doubanBlstSpider.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 基于已有书名列表的豆瓣专题爬虫\n",
    "\n",
    "- 需求：根据含有书名的Excel文件逐条查找补充书目信息，爬区豆瓣上的出版社、出版时间、ISBN、定价、评分、评分人数等；整合到pandas进行专题分析。\n",
    "- 爬虫+数据分析：根据书名爬豆瓣获取书籍属性、整合到pandas，进行分析\n",
    "\n",
    "本来打算直接用https://book.douban.com/subject_search?search_text={0}&cat=1001'.format('书名') 查询结构，没找到异步返回的数据json，绕了一圈后发现https://book.douban.com/j/subject_suggest?q={0}  可以用，就用了，通过比较名字和作者等确定url，然后直接爬该图书主页，然后写入字典。再变成dataframe。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T09:36:36.829909Z",
     "start_time": "2019-02-22T09:36:33.730339Z"
    }
   },
   "outputs": [],
   "source": [
    "#引入所需库\n",
    "import json\n",
    "import requests\n",
    "import pandas as pd\n",
    "from lxml import etree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T09:48:58.955320Z",
     "start_time": "2019-02-22T09:48:58.700941Z"
    }
   },
   "outputs": [],
   "source": [
    "bsdf=pd.read_excel('booklistfortest.xlsx') #读取书名Excel数据，为了简化，这里只用了\"书名\"列\n",
    "#bsdf.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T09:49:00.905526Z",
     "start_time": "2019-02-22T09:49:00.895553Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'龙族'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blst=list(bsdf['书名']) #书名列表\n",
    "blst[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "爬虫方面涉及到的：get，xpath去匹配，对于长度不定的一个html区域，不能写死xpath解析式，通过循环去取数，理清其HTML树结构；\n",
    "\n",
    "一般解析是用BeautifulSoup或xpath，我更喜欢用xpath，因此下面的代码主要基于xpath解析文本。\n",
    "以评分为例，鼠标点击评分部分，然后按Ctrl+Shift+I，或者右键点击检查元素，反正就是定位到评分对应的HTML上，定位到评分的代码部分后，右键，选择Copy->Copy XPath ,例如对于评分来说有：//*[@id=\"interest_sectl\"]/div/div[2]/strong 通过con.xpath('//*[@id=\"interest_sectl\"]/div/div[2]/strong/text()')就可以得到评分数据，返回的是列表，一般就是第0个值。同样，其他地方也是这样，而作者、出版社那几个属性是结构比较散的，需要特殊处理。\n",
    "\n",
    "通过//*[@id=\"info\"]/span[2] 可以确定 出版社 这个属性，但是属性的值，具体是哪个出版社不能确定，这些文字是在info这个节点上的。还是需要建立info的树结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T10:19:17.052405Z",
     "start_time": "2019-02-22T10:19:17.045424Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' 5.9 ']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.xpath('//*[@id=\"interest_sectl\"]/div/div[2]/strong/text()')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T09:50:20.879398Z",
     "start_time": "2019-02-22T09:50:20.871394Z"
    }
   },
   "outputs": [],
   "source": [
    "def getBookInfo(binfo,cc):\n",
    "    i=0\n",
    "    rss={}\n",
    "    k=''\n",
    "    v=''\n",
    "    f=0\n",
    "    clw=[]\n",
    "    for c in cc:\n",
    "        if '\\n' in c:\n",
    "            if '\\xa0' in c:\n",
    "                clw.append(c)\n",
    "        else:\n",
    "            clw.append(c)\n",
    "    \n",
    "    for m in binfo[0]:\n",
    "        if m.tag=='span':\n",
    "            mlst=m.getchildren()\n",
    "            if len(mlst)==0: \n",
    "                k=m.text.replace(':','')\n",
    "                if '\\xa0' in clw[i]:\n",
    "                    f=1#需要m.tag=='a'下的值\n",
    "                else:\n",
    "                    v=clw[i].replace('\\n','').replace(' ','')\n",
    "                i+=1\n",
    "            elif len(mlst)>0:#下面有子span 一种判断是m.attrib=={} 不够精确\n",
    "                for n in mlst:\n",
    "                    if n.tag=='span':\n",
    "                        k=n.text.replace('\\n','').replace(' ','') #不至于下面还有span，懒得用递归了\n",
    "                    elif n.tag=='a':\n",
    "                        v=n.text.replace('\\n','').replace(' ','')\n",
    "        \n",
    "        elif m.tag=='a':\n",
    "            if f==1: #是否可以不用这个if\n",
    "                v=m.text.replace('\\n','').replace(' ','')\n",
    "                f=0\n",
    "        elif m.tag=='br':\n",
    "            if k=='':\n",
    "                print(i,'err')\n",
    "            else:\n",
    "                rss[k]=v\n",
    "        else:\n",
    "            print(m.tag,i)\n",
    "    return rss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "调用上面函数返回的是一个字典，要整合到已有的字典里。涉及字典的组合，查了一下可以用d=dict(d,\\*\\*dw)，其中d是旧字典，dw是要加到d里的新字典，更简便的方式是用d.update(dw)函数，下面的代码就是用的update的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "主体代码如下，这个部分也可以打包为函数，因为jupyter里还是逐步分析方面就没打包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T09:51:31.515042Z",
     "start_time": "2019-02-22T09:51:21.718724Z"
    }
   },
   "outputs": [],
   "source": [
    "rlst=[]\n",
    "for bn in blst:\n",
    "    res={}\n",
    "    r=requests.get('https://book.douban.com/j/subject_suggest?q={0}'.format(bn))\n",
    "    rj=json.loads(r.text)\n",
    "    #对rj进行一下验证和筛选\n",
    "    html=requests.get(rj[0]['url']) #之后再考虑多个返回值的验证\n",
    "    con = etree.HTML(html.text)\n",
    "    bname=con.xpath('//*[@id=\"wrapper\"]/h1/span/text()')[0] #和bn比较\n",
    "    res['bname_sq']=bn\n",
    "    res['bname']=bname\n",
    "    res['dbid']=rj[0]['id'] #不需要存url，存id就够了\n",
    "    #这部分取到info就够了，之后再用高级方法去匹配需要的元素，目前对应不对\n",
    "    binfo=con.xpath('//*[@id=\"info\"]')\n",
    "    cc=con.xpath('//*[@id=\"info\"]/text()')\n",
    "    res.update(getBookInfo(binfo,cc))  #调用上面的函数处理binfo\n",
    "    bmark=con.xpath('//*[@id=\"interest_sectl\"]/div/div[2]/strong/text()')[0]\n",
    "    if bmark=='  ':\n",
    "        bits=con.xpath('//*[@id=\"interest_sectl\"]/div/div[2]/div/div[2]/span/a/text()')[0]\n",
    "        if bits=='评价人数不足':\n",
    "            res['评分']=''\n",
    "            res['评价人数']='评价人数不足'\n",
    "        else:\n",
    "            res['评分']=''\n",
    "            res['评价人数']=''\n",
    "    else:\n",
    "        res['评分']=bmark.replace(' ','')\n",
    "        bmnum=con.xpath('//*[@id=\"interest_sectl\"]/div/div[2]/div/div[2]/span/a/span/text()')[0]\n",
    "        res['评价人数']=bmnum\n",
    "    rlst.append(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T10:07:50.764200Z",
     "start_time": "2019-02-25T10:07:50.665448Z"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pd' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m--------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-7d1ee9147cd3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0moutdf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrlst\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'pd' is not defined"
     ]
    }
   ],
   "source": [
    "outdf=pd.DataFrame(rlst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T10:08:15.037933Z",
     "start_time": "2019-02-25T10:08:15.030947Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T10:26:53.531299Z",
     "start_time": "2019-02-22T10:26:52.374963Z"
    }
   },
   "outputs": [],
   "source": [
    "#一般有多个返回值，简化只取第一个，所以就是用rj[0]['url']\n",
    "#html=requests.get(rj[0]['url'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T10:26:54.546719Z",
     "start_time": "2019-02-22T10:26:54.541733Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://book.douban.com/subject/26943161/'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rj[0]['url']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T10:26:57.895208Z",
     "start_time": "2019-02-22T10:26:57.878254Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Element div at 0x19aad51ed08>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con = etree.HTML(html.text)\n",
    "binfo=con.xpath('//*[@id=\"info\"]')\n",
    "binfo[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "def priceNorm(df):#用apply处理定价列\n",
    "    pri=df['定价']\n",
    "    pric=re.findall('^[0-9]+\\.[0-9]+',pri)\n",
    "    if pric==[]:\n",
    "        print(pri)\n",
    "        return float(pri)\n",
    "    else:\n",
    "        return float(pric[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 简单统计数据分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outdf.to_excel('out_douban_binfo.xlsx',index=False) #输出数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据合并\n",
    "bdf=bsdf.merge(outdf,on='书名',how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(list(bdf['出版社'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outdf.apply(priceNorm,axis=1)\n",
    "#因为存在外国价格，就不直接转float了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "一共有421本书，309个作者，97个出版社；\n"
     ]
    }
   ],
   "source": [
    "# 基本统计值\n",
    "#多少本书、多少个作者（作者国籍)、多少个出版社\n",
    "\n",
    "print('一共有{0}本书，{1}个作者，{2}个出版社；'.format(len(bdf),len(set(list(bdf['作者']))),len(set(list(bdf['出版社'])))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[日]东野圭吾    15\n",
       "刘慈欣        14\n",
       "秦明          9\n",
       "江南          9\n",
       "萧鼎          8\n",
       "雷米          5\n",
       "吴军          4\n",
       "Name: 作者, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#前几位的作者\n",
    "bdf['作者'].value_counts().head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>书名</th>\n",
       "      <th>阅读时间</th>\n",
       "      <th>阅读情况</th>\n",
       "      <th>出版社</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>数学之美</td>\n",
       "      <td>2016-10-20</td>\n",
       "      <td>P5</td>\n",
       "      <td>人民邮电出版社</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>智能时代</td>\n",
       "      <td>2017-06-22</td>\n",
       "      <td>P4</td>\n",
       "      <td>中信出版社</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>硅谷之谜</td>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>P4</td>\n",
       "      <td>人民邮电出版社</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>见识--商业的本质和人生的智慧</td>\n",
       "      <td>2018-10-21</td>\n",
       "      <td>P4</td>\n",
       "      <td>中信出版社</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  书名       阅读时间 阅读情况      出版社\n",
       "104             数学之美 2016-10-20   P5  人民邮电出版社\n",
       "234             智能时代 2017-06-22   P4    中信出版社\n",
       "238             硅谷之谜 2017-07-01   P4  人民邮电出版社\n",
       "385  见识--商业的本质和人生的智慧 2018-10-21   P4    中信出版社"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#前6位都是小说类型的书，可以看一下吴军的是哪些书\n",
    "bdf.loc[bdf['作者']=='吴军',['书名','阅读时间','阅读情况','出版社']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(bdf.loc[bdf['作者']=='吴军',['书名','阅读时间','阅读情况','出版社']]) #print is easy to copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "人民邮电出版社       36\n",
       "EbookPress    30\n",
       "中信出版社         24\n",
       "机械工业出版社       22\n",
       "北京大学出版社       19\n",
       "电子工业出版社       19\n",
       "清华大学出版社       13\n",
       "Name: 出版社, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#前几个出版社\n",
    "bdf['出版社'].value_counts().head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe60d584438>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "press=pd.DataFrame(bdf['出版社'].value_counts())\n",
    "press.plot(kind='bar',figsize=(12,6))\n",
    "#各家出版社的统计分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2015-09'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bdf.loc[0,'阅读时间'].strftime('%Y-%m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_date=bdf.loc[:,['阅读时间','书名']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#r_date=r_date.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#阅读时间按月统计（折线图）\n",
    "bdf['阅读年月']=bdf['阅读时间'].apply(lambda x : x.strftime('%Y-%m'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "read_date=bdf['阅读年月'].value_counts() #每月阅读量，按月计数\n",
    "read_date=pd.DataFrame(read_date,columns=['阅读年月']) #从Series变为DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "read_date=read_date.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe60eb24908>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "read_date.plot(figsize=(12,5)) #没调出来x坐标标签 xticks 不行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe612c52198>]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.xticks(rotation=90)#设置时间标签显示格式\n",
