Caribbean Cruise.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Caribbean Cruise Incident"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have received an anonymous tip that an purchase group employee in the purchasing group for industrial oils has been on a caribbean cruise for the third time in the last two years. Since spending beyond means is a red flag, you begin an investigation. \n",
    "\n",
    "You have requested a copy of the data from the SAP system. You focus on the **purchase orders**. Analyze the data to understand the irregularity. \n",
    "\n",
    "**Can you establish sufficient evidence?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T22:50:25.650751Z",
     "start_time": "2018-04-26T22:50:25.644938Z"
    }
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-20T18:28:35.627732Z",
     "start_time": "2018-04-20T18:28:35.612855Z"
    }
   },
   "source": [
    "Some initialization to make life easier. **Make sure to run the following cell before proceeding.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:57.059671Z",
     "start_time": "2018-05-04T06:45:56.402362Z"
    }
   },
   "outputs": [],
   "source": [
    "#Allow multiple outputs for each cell\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = 'all'\n",
    "#Show simple plots in the notebook\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the following libraries:\n",
    "* [Pandas](https://pandas.pydata.org) is the most important workhorse in data analytics.\n",
    "* [Altair](https://altair-viz.github.io) is a visualization library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:57.891346Z",
     "start_time": "2018-05-04T06:45:57.071338Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import altair as alt\n",
    "#Format number without any decimals\n",
    "pd.set_option('display.float_format', lambda x: '%.0f' % x)\n",
    "#Show altair plots in the notebook\n",
    "alt.renderers.enable('notebook')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have received three tables from the SAP system that contain all the information of the `purchase order` step:\n",
    "2. The header information of the purchase orders is stored in the table `EKKO`.\n",
    "1. The line items information of the purchase orders is stored in the table `EKPO`.\n",
    "3. The sets of applicable conditions associated with the purchase orders are stores in the table `KONV`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:58.571412Z",
     "start_time": "2018-05-04T06:45:57.895652Z"
    }
   },
   "outputs": [],
   "source": [
    "ekko_table = pd.read_csv('https://raw.githubusercontent.com/mschermann/forensic_accounting/master/EKKO.csv')\n",
    "ekpo_table = pd.read_csv('https://raw.githubusercontent.com/mschermann/forensic_accounting/master/EKPO.csv')\n",
    "konv_table = pd.read_csv('https://raw.githubusercontent.com/mschermann/forensic_accounting/master/KONV.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The tables from with huge numbers of columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T22:52:57.969779Z",
     "start_time": "2018-04-26T22:52:57.965164Z"
    }
   },
   "source": [
    "## Understanding the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can find the definition of all the columns in the SAP system using the transaction code `SE16`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:58.595033Z",
     "start_time": "2018-05-04T06:45:58.574727Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MANDT', 'EBELN', 'BUKRS', 'BSTYP', 'BSART', 'BSAKZ', 'LOEKZ', 'STATU',\n",
       "       'AEDAT', 'ERNAM',\n",
       "       ...\n",
       "       'OTB_RES_VALUE', 'OTB_SPEC_VALUE', 'SPR_RSN_PROFILE', 'BUDG_TYPE',\n",
       "       'OTB_STATUS', 'OTB_REASON', 'CHECK_TYPE', 'CON_OTB_REQ',\n",
       "       'CON_PREBOOK_LEV', 'CON_DISTR_LEV'],\n",
       "      dtype='object', length=137)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Index(['MANDT', 'EBELN', 'EBELP', 'LOEKZ', 'STATU', 'AEDAT', 'TXZ01', 'MATNR',\n",
       "       'EMATN', 'BUKRS',\n",
       "       ...\n",
       "       'FSH_SS', 'FSH_GRID_COND_REC', 'FSH_PSM_PFM_SPLIT', 'CNFM_QTY',\n",
       "       'REF_ITEM', 'SOURCE_ID', 'SOURCE_KEY', 'PUT_BACK', 'POL_ID',\n",
       "       'CONS_ORDER'],\n",
       "      dtype='object', length=300)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Index(['MANDT', 'KNUMV', 'KPOSN', 'STUNR', 'ZAEHK', 'KAPPL', 'KSCHL', 'KDATU',\n",
       "       'KRECH', 'KAWRT', 'KBETR', 'WAERS', 'KKURS', 'KPEIN', 'KMEIN', 'KUMZA',\n",
       "       'KUMNE', 'KNTYP', 'KSTAT', 'KNPRS', 'KRUEK', 'KRELI', 'KHERK', 'KGRPE',\n",
       "       'KOUPD', 'KOLNR', 'KNUMH', 'KOPOS', 'KVSL1', 'SAKN1', 'MWSK1', 'KVSL2',\n",
       "       'SAKN2', 'MWSK2', 'LIFNR', 'KUNNR', 'KDIFF', 'KWERT', 'KSTEU', 'KINAK',\n",
       "       'KOAID', 'ZAEKO', 'KMXAW', 'KMXWR', 'KFAKTOR', 'KDUPL', 'KFAKTOR1',\n",
       "       'KZBZG', 'KSTBS', 'KONMS', 'KONWS', 'KAWRT_K', 'KWAEH', 'KWERT_K',\n",
       "       'KFKIV', 'KVARC', 'KMPRS', 'PRSQU', 'VARCOND', 'STUFE', 'WEGXX',\n",
       "       'KTREL', 'MDFLG', 'TXJLV', 'KBFLAG', 'KOLNR3', 'CPF_GUID', 'KAQTY'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ekko_table.columns\n",
    "ekpo_table.columns\n",
    "konv_table.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T22:55:41.657326Z",
     "start_time": "2018-04-26T22:55:41.652350Z"
    }
   },
   "source": [
    "### The EKKO table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our purposes, we use the following columns from `EKKO`:\n",
    "* `EBELN` - Contains the purchase order number.\n",
    "* `ERNAM` - Contains the purchase group employee who authorized the purchase order.\n",
    "* `LIFNR` - Contains the vendor unique identifier that received the purchase order.\n",
    "* `KNUMV` - Contains the link to the set of conditions associated with the purchase order."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T04:05:15.603045Z",
     "start_time": "2018-05-04T04:05:15.597775Z"
    }
   },
   "source": [
    "### The EKPO table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T04:10:17.248578Z",
     "start_time": "2018-05-04T04:10:17.235321Z"
    }
   },
   "source": [
    "For our purposes, we use the following columns from `EKPO`:\n",
    "* `EBELN` - Contains the purchase order number.\n",
    "* `EBELP` - Contains the line item identifier.\n",
    "* `TXZ01` - Contains a textual description of the material.\n",
    "* `MATNR` - Contains the material unique identifier.\n",
    "* `MENGE` - Contains the amount of material ordered.\n",
    "* `NETPR` - Contains the effective net price of material ordered.\n",
    "* `NETWR` - Contains the effective net value of material ordered (i.e., `MENGE * NETPR`)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The KONV table"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our purposes, we use the following columns from `KONV`:\n",
    "* `KNUMV` - Contains the unique identifier for the condition set.\n",
    "* `KPOSN` - Contains the line item identifier.\n",
    "* `KSCHL` - Contains the type of a condition.\n",
    "* `KAWRT` - Contains the baseline of a conditon.\n",
    "* `KBETR` - Contains the effective price."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following condition types are relevant for this case study:\n",
    "* `PBXX` - Gross price\n",
    "* `RB00` - Absolute discounts\n",
    "* `NAVM` - Tax deductions\n",
    "* `SKTO` - Cash discounts related to payment goals\n",
    "* `WOTB` - Effective price\n",
    "\n",
    "The following variable contains all relevant condition types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:58.604350Z",
     "start_time": "2018-05-04T06:45:58.599657Z"
    }
   },
   "outputs": [],
   "source": [
    "cond_types = ['NAVM','PBXX','RB00','SKTO','WOTB']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T04:27:15.987078Z",
     "start_time": "2018-05-04T04:27:15.983309Z"
    }
   },
   "source": [
    "## Clean the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T22:54:20.318069Z",
     "start_time": "2018-04-26T22:54:20.299872Z"
    }
   },
   "source": [
    "**Your task:** Reduce the `EKKO` table to the columns of interest. Store the result in a variable called `ekko`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:58.627173Z",
     "start_time": "2018-05-04T06:45:58.611500Z"
    }
   },
   "outputs": [],
   "source": [
    "ekko = ekko_table.loc[:, ['EBELN','ERNAM','LIFNR','KNUMV']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your task:** Show the first rows of `ekko`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:58.662878Z",
     "start_time": "2018-05-04T06:45:58.636312Z"
    }
   },
   "outputs": [
    {
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      "text/plain": [
       "       EBELN      ERNAM  LIFNR      KNUMV\n",
       "0 4500000000     COCCHI 101999 1000000000\n",
       "1 4500000001    GBI-085 125000 1000000020\n",
       "2 4500000002    GBI-085 125000 1000000021\n",
       "3 4500000003    GBI-085 125000 1000000022\n",
       "4 4500000004  GBIFAC-01 125000 1000000023"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ekko.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T22:54:20.318069Z",
     "start_time": "2018-04-26T22:54:20.299872Z"
    }
   },
   "source": [
    "**Your task:** Reduce the `EKPO` table to the columns of interest. Store the result in a variable called `ekpo`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:58.679871Z",
     "start_time": "2018-05-04T06:45:58.668249Z"
    }
   },
   "outputs": [],
   "source": [
    "ekpo = ekpo_table.loc[:, ['EBELN','EBELP','TXZ01','MATNR','MENGE', 'NETPR', 'NETWR']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your task:** Show the first rows of `ekpo`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:58.705955Z",
     "start_time": "2018-05-04T06:45:58.684788Z"
    }
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EBELN</th>\n",
       "      <th>EBELP</th>\n",
       "      <th>TXZ01</th>\n",
       "      <th>MATNR</th>\n",
       "      <th>MENGE</th>\n",
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       "      <th>0</th>\n",
       "      <td>4500000000</td>\n",
       "      <td>10</td>\n",
       "      <td>Off Road Helmet</td>\n",
       "      <td>OHMT1999</td>\n",
       "      <td>19689</td>\n",
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       "      <td>BRENTCRUDE</td>\n",
       "      <td>20193</td>\n",
       "      <td>4335</td>\n",
       "      <td>87536655</td>\n",
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       "      <td>4500000002</td>\n",
