examples/agc_target_vs_scada.ipynb

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    "## NEMOSIS and Pandas to replicate AEMO's analysis demonstrating the precision of battery dispatch\n",
    "\n",
    "|![alt text](initial_operation_of_hpr_aemo_2018.PNG)|\n",
    "|:--:|\n",
    "|Initial operation of HPR (AEMO, 2018)|\n",
    "\n",
    "This note book demonstrates how to use NEMOSIS and pandas to create a comparison \n",
    "of Hornsdale Power Reserves' actual ouput against it's AGC setpoint.\n",
    "\n",
    "This done using the 4 second resolution FCAS causer pays data published by AEMO. This data includes SCADA readings of plant output and the FCAS regulation set points. The energy market dispatch set points are calculated from the dispatch interval initial outputs, dispatch targets for the end of the interval, and assumes a linear ramp. The net AGC set point is assumed to be the sum of regulation and energy market components. \n",
    "\n",
    "Before you get started:\n",
    "* AEMO's nemweb portal, which NEMOSIS uses, only provides causer pays data for the previous 90 days. Also, the dispatch target data used is provided on a monthly basis, published sometime after the end of each month. This means care must be take in selecting a time period to analyse that has both data sets available.\n",
    "* getting plotly (the graphing package I've used) working in a jupyter notebook takes a little bit of extra work but the process is well documented here: https://plotly.com/python/getting-started/\n",
    "\n",
    "The end result should look like this:\n",
    "\n",
    "|![alt text](agc_target_vs_scada.jpg)|\n",
    "|:--:|\n",
    "|Recreation for 2021/04/27 12:00:00 to 2021/04/27 13:00:00|\n",
    "\n",
    "Interpreting the results, our analysis shows a larger discrepancy between the ouput and set point then the analysis published by AEMO. However, this may be due to difference in the analysis rather than in actual performance. A few things we haven't considered are lag in the AGC signal and the impact of the unit control model. This report by DigSilent for AEMO makes good further reading on the topic (https://www.aemo.com.au/-/media/Files/Stakeholder_Consultation/Working_Groups/Other_Meetings/ASTAG/371100-ETR1-Version-30-20170919-AEMO-Review-of-Frequency-Control.pdf)\n",
    "\n"
   ]
  },
  {
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   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import timedelta\n",
    "from nemosis import static_table, dynamic_data_compiler\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "723da440-92b3-4f41-89af-754ab744541f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify where we will be caching the raw AEMO data.\n",
    "raw_data_cache = 'C:/Users/nick/Desktop/cache'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7116b2b1-430d-4a4e-97d3-b0f9002f5944",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Time window to pull data from.\n",
    "start_time = '2021/04/27 12:00:00'\n",
    "end_time = '2021/04/27 13:00:00'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f5239caa-e27d-484a-ba21-2e3fbfb721b2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieving static table ELEMENTS_FCAS_4_SECOND.\n",
      "Downloading data for table ELEMENTS_FCAS_4_SECOND.\n"
     ]
    }
   ],
   "source": [
    "# Download the latest FCAS causer pays elements file. The \n",
    "# update_static_file=True argument forces nemosis to download a new copy of \n",
    "# file from AEMO even if a copy already exists in the cache.\n",
    "fcas_elements = static_table(table_name='ELEMENTS_FCAS_4_SECOND', \n",
    "                             raw_data_location=raw_data_cache,\n",
    "                             update_static_file=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6961c9a5-a31b-4195-a601-b8fc0e25a5c0",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ELEMENTNUMBER</th>\n",
       "      <th>EMSNAME</th>\n",
       "      <th>ELEMENTTYPE</th>\n",
       "      <th>MMSDESCRIPTOR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>330</td>\n",
       "      <td>SUBSTN.HRN_WF.GEN.HPRG1</td>\n",
       "      <td>GEN</td>\n",
       "      <td>*MMS MarketName*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>331</td>\n",
       "      <td>SUBSTN.HRN_WF.LOAD.HPRL1</td>\n",
       "      <td>GEN</td>\n",
       "      <td>*MMS MarketName*</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    ELEMENTNUMBER                   EMSNAME ELEMENTTYPE     MMSDESCRIPTOR\n",
       "248           330   SUBSTN.HRN_WF.GEN.HPRG1         GEN  *MMS MarketName*\n",
       "249           331  SUBSTN.HRN_WF.LOAD.HPRL1         GEN  *MMS MarketName*"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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    }
   ],
   "source": [
    "# Using filtering and manual inspection find which fcas element numbers belong \n",
    "# to Hornsdale Power Reserve.\n",
    "fcas_elements[fcas_elements['EMSNAME'].str.contains('HPR')]"
   ]
  },
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   "execution_count": 7,
   "id": "64e40fb3-d1b6-414c-86dc-eee11bf2546b",
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    {
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     "text": [
      "Retrieving static table VARIABLES_FCAS_4_SECOND.\n",
      "Downloading data for table VARIABLES_FCAS_4_SECOND.\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VARIABLENUMBER</th>\n",
       "      <th>VARIABLETYPE</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>MW</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Gen_MW</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>GenSPD_MW</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>GenRPF_%</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>GenRegComp_MW</td>\n",
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       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Spare06</td>\n",
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       "      <td>8</td>\n",
       "      <td>Spare08</td>\n",
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       "      <th>8</th>\n",
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       "      <td>ON</td>\n",
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       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>OFF</td>\n",
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       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>ACE</td>\n",
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       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>ACEFIL</td>\n",
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       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>HZ</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>HZNOM</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>SEC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>ACEINT</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>HZOFFSET</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>HZDEV</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>SPD_MWB</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "   VARIABLENUMBER   VARIABLETYPE\n",
       "0               1             MW\n",
       "1               2         Gen_MW\n",
       "2               3      GenSPD_MW\n",
       "3               4       GenRPF_%\n",
       "4               5  GenRegComp_MW\n",
       "5               6        Spare06\n",
       "6               7        Spare07\n",
       "7               8        Spare08\n",
       "8               9             ON\n",
       "9              10            OFF\n",
       "10             11            ACE\n",
       "11             12         ACEFIL\n",
       "12             13             HZ\n",
       "13             14          HZNOM\n",
       "14             15            SEC\n",
       "15             16         ACEINT\n",
       "16             17       HZOFFSET\n",
       "17             18          HZDEV\n",
       "18             19        SPD_MWB"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check which variables are needed. By comparing the contents of the \n",
    "# VARIABLES_FCAS_4_SECOND table and the information provided by AEMO here:\n",
    "# https://aemo.com.au/energy-systems/electricity/national-electricity-market-nem/data-nem/ancillary-services-data/ancillary-services-market-causer-pays-data\n",
    "# we can suppose that variable 2 (Gen_MW) will provide generation and \n",
    "# consumption data and varaible 5 (GenRegComp_MW) is the amount of regulation \n",
    "# FCAS being requested.\n",
    "static_table(table_name='VARIABLES_FCAS_4_SECOND', \n",
    "             raw_data_location=raw_data_cache, \n",
    "             update_static_file=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "496862e9-3006-4483-9240-29e6593e236b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling data for table FCAS_4_SECOND.\n",
      "Returning FCAS_4_SECOND.\n"
     ]
    }
   ],
   "source": [
    "# With the elements and variables determined we can compile the required 4s\n",
    "# scada data. The fformat parquet and keep_csv False is used to save disk space.\n",
    "scada_4s_resolution = dynamic_data_compiler(\n",
    "    start_time, end_time, table_name='FCAS_4_SECOND',\n",
    "    raw_data_location=raw_data_cache, \n",
    "    filter_cols=['ELEMENTNUMBER', 'VARIABLENUMBER'],\n",
    "    filter_values=([330, 331], [2, 5]), fformat='parquet',\n",
    "    keep_csv=False)"
   ]
  },
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   "execution_count": 9,
   "id": "f4f68faf-23a5-4266-ad0c-ad1804d0231d",
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       "               TIMESTAMP  ELEMENTNUMBER  VARIABLENUMBER  VALUE  VALUEQUALITY\n",