    "plt.plot(read_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "bdf['阅读年']=bdf['阅读时间'].apply(lambda x : x.strftime('%Y'))\n",
    "bdf['阅读月']=bdf['阅读时间'].apply(lambda x : x.strftime('%m')) #这里也可以用.month .year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_dd=bdf.loc[:,['阅读年','阅读月']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_dd['val']=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_ee=r_dd.groupby()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>阅读年</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>阅读月</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07</th>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09</th>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "阅读年  2015  2016  2017  2018  2019\n",
       "阅读月                              \n",
       "01    NaN   2.0  11.0   8.0   7.0\n",
       "02    NaN  10.0  23.0  16.0   9.0\n",
       "03    NaN   3.0  17.0   9.0   NaN\n",
       "04    NaN   1.0  16.0   9.0   NaN\n",
       "05    NaN  11.0   4.0   7.0   NaN\n",
       "06    NaN   NaN   7.0   2.0   NaN\n",
       "07    NaN  18.0  24.0   9.0   NaN\n",
       "08    NaN   2.0   6.0  14.0   NaN\n",
       "09   18.0  11.0   8.0  20.0   NaN\n",
       "10    NaN  24.0   3.0  10.0   NaN\n",
       "11    9.0  45.0   4.0  11.0   NaN\n",
       "12    1.0   3.0   5.0   2.0   NaN"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(r_dd,values='val',index=['阅读月'],columns=['阅读年'],aggfunc=np.sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>阅读年</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>阅读月</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05</th>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07</th>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09</th>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "阅读年  2015  2016  2017  2018  2019\n",
       "阅读月                              \n",
       "01    0.0   2.0  11.0   8.0   7.0\n",
       "02    0.0  10.0  23.0  16.0   9.0\n",
       "03    0.0   3.0  17.0   9.0   0.0\n",
       "04    0.0   1.0  16.0   9.0   0.0\n",
       "05    0.0  11.0   4.0   7.0   0.0\n",
       "06    0.0   0.0   7.0   2.0   0.0\n",
       "07    0.0  18.0  24.0   9.0   0.0\n",
       "08    0.0   2.0   6.0  14.0   0.0\n",
       "09   18.0  11.0   8.0  20.0   0.0\n",
       "10    0.0  24.0   3.0  10.0   0.0\n",
       "11    9.0  45.0   4.0  11.0   0.0\n",
       "12    1.0   3.0   5.0   2.0   0.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_dd=pd.pivot_table(r_dd,values='val',index=['阅读月'],columns=['阅读年'],aggfunc=np.sum).fillna(value=0)\n",
    "r_dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2015', '2016', '2017', '2018', '2019'], dtype='object', name='阅读年')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_dd.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_dd=r_dd.loc[:,['2016','2017','2018']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe60e8cce48>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FAoFRYgh1aEGYkr+oSOgCgcDoMJQ6tCBMyV9UJHSBQGB0qNWhncqwu2JhmJK/qEjoAoHA6Nh1UVGHdqhl+IRuSj1dREIXCARGR1l5h2qDnwmVLoqELhAIjAq1OrQsvEO1wZT8RUVCFwgERoWh1aH5UfuLmkItukjoAoHAqDC0OrQgfF3sxZaLQCAQFAdjUIcWhKn4i4qELhAIjAZjUIcWhNpfNOaecfuLioQuEAiMhh1ht3GwsTS4OjQ/an/Rq0beG10kdIFAYBQ8VodWNrg6ND+mUotuXP9qAoHgmeWxd6hx7Z+D6fiLioQuEAiMAmNSh+ZHkiT8XOzFCl0gEAi0wZjUoQVhCobRIqELBAKDY2zq0ILwdbEn+t5DHmZkGzqUQhEJXSAoKQ/vQsptQ0dhFqjVocbQXbEwTMFfVCR0gaCkrHkDfu0JJtAn25iRZZn1IdH4udrj51re0OEUiin4i4qELhCUhLs3IGIPJF6GmFOGjsakORyRyOmb9xja2tfQoWjEFPxFRUIXCErCmVXKZ5UVnF1r2FhMnHm7rlC5QjkGNPE0dCgaeeQvasQHoyKhCwTFRZYhdAX4tIVa3eDcn5BjvAdlxszJG3c4dDWREe38sLGyMHQ4RWLsTbpEQs9PRir8MQBOLjV0JAJjJfIo3ImAhq9Avf6QEgfXDxg6KpPkh11XcLK35pXm3oYORSuM3V9UJPT8HPsZLm+DTWPg4FxDRyMwRkKWg5Ud+PeGWt3BujycXWPoqEyOs1FJ7AmPZ1gbX+ysLQ0djlYYu7+oSOh5eXgPDsyG6p0g4EXY/h/Y/ZWoYhA8JvMhnF8H/n2gXAWwtoM6vSBsI2SlGzo6k2Le7ss42FgyuGU1Q4eiNX4uxt3TRST0vByeB2n3oMsU6L8IGr4Ke2fAtokiqQsUwv+B9GRoOOjxY4EDIC0JruwwXFwmRnjsfbaej+ON1r5UsDFOZWhBqGvRjfVg1DTuc8qClHg4PF9Zmbs3UB7rM0+5tT48DzJToecsUIn3wGeakBXg4Ak+7R4/5tcB7JyVapc6vQwVmUnx4+4r2Ftb8GZrH0OHUiwe+Ysa6cGoSOhq9s+CrDToOOHxYyoV9PwGrO3h4BzlwLTvj2Ah/tmeSe7HwtWd0OaDJ9/YLayg7gvK3nr6fWUrRlAoEfEpbD4Tw4h21aloZ23ocIqFsfuLiuUmwL1IOLFIqVpwqfnkzyRJ2YLpNBHOrIS1b0CWcR6ICPTMmdUg50CDQU//LHAAZD2Ei/+UfVwmxoI9V7G2VPFWW+MWEhWGrxF3XRQJHZR9coD24wr+uSRBu0+g21cQtglWvqIcjgmeHdS15x5Nn37TB/BqrmzFnBMiI01E3kll3eloBjXzxqV8OUOHUyL8XOy5bqT+oiKhJ1xWbpWD3oKKXprHthwJvecqh1/LgpXba8Gzwa1QuH3hycPQvKhUENgfru6CB4llG5sJ8dO+q6gkiRHt/AwdSonxc7UnI8s4/UVFQt89HSxtoc2H2o1vMgT6/QI3D8NvLygd9wTmT+gKsLCGgH6Fj6kXDDlZcGF92cVlQsQlp7H6eBTBTT1xd7Q1dDglxpj9RYtM6JIkeUmStFuSpDBJks5LkvR+7uNOkiRtlyTpcu5n43J11YaYEKWmuOVIKO+q/fPqD4CXfoPYM7Ckt1IhIzBfsjMV4VDtHmDnVPi4KoHgUlv0dimEn/dFkC3LvNu+uqFDKRXG7C+qzQo9C/hIlmV/oAUwSpKkusB4YKcsyzWBnbnfmxa7poFNRWg1uvjP9X8eBq2ExCuwpCckx+g+PoFxcHk7pCZCg1c0j5Mk5XD05iFIiiqb2EyExJR0/jh6gxcaeuDlZGfocEqF2l/UJBO6LMu3ZFk+lfv1fSAM8AD6AuqGJ0uBF/QVpF64cQiubFdK0GwcSzZHjc7w+l+QfAsWd4e713UaosBICF0O9q7K/3dR1Mvdkjn3p35jMjEWHbhGelYOIzua9uocHvuLGmMterH20CVJ8gEaAUcBN1mWb4GS9AHjtRrJjyzDzi+gfBVoNqJ0c1VrBUM2KErBxT0g/pJuYhQYB6l3IPxfCHxJqTcvCufq4NHE5Hu7hN8JZ+axmaRmppZ6rqTUTH47fINege5UN2IDi+JgrP6iWid0SZLKA38CY2VZTi7G80ZIknRCkqQT8fFGstd8ZYdyqNn+E6UXR2nxaAJv/A05mfBrD4g9W/o5BcbBuT+V/9cGL2v/nHrByu9AfLj+4tIjofGhDN06lGVhy/jh9A+lnm/JoeukpGcxqmMNHURnHBirv6hWCV2SJCuUZP6HLMt/5T4cJ0mSe+7P3YECzRVlWf5ZluWmsiw3dXUtxsGjvsjJgZ1ToWI1aDRYd/NWqQdD/wXLcrCkF0Sd0N3cAsMRshzc6oF7fe2fU68fIJnk4ejx2OMM3zaciuUq0t2nO8svLud8wvkSz5eSnsXig9foWtcNf3cHHUZqWIzVX1SbKhcJWASEybL8XZ4fbQSG5H49BNig+/D0wIX1yuqp4+dgqWPZsUsNGLoFbJ3gt75wbb9u5xeULfHhir1cQcpQTVSoAr7tFJGRCTV12xe1j3d3vEtV+6os7b6USS0n4WzjzJTDU8jKySrRnMuO3CDpYSbvmdHqHPLa0ZlYQgdaA68DnSRJCsn96AnMALpKknQZ6Jr7vXGTnaXUnbv6K9UI+qBSNSWpO3rCH8FKhYTANAldAZIF1H+p+M8NDFZMMEzEb3T7je28v/t9/Bz9+LX7r7jauVLBugKfNf+Mi3cu8kfYH8We82FGNv/bH0G7Wq408Kqoh6gNx2PDaOOqRdemyuWALMuSLMv1ZVlumPvxjyzLibIsd5ZluWbu5ztlEXCpCF2ulBl2mggqPdpdObgre+outWDFILiwUX/XEuiHnGwIXQU1ukD5Epz3+/dWhEhnjb/aZePVjXy892PqOddjUbdFVLJ5LCnp4t2FDp4d+DHkR6JToos178rjN0lIyWB0J/NanYPx+os+O0rRzDTYM1M5wCyLFqf2LjBkE1RtBGveUJKDwHS4thfuxxQu9S8K20pQo6vR+42uDl/NhAMTCKoSxE9df6KC9ZOdIiVJ4vPmnwMw7cg0ra3X0rOy+WlvBM19nQjy0SDGMmGM0V/02UnoJ3+F5CjoPEkRgJQFthXh9XXg0xrWvQ0nFpfNdQWlJ2SFok+o1aPkcwQGQ0qs0fqNLjm3hP8e+S/tPdvzY+cfsbMquOLLvbw7YxqN4UD0AbZe36rV3H+ejCY2OY3RnQpoZGYmGKO/6LOR0NPvw75vlYMqvw5le+1y5eGVNVDzOdj8ARwqfRmYQM+kJStdNQP6gZVNyedR+40aWQdGWZaZHzKfWSdn0c2nG7M7zqachebOh4PqDCLAOYAZx2aQlJ6kcWxmdg7z91yhoVdFWtdw1mXoRoXaX/RuaqahQ3nEs5HQjyyE1AToPNkw17eygYHLFBOEbRNhzwyTqn545riwQelt3lCz1H/uzstMWKdBc6D2G72wwWj8RmVZZtaJWSwIXcALNV5gZtuZWKmKFkxZqCyY3HIy99LvMefUHI1jN4TEEHX3IaM71UAqq7thA+BnhAej5p/QU+/AoblQuxd4NjVcHJbWik9pg1dgz1eKAbVI6sZJ6Epwqg6eQYUOScvM5pd9Efxx9CaRdzSoKR/5je7UQ6DFI0fOYdqRaSy9sJRX6rzC1FZTsShGcYC/sz+v+b/G2ktrORVXcPVOdo7M/N1X8Hd3oFMd0xGPlwRj9Bc1/4R+cI6y5dJpoqEjUazr+v4IQcOVrZe/P1SETgLj4e51uHFAOQzVsLrcEx7P/XSlNnvV8cjC5/ProOgSDNwKICsni4kHJrL60mqG1RvG+GbjUUnF//Mf2XAkVe2rMvXwVDKyn3bu+ufsLSISHpj96hwe+4saUy26eSf05Ftw9GeljtitrqGjUVD7lLYeqxySrn9XqY8XGAehqwAJ6muW+m8MjcbZ3pp2tVxZczKSrOxC3pgtrBTj8fAtkG6YW/OM7Aw+2fsJmyI2MabRGMY2GVviZGtnZcfEFhOJSIpg8bknD/lzcmTm7bpCjcrl6R5QRRehGzVqf1Gx5VJW7PtG6cPRwcg6++b3KV35iiITv3VGWNsZErXNnG9bje5V99My2Rl2m+fru/Nqc2/iktPZHa6hT1FgsLInH172fqMPsx4yZvcYdtzcwbigcQyvP7zUc7b1bEt3n+78cuYXriddf/T4jrA4wuPuM6pjdVQq816dqzE2f1Hzta+/cw1OLYXGQ8DJCO2u1D6l1uVh23/gsrocTFKSiUttcK2t+Feqv9ZkriAoPTePwN1r0P5TjcO2nY8jPSuHPg2rUt+zIq4VyrHq+E261nUr+AleLRS/0bNrSqY6LSEPMh/w3s73OBl3kqmtptKvpga3pWIyrtk4DsYc5IsjX7DouUUAzNt9BW8nO3rXr6qz6xg7fi727L0UT3aOjIURvImZb0LfMwNUlkrSNGZavAtNhsKdq0rvkITLkBCutOG9vh+y0h6PtXPOTe61FBWqS27Cd/RStnIEpSN0OVjZg38fjcM2hsbgWcmWxt6VkCSJAU08Wbj3KrFJaVRxLKDMUaVSGnYdma/4jdrrv5QvKT2JkTtGcj7xPDPazqCnX0+dzu9i68IHTT7gi8NfsP7KepzkNpyJSmJGv0AsLZ6d38W8/qLGYNxhngk97gKcWaU4ETm4GzqaorGyAbcA5SMvOTmQdFNJ7gmXHif6Cxue9DK1sgPnGkqSd62dm+xrKb25LU3TWb3MyXwI59dD3T6KdqAQElLSOXAlgRHt/B7tQw8M8mL+nqusORHJ6M6FCGkCByjVVhfWQ9AwfbyCRyQ+TOTt7W8TkRTBdx2+o5N3J71cp3/N/my+uplZJ2bhcs8Od0cb+jX21Mu1jBW1v2hEwgOR0PXG7ulQroLiRlQC0rPTixRalAkqFVTyUT5qPffkzx4k5K7oc5N9fDhEHntSxCJZKM91qfXkqr5KYOkEM+bIxb8hPbnIzor/nL1Fdo5M34aPtxWqOdvTqrozq05EMqpjjYL3j9V+o+f+1GtCj30Qy/Btw4l9EMu8zvNoVbWV3q6lklRMbjmZfhv7k5D5B5+1/wJry2dndQ55ShfjU2hfy/Dtwc0voUedhIuboeOEEu05h98J541/36Cnb08mtphovKVX9i7Kh0/rJx/PeJC7baPeusndxrmyQzkgBqWfzbAdYpsmL6ErlK0rn7Yah20MiaG2WwXqVHmyt/fLzbwZs+I0B68m0LZmAX/YkqQcju6erviNOup+JRt5P5Lh24ZzL/0eC7supIlbE51fIz9+Ff1wyepOnONmfDyjAB+9X9OYMDZ/UfP7i945FexclL3pYpKUnsTY3WPJyM5g9aXVLAhdoIcA9Yy1PVRtCPUHKFU0A3+HUUdgQiy8d1JRy0afFJ6XeUm+BVd3Qf2BGt/kou6mcuLGXfo0fPrQr1uAG5XsrFh5TENNer3+yudzfxU+poREJEXwxr9vkJKZwqLnFpVJMgc4dfMuVy43p6JVVWYcn87DrGerSsvY/EXNK6FH7FG65LX9SNlyKQY5cg6f7f+M2NRYFnVbxAs1XmBB6AJWh6/WT6xljYWlYsDReiy4BSorxWzj6UFhUM6uBjmnyO2WTaG3AOjT4OmEXs7Sgn6NPdl2IZbElEJk/s7VoWpjnYuMLt65yNB/h5Kdk83ibosJcAko+kk64sddV6hka8f0NlOJSonip9CfyuzaxoIx+YuaT0JXGz87eELTN4v99IWhC9kfvZ/Pmn1Gw8oNmdxyMu082zH96HR23jS8bFtnqFTQ+T9Ked7p3w0djeGRZaWzomeQ8oangQ0h0TTyrljo4dfLQV5kZsv8eSqq8EkCB0DsGZ2ZiYfGh/Lm1jextrBmSfcl1KpUSyfzasO56CR2XrzNsDa+tPNuwQs1XmDp+aVcuvtsGaWr/UXTMg3fJtl8Enr4P8pWQodxxT7w2xu5lwWhC+hbvS8DailORpYqS75p9w31nOsxbt+4QntXmCQ1nwOv5rD3ayFkuhUC8WFFrs4vxd3nYux9+hawOldT060CTapVYuXxyMJbqqr9RnXQgfF47HFGbBtBxXL0+sjPAAAgAElEQVQVWdp9KT6OPqWeszjM33OFCjaWDG6lXPejJh9RwboCUw9NJduIe8DrGmOyozOPhJ6TDbumKQ2VGmjukJefm8k3+Wz/Z/g7+T91CGpnZce8zvNwt3fnvV3vceXuFV1HbhgkSdlLv38Ljv/P0NEYlpAVYFEuN9EWzsaQGFQS9CpCNPNykBcR8Q84fv1uwQMqVFGUqGfXlKo52/6o/by7413c7d1Z2n0pVcuXrZjnctx9tpyL5Y1WPjjYKN0aK9pU5JOgTziTcIbVl8xkq1IL1JUuIqHrirNr4fYF6DRB2SvWktTMVN7f/T4qlYrZHWdjY/n0yr6STSUWdl2IjYUN7+x4h9gHsbqM3HD4tIbqnWH/d0r/72eRrAxlpVy7h+IwVAiyLLMxNIbWNVxwraC5nLVXfXcqlLNk5bGbhQ8KHJDrN3q6RGFvv7GdMbvHPOH/WdbM33MVWysLhrb2feLx5/2ep6V7S74/9T1xD+LKPC5DYEz+oqaf0LMyYM+XSp1v3Re1fposy0w5NIWIpAi+bvc1HuU9Ch3rUd6DBV0W8CDzAe9sf6fIBv8mQ+f/wMM7cPhHQ0diGK5sh9TEIvueh0Te4+ad1AIPQ/NjZ21J30ZV+fvsLZIKMz7w7w0qK2UhUkw2Xd1UqP9nWXEj8QEbQqJ5rUU1nOytn/iZJEn8p8V/yMrJYsYx4/eN1wXG5C9q+gn99O9Ky9NOk4pVV70sbBlbrm9hdKPRWokvajvVZm6nudy8f5PRu0aTlleSb6pUbQR1+8LheYpQ6VkjZDnYuyp3KhrYEBKDtaWKbvW06yD4cpA36Vk5rA8pxFTZtpJyjnH+r2L5ja4OX83nBz4nyK1g/8+yYsGeq1haqHirrW+BP/dy8OKdBu+w4+YOdt3cVcbRGQZjadJl2gk9I1U52PNqATW7av2047HHmXViFp29OzOsnvaqvaAqQXzV9itCbofw6b5Pycoxg7a3HSdAZiocmG3oSMqW1DtwaSsEvqRxmy47R2bzmVt0ql350V5xUdTzcKSehwMrjt0s/HA0sL9yhnHjYJHzZeZkMuPYjMf+n10K9//UN9H3HvLnqSgGBXlRuULhxQdDAoZQs1JNvjz6JQ8yDZ/o9I3aMNrQ/qKmndCP/6KY8HaZrLXxc+yDWD7e+zFeFbyY1npasZWg3Xy6Mb7ZeHZH7mb60ekG/w8sNa61lQqPY79AUiErSnPk7FpFOdtQc3XL4auJJKSkPyH114aXg7y5GHufM1GFbM/V6qE0AiuiJj3hYQLDtw3nj7A/eL3u61r5f+qTn/deBWBE++oax1mprJjccjK3U2/zw2nz99H1cy1P0sNMg/uLmm5CT0tSVpU1ukA17fpVZGRn8NHej0jLSuP7jt9T3rrwJkyaeMX/FYYHDmftpbUsDF1YojmMig7jFWHN3pmGjqTsCF2uCKyqBGoctiEkmvLlLOlYTDu1vg2rYmtlwcrjhRyOWtuB//NwYWOhfqNn4s8wcPNAzicoHRM/DfpUK/9PfXH7fhorjkfSv7EnHhVtixzfwLUBA2sPZHnYcs4lnCuDCA2HsfiLmm5CPzRP6TjY6T9aP2XmsZmciT/DtDbT8KtYuh7poxuNpm/1vswPnW/6atKK3ooY6/QySLxq6Gj0z+2LSoVJEavztMxs/j0fS7eAKthYae+9CVDBxorn67uzMSSGB+mFbM3VC4a0ewX6ja69tJY3/n0DK5UVy3ouo5dfr2JdXx/8b/81srJzeLeD5tV5Xt5v/D6utq5MOTSFzBzzVSYbi7+oaSb0lHilMqPuC0rfEi1Yd3kdqy+tZmi9oXStpv1+e2FIksTkVpNp69HWPNSk7T5WWu3unm7oSPRP6AqlE2XgAI3D9oTHcz8tq8DeLdrwcjMvHmRks/lMTMEDqndU/EbziIwysjOYengqUw9PJahKEKueX0Vtp9olur4uufMgg2VHbtC3oQfVnO21fl556/J81vwzwu+Gs+zCMj1GaFiMxV/UNBP6ge8US6+OE7Qafj7xPNOOTKO5e3PGNBqjszCsVFZ82/5b81CTlq+sNDQ79yfEnjV0NPojJ1vplV+zq/KaNbApNAZne2taVy+ZIUVj70rUrFyeFYU17LKwgoAX4OI/kJ5C3IM4hm4dytpLa3kr8C3md56PYznHEl1b1yw+cI2HmdmMLMbqXE1n78509OrI/JD5RN3X0BbBhLG0UOHtZCe2XIrNvUhF3djwFaXHdxHcTbvLB7s/wMnWia/bfY2lSrcdg81KTdpqNNg4KqpbcyVij1JdUoTU/35aJjvC4uhV373EDjySJPFyM29CIu9xMbYQ8VbgAMh6yMmTCxm4eSBX7l5hdofZvN/4fSxUxdvm0RdJDzNZeug6PepVoaZb8UslJUni8+afo5JUTDs6zfQLCQrBGJp0mV5C3/e18rn9uCKHZudk