mustlink_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MUSTlink test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mustlink import mustlink\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instantiate the Must() class - by default this will look for a configuration file, and load user authentication if found. Otherwise specify `config_file` and `url`:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connecting to the server"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m\n",
       "\u001b[0mmustlink\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'https://bepicolombo.esac.esa.int/webclient-must/mustlink'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mconfig_file\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'/home/mbentley/.config/mustlink.yml'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m      <no docstring>\n",
       "\u001b[0;31mInit docstring:\u001b[0m\n",
       "The WebMUST URL instance can be specified, along with a\n",
       "YAML config file containing a user section with login and\n",
       "password entries. If neither of these are provided, the\n",
       "default values are used\n",
       "\u001b[0;31mFile:\u001b[0m           ~/Dropbox/work/bepi/software/mustlink/mustlink/mustlink.py\n",
       "\u001b[0;31mType:\u001b[0m           type\n",
       "\u001b[0;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mustlink.Must?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO 2020-06-17 16:22:49 (mustlink.mustlink): user mbentley currently logged in\n",
      "INFO 2020-06-17 16:22:49 (mustlink.mustlink): 6 providers found\n"
     ]
    }
   ],
   "source": [
    "must = mustlink.Must()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with providers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The list of providers (essentially separate databases) is updated on log-in and stored in the `providers` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['BEPICRUISE', 'BEPIMAGNETIC', 'BEPIMST', 'BEPISVT1B', 'BEPISVT1C', 'BEPITEST']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "must.providers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The provider can be included in most calls, but if you only plan to work with one, this can be set by default using the `set_provider` call:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "must.set_provider('BEPICRUISE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with tables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As well as parameter (TM/TC) data, a provider can also offer tables, for example the Telecommand History. The list is populated using `get_tables()` and stored in the `tables` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO 2020-06-17 16:24:37 (mustlink.mustlink): provider BEPICRUISE has 1 table(s)\n"
     ]
    }
   ],
   "source": [
    "must.get_tables()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BEPICRUISE': [{'dataType': 'TC', 'tableTitle': 'BEPICRUISE TC History'}]}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "must.tables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Information about a given table can be dumped with `get_table_meta()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'headers': ['Name',\n",
       "  'Description',\n",
       "  'Type',\n",
       "  'Subtype',\n",
       "  'Sequence',\n",
       "  'Release Time',\n",
       "  'Execution Time',\n",
       "  'S',\n",
       "  'D',\n",
       "  'C',\n",
       "  'G',\n",
       "  'B',\n",
       "  'IL',\n",
       "  'ST',\n",
       "  'R',\n",
       "  'GTO',\n",
       "  'A',\n",
       "  'SS',\n",
       "  '1122',\n",
       "  'CC',\n",
       "  'Parameters'],\n",
       " 'tableName': 'BEPICRUISE TC History'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "must.get_table_meta('TC')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And values from this table retrieved with `get_table_data()`, including a time range:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mmust\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_table_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mstart_time\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mstop_time\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0msearch_key\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'name'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0msearch_text\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mprovider\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmax_rows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mquiet\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Retrieve tabular data from a WebMUST provider and format into a pandas\n",
       "DataFrame. Columns with 'time' in the title are assumed to be times, and \n",
       "are accordingly converted to Timestamps. Setting max_rows limits the number\n",
       "of rows returned by the API (when this is excluded the API returns a maximum\n",
       "of 5000)\n",
       "\u001b[0;31mFile:\u001b[0m      ~/Dropbox/work/bepi/software/mustlink/mustlink/mustlink.py\n",
       "\u001b[0;31mType:\u001b[0m      method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "must.get_table_data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO 2020-06-17 16:27:40 (mustlink.mustlink): 18 table entries retrieved\n"
     ]
    }
   ],
   "source": [
    "tch = must.get_table_data('TC', '2019-05-01 00:00:00', '2019-05-02 00:00:00') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Description</th>\n",
       "      <th>Type</th>\n",
       "      <th>Subtype</th>\n",
       "      <th>Sequence</th>\n",
       "      <th>Release Time</th>\n",
       "      <th>Execution Time</th>\n",
       "      <th>S</th>\n",
       "      <th>D</th>\n",
       "      <th>C</th>\n",
       "      <th>...</th>\n",
       "      <th>B</th>\n",
       "      <th>IL</th>\n",
       "      <th>ST</th>\n",
       "      <th>R</th>\n",
       "      <th>GTO</th>\n",
       "      <th>A</th>\n",
       "      <th>SS</th>\n",
       "      <th>1122</th>\n",
       "      <th>CC</th>\n",
       "      <th>Parameters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ZCPA17CE</td>\n",
       "      <td>AOCS tcHgaSetFixedAng</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>AACF264A</td>\n",
       "      <td>2019-04-11 18:58:05.430</td>\n",
       "      <td>2019-05-01 09:00:30</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>NONE\\/M</td>\n",
       "      <td></td>\n",
       "      <td>S</td>\n",
       "      <td>SSS</td>\n",
       "      <td>U</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>US</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ZCA0A166</td>\n",
       "      <td>AOCS Determine Hga guidance ET/fixed</td>\n",
       "      <td>161</td>\n",
       "      <td>102</td>\n",
       "      <td>AACF262A</td>\n",
       "      <td>2019-04-11 18:58:05.430</td>\n",
       "      <td>2019-05-01 09:00:35</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>NONE\\/M</td>\n",
       "      <td></td>\n",
       "      <td>S</td>\n",
       "      <td>SSS</td>\n",
       "      <td>U</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>US</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ZCA08A73</td>\n",
       "      <td>AOCS SegTab Load ShmAtGuidCheb Seg Ram</td>\n",
       "      <td>138</td>\n",
       "      <td>115</td>\n",
       "      <td>AACF140A</td>\n",
       "      <td>2019-04-11 18:58:05.430</td>\n",
       "      <td>2019-05-01 09:01:00</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>NONE\\/M</td>\n",
       "      <td></td>\n",
       "      <td>S</td>\n",
       "      <td>SSS</td>\n",
       "      <td>U</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>US</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ZCA08A74</td>\n",
       "      <td>AOCS SegTab Load ShmAtGdCosSin SegP1 Ram</td>\n",
       "      <td>138</td>\n",
       "      <td>116</td>\n",
       "      <td>AACF151A</td>\n",
       "      <td>2019-04-11 18:58:05.430</td>\n",
       "      <td>2019-05-01 09:01:01</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>NONE\\/M</td>\n",
       "      <td></td>\n",
       "      <td>S</td>\n",
       "      <td>SSS</td>\n",
       "      <td>U</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>US</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ZCA08A75</td>\n",
       "      <td>AOCS SegTab Load ShmAtGdCosSin SegP2 Ram</td>\n",
       "      <td>138</td>\n",
       "      <td>117</td>\n",
       "      <td>AACF151A</td>\n",
       "      <td>2019-04-11 18:58:05.430</td>\n",
       "      <td>2019-05-01 09:01:02</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>NONE\\/M</td>\n",
       "      <td></td>\n",
       "      <td>S</td>\n",
       "      <td>SSS</td>\n",
       "      <td>U</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>US</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name                               Description Type Subtype  Sequence  \\\n",
       "0  ZCPA17CE                     AOCS tcHgaSetFixedAng  129       1  AACF264A   \n",
       "1  ZCA0A166      AOCS Determine Hga guidance ET/fixed  161     102  AACF262A   \n",
       "2  ZCA08A73    AOCS SegTab Load ShmAtGuidCheb Seg Ram  138     115  AACF140A   \n",
       "3  ZCA08A74  AOCS SegTab Load ShmAtGdCosSin SegP1 Ram  138     116  AACF151A   \n",
       "4  ZCA08A75  AOCS SegTab Load ShmAtGdCosSin SegP2 Ram  138     117  AACF151A   \n",
       "\n",
       "             Release Time      Execution Time  S  D  C  ...  B       IL ST  R  \\\n",
       "0 2019-04-11 18:58:05.430 2019-05-01 09:00:30  D  D  E  ...     NONE\\/M     S   \n",
       "1 2019-04-11 18:58:05.430 2019-05-01 09:00:35  D  D  E  ...     NONE\\/M     S   \n",
       "2 2019-04-11 18:58:05.430 2019-05-01 09:01:00  D  D  E  ...     NONE\\/M     S   \n",
       "3 2019-04-11 18:58:05.430 2019-05-01 09:01:01  D  D  E  ...     NONE\\/M     S   \n",
       "4 2019-04-11 18:58:05.430 2019-05-01 09:01:02  D  D  E  ...     NONE\\/M     S   \n",
       "\n",
       "   GTO  A SS 1122  CC Parameters  \n",
       "0  SSS  U          US       None  \n",
       "1  SSS  U          US       None  \n",
       "2  SSS  U          US       None  \n",
       "3  SSS  U          US       None  \n",
       "4  SSS  U          US       None  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tch.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                                ZCPA17CE\n",
       "Description            AOCS tcHgaSetFixedAng\n",
       "Type                                     129\n",
       "Subtype                                    1\n",
       "Sequence                            AACF264A\n",
       "Release Time      2019-04-11 18:58:05.430000\n",
       "Execution Time           2019-05-01 09:00:30\n",
       "S                                          D\n",
       "D                                          D\n",
       "C                                          E\n",
       "G                                           \n",
       "B                                           \n",
       "IL                                   NONE\\/M\n",
       "ST                                          \n",
       "R                                          S\n",
       "GTO                                      SSS\n",
       "A                                          U\n",
       "SS                                          \n",
       "1122                                        \n",
       "CC                                        US\n",
       "Parameters                              None\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tch.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parameters can be searched by either name (mnemonic) or description using `search_parameter()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mmust\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearch_parameter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msearch_text\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msearch_by\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'description'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprovider\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "The provided searche text is used to search within the parameter\n",
       "descriptions. Matching parameters are returned in a Pandas DataFrame.\n",
       "\n",
       "Search can be by name (mnemonic) or description. \n",
       "\n",
       "Set search_by='name' or 'description'\n",
       "\u001b[0;31mFile:\u001b[0m      ~/Dropbox/work/bepi/software/mustlink/mustlink/mustlink.py\n",
       "\u001b[0;31mType:\u001b[0m      method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "must.search_parameter?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO 2020-06-17 16:28:27 (mustlink.mustlink): 23 parameters match search text: BERM\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mbentley/miniconda3/envs/bepi/lib/python3.6/site-packages/pandas/core/indexing.py:671: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "params = must.search_parameter('BERM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Description</th>\n",
       "      <th>Data Type</th>\n",
       "      <th>First Sample</th>\n",
       "      <th>Last Sample</th>\n",
       "      <th>Subsystem</th>\n",
       "      <th>Id</th>\n",
       "      <th>Parameter Type</th>\n",
       "      <th>Name</th>\n",
       "      <th>Provider</th>\n",
       "      <th>Unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BERM Busy</td>\n",
       "      <td>UNSIGNED_TINY_INT</td>\n",
       "      <td>2018-10-25 18:02:29</td>\n",
       "      <td>2020-04-24 19:09:06</td>\n",
       "      <td>berm</td>\n",
       "      <td>2866</td>\n",
       "      <td>TM</td>\n",
       "      <td>NBED0009</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MPO-TEMP-BERM-TRP</td>\n",
       "      <td>DOUBLE</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td></td>\n",
       "      <td>30150</td>\n",
       "      <td>TM</td>\n",
       "      <td>NKK19001</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>degC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BERM LCL current</td>\n",
       "      <td>DOUBLE</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td></td>\n",
       "      <td>30151</td>\n",
       "      <td>TM</td>\n",
       "      <td>NKK19002</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BERM mode</td>\n",
       "      <td>UNSIGNED_MEDIUM_INT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td></td>\n",
       "      <td>30152</td>\n",
       "      <td>TM</td>\n",
       "      <td>NKK19003</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BERM-A ON-ST</td>\n",
       "      <td>UNSIGNED_TINY_INT</td>\n",
       "      <td>2018-10-19 18:19:23</td>\n",
       "      <td>2020-06-17 11:58:23</td>\n",
       "      <td></td>\n",
       "      <td>34093</td>\n",
       "      <td>TM</td>\n",
       "      <td>NPWD0403</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BERM-B ON-ST</td>\n",
       "      <td>UNSIGNED_TINY_INT</td>\n",
       "      <td>2018-10-19 18:19:23</td>\n",
       "      <td>2020-06-17 11:58:23</td>\n",
       "      <td></td>\n",
       "      <td>34132</td>\n",
       "      <td>TM</td>\n",
       "      <td>NPWD0603</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BERM-A ON-CT</td>\n",
       "      <td>DOUBLE</td>\n",
       "      <td>2018-10-19 18:19:23</td>\n",
       "      <td>2020-06-17 11:58:23</td>\n",
       "      <td></td>\n",
       "      <td>34381</td>\n",
       "      <td>TM</td>\n",
       "      <td>NPWD2803</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BERM-B ON-CT</td>\n",
       "      <td>DOUBLE</td>\n",
       "      <td>2018-10-19 18:19:23</td>\n",
       "      <td>2020-06-17 11:58:23</td>\n",
       "      <td></td>\n",
       "      <td>34422</td>\n",
       "      <td>TM</td>\n",
       "      <td>NPWD3203</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>BERM-A ON-CT</td>\n",
       "      <td>UNSIGNED_MEDIUM_INT</td>\n",
       "      <td>2018-10-19 18:19:23</td>\n",
       "      <td>2020-06-17 11:58:23</td>\n",
       "      <td></td>\n",
       "      <td>34601</td>\n",
