Code/RNN/1. Time Series.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "## Domain:\n",
    "* Multivariate time series\n",
    "\n",
    "## Data source:\n",
    "* MIMIC-III ICU encounters (n = 59,248)\n",
    "* https://mimic.physionet.org/\n",
    "\n",
    "## Variables (8):\n",
    "* Heart rate\n",
    "* Systolic BP\n",
    "* Diastolic BP\n",
    "* Mean arterial pressure\n",
    "* Respiratory rate\n",
    "* Temperature (C)\n",
    "* SpO2\n",
    "* Glucose\n",
    "\n",
    "## Prediction task:\n",
    "* Given first 24 hours of ICU data, predict eventual in-hospital mortality (Death = 1, Survival = 0)\n",
    "* Binary classification\n",
    "\n",
    "## Model:\n",
    "* Recurrent neural network (RNN) with long short-term memory (LSTM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sqlalchemy import create_engine\n",
    "engine = create_engine('postgresql:///mimic')\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, accuracy_score, roc_auc_score\n",
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "\n",
    "from keras.models import Model, Sequential\n",
    "from keras.layers import Input, Dense, LSTM, GRU, CuDNNLSTM, CuDNNGRU, Masking, Dropout\n",
    "from keras.callbacks import Callback, EarlyStopping, ModelCheckpoint\n",
    "from keras.regularizers import l2\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Extract raw data from MIMIC-III database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "code_folding": [],
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 25.8 s, sys: 4.64 s, total: 30.5 s\n",
      "Wall time: 3min 33s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "df = pd.read_sql_query(\"\"\"\n",
    "SELECT\n",
    "    icustay_id,\n",
    "    time,\n",
    "    (case when VitalID = 1 then valuenum else null end) as heart_rate,\n",
    "    (case when VitalID = 2 then valuenum else null end) as sys_bp,\n",
    "    (case when VitalID = 3 then valuenum else null end) as dia_bp,\n",
    "    (case when VitalID = 4 then valuenum else null end) as mean_bp,\n",
    "    (case when VitalID = 5 then valuenum else null end) as resp_rate,\n",
    "    (case when VitalID = 6 then valuenum else null end) as temp,\n",
    "    (case when VitalID = 7 then valuenum else null end) as spo2,\n",
    "    (case when VitalID = 8 then valuenum else null end) as glucose\n",
    "\n",
    "FROM\n",
    "    (\n",
    "        SELECT\n",
    "            ce.icustay_id,\n",
    "            ce.charttime as time,\n",
    "            (case\n",
    "                when itemid in (211,220045) and valuenum > 0 and valuenum < 300 then 1\n",
    "                when itemid in (51,442,455,6701,220179,220050) and valuenum > 0 and valuenum < 400 then 2\n",
    "                when itemid in (8368,8440,8441,8555,220180,220051) and valuenum > 0 and valuenum < 300 then 3\n",
    "                when itemid in (456,52,6702,443,220052,220181,225312) and valuenum > 0 and valuenum < 300 then 4\n",
    "                when itemid in (615,618,220210,224690) and valuenum > 0 and valuenum < 70 then 5\n",
    "                when itemid in (223761,678) and valuenum > 70 and valuenum < 120  then 6\n",
    "                when itemid in (223762,676) and valuenum > 10 and valuenum < 50  then 6\n",
    "                when itemid in (646,220277) and valuenum > 0 and valuenum <= 100 then 7\n",
    "                when itemid in (807,811,1529,3745,3744,225664,220621,226537) and valuenum > 0 then 8\n",
    "            else null end) as VitalID,\n",
    "            case when itemid in (223761,678) then (valuenum-32)/1.8 else valuenum end as valuenum\n",
    "\n",
    "        FROM\n",
    "            icustays ie\n",
    "            left join chartevents ce\n",
    "            on ie.icustay_id = ce.icustay_id\n",
    "            and ce.charttime >= ie.intime\n",
    "            and ce.charttime < ie.intime + interval '1' day\n",
    "            and ce.error IS DISTINCT FROM 1\n",
    "        WHERE\n",
    "            ce.itemid in (\n",
    "                211, --\"Heart Rate\"\n",
    "                220045, --\"Heart Rate\"\n",
    "                51, -- Arterial BP [Systolic]\n",
    "                442, -- Manual BP [Systolic]\n",
    "                455, -- NBP [Systolic]\n",
    "                6701, -- Arterial BP #2 [Systolic]\n",
    "                220179, -- Non Invasive Blood Pressure systolic\n",
    "                220050, -- Arterial Blood Pressure systolic\n",
    "                8368, -- Arterial BP [Diastolic]\n",
    "                8440, -- Manual BP [Diastolic]\n",
    "                8441, -- NBP [Diastolic]\n",
    "                8555, -- Arterial BP #2 [Diastolic]\n",
    "                220180, -- Non Invasive Blood Pressure diastolic\n",
    "                220051, -- Arterial Blood Pressure diastolic\n",
    "                456, -- \"NBP Mean\"\n",
    "                52, -- \"Arterial BP Mean\"\n",
    "                6702, -- Arterial BP Mean #2\n",
    "                443, -- Manual BP Mean(calc)\n",
    "                220052, -- \"Arterial Blood Pressure mean\"\n",
    "                220181, -- \"Non Invasive Blood Pressure mean\"\n",
    "                225312, -- \"ART BP mean\"\n",
    "                618, -- Respiratory Rate\n",
    "                615, -- Resp Rate (Total)\n",
    "                220210, -- Respiratory Rate\n",
    "                224690, -- Respiratory Rate (Total)\n",
    "                646, 220277, -- SpO2\n",
    "                807, -- Fingerstick Glucose\n",
    "                811, -- Glucose (70-105)\n",
    "                1529, -- Glucose\n",
    "                3745, -- BloodGlucose\n",
    "                3744, -- Blood Glucose\n",
    "                225664, -- Glucose finger stick\n",
    "                220621, -- Glucose (serum)\n",
    "                226537, -- Glucose (whole blood)\n",
    "                223762, -- \"Temperature Celsius\"\n",
    "                676, -- \"Temperature C\"\n",
    "                223761, -- \"Temperature Fahrenheit\"\n",
    "                678 -- \"Temperature F\"\n",
    "            )\n",
    "  ) p\n",
    "\n",
    "ORDER BY icustay_id, time\n",
    "\"\"\", con=engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# rows = 10553011\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>icustay_id</th>\n",
       "      <th>time</th>\n",
       "      <th>heart_rate</th>\n",
       "      <th>sys_bp</th>\n",
       "      <th>dia_bp</th>\n",
       "      <th>mean_bp</th>\n",
       "      <th>resp_rate</th>\n",
       "      <th>temp</th>\n",
       "      <th>spo2</th>\n",
       "      <th>glucose</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>200001</td>\n",
       "      <td>2181-11-25 19:07:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200001</td>\n",
       "      <td>2181-11-25 19:08:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200001</td>\n",
       "      <td>2181-11-25 19:08:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200001</td>\n",
       "      <td>2181-11-25 19:08:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>200001</td>\n",
       "      <td>2181-11-25 19:14:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   icustay_id                time  heart_rate  sys_bp  dia_bp  mean_bp  \\\n",
       "0      200001 2181-11-25 19:07:00         NaN     NaN     NaN      NaN   \n",
       "1      200001 2181-11-25 19:08:00         NaN   113.0     NaN      NaN   \n",
       "2      200001 2181-11-25 19:08:00         NaN     NaN    65.0      NaN   \n",
       "3      200001 2181-11-25 19:08:00         NaN     NaN     NaN     77.0   \n",
       "4      200001 2181-11-25 19:14:00         NaN     NaN     NaN      NaN   \n",
       "\n",
       "   resp_rate  temp  spo2  glucose  \n",
       "0       22.0   NaN   NaN      NaN  \n",
       "1        NaN   NaN   NaN      NaN  \n",
       "2        NaN   NaN   NaN      NaN  \n",
       "3        NaN   NaN   NaN      NaN  \n",
       "4        NaN   NaN  94.0      NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('# rows = %d' % len(df))\n",
    "df[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Resample to 1-hour frequency"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For each ICU stay, convert absolute time of each measurement to elapsed time from ICU admission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 30.1 s, sys: 188 ms, total: 30.3 s\n",
      "Wall time: 29.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df['time'] = df.groupby('icustay_id')['time'].apply(lambda time: time - time.iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>icustay_id</th>\n",
       "      <th>time</th>\n",
       "      <th>heart_rate</th>\n",
       "      <th>sys_bp</th>\n",
       "      <th>dia_bp</th>\n",
       "      <th>mean_bp</th>\n",
       "      <th>resp_rate</th>\n",
       "      <th>temp</th>\n",
       "      <th>spo2</th>\n",
       "      <th>glucose</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>200001</td>\n",
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       "      <td>22.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200001</td>\n",
       "      <td>00:01:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200001</td>\n",
       "      <td>00:01:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>200001</td>\n",
       "      <td>00:07:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   icustay_id     time  heart_rate  sys_bp  dia_bp  mean_bp  resp_rate  temp  \\\n",
       "0      200001 00:00:00         NaN     NaN     NaN      NaN       22.0   NaN   \n",
       "1      200001 00:01:00         NaN   113.0     NaN      NaN        NaN   NaN   \n",
       "2      200001 00:01:00         NaN     NaN    65.0      NaN        NaN   NaN   \n",
       "3      200001 00:01:00         NaN     NaN     NaN     77.0        NaN   NaN   \n",
       "4      200001 00:07:00         NaN     NaN     NaN      NaN        NaN   NaN   \n",
       "\n",
       "   spo2  glucose  \n",
       "0   NaN      NaN  \n",
       "1   NaN      NaN  \n",
       "2   NaN      NaN  \n",
       "3   NaN      NaN  \n",
       "4  94.0      NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For each ICU stay, calculate mean measurement values for each hour after ICU admission (0 < hours < 24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.14 s, sys: 40 ms, total: 2.18 s\n",
      "Wall time: 1.07 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df = df.groupby(['icustay_id', df['time']//pd.Timedelta('1 hour')]).mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <th>time</th>\n",
       "      <th>heart_rate</th>\n",
       "      <th>sys_bp</th>\n",
       "      <th>dia_bp</th>\n",
       "      <th>mean_bp</th>\n",
       "      <th>resp_rate</th>\n",
       "      <th>temp</th>\n",
       "      <th>spo2</th>\n",
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       "      <td>98.000000</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>118.0</td>\n",
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       "      <td>61.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>37.666667</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>200001</td>\n",
       "      <td>4</td>\n",
       "      <td>108.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   icustay_id  time  heart_rate  sys_bp  dia_bp  mean_bp  resp_rate  \\\n",
       "0      200001     0       113.5   111.5    65.0     76.5  22.666667   \n",
       "1      200001     1       108.0   113.0    68.0     79.0  18.000000   \n",
       "2      200001     2       110.0   116.0    68.0     79.0  27.000000   \n",
       "3      200001     3       102.0   102.0    61.0     71.0  21.000000   \n",
       "4      200001     4       108.0   103.0    58.0     69.0  19.000000   \n",
       "\n",
       "        temp       spo2  glucose  \n",
       "0  37.277778  95.333333      NaN  \n",
       "1        NaN  98.000000      NaN  \n",
       "2        NaN  98.000000    118.0  \n",
       "3  37.666667  96.000000      NaN  \n",
       "4        NaN  94.000000      NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Fill in missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For each ICU stay and variable, if no measurement occurred in an interval, use the most recent measurement value (forward propagation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.groupby('icustay_id').ffill()"
   ]
  },
  {
   "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>icustay_id</th>\n",
       "      <th>time</th>\n",
       "      <th>heart_rate</th>\n",
       "      <th>sys_bp</th>\n",
       "      <th>dia_bp</th>\n",
       "      <th>mean_bp</th>\n",
       "      <th>resp_rate</th>\n",
       "      <th>temp</th>\n",
       "      <th>spo2</th>\n",
       "      <th>glucose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>200001</td>\n",
       "      <td>0</td>\n",
       "      <td>113.5</td>\n",
       "      <td>111.5</td>\n",
       "      <td>65.0</td>\n",
       "      <td>76.5</td>\n",
       "      <td>22.666667</td>\n",
       "      <td>37.277778</td>\n",
       "      <td>95.333333</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200001</td>\n",
       "      <td>1</td>\n",
       "      <td>108.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>37.277778</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200001</td>\n",
       "      <td>2</td>\n",
       "      <td>110.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>37.277778</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200001</td>\n",
       "      <td>3</td>\n",
       "      <td>102.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>37.666667</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>200001</td>\n",
       "      <td>4</td>\n",
       "      <td>108.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>37.666667</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   icustay_id  time  heart_rate  sys_bp  dia_bp  mean_bp  resp_rate  \\\n",
       "0      200001     0       113.5   111.5    65.0     76.5  22.666667   \n",
       "1      200001     1       108.0   113.0    68.0     79.0  18.000000   \n",
       "2      200001     2       110.0   116.0    68.0     79.0  27.000000   \n",
       "3      200001     3       102.0   102.0    61.0     71.0  21.000000   \n",
       "4      200001     4       108.0   103.0    58.0     69.0  19.000000   \n",
       "\n",
       "        temp       spo2  glucose  \n",
       "0  37.277778  95.333333      NaN  \n",
       "1  37.277778  98.000000      NaN  \n",
       "2  37.277778  98.000000    118.0  \n",
       "3  37.666667  96.000000    118.0  \n",
       "4  37.666667  94.000000    118.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For remaining missing values, use first available measurement (backward propagation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.groupby('icustay_id').bfill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>icustay_id</th>\n",
       "      <th>time</th>\n",
       "      <th>heart_rate</th>\n",
       "      <th>sys_bp</th>\n",
       "      <th>dia_bp</th>\n",
       "      <th>mean_bp</th>\n",
       "      <th>resp_rate</th>\n",
       "      <th>temp</th>\n",
       "      <th>spo2</th>\n",
       "      <th>glucose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>200001</td>\n",
       "      <td>0</td>\n",
       "      <td>113.5</td>\n",
       "      <td>111.5</td>\n",
       "      <td>65.0</td>\n",
       "      <td>76.5</td>\n",
       "      <td>22.666667</td>\n",
       "      <td>37.277778</td>\n",
       "      <td>95.333333</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200001</td>\n",
       "      <td>1</td>\n",
       "      <td>108.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>37.277778</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200001</td>\n",
       "      <td>2</td>\n",
       "      <td>110.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>37.277778</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200001</td>\n",
       "      <td>3</td>\n",
       "      <td>102.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>37.666667</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>200001</td>\n",
       "      <td>4</td>\n",
       "      <td>108.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>37.666667</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   icustay_id  time  heart_rate  sys_bp  dia_bp  mean_bp  resp_rate  \\\n",
       "0      200001     0       113.5   111.5    65.0     76.5  22.666667   \n",
       "1      200001     1       108.0   113.0    68.0     79.0  18.000000   \n",
       "2      200001     2       110.0   116.0    68.0     79.0  27.000000   \n",
       "3      200001     3       102.0   102.0    61.0     71.0  21.000000   \n",
       "4      200001     4       108.0   103.0    58.0     69.0  19.000000   \n",
       "\n",
       "        temp       spo2  glucose  \n",
       "0  37.277778  95.333333    118.0  \n",
       "1  37.277778  98.000000    118.0  \n",
       "2  37.277778  98.000000    118.0  \n",
       "3  37.666667  96.000000    118.0  \n",
       "4  37.666667  94.000000    118.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Save data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('data/vitals.csv', index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Extract and save mortality labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = pd.read_sql_query(\"\"\"\n",
    "SELECT\n",
    "    ie.icustay_id,\n",
    "    adm.hospital_expire_flag as label\n",
    "FROM\n",
    "    icustays ie\n",
    "    left join admissions adm\n",
    "    on ie.hadm_id = adm.hadm_id\n",
    "\"\"\", con=engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>icustay_id</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>280836</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>206613</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>220345</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>249196</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>210407</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   icustay_id  label\n",
       "0      280836      1\n",
       "1      206613      0\n",
       "2      220345      0\n",
       "3      249196      0\n",
       "4      210407      0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels.to_csv('data/vitals_labels.csv', index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Example data plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(icustay_id, linewidth=3):\n",
    "    fig = plt.figure(figsize=(20,5))\n",
    "    ax = plt.gca()\n",
    "    \n",
    "    data = df[df['icustay_id'] == icustay_id]\n",
    "\n",
    "    ax.plot(data['heart_rate'].values, label='Heart rate', linewidth=linewidth)\n",
    "    ax.plot(data['sys_bp'].values, label='Systolic BP', linewidth=linewidth)\n",
    "    ax.plot(data['dia_bp'].values, label='Diastolic BP', linewidth=linewidth)\n",
    "    ax.plot(data['mean_bp'].values, label='Mean BP', linewidth=linewidth)\n",
    "    ax.plot(data['resp_rate'].values, label='Respiratory rate', linewidth=linewidth)\n",
    "    ax.plot(data['temp'].values, label='Temperature', linewidth=linewidth)\n",
    "    ax.plot(data['spo2'].values, label='SpO2', linewidth=linewidth)\n",
    "    ax.plot(data['glucose'].values, label='Glucose', linewidth=linewidth)\n",
    "    \n",
    "    ax.set_xlabel('Hours After ICU Admission')\n",
    "    ax.set_xlim([0,23])\n",
    "    \n",
    "    legend = ax.legend(loc='center right', bbox_to_anchor=(1.15, 0.5), fontsize=12)\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[200001, 200003, 200006, 200007, 200009]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "icustay_ids = list(df['icustay_id'].unique())\n",
