1_preprocessing_data_updated.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 122
    },
    "colab_type": "code",
    "id": "qWhfG1CLn1-Z",
    "outputId": "4cc0085a-6ee8-4c4a-a42d-062031c07fc3"
   },
   "outputs": [],
   "source": [
    "#from google.colab import drive\n",
    "#drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 544
    },
    "colab_type": "code",
    "id": "RgKbRY1H9jgK",
    "outputId": "123018a8-4ab9-43b8-8a0c-b7e5600eb0df"
   },
   "outputs": [],
   "source": [
    "#!pip install wfdb\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fvu3Ud0kmlkH"
   },
   "outputs": [],
   "source": [
    "import numpy as np, os, sys\n",
    "from scipy.io import loadmat\n",
    "import wfdb\n",
    "import matplotlib.pyplot as pp\n",
    "import pandas as pd\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-bFQXP73mlkP"
   },
   "outputs": [],
   "source": [
    "def writetocsv(PATIENTSDATA,output_dir,csvfilename):\n",
    "    #cols=['ptID','Num_Leads','Sample_Fs','Lead_Name_1','Gain_Lead_1','Lead_Name_2','Gain_Lead_2','Lead_Name_3','Gain_Lead_3','Lead_Name_4','Gain_Lead_4','Lead_Name_5','Gain_Lead_5','Lead_Name_6','Gain_Lead_6','Lead_Name_7','Gain_Lead_7','Lead_Name_8','Gain_Lead_8','Lead_Name_9','Gain_Lead_9','Lead_Name_10','Gain_Lead_10','Lead_Name_11','Gain_Lead_11','Lead_Name_12','Gain_Lead_12','Age','Sex','SNOMED_Label_ID','Label_Name','Label_Abbvreviated']\n",
    "    \n",
    "    #ptDATA_Df= pd.DataFrame(PATIENTSDATA, columns=cols).T\n",
    "    \n",
    "    \n",
    "    with open(output_dir+\"/../CSV/\"+\"/\"+csvfilename,'a') as f1:\n",
    "        writer=csv.writer(f1, delimiter=',',lineterminator='\\n',)\n",
    "        writer.writerow(PATIENTSDATA)\n",
    "    f1.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sDoLOc1wmlkU"
   },
   "outputs": [],
   "source": [
    "def returnlistofcsvfile(output_dir,csvfilename):\n",
    "    savingto=output_dir+\"/../CSV/\"+\"/\"+csvfilename\n",
    "    filelist=list()\n",
    "    \n",
    "    if not os.path.exists(savingto):\n",
    "        return \"\"\n",
    "    else:\n",
    "        with open(savingto,'r') as f1:\n",
    "            reader = csv.reader(f1, delimiter=',')\n",
    "            included_cols = [1]\n",
    "            for row in reader:\n",
    "                if not str(row[0]) ==\"ptID\":\n",
    "                    filelist.append(str(row[0])+\".mat\")\n",
    "        f1.close()\n",
    "        return filelist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ArMfpj9JmlkZ"
   },
   "outputs": [],
   "source": [
    "def writeheadertocsvonce(output_dir, csvfilename):\n",
    "    cols=['ptID','Num_Leads','Sample_Fs','Lead_Name_1','Gain_Lead_1','Lead_Name_2','Gain_Lead_2','Lead_Name_3','Gain_Lead_3','Lead_Name_4','Gain_Lead_4','Lead_Name_5','Gain_Lead_5','Lead_Name_6','Gain_Lead_6','Lead_Name_7','Gain_Lead_7','Lead_Name_8','Gain_Lead_8','Lead_Name_9','Gain_Lead_9','Lead_Name_10','Gain_Lead_10','Lead_Name_11','Gain_Lead_11','Lead_Name_12','Gain_Lead_12','Age','Sex','Length_ECG','SNOMED_Label_ID','Label_Name','Label_Abbvreviated']\n",
    "    savingto= output_dir+\"/../CSV/\"+\"/\"+csvfilename\n",
    "    if not os.path.exists(savingto):\n",
    "        with open(savingto,'w') as f1:\n",
    "            writer=csv.writer(f1, delimiter=',',lineterminator='\\n',)\n",
    "            writer.writerow(cols)\n",
    "        f1.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IizAZoGbmlke"
   },
   "outputs": [],
   "source": [
    "def makealldirectoriesatonce(output_directory,actual_labels,classes,csvfilename):\n",
    "    lead_names=['I','II','III','aVR','aVL','aVF','V1', 'V2', 'V3', 'V4','V5','V6']\n",
    "    tmp_class_names=list(actual_labels.values())\n",
    "    \n",
    "    #print(tmp_class_names)\n",
    "    #print(actual_labels)\n",
    "    if not os.path.isdir(output_directory):\n",
    "        os.mkdir(output_directory)\n",
    "    if not os.path.isdir(output_directory +\"/../CSV\"):\n",
    "        os.mkdir(output_directory+\"/../CSV\")\n",
    "    \n",
    "    \n",
    "    writeheadertocsvonce(output_directory,csvfilename)\n",
    "    \n",
    "    for i in range(len(lead_names)):\n",
    "        if not os.path.isdir(output_directory+\"/\"+str(lead_names[i])):\n",
    "            os.mkdir(output_directory+\"/\"+str(lead_names[i]))\n",
    "            for ii in range(len(tmp_class_names)):\n",
    "                if not os.path.isdir(output_directory+\"/\"+str(lead_names[i])+\"/\"+str(tmp_class_names[ii])):\n",
    "                    os.mkdir(output_directory+\"/\"+str(lead_names[i])+\"/\"+str(tmp_class_names[ii]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wPniE_UVmlkj"
   },
   "outputs": [],
   "source": [
    "def drawandsavefig(PTDETAILSDATA, data, output_directory):\n",
    "    leads=list()\n",
    "    ptID=str(PTDETAILSDATA[0])\n",
    "    num_leads=int(PTDETAILSDATA[1])\n",
    "    for i in range(num_leads):\n",
    "        \n",
    "        leads.append(PTDETAILSDATA[2*i+3])\n",
    "        newdir=output_directory+\"/\"+ str(leads[i])+\"/\"+str(PTDETAILSDATA[-1])\n",
    "        savingto=newdir+\"/\"+ptID+\"_\"+str(leads[i])+\".jpg\"\n",
    "        if not os.path.exists(savingto):\n",
    "            x=pp.figure(num=None, figsize=(4, 4), dpi=300, facecolor='w', edgecolor='k')\n",
    "            pp.axis('off')\n",
    "            pp.grid(False)\n",
    "            \n",
    "            #print(\"Length: \"+ str(len(data[i])))\n",
    "            pp.plot(data[i][:2000], \"k\")\n",
    "            pp.savefig(savingto,dpi=300)\n",
    "            #print(\"Saved to: \"+ savingto)\n",
    "            pp.close(x)\n",
    "        #pp.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Dd7J9wUQmlko"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KAsxtsnsmlkv"
   },
   "outputs": [],
   "source": [
    "def load_data(filename):\n",
    "    x = loadmat(filename)\n",
    "    data = np.asarray(x['val'], dtype=np.float64)\n",
    "\n",
    "    new_file = filename.replace('.mat','.hea')\n",
    "    input_header_file = os.path.join(new_file)\n",
    "\n",
    "    with open(input_header_file,'r') as f:\n",
    "        header_data=f.readlines()\n",
    "    return data, header_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3-mldMV5mlkz"
   },
   "outputs": [],
   "source": [
    "def getactuallabels(classes):\n",
    "    dictclasses={'713427006':'Right Bundle Branch Block','63593006':'Premature Atrial Contraction','164884008':'Ventricular Ectopics', '164889003':'Atrial Fibrillation', '164909002':'Left Bundle Branch Block', '164931005':'ST Elevation', '270492004':'1st Degree AV Block', '284470004':'Premature Atrial Contraction', '426783006':'Sinus Rhythm', '429622005':'ST Depression', '59118001':'Right Bundle Branch Block'}\n",
    "    dictclasses_abrevated={'713427006':'RBBB','63593006':'PAC','164884008':'VEB', '164889003':'AF', '164909002':'LBBB', '164931005':'STE', '270492004':'IAVB', '284470004':'PAC', '426783006':'SNR', '429622005':'STD', '59118001':'RBBB'}\n",
    "    '''for label in classes:\n",
    "        #print(label)\n",
    "        print(dictclasses_abrevated[str(label)] + \" : \" + dictclasses[str(label)])\n",
    "    '''\n",
    "    return dictclasses, dictclasses_abrevated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0m2zaAfCmlk4"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oE6hFhzgmlk8"
   },
   "outputs": [],
   "source": [
    "def get_classes(input_directory,files):\n",
    "    classes=set()\n",
    "    for f in files:\n",
    "        g = f.replace('.mat','.hea')\n",
    "        input_file = os.path.join(input_directory,g)\n",
    "        with open(input_file,'r') as f:\n",
    "            for lines in f:\n",
    "                if lines.startswith('#Dx'):\n",
    "                    tmp = lines.split(': ')[1].split(',')\n",
    "                    for c in tmp:\n",
    "                        classes.add(c.strip())\n",
    "    return sorted(classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gwgT6fUqmllB"
   },
   "outputs": [],
   "source": [
    "def get_12ECG_features(tmp_input_file, data, header_data):\n",
    "\n",
    "    ptDetails=list()\n",
    "    gainLeadDetails=list()\n",
    "    \n",
    "    tmp_hea = header_data[0].split(' ')\n",
    "    \n",
    "    ptID = tmp_hea[0]\n",
    "    ptDetails.append(ptID)\n",
    "    num_leads = int(tmp_hea[1])\n",
    "    ptDetails.append(num_leads)\n",
    "    sample_Fs= int(tmp_hea[2])\n",
    "    ptDetails.append(sample_Fs)\n",
    "    \n",
    "    \n",
    "    gain_lead = np.zeros(num_leads)\n",
    "    #ptDetails.append(gain_lead)\n",
    "    for ii in range(num_leads):\n",
    "        tmp_hea = header_data[ii+1].split(' ')\n",
    "        gain_lead[ii] = int(tmp_hea[2].split('/')[0])\n",
    "        lead_no= str(tmp_hea[8]).strip()\n",
    "        ptDetails.append(lead_no)\n",
    "        ptDetails.append(gain_lead[ii])\n",
