applications/13-data-processing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "defined-charles",
   "metadata": {},
   "source": [
    "# The Power of Parallel Processing feat. multithreading and multiprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "auburn-variation",
   "metadata": {},
   "source": [
    "If you've worked with data, chances are you've had to deal with large datasets. and I mean LARGE!\n",
    "\n",
    "![Large Dataset](../img/large-dataset.jpeg)\n",
    "\n",
    "[Source](https://medium.com/@laxmi.vanam17/large-data-sets-in-machine-learning-4ce3a7916e92)\n",
    "\n",
    "When working with large datasets, one consequence is slower processing. To demonstrate, we are going to try to fetch some metadata about all the connections we have access to based on all the whitelabels we have access to without parallel processing."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "secure-fence",
   "metadata": {},
   "source": [
    "First we'll get our API token by running `%env API_TOKEN` in one cell, then copy it and run `API_TOKEN = <pasted-token>` in another cell to set it as a variable. I've deleted the blocks so you can't see it (but you'll need to do these steps!)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecological-groove",
   "metadata": {},
   "source": [
    "Next, we'll see the list of whitelabels we have access to. For each whitelabel, we will then retrieve a list of connections and put it altogether to give us a master list. This is a part of `get_datasets('external')` though some filtering does happen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bronze-tuner",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>whitelabelid</th>\n",
       "      <th>company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Atom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>AdLoop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Advantex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>WL TEST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Hydropool</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Gourmet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>CHR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>FreeMedia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Metroland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Ocono</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   whitelabelid    company\n",
       "0             1       Atom\n",
       "1             2     AdLoop\n",
       "2             3   Advantex\n",
       "3             4    WL TEST\n",
       "4             5  Hydropool\n",
       "5             6    Gourmet\n",
       "6             7        CHR\n",
       "7             8  FreeMedia\n",
       "8             9  Metroland\n",
       "9            10      Ocono"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import locussdk as locus\n",
    "import pandas as pd\n",
    "\n",
    "whitelabels = locus.get_whitelabel()\n",
    "pd.DataFrame(whitelabels[0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "distinguished-essay",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.41 s, sys: 107 ms, total: 2.51 s\n",
      "Wall time: 37.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import requests\n",
    "\n",
    "headers = {'eq-api-jwt': API_TOKEN}\n",
    "route = 'https://api.locus.place/prod'\n",
    "\n",
    "connections = []\n",
    "for wl in whitelabels:\n",
    "    params = {'whitelabel': wl['whitelabelid']}\n",
    "    r = requests.get(f\"{route}/connections\", params=params, headers=headers)\n",
    "    connections.extend(r.json())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "median-richmond",
   "metadata": {},
   "source": [
    "If you don't believe that it's there, run the next line!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ordered-unknown",
   "metadata": {},
   "outputs": [
    {
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       "      <th>id</th>\n",
       "      <th>set_id</th>\n",
       "      <th>set_name</th>\n",
       "      <th>columns</th>\n",
       "      <th>whitelabel</th>\n",
       "      <th>customer</th>\n",
       "      <th>type</th>\n",
       "      <th>name</th>\n",
       "      <th>created</th>\n",
       "      <th>updated</th>\n",
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       "      <th>0</th>\n",
       "      <td>1576</td>\n",
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       "      <td>COVID Ontario</td>\n",
       "      <td>{'Row_ID': {'hash': False, 'type': 'Number', '...</td>\n",
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       "      <td>COVID Ontario</td>\n",
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       "      <td>9324</td>\n",
       "      <td>google sheet</td>\n",
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       "      <td>google sheet</td>\n",
       "      <td>test gs string</td>\n",
       "      <td>2021-01-21T15:30:02.203Z</td>\n",
       "      <td>2021-01-21T17:03:09.559Z</td>\n",
       "      <td>2021-01-21T17:03:09.564Z</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1569</td>\n",
       "      <td>119</td>\n",
       "      <td>test gs</td>\n",
       "      <td>{'col_1': {'hash': False, 'type': 'Number', 'd...</td>\n",
       "      <td>1</td>\n",
       "      <td>9324</td>\n",
       "      <td>google sheet</td>\n",
       "      <td>test gs</td>\n",
       "      <td>2021-01-20T23:23:57.193Z</td>\n",
       "      <td>2021-01-20T23:24:25.218Z</td>\n",
       "      <td>2021-01-20T23:24:25.221Z</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "     id  set_id         set_name  \\\n",
       "0  1576     122    COVID Ontario   \n",
       "1  1570     120         test gs2   \n",
       "2  1585     123  test new branch   \n",
       "3  1573     121   test gs string   \n",
       "4  1569     119          test gs   \n",
       "\n",
       "                                             columns  whitelabel  customer  \\\n",
       "0  {'Row_ID': {'hash': False, 'type': 'Number', '...           1      9324   \n",
       "1  {'col_1': {'hash': False, 'type': 'Number', 'd...           1      9324   \n",
       "2  {'col_1': {'hash': False, 'type': 'Number', 'd...           1      9324   \n",
       "3  {'col_1': {'hash': False, 'type': 'string', 'd...           1      9324   \n",
       "4  {'col_1': {'hash': False, 'type': 'Number', 'd...           1      9324   \n",
       "\n",
       "           type             name                   created  \\\n",
       "0  google sheet    COVID Ontario  2021-01-21T19:48:09.099Z   \n",
       "1  google sheet         test gs2  2021-01-20T23:30:12.363Z   \n",
       "2  google sheet  test new branch  2021-01-22T15:18:57.636Z   \n",
       "3  google sheet   test gs string  2021-01-21T15:30:02.203Z   \n",
       "4  google sheet          test gs  2021-01-20T23:23:57.193Z   \n",
       "\n",
       "                    updated                 last_sync  is_syncing  records  \n",
       "0  2021-01-21T19:52:42.148Z  2021-01-21T19:52:42.153Z       False   197152  \n",
       "1  2021-01-20T23:30:32.727Z  2021-01-20T23:30:32.731Z       False        5  \n",
       "2  2021-01-22T15:19:17.292Z  2021-01-22T15:19:17.297Z       False        5  \n",
       "3  2021-01-21T17:03:09.559Z  2021-01-21T17:03:09.564Z       False        5  \n",
       "4  2021-01-20T23:24:25.218Z  2021-01-20T23:24:25.221Z       False        5  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(connections[0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cooked-spencer",
   "metadata": {},
   "source": [
    "To avoid spending lots of time waiting around, one way is to optimize the runtime of your code by using parallel processing. Parallel Processing is nothing new to us. Here's an example: Imagine you're an EQ data analyst and you find out there are 3 reports waiting to be done for a specific client. You can either do all 3 reports yourself one by one, or get 2 other data analysts to help you out perhaps by taking one part in each report or doing one report each. Parallel processing is the second option.\n",
    "\n",
    "![single vs parallel.png](../img/single-vs-parallel.png)\n",
    "\n",
    "Python offers this capability of parallel processing in 2 ways, through 2 modules: multiprocessing and threading! You'll see this example again later in multithreading."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prescription-status",
   "metadata": {},
   "source": [
    "# What is multiprocessing?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "recognized-german",
   "metadata": {},
   "source": [
    "Python was designed this in mind: computers would only have one core. One process to be run by the one core at a time. Process is an executable program (so running some code) with its own memory space. But nowadays, that's not the case- computers have multiple cores. Since Python was not designed to have multiple processes run at the same time so there was potential of memory getting mixed up between the processes. It's like suddenly being able to read someone else's thoughts all of a sudden which can get chaotic. So Python has a Global Interpreter Lock (GIL) in place to help manage memory management and run one process with one core at a time. But this means Python doesn't effectively use all of the cores that are at its disposal. That's where multiprocessing comes in: create multiple processes with its own memory space for each core.\n",
    "![Multiprocessing](../img/multiprocessing.png)\n",
    "[Source](https://levelup.gitconnected.com/multi-threading-and-multiprocessing-in-python-3d5662f4a528)\n",
    "\n",
    "Multiprocessing bypasses GIL and uses all cores (one process per core). You'll see the speed difference in a bit through an example with pandas and Modin. An analogy of this would be 3 data analysts working on 1 report as opposed to 1 data analyst working on 3 reports."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "allied-bible",
   "metadata": {},
   "source": [
    "# When would you use multiprocessing?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "noted-genius",
   "metadata": {},
   "source": [