    "icustay_ids[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(200001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rZ1FnQRDmPPHEE7FTpkzJSklJyZ47d24GAJx11lkztVpt7iuvvBLZ+/E2bNgQPXv27Bm33nprcnh4uOmXv/xlYkFBgeqcc85Jj4iIMEVGRhqvvPLKqXV1dXIAWL58+dTKysqQ66+/Pk2r1eY+9NBD8QDwxRdfhObm5s7Q6/WmjIyMmR999NG476RUBLoAIiIiIiIiorFKEASYTCb8+9//BiAN4c7NzQ1wVURjQ8oDH88Zrccq/f1le8/0Gk6nE5dddlnq0qVLm95///3jx48fV1566aUZmZmZ1pUrV7YoFAo888wzFfPnz28/fvx4yNKlS9OefPLJ2EceeaSm6xoffvhhxK5du4pCQ0NdOp1OFARhzu7duwuzsrL8tlHn5+eHrly5sqGuri7PZrMJpaWlIffff3/VkiVLWhsbG+VXXnnl9Pvuuy/x1VdfrdiyZUtJUlKS7sUXXyxdvnx5KwCUlJQoV65cmfbyyy+XXHPNNc0ffPBB2I033ji9sLDwYGJiouNMvy+Bwk5JIiIiIiIion5kZ2dDEKRmqbKyMjQ0NAS4IiLyZ9WqVal6vd7Udbv//vsndx3bsWNHaENDg+Lpp5+uVKvV4syZMztvuumm2k2bNkUBwIUXXmhZtGhRu1KpREZGRufq1atr//Of//ToSHzggQeq4uPjnTqdThxsTbGxsZ0PPvhgjVKphE6nE7OysmwrVqxo0Wg0YmJiouPuu++u/u677/x2Pm7cuDF6wYIFzT/84Q+b5XI5VqxY0ZKVldW+efPm8NP5Ho0V7JQkIiIiIiIi6oder0daWhqOHDkCADCbzVi4cGGAqyIiX958883irg5DQBo+/frrr8cAwPHjx0Nqa2tD9Hq9Z7JYl8slzJ07txUA8vPzVXfffXfygQMHQq1Wq8zpdGLmzJkW7+tPnTq1c6g1GQyGHhNJnjx5UrF27drJu3bt0lksFrnL5UJYWJjT3/3LyspCPv3000i9Xu8JIR0OhzB//vxWf/cZDxhKEhEREREREQ3AZDJ5Qsm8vDxcdNFFkMk4+JCC23AMqR5NKSkpnUlJSbaysrKDvo6vXbt2SnZ2tuWf//zn8cjISNfjjz8e9/777/eYJ7Kra3ooBEHo0VX5i1/8IkkQBDE/P78gISHB+cYbb0Tce++9k/3dPzk5uXPFihX1b731VtmQH3wM419QIiIiAgBY9uxB9RO/h+14SaBLISIiGnPS09Oh0WgAAM3NzSgrm1DZAFFQWLBgQbtOp3M++OCDCW1tbYLD4cDu3bvVO3bs0AJAW1ubPCwszBkeHu7av3+/+tVXX40b6JrR0dGOI0eOqIZSR1tbmzw0NNQVExPjLCkpUT777LMJ3sdjYmLsxcXFnmveeuut9du2bYvYvHlzmMPhgMViET766CP9sWPHlEN53LGGoSQRERHBVlyM8ptvQcPrr6P85pvhslgGvhMREVEQUSgUyMrK8mzn5eUFsBoiOh0KhQIff/xxcX5+viYlJSUnKirKtGbNmpTGxkY5ADz55JMVmzdvjtLpdLlr1qyZsnz58gEnkL3vvvtOrV27NkWv15s2btzYZ/VtXx5//PFTBw4c0IaFheUuXbo07Yorrmj0Pr5u3bqqZ555xqDX602PPPJIfGpqqv2dd94p/v3vf2+Ijo42JSUl5Tz99NPxLpdr6G2bY4ggioOel3PEzJ07V9yzZ0+gyyAiIgpKosuFsh/diI79+z37Yu66E7G33x7AqoiIiMaekydP4pVXXgEAKJVK3HvvvVCphtQgRTSqBEHYK4ri3OG6ntlsLjUajXXDdT0KDmazOcZoNKb03s9OSSIioiDX+NZbPQJJAKjf+BfYa2oCVBEREdHYlJiYiNjYWACA3W5HYWFhgCsiIhq/GEoSEREFMXtVFWqf+YNnW1BK09KIFgvq/u+FQJVFREQ0JgmCAJPJs2gvh3ATEZ0BhpJERERBShRFVD3+G7ja2wEAIVOnIum5Zz3HmzZvhtW9yigRERFJcnJyPKvvlpWVoaFhwCnniIjIB4aSREREQap167/R9uWXnm3Dbx6HftEihJ5/vrTD5ULN008HqDoiIqKxSa/XIzU11bNtNpsDWA0R0fjFUJKIiCgIOZubUfW733q2I667Dtq50hzocfetA9wdIO1f/wdt33wTkBqJiIjGKu8h3GazGS6XK4DVEBGNTwwliYiIglDN08/AWSstnKiIjUXcvb/0HFNnZCD86hXd5z75FESnc9RrJCIiGqsyMjKgVqsBAE1NTSgrKwtwRURE4w9DSSIioiDTvmsXmt5917Md//BDkIeF9Tgn9q67IWg0AADb4cNofv+DUa2RiIhoLFMoFMjOzvZsc8EbIqKhYyhJREQURFw2G6oeWe/Z1l28CGGXXtrnPGV8HKJvucWzXfvcc3B1dIxKjUREROOB9xDuwsJC2Gy2AFZDRDT+MJQkIiIKInUvvYTO0lIAgEynQ8LDD/s9N/qWmyGPjQEAOGpqUP/Xv45GiURERONCYmIiYmNjAQB2ux2FhYUBroiIxoKkpKTsLVu26AHggQceSPjhD384JdA1jVUMJYmIiIKE9fAR1G/8i2c77t5fQhkf7/d8WWgoYu+6y7Ndv/EvcNTWjmiNRERE44UgCD26JTmEmyjwtm7dqsvNzZ2h1+tN4eHhptmzZ8/YsWOH9kyu6R0yDtXvf//7qrfffnvIk87OmzcvQ6VSzdZqtbl6vd40d+7cjF27dmm6jt9zzz2JCoXCczw3N3fGtm3bQk+nxkBiKElERBQERKcTlQ8/DDgcAADNnDmIuO66Ae8XcfXVUKWlSdewWFD7fy+MaJ1ERETjSU5ODgRBAACUlZWhoaEhwBURBa+GhgbZNddck3rbbbfVNDY25p08edL80EMPnVKr1WKgazsdTzzxRLnFYtnf0NCQd8EFF7T++Mc/nup9/LLLLmu0WCz76+rqzGeffXbbDTfcMN3lcgWq3NPCUJKIiCgINP7jTVjz8wEAglIJw+OPQZAN/DRAkMsRd986z3bTe+/BdvToiNVJREQ0nuj1eqSmpnq2zWZzAKshCm4HDx5UA8DatWsbFAoFdDqdePXVV7ecffbZHR0dHUJ4eLjJu9vw5MmTCrVaPfvUqVOKyspKxcKFC1O7OiznzJmT4XQ6sXz58qmVlZUh119/fZpWq8196KGH4gHgH//4R3hqauosvV5vmjdvXsa+ffvUvmq65557Eq+66ipPmOjdyZmQkJCzYcOG6IG+LqVSiZtuuqnh2LFjPh9DpVKJa9asqaurq1NWV1crhvp9C6RxVSwRERENnf3UKdQ895xnO/q2tVBNnz7o+4decAFCzzsP7d9+C7hcqH76aUx++eWRKJWIiGjcMZlMOOp+w85sNuOiiy6CbBBv/BFNCI+Gzxm9x2re29/hrKwsq1wux9VXX51y/fXXNyxcuLA9NjbWCQAajUa84oorGv76179GzZs37yQAvPrqq1HnnXdeS2JiouOOO+5IMhgMnXV1dWYA2L59e6ggCNiyZUtJUlKS7sUXXyxdvnx5KwDk5+er1qxZM+3NN988tmzZstbf/OY3ccuXL089cuRIQX9dmUePHg25+uqr0/7whz+UrV69urGxsVF2/PjxkIG+bKvVKrz22mvRRqOx3dfxjo4O4ZVXXomJj4+3GwwGx0DXG0v4l5KIiGgCE0URlY89BtFiAQCEpE5HzE9/OqRrCIIgdUu6h6e17/haCiiJiIgIGRkZUKulBqampiaUlQ15+jgiGgZRUVGuL7/88pAgCLjzzjtTDAaD6Qc/+EFqRUWFAgBuueWW+n/961/RTqcTAPD2229Hr1q1qh4AlEqlWF1drTx69GiISqUSlyxZ0ubvzYU33ngjauHChc0rVqxoUalU4mOPPVZttVpl27Zt0/VXX1cIunbt2gaVSiUmJCQ4zzvvvA5/5z/44IPJer3epNfrc1977bXYhx9++JT38U8++SRSr9ebkpKScsxms/bdd98tHtp3LPAYShIREU1gLZ98gvYdX0sbggDD47+BEDLgG7J9qGfMQPjVKzzb1U8+BdH9hI6IiCiYKRQKZGdne7a54A1R4MyePdu6efPm0urq6vzdu3cX1NTUKG+//fZkAPjBD37QrtFonJ988ol+//796rKyMtWqVauaAGD9+vVV06ZNsy1ZsiR90qRJ2b/+9a8T/D3GqVOnlMnJyZ1d23K5HAaDobOiokLZX20VFRUhU6dOtQ32a/nd735X0dramme1Wve9++67xTfddNP077//3jP8fNmyZY2tra15DQ0N5p07dx658MILLYO99lgx4PBtQRBeBXA5gBpRFLPc+54CcAWATgDHANwsimKT+9ivANwKwAngLlEUt45Q7URERNQPZ1MTqv/3Cc925A03QDs797SvF3vXXWj55FOIHR2wHTqE5g8+RMSK5cNRKhER0bhmMpmwe/duAEBhYSGWLVsGlUoV4KqIRsEAQ6oDKTc317pq1aq61157LbZr3/XXX1//xhtvRMXHxzuWLVvWqNVqRQCIjIx0vfLKKycAnNizZ4/60ksvzTj77LPbr7rqqtbe101MTLQXFBR4wkGXy4XKysqQ5ORke3/1JCcnd+7Zs2fIK2TL5XIsWbKkbfLkybaPP/447Oyzz/bbXTneDKZT8jUAS3rt+xxAliiKOQCOAPgVAAiCMBPA9QBmue/zR0EQ5MNWLREREQ1a9ZNPwVlfDwBQxMcj9p5fnNH1lPHxiL75Zs927XPPwdUxYZ4TERERnbbExETExkq5h91uR2FhYYAromFj4Yrq48X+/fvV69evjz927JgSAIqLi5Xvvfde9OzZsz1zMa5Zs6Z+69atkZs3b45avXp1fdf+TZs2hR88eFDlcrkQERHhlMvlolwuxVkxMTH24uJiz7sMN954Y8P27dvD33//fb3NZhMeffTR+JCQEPHiiy9u66++W265peHbb78N27hxY6TdbkdVVZX822+/1fR3ny7btm0LPXbsmDorK2tCPfkeMJQURfFrAA299v1bFMWuyTN3Apjk/vwqAG+JomgTRbEEQDGAecNYLxEREQ1C+3ffofmf//RsJ6x/BHJdv9PcDEr0rbdAHhMDAHBUV6PhtdfO+JpERETjnSAIMJlMnm0O4R7HRBE4tR/44jfAC/OAly+S9tGYFxER4dy9e3foueeem6nRaHLPO++8zBkzZnT88Y9/rOg6Z/r06fZZs2ZZBEHA4sWLPSHikSNHVIsXL04PDQ3NPf/88zNXr15de/nll7cCwLp166qeeeYZg16vNz3yyCPxRqPR9vLLL5fcc889k2NiYoyffvppxJYtW472t8gNAKSlpXVu3rz56IYNG+IjIyNzTSbTrL1792r9nf+rX/1qslarzdVqtbm33HLL1AceeODkdddd1zIc36uxQhAH8cslCEIKgI+6hm/3OvYhgLdFUfy7IAgvANgpiuLf3cf+AuBTURTf83G/nwH4GQBEJE6b8//96QOolXKoFLJ+P6qVMqgU3R9VXtshchkE9yT8REREwcplteL4lVfBXl4OANAvXoxJzz83wL0Gr/Gdd1D1yHoAgKDVInXrZ1DExg5wLyIioomttbUVf/jDH9D1Gvvuu+9GZGRkgKuiQXE5gfKdQNGHwKGPgOaKnsfXfg0YjIGp7QwJgrBXFMW5w3U9s9lcajQa64breoFw7bXXphgMhs4NGzacGvhsGg5msznGaDSm9N4/4JyS/REE4UEADgD/6Nrl4zSfqacoin8G8GcAUBnSxL9+U3ompbjrAVSK7pDSX7ip8hV2ugNOtft47/DTVxjadX+GoURENJbUvfiiJ5CUhYUh4aEHh/X6EVdfjcY33oDtaDFEiwW1L7wIw2OPDutjEBERjTd6vR6pqak4evQoAMBsNmPBggWBLYr8c9iA4zuAQx8Chz4BLH5yNqUWqD0ybkNJ6unw4cMhn332WcTu3bs5x8IYcNqhpCAIP4G0AM4isbvd8gSAZK/TJgEYteRZFAGr3QWr3YXmURxlLwiATqVAuEbZ96b1sc/rplcrIZcx0CQiouFhLSpC/at/9WzHrbt32LsYBYUCcevWoeJnawEATe++i6ibboQqNXVYH4eIiGi8MZlMnlAaiv8UAAAgAElEQVQyLy8P8+fPh0w2mKUcaFTY2oDiz6WOyCP/Bjr7rGEiUYcDGcuAGZcD038AhPgdYUvjyN133534yiuvxN95552VM2bM6Bz4HjTSTiuUFARhCYD7AVwkiqL3kuMfAHhTEIQ/AEgEkAZg10DXS4rQ4NeXZcLmcMFmd8LmcMHq46PV7oLN0ftj9306na7T+XLOmCgCrVYHWq0OnGgcWhrqK9CMcAeZYf2EmQw0iYioN9HhQOVDDwNOJwBAe9ZZiLjmmhF5rNALL0Toeeei/dvvAJcLNU89jeSX/zQij0VERDRepKenQ61Ww2q1oqmpCeXl5UhJSQl0WcHN0gAc/lQKIo99CThtvs/TxUshZOblQMqFgFw5unXSiHv++edPPf/88xyyPYYMGEoKgrAJwAIAMYIgnACwHtJq2yoAn7uHLe8URfE2URQLBEF4B0AhpGHdd4ii6BzoMaJCQ7Dmwmmn/1W4OV0ibA4nbHYXrL0/+go5vQJNm9e2d+jZ5zruj96PY3ee/qS3wxFoRvjoxmSgSUQUfBre+DusBQUAACEkBAmPPzZi04sIgoC4++5DyYqrAVFE244daP/uO4See+6IPB4REdF4oFQqkZ2djd27dwOQuiUZSgZA80ng0MfS0OzSbwB/sURkCpB5BZB5JZA0F2BXK9GoGjCUFEXxBh+7/9LP+b8D8LszKep0yWUCtCEKaENG93EdThdarQ40d9h93lr87G/usKPV6hj4AfzwDjQrMPRAU69SeIaXR2pDkBylxeQoLaZEaaXPo7UIU/PdISKi8aDzxAnUbtjg2Y65/Xaopk4d0cdUz5iB8BUrPKt8Vz/5FKZufg8Cn9ATUbCyW6VOrEMfAx2NQEwaEDsDiJsBxGRwCGiQMJlMnlCyoKAAS5cuhUqlCnBVQaCuWAohiz4ETu71f158lhREzrgciJ8lvTgmooA4o4VuSKKQyxAZGoLI0KGnoU6X2G9oOZKBZovVgZYBAs0IrRKTu0JKd2DZtW0IV0Mh5wtPIqJAE0URVesfhdgh/T1Xpacj+tZbBndnl0saxmTvkCZ8V6gAbdSgHzv27rvQ8sknEK1W2IqK0Pz+B4hYsfx0vgwiovGpK4gs+Jc0RNR7jrrD3icKQOQUKaSMnQHEZQKxGQwrJ6DExETExsaitrYWdrsdRUVFMJlMgS5r4hFFoCofKPpICiJri/yfO2meuyPyciDqzEdpEtHwYCgZYHKZMPqBpsWOVtvgAs0mix1Nlmbkn2juc0whE5AUqcFkd1A52SuwZJclEdEQiSLgsEo3uxVwuEPCrrCwn+2WbwrR/s030nUEwLBIA+Fft7qvY/VzXfc+X/MqZV4BXLFhUOGkMj4e0bfcjLo/vgQAqH3uOYQtWQyZRjOc3x0iorGlvyDSLxFoLJVuRz7z2t8VVrpDyrhMKbSMSWdYOU4JggCTyYTPP/8cgDSEm6HkMHE5gYpdUgh56EOgqdz3eTKFNC9k5uVAxmVAmGF06ySiQWEoOY4NZ6BZ22pDeYMF5Q0WVLg/ljdYYHP4XzzI4RJRVm9BWb3F5/EIrbJ7KHiv0JJdluNfi9WOAyeaYT7RhPyKZhRWtkClkGFKdChSorVIiQlFSnQopkRrkRih4RymNPG4XEBHA9BaJd3aqoDWSsDW2jc89BkO9tr2N+n6ABw2Gao/iQUgBwBEprVB0/gp0HgGX1vRh8DJfcDVrwAp5w94etQtt6LxnXfhrKuDo7oaDa+/jpjbbjuDAmjcaihxLyJgBww5QEIOoNIFuiqi4dEVRBZuAQ594j+IjJoGzFwuhYt1R4DaQ0DNIaDhuJ957bzDyk+99jOsHM9ycnKwbds2iKKI0tJSNDY2IjIyMtBljU+OTqDkaymEPPQx0F7r+zyFGki9WHpzNX0xoOH3m2isYygZpAYTaLpcIurabChrsKC8vm9gWdPa/wvori5Ls58uy0mRGp+BJbssxx6r3YnCyhbkVzTB7A4ij9e2+zz3aE1bn30hchmSozTukDIUU2O07vAyFIkRDKhpjPEXNnZtt1YBbdXSR5c90NWiZn8YnDYpkFRoHYjLHky3Ti9yFaBUSx/ba6R9LSeB1y8HLnoAmH8vIJP7v7suFLF33omq9esBAPV/fgUR11wDRUzM0Guh8aWrY+XIp8CRrVL40oMgzamXmAsYTECiiUEljS9DDSJnLZd+xn3NUeewAXVHpd+T2kNATRFQe/jMwsq4Gd3DwRlWjil6vR6pqak4evQoAMBsNmPBggWBLWo86WwHirdJb5Qe+Tdg6/uaEgCgCpcCyMwrgNRFQEjo6NZJ49KqVasmJyUl2Z966qnK0XrMDRs2RL/++usxe/fuPQwAWq02d8+ePQUzZ87sHK0axiKGkuSXTCYgLkyNuDA1zkrpO4Svo9OJisbuwNL7VjGILsvSegtK/XRZRvaay3KyO6ycHKWFIZxddyPJ6RJxtKYV+RXNyDvRhPwTTThU2QqH6/RXme90unCsth3HfASZSrmA5EgtpkRr3YGl1F2ZEh2KpEgNlAwsabiMh7BRrpLe5VeqpbkdFRr35143r+224hY0l37vubth7dWQzcmQ7qdQAUpN93U8272uI1f1XGny8GfAlp9L3yvRBXz1v1J3wspXgLBEv6VHrLwaDW/8DZ3Fx+CyWFD7wgswPProCH6zKGCsLcCxL6SflaP/ln5W/BKlTrG6I0D+2+59ghSeJJoYVNLY5B1EHv4UsLX4Pi9yKjBrRf9BpDeFCkjIkm7efIaVXZ2Vvp5PDzas7JqzkmFloJhMJk8omZeXh/nz50PGxeD8szRIb3AVfSj9P+Ow+j4vNA6YcZk0NDtlPqAY5ZVuaUxLSkrKrq+vV8rlclEmk4mpqanWG264of6Xv/xlrVwuvcn+5ptv+hn3P3i9Q8ahslgs+4d6n48++kh/5ZVXpqvVahcA6HQ6549+9KO6Z5999lTXOYIgzFGr1S5BEKDT6ZxXXnllw5/+9KcTCsXYjP/GZlU0LmhC5EiP1yM9Xt/nmMslorbNPSTcR2hZO0CXZaPFjkY/XZZKuYCkiJ5dllOiuwNMPbssB00URVQ0dEjhY0UT8k8048DJZnTYfb1b35NcJiAjXg9jcjiMkyKQPSkcLhdQWt+Osvp2lNRZUFbfjtJ6C+ra/P97250ijte143hdO4CeQzG6Omp9DQmfFKlFiIJP6gjSXIyWhu6A0RM2VnvtG6GwURUO6BMAfTygNwC6eGmoUK/w0P+2V1jYOxwcgMtiQdXzV3q2wy67DLq1//+Zf00ZS4CffwNs/ilQ9l9pX9l/gZfOB5a/JB33QVAoEL9uHSrWSsO2m959D1E33ghVauqZ10SB11AizYF3+FOg7Fv/v0sKNTBtAaCJAirzpGClT6giAnWHpRuDShorRiqIHIxRCStTulcB7worYzOk/39oxKSnp0OtVsNqtaKpqQnl5eVISUkJdFljS0slcOgj6VbyHz9dwwAiJgOZV0orZifP63cEB9Fbb711dPny5a319fXyzz77THffffdN3rVrV+h7771XGujazlRsbKy9uro6HwAOHToUctFFF82YPXu25aabbmrqOmf37t2FWVlZtv3796svueSSjPT0dNt9993nZ96DwGIoSSNCJhMQH6ZGvJ8uS0unAycaO1Beb0FZr2Hh5Q0WdPbTZWl3Dr7Lckq0tkfHZbB3Wda0WpFf0Yz8E9Iw7PwTTWi0DC6kmRoTipxJUgBpTA7HTEM4NCF9nwxkTwrvs6/VavfMP1pa347SunbP5/1NA+DdUbuj1zG5TAqnu7oqpcBS6rZMjtJApeATlXFvLIWN+gRAl+Del9C9HcDOk9r/ewH2kycBAPLwcMT/+lfDd/GwROAnHwBfPw3s+L304rejAdj0Q+Cc24GLH5VeRPcSOn8+Qs87F+3ffgc4nah5+hkk/+ml4auLRo/LCZzYLYUzRz7zMSzbiy5BGjqXsRSYelHP34tOC1B1QAooT+WdYVCZCyRkM6ik4eOwAcVfDDKIXC6FkcMVRA6Gv7DSbgXqi3sGlQOGlSXSzVdY2bUKeFeHZUw6w8pholQqkZWVhT179gCQuiUZSgKoPyaFkEUfSv/X+BM3UwohM6+Q/v6P1u8eTRjR0dHOH/3oR82JiYnHFi1alLl79+6qs846y7py5cqUpKSkzg0bNpyqra2VX3fddVPz8vJCnU6nMGfOnLaNGzeWTZ8+3Q5IHZFPPvlkYmNjoyIiIsLx0EMPnTz77LMt69atm+JwOAStVpsrl8vF1tbWvPr6evmaNWuSv/rqq3CNRuO68cYb65544onKrg5Nb4IgzDlw4MDBrKwsW1tbm/CLX/wi6eOPP45sbW2Vp6end+zYseOITqfrd7jijBkzOufMmdNWUFCg9nU8NzfXetZZZ7UePHhwzP5RZyhJAaENUQzYZVlWb/G5+M5wdln2Di0nUpdli9WOgyek74O5QhqGfarZzxCIXuLDVO7wMQI5k8KRkxSBcO3pf2/0aiWyksKRldQ3sGy3OdyBpdRVWVrX7u62tKCqxX+9Tpfo+Zn4z9G6HsdkApAYofF0VUpDwqXQMjlKC7WSgeWY01YLHHwPKPum53DqEQkb493BoruzsSt0HCNh42B0HDiIhtdf92zH3X8/FNHRw/sgMjmw4H4g5QJg8xqg1T0qZOcfpU65a14Foqf3uIsgCIhbtw4lV68ERBFtX32F9p07EXrOOcNbG42MrmHZR7ZKw7It9f7PNRiB9KVSGGkw+e/yDdECk8+Wbl0624Gqg2ceVHbNU8mgkobCYeu5avZAQeTM5dLP+1gKQ5Tq4Q8rD3/itZ9h5XAymUyeULKgoABLly6FStX3jb0JTRSB6oNSCFn0EVBT4P/cpLnSsOwZVwAxHG0xHmS/nj1ntB7rwE8O7D2d+y1cuNASHx/f+eWXX+rPOuusHi8ynU4nfvKTn9R98MEHxx0OB1atWpWydu3aydu2bTvW0tIi+/Wvfz35m2++KTQajbaysjJlbW2tfPbs2dannnqqrPfw7TVr1iS3tLTIjx8/fqCmpkaxePHidIPBYP/FL35R17eqbj//+c+TDx8+rPnmm28OJScn27dv3x7qK8js8/04cEC1Z88e3W233eazC3Lv3r3q3bt36x988MGTg/xWjTqGkjTmeHdZzpvqu8uyoqGjR2BZVt+OisaOYeuynBwdislRmnHTZWm1O1FU2YJ8dwBpPtGE43XtEAcxDWSYWuEJH7uCyPgwn2+0jIhQlQIzE8MwMzGsz7GOTifKGtpR6hkK3v15fwGrSwRONHbgRGMH/lvc85ggAInhGs8clr2HhTOwHEV2q/QiyPyWNJG5v+E6g+EdNno6Gg19Ox3HeNg4GKLdjspHHpHmyASgPfcchK9YPnIPmHK+NJx7y+3dHTaVecDL84HLnwVyrutxujozE+HLl6P5X/8CAFQ/+SSmvvceBM6hNTY1lkpzQx75FCj9pv9h2VMvkobvpy/pd37RAYWEjlBQmdvdVcmgkrxNhCByMPoNK49Ki+oMW1g5o3s4OMPKfiUlJSEmJgZ1dXWw2+0oKiqCyWQKdFkjy9YGNJVLt9L/SGFkU5nvcwW59Fwj80ppnsgz+f+FqB9xcXH2hoaGPi/2EhISnKtXr/YMfX744YcrlyxZktG1LQiCuH//fs306dM7p0yZYp8yZYrPJ0sOhwMff/xx1HfffVcYGRnpioyM7LzjjjuqNm3aFN1fKOl0OvHuu+/GbN++vWjq1Kl2ALjkkkt8ryoLoLa2VqnX602iKArt7e2ySy65pOnSSy/tsdrsOeecM1Mmk4nh4eGOVatW1d511139hqKBxFCSxh1tiAIZCXpkJPjusqxptfVZdKesvh3lDR39zm0IDK7L0juwDESXpdMlorimzRM+5p9oxqGqFtidAyeQKoUMWUnhyJkUDlNyBHImRSAlWgthjD7p1oTIMSMhDDMS+gaWVrsT5Q0Wz1DwEvdclqV1Fpxq7vAbyIoicLKpAyebOvDtsZ5dQIIAJEdqkRanQ1q8HunxOqTF6ZEap/M5VJ1OgygC5TsB8yagYIv/lRS7qMJ6djD2Dht17iAyiFZabHj9ddiKigAAgloNw2OPjfzvsDYKuGET8P3LwOcPA85OoLMN+OdPgeNfAUuf7BEAxd59F1o+/RSi1QpbYRGaP/gAEctHMDilwesxLHsrUFvk/9yuYdnpS6R5Ikcy1B9MUHlqvxRG9htUvuXeJ0gdXl3zUzKoDD6eIHKLFKz5DSJTpGHZ4zWIHAylWvr5T8juuZ9h5agQBAEmkwnbtm0DIA3hHvehpLXZHTpWdIePzeXd+/pdAA3SPNqpi6Sh2RlLpecZRCOsuro6JCoqqk8XRGtrq2zt2rXJX331VVhLS4sCANrb22UOhwNhYWGu11577fgzzzwTf+edd6bMmTOn7dlnnz2Rm5vbp0OmsrJSYbfbhbS0NM9q2lOnTu2srq7uNyioqqpS2Gw2YebMmf2HFW7ec0rW19fLb7755snXXnttyocffljSdc7OnTsLs7KyBnW9QGMoSROKTCYgIVyNhPChdVmWN1hQ0dhx5l2W0aHusHJ4uiy7FqIxu1fBNlc04+CpZlg6B7cQTXq8HsZJ4Z5OyPR4/YRZzVqt9L/Qks3hREWDBaV1Fs9Q8FJ3p+XJxg74W0hcFOH52fjiUI1nP8PKYdBwHDC/LQUGjaW+z5l8LpB9jTR/UBCGjYPRWVaG2v97wbMd+z93IGTy5NF5cEEAzrkNmHIu8O7NQMMxaX/eP4CKXdJwbkMOAECZkICom1ej/qU/AQBqn3seYYsXQ6bhi9OAsLZI4cyRzwYelp2QI71ATF/S/7Ds0TBgULlfCiv9BZVdAUtXUCnIpJDEO6g05PDvzEQylCBypnuOyIkaRA7GQGFlzaGei+w0lgwtrBRk3QvsxM7oHg4ehGFlTk4OvvjiC4iiiNLSUjQ2NiIyMjLQZfkmikBHo1fYWNE3gBzoTWVfQvTSG12ZVwCpF/NNognkdIdUj6YdO3Zoa2pqlAsXLmzrfezxxx+PLy4uVu/cubNo8uTJjm+//VZz/vnnzxTdXS4rV65sWblyZUvXvI9r1qyZsnfv3sO9mwIMBoNDoVCIR48eDZkzZ44VAEpLS0Pi4+P7nY8qISHBoVKpxMLCQtW5557bMZSvKzo62rlq1aqGW265ZdpQ7jeWMJSkoDKkLkt3WCndBttl2QRzRVOfY0q5gEmRXSGl/y7L2labZxGarnkgB7sQTUq01h0+RsA4KRyzEn0vRBMMVAo5UuP0SI3r++/c6XDhRKOlx1DwEveclhUNFp+B5UBhZXq8DqlxUliZHq/H9FiGlQCAjiZpqJz5LaBip+9zIlMA4w3SEOCocft/6agQRRGVjz4K0Sb9LVJlZiJq9erRL8RgBNbuAD5ZJ3W8AtKL140XA5f+Fpj3U0AQEH3rGjS98y6c9fVwVFWh4fW/Iea2taNfb7DyDMv+DCj97+CGZactBsKTRrXMITuToFJ0DRxUdi2mw6By/GAQOfwGFVYWdXdY+gsrRZf0pmTDcT9hpTuk7OqwjEmbsGFlWFgYpk+fjuJiaV4hs9mMBQsWBKYYUQTa67w6G70Cx64AsrNPbjM08hAgfJK0YnZ0qvRG19T5PhfJIxpJDQ0Nsq1bt+rvu+++5Kuuuqp+3rx5fUK/1tZWuVqtdsXExDirq6vl69ev98whUFFRofj6669Dr7jiitbQ0FCXTqdzdc31aDAY7FVVVSFWq1VQq9WiQqHAsmXLGh944IGkd955p6S2tlbx4osvxt95553V/dUol8tx7bXX1t1zzz3JmzZtKpk0aZL9q6++Cj3//PMtGo2m3+GQzc3NsrfeeisqNTV1SGHmWMJQkshtKF2WXQGWp+NyEF2WJXXtKKnzPTVEVGgIQuSyfhd28RanV8GYHNHdBXmGC9EEkxCFDNNidZgW2/fdWZvDieO17Tha04aj1a04Ut2KozVtKKu3wOkjrfQOK7cV+Q4r0+L1SIsLorDSaZdWMjVvkoaHOn2E+apwIGuFFEYmn80XhoPU/K8tsHznDndlMhh+8xsIigD9N67SAyv+JIVZH/8SsLdL/9afrpOGc1/1AuS6KMTeeSeqHn0UAFD/5z8j4pqVUMTEBKbmic7lBE7skeaGPPzZAMOy493DspcC0y4a/wFcf0Hlqf3dw7+HElRGp0kBrTYGCI0FQqO9Po8BtNHS5yo9/4YFwpCDyOVS8Mx/qzMzImHlx937J3hYaTKZPKFkXl4e5s+fD9lIdKO7XEB7jVfg6N3t6A4gHWeYXyjUQHiyFDpGTAYikoGIKd37dPGB7bSnoHf99denyeVyUSaTidOnT7fefvvt1evWrfO5GMwDDzxQfd11102LiYkxxcXF2e+4446qbdu2RQCAy+USnnvuuYTbbrttKgBkZmZ2vPTSS2UAcPnll7e+8MILHfHx8UaZTCY2NjaaN27cWL5mzZrJ06ZNy1apVOKNN95Ye/fddw84n+NLL71Ucdddd02aN29eZkdHhywjI6Pjq6++OuLr3NraWqVWq80FgJCQENFkMrW9+eabJb7OHQ8EcTArYYywuXPnil0rkhGNR95dlr0Dy8F0WfZHr1bAOMm9EE1yBIyTIpAQPnoL0VB3WHmkuhXFNW1SWFndhtL6dr9DwX3pHVZ2DQMf92GlKAKVZqkj8sC7gMXH/7uCHEi7BDBeLwUhSv4MD4Wjrg7HLrscrmZpuFTUzTcj/v77AlyVW10x8N5qoOpA976wScDKjRCTzsLx5cvRWSwN9Y644XoY1q8PTJ0Tka1VCmYOfwYc3Tp+hmUHSme79HPatZCO36HfQyAPcYeV7htDzJEz2CAyYorUDckgMvDsHUCde87K2qLu4eB+h4H74R1Wxs3oHg4ekz6unk/Y7XY888wzsFqlJoTVq1cjJSVl6BdyOYHWKt9zOTaVA80nfL8pPBTKUK+wcbJXADlF2hcay9+tcUQQhL2iKM4druuZzeZSo9E4ZhdOobHJbDbHGI3GlN77GUoSjYKuLkvP/JV+uixVChlmJYYhZ1KEeyGacKREh0I2Rlf9DnbDGVZOjvI9Z+WYXg285RSQ/44URvrryjKYpI7IrJWALnZ065tATt7zS7R8Ig19UyYlYdqHH0CmHUMriTtswOePAN//qXufIAMW/Bptrrmo+Pnt0j65HNM+eB+q6dMDU+dE0FgmDck+/Gn/w7LlKqkLMt29WvZYH5YdKCMRVPaHIebQOGzAse3uVbMHCiK7hmYziBzzJmJYKYrSaBGXw/fNaQdcTnz01ffYUyC9UWdKm4TlF8z0up/T/dFr22GTnm81lUurVzdXSKGjy3Fm9arCurscPYGjV/CoieTv0QTCUJLGAoaSRGNUV5dlm82OKdGhE2YhmnHDbnW/IN0HtFUDMRnSvGLRqYDs9AJBq92Jkrp2T0h5tOZMw0o90uJ1YyOstLUBhz6Shmcf3wHAxxekT5TmiDReLw29ojPStmMHKtbe5tlO3rgRugvOD2BF/Tj0CfD+7dIE+W7ilAtQ/mUELLv3AQB0Cxci+aU/BqrC8cczLNs9P2RNof9zJ9qw7EDpbJfCkvY6qfO7vQ5or5U6Udtre+63+178btjIQ6RwUhvtDjJj3eFltNfnXgHncISYougVjvgLWLy37T3Pd/ba9j7eI7RxeoUv7m3v423V0grxDCKDhyes7Fpcxz0cvKEEPp9v+CPIgMipUkAZnuT753lQP6c+QkJ/xwYZpp5APDZiFQBAiU7ciz9DhcHNHz8kmshew6t7BZCaiOF/TBqzGErSWOAvlOSckkQB1jWXJTB+hp+MWy6n9CT35D4phDy5F6gu8P1uc4hOGu7YtULrEIJKtVKOTEMYMg1hPfYPNawURaCs3oKyegu2FXXPj+wdVkpDwUc4rHQ5gdL/SB2RhR9I8wf2ptQCmVdKQeTU+acd6FJPrvZ2VD72mGc7/Korx24gCQAzlgGGb4DNa4DybwEAQtl/EZ8YgxJBBYgi2rZvR/vO7xF6ztkDXCyI9RiW/W/fUyJ0SciWQsiMJYAhNziHZQ+3kFAgafbgzu20uAPKWqC93uvzuuEJMZ2dQMtJ6TYY3iFmSGivAKWfALD38bGqK4icuVxalIhB5MSi1ACGHOnmzTusrCnq7rD0F1aKLqDhmHQbY5JQjRjUow7RsCMERUiFCf3MAeyPNsars3EyEO41t2N4MqAOG/gaRERjADsliWhiEkVp9dlT+6QQ8uQ+ad5DX4HaYPUJKnPdQeWZhQBWe9cCO1JI2TUc/HQ6KzPi9ViUGYdFmfEwTYo4s6H/tYeljsj8d/y8IBakANJ4A5B5BaDqu3gQnZmq//1fNP7tDQCAPDIS0z75GIrIyABXNQhOB/D1U8DXT3q6R07tjEBzqTTkXDUzE1Pfew8CA7RuLhdw/Etg72tSd5iz0/d5PYZlL5ZWN6Xxo7PdK6R0h5aebsy6vp+PdCfmeBAxWeqGZBBJvQ01rBxpMoX7ppTenO3alvfc/m9HKra1pQEAUlTNWG046nXcx3118d0LyURMlv7usxOehoCdkjQWsFOSiCa2thp3+Li3O4jsaBjcfaOmA0lzpCE+1YXSvGJt1X3P62yTur/cHWAAegaViblSWDnEoFKtlGNmYhhmJvbtrBxKWCmKwKGqVhyqasWL248hRheChRlSQHlhWgxCVYP4k99eBxzcLIWRp/b7PicmQ+qIzLmOgcgI6jCb0fjG3z3b8b96YHwEkgAgVwALfwWkXAD886dAayVic1rQUqGB6BRgKyxCy4cfIvyqqwJdaeC1VgH7/w7se12aM8yX0DgpgMxYCkxbwBej41lIqHSLnDK48wcVYtZ2HzvTFXW7CDJ3ONIVssjdwYqiV3jitd3juL/7Dua4+6ZQAZPOYhBJ/vXbWYPa9csAACAASURBVHlEGv7d0dArHFT4vsn97Pd53EdwKMgG/XOa09KCL559FqIootQWjsar3kDkePn/nYhomLFTkojGH2uLFBye3NvdBdlyYnD31RukADIx1/3RJM2701tLpXvRg/3dCyD4Cip98QSVud1dlcPQUdnFV1h5tKYNZf10VobIZTh3ejQuzozDDzLjkRSh6T7osElz1ZnfkoaK+hq6p40Gsq6Rwki+QBxxot2OkpXXwHbkCAAg9IILkPzKnyGMx+97ez2w5efA0a2oydejvlAPAFBEh2H6FzsgUwfh1BXeXZGHP/X9O8dh2XQ6vENMh23Aji3f4YqcP29EI+zvf/87iouLAQALFizAggULAlsQTWjslKSxgJ2SRDQ+OWxA1UGvDsi90lCdwQzLUYcDibOlucGS5kifhxkG97hhBumWsbR7X0ulFFJ2rdA61I5Kg7F7fsozCCr9dVa22xz4z9E6fFFUje2Ha1DX1j38s9Ppwo4jtdhxpBYPv1+AGfE63JRcg0sd2xFT+jEEa1PfB5KHSF+/8QYg9WLpxSqNivq/vOoJJAWNBgmPPjo+A0lAWpBj1dvAzpcQ7VyPpmNaOG1yOOpb0HD/SsQ89U7wdP61VgP73/DfFamJBIyrgDmrgdj0US+PJoChdmISUUCYTCZPKJmXl4f58+dDxjcDiCgIMZQkorHD5ZTmA/LMA9m1EM0gViVUqKXQL9EdQCbNBqKmDW9HX1dQOWOZtC2KQGtld0B5yt1Z2V7T976dbUDZN9KtS4jePezozINKAAhVKbAkKwFLshLgconIO9GEL4tqsK2oGoeqWgEAk4QaXC37L1Y0/gdTm/10fiafLXVEzlrhu4uURpStpAR1f+xenTr2rrsQMikpgBUNA0EAzr0d8innIrb6x6j6WuoMrP+iGBHPzYfix69JnYETkcsFHN8O7P2r/67IKedLQWTmlYAyCDtHiYiCTEZGBlQqFWw2G5qamlBeXo6UlJRAl0VENOoYShJRYIgi0FTmtRL2PinUG8xCNIIciJsJJOV2d0DGZY5+J58gAGGJ0u20gsrWgYPKxFxpzsshBpUymYDZkyMxe3Ik7r0oAQ2734F935uIb9zn8/wKVyz+6boQH+FCJApZuNgeh0U2NRI1Pk+nESK6XKh6ZD3ETqnLVZ2VhaibbgxwVcMoMRcRz36NhiWL0FnbAZdDhrqva5FgWwQs/h1w1pqJMzVAazWQ93dg7+vS37rePF2RPwFiM0a/PiIiChilUons7Gx0TWGWl5fHUJKIgtKAoaQgCK8CuBxAjSiKWe59UQDeBpACoBTAdaIoNgrS2LLnASwDYAGwWhRF36+AiSi4tNX27IA8tQ+w1A/uvlHTusPHpNnSfI0h2pGt93QNKqh0z1N5WkGle57KgYJKpwM49qW0YM3hTxDlsPY5pUMWis/Ec/Cm9fz/x955x7dV3vv/c4a2LHnIsmXZTmI7eydkUkbgEgJlBAIJhBEIKYSmlEIhUGjLBXrLLVzKvZTQXhJCW1oCNPBrSaGMhOYWMkickL0T7yHJU1s66/fHkWRJlrxteTzv10uvs54jPXIc65y3vgOl0jhIkJ/vbFSa90SLAf8W6uY9zWrsXTdvQqe0fPABvPv3yxsMA8svngfFDq/vDyldBszP/RrVDz4IAGg+r0XGOA9UnzwGXNgJ3Pja0I3Q7UpUZOFC4KJ720VFBnkR5Y0enLW5UdfqQ36GBmNz0jAqUwuWISl9BAKBMNyYMWNGREqeOHEC1157LZRKZYpnRSAMfaxW61S73a6orKw8YrFYIhdjEyZMmHT69GnNqVOnjo4fPz7Y0XP0JX//+9/TbrjhhnFqtVoEAL1eL9xxxx0Nr7zySm14DEVRs9VqtUhRFPR6vXDDDTc0/e53v6tmh9l9QCK68g5/D+A1AH+M2vckgB2SJP0nRVFPhrafAHANgLGhxzwAvw0tCQTCSCLgkqVbpA7kt0Brkq6y8ehzQ+nXM9sa0gxVQRGmM1EZXaeyu6Iy3PE7LCptx+SGNUf/kvi5KEauDzn9NmjGX4MbGTVGVbdgx0kbdpy0R9K8w5ysc+JknRO/+fIcTHoVrpiQHenmrVUO/w/JgYSz22F/6b8i21mr74V6woQUzqj/0F9+GbTz58O7dy8gUbAfMqDg0ibg1N+BusPAsk1A4fxUT7PrdBYVqU4HZsi1IoMZY2X5eLI51KRKblhV1uABn6BTlZKhUZStw9icNIwz6zE2R09kJYFAIAwDrFYrTCYTGhoaEAwGcfLkSUyfPj3V0yIQhgVWqzW4efPmzKefftoOAPv27dP4/f6UXThlZ2dzNpvtCACcOnVKedlll02YNWuW96677ooU9t+/f/+JKVOmBL799lv1VVddNX7cuHGB9evXO1I154Gi0ztKSZL+RVHU6LjdNwK4PLT+BwA7IUvJGwH8UZJbeu+lKCqdoiiLJEl1fTVhAoEwCHHVA6c/Aar2yxLScRpdakSjMsryMboOpCGv36c7KOhQVH4bm/7dVVHJqoEEEZEA5OjS6bcDU28B9ObIbhqIpHk/fvUEVDd7seOkHTtO2bH3fCOCghgZ2+AO4P3SarxfWg0lS2NhcRaunJiDKyeYkZdO8rx7i+0/fgnR6QQAKAoLYVq3bsBeO8AL+PpsAz4+WoeTdS5kp6lQmKlBYaYWhZlaFISWaeq+KZFAURRy1j+OsmW3AJIEd60aHpsSupwg0FoFvHUtsOgp4DuPyJ2BByORqMjfy3//EkRF+izzcNq6DP9SLMTpBg5n3q5HWcP5hPIxGUFBxKl6V7svDNrLyjSMzdETWUkgEAhDBIqiMGPGDGzfvh2AnMJNpCSB0DcsX768ccuWLVlhKblp06asFStWNLz44ouRQu0+n496+OGHrdu2bcsIBoP01Vdf3fzGG29U6fV6yeFwMMuXLx9z6NAhnSAI1OzZs92bNm2qKC4u5gBg7ty54xcsWOD66quvDKdPn9bMmDHDvXXr1rLoyMxkTJgwITh79mz38ePHExYSnzlzpn/OnDmuY8eOjYgbrJ6GueSERaMkSXUURYXvcK0AqqLGVYf2ESlJIAw3PI3Ayb8Bxz4Eyr9GpxKSVctiLLoTdmZRj5u6DEtiROV35X2SBDhrYzt+134LeBJ8aRYvJPW5wLTlctOanMldmkJ+hharFo7GqoWj4Q7w+PqsA9tP2vHPU3Y0eqK6efMidp52YOdpB34GYJLFgCtJmnePcX35JVyffRbZtjz3LGh1/zY8iRaRX5ywweVvu4Y6meRTO1OnjAjKsLQMb1uMGjDd+HdXT5oE4w03oPVvfwMA2CumY3ThWVCBFkASgC+fB8r+D7h5I5CW26v32qd0EhXpodPwKbsIG92X4FSZFSgDgK5FilvTNRibo0deugZVTV6ctblR70z8RQORlQQCgTD0mTZtGnbs2AFJklBWVoaWlhakp6eneloEQrc4OWHi7IF6rYmnTh7oyriFCxe633///ayDBw+qp02b5v/oo48y//Wvf52KlpLr1q3LLy8vVx06dOiEUqmUli1bVvTEE0/kbdiwoUYQBKxatarho48+usDzPFauXDn6gQceKNy+ffv58Pkffvhh5scff3y2qKgoeMUVV4x7/vnnc15//fWazuZ29OhRVWlpqX7t2rUJoyAPHDig3r9/f9rTTz/d6XMNB/o69y7R3UhCU0FR1P0A7geAwsLCPp4GgUDoF3wtwKmPgWMfyLXfJCHxOIqWG9HkzWyLgDRPGvhGNMMBigKMVvnRFVHJaoCJ18sisujyXkWZ6VUslkyxYMkUCwRRwqGqFnx5KnGa94k6J06E0ryz01S4YrwZV0404zskzbtTBLcb9c8+F9k23nwzdPP7J3W5IxHZFZo8QTR5gjhc1dLumIKhkJ8RlpRdi7LM/tHDcH76KaRAAP4LNXDe9e8w+t4DKvfIA8r+Bfz2YuCm3wFjr+rRe+4TRBH8+X/Ct2cTdGWfg5ba/9y+ESdgC38F/iHORQAd1wQLy8dxOWkoMbct9ar2/1dafRzO2d04a3PhjM0dSffutqxkaRSZdBiXk4axIVk5LkePQiIrCQQCIWUYDAYUFxfj3LlzAIDDhw/jsssuS/GsCIThwfLlyxvffPPNrEWLFrmKiop8Y8aMiURYiKKILVu2mEpLS0/k5OQIAPDUU0/VrVq1qmjDhg01ubm5wj333BO54P3Zz35Wt2TJkpjOhLfffnvjtGnTAgBw8803N3388cdJv1FwOByKtLS0GZIkUR6Ph77qqqtaFi9e7I4eM3/+/Ek0TUtGo5FfuXKl44c//GFDX/0sBjM9vVO0hdOyKYqyAAjnFlYDKIgalw+gtt3ZACRJegPAGwBw0UUXdT2PiUAgDCwBt9yw4fiHwLntgJCoJjAFjP4OMG6JLCEt0wClbsCnOtyQRAkSJ0AKCBADsUspqIAYmAWJnQ7RfCckIw/J64UEFpAY4BCAQ+c7fY3uMAbAfaBxX34uPNkm1LX6UdPig8PlR0w2qgtAqQMXSh0opyjkGFSwGDXIS9dAqxykqbh9CEVToBQ0KJYGpaCB0DK8HbPO0mj6/ZsQvTQobRZoQxqyf/goJE4EWApUH3SiDvIivj7nwN+PdCwi8zM0+O5UCy4fb4bLz6GyyYuqJi8qQ4+qZh+CvJjwXADgBAllDR6UNXgSHk8WZZlz2x0I/mEzAMD+xttI+/tHoL/5H+BfLwGQAG8D8OdbgIUPAVf8HGD7twkAJ4gob/DgjM2NmuoymM9/iLlN25An1SMtbmyLpMMHwqXYIizCOSm/3XN1Rz4mw6hRYPaoDMweFVtbV5aVsqDskqzkO5eV43L0KDHLy1FZum5FvhIIBAKhZ8yYMSMiJQ8dOoRLL720Tz7/CYSRzpo1axovv/zy8RUVFao77rgjpsNqXV0d6/f76fnz50+M3i8IAgUALpeLfuCBBwp27txpcDqdLAB4PB6a53mEm8/k5uZy4fO0Wq3o9XqTfssbXVOysbGRuffeewtvvfXW0du2bSsLj9m7d++JKVOmBPrgrQ8peiolPwKwCsB/hpZ/i9r/A4qi3oXc4KaV1JMkEIYgnA84+7kcEXnmc4D3JR5XMA+YsgyYdOPgSq9MER1LxC7sC4bXeUgBEVIwSSTqIIACkBd6oKOoMAlAqwi0eoBKD7wDMbkhxxzorpwT2bL99wl5hQIoNoHUTLaMWhdpCuWtPhyzuXCk3gknLyIACVMBBMAgCCAACekGFS4Zb8blU3IwqSAdtJIBmMQyVBQl2F0BVDR62gnLyiYfGtwdX0Mli7LUckV4U6VHesANvq4O7z79Ctw3r8SMSydhZul6KLyh7z13/wYo3wXcshnIHNObHziAWPl4NiL3XChvcGEejuF2ZgfupQ9AQbX/f7hPHI93+CsjUZHWdA0uz9FHRSB2Xz52F1lWZmL2qMyY/URWEggEwtBi/PjxUKlUCAQCaG5uRmVlJUaNGpXqaaUcSZQAQYIkiJAECRBESLy8Le8PrfNxY0LHwIttY8LPw7c9Z+y+qPPC67wY8/qmVZPBZvZvaZ2hSldTqgeacePGBfPz84M7d+40vvPOO+XRx3Jzc3m1Wi0eOXLk+JgxY7j4c5977rmcc+fOqffu3XuysLCQ3717t+biiy+eJLdP6R1ZWVnCypUrm1avXl3U6ycbBnR6tUxR1BbITW1MFEVVA3gGsox8n6Ko+yAXSro1NPwTANcCOAfAC+DerkwiWONGzc92dRxZErnpYkCxVGiZ/MasKzdxyW68CIQRCR8Azn8p14g8/QkQdCcelzcTmHwzMPkmIL0g8ZghhsSJENxBiB5u2EpEwhBEkn83wYmQknwv0BHZABYBWARV8kFOAPubgf3NbcWfKYBSs6A1UQ+tvNRoWEzSsJisYUFbMkEXmUFrFKA1LHw0UOMLoLLJ160oS69CjbcnLMZDhz8EAIzf/gFWU+PQqtIjE8/hZcVvsYg5LA+uPYjAhoU4MO1ZUFOWoTBLi1yDukNJFpaPZ+2ydDwbEnVlDR5wQtuFpQmtuJX5P9zGfolRdPvmUi2SDp+zi3DIvBS6/MlYmJOGewZAPnaXgZCVxdmygB0X6gQ+1tx/slKSJPmGkxPkm1FOgMSLkDixbRm9HrdEkv2JlwIkToLEC4AggdYpwBiUYNKUYAwq0JH1tiWtV4JiyLUkYfAiihJcAR5OH4fW8MMThLfFj2BrAKIzCMnNgfXyUPoFaIIi9JwEowAYAHgAuFjAp6TBaVlApwBrVEKdroEhW4PMHD2yc3VQklItPUahUGDq1KkoLS0FIEdLDlUpKXEi+CYf+AY/+EYf+EYfBBcXJf2ihGKc9GuTjpLcTC55ckZKINf5Q5O33nqrvLGxkTEYDCLHtblHhmFw2223Naxbt65g48aNlVarlS8rK1McPHhQs2zZMqfL5WLUarVoMpkEm83GPPPMM33WjbW1tZV+9913M0tKSnpwhT/86Er37duTHLoywVgJQI/ahYYvKgcUCgmFZazQjJKgXYhUQVfG0ESGEgYJAi83kjj2IXBqG+BvTTzOPBmYcpMsI7OKB3aOPUQMChBdQQhuDqI7tIzfdnMQXEFIgcF7kUEpaFAqBrSKAaViQCnb1mkVG9pHt60PspvjJk8QJ+ucOFnnxAWHB3ySbxcVNIXibD0mWgwYlaVFTieiadAihSNmoyRHSKQgTqgEyqvA19oARgGKVYG1WAGJipwLIUWVTSRA8vEQfDy6+z9DTwGT1SymRkSmBrQlDdQYFj4GaBFENPACbEEO1b4gyj0BnHP68fWo+bjx/C4Uum3Q8gGsPPU5fjv9ZjTBgNXc41gt/gNPsO9CSQlQCV4s/PZxbNn/Ce7l74bAqNvVsvQGhaTyMRoKIhbSx7GS2YHFSaIiHZmz4JlyJ0xzl2O5Pg3Le/AjHQx0JivP2NyRn9kZmws2Z/vIVyUAJS/CXudCS50LR0BBCUAFClqaxmijBoVGNfL1Kpi1ShgUDNIYBnqGBiNKXZKIiaRiqhBDnxMcEpckAABQaJOXBhWYNGWsvAytE3mZHEmSIAVFiD4OopeH6OMh+XiIAUH+vNPEfklCqZgRdx2dUCwmeDi98vUN5eag8AlQBQToORFZoJEFCqbQMh8UmIStAMK0HUsHkM4D4CXAywENHOTYEznqXYJcx6sFElwsBZ+SipGXmgwN0kxEXnbGjBkzIlLy+PHjuOaaa6BU9m+5kp4i8SL4Jj/4Bp/8aPSBb5S3hdZAp70vhypSqq7LCL1i8uTJSVN5NmzYUL1+/fq8efPmTWxpaWHNZnNw9erVDgDOJ5980