    "        \n",
    "    # for testing, we included the mean age of 57 if the age is a NaN\n",
    "    # This value will change as more data is being released\n",
    "    for iline in header_data:\n",
    "        if iline.startswith('#Age'):\n",
    "            tmp_age = iline.split(': ')[1].strip()\n",
    "            age = int(tmp_age if tmp_age != 'NaN' else 57)\n",
    "            ptDetails.append(age)\n",
    "        elif iline.startswith('#Sex'):\n",
    "            tmp_sex = iline.split(': ')[1]\n",
    "            if tmp_sex.strip()=='Female':\n",
    "                sex=1\n",
    "            else:\n",
    "                sex=0\n",
    "            ptDetails.append(sex)\n",
    "        elif iline.startswith('#Dx'):\n",
    "            label = iline.split(': ')[1].split(',')[0].strip()\n",
    "            ptDetails.append(len(data[0]))\n",
    "            ptDetails.append(label)\n",
    "            \n",
    "            \n",
    "            #ptDetails.append()\n",
    "    #ptDetails.append(tmp_input_file, ptID,label,num_leads,sample_Fs, gain_lead,age,sex,gainLeadDetails)\n",
    "    return ptDetails\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_HFsBmwxmllJ"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "APdftlLemllN"
   },
   "outputs": [],
   "source": [
    "def main():\n",
    "    \n",
    "    \n",
    "    DRAW_SAVE_ONLY_IMPORTANT_PICS=1\n",
    "    TO_DRAW_PICS=list()\n",
    "    DRAWN_PICS=list()\n",
    "    \n",
    "    \n",
    "    #classes=get_classes(input_directory,input_files)\n",
    "    classes= ['713427006','63593006', '164884008', '164889003', '164909002', '164931005', '270492004', '284470004', '426783006', '429622005', '59118001']\n",
    "    #print(classes)\n",
    "    actual_label=getactuallabels(classes)\n",
    "    #print(actual_label[0])\n",
    "    \n",
    "    \n",
    "    \n",
    "    input_directory=\"../DATA/Training_WFDB\"\n",
    "    output_directory=\"../DATA/Training_WFDB_IMAGES_Classed_Trimmed_0_2\"\n",
    "    csvfilename=\"1.PATIENT_HEADER.csv\"\n",
    "    \n",
    "    makealldirectoriesatonce(output_directory,actual_label[1],classes,csvfilename)\n",
    "    \n",
    "    \n",
    "    #data=readfile(\"AOOO.mat\")\n",
    "    #header_data=readfile(\"A0000.hea\")\n",
    "    input_files = []\n",
    "    \n",
    "    alreadyprocessed_input_files=returnlistofcsvfile(output_directory,csvfilename)\n",
    "    \n",
    "    for f in os.listdir(input_directory):\n",
    "        if os.path.isfile(os.path.join(input_directory, f)) and not f.lower().startswith('.') and f.lower().endswith('mat'):\n",
    "            if f not in alreadyprocessed_input_files:\n",
    "                input_files.append(f)\n",
    "    \n",
    "    \n",
    "    print(\"Noof input files remaining  :\" + str(len(set(input_files))))\n",
    "    print(\"Noof input files processed  :\" + str(len(set(alreadyprocessed_input_files))))\n",
    "        \n",
    "    '''\n",
    "    if not alreadyprocessed_input_files == \"\":\n",
    "        print(\"Noof input files processed:\" + str(len( alreadyprocessed_input_files)))\n",
    "        #print(input_files)\n",
    "        \n",
    "        set_input_files=set(input_files) - set(alreadyprocessed_input_files)\n",
    "        #print(set_input_files)\n",
    "        \n",
    "        input_files = list(set_input_files)\n",
    "        #print(list(set_input_files))\n",
    "    print(\"Noof input files remaining:\" + str(len(input_files)))\n",
    "    '''\n",
    "    \n",
    "    num_files = len(input_files)\n",
    "    \n",
    "    \n",
    "    \n",
    "    for label in classes:\n",
    "        break\n",
    "        #actual label\n",
    "        #print(actual_label[0][str(label)])\n",
    "        #abrevated label\n",
    "        #print(actual_label[1][str(label)])\n",
    "    \n",
    "    \n",
    "    \n",
    "    for i, f in enumerate(input_files):\n",
    "        #print('    {}/{}...'.format(i+1, num_files))\n",
    "        tmp_input_file = os.path.join(input_directory,f)\n",
    "        \n",
    "        \n",
    "        data,header_data = load_data(tmp_input_file)\n",
    "        PTDETAILSDATA=get_12ECG_features(tmp_input_file, data, header_data)\n",
    "        PTDETAILSDATA.append(actual_label[0][str(PTDETAILSDATA[-1])])\n",
    "        PTDETAILSDATA.append(actual_label[1][str(PTDETAILSDATA[-2])])\n",
    "        \n",
    "        print(\"DONE:\" + PTDETAILSDATA[0])\n",
    "        drawandsavefig(PTDETAILSDATA,data,output_directory)\n",
    "        writetocsv(PTDETAILSDATA, output_directory,csvfilename)\n",
    "        #print(header_data)\n",
    "        \n",
    "        \n",
    "        #annotation = wfdb.rdann(tmp_input_file.replace('.hea',''), 'atr', sampto=3000)\n",
    "        #wfdb.plotrec(record, annotation = \" \",title='Record 100 from MIT-BIH Arrhythmia Database',timeunits = 'seconds', figsize = (10,4), ecggrids = 'all')\n",
    "    \n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gOj_mgLUmllR"
   },
   "outputs": [],
   "source": [
    "def drawandsavefig_temp(PTDETAILSDATA, data, output_directory):\n",
    "    leads=list()\n",
    "    ptID=str(PTDETAILSDATA[0])\n",
    "    num_leads=int(PTDETAILSDATA[1])\n",
    "    \n",
    "    \n",
    "    for i in range(num_leads):\n",
    "        \n",
    "        leads.append(PTDETAILSDATA[2*i+3])\n",
    "        newdir=output_directory+\"/\"+ str(leads[i])+\"/\"+str(PTDETAILSDATA[-1])\n",
    "        savingto=newdir+\"/\"+ptID+\"_\"+str(leads[i])+\".jpg\"\n",
    "        \n",
    "        print(\"Saving to: \"+ savingto)\n",
    "        \n",
    "        pp.figure(num=None, figsize=(14, 6), dpi=300, facecolor='w', edgecolor='k')\n",
    "        pp.axis('off')\n",
    "        pp.plot(data[i][:2000])\n",
    "        pp.show()\n",
    "        pp.savefig(newdir+\"/\"+\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HIMSDJ-OmllW"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RYXa23Gemlla",
    "outputId": "fb8d11ab-d047-4cf6-d0a1-3ecabfe35715",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Noof input files remaining  :6877\n",
      "Noof input files processed  :0\n",
      "DONE:A6032\n",
      "DONE:A6857\n",
      "DONE:A0144\n",
      "DONE:A5250\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-344-8a368ce6202b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m__name__\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m\"__main__\"\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[0mmain\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[1;32m      3\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"100% Complete\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-342-26227943c913>\u001b[0m in \u001b[0;36mmain\u001b[0;34m()\u001b[0m\n\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"DONE:\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mPTDETAILSDATA\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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[0;32m---> 76\u001b[0;31m         \u001b[0mdrawandsavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPTDETAILSDATA\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0moutput_directory\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     77\u001b[0m         \u001b[0mwritetocsv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPTDETAILSDATA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_directory\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcsvfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m         \u001b[0;31m#print(header_data)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-337-9c580aef6ab4>\u001b[0m in \u001b[0;36mdrawandsavefig\u001b[0;34m(PTDETAILSDATA, data, output_directory)\u001b[0m\n\u001b[1;32m     15\u001b[0m             \u001b[0;31m#print(\"Length: \"+ str(len(data[i])))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m             \u001b[0mpp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"k\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m             \u001b[0mpp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msavingto\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m300\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     18\u001b[0m             \u001b[0;31m#print(\"Saved to: \"+ savingto)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m             \u001b[0mpp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\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~/anaconda3/envs/py36/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    721\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    722\u001b[0m     \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgcf\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[0;32m--> 723\u001b[0;31m     \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    724\u001b[0m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m   \u001b[0;31m# need this if 'transparent=True' to