p2/full.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=\"center\">BDCC - Big Data Challenge</h1>\n",
    "\n",
    "<h2 align=\"center\">Full Dataset</h2>\n",
    "\n",
    "<p style=\"text-align:center\">\n",
    "António Almeida, up201505836<br>\n",
    "Miguel Ramalho,  up201403027<br>\n",
    "Tomás Albuquerque,  up201106437\n",
    "</p>\n",
    "\n",
    "This version of the notebook contains the dataset visualization section of the original report, but is executed on the entire dataset (~36GB *uncompressed*).\n",
    "\n",
    "The dataset was uncompressed locally and uploaded to a [Cloud Storage Bucket](https://cloud.google.com/storage/docs/json_api/v1/buckets), namely to `gs://bdcc_up201505836/EVENTS.csv`.\n",
    "\n",
    "This time, we used a [Dask](https://distributed.dask.org/en/latest/) instance running on a cluster managed by [Google Kubernetes Engine](https://cloud.google.com/kubernetes-engine/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip3 install matplotlib dask gcsfs seaborn distributed dask_ml dask-xgboost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initial Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cluster used was comprised of 4 nodes, each with 2vCPUs and 7.5G of RAM - [n1-standard-2](https://cloud.google.com/compute/docs/machine-types). This was the best choice for our use case, given the problem at hand and the quota limitations (which we managed to increase) imposed by GCP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3>Client</h3>\n",
       "<ul>\n",
       "  <li><b>Scheduler: </b>tcp://loopy-echidna-dask-scheduler:8786\n",
       "  <li><b>Dashboard: </b><a href='http://loopy-echidna-dask-scheduler:8787/status' target='_blank'>http://loopy-echidna-dask-scheduler:8787/status</a>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3>Cluster</h3>\n",
       "<ul>\n",
       "  <li><b>Workers: </b>8</li>\n",
       "  <li><b>Cores: </b>16</li>\n",
       "  <li><b>Memory: </b>62.74 GB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: scheduler='tcp://10.52.8.2:8786' processes=8 cores=16>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import dask.dataframe as dd\n",
    "from dask.distributed import Client\n",
    "client = Client()\n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Utilities functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reads a DASK DataFrame from an EVENTS csv\n",
    "def read_events(filename):\n",
    "    return dd.read_csv(filename, parse_dates=['CHARTTIME', 'STORETIME'], assume_missing=True, dtype={'RESULTSTATUS': 'object',\n",
    "       'STOPPED': 'object',\n",
    "       'VALUE': 'object'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization\n",
    "This section contains some of the requested plots and some more high level information about the dataset and its contents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dask.dataframe.core.DataFrame"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#path = './EVENTS_10M.csv'\n",
    "path = 'gs://bdcc_up201505836/EVENTS.csv'\n",
    "df = read_events(path)\n",
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ROW_ID</th>\n",
       "      <th>SUBJECT_ID</th>\n",
       "      <th>HADM_ID</th>\n",
       "      <th>ICUSTAY_ID</th>\n",
       "      <th>ITEMID</th>\n",
       "      <th>CHARTTIME</th>\n",
       "      <th>STORETIME</th>\n",
       "      <th>CGID</th>\n",
       "      <th>VALUE</th>\n",
       "      <th>VALUENUM</th>\n",
       "      <th>VALUEUOM</th>\n",
       "      <th>WARNING</th>\n",
       "      <th>ERROR</th>\n",
       "      <th>RESULTSTATUS</th>\n",
       "      <th>STOPPED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>788.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>165660.0</td>\n",
       "      <td>241249.0</td>\n",
       "      <td>223834.0</td>\n",
       "      <td>2134-05-12 12:00:00</td>\n",
       "      <td>2134-05-12 13:56:00</td>\n",
       "      <td>17525.0</td>\n",
       "      <td>15</td>\n",
       "      <td>15.00</td>\n",
       "      <td>L/min</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>789.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>165660.0</td>\n",
       "      <td>241249.0</td>\n",
       "      <td>223835.0</td>\n",
       "      <td>2134-05-12 12:00:00</td>\n",
       "      <td>2134-05-12 13:56:00</td>\n",
       "      <td>17525.0</td>\n",
       "      <td>100</td>\n",
       "      <td>100.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>790.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>165660.0</td>\n",
       "      <td>241249.0</td>\n",
       "      <td>224328.0</td>\n",
       "      <td>2134-05-12 12:00:00</td>\n",
       "      <td>2134-05-12 12:18:00</td>\n",
       "      <td>20823.0</td>\n",
       "      <td>.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>791.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>165660.0</td>\n",
       "      <td>241249.0</td>\n",
       "      <td>224329.0</td>\n",
       "      <td>2134-05-12 12:00:00</td>\n",
       "      <td>2134-05-12 12:19:00</td>\n",
       "      <td>20823.0</td>\n",
       "      <td>6</td>\n",
       "      <td>6.00</td>\n",
       "      <td>min</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>792.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>165660.0</td>\n",
       "      <td>241249.0</td>\n",
       "      <td>224330.0</td>\n",
       "      <td>2134-05-12 12:00:00</td>\n",
       "      <td>2134-05-12 12:19:00</td>\n",
       "      <td>20823.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ROW_ID  SUBJECT_ID   HADM_ID  ICUSTAY_ID    ITEMID           CHARTTIME  \\\n",
       "0   788.0        36.0  165660.0    241249.0  223834.0 2134-05-12 12:00:00   \n",
       "1   789.0        36.0  165660.0    241249.0  223835.0 2134-05-12 12:00:00   \n",
       "2   790.0        36.0  165660.0    241249.0  224328.0 2134-05-12 12:00:00   \n",
       "3   791.0        36.0  165660.0    241249.0  224329.0 2134-05-12 12:00:00   \n",
       "4   792.0        36.0  165660.0    241249.0  224330.0 2134-05-12 12:00:00   \n",
       "\n",
       "            STORETIME     CGID VALUE  VALUENUM VALUEUOM  WARNING  ERROR  \\\n",
       "0 2134-05-12 13:56:00  17525.0    15     15.00    L/min      0.0    0.0   \n",
       "1 2134-05-12 13:56:00  17525.0   100    100.00      NaN      0.0    0.0   \n",
       "2 2134-05-12 12:18:00  20823.0   .37      0.37      NaN      0.0    0.0   \n",
       "3 2134-05-12 12:19:00  20823.0     6      6.00      min      0.0    0.0   \n",
       "4 2134-05-12 12:19:00  20823.0   2.5      2.50      NaN      0.0    0.0   \n",
       "\n",
       "  RESULTSTATUS STOPPED  \n",
       "0          NaN     NaN  \n",
       "1          NaN     NaN  \n",
       "2          NaN     NaN  \n",
       "3          NaN     NaN  \n",
       "4          NaN     NaN  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ROW_ID</th>\n",
       "      <th>SUBJECT_ID</th>\n",
       "      <th>HADM_ID</th>\n",
       "      <th>ICUSTAY_ID</th>\n",
       "      <th>ITEMID</th>\n",
       "      <th>CGID</th>\n",
       "      <th>VALUE</th>\n",
       "      <th>VALUENUM</th>\n",
       "      <th>WARNING</th>\n",
       "      <th>ERROR</th>\n",
       "      <th>RESULTSTATUS</th>\n",
       "      <th>STOPPED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.999990e+05</td>\n",
       "      <td>999999.000000</td>\n",
       "      <td>999999.000000</td>\n",
       "      <td>999542.000000</td>\n",
       "      <td>999999.000000</td>\n",
       "      <td>847390.000000</td>\n",
       "      <td>999999.000000</td>\n",
       "      <td>999999.000000</td>\n",
       "      <td>999999.000000</td>\n",
       "      <td>999999.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.003201e+05</td>\n",
       "      <td>5800.489858</td>\n",
       "      <td>148498.552431</td>\n",
       "      <td>254446.497683</td>\n",
       "      <td>222180.309609</td>\n",
       "      <td>18182.896204</td>\n",
       "      <td>85.618958</td>\n",
       "      <td>85.618958</td>\n",
       "      <td>0.065634</td>\n",
       "      <td>0.001921</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.892129e+05</td>\n",
       "      <td>3503.306794</td>\n",
       "      <td>27689.259457</td>\n",
       "      <td>31045.418397</td>\n",
       "      <td>2488.229065</td>\n",
       "      <td>2288.817959</td>\n",
       "      <td>778.306520</td>\n",
       "      <td>778.306520</td>\n",
       "      <td>0.247641</td>\n",
       "      <td>0.043787</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>100009.000000</td>\n",
       "      <td>200203.000000</td>\n",
       "      <td>220045.000000</td>\n",
       "      <td>14023.000000</td>\n",
       "      <td>-610.000000</td>\n",
       "      <td>-610.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.500005e+05</td>\n",
       "      <td>2636.000000</td>\n",
       "      <td>128932.000000</td>\n",
       "      <td>225777.000000</td>\n",
       "      <td>220180.000000</td>\n",