rZ8+fIik8k0w2w2c+vWravfvn17j7tQORwOhVarnQkASqVSmjFjhvudd94p6+y8kQDVF+GnvWWaZYL0yaqNqZ7GwNGZDO0oCjSZ6OykZhmRoYQIogBU7JZrRJ74G+BtTDwuq0ROzZ58M2CeMLBzTIB80yJAdHFyVKNbXgqu9pJRdAchBVNzIxuWiJSKAa1kYoQirWJBKekYoUiHRGPsuDYBSQ1FMZcEd4DHV2dC3bxP29HkSVSfVEbF0phoMWCq1YipViOmWI0Ym6OHYpg05AicP4+ypTdBCn1jm/PUU8i8+66YMVK0xOFDEZO8iKCfx5HyJuw714TjFc3gOQGqKDkUXppULMZm6TDaqEGGio2c35kISpUMlSABQQ+koBci50WNOQ/1NAtbUECrJEKPBtzIfAETXQcabtCUG5WSDo9xd+Iw8rt1D2RCK25h/g93KnciX6pvP0BtBKavBGavAswT2x9PIZHIwfgIvy5EEXYnalAICuADAkROBCWIYAdZxMqQgwJovSISdRmRl3HRl0NVXraJxbBU5OT1kGSMf0hx27GFiTuBBuhQNHeMsNQqYiO844+nWGiKogR3kEert2Ox2Orj2slHt5eDARRMoJAFGqZ267JszAAFtkPZmHrayUu9AqyByEtJkrBhwwY0NMh9LW666SZMnz49dfOJFo+NYfmYAvHIUqAYWv67GFpGMh4Z+RhC+6LHgIk+L7TORj1H5Lyo9fB4Njwm9jXZLDUoxdCsjU5R1AFJki7qq+c7fPhw+fTp00dEExZC33H48GHT9OnTR8fvHxRS8qKLLpL27drb+4vobqbtdOsCaDjQoQyNEyKJJEkycaIYXuJkWCJJQPV+uUbk8b8C7gQ34QCQXihLyCnLgNypcufnfp2WBCkgyCIxTja2Cca27X6LpqYp+WZRp+gkKpEGpWLbycaY/yPk/0KXkLt5N2P7STu+PGnHaZur03OGi6iURBEVd94F38GDAAD19GkY/c47oJjkF7pBXsSucw2hZjX1cCZpVmNN1+C6aRZcO9WCafnGHt18S4LYoTyIeXhjj6cqtUmEBBckuAC4ICEQd7ekZGhoFQzSGS+MfBNUXIv8dzEelQFIywG0WQCVwt8rSb4hTHSdk8rIwVQh0oDEyNcrtJIGq2BAKzsu7dPlWqwJvswFS4OiqbYIe2cAgisIwSk/xPB66DOqT4mXlwYl6EiquAJ+NQMPS6GVBloDfDuZ1eJtE1osQ4UaS8mRxKOytCjI0EKXpNxAUrGY4P99r8ViqggLTa2inbBMKjRD5SsoZXKh6eeEuDq7cgkLuysQIxrjf0Q0gPQ4wRiOaIze1x+y0ctS8CtpcBoGopYF0pRgDUoojSpoMzXQZ2mgz1CjpdmPJpsbzgYffM0+cK1BwB0E6xWgDopI4yWkSwDdx/OLyEsVBU4TKy8N2VpkmHXDTl5+/fXX2L59OwBgzJgxWLVqVb++XkQ8NsamW/MNPggtPRSPFMCkq8CaNGCz5AeTrgxJw8QCMVYEtglC0CABNX0EkZKEwUAyKTlo/orTKhYdlb3qD8K1LGIjDELLnsjQuJuGQSdDJUAKiv0SQSYLy6gUUiWRnClHkoC6Q3Jq9vH/B7RWJR6XZgmJyJvlztm9/PCXJElO/UwgFdttu4NyOlB/wFBg9ArQemXbMi3xNq1hye/cAMPQVCSV9IklE1DV5MWOkzbsudCIYzVO1LS0L7ES4EUcqmrBoahGKfGicmq+EWPNerCDWFS2vP9+REiCZWF57vmEQnIgRWQ0FEOD0SvB6LufNtYloelNfLw3QpMGBSMoGJMNEBCKANUAsCIIa+Jx/tADSWrqjnQYKqnYE2gKXkmEixPRwvFwcgKcvAAnJ6A5yMMnSQgACCJ2GYAUabyUaBkEIImQa4txAEJ/GgxqFjkGNcxKFXI0apgNapjTlPI+gwo5afJS3YuoFjZdBaSrgHZ919uQeLFv5aUEiC4OoosDV5s4bZwCkAYJPCT4IYGGCDEk470Q0QIJDRDBA2hBE8pAIS3qka1gYFawyGIYGCgKOhFQ8hKYYOquUSkFnTCqMZzWLUXL0N58QSlCfh5v4r+nHUJTEJU0giwFLw04IaFJEGEP8rBzPFyQ4Ix8QSI/5KZwNKaBQhaUAyIbBRUDSceCNiihMKigzlBDYQwJ7nBkbposibqCRqeEJd/Q4RguKKDB7onIS2+TD7yzd/IyHVRb2rgncdp4PSQ4KcDFUPCqaPAaJiQvVdBkqGHI1sKSq0d6hka+z+jie04V06ZNw44dOyBJEsrKytDS0oL09B5niwIIicdmfyTKMTrdWmj291w8GkPiMSIf1fJ6pnrQ/5wJBMLgYtBIyVQgfxvDAKqBDcOWZajQ/cjQrqbexWwLbaK0H68zpaAQSa3tNRRkMZk0Yi2J0IxvzKBhR+aHou2EnJp97AOg6ULiMbpsuWP2lGVAwXyATv5zkjgBgpeXm8B4QvWevG3rgoeTt728fAPm4fov/ZOlwOhDUSN6hbyuV8iSMU0ZIyEpDUu+XR1CFGRqcc/FY3DPxXJX5UZ3AMdqnTha3YKjNa09F5X5sqwcLKKSs9lgf+m/IttZa+6Devy4yHZYRH58tA6fH+9YRH53mgXf7SMR2Vf0hdCsf/4/4f7XXlAKLZRjxiHrgXWQ/EK7NFDJ7YHY1ABRUEKCth/ezSAmHGnSSdmWcG1sRKIIu14ju13zwFD0YE8QRQnN3iDsrgBsTj/srgDsTj9szgDsLnnpcvrhcAeS1v+Mx+nn4fS7cdbesUCOyMuIqFTDnKbqlbzssL5fgEOryKGF5uBUcmjVcWilebgVPGgvD3WAR2acmIqOjMtA1/5OMaHoOhOA8ejmdSwXenS7amzHJBKLSVOp41Ktu3O9JvFiuy87kkZ0e7mY470TmhJovwA1ADWATACjAQBM6NG/0Fo2KnJWFdtwqQeysS9RKBlY8g29lpeaoAh9N+QlDQrpUrjmpQh4xCh5KeND5LsMgKES30co4zJfIvvYjoMq+vhnbTAYUFRUhPPnzwMADh8+jMsuu6zT8yQhHPHob1/jscXfs4Yx0eIxLByzNEQ8EgiEPmdES8lUIcvQVESGdiA6g6LcRTjUTbito3B8l2Gh3bg+T6mVoiVn756KUtJxF8Pt6w0lqjk05IRmw7mQiPwQcJxMPEadDmn8TRCKboSYOQuiX4TYwkGsrYfo4UJyMVY4ip5+TJkOQSnodkIxnLpGR4vHNOWILG4/UsnSq3DZuGxcNi47sq/RHQgJytYhKyrrn38eokeOflKOHg3Tgw8OeRHZV4SFpvnhNXB9+iHEQAA++3GItyyA8frrE5/E+YDPfwpp31sQoYMo6SFSRkBnAdy29uOVeqD4SmD8NUD6IO5sSqHj2tFDLLKbpilk6VXI0qsw0ZJcWoTlZVhW2qOkZbTMtLsC4LsY0ddTeZmpU8ITFBKKR5e/fRpu90j+ucoCyEySwpsFCmaKhgk0jFL//A744yL94h+ikoE6TQmdQQVjhhpZJi1yzHpYLXrkZmhBD8DvJsXSEQHXHSRJgr3Vh9o6N+rtbjgcXrQ2++Fu9SPgDkLyC0gDBUNcZGn4oemnWo0dysaoeqND6ro0Cd2V/vB+awAAIABJREFUl402N1zR8tIVBOvrvryMIEhylGxPImUT0Q+Sc8aMGREpeejQIVx66aWgKEoWj82BtvqO4RqPjT454rE34jFaOmZpwJrUYDM18mcPgUAg9DODpqZkuNsYYeghiaEGJIFooRktL3lIATF1krOHdEtoauP2DYDoEO1lEA9/AvHE/0FssEGEAaJkgCAZICINomSASGVCVFkhUukQg+yA/WwpJQM6LTaSMVzAPz6qkR7gSGXC8KKrojIRYVE5LV+uT9nfotL5+eeo+eHDbXP/j1fxV9HcJRF57VQLpg9DEZkM+69fQeMbbwAA2DwLij/5BLRanfyEEx8BH/0A8LcmPl4wH5h9DzB5KaDQ9P2ECQNKInkZlpY2px82VwCObsrLgUSnZGDUKGDQKGCMe6RrFUmPGTSKSA3dcNq44AzEpIlHp45LgtTu+gRqFm5KQgMvwB7kUOPjUOEN4rzLhzMtPti8yRuQdYaSpVGQoYmpY1mYqcWoLB0KMjXQDkDtPz8noLo5VNex0YuKUG3HcJ1Hfy+ug7LUCoxP16A4TYNROhXytUrkKllksQzSJAB+oV2kJkWhTTZGdWQfbrIxVXQmL9UBASpeghYUtMCgbwIEhoKoBP4k7UQQ8nXBTVmXIsefBr450OPyCjHi0RSXaj1EG7cQugepKUkYDAz6RjdEShLC9FRyJkrhGbDOcHF0WWhqY0VmJNUolCotxEcuurwQW90QAxQkSTEwb4amQOtCqVZaBRgdC1qniGy3HWMjEpJWkgscQuoYjKJScDpx/rvXQXA4AADbixbg5WnLEo4dqSIyGsHtxvnFV0NoagIAZP/4UZi+972OT2qpBD5YA1R9I2+rjcD024FZq4CcSf08Y8JgRBQlNHmDsrR0+eUoy8h6ALZQ5KWjB/KyI7EYloudicXBiMvPRZq0tDVt8aGqyYvqZm+X0+sTYdKrUJjZJi0Ls3SRdXOaqktRlpIkweEOtM2v0YeKJk9k2+YM9Hh+DE3Bmq6JEaqFUQ2CjNoBuu4i9CkVjR5s2VeFraVVcHqC0IKCBgiJSgr5WiWuKDJhYWEGDAwde+/R4f2I0G81WL9mT+EUWwMAGMfn4VJ+YqfnMEZlW3p1dI3HIdwxmtB3EClJGAwMbik5dbxU+sUHgN4sd7ykyR9OQu8Jd3buqLGCGNdZcjAIzX6FodoEok4RkoshqRhe1ynAaNvWSco0YTgQLyqPVreittXfpXNVLI1JeYZIx+/uiMogL2LX+Qa0PP8cJpTuAAA0qdJw/5Xr4VG2RewREdme5i1bUP/scwAAWq9H8eefgc3M7PgkgZcbe9EMMG4JoBxhtSYJPSIsL6NTxJs8HHSqNvGYPoTEYn8hiBLqWn1x0tKHykYPKpu8aPb2vK54dJSlHFmpRa5BDYfLL79GkxeVTR5UNfng43peC9OgZjEqJEPjxaPFqB4UtYcJ/UOQF/HFCRu27KvE1+fauxSaAhaNN2PlvEJcPt4MphNJLkkSwEttQRJBMRQ4kTgTrCv7wpLTTrXiI5UcsKOQGKwMXAIFGDm6NksDhSmUYh2SkEymmgQEEDqESEnCYGBwS8k8Riq9Xy9vULQsJnXZ8kNvBnRmQB/ajqybAZ0JYAe4MCNhRDA0hCYHmg2A0SlBZ2SATtNERGJYMjLRwlHHynVriOwgEADEisoj1fKyq6JSrWirURkvKsMi8pMjdfjseD0Kq0/jpa9/Gzn3F3Puxi7rNCIiO0HiOFy4cSmCF+SGXRkrVyL35z9L8awIBEIynH4OVXFRlhWN3lCUpW9AUukZmkJeujokG9siMcMPEu1IAIDyBg/e3V+FrQeq0OBuX7LAYlRjxZwCLL+oAHnpA1PyI1pyin4ev317E5pa5WyBGxZdixkLZhPxSOgxREoSBgNDR0p2F7UxJCrNcSIzu/0+pa5vJ04gJCAiNOM7xXYiNKWgAJrxgubqQfsrQaMFNFygKScYygkaTtC0G3T+eNBTrwI19TpQuqxUv10CYVjR4A7I0ZTVrRFh2R1ROT4nDWUNnkiNSIXAYcM/f40Ct5y2/W3BNJQ9/HN8d7qViMgu4Pryn6j+/vflDYZB0bZtUBWNSe2kCARCt4mPsqxo9MZEXHYnyjJNzWJUljaheLSkq0dkFCuhZ4SjJ9/ZV4Fd5xrbHe9u9GRf8tVXX2HHDjnDIj09HXl5eQP22oTELF68GOnp6ameRo8gUrL/+fTTT/Vr164dXV5efizVcxmsDG4pOSZdKv3xOMBjB3zN/fdCCl2SiMvstvWwyFQbAXKzSOgrRBHwNgKuOsBVD7jr5aWrDnDZ5KXtOCAkqYVUuACYsgyYeAOQljOwcycQRjgN4YjKHojKu058ipVntgMAJK0WJZ98DGVubn9Od1ghSRIqV90D7759AAD9lVeiYMNrKZ4VgUDoa6KjLMPC0uYMIDtNiYJMLUZFyUcS7UjoD8obPNiyvxJbS6vR6EkePbliTgEsxv6PnnQ6nXjllVcwGO7VCTJr165F7hC9hhtpUtJqtU5tbGxU0DQtabVa8bLLLmvdvHlzpdFoHBwddRPw6quvZv3hD38wHThw4HSq55IMq9U6dcOGDeVLly519eT8ZFKy/9vgdYWsEuAH8g0HBA7wNMiC0u2Qlx4H4I5eho57GgCpG3VlOA/Q7AGayzsfyyjbR14mSyfXZpI6mCOVaNnotrVJR1eUdHTb5IeYuLtuUqyzgck3y91ijfn9M38CgdApJr0Ki8absWi8ObKvM1FpTdfgNlMQV277Z2Sf5fHHiJDsJhRFwbx+PcpvuQUA4N6xA559+6CbOzfFMyMQCH2JQa3A5DwjJucZUz0VwghltEmHn1wzET++ajw+P1GPLfsqY6In61r9+O/tZ/HqjrO4YoIZt8/t3+hJg8GAqVOn4siRI/3y/ATCcOfdd989u3TpUldlZSV71VVXjfvpT39q+c1vflOTirlwHAeFon+/UOvtawzEHJMxOKRkNIwCMFjkR2eIIuBrahOWEWkZFpiOWLkptP/WKylCEHDWyI/OoGhZTqblAGkWQB9apuW2PfS5ssRkBt+PnJCAsGxMFNEYHenYE9nYETlTgSk3A5NvAjJJiiKBMFhJJipP1bmQrlVgUo4OlXfcCZ8gf3GmmTUL6StWpGq6QxrNlMkw3HA9nB9tAwDYX3wJo99/DxRNUjQJBAKB0LcoWRrXTcvDddPyUNbgwbtx0ZOiBGw/acf2k/Z+j5684YYbMG3aNPj9XcvOIPQOKRBA4Px5+E+fRuD0GfA2W8xx7R13AOTL5SFHYWEhv2jRIufRo0cj/0l9Ph/18MMPW7dt25YRDAbpq6++uvmNN96o0uv1Ul1dHbty5crRpaWlepqmUVJS4tu3b99phmFgtVqn3n333Y73338/y+FwKBYvXtzyxz/+sUKr1Up///vf0+67774xNpvtCCBHFd5zzz2Ov/zlL5nl5eVqj8dz8Jlnnsl9++23TU1NTYrc3NzgM888U3P33Xe3HDx4UP3444+P4nme0mq1MxmGkVwu16HGxkZmzZo1BTt37jRqNBrxzjvvbHjhhRfqGIbBq6++mvX73/8+e+bMmZ6tW7dm3XnnnY4//elP2V988cXpuXPn+gCgpqaGLS4unnbhwoUjeXl5MdIi/vxVq1bZH3jggcb77rtv1KlTp7QURUmXXHKJc/PmzZUmk0lYunTpmLq6OuVtt902lqZp6dFHH639xS9+YduxY4fuscceKzh37pw6Ly8v+PLLL1ddd9113YqkHNqGjKblZjc6U+djJQkIODuIvnTErgfdXZ+HJMqSyl0P1B1OPi5eXoZlZbS8TLPI8pJEXvYP7WRjXERjRDr2sWwEAHV66N89ibw2FgBGa9++JoFAGDBMehW+M1Zuvtb0pz/Ddzj0eaBQwPLcs0Si9QLzj34E12efQwoE4D92DM6PP4Hx+utSPS0CgUAgDGPGdDN6cuW8Qlw2ru+iJ1mWRUlJSZ88F6E9kiDAf+IEPLt2w7NrF7yHDoHlOCTrQiF8+y0wduyAznGosGHtl7MH6rXW/e6KA90Zf/78ecWXX35puPjiiyOibN26dfnl5eWqQ4cOnVAqldKyZcuKnnjiibwNGzbU/OIXv8ixWCzBhoaGwwDwz3/+UxddA37r1q1Zn3322Zm0tDTxmmuuGfvkk09aXn311dpEr/3BBx9kfvLJJ2dzc3N5hUKBkpKSwFdffXW6oKCA27x5c8YDDzww5rLLLjs2a9Ys/0svvVQRn769Zs2aAqfTyVy4cOGo3W5nr7766nEWi4V75JFHGgDgyJEjumXLljU1NDQcCgQCVGtrK/PWW29lzp07twYANm/enLlw4UJnvJAME39+eXm58oknnqhfsmSJq7m5mbnhhhuK169fn7d58+aqv/71r2VWq1Ufnb5dVlamWLZs2dj//d//LbvllltaP/roI8Odd95ZfOLEiWPJXjMRQ1tKdgeKkutEqo2AqQt/3IPerkVfehxdr4NJ5GX/EY6ajU+fjo90dNcPvGxMy5X3KQamex+BQEgtXF0dHL/+dWTbdP/9UJGbil6hyMtD5qpVaHzjDQCA/ZVfI23xVaBVqhTPjEAgEAjDne5ET+YZ1Vg+gLUnCd2Dq6mBe/dueHbvhnf3HgitrUnHUkoltBfNhu7ii6FbuBCq8eMHcKaE3rJy5coSiqLg9Xrp+fPnu1566aVaABBFEVu2bDGVlpaeyMnJEQDgqaeeqlu1alXRhg0bahQKhWSz2RRnz55VTpkyJbBkyZKYaLXvfe979pKSEg4AnnjiibrHHnusIJmUXLt2rS08FgBWr17dHPU8zS+//LLlq6++0o0aNaol/lye5/Hxxx9n7tmz50RGRoaYkZERXLduXf2WLVuywlIyOzs7+PTTT9sBQKFQSKtXr25cuXJl8WuvvVbDMAzee++9rB/96Ef1yX5G8edPmTIlMGXKlAAAaDQa/uGHH7b98pe/TNpla9OmTVmXX35564oVK1oB4KabbnK+/PLLng8++MD40EMPte8eloSRIyW7i1ILKEcBGaM6H8sHZUEZHXUXI8RCD28Xa8EON3kpcADnBThf1NKXYF+iY4m2kzwH+rgQtDo99ucYkY3RP28iGwkEQhuSJKH+2ecger0AAGVxMbIeuD/FsxoeZN3/PbRs3QqhqQl8bR2a/vhHmL73vVRPi0AgEAgjiHD05KNXjZM7d39Tid3n2+69a2OiJ3Owcl5Bn0ZPErqH4PbAu28fPLt2wbNrF4Ll5R2OV40fD93ChdBdfDG0F80GrVYPzEQJfc4777xzbunSpa6PP/5Yf++99xbV19ezJpNJqKurY/1+Pz1//vyJ0eMFQaAA4Jlnnqlfv3593pIlS8YBwN133+345S9/GRF7hYWFkZqAxcXFAYfDoUw2h1GjRnHR26+99lrWa6+9llNTU6MEAJ/PxzgcjoROrq6ujuU4jho7dmzk9caMGRO02WyRwo8WiyXm+a+44gqPRqMRPvnkk7T8/HyuoqJCtXLlynbCM9n5NTU17AMPPFC4b98+vdfrZURRhMFgSNrEpaKiQvmPf/wjIy0tLVIQmud56tJLLx1B6duDBVYpNyLprBkJH2xrepI0oq8/5WVcjcuwbEvLkQUc7w9JPn83pGEH+8LP19eRib0lWjbG/xwi8jGXyEYCgdBtXJ9+CvfOnZFty/PPgVYmvVYhdANGr4fpB+tge+55AEDj/76B9GXLwGZmpnhmBAKBQBhpqFgmNnpyXyX+cqAaTTHRkzZsP2lDnlGNFXMKsXxOPome7GckQYD/2DG4d+2CZ/du+A4dBvjk96JMtgn6kITULVgANjt7AGc7fOhuSvVA8t3vfte9YsWKxh/96Ef527dvP5+bm8ur1WrxyJEjx8eMGcPFj8/IyBA3btxYDaC6tLRUvXjx4vHz5s3z3HjjjS4AqKysjFzYX7hwQZmdnZ20cQlFUZGoqTNnzigfffTRUdu2bTtz5ZVXulmWxYQJEyZJkhQeG3OuxWLhWZaVzp49q5w9e7YfAMrLy5U5OTlcoucPc9tttzW+/fbbmTk5Ofy1117brNVqk0ZuxZ//yCOPWCmKko4cOXI8NzdXePvtt9Mfe+yxwmTnFxQUBG+66abGd999tyLZmK5ApORAwiqB9AL50RFdkpd1cm3ErhAjL3v/NgYtamOCiFEiGwkEQv8ger3gHQ7wdjs4ux28w4HGjZsix9Nvvw3aWbNSOMPhR8att6L57T8hWFYG0e1Gw4bXkfuzn6Z6WgQCgUAYwYwx6fCTayfi0cXj8PlxG7bsax89+cr2M/ifHWdI9GQ/EKyujtSF9OzdC9HpTDqWUqmgnTMnEg2pGje2nQwiDD9+8pOf2IqLi6fu3r1bs3DhQt9tt93WsG7duoKNGzdWWq1WvqysTHHw4EHNsmXLnFu2bDFOnTrVP2nSpEB6errAMIzEMG1Zp5s2bcpetmxZi16vF3/1q19Zbrzxxi7V8nO5XDRFUcjNzeUA4H/+53+yzp07FxETFouFq6+vV/r9fkqtVkssy+Laa69tfvLJJ63vv/9+mcPhYDds2JDz0EMP2ZK/CrBmzZrGWbNmTdbpdMKbb75Z1p2fk9vtZgwGg2AymYSysjLFK6+8EtPZyWQycefOnVMBcAHAfffd1zh//vxJH3zwQfONN97oDAaD1JdffqmfOHGiv7i4uJ3wTQaRkoOR7srLRKni0Y1buiov+wuKAZQ6WQYqNIBCKy9ZTft9kWWifR0d05D6mgQCoU9IJBt5uyOyjw/tE93JG6KxOTkwP/roAM56ZEApFDA//hiqv78OAND83nvIuOMOqIrGpHhmBAKBQBjpqFgG10/Pw/XT83DB4cZ7+6s6jZ5cMacAuUaSItwdBJcL3m++gWf3brh37QJXUdnheNXEidBfvBC6hQuhmT2b1KMegeTl5fE333xz47PPPpv32Wefnd+wYUP1+vXr8+bNmzexpaWFNZvNwdWrVzsAOM+cOaN67LHHCpuamliDwSDcc889juhu0suWLWtavHjxOLvdrrjqqqtaXnjhhS6Ffc2ePdt///332y699NKJNE1Ly5Yta5w5c2bkZuK6665zvfbaa76cnJzpNE1Lzc3Nhzdt2lS5Zs2awqKioqkqlUq68847HQ8//HCHabXFxcXc5MmTvRUVFaqrr766G92bgeeee6521apVYwwGw8zCwsLAihUrGn/3u9/lhI8//vjj9Y8//njBs88+m//II4/UPffcc7b333//3BNPPJG/evXqIpqmpenTp3s2btzY8X/KOKhwuGgqueiii6TS0tJUT2P4klBehhq/hOWlv7WbgrAb0pBRdD5HAoFA6Gf6QjZ2lfzXX0faFYv6YNaEeCRJQuXdq+Ddvx8AoP+3K1Hw2mspnhWBQCAQCO0J8AI+Py7XntxzoX2gCE0BV0zIwR3zCnHpuGwSPZkAiefhO3o0Eg3pO3IEEJKWuQNrNkciIXULF4DNyhrA2Q5OKIo6IEnSRX31fIcPHy6fPn16F2vODR+sVuvU6O7Tg5Vbb711tMViCSZrwJMqDh8+bJo+ffro+P0kUnIk0NXISwKBQBiCDKRsjIZSKMCazWCzs+VlaF07exa0F/XZdR8hDoqiYF6/HuW33goAcG/fAe/+/dDOmZPimREIBAKBEEt89OS7+6uwNUn0pDVdgxVzCrD8IhI9GayslNOxd++GZ+83EF3JHRClVkM7dw70oS7ZypISkpJNGLGcPn1a+emnn6bv37//RKrn0lWIlCQQCATCoCRaNvIOhywco2VjaDlQsrFtmQ2F2QzaaCQXvSlCM3UKDNdfD+e2bQAA24svYfR774Ki6RTPjEAgEAiExBRl6/HUtRPx48Xj8NlxG7bERU/WtPjw6y/O4L+3nxlx0ZOC0wnP3r1yNOTu3eCqqpIPpiioJ02KRENqZs0kTQUJBAAPP/xw3saNG3MeeuihugkTJiRtwDPYIOnbBAKBQEgpos8H74GD8OzZDf/xE5Hoxn6Xje2kYzbY7Gww6elENg4BuNpanF9yDaSgfM2V91//BeN1303xrAgEwkhFEgRwVVUInDuHwLnzCJw/D9HtBmMwgDYawBiMYAwGMEYDaIMBjFHeDq+TOncjk0TRk9FY0zX4TokJY7J1GJ2lQ1G2DoWZWqgVQ7uWvsRx8B050paSffQoIIpJx7O5udCF6kLqFiwAm5k5gLMd+pD0bcJggKRvEwgEAmFQIAkC/CdOwLN7Dzx79sB34AAkrssN2tpBKRQdRDWaiWwcpijy8pC5ahUaN24EADh+/WukXfVv5MaeMOKRJAmixwOAAqPXpXo6w4528vHcOQTOnUPwwoXIlyQ9gVKro6RlSGAmFZrpYIzh40YSJTaE6Ur05HulsVGDFAXkGTUoConKMaa2R36GBiwz+LIGJEkCV1EB965d8OzaDe8334T+TiWG0mqhmzNHrgv5nYuhHDOGXMMRCMOUXklJiqIeAbAGgATgKIB7AVgAvAsgE8BBAHdJkjRkQkcJBAKB0LdIkgSushKePXtkEfnNNxBbWzs9L0Y2dpBKTWTjyCXr/u+hZetWCM3N4Gpr0fz228hasybV0yIQ+oWwbOQjpSxCS7s9ss457OAdDZC8XgAAk5EBRUEBlPn5UBQWQFlQAEV+AZQF+WBzckAxQzvaqj+JyMfz5xE4K4vHwPnzsnwMBPr+9fx+8H4/eLu92+d2KDSNRllqhoVmaJ0IzcGFimVww/Q83JCk9mQ0kiTLypoWH746GxusxtIUCjO1GGPSYbQpVljmGtSg+zkVXBJFCC0t4G02uda3zQb/0WPw7NoFrraDnhsUBfWUKZFoSO2MGaDI7yaBMCLocfo2RVFWAF8DmCRJko+iqPcBfALgWgAfSpL0LkVRvwNwWJKk33b0XCR9m0AgEIYXfHMzvHv2REQkV1PT4XhlSTF0CxZCN28uFAWFRDYSukzTn/8M2/O/AADQej2Kv/gcbEZGimdFIHSdnsjGPkGhgDIvD4rCQigL8qHIL4CiID8iLkdKlGVfykfWbIaqpBjKkhKoSkrAZGRAdLogOJ0QnK0QW53yemtr7LbTCfB8P73DjmknNI1RUZlGI1RFRVBPngxFfj75TB5gAryAfWVNuODwoKzBgwsNHpQ3eFDd7IXYg1t4tYKORFbGC8ssnbLTf1/BHf471SYcebsjIiB5ux2cwwF0MfuFzbPIzWkuvhjaefPIZ3c/QtK3CYOB/krfZgFoKIriAGgB1AG4AsDK0PE/APh3AB1KSQKBQCAMbUS/H94DB+QuiXv2IHDiZIfj2exs6BYugHbBAugWLIQixzxAMyUMNzKWL0fz239CsLwcotuNhg2vI/enT6d6WgRC6mQjZNEEQei4NAbHIVhRgWBFBRIlUTKZmbKkjJOVQzXKUhIEcNXVsnQ8K4vHSNp1d+VjdjZUY0tk+VhcAtXYEqiKi8EYjT2bmyRB8nojglJobYXodEKISMtWiK2tcdt9IzS7GqFJG41QT5oIzZQpUE+eTETlAKBiGVwyNhuXjM2O2R/gBVQ1+VDW4EFZgxtlDV6UNbhR3uBFvdOf9Pn8nIhT9S6cqo/tZM2KPAokLyYrgxhL+1AgepDDuWD0tELraoIUbizYQbp1V6C1Wmjnzw81qFkI5ejR5PeHQCD0XEpKklRDUdR/AagE4APwOYADAFokSQp/MlYDsCY6n6Ko+wHcDwCFhYU9nQaBQCAQUoBcF/JkKBJyN3wHD3ZYS4vWaqGdOxe6hQugW7AAypISciFK6BMohQLmxx9D9bofAACa330XGXeshGrMmBTPjDBcSbVsjK6Vq0hS3oLW6wFJkiOXqqoQrKoGV12FYGWVvF1dDaGxscPXEpqaIDQ1wX/4SPt5KBRQWK1yanh0lGVhIRTW/JRGWfaLfCyWIx97Kx+TQVEUKJ0OtE4HhcXSrXM7FJrhiMx2grP7QlNsbYV3z1549+yN7KONRmgmT4pISiIqBwYVy6DErEeJWQ8gJ+aYN8ijvMErC0u7E3WVNrRU1cBXZ4OypRFZvlZk+Z3I8oeWvlakB5PLxp4UKqANBihyzGCzzWBzcqDIt0I3bx4006eDUih68IwEAmE402MpSVFUBoAbAYwB0ALgLwCuSTA0YXC5JElvAHgDkNO3ezoPAoFAIAwMwcrKSHMa7969EDqqC8kw0EyfDt2CBdAtXADNtGnkQpTQb+ivuALaiy6Ct7QU4HnYf/UizE+sj6QhUizp60foGhLPg6u3gauujpONjlBqYj/LxlCtXEXCpl2ybOyy8KEoKHJzocjNhXbOnHaHRY8HweqaOFlZBa6qGlx1dYdRlhLHIVhejmB5eRejLAsj0Zas2dwnUZZDUT72B30tNKOlJt/QgMCpk/AdP5GwFrTY2ipfF+zeE9nXTlROmQKF1UpEZR8jp1KH0qZtNnB2O3ibnEKttdkwzmFHkaOhy6nUXSFIs2hUG9GoMchLtQFcRhYUZjP0+RZkjbIir7gAo6xZyM/QQskOvoY7BAJh8NGbq/R/A1AmSZIDACiK+hDAQgDpFEWxoWjJfAAdVLQlEAgEwmCFb26Gd+/eiIjkqqs7HK8sLpZTchYsgHbuHDB6/QDNlDDSoSgK5ieeQPmttwIA3Dt3wr1zZ+Q4rdWCjtRJa9/woV3zh/A4IjSHJYLLlTR6kKut7dPafvGyMRLdGCcd6bS0AZc2tE4H9fhxUI8f1+6YJIrg7XYEKyvBVVW3yco+j7IsaIu2LChIGGUZIx/ju133QD4qS4qhKhkry8eSYlk+pqd363mGC10VmpIkgaupgf/YcfiPH4P/+PFuiUrGaIQ6IiqnQD1lMhGVSZAkKdS8rQ5cXS34unrw9ljpyNtsEPvyixGaBpWZCT7DBHdaOprURtQp9CiHFmcEDWyKNDRojHArNHLr70TYAdhbgf3y7wQl+O7qAAAgAElEQVRDUyjI0GC0Se4Qbk3XwJqhiSy7UsOSQOgJWq12Znjd7/fTSqVSomlaAoCXX3654sEHH2xK3ex6h8lkmv7nP//5/NVXX+1O9Vz6kt5caVcCmE9RlBZy+vaVAEoB/BPALZA7cK8C8LfeTpJAIBD6A4nnEaysRODMGQTOnAXvcIDJzJRvHM1mKMI3jCbTiOgAKPr98B08KNeF3L0H/pMn5RaPSWCyTaFISFlEKnJyko4drriDblS7q1HtqkaVqwo17hrQFA2z1gyTxgSzxgyTVl4aVUZyAd6PaKZOgeH66+Hctq3dMdHrhej1gq+r6/bz0jqd3MHWENX8IZnQDIlM2mgEk5ZGhGaKaIt2rEKwSpZpwarKiFTrMMq7i1AqVVvKdAep1KmQjX0BRdORKEvMndvuuOD2gKupbpO74SjLyipwNTW9jrKUIyqzEayu6ZF8ZLJNIek4Fqri4rbIxxEqH3sLRVFQ5udDmZ8Pw5KrAYREZXU1/MePRx7JRKXQoagM1agcIaJS9PvB1dWBr68PicewfKyLbPdld/f4VGrWbAabY5b/XoW3s7KSfl6JooTaVrl+ZXlUs52yBg+qmn0QknTcEUQJ5Y1elDd6ATjaHVexdIyozEvXxGznGtVQMCTSktB9vF7vt+F1q9U6dcOGDeVLly51dXTOYIDjOCj6OatsIF6jJ/SmpuQ3FEVtBXAQAA/gW8jp2B8DeJeiqF+E9r3ZFxMlEAiEniKF6mqF5WPgzBn4z55B8Nz5DusgRsNkZMSl0WVH1oeqvJQEAf6Tp+DZs1uuC3mg47qQlFYL3Zw5cl3IhQtHRF1IQRRg89pQ7apGtVsWj9Wu6sh2S6Cly8+loBXI1mQjW5vdJi2j5GW2NhvZmmwiL3tB7tNPgaIpBM6dj6Qjik5nh3K9M0SPR64fWNtPQtNojI3SJEKzS7SLdozIx6peRzsy2SYorflgc3OTplIPVdnYVzB6HZjx46EeP77dMUkQ5CjL8L9JSFaGoy2Fpo6DVISmJvg6GROZB5GPKYOiKChD0a6GJUsAJBGVx47Lf4fjSC4qJ0elfg8tUSmJIoTGRlk0JhGOnf3+dxVKqYxIxXjpqMgxt5V80Gh69To0TSE/Q4v8DG27hjucIKKqKVS/MvQob/SgzOFBbWvyhjsAEOBFXAhJzoSvSwE5BnVEVEZLy/yQxNSpyOckofvwPI+f/OQnli1btpg8Hg99ySWXODdv3lxpMpmEb7/9Vj1nzpzJL730UsULL7yQFwwG6X//93+vmjJliv/BBx8cXV9fr1ixYkXDpk2bqgHgxRdfzP7www8zioqKAh999FFmTk5O8LXXXqu45ppr3ABgs9mYtWvXFnz11VdGlmWlO+64w/Hiiy/WMQwTOXfs2LH+v/3tb5lr16613X777c0PPPDAqNOnT2tompYWLVrU+uabb1ZmZGSI11xzTVFTUxO7dOnSsRRF4amnnqqZMGGC/9FHHy2srKw8Fn5/0dGU3//+961VVVVKURSpnTt3Gl966aWK+++/vynZ+0/Vv0mv/idLkvQMgGfidl8A0P4rVQKBQBgABJcLgbNn4wTk2YTf3HfreZubITQ3I3D6dIfjIvIyLl2PNZvbbmxTKC+DVVVtdSH37Om8LuS0abF1IYeQdO0q0dGO4YjH8Hatpxa82DepnJzIodZTi1pPx1VNksnLbE12ZD+Rl4lh0tOR96tfxeyTRBGiyxWql+aE6GyNrMc3gBCd0d1tB6nQNKYnFJzDTWgminaU16vBVVb2KtqRUirllOF8OWVYWdjWVVphtYLWavvwnYw8KIaBwmKR04E7i7KsrGr7d62qQrCmJmENvIh8jK/5SOTjoKIrotJ37Bj8x090ICrlL0rDxIjKUOdvhTUvJZ9/oscDLhLhWCtHPEbkYx24+vo+qeFI63RQ5OWBzbNAkWuBIjcU0Whuk460MfXXAAqGRlG2HkXZ7cv1+IICKppkQVnZ5EVtiw81LT5UN8tLl7/jaytRAupa/ahr9aO0ojnhmHStQhaVIUmZHx11SVLEB5SXV1w3e6Be68fv/f1Ab87/+c9/nvvll18avv7661Nms5m/6667Rq1du7Zg69at5QAgCAKOHDmiqaioOPrhhx8a1qxZU3TppZc6v/rqq1Mul4uZPXv2pJUrVzZfccUVHgAoLS1NW7p0afNbb71VuXHjxszbbrutpKys7EhmZqZ46623Fo0ZM8ZfVlZ2tKmpiVmyZMnY0aNHBx966KHG8Lm33npr01tvvVXp9/ups2fPqn7605/WLl682G2325nrr7++5Omnn7a8/vrrNf/4xz8uxKdvb9261dDZ+/3ss88y3n777fMfffTRBZ/PR3X2/lPB0L9qJRAIIxIpGESgrCwkH2UB6T97pts3/2xODlTjxkE1diwU1jwIzS0xTQ14ux18YyMgil16vi7Ly8zMuMibtvW+lJd8czO833wTikbY3XldyKIiOR174QJo58wBk5bWq9cfDMRHO8bLx+5EO8ajpJWwplmRr89Hflo+rHorAKDB1wC71x5ZOnwOeLjk3S2j6aq8VNJKZGuzYyMu4+SlWWuGQWkY0RflFE2H6kgagYLunTt8hKaxw4jNVAlNwe0GV1nZPtqxugpcTR9EO4abrOQXxMhHNtsEiiZpgamiS1GWlVXg7XYoLLlQlZQQ+TiE6VBUHgvXpzzePVGZng71pEkRSdkXolISBPAOR1Qtx+hoR/nR2y+45ckzUOTkyMLRkhcS+LlgLaHtPMuwuPbSKBlMyDVgQm5iZ+L0c7KobPahtsWH6tB6TYu8bXcFOv0IbfFyaPFyOF7b/vcGANQKui3CMirSMryPpIiPTP7whz9kv/XWWxdGjx7NAcB//Md/1M6cOXOyKIrl4TEvvPBCrUajke64447WNWvW4K677mrMzc0VcnNzhVmzZrlLS0u1YSmZk5MTfPLJJx0A8IMf/KDx1VdfzfnrX/9q/M53vuPZv3+//osvvjinUqmktLQ0ce3atba//OUvmWEpmZ+fH/jxj3/cAAB6vV6aOXOmf+bMmX4AKCws5NetW2d7/fXXe1Ufa86cOa4VK1a0hl+jo/dPp+jaiEhJAoEwqJFEEVxtbYx8DJw9g0BZebduWGm9PiIfVePGQh1a78qNjiQI4Bsb5S6s4W6s0dIyvN4deRlqAtBreRlOGw/VBxEDAfgOHIBnj5wS5T9xouO6kKboupDz5fphQ5D4aMfIursaNe6aXkU7ZqmzkJ8mS8ewfAwvzVozaKprH+BezguHzxEjK6OlpcPr6Ja8DIpB1LhrUOOu6XBcvLyMjrYML4m8TEyvhabbHepmOwSFZjgi02DosdCUBAF8fT2C0TUdo6LihJaefyHQLtox3CglVPeORDsOTWKiLAnDlhhRec01AEKisqoqqj5lB6KypSWxqIxO/Y4TlYLLlVQ48nV14Gw2QOh99iJjNILNy4v8HivyLGBzcyPCkc3O7pPu80Mdg1oBQ64iqbQM8ALqW/0RUVkTJy1rW/wICh1fc/s5ERccHlxwJE8RzzWoI5GV0dIyP7SuVRJdMpwQRRE2m025bNmysXH7KZvNxgIAwzDIzc2N/DFQq9WixWLhorfdbnfk4j83Nzem9lV+fn6wpqZGcf78eWUwGKRNJtP08DFJkqjCwsJIbQOLxRJzbnl5ueLBBx8sOHDggN7r9TKiKCI7O7tX4ddWqzXyGp29f4vF0ned/roB+V9GIBAGDXxzMwKnQ/LxbFhAnu1eh0GFAqqiopB8HBcRkKzF0mPhQjEMFKH0a2By0nEJ5WV81KXD0X/yMisTwcqqDgukU1ottHMugn7hQmgXLIBq7NghIaIEUYDda49JrY6Wj82BxKk9XSE+2jGyDK1rFX0jN7QKLUYpRmGUYVSH41ItL/P0ebh9wu24atRVXX5vhPZQNB2RegMSodkqjxFdrtQJTb0+IilFrwdcbV2vUhkZk0mWjoUFbdGOIfnIZmeTaEcCYRhBURSU/5+9O4+To6zzB/55qqrPuY+eKzOTSSYkIdckMSIBxQs8VndxRXZXRFFwXXHXA3G5PMAfoBD9iSK7KK4CKp6geLC/9Vg5VoIskIScJJlJJtdM5j77rqrn90dVV3fP2XN2z8zn/Xr1q6qerqp+Jpmj69Pf56n6erjr68cPKvcfQOTgBEHls88i+OyzTptaXAy1vAz62Q6YwzO/Wa1wueyKxpTAMVHhWG3dGErJy5v8RDQpj6ZieVkelpeN/e9pmhLdw1GctkNKJ7xMCTEzGSLeNhBB2wRDxEv8Lqey8hNvPgcblhXN+GtbjGY6pHq+KIqCioqK2E9/+tOW173udaMuMM+ePTvlfOzs2bNpw9rOnDnjXrZsWXzFihUxv99vDAwM7BmvAnHkNdgnPvGJWr/fbx44cOBAIBAwHnjggZI77rhjWcr+aW/w8vPzzUgk4pw8Go2KgYGBtE89Ul9jsq8/WxhKEtG8M8NhRJtbktWPR615H42u7imdx1VbOyp8dDc0OFWD8y3j8FLXoff2jh9aziC8HEVV4du40bk5TS7PCxk1omgdaB01r+NsVDuWektRV1A342rH+TCT8DIRWM40vHzh7Au47JzLcON5N8KnzWySfJq6eRlyPjBgbadWaM400Bweti782yaeeiBBuN1w1dY6Q6zd9XWsdiQix2RBZWJ+ysiBA9bvrxGM/v4pVWSrZWVjB441VptaWsoPQ3KEoghUFHpRUejF1vqSMfcZjMSd4eGJwDI1xOwcmvwu532hOPrsIeIffUPjbH8ZlAVXXXVV14033rjs+9//fuuqVaviZ86c0Z5++um8K664YlrzM3R0dLh37NgRuO6667q+853vlLa3t7svvfTSwbKyMqOpqSl47bXX1t51111thYWF5sGDBz1tbW2uxJyQIw0PD6u1tbWxkpIS4+jRo+5vfvObaUPYysrK9KNHj3oSxzc1NUUGBga0X/3qVwVve9vbhq+//voaKeWE1Saz/fXPBoaSRDRnpGEgduJkWvgYPXIUsZMnp3ThqxYX28FjSvi46hyo+Qvz02ihaZmHlz2944eWnZ2Id3XC6OlNCy/dK1c6N6fxn3deTs5NFDWiONJ7BAd7DuJg70Ec7DmI5r5m6HJ6waNLcWFZ/rLRweMsVzvmkumEl05omUF4+djRx7Cncw92vH4HVpesnssvhWbRnAWaAwMjhplnFmg61Y6JKseU8JHVjkQ0VRMGlfv3O8O+RwaVwut1AketJlHtmAwctaoqKB5Ptr4smgOFXhcKq104t3r8IeLt/ZG0OS2dALM/jPYRQ8Rri/kh7WJw++23n7311lvx5je/eU13d7errKwsfvnll/dMN5Tbtm3b0J49e/wlJSVbKioqYj/60Y9aysrKDAB47LHHjn3yk5+sXbNmzYZwOKzU1dVFb7jhhnGHotxxxx1nrr766hWFhYVbVqxYEXnXu97V94Mf/KA88fwNN9zQfsstt9TdfPPNdTfffPOZW265peuOO+44ec0116yUUuK6665rLy4unvBiara//tkg5Aw+EZ8t27Ztky+++GK2u0FE0ySlhN7Z6dzt2gogjyLa0jLhUOKRhMdj3VVzRACplpcviCHG2eKEl91d0MrL4aqc0XzIs262AshSbylqC2qt4DHHqx0XomA8iI5QB+7fcz/+q/W/nHaP6sENr74Bl6++nD+HNC5pGNYcmnaIKVwuuGuXcSgjEWVFIqg0g0FoVVVQi4v5N4ymJHWI+Jm+MN6xsRqKsjC/h4QQL0kpt83W+V5++eXWpqamqQ1xW4R27NgRePzxx0t27tx5JNt9WQhefvnl8qampoaR7ayUJKKMSV1H7OQpxI4fQ7TlGGItLYgeP47YsWNTm6tHUeCurx8VPrrq6jj59zQITYOrsgKuyopsd2XGAWRdQR2WFy5PDx4XcbVjLslz5WFl0UrsuGgHttdsx5ef/zIiRgRRI4rb/3I7/tL+F9x2wW0odI9dcUBLm1DVaVdoEhHNtkRFJdF0ZTJEnIhmjqEkEY1iBoOIHm9F7FiLFT4eO4bosWPWsOsp3rRACwRSwkcrgPQ0NkLxeueo9zRfokYUR/uO4kD3gWkFkMsLl2Nd6TqsK7Me55adiwJ37g01X2qEEHj3Oe9GU6AJn3n6M2jubwYA/OHEH3Cg+wB2vH4HmgJNk5yFiIiIiIhoYhy+TbRESSlh9PTYoWMLoseOO5WPevv07ro6cui155xzoJXwk8XFIBFAHuw5iAM9BxhALhERPYKvvPAV/OzIz5w2Vaj4+JaP40MbPsQh87QodQQ7cGzgGIo9xajOq0aRp4jDPomIaMHi8G3KBRy+TbREScNA/PRpK3xMDLu2Kx/NwcEpn0+rrISncSXcKxvhXrkCHnupBQIL8qItFA/h+MBxNPc3ozvcjUJPIUq9pSj1lqLEU4JSXykKXAUL8mubrpgRw5E+ewi2HUJOJYCsL6jH+rL1TgC5tmwth/wuUF7Ni89v/zzOrzkftz57K4biQzCkga/v+jqeb38eX3rdl1DuK5/8REQ5ypQmWvpbsLtzt/M4M3wmbR+f5kNVXhWq86pRlVflrCcelXmV8Ki8QQYRERHRVDGUJFokzHAYsePHrYrHlMrHWGsr5BSHXEPTrDkfG1fCvWKlHUJa6wv1jtep4WNLfwtaBlrQ0t8y6uJzLJqiodRTihJvCUq8JcnQ0l4f2baQQsyRAeTBnoM42nd0SgHkurJ1TgjJAHJxumT5JVhXtg43PnMjXu56GQDwXPtzuOzXl+HLr/0yLlh2QZZ7SJSZqBHF/u79aSHkUGxowmPCehjHB47j+MDxcfcp85aNDi3zq522Um8pK4uJiBaIUDyEAz0HsK97H/Z17cOJoRN47K8fWzDv74kWEoaSRAuM3tdnDbNOnevx2DHE29qAKU7HoPj9cK9cmV752NgId10dhMs1R1/B3EoLH+3gMdPwcTy6qaMz3InOcGdG++dqiDlmANl/FLo5tQAyEUIygFxaluUvw4NvexD/vuff8d1934WERG+kF//0x3/C1Ruuxr9s+Re4lIX5e4MWr/5IvxU+du3G7o7dONBzAHFz4g/qvKoXq0tXIxgLoj3YjpAemvR1eiI96In0YH/P/jGfdytuJ6yszKtMq7Ssyq9Clb+KN/MiIsoC3dTR0t9iBZDd+7C3ay+ODRyDKc20/U4NnUJ9IW+eRDTbGEoS5SBpmoi3tVnh44jKR6O/f8rn0wKBZPiYUvmoVVYu2E/8Zit8VIWK+sJ6rCpehaq8KgzFhtAX6UNfpA89kR70RfoyuiBNlQshZsyIWTehsed/nEkAmZgDkgEkuRQXPrn1k3h11atxy//cgp5IDwDge/u/hxfPvoi7L7obtQW1We4lLVVSSpweOo3dXbuxq2MXdnfuxrGBY5MeV+otxZaKLdhSsQVbK7ZibelauFSXc87B2CDOBs/ibPAs2oPtziOx3RnqHHXxOlLMjOHk0EmcHDo57j6JOSxHDhFPrJf7yqEq6tT+UYiIyCGlREeoA3u79joh5MGegwjr4UmP3du9l6Ek0RzgjW6I5oE0TZihEMxgMOURghlKbuvdPcnKx+PHIaPRqb2IosBdVwd3YyM8K1fAvTKxXAm1cOGGSYnwsWWgJTn0eobhY2NxIxqLGtFY