pm25_features.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature engineering\n",
    "\n",
    "After removal of anomalies and imputation of missing values, we can begin analyzing the dataset itself"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>season</th>\n",
       "      <th>PM</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>HUMI</th>\n",
       "      <th>PRES</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>cbwd</th>\n",
       "      <th>Iws</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01 00:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>129.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.79</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 01:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>129.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-01-01 01:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 02:00:00</th>\n",
       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>129.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1019.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.71</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-01-01 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 03:00:00</th>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>129.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1019.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-01-01 03:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 04:00:00</th>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>129.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12.97</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-01-01 04:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     id  year  month  day  hour  season     PM  DEWP  HUMI  \\\n",
       "date                                                                         \n",
       "2010-01-01 00:00:00   0  2010      1    1     0       4  129.0 -21.0  43.0   \n",
       "2010-01-01 01:00:00   1  2010      1    1     1       4  129.0 -21.0  47.0   \n",
       "2010-01-01 02:00:00   2  2010      1    1     2       4  129.0 -21.0  43.0   \n",
       "2010-01-01 03:00:00   3  2010      1    1     3       4  129.0 -21.0  55.0   \n",
       "2010-01-01 04:00:00   4  2010      1    1     4       4  129.0 -20.0  51.0   \n",
       "\n",
       "                       PRES  TEMP  cbwd    Iws  precipitation  \\\n",
       "date                                                            \n",
       "2010-01-01 00:00:00  1021.0 -11.0     1   1.79            0.0   \n",
       "2010-01-01 01:00:00  1020.0 -12.0     1   4.92            0.0   \n",
       "2010-01-01 02:00:00  1019.0 -11.0     1   6.71            0.0   \n",
       "2010-01-01 03:00:00  1019.0 -14.0     1   9.84            0.0   \n",
       "2010-01-01 04:00:00  1018.0 -12.0     1  12.97            0.0   \n",
       "\n",
       "                                    date  \n",
       "date                                      \n",
       "2010-01-01 00:00:00  2010-01-01 00:00:00  \n",
       "2010-01-01 01:00:00  2010-01-01 01:00:00  \n",
       "2010-01-01 02:00:00  2010-01-01 02:00:00  \n",
       "2010-01-01 03:00:00  2010-01-01 03:00:00  \n",