       "      <td>16037.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000e+05</td>\n",
       "      <td>5885.000000</td>\n",
       "      <td>142471.000000</td>\n",
       "      <td>256064.000000</td>\n",
       "      <td>220277.000000</td>\n",
       "      <td>18462.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.499995e+05</td>\n",
       "      <td>8799.000000</td>\n",
       "      <td>172420.000000</td>\n",
       "      <td>284784.000000</td>\n",
       "      <td>224684.000000</td>\n",
       "      <td>20652.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.058971e+06</td>\n",
       "      <td>10694.000000</td>\n",
       "      <td>199622.000000</td>\n",
       "      <td>299972.000000</td>\n",
       "      <td>228377.000000</td>\n",
       "      <td>21545.000000</td>\n",
       "      <td>598185.120000</td>\n",
       "      <td>598185.120000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ROW_ID     SUBJECT_ID        HADM_ID     ICUSTAY_ID  \\\n",
       "count  9.999990e+05  999999.000000  999999.000000  999542.000000   \n",
       "mean   5.003201e+05    5800.489858  148498.552431  254446.497683   \n",
       "std    2.892129e+05    3503.306794   27689.259457   31045.418397   \n",
       "min    1.000000e+00      23.000000  100009.000000  200203.000000   \n",
       "25%    2.500005e+05    2636.000000  128932.000000  225777.000000   \n",
       "50%    5.000000e+05    5885.000000  142471.000000  256064.000000   \n",
       "75%    7.499995e+05    8799.000000  172420.000000  284784.000000   \n",
       "max    1.058971e+06   10694.000000  199622.000000  299972.000000   \n",
       "\n",
       "              ITEMID           CGID          VALUE       VALUENUM  \\\n",
       "count  999999.000000  847390.000000  999999.000000  999999.000000   \n",
       "mean   222180.309609   18182.896204      85.618958      85.618958   \n",
       "std      2488.229065    2288.817959     778.306520     778.306520   \n",
       "min    220045.000000   14023.000000    -610.000000    -610.000000   \n",
       "25%    220180.000000   16037.000000      17.000000      17.000000   \n",
       "50%    220277.000000   18462.000000      56.000000      56.000000   \n",
       "75%    224684.000000   20652.000000      97.000000      97.000000   \n",
       "max    228377.000000   21545.000000  598185.120000  598185.120000   \n",
       "\n",
       "             WARNING          ERROR  RESULTSTATUS  STOPPED  \n",
       "count  999999.000000  999999.000000           0.0      0.0  \n",
       "mean        0.065634       0.001921           NaN      NaN  \n",
       "std         0.247641       0.043787           NaN      NaN  \n",
       "min         0.000000       0.000000           NaN      NaN  \n",
       "25%         0.000000       0.000000           NaN      NaN  \n",
       "50%         0.000000       0.000000           NaN      NaN  \n",
       "75%         0.000000       0.000000           NaN      NaN  \n",
       "max         1.000000       1.000000           NaN      NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.compute().describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Plot of values per item\n",
    "- For a given item, i.e., `ITEMID`, get its respective rows\n",
    "- Plot its items, i.e., `VALUENUM` entries\n",
    "- A scatter plot was used as it represents more appropriately the data presented"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def item_hist(df, item):\n",
    "    df[['ITEMID', 'CHARTTIME', 'VALUENUM']]\\\n",
    "        .query(f'ITEMID == {item}')\\\n",
    "        .compute()\\\n",
    "        .plot(x='CHARTTIME', y='VALUENUM', style='.', figsize=(14,7), title=f'Values for ITEMID {item}')\n",
    "    \n",
    "    # display options\n",
    "    ax = plt.gca(); ax.set_xlabel('Time'); ax.set_ylabel('Values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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f/Gu99dYbTZ7/lVeO1qJFC2QYhpYs+VQjRoxsjmq7abVEasqUKRo2bJiysrK0fft2SVJZWZnuuusujRw5UqNHj9bvfvc7lZaWOqfJy8vTmDFjNHLkSN1+++0qKSkJqgzNoyV+9QAAAIB52/aWqbbOLsOQ6urs2ra3LPBEQbrqqrHKyVmo2tpaLVnymfr27aeUlFSVlZUqN3e9hg2r73Y3atRVQd2V8vXyBtfPPbv2de+e5fbdsWOv0Y4dP2jz5u98zsPf5+edl638/O368svl6tr1bMXHmxsjKhitlkgNHz5cH3zwgdLT052fWSwW3XnnncrJydG8efN05pln6sUXX5RU/5Dbo48+qkmTJiknJ0fZ2dlBlaF5tNSvHgAAADAvq3OiImxWWS2SzWZVVufEZpt39+49lJx8hlavXqmFC+fqqqvGSJIWLVqguro6/epXE3TddaM1e/bHWrLks4DPGyUkJKiiwv3asaKiXImJSUHXKSIiQnfc8Ru9+ebrHvNO1JEj7vMuLy9XQoJ7e1gsFg0bdpmmTn1Oo0aNDnq5ZrRaIpWdna20tDS3zxISEjR48GDn3/3799eBAwckSZs2bVJUVJSys7MlSRMmTNDixYsDlqF5tOSvHgAAADCnW3q8Hr1xgMZf1FWP3jhA3dLjm3X+V101Rn/721vat2+vhg69WJK0cOE8vfDCi/roo3n66KN5+ve/F6p373O0fPlSv/PKzh6s+fNnO1/2sHLl17JarcrIONNUnS677ApVVFQ43x4o1d9pWr16pYqKCiVJR45UaOnSz3T++UMaTT927DWaOPFXGjz4AlPLDVbYjARmt9s1c+ZMDRs2TJJ08OBBderUyVmelJQku92u8vJyv2UJCc1/2+7nyPGrR12dvdl/9QAAAIB53dLjmz2BcrjsslGaPv0vGjv2GkVGRmrz5u909OgRDRw4yON7V2jBgrkaOfJKSdIDD/xWNpvNWf73v8/SrbfeoenTX9Xtt98ki8WquLg4Pf/8NEVENKQe77//d82bN8f597hx12jcuOvclmW1WnX33ffq8ccfdH6WmdlF9933kJ544mHZ7XYZhqFrr71B2dnnN1qnlJRU3XTTrafWMH5YDMMwWmzuXgwbNkwzZsxQjx493D5/+umnVVhYqNdff11Wq1U5OTn6+OOP9dZbbzm/069fP33xxRdavXq1zzISqeazdXepNu04rHPPPkM9M4O/FQsAAADfNm/eok6dzgp1NeDiwIE9Ouec3qamCYs7UlOmTNGePXs0Y8YMWa31vQ3T0tKc3fwkqbS0VBaLRQkJCX7LzCgpOSa7vVXzSKeUlFgVFx8NybKDldwuUpf0re+OGe51PV2dDnGAlkccwIFYgEQc/BzY7XbV1tr9ficiwhrwO2g+dru90X5ntVqUnNze5zQhf/35yy+/rO+++07Tp09XmzZtnJ/36dNH1dXVWreufgTkWbNmadSoUQHLAAAAAKCltdodqeeee06ffvqpDh8+rNtuu00JCQl65ZVXNGPGDGVmZmrChAmSpIyMDE2fPl1Wq1VTp07V5MmTdeLECaWnp2vatGmS5LcMAAAAAFpaqz8jFU7o2odQIw4gEQdoQCxAIg5+Dg4d2qMOHTr7HBNJomtfazIMQ4WFe9Wxo/tza2HftQ8AAAD4OYmIaKPKyiP6Gd/PCBuGYaiy8ogiItoE/rKHsHjZBAAAAPBzkZiYorKyYh07Vu7zO1arVXY7d6RaQ0REGyUmppifrgXqAgAAAMAHmy1CZ5yR5vc7dPEMf3TtAwAAAACTSKQAAAAAwCQSKQAAAAAwiUQKAAAAAEwikQIAAAAAk0ikAAAAAMAkEikAAAAAMIlECgAAAABMIpECAAAAAJNIpAAAAADAJBIpAAAAADCJRAoAAAAATCKRAgAAAACTSKQAAAAAwCQSKQAAAAAwiUQKAAAAAEwikQIAAAAAk0ikAAAAAMAkEikAAAAAMIlECgAAAABMIpECAAAAAJNIpAAAAADAJBIpAAAAADCJRAoAAAAATCKRAgAAAACTSKQAAAAAwCQSKQAAAAAwiUQKAAAAAEwikQIAAAAAk0ikAAAAAMAkEikAAAAAMIlECgAAAABMIpECAAAAAJNIpAAAAADAJBIpAAAAADCJRAoAAAAATCKRAgAAAACTSKQAAAAAwCQSKQAAAAAwqVUSqSlTpmjYsGHKysrS9u3bnZ/v2rVLN9xwg0aOHKkbbrhBu3fvPuUyAAAAAGhprZJIDR8+XB988IHS09PdPp88ebImTpyonJwcTZw4UZMmTTrlMgAAAABoaa2SSGVnZystLc3ts5KSEm3ZskVXX321JOnqq6/Wli1bVFpa2uQyAAAAAGgNEaFa8MGDB9WhQwfZbDZJks1mU2pqqg4ePCjDMJpUlpSUFKrVAQAAAPAzErJEKhwkJ7cP6fJTUmJDunyEB+IAEnGABsQCJOIA9YiD8BayRCotLU2FhYWqq6uTzWZTXV2dioqKlJaWJsMwmlRmVknJMdntRgusXWApKbEqLj4akmUjfBAHkIgDNCAWIBEHqEcchJ7VavF74yVkrz9PTk5Wr169NH/+fEnS/Pnz1atXLyUlJTW5DAAAAABag8UwjBa/JfPcc8/p008/1eHDh5WYmKiEhAQtWLBAO3bs0BNPPKEjR44oLi5OU6ZMUdeuXSWpyWVmcEcKoUYcQCIO0IBYgEQcoB5xEHqB7ki1SiIVrkikEGrEASTiAA2IBUjEAeoRB6EXtl37AAAAAOB0RSIFAAAAACaRSAEAAACASSRSAAAAAGASiRQAAAAAmEQiBQAAAAAmkUgBAAAAgEkkUgAAAABgEokUAAAAAJhEIgUAAAAAJpFIAQAAAIBJJFIAAAAAYBKJFAAAAACYRCIFAAAAACaRSAEAAACASSRSAAAAAGASiRQAAAAAmEQiBQAAAAAmkUgBAAAAgEkkUgAAAABgEokUAAAAAJhEIgUAAAAAJpFIAQAAAIBJJFIAAAAAYBKJFAAAAACYRCIFAAAAACaRSAEAAACASSRSAAAAAGASiRQAAAAAmEQiBQAAAAAmkUgBAAAAgEkkUgAAAABgEokUAAAAAJhEIgUAAAAAJpFIAQAAAIBJJFIAAAAAYBKJFAAAAACYRCIFAAAAACaRSAEAAACASSRSAAAAAGASiRQAAAAAmEQiBQAAAAAmhUUitWzZMo0bN05jx47V6NGj9emnn0qSdu3apRtuuEEjR47UDTfcoN27dzun8VcGAAAAAC0p5ImUYRh67LHHNHXqVM2ZM0fTpk3T448/LrvdrsmTJ2vixInKycnRxIkTNWnSJOd0/soAAAAAoCWFPJGSJKvVqqNHj0qSjh49qtTUVJWVlWnLli26+uqrJUlXX321tmzZotLSUpWUlPgsAwAAAICWFhHqClgsFr3yyiu69957FRMTo8rKSr355ps6ePCgOnToIJvNJkmy2WxKTU3VwYMHZRiGz7KkpKRQrg4AAACAn4GQJ1K1tbV688039cYbb2jgwIFav369HnzwQU2dOrXFl52c3L7Fl+FPSkpsSJeP8EAcQCIO0IBYgEQcoB5xEN5Cnkh9//33Kioq0sCBAyVJAwcOVHR0tKKiolRYWKi6ujrZbDbV1dWpqKhIaWlpMgzDZ5kZJSXHZLcbLbFaAaWkxKq4+GhIlo3wQRxAIg7QgFiARBygHnEQelarxe+Nl5A/I9WxY0cdOnRIO3fulCTt2LFDhw8f1llnnaVevXpp/vz5kqT58+erV69eSkpKUnJyss8yAAAAAGhpFsMwQnNLxsXcuXP19ttvy2KxSJLuu+8+jRgxQjt27NATTzyhI0eOKC4uTlOmTFHXrl0lyW9ZsLgjhVAjDiARB2hALEAiDlCPOAi9QHekwiKRChUSKYQacQCJOEADYgEScYB6xEHohX3XPgAAAAA43ZBIAQAAAIBJJFIAAAAAYBKJFAAAAACYRCIFAAAAACaRSAEAAACASSRSAAAAAGASiRQAAAAAmEQiBQAAAAAmkUgBAAAAgEkkUgAAAABgEokUAAAAAJhEIgUAAAAAJpFIAQAAAIBJJFIAAAAAYBKJFAAAAACYRCIFAAAAACaRSAEAAACASSRSAAAAAGASiRQAAAAAmEQiBQAAAAAmkUgBAAAAgEkkUgAAAABgEokUAAAAAJhEIgUAAAAAJpFIAQAAAIBJJFIAAAAAYBKJFAAAAACYRCIFAAAAACYFlUjNnz9fO3bskCTt3LlTN910k371q185PwMAAACAn5OgEqlXXnlF8fHxkqSpU6eqb9++GjRokJ5++ukWrRwAAAAAhKOIYL5UWlqqM844QydOnND69ev1l7/8RRERERoyZEhL1w8AAAAAwk5QiVRSUpL27Nmj7du369xzz1WbNm10/PhxGYbR0vUDAAAAgLATVCJ177336pprrpHNZtPLL78sSVq5cqV69uzZopUDAAAAgHBkMYK8rXT8+HFJUnR0tCSppKREdrtdKSkpLVe7FlZSckx2e2juqqWkxKq4+GhIlo3wQRxAIg7QgFiARBygHnEQelarRcnJ7X2XBzuj6upq5eTk6O2335Yk1dbWqq6u7tRrCAAAAACnmaASqTVr1uiKK67QvHnz9MYbb0iS9uzZo6eeeqol6wYAAAAAYSmoROqFF17QK6+8onfeeUcREfWPVfXr108bN25s0coBAAAAQDgKKpEqKCjQBRdcIEmyWCySpMjISLr2AQAAAPhZCiqROvvss/XVV1+5ffbNN9+oR48eLVIpAAAAAAhnQb3+/IknntBvfvMbXXLJJaqurtakSZO0dOlS5/NSAAAAAPBzEtQdqf79+2vu3Lnq1q2brr32WmVkZOijjz5S3759W7p+AAAAABB2grojJUkdOnTQXXfd1ZJ1AQAAAIDTQlCJ1KOPPup8yYSnqVOnnnIlTpw4oRdeeEErV65UVFSU+vfvr2effVa7du3SE088ofLyciUkJGjKlCnKzMyUJL9lAAAAANCSgurad9ZZZ6lz587Of9HR0fryyy8VHx/fLJWYNm2aoqKilJOTo3nz5un++++XJE2ePFkTJ05UTk6OJk6cqEmTJjmn8VcGAAAAAC3JYhiG0ZQJN23apOnTp2vGjBmnVIHKykpdfPHF+uKLL9SuXTvn5yUlJRo5cqRWr14tm82muro6DR48WJ9++qkMw/BZlpSUFPSyS0qOyW5v0uqfspSUWBUXHw3JshE+iANIxAEaEAuQiAPUIw5Cz2q1KDm5vc/yoJ+R8tSrVy+tWbOmqZM77du3TwkJCXr99de1evVqtWvXTvfff7/atm2rDh06yGazSZJsNptSU1N18OBBGYbhs8xMIuWvYVpDSkpsSJeP8EAcQCIO0IBYgEQcoB5xEN6CSqRWrlzp9nd1dbUWLFigbt26nXIFamtrtW/fPvXu3VuPP/64vv32W91zzz169dVXT3negXBHCqFGHEAiDtCAWIBEHKAecRB6zXJH6o9//KPb3zExMerZs6deeumlU6udpE6dOikiIkJXX321JKlfv35KTExU27ZtVVhYqLq6Omf3vaKiIqWlpckwDJ9lAAAAANDSgkqkli5d2mIVSEpK0uDBg/X1119r6NCh2rVrl0pKSpSZmalevXpp/vz5Gjt2rObPn69evXo5u+75KwMAAACAluTzZRN2uz2oGVitQb34z699+/bpySefVHl5uSIiIvTAAw/o4osv1o4dO/TEE0/oyJEjiouL05QpU9S1a1dJ8lsWLLr2IdSIA0jEARoQC5CIA9QjDkIvUNc+n4lUz549fY4dJUmGYchisej7778/9VqGCIkUQo04gEQcoAGxAIk4QD3iIPSa/IzUkiVLWqRCAAAAAHC685lIpaent2Y9AAAAAOC0EfQ4UkuWLNHatWtVVlYm196AU6dObZGKAQAAAEC4CupNEa+//romT54su92uxYsXKyEhQStWrFBcXFxL1w8AAAAAwk5QidTHH3+sv/3tb3ryyScVGRmpJ598UjNmzND+/ftbun4AAAAAEHaCSqSOHDmiHj16SJIiIyNVU1Ojvn37au3atS1aOQAAAAAIR0E9I9W5c2f98MMP6t69u7p3766ZM2cqLi5O8fHxLV0/AAwUskQAACAASURBVAAAAAg7fhMpu90uq9WqBx54QGVlZZKkhx9+WI888oiqqqo0efLkVqkkAAAAAIQTv4nURRddpDFjxmjs2LHKysqSJPXr10+fffZZq1QOAAAAAMKR32eknnrqKe3fv1/XX3+9xo8fr7///e8qLS1trboBAAAAQFjye0dqxIgRGjFihI4cOaKFCxdqzpw5evHFF3XhhRdq/PjxGjZsmCIjI1urrgAAAAAQFoJ6a19cXJwmTJigmTNnauHCherTp4/++7//W0OHDm3p+gEAAABA2AkqkXI4efKkNm3apI0bN+rw4cPOV6IDAAAAwM9JUK8/X7dunebMmaNFixYpOTlZY8aM0eTJk5Went7S9QMAAACAsOM3kXrttdc0Z84cVVRU6IorrtCbb76pgQMHtlbdAAAAACAs+U2k8vLy9OCDD2rEiBGKiopqrToBAAAAQFjzm0i98847rVUPAAAAADhtmHrZBAAAAACARAoAAAAATCORAgAAAACTSKQAAAAAwCQSKQAAAAAwiUQKAAAAAEwikQIAAAAAk0ikAAAAAMAkEikAAAAAMIlECgAAAABMIpECAAAAAJNIpAAAAADAJBIpAAAAADCJRAoAAAAATCKRAgAAAACTSKQAAAAAwCQSKQAAAAAwiUQKAAAAAEwikQIAAAAAk0ikAAAAAMAkEikAAAAAMIlECgAAAABMIpECAAAAAJNIpAAAAADAJBIpAAAAADAprBKp119/XVlZWdq+fbskKS8vT2PGjNHIkSN1++23q6SkxPldf2UAAAAA0JLCJpHavHmz8vLy1KlTJ0mSYRh69NFHNWnSJOXk5Cg7O1svvvhiwDIAAACgueUXVGjByt3KL6gIdVUQJsIikTp58qSeeeYZTZ48WRaLRZK0adMmRUVFKTs7W5I0YcIELV68OGAZAAAA0JzyCyo0bWauPvlyp6bNzCWZgiQpItQVkKRXX31VY8aM0Zlnnun87ODBg867U5KUlJQku92u8vJyv2UJCQlBLzc5uX3zrEATpaTEhnT5CA/EASTiAA2IBUjEQbhZvvGg6ursMgyprs6u/SVVuqB/RosvlzgIbyFPpHJzc7Vp0yY98sgjrb7skpJjstuNVl+uVL9jFBcfDcmyET6IA0jEARoQC5CIg3CUkRwjm80q1dlls1mVkRzT4tuIOAg9q9Xi98ZLyBOptWvXaufOnRo+fLgk6dChQ7rjjjt0yy236MCBA87vlZaWymKxKCEhQWlpaT7LAAAAgObULT1ej944QNv2limrc6K6pceHukoIAyF/Ruruu+/WihUrtHTpUi1dulQdO3bUO++8ozvvvFPV1dVat26dJGnWrFkaNWqUJKlPnz4+ywAAAIDm1i09XlddkEkSBaeQ35HyxWq1aurUqZo8ebJOnDih9PR0TZs2LWAZAAAAALQ0i2EYoXlIKAzwjBRCjTiARBygAbEAiThAPeIg9AI9IxXyrn0AAAAAcLohkQIAAAAAk0ikAAAAAMAkEikAAAAAMIlECgAAAABMIpECAAAAAJNIpAAAAADAJBIpAAAAADCJRAoAAAAATCKRAgAAAACTSKQAAAAAwCQSKQAAAAAwiUQKAAAAAEwikQIAAAAAk0ikAAAAAMAkEikAAAAAMIlECgAAAABMIpECAAAAAJNIpAAAAADAJBIpAAAAADCJRAoAAAAATCKRAgAAAACTSKQAAAAAwCQSKQAAAAAwiUQKAAAAAEwikQIAAAAAk0ikAAAAAMAkEikAAAAAMIlECgAAAABMIpECAAAAAJNIpAAAAADAJBIpAAAAADCJRAoAAAAATCKRAgAAAACTSKQAAAAAwCQSKQAAAAAwiUQKAAAAAEwikQIAAAAAk0ikAAAAAMAkEikAAAAAMIlECgAAAABMIpECAAAAAJNCnkiVlZXprrvu0siRIzV69Gj97ne/U2lpqSQpLy9PY8aM0ciRI3X77berpKTEOZ2/MgAAAABoSSFPpCwWi+68807l5ORo3rx5OvPMM/Xiiy/KMAw9+uijmjRpknJycpSdna0XX3xRkvyWAQAAAEBLC3kilZCQoMGDBzv/7t+/vw4cOKBNmzYpKipK2dnZkqQJEyZo8eLFkuS3DAAAAABaWkSoK+DKbrdr5syZGjZsmA4ePKhOnTo5y5KSkmS321VeXu63LCEhIejlJSe3b9b6m5WSEhvS5SM8EAeQiAM0IBYgEQeoRxyEt7BKpJ599lnFxMTo5ptv1meffdbiyyspOSa73Wjx5XiTkhKr4uKjIVk2wgdxAIk4QANiARJxgHrEQehZrRa/N17CJpGaMmWK9uzZoxkzZshqtSotLU0HDhxwlpeWlspisSghIcFvGQAAAAC0tJA/IyVJL7/8sr777jtNnz5dbdq0kST16dNH1dXVWrdunSRp1qxZGjVqVMAyAAAAAGhpIb8j9cMPP2jGjBnKzMzUhAkTJEkZGRmaPn26pk6dqsmTJ+vEiRNKT0/XtGnTJElWq9VnGQAAAAC0NIthGKF5SCgM8IwUQo04gEQcoAGxAIk4QD3iIPQCPSMVFl37AAAAAOB0QiIFAAAAACaRSAEAAACASSRSAAAAAGASiRQAAAAAmEQidZrKL6jQgpW7lV9QEeqqAAAAtAhf1ztcByEchHwcKZiXX1ChaTNzVVtnV4TNqkdvHKBu6fGhrhYAAECz8XW9w3UQwgV3pE5D2/aWqbbOLsOQ6urs2ra3LNRVAgAAaFa+rne4DkK4IJE6DWV1TlSEzSqrRbLZrMrqnBjqKgEAADQrX9c7XAchXNC17zTULT1ej944QNv2limrcyK3swEAwE+Or+sdroMQLkikTlPd0uM5cAAAgJ80X9c7XAchHNC1DwAAAABMIpECAAAAAJNIpAAAP2uMR4PWRLyd3lpz+23dXUqshDmekQIQdvILKniIGK2C8WhaD/s18Xa6a83tl19QoRdn5aqmllgJZyRSAMIKFxpoTd7GoyHemh/7dT3i7fTWmttv294y1dYSK+GOrn0AwgoDLaI1MR5N62C/rke8nd5ac/tldU5URASxEu64IwUgrDhOVHV1dk4eaHGMR9M62K/rEW+trzm7lLbm9uuWHq/n77lQqzYWECthzPbUU089FepKhMrx4ydlGKFZdrt2UaqqOnnK88kvqNDKzYdktVqUFNfW+feBkkpt3lXq/NzbdPO/2a1v8w+rXXRko+94ztfXZ4EszyvQx1/s0MlauzI7xgVcRjBlwXCdvvToiUb//23+Yf3j8+1aumG/bDaLdh86qvdytmnjjhIlx7cNapmnWkepPg7+vmCL3vt0q8qOntCW3aXO/z+nS5Lf5frbxt7a3Vt9/W0fMwK1hWe5v+UmxbVVbEykaursuvz8zopr10bzv9mtL789oG17y9QuOtLrNjW7HT5clh+wrYNZt2D5a4Nzzj7DeTx4a+5mvbtoq/YXHdPArFRT83bExLf5h7V4zR6/2zXQ+vuKl/dytmnld4eU+8Nh5/bw1i6O9ftuZ6ney9mm2St26cDhSg3MSlV+QYWe/791+nBZvr7NP6yL+6e7tUdtneH1uOZrG7w0K1d/X7xN2/eW6Rd90ryur792TYprqx5nJvjdvh8uy9db877TsvX7FRlpC2p/8Xcc9bWNfJ0bPI9p/o7fgeoQbOybWR/PbbU8t0AfLs93LiMprq16nZWolIRojRnaRZIC7leucT3v6936dO1eGVJQ5xJ/MdNcxz3PeXnGraMentvKNd581dMRB811/PEn2OsGf3y1qeOY4Ti3ejte+4ohq9WijTtLTmlbLc8r0Bv/3qQtu8u0YtNBbdxxWIaktd8X+dwHArX5M++uVV5+idZ8X6ieZyUqKa6t7pq6VLO/2qX53+zSmAu7+GwPs9vzrPQEpSfFBDwONnWfRmAWi0UxMW18lxtGqFKJ0CspOSa7PTSrn5ISq+Lio6c0D8cByjAkq9Wimy/voZmf/6CaWrskySIpIqJxX/T8ggpN+ccG1dXVr3uEzaLHJp7n/I63vuySTPVvzy+o0KJVe5T7w2HnZ7+6IkuX9E/3uQxfy79xRHdVHq/x+ovM8rwCrd9WpIFZqV7nbbVaZK8z5NjKETaLauv8b3Ob1aLHbzov4Po1R3//Bav36uNl+V7LRg3urOsv7eZ1ud628f7iY1q/rUix0W20akuhc5pfXZGljJT2jeq7v/iY/m/xNrfvOdrQjEBt4Vk+YmCGFq3e63O5nttPhqE6e8PybNb6A1ud3ZDVapFFUp3dCBgrrj5clu9WB29tLUmPTv9aJUdPSJIivexLTW0jzzb47XX9NLBbst6au9lt252Z2l63jMwKKhYdMeHJ23YNtP4vzcrV5t31Xa8iI7zHi4Pn8UOq3y+9fVeSzslMdM7bITk2ytnOUv0+aDcatunMz3/wGV+udXXM/+EJA9zm79muQ3p30N1jzvFaP28820sKvL/4O456bivXeXk7N/jbJ7y1v7865G4vDir2g5nX/uJj+uDT7bLbDUVENGwrz/XzXIZnzHrbHv7iOtC5RPJ9vvKMzaYe97zNyzVuHfUwe651lKWkxGpl3v4Wf67M3zkl2GX5alPPz62W+msVx/Ha27byFUNSfRzFtI0IeHx/9t212lN0TB0SonWorCrgD+au8RnofPa7l79Q1Yk6t+mtFsn1UtIiyXWRjvZoynVDSWWNVm0sULvoSH3w6XbV2Y1G1yjBns/QNFarRcnJ7X2Xt2Jd0IzyCyr0f4u3OQ8Qdruhhd/sVq3LFach733Rt+0tcx7YJam2znD7jre+7Gb6tzsOFq5JlCSt31bkdxneympr7frg0+365MudmjYz1+0VoI6D9OZdZfq/xdu0PK/Ay7wNtwNaoCRKqr8oD9R/v7n6+3+z6aDPsvXbixp95liug2MbL1q1x9kWrheMUn27e6uv6/ZwfK8pArWFZ/mqLYfcyr/69oDP79vr3JMoSaqz129HR7nj//3FiifPtvXW1n98a5XbxX1NbdO3s2cbeC7vm431bbBpZ4nb5/uKjgVcF8+Y8ORtu/pb/7fmbnZLTBzr7Ss+PI8fvpbpsH1/43VxbWepfh90ttW2Ir/x5Tk/b/P3bFfPvwPxFh+B9hd/x9FTmZfnPuGt/f3VIZjYD2Ze32w6qPd/vLAzJNW6bKtG6+exjG17y9wulFdtKdSHHj8o+YvrQOcSf8ek5jrueZvWNW4d9TB7rnXVGs+V+TqnmFmWrzb1/NxuNBy7fW2r9duKfP4otHjN3oDH92ffXatdh47Kbjd0sDRwEiW5x6e/Ns8vqGiURDnWy5XnIh3tYHZ75hdU6I8zvtYnX+7UeznbVPfjgurshhat2uO1/t7+RssikQoTZscl8LYDnqitU4TNKsuPf1t8PKCY1TlRNpvF+XeEzeL2HW8PU5p5wNLXCdC1O42/+bmWWawW2T1OTg6+Dt6u0zeFzWoJ2H+/uR44/cW53rshSdLAHo27dTmW67mNy4+daPRd53yyUr3W17N7U7DdyHzVyVdbeJanxEe7lSe0j/L5favNIpvHUcpmrY9ZR7nj//3FiifPtvXW1ofKqryua1N4toHn8n7Rt5Mk6dyuyY2mDbQunjHhydt29bf+3pIMb/Hi4Hn88LVMhx4ZjX+BTY51jwGb1dLQVlmpfuPLc37e5u/Zrt7a2R9v8RFof/F3HD2VeXnuE97a318dgon9YOYlya1Hh9VicW6rRuvnsQxv9fW8+PMX14HOJf6OSc113PM2rWvcOuph9lzrqjVebODrnGJmWb7a1PNzq6Xh2O1rW/nbHoahgMf3PUXHgq63s749grs28bVMz2sNz5h1rJPZ7en61j7PhND1nN/UfRrNg2ekwuAZKccdnM27S7Vqc6F6/djn1h+r1aIVHnczhp+XoWsvOVupidEa1CtV3dLjNWZol0a3jpPi2qp3ZpJsVosyO8Zqwogebt/x7MveLT3e62f+6rZqc6FkGLJYpcyOsRoztItb9wl/83Mty+6Zqi27yyTDkM1m1ZihXZxtc7LWrm/zGy76Rg05S5kd49ymj2/XRvuLK53fGdK7gwb37qDMjrGqrK5RbEykrrm4q/p1O0NHKk+qS1qcfn1lr4C32820hz8XDshQecVxHak6oYv6ddIZ8W1VdvSEBvZI0a+u6OlzuZ7bODE2yq0thvTuoPj2bTRqyFm6pH+61/pmdoxTfPs2qq2zO7/XFIHawrO8d5ckff3dwR+7pEq3XdnLLd5dvz92aFcN7dtJNqtFibFR6tk5QTeO6KELz01zljv+31+seDqnS5JO1tQ5291bN4g1Wwp17HiN8+/k2ChNGN69Wdrol307ubX92Iu7qarqpAZmpaqwtEqHK6pltxvOixp/6+ItJjI7xiqqjdXndvW3/vuLjrntM+dkJurqX2Q64+VI5UmlJkSrc4dY9eyc0Oj4Icktts7uFK8jlSdlSBrUM1W/v7avzumSpO92luhkTZ0yO8bqubuHuLXHlUPOcrbVeT1S/MbXL/qkKX9/ucqPnVSvzgmNuvVJcrarY98y063Ptb0KyyoVFx2p6y7tFnB/8XccTU2M9rmNvD0j5Wuf8Hb8DlSHYGI/mHmd2SFWq7YUyvixi+3Nl/fQJf3TnesX366NTtbWeV1GUlxbFZZWucXZRf06uT3b4RnXVotF0VG2oM4l/o5JzXXc8zYv17h11MPsuVaq723xz8+3KzrKplGDzzrl84w/vs4pZpblq01djxld0uJ025W9nMdrX9vqvB4p2ph/WOXH3PeBczIT6z8LcHz/9gf3adOSYpSdlaLMjrHqdVaic5/rlNzO6z7gL3asVotWbSl0/oAQabPoiZsH6tejemn+N7tkGPU/9P318WFe28PsdYNjecaP11Ku16tjhnZxPnvV1H0aweEZKT/C5RmpBSt365Mvd9ZfWFqk8Rd11VUXZAacR35BhT5alq/iiuMa0rujqZ0n0FtsvD17ZMapTh9sXYNZzofL8rV+e5EG9kg95QNMU97+428a1zj4cFm+Fq/ZK8No2vM4zdnmLa2lBuZs7vn+8a1VOlhapdjoSP3+ur4t9tYkb8/FNGfcmvXSrFxt31+hHhnxXhOT5uC5rU51P29prTWYbHM8P9tSHM8MJsdGadpvLzzlNgn1sTkcNeczXGa11DHnrbmbtWlnic7tmhz0jxgvzcrV93vLFN0mQtdecrbzGaNA2zi/oEL/79+bVFF5Umd1iNWffj2o2dbDMf/WjDPHM1JZnROVu71Y67cXqWtavNJT2p32sX66CPSMFIlUGCRSjjtSjtfCtvRAhYEeeDzVA/lPdeDFpqxXoGkcceDZ5hZJ11wcXEKNltNasex58RzKi6nmXmdvFx7eXijj64USoWwL13VorWNauCZSri9ekeRMpsLBT+mc88e3V+lgSUO34rTkGD1/15AWX25LvbDgVF/0YsZPKQ4cHMeD5ngpCJqGl02cBhzjEoy/qGur7BSBHng81Ydxf6oDLzZlvYKdplEbW5r+PA6aT6hiuTkfiDerOdfZcfL3fEDc2wPmrfFygKb6qR7TzPB8IYjn36HE9jl1LfXCglN90YsZrRUHZp9pbw7N8VIQtAwG5A0T3dLj3V5l2dTb645pPW/9uv4q7HjgsbbWLlksahcd6TaPgVmp2ryrzO1vM11rmmPgxVO5fd7ct94d6945Ndb0emV1TnS+gt3i8RKL/IIKLd94UBnJMY3a/IrzO0uq7/a5YVux9h+u9NvNynOd/W0vx3fbRUdq76Gjqqg8qfh2bfSLc9Ocr1B3TJdfUOF8s6DjxRhNaVvPmDazjVqrK0V+QYXeW7xVxRXV6t/tDN095hy/sXyq3WBc1yslJdatLDbavT+2Y+wls+3QlDoGu/8Gqk9+QYXmrNjp/AW1ptauj5bl64mbB6pddKTzFcEWa/1LCrbvq3BbpmP+nVNjGx2PPC3PK9Bna/dJki4bdKbbq7GbI3baRUfKarHILqNVB5Nt7q7Egfjr0un5inrPF4QEqkNL7seuMWuxWlRSUa38goqAy/E8TvrbXxxDepQfO6Ff9uvk9zzY1K6x+QUV6pgU43ZH6rJBZwY9/akY2CPV7Y5UQruogG0Y6PiSX1Chdm0jVFld6/wss2OsFqzc7RYH/s5XrssY0CPFbwz5iwMzXYm9rYfju7nbi51d8F2fDXScKx3n0+Ly49p96KjO7ZqsHp0Tmtw1eevuUufrz60Wi+pcOpF5XlMgNOjaFwZd+1ydyu11b2OdREZ47zbjOf6Ht+59jh1fkumuNaeaCDX19nxz39r37FYU7DgWrvWZ+o8Nqq0z3MYQ8dad0zWJcYz75PkaWG9j5JgZp8nf2CyeY2GMGtxZn67b53x9r+v4TWba1jMuh/TuoPXbi4PaRq3VVSO/oEIvvLfe7TNHFxRvsXyq3WA81+uF/7xQye3qf9DwjLkhvTto2MAM0+1wKnUMJkkKZuwwb3E2pHcHrd1W5BJX9WOiSA1JuqRGMb236KjXCxFvY1b5GjvtVMYAq6m1u104tRTPrjzN2ZXYn2DG4/J8RirYOkjmxiFsCseF7IpNB4M6RnnGjef4Zp5jC/35g/Wyu4Szr/NgMO3oq/6ONrJYpG4ZCRrSu0Ordun97/fXO19oEKjrWKDji+d4lZLUpWOs9h+uDHo8Q89lWK0WGYb/bestDjyvgQKNTec5P9ftYvc4pNmsFt10eQ998Nl2t3X1xUzXZNfrB4ul8Zv7/I0hh+YTqGsfd6TCjLfb674ufjzvFniOzyPVj63j2m2mtrZ+/I/k+LbOMQlqfxwnxnVnvKR/unNnf+mfue512lYU8EDgeCiyqrrW9E7u7fa8Yx6BHlr1N603gS4YPbsR5eUfDrq/uuNFBQ72H8en6pYe71ZP/VjPqy7IdLbrgpXuY4I5bN1XHnCdG8WQy/byNzaL528K67cXuZ0Y6if7cRyLINrWdT6uNu0saRSPvubja3t6xv6pnki8dY/YtLNEy/MK9NW3B5TQPsrtlz8z+6krx6+rCe2i3NZr047DuqRv/R0/z5g7evyk6bj2VsdVWw55/RGgKXetAtXHX5xt2lniEVeGM/4d05ZUVLvN/1Bp41fRO9fT2zhZ24pUebzGazcfsz/wuK2LYajS5U2OLakp29zbWE/Brm8w43E1td6STmlbeItRz7sYjuOq51hOvubtGTee6+u6T2/bW9boAtrXefD7Pe7Hku37K/zecXGUtYmwOdvIImlInzTnMaE1eB4DHWOD+dpWnseXL/IK3LaR5xhaklR5oqb+dd5q2D5bvTxa4Ggjz2U4fvj2tW1d29I1Dr769oDzR53a2vq/g9m3lucVaPHqPc5pvd12sBuG1m8rCiqJcqxfRkr7gLH/4bJ8LcstcI596W3ZjnHJSKRCi0QqzKTER6uorNrtb288f+35auOBRgd6qf5g2Dk1Vlv3ljsHp12x6aAuyz7T7Tue3ftceevq54/rr0iO/5r5td5X1yLXh1Yd//VMpsx0Kwzm11vPdT9YUqXleQUBE0nPJEpyHzciUD0d5Z6/6NvtRqPuFp7z8uyi4W3MFcfJzJXnHamBPVLd7kg5mB1nxLM+53ZNdt6RcMSjr2TIWzt5xv6KTQdP+Vc5b+uSmhDt8kvpUW3cUeIcTb5RGwcxbofrflFUVi2r1SLLj13Fzj37jIZ5ednfMlLam+5W6lnHisoaffLlTrdY97avDuiREnC/aGr8Su7bX2oYW8d1f7RaLbL9OC6YLHIO7u1oF9f9z7O9fLVZu+jIJt0VaY6uyk3RlOV6dm0K9u6MVD/+luudFM/xuFxfNlFy9IQenf6117tSvurd1G3hLUZTEhv2TdeYMNNmnnHjuf6eYwtZre53I7ydBz9clt/oB6kz4tp6ravU+K6YzWqR4eWY0Bq87bOGIVVV13rdVp7Hl6oTdao6Ued2HLHZLG7nD8e1jes5pF10pM/rC89lWK0W5+vPPbetr7a0WC3aW9jQ+8eQtLfwqPP44itOvL0AymJtfEcq4sexrxzXWIF0To0NGPveehf5UuAyhABCg3GkwmAcKVcrNx9ScXlDIpWaGK1f9Gn8q9TKzYfcDj6+1sNikfqenayU+GjtPtRwMGkTadXh8oaDWrf0ePU4M8HrPMyOu/Hep1vd+kQfqTqhEdnB9/P2NdbCu4u2uh3ky46e0JVDzgpqWm9Wbj6kzbtL69vOMJSSEN2oDTI7xmnN9+7jCdXW2b1uE1f/+Hx7o8+euKnhYt9Rz8z0BI06v3OjerqO7XGopFLVJ+tHU7da1KiegcYo8jbmimPMkMT2UQ3jM13WQ106xTmnu+qCTPXOTNL+4mPOcTksknp3SdTtV/UOOnHxHOPiV1f0VPnRE27x6K3tva1bt/T4RrFvN+r3kx5nJii/oEIrNx+S1WoJOBab53LO6ZKk3QeP6GStXdlZqTJkuO2LhstymjJuh+d+kdC+ja4ccpbGDO2iAT07OI8H3va3poxb5lrH9DPaqeRIdaNY97avtmsbGXC/CHbssG37ytzmnxwXpT/cPNDr2Dqu+6NF0i/7dtJ5WSk6fqLWbVwYz/3P0V5FZced48J5a7ODJZUB18ub5hozLliOc0NTlus6TUp8tHYdOhr0+gYaj2vW0ny3v4+frNPYoV381sHXWEFmtoW3GK2oPOm2bzpiwkybee5nE0f08LlPO8aDOna8xutYVr7qGmmzKKNDe691laSPv9jhVpbZMVbDzstodExoDY62Kz1a7ayTRVKbNlYVlh1vtK1cjy+GYajGJYk4UnVC/3Fpd+d+brFIFT7OIf6uL7wdZ31tW19t6Xnt46iD4/jiK04855ea2Fb3XdfPeY7MzkpVv27JzrGvHOuaGBul6Cib2zFrQPczlBwfpVE/Xq8Ein3POPLnZG2dqesrmBdoHCnuSIWR/IIKtYmwuX3m6+5PVudE2ayO9yXQ1AAAIABJREFU7la+WX98mURJRbUibA2/wLj+GmcY9XekXB8Ade2KkJHSXpXHazQwK1WVx2ucd0R8dVfomhbvdleta5r7QcqzW9bS9fsbdddzfFZQXOk8yLWNtLkdXOJi2ujZv69VpM2qTme0c97V2F98TFt/fJlCt/R4Z3fA1IRoxURHSIZUXHFcXdPinb/AGar/5U1q/KDwZYPOdPtlyrFN/D282jExxu2OVKzLHT/X9U9OjNGcFTu9dvlwvIAkq3Oipvxjg9cXVkjuXVwcbeXYZhkp7RvV0/Evv6BCy9bvV3FFtc7uFOd8AUXPzonKSGnvrMONI3o4+2kbknYUHNHMz7YrJjqiUb09X2Th2MbXX9rN+ZKJBSt3q3PH2PqXcNgN2Q1p6Yb9qqqu9fkwcUlFtb7ZdFC524u1dW+Z80UFUv1JceeBI1qeV9Con/rfnhgmfzzb5pYrejr/3l98zC1hs1jq9xPHNHsLj+pIZY2+21WqAT0a7hK6znPp+v1at61INqtVnZJjJDXsF0N6d3R2Z9u6u1QLvtrhbC/XrrWOef513maVHKlWXEwbZabFOV8Q4nkR4Bm/jnafNjPXeSfS0V4J7aIa7aveHthenlegyuM1ahcdqcrjNW6x5Nmerg9ce/6iXFtr12sfb9S5ZycrOb6t23b2fAGF4+Umm3a4v+XL85joiH/Xl0w4eNbR+ZxBgLdiusaxY30Liiu1YOUepSRE65aRWY32eUmNppHkfEFBh8QYFZZVKaF9lDomxWhv0VF1To11dreUpI+W5avgcKWi29h05S8ynV3WXNfT24sRUhKjG3Vzyy+o0NffHVJNrV12Q/rq2wNu9fT2IohdB4+optau/IIKt4f0v9l0UFERVp1w+SHL38smvMWGK9e7H3ZD+viLnfpm0yH94tyOjY6nnueTY1U1jXpquMaEY9nenudyfXFQTNuIRse7AT3qE/fjJ2qVX1DR6OU7v7+2r559d63ey9mm93Pqzwk2q0W1dkMJ7do4u8s7JMZGNXpZSmx0/XkroX3jMsdLLJbnFWjSm9/o3K5JPrsCBnpxgSM+2kVFqrzyhFLiozWkT0dnbHo77+4vPqbtexu6j0dENNxt8Tz/5BdU6FBpldq1jVRKfLTXu3mu5xrHOcxqs2js0K7qlh6vl2blasueMlmtFp2dVj+4rOeLKK6/tJsG9EjRolV79Oac79Qm0uY8tzvq4e2lNI62/N3LXzT6odmQFB31/9u78/ioqjRv4L/aUqlKKvsesoeERWQniNCgLZvSoIKtMK2ijo6+rS8zbug4PaLtp226Pz2t0+KC7Yhtq3GQdnldaERFQBZplgQSthCSkITseyWVWt8/Kvfm3lu3KomoRcLv+w8kqeUuzz3nnnvOeY5edXkR4fOUQzYWFWSI+yNd06q6sUu8nmZNSBbLgMr6k3C7vYvOT8iJxdEzzdhVVIuOrv4H+G4PsOdYHcJMBnkvu6LcDGQwoyHoh8VkExdJsgnlhMaMREvAzEBqEzn90Wk1cHu8q8/PmZAsNl6kaztoNd5yQy1ZgU6rESsIYQKqv4QGatulTLIQaLtnjksEAJ91J5S/U6PXaTB/Wppsu7KSLDhb539NFuXfk2PkDaDxmdHotjllr8lKsmDl/LwB18F5/ZPj4mdpNBBf529Sqr9JqP4SVgDqa+xIJ9l7h4/BZ3iPWnIFgXKS8UDnTHru1RIMCNsM9E84B9R7UZWTiQEMOs6ljSspf42pwaxlVN3Yhc8PnPPO0fEAWp33eDoV26PVAI/9YqpsH9X2LyvJAmuvQzbXQ3p+Ad8JxIHOlfK1gSa6D2a4iPRcCRO2lfvqbxK6Wpzo+obCqJ0X6ecA8vOsNoFbA2CRYkL7UNaYGuxrpQtjC9+rNgz2FwvzxXgRrjOnZFu1Og08fQ8KBqLTeTN7Kl8qbONAiRHU3gP4xoOm77uk5QEAv4lBbluU77e8ykoa/GKnasOo3/n8lGrZrEyQFGi0hcfTn6xEGotqa15dd2WmT1ISoD8OlWWzcqjzbYvysetIbcD6RI1Oq8GC6WmoauiExRQiq8ekfxMaRoHidCgx7O9aV4vnrCQLbA6XLFsg4K0P77hurE/9A8An+cb4zGg0tvf4zXio/IwPd5WrxrBa/aP8LmHflXWdcL3ptMDaf5qKF7YUo6Pb/5xG5fELlCQnUNIm4XoX6lplUgp/dZO/z1det4N9H/0wmGximJBO0NUAmJIX75PCV/qkU20ipz8ucYKmB+1WO3JTI/H85iLZa4QKw+F0Y6uiAJY+ZfPA+1T5q8M1stcIE0TVtks6IfKzfZUBt/toeTMcim62wa474XR5sLOoVva7yoaugO+pbpKPL65rlVckJRXe3g/lZ+45el4sRJXr4Didbny4uxx3XDcWJ6ta8bed5bL1cvzt/66iWoyKD5f11r3z+SlU1Hf2P8FyyyeXKidMb9lxBqlxYZJt6/8uaVKRQGtPeOCNgze3nkCv04WoMGPABw7Sc6+WYMDp8uCzfZWoaepSraCklJOJm9ttg45zf69S9q4KTw9PVrXKUnPLJiRLEoB8dahGPP7+tsXtAT7cXY74SFPAm7/qxi688shVYs+c2rXsdHlkSQICnSuny4MPd5dj2exsAMDxKnkyEmECfVlN+6CecArXarfNiZ1FtaqNAH+T0NWu/UA95tIJ54D82LrcvhO4PQDMofIqa5fien/vqzLMm5SKPxQexvG+nkudTovRqZFobO+RvVYtWcCOIzU+x0ntVLo9wKd7KiQT5z0+71FrGPnj77XCNm7ZcUb2e2VCA7X3APAZ0uQ9d/JrDIDf6zJQeTWUBoVaAgpl2StwOt2yCf7+CNeYx9NfJgq9Z2prXvlbf0wo75SNLJ/kOycbUFk/9IWShVhu7bJDp9X4/K2upRsP3TwZ/7z+S9WG3gc7yzFvUio2flSCb0/4TyQk+32ANaDUzqa/c1nf1iMm8AD665/mdptPw+bs+Q5oNBq0qqwv5j3/3s8Qyjd/DQVpuSDUVWrzv30TyvTvmdvj/c5AjSgA+N++4arCMZTW60pCWfP5gXPo6nHI55JB/v1qPWAD+fzAOfGBpLIcD2RXUa3YQ72vtA7xkSasuCqXCSh+RGxIXST8TZL1t5p1RIDkEIEcPt2EHUdqEGEO8TsGd6CL3gOIc3YEwtAKteEymr7f7zhSI04a9ychyuRTqCdEeYdxDKbiVu5TRkJ4wPcpJxjHhBt9KmHl8UiMMmFXX2MH8D6tENbBEW6sSs+24tS5w1h5zWjZeVUOP5CqrOvE+rcOijefO47U+rxGOTG2u0e+v1ab02+2LQ/6k4oESi4iONc3iVVIjOCv81p67tUSWWglyQIGS6v1Dknd8nX5kN6nRjrRW+id1eu0PpPpz56XTEjuG+668aMSnBugMS4oPdsKra4NWg3gr+3ncHnw600HZCmAV14zGjqdRrzR0Gnhk7p3oO89UXUIGsCnwRvXN0/spfePDmoftBrv5OWBen/VJqGvvGa0z4R84b5RrUGmTFqiTO+rjG2dyjpsyhvb7l4X7v/j1+ju7S+fXG636k2b2rBptZtttbTDANDU4X9BWo2m730DDL0G+p9mqzWm0hMs+EPhYZ9yLVAvl3S/JmTHys6l0CMlnWRf3eg/vqWJitQMJvEOoF6/mQ6cU71p9QCyoXz+CLHlLwmNVKzFqJqUZCgCld0Daeiba6N2W3/4dBPu/O2Xft/b0e3w6WET+Bv6r0xaJeUvntVEh4X4nLswkwEf7D7r81rhmlNLBNWtiF+1uk3cPvgmZlKWK4A8oYyyzhHKb7NRJysLlGx2l1g/jIoPx85i/9tVcb4TZ8/7NnR9tn8Ix1fqfHM3Nn9Vhu0Hq4c0UqqyvkuWiKu1047fvnVINiebflhMNhGkgY3KZBMxEaGw9TrRbu3F7AnJmHN5CoD+hAhSbrcHPXb/hcNAGlp70NRhk12soSE6nyE8ahKiQ30q9cmj43Dz1aPF/fhkb4WsotdogDsWj/WZvBltCcGDN0+Cw+lGa2cv8kZFos3a6/P5qfFhMBi0svdKPyM9IRwRYSHiZFZhO5fPy8Hti8agvqUb9a09PoVThNmAgnGJyEyywBiixeKZGUiNDwv4tFenBdISw9HQ4n26rQEwMTcO4SYDpuTHw+lyo6nN5i3UPR7kpEZi6ewscYLspk9P+Gy/0Cj1YOAC+BcL8jAlL178+d0vywZ13gDveYgON6K6sQt1zd0+T6vVzq3AaNBh9oRkhIXq0W1zYlRcGFLjw2STg2MiQmExG8SJuKPiw5CZZAE0kE28HYyfTEyBXqcNeC6ULGYD7A55bavcJ/H4ejyw2pwBn3rnpkZiT98ck0HzAIkxZllyEqWObgfcfal5hRhZveQyOOxOZCZZkJFokSUJyEmNxA0/yRZ7ivx9r1rdm5EUjlmXJWPzV2U+N+nJsWY4XW5Z/ERZQtDRbR/URGeX242Wzl7ZdiZEySd2z52UgiiL0Sd9ucWsx7jMGNy+eIyYjKD4TJMsTpQxM3diCq6aMkr8+anXD6DX4XtuHANcDwnRofjJRG/5qkxKYne6UVTW3wM+PjMacyam4IafZMPhdA8qQ1ZyrBkLpqf5HIvIMINse4Wya0J2LFZek4c5E1NwvKJFduNnMRtQWtk64PBAo0ELg16LafkJWD43R/z91PwE1Ld0o6ndhuTYMPzyxgkwG/WoaeyCTquB0eCdk3vCz3U2Kj4MK6/JQ31LN9qtdp/tGEziHUA9AcX7O8t9Pm98VrSYBGkgcyd5kwV4PB78/dsqnK5uVy1nhDlSTpcHe47ViWVAtCUEEWZDwFi/bVG+mAQBCNwTqBQaokN2coTqcRsqZX2v1WrERdvVEusok1bpdBqMigtDTmokkmLMGJMeBQ28ZVGgOic7NQJLrsiUnbtj5c2yh05qqhutKKtuw7HyFmz67ARqmqzodQzunmW8JBHFExv3Yeu3VYgJN2JMRjQcThfMRj3GpEeLc4KVCTIEuamRGD0qalDnrMNqR3lt+6Aa8IEIiTSa222D7o2WbUd3b8C6Q43H4028pcy2KCRGogs3ULIJNqQukobUjiM12PJ1Oaw27yTXyPAQZCZFQKvVYF9JPTzf41yurh6HT6NCg8BPOQHv/IkbfpKDo+XNsuO2YEYaMpMixJ9PnWvzKdQiw0OQEhcmu0n5+dW5mJIXj6n5CchLi8L/21OBTpWu+MUzM2DQaVFWI+9p0Wk1+NefT8LS2VlIjQ/3PpHxeBcYfmD55WKDY2p+AsZmROObY+dl2+10eVBa2YqKuk7ced04TMmL9x7v0nq/T4Q8HvRnNNJ4nyQ3tPagtKIVpRWtWDAjHaUVrWKKVuGmIS8tCo++tMencLU7XbJtEuaq+VNa0YqxGdGIiQj1JotQDLFUo+17Oq7VanCuoQulFa0+jSitBrhxbg6OnW3xu15FTZMVd143Dr9YkI+5k1Ix67Jk2Xkvq2nHKx+VoL7Vu6L7LT8djaumjIIHkJ33wQgL1ePKy1OwW9Lz54/wlN3ucPkeX4dLNa71ei0m5cahOsCNcWaSxSdj3GBYbYErwsxEC7psTlmMTB6TiNEpEZiYG4cwkwH7Sup9YigzOQJ7SvpvBHVaiEOFtLq+dOGKfW1ss2HZ7CwUlTX57Id3WIz8DZNy45CdEulzranp7LZDo9F456n0TSBPS7RgT0kd0Pe7ORNTsP1gtU9M2R3etaGyUiLEGFKLE43GG5t6vRa3/HQ0Wjp7sbekDq9+VII2q/p5MRt1fhtTBr0WN/wkGx99U4GSihbsK6kXryfAm8mtvqVbjIu2Lrt4/KfmJ8DucMmOjbenVv4dN87NxoLp6d7zKCmTVlyVK5YNGi3Qa3ehuaMXNU1WXNmXNCQrJRJ7JOVUXUv3oG7CXW4PnC4PqhutYt0hmJqfgOuuyERaQjg+21eJXcXn4XB5s6yV1bQjMizEm5VN5YuqG7tgNuqx/3iD6nYsnpkh+65AYiJCkZcWhZiIUGz+qgwnz8ljbOa4RCyemYF9pYOr71bNz0NxWTMOn26Cw+lGh9UuG4at12nw2C+m4pafeh/y7S2pQ6nkptpmdwVsRBl0Gjyw/HKxnNNqNT51SCDOvqH0Ho/ngu8zYi1GWWNqXEY0DpxsVI1hwPeBwKIZ6Sgub0FNkxV1Ld6HaG1dA9//COdXOHctnb14+4vTg9qfxjYbqhutcDjdg2pECde5tBElzDHusbugAfDPPxuPrw7XoLbZKu53bmokEqLNsrJR31cexUWasKt44Dqks9uB+lb50F+txjsns7K+E/CoD73VSSpsjWT7S8pbvtPD7p9MTEFFXacY/xrlvAJ4Y8EuGT6u12kweXS8rC7TajW4fk72kDLXkn9sSAVwMTWklL01ynSuCdEmnKltl81X0kBYV8H/92g03h4jjcb7dNNiNqg+8XB7gH+/daqY/jk/LQoJ0ab+FOnwXuTLZmfLUldr4Js6fdZlydj+D/mwDafLjVXX5PlNc6rsebOY9YiNCBVTGVfUdcieLCXHmnH/8st90okHSscsTV+blRyB8y3dPilIpcc7MiwEdqfLby9AVrIFl2fH+vQeSHugpNuhNhxCee7mTkpBZpIFmUkWrJqfh+qGLrRLbxgl27q3pE62mGF0eAhyUiMwLT8BvXanmMb71kVjfNMhK0RZQvAvSy9DRFgIOqx2ZCVHIDPJIn/S1ffdtc1WbPn6DOxOt+wmyl86eWl622n5CbCYDWho9b/AKuCthB1ON27r61F0ezy4anIqWjtsPhXU+KxojE2PRoXK/AW3x7enSpjMvnxujiy1rsvtljU2rDaHOLwR8O21TY41Iz4qFCajXvWa0gCYNykFYzOiUVnXAQAYlxmNx34x1SdWpeWBv1jeW1LnvRFH//V4yzV5iI8yYdnsbFw5IVn1pmHZ7CzMnZSK4rImtFvtCA3RiWt4KevpgnGJuH5ONuwOF8pr22XhGRaqQ1ykqX9fPf3/aLQazO7Lsrfn6Pm+Cd8aRIaFBHx6Le3RyEyKwDfFtbIeGaF3Z2lfmu3fv3MYJYpeG4FBr8XY9Cg8c/dMn/LHu/16PHjzpICpt8tq2vHuV2WyOYnSJ7tq6ZgTok2yXm1pD630PE7Ji8fYjGhoNRpU1nWKDROPx4MESflTWumdg6JGr9MgMjwEV01OxawJSTjfbPVpDHRY7ZirGG4nDBGvUZmXZHe68MDyy8UlETqsdtl10K4YJRCi1yIh2iSWzYK7fvslPtx9Fh/tPquaFl1Kmd7ZaNDiyTtm+CzP4HS5fR7KWUwGPNyXiEC5JEZoiLfnXJpWXyA8lBzMsCmDToNXHrlK9ruYiFCfZRsG4vb4H2VgNGh9svwJZo5LhNXmgNvjwfQxCXj81qmob+lGa2cvpubFIzslImAKbWVK8fLaDtVzD3jLgKxkCyblxmGVYgkM5bBNZRmk1/k+vPEnRK9FemI4spIjcOd1Y2Vl1b/fOhWd3Q6EGLSIthiRmRThs4SI1eZAQpRJdb+F+l25pEJMRCj+/m2V31Eb0ZYQjM2I7k8KJTkWK+fnYc7lKeI1bLPLH6oJ9yBCrOb21f25qZE4erZZFrfJsWYsnJEmvk5aXkzMjZMtOSKNf+8csTZZOdzrdOOxf5oi29f509Ngd7jQ3NGD9IRw3LN0/EUxrO+7LkdysWH68+FCcZ2rpXNVzl0oGJeIe5aO98nwkpVkQXWTVRzTvHhmht8sb1K5qZF46q4C8eeymnacOncYLpc3Re2OI7XYW1KHh26ZjG+O1QVc9HD5vBzVlOHStM7S1MHKcdgPLJ/os+isQe8dC63RajB/eppPQeEv5a70ex5Yfrn4u2NnW1T3Qfk5v950QHWe1bmGLsyZmAL9MfnYf+G9QmpUIUGIMguUysMm2Owu2djyX62eLt4EKbdVeczuu2GCLGWs8tgI6ZBdLrfPMMKZ45JQVtMuyVpnxSMrJ+PqqaNk391tc4rzlpSLSwZaDFN63j/ZW4EjZU0DDn04Wt6Me5aOF7PhCfulPB5CooVvVIbhRZgNuHJCsiyBgMPpxjvbT2NUfLiYlh3wXhuBxsAnx5pR3dh/Xd1x7VhxGQCfhSD7epOE9OTStO7C+QhU0an9XXl8hc+W3SwqYkw6t13IsCY9fpq+bHPKhSmFlMPS47zmpkkAIP4OGo345NQjmYQu3LQIN6xCmns1yjke186SZ1aTpjT/ZG+F30QeQnaxk1WtKKtp9yl/ACA1Lkz1OErj1DuxXf4Fygcp0pgBMKTzKE6eV+yDdBvmz8iQpaCWxpMyU+Ko+HA8+9eDsmMSFe6blvxkVSucfoaoTs1LkG2nkAnN3wLft1wz2ifTmTSrpAfeRtVrfdkypWnaq+o7caq63WeO79WSIZvKbRHqILX9V84Bm5Qbh9sWjZF9tnQ5gEdWTsZn+yoDztmUZrpUmjUhGd8cq1Nd0HwoDHrvkh3KhwwGSba6+HgL9h6pFmNaWi+U1bQPuPCwUOYGmpvcn022v9EppPdWo7x2lHOMA5mSF49oixEHTzXg8KlG/M9jV4t18+FTjeI2CmWwxWSQJYtIijYHvHb9lamTcuN8MhQD3nlY910/AQBk9wIrFQ1w4XPDTAZZ/SDcg6h9p3I+ntrSDFLSvwmfueNIDU5UtSIuSr5ERUZCuOr3KsulYFPL1HkxNO5+COyRugh6pDZ/VYY9Jf0X+vjMaKy6Js/nPcJ4d+GplFCwJkSbxSFQGg1w3w0TcOWEZNkTbeHJQHZKJLJSInD6XJts+Mv4zGifse7C08FvjvYPZ3C5Pfj2eD0eXjk54KKHAy3iK1xkJRUt2HOsDloAsy9PFnt01HqULGYDjpW3wOP2yIa4BSL9HukQiEA9WN5hlv09LsLT/I5uB8JDDbBLbkjUeqCE7zx2tgVFZc04UdmKfaX1WPtPU7zHEt7C/LXHrsY3R8+ju7f/Rk1tcT1h3x0uNxbMSBeHLAr7UNfc7S2stFqMz4rxeyyk+3z9nGyEmwyyJ+tqPUozxyXJjtO3x+v9Li6pdkyFuKtttqLkbAu0Wg3iIk3i0DWtTgOtxnd4FABM7Rv2GWg/lAt+JkSbUFnXAYfLgwizAc/93zliL8L5Zqusd2138Xl8sPssPt1bgZ9dmeUTszmpkbLhMUtnZ2FxQYZPzCjfd+3MDNV4EGK9rbMXYSaDLHb9LdAt1dLZCw2AjCQLbvnpaNU1nOKjTOJ8F60G+PNa39Tv0uMn9GSpXQcDLaw6bUyCbBjrlPx4fLy3sn/ooU6DW67JQ2ayBcfKvb3NBr0WC6en+fTeCAKVG0KPgjBULjosBDaHS2xESa/zxQUZyEqJQHVDlzclsQdo7rBhX0k9rpyQ7HeftVqNz3BSY4h2UPOABkttiJh0iOPE/EQYtPAbT1IxEaGIDA9Bcd9aWzqtBquvHetTLtY2W2WxbDbqEGrU46dTRsluvoQb2yn58WLZFmiBbyG21RrKy2ZniWm4hSHrjW02cY5vrMUIS5gh4ILWA4008FcnCoSHjG63d3Htlg4b/vXnk2Q95BNzY2HrdcJqc6ouRKy2PdIeg8wki9+hsFotZENuYy1GXHuFdxHuaItRdk4mj44T5wwC3mQ/v379W1m50drVi5KzLYiLNPmNYem52VtSh/2l9T49KdPyvY2a/LQo1bJEoKwLledjyaxM1Ld0o6G1B+EmA27+aS6cTre4qHNOSqR4bqItRlks1Ld0472vz6CkogWnFQmSTlS2ynqdI8wG/P7/XCn7fmFuXHVDl9+kG4BvjAgjNPwtFu3vWAx0T/NdX+vvuP9l60k0ttlgtTkRazGi1+lGZuLglxwItr0ldSg529L30La/1304GqhHiutIXQTrSAXK2ENERERENBg6rXzpiRCDFlNG+z5oAOCT5XTy6DjUNHaho9uBSblx4nuEJSVMIXosn5cjG1n03ldlaGzvwcxxSeJDEeVaZsN5vauB1pFiQyrIDSk2ooiIiIjox5IQHYqmNtuAc9wizAb09Dp9Evgoh5ELYi1G/Mv1l6kuIi+kozcatMgdFSkuQn2xG9EL8p49exaPPfYY2traEBUVhfXr1yMzMzPYm0VEREREdFEabKp3f4sa+2uANXf2qjaigP61xnodbpScbfWZZz1cDeseqdtuuw3Lly/HsmXL8OGHH2LLli34y1/+Muj3B7tH6sX/PSzr+iQiIiIiulQYDVq89NA88WfpSK3/ecx3nu+PbaAeKe2PuC3fq+bmZpSWlmLJkiUAgCVLlqC0tBQtLS0DvPPiwUYUEREREV2qeh1u3PeHHQB8cwYMh+kvw7Yhdf78eSQmJkKn867MrtPpkJCQgPPnB158jYiIiIiIgq/Xob48w3AwrOdIXahAXXVERERERPTDMht1iI+3qP7N3+8vFsN2jlRzczMWLlyI/fv3Q6fTweVyoaCgANu2bUNMjP+1dOSfwax9REREREQ/BJ0WiI0MRWtHLxwuD4wGLeyO/kWth/scqWHbIxUbG4uxY8fi448/xrJly/Dxxx9j7Nixg25EXSykQSIshthtc6KqoRPpCRaYQ/UIMxmw71gdzrdYERVmRE5qJNKTLKiq865DNWtCMnJTI/GHwsM4ca4NESYDJubGYdaEZHx5sBpHy5uRmWRBiEGHtq5ezJmYglHx4fhsXyXqWrqRFGPG4pkZAIDP9lWirasXY9KjYQ7ViyuH7+lbpFL6vTa7C+Xn25GdHInU+DCEmQyw9jjEf2sarSg/3474SBNaOnsBADEWI2qbrYiPNGHFVbni4ndPbNyH8y3dAICEaBNS48IQGRYi7sOhU40wGfVYNicLo+LDsefoebRb7ahp7EJblx1hJj2iwo3ivp2sapVte22TFQ6X2+fvwveX1bTL9tHa40C3zYkjZd7V1jMSLejssWNqfoL4/jCTQTwWyvMhPWbSn9utdkRDryg0AAAS+ElEQVSGhYjfAa0Wh07UIyrciAk5seLxU55bAPj1pgM4W9eJ0BAdkmPMaGjrwYTsWFw9dRROVrWiud2Gr4tqxYU+w0L1ssUuhfgSzo90/+ZPTxOPa22TFZ09DlhMBqTEhYnbeuhkI6oauxBrMWJqfgKOlDXB2uOAQa9FeqIFE3JiUVXXiXarHdYeh3i8AeDzA+fQ0mGD0+WGTquFXqfBqPhwzLwsSdxXk1GPE1WtiAo3YvHMDLzz+Slxf6+anOoTj8KxNBn1qGroFFOpbv6qDAdPNWBqXgIm58XLYkHY/6NnmsVrobG1BwdPNYhxrHyttceB/PRofHmwGkfKmhAfZcJVU1J9zlFZTbt4/SRGm1FZ3wm7wwWdVgOHy42Z45L8bg+0WoyKNYufI42d6sYuHDzZIO6fcB6F+FX7XuGcAsCuoloxvpQxqoxj6b4I31Hd2IVdRbVwOt3Q67Vi2dBtc+JEVSsMOi1S4sJU35ubGokdR2rwwc5y2Bwu2Vomm78qw77SOsRHmjDzsiRZ2SE97rmpkdj4UQmOljdjQnYs8tKj8PmBc6hr7oYH3ieZWSkRPql0pcdRiC1/2yr9PmV5J8SK9JwK8b51XyWaO2yIMIcgM9m7oK61x4HmDm9GLOF1avuiVia/91UZWjp7kZsqL1Ol50G6D2rljFqMS4/LjiM1OHiyAfBAVhYfPtUou26UZUGYySCWXcJ1Li3L1Oot6bYfPNmA9AQLenqdsvJYLaaF7RTizun2yOopaT0pPe87jtTg8wPnxBiaPz0Nja092H20Fka9DtfOyvT5Pul+x0ebxGtNrZ4Qjp10Pzp7HOK2qS2SLY0vtbJHeg1I/zZhdALe3npc/Fm4loXz39jWgzO1HYiPMuHWhfmysqO2ySqLQeG4CbHS2NaD09XtMOi1yEuLUi27x6RHo6dvsXi1e4kzNe1wutyIjQgV70mEOPjm6HlYbQ4Y9DpEW4yYlBsniwll3al2zUtjQKgTqhu78MmeCtidbsyekCyWp4FiTVqu+SsblHErPRbC+ZWWn8I1IMSxskwUtjdQrCq3QzgOyvuS6uZuNDZbVWNdiFUAsjpisNSuu4vBxdB4Goph2yMFAGfOnMFjjz2Gjo4OREREYP369cjOzh70+y+GHim6tH2fcVBW047fv3MYLpcbOp0Wj6ycfFEVjuQfywMSMBYIYByQF+Mg+EZsjxQA5OTkYPPmzcHeDKKLQm5qJB5ZOfmifMJERERENNIM64YUEcnlpkayAUVERET0Ixi26c+JiIiIiIiChQ0pIiIiIiKiIWJDioiIiIiIaIjYkCIiIiIiIhoiNqSIiIiIiIiGiA0pIiIiIiKiIWJDioiIiIiIaIjYkCIiIiIiIhoiNqSIiIiIiIiGiA0pIiIiIiKiIWJDioiIiIiIaIj0wd6AYNJqNZf099PFgXFAAOOA+jEWCGAckBfjILgGOv4aj8fj+ZG2hYiIiIiIaETg0D4iIiIiIqIhYkOKiIiIiIhoiNiQIiIiIiIiGiI2pIiIiIiIiIaIDSkiIiIiIqIhYkOKiIiIiIhoiNiQIiIiIiIiGiI2pIiIiIiIiIaIDakfENc6JoBxQF6MAwIYB+TFOCABY2F4Y0PqB+RyucT/80K5dGk0mmBvAl0EGAcEsF4gL8YBCVg3DG+6devWrQv2Row0H3zwAX71q1/h5MmTaGhowPjx43mhXIK2bNmC3/72tzh9+jRcLhcyMjKCvUkUBIwDAlgvkBfjgASsG0YGNqS+Z3v27MFLL72EtWvXIjExES+88AJCQkJw2WWXwePxsMC8RGzduhWbNm3CmjVr0NnZiS1btsBkMmH06NFwuVzQatkZfClgHBDAeoG8GAckYN0wcuiDvQEjzZ49e7Bw4ULMmDEDAGAwGLB27VosWLAA0dHRQd46+rF8/fXXWLFiBa644grMmDEDCQkJePbZZ3HttddCp9MFe/PoR8I4uHTZ7XaEhIQAYL1AXt988w3jgACwbhhJ2OS9QO+99x4OHjyIxsZGAEBaWhp27twp/n3u3LkYP348/vjHPwIA3G53ULaTfljbt2/HmTNn0NXVBQDIzc3FkSNHAAA6nQ7Lli1Deno642CE27hxI3bs2CH+nJ+fzzi4BG3cuBHr1q1DZWUlACArK4v1wiXojTfewJYtW1BUVAQASE9PZxxcoj799FOcPn1avEfIyclh3TBCsCH1HZ04cQLXXXcdPvvsM7z33nt4+OGH0dvbi9mzZ8NiseDTTz8VX/v4449j586d6OjoYHftCLN9+3bceOONKCwsxPr167Fhwwa4XC7k5eXB4/Fg37594mvvv/9+HD58GFarlXEwAh0/fhzPP/88nn76afF3aWlpAMA4uAR4PB7U19fjjjvuQFFREe666y6MGjUKADBx4kRERESwXrhEHD58GDfeeCP279+PqqoqrFmzBna7HQUFBYyDS8zRo0exYMECbN68Gc899xx+97vfwW63Y/z48XC5XKwbRgCeqe+ouLgYS5cuxWuvvYannnoKZrMZjz/+OFJSUjBz5kx8+OGH6OnpAQCYTCZMnz4dTqczyFtN36fi4mK8/vrrWLNmDf785z9j1apVqK+vR1VVFfLz85GUlITt27eLcQAAqampMBgMzNI0AplMJqxZswahoaFYv349AGDMmDFITExkHFwCNBoNzp8/j7S0NGzYsAE5OTniOU9LS8OMGTNYL1wCXC4Xtm3bhttvvx0vvvgi7r77bsyYMQNutxvx8fGMg0uAtFzftWsXVq9ejddffx0PPvggmpub8bvf/Q5XXHEFEhISWDeMAGxIfUeHDh1CXV0dACAkJATr16/Hrl27UFRUhBUrVsBoNOKRRx5BbW0tXnnlFXR2dsJisQR5q+n7lJqainXr1mHu3LkAgMsvvxylpaXQ6/VISEjA7Nmz0d7ejqeffhp2ux2ffvop3G43QkJCOKl4BCopKUFHRwc2bNiAN954A4A3RhgHl469e/eK/3/qqafw6KOP4sknn0R9fT2WL18Os9mMhx9+mPXCCOXxeKDT6VBaWordu3fDbrfj6aefRnFxMV577TW0tLQwDkY4p9MpK9ePHj0qTv3IycnBI488gi1btqCqqgo33HADWlpaWDcMc8zaNwivvfYa6urqMHr0aNjtduh0OoSGhmLr1q2YN28ezGYzjEYjuru7UVhYiFWrVmHu3LkoLi7G3/72N4SHh+OZZ55BaGhosHeFLoA0DgAgNDQUcXFxALxPIZuamrB//34sXLgQFosFo0aNwuTJk/HFF1/g7bffRkREBJ588kkYDIZg7gZdIGkcCOPYNRoNKisr4Xa7MW/ePJSUlOC5555DcXExfv7zn6OgoABffvkl3nrrLcbBCCGNA4fDAZ1OB4PBgHfffRclJSWIiYnB6tWrceTIEbzxxhu4/fbbMWfOHBw9epT1wggijQMh29rYsWNx+PBhrF+/HhkZGVi7di2+/fZbbNq0iXEwgr355pt48cUXce7cObhcLqSlpaGnpwenT5/G9OnTERoaiqioKLS1teHLL7/EqlWrMGXKFN4jDHMaD/sPVXk8HjQ1NeE//uM/cOTIEVitVhw6dEjMwnTu3Dm8+uqrSEhIwP333w8AqK6uxuOPP47f/OY34tyI7u5umM3moO0HXZhAcSCkq3W5XNDpdPj73/+O999/Hy+//DIAiE8Z7XY7ent7+cRxGAsUB263G1qtFm+//TZsNhuuvPJKrF27FmVlZVi3bh1WrFgBwPuksqenh3EwjA1UL9TW1uKFF15AcXExPv74Y/F9s2bNwn//939j2rRpAFgvDHeDKQ8qKyvx/PPP47/+67/E9xUUFGDDhg2MgxGmsrISTzzxBCIjI3Hbbbdh27ZtaGlpwX/+53+isrISmzdvxqRJk3DTTTfB7XZjz549eP/99/HEE08gJiYGLpcL3d3drBuGKQ7tU9HV1QWNRoPo6GjMmTMH+/fvx7Rp0/DMM8+Ir0lJScG8efOwe/ducTjH6dOnkZycLDaiALCQHMYGigPhGYSQqvTIkSO49tpr0dXVhUcffRSbN28G4B36yQJy+BpsHOj1erzxxht48MEHsXbtWtx777147rnnxM/R6/WMg2FsMPVCUlIS5s+fj7a2Nhw4cAAAcObMGUybNk3syQZYLwxngy0PLBYLmpubcfz4cQDAyZMnMXXqVGRnZ4ufxTgY3oQMfD09PVi8eDE2bNiAgoICLFmyBO3t7XC5XBgzZgzy8/OxZ88eFBcXQ6vVoqGhAWFhYYiJiQHgvYdg3TB8sUdKwul04k9/+hMOHTqEqVOnYvbs2eKTo4qKCixevBiffPKJWBA6nU4UFhaisLAQY8eORXFxMe68807cfPPNwdwNukBDiQO32w2NRgOHw4F77rkHNpsNdrsdixcvxt133x3kPaELMdTy4