3IiGwgbn4nM8ET2CvkgfeqO91jKSXCbWZxJiTlVqiJkaWhZ5inA2eHbKAWRdQV3aHJAMICkT3eFufPbPn8XOtp1OW4GrALdecCve2vDWLPZsYTsxeAK7Onah0F2I2oJa1BbUIs+1MKfNmGu6qeNw72Hs7tyNXZ1WCNkdnnze/YbChmQIWbkV9QX1M/qATjd1dIe7rbByeHRo2R5sn3SIeCY0oaEyr3Lc0LI6rxr57vwZvw4R0WIxHBvG/p792N+93wkiM/k7oQgFjcWN2FS+CRvKN2Bj+UY0FjdCUxZmTRdvdEO5YLwb3TCUJBpDJiGi0xYMOu1GMAgZDMEIpT8vw5N/+pYp4fPBs2LFqMpH1/LlUNzuWXud+TZe+Ng23AaJzH9PpYaPK4tWOiHk8sLlcKvz8++TGmL2hnvRF00PLUcGmnMdYqaqK6hLmwOSASTNhClNPHTgIXxz1zfT5iG9fPXluOHVN8CrebPYu4Xl+MBxPLD3Afzn8f8cVXVX6i1FbX4tlhUsQ21+LeoK6lBbYC0DvsCSqZ4LxoPY27XXCSH3du2dtLpFExrWla2zQsjKLdgc2IwyX9k89TgpGA+mV1oOp4eWHaGOjD9ImkiBqwBV+fZNefxVqM5PDy0D/gCnWSCiRSluxnG076gTQO7v3o9jA8cyuo6o9FdiY/lGbAxsxMbyjVhftn5RTanBUJJyAUNJWtRyOUScLrWszAofR1Q+atXVEMrCnSw/FA/h+OBxtPRb4eOx/mNo7m+edviYqHjMRvg4WyYLMUdWZmYyxARIBpCJEJIBJM2VvV17ccMzN6RVMK8qXoWvXPQVrCpZlcWe5b6JwshMuBQXluUvGxVY1uYv/CrLzlCnczOaXR27cLjv8KT/RvmufDRVNGFrxVZsqdiCDeUb4NN889Tj6TOliZ5wz+gqy+Hkdl+0b8avowgFAV8grcpy5I15Ct2FC3Zql/kkpcRAdABd4S50hbrQGe5Ed7gbnaHkMmpE4Vbd8Kre9KXmhVuxl+M9b297VI/10DzJ9ZTHUvlQgiiVlBJtwTbs69qHvd1WAHmo5xAiRmTSY/2a36l+TASRFf6Keeh19iy1UPJ3v/td/k033VTb3NzsVRQFjY2NkXvuuefk61//+kkrQX784x8XfelLX6ppbm72ejweedFFFw3cc889pxsbG+MA8JOf/KToK1/5StWRI0d8Ho9HXnzxxf3333//qZKSkqm/iVtiGErSomFGIogeOYLIwYOIHDiIyMGDiB45MvU7TM8z4fdDyfND9edBybMffr+zrhYVwt3QYN/legW0kpJsd3lGZjN8rCuoc0LHVcWrsLJ4JRoKGxZc+DhbnBAzUW0Z7XPCzCJPkVUBWXouijxF2e4qLSGDsUF8cecX8fsTv3favKoXN553Iy475zKGHCNMFEa+uurV8KgenB46jTPDZya9MctEFkqVpSlNHB84bg3D7rAqITOZpqPSX4mtlVudEHJV8aqc+ZpmW1gPoyPYMWpouLM93I6YGZvx6/g039ihpf2ozKtc1H9/Mwkbu0Jd6Ap3zehnc7ZoijZmWOnRPDMOQ32aDw1FDQv6ww1aHAZjg9jftd+ZB3Jf9z70RnonPU4VKs4pOScZQJZvxIqiFYv278R4llIo2dvbqyxfvnzTV7/61ZPXXHNNbyQSEb///e8Lli1bFn/Na14zYWXHgw8+WPLxj3+84Stf+cqJ97///X29vb3q9ddfX7tz586CPXv2HAwEAsa3vvWt0vLycv2tb33rcCQSEZdddtnK2tra6I9+9KPxJ40mAAwlaYEyhoOIvnIoPYA8dgwwjDl/7clCxCm1+X0LurpxPDEj5lwgtQXbrOHXdgg50/Ax8VjK4SPRQiOlxGNHH8Nd/3sXokZyXty3NrwVt26/FQXugiz2LjdMFEZeWHMhPtr0UWyu2Oy0GaaBrnAXTg2dwumh0zg9fDptmclF2XiyWWUZM2I40HPAuSHNnq49GIgOTHiMgMA5Jec4N6TZUrEF1fnVc9bHhUZKib5o35jDwxPrmcyllolyX/m4oWVVXhVKvaU590HEQgsbc4UiFKwpWYOtlVuduVgXe1UZZVfciONI3xHs7d6LfV1WANk62JrRsTV5NdhQvgGbApuwsXwj1pauXVTDsKdrKYWSzzzzjP8d73jH6qGhoT0jn7v33nvLHnroocDGjRuDv/jFL8rKy8vjX//6109eeumlQ6Zpora2duPVV1/deccdd3QkjjEMA2vXrl3/jne8o+/rX/9628hzPvzww8V33nlnzZEjRw7O9de20I0XSi7MmVppUdL7+hA9ZAeQdggZO3Ei4+NHhYiZBohLKEScCikleiO9Y17UzOTiJjV8XFmcnPOR4SPRwieEwHtWvwebA5vxr8/8K5r7mwEAv2v9HfZ378eOi3ZgU2BTlnuZHVMNIxNURXWCn1dXvXrU88F4MD2sTFmfrMoybsbROtg67sVeosoyccOdxHpdQR0q/BVQROZ/JweiA9jTuQe7OndhT+ce7O/eP2lVn0f1YGP5RueGNJsCmzgFxQSEECj1lqLUW4r1ZevH3CdmxNAR6hhzeHjib30mU4R0h7vRHe7Gvu59Yz7vVtzWfJZ+O7TMT78pT1Ve1awNq89m2JjvykfAH0DAF0DAH0CFrwLlvnJU+K2l3+VHzIghakSdR0SPIGbEEDGSy6geTdvHeYzVrkcRNa1lJkNVZ8qUJg71HsKh3kN45NAjAIBl+cusDwYqrQ8IVhStmNLvA6IEKSVOD522Aki7AvKVnlcyqvrOd+WPGoZd7iufh17TSKdv+p9Xzddr1d71upcmen7Dhg0RVVXx7ne/u+Ef/uEfet/4xjcGA4GAU9G0d+/evEsvvbS3u7v75e9///vFV155ZWNzc/O+trY2V3t7u/vKK69MmytFVVW8853v7HvyyScLAYwKJZ9++umC1atXz/0v40WMoSRlRbyzMxk+2g+9rT2zg4WAu6EB3nXr7Me58J57LtTi4rnt9CIT1sNpIePI8PFs8GxapdNUJcLHRMVj4sYzK4pWMHwkWuRWlazCj97xI+x4YQcePfIoAODM8Blc9f+uwie2fgJXrb9qyVzAtg604tt7vz3lMDJTea48rCldgzWla0Y9N26Vpb0+WZVlYoqIvd17Rz2XqLJMDSsT63UFdeiN9Do3pNnTuccJqCdS4ilxAsjNFZuxrnQdXCpvyjKb3KobdQV1qCuoG/N5KSUGY4Ojh4cP2+8XQmfRGeqcdG7PmBnDicETONw5sbgAACAASURBVDE4/ofLJZ6S0XcSz0+ul3nLMBQbylrYmAgXndDRF3DCxtTQMZuklIib8QkDz5gRQ0SPZBZ4GsmwM2bE0B/tx/GB46NGvpwZPoMzw2fwm2O/AQAUugvT7mi/vmw93+vRmPoj/djXvc+6GY09F2R/tH/S4zShYXXp6rQAsqGwYcm8l6DMlZaWmn/6059eufPOO6s+/vGPN/T09LguuuiigYcffrjVfj7++c9/vlNRFPzjP/5j37333lv16KOPFq1atSoGAPX19aP+oFRXV8f7+vpGZWe//OUvCx999NGyP//5z4fm/AtbxBhK0pySUiJ+pg2RgwdSAshDMLozrLBTVXhWrUoLID1r1kLN59w2EzGlie5wd/KunvYQ67meML+2oNapfGT4SLS0+TQfbt1+K86vPh+37bwNw/Fh6FLH1176Gp5vfx53vPaORV3R0DrQigf2PoAnjj8xJ2FkJqZTZXlq+BTODJ2ZcZVlJpYXLneGYm+u2IyGwoacG/K71AghUOQpQpGnaMygGwB0U0dXqGv0nJYp60OxoUlfqy/ah75oHw71zt+1XGrY6ISOdpVjauiY7bAxU0IIuFU33KobBZib6TEGY4PY27XXmWphX/e+UR9aD8YG8fTpp/H06acBWJWy68vXp/18c57rpSVuxJ3fC0f7jzp3wz45lNm0e7X5tWl3w15buhZezTvHvabFYuvWrZHHHnusFQB2797tff/737/iYx/7WN0ll1wyWFFREVdSRkTW1tZG29ra3BdccEEIAE6ePOlau3ZtWqlue3u7q6SkRE9t++///u+8q6++esUPfvCDlk2bNk2/kocYStLskaaJWOuJ9ArIQ4dgDkw8R1SCcLvhWbPGCh/PPRfe9evgWb0aisczxz1feILxoDVfVOjsmHNHdYQ6oJv65CeaRIGrIK1qIa2aIa8KAX8ALoVVLEQ0vrc2vBXry9bjxmdudCrunm17Fu/59Xvw5dd9Gdtrtme5h7MrF8LITE21yvLUkBVYZlJlOZImNJxbdq5TTbW5YvOiDqUXM03RrOHYE8znORwbHnP6l8R6R7ADupz5+5SEPFfemOHiyNBxoYSNuaTQXYjXLnstXrvstQCssOlg70FrOgY7qBz5QXfMjGF3527s7tyN7+F7AIDGokZnuPeWii1Ylr+MH0IsYMOxYbQF23A2eBZtw21oC7Y5xQ/tw+3oCndlPLd8obswLYDcUL4Bpd7SOf4KaDZNNqQ6m7Zs2RK54ooruh966KHAJZdcMtjZ2ekyTROJYPLMmTPud77znf1NTU2RysrK+A9/+MOSkXNK/va3vy15+9vf7vyie/bZZ32XX375qn/7t39rvfTSSyf/FI4mxFCSpkXG44geO+bcfCZy8CCir7wCMxTK6Hjh91vBY2oAuXIlhIsBV2oFwnhv6DOpQJiMJjRU5lWOChpT1/Pd+bPwFRHRUldbUIuH3v4Q7tt9H76337pA7Yn04J/+8E+4ZuM1+Njmjy34DzgWUhiZialWWZ4aOoXTw6edKku36sbmwGZsrtiMrRVbsaF8AwOhJSTfnY9V7lVYVbJqzOcN00BPpGfU8PDUKWT6on0Tho2poSO/t+aPS3WhKdCEpkATrlp/FaSUaB1sdeaM3d25e8xh+y0DLWgZaHGm9Aj4As5w7y0VW7C6ZDU0hZemuUBKiZ5ID9qG25yQMTV0bAu2TftaRFM0nFt6rjMX5KbAJtQX1DOgplmze/du7+OPP170gQ98oLexsTHe3NzsevTRR8u2bt0aBIDe3l7XnXfeWXHDDTd0PfLII8XHjh3zXXbZZQOKouD2228/dd111zXU1tbGr7zySufu28PDw8ott9zSAQAvvPCC92/+5m9W33333SevuOKKzKqvaEIzuvu2EKIYwH8A2ABAArgawGEAPwXQAKAVwN9JKSccJ8q7b+c2MxpF9MiR9ADyyBHI2OQTEAOAUlRkz/u4zhmG7V5eD6Gqc9pvKSUMaUA39dFL04Au7WWiXerOc4n9Es8l9k/djptxZ92Q9nbqsXKCc43xfDAezHiupkwUe4pHB4121WOVvwrlvnKoytz+HxARjbTzzE7c/Oeb0yrtmgJN2HHRDtTk12SxZ9MzURh5Qc0FuLbp2gUVRs4GU5oQELzIpBnRTZ0h1QLVHe7Gy50vOyHloZ5Dk1bG+jU/NgU2OTfQ2VS+iWHzHIkbcWu0VUrImAgezwat9kxuNDMRAYGA35riqa6gzgkh15au5fROWbCU7r59/Phx1z//8z/Xvfjii/lDQ0NqQUGBcfHFFw/cd999p374wx+WPPTQQ4ENGzaEfvnLX5aWlZXpX/va106++93vHkwc/8Mf/rD4rrvuqm5pafG63W550UUXDdxzzz2nV61aFQeA97znPQ2/+MUvyrxer/Omr6amJtbc3HwgG1/vQjLe3bdnGko+DOB/pJT/IYRwA/ADuAVAr5TyLiHETQBKpJQ3TnQehpK5wxgOInr4lWQAeegQos3NgGFMfjAAtbwc3vXrklWQ69bDtaxmTi5MusPdeK7tOTzX9hxe6ngJQ/GhtJDPkJn1eSFyK+60O1iOvKtlpb+Sb+SIKGd1h7txy//cgufan3PaCtwF+OIFX8Qlyy/JYs8yxzCSiCgzoXgI+7v3O0O693TtQTAenPAYVahYU7rGGe69pWILAv7APPV4YQvGg2NWObYFrbauUOZDq8fjVtzO9Ud1njWdQ01eDWrya6wqe38Vb1SWQ5ZSKDmRe++9t+zhhx8uf+mllw5nuy9L0Xih5LQ/fhRCFAK4CMAHAUBKGQMQE0JcCuAN9m4PA3gKwIShJI1PSgnE45DxOKSuW8uR63EdMh6DjMeBtHZ7v9hYx1tL2MfrXV2IHDqEWGsrkGFQ7aqpgWfduSk3oVkHV0XFnP1bRPQIdnXuwnNtz2Fn204c6TsyZ6+VbWXeMucPfGqlY3VeNSrzKlHqLeXd5ohowSr3leNbl3wLD+5/EN/c/U0Y0sBQbAiffurT+Ps1f4/PbPtMzk5ozzCSiGhq/C4/zqs+D+dVnwfAGr7f3N9sVVJ27Mauzl3oCHWkHWNIAwd7DuJgz0H88NAPAQB1BXXJu3xXbMWKohVLriI7MbQ6tbJx5JyOg7HByU80iQJ3AWryapywMTV4rM6v5rUIEc2amYyJWAmgC8CDQogmAC8B+CSASillOwBIKduFEGOmVEKIjwD4CADU19fPoBuzIxH+mbEYZCQCGY3CjEatZSQCGY1BRiMwI1HI2Ii2aDQl4EuEhOMHgYkHxt0vuQ599iYBnwl3Q4M1BNsOHz3nngutpGROX1NKiSN9R5wQclfnrlF3+5uMKlSoQoWmaFAVFZqwl4rmtCfanG37ucR+mhhje8Q5xlumtaW+fsoy8ZxH9aDSX4nKvEp4VN7ch4gWN0UouGbjNXhV5atw4zM3oi3YBgD46eGfYlfnLnz1oq9iZfHKLPcy6cTgCTyw9wH89thvGUYSEc2AqqjOTbbeu/a9AID24XZnuPeuzl1o7mseVc13augUTg2dwq9bfg3Amqpoc8VmJ6RcV7YuJ4cGSymhmzoiRgRRI5p86NH07ZT2iBFBzIghrIfREepIq3yczaHViZCxOq8aNfk1ThEE55Unovky7eHbQohtAP4C4EIp5fNCiG8AGATwcSllccp+fVLKCdOr1OHbUkpA151AUEYiMJ1AcEQQ6ISG9jJqPx+JJNej9vEjg8a0fa02mDOfx2/BU1V4Vq60wsf1dgC5di3U/Pn5w9QV6sJf2v+CnW078Vzbc+iJ9Iy7ryY0bK7YjO0127G9ejvqC+vTA0WhLblPT4mIFqLB2CBu23kb/nDiD06bV/Xi5tfcjL9d9bdZ/V3OMJKIaP4NxgbxcufLTki5v3v/pMUJbsWNDeUbnBvoNAWaUOQpcp6fTjiYWE+EhBHdXhojluO0J46fjfniM5UYWl2VVzVmtSOHVi89HL5NuWDW55QUQlQB+IuUssHefh2AmwCsAvAGu0qyGsBTUso1E51rY0GBfGz9BqdKkeHgCJoG4XJZj9T1Edtwpe439j7C3gcj9lPy/PCuXQvP6tVQvPM3ZC6iR7CrYxd2tu3EzvadONp3dML9VxStwAU1F2B79XZsq9qGPFfePPWUiIjmkpQSPz/yc+x4YUfahefbG96Oz2//PArcBfPaH4aRRES5I27EcbD3oDPce3fnbvRH+yc9LuALIG7GsxIOzpXUodWp1Y4cWk3jYShJuWDW55SUUp4VQpwSQqyRUh4G8GYAB+3HVQDuspe/mvRccR1G/+R/VOacpkFxuyG8XgiPB4rHA+HxQHg9UNweu90NxWM/7/VAuO19PO6xg0D3iOBQGxEcuscJD7WU8HARVfuZ0sTRvqNWCNm2E7s6dk04BKHIU4Tt1dudasjq/Op57C0REc0XIQT+bs3fYXPFZvzr0/+KYwPHAAD/r/X/YV/3Pnzl9V/BhvINc96PicLI7dXbce3ma7GlYsuc94OIiJJcqgtNgSY0BZrwQXwQUkq0DrZalZQdVkh5cujkqOO6wl1Z6O1omqLBo3pGP7Qx2uyHV/PCrboR8AU4tJqIFq2Z3n17M4D/AOAGcAzAhwAoAH4GoB7ASQCXSyl7JzrPBq9P/ryhIdkwnXDQY7d5vVZQ6E0cl95u7etJntfrTZ5fm8kUmzSerlAXnmu35oX8S9tfJh6SrWjYUrEF26u344KaC7C2dC1URZ3H3hIRUbaF9TDu/t+78djRx5w2TWj41Ks+hfeve/+cVIAwjCQiWti6w93Y07nHuYHOK72vQJfp8/NrQhs7CJwgHBzv+URo6FVHLDVv2n5u1Q1N4XUmZQ8rJSkXzPrw7dn0qqYm+fyTTzIcXCTCejg5JLttJ5r7myfcf2XRSmyvsULIbZXb4Hf556mnRESUy/7r+H/hi899EcPxYafttcteizsuvANlvrJZeQ2GkUREi1NYD2MgOsBwkJY8hpKUC2Z9+PZsEi4XtNLSbHeDpsmUJo70HXFuTjPZkOxiT3FySHbNdlTlVc1jb4mIaKF424q3YX35etz4zI3Y170PAPDnM3/G5b+5HF9+3ZfxmurXTPvcJwdP4tt7v40njj0BQxppzzGMJCJa+HyaDz7Nl+1uEBHRBHIilKSFpzPUiefa7CHZ7X9Bb2T8EfqaomFrxVYnhDy39FxOvkxERBmpK6jDw297GN/c/U08eOBBANYcYf/4+3/Ehzd+GB/b/LEpVb4wjCQiIiJaui677LKGZcuWxe699962bPeFGEpShsJ6GC91vORUQ042JLuxqNEJITkkm4iIZsKluvDpbZ/GedXn4bN//ix6I72QkPjOvu/gxY4Xcffr7p70RmgMI4mIiIiWhgceeKDkvvvuqzx69KjP5/OZtbW10SuuuKLnhhtuyI27X5GDoSSNyZQmDvcetkLIdmtIdtyMj7t/iacE51efzyHZREQ0Z1677LV49K8fxc1/vhnPtz8PANjduRvv+c178H8u+D948/I3jzpmojDy/OrzcW3TtdhauXVe+k9EREREc+vWW2+tvO+++6q++tWvnnzXu941UFRUZD733HO+u+++uyoajXIuzBzDUJIcHcEOPNf+HJ5re27SIdkuxYWtFVtxfs35zl2yOSSbiIjmWsAfwLcv/jYePPAg7tt9HwxpYDA2iE899Sn8w5p/wGde/Rl4VA/DSCIiIqIlpqenR92xY0fN/ffff/yDH/xgf6L9wgsvDP/6178+PnL/e++9t+zhhx8uf+mllw4n2oQQr9q3b9/+DRs2RIeHh8V111237IknnigZGhpSV69eHX766aeP5Ofny0ceeaTo1ltvre3o6HCde+654W9961sntm7dGgGAz372s1Xf+c53KoLBoBoIBOLf+MY3Tlx66aVDhmHg85//fNUPfvCD8qGhIe2CCy4YfPDBB09UVlYaI/u2VDCUnKFgPIjucDeiRhSmNGFIA6ZpLSUkDNNItttLKWXa9rjts3UeacIw7fOknDd1/9PDpycdkr2qeBXOr7ZCyFdVvopDsomIKCtURcWHN34Y2yq34YZnbkB7sB0A8JPDP8Guzl1YW7qWYSQRERHRPLjtttteNY+v9dJEzz/55JN58Xhced/73tc/0X6Zuvbaa+sOHz7se/bZZ1+pq6uLP/nkk3mqqmLv3r2eD3/4wyt/9KMftfzVX/3V0O23317xrne9a9WRI0cOHD582P3d73634n//938PNTQ0xA8fPuzWdV0AwJ133lnxxBNPFD/11FOHa2pq9Guuuab+wx/+cP1vfvObUYHpUsFQchzBeBCdoU50h7vTll2hLnSFrUdnqBNhPZztrs6ZUm8pXlP9GlxQcwG2V29HZV5ltrtERETk2FyxGT//65/jtp234Y8n/wgAONJ3BEf6jqTtxzCSiIiIaPHr7OzUiouLdZfL5bRt2bJlbXNzszcejyu//OUvj0xweBrDMPDzn/+8/Mknnzy0YsWKOABccsklQQD4wQ9+UPrGN75x4G//9m8HAeCLX/xixwMPPFD5xz/+Mb+hoSEWi8XEnj17vNXV1fqaNWtiiXM+9NBDgXvuuedkY2NjHAC+/OUvtzU2Nm6Mx+PHU/u8lCy5UDIYDyaDxVAyXEzd7gp1IaSHst3VeZcYkr29ZjsuqLkAa0rXcEg2ERHltCJPEb72hq/hZ4d/hh0v7EDMdN73MYwkIiIiWkLKy8v1/v5+LR6PIxHy7d69+xUAqKys3GQYhsj0XGfPntWi0ahYt25ddORzbW1trrq6OudNp6qqqK6ujp06dcr1zne+c+hLX/rSqdtvv73mAx/4gO+iiy4avO+++041NDTE29vb3e973/tWCSFk6rGnT592JYLPpWbRhJKheGjMcLEzbFU3JiodZzts9KgelPvK4dN8UIQCVahQhOI8EttOu6JAQUq7Msn+E60rKhQoaeeZ9PXHWc9z5WFjYCN8mm9W/32IiIjmmhACf7/277G5YjO+sesb8KgevH/d+xlGEhEREc2xyYZUz6c3velNQZfLZT7yyCPFqXNKjic/P98Mh8NOJdbJkyedjKyqqkr3eDzy4MGDnu3bt6cNka2pqYkfOHDACU9M00R7e7u7rq4uDgAf/ehHez/60Y/29vb2KlddddXyT33qU7WPP/748crKyvgDDzxw/C1veUtwdr7ihS/nQ8mJwsbucLe1PodhY4W/AgFfAAF/IG1Z4a9Aua8che5CCJFx2E5ERERzZE3pGvz7xf+e7W4QERERURaUl5cb119/fdv111+/XEop3v3udw/k5+ebzz//vC81fEzYtm1bqLm52bdz507f5s2bIzfddFNN4jlVVXH55Zd3f/rTn6778Y9/fLy2tjb+1FNP5V144YWhK6+8svf8889f96tf/argbW972/Cdd95Z4Xa75cUXXzz88ssve06cOOG+5JJLhv1+v/R6vdI0TQDAhz70oc7Pfe5ztQ0NDcdXr14da2tr0/70pz/lX3nllbMyB+ZClBOh5EB0AA8feDhtvsbEejA+uwEyw0YiIiIiIiIiosXnjjvu6Fi2bFn8a1/7WtXHPvaxBq/Xa9bV1UW/8IUvnL744ouHH3zwwbLEvps2bYped911be94xztWezwe+YUvfOH0j3/840Di+fvvv//UJz7xidrzzjvv3HA4rKxZsyb81FNPHWlqaop++9vfPv7pT3+6/sorr3StXbs2/Pjjjx/1er0yEokon/3sZ2vf+973ejVNk1u3bh1+8MEHTwDA5z73uU4ppXjLW96yuqury1VaWhq/9NJL+5ZyKCmklJPvNcd8K3xy1W2rZnSO1LBxzNDRXmfYSERERERERERLgRDiJSnlttk638svv9za1NTUPVvno6Xh5ZdfLm9qamoY2Z4TlZITcStuBPwBho1ERERERERERESLRE6EkoXuQrzv3Pelh44MG4mIiIiIiIiIiBalnAgl6wrqcNN5N2W7G0RERERERERERDQPRt19iIiIiIiIiIiIaAymaZoczkoZs79fzLGeYyhJRERERERERESTEkKcDYfD3mz3gxaOcDjsFUKcHes5hpJERERERERERDQpXde/2Nra6g4Ggz5WTNJETNMUwWDQ19ra6tZ1/Ytj7ZMTc0oSEREREREREVFu27p16+927dr1Ly0tLbdKKavAYjcanymEOKvr+he3bt36u7F2YChJREREREREREQZsQOmMUMmoqlgok1ERERERERERETziqEkERERERERERERzSuGkkRERERERERERDSvZhxKCiFUIcRuIcRv7e0VQojnhRBHhRA/FUK4Z95NIiIiIiIiIiIiWixmo1LykwAOpWzfDeAeKeU5APoAXDMLr0FERERERERERESLxIxCSSFELYB3APgPe1sAeBOAR+1dHgbwrpm8BhERERERERERES0u2gyP/zqAGwAU2NtlAPqllLq9fRrAsslOYug6Brs6Z9iV7BKqAlVzQdU0ZykUTtlJREREREREREQ00rRDSSHEOwF0SilfEkK8IdE8xq5ynOM/AuAjAFBbUoTv/MvV0+1KzhJKMqhUNA2qyw4tVc1us7ddKfukhJpKynra/qOOSa5rmivtPMqI/dPaNA2KqsEqcCUiIiIiIiIiIpofM6mUvBDA3wgh/gqAF0AhrMrJYiGEZldL1gJoG+tgKeUDAB4AgLrS4jGDy4VOmib0WBR6LJrtrkwoEU4qmgpFUaGoKoSqQlXtdbvNeShq2r6p7UIdY9+RbSP2Ve3XG3PflNcSE722qkLVXNDcbqguFzSXtWTgSkSzzTBMxEI6oiEdkWAc0ZCOaCix1KFqCvKK3fAXeZBX5EZesQdu70wHJhARpTNNiVhIRySU8nsoaC0jIR0ut4pAfQHK6/L5O4iIiIhy0rTfoUgpbwZwMwDYlZKfkVK+TwjxcwDvAfATAFcB+NVk51I0DQXlgel2JfskIE0Duq7D1OMwdB1GPJ7tXmXM0HUYug7kdnY6LYmA0gor3dBcLqhua6m53M669ZwbmtuV3M8+Lm095TzW8YkA1Do2dZ1VqES5Ky1YHOeCPhrSEQ0mw8ZE8BiPGlN+Pc2jWgGlHVT6iz3IK/QwvCRa4sYMFlN+50SCqW320m6LRTL8XSSAkko/AvUFqFheyKByiTFNA0Y8DiOuw9DjMOJx6PF4ynrMet7ettZ1pw0AhFAgFAEhFEAIZ10I4TygJLYT+ybXgZHHpO8DRUCM3EexX8ted/YV6f0RI/tjT5+VOAbCOvdCJSEhTROmrsM0DZiGCdMwYBo6TMOANE0Y9nPSSDyf2Ddl2zDsfUwYhm7va8Acde7kvqnbctS+4x/r7JvYljLt/9BZV5LriqI43wuJ9bR9lJTvN0VN/r/b68oY53ReS0l8D6XvozjfI8rYxyeOsY9fs/118BcWZftbgmjRmYt3IzcC+IkQ4g4AuwF8d7IDAvUN+Mi/PTgHXckeKaX9RyLlTYCuw7QDQGt7ZJsdaibeDKS26clzJI9JvmkwdR2GMWL/+Mj9R7+uaUz94nohSby5ioaC8//iQlhBZyIIdVvBp6K5AKiQpgLTUKDrCvSYgDQFPH4XvHkuePOtperivKTzTUm8UVHV5BsWRU0u1cQbmRHPqyn7qWM8ryh2tW+yzXqd5POJ1xx5/rT2lH4l9nHal1gIbhqmc/E+OlhMCRdnKVicCT1qYKAzjIHO8IT7TRRe5hV54Gd4SZRzJgsWoynBYmScYFHK1EFDcpLlGG1yrH2SetvC6G3rweG/2A0CKA74UFaXj/LaApTX5qN0WR7cHv5umU2mYaQHgU7oZ7elBoEjwsLUkNAKDvW05/TUc8Vi0FOCxNTXWezv9Ynmy7I16xhKEs2BWXnnIaV8CsBT9voxAOfNxnkXMiGEMzzZ5cl2b8YnTROGYcDU49YnXKYxZ5/EGc4nZ9P4JM75NG/8T+ISb870WAxGPGZVf2b1H1cmh+9nmImGB+a2S7TIJaoVFjI55mpKQy7O9iGmMqPy+OewDZ8daz8xatOpTkmsKwIiUUEiYFWniJT6EPt7Y6zvEactsc84vSSajAQgDQmZiz+qUyCltD9gls46ZHLd2k4JE0eFghkEiVn+fdbRD3QczWoXiIgWDIU3sSWaE/w4dIkTigJNUQCXK9tdmXXSNK1PoGOJT5hj0EesW8sY9Lj1SO5rLa2A026LxaCnDHPRYzHEI1FEw1HEo1Ho0RgMPQbT0CFNHYCZ7X8CWmoSF8o0z+T8ZwtWPpKTES0REeWW1PnWVc0F1WXfKNPlGuM5FzT7JpmK5oIQsMN5MyWoN633HKZpfRBhmsmw3lk3nZFjGLGPTN3HCf7NZPCf8noY4zhAjtmntH1kynkXuORc+hoUNTFqJmVe/gzm/J/KvqPm/B9nPv9R5x7zXgJa2veQaSa/d0wz9f/M/l5IrCfaTQlpGs73gGk/DzO5LlPPM8Y5rdc0k8en7pd2fPK1nGOc15HwFhRm+1uBaFHKiVBysCeC537ZYg1LK/Igr9getlbkhuZSs909WqCEosDl9sDlnlmpajxmYKAzhP6OMPo7gujvCKOvI4ThoRCiIbsaUwCKF0j9/Mx6E2QAUgegQ0oDgA5FmPAXqcgrUuEvUuDLV+HNExCKiaHeCIZ7IhjsjWC4NwrTzCxy8Oa7UFjqRUGZ/Sj1wuXlz06CNCXCQ3GEBmNpj/BgFNFw6rAm6TxcHgV5xS7kFbrgK9TgL9Dg8asAJEzDhDQN+41NolrXWpcp62biDZfTbiTfhDnHj2g3UtrNqbUTEdEikJgDTySqmK0q6GS7/Zy9bhU4p1ROJ/aZhGlI+4LbesgM33MAViW2Yj8S6yyxHk0oih3yJQPAUdtacqm507etwHDE+hjHpQWLduCY2JdznBMRUS7LiVAyMhzHrt+dGPM5j19LCSk9dmjphr+Q4SXNHtOUGOqJUQkzbgAAGm1JREFUoL8jZD06Q876cN/07gCUV+xFcYUfxVV+FFf4UVLpR3GlHwXlXqjq5OX/hmGity2IrpND6DoxhM4Tg+g5E4Shjw6f9DjQ22E9EgrKvKioL0BgeQEq6gsRWF4Ab97iq4hNkFIiNBBDf0cIfSP+Dwe7I5CmC4B/1HHaOJm1BDDcbz0SFE2gtDoP5bXWHFxltfkor83PiX/XZJVBdvsw1BNB16kh6/v21BC6TgwjEszsxl8ev4ayZXnwF7jh8Wvw+F1w+zVn3ePX4LWXnjwX3B7VnkA/NyWrVqW97Txht44Y9uks0tvTi1+tSoFYWEdwIIbQQBShwagdtEcRHIghbK+HBmMw4iN/X4z1DTKybXa/iYQQUF0KNJcCxaVA0xSoLgWqZrfZSzWtXVjtmgrVrUBVBTR3Yl8VqiacfVWXap/TblMVaB41k0yGpkBKoK8tiFOv9OLUwV70nBked1+3T0NpTd6CvrkEYH0dHp8Gt88FT5699Luc30Nuv8t5XlHFOAEi0sLE8QLEXAiN4jED3aeG0XVy0HrfcXIIfe3BzIbiJ26mk3jPwZvpUA4wTWlPa5L9ny8iIhqbyIWhfvWBNfLGy+6f0Tk8eVryxgAML2kMUkpEhuNWYJX66AxjoCsEU5/6z4LLo6LYDhuLK3wp6364fbP/Rjw1qOw8MYSuCYLKsSyGoDIW1tMCx8T/YX9HaFo3L1E0gaKA9f/nzXOhtz2InjPD0GOZVx7ml3isgHJZvhNUFlX4rcqRRcoJIE9aF66J8HwqAWR5XQEqlhfYd4QtQGG5jxcOs8gJL/tjCA5EERqwQstgf9QJNIN22+jwcmFz+zRUrSxC9aoi1KwqQsXyQmhu/v2fKj1u4PQrfWjd14MT+7on/JCuuNKPhk3laNhYhurGIigZfPhGuW9Wg8rl1g11GFQuPaYpoccMGLoJPWbCiJvQ44mlMe620xYboy1uwogbI9rSz2UaEp48DRsuWobNF9cvuPe8RLNFCPGSlHJbtvtBNJacCCU3rG2SD/7fx5MXSYkLpsHYlIaSZCI1vLTuZDo6vMwr8vCuxwvYWMOtEyGWM9x6CoQiUFjuRUmlH0WVdsVjhRU++ovcWQ9RnKDSvljoOjGI7jPDGYesBWXeZDCUI0GlYZgY6o6MESCHEBqITeuc+SWetNA4UcFaUOYdFR6apsRgVxjdp4fRfXoIPaeH0X16eEpVs5pbQWlNvl1Vme+ElnMRVs81KSWGeiPJ77EpBpBun2Z/f9mBOAPInCKlRDSkIzRghZfhoVjKheDEF4zORWLqxaa91HUTRszIiRueKKpAxfICVDUWo7rRCit9+e5sdysnBQeiOLG/B617u3HqUO+4H9AIRaBmVZEdRJajuHJ0JTotTvGoge7TDCoXKyNuTSc00B3GcG8E8agxK+Fgtrm8KpreVIemN9dl/X0u0XxjKEm5LCdCyW3btskXX3xxVLtpWpVtQTuoTFwwJSo9ElUf8xleOusML+eVaZiIhnREgnFEgtYyGozb23FEhuMY6ArPaLi1v9CdDK1Sqh8LA76MhlvnEkNPqaicRlBZWO5FoH5ug0opJUKDMfSftcLGvo4QBuyqx8GucMbzaaby+LVk6FjpQ3FlHoorfSiq8MM1C1VSkWDcCSi7zwyj5/Qwetsyr1QFrH/bsmX5aUPAC8u8OTMM2QkgE9MGMICkGbAmi5cwYomL2MwCzcRF7VgXvWnHx0a3xe2L5smUVPlR3VhkBZWrilAUWJrfp1JKdJ8eRuvebrTu7UbniaFx9/X4NdSvL0PDpjLUryvjhT05ZiOorFhuDftmUDm3EiOHBrrDGOwOY7ArYi27wxjoCmO4P7qo76LGcJKWIoaSlMtyOpTMlBNe9kftIWrzH15681xwe1W4vIn5h6x1t9dat5Ya3N6UdZ8KVVOW1EWQaVrDCSPDcURCVpgYDSW3o8NxREIjQ0cdsfDUKxzHonlUu9LRl6x6nMPh1rkkW0FlLKKnzNMZTqt6jEemMdxaFSgK+EYFyCWVfnjzXfP+82QaJvo6Qk5Y2WMHllOp6HR51bSh32W1+SiryYfLM7fDTUcGkInvjcjw9ALIQH3Bkg12KHdIKTHQFUZ78wDaW/pxtmUAfWdDkx7nK3SjprEI1auKUdVYhPK6/AX3gVSm9LiBM4f7rSAyk2HZG8vQsKmcw7JpSlKDyk77b8xUg8rCcl9yaqYRc8z7C138fhyHoZsY6rGqHQe77PCxO+IEkdN5/zUjAtbcwS5rXuDkur10W9dEmtued9htzxXsViY+LnGsa+z9hCLQsqsTL/z2+Ki/AwwnaSlhKEm5bFGEkpnKZng5HkUVyeDSZ4eY3pR1X0rQaYeeTriZss98h5tSSsQihh0qxlNCRn3C7WhIn/NPXxPDrYtThlknQqtcGG6dS2YzqAzUF8DUZVrVY1/HLAy3rkgPHwtKPQviIiQ0GEPPmZSg8vQw+tqDmVeACqAo4EsZ/m1VjuSXeKb1PcwAkpay8FAM7S0DaG8ZwNmWfnSeGJp0OKHmVlC5whrqXd1YhKoVRQv6w6upDstevrEcKzZxWDbNrkRQ2Xli0Pp7NJWgciQB+ArczuilvCI3/MWeUXPML8bwUkqJSDDuVDkOOMFj2Bp23TeDakdhvQcrKvehoMwLj89lB4cpQaBrZBBo33QssZ+WHhYmbwSVHaYpGU7SksZQknLZkgolM5WL4eVkFFWMEVamVmamh57Jik4NLq8KRREpw6MTVYqjKxYjQTuEDOrz+7ULOHfe9ebbd7/MT98uKLVCrMJyH1Rtcb35nE9pQaV90TCVoHIq3D57uHWlz5qzs8KPkio/igL+Oa8SzAZDN9F3NmjPVZkMKzMNBoHEHapT5qmszUdpTV7aTbyklBjui1r/fwwgiUbRYwY6TwyirXkA7c0DOHtsYNKKfCGAstp8VK+y56VsLEZ+iWeeejx1Ukr0nLGGZR/f24PO1sFx9+WwbMqmeNRA96mUOYtnElSOJTW8LPYgrzA9vMwr9sBfmHvhZaLaMRk2RjDYFZ6VakeXR0VhwIeich8Ky70oLPc52wWl3kU7PRXDSVqqGEpSLmMoOQOp4WUsrCMa1hGP6IhFDMQi1pDjWNhejxjWdqLd3s6FiZ/nm9unwZunwZvngifPBW+eC16/Bk++KyVk1OBN2Xb7tEV9J+NclwgqUysbMg0qxxtuXVzhh69g/odb5xopJUIDsVE31envCGV8QSYUgeJKP8qX5SEa0qcRQOY7Q/ID9QUoKvflzByXRPNBmhK97UG0N/dbQWVLP4Z7J5+fuKDMa1dSWvNSllblZfVnxxmWvc+aH3KiYdlFFT40bCrHio3lqFpVtGiHqtPCFI8a6G0PphUDpBUK9EcRHo7P7ugbAfgL3PBPEF7mFXngK5id8HJUtWN3OG1ux2BfdPrBrADyiz0oCviswLHch8KAFT4WlfuyMt1NLjFNiZaXOvHCEwwnaWlgKEm5jKFklulxwwku4xED0bAVWi6EcNPlUeGxw8XEwwoZtRHbyTaPX8upT6Fp+kYGlT1nhqG61OQ8nXYFZEGpl//n06DHrAuy1KrKnjPD07qDfEIigAzUFzp3XGcASTS2od4IzrYMWEFlywB6zgxPGoB4/BqqGovsO3wXo2J5QVoV81wIDcacEPLUK33Qo2NXTwlFoLrRuls2h2XTYmAYJsKDMQT7E4FlMrwM9scQGpyn8LLIY62nzHuZCC+ltH6XJOZ1HOhOVj4OdoURm2m1o13p6ISPS6DacTZNFE66vSo2MZykRYKhJOUyhpKLgJQShm5OGG46VZxhIz3QtNdNQ1pDokdUMI4OHe1tv4tvdojmWWJYthVSDjmB5UBXeNQFV1oAaQ/DZgBJNH3RsI6OY9a8lO3N/eg4Pgh9krt8K5pARX2hVU25qhjVK4vgzZ/ZxW1yWHYPWvd1o6N1cNzAxRmWvbEM9es5LJuWpozDy6HMp1LJRKIQcabVjsmw0YtCO3xktePsYjhJix1DScplDCWJiBa4WERHb1sQve1BuNxqcg5IBpBEc8YwTHSfHEZ7S79zp+9MQo2S6jy7ktIa9l1Y7p00WNDjBs4cSblb9gRDyzksm2h6JgsvE22zGV5qHjU5r6Mzx6MPRQFWO2YDw0larBhKUi5jKElEREQ0Q1JKDHSGU0LKAfR3hCY9zl/ktuaktIPK8tp8KKqC0GAMJ/Z3o3VvD04e6p18WPbGcjRsKkNJVd5sf2lElGJK4eVY1Y4pwSOrHXMTw0labBhKUi5jKElEREQ0B0KDMZw9Zg33bm8ZQNeJIZjmxO+7NI+KwjIvetuD4w7Ldvs0LF9fioZN5RyWTZSjDMMEJKBqrHZcqBhO0mLBUJJyGUNJIiIionkQjxnobB10hnufbRnI+EYXRQFrWHbDpnJUc1g2EdG8MU2J5pc68OITrQwnaUFiKEm5jKEkERERURaYpkRv27Az3Lu9uR/DfdZ8kUIA1auKOSybiChHTBpOvrkOTW9iOEm5h6Ek5TKGkkREREQ5Yqg3gsGuMMpq83lhS0SUgxhO0kLDUJJyGUNJIiIiIiIioilgOEkLBUNJymUMJYmIiIiIiIimgeEk5TqGkpTLGEoSERERERERzYBpSjS/2IEXnmhFfwfDScodDCUplzGUJCIiIiIiIpoFDCcp1zCUpFzGUJKIiIiIiIhoFk0YTvo0NL2pFk1vroPHz3CS5hZDScplDCWJiIiIiIiI5gDDSco2hpKUy6YdSgoh6gB8H0AVABPAA1LKbwghSgH8FEADgFYAfyel7JvoXAwliYiIiIiIaLFiOEnZwlCSctlMQslqANVSyl1CiAIALwF4F4APAuiVUt4lhLgJQImU8saJzsVQkoiIiIiIiBY705Q4+kIHXvxPhpM0PxhKUi6bteHbQohfAbjPfrxBStluB5dPSSnXTHQsQ0kiIiIiIiJaKhhO0nxhKEm5bFZCSSFEA4BnAGwAcFJKWZzyXJ+UsmSMYz4C4CMAUF9f/6oTJ07MuB9EREREREREC8Vk4WTVyiK4fSrcPg1urwa3V7WWPs1q99rtKftobgVCiCx9RQuHlBKGbiIeMRCLGIhHDcQjOuLRlO2otb3h9bXw+LRsd3laGEpSLpvxT5UQIh/AYwA+JaUczPSXn5TyAQAPAFal5Ez7QURERERERLSQKIrAmtdU4ZxXV44KJ2NhHScP9Ez5nEIRKeFlSojpVeGyg0uPT4VrjEDT7bXXfRo0V26Fm9KUdlBoIGaHh3E7PIxF9eT6eAFj4phocj9pZhZFrNwcWLChJFEum9FPlRDCBSuQfERK+Qu7uUMIUZ0yfLtzpp0kIiIiIiIiWqwmCienSpoS0ZCOaEifUZ/Sw83UysyUENMONz32tsvex+PT4PKqUFUlGSDaIWIsOiJQHBkwRpIViqmBoh4zZ/T1zEQ8amTttYkWs2mHksL6yOS7AA5JKb+W8tSvAVwF4C57+asZ9ZCIiIiIiIhoCUgNJ7tODCE8FEMsoiMW1hGLGMml06YjFjbS9jHisxPezVa4mcsUTcDt0eDyqHB5Vbg8qlVR6tHStr35nNuTaC7MpFLyQgDvB7BPCLHHbrsFVhj5MyHENQBOArh8Zl0kIiIiIiIiWjoURaByReG0jk3Ok6gjGtYRHyO4nM9wczZpHhVujzoiREwJEFPCxLECxrRjPCpUTcn2l0S0pE07lJRS/hnAeBNMvHm65yUiIiIiIiKi6VE1BWq+MuPqvgnDzUSwOVG4GTFg6qYdBmopIWEiHNSsgHFEUDjetsutQii5M8clEc0cZ2olIiIiIiIiojSzFW4SEY2HtcpEREREREREREQ0rxhKEhERERERERER0bxiKElERERERERERETziqEkERERERERERERzSuGkkRERERERERERDSvGEoSERERERERERHRvGIoSURERERERERERPOKoSQRERERERERERHNK4aSRERERERERERENK8YShIREREREREREdG8ElLKbPcBQoghAIez3Q8imrFyAN3Z7gQRzQh/jokWB/4sEy0O/FmmmVoupQxkuxNEY9Gy3QHbYSnltmx3gohmRgjxIn+WiRY2/hwTLQ78WSZaHPizTESLGYdvExERERERERER0bxiKElERERERERERETzKldCyQey3QEimhX8WSZa+PhzTLQ48GeZaHHgzzIRLVo5caMbIiIiIiIiIiIiWjpypVKSiIiIiIiIiIiIlgiGkkRERERERERERDSvsh5KCiHeJoQ4LIRoFkLclO3+ENHUCSFahRD7hBB7hBAvZrs/RJQZIcT3hBCdQoj9KW2lQog/CCGO2suSbPaRiCY3zs/ybUKIM/bf5j1CiL/KZh+JaGJCiDohxJNCiENCiANCiE/a7fy7TESLVlZDSSGECuDfALwdwDoA7xVCrMtmn4ho2t4opdwspdyW7Y4QUcYeAvC2EW03AfhvKeU5AP7b3iai3PYQRv8sA8A99t/mzVLK/5znPhHR1OgArpdSngvgfAD/bF8b8+8yES1a2a6UPA9As5TymJQyBuAnAC7Ncp+IiIiWBCnlMwB6RzRfCuBhe/1hAO+a104R0ZSN87NMRAuIlLJdSrnLXh8CcAjAMvDvMhEtYtkOJZcBOJWyfdpuI6KFRQL4vRDiJSHER7LdGSKakUopZTtgXSABqMhyf4ho+v5FCLHXHt7NIZ9EC4QQogHAFgDPg3+XiWgRy3YoKcZok/PeCyKaqQullFthTcXwz0KIi7LdISIioiXufgCNADYDaAfwf7PbHSLKhBAiH8BjAD4lpRzMdn+IiOZStkPJ0wDqUrb/f3v3H6pnWcdx/P1xmybLMhVKrbkpisqiYRkFG60fiKRUzjKXkhXmsEgSSpaQ9U9oKmVmEGg2y5mFphtaJqgDTUkxdEfNsnT4x2BqlJXW2Drf/rivMx8fz6/ndDxnc+8XHJ77ue7ruu/v/dwc7sP3fK/reSuweZZikTRFVbW5vT4D3ES3NIOkXdOWJAcCtNdnZjkeSVNQVVuq6r9VNQxcic9maaeXZB5dQnJtVf2yNftclvSaNdtJyQeAw5MsSrIncCqwfpZjkjSAJPOT7DOyDRwHPDL+KEk7sfXAGW37DGDdLMYiaYpGkhjNSfhslnZqSQL8CPhDVX2nZ5fPZUmvWama3dnSST4MXAbMAa6uqm/NakCSBpLkULrqSIC5wHX+Hku7hiQ/A5YDBwBbgG8ANwO/ABYATwOfqCq/QEPaiY3xu7ycbup2AZuAVSPr0kna+SRZCtwNDAHDrfl8unUlfS5Lek2a9aSkJEmSJEmSpN3LbE/fliRJkiRJkrSbMSkpSZIkSZIkaUaZlJQkSZIkSZI0o0xKSpIkSZIkSZpRJiUlSZIkSZIkzSiTkpIkadKS/Kvv/WeSXDHDMcxN8lySC/valyV5NMlDSY5K8qlpONemJAe07bckuT7JX5I8luRXSY5IsjzJLX3j1iT5+CDx9/V5xTEnEetVSY4ecMxHkqweZIwkSZI0HUxKSpKkWZdkzgDdjwP+CJySJD3tpwGXVtUS4M3AQEnJ8WJo57kJ2FBVh1XV0cD57TyDGiv+/0tVnVlVjw04Zn1VXTRdMUiSJEmTZVJSkiRNiySHJLkjycb2uqC1v6xqcKTaslUD3pXkOmAoyfwktyZ5OMkjST45xqlWAt8Dngbe0451JnAKcEGStcBFwLJWNXlukjlJLknyQItv1WgxjHN57we2VdUPRxqq6qGqunsKH9Ur4m+xHJ/k8ST3ACt62r+Z5Jokt7fKzRVJLk4ylOS2JPNavw1J3tWudU37DIeSnNv2n9MqPDcmub617ah0neD+XZ7k3iRPjlUBKkmSJA1i7mwHIEmSdil7J3mo5/1+wPq2fQXwk6q6JsnngMuBj01wvHcDi6vqqSQnA5ur6gSAJG/s75xkb+CDwCpgX7oE331VdVWSpcAtVXVDkuXAV6rqxDbuLOD5qjo2yV7Ab5Pc3h/DOHEuBh6c4FomNFb8SV4HXAl