    "This method is great for CPU-intensive processes like deep analyses, heavy filtering and data manipulation. By having multiple processes, you lighten the load instead of one process doing all of the work, like in the data analyst scenario."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "indirect-registrar",
   "metadata": {},
   "source": [
    "# Mulitprocessing in Python for Data Analysis\n",
    "## Introduction to Pandas Data Frames\n",
    "Pandas is a highly optimzed library written in C or Cython that provides data structures along with the neccessary tools to load and operate on them. It includes two principal data structures, `series` and `dataframes`. The dataframe is two-dimensional, size-mutable, potentially heterogeneous tabular data which resembles a standard spreadsheet. \n",
    "\n",
    "Python with pandas is in use in a wide variety of academic and commercial domains, including Finance, Neuroscience, Economics, Statistics, Advertising, Web Analytics, and more because it supports the following features and more: \n",
    "\n",
    "- A fast and efficient DataFrame object for data manipulation with integrated indexing\n",
    "\n",
    "- Tools for reading and writing data between in-memory data structures and different formats: CSV and text files, Microsoft Excel, SQL databases, and the fast HDF5 format\n",
    "\n",
    "- Flexible reshaping and pivoting of data sets\n",
    "\n",
    "- Columns can be inserted and deleted from data structures for size mutability\n",
    "\n",
    "- Aggregating or transforming data with a powerful group by engine allowing split-apply-combine operations on data sets;\n",
    "\n",
    "- High performance merging and joining of data sets;\n",
    "\n",
    "- Hierarchical axis indexing provides an intuitive way of working with high-dimensional data in a lower-dimensional data structure;    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sublime-commonwealth",
   "metadata": {},
   "source": [
    "# If Pandas is so great why do we need anything else? \n",
    "The popularity of Pandas is largely due to it's maturity and time in use but, it was written to use a single processor and loads the entire dataframe into memory thus, severely lowering it's scalability and efficiency. In the last few years the open source community has developed modules built on top of Pandas like Dask, Ray, and Modin which run python code across compute clusters or parallelize code to run on several clusters. RAPIDs for example, is a partial Pandas replacement that runs code on GPUs. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alternate-transsexual",
   "metadata": {},
   "source": [
    "![ModinVDataframe](../img/mostcommonpandas.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "compact-mailing",
   "metadata": {},
   "source": [
    "## Introduction to Modin\n",
    "\n",
    "Modin was designed for terrabyte sized data sets by splitting the dataframe into smaller more manageble units allowing it to be loaded into separate processors and memory thus, parallelizing your Python code. \n",
    "\n",
    "It covers 90% of the Pandas API with a focus on the most commonly used Pandas methods. Modin is a superset of Pandas which means the remaining falls back to native Pandas. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "increasing-greeting",
   "metadata": {},
   "source": [
    "![ModinVDataframe](../img/PandasVModin1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "married-review",
   "metadata": {},
   "source": [
    "## Using Modin\n",
    "\n",
    "Modin includes a scheduler that abstracts the hardware from the interpreter so that you don't need to manage it within your code.\n",
    "\n",
    "You can learn more about how to install and use it at https://github.com/modin-project/modin."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lonely-people",
   "metadata": {},
   "source": [
    "![Modinarchitecture](../img/modin_architecture.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caring-stack",
   "metadata": {},
   "source": [
    "Run the next few cells to see the runtime when using pandas. Once you're done, re-run these cells using Modin!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "suburban-longitude",
   "metadata": {},
   "outputs": [],
   "source": [
    "import locussdk as locus\n",
    "import pandas as pd\n",
    "# import modin.pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "private-cliff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'date_range_type': 'week',\n",
       "  'date_range': {'date_type': 2,\n",
       "   'start_date': '2020-01-01T00:00:00.000Z',\n",
       "   'end_date': '2020-01-05T00:00:00.000Z'}},\n",
       " {'date_range_type': 'week',\n",
       "  'date_range': {'date_type': 2,\n",
       "   'start_date': '2020-01-06T00:00:00.000Z',\n",
       "   'end_date': '2020-01-12T00:00:00.000Z'}},\n",
       " {'date_range_type': 'week',\n",
       "  'date_range': {'date_type': 2,\n",
       "   'start_date': '2020-01-13T00:00:00.000Z',\n",
       "   'end_date': '2020-01-19T00:00:00.000Z'}},\n",
       " {'date_range_type': 'week',\n",
       "  'date_range': {'date_type': 2,\n",
       "   'start_date': '2020-01-20T00:00:00.000Z',\n",
       "   'end_date': '2020-01-26T00:00:00.000Z'}},\n",
       " {'date_range_type': 'week',\n",
       "  'date_range': {'date_type': 2,\n",
       "   'start_date': '2020-01-27T00:00:00.000Z',\n",
       "   'end_date': '2020-01-31T00:00:00.000Z'}},\n",
       " {'date_range_type': 'month',\n",
       "  'date_range': {'date_type': 3,\n",
       "   'start_date': '2020-01-01T00:00:00.000Z',\n",
       "   'end_date': '2020-01-31T00:00:00.000Z'}}]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report = 2163\n",
    "locus.get_aoi_frequencies(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "organic-baseball",
   "metadata": {},
   "outputs": [],
   "source": [
    "date_range = locus.get_aoi_frequencies(report)[5]['date_range']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fancy-musician",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 571 ms, sys: 44.1 ms, total: 615 ms\n",
      "Wall time: 657 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df = locus.get_dataset('eq', report, enhancement = 'aoi', date_range=date_range, resolution='postalcode' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "rising-symphony",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12514\n",
      "51\n",
      "CPU times: user 7.33 ms, sys: 0 ns, total: 7.33 ms\n",
      "Wall time: 6.17 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df.head()\n",
    "print(len(df['geo_id'].unique())) \n",
    "print(len(df['poi_id'].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "rational-organ",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 22.2 ms, sys: 144 µs, total: 22.4 ms\n",
      "Wall time: 21.4 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "visit_per_pc = df.groupby('geo_id').sum()['visits']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "legitimate-fifty",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9.46 ms, sys: 99 µs, total: 9.56 ms\n",
      "Wall time: 8.01 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "63"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "df_top = pd.DataFrame(visit_per_pc[visit_per_pc.gt(visit_per_pc.quantile(0.995))]).sort_values('visits',ascending=False).reset_index()\n",
    "len(df_top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "brutal-symposium",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 117 ms, sys: 11.4 ms, total: 129 ms\n",
      "Wall time: 11.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "top_geojson = locus.get_geojson_pc(list(df_top['geo_id']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "attended-certificate",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.33 ms, sys: 21 µs, total: 4.35 ms\n",
      "Wall time: 3.21 ms\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>geo_id</th>\n",
       "      <th>visits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L0K1S0</td>\n",
       "      <td>562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L0S1N0</td>\n",
       "      <td>525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>L4B4H6</td>\n",
       "      <td>475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L0S1J0</td>\n",
       "      <td>473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>L0G1M0</td>\n",
       "      <td>471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>L0L2L0</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>M1B3S3</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>L4B3P9</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>L6A1E9</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>L4B2G2</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>347 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     geo_id  visits\n",
       "0    L0K1S0     562\n",
       "1    L0S1N0     525\n",
       "2    L4B4H6     475\n",
       "3    L0S1J0     473\n",
       "4    L0G1M0     471\n",
       "..      ...     ...\n",
       "342  L0L2L0     101\n",
       "343  M1B3S3     101\n",
       "344  L4B3P9     101\n",
       "345  L6A1E9     101\n",
       "346  L4B2G2     101\n",
       "\n",
       "[347 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "df_demo = pd.DataFrame(visit_per_pc[visit_per_pc>100]).sort_values('visits',ascending=False).reset_index()\n",
    "df_demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "spiritual-negative",
   "metadata": {},
   "outputs": [],
   "source": [
    "# locus.list_enrichment('demographic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "changed-compound",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# df_enrich_income = locus.enrich(df_demo, 'demographic', 'Household Income')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "competitive-working",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# df_enrich_income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "spoken-hamilton",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# df_enrich_education = locus.enrich(df_demo, 'demographic', 'Education Breakdown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "historic-breakdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# df_enrich_education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "equal-hunger",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# pd.DataFrame(df_enrich_education.drop('visits',axis=1).mean().reset_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "annual-gravity",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# df_enrich_age = locus.enrich(df_demo, 'demographic', 'Resident Age Breakdown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "behavioral-petite",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# df_enrich_age"