MCBA6itrcWyZcvEz3jzzTdx6623BmX76fsx1Dhwu90oLCzEhx9+KA7xuuGGG7B69eog7gVdqKHGQX19PV599VXs378fV1xxBb799lssW7YMd9xxRzB3g74H0lgoKCjA6NGjsXDhQjidTuj1enR1deG2227DSy+9hMTERDQ0NGDLli344IMPUFBQgL179+KBBx7A0qVLg70r9D1gQ6rP4cOHsW7dOkyfPh1LlizBjh07cPToUWzYsEEcu/zEE0+goqICb731luy9ZWVlKCkpwZw5c8QnDDQ8fdc46OrqwpIlSzBnzhw8/PDDiIyMDNYu0PfgQsoDQL4YKw1fFxIHFRUVOHbsGGbNmsV6YZj7rnFgt9uxdetW1NXVYcWKFYyDEUAaC9dddx127tyJoqIivPzyy2KZ/8UXX+Ddd9/Fxo0bZe/9xz/+gbKyMsyfPx+xsbHB2Hz6AeiDvQEXC4PBgJUrV+KWW24B4H2qeOrUKbS1tSE+Ph46nQ5PPvkkZs6ciUOHDiE8PBzl5eVYtGgRcnNzkZubG+Q9oO/DUOMgLCwM586dwzXXXIN3330XiYmJQd4D+j58l/KgoqICCxYsgMfjYSNqhLiQeiEzMxOZmZnB3QH6XnyXeuHs2bNYtGgRex1GGGUsAN5hmy0tLYiLi4Ner8ehQ4cwefJkAEBRURFsNhsKCgowbdo0sReTRg42pPrk5+cjOztbTCBgNpvR0NCAuLg46HQ68eZo9erVWLVqFdLT0/Hoo48Ge7Ppe3YhccBG1MhxIXHAtLUjB+sFAhgH1E8ZCyaTSRYLANDR0YGkpCQ8++yzKCoqwr/9278Feavph8SGVB+DwSBLOXns2DFkZ2dDr/ceIo1Gg3fffRcffPAB7rvvPqxZsyZYm0o/IMYBAYwD8mIcEMA4oH4DxUJbWxvef/99pKSkYPny5SgsLAzWptKPhA0pBSGVdUlJidgFu23bNowdOxbjx4/HW2+9heTk5CBvJf3QGAcEMA7Ii3FAAOOA+vmLBb1ej4ceegiLFi1iLFwimP5cQeimb25uRk9PDx566CH89a9/hc1mw2WXXcYL4xLBOCCAcUBejAMCGAfUTxkLDz74IAoLC5GSkoI77riDsXAJYY+UivLycmzbtg11dXW46aabcNNNNwV7kygIGAcEMA7Ii3FAAOOA+jEWCGD6c1VdXV14++23sXr1ambfuoQxDghgHJAX44AAxgH1YywQwIYUERERERHRkHGOFBERERER0RCxIUVERERERDREbEgRERERERENERtSREREREREQ8SGFBERERER0RCxIUVERJeMl19+GU888USwN4OIiEYApj8nIqIRY/LkyeL/e3p6EBISAp1OBwB46qmnsHTp0mBtGhERjTBsSBER0Yh09dVX45lnnsGsWbOCvSlERDQCcWgfERFdMv70pz/h4YcfBgBUV1cjPz8fW7Zswdy5czF9+nS88847KC4uxs9+9jNMmzYNTz/9tOz97733HhYvXozp06fjrrvuQk1NTTB2g4iILgL6YG8AERFRMBUVFWHbtm04cOAA7rvvPsyZMwebNm2C0+nE9ddfj0WLFmHGjBnYvn07XnnlFbz88svIyMjAxo0b8dBDD6GwsDDYu0BEREHAHikiIrqk/fKXv4TRaMTs2bNhNpuxZMkSxMbGIjExEdOmTUNpaSkAoLCwEPfccw9ycnKg1+tx77334vjx4+yVIiK6RLFHioiILmmxsbHi/41Go8/P3d3dAIDa2lr85je/wfr168W/ezwe1NfXIzU19cfbYCIiuiiwIUVERDQIycnJuPfee5n5j4iIAHBoHxER0aDccsst2LhxI06fPg0A6OzsxGeffRbkrSIiomBhjxQREdEgzJ8/H1arFQ8++CBqampgsVgwa9YsLF68ONibRkREQcB1pIiIiIiIiIaIQ/uIiIiIiIiGiA0pIiIiIiKiIWJDioiIiIiIaIjYkCIiIiIiIhoiNqSIiIiIiIiGiA0pIiIiIiKiIWJDioiIiIiIaIjYkCIiIiIiIhoiNqSIiIiIiIiG6P8DDgXhdFfs+3IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "item_hist(df, item=223834)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram of values per patient\n",
    "\n",
    "- For each patient, i.e., `SUBJECT_ID`, get its hospital admissions, i.e., `HADM_ID`\n",
    "- For each hospital admission, plot the entries for each item, i.e., `VALUENUM` for each `ITEMID`\n",
    "- A scatter plot was used as it represents more appropriately the data presented"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get histogram of values per patient\n",
    "def patient_hist(df, patient):\n",
    "    df[['SUBJECT_ID', 'ITEMID', 'VALUENUM', 'HADM_ID']]\\\n",
    "                .query(f'SUBJECT_ID == {patient}')\\\n",
    "                .compute()\\\n",
    "                .groupby('HADM_ID')['ITEMID', 'VALUENUM']\\\n",
    "                .plot(x='ITEMID', y='VALUENUM', kind='scatter', figsize=(14,7), title=\"Values per patient\", c=\"b\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "patient_hist(df, patient=2090)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram of frequency per item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get histogram of frequency per item\n",
    "def item_hist(df):\n",
    "    df[['ITEMID', 'VALUENUM']].compute().groupby('ITEMID').count().hist(figsize=(14, 7))\n",
    "    \n",
    "    # display options\n",
    "    ax = plt.gca(); ax.set_xlabel('Items'); ax.set_ylabel('Frequency'); ax.set_title('Frequency per item')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following call fails due to a known, yet *under-documented* [issue](https://github.com/dask/distributed/issues/1674)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "distributed.client - WARNING - Couldn't gather 59 keys, rescheduling {\"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 163)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 219)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 203)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 451)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 195)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 275)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 299)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 331)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 19)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 131)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 115)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 251)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 67)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 51)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 27)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 435)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 371)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 35)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 419)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 139)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 427)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 459)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 187)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 363)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 291)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 179)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 227)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 75)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 387)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 347)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 355)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 59)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 147)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 3)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 323)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 99)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 339)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 403)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 171)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 379)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 211)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 443)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 395)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 315)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 267)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 307)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 243)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 83)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 43)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 123)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 283)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 91)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 155)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 411)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 259)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 235)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 11)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 467)\": ('tcp://10.52.4.3:44349',), \"('getitem-2b1aa818345efaefa437f7ff9ab330ed', 107)\": ('tcp://10.52.4.3:44349',)}\n"
     ]
    },
    {
     "ename": "CommClosedError",
     "evalue": "in <closed TCP>: Stream is closed: while trying to call remote method 'gather'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mStreamClosedError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/distributed/comm/tcp.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, deserializers)\u001b[0m\n\u001b[1;32m    179\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m             \u001b[0mn_frames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_bytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m             \u001b[0mn_frames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstruct\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munpack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Q'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_frames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1132\u001b[0m                     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1133\u001b[0;31m                         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1134\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mStreamClosedError\u001b[0m: Stream is closed",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mCommClosedError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/distributed/core.py\u001b[0m in \u001b[0;36msend_recv_from_rpc\u001b[0;34m(**kwargs)\u001b[0m\n\u001b[1;32m    579\u001b[0m                 \u001b[0mcomm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlive_comm\u001b[0m\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;32m--> 580\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0msend_recv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcomm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcomm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    