       "2010-01-01 04:00:00  2010-01-01 04:00:00  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "from tqdm import tqdm\n",
    "from pm25_hyperparameters import *\n",
    "\n",
    "DATASET_PATH=\"BeijingPM_imputed.csv\"\n",
    "\n",
    "data=pd.read_csv(DATASET_PATH)\n",
    "data.index=pd.DatetimeIndex(data[\"date\"])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Holiday and special event features\n",
    "\n",
    "We used the open-source package [`chinese-calendar`][1] to generate attribute `is_holiday`. We also used manually collected dataset to generate `is_special_event`.\n",
    "\n",
    "[1]: https://github.com/LKI/chinese-calendar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/tqdm/std.py:668: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n",
      "  from pandas import Panel\n",
      "Generating datetime features: 100%|██████████| 52584/52584 [02:49<00:00, 309.51it/s]\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_holiday</th>\n",
       "      <th>is_special_event</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>2010-01-01</th>\n",
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       "      <th>2010-01-03</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "            is_holiday  is_special_event\n",
       "date                                    \n",
       "2010-01-01           1                 0\n",
       "2010-01-02           1                 0\n",
       "2010-01-03           1                 0\n",
       "2010-01-04           0                 0\n",
       "2010-01-05           0                 0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
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    }
   ],
   "source": [
    "from datetime import date\n",
    "from chinese_calendar import is_holiday\n",
    "\n",
    "datetime_filename=\"BeijingPM_datetime.csv\"\n",
    "\n",
    "se_map={\n",
    "    \"meeting\":1,\n",
    "    \"visit\":2,\n",
    "    \"celebration\":3,\n",
    "    \"sport\":4\n",
    "}\n",
    "\n",
    "if os.path.exists(datetime_filename):\n",
    "    print(\"Reading existing features from %s\" % datetime_filename)\n",
    "    datetime_fea=pd.read_csv(datetime_filename)\n",
    "    datetime_fea.index=pd.DatetimeIndex(datetime_fea[\"date\"])\n",
    "    datetime_fea.drop(\"date\",axis=1,inplace=True)\n",
    "else:\n",
    "    sedf=pd.read_csv(\"Beijing_events.csv\")\n",
    "    sedf[\"date0\"]=pd.to_datetime(sedf[\"date0\"])\n",
    "    sedf[\"date1\"]=pd.to_datetime(sedf[\"date1\"])\n",
    "\n",
    "    def gen_datetime_features(row):\n",
    "        dt=date(year=row[\"year\"],month=row[\"month\"],day=row[\"day\"])\n",
    "        row[\"is_holiday\"]=int(is_holiday(dt))\n",
    "        x=sedf[(sedf[\"date0\"]<=row[\"date\"]) & (row[\"date\"]<=sedf[\"date1\"])]\n",
    "        if x.shape[0]==0:\n",
    "            row[\"is_special_event\"]=0\n",
    "        else:\n",
    "            row[\"is_special_event\"]=se_map[list(x[\"type\"])[0]]\n",
    "        return row[[\"is_holiday\",\"is_special_event\"]]\n",
    "\n",
    "    tqdm.pandas(desc=\"Generating datetime features\")\n",
    "    datetime_fea=data.progress_apply(gen_datetime_features,axis=1)\n",