       "992  2021-04-27 12:55:03            330               2    0.0             0\n",
       "995  2021-04-27 12:55:03            330               5    0.0            -1\n",
       "996  2021-04-27 12:55:03            331               2    7.6             0\n",
       "999  2021-04-27 12:55:03            331               5    0.0            -1\n",
       "2512 2021-04-27 12:55:07            330               2    0.0             0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scada_4s_resolution.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e8afe2ff-c253-4630-9127-fa01318315ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling data for table DISPATCHLOAD.\n",
      "Returning DISPATCHLOAD.\n"
     ]
    }
   ],
   "source": [
    "# The FCAS 4 s data does not provide the plant dispatch targets for the end of \n",
    "# dispatch peroid in a user friendly format, so this data will be sourced\n",
    "# from the table DISPATCHLOAD.\n",
    "targets_5min_resolution = dynamic_data_compiler(\n",
    "    start_time, end_time, 'DISPATCHLOAD', raw_data_cache,\n",
    "    select_columns=['SETTLEMENTDATE', 'DUID', 'INITIALMW','TOTALCLEARED'],\n",
    "    filter_cols=['DUID'], filter_values=(['HPRG1', 'HPRL1'],))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1c31fae8-e91d-4e0b-81ed-1de302bf0a96",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2704884</th>\n",
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       "      <td>HPRG1</td>\n",
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       "      <th>2705241</th>\n",
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       "      <td>HPRG1</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>2705242</th>\n",
       "      <td>2021-04-27 12:10:00</td>\n",
       "      <td>HPRL1</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2705598</th>\n",
       "      <td>2021-04-27 12:15:00</td>\n",
       "      <td>HPRG1</td>\n",
       "      <td>2.6</td>\n",
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       "             SETTLEMENTDATE   DUID  INITIALMW  TOTALCLEARED\n",
       "2704884 2021-04-27 12:05:00  HPRG1        0.0           0.0\n",
       "2704885 2021-04-27 12:05:00  HPRL1       73.4           0.0\n",
       "2705241 2021-04-27 12:10:00  HPRG1        0.0           0.0\n",
       "2705242 2021-04-27 12:10:00  HPRL1        7.7           0.0\n",
       "2705598 2021-04-27 12:15:00  HPRG1        2.6           0.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targets_5min_resolution.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d9daa988-7c47-405b-822d-d934c8a5fe69",
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       "      <th>TIMESTAMP</th>\n",
       "      <th>ELEMENTNUMBER</th>\n",
       "      <th>VARIABLENUMBER</th>\n",
       "      <th>VALUE</th>\n",
       "      <th>VALUEQUALITY</th>\n",
       "      <th>DUID</th>\n",
       "      <th>variable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>2021-04-27 12:55:03</td>\n",
       "      <td>330</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>HPRG1</td>\n",
       "      <td>Output MW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>2021-04-27 12:55:03</td>\n",
       "      <td>330</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>HPRG1</td>\n",
       "      <td>Regulation Component MW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2021-04-27 12:55:03</td>\n",
       "      <td>331</td>\n",
       "      <td>2</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0</td>\n",
       "      <td>HPRL1</td>\n",
       "      <td>Output MW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2021-04-27 12:55:03</td>\n",
       "      <td>331</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>HPRL1</td>\n",
       "      <td>Regulation Component MW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2512</th>\n",
       "      <td>2021-04-27 12:55:07</td>\n",
       "      <td>330</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>HPRG1</td>\n",
       "      <td>Output MW</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               TIMESTAMP  ELEMENTNUMBER  VARIABLENUMBER  VALUE  VALUEQUALITY  \\\n",
       "992  2021-04-27 12:55:03            330               2    0.0             0   \n",
       "995  2021-04-27 12:55:03            330               5    0.0            -1   \n",
       "996  2021-04-27 12:55:03            331               2    7.6             0   \n",
       "999  2021-04-27 12:55:03            331               5    0.0            -1   \n",
       "2512 2021-04-27 12:55:07            330               2    0.0             0   \n",
       "\n",
       "       DUID                 variable  \n",
       "992   HPRG1                Output MW  \n",
       "995   HPRG1  Regulation Component MW  \n",
       "996   HPRL1                Output MW  \n",
       "999   HPRL1  Regulation Component MW  \n",
       "2512  HPRG1                Output MW  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add some more readable descriptors to the 4s data.\n",
    "elements = {\n",
    "    330: 'HPRG1',\n",
    "    331: 'HPRL1'\n",
    "}\n",
    "\n",
    "variables = {\n",
    "    2: 'Output MW',\n",
    "    5: 'Regulation Component MW'\n",
    "}\n",
    "\n",
    "scada_4s_resolution['DUID'] = scada_4s_resolution['ELEMENTNUMBER'].apply(lambda x: elements[x])\n",
    "scada_4s_resolution['variable'] = scada_4s_resolution['VARIABLENUMBER'].apply(lambda x: variables[x])\n",
    "\n",
    "scada_4s_resolution.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fd9dc7e3-122f-42df-8cd9-f93938a82031",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th>variable</th>\n",
       "      <th>TIMESTAMP</th>\n",
       "      <th>DUID</th>\n",
       "      <th>Output MW</th>\n",
       "      <th>Regulation Component MW</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-04-27 12:00:03</td>\n",
       "      <td>HPRG1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-04-27 12:00:03</td>\n",
       "      <td>HPRL1</td>\n",
       "      <td>76.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-04-27 12:00:07</td>\n",
       "      <td>HPRG1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-04-27 12:00:07</td>\n",
       "      <td>HPRL1</td>\n",
       "      <td>78.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-04-27 12:00:11</td>\n",
       "      <td>HPRG1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "variable           TIMESTAMP   DUID  Output MW  Regulation Component MW\n",
       "0        2021-04-27 12:00:03  HPRG1        0.0                      0.0\n",
       "1        2021-04-27 12:00:03  HPRL1       76.8                      0.0\n",
       "2        2021-04-27 12:00:07  HPRG1        0.0                      0.0\n",
       "3        2021-04-27 12:00:07  HPRL1       78.1                      0.0\n",
       "4        2021-04-27 12:00:11  HPRG1        0.0                      0.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resahpe the 4s data.\n",
    "scada_4s_resolution = scada_4s_resolution.pivot(index=['TIMESTAMP', 'DUID'], columns='variable', values='VALUE')\n",
    "scada_4s_resolution.reset_index(inplace=True)\n",
    "scada_4s_resolution.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d7e9aca6-aac4-4df7-9032-56676191516d",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>TIMESTAMP</th>\n",
       "      <th>DUID</th>\n",
       "      <th>Output MW</th>\n",
       "      <th>Regulation Component MW</th>\n",
       "      <th>SETTLEMENTDATE</th>\n",
       "      <th>INITIALMW</th>\n",
       "      <th>TOTALCLEARED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-04-27 12:00:03</td>\n",
       "      <td>HPRG1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2021-04-27 12:05:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-04-27 12:00:03</td>\n",
       "      <td>HPRL1</td>\n",
       "      <td>76.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2021-04-27 12:05:00</td>\n",
       "      <td>73.4</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-04-27 12:00:07</td>\n",
       "      <td>HPRG1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2021-04-27 12:05:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>2021-04-27 12:00:07</td>\n",
       "      <td>HPRL1</td>\n",
       "      <td>78.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2021-04-27 12:05:00</td>\n",
       "      <td>73.4</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-04-27 12:00:11</td>\n",
       "      <td>HPRG1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2021-04-27 12:05:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "            TIMESTAMP   DUID  Output MW  Regulation Component MW  \\\n",
       "0 2021-04-27 12:00:03  HPRG1        0.0                      0.0   \n",
       "1 2021-04-27 12:00:03  HPRL1       76.8                      0.0   \n",
       "2 2021-04-27 12:00:07  HPRG1        0.0                      0.0   \n",
       "3 2021-04-27 12:00:07  HPRL1       78.1                      0.0   \n",
       "4 2021-04-27 12:00:11  HPRG1        0.0                      0.0   \n",
       "\n",
       "       SETTLEMENTDATE  INITIALMW  TOTALCLEARED  \n",
       "0 2021-04-27 12:05:00        0.0           0.0  \n",
       "1 2021-04-27 12:05:00       73.4           0.0  \n",
       "2 2021-04-27 12:05:00        0.0           0.0  \n",
       "3 2021-04-27 12:05:00       73.4           0.0  \n",
       "4 2021-04-27 12:05:00        0.0           0.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge the 4s and 5min data such that each 4s data point is paired with the\n",