8APgz8PO+oYfRJUaPBu4DTq6q85LcBJwA3NzTdwlwcFUtbufct7WvBhZV1daetl7j3b8DgaXAkXT3+4YpfgSSJEkSYKWkJEkazL+rasnID3BBz773Ate17Z/SJbEmcn9PMnAI+FCSbydZVlXPj9L/ROCuqnoRuBE4aZJTv48DPt0Sqr8D9gcOHyWGqagB2seK/0jgqap6oqoKuLZv3K+rahvdZzQHuK21DwEL+/o+CRya5PtJjgf+0do3AmuTnA5sHyW28e7fzVU13KaHT2XKuiRJkvQyJiUlSdKrZSQpt532N0dbQ3HPnj4v7Ohc9SfgnXSJtguT9CY8R6ykS1xuoqtc3J+ugnAiAb7Uk1BdVFUjlZIvjDewebTFNpq/Am/qa9sPeG6UvuPFP1ZyE2ArQFUN000jH+k7TN/Ml6r6G/AOYAPwReCqtusE4AftOh5MMtGMmd54tvZsT9s6mJIkSdp9mZSUJEnT5V7g1LZ9GnBP297ESwm9jwLzRhuc5CDgxaq6FrgUOKZv/xvoqvcWVNXCqlpIl3RbOcrh/gns0/P+N8DZPesvHpFk/gDXdiewV5LP98RzbJL3AU8AByU5qrUfQpcU7J3mPlH8jwOLkhzWuo92TZOS7tvC96iqG4GvA8ck2QN4W1XdBZxHN3X89X1Dx7p/kiRJ0rRzTUlJkjRdzgGuTvJV4Fngs639SmBdkvuBOxi7MvHtwCVJhoFtwNl9+1cAd1ZVb9XeOuDitk5kr43A9iQPA2vovlhmIfD7Vq35LBOvd7lDVVWSk4DLkqwG/kOXbP1yW6PxdODHbW3IbcCZo0w/HzN+4AvAWcCtSZ6jSwgunmx8fQ5usYz88/lrdFO+r23rdAb4blX9ve/Lv8e6f5IkSdK0y0uzfyRJkiRJkiTp1ef0bUmSJEmSJEkzyqSkJEmSJEmSpBllUlKSJEmSJEnSjDIpKUmSJEmSJGlGmZSUJEmSJEmSNKNMSkqSJEmSJEmaUSYlJUmSJEmSJM2o/wG5oDZg9FXj4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(200003)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(200006)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(200007)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NGgW1Wi1jdtTQ4MGDERcXh/z8fFRXV+PkyZMYO3as3GkREREREXUKLEoSERG1gerqavzyyy84ffp0o7iTkxOmT58OOzs7mTKjh6lfW3Lbtm0AgJMnT2LYsGHsliQiIiIiagEsShIREbWyq1evYteuXSgpKZFiarUa48aNQ2BgIBQKhYzZ0aN4enoiLi4OBQUFqK6uxqlTpzBmzBi50yIiIiIi6vBYlCQiImolZWVl2Lt3Ly5cuNAo7u7ujtDQUFhbW8uUGT2p+m7Jn376CcC9bkluQkRERERE9GxYlCQiImphoigiJSUF+/btQ2VlpRQ3NTVFSEgIvLy8IAiCjBnS06jvliwsLERVVRVOnTqF4OBgudMiIiIiksXZs2cnqVSqj0RRtAfAKT/0MHpBELK1Wu2f/Pz89jV1AIuSRERELaiwsBC7du1Cenp6o/hzzz2HSZMmwczMTKbMqLmUSiVGjx6N7du3AwBOnDiBYcOGwdjYWObMiIiIiNrW2bNnJxkbG3/l4uJSY2JiUqRQKES5c6L2Sa/XC5WVlVYZGRlfnT179rdNFSYfW5QUBGElgFAAuaIoet733LsA/g6ghyiK+YKh7eNLAFMAVABYLIri2Zb4ZoiI6OH0ej0uXbqE8vJyuVPp0kpLS3HixAlotVopZmVlhWnTpsHd3V3GzOhZeXl5IS4uDkVFRaiqqsLp06cxatQoudMiIiIialMqleojFxeXGjMzs8rHH01dmUKhEM3MzCpdXFxw/fr1jwA8fVESwGoAXwFY2zAoCIIzgAkAfm0QngygX92foQC+qftKREStRK/XIyYmBqmpqXKnQg0IgoChQ4di7Nix7KjrBJRKJUaNGoXY2FgAwPHjxxEYGMj/tkRERNSliKJob2JiUiR3HtRxmJiYVNVN9X/AY+f+i6J4BEBhE099AWA5gIatujMArBUNTgKwFgTBoRk5ExHRE9Dr9di5cycLku2MnZ0dlixZgpCQEBatOhFvb29pc6LKykokJCTInBERERFRm1NwyjY9jbr/X5qsPzZrTUlBEKYDuC2KYsp9C/U7AbjZYHyrLnanOdchIqKHE0UR+/btQ1JSkhTr168fd3SWmb29PXx8fKBUKuVOhVpYfbfkzp07ARi6JYcMGcLCMxERERFRMzx1UVIQBFMAHwCY2NTTTcSarKALgvAbAL8BgN69ez9tGkREXd7hw4dx6tQpaezj44Pp06dDoeAGeEStxdvbG0eOHEFxcTEqKiqQmJiIESNGyJ0WEREREVGH05xPrm4AXAGkCIKQAaAXgLOCINjD0Bnp3ODYXgCymjqJKIrfiqIYIIpiQI8ePZqRBhFR13Xs2DHExcVJ40GDBmHatGksSBK1MpVK1WiDm2PHjqGmpkbGjIiIiIionpOTk9f27dstGsZWrFjR3d/ff0BrXVMQBP8LFy606NSZpr6PzuipOyVFUTwPoGf9uK4wGVC3+3YsgN8KgrAZhg1uikVR5NRtIqIWlJiYiP3790tjd3d3zJw5k9OFidqIj48P4uPjG3VLBgUFyZ0WERE1UFtbi7y8PLnTaBE9e/aEStWsldeIqBXV1tZCrVa32es6o8f+yyYIwiYAYwDYCoJwC8BHoij+8JDD9wCYAiANQAWAF1soTyIiAnDu3Dns2rVLGvfp0wcRERF8o0rUhlQqFUaOHIndu3cDMHRLBgQEwMjISObMiIgIAK5evYpt27ahqqpK7lRaxJtvvgkbGxu50yDqNDIyMtRLly7tffr0aXNTU1P9a6+9lvPhhx/mAsChQ4dM33777d7Xr1/XaDQa/eTJk+9+++23NzUajQgYuiI//fTTX//1r3/ZabVawcHBoQYAhgwZMkgQBHz55ZcZr7zySqPdyVesWNF99erVPXx9fcu3bt3afdGiRblLly4tePnll/tcvnzZVBAEcdSoUSUrV6781dbWVhcWFuZ6584do7lz5/ZTKBTiO++8k/XnP/8555dffjF79913ndPS0jSOjo41n3/++c3Q0NDStv8JtpzHfooVRXHeY553afBYBLDs2dOitpSSkoLs7GwMGzYMVlZWcqdDRA9x+fJl/PTTT9LY0dER8+bNYyGESAa+vr6Ij49HSUkJysvLcebMGQwfPlzutIiIurykpCTExsbC8NGUiFqby/u7/dvqWhl/nXrmWc+h0+kwdepU98mTJ9/dsWNHenp6unrixIkDBg4cWBUeHl6iUqnw+eef3xw9enR5enq60eTJk/t99tlnPf74xz/m1p9j586d1qdPn75kZmamNzc3FwVB8E9ISLjo6elZ/bDrnjt3ziw8PLwwPz8/ubq6WsjIyDD6/e9/nx0SElJaVFSknD59utvy5csdV65ceXP79u03nJyczL/++uuMsLCwUgC4ceOGOjw8vN+///3vG7NmzSqOjY21XLBggdvFixcvODo6ap/15yIXttZ0cUlJSdixYwcA4NKlS3jxxRdZmCRqh65fv44tW7ZIb7B79uyJBQsWQKPRyJwZUddU3y25Z88eAPe6JTkVh4hIHqIoIj4+HgcPHpRipqamneKzDZfoIXo6kZGR7kqlUrozodVqhUGDBlUAQFxcnFlhYaHqH//4xx0AGDRoUE1UVFTepk2bbMLDw0tGjRpVUf+6AQMG1CxevDgvPj7eAoBUlHz//fez7ezsdE+TU48ePWo++OCDXABQq9Wip6dndX0R08TERPvWW2/lfPrpp44Pe/3333/ffcyYMcVz5swpBoAXXnih5PPPPy+PiYmxeuONNwqeJpf2hEXJLiwnJ0eaegYAd+/exZo1a/Diiy/CwqLTr6dK1GH8+uuv2Lx5M3Q6w+89GxsbREVFwdTUVObMiLq2+m7J0tJSlJWV4cyZMxg2bJjcaRERdTl6vR4///wzEhISpJidnR0WLFjAzzVEXdDGjRvT6jsMAcP06TVr1tgCQHp6ulFeXp6RhYWFT/3zer1eCAgIKAWAc+fOGb/11lvO58+fN6uqqlLodDrUFzTrubq6PvUuhw4ODrUNx7dv31bVTyGvqKhQ6vV6WFpaPrTQmZmZafTzzz93s7CwkO60aLVaYfTo0Z17+jZ1TlVVVYiOjoZW27jLt7CwEGvXrsXixYthZmYmU3ZEVO/OnTvYsGEDamsNv8MsLS2xcOFCvsEmagfUajVGjhyJn3/+GYChW9Lf35/dkkREbai2thbbtm3DpUuXpJirqyvmzJnDGSVEbaAlplS3JRcXlxonJ6fqzMzMC009v3Tp0j5eXl4V27ZtS+/WrZv+448/7rljx45uDY8RBOGprysIQqM1Jd5++20nQRDEc+fOpdrb2+vWrVtn/e677/Z+2OudnZ1rXnjhhYLNmzdnPvXF2zGF3AlQ2xNFETt37kRBgaHDV61WY8KECdJfrLy8PKxbtw6VlZVypknU5dX/XayuNixNYmZmhoULF8La2lrmzIionp+fH8zNzQEApaWlOHv2rMwZERF1HZWVlVi3bl2jgqSnpyfmz5/PgiQRNWnMmDHl5ubmug8++MC+rKxM0Gq1SEhI0MTFxZkCQFlZmdLS0lJnZWWlT0pK0qxcubLn487ZvXt37dWrV42fJo+ysjKlmZmZ3tbWVnfjxg31F198Yd/weVtb29q0tDTpnC+//HLBgQMHrGNiYiy1Wi0qKiqEXbt2WVy/fr1D3w1nUbILOn36NFJTU6XxtGnTMGLECMycOVOKZWdnY8OGDVIxhIjaVlFREdauXYuKCsNMAY1Gg6ioKNja2sqcGRE1VN8tWe/o0aMPzEIgIqKWV1xcjJUrV+LXX3+VYsOGDcPMmTOhUnFCIBE1TaVSYffu3Wnnzp0zcXFxec7GxsZnyZIlLkVFRUoA+Oyzz27GxMTYmJub+y5ZsqRPWFhY4ePOuXz58qylS5e6WFhY+Hz//ffdHnc8AHz88cdZ58+fN7W0tPSdPHlyv2nTpjXasfu9997L/vzzzx0sLCx8/vjHP9q5u7vXRkdHp/31r3916N69u4+Tk9Nz//jHP+z0ev3Tt222I0J72JUsICBATExMlDuNLuHWrVtYuXIl9Ho9ACAgIAChoaHS82fPnkVsbKw0dnFxQWRkJHf3JWpDJSUlWLlyJe7evQvAUPRYtGgRevXqJXNmRNSU2tpafPnllygrKwMATJkyBYGBgTJnRUTUeeXm5mL9+vUoKSmRYhMnTkRQUJCMWRG1T4IgnBFFMaClzpeSkpLh7e2d31Lno64hJSXF1tvb2+X+ODslu5CKigps2bJFKkg6ODhg0qRJjY7x8/PDlClTpHFGRgZ+/PFHdn0QtZHy8nKsXbtWKkgqlUpERkayIEnUjqnVaowYMUIas1uSiKj1ZGZmYuXKlVJBUqFQYObMmSxIEhF1QCxKdhF6vR4//fQTiouLAQDGxsaYPXt2k4vxBwYGYsKECdL4+vXr2LJli7TzLxG1jqqqKqxfvx75+YYbjwqFAhEREXB1dZU5MyJ6HH9/f2mDuJKSEiQnJ8ucERFR53Pp0iWsXbsWVVVVAAAjIyPMnz8fzz33nMyZERFRc7Ao2UUcO3YM165dk8YvvPACbGxsHnr8iBEjMGbMGGl85coVbNu2TeqyJKKWVVNTgw0bNuDOnTtSbObMmRgwYICMWRHRkzIyMmrUpRMfH89uSSKiFpSQkIDo6GipUcLMzAyLFy+Gm5ubzJkREVFzsSjZBWRkZODgwYPSOCgoCB4eHo99XXBwcKPpaKmpqYiNjWVhkqiFabVabN68GTdv3pRi06dPh6enp4xZEdHTGjJkCExNTQEYNmBISUmROSMioo5PFEUcPHgQu3fvRv1+CDY2Nnj55Zfh6Ogoc3ZERPQsWJTs5EpLS7F161bpF3jv3r0xbty4J3qtIAgYP358o8X6k5OTsWfPHrSHDZKIOgOdToetW7ciPT1dik2aNAl+fn4yZkVEzdFUtySXPiEiaj6dTofY2FgcOXJEijk6OuLll19+5KwvIiLqGFiU7MT0ej1iYmKk3UBNTU0xa9YsKJXKJz6HIAgICQlpVCBJTEzEf/7zHxYmiZ6RXq/Hjh07cPnyZSk2duxYDB8+XMasiOhZNOyWvHv3LrsliYiaqaamBps3b0ZSUpIUc3d3x6JFi6Q1fImIqGNjUbITO3ToEDIyMqRxeHg4LC0tn/o8CoUCoaGh8PLykmInTpzAoUOHWiJNoi5JFEXs2bMH586dk2JBQUEYPXq0jFkR0bMyNjZudGPhyJEj7JYkInpK5eXlWLNmTaM18X18fDBv3jwYGxvLmBkREbUkFiU7qWvXriE+Pl4ajxkz5pkWgVYoFAgLC8PAgQOl2JEjRxpdg4iejCiK2L9/PxITE6WYv78/JkyYAEEQZMyMiFpCYGAgTExMABi6JRvefCAiokcrKirCypUrcfv2bSk2atQozJgx46lmfBERUfvHomQndPfuXWzbtk0a9+3bt0W6r5RKJcLDw9GvXz8p9ssvv+DkyZPPfG6iriQ+Ph7Hjx+Xxl5eXpg6dSoLkkSdBLsliYia586dO/jhhx9QUFAgxSZPnoxx48bxfRIRdRhOTk5e27dvtwCA999/337OnDl95M6pvWJRspPRarXYsmULKisrAQAWFhYIDw+HQtEy/6lVKhUiIiLg6uoqxfbu3duo44uIHu7kyZM4ePCgNB4wYADCwsJa7O8oEbUPgYGB0Gg0AAxdP+fPn5c5IyKi9i09PR2rVq2S1sNXKpWYPXs2hg4dKnNmRNSR7Nu3z9zX19fDwsLCx8rKysfPz88jLi7O9FnO2bDI+LT++te/Zv/444+ZT/u6wMDAAcbGxn6mpqa+FhYWPgEBAQNOnz5tUv/8O++846hSqaTnfX19PQ4cONDhFtzlp+BOZv/+/dJUB0EQMHv27BZfCFqtVmPu3LlwdnaWYrt27eJi/kSPkZSUhL1790rjvn37PvXmU0TUMWg0GnZLEhE9ofPnz2P9+vWoqakBYOg4j4qKwuDBg2XOjIg6ksLCQsWsWbPcX3311dyioqLk27dvp3z44YdZGo2mQ+7S+5e//OXXioqKpMLCwuSRI0eWLly40LXh81OnTi2qqKhIys/PTxk6dGjZvHnz3PR6vVzpNguLkp3IxYsXcerUKWk8fvx49O4MSC/FAAAgAElEQVTdu1WuZWxsjPnz58PR0VGKbd++Hampqa1yPaKOLjU1FbGxsdLY2dkZc+fOhVqtljErImpNQ4cOlbolCwsLceHCBZkzIiJqf06cOIGYmBjUf5C2sLDASy+9BBcXF3kTI6IO58KFCxoAWLp0aaFKpYK5ubk4c+bMkqFDh1ZWVlYKVlZWPg27DW/fvq3SaDR+WVlZqjt37qjGjh3rXt9h6e/vP0Cn0yEsLMz1zp07RnPnzu1namrq++GHH9oBwIYNG6zc3d0HW1hY+AQGBg44e/aspqmc3nnnHccZM2ZIxcSGnZz29vbPrVixovvjvi+1Wo2oqKjC69evN3kNY2NjccmSJfn5+fnqnJwc1dP+3OTUoZKlhysoKMCOHTuk8YABAxAUFNSq19RoNFiwYAHWrFmDnJwciKKImJgYqFQqDBgwoFWvTdSRXL16FTExMRBFww06e3t7REZGwsjISObMiKg1aTQaDBs2DIcPHwZg6Jb08vLicg1ERAD0ej0OHDjQaJ1tW1tbLFiwANbW1jJmRkRP5f9a+bfdtYrPPOppT0/PKqVSiZkzZ7rMnTu3cOzYseU9evTQAYCJiYk4bdq0wlWrVtkEBgbeBoCVK1faBAUFlTg6OmqXLVvm5ODgUJOfn58CAIcOHTITBAHbt2+/4eTkZP71119nhIWFlQLAuXPnjJcsWdJ348aN16dMmVL6P//zPz3DwsLcr169mvqorsxr164ZzZw5s9///u//Zi5evLioqKhIkZ6e/tgPhVVVVcLq1au7e3t7lzf1fGVlpfDdd9/Z2tnZ1To4OGgfd772hEXJTqC2thbR0dGorq4GAFhbWyMsLKxNFoM2NTVFVFQUVq9ejfz8fOj1ekRHRyMyMvKZdvvu8kQR0GsBXS2gq6l7XFM3rgX0dXFdXVxf+2TP3X+uh5634bjha2sfct5aQNQBTn7AhP8BHH3k/gm2Gzdu3EB0dLR099/W1hZRUVHSzrxE1LkNHToUJ06cQHV1NQoKCnDhwgU899xzcqdFRCQrrVaLHTt2NFpv19nZGfPmzYOp6TMt/UZEXZiNjY3+4MGDlz/55BP7N954w6WgoEA9evTo4jVr1mQ4OztrX3rppYLIyEi3r7766rZSqcSPP/7Y/Xe/+102AKjVajEnJ0d97do1I09Pz+qQkJCyh11n3bp1NmPHji1+4YUXSgDgT3/6U863335rd+DAAfPQ0NDSh72uvgi6dOnSQgCwt7fX2dvbVz7s+A8++MD5o48+6lVVVaXQaDT69evXX2/4/J49e7pZWFhYqdVqsX///pVbtmxJe9qfmdzaR1Ey+zzwNxe5s+iwfq4JQo6uPwBACR0iKtfBZMU/2+z65gAWiqZYJYSgSLSETqfDpnWrsMBoP1yUOW2WR4cn6hsX/DqiG0eA78YCgUuB5z8AjJu1FnCncevWLWzatAlareFmlbW1NaKiolp8nVciar9MTEwwbNgwxMXFATB0S3p6erJbkoi6rOrqavz4449IT0+XYh4eHggPD+eyNkT0zPz8/KpiYmIyACApKUkTFRXl+vrrrzvv3LnzxvPPP19uYmKi27Nnj0WvXr1qMzMzjSMjI+8CwEcffZS9fPlyx5CQkP4AsHDhwrxPP/00u6lrZGVlqZ2dnWvqx0qlEg4ODjU3b9585D9iN2/eNHJ1da1+0u/lk08+ufnOO+/k63Q67N+/3zwiIsJ9//79V4YOHVoJAFOmTCnasWPHjSc9X3vUPoqSei1QWSR3Fh1SMgbiLPpL4xAchmN12xfHLVGEhdiCVZiNElhCCxU21ozDQsSgF5r8e0ydlagHTn0DXNwBTP4rMHA60AZdu+1NTk5OowXbzc3NsXDhQlhZWcmcGRG1tWHDhuHEiROoqalBfn4+UlNT4eXlJXdaRERtrrS0FBs3bsSdO3ekmL+/P6ZOncqbNUQd1WOmVMvJ19e3KjIyMn/16tU96mNz584tWLdunY2dnZ12ypQpRaampiIAdOvWTf/dd9/dAnArMTFRM3HixAFDhw4tnzFjxgOdj46OjrWpqanS1De9Xo87d+4YOTs7P7K7yNnZuSYxMfGpO1SUSiVCQkLKevfuXb17927L+qJkZ/DYoqQgCCsBhALIFUXRsy72dwDTANQAuA7gRVEU79Y99wcALwPQAXhTFMV9rZR7l5eD7tiFcdLYC5cQgHOy5dMNJViEGKxCBMpghhoYYT1ewCJshQPyZMurwxKUgNIIUKoNfxTqurGq8WOlUd24/o8RoFC1wmsbjps4b1ku8PNyIP2wIf/SLCB6IdBvIjDlH0C3PrL+ONtSfn4+1q5di6qqKgCGTqmFCxfCxsZG5syISA4mJiYYOnQo4uPjARi6JQcPHswP4ETUpRQUFGDdunW4e/euFBs7dixGjx7dJstOEVHnl5SUpNm+fbvVwoULC93c3GrT0tLUW7du7e7n5yetxbhkyZICPz+/wWZmZroffvhB6jLctGmTlZeXV9WgQYOqra2tdUqlUlQqlQAAW1vb2rS0NGMApQCwYMGCwmHDhg3asWOHRUhISNknn3zS08jISBw/fvxDp3wDwEsvvVS4YsUKh++//77bokWLigoKCpTp6elGQUFBjy0yHjhwwOz69esaT0/PTlOQBJ6sU3I1gK8ArG0Q2w/gD6IoagVB+BuAPwD4vSAIgwDMBTAYgCOAA4Ig9BdFUffIK9h7AcsPNiP9rqu6ugbR636EttDQYWrbvRtCo76EIPPGGd0BLMwvwOpNMaiorEIVNFhnsgSL54Wjp+1jN5Wi+uKfQg10tA+rJt2AqO3AhRhg7x+A8lxD/Np/gK+HAmN+Dwz/reH768Tu3r2LtWvXorzc8HvP2NgYUVFR6Nmzp8yZEZGchg8fjlOnTqGmpgZ5eXm4dOkSBg8eLHdaRERt4tatW9i4cSMqKioAAIIgIDQ0FP7+bbc/BhF1ftbW1rqEhASzb775xq60tFRpYWGhGz9+fPFXX311s/4YNze32sGDB1dkZmYaT5o0SSoiXr161fjdd9/tXVhYqLK0tNQtXrw4r359yPfeey/7vffec/7Tn/7U6+23377z8ccf5/z73/++8c477/ResGCB2sPDo3L79u3XHrXJDQD069evJiYm5try5ct7/e53v3MxNzfXffDBB7cfVpT8wx/+0PvDDz90BgyF0ffff/92REREScv8tNoHoX432EceJAguAHbVd0re99wLAGaJoji/rksSoij+pe65fQD+ryiKJx51/oCAADExMfHps++i6ne5vnDhAgDD9vCvvPJKuyp63LlzB2vWrJE6xczNzfHiiy+ie3cWJruEyrvALx8DiSsBNPg3psdAIPQLoM9w2VJrTaWlpVi1ahUKCwsBACqVClFRUejTp+t0iRLRwx04cABHjx4FAPTs2ROvvvoquyWJqNO7du0aoqOjUVtrmNWoUqkwe/ZsDBgwQObMiLoGQRDOiKIY0FLnS0lJyfD29s5vqfPJYfbs2S4ODg41K1asyJI7l64iJSXF1tvb2+X+eEu8E34JwM91j50A3Gzw3K26GLWghIQEqSAJAKGhoe2qIAkADg4OWLBgAYzqOjfLysqwdu3aRtM1qBMzsQZC/xd4eT9g12DdtLxLwKoQYMdvgYpC+fJrBRUVFVi3bp1UkFQqlZg7dy4LkkQkGT58uLSJQ25uLi5fvixzRkRErSspKQkbN26UCpImJiZYtGgRC5JEJJsrV64Y7d271/r111/v0IXVzuKZipKCIHwAQAtgQ32oicOabMUUBOE3giAkCoKQmJfH9Qaf1O3bt7Fv371lOv39/eHt7S1jRg/Xq1cvREZGQqUyrBJQXFyMNWvWoKSkU3Ub06M4DwF+cxiY+AmgbrCeb9I64KsAIHkj8ATd2u1ddXU1NmzYgNxcw5R1QRAwa9YsuLu7y5wZEbUnZmZmCAwMlMZxcXHQ6/UyZkRE1DpEUUR8fDx27NiB+pl5VlZWeOmll+Ds7CxzdkTUVb311luOvr6+g19//fVsDw+Pmse/glpbs4uSgiAsgmEDnPnivTngtwA0/C3TC0CT7bCiKH4rimKAKIoBPXr0aOoQuk9lZSW2bNkCnc6wRKe9vT1CQkJkzurRXFxcMG/ePNQvEFtUVIS1a9eirOyR679SZ6JUAUG/BZadAjxC78UrCoDtrwGrQ4G8q/Ll94xqa2uxadMm3L59W4qFhYVh4MCBMmZFRO1VUFCQ1C2Zk5ODK1euyJwREVHL0uv12LNnD3755RcpZmdnh5dffhn83EdEcvryyy+zKioqkv72t79ly50LGTSrKCkIQgiA3wOYLopiRYOnYgHMFQTBWBAEVwD9AJx+9jRJr9fjp59+kqY/GxsbIyIiQvpg0565ubkhIiJCWjcrPz8f69atkxa6pi7C2hmYuwGYuwmw7HUvnnkU+CYIOPhnoLZjbSSm1WoRHR2NjIwMKTZ16tR2271MRPIzMzPDkCFDpHFcXByeZH1vIqKOoLa2Flu2bEFCQoIUc3FxwYsvvghLS0sZMyMiovbosUVJQRA2ATgBYIAgCLcEQXgZht24LQDsFwQhWRCEfwGAKIqpAKIBXASwF8Cyx+68TU/k+PHjuHr1XjdZWFgYbGxsZMzo6QwYMADh4eEQBMMM/5ycHKxfv17aCIe6EI8phq7JoDcAwdBBC30tcOTvwP8bDqT98ujXtxN6vR7btm3DtWvXpNj48eMbFRuIiJoSFBQkLW2SnZ3Nbkki6hQqKyuxfv16XLp0SYoNHjwYCxYsgEajkTEzIiJqrx5blBRFcZ4oig6iKKpFUewliuIPoii6i6LoLIqiT92fVxsc/4koim6iKA4QRfHnR52bnkxGRkaj6Q/Dhw/vkFNDBw8ejLCwMGmclZWFjRs3oqaGSzl0OcbmwMQ/A0uPAL3ura+GohvA+pnAlheB0vbbUa/X6xEbG4uLFy9KsVGjRmHkyJEyZkVEHYW5uXmjGxiHDx9mtyQRdWjFxcVYtWoVMjMzpdjQoUMRHh4u3YQhIiK6X0vsvk2tqKysDFu3bpU+rDg7O2P8+PEyZ9V83t7eCA29t67gr7/+ik2bNkk78lEXY+8JvLQPCP0noLG6F0/dBnw1BDj9HaBvX83Woihi3759SE5OlmJDhw7F888/L2NWRNTR3N8t2XA2BBFRR5Kbm4sffvhB2vAPACZMmICQkBBp+SYiIqKm8LdEO6bX6xETEyNtCmNqaopZs2ZJm8Z0VAEBAZg0aZI0vnHjBqKjo6HVamXMimSjUAABLwK/TQSem3MvXl0C7HkX+H48cCdFvvzuc+jQIZw6dUoa+/j4YNKkSdLSBERET8LCwgIBAQHSmGtLElFH9Ouvv2LlypUoKSkBACgUCrzwwgsYMWIE3xu1BL1e7gyIiFoVi5Lt2OHDh3Hjxg1pPHPmTFhZWT3iFR3H8OHDG3WWXbt2DTExMdLO4tQFmfcEZn4LLNwB2Ljdi2edBb4dA+z9A1BdKlt6AHD06FEcOXJEGg8aNAjTp09nFwARNcuIESOkbsmsrKxGa9R2NLW1tcjLy8O1a9eQmJiIGzdusMhK1MldunQJa9euldaIV6vViIyM5IZ/LaU0B/hhPHBtv9yZEHU6kZGRvd977z2HtrzmihUruvv7+w+oH5uamvpevHjRqC1zaI+4wEc7lZaW1qj4ERwcDHd3dxkzanmjR49GbW0t4uPjARje2Gzfvh0vvPACizxdWd8xwGvHgWP/BOI/B3Q1gKgHTv4/IHU7MPlvwMBpQBvffU9ISMCBAweksbu7O2bOnMn/V4mo2SwsLODv7y91X8fFxaFfv37tsrtIp9OhpKQERUVFuHv3rvS1/nH9rI6GPDw8MGPGDJiYmMiQMRG1poSEBOzZs0e6+WBmZob58+fD0dFR5sw6iYLrwLoXgLuZQPRCYNEuoJe/3FkRdQhOTk5eBQUFaqVSKSoUCtHd3b1q3rx5Bf/1X/+VVz/rdOPGjb8+63VWrFjRfc2aNbZnzpxp1o6FFRUVSU/7ml27dllMnz69v0aj0QOAubm5bv78+flffPFFVv0xgiD4azQavSAIMDc3102fPr3wX//61632ur5v+8yqiysuLkZMTIw07tu3L4KDg2XMqPU8//zzqK2txcmTJwEA58+fh1qtxrRp09rlhzJqI2oNMOZ9wHMWsPsd4EacIV6aBURHAf1DgMmfAd36tEk6KSkp2L17tzTu06cP5syZw4XbieiZjRgxAomJidDpdLh9+zbS0tLQr1+/Ns9Dr9ejrKzsgWJj/deSkpKn7ny8fPkycnJyEBERAQeHNm1GIKJWIooiDh061Kh5olu3boiKioKNjY2MmXUit84AG2cDFQWGsbYaKLjGoiTRU9i8efO1sLCw0oKCAuXevXvNly9f3vv06dNmW7duzZA7t2fVo0eP2pycnHMAcPnyZaPg4GAPPz+/iqioqLv1xyQkJFz09PSsTkpK0kyYMGFA//79q5cvX54nX9YPx0/U7YxOp8OWLVtQWVkJwNBF0Zm7sQRBwKRJk6DVapGYmAgAOHv2LNRqNUJCQliY7Ops3Q3Tuc9vAfb9N1Be9+/o1b3AjSNA8O+B4csApbrVUqjv4K3n6OiIefPmQa1uvWsSUddhaWkJf39/nD59GoChW9Ld3b3Ff/+JooiKiooHio31Bci7d+8+0xIqgiDAysoK1tbWUKvV0lT0oqIi/PDDD5gyZQr8/Pxa6tshIhnodDrs2rULSUn3mnscHR0RGRkJc3NzGTPrRK7tN3RG1lYYxioTYPZqYECIrGkRdVTdu3fXzZ8/v9jR0fH6uHHjBiYkJGQPGTKkKjw83MXJyalmxYoVWXl5ecqIiAjX5ORkM51OJ/j7+5d9//33mW5ubrWAoSPys88+cywqKlJZW1trP/zww9tDhw6teO+99/potVrB1NTUV6lUiqWlpckFBQXKJUuWOB8+fNjKxMREv2DBgvy//OUvd5raF0QQBP/z589f8PT0rC4rKxPefvttp927d3crLS1V9u/fvzIuLu6qubn5I+8Ie3h41Pj7+5elpqZqmnre19e3asiQIaUXLlxot9NWWJRsZ/bv349bt24BMLzBnzVrVqf/JS8IAqZMmYLa2lqkpBg2NDl16hRUKhXGjx/PwmRXJwjAcxFAvwnAgT8BZ1YZ4rUVwIGPgHM/AqFfAL2Htfilr1+/jq1bt0rdQT179sSCBQug0TT5bz4RUbOMGDECZ86cgU6nw61bt3D9+vVmLdlSVVXV5NTq+sc1NTXPlKe5uTm6desGa2tr6Wv9Y0tLy0Yb8V24cAGxsbGoqamBVqtFbGwsbt68iSlTpvCmDlEHVFNTg61bt+Lq1atSzM3NDRERETA2NpYxs04kaQMQ+wYg1t0gMrEBIqMB5yHy5kX0GF5rvNqsjff8ovNnmvO6sWPHVtjZ2dUcPHjQYsiQIVUNn9PpdFi0aFF+bGxsularRWRkpMvSpUt7Hzhw4HpJSYniv//7v3sfO3bsore3d3VmZqY6Ly9P6efnV/X3v/898/7p20uWLHEuKSlRpqenn8/NzVVNmjSpv4ODQ+3bb7+d/6j8XnvtNecrV66YHDt27LKzs3PtoUOHzJ5kg+Pz588bJyYmmr/66qtNdkGeOXNGk5CQYPHBBx/cfsIfVZtjUbIduXjxojSNGQDGjx+PPn3aZnqq3BQKBaZPnw6tVovU1FQAwLFjx2BkZNRpp67TUzLpBkz7J+ATCex6G8i5YIjnXgRWTgL8FgLj/wSYtszUoczMTGzatEnqHLKxsUFUVBRMTU1b5PxERPWsrKzg6+srzRiIi4uDm5vbAzflamtrm5xaXR+rn2XRXCYmJg8UG+u/WllZPVUx0dPTE3Z2doiOjkZenuF9clJSEu7cuYOIiAhO8yTqQMrLy7Fp0yapcQIAvL29MX36dDzJh2Z6DFE0rKN+8H/uxax6A1HbANu2X86DqLPq2bNnbWFh4QP/aNnb2+sWL14sTX3+P//n/9wJCQmRNqQRBEFMSkoycXNzq+nTp09tnz59aps6v1arxe7du21OnDhxsVu3bvpu3brVLFu2LHvTpk3dH1WUrJsta3vo0KFLrq6utQAwYcKE8ocdn5eXp7awsPARRVEoLy9XTJgw4e7EiRMbLe49bNiwQQqFQrSystJGRkbmvfnmm48sisqJRcl2oqCgADt27JDGAwYMQFBQkIwZtT2lUomZM2dCq9XiyhXDzYZDhw5BpVJhxIgRMmdH7YZzIPCbOODUN8ChT+9Nbzm7Fri8G5j4CeA995k2wsnKysLGjRuh1WoBGKZXLly4EBYWFi3xHRARPWDkyJE4e/Ys9Ho9bt68icOHD0Ov1zcqPDa1mczTMDIyeqDY2LAA2dLdTj169MArr7yCnTt34vz58wCA7Oxs/Pvf/0ZYWBgGDhzYotcjopZXVFSE9evXo6CgQIqNHDkS48aN42ymlqDXAT//Hkj47l7MzgtYsBWwsJcvL6JOKCcnx8jGxuaBtWpKS0sVS5cudT58+LBlSUmJCgDKy8sVWq0WlpaW+tWrV6d//vnndm+88YaLv79/2RdffHHL19e36v7z3LlzR1VbWyv069dPmpri6upak5OT88i7utnZ2arq6mph0KBB1U/yfTRcU7KgoED54osv9p49e7bLzp07b9Qfc/LkyYuenp5PdD65sSjZDtTW1mLLli2orjb8P2NtbY2wsLAu+YteqVRi1qxZ2Lx5M65fvw7AMKVdrVYjMDBQ5uyo3VCqgKA3gEFhwM/LgSt7DPGKAmD7q0DyBsOU7mbcXc7NzcW6deukv49mZmZYuHAhrK2tW/I7ICJqxNraGr6+vjhzxjArKS4u7qnPoVQqmyw21n81MTFp8/cWRkZGmDlzJnr37o29e/dCp9OhuroaP/74I4KCgjBu3Dh2WhG1U9nZ2Vi/fn2jGyKTJ0/G0KFDZcyqE6mtAra9AlyKvRdzHQ3MWQ9orOTLi+gpNXdKdVuKi4szzc3NVY8dO/aBO7wff/yxXVpamubkyZOXevfurT1+/LjJiBEjBtUv4RUeHl4SHh5eUr/u45IlS/qcOXPmyv3vqRwcHLQqlUq8du2akb+/fxUAZGRkGNnZ2TXZWVnP3t5ea2xsLF68eNF4+PDhTzXtpXv37rrIyMjCl156qe/TvK49YVGyHfj555+RnZ0NwPCBYvbs2TAxabfrkLY6tVqNOXPmYMOGDcjMzAQA7NmzB2q1Gr6+vjJnR+2KtTMwb5OhQ3LPcqCkblpRRjzwTRAw4nfAqHcA9ZP9fSosLMS6deukKZAajQZRUVGwtbVtre+AiEgyatQoJCUlQa/XN/l8w81kmup2NDc3b5cb4wmCgCFDhsDR0RHR0dEoLi4GABw/fhy3b9/GrFmz2IlOJLPq6uoHloVITk6WbtLWz2gaPHiwzJl2EpV3gc2RQOaxezHPcCDsG0DFNTqJWkphYaFi3759FsuXL3eeMWNGQWBg4ANFv9LSUqVGo9Hb2trqcnJylB999JFj/XM3b95UHTlyxGzatGmlZmZmenNzc339zVQHB4fa7Oxso6qqKkGj0YgqlQpTpkwpev/9952io6Nv5OXlqb7++mu7N954I+dROdbVgPLfeecd502bNt3o1atX7eHDh81GjBhRYWJi8siNboqLixWbN2+2cXd3f7Y1fGTEoqTMUlJScPbsWWk8adIkODk5yZhR+2BkZITIyEisXbsWt28b1mSNjY2FSqWCl5eXzNnR/fR6PcrLy1Fb+8ibQK2n53Agcp9hSnfSBgB6QAfgyL+B5N3A8x8AvYc/8hT1nTulpaUADP8PLliwAPb2nDpDRG3D2toas2fPRlJSEjQazQPdjvdvJtPRODk5YenSpdi2bRvS0tIAGNbv/de//oXZs2fDxcVF3gSJOrGGa9I2tS7to9akNTY2xty5c+Hq6tqGGXdixbeBDbMM66LXG7YMmPhnoB3eWCLqiObOndtPqVSKCoVCdHNzq3r99ddz3nvvvSY3g3n//fdzIiIi+tra2vr07NmzdtmyZdkHDhywBgC9Xi/885//tH/11VddAWDgwIGV33zzTSYAhIaGln711VeVdnZ23gqFQiwqKkr5/vvvf12yZEnvvn37ehkbG4sLFizIe+uttx67nuM333xz88033+wVGBg4sLKyUjFgwIDKw4cPX23q2Ly8PLWpqakvABgZGYk+Pj5lGzduvNHUsR2BUN+SKqeAgACxfnH3riQ3NxffffedVMjx9PREeHh4l5y2/TCVlZVYs2aN1EkqCAIiIiK4DpUMRFFEWVkZCgoKUFhY+MDX+vUXOwOVSoX58+fzzTcRUSvQ6/WIj4/HoUOHpJggCBg3bhxGjBjB90FEzaDT6VBcXPxA0bH+cXPXpLWwsMD8+fN5k7al5F4G1s8EShpshDvxz4ZliYhaiSAIZ0RRDGip86WkpGR4e3u3241TqH1KSUmx9fb2drk/zk5JmVRXVyM6OloqSHbv3h3Tpk3jG/H7mJiYICoqCqtXr0ZeXh5EUcTWrVsxd+5c9OvH3ehamiiKKC8vf2jhUbZOyDakUCgQERHBgiQRUStRKBQIDg5Gr169EBMTg4qKCoiiiAMHDuDmzZsICwvr0svYEDVFr9ejrKzsgQ7H+sclJSV4lmaT+jVpG3Zod+vWDe7u7i2+CVaX9etJYOMcoKpuk1+FyjBd+7kIefMiIpIRi5IyEEURu3btQn6+4eaCSqVCREQEf+E/RP1GI6tWrUJhYSF0Oh1+/PFHdrI1kyiKqKioeKDgWP+4pqbm8SdpgkajgUajaeFsn5GoByoKgdryxnGlEWDa3fC1ASMjIzz//PPo379/GyZJRNQ1ubm5YenSpdi6dStu3rwJALhy5Qq+/fZbREREwMHBQeYMidpO/fuz+4uN9V+Li4uh0z2waewTa7gmbVMbYrXXNflIwHoAACAASURBVGk7jUu7gJiXAW3dhr1G5sCcdYDb8/LmRUQkMxYlZZCYmIjz589L49DQUNjZ2cmYUftnYWGBRYsWYeXKlSguLoZWq8XGjRuxcOFCODs7y51eu9Sw8Hh/12P9ouVPy9jYGN27d4eNjc0DX01NTVv4O2hB1w8Bu/8LKDTs6A4dgDIFMPQ1YOwfAGNusEBEJAcrKyssXrwY+/fvx8mTJwEARUVF+P777zF16lT4+fnJnCFRy6mqqmpyPcf6x886I8Xc3LzJTbC6devW4dek7dASfgD2vGu4WQ4AZj2A+VsBRx958yIiage4pmQby8rKwg8//CDd6fTz88P06dNlzqrjKCwsxKpVq6TNSIyNjbFo0SI4Ojo+5pWdU2VlZZPTrAsKClBVVdWscxoZGT2y8NhhlxiorQKOfgEc/V9Ad68bVG/hiIwhf0Sy2UgUVtTCydoEvbubok93M5gb874NEVFbSU1NxY4dOxp17Pv4+GDKlCkwMjJ6xCupIa1Wi8uXL6OoqEjuVLo0URRRVVXVqADZ3Pdm9UxMTB4oNtZ/tbKyglqtbqHsqUWIInDoU+DIZ/diNn2BBdsAG872orbDNSWpPeCaku1AZWUloqP/P3vnHR9Fmf/x98zWbElI7yGFntCrBWwHKqeCRsDDgiIqJ6ee3VPvvOKd1zy9+8nZ2+FZELvYDhXFgvQivaRAei/bd2d+f8xm0yFAIAk879frec3MM888893NZnfmM9+yNCRIJiQkcOGFF/awVX2LqKgorrnmGl566SUcDgcej4clS5Zw7bXXnrTepm63u0Phsbq6GqfTeVRzGgyGToVHq9Xad4XHTnD7AhRW+9gfey3VY8czftsfGejQqt7LDcVkfrGQfYExPOqbRxGxoeNibEb6R1vpH6WJlP2jLcFmJdJiOOneJ4FAIOhJsrOziY+P54033qCiQiuQuWnTJkpKSpg9ezbR0dE9bGHvxuv1smHDBr777jvq6+t72hzBUWA0GtuJjS3zPPa6NDmCzgn44cNfwsYlzX1JY2DuUrDFdn6cQCAQnGIIT8kThKqqvP766+zatQvQPPxuvPFGcYF9lJSWlvLyyy/jcrkALe/knDlzsFqtPWzZ0eP1ejsMt3Y4HIc/uAMMBgNRUVGh1lJ8tNlsJ52gpigqRbUu8iodobavopG8SgdFtS5af9WpzJS/5UHDK8RIzTduTtXEi4Hzed4/nWrCD3k+u1kfEij7R1lID4mWVuLsJmT55Hp/BQKB4ETh9Xr58MMP2bJlS6jPZDIxY8YMhg0b1oOW9U7cbjdr165l9erVR33NIDgxNBWT6czbMSws7KS7Pjsl8Tph2XWw+5PmvgFTYdZLYLL1mFmCUxfhKSnoDXTmKSlEyRPEt99+y//+97/Q9uzZs8WF9TFSVFTEf/7zn6POj3gyoNfrOxUe7Xb7SXdhq6oqNU4feZWN7KsIio9NyyoHXr9yRPOF08gDxjeYI3/eqt+NkdcD5/GUbzqlHPmDA7NBJi2qWbDsH9MsXCb1M6PXiUTyAoFAcChUVWX9+vV8/PHHrYp7nHbaafzkJz8RufHQckevXr2aNWvWtAsLtlqt5OTkiHDeHsZgMLQSIEUxmVMARxW8NgcOrm3uGzkXLvkX6MT/o6BnEKKkoDcgwrd7kIKCAlasWBHanjRpkhAku4Hk5GSuvPJKlixZcsyJwXszOp2OyMjIDsOt7Xb7SXlx6/IGWng8NrK/0sH+oPhY5zryv7UsQUqkhYwYK5mxVjJjrGTE2MiItZIYPhsOroHld0DZjwCY8XKt7mPmGVZQlnkZa5Lmsc0TRUGlk4JqJwVVDpzezitgun0Ku8sa2V3W2G6fXpZIiQwjLdpKerSFtBZelqlRFswGcaMtEAgEkiQxbtw4kpKSWLp0KbW1tQB8//33FBUVcfnllxMefmiP9pOVhoYGvvvuO9atW9fu+ic8PJwzzjiDMWPGCEFSIDjR1BTAK7lQtae578w74LzfwEnmKCAQCATdhfCUPM40Njby9NNPhwqzpKSkcO2116LXCz24uygoKODTTz8NhXL3VfR6PZGRka2Ex6ioKCIiIk5K4dEfUCiqdbE/6O24v7Ix5PlYXHd0ieBjbEYyY2xkxFjJiLWSEWMlK9ZKapQFk/4wYp8SgB0fwKq/Q+nW1vskGXIuh8l3QtwQVFWlstFLQZWD/ConhcFlk2BZ6zw6kVySIDHcTFq0JlSGllFaLku7WdxgCgSCUw+n08k777zDnj3NN/pWq5XLL7+cjIxTp1hETU0N3377LRs3bmzlPQpazu0zzzyTESNGiGtMgaAnKN2qCZKNZcEOCS78K0y8sUfNEghAeEoKegdHHb4tSdILwEVAuaqqOcG+KOANIB3IB2arqlojabGi/wSmA07gWlVVNxzOuJNVlFQUhVdeeYX9+/cDWsW8hQsXEhER0cOWCQQnhibxLq/Swf5gfsf9QQ/IgioHvsCRPxSxGHWa6BijeTxmxmoiZHqMlYiwbhDtVBX2/E8TJw/80H7/0Is1cTJpdKdT1Dl9FFQ7KKjSREpt6SS/ykF5w9GnG4i2GpvzWLYoutM/ykKU1XjShesLBAJBE4qi8O233/LFF1/QdO0qSRLnnnsuZ5xxxkn58K6JyspKVq1axdatW1GU1mlK4uLimDx5MtnZ2Sf1e9DbUVUVt0/B4fXj8PhxeAI4vX68gSNLKyPofqKtJgbFH+dc6vu/gjeuAk8wT7nOCJc9C9kzj985BYIj4FQTJZOTk4eXl5cbCgsLtyQmJvqb+ocMGTJs165dYTt37tw6ePBg74my58MPP7Rfcsklg8xmswJgs9kCV155ZeVjjz1W3DRGkqSxZrNZkSQJm80WuOSSS6qfeuqpgyfTg8ZjCd9+CXgC+E+LvvuAz1VV/bMkSfcFt+8FLgQGBttE4Mng8pTkq6++CgmSAJdddpkQJAUnJQFFZVdpA3srGoM5HhtDHpANHv/hJ2iDTpZIi7KEhMcmr8fMGBvx4abje2EpSTBoGgycCvnfaOLk/pXN+3d8oLUBP9HEyf6nt5siwmJghKUfI1L6tdvn9PoprHa2EywLqh0U1bhQDqHTVjm8VDm8bCisbbfPbtKTFm0hJTKM+HAz8eFm4uym0Hp8uImIMFExXCAQ9E1kWWby5MkkJyfz1ltv4XA4UFWVzz//nAMHDnDppZcSFhbW02Z2KyUlJaxatYrt27e325ecnMzkyZMZNGiQECOPEFVV8fgVnN6AJiB6m0XEloJiY6ivadvf6hinJ9Bqeajfb0HPkhVr5fKxqVw6OpmEiG6uYP7jW/D2TaAEo2RMEfCzVyH9zO49j0AgOCKSk5O9L7zwQtQDDzxQDrBmzZowt9vdYz+YsbGxvrKysi0AO3fuNJ511llDxowZ47z66qtDN3Zr167dnpOT49m4caN56tSpgwcNGuS55557KnrK5hPFYUVJVVW/liQpvU33DODs4PrLwEo0UXIG8B9Ve4S9WpKkfpIkJaqqWnKoc/irXFS+tO3ILO/lFLpK+apsVWh7XMRQIr/1UvntyfU6BacmAUWl3uWj1uWjzumlzu3HH/TeSAi20wAwBFvHmHQyFqMei1EXbHosJh1mgw65STyrVKGyEWgEyqg6ni9MAsmkQzbpkEx6ZFN/pMx/IyccQNr3AXLxN0iSCxkn0p6NyHtykdJGI511B2Sd16V8QRajniEJ4QxJaJ8LzevXQtrzqxwUVDqC4eCaeHmg2nVIj4sGj59txfVsK67vdIxRL7cQKk3E2ZsFy1BfuBm7Sd8t4qUaUFDcAVRPAMUTQPX4W2z7Ud1N/QGUuhrUigOobjeY7GCNBYNF5GASCAStCAdmRZ7Dp/7vKfFovwi7d+/myUef4ILY04gzRfasgd1AibuKdXU7KHC1v3xONscyLmIoKfo4pNUBqlfvAJqFNodHE88cHj/+oEomSxKSBBKad2loGepr3y+j7ZDbHdfZekfn0M5NcL6uHNcRiqoSUJqbP7SudNDXoqkd9/sVFZVDK4hGICrYDo0cbCK9Sq+mQoFPCvj6kwKirCYSI8zE2Ezo5GO8xqjJh4p84Ffatt4MUWNhpR3o3nu+yMsGogs3duucAsHJzOzZs6tee+216CZR8rnnnoueM2dO5V//+tfkpjEul0u67bbbkj/44INIr9crn3/++TXPPPPMAZvNplZUVOhmz56dsWnTJmsgEJDGjh3b+NxzzxVkZWX5ACZMmDD4tNNOa1i1alX4rl27wkaNGtW4bNmyvJaemZ0xZMgQ79ixYxu3bdvW4VOS0aNHu8ePH9/w448/nlxPWzvhaH1B45uERlVVSyRJigv2JwMHWow7GOw7pCipuAO4d1YfpSm9DwduPjOt0a6wgKRAJCPKEnGXnTyvUSAIC7ZEAHTBdoQEAJcKLj/gB7TQ5hPmS39EXBBsbdgD0h4nku4jZKsVKTyiWdQ064Iipx7J3CR2BrdNunb7DQY5FJrO4NanCSgqpfXukFipCZddK7zThNevcLDGxcGazvOvykCkXkd/m4lkq4mkMCPxJj0xRgNRBh39dDJ2WcaKhM6vaKKiOygyNomPwW38R+o20rLSuYemz4NAIBC0RA9cyAjW6Pfxo74QgHq/g7eKv+A0/yAGB5KQ6FsPNFRUiuUaNunyKdHVtNufGohmlD+deHc/qAUP7ccAWIOtt6IG29FylFcbBGXPYziz4KTBoYDDiQ8nx14mMxyY0LzpBfJ8QPff86m+w1/nCQQ9xY4hQ8eeqHMN3bljfVfGnX766Y1Lly6N3rBhg3nEiBHu999/P+rrr7/e2VKUXLRoUUp+fr5p06ZN241Go5qbm5t57733Ji1evLgoEAgwb968yvfff3+/3+9n7ty56TfddFPaihUr9jUd//bbb0ctX758T2Zmpvfcc88d9Ic//CH+3//+d9HhbNu6datp3bp1toULF3boBbl+/Xrz2rVr7Q888MBh5zoZ6O4A9Y5+7Tu89pAk6UbgRoDhCYM7GtInUVD43Pgjbkn7mbOoRs72ZSOLCyGB4KRFxYIaAKUeqG9fcbvLhDw124iYZj2SUYfVrCPbpGO4SY8U1Q85MRrJrEcyytQFFIqdXiob3NTWemisc+No8OJy+PC6fCguP7qAihUJK2BBwoKEFQkLBJdaww/UArUBoGMB8+hKEQkEAkH3ICMzyT+QeCWCrw3b8UkBApLCN4adlMm1nOEbgv4o5asTiYpKoVzJJn0+FXJ9251kKHGM9KcTo9p7xkCBQCAQCPoos2fPrnr++eejzznnnIbMzExXRkZGyPdFURRee+21mHXr1m2Pj48PANx///0l8+bNy1y8eHFRQkJC4Nprrw2FVv/6178uueCCC1oJVz/72c+qRowY4QG47LLLqpcvX94+d1eQiooKg91uH6WqquRwOOSpU6fWTps2rdWN46RJk4bJsqxGRET4586dW3Hrrbf22ryd3cnRipJlTWHZkiQlAuXB/oNAaotxKUBxu6MBVVWfAZ4BGJMzSo2eN+woTeldfLHpG8p31QFaGMrMcy4iNS75MEcJBD1HQFE5WO1id3kDe8sb2VPeSJ3r8M+Ok/uZGRBnZ2C8jYGxNqJtpr4faauoKF4F1e1vDit2t/AAbLvt9qF6uzGJlAqqO0DAHYC6Iz88NtjaI6MFo51Y/Ki4ABcqep0LG7XY1HJ0NDaHweNEllxIsgc5IQspfgBU7tKqWPoPIX3ak7RiQ0mjIW4I6ETonEBwKhLNMDIbRvHutx9RUaeFc+/RlVIb5WPmGRcSZe+d4dyKorDr4F6+374uZHczEn45nmJ3PLsVC58CWv3IQxOml0mO1HILp0SG0c9iQFUhoKooikpAgYCqEFCaQ6IVRSWggqIqKIo2trlfDY6jeTu4bO7TjlPUpv0tz9dyjhbnajmXEtx/iJ9SWQKTXsZs0IWWZr0Os0HG1KLPZNBhDq3LmPQyYcH9JoMueJyMSa9Dr+vrFyyCo6Gq0cP3+6r4dl9Vh4UHJWBoYjhnDIhmTFokRn2b9HM+F6z6B5Ruae5LGQ9n3KoVtzmOyHYRui0QHCkLFiyoOvvsswcXFBSYrrzyylY/tiUlJXq32y1PmjRpaMv+QCAgATQ0NMg33XRT6sqVK8Pr6+v1AA6HQ/b7/TQVn0lISAjdNFssFsXpdHaas7JlTsmqqirdddddlzZr1qz0Dz74IK9pzOrVq7fn5OSccqFiRytKvg/MA/4cXL7Xov8XkiS9jlbgpu5w+SQBZKOMKd1ylKb0Hnbu3s3aXRtD2+dOmcKACQN70CJBn0KWkYzHv4Ky0+tnU2Eta/NrWJtfzYbCmsOG/hp0EiNS+jEuPZLx/aMY2z+SSKu4OAJQFRW1phTluxdQN72L4gNVtaAShqJaUEyJqGnnoESPQPVJIUFTcQdQvYFWAqjq6yVVOo06VIOMXy/h0Um4JBWHqlKvKNQGFKp8fio8fhpUBQfgQMUZbA7AiYqLAKOkHczUfcuFujWESx3fUP+gDOG9wBl85J2AriqaWK8Jm2kQ4Sk/ZURgB6M9axjW+AOxnoLWBzqBve/BXgjoLXhSz8SfNRX9oGmYY/ojH2ueKEHfRQmgJbETxT96CkXRMvYdc762LpJMNDeMuonly5ezefNmACrqqnh5xVJmzpzJsGFH8OBbUZoLVuhN3W5rjcPNyu/WsX3jGnzO1p6RAVViTyCGHwOJNKpN526fmkovS2TF2hicYGdIop0hCXYGJ4STFGHus8XMVFVFUcEfzBPpD4qYTUJkX31dgt5FCjBrfBKXqyrrC2pYtv4gH24pobFFUcZvS6p5rqQam0nPT4cncvm4FMb1j0RyVMB/50LF5uYcAmOvg+l/AN3JUx1XIDgauhpSfaIZNGiQNyUlxbty5cqIV199Nb/lvoSEBL/ZbFa2bNmyLSMjo51Hzu9///v4vXv3mlevXr0jLS3N/91334WdccYZw7TyKcdGdHR0YO7cudXz58/PPObJTgIO+w0qSdJraEVtYiRJOgg8hCZGLpUk6XqgEJgVHP4RMB3Yi3bLeF1XjHBv38Gu0WOO2PjeRKPVymfnTwOjJtQkFRUR/fOb2dXDdgn6GLKMbDYjWSzIYWGhJlnCkM0t1sOC+y3BvrBgX8vt4Bw1AR2bKt2sKXGyrrCOH4vrCRymRKTdrGds/0jGp0cxPj2KESkRmA29PwyuJ5BkCSk6EfniB+C8n8OaZ2D1k+AOevsHgLxXoCwGTrsZxt8A5vZFbkArDNPWKzMkWLbM29iyWEybbUmWgiHdWs7KphBwyaxvldOyKZ9lU3h4aJxRh9QFIUFVVWqdPsoa3JTVeyird1Ne50JXtpkBZR8ztuFLopSOcyptU/rzXuB0PgicTknLPJLB6uJNfEESMBOYSYpUztnyZs6RN3G6vI0wqXmczu/EkvcZ5H0GK+5mp5LKd9IoftCPY68pG7PZjDVYRMlq0mMNFlWyhra1Yks2kx5LcH+o36TDatRjNoib4h7H0wiNZc2toQwaS6GxHBqCy8ZScFSC0QqJoyB5NCSPhaQx0C9NFE7qZlRVpbzBw87SBnaV1rOztIF9JdVIFTsJV+oIN0n0M0lEmGTCTRLhJgm7USLcCDY92IxgNYBVDxaDigEFSQ2A4tdawKeJzE3bir+TbR9Gxc/MgJ+0qCg+qu5PABmv18vSpUuZFJbH1LBt6BTv4edrmXUoIhUSRkDiSEgcoa2HJ3Xpc+T1K+yvbGRXaQM7SxvYXVxLY/FeUn2F2KTWmZN9qsyuQCzb/Am42ni1J/cLY3CCXRMgg8vMGFt7D64+jiRJ6CTQyeJaQ3D8kSSJcelRjEuP4qGLs/lseynL1h/km72VNOkNjR4/b6w7wBvrDnB6ZC1Pqn8kwt0ivds5D8CUu8XvikDQy3nxxRfzq6qqdOHh4YrP16w96nQ6rrjiispFixalPvvss4XJycn+vLw8w4YNG8Jyc3PrGxoadGazWYmJiQmUlZXpHnrooaTusqmurk5+/fXXowYMGNB5ov9TiK5U3/5ZJ7vO62CsCiw6VqP6GgFZ5tszzsAXFCQtDgcTflgjskgKjhxFQXE6wemkO9NZpwXbJbIet86IW28MLT06I6rZjDXCRkRkODExEURaItA5LMj7w5BLw3CHheE1txRBmwVQTSi1IBkMQrSxRMHZ98Fpi2DdC/DdE+AIZrdwVsLnv4dv/gkTb4SJPwdrdKvDJZ2MZJGRLb0/FFmSJCKtRiKtRobo90LxMtj+JlTt7XC8155KcepFbI8+nz1qCo0NbrLr3cQEBc3KRg+H0soPqnG8EpjKK4GpmPAySd7B2fImzpE3kS6XtRo7RD7AEA4wP/AB9Y4wvmkYzpfKKL4KjKScowvllCXaCJuakGk16VsJnTaTjoSIMLKTwhmcYBdi/uFQFHBWBYXG0qDQ2FZ4DDbvEeRr9TZCwTdaa8ISrYmTyWMheYy2bus44YGgPQ6Pn11lDewqbQgKbfUcLCkj0b2PbDmfHDmfG6R8BkhFGPTBXzAVLQHtCUpCKwFjgUTiWMpF1BIBwGpXBkUuI7NYTjiOrk9Yd0Bru5Y391miWwmVasIIinVJ7CprZGdpAztLtPdnf2UjvoCKngCDdeXk6EtJlfytsq97VR07AnFs98djNIeRk2IPCpDhDE2wMyjBTri59/8eCAR9mTCjjhmjkpkxKpniWhfvbCzirfUH2V+pfVeMkPbxL+ffiJA0z2YFmY0jHmLoab/Acqpf9woEfYDs7OxOw6EXL1588J577kmaOHHi0NraWn1cXJx3/vz5FUD9fffdVzZ79uzMmJiYUXFxcb5FixaVrlixotOckYejoqLCYLFYRgMYjUZ11KhRja+++mre4Y47FZC6w/30WMmxWNRlg/pusZu1I0ewLyMDADkQ4LxV3xBdW3uYowSCNgQCqL5jrwPYY+j1WEaNIu6euwkbMaKnrekd+Fyw8RX49p/ajW1LDFYYdx2c9gsIT+wZ+46FhlL48S3Y+iYUb+x4jDUWsi+D4bMgZdwhvQn8AYUqh5eKBg9ObwCH14/TE8Dh8Wvr3gCNHj9Ojx+Ht6k/gNPjJ9xZyCj3Gsb71zFG2Y5J6vz/aJvSny+VUXwZGMUmdQCB41gIQydLDIyzMSwpnOykCLKTwhmWFH5qiAw+dxtxsYUnY8izsUxbV3uwomhEmuZN2SRWJo0C06ldUMQfUMirdAS9HzUvv11l9TirSzXxUcpnmJxPtpRPRpsHAr0JFybe4Xx2kxXqs+Igl4/J5MAhjgRkg/a5VLuWUqNRNbNDTeNHJYPtan+2KekUqAkM0FUxTF+GSWr9GfdJBvwxA0gbPIJhKdEMTrCT2IdDrwWCkw1VVdlQWMuWlcuYk/cAFjRNw6Ua+YXvFj5XxmI16pg+PJHcsSlMSI8SaWMEvRpJktarqjquu+bbvHlz/siRI0+JIiyC7mPz5s0xI0eOTG/b3ytEyXHjxqnr1q3raTPaoaoqHo+HxsZGGhoaaGxs7HC9oqK5kvv06dOZMGFCD1ot6Muofj+Ky4XicqEGl4rTheJyorpcuOobKSiq5kBxFWXlNVRX1aPzejD7vZgDXsx+D6aAD3PAiyngxez3EhbwEqb4MPq9SCfi/12S6Hd5LrG3344+Kur4n68v4PfC1qVacvTqfa336Yww+io445cQ2b9n7OsqrlrY8YEmROZ9TaswxyaMNhh6MQy/HDLOPvF5lrwOAvu/JrDrU3T7VqCr71x8cOvt7A+fyA7bJDabxlGm2DVB1OPH4Qm0EkO9/u7L95kWZSEnWRMqNcEynDi7udvmP26oKrhqOhEX2wiP7qOo1HQ4dEawJYA9HmzBZk8AW5zWb4vTtq2xmh1F66F4AxRtgOJN4OmKTRLEDGrtTZmQc1xyC/Y0qqpSVu9hZ2l9C+/HBvaWNxCnlJMt5ZMt55EtFZAt55Mg1XR57kC/DKTI/vjR4VVkvKqEJyDjViTcAQlXQMbll3AFwOEDhx8afeBVZALo8KvBJdrShy64rSOAjF8NLmm5bDFebdqWiNN5SNU5Qs9EVBWK9bG47CnYLRYirGbsNguR1jAibGFE28xEWoyE6RUq92/FVbgBY8VWYht3k6XkYZcOHWnViIXvGcNadSReqXUodpjFyplnnsn4cWMxGkVeZoGgV7PpVXj/lmBaB2iU7cxz38l6ZVC7oalRYVw2OoXcMSmkRff9OgmCkw8hSgp6A0KUbIGiKDgcjkOKjU3bfn/7BOOdkZOTQ25urnjSLeg2yhvcrAsWpFmXX8P2ksPngww364N5ciKZkB5FTrKWD1JVVVS3OyR0qi5nO9FTcQX3N207XZ1su1CdztC64nJBm/8VOSKC2NtuJXLOHCSdCGEFtNxl29/VxMmyH1vvk3QwYjaceQfEtr/g7TF8btjzKWxZCns+g4C3/RidEQZO04TIQReAIezE29kRqgqVuzW793wGBd83F7DoiKQxMHCq9lqSRkOL3Ga+gBISLJ3eZtHS4Qk0b3v8NHj87K9oZFtxPXmVXQ8TjbWbyGnhUZmdFEFqVNiJ+T0J+IJi4uHyNZZB4DgUBDRHtBcbQ4Jj03o8mPsdfe4uRdEeCBRtCAqV66FkS9dej2zQhMmkMc1CZezgVp+P3k6D28fusobW3o+lDTS4PGRKxeRI+WQHvR+HyQX0k7r22VVlPcQORkpokXMxYXineXMPOZeq4vQGqHZ42zenl+rG4NLhpSaYf7bO1bXoggS5nrMM+wiTmn+nDgQiWOXLxHsENR8lFNIkTbDNCb1f+cRK9dRi5zvGsoHh+NvMGUktZ7KWkexEH53ROkdl4kgt9YdAIOgdqCp88xh8/rvmvog0uOotSo1pWnj3hoPsLe84pcjEjChyx6YwfXgiNpMogCPoHQhRUtAbOCVESa/X26k3Y0ux0eFw0N2vOzk5mWuuuQaT87jwBgAAIABJREFU6eTzphCcGFRVZX+lg3X51azNr2FdfjX5VR1XLG5Jcr8wxqdHMi5YlGZgnK1HQki8hYWUPfJnGr/8slW/edgw4n/9IJbRo0+4Tb0WVYXdn8DXf4eitt99Egy7BCbfqd2s9gQBP+R/DVuXaZ6RnvoOBkmQfqYWmj3sEgg7ulyNJxR3PeR9BXv+p7WG4s7HWqJhwE80gTLr3KMSDRo9fnaU1LOtqI5txfX8WFzPnrIG/Id5sNCE3axnWGI4OcnNQmVWrBW9rosFLjwNLXIyduTZGFw6q+jQ6/VYkHRBD8ZOvBmb1m3xYOghL9GAD8q3a0Jl0XotDUH59q6F7BqsWqh30uhmr8p+/Xu84IGvVei15gG5o6SBoloXJrwMkg6SI+cFvSALGCIVtioadShUfRhSQk5QSAsKanHDeu7vhxZqXuP0UeP0UtXopcbZXtBs2ud0NDDMs4M4qSF0fINiZKVvAFWqtUvnM+pksuJsoYIzgxPsJBq97Fy3ii3bd6O0ubaMpYrJrCGbXegO9T8WntJGqBwB4ck9/nkSCE45lAB8cp9WtLCJ+By4clmrVDuqqrL5YB3L1h/g/U3F1LvbO7GEGXRcODyBy8ekMCkzWoR3C3oUIUoKegO9WpSMSB2iTrrjmY53qip61YdR9WJQvBhUT2jdqHqCSy8GxYO+W0uDaASQ8clGvJIJn2RsXpeNeCUjXjnYLxnFxaPgmKho9FDtOPTNoSTB4Hg744OekOPTo0jq10u80oI0rFxJ2R//hO9A67DZiMsuI+7OO9BHR3dy5CmIqmoi2dd/h/xV7fcPnAaT74K0iSfGlqINWpj5j283F+hpS+JIGD4bci7TqtD2VVQVyrZpHpR7V0Dh6s5zG0oyJI/T/h4Dp2qigXx0lW89/gB7yhrZVhwUKovq2FHSgMvXtd8vsx4mxCmMi/GRE+5iQJiDRF09Bmd5+zBq3xEU9OgqRluzwNjOszE+KDbGa6LuUb5HPYrXoXlQNoV9F62Hmi7mILdEN4uUTV6VtrjjYqaqqpTUuVt4PWqVr/dXOPAGFOw4GRYMu86W8xkm5TNQKkIvdTENgblfa0++hBEQM7BPeYd2hN/v59P/rWDtD6tDfZIsEzFwAu5+/alx+ELemI1uP6lRYa0Kz6THWDEEHwqUlZWxatUqtm3b1u5Bd2JiIlMmjWGw3YlcuhVKNkPpFs1zu4t5KpsL6rT4O0Rl9c3/K4GgL+Bzwzs3wvb3mvvSJ8MV/9U8+jvB7Qvw+Y5ylq0/wFe7Kzos2pfcL4zcMclcNiaF9JiuPQQRCLoTIUoKegO9WpS0J2aqp8//NWGSjzB82lLyYZG8mPGjk7rfRreqx6UacKkGnBhC6y7VgAsDzuC6Dx2IOtqCHsKolxmV2i/kCTkmLZKIsN5fJEPxeKh6/nmqnn4G1dMcHimHhxN7661EXjEHSS9CWlpxYI0mTu75tP2+9Mma52Tm2d3/8KNit5YjcuubnYsvUZmaR2TO5b0rtLw7cdXC/i+bvSg7E2VBE90G/EQTKDPPgbCjLsQHQEBRyS+tZH/efkoOFlBVXoi7uhirr4o4aomV6oiTaoiTaommvuvCUpeRwBrT2nsxJDDGtQ6jNtm6+dx9AGe15kUZEio3aJ6nXSEiNShUBgvpJI464tDmereP3S1CrpsqXzd55sRQR46cx7BQCHZBu4r0h8Se1NpDL2EE9Es7qR+07tixg3fffRdPi9+nESNGcNFFFx021+PBgwdZtWoVu3btarcvLS2NKVOmkJWV1XHqBa9T88ZtEilLNkPZ9q6nRTBYtfD4ln+v2KGgF/kpBYJjwlULr18JBd8092VfCpc+fUQ5hcvr3by7qYhl6w+yu6zj8O7x6ZFcHgzvtp8Khe8EvQIhSgp6A71alExKSlJvvPHGY54noEq4DiEwulRt3Y0BBfGkWdD7iAgztAjFjiQnOQKTvu96pngPFlH250doXPF5q37TkCEk/ObXWMaM6SHLejElW2DVo8En9W2+n5PHap6Tgy88NsGgvlirnL1lqXZj3BHWOMjJhRGzNK+vk1igaIeiaO/Lnv9pnpQH19JpeLOkg7RJzaHe8dm0qqjhqjlEnsYW/V0qwnJkeDDQoI/GZ4lDHx6PNToFS1RS+5BqawzoxI3REVFfrHlRhnJUbuzS31BFwhORSUP0COoih1PVbzhVtoG4FANufwCPT8HjV6hzaTkgd5VqoddNR6dIFa3yP2bL+cRLtV23OyqrjQA5EmyxR/ce9HGqqqpYunQpZWXNAm5cXByzZ88mJiam1VhVVcnPz2fVqlXs37+/3VxZWVlMmTKF/v2PolhZwKd5UJZsaRYqS7d2kjajA2QDxA1t/nsmjtDCTU/FBwgCwdFQXwyv5GoPDJqY+HM4/09H7Zmsqio/FtWzbP0B3ttcTK2zff5bs0HmguwELh+bymlZ0ehEeLfgOCJESUFvoE+LkgajEbPFSpjF2rwMs2K2tu4zmsyiyIygz2LW60iLspyUOWcaV62i9OGH8RUUtuqPmDGDuLvvQt/mBlCA5sH4zWOw5Y32YcVx2TD5Du0pflfDKV01mtC5dRnkf0OHIpspHIZeEqycPaXPh2p2G85q2PdFc6i3s6rzsfYkLay9SXDsqDDQMeIz9qNBH0U5/TjgtZPntlGm9qNC7UcF/SgPrtdjoa2nf4zNRHZSeKj6d3ZSOGlRlpPyt1NVVXwBFY8/gMev4PZpS034a9PnV/D4OugLCYXNgmFH83l9PmJ9xQzw72aIsoccdS/DpHzM0uELsXhVHTvVNLYomWxWs9iiZJKnJpIulYaEx2ypgGFyPhHS4fMMAyDrNQ+6lgJkfM5RFaA5mfH5fCxfvpxNmzaF+oxGIzNmzCA7OxtVVdmzZw+rVq3iQJuUJABDhgxh8uTJJCcnd69higK1+S2EyqBYeSgP7lZIED0A4odpuU4j+2vLfv01L9gezAMqEPQqKnbBksug/mBz39Tfw+m3dtvDWI8/wJc7y1m2/iBf7qrosGBlYoSZy8YkkzsmhcxY8UBB0P0IUVLQG+jVomRmZqb62GOPYbPZsNls2O320LrNZjtsKI1AIOj9KF4v1S+8QOVTT6O63aF+2WYj9tZbiJw7V4R0d0RNAXz7T9j4SvsQv6gsOPN2GDGn4/A9r1MrqLN1mSaodVR5WmeCQedr4dkDp4mb1cOhBLRQ3iYvyuIN3TOvrA+GSHeWr7FFaHWbULJGj5+dJfWhHJXbiuvZU96AL9DFgjomPUOTwjWxMimCwQl2dLKEP6DiVxQCiopfUVssFfwBtVW/L9DJOEUlEGgxTlFabbca17Qd3B86d+hcHZ2jg3MHFLwBTUDsyUscPX4GSQcZIe9npLSPkfJ+BkkHuj/83mDRBMeWAmTcsCMKOTzV2bBhAx999BF+f3OxilGjRlFaWkppaetQfUmSyMnJYfLkycTFHZ+coZ3SUBoUKjc3C5W1BUc+jy0hKFSmtREt0yAiRXhOC04NClfDq3PAHfQ2l/UwYzGMvOK4nbKiwcN7wfDunaUNHY4Zk9aPy8em8tMRiX0iZZOgbyBESUFvoFeLkt1VfVsgEPR+fEVFlP3lrzR89lmrftOgQST8+kEs48f3kGW9nIZS+O7/YN2L7QuYhKfAGbfBmKu1UL68lc2Vs70d5DSSZM0TcvgsGHLRMedEPKVpLIe9n2sC5b7Pwd0mhNdo7yA/YweVqMMiu7WARVNBne3F9fwYLKqzo6Qep7f7C8IJOkaWwGzQYdLLmPQ6TAaZcJ2PYVI+Q5Q9DPLvYYBvN/H+oq5PGhbZOvQ6cYTmESe8mo+ZkpISli5dSk1NTYf7ZVlm1KhRnHnmmURFRZ1g6w6Bq1YL927yqCzdonl/dVa463BIOq3yd0uhsmk9sr/2fSWK7Qj6OjuXw7L54A8+JDdYYc4SGHDeCTNhW3Edy9Yf5L1NxR0WujTpZaZlJ3D52BTOHBAjwrsFx4QQJY8/n3zyiW3hwoXp+fn5P/a0Lb0VIUoKBIJeReM331L28MN48/Nb9YdffDFxd9+F4UR7oPQVnNWw+klY83R7Acwaq+UxdHZyjZA0Jliw5jJNDBN0LwG/5sHk9zZ7Ohp7T5XNgKKSX+Xgx6I6thdrnpXbiuuo6SDX1cmCXpYw6eVmcTAkErZc1wTDVuP0TeNlzKH9zcd2NF/bPr0sdS0s3lWjed82FdEp3gANJZow1Kr68gitcM5JGGrfW3C5XLz77rutitjo9XrGjh3L6aefTkRE5xV4exU+l1ZAp3qf5m1fmx9cFkLdwaMXLAF0Ru1z2FKo7JcG/dK1dUu0+IwKejfrXoTld4Aa9Fq3xsLcpVpBsh7A61dYuUsL7/5iZzn+DsK748NN/HHmcH4yLL4HLBScDJxqomRycvLwqqoqgyzLqsViUc4666y6F154oTAiIqK7q0V2G//617+iX3755Zj169e3r6TXS0hOTh6+ePHi/JkzZ3bs6n0YOhMlRaykQCDoEWxnnoHl/feofullKp98EtWlFXOo/+ADGr/4gphf/IKoq65EMojQlVZYouDcB+D0W2Dtc/D94mYR0lHRfnz0ABg+W8sTGZ11Ym091dDptWJEvRSdLJEVayMr1saMUVoOPFVVKalztwr9Lqx2IEsSOllCLzctZW2pa97WyxI6XcsxErqm/uC2Xtd6u3n8YcaFztWFcbLczg6jXsaok9Hr+oBHV1gkZJ2rtSb8HhF+3QOEhYVxxRVX8MMPP7Bt2zbS09OZOHEiNlsfy/FmCIOUsVprS8AP9UVa2HeTUBlaL9AE8UMR8GpiZ/W+Ts5t7Tw0PLI/mPuIsCs4+VBVWPln+OrPzX2RGXD12xCV2WNmGYMekdOyE6hq9PD+5mKWrT/ItuLmYldl9R4S+4n0OgLBkfD666/vmTlzZkNhYaF+6tSpgx588MHE//u//zuC8JTuw+fzYTjO97THeo4TYWNnCE9JgUDQ4/hKSrSQ7k8+adVvGjiA+Ad/jXXihB6yrA/gdcKG/8B3/9JuNAHsiVrl7OGzIHGk8FoRCAQCQdfwuTVvypB3ZRvx8lCFvrqCuV8boTK9tcelIaw7XoVA0JqAX/OO3PByc1/SaJj7Jthie86uQ7CjpJ631h/k3U1FxNhMfPLLKT1tkqAPcyp6Srb06Fu4cGHKzp07zStXrtwL4HK5pNtuuy35gw8+iPR6vfL5559f88wzzxyw2WxqSUmJfu7cuenr1q2zybLMgAEDXGvWrNml0+lITk4efs0111QsXbo0uqKiwjBt2rTa//znPwUWi0X98MMP7ddff31GWVnZliYbrr322oo333wzKj8/3+xwODY89NBDCUuWLImprq42JCQkeB966KGia665pnbDhg3m0047bZjf75dMJpOi0+nUhoaGTVVVVboFCxakrly5MiIsLEy56qqrKh955JESnU7Hv/71r+iXXnopdvTo0Y5ly5ZFX3XVVRWvvPJK7P/+979dEyZMcAEUFRXps7KyRuzfv39LUlKSv+V71Pb4efPmld90001V119/ff+dO3daJElSJ0+eXP/CCy8UxsTEBGbOnJnx/vvvRxmNRlWWZfWOO+4ofvjhh8s+//xz61133ZW6d+9ec1JSkvfRRx89cNFFF3XoSSk8JQUCQa/FkJhIyuOP4fhuFqUP/xHv/v0AePbspXDePMKnTyfu3nswxIuwlXYYLTBpIYybDwdWa6F1KeNFjjmBQCAQHDkGM8QM0FpHeBo0gbIjL8uaAvAeJqLLXQsltVqRno6wxrUQLVPBZAe9uUUzNa8b2vaZQB/WvK0ziIdyAu3h7bL5sPvj5r6s82D2f8DUe72ghyaG8+BFw7j3wiGU1rkPf4BA0IMsXvjFCQsVWvTUueuPZPy+ffsMX3zxRfgZZ5wR+oFatGhRSn5+vmnTpk3bjUajmpubm3nvvfcmLV68uOjhhx+OT0xM9FZWVm4G+PLLL60t0/EsW7Ys+tNPP91tt9uVCy+8cOB9992X+K9//au4o3O/9dZbUR999NGehIQEv8FgYMCAAZ5Vq1btSk1N9b3wwguRN910U8ZZZ53145gxY9x/+9vfCtqGby9YsCC1vr5et3///q3l5eX6888/f1BiYqLv9ttvrwTYsmWLNTc3t7qysnKTx+OR6urqdC+++GLUhAkTigBeeOGFqNNPP72+rSDZRNvj8/Pzjffee2/pBRdc0FBTU6O75JJLsu65556kF1544cC7776bl5ycbGsp9ubl5Rlyc3MHPv3003mXX3553fvvvx9+1VVXZW3fvv3Hzs7ZEUKUFAgEvQbr6aeT+e47VC9ZQsXif6M6nQDUf/QRjStXErNoEVHXXC1CujtCb9SK1wgEAoFAcLww2SE+W2ttUVUtR2pbobJJvKwtbC4s0hmOcq0dXNsNxkpdEy+blocdF+wzdHBsO9HUJATR3oCzWquwfXBNc9/In8El/9dnqswbdDKpUZaeNkMg6HPMnTt3gCRJOJ1OedKkSQ1/+9vfigEUReG1116LWbdu3fb4+PgAwP33318yb968zMWLFxcZDAa1rKzMsGfPHmNOTo7nggsuaFU19IYbbigfMGCAD+Dee+8tueuuu1I7EyUXLlxY1jQWYP78+TUt5ql59NFHE1etWmXt379/bdtj/X4/y5cvj/r++++3R0ZGKpGRkd5FixaVvvbaa9FNomRsbKz3gQceKAcwGAzq/Pnzq+bOnZv1xBNPFOl0Ot54443oX/7yl6WdvUdtj8/JyfHk5OR4AMLCwvy33XZb2Z/+9Kekzo5/7rnnos8+++y6OXPm1AFceuml9Y8++qjjrbfeirjlllu6HFohREmBQNCrkIxGoq+/nvCLLqL8L3+l/qOPAFCcTsr/9jdq336bhF8/iHXSpB62VCAQCAQCQQhJ0vIeW6K00Ni2KIomOIaEyvzW4uWxFuFphwp+l9Z6Ar0ZZD0gxMkeI+DR8qA2cebtcN5DQjAWCE4BXn311b0zZ85sWL58ue26667LLC0t1cfExARKSkr0brdbnjRp0tCW4wOBgATw0EMPld5zzz1JF1xwwSCAa665puJPf/pTSNhLS0sLfalkZWV5KioqjJ3Z0L9//1bVJJ944onoJ554Ir6oqMgI4HK5dBUVFR1qciUlJXqfzycNHDgwdL6MjAxvWVlZ6IlKYmJiq/nPPfdcR1hYWOCjjz6yp6Sk+AoKCkxz585tJ3h2dnxRUZH+pptuSluzZo3N6XTqFEUhPDy80x/mgoIC48cffxxpt9tDCaP9fr80ZcqUIyqEI0RJgUDQKzHEx5P8j0fpN3s2pQ//Ae9eLam+d98+Cq+9DvuFFxB/770YEkQVaYFAIBAIej2yDPYEraVNbL8/4IeG4mahsq4IfE6t8JPfHVy62my7tTyYLbebxildjhw7PhzOK1RwApHgwr/AxJt62hCB4KTkSEOqTyQ//elPG+fMmVP1y1/+MmXFihX7EhIS/GazWdmyZcu2jIwMX9vxkZGRyrPPPnsQOLhu3TrztGnTBk+cONExY8aMBoDCwsKQCLl//35jbGyst+0cTUiSFCrgsnv3buMdd9zR/4MPPth93nnnNer1eoYMGTKsqcaL1OZhSWJiol+v16t79uwxjh071g2Qn59vjI+P93U0fxNXXHFF1ZIlS6Li4+P906dPr7FYLJ0WkWl7/O23354sSZK6ZcuWbQkJCYElS5b0u+uuu9I6Oz41NdV76aWXVr3++usFnY3pCkKUFAgEvRrrpIlkvvMO1a/8l8onnkBxOABo+PgTGr/6mpifLyR63jwkY6cPqQQCgUAgEPR2dPpg1e40YPKxzxfwa55yIfGyA0HT34Gg2R3jAp3eowpONPZEuOARyL60py0RCAQ9xK9+9auyrKys4d99913Y6aef7rriiisqFy1alPrss88WJicn+/Py8gwbNmwIy83NrX/ttdcihg8f7h42bJinX79+AZ1Op+p0zbn6n3vuudjc3Nxam82m/OUvf0mcMWNGzSFOHaKhoUGWJImEhAQfwD//+c/ovXv3hqq7JSYm+kpLS41ut1sym82qXq9n+vTpNffdd1/y0qVL8yoqKvSLFy+Ov+WWW8oOdZ4FCxZUjRkzJttqtQaef/75vCN5nxobG3Xh4eGBmJiYQF5enuGxxx5r5f0TExPj27t3rwloALj++uurJk2aNOytt96qmTFjRr3X65W++OIL29ChQ91ZWVntBN/OEKKkQCDo9UgGA9HXXUv4T6dT/re/U//BBwCoTicVj/6DurffIf7BB7CdcUYPWyoQCAQCgaBXoNNrzWg98edWlGDocJfvyQTHC6O1zxb/U9xuyv70COHTLxRpiwSCYyApKcl/2WWXVf3ud79L+vTTT/ctXrz44D333JM0ceLEobW1tfq4uDjv/PnzK4D63bt3m+6666606upqfXh4eODaa6+taFlNOjc3t3ratGmDysvLDVOnTq195JFHSrpiw9ixY9033nhj2ZQpU4bKsqzm5uZWjR49OpSv8qKLLmp44oknXPHx8SNlWVZramo2P/fcc4ULFixIy8zMHG4ymdSrrrqq4rbbbjtk1fOsrCxfdna2s6CgwHT++ec3HmpsW37/+98Xz5s3LyM8PHx0WlqaZ86cOVVPPfVUqNLs3XffXXr33Xen/u53v0u5/fbbS37/+9+XLV26dO+9996bMn/+/ExZltWRI0c6nn322cIjOa/U5C7ak4wbN05dt25dT5shEAj6CM61ayn9w8N4du9u1W+fNo34++7FkNRpPl6BQCAQCAQCgaBX483P5+Avb8ezcye6mBgy3n4LQ1xcT5sl6KNIkrReVdVx3TXf5s2b80eOHHlIcexkJDk5eXjL6tO9lVmzZqUnJiZ6OyvA01Ns3rw5ZuTIkelt++UesEUgEAiOCcv48WS8/Rbx9/8K2WYL9Td89hn7fnoRlU89jeIVoVMCgUAgEAgEgr5F/SefkJd7OZ6dOwEIVFZS9957PWyVQCDoC+zatcv4ySef9Lv55pv7jGgsREmBQNAnkfR6oq65hqyPPyJixoxQv+pyUfH44+RdfAmNq1b1oIUCgUAgEAgEAkHXULxeSh/+I0W/vD2UQ10yGkn47W+JXrCgh60TCAS9ndtuuy1p9OjR2TfffHPpkCFD+oyHTq8I344bHKfOeXJOT5shEPQoEhJ6WY9e1mOQDRhkQ7v1pu0uLXUG9JK+1bpBZ8AgGbTtDo6Rpb77nMK5YQOlv/9D6KlyE7afnEf8fb/CmJLcQ5YJBAKBQCAQCASd4z1YRNHtt+PeujXUZ0hNJfnxxwjLzu5BywQnAyJ8W9Ab6Cx8+5gK3UiSdDuwAFCBrcB1QCLwOhAFbACuVlX1kCptg7eBlQdWHospAoGgG5AlueuiZxuh1CAbGBo9lFmDZhFhijjhtlvGjCFj2ZvUvP4GFf/8J0qDluqjccXnOFZ9Q/RNNxJ9/fXIJtMJt00gEAgEAoFAIOiIhi++pPi++1Dq60N99qlTSfzTH9HZ7T1omUAgEBx/jtpTUpKkZOAbYJiqqi5JkpYCHwHTgbdVVX1dkqSngM2qqj55qLnCMsLUAb8dcFR2CASC3kWYPoxZg2Zx9bCrSbAm9IgN/qoqyh/9B3Vvv92q35CWRvz9v8J+9tk9YpdAIBAIBAKBQACg+nyUP/441c+/0Nyp1xN/z91EXn01kiT1nHGCkwrhKSnoDXTmKXmsouRqYCRQD7wL/B/wXyBBVVW/JEmnAb9VVfX8Q801eMRg9ckPD6lbCgQnPaqq4lf9+BQffqXFMuDT+lsuW+zvqK+j5aH2NS27E72s5+LMi7ku5zoyIjK6de6u4ty4kdI//AHP9h2t+m3nnEP8/b/CmJraI3YJBAKBQCAQCE5dfKWlFN1xJ64NG0J9+sREUh77B2GjRvWgZYKTESFKCnoD3R6+rapqkSRJfwcKARfwGbAeqFVV1R8cdhDoMJGbJEk3AjcCpKWlcW7auUdrikAg6AZUVSWgBtqJlYcUMlsIpVXuKl7b+Rp7a/cC4Ff8vLP3Hd7d+y4/6f8Trs+5nuyYE5sTxzJ6NBlvvknt0qWUP/5PlLo6ABq//BLHt98SfcMNRN+wANlsPqF2CQQCgUAgEAhOTRq/+Zbiu+8mUFMT6rOeNYWkP/8ZfWRkD1omEAgEJ55j8ZSMBN4C5gC1wJvB7YdUVR0QHJMKfKSq6vBDzTVu3Dh13bp1R2WHQCDoPaiqytcHv+a5rc+xqWJTu/0TEyeyYPgCJiZMPOEhKf7qaioee4zaN5e16jekpBB///3Yzz3nhNojEAgEAoFAIDh1UAMBKhcvpvLJp6DpHlynI/aXtxF9/fVIct8tOCno3QhPSUFvoDNPyWP55vsJkKeqaoWqqj7gbeB0oJ8kSU0emClA8TGcQyAQ9CEkSeKs1LNYMn0JL1/wMlNSprTa/0PJD9zw2Q38bPnP+F/B/wgogRNmmz4qisQ//IH0N17H3KKKoe/gQQ7efDMHblqIt7DwhNkjEAgEAoFAIDg18FdWUnj9Air//WRIkNTHxtL/pReJueEGIUgKBIJTlmP59isEJkmSZJE0l6fzgO3Al8DlwTHzgPeOzUSBQNAXGRM/hsXnLWbZxcuYnjEdWWr+utlWtY07Vt7BzPdm8s6ed/AFujef5aEIGzmS9KVvkPDb36KLaK4S3vjVV+y/6GJKfvMQ7p07T5g9AoFAIBAIBIKTF8cPa9h/6aU4V68O9VlOm0TGO29jGT++By0TCE4+LBbL6KYmy/JYs9k8pmn7ySefjOpp+46FmJiYkZ9++qmtp+3obo5alFRV9QdgGbAB2Bqc6xngXuAOSZL2AtHA891gp0Ag6KMMjhrMX6b8hQ8v/ZA5g+dg0plC+/Lr8/nNd7/hgrcv4OVtL+P0OU+ITZJOR+QVc8j85GP6zZkDwVBy1euldunVMlxMAAAgAElEQVRS8mZeSv7cK6n7cDmq13tCbBIIBAKBQCAQnDyoikLlU09TeN11BCqCka6SRMyiRaQ99xz6mJieNVAgOAlxOp0bm1piYqL39ddf39O0/fOf/7y6p+3rDJ/v+DvpnIhzHA3H5CeuqupDqqoOUVU1R1XVq1VV9aiqul9V1Qmqqg5QVXWWqqqe7jJWIBD0XVLtqTw46UE+yf2EG4bfgN1gD+0rd5bz93V/Z+qyqSzetJgad80hZuo+9JGRJP7ut6QvXYp55IhW+1wbNlB8113sOedcyh9/HF+xyEQhEAgEAoFAIDg8/poaDixcSMXjj4OiAKCLiiL1uWeJveUXSDpdD1soEJya+P1+7r777sSUlJThkZGRIy+55JKMyspKHcDGjRvNer1+7GOPPRYTFxc3ol+/fqMef/zx6BUrVlgHDhyYbbfbRy1YsCClaa6//vWvsZMmTRo0d+7c/jabbXRWVlb2xx9/HPJkLCsr01166aXpMTExIxMSEkbceeediYFAoNWxV199dVp4ePioBx54IHHjxo3mCRMmDI6IiBgVGRk58rLLLkuvqamRAS688MLM6upq/cyZMwdaLJbRDz/8cNyyZcvC09LSclq+vpbelDfffHPyxRdfnPHTn/4002q1jn722WejDvX6e4qjrr4tEAgER0NMWAy3jrmV63Ku483db7Jk+xIqXdrT43pvPU9tfoqXfnyJ3EG5zBs2j0Rb4nG3KWx4Dumvv45r/XpqXn2N+s8+A78fgEBVFVVPPU3VM89iO/ccIn/2M6ynnSZy/wgEAoFAIBAI2uHcuJGiO+7EX1IS6gsbO5bkfzyKIT6+By0TCI4Pj865aOyJOtedb3y4/liO/81vfpPwxRdfhH/zzTc74+Li/FdffXX/hQsXpi5btiwfIBAIsGXLlrCCgoKtb7/9dviCBQsyp0yZUr9q1aqdDQ0NurFjxw6bO3duzbnnnusAWLdunX3mzJk1L774YuGzzz4bdcUVVwzIy8vbEhUVpcyaNSszIyPDnZeXt7W6ulp3wQUXDExPT/fecsstVU3Hzpo1q/rFF18sdLvd0p49e0wPPvhg8bRp0xrLy8t1F1988YAHHngg8d///nfRxx9/vD8mJmbkf//7333nn39+I8CyZcvCD/d6P/3008glS5bse//99/e7XC7pcK+/JxB31QKBoEewG+3Mz5nPJ7mf8JvTfkOqPTW0zx1w898d/2X629N54JsH2Fe777jbI0kSlnHjSP7Howz88gtib7sVfUJC8wBFoXHF5xy4fgH7p/+U6pdfJlBff9ztEggEAoFAIBD0flRVpeqllyi4+ppWgmT0DQvo//JLQpAUCHoBL7/8cuwjjzxyMD093WexWNQ//vGPxcuXL49Ugh7NAI888khxWFiYeuWVV9YBXH311VUJCQmBgQMHeseMGdO4bt06S9PY+Ph473333VdhMpnUX/ziF1Xx8fHed999N2L37t3GtWvX2p555pmDdrtd6d+/v2/hwoVlb775ZiivZUpKiufOO++s1Ov12Gw2dfTo0e5LLrmkwWw2q2lpaf5FixaVfffdd3aOgfHjxzfMmTOnTqfTYbPZ1K68/hON8JQUCAQ9iklnYtagWVw64FJWFKzg+R+fZ2e1VmjGr/p5f9/7vL/vfc5JPYfrh1/PyNiRx90mfWwsMT//OdE33EDDl19S8+qrOL9vTk7uzc+n7JE/U/74P4m46CIi5/4M89Chx90ugUAgEAgEAkHvI1BfT/H999O44vNQnxwRQdKfH8F+zjk9aJlAIGhCURTKysqMubm5A9v0S2VlZXoAnU5HQkJCoGmf2WxWEhMTfS23GxsbQ859CQkJrQoQpKSkeIuKigz79u0zer1eOSYmJnTzqv4/e/cdHkd1qA38PTOzRc3qtqziIhkXYmPsOBAwdoBAQjCxHGpwSEIgXwipECeESye2SSAECOFecp1Ct+FSjAm5NyGAwYAJYAPGuEtyldzU++7OzPn+mNnZolVfaVXe3/PomZkzZ2bOSrta7atzzkgpJkyY0B7cHj9+fMSx+/btc1177bVFmzdvTm1tbVVN00Rubm6/JoIsKChwrtHd4x8/frzen2v1FUNJIhoSNEXDeZPPw5cnfRkbqzbiz1v/jE1HNzn71x9cj/UH1+NzeZ/D1TOvxun5p0PYN6gZKELTMObcczHm3HPhq6hA3Zqn0bB2LczmZgCAbGtD/bPPov7ZZ5E0Zw4yly5F2pe/BMXtHtB2EREREdHQ0PbpNlRedx0Chw45Zd6TTkLh/ffBVVCQwJYRDY7+DqkeLIqiYOzYsf5nnnmmfMGCBR3usHrkyJFe52NHjhyJ+OBXWVnpLigoCEyePNmfnJxsNDQ0fKx0Mu1X9GfZn/zkJ4XJycnmtm3btuXm5hqrVq3KXLFiRUFYfRlePzU11Wxvb3dO7vP5RENDQ8T8kOHX6O7xJwqHbxPRkCKEwPyC+XjkvEfwxFeewFlFkf9d/uDIB/j+q9/HpS9fin/s/QcM0+jkTPHlKS5G3s034YQ330DenXfCM21axP62jz5C1S9+gbKzzsax+3ljHCIiIqKRTEqJujVrsP/yyyMCycxvfROTnnyCgSTREPTtb3/7+C9/+cuCsrIyFwBUVlZqq1evTu/r+Y4ePeq+5557cgOBAP7rv/4r6/Dhw+7S0tLG6dOn+2fPnt1y7bXXFtbV1SmGYWDr1q2e4E1oYmlublZTUlLMzMxMY8+ePe4//OEPeeH7s7Oz9T179niC27Nnz25vaGjQ1q1bl+bz+cSyZcvypZRd9tqJ9+OPB4aSRDRknTz2ZDx49oN4sfRFLC5ZDE2E/nm1s3YnfrHhF/jqi1/Fs7ufhc/wDUqblJQUZF52KSa/uBYTn3oSYxYtAlwuZ79RU4Oa//5vlJ1zLg7+8EdofucdyATO0UFERERE8WU0t6Bq2c9x5M5fQQas0ZVKaioKfv975N10EwRHzRANScuXLz9y1llnNX3xi1+clpKSMue0006bvnnz5uTuj4xt3rx5TR9//HFyZmbmnHvvvXf86tWry7Ozsw0AeP755ysaGhrUadOmzczIyDj5sssuKz58+HCnvTFXrFhR+f7776eOGTNmzpIlS0oWL15cF77/hhtuOLxixYqCtLS0k++6667c8ePH6ytWrDhw9dVXF+fn55+Ul5cXyMjI6HIIdrwffzwIKWX3tQbYvHnz5KZNm7qvSESjWlVzFR7f/jie3/082o32iH05STn45onfxKVTL0Wqu9N/QA0I/fhx1D//POqefgb6kSMd9rsnTkTm0suRvmQJ1PSE/iOKiChuTL8fRnU19OPHra/qaujHrCUAqJmZUDMzoGVl2etZ0DIzoGZlQUlKSnDriYj6pn3XblT+9Kfw79vnlHlmzEDhA/fDPXFiXK8VMAKAAFyKq/vKRJ0QQmyWUs6L1/m2bNmyb/bs2dXxOt9wdc899+S++OKLmRs3btyd6LYMB1u2bMmZPXv2pOhyhpJENOzUttdi9Y7VWLNzDRr9kXfATnOl4evTv45vzPgGspOyB7VdUtfRtH496tesQcvGdzvsF14v0r96ATIvvxzeE08c1LYNVXpNDdq2bkX71k/RtvUTBKqqoOXmwpWfD1dBAVz5+XDbS23cOAiNUyETDSQpJcymplDAGAwbjx+HXn3cCSCN49UwGhr6fB3h9ULNyoSWkQnVDi21rEwnvIwIM7OyoI4ZA6Gq3Z+YiGgA1T//Ao4sXw7ZHvrneMall2LcTf8BxeuN67X21O3BzW/fjLMmnIVrZ18b13PT6MJQcmAwlOydzkJJfrojomEny5uFH835Eb4z8zt4bvdzeHzb4zjWdgwA0BRowp+2/gmPb38cX5vyNVw580oUpA7OnD6RN8bZi7qn16Bh7Yswm5oAALK9HfXPPof6Z59D0sknI3Pp5Ug777xRc2Mco7kZ7Z9uQ/unW9Fmh5B61eEO9fxl5bFPoKpwjRsXCiwLQsGlKz8f2vjxo+Z7SdRbUteh19Q6wWJED8fj1RHho/QN/HQYsr0detXhmL8DYhICakaGHVJmQnPCy+gwM5O9MYko7sy2Nhz51XI0rF3rlInkZIy/8w6kf/Wrcb2WYRp4bPtjeOijhxAwA9hTtwcLCxfiM9mfiet1iIiGAvaUJKJhz2/48XLFy3jk00ewr3FfxD5VqDhv8nm4auZVmJo5ddDbZra2ouHll1G3eg18O3d22K9mZSHj4ouRedmlI2pCdNPvh2/nTrR9shXtW7ei7dNP4a+oAAbyPUcIq5dlMKgMXxZYwWW8ezEQJZrZ2hrZm/F41HBqe2nU1MT/9aco0LKzoeXmQs3NgZabCy3HWgpFgV5bC6OuHkZtLYz6Oui1dTDq6mDU1jpzsA0k9sYkonjwVVSg8qfXwbdnj1PmOWEKCh54AJ6Skrhe60DjAdzyzi346NhHTplbceOO0+/AV0viG37S6MGekjQUcPg2EY14hmng9YOv489b/4ztNds77P9C4Rdw9ayrMWfsnEFvm5QSbR99jLrVq9H4z38C0R/IFQWpZ56JzMsvR8r80yGU4XMfMmkY8JWXO0Ow27d+ivbduzs+xhiExwPviSfCO2smkmbNgntyMYyaavgrK6FXVcFfWYlAVRUCVVUwjvf/bx81OzsqsMwP2y6AmprS72sMJdI0Yba2wmxshNHUDLO5CUZTE0xnvRlmUxOMZrusqQlma2uim91/imKFS5oKoWoQmgqoGoSqWuuaFrvcXoemQmiuyHJNBdTw89nrLq1juWafxy4Tmha7XA22JayOokCaJoz6+qhejJFDp4Nho9nSEvdvn0hKsgLGsJDRWR8bKlMzM/sU4kkpYba0wqirhVFXFyO8DNuuq4NeVwezsbH7E/eXokBNT7dCyowMCLe78+dD+PMr4rmmRT3vBu45A1WFcNntii4XXd58k4h6qOHlv+PIbbdFvDeml5Yi7/bboCTH794QUkr8z67/we82/w5teptT/pnsz+CuM+5CcUZx3K5Fow9DSRoKGEoS0aghpcS/D/8bf/n0L3jv8Hsd9s8dOxdXz7oaCwoWJOSDm15djfrnnkfdM89AP9xx6KJr4gRkfv1yZFz4tSF3YxwpJQKHDlm9H7d+ai23b4fsSZClqvCccAKSZs2Ed9YsJM2aBc+UKRCunk3ebvp8VkBZWYVAVaW9rELADi71o0f73RNMSU93gkp3dI/L/Hwo6emD9pyRpgmzpcUKDXsYKBrNUWXNzQPbO5XiL/gPCdOM+6nVrKxQyBgMGMOCRzUnB1ru2CEZzstAwApq6+pg1NaFhZf2dl0d9LrIMHMwemMOWaoKNT0d7uLJ8BSXwFNSDLe91MaPZ2hJ1A3T58PRX/8a9U8/45QJjwd5t96C9Isuiutr6EjLEdz2zm1493BoPnJNaLhm9jW4etbVvMkN9RtDSRoKGEoS0aj0afWn+Ounf8Wr+1+FROTvuxMyT8DVM6/Glyd9GZoy+FPsSl1H8xtvoG71GrRs3Nhhv/B6MeaCRci8/HIkfSYx8wjp1dX2jWhCIaRRX9+jY90TJ9rh40x4Z50E74zpAzrHm/T7ETh61Aorgz0sw5dHjgCG0a9rKCkpnQ4NdxUUQM3KghCiY6DY1GgFis3NUcGitR4rWDRbWhgoUreEyxVz+LS1bi9zc6BlZ/f4HwAjgdUbs8UZLq6HhZdGXeR2MMwclN6YQ4BIToZn8mS4S4rhKS6xliUlcBcVjarnCFFn/AcO4NB118G3fYdT5p44EQW/fwDe6dPjdh0pJV6ueBm/fu/XaAo0OeUl6SW4a8FdODGbN0Wk+GAoSUMBQ0kiGtUqGirw6KeP4m8Vf4Nu6hH7ClIL8J3PfAelU0rh1RIz56CvYi/qn3ka9S+sdW6MEy5p9mxkfmMp0r78ZSgez4C0wWhqQvu2bVYI+Yk1D2SsnpyxaGPHwnvSLCTNnGUNxZ45c+j18tR16MeOOcPBIwJLu9dlf3tWCa8XQtOGVKAokpOhpqZCSUtzlkpaKtTUNKssLRVKahrUMda2kpwMiOEzfUBM0oTUDcDQIQ0DUtcBe+mU6wakESyPWtcDkeW6YZ0nbB2GDhnQO5brwbKwdV2PXR6jTpAyZky3w6e13FwoY8aw11ucOL0xa+tgNNRH/mwing/dPH+i63T2/NHt52Ss8ujnRqzyTp5jfe5lq2lwT5wIT3FxKKgsLoZn8uS4DlMlGsoa//UvHL7p5oi/xdLOOw/jVyyHmpoat+vUttdi+bvL8eqBV50yAYErP3Mlfjjnh/CoA/O3Ho1ODCVpKGAoSUQEa4jME9ufwLO7n42Yswew7uq9ZMoSlJaUJmzunm5vjJOZiYyLL0bGZZfBXdj3G+OYPh98O3ZYvR/tu2H7Kyp6dKwyZgySZtpDsE+aBe/MWXCNG9vntgwV0jShV1eHwsqI4NIOLdvauj9RHCnJyZ2GiEpaKtS0NCtQTLPDxlS7LBhApqZac9PRsCCltAIl02SPNeozaZqAriNw7Dj8FeXwlVeEluXlMBoaen1OV34+3CUlHQJLLTNzAB4B0eCTfj+O/e4+1D72WKjQ5cK4G3+JzKVL4/rPn9cOvIZfvfsr1LbXOmWFqYVYecZKzB03N27XIQpiKElDAUNJIqIw9e31WLNrDVbvWI16X8fhyCflnITFJYtx3uTzkO4Z/B5/zo1x1qxB0z/+0bEHnxDWjXGWXo6U+fO7vDGONAz4ysqt8NG+G3b77t0RvbI6I7xeeE880RqCPdMKIV0TJozKnllSShh1dZHDw6N6XJrNzU59BopENNRIKWHU1sJXXg5/RYUTVPoqKqAfOdLr86lZWXZQGTVvZV7eqHyfoOEpUFWFyut/hrYtW5wyV0EBCh64H0mzZsXtOo3+Rtz9/t14qfyliPJLp16KZfOWIdnFHsk0MBhK0lDAUJKIKIbWQCvWlq3Fo9sexZGWjh/I3IobZ004C4tLFuP0/NMTMvekXlNj3xjnaehVXdwY52tLoKSnI3DwoD0P5KfWcvv2nvXwU1V4pk5F0qxZzt2wPVOmMBjrBaOxEZDSChT7cFdiIqJEMZpb4N9bYQWW5RXwVViBpf/gwV7Px8t5K2m4aH7zTVTd8MuIHsSpZ5+N/F/fFddpaN6tehe3vnMrjrYedcrGJo3Fr+b/CvML5sftOkSxjLZQ8p///GfqjTfeWFhWVuZVFAUlJSXt999//4EvfOEL3d6Zc82aNel33XVXfllZmdfj8ciFCxc23H///YdKSkoCAPD000+n//a3v83bvXt3ksfjkeecc079ww8/fDAzMzP+dygcYRhKEhF1IWAGsLFyI9aVr8P6g+s7zDsJADlJOfhq8VexuGQxpmROGfQ2SsNA85tvou6p1Wh5550O+4XHA8Xr7fHQPPekSWE3opkF74wZULyJmVOTiIiGJun3w79/P3zlFfBVhAWWe/dCtrf37mQuF9wTJnSct7K4eEBvhEYUTeo6jj/4B9SsWhUqVFWMXbYMWd+5Mm49fVsDrbh/8/14etfTEeWLihfhP075j4SMxqHRZzSFkrW1tcrEiRNPuvfeew9cffXVte3t7eKVV15JKygoCJx66qld9tJ45JFHMn/84x9P+u1vf7v/m9/8Zl1tba26bNmywo0bN6Z9/PHH23Nzc40//vGPWTk5OfqXv/zl5vb2dnHRRRcVFxYW+lavXn1gsB7jcMVQkoioh+rb6/G/e/8XL5W/hG0122LW+Uz2Z1A6pRRfmfQVZHgzBrmFgG/vXtQ//Qzq167t0R1jtXHjnPkfraHYM6GOGTMILe2aKU3sqNmBDYc2YMOhDdjftB9FaUWYnjUd0zKnYXrWdEzNnIpUd/wmlyciov6TpolA1eEBnbfSlZcHNTcXits9AI+ARqvA0WOoWrYMrWGfP7Vx41Bw/31Inhu/OR0/PvYxbn77ZhxoCmUVGZ4M3HbabTh34rlxuw5Rd0ZTKLlhw4bkRYsWTW1qavo4et+DDz6Y/eijj+bOmjWr5YUXXsjOyckJPPDAAwdKS0ubTNNEYWHhrKuuuurYihUrnC7NhmFg+vTpn1m0aFHdAw88UBV9zsceeyxj5cqV+bt3794+0I9tuOsslOSYPCKiKBneDCydsRRLZyzFnro9eKn8Jbxc8TKq20LvvdtqtmFbzTbc88E9OKvoLJSWlOL0gtPhUgZnWJpn8mSM+48bkfvTn6Dh73+3boyzYwcAQElPt29EMxNJJ50E78yZcI0dOjeiaQm04N2qd7Hh0Aa8VflWxPcVALbXbMf2msj39cLUQiuozLKCyulZ0zEueRznLCMiShChKHAXFsBdWIDUhQud8r7OWxmcp7flrbc67FPS06Hl5kDLyY28G31urlUevBt9WhrfF6hLLe++i8qf/wJGTY1TlnLGGci/525oWVlxuYbf8OO/Pv4vPLLtEZgyNKLzzKIzcftptyMnKScu1yEaKg7d+NZnB+tahb9ZsLmr/TNnzmxXVRUXXnjhpK9//eu1Z511Vktubq4zB8knn3ySUlpaWltdXb3l8ccfz7jiiitKysrKtlZVVbkOHz7svuKKK+rCz6eqKi644IK69evXjwHQIZR8880306ZOndrLYQMUjqEkEVEXTsg8AcvmLcNP5/4UG6s2Yl2ZNbw7YFo3ntFNHf/a/y/8a/+/kOXNwgXFF2BxyWJMy5o2KO1TkpORecklyLj4YgQOHgQAuIqKhtyHsn0N+6zekJUbsPno5pjD47tyqPkQDjUfwqsHXnXKxrjHRASV0zKnoTijeNCCYSIi6kgIAS07G1p2NlJOOSViX1/nrTQbGuBvaIC/rLzra3s8VliZkwNtrBVUqjk5YUGmvczO4nzJo4w0DFT/8Y+ofug/geBIQUVB7o9/hOxrrunyhoG9sbN2J256+ybsqdvjlKW6UnHjKTdiccniIff3GdFIk5WVZb7++us7V65cmffjH/94Uk1NjWvhwoUNjz322D57f+DWW289pigK/t//+391Dz74YN5zzz2XPmXKFD8ATJgwIRB9zvHjxwfq6uo6vGmsXbt2zHPPPZf99ttv7xjwBzaC8d2YiKgHNEXDwsKFWFi4EA2+Bvxj7z+wrnwdtlZvderUttfi8e2P4/Htj2NG1gyUTinF+ZPPR6Y3c8DbJ4SAe8KEAb9OTwWMADYd3eT0htzfuL/TuhmeDJxRcAYWFi7E7NzZONh0EDtrd2J33W7srN2JivoK6LJjiNnob8T7R97H+0fed8pcigslGSXO0O9pWdMwLWsaxrgTP1SdiGi0U1NTkDRrVoc7Gpt+PwJR81b69+2Dfvw49JqaHt9oR/p8CFRWIlBZ2XVFIaBmZUX2uAwux0aWKSkpfX24NEToNTWo+sUNaNm40SlTc3JQcO9vkfL5z8fnGqaOv376Vzy85eGIf7yeOv5ULD99Ocanjo/LdYioe3Pnzm1//vnn9wHARx995P3mN785+Qc/+EHRueee2zh27NiAEvZPiMLCQl9VVZX79NNPbwWAAwcOuKZPn+4PP9/hw4ddmZmZER9GXnvttZSrrrpq8hNPPFF+0kkn+Qb+UY1c/QolhRAZAP4MYCYACeAqALsAPANgEoB9AC6VUtZ1cgoiomEn3ZOOy6ZfhsumX4aK+gqsK1+Hl8tfxrG2Y06dHbU7sOP9Hbh3071YWLAQpVNKsaBwwYjuxXe89TjeqnwLGw5twLtV76JV7/wGd9OzpmNBwQIsLFyIWTmzoCqhO1Xnp+bj1PGnOtt+w4/y+nLsrN2JXXW7sKvW+moKNHU4b8AMYGftTuys3Yl15euc8oLUAkzLnOaElNOzpiM/JZ89FoiIhgDF7YbnhBPgOeGEDvukYcCor7cCyuPV9vI49Gp7vfq4s0+2dntjVfukEkZNDYyaGnT3SVJJTrZ6XAaHiUcPIbdDTDUzM2697Sh+WjdtQuXPlkE/FvobLfmUU1Dwu3uh5ebG5Rp7G/bilrdvwSfVnzhlXtWL6z97Pb4+/etQBJ8XNLJ1N6Q6kebMmdO+dOnS6kcffTT33HPPbTx27JjLNE0Eg8nKykr3BRdcUD979uz2cePGBZ588snM6DklX3755cyvfOUrTqb1zjvvJF1yySVT/vM//3NfaWlpxw8k1Cv97Sn5ewD/kFJeLIRwA0gGcBOA16SUvxFC3AjgRgC/7Od1iIiGpOKMYlz/2evxkzk/wb8P/xvrytbh9YOvw2dYH3N0U8frB1/H6wdfR5Y3C+dPPh+lU0oxPWt6glvef6Y0sa16GzZUWjepiZ4HMlySloRTx5+KhYULsaBgAfJS8np8HbfqxozsGZiRPcMpk1KiqqXKCiprdzk9KyubY/eOqWyuRGVzJV4/+LpTluZKc0LKYM/KkowSuFXeUIGIaKgQquoMB0c3b51mS0tkYBkeZIaVGbW1Pb6+2doK//79wP7Oe/wDADTNamfYXJdK2hiA//tKGLOxCfUvvBDR0zb7+9cg90c/isvwfVOaWLNzDR7Y/ADajdCUciflnoSV81diUvqkfl+DiHrno48+8r744ovp3/rWt2pLSkoCZWVlrueeey577ty5LQBQW1vrWrly5dgbbrjh+FNPPZVRUVGRdNFFFzUoioLly5cfvP766ycVFhYGrrjiCufu283NzcpNN910FAA++OAD7+LFi6fefffdB5YuXdr7u7pRB32++7YQYgyALQCKZdhJhBC7AJwppTwshBgP4A0pZZeTq/Hu20Q0kjT6G/HPff/EurJ12HJ8S8w60zKnYXHJYiwqXoTspOxBbmHfNfmb8G7Vu3jz0Jt4u/Jt1LZ3/sGuMLXQGfI+L28ePKpnwNvX4GvA7rrdTlC5q24XyurLejyHpSY0FGcUO3f9Ds5VmYg7rBMR0cCQgQD02lrox8J6WoaHlmFBpvT7uz8hDXlqRgby77k74qZM/VHVXIVb37k1YkHz2s8AACAASURBVAoZTdHww5N/iCs/cyU0hbOk0dAxmu6+vXfvXtcPf/jDok2bNqU2NTWpaWlpxjnnnNPw0EMPHXzyySczH3300dyZM2e2rl27Nis7O1u/7777Dlx44YWNweOffPLJjN/85jfjy8vLvW63Wy5cuLDh/vvvPzRlypQAAFx88cWTXnjhhWyv1+vcxSo/P99fVla2LRGPdzjp7O7b/QklTwawCsB2ALMBbAbwUwCVUsqMsHp1UsoOE6oJIb4H4HsAMGHChM/u7+6/j0REw9Dehr34W/nf8FL5SzjaerTDfk1oOKPwDJSWlOILhV+ASx1aw7ullNjbuBdvHbKGZX949MOY8zsC1mOZM24OFhYsxMKihZg8ZvKQGB4dMAKoaKjArrpdET0rG/2N3R9sy0vJw/TM0ByV0zOnoyCtgEOyiGymNNESaEGTvwlNfmsk0xj3GKS505DiShkSvwuIektKCbOxMSq0jDWEvBpmAzvMDFVJJ5+Mgvvvg2t8/+d1lFLixbIXcfcHd6Ml0OKUT82cirvOuGvQbnRI1BujKZTsyoMPPpj92GOP5WzevHlXotsyGg1EKDkPwL8BzJdSvieE+D2ARgA/7kkoGY49JYlopDNMA+8feR/rytfhtf2vRQzzCcrwZOD8yedj8ZTFODHrxIR9iPcbfmw6sgkbKjfgzYNv4lDzoU7rZnmznJvUnJ5/OtLcaYPY0r6TUuJo61Fn/slgUNnVY42W4kpx5qkM9qickjllUHqEEg0En+FDo68RTf4mNPob0egPrQfDRqfMF7m/OdAMU5oxz6sIBWnuNKS50jDGYwWVY9xjnNAyepnmtuoF63BKBRoOTJ8P+vFqGNXHEbBDyx7PcUkDxlVYhLQvng3h6v8/favbqnHnxjvxxqE3nDJFKLhq5lW4dva1/F1FQxZDSQtDycTqLJTsT7/yQwAOSSnfs7efgzV/5FEhxPiw4dvHOj0DEdEooSoqTss/Dafln4bmU5vxyv5XsK5sHT489qFTp95Xj9U7V2P1ztWYkjEFS6YswaLiRchJyhnw9h1tOercpObfh/+NNr2t07ozsmY4w7Jn5swclr0FhRDIS8lDXkoeziw60ylv9jc7d/0O9qwsqyuD3+w4fK8l0IIPj30Y8TNUhYpMbyY0RYNLcUFTtIj1rpY9qRNrGXOf6oImrKVLuKxtRXPKgnWH48+OOmeYBpoDzVZgGAiFhxFhYmdho68x5vM8HkxposHXgAZfA9Dc++M9qqfz8NIOLsPDzvB9qa7UiBtpEQ0UxeOBu7AAKCxAUqIbQ3H3z33/xIp/r0C9r94pmzhmIlbMX4GTx56cwJYREQ1vfe4pCQBCiLcAfFdKuUsIcQeAFHtXTdiNbrKklDd0dR72lCSi0epA4wG8VP4SXip/CYdbDnfYrwoV8wvmY3HJYpxZdGbceuEZpoFPaz7FhkMb8Naht7CjdkendZO1ZJyWfxoWFi7EGQVnYGzy2Li0YbjQTR37GvZhZ12oR+Wu2l2o89V1f/AQpwglZsipCWupChWqokIVasR2sE6sfcHtzvZHl3e2z9nuwTWD5cGy4R62mtLsUe/E6GCxOdCHxC+OkrVkJxAE4LStq39yDIY0V1pED8zwHpsRwWZUoJniSkGSlsRQk2gUa/A1YOV7K/F/e/8vovzy6ZfjurnXIdmVnKCWEfUce0rSUBD34duAM6/knwG4AVQA+A4ABcD/AJgA4ACAS6SUXd7ijqEkEY12pjTxwZEP8FL5S/jX/n/F/BA/xj0GX5n8FZSWlGJmzsxeD+9u9DdiY+VGbDi0AW9Xvt1lqDYhbYJ1p+zCBZg3bh6HJEWRUuJY6zHsqtsVcVOd/Y2cH5mGL03RejSsOtbw61R3KlxK7OGRATMQ0SOzsx6bnQWxnc1jO1i8qhfJrmQka8nOMsWVgmRXMpK0pIjt4HqSyyqPtc+reYd9cE40Grx16C3csfEOHGsLDfzLS8nD8vnL8fnxn09gy4h6h6EkDQUDEkrGC0NJIqKQlkALXtn3CtaVr8Pmo5tj1ilOL0bplFJcUHxBpz0XpZSoaKjAhkMbsOHQBnx07CMY0ohZVxMaPpv3WesmNYULMSl9UrwezqjSGmhFS6AFATMA3dRjLrvap5s6AkYAugwtY5UFjG7O0ZPrmIFEf7toAMTqBdjlkGdXKGz0qt4hd0MaKSXa9LaY4WVEsOmLHWwmuvdoZ2KFmV0FnymulI77tGQkuax9Q/FnRzRctQRacO+me/Hc7uciyheXLMaNp9w4bObPJgpiKElDAUNJIqJh6GDTQefu3ZXNlR32K0LBafmnYUnJEpw14SwAwPuH37eGZVe+FfOYoGxvNhYULsDCwoU4bfxpSHWnDtjjoKFHSglDGk5IGR5mmqYJXeowTAOGNJxw1Nk2dRjSgGEakfXs88TaF35c8FzRx0Yf59QPP0931zR1SCT+b5v+EEJ0OuS4q16MnD+xI2eezehAswc9NlsDrWjVh8eNSgREZE9NLQnZSdmYnD4ZxenFzleGN6P7kxGNYpuPbsbNb98c8fdTljcLt592O86ecHYCW0bUdwwlaShgKElENIyZ0sTmo5uxrmwdXtn/Sszh3WmuNOhS73L+tpnZM52b1MzInsEhhEREXTCliXa9Ha16qxNStgRanPXoZcS+YHnYvja9LaFzbGZ5szoElcUZxRiXPI49LWlU8xk+/OHDP+Dx7Y9H/GPrnAnn4NbTbkWWNyuBrSPqH4aSNBQMxN23iYhokChCwefyPofP5X0ON516E1498CrWla3D+0fed+o0BZo6HJfiSsHp+adjQcECLChcMCh38iYiGikUoVg9EF3JiNctlQ3TQLvR3vNwM9Y+e7tNb0NLoAU+w9eja9e216K2vbbD1CDJWnIorMwodtaL0oqgKfy4QCPbtuptuOntm1DRUOGUpbnTcNOpN2HR5EUM7ImIBhD/yiAiGmaSXclYXLIYi0sWo7K5En8r/xvWla3DoeZDAIBJYyY5vSHnjp0Llxr75hNERDT4VEVFipKCFFdK3M6pm7oTUAYDy8Mth1FRX4GKhgrsbdiLvQ170W60xzy+VW/Ftppt2FazLaJcUzRMTJsYEVROTp+MyemTkaTFKaUlSpCAGcCfPvkTVn2yKmLO7fn583HH6XcgLyUvga0jooFy0UUXTSooKPA/+OCDVYluCzGUJCIa1gpSC/D92d/HNSddg70Ne+FW3ShMK0x0s4iIaBBpiubMLRo0M2cmMDFUx5Rmh6CyosFab/A1xDyvbuoobyhHeUN5h335KfmYnDEZJeklTg/L4vRipHvS4/74iOKtvL4cN719E7bXbHfKkrQk/Hzez3HJ1EvYO5JomFu1alXmQw89NG7Pnj1JSUlJZmFhoW/p0qU1N9xww/FEt40iMZQkIhoBhBAozihOdDOIiGiIUoSCgtQCFKQWYEHhAqdcSona9trIoNIOLo+2Hu30fFUtVahqqcI7le9ElGd5s5z5KsOHhHPeShoKDNPAkzuexIMfPgi/6XfK54ydg5XzV6JoTFECW0dE8XD77bePe+ihh/LuvffeA0uWLGlIT08333333aS77747z+fzcS7MIYahJBERERHRKCWEQHZSNrKTsvG5vM9F7GsJtHQIKvc27MXBpoMRw13DBeet3HQ08iaWnLeSEu1g00Hc8vYt+PDYh06ZS3Hhx3N+jG+d+C2oiprA1hFRPNTU1Kj33HNP/sMPP7z3yiuvrA+Wz58/v+2ll17aG13/wQcfzH7sscdyNm/evCtYJoT47NatWz+dOXOmr7m5WVx//fUFf//73zObmprUqVOntr355pu7U1NT5VNPPZV+++23Fx49etQ1Y8aMtj/+8Y/7586d2w4AN998c96f/vSnsS0tLWpubm7g97///f7S0tImwzBw66235j3xxBM5TU1N2umnn974yCOP7B83blzsN9VRgH8BEBERERFRBymuFMzMmWkNBQ8TMALY37jfGf4dDCv3NeyLy7yVxenFmJQ+ifNWUlxIKfHcnufw2w9+iza9zSmfkTUDK89YiRMyT0hg64iGvzvuuOOzg3itzV3tX79+fUogEFC+8Y1v1HdVr6euvfbaol27diW98847O4uKigLr169PUVUVn3zyiee73/1u8erVq8vPP//8puXLl49dsmTJlN27d2/btWuX+y9/+cvY999/f8ekSZMCu3btcuu6LgBg5cqVY//+979nvPHGG7vy8/P1q6++esJ3v/vdCX/72986BKajBUNJIiIiIiLqMZfqwpTMKZiSOSWi3JQmqpqrYg4Fb/Q3xjxXV/NWuhQXPKoHbtUNr+qFW3XDo3qcMo/mgUcJ21Y9Vln4dthXsCx4vlj1gtvsNTcyHGs9hts33o63K992ylSh4ruzvotrTrqGNwMkGmGOHTumZWRk6C5X6LU9Z86c6WVlZd5AIKCsXbt2d0/PZRgGnn322Zz169fvmDx5cgAAzj333BYAeOKJJ7LOOuushq997WuNAHDnnXceXbVq1bhXX301ddKkSX6/3y8+/vhj7/jx4/Vp06Y5c0U8+uijuffff/+BkpKSAAD8+te/riopKZkVCAT2hrd5NGEoSURERERE/aYIBYVphShMK8TCwoVOuZQSNe01VlBZXxHRw/JY67FOzxcwAwiYASAwGK2PpAmtQ8AZHY52FXx6VA+HpSdYm96GJ7Y/ERGIT06fjLvOuKtD718iGhlycnL0+vp6LRAIIBjyffTRRzsBYNy4cScZhtHjyY2PHDmi+Xw+ceKJJ/qi91VVVbmKioqcsFFVVYwfP95/8OBB1wUXXNB01113HVy+fHn+t771raSFCxc2PvTQQwcnTZoUOHz4sPsb3/jGFCGEDD/20KFDrmDwOdrwnZKIiIiIiAaMEAI5STnIScrpMG9ls7854k7gwV6Wh5oOdTpv5WDQpQ49oKMl0JKwNlB8XTHjCvx07k/h1byJbgrRiNLdkOrBdPbZZ7e4XC7zqaeeygifU7IzqampZltbmxLcPnDggJOR5eXl6R6PR27fvt1z2mmntYUfl5+fH9i2bZszx4hpmjh8+LC7qKgoAADf//73a7///e/X1tbWKt/+9rcnXnfddYUvvvji3nHjxgVWrVq190tf+hLfXGwMJYmIiIiIKCFS3amYlTsLs3JnRZRLKeE3/fAZPvgNa+nTfdYyvKyT7fAyv+FHu9EeUcdv+NGut0fU8Zmha0jITlpMw01BagGWz1/eIRAnopEnJyfHWLZsWdWyZcsmSinFhRde2JCammq+9957SeHhY9C8efNay8rKkjZu3Jh08sknt9944435wX2qquKSSy6p/tnPfla0Zs2avYWFhYE33ngjZf78+a1XXHFF7ec///kT161bl3beeec1r1y5cqzb7ZbnnHNO85YtWzz79+93n3vuuc3JycnS6/VK0zQBAN/5zneO3XLLLYWTJk3aO3XqVH9VVZX2+uuvp15xxRVxmQNzOGIoSUREREREQ4oQwhkGPdiklNBNvdOQs9PgU293glSf4YNu6oPedoqUn5KPJVOWINWdmuimENEgWbFixdGCgoLAfffdl/eDH/xgktfrNYuKiny33XbboXPOOaf5kUceyQ7WPemkk3zXX3991aJFi6Z6PB552223HVqzZk1ucP/DDz988Cc/+UnhKaecMqOtrU2ZNm1a2xtvvLF79uzZvv/+7//e+7Of/WzCFVdc4Zo+fXrbiy++uMfr9cr29nbl5ptvLrz88su9mqbJuXPnNj/yyCP7AeCWW245JqUUX/rSl6YeP37clZWVFSgtLa0bzaGkkDLx/wWcN2+e3LRpU6KbQUREREREREQ0YgghNksp58XrfFu2bNk3e/bs6nidj0aHLVu25MyePXtSdHmH7qtEREREREREREREA4mhJBEREREREREREQ0qhpJEREREREREREQ0qBhKEhERERERERFRT5imaYpEN4KGD/v5Ysbax1CSiIiIiIiIiIi6JYQ40tbW5k10O2j4aGtr8wohjsTax1CSiIiIiIiIiIi6pev6nfv27XO3tLQkscckdcU0TdHS0pK0b98+t67rd8aqow12o4iIiIiIiIiIaPiZO3fuPz/88MMflZeX3y6lzAM7u1HnTCHEEV3X75w7d+4/Y1VgKElERERERERERD1iB0wxQyai3uh3oi2EUIUQHwkhXra3Jwsh3hNC7BFCPCOEcPe/mURERERERERERDRSxKOb7U8B7AjbvhvA/VLKEwDUAbg6DtcgIiIiIiIiIiKiEaJfoaQQohDAIgB/trcFgLMBPGdXeQzAkv5cg4iIiIiIiIiIiEaW/vaUfADADQBMezsbQL2UUre3DwEoiHWgEOJ7QohNQohNx48f72cziIiIiIiIiIiIaLjo841uhBAXADgmpdwshDgzWByjqox1vJRyFYBVADBv3ryYdYiIiBJBSgkpTUhTOusw7TIpIU0J0zQAae83zVA9aR0PSFgLCRl8K5R2GaRdB/a7pL0tZehNU4bqBPeFty/yvB2vaZ/VWpdh687u4DUR0T6HEBDBt3UBWIMhBKyFsIuF9c4frBssF/ZeIRA6hbVfCIGwSs62VVeE/pBw6oadI6oNofaJiGt2+P5Ls+Njlmbo+x38OYZ9n6Vp2t8zROyL+J5Gndc53j5vh+eD2fXx4T/z8J+DsxqrzFkXHQ8J/zlFnyTivNE/k4gGRF0HkT/TqIt2aKPz3Ihsa8TPMKxNoedJ1LVF6DkU0Z7w55RT125ZxL7wNoQ9zg7XDLZPhP0OCL3uQ+tm6GcW9vp36pgxjunyd4rpPIdCx5odjpGmBLo6Jqw+Yjy/rC10eK6F9gd/RyHs+Y6wuuG/Y8J/P3X9OyZ0bDe/55zrIuy11MnvuahzdnaO3j/OsMcQ9Zq3rxjxuzzm9yD6dUyJoyhQNS3sywVFc1nrLpdTrtj71Kh9SthxoeMjjw3ui31ea11E/mIlIiJbf+6+PR/AYiHE+QC8AMbA6jmZIYTQ7N6ShQCq+t9MIqLRTUoJ0zBgGjpMw4RpGjB13V4a1tIwIA0Dpml/IDbNTtYNmLKr/db5uz5H7Dod6xrdnCNGnR4EgrE+yCMsEDCDgUE3H9o7OzcRERFRvCiq1kXYGQozlagANRhszr/sCqRmZiX6YRARxV2fQ0kp5X8A+A8AsHtK/lxK+Q0hxLMALgbwNIBvA1gXh3YSEfWLNE3ofj8Cfh90vw8Bnw+63w/d57PL/ND9PifUM3TdCu+MGF9OIGiGBYWGFRY662F1jVBgaDhL3blWzOPDw0bdYFBGRERENExZf9/pCPja+3T8KaUXxblFRERDQ396SnbmlwCeFkKsAPARgL8MwDWIaASQUsIIBKxQ0BcVFtpBYcAuj97nlNtlTrDohIyR+4xAINEPl4YZIRQIxRrGKYQCKNZSCGGXB9cV6ys4bDRieGmsocZhQ0jDh7x2OBYRw1C7HE6NsGt0M7S543Bq54TOtSOHi0cNQ3dGR3Y+RL1Xw8W7GWYZPsyzs3N0GCoa/v1RoocPi44/K0WJ/D6HDR23ipQOw9SdOvawYqGEfqahfVHDiCPOG3l8xzqI+H5EDL2NKgsfKho+1LXDOcKHpXYoizGEP/xn0YN2RA89H+yhstHPo5jDgTs5R8fhwPbzIux3gPN6D/6MnPWoep38fghuI+J8sc8drKcoinNO9OAY6zkU1sbo51dQN79vYg2172zYu1MW9TsmfDvy91HXv+cQvFLU6zBUN/R7zr5Kx99z9kmcfaFGDvqUApRY0v4HshEIwNB1GEbAWTd13SoL7tMDMPRAqDxsn6kHoNvL8H2hugH7yz5vIFTPNPTuG9oN1eWKw3eDiGjoiUsoKaV8A8Ab9noFgFPicV4iGljS7hFo6NYfaHog9MeYHgj+0Ra2DP6RFVGuO9sBv7/LYLDDPr+f8y71gqKqUBQVQlWhqpHL4D5FVZ0PwUrww3DYurVUI/eLWHXVTs/RkzqhMrWbc3RSpydBYKwP+dH1gx/oI47pWdhIRERE1F9SSifYNKIDSz08EI0OO0P7klLHJPphEBENiIHoKdlrgfZ2VO3ekehmECWUaZrOf1b1YOAX/p/bWGFgWLnu/BFj1dEDescwMeo8pmEk+mEPKs3tgebxQHO74XJbS83jgcvttva53FA0zQr4giGfFhkEWvs0J/xTNc0K1dTQV0RQqGpQVMUODO11VYuoH32tiOPCrsWgjIiIiGh4EUJAc7kA9nYkIupgSISStVWHsObWXyS6GUSUAKrLFRYSeuyQMBQYdtjnscJDa5+9bYeK4esR2/YxDPWIiIiIiIiIhoYhEUoSUeIE7wKoai5r3eUKrYeVa52Uh+prTlgYChW9HXslBsNEtweq2wVFURP9LSAiIiIiIiKiQTYkQkmXx4vxJ0xLdDOIEksIaN0Eg8FyzeWCYpdrrrB6wePskDD8PLGOUVSNvQeJiIiIiIiIaNANiVAyq6AQS1f8LtHNICIiIiIiIiIiokGgJLoBRERERERERERENLowlCQiIiIiIiIiIqJBxVCSiIiIiIiIiIiIBhVDSSIiIiIiIiIiIhpUDCWJiIiIiIiIiIhoUDGUJCIiIiIiIiIiokHFUJKIiIiIiIiIiIgGFUNJIiIiIiIiIiIiGlQMJYmIiIiIiIiIiGhQMZQkIiIiIiIiIiKiQcVQkoiIiIiIiIiIiAYVQ0kiIiIiIiIiIiIaVAwliYiIiIiIiIiIaFAxlCQiIiIiIiIiIqJBxVCSiIiIiIiIiIiIBhVDSSIiIiIiIiIiIhpUDCWJiIiIiIiIiIhoUGl9PVAIUQTgcQB5AEwAq6SUvxdCZAF4BsAkAPsAXCqlrOt/U4mIiIiIKFpLgw/HDzSh+mAzqg824fihZgR8BjxJGjzJ9leSBk+yC25n3doO7ncnafAmu+BOUqGo7LdAREREA6/PoSQAHcAyKeWHQog0AJuFEP8CcCWA16SUvxFC3AjgRgC/7H9TiYiIiIhGLyklGqvb7QCyCcftELK10R+zflsn5d1xedWI4NIKLLWwQDMUZlqBZmjb5VEhhOjPwyQiIqJRos+hpJTyMIDD9nqTEGIHgAIApQDOtKs9BuANMJQkIiIiIuox0zBRd6QVxw82ofpAs7U81Ax/mz7g1w60Gwi0G2iu8/X6WKEIeJKsANNr98B0QswkDZ6Urnttqhp7aRIREY0W/ekp6RBCTAIwB8B7AMbZgSWklIeFEGM7OeZ7AL4HABMmTIhHM4iIiIiIhh3db6C6shnVB+3w8UATaqpaYATMHh2vuRXkFKYipygNuUVpyJ2QhqQ0N/xtOnytAfjadPharS9/WwDtrTr8rbpdHgjbZ5VB9v2xSFOivSWA9pYAGvtwvOZSnLBSKOxxSf2jago8yRq8KS54U1zwpITWrW0XvHaZJ1nj1AVERIOs36GkECIVwPMArpNSNvZ0uIaUchWAVQAwb968fvzpQ0REREQ0PPhaA86w6+MHrXkg6460Qpo9+3PYk6IhtyjNCiAnpCK3KA3pY5OhxArwMj29bp80JfztoRDT12YFmO2tATvkDJZbYaYTbrZY4afu71mQ2hk9YEJv8KO1oW9Dz4n6w52khULKYHiZrMGT6oI32QowPWGhpjfF6vEb8/VHRETd6lcoKYRwwQokn5JSvmAXHxVCjLd7SY4HcKy/jSQiIiIiGm5CN6AJzf/YWN3e4+NTMz1270e7F+SENKRmegZ0zkahCHsotatPxxsB0woy2+wgMyzcdHpltunwtVi9Nn0R+/Ueh7NEA8FvP3d78zqFADxJUUFmRLAZe5/by97ARET9ufu2APAXADuklPeF7XoJwLcB/MZerutXC4mIiKgDwzBhBKwvPWpp6IbV28hvwtCj6xh2HavMOcZeNw0TQhFQ7C+h2utqcFuJuU+E1XG2o/epAoqiRB4bfY1Yx4atq6rS4VghwBtrUEJJU6Kxpg3Hg3M/2iFkj280I4CMscnIKbJ6Plo9IVORlOYe2IYPANWlINnlRvKY3rddSomAz4jLMHIiAND9JnytAXtKAR3tLQH4WmJs24F5n55zEk64juNtPT5MCEQElZ6UYG/MjsPMx00eA3dSXGZeIyIaUvrzm20+gG8C2CqE+NguuwlWGPk/QoirARwAcEn/mkhERDR0GYYJ3Wcg4DOh+w3oAaNjSOis2/v0GEFicF23zhMeJkbUscvZmyiSE252FYzGWFdUJSJQ7XQ9KpyNfU6l84C2q3NGnF+BEFY4YxoSpikh7aWzHtw2JKTZxbohYZpm5+fp0TnNGOfsuB29LoSAy6vC5bG+3F4VLo/mbEfui13u8mhwe1VoHnVIDY00DBP1R1px/EBo+HX1wSb4240eHa+oAln5KaEh2EWpyC5MhdvLwEEIAbdXg9urITUz0a2h0cY0pdXDt9kKKdubraAyuO1rtuZjDYWaVrDZ15tPSQnr3M2BbuteevPnkFuU1qfrEBENZf25+/bbADr7C/GLfT0vERGFSFPClBIwAVNaH/alKSElnOAAEqEgwA48VM0KR1Q1FJKMVlJKK9jzGQj4DAT8BnSfaS/tbX8oVAzViV034AuuW+c0GQ4OCaYpAVPC6P6zHQ2S9pb4/TA0lxIWWGp2yBn21Wmw2Uno6VZ79Hsx4DdQU9mM6gOh4dc1lS0w9B7egMajIqcgFbkT0pxekFn5KbzDNNEQpCjC6ZnYG6ZhWuGlHVKGh5bhoWZ0yBno4T8yAPS6TUREwwX/JUtEw5ppyqheZn3vpWbophMCStMKs6wQMLRuhpfLGPtilMuw0NDZF1XPDAsaw4PHeAkfPhsMKhXN6pWl2r3FVC3Ucyw82AyWqaqAEhV2OufQ7F5imoioF1k38tgO59cEhBCRIWF4EBixjAoVgyFhVN1g2UgcAigEoLpVaJoC1aVAc0UuVU2B5latZbDMhPeowwAADKZJREFUpVj13XaZFn6cCtVl/UykRFhPOjO0bnTeM67zXn1mx955XfTw69E5o84fz9cKDU3BHsNtTXEKOgXgcqsxgkwrwBQCqKlqQf2Rlh4/v7wpLit4nBAaft3pDWiIaMRQVAVJae5eT7dg6OFhZifDyu1thpJENFINiVBS2l3liWj4krD+U6z7zYj56sLnsAsNTe28Tvjw1I7z5IWGtQbLTINpRE9IU8Kwe5EF0PP/zFP3hCLgcivQ7N5XmluNGRIGw8PwADAYDKouBZo7OiRUoGqqUx59TkVlT6sg558J0aFm9DDjmIGq2emQ5M6HRAfXzR6FqEaPhlnbAav9D44u5+ZUoufp7Go4eCfD1bsbWh59flVA2POBdtWu4LZpSAR8uvXPgXYDfnsZCPZY9unOduS+0DHBfbpvAH5nSTht6YvUTI/d+zF0E5qBvgENEY0sqqYgeUzf5l8lIhophkQoefxgM/50/YZEN4OIaEgSioBQrLm2hCKgCLtM2OVh65DWf95NOwgxDROmzuBWdSlWYOhRnN5Rmh0gujzBfWrE0uVRYpRZIaHmDg0BVewenpQ4QhFQIQA10S2hSPH5oC1N6fSYDg82/e16KOSMCDwNBML2+WMEnnqgZ8OvgzegCR9+nVuUBm8qey0RERER9deQCCWJiPojYmiqS4HqUjv0QIvopaYp1rBXV1QPNE3pEPIJEeo1JIJhYNi6IjruUxQBCPvGGyIyUOz5OUPt6K/gMHIj2FPMDioNe2kFmHaPLyfQDG6HrdtLQw/rIWaY9nZ4vVjnidyOPEfoPNKU0NyKExqGLyN6Izq9EiPrhtaViOM5fJJo+BJK6OYnSI/POU1TQncCS71DsGnoJtLHJiO7IIU3oCEiIiIaIEPiryyhAC4vuzcQDXeqGhkORsxtFzVsNXw4a/Sw1YjhrVF1ooe+Kip7qXVHiOBdiRPdEiKioUFRBNxJGtxJGgBPoptDRERENCoNiVAytygN33vgC4luBhEREREREREREQ0CzpJPREREREREREREg4qhJBEREREREREREQ0qhpJEREREREREREQ0qBhKEhERERERERER0aBiKElERERERERERESDiqEkERERERERERERDSqGkkRERERERERERDSoGEoSERERERERERHRoGIoSURERERERERERIOKoSQRERERERERERENKiGlTHQbIIRoArAr0e0gon7LAVCd6EYQUb/wdUw0MvC1TDQy8LVM/TVRSpmb6EYQxaIlugG2XVLKeYluBBH1jxBiE1/LRMMbX8dEIwNfy0QjA1/LRDSScfg2ERERERERERERDSqGkkRERERERERERDSohkoouSrRDSCiuOBrmWj44+uYaGTga5loZOBrmYhGrCFxoxsiIiIiIiIiIiIaPYZKT0kiIiIiIiIiIiIaJRhKEhERERERERER0aBKeCgphDhPCLFLCFEmhLgx0e0hot4TQuwTQmwVQnwshNiU6PYQUc8IIf4qhDgmhPg0rCxLCPEvIcQee5mZyDYSUfc6eS3fIYSotN+bPxZCnJ/INhJR14QQRUKI9UKIHUKIbUKIn9rlfF8mohEroaGkEEIF8J8AvgLgRACXCyFOTGSbiKjPzpJSniylnJfohhBRjz0K4LyoshsBvCalPAHAa/Y2EQ1tj6LjaxkA7rffm0+WUv7vILeJiHpHB7BMSjkDwOcB/ND+bMz3ZSIasRLdU/IUAGVSygoppR/A0wBKE9wmIiKiUUFKuQFAbVRxKYDH7PXHACwZ1EYRUa918lomomFESnlYSvmhvd4EYAeAAvB9mYhGsESHkgUADoZtH7LLiGh4kQBeEUJsFkJ8L9GNIaJ+GSelPAxYH5AAjE1we4io734khPjEHt7NIZ9Ew4QQYhKAOQDeA9+XiWgES3QoKWKUyUFvBRH113wp5VxYUzH8UAixMNENIiIiGuUeBlAC4GQAhwH8LrHNIaKeEEKkAngewHVSysZEt4eIaCAlOpQ8BKAobLsQQFWC2kJEfSSlrLKXxwCshTU1AxENT0eFEOMBwF4eS3B7iKgPpJRHpZSGlNIE8CfwvZloyBNCuGAFkk9JKV+wi/m+TEQjVqJDyQ8AnCCEmCyEcAP4OoCXEtwmIuoFIUSKECItuA7gSwA+7fooIhrCXgLwbXv92wDWJbAtRNRHwRDD9jXwvZloSBNCCAB/AbBDSnlf2C6+LxPRiCWkTOxoaSHE+QAeAKAC+KuUcmVCG0REvSKEKIbVOxIANACr+TomGh6EEGsAnAkgB8BRALcDeBHA/wCYAOAAgEuklLyBBtEQ1slr+UxYQ7clgH0ArgnOS0dEQ48Q4gwAbwHYCsC0i2+CNa8k35eJaERKeChJREREREREREREo0uih28TERERERERERHRKMNQkoiIiIiIiIiIiAYVQ0kiIiIiIiIiIiIaVAwliYiIiIiIiIiIaFAxlCQiIiIiIiIiIqJBxVCSiIiIekwI0Ry1faUQ4qFBboMmhKgWQvw6qnyBEGKbEOJjIcQMIcTSOFxrnxAix17PE0I8LYQoF0JsF0L8rxBiqhDiTCHEy1HHPSqEuLg37Y+q0+GcPWjrn4UQJ/bymMVCiBt7cwwRERERUTwwlCQiIqKEE0Kovaj+JQC7AFwqhBBh5d8AcK+U8mQA4wD0KpTsqg32ddYCeENKWSKlPBHATfZ1equz9veLlPK7UsrtvTzmJSnlb+LVBiIiIiKinmIoSURERHEhhJgohHhNCPGJvZxgl0f0Ggz2trR7A64XQqwGsFUIkSKE+LsQYosQ4lMhxGWdXOpyAL8HcADA5+1zfRfApQBuE0I8BeA3ABbYvSavF0KoQojfCiE+sNt3Taw2dPHwzgIQkFL+MVggpfxYSvlWH75VHdpvt+U8IcROIcTbAC4MK79DCPGYEOIVu+fmhUKIe4QQW4UQ/xBCuOx6bwgh5tmP9VH7e7hVCHG9vf8ndg/PT4QQT9tlTk/Xbn5+DwohNgohKjrrAUpERERE1BtaohtAREREw0qSEOLjsO0sAC/Z6w8BeFxK+ZgQ4ioADwJY0s35TgEwU0q5VwhxEYAqKeUiABBCpEdXFkIkAfgigGsAZMAK+N6VUv5ZCHEGgJellM8JIc4E8HMp5QX2cd8D0CCl/JwQwgPgHSHEK9Ft6KKdMwFs7uaxdKuz9gshvAD+BOBsAGUAnok6tARWMHoigHcBXCSlvEEIsRbAIgAvhtU9GUCBlHKmfc0Mu/xGAJOllL6wsnBd/fzGAzgDwHRYP+/n+vgtICIiIiICwJ6SRERE1DttUsqTg18AbgvbdxqA1fb6E7BCrO68HxYGbgVwjhDibiHEAillQ4z6FwBYL6VsBfA8gK/1cOj3lwB8yw5U3wOQDeCEGG3oC9mL8s7aPx3AXinlHimlBPBk1HH/J6UMwPoeqQD+YZdvBTApqm4FgGIhxB+EEOcBaLTLPwHwlBDiCgB6jLZ19fN7UUpp2sPD+zJknYiIiIgoAkNJIiIiGijBUE6H/TeHPYeiO6xOi1NZyt0APgsraPu1ECI88Ay6HFZwuQ9Wz8VsWD0IuyMA/DgsUJ0spQz2lGzp6kDbNrttsdQAyIwqywJQHaNuV+3vLNwEAB8ASCn/f3v382JTGMdx/P2ZkoUff8AII6UUqSlrf4CFWBEbhRpKNsTCP4BiY0VYzNZiFhYWhoUsFMVK2diPopRodL8W54w53e5tRl1n4/1aPfc855y+T2f3vc/3+wxoyshX7h0wVPlSVV+AA8AL4AJwv506DNxt1/EmyVoVM914fnbGE+uDKUmSpP+XSUlJkjQpr4Dj7fgk8LIdf2I1oXcE2DDq4STTwPeqmgduAbND81tpdu/tqKqZqpqhSbqdGPG6b8CWzu+nwFyn/+KeJJv+Ym2LwMYkZzvxHExyCPgITCfZ217fSZMU7Ja5rxX/B2BXkt3t7aPWtC5pTgufqqrHwHVgNskUsL2qngNXaErHNw89Ou77SZIkSRNnT0lJkjQpF4EHSS4DS8Dp9vo9YCHJa+AZ43cm7gduJhkAy8Dc0PwxYLGqurv2FoAbbZ/IrvfAryTvgEc0B8vMAG/b3ZpLrN3v8o+qqiRHgTtJrgI/aJKtl9oejaeAh21vyGXgzIjy87HxA+eBc8CTJJ9pEoL71hvfkG1tLCt/Pl+jKfmeb/t0BrhdVV+HDv8e9/0kSZKkictq9Y8kSZIkSZIk/XuWb0uSJEmSJEnqlUlJSZIkSZIkSb0yKSlJkiRJkiSpVyYlJUmSJEmSJPXKpKQkSZIkSZKkXpmUlCRJkiRJktQrk5KSJEmSJEmSevUbkKFs9zTlY0EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(200009)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Load data and labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>icustay_id</th>\n",
       "      <th>time</th>\n",
       "      <th>heart_rate</th>\n",