reset colors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    725\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py36/lib/python3.6/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[1;32m   2201\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_visible\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframeon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2202\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2203\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   2204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2205\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mframeon\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py36/lib/python3.6/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m   2091\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2092\u001b[0m                     \u001b[0mbbox_inches_restore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_bbox_inches_restore\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2093\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2094\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2095\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mrestore_bbox\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py36/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    356\u001b[0m                 \u001b[0;34mf\"%(removal)s.  If any parameter follows {name!r}, they \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    357\u001b[0m                 f\"should be pass as keyword, not positionally.\")\n\u001b[0;32m--> 358\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    359\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    360\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py36/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_jpg\u001b[0;34m(self, filename_or_obj, dryrun, pil_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    598\u001b[0m             \u001b[0mpil_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"dpi\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\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    599\u001b[0m             return background.save(\n\u001b[0;32m--> 600\u001b[0;31m                 filename_or_obj, format='jpeg', **pil_kwargs)\n\u001b[0m\u001b[1;32m    601\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    602\u001b[0m         \u001b[0mprint_jpeg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprint_jpg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py36/lib/python3.6/site-packages/PIL/Image.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, fp, format, **params)\u001b[0m\n\u001b[1;32m   2132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2133\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2134\u001b[0;31m             \u001b[0msave_handler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\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   2135\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2136\u001b[0m             \u001b[0;31m# do what we can to clean up\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py36/lib/python3.6/site-packages/PIL/JpegImagePlugin.py\u001b[0m in \u001b[0;36m_save\u001b[0;34m(im, fp, filename)\u001b[0m\n\u001b[1;32m    761\u001b[0m     \u001b[0mbufsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mImageFile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMAXBLOCK\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbufsize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexif\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextra\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[1;32m    762\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 763\u001b[0;31m     \u001b[0mImageFile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_save\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"jpeg\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrawmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbufsize\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    764\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    765\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py36/lib/python3.6/site-packages/PIL/ImageFile.py\u001b[0m in \u001b[0;36m_save\u001b[0;34m(im, fp, tile, bufsize)\u001b[0m\n\u001b[1;32m    526\u001b[0m                 \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode_to_pyfd\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    527\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 528\u001b[0;31m                 \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode_to_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbufsize\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    529\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    530\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"encoder error %d when writing image file\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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CdYHctH/aTvCGoBQYqAjeoB+Qm/ZN2wneEJTTaaFiIgoEb3LT/mk7wRuCEjSgYsAF6gK5af+0neANQSkwUNEfwAQUuWn/tJ3gDUEpMFDx6AWoC+RmApa2E7whKAUGKvoD6Afkpv3TdoI3BKXAQMVKH+gH5Kb903aCNwRlay1UDLhAPyA37Zu2E7whKCveUNEfQD8gNxNPtJ3gDUEZYEHFgAv0A3IzLqLtBG8IygALKgZcoC6QmzpA2wneEJQCAxVnHoC6QG4mnmg7wRuCUkCgInCA4EFu6gBtJ3hDUAZYUNEfQD8gN+2fthO8ISgzu1Ax4AJ1gdy0b9pO8IagDLCgoj+ACShyUwdoO8EbgjLAgor+APoBuWn/tJ3gDUGZ2YWK/gD6Ablp/7Sd4A1BeX0SVPQHsOJHbto/bSd4Q1BmdqGiP4DgQW7qAG0neENQCgxUBA5QF8hN+6btBG8IStCAiv4A+gG5af+0neANQVnZgIr+AIIHuakDtJ3gDUEpMFAROEBdIDd1gLYTvCEopzhDReAAwYPc1AHaTvCGoBQYqAgcoB+Qm3ERbSd4Q1AKDFS0dxC8yU37p+0EbwhKgYGK/gAmZMlN+6btBG8IygALKoI36Afkpv3TdoI3BCV4Q0V/AMGD3NQB2k7whqCcag4VgQMED3LT/mk7wRtGlAJDJgZcYAKK3LR/2k7whoD6KR4KDJkYcIF+QG4mYGk7wRsC6mebuQJDJto7CB7kZuKJthO8ISAr3lBnwAX6Ablp37Sd4A0BCd5QZ6UPBG9y0/5pO8EbAnKiOdQZcIEJKHLT/mk7wRsCsuINdYI36AfkJnjTdoI3BORwNagz4ALBm9y0f9pO8IaArHhDnQEXmIAiN+2fthO8ISDBG+oMuMAEFLlp/7Sd4A0B2WoOdQ4chLH7gX7CKDPuoe0EbwhIcYE6K96gH5Cb9k/bCd4QkK3mUGclD2y1JTftn7YTvCEgW82hTp8AwYPcrHjTdoI3BGTFG+r0CRA8yM3EE20neENAQgbUWfEGwYPcTDzRdoI3BCRkQJ3JKBC8yU3wpu0EbwhI8YA6h6uB4EFu6gBtJ3hDQFb3oE6fACve5GbiibYTvCEgW82hTp8AwZvctH/aTvCGgKzuQZ3gDVb8yE37p+0EbwhI8IY6fQKs+JGb4E3bCd4QkANEoM6KNwje5Kb903aCNwRkdQ/q9Amw4kdu2j9tJ3hDQEIG1NkFAlb8yE0doO0EbwjItlqoMxkFgje5WfGm7QRvCEjIgDqTUSB4kJv2T9sJ3hCQkAF1+gRY8SY37Z+2E7whIMUD6uwCAcGD3Kx403aCNwQkZECdFW8QPMjNxBNtJ3hDQEIG1JmMAsGD3LRv2k7whoCEDKgzGQWCN7lp37Sd4A0BCd5Qp72DrebkZgKWthO8ISDFBer0CbDiTW4WJWg7wRsCUjigzoALBG9yMwFL2wneEJCQAXUGXGCrObkZG9F2gjcEJGRAnT5BdkIH2ekDtJ3gDQEpLlCnT5CdySey0wdoO8EbAhIyoM6Ai+zUBbLTvmk7wRsCEjKgTnsnO3WB7PQB2k7whoAUDqgz4CI7fYDs7Pqg7QRvCEhxgTp9guz0AbLTB2g7wRsCsrIBdfoE2ekDZKcP0HaCNwRkVhfqDLjITh8gO2Mj2k7whoAMsKDOgIvs9AGyMzai7QRvCMgAC+oMuMhOHyA77Zu2E7whIMUF6kxGkZ3gTXbqAG0neENABlhQ10+fgFEmdJCdsRFtJ3hDQAZYUKdPkJ3QQXbqAG0neENAigvUCR1kpw+QnT5A2wneEJDiAnX6BNmZkCU7fYC2E7whIMUF6vQJsjP5RHbqAG0neENADpKCOqGD7PQBsjM2ou0EbwjIrC7U6RNkpw+QnT5A2wneEJCVDaiz0kF26gLZ6QO0neANAZnVhTp9guyEDrJTB2g7wRsCMsCCOn2C7Ppp33aGMMoEb9pO8IaAFBeoE7zJTh8gO32AthO8ISCFA+pMRpGd0EF26gBtJ3hDQAZYUKdPkJ3QQXYepaDtBG8IyAAL6vQJsjP5RHbqAG0neENABlhQZ6WD7NQFshO8aTvBGwJSXKBOnyA7fYDsTD7RdoI3BKS4QJ0+QXb6ANmZfKLtBG8ISOGAOqGD7PQBstMHaDvBGwJSXKDOSgfZ6QNkpw/QdoI3BKS4QJ3JKLLTB8jOIZu0neANARlgQZ3JKLJTF8hOHaDtBG8ISHGBOqGD7NQFstMHaDvBGwKynQrqDKbIzuQT2ekDtJ3gDQGZ1YU6Ay6y0wfIztiIthO8ISADLKgz4CI7fYDsjI1oO8EbAjLAgjoDLrLTB8jO2Ii2E7whIMUF6oQOstMHyE77pu0EbwjIAAvqTEaRnbpAdvoAbSd4Q0AKB9QZcJGdySey0wdoO8EbAhIyoE57Jzt1gewEb9pO8IaAFBeoEzrITh8gs37btj7AVBK8ISADLKgzGUV2+gCZCd5EIHhDQAZYUGcyiuz0ATLrp/2Xog8wtQRvCMgAC+r0CbLTB8hM2yYCwRsCUmCgzi4QstMHyMyKNxEI3hCQlQ2o0yfITh8gM894E4HgDQFZ2YA6fYLsBG8yE7yJQPCGgAywoK7fbYYwqkw+kZmt5kQgeENACgfUCR1kZ0KWzKx4E4HgDQGZ2YU6oYPs9AEyMy4iAsEbAlI4oE7oIDu7PshM2yYCwRsCMrMLdUIH2Zl8IjNbzYlA8IaAFBioEzrITh8gMwsSRCB4Q0CCN9RZ8SY7fYDMjIuIQPCGgMzsQp3XiZGdFW8yMy4iAsEbAjKzC3VW+8hO8CYz4yIiELwhIKt7UDHgApNP5KYOEIHgDQEpMFCxxRCseJObBQkiELwhIEEDKiaiQPAmN3WACARvCEiBgYqJKLDVnNzUASIQvCEgBQYqJqLAije5qQNEIHhDQAoMVDzbB4I3uRkXEYHgDQEJGlCxAwRsNSc3dYAIBG8IyMwuVPQHsOJNbuoAEQjeEJCZXajoDyB4k5udgEQgeENAZnahIniDrebkZlxEBII3BCRoQMWAC/qrC1YFGVXqABEI3hCQAgMVE1Fgqzm5qQNEIHhDQFYtoGIiCmw1Jzd1gAgEbwhIgYGKiSiw4k1uVryJQPCGgARvqBhwgeBNbsZFRCB4Q0CCBlQMuMBWc3LTtolA8IaABA2omIgCK97kpg4QgeANAXmmFSoGXCB4k5sFCSIQvCEgBQYq+gPYak5uJmCJQPCGgBQYqOgPYMWb3EzAEoHgDQEpMFDx6AUI3uRmXEQEgjcEZIUPKgZcYKs5uRkXEYHgDQEJGlAx4AIr3uSmbROB4A0B2VoLFRNRIHiTmwlYIhC8ISBBAyoGXGCrObkZFxGB4A0BCRpQ0R/Aije5Cd5EIHhDQAoMVPQHELzJzQQsEQjeEJACAxX9AWw1JzcTsEQgeENACgxUHDYIVrzJzQQsEQjeEJDCARUTUSB4k5u2TQSCNwRkZhcq+gPYak5uJmCJQPCGgBQYqAjeYMWb3NQBIhC8ISAFBiomokDwJjd1gAgEbwhIgYGKiSiw1Zzc1AEiELwhIAUGKvoDWPEmNwsSRCB4Q0AKB1T0BxC8yc1rJYlA8IaArPBBRX8AW83JzYo3EQjeEJACAxX9Aax4k5s6QASCNwRkhQ8q+gMI3uSmDhCB4A0BmdmFigEX2GpObuoAEQjeEJACAxUTUWDFm9zUASIQvCEghQMqJqJA8CY3dYAIBG8ISIGBitfIgK3m5GbFmwgEbwhIgYGK/gBWvMnNBCwRCN4QkBVvqOgPIHiTmzpABII3BGSFDyoGXGCrObkZFxGB4A0BKTBQ0R/Aije5mYAlAsEbAvIsE1QMuEDwJjd1gAgEbwjICh9U9Aew1Zzc1AEiELwhIDO7UNEfwIo3uakDRCB4Q0BmdqHi0QsQvMlN8CYCwRsCUmCgYiIKbDUnN22bCARvCEjQgIqJKLDiTW7qABEI3hCQrbVQMeACwZvc1AEiELwhICveUNEfwFZzclMHiEDwhoDM7EJFfwAr3uSmDhCB4A0BmdmFigEX9NcPPKbEqDIuIgLBGwISNKCinYOt5uRmXEQEgjcEZGYXKgZcYKs5uakDRCB4Q0C2C0LFgAsEb3LTtolA8IaArHhDRX8AwZvcTMASgeANAQkaUDHgAs94k5s6QASCNwSkwEDFRBRY8SY3dYAIBG8ISIGBiokoELzJTR0gAsEbAnK4GlQMuMBWc3JTB4hA8IaArHhDRTsHK97kZlxEBII3BGRmFyr6Awje5KYOEIHgDQGZ2YWKARfYak5u2jYRCN4QkKABFRNRYMWb3IyLiEDwhoAEDagYcIHgTW7qABEI3hCQU82hYsAFtpqTmwUJIhC8ISAFBir6A1jxJjcTsEQgeENACgxU7AABwZvcjIuIQPCGgKzwQUV/AFvNyU0dIALBGwIyswsV/QGseJObOkAEgjcEZGYXKgZcIHiTm0eOiEDwhoAUGKiYiAJbzclNHSACwRsCUmCgYsUbrHiTmzpABII3BCR4Q8WACwRvclMHiEDwhoAUGKho52CrOblZkCACwRsCUmCgYiIKrHiTmzpABII3BKTAQEV/AMGb3CxIEIHgDQEpHFAx4AJbzcnNBCwRCN4QkAIDFf0BrHiTm9esEoHgDQFZ4YOK/gCCN7mpA0QgeENACgxUrHiDrebkpg4QgeANwQxSNBQYMjDgAlttyU0dIALBG4IZZHClwJCBdg6CB7nZCUgEgjcEo2hAncABgge5qQNEIHhDMFa8oc6AC/QDctP+iUDwhmA84w11VvpA8CA3dYAIBG8Ixoo31AkcIHiQm8MFiUDwhmCseEOd4A36AbmZeCICwRuCseINdQZcIHiTm/ZPBII3BKNoQJ0thmACitwEbyIQvCEYW82hzoAL9ANyM/FEBII3BGOrOdQZcJGdCVmyM/FEBII3BGOABXUGXGSnLpCdOkAEgjcEY8Ub6qx4k526QHbqABEI3hCMlQ2os9JBdoI32TlkkwgEbwhGcYE6wZvsTMiSnTpABII3BGOABXW2GJKdFW+yUweIQPCGYAywoM4uELJTF8jOijcRCN4QjBVvqDPgIrtB2raJKkaROkAEgjcEY2UD6mwxJDt1gezUASIQvCEYAyyos9JBduoC2akDRCB4QzC2CUKdARfZeQSJ7Kx4E4HgDcFY2YA6Ay6yUxfIzqIEEQjeEIwBFtRZ8SY7dYHs1AEiELwhGAMsqNPOyc5Wc7Kz84kIBG8IRvCGOisdZKcukJ06QASCNwTjOSaoM+AiO8Gb7NQBIhC8IRgDLKizxZDsbDUnO3WACARvCEbwhjorHWSnLpCdOkAEgjcEY4AFdQZcZKcukJ3H8IhA8IZgDLCgzhZDsrPVnOzUASIQvCEYwRvqrHSQnbpAdnY+EYHgDcEIGVBnwEV2gjfZWfEmAsEbgjHAgjoDLrKz1ZzsTMASgeANwQjeUGfARXbqAtmpA0QgeEMwBlhQZ8BFduoC2dn5RASCNwRjgAV1Blxkpy6QnQlYIhC8IRgDLKgz4CI7dYHsHDxLBII3BKO4QJ0Vb7ITvMlOHSACwRuCMcCCOpNRZKcukJ2dT0QgeEMwBlhQZ8BFdqtWrer7u/oBo0gdIALBG4IRvKHOFkOyUxfITh0gAsEbgjHAgjorHWSnLpCdOkAEgjcEY0sh1BlwkZ3gTXZWvIlA8IZgHCQFdQZcZCd4k50JWCIQvCEYAyyoM+AiO3WB7CxKEIHgDcEYYEGdARfZqQtkZ+cTEQjeEIwBFtQZcJGdukB2dj4RgeANwRhgQZ0BF9mpC2SnDhCB4A3BGGBBnQEX2akLZGfnExEI3hCM51mhTvAmO8Gb7NQBIhC8IRgDLKgz4CK7VatW9f1d/YBRpA4QgeANwQjeUGfARXbqAtnZak4EgjcEY4AFdYI32akLZOcxPCIQvCEYAyyoG2SbLYwidYHsTMASgeANwRhgQZ0BF9mpC2RnqzkRCN4QjO1UUCd4k53gTXbqABEI3hCMARbUGXCRnbpAdla8iUDwhmAMsKBO8CY7dYHs1AEiELwhGAMsqOv3cDX9gVGlLpCd4E0EgjcEY4AFdQZcZKcukJ2t5kQgeEMwBlhQ58BBslMXyE4dIALBG4JRXKDOivcLBvQAACAASURBVDfZCd5kZ8WbCARvCMYAC+oEb7Lr95yDUvQDRs8gbVr7ZyoJ3hCM4A11Dlcju0Hqgl1TjBrjIqIQvCEYBQYqnU7HFkPSUxfITPsnCsEbglFgoGKLIagL5KYOEIXgDcHYJggVgQP0A3LT/olC8IZgFBioDHKoFIwqdYHMrHgTheANwRhgQUV/AP2A3OwEJArBG4IxwIKK/gD6Ablp/0QheEMwCgxU9AfQD8jNVnOiELwhGAMsqOgPoB+Qm/ZPFII3BONZJqgMcriaARejSvAgM+2fKARvCEaBgYr+ACagyM1Wc6IQvCEYAyyoCN6gH5Cb9k8UgjcEo8BAxaMXoC6Qm/ZPFII3BKPAQEV/AP2A3LRpohC8IRgDLKh49ALUBXLT/olC8IZgbK2FigEX6Afkpv0TheANwSgwUNEfQD8gN6eaE4XgDcEYYEFFfwD9gNy0f6IQvCEYBQYq+gPoB+RmxZsoBG8IxgALKoMcrgajSl0gM+2fKARvCEaBgYr+APoBuWn/RCF4QzBONYeKARfoB+RmXEQUgjcEY4AFFf0BvM+e3NQBohC8IRgFBir6A+gH5GbiiSgEbwjGAAsqBlygLpCb9k8UgjcEo8BARX8A/YDctH+iELwhGAUGKvoD6AfkZucTUQjeEIzTO6GiP4DgTW7aP1EI3hCMAgMV/QH0A3LT/olC8IZgFBio2GII6gK52flEFII3BGOABRX9AfQDcjMBSxSCNwRjgAUV/QH0A3LT/olC8IZgFBio6A+gH5Cb9k8UgjcE41kmqBhwga225Kb9E4XgDcEIGlAZZMAFo0pdIDPtnygEbwhGgYGKlQ5QF8hN+ycKwRuCUWCgIniDukBu6gBRCN4QjAEWVAy4QF0gN2ffEIXgDcEYYEFFfwD9gNy0f6IQvCEYM7tQseINgge5af9EIXhDMAoMVARvUBfITR0gCsEbgjHAgorXiYG6QG7aP1EI3hCMAgMVKx2gLpCb9k8UgjcEI2hAxYAL9ANyMy4iCsEbgjHAgooBF6gL5Kb9E4XgDcE41RwqgjfoB+RmXEQUgjcEY2YXKgIHqAvkpv0TheANwSgwUBG8YbC6YHWQUaMOEIXgDcEI3lDRH0A/IDftnygEbwhGgYGK93iDukBu2j9RCN4QjG2CULHFEAQPclMHiELwhmAMsKBiwAXqArlp/0QheEMwCgxUBG9QF8hN+ycKwRuCUWCgoj+AfkBuzvogCsEbgjHAgooVb1AXyE37JwrBG4JRYKAieIO6QG7aP1EI3hCMU82hYoshmIAiN8GbKARvCEaBgYrAAeoCuakDRCF4QzAGWFDRH0A/IDftnygEbwhGgYGKlQ5QF8hN+ycKwRuCUWCgIniDukBu6gBRCN4QjAIDFf0BBG9yc+gsUQjeEIxTnKEieIPgTW7aP1EI3hCMoAEVAy7QD8jNuIgoBG8IRoGBih0gIHiTm/ZPFII3BNLpdBQYWIeJKBA8yE37JwrBGwIZdHVPgWHUWfEGwYPctH+iELwhEMEb6vQJEDzIzc4nohC8IRCre1A36GtkDLoYRYIHmZl4IgrBGwKxugd1+gQIHuTmPd5EIXhDICtXrhzo+wZYjDrBGwRvcrPjgygEbwhEyIA6j1+A4E1u2j9RCN4QiOANdfoECB7kpv0TheANgQgZUKdPgOBBbraaE4XgDYHYVgt1TjUnu06nM1C71gcYNSaeiELwhkCs7kGdPkF2+gDZCd5EIXhDIE41hzqhg+zs+iA7wZsoBG8IRMiAOn2C7PQBsvMYHlEI3hCIARbUGXCRnbpAdla8iULwhkAMsKDONluyUxfITvAmCsEbArG6B3WD9olBgzq0neBNdl4nRhSCNwTicDWoEzrITh8gOyveRCF4QyAGWFCnT5CdPkB2gjdRCN4QiAEW1OkTZOecA7LzGB5RCN4QiJABdYOGDs94M2rUBbIz+UQUgjcEYoAFdfoE2ekDZGdClSgEbwhk0MPVYNQJHWSnD5CdFW+iELwhEAMsqNMnyE4fIDt9gCgEbwhEcYE67/EmO3WB7Kx4E4XgDYEYYEGdARfZqQtkpw4QheANgRhgQZ0+QXZCB9mpA0QheEMggx6uprgw6mw1Jzuhg+xMPhGF4A2BGGBBnT5BdvoA2QneRCF4QyAGWFCnT5CdXR9kN2gfgKkieEMgQgbUDRoihA5GjbpAdla8iULwhkCsbECd0EF2+gDZCd5EIXhDIA5Xgzqhg+z0AbITvIlC8IZADLCgTp8gO6GD7NQBohC8IRDFBeo8fkF26gLZmXwiCsEbAhEyoM6Ai+wEb7JTB4hC8IZADLCgMp6JJX2CUaMukJ0+QBSCNwTicDWojOfdrXaBMGqEDrLze50oBG8IxAALKuMJ3voEo0ZdIDtbzYlC8IZADLCgIniDugD6AFEI3hCIw9WgMp72rU8waqz2kZ0+QBSCNwRiVhcqVrxBXQDBmygEbwjE4WpQEbxB8AbBmygEbwjEAAsqTjUHdQH0AaIQvCEQz3hDxYo3CB1gxZsoBG8IxAALKuOZWNInGDXqAtlZZCAKwRsCMcCCihVvUBdAHyAKwRsCUVyg4hlvsM0W9AGiELwhEKeaQ2XQ/lCKPsHoMSFLdoI3UQjeEEjTAGvatGldP7e6xygTvMGELJh8IgrBGwJpKi4zZ87s+rniwigbT/A2GcWoEbzJzoo3UQjeEIjgDRUr3iB4k1un07HiTRiCNwQieENF8AbBm9y8VpJIBG8IpGmANWvWrK6f21bLKLPVHARvchvP2y1gqnRfJgNaqanANAVvAyxGmRVvELzJbTzBO1MfWLZsWfne975XHnjggbLPPvuU173udVN9S6kJ3hCIreZQEbxB8CY3wbvZokWLystf/vIyb968Jz475phjyn/9139N4V3lZqv5CDn77LPLHnvsUaZNm1amTZtW3v72t5ff/va3U31bTCDBGypNgWPGjBmNf8ZWc0aN4E1mJmCb/du//VstdJdSyne+851yySWXTNEdIXiPiB/84Afl8MMPLzfddNMTn82ePbsceuih5dxzz53CO2MiCd5QGfTMg1L0CUaP4E1mVryb/fd//3fXz88888zJvRGeIHiPgE6nUz772c92vbZ69erymc98ZpLviGFxuBpUmvrDzJkzy7Rp07peyzLgIg/Bm8wE7+7+/d//vdx7771dr33pS1+a5LthLcF7BNxzzz3l1ltvbbx+2WWXlUceeWQS74hhcbgaVHoF7+nTu5c3fYJRI3iTmeC9oUceeaR87GMf6/mdn//855N0N6xL8B4B1113Xc/rnU6nXHzxxZN0NwyTreZQGc+Kd8ZdICtWrPC7YIQJ3mTmGe8N9XN42le+8pVJuBPWJ3iPgLGCdymlXHjhhZNwJwyb4A0VW817W7p0afnwhz9cttpqq7LFFluU97znPWXRokVTfVtMMMGbzLzHe0NnnXXWmN+58sorU05ETzXBewTMmTNnzO9ceumlk3AnDNugW839UmWU2Wre28c//vHy9a9/vSxdurQsXbq0nHrqqeXoo4+e6ttiggneZGar+YZuvvnmMb/z8MMPl1tuuWUS7oZ1Cd4j4NRTTx3zO9ddd93I/6LJoFfQ6Mb/54wyW82b3XPPPeWrX/3qBp+fddZZjQfuEJPgTWaCd92qVavKQw891Nd3586dO+S7YX2Cd3Bf+9rX+vreI488Uu65554h3w3DZqs5VGw1b/b5z3++6yTD6tWr+9olRRyCN5l5xrvuoYce6vt/37qvIGZyCN6BrVy5snz0ox/t+/u33XbbEO+GySB4Q8VW8+46nU45/fTTG68ff/zxZfny5ZN4RwyT4E1mVrzrHnjggb6/e8EFFwzxTuhG8A7s5ptvHmjb5IIFC4Z4N0yGFStWdP1800037fp5lm215GSreXfz588v9913X+P1a665puyyyy7l+uuvn8S7YlgEbzITvOsGCd7nnntu47iS4RC8Axv0Ob0777xzSHfCZGkaYG2yySZdPx/l4gK2mnc3e/bsMb9zzz33lPe9730pJiJGneBNZoJ33R//+MeBvn/OOecM6U7oRvAObNDgffvttw/pTpgsTTOTTaeawygTvLv7+c9/3tf3rrnmmnLRRRcN+W4YtqZ+kPlxC/IQvOsGWfEupZQrrrhiSHdCN4J3YIMGbwfqxDfoincpo11gyM0z3t0NcmDOr371qyHeCZOhqR80Tchm6APrW716dbnhhhvKDTfc4L3PI2Y8h6uNskFXvL1SbHIJ3oENGryvv/56BSe4phVvwZuMPOO9oT/96U89n+9e3/z584d3M0wKwbu3yy+/vOy8885lzz33LHvuuWfZbbfdyuWXXz7Vt8UEseJdN+iKt+A9uQTvwJqC90te8pKunz/++OMDDchon/GseI960CAvW803NOgjRd7pHZ/g3ezWW28thx56aO11qvPnzy/veMc7ypIlS6bwzpgognfdoMF74cKFQ7oTuhG8A2saMB144IGNf8YBa7GN5xnvUS4w5Gar+YYGDdKCd3yCd7Njjz22PProoxt8vmDBgvLjH/94Cu6IiSZ4191///1dP3/+85/f9fMHHnhgpP97tI3gHVjTgGnPPfcsW2yxRddrgndsnvGGiq3mG7r66qsH+r7gHZ/g3d3NN99czj///MbrZ5999iTeDcMynme8R7kPNO16etGLXtT18xUrVpTFixcP85ZYh+Ad1MqVKxsHTM985jPLzjvv3PXab3/72yHeFcNmxRsqtppv6JOf/ORA31+8eHHXFUHiELy7G2u8c+ONN07OjTBUVrwrjz/+eLn11lu7Xtt///0b/9yg29MZP8E7qAULFjQW22c/+9llr7326npN8I7NM95QsdW87q677mq89sIXvrDx2h/+8Idh3A6TRPDubqwD1K6//nr1cQQI3pVXvepVjdde8YpXNF775S9/OYS7oRvBO6hepxDuuuuu5Y1vfGPXawsWLBjZXzijrtPpONUc1mGred25557beO31r39947V1D54inkEnZEe5D6xr3rx5Y35nv/32K0uXLp2Eu2FYBO815s2bVy677LKu12bMmFH23nvvsvnmm3e9/pvf/GaYt8Y6BO+gmoL3M57xjLLFFluUXXbZpev1pUuXepYjqF6DJcGbjGw1r2vaYljKmkOmtt56667XBO/YrHh3189rkq688sryla98ZRLuhmEZtP2XMpp94Kc//WnjtX322adsttlmjROwF1544Uj+N2kjwTuopoLy3Oc+t5RSyo477tj4Z20rjKlptbuUfAWmyZIlS8r5559fTjrppHLSSSeVX//61+Wxxx6b6ttiSGw1r2s6PHOPPfYou+yyS3nGM57R9boD1mITvDf04IMP9v361NNOO23Id8MwNa149xoXjeKuj+uuu67x2hve8IZSSimHH3541+v33Xdfz0eVmDgzp/oGGJ+mlY3ddtutlDJ28N59992Hcl8MT6/gbcW7lN///vfl9a9//QYhYvvtty+/+MUvyktf+tIpujOGxVbzuqbg/Za3vKWUUspOO+3U9UCpu+++e6j3xXAJ3nXXXntt2Xvvvfv+/s0331xWr17dOFlHuzUF75kzmyPOKPaB66+/vvHahz/84VJKaXwMtZQ1C3pNBzMzcfyWCWqsFe8nPelJjdsKrXjH1OuVGdkPV+t0OuWoo47qunJ3//33l3e9610jWWiz6xU4sm01f/DBB8tVV13V9dpOO+1USinlWc96VtfrVjpiE7wrK1eubFzVa7Js2TJ9IDDBe4077rij6+df/OIXy1Of+tRSSinbbLNN2W677bp+r9ejSkwcwTuglStXNnawtcG7lOZVb8F7+G6//fZyyimnlIsvvnjCgq8V7+46nU754Q9/WK699trG78ybN8+rY0aQZ7wrZ555ZuPviLW7PZqCtxXv2ATvyvnnnz+uEO2NL3H1qgNNRq0PLFq0qCxatKjrtX322af27+vmhHUde+yxE35fbEjwDmj+/PmNv2jWbjUvRfCeKp///OfLrrvuWo488shy0EEHlTe96U1lyZIlG/1zx7viPWoFZl0rVqwo73vf+8oRRxwx5nebJquIyzPelbPPPrvr5895znPKy172slKK4D2qBO/KOeecM64/d955503wnTBZPOPd/JhRKWteMbyuPfbYo/G7xknDJ3gHdPXVVzde23XXXZ/4Z8F78p1zzjnl7/7u7zb47Fvf+tZG/2wr3hs6/fTTy8knn9zXdx0gNXo8473GqlWrykUXXdT12rve9a4nJiHWbjlf38KFC3v+fqHdBO9KrwBSSikHH3xw18/nzp07jNthEowneI9aH2h6D/f06dM3OFTzwAMPbPw5JqCGT/AOqGmAtdtuu5UtttjiiX8XvCffP/3TP3X9/Ac/+MFG/2zPeG/orLPO6vu7gvfosdV8jfnz5ze+JvK1r33tE//ctOLd6XT0j8AE70qv4H3CCSeUo48+uuu1W265ZaT/u4wyz3iXcu6553b9/MUvfvEGvwfWrQnr82rJ4RO8A7riiiu6fv7KV76y9u+C9+S6+OKLGw83uvbaa8sjjzyyUT/f68Q2NH/+/L6/K1iMHlvN1+i1VXzd5/uagvdYP4P2Wr16dePkasbgvWDBgsZrxx9/fOPzrY8++mj54x//OKzbYoiaxkZZFiRWrlxZLrnkkq7X3v72t2/w2Q477ND4s4yThk/wDubiiy8ul156addr678+Q/CePJ1Op7z5zW/u+Z2NPTHSM94bGuQQnYceemiId8JUsNV8jaZViq233rq2C2rLLbcsm2++edfvCh0xNa32lZIveD/++OONv+fPP//8UsqaMw+a3H///cO4LYZs0B0fpYxWH7jzzjsbzxE69NBDu37+kY98pOvnVryHT/AOZO7cueWggw5qvP6CF7yg9u9NwXvRokVl6dKlE3pv2d16663lwQcf7Pmdsa6PxYp33cqVK8sDDzzQ9/cffvjhId4NU8FW8zXe8573dP18/Wf7pk2bVp72tKd1/e4gfYn26DUhmy1495pcXbvK19T+S9EHomrqA1kWJG6//fbGa3vuuWfXz9evDWtZ8R4+wTuI5cuXbxCs19dv8C7FtsKJ1s8s4cauuFrxrrvzzjsH+t8meI+epsmoTFvNr7nmmsZr3QZXTcHDindMgnelV41d+x7jTTbZpGy55ZZdv6MPxJR5xfvhhx8uhx12WNdrW221VW3H07oE76kjeAcx1iFST3nKUzboSE0n2JbiBM+J1k/B3tjgPd5TzUdxa20pzYcMNtnYZ+xpn+xbzVetWvXEO7q7Wf81MqWUst1223X9rtW+mATvSq9dZdtss80T/6wPjJbMdaDplP5SmsN1KaU885nP7Pr5gw8+WJYtW7bR90UzwTuIsYL37rvvvsEvmM0337zxeaYbb7xxom6N0l/w3tit5la86/7v//5voO97P+Xoyb7V/Cc/+UnP6+sfuFlK84q351tjErwrTZPbW2yxRdl0002f+He7PkZLr51Po1wHTj755HL99dc3Xu8VvHtdcw7UcAneQcybN6/n9aYDFF74whd2/Vzwnlj9DFqHteI9bdq0MmPGjMY/NwoFppumFe/nPe95jX9mYw+4o10yB+9Op1Pe8Y539PzOa17zmg0+e/rTn971u01vZKDdBO9K0+T2tttuW/v37bffvuv3brnllgm/J4YvYx1YvXp1+dSnPtXzO4ccckjjtV7B23bz4RK8A+h0Oj0LwjOf+czyoQ99qOu1PfbYo+vnN9xww4TcG2v0M1O+sStK43l1UinxC0w3nU6nMUQffvjhjX/uiCOOGNYtMQXG0ydGZYvhr371q57Xt9xyy66Dq6at6TfddJNzEAISvCtNwXvt891rNZ2Xc9111034PTF8GV8reffdd/c8VG2HHXZofGd9KWseT216/lvwHi7Bu+VWrlxZvvjFL5bFixd3vX7QQQeVyy67rPH9rE3B+6abbhqZAWgb9BO8TzvttI0q7E0r3rNmzWqc1S1ldILGuhYvXtz4Gp3Xv/71ZebMmV2vXXXVVT0HqsSScaVjrTPOOKPn9abJ1V5vxpgzZ85G3ROTT/CuND2jvf7W8r322qvr9+yIiqnX4Wqj+oz3nXfe2fP6VVdd1fgcdylrdko6YG1qCN4ttnLlyvLa1762fOxjH2v8zuzZs3t2rqat5kuWLCkLFizY6HtkjX6fDdtnn33K7Nmzx/V3jCdklDKag6xe2/Z32mmnDVY41nXttdcO45aYAlmDd6fTKRdccEHj9b/+679unIzdeeedawdNrUvwjkfwrjQF7/UPU2s6eHbJkiU9DzGlnTLWgV7j9+XLl/fcSr6W4D01BO8WO/HEE8v555/feP2pT31q4+mca+2+++6N12w3nzj9Bu+VK1eWj370o+OabR3vinf0AtNNr+C97bbblk9/+tON1wc9lI32yrjFsJRSzjvvvHLbbbc1Xv/GN77ReG3atGmNK3622sYjeFea6vD6K95bbbVV489YtGjRhN4Tw5fxcLWmc5/23HPPnq9RW1fTol0/r8dl/ATvllq9enX5wAc+0PM7L3jBC3oGrlLWPMex8847d73mgLWJM8hpqHffffe4/ttb8a40zfZOnz69bLXVVuWYY45p/LODvoaMidP0eMB4ZX2NzIknnth47ZJLLhnzz7/oRS/q+rngHY/gXel3q3mv4O21k8Nx++23l//3//5fOeqoo8pXv/rVCT1PIuME7Gc/+9mun++yyy59/4wddtih6+dO9x8uwbuleq10r9UUqNfX9Jy3d3lPjNWrVw/8/s+xns/ppteKd6bD1R566KHy1re+teu1bbbZpkyfPr3MmjWrfPKTn+z6nd///vfDvD26OPHEE8tuu+1WNt9883LIIYdM2OnBGbcYllLKNddc0/Xzgw8+uOy///5j/vmmFe85c+aMxMREJoJ3pd+t5ltvvXXjz7DiPfGuvvrqsu+++5ZPfvKT5aSTTirHHXdcOeCAA8p99903IT8/2wTsscce23itaVK1m6bX6nmf/XAJ3i3Vz6rcPvvs09fPatpuftNNNw10T3T30EMPDfxLfOHChQP/PeNd8Y5cYLr5+Mc/3nht3QFWU/+47bbbyqOPPjrh90V3P/3pT8v73//+ctttt5Xly5eXCy+8sBx88MHl8ccf3+ifnXGlo9PpNG4FfNOb3tTXz2gK3o899pj33QcjeFf6XfF+ylOe0vgzBO+J95nPfGaDx8Pmzp1b/vM//3NCfv54DleL2gdWrFhRvv3tbzdeb/rd3k3To6pWvIdL8G6pfk7X3G+//fr6WU2vzpg7d27YXz5tMp5fUnfffffAf8Yz3muCQa+ic8ABBzzxz71mfufPnz+Rt0UP3QZXCxcuLGeeeeZG/+xsKx2lrDkccOnSpV2vHXjggX39jBe+8IWN/30csBaL4L3G8uXLG0Pz+sF7xowZjeFb8J5Yq1evLueee27Xa02fDyrTzqexduy97nWv6/tnNa14O3h5uATvlrr++ut7Xt9nn336HmQ1Be+HH354o98tzfiC93gOr8j+jHen0xmzzR911FFP/HPTqc6l9D6cjYl1zjnndP38S1/60kb/7EwDrlJKOfXUUxvfw11K82E563vyk59cdt11167XPOcdi+C9RtM7vEvpvrLXtN28189hcHfffXdZvnx512u/+93vJuTv6HW42qjtfDrvvPMar73sZS/ruZtjfU3Bu5RSLrzwwoHui/4J3i10wQUX9Bz8/OVf/mU577zzxjxYba1eJ5sfcMABYX8BtcV4BqoTveKd4RnvU045peds79FHH11e+cpXPvHvm266adliiy26ftfganL0ejVP03PKE/HzR3HA9eMf/7i85z3vabw+Y8aMxsNyumnaEXL11VcPfG9MnV7Be+bMmV0/j9oHmtx///3lve99b+P1bgHj6U9/etfv3nXXXRN2X5Ry880397zetHtnEFl2Pt12222NZ9eUUsrPf/7zgX7e9ttv33jtne9850A/i/4J3i30rW99q/HabbfdVk4//fSy7bbb9v3ztttuu8YOdvvtt0/YrGNW3/ve97p+3us9ipO54h2xwKzv1FNP7TmwKqWU73znOxt81vQ+byvek2Osdr5s2bJx/+zVq1c39olNN910ZAZcS5YsKQ8//HB529ve1vN7++23X9lkk036/rlNzwL+4he/ED4CaZp8mjFjxkju+ljfqlWrytve9raeB9J2Gy89+9nP7vrd8Rx8SrOf/exnPa9PxH/vLDuf/vmf/7nn9V5Bups/+7M/a7x23333lcWLFw/08+iP4N0inU6nnHDCCeX000/ven3//fcf6FUB6+q1Rfd//ud/xvUzWVM0mlaI9tprr/L973+/6zXPePfv7rvvLkceeWTP75xwwgld/xs0TVAJ3pOj1/vUS9m4E+Z7raZvsskm4Qdcy5YtKx/84AfLlltu2TiBtK53v/vdA/38gw46qOvny5cvb5xMpH2a+sEo9IF+nHnmmT0Po91hhx26brlvCt4nnXRSueKKKybs/rIb6/WGE3GYY4bD1VatWlVOOumkxuuDPNu91qxZs3puN7/ssssG/pmMTfBukX/5l38pn/jEJxqv99pmOJZendJBU+PzwAMPND4/X0opRx55ZOMzlw899NDAW6wyPuP9+OOPl7333nvM/w1Nr9doCiy2mg/fWAOFUkq5+OKLx/3zm54bLGU0QsdnPvOZ8s1vfrOvd59vuummA28NfNWrXtW4QnLppZcO9LPordPplB/84Adlr732Ks9+9rPL3/7t307Y5F9TPxiFPtCPXjsESynlsMMO6/p5U/AupZSXv/zl5Ytf/OJG3RdrjLWiPczgHf2Ro/vvv7+cffbZ5eqrry4//OEPe3538803H9ff8f73v7/xmjcfDYfg3RKLFi0qJ5xwQs/vNBWQfvRaMbS1anCr7+lnRwAAIABJREFUVq0qr3rVq3qG5yOOOKLnAV+DbjfPuOJ9/PHHj/lOyWuuuaZss802Xa81rXiPZ6s/g5k7d+6Y3xlm8G4acEXYar548eKBDp/79Kc/3fhqmCYzZ84sb3zjG7te6+etGvSn0+mUQw89tLzvfe8rc+bMKQsWLChf/vKXy1/91V9NyO/m7MH7ggsu6Hm9aeyzxx579Pxzn/jEJxw+u5GWLl065n/D22+/faP/nl5nfUR85Ojhhx8uz3rWs8rTn/70cvjhh5d99tlnzB1Nb3nLW8b1dx133HGN18barcD4CN4tcf755/d8t/D06dN7Po8xls0226x87GMf63pN8B7cb37zm3LDDTc0Xn/FK15RZsyY0fOU4UFnesczq1tK3EHW/Pnzy5e//OUxv9drAPWc5zyn6+eCxfD1s11z9uzZ4y7uo7zi/ZOf/KTv95zPnj27/MM//MO4/p43vOENXT+/4447Nur5eyrHH398+c1vfrPB5z/72c/K//7v/270z88cvMcKT1tvvXV59atf3fXaWO87fvzxx8d8Ppne+pl8nYjJjVF6xnvRokXlWc961kCLA9tvv31585vfPK6/b8cddyxvfetbu147/fTTvV5vCATvluj1jFIpaw7qano1SL8OPfTQrp/ff//9fW1npDLWyb9rT0zdYostGl9b0uu1EN2Md8W7zTO7vXzhC1/o63u9DpR67nOf2/XzW265ZVz3RP+uvPLKvr536KGH9px0bDLKwftHP/pRX9+77rrrGgdN/Xje857X9fOVK1eWq666atw/lzXuu+++8q//+q+N1//8z/98o38/Zw7eCxcu7Hn9wAMPLDNmzOh6bfvtty8vfvGLe/55z7hunH4mzsfa0daPUQre3/3ud8uSJUsG+jMnnnjiQK8RW98BBxzQeO3rX//6uH8u3QneLTHWsxS9tiz3q+n1GatXr56QX36ZjLVLYN3/1k3Pkg260jee7VSltLfArK/T6ZSvfe1r5TWveU3Zd999J+QX/m677db18wcffHBcYY/+XX755X19b+nSpWM+v9bNeLeaR+gP/byi8Hvf+17jK8H6tfvuu5cnP/nJXa9528XG62dFe9BXAK0vc/Aea9fYBz7wgZ7XP/7xj2/Uz6fZ0qVLGw8KXte5557b86DMfvQ6XC1aHZg9e/ZA3z/66KMbdy71a99992281uvcKcZH8G6BJUuWlHPPPbfnd5pWJgbRFLxLKeWXv/zlRv/8TMYK3jvuuOMT/3zwwQd3/c5Y77dc33hO7iylvQVmfccdd1z5yEc+Un7961/3vVr693//9z2v9zpAx3Pew3PXXXcNdDLwhRdeOPDfMd4V77bvAFm5cuWYbfO4444rRx111Eb/XTNnziyveMUrul679tprN/rnZ9fP5EW/uxuaZA7evSb3PvGJTzSeYbDWO9/5zlqtXp8FifG74YYbev6OXqvT6ZRtttmmnHHGGeP+u0blGe9ly5YNPOHZ6xntfr385S/veX2sfMJgBO8pdOedd5bvfve7Zb/99hvzu29605s2+u/rdfjORHTeTBYsWNDz+rrbP5uC94MPPjjQc5S9ikv0Z7wffPDBMU+nXd+sWbPK0Ucf3fM7vd6l7l3FwzPWgUfr63eiZV2jutX83nvv7fnoz2te85qBDl4by0tf+tKun19zzTUT9ndk1c8zrt///vc3qk1mDt5nnXVW18+f+9znls997nM9J6TX6lXLH3nkkXHfW3b97NpZ67HHHitHHHHEuM9eGZWt5uecc85A33/1q1895lkF/XjSk57UeBZCKROTP6gI3pPoT3/6U7n22mvLAw88UE477bSy6667lmOOOaZcf/31Pf/c7rvvPiENf9asWY2nPD/88MOtnAFsq14r3m94wxtqW0D33HPPxu/+4Q9/6PvvHO+Kd4T/X6+88sqBtpu96EUvKr/61a/G3AnypCc9qXHCaTzvUqc/gwy6Sill3rx5je27Sa9Jq8hbzT/0oQ81Xnv3u99dzj777L4CRb/23nvvrp/PnTt34FceUtfvWRLf+c53xv13jOfsj7b3gW7uuuuucuWVVz7R7++4447GnTJN59l0M3PmzMYt0Q6WGr/xnKNyyimnjOvvGpXgPehrHCfy8L9eP2vlypUezZtAgvckOfnkk8v2229f9t5777LddtuVd7/73X0daLbTTjuV008/vWy66aYTch9/+tOfGq95Z19/Fi1aVBYvXtz12qxZszYYRPVadR3kdUqj/Ix3vyH4Qx/6UFmxYkW57rrryiGHHNLXn2k6H0HwHp45c+YM9P0VK1YM/FqZUdtqfskll5Rp06Y1DoCe9rSnlVNOOWXCasFaL3nJS7p+vnr16jEnhWm2bNmyMXdGrfXlL3953L+nR33Fe+nSpeWYY44pO++8c9l3333LLrvsUg499NCyyy67NP6ZQU943mqrrbp+vmjRolD/rdpkPK8JG+8um1F5j/dYhwWu65BDDimbbbbZhP3dm222Wc+FjH7PbGFsgvckmDdvXnnve9/b9+th1tptt93KHXfcMebJm4P49re/3Xht0K2+Wc2fP7/x2q233lp22GGH2mdPecpTGgt7r/err69pha/XAKuUdhaY9fUbgo888sgyc+bMgX624D35mgLHMccc0/hnBp34awoc06dPLzNmzAgVOn784x/3PFm2lMHDRL922223svnmm3e95jnv8bv33nv7bms33njjuN+0MOrB+5vf/Gb57ne/+8S/33vvvWMeWvcXf/EXA/0dTW8eWbVq1cAnTLPGeA6mG++rbcezG7CNE7CD7ID81Kc+NeF/f6980Ov1uQxG8J4E4zmxt5Q1rxhrehXGePU6kOc//uM/yh//+MeN/jseffTR8jd/8zfl+c9/fjn44IPLT37yk1LKmvdiXnTRReWqq64qnU6nLFq0qJx33nnloosuGnhS4v+zd+dxOtX9H8c/15h9xswwM8ZOSRHJGlnKllAi0l1RRLq1CN1tRBvKHUn7Xb/qjrTcQne6dYcoZElSSimyFCpjG8sMZrnO748eM/cM53uuc53rnGs51+v5eFyPR53t+s7lOtc57/PdQkn15C82Nla3dtvj8RgOXrFv3z5T76tq9pmUlBQVwTsmJsZSf6Y6deroLqePt3N+++033eVdu3ZV1lTZFbxLa4QjpaZD0zRT83AHOnKtSqVKlZTnFf28rVOdAypWWxe4OXj//PPP8re//c2vfdq2bet3VwzVg3GRwAe/i1b+DK5Zavv27Zbey0prwHA8B8zWeE+ZMkW6dOli+/t37txZuY4WsfYheAeBlQvqZZddZjgKeSAGDRqkXPfcc88FdOxTp05J9+7d5fnnn5etW7fKqlWrpH///tKvXz9JSkqSTp06SevWrSUmJkYyMjKkZ8+e0qlTJ2nQoMEZc2MfPnzY9OBjeXl5QXuCqZrb88ILL1TWxnbo0EF5PLOjm6uCd3JycsQPrmYmeDdu3FhZM2dEVeNN8HZGQUGBclCimjVrSqNGjXTX2RW8S+d1j5Sajl27dpmq7ezYsaNjZVD186bG2zp/g7fVqavcGryPHTtmeN1Uady4sd/7GAXvoUOHyrJly/w+ZjTzNU2byrFjx0yNhH46N/TxPn78uM8uWjVq1JD33nvP8udrxm233aa73N9xW6BG8A4CK19YX9NgBKJTp07KdZMmTZIWLVrI1q1bLR37jTfe0A2mH3zwgeF+v/32m3Tq1Ek0TZPdu3dLhw4dpGrVqpKeni4jR46s8ETT6/XKf/7zH5k6darMnDlTevToIVWqVJH4+HgZO3asXyOF++vbb7+t0OytPNW0PCIio0aNUq4z28TQao13uAWN0xUWFpqarsLqnMWqGu8dO3aE3cXXDYwCh1Hw9ndEW6vBO9z+zc1cHzp37mw4K0WgVP28v/32W1NjkeBM/nZlIXhX9PTTT0tubq7f+1kJ3pmZmYbXUCea9brV888/L0888YTl/Q8dOuT3Plb6eIfLfVFxcbHce++9UrlyZeU277//vuzdu1d27Ngh11xzjaPlUV2fV69eLePHj+d6YAOCdxBUq1bNr+0zMzNtmaNVZcCAAYbrv/nmGznvvPPE4/H4Partm2++ablcBQUFEhMTI82bNy+by/DUqVPy8ssvy0MPPSSnTp2Sjz76SBITE6VPnz4ybtw4GTt2rCxdulRE/uyPNXPmzLIf/b1798rVV18tZ599tnTp0iXgp9YFBQUycOBA5XpVzaqIlD1E0GM2cLi1qfmTTz5partevXpZOv4555yjuzw/P9+vPlUwx2gKpZo1a0rDhg111/nbx9UtwdtMTf9DDz3kaBlUwTs/P99y889o529TW4J3RVbndW7SpInf+yQkJCh/l0T+DB2qAVX9tWbNGunatavk5ORIr169TLd4iwSl3QyNtGvXznC93cE73M+B5557TqZPn264zTnnnCM1a9aUxMREx8tj9ODqiSeekKpVq8rZZ58tjRs3lilTphDELSB4B4E/PySNGjWShQsXSlpammPlycrKMj1vd3Jysukmc5qm+TVKt4re5zV16lQ5++yz5YorrvA57dSLL74o3377rTRq1Ej+/e9/y86dO+Wzzz6T7t27y5IlSyyXa86cOYYtAXx1DejZs6fucrMjQLsxeBcWFpp6Ol6/fn259tprLb2H0Q2V1XlDoaaqwa1fv76kpKQo/z3279/v182t0WCDIpHTx9tXzegLL7zgSH++8po2bar8vD799FNH39uNioqKyh4Im2VlFGgR9wXvwsJC2bJli/zwww9+75udnS3du3e39L6+WlStXbvW0nHLW716tXTo0EE+/fRTyc3NlY8//lgaNWoke/fuLdumpKREfvzxR9Njv4ST0vF8jEyZMsVwvZ3BO9yn1Dt16pRMmzbN53anD9jrJF8tRo4ePSo7d+6UH3/8USZMmCD33ntvkErmHgTvIDh48KCp7SZNmiQ//PCDz9Ft7eDrCVt5999/v89tfvrpJ8OmMnYw+wBg//79cuGFF+rOO3j55ZdL+/bt/e5PqmmaTJw40XAbX8FbNa3YokWL5Morr/Q5Z6hR8I7EPt5HjhyRhIQEn6PGNmjQQJYsWWL5aW9mZqZUqVJFd52qvz6sUwXv0hvbs846S7mvP4M75ufn6y4v/Z5ESh9vo9+1ffv2ye233+54GZKTk5VNDBcuXOj4+7vNunXr5MCBA7rrbrzxRt3lu3btsvRb7Zbg7fV6Zdq0aZKQkCDnn3++3/t7PB556qmnLE+356sJ76pVqywdt9TGjRuV4zTUrl1bXnrpJfnwww+levXq0rhxY6levbq0bt1apk6dKnfeeac88cQTlkf9Dpa3337bcH1mZqZ07drV8KGK2fvl8owGVwvHB7D5+fkya9YsqVy5sqlWd1WrVg1Cqf5Uq1Ytv1rpPv300zJ58uSwu66GM4K3g0pvmswGxq5du/o9GqdVsbGx0qdPH1PbzpkzRxYtWiQLFizQvTH2er3Su3dv5Y1wuFm7dq1cf/31fg3isXnzZp/9zawGb5E/w7dqUItSburjvXfvXuUULqWysrJk3bp1smXLFsNaazNUzc3vu+++gI6LM6mCd+nI2ZmZmcp9/antUNWOl3bpCMfQsXHjRpk/f36FATdV83YPHjzY725KgWjZsqXu8iVLljg6boYbqVrSpKSkyPDhw3XXnTx50lKtt+o6Fu61faVyc3Pl5ptvlkqVKln+Pe7SpYv8+9//Vj7UMGPAgAFlrWX0WBmlW+TPQWKnT58urVq1Mtzu9ttvl6uuuqrCA5uvvvpKxo0bJy+88IKMHz9e6tevLx6Pp+z1n//8J2z+Lc2M1VLa0rJx48bKrnmqB1YqXq9X+RkYNTUP9n3R1q1bZc6cObJ06VLp0qWLDB061GfrTZE/W+wZVazYzePxGI4DpWfixIlSqVIlue666yw9OIk2BG+HvPXWW1K9enV56aWXTG2fnp7u84fZbg899JDpUaKvvPJKGTBggFSrVq3Cj6umafL6669bbiYXKps2bZIxY8bIxo0bTf34+fr7YmJilOGuVK1atQzXz58/X7eWvpSqZjg5OTkimpqXlJTI9OnT5eKLLzbsD19qx44d0rZtW4mLiwv4vY2Cu78XeqgVFhYqu2OUBu+0tDTlNIn+XLRVwbu0m0441XRomiY33XSTtGrVSq655hq54IILym6eVb8//t78BEo1oGdxcXHE/b6HmqqZcMOGDaV58+bK3+sVK1b4/V6qqTgTExPDPnhrmibXXnutvPHGG37tl5ycLMOGDZOCggLRNE2WL18uV111VUBliYuLMxzTxso86/Pnz5eqVas61hy3T58+csstt1QIkZqmySeffCJt2rSR5s2by8CBA+WTTz5x5P3Lv2ffvn0Nt4mJiZHBgweX/b/qoaLZKbVKqZqZi4RPH+8nnnhCzjvvPLnxxhulR48efj3EcXowNT233nqrpf3+9a9/SVZWlqxcuVJWrVolGzZssLlk7kDwdsCePXvkpptu8mufa665xnITKatat24tX3/9tc/BLk7Xq1cvefbZZ2XLli1y3nnnyYgRIxwqobNeeukladWqlWRmZvrsm+SrH2a3bt189suvX7++4frCwkLZtGmTcr1RjbeRcLjJ0jRNBg8eLPfee6+sW7fO5/YdOnSwteuC0YA7zE9pnz179igHWyltPurxeJRN5+yo8S49D8OlpkNEZNasWX4PPGnm4ZSd+vXrp1y3bds2KSkpCYvfkkigCt45OTmSnp6u7E/8j3/8w+/P2ErwDpdWUKtWrfLrYcOAAQNk7969kpeXJ6+99prPa5+/YmJi5N///rfuup07d/r1YHD9+vVBCU2vv/562awxhw4dkkaNGslll10mGzZskE2bNsm8efOkZ8+e8s477zhWhvXr1/us7Z49e3aFbkaqFoD+TsMX7sH7+++/tzz9V//+/R0fWFPPZZddJo899pjl/S+99FK55JJL5IYbbrCxVO5B8HbArFmz/L6wOTmKuZFzzz1X1q5dK2vXrvUrgI8ePVrOP/98U0+Bb7nlFnn99dcDKaajjh07JgMGDJBVq1bJL7/8Iq+++qo8//zzFUKwUfCOiYmRRx991Of7tGjRwrApm4jIFVdcobu8uLhYeYEpvfkI9QXGyOLFi+Xdd981vf0ll1xi6/urmneK/HlDAHsY9UEs/+AplME7FOfDc8895/c+gXav8FdCQoLy4WDfvn2lSpUqUrVqVbnzzjuVYQ9/9jedOXOm7rrS7kjdunXTXf/ll1/K4sWL/Xo/1b+Fr7E/wsGECRP82r5p06ZSs2ZNW1pBqRi1XLvuuutMHePEiRPStm1bu4rk05w5c0RE5N5779VtcVRSUiI33HCDeDwe6du3r+2tvObNm2e4Pi8vTwYNGlRhWTCCd1xcXMhbPvk7Ov/AgQNlw4YN8uuvv8r8+fODMpL56Twej0ycONH0wL/wT3j/KkegoqIivy8mrVq1CsqAakbatWsna9euVY68bVVsbKw89thjykAZLjRNk0suuUTq168vI0aMkFGjRkmLFi3k8ccflwMHDij7YYqIvPvuu4ZzeJdKTEyUrl27Gm5z5MgR3dFwjZrAlQbvcJ6v0t/aPrub2ebk5Cg/n1dffVVZywH/qJ6SV61aVVJTUyv8v57yo/v6ohqMsLSPd6hvuAoKCmTz5s2yfv162bhxo1/7VqpUyWcLGSeophUT+fMBZV5enrzwwgsyduzYIJYqsjzyyCPKdaXB26gmyN+aSSs13iKhfyC7YcMGvwcs8zXyuB0aNmyo7IL3ySefyLFjx5T77t27V9avXy+vvfaaU8XTtWDBAvn1119NPUReuHChtG/fvsKDywMHDsjSpUtlyZIlsnv3br/f32hKtH//+9+6U6mqgrev1oWnM+oqGA59vFVjnqg8+uij0qpVK6lTp45DJTKvadOmtmcCELxttXbtWuXoySotW7aU2bNnh82TaV9zfPvr9ttvlxo1akh2drbUrVtXd5u7775bxo0bZ+v72kHTNHnwwQclOzu7wmBI5d1zzz2Gc3uf7tFHH1X2by3Vo0ePM57i5uXlKbcvbZIdTjV8p/PnBqtly5Zy+eWX214Go5vdkSNHGj45h29//PGHfPbZZ7rr6tWrV+H/VTcVa9asMf1+4dzUfNWqVdK4cWO54IILLNV8NWvWzNFaPZX+/fub2u6111474zfJ6/XKa6+9JkOHDpVHHnkk7EdgdkJhYaGytltEyq6BrVu3Vt7QmumKU57qoWw4B++TJ09KmzZt/N5PNSq4neLj4w3LlpaWJrNmzaqwrKioSIYMGSK1a9eWtm3b+pzL2gn16tUzfQ3btm2bzJgxQ7xer9x7772SnZ0tPXr0kMsvv1zq1q0rI0aMOONYmqYpvzOqlo+xsbHKvt+qa8D27dtN/Q2lwr2p+TfffGN62wEDBviczivYgv0QKSposMWvv/6qpaamaiJi6jVz5kztl19+0YqKikJd9ApOnDihNWjQwPTfYfR67LHHtJKSkrJjT58+/Yxt+vTpo3m9Xq2wsFB7/fXXte7du2v9+/fXnnzySW3gwIG6x61UqZIWExNjSxkDfT3zzDN+f8Zff/21lp6ebnjc1atXn7GPatvc3FxN0zQtLi5Od/3HH38c2JciQF6vV0tJSTH1ed53331afn6+I+V48cUXDd97zZo1jrxvtNA7v0tfI0aMqLDtjBkzdLeLiYnRfv/9d1Pvp/qdmj59uqZpmnbDDTforh8+fLjtf3t5J0+e1GrXrh3Q78prr73maBlVjh07piUnJ5sq46JFizRN+/P8Xrp06Rnrc3JytB9//DEkf0eovPfee4af2VdffVW27dy5c5XnwIkTJ0y/Z926dXWP889//lNbsWKFsizFxcVOfASmXHvttX6fE5deemnQyvf555/7LM/ixYvLtp82bVpA53uoXg8++KBy3XPPPacdPXpUmz17tjZ8+PCy71lmZqb28ssva16vV9M0TTt+/LgWGxure4y5c+cqP2Oj7+aBAwdM/1vt2bNHeZyffvpJu/DCC3XXzZgxw/oXxKRff/3Vr3+P9evXO14mK3z9rqleDRs2DHXRwxLB2yb333+/qS9is2bNtNmzZ4e6uIZ27Nih9e7d2/KPedeuXbUtW7accVyv16s988wz2jnnnKNlZWVpd955p3bw4EFlOU6cOKGNGjVKy8nJ0URES0tL0wYMGKDl5uZqv/zyi9anTx9TgW748OFa7969HQnr8+fPt/w5Gx339EC/bNky5balD2/i4+N11//3v/+1XMZAeL1e7YcfftDefvttn59jQkJC2YXcKdu3bzcsg9FNAowVFxdrTZo0UX62n376aYXtjR4kvfHGGz7f7/Dhw8r9582bp2mapg0aNEh3/bBhw5z4CMrMnz/f8u9J9erVtRdffNHR8vnSq1cvU2V9+OGHNa/Xq/Xv31+5zciRI5Xvc/z4cW358uXa1q1bHT/3g8Hr9Rp+XtnZ2RXC7o8//qjc9uuvvzb9vtWqVdM9xjvvvKOtXLnS53XDaX/88Ye2ePFibffu3Zqmadrvv/+uDGqqV3p6urZhw4aglLeUrzJ16tSpbNvWrVv79ffExMRoV155pV+VNaF4nXXWWcp1Cxcu1DRN0z755BPlNt9//73y8/3tt9+U+51+vTCya9cu5XG2b9+utWjRQnfdU089Zfm7YdaCBQtMf9ZjxoxxvDyBmDVrlta0aVO/vj8Eb30Eb5uoTu7SV+vWrUP6hNmKF154wa+T7PHHH69Qw20Xr9erFRcX696ceb1eraCgQLe2t1atWhU+89L9vV6vYe2cP68vvvjC8t/19NNPGx67PFXtSHp6etk2iYmJutt89NFHlsto1dGjR7Xu3bub/hwvuOCCoJTr5ptvVpYhISFB27FjR1DK4TZvvfWW4b/v6eeu1+vVGjZsqLutmRppo5u9Xbt2aZqmaTfeeKPu+qFDhzryGWjanzXGI0aMMP29X7FiRdlvmxO/nVZMmjTJdPlVtUmlr3PPPVf3Pf71r39pVapUqfBvUlBQEOS/1F6fffaZ4WdxeiuGoqIi5cPSOXPmmH7fypUr6x7j/fff11atWqUsT2Fhod0fQQVer1d7+OGHNY/Ho4mI5vF4tIceekh75513DD+n0gdPX3/9tTZlyhRt6tSpZa26gslX5YPH49GOHTumaZrvkF7+9fjjj1e4Fzl06JB29dVXl61X/XuWvlQtAYP96tKli6ZpmvbUU0/prs/MzDR8oOb1epUt/26++WbT/04///yzsoy//vqr1rJlS91106ZNC+DbYY6qZdfpr969ezteFrucfp0qLi7WSkpKtJKSEq24uPiMF85E8LaB0QW09LVgwYJQF9NvJ0+e1Jo1a6b797Rt27bs1a9fP+3dd98NaVn379+v3XbbbWXlu++++7TDhw8rt//ll18CvvAkJiYG1Cw6Pz/f8Pg//fRT2bb/+Mc/dLc566yzyrZJSkrS3eY///mP5TJaNWbMGL8+y1tuuSUo5SopKfFZllA8qDDr8OHD2quvvqpNmTLFsDYh2IxqfLZt26a7z+233667fcuWLX2+39SpU3X3zcrKKrvZu+mmm3S3GTJkiJ1/uqZpmnbgwAGtT58+ZSHDzKt8s+NwonrIZ+UVExNzxvFXr16tu+24ceNC8Nfa5+GHH1Z+DhdddJHuPqoHFw888IDp9zXqYmTUZPrUqVN2/em69Lof+HqNHj3a0TL547///a/P87l+/fraX/7yF1N/W2pqqnby5Enl+x04cED7/vvvtaKiIuXvaZcuXbQ1a9ZolSpVsu0cDeR18OBB5brLLrvM52eseriRmppqurvFli1blGX47bfftFatWumue/LJJ01/F6waNWqUqc8R0YV/cRv89NNPPn9wDx06FOpiWrJz505t2LBhWp06dbQ0TdVJAAAgAElEQVT27dv71QQonHm9Xq169eoBXXQGDRoUcDmmTJmiPP5tt91Wtp2qFqp169Zl26j6Zn744YcBl9NfderU8euzXLt2bdDKZqZ/4el97MPBtm3bzujX3LhxY23ChAnaN998E7Lmuka/f3379lXu989//lN3n8qVK/v8W1T9t3v16lW2zZAhQ3S3uemmm2z720sZNbfWe3322We2l8EuGzduDOh38fTX/v37y47tq2VEOH8uvnTt2lX5d6maSau6Q1x55ZWm3rO4uNjws1Q95BARwxBohzvuuMOv70liYqKj5bHi/fff1y6++GJbzoP777/f9PvOmTNH9xgPP/ywpmmatmTJElvPUSded911l8+/87nnnlPuv2LFClOf1Xfffac8Rm5urvIhxt///nfT/x7+OHXqlLZ69WrtjTfeMPU5vfDCC46UA+GL4G2D5cuXG55YwXiyBv/dddddli8q6enp2s6dOwMuw+7duw3fp/SBzdChQ3XXl79BU/V3L+2LFSyFhYWmP8fs7GztrbfeCmr5Ro8e7bNc7du3D2qZzLjlllsMy3zVVVdphYWF2u7du33eVOfl5WkbN270axAblR49eijLZNQv06g27o8//jB8z86dO+vud++995Zto+pWcOONNwb8N5fn6xw+/XX33Xfb+v52O3r0qOXfRaPvZv369X1uV6VKlYh8SH3y5EnDQelUHn/8cd3ty7dkMmLUamrdunXamjVrlOv9GcDNCl9Npk9/TZ061dHyBKJbt24BnwNLly41/X56td716tXT9uzZU7bNQw89ZPt5aufrpZde8vl3HjlyRLn/5MmTTX1WRuOFHDp0SLvooouC9n3bs2ePz+43pa+UlBRtwoQJYdPFCMETHnNYRSBN02TRokVy9913G85RvWjRIrn33nuDWDKYNX78eMnMzKywLDMzUzm/ZKkxY8bIt99+a8s8u7Vr1y6bDkxP1apV5ejRo/Lzzz/rrm/YsGHZf4d62oxSf/zxh6ntbr75Ztm3b5/hNF9OqF69us9t1qxZIwcPHgxCacz74IMPDNcvXLhQ4uPjpU6dOmVTCX3++ecVtjl58qR0795dMjIypGXLlpKVlSXnnXee5ObmWi6XaiqZqlWrSosWLZT7lf/unm7MmDGG7/n777/rLi9/7gZrOrGVK1ea3jYnJycsp04sr3LlymXzTdtl4cKFsmvXLp/bHT58WKpWrSrt2rWTw4cP21oGJy1evFgKCgp0161du1a5X9OmTXWX79y5U44fP+7zfVVTiYmIJCUlhXQ6MaP5rvV06tTJoZIEzsrUZ+W1bdtWunXrZnr72NhYWblypTz++OMyYMAAmTBhgqxevVpq1apVts1f//pXSUlJUR6jdu3aMmXKlIDKHQgz02KlpaVJr169dNeZnYLU6nRidl8HNE2T/v37y6ZNm3xu+8orr0heXp5MmjQpbKYSRvDwL27RmDFj5Morr5Snn35aefFr2rSp9O7dO8glg1k5OTmycuVKufrqq6Vp06Zy9dVXy4oVK2T48OG621944YXi9Xrl6aefVs5JbsWePXsM16enp58RoEqVDy+qH/BgzFtc3t69e01t161bN8MbQ6fUqFHD1HZbtmzx67glJSWyYsUKeffddwMKsnpOnTol+/fv93u/Tp06yauvvlr2//fff78sW7aswjZbt26V2267zXK5VPM1p6WlGd5UZGdnKwPeu+++a3hDpXq4U/6hipMPooqLi2Xt2rXy0ksvyaBBg0ztM3DgQFm+fLlkZWUF/P5OM3ooEgxffPGF/O1vfwtpGfyxevVq3eVpaWnSqlUr5X5NmjRRrjPz+2MUvBMSEsJ2Hm89rVu3DnURlKzex9WpU0dGjhwpS5Ys8ftal5SUJOPGjZN58+bJpEmTKoRukT8fMhrNET18+HAZP3685ObmyhtvvGGl+AFp1KiRqe1UD1zWrVtn6t7F6DoRFxcXtAqJ1atXy/r1631ul5CQIDfffLPExsba+v6IHARvCzZs2CDPPvusz+181Zwi9M4//3xZsGCBfPfdd7JgwQJp0qSJjB49Ws4666wK2yUkJMgzzzzjSFBMS0uTc88919K+559/ftl/h0uN99NPP21qu/JlDyYzNd4if9bSma29P3TokPTo0UM6d+4s119/vdStW1dmzZoVSDEr+O233yzvO2LECKlevbpcfPHFyt+tBQsWmP5by9u5c6fy5uiZZ54x3Nfj8Uj79u2V62vXrq0bLE6cOCFHjhzR3af8QxVV6A/0fDh27Jj06dNH2rdvL7fffrvP7S+44ALJzc2VuXPnhuw77y+z54iT/vnPf8rTTz8ddgFRz759+3SXX3755RIXF6fcr379+pKcnKy7bvPmzT7f16hVQHp6esiCt7/HHjZsmMTHxztUmsB17NjRr+1btWolhYWF8uuvv8pLL70kaWlpjpTrnHPOkZKSEunSpUuF5fXq1St7cJWdnS1Dhgzx+Xtsd7mqVatmalvVZ3vkyBHZvn27z/2LioqU64xqvO3+/n/22Wemtnv00UcJ3VGO4G3BjBkzTG1Xp04dh0sCJ2RmZsqGDRtk8uTJMmDAABk7dqx8+eWXcumllzr2nlaeqMfFxclFF11U9v+hDt7FxcUyfPhwmTt3rs9tK1euLM2aNQtCqc5k9oHYtGnTpFatWjJy5Eg5evSoPPXUU3LXXXfJe++9JydPnpRZs2bJbbfdJjNmzJDHHntMli9fXrbvqVOnZOjQofLDDz+ULVu6dKncddddMnjwYJk6daoyTH///fcyfvx4GT16tNxxxx0ybNgw6dq1a0B/8759+2TdunWG29StW1duv/12+eCDD3S/M8XFxWcsVzUzF/kzdPjSt29f5bp9+/ZJcnKyHDp0SEREdu/eLY888ohhk00zNd6BtgAZO3asfPzxx6a2ffLJJ2XVqlWSnZ0d0HsG21VXXRXqIoiIyN133y0xMTGmP29fPvnkE7nrrrvk1ltvlbffftu238YDBw7oLvf1ACMmJkb5MMZM8C49N/RUrVo1ZMH76NGjfm1v9mFtqHg8HtMtjqZPny6rVq0yfOBip5iYGFm6dKm88MILMmrUKHn88cdl8+bNZ3RhGzVqlKxYsULZtPvmm2+2rUwPPPCA6UqKFi1aKB+SmmlubhS8K1Wq5NgD2NP9+OOPPre54IIL5P7777f1fRGBgtynPOLt3bvX9FQOzzzzTKiLiwhhNC+x6nXxxRdXOEb5eXHLv+bNmxeUv8GfKcQGDx4clDLpKS4uDnhEe39ec+fO1R29vkaNGtrWrVsrlE012FKwX+UHKdu3b5/Wp08fLTExUatRo4Y2adKkslHH//rXv+ruX69ePVP/Fmamd7vqqqu0zZs3K+d8Lf86cuRI2bFVZbv22mstf3dyc3OV0zed/tq9e7fl9wm1ffv26Q7W2LlzZ23x4sW6f2/9+vW1unXrOvq9fOKJJ7QnnnhCe++997Tjx4/79Tf9/e9/P+N4ffr00Z5//nmta9eu2rRp0yqMwO4PVXkfeeQRn/uqBs5s3Lixz30XLFigu29qaqqmaZr25ZdfKst29OhRS3+rGb5mevH37wwXvqYhXbx4caiLaMr27du16667TmvTpo125513ar/++qtWUlKiTZs2LeBR3K0MJtykSRPl8XwNFPrRRx/p7hcfH69pmqZ17NhRd/2jjz5q6bNT8fW5NGvWTCsqKrL1PRGZCN5+eu+990z/ALll6i04z+v1+j03Z/lwpGmalpmZqbvde++953j5fY3sX/6VnZ2t/fzzz46Xycjs2bO1mJiYgG4w7Hhdcskl2saNG7Xi4mJt5syZIS9P+dfw4cO17Oxs3XUzZszQnn32WeW+3bt3N/1vMWzYMJ9lUd08lX8lJSVVmIbstttu091u4MCBlr83CxcuNPXZdezY0fJ7hItp06ZV+JtSU1O19evXa16v94zp08466ywtLy9Pu/zyy4P2/WzYsKH2yy+/mPpb9u7dq8XHx5s6rtFI/Ho+/vhj5bGeffZZn/tPnz5duf/nn39uuO///d//6e5Xt25dTdM0bcOGDcpjl39IZad58+aZfjglYu7hRDj5/ffftdjYWN3fn8OHD4e6eLbKz8/XGjVqZPrf8sEHH7T0PqqpH0X+vNYYef/993X3q1y5sqZpmtapUydHv3der1cbO3as4efSu3fvkE33ifBDU3M/bdiwwdR2tWvXlg4dOjhcGriFx+PxexTYnj17nnEMPU4PrpaXlycDBgzwuV2vXr1k8uTJsmXLFmnQoIGjZfLlxhtvlNWrV8t9993nd/89O61cuVJatmwpsbGxPkfyDrbXXntN2bzy7rvvlrvuuku5rz+Dc40dO1ZSU1MNt1ENLlhejRo1KpwDTpwP3377rantAu0aEA7uueeesnNk4sSJ8tNPP0mbNm3E4/HIvHnz5O2335b77rtPnnrqKdm8ebOkp6fLNddcY/r4p4+j4a9t27bJ3/72N1m2bJmMGDFCxo4dq2zu+c4770hhYaGp495xxx2mm6HOmjXrjN/h8tLT030eo3x3odN17NhRhg0bphwxXdXUvGrVqiKiPgdExJGm5nPnzpVrrrnGsPnv6exs4hwM1atXl6lTp56xfMyYMZKRkRGCEjknOTlZvvrqK8MxjYYPHy6jRo2SRYsWyeTJky29j9E1ePbs2Yb7njp1Snd5YmKiiDjbBe/o0aNyzjnnGHaVuPPOO2XhwoUhGUgWYSrEwT+iFBcXm3rql56e7tecjUCp//73v1paWprP71hCQoJWXFxcYV9V7eS7777raJmff/55U+dFuDLT3JmXf6833njDr3+D7777LuD3PH3u9TvuuEN3u/79+1v+rgwaNMhUWb7++mvL7xHJ8vPztWbNmik/l169emlXXnml9vHHH2uapmmnTp3SpkyZotWsWdOW7118fLz2ySefnFGuyy67zK/j7Nixw+ffum3bNs3j8Rge56OPPvJ5HK/X67M8Tz31lO6+9913n+723bp10zRN0zZu3Kg8pt21s4WFhVq1atX8+pxr1qxpaxmC6b333tP69Omj9ejRQ3v55ZddX6Pp9Xq1V155RevevbvWpk0bbfTo0drBgwdtOfaxY8eU3xGPx6MVFBQo933jjTd096tdu7amaZrWuXNn3fUPPfSQY2Uu/3KqZQkiFzXefvjkk0+U6yZOnChLliyR+fPny7Zt26R79+5BLBncomfPnrJ161Z5/fXXDbd79tlnpVKlShWWhWpwtTVr1vjc5u6773a0DIGIiYkxnPInXLVv3z7oU8WZ1a9fP7+2b9q0qV+1ZHpOn5bGifNhx44dPrcZMWKENG/e3PJ7RLLk5GRZtWqV3HjjjWXLKlWqJBs2bBBN0+Sjjz6SDz/8sGzgvfj4eBk/frzs3btXJkyYEPD7FxYWSvfu3aV169bSpk0befjhhyU/P99wLm09ZgZ1evPNN31+ly6++GKfx/E1ur+IyD/+8Q/d5eFQ452bmytjxoyR+Ph4v6dRDPW0dYG45pprZOHChbJ48WK59dZbXV+j6fF4ZMSIEbJ06VJZv369zJw5s+x7FqjU1FRlrbGmaYaj96tqvBMSEkTEuZaApw9ep6dly5aOjWiPyEXwNuHUqVPy1FNPGTYp69Gjh1x22WXSv3//iBvBFuElJydHbr75ZsnPz9ed43LQoEFy6623nrE8WKN3iohs375d7r33Xrnsssvk7bff9rm9mSaXoWQ0UnY4Ovvss+Xzzz8Xj8cjCxcuPOMhTCg98MADlv69Y2NjLTdVTE1NPeOccGJe+507dyrX1a9fXz744AN55ZVXLB/fDdLS0mT27Nmi/TmGjBQXF5t6sDVp0iTbbuS/+uor2bBhgzz22GOSmpoqx48f92v/IUOGSNeuXeWKK66QyZMn63YDMppDWURk6NChppsenz4d1Om2bdum+zeEMnifOHFC/v73v0tOTo7lqaoiOXjDXn/5y1+U66wEbyebmn/11Vc+t/F4PPLQQw9Zfg+4F8HbB6/XK71795Z77rnHcDujflqAFcnJybJy5Ur57LPPZPjw4dKvXz+ZO3eucn7oYPXx/vHHH6Vt27Yyffp0w1Yg5bVu3drWMtjNTB/1UGvSpIlceeWV8uqrr8rPP/9c9u/dp08fWb16tdx4443St29feeaZZ5Sh02nJyckyatQoy/sPHDjQ77KnpaXJpk2b5Oyzz66w3O4broKCAuVc54sXL5adO3eGzTRckeqpp54KdRHKfPrpp/LRRx/JxIkTpVOnTmfc/Pvq7++r1VJ5Zn5/9KYXO3jwoO62mZmZIuJc8C4qKpKePXvKAw88YPkYIuEzbR1Cr0qVKsp1eXl5ynUnT57UXe6rxjuQ7/8HH3zgc5vly5cbTpeJ6MUs7j4sW7aswvy8epo2bSrx8fFBKhGizaWXXmpqDvFgNTWfOXOm8oZPT0pKSkgHMDPjoosukltvvTVsayu/+eYbufDCC5Xr27ZtK23bti37/5KSkpA073/mmWdMz5Ou59xzz5UJEybIY4895nPbDz74QC666CLlXMl2nw+7du1Srjs99MOaa6+9Vt555x1ZsmRJqItSwaZNm+S2226Td999V0RE1q1bZ/h9+Oqrr/xqetyiRQtJSEhQ1t6J/Pn9a9euXYVloarxXrZsmaxcudLy/iIi/fv3V84pjeiTmJgoiYmJukE6kKbmTrQE3LZtm+H6X375RerWrWv5+HA3grcPy5Yt87mN0ei+QLAEK3j7ehB1unHjxkVEP6eXX35ZBgwYIN99950sXLjQ8o3lhAkTZOLEidKiRQv54YcfLJfnoosukiFDhsigQYP8bro9duxY6dixo3zyySfyww8/yJw5c5TbzpgxQzp06CDVqlWTDz/8sEJz6i+//FIOHjwoW7ZsMXy/YcOGyX333SfnnXeeX+XU8+ijj8qaNWsMW1O88sorPmvL7L7hUjUz93g83GTZJDk5WT788EP56KOP5IsvvpD69etL9+7dZfny5XLPPffI0aNHQ1a2f/3rXzJ8+HBp1KiRYd/tRYsWScuWLf0+/qeffmrY1/u33347Y5nqAajTwXvFihV+73PeeefJX//6Vzlw4IC0a9dOrrjiipC1zEF4ysjI0G1V5ERT80BaAhoF70OHDhnW3gMEbx9U05OUV79+fecLAvgQjOBdXFxs2M+1vAEDBsj1118fEc24S/Xo0UN69Oghbdq0MdXK4HTz5s0r+3vnzJkjXbt2NWwmp1KtWjX59NNPJTk52e99S7Vp00batGkjIiKjR48u++9STZo0kW+++UZiY/93GVA1E//LX/4ic+fO1V33yiuvyIgRIyyXU88TTzyhDN6ZmZmmpq2y+4br+++/111ep04dWjzZKD4+Xvr161dhgL4GDRpIjRo1pE+fPiEs2Z+/D75+z6x2q2ndurVUq1ZNOUDZ6cHb6/XKgQMHdLd1Onirphk08vzzzzPoLAxVqVLF7+AdiqbmqtYuY8aMIXTDJx43+uCrpkdE5IILLghCSQBjwRhcbdeuXVJcXOxzu4yMjAohNNL4OqdP/6xnzpwpR48erfD3tmjRQvbv3y8rV66UN998U5YtWyYFBQXi9Xrl008/lalTp+o2y05KSpL/+7//Cyh0n65169aiaZrs3btXlixZIvv375fNmzdXCN1GevToobu8Vq1acsstt9hWzlKtWrVSBphXX33V1M2N3Tdc//rXv3SXn3POOZaOB/906NAh1EUQEZH58+cr18XFxUlWVpal48bFxRl2ddm7d2+F/8/NzVXOTV6rVi0RcS54qwK/ym233Ubohk+lYxOcbvTo0cp9rI5qbvX7X1RUpGxpcsUVV1g6JqILwdtAYWGhbN++3XCb7OxsycnJCVKJALVgDK72xRdfmNqu9MYvUlWpUkXq1Kmju27cuHHy448/ynPPPSdvv/22nDx5UkaPHq07vUhsbKx06tRJBg8eLF27dpWkpCTxeDzSuXNnuf/++2Xv3r2yadMmmTFjhkycOFGef/552bx5s2ODDtWsWVMuu+wyv8PBddddd8bI1HFxcfL88887Mo2Ox+ORDz/8sELgT0lJkd27d5ueqszOB1E//fSTbNy4UXdd165d/T4e/FelShWpVq1aQMcwM3ZAIGrXrh1Q8+m+ffvKtddeq7vu3XfflSFDhsj3338vTz75pOEDn9KuD+EQvD/77DN58cUXLb8XoofRWBmq75yvpuZ2z25h1NqDLAAzaGpuYNu2bVJSUmK4zcCBA10/fyMig9NNzffv3y+DBw82ta0b5jEeNGiQTJ06tcKyhIQEGTZsmJxzzjm2TYXTrFkzadasmS3HckpKSoqsWLFCXn75Zfniiy+kZs2acv311zs6m0P16tVl8eLFcuLECUlISPA70Nj5IMqoT+8NN9zg9/FgTU5Ojt9zRZdatGhR2Qwl9erVs9Rc2hc7RjHu1q2bslvH7NmzZfbs2Yb7JyUllU1pGuqm5tddd52lLjuITkbX1GXLlulOORbspub/+c9/lOsI3jCD4G3A18iqF154oUyYMCFIpQGMORm833zzTbnppptMb683/3ik+dvf/iafffaZrFu3TkT+rL2eOXNm1DYtTklJCclI6UlJSZb2s+t8OHbsmLKPYZs2beSss87yu2yw5pJLLpHvvvtOd11KSork5+frrhs1apT07t1bRP78PuXm5sqhQ4dk69atcurUKencuXPAZWvVqpWMGzcu4OME+vtSr169su9+KGu8GzZsKE888YTl90D0adKkiXKdakCzYDc1HzlypO7ymJgYZVN5oDyCtwGjJ1uLFi2Szp0729oPEwiEU328d+zY4VfoFhHdJ9ORJisrS1atWiVLliyRffv2Sbdu3Ri9OoLYdT588803ynWXXHKJX8dCYAYPHiwvvPCC7vIZM2bIsmXL5NixY7Jy5UpZuHChxMXFyciRI3UfkFetWlXatWsneXl54vF4AvqdrFu3rqxatcryQ6LyAg3e5WcWcCJ4FxcXKx9EDR06VBo3biznn3++XHrppbrdbwAVo+nlfv75Z93lqodtpeeincF7z549yv1q1qwplSpV8vuYiD4Eb4WioqKymq7TPfbYY2VPz4Fw4VQfb70bXSPdunWTjIyMgN4zXMTGxnKuRyi7zgfVDZ+ImO56AXu0a9dOXn75ZRk9enRZE9MePXrIc889JxkZGXLdddeJiPg1yn5GRoZcccUVZzxob9CggfTo0UNeeukln8cYP368LaFb5M8b+EAeBDRu3Ljsv50I3ocOHVLuO3r0aFd0M0JoJCYmSqdOnWTVqlVnrJs1a5b07dtXrr766grLVbOGlN6D2NnHe8eOHcp1DKwGsxhcTeGHH36QgoIC3XWqEX6BUHKiqbmmafLWW2+Z3r50NHMg1Ow6H4yCNyEj+G699VbJy8uT9evXy2+//SaLFy8O+EHftGnTKtQUZ2VlyZw5c2TSpEllgzSpxMTESM+ePQN6//JiY2PL+mhb4XTwNmpmHki5ARExHFi0f//+Z3TnULW+KP1NsPO+yGhsg8mTJ/t9PEQngvdpNE2TDRs2KG+oPB6PtGjRIsilAnyzO3h7vV7p06eP7Nu3z+e2NWvWlO7du8uKFStcU9uNyGbX+bB7927d5W7oThGpEhISpE2bNlKjRg1bjteoUSP56quvZOnSpTJ//nz55ZdfpF27dpKZmSlTpkwx3PeWW26RevXq2VKOUoGMjN6oUaOy/w528KaPKwJVOge9ytSpU8Xj8cill14qW7dule+//153u9IpJ+28L1J992vVqmV5GkFEH5qan8bj8Uj79u2V688++2yJj48PYokAc+wO3g888IAsWrTI53aTJ0+WBx980NJ7AE6xq4+36sGT3hzsiFwpKSm6c03ffffdctFFF8nixYvltddek99//11E/qyZvuWWW+TZZ5+1vSzVq1eXP/74w9K+Tgdv1ajyqampPlsHAL6YfXizcuXKCq1UTlcavO0c+0YVvLkWwB/UeOuIi4tTrjtx4kQQSwKYZ+cF5vjx4/KPf/zD1LZGD6qAULGrj7cqeAc6pzQiR8eOHWXSpEny22+/yalTp2Tz5s2Sl5cnL730kuH9glV33nmnpf06d+4saWlpZf/vRPBWdb2oXr26peMB5fmq8TbLV1NzK328VU3N6WIBfxC8dRiNTNilS5cglgQwz84LzLfffivHjh3zuV39+vUZ2Rlhya4WIKrgzZyt0Sk+Pl6aNGkiKSkpjr3HX/7yF2nQoIFf+8TExMjEiRMrLHMieG/ZskV3uVHtI2CWXcHbiabmqv7kdLGAPwjeOoz6V1l9Eg04zc4LjGrOzPLatGkjS5cuZQoNhCU7WoB4vV5lLQfBG05JTU2VFStWyCWXXCIej0cSExPlr3/9q+zevVt3yqWaNWvKBx98IF27dq2w3Ing/cMPP+guLz+oG2CVXb+rDRs2FBF774uOHj2quzw9Pd3vYyF60cdbh1HwZh5fhCs7LzBGIzmLiGzdurXswgaEIztagBw/flxKSkp01zGYDpxUq1YtWbFihRw5ckQSExMlISFBREQ++ugjKSgoEK/XKzExMXLw4EGpXbu27vfd7uC9YsUK2bBhg+46gjfskJWVJR06dJDVq1dbPkbjxo3LpvezswueKniX794B+EKNtw6j4O1k8zIgEHZeYIyCd7Vq1QjdCHt2PIgy6m5RuXJlv8sE+Cs9Pb0sdJdKTk6W1NRUSU5Oljp16ii/63YG74MHD0rnzp2V68sP6gYEYvz48YbfXV/ef//9sv+2swsewRt2IHjrMGo6S/BGuLLzAmPU1HzYsGF+Hw8INjseRBG8EcnsDN7z5s0zXE/whl169+4t77//vtSvX9/S/uVbptrZEvDIkSO6ywne8AfBW4dRjXdsLK3zEZ6C1dR83Lhxfh8PCDY7HkQRvBHJ7AzeqrZHtQQAABy9SURBVEHVRES6du1q26BYgIhI3759ZefOnXLgwAEpLi4WTdMMv4OlqlWrVtbMXCQ4fbwJ3vAHwVuHUfAGwpVdF5j8/Hzlk90VK1ZwkUFEcLqpeWpqqt9lAoLJzuD966+/Ktc9+uijfh0LMCszM7OsFWqjRo2kf//+hts3bdq0wv/TxxvhhoSpg+CNSOT09EkiIjVq1PDrWECoOBm8U1JSGM0fYc/O4P3LL7/oLu/SpYt07NjRr2MBVvmaL75Tp04V/t+uLnhFRUVy4sQJ3XUEb/iDhKmDftyIRHY92TUK3kyhhEihOh/saGpOM3NEAjuD9969e3WXDx482K/jAIHw9fDfbPD29/tfUFCgXEdmgD8I3joee+wx3eWXX355kEsCmGfXk11V8E5MTCRwIGI4WePNeYBIYFfw9nq9cuDAAd11vmogATtdccUVynVxcXHSrl27CsvsCt4nT55UrivfpxzwheCto2fPnro3VgMHDgxBaQBz7LrA5Obm6i7PyckJaIoPIJgI3oh2dgXvvLw85Xz22dnZfpcLsKp58+bSvXt33XVDhw49o/bZrpaAqmbmIgRv+IfgrSMtLU2WLVtWNpVBcnKyPPTQQ0yjhLBmV/A+ePCg7vKsrCy/ywSEipPTiRG8EQnsCt779+9XrqtWrZpfZQIC4fF4ZMGCBdKvX78K3+/+/fvLtGnTdLfX429LQII37MLcWApt2rSRHTt2yJ49eyQnJ0fi4+NDXSTAkF1Pdg8fPqy7nOliEEnsuOFSjWJL8EYkMBoo1p/zQNUKSoQabwRf5cqV5f3335e8vDzZtWuX1K9fXzIyMnS3tatCguANuxC8DXg8HqlTp06oiwGYYteTXVXwrlKlit9lAkKFpuaIdkbB244a7+TkZElOTva7XIAdMjIypHnz5obbELwRbmhqDriEXReYQ4cO6S6nxhuRhKbmiHZ21Xirgje13Qh3Tvfx9ng8tIiFXwjegEvYFbyp8YYb2NECRBW8mbcVkcCojzfBG9HA6T7eiYmJDDoLvxC8AZdwusab4I1IQlNzRDuna7wZWA3hzunpxGhmDn8RvAGXYHA14H9oao5o53Qfb2q8Ee6c7uNN8Ia/CN6AS9jVpIoab7iBk03NCd6IBPTxRrRzuo83wRv+IngDLmHHk92ioiI5fvy47jpqvBFJaGqOaEcfb0Q7p/t4E7zhL4I34BJ2BI28vDzlOmq8EUkI3oh2Ts/jTfBGuKOpOcINwRtwCTuaVKmamYsQvBFZVOeD2cBRWFgohYWFuusI3ogEdvTx1jRNDhw4oLuOwdUQ7gjeCDcEb8Al7GhSpRpYTYSm5ogsgd5wqWq7RQjeiAx21HgfOXJEioqKdNdR441w53Qf78TERL/LhOhG8AZcwo4nu6rgHRsbKykpKZbKBYQCwRvRzo4+3qr+3SIEb4Q/u/p4M50Y7ELwBlzCjuB95MgR3eXp6emGN3FAuAm0puPo0aPKdWlpaZbKBARboMHDqPtRZmampTIBwUJTc4QbgjfgEnZcYFRhIz093VKZgFAJNHBQ4w03CPQBlKoVVKVKlTgPEPYI3gg3BG/AJQIdTErEuMYbiCSBBg5V8I6NjZWEhATL5QKCKdDrgip4V61alVZQCHt23BeJELxhH4I34BJONjWnaS0ijVOBIyMjg8CBiBHoeaBqas4sF4gE1Hgj3BC8AZdwuo83EEmcChyM7o9IEmiXC9UDKII3IgHBG+GG4A24BMEb+J9Ag/fBgwd1lxO8EUkC7XLBAyhEMrumE2NUc9iF4A24hB0XGII33MKp0ZwZyRmRxKkuF9R4IxLYNZ0Y83jDLgRvwCXsuMAQvOEW1PQBdLlAdKOpOcINwRtwCZqaA/9DU3OAPt6IbgRvhBuCN+ASTs7jzajmiDTU9AG0/EB0s6uPN8EbdiF4Ay5BjTfwP04Fb/p4I5LQxxvRzOk+3gRv+IvgDbhEoDdYmqYRvOEa1HgDzOON6GZHhYSmaQRv2IbgDbhEoBeYEydOSHFxse46gjciTSA1HV6vl+ANVwjkPDhx4oRyGiXOA0QCO4J3UVGRcnuCN/xF8AZcItALjKq2W4TgjcgTSE3f0aNHldvR1ByRJJA+rqoBBkU4DxAZ7OjjrartFmE6MfiP4A24BMEb+J9AbrhUtd0iNLFFZAnkARTBG5HOjj7eRsGbGm/4i+ANuESgT3YJ3nCTQGu8VTIyMiyXCQg2p4I3D6AQCexoak7whp0I3oBLBPpkVxW8PR6PpKSkWC4XEApOBW+m1kMkCeS6oAreaWlpEhcXF1C5gGAgeCPcELwBlwj0AmM0h7cqxADhyug76+ucUJ0LSUlJBA5EFCf6eNPMHJHC6T7eBG/4i7tpwCWc6uNNM3NEIqPg7au2z+ghFBBJnGhqTvBGpKCPN8INwRtwCaf6eBO8EYlUN1wivm+6VOcCwRuRhuCNaGZHU3PVlHoxMTG0gILfCN6ASzjVx5vgjUjkRFNzgjcijRN9vAneiBR2BO/8/Hzd5UlJSYYPeAE9BG/AJQK9wKimUGIUZ0QimpoD1HgjutnRx/vw4cO6y7k3ghUEb8AlAg3eBw4c0F2elZVluUxAqBC8AQZXQ3Szo4+3KngzpR6sIHgDLuFU8M7OzrZcJiBUCN5AYDXeqlZQBG9ECjuamqvOA4I3rCB4Ay4RyA2WiMj+/ft1l1PjjUhE8Abo443oZkfwVtV4V61a1VKZEN0I3oBLBNqXiabmcJNARjVXBW8GGkSksfpA1uv1KgMHwRuRwo4+3tR4w04Eb8AlAqnZ8Hq9kpubq7uOpuaIRNR4A9aDR15envI8IXgjUjjZx5sab1hB8AZcIpAnu/v375eioiLddTVr1gyoXEAoBDKdGPN4wy2s1nirmpmLELwROejjjXBD8AZcIpA+3rt371auq1OnjuUyAaFCjTdgvcaP4A03oI83wg3BG3CJQJpU7dmzR3d5fHw8fbwRkQjegPUHsqqwERsbK6mpqQGXCwgG+ngj3BC8AZcI5AKjCt61atUyDDBAuLIavE+ePCknTpzQXcfgaog0Vq8LRgMMGg1cCISTQPt4Gw0ySPCGFdxRAy4RSFNzVfCmmTkildXgrbrJEqGJLSKP1esCI/vDDQJtan7s2DHluUJTc1hB8AZcIpAnu6o+3rVr1w6oTECoWJ1OTNWsUIQbLUQeu4M33S0QSQIN3kYPYqnxhhUEb8AlnGhqTvBGpLI6qrnRoFIEb0Qaqw9kGdkfbhBIS0ARgjfsR/AGXCKQC4wqbNSoUSOgMgGhYrWpuarGOyUlRRISEgIuFxBMdvfxJngjkjhZ452RkWGpTIhuBG/AJQJpaq66yeLCgkhld/CmthuRyOiBrKZp8vXXX8vPP/8sxcXFFdYTvOEGgQbvvLw83eWVK1eW2NhYy+VC9CJ4Ay4RSFNzbrLgNlaD97Fjx3SXM6gUIpHqPNi+fbu0aNFCWrZsKQ0bNpSOHTtW6HLENQFuEOh0YoxoDrsRvAGXsNrUXNM0brLgOlaDd35+vu7ylJSUgMsEBJuqxm/OnDmyadOmsv//4osv5M477yz7/+PHj+vuV7lyZXsLCDgo0OnEVMGb1oCwiuANuITVC0x+fr7y6S/BG5HK6qjmBG+4idEDqNMtWrSo7CEs5wHcwKmm5tR4wyqCN+ASdg+iI0LwRuSixhvwL3gXFxeXTS3JeQA3cGpwNYI3rCJ4Ay5h93ytIgRvRC6r04kROOAm/gRvEZF9+/aJCOcB3MGpPt40NYdVBG/AJaw2NSd4w42o8QaMu1zoKQ3eBQUFuuuTk5MDLhMQLIH28aapOexG8AZcwu6m5h6Ph7CBiGU1eKsGlUpNTQ24TECw+Vvj/f3334sID6DgDjQ1R7hhEjrAJaw2NT9y5Iju8rS0NL9rS4BwQY034H/wnjJlimzbtk0ZODgPEEkI3gg31HgDLmF3U3OamSOSEbwB/4O3iMjcuXOV6zgPEEkC7eOtampOH29YRfAGXMLupuYEb0QyphMD/O/j7Qt9vBFJAunjrWkaNd6wHcEbcAm7RzVPT08PuExAqDCqOWCtxtsI5wEiSSBNzY8ePSqFhYW66zIzMwMqF6IXwRtwCZqaA/9DU3OA4I3oFkjwLh3hX09OTo7lMiG6EbwBl6CpOfA/ZoN3UVGRrFu3Tnbt2iWaphG84SoEb0SzQPp45+bmKtdVq1bNcpkQ3RjVHHAJq03Njx07pru8cuXKAZcJCBWjwDFy5EipU6eOfPnll7J///6y5VdccQXBG65id/CmjzciSSB9vFU13ikpKVwPYBnBG3AJqxeYgoIC3eVcWBDJjALHd999J999990ZyxctWqTch/MBkcjO4O3xeCQxMdG24wFOc6KpOc3MEQiamgMuYbVJlSp4U7OBSGb3aM4Eb0SiSpUq2XaslJQU288rwEkEb4QbgjfgElabmhO84UYEb8D+4A1EkkD6eBO84QSCN+ASdjc1J3gj0tnZzJbQgUhE8EY0c6KPN8EbgSB4Ay5BU3OgIjuDd2pqqm3HAoLFzuDNNQGRxomm5oxojkAQvAGXoKk5UJGdwTspKcm2YwHBQo03olkgwfvQoUO6ywneCATBG3AJmpoDFdkVvNPS0myflgkIBoI3olkgfbyPHz+uu5ypVhEI7iQAl6CpOVCRXQOspaen23IcINgY5wDRLJA+3vn5+brL6XaEQBC8AZew0tTc6/XKiRMndNcRvBHp7AodBG9EKvp4I5oF0tRcVePNAygEguANuISVJ7snT55UruMmC5HOruCdkZFhy3GAYKOpOaKZ1eBdWFgoxcXFuus4DxAIgjfgElaamquamYsQvBH5qPFGtCN4I5oZXQOM7o1UzcxFaGqOwBC8AZew0tSc4A03I3gj2hG8Ec2MxvmwGrw5DxAIgjfgElaamhO84WZ2hQ6CNyIVfbwRzawGb1X/bhGCNwJD8AZcgqbmQEV2hY6srCxbjgMEm5lzoHnz5nL11Vf73I7AgUhDjTfCDcEbcAlqvIGKCN6IdmbOgaSkJElLS/O5HYEDkcaou5HRvRHBG04heAMu4auPd35+vhw6dKjCOlXwjo2Nlbi4OHsLCASZXX28s7OzbTkOEGxmgndycjLBG65kd1Pz+Ph47o0QEII34BKqkHH48GHp16+fVK5cWbKzs6VXr16Sl5cnIurgTW033IAab0Q7s8HbzDgGXBcQaexuas7DJwSK4A24hOoCU1JSIh988IFomiZer1c+/vhjGTx4sIgQvOFudgVvarwRqexsal65cmU7igQEjd1NzZlKDIEieAMu4U+z2v/+979y8OBB5cWF4A03oMYb0c7OpubVqlWzo0hA0Ng9jzc13ggUwRtwCX+Ct9frlS1btlDjDVczc06MGjXK5zYEb0QqO4N3Tk6OHUUCgsZqjbeqjzfBG4EieAMuYdSXSU9eXh7BG65mJnQ0bNjQcH1qaqokJibaVSQgqMw8fDITvD0eDw+gEHHsbmpO8EagCN6AS/g7gvO+ffsI3nA1M8G7Xr16huvp341IZuYcSE9P9xm8s7KyJDY21q5iAUFBH2+EG4I34BL+Bu/c3FyCN1zNTOjIysqSpKQkw/VApDJzDqSlpfkM3rVr17arSEDQ0NQc4YbgDbiEv03NCd5wOzMPo5KSkgz7rtKvFZHMrhrvCy64wK4iAUFjdF9EU3OEAsEbcAl/a7w3bNggR44c0V1H8IYbmAkdiYmJUr16deX6Jk2a2FkkIKjM1nhXqVLFcJtmzZrZVSQgaIzui0pKSmTjxo2yZcsWKS4urrCOpuZwCsEbcAl/g/fnn38u77zzju46gjfcwGzwrlOnjnI9gQORzGyNd0ZGhuF5cM0119hZLCAojO6LmjdvLq1atZLzzz9fOnbsKDt37ixbR403nELwBlzC36bmRgjecAOzwbt9+/bK9UbrgHBntsZbRKRz586666+//nqfgxAC4cgoeO/bt6/sv7/44gsZPXp02f/TxxtOIXgDLuFvjbcRgjfcwGzw7t69u+66jh07Sv369W0uFRA8/gTvMWPGnLEuPj5eHnzwQdvLBQSDP/dFH374ofz+++8iQo03nEPwBlyC4A1UZOacSExMlKZNm8r1119fYXl8fLy88MILThUNCAqzTc1FRFq2bClvvPFGWX/v2rVry+zZsxnnABHL3/uiTZs2iQh9vOEcJmUEXIKm5kBFZkJHQkKCiIjMmTNHOnToIJ9//rmkpqbKmDFjCByIeP7UeIuIDBkyRK6//no5evSopKWlSXx8vJPFAxzlb/D+7rvvpGfPnjQ1h2MI3oBLUOMNVOQrdMTHx5edNzExMXLHHXfIHXfcEYyiAUHh6xyIi4uTxMTECsvi4+OZvx6u4O990bfffisiNDWHc2hqDrgEwRuoyFfoOD1wAG7j6xxIS0uztbUUEE6sBG9N02hqDscQvAGXsPPmiYsL3MDXTRfBG27nK3iX9u8G3Mjf4P3TTz9JQUGBaJqmu54abwSK4A24hJ013pmZmbYdCwgVarwR7XxdF8r37wbcxt8KiVOnTkmjRo2U6wneCBTBG3AJgjdQEcEb0Y4ab0QzK/dFe/bsUa4jeCNQBG/AJexsal61alXbjgWECqED0c5MH2/AreyskBChGx4CR/AGXMLOC0zpPK5AJPN1ThC84XaxscaT13AOwM3sDt7UeCNQBG/AJey6wKSmpjJ3K1yBGm9EO1/dKajxhpvZHbyZ8QWBIngDLmFXU3P6d8MtfAXvjIyMIJUECA1fNXQEb7iZ3dOs2h3kEX34BgEu4StkmEX/brgFNd6Idr5q6DgH4GZ2BmWamcMOBG/AJQjeQEX08Ua0o8Yb0YzgjXBD8AZcwtcgOqU+/fRTw/U0NYdb0NQc0c5XWODhE9yM4I1wQ/AGXMJsjfe5555ruJ4ab7gFTc0R7RISEgzH/6DGG25mZ/BmKjHYgeANuITZGu/q1atLVlaWcj3BG25B8Ea083g8ommacn1OTk4QSwMEl12DzopQ4w17ELwBlzBT4x0fHy8xMTHSuHFj5Tb16tWzs1hAyBC8AWM1a9YMdREAx9hZ4831AnYgeAMuYabGu3RO1/bt2yu36dChg21lAkLJ100XfbwR7ajxhpvZGbwZ/wZ2IHgDLmGmxrs0eA8fPlz3gtSgQQPD2nAgklDjDfzZvUglLi4uiCUBgsvOpuZ0w4MdCN6AS/hT492wYUN5+umnK6yLi4uT6dOn2/qEGAglgjcg0rt3b93lV111VZBLAgSXx+OxLXwTvGEH7rABl/AneIuI3HXXXbJ8+XK544475J577pE1a9ZIv379nCwiEFS+gjcjOiMajBw5UpKTkyssi4+PlzFjxoSoREDwmKlMmDx5ss9taGoOO5gbBhlA2POnqXmpLl26SJcuXZwqEhBSRudESkoKzWwRFdq0aSPLly+X5557TjZu3ChNmzaVO++8Uy655JJQFw1wXExMjJSUlBhu07x5c5/HqVKlil1FQhQjeAMu4W+NN+B2SUlJynU0M0c0adu2rbRt2zbUxQCCzkyNd8OGDSU1NVWOHz+u3IYZX2AHmpoDLmGlxhtwM6N5VwneAOB+ZoJ3UlKS4WwvIiLnn3++XUVCFCN4Ay5h5uJC8EY0MQreTCUGAO5n9t6oa9euhtucPk4CYAXBG3AJj8fjs9ab4I1okpqaqlxHjTcAuJ+Z4J2QkGA42GCPHj3sLBKiGMEbcBFf/bwJ3ogmNDUHgOhmZjqxhIQESUhIUM7sMmTIELuLhShF8AZchBpv4H+MaryZSgwA3M9MjXd8fLyIiIwfP/6MoN6kSRO59tprHSkbog/BG3ARXzXeRqM8A25DjTcARDdfwTs5ObksbLdp00ZWrVolnTp1ksaNG8vNN98sS5cuNTVrDGAG3yTARXzVeFPLh2hiVOOdmZkZxJIAAELBTPAur0OHDrJy5Uoni4QoRo034CK+nsoSvBFNSpsP6iF4A4D7+QreRi2jALsRvAEXocYb+J9atWopw3fPnj2DXBoAQLD5W+MNOIngDbgINd7A/yQnJ+tOA9OpUyepU6dOCEoEAAgmarwRTgjegItQ4w1U9M9//lPatm1b9v/NmjWTOXPmhLBEAIBgocYb4YTB1QAXocYbqCgrK0vWrVsnP//8s3g8Hjn77LNNzesKAIh81HgjnBC8ARfxVePNFEqIVuecc06oiwAACDKCN8IJTc0BF/FV481IzgAAIFr4auFEU3MEE8EbcBGj4O3xeKR27dpBLA0AAEDoUOONcELwBlzEqKl5jRo1DOc1BgAAcBMGV0M4IXgDLmJU4123bt0glgQAACC0qPFGOCF4Ay5iVOOdk5MTxJIAAACEFjXeCCcEb8BFjGq8s7Ozg1gSAACA0KLGG+GE4A24SEZGhnJdVlZWEEsCAAAQWtR4I5wQvAEXqVmzpnIdwRsAAEQTarwRTgjegItUqVJFuY7gDQAAognBG+GE4A24SIMGDZTrGjVqFMSSAAAAhBZNzRFOCN6AizRv3lx3ec2aNaV169ZBLg0AAEDoGA06K0KNN4KL4A24SOvWraVly5ZnLH/yyScNpxoDAABwm/j4eMP11HgjmAjegIt4PB5ZsmSJDBgwQKpVqyb16tWTZ599Vm644YZQFw0AACCofAVvarwRTMbtLwBEnMzMTJk3b16oiwEAABBS1HgjnFDjDQAAAMB14uLiDNdT441gIngDAAAAcB1qvBFOCN4AAAAAXMcoeCckJDDwLIKK4A0AAADAdYyCN83MEWwEbwAAAACuYxS8aWaOYCN4AwAAAHAdarwRTgjeAAAAAFyHGm+EE4I3AAAAANehxhvhhOANAAAAwHUI3ggnBG8AAAAArmMUvDMyMoJYEoDgDQAAAMCFjIJ3lSpVglgSgOANAAAAwIWo8UY4IXgDAAAAcB1qvBFOCN4AAAAAXIfgjXBC8AYAAADgOunp6cp1NDVHsBG8AQAAALhOx44dJTExUXdd8+bNg1waRDuCNwAAAADXqV69ukydOvWM5T169JCGDRuGoESIZh5N07RQFwIAAAAAnDBnzhx58cUXJTc3V3r37i1Tp06V5OTkUBcLUYbgDQAAAACAg2hqDgAAAACAgwjeAAAAAAA4iOANAAAAAICDCN4AAAAAADiI4A0AAAAAgIMI3gAAAAAAOIjgDQAAAACAgwjeAAAAAAA4iOANAAAAAICDCN4AAAAAADiI4A0AAAAAgIMI3gAAAAAAOIjgDQAAAACAgwjeAAAAAAA4iOANAAAAAICDCN4AAAAAADiI4A0AAAAAgIMI3gAAAAAAOIjgDQAAAACAgwjeAAAAAAA4iOANAAAAAICDCN4AAAAAADiI4A0AAAAAgIMI3gAAAAAAOIjgDQAAAACAgwjeAAAAAAA4iOANAAAAAICDCN4AAAAAADiI4A0AAAAAgIMI3gAAAAAAOIjgDQAAAACAgwjeAAAAAAA4iOANAAAAAICDCN4AAAAAADiI4A0AAAAAgIMI3gAAAAAAOIjgDQAAAACAgwjeAAAAAAA4iOANAAAAAICDCN4AAAAAADiI4A0AAAAAgIMI3gAAAAAAOIjgDQAAAACAgwjeAAAAAAA4iOANAAAAAICDCN4AAAAAADiI4A0AAAAAgIMI3gAAAAAAOIjgDQAAAACAgwjeAAAAAAA4iOANAAAAAICDCN4AAAAAADiI4A0AwP+3X8cCAAAAAIP8rUexrywCABiJNwAAAIzEGwAAAEbiDQAAACPxBgAAgJF4AwAAwEi8AQAAYCTeAAAAMBJvAAAAGIk3AAAAjMQbAAAARuINAAAAI/EGAACAkXgDAADASLwBAABgFDnp9/pkl+RAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if __name__==\"__main__\":\n",
    "    main()\n",
    "    print(\"100% Complete\")"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "1.preprocessing_data.ipynb",
   "provenance": []
  },
  "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}