    "# initial output and dispatch target for the current dispatch interval.\n",
    "scada = pd.merge_asof(scada_4s_resolution, targets_5min_resolution, \n",
    "                      left_on='TIMESTAMP', right_on='SETTLEMENTDATE', by='DUID', \n",
    "                      direction='forward')\n",
    "scada.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "591ff887-3f25-4e6a-95a7-8f3b67133ed2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the fraction of the linear ramp from the initial operating level\n",
    "# to the dispatch target that the unit should have completed at a given \n",
    "# TIMESATMP.\n",
    "scada['fraction_ramp_complete'] = (\n",
    "    1 - ((scada['SETTLEMENTDATE'] - scada['TIMESTAMP']) / timedelta(minutes=5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c18209e7-a5ac-4a27-9f79-1facc57d7645",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the dispatch target on a 4s basis based on a linear ramp between\n",
    "# the initial operating level and the end of interval target.\n",
    "scada['linear_ramp_target'] = (\n",
    "    scada['INITIALMW'] + (scada['TOTALCLEARED'] - scada['INITIALMW']) * \n",
    "    scada['fraction_ramp_complete'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "04032e0b-2197-4c0f-be29-d2c1c4d3d8e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adjust values to account for the fact HPRL1 is a load.\n",
    "scada['linear_ramp_target'] = np.where(scada['DUID'] == 'HPRL1',  \n",
    "                                       -1 * scada['linear_ramp_target'],\n",
    "                                       scada['linear_ramp_target'])\n",
    "scada['Output MW'] = np.where(scada['DUID'] == 'HPRL1',  \n",
    "                                -1 * scada['Output MW'],\n",
    "                                scada['Output MW'])\n",
    "scada['Regulation Component MW'] = np.where(scada['DUID'] == 'HPRL1',  \n",
    "                                      -1 * scada['Regulation Component MW'],\n",
    "                                      scada['Regulation Component MW'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e80e8feb-e18b-45be-a4d2-c0037fe67961",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine generation and consumption data.\n",
    "scada = scada.groupby('TIMESTAMP', as_index=False).agg(\n",
    "    {'linear_ramp_target': 'sum', 'Output MW': 'sum', \n",
    "     'Regulation Component MW': 'sum'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "72431fea-18a2-4756-aaca-87b62f4f5880",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the combined linear ramping and regulation target.\n",
    "scada['AGC Setpoint'] = scada['linear_ramp_target'] + scada['Regulation Component MW']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b775d911-11dc-4d8c-8319-4257efb96727",
   "metadata": {},
   "outputs": [
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OuUBr734usvLhQf4VAFT611CVHACVqljCfzkAlf41VCUHQKUqlgimHGmEymCUiT4Xux1poy+RuyfCU+lOr3Z3AyqtodIIcAxuAuas4M6nOWyTCxAubrkdaRttSsU+A6hl1AkkoFL2UorMAJW2sip1A6BSKXP4Kgyg0pd8SiUGVCplDl+FAVT6kk+5xIBKdUwCqFTHFrGUpJqhUgYxs91SzaCSjcSQJq+zFNCWXb6Esm+/RsWBg6i4Tc9Q7CmgUs6cn18JKlXdXCgUgVKQKaAyBUZc/wqAyvTYElCZHlsCKtNjS34TQKU69gRUqmOLWEpSrVApQ6HVZjZGqBRAZ/QGCiAVm/mwIRtvf4a0fI0rm2Y/eY+yi/9BraOOJ8pkTdOahblySC6v+2SvJe/+arxUWAfqSogqvxlQmZ4KAKhMjy0BlemxJaAyPbYEVKbLlnG/DcJffVoAULlhh9TMmtWUe+EJKvTfi7SGviQ26RFQZty0R0jfcuhxlP3ondLmOPx7197B1mbqePYoPUsrCJRBWDY7oNJnI1AsOaBSMYP4KA6g0od4iiUFVCpmEB/FAVT6EE/BpPBUKmiUhBYJUOnTcIDKNgE5hFQOLZV/lgHRCuyEGQTgufUOWuXbdOndVNx2ez17J1Cprb/PCJ0qHoPis+qmNjmgMj2mBVSmx5aAyvTYElCZHlvymwAq02XPON8GUOlT/WqFSqPX0QiVDJL5GdN1kDN6Hfl3/LfitjuQ1nkjqn1ysm4FDost7LCrHorqFiqtvKCcT/5vM6k4cH8qDNxfB9/iDrtS6657EzU16s8S4bgc/ipDpQBcPbz3zKuJikWi+o4+awySh60AoDJshaPLH1AZndZhPwlQGbbC0eUPqIxO6yieBKiMQuXqeAag0qedAZVtAsqb3fDPlaDSKLlY58hpMksWWkJlpnENZT/9iAq9dibK50ub66w76zrKz35EB1iz3VvF84xeUCOI8i6w2Q8XlcJwuTzyOk/Oxy3s+qxeSO5BAUClB9EUTQKoVNQwHopVVVDZ3EgdzzlCV6lx0lOkWZzj7EFGJZIAKpUwQ2CFAFQGJmXVZwSo9FkFqgoqpQ9l636HUP7VmSX1ms68lurvvLT0swxkdkdyZNauoczKL/QwVeM6TJGhcddWI8QKmFx37o2U+fBtyklwaDSx1RpPJ1BptSmRz2qE5AEqAKgMUMyYswJUxmyAAB9fTVApT1imcSISUBlgw1AgK0ClAkZISREAlT4NWU1QaXYch5V8wivIf7eDSjkPq/Mr7aBS5CE/y2ynV75PhOMa11gyVHL4qwiJFWDM98mXm/fxWb2Q3IMCgEoPoimapCqhslikjmeNpNbhR1Hzkacoahn3xQJUutdM1RSASlUts75czU2Un/0nKux3CGmbbmZbWEClrUS4waECgEqHQlndVk1QaQZp68bfRJkli3QQky8BZG49e3ZQKTySZmGuxmdZQeXa384gqqltt3HPuvNuJqbKutsupLW3PE7UoVPJcwqo9NlQIkwOqKwgdqFAlMtFaA1/j6pGqLTqA/0pGX/qaoJKeRIUnsr46161lcC4aaLd+6cdKh9/Zh5dduPvSzKcdOwoOu/Uo+xkifTvq75dTWdcfBvtsVu/dmVbuvxz+sWFt9DpY35Mhw7di266+xE67sjh1KvHVnoZ/7Hwfbr1d4/SXdefS5tu3KX0u3mvvx35ewIqfVabaoFKq51T5XWQZVB5zg1UN+kifY2jOIfSidTygIrvbz7pUtI26lraWbb5hPOpdvotpaxkuDRuCGT8sHMirWFHKvTfQ09vfKe7u02kwSf3p4aGDSU12wDI9XEnTl4c9wSmAKDSWkqeaCnsOYzWnXhxYHqHmVE1QyXrmqaoiGqFSrffwDDbU1B5w1MZlJLh5COPfZyMV9IMlbfe+yit+M9KunLCOKqvr9UF59/xpRJYMlRed/uDtPr7tXTtJaeU4JDLyVD87Itv0MFD9qQjDx2kl3/QXrvQHgP6ld7n9w/NoCmTLi77nXxPODWtfa6ASp9KAypv1sHRKjTWyyyt7GEU6cXveJCVXfxPqr/jEt1y/Het985UO/k6WnvDnyiTzZYs6qRjlZ/FUPlx7UC68vLWsloh7vF63InPKobkLhUAVJoLJk+iJAVWAJXPuaz96t5erVCZtskBfh9ApbrtjEsmj2uqGSrZw8dePSOkMcBdet19dMHpx5S8fVYW5Tw+/OTfdPCQNmeEn6tSXgIqt2/YmnbbZYcSHMq//0HXTXSoZM+k8EI2NTXTXdOepJ37NNDiJct0UObfGb2ZfsrtJi2g0o1aJvdWC1TKG+jIoa7y4DT/ykzKrlxB+VkPlZRqvOUvpHXs5EplXk9EHKa3/pgR7hRlqJQHx5WgVfYyWnWscuc7ofsL+jMbemg0bkzb8/nKfLGcsqv+S5ml73o67sTVy+Nm3woAKtskzL36DNU+cT813vQYUSZb5plXESq1DFGGFzVLVzVCpZN+y3cjiSGDaoJK49ILFdubnyoAqPSjXvhp203MjzqeMoUCabk8USbTrgBBeSoLSz+gljdfDf8FTZ5Qf9S4dr+V4cv4R9nbZ/T8iXQ/Png/Pez08y9X6smvuvAkGrhzb/rLs6/q3s8Zc14r/Z5hTwDgJWcfV/I0iry7bbJRu7w4jbhE2qMOH0Kz5r6hAy97VkVZGrbZUr+V0zCcPvj4HP2ez7/8Sv/3yceOooefnEtnjP5R6XcijygNAqj0qXa1QKXw+hmP2TB+LOUBUcuhx1HL4WPdK1xoJcptODJE3umV/y1DZaVZOHEeJv9/zZ3PlnkxRaFkb+ade8/VN+pZtjxDQwYXaejgYlnZxbu1jDyWtFyOCiN+nrqt4t0bS70UgMo2m4i6LSZenLYblSwKqGyLBEnDBahMgxXb3gFQqbYtjVApdsPnvqTlqNOpuE3vshcIEipXX3xS5OLkevWlLtdvWDMpCsBho8s+W2Ea5sp/E5BmBZXs9WOo+/Tf/9Vhji8Gukuvv5+uvfhk3csp1kKed9pRxF5GDmE1g0oOVTXmJQsloHL8KUfR/Q/NKK2ZvPeBp2j4oB/SgsUfl8oreyLF7+W1liu/+a7kyYzaGIBKB4qzAS+/eXJpVkKOW64WqKx57HdU8/yf9fWR8tmNlaDSSdhFJfmNs73y2hT5bEvbQVexSCSFxcrPFKDIYa+zdr+VDhxcoMnT8votRrA0rsHUB+sHH02Z774hrdsWDmoSbolCgVRDZWsr1d7zG2o5+Vek1XWoKKcctt162OiyTae8hKVHYTvjM6odKpNiJyd1o1qg0rgvAP/s91voRN8o7wFURqm2u2dZ7X8h59J4xe9J23K70q+ChMo0eSqtoFJ4CcUaTQGoB+430DdUMpC+8OoC3Tac3x/+OEv3Pj4z9/USVPI/BAi/9faHOnQy4IpyMEjHsZ6SywWodNBe5RkNo3u7GqCybCexu2ZTdum7VPPXP1Bx98HUMvjwMgWD9Ibk5r9MdfddVcrfM1RWsLERKk8cU6Bly8gSLM13lM3Q2rtnO6hJuCUKBdIMlUbvY+br/1DttJuJz2eVL7MQ8STuSAmoPIF4QiANVzVCZWGHXVO5ZAJQqW6LlCc1jMehiVIbJ6uCgkrVVHG6ptKtpzJsqFz1zWo9pHXwPgPpq6+/1b2kMoewzuz1fPSpF3XJhWeU35dDc9esaSzbHTZKuwAqbdQ2W/DKFZDjm9nQ1QSVzT8aR60j/7eiYkFvBiKH08pQmV34N6p5fQ6tO/VyX+0l881X1OGS/6Xbu91BvQ7akQ5cH/I696UsvfhS26Y/ssfS8piSu9OzoYYvQRVIXA1QyTJzlIARMoX8Zu3Gqi0pYDLLIgAqAZUq10+zssmDekBl0qyX/PJaQWXzyb+m2vuv1l/QuCNxWqGS39XJ7q/ymF6k4f8LT6V8NIc43uPaS0/RN9Mxhr/KGwDJfxPhr1bHfBgdVlymmc+/RvfceH6ZF1IOw+X1nocM27sU3iuet+3Wm5XtdhtlrQZU2qhttvBWnjH4dk1LlPaK5VnaDeOJ3l9AmYsmEvUbWLkM7y8g/X52g/+hbeMbP5f2lylET05ty6LfwLYyBHw9+zzR7OeJRgwjOnjYhszF7/k3Z5ysUe9eGdKmTyJ64wWiYT/eUK6A3jXg16ra7Dp3yFPjugIVioZdX1KgiHbigaW3mH38C9T7T+Opd3NbqMzHu45t+/f7bT/ztTK3FXUrfG765tP3foI2XrWEOnXJUUPzO+3u6d3TWr/p3x9HBw6poXc/IBq0r0YdO7Tf/MGN3N+tJlr8HtE+exLN+xvR/vsQLVpM1LdXjrpspFFLa/n6Zjd5J+1e0d/q5W7oQzRgH8oecgxpdfVJe5Wy8naoy1GxqNG6lpTbUnwD+Xv1ozFt38OQvl1xVYi6mixlsxm9n8WllgKlMdOPxrQVbP34SYzfSt+Q9WO5ppOvoi2321Stlwi4NMZzKnnDHXmTHAGKYkMe9vytbWyiU48/vASNi977uLRRz++m/1U/+uPl1xbpJZXzk5+164699RBWsZurgD6Rl9lGPcLrKDyR4igUo6dSLMvjjX3E0SJclriPSwFU2lRergQiplnET1faUSrgthB7do8+WaCDHhysl2OTP71iW57WxW/R9/93NuV33o06X3G77f12N4j8+L66n51IHX4e3CLwJ2e2fRCfmtU2yLngrBz13WHDkSR6fzyzYPp3uVxOtbF7V/wdClRSwFjnZnceQyO+Xz/hYkjIa4Qnd72WBn//x3b3VErn1AKcPx/Bk9da6JSvL6S+O2So869vI8rlnGZRdt/90wv02j+LdMFZebrpjlY6+9Q83X5vKw3YOaP/u5ou7j/Z1vIVVH9aTTrG8a5rGzV65dL7ac9/Ty19r775+f6Ov59xlBnPNFdgzVqie6e20lmn5Kgm72/SzIvG3192OlE+T7kdN0zkd/jJCUQ1bWctat+uojW3tUVq8T01O++u/7vpscl6/8Hjr0ynLrT6wrFlYydRH0WZ+Hvw88mneCliVaaRd14VTFCVQli8NKDSpjbYeSrXNJWfaZimyvXRxxrdfi/RzV+0eUdqprXFb9tdxSemUGbAXpTptaPdrY7+Xlz8JmkfLKLcj07Qd4X1e818TqOPlpL+n7gOH5mlYUM0s922ie+fNaftzkk3lH9cio/eR4WnHqT8JbdRRur8/ZYR6b0rwB6Rdc1FKmrp8lRq7y2g1uvOLQnDYMeeyaUDxlKfQwdS8b02EPlmiwG0asvdSvd1XfEWre3SnXKtTZR/+2/0/f5H0bZ/upA4P3Fx3c30rbzLqGgDAmT5CJ4Rq6eUoDX74zGUO/JET4a7/Z62Nrl9r7Z2Kf7fp3eGfnlaJpVeZyuh2MaybcR9TvtfTwaIIFFtPkscPNBaSK+nkutxr4VtbUK0B2FPP+0jAvO4ekQ+l6Vshqg5xREE4rs/cjjRIQdFC5XGvl7up7O9dyLt/YWk1dSY9hPt+osV/ybacuuSfUV9FN8P/oMTh4GrCpLimwGVlY0LqLSp/NW8ppLXFX763CI6feX4djH4Sewz5HWSovxivWT3rh1oxapGsuKQyVNz+lEjxjMs5aNWbHehTaJoCSxzWtdUyptKiZBXNo/XnSV5fTDX2cL/HEitg0bZWlq0ATHJZIRKPzuVyu1LtDP+/w69iU45UaOm5uoJsxN9CttG3mgj6WcdVsNGPZdfmde/l9w+RbuUz3hOy6ZL1bBRjxgvmB0vZttZ+rxB7gN4XS5f8vngcvZcz/Izppc9kY9yK27fv10pxDtltQIVMzn6Wb83aY9/T6duV9/ps8TVkxxQCaj0Xdvls27YcykvxE3zRj080Mt9uDDRUMmdKF9i0x3+N4NhQ4NWdg6lHVRyOjHwlT8ygErfzSvwDNIIldnPPqF/Pfkq9XlnKi3pP0b/v7g8w0axQJR1Hq4q6r8YNHPYFF/CcxkGVHL+N15dnVDZfOgFVzkAACAASURBVMbVlPnyX1Tz53t1nT3bOfAW5i3DtEOl2DXcCJXyxikMAGm4AJXhWVHepVueMDTbJNC44Y5VqYwT6jyG4UucxZ3mjXrCsxRyNlMAnkoH9aJaz6nkWVcR3ua083IgZyS3cCe6bFlG9y6Ky9iRygVxApVmR42kcRY6EgOF+JC0QWXu/flUN+mikmL6wLRxDeXnPk4th51AxR0qh60GJbWAylO7TdGhFlAZlLLl+YjBo4DItExcpR0qxcDdCJVsXaNNw6k50eUKqAxW65qXn6aahyeVZdo69CfUfNQZpd/JsCl+2XTeLVRc78k0KxGPWZYu37CTPd9j5nkFVAZrz2rODVDp0/pp9VQKgBJQ6ccL4VNiV8krhbhWysgJVHJ6I1iyPhyWkhR9XImZ0JvTBpXGwYTXcFe/5jSDyt7NC0u7z/qZeLIKf+UyV5OnsmbmQ/oZwHwBKv3W2GjTG8PD5XZamhg47f+oMGDfaAsWwtMAlcGKauzjzcYT8pFQ+t9HnUCmm0CsL5qZd1J4Jo2lB1QGa89qzg1Q6dP6aYVK0SElASqdhrgGAZWch9xZe9FHzFq3HHo8tRy+fttvn/UQyTcokCao5Lq24+Pnl8CN3zJuqDxpn/l6mXijB77E+k5Apf9WKA8uAZX+9YwyB47s4UusOZbDlWUgaLz9GdLyNVEWLfBnpR0ql/0rQ+++m6HX3siaevaCFtRJNIJchyp9A9zApHgPQGXQFq3e/ACVPm2fVqg0rp+KayBbyTxuQ1yDgkoZLP1ApZ9BuM9qm+rkaYFK4xotYbS41taJPgFQGV7zMQuTTEuIfZrDX0Vb5QkWs43tMmvXUO09V+gbL6UhqiXtUMkTBNksUbFoHi4adA/gKDx6XSPV3XUZFbbfhVpNJqO9wCSgMmhLIj9Apc86kFaoFLOuZutDfErmO7nXENcgoVKAZe3T0/SNStwMFOQF93EBgm8jKJxBGqBSDrPWPR8bbUr03SoqbtOTmn7VtnFL1Je8UdVhjwzTHy9vS+9nkgThr6QDB3ssjDoCKqOu6e6fZ5wAeu/IW6jHQW27doorLXbk96kGqBR2C2z316Y1RPWdyuqEpmmUIY06nnGw/nsv4wG7TXic1GZ4Kp2ohHucKACodKJShXvSCJXiA8m7pB4y/7yy7dF9yuU5eRAhrpUe7nRNpTGP5c8tKoUCbn32CUSFAmlbNZDWZdO2W1tbKT/rISr235OKDf30XwEqPVcDRwmTDpXGdbsMcH6AzZFoDm6yg0qvgyJOB6gEVDqogsreYozsMYNKue/3Ag8qvXw1QaXxGDEvdsg0rqEO5/2Y1p11DRV23rOUhTwWcNvHO92Ex0l5AZVOVMI9ThQAVDpRqcqgUsx8qQCVQYa4hgGVwrsg8ta23I4yK/5FTefdrO/KWbZG6o5ZRLkcoNJnm7NLnnSolAHrlH3mm3qv7DQI4+9yuc56baj+CNlTGSRUyuWvlo16rNZVpcXDlebwV2NkD6AyjB4oujyFPfmJQUClaMNGcPQKlX5CXc1UBFRGV7fS/iRApU8Lp9FTKQaP40a30k43HKQrFPXMahghrmFApTz7LOcvPh4yVDYfew7V/ukuotaW0q1R6+qzuicieZKhUga3cWMKliGRcRhCLtuhb55HvVoWtCuG1/ps9FQmCiqLRco/8wDVPvtHarrot1Tcprdr81Q6y9BqQOr6ITEnSCtUypE9p389Xm+zVlDpZEOWmM3k6PHwVDqSqXSTsLsMlcYJaSdLaIKGSVFAQKU7e+JuawUAlT5rRxqhUszSyVD57kXPUUODT7Fskocd4hoWVMq7ssnP4AG2DJX8QeEPiXypuAFSuFYOP/ekQqURKFkplYBCHtCItdZGa3qtz0mGStnb4GRgaNYCKkGlPHHlFdrDb3X2TwBUUul74LWd2KsczR1phsrmFqKrr2vbyZevIDyVlaCSxwWFgftR65AfWx4R8uWXGVq5MkOv/aPt7O3A1nmuf0dAZTTtphqeAqj0aeW0QaU868qeEjFgChMqowpxDQsqM99/S7X3XaVnb4RGM5AEVPpsdDbJkwiVMrDxZI6YwBHtT4VBaCWoFPXcazmTCpVGb4PbdVFclZ99Lkdd5kyj4d9NsdzwS6V64LX1phUqzSJ77th7LvH303jBU+m19kST7m+vZ2nWs9myhwUBlaL9mnkqK/UZRs8kl4WhUv5GBKEMoDIIFZEHKwCo9FkPAJXeBYw6xDUsqBT5igHmt7kf0MaFrxwJ49Wz4SjzKr0paVBpBZR23quozWt2Pqsog1+olNcwGd9L5TWVso3EhJJbbyJDidgQzao/SMO6yrRCpVlkjxVUpsGO3D7T6qkUEwRyH+QXKo0TT6J/qFQXzDbh4TIBKqP+6uF5bhUAVLpVzHB/2qBSnnXt3dy2xT1vxrFu/E2BhL/GGeIaFVSyXh/XDtCPGrG7AJV2Crn/e5KgUgY1OaTJ6UHX7tXxngJQ2V472fPE/+bLrbeWocTu6KY0wEgaodIqsgdQ6b2fiTOl2eRW1FBptm5y2bK2sFdxXXl5a6AywVMZqJxVnRmg0qf50waV8qxrkFCpQohr2FDJ+dc+cCu17juSsu/+k2pnTLetXYBKW4lc35AUqDQeHTJ0cFF/V6uZbddCBJzACip5EqWhQdPL7RaoRBGT6qmUoTI/Y7prDbgOTJmWoV+snFDx6CZAZcCVOaDsAJUbNp0LSNLYspH7Y7kQYUClVixS/aQLy/qLSpvwGD2ogMrYqgkebKMAoNJnFUkTVBo/kGIg48dTqVKIaxRQKZ5hBAOrZwMqfTZAk+RJgEoroMx/uIBqb71Afysv6/OCV3NDjoDK9uqKtVIc0uYF/L5//S3afMqFpYytoFy1UGgv9SyNnko5sofXQYv6YOWpTIMd2fZpDH+1gsqfN99BP+z2EbUecSIVeu/suurLUSd6n37m1dTxnMNL+fBOwb//++6ln8024QFUupYdCWJSAFDpU/g0Q6WYhb+720QafHJ/x+Gvqoa4RgmV/Cx5V0jjsxkm2ZOpGjj4bA5KJFcdKq2Aklqaqe6OS/XZaxXrhQyVvZsX6CGbfFWzp9IvVMq7Q7OWldZjys9SoqG5LETaoVKO7Jm5+62mG/XI3wW3a29dyh3q7dUElTd/cWDZJF/2w0WU/WABabxra58BtjoboZITiPXX3Hc+2PNW+u67jL5ekiM+RMSKnLFxch6eSlvZcUNMCgAqfQqfJqi0mnVlqNz2oF1NOztjx2eM/edZN77MOkqf0geavHvXDrRiVSNpWnDZigGj8YB4fgKgMjidjTmpDpXcznotfpjW9d2DDj6pp178zHerqMNFPy+9iooDTiuonN15jL7FPU+SePW8Jyr8VdMo9+EiKvTuTx3PGqnbjO3lxQslQ2VLbRdqmfS4ZcMAVIbXZ3jJWZ4c4kG+sD/3906g0muouJeyBp2mWqBSnjzTum5OmbVriJrWuOqn5TauddmEMqu/0dM/vtOt9LdVu5XyqnRMiNz3+g3HNasLWFMZdAup3vwAlT5tnyaolNdTyqE8E7q/UPFcpKSEuFYydRhQKZ8x+PznA8s27gFU+mx4FZKrDJVi4sY4+y0GHlwvnM6Ah6egec5WUMmTTiftPb9qoNLoXZS9yk7Bb/Vqoq9XZWjH64brYl/Q/XkaMlijAwdbz2ol/TiKtHkqjctFnEKllzDpqNu63fOqESrNNHEyiWbsLzgfnnjgflO+AJV2tQ5/T4ICgEqfVkoLVFb6QHLnZ+zwkhjiGjVUaoUCdbj2NFp3+pX0/OKt6YM5n9Bujc/T0DUP6xuaiN0iVfRK+WwWsSZXFSoFUMqz3yyUmGDgf6tcF4yTRwKMAZUD9PbMl1Oo5Ak8UQ/EANPuQHNAZazdSruHGyN7ZKjkNmEVogioVMuOojRmayr75xbQ2M/awvzlSxyh5BUqeaLeeFVq/3LZ4KlUs/6gVG0KACp91oRqg0rVd3H1as4wPJXGsrB2H875mEatvpeW/OQGOuyRYfotKoOEVz3jTKciVMpAdtI+82nHx883HagIOIlTP6tnAyrblKnkqXQCfmKA6BYqkw4jafNUeoXKssmHiU8S1XdUsblXLFPaPJV/fz1L776XoeX/2nBsx+GHFmmLv0+lfu9O8wyVcl8hlsGYeSn5AYDKxDUDFNhEAUClz2qRFqg0fiDFAIbXS83uMrZ06K4sl93Muk9pI00eBVTyC8kDc+HpAVQGa2rVoFK2+bjRrSQ29BCz3eLtVV9jZYTKC/47ljoVv6XfbPEEXbv75KoNf5W9FU7AT/S1I1ZP0UPi5T523JiCZWNwknewLSnY3NIElcb1lKyUU0+lcWIiif1/2qDSbE03e5oFFJb66s4bEWVz1DzosIr9nZh8P2T+eXpEwtMbnU61xbVl7d3YuuzGU6KMdvd5abVYU+lFNaQxUwBQ6bNepBIqt2qkjuccoSszc/eJ9PwXA0sqVdqhzKeUsSaPCiplsARUhmNylaDSCJS8VlmGA97wRewEmDSolK1XTVBZ2tW5po6aR/ycWg8bXZKiDPwOPZ4om21XycXgkHfP5QEnh0rqO+j20Cx3DOVMAJXh9BdecjUuF+E8RL0QkwSVdujMrPySOvz6eP3Rqrd7M33SBJVWR4nIUNl83HlU+9Bt1Hjjo6R13qjUFo27dLe2EuXzRGZtvIXq6KyVZ9Gvt5xBzZn6drLyhCN/H6wu8S0BVHppsUgTlQKASp9KpwEqjbOucsjGF1c9Tbfc1UkPzeBL9V1cvZozSqgUYMkhkDyo5EFFsdPGVH/HJdR09XTScnmvr4F0RKQKVFqeRfn0tLJZbh0qa2qp0HNHpe1ntiGXKHA1QqWZh8l4Pq1xzZVcJ27ocq4+oeAUKr3sLqtShUqTp9Iq9JX1vrvrRPq4bqDlmkphkyRPEqQJKq36NYZKMVFgBH+ztthaILrymjwNHlSkl+a1TSaJiWOzNZTGtmkHld+vIXriySyNGlmkrl2DbdnwVAarZzXnBqj0af00QaWYKRdQ2XTWdVTc+X98KpSM5FFDJavyxTkX6FD56sBf0d5rn9YHmE4W/idD0fhKqQJUWp5FKa3JS5qHAlDZVqftNuMxnk/bMuxn1PKzX+hpZQ/XWa8N1X8nBpx2nkpAZXx9ivHJVstFWkYcTZe8fZp+u91ZgoBKNewpbGksjQyVxgkkuS2+PvQW2rW/Rv/6lGjytHzZUqEgoTJMtQCVYapbXXkDKn3aOw1QaXU+ZRLXeng1ZxxQabaIH1Dp1YIb0sUNlZWAkkvpZDMX/yoEn0MlqDy12xTq885Uz5MiSTqn0g4qs59+SNmFf9fPshShzaIvFRoO676ADpk/Xg975TMNly1v2yDEDkTEs5PYT6TJUynqq7CX3KZ/9cgPHdkyyZMEafJUGvue2hqi5haia455U++ruY12n3RTWYcqRyTwpND++xapz/bFMqg0bsRl1yPbeSrt0vv5O6DSj3pIKysAqPRZH9IGlfImIirvROnTbO2SxwGVmdYWqr39ktLAkwtlXKMR9HtWQ35xQ6WYpBGep/xrz1HtgxOpceKTpOVrUgmVYkdbr/U3TVAp2hgPPPMzpuvtW3ilRd0Qen212QDKXXoLXXNjrgQiLa1ENRYR8LWP3U355x9PZD+RFqg0W08pQ+VzX+xGL76Urbibp6gjdhMUqvbXaYFKs/WUR/20QJ06arRDy0JLqGS7CNsxVHJfP3RwAVCpaoVFuSJTAFDpU+qkQ6VxPWWSZ0/9mDIOqOTyihAouezV5CH2YzOrtHFCpREouYxGz2RSB5KVPJVhQuVp4zTaahvrXVHDqEOV8nRjP2OIo4Bn4xpUYwjeFb9qpVwbZ7a73Dw/am0qPS8tUClsxfsMDN/hE6q/+hQiTdNfnftuNxuqJNWWaYFKsz5NeAzlHfC3OfN4fROdqQ/kqbFRo9NOKQAqVepcUBZlFABU+jRFWqCy5FUxbCLiU57EJI8LKqlQoI5njSzT6b0jb6EeB+2aGO1UK2hcUGkGlPKMtqyTV49enFoDKtvUdwMCYmDK9v5g5JU0+dGNdK/G6V+Pb7eGWgbLSjs8JjV8Om1QyfDR94kJpUgT0abNPJlW7dZNXYqz7RufnRaolPtsEYJuhEreSGvwyf11qJTDnit5Kr9Z+h/q0zyffv7NjaUjg+zsh/BXO4Xw9yQoAKj0aaWkQ6VxPWVSByw+zUixQSUXvGkNZQpFyr3whL4rqPwR8/te1Zg+Dqg0OzqEtTfuBirskbRNerjcgEr3UEnNTdTxnMP1hEv6j6F7V7ad+Sug0lgPXn4lQ3Pm5iqGTiZ1g5e0QKUMFvK6eHmiyLjm0qofTur3Ni1QKZ/9yCHLfF15/irKv/AEZVuaKT/7j2XfYzPb8/e6sMOAUvgr53HTF0MpQ23ea7G7s923GFBppxD+ngQFAJU+rZQWqDxpyHvUs4dGNY/fW7YGyKc8iUkeK1SuV0kMFnljAL54oT913pjW3vRYYnRUoaBRQ6UVUJpBpX58zHbbk1bfSQWpXJXB6jw3zqRawl+9LA/ILXqN6u6+TN/w48v8drTJyAOp7ztTLPtZOyABVLqqtoHebPRCyjv9yhMEcohspWO4AJWBmsdVZrItGxo0mvdikSZ8dSJ127KWsp8tLeXFayYF8FWCSs5DgKnY9RVQ6cokuDkFCgAqfRoxyVApOtUOxdV01ZdHlClRbev6VIJKY5UUtuhw1cnUfNQZVOi3u89am+7kUUJlJaBklQUAaPlayrQ262uuknoBKjd4nt2GLxuPGRF1wKw+iIFrJc9FEsMm0+CplGFx5Jd36B6t4ja9qNBrZ2r531+WmrbTdZVJXVOfBk+lsBFHDjAQ1j49jUZ8P7Vd92wFlfKaS45CEFApdn0VGcFTmdQvHsrtRQFApRfVpDRpgMoRq6e060yTPPj1YlIVoFIOlZzdeQzxTrz8gRK78PKsNl/VZhu39owKKu2ODuFylzwR59xIxR59SevQ0e3rKHN/JahkANrphoM8189Ku7+qtFGPF0+lXA9kY1odC+IESES9cgu3cVamNEGl8MyznmY2cLquElAZX42Ul/4sXZ6lHR8/vy06SLo4uoCh0Oip5J+3f2eavlyFv9WVoFKcQyuyZYgV6zflZyH8Nb66gCcHpwCg0qeWSYZK0amevnJ8WWeapIGKT/OVkqsAlVwY9kAs2fgAurfj/5Gwy0c/uZZ6Lv5juzPvgnr3tOUTBVSKQWONto4GDcrQgf0+pQ43nE1rr5muhyyLK6nhbWZ1AlDp3VOZ/egdqr9lvD4A5U14tD4DdBgxu5xApTwBlZRJpjRApZj8EGcYVvpW2oUxs+0BlfF9fWT7cJsLCiqNk/ROodLujNowlcI5lWGqW115Ayp92htQ6VNARZKrApV8duXcV+vohZeyZOZBZrkqbfIiIKblJydTy4ijFVE32mKEDZUCrnZtfIlGf/Mb/eV4cMkD/ZL3SdMo07KOOqzfpCUpA/9KlgJUeodK1tUJZPB9xmOerGyStBDYpEOl7H08ZZ/5ehRCJah0uq7SGBqdhL4iDeGvwj4Mc2zbuokXWHoqxW7Mcmi68FSyN3Pm7reWwl/l77bwdMpt2MpTCaiMdpyAp4WjAKDSp65JhkrRQd7Q5dySF4zlsArL8imV0slVgUoWSXgqrKDSyj6y96Iavc2igoUNlX+YmqNTXj+UvsltTpu3/qusXgvbWO0KqXQjsCkcoNI7VDoNhxQmcAKgSfOCJx0q5TV4p3b7gx76WOlb6cTjzPY2QmUSdoZOI1SK8H0xSVjYcxjNaRxBc/9WV9qNWYZKXp7CbZDBcU7n4+nQjZ6jO5rOpxtWjNCb8X869aOv9/kZ3b94WFnPCqhM8lcQZbdTAFBpp5DN35MKlfIg56zXhpa9ZRI+aj7N1i65ilApFvzzR+uFzsfQAWseoz7r/mk5O248viIJM95B25HzCxMqeXY79+FCPTTZ7BIwLw8UW4YfRS0/PTWMV400T0BldFDpBEgAlZFWf5LX4InzKSt9K51OJMgTUPxGSfj+pg0qjd9O0Y/L7bBXjyJNnpbXKx2vfxRQyT/zN5q/1+L//Lt79ppJg4bkS2lEbQVURttu8bRoFQBU+tQ7qVApPpDDui+gQ+aP10FF69mPcnP+TI2/nUGUbTuzqVoulaBSHryf3XghbX7OL+j5D3rq25WLrcr1GfKDjyaqqSuZyDg4AVQWA62+os0Yd/eTH8LtqHXUCbahcYEWLKLMAJXtoVIMOu022XAKGMKUTqAyaUeLJN1TaXachB0AOvE4Z77+D9VOvYkyxQJlP3obUBlRfyaHv3qByoaG9l5mUfQpm15F3/fdr+zsSkBlRIbFY2JVAFDpU/6kQ6Xf8+V8yqdMclWhkmc1x40p6DrxQNO47bkMjoDKtuoUhqdSPjrk1G5TqM877beeF5VZrK+0G3AqU/kdFgRQuWFjFZ7U+aj/6JIXgtvp0MEF4oGm2SWHTor2XEl2J+sqAZUOK24AtxknBZx6iZ2uq+QiOs0zgNfxnQU8la16W//szgdMvwW8Y2xhhwGWUMkGMO4AizWVvqslMlBAAUClTyMkFSrd7GLnU6JEJE8CVLKQD0xpoYbFfywdASNDpQi5TCvUOK1IQUOlAII6rZGOG1NDIvSNyyO0lsuWVv0BleVQee/KE8sGhvIEkLGuOvE8GtPYebkAlU57BP/3Cfv13LaFzvj4BKK6esp8vtz2eCc3dk+SPZMMlV/+J0N33ZOnLTYvUn096ZO2Xj2Vf5n8Xzr2H8e2q2C846uYaBIhs+Im/j2g0n+bRA5qKgCo9GmXJEKl213sfEqUiORJgUoWkwebYhMfeaMIQGVbVQsSKsWgMK+10PUrRugbc+Q+XFTa2EocC8GDEuOVtvDjsKCyUr6sqUrnVMoDfwGVvDMkh6YLqCwW268ecAMXoh7ZebnkgXDj1Q+Q1m0LpfvaJIe/ClvI51Oy2HZt3E3YM6AymuorR52INmsFlbL9OBJBXlPJnkquF/9e1qwX/LoVI/X/ix1fAZXR2BNPUUsBQKVPeyQRKuXBCsOJ3S52PiVKRPIkQSXbj8MvR3w/tbT7oHwoe2GHXSvatGbmg1Tz/J9p7c2PJ8I2bgsZFFTKgw+x9thYlnXn3ECZj97R9W4Z9lPKfvqRDpxp2ZxHfl9AZbmn8tL543R5eD0lDzZ5ENmpE9HidzOlw9Kff5HXpmfopZczpXutQmSNdcsORPn4oQ5nH6onS8KO3UmGSuE1vnb3yXpb58vpDtt2Hmdhd0Cl257e2/1mUGk8L1S0JydQKcJYxX4HgEpvdkGqdCgAqPRpxyRDJQ+GxFlLSRiU+DRVxeSqQqU4H0suvLwDKX/A3jvyFjqo+1ulzWHsoFKs3UmrzYOAShmg2AZi8kW2Q+PNj5PWqUvpAHOhp1YoUCaXC7O6xpK3HVQ62RHTrOBJ9FQu6T+G7l05tuSdFOAgv5/ZLo9X/KqVnFYNJ16u3PvzqW7SRY4BJ5aKs/6hSYVK2Q6nfz2+LEqB103bXXYeZ0ClnYLB/j0sqBx5wPdUnPEYvdzpZ9SU7awfQyLvGCveAuGvwdoTuamlAKDSpz2SCJVedrHzKZPyyZMGlTw7KmZGL9zyObpxxUGl2XOnUOl0pl154xkK6BcqjUA5dHCxtIkGg6PWZ4A+iC9d6xqp47lH0Lozr6ZC/72SJpfj8gIqN3gqZ3ceQ7O7jG13fl0lMc0miOzEt/NyyREKTgDH7nlh/j1tUOl0Us7O4yw0T5Itk7ymUkA+6y7CX916KkVblvMSYfDCnlZQyb9ftiyDjXrC7GyQd2wKACp9Sp80qPS6i51PmZRPrhJUslhiMGnlqWSo5LMS+XgL+RLQw95IK2iUz1C0WxOkvOFMCugXKsVAQd54xXZnxkIrUa7tDLO0XoDKDTt08u6OHCUgjhIxeir593NfypUNHL1ApRMvl2jPqrflpEKlfD7lTje0Td7x5RQqnXicOT9AZTQ9p4pQWWmTryhU2apbhygeg2dUgQKASp9GThpUGgcptoNln/okJXkSoVJs1iNrvPyMP9Bmu2xDlQaagErrWsntY/lyomFdXqYDxu9XujEpA/cw2xugsj1UimMAjFApfi+H2nmBSiderqTUzaRCpbCtmMQrbrcDUW09NXHoq8PznO08zqLdJsWWafNUinFQywGHU83LT5FY2iBP8Iq10/w7M0+lMdxd3GPsG2RPpUgDqAzzy4W8o1QAUOlT7aRCpZhhB1S2VYAkQaUYaHYqfkM/6fUG7dT1M/q4biD9/u+76+/CA1qnUJm2sxT5/b16Kp+a/AUtXZaln397s+4BFp4IERqV1nBhp11gJajkgdIh88/TvS1u61SS1lSK/lJ4KsOGSideLtHW3eru1O5B3ZdEqBT6yxN4rcOOpOafne5KFiceZ84QUOlKVk83m3kq5XEQZTNU2H7XUt4CCu2g0lgYQKUn8yBRwhUAVPo0YNKgUp4xTVK4jU8z2SZPIlTyS4nJAf63HLZ51mtD9Xc2G2jKnkoVB6KZb7+m3LynqXX4T4nqO9nazniDF6hkUD/skWHtnrXuotup7oazLbV0XbgEJwBUlnsq+XBzPuOOr7A8lXLeVoejJ2ViMMlQKbyUXvtLJx5nQGU0naMdVJatl5fatuyJNPNUAiqjsR+