       "      <th>sys_bp</th>\n",
       "      <th>dia_bp</th>\n",
       "      <th>mean_bp</th>\n",
       "      <th>resp_rate</th>\n",
       "      <th>temp</th>\n",
       "      <th>spo2</th>\n",
       "      <th>glucose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>200001</td>\n",
       "      <td>0</td>\n",
       "      <td>113.5</td>\n",
       "      <td>111.5</td>\n",
       "      <td>65.0</td>\n",
       "      <td>76.5</td>\n",
       "      <td>22.666667</td>\n",
       "      <td>37.277778</td>\n",
       "      <td>95.333333</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200001</td>\n",
       "      <td>1</td>\n",
       "      <td>108.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>37.277778</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200001</td>\n",
       "      <td>2</td>\n",
       "      <td>110.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>37.277778</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200001</td>\n",
       "      <td>3</td>\n",
       "      <td>102.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>37.666667</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>200001</td>\n",
       "      <td>4</td>\n",
       "      <td>108.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>37.666667</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   icustay_id  time  heart_rate  sys_bp  dia_bp  mean_bp  resp_rate  \\\n",
       "0      200001     0       113.5   111.5    65.0     76.5  22.666667   \n",
       "1      200001     1       108.0   113.0    68.0     79.0  18.000000   \n",
       "2      200001     2       110.0   116.0    68.0     79.0  27.000000   \n",
       "3      200001     3       102.0   102.0    61.0     71.0  21.000000   \n",
       "4      200001     4       108.0   103.0    58.0     69.0  19.000000   \n",
       "\n",
       "        temp       spo2  glucose  \n",
       "0  37.277778  95.333333    118.0  \n",
       "1  37.277778  98.000000    118.0  \n",
       "2  37.277778  98.000000    118.0  \n",
       "3  37.666667  96.000000    118.0  \n",
       "4  37.666667  94.000000    118.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VARIABLES = ['heart_rate', 'sys_bp', 'dia_bp', 'mean_bp', 'resp_rate', 'temp', 'spo2', 'glucose']\n",
    "\n",
    "ts_data = pd.read_csv('data/vitals.csv')\n",
    "ts_data[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>icustay_id</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>280836</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>206613</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>220345</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>249196</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>210407</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   icustay_id  label\n",
       "0      280836      1\n",
       "1      206613      0\n",
       "2      220345      0\n",
       "3      249196      0\n",
       "4      210407      0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_labels = pd.read_csv('data/vitals_labels.csv')\n",
    "ts_labels[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts_data = ts_data.drop(columns=['time'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Normalize features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standardization: $x' = \\frac{x - \\bar{x}}{\\sigma}$\n",
    "\n",
    "Note: this should actually be done **after** the train/val/test split, calling .fit() on training values only, and then .transform() on all 3 sets. For this example, we're scaling based on all values, but in practice, the scaler should not know about the validation and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = ts_data.copy()\n",
    "for variable in VARIABLES:\n",
    "    scaler = StandardScaler()\n",
    "    data[variable] = scaler.fit_transform(data[variable].values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>icustay_id</th>\n",
       "      <th>heart_rate</th>\n",
       "      <th>sys_bp</th>\n",
       "      <th>dia_bp</th>\n",
       "      <th>mean_bp</th>\n",
       "      <th>resp_rate</th>\n",
       "      <th>temp</th>\n",
       "      <th>spo2</th>\n",
       "      <th>glucose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>200001</td>\n",
       "      <td>1.037161</td>\n",
       "      <td>-0.348265</td>\n",
       "      <td>0.317805</td>\n",
       "      <td>-0.088839</td>\n",
       "      <td>0.690817</td>\n",
       "      <td>0.546693</td>\n",
       "      <td>-0.538583</td>\n",
       "      <td>-0.022463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200001</td>\n",
       "      <td>0.797887</td>\n",
       "      <td>-0.277900</td>\n",
       "      <td>0.529442</td>\n",
       "      <td>0.076864</td>\n",
       "      <td>-0.165601</td>\n",
       "      <td>0.546693</td>\n",
       "      <td>0.241495</td>\n",
       "      <td>-0.022463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200001</td>\n",
       "      <td>0.884896</td>\n",
       "      <td>-0.137170</td>\n",
       "      <td>0.529442</td>\n",
       "      <td>0.076864</td>\n",
       "      <td>1.486063</td>\n",
       "      <td>0.546693</td>\n",
       "      <td>0.241495</td>\n",
       "      <td>-0.022463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200001</td>\n",
       "      <td>0.536862</td>\n",
       "      <td>-0.793909</td>\n",
       "      <td>0.035623</td>\n",
       "      <td>-0.453384</td>\n",
       "      <td>0.384954</td>\n",
       "      <td>1.031733</td>\n",
       "      <td>-0.343563</td>\n",
       "      <td>-0.022463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>200001</td>\n",
       "      <td>0.797887</td>\n",
       "      <td>-0.746999</td>\n",
       "      <td>-0.176014</td>\n",
       "      <td>-0.585947</td>\n",
       "      <td>0.017917</td>\n",
       "      <td>1.031733</td>\n",
       "      <td>-0.928621</td>\n",
       "      <td>-0.022463</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   icustay_id  heart_rate    sys_bp    dia_bp   mean_bp  resp_rate      temp  \\\n",
       "0      200001    1.037161 -0.348265  0.317805 -0.088839   0.690817  0.546693   \n",
       "1      200001    0.797887 -0.277900  0.529442  0.076864  -0.165601  0.546693   \n",
       "2      200001    0.884896 -0.137170  0.529442  0.076864   1.486063  0.546693   \n",
       "3      200001    0.536862 -0.793909  0.035623 -0.453384   0.384954  1.031733   \n",
       "4      200001    0.797887 -0.746999 -0.176014 -0.585947   0.017917  1.031733   \n",
       "\n",
       "       spo2   glucose  \n",
       "0 -0.538583 -0.022463  \n",
       "1  0.241495 -0.022463  \n",
       "2  0.241495 -0.022463  \n",
       "3 -0.343563 -0.022463  \n",
       "4 -0.928621 -0.022463  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Build dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data dictionary: {icustay_id --> sequence}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict = dict(data.groupby('icustay_id').apply(lambda group: group[VARIABLES].values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.2764338 ,  1.69231454,  0.38835094,  1.18155077, -0.16560095,\n",
       "         0.06165535,  0.53402351,  0.04967756],\n",
       "       [ 1.31993801,  1.87995403,  1.30544475,  1.84438108, -1.26671035,\n",
       "         0.06165535,  0.53402351,  0.04967756],\n",
       "       [ 1.66797174,  0.80102697, -0.66983421,  0.09895541,  0.38495376,\n",
       "         0.06165535, -0.05103442,  0.04967756],\n",
       "       [ 1.10241694,  0.94175659, -0.45819718,  0.29779859, -0.16560095,\n",
       "         0.06165535, -0.05103442,  0.04967756],\n",
       "       [ 1.31993801,  0.47265787, -0.3876515 ,  0.12105382, -0.16560095,\n",
       "         0.06165535,  0.53402351,  0.04967756]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dict[200381]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 8)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dict[200381].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24, 8)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dict[200001].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Outcome dictionary: {icustay_id --> mortality label}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "outcome_dict = dict([x for x in ts_labels[['icustay_id', 'label']].values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome_dict[200001]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome_dict[280836]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Build X and y from dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "icustay_ids = list(sorted(list(data_dict.keys())))\n",
    "\n",
    "X = [data_dict[icustay_id] for icustay_id in icustay_ids]\n",
    "y = np.array([outcome_dict[icustay_id] for icustay_id in icustay_ids])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10. Prepare input data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pad sequences to uniform length (max sequence length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    59248.000000\n",
       "mean        20.791098\n",
       "std          5.446613\n",
       "min          1.000000\n",
       "25%         21.000000\n",
       "50%         23.000000\n",
       "75%         24.000000\n",
       "max         24.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence_lengths = [len(x) for x in X]\n",
    "pd.Series(sequence_lengths).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(59248, 24, 8)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_padded = pad_sequences(sequences=X,\n",
    "                         maxlen=max(sequence_lengths),\n",
    "                         padding='post',\n",
    "                         truncating='pre',\n",
    "                         value=999)\n",
    "X_padded.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split into train, validation, and test sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 80% train, 20% test\n",
    "* Use 20% of train for validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Train examples = 37918\n",
      "# Validation examples = 9480\n",
      "# Test examples = 11850\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_padded, y, test_size=0.2, shuffle=True)\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, shuffle=True)\n",
    "\n",
    "print('# Train examples = %d' % len(y_train))\n",
    "print('# Validation examples = %d' % len(y_val))\n",
    "print('# Test examples = %d' % len(y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 11. RNN (LSTM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model using Keras functional API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model(lstm_units=64, dropout=0.2, input_shape=(24,8), mask_value=999, optimizer='adam'):\n",
    "    model_input = Input(shape=input_shape)\n",
    "    mask = Masking(mask_value=mask_value)(model_input)\n",
    "    lstm = LSTM(units=lstm_units)(mask)\n",
    "    dropout = Dropout(rate=dropout)(lstm)\n",
    "    model_output = Dense(units=1, activation='sigmoid')(dropout)\n",
    "    model = Model(model_input, model_output)\n",
    "    model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Build model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = build_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 37918 samples, validate on 9480 samples\n",
      "Epoch 1/5\n",
      "37918/37918 [==============================] - 8s 218us/step - loss: 0.4396 - acc: 0.7801 - val_loss: 0.3877 - val_acc: 0.7742\n",
      "Epoch 2/5\n",
      "37918/37918 [==============================] - 7s 192us/step - loss: 0.3687 - acc: 0.8458 - val_loss: 0.3583 - val_acc: 0.8983\n",
      "Epoch 3/5\n",
      "37918/37918 [==============================] - 6s 169us/step - loss: 0.3560 - acc: 0.8705 - val_loss: 0.3428 - val_acc: 0.8970\n",
      "Epoch 4/5\n",
      "37918/37918 [==============================] - 7s 189us/step - loss: 0.3359 - acc: 0.8893 - val_loss: 0.3126 - val_acc: 0.8944\n",
      "Epoch 5/5\n",
      "37918/37918 [==============================] - 7s 194us/step - loss: 0.3285 - acc: 0.8927 - val_loss: 0.3262 - val_acc: 0.8963\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f32513b5400>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x=X_train,\n",
    "          y=y_train,\n",
    "          batch_size=256,\n",
    "          epochs=5,\n",
    "          validation_data=(X_val, y_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 12. Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get prediction probabilities for test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02626532, 0.3542437 , 0.17709428, 0.15505192, 0.2001451 ],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_proba = model.predict(X_test, batch_size=256).flatten()\n",
    "y_pred_proba[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Round probabilities to get predicted class labels (0 or 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0.], dtype=float32)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = np.round(y_pred_proba)\n",
    "y_pred[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy = 0.898\n",
      "Test AUC = 0.708\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.99      0.95     10541\n",
      "           1       0.70      0.13      0.23      1309\n",
      "\n",
      "   micro avg       0.90      0.90      0.90     11850\n",
      "   macro avg       0.80      0.56      0.59     11850\n",
      "weighted avg       0.88      0.90      0.87     11850\n",
      "\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "auc = roc_auc_score(y_test, y_pred_proba)\n",
    "\n",
    "print('Test accuracy = %.3f' % accuracy)\n",
    "print('Test AUC = %.3f' % auc)\n",
    "print()\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check how many predictions were made of each class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    11597\n",
       "1.0      253\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(y_pred)[0].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 13. Model with class weighting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit-learn 'balanced' = n_samples / (n_classes * np.bincount(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.890275\n",
       "1    0.109725\n",
       "dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(y).value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.56169822, 4.55198079])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_weights = compute_class_weight('balanced', np.unique(y_train), y_train)\n",
    "class_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.5616982194175333, 1: 4.551980792316927}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_weights = {i: class_weights[i] for i in [0,1]}\n",
    "class_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 37918 samples, validate on 9480 samples\n",
      "Epoch 1/5\n",
      "37918/37918 [==============================] - 8s 200us/step - loss: 0.6595 - acc: 0.6095 - val_loss: 0.6468 - val_acc: 0.6679\n",
      "Epoch 2/5\n",
      "37918/37918 [==============================] - 7s 182us/step - loss: 0.6236 - acc: 0.6283 - val_loss: 0.6252 - val_acc: 0.6806\n",
      "Epoch 3/5\n",
      "37918/37918 [==============================] - 7s 190us/step - loss: 0.5885 - acc: 0.7080 - val_loss: 0.5548 - val_acc: 0.7341\n",
      "Epoch 4/5\n",
      "37918/37918 [==============================] - 7s 173us/step - loss: 0.5799 - acc: 0.7184 - val_loss: 0.5886 - val_acc: 0.7238\n",
      "Epoch 5/5\n",
      "37918/37918 [==============================] - 7s 196us/step - loss: 0.5755 - acc: 0.7169 - val_loss: 0.5670 - val_acc: 0.7138\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f324efb7748>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = build_model()\n",
    "\n",
    "model.fit(x=X_train,\n",
    "          y=y_train,\n",
    "          batch_size=256,\n",
    "          epochs=5,\n",
    "          validation_data=(X_val, y_val),\n",
    "          class_weight=class_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy = 0.708\n",
      "Test AUC = 0.773\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.71      0.81     10541\n",
      "           1       0.23      0.68      0.34      1309\n",
      "\n",
      "   micro avg       0.71      0.71      0.71     11850\n",
      "   macro avg       0.59      0.70      0.58     11850\n",
      "weighted avg       0.87      0.71      0.76     11850\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_proba = model.predict(X_test, batch_size=256)\n",
    "y_pred = np.round(y_pred_proba)\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "auc = roc_auc_score(y_test, y_pred_proba)\n",
    "\n",
    "print('Test accuracy = %.3f' % accuracy)\n",
    "print('Test AUC = %.3f' % auc)\n",
    "print()\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    7903\n",
       "1.0    3947\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(y_pred)[0].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14. Callbacks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Value calculation (e.g. AUC) after each epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AUCCallback(Callback):\n",
    "    def __init__(self, val_data):\n",
    "        super(AUCCallback, self).__init__()\n",
    "        self.X_val, self.y_val = val_data\n",
    "        \n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        y_pred_proba = self.model.predict(self.X_val, batch_size=256)\n",
    "        auc = roc_auc_score(self.y_val, y_pred_proba)\n",
    "        logs['val_auc'] = auc\n",
    "        print('* Val AUC = %3f' % auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 37918 samples, validate on 9480 samples\n",
      "Epoch 1/5\n",
      "37918/37918 [==============================] - 7s 195us/step - loss: 0.6603 - acc: 0.5926 - val_loss: 0.7051 - val_acc: 0.6341\n",
      "* Val AUC = 0.682516\n",
      "Epoch 2/5\n",
      "37918/37918 [==============================] - 7s 193us/step - loss: 0.6419 - acc: 0.6218 - val_loss: 0.6004 - val_acc: 0.6304\n",
      "* Val AUC = 0.731607\n",
      "Epoch 3/5\n",
      "37918/37918 [==============================] - 7s 190us/step - loss: 0.6049 - acc: 0.6595 - val_loss: 0.6434 - val_acc: 0.6994\n",
      "* Val AUC = 0.742208\n",
      "Epoch 4/5\n",
      "37918/37918 [==============================] - 8s 198us/step - loss: 0.5894 - acc: 0.7029 - val_loss: 0.5583 - val_acc: 0.7540\n",
      "* Val AUC = 0.764076\n",
      "Epoch 5/5\n",
      "37918/37918 [==============================] - 7s 195us/step - loss: 0.5815 - acc: 0.7127 - val_loss: 0.5814 - val_acc: 0.6942\n",
      "* Val AUC = 0.754997\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f324efdf630>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = build_model()\n",
    "\n",
    "auc_calculator = AUCCallback(val_data=(X_val, y_val))\n",
    "\n",
    "model.fit(x=X_train,\n",
    "          y=y_train,\n",
    "          batch_size=256,\n",
    "          epochs=5,\n",
    "          validation_data=(X_val, y_val),\n",
    "          class_weight=class_weights,\n",
    "          callbacks=[auc_calculator])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model checkpointing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpointer = ModelCheckpoint(filepath='model.hdf5', save_best_only=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Early stopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "early_stopper = EarlyStopping(monitor='val_loss', patience=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 37918 samples, validate on 9480 samples\n",
      "Epoch 1/1000000\n",
      "37918/37918 [==============================] - 8s 214us/step - loss: 0.6474 - acc: 0.6135 - val_loss: 0.6571 - val_acc: 0.6084\n",
      "* Val AUC = 0.726266\n",
      "Epoch 2/1000000\n",
      "37918/37918 [==============================] - 7s 181us/step - loss: 0.5990 - acc: 0.6813 - val_loss: 0.6318 - val_acc: 0.7036\n",
      "* Val AUC = 0.738424\n",
      "Epoch 3/1000000\n",
      "37918/37918 [==============================] - 7s 186us/step - loss: 0.5875 - acc: 0.7013 - val_loss: 0.5863 - val_acc: 0.7103\n",
      "* Val AUC = 0.770618\n",
      "Epoch 4/1000000\n",
      "37918/37918 [==============================] - 7s 181us/step - loss: 0.5793 - acc: 0.7187 - val_loss: 0.5631 - val_acc: 0.7273\n",
      "* Val AUC = 0.760656\n",
      "Epoch 5/1000000\n",
      "37918/37918 [==============================] - 7s 187us/step - loss: 0.5706 - acc: 0.7245 - val_loss: 0.5920 - val_acc: 0.7136\n",
      "* Val AUC = 0.772701\n",
      "Epoch 6/1000000\n",
      "37918/37918 [==============================] - 7s 179us/step - loss: 0.5739 - acc: 0.7026 - val_loss: 0.5893 - val_acc: 0.7094\n",
      "* Val AUC = 0.766229\n",
      "Epoch 7/1000000\n",
      "37918/37918 [==============================] - 7s 189us/step - loss: 0.5779 - acc: 0.7005 - val_loss: 0.5346 - val_acc: 0.7467\n",
      "* Val AUC = 0.772331\n",
      "Epoch 8/1000000\n",
      "37918/37918 [==============================] - 7s 191us/step - loss: 0.5757 - acc: 0.7012 - val_loss: 0.5632 - val_acc: 0.7310\n",
      "* Val AUC = 0.768480\n",
      "Epoch 9/1000000\n",
      "37918/37918 [==============================] - 7s 180us/step - loss: 0.5771 - acc: 0.6985 - val_loss: 0.6093 - val_acc: 0.6713\n",
      "* Val AUC = 0.758113\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f321480e400>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = build_model()\n",
    "\n",
    "ac = AUCCallback(val_data=(X_val, y_val))\n",
    "mc = ModelCheckpoint(filepath='ts_model.hdf5', monitor='val_auc', mode='max', save_best_only=True)\n",
    "es = EarlyStopping(monitor='val_auc', mode='max', patience=4)\n",
    "\n",
    "model.fit(x=X_train,\n",
    "          y=y_train,\n",
    "          batch_size=256,\n",
    "          epochs=1000000,\n",
    "          validation_data=(X_val, y_val),\n",
    "          class_weight=class_weights,\n",
    "          callbacks=[ac, mc, es])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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