8+m+T0l8mMicDnNwstGPJ6fZqEltKyndGC/9q/hrmiOhK5Q3rVrdNQ7bfkHxDS1udUt++jXywNpCVWhbXdmzOX9U9uSty0upYF2B5b2W06Val1JdU9f8dug699OzGNVRc/MyTVSxr8KYxmM4GH2Qf6//q8PojAc/F3uuJ6aSnWO4NyzTSugJV+D0H8oBXkXvIof/cPoHjtw6woQWE/TeUtRs1KTN3wb7ykrnSnNbSaUlQ9hmpS95EQ3cNoQ89g0tDZXsrelWrwp/nYp6qhtfWlYaEw9PYoa9ijapaSzv9CPVKxZ/S0OfPEjPYtHBa3SuU5mAqqXb/nm59svUc67HjGMzTPfvQwN5/UUNhWkl9N3TwdJG6XdeBDtu7GDRuUUE1wrWqdu5JvKqScfsGmOaalJre8X1/sZBxfTBnLiwXjl7KaKBW2Kub2jvBlV14lg1KMiL5LQs/j33+M4tJiWGwVsGs+nqJkZW78/3cbepcHlHqa+la/44eoN7qZmM6lTy1bkaC5UFk1tNJik9idknzU/Iltdf1FCYTkK/dUaRSrd4t8jDrIikCCYcmECgSyCfNfusjAJUUKtJT98+bbpq0sZDlDsgc1ulh65UzLQ9m2ocVpBvaGlo4edMNWc7VuQ27Dpy6wgDNw8k6n4U8zrP493Wk1G51CpRbxd9kpaZzc/7rtGmhkup71TU1HGqw+C6g/nz8p+cjDupkzmNBWNo0mU6CX3Xf8GmonJwp4EHmQ8Yu3ssNpY2fNfhO6wtrDWO1wfdfLoxrtk401WTWlpDh8+UXuFhGw0djW64e12562gwqEhV8YaQGGq5lX/KN7SkqFQSA4O8OHotkW+PLuTt7W/jYuvCiudX0M6zXa7f6AC4cUjxGzUSVh2PJCElnfd0sDrPyzsN3sGjvAdTD08lIztDp3MbEpfyhvcXNY2EfuMwXN4GbcaCbcVCh8myzMQDE7mZfJNv2n1DFXvtminpg1f9X+WtwLdMV01af6DiUr9rGmSX/C4jNcNI7lBCVwKS8ro0oPYN7duw8O6bJaFXA2dsPVaw9OKPdPbuzB89/6CaQ7XHA+r1B2S9+I2WhIysHBbuvUqQTyWa++q2mMDOyo6JLSZyLekai84u0unc2pAj5xD3IE7nJhxqf1GR0Iti93Qo7wbN3tY4bPG5xey4uYMPmnxAM/dmZRRc4YxpNOaRmnTNJd36SOodlYViMp1wSanbLgHXEh7QbPpOvttuYEsyWVaqW3zbQkUvjUM1+YaWlJvJNxm7700sHc5ice95vmr9zdPNtdR+ozpwMtIFvx68xq2kNEZ1rKGTc4T8tPFoQw/fHvxy9hcikiJ0Pj9AenY6l+5e4t/r/7IgdAGf7v2U4I3BNPujGV3WdtFLe18/1/IGNYwusihbkqTFwPPAbVmW6+U+5gSsAnyA68BLsiwXYtGiA3p+ozSOsi68w9zhmMPMPT2Xbj7dGFx3sN5CKQ5qNemdtDtMOzINJxsnOntrbtVqVPj3Vlrs7pkBgcGKklRLZFlmwrqzpKRnsXDPVYIbe+LtbJgOgdw8rGy5tB9f5NCNoTEafUOLy76ofYzfPx6VpGJknRnMXCez6+JtegS6Pz04MBi2fq74jWqhsdAXYbeSmbXtEt0C3GhfS3/mGZ8GfcqB6AP89/B/WdxtcYnfOJLSk7iWdI1rSdeISIp49Dk6JZocOQcACYmq5avi6+hLc/fmxKXGsTJ8JW092yrbXjrC18WedaejScvMLna7CF2gjcpmCTAP+C3PY+OBnbIsz5AkaXzu90UXhpeUyv7KRyHEpMTw6b5P8XP044tWX+hlRVFS1GrS4duGM27fOH7u+jON3RobOiztkCToPAl+fxFOLlFKGrXkr1PRHLqayJhONfjfgWt8+U8YC19vor9YNRGyXOls6N9b47BLcfcJu5XMlN51S33JHDmHn8/8zPyQ+dR2qs3sDrNxt/fg9127WHE8suCEHtAPtk5QVukdPy91DCUhLTObsStDcLSz4qt+9fX6t+Ri68JHTT5iyuEprL+ynhdrFm5QI8sycalxjxP2vQiuJSufE9MSH42zVllTzbEa/k7+9PLrhZ+jH76OvlRzqIat5eOGYunZ6UQkRTDp4CT+6vuXzjQqef1F/d11cwZTHIpM6LIs75MkySffw32BDrlfLwX2oM+EroH07HQ+2PMBWTlZzO4w22B9ojWhVpMO3jKY93a9x2/df6NGJd0eNOkNv47g0xb2fQONXlPKGovgzoMMpv19gSbVKjG2Sy2sLVV8u+0Sh68m0rKEMvoSk5EK59crRh7lNHfX1NY3tCjuZ9xnwoEJ7I7czfN+zzOp5aRHyWRAUy/m7rpM5J3Up+8CHNxz/UbXKofSBliYfLM1nPC4+/w6NOgpNyJ98GLNF9l4dSPfnviWdp7tcCjnQGRy5KNVtjqBX0u6RmpW6qPnVbCugJ+jH2092+Ln6PcocXuU99BKYVvOohxftfmKQX8PYuqhqczpOEcnb155/UWNMqEXgpssy7cAZFm+JUlS8XqL6ghZlpl2ZBoXEi8wt+Ncnbiep2ZkMXnDeXo3qEo7Hd5uqtWkr//zOu/seIdlPZcZ9NBWa9Sr9EVd4cgCpYlXEUz7+wL307L48sVAVCqJt9r6seJYJF9svsDm0W2wUJVhogr/BzLuF9lZsTi+oZqIuBfB+7vfJ/J+JOObjeeVOq88kSgGNPVk7q7LrDkRyYfPFdB0q14wbBqjdIP0KNs7uYNXElh04Bqvt6hGx9pl8yetklRMbjmZ4E3BvLDhBVIyUsiSHx+ku9m54efox4s1X8TXwRe/ikridrZxLnUCru1Um/cbv8+3J74t8g5BW/Ku0A2BbhubFIAkSSOAEQDe3kWrO4vDmktrWH9lPSPqj6Cjd8dSzyfLMuP/PMvG0Bg2nYnhj7da0KSa7jwb1WrSN/59g3e2v8PSHkuNpvmSRryaKYYMB+dC0DCNhsoHryTw16loRnWsTu0qSt8PGysLPutZh/eWn2bNiUhebqbb3wONhCwHR2+o1kbzsFzf0NGlKNHbeWMnnx/4HBtLG3557heCqgQ9Ncazkh3tarqy+kQU73ep9fSbW90+8PdHSpO0MkzoSamZfLQ6FD9Xez7vWfj2pj7wq+jHhOYTOBB9AF9HX3wdffFz9MPH0Qd7K+07O5aE1+u+zr6ofcw4NoOmbk3xctB8aF4Uan/RqwaqRS9plUucJEnuALmfbxc2UJbln2VZbirLclNXV92teEPjQ/nq2Fe09mjNyAYjdTLnrwevszE0hhHt/KjiYMOwpce5clu3/zG1nWrzfcfvTU9N2mkipCcrSb0Q0jKzmbDuLD7OdozuVPOJn/UKdCfIpxLfbgvnfloZ9cVOvgURu6HBwCL9ZjeGFs83NC/ZOdnMPTWXsXvGUr1idVY9v6rAZK5mUDMvYpPT2HupgD8b20pKJ8hzfxbLb7S0TNxwjoSUdOYMbIitddkf5vWv1Z/ZHWczpvEYelfvTYBLgN6TOSh3CNPbTMdCsuCzA5/pRAhoSH/Rkib0jcCQ3K+HABt0E452JDxM4MM9H+Jm58bMtjN10pXuaEQi0/8J47m6bozvXoff3myOpUpiyOJjxCbpNuk2c2/2SE06bt8401CTVqmnVGEcXQj34woc8sOuy1xPTGX6i4FPnfBLksSk5wNIfJDBvF1l1JHyzCrFian+yxqHlcQ3VE1SehKjdo3il7O/0L9mf5Z0X1LkVlpnfzdcypcrvK1uYHCu3+ihYsVSUjaERLMpNIaxXWpS37NwnYe5UsW+ChNaTCA0PlQndfGG9BfVpmxxBcoBqIskSVHAZGAGsFqSpGHATUCzU4AOycrJ4pO9n5CUnsSynst0smURm5TGqOWnqeZkx6yXGqBSSXg727FkaDMG/nSYN349xqq3W+Joqzv7r24+3Uh4mMCMYzOYeHAirapqZ6OnC9zs3Gjs1rj4dmYdPoPz62D/t0opaR7CY+/z094I+jX2oHUNlwKfHujpSHBjTxYfvMagZt74uOhxBaauPfdsBi6at1GORCQSfz+92EYWl+9eZsyuMcSmxjKp5SQG1NLuz8DKQkVwE09+2R/B7eQ0KjvkaxSW12/Ut22xYiou0fceMnH9OZpUq8Q7RfiEmjO9/HqxN3IvC0MX0sajTama+eX1Fy2Lg+W8aFPlUthpkkEKqmefnM2JuBN82eZL6jjVKfV8GVk5vPvHSVIzslgxvDkV8qzQ6nk48tPrTRm65BgjfjvB0jeb6bS29FX/V0l8mMgvZ3/h74i/dTavNjhYO9DBqwOdvDvRqmqrJ0q6CsW5OjR6HU78Ci1HQSUfAHJyZD5fd5YKNpZM7KW55O+TbrX55+wtvvwnjJ8Ha+6pUipiTkP8RXi+6CZQat/QTsXwDb2RfINhW4dhqbLk126/0rCyds5ZagYGebFw71XWnIx6ui2ttR3U6QUXNkDPb5VWDHogJ0fmo9Uh5OTIzH6pYYn7vpsLE1pM4NTtU4zfP57VvVdr9zdRAH6PDkZTcLLXT8vuwtD7oagu2XJtC79d+I1BdQbRu7rmmmJt+e/mC5y+eY/5rzYusHl/m5oufDugAe+vDOGDVSHMe6WxTqs0xjQew8DaA8nIKaOeFjJcuneJXTd3sTtyNxuvbsTW0pbWVVvTybsT7b3a42Ctodyq/afKQeOemfDiAgCWH7vJyRt3mTWgQZErksoONozsWINvtoZz6EoCrQpZzZea0JVgUU6p7dZAelY2W87F8lyAm9Zv1gkPE3h7u1KT/2v3X5+U8GuJr4s9LfycWHU8knfbV0eV/3cqcACcXQ1Xd0LtHsWeXxv+dyCCIxF3+Lp/fcOJvowIx3KOTG8znbe2vcWsE7OY2GJiieZRly5ejX9Ak2oioRfI5buXmXxoMg1dG/JJ0090MueaE5H8fuQGb7fzo2dBQo9c+jb0IP5+OtP+DmPqpvNM7ROgU8GFm72bzubSBi8HLzp7dyYzJ5MTsSfYeXMnu2/uZsfNHVhKljRzb/bINszVLt9BtkNVaDYcjsyH1u8TZ+PDzC0XaV3DmX6Ntet/MqyNLyuO3eSLzRf4e0xb3ZcxZmUo2xV1emrs/QOPfUO17d2SkpHCyB0juZN2h8XdFpcomasZ1Myb91eGcDgi8eltKrXf6Nk1eknoYbeS+XbrJZ6r68aApp46n99Uae7enMF1B/Pbhd9o59muRCpSQ/qLmsQ9VnJGMmN3j8Xeyp5ZHWZhZVH6vexz0UlMWH+OVtWd+aRb0Sa8b7X14+12fvx2+Abz91wt9fWNASuVFS2rtmRii4lsH7CdZT2X8XrA60SnRPPfI/+l85rOvPbPayw5t4TI5DwHeG0+VPZ4d09j6qbzpGfnMO2FQK3f5GysLPi8pz8XY++z8vhN3b+wy9vg4Z0i+56DIibS1jc0IzuDsXvGcvnuZb7r8B31XOqVKsxuAVVwtLV61Fb3CdR+o+FbIF23lVZqNaiDrRVf9dP+/+1ZYUzjMdSsVJNJBydxJ+1OsZ+v9he9ZoCeLkaf0GVZZsKBCcSkxDCr/Swq25Ve8HD3QQZv/34SF3trfhjUSOu9w3Hd6/BiIw++2RrO6uOFVCiYKCpJRQPXBnzY5EM2vbCJdX3WMbLhSDKyM5h1chY91/Wk38Z+/BjyI+HpCcgtR0HYJqLOHWRMpxqPBBXa0qNeFZr5ODFr2yWSdV3GGLpCcV2q3knjsJT0LK19Q3PkHCYemMjRW0f5ovUXtPHQXNeuDTZWFrzYyINt5+O486CALbd6wZCZqiR1HfJtrhr0mwH1cS6fT0SVfAuO/QK/9YU5gRC2SafXNgXUKtLkjGSmHppaomoVP9fyRCSUfS260Sd0SZJ4scaLfN7ic530QMnOkRmz8jTx99NZ8FqTp3+hNaBSSczsX5+2NV34bN1ZdoYVXL5n6kiSRI1KNXinwTus7r2af/v/yydNP6GCVQV+Cv2J4E3BdE/YzTSnygyqtIq32vqW6BqTetflbmoGP+y8rLvgHyTCpa1Q/yWw0LyjuO18rFa+obIs883xb9hyfQsfNPlAZ+c3oGy7ZGTn8NepAvqge7cEBw9l20VHHLySwP/yq0ETr8KBOfC/LvBdHfjnY6Uvu3UFWPUa7PxvmdbEGwO1nWozptEYdkXuYv2V9cV+vqH8RY0+oQN08u6kdUlYUXy3PZz9lxP4om8ADbyKX3Nrbali4WtNCKjqwKjlpzh5Q39NJo0Fj/IeDA4YzNIeS9n90m6mtJxCTqYbayrY8qVbMt3XtGfq4akciD5AZrb2q+16Ho681MSLJYeu626/8dxayMlUjCyKYENIDB4Vi/YNXXJ+CcvClvGa/2sMDRiqmzhzqV2lAo28K7LyeOTTK0GVSumTfnUnpBb/1j8/j9SgLnZMaJwJu6bDjy3gh8awYzJkZyoCspFH4b0TMHwXNB6slKkufwkemv/vel4GBwymWZVmzDg2g8j7xbsjN5S/qEkkdF2x9XwsP+6+yqBmXqWSn9uXs2TxG0F6U5MaM862ztSy68LVsy/Tq8KPfJ2cTdP0DP6J+Id3d7xL+1XtGbdvHNuubyM1M7XI+T7qVotylhZM/ztMNwGGLIcqgYoQSgNq39A+DTX7hm66uonvTn5Hd5/ufBL0iV72mwcFeXPldkrBi4PAYMjJUvxQS0NONr+uWM7bD3/hX97DZnEHJVHbOUP3GTD2LLy9F9p9ApXrKD18rGygzw/w/ByI2As/d4DYc6WLw4TIqyL9fP/nxRIAGspf9JlJ6FfjU/hodSgNvCoypU/JRQNqXMqXe0JNGpdsIhL+UpKVncP4v87gXL4c43q1oEerT/n2xiX2NZ7AvE7z6FKtC4djDvPR3o9ot6odo3eN5nzC+ULnq1zBhlEda7AjLI4DlxNKHpgsK1stt0K0OgzVxjf0QPQBJh2cRPMqzZneZjoqST9/Lr3qu1O+nGXBytEq9cGlFpz9s/gTZ6XD5e2wcTRpM2syNnIMgy13YO0eoCTqjy/D0L8Vn96KGhY4TYfC0H+U+RZ1NTrvU32iVpGGxIcUS0X6qElXGfd0eSYSekp6Fm//fpJylioWvNqYcpa6EQep1aT3UjMYsvgYSQ/LqEeJAVly6DrnY5KZ2idAUc42fBWc/Ci3ewbtPdryResv2PXSLhZ3W0xwrWDOxp9l8JbBrLu8rtA5h7b2wcvJlv9uvkBWdk7xApJluLQNFndXtgUcPJT98yLYGKrZN/Rcwjk+3PMhNSrVYE7HOXr1prUvZ0nvBlX5+2zM0wfEkqQcjt44qJi8FEV6iqLmXfsmfF0d/ggm59xf7E6vzbcO45E/uQqvrla2UuyLoQHwagYj9oJ7A/hzmNK3vRTWhKZEL79e9PDpwcLQhRoXJ3lR+4uKFbqOkWWZT9aEEhGfwg+vNKJqxZKpvwpDrSa9Gp/CiN9OkJZpvodHkXdSmbXtEp3rVKaHuomVhRV0nAC3z8N5xQ/TUmVJUJUgxjcbz7q+62js1phJhyYx7ci0AvfYbawsmNDTn/C4+6zQtnooJ1vx31zYFpYPgORoRVU5+mSRiSr63kOOXy/cN/RG8g1G7RyFk40TC7osoLy15j7qumBQMy/SMnPYEBLz9A8DgwH50b/vUzxIhNPLYPlA+NoP1ryhbJHUe5GcQasZ4ryCj3PeZ8CQMVjalaJVRgU3GLwRmo2Aw/Ng2YvwoBR3VSbEhBYTcLZ1Zvz+8TzMKnpf3FD+omaf0H/eF8GWc7GM71GHVtX1o0pUq0mPXrvDh6tDyvxkuyyQZZlJG84hSfDFC/We3EsO6Adu9XINpZ9M2JVsKrGgywKG1hvKqvBVvLn1TW6nPt1lsFtAFZr7OvHdtnCSUjXc6WRlwKnfYV4QrB0KWWnwwgIYc1oRPFkV/Ya9KVRJmr0LMLJQq0BlWWZhl4W42OpJyZqPQA9H6ro7sLKgmnTn6ooVYN5ql6QoOLIQljwP39aADaMg7rzS3viNf+DjS9DnBxbF1WT/tftM7h1ANWcd9M6xtFb6+LywAG4eVfbVY06Xfl4jR60ivZ58nVknZmn1HEP4i5p1Qj94JYGZ/16kV6A7w9v66fVafRt6MLGXP/+cjWXqpvMG6bSmT/4+e4vd4fF82LUWHvnvclQq6PQfuHtNWSnmw1JlyYdNPuSb9t8QfjecgZsHcvr2k0lAXcZ472Emc3cVUMaY8UAx2JjbEDa+p7gPvfQbjDoKDV9R7hS0ZEOI4huaX+6eVwX6Y+cfdWKYoi2SJDGomRfnY5I5G5X09IDAAXArFLZPUpLo7AD4d5yyQm77kbIdMvYsdP8KfFqDyoKwW8l8szVcP2rQhq/AsK3K14u6KYfRZo5aRboqfBX7o/YXOd7XxZ7oew/L9K7dbBN69L2HjF5xmuqu5fk6WL/eiGreauvHCDNTk4JS7jZl4wUCPRx5o5VPwYNqdQOv5rB3JmQWfEva3ac7f/T8A1tLW97c+iYrL6584o0voKojLwd5sYK0r0gAABLkSURBVPTQ9ccGAQ/vKfZ3cwLh3/FKQ7DX/lQSWN2+UMzWyZdzfUP7NnhydZ6ZnfmECjTQNbBY8+qCPg09sLFSsaIg9WxAP1BZwsHvQbKALlPgvZMw6ohSali14ROWdWWiBq3aCEbsAe/msP5d+Ptj5Q7KjHmkIj1UtIpUfTB6PbHsVulmmdDTMrN5d9lJMrNyWPh6E+zLlV3LmvFmqCad8e9F7jxI56t+gYUrKtVWdfdvwfH/FTpXzUo1Wfn8Slq6t2T60elMOjSJ9Oz0Rz//sGttbKwsmLfxEOyYoiTyXdPAoym8uVWptqjRpcR+mxtDn/YNzZFzmHBwAkdvHWVq66k6UYGWBEdbK3oGurMxJIbUjHwHjg7uypvYh2EwfCe0+UBjW+BHatDgAtSgusTeBV5bB61Gw/FfYGnvQvvlmwNqFWlSelKRKlJ1k66y3HYxy4Q+ZeN5zkQlMeulBlR31f+BVl7MTU16/PodVhy7yZutfannUcSBmk8bRW6//ztISy50mIO1A/M6z+OdBu+w/sp6hmwZwq2UWwC4Zsex0vNPvrr5CvKBOUryfnu/Upnh3aJUr0WWZTaEPOkb+kgFem0LYxuPpU/1PqW6RmkZ1MyblPQsNp+59fQPq9RTmqMVwaFcNehrLbzpWIyWwCXGwhKemwb9F0HsGfipHUQe0/91DYS2KlJD+IuaXUJfcewmK49H8l7HGjwXYBgTZmtLFQtea0Jdd0VNeuqmaSrsMrJy+Pyvs3hUtOWDrrW0e1LnSUpjrMM/ahymklSMajiK7zt+z/Xk6wzcFMyxta/A3EYE3PqLHVbtGWo/n6x+i8C9vg5eDYRGJXHzTiq982y35FWBvlnvTZ1cpzQ0rVaJGpXLF3w4qgVJqZl8tEbxBp3QU3Nvep0TGAzDtiuCpF97wonFSlmpGTI4YDBBVYI0qkgN4S9qVgk9JPIekzecp10tV+0TkJ4oX86SX4cG4eZgw5tLTFNN+tPeq1y+ncK0F+ppv21VtRH491HK2h4kFjm8k6UTK1TeVExJZETKGX4L6AxjTmP14o/sSXRkeQkTW0FsCInG2lJF99ySS7UKtJtPN72pQIuLJEm8HOTFqZv3uBR3v9jP/8+Gc8TfN5w3KFXqKfvqfh1g8wewcTRkmp/oTiWpmN66EBVpRqpygH12LZ9YryX46gSY37JMtqLMJqEnpKTz7rKTVHYox9yXG+q+x3YJUNSkzUxSTRoRn8IPu6/Qq7578W/bO01UugQe+K7wMTcOwbL+8HN7fK8dYYXPS3T0bMc3KWGMO/MDbWs50NLPme+2X+JeaukP2tS+oR1ru+JgY8XB6IOPVKBftvlSbyrQkvBiIw+sLCRWFuY5WggbQqLZGBrD+50N7A1qWwleWQVtP4bTv8OvPZQySzPDXbJmQo2XCIkPYfFfLyu/z3MC4cuqyrbTn8N44f5KPNOvQMVqyt+EnpHKsryuadOm8okTJ3Q+b1Z2Dq8tOsrpm/f4891WRe/1ljHnopMY+NNhvJzsdO5Nqg9kWeaVX45yLiaJnR+1p3IFm6KflJ/1IxWJ+JjT4Oihnhiu7ID9s+DmYbBzUazsgoaBjSOyLLPo3CLmnppLzUo1GR0wjTf/F8GQVj5M7l26dg0HryTw6v+OMv/Vxni7J/Lm1jfxruDNku5LykQ4VFxGLT/FwSsJHPmss1ZOStH3HtJ9zj5qVi7P6rdbGo+dXNhmWPcOWJaDl5Yq5yymRE4OJEVCwiXlIz788depyh3op67ObLe3Y1l6BQKc64JrbaVdg0stfg23YOo/Vzj1n66l8heVJOmkLMtFejaajGORJr7eGs6RiDvMGtDA6JI5KGrSha834c0lx/XiTapr1p6M4nBEItNfrFeyZA7QfhycWQ37voZe30HYRiWRx54FB0/o8Q00ek3xz8xFkiTeCnyLOk51GLdvHBOOvUWnxu/y+2GJV5tXo0blkidetW9ojaoPGb6jbFWgJWFQkDd/n7nF1vOxRbop5eTIfLw6VPEGHWhk3qD+z4PLLlj5CiztA92mQ/N3SlylpDey0pU2wgnhkHA5N3GHQ8IVyKsMtXMGl9pQ5/ncxF2bCQ7unNr3PuMdbVnde94TXqQ+dxURXVn5i5p8Qt98Joaf90UwuGU1+jcxXiuttjVdH3mTfrg6hB8G6dabVFckpqQz/Z8wmlarxKCgknekpFI1aPqmUsJ4bT/cuQrONaHvfEUko8H4uI1HG1Y+v5Kxu8dy7O7X2Lp2579/O7N0aPMShaL2DW1ftxxj9ox8pAJ9yl7PiGhV3RkvJ1tWHossMqEvOnCNwxGJzOwfqBs1qK5xraW04l3/rqIliD4Fvb9/4s28zHh4T0nYCeG5STv367vXQVb3EZKgopeSuH3aKfG75K667Z92tnIEprWZxvBtw5/yIlVXukSUkb+oSSf0S3H3+XTtGZpUq1Sk27wxkNeb1KW87r1JdcG0v8N4kJ7FV/0CnzYuLi7tPlaMjq3tYcBS8O+ttRDIq4IXv/f4nSmHp7CFLRxNvsmWCzPoUden2GHsCY/nfkYKERYLuZN2h0XPLSpTFWhJUKkkXg7y5put4VxPeIBPIY5QedWgLzX1KuMoi4GNA7z0u3KXtns6xIfBwGWKUEzXyDIkx+Rbbeduk6TkOZi0sAbnGkpHy3rBj7dKnGsU+82mhXsLXq/7Or9f+J32nu1p69kWAM9Kir9oWTXpMtmEnpyWydu/n8TO2pL5rzbG2tKIbjM18FZbP27fT+fnfRG4OSitY42F/ZfjWXc6mtGdalDTrULpJyxfGT66pMjyS/DGZWdlx8y2M/GvFMB3J7/js8PDqVHlJ2o6Fa+Nw/qQmzhU+4Nbqdf4odMPBlGBloTgJp58t/0Sq05EMq57nad+npaZzQerTMgbVKWC9p8oqtY/hyktDPovghqdSzZfdibcuZabuC9B/KXHSTwjT1VZOcf/t3fvUVVWaRzHv/twVQQFQcALiAnIxXSViImKJKJWpo02KzKzRrvoON6mVhN5yZFZsSpmrGxW45SNZTdnmZXpiHi3pWY6ySCiZiZoegA1wwuXkD1/HEBTEeUcLu/L81nLP3h5j2fvxeHH++53P3vbrrK7DwXf0MvB3S64