       "      <td>TM</td>\n",
       "      <td>NPWU2803</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BERM-B ON-CT</td>\n",
       "      <td>UNSIGNED_MEDIUM_INT</td>\n",
       "      <td>2018-10-19 18:19:23</td>\n",
       "      <td>2020-06-17 11:58:23</td>\n",
       "      <td></td>\n",
       "      <td>34635</td>\n",
       "      <td>TM</td>\n",
       "      <td>NPWU3203</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MPO-TEMP-BERM-TRP</td>\n",
       "      <td>DOUBLE</td>\n",
       "      <td>2018-10-19 18:19:18</td>\n",
       "      <td>2020-06-17 11:58:02</td>\n",
       "      <td></td>\n",
       "      <td>34937</td>\n",
       "      <td>TM</td>\n",
       "      <td>NRUD1076</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>degC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>BERM-DG</td>\n",
       "      <td>DOUBLE</td>\n",
       "      <td>2018-10-19 18:19:18</td>\n",
       "      <td>2020-06-17 11:57:46</td>\n",
       "      <td></td>\n",
       "      <td>36508</td>\n",
       "      <td>TM</td>\n",
       "      <td>NRUD9048</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>degC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>MPO-TEMP-BERM-TRP</td>\n",
       "      <td>UNSIGNED_MEDIUM_INT</td>\n",
       "      <td>2018-10-19 18:19:18</td>\n",
       "      <td>2020-06-17 11:58:02</td>\n",
       "      <td></td>\n",
       "      <td>36945</td>\n",
       "      <td>TM</td>\n",
       "      <td>NRUG1076</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>BERM-DG</td>\n",
       "      <td>UNSIGNED_MEDIUM_INT</td>\n",
       "      <td>2018-10-19 18:19:18</td>\n",
       "      <td>2020-06-17 11:57:46</td>\n",
       "      <td></td>\n",
       "      <td>37943</td>\n",
       "      <td>TM</td>\n",
       "      <td>NRUG9048</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>BERM ON Saved</td>\n",
       "      <td>DOUBLE</td>\n",
       "      <td>2020-04-25 04:08:06</td>\n",
       "      <td>2020-04-28 04:01:40</td>\n",
       "      <td>simbiosys</td>\n",
       "      <td>72129</td>\n",
       "      <td>TM</td>\n",
       "      <td>NSSPY038</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>BERM-A ON-PW Saved</td>\n",
       "      <td>DOUBLE</td>\n",
       "      <td>2020-04-25 04:08:06</td>\n",
       "      <td>2020-04-28 04:01:40</td>\n",
       "      <td>simbiosys</td>\n",
       "      <td>72148</td>\n",
       "      <td>TM</td>\n",
       "      <td>NSSPY057</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>BERM-B ON-PW Saved</td>\n",
       "      <td>DOUBLE</td>\n",
       "      <td>2020-04-25 04:08:06</td>\n",
       "      <td>2020-04-28 04:01:40</td>\n",
       "      <td>simbiosys</td>\n",
       "      <td>72149</td>\n",
       "      <td>TM</td>\n",
       "      <td>NSSPY058</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>MPO-TEMP-BERM-TRP-Raw</td>\n",
       "      <td>UNSIGNED_MEDIUM_INT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td></td>\n",
       "      <td>52658</td>\n",
       "      <td>TM</td>\n",
       "      <td>RKK19001</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>degC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>BERM LCL current-Raw</td>\n",
       "      <td>UNSIGNED_MEDIUM_INT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td></td>\n",
       "      <td>52659</td>\n",
       "      <td>TM</td>\n",
       "      <td>RKK19002</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>BERM-A ON-CT-Raw</td>\n",
       "      <td>UNSIGNED_MEDIUM_INT</td>\n",
       "      <td>2018-10-19 18:19:23</td>\n",
       "      <td>2020-06-17 11:58:23</td>\n",
       "      <td></td>\n",
       "      <td>53238</td>\n",
       "      <td>TM</td>\n",
       "      <td>RPWD2803</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>BERM-B ON-CT-Raw</td>\n",
       "      <td>UNSIGNED_MEDIUM_INT</td>\n",
       "      <td>2018-10-19 18:19:23</td>\n",
       "      <td>2020-06-17 11:58:23</td>\n",
       "      <td></td>\n",
       "      <td>53277</td>\n",
       "      <td>TM</td>\n",
       "      <td>RPWD3203</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>MPO-TEMP-BERM-TRP-Raw</td>\n",
       "      <td>UNSIGNED_MEDIUM_INT</td>\n",
       "      <td>2018-10-19 18:19:18</td>\n",
       "      <td>2020-06-17 11:58:02</td>\n",
       "      <td></td>\n",
       "      <td>53474</td>\n",
       "      <td>TM</td>\n",
       "      <td>RRUD1076</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>degC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>BERM-DG-Raw</td>\n",
       "      <td>UNSIGNED_MEDIUM_INT</td>\n",
       "      <td>2018-10-19 18:19:18</td>\n",
       "      <td>2020-06-17 11:57:46</td>\n",
       "      <td></td>\n",
       "      <td>54252</td>\n",
       "      <td>TM</td>\n",
       "      <td>RRUD9048</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>degC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Description            Data Type        First Sample  \\\n",
       "0               BERM Busy    UNSIGNED_TINY_INT 2018-10-25 18:02:29   \n",
       "1       MPO-TEMP-BERM-TRP               DOUBLE                 NaT   \n",
       "2        BERM LCL current               DOUBLE                 NaT   \n",
       "3               BERM mode  UNSIGNED_MEDIUM_INT                 NaT   \n",
       "4            BERM-A ON-ST    UNSIGNED_TINY_INT 2018-10-19 18:19:23   \n",
       "5            BERM-B ON-ST    UNSIGNED_TINY_INT 2018-10-19 18:19:23   \n",
       "6            BERM-A ON-CT               DOUBLE 2018-10-19 18:19:23   \n",
       "7            BERM-B ON-CT               DOUBLE 2018-10-19 18:19:23   \n",
       "8            BERM-A ON-CT  UNSIGNED_MEDIUM_INT 2018-10-19 18:19:23   \n",
       "9            BERM-B ON-CT  UNSIGNED_MEDIUM_INT 2018-10-19 18:19:23   \n",
       "10      MPO-TEMP-BERM-TRP               DOUBLE 2018-10-19 18:19:18   \n",
       "11                BERM-DG               DOUBLE 2018-10-19 18:19:18   \n",
       "12      MPO-TEMP-BERM-TRP  UNSIGNED_MEDIUM_INT 2018-10-19 18:19:18   \n",
       "13                BERM-DG  UNSIGNED_MEDIUM_INT 2018-10-19 18:19:18   \n",
       "14          BERM ON Saved               DOUBLE 2020-04-25 04:08:06   \n",
       "15     BERM-A ON-PW Saved               DOUBLE 2020-04-25 04:08:06   \n",
       "16     BERM-B ON-PW Saved               DOUBLE 2020-04-25 04:08:06   \n",
       "17  MPO-TEMP-BERM-TRP-Raw  UNSIGNED_MEDIUM_INT                 NaT   \n",
       "18   BERM LCL current-Raw  UNSIGNED_MEDIUM_INT                 NaT   \n",
       "19       BERM-A ON-CT-Raw  UNSIGNED_MEDIUM_INT 2018-10-19 18:19:23   \n",
       "20       BERM-B ON-CT-Raw  UNSIGNED_MEDIUM_INT 2018-10-19 18:19:23   \n",
       "21  MPO-TEMP-BERM-TRP-Raw  UNSIGNED_MEDIUM_INT 2018-10-19 18:19:18   \n",
       "22            BERM-DG-Raw  UNSIGNED_MEDIUM_INT 2018-10-19 18:19:18   \n",
       "\n",
       "           Last Sample  Subsystem     Id Parameter Type      Name    Provider  \\\n",
       "0  2020-04-24 19:09:06       berm   2866             TM  NBED0009  BEPICRUISE   \n",
       "1                  NaT             30150             TM  NKK19001  BEPICRUISE   \n",
       "2                  NaT             30151             TM  NKK19002  BEPICRUISE   \n",
       "3                  NaT             30152             TM  NKK19003  BEPICRUISE   \n",
       "4  2020-06-17 11:58:23             34093             TM  NPWD0403  BEPICRUISE   \n",
       "5  2020-06-17 11:58:23             34132             TM  NPWD0603  BEPICRUISE   \n",
       "6  2020-06-17 11:58:23             34381             TM  NPWD2803  BEPICRUISE   \n",
       "7  2020-06-17 11:58:23             34422             TM  NPWD3203  BEPICRUISE   \n",
       "8  2020-06-17 11:58:23             34601             TM  NPWU2803  BEPICRUISE   \n",
       "9  2020-06-17 11:58:23             34635             TM  NPWU3203  BEPICRUISE   \n",
       "10 2020-06-17 11:58:02             34937             TM  NRUD1076  BEPICRUISE   \n",
       "11 2020-06-17 11:57:46             36508             TM  NRUD9048  BEPICRUISE   \n",
       "12 2020-06-17 11:58:02             36945             TM  NRUG1076  BEPICRUISE   \n",
       "13 2020-06-17 11:57:46             37943             TM  NRUG9048  BEPICRUISE   \n",
       "14 2020-04-28 04:01:40  simbiosys  72129             TM  NSSPY038  BEPICRUISE   \n",
       "15 2020-04-28 04:01:40  simbiosys  72148             TM  NSSPY057  BEPICRUISE   \n",
       "16 2020-04-28 04:01:40  simbiosys  72149             TM  NSSPY058  BEPICRUISE   \n",
       "17                 NaT             52658             TM  RKK19001  BEPICRUISE   \n",
       "18                 NaT             52659             TM  RKK19002  BEPICRUISE   \n",
       "19 2020-06-17 11:58:23             53238             TM  RPWD2803  BEPICRUISE   \n",
       "20 2020-06-17 11:58:23             53277             TM  RPWD3203  BEPICRUISE   \n",
       "21 2020-06-17 11:58:02             53474             TM  RRUD1076  BEPICRUISE   \n",
       "22 2020-06-17 11:57:46             54252             TM  RRUD9048  BEPICRUISE   \n",
       "\n",
       "    Unit  \n",
       "0    NaN  \n",
       "1   degC  \n",
       "2      A  \n",
       "3    NaN  \n",
       "4    NaN  \n",
       "5    NaN  \n",
       "6      A  \n",
       "7      A  \n",
       "8    NaN  \n",
       "9    NaN  \n",
       "10  degC  \n",
       "11  degC  \n",
       "12   NaN  \n",
       "13   NaN  \n",
       "14   NaN  \n",
       "15     W  \n",
       "16     W  \n",
       "17  degC  \n",
       "18     A  \n",
       "19     A  \n",
       "20     A  \n",
       "21  degC  \n",
       "22  degC  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO 2020-06-17 16:30:38 (mustlink.mustlink): 144 parameters match search text: NBE\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mbentley/miniconda3/envs/bepi/lib/python3.6/site-packages/pandas/core/indexing.py:671: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    },
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Description</th>\n",
       "      <th>Data Type</th>\n",
       "      <th>First Sample</th>\n",
       "      <th>Last Sample</th>\n",
       "      <th>Subsystem</th>\n",
       "      <th>Id</th>\n",
       "      <th>Parameter Type</th>\n",
       "      <th>Name</th>\n",
       "      <th>Provider</th>\n",
       "      <th>Unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BC Status Word</td>\n",
       "      <td>UNSIGNED_MEDIUM_INT</td>\n",
       "      <td>2018-10-25 18:17:05</td>\n",
       "      <td>2019-03-29 19:36:15</td>\n",
       "      <td>berm</td>\n",
       "      <td>2855</td>\n",
       "      <td>TM</td>\n",
       "      <td>NBE22911</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1553 bus type</td>\n",
       "      <td>UNSIGNED_TINY_INT</td>\n",
       "      <td>2018-10-25 18:17:05</td>\n",
       "      <td>2019-03-29 19:36:15</td>\n",
       "      <td>berm</td>\n",
       "      <td>2856</td>\n",
       "      <td>TM</td>\n",
       "      <td>NBE22912</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Number of data words</td>\n",
       "      <td>UNSIGNED_MEDIUM_INT</td>\n",
       "      <td>2018-10-25 18:17:05</td>\n",
       "      <td>2019-03-29 19:36:15</td>\n",
       "      <td>berm</td>\n",
       "      <td>2857</td>\n",
       "      <td>TM</td>\n",
       "      <td>NBE22914</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CMD Counter</td>\n",
       "      <td>UNSIGNED_MEDIUM_INT</td>\n",
       "      <td>2018-10-25 18:02:29</td>\n",
       "      <td>2020-04-24 19:09:06</td>\n",
       "      <td>berm</td>\n",
       "      <td>2858</td>\n",
       "      <td>TM</td>\n",
       "      <td>NBED0001</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CMD Size</td>\n",
       "      <td>UNSIGNED_TINY_INT</td>\n",
       "      <td>2018-10-25 18:02:29</td>\n",
       "      <td>2020-04-24 19:09:06</td>\n",
       "      <td>berm</td>\n",
       "      <td>2859</td>\n",
       "      <td>TM</td>\n",
       "      <td>NBED0002</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Description            Data Type        First Sample  \\\n",
       "0        BC Status Word  UNSIGNED_MEDIUM_INT 2018-10-25 18:17:05   \n",
       "1         1553 bus type    UNSIGNED_TINY_INT 2018-10-25 18:17:05   \n",
       "2  Number of data words  UNSIGNED_MEDIUM_INT 2018-10-25 18:17:05   \n",
       "3           CMD Counter  UNSIGNED_MEDIUM_INT 2018-10-25 18:02:29   \n",
       "4              CMD Size    UNSIGNED_TINY_INT 2018-10-25 18:02:29   \n",
       "\n",
       "          Last Sample Subsystem    Id Parameter Type      Name    Provider  \\\n",
       "0 2019-03-29 19:36:15      berm  2855             TM  NBE22911  BEPICRUISE   \n",
       "1 2019-03-29 19:36:15      berm  2856             TM  NBE22912  BEPICRUISE   \n",
       "2 2019-03-29 19:36:15      berm  2857             TM  NBE22914  BEPICRUISE   \n",
       "3 2020-04-24 19:09:06      berm  2858             TM  NBED0001  BEPICRUISE   \n",
       "4 2020-04-24 19:09:06      berm  2859             TM  NBED0002  BEPICRUISE   \n",
       "\n",
       "  Unit  \n",
       "0  NaN  \n",
       "1  NaN  \n",
       "2  NaN  \n",
       "3  NaN  \n",
       "4  NaN  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "must.search_parameter('NBE', search_by='name').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having identified a parameter of interest, the `get_data()` function will return parameter values for a given time range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mmust\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mparam_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mstart_time\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mstop_time\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mprovider\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcalib\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmax_pts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Requests data for a given parameter and time-range. Minimal checking is \n",
       "currently performed on the times and return codes. Data are formatted into\n",
       "a Pandas DataFrame with time conversion to UTC performed\n",
       "\u001b[0;31mFile:\u001b[0m      ~/Dropbox/work/bepi/software/mustlink/mustlink/mustlink.py\n",
       "\u001b[0;31mType:\u001b[0m      method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "must.get_data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO 2020-06-17 16:30:20 (mustlink.mustlink): 8038 values retrieved\n",
      "CPU times: user 54 ms, sys: 5.12 ms, total: 59.1 ms\n",
      "Wall time: 307 ms\n"
     ]
    }
   ],
   "source": [
    "%time data = must.get_data(param_name='NRUG1076', start_time='2019-02-01 00:00:00', stop_time='2019-02-28 00:00:00', calib=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = data.plot(figsize=(14,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO 2020-06-17 16:30:47 (mustlink.mustlink): 60 parameters match search text: speed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mbentley/miniconda3/envs/bepi/lib/python3.6/site-packages/pandas/core/indexing.py:671: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Description</th>\n",
       "      <th>Data Type</th>\n",
       "      <th>First Sample</th>\n",
       "      <th>Last Sample</th>\n",
       "      <th>Subsystem</th>\n",
       "      <th>Id</th>\n",
       "      <th>Parameter Type</th>\n",
       "      <th>Name</th>\n",
       "      <th>Provider</th>\n",
       "      <th>Unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Speed Profile S value</td>\n",
       "      <td>UNSIGNED_INT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>4206</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCAC0065</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Speed Profile duration</td>\n",
       "      <td>UNSIGNED_INT</td>\n",
       "      <td>2018-10-19 18:20:54</td>\n",
       "      <td>2020-06-17 07:13:02</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>9964</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCADAB94</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Speed Profile duration</td>\n",
       "      <td>UNSIGNED_INT</td>\n",