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       "      <td>20013</td>\n",
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      "text/plain": [
       "        EBELN  EBELP            TXZ01       MATNR  MENGE  NETPR    NETWR\n",
       "0  4500000000     10  Off Road Helmet    OHMT1999  19689     25     1500\n",
       "1  4500000000     20      Road Helmet    RHMT1999    150     25     3750\n",
       "2  4500000001     10  Brent Crude Oil  BRENTCRUDE  20193   4335 87536655\n",
       "3  4500000002     10  Brent Crude Oil  BRENTCRUDE  20103   4360 87649080\n",
       "4  4500000003     10  Brent Crude Oil  BRENTCRUDE  20013   4340 86856420"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ekpo.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T04:20:41.861876Z",
     "start_time": "2018-05-04T04:20:41.849690Z"
    }
   },
   "source": [
    "**Your task:** Reduce the `KONV` table to the columns of interest. Store the result in a variable called `konv`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:58.717576Z",
     "start_time": "2018-05-04T06:45:58.708797Z"
    }
   },
   "outputs": [],
   "source": [
    "konv = konv_table.loc[:, ['KNUMV','KPOSN', 'STUNR','KSCHL','KAWRT', 'KBETR']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your task:** Show the first five rows of the `konv`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:58.742669Z",
     "start_time": "2018-05-04T06:45:58.721104Z"
    }
   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>KNUMV</th>\n",
       "      <th>KPOSN</th>\n",
       "      <th>STUNR</th>\n",
       "      <th>KSCHL</th>\n",
       "      <th>KAWRT</th>\n",
       "      <th>KBETR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>PR00</td>\n",
       "      <td>20</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>930</td>\n",
       "      <td>SKTO</td>\n",
       "      <td>6000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>940</td>\n",
       "      <td>VPRS</td>\n",
       "      <td>20</td>\n",
       "      <td>1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>PR00</td>\n",
       "      <td>50</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>930</td>\n",
       "      <td>SKTO</td>\n",
       "      <td>15000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   KNUMV  KPOSN  STUNR KSCHL  KAWRT  KBETR\n",
       "0      1     10     11  PR00     20   3000\n",
       "1      1     10    930  SKTO   6000      0\n",
       "2      1     10    940  VPRS     20   1400\n",
       "3      2     10     11  PR00     50   3000\n",
       "4      2     10    930  SKTO  15000      0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "konv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Focus on Brent Crude Oil"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T04:29:31.332488Z",
     "start_time": "2018-05-04T04:29:31.319713Z"
    }
   },
   "source": [
    "**Your task:** Filter the line items that contain orders of Brent Crude Oil (`MATNR`: `BRENTCRUDE`). Store the result in a variable called `ekpo_bco`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:58.762875Z",
     "start_time": "2018-05-04T06:45:58.747539Z"
    }
   },
   "outputs": [],
   "source": [
    "ekpo_bco = ekpo[ekpo['MATNR']=='BRENTCRUDE']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T04:29:31.332488Z",
     "start_time": "2018-05-04T04:29:31.319713Z"
    }
   },
   "source": [
    "**Your task:** Show how many purchase orders contain orders of Brent Crude Oil?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:58.796574Z",
     "start_time": "2018-05-04T06:45:58.783178Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ekpo_bco)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T05:33:31.215339Z",
     "start_time": "2018-05-04T05:33:31.206642Z"
    }
   },
   "source": [
    "## The value of the Brent Crude Oil purchase orders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your task:** What is the overall value of all purchases order of Brent Crude Oil?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:58.818998Z",
     "start_time": "2018-05-04T06:45:58.807321Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2536790085.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ekpo_bco['NETWR'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T04:37:46.989304Z",
     "start_time": "2018-05-04T04:37:46.971051Z"
    }
   },
   "source": [
    "**Your task:** Show how the effective net prices (`NETPR`) of all purchase orders of Brent Crude Oil changes over time. *(Assume that the order of purchase orders represents time)*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:59.250509Z",
     "start_time": "2018-05-04T06:45:58.823656Z"
    }
   },
   "outputs": [
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pwdMLm9lE9w8mAAfAAVk5n/qEfoXmf7RPPLM41upQTxAwUqFjz3KuZ1HY+wjKNWy7Qeq3O7Dc1AkqpwD4EQA+M7N2xpKnwT/lzGS+AuBzAJYD4K361dnNz/AB7QBGOsEpr2EgAHc7s6B1j0uSwDKIu7jXiYPzu99la0sIUzO8hatwFR/Q7wNBmE4+o39L1Vm5D6CPQanXQbSxUmr5P998e/SiH3+Tv/ONXEG0ugrp7bUSS7d7iAbfpKpzS7l0r2sdDwWMaPXQbpAi7QwseT0kz1R+2VkjeTuA3YbMWHJgyYetz3ReefNsZA+AK53X3Ty7uT0A3ma/FYBzATzsBJb8Gpxfsb+Uz+fPUUqt1rlTpnA8K5cQEAJCQAgIASHQDgJ8/vVl857GO0tXYLP1u3FCcnv84LYn8e+3l+GMQ3fExzbkl5XRue545B9YsOjv2HRMN6YfvhMsnuZaA6+xY8euduetQMEB4VMAHnNmHxl9P4B0TXBZcoLDx53X3U8A2B/ArQD2cQJSLr+Z88r7ZWemsstZa7kXgLrZ7WXG0oynx+FXlRkywa0K0+DsmtUUrsJVfEC/D/hhmsj2jyNl3z14iozCA5XuFRMW9B71diJTLBAhpYBvlnOpH+tXudKiH61eNBx01pxdOmxrEQE2kb3rvJnTnvFSz0sZ3Vq9tBm0TDtnLPnVNW/O4WnuHQHw7CSvm1zqrI3kTTg8U8kLefmzTwNgB+NgkTf0fA3AgwAuBfBrXpIB4GTndTrPXt4BYCcAS+rBkcAyqMs0rxcH5zdDJrhVYRqcnQSWZtgJV+HaiIDX8Wri9OJeFqm7iWi0gvrN4mUdE35/cc/g93UiUziDiC6EUteU8ulvmKLtVauX9qf09o9csrTyKEDbQeH0Uj71Ay/1vJbRqdVrm0HLhQ0stwFgNWn8hZpUQM008gLdawAcAGAsn38JYAcn8OSNO/z/+eLd35yjaXcA1cWw1wFgx+PA8noAX3HK7urMhtZtVwLLoC4jgaUZco2txmFAaTUzL+0JVy+U/JcRrv6ZeakRFa5edCanFw4A0TwQuqGwoPLeOocuuGzSsioHPlUGoHsB9cdSLs3f90YuL1q9NpzMFL8PwmlKqd+V86l9dR+xqFOr13sKWi5MYMk7rzlfZLNrAwCLfYpjuzxDyemHqht51gXA2eqr+SrZJC+8WNvZGV7bxMYA3nPTJoGlz17xWDwOzu/xVltWTJiaQS1chav4gH4fcGM6KVP8EkHNJaIRCpj7btcm0xb27lf73Y69T+3v3mCU/R6g7FcXv91lagOPm1avdBKZwj4A3Q+o9yx07DAv3/NXr3W9ltOl1Wt7YcqFCSx58w3v0ub/Nrp4tvH9XyEeha7nBIy8C9zYJYGlGbRxcH4zZIJbFabB2TWrKVyFq/iAfh9oxjSRLU4hqOLKIwgxu5ybenSjmb1EtvgsAdsQsOe8XIo35Wq/dPQ/nxI0smPkUyBsrmz19fKsNL9F1X7p0KpdVAODYQJLNsmzhpyYnF+H8//4dXT14oAz0JmYrbh5CSzNUI6D85shE9yqMA3OTgJLM+yEq3BtRKDReJXIFr4KRdcSgZTCleX81OObvS5OZgqzQTRNKXy7nE9xJhjtl46xNZEp8j19TSncWc6nDtIu0jGoQ6spbUPthgks+ZU17+ieBYDXWp42xDgnL+eE5XV3ZbfqBhu1I4GlmR6Ig/ObIRPcqjANzk4CIDPshKtw9RNYJqYXTyELF3MdpdQF5Xz6bDeCyUzxNBC+rxSuK+dTX3crH+TzsGNrItM3gUjdoaDetKlj2wUze/4VRIeXOmG1emlDV5kwgSWf4Z0DsAAAJzrnzTK8o7t6ccofzjvptg5T1734siOBpS9cngvHwfk932yLCgpTM6CFq3AVH9DvA0OZJjLFc4kGYwEoG6d6Pc96Uqbvcxap+6DwaCmf+qR+peHSDSWys8cAHfy6fiNb4ZD5+dQvTGis2oyDr/rNY8lHMHEuSX4VzjOUT5sErMO2BJY6KK5uIw7Ob4ZMcKvCNDi7ZjWFq3AVH9DvA7VME5niFUQ4TqnB2OB4P6+0W7GBJ0z/JzLF24jwJSg1p5RPH6mf5KoWw2g1rW2o/TAzlmyL11j+0MkpWWubj1zkE3Vea/UNeW1PAkuvpPyVi4Pz+7tj86WFqRnGwlW4ig/o94GVTLeyEtnitQT66uA8JeiYUi41229ryWzhac4LaZPaa/7MNOet1noF7f9ktsiB5E0K6lW7q7IdJ3XXKqyOsaBaTeuqZz9MYMmzmtW8kRxIch5KfhV+hJNL8jkn8fkqaQTacZP12pTA0kxPxMH5zZAJblWYBmfXrKZwFa7iA/p94PkXXlSnXPvwXAKmKGCFUjgi6GviRKbwMyI6CkqdUMqnL9etNkj/Tzjr5k077YFnCLQOoPYr5dJ8NLXxK4hW46IaNBAmsFwHwB8BzHdOvOFzu6vXt5zzvJsmKW/XTXO7EliaoR8H5zdDJrhVYRqcXRwCy0mZvp7ph+3QN/7TO/tdomQGnItV8Vcz2KPAddw3rh6x/y6bDzz9yn+gFJbZwKEL8inehxHoqm76UcBPyrnUsYGMNKkUhGkyW7wXwPiVO9tT39atqZG9IFpbpW1oO2ECS97xzWspP+UcsVhrm0/Seb7BZ+2611XalcDSTDfEwfnNkAluVZgGZxf1wHJypn9zm+wXaTBfsEqVcuk+MzT0WRV/1cey1tJw5zq4JnKkzafpHKCUehcWJpZnpn8ThsbkTP++iuzfKIXHy/nULmFs1avrl2kiW/w2AZcrhRff7V6y08LeY2s3LOuWt4o9v1qNign449LLL+NmM5Z8jjefnsNndT/Zzhts1LYElmZ6JQ7Ob4ZMcKvCNDi7aAeWihKZ4v1EtM8H96F+2P1ix2lz5/ZUTyUzAyeEVfHXEPCaVB3OXCf23rROx5LOO0HYa9QIC0uXY49yrqd67HJgICs38FQGM8u8saxj7epZ4oENDqnoh+nEM4tjrQ71BAEjFTr21HF/fu7Dj1Y/dk2UDTNjWbvG8lYANzjJ0g8GwNPDssZSU4/FwaE0odBqJipco6KTO0e06nPRRLaQIVCONwgc8unNN73twb+t4JlLBfWbpTTq0F/NPOwNfa3psyQ+oI9lraXhyvWAGbds0K2W3QPQLoB6Y/rhO23wuU/pW7aRyBafJGAHkLVPaWbP73XS9cy0t9dKLN3uIQLGQSFXyqdm6NThxZZnrV6MGS4TJrBkaR8CBpOe8gxl7SW7wjV2XBwcSiMObaaiwjUqOiWw1OaaOOisObt02NYiKHRYlrXfJV8ft/CEqx/e27JwO+fNU0q9QqBkKZ96VF+reiyJv+rhONTKcOR64Bm3bDyyY9n9INoaSv0Dtj3+0m/t9dzYsWO9vPX0BCqZKf4UhGOUwknlfOoyT5U8FvLKdFKmmLUIMwH1zDtdH9556NnmHpsLVcyr1lCNaKocNrCsytgKwBbOP/4peSw19Y5jJg4OpZeIHmtR4RoVnRJY6vHLKb39I5csrTzKaVYU1MXlXPq0qg+s3JG6fB7PnPDmCChMK89K3aKnZT1WxF/1cBzugSWv/1WwF4KwJRReJljj5+V7/qq7/yfNKJxkKboUCjeW8ilOY6jt8qL1/R95ACqWPe6OC6bxiYMtv7xobbmoBg3qCiyHy/141iFrLD2j8lUwDs7v64ZbUFiYmoE8XLkmssWLCDgdCs92d1u7zO3tGajV6gSe1wJ0NJNRwPfLXc9MR29vbWYOM9A8WB2uXOtJF60eOrROkeSZs7eGZS0E0WZQ6vmByqh977roMD7GWftymESmsA8R/VYBT5VzqR2DKa5fy63/x/fe27n20n8+PphLU2HG/HyKTxtsy+WmtS2iJLBclYAElmbcMA7Ob4ZMcKvCNDi7ZjWHI1f+EgXofhAqSlm7z8/3PN7oyzqZLZ4AhUtA6ADUPZ1Luw6/7ZJD3zJDy7vV4ci1kXrR6r1fqyUnzujfoUNVfg3QBoB6bAmN2r92va9upgeecePokZ0j3+FE691dHd38Q8u/6mCBZTJTnAlCVgGLyl3P7NnOH2+6uepi6OcHm7b1ESbFh7EtgWUYeo3rxsH5zZAJblWYBmcXpcDS2Vn7OAibK6hsOZfOV/U38oHEjMJnSeFW/pJXUH+xlErMy09rayYO8df4+msi2z+OlH03COtB4YFK94oJQ0+dMdH/iUzxBSKMtSvWbvMv7PmTLsLNtA7eKyoPKWDArtBOCy5Mvair3SB2THANosNLHV2vwjudE3dSAAoAODB9AMCfvYhoRxkJLM1Qj4PzmyET3KowDc4uSoFldZMCf2GX8lN55pLPVx68mvnAxOk3f7SDlpdA2BUKS5TCUe1cdyn+Gk9/5R8xsLGAiEZzZoLFyzom1Ev/Y6L/k5nirSAcAoUvl/KpG3URbqR1fO8NXaOXdD/BwaziM85zqSt0tRnUjgmuQbW41dMVWPYCOMdp7CsA9gRwkJPHcombiHZ8LoGlGepxcH4zZIJbFabB2UUlsJyUKX7JItzGiaWXd2L7u85P/61Wu5sPTDxx/qiO0f/5KUBTV9ZTM0u51Nm1wakZiqtbddPaKh1e2hGtXigBEzPFiRZwKxFGQWFB5b11Dl1w2aRl9WqbYJrIFM8lwveUUheV8+kzval2L9XwTUCmeAURjgOwsJRL7eduyXwJE1xNqdYRWI4GwLukOLjkixOn3+Xksax3Ko+pe/FlVwJLX7g8F46D83u+2RYVFKZmQA8XrhNn9G9kqcqzBBpjQx07P5f+ydA79qp1UrZwqqXoQl53qRTutLtX9Ax9VWmG5gdWvWo1rcOLfdHqTol/9BDh5ytzqGLuu12bTGuWbscE0+T0whGwaC77dDmf4kkrLVc9rclsYTxA9yqot1dYI7e784LDX9XSWEgjJriGlNSwus7A8lQAowBs4pwfzusRtnUCTFP6A9uVwDIwuqYV4+D8ZsgEtypMg7NrVnO4cE1kincQYQIU5pXyKT5gYrXLj9bBdZc23c7r4PjouYqyEnfM6nnWDMVwWlulqVE7friuiVqT2eKRgLoRIEsBs8u5qUe7zYKbYDppRnEbS+FZPiygnEtvpqsvhmrldc7W0o5nCLQpgKNKudRsXW2FtWOCa1hNfp8rP5t3uOylAE4c0shL8ipcT7fFwaH0kNBrJSpco6KTe0e0+vPRSZniNy3CVQr4F1DZtpw78s2wgSXXn3T2zVtYKwbKINqRX68ri1LzZ6ZK/tQFKy0+EIybW61Wc01kivwq+HIikFK4spyferxbUGlyDEhmi/zqfaRCZf1Gz4kbw6GfD2WazBRmg2iaUvhFOZ86xK89k+Vb3f9h7kXHjCW3zzOVJwGY5YjpBzAdwF/CiDNZV2YszdCNg/ObIRPcqjANzq5ZzXZz/eDsYeoCMKGUS/ESorpXEK18zvL6I+2biHCYUlBE6n9LuXR1yZIZqPLjIhZcE9OLp5A1eKoelFIXlPPps73eWBBf9WI7kSk8TER7AGq/Ui690EsdtzK1Wt9f5+zyI8/NpqnPTXE1oTdMYFndCT6ygTD++xwA75kQHtamBJZhCer7AjSjxN1qVB7UqOhk4qLV3e8GS9SePQx1VSmX5tmhhlcYrslM33SQPZNfZwIoDawYSN110THvelTqu1gYrb4bC1lBtK4OMJEpnE9EZw0GlTZOLc9KXeIHsymmyUzxehCOtUmdPH9m+od+NDUqW9Vau85ZKTqonJ/Kx1IPq8sUVxM3GSaw5E07/Lp74wbC/usc81j31Y6Jm/FjUwJLP7S8l42D83u/29aUFKZmOLeTazLT9x2QOk9BvfRu19IdF