zl2tbsX0O6az48QO5m6fy4r7V+Dj7tPo+4saMtArKzWzPsni2JmLfPhEP/y96jnO20T+NLwHhcWlvJJxED9Pt2ZxZVX6yyVmf7aPEF8Px/6RucHQys1QSvF4zwmUXAjgzZx5PLQ6mVfj00gISrip1xeXlrPlpzdw8vyO+f1Ta66cjMDfy52E8A78e/dxZg0Nw+WqsfFXMw5ywHqOdx+LadhqUEcLHQpPbIJPxsMHY23rAA2YWfsf/bJzVUMjVz2UPHPEtvlHNa9OtsDuPa5qmKRqqKRNh0YZs3dzciNtYBrJq5OZv30+CxMW2lZdbMT9RQ0Z6H/ffJj1uQXMGxlJ35CGH5dyNItF8fLYXpy+UM7zn2bT3sOVIRH+Tdqm1zd8R97pi3z4RGyzfGA7OTaJTfsqOFCyiGmbpvF0r6eZ3GvyDacbaq15ZkMqTp57ebDbU4zqPqoRW+wYyX27sD63gA25hTXz56EJqkEdrf1tMCkTPp8KG+bbVmxMWgBn86+42q76V3zFOvAWZ/DpZgvrmgeTobav3RxwV2mn6irS9D3pfHb4Mx4IfYBuvh5sOVTEpUrd4M/NDBfomw8Wkp55iNG9O9a+UJQBVFeTJi/eydPL9hAf5kdSVACJEf52TW+qjwPWYhZvPcLYOzs32BLD9lJKseC+gdzz+i9E9FzPW1lvsf/0fl4a+BJertffpGJpzlJ2nFqJ64VBzI6b0sgtdoz4MD8CvNz5+Jv8mkCvqQb1bYJqUEdy9YCxS2zFaOvn2WZC1XyvjS2ouw6whXX1MIl3iN13fQ3t0ahH2frjVtJ2pdEnoA8hvpf3F+3i07APgg0V6MfOXGT6x3sJ9/fkpd80zgqKDamNmzNLf9eXRRsPk5FjZX1uIRYFsSHtGRblT1JUgMM35LhaZaXm+U+z8Wrlwgv3RDToe9mrR4AXD/e9jY92uTDjgRiW5C4k+ctkFiYsJNQ79Ffnrvp+Fel70qkovp1x3X+PxWKMZyxXc3ay8GCfzizadJgTZ0vo2K5VTTXoisn9m6Ya1JGUgrhpEHSX7Srdt7stuL06Nb+pjTepuop0zBdjSNmWwuw73uCZpLBGufM1zKe8pPwST72/B601/xh/p/E/yFV8PFyZOzKSr55LYNXUAUwZ3J1T58t4cdV++qdt5P5FX/HmpsMNtnTAB1/n8W3+WWbfG4F3I98Z1MfMxDBauzrzTXYE7wx7h4sVFxm3ZhwZRzNqzqmuAg1qfTslJ37LqN7Ndzrrzah+xrJ897GaatBpQ0Lp1aUJq0EdrUsMxD5pW9ytbWfDhnm1wDaBpPRLYW/RXrYULmfq3aE1C8I1JENUimqt+ePyLFbu/ZElE2KMOWZ4i74vOk9GjpWMnAKyjp0F4DY/D4ZHBzAsKoCendrafYdSUFxKYvoWenVpx/sT+xrmjuftbUdIXZ3Lu4/FEBUEszbPIqsoi8ejHycxKJFJ6yYR5BlE5YnJnC9xJmPGIMP0rTbj3/mag9ZzlPxyqflVg4rr0lrz3NbnyMzLZNk9y4jyrX+1881Wihoi0N/bcZS5n+cwMzGM6YmhdZ5vNid/LmFdTgEZOVa+/uEMlyo1Hdu6kxRlC/eYrt71+uWevGwPGw8Usm7moOZZkFKL8opKhi/cCgoyZgwCLpG2K43lh5ZjURYCPQJ5Oe6f3L/wfzw7LLxZTQ2trzXZJ5nywX/xcHVizfSBhvp5tWQ/l/3MmC/G0Mq5FctHLv9VFemtME3pv9aab/PPMqRHB/5wt/F/MesjsG0rJvTvyoT+XfnpQjkbDhSydp+Vj3bl86/tR/Fu7UJihD/DowOI6+57U2N1mfsL+M8+K88OCzdcOLg6W3jh3ggmLt3NezvymDgghDl3zSHaN5oV360gNS6VtXttC3pdb99QI0qM8GdopD+je3cy3M+rJWvr1pbUAamkbEshvzifcJ/wBn0/Q1yha60pq6hsltPpmtKFsgq2HipibY6VjbmFnCurwMPVicHhHUiK8ufuHh3wdL92IbDzZRUM/esWvNxd+HLagGvmNxuB1ppHl+wi69hZNj+bcM3MoBGvbcPdxcLKKXFN1EIhLiutKMXduf71Mqa5QgfblDUJ82t5uDkzomcgI3oGUl5RyY4jp8nIsbIup4DV2SdxcVLEdfdlWFQAQyP98a0qPklfdxBrcSmLHr7DkGEOts/EnPsiGfHaNv6WeYgFo6Nrvle9b+i8kQae0idMxZ4wvxWGCHRRN1dnC/FhfsSH+bFgVDTf5v9ERo6VtTlWnv80m5SV2cQE+xDbzYel24/ySGwwdwZ7N3Wz7RLm78m42CCW7czjkX7BhAfYCksu7xsa2MQtFKJxGWLIRdSf1prck+eqZsxYOWA9h7+XG5mz4vG6znCM0Zy5UM7gVzZxe2fbTB2Awa9upot3a5ZNqt/qjEI0N40y5KKUGg68BjgBb2ut0+z5/4TjKaWI7OhFZEcvZg4NI//0RVydLaYIc7DN45+RGMafv9zPhtxCfD3dyDt90RQzW4S4VfUeQFVKOQFvAiOASCBZKSWDls1cUPvWBLQ11mJmdRl/VzDd/Dz4y5pcVuw5/qt9Q4VoSex5ItYXOKy1PqK1Lgc+Boy3+pEwPBcnC3PujeSHUxd4f2dezb6hQrQ09gR6J+DKXWyPVx0TotEl9OhAfJhtB6K6dvgRwqzsCfTr1VJf84RVKfWkUmq3Ump3UVGRHW8nxI2ljo5m4oAQhkSYf2kIIa7HnkA/Dly5M0Nn4MTVJ2mtF2ut+2it+/j5Nd89HIXxdfFpzZz7InFzlpoF0TLZE+jfAKFKqRCllCvwEPBFHa8RQgjRQOo9bVFrXaGUmgpkYJu2uERrneOwlgkhhLglds1D11qvAdY4qC1CCCHsYMyFPIQQQlxDAl0IIUxCAl0IIUxCAl0IIUxCAl0IIUyiUZfPVUoVAXn1fLkvcMqBzWlOzNw3MHf/pG/GZaT+BWut66zMbNRAt4dSavfNrAdsRGbuG5i7f9I34zJj/2TIRQghTEICXQghTMJIgb64qRvQgMzcNzB3/6RvxmW6/hlmDF0IIcSNGekKXQghxA1IoAshhElIoAshhElIoAshhElIoAshhElIoAshhElIoAshhEnYtQWdEEahlHoR6AdUVB1yBnbWcozrHddav9gYbRWiviTQRUvykNb6LIBSqh0wo5ZjtZ0rRLMmQy5CCGESEuhCCGESEuhCCGESEuhCCGESEuhCCGESEuhCCGESMm1RtBSFwHtKqcqqry3A2lqOcYPjQjRbssGFEEKYhAy5CCGESUigCyGESUigCyGESUigCyGESUigCyGESfwfh/a+dzXU6ZAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r_dd.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe60e741e80>,\n",
       " <matplotlib.lines.Line2D at 0x7fe60e741f60>,\n",
       " <matplotlib.lines.Line2D at 0x7fe60e7480f0>]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "plt.legend(r_dd.columns)\n",
    "plt.plot(r_dd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe60e6f8e48>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YROJOZtGkjq8lGQBe+uUl9qbudeiYLQJb8H8d/6/Y18fHx7NlyxY6derEsWPHCAmx93oOCQnh+PHjF31u/fr1mThxIg0bNqRatWoMGDCAAQMGlCn/+WhmWERERCqN3IJC3l17iO5Na9Oqfk1LMkSFa90wQFZWFsOGDWPatGn4+fmV+PlpaWksWrSIQ4cOcfToUc6cOcNHH33k8JyaGRYREZFK46vNSZzIzOW1EW0ty9C4tg+BPp7EHE5jZMeGluUoyQyuo+Xn5zNs2DBGjx7N9ddfD0BwcDDJycmEhISQnJxMnTp1LjrG8uXLadSoEUFBQQBcf/31rFu3jptvvtmhWTUzLCIiIpVCUZHJ7NVxtKrvR9cmtSzLYRgGUWEBVXYTnWmajB07lsjISB5++OHfHx88eDBz584FYO7cuQwZMuSi4zRs2JANGzaQnZ2NaZqsWLGCyMhIh+fVzLCIiIhUCj/sPkbcyTPMHNUOwzAszRIdFsCy3cc4kZlLkK/rWruVB2vXruXDDz+kdevWtG1rn6F/4YUXmDRpEiNGjGDOnDk0bNiQ+fPnA5CSkkJ0dDQZGRnYbDamTZvG7t276dSpE8OHD6d9+/a4u7vTrl07xo8f7/C8KoZFRESkwjNNkzdXHaRhYHWualXX6jhEn1s3HHs4jYHlII8rdevWDdM8fyeNFStW/O2xunXrkpiYeN7rJ0+ezOTJkx2a76+0TEJEREQqvF8OpbI14TR3dm+Eu5v15U2r+jXxdLcRe7hqLpWoSKz/2yIiIiJSRm+tjqOWjyc3RDewOgoAXu5utAmtyaYq3lGiIlAxLCIiIhXavpRMVu49zpgrwvH2cLM6zu+iwgLZdTSdnPxCl973QksUKiNH/LeqGBYREZEK7a3VB6nm4cYtncOsjvIn0WEB5BeabEs47bJ7ent7c+rUqSpREJumyalTp/D29i7TONpAJyIiIhVW0umzLN56lFu6hBHg42l1nD+JCjt3+MbhNDo1dk2rt9DQUBITEzlx4oRL7mc1b29vQkNDyzSGimERERGpsN5dcwgTGNutkdVR/ibAx5OIIB+X9hv28PCgUaPy92dRnmmZhIiIiFRI6dn5fPrLEQa3qUdoQHWr45xXh/BAYg+nUVRU+ZctVFQqhkVERKRC+nBDPNl5hYzv0djqKBcUFRZARk4BB05kWR1FLkDFsIiIiFQ4OfmFvL8unl7Ng4gM8bM6zgVFhwcCEKMWa+WWimERERGpcL6ITeRkVh4TekRYHeWiwmtVp5aPJzE6fKPcUjEsIiIiFUphkcnbP8fRpoE/nRsHWh3nogzDIDo8QDPD5ZiKYREREalQvtuZwuFT2dzVozGGYVgd55KiwwI5kprN8cwcq6PIeagYFhERkQrDNE3eXHWQRrV9GNCyrtVxiiUq3N5vOFazw+WSimERERGpMNYfPMWOpHTu7N4YN1v5nxUGaFWvJl7uNmIOqxguj1QMi4iISIXx5uo4atfw4vr29a2OUmye7jbahPq79PANKT4VwyIiIlIh7D6awepfT3B713C8PdysjlMi0eEB7Dqawdm8QqujyF+oGBYREZEK4a3VB/HxdOPmTmFWRymx6PAACopMtiactjqK/IWKYRERESn3ElKz+WZ7Mjd1akjN6h5Wxymx9g3PbaJTv+FyR8WwiIiIlHtz1hzCZsAd3RpZHaVU/Kt70rRODW2iK4dUDIuIiEi5lnomj3mbjjCkbX1CalazOk6pRYcHEns4jaIi0+oo8gcqhkVERKRc+2B9PDn5RUzo0djqKGUSHRZAZk4Bvx7PtDqK/IGKYRERESm3zuYVMnddPP0i69A02NfqOGUSfe7wDR3NXL6oGBYREZFy6/OYBNKy85nQM8LqKGXWMLA6tWt4Eat1w+WKimEREREplwoKi3j75zjaN/QnOizA6jhlZhgGHcID2KTDN8oVFcMiIiJSLn27I5nEtLPc1TMCw6gYRy9fSlRYAIlpZzmWkWN1FDlHxbCIiIiUO6Zp8taqOCKCfOgXGWx1HIeJDg8EtG64PFExLCIiIuXOmgMn2Z2cwYQeEdhslWNWGKBlPT+8PWzE6PCNcuOSxbBhGN6GYfxiGMY2wzB2GYYx+dzjjQzD2GgYxn7DMD4zDMPT+XFFRESkKnhz1UGC/bwY0q6e1VEcysPNRptQf22iK0eKMzOcC/QxTbMN0BYYaBhGZ+Al4DXTNJsCacBY58UUERGRqmJHYjprD5zijq6N8HJ3szqOw3UID2TX0QzO5BZYHUUoRjFs2mWd+9bj3JcJ9AG+OPf4XGCoUxKKiIhIlfLm6oP4erkzqlNDq6M4RVR4AIVFJtsSTlsdRSjmmmHDMNwMw9gKHAeWAQeB06Zp/vaWJhGo75yIIiIiUlUcPnWGpTuSGd05DD9vD6vjOEX7hgEYBsRoqUS5UKxi2DTNQtM02wKhQEcg8nyXne+5hmGMNwwjxjCMmBMnTpQ+qYiIiFR6b/8ch7vNxu1dw62O4jQ1q3nQrI6viuFyokTdJEzTPA38BHQG/A3DcD/3o1Dg6AWeM9s0zWjTNKODgoLKklVEREQqsZNZucyPSeS6dvUJ9vO2Oo5TRYcHsPlwGoVF551LFBcqTjeJIMMw/M/9vhrQD9gD/AgMP3fZGGCRs0KKiIhI5Td3XTx5hUWM79nY6ihOFx0eQFZuAftSMq2OUuUVZ2Y4BPjRMIztwCZgmWma3wD/BzxsGMYBoBYwx3kxRUREpDI7k1vAB+sP0z8ymIigGlbHcbroMPvhG7HqN2w590tdYJrmdqDdeR6Pw75+WERERKRMPtuUQPrZfO7qFWF1FJcIDahGHV8vYg6ncUuXcKvjVGk6gU5EREQslV9YxJw1h+gYHkj7hgFWx3EJwzCIDg/QsczlgIphERERsdQ324+SdPosd/Wq/GuF/yg6LJCk02dJTj9rdZQqTcWwiIiIWMY0Td5aFUez4Br0albH6jguFR1unwXX7LC1VAyLiIiIZX769QR7UzKZ0CMCm82wOo5LRYb4Uc3DjVj1G7aUimERERGxzJs/HSSkpjeD2tSzOorLebjZaNvAnxh1lLCUimERERGxxJYjaWw8lMrYbo3wdK+aJUmH8AB2H80gK7fA6ihVVtX8myciIiKWe2tVHH7e7ozs2NDqKJaJCg+kyIStR05bHaXKUjEsIiIiLhd3Iovvd6dwS5cwanhd8tiDSqtdQ38MAy2VsJCKYREREXG5t3+Ow8PNxm1XNLI6iqX8vD1oHuyrTXQWUjEsIiIiLnU8M4cFsUkMjwolyNfL6jiW6xAeyObDaRQUFlkdpUpSMSwiIiIu9f7aePKLihjfvWodsnEh0eEBnMkrZG9KptVRqiQVwyIiIuIymTn5fLjhMFe1qkt4bR+r45QLUWH2wze0VMIaKoZFRETEZeb9kkBmTgETekRYHaXcqO9fjbp+3sSoGLaEimERERFxibyCIuasOUSXxrVo08Df6jjlhmEYRIUHEBuvjhJWUDEsIiIiLrFoaxIpGTnc1Uuzwn/VISyAo+k5JJ0+a3WUKkfFsIiIiDhdUZHJW6vjaFHXlx5Na1sdp9yJDg8EIEazwy6nYlhEREScbuXe4xw4nsVdPSMwDMPqOOVOi7q+VPd00yY6C6gYFhEREad7c9VB6vtX45rLQ6yOUi65u9lo19CfmHgVw66mYlhEREScKiY+lZjDaYzr3ggPN5UeFxIdFsjelAwyc/KtjlKl6G+kiIiIONVbq+Pwr+7BjR0aWB2lXIsOD6DIhC1HTlsdpUpRMSwiIiJOc+B4Jst2H+PWLuFU93S3Ok651q5hADYD9Rt2MRXDIiIi4jSzV8fh7WFjTJcwq6OUezW83GlR14/Yw+oo4UoqhkVERMQpUtJz+GpLEiOiG1CrhpfVcSqEDuEBbDlymoLCIqujVBkqhkVERMQp3lt7iMIikzu7N7Y6SoURFR5Idl4he5IzrY5SZagYFhEREYdLP5vPxxuPcM3l9WgQWN3qOBVGdFgAADFaKuEyKoZFRETE4T7ZeISs3AIm9NCscEnU869GvZre2kTnQiqGRUQqq8ICqxNIFZWTX8i7aw/RvWltWtWvaXWcCicqPJCY+FRM07Q6SpWgYlhEpDLa8Aa8dhlkJFudRKqg99bGcyIzlwk9IqyOUiF1CA/gWEYuiWlnrY5SJagYFhGpbDJTYMVzkHUMVk2xOo1UMbGH05j6wz6ualWXrk1qWR2nQoo6t244VkslXELFsIhIZbPiWSjKhxbXwuYP4cSvVieSKuJ0dh4PfLqF+v7VeGn45RiGYXWkCqlFXT9qeLlrE52LqBgWEalMjm6BrR9D57th0HTwqA4rJludSqoA0zSZOH8bxzNzmHVTO/y8PayOVGG52QzaNfQnJl4zw66gYlhEpLIwTfjucfAJgu4Twac2dH0A9n4DCb9YnU4quTlrDrF8z3H+eXUkl4f6Wx2nwosOC2TfsUzSz+ZbHaXSUzFc0Zw9DT++ACf3W51ERMqbXV/BkfXQ50nw9rM/1vke8KkDy562F8siTrDlSBpTlu7lypbB3HZFuNVxKoXo8ABM0/5nK86lYrgiKSqEBWNh1UvwxhWw8nnI105TEcH+b8GypyG4NbS75X+Pe9WAXpPgyDr49Xvr8kmllZ6dz32fbKFuTW9eHt5G64QdpG0Df9xshjbRuYCK4YpkxbNwYDn0ewYuGwqrX4b/drE/JiJV2/rXIf0IDHwBbG5//ln7WyEwApY/Y39TLeIgpmky8Yvf1gm3p2Y1rRN2FB8vdyJDfLVu2AVUDFcUO76AtdMg+g7o9g8Y9jbcuggMG3w0DObfbm+nJCJVT0Yy/PyqvXtEox5//7mbB/T9F5zYA9s+dX0+qbTeWxvPst3HmHRVJG0baJ2wo0WHBbI14TT5hUVWR6nUVAxXBMnbYNF90LALDHzpf4837gV3r4Ne/4S938KsDrBxtmZ+RKqalc/ZW6kNeO7C11w2BOpH2fccaHmVOMC2hNO8uHQP/S8L5o6u4VbHqZSiwwM4m1/I7qMZVkep1FQMl3dZJ2DeaKheC0Z8AO6ef/65hzf0+j+4Z739hW7po/B2H3t7JRGp/P7YSi2w8YWvMwzoNxkykuCX2a7LJ5VS+tl87v1kM3V8vfmP+gk7TXRYIAAxWjfsVCqGy7PCfJg/Bs6cgJEfQY06F762VgTc8hUMmwMZR+0F8ZLHICfddXlFxLVME5ZO+l8rtUtp1B2aDoCfX4GzenGV0jFNk//7Yjsp6TnMvKkd/tU9L/0kKZW6Nb2p71+NWB2+4VQqhsuz7ybB4bUweBbUa3fp6w0DWg+H+zZB9Fj77M+sjrDzS7VUEqmMdn0FCRv+3ErtUvo+DTkZsOY152aTSmvuuni+25XC/w1sQfuGAVbHqfSiwwOIiU/D1Ou406gYLq9i34dN78AV98PlN5TsudX84ZqpcOcK+2zyF7fDx8MhNc4pUUXEAhdqpXYpdVtBm5Gw4U1IT3RePqmUdiSm88KSvfRtUYdx3RtZHadKiA4P5HhmLgmpWuvvLCqGy6MjG+DbiRDRx77Gr7TqR8GdP8LAKXBko70N2+r/QEGu47KKiDXWz7pwK7VL6f1PwIQfX3RKNKmcMnLs64Rr1/Bk6g3qJ+wq0WH22fcYLZVwGhXD5U16Enx2C/g3gOHvlvxF7q/c3O0ba+77BZoNhJX/hje7waGfHZNXRFwvIxl+fu3CrdQuxb8hdBwP2z6BY7sdn08qHdM0mbRgO0mnzzLzpnYE+GidsKs0C/bF18tdm+icSMVweZKfA5/dDPnZMPITqObAtVh+9WDEXLhpvn1meO618NVd9m4VIlKxFKeV2qV0fwQ8fe2H+YhcwkcbDrNkRwqPXtmcqHMdDsQ13GwG7cICiNXhG06jYri8ME34+kE4uhmunw11Ip1zn2YD4J4N9hfCHV/ArGj7+uQiNfQWqRCSNhevldqlVA+Ebg/Br0vh8DrH5ZNKZ2dSOs99s4fezYMY370Mf+ek1DqEBfDr8UzSs/OtjlIpqRguLzb8F7bPsx+g0eIa597Ls7r9NKq71kBwS3sR/u6VkLLTufcVkbIxTfju8eK3UruUTneBbwgs+5c6zsh5ZZ5bJxzo48krI9qhGE1FAAAgAElEQVRis2mdsBWiwgMwTdh8RLPDznDJYtgwjAaGYfxoGMYewzB2GYbx4LnHAw3DWGYYxv5zv6q/Smkd/BF+eNK+/q/Ho667b50WcNu3MPQNSD0Ib/Ww58jNcl0GESm+31upPVX8VmoX41kdej0OiZtg7zdlH08qFdM0efzLHSSm2dcJB2qdsGXaNvDHzWZoE52TFGdmuAB4xDTNSKAzcK9hGJcBk4AVpmk2BVac+15KKjUO5t8GtZvDdW+CzcWT9YYBbW+C+2Kg3WhYNxNe72Q/3llEyo/8s/YZ3ODW0O5mx43bdjTUbgbLJ0NhgePGlQrv441H+GZ7Mo8MaEaHcK0TtlJ1T3da1vMjRuuGneKSlZdpmsmmaW4+9/tMYA9QHxgCzD132VxgqLNCVlq5WfajlgFGfQJevtZlqR4Ig2fCHd/bZ5zm3QSf3gSnE6zLJCL/s34WpCfAwBfL3mXmj9zcod8zcGo/bP3IceNKseUX5pNXmGd1jD/ZdTSdZ7/ZTc9mQdzVI8LqOAJEhQWwLfE0eQXa4+NoJZqGNAwjHGgHbASCTdNMBnvBDFzkrGD5m6IiWHgXnNgLN7xfto0wjtSwM0xYDf2fhbgf4fWOsHa6/WhoEbHGb63UIgfZj1R2tOZXQ4NO9r7DedmOH1/OyzRNfoj/gYELBnLD1zdw8uxJqyMBkJVbwH2fbCGgugevjmijdcLlRIfwQHLyi9h1NN3qKJVOsYthwzBqAAuAh0zTzCjB88YbhhFjGEbMiRNq4/W7n6fCnq+h/3MQ0dvqNH/m5gFdH4R7N0KjnvaPZt/qaT+4Q0Rc77dWav2d1AbNMOwH/GSl2DfzitMlZCZwz4p7eGTVI9T0rknymWTu/OFO0nKs/RjcNE3++eUODp86w4yR7ahVw8vSPPI/vx2+Eat+ww5XrGLYMAwP7IXwx6Zpfnnu4WOGYYSc+3kIcPx8zzVNc7ZpmtGmaUYHBQU5InPFt/db+PF5uHwkdLnX6jQX5t8QRn0KN34MOafh3QGw+AHI1gJ+EZdxVCu1SwnrYp8hXjsdzpxy3n2quPzCfN7e/jbXLbqOzcc281iHx/j82s+Z0WcGCZkJjF82nvRc62b+5m1KYPG2ozzcvxmdGteyLIf8XR0/bxoEVtO6YScoTjcJA5gD7DFN89U//GgxMObc78cAixwfrxI6vhe+HA/12sGgafYZmfLMMCDyWrj3F+hyH2z5yN6beOunasUk4myObqV2KX2fhrws+PkV59+rCtqUsonhXw9nxpYZ9AjtwaKhi7jlsltwt7nTOaQz03pP4+Dpg9y17C6y8lzf1WdPcgbPLN5F96a1uadXE5ffXy4tOiyQmMNpmHr9dajizAx3BW4B+hiGsfXc19XAFKC/YRj7gf7nvpeLOZsG80aBR3X7bKtHNasTFZ9XDbjyeZiwyj47tfAumDsITuyzOplI5eXoVmqXUqeFvbvEprch7bDz71dFpOak8sSaJ7jj+zvILczl9b6v82qvV6nrU/dP13Wr341Xe73K3tS93L38brLzXbd++0xuAfd+spma1Tx47Ub1Ey6vosMDOJmVy5FUre13pOJ0k1hjmqZhmublpmm2Pfe1xDTNU6Zp9jVNs+m5X/XZ+cUUFcIXY+3dGW78EGrWtzpR6dRtDXf8ANdOg5Tt8EZXWPGcve2TiDiOs1qpXUqvx8GwwY8vuO