       "      <td>2018-10-19 18:20:54</td>\n",
       "      <td>2020-06-17 07:13:02</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>9968</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCADABA4</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Speed Profile duration</td>\n",
       "      <td>UNSIGNED_INT</td>\n",
       "      <td>2018-10-19 18:20:54</td>\n",
       "      <td>2020-06-17 07:13:02</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>9972</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCADABB4</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Speed Profile duration</td>\n",
       "      <td>UNSIGNED_INT</td>\n",
       "      <td>2018-10-19 18:20:54</td>\n",
       "      <td>2020-06-17 07:13:02</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>9976</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCADABC4</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Speed limitation state</td>\n",
       "      <td>UNSIGNED_INT</td>\n",
       "      <td>2018-10-19 18:19:16</td>\n",
       "      <td>2020-06-17 11:58:20</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>12052</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCAT4540</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Speed limitation state</td>\n",
       "      <td>UNSIGNED_INT</td>\n",
       "      <td>2018-10-19 18:19:16</td>\n",
       "      <td>2020-06-17 11:58:20</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>12068</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCAT4B70</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Is trq cmd / speed cmd</td>\n",
       "      <td>UNSIGNED_TINY_INT</td>\n",
       "      <td>2018-10-19 18:19:16</td>\n",
       "      <td>2020-06-17 11:58:04</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>12093</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCAT6B20</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Filt dur nz speed surv</td>\n",
       "      <td>FLOAT</td>\n",
       "      <td>2018-10-19 18:20:54</td>\n",
       "      <td>2020-06-17 07:13:02</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>12107</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCAT7220</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Filt dur over speed surv</td>\n",
       "      <td>FLOAT</td>\n",
       "      <td>2018-10-19 18:20:54</td>\n",
       "      <td>2020-06-17 07:13:02</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>12108</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCAT7230</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>SG1_Speed RW status</td>\n",
       "      <td>UNSIGNED_INT</td>\n",
       "      <td>2018-10-20 00:20:11</td>\n",
       "      <td>2020-06-17 11:58:22</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>12328</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCATA340</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>SG2_Speed RW status</td>\n",
       "      <td>UNSIGNED_INT</td>\n",
       "      <td>2018-10-20 00:20:11</td>\n",
       "      <td>2020-06-17 11:58:22</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>12329</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCATA350</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Rw1 speed</td>\n",
       "      <td>FLOAT</td>\n",
       "      <td>2018-10-20 00:21:51</td>\n",
       "      <td>2020-06-17 11:58:39</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>12378</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCATABD0</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>rd/s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Rw2 speed</td>\n",
       "      <td>FLOAT</td>\n",
       "      <td>2018-10-20 00:21:51</td>\n",
       "      <td>2020-06-17 11:58:39</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>12379</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCATABF0</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>rd/s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Rw3 speed</td>\n",
       "      <td>FLOAT</td>\n",
       "      <td>2018-10-20 00:21:51</td>\n",
       "      <td>2020-06-17 11:58:39</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>12380</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCATAC10</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>rd/s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Rw4 speed</td>\n",
       "      <td>FLOAT</td>\n",
       "      <td>2018-10-20 00:21:51</td>\n",
       "      <td>2020-06-17 11:58:39</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>12381</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCATAC30</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>rd/s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>SG1speed thd min rw1</td>\n",
       "      <td>FLOAT</td>\n",
       "      <td>2018-10-22 13:45:03</td>\n",
       "      <td>2020-04-24 01:20:05</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>12386</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCATAD10</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>Nms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>SG1speed thd min rw2</td>\n",
       "      <td>FLOAT</td>\n",
       "      <td>2018-10-22 13:45:03</td>\n",
       "      <td>2020-04-24 01:20:05</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>12387</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCATAD20</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>Nms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>SG1speed thd min rw3</td>\n",
       "      <td>FLOAT</td>\n",
       "      <td>2018-10-22 13:45:03</td>\n",
       "      <td>2020-04-24 01:20:05</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>12388</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCATAD30</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>Nms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>SG1speed thd min rw4</td>\n",
       "      <td>FLOAT</td>\n",
       "      <td>2018-10-22 13:45:03</td>\n",
       "      <td>2020-04-24 01:20:05</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>12389</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCATAD40</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>Nms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>SG1speed thd max rw1</td>\n",
       "      <td>FLOAT</td>\n",
       "      <td>2018-10-22 13:45:03</td>\n",
       "      <td>2020-04-24 01:20:05</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>12390</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCATAD50</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>Nms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>SG1speed thd max rw2</td>\n",
       "      <td>FLOAT</td>\n",
       "      <td>2018-10-22 13:45:03</td>\n",
       "      <td>2020-04-24 01:20:05</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>12391</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCATAD60</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>Nms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>SG1speed thd max rw3</td>\n",
       "      <td>FLOAT</td>\n",
       "      <td>2018-10-22 13:45:03</td>\n",
       "      <td>2020-04-24 01:20:05</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>12392</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCATAD70</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>Nms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>SG1speed thd max rw4</td>\n",
       "      <td>FLOAT</td>\n",
       "      <td>2018-10-22 13:45:03</td>\n",
       "      <td>2020-04-24 01:20:05</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>12393</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCATAD80</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>Nms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>SG2speed thd pos rw1</td>\n",
       "      <td>FLOAT</td>\n",
       "      <td>2018-10-22 13:45:03</td>\n",
       "      <td>2020-04-24 01:20:05</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>12394</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCATAD90</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>Nms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>SG2speed thd pos rw2</td>\n",
       "      <td>FLOAT</td>\n",
       "      <td>2018-10-22 13:45:03</td>\n",
       "      <td>2020-04-24 01:20:05</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>12395</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCATADA0</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>Nms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SG2speed thd pos rw3</td>\n",
       "      <td>FLOAT</td>\n",
       "      <td>2018-10-22 13:45:03</td>\n",
       "      <td>2020-04-24 01:20:05</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>12396</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCATADB0</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>Nms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>SG2speed thd pos rw4</td>\n",
       "      <td>FLOAT</td>\n",
       "      <td>2018-10-22 13:45:03</td>\n",
       "      <td>2020-04-24 01:20:05</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>12397</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCATADC0</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>Nms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>SG2speed thd neg rw1</td>\n",
       "      <td>FLOAT</td>\n",
       "      <td>2018-10-22 13:45:03</td>\n",
       "      <td>2020-04-24 01:20:05</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>12398</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCATADD0</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>Nms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>SG2speed thd neg rw2</td>\n",
       "      <td>FLOAT</td>\n",
       "      <td>2018-10-22 13:45:03</td>\n",
       "      <td>2020-04-24 01:20:05</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>12399</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCATADE0</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>Nms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>SG2speed thd neg rw3</td>\n",
       "      <td>FLOAT</td>\n",
       "      <td>2018-10-22 13:45:03</td>\n",
       "      <td>2020-04-24 01:20:05</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>12400</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCATADF0</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>Nms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>SG2speed thd neg rw4</td>\n",
       "      <td>FLOAT</td>\n",
       "      <td>2018-10-22 13:45:03</td>\n",
       "      <td>2020-04-24 01:20:05</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>12401</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCATAE00</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>Nms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Is Wheel speed held TTP</td>\n",
       "      <td>UNSIGNED_TINY_INT</td>\n",
       "      <td>2018-10-22 04:45:29</td>\n",
       "      <td>2020-02-26 11:59:57</td>\n",
       "      <td>AOCS</td>\n",
       "      <td>12729</td>\n",
       "      <td>TM</td>\n",
       "      <td>NCATE220</td>\n",
       "      <td>BEPICRUISE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Description          Data Type        First Sample  \\\n",
       "0      Speed Profile S value       UNSIGNED_INT                 NaT   \n",
       "1     Speed Profile duration       UNSIGNED_INT 2018-10-19 18:20:54   \n",
       "2     Speed Profile duration       UNSIGNED_INT 2018-10-19 18:20:54   \n",
       "3     Speed Profile duration       UNSIGNED_INT 2018-10-19 18:20:54   \n",
       "4     Speed Profile duration       UNSIGNED_INT 2018-10-19 18:20:54   \n",
       "5     Speed limitation state       UNSIGNED_INT 2018-10-19 18:19:16   \n",
       "6     Speed limitation state       UNSIGNED_INT 2018-10-19 18:19:16   \n",
       "7     Is trq cmd / speed cmd  UNSIGNED_TINY_INT 2018-10-19 18:19:16   \n",
       "8     Filt dur nz speed surv              FLOAT 2018-10-19 18:20:54   \n",
       "9   Filt dur over speed surv              FLOAT 2018-10-19 18:20:54   \n",
       "10       SG1_Speed RW status       UNSIGNED_INT 2018-10-20 00:20:11   \n",
       "11       SG2_Speed RW status       UNSIGNED_INT 2018-10-20 00:20:11   \n",
       "12                 Rw1 speed              FLOAT 2018-10-20 00:21:51   \n",
       "13                 Rw2 speed              FLOAT 2018-10-20 00:21:51   \n",
       "14                 Rw3 speed              FLOAT 2018-10-20 00:21:51   \n",
       "15                 Rw4 speed              FLOAT 2018-10-20 00:21:51   \n",
       "16      SG1speed thd min rw1              FLOAT 2018-10-22 13:45:03   \n",
       "17      SG1speed thd min rw2              FLOAT 2018-10-22 13:45:03   \n",
       "18      SG1speed thd min rw3              FLOAT 2018-10-22 13:45:03   \n",
       "19      SG1speed thd min rw4              FLOAT 2018-10-22 13:45:03   \n",
       "20      SG1speed thd max rw1              FLOAT 2018-10-22 13:45:03   \n",
       "21      SG1speed thd max rw2              FLOAT 2018-10-22 13:45:03   \n",
       "22      SG1speed thd max rw3              FLOAT 2018-10-22 13:45:03   \n",
       "23      SG1speed thd max rw4              FLOAT 2018-10-22 13:45:03   \n",
       "24      SG2speed thd pos rw1              FLOAT 2018-10-22 13:45:03   \n",
       "25      SG2speed thd pos rw2              FLOAT 2018-10-22 13:45:03   \n",
       "26      SG2speed thd pos rw3              FLOAT 2018-10-22 13:45:03   \n",
       "27      SG2speed thd pos rw4              FLOAT 2018-10-22 13:45:03   \n",
       "28      SG2speed thd neg rw1              FLOAT 2018-10-22 13:45:03   \n",
       "29      SG2speed thd neg rw2              FLOAT 2018-10-22 13:45:03   \n",
       "30      SG2speed thd neg rw3              FLOAT 2018-10-22 13:45:03   \n",
       "31      SG2speed thd neg rw4              FLOAT 2018-10-22 13:45:03   \n",
       "32   Is Wheel speed held TTP  UNSIGNED_TINY_INT 2018-10-22 04:45:29   \n",
       "\n",
       "           Last Sample Subsystem     Id Parameter Type      Name    Provider  \\\n",
       "0                  NaT      AOCS   4206             TM  NCAC0065  BEPICRUISE   \n",
       "1  2020-06-17 07:13:02      AOCS   9964             TM  NCADAB94  BEPICRUISE   \n",
       "2  2020-06-17 07:13:02      AOCS   9968             TM  NCADABA4  BEPICRUISE   \n",
       "3  2020-06-17 07:13:02      AOCS   9972             TM  NCADABB4  BEPICRUISE   \n",
       "4  2020-06-17 07:13:02      AOCS   9976             TM  NCADABC4  BEPICRUISE   \n",
       "5  2020-06-17 11:58:20      AOCS  12052             TM  NCAT4540  BEPICRUISE   \n",
       "6  2020-06-17 11:58:20      AOCS  12068             TM  NCAT4B70  BEPICRUISE   \n",
       "7  2020-06-17 11:58:04      AOCS  12093             TM  NCAT6B20  BEPICRUISE   \n",
       "8  2020-06-17 07:13:02      AOCS  12107             TM  NCAT7220  BEPICRUISE   \n",
       "9  2020-06-17 07:13:02      AOCS  12108             TM  NCAT7230  BEPICRUISE   \n",
       "10 2020-06-17 11:58:22      AOCS  12328             TM  NCATA340  BEPICRUISE   \n",
       "11 2020-06-17 11:58:22      AOCS  12329             TM  NCATA350  BEPICRUISE   \n",
       "12 2020-06-17 11:58:39      AOCS  12378             TM  NCATABD0  BEPICRUISE   \n",
       "13 2020-06-17 11:58:39      AOCS  12379             TM  NCATABF0  BEPICRUISE   \n",
       "14 2020-06-17 11:58:39      AOCS  12380             TM  NCATAC10  BEPICRUISE   \n",
       "15 2020-06-17 11:58:39      AOCS  12381             TM  NCATAC30  BEPICRUISE   \n",
       "16 2020-04-24 01:20:05      AOCS  12386             TM  NCATAD10  BEPICRUISE   \n",
       "17 2020-04-24 01:20:05      AOCS  12387             TM  NCATAD20  BEPICRUISE   \n",
       "18 2020-04-24 01:20:05      AOCS  12388             TM  NCATAD30  BEPICRUISE   \n",
       "19 2020-04-24 01:20:05      AOCS  12389             TM  NCATAD40  BEPICRUISE   \n",
       "20 2020-04-24 01:20:05      AOCS  12390             TM  NCATAD50  BEPICRUISE   \n",
       "21 2020-04-24 01:20:05      AOCS  12391             TM  NCATAD60  BEPICRUISE   \n",
       "22 2020-04-24 01:20:05      AOCS  12392             TM  NCATAD70  BEPICRUISE   \n",
       "23 2020-04-24 01:20:05      AOCS  12393             TM  NCATAD80  BEPICRUISE   \n",