    "    datetime_fea.to_csv(datetime_filename)\n",
    "\n",
    "datetime_fea[::24].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lagged features\n",
    "\n",
    "In this section, we generate features from original attributes of our dataset via sliding window mechanism. Specifically, we incorporate metereological parameters from the past `lag_hours` hours into each entry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading existing features from BeijingPM_lagged.csv\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>TEMP_lag1</th>\n",
       "      <th>DEWP_lag1</th>\n",
       "      <th>HUMI_lag1</th>\n",
       "      <th>PRES_lag1</th>\n",
       "      <th>precipitation_lag1</th>\n",
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       "      <th>DEWP_lag2</th>\n",
       "      <th>HUMI_lag2</th>\n",
       "      <th>...</th>\n",
       "      <th>precipitation_lag47</th>\n",
       "      <th>Iws_lag47</th>\n",
       "      <th>PM_lag47</th>\n",
       "      <th>TEMP_lag48</th>\n",
       "      <th>DEWP_lag48</th>\n",
       "      <th>HUMI_lag48</th>\n",
       "      <th>PRES_lag48</th>\n",
       "      <th>precipitation_lag48</th>\n",
       "      <th>Iws_lag48</th>\n",
       "      <th>PM_lag48</th>\n",
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       "      <th>date</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th>2010-01-01</th>\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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-02</th>\n",
       "      <td>-5.0</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.89</td>\n",
       "      <td>129.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-03</th>\n",
       "      <td>-6.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.43</td>\n",
       "      <td>90.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.92</td>\n",
       "      <td>129.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.79</td>\n",
       "      <td>129.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>-11.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.30</td>\n",
       "      <td>79.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.68</td>\n",
       "      <td>159.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.79</td>\n",
       "      <td>148.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>-15.0</td>\n",
       "      <td>-26.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1035.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>198.45</td>\n",
       "      <td>30.0</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>-24.0</td>\n",
       "      <td>39.0</td>\n",
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       "      <td>0.5</td>\n",
       "      <td>61.69</td>\n",
       "      <td>65.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>58.56</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 336 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            TEMP_lag1  DEWP_lag1  HUMI_lag1  PRES_lag1  precipitation_lag1  \\\n",
       "date                                                                         \n",