6elVSRWcSX+79k8MLBLIlbwrjW4/hqyFf0CO1xwef0atCLl3u+zI6TO7hv5X2cLXD+v62mafLkwp3EnzzD9JHtqK11wuVW9LmjsDdpqYRD6QQ6V1n+DBxcAddMtXdsqMhsNoi+He6LhVbD7JsB/9sZ9i+3OplI5eGsVmqXUrO+/WS67Z9Byg7X3beS+TXtV2777jaeXvc0ETUjmD9oPg+2f5Bq7pf+RLB/WH9e6PYCMSkxPLjyQXILc52adX5MIl9tSeKhfs3oEqF1wuVZ0zo18PN2J1aHbziUimFX2D4f1s2A6LEQdZvVaRynRhBc/xbcuhhsHvDxMPh8jL0NlIiUnrNbqV1Kt4fAu6b9TbyUSHZ+Nq/GvsqNX9/IofRDPNf1Od4f+D5NAkq2BvfqxlfzbNdnWZ+8nn/8+A/yndTecl9KJv9avJOuTWpxb2+tEy7vbDaD9mEB2kTnYCqGne3oFlh8HzS8AgZW0mXVjXvC3Wuh95OwbynM6gAb37IvDRGRklvxrHNbqV1KtQDo/ggcWA5xq6zJUAH9lPATQxcN5b2d7zG4yWC+Hvo1Q5sMxSjlRumhTYbyry7/4uekn5m4aiL5RY4tiLPz7OuEa3h5MO3GdrhpnXCF0CE8kP3HszidXb4OaqnIVAw7U9YJmHczVK8NIz4A90p8rru7F/R8FO5ZDw06wNLH4O0+9rZQIlJ8SZth2yfOb6V2KR3Hg18oLH9anWMuIeVMCg+ufJD7V96Pj4cPcwfOZfIVk/H39i/z2Dc0u4HHOz7OyoSVPP7z4xQUOe7I7KcW7uLgiSxmjGxLkK/WCVcUUeo37HAqhp2lIA8+vxWyT8HIj+1LCqqCWhFw85cw/F3ITLYXxEsehRydmCNySQ5upZaTX4ZPZzy8oc8T9k+3di8sc5bKKL8on7m75jJ44WDWHV3HQ+0f4vNrP6d9cHuH3uemyJuYGD2R7+O/56m1T1HogE/d5scksGBzIg/0acoVTWo7IKW4SptQf9xtBjEqhh1GxbCzfDcJjqyDIbOgXlur07iWYdg31t23CTreCb+8bV86sXOBZphELmbXlw5rpfbGTwfp8O/lxJ88U/pBLr8R6lxmX7ahI9n/ZOvxrYz8ZiRTY6bSsW5HFg5dyNjWY/Fw83DK/ca0HMP97e7nm7hveHbDsxSZRaUea/+xTP61aBddGtfigb5NHZhSXKGapxst69ckVuuGHUbFsDPEvAcxc+CKB6D1cKvTWMe7Jlz9H7hzJfjWhS/ugI+uh1MHrU4mUv7kn4VlTzukldqJzFxmrtxPZm4BTy/eVfoG/TY36PcMpMZB7PtlylRZpOemM3n9ZG5ZegvpuelM6zWNmX1mUr+G89tljr98PBMun8CX+7/khY0vlOp/1+y8Au75eDM+Xm5MH9lW64QrqOiwALYlnia3QHtzHEHFsKMd2WBfFtCkn/1FRKB+e7jzR7jqZUjYBP/tAqtehgLntgsSqVAc2Ept5sr95BYUMaZLGKt+PcH3u1JKP1jTARDWFVa9BLmuPxWtvDBNk68Pfs3ghYP5av9X3HrZrSwauoi+YX1LvUGuNO5tey+3t7qdz/Z9xsubXi5xQfz0ol0cOJHFtBvbUcfP20kpxdk6hAeQW1DEzqQMq6NUCiqGHSk9CT67BfwbwLB3XNsbtLyzuUGnCfalEy2uhh+ftx/YoZ3qIg5tpRZ/8gyfbDzCqI4NeOray4gM8WPy17s5k1vKjVeGYe9qceYErH+9TNkqqrj0OMb9MI5/rvknoTVCmXftPB7t8Cg+Hj4uz2IYBv9o/w9ujryZj/Z8xLTN04pdEC+ITWR+bCL39W5Ct6ZaJ1yRRZ07fEP9hh1DxbCj5J+Fz0ZDfjaM/NTemkj+zi8EbngfRi+wt476YDB8OR6yjludTMQ6v7dSe67MQ039YR8ebjYe6NsUdzcb/x7akuT0HGas3F/6QUOjIXKwvV961okyZ6wocgpymLllJsMWD2NP6h6e6vwUH179IS0CW1iayzAMHuvwGCOajeDdne/yxrY3LvmcA8czeXLhTjo1CuRBrROu8IJ8vQirVV39hh3E3eoAlYJpwtcP2nddj/wU6lj7D2WF0LQf3LMBfn4F1kyDX7+DlteBbz17wez7h6/qgfbZKZHK6LdWal0fgsBGZRpqW8JpvtmezAN9mlDH1/4ReFRYICOiQ5nz8yGGtQ+lWbBv6Qbv+y/Y+y2sftm+F6CSW5u0luc3Pk9CZgLXNr6WR6IfoXa18jObahgGT3R+gvyifN7Y9gaebp6Maz3uvNeezSvk3o+3UN3TjRmj2uHupnmwyiAqLIBV+05gmqZLl+pURiqGHWH96/ajS3s/YV8CIMXjUQ36PAmtb4AfnoQ9X9tb0f2Vm6d9A55vyLlf6/3v+98L57rgVcoXeRGr/KmV2iNlHMpkytK9BPp4cmePP/cnnnRVJD/sPsZTC3cyb3zn0r1w1m4K7W+FmHet74HsRCeyT/Dyppf5Lv47wv3CeWfAO3QK6WR1rPOyGTae7vI0+UX5TN88HQ+bB2NajvnbdZO/3sW+Y5nMvaMjwVonXGl0CA/ky81JxJ/KplFt1y/ZqUxUDJfVwZWw7Cn7Wj8H9AWtkoKaw+j59t8X5EJmyrmv5D98pUDGUTi2Gw6sgLzzbOTx9D1XJNcFvz8UzL5/KJh969oPCBEpD35rpTZoRplbqa3ef5L1cad4etBl+Hr/ub1XoI8nj13Zgn9+tYOFW5O4rl1o6W7Sa5L9jf/Kf9t7iVcihUWFfLbvM2ZumUleYR73tr2XO1rdgadb+T4syc3mxnNdnyOvMI+pMVPxsHlwU+RNv/984ZYk5m1K4N7eEfRsVkX63VcR0ecO39gUn6piuIxUDJdFahzMvx2CWsDQN8Gmj57KzN0LAsLsXxeTm/m/gjnjDwVz5lH7r0fW238tPM9xldVr/blAPl/h7FNbGyDFuX5rpVa37K3Uiorss8INAqsxutP5/78zskMDPotJ4Plv99KnRTA1q5WiH65vXehyL6z+D1xxP9RrV6bc5cWuU7t4dv2z7D61my4hXXiy85M09Gtodaxic7e5M6XHFPJ/yufFX17E082T4c2Gc/BEFv/8agcdwwP5R79mVscUB4sIqkHNah7ExqcxIrqB1XEqNBXDpZWbCZ/eZF/LOvIT8KphdaKqxcvX/lX7IhtBTBOyU/88w/zXwjll+7nNe3/ZjW24/W8m+U8zy39ZmuHtr/XMUjrrzrVSG/pGmd94Ld52lD3JGUwf2RZP9/O/KbfZDJ4f2orBs9bw6g/7mDykVeludsUDsGkOLH8Gbl1U+tDlQGZeJrO2zGLevnkEegfyco+XGRg+sEKuv/SweTC151Qe+vEhnl3/LJhuvLO0Fl7uNqaPaqt1wpWQzWYQFRZAjDpKlJmK4dIoKoKv7oKT++xHD5dx00tJpeWkEeCtbhWXZBjgU8v+VfciL/yFBZB17MJLM04dhPg1kHP67891r3b+Ivm3Ajqohf3+In+UkQxrXnVIK7XcgkKm/rCPlvX8GHR5vYte26p+TW7pHMaHGw5zQ3QDWtWvWfIbevtBz8fsp2weWAFN+pYyuXVM0+T7w9/z8i8vc/LsSW5sfiP3t78fP8+yLVWxmqebJ6/1fo37VtzH5A1PczZ7JG/fOI6QmtWsjiZOEhUWwMq9x0k9k0egT/le0lOeqRgujdUvw95v4MoXIKK3y25bWFTItM3TeH/X+wxqPIh/dvonNTw1I11mbu5Qs77962LysiEr5X9F8p+K5xR7N5GMJVBw9n/P8fSFsd9DcEvn/jdIxbLiWSgqcEgrtY82HCEx7SwvXt8aWzFOE3t4QHO+3ZHCkwt38uXdVxTrOX8TfQds+C8sfxoa965QS8QSMhJ4/pfnWZu0lsjASGb0mUGr2qWcJS+HvNy8uDLocdYceIjq9T+jwLsD0N/qWOIkHcJ/6zecRv/Lgi1OU3GpGC6pPd/ATy9Cm1HQ+R6X3TYzL5PHVj/GmqQ1dKrbiW8PfcuW41t4qcdLXB50uctyVGme1e076C+2i940ISfdXhynJ8Kie+HTUTD+J3uLOJGkWIe1UsvIyWfWyv10a1Kb7k2LtzmqZjUPnrimBf/4bBufxSQwqmMp1sa6e0Gfp+DLO2HnArj8hpKP4WJ5hXm8v+t9Zm+fjbvNnUkdJ3Fj8xtxt1Wul8FDJ8/w9Fe/EhnyENWD3uOxVY/xWu/X6NWgl9XRxAkuD62Jh5tBzOFUFcNlUHHezpcHx/fAVxOgXnu4dprL1ooezjjM6CWj2XB0A091fop3rnyH9658j0KzkDFLx/DOjncoLNL55OWCYUA1f3uv6ab9YOTH9pnj+bfZl2NI1fZ7K7U6ZW6lBjB7VRxp2flMuqpkvc2Htq1Pp0aBvPTdXlLPnGeTaXG0Gm7f/Lfy2XJ/tPqmlE0M/3o4M7fMpGdoTxYNWcToyNGVrhDOyS/kno834+FuY9aoLrzR77+0CGzBwz89zNqktVbHEyfw9nCjVf2axOrwjTJRMVxc2an2GT6P6nDjR+Dhml6N64+u56ZvbyItJ43ZA2YzovkIANoHt2f+oPn0adiH6ZunM37ZeFLOpLgkk5RAaDRc+xocWgXL/mV1GrHari8hYSP0farMrdSOZ+Twzpo4BrepV+K1v4Zh8NzQVmTlFPDS0r2lC2CzQb/JcPoIxLxXujGc7NTZUzyx5gnu+P4O8grz+G/f//JKr1cI9qmcM2j//nY3e5IzeHVEG+r5V8PX05c3+79JhH8ED/74IBuTN1odUZwgOiyA7Unp5BZoUqy0VAwXR2EBLBhr/9j7xo8uvbbUAUzT5OM9H3P38rupU70On1zzCR3qdvjTNTW9ajK151SeveJZdpzcwfCvh7PiyAqnZ5MSanczdJwAG16HrZ9anUas8sdWam1Hl3m415bvp7DIZOKA5qV6frNgX8Z2a8RnMQnElnY3ekQfaNTTvo8iJ6N0YzhBkVnEF79+weCFg1lyaAl3tr6Tr4Z8RffQsm1WLM++2X6UjzYcYUKPxvRp8b9iv6ZXTWb3n00D3wbcv/J+Yo/FWphSnCE6PJC8giJ2JqVbHaXCUjFcHCuesR+ucc0r0ND5JxHlF+Yzef1kpvwyhe6h3fno6o9o4Hv+HoKGYXBd0+v47NrPqOdT7/e2Omf/uIlLrHfl8xDe3X5sd6JejKqk31qpXflimVupHTiexecxCYzuFEbDWtVLPc4DfZsSUtObJxfuoqCwqOQDGAb0e8Z+cuS6GaXO4Uj7Uvdx69Jbmbx+Mk0DmvLFoC94oP0DVHOvvB0V4k+eYdKCHbRv6M/EK//+5ijAO4C3B7xNcPVg7ll+D9tObLMgpThL1O+Hb2ipRGmpGL6U7Z/DupnQYRxE/f2YS0dLzUll3A/jWLB/AXe2vpPpvafj43Hpk2Ua1WzEx1d/zG0tb2P+r/MZ+c1I9qXuc3peKSY3D7hhLvgGw2c3Q+YxqxOJKzmwlRrA1O/34e1u474+Tco0jo+XO/+69jL2JGfwwfrDpRukfntoeb39WPpM65ZqZedn80rMK9z4zY0cyTjCv7v+m/eufI8I/wjLMrlCTn4h936yGTebwcyb2uNxgX7CtavV5p0B71CrWi3uXnY3u07tcnFScZbaNbxoVNuHGBXDpaZi+GKOboHF90NYVxg4xem325e6j1HfjGLXqV281P0lHmj/ADaj+P8Tebh58Ej0I7zV/y0y8jIY9e0oPt7zMaZpXvrJ4nw+tewHtOSchs9vKfebjsSBVkx2WCu1zUfS+G5XChN6RlC7RtmPFh/Yqi49mgXx6rJfOZ6RU7pB+j5lP+1x1UtlzlMaK4+sZMiiIby/632GNhnK4qGLGdJkSIU8PKOkXliyh11HM3jlhjbU97/47HewTzBzBszBz8uPCcsmaMKkEokKC2DzkTS93peSiuELyToO80aDT5B9Rs+tFEeXlsCKwyu4ZektFBQV8P7A97m68dWlHuuKelewYPACutTrwpRfpnDvins5dfaUA9NKqdVtDUP/a99EtWSivbuAVG5JsbDtU3srxjK2UjNNkylL9lK7hhdjuznmsB/DMHh2cEvyCot4fsme0g0S2Njeezh2Lpw84JBcxXE06yj3r7yfB398kBoeNfjgqg945opn8Pf2d1kGKy3ZkcwH6w8zrlsj+hWzrVZIjRDeGfAO3m7e3PnDnRw8fdDJKcUVOoQHkHomj7iTZ6yOUiGpGD6fgjz4/FZ7B4mRH0ON4vXvLA3TNHlr21s89NNDNPFvwqfXfuqQBvCB3oHM6jOLSR0nsTF5I8MWD1NrnfKi5XX2tlqbP4BN71idRpzJwa3UVu49zi/xqTzYryk+Xo5rCxZe24e7ekawaOtR1h04WbpBejwGHtXss+BOll+Uz3s732PooqFsTN7Iw1EP8/mgz2lXp53T711eHDmVzf99sZ02Dfx5bGDJWuuF+oYy58o5uNvcGffDOOLT450TUlwmKszexz4mXkczl4aK4fNZ+hgcWQ9DZkFIG6fd5mzBWR5d/Sizts7imsbX8O6V71Kneh2HjW8YBqMjR/PJNZ/g7+XPXcvv4j+b/kNeYSn7iorj9H4Sml5pP9I2Xm9SKq2dCxzWSq2wyOSl7/YSXqs6Izucf0NtWdzTK4KGgdV5atFO8gpKsZmuRhBccT/sWQwJmxye7zdbj2/lxm9u5NXYV+lUtxMLhyzk9la342Fz7qd35UlugX2dsGHArFHt8HQv+Ut5mF8Y7wx4hyKziLE/jCUhM8EJScVVIoJ8CKjuoXXDpaRi+K9i3oXY9+ynQ7Ue7rTbpJxJYczSMfwQ/wP/iPoHL3Z7EW935/Qubh7YnHnXzuPG5jfywe4PuHnJzRxKP+SUe0kx2Www7G0IaGT/FOL0EasTiaM5uJXal5sT+fVYFo9e2eKCm6TKwtvDjcmDW3LwxBneWRNXukG63GtfWrb8aYcvAfo17VceXPkgtyy9hcy8TKb3ns7MvjOpV6OeQ+9TEby4ZC87ktL5zw1taBBY+m4ijf0bM7v/bHILcxn3/TiOZh11YEpxJcMwiAoLIPawiuHSUDH8R4fXwZJHoUl/6Ou8AxK2Ht/KyG9GciTzCDP7zOSOVnc4faOHt7s3T3Z+kum9p3P0zFFu/OZGvtz/pRbbW8m7Joz61L7xaN5oyMu2OpE40rpZkJFo33xbxlZqOfmFvLrsV9o08Ofq1nUdFPDvereow5Utg5m54gCJaaX4++jlCz3/Dw6vhf3LHJLpUPohHlv1GMMXD+eXlF+4p+09LBqyiD4N+zhk/Irmu50pvL8untu7hnNly7L/XWge2JzZ/WeTmZ/J2O/HcuyMOt1UVFFhgcSdPMOpLG3OLikVw79JT7TP0PmHwbB3yvzidSGLDiziju/voJp7NT666iN6NujplPtcSJ+GfVgwaAGta7fm6XVP88iqR0jPVaNuy9RuCsPmQMoOWHyfNtRVFhlHz7VSGwzh3co83Afr40lOz2HSwBZOf+P8r0EtAXj2692lGyDqNvuGuuXPQBmOiU/ITOCJNU8wdNFQfkr8iXGtx/HdsO+4u83dVPco/WxoRZaQms2jX2yjTWhNHr8q0mHjXlbrMt7q9xZpuWmM+2EcJ8+Wct24WKpDuL3fsGaHS07FMNg/zpx3E+Tn2Gfqqjl+J3JhUSFTN03lybVP0q5OOz695lOaBJStR2hpBfsEM7v/bB5s/yA/HvmR4V8P16lEVmo2wL6mdOcCWDvd6jTiCCuePddK7dkyD5Wenc/rPx6kV/MgukTUckC4i6vvX40H+jblh93HWLm3FLOEbh7Q5yk4vgu2f1bip6ecSWHy+skM/mow38d/z82RN7P0+qU80P4BanqV7NjpyiSvoIj7PtkMwKyb2pdqnfDFtA5qzRv93uBY9jHu/OFOUnO0EauiaVW/Jp5uNmJUDJeYimHThMUPQPJ2+xrOoNIdbXoxmXmZ3LvyXubunsvI5iN5s/+blrf+cbO5Ma71OD646gM8bB7c8f0dvL71dQqKCizNVWV1e9h+cMHyZ2D/cqvTSFk4sJUawH9XHSAjJ5/HrixZx4CyGNutEU3q1ODpxbvIyS/F7O5lQ6FeO1j5vH2SoRhOnj3JlF+mcPWXV7PwwEKGNxvOkuuX8GiHR6lVzflvAsq7l77by7bEdP4z/PIyrRO+mHZ12jGrzywSMhMY/8N4fWpYwXh7uNE6tKY6SpSCiuH1s2DH59D7CWh+lcOHP5xxmJu+vYmNRzfyVOeneKLzE+Vq13ProNbMHzSfaxtfy5vb3uS2724jKSvJ6lhVj2HYu5cEt4Iv7nBpr1ZxIAe3Ujt6+izvrY3nurb1uaxe2bpRlISnu41nh7QkIfUs//2pFH1obTboN9m+ZnrT2xe9NC0njVdjXuWqBVcxb+88BkUM4tvrvuWJzk84tLtORfbDrhTmrDnEbVeEM7BViFPv1TGkIzN6zyAuPY4JyyaQmZfp1PuJY0WHBbAzKaN0b2KrsKpdDB9YDsv+ZV/X12Oiw4dfd3Qdo74dxenc08weMJsRzUc4/B6O4OPhw/PdnmdK9ykcPH2Q4YuHs/TQUqtjVT2ePva+1m7u9mU7ORlWJ5KScmArNYBpy38FEx4e0MwB4UrmiojaDGlbjzd/Osih0jTyb9wTmvSD1VPh7Om//TgjL4NZW2YxcMFA3t/1Pv3C+rF46GImXzG5SnaIuJDEtGwmzt9G6/o1efxq13w6cEX9K5jWexr70vZx9/K7OZOvgxwqiujwQPIKi9iRpFn9kqi6xfCpg/YZuKBIGPqGfWbOQUzT5OM9H3PP8nsIrh7Mp9d8Soe6HRw2vrNc0/ga5g+aT2P/xjy2+jGeXPOk/hF0tYAw+4mHpw7Al+OhqBT9XsUaDm6l9uuxTL6ITeTWLmGEBlizYeyJqyPxcrfx9OJdpes80+8ZyEmHtdN+f+hM/hlmb5/NwAUDeWv7W3St35WvhnzFi91fpKFfQ4dlrwzs64S3YJow66Z2eLk7Z2P3+fQI7cHUHlPZeXIn9664l+x8dbupCKLC7JvoNmmpRIlUzWI4N9M+82bYYNQn4FXDYUPnF+bzzPpnmPLLFHqE9uCjqz8i1DfUYeM7W6hvKHMHzmXC5RP4Ou5rRnw9gp0nd1odq2pp1N3ejuvXpfDTC1ankeJaN9NhrdQAXv5uHz6e7tzb25qNtgB1/Lx5eEAzVv96gqU7U0o+QN3WcPkI2PAGZ8SkfrwAACAASURBVE8dZO6uuVy14CpmbplJVJ0o5g+az6u9XiXCP8Lx4SuB/3y/l60Jp5ky7HLCavm4/P59w/oypfsUthzfwgM/PkBOQfHWf4t1An08aRzkQ6wO3yiRqlcMFxXBlxPg5H644X0ICHfY0KfOnmLcD+P4cv+X3Nn6Tqb1noaPh+v/ASsrd5s797W7jzkD5pBbmMstS27h3Z3vUmRqltJlOt4J7W6G1f+B3YusTiOXknEU1rzmsFZqm+JTWb7nGHf1iiDAx9MBAUvvls5hXBbix7Nf7yYrt+QbbPN6PMYnPt5c/e0IpsZMpUVgCz6++mNm9p1Ji0DXbQqsaFbsOcbbPx/ils5hXHO5c9cJX8zARgP5d9d/80vyLzz000M6wbQCiA4LIPZIGkVFatVZXFWvGF71Euz7Fgb8Gxr3ctiw+1L3MerbUew6tYuXe7zMA+0fwGZU7D/e6LrRLBi8gN4Ne/Na7GuMXzae49nHrY5VNRgGXPMqhHaAr+6GY7usTiQX48BWaqZp8uKSPQT7eXFH17J3oygrdzcbzw1tRUpGDjNW7C/28/KL8lnw6wKu+eluXgz0o+HZTN7rNJnZA2ZzedDlTkxc8SWdPssj87dxWYgfT1zjuH7CpTUoYhDP/D975xkV1dWF4ecCAiIIimDDXlBQKaLYe6+xxRZb7EaNGnu+qEnsLRp777FFjSZ2jb2hKCgW7CgivdcZZu73Y7DFBjIwA5xnLRYw5d59KHPf2Wfvd9eaxoUXF/jhzA8o1UpdhyT4BG4l8hMZr+RxaKyuQ8kyZG21llbu/g1nZoNTD6gxVGuHPeF3gl6He6GSVWxqsYmWpbTvSqErLE0sWVB/AdNqTuNmyE06HejEqWendB1WzsDIBLpu1TRibe8O8aIGTC/xT7FSq/mdVqzUjt0J4vqzSEY3KU9u48yrEf0UVUvko6tbMdaff4Jv4KfdBVRqFX8/+pt2+9ox7dI0bHLbsKrufDaGJ+B2489MijjrolSpGfHHdZJVMst6umKaSz/+BjqW68iP7j9y+vlpJpydIGw49Ri3kq/qhkWpRGrJOWI46I6mPKJoVWjzm1Ya5mRZZoX3CkafHk05q3LsaL0DxwKOWghWv5AkiU7lO7GjzQ4K5SnEyFMjmX55uqgfywwsCmkEccxL2N0HVOICpFfIMhyZqLFSqzMm3YdLVqmZe+QeZWzy0LmqfvUaTGhZAXNTI37a7/PBZjq1rObI0yN0PNCRyecnY25sztJGS9nWahu1SjdHqv29Zlfu2WUdRJ91mH/Ml+vPIpnVsTKlCuhXmV23Ct0Y5zaO437H+fH8j6jSMWFQkHGUKpAH6zzGXBNiONXkDDEcHw47umsa5bpuhVym6T+kMp6xZ8ay3Gs5bUu3ZX2L9diY2WghWP2ltGVptrXaRm+H3uz03Un3g925H3Ff12Flf+zcoM0ieHIWjv+k62gEb+OzB/w9tGal9qenP49C4hjfogJGhvr18pw/jzETWlTA40k4+2688SKXZZlTz07R5e8ujDszDgmJBfUXsLPNTuoXq/9mfHSNoWBeSGNnKcaOf5B/7wWx6sxjeroXp62TftrL9Xbszfeu33PoySGmXpwqekn0EEmScC2RD08/sZuYWvTr1TYjUCVrLNSiAzRCOG/6X2Bexr6k75G+HPc7zpiqY5hRZwYmhiZaCFb/MTY0Zly1caxosoLwxHC6/9OdP+7+8WW2S4LU49IT3IfA5eXg9YeuoxEAKOJTrNSqaMVKLUGh4rcT93EtbkUzh4JaCFD7dHUrhnMxK2YeuktkvIILLy7Q42CP104Ds+rOYk+7PTQr2ez9ngnjPNBgosaH2feQbhagx7yMSuCHXd5ULJyXn9o46DqcTzKg8gCGOQ1j/6P9TL88Xbz+6yFuJfLxNCyekJgkXYeSJcj+YvjEVHh8ClovgGLV0304r2Avuh3sxvOY5yxtvJR+lfq9yXzkIOoUrcOednuoXrg6szxmMfLfkUQkii2ZDKXZdChVD/4epalTFeiWS0tTrNRmacVKbf2FJwRFJzGpVUW9fU0xMJCY/lUlItX3+GpvD4acGEJYYhi/1PqF/V/tp03pNhh+6mfh0gusy8GJn0XJz1skq9SM+OMGimQ1y3q46E2d8KcY4jSEAZUHsPv+buZcnSMEsZ7hVjI/AJ5+4rqcGrK3GPbeoblgVRsIrr3Tfbi/Hv7Ft0e/JU+uPGxrtY16dvW0EGTWpUDuAixvvJwJ1SZwIeACnQ504lLAJV2HlX0xzAWdN4JFQdjZE2K+wPc1nYTEJDF53y0eBOXwEa1atlKLiFOw8vQjmlQsSLWUi5g+4h3izeLbY8ldYjUhiQH0sx/DPx3+oUO5DhgZGH3+AIZG0GQqhPqCt9jhAE