wbAHgDOb5zilw+V2/kA9mycfyCAX1hrl1hrFwCYB1fv+h7GmJXgVqj7AQBYa5daa59DqkM+RgAsb4wZAbACgIXow3o0EQh/bQCPsf8fbxwbKDS6jVsDuAnADGvtQsA1CgBWb5ymldVELsNvAvgMgPHG/68H8Jy1drTxP3/W/yuHxu/PN86fyOWzPoBFAH7YMHudaYxZEakO/R+stU8AOBnAX+GI/nkAt6IP69FEIHwjHBso1yNjzOvglpT8hLX2haJThWO24HhfwxhzAICnrLW38sPCqTbw24QsnwZGAGwD4DRr7dYAXoYz4WgYuDJqjF8cCGeGWQvAigD2F07t+Xo0EQj/cQBvYP+vA+BvXcpLx2GMmQRH9j+z1l7YOPz3Rjcbje1TjeNaWU3UMtwJwDuNMY/Amfr2gFP8Kze65kD+Wf+vHBq/TwOwGBO3fAD3bI9ba29q/H8+XAOQ6lCGvQAssNYustYuA3AhgB3Rh/VoIhD+LQBmNUbMJ8MNklza5Tx1BA274A8A3Get/R/206UAyEviCACXsOPvb3havAXA843u+pUA9jHGrNJQM/s0jvU1rLXHWWvXsdauB1cvfmetfS+A3wM4pHGaXz5Uboc0zreN44c1vC9mApgF4OYOPUZbYa19EsBjxpiNG4f2BHAvUh3i+CuAtxhjVmh8c1RG/VePuj0CXscfnOfAg3Cj3sd3Oz8dfO6d4bqEdwK4vfH3Njh74W8BPNTYrto43wA4tVFOdwGYzdL6ENwg0jwAH+z2s7WhrHZD5qWzPtyHNg/ALwFMaRxfrvH/vMbv67Prj2+U2wMA9u/289RcNlsBmNuoRxfDedmkOpQvoy8CuB/A3QB+Audp03f1KM20TUhISBgQTASTTkJCQkJCBBLhJyQkJAwIEuEnJCQkDAgS4SckJCQMCBLhJyQkJAwIEuEnJCQkDAgS4SckJCQMCBLhJyQkJAwI/j/VrOKJEYvdhwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(u[1,:], 'r-')\n",
    "plt.hold('on')\n",
    "plt.plot(u_detrend[1,:], 'b-')\n",
    "#plt.plot(Output_dissip,A[:,0],'ro')\n",
    "plt.xlabel('burst time')\n",
    "plt.ylabel('u')\n",
    "plt.show()                                                                                                          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "object of type 'PCA' has no len()",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-106-cd81eca39115>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPCA\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_components\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\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[0m",
      "\u001b[0;31mTypeError\u001b[0m: object of type 'PCA' has no len()"
     ]
    }
   ],
   "source": [
    "for i in range(0,len(jt)):\n",
    "    X=[u[i,:], v[i,:]]\n",
    "#print (X) \n",
    "    pca = PCA(n_components=2)\n",
    "    pca.fit(X)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'butter_lowpass_filter' 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-111-e509de21e0aa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;31m# Filter the data, and plot both the original and filtered signals.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbutter_lowpass_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcutoff\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\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     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\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 'butter_lowpass_filter' is not defined"
     ]
    }
   ],
   "source": [
    "# Demonstrate the use of the filter.\n",
    "# First make some data to be filtered.\n",
    "T = 5.0         # seconds\n",
    "fs=9 \n",
    "n = int(T * fs) # total number of samples\n",
    "t = np.linspace(0, T, n, endpoint=False)\n",
    "# \"Noisy\" data.  We want to recover the 1.2 Hz signal from this.\n",
    "data = np.sin(1.2*2*np.pi*t) + 1.5*np.cos(9*2*np.pi*t) + 0.5*np.sin(12.0*2*np.pi*t)\n",
    "\n",
    "# Filter the data, and plot both the original and filtered signals.\n",
    "y = butter_lowpass_filter(data, cutoff, fs, order)\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(t, data, 'b-', label='data')\n",
    "plt.plot(t, y, 'g-', linewidth=2, label='filtered data')\n",
    "plt.xlabel('Time [sec]')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "\n",
    "plt.subplots_adjust(hspace=0.35)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "list indices must be integers or slices, not tuple",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-102-f485dc2f9eef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m# plot data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m[\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[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\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     10\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mlength\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvector\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexplained_variance_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents_\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     11\u001b[0m     \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvector\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m3\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlength\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: list indices must be integers or slices, not tuple"
     ]
    }
   ],
   "source": [
    "def draw_vector(v0, v1, ax=None):\n",
    "    ax = ax or plt.gca()\n",
    "    arrowprops=dict(arrowstyle='->',\n",
    "                    linewidth=2,\n",
    "                    shrinkA=0, shrinkB=0)\n",
    "    ax.annotate('', v1, v0, arrowprops=arrowprops)\n",
    "\n",
    "# plot data\n",
    "plt.scatter(X[:, 0], X[:, 1], alpha=0.2)\n",
    "for length, vector in zip(pca.explained_variance_, pca.components_):\n",
    "    v = vector * 3 * np.sqrt(length)\n",
    "    draw_vector(pca.mean_, pca.mean_ + v)\n",
    "plt.axis('equal');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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