/Yeu9RUYMl2J2bn7N+hrJud/IPPVmwkTOXlaydXv54iWmuJKUpk+vjV93E8ww1Os5NPXTlcmNbMol5byqX+x6+ueuWr/V9d56ygri/n0l/TYVu3jTj4qtc1lrzAlR3w5wCf/gBONdQ83jMAACAASURBVMSBJJ/nya/GtwEg6YZCelgcHCokAiPVo8I1Kjq5k0Sru6tWzx4mKIsq9Kl5F6Yecaulg+sq6y6h3oayekzMzOjQ6sZD1+ei1SG5cgb9RgJ4s44N0DFBN66YYpqcXjgAFt2toB4s59J76fAB1nriNQ//jwVcA4W/DlQGdjA5mx9GsymuYTQ1qhtmxrJqk9cHLQLACUQvd/44FsALACTdkIZei4NDacCg3URUuEZFpwSW7i7KiZfXXtr1Rz57GFDnel3zqMsHeN3lBqNsXgOf5ABCKXy3nE9f4K7cewldWr23GLykaAX4POzRS177ORG+pIAVSuGI+fnUL4JSNcU0kZ09htCxmDejlfPptYPqq633h0efVufM+dO7AK0FqH3L+fTvdNg1YcMU11Zq9TpjyZo4sHwQwIcBnA6AD6L/FoBDAWwF4GUTwsPalFfhYQnWrx8H5zdDJrhVYRqcXbOa7eCazBYuBegkKDxS6n7mU17PHtatNZEpnAXQ/zk7fm9ZPGAdVe8UlSDkdWsNosFrnTVd62Bi/bXe5lNtJiqFZTZw6IJ8aoFXfvXKmWSazBb+CdAmlQo+EX4ph6ITflCy//z6u1BQF5dz6dPC3Lfpuia56tauY8aSNX3aOcKxVh930uCusuF4SWBpplfi4PxmyAS3KkyDsxtOgWVN4uWlgL1jOXckr1H3dJnwgUSmbwLI7ifQOlDqSbtzZGL++YeHzuRhQqsnSAEKrclaDzzjxtEjOkcsINBneRYQFiaWZ6Z/EwDjKlVMMk1kCncR0RfJpkPnzZp6WxityUzxNBC+D6hnurs6dp3b2zMQxp7puia56tauK7BkXWsB2BoA7zzkTPX/1C1Wpz0JLHXS/MBWHJzfDJngVoVpcHbDJbCsTbysFL7td1OEKR/glEcdFsogbAuFtypkH74gN+2eMMRNaQ2jqVHdNVXrSn/svIeAcXzCDNCxfznXw0vaQl8mmSYzxe+DcJpSOKecT/1vULGTZ8zZTinrUSKMXE72rndcMI1PDxzWl0muum9cV2D5beeUHbtGIOe25FyW7+gWrcOeBJY6KK5uIw7Ob4ZMcKvCNDi74RJYJrKFOQRK83G3pVxqgt87MukDPHM1snNkcXDdpUJFQWXL+fRFfjVWy5vUGlSTBJYfEDhgxi0bdKtl9wC0C6DeWGGp/XUGVib7PzGj78uk1E+g1M9L+fSUIP7grCl9hAg7Txz3ERw/5bN+lv4FaVJLHZNctQisMaIrsLwWwNAt+rzWcjtnl7hu3aHtSWAZGmFdA3FwfjNkglsVpsHZDYfAclKmr8ci1aeg3rSpY9sFM3v+5feOzPvAYKqZ7xLhXNamFG6x31tn2oLLJvFpJ74u81p9yWlaeE3TevD0wmY2YSGItoZS/4Btjy9deOTz+oiazQwx+czi7qoDi6DwbCmf4vjC95XIFM8lwveUwuOX/M8eO2/9iU9IYOmbYvMKugJLToZe7ZwKnyIBgBOYcscv16xZizkJLLVgXM3ImjZQm6G4qlVhaoZyK7hOOOvmTTvtAT57eB0oOriUnzovyN20QivrmjSjmCRbFYloNBQeragRyQWzDn/Fj+ZWafWjqVHZNUnr5DP6t1SdlfsA+hgUXiZY4+fle/6qg2OtDZNMp/T2j1yytDKYwrC7q6Pb77rIRLZ/HKHyEEAriOxdL/n6p58eO3asBJaanUBXYMmn8FSvFQB2d1IQcR5Lrb+GdN2/BJa6SEoQZIbkB1ZNDtS6tYvWVYkms8V7AYxXUD8t59Kc5zfQ1UquB03v37aD7DIRxvKrUkU41M+mjlZqDQSzptKaojV55uytYVn3g2hjKPX8QGXUvndddBi/VdR+mWaazBae5nRdCtYeftaFcqqv0Uu6n2C/rp4oZFqrTrhx0Oo3gpdX4To9aIitODiUQTyBTUeFa1R0ckeI1g/cMZEpnkjEb27U3wZWLN8+TOLlVnMd3NyxpLOfCBMGcxuSOt3rMXqt1hp4AFhD/JUT8nfaxGsqNwDUY0to1P6/mnnYG2G4Natruv8TmUIfEfXAxldLs1I3eL2PZKZ4CQgnK6V+V86n9gVImdbqVZuXcnHQ6jew5NRCnAydj3Hki1+HFwD82guwdpSRGUsz1OPg/GbIBLcqTIOza9cXIO+2tjrUE1A0ylbYZ8Gs1ANh7qI9PqAomSn+L4i+s1K76uvu6jjG7fVje7QGoxt3rROnF/eyLHXnyqUYeKDSvWLCgt6j3g5Gy1st00xrjkP1nHvy/VRfSr1roWOH6hIA01q9EfNWKg5a/QSWvPv7ZCdJ+n3eELW/lASWZvogDs5vhkxwq8I0OLt2BJaDu06XvvYAp3KxFWbNz6cyYe+gnT6wyrrLlTdyM5RarAiLeXMmKSyGwpsVSy2mSufic4/a+Y/nzX5undtnfak60RD29o3VbydXvzflV2tiRuGzZNOdIHQrqN8sXtYxQVcS/HY8V9U2EzMKB5OiXyjg7nIu9UU3jk6+zucJtKmC+lo5l76+WscvU7e2TH4eB61+AkteX/lL58Sdac5spUm+WmxLYKkF42pG4uD8ZsgEtypMg7NrxxdgIlv4XwJ9VwFPrNVljXOb4fNyd+32gcF1l1aFZ7628KKXy/Ar9PcDT1KLAXpTqZX/JVKL7WpgamMxWR2LCSveXLa8a/HA2mMWL+zdj+sav9rN1c8N+tE6MVOcaAG3EmEUFBZU3lvn0CA7/P3oa1WwtnITkv0SlHq9lE9v4qYxkS3eQMBXlMKd5XzqoNryfpi6tWP68zho9RNYMs8fADgVACfWfdI55rEbAOe3HJa/WiWwNPMYxMH5zZAJblWYBmfX6sCydtfpCtva5Y5ZPc/qUD8cfOCQU25db0X3wD7KxvrKste3FNYHMEYprE9E60NhfUVYf+2uzm3eXbKiAkJH4HtXg98bbypgMQehHIw2myXFyI43O5d3LvY7SzocuHpl5FVrIlvk/I5zCOhUwNx3uzaZ1qpAne/Fq06v912vXCJTeIczFyhU1i/njnyzkS0+XYpI3dEo1VcrtIa5z7gFwX4Dy8udILKWg+Sx1ORR4vyaQA4xExWuUdHZqi8VXd6gm2vtrlMonF7Kp/gHt5ZLt1YtohoYqWrlDUBqacf6BIzpGAxGaX0OQi3i/9rrAzQGpPjz9ZWi9UEYLOuc4hZI4iqzpMCbICxuNEvKgevZPTv/trfw3CatnCUNdGMeA7ZEtvBVKLp28Ax4YHY5N/Vo3qQStM0g9Vrhq8ls4XcA7Q1bfaE0K/2rusFndvYYoONZAjayFQ6Zn0/9Ymi5VmgNwrBenTho9RtY6mLXMjsyY2kGdRyc3wyZ4FaFaXB2zWrq5prMFq4E6FuAuq+US+2n8wtdt1YzRFdaDat14onzR9kfWrK+pSpjOm2sb1uVlQGposEZUZ4l5dlRIg5I1WBAuvJvtB4HVIHuTeHRUj71yUB1W1TJjWsiUzyOCFewHKVwZTk/9XidPuj1Nt10erXTrFwiU7yKCN+0oU6bn0tfXDewzBRvI8KXlFI3lfPpo+MarOngqdtGIx/w+nDy+eDfBbAjgEOd9ZWceogTmPKC9fd0C9ZlTwJLXSRXtdOKQUWX8qhojYpOHUGFrr71Ykcn15pXbm+vsEZud+cFh7/qRYPXMjq1em0zaLm2ae3ttSb8Z4f1MArrjxgMSFcMBqPgGVIoDjwHZ0c5EB0MSEFjiLD9YCAG/KucS20c9J5bUa8Z10S28F0CDZ6drZS6oJxPn90KTe0K1pKZwvEg+hEUbijlU18dqiOZKR4Dwk8V1Kt2V2W7Rjvh2+arATonDlq9BpYzAFwAgM+ZPRIAnxXOJ+6cCIBPmKgGm24YeQMQL8L9+5CCvEZn7Zq/8YLud51/bwRgHQB/wcpF4nyxHV5czueTv9asUQks3bok2OdxcP5gd26uljA1w1YX10TNKzcAR5Vyqdm6FevSqltXuwILXffxwgsvqpOveXjwVfE/3/zPqEU//uawPCmO9TXygUSmeDERThkMKm2cWp6VukQXnyB2WuGrgzveFf1aKfWHcj7NqQ7fv1Y57Qpqv1IuvbDRfbRCaxCGUX+uwsxYdjmn69wBYPqQ3eB8bjjPXO7kbOZxY8sB6lEAdhliZ2sAzwHg9AAceP4MQB+fPAaAE6POAXAwgHEABgDcBeBRPukCwHlOwFu3bQks3bok2OfyoAbj1qyWMNXPtNkXtd/WEs4rNyjMK+VTPB5pv8QHtCMdNMhcT/rxQ68Q0UcHOtTmd52f/puZlsJbXd0HFCWyxWsJ9FWleNIVx5fzqSvDtxTOQit8lVMIjewcyRNIA6VcilMevn99cNoVrijnUsfL2BquP4PU1hFY8poO3rxTe33COcpxLye/ZTNtnwfAvyh4hvMQZ9azWv4LADZ1Asrq3zig5Z3nezspjk4CMAIAv3ris8m/B2A0gJcAcGBaNxmsBJZB3MW9TisGFXcV3kpERWtUdOoM1rz1YLhSOrhOmlE42lJ0I79yA+wdm+1QDaNWh9Yw7fupGzWtJ/34oYeIaE/Y1qdLs3oe8nOvrSy7CtfeXiuxdLsiAVMG5ylBx5iYKQ9yf63q/2S28Fc+99wmbDt/ZoonoFA97UopvPhu95KdFvYeu1QCyyC9GK6OjsDyCQC8xqH6ippfo/MM5PkA3M4K56CRg0pOj/AjAPsNmbFMO7OSfJe8y5xnIjkNxZ01s5ufAXAggJFOcPo7YHAB993OLGjdtU4SWIZznEa1WzWo6FAfFa1R0bmmBZaTM/2b21R5nE81IbLGz5vZY+yACPEBHU/86jaY68k/fvg2EA4hmw6dN2vqbWZaCm+16gPjvnH1iA+PWXfu4MYUPm5T4Yh6O57DtxjMQqt8NZktlvntpQ06Yn5u6s3V064IGKnQsaeXc8RbpTUYyVVrxUGr1zWWxwErd6EBuAzAfwBwkvStPKyx5PWQHFR+2VkjeTuA3YbMWPIr9X86geThAL7F0/0ArnKCTF7TyYuvWQe3eS6Ah53Aktd9coD7Uj6fP0cp1Tu0c6dM4XhWLiEgBISAPwJKAZfc/hT+/Pq7+OwOG+OIz3jOG+6vISltnED//X/Bb59+HVM+swX23WFY79/BwAob19z5PJ77x9vosAhfP3Br7PCxdY0zGo4NzHvoFdz96KuYsNtmOGjcZvj+bU/hlX+/hy/s+mFM3vNjw1HyGqVp7Nixq8WRXgNLBsXb+G8cQizvrHGszmLWA8oB4VMAHnNmH7lMPwCepeSAkS9eO7HM+f/89DwCYE8ACwDs4wSkXH4z55X3y85MJb8u57WW/Cq+bvJUmbE04+Nx+FVlhkxwq8I0OLtmNcNwTWaKp4HwfSg8+073kk+6vXILewdhtIZt22/9qGk9+cd/+C5InQel/q+UT3OWk2F5Pf3s8+rM6xc9AMJeSql3YWFieWb6N8NNbKv6P5kpTAPRbAV1K0CLCPg/pfD4u92b7O41IXyrtOroozho9RNYMjN+Db2hM1PIC2p55tLt4jqcDJd34XG6It7sw+smeU0Er43kgJMfmjMAPOC87uYZSN648wcAPJv5IIBLAfx6ZcaIwTPL+XU6z17ypiLePMSpj1a7JLB0655gn8fB+YPdublawtQM26BcJ8+Ys51S1qMKsJSydp+f73ncjMIPrAbValpXPftR03rSNQ99jUDXKYXryvnU19vBzK1NPvXowx/teJNn5BTU20DH/l5e9brZNfF5q/o/cVZxJ7Ix+OzxkgACbCJ713kzpz3j9b5apdWrnmbl4qDVb2AZlhtv9rkGwAEAxgIoAdgBwGcB3OsY5zWWvJmHHWl3Z0c6f3QdgG84gSXvHv+KU35XJzitq00Cy7BdVr9+HJzfDJngVoVpcHYmBupEpngfET7Xyhku8QFzPnDiNQ9OtGAt4DO1S/kUT1wMq+vAM27ZeETnwL00+J2o3lhhqf3vuGAaT7wMy6tlvtrbayWXblepQlBQ2XIuzW9LPV8t0+pZUeOCcdDa6sCylibv6ObZS04/xE7DM5ucy5J3d1fzVXJ5Ts7Of+eAs/biRTKcmJ1nThteElhq8PQ6JuLg/GbIBLcqTIOzMxRYvkWEdd/psj60sLen6TijS7n4gC6Sq9pxNu98EoQ/YRievsMbxBTshSBsuU53J95+Z9k2pQuPfN4MDT1WW+mryWxx5XGVCv8t5aeu6/ekoVZqDUs3Dlr9Bpb8SpsH2NrkspzA/N/OTKIfpus5AeMrfir5LSuBpV9i3srHwfm93WnrSglTM6yDcN371P7uDUbZ7ymoN8u5NB8l2JIriNaWCIvBj8sTrl20cYeyX4dSr5fyac6XPCyuyWf0b6k6KveDaDMovNw7bdct99h1e7/fzS2/l1b6aiJbvAlQh9sV2mnBhakX/d5sK7X61Ta0fBy0enXeDzk5IwtOLkte68g5JTk4/L1zCk7dzTNhIYetL4FlWIL168fB+c2QCW5VmAZn16xmEK7vr+tq8exWEK1mqLlbjZ7WraxEtm8ACh3l/NQOv7Ne7kT8l5g4o3+HDrtyL4g2hlLPD1RG7Xvlcbu+Vm+nrX/rZmu0sv+Tmb7pIPvtUi7NmWJ8X63U6lvckApx0Oo1sOT1jHwCTr2LX1FzwnIJLEN6VBwcKiQCI9WjwjUqOrmT4q41OaNvEpQqmzxlp56zx52rkQfcg9Eq12Sm+BcQNq/YIz62YNbhRt+WuclKZPvHkbLvBvEEjXpsCY3a/1czD3sjKj7QSp28VGBevuevbkwbfd5KrUE1VuvFQavXwJIDx08C4BQNtwD4o7MmkllwYMkbb1augRhml8xYmumQODi/GTLBrQrT4Oya1QzCdVKm+E2LcBWUuryUT59gRtnqVoNobZW2oe1EUWsyU/w9p/Fp9+k7E6cX97JI3U1Eo6HwQKV7xYQFvUcNnh4XFa5R0RklpnHR6jWwrI4pnJPytZrTd6p/r+agbNcY17BdCSzNdIkMKvq5ClP9TIMO1IlM4XwiOivIDtQwdyE+EIZe47pVrols4RYCHWorHNKuU2yS0wsHgGgeCN0K6jeLl3VM+P3FPe+ny4uKD0RFZ9AxwIwnuluNA1e/gSWf8X3rEDTyKtzdVzyViINDebrRFheKCteo6FwTBupEpvAzIjrKBk2bn5vKa8tbcokPmMH8wavwwo9AdLxS+HY5n7rSTGuNrU7KFL9EUHOJaASnPaq8t86hCy6btMrETFR8ICo614TxqtV+XG2vkQ/4CSz5lBtOVL6ls96yemoOpwPi5OZ8tvewu2TG0kyXyKCin6sw1c806JdKNYdlhWjfBTOn/taMstWtig+YIf3+jGWmcBYRna+gzivn0t8z01p9q8ls8UhA3QiQpYC573ZtMq3eyTFR8YGo6Aw6BrTSN2rbigNXP4El5518CEDWOR+8Xdx9tSuBpS9cngvHwfk932yLCgpTM6CDcE1kiy8T8HFS1hZhNg34vaMgWv22oat8FLVOyha+YoFuaPXpO4ls4atQdC0RSAGzy7mpRzfalR4VrlHRKYGlrife+w9hP4ElW73aOZ7xHAADztGOvGnnL0OSmpu7E5+WJbD0CcxjcRlUPILyUUyY+oDlo6h/roralZbGv1YfIDQXjaLWZLZ4IIA7Acwv5VIJzUjqmktML55CFi7mD5XCleX81OObpTqKCteo6JTA0pyX63gVzur6APTUkbkBgMXm5Ae3LIFlcHbNasqgop+rMNXPNMiXysHTC5vZFv0dUH8r5dKbm1FV36r4gBnaVa6TMv07W2Q/phT+VM6ndjPT2gdWE5niuUQYfOWulLqgnE+f7dZmVHwgKjqDjAFufWTy8zhw9Ttjua8zY1mbWqgTwAIAw3JnuASWZh6BODi/GTLBrQrT4Ox0/gjiVDAdFn6vlPpdOZ/+jBlVEli2g+sBM27ZoFsN/BtQr5Vy6Q+b1JDIFK8gwnGDQaWNU8uzUpd4aS8q40BUdEpg6cXrgpXRNWPJQeQRAFIAeKckB6YPAPhzMFnma0lgaYaxDCr6uQpT/UyDfKlMyvT1WKT6lEKxnE+lzaiSwLJdXBOZwgBAneZO3+GlFMVrCfRVpQbzOx/vZwd6VMaBqOgMMga00jeHthUHrn5nLHsB8PpKvvg0nj0BHARgJwDv5+FqZ6cMbVsCSzO9EQfnN0MmuFVhGpydzhnLRLbvdIK6yFaYNT+fyphRJYFlu7gmssXXCdiIltsbzfv+tH/r1DG+997O0Utfm0PAlMF5StAxpVxqtp82ojIOREWnBJZ+vM9fWR0zlrwr/DEAHFzytQ6AuwA8B+BTAP7gT1JrSktgaYazDCr6uQpT/UyDfKkks4VLAToJwImlXOpHZlRJYNkurols8VkCtlkOa+s7cz0v6NSRzBZnAThTASuUwhFBkrBHZRyIis4gY4BOn/BrKw5c/cxYVgPLUwGMArAJ76zjE6gAbOsEmH4ZGi8vgaUZxHFwfjNkglsVpsHZ6ZyxTGaKt4JwiCL1pfLM9O1mVElg2S6uiUzhQSLak4A95+VSD+vSMXlG/+dt276XUwrZpI6ZPzP9syC2ozIOREWnBJZBvNBbHR0zlhyEXsq/4oc0+ZK8CvfWCW6l5EF1IxTs86hwjYrOuA/UyUxxEQi72xVrt/kX9vwpmNcFqyU+EIybW61arolM8Q4iTAAwoZRL8Vu30FciO3sMYD1JoE0V1PfKufR5QY1GxQeiojPu41VQP9NRT0dgyTp4ppJfEfF0P1/9AKY7eSx16NRuQ2YstSMdNCiDin6uwlQ/0yC+mswU/gWiDZfQyA1/NfOwN8yoqm9VfMAM7SGBZYGIN6CqVCmX5hR6oa9kpng7CJMV1IPlrmf3QW9v9WQ637aj4gNR0RlkDPDdaRorxIGrn1fhHFSe7BzreJ9GjkZNSWBpBm8cnN8MmeBWhWlwds1q+uHKmy/WXvracigsKeVTfFxtSy8/WlsqrE5jUdX6QRogdVwpl74qLEc+VYdA10HhvwOdase7zk//LYzNqHCNik4JLMN4Y/O6OmYsOdXQLwG8DmAagIo5ufosS2Cpj2WtJRlU9HMVpvqZ+v1SmTSjuI2l8Cygninl0tubUdTYqviAGeKrzlgWzieiswA6q5SbOjNMi4ns7K2grMeIaLQNOmJ+burNYez59dewbYWpL74ahl68xwA/M5ZM4gcAePPOPQCeBNAFoBvAtwH81wzmcFYlsAzHr1FtGVT0cxWm+pn6/aJOZPu+QFD8A/quUi7F6/BaeokPmME9JLA8g4guBHBhKZfipVyBrsHZ7SWvPcjrcRXUT8u5NKfgC31FxQeiotPvGBC6A0MaiANXv4Hl5U4QWYuOZzC3A/BmSJ5GqktgaQSrrLE0gDUOA4oBLKFN+uFafa2pFK4r51NfD924TwN+tPo0rb14VLUmphe+RhZdC6WuKeXT3wgKJpkpnAei70Dh5YHKwM53XXTMu0Ft1daLCteo6JTAUodX1reh41X4UMsbA3gbwFJzssNblsAyPMN6FmRQ0c9VmOpn6vdLJZntOwdQvUrhnHI+9b9mFDW2Kj5ghvgqM5bTi4eRhZuh1M9L+fSUIC2uPPZT/VaBbBtqzwW59B+D2Iny2Cq+qqvHV7UTB65eZyy3BHCtk1boBADHApjo4NjL2dBjhnJIqxJYhgTYoHocnN8MmeBWhWlwds1q+uGayBau46P4bKhj5+fSPzGjSALLdnJNTO/bjyx1DxR+VcqnvuBXy8Tem9bpWNrxBEAfU8B3yrnU+X5t6PJXne36teXnufJrW3d50aqb6Ep7YWYsOfi8BsDXhki7DsBkAO9IHks9nSbOr4fjUCtR4RoVnc0GFDM9GM6qH66JbPGXBHwBtvpCaVb6V+Fa9l/bj1b/1vXWiKrWSWf2f9LqsP8IhUdK+dQ4v1SSmcJsEE3TkVqoXttR4RoVnXEer/z6ru7yYQJLPnHnIQB83mkOwK+dE3cucHaHXyxrLPV0lzyoejhKYGmGY63VuPpq9bg/VCrblC488nnzJFdtIa5cW82x2Rgw6eybt7Aqy//MayNL+dRWfrQlssUpxPmbNaUWksDSD/3gZeW5Cs6uWc0wgSXv/F4EgANIfh1+tXNmOG/kOcT5t2ze0dBv4vwaINYxERWuUdEZ5xmARLa4nIDOd7o2GbGwd78VZjyysVXxATPEa7l+IdO/bhfZb0HhrVI+NcZriweeXfjYiAqeINA6ulILSWDplX64cvJchePXqLaOwJLzc3FweQOAPwDgwJLXWmZkxlJPp4nz6+EoM5ZmOMZ9xnLy6XM2VCOsfyngX+VcijcntvySMcAM8qFcE5ni4Mk45fzUDoCUa6u9vVZyyXa/BWEvBfyknEvxd5+RKyo+EBWdcf4hbMQBfRgNE1jyq/DHADR6ZSDphnx0RLOi8qBqAjnETFS4RkVnXAfqyWcWd1cdWKSAReVcag8z3tjcqviAGep1Ass3iLB+59JRY2675NC33FpNZvq+A1Ln6U4tVK/dqPhAVHTGdbxy89lWfB4msOQTd44CMBJAvV92/HfePaklh5duGLIrXDfRlfZkUNHPVZjqZ+rHVydP7ztEWepWBXVrOZc+zIwaCSyHA9dEpvgCEcbSCmureRf1vNxME//gsDvwIJfRnVpIAsvWeIOMrWY4hwkszShqkVUJLM2AlgdVP1dhqp+pn8By0ozCSZaiSwH1w1IufbIZNRJYDgeuiUzhYSLagyoYN+/C1CONNB14xo2jR3SOeIxAWymlzi7n07xp1egVlXEgKjr9jAFGO9aj8Thw9ZrH0iOS4VdMAkszfRIH5zdDJrhVYRqcXbOaXrkmssWLCDhdgc4o56Z+34waCSyHA9dEpnAXEX3RLa1UNa+pUvh1OT91vKf1mCFv0Ku/hmwmdPWo6JTAMnRXNzQgM5bm2GqzLA+qNpSrGIoK16jojOtAncgUrzRfxQAAIABJREFU+oioRwE95VxqrhlvlMByOHCt9jVsNaU0K/3zepqSmb7JIHU7pxaybWub+Rf2/LMV2qMyDkRFZ1zHq1b4olsbYQJLXmN5MID7AOwG4HEAr7k1OFw+lxlLMz0hg4p+rsJUP1M/XyrJTPH3vOu3YmPvBbNSD5hRI4HlcOCayBSvIsI3odQ3Svk0HwCyyjXhrJs37bQHniTQGJOpheqxiMo4EBWdfsaAdvjm0DbjwNXLq3DeFf6Sk8MyCeAXAHhNiuUA4cCzBOC94dApQzVIYGmmV+Lg/GbIBLcqTIOza1bTK9dkpvB3EG1m2eojt89K/8OMGgkshwPXZKY4E4QsFGVK+amzVtWkKJkt3gvQ5xXU9eVceuipc0Zvwau/GhXhwXhUdEpg6aEzAxYJM2PJwSe/Fjq8SdsbAFgcUJvRahJYmsErg4p+rsJUP1OvXyrje+/tHL3ktQEQKuXc1JGtWEsX5dkqr1zN9Kh/q0OfrWSmbzpI5QE1s5RLn1VrMZktnglgVitSC0XZB2S88u+HXmrEgauXGUtm0QHgwwBuAvAjAL8HMKIG0t8GMzEMw0sCSzOdEgfnN0MmuFVhGpxds5peuB6ULXy8E/RykGP+dKr2olVne2FsRVlrIlv8Bg2eIqeuKuXSx1U5TMr072yR/QcFWK1ILSSBZRgP9F43yr7q/S5bXzLMjGWt2rUAfATA5wGMcl6Jc5A5bC8JLM10jTyo+rkKU/1M2aIXrpMyfZ+zSN23cvdvise3tlxetLZFWJ1Go6z1/TO/ofpKuXSKb2987w1day/p/hMI29oKM+bnU7l2sI4K16jo9DoGtKOvo/zDohlXrzOW1fufAqB/CAxOy8GvDtyPxWpDz0lgaQa6DCr6uQpT/Uy9fqkkMn1HEamfKaVuKufTR5tR4m5VfMCdUZASQ7kmsn1fIKhfArirlEtNYJuJTPEKIhzXytRCUQ4sxFeDeKJ7nThw9RNY8qvv3wL4M4Be56QdHoDPA/AJnhhwR9b6EhJYmmEeB+c3Qya4VWEanF2zml64JjKFs4jofKXUBeV8+mwzStytetHqbqU1JaKsNZHtH0ew/wDg4VIutWcyWzwQwJ0K6k1V6dihVamFJLAUXx1KIMrPVfVe/ASW1TPDjwRQTcXB6y5fBbB3zd9a4ykeW5HA0iMon8Xi4Pw+b9l4cWFqBrEXru+nn4E6rpRLX2VGibtVL1rdrbSmRJS1JrKztyJ08GTICxWy9rGU/SQBGymlkuV8utwagvVbiQrXqOhkyqLVjEfrWGPZBWARgCcBfBfAEgBpAPzrficAfzUjPZxVCSzD8WtUWx5U/VyFqX6mXr9UktkiBxOTQJQozZw634wSd6viA+6MgpRY/VX47DGEjsWAegMg/l47UClcV86nvh7Evs46UfGBqOj0Ogbo7MMwtuLA1c+MJbM6BMCtQ6DxLnE+V9fLrnDOebkJgL83Af8xAK/UrNncCMA6AP4CYIVTj+1sAeAdt2TtEliGcfHGdePg/GbIBLcqTIOza1bTC9dEtvg4ATspCzuXL0g9YUaJu1UvWt2ttKZEtLVyrsq+D76zFF4eqAzsfNdFx7zbGnrRH1uj3f/t7uXo93+zgN1vYMm2tgSwv5OC6DGfr8BnADgKwC4AKnXQ8m7MeU7Q+ObgDAJwA4A5zuk/4wAM8IJrAI/yRj5njWexUTdJYGnmAZJBRT9XYaqfqdfZimSm+B4I3UuVtd7d+Z7/mFHiblV8wJ1RkBL1uCazxfc3nFagdl+QS/8xiG3ddaLiA1HR6XUM0N2PQe3FgWuQwDIoLw4aFzqBI898Dp3h5PWaHCzytZ3zqp1fu/P6zdcBnOTkzuQ1nfz59wBUTwXaGsDb9YRJYBm0u5rXi4PzmyET3KowDc6uWU03rl/I9K/bRfZbUFhSyqc4pVrbLjetbRNWp+Goa30/sFT4QSmfOn24sI0K16jolMDSnGfrWGMZRt2mTlDJ6Yr41fl+Q2Ys+XjIXwP4pjNDyekfOGi8s2Z28zO8DgbASCc4/R0ADozvdmZBOeBc7ZLAMky3Na4rg4p+rsJUP1MvXyqJs4o7kY3HFfBEOZfa2YwKb1bFB7xx8luq7oxlprhIAe+U81PHt+ukpXr3ERUfiIpOL2OAX38yWT4OXFsxY8nrIXmm8svOGsnbAew2ZMaSX5G/7Bwd+RSATzmJ2K92Xnfz7Ob2APiEhK0AnMtpIpzAkl+Dc/2X8vn8OUopToW0yjVlCsezcgkBISAEVifw1N/+g6vveA7bf3RdfGviNoJoDSFww90v4PB9tsA6a9UeIreG3LzcphDQRGDs2LGrxZF+A0sOEo8AwCcVFJzAjlMPcW7LRhcHhBws8npMXlvJFydZ5x3lHDBWX2dvXGOAN+/s5WwU2scJSLn8Zs4rbw5CeaaSd6rz63Muy2syV7tkxlKT9wwxE4dfVWbIBLcqTIOza1bTjWsyW/gWQFcqhavL+dS3zKjwZtVNqzcrrSkVda2Tzuz/cDvzVTbqpahwjYpO5ixazYwJul6F82zgOY7ErwDYE8BBTrohTj9U7+JX12MALAewI4BrnXWTSwHw2kgODDmo5B3fHwJwv/PKm4NHTgPxNQAPArjUeV3OC655Fzq/TufZyzuatS+BZWsdykxr4axGZVCJis64DdTJbOECgGYopc4u59MXhPO2cLXFB8Lxi3qwFqVnS3xVfFVHYFlNkF591cwpgHh39nPOq2s+xcDt4hN6rgFwAICxAEoAdqhZb8kzkBxEftaZmdzdCS7Z7nUAvuGkIboeAAe2fO3qzIbWbVsCS7cuCfa5DCrBuDWrJUz1M/XyRZ3IFm8i4Eib1DHzZ6Z/ZkaFN6viA944+S0lXP0Scy8vTN0ZBSkRB65+XoVXA8tTAYxy8lFyImE+vWBbJ8D0w5Ht8ewlpx+ql3qoaot3aa7t7Ayvtc+znO85uSwbtiuBpZ8u8V42Ds7v/W5bU1KYmuHsxjWRKd5HhM8RWePnzey5z4wKb1bdtHqz0ppSotUM56hwjYpOLz8uzfRkMKtx4OonsOSy/Dr6xCG4XnJ5Fd6I7npOwMjrKY1dEliaQRsH5zdDJrhVYRqcXbOablwT2eLLBHycVlhbzbuoh5fgtO1y09o2YXUaFq1meiMqXKOiUwJLM37ajKufwJLt8Ewl55Oc5UjlTTjTnVNxzKkPYVkCyxDwmlSVQUU/V2Gqn6n7l4qiRLZvAAod73ZvMnJh737V073MiHGxKj5gBrtw1c9VmOpn6j5emWkzqFUdayy5bd6Ew4Ekn4bDCcn5nPDz6rymDqpTez0JLLUjHTQog4p+rsJUP1M3X02e3f8RVOxXFNSr5Vyas0609RIfMINfuOrnKkz1M3Ubr8y0GNyqjsCSZzerm2Z4Uw2n9+HXRkFfhQe/Gx81JbD0ActHURlUfMDyWFSYegTls1gzrhOnF/fqsPB7BfVgOZfmtGVtvcQHzOAXrvq5ClP9TNfEwJI32zwE4GJn0w0z+LRzVjgnNPeyK9xMTzSxKoGlGeQyqOjnKkz1M3UbqCdl+nosUn0KmFvOpXrMKPBuVXzAOys/JYWrH1reygpTb5z8looDVz9rLKuB5WwnuOQ8lOMB3APgkzXnfPvlaLS8BJZm8MbB+c2QCW5VmAZn16xmM66JTOEMIroQw+S8aPGB1vuAmRaDW42KD0RFp9uPy+A9ZaZmHLj6CSyZIh+dODSBsLwK1+RfcXAoTSi0mokK16jojNNAncgUf0iEE6FwSimf4qwXbb3EB8zgF676uQpT/UzjMrb6DSy5/KEADgewrpPMnM/zft0M4vBWZcYyPMN6FmRQ0c9VmOpn6jZQJzLF24jwJUAdVsqlbzWjwLtV8QHvrPyUFK5+aHkrK0y9cfJbKg5cvQaWnKR8AIDl/I+PVaxefH74u37htaq8BJZmSMfB+c2QCW5VmAZn16xmM67JTHERCLvbtvrU/Fnptq8TFx9ovQ+YaTG41aj4QFR0uv24DN5TZmrGgauXwLJ64g7nrtwGwGlDcPJs5XbOLnEzpENYbRRYTpnS3zF3bk+zE39CtBqsahwcKtidm60VFa5R0RmngTqZKfwLRBsOrBi5yV0XHdb2Ny/iA2bGAuGqn6sw1c80LmOrl8CSk6LnACwAsKlzNjdv3KlefL7399yOVjTTBe5WGwWWyRl9k0ozp/KRlMPmkgfVTFdEhWtUdMZl8Nv71P7uDUbZ7ylgRTmXGmHG+/xZFR/wx8traeHqlZT3csLUOys/JePA1UtgWWXCAebJzrrKtp6n66eT6gaWvb1Wcsm2faV8eoofW6bLxsGhTDMKYj8qXKOiMy6B5UHT+7fttOxnALxQyqW2DuJbuuuID+gmutKecNXPVZjqZxoXX/UTWPJayl86G3WmARhWr5EbdXG9wHLyjDnbKZt+U8qnNzLjGsGsyoMajJtbrahwjYrOuAx+k6bP+aJlWXcB6p5SLn2Amx+14nPxATOUhat+rsJUP9O4jK1+Aku+5x8AONXJXfkkAH4N3g3g2wD+awZzOKv1AstEtjiFgH67Y8TH559/+F/CtaCvtjyo+ljWWooK16jojMvgl8gWvkqg6xTwk3IudawZ7/NnVXzAHy+vpYWrV1LeywlT76z8lIwDV7+B5eVOEFnLKXKbdxKZ4rlEvC5UpUq5dJ+fTjdZNg4OZZJPUNtR4RoVnXEJLJPZQi9A5yio88q5NK8Tb/slPmCmC4Srfq7CVD/TuIytfgJLTjk0BsASAIvNINVvtd6MZTJbdNIlqWdKufT2+lsNZlEe1GDc3GpFhWtUdMZl8EtmiteDcKwN/M/8XOpaNz9qxefiA2YoC1f9XIWpfqZxGVu9BpafAXB/DcYTAPDs5bC/mgWWSmFZOZ/i1/nD4pIH1Uw3RIVrVHTGZfBLZop3g8BrKyeUcqm7zHifP6viA/54eS0tXL2S8l5OmHpn5adkHLh6CSy5zDUAepxgkv+7NgDeRfm2H2DtKDs0sJzS2z9yyVJ7WVXLq4v/M3LRj7+5vB3ahrYZB4caDhyjylX634z3NOKayBafJWAbInv7eTOn8e7wtl/iA2a6QLjq5ypM9TONy492L4Elz+g9CODHTmDJydCfdvJZPmYGrT6rQwPLyWcWd1cdWFRtQcHao5zref/f+lr2b0keVP/MvNSICteo6IzL4JfIFpcT0PnGMmut31/cw0t82n6JD5jpAuGqn6sw1c80LmOr18CSA6+LAfA6pI8DeBnAbgD+ZAatPqtDA8vEjL4vk1I/eb8FpU4o5dPD4rW+PKj6+r3WUlS4RkVnHAa/iTP6N+pQ9utKYXE5n9rAjOf5tyo+4J+ZlxrC1Qslf2WEqT9eXkvHgavXwJJnLD8MYJ7z3wQAnq3kv28M4CsA3vIKrpXlVgssM4ULiegMKLwFwnqA+lkplz6mlZoatRUHhxoOHIdqiArXqOiMQ2CZyPaPI9h/UAp/KudT/CN5WFziA2a6Qbjq5ypM9TONw9jK9+A1sOQZyx0aYIxUuqFEtrCAQAcp4PsEnK6A58q51LZmXMSfVXlQ/fHyWjoqXKOiMw6D3/spxxTmlfKpg736kuly4gNmCAtX/VyFqX6mcRhbvQaWXI5TDTULQt81gzi81dVmLLPFVwj4CBQ+qaAeJqIRla4V6y7oPcrTRqQDZtyyQZdadtlaL3YcPXduj9bTh+RBDd/f9SxEhWtUdEZ18Jsypb9jyZaVQ5VFpxDAmS4ApS4v5dOc5WJYXOIDZrpBuOrnKkz1M43q2DqUhJcZSzP0WmS1NrA88IwbR4/sHPkOgIFS1zPdiaXb/o5An1aKDirnp97pRVIiU/gZER0FhRtK+dRXvdTxWkYeVK+k/JWLCteo6Iza4PfEU8+pzE8XnUl8ahjRZiu9R9lK0Tx04DvlC1JP+PMoc6XFB8ywFa76uQpT/UyjNrY28oE1KrBMzCh8lhT9GlCPlXLpXROZ4g+JcCJAvaXc1HPd3GTyjP7PK2UvrCk3vZRLXehWz+vn8qB6JeWvXFS4RkVnVAa/STOK21i2OmlEZ8fxyyu2E08OHj17vaLKD8u5I1/y50nmS4sPmGEsXPVzFab6mUZlbK3euQSWABKZ4nFEuAJKzSnl00cmphe+RhZdqxSK5Xwq3cxN9j61v3uDkfaTIGwJpf4Nog2VglIWDp4/M1XS4WLyoOqguLqNqHCNis7hPvglsn1fgLJPAWgSkbOER+Flm9RlqqtynddlL2a8sblV8QEz1IWrfq7CVD/T4T62Dr1jHYFlNVE6pxx6wGlgHQC/B7AvgDfNYA5ntfZVeCJbvJyAbwN0Vik3dWZiet9+ZKl7lFIPlfPpTzdrKZktzgJwJu8iXesla4/3xtpXEPANKCyxlfrc/FnpP4RTCsiDGpZg/fpR4RoVncNx8Fv5w69yFECng/D+ZjwF9Zuvf3Hrzx76m1IHenudaUszfqbDqviADorR/XE5HJ+tRj0iviq+GjawPALA3AYYI7MrfHKmf1+bKodZquOWefme+w/KFj7eCXpZAf8q51KcNqnuNTkzZ0cF61H+kGDvOi8/7Un09lrJpdtx+qVJPINZUSN3WzDr8FfCuJo8qGHoNa4bFa5R0TmcvvwOnl7YrGLRt0nh+JXpwwavAUD1VYCLF+TSfxSua/ZzNZz81UtPRMVfo6JT+t+L1wUrEzaw5PN0vwlgCoB7ALzoyBgN4FkAFwBYEUya2Vr1zgqvbTGZKa4AoeOdriXdC3uPXTpUzeBO0rGVhwHaTUHly7l0tlrGeT1+Pwi7A+o1oo6p82b23Bf0juRBDUqueb2ocI2KzuEwUE+aXtiDLDoFwFQ+PWfQA3iJCnCVbXdcPv/Cnn9WvUK4rtnP1XDwVz89EBV/jYpO6X8/3uevbNjAstoaB5Z82o4FgFMMcYoeT2l6/MnVV9otsExkii8QYewKy971jgumrXZEZTJTPA2E7yvgz2t1WdvO7e0ZqFU3+fQ5G9qdVj8R9hv8boO6HbBPDbIxQB5Uff1eaykqXKOis90DdSJTOIuIzq/2sQKegK0usZese9OCyyYtG+pFwnXNfq7a7a9+6UfFX6OiU/rfrwd6L68rsOQTKh5xmj0J4Jk6PAVA285o77fkraR7YFm4i4i+aCscMj+f+kWt1YnTb/5oBy1/DoRuW9Hn5+en/rpRq8ls4VAFXESgrZRSy0H40TLVce7d+Z7/eFMqayy9cvJbLioDYFR0tnugTmSKVxENvkGBAn2xnJt6dzOfEK5+nxhv5YWrN05+S0WFa1R0tnu8imv/N+PqJ93QCAC/dSDxWsL5AEYCyAHYEcDf/AJsRXnXwDJbvJo34dhQp83Ppfk89PevZLbIuS0PhFLXlPLpb7jpndLbP3LJUvtkKHwXhA/xqzlFdM5aL1pXe0mmLg+qG+Fgn0eFa1R0tnugTmYLNwJ0NBS+XMqnbnTzCuHqRijY58I1GDe3WlHhGhWd7R6v3Pp76Odx4OonsOT1lPyq+HAAmwD4BOeDA/AegL1rdor75Wi0vHtgWcgQKKeAH5VzqROrYiZl+9IW1BxeO/lOV8cnFvb2cGJ1T9eBZ9yy8ciOZf8HwtcAsgD1DGycUJqV/lUzA3FwKE+AWlwoKlyjorPdA3UyW/w5j0MK6CnnUo02Fb7vZcLVzAMnXNdsrtL/a3b/N/se8BNYdgF4EMDzAF4FwGuZeLH8qQB2AvAvM5jDWXUPLItTCOjnGdhSLpWotpbMFF4D0cY2YXLQPJWDu8mJLgfo82xXKVUm2z61dOGRzHC1Sx7UcH3dqHZUuEZF5zAILMucjUEplSzn0/z/m17C1Y1QsM+FazBubrWiwjUqOts9Xrn199DP48DVT2DJ98/n694/BASfWMP/U34BtqK8W2DJu0stix5WCk+X86kdWFMiO3sMoWMxJ0Av51O8USnUNXT9JYGuWArrnKHrL+PgUKFAGaocFa5R0dnugTqZLd4LYLyyaf/yrKn8/yWwdINg4HPxVwNQOeXKiy+qsWPH+v1uNiOmidWo6Gz3eOW3Y+LANYjzbgjgkwBGAeAj0Z72C66V5d0Cywmn9q8/YpT9hoJaWs6luwcDy+nF7cnCU7zbtJxL7axD79D1l0phMRHO6X7RurK6/jIODqWDlW4bUeEaFZ3tHqgT2cIDBPp0xcbeC2alqoc1NHQb4ar7iVppT7iu2Vyl/9fs/m82BngJLDmA5FNnBoOuOhf//dvA4Bm8w+5yCywHA8lM8S0irGvZ6iO3z0r/Y2J2zv4dsH6lgLvLudQXdd5Us/WX8qDqJP2BrahwjYrOdgcViUzxMSLs3ChF2FAvEq5r9nPVbn/1Sz8q/hoVndL/fj3Qe/lGPuAlsOS1lYsADL4mdi4+bad6Ug0HlFtE4UjHRriS2cIjnAC9QrTvgplTf5vMFKaBaLZS6qZyPn20d8zeSw5dfwmFBd+ZuvPEvXbf0UufeG/IUEkZVPSDFabemCazRV6j/AlUKts0Wq9ca0m4euPqt5Rw9UvMW/mocI2KTgksvfldkFJhAktur2PwNMOVp+uMAXA8gPOcWUoOvG6P6hpLvrlEpngzEQ6zSR0zf2b6Z4ls3+kEdRHn5yzlUtODAPdap3b9JRFg27hsxYDVe+fFPYu92mhHORlU9FMXpt6YJjOFv4NoM3RYHy2d3/N3t1rC1Y1QsM+FazBubrWiwjUqOiWwdPO44J+HDSyrLScB8PnYfJ0G4Lqon7yzMrAsXEhEZwDUW8pNPff9fyucXsqnfhAcu7ea1fWXozqtWctW2Hx6z5uk6Nx3uje5fGHvfsPyqEwZVLz1rZ9SwtQbrUS2sJhAYxQq65dzR77pVku4uhEK9rlwDcbNrVZUuEZFpwSWbh4X/POwgeUuAArO63DOXfldAP8ILqd1NT2usTyOCFdA4cZSPvXlRLZ4EwFHQqkjS/n0nFap/dMTz6rv/OyRa97Pf6nwrII63UtKlVZprLYjg4p+4sLUG9NEpriUCKPe6VrSvbD32KVutYSrG6FgnwvXYNzcakWFa1R0SmDp5nHBPw8TWFYTo2/lNH+Pc074R5x/rwdgD49rLDud5Or1Xl9tBGAdALx+s3YjUPXvf3FexXOzbIfXdXLS8teaYfESWCazxf9v71zA5CqqPP6vnhATiCAi8vBNdAFBRVkfoC4gCJIZgvKY9ADrCwQVRVTIdERgFE26w/vlrjwXBWYyIArpSYBFAQUUgV15P1bAN08RYiAJyXTt95/UJZ1O99y6t2/d23Xn1Pftt5ipqnvur06dPvfU4+wJ4DpA/7pa7tu5p3/oBijsPora7ovLB/F9UynBIDXZf/lzVah9ZeG8gx5KRRCLh4hRsYAUsYowtQPWUxoau9qsWi5a7UcWrnZco9YSrlGJ2dX3hasvcopjaad3cWq141jy8A5z8fL0Ny9Bp6NXX6I4lnMAHAKAEdDRuk548vonAH4AoB/A7gDo0M0AcDEARg1nAtgRwMsArgdwN++yM3s9h1pBsXEsu0uXbaXQ9agGnhkpF1/f0z94H5TaTtfwzpH5xdSuU2ocpPr9l9AY1Qo/XLWicHwn7L8UoxJnGo7fRpiGM93zmB9tMHnSZH5QvlQtF/nRG1qEayiiWBWEayxsoY184eqLnOJYhqpc7ArtOJaxH9rQkNlnbjJ7ND8JoGb+zqjDYgCfN8vrHwVAR3MugPtNukhGMY8CwHzlzPqzDYATAPCHhXdpvqPVXk8bx/LAA4e7lm1VWwGFrtEpqzYqLJv0uFJ4re0erqQANRukdfOP4/ma0t99acrmZ2e5/1KMSlKjvqYfYRrOdJ9vXv46vV7hGWj9bLXS1/iR27QD4RrONU4N4RqHWngbX7j6Iqc4luE6F7dG1o7lFsapPBDAOQB2a4hYBqfO9zUO47cB/O/q5elXopvM+sMl68nGOb3NnFRnNJVRUDqc6xQbx5KNuktDDyvgX6AKO0PXbtNarxyp9PFZqZXxJmrj/ZcaeERpdUy1Mis4TJWanDJR3aAWQx3OdZ/+4TdrVfsjNP5UrRS5HSa0CNdQRLEqCNdY2EIb+cLVFznl9ypU5WJXyNKx5H5IRio/Y/ZI8mqi99ZFLIOXYr1DAfD6Iub//RmAH5rlbkY3twXwJQDc68kUkncYx5LL4Fxif6xSqZyotR5opHTggfRnxy8/vPYRPPDnFzDzA2/CNb/9MzaeNhkDfe8Ja5b635/8xzIM3/IHPPokVwOBf9lyQxzw4Tdjs9e0ur8+dRHlgULAGYGnnl+GuVfch9dvNAXH9SaSFMuZrNKxEBACQiDvBJqlH7Xa/N4EDCN5zKEdeiLTOIQPALjHRB/Z3TCAPuNc0qE8AMBPAawAsDmAnwP4kPn/OxuHlPW3NEvej5t9n9z/yb2WrNv02hHriGX/0OlK4WieCQDQo6FvHyn3sd/USpQvwH1mL/hkrVA7VUFtxf2XUPp8tVIfv/DUg55NQ+AosqYhz3jP8EVWX+TMMgKwd2nwvV1Q/wPo/62W+95no1vC1YZS9DrCNTozmxa+cPVFziztlc14N9bJA9eojiXzhJ8LoBfAFwHwZPgt5jBNK4Z0Qnmp+koA2wG4wOybpFPKvZF0Ou8E8GnjJHLJm4d46NTx3xnFvB3AmQB+aS5i/5pZTmf08loA2wNY1kwAW8eyp3/wSCh1jtZ4gekdtcbVI5Ui94KmVqIqVJP84y9AqZOefO75s+467wjydlaiyupMEIuOfZHVFzmzNNR7z1nw4S6tb9Fa3zZS6aOtCC3CNRRRrArCNRa20Ea+cPVFziztVehgN6mQB65RHEvWvQLA/obF5wBMB3C4OUwTelHxWBo24Hxz6pttGR1kqkieAud+yqB8BMCtABiRYDpJFl7GzmfxqhHepflZ8+9cr2Y0tGmxdixfuXJodTda44cjlSKd59RKXIXi/sv1Jr18koI+DFCMJP/la6H9AAAgAElEQVS+pnHMokrxalfCx5XVlTzj9euLrL7ImaWh7i4t2ENB/7cGbhgpF3nIL7QI11BEsSoI11jYQhv5wtUXObO0V6GD3aRCHrhGcSyD+yzpUG5k7pH8MYDnTQTyNxEhsj9GL3nwhlcPrQ+AS9tL6u6rZJf892nmfsv6RzBX+UvmLsuWj7Z1LPc5ZvhtelKNJ8xNWZ2FJ+I7tVW9XYVa5/5L6JuV1kcurBzE0/WJlnZlTVSYkM58kdUXObM01N1zBmcqra7W0NeMlPt42C+0CNdQRLEqCNdY2EIb+cLVFzmztFehgy2O5ZjTx+gho4VcxqbDx0gho5hc4v5zRIi8/5IO418itotU3daxZKc9/UOreOUQ/7um8cVFlSIPD6VWkpqoa+2/hK5pqAsKK2vHJbn/MilZ04Dri6y+yJmloe4pDc4C1BCgF1TLfUUb/RGuNpSi1xGu0ZnZtPCFqy9yZmmvbMa7sU4euEaJWPL9DzNL2fUsfgXgYw1Rxjg8nbSJ6Fg+BIWtjWP5SZdLyc1eNkmFWmf/JfQSBfW9J5574Ywk9l8mKauTga/r1BdZfZEzS0PdM3vocyjgImhcXK0UefdtaBGuoYhiVRCusbCFNvKFqy9yZmmvQge7SYU8cI3qWDLlIrPm7GUy8TCCyYw5zIbTkSWSY1ka4p2QPWOOpdIfWjSvj4eGUisuFKpx/6WGfgw1dezI/OJV7byYC1nbkWe8tr7I6oucWRrq7tLQlxVwrgZ+MFIuHmmjM8LVhlL0OsI1OjObFr5w9UXOLO2VzXg31skD1yiOJevyzkhmv+Gpbe6pXH2ZYgeXKI5l95orh6B04S0LK71/SvPVXCpU0vsvXcqaNHNfZPVFziwNdXf/4DFKqZO11qeMVPqOtdEV4WpDKXod4RqdmU0LX7j6ImeW9spmvCe6Y8n3/waAU+tAzDd7LBm55GntjiuRHEsTDeFLPPHcC5OTWDKOAiSNidq4/xJQF728avJx15+yH9NmWpc0ZLUWJqSiL7L6ImeWhrq7NHi8gvqu1vjuSKV4oo2OCFcbStHrCNfozGxa+MLVFzmztFc24y2O5RoC3IfIaAHvmPynOSFuc91QHM5ttYnkWPYPFZXCIB9YLRejRHPbkjFonNZEbZJ//J81YK5+acPTF589g5fUh5a0ZA0VxKKCL7L6ImeWhrqnf2geFEo1jTmLKsWyxfBDuNpQil5HuEZnZtPCF66+yJmlvbIZb3EsgTeZq4WY6zu445FX9OxgHMw4HJ22ieRYzh7aVhV4Ybv+e7Xcx8vgUy1pT9R17r/UeBxaz67O77sy7MXTljVMnvH+7ousvsiZpaHuKQ2eCaijakp/bdG8vrNs9EK42lCKXke4Rmdm08IXrr7ImaW9shlvcSyBBSbrDlmcYA7uPNipy+AUMopjyfqrrxzSN1fLfby0PdWS1URt3H+pgVuVxpHVSpHpMpuWrGSNMyC+yOqLnFka6p7+wfOg1Beg9eHVSh+TLYQW4RqKKFYF4RoLW2gjX7j6ImeW9ip0sJtUyAPXqMu9XwbwOwB3mBSNcbil2iaGY/kQoO+qVvoOTlVQIPMlu3X2X2p1ycujk0vN9l/mQfnTHt+w5wnTMEJAd2noUgVwbh5SLRcvC2+R/byykTGoIzoQhZZ9XeFqz8q2pjC1JRWtXh642jqWTLv4nLliiBel1x/U4YXij3SqoxnZseSVQxqPVCvFb0ZTh/Zrd4JCrbP/EliqtZ5Xe2mjU+v3X3aCrLbEfZHVFzmzjAB09w/9RCnsB+j9quW+n9rogHC1oRS9jnCNzsymhS9cfZEzS3tlM96NdfLA1caxZOpFXivUD2Dbuhzd9Tw2MY5nHI5O20R1LLtLg6cpqCeq5eLJTgXr8BB4k/sv/6h1YfaiyqxhmahuNCMPBsUNmTW9dpcGFyuoT9RQ23tR+aBrbZ4nXG0oRa8jXKMzs2nhC1df5JTfKxuti1enlQ7YOJaTAHwcAA/pbAyATuTKOjF4r+UNAKxOE8cTP36r6I7l0Jeh1ZKRyqxL4z81XstOnKit9l+eefj7fzd9+nQb/YkHI8FWnci12ev5ImeWhrqnNHgToHapabXLosqsX9qoiXC1oRS9jnCNzsymhS9cfZEzS3tlM96NdfLANYpjwLrcLH+BuRydPJiJ59cAPgLA++uG+EIzZl/+cVXo0iPlWXSWUy2drFAz+of2VUozmruV1tAf+JdN1G2PLtnyurn7P5EqpBgP62Su9a/ji5xZGuqe0tBvAbxfAR9YWC5yr3doEa6hiGJVEK6xsIU28oWrL3Jmaa9CB7tJhTxwtXUsDzAXoTfjxIu1t8mLY/mJ0uBbuwpq2sjc4n1xlKKdNp2uUNx/+dKy0aMU1AlQeLXW+kWFQnnp1JdOuWngc8vbeXeXbTuda/DuvsiZhaGeOXtwy2vm9/2tuzR0rwK2V7q2/cLKQffb6I1wtaEUvY5wjc7MpoUvXH2RMwt7ZTPOrerkgautY8mrd44AcCCAX/AAs4HC/ZcPA5gLYFU7MF21jboUTjm6S5dtPFI+OPUIrC8Ktfec4U0/uvUmT9/20NM1QBWg8aeaUqVF5Vljl8t3WvGFqy9ypm2oVx8oG/1Ntdz3vu7S4KOMmqtVha0WntL7uI2uCVcbStHrCNfozGxa+MLVFznTtlc2YzxenTxwtXUsAw50LLn89Id24aXVPo5jmZZsjc/xTaGOPu/27bVS53LPG99FQ98OdB05Uu5lis+OKb5w9UXOtA11T//QqVD4xsoVhU0mvWr0PgW1RW20sMWik3uftFEy4WpDKXod4RqdmU0LX7j6Imfa9spmjMWxXJvAewHwJOZeAJ4HcA+AvQHc2i5MV+3FsXRDtt6oNO6/VEpfurIwub9T9l/6YgB9kTNNQ73PnOFdtK7dxGdy+buGwq1KYaPRKas2WjxwyBIb7RauNpSi1xGu0ZnZtPCFqy9ypmmvbMY3rE4euEaJWLIuD7R8DMB25nqhIDML//ezYcCy+Ls4lm6oNyp/4/5LAC9pjcqLU5fNz3r/pS8T1Rc5bQx195zBmagVVoxUZl0XVwO5JQUo3M8IJfuo1Wp7KqVGlFLrPfHcC5PvOu+I+tspWj4mT1zjsnTRTri6oOrPhf4y/hN7/Mf7HYjiWHI/JSOURwNYaJC+FQD3Oe1Ud1LcDe2YvYpjGRNcSLNWRoX7Lwu12klK6S9w/6XW+i9KqZJtlhQX0vpiAH2RczyDQmdQ6cI5UOogaP306NTRd9hGFhvHvqd/6Boo7BP8u4Y+VEFdCI3RaqXIa9CsSh64Wr1oypWEqxvgvnD1RU6bD2E3Ixmv1zxwjeJYMuMO984xN/hJAF5gam0AZwPY2mTfiUfSYStxLN3ADVP+de+/zG7/ZZisbghF79UXOVsZ6u7Sgj0UapcCarM1ziAuGykXD4lKo6d/8AtQ6jwNPAONa5TCoYCeB6g5GnrJSLlvI9s+fedq+55p1xOuboj7wtUXOcWxdKOn43GN4liyn30B/KxBTEYv98vTqXB3wzB+z3mcqI37L6Fwea22Xmnx/P3/khZnX7j6ImejQZk5++pX1wrLTgNwGP82dohLF06Eqv1MQU0Z1ZixuFJcbDve3aXLtjJL4PyY3UtrbK0UztJaDyulegH9VLXct7ltf75ytX2/rOoJVzfkfeHqi5ziWLrR0yQdS/b1NgD/ZvKG8x45HtypuRO9vZ4lYtkev1atoxiVdfZfaizTwMmFqRtUFg7s85IbCdf0GkVW17KM178vctYblL3nLPhwl64NAupNGno57zitTnnoVAwM1Hr6h74GhTO4JL7y5a5trzu997kwvrsO3Dhp2rKnbofC+zRwzki5+NUZpQX7F6Cv1MBdCtgRGo9XK8WtwvoK/u4jV9t3y7KecHVD3xeuvsgpjqUbPU3SsXwdgHMB9AL4IoA3ALgFwPXuRG+vZ3Es2+OXhGMZ9LHO/kvgr1rpOYvmFbl8qt1IKpvhXXB98OH/08defOdZWquvKjU2eHeN1goHXzu/l/famqJVd//QLUqpnaHxs2ql+KkwWXr6h+ZBoQSNh5dOXbYDD371zBneCbp2GzSeh8JrNPDASLnIA4NWRX4ArTBFriRcIyOzauALV1/kFMfSSu1iVWqlA1GWwln3CgD7Gwk+B2A6gMPzlHknFt2EGk2Uidok//hdWukjF83ruz0hlGt14wtXX+TsLg3vuOmG69357JIV5PwyoAaqUx6sMErZOH779A+/uYbRB5RSG2iNvpFKcajVGPNqoVqtdqNSWFnThX9dVOm9l3WZDWsS1CuXoWut7xyp9L3fVld84So/gLYjGr2e6EB0ZmEthGkYoXh/zwPXKI5lcCqcDiU3zr8FwI/NfZZyKjyeDnnpACX1A1i//5J9ao2hml7v2KT3X/oyUTtdzrEMOMtq34HSs03GpbtX6cKstaOU606E7tmDh6qCukBrPLdydPK215+yH9PArlX2Hrh0w8LyrofGrhbS+Ga1UuSezbEytjy+/KlXrhbSGr8cqRTHLuW3KZ3Otf4dRFabEY1eR7hGZxbWQpiGEYr39zxwjeJYBqfCLwLAvNDrm+uHGMXkstSf42F020qWwt3wTUr5m+2/BPSpauq0eUntv0xKVjck1/TayXL29A+9B9CXQanttNYru9//xvWG71u13k0Du1mlcu3uH7pWKewFjcXVSnFGI8vu0tCwGksZq2+ulvt2bfx7T2nwWUBtYj5ArhupFD9hOx6dzLXxHURW21GNVk+4RuNlU1uY2lCKXicPXKM4liTEU5/nN6D6lbk03eoHJjrm9lqIY9kev1atk1b+xv2X0PpvulD41si83h+1u/8yaVndEO3MvaCMFm6w/Mk5gDpBAZOgcbdC7eAzDv/gfdOnT7e2H2Pjq0cfVlAba6U+OzJv1iUBx+45Cz6jtP4vDf2PVYXJ2zXL2NTdP3SPUnjXmGMJ/dORch9vorAqvow/X0ZktRrSyJWEa2RkoQ2EaSiiWBXywNX6h6GO0EdMSsep5l7Ln6zeZ9WZRRxLN+PiSvld7L90JWvSZDtNzk/MHt56kqotgMJ7NLBKQX9/6ZTNv8coZRxZu/uHikphkPdQdtWw7TXz+/42drWQLtzDPZjQama1MitIvrAW3lcinmOepb68Wuk72JZ/HFlt+066nsiaNNHV/QnX5LkK0+SZ5kVXbRzLYAn8CwA+D+BHAH4JRi/WlI6MVlI8cSz9VP5191/qYT1p8uxF39//j1HfyBcD2DFyDgwUupdv+3UFPRfAZGh9P6AOrlaKQQrX2D/U3f1DP1OK9+HqX1SnPPzxnmXb3MGrhaD1+dVKHw8CNi3dpaGLFfBZ88cLquUi7ZFV6RiuFtKKrBaQYlQRrjGghTQRpskznYiO5TtbYORG/G0A/MMN5vZ6FceyPX6tWqdhVMb2Xy7XX1VanwiFV/OeRGictnJ05dzrT/n0i7ZvloastrKMV68T5OQJ7C5gSEF9kFFKAJUnn3vhO415uePKujr/d9fDCthUazypFDbX0I+9OGX5duPllO/uH/y+Uupbq/nps6rlvq/ZMo8rq23/SdYTWZOkuaYv4Zo8V2GaPNOJ5FjyXbn8zaUn3l15NVcWABQM1lcBmA1gqRvM7fUqjmV7/LJ0LINnr5t/HE9C6eNGysWLbfZf+mIAs5azu3/oSwo4FQpTeY8kgFn1Ucp6XWhH1u7+BXsppa8N+lOj2HHhycX/GU9Te/oHj4RS54y5ldCVkXJfyVaz25HV9hlJ1RNZkyK5dj/CNXmuwjR5phPJseSS90wANwNglOAqAL9zgzT5XsWxTJ5pVsrfuP+SB0lGC+rIxfNmMftTy+KLAcxKzpmzB7esFdRlvNkHGqMATp46tXDiFQO9LfdOtytrd2nwQgX1+ZrGnEWVYjlMS3tKg58CFG0Pr6U6caRS/G5Ym+Dv7cpq+5wk6omsSVBctw/hmjxXYZo806x+W+O+SSsdsNljyfsrHwNwAYAeE7FkdCGIWNLxrAJwnpovzsuLYxmHWnibLI1K4/5LaH1lbdLkY1rtv8xS1nCSa2pkIefYieyaPptbDRil1Kpw8Ei5964wuduVdc9jfrTBel2TfzRSmXWATdR5xpzBDxa0+o2Ra3a1XDw5TEZxLG0JxavXrg7Ee2q8ViJrPG55+GDPi7OW/Ai232M7jmVjxp1m0vB+udA8wO2/RvQexLGMzsymRdaGep39lxoroPQZK1etPKlx/2XWstrwTNv4cXtBV612CRT2BnRNa5y2/tSu48aLUta/R9pMmcFHq9rqg1taf6Va6WNqWauStqxWQrWoJLK2Q691W+GaPFdhmjzTtH8H2n2DdhxLPrsLwOYALgXAfU6/BrBenVC8HH2ddG7tCp1Ee3Esk6C4bh+dYlTWuf8S+qka1LcXlWddGETCOkXWsJFIS87uOYMzUVMXK4XXAvh9TelDoqbTTEvWgNla2Xdq+Hx1PvfX2pW0ZbWTqnktkbUdeuJYuqEnuipco+mAzVI4e9wCwDJzXyX/vzaPeSOAtwO4rVPvshTH0s2U6LQfwPH2X3aarK1GxLWce319+LWTJtfOVQrF1VFKddaLU5fNGe80dlayNntuT2lotd2RiKWbSR2xV9f6GlGccauLrEnSXN2XME2eaV642jiWQY7wH4AnRoH5AB4xey55Uvw/TN5wuW6oTT2TidomQABN8o9fdWLfu/f7wA7vtNH19gVoowdX47/rwPC0DZaPfl0BRwJqMw38QStdjBqlrH81V7KOh2+NY6mOr1Zmfc8WdRay2srWWE9kjUtu/HbCNXmuwjR5phPJsQwuSKdjyX1NPMTDi5LPBnCA+Te5xzIBHZOJmgBEAI37L7sKCqM1fXKhNvWka+bv+89knpJ8L0mP/8xvXb5ZbbRwNOhQ8h5QDQ2Fc59bUZj969N7ufIQuyQtq40gPaWh6wDsqaE+PlKedYNNm7wYatt3TbNeFjoQ9/1E1rjkWrcTpskzzYu9soniNHMs/9c4lOJYJqhbMlEThAkg2H9ZKOAIzUVUrZ/WSh0/MuWhCzAw0HF7gpMa/72PHZpeKOBYpfC5scw5q+9+vLZWU99ZPL8YnKxuC3ZSsrYlhGVjkdUSVMRqwjUiMMvqvnD1Rc68OGuW6pNqtVY6EMWx5JI3I5bnA3jQLIsfYv6/RCwTGE6ZqAlAbNLFrXfep+ddee/NgNpl7M9a368KXUcunNfLu1k7prQ7/vscO/S+WkH3A+pApaDG7qRU+sqa7vr+okrvvUm+aLuyJilLWF8iaxiheH8XrvG4hbXyhasvcopjGaZx8f/ejmPJPZa/BSApHePzt2opE9UKU+RKAdcm+y+vhhr9xkj5YN7TmnmJO/49swd31wVVUsAeq/1mXr2ESwqrCuWFp/Q+7uLF4srqQpawPkXWMELx/i5c43ELa+ULV1/kFMcyTOPi/70dx5IpGy8y1wsFp8HrJaHjycjl8xbi8TL1zQD8tUndVwN4g8k5/lTd3zcFsCEA3mHH3MUs7OctJo1kfd11upVT4RajEqOKr0alcf8lbzPQwNnrP1rov+KKXmadyaxEYjowUOh5aev9tFInKIV3jTmU0Eu0Vv85qat22jVzDxp3XrT7kpFkbfdhbbYXWdsE2KK5cJ3YXGX8J/b4j+ew2yyFJ0lvjnFC3w2MpY4LCn8Y7wHA1G69AOYCuJCHfAHwvrrLTVrJHc21RtebA0S7AjgJwFArIcWxTHL41vTlu1FZc/8ljuBbrSrU3nPt3IOog5kVG6Z7f3XRqwobLPkMoPsV1FZjwnLvKHDmCnSde0Ol94U0XsBG1jTksHmGyGpDKXod4RqdmU0LX7j6Iud4DpDNeKRdJw9c03Qsub/tJgALAXyy4UL1awHMM/nIGaHkAQM6kUwttxOApwEcZaKmTwDgns4TAATpJt8BYEkzBRDH0s20yIPyk0xPafCmsb2XWh9erfRx/3BmZTymew9cumHXsvW+qJU+RgGcI7xM9g9K61OWTl1+YZy7KNt50byMfzsMXLQVri6oyp2LLqiKrrqgmg9dTcux5AXrdCoPNJl7dmuIWPKHkkvpK43T+R2Tl5wOZxDd/DCvGjGnXOmc8lJ2ys9rR7gUT4dznSKOpSj/eAawp3/BbChd0RoXjlSKh7mhZddrMzlnHDu8eaFQ+1pwZdDqACXuVQqVqY8WhrJavpcfFbsxjVpLuEYlZldfuNpxilJLmEahZV83D1zTcCy5H5JO5WfMHslrALy3SQpI7rHkBez7AviI2Uf5QwBc7ubVMNsC+BIALv/R8bzDOJZcBucS+2OVSuVErfVA4xAeeCD9WSlCYF0Cjz65FGctfBBbbDwVpQO27xhEzyxZgZ/f/QR++8izvINzTK63bTYNe+ywBbZ/82s6Rk4RRAgIASEgBCYugenTp6/jR6bhWNIhfMDsoWT0kWUYQF+dc8nUkMw3/k0T0XzZLHPfCGBn45Cy/pZmyZsnXRmp5B2bvKz9Q+bQzzqjKxFLNwqfh68qktnp68NTN3nV6FL+999XdE1r9+LwdmiT6dH/eceOa10ZtLrDRUoX5i2s9N7STv9Jts3L+CfJJIm+hGsSFJt8QD76qG72A+jmae316osO+CInR0NkbU8nW7VuxTUNx5IXNG9slrm3M5l7uG9yOQDujaRjyMgk91Py9DlPoVMungDnvx0K4HYAZwL4pclTzqVBLqczesnlcoaammYSEccyXYVy87T2eg0zKj2lwbsB9W5A71Yt9zG6nnrpLi3YY+s3vPq/H/mr2SrMOyihF9TQVU76DsokXi6MaRLPSKoPkTUpkmv3I1wnNlcZ/4k9/uM57Gk4lvX0324uWN8dwHQAVWDsqpRfAOAeyqD8yjiO7zHOJf+dp8QPN44lHdDPmsqs0/I0rziWovxhBrCnNMRDO4dBq/5qZdZ8N8Sa9Nr8yqDl0OriAgrlhZXeP6UmS8QHhTGN2J3T6iKrG7zCdWJzlfGf2OPfSY5l/UjwRDfzjvPgzXj3B64PYJo5GV7f/vUAXjJ3WbYcYXEsRfnDDOCM0tBhBeB8rXHVSKW4vxtia3ptdmWQ1njh4ztssdF19zz1+sXzep9xLUO7/Ycxbbf/JNuLrEnSXNOXcJ3YXGX8J/b4d6pjyRMIdBj/4mZ4VvcqjqUbunkyKjP6h99VULV7NPDXkXKR+32dFF4ZVFje9SVAfTO4MgjQT9W0OmOSnnru6Udsv0T2gSWPPk+6mjyd+D0K1/jsxmvpC1df5BzPAXIzgu31mgeuaS+Ft0c8RmtxLGNAs2iSB+V/5TW5JL1sm6VQmKpW1jZdeOpBz1ogsK7yypVBCl8xH1PMkvOYAk4efXGjixefPWOFGD9rnJEr5kpXI7+9uwbC1Q1bX7j6IqfYVjd6Oh5XcSzdMY/cs0zUyMisGthw7SkN8QaCXQG9X7Xc91OrjkMq7VUafvsk1I5Vq/cD8xAbtwjfU0OhvGjKgwswMMBrtF4pNnImIVcSfYisSVBctw/hKlx90QFf5BTH0s2cEseyVPLCgZaJ6mYC2HCd0T9UKSjMhka5WinyXtTYpbs0vCNQ64fGAUqN3XDACOWvlCqUq/NmLWrVsY2csYVKuKHImjBQ051wFa6+6IAvcopj6WZOiWMpjmXimpU3o9I9e2g/VcBPtMaNI5Xix+IA45VBq3N4Y48xZ1JDK6A6qjF38fwi05SOW/LGNOx90/q7cHVDWrhObK4y/hN7/MWxFMcy8RmQN6PSc9zwGzBa+ws0llWnPjStcZm6JcCBgUL38m32h8bxSo1dncUc3qug9VCt0DVv8bxeJgewKnljavXSKVQSrm4gC9eJzVXGf2KPvziW4lgmPgPyaFR6+gefgVKvU7q2/cLKQfePB63ZlUF0SgF9kULX/Dh3UOaRaeKKF6ND4RoDmkUT4WoBKUYVX7j6Iud4DlCM4XHeJA9cvdh72M5Iyqnwdui1bpsH5