       "2010-01-01        NaN        NaN        NaN        NaN                 NaN   \n",
       "2010-01-02       -5.0      -17.0       41.0     1020.0                 0.0   \n",
       "2010-01-03       -6.0       -8.0       85.0     1027.0                 0.0   \n",
       "2010-01-04      -11.0      -12.0       92.0     1023.0                 0.0   \n",
       "2010-01-05      -15.0      -26.0       38.0     1035.0                 0.0   \n",
       "\n",
       "            Iws_lag1  PM_lag1  TEMP_lag2  DEWP_lag2  HUMI_lag2  ...  \\\n",
       "date                                                            ...   \n",
       "2010-01-01       NaN      NaN        NaN        NaN        NaN  ...   \n",
       "2010-01-02      0.89    129.0       -5.0      -17.0       38.0  ...   \n",
       "2010-01-03     55.43     90.0       -6.0       -8.0       85.0  ...   \n",
       "2010-01-04     14.30     79.0       -9.0      -11.0       85.0  ...   \n",
       "2010-01-05    198.45     30.0      -13.0      -24.0       39.0  ...   \n",
       "\n",
       "            precipitation_lag47  Iws_lag47  PM_lag47  TEMP_lag48  DEWP_lag48  \\\n",
       "date                                                                           \n",
       "2010-01-01                  NaN        NaN       NaN         NaN         NaN   \n",
       "2010-01-02                  NaN        NaN       NaN         NaN         NaN   \n",
       "2010-01-03                  0.0       4.92     129.0       -11.0       -21.0   \n",
       "2010-01-04                  0.0       2.68     159.0        -4.0       -16.0   \n",
       "2010-01-05                  0.5      61.69      65.0        -6.0        -7.0   \n",
       "\n",
       "            HUMI_lag48  PRES_lag48  precipitation_lag48  Iws_lag48  PM_lag48  \n",
       "date                                                                          \n",
       "2010-01-01         NaN         NaN                  NaN        NaN       NaN  \n",
       "2010-01-02         NaN         NaN                  NaN        NaN       NaN  \n",
       "2010-01-03        43.0      1021.0                  0.0       1.79     129.0  \n",
       "2010-01-04        38.0      1020.0                  0.0       1.79     148.0  \n",
       "2010-01-05        92.0      1027.0                  0.4      58.56      63.0  \n",
       "\n",
       "[5 rows x 336 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lagged_filename=\"BeijingPM_lagged.csv\"\n",
    "\n",
    "if os.path.exists(lagged_filename):\n",
    "    print(\"Reading existing features from %s\" % lagged_filename)\n",
    "    lagged=pd.read_csv(lagged_filename)\n",
    "    lagged.index=pd.DatetimeIndex(lagged[\"date\"])\n",
    "    lagged.drop(\"date\",axis=1,inplace=True)\n",
    "else:\n",
    "    print(\"Generating lagged features\")\n",
    "    df_list = []\n",
    "    for lag in range(1,lag_hours+1):\n",
    "        df_shifted = data[prim_fea].shift(lag)\n",
    "        df_shifted.columns = [x + \"_lag\" + str(lag) for x in df_shifted.columns]\n",
    "        df_list.append(df_shifted)\n",
    "    lagged = pd.concat(df_list, axis=1)\n",
    "    lagged.to_csv(lagged_filename)\n",
    "\n",