       "24 2020-04-24 01:20:05      AOCS  12394             TM  NCATAD90  BEPICRUISE   \n",
       "25 2020-04-24 01:20:05      AOCS  12395             TM  NCATADA0  BEPICRUISE   \n",
       "26 2020-04-24 01:20:05      AOCS  12396             TM  NCATADB0  BEPICRUISE   \n",
       "27 2020-04-24 01:20:05      AOCS  12397             TM  NCATADC0  BEPICRUISE   \n",
       "28 2020-04-24 01:20:05      AOCS  12398             TM  NCATADD0  BEPICRUISE   \n",
       "29 2020-04-24 01:20:05      AOCS  12399             TM  NCATADE0  BEPICRUISE   \n",
       "30 2020-04-24 01:20:05      AOCS  12400             TM  NCATADF0  BEPICRUISE   \n",
       "31 2020-04-24 01:20:05      AOCS  12401             TM  NCATAE00  BEPICRUISE   \n",
       "32 2020-02-26 11:59:57      AOCS  12729             TM  NCATE220  BEPICRUISE   \n",
       "\n",
       "    Unit  \n",
       "0    NaN  \n",
       "1    NaN  \n",
       "2    NaN  \n",
       "3    NaN  \n",
       "4    NaN  \n",
       "5    NaN  \n",
       "6    NaN  \n",
       "7    NaN  \n",
       "8      s  \n",
       "9      s  \n",
       "10   NaN  \n",
       "11   NaN  \n",
       "12  rd/s  \n",
       "13  rd/s  \n",
       "14  rd/s  \n",
       "15  rd/s  \n",
       "16   Nms  \n",
       "17   Nms  \n",
       "18   Nms  \n",
       "19   Nms  \n",
       "20   Nms  \n",
       "21   Nms  \n",
       "22   Nms  \n",
       "23   Nms  \n",
       "24   Nms  \n",
       "25   Nms  \n",
       "26   Nms  \n",
       "27   Nms  \n",
       "28   Nms  \n",
       "29   Nms  \n",
       "30   Nms  \n",
       "31   Nms  \n",
       "32   NaN  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = must.search_parameter('speed')\n",
    "params[params.Subsystem=='AOCS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO 2020-06-17 16:30:51 (mustlink.mustlink): parameter info for Rw1 speed extracted\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Description                 Rw1 speed\n",
       "Data Type                       FLOAT\n",
       "First Sample      2018-10-20 00:21:51\n",
       "Last Sample       2020-06-17 11:58:39\n",
       "Subsystem                        AOCS\n",
       "Id                              12378\n",
       "Unit                             rd/s\n",
       "Parameter Type                     TM\n",
       "Name                         NCATABD0\n",
       "Provider                   BEPICRUISE\n",
       "dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "must.get_param_info('NCATABD0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO 2020-06-17 16:31:20 (mustlink.mustlink): 546525 values retrieved\n",
      "CPU times: user 1.87 s, sys: 247 ms, total: 2.12 s\n",
      "Wall time: 12.6 s\n"
     ]
    }
   ],
   "source": [
    "%time data = must.get_data(param_name='NCATABD0', start_time='2019-02-01 00:00:00', stop_time='2019-02-28 00:00:00', calib=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 546525 entries, 2019-02-01 00:00:09.678000 to 2019-02-27 23:59:53.237000\n",
      "Data columns (total 1 columns):\n",
      " #   Column    Non-Null Count   Dtype  \n",
      "---  ------    --------------   -----  \n",
      " 0   NCATABD0  546525 non-null  float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 8.3 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = data.plot(figsize=(14,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This returned a *lot* of values. If you don't need them all, you can limit the returned parameters using `max_pts`. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO 2020-06-17 16:32:38 (mustlink.mustlink): 2000 values retrieved\n",
      "CPU times: user 30.5 ms, sys: 2.87 ms, total: 33.4 ms\n",
      "Wall time: 5.01 s\n"
     ]
    }
   ],
   "source": [
    "%time data = must.get_data(param_name='NCATABD0', start_time='2019-02-01 00:00:00', stop_time='2019-02-28 00:00:00', calib=False, max_pts=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2000 entries, 2019-02-01 00:07:59.678000 to 2019-02-27 23:59:13.237000\n",
      "Data columns (total 1 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   NCATABD0  2000 non-null   float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 31.2 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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b+6LHa8vCb/WGbmeeTfgSKYVoDpVet1DU2ESLUkBfKX0fn9UbugEArR29OOuIKdh/4mjnuQghQxcKH0IIIbnHtdCuhtdWbAAAvFp2bIBYQISODhCETwFAd28JnT1FAEBbZ29ivDUbe6L5bSyLhPauvkQ7SaEsApQSIiGl3bDG4E98jwhdq8b7ct2zWGh4aCyrjUpV05rK7fpKPlyVpcPrKykV5WZVcmFK5YYFS0M/4+t/7jF7ZGqXN4olH5ff+ybefj8pXgnJExQ+hBBCSEbCdXXWhTQA9JRD14Y12P/kSiFQzZ5D4TI/bTqh8Okt+fBQvdPlEieh0GgQQqOSK9NYvv5iSTnvXziOr+J2leYfDtdgmUNfhsIGE7ffBmNGDqvYLo/c9OxS3PTsUvxWOIKE5BEKH0IIIbmnVjk+WhiWQxnIhXqYsxMKkTSqEVJe6g/J/rHjk23/GkB3eVwCTEWCJDuNDWXHx/edm4k2hOLSjx0fl6byy0UQgNgtMrGdb3Nv7Ib9+V8Ot58kxxRLPn5Qzs+ausO2AzwbQvqXDyx8PM/bxfO8WZ7nLfA8703P884rPz/W87xHPc9bWP53jOhzied573qe97bneX//QedACCGEbA1CN0LBXiXMJNw/psERr1WN8ElrmdY9dD+KLutGGyOWOp7nOUuAh0NWIyibCtkcnyjUzVexwHIIn6Ifj2cLddPC8MSpv1UulAAEjs9Q4tH5a3DjM0s+8DiXiep3HzAFjpBBTy0cnyKA7yql9gXwUQDnep63H4CLATyulNoTwOPln1E+9gUA0wCcCOBXnuc11GAehBBCSL/iiVA3l2sRklUYBPk6W7DqdAgQlfLIRdFXUVMP7r2PQqFRjWALRUnJd9+7sJ2YjtXJCedQKReomBLq9peXV+Dxt9YCAG748owKsx9cdPQU8Y1b5uD7981PHFNK4a65K7A+JafMZHNvCX98YZnoW9NpEjLo+MDCRym1Win1SvnxJgALAEwEcAqAm8vNbgbw6fLjUwD8SSnVo5RaAuBdAId+0HkQQgghNmq1oItC2JTbSalWw2QRUWlEzovjAuUh130ollQU3uZ5blEThvlJcVINrsttiIo3iByfCo5PqYKrZt7fYsnHv/2/oGLew+d/HJ/Yd0ft+GBf/3975lzrsVuffw8X3P5qajW+7r4SFrd0RD9fWi4hft4n9gTgDt8kJA/UNMfH87zJAD4C4AUAOyqlVgOBOAKwQ7nZRADLRbcV5efSxjvb87w5nufNaWlpSWtCCCGEbDUKse5BKUPCPJBNBFXjnMhxSxkEj8o4Bxm658FzLv7D9bFcKFc6hQxbcy2wyxFxRlU3++glXxZBSBeBfaX42hSAj/7ocQDAWUdMwd4fGqW1rXQdvq9w5UNvRZu5ZmX5ui58/743USxlz7lKY83GbjxRdqrM8DylFK4slzMfNbwx0fczv3oWx/7sSQDAus5e3PHKCozepgnfDoUPdQ/JOTUTPp7nbQvgDgDnK6VcnwZpnympv2pKqd8ppWYopWZMmDChFtMkhBBSl9RmRReFuvkqc45Plhn4GV2Z4HjcIBQQri5ZNVWxpAx3yOH4yCIIVd5aD55TsBW8ONQtiyD0faWV9U7rIh2fJa2daO0IwsAu+od9sk+8zJ1zV+JXf1uE3z25uKp+R145Czc+sxTvretKHJu3vB1vrtqQaZxTy5u+ThjVnHiNrnn0HWzqDsqnm6GTS1o7sWB1sDxTSuH4qwMBdN0/fWSLqhUSMhSpifDxPK8Jgej5o1LqzvLTazzP26l8fCcAa8vPrwCwi+g+CcCqWsyDEEIIcfFBc7fDUDeFbOFpSmU755buMxS5Oindw7A1WZ3NHerma1XUXJe3JTk+af3TiISPrzLl+BR9pVXRSxs7LSzx6QuPQZOjxHgaJV9FIXKTx4/M3G/hGvv+OL1FH5/+5TM46drZFcdZ3NKBle2bAQBH7zVBE7zdfSVc+8S70c/mffjxgwuix4taOqJ9pY7ccwI8z4Pn1W6/K0IGK7Wo6uYBuAHAAqXU1eLQvQC+XH78ZQD3iOe/4Hles+d5UwDsCeDFDzoPQgghxEbNcnxEOeswYT4tr0Su06NTZ9gQtFrCxW3WHB9XW4VYJHnhE9bz6v9WQim9NLXLLGsQRRCyVHUz3ae0KZnFDc46Ygp2GTsi09wlD76xOnpcaVNVybmiepx5+/+/P75s7ff0whYccsVjaO0INroNc3Ju/tqhZaESt72uLHpOmb4zAP21Wd/ZG220CyAqivDYdz4ursdjqBvJPbVwfP4OwJcAHOt53rzyf58E8GMAx3uetxDA8eWfoZR6E8CfAcwH8BCAc5VSpfShCSGEkMFD2uaalcjSzFxwZvnmXRYgcIfR6UdtQysFw/FxjBoJH5Hj4xQn8WMP7tykgrjHWXJ8iiLUzZxT3EZXWheeWH2IW7Hk41u32YsKhHT0FLUcppffW4931nRg+xFNibYtm3rw2IIgIObw3ccljp/5+xfRsqkHS1s78cRba/DsojYct++OOGqvCeU8rOA8m3tLuG5WIHz++1P7A9DfQxfd8Rq6eks49SNBSvXTC1tx6kETsccOcX5TwfGad/QUK143IUOBWlR1m62U8pRSByqlppf/+6tSqk0p9Qml1J7lf9eJPlcopaYqpfZWSj34QedACCGEuKjVF9ly+R2HmTnclrKPUmkOSqnUsSsRra/TQt1EGJwXNbNvS6ofcxc3iIrbKZVJpPlKH91Z3KA8dlC0QDtd+thiLOksScxQN9dmsja+8+cgxO3je9lzjjds7sP+lz2Mnz36djSfz/46yMm5JMoniufyw7/G4Wemtnt37SbtWr520xwAwI9OPSBqHx7/zK+eAQBcetK+aGgIq+IFxx58fTUemb8Gh+8+DvvsFAudiwzx51kcn9/PXoL9L3sYy1NykwgZatS0qhshhBCSZwpywZ+xz5Y4PtndpMqiytYn+bzu+GQTNFnPGT+u5CZJLReXs66U4xP3SRU+GSvw2egt+rj31SAd+bdfPNjaLtxQdN7ydgDAU+XwsinjR2LEsKDKWji/tRu7cdfclQCAQyaP0cZRSuG4q5+Kfn7ojfcBAB+bOg4TRjUDKL9GAN5ZswlvvR/kEH39iCnRvfDLovScPwZhdleedmDknB2553jsuN1w7ZyFlNdcKYX/vj8Ii1vfVXlfIEIGOxQ+hBBCSEY8UXEsi+iw5ZyYmEIgq6Bw7eNjK3xgd3xivApziIRG5nnqDbMWhgjdHFcYXclX0esS3O/KoW7V8uc5wS4cN371EDQ2pE9mU3cffj87ED5777gdAOD6p4PKbw98+wit8hwAHPrDoKT27750cDT3kB8/9JY29vXlca887UDxrAelgF//bREA4I9nHQbP8yJxo4Co7PWhk8dil7EjsKzs2nw0JawuyPHR792//+U17biku6+0xblphAwUFD6EEEJyT82LG4jlddrYYZWxklKZNoU0F5BZHBHZzim+jKP2HB89LM/laUVlvYXz5cHLXGTBeX2hYIOKcoFcoW7Je5dsE4a6jd6mCcOqrOS2vrM3KipwtCPM7cgrZ2FjuZS0rxSufuRtPL2wFReduA9GDGuMKwKqIIwt5IRpH9LydXxf4bflUtm3n/3RqN05R0/FpDFxQQbPA1o7enDX3JU4fcYu+Ls9xkfPA8F9ueTO1wEA1/3zRwAAI5sD1+molOswixt095Xwl5dXRD9rr5+vsM/3HnIWZiBkMJLc3YoQQgghqeiOT+X2MiTOWSvAdGWqdFKqGdsmOmSomy1XBijvm4NkAQLXPPQiCJWqh8XXFIe62VtL4SPD47Q25VC3G748AzMmj3WdPMHtZbfnh585oPz6J8d/YXEb2rv6AADbj2jC5t4Sbnp2KQDgnz+6a+IawjC2O845PHhC5OuE+UEX/8M+kVABgPOP21M7p7wlZx+1e/x8+cD1Ty/G+q4+nP3x3bHDqCCs7bxP7IkT9tsR+08cnbgGMwTxojsCt2fvHUfh7TWbNCH8t3cCJ+nhN9ckxiFkMEPHhxBCSO7JnpHjRg/xyuLkZM3xMVyZjPPNEsGloJfhrtQ2mI+9bUkpbTxtQ1VLJ98I+XOFSMWOT/K59LHlfGzFDYIblVZ63DWHto4e/PjBt3D47uPwT4ftmtoGAE7/3fMAgFn/djQKnheJpa/+3WRsN1yv5jb73bis9MG7BSIslFNdvUX8clYQunbm4btp+ww1NzZo42zYHAutqRO2jZ4PQ9LWl4XYvx67R3RseFMDPrKrnk8k+4XXtK6zF/fMC3Ka/v3v99au1/dVpkIPhAxGKHwIIYTUDS7nIAtxnoPdEZHnKflxUJxLzMjFe/BzejspMjx4ItTNJSSyhrrp87GJmJIoJiBD3QB7mWptHx9UED4p83Y5ROYGpmnhdn1lxyfThqXihfjiDcE2g2cdOSU+bDR/YXEbAGDaztthyviR6C3GavS//nG/RL//KRcLePrCYxKnPO3XzwEArvrchzFiWCOayvlEp8+Q+74HPPl2CwDglq8dqj0vc3FOPWgiRg1PltFOo+DFr8tHfxTkH/3iC9NRMG7ZZfe+GblbjdVsZkTIIIDChxBCSO6pVY5PiO87igSIk6mM1Q2SxQ3s4Whp7dyOiDGGZUIKcWlq06GxzU2Go8lwLeccKu0RJOcUNUu2l2WvdQcqOU61jg8ALG3txILVG/Gh7YbjE/vuaG0Xuj1Xf346gHjPm3OOnqpVo5PCdvou2yc2UN3UXcT81RsBAJ89KNhvZ/cJ2+K2sw7DDz6zf+K8H95lewDAtJ31sDV5hVd+9kBkpaEQCOkV67si8XbK9InR8dCR+sPz7wEApk4Ymfl1JGSwwBwfQgghJCPSvcninJQy5vgkxIklhM1W/S1t6FiAZcvDMZPXXYUK5EauWYoWBLlO2RwcJcRcdL9T2nvljWykA2WbQ1jOuhqHIqyW9oevH2pt8/TCwHXZYVQz9v7QKO3YheUQMTHj6NHvv3KIccTDgrLoufErh2iC6WPlogUmv/nSwWjv6k2IubDrTqOHo7GKQg5h7tXRP/0bgCAfKpwbELwuZ90c7CX07WP3wFMLWzNXHyRksEDhQwghJPfU6ovpcKHnK5dzorevNsdHOcLMzPEiceIUEnoImtWpMubjEkhxqJsoQAB7CJs5VqZQNyHY0loXPKAEs7hB+qsStskqBFo7evDUwhacceiu2HPHUdZ2XyqHwj143pGJY+beQ0taOwEAR+89AWNHDtOO9ZVipZtWcS2NbZsbsW1zchnneR6uP3MGDpyULGDgouABr69sjyrgRS5X+TJaNvXg2UVBWN93Ttgbs99tzbTXEyGDCQofQgghJCNKLPKzVDDzZY6Py/ExHB678LE5Psn2SvwrnSH7BqYqU9U6BUSLYXMjV5ue0aq6IblfjD6P+F9ZZc7EK5cEMEPd0sbuC4VPRsfnzVWB+3L6IcncmpBflR2h6btsj3HbNkfP//cp0zB53MhE+7Ejg1ybr3xscuLYnPfWAwj2CSrUIG/muP3soXk2Cp6HN1YG1z3r346Ong9nc+MzSwEAt33jsKg9Q93IUIPChxBCCMmI3FAzU6ib7y6CEI1rETSusWW/tHPoAiKb4yOLFrjC1qTjk8hpSsEUUq6NSWVooDKe09vFYYfRBqZIvxelsqNi23xU8l5b4MzsPHo4PuxwTTb3lQAkQ+HOPHxyavvPz9gFB0zcHvvtvJ11zI9NTW4surVYvaEbAPD5GZMwZXws3MJ7+9ziNhy/34742NQg9K7geZmqChIymKDwIYQQkntqVc46FCRyw8m0c8kcmCzfivtKz1Oxja3vh5NxA9NyHkz0s2WxqhT0stC28WDP8bGGukGft636m3kelVHYyTLj6cUNgiezFDcIK5b97swZiXA1k88dPClz1TTP86yi539OmYb2rr5EyeqB4Pzj9rIe++bH9f2C6PiQoQaFDyGEEJIRuaFm1rLQWZaGieIGjpAxldIuzWmJHRF9/AqSJpqPPSROfxyJQS+bU+XBvf9QLHZUBcdH/KuVAk+2DoVPk1mb2YFZdS2N//l0stralvAli0s0EOw0erj2c3g/x4xo0jZ/LXieM1eLkKZjnKsAACAASURBVMEIy1kTQgjJPbX6YlqJRX4WfIVohe6aQtZy1uY6M8vCU6k4tCz82dYuvK7AqbINaDhDmULd9Hah4+O6jSr6n0XYCYGU6GcQ3qeGDKFuIaO3qezkDG8aeIem1pgu14ayA2Zu4Foo0PEhQw86PoQQQuqID5Y4Hjs+ntOVCZEbmLowc3BcG5imlW4uOJSYrBIn82YS7RDvjeNLxZZop4yqbnI+tjnoojFLcQMod4himhukoDSRFxJWTavFhpsNBQ+f+vDO+MxHJlZunAM+ecBO2Njdhy8cYggfFjcgQxAKH0IIISQjcY6Po5y10osEuMLRQkpmVTeLgjA3Fs3iQCVC3RwhbJ4IdXOJGJnjI2dky90xF8iue5G2V5Irxwdi3rZho3LWGULdfnzqAWhusrfzPA/XnvGRiuMMNe771hGpLtewxkJqwYZw3x9ChhIUPoQQQkhGMuX4yPZbWNUti5ukzSfFyZKiQdsnyDGHUBc49/GBWf0N0Rxsgs0cK/zZvZGpdHXcQsmrkOPTV8UGpl84dNeKbfLIAVuw7w/38SFDDeb4EEIIyT21y/GJhYY1ZEzLZXHn9oT4Smm5FfZS0nqfSDg4HR+VPcdHFjewOlr6WGaxg0rzThNpcuxIsEEXb26nyj2Hku+j4KEme+SQgAIdHzIEofAhhBBCMiIrmFkX4vKxkdRvw2zi2kNH9okdH/s8ZOU1BXvOkUKsIJRS1spriZyalKIFJrZqdMl2htgRm79mEa8yn0nSV1KZwtxIdgosZ02GIPwUIIQQQjIihYbV8RGCIShuII5ZFopmdTZ7OJqsZiYcqAo5PiVho7gdn/A8DoGkRPU33wjtc1Z1QzRXl1smc3zia3AU4Vayylx6+FzJV5n28CHZYY4PGYowx4cQQkjuqfUGpoArxyfOOSklyj3bxjXDxyoXCVBiPu4cn3gOrtwdIK4OV/Ld1xfv96M08eVywcLXwIPr+nTHR3/ePrYM0UuTVUVfobGKUtakMq7XkZDBCoUPIYQQkpHIbRH/T7bRH+uhW7Zx9Z9t36RL4eOLsK7KVd2CdiVn7o6+j491Tau1SwqU9LH18exDq/Q2FicnHLvS/kPFkl+TUtYkhuWsyVCEwocQQkjuqdX6TNsPx+qIiJwakdti5sOkjQu497nRnlbS8bEjRYe7Wlu8P4+ZK2PuM2RrZ9tQVQvRM69Dm6sQPNo5XblJpruVbFPyFRobGN1fSwqF2v1eEbK1oPAhhBBSN7ickSzoRQLSkaFlpkCyCx/ZH9bCAr4hBsJqbe4NTEV/v1KujKzqlj6G7gzZry9ZiEGO5RB20T3Wn3eLpVhUpRY38BUdnxrj0fEhQxAKH0IIIbmnVsuzLI6PJmKQrTKZ6ZRkEUjBJqOuUDdxXnEed65MfH7ZzhRc8rFtbyNTEIX9XK6TbGeGxtnX2JUdrWLJZ45PjSl4Hh0fMuSg8CGEEEIyIoqjOQSEsgoku2uhjJ/T25lFEOLy2o7iBuKx6dDY5uCrpMiS4+r7/cRIASefl7k3rrWyGbYm5+YK/wuPSDEoKfosZ11rWM6aDEX4KUAIIST3hIvolk09eK+tEwDQ3VfCK8vWR21eWroOfaUgxuyeeSvxt7fXAgAWrN6I/3tqMQBEoWWuQgWyZLQMibOFYQHJqmWuzUOVaJOpuIHhtjimrfWRz5jV5OLy0aKqGzy4Q90q3zvzPCrlcdq8szg+LGddW7iBKRmKUPgQQgipK4766d8AAJfc+TpO/dWzWNW+GQtWb8TnfvMcfvTXtwAA5/1pHr5y40sAgH/4xdO44q8LAEjnw1UkwHR50l0QibnBp72qmxwXQnQYc1C64IryZjI6JyVfJc4lx9ZD4izzM8aOc3cc907MQbpl8lrT+8R2UppoLDLHp+a4inAQMlih8CGEEFKXhG5PT9HH+q5eAMD81RucfXTXwr4QT3ss+wfHdEdFDy2zOUNm+Ffw2Ax1M+egOScOqyre78dVtEAUQfBNMaa3k/2jMRz3DkLgmI6PtZw14gu0l7PmPj61hjk+ZCjCHB9CCCG5J219ViwFzzYWPDREG3dmW8kZkWAaWoI+DNEgqrUFAiIOA4sX/PYCBLLamxzbXNLrwsmoMue4pthtsRc30PpAFzHGWfV2KvnYRDpf2j1wTNzM8Um7dyXm+NQc5viQoQg/BQghhNQ9Yf5HJeFj5qCkoS3sRSiYzLUJ20UhY0b56iwbmOrFDez9pXuj4AgZE/NOOlCiXcLJSZ+DrZ0Mw3PNwXzeJb7C3Cub49PncwPTWsMNTMlQhMKHEEJI7qm0PouEj6OdzI9x56no+TXWHBilxL45eu6Nn8F50ktOe9ZjplmSxfHxjXA0s/CCLG4AQ4ylj21WussSymfk+FjnLV2iYIYmJV+xuEGN8VjcgAxBKHwIIYTUPbHjY9k5FLrDUjHHJ2XBngh7QxAuBLiLBEh8MaBSsUPlWtNLkaZUBcEW5fg4ihYofQPTLDk+Wa4tbBffLxkyaC/KIMeXeU+SYkmhqYFLnlpS8OwClpDBCnN8CCGE1AHJBVrsWgRhOwBQsuseIw/H7SLJBbtLDAT74ajEfjh2R0SeIx4vGepmuiD67NLnLBwf3xQ+Zohe7FRlq1oHzS2zIR2kRIqPVbDFY/oq3S0r+ixnXWtYzpoMRfj1ByF1QEdPEZu6+wZ6GoQMKqRYKERVyhyLcsS5OK7QK+mWaO2MEC8FFUWomaFurrGjNkJ0eIlQN33ekRCrICDi6mj6uczz2h0feV69j21TV20O8RQS5bBdG8bK3KS0VkVfoYlV3WoKixuQoQgdH0LqgA9//xGUfIWlPz5poKdCyIBQyZ0Jl8vFiqFu6U6O7VxmykkiZEw8L8WSfa8d3T0K5+0ZYUe6+BKPkR4KFh7T+ruuSRzQ2kGfXzyeXRRJTNEohaHtJTTPk3bviiXm+NQaz/My5aIRMpig40NIHZC1RC8h9UQcrgXhGNjbaxXDHI6P5loIcSKdl2gOskiAdi7bHOR5REU12HNqtLkpu+iAETJmK2edFGzpqkgXTkZVN5frFPVX2a8pcpPSX5iSr9DIHJ+awn18hg4tm3rw6vL2gZ7GoICfAoQQQuoaGYZV6UsCmeNj31fGntdjDQtTepiddQNTP31chWQ4mmynVXhzhZlZxIl5W2Q1OpsbZOb+ZMmP0vZAkqLR4YLJ8wbtkseLLGddcxjqNnT4+WPv4Kxb5gz0NAYFFD6EEEJyT9ryTOaphOs3l/DRQt3EAj3tXLExZOyHI4WLkQNjEw0S2/484Xn1Y6F7o7T5uJ0TiD5CiGn3RemOjyW8TZ+PJe/JMQc5nv1u60LKnHcIQ91qT6HA4gZDhYVrOtDTV6qqj1IKb72/sZ9mNHBQ+BBCCKlz3KWSQ7RNOOEKvRLiwhFaZooEzWGxpBpJwSVzk8xQN/d+ODbBFo8XVEezzFUhinWTIWye5zlzfMI5mZXg9OvTXSf5vC2fRN4Ts9hCSNFXaCpwyVNLPDo+Q4bFrR1V95n54nKc+POn8ey7rf0wo4GDnwKE1BHcc4HUK663vlwsm2WhtTEgHZ9KeTRxH7mQt1VuM0O5rKFuhgCxFQxIzMEiSBLzDp0TX5dHyXLW8fPWe6vN1djU1dpFL8OthRY6TqOH6KU5Pj4aWNWtpjDHZ2iwoasPrR29Vfd7fMEaAMDGnFWEpfAhpI54Z0313/oQkneCxZsedpbeTlZHcy3eo0ETIWzWzT5hCpV0smzimRjbmLe1vzheMgSE6SDJx9ZjhtiSotGGbZ8iUzSaEw+P2IRYn6/QxFC3msIcn6HBorLbU+0rtWxdFwBgzIhhNZ7RwELhQ0gdMGX8SADAA6+tGuCZEDIwpIkZ6VpIx8eW56NtwlkpXEs8jkQQkmInrCynb9xpH1tzhfwKRRTSRJrTqdLzcMx8ongIJYobGGNo4+nzVtokXHMQrpoY1yrsoAu79HLWPhoY6lZTgg1MKXwGO4tbOreoXyh88vYK81OAkDpg7MjgG5v7X1/NcDeSSyZf/AC+d/cbW9RX5o948FC0b3Qj9vFxh2uJLg7HR28bl3u2l7PWcmBgd1h8/YBRJMAi2MT/YTw2BZYmGqN7lxR2+ryloLGLS3k9eq6UPp7+WEX90z7ifAU0MtStpngeixsMBRa3bFmkR08xSPLLm7il8CGkDgg/uBa3dGL+6vxVaSEEAP7w/HvWY2l/urWSzOJ5e36NsRC3OBC+D8050R8bsVzaHMPFu5EL5Ag5s4oBMXwy5Cz18gyB5HB8lL7/ULpUSs7bT7knaZPQ7peQY9b8KMTjmfdOwnLWtSW8m/wybXCzaAuEj8zrydvLS+FDSB2gFHDgpNEAgJOunT3AsyFk8BEulj0PVscnKSaSbk14TKu8Zl2wGyLG4vgk3Q19zubcgLT9foRTZQvRMxwea74OlLb5q+kupaGFCVr9Hr08thQ0ifmYAlC2s4xN4VNbCpZwRzK42JJQN9mHjg8hZMihkL8ERUI+KGmVyTxUKJtsWQPYFuKAGepmCpKwTeyIyD140vpbQ90c+T42YZCYtzindX8eJfdAcgkkiVmtzT4HXzTU5qM5SPrIerU9i+PTwCVPLSkI148MTkq+wnttQa5ONck6Mjwuby8vPwUIqQOUUvA84Jyjpw70VAgZEFx/vJWSjo/nLG4QOScqKS7Szme6N9reOEZ76RLZQ92EhFDGJqNWN8gllow5iIY218m8Tlly2u5Oxe1cm57KPX6UOGiGI9pKd7vKa3MD09pSKNDxGeysWN+F3pKPUc2NVfWj40MIGdIoFXyT3VjwnPuUEDLUsYmWVKyuRTq2xXbFY1qIV7qgEZoqEB1WgWQXMTCuIa2YgFJ6Q1u4XDJEL93R0vbncbg/QREEOTa0Y+ljC0Hju9yy+GfbBqYA0MTiBjVFun5kcBLm9+y+w7ZV9ZMbnubt5aXwIaQO8JVCwfPAP/skj8gF+hsrN6S3ccR5KAjHB44/9HJRbizera6FMZhNYEnXKeFuGM5JHBJnhI+5BI1FLJkukSY6bM6QEHBmjo92taabBJXSKCkgZS6Q1s76uoicKmUvbsBy1rUlzPHJ28I4T4TOzdTylhbV9JswqhlA/oQtPwUIqQNkTH7OPsMI0Rbvc5etz9ZHS/4XIsaziyRfOBCm0LAKGnGsopgQusAMVYvnEPsySgyeFGKiVyJMTF5TulNlXp9EXocUS+YCyTyPL67PJuxkaKDpLGl7GFncMvkambC4QW0p0PEZ9Cxq6cSYEU3YfsSwzCk+vq+wpLUTe0wIXKK8hTJS+BBSB0TfZTPOjeQQufB6dUW642P+1U8u+GPHx7ZCkOFfSeckXUi5nZj0ELtEcQPjPGkCwgyv0woayDHMcDTDojHLcqchzysdKOWYtzwmN2sNr0m20/dKSr/fibGjx/bNX7mPT20piHLwZHCyqKUDu0+oLsxtZftm9BR9TN0hcIny9vpS+BBSByilUPC47wLJJ/IP811zV2bsI6u6xYt8z/PsOT6WxTbgeqyLIFvpZ23BL8RN2hziIgHuXBl9rrGAkO1Ktj6G35Nwk6TTJBwo89q18SyOj8QVbifR56agFU6w9KHjU1s8lrMe9Cxu6cTUCSOr+s5zcWsQHhc6PnlbLlD4EFIHhKFuDHcjeSTL+9lsYjo0MsfHhp57Y7gjFgcpq0AK24b/2sK6pOMDQ9C4HCiIsWW7UkleEQzRoQsXrZ28VhW3sbkysjiBOYdkPpIUVfFYrlA3JZ63b2DKJU8tYTnrwc3G7j60dvRU7fiEpaz32GEUgPy9vvwUIKQOUAiLG/AbT5I/5N/l5sZsf9Z8pbRvrG3uhnkeKQy0Y7IKmzLcDcMtSTuPKTIyhXWZc7OE1Nk2VwVcjo87tCzKqfFlIQd9VnJ+WiEG6NdrFljQhJ1NVGn32xRsSIWhbrWFG5gObsLCBrtvQWGDUc2NorhBzac2oFD4EFIH+EpP78nZ5xipc8JFfsHL/t6WZa9l7o7nKG7gEgaagIDhiCB9ka+gNBc2qurmJ0WDft6wnRQDunozw9vkfGS7xH3QnKp0ESOPJHN85Bz0/mnV6BJzSMw7no81RE/Mz+X4cB+f2sLiBoObRWvLpayjkLVsr9Pi1g7sPmEkwv1+8/b6UvgQUgcopeDBE4usfH2Qkfom/MPsWtiab3mZ46PE4t2D58grMfasEcv3oqgEkAgTczkn0di6u6En/8s+6bkyiZC6RB+V2i4hOoSQMs+bdn1aTk7GvCDj8oyxZY5PPFlDr6UUkxDXZ3N8GOpWUzwWNxjUvLCkDQUP2HXsiKr6LW7pLIulfL6+1W3lSggZkiiUc3zEz4TkhUi0eNktn+RCXhyz9on7mY6Pb4a6idGsuUDm2AjFhHnMdE6Si3z5fHietGsy551wTmzuDfTHUgD6SO8j0avMqcSx9LEBJRwx232U8/OVKb9iWNygtnAfn8HN6g3dGDmsEcMaC5mD3N/f0I3VG7rRW/KR118Xfv1BSB2gVLAoZDVrkkvKC6+CK0zNeF46HTI8yvPsjmiigIFDQEROhVzwG2FYZl6PDBnTz2tci+awhCLIXeFNzkELvTP3M0oJMzPnZAoued2uMEFNsGnXp89Hc7SiY/r1JU4j7p0tJ4E5PrWFoW6Dm/faunDU3hOq6jN/dbAdwG5jR+S2XDmFDyF1QBDqJn8esKkQUnOiUDfPHqaW7BM/Nt0Se6ib/lhbvBtCSnMtLIMkXBUpTiwiBoaAsIeCSZfHHoJWMlSCLtKQihRIJT8WcwmXybgG3alKd2+U/L8m2PTrM4syxG6Zsr5+DHWrLSxuMHjpKZawYn2XVtEty8v0/oYeAMA/HbZr/Ppa9vMaqvBTgJA6QCH4di6MybZ9K0vIUCQqblDItgdP8HNcWCDrN5oJ50T0KxpKSivJLMSJa38euey3CwMzz0jOLd0ScQmNYqKwQGJSyfHEIe2xeY+1Oei5QBK5sLJeK0xxqZ8n7OYsZ03Hp6ZEvz9UPoOOZW1d8NWWVHTrwPCmAnYevU3mz8e1m7rx/OI2bO4tbel0tyoUPoTUAbJ0L0DHh+SLcN3VUPCsYWom0jGQLorncI3MMDHNgfDTn5duRDDXdKERLOzjNjaHJuHyRC6K6RJpl2d1oEyhIcf2zfPKMYTYiZyclGvX5qCS/c05wHR5jDGiPpaiDOYcJMzxqS3M8Rm8LApLWU8IhE/WMPfFrZ2YPG4kCgUPhUK21/epd1rxhd89j/c3dm/xfLcmFD6E1AFKBYUNmOND8ki4+G/wHI6P8XNQ1S2OYQ9Fh+tXRC7ezUFdgsYWriWFBmR+jUMYKPFzssy16GOE3sWCRp+EtZS04RIl5q39LK7B0scXIXHKGMPccyjtXGZ+VHIDUxX3t6zUWM66thRyWu44DyxpDYTPlC1wfKaWw+PC35ZKr+/S1k40FDxMGrNN1fMcCCh8CKkDlILm+BCSJ8I/yy63xiThGGjjpQ+ScD1Eu+S+QMmxgwIGFqEhxjcdH9N50dwbobAS+wVpc1CJx4l5G+6KzfGRoXyAKCUNHVMEKfG8LtLk0LI0tR4mqAtNfW5xYQjm+Gwt8pr8ngcWt3RgwqhmjBrelLlPT7GEZeu6MLXsEkWOXoV+S9o6MWnMNmhqGBq/XyxnTUgdEOYzhN9w8+8UyRPxPj7V9ZEx7HpVN3sfmaci29k34YzVSZqLooeMQYwhRIwhqtJCy0zHx17dzu0mxfMxw+1sIkYfyxqapjJWdVP6NaXttZScd7YcH+qe2uKxuMGgZUlrZ8LtqfR3P8oLKjs+Wav2LS2Hxw0V+DFASB0QLiCiDUwrfodDyNAhyvGJcg6S72/zuUQOjBQ+lvMknZMYszy2dGL8aFFuukYyeT8OdUvsUWM6Pir5OLyO9Lnqm4LqYiV9PuHP1rE1wZbuJiWuVbo3guRmpJb+0Nvp5ynfO99RzprKp6awnPXgZXFrZ+TcANkiPpJ5QZWFrVIKb67aWHVI3UDCTwFC6gClAtuawW4kj4SOSNZkXMB0aMwyzOkDuPbxceXbSHljE1xK2Cjm6ZNCSAgaTdykiy9NqABWwWbOxxRc2hyEo2UTYoB5v5JCzLw+rZ0RRifvXckosatdn+X1GyKROEOG5sYGAEBvMWf1joc47V29WNfZW31+T2sHgDgvKBS2roIxqzYEBQ3Gjhy2BTMdGPgxQEgdIMN6AIa6kXwRvp/D5PW0t7f5nOkYhItqD/YCCfJc0qEBkpXJ5MM4DMu+X5ASY7hCxqRwMQWNHgpm9okb6teuN0xzoExMseOLOdjEUhCCljKfxFwteyAZ8064RPKx5QVsoONTU5obg/vZUxwaZYzrhcXlwga7j9+2QkujX0sndhB5QZHj47B8lpRdokOnjN2SqQ4I/BQgpA5QCEJ44lA3QvJDuOwtOELdTEpKX3jbcnxMgZQmOsIx5Hx0VyYey3RETFETn8cmqmSOjymibGLALpBsRRnSrkmeKxZIwv0xQ+Ug9npRsp3dLfP9+DKkkJKCD0BCYGlOleUTroEFXmpKJHz66PgMJhaXxciUCdVXdNtd9ClkWC8sMVyioQCFDyF1gFLBtzdxcQNKH5IfwgW68w+18aStkpjpGSUW6JaFuKs6mnR8EkJKuR8HP0txkj6eKVrMUDv98i1ui0PY2Vys5LztLlF4z80W+mthzkeIKpvjI/opI5xQQsOntjQ3BaFuPQx1G1Qsae1AQ8HDrmNHaM+7cnuVUljU0hkVNgCy5fgsbu3EiGEN2GFU8web9FaEVd0IqQOUUtzHh+SWuKpb9je43NQ3sXeMxY0wzBqtobUymbF4N0PQ4se6kyOP2Us368/rfYz5RH0c+TViUNM50e9PLDX0PBxDXqlASIa3Ki6C4BAxSj+rUukbL9vyrRKCTcDiBrWFoW6Dk1/OWgQAWnnpSp+M6zp7sWFzH3Yfn+L4OL4oDavHDaXtMvgpQEgdIENOwp8JyQvhH2bXTvKVvu2M4tiNP+DJqmfp4sR0aGSIFuSi3OImyfwas/pbsupZcg6GDtPziTShYhdIegiby0FKnsvsH44RtTHLfVuuT3OxtPm4cpiU0S79tabuqS2x8KHjM9SZu6wdAIxQt8r7NKWVzR7s8GOAkDpAKRV9iAU/D+BkCKkxcahbGKhW+Q0euhHh47i4gWPx7nAWkqFXwt0Qz9v3oolPbJZutoWCQc5BpQkkMR/rvPVrtYWtafdUChxDnGjNDLGUxdGSZ3KF0VkrwRlukoSOT22JQt2Y4zNo2NwbuG/fOX6vqvotW9cFANhvp9HRc4UKoW69RR/L13VpLtFQgJ8ChNQBvgr38WF1A5I/wnVuuK7NIuyNgCqHe5PeKxAT8cHEBqYq2c6vEOKlbUxqdXyM6m9ayJmcqX4NmpPjcGJkfzPETt4Fvcx0cj5hH098a5wWomdenyYuhT9l5u7om7rq87a9/ixuUFsY6jb4WNKq78UjcX0uLmrpwOhtmrDjdnGujtzgOY1l64INT6stojDQUPgQUgeocj4D/+yTPBLl+DgWtubf7mBRXu7vx+Ik2MDU7izEj+1iSRtBuElpc5CPw5/Nb1ht/RJOjk3QGGIki1MFo51VSBnxaKZAis9jhLDZRIy4DtPxgTGe7GRzsSQ0fGoLQ90GH+FePIlS1hX++C9q6cDUCXquTvQ9qeX3aWlZZE2psmz2QMOPAULqAAUzx4eWD8kP4eK9mg1MTZcgKmcNUxjoj6XTIdtJx8fM0YkFjbuwQCw69Asw2/lSQMj+lnnroWD6tZeMhtJt0QVTunjS5oMUgSSUi9V1Mu5JqrBLaRedB4Zgs3y+MdSttoQbmFL4DB6iUtbVbl7a0ompE3QBU2l7gNBdmjKOjg8hZJChVLAxY6VvcAgZioTv5wZHjk/ae16GctjC2+wbZeqL/KQzFIoBGY6WdEHS5miWvbbNQRMJxgWWEsJAnid9DqaDJDcc1eYpxtBdmeTmofFc4x9Mx6fkp89Vc7FMl8gpxJAKdU9taWoI/qb09DHUbbCwqKUDE7ffBtsMa8jcZ1N3H9Zu6tFKWQOVc3wWt3Zi7MhhGD2iaYvnOxDwY4CQOiAIdRPJ3AM6G0JqyxY5PkYcV1zUzdOdDvFltm8ssO37yphFAuJzuhbvsdBwh27JOcj/mwJJts86B+1UFpcoWbBBRfO0hdXZ9t1JzjU+mXYfEw4UNDQBaHkD0PGpLZ7nobmxQMdnELG4pTM1v6dSHwCYavQrVMjxWdLaMeQqugEUPoTUBQrBh5hXwbomZCgSvp1dG5imuRYhQeJ98Ewy1E0XNDashQCMx/rP5thlAeG7hIEMH9MdGquLovSZ23OYzKIFWif9oRRSYlxdT9qcHH2y+rXq55Tujz3UTXfVrI4PkxxrTnNjwxYJnz+9uAx/eG5pzedTzyilsLilw1plzfbptailnBdkOD6VNjAdiqWsAQofQuoCuVkjIXnD3MA0i7APwz+D/pXHDjqJxYMjPE5b5CMWE74hQJJ5OKK/xfGR1c0SG4aKx4nwMZmHYx1bzsfMw0l3hrSxjfuYzN1Jd5D0/ChdxGjXJ05gVtGTBSRs31DzM7D2BI5PdaFu76zZhIvvfB3fu+fNfprV1qezp4izbn4Jc5auG7A5rN3Ug87eUkLAAPFnXRqLWjrQWPCw27gRyX7mN0FlNnb3Yc3GHkzcfpsPNOeBgMKHkDogWOSJKi0DOhtCaktyH58kSTGkOwZhZbGCYwNTX6gGBV14aDk1lkW+mbuTFAoq7u8UHcIZsjlIjuezHDOdk5Jh/6SdyxQ0NtGYFF+WOQhxafYxhaYWHgeytWhuKlS1j49SCidc8xQADMlFs42rH30Hjy1Yi/tfWz1gcwidG7NIQSUWt3Ri17Ej0NSQlAQFz0v9Yui+V1cBADZs7qt+ogMMhQ8hdYACtHLWjHQjeSJchDdUleMjixuIW5oGoQAAIABJREFURb5nLqpV+uNKAiLqk7JIR3heQyxZx9b7hxuaVqqOljZXUxgkCwuoRB9zbE3gmA6Lma8jx1bJ583rk66YPp9sYtDl+JDaU22o2xUPLIgejxre2B9T2uosXLMJN8xeAgDYafTwAZtHmKtTbY7PopaOVJcICMJD036fwqfO/vju1U1yEEDhQ0gdEBY3gKPqFSFDlXBR3lCFpak1MRbVtiR6XdC4BIRR3CCRIBPOO10YmAsNW+ieS3wlqrVpgiY+aOYCpfVxzVXeB3con726nTUsT0E4ObHgS16ffr/58bb1qCbUbe3GblxfFghH7TVhq30B98Brq7GyfXO/jO37CseXHSxgYN96i1s6sU1TAz60nUV8pUyu5Cssbe3C1B3SxZJncXyWtnaiubFgP9cghsKHkDogCnUb6IkQ0g/Em486ylkn+sS/D5rjA3sYll4+uoIjEh1UesiYNRzNvh+ObWxf2du5yj3LPglhp+IfnOFomsOS0h8pG5Na750uvnTHR54zpmQoO/0+JF//BlY26Beqqep2xv89DwD4xRemY2Rzw1Zx5l5fsQHn3vYKrnhgfr+M/593vw4A2HOHwDEZSLNxcbnKWiHlve55QG/Jx0NvvK89v2J9F3pLPqZaNiEteOlfvCxt68TkcennGuxQ+BBSBygYuQv8RpTkiMjx2cK/aDInJ7mIFu1gOCfSETEX+fKxQ0il4RudzApm8alc4kTvo4sO4baY7eS1Wh2k9PFkIYdkH7v4cuf4WK4p4UClC8oQCp/+obmxIVOOz20vLMOilk58eNJofOrDO8PzPO291x+UfIWTr5sNANhhVHXOxGPz1+DmZ5c626zZ2I2ZLy4HAMw8+6MABjaaIksp63+59WXt5ygvyOb4wEv9nFrS2onJ45PFEIYCFD6E1AF+OdSNxQ1IHsmS42M+p5SKfiH0/XmMxbul7JlcyAdjGEJFO1/6eDYx4XIwTLfFJr5sDpZbLDnETmIO8bzTnk/rI3+wuWVJoZl+HckwuvhxWmhOAyu69QvNTZVD3XqLPv7jrsAZufWsw+B5Hho8r9/dkW/cMid6PLI5+4aey9d14axb5uCye91V5468chYA4PozZ2Db5iBfaWs4PkoptHX0aM/1FEtYsb7LmqtjI8oLcjo++nMlX2H5us2YPARLWQMUPoTUBXGoW/bkb0KGCnLzUSCbsFfG47iiml106KFu+llsC/akQEofW4a6+YYwMH9f43LW+oXYx1aGWBLtEsUN4nNaBZI4rz5vvY/5jb4eomcIxZTHcIznDuVLvgPo+PQPWULdvnrTiwCAbx2zB0YNbwIQLNSXtHait582P31nzSY88dba6Oesf/N8X0WCxsVDb6xGb9HHsIYCjttvR2xNXf39++bj4B88hvWdvdFzy9q64KvkJqSVWNTSgbEjh2HMyGGpx9Oquq1q34zeko8p4yh8CCGDEJn/wC89SR6J9vFxbtBrLsLlY6WJCdsiyRQDyrFgl21sfaxVz2AXBnI8MwRFFxD6dJTl+hLOUNROL99Q8mU7o1BB1MxR3EA6Q4alZXWWEuNZ7kk8betrR+HTPwyrUNVt3vJ2PPNuGwDg/OP2jJ5/+M01AIBH5r+f2u+D4PtxyewnvnsUhjUUnHt1SX7x+MLosa3cdk+xhAtufzUY/9+O+mCTrZLl67pwUzkEr6OnGD2/qOzc2DYU7RRtJYvWdlo3PAUCU9z8nFnSGpyLjg8hZFASfmbpKT60fEh+CP8wF6oI5UyWpg4dEbmMTroe2RwRsY+PcS7NldHERNzOdHxsxQ1c7pQtNM289sS8M1xfeDz8V3PLXK6MbT6mm5RyrcF4yfNHj6P7rVLFD4VP/9DcWEBPX3qom+8rfPqXzwAAnr34WDSmJOGNHZHuNNjGu2feSmzsdu8d87NH3wYAHDBxdBD65WX7m9fRU4yEzynTd7a2O/HnT2NzXwk/Pe1ATBoT5LnE0RT9+7fV5kYtbg1ydWzCZ/WGbms/174/hYKXuKZ31mxynmuwQ+FDSM4JP7I8cB8fkk/Ct3OhihwfKFnVzUzqT46dfGy4ER+4uIFwnVyCRswjmKtdpMm5xqFpVYgqOVfL9cnzJm6xQyxJ5FxLzvNkE2xpUPj0D65Qt/NunwcA+MQ+O2Bnwz25+vMfDh5U8bJcP3sxzvvTPNz2wjJrm1Xtm/HLWYsAAH8553AA5S9EMvzNO+HqJwEA//Pp/dHUUEgVMfOWt0eOx2kHT4qeD79YvOqRd7JcyhbxxFtrtJ/l78Dilk7sMKo5CiU0Wb0hWc57Q1cfWjt6rYUNgODlMd2yH5T3YtphVHPGmQ8uKHwIyTnhh3eBxQ1ITomKG1SxT5UpaMKFvbnWSTgnmjCI25UMMaGHrcnx9Hba8xZhkMwnUtHz8ogpGrR5y9A0OZaxqpGiyl50QBaAcOQ9OcPjpNixn0dOVl5fQiDJ+52yYGVxg/7BtoHp0tZO3PfqKgDAb750cOJ4JIQy/jFq7+rFD//6FgBgk8Px+dxvngvO+cWD0dwYFDSwVSaTPPVOC1aVXZEvHrYr0rRSX8mPHKy/fvvIKKcwOEf/0t1XwtduCoo1XHTiPgD03/HFLR3Oim6r24Nra2qIZ7qo7BLZChsAQY6P+XkaXrY3RH+nKHwIyTlx4jeE50NIfggX2A1VxLopFf8B980cH9lO62M6L8pyTBYTMHJ8bH2kOKnoyiQfh9eR2geG2yJFhzU8Tve03GFrKjqnLgaN+2V1nVzOkuWaTHEY3bv0l5+OT/+QVtVNKYWjr/obAODx7x6FppQQt0IVhUiAWNAAQEd3er7KPfNWYmX7Zuw8ejhO3P9D0fNeSmUySXdfCeffPg/DGgt49b9OiPJhzT7XPfEuAOCE/XbEfjtvpx37ICKgw5J/I7nsnqDC3Dc/vjt2Gh2U5pbTW9za6azoFgpNWdZ70dqwlLW9X9oGpttv04QzDt214pwHKxQ+hOSccEEgP5j7Ow6ZkK1JlONTsC+mXO94bYFtChVDQMTP6+1sDkTYNp4rUh/Ln11Vz4J9fFT0WKX0D4/p1xfPxc8476wbosYhegpuYSfnJ8b2bdfquo+GSFNxL+b4bD2aGwvoKyntfRTmyew6doQ1f0R+6VCJB15bjYVrOzBu5DCM3qYp9Xe5t+jjvD8FoXX3f/tI7VjgWtj54vUvYF1nLy49aV+MHhGEinnQnY7FLR34xeMLMW7kMPzqnw9KjLGlb6+/vLwC+1/2cJQ3k8a85e24fU6wX9DF/7BP4t6t6+xFe1efs0jBjV89BDuPHq79Hixq6URTg4ddxqQXcQCSG5hu6OrD+q4+TBmie/gAFD6E5J7wM8sL6llrzxGSB6INTKOqbpX7KCiR86a0hbz+xYB9wW8XHaYjItvpIkY+lhuByk6ukDj7Bp/QnteKLVhukBJzSoggo+y1fWzLvLWQOHu7knENNnFpd5OCc5kLUeqe/iEMJwvLUnf1FvHzxwLh8+h3Pm7tlzXftOQrnHvbKwCAx75zVOq+MgDwz9c/DwA48/DdMNYozRzkqaSfaP6qjZjz3noAwJc+ulv0fKGgn+fYnwX5P7/+4sGpRRo8z8PX/m5KtJ9PFjp7ivi3/xdUh2vr6E1to1RcIOL+fz3C+AIz+PfOV1YAALbbJj2/BwDGb9uMw3YflxBzu40bmXo9IQXP00JWl7SVK7oN0VLWAIUPIbknEj4MdCO5pez4RJFuyUVOMm9Gf2zbx8cMydLGtjgQchBzQ1RnCJt43hQX0WNxLlmUITwWjW0rEmAKMYfjI1vaS06LY457FzyW87a5N+nnN+dgu3fhz40FfXlDx6d/aG4M7nMY7vb53wYhad//1LRIFKWRNd/0rJtfAhAImjEjh6XmnMxb3o6Xlgbi5fKTp6WcLF0s+b7CJ699GgBwxzmHG+FqcYjX4wuCogLDGgo4dMpY5zVVE03x3/fNjx7b3p+XlzdR/ciu22P/iaMBxGGC4d1bszHI39l/59HO85lnWNTS4XSJgnPpv59LW91ls4cCFD6E5Jw41G3oJiMS4iJcoLiqupkoxL8PejGC9FCpsI+yLPLN+ehujjimCY10AWE6OfokjApmFkfEvodORlcGSvum1yZOtDC6xNgWV8bRzrwG135G+rxVPDaAxgb9s47Cp39obgqFj49FLR14Y+VGAIFQcRFtNuz4ZZ2/aiNmvd0CALisLGiCfWXiNiWjZHYh5XUuWP7ufe+eNwAA+3xoFA7eTRc0QReFjp4ivn5zUFTgqQuPcV8TKgu5kKfeaYnC14D0+7B2Yzdufu49AMDMb3zUmFt8H4q+wohhDdh3p1EVzxuepljysWxdlzMvKDiXnuOzpLUTngfsMpahboSQQUr4QSf/HjDUjeSJxAamKW3Mb4nNhbPu+KSLFtuGo4BD0ChzkZ+cd9gO0Rzsyf+mgJBYN/iUws5widyCxnLMvD+aA+WYQ/TYcHJsOT7mfZDtbFXdyn1ModNQ4HKnPwhdnZ4+H2s39gAALj95v4pfslUKdSsJN+aZi48Vr6en9bnwL68BAI7cc3yiZHZ0rpRNOFs29eCP5bLYfzr7o8k+5bntf9nDAIJ9fT40eniinXmeTF+6KIUzf/8iAOBnnwvKeqdtsPrP178AALj2jI9geFPsnhWMkN6lrZ3YbdzIyl9sivktX78ZfSXlrAQHlK9J/E4vbevEzqO30eYz1KjJJ4Hneb/3PG+t53lviOfGep73qOd5C8v/jhHHLvE8713P8972PO/vazEHQkg64Qe+to9P5u+lCBn8RDk+hcrfIoco0c5XuttiiWZLLN6tFccgF+KGeJJ9fP15zTmR12dsdKqXs7YJF0MswX5Mn3fsnNhyb8ywt/ie2J0cifmsLsSM+ViOmQtFWSobUIlKYo40BvIBkKFu4e/fHjtUdh4ix8fyt+iC8h5Ax++3IyYKQRM6MQCwYn0X7ijnt9z4lUPs50JSkBxTrjr3my8ejO1TNlENFvzAuHK+UChQXHgpYXhphMUfpFgz+93+0jIsXNuB6btsj5MP3ClxPUD8+/VeWxcmj6vswMhg9yXlUtZTKwifgqcLzaWtnUM6zA2oneNzE4ATjecuBvC4UmpPAI+Xf4bnefsB+AKAaeU+v/I8b+hKR0IGOeFnlif38aHuITki2qvKEc6UeM9LQSLUTkIkaO6GK5Hf4m6IPkG79D4wBI3EFBBZhEayIILS+uoH48easLOObcwnFHlmTo62vYu4D0q/xlJmt8ziDMk5lPskHB+G+fYLsfDxo9cuy612OT7L13Xh3vIeQGYFNSliTv9tUNDg/86cUTFBX743Z729NiohLcte6+fxoJTCsfvsgJ1GD3eOnzY3GyvWd0XFH373pRkw0nUABGFoF93xOgDgD18/NOHkyL/jxZKP5eu7MDmDGJHDLG4JcnVce/gAeo5PseTj1RUbsClD+e3BTE2Ej1LqKQDrjKdPAXBz+fHNAD4tnv+TUqpHKbUEwLsADq3FPAghScIP4nBvAkLyhhnOmSncROSz+CpepJuiw+74mOPpj7WNTi0DGrpHm495Xn3eNvGlt9P6yGuw9pEixnSn0kWVPJd5T+xi0HSg4seaCBL/D9pZ7p32ugSvq6mBmePTPzSXQ556iqX4S7YM/cxwrRClFI68chYA4PdfmZFw7sJwsiffacHK9s04cNJoHL/fjs5zybygzb0lfPXGoGDCk/9+tLNP+D7N+s7xKpTNBoAjfhJc22Un74dthjWk7mf0zT+8HPz78d0xaniyUpt0y1a1d6OvpDI5PkD8O7SopRNjRjRhzMik2yUpiByfd1sCl2i0o3rcUKA/zd8dlVKrAaD87w7l5ycCWC7arSg/l8DzvLM9z5vjed6clpaWfpwqIfkl+hZOPjcwUyGkX4j28alS2UtXxpXrEp9H9jUqqpm5KfIcljGszkmlXBkVP59JnIg+5rUmxYlK9DHHs7kySiWPpbcz9kByhdFZ2pWUfoFSDALJzZopfPqHyPHp8+PXKovjY9nH54bZS6LHx+6TFDTh/jpfLufI/N4R4hafKw7XOuHnQVnqMw7dBbs5SjKH7o1S2YsCVarqNvPFIKdo+xFN+OrfTYn6APF9eHftJjz+1loAwL///d7WuaE8v6VVlJeWxRcWt3RkC1kTjs+yti4AwHeP36tyv0HMQES9pr2DUt8pSqnfKaVmKKVmTJgwoZ+nRUg+iR2feDHADUxJnoiqujn28TGfkuFfsny0tviH8VhYC64wLO2YsodomYt8rcCCw5Wxt7OdJxZIpgPlrv5mGc/X748UVTahIp2qrNXawnmkzdXmJsEYO4TCp38Ihc9XbnoJGzYHe9FUs3GCfKW6eov4wQMLAABv/Y+ZPRFgFhAYv21zxXMEIkZh4ZpNWL5uMwDgh585wN3H8xJfQGQ7T/qxjd19uOTOIHzt8e8cpfWB6Hfc1U8BAG752qHW8DpPfM7NXdYOAJlD3cLzLG7trFjRDSh/ppb7RCJriOf4ZN9pqXrWeJ63k1Jqted5OwFYW35+BYBdRLtJAFb14zwIqWvCz+GCCHWj7CF5IqrqVohDQCphhq1FYsJYvLtC3ezFDdKFgdnOzFNJe5xoJ+cAuzCA0h9qIWyZ3BtlLSaQyB9KmZt73vZ7nBSQFsFl9jeuz/ySnsKnf5AbmL6+cgOAjDk+KWGpn/nlswCAS0/a11o1rFAOJ9t+RBM+9eGdM80xXPD/9fX3AQA/+ewBlavOefH7KquRLDRCgt89uRhA4OKME2JN/k0OHaFJY7bBx/eyf9kfvpVXtnfhmsfeAQDsMCqLAAw6buruQ8umnooV3cJzhb+TS1o7MW7kMIa6ObgXwJfLj78M4B7x/Bc8z2v2PG8KgD0BvNiP8yCkromquvHvPskp4eK4wbGPT5qTIBf5mpNjO495xCEA5OOEa5T6OO7nK3PTU4uQUqaosrkougNldVv0S7KG8jmFmE0giQHNe1yyCRrjjtv2+9FzmFRqXka1YZAkG8Ob4mVkmI9TTY5P+E54blEb3l6zCQDwtXIYmA2//L7P+oqGxQ322jFwOD68y/YV+wQhdcHsMgufckEEk1lvr8V1s97FSQfshHOP2UPvI/YzuvTuoDDyA/96pPs85fls3FwUz2WbpILC3fMCr2F1e3fF9kGOT3BNi1uGfkU3oHblrGcCeA7A3p7nrfA87+sAfgzgeM/zFgI4vvwzlFJvAvgzgPkAHgJwrlKqVIt5EEKSRKFuKc8RkgdkOCeQzdGUYSxywa+QFEnyPHHRAnuOTyL53ypORBtxzBmOZhEWrjno12d3oDTHxxjPKU60e+IQdinnCeZgmbfRruTYUNU8l7kQbKTj0y9MGT8S/1gutxy+dlkW4XITzmLJxxn/F1Roe/nS45zVGb1yoopSKnvuDfSS9VlEcJivo5TKHLqX5vgUS35UTOHCE5M5OzLU7fh9d8TIYQ0YPcLtqJjz+crHJmefnwKmlPOBTpjmLgoR9ImLGyzJQSlroEahbkqpMyyHPmFpfwWAK2pxbkKIm3AhElR1S6mdScgQJ9rHJ8Nu8CHSnfB9c/Eu2hkiwSaQnKWWU+ZqzlOe10z4duXXyNGlOLHlzZi3Rp9rPF5SnFj6mPdH/pzIBUp/XaxhggkBaBNfxuvCHJ+thud5+KfDdsX9r63O9HsX9UPszv7rzLkAgM98ZKIWBpZ+vuTvVJY5KiWLoGSZX3ye7I5P8vcrzFk66cCdUospyApthQKwk2UTVr1P8G94PYdMHpttfsZ1hGGKLgplAdjRU8TaTT2YkiE8brDDLb0IyTny23DX3gmEDFWSOT6VUeIbYHPhDNtCXLglLmGQEDQZHBsppJxjG9fgcm9Sx07MD1rD2PGxV5azlcA255eoLBedU1+6WivBOcSSq7JcWl4GhU//EYqYasKqY3dW4cE3gtybq7JsEhqGk6W8xq5zyWqGWR2p8H2V+Z1jjNve1Yubnl0KAPjF6dPTu5T/VSr44iKLMxnOP3bYsk4w+K0Li45k2dQ3LAW+tDUobDAlQ/W4wQ6FDyE5Jw51Y3EDkk+iDUxdVd0Sz8kqY/rK3XQdUh878k+SQsUmIIzzWkSHTRhIB0o/S3JsiX5NSmumRBu5AalvcZPC+aYN7ireYA91s4gy41iiYlx0p1Xqt/QUPv2HDFsDsub4BP8qBZx60ERM3H6bTK+Rvr9ONWWm4/dTtlA3L3ovZc0Pi0VMcKKP/fgJAIGgs1Vok59ZJV9lm1v530jIZZpd0DL4vdY/L10UytXtHn4zEKdDvaIb0L9V3Qghg4BwQVDwsv+hIGQoEYW6RQun6qS9mduiHdPaGWWhrU5OuhuRdt74PK5NRm3CwFEEIeGcxG3sFdpkyJ8uaGyOFoy5lixzkD9nDqOD6z7Igc2xk3kZDQV+z9tfhAvocEGdTSfELpFS2YVpvL+OqrroQNWhbqr8nqvCWUK530NvrEZXbwmNBQ+nHTypYh9fqcDxacgmRoLzVHO/w3YqConNJjSDHJ/7X1sNABi3rXvD06EAPwkIyTlpdjhD3UhekIt1+S1y5X7QxIAyjsnxo8fiWPCtc7ro0HNb7E5McgNTldrHNh8g29i+H8/VnLd1PxzYBYlrI1d7LlDcspqNVyVZBFLkBhhrupK0r0hNSW5Gmr24QdgvqyHneaLaWsb5FcouUXXFDbwo7DXreUKx3ef7+N49b2DksAbMu+yETH0VgGJWx8dw2LISjlysQviE5axPPyTYhWZU89AuZQ1Q+BCSe6JvhbRQNyofMvS5/unF2O+/Hsam7qCsa8GR42O+56UACBbOodOhrMLJ5fLYFv8Q3zQHx2wuSrbqbdmrnsm52XOTbDrDvA/uUtnpLo8tF8jl5CRKd2cRlykOlLmkm79qI0j/EIdelX/O6KgA1YV4hWOH783sTodXruqW3SGJzoPs1eNCHbG0tQutHb04/ZBdsW2zO7BKukS+UtlcmPK/1QjNECXuQzbhoxeGyENVeIa6EZJjlq/rwl1zVwJgcQOSL9Z39kYVk9o6gx3j46pulftLAeALxyfhtojHWfNP3OFa9vHiMDp9FnbhZM8zspWSNq/JViTAbOksbmAZz7rfjzEfTbCJx4mQOKuwk25ZmOMTr9DOOmIKjtrbviEk+WCE9zoMocqW4xNXM1MKzhLW2rkQ/65UU85ad4aziYvw/ZfZ8Sk3LJbfqAftVnm/ILmfUbGUUfiY97uqIg9xv4aM9yEMR6zmXIMZCh9CcorvKxx55azo56Cc9QBOiJAacsRPnogeR7H7jkWDKVTMnJrwB5eDYYoC2745CVdGm4fD3bA4Is79eRxix9ZOHtSEhrgm5bwm45w2Z8gI+YvbZXSTjOtwuU5Rhb5wgRZ3w6X/uB9I/xG7FuFCvIpwLX9LQt2q28A0XPBXvY8PULWzBMj3YHVha75SaMyQiyZdouA8Gedn5jpV4/hUURBhsMNQN0JyyjdvfVn7WX5c0fEhQ5n7X1uFzt543+s4aTn+FrkSUuD40m1JDYqLz6Mn/8vxXIImXWhYNw81pu8UHZZ2W5K7k3BlLOdy7VNkDbdTemEIiVXYKf3VcJbrTglbJFuHRKhbpj5xWGpVoW4Q79NqxJKKk/qzFTfw4lC3KosCVXseIA75y1LcIFHVrYowQTm/TI6PpzvLFD6EkEHJ6ys24NH5a7Tngi+Ssi8MCRmMdPeV8K3bgg0Pzz9uTwDxYttV3CDp+MgFe7b9eRL9ZTtfP2bD7W6kOzZbUu5Zn7fdlUnMNbwP0Ku/6ZujiubKFC6V3RspVIBkaeq0PsG5bPdBJYQQC1huPcLFcClaiFfuI10iX1VRMtqLHdrM5ayhv08z7+ODah2f4N9SlecBgvd9VgEYOjXVFjcIzoOqqrqZOT55qApP4UNIzugr+Tj5utkAgBOnfSh6XituQN1DhijHXf0kAOBbx+yBXceOABCHYUQbmGZ4f8vFsplXIuWAXPDrjoh9g89EH3NRHvXRz6s5SFmdDk18pTs05tgll4CwzcchnPR26XOAce90IZZ+nuQ9qTyHaHNLkK1FItStmmR7lEPdMq5GPYT762QvZx0v3oOfsxYQMN9/WeYGSIGVpQ/KfYLfy0wbmJb/rXYfn/Caqgp1KwTt48IVQ/83i8KHkJxx2A8fBwD8/bQdcciUsdHzsrgBIUOR5xa1YcX6zQCAC47fK/ENqyvULW0BoyXEWxbfiWIC0rWwLN5tRQvMsfWQLLP6W3q7rKFgLlfHfq3ynrjOCyuaS2QJ+UuKJce8ra+Ffl7TTcrDAm2oEC74wy8Qstz6qAKjCt73WcKuwrHDLxOqyfHxVXX7+IQXETg+1YaSoXye7IUKFP5/9t47XpKruhZep/pOntFIo1EYjcIoCwUkIQkQiGAZgwLZNulnzPvs92F4xtj4vWeCgQ8bjHkGbAEPgwXPxvAZTDbIBIEEEkIB5UFIKI7SKI5GM5ocbne9P6pP1d77hD6n7tzQfffST3Oru07YVV3VvVetvfcBxrtlEhlxc6qSzCMhf9XrlHNeGFOH+I7KLaXER6EYIVx+5zo82a9w9YnXncq+4NURUAwzyrLE6z93DQDg6285E53CEIcrX/EBGp+a5fiIzsFcEkcRCZAb+Tqgtsh+sUVLQ2OHyA7NTYrZEFtENVRKGiLMjKtJgWPrybynAKERxC6kblGGlOsUKyYOdx2fhD79v1aJSScXZB2frIIIyFItmhZllqICZOb4sJC/RMXHLhhbH0+igda+WvFJa9/r31OjkN8DaFU3hWJkMN7t4U3/fC0A4BOvOwXz53SYpG/gVp1RKIYFX772AQDAUfsvxhmrKiVTOlw5P8zUsWfbiIRUSbVF+t7ePuEKZtGwrgQ1qeeM7d+WY6etCxQO5XPLWSeQKmJD7By3qurmUeyMAX7y318AxdQhax2fOrelIrHJVd1g76lzXLZuAAAgAElEQVT0ogO0D5BGSOq8pV660tFGiaEEcLyXqPj0/+YQTQt7voF0xadEXuW9mQ4lPgrFiOAfLrkTAPDCY/fDK05ZCYA7gmwdHy1uoBgijHd7+Mtv/woA8J0/fq6zv9u/nOPFDYS6Qhx7J7clQjooQUKQGPC5QqQhVpo6ddHTJNKBVAIhFK0E8uUqQyAvwFDvE31YSFyALMmxB+VHGRgcsd9iKCYfbXJ8aDWz1IU77VxWJUovgd3MA6SXswasGpU4D2SRh5xQtxK9XlrIn6P4pBJAA6DMLW5g1ajRiRrRUDeFYgSwacdufPqn9wAALnzj6fX79Iut0HV8FEOKN3zuFwCAP3r+EVhEVkKvHQCxxkR6Oev+Nui2UDpEMgkRRIIOf1cuwkn6hxcZDas3McUovE+MnZBnJAljSNEKrT8kbZCqDB0rSGgC6wo5YzsFJDg51O+6qUMdcpqhdBQ1sSj7qkoG8aEvEtCoFs3rgfOgsS+LWICGuqURC6BRfFJC3epzl1M/HO7nlLqOT53jkzbNjIcSH4ViBPA7n7kKAPBXLz8Bc8ea25rl+JD2GuqmGBZs39XFtfc9CQB45znHsX0FcTQK4y7sRyHfoqShcgRCSoefnMRUFEkM6OTBPsLGmJPvm6fqQ7Z7bFeYnLB24vhSQt3EieUlsP3tXLsDZClCGkO5Sb7xFZMLmytildckPkJC3coyLeyq6maIspQGWdwgr9x2/gKmOdXWGjLSD/lLCnXLn8eiRO46Pga9XtVvVHJ8lPgoFEOOG+7fgDsf2wIA+P0zD2P76BfV1l1dFletUAwD3v2tXwKoChpIp8A6AD+89VH2lD/l+g4pDpKAhNeviSTeO857GoHoSUN8tkaUk6DCEiNzsh09vgBZcVUnP0mL5gWRfaz8NzmeeLhdvN2ohOUMA1xHPN15R5+QJJezNvlJ/VWOT3ON5FRb62UoHU1xg5x5qr9lWaaXsyZheNTWlH52HiCxrLcljRl5WDMdSnwUiiHGrvEefruv9vznn5zlfAHSL7a1G7Y56wwoFDMZ96zbgv+4+WEAqAsaUMjf++j1Ld6iyc4sd0c68o66UdbDxcgOn8vfrlUejrQnqCaR98XBh5WYZjyHVJE+bnEDv92hSnJSyXFsCIwdz2Hi+0bERxsKtErqJw8pumXawp1ARS5yCFY1l2FJ/TkOfC9D8nFD3dLnsUpMm3LWqTB2nm468SlYTtVo3FVKfBSKIcY/XnY3AOB5Ry/HiSuXOvvpF1W3V2qNV8VQ4Tc/fjmASu3xgV7O5z99BQufGQTqVHNHnPeWDjUjSKRdvOoZ/PukgpRAdtwQr1CfsCoTJHas4AM3vE1uUnhdoTJ5bIogQfIQqRHx0YYCdT5Mj7+Ooc7Hy3Wq+4uR9jdTu9QJ+nTuQX2AvnqYNg2p0FbyNyKoiU5Z/UanLWDaLtRNrnuWXtWtHyY4IveUEh+FYohxwSV3AQA+/6bTvfvp06PNO8br7b/74R3YvGP35BqnGHqc+4kr8MWr75u2+VcsnQ/Ar/YAzQ/5WGHw6Tc8g5WGTYF1HNjimqUkGs22HDbs8EtiMJjEpI4niQXtEwrL65VlkKS5BInYjfB4zO5gKF/E7oQ+tVED27lESonP1EGWlc8p45wbRlWY5jrPISQVwcqxr1GPc3N85ILKg2wDKtvGe2nKlxvqlmYf0D8PVpFKLm7QbI8ClPiMCMa7Pfajp5g87BrvOZWe5H4fdo5393iI2bOPWIZli+Zi3ljHu58+0fkvz1lVf8levWY9vnj1/XvUFkU6fvXQU7h//dY9Mla3V2J313/NTQS9XolfP7IJ7//Orc5843tgvrIsg/eKxRmrlmHVvgsjLaoreuU+C6pXkR9mp9IbcfJt8m7/bQaed0PKXiMxx8cZM+zkc9UiUclhtvr7y6+dsBLDzwNVTlKrv4WJD2eQPpJH8zfsuKHjY3lBpfu5KaYSTYI+fR3tQYoH9NqGuiWTpUa1MCYtJ4YWTslWVDKqusn1jNKquuXnVFVzVeehW+aVD7eFITTHR5GFT116F75w5b0D2/V6JR7btMN5f7zbw6NPue9bHP/+i+tcD8Xk4pj3/gB/9tWbvfsuWv0wjnnvD7Bm3Zb6vcc27cDmHbtx7Ht/iM9evgYbt+3C9l3d7Hk379jtqDSL5o7hoL3nB/t0+nf48Sv2wqrli9gXfuoXXww7dnfx5NZd7L0ntuzEzvH48f1izXrc+vBTreddt3knvnPzQwPb7Rrv4YktO1vPM1l46ad+jhd89DLvvg1bd2HH7vTr4+1fuQlH/+UPsm3o9kq8/Ss34cYHNnj3b9457n3/DZ+7BkcNmC/2XWXx9z++E8e89wcDr5UUJ+WAJdU90LQc7AZT2kJLMrtKB1ctqDLE82v86kbIyZfbbjhaSh+pJvltsCRm0NjMBvGUN76WEBmv59+mcEpgE0cxtjCpJGl0283xGREvbQhQCHKcpaigH+qW7IjTULcM572XSbBoqFsGKQNA7Evp04T8jffSCEmseuUg+8qyyvFLraJX9M93zrmb6VDi0wI7x7v4xCV34altlRP61Lbd+NSld0Wfgn78x3fiAxfdhmvWrMcrPn0ltu2qnIpHn9qBxzdXTkK3V+J5f/dTPOvDl+JXDz2FB5/chg19p/Ir1z2IZ//tpbjqnifqMddv2Yl3fuOXWL9lJ3Z1e7j5wY3YunMcf/fD2/HRi2/HVuG4/Oqhp/C16x6sX2/bNY6bH9yICy65E7vGe/jVQ095FYmHNm7HZy+/h+37lyvvxbdvWouzP34Z1m/Zidse3oSvXf8gfv3IJu9T3HWbd+KRp7bXr8uyxN//6A5cdXdzPL75/+Omh3D1PesBALc/ummgk/TPP78XDz65rTpn1z6AZ3/40jpUZ9uucVxwyZ3YsbuLW9b6j/XBJ7dh47ZdtY0X/uweXLT64Xq//Ty+u/phbNk5jj/4wnV47kd+Uvf51o1rAQC3PbKptvlZH74Uf/O9X9fHc8pf/xivu/BqZ+6d411ccMmd2LpzHHc/vgWbduzGLWufwtevrz6zZ3zwx3juR37C+gz63uv0S+WsWl49NadfW7sHPG2n2LB1Fz5xyV2OqviH/3odnvHBH+NPvnIT1m3eiTse3YzTP3QJ3vcf1WKTqx/cWNtvcdMDG/DaC6/B+Z/8Oe59Yiv+5cp7iTPpP6If3foozv3EFbjh/qqs8dkfuwx/+u834+GN2x0CddvDzXXy7m/dgtM/dImjiPz0jsdx6a8fSz5+itsf3ZRFTCw2bN2Fn9z+GCO99J7YvquLux/fjFM/+GO89sJrADTX49oN23Dhz+7xjvu9Wx4BUH1/bNk5XpPusixxy9rm3Gzesbu+/gHg8c078N3VD+NN/+daNt5tD2/C7m4Pm7Y3JLvXK/HLtRsBAL+490nW/ls3rsVrPns17nh0M3aOd/H5K9bg2X97KS799WP4zs0P4fr7eHuLr1z7AADgiS3VvfPk1l349E/vDlb48mFxf02fs5+2P4AB5aw9KkWt+BDnXZKEUNlkqeTEixYkqDcoEVRiPP3teiYpNlSRYPHx5PFZ20kKQtBuqgylKD6lsMfu6xCn1teuLF0n225L8jUiPtpQQJZxTurT9z7LzFA3qvgkzwVT3185ZbMB6/CnTtQPdcsobtDcX5WqkkR8+n9zlS+QezSvip5dwDRxnhmOscFNFBJfv34t/uGSO7F9dxfvOvc4fOxHd+BL19yPg5ctwHknrcCFl6/B6auW4cwj98X2XV3c8lDjfLztyzfhiS07cdXd63HaYfvg2X97KQDgvo+cj6vueQIPbawcoa9e9yC+dM39OGL5Inzv7c/DdX1n4w2f+wWu+IvfwIFL5+PPv7Yal9+5Dl8ljuUnLr0LF/5sDQBg684u3v/S41EUVd37l37q5wCAs45ejr0XzsEH//M2fOXaqu/aDdvxjRvW4v972fF4/TMPxfw5HYx3e9i+u4vf/ser8OimHXjJCQeiYwy+u/ohfOxHd9Zz/vzuJ/Cn/94oIG95wZF48/OPwMK5HRTGoNsrccbfXAIAuPjPno9Vyxfi4Y078Mmf3I1P/uRuXP/eF+Gux7bg9Z+7Bn9y9lF46wuPRGEM7l+/rVZW7vzQuTjngitwwkF74dv/7bmYO1Zgd7eHLTvGMW9OgYVzx/Clq+/DX//nbfjM5ffgqnedjXd/6xYAwPu/cyvOO2kFPnfFGvzT5WvqvJi/fsUJeN0Zh9br3ljieeBe8/Gzv/gN/Orhp/Dh798OoMo1OH3VMvySfJYX/PhO/OT2xwEAP7r1Mfzu6QdjTl9iWbthOz516V34+I+r82Q/o/lzqv2r1z6Fjdt2Ye+Fc+u5L1r9CC645C5s2LoL/3r1/Vg8bwxb+uT1laeuxO5uid3dcax61/fwT288DS854UAAcZn7mYcvw1tecCRec/rBzr5tfQf4j//tRuzq9vC536/yhHbs7mJOp8DFtz6KVfsuwhH7LcJ/+7cbcfWa9Tj10L3x/GP2q8e48u6KlF60+mFs2r4bl9+5DgDw0zvW4aGN2/GKT18JAPjNpx2AZYvmoixLvOofG2XyLV+6AXc8thn7Lp6HUw/ZGy//3z/H+156PF516sr6x3TLznF86Hu/xgNPbsPV96zHg09ur9WID3z3Vvzotsfwzbc+B6cdtg/Wbd6J8z55BU49dG98663PwTf7RPSux7Zg1fKFmNspMNYp8P/8y3WV3W87C8ceuARzOgabd45jvFtiyfwxzOkUKMsSm3aMY/6cAvPGOtg13sOO8S7OueAKHHfgEnz3bWfh2nufxEF7z8fhRE3bsHUXlswfw1inwI7dXVx/3wYcsNe8/gOPLl54bHP+XvjRy3D+01fgPec9Df/w4zvxb7+oyMDqBzdi047deN7f/RT7L5mHbq/E+q276uvxc79/Os4+bn+Mk0fqG7btwpv++Vrc+vAmrPnwebjolw/jT//9Znzy9afixccfgG/esBYXXHIXdo738M5zjsP6PuGgys5dj23GeZ+8Am9+/hE4/6QV9fsf/v6v8fmf34vv/PFz6/c2btuFJfPn4M+/thoA8JILfoaXnXxQ/aDg2vuexD9dXn0X3f7Bc2AMMG+sg4c2bseadVvqe+XxTTuw76K5+PiP7sC//eIBfPTiO/DZ33sGzjmxmj/2W/ucI/fFD//seTj2gCXs/RS3qKT/CiWHqxGkj6PkhFSQiFMeGbtE5WT1ynA7Gm8fW8eH856IciKZDtksa+UkRsqq1oUx6CYca2F84YTVX+tkcRv4vFYVkoqWJFkj4qMNBRpHnL9O6WPVhJzQqzrHJ6u4QV7uFyXyyaFk/b82xydJKSLKUrriwwlWzrVe9vvlKj5lhvI106HEJxM3PrAB7+0/zf7s5ffg69c/iPV9FeAdX12Nd3x1dbS/Dbv5r1+8nr2/6l3fY6+/dE2Vf7Hmia142vt/yPY97+9+Ghzfkh4A+MJV9+ELV93ntHmOUA0A4Bs3VA7iX110G/7qotu8Y//Gxy7zvk9JD1Cdl89e7n86/ZILfua899p/uhr3rKvyHT71k7vxqZ/c7bQ55r1VaM2tD2+qt0NYt3mnE/pz+ocucdq9/zu3OvkLAPDoph3OHL/zWVeh+fzP7623/+Kbv8RffPOX9euP/OB21tb+Jq8mT+BP+esfe+3/137uzRbikFqCZfH169fWxCeGpQvm4F3nNos+0u+t7bu62L6rWysG8hr04ff/+VoctHQ+Xv/MQ2uSY0Ffr9u8Ex/6z+Y6etmnfo5TDt0bv1jDn/7f8dhmAFW4lsWff201/vxrq3HaYfvghvt5GBYl3ADwo9sq1UaGed70wEYc/u7v16/P++QV3uN52f/+uff9c088ED/41aPefQBw+6ObnWvkT84+ynvtSlx2R3Oedo738K0bH8K3bnTD9p7+gR8BAB7f7Ibq/b/i+wPg99ER72mOnZ5bAPjMZffgM5fx+1N+9hf+bA37LrHXuiWygP/6peqoJT0AcNz7fui0taBE2OLOx7bgnBODXWoUhcFxB+5Vv6ZhIxLyLUpwYkSlyxxq0sejjtTtevx9rmJEVJCSrJROx/OoP5ZA+ELGvGOz8QJjg5+TEv4cH1/Bh8aRjStLzUrwrg1OqJunXRVuU3rPHX09Mo+nhwBucYN0573JvUlXYvJzW/p5fGV+gn6vh2Rm4ZT1TulDiiik2ieLGyTbh+pLI7Vstp2rul9HJ8dHiU8mLhMO6HqR36DIh3XWFGH80ZduYK+PPXBxq3Goc7B9V9ch1RRLF8zBU9vdym8PP7WjVrIAYG6nwC5PmCclDg9t3F6rmamgpOeAvebhsU17NlcnZDeAKOkJIYX0TCZG6T7yEZIU5KxvIcPWQLaD688gXP2N5jekLmDK5y37oWWVYx9fS6jJdQgRpNQ1gkKkzPbx5/i45MTa3Q2Rr/5fnzJkm1m1i9nAjs/j9JF2jVqmis9UQj5wyFd8cspZ83st1b4SvbyQOnLdp0eSWSWmep1DYsb7a+vkFDdo8/2YE1Jn57Jq2ajk+CjxycRxK/bC2cftj53jXVx593q885zjcPjyhThiv8U4ZJ+FeODJbVi9diMOXbYQO8d7OHr/xfj2TQ/hoxffAQC47i9fhLHC4L71W7Fi6QKs27wTc8YMvn3jQ9hvyTw896jlWL9lFy759WM4Yr9F2Lqzi/NPWoHbHtmEC392D258YCP++DeOxLknrkBZVkrAzQ9uwJ+96Bj8y5X3YsfuHsY6BueeuAJ//OUbAQAfftVJeM+3q7Cvi952Fp7YuhP7L5mH8z9ZPe3+21efhDmdAiv3rioj9coSG7ftxoFL5+G3P3M1jj1gCV5x6kH44lX349FNO/Da0w/Ba844BD+9/XE896jl2DHexX6L52HNE1txyD4LMH9OB4vmjuHmtRtx28Ob8IpTDsJ//9pq3PbIJrzx2YfhuUctx8mHLMXDG3fgsjsedxzGb771TIwVBUoAey+Ygye37cK8sQIGBo9t3oFN23fjkGULcdLKpZjTKXDl3U9gx+4uzjxyX3zy0rtx9P6LcdT+i7F8yTxcf9+TWDxvDDc+sAGbto9jvFdi7YZtOP6gvXDSyqU454QDcfGtj+GgvedjwdwOur0ST2zZhZ27u5g3p4NjDliMj158B47cbzEefWoHzjxyXyxdMAf3rNuCo/ZfjMc27cAN92/A/39NFaJ0wWtPwcmH7I0ntuzE2g3b8I6vrsbbzz4KZx29H5Yvnot/uOQuvP6MQ9ArK2LRK0s8vHE79t9rHk45ZB9cs2Y9FsztYMXS+bj3ia1Yu2E7PnvZPVjzBHdq91s8D0D+F9+GbQ1R/8kdDYn/0CtPxBevvg//7YVHYdXyRThgr3lYsXQB63vfE1tx+6ObqzyV3T08bcUSnHboPnjWEfviktsew9Vr1uPNzz8C88YKXH3Perzn27dg5T4L8I23PAdv/D+/wHX3bcAHX3kifu9Zh8IYg/d8+xZ8uR/add9HzsdXr3sABy5dgGcdvgx/ddGt2Gv+HPzW8QfglEP2xlinwHi3hz/81+txyLIFWLXvIhy8zwJcs+ZJvO6Zh2Djtt3YNd7DgrkdGABH7rcYb//3m3DSyqV48QkH4qLVD+PZR+yLsY7B3E6BTmFwxqpl6BQGO3Z3cfWa9Thp5VIsmT+GP/3KzbjtkU34+9ecjNNXLcOadVuwaN4Y7lm3BYvmjmHZork4ZBmvNPbtm9biHV9djVc/YyU+/rsn49ePbMbDG7fjsH0X4ugDluDOxzbji1ffh3e86Bjc9sgmzBvr4N4ntuCd37wFbzrzMCxdOBfrNu/EMw7dGy88dn8sXzwXH/vRHfj+LY/i9MP2wctPOQiHLVuE/7j5IWzf3cXieWM4aeVS9MoSR+63GGue2IrbH9mEYw5cgo3bdmHrzi6O3G8xNmzbhUtuewx/cNbh/XZbsG7zThhjUBhg4dwOvnDV/XjeUctx/tNXoFtWeUE/v/sJvPj4A2CMwSs/fSXedOZhePkpK3HD/U/i5IP3xi/ufRLX3fck5nYKXHr743jZyQfhNacfjMIYnLhyKS6743F85rJ78PbfPBpzOwWKApjb6WDH7m6tdn/xD56Ja+99EsetWIJlC+fiDZ//RX0+mVuc8VtbO1OeffJWoeFtTIEQZIJxY7LPdcqpakG7yER+t09tX+k+Pa/2+ZST/vsBguSG3vkJSZCIlQ2ZkHYzstT/vygAdAfZ0IQdhRQfyUIlCfU5fWXf2CYMTstZTyXalLOuP8d+v05Wjk+znYKiAMpuHsGixCy9elz1N6ectW1jw5aTFjDt/81Wvvp/80LdSI5PUo+ZDyU+mTjvpBU4j8S+Sxx74BIceyCPN3/e0cvx0YvvwNIFc7Dfksph3WdRldtxYH+dineftxfrc9bRy9nrQ/ddiC9cdS8A4OzjDqgXqzzp4GbRygtedyrrs/9eZ+Ir1z6A151xCF5w7H644f4NrP2HXnkiNu3Yjdc/89Dg8Xz6Dc/AGav2wf57zcerTl2J9/3HrXjPeU/D0oVzcNph+7C2cgHNQ/ddiJeffBAA4E3POQzv/OYteNvZR+GAvapjXrF0AZ5x6N7YsG0Xzli1DIvnjeGKu57AaYfxNTtWYVG9fTz4eQKA5x7VnCsa1gUAK09ZCaDKMQnh/KeHP08A+PvXnBKd81WnHlwTn5effBCKwuDw5YtwxqpleNWpPLfmU6/nnxEAnHzI3t5xLfH43dMOxov/4We46/GmUhz1DXJ+4NeRsCm7/au/egkWzxvD7z37sGjfVcsXYdXyRTjnRDfE7kXHH4AXHd+c43NPWoFzyX3y9bc8x+nzoVeciC//4gH84VmHAwBee0ZzHf7tq5/utB/rFPjXP3gme8/mgfjwpT98Vr19CjnHEvPndPAbx+5fv/7sG09j+4/Yr1LX7HXrw6tOPZh91scftBeOP6i5Vo85YAk+9MqTAADPO7rK8Xnm4cvYMUv8z5cch//5En49v/03j/a2PWTZQryA5F5R0O+rEw5yF7mV99tzj1rOrsP7PnI+aVvd8886Yl8AVbXJr12/Fq9+xkrMn9OUVH/FKSvxiv69J/Hl//osLFs8F8cduBfLF6Owjk0erUf9y5z6PKAmMUSVKcXEcaWD7KM5LMIAThT8aovd1+lnfYcIEiUJIfJFD65TGBaiJ8fmBETYSYhGvDT14HY9Yg8zENxhZsQOHJWy5Npt7aVq0KiE5QwDZBnnFEeckqVu4vo1AOq8YTrGwLmQT4hb5fjUDwnS7bNNdmcoPlL1zPEBavUmQ/GxD4Y0x0eRjMP2rRx3X9hQDj72uyfj81fcG3XiKM5Ytaxe+G/l3gtqRcdikKMLcFKwYumC4EKZg/DaMw7Fq5/RJP9bGGNqhxCIE5SZjG++9Uxceff65C+THBhj8P0/fR4+/P1f41+uvA8Ad1ByYB1aG+b1nCP3rStjTTWKwuCuvzk36YteMTMx1inwhmeFyZsPzzlquff9JfPG6kILlDjkXB1NidzBN0iJxkGhio8sZ+2UmCakjIsT1tmReSqoc2VsWI+FLxytMO4+n4pi1REequaOXecCwd0n24XsDo1NbW+UmEH2WILkOabCsHbhHB+pfPXPXdEPE8ToOGnDACM++1xHvPnsUuZqoXTYeyDT4Qcq9SaHYAF5xRckaUwJQavvgdyFXPvnIUfxMcbmNqZXgpvpGJHDmNlYumAOAOC5R+07oXEO3mchPvDyE/bI+ivTAUl6RgmnHbYs+DR+T2BOp8C7zj0OX3/LmQAaZyyX/zxtxV647yPn4/RV1ZP7mIoxFZjTKdRBUQAAXnPGIfV27VhnXuD1peTp5y5g6s8zkV2lg05VIvicdwN0hfNfkjyB6Ho/8BMIX9hb7cwNyB8yfcYl55VlqmWVuJqIFS7RoMdq21EHzheuJ59O+9QpuUq8e3xuf7s4ZQle6lq/VaYOMvQqqQ/5gHJKRtsHCHKMeB8DWykxdx2fnFA3eK7NwV36oW79mNoc/y6nmASdq9tLn8fmEmqOjyIbN77vt7BwbmdwQ4UigHljHTxtRRU+RX9f2nwVLeiHJU038VEoLP7yvKfhzc8/Amf9r58wQpBDjGM5PhIladgjKk8o/6RqLhYM9ShDHeOWmQbi1czo2NSxtySDqzXNeOO9nne8QSFjAC0L3ThpPSefyVVYDDm+RpVpkqWtg9QtS/bkW1a68ilfNEyQ5gI1280Te2srLWhR2DDBjKf0ioljQjk+maFutKpbsn3VRHkEq/+3TTnrnPNg1zPanaH41Nd8klUc1fnuZRQ3qPqMUo7P6D6Cn2FYtmgui4FXKNrAFwbTBjv6C3wesNe8iZqkUOwRFIXBAXvNZyuzp4SsUciQGwonJ4QQCl5+WpSzpguqesawoIREKjQ0pj5UtMDaQ9sZ4hw283BCw8PjqA3Ndj02y4HhIWeULFGbXGWIz2OFL6pUSXvowqT2+PyLskrFp2Rj0wUoORHjNvR66WFQiomjWeyz/zpJ6UDdp0eUxYH9DP/sU2DvlUoxzJCWkEeim/V17Lzp5yFH8anD8NqsZ4RKkU5WfNCU1x8VxUeJj0IxRPDFx7fBpu1VLoUqPoqZBlkcIOentnkSOvgGocUJZOW18HZ4fR2mthA1IkROADiLepaEkDCi4ilGQAmiJEj0HNYEgpGdkikltp2vGp0R3zk0h6kOO2LH1zwh52Pz8B8aQkRD59zcnZKPHcnx8YbRKSYd8lpKuWfpQ4ocJcb2SZ3HzpW7Fk1NzPwrHsT7kGszxTagWrwUQFLujcy7y4nEK0tklfVuSOPoqKhKfBSKIYT9Ym37+75pR1VoQxUfxUwDdZJzr+861M2n+Hhel579ZcmJE13A1JITC5or40vQt44GV0S4Iw/w/Bo6nnTyqZ2VQ8JzW2LkhFdec3OJqjE8CpRDkKTiw7BSymYAACAASURBVIsJlPCHPvXYPGDhTY3D7CpV9v3g+e7bYLdrjIiTNgyQ4YtJIV79v/W6Msm5N/zaTrWvLJEXUkeu++QcmvpBhb2PchSf/jWcVM5aqp5J5tUNu730dXyMMej1Svb9MexQ4qNQDBG8Xzwtvow2bquIz/5LVPFRzCwYcFUk5/LOaWtVArvNqrUxcsPJAB/Ddd6pw96oLX7SIUPGqmPwEaTm+JocGNMUThChLwVRnWioG3XMfOTLLVrgyfEBJ2lSlaHOpc9uOp5LYojaV6t3ZHvg+W6OaTRctOGAvP5yyllbBSI9QZ9cVxn2VcpSzjo+zTWcySvIOj7pfazik7aAKfq2tXvy2c0kMSX64aMjclMp8VEohggFcVwmgve99HgsXzzPKXGuUEw3qCObj0Z1cOAZlK7dUxLmUwa60SII1Ws37ISHa3lIhyfMjCoiNFzG5+T7ihb4SUw/dMYqUKUgHUXj2NmJ/QpUvx0J3/MdX4fY6le0wMbjYXQNmbOobSAqjy9k0D7NZ7aWo+OkDQN8Ct/gPs31kkNICkNzW9KVmDrULdHr9d2vA/tYstTj904M9QKmOVXdxD2QSs3q75YsxQeQhU6GHVrVTaEYIsgnqW39w9857WD8zmkHD26oUEwxqGOdHeomCEUMTNmh6g99Af6QQapBjdrSECInzKzf0XX4S97OQzR8a9ZQJceaKsPWjNi2tlNC4idVzdjd0ub4NH2M6T9xdyqq8VwgSWg6grA1NqDuAwBF4SneAH4empA6Shq5I5xTiUsxcdTXWLYS0+SPpK52QasK5oS6Afy6H4Q26qER13MOT6hzfDJC3bLPA1GX0qu6mfzCEDMcqvgoFEME+WQN0FB2xWihKHg56DYObApfootjUh3HKhgWLOHfs08WIKA5MDUxgN/hb8QWN1yLq0R2PLHAZ/8/WbTAkLwXGhLHw+0ae+y8TilplJywCRsoCbFP0rmi1ZyHGDmJ5fiw8DZ6Tuz5LtycD0rYFJMPJxctg5BUIWg55azzQ93stdQ61C1ZWaradck9OrhP9TenuAENK61sTQNdYDXvfOcVhpjpUOKjUAwRaAKxQjGKoI5Ndjlru+Hp5huJr91Tetux4gZCSeqVbg6MDBOzY9MqVm6Oj0sMfPk1NqematfkKTFVpra1GbsmdkQR8+UZ+SqvyYpqRo4t+vAcn/6+gpaztuPxEDZ7TKxMdV04IX6+u8Qee3xKfKYOVFGpXueoCWU/fyQ99KpHL5iMPlmVydooPv2/OWWm2yxg6qu0mIO84gaoSeOo3FNKfBSKIQN1NlJCehSKYQKt0gXkFjfgYVAxWNJQtafvy9LWJesDts9XHc2IJ7688hpXUTxhZhHlRJITJxSsHx5nn9LW7UrXVl9+jRPCBs/DFgPXhjJAlkhYnkuqeGlqG0bHSZXPbnlOmvPYIQ6hhrpNHXwKX2q/XpkX6gaiCiYTEgPnOh08S4Wq8EL6PLZP9TpdvdndZ+9poW4Vcn/9rTndXnoVPV+BlmGHEh+FYsggF0gcke8ihQKADaWqtnN5vVQ7KOR7PLyNbIOTHbqAqS1uIPNe7DbQz1Oh6/iUUqnwJ4NL5YQrQ01ISyg0jdljBDGw54cpJ/y8yKIM9rw0Tm315JvaQBcPjasybniS7GPgC+WzRJG34+fODb0bpTVHhgEyxCsn9Kos86qMGc91n2JfvZBwRh/A3h+pZImfh7x1fPIXMO1lh7qhniu1yANqcqo5PgqFYppgiBOhUIwa5AKmOZBPnmOQ4W21+lNGcnzgEhJf6FVMEWF9PHkq/nLPfDw+tsjxgfGQE5kLBEZo6vHEE3uf2kKJqQ2jkxXj3EVG3bV2+Do+qMPofAuTyrFpuBMr+FA0diumDi6RT2UX/QcQmUpMbkidVRJzc4mAzKpuQvFJyvHp/+1m9IFzvtPss+iWidXj0D/HJbJI40yHEh+FYshAV2xXKEYNhjiy2aEckXLWkkyVpJ0kO9FQN1CnyM1TkQTChs75SzJTstT0acZriEozHt8uhT2Q5KSghK2ZlxYMqGzgylLTrtk2MI46VdnAFaTGEW7OCSV58tzVSg757OtqeeRJc6+HWnWi59tHGkfl6fQwoL5eevz1IFSkNcPh77fLyaGp2uVXJqvDwnJUov7fnFA8RzFOIUuS+KQSQHtMvV6ewmbvw6QeMx9KfBSKIcNEnogrFDMdbo5P+s+tVDEGoVZ56AtAKD6U+Nh8HRoKxtUN6yg0x8KPg5VkjoSZsSesZTOG4yARZ86SIKqIdFg4WlipYmMzckLIl7FP3Bu7/YpWc77qdiL8RxZYMIY7VpLk1eMZ9zxQElSfYyimCrQgR/U6sR9MXc0sPQTNfQAxCPb6y6lMRsl7NrHIUL58iw8PgixMkoo6rK5bJi2Uau1pHm6Mxl2lxEehGDI4OT7TaItCsadB1Y+2/N6r+MjXZXg/JU5uqFvJnCJZmpo+kW7CtUoWhuWuvO6Ws6akoyFSnjwc2qcfPiYVEbkQaK9HiiiQMDGq0Fj7jCATMqfGkipeKrvvzDFSNWhs48/xoUQKjeJDVTAZ8jNKFaiGAa7SkU4U6nslo8pYboiXvV7yFkq1KmNOvljTpxojsRdROlNOXf08pGWoW846PlWJ+ZKtKzbsUOKjUAwZ6I++QjFqoI4NkEfseZhYHKzyW8krudH5aXEDXzEBayElLo1SYfNrBuXAuMUNvAt8GrfymlRvjJHkxIb1lWJs/pSel+GuT4sTwsYUn6J/Jgmx8+cwufkfTphgPTY/J75zJ51fXxluxdShER/zTrxBZm4LrCPebKegLpvdIk+lVY5PhuID9M9Dq1C3NLsk7AOSJNv63x+jVDBEiY9CMWSgoR36A68YNdDrOxeNIzR4gB4lNHIf40Rc/eGhbqF8nb7jU48R6gPSp38MpJ2v5PQggmQVEU6+fDbwcBmfDQ6pEjk+NKfKV3LaX5QBgbEtiQHrI201/Xm5osXHthXoFFMDuthn/40kUHU0uZw1fTCS+iH3FZVuLz33y7BrLp3AAECXLGacOpf8zoiBFgip+qfOU/3NU3yaIiNKfBQKxbTAeSI+Kt9GCgXAHOvc8gYxxUe+x8kNf1rNFB+yLR1xmqdCiYbjvIsS2K7awpUO204+2aVloWmoGs2xkGsZ0QVMjXdsn3JCbZV9DLgqw0marNZWnwePKiPtqQgNUapKfu7qMEOhkMkkby1uMLWoP8dMRxwGJMcnzxG322l93IcOKfP4X0T6eB5UpIAVeciQpOQDiIH2gdyjyaGF7kOQYYcSH4ViyFCtgq5Sj2I0QZUHIO8pY05bVrQActFS2o6+L0PdXMVHhmtBqC1yTZ9qPDdBXxIsO69vrR226GndrhmvIid+8uVVfMgTfFmIwafKAEBnIKmyfZpzSRUxqyZxu93z3ZTrdu1pQq4093Eq4Xzeqf2ArMU+bbs2xMKu45OsdJBmqcdjh85WYmCy1kCi13kOGsWnl7yAqW2Ws9bSTIcSH4ViyEB/9BWKUQN17nP5fbSctXjtKD6B8LZ4OWtPvk7BnbkSbj5LG7Wl2keqxJHjoE9yrSpTEuewUk78BRHildd4WF5DVKgqw4kYrwTnUWWsMsTOXTg8TpJGY/j5MqIPkJuQrpgoZKhbDomxik8n8fOin3cywTK2uEFGqBsZvU0JbCCjGIBpwuNSurQNdbPo9jLX8cFo3VNKfBSKIQMt95ubTKpQzHTIHJ82ik8KYZKEhnahpKiutgRKboiTXzR9bLgWX3enCnbzLx5a/fURA0Ygek27UjiX7ho6Ujlp9lFi4K7j49rAQ/n6hQUcu5vtph0/vo4xTqUrWlnOqj+GtDNWLQO3G+DnmOX4sAVMR8RLGwK0VXwKk1/VjYZ75igqVXGDvEprTf/0eQDkV3UD2IOKlPZAm/NQIXcdHzvXqISPKvFRKIYMNIcA0J93xWiBPtHNDuXo//U9EJDhoTzUTRYxoPvc96ny0eQPNGFmch0fmgND+1CVx13A1B8yJlUZqbBU5Kvp0yncxGmfKkO/V/whcTSMzqfk+PvUdvfn9uVH1dXa0LQz7NzZc9IQO381On4eFFOD5n7ov04mFwbjfVaf7ohTJSZtnqKwqm5GNTM2Z9o8kPdrRtW5Wr1JmYbcQznzNKFuZXIxCRq+l1sRb6ZCiY9CMWTQBUwVowxH8WlB7dMUH7otSVF821ehrXbKwdWWJszMjtGEa1H1xkn+F/PYfb4cH/ZUFn1yQsPtfISmLo7gC3Vz1almAVOpaMHt4zlfvna+PmDnrlLLmvHKumqdL3/I+MZTTDqcpP6MKmhdomam9mm20xlJ7jo+jGAlz1IhJ2zNtssJj3PXAUu1z/Tty8l1avpojo9CoZgWVE5Nta38RzFqoIpCLsGvlYGEtjJPLkSEqHMhq1b5FBEIElPadW5EaJl03kvi8PvGtvvchUCl0mHAyz1zBcoeK610lWqDT/Gpj9W3jg/Z51OGmKJlPGNDFHno95eV82IV8RSTj9ZJ/QbOZze4E++fOg/66/jk9bHbeSRBPpwY2A95ahkl+Kl9KNqQmKq4Qd48MxVKfBSKIQPN8QHyv/QUipmMiVR1Q+3MD6Y+vqIFFr3APhqGZdvxGHj7FJsTGqb49Eg+izdkzDTtxJPdgaFl/XAUNxwN9XbThx+TX03ieTNOjk/hV8EMOdbQ2DyHqToGqt5wtYyfb1fxCRM7xeRDVv7L6VeXs85MtqfzDoK9lrIUn8B2Sh95jw7sR0LdcghJmyIPQK7iQ+cajXtKiY9CMWSQjqFCMUowpiEluZd5DknqipuI5/KAbLvqj69gAF+TB/1tX46Ph0AIhx+ePvb4nJCxHidf1jXkBMKntvCn00k5PoYTtirsTRC2Ml4qWxY0sO0qQsPHs2oZD+UzznmQ504Vn+lBtsOPKtEeyAlB4/3T5qmuqzJDtTAtJqLEAsgjWblha4XJU4koxjMUH/lQZRSgxEehGDLQJ5wKxaiBlmwG8p4x2ra+28NVdcg+sY4PJzvuGLQ4gVQZ+Dojtp9YCBQ+VUaoFnBj+ZlKJJQYexxNuB8lJ2Bj+8tHu8oJJUi+81OF0aHeBmhJ7VBRBp9aRgpDRBQfS4K4WhayG4opBL2ec5LtG5KQOI/onzpPCX6/Zs2TnLPEHybkVJDr5ZIlcg+0QXo56wo5ZGmmQ4mPQjFkoM6B8h/FqIE6sm1CZwB/VTcJGc7GegRIkHTyHQJheLx+0fe4eLgW4BYJ8CX/exY6lWOD5BZZe8R4zCElRIz2sePJJ8i0/C8nbNTu6kWnJie8vzu2IWM3JK8pDNE4gFYtYyXDnWMyTu5VdYpHw0kbFlCVMV3xaUK82i0smq5alGWVG5u7Jo/cTunTq4sbpJOYrrhHB/YB/W5Kn8cit6x3r9cURBl2jMhhKBSzBzShGNAfeMVoQZZrz3l0H1V8BBkqBaEJFTSg3eRaG5Kc+Jxyqya5RRD8agsPM+O2e9fagQgfq8kN6j4y9MaSDmOa4/MpJ6DkpK8scfWGk6Dm+HztLEFqjo+X+K4OntlQumWvYXyhfH0bRMEGxdShTRW0wqDO8UnP1yHzZPz09cqS3VOD0C6krkI3U3GslK9qOycErZdbPY7NmXe+u2U5Mr6GEh+FYsigOT6KUQZ1mNv0BdIc354gNGVgnzfHh4WZNftMreTYkJ9m/RCmRhjfQqAuOWkKHTTHR0PB7Ly1c8KKBDRP0n