2ZycxD97jmF8HMjpUpbaM93/uMRJIkRrqMpLdDb7bd3cZvnr9lG0EsyzJRSVE8iHjAhRcX2PdgH6tvrmb65emMPTOWyy/1v+69UtG8GBsZcE0M30gVqXj1ysLEhUDJuprMTTpQqVUs9FzI5jubcS/szoL6C7A0sdRSkFkbSZL4xuEbqhWqxriz4xh0fBD9HPsxwmUEuQxz6Tq87Ecea+i2HdY1hZ29oO8/GteJTCA8TsE3a6/gGxTDqXvB7BtWm0KW6a+/z5Kc+FlrVmoAy049JE6RzPgW9lo5nra5G3aXpV5LOet/lvym+RnhPIZV/xTkTJIlo6qn8f+8QhuNZeCpWVCpMxjrZrqePqBWy0w54MPWy8/oW6sk7Z2L6jqkNCFJEmPdxqJQKdhwewO5DHMxwmWErsP6JPHKeEISQgiODyY4PpiQ+BCCE976Oj6YkIQQklTvlxdYmlhigAEnn51kVt1ZtCjZQgcrSB0mRoY42VlyTWSGU0X2FsO1RkCNYenawoxWRDP+7HhNbVyFHoytNpZcBkLk/Rf7/PbsbLOTeVfnseH2Bq4EXmFuvbmUyFtC16FlPwpVgq+Ww+6+cPAHaLdEq+PEP0RkvEYIPw2L4+d2jsw76kvfDR7sGlKTvKY57P/B3xNu7oA6o7VipeYfEc/mS350rmpH+YIWWghQezyMeMhy7+Uc9ztOXuO8fO/6PT0q9MAslxm26heM2unFjqvP6eGehjHKkgRNfoaNreDKSqibfheOrIgiWc0Pu7352zuAIfXLMEFP3wh9DkmSmOQ+CaVayeqbqzE2MGaw0+BMj0OpVhKWEEZQfBAh8SGvP4ckvPV1fAgxyvd3tXIb5cbWzBZbM1uq2FR5/bWNmQ22uVM+m9liYmhCtCKaESdHMP7MeKKTovna/utMX2tqqVoiP+vOPyZRqcoSpTe65LNiWJKk9UAbIFiW5Uopt+UHdgIlgafA17Is6+fbj3QI4adRTxnx7wj8Y/yZWnMqnct31mJg2Y/cRrmZUnMKtYrUYurFqXT5uwuT3SfTvkx7va2BzLI4doBAHzg3Hwo7aSbWZRDRiUr6rPfgYXAsa/q4Ub+8DaVt8tBvw1WGbvVkQ9/qGBtl74qr12jZSg1g4bH7SBKMalJeK8fTBn7Rfiz3Ws7hJ4cxy2XGUKeh9HLohYXxG7He3rkIO64+Y86RezR3LIi1eRp2KErWhvIt4PwiqNoXzPS3NCQjSFCoGLLVkzP3Q5jYsgJD6mftcdQGkgFTak5BqVay1GspJoYm9K3UVyvHVstqIhIj3svmBsUHEZIQ8vrriMQIZN4t0zCSjLAxs8HGzIYyVmWoUbgGNmY2FDQr+Fro2prZkidXnlRfo/Ia52Vl05WMPTOWXy//SrQimv6V+uvlNa5ayXysPCPj/TwS99LWug5Hr5E+V+MjSVI9IBbY/JYYnguEy7I8W5KkiUA+WZYnfO5kbm5u8rVr17QQdsZz8cVFxp4di5FkxMIGC3Er5KbrkLIUgXGBTD4/mauBV2lRsgU/1fyJvMbpt54SvIVarbEMfHgCeu/XSu3qf4lNSqbPeg+8n0ey8puqNHnL5WCPpz8/7Pamg0tRFn7tpJcXA61z60/Y01+TjddCH8KdgGhaLznHoHqlmdRS95PGXsS+YJX3Kg48OoCxoTHdK3Snn2M/rEytPvj4B0ExtFx8jo6uRZnb2SltJwu6Aytra3bvms/QQvRZg6h4Jd9uusqNZxHM7FCZbtXTkFXXc1RqFZPOTeLw08NMrD6RnhU/7rIiyzKxyth3yhTeLlUITgh+ndn974APCYn8pvnfZG/NbN/J4Nqa2WKT24Z8pvkybBKsUq3kf+f/x6Enh+jj0Icf3H7Qu9fAyHgFzr8cZ1xze75rWFbX4WQ6kiR5yrKcKvH22cywLMtnJUkq+Z+b2wMNUr7eBJwGPiuGswKyLLPt7jbmXZtHGasyLGm0hKLm+lPHJcsy/hEJ2OXLrXf/eG9TKE8h1jRdw3qf9SzzWsbNkJvMrjcbF1sXXYeWfTAwgI6rYU1j2NUbBp0GK+1dWBMUKvpvvIrX80iW9XB5RwgDdKpqx8uoBOYfu09hS1PGt6igtXPrJVq2UgOYe/QeeU1zMay+bi9UQXFBrLm1hj0P9mCAAd0rdKd/5f4UyF3gk88rV9CC/nVLserMY752K/a6gz1VFHTQTAP1WA3ug7X6t6uvBEcn0nu9B49D4ljWw5WWlQvrOiStYmhgyIy6M1CoFcz2mE1CcgJ2FnYExwW/m9lN+TohOeG9Y1jksngtcqsVqvZa2L4SubZmtljnttZ5uWIug1zMqjsLSxNLNt3ZRJQiiqk1p6aukTSTsDIzpqytuWiiSwWfzQwDpIjhf97KDEfKsmz11v0Rsizn+9xx9D0zrFApmHFlBnsf7KVhsYbMqjuLPLn0ZwKQIlnN1AO32e7xjGYOBZnTqQr58hjrOqzPcjPkJuPPjudl3EuGOA1hYOWBevWCkeUJfaARxPmKw7fHtNKQlKhUMWDTNS4+CmVRNxfafWQAgCzLTN7nw3aPZ/z6VSV61cjGNeJn5sKpGdD3oFay8BcfhdJjzRUmt6rAoHq62SYPTQhl3a117PLdhVpW07FcRwZWGUihPIVSfYy4pGSaLjxD3ty5+GdEnbQNC4nyh99doVIn6LDiC1aQdXgWFs83664QGpvE6l5u1Cn36TcaWRmlSsno06M543/m9W0mhibviNrX5Qq5bV5/XSB3AcxyZa2GyleTaFd4r6BRsUbMrT9Xr+YOTNxzk0O3XuI1pRkGBvqbQMsI0pIZznAxLEnSIGAQQPHixav6+fmlJq5MJywhjNGnR3Mj+AYDKw9kuMvwDNte+RLCYpMYuu06Hk/CaeZQkFO+weTPY8xvXZ2pVUb/X1RjFbHMuDKDfx7/g6utK7PqzqKIuX5OWMqS3D8Gf3ytqSXuvD5dDXVJySqGbPHk9P0Q5nV2+uxY4GSVmsFbPDnlG8yqXm401dOBEekiOgCWVIWyTaDrlnQfTpZlvlp2gZCYJP4d2yDTm1uikqLY4LOBP+79QZIqiXZl2jG4ymDsLL5sBPQRn5cM2Xqdn9o40L9OGpsKj/0EF5fAkPOa5tBsyL3AaHqv80ChUrOhbzVcin82d5TlUaqV+IT6YJ7LHFszW/Ia59Xr3cz0su3uNmZ7zMa9kDuLGy3Wm0Ta7mvPGffnTY6Nrqd3DboZTVrE8JeqvSBJkgqnnKwwEPyxB8qyvFqWZTdZlt1sbPRzXPG98Ht0P9idO2F3mFdvHiNdR+qVEL77Mpr2yy7g9TySRV2dWd3bjX3DapPHxIiea68w98g9lCr9HolpbmzOrLqzmFlnJr4RvnQ+0JmjT4/qOqzsQ/lm0HgK3N4LFxZ98WGUKjXD/7jBKd8QZnao/FkhDGBkaMCSHi5ULmrJiO3XufFMP3tp08UrK7Vmv2rlcIduBeLtH8XopuUzVQjHKGJY7rWcFntasN5nPQ2KNeCv9n/xa+1fv1gIAzR3LEQDext+O36foOjEtD25zmjNKOuTP3/x+fUZT78Ivl55CUmCXYNr5gghDJoyAhdbF8rlK4eliWW2FsIAPSv2ZGadmVwLukb/o/31ZgjVK9/ya0/1Ix595UsV3wGgT8rXfYD92gkn8znud5zeh3ujklVsarmJFqX0yzfw6O1AOq24iCJZze7BNfnKRVO/XKmoJf+MqMPXVYux/PQjOq+4iF9YnI6j/Txty7Rld5vdlLQsydgzY5lyYQrxynhdh5U9qDMaHDtqhNv9Y2l+erJKzagdXhy/E8Qv7R3pnobGHjNjI9b1rYathSn9N13jaaj+/y2mmldWajW/g3wl0304pUrNvKP3sC9oQUfXLxegaSFeGc/aW2tpsacFK7xXUKNwDfa028PcenMpZZl+ezhJkvi5nSMKlZrpB++m7clm+TXOHA+OwZNz6Y5Fnzh7P4Rv1l4hfx5j/hxSK8dl5nIabcu0ZVHDRTyMfEifI30IjMv8wUj/pYS1GQXMjUXd8Gf4rBiWJGk7cAmwlyTJX5Kk/sBsoKkkSQ+ApinfZynUspoVXisYc3oM5azKsaP1DhytHXUd1mtkWWbpvw8YvMWTcrbm/D2iDk7F3u3oNjM2Yk7nKizr4cqT0DhaLT7H3uv+Ooo49RTLW4xNLTcxsPJA/nr4F13/6cqdsDu6DivrI0nQfplmq3nPAAh9mOqnqtQyY3d7c/DWS/7XuiK9a5ZM8+kLmJuw6dvqyLJMnw0ehMW+b1qf5XjbSq3uD1o55I6rz3kaFs+ElvYYZnANX2JyIptvb6bl3pYsvr4YJxsndrTZwW8Nf6NcvnJaPVcJ6zwMa1CGv70DuPAwNG1Pdh8MeYvCiaman3k24J+bAfTfdJVSBfKwe0gtiuXPWrWwgi+jQbEGrGyykpD4EHof7s2TqCc6jUeSJKqWyCeGb3yGVNUMawt9aaCLV8bzvwv/47jfcdqVaceUmlP0quA9QaFi/J6b/O0dQHvnIszpVOWzW6kvIhMYvcMLj6fhfOVchF++qpQlhiFcDbzKxHMTCU8Mx62gm16Vp2gDMyMzKlpXpJJ1JRysHT5qUaVVIp/B6gZgZg0DTmq2oD+BWi0zce9Ndl3z14oFz/VnEXRffZkKhfOyfaA7ZsZZuFnytZXaUnDtle7DxSUlU3/eaUrb5GHnoBoZunV83O84s6/MJjghGPfC7gx3Ho6zrXOGnQ80jZfNF53F0EDi8Pd1MTFKQwnIja2w/zv4ejM4tM+4IDOBP64848e/buFWIh9r+1TDMrf+vxYLtMvdsLsMOTEEgBVNVuBg7aCzWNacfcyMQ3fx+LExthY5Z2qo1hvotIU+iOGXsS8ZeWokvuG+jKk6hj6OffSqlullVAKDNnviExDF+OYVGFK/dKrjU6lllp16yOKTDyhiZcribi64ZoH6tMjESBZ4LuBx1GNdh6J1opKi8It+0zRqZ25HpQKVcLR2xLGAIw7WDhnTaPHkHGxuD+WaasY3G3z4TYYsy/y0XzMOdmTjcoxpqp3BD8duBzJkqycN7W1Z1atq2hwG9AVFPCytptnGH3Q6XQN8XvH7yQcsPH6fvcNqZdj/ZrwynrlX57LnwR4crB34oeoPVC9cPUPO9SFO+QbTb8PVtL+xUqtgRW1QKeC7K5AFx7nLsszy04+Yd9SXRhVsWdbDldzGYvJXTuVp1FMGHR9EtCKaJY2WUK1QNZ3EceNZBB2WX2RFz+xn5/cphBj+CDeCbzDq1CgUKgVz6s2hnl09ncXyIa4/i2DwFk/ik5JZ3O19X9fU4ukXzvc7vHgZlcjoJuUY2qBshm/HCj5OtCKaO2F3uB16m9tht/EJ9eFl3EtAYx5fyrLUOwK5Qv4K2tmp8FgDh8ZC3bHQ+Kf37pZlmV/+ucOGC09fj4PV5hvDzZeeMmX/bXq4F2fGV5X06k1nqnhtpXZIMzEtnYTGJlF/7inqlrNhZa+qWgjwfe6E3WHC2Qn4RfvRv3J/hjkP04kfq8aNJJjjo+unrTzA9zBs7watF0K1/hkXYAYgyzIzD91lzbkntHcuwvwuTuTKim8CBVolMC6QwccH8yL2BfPrz6dBsQaZHoMiWU3laUfp6V6CKW11l6HObIQY/gD7Huzjl8u/UCRPEZY0WkJpq9I6ieNj7L3uz8S9tyiU15S1fdzS3WgRnajkx30+/O0dQPVS+VnU1ZkiVrm1FK0gvYQlhHE77PY7AjksMQzQjBAtl68cjgUccbR2pFKBSpSxKpN2USPLcGAE3NgCXTaB41dv3SUz+8g9Vp15zLe1S/FTm4oZIlZnH77HyjOPst4EpFdWauWaarbttcC0A7fZctmPY6PrUcbGXCvHfIVaVrPlzhYWXV9EfpP8zKw7E/fC7lo9R1oIiEyg8YIz1ClXgDW90zC9U5ZhQ0sIewQjb4CJdn9OGUWySs3kfbfYdc2fPjVLMLWtY47zdBV8nIjECIadGMbd8Lv8WvtX2pZpm+kxfL3qEklKFfuHa39Sqb4ixPBbJKuTWei5kC13tlCjcA3m15+PpYllpsbwKVRqmblH7rHq7GNqlM7Pip5VtTZIQ5Zl9l5/wZT9PhgZGjCnU2VaVMo5WyRZCVmWCYoP4nbobXzCfPAJ9eF22G1iFDGAxrDePr89lawrvc4il7Qs+fka6+Qk2NgGgnyg//HXPq4Lj/ny+78P+aZGcX5tn3FZW7VaZswuL/7yCmDh106Z5p6QbvYO1tjUDb+qFQcJv7A4miw8Qxe3YszsUDn98b1FaEIo/zv/Py4EXKBRsUb8XOvnzKlN/wwrzzxi9uF7rOvjRuOKadjleu4B65pCw/9B/XEZF6CWSFSq+H7HDY7eDuL7xuUY1aRc1tsFEWQ4cco4vv/3e64EXmFCtQl84/BNpp5/7pF7rD77mFvTmueY0h0hhlOIVkQz/sx4LgRcoEeFHoyrNk6vJp9FJyr5frvG0/WbGsWZ2tYxQ7bVnobGMXLHDW76R9G9ejF+auOQtZuacgiyLPM85vlrYewT6sPd8LuvR5jmyZUHB2uH1+UVlawrUdS86PsX4phATUOdYS4YeJqlV8KZf+w+Xd2KMatj5QzPYCmS1fTd4IHHk3A29quu/5O3/D1hbSONVV2TaVo55MjtNzh+J4gz4xpgm1d7DSxn/c/y04WfiFPGMb7aeLqU76I3QkyRrKbV7+dISlZxfHT9tPkp7+gJj8/A916QR3//XmKTkhm0+RoXH4Uxta0D/Wp/gU2dWg0RT8AkL5jrpxe/QDskqZKYcHYCJ5+dZHCVwXzn/F2m/b+euhdMv41X2T6wBjXLWGfKOXWNEMMpTDg7gWN+x/jR/Uc6l++caedNDU9D4xiwWePHOrWdY4aPsVUkq/ntxH1WnnlE6QJ5+L27C45F9CdDLkgdKrWKJ1FP3mSPQ2/jG+GLUq0EwMrE6h1x7FjAEVszW43A29CSF3mdqPdyBO1dijOvi1Om1ZJHJyr5euUl/CMS2DW4Jg5FPu1woTNkGdY1g4inMPI6mKTfF9bnRRRtlpxneMOyjG1un/4Y0VxUF3kuYuvdrZTLV4559eZRxko3I50/xaVHYXRfc5mRjcoyplka1h5yH5a7Q/VB0HJOxgWYDsLjFPTb4IFPQDTzu1Shg0sqdj1kWTOCOuA6BNyAF9chwAuSosDQRFMnXWc0mNtm/AIEOiFZncwvl35h38N9dLPvxiT3SZniohQVr8Tpl2OMbVae4Y20a6uorwgxnEJwfDDPY55TtWDGNKt8KRcehjJs23UkCZb3dM3UccoXHoYyeqcXkfFKxrew59vapURtWxZHqVJyP/L+O/XHjyIfoZJVANjmtsWxgCPmgdG0fn6YcNPmtBy+MdMdHl5GJdBh2UVkZPYNq62fNexatlID+GbtFW4HRHFmfEOt2B0+inzEhLMT8I3wpWfFnoyuOlqvrCH/y6gdNzh0K5Cjo+tRqkAanFMOjASvPzSlKvnTPxhEm7yMSqDXOg+eh8ezrIfrx5udY0M0wvdFivgNuA5xIZr7DHJBQUco4qL58PcAr+1gZKJ5E1D7e42TiSDbIcsyCz0XsvH2RlqWasmMOjMypdG12W9nKGyZm03fZp67jC4RYlhPkWWZTRef8uvBu5SxycPa3tUobp35RuzhcQrG/3mTE3eDqF/ehvldnLCx0N+LqSDtJCQn4Bvui0+oDz5hPlzy9yJc8eL1/UWNrahUxP119jjDLN7+w73AaLqsuERhK1N2D6mlX/6rGWCldu5BCL3WefBTGwf610mfoJNlmd33dzPv6jzMcpnxa+1f9c4R50MExyTSeP4ZnItbsfnb6qnfFo5+Cb+7QMU20GltxgaZBh6HxNJrnQfRCUrW9nHDvXTKlnNCZIrgTRG9L25AdMoQJMkACthDUdcU8euqEcK5/lMyE/oQzszWvCkzNtdMPaw5DEzFLl52ZN2tdSy6voi6ReuyoMECchtlbIJg0t5b/HMzAO8pzXJEEkyIYT1Ekaxm6gEftns8p0lFW37r6oyFDodiyLLM1ivPmP7PHSxMjZjfxYkG9mJrLjvyp6c/Y3d7U9c+D4MbG+F7ajK3419yO39RAlIcLF5ZvL0usShQCft89pgaad+g/eLDUPps8MC1eD4296+etsEMGcnpOXB6ptas1NRqmbZLzxOVoOTkD/XTtc7IxEimXZrGyWcnqVm4JjPqzMDGLOvUl2688IRpf99hWQ9XWldJQxPvyV/g3AIYfBYKO2VcgKnE50UUfdZ7YConsqW1KaUV999kfcMfvXlg/tIawVvERSOAC1VJmzNG8F04NRPuHgBTK6g9EqoPzjLuGoLUs/v+bn699Csuti4sabyEvMYZV0K2x9OfH3Z7c2RUXSoU0tNSNS0ixLCeERabxNCt1/F4Gs6wBmUY28xeb96V+QbGMHL7DXyDYvi2dikmtLTXH3EiSDf7vV4weqcXtctqLK5McxlCfLimoU6lIKz3Pu4kheITpqk//q/FW9l8ZV/buzlaO1I2X1mtbOft93rB9zu8aOtUhMVdnXX//xD1XFok5wAAIABJREFUApa6adVK7dUaF3V15iuXol98HI+XHkw6P4nwxHBGuY6il0OvLDepMVmlpv2yC4TFKjjxQ33MTVLZwJsYBYudNKKy176MDfJjJCdBkA9PvM/hdeUUVQweUxp/JFmtuT9v0Tei91XJQ24tDVQJ8NKI4gdHwawA1B0Dbt9CLj0sMRJ8MUefHmXiuYmUsSzDyqYrKZA7Y0onn4XFU2/eKaZ/VYlvMrhPSR8QYliPuPsymgGbrhEam8TczlVo7/zlF8WMIlGpYtahu2y65EfFwnlZ0t2ZsrbpbxwS6JbDt14yfPsN3ErkY2O/6u/a6QT6aOyrCjpC34OaOkXetXh7VX98O+w20YpoQGPxViF/Bbrad6VVqVYYpqOU4JX11uB6pZnUqmK61vrFJEbDwxNwZZVma1tLVmqKZDWNF57G3CQXB0fU+SKxr1QrWeG1grW31lIibwnm1Juj05Gu6eX6swg6Lr/IwLql+LF1GtZxaRkcnQy9/oIyDTMuQABVMoT6pmR7U2p9g25DSoNqhGRJ7pJumBZ3e5P5tfiy4Uhp4vlVODUdHp8Gi8JQ9wdw7QNG2rHhFOieiy8uMur0KArkLsDqpquxs9C+DaUsy1SfeZLaZaxZ1M1F68fXN4QY1hOO+AQyZpcXFqZGrO7lhlMx3Xt/foqTd4MY9+dN4hXJTGnjSPfqxfTGpkmQNo7fCWLoVk+cimnqNPN8KBN3Zz/s6g0uvaDdEvjI71qWZfxj/F87WFwMuMjDyIeUsizFMOdhNCvR7IsylbIsM/XAbTZf8mNaWwf6fokt1ZcQHQC+h+DeIXh6TjP+N3d+aPQ/rU0923DhCT//fYdN31anfvm0lzM8j37OhHMTuBV6i47lOjKh2gTMcmV+f4G2mbT3Jruu+XNoZF3sC6XyDXdyEixxA7N8MPD0R0eLpxm1GsIfv+vsEHgTlPGa+03yQhFnfA3LsfiuBQpbJ+b2b01+cx32Vzw5p5mK+OwSWBaH+uPBqTsYCqvM7IBXsBffnfwOU0NTVjZdSbl82nd9GLrVk1svojg/oZHWj61vCDGsY2RZZum/D1lw/D5OxaxY3asqBbXoLZqRBEcnMmaXN+cfhtLCsRCzO1XGykxkH7ISp32DGbTZk4qFLdgywP3TDgb/Toez86DlPHAflKrjq2U1/z77l2Vey3gY+ZDy+coz3Hk4DYo1SPObJ5VaZshWT07cDWJFz6q0qFQoTc9PFbIMwXc04tf3oEb4AOQrBRVaaz6KuWulYQ4gJlFJ/XmnqVDIgm0D3NP8M/n70d/MuDIDA8mAqTWn0rxkc63EpQ9ExClotOA05Wwt2Dm4Rup/Nt47YN9g6LQOKn+BTaYsQ9Tzt+zMrkOAt8bSDMAot6Ym+XW5gyvkL836i3788s8dapWxZnVvt9SXd2QksgyP/tX87wZc19QnN5gElTpp7W9YoDseRDxg8PHBJKmSWN5kOU422q2VX3vuMdMP3uXK5MZZRpd8KUIM65AEhYpxf3rzz82XfOVchNmdqqTNbF4PUKtl1p5/zLyjvljnMeG3rs45xqQ7q3PhYSjfbrxKWVtz/hhQA0uzz9T3qtWwowc8OAa990Opuqk+l0qt4sjTI6zwXoFftB+VrCsx3GU4tYrUSpMATFCo6LH2MncCovljoDtVS2jBTkqVDM8vvxHAEU81txd1A/uWGgFsU+Gj2fD08Gq634Hhtalil/rdoFhFLNOvTOfg44O42royu+5sCptnv4mRO68+Y8KeWyzo4kSnqqncClarYVU9UMTAd1c/Xx4QG/yW6E0RwPGhmvteWZq97exgU+Gd7Kosy/x24gG/n3xAc8eCLO7mon+v47IMvoc1NcVBtzRraDgZKrTVXvZcoBP8Y/wZdHwQoQmhLGq4iFpFamnt2N7PI2m/7ELam1mzIEIM64iXUQkM2uyJT0AUE1pUYHC90lm6zOCWfxQjd9zgaVgc3zUoy/dNymXIhDyBdvB4Ek6f9R4Uz2/G9kE1yJ/asd6J0bC2McSHaSzFrIqn6bzJ6mT+fvQ3K71XEhAXgKutK8NdhlOtULVUHyM8TkGnFReJiFewZ2gtyth8Qdd8UqwmY+Z7CO4fgYQIMDSGUvU14te+JVhkQOb5LYJjEqk/9zSNK9qytIdrqp/nHeLNhLMTCIwLZIjTEAZUHqBX0zK1iVot03nlRfzC4vn3hwaff8P2igcnYFun93cxEiLeWJq9cnaITrERlAw0IvFVY1tRVyhY6XWN/Mfim/a3pnznazc7ZnaonOme3GlCrYa7+zWiOPQ+FKqsGWVdvnmGvNkTZA6hCaEMPj6Yx1GPmVN3Ds1KNtPKcZUqNVWmHaNrtWJMa+eolWPqK0IM64DrzyIYtNmTRKWKxd2caVwxE5oqMoG4pGSmHbjNbk9/XIpbsbiri068kQWfxtMvgt7rrlDI0pQdg2qm3Tc69CGsaQT5isO3R8E47Z7DSpWSvQ/2svrmaoITgnEv7M5w5+E42zqn6vl+YXF0XH4RMxND9gytha1FKrbwYoLg/mFNBvjxaVAlaayoyjcH+1ZQtrFWpsillv/9dYsdHs85MaY+JVMxYEKlVrHOZx3LvZZT0Kwgc+rNSfXPKytzOyCKtkvO09O9BL9+VSl1T5Jl2NRWYztWZ/QbP9/wx28ek05LM6VKzdjd3uz3CmBQvdJMalkh6yQ01Cq4tRtOz9aMdy7qBo1+hNINhSjOokQrohl+cjjeId78VOMnrU3S7bb6EnFJKv4eUUcrx9NXhBjOZPZ4+jNp7y0KWZqyto8b5QtmPyeGv70DmLzvFrIM07+qlC6rKIF2uekfSc81V7A2N2bn4JpfXgf24ARs6wyOHaDz+i++gCYmJ7L7/m7W3lpLeGI49ezq8Z3zd6lyQvB+Hkm31Zcpa2vOjkE13m/8k2VN9uveQU0G2P8aIGuy2fatoUIrKF4TDDPfw/txSCxNfztLT/fi/NL+8wIvMC6QyecnczXwKi1LtuR/Nf+XoR6j+sa0A7fZdOkp+79LQznJC09Y0xiQIa8dFHF+U+NbxDldlmYJChXDtnlyyjeECS0qMLSB/o