    "lagged[::24].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistics over rolling window\n",
    "\n",
    "In this section, we extract local minima, local maxima, and mean from rolling window."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading existing features from BeijingPM_stats.csv\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .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>TEMP_mean</th>\n",
       "      <th>DEWP_mean</th>\n",
       "      <th>HUMI_mean</th>\n",
       "      <th>PRES_mean</th>\n",
       "      <th>precipitation_mean</th>\n",
       "      <th>Iws_mean</th>\n",
       "      <th>PM_mean</th>\n",
       "      <th>TEMP_max</th>\n",
       "      <th>DEWP_max</th>\n",
       "      <th>HUMI_max</th>\n",
       "      <th>...</th>\n",
       "      <th>precipitation_max</th>\n",
       "      <th>Iws_max</th>\n",
       "      <th>PM_max</th>\n",
       "      <th>TEMP_min</th>\n",
       "      <th>DEWP_min</th>\n",
       "      <th>HUMI_min</th>\n",
       "      <th>PRES_min</th>\n",
       "      <th>precipitation_min</th>\n",
       "      <th>Iws_min</th>\n",
       "      <th>PM_min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-02</th>\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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-03</th>\n",
       "      <td>-5.833333</td>\n",
       "      <td>-13.333333</td>\n",
       "      <td>59.218750</td>\n",
       "      <td>1021.041667</td>\n",
       "      <td>0.008333</td>\n",
       "      <td>20.841875</td>\n",
       "      <td>135.291667</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.4</td>\n",
       "      <td>58.56</td>\n",
       "      <td>181.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.89</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>-7.000000</td>\n",
       "      <td>-9.270833</td>\n",
       "      <td>83.906250</td>\n",
       "      <td>1023.833333</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>48.196875</td>\n",
       "      <td>109.479167</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.2</td>\n",
       "      <td>127.84</td>\n",
       "      <td>181.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.89</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>-10.250000</td>\n",
       "      <td>-15.895833</td>\n",
       "      <td>66.083333</td>\n",
       "      <td>1026.208333</td>\n",
       "      <td>0.225000</td>\n",
       "      <td>94.028958</td>\n",
       "      <td>53.229167</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.2</td>\n",
       "      <td>201.58</td>\n",
       "      <td>107.0</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>-26.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.89</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            TEMP_mean  DEWP_mean  HUMI_mean    PRES_mean  precipitation_mean  \\\n",
       "date                                                                           \n",
       "2010-01-01        NaN        NaN        NaN          NaN                 NaN   \n",
       "2010-01-02        NaN        NaN        NaN          NaN                 NaN   \n",