581\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mRPCClosed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCommClosedError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1132\u001b[0m                     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1133\u001b[0;31m                         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1134\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1140\u001b[0m                         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1141\u001b[0;31m                             \u001b[0myielded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1142\u001b[0m                         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/distributed/core.py\u001b[0m in \u001b[0;36msend_recv\u001b[0;34m(comm, reply, serializers, deserializers, **kwargs)\u001b[0m\n\u001b[1;32m    456\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mreply\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 457\u001b[0;31m             \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mcomm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeserializers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdeserializers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    458\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1132\u001b[0m                     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1133\u001b[0;31m                         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1134\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1140\u001b[0m                         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1141\u001b[0;31m                             \u001b[0myielded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1142\u001b[0m                         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/distributed/comm/tcp.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, deserializers)\u001b[0m\n\u001b[1;32m    200\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mshutting_down\u001b[0m\u001b[0;34m(\u001b[0m\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;32m--> 201\u001b[0;31m                 \u001b[0mconvert_stream_closed_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    202\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/distributed/comm/tcp.py\u001b[0m in \u001b[0;36mconvert_stream_closed_error\u001b[0;34m(obj, exc)\u001b[0m\n\u001b[1;32m    128\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 129\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mCommClosedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"in %s: %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    130\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mCommClosedError\u001b[0m: in <closed TCP>: Stream is closed",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mCommClosedError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-b901106d30a0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mitem_hist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-19-76b8dfdd2e40>\u001b[0m in \u001b[0;36mitem_hist\u001b[0;34m(df)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Get histogram of frequency per item\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mitem_hist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\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;32m----> 3\u001b[0;31m     \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ITEMID'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'VALUENUM'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ITEMID'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;31m# display options\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/dask/base.py\u001b[0m in \u001b[0;36mcompute\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    154\u001b[0m         \u001b[0mdask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m         \"\"\"\n\u001b[0;32m--> 156\u001b[0;31m         \u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraverse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    157\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/dask/base.py\u001b[0m in \u001b[0;36mcompute\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    396\u001b[0m     \u001b[0mkeys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dask_keys__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcollections\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    397\u001b[0m     \u001b[0mpostcomputes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dask_postcompute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcollections\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 398\u001b[0;31m     \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mschedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdsk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    399\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mrepack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpostcomputes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    400\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/distributed/client.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, dsk, keys, restrictions, loose_restrictions, resources, sync, asynchronous, direct, retries, priority, fifo_timeout, actors, **kwargs)\u001b[0m\n\u001b[1;32m   2336\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2337\u001b[0m                 results = self.gather(packed, asynchronous=asynchronous,\n\u001b[0;32m-> 2338\u001b[0;31m                                       direct=direct)\n\u001b[0m\u001b[1;32m   2339\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2340\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfutures\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\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;32m/opt/conda/lib/python3.7/site-packages/distributed/client.py\u001b[0m in \u001b[0;36mgather\u001b[0;34m(self, futures, errors, maxsize, direct, asynchronous)\u001b[0m\n\u001b[1;32m   1660\u001b[0m             return self.sync(self._gather, futures, errors=errors,\n\u001b[1;32m   1661\u001b[0m                              \u001b[0mdirect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdirect\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocal_worker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlocal_worker\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1662\u001b[0;31m                              asynchronous=asynchronous)\n\u001b[0m\u001b[1;32m   1663\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1664\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoroutine\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/distributed/client.py\u001b[0m in \u001b[0;36msync\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    674\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    675\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 676\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0msync\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    678\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\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;32m/opt/conda/lib/python3.7/site-packages/distributed/utils.py\u001b[0m in \u001b[0;36msync\u001b[0;34m(loop, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    275\u001b[0m             \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    276\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\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;32m--> 277\u001b[0;31m         \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    278\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/six.