23ThUqpWZy35m5mqEfJWpDwx+14p0tyHwSkhMYc3oM51+cZ5TrKPpXTn+T7/yjvqw484ibU5t9uLk6myDEcCahUsvMOXKP1WcfU7O0Nct7upIvtVvTWRD/iHhG7fDiml8EHV2K8nN7R50ODhHAnYBouq+5jIWpEbsG10z/iOPzv8GJadB4qsbTNB3EK+PZfm87633WE62IpknxJgxzHvbZDumTd4MYuPka9crbsLa3G0aSDM89NLW/9w69GW5Q2Dml/KGVpgZUx9mvYds8OeMbwulxDT+bmT/hd4KpF6eiVCv50f1H2pVpl3UykFoiOlFJ4wVnKGJpyt5htTFMrf1cyH3NRDYtWppFJSgZsOkq1/wimPFVZXq4p61USC9JTgLPTXBuPsQGQZlGmvIJu6q6jkyQRpQqJT9e+JHDTw7Tz7Efo6uOTtfrxSnfYPptuMofA9ypVTZjPI31ASGGM4HoRCUjt9/gtG8IvWuW4Kc2DjminjZZpWbpqYf8fvIBdvnM+L27C856bhmXXbkfFEO31ZcxMTJg1+CaFMuvhfIVWYY9/cFnL/TYBeXTX6cWo4hh652tbL6zmThlHC1KtWCY0zBKWpb86HN2XvTlxD87GFzwHlWTPJDiQzWNT6XqasSvfSuw1J/diRvPIuiw/CKjmpRjVJPyH31cQnICc6/O5c/7f+Jo7cicenMokTf7m99/jFeDSXQ5BCA4JpE+66/yMDiGRV1dsl9TkSIerq3TvNGND4PyLTWNdoWr6DoyQRpQy2pmXpnJTt+ddCzXkSk1pnyxz3tUghLnX44xukl5RjbWvn2bviDEcAbzJDSOAZuu4hcWz8/tHenpnvMuZlefhjNqhxdB0YmMblqeIfXLpD6zI0g3j0Ji6brqMgYS7Bxck1KpqE9NNYp4WN9c48Aw8F8ooJ0Xy6ikKDbe3si2u9tIUiXRrkw7hjgNoah5iqiNC9V0x/segkenIDmBaNmMl7Z1sa/fFco20WQE9QxZlum2+jKPQmI5Pa7hR+23fMN9GXd2HE+jntKvUj+GOw8nlw7KOfQJWZbpseYKd15Gc/KH+hTIZA/f5+HxfLPuCsHRSazqVZV6X+AJnWVIioErK+HiEs1kP4f20GAy2FbQdWSCVCLLMsu8lrHq5iqaFG/CnHpzMDb8st3oFovOYmNhwpb+7lqOUn8QYjgDOf8glO/+uI6BBCu+qUqN0jnXciwqQcnkfbc4ePMlNUtbs7CrE4UtxZjQjMYvLI6vV11CpZbZMahGxkwLjHymGdmcOz8MPKlVERqWEMY6n3XsvLcTtaymo0U5BoaHU+h5Sv1vXjuo0ArZvhXjr5mz+0Yw8zpXoYtbMa3FoE1ebTn+0t6R3jVLvne/LMtsu7uNhZ4LsTKxYmbdmdQoXCPzA9VTHgbH0HLxOdo7F2V+F+16qn6K+0Ex9Fp3hUSlmvV9q1G1hJZGKOs7CZGaqX6Xl4MiDqp8DfUngHUWrZHOgWy5s4W5V+fiXtidxQ0XkydX2pMhP+67xX6vALynNsu2iSwhhjMAWZbZePEp0w/epayNOWv7uGlnWzqLI8syuz39mXbgNsZGBszpVIXmjhlrX5WT8Y+Ip+uqy8Qrktk+qAYVCmVgw9XT87C5vSYj2227drxL1WpNE5TvQYJ8D7JGHcIeC3MMkPg6rz39XUdSoETd1/W/imQ1/Tdd5dKjMNb1rfZF09wyEpVapvXv50hQqjg+uj7GRu/+jEITQvnpwk+cf3GeBnYN+KX2L+QzzSGiKw3MOXKPFacf8eeQmriV1ILP9Ge48SyCfhuvYmxowJb+7qmfhpediAuDi4vhymrNFEbnHpqJdmm0VhTohgOPDjDlwhQcrB1Y3ng5VqafKVdUqzVe2zEvIfolN+7c4YznLfpUMiGfKgzMCkCHFZkTfCYhxLCWUSSrmbLfhx1Xn9PUoSC/dXXWj0lEesTjkFi+3+HFrRdR9HAvzk+tHchtrGcm9Vmcl1EJdF11mch4BX8MrEGloplQMuCxBg6NhbpjofFPX3YMZSI8OaNxgLh/RNPMIxlqutvtW/OimCur/Q6y/+F+jA2N6V6hO/0c+71+cY9JVPL1qss8C4tj5+CambPuVLLH058fdnuzpLsLbZ2KvHPfhRcX+PH8j8QqYxnrNpau9l1zXJNcaolXJNN04VksTI34Z0SdDPX1PfcghMFbPLGxMGFrf3eR1IgJgvML4dp6Tc9A1b5Q9wfIm81qp7Mhp56dYuyZsdiZF2aV6yQKJSs0YjfmJcQEakbPxwRqPmIDQZ38zvPVskSSSX5yW9tpbAjbL9XRSjIGIYa1SFhsEkO3XsfjaTjDG5ZlTNPyGGTTLYX0okhWs+CYL6vOPqasrTm/d3PBoUjOsYrKSIKjE+m2+jLBMUlsHeCeeU2Lsgx/j4Trm6HLRo3tWmqID4f7RzUOEA//BWUcGJtrsswVWkO5pu/ZYPlF+7HCewWHHh/CLJcZvR1608uhFxbGFgRFJ9Jh2QWUapm9Q2vphYBJVKpovOAM1ubG/DWs9uvXBYVKweLri9l8ZzNlrcoyt97czzpoCOCITyBDtnryv9YVGVC3dIac49Ctl3y/4wZlbMzZ3L966ryscwpR/nB2PtzYAgZGUG0A1B4F5vq1G5OjUCa8EbYpGd13vo95yVVFGCOsLbBUqVkdGEyJ5BTBa2oJFoXf+igEeYtoPlsURrYoRO2lPriVLsjv3V10u84MQohhLXEnIJqBm68RGpvEvC5OtPtP5kfwYc49CGHMLm+i4pVMalWBvrVKioxYOgiLTaLb6su8iExg87fVM2Ub+R2Sk2BjGwjygf7HNBOuPkT4E03z271D8OwSyCrNi7B9S40HcKm6n5z89YqHEQ9Z7r2c437HyWucl36V+tGjQg/8w1V0XnERGwsT9gythZWZbm0M1557zPSDd9+xJ3oc9ZgJZydwL/we3ey78YPbD5gaCcGVGmRZ5tuNV/F4Es7JHxpQyFK7P7ftHs/4cd8tXIvnY13faljmztnNix8l/InGo/jmDjDKDTWGQM3hYJbJrzvZGVUyxAV/VOC+zuomRr7/XCPTNwI3r+bz7VyGDH15FEkyZGWNn6loVxuMP58w+G7bdbyeR3JhYqMMWKTuEWJYCxzxCWTMLi/ymuZide+qqTeFFwAaATf+z5ucvBdMQ3sb5nVxyvRO8exAZLyCbqsv8yQ0jo39qlOzjI4aNmMCYXVDMDSCgachj7UmaxxwXSN+fQ9B8B3NY20dNNZnFVpBYZcvrjW+G3aXZV7LOON/hvym+fm20reUMm7KgI3eOBWzZEt/d0xz6aYUJypBSf15p6hiZ8Xmb6sjyzJ7H+xlztU5mBia8GvtX2lQrIFOYsvK+IXF0fS3szRzKJimcdafY8XpR8w5co8G9jas6FlVlHClhpD7cGY2+OwBk7waQVxjKJiK3b6PIsuaXbHX4jbgw1nduGCQ1e8+VzIE84KvBe6rDO57WV1Tqw96qj+JesKg44OIVcSytPFSqhb8vJ/0+vNP+OWfO1ya1ChbNr8LMZwOZFlm6b8PWXD8Pk7FrFjTqyq2XzrRK4cjyzKbL/kx49Bd8prmYsHXTnrXAKXPRCUo+WbtFXwDY1jbx033tk8vPGF9SyhaFWwramzQYgJAMoDitTTi174V5C+l1dN6h3iz7MYyLr28hE1uG2pad2XbiUK0qmTH0u6uOilbmnvkHstPP+LgyDrYWcPPl37muN9xahSuwYw6M7A1s830mLILi0884LcT99na35065dI3EECWZWYfvseqs49p51SE+V2c3mtyFHyGQB84PQvu/aMpbao9CqoP/KKR7VmapNiP1OO+fPd2leL955pZvyts38rqar4vAnkKwBf6Br8iMC6QQccHERAbwIL6C6hfrP4nH3/LP4q2S89/sOchOyDE8BeSoFAx9k9vDt58SQeXoszqWFlnmafsxL3AaEZuv8H9oFgG1CnFuBb2mBiJn+uniE1Kpte6K/i8iGJVr6o0qqC9aVvpwms7/DUEcuWBso005Q/lm2fKFurVwKssvbGU68HXsTCyJeRZXb6p1JGpbTN3eEBgVCIN5p+ihWMhvmmgYuK5iYQlhDHSdSR9HPtgIAmxlR4SlSqaLzqLoSRxeFTdL36tUKllJu+9xc5rz+lVowQ/t3MU/R7p4cV1ODUTHh6HPDaaJruq/SBXFk8WJSs0zWXvCNz/ZnQDQRHz/nONLVLE7Lv1uO8K30KpKg/TFuGJ4Qw7MYx74feYXmc6bUq3+ehjk1Vqqvx8jK/dijGtnWOmxZhZCDH8BQREJjBoyzVuB0QzoUUFBtcrLepctUiiUsWMg3fZctkPxyJ5+b27C2VszHUdll4Sr0imz3oPrj+LZFkPV1pU0jOrutAHYFlMJxdBWZa5FHCJJV5L8An1Qa2wpmXR3sxp0eeLpzGllYl7brL3+jP6tL7ProebsDO3Y269uTgWyH4XE11x2jeYvhuuMq65Pd81LJvm5yclqxi1w4vDPoGMaKRpfBav51ri2WX4dzo8PafJaNYbCy69wEi3NfzvoVZDXMgnShZSvo4Pff+5hsZvCduUzO3b378Svib6ackXp4xj5L8j8Qj0YGL1ifSs2POjj+2x5jJRCUoOjqybiRFmDkIMpxFPvwgGb/EkUani9+7O+pOFy4YcvxPE+D+9SVSqmdrWga7ViomL1FskKlV8u/Eqlx+Hsbhb9ty60gayLHPq2Wkmn55HHM+xNS3BBPeRNCnRJEMzsw+DY2i+9C+K2f9FWPJ9vir7FZOqT8Isl+7dLbIbQ7d6cso3mOOj66fJPSQuKZnBWzw5/zCUn9o40L+Odst2BCk8PqMRxf4eGm/i+hOhSldNX0FGIsuaCXqfLFkI1Fg4/sdKDCQwt31f4P63ZMEs/wfrcrMSSaokxp0Zx6nnpxjmNIwhTkM+eK1deMyXpacecnNa82xnGSvEcBr409OfyXtvUdjKlLW93ShXUD/f6WUnAqMSGbPLi4uPwmhVuRCzOlTB0kx0diclqxi42ZNzD0JY0MWJjq52ug5J74lXKOmwcSX+/IWBSTAV8lfgO+fvqG9XP0PeZHXYtIQH6s1YmBgxpeYUWpZqqfVzCDQERCbQZOEZapUpwNo+qbqeERGnoO/Gq/i8iGJupyp0qir+hzIUWYaHJ+DfX+GlN1iXhQaTwLHjlzXOvm0l9iGB++q25IT3n2tq9UELsXdKFswLZrxY1yOS1clMuziN/Y/206NCDyZUn/BesuDM/RD6rPfQSo2+viHEcCpQqWVmH77LmnNPqFXGmmU9XMmXR8+2ebIxarXMqrOPWXDMF1sLExZ1c6F6qZxr3aNIVjNsmycn7gYzp1NlulYTU6BSS0Scgo4rzxOqvkyhkmcJjH9B5QKVGe4ynJqFa2pFFMcp4/jh5FQuBB2loLE9m9oupqh5US1EL/gUq848Ytbhe6zt7UYTh0/v2AVGJdJr3RX8wuNZ1sOVpp95vECLyLJmqM6pGRpXGVsHaDgZKrTRZFhVyZpM7Ycazt4WvZ+yEntP4L6V1TUvlCorsZyIWlaz4NoCNt/ZTOvSrfm19q/kMniTfIpJVOL08zFGNi7HqCbldRip9hFi+DNEJyoZ8ccNzTuimiX4XxsHcmXgxCPBx/F+Hsn3O27wLDyepg4FcS6WDyc7SyrZWZLXNGdki5NVakZsv8Fhn0B+be9Ir5oldR1SluN5eDwdll/E2EhmUKtw/ri/jpdxL6lasCrDnYfjVih1mcUPcSvkFhPOTeB5tD9GMc04M3A6eU2zeNNQFkGpUtNq8Ztx1x+zRHsSGsc3a68QlaBkTW833VkQ5nTUari9V+M+EfYQLIuDKglig4H/aA3J8E2D2UcGQ2BRWDM8IouXLOgaWZZZe2stv9/4nXp29VhQf8E7/uctF5/DOo8xWwe46zBK7SPE8Cd4EhpH/01XeRYWzy/tK9HDXWTgdE1sUjLzjtzjX99gnoe/2f4qXSAPVewsqWJnhVMxSxwKW2Y7f1CVWmbUTi/+9g7I0MlbOQGfF1F8veoSJa3zsHVgVY4+28+am2sISQihZuGaDHcZThWb1DtPqGU1633Ws+zGMixy5efF/Y782qKteM3IZC4/DqPb6ssMb1iWsc3t37v/dkAUfdZ7oJZhU7/qVLbTn3HdORZVMtzaBXf/0dTffqhsQQtWYoK0sct3F9MvT8fF1oWljZdiYawpC1184gFBMYnM7PCRgUpZFCGGP8K5ByF8t+06hgYSK76pSo3SInugb0TEKbj5IoqbzyM1n/0jCYpOAsDQQKKcrTlOdlZUtrPEyc4K+0IWWdY3VK2WGffnTfZc92d8C3uGNUh717zgXU77BtN/0zVqlbFmfd9qqGQFO313su7WOiKSIqhvV5/vnL+jonXFTx4nKC6IH8//yJXAKzQt3gxvrybIqtwcG10PI7GLlOmM2enFPzdfcnhU3XdcaDyehNN/41UsTI3YMsBdONQIBJ/hyJMjTDo3ibL5yrKiyQoK5M5edcJvI8Twf5BlmY0XnzL94F3K2ZqzprdbmrqTBbolKDqRm/4aYeyd8jkyXgmAsaEBFYvkpUpRS6rYWeJUzIoyNuYY6rmfqCzLTN7nw3aPZ4xqkv1qtXTJrqvPGb/nJp2r2jGvcxUkSSJeGc+2u9vYcHsDMYoYmpZoynfO31HGqsx7z//32b9MuTgFhUrBpOqTUERUZcLeW6z8xpUWlQrrYEWCkJgkGi04jZOdFVv6V0eSJP69F8TQrdexy5ebLf3dKWKV/SZoCQQZwfkX5xlzegw2uW1Y3Wx1tu1/EGL4LRTJan76y4ed157T1KEgv3V1znb2ITkNWZbxj0jA2z/ytUj2eRFNbJLGRsfM2JBKRTTi+FUGuYS1md5YuMmyzLQDt9l0yY9hDcowrrm93sSWXVh04j6LTjxgZONyjGn65o1GtCKaLXe2sOXOFuKV8bQq3YqhTkMpkbcEicmJzL82n52+O6mYvyJz6s2hsFlxGsw7TSFLU/YNqyV+Tzpk86WnTNl/m6U9XEhWyfyw2xuHwnnZ2K8a1mLUu0CQJryCvRh2chi5jXKzuunqDyYGsjpCDKcQGpvE0K2eXH0awfCGGuN1MYEoe6JWyzwOjU0Rx1F4+0dyJyCapGTN/Pe8pkZUsbN6pwa5UF7TTBc3siwz85DGxWRAnVL82LqiEFgZgCzLTNhzk13X/JnVsTLdq79b5xuZGMmG2xv44+4fKNVK2pRug0+oD4+iHtHXsS8jXUaSyzAXK04/Ys6Re+wcVAN3UValU1RqmfbLzuMXGk9MUjI1S1uzundVLHJIo61AoG18w30ZcmIISrWSFY1XUNlG1AxnCpkthkft0HToz+/iJIYX5ECUKjX3g2JeC+Sb/pH4BsaQrNb8zRcwN8EpRRxrRLJlhmeY5h/VGJz3rqkZDyuEcMahVKkZsOka5x+Gsqb3h0dahyaEsu7WOnb67sTSxJIZdWZQq0gtACLjFdSde4rqJfOzrm+1zA5f8AFuPIug88pLNKpgy5LuLpjmEg1YAkF6eB79nEHHBwFwoMOBd2zXsjpCDKcQFpvEy6hEKhUV3cUCDYlKFXdfRr/OHt/yj+JhSCyv/g2KWuV+kz3WssXb7ycfsPD4fbpVK8bMDpXFLkUmEJeUTNfVl3gUHMfOwTWoYmf1wceFJYRhbGj8ursaSMngP+bI9/WwLySG8egLwTGJFMhjIv5/BAItERIfQnhiOPb533drycoIMSwQpIHYpGR8UpwrXmWRn4XHv77/bYu3KnaWOBZJu8XbyjOPmH34Hh1dizK/s5O4kGciwTGJdFx+kUSlir1Da1Pc+vPNsy8iE2g4/zTtnIowv4sFmnwcAAAKoElEQVRTJkQpEAgEAm0ixLBAkE4i4hTcevHGweKWfxSB0YnAG4u3NxnkT1u8rTv/hF//uUNbpyIs6uqs904X2ZFHIbF0WnGRfGbG7Blai/yfmTY5drc3B7wDODW2AUWFS4FAIBBkOYQYFggygLct3l59jnjb4q2wxTtNemVtzfnD4xk//eVDc8eCLO3hKiYd6pBrT8PpsfYKlYrk5Y+BNT5ab3ovMJqWi88xsG5pJrf6tB+xQCAQCPQTIYYFgkzglcXbGw/k9y3e4hUqGlewZcU3VbPscJDsxOFbLxn2x3WaORRkec+qH8zSf7vxKteehnN2fEOszD6dQRYIBAKBfpIWMSwMdwWCL0SSJIrlN6NYfjNaV9EMY9BYvMW9zh4bGxkwpml5IYT1hJaVCzOljQM//32HX/6+zbT/OHpcfhzGv/eCmdiyghDCAoFAkEMQYlgg0CIGBhJlbc0pa2tOR1c7XYcj+AD9apciIDKBNeeeUMQqN4Pra8zmZVlm9uF7FMprSt9aJXUbpEAgEAgyjXSlqyRJaiFJkq8kSQ8lSZqoraAEAoEgI5nUsiKtqxRm1uF77Pd6AcDR24F4PY9kTNPywr9WIBAIchBfnBmWJMkQWAY0BfyBq5IkHZBl+Y62ghMIBIKMwMBAYkEXJ0Jikhi725v8eYyZe8SXcrbmdHQtquvwBAKBQJCJpCczXB14KMvyY1mWFcAOoL12whIIBIKMxTSXIWt6uVHSOg991nvwODSOCS0qYCQcPwQCgSBHkZ5X/aLA87e+90+57R0kSRokSdI1SZKuhYSEpON0AoFAoF0szXKx8dvq2FiYUKN0fhpXtNV1SAKBQCDIZNLTQPehyQHv+bTJsrwaWA0aa7V0nE8gEAi0TlGr3Jwa2wADSXrHWUIgEAgEOYP0iGF/oNhb39sBAekLRyAQCDIfM2NhrCMQCAQ5lfSUSVwFykmSVEqSJGOgG3BAO2EJBAKBQCAQCAQZzxenQ2RZTpYkaThwFDAE1suyfFtrkQkEAoFAIBAIBBlMuvYGZVk+BBzSUiwCgUAgEAgEAkGmIjyEBAKBQCAQCAQ5FiGGBQKBQCAQCAQ5FiGGBQKBQCAQCAQ5FiGGBQKBQCAQCAQ5FiGGBQKBQCAQCAQ5FiGGBQKBQCAQCAQ5FiGGBQKBQCAQCAQ5FkmW5cw7mSSFAH6ZdsLsTQEgVNdBaBmxpqxDdlyXWFPWITuuS6wp65Ad15Ud12Qvy7JFah6YrqEbaUWWZZvMPF92RpKka7Isu+k6Dm0i1pR1yI7rEmvKOmTHdYk1ZR2y47qy65pS+1hRJiEQCAQCgUAgyLEIMSwQCAQCgUAgyLEIMZx1Wa3rADIAsaasQ3Zcl1hT1iE7rkusKeuQHdeVo9eUqQ10AoFAIBAIBAKBPiEywwKBQCAQCASCHIsQwwKBQCAQCASCHIsQw1kASZJaSJLkK0nSQ0mSJqbcti3lNh9JktZLkpRL13GmhY+saZ0kSd6SJN2UJOlPSZLMdR1nWvnQut66b4kkSbG6iu1L+cjvaqMkSU8kSfJK+XDWdZxp4SNrkiRJmiFJ0n1Jku5KkjRS13GmlY+s69xbv6cASZL+0nWcaeEja2osSdL1lDWdlySprK7jTAsfWVOjlDX5SJK0SZKkTLU+TS8p16FgSZJ83rotvyRJxyVJepDyOZ8uY0wrH1lTF0mSbkuSpJYkKUtakX1kXfMkSbqXcv3dJ0mSlS5jzHRkWRYfevwBGAKPgNKAMeANOACtACnlYzswVNexamFNed96zEJgoq5j1ca6Uu5zA7YAsbqOU0u/q41AZ13Hp+U19QM2AwYpj7PVdazaWNd/HrMH6K3rWLXwu7oPVEx5zDBgo65j1cKangPlUx7zC9Bf17GmcV31AFfA563b5r56HQcmAnN0HacW1lQRsAdOA266jlGL62oGGKV8PSer/a7S+yEyw/pPdeChLMuPZVlWADuA9rIsH5JTADwAO51GmTY+tqZo0GTogNxAVuvu/OC6JEkyBOYB43Ua3ZfxwTXpOKb08rE1DQX+397dhFpRh3Ec/z5RCUEalWZlYKTRqlZJBFEIYYS9UaESIZVBCxfZRqSFIgRCCxdBBYFiIGHQm2RakgtdaC8LhcJeXFhIlgsLClGw+7SYuWUxc9RzrjN3mu9nc+/9M1x+DzNnznNmnnPO2swcA8jMYy1mHMbAfRURlwPzgS5dGa6rKYGp5TbTgJ9ayjeMqpoeBU5l5nflNjvLtc7IzN3A8f8sPwRsKn/fBDzcaKgRVdWUmQcz89uWIk2Imro+yczT5Z/76FZPMTKb4cnveoorBuOOlGsAlOMRTwI7Gs41itqaImIj8DNwC/BK89FGUlfXcmBrZh5tJdVoBh1/L5W31NZHxJTmow2trqabgEUR8WVEbI+Iua2kG97AcwXwCPDp+IvOjqiraRnwUUQcoTj/rWsh27CqapoJXHLGbffHgBuaDnYBXDN+3it/zmg5j87N08D2tkM0yWZ48ouKtTOvmL4K7M7MPQ3lmQi1NWXmU8B1wEFgUZOhJkBVXVOAx+leYz+ubl+tonjBcjtwJbCyyVAjqqtpCnAyi68kfQPY0Giq0Z3tXLGEYqSqS+pqWgHcn5mzgI0UY1VdUVXTGLAYWB8RnwO/A6crtpMuqIh4keLY29x2libZDE9+R/j3FYJZlLcEI2I1MB14oYVco6itCSAz/wS20LHbhFTXdRiYAxyKiMPAZRFxqPloQ6vcV5l5tJzSOUXRjMxrJd1w6o6/IxQztQDvAbc2nGtUg84VV1Hso20t5BpFVU3HgNsy87NybQtwZ9PBRlD3mNqbmXdl5jxgN/B9K+km1i8RcS1A+bNro0e9EhFLgYXAE+UIZm/YDE9+XwBzI+LGiLiU4urB1ohYBiwAlozPOHZIXU1z4O+Z4QeAb1rMOIyqut7PzJmZOTszZwMnMrNL73yv21fjT3BBMQf41YD/MdlU1kQxSzu/3OZuijdpdUldXVDcnfgwM0+2lm44dTVNi4iby23upbiT1BV1j6kZAOXI0Urg9RYzTpStwNLy96XABy1m0QARcR/FcfdgZp5oO0/TOvXRLX2UmacjYjnwMcW7kDdk5tcRcQD4Adhb9CO8m5lrW4x6zqpqongy2xMRUyluIx6geENTZ9Ttq5ZjjWTA8bcrIqZT7Kv9wHNt5jwfA2paB2yOiBXAHxRzqZ1xluNvMd2aqwVqazoQEc8C70TEGPArxYxjJww4/l6OiIUUF6ley8xdrQY9TxHxFnAPcHU5y72a4ph7OyKeAX6keFHWGTU1HacYe5sObIuI/Zm5oL2U56+mrlUUo2I7y55iX2Z25rw+Kr+OWZIkSb3lmIQkSZJ6y2ZYkiRJvWUzLEmSpN6yGZYkSVJv2QxLkiSpt2yGJUmS1Ft+zrAktSgi1gB38M/X714M7KtZo2o9M9c0kVWS/o9shiWpfYsz8zeAiLgCeL5mrW5bSdKQHJOQJElSb9kMS5IkqbdshiVJktRbNsOSJEnqLZthSZIk9ZbNsCRJknrLj1aTpHYdA96MiLHy74uAHTVrDFiXJA0hMrPtDJIkSVIrHJOQJElSb9kMS5IkqbdshiVJktRbNsOSJEnqLZthSZIk9dZfI4j7IV2OAxMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "r_dd.plot(xticks=range(1,13),figsize=(12,5))#;plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>书名</th>\n",