       "2010-01-03  -5.833333 -13.333333  59.218750  1021.041667            0.008333   \n",
       "2010-01-04  -7.000000  -9.270833  83.906250  1023.833333            0.233333   \n",
       "2010-01-05 -10.250000 -15.895833  66.083333  1026.208333            0.225000   \n",
       "\n",
       "             Iws_mean     PM_mean  TEMP_max  DEWP_max  HUMI_max  ...  \\\n",
       "date                                                             ...   \n",
       "2010-01-01        NaN         NaN       NaN       NaN       NaN  ...   \n",
       "2010-01-02        NaN         NaN       NaN       NaN       NaN  ...   \n",
       "2010-01-03  20.841875  135.291667      -1.0      -7.0      92.0  ...   \n",
       "2010-01-04  48.196875  109.479167      -4.0      -7.0      92.0  ...   \n",
       "2010-01-05  94.028958   53.229167      -6.0      -8.0      92.0  ...   \n",
       "\n",
       "            precipitation_max  Iws_max  PM_max  TEMP_min  DEWP_min  HUMI_min  \\\n",
       "date                                                                           \n",
       "2010-01-01                NaN      NaN     NaN       NaN       NaN       NaN   \n",
       "2010-01-02                NaN      NaN     NaN       NaN       NaN       NaN   \n",
       "2010-01-03                0.4    58.56   181.0     -14.0     -21.0      25.0   \n",
       "2010-01-04                1.2   127.84   181.0     -12.0     -15.0      42.0   \n",
       "2010-01-05                1.2   201.58   107.0     -17.0     -26.0      36.0   \n",
       "\n",
       "            PRES_min  precipitation_min  Iws_min  PM_min  \n",
       "date                                                      \n",
       "2010-01-01       NaN                NaN      NaN     NaN  \n",
       "2010-01-02       NaN                NaN      NaN     NaN  \n",
       "2010-01-03    1014.0                0.0     0.89    63.0  \n",
       "2010-01-04    1020.0                0.0     0.89    53.0  \n",
       "2010-01-05    1020.0                0.0     0.89    20.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats_filename=\"BeijingPM_stats.csv\"\n",
    "\n",
    "if os.path.exists(stats_filename):\n",
    "    print(\"Reading existing features from %s\" % stats_filename)\n",
    "    stats=pd.read_csv(\"BeijingPM_stats.csv\")\n",
    "    stats.index=pd.DatetimeIndex(stats[\"date\"])\n",
    "    stats.drop(\"date\",axis=1,inplace=True)\n",
    "else:\n",
    "    print(\"Generating statistical features\")\n",
    "    rl=data[prim_fea].rolling(window=stat_wnd)\n",
    "    stats=pd.concat([rl.mean().rename(lambda x:x+\"_mean\",axis=\"columns\"),\n",
    "                     rl.max().rename(lambda x:x+\"_max\",axis=\"columns\"),\n",
    "                     rl.min().rename(lambda x:x+\"_min\",axis=\"columns\")],axis=1)\n",
    "    stats.to_csv(stats_filename)\n",
    "\n",
    "stats[::24].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features from `PM`'s frequency domain\n",
    "\n",