19jOFPjXmIHiV2pA/c0LlQRTUDkKfYvoVOXeVMqgZln/Kx/ChwpYnawFSD0fDRhgZtnGqa1N9J7uOfc2CfMjfULZ9g0cIkOWFh1XdBnuJDH5xknQfPdgq6vfw+MxVKfBSKIQN1alJCehSKYQINi8q9uusn/p59bo6PCGEbQHYAQkB8OT7992koWFH01SSIPBW4IWhucYOSbQMJBMmrOsElND2ryshcGXt8zbHLCnZ0vND6PJac9DxOLVe0iJLTP3e0Xdk/eYV4nys+blEGegyKqQFTYjL65eb4oAUhsWGTvTI9pI6Vzc7IWQKsOpKOgoS6pZpXeO7rHKQfk/tdN+xQ4qNQDBnok+L6DYViREB/0IG8y7tRfFzqMy5k0jL4Qiby02auukHJiSUGbB2fsnTKQgOuMgQPgXCqoxWAVFicwgnGBFUZpoh5CERJ5mlsoOehH2bmWa+Fxf8TMij3+dQkq5zRz7rO8QF/P0bseBiUfjFOKVqoCVWIV57zzpulqjdNqFsOWfLPGZ8HaKH4GFplMl2Ryo384PdXXp/uCFV1G5tuAxQKRR7kOj4KxSiBqhBtQ958cMpXS8UntM9DgqJqiyxagIowuWWhaTnaQWWhqeID0Y6Hj1X7GptpDoxvMVILSdia/twB9BVikNsAf0DD2zXnUuaFGDFeRRp9TiS3wRfqNiI+2tCAkdNEp5/mn+VWGavmSbTN2JDTvBLT+fNUDXNzfABDqrqlzkXC41JnaUHm2PpjI/IwQYmPQjFkoEmNCsWogYZcAXlx5bWi4dk33uU3Db2HJMGS+T9ymypLjtpiuCpjHS5OkNwEfd8aQajHtsfnIxDSBo9yUso+VmExACipcp++yxAm+/Qc4EqO0y4QikND/nwhbPR1rfiwdm5InAwTBPKuG8XE0eZ8U6VjMosO2Gu7dXGDTDWql5kPQ6/hNqQxhwDW25mhbvlkbuZCiY9CMWSgDsUefCCuUMwIGJjWF3btNHi6y1A3X5lq3z5vDkxAbbEOv1Q6SrhO+dZdXWzd1a3bSTUJoCTIfbIrq60BtLgBfbLrV5OMjXVj7fi81la2Tc8DXFttO1qqmO7zqWV2LCPalR5n0JIv2s5HsEbERxsa2Gshy+FHc18WyYoPv7ZTJ6pyfFoWN0ibheXG5YS6FQbZOT70wUmbamu5BNB+t4wCNMdHoRgyyHK/o/FVpFBUoHksQN71bdv6QkEl8elS4lOG83p4Dowvx4c/gvYmeQ8IsWFkx6Oi+BZE9M1jc2X42H41yVWG/FWifCFsMmfIaw8hgB12fM0x+cgStcfa6rQTr6WiVb2v34xTCXu6s+5X45ZaT50nZy5bGn2q1vHJJQmGhLolkxiTHwrMRs4M+ctRy2Y6lPgoFEOG6stHpR7FaII+wW+raPr6jXdljg/ZrgsnN+/ILZ8qU8W9h+3gi5E270vnKxRW4xQ3CHh9Tu6OsEH273qeSFchtL4cHzqlCeb4SJWJEyQ/oXGeqnuOieZH1TbAPUeOPVBMJey5zyGcbULd2hYd6NX5Yul9mu08zYcuKpzUyyB7AdNQNcOBE9X9U7v0vzN6uo6PQqGYJtAvPKU/ilGDo2hmOlKA/77oisS4XhkmQr78H+N5wioVFgNJAJq5YmFYIYed9pftfCpRPQ8lEKyiGm0nFRs/wXKKGyCkQJF5wENyg8qQIHz82BvSJwmftNs39oj4aEMDI/6m9rFKbOo6Pqx/hhJThbqlK0ttq9QBTQ5dKgz8hUkGzSW/w1LmabbzlK9eKSo3DjFG5DAUitkDp9yv/sArRggGDXHJrV5Yr+Pj6bbbyfFptmV7f46Pqa2pF9SEq3r4iUs88T5UJGBQtTbZrqksR+eKjCfyJXxExa28NpiI2bC1QTlDThid5/jo4q+1DaDtAkRKmc/UosXp9uWfDe5Dp0zrY8NUe2WZ7LxPJMen2yuTc5YA/z2aMpfvfk1FuvLFvyNGAUp8FIphg8mv369QDAsKoazkhowAfsLUdULdEnN8eo1dUumQOT6uihImBgctnd+8DhQJoPPExqOobAjNK0iaIBSx8tN1H7gltQGhLHkKFdB93j5GOlnVX6cIgnFf+48ViilErfhkKh3ZVd0gLtrEeXqley3F0CZfjBcPSQd/0JDWx1fafjLmCT1sGWYo8VEohgzSMVQoRgk0xycX9nfZr/jwULfYFGVA8WlIEHFwhB8WDEdz1I3BzrssbhByXGKqUzQcjYzlW+jUb3dgPBmORm0ojLdPjKgE86MQPo+j4pgNI+znkKO0tVrHJ5/31J2ycnxaMGe65k3uAqbNGOnnIb+4Qb56E7v3hhVazlqhGDLQMq/OsuYKxZDDEIKRzX9qxceFXMCUlbN21vHhalBll0tAZM5AKNTN2Y4pIh4y4FdiAqoMwo6UtE3O5VOWYk+JabuOmCekOgWPNXBOSuccR0L+dAHTaUP9meQoPgYY79/syaoF65/nvPuKesRs823Hbasajmcu9tkmrA5UeU3uQ3q3UNhG5cGCEh+FYsjg5PjoT7xihGAMJy55oTOCKRDIqm7xdXzcdsypDjxBjpGOZOfdo6Kk5vjUY3tCxpx2IIoPLPFx+6SHmYGBKT6MnPjbyHMSVpbSz7Fi6tCC9wCYmgVM7f3Q7eas4+P2T7Utt7hBm7C6wmBKFjBFC9tmOjTUTaEYMlQx9hrqphhN0FDO3Mu8Vkg8+8adqm50PpnjEwh1c3J8fM62/wmpG2Y2WBGROT5JuTvJapKpTbW5OzEb6rGbl14yWG2DIVmpCtjgHF+Q2HFbFVOHppx1eh++aG4qucgnt7bdeEYIWhvFxyJ3zZuJHBOQQczIdpvy4bqAqUKhmBYY0zhpuVWvFIqZDpnjk+PA2pYp6/jQNWvsOj50jRmLZh0ft9yzLFONCOkohCcVrHSWuIBpyFlySUd4Wyo+XnvEgElqi0jySVNo5Lnzb7vKkCo+MwH2fOfl+GCCC5imqyOADXVLtc54tgb0sPNkL2DaIF2Ryr/A24Xv5feZ6VDio1AMGViOD0bny0ihAHi1ouxy1iQETWI8kONTFKZWfCTRqLabsWvlpb/PWcfHuE66tM32d9QXz3ZsAdNB4XEWNGmcO1WmSUg3YQIRDzOj84h21IbIeI01HNHCEKF2TIGCYkrRRvHxb8dnaaOOVA1zykxPxLbcqm4tCtUF76NUtCGao5Ljo8RHoRgyGJO/cJlCMTSQ13eOIxXZ5xY3qP4WBmR9nuovJTQlVVs8BETOKcob1FsxYhAiMXLwoMOfTKqEDeQpfcgeh7AlhOjFiFTINsg+AcJWKUMBIgWKEfHShgTNtZTRJ3AtpcyTOxdQfQe0UzrybOtlFzdo0CrkL3meialEGuqmUCimBRMp96tQzHQUpmEiezLHZ3e3x9o1IWx9xQeNM0HX5/Hl+NDff6qoGEhHgdiGcLtgbkvg+OTYbnEDOnagHd1nwmQnHo4WIDQy5C+4QCu3M0y+Yu3C5EsxdbCnO8vhD1wjKfO4L8Jok6cSuw9DqEPdMhZKdWxqc92mMx//nBGE7vFhhhIfhWLIQJ025T+KUYNbtTAdlkD47guq+Bg0So4dvwp1q7Z7dJus3eOrepa8gKn4tQ0t/hkiGtXfPCXHZx9vV71hpD0R0kERzcNhLf1jtyJVgvmEiaZiKtFK8WlBSEIPDFL7tKkel3pQTahbnrrSxr4cYlXPE5gzuf+I3FRKfBSKIQN1wIDR+TJSKACZ45OJmOJDbhpeoc30ezQqT0mKFlCVx+YcBfNKRNAYK90snXyE9nkPyaMm+Z3GwiEQ/j4yV4L7eX6iImoWxPNwIoTLb084vEj6oMFj1xyfaUP9OWSc9xbcol2OD9luUzwgu5x1L7OqW+CBxp62j/dPbBd4IDLMUOKjUAwZjMlfsVmhGBYYqfi0+LH13R9dUs66MM0iqdX95BY3oEqQ7JOa/B/LRUkhMVSJkWQptVR2TMlhp5aOF1KgbMe6XWjsxgI5TyishypQcrzQ2G67iTmDivYw4m9Sn8C9ktwncZ424Vohsh7t0/+bs1CqHD+9zLS/f3we/70b7cPmHI17SomPQjFkoFXdlP4oRg0GpGphyxwfH2g5a0quisL09R7yxJaQIKvy0AcOUdUiosTQ+dMqtIUdylDyPzJCxpqH9CapAEGMdLhV69ztmN1ugYUYGQy1o/ZAMYWgBD25D9lOD0GbGIlpo1rkqiNlmdEJ8t5J69gqBymwnTzPiNxUY9NtgEKhyIObAzEaX0YKBVBd32XL69u29K7jI0LdqJJTljyEjS5AyBQfUtq6nnMPqBEpTr6jJtEBIk/B4+W1Tf1+yCmSjmKKUkVHcRdHDdtGh2BFIyRhC4Q7tXEGFXsWOb5xm6ID7Dpto3RMpuITuBYH9guMkTpXOxWrRZ+kHjMfqvgoFEMGutq1QjFqkDlsOaifuHqkovEuDXWrwlHstq+ctazqZgxRf+icMo0/qLawTuG8Hkmk7JN0oYmEnhJLfya0jk9UlYkRMdB2/m2uJuWTJXkcnHyZoAOXGr6j2POgn3duH6BtcYPEech2srLUJsenxTxAO5LVJuysTdVDfkyjcX8p8VEohgwGzRNxzfVRjBqKoiEb2QuY9v8OUnzYYqSmKWfNyE5E8QmrKOGnt5JAhNSgWMnpEBlwxgqEp4TzcIywwT+2cQibn3RQuwuhggWLFhhOaDqhcyJ8r9SFXBWTi0aZzFE62jji+X3aXBdtwuPahKy5c6X1m0i57Zx5JhomOBOhxEehGDLQMB0gL7RAoZj5MIzu5FzftWLj2eeWs662O/1QN1rO2qv4AE5IXLVNnb5wSBsP3Qo/uXaqqAVIVjtVRrRLUGWc9XngbxfMBRBkKb44KhmbKlW0Oh7keP5txdTCXhlZn0ALpYO3SyQJoXy4WJ8WMkzbHLOJkpg2KlH6PP6HG8MMJT4KxZCBPhFXKEYNNMcnewHT/s+5r99uUdygFKFusoQ1XdOnatc8cOB+PVFOYILOnNtnsPNOiw4YcnzW7nqblc2OkRjRzo5n6n/6+8JqC1NlWPU3enzh0Kf0inh0W9gQGC9WFlwxubCfZZ7DT7cTFQi6nUyW6AOI1D7+OWNoS8JD13PqXG0KIrQ536MCJT4KxZCBhukoFKOGiaxT1Sg+A8pZF6JQQR3q1u8fzPEhfcicoRAXTgb4gcSLDtB+pE2QDIRVmSCBIPtcxce/LY8jVrWuVgAcOo5nGAAAIABJREFUIoaBfZx9Yv6wCua3UzH5aK6tHIe/hSPeipDQ7ckkWPl9ZONk9abNAqb8Rkrs047MzWQo8VEohgwG0HLWipEFrVrY9vr25vgQxYctYFr055Hr+BAS5PQRZIKSk3A4Gu0D4eyE2zWKTyzMjOyQRCwSZmbIdoyQsLEiSgw1oiFVkaIMgqikECTZL0a+FFMHRtBT+0QIdriP/1qKYcLr+EyiolKNT+dN69fhBiZBqrIp4N85iZ1mOJT4KBRDhsIYJTyKkYUtNlC/bpkkLMHLWTchbHV4HKTK03+fEKRez83xoWRHiDLxkDHSLpaHU0ejiWMLF0dIHzulMpxUieh4NGzIyWGi9gRJVYC8eY6Jjy3Oka8dFFMKw/6kdQlcZ8l9EueJKZiRmbxzxucJX8/xfnnzVG2bxp3EydqoN6HFjIcZSnwUiiGDXMdHoRglGHJ951YtjJGkcRLqBhie41OWbK5e2Tghg3N8aFhXYnU0R92IOflkHtouthBogiIic5Gi4XbEMUvLTWrGkGMFE8BN/sKrcgzN8Zk+1NdplsOf/3m1qgTXwuGfKMHKORFNyfp0tCFzU3W+ZzqU+CgUQwYacqP8RzFqmEjVwiY8zbOOj6P4NOqNs44PrPMOXva6biccKfK0O9V5D5MdTmIswZGLo4bOiyQnUonx2ubYKscj/VghhTBho2NzVcav1lACKds5xI72C6pqI+KlDQkmer7bEZK0Pp3AdRqdJzBntE/g2k6dK0dRaRNW1ybXKRTaOsxQ4qNQDBsMQB9ej8qXkUIBTCzHx94Jkvf0eqVDpnpEvZHr+JT9HB8DsLV7KFmiY1EDOLmg20JFodsJIXExUhV7khtyFOl4UVUGZBFVQapi66MYYnhYoeFqEj0poYpx8kTQXR1R9loxdajDtbJCU6eGkITIenSeFiSah4HmnAf+N22ufCVmon00x0ehUEwLRiXOVqHwQeb45PYFXMK0m4W5yUIFBiX4Oj69sqwdfrp2D13Tp56TvDZib7SwQIAMhBYMdZSXUB/whinlo2OkipOvcDgaU5ZYcYMIsSNusuA9wRwGdr5Fp1Aon2LyQav4pfch2y0Un1RMVB1JRYeFn6b3C4WFxvs025Op3vDvnNG4qZT4KBRDBs3xUYwyzASub/uzLLvTxUsBHk7XKUxf8SmJ4tOM12O5QKi3G3uJkx8jNNJ5Dzhj3Lloqq1VYwf6BAoLSFsLSU6IsxorJU2fSAfJHLWajFcUshqdaEecvpSwNXa+IVQsQaoUUwf6maSiTZ7KxHN8pmaeLBIj/qagHZnj93UKQt9nwwwlPgrFkIHl+EyzLQrFngYlJW35vVzHhy5eCnByZXN3SlHQwCoidL0fWtqajkVXrQ+tJRMLdYuv+G7qNqGnr9w5iRQJoKMa2s8EbZDHx2wIFVggYxsxsSSAdDt0TmQIW+0oGlmGG4ppRo7Dz/PPEhWIFkpHq3V8Avdx+jxJXdhcWTk+oVDQWJ8WJCb0YGGYocRHoRgyUGcM0IgOxWhBKpptwj9SFB+2Jo8vxweot6VdzoKhVBEJ5MCEwsysPb5tY0juhJByomWqQypIKIQtYg8LnYuRKuEoUnISVJNM6hpIklQRIpZgj2Ly0cYpbqPE8P5p7SZ6XbSZJ28dH5M1jxy/zXpG7Uhjmm0zHdNGfIwx5xhj7jDG3G2Medd02aFQDBsmkgOhUMx00Cpr2cUNLHER7493e6JdUyDE5vi4C5hWDrtvAVNXEeGv62MJVFSLkRMZPtY8DebHFFROjDuXb2zACALR7Nw13pwvRpAQzhkKlsoGguQEZJ9brY1uS2JHbQufO8XUgX4mqWijxDikfJLmCd1D8XnaER+r3mSRpX7bNsoSkHF/tDh3Mx3TQnyMMR0AnwZwLoDjAbzeGHP8dNiiUAwb7LojALSetWLkYCAUnzaDiPti3Jvj4+bu0FA3O7mv7LV8CtoQiLDzLp/QhkJV3HydhnaEyZIgBoGntLFFVGmfb9ywFvDsk33CxQ3IOXH6sKGbYzL8mOh4HXHuKFkKlgIfDR9taGCv5/aqRVof+dAhd54i0ettcy21sQ0gik96l/q6b1sCO724gf8ByzBjbJrmfSaAu8uyXAMAxph/B/AKALdNkz0KxdCAPnkG9AdeMVqwimZZ/dOiP3DPuq24+NZHURiDM1btg91S8YHI3UGVF2SdgZ6nnLUhBEnmJjAFIhiiJVULv2PFyAhx7IuIkiMJVmgdHydshRGagHJiEgmX04dsBxxCQ20QxxSsRgdOlhCyZ2TctOFA47znKB35CokJbEfnaaHEyPtwsuYB+IOFVNjx2xOftD4xtXZYMV3EZyWAB8nrtQCeJRsZY94M4M0AcOihh06NZQrFDId1xnaN97B67VN4/jH7TbdJCsUeg/1xvXrNeqxe+xTOWLVPVv+9F8zB9255BN+75ZFwIyOrtZUoCcmwhQ6MCa3dE34KGlZbeJuYGsTbhZSTWB8E9nFiYOh2yB5yTIJnRPOMKDehCNogmA9zil3mU28GiyCMho82NJiI8w6kk5g2SgxXVxOJT4CsR+cJ3BupyMmTKlqc7zYkZhTz5qaL+PhOn/NoryzLCwFcCACnn366xvQoFLBPxEu859u3AAB+due6abZIodhzsD/oH/jurQDynAEA+NE7XoDHN+8AAGzYuhs3PbABvRKYN6fAR35we92ujmbrj0+LG9Tr+MCf1+OEV5lmO5TIL8tUhwhJiHQURpIT/7b8eU0qEiBJVaA8NlVyZLtQ1TpXTfKTNAN57vzHIMlSuJy1YiphxN8UTDT3JnW20P0VQxt1o73iY/p90udq+uSTJSCHaNL+o3FXTRfxWQvgEPL6YAAPT5MtCsVQoTAGm3aM13H4f/T8I6bZIoViz8H+tt752Bacd9KB+B8vPjar/35L5mG/JfPq12cdvbzetsTHAOj1iOIDAGVZOwa9sqyJhk/xCS/wGVZbHMWH2MzIhOjP1JYEUiV9k2CYGbjdSfk6zly0jzi+uk+YsNFjiqlg7tj+8YrQwSomHzWJTj/vnch1G5zGnXIgQmGT6fPkk7J26k2bPsld2uUtsf7pc81kTBfxuQ7A0caYwwE8BOB1AN4wTbYoFEMF+sX90//xQhy+fNH0GaNQ7GHQH+d3n/s0HLJs4aTMwYoWlABNrq9fA4GCBgFiECM0Dpnwj+cqJ41DGfLr3UpwfuUjdeHV9JLTfDxmA+3DbGhe3fHYZhYiRdt1AueEkq9BpEoxdTDib1Ifet8ke+L+azuG0HUan8e7Ge/Sv4/KMlO96f/N6TPRHJ82CtuoKD7TUtWtLMtxAG8DcDGAXwP4WlmWt06HLQrFsIF+9yjpUYwa7I/rKYfsPSmkB0CdJ0fno85KWTbOSFmHuvmfltIQNEkMYk5DuDAARDv/2CHlpYgQlZjTR/es3Hs+t8faAMOVmKDq1JyTQhhBCeAVdz1B5uckBsFjhTjfxJ5A+XDF5KM+31nOu/2b3okrEGn92ik+7a6lNoTEtDh5Nflvcb5zphrFSonTpfigLMvvA/j+dM2vUAwr7lm3FQDwxmcfNs2WKBR7HvbH+eUnHzRpcxhDQtg8ic9VVbeirwxxu2g7O1bjtpjgU9VQqFtMEaGOvUOciN3S4a+JAcIOXDWv/4n7y09ZidVrn6rHaDrxN6KqE50X/nZ0H7Wnahc4dyTLxzjHZFg7xdShUeHy+7RROnLmmkqlozBAF0gumw20U3zYg4VETLS6nSo+CoViWrBx2y4AwNvOPmqaLVEo9jz2WTgX88YKvPTpKyZtDrkwaf1+f7MqbhD+0Y+tWcOVCj4n3WbkhPYpeB9KDJBCDBipilVrCytVHf5Ynecw0V2hp+KOKuO3wdroG4Pm9TjFFuqxTdARHhEfbWhAr7lU2M81h6TG1NHgPIH7KzpPYM6B/ervgpxjIt8fiWhDGtmcqe1U8VEoFNONT7zuVPxy7VM4YK/5gxsrFEOGVz9jJV5w7H7YfxKvb6r4+Kqr9fo5PrFwK/oiTAbCaosdvAqV8xMa6uTLqm7hSnDNeMZpB++2tNvNR2ocM+ZwSSXG2DBBcQzw22DHl8fjHFPoPHITWqkBij0DSuRT0SpBn22ndeTEOVHpaHkttQrfa9FnoguYquKjUCiGBocsW4jzJ/FpuEIxnRjrFFNC6uk6PhZNjo+r+IRUC7ewQKgdd95DTp9TJICSGNIumLBtpHPot4eGt8n8mvi6QE0bZjqZN3XsP/+tY+pOsXPXMfyI6jGMaKcezbSBhk2mol0+jH87Po8758B5QpMOQKc+puQuTFFNRZPjk0F8Wqxz1SY/aqZDvyYUCoVCMevgC3VrcnxsmJifuDg5J8TjD5ETJ4SNEoiAEiMJUgr5ihdb4E++KVEJLrZKw+3Ytut0UZJGxwoRsU6RFsrnlLNmNoSOD4qpRM1FM0hC0YYk5H+wIdUz3ofOmY6JkLk24XGt86PanIcRuamU+CgUCoViVqFaBLja9j0NrnN8AmTAdSCIKhMkJ1KVoUqOvx1dENVdHJUNF9gesIYOU1v8CgtTb0zYsTPgJI2uTRI6J1wZCp9vVwWjY4fPnWLqwAlsYp9+26wQKvFgIAXtihu0I9H0oUMqJlKhrf0Cpi0IZHaPmQklPgqFQqGYVTAAupEy1b1eyZQFIOw8FYY7O+Ey1dx5twqQW+Za2Gqa92PqDQ+daxhNiFTxAgTCBpnjQ20hcgsjWYKYUVLFx6YmCOUrYINDxOi8tM+oeGZDiDb5H0XoIolAXnNp89A+ifO0JAlFrWKl92lDANsUN2hD5tqQxpkOJT4KhUKhmFUwJpTjU70ovX1iqkx/G2HHrCOcBl5K2t+HKjFGuF8pyolUUaQzR1WnWD5SM15TRc0Zu/6Ht4MJnzsaBifHY+0cstScOx5O2O4pvWLiaBOuNdEE/dRe4XyxMPh9nDgRWhKSNvlR/XsiKzyO9k9Wvtw5hx0jchgKhUKhUKTBGH+Oj920lcmSK6JRpy/gmDmhW8TpCzoklBiIOWXYGgstC/QJFi0Q5KsjiAaIY2abuVXmuK2U0HC7OQmiJCYUyhdeNylCSEcmMGc40EK8IepI/jxyOzoPI9GpDn8+warmcuccPFl+nzrHJ8OL1xyfCkp8FAqFQjGrEAr/2rxjHACwZed4PNSt4P15vg7tQ+aUoW4B5UTG4dMnuyEnH9SGgEJj56LbxrPtju13NiVRcdcFsk5tPMeHHW2wHXe4KNEMFmUYDR9taNCGaE64qlvinHyR4sR5AnMO7FcT+fRO7RZ/5X1z5qH9ByG0EPEwQ4mPQqFQKGYVQk+Nb7h/Q1I7R+kw7jYQDr1y1ZFYO/9T8ViYWSiMzglnC4agcWensTUS6mZoP0GCAkqOVKdCzmZH2MP+jditmDrQzzsVVKVMnidwr8TnCZPo4DwtCFY1F/+b0yfv3E2MNKbeIW1Kgc906AKmCoVCoZhVCKobtI3hOkg4pIqOa4JOllRbQJy+UDgJJReSxMiwNV/ZawPutMmy0HSsmNriI32+qm6UxDQEKfH4xL4OU9X8ttLjdo8JimlAznm3hDZPUSHbiX3arePTYiK0IyTNecghWPnnrgjcUzGEvheGGar4KBQKhWJ2ISHkQyoatF2wyphQLYKhbkTpkPOHihbQkDpfPxZul0qqyDa3AaIdHbthKs7CqVQNoscQGLsgDFD6VLG8DHoUwVLgqvlMKWgRjvw+bSfNmwdo5/Dn2DeRamttcp1al7NO7pd/7mY6lPgoFAqFYtaCOg4vOeGAett12Dkhoe+HysTKEC2qbtCV10OOmbtgaMAe8HLWoZwjVtFKKCeSmNHj4WFrxB4yNrPVRM5XwedpzolfWXKOVdoTaaeYOkwkXCtP8cknt+3W8fHPmdovp49VYvNC6lqQpRb5OuwwRuSeUuKjUCgUilkF9ltOftkXzh0j7/M+KeEyTBGBx2FnDd1xfWMz9cbzfj12IMSGEbHQ+jzgRQJYO0l2rN1ShaGEhJCYmFLFylkb7phRkiarzPEy3Hw8xfTAiL8psJ9X6asfP2AeIJ0wSYU2bZ58kgC0DHUr8gngRBcwTe2n6/goFAqFQjHkCIWw8fCWcJhYLOckPA8p3Sy0nBihoU/Fw4oPV0G4F+p3+ihZioWj8XVz4uv4GI+t0m4jjAiqN6JyHreb2+cdWzGlMOwCTENqaWnWR1z3KQjdN6l92hGSnD7N/TWZfdosYMq/F0bj/lLio1AoFIpZhdCPuZuw73cUZJIwVTqC4wXIBe0v+1AOI20LqkmR8Diu+EjCRewOVaMj9hih5IQKJLjKmTh3tXoj1zPy95EkL3h8I+KkDQuM+JuCNo50mxC0EImOzhOYc+BcVvHJYD72up3svKA25d5DD4mGGUp8FAqFQjGrEHLypWMQUhZccuIqHYAntMzjsLtqCzeCKywBW2Hq4+BKTpiIcRtEqJskgMzuxuFyFCTfsQpX2FWTmk5hhU2SPEKWgudEMZVoIfg0oW458wS2U+aR29F52D2eflDtzoP/IUEMNvyz7To+qcfUpnz4TIcSH4VCoVDMKoSe5obUlmg7Ga4V6EMVEkqWZHEEx3k3zZx8bN6QkQ4yf4hUcbWFGxsuZy22weENiTMIkhinelxKO3JQxkSUoRFx0oYH5FpKhH0wkJXjY/zbMbRaxycwZ+pcbXJ82pCYNiF1ANiirjG0UdhmOpT4KBQKhWJWIRS+4eafEOcd/j5BBQOu4kMd9kYRCTsXbF7BxEKkoaBETIzthIIFFJKi4O8zRSugbhn4nbCBRRnq4+HteEK6n5C6585/PIrJR6vFSFs50i1Ui8B9k9onB23C1uriBhnz0IcM6Z3oZptzNxp3lRIfhUKhUMwqhMLWQqFS0sGW4WhcYQkpJ9Rh5wUDQqSK5gwVkbFpPzaPkUSMdeF2R5QTn+NI1+Cx7eqn/oaE7yA2NieXzLaQs2r8ypJvbMXUgZLtVDSfa7rkY8S1kDZP07CTyEhC6mNqvzbqTV5xAztfcpcJh/xpjo9CoVAoFEOOkFPEVBkRjiaVhdA+GVPPQuKo4xJ03qlD6S8eYO2joUZ8IdGEsY0kgMJudqx+e0BeM9WJEDF3bPBtzifZ8bF54YeGuk0fqBKYilbFDeh2YvdO4NpOnrMFuWi1js8kn7s294d8EDMKUOKjUCgUitmFgIMdc8qDzrujsISJlK9PrFob7VcUYafPgDtchrYh7dyqboZsUxuErUSVCYX/8WOSio//qTEnSIY5z3LtH3pMYcLGz4li6kCJaSpsQY68HB//PRqdZ4KEuE1xg1blrDNsaqO+8O+WtAG0uIFCoVAoFEOOGIlptsX6PNRhJ7+chWAAdOxOIFcGEMn/1DaH0BByEiJpJuSUiLyZGBFjx8edy8ZqrurwmXhiNyeDdC6/3Qb+7aZd4xyG1jaSOVqKqYNpLpJktHPe6ZyJzru8XzPR5lKa7FC3NqGc/PujTZ/RuKeU+CgUCoViViHkAATLVAtNJFT9TTrs4epoYAQiRmh824AoQGA46eCKVvOiI+1mRCNwfOQApRJjQsdHiErBzXEVHzRGULLknDtiDyViwVBFKKYSrEJgIuxnl1XOOvCQIWUeuZ08Z3aPdsUNygzpa+KhbomkkfYfEcYwIoehUCgUCkUaUoobABCOPLx9YuFfY8RT4CFxgixRexihMexpcJh8+QssVISoGS+UwwQjlCEREkPH9hVRaI6jsY2FPkVIVUHaBRUf8tqATxwiVSPycHp4QK6LVMj1otKm8T8YiKFNUj+bs1Wf9E72vuxlMMCJHsdkr2c0k6HER6FQKBSzCqGwrnAVNulgB9QIOZ4MdSPhWjxvhrbjzkWjDIUdFxkKxggEI1X8WNm6QhGlqRnbX8BAtiso4TLxc8dD/vwklNkqiF1QqRoRJ21YQEMyU9GKnEbulRAmXu0vv0+bULcc5WvCik/iMbUhSzMdSnwUCoVCMStw0sqlAMI/5jy3xQTJSbDsdUQ5ccK1jL+PQ2jIPHIugLd1+gi75RN2Xx/ADRkz5EV4HR9x7hipcue0+0ikG7OHWuSEFnr6OGOPiJM2LKDXXCpqhz8jxIvfA3nzVNvJU2XPw+dMb2tzAbPOwwRtSu0f+84ZVijxUSgUCsWswDfeeiZu+cCL00LdGOkQYWZiIVDqiFOPYqwjVJS6XTOeVHJkaAnP3eEEwGuDCDMLEhrhxMicIQp6fAXZlmFHdoyCbMuxpOrkK/IgCznQc0fD8mQuUOoaLYo9D05a02CvkbwcnzZKB93O79/mqmql+GSFurU4joCqm9pnVG4vJT4KhUKhmBWYN9bBkvlz2HuhUKkb7t9A2niUirqPUEHYPr9ywhQfSaqk4hNQZRzi4gkZY4QBHiWHhscJxYbOw+gTI2LMBDIvJXbx46M5PpRhRcMOGflyzwHgmKaYZFDSmoqpWsdnwsUNWvVJb9vk+OQUN8i1iCP1mNpU0ZvpUOKjUCgUilmFEImhP+zdXsnDsEi7cCnpQSWnG6IRWnndXceHkImQ3eCOJycGfkWEh8TJxUjF2Ia0IzukE1r2n907C5hS8iWeIPsVH3l80u6mj8yjou0UUwcaFpqKiZaWbpOnYlp4vW0upZxja6q6ZYw/QeaTXNwg8NBimKHER6FQKBSzFtGqbvav8N7lQqD1tkepoKPxcLSG0FDPKloqG/6x6bxcJYoQMTKGQeO4+samfeoxStFHkpOa5BlBSCRlo33883JyIwosCHXKZ49i8jERctA2xKtduNbkqjfNPDnj989DxvgTVV+S74/A98cwQ4mPQqFQKGYVQnHrQbUFYTVBKjm0XcxJM3WbcDtKBtwFQ4UzR0iMbx7AzfFpiJ2rIA2yJwZa1U32kaWFm+OKl+v22SAry7Ecn9Hw0YYGbRzxzgTVl3aVzVrM2Yb4ZExkVdC8dXyyTWrVP/QwYpihxEehUCgUswrMeWLJ9oF2jvPuJxDVa/+chdOOKDQBe2hxg6giIhQfO2IVCtY05FXmhCoTVHmIOoVYRbvmyT0nVRFVxhhWBCF87uSc9Jz4P78R8dGGBlRlTO+T7/Bjgp9xu7yiNipRBvGpq7qljz9h9SWx+8RLgc88KPFRKBQKxaxCOKRKKj79v/AoLCD7qLoRbeeSmII4/9VrPj8P6wodT7PPWV/HczyyD4x07vgAjMRE1CnruLFqbWK4kOJjSLMSsnIen4edO3FMvm3F5KO5/tL7tFm/ps0Cpr45s5DRhZZxT0VzHqZS8UkbgCtsE5tzpkCJj0KhUChmFfiPuZ+oVO0sURG5MoHSz0Zk/DsOO2nXbKfZIFUnqUBxkkbmDCo5nNiF1viQ7by2yW1ihBNGF1KdpK2BPnRfUUii6e+vmHzYj2gsJ8SrTahbgOimoo3zPtHFQgehqeqWPv7Ec3xS52m2NcdHoVAoFIohBHUaZJUx3q75y/UQTlRouxCJYdXWDB07pqKIRP6AosHIEyNBlAal5RIBLumo95n4GM08QsmJHV89dOQ8BOxx1K1Af8Xkg5YvT+7TKuxsYmjjvM9pwZbyFjDtKz5ZOT4TOxPpis/o3VNKfBQKhUIxa5HyRNM47cQ+mnOCQDtjW3Oi4vbhhIbm10ib6j6ipDOzJ6jkcFIE5uAIGwLOj783D8tzcqDEdkpZ76ASF7NnRJy0YYE93zmLyE7Vmjq8f36fsQxpipLyVLSrbpfe1odU89zvjOGHEh+FQqFQzCoEiQbZ/q3jD+B9qCMu18MhCkuIaNDXlOzQfBhpT/W6+RskMU74mLUndqzc7ljRAnZ87IB4O/vE2pBjcu3mx8qVobANXnuMLJxA+4+GkzYsqB3+LOLT35hShz9/gLFOG4KW3rZWfDLGn6ji02YNJM3xUSgUCoViCBH6Mafbc8cKkn8iFQc6ligSECMk9H3i8IdskH1COTWSOLE+kfAxQ94PrXXC8pFMuMw0U4xAVajYuaPKF+8Pdk789tCy2dIeVXymFvbz62Sc+LqMc9Y8OVbtGeTkLVm0UXx6GZLPRM9D6kKuoe+ZYYYSH4VCoVDMMvjVmyCBQCxkrHmjIhPCga+3eQGC0Do3sj9N/qfzx0LOgusPCWLgU6B8c7HjC50H0ziwBWEusXA7twJdgHw5x25IfyU7MwH21OeQhHZha1P/IY8V+a5yTpcmxydj/AkrPqnz7Lk5ZwqU+CgUCoViViFEGvgaOpIY+H/0nTVrQkqF6B5a24aHj/FCBUG7CQFg8xhJ5vj8Ta6MrFonzkN/u1OYoLMqz2NN7OSxOsTOHmuEzEk1qD4eMzLhN8OOiYS65ST1T4fvPadFqFue4lP9zStnPbETkVzcIHRTDjGU+CgUCoViViHkVIfW0HF/8XkfllMTIDsFDVUz7j5fH0q4CqHK8FAw//FIhSaY4yMIkpyH59R4TYAxplnAFAgTGmFrk+NjHFLjP77w2MzsEXk6PSywn15WqFsL1tom7GyiyCluYJFz/RWtFJ9cizhSzeP35MTmnClQ4qNQKBSKWYUQGXBD3RoVJRbiRbdDzjtz2GEQzOsB304KiRNeDCUqvvftPqkOee0WOTVyDK/dhhY3CHtLlHDR4yulrZD2NGOnlMBWTD7aKD5tyOmcNov/TBDtcnzS29a5TlO4jk+64pPfZ6ZDiY9CoVAoZhVCi146ZaqpkgO+j47Fnf7ANulH81TcPnyA2qEkRKwsw/MwZSkytkFzHqSS4yosDdGIqVM2VIcXWAg7ae7Cq5zgUHt8Kk+1gCm8fUbERxsa2NOdw0uaEK90tAk7myjakK02C5jmreOTbVIrhIqeDDOU+CgUCoVi1oKXpg5tRxxp4tg7zrszRtOu6W4cQkK3g6oTPYagshQmA0awuVBZb9rMUXzEcVi/rWDHGieGNDeJ7aPeiZGkqLE5SMRU85lS2M8xpxBAm/Vr2hQamCjalLPOISZNVbf8Pm3Rpvv2WlY8AAAgAElEQVSI8B4lPgqFQqGYXUgJdaNhawZSReF9DHk/pCBBEJCQDdK5sH6eG7YmFBEyC1dbwvOwPgG7WYEFElomw9G4bWSfQ5a43QUZm41BtiUpi6lqvvcVkw+qTKaiVY7PdCg+LchWTihas45PhuIzQe+93eKxE5tzpkCJj0KhUChmFUJOvrMdUiPAt5nCEnDyuRIjlAqE+9i91diBPkHFR1aZ4/MwtSWmVFHVCRQhNUksTBoMQZMLmEZUJ3KOaf5QTKlSTB3q4gYZXmUbR3o6cnw6k1zVzR5SjuJTP4DIiROk/Vv00VA3hUKhUCiGEKH1YugTaLZwZ2wsShoMbx3MGRIEIhau5XuS7hAxh+wECE2IqIjXTrEFYkOI5AF0HR9aECFWhpvMRc9J6a4fUjcz1B4+vyo+04f6Os0qZ52vdExLVbdJLm7QJuRvqspZ78k5ZwqU+CgUCoVidkGQhmabkJaCEpVw9bCCsBipsECM3aybw0PBQk6SIfskWZLkrS4l7ZAq/7FS89zwMan4NGM1zqprK83xsQ6wY7cME6wVH8POiauWuQpSLMdHNZ+phT3bbcpZZ+X4TIPiM4rFDdpwmFG5o5T4KBQKhWJWIaS2yJycAIdxEu2pGhFUcuAhTJ7BHSWGOfnV+2XJ+wTX8RGExinXTdWbgFIFoToFz0mALElnSaoyvnPnK5vtI2mDCJti6mDPd4460sZ5nzsdoW45Jbrt3zaKT4ZNbfpQtCmHPSr3lBIfhUKhUMwqhEq0uiWUiWoRUk4EUYmtC+QfIGaDKJZQ+B17SWJY5F0wD0eQjqjiY+r3g+3E8TTnLlyMgCo2hownlTMa3hZSoCRGxEcbOkz2Oj7TUdygDXIUnybULUfxmfrzMCqLAivxUSgUCsWsglQd6LYxgfcDYzHSIQmEmIeqIEHy5djTKD4pyfucqIRVJl5G2yCkfBliEyWDvuOzz5+ZKiNaSuepqVrHz70kgPzckfcDJ2JUnLRhgU3Mzwl1a6NaTEeOTxu0CnXLGj/TIEUNJT4KhUKhmLVwnPz6fepsm6AjbUCIBsKhZayPCVeTk8pJqI+bK0Ps8agjcuyY4hMqH+2EujG7+To+tEx1yAd0cnyInfL8GHJQdfhfoQ7gTEH92Wd8IDkkyWJYCG3OdVlXdcso65ZznhUcSnwUCoVCMasQIyc89KppHwrXckK5ZOybr4/MHwoRmoKWbnZzhmi7ZluOHVCTpH0he8gxuTaExiaFHAT5opBha3YQp5iEzKkipCo0urqFU4ten/lk5cPM8A9p+eJ5rfvmELR2OT6ZBilqjE23AQqFQqFQTCVixIA61awKG+sf6sMbhosONK+N3Efm4WF0RozH29H+jfoTJ0G8yhxXkPzHl7Z4KFN8xNhg7UiInQxnY3b7VSypDEH0UUwdLPHJKm5QV+uYDIsmjov/7Hl4YsuuVn3zFJ8+8Wmxjo8iH0p8FAqFQjGrEM6VsVShFAn1JuhJF5RASEUklA+DWGgZt9OQ92MqCB0glOPDkZibxELYIqF8pvFf6fkaqPgwghSwh5GdZqfMe5JjK6YOlvhk5bbUSsfMZD77Lp6HfVuqPq3WM8pgPnp5t4eGuikUCoViVoGTCfI+ITtOQj1EOzJa85orLCGFxiUxdB9VXsh6OAivWRNaI8ggnmdEyYRUmmg765jKUDcaYlcRJNrOf+wUheGfBTseoUDxRVndY3COT13DKUVd3GCSy1kPC9ooPhkpPooJQImPQqFQKGYXPCqDfV2E9iVuh/J9ZIGEENGAeN/U22FnSpIMQ18E4Faj85MqIJy4nkLE4mbI4gbG254Sz1Rip7xnalGT3kkuZ22x35L2+TdTgZxjs8RnzpCU6h52aKibQqFQKGYV2Lo0zEGnFcPIPhNWEJz8mlDCv6NoNO9HFRGidISIRmi9IEneKKitRSHzh4TiUzbbNCwnWGxBKj7Bc+dX32Rrh6TRz095z4xAr1f9zStnXf3NyW0BgKvedTYWzZvZ7mvO9be7W528mX5MowI9ywqFQqGYVWAOtlB8fM63qwxJckHCsAKEpCIQpXdfOBxNlJIO2SCOgeXXxELd6NgBV83AkFA3mePD25b1+2FCwm2QpNF9Xx4HLRkeJXaa5DOl6JEwx1S0Wb8GAA7ae0Fmj6lHzvW314I5AID/8pxVk2SNgkKJj0KhUChmJXw5InU5a6YyxMpPh3NlYkQl1E7aQkO8UhQfbk8kB0YQmDCBAFufh6otoUIOIESlLMNGFMTAmOLjnH/PPI7d/ikVkwT7MeSQGCWnFfaaPwf3fPi8kc55mklQ4qNQKBSKWQVGVEKkQTjijqrSR1WSmfbxKzmSBIWICwUlJA7RoMfDbA2XnKag7WIKjTFcyWnCk9xQt1D1tyD5Ynk94Rwf9p44Vi1nPTNg1ZtuziKc5Fqa7cgpCqGYGLS4gUKhUChmFXi+iHi/JgNgjnisSphv0VP72rtNx44474UgA6E8HFlWWhZV8MI0Vg0KdUOd4xNZx8dAVHULqEHgfYznJMWUAEP2xz4Xreo2tWhTklmdfcV0QBUfhUKhUMwqhNa5oe4/zyXhDMkJdaPhcQi14+pIkiojVCFaMSs0D1VbytjYZN/AUDfQ/I3mWLk9nIiFSJ8c2/i2vfY289JxVfGZGWhTkjlnzZ+J4lmHL8P6re0WI20DVbFmLpT4KBQKhWJWgREatqMpxxssEW0b9kHDv6q2fqXDDU0brIjIULKkHB8yV1nGCA0tlR2vjtYjIWw81E009NgaI1/cbr5Wj2tvM7ZPJVJML+xnmRPq1iYvqC2++kdnTsEsUMY9BNBQN4VCoVDMKvBQNz9pgJH5P37SYZywrvic1TzhBUMh3qer2sdKTjN7AkoMH5soPoUJ2iBD2GI5TGW9TVSnMhKOZuRnEbaZK0MNQQra7X1XMVmwn0MvQ+lowuMmxSSFwgslPgqFQqGYVWhKI7vhaHQfez80FgyKwraLhF6JQgoF+fVNWZi0eh0mSIwYJIW6kTWLHEWLt6SERiphTSvUzEcSpJji0+Q6cRLkWEHzeohiF7Rbmc+UotO/nnMUn5w1fxSKPQUNdVMoFArFrEJDEsK5KLKUtOPk12NQImUQ9LiJcuIqTSFFxIjXwaHZWDVBKst4fo1VfGKhbiZcrS1UwMBZEDVkA4TiY+Ltq32c5IUVH3WqpxJWZexmyDfGAIvmdvCuc4+bLLMUCgdKfBQKhUIxq0CrsAV5igzDCoSZ0Qpmsh2f07/A56A+PrtDx2PHs0/Se2U4f4hStE5hou0auzmhCJbXlq+DYXSc/PkKGDRtm78p507FhKmFveZywtaMMbj1r8+ZJItGG4vnVe77IfvM/MVcZxqU+CgUCoViVqF2iqXyYkQ+C3k/FuxWr+ODiLphTB0GJIlGEYh1k++nER9a1S2s+IAqVZFQN0MkH1fxoe14jo+1vUQZDv9LJDF2f2N68wHqAqYzA22KGyja4+gDluCf3ngazjpq+XSbMnTQHB+FQqFQzCqE192RBKIJYYuFunHFJ6Kc2JCxIq3cs6v4+NuZojkQA540HsvxacZNU3zk8UnFxkcagfB6LZWdYXXHa0+iWhZbC0ix5/Gykw8CAJxz4oHTbMnswUtOOBCL5ql+kQslPgqFQqGYVahzWyQBEQqG8bwvX/OwtfBipI6aJMbw9zHR177+xvA1VVIqqnWKiJ5leI5PwewW7Yg9ta2Dwu3IuYuWs2ZFEPzhdhQd9W6mFMceuAT3feR8HHPAkuk2ZVpxcD/0bOFcJSQzFfrJKBQKhWJWIeREG9EmlGwvS2AzBSlENGDq9XA6huazxKq6yVC30PHweehaOyZAAEKqlc/uegHTIpzjw/o4xM5vg1NAgszpG7Me29PHN7ZCMdX4X7/9dJx34goce+DsJoAzGfpMRKFQKBSzCnVOjshZgeGJ/LVbHQsFo8QlGnqFhkBIZShIDMQ80ZCxpk+IzHF7aDnrdlXdYiFs3KbBjE3m+4SayrDDsOKjxEcx9Vg8bwznP33FdJuhiECJj0KhUChmFaiSI9US2cbbTjjeqepCj+b4JDjvyYqPJEiBPBw5Vh3qNsB+S3wkUZTbTbvUHB/+WTTv+xSfRiGrSVCEaKrio1AofFDio1AoFIpZBVqMINYmveKYDXVLVU6q1tIeXx/fPF5bm1YsvyWs+DT7ioiaxHN3wqFucl2iusCC5ziasXkJaxr+57Qlf1POtxIfhWLP4ISD9ppuE/YoNMdHoVAoFLMLptkIKRi0WaX4BNQgEuo2KFfGUohOES4SQOEqPoMJkiGhbk4on7SIhvyFWhlarS22gCl5vwBjXGFFq+lXlmUwp4q+WRFI/0KwFBrqplC4uPJdZ2PT9t1ZfS5621mTZM30QImPQqFQKGYVuOLjz4eRTn5MtQiVx+Zz8j6yFHTMTt8YoXbGeT2YLHWKiFIl5pf5RLQdz2FqdoZIiLStCWHzhLqRrZK8F7Jbq7opFC5W7r0AK/fOW/Q0pAYPK/SrQaFQKBSzCmzBUUFImu3GMZeKiBHOP1V8wkSDEAEynoEJVl4rSHhc9TqsnNA2oeNj9oCH3sUI0v9t786jJavqQ49/f3Vvd6NNo4CAyKCCdEC0xdARIyIqupyf83MMalRUnIfnhEmcx6fvqTEOyXuSvCQOzyzUh1OWLic0ihCnoCIqzkZRBhVk6L6/98c5VXXq3Kq61X373jpV5/tZC7rqnF1V+9Zv1b3nV3vv3x69xicGbg8+3/D+1Z+7e6o6JW5Y8+oan2p/Jl0fJUlg4iNJapnqWpLBhGZ4BbNlCVLtuSZa41O5vbv7+IxrV12PNMk0r3rSMrpUdgytRpe1doOP7/enmMI2Oqnqnssx+/0MPvPga496hFPdJA1j4iNJapXuNfHARpssT4KqycSoS+zqup56IlVv13v9Tiybnjaun/3HjWhYe53q6Eg9WRqWzC3Uh2hq7UcVZRhsF4NFEKpT6SYYlclKu3pSVX3V6pqjqL3OqOeWpC4TH0lSu1TX5Cw/XN6uVBkbUwRhoLgBo0ctBqeCDSYak0zXGjeta+B1GBy9Gb02qfqYMVPdqFZ1G72pa1U9oRld8GH5WiPoFy8Y1t9Ro23L+uCIj6QhTHwkSa0yOjEYMQqzbM1KtV30L7Jj9FS3qoVOdQRj8lGLia7lJxyBqldkG/fc1RGWapnq5Q2H93uSny8ze+/j8BGf/s+00vPCynsTSWonEx9JUqsM7DFTSw2qpZt7JZRZXvhg2O0xM8YG1Ku6rXYfn6p6RbX6YwZHuCZYXxO1EZ8Ve7C83+P2CKo+Yy9ZGZpV9Z9ryIDQkHYrt5HUPpazliS1yujpbYO3R42cdGpJSzdZWojRU6+q6qMRI0dEagnDuKluUfm3XmZ6aPt6UjXyeWNgJGdcH7pFEEYmNMv6UC2C0E9WhuU1S5X3uPc6I3viiI+k4fxORJLUKr0pVbUr7MHkpppMjNm4s/I8nU4MTGEb+fqVQgD15xtsN/7+MAMJ25Aqc9UKdF3VUtLDnm9U0YJRBqewje9391SS/eIGQ4Z0ukcWKyM+Y6e6ucZH0hAmPpIkUZvOVikSUF+HU18j1L0oX+hMVoCgPg1r3MakkyRI1b4HMXDRP2qqW30Ua2RRBqprfMb/bMNKga/0uKHFDYa06434dCobmI55XosbSBrGxEeS1Cr9NT65rLpZ/6K6PtVteDIR9EcgFjuTFTdYvvh/0jU+o59zkhLPu/Pc9TLVE1WWqzWZZPSlmOo2ut3SUtGLxYXKiM+Y57OctaRhTHwkSa3S24Qz6wkNlduDG5MOlp+utOv0RyNG7QlUN+k0rOVV3SZLOqoJ27DzRf8Gp6ONXuPTP9dZIbGrJo1VY6e6VRr3p7otb1fmPSx0Or01PuPeZNf4SBrG4gaSpFYZWdCgkg5E5dy4aWEDxxk9cgKDa2WGrWOp25XEp5qkVfcVGt2eWjI3etSp+9wrrfEZqIjXPQYjR7QGHks/IVwa8t5UixtMwqpukobxV4MkqVXqe9hU9ZKTTn0tT/9O/fjgNLNY1n7c64/v5/j7w0SMv+jvpnb15xo31S0qtyfbRLUvM8eOcFXP9DcwXd4us9+m14cxuaMjPpKGMfGRJLXWyKluRGXK2OD+NZ1aEtSb4sVkycmkic+ywgQTl7Oe5PmrozK5bD+jrg0L/eltk+7j06mV9Z7kPckcPwWwm1wuLvRHtHYujc58rOomaRinukmSWmVgA9ORIx3V6WPjp8f1yyvHyASialzVtcF+jr8/zECSNmE1tXFNFzpRm+o2Jjnp9XMX1ib1Bm+yH5ehU936/em2G5P3TDS9TlL7OOIjSWqVweIGffXiBgPlrCst61Pluovtq2tgVlxfM1E/d724Qbcfo1984J8V+7Sh0+k9X71MdV0vAVxWrnvlhU/VMtzjylkvDiQ+K6+TkqQqEx9JUqtELXHp3aY/b62exFTb1ROf3sjDwHqY0a+/K1Xdqtf2k+xNUx2pGt9ucHRq1EhVsSlrd8rf+ISu/1x9yfhEbCn7SeMkVd2qydckBSIkqcrER5LUKv0L8aR6mT7pGp/B27V9ZSZc4zPJJfuulIXuquYCw5rHiHNjZ6P1EsCYKKmqrwUaN+Kzc6n4d6ETvaIMOeTdGVzjM3pkSJLGMfGRJLXLqBGfgVGdyn44vf+Neky/qlv/MePW7kw+4jPp4wZP5ZBjg+2q5zInK5VdFEFY2cBz5fikaqlSAntcVbdeOetOfwqiU90k7apVJT4R8dCIuCAiliJie+3ciyLiexFxYUTco3L8+Ij4ZnnuLeEKREnSOhq5xqc2+jO4j8/wggTV6WgrlTbIyrSuXeln9bVWMun0r2L0pnp/XNvuc1dfZ0z7ZYUTxo34VBOacWt8in8XO53e840rbiBJw6x2xOc/gAcBn6sejIhbAg8HjgXuCfxNRCyUp98OnAYcVf53z1X2QZKkiU0yVaq+Z83I6m9UFvVPuL5moKrbBP1cqQ+7Y9KqbtVzkw6w7Eo/q2t8hiVY9XYLlbi4xkfSrlpV4pOZ387MC4ecuj/w3sy8JjMvBr4H3C4iDgb2ycx/y+I31j8AD1hNHyRJ2hUDozwjCx0MrP4ZmaAsq+o27EVqOhETLVCpT6mbdIrcwJqjmqVyTc3iQj2pWnlqXnVq2biuTNrP6nN2Bt68YWt8ytetbCzb/VkkaVJrtcbnEOAnlfs/LY8dUt6uH5ckaV0MjORUjgeDe9F0q6iNG8mJiN6Uq+r0sbEjORPOdau/5riHTba1KOwos4WNC5P/+R+2pmbcYEsngmt3FK+zaXH86/SmulWqzA177qysBbKctaTdteIGphHxSeDGQ06dkZkfGvWwIcdyzPFRr30axbQ4Dj/88BV6KknSBEYVNBjYWHSyPXmgcqE+4VS3Sdf4LH/cBGt8VjjfTdI2LHYmTJWqxQ0m0wm4esdOADZtGJ/49MpUV4oWbBySLD3+pCN4y6cuYtNih/ve5mA+feGveN49/giA5959K0cdtAWAzz//Lvz88j9M2FNJbbNi4pOZd9uN5/0pcFjl/qHAz8vjhw45Puq13wW8C2D79u1+tSNJWrXqGpFRG5NWRxaqSdAw3aluwWTrWxZiMMEap1raedIZZP1thUY/YFdGfIbtmzN+TVCwWGYx+15/Y+/4rQ+5AQA3ucFe/PyKqwF42J8cxgfO/yl3OHJ/9tu8kWffbSv3vc3BAHzuv92l99jn3H0rz7n7VgA2LHR4+6OP7517+ilH9W4ftt/1OWy/60/8s0lqlxUTn930YeCfI+JNwE0oihicm5k7I+J3EXF74MvAqcBb16gPkiQtM7jGp397YCSmsuRkpWpt3Uyjut/PSonB7tjVx41rXU18VioSUC060B2NOWzffnKxz17FpcSbH34c7/jsDwB48B8fym+uvJY/P/HmAJzzgrv0kqCPP/tOXHnNDgD+5Gb78cPX3qf3XM+8Wz+JOXx/ExhJe9aqEp+IeCBF4nIA8JGI+Fpm3iMzL4iI9wPfAnYAT83MneXDngKcCVwP+Fj5nyRJ66K/UWbteMTQtSQrj/gUihGf0Q3764d2ucsrGrbp5zidTn9UptrnIw7YDMBtDrshX//J5QC87sHbeMMnLmTboTdk42KHNz/8OO5y9IEAXPCye/Sq1N3/uEO4/3HFst3FhQ6n3/kWvec9dCBR2sA+e23Y1R9RklZtVYlPZp4FnDXi3KuAVw05fh5wq9W8riRJu6s/1W30BqZBP0Epjo9JaLK/YWh1lGiUiNjlRGUlv7+m+G5x702LXLezKCywZa/+n/hukvPMU47izZ+6CIDTTj6Sy666jsedeDMAPvjUE7lpOU3sg6ffoffYow7awrtO7W/V101uADZvWquJI5K05/kbS5LUWvXNSKu3u+fqm33W9RboTzgVrV5Jbk944b2O5kX/8g1uceDebFrs8KJ7Hc1Dji+W1P7bi+7KXovFVnrPvvtWnl2uldl70yKveED/e8jjDrthv1/uLS5pDpn4SJJaZbC4Qd9Crapbf6PTHJug9IsJ7Jn+7b95I7+58loA7nTUAbzzsz/ghJvvD8CDbnsI9751sfj/o884iSuvLdbKnLz1AL74olN6z/Gkk4/s3T74BtfbMx2TpBln4iNJapWR09tqtwfvrzzVDcYnQSfcfD8+ccEv2bjY4aB99gLgVmWls5ff/1gOL6eZfexZJ/Gzy4qSzCfe4kZc9Kp7saEsRvCmhx3Xe75b3mSfkX2SJC1n4iNJapVRU9IGNzbt7yuTOX5K2v6bi2ple29aZMNC0fKexxbb3z3idodxxR+uA+DND78tP770KjZvWuTYm9yAs59+R445uEheTv3Tm/We78Ate3Hglr169zfsQulpSdJoJj6SpFbpJTSwbB+fauW1GFEEAeBuxxzIJ7/9KwBeeK9j2HrjLdz16AOJCM598SnsWyZDr3nQtt5j9tqwwNZyo03oj/ZIktaHiY8kqWWGbyDaqU1tG1zjM5j5vPPPtrNjqaiedr2NCzzqhJv2zh24z15IkprHxEeS1Cq9BKc2hW2wwls/KRo24rPQCRY6C2vaT0nSnuXEYUlSqwwUKhg54rM2G41KkqbHxEeS1Crj1vj0Vae67blS1ZKk6THxkSS1ysA+PpWEprqPz7LiBntsq1FJ0rSY+EiS2mX4TLdl+/b086B0xEeS5oCJjySpVQansNXKWWf3dj8pGlbcQJI0e0x8JEmtEiNuL9vAtFNJkJzqJkkzz8RHktQqAwnOmKpuMWItkCRpNpn4SJJapVfVLQePRy0hGqz+JkmadSY+kqR2GShuMJjSJEU21IlKOWvX+EjSXDDxkSS1Sr+4QY4cyomonzLzkaRZZ+IjSWqVUSWsqwZGfMa0kyTNDhMfSVKr9Kq1jR7wIegnO5npeI8kzQETH0lSq4wqYQ39ggdRGfHp3pckzTYTH0lSq0RlCls98em3GZzeZtojSbPPxEeS1CrV/Xo6I/4KWtVNkuaPiY8kqVWq09YWRo34UFnjQy4rey1Jmj0mPpKkVumN+OToqW6O+EjS/DHxkSS1SnX0ptMZvcanmvhIkmafiY8kqVWqU9jqstLGUR5Jmi8mPpKkVplkJKcoZ13cTtIkSJLmgImPJKlVJkliOlEpe53u4yNJ88DER5LUKp3KPj6jBDHQzrRHkmafiY8kqVXGDt6U2VAn+tXfAlgYUQRBkjQ7THwkSa0yUQ5TKW5QLW0tSZpdJj6SpFbpr90ZMtmtN8oTvXYLnWDRER9JmnkmPpKkVhmbwlSmunXbRcDCgomPJM06Ex9JUqtMVNwgolfuuhPBglPdJGnmmfhIklpl0nLWO5eKzGehExY3kKQ5YOIjSWqViTYwJVgqG3TCNT6SNA9MfCRJrTJuxCfLCXDRgaXKeh9HfCRp9pn4SJJaJSaY6xb0q751IiZ6jCSp2Ux8JEmtMsngTSeiP+LjX0pJmgv+OpcktUqUharHrduJgJ3liI+jPZI0H0x8JEmt0s13FsfszdOJ6E11s5S1JM0HEx9JUqt0R3AWV5jD1q/qtuZdkiStAxMfSVKrdAdwhlVqq25aurTUvy1Jmn0mPpKkVukmNxvGTHWL6I/4uMZHkubD4rQ7IEnSetpRDuUMG/HZd/NGLvndNQAcvt/1Adh26A0AOHDLJu5x7I3XqZeSpD3NxEeS1CrdYgU33W9z71g3B/qXJ9+Bc773azYsdDjhiP35+LNO4o8O2gLAuWfcbd37Kknac6Jbtabptm/fnuedd960uyFJmgMf+cYvuMOR+7Pv5o1cec0OADZv8rtASZp1EXF+Zm4fds7f8pKk1rnPtoN7t014JKkdLG4gSZIkae6Z+EiSJEmaeyY+kiRJkuaeiY8kSZKkuWfiI0mSJGnumfhIkiRJmnsmPpIkSZLmnomPJEmSpLln4iNJkiRp7pn4SJIkSZp7Jj6SJEmS5p6JjyRJkqS5Z+IjSZIkae6Z+EiSJEmaeyY+kiRJkuaeiY8kSZKkuWfiI0mSJGnumfhIkiRJmnsmPpIkSZLmnomPJEmSpLkXmTntPkwkIi4BfjTtfpRuBPx62p3QWMZoNhin5jNGzWeMms8YNZ8xmg2TxOmmmXnAsBMzk/g0SUScl5nbp90PjWaMZoNxaj5j1HzGqPmMUfMZo9mw2jg51U2SJEnS3DPxkSRJkjT3THx2z7um3QGtyBjNBuPUfMao+YxR8xmj5jNGs2FVcXKNjyRJkqS554iPJEmSpLln4iNJkiRp7pn4aOZFREy7D5K0liJiw7T7IM0Lrxvay8RniIhYKP/1g9FQUXh2RByaLlRrrIg4KiL2mnY/NFpEbIuIvafdDw1X/q57KfCs7v3p9kijeO3QbF43zIa1/hyZ+FRExGMj4qvAM6fdF40WEacCnwZuC/zWPzLNExH3j4jvAy8H/i4i9pt2nzQoIh4VEb5VhHMAAA5ESURBVN8AXga8LyI2TrtPGhQRj6b4XXcq8GgAL9iax2uH5vO6ofnW63Nk4lOKiKOB04GzgTtFxBGZmRHhe9QgEXEicCbwvMw8NTN/270Q8BdZM5RJzhOAR2bmI4BfAWdExNbp9kxdEXEv4EnAUzLzgcCRwP3Kc36OpiwiFiLi8cATgedn5hHAzyLi2Cl3TTVeOzSf1w3Nt56fo1Z/MCNiS/d2Zn6H4lu1/wF8C3haeXxpOr1TVy1OXwC+AhxTnnthRNwvIvb2m9DpqcaoewjofnbeCzwYuLejCtPTnT5Q+kxm3ikzvxARNwB+ULYJP0fT041RZu4EPpSZJ2fmuRFxDPA7is+Vpqw6NdRrh2aqxcjrhgaa1jV4axOfiHgh8NWIeF1EPLY8fGFmXgqcBRwZEXcq27b2fZq2WpweXx4+Hfj7iPgacEPg6cAbym8MtM4qMXp9RDyy/Ax9E3hMROwLbAfOA24MHDLFrrZWRLwc+MuIOKA8dE15/CDgo8DlFMmpn6MpqcToQIDM/HV5PDLz28DNgOPKY/5NmpKIeD7wmfL33alQXLR57dActRg9rjzsdUODTPMavJUfyoi4K3Bv4O7Ax4HXRMS2Sub/bYq5oE+CIuOsfVuqdTAkTq+MiD/OzPMpfmk9ITNfCDwKOBi46dQ621K1GH0MeGM5pe0dwLXAPwInAn8F3B7w27V1FBGbIuJFwGOAbRTz23vfomXmL4F7Z+YjKb5h2wocMaXuttKQGB1Xa9L9O/1PwB3B0YRpiIj9I+JMii9yngCcCzw1Iqpf5njtMEUjYvTkiLhp5brh8V43TNe0r8FbmfgAG4CvZubFmflp4M3AayrnrwQ+APw+Il4REa+n+LZN62tYnF4NkJlvy8zzytuXAJcCLqBff/UYvRV4Y2b+KDOfQ/FH5lGZeQHwU4zReruOYs70LYEvAXeJiJtXG2TmFeW/l1Ksx9p3vTvZcmNjVE57g2KU7oootPVv9zRdCfxrZv7XzPwa8CmKke1Da228dpieUTE6BHrXDeeXt71umJ6pXoO39Zfn9YH9oyyzm5mvBQ6OiIeW9xO4Grg18BTgksz8/rQ622LD4nRgN05QLKSPiDdSfFP6lel0s9XqMXo1cEhEPKy8/58RcVhEvI3ij8+F0+tq+5QjA9/NzCuB91FcpN0uIjZBf2Fv+Tn67xSjDX6O1tEEMep+0/kd4HFZcMRnnWXm1cD/qxzaQfF5+QUMrI/z2mFKxsTop9V2XjdM3VSvwec68akunKp+Q5aZZ1FUMbpvpfnrgedU7r8GuAA4PDPfsMZdbbXdjVP5reh7KL49ODkzv7cuHW6h3YjRsyr3/xpYAO5TXtxpDYyJ0TXlvz8EzgFOBo6utN0GvJ/+5+i769Tl1tmdGFVGfL4IvDoiFrsJq9bGmDj9rtJsf+BXmfnj8lx3ms7r8Nphze1OjMq2R1AU3PG6YY1FZRuLJl2DL+7JJ2uKKEq1Ph/4SURcmJmvqswRXCz/yLwZeEZEnFf+sfkicFJEbCk/OE8vvz3QGlllnDZSfNP2iHKKjtbAKmO0d2b+niJGV03th5hzK8Qou7fLC+j3ALcCji0Tnssy8+yIeEQ59UNrYBUxujVwZWaelZnXAf9zej/F/NvFOB0KXFY+7h7l+X8FnpaZf5jaDzHnVhmjazLzM+Xvu99M76eYbxFxT+DFwA8j4pLMfG4Zlw6wYdrX4HMz4lPOe16IiCdTbJr4BuBtwJ9GxJ9D8c1ZZl5TZvzvAz4DvCQingq8C1jofltg0rM29mCcrs3Mq0169rw9GKPfl21NevawXYjRUhmjLeWx3wIXlW3/irLkuEnPnreHYvRSygp8Whu7GyfgTsDGiHg78BcUU3Mw6dnz9lCM/hLYWbY16dnDKjE6jeL31huBlwDHl8kqmbnUhGvwuUh8Ioq5tWWG/2OKjRM/mplfBj5JUbqwuyncC4EvU1THeSPwbuB2wKcy88nT+QnawTg1nzFqvt2I0RcoLhAiitKtzwLekJm3yMyPTuvnmGfGaDbsZpxOLB9+LHAC8J3MvGNmfm4KP8Lc24MxOjEzPz+FH2Hu1WJ0DnDHzPwQxZcBvwIuKEfkiIgzmPJ1w8xPdYuIpwGnRMTngX/IzI+WH4DFzNxBsWHVd8rmBwJXAFsz87Ly2Bci4kvZn0etNWCcms8YNd9uxuiW3RhFxA+BW6drrdaMMZoNq40TxZTEp2bm5eve+ZYwRs1XidHngPdm5rfK47elGJVbBF4A/AF4Hg24bpjpEZ+IeCDF3gdvoaj+8NKIOK72Bm6iyC7JzF9k5tsz87LywxPlcS/U1pBxaj5j1HyrjNFieexqL6jXjjGaDauM04by2NleUK8dY9R8tRhtA86IiNuUp68AHp6Zt6NIfJ4YEbfNzL+e9nXDTCc+FEOYb8+iDvhLgYuBZwBk5o4oyoEeBpwfEYdGxBOhNyy3MzPdTHF9GKfmM0bNt5oY7ZhSn9vGGM2G1cTpuin1uW2MUfMNi9GzADLzB9mvePh7ijU9+8H0rxtmIvHpZoVD7v8AeCRAZv4I+AiwOSLuX54/mqKc4TOBD+NGVWvKODWfMWo+Y9R8xmg2GKfmM0bNt4sxun5E/Jda+5dQrLe6oGw71S9KZyLxobYWqfKmfQC4qvJB+AVFlYhjysAcQTEH9OYUe4i8rvZ47VnGqfmMUfMZo+YzRrPBODWfMWq+XY3RLaEoPR4R5wBbgYdk5n+uT3fHa3TiExG3j4h/Al4WEUdFvypENwiXAWcBTymHzq4A9gauVwbmexTVJZ6Smb+Yxs/QBsap+YxR8xmj5jNGs8E4NZ8xar7VxKg8/23gyZl5apNi1NjEJyJuBbwVOJuiHN5pwKlQzO8sm10P+ARFlvmuiLgJcFvgurLdNzPzK+vc9VYxTs1njJrPGDWfMZoNxqn5jFHzrTJG15btfpiZ/7HOXV9RYxMf4PYUtdffA/wtcBXwqCg2PiIiXkGRaR4EPBf4JfDPwOXAa6bS43YyTs1njJrPGDWfMZoNxqn5jFHzrSZGr51KjyfUmMQnIk6OiBMqh74CHBYRt8iitOcSxRv6mIjYDBwJnJ6ZX83MSzPzJcC9M/P0tKTumjFOzWeMms8YNZ8xmg3GqfmMUfO1KUZT38A0IrYAfw/cGfhgRFyUmZcC3wfOBf53RFxK0dd/BLYDf8jMR5aP72TmEkBmXjWFH6EVjFPzGaPmM0bNZ4xmg3FqPmPUfG2MUeSUC2BEUYv9iRRl8e4A/CQz31k5vw24eWZ+KCK2A6/IzHuV53pvuNaWcWo+Y9R8xqj5jNFsME7NZ4yar40xmsqIT0ScCvwI+HpmXh4Rf0cxjHYj4I4RsTUzvwuQmd8AvlE+9K7AlyIisjBzb/gsMU7NZ4yazxg1nzGaDcap+YxR87U9Rus24hMRAdyYYvHTEsUw2mbgmZn567LNUcBjgKsz85WVxx4PvBHYCZyWmd9fl063kHFqPmPUfMao+YzRbDBOzWeMms8Y9a1LcYOIWMgiw9oC/CwzTwFOBy4FekNqmXkRcD5wk4i4RUR0a4H/EPirzDxl1t/wJjNOzWeMms8YNZ8xmg3GqfmMUfMZo0FrOtUtik2OXg4sRMRHgX0oMkYyc0dEPAP4eUScnJmfLY+fFRHHAB8H9o6Iu2bmt4DPrmVf28w4NZ8xaj5j1HzGaDYYp+YzRs1njIZbsxGfiDiZInPcl2KH3VdQbDx1l4i4HUCZgb4ceGnlcQ8FzgA+DWwr33CtEePUfMao+YxR8xmj2WCcms8YNZ8xGm3N1vhExEnAzTLz/5T3/wb4JvAH4OmZeXxEdIADgbcAL8jMi8vHkZmfX5OOaYBxaj5j1HzGqPmM0WwwTs1njJrPGI22lmt8zgfeHxEL5f0vAIdn5pkUw25Pz6IixKHAzsy8GIo3e57f8AYyTs1njJrPGDWfMZoNxqn5jFHzGaMR1izxycyrMvOa7O/genfgkvL244BjIuJs4D3Av69VPzSecWo+Y9R8xqj5jNFsME7NZ4yazxiNtub7+JTZZgIHAR8uD/8OeDFwK+DizPzZWvdD4xmn5jNGzWeMms8YzQbj1HzGqPmM0XLrUc56CdgA/BrYVmaYfwEsZeY5bXvDG8w4NZ8xaj5j1HzGaDYYp+YzRs1njGrWZQPTiLg98MXyv3dn5v9a8xfVLjNOzWeMms8YNZ8xmg3GqfmMUfMZo0HrlfgcCvwZ8KbMvGbNX1C7xTg1nzFqPmPUfMZoNhin5jNGzWeMBq1L4iNJkiRJ07Qea3wkSZIkaapMfCRJkiTNPRMfSZIkSXPPxEeSJEnS3DPxkSRJkjT3THwkSY0WES+NiOeNOf+AiLjlevZJkjR7THwkSbPuAYCJjyRpLPfxkSQ1TkScAZwK/AS4BDgfuAI4DdgIfI9iU77jgLPLc1cADy6f4m3AAcBVwBMz8zvr2X9JUvOY+EiSGiUijgfOBE4AFoF/B94BvDszf1O2eSXwy8x8a0ScCZydmR8oz30KeHJmXhQRJwCvycy7rv9PIklqksVpd0CSpJqTgLMy8yqAiPhwefxWZcJzQ2Bv4BP1B0bE3sAdgP8bEd3Dm9a8x5KkxjPxkSQ10bDpCGcCD8jMr0fEY4E7D2nTAS7PzOPWrmuSpFlkcQNJUtN8DnhgRFwvIrYA9yuPbwF+EREbgEdV2v+uPEdm/ha4OCIeChCF26xf1yVJTeUaH0lS41SKG/wI+CnwLeBK4PnlsW8CWzLzsRFxIvC3wDXAQ4Al4O3AwcAG4L2Z+fJ1/yEkSY1i4iNJkiRp7jnVTZIkSdLcM/GRJEmSNPdMfCRJkiTNPRMfSZIkSXPPxEeSJEnS3DPxkSRJkjT3THwkSZIkzb3/D0LGxrqpN94jAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = data.plot(figsize=(14,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is also a basic plot function built in which wrapped up the call to retrieve data and tries to pretty print the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO 2020-06-17 16:33:45 (mustlink.mustlink): parameter info for Rw1 speed extracted\n",
      "INFO 2020-06-17 16:33:48 (mustlink.mustlink): 2000 values retrieved\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = must.plot_data(param_name='NCATABD0', start_time='2019-02-01 00:00:00', stop_time='2019-02-28 00:00:00', max_pts=1000)"
   ]
  }
 ],
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