   ]
  },
  {
   "attachments": {
    "74afa294-970b-4f0f-ae35-64486699a156.png": {
     "image/png": 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"
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sWURpbWVuc2lvbj4yMTY8L2V4aWY6UGl4ZWxZRGltZW5zaW9uPgogICAgICA8L3JkZjpEZXNjcmlwdGlvbj4KICAgPC9yZGY6UkRGPgo8L3g6eG1wbWV0YT4Ki/DWVwAAABxpRE9UAAAAAgAAAAAAAABsAAAAKAAAAGwAAABsAABRIk2lTUcAAEAASURBVHgB7J0HeFRF98ZPCjX0TgDpvRg6qCBFEMSCYAEbKLbvsyv28imKotg7gvC3V1BRVEARkSZd6b333iH1P+9sZjN7czfZEkLYvPM8yW0zc+f+7u7e+86ccyYqTSVhIgESIAESIAESIAESIAESIAESIAESiAgCURT6EXEfeREkQAIkQAIkQAIkQAIkQAIkQAIkoAlQ6PODQAIkQAIkQAIkQAIkQAIkQAIkQAIRRIBCP4JuJi+FBEiABEiABEiABEiABEiABEiABCj0+RkgARIgARIgARIgARIgARIgARIggQgiQKEfQTeTl0ICJEACJEACJEACJEACJEACJEACFPr8DJAACZAACZAACZAACZAACZAACZBABBGg0I+gm8lLIQESIAESIAESIAESIAESIAESIAEKfX4GSIAESIAESIAESIAESIAESIAESCCCCFDoR9DN5KWQAAmQAAmQAAmQAAmQAAmQAAmQAIU+PwMkQAIkQAIkQAIkQAIkQAIkQAIkEEEEKPQj6GbyUkiABEiABEiABEiABEiABEiABEiAQp+fARIgARIgARIgARIgARIgARIgARKIIAIU+hF0M3kpJEACJEACJEACJEACJEACJEACJEChz88ACZAACZAACZAACZAACZAACZAACUQQAQr9CLqZvBQSIAESIAESIAESIAESIAESIAESoNDnZ4AESIAESIAESIAESIAESIAESIAEIogAhX4E3UxeCgmQAAmQAAmQAAmQAAmQAAmQAAlQ6PMzQAIkQAIkQAIkQAIkQAIkQAIkQAIRRIBCP4JuJi+FBEiABEiABEiABEiABEiABEiABCj0+RkgARIgARIgARIgARIgARIgARIggQgiQKEfQTeTl0ICJEACJEACJEACJEACJEACJEACFPr8DJAACZAACZAACZAACZAACZAACZBABBGg0I+gm8lLIQESIAESIAESIAESIAESIAESIAEKfX4GSIAESIAESIAESIAESIAESIAESCCCCFDoR9DN5KWQAAmQAAmQAAmQAAmQAAmQAAmQwCkX+hs3biRlEiABEiABEiABEiABEiABEiABEiCBXCJAoZ9LoHkaEiABEiABEiABEiABEiABEiABEsgNArkm9MuXL58b18NzkAAJkAAJkAAJkAAJkAAJkAAJkEC+JkChn69vPy+eBEiABEiABEiABEiABEiABEgg0ghQ6EfaHeX1kAAJkAAJkAAJkAAJkAAJkAAJ5GsCFPr5+vbz4kmABEiABEiABEiABEiABEiABCKNAIV+pN1RXg8JkAAJkAAJkAAJkAAJkAAJkEC+JkChn69vPy+eBEiABEiABEiABEiABEiABEgg0ghQ6EfaHeX1kAAJkAAJkAAJkAAJkAAJkAAJ5GsCFPr5+vbz4kmABEiABEiABEiABEiABEiABCKNAIV+pN1RXg8JkAAJkAAJkAAJkAAJkAAJkEC+JkChn69vPy+eBEiABEiABEiABEiABEiABEgg0ghQ6EfaHeX1kAAJkAAJkAAJkAAJkAAJkAAJ5GsCFPr5+vbz4kmABEiABEiABEiABEiABEiABCKNAIV+pN1RXg8JkAAJkAAJkAAJkAAJkAAJkEC+JkChn69vPy+eBEiABEiABEiABEiABEiABEgg0ghQ6EfaHeX1kAAJkAAJkAAJkAAJkAAJkAAJ5GsCFPr5+vbz4kmABEiABEiABEiABEiABEiABCKNAIX+Kb6jH330kQwfPlyf5bfffpNKlSqd4jOyehIgARIgARIgARIgARIgARIggfxMIM8J/aRDIvv/iZJj26IkrkqalG4uEhuXFvA9Oq7K7V8cJcmqnmK1VfmENImKDri4N+PixYtlypQpevv666+XMmXKeI8FszJmzBh55ZVXdJGJEydKlSpVginOvCRAAiRAAiRAAiRAAiRAAiRAAiQQFIE8JfT3/h0tCx+NkZP7orwXUahMmrR8NVkLdu9OPyvrPoqR5a/F+Bwtk5AqLV5KkUIVAu8sQAVfffWVPPvss7qu7777TurWretTb6AbFPqBkmI+EiABEiABEiABEiABEiABEiCBnCCQZ4T+8e1RMqVnAddrwoh+p/FJUqis62G9c+fUKJl3r3v5sq1Spe0HyUGN7OeU0N+1a5ds27ZNt7FJkyYSGxvr/yJ4hARIgARIgARIgARIgARIgARIgATCJJBnhP7qETGy6j3PaHyVnilSuWeqbBkXIzumeuzuGw1OlprXpfq93Nk3xcreBZ689e9MkbjqabLq7Wg5stGz77zPk6Rko8BH9XNK6PttMA+QAAmQAAmQAAmQAAmQAAmQAAmQwCkgkGeE/iJlsr/1F4/Q7z4tUQqUEDmxI0p+7+EZpa/WO0WaPZ3iF8HEcwtI8tEoKdkwTc77Iknn2/azcgV4zDOCnvBcslS52H9Hwe7du2XcuHHe+hctWiR//fWX3r766qulQoUK3mNYadu2rSQkJPjsw8axY8dk7NixmfZjR9++faVo0aKZju3cuVO+//57vb9jx47y7bffysKFC6V58+aC+AA494cffigzZszQsQIuu+wyufDCCzPVY3YgvsCsWbNk5cqVsmbNGqlVq5Y0bNhQevbsKdWqVTPZuCQBEiABEiABEiABEiABEiABEohAAnlG6C8dFiMbvvQI/bYjkqVc21TZOSVa5t3vEep1bkqR+nf7F/p/XharR+9h5t/l5yTdUbD8lRhZ96mnzjZvJ0n58/yP6EMc9+/fP+BbfN9998mgQYMy5YeZfvfu3TPtxw5/wfjmz58vAwYMcC1To0YNLfTnzJnjc3zUqFHSrl07n31JSUny/vvvy4gRI3z2mw10MgwdOlS6detmdnFJAiRAAiRAAiRAAiRAAiRAAiQQYQTyjNDf8Xu0zH8gw3+9WPVUr9k9mLdT4r+sEv8mJR0UZQEQrf3uq1+VKsteipH1n3tEPfIUrZomx7ZkBPXrNjVRCpYypUWObo6SLd9HS/lz0qRMy1TZsWOHfPzxx94MEP4YVUe66KKLpFy5ct5jWOnUqZO0adPGZx82Dh06JC+//LJ3/5IlS2TVqlV6O1Ch37t3bz0ij5F+kyD4GzRoIL/++qve1adPHxkyZIg5rJeDBw/2HoeoRz2VK1eWdevWCQIKmvTJJ59oawGzzSUJkAAJkAAJkAAJkAAJkAAJkEDkEPAK/ZMnT8qmTZvk4MGDEh8fr+d7j472+LeHc7kbN27UxcuXL59tNQsejJXtkzOfs9rlymz/f2o0Xw3IY+q9TeOiZct4j6gvUlGN4E9MkuTDIn/1L+Aj7s0JE4Yqs/1eqZKaKLJrWrRs+jZads/2nKd631Rp8mSyyepd5pSPfiBR9+0R/Ycfflib60Pkd+3aVbcHU/v98ccfEhMTI08++aQW7TDB/+WXX7ztnTt3rtx44416G0H/3nrrLbGZL1u2TK666ip9vF69evLNN9/o+rwVcIUESIAESIAESIAESIAESIAESCAiCGihn5aWJj///LMkJ2cIXohB+HWHm4IR+mlqwB4ifPuvKojehigpXidN4nulSaVOKbJV7dv4eUZwvbItUuWsK1OlYuc0iSnsMcmHj/6akdGyR03Tl7hPtL9+rYEpUlBF69/yQ7Rs+CJa+/HjmuJ7pEq1PqmCiPxRmfsWcnV6PVvow6qgRYsWGjssBuDzf8EFF8jrr7+u92E0/sUXX9S+/rY5f79+/QTWA0gY9a9atapet/89+uij8uOPP+pdn332mZx99tn2Ya6TAAmQAAmQAAmQAAmQAAmQAAlEAAEt9DEFHIK32QnTwPXq1cveFdJ6MELfeYJ9avR+89iM0Xv43591hRLovVOlWE3//vaoJ03F49uJ0ftvMkbvYc5f4+oU1XmQKoXKOM/mu326RvQREBCdLEgY0cfIPkzwn3vuOb0PJvgY1Uf6999/BVYXiYmJ3s6BihUryiuvvKKP4x86cUxasGCBvPbaa3rzqaee8o7wm+NckgAJkAAJkAAJkAAJkAAJkAAJnPkEtNA/fvy4TJo0yedqSpQoIZ07d/bZF8pGsEI/6ZDINjV6v+FLNXq/zjPUrkfvlR8+AvRh3wnlul70rDQp0SBNojPc+nXz4Hu/dXyU9tfHCD8Spuur1jdNTbmXKodXRknycWUtUDtNitXKEME6o/XvdAn9n376SeCPj4To+mvXrpVrrrlGHnvsMb1vwoQJAvN+JIzowxd/w4YNcvHFF+t9gf674YYb5KGHHgo0O/ORAAmQAAmQAAmQAAmQAAmQAAmcIQS8PvrLly/XojIlRc1BHxenR4jhGx5uClTopyoX/CXPxcjm7zy+9xi9r65M86te5hm9hzk+puA7uc8j3tGuEvXSpPmLyXp0/9jWKFk6NFp2zfSU16P3/VK0b37B0iIbPouWpcN9ewUqd0+VZso/P7Z45qs8XUIffvdmCjwE3EMgP1uU4/iDDz6oG2yE/vTp0+X222/X+yD8nVMBZr460R0Dpozbce4jARIgARIgARIgARIgARIgARI4Mwl4hT6aDzNvBOUrXLhwjl1NoEIfJ/zrqgJqWjxlnq8EfqVOqRJdyNMMiPg/ehVwbRMEfcdvkiXlRJpM7lxQqvZSo/d9VCR95cMv6X0CO35TEf0H+4p8UxlG+xNeyDxt35kk9BctWiTXXXedviQE5HvggQfM5XFJAiRAAiRAAiRAAiRAAiRAAiSQzwj4CP1Tce3BCP2UE1HewHp2W1a8ESNrx3hG6jEKX7FDqmxSvvv7FnlM+xOeVVH1L0lVYt+9/HQVjf/gco/qr3e7Cs5XJk1Wv59hHdDpxySJq+Zrxn8mCf19+/ZJx44dNbJLLrlEXnjhBRsf10mABEiABEiABEiABEiABEiABPIRAa/QR0C3zZs3e6fXQ1C3qKgMM/lQmQQj9P2dY949sbLzT4+o7zErSWKKpMmhFVHyVz/PKH/N61Ok0QOZR+V1fUq/T2heUK/C17/daM/MAojAv/RFzyh/69eTpEInX6H//fffyxNPPKHLffrpp5KQkOCveVnuD3Z6vVBM99EAE6Ef9w11FCzoueYsG8eDJEACJEACJEACJEACJEACJEACEUdAC3236fXq168vDRo0CPuCc0LoL3wkVgfoQ2PO/y5J++RvnxwtCx70CPX6d6RInVv8CH1VZuK5BfS0eoXUSH7XiUkSpfoHljwfIxu/9lgJtP8wWcq0VKb+Vpo9e7bcfPPNeg+i3F999dXW0cBXc0voDx8+XD766CPdMATuQwA/t4R7jXgMjRo1cjvMfSRAAiRAAiRAAiRAAiRAAiRAAmc4AS30MYUbhK2d8sL0eqY9mCJv8dAMH/syCales33kaT86Sfnk+47Im7JYLngoVrZP8lgEQOwXKidyaJXHWgFB/7oo8V+gmF1CZMeOHXr+euxFgDvMQY+OD6wjIVBh8eK+Ufwgonfv3q2Pm3+Yr/7DDz/Um7AMiI+PN4d0LATMbjB//nwZMGCA3h/qiP7hw4f1dHzHjh3T9cBnH2K/SpUqkpycLFu3btVR+tEGROlfsmSJtx1cIQESIAESIAESIAESIAESIAESiBwCWuhDHE6ePNnnqk7X9Ho+jUjfQET+2TfGyv5/PWLdzlO9b6o0UZHzs0ondkTJn31j9ai+M1/z55Ml/iLf0XyT5/nnn5fPP//cbPos77vvPhk0aJDPPrtzwOeAn402rVvL6DFjckTo4xTorLn77rvFiH0/p9W7KfSzosNjJEACJEACJEACJEACJEACJHDmEvD66C9dulRPr4dRaYxat2jRQsqWLRv2leWE6T4akXoyStaMitIj80c2RkvJhio6fx8VYV8J/ajM+j9Tu0/uipIVb0bL7pnReoq+sq1Spe7tqYKlv4SpBmfOnCkYBV+4cKGPgB48eLAMHDjQp+iuXbukS5cuPvuy2oBf/ejRo8WOmj9x4kQ9Co9yV111lSxbtkyP9psp9XDcRNU30+vZ50BgvmHDhsnPP/9s7/aud+jQQbp37y6XX365dx9XSIAESIAESIAESIAESIAESIAEIoeAV+jjklJTUwVB+U7X9HqRg/X0Xwk6KbZs2SLoaImOjhYE6YMZv3E9OP0tZAtIgARIgARIgARIgARIgARIgAROBQEfoX8qTpBTI/qnom2skwRIgARIgARIgARIgARIgARIgAQijQCFfqTdUV4PCZAACZAACZAACZAACZAACZBAviZAoZ+vbz8vngRIgARIgARIgARIgARIgARIINIIUOhH2h3l9ZAACZAACZAACZAACZAACZAACeRrAhT6+fr28+JJgARIgARIgARIgARIgARIgAQijQCFfqTdUV4PCZAACZAACZAACZAACZAACZBAviZAoZ+vbz8vngRIgARIgARIgARIgARIgARIINIIUOhH2h3l9ZAACZAACZAACZAACZAACZAACeRrAhT6+fr28+JJgARIgARIgARIgARIgARIgAQijQCFfqTdUV4PCZAACZAACZAACZAACZAACZBAviZAoZ+vbz8vngRIgARIgARIgARIgARIgARIINIIUOhH2h3l9ZAACZAACZAACfglcPz4cVm0aJE+fvbZZ0vRokX95uUBXwJz586V5ORkqVWrllSsWNH3ILfyHIElS5bIvn37fNpVsGBBadeunc8+buRfArt27ZIVK1ZkAoDfxpIlS2ba77Zj4cKFcuLECalZs6ZUqlTJLYvffbt375Y1a9ZITEyMtGnTxm8+HgiNAIV+aNxYKgACeJlKS0sTPFRiY2MDKMEsuU0ALwCJiYmZTluiRAm+/Gaiwh0k4EvgRFKKpKSKREeJFCkY43uQW3mWwLvvvivvvPOO/o37448/pFixYn7buuHAJtdj8cUrS8GYAq7HInXnnDlz5MYbb9SX99lnn0lCQkLIl2reDwoUKCD4CyaZsihTqFAhLRCCKZ+f8t56660yY8YMn0tGxxY6bLJLKceOSZrq1EGKiYuTKCXEmHKfQFpKiqSePKlPHJNNpyTyphw9qvNGqffu7PIj448//iiPPPKILmP/+/DDDwPuELrjjjtk6tSp0rx5c/nkk08kKko9FANM6Iy6+uqrde4PPvhAzj333ABLMlsgBCj0A6HEPEETgHhs0aKFLnfDDTfIQw89FHQdLHDqCVx88cWyYcOGTCfCj/Z//vOfTPu5gwRIIINA9xdmyIyVe6VBleIyf2jnjANcy7MEdu7cKV26dNHtGzx4sFe4ujV4++GdUuf9c9wOydg+o6RH7eDvedzw2rq+S+t2ly96v+dad17cmaIExBVXXCGrVq2SDh06yPvvvx9yM1NTU6VZs2a6PF7wn3zyyWzrQicDOmeWL18ux5QANWnYsGGC5xiTOwFb6EOEIWGUFiyzS/9c3V+OLl+mszV69x0pdd552RXh8XQC2z7+RDa8/LLeajl5khQKw/ply4ejZdMbb+i6Wk2aLAUrVfDLef+0abL8zrv08biGjeTsr77wm9ccmDVrlvfzsGfPHtm8ebM+FIzQX7ZsmVx55ZW63KuvvioXXnihqT6g5V133SVTpkyR2rVry3fffcfOu4CoBZYpTwv9X375RT9UihQpIvixYjpzCNhC/7rrrnPtLTxzriZyW3rZZZfJ2rVrM10ghX4mJBG3Y8/hRHl70jp9XRc3ryStapWKuGs81RdkhH7dSsVk0TCPeDzV52T94RF44okn9IskRjX//PPPLC2XdhzZJbXfa+96wnCFfs9aXeTbviNd687pnY/+8bwcSToqzSs2lZvO7hdS9ePHj5dHH31Ul/3qq6+kSZMmIdWDQsEK/b/++stvx/OpFPqLFy/W4gNtvv7666VMmTJYPaOSEfpws4CQCibZQr/h229J6Y4dgymer/NuHfN/svG11zSDFr/+IoXj40PmsXXUh7LxzTd1+ew6DUIR+nbDfv/9d7n77rv1rmCEPgrcc8898ttvv2mXnokTJwZlqYMOPHQkIj377LPSp08fvc5/4RPI00Ifve2//vqrfhCjN5fpzCFAoX/m3CvTUvTkdurUSW9S6BsqkbtctuWQtH5iqr7A1wc0k1s619Dr/Bc4AQr9wFnlhZz4jTv//PN1U2655Ra59957g2rWJ4u/ldt/fViXOZOEfsmX60lyWoqcW7W1TOr/ZVDXbDJffvnleuAFAh9CP5wUrNC/6qqrBCOGSLhvMO0tXbq03oY/cJwyKz8VCdcJ0YGEUca6deueitOc0jop9E8pXr+V50ehP3v2bBk0aJBmEsqoPgYF4etfvXp1mTBhQlDm/35vBA8IhT4/BKeMwOrVq7WPPh7I5cuXP2XnYcU5Q4BCP2c4nim1UOiHf6c27D4mR04kS5FCMVK7wqkRG+G3kjUYAhihwgso0vfffx+0cMsJoT9t02xJUaI7vlhFqV+2jmnaKV2GK/T/+ecfueaaa3Qbn3rqKa8/baiNDkbow2UAQcGQWrZsKR999FGopw26HIV+huk+R/SD+/gk7t4lJ7du14XimjSW6DDiVJ0pI/r4rmKwCLGfEFRvzJgxQUH75ptv5Omnn9Zl8D1v1apVUOWZ2Z0Ahb47F+4lgXxHgEI/f91yCv38db95taJ98+GjX69ePT1CGyyTnBD6wZ4zJ/KHK/ThQz9u3DjdlOnTp3tH00NtWzBCHxHBTUwFxI2BtVluJQp9Cv3c+qxldZ4zRejjGl5T7gqjRo3Sl/Pzzz/r0fmsrs0+duDAAW8gvksvvVReeOEF+zDXQySQp4Q+eo1h+mESIkGaQGEI1OBMMCWrUME9KAXKwVcEU0asW7dOqlatKg0aNNBBZJo2beqsSm/b50fv9cqVK7UPH6KTYlQawWN69Oihp49wrSAHdp5UkTV/+OEHbb6C89evX18HtUOwmZkzZ8q2bdukVKlScskll/g9G/zKEFwD5TFlBabBadiwofTs2VOqVavmtxwOHDp0SH766SfNDUF34MeIsgjicsEFF/gti548jJYgyr4zgfc555zj3J1pG3XArwfXiWAg+NKXLVtWKleuLK1bt5bzVCCYcuXKZSpndoTSdpT54osvdBWov0aNGoIozHAVQSTQs846S5+7X79+ER8cJFShH8p3zdwzs8TnGtzxecXnDp8FsMcITq9evTJFxZ6mAs4gtgBmdLj22mtNNd7lkSNHBL3DSPjs+PMn3bJli863fv16wTrqw28KAsLAxBefXUz5cirSgvUHZPyCHTJz1V5d/Xn1y0qvhEpSpUxhGTt3m953YbOKUqei+0jxsZMp8u3fW+WfTQf1X0pqmrSoUUra1S0jV7SposzeMrf61QlrJEkFwkLavv+EjJyyQa93b1ZBl9Mb6f9qqRHqK9tWsXfl2PqPC7bL1GV7ZPaafRJfurCcW6+sXKHOtevgSZml9iHd2LG6FFUj5W5p3roDMmXZLlm86ZAs23pYGsQXl7Orl5Cr2laVGuWznirtwLEk+XLWZvl34yFZotwXihUqoMu2r1dGLm1R2e103n2fTt8sW/cf926blQolCsmN51c3m36XKSlpMm7eNvlt8S5Zr6wB9h5JlIolCkvVsoWlY4Ny0q1pealYsrDf8qG2/Z3J67TlQb3KxaR3y3j59Z+dMm3FHpmhPnslixSQlrVKyz09akvpuOCin/ttqHXg2zlbZe3Oo1KyaKzc2qWmjJ2zTf5cvkfmrt0v9dV9a1O7tFx3XjUpVTT7c2/ac1wmLNouy7Yc1vcOn3lYUpyjvjtXt6sqJYr4n91l48aNctFFF+mWwbz0/vvvt1oZ2GooQv/ntb/Lgh2LM50goWITubiO/2eqKZCSmiIfLf5Gflw9SdarGQAOnDwk5YqWkarFKku3WudL73o9pEpx3+msxvz7pazYu8ZUIW/P84yslSlSUq5p7Ov7Gi0x8kJnj++9t4BjBcH3MErXqFEj7++qI4vrJkQ63if+/fdf/e6CdyhM6wbhnlUwPkTtNgH3Dh48KB9//LGuv436LW/rmBaua9euUqdOHdfzB7sT03yZDg2UxRSMiA+AhKCBzvfNtm3b+p15IJTnon3+zp076xkF8E6E9+KkpCTNrKPyk8d5A025bbqfmnhSdo//UQ4vWCRHV62QuHoNpFjL5lJBPccPqHe7E9u2S4FSJaW8vwCK6h1yn4refkR9Zo4sXyFJe3ZLnPrc4a+CEn/ZRZE/vn6D7FN+5kdWrJQTG9ZJoSpVJU69+5dW73fFmoYeVyI73ilqlqmdYz2dYc68Fftcnm27UWafegc6+Pffclhde4FSpaVE61ZSTr237/lpwhnho49rwPfl9ttvx6r873//E7jdBJPwPcO7N7QH3sODid4fzHnyU948JfRhqjF8+PCA+X/66aeuP7J4sLhNFWEqvu+++3Sk3ejoaLNLL//v//5PXk6Pkvnf//5XMAWPW0K0WYjCnE4QtghmMX/+/ExV48GIBx6OQawjUKEz4UGAto0YMcJ5SG/jizN06FDp1q2b6/GlS5fqIBwY8XBL6OSAWY3bVER4KPub/zKQYHz79++XgQMHugaGM23By8Z7771nNn2WobYdD2MTsRefCwhIN/7wCcTn4VSJPp+LOU0boQj9UL9r5hLRMYQXKzwQskp4eBifTOTD9xvnRsJDwZk2bdrkfal/4IEHXCNro4MH34esEqaQMmajWeUL9tiXM7fIoA8WuBZ7ok99eW7cSn3s//7T0lVs/7PxoAx4b76s3nHEtY6LmleUD25ukUm4xQ0c75rfbecFSnT+8EB7t0Mh74PQfeSrJfLupPWZ6qhZoagSuqrdv3uOrXy1m1QtU8QnX1JyqrwwfpW8qP7cUjEl9EYMSpDereLdDgs6V/q9NUe27jvheryv6mx4e+DZfgVjxyHq90F1MjhTIMH4EPzwwmEzZIXqmPCX0OHy3f3tXA+H0/aK//lZjhxPlktbVpY6leIEHT7OhA6mP57ooDqafJk78wW7ffmrs2XSv7ukvOoM6dsmXt7/LfO9B79xD7STWn46adB//NG0jXLHmH+yPP3Gt3pIueIFXfPAxxqB+JAw6tS9e3fXfFntDEXoXzXuNpmw9rdM1QYSjA/BANt9dLHsPubpDMxUidpxdoVGMnPAjz6Hzvv4Mlm4M/Pvok8ma+Pog5kDsprD+C3FIAESBkAef/xxcyjLJXzq8cLvnMMdhez3K7eo+3iXMEI/y5OogzkZjA+DJP3798/ulN7jeGcwPsnenWol1OciOkSMiwRmK/r2229dOeA98eabbw5IBOWm0E8+eEBW3HOfHFqQ+dlWRnVcJKt3WBwrrMR3i18m2Mj0etLefbJGvWPuV0Ey3VIRNRBT/9WXpWidum6HZc+En2TVo/4/n2cpblVuHChRjnd/18qC3HlSDVbM7+H5njiLZheMD1PibVDaZ/vnnkEnu3zBShWl9Dnnys50i5q8HIwP7ca7vNFHGKR56aWX7MvJdv3FF1/0duwhACgGXZjCI5CnhD5GzjGqZxJ6Mo3oxI+eM0FAxjsiWUKo4SFiEqZ4QAAVjNbBJ8+khx9+WEdRNdtY2kLf7McIAHqL8dCChYBJsDaoWbOm2cyR5U1qfto56XObQsxDkKM3a9KkSd7pLnAif0LfBC9EHoj63r1769FwWDTgJcck9JabaVbMvr1793qDFGEfjqPXGCOjML8xD2t0OLyZHv3TlMUSnQx4eTIpWc29+vnnn+vNQIQ+xBjuNxLMKvFigbncMSIA9hB6ENtunRjhtN0W+vrk6h+uHSPJqNfmhus2JoQmb6jLg8ui5N+nY4MuXve2FKnU1TMiG3ThbAoEK/TD+a6ZpqDzxO5Qw2iFGX3HyL75ziFasD2aEq7QR912VFdEe4X1DIJI4sV28uTJ+jPvrzPRtD+U5V9qOrYealo2k/oo8dP0rBKyZPNhGatG6O3kJvQXqxH8dk9lvAid37C8dGxYRpJS1AumGilfsvmQrqJL4/Ly44O+Qv2Jr5dJMiZ+V2nHgRPyzd/b9DpGs1vV9I26X1eN/g7qVEMfz6l/w8avlGfTOzEgyvu1ryqVShWS35fsllmrPCP55lxuQv8G1blhGKH8dedWk2pli8jK7Ufk42mbTFH57fHzpL2ybLDTrkMnpebdnt8Y7Mc1g92h44nyzextslsdR7q4RSX56u42et35DyPjm/cc8+4eo84JAR2I0L/+3XkyTo1mIzWpVkJZXcRL6WIFZZuyrFi44YAWw/46V8JtuxH6puHoVMFMC0jmGrB+14W1ZFj/nB31MkIf9SPhvl1zTjUl/AtqywLTcYKOhmXDL5DYGN8OeJR5/oeVMvS7lVjVqcfZFaVFzZL6+QirjvHzt+v9a17vLpVLuVtEDBkyxBtEDkF+s7NuSz+VzyIUof/ego/l9/WeUWFU9su6KbrOQIR+9y/6yYwtc3X+SsUqyFUNLpGyRUvLpoNbZd72f+SfXcukaYWGMnuAp9NTZ1T/3pgzUh1bbjblq+U/6PViBYpKrzrdvPuxEqNEz8iLPNOA+RxI38Dz/8EHH9Rbzz//vGCmluwSLOVsKz6MxLdR7xOw3LJHzFGPm9DHMwHvHkioy7y74dlgpu3VB9U/dNTD0iAn0o4dO7wiA/VB+CM4GBLeBZ0WhfBHdg5whPNctIW+Pqn6h3cRWEHgXRjC36TnnntOv+OZbX/L3BT6S5WlzMG583RTIObLdleftSiRvRMny4mtW7xNdBP6GBFfqD5biTt26nwQ9WW6dJbYuGJySN2D/cplBCmmWJy0VN/fWPV+aCc72jz2l7uwu+4QwHl3fe/5/GN/DTXVc/x1ma0AcSyclKw+pxtfzXgHPrJksbJoWK2rzE7ob35/hGxOH1jE9VVQFruxynJ339Rp3ukNTdvyutBHO/GejM8rZqkwFjGm/dktQ/m9ya7O/H48Twl9580wwtWYcDiPO7cxOoiHhonOip4kY6qHvPjBxhQpSKgTU+tg6j6TnEIf1gWmJxt5MLpnfEYwX2R2o5Cm3kCWdtvw0Bo5cqSe6xRlMdJ/0003aZNmbLsJfXSS3Kg6CpDwMHzrrbd8AuCBiTGhgZCGWbM9Og2RDtN7JPQoQ0gZiweIbZwfohjp66+/zvbBGkzUfdsaAPcFnT3OKLow5Ycbhps1Qjhtdwr92267Te68805vTzk+I8Yn0F8nh4YS5L+9c6Nl9i3BC/2mTybLWX1Pv9AP97sGXLin5vuF+/7666/7vBwiD0Q3oh5j1AZuHCaFK/RhGWLmEXYblUFHFaaZQaeP3cFgzh/O8pLhs2TK0t26ilG3tpD+51T1VveREo7/Hb3Iu+0U+hjZ7PnSTPlLmT4jvXdTgtzQ8Sxvfox43zpqoXw929Nh8MXdrf2ao+e2j/5hFbSuzn2TtDCG2Pvt0XNVB0dJ3XaYYOO6YRpvklPo/6VMzXsMm6kPt1RTAX6txHglS9Qt3HBQznv6T30cQnrm0+er3zj1lpmenvxmmXck+/YLasrwa5tIdLp/w3bV6dFT1W0sJKarss1reNpmyrstA426fzQxRSrc6hnBwrWvea27FC/s+/1ft/uYcic44GqNEG7bbaGPzoTP72wjceluEduUK0Ld+yZ7L+/wmEu8XLw7w1ixhT6ufaqyGmhYpbiuERYeNyrLFtN5M+b2FnKVMsG3E7g0fdDTyY7yn9/RWro28Q3uunbXUbn3o3/lw9taCNwo3BJ+x6dOnaoP4XmJ35xgUyhC33mOuOGeEarshP7RxKNS4Y1munhsVIxsunOulCzs+5lcqczz52xbJNc3vcJ5Gp/tcHz0YTaPUTakDz74wOtD63MCxwbeX95In/cbrlX4vTYmuDCHR+e/SW5C3xzDEuLbuA1ilgSMZOdWCtZHP9znolPo410MUxoadnAvNa5qeA+E5YD9HufGJRyhn7R/n6QmJulqCyrRFlWggNsp9L7DCxfJ4gED9Drmb2884n0lVj2fV4z0Lx10s1f4ugl9W+xW6nuF1Hj0IYkumPFd3qFmQFg39Hldf/x11yvBPjijLerB+E+/a7yiuN5Lw6ScNbputw1CurUaOIgu7N4hmFFpeGuBRt1POXJY5nW/UFKOHNWdGE0/+VSK1q6lT56q3kPWqPu/Z+Ikb2OyE/ppJxMl8cB+nT+6YAEpUNq3w9tbkZ+VcKbXM1ViYNZYxqKzzGgJczyrJcz1jZbBu1luft+zateZfCyihL79APE3+gvxb/y90DuNgA8m2UIfPdCjHREj8SOOjgMIFCT4kmPUOSfSQ6qXET1ZSKNHj87US4wRTTO3pZvQhw+5MWHGaAViEjgTHhiwRECyTZIRFwC9xkhuHSDYj5FVMxURLAXQm5xVCkbob9++3Svg8UCH4As0hdt2W+ij9xHXCT9tO0GM4p7DhAjxE3IinelCP9zvGhjCBNTwhEuImUM1EL7hCv1nnnnG62uKTi/EociNtGLbYWn5mMdqCSPKvz12XqbTtnz8D695t1PowwQawgkJYvWV65pmKg+xmvCIZ9TQ3wgxCuW20B/950a5K930+vHL68tjl9X3abtz1Nop9G2z+aXDL3D1xb9ZicYvlFsE0h9PdtD+31g/ocwdyt6SIbTXv35hJv//8coaov+bntFT+IyPuLk5imaZAhX6m/celwYPeMQ0zOe/uKt1lvXaB3Oi7bbQnze0s1dom/Pc+/G/3ngNWZm/m/zBLG2h/1TfBvLwJfV8itts0IEz7amOPsfRcfVZegfQ2zeeHVAsBJ8K0jcgKM0zEq5eoaTcFPoblD9+45GddTPbxjeXKddmjOYG2/ZwhD6exxDuSOjkb9y4cZanx3sSrAGN6T1e3J2dKui8hYhGiiShH+5z0Sn0s2OHjhfbcsLtxoQj9N3q87dv1UOPyB41XzxS41FqoEq5X9gJ/ucr7rlX73IK/SQlTOd27KSPxdWrK82UW52zUwHm7fPVu5gZ8W+/cIFExcToMocX/SOL061+S6l4UI3ez+ziueGll2Xbp5/o/HWHPivlL8l499c7c/hfoEJ/x9ixsu6ZIfrsZ6nOyKq33erTkpNqZHx+t+7efdkJfW/GEFdyQujDtcRYY0InlSzp20GZVdMQW8xYW8Kd11gTZVWGx7ImEFFCH/4cjz32mL5i9EDDP8SZ8KCHKEbCfKz4QJpkC32Y+ZkPmzmOpW1qnJMCAeZwCC4GsYlRB2cPGEzjjbm9U+jborpixYryyiuveJuMh65JC5RvlDGvxxQ5ZoTfHlmF2ILocib7/LAY+PLLL51ZfLbtNmVnum/XjUpQtzHf9qnUZSPctttC398LB0b4cU/wsoIHb06kE2pAd+cfmU1Us6u7bKs0KVYr455mlz+Y48GY7of7XUO7YHIJ/uA6Y8YMKZDFaIHzOsIV+nhBMi4o6LjC74bzZdR5zpzYRhC6fuli0p9ogf80RnCRnELfNn1/oV9jaVOntLdZ5quOT8etIxfI+l3HtJn0zvcu8uaxV3Jb6D/yxRJ5a+I63YQFL3SW+pU9o7p2my57ZZYKVKe+HCrZQj8xKVVK3+IxT4aJ98f/beUtZq4bO2D+b9i9ObCZ1/XAHhW+sVN17YfvrSB9BdYQpW72nMNNcDrzYztQoW/XjXIQszhHICkn2m6EPkz2l7x0QabTvj1pnTz8+RK9f85znaRx1ZzpwEaFttBfNKyLdnNwNuCC56d7XTeO/p/vCzg6rdB5hdH8LcoHv0Bs8L+bOB866RGQD99zjOiHknJT6CclJ0qp1zI6ICf2/0LOq+orngK9hnCEPuKYGDc8dMxmF/gOFojGRxfM3Xx0582bJ3iJR/L33NUH1b8zaUQ/3OeiLfTxfIQlmzP9rYK1mbgAGLwxI/zOfGY7t4T+osv7yDH1DlugrHIDU9ZwTj/4NPUOO6ul53fbKfQPqs/D0psG6SbD5L7ytRkWHyrCs7kU2f7Fl97OhATls160Tm19bPeP42X140/q9brPv6AC/WV+5h1dslT+URYSSFVvuVnOcgnwrQ/m0L9AhT5887epUXykhB++l6IuLsG2S8SZIPTxPmUGcSD4EVA70GTHBIHltJseCbQu5vMQiCihb5vj+vOtRaAIBHVDcj6EbKEPM3a3yKbwFTMBfd5++22Bj1a4CWLczASAwF8YbXdLZmTZKfRtsepWzm0fTGtgRYBkm/27mTGb8qYzIhDBG4zQR/22RQO2a9SooXuqEZkX1gb+fijCbbvNDhYTeCg6E+638RE0I0LOPJGwHYzQD/e7BtP4hIQEjQ0+jrBiCSaFK/RXr14tmLXDTogPgHlb0cmETrVgOh7serJah4/3Q595BNVPyn++s/Kjd6ZvlJ/+QOWLjuQU+gjgh0B+waRNb/eQssoX3JlyW+hf/eYcFUNgh27G7hG9Mo2o48A9amR51JQNOo8t9G0rBX0wgH+2v7lt9v/slY3k/l51XGsw1hQQlf46SOyCgQp9lBn43jxvTARsw68fJuitVcT7c1XUeMQacEs50XYj9BGT4OeH22c6DVwmblMj50hTlSVEaxUJP6eSLfT3fNBLihSMyVS1PWq/5Z2e3iCSiCdRcpCn88Vf2zNV5mcHzKBh+oyE33FjDu0nu+vu3BT6aEDPr66VaZs8FjzYRtT8ztXPk3OqtJIetTpJjVJnYXe2KRyhDzdABPlFguDPLjjp8uXLBS/oSHCFc5sxCb76JhhiJAn9cJ+LttBHrCk73pQGqv5BCOHdFWmAMpXPbsQzV4S+eoedebbneV5cvbM1TR851420/i3o2Uv76juF/k4VQ2rt/562cma/2uCN1wUB/pBss/+mytWkeMLZmSqAG8Lc8z35y/e6SOqe4qnbAhX6K++9X/ZO+V23t+3fsyXGcic2F7FWDTzu/Has3jwThD6+87BCRkLHlFsAb33Q5R/cjM3vB8z2oUmYwiMQUUIfPT8mWAnM4DE9lzNh2i7zoMLLPALTmWQLfQSMgS+7M9km7JhbFg+pcNPhw4elfXvPy1dWfuAYgceXwCn0Ma+tmc4CIjwQn2L0FpsyEyZMEAQnRMoqwAssIDBtHxJ65Atn4eMUrNDHKACsJczIgT6J9Q+uGLCygMWCncJtuy300QtpIt7a5zgVQj8tVSTlRJR9moDWowulSXTm9+SAymaXKRihH+53DcExMYsDEgQ3TDmDSeEKfZwL8RdeffVV15ke8D3Cd6Jv377BNCvbvLav9cxnzlfTumU2acPUZ31f+1vX5RT6tvk6Rmdjo7Mf3fz9ifPyhNA/V/nPL1J+9EjOUVu9U/2z+dhCf7Kajq73Kx7BAxFeOYsp6Exd/c6pIo9c6nEP+GrWVrlphKfzBCb5MM13S3b8BH+i1C4XjNDfr6bSe+77la5R51En3CwQcyG+tK/gz4m2G6Hvz23AFvq2y4N9raGu20Lf331/8PPF3pkY5iqLgkbpFgUblH9+43T/fMSiAJ9Qk21OGuzLpzlnbgv9XUf3yOApQ2TsigmmCT5LBOL7qvf7Ur1kZlc9O2M4Qh/PZDM7CQS/GSix67fXEXwL890j+ZvxxI7LE0lCP9znoi304drmNgOAbTGBZ6iZKcq+B/Z6bgj9FPUO+/e5Hjc0iG+IcLf0z9X9tR+9U+hvUoGOt4z6UBeBRUBs8ewtimo8/JCUVu+FSLYQbqEi7xdW8QucKU1NUzkroYXeXbx5gjRVs3ydyhSo0DdM0JZz/vV0RDrbtfntd2SzskJEOhOEPqx4TRDLYDtVMZWksVhBJ5ax/HEy4XbgBCJK6CN4HqboQ/In1I+ryJ6YVxsJDyz0wJpkC33bh90cxxKR4fHwQkJgvqzms9eZAvhndz64xQYwVRhTZ6fQt/3CEMTCtM+Uy26J4Hem1x2i1rg2OMvBRMyMiOCB5HQvsPMHK/RNWfjrw/wN1+SM1gkf+bHKnyk2NtZk14H7wmn76RL6e+epYHw3Z1yH94KyWWn2VLJU66N6CU5BCkboh/tds19WEGDRuJQEelnZCX37vvp72cS58N3D5x8dWHDPMLN8mHY4A3Ka/aEubbP8X1Uwug5qJNeZflARxK95y2Na7BT6tmn71nd7BjT3uLN+s53bI/p22w+MutjVBNsWfLbQn71mn3R9brpu+r0968jQqxuZywhoOWHRDrnq9Tk672s3NNXzubsV7PzcXzJnzX59KJCgdMEIfXM++KR/PnOzzF69T0faN/uxbKCC1P095HyfyPM50fa8IvT9MX3g08XeDhA7/gI6R6re+atG1Lt1vHx2h8f012YW6Dqe17D0QwrEBN2t3twW+qYN8Nd/b+HH8temv3WkfbMfy/JFy8qq22dIwRj/gdLCEfoY3DAujoHMiw0XQTNLkj8LQfv3P5KEfrjPRVvo+3tuITgyBoSQAukkzw2hD//5Wc09Irqkmvu9cXpgZ91I69/CSy+T48pdzyn0t44eIxvTYzM1fPstKa2s64JJG9W02Fs/9gzaJaiZCYoqP39nSj1xQma3aat3l1bTYzd89x1nlhzdDlToL7v5FjmQ7hLafpGKO+AykmOb958JQh/aCjN1QasgZlgwyZ4GFd8nY70STB3M60sgooS+HRV/1KhRekoS38sVPc2eGUmEoDVm+MhnC32Yq3VONwuy67Dn3nYLmmfnDWbdn1m+qcMOcOMU+vhCwewYCR0PZmYAUza7pW1qh1F++KS7pa5du2oh5Dy/W95Qhb5dF14G8COBUVcT2McZfCbcttuCMDdH9PN6MD5/Jpfm/oT7XUM9Zq5kWM44p1wy5/G3tK0sENXVaYaLiK8wa0Ty98LkrDs1NVV3MOF7DdGPlJUrDY6jgwDWCSahHXj58hcJOSuzfFPHCDWP/P2fLNabTqEPP2r4UyP583fWBwP4l9tC359Zvt3Ua9+ZJ9/P3aZ32UIfU9/VSJ8aD7MUYLaCYNI/Gw/KOf/7Uxd5RAUBfFIFA3RL9e6fJFv3nRB/vuzOMqEIfbuOvUrIjp2zVVkyLNezEeDY+MHtfaLK50Tb84rQ36LcSDCloDPZ933vyF5SuECG2ZJpO2ZS+PvZTs6iAW//8ssvgpl8kPCMtAPxBlqJLfQ/7/2uXFb3wkCLevMFGnXfW8CxsvPobvlo8dcydPobkpyWoo9m15ZwhD46P21hmV0gXttay3YRtC8D8YjMNH2RJPTDfS7aQt+fWb49NSxmkjDWEzZfez03hD7O588s39sWZd7/txqBR3R5p9CH6TpM2JHqDHlGKqi4OcEkzD+/Pj2eQeMPRkhJNR2hM51Qz+kFF/XSuyupd/9ajz3qzJKj24EK/bVPPyM7lQUxUus//3CNkm8HOszrQh8xNaAVkBDnLFhrTXsaVAysugUW15XzX8AE8rTQt6NyYyTZ38uzuVpEizQ9z/6ECqLOI4AJ0v3336+njTPlbaHv70fWTPmHMjAbr169uike1hI/1mYEG6MNGL22k3NaFby02MmIJpi245gzcryd17luxy3wJ2zsoHf+ZjSw680JoW/qG6NmPzABBp0WB+G2nULfUBaxgyLClcREWs7IkbEW7ncNNWGqS2Pe5faZzzhb5jV75ASB/JxRXeHCAzNKpECFvs6s/h09elR38qFzKbt4FLY5sCmf1dRdc9bul87P/qWz9m1bRT7+T0tTzLu0TZ2dQv+DKevlvo89nQDvKjPmAdbUet4KAlzBlGTNHvpd587Kbz3A6rLN9vova+TxrzxBBu1AeabgsZMpUvPeiV7Bawt95DGCD8H4lrx4gRQskL3bgql7z+FEqX6XZ2QBAQz/UFO8OZMd9C6r2QrscuEKfVOXzcZpcZATbTfsTrfp/jf3tJWLmvu6X2GKvdpq2kV05pRXU+NteNNXPNuB+uY/r8yC4zMHcTQcs1raghUuWni/CDZNXDdV+owdpIvd3epmeaFz8GIhXKFv2vzkny/Jq3NG6M3/dbhfHmp3hzmUaVn21UZyIuWkwNR/9gBPzINMmbLYgcERvAPgfcfMDuQvu/3s99eJa/8+52Whb8dk8hf3yeYQ7nPRFvr+2NlT/mXlamnalVtCf/l/7/DOdW8HyjPtOKKsQP9V0+IhOYX+MTXf/CIVCBqpfK+Llf/8UL0e6L99yh98xb0eX+5qKs5StTszfxd2q6kIVz/m+c5Xv+9eqZI+HXWg5wg2X6BC344v0PCtN6X0+ef7nArWEvPUbFRJe/fp/Xld6NsWQKF0qKID1gQmN5rIBwg3giaQp4U+5rk2pvUw56hbN7M5jn3FtukxXtAxKoelSTDThfm5CaiGH0x7qhhb6KMcTHrt+dxtkymMagcyh6k5d3ZLiHMTVAUm+pj6z5jGY6QRx9C7heQ2om4LH38j0ygLywCMgjdqlGH6in142ML/H8kt6j2ENgQ3EgLnGdM8vcPln/2wzy7qPgKzHTlyREqVKuVSk+iOC8MGvYN2ELVw2366hH7y0Sg5tMb1crPcGVctTQqVyTJLWAeN1QYqwah4oUKFXOsL97uGSu2XPQS1xLzLbp15mAoLHV92TAhMkWmiOcPiwwR2Qr34nqMnGQ8LJDehv3v3bilXrlwmSwDkx/cNDxt8NjALxrRp07DbNQUr9DFfPKbXQ3A5JGeEc3v0FsedQt+e8xyiaMlLXaWYYz52lEPCaDHEs78gbzhW/jaP72+fNvHyiRXJ3lNDzv7ftOe4NBw8WVeKQHR/PtVBShbNMDd+//d18sAnS7wndQr9R79YKm9O9NzTV65vIrd3reXNa6+onzNZpEbwm9fIiH+Afec9kxEjwC3qPTohILiRhvVvIgjml10KVOgjqNzh48muo9k4x7dqVH/Au54YAvBDhz+6STnR9rwi9Ls3qyDj7munvnfm6kTsmShuUjMivDXQN5DWGDUt453p0zKik+DLO9uo3wmrgvSqFqw/oKcNdAv2Z86GZwdGRNEhjhdS84w1x7Nbrt6/XhJGXaCzNS5fX+YM/Dm7IpmOByr0E1OS5MCJg1IhrlymOrBjzL9fyp0TPcLlhU6Pyd2tPR0Qbpmbjewiaw9slMIxhWTb3QulUKz777pbWezDb61xiwxklA1B5MzvJnz8EVTXJPw+w/fcvG/kZaEPf2Ezj3cgMZnCfS7aQh+8nC6kTnb+plI2rLHMLaG/Z+KvsurBh/WpIdbrDH3WG3k/TT1TV6uYN2Y+eKfQh//8v/2v0/77qOBs9W4e17CBrsv5LzXxpJzcuFmK1K3jPZS4d4/M69xVb8cUi5NWkybrOelNBojlxWpg4YiKvI+E6fuKNc54Bzb5cnIZqNC3ZwMooyxzGyixb/9A2tYOaF9eF/oYYMGMZEgIyOeMq6UP+PkH111MsY0Uamesn6rz9e48LfRt8YveTURFhxmHiYaNSOzOkWtbkCJ6Nno8a6rpKhDlFcfwcEeCqTuCv9nJFvrYj9FtlEdQP4gGiE0jHgLpSbXrzm4dP+AQ+Og1R0IPOqb9QoLPut2L7ib0EdAPIs2YuENc44tSpUoVgZDeunWrnhoOvdIQMKazQ59A/UNgMpiBIaGTA1OPIVghRjfxpYUrgznm7EDRBxz/ghH6JgI63A7wIoZ7DdGPLz3EJl4y4J6AhJcMXJOdwmk7WIA7kr8OEttM3MnNbkckrEM0mwj4+JwhAKOxWomPj/eZ/SCc7xpYOV9aMLsCvmOYvgkm8IgujA4+iHo8MOwgk7aFCx4k+Iyg0w6fGXR6mZdMnMdN6GMf5mvFSyZ+C3Bt6DTC/cU58Yfkb7pJfVD9C1booxyi5iN6PhICyw1T0+TVV6OUa5X4H6xM848oQWiSU+hjvz36CxPzl69tKu3qltH++jsPnpCV247IZzM2CwKsvXRtE7mjm3/B2uSh3/Q0fKj3iT71pWvjCjriOfjHFYqRyqUK41COpf+OXiQfTduk64Mp9iOX1ZNyxQvJ9JV75LlxK33O4xT6B48lST01F73h89/uNeX2C2pJjbJFJUm9SG7KfK0dAAAQ4klEQVTcc0z+XL5HB3VDR4oz8Nsvi3bKFa97ghyC+5d3tpb29cpoAT5aickhY1fo8+PYujculDiX6PA+DVQbgQr9pVsOSZsnpgrcDq7vUE2aVCupAyTCX3/Gyr3yyJdL9Yg26l/2cjepXs43IF+4bc8rQh/Xd0uXGvLwpfX052zqsj3ewJM45uaOghH/js9O8wZyxAwFL1zdWAXsK67fh9fuPCqf/LVJT9245nU1NVcWn1nMA//MM8/gVNpf30xZq3cE8C9VRVFt8H4H2Xpkh87dsFxdub/1bVI+rqzeblW5mZQunNFhfVAJ9UOJR31qbjDCY03SoVpbGXnRyz7HKsdVUPEZYvW+BTsWS4dPekvX6h3k9pbXS8tKzaRiXHmBv/6vyrLg8anD9Cg9Mi+6+TepW7qmT132xrU/3CHfr/JYtJxbtbXc3+Y2qVK8ksQof+DoqGhpUDZDNNnlzPqaNWu8pvb33nuvfi6YY25L23UKnaV4VoA1BkuwbgYtUDYvC317aj+8F8EatEGDBt4BJFxb8eK+FibhPBedQh/nxOxOYLd3717txmjeBQN108wtoQ8xvfDiS3VUfdzXst0vlAqXX4ZV2aNG03dPyOgUcwp95LFH/LFdSwVHLtOlkxQsX0FSjh6R45s2y/4pf8i2zz+Tkq3bZAr4t/G11wTiGqlYk8ZS95khUrhWTTmp3gk2vvyqN7K9FtMqDkCOJvXukLhnt0+VO5Q7wZYPR+t9mAmgUJXK3uPRhQpLbIkS3m3bT79y/356+r8Y9bk6MGu2rLj7Hm8+rORloX9CxUGAfz50CAQ7Bm+CSXj3xPcHCQNBDRs2DKY48/ohkKeFPoSAPTLnvAY3Uyr8GMLsHgLOX8KPMz5QzvlgnULfX3kIEpg1OzsZ/OUPdD96uOEjb0StXQ7iF0Ho3KLum3zofUZniBH7Zr/b0ilYIXLglmA/gN3KQVAFEhwjFKHvdj57HyJxuk21EU7bKfRtwiKIi4DRbLfPINxLTGcQSoXzXTNnxegaxLLp4DL7nUun0MdxOzikMz8sBNAhheRP6Gf3WcdLFoI/osPDXwpF6EO43P/ZYu80cs66MZr7cboYdhP6mFN+0MgFMm6Ox5fdWd7ezk7ofzdvm1z39jy7iHc9UPN1b4EAVmCGfuUbf3sD3tlFILC7N1UjvunX5RT6yPvH0t3S7+25XrFvl3euO4U+RsZvUFPcmfqd+c22G3NzzLkMVug7yzu3Me0f3CicKdy25xWhj3tsOmqc1/hAr7oy5Er3F7slmw9Jv7fmeDulnGXNdnZCH5ZjZtpcxOjBKG2wadK6P+XysTe5Fvvq8hFycR3PiBQy9P/+PzJ+9STXvG47x/X9UC6s1UkfMkLfLZ+974amV8p7PYbZuzKtYzQfo/r+0tEHPZYy/o5jPwL9zlFBwwIx30d+u4Mc23ZC56wJfJqXhT7aDOtKf7MBuQUbDOe56BT6NjN7He+weP91m1nKzof13BL6ONcR9Q67XA0YGTNz7DMpDgHyVMDIo8uXZTLdN3nsUXCzz23pFtk/Ub37L73xJh3sz60M9iGif5MPRvpYA/jLG8z+kyqOxfxu3QMu4gxYeFRZ2S5R77eIX5BdystC3zbbRzwtuPkGk6AtNm7cqKc4hsU1U84QyNNCH5cI0YqRfdx0Y+plLt1pEmb2o8wwFZjDLcAXRu9g/l22rKcH3pTB0hb6qBs9S+iZthM6HvAAy2mRb84By4PXVfRRiHaILTwQ8WWBwEJMAYxkwuweIxNuCWVw7abX15kHvW0wc7bN300eCGY8PF588UWzy7usUaOG3m+7OngPuqycPHlS0CGClJ3pPtqMUVj4t7l1UoABAgQieI8/U8tQ244flV69eul2Ov3/9U7176mnnvJ+lpwdJCZPJC0h9mHBgSmo7A4zt8A/oX7XbF6oA6MWGLl3JohtTK+CTh6nGwFGhyDijZ+/KQu3EpiGIsAlEqwETGA+kwexOuCigu+TW8JoCTo2snuRwnfS2WGQlY++fa6Rf2yQr2ZtkVmrPNYqHRqWk2vViC+mV7v05Vk669j72kqPs319mk0d41TQuke+WKKDx5l9Zgk/9r6tq8ggNXpap2Kc2e26hLsAAgCOVfXZIgxm1t/d3861TDg74TLw7HcrBKPUGHmH+OvSqLzc1aO2/Kam0Xtx/Cpd/TY1q4Bt2m/OCV/uB1VHyTd/u3d0oN2Xt4r3MX83ZSGY3/1tnTz0WYaLgDkGd4LRt7WQFjUzRmTNMX/LC56fru8fymI02l9Cmx9Vo/Y/Ltzhw9jkx/16oncDuU6N9kfbdu0mg1qG03YEMkQb/Llo2FYmbm4NVjOCXjUxJ+Bq8sMD7XRHDwIe2skZl8A+ZtaPJqbIkG+Xe4NRmv1Y4jN0t3K1QGeBHcjPzmPWMcMHgvUiwQcUoinYNG/7PzL49yGyeNdy76g66vi2z0jpWTvjc3DdD3fKd6t84+lkda7xV/6fdK3hGfHfcWSn3Przw/LnppneoHt22WIFisrj594rd7a6UY/K28fc1rcc3CqvzRslny7+Vo4kHfPJEojQByszJW8g0+zhmYx8cMG0E9yw8Htvfp+dQZHtvFi3XSadcZWceU/FNgacZs6cqd+N8Kyx31EwOILnkzOF+ly0hT7eU+G2hueUnTC6j3dT28LNPu5cz02hj3OfVO+wm9T7wwH1DgvBX7BSRSl9zrlS7fb/yMrBD8hh5asf17CRMs//wtlUvX10+QpZ++wQr5m9nQlm+WU7dVHB+i6TEm1a24f0eoqaVWvDiy95g9vZGTCSX+uZp6Wgy7u/nS+U9cTdu2Re124BFy3Vpo00GjXSJ/+JjZtUnIF75Vi6y6E5eJZ6948qWEA2vva63tXq98naysEcz+kl3sMxYIj0oZo9oZ1LYEN/5zTT6mFgEiPybq6Y/soisLFxk4FFsQno5y8/9wdOIM8L/cAvJXNOjCpDqGC0EGb+MOEvUqRI5ozpe2yhD6GMl3yIHkT1hnlW/fr1syzvt+IQD+BhAaFjEjopIIoDmTsVDydEv4WQhTiGWIbJu12fqde5hI8yTP3Xr1+v88PywZ//vLOs2QZ3YxLvbyTe5DVLnBdthv80guyVUKZNlSpV0u0O9AcjJ9pu2sNl4ASC/a651Yx7B9N7fGbx+YU5Pb6DxlXHXxl8VtetW6d97mFWGehnBfXB5QWfOfhWwsXFfE+cwf3czp1T+5KSU7UJcmxMtK7SntN83tDO2u84q3MdUCbtq7YfkR3KbL+CElPxpYpov3w/ejGrqnL92HEl3grGRisTYo/P9a2jFspn0zdr4bbzvYuybA8sI9Yrc/01qrMA5eNLF1Ym70X9xiywK0tVImSDihmwatthnb+xmtbOLRq8XcZt3YyUJ6h4ADOePt8ti88+nHe9mht+x4ETAuuG0ipGQZWyRbT7gZvfuU/h9I2cartb3adiny30TaC9DYrB0q2HpIa6Xw0qF3f1uffXFlz/5r0n9H1HzIuzlJtD7QpxrtM1utVx6NAhr5sbXizdrMTcyp2ufXAXWLVvnWw+tE12Hd0jZYqUkpqlzpI6pWp4zfxzq23ghRdyuEVCgMLFJ7uEIK8w/ccIPgYpAhWo2dV7JhwP9rloC33jHoopoTHIBY54vgX7LmaEvs0LVmrBTntmlw90HcI7xnrnnqtmsoL4h1l//ZdfyrIaTIcH8Xti6xaJLVZcCpQvL4WrVpGoAhlxXfxVkHYyUY6r94gTWzarjoZKUrRWLYkunLNuaP7OHe5+xBs4pjo7YNofp0zXA7necM+JwQp0ojlTMELfFuqwmDaWU846/W3D1RgDLxggRDyQQH5b/NXF/b4EIlro+15q9ltuQj/7UrmTAw9K47Pvb0aB3GlJYGdBXAH4vCMFEsQmsFqZiwQin8AN782XsX9v1Re6a0Qv7Ssf+VctApcE+OBj5NlfZPy8xMGO0u9vBoW81N7T1RY3oX+62mLOCwtBTOOENH36dCldurQ5xGUWBOBqZawBAxnVz6IqHnIh4Cb0XbIFtctN6GPAB5ZnuZmOr14jC/v21af0Fxk/N9vDc2UQgIXjI488krEjfS0YoW9G8zEVp4nplalCPzvsTgLEBLODhfspwt1BEKDQt2CFIvTRS42eqFBTt27dvF8w+BRjRBMmbXavLawK0NsG/zgkfClhnZBX06JFi7Rfm3G1cIulkFfbznaRQG4QGKxMzy9vHS/tVRA921Tb9pkPZb74U9F2RPrvlD4lYCj1X9aqsgy/pqkuiin9MGKPa4O5u0kYnX/im2Xy5q8eX+H3ByWooHUZkedNvryyxIj0M2OXy9ezPR0yuTE9YW5fO9wSNimLiVASzPSNhUNeFPqwGIIlEBIs3YKxAgqFRySVgXshrJ/g/mjPSnS6rxEuGZgJKdSEWYWyc9UKte5Ay50KoQ9rNdvdAG2BlRysXHM67VdBnU+qedTLXthdCpTK6DxLPnhAVj3woBxIf4dtrqaQLlKzRk6fPuj6Nio32d3KejfU1ESZthdW1hFnesLnA58TZ4JFbaBuyuZ3Aa5QxYplPNuddbptI+g3Ylvgd9gZbNstP/cFR4BC3+IVitCHW4DxNbOqCnjVNsNH7xkeVkjww0LQG5gZwl/f/FAjABkiv+alhIfTw2rqFCSYqpkgO9huo3yRTBR3bDORAAmIxA0crzFAELVV87qXUibcy5UZ+fx1B7x4VrzSze/UeN5MubACUdv4Qc9sJaGczh7tnr1mn3R9brqupoEyl2+qI6dHydx1+72B1rB/zpBOQZlzh9KuYMrAvaL5Y1O8RdbvyhDA8A9fpe6VWzwBb4EzcMX49YfSdDAxrhd5UeiHck0sk7cJwHLQzJYSSkt/UOIT8QNOZzoVQj83r2fr6DEC8YxUvHmCFFHvsMkHD8nBuXO8geYqX9NfarqMHudmO8251vzvadmVPsOO2RfMMmHcOCla5/R+ZoJpL/PmTwIU+tZ9D0XoQ4hDoIea4Pdvotjbc4O71YdpbQaqwC+Ivp+XEoK2wQ/fmdq3b69H9ssr3yomEiCBDAJG6GfsyViDv/cHNzeXxlUzpt/JOJr7a4gB8MqE1SGfuNlZJeXKtlV0eXRkdBwyzW9dV7WrIq9e1zQkf3m/lebAAcQSKHfrhEw1IYjeZ3e0lta1M0avMmU6Q3e8OmGN7D+WGFLrMTXjI5fW12Up9ENCyEJBEpg0aZIOXhdkMW92vFudbheOM13ob//kU1mvAiv7S9VVkLfKAwdIdB55h907WU3XqgIehpriB9wgBUoHH8wz1POxHAmEQoBC36K2VkW7ROA9JESmDyRwnVU8R1Zhuo8fe8zfisB7MOGHmT6CsMCMJi8mmPzAz9EkmPOhZ7yGitTvL0q+ycslCeRHAieS1JzDGw4K5lffffiknExKUwHFikrdysWklYr6bgLzRSIbdBzMWbNf1u46ovzxE1UQtShtxt8wvnie6dxwckcAuK9mbRUEf0MqqNtcXBrEF5MiBWOc2bltEZi6bLdsUVH24wrH6NkQrENcJQESsAjATdNMC9uiRYvT7kpgNS3gVZjuH1HvsCe375Ck/fsktmQpFQyvphRV77CFVFBoJhIggdwlQKGfu7x5NhIgARIgARIgARIgARIgARIgARI4pQQo9E8pXlZOAiRAAiRAAiRAAiRAAiRAAiRAArlLgEI/d3nzbCRAAiRAAiRAAiRAAiRAAiRAAiRwSglQ6J9SvKycBEiABEiABEiABEiABEiABEiABHKXwP8DAAD//292MXEAADmTSURBVO2dBZjstvW3dcPQcG6YmZnTJDcMbajhpOGkYWZsGBtmZmZmZmamBhpmxu6nV/8efxqPZ8Yz45317P70PDO2ZVmWX8mWjnR0NKjHO9eL7t133w2xDx48uBfvoqhFQAREQAREQAREQAREQAREQAREQAQgMEiCvgqCCIiACIiACIiACIiACIiACIiACPQfAhL0+09e6klEQAREQAREQAREQAREQAREQAREQCP6KgMiIAIiIAIiIAIiIAIiIAIiIAIi0J8IaES/P+WmnkUEREAEREAEREAEREAEREAERGDAE5CgP+CLgACIgAiIgAiIgAiIgAiIgAiIgAj0JwIS9PtTbupZREAEREAEREAEREAEREAEREAEBjwBCfoDvggIgAiIgAiIgAiIgAiIgAiIgAiIQH8i0DFBvz9B07OIgAiIgAiIgAiIgAiIgAiIgAiIQFkJSNAva84oXSIgAiIgAiIgAiIgAiIgAiIgAiLQAoFeF/RbSJMuEQEREAEREAEREAEREAEREAEREAERaJGABP0WwekyERABERABERABERABERABERABESgjAQn6ZcwVpUkEREAEREAEREAEREAEREAEREAEWiQgQb9FcLpMBERABERABERABERABERABERABMpIQIJ+GXNFaRIBERABERABERABERABERABERCBFglI0G8RnC4TAREQAREQAREQAREQAREQAREQgTISkKBfxlxRmkRABERABERABERABERABERABESgRQIS9FsEp8tEQAREQAREQAREQAREQAREQAREoIwEJOiXMVeUJhEQAREQAREQAREQAREQAREQARFokYAE/RbB6TIREAEREAEREAEREAEREAEREAERKCMBCfplzBWlSQREQAREQAREQAREQAREQAREQARaJCBBv0VwukwEREAEREAEREAEREAEREAEREAEykhAgn4Zc0VpEgEREAEREAEREAEREAEREAEREIEWCUjQbxGcLhMBERABERABERABESgXge+++859//33VYkaYYQR3BhjjFHlLw8REAER6K8ESi/o//TTT+61115zL7/8snvvvffcWGON5SaaaCK3wAILuNFGG60qX1588UX366+/VvkPP/zwbtJJJ3V/+tOfqs7h0dPT45599tmwHWmkkdx0002XGe7f//63+/LLL8O5mWaayQ033HCZ4crg+fzzz7uVVlopJOW4445zyy+/fBmSpTSIQCaBr776yn322Wfh3JhjjunGHnvszHBZnry/b7/9tvvjjz/coEGD3JRTTumGGmqorKBVftyXd5/r+bbMOuusbrzxxqsKJw8R6GsC//3vf90TTzwRyur777/vRhxxRDfZZJO5mWeeOWx7K33Uw6+88op744033KeffuoQpMYff/xQp84555yZdXGttLTzrtaKU/4iEBM44IAD3Lnnnht7hf25557bXXbZZVX+Zfboi3rx888/dy+88EJ437/55hs3/fTTO9q7k0wySe56tcxMlTYRGEgESivo0xg4/fTT3eGHH14zP7bZZhu3ww47VJznQ/7FF19U+MUHdBRsvPHGbv311w+NJDtH54AJ91NPPbW77bbb7FTFlnvedNNNwe/OO+90U0wxRcX5Ig7oSDj77LNDVEsuuWQQPFqJ95lnnnGrrLJKuPToo49OhP5W4tI1ItBbBP7zn/+4s846q6JhtsEGG7h999234S0R7HlXTzzxRPfqq68m4R9//PGGHQVcu9dee7nLL788uc52Fl54YXfyySc7Ov3kRKAMBJ577jn3z3/+09GBm+XWWmstt+uuuzYldGfFk/Y79dRT3RFHHJH2To5HHnnkUA9Tpw499NCJf3qnnXc1HZeOmyPAt+zHH3900047bcc7/Cm3d9xxR0jwhhtuGAZrmkt986EPOuigpA0VX91Ngn5f1Iu0u6+44gq3++67x9iS/fnnn9/xPRhllFESP+2IgAiUm0ApBX0qpN122y0RqA0hvYmM6pubb7753MUXX2yHYdtI0LfAxHXDDTckH6wyCfqvv/66W2aZZUJSDzzwQLfOOutYspvaStBvCpcCd5jAm2++6c4444zQsEjfupGgz/t6/fXXBwE//iZYPI0E/d9//93tvPPOIQ67BoHlhx9+sEPHSOU555xTUwsoCagdEehlAmiSoZFl5ZOyOssss4TjWPBffPHFQwc5Wi1FuSyhKf2ucK/tttsu/NL3bfddTcen4+YJoPFB2VluueXCN7P5GFq/4qKLLnL77LNPiOCWW24JnQ2tx9balbwX77zzjusGQb8v60U63A8++OAEMpptaO7QljTHgNiFF17o0LqTEwERKD+BUgr6+++/vzvvvPMCPRoU9CDS6GZ+1W+//RZGNA477DA37LDD1hX077333hAHvZQfffSRe+CBB9wpp5yS5Mqqq66ajFT0R0GfDhMb5Zx88sk1Ny3Jee30NQFUkKeaaqqKZMTCQyNBv9EoYyNBn04+BBMcjRm+N2jyfPLJJ26zzTZLRk133HFHt/XWW1ekUwci0GkClMNrr7023PYvf/lL0HQzbRMEA0bzTZPtzDPPdIsttlhhSaTx/9Zbb4U4qYdHH330MMXtpZdecvfcc4879thjk3vxXs0444zJMTvtvqsVkemgJQIS9LtD0O/LehF1/XnmmScpX2jJ0TGEoy2JttDNN98cjtGkRbtVTgREoAsIeCG4VO7jjz/u8UJp+Pk5QT2+gZGZPq8G2OPnDFadm2uuuZLrq056D98wSc5zn2+//TYE++WXXxL/pZZaKuvS4Ocb/Um4WmmreXHOE94mQXIP33Oa8yoFE4HuIcD7a+/52muv3fPwww/3+FGDxM939tV9GK+KGsLyjfD2J3r8PMaejTbaKLnez/Wve/26666bhPXz8yvCkhZLm1dV7CGtciLQVwR847+Hcm5lkrKedl7dNjnvBe/06V495v2ztJ1//vlV92r3Xa2KUB5NE7Dys9VWWzV9bbsX0Iax8uEHHtqNrqXrfcdXSMPqq6/e0vWduqgv60XeXcsnr0la9che2O+x9rXqxSo88hCB0hIo3Yj+IYcc4hiRwNVSBazXfxKr7mNcK8thoM7UHa+55powB76vR/QZ9UCdGMeoIupuuCFDhrg55pgj7NsfRgVrGda78sorg6EkC2vbRRZZpKY9geuuu85h2GmGGWZw9OpyzFzLpZdeOkwbQBOCeczvvvuum2222cJ8zFqWaz/88MMwZxrDTWgToN5FvKjOMRpUz/lKLvQYcz/u9fXXX4d51hNMMIGbd955Hc8wePDgelG422+/PRiqskCwi3upzb+oLc94//33h+jQEMlSZ2N6CRaAmS5iUzLS94f/JZdcEkbO2MfI47jjjhtGmRmdw0Bcvfmv7XDnfuQ5jh58yhdzKm+88cbAEoNfjHavueaaIf/TaW/l2H8R3bbbbuuYs2nlm7mcK6+8coiu0Yg+o5sY7mMk0wxsYnvDd+SF6+uN6DP3caGFFgrhUEO0UQo8GFFZY4013FNPPRXO88e7yNzEIlyR71q7ZaaI52k1jmbTTn7yw62wwgrhXap1b94jRreHGWYYt/nmm1cF413EIBcGGEkH3360Ong/sc3AlDAbLa+6+H8e2FDhOnOMsk888cR2WOgWLTbmVuOwMYMxvrTDCC1ccGzjUfZ02KKPH3vssfAeEi/TzJhuFrt23tU4nmb3eYcfeeSRYLNgmmmmcRdccIH74IMPQh6jtUM9y9QcwnF+0003rdJGsHs2W17tur7a8u1muoc5bPTgvCCXfGPtHPXKFltsYYdVWxg+9NBDwSAy7QM0sTDO9te//jWodacvwFhjbPfk6aefdqZdSfmgXosdhpWtDjB/vsMYYL7vvvtCO4L6DYOQ5BPfbN5R2iJ5Xauq+51uT/RlvRhP0eGdZWpQ2lGOGOnH0a4hH4pwqheLoKg4RCCbQOkEfVMxI7lUDjS+mnF5BP2ddtrJIeDjTD2prwX9Zoz60RjNsijL81Bpeq0IditcPWN8VL5U5llutdVWq5pDjcCOwZa0Q21zzz33TOaRps8j2KHulSWwYoAQoQ2rzrUcnR5mpLBWmC233NLdeuutyWmOmYvdW44GjRmuyVJb5b5WJmulnwYoRrbquauuusrNPvvsmUHa4U6EdKxgSAtHhxMCVS3OdJCZYB0uKPCvGUE/67Z5BX0Ee1PHp4G7yy67JNFdeumloQwnHn6nSPX9ot61dstM/Hyd3m8l7QgaXgsjJHWTTTapyiN7Br59fANxf/7zn50fpbJTYYvQQVm3ue4VJ/93wPv8j3/8I+tU4pf+XjP1wzqPkkAF7tg3hOktvIPpOfjxO5wu0wUmIzMqvj82DYZv/Pbbb58ZLvbM+67G1zS7f9JJJ7mjjjoq8zK+xXTSxnUlbOkoTK/20Up5zbxpBz1jvnlumzUoQgcTRhiZupHl4HX88ce7RRddtOJ0/B2vOFHjAJXwdIcc72DcWZB1Ke04rstqT6TDtyrod7o9kU43xzHPRh3gWdfHZaFeB7jXiEs6ZGgPpjtkiJuBJPILt99++7n11lsv7Lf7p3qxXYK6XgRqEyiVoM88IK9iFlKLgI+g36yzBhHXZVVe+DNiZ6MiCDRU+n0t6LO6ABUrjpFKGk84hGpGc2NHI9OrO8deyT4Vsy3/R8+6Mcwr6NPjzwi8rSxgEWO9n7hsHiij2CxFZi42uIMfo0r0vJuGgF2HUJU1eoDgZSOrXMdoAcsnMurCaBX3rtfBYenodMXcrqBPY9PmwfEMjJozWuKnkgStBjotYEcFmx71IHy73IkjFhLQ4rAVJ8gHRlDQrGDUC+GoPwj6CH80UnDW0cc+7wsNQp6T985G9WnMWHjCtePiBk2r71q7Zaad9Ld7batpR9sHQRrBDAEDgR0bLWnHSi3Yb8HFecsx31c6AexbhICy4IILhngw6MjINN+aLMGD62PXaUE/Hm3717/+5f72t78lyYENHROmzVLrW5FcUOAOHbN0nJjAjKYEdXAjl1f4aBRPvfOxoE+ZQQuOjrzYUaewVKAZG0vXk62W1/gefbFPPsSd5tZxi+ZKXN+QNpYhpYM+7WhL0PGLgx91OsIfgqeVNc5hFNXabRxjDynuHCC8fUvJg7RWHt/ctMaUDcZwX9pnvG+0BxjZ59msoy5vp5YE/caabvE7+eijj7pxxhmH7KxwsaBPm41O8CLcQK8Xi2CoOESgJoEyTSpgzrvNEWp1LpXNISKeLOcbiMk9COMbfSFYf5yjHz+r12DIwhH8mCMNC+bx+Q6P4Lf33nsnnLwqbPDz6vSJn6/ck/h8x0Iyh5Q4fMWenGPHC/s9NkeO+/jKuuK8r7STeLneN7wqznPAvb3F3ir/tIev+JO4uNeRRx6ZDlLosW90JPfzQkJm3FYmvZp61fl4fqs3FFl13gsnPb7Tpcc3pKvOtcvdIvSdNskzwIz5d75hZqfD1gv7Pcz9/fnnnyv8izxgrjz359dojn7WffPO0fejfMl9HnzwwSQqPxoZ/Hl+7H9YWihTRbl23zXS0U6ZKeo5Wo2nnbR7wS3JE69SW5UE5rLbd4bviFf1rQgT217wwmnFOTug3KfLvp2Lt1Y2bMs71JuOd5002/28wNTD94L3hGc1/6y5tUWmy3ds9viVMnp8x1f4Tth92TbzzuZ9V9tJu+/oSbj4NcFDVL4DPfHzWgjBj3nRvPM8A/Ve7Nopr3E8fb1vZSTvHH1snFjecq3v8Kh4BN9xkJz3nUwV59IHrczR9xoyPbRZ0u8wcfMuWJ1KGmk/NHL2XWi2Xdnp9kTWc3SqXuTbYXnuO/SzktIT15177LFHZphWPAd6vdgKM10jAnkJYD23NI5Gt31oMHrXiosrAIQkfggnCIp+9LOiUeRVxZNbSNCfvIePrTk//yrJCxNgaUhb/sRGAr02QuKPUJrlYmNRfpSgIoifM51c71XxKs41e9DpirldQd+PpCTPbpzzPnO73O0+aUHfGsV2vlPbTjVo/NKdCXPuifMjVIkf3yEMnllZ97YXCkNgDZpW3zUS0k6ZKexBWoyonbT7kcIkT7KEi9iYY5bAS+ek5SmCRDvO4rFtbwv6ltb4e2P3ti1CLN/o3nQmENs9bZunEzZOV6cFfW//JtzeL12bWQYoTzxLWhBup7zGz9vX+80K+nQiWd5mdUDzPBgttjC0r2q5VgT9WnGZv9fcSe5t33A7l7WVoP9/Hej1jNTGxvj8EntVGGNjfOS7n0JVFaZVj4FeL7bKTdeJQB4CpRL0ERKt4qCCbcXFgr7FlbWl4vOGeZJbSNCfvCceuYwbxXElbnzjyp8OE2Ps53aFETEbGXvyySd7+HnV+yRMuhMHLQK7nm1aIyDJpBw73Je02y9rZYYc0eQOEje8awnqxixrRD8edfLTGnKNTlji2uVu8cSCPg3wvnKdEvRpzFt5Y6SKESETYLw9h/D4jCRZGBqJRTlr0LT6rpGOdspMUc/Rajztpp3GpeWLn9ZTkQxv5yI5l/Xe+/mpyflll122h46DVp2fUpN8Y/jW1GtAt3qP9HWxcGMM4q03VtqDFldvuhVXXLGis9zuzzcujyaEpa3Tgr7dN+5U9nZPzLvHqyCHssFqHLFrt7zGcfXlfrOCPnWR5W3cToqfwRtNTsLU6+hqV9BHwPSGBUM7wquU9/BDu8zSR9uikWtV0O90eyLrOTpVL/qpawlT2PKNM0cdGdebnI8Hyixcq9uBXi+2yk3XiUAeAqUS9P0cyeRD0+rIrglVVglkbWkQplWhJehPHho7Vmj8PO0kL2JWJhChwmXOGhFZrLP8spYvNLVpC0/FTD6hvkfjrKyuXUEfQdOe2bY0gtF6QNXYplJkPX8R3Ik3FvS9caWsW3XEr1MNmnhaCg0504yAJ2qhOLaWH82qe9aDZQ0aBAtzzbxrXNNOmbF79tW23bTfeeedSb74ecDJY9AxY+8Dau1ZDoHBvl+Wt2hr8C1jKsA333yTdVkp/GIVcp6PDlW0TpgG5Y3FJUx4rlodjkU+COru3NvPaa+4d1YHS9Z9Oyno0yYwx1Q9y/tY+ww1ZPy97QMLGrbtlteKyPrwwN6NtMZCrSTxzTNOpg2RDht/t2x6XzoMx60I+pQvv/JLyA9LR63tXXfdlXXbCr9WBf2KSProoFP1Io9nS2Eaa76XfG/smM5E26fNVpQb6PViURwVjwhkESiVoM/IsX1E0hVuVuKz/GJB30Z12dLr643T1BwxjUeVswRRuxej0ZZGbAr0hnvttdeSe8Qq8s3eq9k5+vTim4sb1MyxN2cfem+oJ3j5paqStMKFCrXRL2tkG8EKwd7YprfMT21nBM7SX/S2XUGf9NBQidUg42engeYNSFUluyjuRBwL+ldffXXVvTrl0akGTTwaFAtQfCfMMYJk+ZDWQLEwrWytQdPKuxbfr5UyE1/fl/vtpJ2pWPaN5ztjDtVxy696QgffVr4/Fja9ZZ55b9qhsPQ2s41H2vgeZHVIxM/faa0c5u0bx6wpFVnP2leCfjwlJx6xtM4/NBbSrp3ymo6rr46bFfStQyzuJEmnHU09y3e+qbVcK4J+PF/c7kFarG1h3wDO0eHQyHEdYYvstG10z6LOd6peJL10sDCtyZjHW8oQ2pbm542eFvWIYdoo8Q7kerEwmIpIBFIESiXox6Pq9SqY1DNUHMYVQMWJBgd84OwDllfQ95aaG8Ta2uluEvTp7TduqMO26xi9P+GEEzIb4+QLDf0yuSIEfZ4HjjQ8aXBaI8u4smX+beyK5B4L+nkaTXE6itzvVIMmbnga43TnU9ygyZrv3epzFyXoc/9my0yrae6N69pJezyKTJnBxYJjliCcfgbm8x966KE9CHZWBmybNsiWvrbTx3GnK9ontVxc98G3k84ESRjmcXF+9da0B1O7j9sSrQj6PE875TUPj94OY/mTd0Sfupy85LpaLrapdNppp9UK1vSIftyBQN5RJ6Xr/XiaZ546S4J+4zn6cQYyqEJ7hLYYneF+laqgXRgbzPZLPMeXtLWverEtfLpYBOoSKJWgT0pjIYfGWLMubuw0e6019Bi1ruXqzRGtdU2z/t0k6PNsNhodN6iafeas8Izyo5ZqjRTyp95cwKw4etsvNjJoQkd8TxooVq7SwmQcLt6n04mKNS5rWRouRXEfaII+DRjLE9u+//77cRb0xIaJYnsUFYFaOCiyQRPfPm+Zia8py36zaY+1LdACYmqR5WM8IpT3+Wi8MjplcbBFY6YsLi6L7NdyfCPsGeLpVnF4hDM0HuyHtlARnQKxei/CdCPXbYJ+/DzNltf42r7atzo0r6BvxgkpT0x5yXJMq7PyFk+DSIeNO1aZCtHI+aUkk3hr2ZxgapvduxlBP6sebZSevj7fqQ7wPM/JihvNcM8TJ2FUL+YlpXAi0DyB0gn6scGhVkZW2hH0rZOhXi82czrtQ5e19EvzWVB9RdyQbUfI6ITqPqmPjbS8+eab1Q/Upg+jBcYcwb+eY64l+We/dvjVu4+di0fb2E87potY2vMK+hYHywxaAy2rTBbFfaAJ+vH8e/Ima2Qi7mQpchWC3mrQ5C0zFo4t3we+efarp8kUX9db+43Ke3xf48h7waiTvWMY3GvVxSr9jeaa0xls3xi22JfpLceUEnu+vCP6lPEsh+0bi8u2eZYny4rL/OgosLjY1hIMLTzbbhb07TmaKa9MDaTM2LvGlhUAOuWsXcQUuDwuVp2v1bm+6667JvleSyDnXnFneB6DjXG9Vqss2ftPecsj6MeGa9Eczes63Z7ISldZBH3ec8otzPnmtfvdiJ/V8jPuqI3bVvWmj8bx1Npv5l2tFYf8RaBbCZRO0GckhY+INRyy1ksGNg2ZeE6tZYBVaFzfrIt7sRn5SLt33nknSReVdm85Kjd7/rw98Flp6ZSgj9EcSy/WxOst8cToWbqCYNS7VsOU54rXPmY+aD3X6eX1WG/Wnp3RxbTDz85nCfpYDq/Fi5EjUznM0pZol7uldaAJ+jx3bGsjXaYwZmZ5VsR0FOPMtogGTTtlJk7LQw89lDynPW98vjf2i0p7rLpraeebXOtd4lm+/vrruvPvbZ428TWyv2L3tG0tYagIhi+//HKST3wHaLSmXbyqCd+MWq5ZQZ9vNVbO+RbVcrHNi7xzoLtF0C+qvMareFiZKbIDsVbemL8NUFB+8gi6d999d1Lm6PRMv1dpuxHpOt3uyzZW8c9jcyjuZIjtKFiccX0FyzyCfrykap7OBrtXp9sTdt9420lBP53Plg6EfOyXWNll+lSRrkz1YpHPpbhEoAwESifoAyVWy+LDwhwhPs7M5UOdn2Vd6AzIWt6jHUE/Xn6J+9ITjXDP8jJUOHHcnOtNR6PVPqo8P0I7jU/SU0stE2GZc/ajArQ4sFBt/mxpwJgr4iMbj4axRBECMB0WVBDYMsBYFP6kJ93AMcvGWCLH0rwJ/czXRz0w5p5WsbZnsG2nK2YaOHHHFOWCzirmuMVziXnuLEGfjhwa5meffXbIW0Z+aIihuh+PmDCykOXa4W7xxQ2nPI0mu67dLflMw89+scomDVPzZ0t5SjuEmziMdYrA+qSTTkrOXXTRRVXqybFgRHg6DbHITaPURi3wr6cqnU5PnuMi3rV2y4ylsy8E/aLSjsG8+L0jr6g36jnKCtcwKs43iHcXAZbvT1znkP+1GrwWP/eLf70p6NMRanOmuSfqx7wPfCu+/fbbUHZjFtQXtVyzgr5pl1EfYZH7nnvuCSPR1EW8Q3HnOGmrZcyznXe11rM08i9ijn5R5bWvBf24E4tnYtUGtO9oT5DHaUf5j6djUP+wygL1OfZL4m8t7bF6juvsXaGc0rFKZyr35pe2qcGyhxaecs/ILm0JOupimzgWJk+dFXdckAbeEbRwaPPV097pdHsCjn1ZL9Le4vtCHvCNRCORujHumIM7bcgiXZnqxSKfS3GJQBkIDCIRrmSOJPnKw3ljSXVTNt9887mLL764Iszcc8/tfIM9+L399tsV5/Ic+J5f54W1ukG5h5/j6IYaaqi64do56QVj5yvkzCgWXnhh59WNq84NGTLEeaG6yr+Wh1cddMMMM4xbZ511nK/4nReunLemH4J7S8Nu0003Dfte6HRjjTVW2Ld7+ArQeTWrJGovgLvNNtvM+UZz4ldrx1cczle2yWlvk8D5Cj05rrXjG6nOC7+1Tgf/Lbfc0vlOmSQMx35t9OS4N3a8QO98ozIzasqKH5FzXqhwsPMCfUU4P7Lsbr755gq/9MHII4/s/Aimm2SSSdKnXDvcLbIHHnjAeZXOcOgFHudVuO1Ur259Y8+tsMIKue6x3XbbOX6xo3xSTvM48mCEEUaoCOqXEnR+NLLCLz5YZZVVnJ+77YYeeujYu639It61dsuMPYAX2tzGG29sh2HbyjezIoIGB0WlndsccsghoZ6wW/pOQjfeeOPZYdXWd/i4ffbZp8o/7UHd4wWZtHfF8RRTTFFx7C1Vu4UWWqjCr8gD3+B2Sy+9dMMo55xzzlA38V3Pcny3vWBUcYr3cKSRRqrwswO/Eo5bdNFF7bDu1ne8u4MPPjgzTLvvamakDTx9Z5/zSyeGuos6DOeFRTfHHHOEfT+tK2FKuaB8zDLLLO7aa68N5/krqrx6YdbNPvvsSbzs8E2ffvrpK/x668B3mtctn1nvvR9ccRtssEGou2qlC160wWqVH7tuv/32c77T1A4rttTp1O3mfMeW49tLuazllltuuaTezFtneWHV+Q6nqih5Z2q1+fqiPdGX9aLvqKvbXqId6Ofpu9lmm62KYzseZaoX23kOXSsCZSRQSkHfQPkRJ3fkkUc6P3phXskWQZHKgQ9+7BZYYAHnexuDV1blFYfN2vcjPM73ajqvppRZwVEp+VFUN/zww2ddXqjfSy+95Pyc9NAgQFA0lyUwcg7/ZgR9Go8IMeutt57zI5nOq10GwYa4YiGACn+MMcbA2y2++OLO98K7bbbZxu2www7Bz/6ooGk8URHE6bXz5Bn5xUd9lFFGMe/QMUOj3U/TyLyOxjv3ovJv1LlCumhAmctKp50rautH1pxXNwwNxThOOmQQJpdZZplQJmkwe82KOEgoa36k0XlNlQp/O1h55ZWdX6/WTTrppOZVtW2Vu0XEe+a1LcIheUced8JRvv2oUa5bbb/99oFDHDhLaInPx/t0QA033HCxV9in44Uya52DeNKxgtBCp1+RQj5xF/Gu8X1qt8yQlmOOOcb5+e3sBkejft9997XDXtkWlXYSx3dptdVWC+ms9U2MH8KrwIbn5duW5Wjw77TTTo4O5EZu5plnrvhW8Z1ecMEFG13W1nk69RBc6ShNO8os30i+rfXqpizBtZ6g7zUGQmfYfffdF7776ftyPPXUU7vdd9+9bodAEe9q1r3r+SEA0nFN/UEnEC4WuL3tF7fkkksGfxNEEcb9aGbw46+o8hp/Y4kXZnTwFv19Ie5azmvxOW980flR8aSNZGFrtZUoc17TLrxrFta2XpsjvC/DDjusedXc0q6iQ5kBCt7buH3gtQWSQQWLgLRSp6Y7wRE0+UaNOeaYTddZpMHbgAodXXGbks54mGS5vmhP9GW9SN7wzNaGjpnwjaUjb/zxx4+9C9kvU71YyAMpEhEoEYFSC/rGCUEKodSr0Luxxx7bTTDBBL3ysbH7saVSoBecEQ2vrhYErYkmmihTWIiv075jOoijoqbx4FXu3LjjjhvyzLQCajHy6oJhhNrP/3Nefc2NOuqo4Tq4d7JBVCt9jfz91BKHlgSdEbPOOmvDUY44PhrUNKqIg/JG45TnHn300eNgdfdb5V430gFykg4y8o4OlSmnnLJhh1IZsLRbZug4izuY0OrhXe2EazftpNEEOfa9WnnoUGO/kaM+4bvOu+bnu7vBgweH70w9bYBGcXbyvJ8aFOpC6sMRRxzRTTzxxCH9tUbxi0qbN4gVvut8m/muUw9POOGEQegq6h5ljafd8prW+kLgXGKJJcr6uFXposzRBqNDdLLJJnNefbsjdTICJwMLfqqOm2GGGTr2faoCMMA8+D76KRfh+4j2UqfyuwjM7b6rRaRBcYhAmQh0haBfJmBKiwiIgAh0OwEEXDqjzOWZFmNhy7Cl4Y+qPIIHo9mMRGVpbJQhrUqDCKy00kqJZiLq7mgKDBo0SGBEQAREQAREoFcJSNDvVbyKXAREQATKR4C5qsxZNffkk092zcgsGj9MOWBONY6pLUztkBOBMhJghDGe09zb9hzKyEBpEgEREAER6BsCEvT7hrvuKgIiIAJ9RoC5y6i+47IMHfZZwurcGAEJg16o8ppjOhDGNxtNC7Lw2opApwnEnWr1DL91Ol26nwiIgAiIQP8nIEG//+exnlAEREAEKghgf4T5r6gPTzfddE3Zk6iIqIMHcecEt2VOPauf1DNU2cHk6VYikEmAEX2z3YJNg07ZwchMjDxFQAREQAQGFAEJ+gMqu/WwIiACItCdBLAOz+oJWPlmWbKpppqqK4wmdidtpVoEREAEREAERKDbCUjQ7/YcVPpFQAREQAREQAREQAREQAREQAREICIgQT+CoV0REAEREAEREAEREAEREAEREAER6HYCEvS7PQeVfhEQAREQAREQAREQAREQAREQARGICEjQj2Cwy7rMv/76a8rXuVFHHTWs11x1IuXx0UcfpXz+73Dw4MFumGGGyTwnTxEQAREQAREQAREQAREQAREQAREoikBpBP2nn346PNPoo4/upphiiobP19PT45555pkQDiEcw0xFuMUXX7xi+SaLk3WaWa+5nvv888/dPPPMkxnk4osvdvPNN1/mubJ6nnXWWe7ggw8OyXvooYfc+OOPX9aklipdWDT/4YcfQpommmiipiya//zzz+69994L144wwghukkkm6bVnI42vvfZaw/jHHnvsXk1HwwQogAiIgAiIgAiIgAiIgAiIQFMESiPom3A/ZMgQd/bZZzd8iK+++sqxJi1u0UUXdQilRbill17avfHGG1VR5RH0v/zySzfXXHNVXYtHJwX95557zt1xxx0hHRtuuGHLa0yffvrp7rDDDgvx3H///Q6hVa42ATqeWJvc2BPylFNOcZSpRu777793l156qTvttNOCVgnhJ598cnfXXXc1urTl848//rhbc801G16/zjrruAMPPLBhOAUQAREQAREQAREQAREQAREoB4HSCPozzzxzGAXNK9ywzNJyyy0XKG6yySZuzz33LJzoZ5995uadd94Qbx5BP52AW2+91W255ZbBu5OC/kUXXeT22WefcN9bbrnFTTvttOmk5TqWoJ8Lk0Pb4aSTTnKPPvpo1QWNBH06rC644AJ3xhlnJFoAFkned8HCN7uVoN8sMYUXAREQAREQAREQAREQge4gUBpBf4011nBPPPFEoPbWW2+5QYMG1SV4zz33uI033jiEQb18rbXWqhu+lZMDXdD/5JNPHGrouFlmmUU2BjIKEVNOVl111YozI488ciK0NxL011tvPffggw8m14833nju448/DsedFPR33XVXhzZNlhtjjDHcuOOOm3VKfiIgAiIgAiIgAiIgAiIgAiUkUBpBf6+99nKXXHJJQPTkk0+6Mcccsy6ueNSa/fnnn79u+FZODnRBvxVmA+2ap556yq222mrhsdluttlm7u6773aHHHJI8Msr6DMXf+utt3YrrrhiooHRSUH/xBNPTDRkBloe6nlFQAREQAREQAREQAREoL8RKI2gf+aZZybC0Q033OBmnHHGwBoL+H/88UfYH2644dzQQw8d9o888sgw/5mDBx54wE044YTB/7///a97+eWX3X333edQ7//www/dTz/95KaZZho33XTTBYN4s802Wwjb6K9bBP1PP/3UXX755cnjMMp87733hmPmV6dHYxdYYAE3xxxzJOFt58cff3SXXXaZHVZs0bgYaaSRKvw4YPT5yiuvDP7YSmCeOcIv9hOwD8C9mXfOHP+xxhrLrbLKKnUFSuwLMML9yiuvBFsJU045ZSgLyy+/fC6DcLfffrt7++23k3TynLUMJCaB2th5/fXXg/0Fpo+YDYO4LDcS9CnHlHXm8VvZNnsVA0XQp7xec8017v3333esWoFxTTQbeE/RMqAMyImACIiACIiACIiACIiACOQnUBpBH6Njm266aUg5gtJiiy0W9hklRXDEYRhu9dVXD/vMmb/++uvDPsbzTEjafffdK4TeECD1t9NOO7nNN988uSZ1OjnsFkEf4XjllVdO0t1oBzVtnj/tUNNfaKGF0t7huJYxPqZb0AmQ5RBUEdgeeeSRitPMSV9wwQUr/H777Td3wgknOEaWsxzq8AjFyyyzTNbpxA+bCNhGMMfxzjvvbIcd2TYj6GclaKAI+nTg8S7ae5zFAr+446ZWGPmLgAiIgAiIgAiIgAiIgAj8fwKlEfQR1s06ORa+GYlG+IsNyTEP35Z7sw4ARulvvvnm5IkQHBgdRDBkNBChabTRRgsj+4xW27JnW2yxhdtll12S67J2ukXQZxQ0XnUAwd86RxgJHzx4cMXjsYRg1lSHb775xh166KFJ2Oeffz5oReCRV9Bnvjoj8jbPnGsR+GeYYQZ30003cRg6a8yaf/Dwf9tss01ynrxj5H+CCSZw2Gu44oorLFjYt9UWEs9oR4J+BKPBbmyMD6ZozPB+TDbZZEEDZpFFFkk0axpE1dLpePoN2h6802jmfP311yHfr7322hCvBP2W8OoiERABERABERABERCBgUzAr0dfCufVxnu8QBh+hx9+eEjTiy++mPhxbqmllkrS6pexC+f8vObEj53zzjuvxwv6PV5dv8KfA7/8XY9dR3xeqKkKE3t4lfjk/scdd1x8Kte+t3ifXO9HtXNdU0SgCy+8MLmvn77QcpRe5T6Jx6tVZ8bjhcUkjF8WMYTxHQ+JH7x///334O81CYK/FyAr4oKN5b2fo97jjQBWnH/hhReS88suu2wSX0Wg/x34DpwkLHF6LYCsYL3q5y3oJ2nw2gVN38tYeK2Wpq9t5oLHHnssSafdM73lXeTd7A3ntXOS+/uOqapbeIG/x9vtqPKXhwiIgAiIgAiIgAiIgAiIQH0CpRnRp7OFueOMBDMK7QXrMF98jz32CKPzNhLPaPXwww8f5ttzzXbbbRd+7OdxLGNmo9aM/M8666w1L+uWEf30A8Qjpb29vF6suo+dAC/Yh+TYcoloaTBPHXfOOeeE9dgZsffCe/Djb6WVVnJoD+CwrTDxxBOH/fjPNDXwu+qqq9zss88en072NaKfoGi4kx7RZ5qF7wwLeWHvG5H8/e9/dwcccEDD+JoNYO875eHZZ59tOJWm2fgVXgREQAREQAREQAREQAQGKoFSCfobbbRRMCKHEIcwZ/PtmU/OvG6ED9ajR70XtWLc0UcfHQTFrAzECB+G6j7//HPnR5VDENY6pxMBh+Bp8QSP1J8E/dODXQSw5FHdZwoFUylwJsShyn/EEUcEP1Twd9ttt7D/5ptvuqGGGsphbNGuQdCM5+j7PqoQlj86FCyegw46yK299trJuXgHw262JCD+U089dRJ/HK4397tljj5TPhD2l1xyyQpDiz///HOYCnLUUUclmDC4mGXAMQnQwg7Tc8x+A9N11lxzTQn7LXDUJSIgAiIgAiIgAiIgAiKQJlAqQZ/598w1Z74ugh1zyd95551gtf26664Lc/EZ4acjwIzypUd3sbrP8maMIj/zzDPp5604jo3+VZz434EE/eYE/TvvvDPYRAAfI/nYXWCd+P322y8QJQ932GGHsO+nZQThkvnXSyyxRPDL+0eH0N577503eMfDdYug3wjM/vvv7/xUmBAMoRxhvEjHcposq2mO937hhRcOHQpY3Meuw6BBg+y0tiIgAiIgAiIgAiIgAiIgAjkJlErQZ7TeBDg/f9jNO++84TEY9WO0GEEDq+t+nnairv/kk0+6McccM3lcRnv9XPHkmB0ECJbswn377bfuiy++CPunnnqq8/P+w37WnwT95gR9lvRjPXjccsstFwz5xUL5jTfe6LbddttwHtV9VLZR1WcZPhzH44wzTtiv94eqP8b7yur6i6D/wQcfBMEbzhjri40iFsEeq/to1aBhE08VsLgpS2jsFK1JYPFrKwIiIAIiIAIiIAIiIAL9lkD9KfydPfvQQw8lxrkwwoVhMG8dPiTCj86HY4y7+dH6sD/TTDNVJNAL/cn1hLvtttt6vOX+ijDe8nsShvP1nIzxNWeM7913301wYjSP/POdM4nfDTfckLD//vvvgz9G2MwAnLedkITt5p1uMcbXiLEXxJO8Sb9rja5t5jxlAQOS3t5GD/ex8mDb9957r5noFFYEREAEREAEREAEREAEBjyBUo3oxyOILP/GSL7N8f7ll1/c9NNPHzpcUO9lznh6lNFU/wmUVum3nhpvSd55S+LhsJkRfW/d3+24444WTa4t67ljHA6HCnStNepzRdZEoL4yxtfKiD7aFXPPPXd4upVXXtnF88KbeORSBe2LEX3fSeb8yggJB1TeeXeGHnroxK/ZHWxczDjjjOEym07TbBzNhsc+AOXomGOOCVM/uL6Vd6/Z+yq8CIiACIiACIiACIiACPQrAmXq6mAZNhvFs228vFa8HBfnvWG3iuRvtdVWyfW1lgTzRtySMI1G9L2huCTsuuuuW3GvPAfe8F9y/cknn5znkkLCeBXr5L5Zy5blvUmzy+u1MqJPWmwUF+0N36GTN3mZ4bwNhxAfcfJD+6PTri9G9L3ByiTP7d3x6vBtPXq87CHxd9J5K/zJ8/Bey4mACIiACIiACIiACIiACOQn4PIH7UxIP2c+aeAjsLzyyivJjVkT3YQYtn5EPjnHDmridj5r/XKvBZCcJ1wjQZ84ET4tTj/aiFdux9rzdm0rHQW5b5QK+OCDDyb3RSW6VdcpQd/bVUjS6zUfaibXG1rs8XP7a57nxBZbbJHEBXvKTKddNwj6dKp5g4g10dBRFr+L6Xet5oVNnPBLadYM7VcESPLRa9LUDKcTIiACIiACIiACIiACIiAC1QRKpbqPqgRG1vw8+kRrAsvtpn58zz33uI033jg5h2V9rLubu/rqq93OO+8cDlmyjbXXWeaNJdy8UB+W67OwbBup7hMGNX/U/XEYB0MV3wuQ4Zhl/iaYYIKwn/XHCgAYjsPCPI7pCOuvv74bY4wxwjFpHGWUUcJ+kX8sm7bggguGKDFwt++++wYL5uzjMF5oxgmDh//zRSMsRWjHbJluACMcy6vxvOZGGGEEN9poo4XVEdZYY43g3YrqPhdiIJH0mkG2DTbYIHCaaKKJwrKIqKSzLCKG21iFAUv9tRz5w5QJcxxbmTC/orevvvqq85oTSbRwuOuuu8Ix6vOzzjprco4VBsYdd9zkmJ3LL7/ceVsSid8+++wT9skvlpg0h6FClsLLcr6DI5Tx+JytbBD72f7XX38djNxRBtdaay031VRThbLs58s737kWjOB5QTwEp9xjDHOkkUayywvZzjzzzI4fSyUyDQcu3333XWDJu21MUeNfccUVC7mnIhEBERABERABERABERCBgUCgdIL+scce644//vjAnrn45557bpIP8XxuPG+55RY37bTTJucRllZZZZVEsE5ORDtYg0doweUR9L/66qtgmd8s9UdRBQvy22+/fexVtc865awPnuUuuOCCRCDPOt+OH0vanX/++ZlR7Lrrrs6rYlecizsHKk7UOJhvvvkcqySwDGK7gj63YI45aTJhv8Ztg3fZBH1WeWC1hzzOa1iEzqc47BRTTBEf1txP26SIA7Yq6MdxZO3T2UCHT29YvkfIb5TfPDPMhh9++KzkyU8EREAEREAEREAEREAERCCDQOkE/WuvvTYxeofxOwxxxW7IkCHOW+EOXlkjlp988klYhs+EebsWY2KMbDOa7dXog7dXsXaLL764Bam5Rdhnma+HH344jChbQIR8Wy7O/LK2jPgyKvn0008nS/sRDqN5jPL3hmPpsgceeCB0lDAyGgtUfh6723TTTStuC7dm0kJY0s8zMWqNw0Aio/C4FVZYIXS4oIFha6WTJ5afWXlHZ8oBBxzgvHX+EEf6j7xnacXVVlstfSo5TmuEcLzDDjsk53tjhw4VOlbyODpH6CSJXV5BH6OFl112WXxpsg/XdJnPYmwXYGiP8nvHHXeYV9XW28Rwu+yyS1iesupkAR5HHHFE0EJASyPL8W5RfnpD6yXrfvITAREQAREQAREQAREQgf5CoHSCflFgUTtGgMCK9wwzzFClLl3UfRRP8QTopEBdn/wbaqih3HjjjRc6EGzqQfF3HLgxogUDZzpZvvnmmzByPumkk7qJJ57YDTvssB0BQycTGiWff/65G2aYYcIUETqMRhxxxI7cXzcRAREQAREQAREQAREQgf5GoN8K+v0to/Q8IiACIiACIiACIiACIiACIiACIpCHgAT9PJQURgREQAREQAREQAREQAREQAREQAS6hIAE/S7JKCVTBERABERABERABERABERABERABPIQkKCfh5LCiIAIiIAIiIAIiIAIiIAIiIAIiECXEJCg3yUZpWSKgAiIgAiIgAiIgAiIgAiIgAiIQB4CEvTzUFIYERABERABERABERABERABERABEegSAhL0uySjlEwREAEREAEREAEREAEREAEREAERyENAgn4eSgojAiIgAiIgAiIgAiIgAiIgAiIgAl1CQIJ+l2SUkikCIiACIiACIiACIiACIiACIiACeQhI0M9DSWFEQAREQAREQAREQAREQAREQAREoEsISNDvkoxSMkVABERABERABERABERABERABEQgDwEJ+nkoKYwIiIAIiIAIiIAIiIAIiIAIiIAIdAkBCfpdklFKpgiIgAiIgAiIgAiIgAiIgAiIgAjkISBBPw8lhREBERABERABERABERABERABERCBLiEgQb9LMkrJFAEREAEREAEREAEREAEREAEREIE8BCTo56GkMCIgAiIgAiIgAiIgAiIgAiIgAiLQJQQk6HdJRimZIiACIiACIiACIiACIiACIiACIpCHgAT9PJQURgREQAREQAREQAREQAREQAREQAS6hIAE/S7JKCVTBERABERABERABERABERABERABPIQkKCfh5LCiIAIiIAIiIAIiIAIiIAIiIAIiECXEJCg3yUZpWSKgAiIgAiIgAiIgAiIgAiIgAiIQB4CEvTzUFIYERABERABERABERABERABERABEegSAhL0uySjlEwREAEREAEREAEREAEREAEREAERyENAgn4eSgojAiIgAiIgAiIgAiIgAiIgAiIgAl1CQIJ+l2SUkikCIiACIiACIiACIiACIiACIiACeQj0uqD/7rvv5kmHwoiACIiACIiACIiACIiACIiACIiACBRAQIJ+ARAVhQiIgAiIgAiIgAiIgAiIgAiIgAiUhUDHBP3BgweX5ZmVDhEQAREQAREQAREQAREQAREQARHotwQk6PfbrNWDiYAIiIAIiIAIiIAIiIAIiIAIDEQCEvQHYq7rmUVABERABERABERABERABERABPotAQn6/TZr9WAiIAIiIAIiIAIiIAIiIAIiIAIDkYAE/YGY63pmERABERABERABERABERABERCBfktAgn6/zVo9mAiIgAiIgAiIgAiIgAiIgAiIwEAk8P8A9E/mgkwE7ZIAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "id": "aware-halifax",
   "metadata": {},
   "source": [
    "Now go back to the first cell below the Modin image and comment out `import pandas as pd`. Comment back in `import modin.pandas as pd` and see how much faster it runs! You should see a drop in runtime like this:\n",
    "\n",
    "### Pandas\n",
    "![pandas geojson method 11.5s](../img/run-pandas.png)\n",
    "\n",
    "### Modin.pandas\n",
    "![pandas geojson method 1.5s](../img/run-modin.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "respiratory-plaza",
   "metadata": {},
   "source": [
    "# You mentioned threading, what is multithreading?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "invisible-specialist",
   "metadata": {},
   "source": [
    "If the tasks aren't CPU-intensive such as data fetching and other I/O operations like web scraping or reading files, we can leverage threading. Threads are components of a process that work in parallel and share the same memory space. As mentioned earlier, a process is executable code with its own memory space. When data fetching, one process is enough (instead of multiple processes) since the processor will be waiting around for data to come back so we can fetch data in parallel by using threads. Python will execute one thread at a time utilizing the one process.\n",
    "\n",
    "An example is: think about multiple people editing a google document in real-time. The google document is one memory space and multiple people are adding text to this document. Think of each person as a thread. In our data analyst example, it's like 3 analysts working on one report to speed up the process. In multiprocessing, it'd be like multiple google documents being edited by 1 person each or in our data analyst example, 1 data analyst doing 1 report each.\n",
    "\n",
    "### Multithreading\n",
    "![Multithreading](../img/multithreading-analogy.png)\n",
    "\n",
    "### Multiprocessing\n",
    "![Multiprocessing](../img/multiprocessing-analogy.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "after-perfume",
   "metadata": {},
   "source": [
    "# So when would we use threading?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "angry-leave",
   "metadata": {},
   "source": [
    "1. Creating processes can be a bit slow so if the code that you are running is not CPU-intensive such as data fetching, threading is a great option. Creating threads is much more lightweight than creating processes. \n",
    "2. Sharing objects is easier between threads since they share the same memory space so if you're fetching from many different areas and combining the data, threading is good.\n",
    "![Multithreading diagram](../img/threading-diagram.jpeg)\n",
    "[Source](https://timber.io/blog/multiprocessing-vs-multithreading-in-python-what-you-need-to-know/)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bottom-assurance",