W98u+7S4FUK6lMa+tCRch8v4V+nNL0ySON5Df2DWqHrjHbuoMwjUzfaF61X4RqNl21t4WpLKlo9X7j6Iqc4ltH0L0rtVjogjmUUio7rykR1A9iWa3dpsF9BlTVw3ki5eES9NLwySHWNHq2gjnzlyiCNJxXU6QU95T+umb/vP9uV3lbOdp+TRHuRNQmK6/YhXIWrLzrgi5ziWLqZU+NxFcfSHfPIPctEjYzMqoEt157S4K6AulFr/G6kUnwvO29+ZRB+r4GTay9ueElwB6WVICGVbOVM4lnt9iGytkuweXvhKlx90QFf5BTH0s2cEsdS9lgmrll5NCo7fX146iavGl1KWEp37VJTtaPWujJI43dK6XJ1ysNXWB/uiUA+j0wjvL6zqsLVDVrhOrG5yvhP7PEXx1Icy8RnQF6NSndp6F4FbL82MH2z1oV5I5VZ1yUOsq7DvDJ1ycymb+FqQyl6HeEanZlNC1+4+iKnRCxttC5eHdljGY9bqq1korrBHYVrd//QBUrhUEqiNa7WWn9v0fy+O91ItnavUeRMQ57xniGyuhkB4SpcfdEBX+QUx9LNnJKIpUQsE9esvBqVGaXBzxa02m2VLsy9dn7vw4mDG6fDvDJNk2GzZwlXNyMgXCc2Vxn/iT3+4liKY5n4DBCjkjhSCNPkmUq0wg1T4SpcxV650YE8cJVT4W50I1aveVCoWC/uuJEvXH2RU5wKdworOuCGrXBNnqswTZ5pXmyrOJZudCNWrzJRY2ELbeQLV1/kzIvxC1WcDCqIDriBLlyT5ypMk2eaF9sqjqUb3YjVq0zUWNhCG/nC1Rc582L8QhUngwqiA26gC9fkuQrT5JnmxbaKY+lGN2L1KhM1FrbQRr5w9UXOvBi/UMXJoILogBvowjV5rsI0eaZ5sa3iWLrRjVi9ykSNhS20kS9cfZEzL8YvVHEyqCA64Aa6cE2eqzBNnmlebPJ9nEcAABYLSURBVKs4lm50I1avMlFjYQtt5AtXX+TMi/ELVZwMKogOuIEuXJPnKkyTZ5oX2yqOpRvdiNWrTNRY2EIb+cLVFznzYvxCFSeDCqIDbqAL1+S5CtPkmebFtopj6UY3YvUqEzUWttBGvnD1Rc68GL9QxcmgguiAG+jCNXmuwjR5pnmxreJYutGNWL3KRI2FLbSRL1x9kTMvxi9UcTKoIDrgBrpwTZ6rME2eaV5sqziWbnQjVq8yUWNhC23kC1df5MyL8QtVnAwqiA64gS5ck+cqTJNnmhfbKo6lG92I1atM1FjYQhv5wtUXOfNi/EIVJ4MKogNuoAvX5LkK0+SZ5sW2imPpRjdi9SoTNRa20Ea+cPVFzrwYv1DFyaCC6IAb6MI1ea7CNHmmebGt4li60Y1YvcpEjYUttJEvXH2RMy/GL1RxMqggOuAGunBNnqswTZ5pXmyrOJZudCNWrzJRY2ELbeQLV1/kzIvxC1WcDCqIDriBLlyT5ypMk2eaF9sqjqUb3YjVq0zUWNhCG/nC1Rc582L8QhUngwqiA26gC9fkuQrT5JnmxbaKY+lGN2L1KhM1FrbQRr5w9UXOvBi/UMXJoILogBvowjV5rsI0eaZ5sa3iWLrRjVi9ykSNhS20kS9cfZEzL8YvVHEyqCA64Aa6cE2eqzBNnmlebKs4lm50I1avMlFjYQtt5AtXX+TMi/ELVZwMKogOuIEuXJPnKkyTZ5oX2yqOpRvdiNWrTNRY2EIb+cLVFznzYvxCFSeDCqIDbqAL1+S5CtPkmebFtopj6UY3YvUqEzUWttBGvnD1Rc68GL9QxcmgguiAG+jCNXmuwjR5pnmxreJYutGNWL3KRI2FLbSRL1x9kTMvxi9UcTKoIDrgBrpwTZ6rME2eaV5sqziWbnQjVq8yUWNhC23kC1df5MyL8QtVnAwqiA64gS5ck+cqTJNnmhfbKo6lG92I1atM1FjYQhv5wtUXOfNi/EIVJ4MKogNuoAvX5LkK0+SZ5sW2imPpRjdi9SoTNRa20Ea+cPVFzrwYv1DFyaCC6IAb6MI1ea7CNHmmebGt4li60Y1YvcpEjYUttJEvXH2RMy/GL1RxMqggOuAGunBNnqswTZ5pXmyrOJZudCNWrzJRY2ELbeQLV1/kzIvxC1WcDCqIDriBLlyT5ypMk2eaF9uatmM5CcBmAP7aYkheDWAVgGV1f98UwIYA/mj+xj+xn7cAWArgqfGGt1wu61KplPZ7xtI4maixsIU28oWrL3LmxfiFKk4GFUQH3EAXrslzFabJM82LbU3b4ZoD4BAA7wYwWjcsXQBeA+CHAE4DcJv52wwAFwO4HMBMADsCeBnA9QDuBrArgJMADLUaYnEsRfl9MYC+yJkX4+dmZrTXq+hAe/xatRauyXMVpskzzYttTdOx3AXATQAWAvgkgFrdsLzVOJSfAvB+AHcCmALgfgA7AXgawFEA1gPwBIBtAJwAYAMAjwF4B4AlzYZZHEtRfl8MoC9y5sX4uZkZ7fUqOtAeP3Es3fBr1qvoqhvWeeCalmO5hXEqDwRwDoDdGiKWwQgNGgfzDgBvBHBdXXTzwwD2BDDZOKeMalL+G0wUlA7nOkUcS1F+XyaqL3KKY+lmTglX4eqTDoi9cqOveeCahmPJ/ZCMVH7G7JG8BsB7GyKWwQgtAHAKADqW25qlcS53M7rJ//0lAFsB+I6pQ/m5DM4l9scqlcqJWuuB+uGePHmyfvnll9N4TzdaJr0KASEwYQhMmzZNL126VOzVhBlxeVEh4C+BTTfddNWhhx7KleS1ShoGjA7hAwDuMdFHCjAMoK+Jc1nvWHKZ+0YAOxuHlPW3NEvej5tIJZfLudfyQwD+4XvE0qfoqsiavDEQpskzZY/CVbiKDiSvA8I0eaZ5sVdpOJZcut4YwEoA2wG4wOybXG72RtIxbBaxpNN4F4BDAdwO4EwAvwSgAXzNLKczenktgO0bTpK/0qEovyi/Lzrgi5x5MX5uZkZ7vYoOtMevVWvhmjxXYZo807zY1jQcy3r6bwdwPoDdAUwHUAXwzrr9lnQ6eTKcS+Es7zPOJf/7QgCHG8fyIgCfNXXeY6KhTUdZlF+U3xcd8EXOvBg/NzOjvV5FB9rjJ46lG37NehVddcM6D1zTdizrR4JL3XQkef1Q/dVDjaO1PoBp5mR4/d9eD+Alc5dlyxHOwyC5Ud/2ehWu7fETQ508P3Eq0mMqHxfuWPtiW32RU3Q1fV3N0rHkvZV0GP/i7rUBHujp7+/nYZ+OLyKrmyHyhasvcnKURNaJrauiA27G3yeuYgPc6EAeuGbpWLoZFelVCAgBISAEhIAQEAJCIBMC4lhmgl0eKgSEgBAQAkJACAiB/BGYSI5ls5zjWY8oc6O/wVyVVJ/z/E0ACiY/etYyNj6fsnH7Ak/ns3SirK8DwP97EsDzRk7r/PIpA2/Gr9Nk5dz5e931YK3k64Q5RhmeqRvDVnOsE2UNxO60Ocbx3hDAc3Vcm82xTrAHHO9VDbeEcKxfa7K08XYSlqznWBTbz7q8au9vAP6Zsn3i45jgZCMAf214Pm9l4e8Us98FmfQ6VdbgPfh7sMwwzNoGNOP3KgBvM79b/P0KiktZx7ORsebNRHEsm+UcDxyODObp2CPfZU6zlwH0AphrTr7zgnfey8kfchryo0MON6UpP9NyMiXnW4wz3Imy8iYBXsh/KgDKxxShvEfVOr98SkD5w8bbDXg4bYUx3AcYQ90psvKqMDo5i8wVYdRHyttMvqznGOXiDRE8EPhuM2dazbFOlDVQu06bY3QomDFtMwDfN0I2m2NMw5ulPegCwH37vFXkNADMzMbyeQBHmqvqDgLwr8a2ZjnHotj+NxvHbT6A2UbHeSd0WoW/TWcbW1Uy/P7HZNDjzS50KvcC8EEAdFD4vztJVl5ZyLK5SQfNFNG/AZClDaDfxQyEjfx4iJnz6AoA+wD4AYBzHcvaShfbmjcTwbFslXOcjkeWhfdvzgNwMwB+jVDZZwK4ylzBxC9ApqvkZA6uX8pSXk7M4M5R5mrfBMBIh8kaXJj/ERO1Yg56/iByAlvnl08JMvPb/8zcwcpH/grAp81HRafIugeAk0y2K8rEJAT8cW6Uj/fIUn9ptJ8GcBQAZmNIc459A0C/ufP2kyaC0myO7WiuMOs0WYMfv06aY4ye0Kn4AoCvmB+5VnOMH8JZ2gPOdTqUnzIfk/yBpqz/Z+bYCwCOB/CgSQuc5RyLYvup1/9pfieYse6/xslcl7Tp4s0tS42t50cl7QEdzdON7WLyE/5O8XeMDtz+HSgrryhkVPXnAJjFj4GG+wDcn6G9Ijfa/vH48UOZH5lMEuNS1lY28t525s1EcCxb5Rw/MelZGLE/OpOMmnJphj+EPLnO1JSMunCisvALlROWkyLLwonJy+mPAHCx+UJlVHWHDpM1uFSfl+8zqkIjzB/Eb0fJL58S6IApow+ch/xypOGjI8eoMCMu/Hd+XPBKridSkqvxMXQQaYiD7FbUzUb5jgXw47pI4YcB7Akg7TlG5+Lquh/eZnOsxyRVCKKanSJrJ88xOgxkyw+FVnPs3zrEHgwaBzP4GOfHBuXnncmMXNJm8cMnyzlma/t/B+B7ALrNhzITjXA1hh9HXO5Po/BaP34s0hYxSckfzccGo8P8d26R4EcxfxvO6kBZqbMnmODM3sZO0ZZel6G94kpQI7/DDEM6vrRN3G7GOUX/wKWszXSRHzC06bHnzURwLFvlHKdxybpw6YCKvy8ARtn4I8dIYBDp4SXwS0wUM0tZ6fAyjSZD9FxWpvLv14GyBtFpbh/4b7Mkw72rjE41zS+fIVT+SDCqwiWRl43x4zLdf3SYrI1pU/kD3ciSy8+MBtExZgSDc+5L5gc8TcSMQjEtLA1j/Z6v+jnGLQhcLu00WTt5jh1slhIDx5IRlMY5xv13nWC76tMC8/eN20143zGdHzpGRQDf7IA5ZmP7uUWGy8p0NLhawAgiP/D5kRfsFU1jfnHPHyNsfwZAXWD0l2U3AL8A8C3jbDIa3Gmy8kOCMjNyGXx0MArbCTagnl8FwHlmHy0/3rl6SYeYEWL+Jri0V426+Pt2581EcCxb5RxPc5mu2eRnJJUTlUYucC4YteJSI39kWBq/vtMwIo3PID/um+GXa1D4NUUDzehPp8n6EICtzY9JEMHiPhW+A6N/ofnlU4LMjwl+SDAqzRJEApn2lE58p8jayIvLoo3yfQzATwHsbCIpfeawQdpzjNsLrqxzLJvNsU6xB/WyTu3wOVbvWJJfszn27wA47lnbg3rHkg4Rl/o4pxjh41IuP4qfzXiORbH9jBwxhTGjbMxc95MUl8LrnUfaem51CArHmR8XdHjoUNKXIPtOkpUyMTLNiC+jq8FvGO3uGRnbq0Z+5MqPdv47l6GDlSLXtrWZLrY9byaCY9kq5zh/gLIs/GLiMje/qLmXiWMR7LWkIeTJNS6H8kAH9wllWTghaZj5ZXOLWebkcjP31XWSrIxGkRmjZWRLI8d/+0OU/PIpgeb+GW7O5pI9I5aLzeEH3hJAufk1y1OD/GHkHsbgJGNK4r3ymEbHkssjjfJxae5WAIeaPY6MDDGykvYca4xYNptj1GPqRqfJ2slzrN6xbDXHuDe8E+xBvWNJe/qo2QfOj+Evmqhf1vYgiu0/BsAjZhWLW6Z46ITRtzRKsALEYAfnDPf90VbxlDjHOohO8sPoRbMHt9NkpWx00ngwZtg4lFzNytIG0HFrxo/boKivXAHiYShGMBmd5sqWK3vVTBfJrK15MxEcS07AZjnHg6WyNCZo4zNonLlXhl9OQeFSDZ2Jz5jT4fx3/vePshCwxTNpaG4H8FGzRM+TY8zh3kmy1o81o5SMovFr1Tq/fEq8qQM8GMEfOxYaPY43l786SdZgzBmZ4HJcsLzIbRos3BPMfaKdMMcY0SE7yso9i63mGGUOTotSf/lDnbY9qJe1/tmdOMf4ccubIIIIdLM5xi0nnWAP+KPMH8tgjyUPxF1idJXOJbfFcNk+qzkW1fbTOaYev9NEtfkRl9aNJvzI5Qnw+tUq7ll/2Gw1qjeVHzAf8J0mK1ergkK94LIy965maa8YOadz28iPK0E8zMuxZuFYk78rWcfTRX5Mxp43E8Wx5CC1yjmekh8R6THcf8fCH/JOL50oKzdH86sr2AsUMLTKL58ycG5+5zz0QdZ6NM1Y+jTHfJLVpznWibJyrBm14n714P5d6nIn2oNm/HiVEu8T5O9BWod24ppBn2TtVBvAK74YAa4f6yxkjT1vJpJjGXeiSDshIASEgBAQAkJACAgBCwLiWFpAkipCQAgIASEgBISAEBAC4QTEsQxnJDWEgBAQAkJACAgBISAELAiIY2kBSaoIASEgBISAEBACQkAIhBMQxzKckdQQAkJACAgBISAEhIAQsCAgjqUFJKkiBISAEBACQkAICAEhEE5AHMtwRlJDCAgBIdCpBLY011X9HQCTFkgRAkJACGRKQBzLTPHLw4WAEHBMgBeOMwsQU/kFhZcOf9VkaUri8cyIwsvug0wkSfQZ1gezMV1tsjMFdT9lcjqHtQ0uYafMvFBcihAQAkIgMQLiWCaGUjoSAkKgAwkEKV1fB2DI5Ipnph1mY2I6UuaNbrcwMw0zfDBV20vtdmbRntlY7jMXfH8DwDMAfmza/RsAZvEar/CycKaJY8q4+swkFo+WKkJACAiB8QmIYykaIgSEQJ4JBNG5BwD0mRdlOsqLAewA4GMAmP2IeXrpcDGKx7zti8x/M/3fuwDMBfB/AI4D0G9Ssp1u6tKxvAJAxfyN7Y8C8HvT//kmssj0kfNMHt53mPqMMjK92/dNOjfmYWYdpvdkP8cCuL9hgJi7+bKGlK9MEUn5FgLoBXCWSV0YyP4nACcDYK535qjnOzA9Hx1LpoxjfaaY5ft/BwCX1o82cnOJnTmgmXaUf+O/0zFnH0w5m3Y6zDzrq7ybEPCegDiW3g+hvIAQEALjEAgcyz+bnODMj/ttAF8AsDWA2SbyRwePdX9rHC86gcwnzry9dKKYf5zOJx0ztqXz9nEA041jRseSjuBVAOhIMjr6LZPf+RwAVeOE3W76uBXAqHFAjwCwDwAub99g8sXT0WS+3l3MvzMqGRQ6gbMAbFOX9pW50X8O4J/GgeZ71MtOGehEM8L5UQB8XzqWdKAfM+/M9nQ+6Uyz7pkAvmz6pKPLHOtsT0eaz+bf6ZzfLRooBISAEAgIiGMpuiAEhECeCQSOJfc/1hdG5r4OgFHHtxpHixFLOn5cIg4cyysBnAhgAwD3GKexDOA1xtFiNJFOJyN/dAzpAHLfIvNQ0wHby/TPqOj3TNTv3wHcYvZ9Uo4/mmjlUgAPArgDwHwA3QAYXd0JwG/qhOez2Dejnsx/zUJbfgmAD5r/o+MayM4o6N+MU8j3ZT7qhwB81zimjH4yism9qCcA4LaBbQEMGKd5D3MwiO/NKOVPjDwPA7gWwMo8K5C8mxAQAtEIiGMZjZfUFgJCwC8C9RHLI43oXNp9yvw3o39vNo7lJsYxZCQvcCzpiNFRpDPGfYmnGidsMoA3mmjmJ4xjGUQQ2Sed1WMA0PmiI0an73gATwLgYZ+tTJSSEcl9TVRwJoAbAVxklsf5DP4fl8rpfAYlWMrfHcAvzD8G8tHRZTSTsgayU05GbBlp5bsE+07pTP7DLKszuvq4eR6feTkAOpJ8D8rL5W4+40AAO5roLx+9t3Eu/dIKkVYICAFnBMSxdIZWOhYCQqADCAROFA+7cI9l/X5A2j86cTMAFAEwMkcHq96xpPPFCCGX0G8C8BYAdAAPMRFAOpPcx8g6jY4lD9RwXyKXz+nwMSLIPrj0TYeT+zzpqJbqlqjphHJ5mtFCLo/TGWXEksvVQQkikFz25lI1neQzzNI3nc3bzDJ+vez8N7aj3FwK57I+35PRUUZp+d8jRg46vXQguWc0cCy12Uf6BsOKMg3WOasdMNQighAQAp1AQBzLThgFkUEICAFXBILrhhiNa3Qs+UwuHQfLzIwqci8hI3uXGoer/koeOpDcA8llbhY6owvMsnSjY7mZudLo1yY6yX2adA7ZlnsV2ZZ7MYPC6CD3fv4rgGvqnsFlc8rSWLhUzaVu7qNkoZP5aXPdULPrhCg7l/LrSxDB5DN4CIeFctKh5R5NRl75HgE3Hu7hEn5QuKeUB4medzV40q8QEAL+ERDH0r8xE4mFgBBIlgCXfnn4xeaCcdbl/kpeK8Q9kWGlC8BGAJ5rUnF9cxKd0cBgrySrBW1WAHgx5AF0VGnHeYp7VUhdyj5tHFnokFKO8fqhbOyD5YWwl5e/CwEhMPEIiGM58cZc3lgICAEhIASEgBAQAk4IiGPpBKt0KgSEgBAQAkJACAiBiUdAHMuJN+byxkJACAgBISAEhIAQcEJAHEsnWKVTISAEhIAQEAJCQAhMPALiWE68MZc3FgJCQAgIASEgBISAEwLiWDrBKp0KASEgBISAEBACQmDiERDHcuKNubyxEBACQkAICAEhIAScEPh/miPNaWWVUiQAAAAASUVORK5CYII="
     },
     "metadata": {
      "jupyter-vega3": "#287533ef-bec5-4b23-abff-8d1333d58c45"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "v_netpr = alt.Chart().mark_line().encode(\n",
    "    x=alt.X('index', axis=alt.Axis(title='Purchase Orders')),\n",
    "    y=alt.Y('NETPR', scale=alt.Scale(domain=(4100, 4700)), axis=alt.Axis(title='Effective Net Price of Brent Crude Oil'))\n",
    ")\n",
    "alt.layer(v_netpr, data = ekpo_bco.reset_index(), width=600, height=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Reflect:** What is your interpretation of the effective net price?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T05:34:05.479375Z",
     "start_time": "2018-05-04T05:34:05.472850Z"
    }
   },
   "source": [
    "## The volume of the Brent Crude Oil purchase orders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your task:** What is the average order volume for Brent Crude Oil?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:59.273019Z",
     "start_time": "2018-05-04T06:45:59.260165Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19977.724137931036"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ekpo_bco['MENGE'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T04:37:46.989304Z",
     "start_time": "2018-05-04T04:37:46.971051Z"
    }
   },
   "source": [
    "**Your task:** Show how amount of all purchase orders (`MENGE`) of Brent Crude Oil. (We assume that the order of purchase orders represents time)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:59.323120Z",