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(tp, value, tb)\u001b[0m\n\u001b[1;32m    691\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    692\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 693\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    694\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    695\u001b[0m             \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/distributed/utils.py\u001b[0m in \u001b[0;36mf\u001b[0;34m()\u001b[0m\n\u001b[1;32m    260\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\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[1;32m    261\u001b[0m                 \u001b[0mfuture\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_timeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimedelta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseconds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m             \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    263\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    264\u001b[0m             \u001b[0merror\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\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;32m/opt/conda/lib/python3.7/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1132\u001b[0m                     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1133\u001b[0;31m                         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1134\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1135\u001b[0m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhad_exception\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1139\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mexc_info\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\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[1;32m   1140\u001b[0m                         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1141\u001b[0;31m                             \u001b[0myielded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1142\u001b[0m                         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1143\u001b[0m                             \u001b[0;31m# Break up a reference to itself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/distributed/client.py\u001b[0m in \u001b[0;36m_gather\u001b[0;34m(self, futures, errors, direct, local_worker)\u001b[0m\n\u001b[1;32m   1527\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1528\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gather_future\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1529\u001b[0;31m                 \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1530\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1531\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'status'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'error'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1132\u001b[0m                     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1133\u001b[0;31m                         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1134\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1135\u001b[0m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhad_exception\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1139\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mexc_info\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\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[1;32m   1140\u001b[0m                         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1141\u001b[0;31m                             \u001b[0myielded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1142\u001b[0m                         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1143\u001b[0m                             \u001b[0;31m# Break up a reference to itself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/distributed/client.py\u001b[0m in \u001b[0;36m_gather_remote\u001b[0;34m(self, direct, local_worker)\u001b[0m\n\u001b[1;32m   1576\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1577\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# ask scheduler to gather data for us\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1578\u001b[0;31m                 \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscheduler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgather\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1579\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1580\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gather_semaphore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\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;32m/opt/conda/lib/python3.7/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1132\u001b[0m                     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1133\u001b[0;31m                         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1134\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1135\u001b[0m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhad_exception\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1139\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mexc_info\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\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[1;32m   1140\u001b[0m                         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1141\u001b[0;31m                             \u001b[0myielded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1142\u001b[0m                         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1143\u001b[0m                             \u001b[0;31m# Break up a reference to itself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/distributed/core.py\u001b[0m in \u001b[0;36msend_recv_from_rpc\u001b[0;34m(**kwargs)\u001b[0m\n\u001b[1;32m    581\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mRPCClosed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCommClosedError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    582\u001b[0m                 raise e.__class__(\"%s: while trying to call remote method %r\"\n\u001b[0;32m--> 583\u001b[0;31m                                   % (e, key,))\n\u001b[0m\u001b[1;32m    584\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcomm\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m  \u001b[0;31m# mark as open\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mCommClosedError\u001b[0m: in <closed TCP>: Stream is closed: while trying to call remote method 'gather'"
     ]
    }
   ],
   "source": [
    "item_hist(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Items's values over time for a given ICU\n",
    "- For a given Intensive Care Unit instance, i.e., `ICUSTAY_ID`, get its entries\n",
    "- Plot its items' values, i.e., `VALUENUM` entries, over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def icu_hist(df, icu):\n",
    "    df_icu = df[['SUBJECT_ID', 'VALUENUM', 'ICUSTAY_ID', 'CHARTTIME', 'ITEMID']]\\\n",
    "                .query(f'ICUSTAY_ID == {icu}')\\\n",
    "                .compute()    \n",
    "    sns.relplot(x=\"CHARTTIME\", y=\"VALUENUM\", col=\"ICUSTAY_ID\",\n",
    "                # hue=\"ITEMID\", #style=\"ITEMID\", # size=\"VALUENUM\",\n",
    "                kind=\"line\", legend=\"full\",\n",
    "                height=8, aspect=2, \n",
    "                data=df_icu);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "icu_hist(df, 261926)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning Task"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given the size of the dataset and the available resources, running the Machine Learning task was deemed as not viable within reasonable time - all attemps failed through inconsistencies caused by running out of memory.\n",
    "\n",
    "However, the setup proved itself viable, and for this use case, it's simply a matter of scaling up the cluster be comprised of extra nodes and/or vCPUS/RAM. This was not possible for us given quota constraints imposed by GCP."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# General Remarks\n",
    "\n",
    "Below are some screenshots/videos showcasing the process involved in the development of this report."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Video"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing Task Distribution/Execution\n",
    "\n",
    "The runtime distribution and execution of the scheduled tasks can be **visualized and monitored** through Dask's web interface, which proved useful to debug the processing of long jobs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video src=\"./res/tasks.m4v\" controls  width=\"750\" >\n",
       "      Your browser does not support the <code>video</code> element.\n",
       "    </video>"
      ],
      "text/plain": [
       "<IPython.core.display.Video object>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Video('./res/tasks.m4v', width=750)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Monitoring Workers\n",
    "\n",
    "Additionally, workers should also be monitored, namely to assess whether the current allocated resources are capable of handling the job's load. This helped us reaching the final form of the setup, as we were iteratively adapting the cluster's and workers' specifications to reach a *sweet spot* in terms of performance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video src=\"./res/workers.m4v\" controls  width=\"750\" >\n",
       "      Your browser does not support the <code>video</code> element.\n",
       "    </video>"
      ],
      "text/plain": [
       "<IPython.core.display.Video object>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Video('./res/workers.m4v', width=750)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing a Task's Graph\n",
    "\n",
    "A task's logical execution can be visualized through a generated graph, from which we can identify possible performance bottlenecks.\n",
    "\n",
    "![a task's graph](./res/graph.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Remarks about this notebook\n",
    "This notebook focuses on distributing computation for plots and statistics, for the complete dataset. \n",
    "\n",
    "The [`sample.ipynb`](sample.ipynb) contains remarks from the overall project and also, the ML task code and results. "
   ]
  }
 ],
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