eorYCgEqf9kkSVBpnveGB2WB8VaFShkZRWjmkzvh38cEUgyDjuh/jeVxm64Lyr8yk4nbbkx7m5fDKfvYJZRfMo9aDjyaqqXOYyvw2MUjmv3pZJ+YWKlnPT59bpK9Rtbqq3UvJugAq44FKeU2f2XEkSTmKIolQKbQXm6J56Y/ktmMX5piUCYK0hr+aTRrI31vxffALlZwPn217wH4avfxqhgYO0OjIH7VNUMVxYU1lHKqn85mASp92TRJUis5RaW5k3QAAIABJREFUfNiS8gHzaSJHydMClcZwLS9Q6TYEK7NyBXX49Qm6zkHAl981n26gUrQJGcILI/+XcrMedn0enaOKlvCbzI7O4FeqlvBXUTcndH+htHMkv3+Ynko7LxegMrxGxXblUHjuH/z2bU7WVSblm5wWqBRwWEl3s8m0oKCS8+m7g0ZbbqE5PmoojNoOqAxD1erME1Dp0+5JgkrjjHdSPmA+TeQoeVqgkl/W6HljsOSLD+1eccNztOVF1jsYyp5MJ7Py2cX/oPo7Li1p7HfgxRlFBZXyDLQxvE3eYt5ryJujipewmwCVbW3HC1SaRR04Mb9xMtCYBlDpREX39wiYCAoqnayrTIotAZVF4qOmrPpDMdFmdg+DJF/sqfSyeZf7mmyfAlBprxHucKYAoNKZTpZ3JQkqjTOlbj1SPqVSOrlqUFkp5K1S+KsQ2WqNoNEIDJyFg44ira5tS3EZKp3AlByqyumDhkonZTC+kxNPpfEsSuMmM251ULpyB1i4SlDJm5nwxIXToxZEsZK0UY/sqZQHhE48lV6hknWq5OVKytr4pIW/2kV9uG1Wdh5nzg9Q6VZV9/cbz5Y8OPsU1Tx0m56R1USq2Q6ugEr32iNF+hUAVPq0cVKg0rieMikDEZ/mcZw8bVDJ9l66vG2tYO91C4jXYvV5Z2pJDz5jTz7jcu1tfyWq61Aa1PCNxS22peyXn1YERSNUVvowOzWG342E7KDSCJQ8ODDz2mPSpb3FAJUbPJVRQmWlSaak9OVJg0rj+nQvE1xyC3Kyk29SbJkmT6WYUARUOv1C4z4oYK0AoNJn7UgKVBpDqMSAOQjPkk8JlUiuGlTOfDZLr72RpSt+1druKDQZiqx2hBSiyl5NXksrdoWd0/kEGv79dP0Ad4bLJf3HUOuo46nvExN0b6XxsvI+yWGiIo2TsFkroxs3EvIykLODSnmXXLF7pxlA1k2+jgr9dqPWfUcqUUdVKIQRKsWOiAxYYXkqhw/V6ID949vEQugu6ia3GT5SJEqotPNyJWECpNqhkuuRk3WVSbBlGqGy0rfGi6dSRCYY0555WoE2+4FGr/wtS4P2K1ImE3/PjvDX+G2QlhIAKn1aMilQKQbSJx6zlnZY8khpE5Kmi26nYkM/nyokP7lqUMmKahqZfnDcQCXnI4OlOG+t6ZzraWn9Dyk/4wHdgzm78xh6rstoOm3l+WUeTGHZ0uSDoVBhQ6XbUEoubyWoNANKTpOEgZwKrQxQOUGfiAFUuq+NSYJKuY/1u/OrrJSTdZVJOHcUUNl+TaV85AjbXEDlgw/nqL6eqEsXjfI1pK/FVO0CVKpmkeSWB1Dp03ZJgUoxGLyhy7klT5QXL5BPuZRNriJUWonlFio5H3kdyVVj3iWtRx89e9n7wj/LIbH8M8Ok8FyuvfNZ6njmwdQyaBS1HHuuTr35GdOp5qW/Uqa1lahpjZ6nF0+lVixQJpOl3IeL9FBUcbWDymJRLwNfTefdTMXeu1DdpAv1n9edfS1RvtYSKq2AElDpvFlWM1Quf24R7fj4+SWolNdIhr2mki1UycuVhEmRJEKlHN3hpV8ztiw7jzPfX3bU1/o+zXkLjebOJEOl3Fad7lrtx1MZjUX8PQVQ6U8/pN6gAKDSZ21IAlTKazlO/3q8DglNEyZSsXd/n2+fnuRph0oxKDU7D0tew8j3/a7rrXTa1+fpxpXXXvIkhAA+GTaFF9PpwDbT3EQan2WZyVBm7RrKzf1zyXMuP4Ofb3ckiny/uNfMU9luc6MeRap55kFqOfT4EsgiFNy+PVczVL503zt0yPzxsUOl2YY/SdjJO0lQKSagTtpnvj6REFTf4GRdpTzJFdRz7Vu2uzvSBJWHPTLMdjLUrN/jpROVdn/1szGXO2v4vxtQ6V9D5NCmAKDSZ01IAlTK6ykFVMJLWW74aoBKq6ouQ6UYxIhBqgyVMkjKeRnT2NUtfp62cVdquvoB6nD2oaRt8gPKfPOVniWDIe8gKq4SVDatoY6XHEfNR59BtVNvKv1dLpMVVJrtlivvsqj1GaDDsqoDOJ9dVKDJqxkqP7tzQ6j47C5j6Te83jnXJq+si3zAvZOdmp0aqJKXKwm7hgIqy+tKpfXwmW9WUodLjrGFHad1J+j7qg0q5Ugf1lK0cW73G29E9O137RUGVAZd65BfEhQAVK630tLln9MvLryFPv9ypf6bKZMupj0GtK01bGpqpstvnkwz5rzW7m9JgEp558CdbmjbvTCIUJ4kVHCnZUwSVPI7XXlNngYOKNIRh/lfn5FZs5pyLzyhS9V6yHHEpzCLQSpv4MOe7a6Fz2nTwn8s5eT65MRbYjyqQw515cwFJJaF3Up5V7KngELZU2k1qBdlZRAFVDptJeVh1GJwtWx5Rt+0Ju0b9bxywQM04vup+sRH8yGjS0DJOsiDTkCleX1KClTKywuuOebNwCecnKyrZAWdRn44b73B3Qmo1Eh4Krm9NzRo+rmT8gWoDK6+IafkKACoXA+Nd017kk48eiRtunEXYsC86e5H6NpLTtF/fvyZebpFjzx0EK36djVdd/uDdMnZx+l/SwJUill07uQAleaNM2lQGXYXk1n5JdVPPJ8ar5hM+Wc3bOwkP1d4Md14KmWoNHol5bzlv8nAavfefK+Aymfnth0wzdeZG91O3X92MBW36aX/DKi0U9L87zKkVzNUth42ukygKKCSH2i1rhKeSm/12SyVHKJ6yj7zA4dKJ+sq5T7KLvIjuDd3nlNaoNLpmKiSpxJQ6bze4M70KwCoNLExeyYZKo87cjhttcUPSv/u1WMr/e5b732UGrbZUodM1aEy7A9kWpoIoNLakvLGEYUddtXDUxko+eKNffjf68bfVDqOpNIgSIbKsnWZ+x9KuVeeKRWCoZIvflZhr+GUe31OWQGtQnH52V333JPmL2qh+6a0AeWw7gv0tXB8CQ+98ALAU+m+BV9xVZ66dSX6amVbGFg1eCq5H62beIFe383qd9xQmYTzDZPiqZQ9iV6975ValdN1lSpPFAAqyz2VZusr4al0/21BiuQrAKg0saHsjeQ/y55J/ln2XAIqk98I+A0AldZ2zDSuobpJF9G6CbdSZsVnVH/NL/QwVYZJPo5EHLEgjiupdAyIfASJAEPeVTb/zANlayl54J5ZsrDsd3IJjVApfuZn/3fvk2niXW1hwRyWeVD3t0obDHG+fBnDbvl3Xo4vSUftd/8Wq78n/Zy1uro2b7BR58bbZ5CWr3WUsRxuaJZAhXMq2bvEG7bEDZWVvFwqh0uyXZMGlQwF278zTe+Dgu4bnJxXCah01H1UvOnPT+To3fczdNklraX75PXPTjdiMvNUDh1coMnT8mXrK7fbVqMdehdp8AGa/8JHmAM26olQ7JQ/qmqg0rguUth11PC96coJ46i+fsMAiD2Rg/baRV9TyYD5hz/OojNG/6h0zz8Wvk/zXn+bzjv1KCoU1e48brmzQEs+0uj8M7PUu3khrb3yl5TbeTfqeNlvU1613b1eLptR3pbu3ii8u1sXvUH5XfekdY9OpuY//6EdVC7b7USq++lY6rN9+RoTLpFII5euyyPzqPDuW3rdFFfHy39LrYvf0vPni+ssX4XFb+n/r/3piaW/ib/z3/i+33WdSB98pNFhB2fp8BFEa685tyxdfufdyp4l8qv78fFENc5AKDx1k5XzU7OK9PSzRV3rwwatpe9POqRkn7qjxjl6mSUfFemWO63XBut2HNm+LjnKPKCbuB8d9vq5OlRy3czt1FYfxSX6Wf65z/YZOv/Mth18hD78b+6DzdqEmyLKehs1WX3MID0rbk8qXtkMEX8t+ahbla9fjG8DkHsm5kv9Ffc3Tuuzk3cT9aVS3Zb7RNVsmskQZYhI8eEPybYUdhG/45/POXARbfvQObZjIrkdG+0rbLhsuUbvfajRIcPj7auc1D/jPTz+wQUFglCgaqDSqViyF5LTGNdQ8u/ke75c1eQ061ju+9Vv2gY3J48p6FBZc+v5VOw7gFrOuyWW8qj60M03qaf/ftuk/IBHJf0yK1dQ7aXHU+Hw0fThzqMpN2M67fD2VJrdeQzx7pg9GzQaNrhIPXtuKHXuqamUf3rD7q78l3X3zCEqFKjujLbzJ0X9lO/l3/GV/WCh/n+uv1yXxcV51P1iuP7jvfu/SEs+1vQ63+eJ80tp+G+th7WF1cplQHvwXquefzFDc1/M0tAhRRo2RKPsor9TzZ2X6ToXDh/jKONPPiG6f+r6bVRNUhw0jGjIoIKjvMK66f4pWRr55nk6VHLdE/VRPI///smytoEZ1/uTx7ZBstBH9MFyW/BSVlmra35Trgm3B24fZuXz8qyg03TpWEOFgkZr123wGgX9DL/5CX2FDUUf5KY+OymDsd1YpRF9mt5HKnR1rMtTLpeh1WtbFCpVeVHktsLfAtH2xJiI7z5l3/nU98/2YyK5HctPEf2esiI4LNgWm9Y7vBO3QYHKCgAqJX0YFpd9tkL3QIpLXl+ZtDWVxrUbKofTxN1QEf7qzQKZz5eTtlWPUmIRgifWXPIfeGc8cfHaL/mSj/HIvfcmZT5eTK0jjyHK1+q7zsrnYmrb9KL83CdKoWjyUSi8VlLUbwG1445ppL4zLtHzaTlsNNU8PU0P2xXrQkU5gg5t86ZkMlMZd9f1Ei6YhPBXDpk7feV41+Gv8rsFtcYqqZv1JCH81bgza1jfTKfrKp3sqB1Hz5GENZVyyKrc9ryEv8r9XMeOGu25h0ZDB/vfeT0O25k9E+Gvqlgi+eUAVK63oRlQCvPKf2PP5aXX3UcXnH4MMWSqvKYSUOm8gQIqnWtV6U55zaSTHCudDWmESn09ZNMaovpOetaVoPLjuoE6BGgbdaXMd1/rG/SI+43rMVXcXdGJdircY9wN1ukaJbnsSYHKm784UC+22XFMVhv1hAGVVusqRdtrOfQ4ajl8rArVo6wMSYJKASFhQl2S11UmHSo32USjb77J0JU/f5M6TrI/o1ju5yqdL6pco3NYIEClQ6Fwm60CgMr1Ia5nXHwbLXrv4zLBrrrwJH2H16SeUymfT9nQsOEYBQyi27cLQKVtX+H8htZWyi1dXHb/3JdylFuyQD/nT77svITyLq3GYxxkSOQ6LSBU3pmWnyXA1QiVrXsdRLmP3qbGq6YR8SIhXK4VKBSIfj81R/kc6bvAcngow3ylyQLjQwCV7mS3g0rOza5duXtiMHcnASqNoBcmVDo5rzIsT6lfiyYVKkVfwxuLDRlUpNpnnG3EVCwSLf9XRg9tT+MFqEyjVeN5J0ClT91V9lTK51MCKisbGlDpsyE4SL78uUX6DppBQ6U8SSJ7L8VzjOdoit9jcsWB0VzcwrDz6XOLUgeVYiDq11MZlIejUuhkftbDVPvkZECli3orbjXTNUyodHJeJaDSgyHXJ5GjBxgiOVxVhkr+WVV9vb+1t5SASm+6IVV7BQCVPmuFqlAZ9QfSp4yxJwdURmOCjmePopbajlSz9hv9gXd3m6jvHis++sZSCEg0A0Bez5nJ56i4+TalZGIQyL8obt1A2X8vo6YLJlGx106lAYS42SyMMRoV0vsUHigf9sgw/QXfveg54sksu0t1TyWX76X736kIy07CX4OCStbTKnRS5fMqVfdUmnkOw4RKJ+sq5TOCxXFIdu0pir8nzVMJqKxcKwCVUbSa6ngGoNKnnZMElaqfY+bTFL6SAyp9yecqcfbbr6n+4qP1NHftPYeWLt+w82dDD94AoVCCkUpQafZQ4zmY6355A1GuLX/jek9ApSuzOb5Z2GxC9xfICUgZ12UaHxT3OZUqQmWl0ElV+/mkQKW8qUuYUFlpckC0AVUnCdIClWHb13GnGfONgMqYDZCixwMqfRpTVagsW0/5g+8os+Zb6vCbtnPjMJhub3RApc+G4CM5D9p5zSWvyROXgMu+T0zQ10o6DVX97h+LaMvJbSG2jb99mrSaurKSGTf38VFsJLVQQIZKJzueAirdV6VKoZOASvd6WgFe2Foa9z0wlhxQ6c2WnMpJ+Cugsk1fQKX3eoaU5QoAKn3WiCRApRiY86u62UDDpzSJSg6ojN9cZnC5z8YLaHjNM1RzxoWOCsh5PDRlHfXsmaH/HVPbPk2xqG/OU+i1E1G+xlGeuMmdAjJUWoU1yzlWgsqc1kpjtnuGGk481F0hArzbyVrRqMNfK4VOqjpQVtlTaaZndvkSqr/+TL0mhTUR62RdZdhg66WpJMFTKR8dYhX+qqK2XuzhNw2g0q+CSC8UAFT6rAsqQqW8RonDz2TvDKDS3OCASp8NIcDklTyXdmv0RN3v1aDR2NHlh8MHWERkVUEBATW8Vrb5sBOox0G7VtSrElSOWD1F3zG46Yr7qbjlhvNQozSAE6iU34G97OPGtNU9Y18cZLmTdl6lylAp7CfbTl6fHTZUys811hEVwQdQGWRLjj8vQGX8NkhLCQCVPi2pMlSKD5X4KDXe/gxp8M6YWhxQ6bMhhJTcCBzGNZfGxwIqQzKEi2zzcx6l2j/fq6dwMolVCSr5eBI+piTOIzLYC9nnnak63FqVQ34H2TsbJlRaratUdUdLlaHSLAy1BHN3zCqty3bRDBzfandepYDbONuA8WWSBpViLCTv/npQ97eItXXSRzk2ZkJvBFQm1HAKFhtQ6dMoKkKl/IHs3bwQHacDGwMqHYgU4y1O4RJQGaORpEfLZ4Z2n3STZ08loNJaOqvQSVXX4akMlUawi1JDu3WVKk4SJBUq5YkYQOWGvgVQqcZ3Mw2lAFT6tCKg0qeAiiQHVCpiCJtiGENjjZ5LQKU6drQL2ysUiNY2Zugfb2boxZeypgUHVNpDpTF0UgARpwwrbNNLLVMVKs3WU0Z5lIfdukrZnqp4K5MIlQ0NWqmf4Q3E3G4C56XOJyUNoDIpllK/nIBKnzZSDSqN4VYqznL6lDyU5IDKUGQNLVOzdZccdsgXA8qwIRoNPgBrKkMzgIOMBVSanVcp91MMRfLOv3LWqkDlIfPPqxiGG0f4K+tkFTppB/QOzBf4LapCZaX1lE53nfYjltnzjfmJEFhVQjWTBpWyniI8XdUNrfzUJa9pAZVelUM6owKASp91QlWoFLPXgEpnBgZUOtNJtbvM4JLLCKiM31ICbJ4+5nkauh74Rank3VIrlfTmLw7U/xzXYLr299fSrFUH0o6fPq5DpRVkxAWVVqGTKvb7qkKlmYZRA4fdukpuAypNFKgOlfKklRlQqqZn3L01oDJuC6Tn+YBKn7ZUDSqNH8ioP44+5YwtOaAyNukDebARLgGVgcjqKxPR97x35C3tdoBNAlTK60JZCBWh0ip0ElDpvOqaAV3UAGe3rpLfRqVveVKgUg4Nb20lyuc31Iuobey8RkZ/J6Ayes3T+kRApU/LAip9CqhIckClIobwWQyGy9Xf1dLAAa1U0NrCYXHFo4AYBM/cfSINPqV/WSHkM+QqlS5OT6WAyqW1u5JGWaWh0mpdZVweXjObquiptDrvM2rgsFtXyXqqNFGQRKg01smobRxPL+zsqYBKZzrhLnsFAJX2GlW8QyWoNNu+XqXZTZ9Sh5ocUBmqvJFmvtkmdfTN6hZqKQAqIxXe8DDR99zdbSKNuboNKjWNaOknGZr6QI4YhOTNM8qTa5TTCnTDioP0X8cBR/IGKaJsay2Ol4gr/JXLlZR1lUmByih3fhX1ysm6SkCl897MarJA5BCHjZ2XPvo7AZXRa57WJwIqfVpWRag0O8A5ig0HfEoZa3JAZazyB/pwQGWgcnrOTAyCP64dSOJYETnsVUDlSy8RjVz9B5rV5UTdI8jX2FWX0c5Nr1KGtNLzo97J1AiVzaMnUOs+B5vqoQJU8o6WDQ0biqeaJ0ZFqDQLO40LOOzWVcZVLrMKn3RPpUpaeu5gA0wIqAxQzCrPClDpswKoBJVmh2GrNrDwKXdoyQGVoUkbecaAysglN32ggEr+Ix+FMLvL2LKjQxiC+KqbeIEeWjq78xj9Hr5E2KvIuOSpbFpDNbMfpZYj2u4L8zJCZaWJuTih0ip0UrUoFRWhUoCcDORx6eZkXaUq33MVobKllahm/ZpJeCrd9YyASnd64W5rBQCVPmuHilApfyBV+Qj5lDn05IDK0CWO7AGAysikrvwgTaPvH36INp83Rb9vQvcX9P8fcViRBuxSpJqatuRfnLMBKp/rMpqO/e4G2m3N7LK8GSpbR52gb1bCVxSRFzIU2z1TRaiUy1/s0YeaLr4z1oqhGlRagUdcUOlkXaX4nkdR/ytVFtWgsqVVo6uuraEDBhVp+IFFAlS6a+qASnd64W5AZWh1QCWoVGEXu9CEDjljQGXIAkeYPaAyQrFtHsUD5dqnp9GI76fqnsgl/cfQuGPXUsdzj6B1591KmffepNoZ00u5cKgsey3trigOgTdCZaXwWzuoNG6kY/d+bv9u1vdnP3qH6m8ZX8oq6vBh4zskASpzi/5OdXdfrhc9ar2crKuMC3iNtlQNKo3aASrd9SCAyvZ6aZpGmUzGnZC4m+Cp9FkJVIFKVXax8ylnbMkBlbFJH/iDAZWBS+o5QxkqOZN3L3qOtn9nWhlIypnbQSV7LDksNSlQye/GwBcVVBrXVfLzVYlWUQ0qjeGm+cVvUO0dv9KrYxT1y6xR2a2rVGWzHtWgUthStDM7ry/WVJbXPkCl508cEhoUAFT6rBKqQKXZekp0nM6NC6h0rpXqdwIq1bGQmOwSayR5sJ5tXEP5uY+XCtl48Am06vVFtNU3C/Xf6aGuI35OdesH+N9nN6XOxVV6yCtf7K1xNehvbqTcBwupsMveroQJwlMZFVRWGkSr4t1SDSqN6ymFTq7qlqsaZX+z3bpKQKW5hsKWgEr7OmZ2B6DSm25I1V4BQKXPWqEaVMoz1YBK58YFVDrXSvU7AZXqWEhA5YjVU/QQWOMlNuDhviqzZCHlPlxES/c/i7beo4GWfdBEDz9MdECX1+iA4bVUGLCv7qXkwb+bI0ZqH7yV8q/MpLWT/kpU28GxOAIyBOgKqLXKQAxshwwu0tDBG46zicJTCah0bFb9RrPIHhXg287DBqisDJX81ysvbyU7OPfSj7irYcm6G1CZLHupXFpApU/rqAKVZmEz6DidGxdQ6Vwr1e8EVKplIe6burcspfO/OqlUsELPfqR125Kax11KZLFuRQz8e/XUaOwJBT2tvCOr0zVvXmHBLVRaDWSjgEqzM4qF2KqAiEqeStWh0ipcWpVvumrhr2L8w3WeJ9YnT6sccq5Km1ClpwZUqmKJ5JcDUOnThipApdV6SlU+QD4ljiQ5oDISmSN5CKAyEpkdP+Q31+SJtzv4zS9XktZlY32NX2G3/WndqVdUzMMMKjmB2zWCUUHlf78imjU7S/97dJHyuQ2vFgVU8tOs1uOpMoBWCSrNJgDc1ivHDcDljZXWVaryTVcJKuUJFZaagXzZ8gwZIwZkM6jSJlxWjdBuB1SGJm3VZQyo9GlyFaDSbD0lvxY6TufGBVQ610r1OwGViluoZR1RvtbSQylKHxRUej2Gwa2n0kr1qKDSKnRSle8AoNJZu7QL3VQBfpMAlWabVgkLqNImnNWI8O8CVIavcbU8AVDp09IqQaWxE/U6Q+9TkkQmB1Qm0mymhQZUpsOWQUOl2w1YRP/5XbYbfXDkjbTzsO08CRs3VKri3VIFKs1ChVXRiCuY3bpKFb7rKkOlaKS8ttLqAlSWKwOo9NS1I5GJAoBKn9VCBag0DZcpFqnjmQfrbxf3Qck+JY4kOaAyEpkjeQigMhKZQ3+IFVS6HVQLz45XqJzQ/QV9nVZDg7dXjgoqrdZVelmH6u1NK6dSDSrldYsqQqXVukoBROvOuMr1jsZB2VV1qLQ7wgdQCagMqi0gn3IFAJU+a0TcUGm1nlIO3XK6oYVPKRKdHFCZaPOVFR5QmQ5b+oHK7GdLqebRu6iw13CqnX6LLohTqGxu0ejmiXm6+pOhejq/UHnHPTkaPkSjfn037AgbloWs1uOpEDKpClSahZeqBJVcNyqtq5SPumm8/H7SuvcIqzpZ5qs6VMqTQLVTb6TWIT+mYo8+pfcBVAIqI280VfJAQKVPQ8cNlVahMmIQ0XTuTVTsO9DnW6Y/OaAyPTYGVKbDln6gUp5UE2o4hUrRp4qzNf1CZZTWsPsexDnBqDJUqgYZFddVakWqu+1CfSdkp3U66DqYFKi08tK7jXYIWj/V8kP4q2oWSW55AJU+bRc3VCZh1tWnxJEkB1RGInMkDwFURiJz6A+JCypFnypDZaX1WaEL4eIBVlCpwiBaBai0ChEW+sQFaUYT262rjNuzqhJUCq1kDUV7laFSXgakQntw0axDvzVqqNQ0233aQn9nPCAcBQCVPnWNGypxPqVPA65PDqgMRkcVcgFUqmAF/2XwA5UiUkMuhVNgSDJUWi2HUMETpxJUGtfcqQYZApQqrQ2MM6RZZaiUNZNDhbkvaLzmQdK6bk6q2dt/b+kvh6ih0l9pkVplBQCVPq0TJ1Tarad0OojyKUEqkgMqU2FG/SUAlemwpRVUOgEkP1DJE3W9mxfQ6SvH08e1A2nm7rfSuDGFxIhqNtHoRLOwX1AFqLQ6fktFyKi0rpJtFWeZVYTKHttqtPzT8vMpjWHwPCbiq3bGdP3/2MSwrdUDKsPu/aonf0ClT1vHCZVWs5lxfmx8yhlbckBlbNIH/mBAZeCSxpJhHFApnilDZfdJN8Xy/l4fagZOgMo2Na3WKsbp9bOys915lXHaVEWoHDK4SJt102iX/lpJUjEWKvQZoK9BFf8XN6y9/RmifI3XppaadIDK1Jgy9hcBVPo0QZxQafeBxCycc+MCKp1rpfqdgErVLeSsfEFDJQ8ouU+0u4yeyqRBpdl6vLjX4LHmKngqVd4d11gv7dZVCqh0Wq/t6r2bv6sKlUMHl++wLK+VFd5Jfk89iuuQ44hyOTevndqSFnZHAAAgAElEQVR7AZWpNW3kLwao9Cl5nFCZpA+kT5lDTw6oDF3iyB4AqIxM6lAf5BUq5c055AI6HXwnHSqtlkXE7Y2LGyqtdFEBuM0aksrrKpMClaLO82QSA6a4MOFeXuMAlaF+yqoqc0ClT3PHBZVJ+0D6lDn05IDK0CWO7AGAysikDvVBgErv8ppNOMZ9dnHcUGm1njJOj5+dhe3WVcY1UaASVFYKE5b1kddZx3m0jp3N4/g7oDIO1dP5zFChcs3aJnr9rXepf99etFm3jSmTyaROxbig0moWU9VZV9UND6hU3ULOyweodK6VynfGCZUjVk+hEd9Ppdmdx9D+Nx2vskymZTM9amrBK1R3z//p98fhqVEFKseNbqWGhg2yqbwHgd26yrjKngSoNI6FBFRiA8P2XQagMnFdvLIFDh0qb7zrYZr90j+ptiZPBw/Zg4bsuxvt0q8ndencUVlR3BQsLqi0+tjEuXjfjW6q3QuoVM0i3ssDqPSunUopraDSbuLMLvw1//ocat1ruOWrsnco6VBptR4vzu9D3FCZxOUidusqAZXWmy8Z+4max35HNfNm0NpJT6nUzSlRFkClEmZIRSFChUqhUKFQoM+/XEmvv/UePfviP+jNhR9Qj223pIMG/ZD232sX2nH7HlRTk0+koHFBpdUHMq6PTCKNJxUaUJl0C24oP6AyHbYMAypbR52gr62q5K1IE1QazzmMM9QzTqhM6nIRu3WVcU0SJNFTmY5eMZy3AFSGo2s15hoJVBqFZchc9umX9Pc3F9OsF96gQqFId11/Lm26cZfE2SAOqLT6QLJ4gEpvVQhQ6U03FVMBKlW0ivsyeYVKGZzkYwR4ox6+xO+sdoJNA1Tye6rmmYsTKpO4nlK0mErrKgGV1p7KuLRx39PFnwJQGb8N0lKCSKGyWNTo8y+/ormvzKenn/s7ffnVKjp02N50yNA99XWX2Wzy1lzGAZWVZi8Bld6aJqDSm24qpgJUqmgV92XyC5Uthx5P+Zf/Si0/OZlqp9/argBWm3W8csED+npKvu7uNpHGXN3ffeEVSGG1RCKub4QKUCmvp5Q3LopjjanTKlJpXWVc4KSSp1JAt9VaWayhtK9pgEp7jXCHMwVCh0r2Sn74yb/pyWdfpWeef406d+pAo4btTcMP+B/q3aM75RJ+TlAcUOl0tzNnVQB3sQKAyvTUA0BlOmxpBZX8dpV2vTQbaMs7Pwp1TEGipZk6/nJUScBLt3yGfn1FMg9HV21dZZxQafT2yetuVQZKroiV1lUa1w+vvfNZomw29A5AFahE1FYwpgZUBqMjciEKFSq//W4NnXnpbbSmsYmOP/IgGrTXrqnbBTYOqLSambMbbKHCWysAqExP7QBUpseW3Nf16qnR2BMKZS9VCSrNPHFOoVIM0j+uHUj3dLuZipSjKy9vTaSgVhEtcXm34oJKM/Cw2+xJJYM7XVfJZY4KkFWBSkRtBVNTAZXB6IhcQobK5pZWevbFN+jxZ+bRko8/1aFy+AE/pD0G9KONN+qUCv2jhspKM3NJ+lCqZnxApWoW8V4eQKV37VRLGSdUcugrX0mFSi672Xq8uL4TcUGlGXjEpYHX9mV3XmXUEwWqQCWitrzWqPJ0gMpgdEQuIUOlLPDaxiZasPhjPQT2+Vfm07bdN6PDDtqHhu6/O221xQ8SuZ6S308lqBQz9LwhhdUmFKj05goAKtNTMwCV6bFlGFDJ/SNDhZlXRwzO+XzK2V3GpgYqjevNKnl6w6o9cUGl6ZmdSxbqm9ol5Vtpd15ltUKlFWzHuctxWO0nzHwBlWGqW115hxr+aiWl2P31hb+9RS/+bQHlczmadNXZifReRg2VVh+XmjmPUs2f79UljyoEJk1NBVCZHmsCKtNjSyuorLTZTKXwV4aIwg67Uu2M6e2OFdE0osJj02mjudNIQKXxSI6kKWu1Hq+aoNIMPKKGML/1xu68yqg9ryp4Ks2itrKfvE/1N55dkjspkwZ+64ff9IBKvwoivVAgUqhkb+XiJcvoldffpjnz3qSvv1lNew7sRz8auR/tv+euVJvAsyqjhkq7nc7YsFa7GqLaWysAqExP7QBUpseWfqHy/td2p1GHFKn7J3Mo/+pMaj76bMrNf8kUKv/6dJa6vjRN3/m1WqCy5dhzqLDj7lT8wVahV5o4PJVWy0XSCpVRff9VgEqzY2LkHX11oDzrWqKa2tDrdtIfAKhMugXVKX+oUMlHiLz34TJ66e8L6bl5b9LS5Z/T7rv0oaH77UZD9h1IW23RDbu/uqgLldZTxjHz7KLoyt8KqFTeRI4LCKh0LJXyN3qBSrkv5PRDBhdp6OBi6V2tgIIHqYfMP496Ny/QjxLhzXqS7qnklzbz1MmD76hAJA6oVG2zIj8Nzm5dZZRjAJWg0uyYGERruatpgEp3euFuawVChcpV366mX9/we9q+YWs6cN/daOe+DVSTQG9kpQoUpafSCiqjDn1JY4MCVKbHqoDK9NgSUOnflmbRLbn586juvitLmUcR3RIHVFotF0map1KeHDCujxVGjPL80bihUoyF+N3ljbSi1MB/y1QnB0ClOrZIeklChcqki+Ok/FFCpd1h1jjk14nFzO8BVHrXTrWUgErVLOK9PFZQWQkKhMfmrn3n0tJPMu08lVaTcGn1VFqtx8u0tlDt7ZfomxalFSqtvHtJhA+7dZVRgrIqUGmMJIjSW+u9V1MvJaBSPZsktUShQiUfKfLO+0tpXXNLRX3qamuof79eWFNpU4vs1lMi5MN7MwRUetdOtZSAStUs4r08dlBp3IhDPgx+QvcX9Acbw18BlRvsESVcRe2prLRcJMr39l77y1M6hcooNqeJGyor7ejLqkUxSRKUXVXIB1CpghXSUYZQoZLDX8+4+Dba/Aeb0PY9t6ZMJmOq2kZdOtFPDz2AOnWsT5yqUXkqsZ4y3KoBqAxX3yhzB1RGqXa4z7KCyuzHi6n+5nP1h8sRGgIYeT2kOGfSDVSe9dpQPU8BpGlYU2kVKsjvGSVcASr9txVV1lXGDZWmId0JOybGf20ILgdAZXBaVntOoUIleypf/vtCmvbYs7rOY48eSfv+T3+qr0vPblxxQyXWUwbThAGVweioQi6AShWsEEwZrKCSc8+/8Beq/dOd+oOEZ8INVHI6ObqDvR9phEp+T7sw0CiWTkQNlZXOdowSpoNpCW25WEUriWdEFf4ZJ1Rib4kga1RbXlFCJR/dZOFfCv7FkGPkCoQKleJtNE2jf3/xFf3p6RfpyVmv0OB9BtBxRx5EO/TchrJZc+9l5Ep4fGBUUGn1gQRUejScIRmgMhgdVcgFUKmCFYIpQyWo5CcYB9FOoFJOJ4cKphkqrUIno1yHFzVUVgKwpEKlXQhsVO8VJ1SmaUffYHpJ/7lECZX+S4scVFYgEqiUBWhpaaUFiz+i6Y/Npk+/+C+N/tkIGn7A/1CXTh1U1smybHFDZVQfkUQax0WhAZUuxFL8VkCl4gZyUTw7qDT2f06h0mwybuqv36HTV47XjxIRobNpCH9luasNKistF8l+9jHVX3OaXguTtvbODiqjmiSIEyrTtKOvi64w1FsBlaHKW1WZRw6VsrqLlyzTjxypr62lu64/lzbduEvixI8CKlVZE5M447goMKDShViK3wqoVNxALornGCrPuYEyH71Dme9WUc28p2nm7hPp+S8G6k8yrqkUjzd6OdMMlVbfkKgghDWP0lPpZJOeJEIll7nSusqo7BknVFq9f1Tv7qL7SsytgMrEmEr5gkYOlU3rmulv/3yHpvxxFv17xVd6GOwRI/alH3TdWBmxbr33Ub0s5516lP7/pqZmuvzmyTRjzmv6z1MmXUx7DOin/ztKqDSbNY9qDYUyxgmpIIDKkISNIVtAZQyih/RIp1BpfLwMlVbeRmPf+coFD9CI76fS7M5jaHaXsXqWafFUWsGIvFtu2F67KKGy0nrKkt1vn0mUz4dUc8PLtlJYb1RgFRdUOpkswC747useoNK9ZkhhrkAkUFksavThJ5/RX2bOo2fmvk777bkL/eSQ/Wn3/jtQLpdTyjb/WPg+zZr7Bm2x2aZ06vGH62V7/Jl5+v+PPHQQ8Y62193+IF1y9nG6ZzUKqGz3gSwWqeOFP6PC1r30M8ai2EJcKSOFUBhAZQiixpQloDIm4UN4rFeofKT3RPrn2jZPpR1UNt7wJ9I22pS+OOcC6t28QA995RDYtEGl+I4YPbcCssIejEcJlVbgJaAryd/MSiGwUe2xECVULnw7Szv2K1JtDVEaN18Kodt0nSWg0rVkSGChQKhQybu/zpz7Ok3540z98aOPOljp9ZMCGI86fAi99faHOlSyl/Kmux+h444cTr16bKW/B3syG7bZUofMOKBSrCMSNg17MFANrQdQmR4rAyrTY0s7qBSQYHxjcSRIJTCU+9E1d8yiFeddnGqojHtdZVRQaeXNkutKFLvdhtUKqwkq+Yjzq6/LlyaG0rj5Ulj1xE2+gEo3auHeSgqECpVJO6fy3geeouGDfqjrNWfemzpUGj2T/DfZcxk2VJqthREzyzzbyhdDJS5/CgAq/emnUmpApUrW8FcWN1DJ/eEXKzL0Ju1P8zr9tPRgK0+lDJUMGbUzputpnACpv7eKJ7UdbIUNWnFDZckje+LF1LrHUMtzs+OxjrunVlpXGcWSmKg8lXKdHTq4QJOnbQBMo2LYtNBdHZLvBlR61w4pyxUIFSrZU/nO+0tpHU83Vbjqamuof79eVFsT3voG47pIUZxRw/emKyeMo7c/WEqf/vu/uvdx6fLPy6DyD3+cRWeM/hHV17edr8khsvNef1tfc9nSWgy1Ti35SKOJdxepT+8MjT8jS4V3F1DT1b+k3E67Uf2vJ4X67GrKvCafpZZCkUirprdO57vmc1kqFDXio4xwJVuB088vUJ/tMzT+9KzpizQ/NplaHp+i/437xLu7TqQlH5fbXfSdxgwK/3yFmm69tOzX8s6v/AertElVlfXk6+5bNiw7ERrWHDmWan82LrRXy2UzevfKy2HCvCbeVdTrANcZrjt8pe27Kewov6PQdM2xB+j/7PTQy6HJzEfBsbLcz4Z5ifEPP2PUiAzNmK1Ztsmmq8+hwrtvUf2vf0u5ndrC13E5U4DHP7igQBAKhAqVQRQwijzYGymDoxEq5TWUXB7ZU/nfb9eFWsT7Jmdo6bIMnXJikXr1JMp8sJDyN59HWt8B1Drh1lCfXU2Z/2CjOlr53TowZQqMvmmXGlq9ppVaQx7wpEAq5V/hksuz1Lsn0cknmk/eZf86lXJPTWsDh8NH00X/PLHdO/Vq0OiUcSaDX02j7MyHKPfE5FKaK7b4C63Jbtg0btiBGg0/MNyBc5RGEN8T+b2EhmF/Uzp3yFOhoFFjcxvYhnUZv5n8nLR9N+e8kKHnX8iQWf3k8QG/L48P2KZhXB1qc5TLZej7xtYwsi/lKd6Tf8HtWB4LGR9cc8ow/Vct9z0fapnSmPlmG9el8bXwTjEoAKhcD4mX3fj7dvLvumNvuvX/zqD7H5wR25pK48L0qBbix1AXY30kwl9jlT/QhyP8NVA5Y83MTfgrLwP41SNtyxfkq9IOrvI6O3nXV5He6jiSWEXx8XCz9XhRfVOiCH+1OjolbaGRldZVymHdYe3oG1X4q3hPucpfebk5yEYR9uuj6SmdFOGvSpsnUYUDVJqYS/ZU8p/ZM7nssxV6uCt7NS+97j664PRj9I17wlxTafaBjGoAkKhaHEBhAZUBiKhIFoBKRQwRQDHsoFI+EsMLVMrp5V1f0wqVZusqo/qmRAmVxomEtEEl10+rdZXZD96i+tsu1KtwWJv4xQWVlSaIAJXeO1xApXftkLJcAUClA6iM65zKOAcA1dZQAJXpsTigMj22dAOV7170nL6Rh/GqNBCVoVLeoCetUGkFI1EMyKOASqsjJ6J4v6hbXZznVcYFleNGt1JDg7nSYdmYg9/bVuam9wJUpte2Ub8ZoNKn4mF6Ks3OFYvqcGOfsiQuOaAycSazLDCgMj22dAqVvPPrBz+52TVUslIdzziYqK4DTdj06XbCpS38lV/QDLzCGpDLggIqg22XlUJg5fM4m8++jrR8TaAPVw0qo/K2ByqiQpkBKhUyRsKLAqj0acAooFKenQNU+jSYRXJTqOTpyfTs0RGOcArmCqhU0Cgei2QHlZxtzUtPUaHfQFrauK0nqKTWZqJcDV1+VfuBdxqh0gxG0gCVVusp0woclaCSCgXqeNZIvdWFcVRMVFApJkD4PZxEHPDkEo5Yc9/ZAirda4YU5goAKn3WjDCh0mzNBKDy/9s78zg7inJhvzOZJJMJAcMiEoOEAIoLgguCC1tYTUC8KLuswSAgBAJEiIAKVwgQNrkkyBIgGAXhQ5BFNMgFcYGLiAgIymK4COJFxIBJJpOZc75fddInPZ3eq6pPnT7P+UfJdL1V/bx1TvfTtbRmwvJIpZ2qiGqZAFJpGXCJ4bNIpd+coFQEm5h0Mxo8zv+9Df5blaUyyMWXyqXnfl/qo9ezkmHbI5Vx7+GsqlSqJCW9r7Lzlb9I939O8XJpem1lM6Qyaeqrv2YWqSz21UUqi3Gj1OoEkErNXmFLKpv9ompNLC1XnOmvLZey2AYjldXJJVJpJ5dhGSljx1DbUhm1XETRq/KD2KR1lTbPvWypPPrLA/LuDaKnDQV3cF7+ucNl+WcPtvOlqXBUpLLCyS351JBKTeC2pDLuAlnFXew0U2CkOFJpBKMTQZBKJ9JgpBFlSWXcKGcVRypVYlZbV9m/XHqOn2hlVMvvCGVJZXhEq8rXzMQpsBaFumypjHuNiOpbVc6vkR/RDEGQygyQOCQTAaQyE6b4g2xLZTtdIDVToVUcqdTC51RhpNKpdGg1BqnUwhdbOEpGbI/o2ZbKuKmgVZaOdpfKKk9ttvPNj46KVJZJu9p1IZWa+bUlle14gdRMhVZxpFILn1OFkUqn0qHVmBvnd8lArS6HHzKQGkdnTWW7jVQmSaUC3fv1q6Q2duNU5nkOsCmVcctF2mEkK2ldpa0HBWWNVCadWzC3rKXM801c/VikUo8fpVcRQCo1e4MNqWznC6RmOgoXRyoLo3OuIFLpXEoKNwipLIwuteBqN+y1mvQct7tXzsaOoTalMm65SDtJZdRGNrZG8lyTStMbEaV+eSp2AFJZsYQ28XSQSk34NqSynS+QmukoXBypLIzOuYJIpXMpKdwgpLIwutSCUZu8+BKiCi+ZsyA1Rp4DypDKKLEq43UpeTiYPjZpCmwrSqW6/9l887p8apta4u62ts7NdH5aIR5S2QpZao02IpWaebIple14gdRMR+HiSGVhdM4VRCqdS0nhBimpfO4FkaSNOvzgTH/NhzlORmxJmE2pTJomaet88tG2d3SVpDL4HVb3P3PndXngor7/tqb22suUu5GRSndz02otQyo1M2ZDKuMukDyZ00xWQnGk0h7bsiMjlWUTt1cfUmmPbZyMBG/Wl0/8knR0dhpphC2pTFouYnPk1QgUQ0HKvmewNf01Sirj3jOLVBrqPCKCVJpj2e6RkErNHmBaKrNcIFmUrpm0iOJIpXmmzYqIVDaLvPl6TUhl3EhHsLVxo5xJL1w3f7blR4ySkaCImbzW2JLKZozUlZ+p5BqT3ldpY6S2DKn0zzjuO4hUmuuFSKU5lu0eCanU7AGmpTL2Atm3VHqmfs5rrY1NFDQxtHxxpLLlU9g4AaSyOrlEKu3mMnaE66lHZfgVM7zKTa2ttCWVq71zM4DMFyqTcmw3I8WiJ4l1K0mlOnu/T/ok4qa+V/lVMcV6QfFSSGVxdpQcTACp1OwRpqUy7gLpP5VTzV166Y+lPnyEZsspHiSAVFanPyCV1cmlTalc3i/yi190ys4TatKuI5VlyogtqUyb+uk9iD3wBOnffq/qfDFCZ1JmHlXVtkYqw1IZN/VVHYdUmuvOSKU5lu0eCanU7AGmpTLuAslUD81EpRRHKu3yLTM6Ulkmbbt12ZTK4I34+I1qjU1BgmdU9emvWWTE1OsabEgly0VW9da4ewd/pFIdaWrU2YZULusT+eWvO+XBX6xaw5v0/UMqzf32IpXmWLZ7JKRSsweYlEreT6mZDI3iSKUGPMeKIpWOJUSjOc2Syukn9cv//K5TJuxQ02i9+0WDI7ThaYamb9ptSKUvxVEjWqbb73o249ZVDr3zBhl6z/e85rsslf4sLZ9z0iglI5VmeyNSaZZnO0dDKjWzj1RqAnSkOFLpSCIMNAOpNADRkRA2pTL4PuDwSGWWV5g4gki7GWXNjrEhlUnrKdtNKpNGnU2zsDFSGZbKPSfW5BMfj3+oY2OtqPaXqUUDIJUtmjgHm41UaialLKnkB1QzUSnFkUq7fMuMjlSWSdtuXXmk0r+pjmpRlCQilStIZXm1SP+eh2on2oZUtvP7KcMJSZJK08tnbEtlloc63BNpfyUbAZBKcyzbPRJSqdkDkEpNgI4URyodSYSBZiCVBiA6EgKptJ+IVpXKpOUiilo7SkdZo862pTJtLbM/8qrybGpKr/1vmrs1IJXu5qbVWoZUamasDKn03xtW9W3RNVOhVRyp1MLnVGGk0ql0aDUGqdTCl6lwnJyZvu6YHqlMWk9puu2ZQDpwUNy6Sn+k0tQ9hA2pDL5KJEkqg+9R7T1hptTe/zEHyLd2E5DK1s6fS61HKjWzgVRqAnSkOFLpSCIMNAOpNADRkRBlSaU63QceXLXrZJbpd44gMtKMqBEu02JmWiqT1lOabrsRyCUEybKbr4mRvWZJZefzf5Ahf3hEhi74oZjalbiEtDhfBVLpfIpapoFIpWaqbEjljjvUBu06aHo9hOYpV7I4UlmdtCKV1cklUllOLoPrS4M73pqcQmpaKpPWU7brNTOLVJqQsaZIZf9y6Tl+otRH9EjH0iVIpcGfBqTSIMw2D4VUanYApFIToCPFkUpHEmGgGUilAYiOhChDKtWrC8aNq7f1SGWcjLgqlWnrKf01d32TDhETmww58nXI1Iwy1lU2QyqD014VCBNynAloGxwUJ5V1Eelog/PnFM0RQCo1WZYhlaa3A9c85UoWRyqrk1aksjq5LFsq1SyRddauy5ZbqNup9vnESVqrS2U7ikfaukoTom1aKv3+94HN6zJ6tMjuuw6s9uULS6WJabzt8w1PPlNGKukJpggglZokTUjlvxeLzLpkqOy9Z7/86MdDJDz9FanUTFKG4khlBkgtcghS2SKJytDMsqUybdfJDE1u2UOiRrhMXntMTn+NW0/Z9dsHZMhDd4kSEPVpR/GIG3U2uc7UllSG732CXyZ/SrP/b+2YW1s/LkilLbLtFxep1My5Can0L5BqGtbClzpWk0qTT4s1T7eyxZHK6qQWqaxOLpHK8nIZJWquSmXcaJx/rVTUTO10Wl4GzNSUZV3l0tNnS/09mxWuEKksjM7Jgkilk2lpyUYhlZppMyGV/gUSqdRMhkZxpFIDnmNFkUrHEqLRHKRSA17OolEyYnLDG5MjlUm71fqn3XfgVOnffs+cFFr/cH8qqTqT8C7Gpt7v2AypDLa9XR8Y2OqdSKUtsu0XF6nUzLmuVAYvAFFSaXLKiuapVro4Ulmd9CKV1cklUlleLqPe+2jy/YampDJu/eegzXn2OEikq6s8eI7VFLszbn+f9Bw/yWutzvRR01KZ9HoYHy1Saa+TIZX22LZbZKRSM+MmpdJvSnBdAVKpmaCMxZHKjKBa4DCksgWSlLGJvlSeetKA9C6ry3rrxhf0pSjqiKj3TgZniPi7v7bzmkrFLUpGTC2/KEsq23FznnCfT5oCa2JKs0mpTBpZDZ4XUpnxR7PAYUhlAWgUiSSAVGp2DF2p9J/QBZsRlEqT0480T7XSxZHK6qQXqaxOLn2pVGfU0SHyrTP7Y08uSSq/8B8Dq+3oGpRKFVStZ293qbS5rhKpLO972UpSGdxT4sjDVt/1lZFK+/0GqbTPuF1qQCo1M60rlf6NTZxUtvP7tjRTk6s4UpkLl9MHI5VOpydX44JSqQpGjTj6AZOkUh0TFkakcvVURMmIqWuQaalkl/T4r1KWdZU6I7qmRiqD7Yx9oNPXKz1T9xp0siZei5Lrh6jiByOVFU9wiaeHVGrC1pHK4A9qmlTqXAA0T7EtiiOV1UkzUlmdXJqUyrCEhDdIS5PW6lCNP5OkdZW6N/ImpLI2IN5rt554kl3S0/pj3LpKE7OfTEllllHK4KtEapt+WGrvWEeWHzRV6iNGpiHg7xkJIJUZQXFYKgGkMhVR8gFIpSZAR4ojlY4kwkAzkEoDEB0JUYZU+qeqNkpLmn7nCBLrzQjLiAkJUY02IZXB5SLhhwSm1n5aB1xSBXFTYE3k04RUZhqlFBETa0BLQt6y1SCVLZs65xqOVGqmREcq/Qukujg+8GBnoyXBaSBcKDUTlLE4UpkRVAschlS2QJIyNjGPVEatTx87ti6L3uyQtxevqDA4fTa89ACpXMEo/A5IU5vFmZbK8HRJrpWDv1SuS2WWUUp1Rkhlxh9LjcOQSg14FB1EAKnU7BBIpSZAR4ojlY4kwkAzkEoDEB0J8ZvfDJWfLKg3WpO0pjJKKn1RDItSUJ784O2+SY/PIUpGTAibrlSGl4sE82VKfB3p9kaaEbeu0oWRyrjXwkSduN9elgAZ6RaRQZBKe2zbLTJSqZlxHakM3ujMnbfqnVqMVGompUBxpLIANEeLIJWOJqZAs1796zC5+oaajBkj8vLLyRv1JElllCiFRyqRyhUJahWp/OYZ/dK5coIPUhn95YpaV2mCle701+AsrQk71GJ/GTre+Lt0/fftMvTntwpSWeAHNGMRpDIjKA5LJYBUpiJKPsCmVJr48dc8vbYpjlRWJ9VIZXVyufaoYbJk2YDMvjr9lR9ZpDI4xTUslUmjoNUhmu1MwjJiYgqi7kiln98D9xuQri6RzTZdNQOO1h8AACAASURBVILNtTI6r1EPCHxWqkRRUTMllWkPcrwRcvUuoXpdlsxZkK3zclRuAkhlbmQUiCGAVGp2jaJSGZz+MWGHAYkaqeRCqZmcHMWRyhywHD8UqXQ8QTmapyOVa/TUZdPNRPbZe8W778KiFJRK1lMOTkp4unBjyuSBJ0j/9oNf75A1naakMkpEuFZml0p1pO4UWF2pjNuZNnwW/rRr9e9IZdZvWv7jkMr8zCgRTQCp1OwZRaXSv8lRNzORUvmemvQct7vXuoH3buk9UeRjjwBSaY9t2ZGRyrKJ26uviFSq39SFL3XEvpfSH5FEKuPzFh7hCr7Wofesa6S2wUa5k64jlUnvXTQhSblPpkUKxHHTlXAdqcy6njI4oopU2u1wSKVdvu0UHanUzHZRqXzzTZFLLu+SOKncpO8Jb9cz9ek94yqpvXtjzZZSPIkAUlmd/oFUVieXJqXSnz7pj3QFpTL8eorqECx2JlHTJv0psEXfV2lCKuNGlHVH3opRao1ScaOCOpsv6Uhl1vWUQankwbrdvoZU2uXbTtGRSs1sF5HKxUs65PxZQ7ya06SSH1PNBGUsjlRmBNUChyGVLZCkjE20IZVKIMdvVItccpCxWZU/LGqES1fcdKQyTURMrPmsalLjXi3iS2WRdZUmpDJtPWVQKvt3+rz07XdcVVPU9PNCKpuegso0AKnUTGURqQy/wDnqBmfTp+bJsLtvZOqrZn6yFkcqs5Jy/zik0v0cZW2hSakM3lz7v7k9PXVZsqRDgjuJZm1b1Y8Lj3D5Uln0QacJqYwTEaQyvjfGSaXOyLOOVGZdT9l4iDHlG9L/kc9U/evW1PNDKpuKv1KVI5Wa6TQhlWpL7eBULHXh9KWy6FQjzdNqu+JIZXVSjlRWJ5dFpHKvSTX52YJOmX5yvwwduopFlFSqUctPbTsg3cM7qgPN0JmEZUR3HZ6OVKaJiM5UTkO4nA1j432VRaUy63pKBZP3U5bXpZDK8lhXvSakUjPDeaUy7gXOSKVmIjSLI5WaAB0qjlQ6lAzNpvhSec+CujzwYKckrX0Mr5mMqjr8bmDWUmYf4WqWVKaJiG67NLtoSxSPknKd6cxFpTJtGnMQJqPP5XUtpLI81lWvCanUzHBRqVxrTZFFb4mccfpyGTa0Y7WRyvf96BRRF8si6x00T6ktiyOV1Uk7UlmdXJqWSn/0zd8hFqmM7ytRI1w6I4JFRyrTRASpTP++J72vssh0Zl2pTFtP2bnon9J92v7eiXEPlJ5f3SOQSl2ClPcJIJWafSGvVAafpm+00Yr3+qpPeKQSqdRMTM7iSGVOYA4fjlQ6nJycTUMqcwIzfHh4hKuZUpm2npKlIvHJT5JKVSrvOyCLSmXaNGb/DPxRSq9ts38q0tFpuGcTLkgAqaQ/mCKAVGqS1JHKceNWVR6USvUeNaZ+aCYmZ3GkMicwhw9HKh1OTs6mmZbK8PIDRiqTExIeJWyGVCaKyLKlMnz2mczqSflexa2r9POZV8iLSGXaNGb/FIK7vuaV3Zw/Lxy+kgBSSVcwRQCp1CSZRyqTXuCMVGomQrM4UqkJ0KHiSKVDydBsimmpVM3h/ZTZkxIe4dJ52Flk+muaiAwa0ZqzIPuJteGRUXLefd6x0vm/z3k08ghcEakMTj0/8rCB2Awwnbn8zolUls+8qjUilZqZLSKVUS9wDkulzhNhzVNqy+JIZXXSjlRWJ5c2pDL8Sie1+zafaAJhqdPZ3KWIVMa9DsNvbUNyp18uAxtvThoTCMSxLJLTIlKZZSMt1fwi7SHxegSQSj1+lF5FAKnU7A15pDJpwwGkUjMRmsWRSk2ADhVHKh1KhmZTwlKZtMFH1ptW/+ZaNY3pr+kJCo5w6dzwF5HKtJzy8DU9f/4RzZbKTOsp63UZdvu10vWzm9mgJ3tqtY9EKrUREmAlAaRSsysUkcqoG6OgVH77gMe8NZVFdmXTPJ22LY5UVif1SGV1chmWSnVmxx49IO9av77aSaYJSPjmGqnM1k+CD0N33eDxwtemIlKZJCKsvcuWP/+ouOU3Raab5h2pTJvG7Lex++qzpfN3D3n/ya6v+fKrczRSqUOPskECSKVmf8gjlUkXyOCULKRSMykFiiOVBaA5WgSpdDQxBZrlS+Wzfx6QufO6vAhRo4tJ69XD1TJSmS8R4RGuoqODeaUyTUSKyFC+M6/e0VH3IEU45pXKrOsp/b6lyOdZ41m9TJV7RkhlubyrXBtSqZndrFKZdoEMSuXM939Huu7/keTdkU3zVNq6OFJZnfQjldXJZV6pjFqvHqYRFNC09+VVh2TxMwlfu4pu1pNXKtNEpIgMFadQjZJxU2DzPijIK5VZZxF47ejslIGtd5Jlh59WDegtcBZIZQskqUWaiFRqJiqrVKa9wNn/+zFvnCSb9P3eaxXTPzSTk6M4UpkDluOHIpWOJyhH83ypXNI7IOdfNESWLu2IHKlM+30NV3nOuUNlyy0H5HOT2KQnSzpMrKvMK5VpIuLLLQ9fs2RwxTHNksos6yl5SJA9j6aPRCpNE23feEilZu7zSmXck3H/x96Xyv49DpK+vY/QbB3FsxJAKrOScv84pNL9HGVtoS+VvX0DkjTbI01AstbHcdEEgnw3fWqeDLv7Ru/A3m/Oldr6G2bCllcq00TEH13j4Wsm/N5Bae+rzDrlNM9IZdosLb/1SGX2PJo+Eqk0TbR94yGVgdy/+NKrcvT0i+TVv78hH37/JjJ75okyeq1R0tvbJ2fNmit33/ewd/T1l50mW2+5YvvyrFKZdoFUsVhP0LwvIlLZPPama0YqTRNtXrysUpnl97V5Z9H6NQ8a4fr0Muk5fqJ3UnlGCfNIZZqIICDF+1TUdyXvlOY8Upk2jRmpLJ5LUyWRSlMkiYNUruwDSihnzLxGzj3tKBm/0ZhBPeO2e1bsRrbPxO3kzUVvy3mXz5fTjz/YE84sUpl2gVSxgy9xXn7QibJ8u0n0zhIJIJUlwrZcFVJpGXCJ4YNSqaqNuiHO8vtaYpMrWVVYDIq8WiSPVKaJSF4JqmRSCp5U1BTYvDzzSGXWWQQ8KCiYUAPFkEoDEAnhEUAqV3aEi6+6RbbbZovGCKTfP9Qo5YVzbpKD99mlIZvq2HFj3+VJZhapzLLeJ+9CefqvWQJIpVmezYyGVDaTvtm6kUqzPHWiBYW+iADkkco0EckrQTrnXbWyUVKZ9yFBHqnMOosgbxuqlpdmng9S2Uz61aobqVTrQnr7ZPa8O+SD7x0n0755hZfhyQdNkmlT9l1tZFL9LThymUcq49ZT8r6t5n+pkMrm58BUC5BKUySbHyeLVGZ5aNf8M2n9FgRFb5O+J3K/rzKPVPoiEnfN5CFs8f4Uta7SF7qs78bOKpVZZxH49auzyjOlujgFSgYJIJX0B1ME2kYqw+sifYCTdtlWpk3ZT076xhWy9Uc290TSP3bfvXaUTce9W667+V459tC9pbt7mFfs0SeelYceedI7dtny9N0DjztlwCt3xawhkXmr/fH3suzbJ0jnBz4iw79+mancEicHgeFDOzPlMkdIDm0SgWFdHdI/UJdavUkNoFpjBIYO6ZCBel1qK39mo35LL51Tk+deqMuJX+mUzTbtMFY3gQYTuPtnNbnnZ3WZuFuHTNqtU5YevL13wIj5v8iEqmtIh9TrIgMpX8znnq/LpVfWZLNNOuTEYzpXi831MhPuxIOivkd+PodN+7bUFj4nQ79wZGyMIZ0d0tEh3u9s0ictl6qsX68fp/vqn0hHz0j9kyRCZgLq/ocPBEwQaBupTIIVXiepjvXF8Yj99xi0hlL9LThS+cZbyxLz8OJfRL47t1PGj6vL0ZOjf4A77rhBOu+cJ7W9DpX63oeZyCsxchJYe9RwefPfy7ybHj6tTWCtNYbJv5f0y4BvIq19Om3d+lE9Q2VZX036+lc8mPvutR3y4sIOOfrImozfeAWar5254obo/HPSH/C1NUzNk19wf4fc998dsstOddl1Ql2GHLWzF3Fgzk9Ehq544Jr0UaNbSijVTr5Jn7Rrpn+9rL9vS6mdenFatfw9gkA4l+qQzgunScefnmgcnXQ/0j1siCixXNzbn8g36vsaLuD3I+5/mtdV11lzePMqp+ZKEUAqV05/Da+bVFL58iuvy8QJ22itqUzbcED1JtaHNP87xfTX5ufAVAuY/mqKZPPjhKe/htfaZZ1e1/wzqUYLguvjgpvLZXkVRdbpr0nrKYe88LQMn3WiB5NXiRTvU0nrKv2oSVNhs05/TZvGXGRtbvGzpmQcAaa/0jdMEUAqV5JUo48L//raatNf1atDgn9To5ozzrtaTj3mAG/jnrQ1lWkbDqjqed+Wqe5cPA5SWZydayWRStcyUrw9YakM3wwjlcXZFikZlIRNn54nw+5a8b7KLIKXVSqTRCSvyBY5x3YoE7WuMri3g88g7mFBFqnM8t1EKt3obUilG3moQiuQykAW1a6u137/bu9fzpk+2dvdVX103lOZtvMZP6pufI2QSjfyYKIVSKUJim7ESJPKqBEXN1pezVaEefuSl2VzlSxSmSYizOox16+i7k1GnLqPDHx8gnS8ulDUvUncwwKk0lweXIiEVLqQhWq0AanUzGPSSGXaBVJVjVRqJsBQcaTSEEgHwiCVDiTBUBPSpDLLTBBDTSGMiISlMrhraP+kQ0RNmYz7mJBKdn011w2THsikyXsWqczy3eQ1IubyqRMJqdShR9kgAaRSsz8kSWWW9ZT8qGomwFBxpNIQSAfCIJUOJMFQE8JSGX5Ql+XG1VBTCLOSQHCEK/gqCPXnpBHLLFKZuJ7yz/lfY0LS4gkkSWXafUkWqUxbT6lallYP+SuHAFJZDud2qAWp1MxyklRmueFJeyKo2TyKZySAVGYE1QKHIZUtkKSMTUyTyrTlBRmr4bAcBAbJwtjl0nP8RG+EUs26UZ+4dXhIZQ7IJRwata7SrzZN9tKkMsssLVUX9z8lJDpDFUhlBkgckokAUpkJU/xBSVKZ5YaHH1XNBBgqjlQaAulAGKTSgSQYakJYKlXYoNTMndcl4zaqy5GHJb+mwlBzCBMxBbajf7nUu4amCkKaVCZJjgKfJjokJz+BuHuUNNbaUlmvSc+xuzcanGWjp/xnR4msBJDKrKQ4Lo0AUplGKOXvcVKZ6UndwID0fHUPr4YsW7JrNpXiCQSQyup0D6SyOrlEKt3LZdy0ybQHpFmlMu4hQZrouEfK/RbF5TKNdZpUps3SCu40m2WTJ/dJtnYLkcrWzp9LrUcqNbMRJ5VZ1lOyPbomfIPFkUqDMJscCqlscgIMVp8klTvuUJMHHuxkpNIg7yyh4kYUdaUyTUTS4mdpO8cMJmBLKtPWUwY3eFKjlHyaSwCpbC7/KtWOVGpmM04q0y6QwSd1vSdfIrVNP6TZEorrEEAqdei5VRapdCsfOq2Jkkr/t9WPe+Sh/TJunE4tlM1LIGraZNroVtpIZdo1E6nMm6X04+NmVKXtSp80UpllllZaX0lvOUeYJIBUmqTZ3rGQSs38x0ll4nrK/uUy/PLTvY0N1AYHPKnTTIKB4kilAYiOhEAqHUmEgWYglQYgWggRNcKVJgpJUpm2nlKdAlJpIZGBNcpnn9XfqMC2VJJLO7ksGhWpLEqOcmECSKVmn4iSyrQndcFt2JdedLvUe0ZqtoLiugSQSl2C7pRHKt3JhW5LkEpdgnbK25LKpE2X/HdUsqmL2Zz6I8RqOvmEHWpecB2pTBtx5gGB2fyZiIZUmqBIDEUAqdTsBzpSuXzX/WX5PkdptoDiJggglSYouhEDqXQjDyZaESWVvtD48YMjLCbqJEY6gagHpzojlVlExJdKNrVLz0+eI+LWVSbxTpr+Si7z0HfjWKTSjTxUoRVIpWYWo6Qy7Uc17eKr2SSKFyCAVBaA5mgRpNLRxBRoVppU8jqRAlANFQkv8Ugb3Uqa/pp2zVRNRioNJS4UJm5mlc+79+zrpetX90rf5yc3SsZJZZZpzOTSTh51oiKVOvQoGySAVGr2hyipTNv5jPUEmtAtFEcqLUBtUkikskngLVSLVFqAaihkeNpkUanMIiJpsQ2dUtuGidoDwpdKte+D4h+cdpwmlUkPe8ile90MqXQvJ63aIqRSM3NhqUxbT6mqQyo1oVsojlRagNqkkEhlk8BbqDZKKoMSElwHZqF6QiYQCE+bTJOFuJHKLNfMtNgkSo9A1LpKXyr9yFmkMsuIM7nUy5WN0kilDartGROp1Mw7UqkJ0JHiSKUjiTDQDKTSAERHQkRJZX+/yNnndnkt5HUizUtUlAwmTVGNk8oooQmfFUtG7OY5al1l8D3aqva+SYdI/56Heg2JG6nMIpXk0m4ui0RHKotQo0wUAaRSs1+EpTLLjyprQzShWyiOVFqA2qSQSGWTwFuoNkoqVTVLlnRIT0/dQo2EzEMgPG1SRyqTHhAwuydPVvIfm7Txkh8tTSqzTGNWschl/vzYLoFU2ibcPvGRSs1ch6UybT2lqg6p1IRuoThSaQFqk0IilU0Cb6HaOKm0UBUhCxAIjzIWkcrEdzqvbBMiUiA5OYuE8xB89ZkKlVUq0zbPIpc5E1PC4UhlCZDbpAqkUjPRQanMsjYEqdQEbqk4UmkJbBPCIpVNgG6pSqTSElhDYcPTJpOEIWr6K9dMQ4kwECb8gCCvVGaZpeXHVJv/qDWafNwggFS6kYcqtAKp1MxiHqnsfPGPMvSOud5OavyoaoI3XBypNAy0ieGQyibCN1w1UmkYqOFwvlT6o1N5pTLLeko2djGctJhw4QcENqSSUcpycpm3FqQyLzGOjyOAVGr2jaBUpj2pCy58Ryo1wRsujlQaBtrEcEhlE+EbrhqpNAzUQrjgtEn/GhecKulXGTVSmXbNVGWRSgtJiwgZHjXu6O+TEcdPahwZvGcJb9STdT0lS3/KyWXeWpDKvMQ4Hqm01AeCUpm2nrLxlG7q+TKw+UcttYiwRQgglUWouVkGqXQzL0VahVQWoVZumaAYbvrUPBl2942D1t8lSWWW9ZRDb7tahi74YWTMcs+0+rWtlo+BAemQuoz46mcHza6Kk8qk9ZSdzz8p3RdNY5aWg90IqXQwKS3aJEYqNRPnS2WWtSE8pdOEbbE4UmkRbsmhkcqSgVusDqm0CNdQ6OC0yd3evj6zVGa5Zspb/5Ser+3vtTT4nkRDTSdMiEDcdGR175I0UpllGrN//8MsLfe6HVLpXk5atUVIpWbmkEpNgI4URyodSYSBZiCVBiA6EgKpdCQRCc0Irqucss51maUy6t2I4WqCS0aWzP6pSEen+0BauIVxOckqlXGvhQlOYe494XzpGDKkhSlVr+lIZfVy2qwzQio1yftSmWVtCCOVmrAtFkcqLcItOTRSWTJwi9UhlRbhGgztT5v89gGPee8hjBqNCq+pzHLNZGMXg0nKECpu9Dh87xKe/po2jZl1sRngN/EQpLKJ8CtWNVKpmdCsUsmPqiZoy8WRSsuASwyPVJYI23JVSKVlwIbC+4I4+ZO/k/ffdnImqUwTEdU0pNJQgnKEicpLklRmmcbs7yQbtYFTjqZxqCUCSKUlsG0YFqnUTLqSyiw7nyGVmqAtF0cqLQMuMTxSWSJsy1UhlZYBGwofnDa55007e1GXzFkwKHpwpDKLiKjCzO4xlKAcYaLWRyZJZZb1lDwcyJGAJhyKVDYBekWrRCo1ExuUyqSdz5BKTdCWiyOVlgGXGB6pLBG25aqQSsuADYXPK5Xh91tGNYNrpqHk5AwTta4yi1TGradU1SOVOZNQ8uFIZcnAK1wdUqmZXCWVWdaGdP7xt9J9+elsp63J21ZxpNIW2fLjIpXlM7dVI1Jpi6z5uP60yfNHnei9WzI81TE4UpnlmolUms9RlohRo8hhKQyuqcwyjZkR5yzkm3cMUtk89lWrGanUzGgmqezvk56VLxFmTYEmcEvFkUpLYJsQFqlsAnRLVSKVlsBaCOvLxZR1rpf3PnWDV0PweheUyiwiwjo8C0nKGDKcn2AuBnbfX3pGjZSuIR3yxNPLZe68LkmapaWqRCozgm/SYUhlk8BXsFqkUjOpv35sqfejqj5nn9UfGc1/4qr+uPTSH0t9+AjNWilumgBSaZpo8+Ihlc1jb7pmpNI0UXvx/GmTO23XL3v8/hRvtFJ9/LWVvlQ+/Wx/JhFhyqS9XKVFDq+T9KXSL9cx+07pWmOUXDK7Jgtf6pAdd6jJhB1qsWGRyjTizf07Utlc/lWqHanUzKYvlUlP6rg4akIuoThSWQLkkqpAKksCXUI1SGUJkA1VEV6LFxYJXyrv/GlNHniwM/Po1rKTZnnLRviURyCcy47+5TLi+ImrGrD3YTL0C0c0pDJpPaUqhFSWl7siNSGVRahRJooAUqnZL/7z4j7vSV3Sj6r/g8rFURO2xeJIpUW4JYdGKksGbrE6pNIiXAuhg9Mmww9Tfam8/Lv11GsmImIhOTlCRq2r7Hz6t9L1/JPSde/3RVZK5dSv1b2ocbO01N9YG5sDfJMORSqbBL6C1SKVmknNI5XhLdY1q6a4QQJIpUGYTQ6FVDY5AQarRyoNwiwhVBapPHnGioYkPYhFREpIVkoVUeteG9Ng9z5MXtrqcLn8KkkdcSaXzc9lWguQyjRC/D0rAaQyK6mY446aujzxSR0/qJqASyqOVJYEuoRqkMoSIJdUBVJZEmhD1QSnTe729vUy7O4bG5v1qJHKPz1Xk9nXdKSKiC8vatqrmuHDp3wCUe+fDErlfWseLvfeJ6nrKbkHKj93eWtEKvMS4/g4AkilZt9QUsn7KTUhOlAcqXQgCYaagFQaAulAGKTSgSTkaIIpqWQfghzQLR0a9b7KxqaDm28lV65ziTz/YvKIs2oaUmkpQQbDIpUGYbZ5KKRSswMoqUza+YwfVE3AJRVHKksCXUI1SGUJkEuqAqksCbShavy1eCrcuR+d641U+qONaqTyO1fW5YW/pIsIUmkoIRphotZVBqXylEWXeNGT1lOqv/NqGI0klFQUqSwJdBtUg1RqJllJJWtDNCE6UBypdCAJhpqAVBoC6UAYpNKBJORsQnAtXnDXTyWV/sYu7BaaE2qTDo9aV+nn9JQN/jt1GjNS2aTE5awWqcwJjMNjCSCVmp3jyycul2+dGf1+Sn5QNeGWWBypLBG25aqQSsuASwyPVJYI21BVwWmTe960sxdVbVL3+mtdbOxiiHFZYaLWVSKVZdEvrx6ksjzWVa8JqdTM8Iuv9Ep394pttaM+TP3QBFxScaSyJNAlVINUlgC5pCqQypJAG6wmbl3lG+N3lktuGSvvGdcpRx42EFsjS0YMJkMzVNS6yqBUJi398atmKrNmEkoojlSWALlNqkAqNRP96htLEyPwg6oJuKTiSGVJoEuoBqksAXJJVSCVJYE2WE1wLd6Uda5rrKtUsvhIzyR5/XPTZMIOtcgaOwb6ZcRXP+v9jZ1fDSalYKiodZV5pTI4BbpgMyhmmQBSaRlwG4VHKjWTnSiVy5ZKz4mf82pQ26KriyQfNwkglW7mpUirkMoi1Nwsg1S6mZe0VkWtq1RlXhi2lTyzz0WxUtnYCEZEls66Veoj10qrir9bJhBeV+k/KJ+zziWy4a4fjs2lahajzpaTYyg8UmkIJGEEqdTsBElS2Xink7pAXn6P1LuGatZGcVsEkEpbZMuPi1SWz9xWjUilLbJ24wbX4n32d9M8ufA/dx3w89VEpF6rydB7vidDnvuDdywPYe3mJ0/08LrKPFLJTK08pJt3LFLZPPZVqxmp1MxoklQ2flCPO1cGPrS1Zk0Ut0kAqbRJt9zYSGW5vG3WhlTapGsv9qC1eNsPiCxfJiOvPFPqz/xe/vi1BTJu3OC6/Wul/69Ipb3c5I0cXlfpPyz/2RqHSd+ehyaOVCKVeWk353iksjncq1grUqmZ1UxSydRXTcr2iyOV9hmXVQNSWRZp+/UglfYZ26ghaS1elDCGpVLtFsvHDQLhXGaVyq7HfiHDrjnHOwny6UYu41qBVLqdn1ZqHVKpma1IqRwYEFnylvRM38+LzlNXTcglFEcqS4BcUhVIZUmgS6gGqSwBsqUqwmvxRn7nVG+kMng97Hz+Ka/27otO8v538RU/lQ6pi3QOsdQqwhYhEMxlVqn0N+hhw6UixMstg1SWy7vKtSGVmtmNksruq8+Wzt891IjMUzpNyCUURypLgFxSFUhlSaBLqAapLAGypSr8tXhHHtrvTXd97cr5Mv6J66Vv0iHSv+ehXq2+eKj/j3xYSoSBsMF1lbu9fb23o6+a/jr2uC+tNpVZVRfccIn7HwMJsBwCqbQMuI3CI5WayY6SyuCFUoXnR1UTcgnFkcoSIJdUBVJZEugSqkEqS4BsqYrwWrw0qVz2xa/IwM5fsNQawuoQCOZy1w0eFzVdWe3ku+ykCxOlkgcFOtTLK4tUlse66jUhlZoZDktl8AkdT1814ZZYHKksEbblqpBKy4BLDI9UlgjbcFW+iIzbqC5HHjaw+khlvS7DLz3VG9Xiwath+IbDBddVfvmTv0uVyq5f/kSGzb+Y0WfDebAVDqm0Rbb94iKVmjlHKjUBOlIcqXQkEQaagVQagOhICKTSkUQUbEZwLV5wpFKNSI6Y9nmprTtG6muv562z5OM2AT+X3z7gMU8q1SdqJ9+OhX+SEed/1ft7cKqz22fX3q1DKts7/ybPHqnUpPm3v/5D6iNGNqIM+/6l0vXQ3Y3/ZvqHJuCSiiOVJYEuoRqksgTIJVWBVJYE2lI1wXWVr9z/tOz66ImeaPjvo1TVco20BN9w2GAuP3D+roOksuPtf0n3xSdL76mXyZD7b/PWXKrPkkvuEOnuMdwSwpkmgFSaJtq+8ZBKzdz/a7/PrJq68/a/pGf6voMi8qROE3BJxZHKkkCXUA1SWQLkkqpAKksCbama4Fq8lxf8QY55Y8Uur8EPUmkJvuGwNzaNcAAAHAFJREFUwVzu+fuTRZ5dtZOvvyOsut9RHyWVfZO/Lv0f39FwKwhngwBSaYNqe8ZEKjXzrqRSTd2pbbiZDPn5rd6Pae0975XO//2zFxmp1ARcUnGksiTQJVSDVJYAuaQqkMqSQFuqJriucuFLHTLrbzs1aqqvOVo63nqTa6Ql9qbDBnP52SXzZJM/rNrJN/ie0eWTDpGhd9/Iq9RMJ8BiPKTSItw2C41UaiZcSaX/6d91P+la8ENvOo/afEB9eEelJuCSiiOVJYEuoRqksgTIJVWBVJYE2mI1/lo8VYUvleoaqT7qOsmDV4vwDYf2c3nMejfESqVfJfc+huFbDIdUWoTbZqGRSs2EB6XSD6Uukv6aAn5YNQGXVBypLAl0CdUglSVALqkKpLIk0BarSZNKrpEW4RsO7a+r3KTv942pzGrnXvUatfq675KOf7zWqJEdfQ3DtxgOqbQIt81CI5WaCUcqNQE6UhypdCQRBpqBVBqA6EgIpNKRRGg0w582qUKoNZVKSNSD1/q498nwK86QJZf+WGT4CI0aKFoWgWAu/VFnXyrV6PPAZh9etUnPnAVlNYt6NAkglZoAKd4ggFRqdoY3zjjOi+BPd1X/PzhSydM6TcAlFUcqSwJdQjVIZQmQS6oCqSwJtMVqgiLyxUUXy7ZL7mwsC+kY6Jf6kC6LtRPaJIGoBwR+fKTSJOlyYyGV5fKucm1I5crsvrnobTn2tEvlD8+84P3LOdMnyz4Tt/P+f29vn5w1a67cfd/D3n9ff9lpsvWWm3v/X72nUgml/94m9W9qOo//30hla3x9kMrWyFOWViKVWSi1xjFIZWvkKa2V/hTYzTbql8M+9qjUPrR1WhH+7igBP5fj+p6Sr75xfKOVSir7Jx3i3fuwo6+jyYtpFlLZWvlyubVI5crsXHzVLTJu7Ls8kVSCOeO8q+XUYw6Q8RuNkdvuecg7yv/beZfPl9OPP1hGrzUqUiqVSI44ZR8Z+Oj20nfQiS7nn7atJIBUVqcrIJXVySVSWY1c+iKyycYihx3SX42TatOz8NdVqtM/+6x+6XzqUelc+Iz073moR2T4Nf8pAzvsJf2brdiMiY/7BJDKfDmqi0hHviJtczRSuXIk8sI5N8nB++ziSaT6KMncbpstZIv3jZeov/kCqkYq1UctVPc/jE623vcHqWy9nMW1GKmsTi6Rymrk0p82iVS2fj6DU2CVVPJpfQJIZevn0JUzQCpXZuLRJ56VW+58QM4+5Uh58k8vNv7/0mXLJDgyqQ4Pjlwila50Zb12IJV6/FwqjVS6lA29tiCVevxcKY1UupIJ/XYglfoMXYuAVLqWkdZtT9tIZXhdpJ+ySbts64lkd/cwUWJ5+NSZEvw3NRX2upvvlWMP3ds7Rn3UcQ898qRMm7Kv9PYNeP/W+6UdvP/t/MBWMmzGZa3bI9q05d3DhjRy2aYIKnPaw4Z2yvL+utTrapIKn1YmMLSrUwZqdanVyGUr53FgQOTE02vy6U90yAFfZOJYK+dStX3qaTXZfNNOOeaoVj8T2q8IqPsfPhAwQaBtpDINlpruqj5KFF986VWZMfMaOfe0o2T0O0YljlT+8+2+FaEXvyVy323Ssf2eUh+9blp1/N0xAqPXGCb/WtwneIhjiSnQnLVGDpXFS/ulHxEpQM+tIqNGdMmy5TXp66+51TBak5vAyO4hMlATHt7lJudeASUhQzpFFveueKjOp7UJqBkhfCBgggBSKeJtzBOe4qpGI19+5XWZOGGbTGsqTSSDGM0jwPTX5rE3XTPTX00TbV48pr82j73pmtXDnv6BuizuZR2eabZlxxvZ3SVdQzpk0eLlZVdNfRYIMP3VAtQ2DYlUrpTK4G6vqi8Ed4NVaygX/vU1bxQzvDOsv6ayTftPZU4bqaxMKgWprE4ukcrq5BKprE4ukcrq5FKdCVJZrXw282yQypX01ZTXo6dfJK/+/Q3vXyYfNMmTSPVJe09lMxNI3WYIIJVmOLoQBal0IQtm2oBUmuHoQhSk0oUsmGkDUmmGoytRkEpXMtH67UAqNXPISKUmQEeKI5WOJMJAM5BKAxAdCYFUOpIIA81AKg1AdCQEUulIIgw1A6k0BJIwglRqdgKkUhOgI8WRSkcSYaAZSKUBiI6EQCodSYSBZiCVBiA6EgKpdCQRhpqBVBoCSRikUrcPIJW6BN0oj1S6kQcTrUAqTVB0IwZS6UYeTLQCqTRB0Y0YSKUbeTDVCqTSFEniMFKp2QeQSk2AjhRHKh1JhIFmIJUGIDoSAql0JBEGmoFUGoDoSAik0pFEGGoGUmkIJGEYqdTtA0ilLkE3yiOVbuTBRCuQShMU3YiBVLqRBxOtQCpNUHQjBlLpRh5MtQKpNEWSOIxUavYBpFIToCPFkUpHEmGgGUilAYiOhEAqHUmEgWYglQYgOhICqXQkEYaagVQaAkkYRip1+wBSqUvQjfJIpRt5MNEKpNIERTdiIJVu5MFEK5BKExTdiIFUupEHU61AKk2RJA4jlZp9AKnUBOhIcaTSkUQYaAZSaQCiIyGQSkcSYaAZSKUBiI6EQCodSYShZiCVhkAShpFK3T6AVOoSdKM8UulGHky0Aqk0QdGNGEilG3kw0Qqk0gRFN2IglW7kwVQrkEpTJInDSKVmH0AqNQE6UhypdCQRBpqBVBqA6EgIpNKRRBhoBlJpAKIjIZBKRxJhqBlIpSGQhGGkUrcPIJW6BN0oj1S6kQcTrUAqTVB0IwZS6UYeTLQCqTRB0Y0YSKUbeTDVCqTSFEniMFJJH4AABCAAAQhAAAIQgAAEIACBwgSQysLoKAgBCEAAAhCAAAQgAAEIQAACSCV9AAIQgAAEIAABCEAAAhCAAAQKE0AqC6OjIAQgAAEIQAACEIAABCAAAQgglfSByhHo7e2Ts2bNlW0/+kHZZ+J2jfO77Z6H5MwLrvX++8Pv30RmzzxRRq81KvL842K8+NKrcvT0i+TVv7+RGqNyYJtwQlnyoJp1/WWnydZbbr5aC4P5Ch/nx777voe9cnExmnDala3y4qtu8c5t2pR9G+eYlKMgiPBxkw+a1IhDLsvvMlG5fHPR23LsaZfKH555wWvQOdMnD/oNjmql+l1++HdPy9mnHCnd3cO8Q1Tsa79/d+YY5Z99tWp89Iln5eIrbxl0Tcyay/B3L/xbSi6r1Vc4GwgkEUAq6R+VIuBf4Ba9tVh23/ETjRsadUN630OPyZQv7eWdr7qRWfjX1wbd3Pog4mKof79wzk1y8D67yPiNxoi6ED/0yJORMSoFtUknk5SH2fPukCP238N7KKByq/Jy7ulfHvSQQJVPOk71AfVRDx7UDdR5l8+X048/OPZBQ5MwVKZadXP5P48/IxM+/ZHG9zAtR8GTn3/bfTJx5228/Ph9Y9+9dvQeJpDLcrtJVC5VC6763p2yy3Yf834f1XdqxnlXy6nHHOD9d9RHfXdVXkeOHCHHHrq3J5XB39Xwb265Z9ketSneM869Wj6yxWaDfv+y5jLpt5Nctkcf4iwh4BNAKukLlSKgLmIvv/K6bPju9bz/DY5UBk/Uv5lRNzz+03H/73ExwmWy3DRVCm7JJ5M1l1lvPIPHjVl/3UEPCNSpqRvlcWPflTqyUjKGSlTnP9RRwhF8uBM+uay59PO13TZbyBbvG08uS+wlWXMZzFHULAI/13tM+IT3cM6XSvU9VHn1yyQ9ACzxtCtZlf9Q58C9J8gP7ri/8aAu6mTDefGPUdfB626+t5G/YFlyWcluw0lBIJYAUknnqCSB4MhF1AkGn6DGyWE4RnhkMs8NcCUhl3RSabkMPilXTYobHQkfFx6ZTKunpNOtdDVpo/vhUY840Q+OTitg5LL8bpOWy/DvYziX6vumHv5tOu7dDSlRZxGcDaL+O62e8s+8ejWmzdQI5zIo+uFp6f7SkhHDh5PL6nUVzggCiQSQSjpIJQkkCUL4AppHKsOjn3FPbysJtUknlSZ7wRwkjR6Hjws/Xefm1X6C0xiHv09hEfHXZ41Zfx357gUnN6ZZkkv7uQvXkJbL8Pc2mMvgcoTgSJeqIzhlXf130qyS8s+6mjWmSWU4l0mjx/4a2RnHf0nm3vyTQaOf5LKa/YezgoBPAKmkL1SSQJyI5BldZKTSja6RJJVpwumfQfi4qJuorLHcoNKarUgSkTz8gxs47fTprRipbEJ3SMpl0t/C62jDUslIZfnJzLouMkvL/FgnTdlXrpl/d2MPAkads9DjGAi0NgGksrXzR+tjCETdoIY390iDF47Bmso0Ynb+HicbWddaRR0X9XCBNZV28heMGicbWXMZFUutxQuLCLlsXi5Vjm+584FBu7mG83b41JmrNdAffb79p79iTaX99A2qIU4q03IZ1cxgLDWDgPWxJSeT6iDQRAJIZRPhU7U9AmERSRLKrNNfwzGKXHDtnXF1I0dJZZyEhHOZNk3L3wGYTZfK6T9RUpmUI18OJ07YRpYuW9bYmTf8qplgDHLZvFwm/SbGiX54o5dgDHUm6vVQ/i6/5ZxZ+9USJZVJuUz7ziqC6rVB5LL9+hJn3N4EkMr2zn9lzz5q6mr46bj/ZHz0O0ZFbu4SJTO8p7L8LhM1dTX4Ljy/ReqdeGoqpL9Rj8pr3HFqV2DebVh+LsNSGX4XXjCXKkdBqVRy4b9TVB3HeyrLz194xDH4SqWo9xUG85RVKlUZ3m1Ybm7DUpmWy6BUqu908Noa/F6Sy3LzSG0QaDYBpLLZGaB+CEAAAhCAAAQgAAEIQAACLUwAqWzh5NF0CEAAAhCAAAQgAAEIQAACzSaAVDY7A9QPAQhAAAIQgAAEIAABCECghQkglS2cPJoOAQhAAAIQgAAEIAABCECg2QSQymZngPohAAEIQAACEIAABCAAAQi0MAGksoWTR9MhAAEIQAACEIAABCAAAQg0mwBS2ewMUD8EIAABCEAAAhCAAAQgAIEWJoBUtnDyqtR09a6ri6+8RWbPPLHxgnPd8wu/A0+9x1C9+y74rslgHeH3a4XrD5e7/rLTZOstN/cOS3p/pXqn15kXXOsd9+H3b7LaOdo4d112lIcABCAAAQhAAAIQgEBWAkhlVlIcZ5XAVd+7U/7++puyx4RPNERNt0IVc5ftPibjNxojSjBnnHe1nHrMAd5/hz/qZdvbbbNFbN3qZdCz590hR+y/hye9SiIvnHOTnHv6l2XE8OHe/z94n1282MEXvKvj7nvoMZnypb28KoMvjVb/rY6dce7V8pEtNpPTjz/YmFDrsqM8BCAAAQh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       "                            \n",
       "var gd = document.getElementById('2e0aeb6e-0bc2-4af8-9f8a-b1f4d02e65aa');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
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       "    x.observe(outputEl, {childList: true});\n",
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     },
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   ],
   "source": [
    "# Plot the results.\n",
    "fig = px.line(scada, x='TIMESTAMP', y=['AGC Setpoint', 'Output MW'])\n",
    "fig.update_layout(yaxis_title=\"MW\", legend_title=None)\n",
    "fig.show()"
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