     "start_time": "2018-05-04T06:45:59.278088Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}}, \"layer\": [{\"mark\": \"line\", \"encoding\": {\"x\": {\"type\": \"quantitative\", \"axis\": {\"title\": \"Purchase Orders\"}, \"field\": \"index\"}, \"y\": {\"type\": \"quantitative\", \"axis\": {\"title\": \"Order Volume of Brent Crude Oil\"}, \"field\": \"MENGE\", \"scale\": {\"domain\": [19500, 20500]}}}}], \"data\": {\"values\": [{\"index\": 2, \"EBELN\": 4500000001, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 20193, \"NETPR\": 4335.0, \"NETWR\": 87536655.0}, {\"index\": 3, \"EBELN\": 4500000002, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 20103, \"NETPR\": 4360.0, \"NETWR\": 87649080.0}, {\"index\": 4, \"EBELN\": 4500000003, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 20013, \"NETPR\": 4340.0, \"NETWR\": 86856420.0}, {\"index\": 5, \"EBELN\": 4500000004, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 20154, \"NETPR\": 4307.5, \"NETWR\": 83319972.5}, {\"index\": 6, \"EBELN\": 4500000005, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 19985, \"NETPR\": 4277.5, \"NETWR\": 85485837.5}, {\"index\": 7, \"EBELN\": 4500000006, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 20003, \"NETPR\": 4322.5, \"NETWR\": 86462967.5}, {\"index\": 10, \"EBELN\": 4500000009, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 19775, \"NETPR\": 4342.5, \"NETWR\": 85872937.5}, {\"index\": 12, \"EBELN\": 4500000011, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 19995, \"NETPR\": 4332.5, \"NETWR\": 86628337.5}, {\"index\": 64, \"EBELN\": 4500000061, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 19903, \"NETPR\": 4205.0, \"NETWR\": 83692115.0}, {\"index\": 66, \"EBELN\": 4500000063, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 19938, \"NETPR\": 4165.0, \"NETWR\": 83041770.0}, {\"index\": 91, \"EBELN\": 4500000085, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 20067, \"NETPR\": 4215.0, \"NETWR\": 84582405.0}, {\"index\": 104, \"EBELN\": 4500000097, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 20228, \"NETPR\": 4252.5, \"NETWR\": 86019570.0}, {\"index\": 108, \"EBELN\": 4500000101, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 20028, \"NETPR\": 4227.5, \"NETWR\": 84668370.0}, {\"index\": 113, \"EBELN\": 4500000106, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 19918, \"NETPR\": 4257.5, \"NETWR\": 84800885.0}, {\"index\": 114, \"EBELN\": 4500000107, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 19810, \"NETPR\": 4215.0, \"NETWR\": 83499150.0}, {\"index\": 115, \"EBELN\": 4500000108, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 19987, \"NETPR\": 4330.0, \"NETWR\": 86543710.0}, {\"index\": 123, \"EBELN\": 4500000116, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 20184, \"NETPR\": 4345.0, \"NETWR\": 87699480.0}, {\"index\": 127, \"EBELN\": 4500000120, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 19864, \"NETPR\": 4492.5, \"NETWR\": 89239020.0}, {\"index\": 140, \"EBELN\": 4500000133, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 20260, \"NETPR\": 4572.5, \"NETWR\": 92638850.0}, {\"index\": 156, \"EBELN\": 4500000147, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 19856, \"NETPR\": 4517.5, \"NETWR\": 89699480.0}, {\"index\": 181, \"EBELN\": 4500000173, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 19780, \"NETPR\": 4510.0, \"NETWR\": 89207800.0}, {\"index\": 182, \"EBELN\": 4500000174, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 19916, \"NETPR\": 4467.5, \"NETWR\": 88974730.0}, {\"index\": 183, \"EBELN\": 4500000175, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 19929, \"NETPR\": 4402.5, \"NETWR\": 87737422.5}, {\"index\": 193, \"EBELN\": 4500000185, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 19815, \"NETPR\": 4462.5, \"NETWR\": 88424437.5}, {\"index\": 228, \"EBELN\": 4500000220, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 19860, \"NETPR\": 4605.0, \"NETWR\": 91455300.0}, {\"index\": 230, \"EBELN\": 4500000222, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 19960, \"NETPR\": 4562.5, \"NETWR\": 91067500.0}, {\"index\": 233, \"EBELN\": 4500000225, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 19853, \"NETPR\": 4490.0, \"NETWR\": 89139970.0}, {\"index\": 257, \"EBELN\": 4500000249, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 20058, \"NETPR\": 4640.0, \"NETWR\": 93069120.0}, {\"index\": 264, \"EBELN\": 4500000256, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 19919, \"NETPR\": 4607.5, \"NETWR\": 91776792.5}]}, \"height\": 300, \"width\": 600, \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.3.0.json\"};\n",
       "var opt = {};\n",
       "var selector = \"#25d52116-3e96-48ef-9f31-b47279b62153\";\n",
       "var type = \"vega-lite\";\n",
       "\n",
       "var output_area = this;\n",
       "\n",
       "require(['nbextensions/jupyter-vega3/index'], function(vega) {\n",
       "  vega.render(selector, spec, type, opt, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== 'scripterror') {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ]
     },
     "metadata": {
      "jupyter-vega3": "#25d52116-3e96-48ef-9f31-b47279b62153"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "jupyter-vega3": "#25d52116-3e96-48ef-9f31-b47279b62153"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "v_menge = alt.Chart().mark_line().encode(\n",
    "    x=alt.X('index', axis=alt.Axis(title='Purchase Orders')),\n",
    "    y=alt.Y('MENGE', scale=alt.Scale(domain=(19500, 20500)), axis=alt.Axis(title='Order Volume of Brent Crude Oil'))\n",
    ")\n",
    "alt.layer(v_menge, data = ekpo_bco.reset_index(), width=600, height=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Reflect:** What is your interpretation of the order volume?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T05:37:13.871861Z",
     "start_time": "2018-05-04T05:37:13.867298Z"
    }
   },
   "source": [
    "## Integrated analysis of `EKKO` and `EKPO`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T04:37:46.989304Z",
     "start_time": "2018-05-04T04:37:46.971051Z"
    }
   },
   "source": [
    "**Your task:** Left join the `ekpo_bco` and the `ekko` tables. Store the result in a variable called `ekko_ekpo_bco`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:59.341210Z",
     "start_time": "2018-05-04T06:45:59.326982Z"
    }
   },
   "outputs": [],
   "source": [
    "ekko_ekpo_bco = pd.merge(ekpo_bco, ekko, on='EBELN')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T04:37:46.989304Z",
     "start_time": "2018-05-04T04:37:46.971051Z"
    }
   },
   "source": [
    "**Your task:** Show a sample of the `ekko_ekpo_bco` table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:59.381604Z",
     "start_time": "2018-05-04T06:45:59.347239Z"
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EBELN</th>\n",
       "      <th>EBELP</th>\n",
       "      <th>TXZ01</th>\n",
       "      <th>MATNR</th>\n",
       "      <th>MENGE</th>\n",
       "      <th>NETPR</th>\n",
       "      <th>NETWR</th>\n",
       "      <th>ERNAM</th>\n",
       "      <th>LIFNR</th>\n",
       "      <th>KNUMV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4500000225</td>\n",
       "      <td>10</td>\n",
       "      <td>Brent Crude Oil</td>\n",
       "      <td>BRENTCRUDE</td>\n",
       "      <td>19853</td>\n",
       "      <td>4490</td>\n",
       "      <td>89139970</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4500000002</td>\n",
       "      <td>10</td>\n",
       "      <td>Brent Crude Oil</td>\n",
       "      <td>BRENTCRUDE</td>\n",
       "      <td>20103</td>\n",
       "      <td>4360</td>\n",
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       "    <tr>\n",
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       "      <td>Brent Crude Oil</td>\n",
       "      <td>BRENTCRUDE</td>\n",
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       "      <td>1000000080</td>\n",
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       "      <th>22</th>\n",
       "      <td>4500000175</td>\n",
       "      <td>10</td>\n",
       "      <td>Brent Crude Oil</td>\n",
       "      <td>BRENTCRUDE</td>\n",
       "      <td>19929</td>\n",
       "      <td>4402</td>\n",
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       "      <th>24</th>\n",
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       "      <td>10</td>\n",
       "      <td>Brent Crude Oil</td>\n",
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      ],
      "text/plain": [
       "         EBELN  EBELP            TXZ01       MATNR  MENGE  NETPR    NETWR  \\\n",
       "26  4500000225     10  Brent Crude Oil  BRENTCRUDE  19853   4490 89139970   \n",
       "23  4500000185     10  Brent Crude Oil  BRENTCRUDE  19815   4462 88424438   \n",
       "1   4500000002     10  Brent Crude Oil  BRENTCRUDE  20103   4360 87649080   \n",
       "10  4500000085     10  Brent Crude Oil  BRENTCRUDE  20067   4215 84582405   \n",
       "16  4500000116     10  Brent Crude Oil  BRENTCRUDE  20184   4345 87699480   \n",
       "8   4500000061     10  Brent Crude Oil  BRENTCRUDE  19903   4205 83692115   \n",
       "22  4500000175     10  Brent Crude Oil  BRENTCRUDE  19929   4402 87737422   \n",
       "24  4500000220     10  Brent Crude Oil  BRENTCRUDE  19860   4605 91455300   \n",
       "14  4500000107     10  Brent Crude Oil  BRENTCRUDE  19810   4215 83499150   \n",
       "7   4500000011     10  Brent Crude Oil  BRENTCRUDE  19995   4332 86628338   \n",
       "\n",
       "        ERNAM  LIFNR      KNUMV  \n",
       "26  GBIFAC-01 125000 1000000252  \n",
       "23  GBIFAC-01 125000 1000000202  \n",
       "1     GBI-085 125000 1000000021  \n",
       "10  GBIFAC-01 125000 1000000104  \n",
       "16    GBI-085 125000 1000000128  \n",
       "8   GBIFAC-01 125000 1000000080  \n",
       "22  GBIFAC-01 125000 1000000192  \n",
       "24  GBIFAC-01 125000 1000000241  \n",
       "14  GBIFAC-01 125000 1000000126  \n",
       "7   GBIFAC-01 125000 1000000030  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ekko_ekpo_bco.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your task:** How many vendors (`LIFNR`) deliver Brent Crude Oil?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:59.397219Z",
     "start_time": "2018-05-04T06:45:59.384766Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([125000.])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ekko_ekpo_bco['LIFNR'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your task:** How many purchase group employees (`ERNAM`) are responsible for ordering Brent Crude Oil?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:59.413631Z",
     "start_time": "2018-05-04T06:45:59.402275Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['GBI-085', 'GBIFAC-01'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ekko_ekpo_bco['ERNAM'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your task:** Are there any differences in the average order volume between the purchase group employees (`ERNAM`)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:59.435134Z",
     "start_time": "2018-05-04T06:45:59.420155Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ERNAM\n",