   "metadata": {},
   "source": [
    "# When should we not use threading?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acute-habitat",
   "metadata": {},
   "source": [
    "1. There is overhead associated with managing threads\n",
    "2. Increases the complexity of the program, which can make debugging more difficult"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sustained-roman",
   "metadata": {},
   "source": [
    "# An example of multithreading using ThreadPoolExecutor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ancient-nirvana",
   "metadata": {},
   "source": [
    "We use multithreading quite a bit in locussdk as well as the backend providing the data into locussdk for LOCUS Notebook. \n",
    "\n",
    "`ThreadPoolExecutor` from the in-built library, `concurrent.futures` is one way to use multithreading. What it does is it creates threads and groups them altogether in one pool of memory. Based on the number of threads, each thread simultaneously takes on a task. Once it's done, if there is information sent back, the information is stored in `as_completed`. One example is getting a list of all external datasets a.k.a the connections made in Connection Hub in locussdk.\n",
    "\n",
    "Let's first create a helper function for fetching connections for a single whitelabel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fifth-report",
   "metadata": {},
   "outputs": [],
   "source": [
    "route = 'https://api.locus.place/prod'\n",
    "def get_connections_per_whitelabel(wl):\n",
    "    params = {'whitelabel': wl['whitelabelid']}\n",
    "    r = requests.get(f\"{route}/connections\", params=params, headers=headers)\n",
    "    return r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abroad-entrance",
   "metadata": {},
   "source": [
    "Now we'll use ThreadPoolExecutor to do the same thing that we did in the beginning with the for loop, except there's a few things to note:\n",
    "- we need to create a variable for collecting all of the tasks a.k.a the queue called `conn_tasks`'\n",
    "- we need to specify how many threads can be created based on the workload provided (the queue)\n",
    "- `executor.submit` will execute our helper function which is provided as the first argument. The rest of the arguments are for our helper function (which requires one). So if our helper function required 2 arguments, `executor.submit` would have 3 arguments, not 2.\n",
    "- as_completed will contain all of the results from each thread that's done their task from the queue (which we provide as an argument)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "answering-sydney",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.47 s, sys: 171 ms, total: 2.64 s\n",
      "Wall time: 5.92 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "from concurrent.futures import ThreadPoolExecutor, as_completed\n",
    "\n",
    "import requests\n",
    "\n",
    "headers = {'eq-api-jwt': API_TOKEN}\n",
    "threads = 8\n",
    "connections_pp = []\n",
    "conn_tasks = []\n",
    "\n",
    "with ThreadPoolExecutor(max_workers=threads) as executor:\n",
    "    for wl in whitelabels:\n",
    "        conn_tasks.append(executor.submit(get_connections_per_whitelabel, wl))\n",
    "    \n",
    "    for future in as_completed(conn_tasks):\n",
    "        connections_pp.extend(future.result())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "listed-child",
   "metadata": {},
   "source": [
    "Wow, that was fast! If you don't believe it's the same result from the beginning, run the next cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "central-disaster",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>set_id</th>\n",
       "      <th>set_name</th>\n",
       "      <th>columns</th>\n",
       "      <th>whitelabel</th>\n",
       "      <th>customer</th>\n",
       "      <th>type</th>\n",
       "      <th>name</th>\n",
       "      <th>created</th>\n",
       "      <th>updated</th>\n",
       "      <th>last_sync</th>\n",
       "      <th>is_syncing</th>\n",
       "      <th>records</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1576</td>\n",
       "      <td>122</td>\n",
       "      <td>COVID Ontario</td>\n",
       "      <td>{'Row_ID': {'hash': False, 'type': 'Number', '...</td>\n",
       "      <td>1</td>\n",
       "      <td>9324</td>\n",
       "      <td>google sheet</td>\n",
       "      <td>COVID Ontario</td>\n",
       "      <td>2021-01-21T19:48:09.099Z</td>\n",
       "      <td>2021-01-21T19:52:42.148Z</td>\n",
       "      <td>2021-01-21T19:52:42.153Z</td>\n",
       "      <td>False</td>\n",
       "      <td>197152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1570</td>\n",
       "      <td>120</td>\n",
       "      <td>test gs2</td>\n",
       "      <td>{'col_1': {'hash': False, 'type': 'Number', 'd...</td>\n",
       "      <td>1</td>\n",
       "      <td>9324</td>\n",
       "      <td>google sheet</td>\n",
       "      <td>test gs2</td>\n",
       "      <td>2021-01-20T23:30:12.363Z</td>\n",
       "      <td>2021-01-20T23:30:32.727Z</td>\n",
       "      <td>2021-01-20T23:30:32.731Z</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1585</td>\n",
       "      <td>123</td>\n",
       "      <td>test new branch</td>\n",
       "      <td>{'col_1': {'hash': False, 'type': 'Number', 'd...</td>\n",
       "      <td>1</td>\n",
       "      <td>9324</td>\n",
       "      <td>google sheet</td>\n",
       "      <td>test new branch</td>\n",
       "      <td>2021-01-22T15:18:57.636Z</td>\n",
       "      <td>2021-01-22T15:19:17.292Z</td>\n",
       "      <td>2021-01-22T15:19:17.297Z</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1573</td>\n",
       "      <td>121</td>\n",
       "      <td>test gs string</td>\n",
       "      <td>{'col_1': {'hash': False, 'type': 'string', 'd...</td>\n",
       "      <td>1</td>\n",
       "      <td>9324</td>\n",
       "      <td>google sheet</td>\n",
       "      <td>test gs string</td>\n",
       "      <td>2021-01-21T15:30:02.203Z</td>\n",
       "      <td>2021-01-21T17:03:09.559Z</td>\n",
       "      <td>2021-01-21T17:03:09.564Z</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1569</td>\n",
       "      <td>119</td>\n",
       "      <td>test gs</td>\n",
       "      <td>{'col_1': {'hash': False, 'type': 'Number', 'd...</td>\n",
       "      <td>1</td>\n",
       "      <td>9324</td>\n",
       "      <td>google sheet</td>\n",
       "      <td>test gs</td>\n",
       "      <td>2021-01-20T23:23:57.193Z</td>\n",
       "      <td>2021-01-20T23:24:25.218Z</td>\n",
       "      <td>2021-01-20T23:24:25.221Z</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id  set_id         set_name  \\\n",
       "0  1576     122    COVID Ontario   \n",
       "1  1570     120         test gs2   \n",
       "2  1585     123  test new branch   \n",
       "3  1573     121   test gs string   \n",
       "4  1569     119          test gs   \n",
       "\n",
       "                                             columns  whitelabel  customer  \\\n",
       "0  {'Row_ID': {'hash': False, 'type': 'Number', '...           1      9324   \n",
       "1  {'col_1': {'hash': False, 'type': 'Number', 'd...           1      9324   \n",
       "2  {'col_1': {'hash': False, 'type': 'Number', 'd...           1      9324   \n",
       "3  {'col_1': {'hash': False, 'type': 'string', 'd...           1      9324   \n",
       "4  {'col_1': {'hash': False, 'type': 'Number', 'd...           1      9324   \n",
       "\n",
       "           type             name                   created  \\\n",
       "0  google sheet    COVID Ontario  2021-01-21T19:48:09.099Z   \n",
       "1  google sheet         test gs2  2021-01-20T23:30:12.363Z   \n",
       "2  google sheet  test new branch  2021-01-22T15:18:57.636Z   \n",
       "3  google sheet   test gs string  2021-01-21T15:30:02.203Z   \n",
       "4  google sheet          test gs  2021-01-20T23:23:57.193Z   \n",
       "\n",
       "                    updated                 last_sync  is_syncing  records  \n",
       "0  2021-01-21T19:52:42.148Z  2021-01-21T19:52:42.153Z       False   197152  \n",
       "1  2021-01-20T23:30:32.727Z  2021-01-20T23:30:32.731Z       False        5  \n",
       "2  2021-01-22T15:19:17.292Z  2021-01-22T15:19:17.297Z       False        5  \n",
       "3  2021-01-21T17:03:09.559Z  2021-01-21T17:03:09.564Z       False        5  \n",
       "4  2021-01-20T23:24:25.218Z  2021-01-20T23:24:25.221Z       False        5  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(connections_pp[0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alleged-guide",
   "metadata": {},
   "source": [
    "### Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "marine-enemy",
   "metadata": {},
   "source": [
    "Parallel processing is powerful when dealing with large datasets whether it's coming from APIs or internally as it can significantly speed up the processing of them. Multiprocessing is useful when running complex logic or loading a large dataset whereas multithreading shines when data fetching from an API. It may not always be necessary to utilize parallel processing but when the going gets tough, the tough get going... to doing things in parallel!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "diverse-damage",
   "metadata": {},
   "source": [
    "# Sources"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "white-potential",
   "metadata": {},
   "source": [
    "- [https://blog.floydhub.com/multiprocessing-vs-threading-in-python-what-every-data-scientist-needs-to-know/](https://blog.floydhub.com/multiprocessing-vs-threading-in-python-what-every-data-scientist-needs-to-know/)\n",
    "- [https://timber.io/blog/multiprocessing-vs-multithreading-in-python-what-you-need-to-know/](https://timber.io/blog/multiprocessing-vs-multithreading-in-python-what-you-need-to-know/)\n",
    "- [https://www.educative.io/edpresso/what-is-multithreading-in-python](https://www.educative.io/edpresso/what-is-multithreading-in-python)\n",
    "- [Modin official repository](https://github.com/modin-project/modin)"
   ]
  }
 ],
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