       "      <th>作者</th>\n",
       "      <th>阅读时间</th>\n",
       "      <th>阅读情况</th>\n",
       "      <th>出版社</th>\n",
       "      <th>评分</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>大数据时代</td>\n",
       "      <td>维克托·迈尔·舍恩伯格</td>\n",
       "      <td>2016-10-25</td>\n",
       "      <td>P4</td>\n",
       "      <td>浙江人民出版社</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>删除：大数据取舍之道</td>\n",
       "      <td>维克托·迈尔·舍恩伯格</td>\n",
       "      <td>2016-10-27</td>\n",
       "      <td>P4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>鲜活的数据：数据可视化指南</td>\n",
       "      <td>[美]Nathan Yau</td>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>P3</td>\n",
       "      <td>人民邮电出版社</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>大话数据结构</td>\n",
       "      <td>程杰</td>\n",
       "      <td>2017-01-28</td>\n",
       "      <td>P3</td>\n",
       "      <td>清华大学出版社</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>利用Python进行数据分析</td>\n",
       "      <td>[美]Wes McKinney</td>\n",
       "      <td>2017-04-27</td>\n",
       "      <td>P3</td>\n",
       "      <td>机械工业出版社</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 书名               作者       阅读时间 阅读情况      出版社   评分\n",
       "108           大数据时代      维克托·迈尔·舍恩伯格 2016-10-25   P4  浙江人民出版社  7.5\n",
       "109      删除：大数据取舍之道      维克托·迈尔·舍恩伯格 2016-10-27   P4      NaN  NaN\n",
       "120   鲜活的数据：数据可视化指南    [美]Nathan Yau 2016-11-07   P3  人民邮电出版社  NaN\n",
       "169          大话数据结构               程杰 2017-01-28   P3  清华大学出版社  7.9\n",
       "219  利用Python进行数据分析  [美]Wes McKinney 2017-04-27   P3  机械工业出版社  8.5"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据相关的书籍\n",
    "bdf['databook']=bdf['书名'].apply(lambda x: 'Y' if '数据' in x else 'N') #也可以用find()\n",
    "dsbook=bdf.loc[(bdf['databook']=='Y'),['书名','作者','阅读时间','阅读情况','出版社','评分']]\n",
    "print(len(dsbook))\n",
    "dsbook.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe60e49b390>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#评分分布（箱线图）\n",
    "b_rank=pd.DataFrame(bdf['评分'])\n",
    "b_rank.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015         AxesSubplot(0.1,0.679412;0.363636x0.220588)\n",
       "2016    AxesSubplot(0.536364,0.679412;0.363636x0.220588)\n",
       "2017         AxesSubplot(0.1,0.414706;0.363636x0.220588)\n",
       "2018    AxesSubplot(0.536364,0.414706;0.363636x0.220588)\n",
       "2019             AxesSubplot(0.1,0.15;0.363636x0.220588)\n",
       "dtype: object"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b_rk=bdf.loc[:,['阅读年','评分']] #根据年对评分进行统计，排列还需要加强，多设置些参数\n",
    "b_rk.groupby('阅读年').boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>书名</th>\n",
       "      <th>作者</th>\n",
       "      <th>阅读时间</th>\n",
       "      <th>阅读情况</th>\n",
       "      <th>出版社</th>\n",
       "      <th>评分</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>动物农场</td>\n",
       "      <td>[英]乔治·奥威尔</td>\n",
       "      <td>2018-11-13</td>\n",
       "      <td>P4</td>\n",
       "      <td>上海译文出版社</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>白夜行</td>\n",
       "      <td>[日]东野圭吾</td>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>P5</td>\n",
       "      <td>南海出版公司</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>码农翻身</td>\n",
       "      <td>刘欣</td>\n",
       "      <td>2018-11-05</td>\n",
       "      <td>P4</td>\n",
       "      <td>电子工业出版社</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>图解密码技术</td>\n",
       "      <td>[日]结城浩</td>\n",
       "      <td>2018-01-15</td>\n",
       "      <td>P3</td>\n",
       "      <td>人民邮电出版社</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>逻辑与哲学</td>\n",
       "      <td>王路</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>P3</td>\n",
       "      <td>人民出版社</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>从一到无穷大</td>\n",
       "      <td>[美] G·伽莫夫 / 校注 吴伯泽</td>\n",
       "      <td>2016-04-11</td>\n",
       "      <td>P4</td>\n",
       "      <td>科学出版社</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>超越时空</td>\n",
       "      <td>[日]加来道雄</td>\n",
       "      <td>2016-07-14</td>\n",
       "      <td>P4</td>\n",
       "      <td>上海科技教育出版社</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>永恒的终结</td>\n",
       "      <td>[美]艾萨克·阿西莫夫</td>\n",
       "      <td>2018-08-23</td>\n",
       "      <td>P5</td>\n",
       "      <td>江苏文艺出版社</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>轻松读懂财务报表</td>\n",
       "      <td>张昕</td>\n",
       "      <td>2018-07-16</td>\n",
       "      <td>P3</td>\n",
       "      <td>北京大学出版社</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>小王子</td>\n",
       "      <td>[法] 圣埃克苏佩里</td>\n",
       "      <td>2016-02-16</td>\n",
       "      <td>P4</td>\n",
       "      <td>人民文学出版社</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           书名                  作者       阅读时间 阅读情况        出版社   评分\n",
       "398      动物农场           [英]乔治·奥威尔 2018-11-13   P4    上海译文出版社  9.2\n",
       "227       白夜行             [日]东野圭吾 2017-05-01   P5     南海出版公司  9.1\n",
       "393      码农翻身                  刘欣 2018-11-05   P4    电子工业出版社  9.1\n",
       "288    图解密码技术              [日]结城浩 2018-01-15   P3    人民邮电出版社  9.1\n",
       "205     逻辑与哲学                  王路 2017-03-22   P3      人民出版社  9.1\n",
       "51     从一到无穷大  [美] G·伽莫夫 / 校注 吴伯泽 2016-04-11   P4      科学出版社  9.1\n",
       "64       超越时空             [日]加来道雄 2016-07-14   P4  上海科技教育出版社  9.0\n",
       "356     永恒的终结         [美]艾萨克·阿西莫夫 2018-08-23   P5    江苏文艺出版社  9.0\n",
       "346  轻松读懂财务报表                  张昕 2018-07-16   P3    北京大学出版社  9.0\n",
       "40        小王子          [法] 圣埃克苏佩里 2016-02-16   P4    人民文学出版社  9.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#评分 top 10\n",
    "bdf.sort_values(by='评分',ascending=False).head(10).loc[:,['书名','作者','阅读时间','阅读情况','出版社','评分']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "#根据页数反算这些书一共有多少页\n",
    "def bookpage(x):\n",
    "    res=0\n",
    "    try:\n",
    "        res=int(x)\n",
    "    except:\n",
    "        res=0\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "bdf['页数']=bdf['页数'].apply(bookpage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "bdf['页数']=bdf['页数'].apply(lambda x : x if x<1000 else 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "c_nna=274"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "419 274\n"
     ]
    }
   ],
   "source": [
    "print(len(bdf),bdf['页数'].count())\n",
    "c_nna=bdf['页数'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "419"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bdf['页数'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "278622"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bdf['页数'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "664.9689737470167"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bdf['页数'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "284.3649635036496"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page_mean=bdf['页数'].sum()/c_nna\n",
    "page_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "119148.91970802919"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page_mean*len(bdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 试代码区域"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "返回的是json。\n",
    "rj里面可以提取的属性：\n",
    "- \"title\":\"书名\",\n",
    "- \"url\":\"对应书的网页\",\n",
    "- \"pic\":\"书的封面图资源位置\",\n",
    "- \"author_name\":\"作者\",\n",
    "- \"year\":\"出版年份\",\n",
    "- \"type\":\"类型，书籍一般标为b，应该是指book\",\n",
    "- \"id\":\"书的豆瓣id，可以对应其网页\"\n",
    "\n",
    "eg:\n",
    "- 'title': '未来简史',\n",
    "- 'url': 'https://book.douban.com/subject/26943161/',\n",
    "- 'pic': 'https://img3.doubanio.com/view/subject/s/public/s29287103.jpg',\n",
    "- 'author_name': '[以色列] 尤瓦尔·赫拉利',\n",
    "- 'year': '2017',\n",
    "- 'type': 'b',\n",
    "- 'id': '26943161'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T09:57:00.024998Z",
     "start_time": "2019-02-22T09:56:59.903837Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'title': '未来简史',\n",
       "  'url': 'https://book.douban.com/subject/26943161/',\n",
       "  'pic': 'https://img3.doubanio.com/view/subject/s/public/s29287103.jpg',\n",
       "  'author_name': '[以色列] 尤瓦尔·赫拉利',\n",
       "  'year': '2017',\n",
       "  'type': 'b',\n",
       "  'id': '26943161'},\n",
       " {'title': '未来简史',\n",
       "  'url': 'https://book.douban.com/subject/26945094/',\n",
       "  'pic': 'https://img1.doubanio.com/view/subject/s/public/s29317658.jpg',\n",
       "  'author_name': '[以色列] 尤瓦尔·赫拉利',\n",
       "  'year': '2016',\n",
       "  'type': 'b',\n",
       "  'id': '26945094'},\n",
       " {'title': '未来简史',\n",
       "  'url': 'https://book.douban.com/subject/4891197/',\n",
       "  'pic': 'https://img3.doubanio.com/view/subject/s/public/s6263884.jpg',\n",
       "  'author_name': '(法) 阿塔利',\n",
       "  'year': '2010',\n",
       "  'type': 'b',\n",
       "  'id': '4891197'},\n",
       " {'title': '未來簡史',\n",
       "  'url': 'https://book.douban.com/subject/30349873/',\n",
       "  'pic': 'https://img3.doubanio.com/view/subject/s/public/s29893492.jpg',\n",
       "  'author_name': '賈克‧阿塔利',\n",
       "  'year': '2018',\n",
       "  'type': 'b',\n",
       "  'id': '30349873'},\n",
       " {'title': '未来人类简史',\n",
       "  'url': 'https://book.douban.com/subject/26977428/',\n",
       "  'pic': 'https://img1.doubanio.com/view/subject/s/public/s29485158.jpg',\n",
       "  'author_name': '[以] 尤瓦尔·赫拉利',\n",
       "  'year': '2017',\n",
       "  'type': 'b',\n",
       "  'id': '26977428'},\n",
       " {'title': '新周刊丛书：未来简史',\n",
       "  'url': 'https://book.douban.com/subject/10530993/',\n",
       "  'pic': 'https://img3.doubanio.com/view/subject/s/public/s24642312.jpg',\n",
       "  'author_name': '新周刊',\n",
       "  'year': '2013',\n",
       "  'type': 'b',\n",
       "  'id': '10530993'}]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r=requests.get('https://book.douban.com/j/subject_suggest?q={0}'.format('未来简史'))\n",
    "rj=json.loads(r.text)\n",
    "rj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 419 entries, 0 to 418\n",
      "Data columns (total 21 columns):\n",
      "书名          419 non-null object\n",
      "阅读时间        419 non-null datetime64[ns]\n",
      "作者          391 non-null object\n",
      "阅读情况        404 non-null object\n",
      "备注          186 non-null object\n",
      "出版社         356 non-null object\n",
      "出版社省份       282 non-null object\n",
      "出版时间        345 non-null object\n",
      "分类          373 non-null float64\n",
      "来源          373 non-null float64\n",
      "字数(千字)      65 non-null float64\n",
      "页数          274 non-null object\n",
      "ISBN        257 non-null object\n",
      "外文书名        115 non-null object\n",
      "评分          215 non-null float64\n",
      "评价人数        227 non-null object\n",
      "豆瓣id        235 non-null float64\n",
      "阅读年月        419 non-null object\n",
      "阅读年         419 non-null object\n",
      "阅读月         419 non-null object\n",
      "databook    419 non-null object\n",
      "dtypes: datetime64[ns](1), float64(5), object(15)\n",
      "memory usage: 68.8+ KB\n"
     ]
    }
   ],
   "source": [
    "bdf.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 参考文献\n",
    "\n",
    "- https://lxml.de/api/lxml.etree._Element-class.html\n",
    "- https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
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