       "GBI-085     20119\n",
       "GBIFAC-01   19914\n",
       "Name: MENGE, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ekko_ekpo_bco.groupby('ERNAM')['MENGE'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:59.492981Z",
     "start_time": "2018-05-04T06:45:59.440826Z"
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4500000107, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 19810, \"NETPR\": 4215.0, \"NETWR\": 83499150.0, \"ERNAM\": \"GBIFAC-01\", \"LIFNR\": 125000.0, \"KNUMV\": 1000000126.0}, {\"index\": 15, \"EBELN\": 4500000108, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 19987, \"NETPR\": 4330.0, \"NETWR\": 86543710.0, \"ERNAM\": \"GBIFAC-01\", \"LIFNR\": 125000.0, \"KNUMV\": 1000000127.0}, {\"index\": 16, \"EBELN\": 4500000116, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 20184, \"NETPR\": 4345.0, \"NETWR\": 87699480.0, \"ERNAM\": \"GBI-085\", \"LIFNR\": 125000.0, \"KNUMV\": 1000000128.0}, {\"index\": 17, \"EBELN\": 4500000120, \"EBELP\": 10, \"TXZ01\": \"Brent Crude Oil\", \"MATNR\": \"BRENTCRUDE\", \"MENGE\": 19864, \"NETPR\": 4492.5, \"NETWR\": 89239020.0, \"ERNAM\": \"GBIFAC-01\", \"LIFNR\": 125000.0, \"KNUMV\": 1000000138.0}, {\"index\": 18, \"EBELN\": 4500000133, 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       "var opt = {};\n",
       "var selector = \"#f0da4243-852b-4a5a-b74d-88fcccb4b9d8\";\n",
       "var type = \"vega-lite\";\n",
       "\n",
       "var output_area = this;\n",
       "\n",
       "require(['nbextensions/jupyter-vega3/index'], function(vega) {\n",
       "  vega.render(selector, spec, type, opt, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== 'scripterror') {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ]
     },
     "metadata": {
      "jupyter-vega3": "#f0da4243-852b-4a5a-b74d-88fcccb4b9d8"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "jupyter-vega3": "#f0da4243-852b-4a5a-b74d-88fcccb4b9d8"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "base = alt.Chart().mark_line().encode(\n",
    "    x=alt.X('index', axis=alt.Axis(title='Purchase Orders')),\n",
    "    y=alt.Y('MENGE', scale=alt.Scale(domain=(19760, 20250)), axis=alt.Axis(title='Order Volume of Brent Crude Oil')),\n",
    "    color=alt.Color('ERNAM', legend=alt.Legend(title=\"Purchase Group Employee\"))\n",
    ")\n",
    "alt.layer(base, data = ekko_ekpo_bco.reset_index(), width=600, height=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of the Conditions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your task:** Filter the conditions for the purchase order of Brent Crude Oil? Store the result in a variable called `konv_bco`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:59.506414Z",
     "start_time": "2018-05-04T06:45:59.498785Z"
    }
   },
   "outputs": [],
   "source": [
    "konv_bco = konv[konv['KNUMV'].isin(ekko_ekpo_bco['KNUMV'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your task:** Show the first five rows of `konv_bco`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:59.537802Z",
     "start_time": "2018-05-04T06:45:59.511636Z"
    }
   },
   "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>KNUMV</th>\n",
       "      <th>KPOSN</th>\n",
       "      <th>STUNR</th>\n",
       "      <th>KSCHL</th>\n",
       "      <th>KAWRT</th>\n",
       "      <th>KBETR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1000000020</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>PBXX</td>\n",
       "      <td>1000</td>\n",
       "      <td>4336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1000000020</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>RB00</td>\n",
       "      <td>87561655</td>\n",
       "      <td>-25000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1000000020</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>NAVM</td>\n",
       "      <td>87536655</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1000000020</td>\n",
       "      <td>10</td>\n",
       "      <td>35</td>\n",
       "      <td>SKTO</td>\n",
       "      <td>87536655</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>1000000020</td>\n",
       "      <td>10</td>\n",
       "      <td>60</td>\n",
       "      <td>WOTB</td>\n",
       "      <td>1000</td>\n",
       "      <td>4335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         KNUMV  KPOSN  STUNR KSCHL    KAWRT  KBETR\n",
       "52  1000000020     10      1  PBXX     1000   4336\n",
       "53  1000000020     10     11  RB00 87561655 -25000\n",
       "54  1000000020     10     21  NAVM 87536655      0\n",
       "55  1000000020     10     35  SKTO 87536655      0\n",
       "56  1000000020     10     60  WOTB     1000   4335"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "konv_bco.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your task:** What is the interesting condition type?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:59.564500Z",
     "start_time": "2018-05-04T06:45:59.541876Z"
    }
   },
   "outputs": [
    {
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       "      <th>KSCHL</th>\n",
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       "      <td>29</td>\n",
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      "text/plain": [
       "       KSCHL\n",
       "KSCHL       \n",
       "NAVM      29\n",
       "PBXX      29\n",
       "RB00      11\n",
       "SKTO      29\n",
       "WOTB      29"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "konv_bco.groupby('KSCHL')[['KSCHL']].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your task:** Add dummies for the condition type to `konv_bco`. Store the result in a variable called `konv_bco_d`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:59.604922Z",
     "start_time": "2018-05-04T06:45:59.573142Z"
    }
   },
   "outputs": [],
   "source": [
    "konv_bco_d = konv_bco.join(konv_bco['KSCHL'].str.get_dummies())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T05:48:03.153060Z",
     "start_time": "2018-05-04T05:48:03.142157Z"
    }
   },
   "source": [
    "**Your task:** Show the first five rows of `konv_bco_d`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:59.663675Z",
     "start_time": "2018-05-04T06:45:59.615210Z"
    }
   },
   "outputs": [
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       "      <th>55</th>\n",
       "      <td>1000000020</td>\n",
       "      <td>10</td>\n",
       "      <td>35</td>\n",
       "      <td>SKTO</td>\n",
       "      <td>87536655</td>\n",
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      "text/plain": [
       "         KNUMV  KPOSN  STUNR KSCHL    KAWRT  KBETR  NAVM  PBXX  RB00  SKTO  \\\n",
       "52  1000000020     10      1  PBXX     1000   4336     0     1     0     0   \n",
       "53  1000000020     10     11  RB00 87561655 -25000     0     0     1     0   \n",
       "54  1000000020     10     21  NAVM 87536655      0     1     0     0     0   \n",
       "55  1000000020     10     35  SKTO 87536655      0     0     0     0     1   \n",
       "56  1000000020     10     60  WOTB     1000   4335     0     0     0     0   \n",
       "\n",
       "    WOTB  \n",
       "52     0  \n",
       "53     0  \n",
       "54     0  \n",
       "55     0  \n",
       "56     1  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
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    }
   ],
   "source": [
    "konv_bco_d.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your task:** Filter the sets of conditions that contain the condition type of interest. Store the result in a variable called by the name of the condition type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:59.689305Z",
     "start_time": "2018-05-04T06:45:59.673665Z"
    }
   },
   "outputs": [],
   "source": [
    "rb00 = konv_bco_d.groupby('KNUMV')['RB00'].sum().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your task:** Left join the condition type of interest to the other tables. Store the result in a variable called `eek_bco`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:59.715952Z",
     "start_time": "2018-05-04T06:45:59.693271Z"
    }
   },
   "outputs": [],
   "source": [
    "eek_bco = pd.merge(ekko_ekpo_bco, rb00, on='KNUMV', how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is the fraud?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your task:** What does the fraudster exploit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:59.744446Z",
     "start_time": "2018-05-04T06:45:59.721711Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20003"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "20260"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eek_bco[eek_bco['RB00']==1]['MENGE'].min()\n",
    "eek_bco[eek_bco['RB00']==1]['MENGE'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:59.771874Z",
     "start_time": "2018-05-04T06:45:59.750007Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19775"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "19995"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eek_bco[eek_bco['RB00']==0]['MENGE'].min()\n",
    "eek_bco[eek_bco['RB00']==0]['MENGE'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your task:** Who is the person of interest?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:59.801166Z",
     "start_time": "2018-05-04T06:45:59.776502Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>19892</td>\n",
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      "text/plain": [
       "      MENGE                 ERNAM\n",
       "RB00                             \n",
       "0     19892           [GBIFAC-01]\n",
       "1     20117  [GBI-085, GBIFAC-01]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
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   ],
   "source": [
    "eek_bco.groupby('RB00')[['MENGE', 'ERNAM']].agg({'MENGE': 'mean', 'ERNAM':'unique'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your task:** What is the financial damage?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:45:59.824970Z",
     "start_time": "2018-05-04T06:45:59.807261Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "450000.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs((len(ekko_ekpo_bco) - len(konv_bco[konv_bco['KSCHL']=='RB00'])) *\\\n",
    "konv_bco[konv_bco['KSCHL']=='RB00']['KBETR'].unique()[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:16:12.187499Z",
     "start_time": "2018-05-04T06:16:12.178351Z"
    }
   },
   "source": [
    "**Reflect:** Can you explain the fraud?"
   ]
  }
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