    "Time series can often be decomposed into trend and seasonality, so we perform short-time Fourier transform on the PM2.5 series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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D0w6zvZYy7HUZJjz38fd8uV2vi/e6FDud71zW52PoPJUhsvNOZ/w12kEUalpEREREZBPUQBYRERERieyIBrKZPcfMPmdmt5rZq8ds90IzczO7ZivzJyJyIVirLjazGTN7V7H+w2Z2ZbG8aWbvMLN/MbObzeyntjrvIiLTtO0NZDNLgbcA3wo8EXipmT2xZrsDwA8DH97aHIqI7H0T1sWvBE65+9XArwFvLJa/GJhx9ycDXwH8QNl4FhHZjba9gQw8A7jV3W9z9w7wTuD5Ndv9HKEyXt7KzImIXCAmqYufD7yj+P3dwLPNzAAH9ptZA2gDHeDM1mRbRGT6dkID+RHAndHnu4plFTN7OnC5u793XEJm9ioz+5iZfazLyvRzKiKyd61ZF8fbuHsPmAeOERrL54B7gS8Bv+zuJ4cPoDpaRHaLndBAHsvMEuBXgR9da1t3f5u7X+Pu1zSZOf+ZExERCL3PGXApcBXwo2b2qOGNVEeLyG6xExrIdwOXR58vK5aVDgBPAv7BzL4IfBVwnV7UExGZqrXq4oFtiuEUh4ATwHcBf+XuXXd/APgAoDpaRHatndBA/ijwGDO7ysxawEuA68qV7j7v7he5+5XufiXwIeB57v6x7cmuiMieNLYuLlwHvLz4/UXA+93dCcMqngVgZvsJHRmf3ZJci4icB9veQC7GsV0LvA+4GfhTd7/JzF5nZs/b3tyJiFwYJqyL3w4cM7NbgR8Byqng3gLMmdlNhIb277r7jVtbAhGR6WlsdwYA3P164PqhZa8Zse03bEWeREQuNGvVxe6+TJjSbXi/hbrlIiK71bb3IIuIiADg+epltsk/U3VprmNfazQH01ojPc+y1Qu3qAyee5WHKh+egyVYmmJp2s9LXVnWca62qpxlmbBkVfpJe5akPRvKVW5Tlqvctlw26ngj1lVpblRduuWyoXW153JUGufTiOtniWGJDWzjuffvt/IarfdYm71f1nvIdV5TNZBFRERERCJqIIuIiIiIRNRAFhERERGJqIEsIiIiIhJRA1lEREREJKIGsoiIiIhIRA1kEREREZGIGsgiIiIiIhE1kEVEREREImogi4iIiIhEGtudAREREWAwDHJprXC7a62vS3OjJkmrLrTxeo496bajjjO8vPhsaStsknU2nre11B1/GmnG/0bp54uLA5taYngW7VduG4c0Hj43w8sG1o0IAT2pumtR5TNKe9I81aU5TTXpWpquzmuUh4H1w6GjR53XCUK2nw/rDYmtHmQRERERkYgayCIiIiIiETWQRUREREQiaiCLiIiIiETUQBYRERERiaiBLCIiIiISUQNZRERERCSiBrKIiIiISEQNZBERERGRiBrIIiIiIiIRNZBFRERERCJqIIuIyPaxJPwAeB5+Jtl2UuPSHE6rJm3vdfur0xRL07WPtxmTlM+S+uOUy4fOadJqkXd75N3emsdas3zj8lQcb5rG5Sdpt0nabazRDIfOssH9yryU98Bw3sbdG5ssh6Upltjqc+x5P5/l8eNt6paV13PUdR848Aa+I+V+NeJzOnZ9mbe1vsNTvj/WI5mdWd/25ykfIiIiIiK7khrIIiIiIiIRNZBFRERERCJqIIuIiIiIRNRAFhERERGJqIEsIiIiIhJRA1lEREREJKIGsoiIiIhIRA1kEREREZGIGsgiIiIiIhE1kEVEZFtU4YCHws8OhBdeK2zuJKF169bXhe1dIwyu97oDoafrpAcOjM/LqPyMWb4q3PJwKOJqd+uvj8qyKsQ0/VDIycwsycxsP+k1QguPVB5vI2GOq0xNsG98jczArF/ueLMs66c37nxvMMRybVpD+3uWjQ4JDqSHDpEeOjSQ/7pw5pYUZZwkTPNaoZ7H7VeTx9rzM3AN1giBvZn7Ycq805ksXHxh5+RcRERERGQHUANZRERERCSiBrKIiIiISEQNZBERERGRiBrIIiIiIiIRNZBFRERERCJqIIuIiIiIRNRAFhERERGJqIEsIiIiIhJRA1lEREREJKIGsoiIbAvPssHQr5ZgaYrnXm0zEEY4DqM7aQjbMgxuXWjm4ZCz6wkBPWLbfGl57eOMK0NNyN7asMdxGkU6nmUk7Xb/nBbrLLHBZXF+V5bJV5anFxJ4I2GOx+zruddfJ0vIFxfJFxfHh4UeFwZ5TFhmazTXzO7AvTsqpHR0XpPZmX54dULoY+90xl9forDVIzMyIkT5BGUYmU55fM9JWi2SVitsUhcGO/r+rrKZ++E8UKhpEREREZENmloD2cz2m9lkzXIRETkvVBeLiGzehhvIZpaY2XeZ2XvN7AHgs8C9ZvYZM/slM7t6etkUEZE6qotFRKZvMz3Ifw88Gvgp4GHufrm7Xwx8DfAh4I1m9rJJEjKz55jZ58zsVjN7dc36f29m/2JmnzSzfzazJ24i3yIie8lW1sUzZvauYv2HzezKaN1TzOyDZnZTUV/PTqNwIiLbobGJfb/R3bvDC939JPDnwJ+b2ZojxItHgW8Bvgm4C/iomV3n7p+JNvtjd/+vxfbPA34VeM4m8i4isldsZV38SuCUu19tZi8B3gj8OzNrAH8IfI+7f8rMjgGr8iQisltsuAe5rkLeyDbAM4Bb3f02d+8A7wSeP5TOmejjfsAREZEtrYuLz+8ofn838GwzM+CbgRvd/VPF8U64+5jX7kVEdrbN9CADYGa/ATye0Gj9FKG395PrSOIRwJ3R57uAZ9Yc5z8CPwK0gGeNyMurgFcBzLJvHVkQEdndtqgurrZx956ZzQPHgMcCbmbvA44D73T3N9XkUXW0iOwK05jF4jPALwH/D/AA8Idmdu0U0h3g7m9x90cDPwn8zIht3ubu17j7NU1mpp0FEZGdbEvq4hEahDHP3138+x1m9uzhjVRHi8huseke5HJscOF6M3sz8FHgzRMmcTdwefT5smLZKO8EfnNdmRQR2eO2qC4ut7mrGHd8CDhB6G3+R3d/CMDMrgeeDvzdesshIrITbLqBXDKzfw9cDRwAzqyxeeyjwGPM7CpC5fsS4LuG0n6Mu99SfPw3wC2IiMgq57MuBq4DXg58EHgR8H53L4dW/ISZ7QM6wNcDv7apgoiIbKNpRtK7HriZ0OvwC5Pu5O494FrgfcX+f+ruN5nZ64oZKwCuLaYO+iRhHPLLp5hvEZG95HzWxW8HjpnZrYS6+NXFvqcIswt9FPgk8Al3f++UyiMisuXMfXMTQpjZnwGvcfebi88JcIO7P3UK+duwg3bUn7l6CJyIyHn3t/7uj7v7NVt5zJ1aF49S1tGWhqB/nmVgCXg+sJ01mnhv9SQco5aPNJz2JMeyhKQ9S764WK0vea9bm8bEy0ZlM+0HQfQsTASSzMySryyPLhcMpD9wTuvKVUharer3vNOZKH/nRXF+LE3xLBvIvzWaVTkGd7F++dIUzz38O1zOmvMzUZbK4467lhb1MY7abujal2Vcte9wPtd5z9Sdo9UbjklzzLq672h57+TdXrG7rbp2687DFii/w3/T/ZM16+hpDLH4A+BdxVQ/HwfmgO0rvYjIhUl1sYjIlEzjJb3rgOvM7CnAlxOGbVy/2XRFRGRyqotFRKZnww1kMzOPxme4+43AjeO2ERGR6VJdLCIyfZt5Se/vzewHzeyKeKGZtczsWWb2DvQynYjI+aa6WERkyjYzxOI5wP8O/EkxLdBpoE1odP818OvufsPmsygiImOoLhYRmbINN5DdfRl4K/BWM2sCFwFL7n56WpkTEZHxVBeLiEzfVAKFuHsXuHcaaYmIyMaoLhYRmY5pBgoREREREdn11EAWEREREYlsuoFcvD19ZBqZERGRjVFdLCIyPdPoQb4E+KiZ/amZPaeI4iQiIltr99XFluBZVh9KOE1XhdAtlwGrw/UW6a1aVu6X2MB6S2zVvtZsrNoP92of73XDT11Y7CLtpD1bU8zVxxrFswzPHc/701YPh0+2NK1C5sbnpAz/W53TqLxJu03Sbg8cuwwTXJV1jbzVFKy//Xr3G/hoVb6t0RxI13vdsN7zgR/PvdrOGs2wrNclmZkNP+12KHezsXZo47q8l8eqW16zLA5DPpDu0PbxfZvMzpDMzgxcw7pzao1mffpxmpMYFUq6OH+rlhf32fB3tAzt7bn3w5UX+R71fV4rD1tlzfxFNt1AdvefAR4DvB14BXCLmf28mT16s2mLiMhkVBeLiEzPVMYgFxGa7it+esAR4N1m9qZppC8iImtTXSwiMh2bnubNzH4Y+F7gIeC3gR93966ZJcAtwE9s9hgiIjKe6mIRkemZxjzIR4HvdPc74oXunpvZt08hfRERWZvqYhGRKZnGEIsrgfnyg5kdMbPfAXD3m6eQvoiIrO1KVBeLiEzFNBrIT4lDmrr7KeBpU0hXREQmp7pYRGRKptFATuK5N83sKFMKYS0iIhNTXSwiMiXTqDx/Bfigmf1Z8fnFwBumkK6IiExOdbGIyJRsuoHs7r9vZh8DnlUs+k53/8xm0xURkcmpLhYRmZ6pPH4rKmFVxCIi20h1sYjIdExjHuQZ4IWEN6ir9Nz9dZtNW0REJqO6WERkeqbRg/zfCVMLfRxYmUJ6IiKyfruuLk6a/T9BeacDnmNpiueOZ9nqHax8rzwDzweXed5fNsSzDEvTmrQGj5Evr6xK06N8Dac5kFya4r0ulkTvvlsymKc4rzUsTbGZGfLFxYHtPffBY+de5T3Oh2cZ1mhW6XvuIc1mA4o0LDGsMYv3umH7ViuU/dwiyb59+MoKkPbTHiqDNZqhnGk6eA5GlGkSq8oQnevyfhhgSXW+AfKV5Wp5ucxmZsCScF+NMu56lPdH3TUsP0fLy+MOFiysT9rtkM+lpWq5NZrh3gLSi4/Tu++Bqrzlv2W5qvNTHns4T+PKN7xdnEaRl+p65t7Pc6tF3u2RtNLBsnmOZ5DMNEOZVpZJZmarvFb5bzT712VU3spzNC6fkxguU5nu8GaNZv/DBElPo4F8mbs/ZwrpiIjIxqkuFhGZkmlM8/Y/zezJU0hHREQ2TnWxiMiUTKMH+WuA7zOz2wiP9Qxwd3/KFNIWEZHJqC4WEZmSaTSQv3UKaYiIyOaoLhYRmZJpDLH4EvC1wMvd/Q7AgUumkK6IiExOdbGIyJRMo4H8VuCrgZcWn88Cb5lCuiIiMjnVxSIiUzKNIRbPdPenm9kNAO5+ysxaU0hXREQmp7pYRGRKptGD3DWzlPA4DzM7zkQzzImIyBSpLhYRmZJpNJB/A3gPcImZvQH4Z+Dnp5CuiIhMTnWxiMiUbHqIhbv/kZl9HHh2segF7n7zZtMVEZHJqS4WEZmeTTeQzew1Q4tebGa4++s2m7aIiExmN9bF1m6Tn1usPqdzc3iew8oKXkYfjkLOxiF3LbGwbDgMMYPhidODB8jPLQ6EM7ZGM+wfhUu2xKp9ktmZKh07eID89Dy+skKyb184Zq+Hl2GDbTC0tD38ErhloZ9mtjpk80CYZht8kJsvLpLOzQGQLSyAJSTNxkDI5JBuP9/hPCR47iTtZhEuOoTyzrs90osvwovz7AtdbH8bP5ORzrWhDA2cGN7pDoQbLhIevAZF2OGqTMU2ZWjiap9RqmvXDyddhshODs7hC+cGyuq5kx46SDZ/pro23u1haUp6aZikJX/oJPnS8sA1TOf2h3N54ADZ2bOrwkqvCpU9pDZ0dLF/XfjrEMK72Q91Xf5uCcnRI2Gjk8Vxuz3yTofGRUdDkmcXSFqtkHYR+jtbWKjSTopleL4qJHt57DJE98B9nqZ4b0QI5qFQ7Z5lJK1WlabnHsrUbNA4eFHI08lT1bqkyHuyvIJ7DuU1K9LLO50q3/nQOjzv37cZmw+fPVS2pN3uh/Ue2n7cNR82jZf0zkW/zwLfDqjXQkRka6kuFhGZkmkMsfiV+LOZ/TLwvs2mKyIik1NdLCIyPdN4SW/YPuCy85CuiIhMTnWxiMgGTWMM8r9QTCtEGFR0HPi5zaYrIiKTU10sIjI90xiD/O3R7z3gfnfvTSFdERGZnOpiEZEpmUYD+YXDC8ys+t3df3UKxxARkfFUF4uITMk0GsjXAF8JXFd8fi7wEeCWKaQtIiKTUV0sIjIl02ggXwY83d3PApjZa4H3uvvLppC2iIhMRnWxiOPmWCUAACAASURBVMiUTGMWi0uATvS5UywTEZGto7pYRGRKptGD/PvAR8zsPcXnFwC/N4V0RURkcqqLRUSmZBqBQt5gZn8JfG2x6Pvc/YbNpisiIpPbjXWxL68MhLzNl1dIDs6RLy1X24TwwYNhgoF+KOohSasVQtsWYW2zM2dXhab1Xgjpm7TbVUhfm5mBThfv5VWY2mRmFrMQghnA2rMhgcWlfgjbMnRx0sDSFvnd9/aPE4UjLsNOh7xnq/YP2ydYo0m+HEJFW6MJ0A/hTD9EchnK2NIyZHNIM19c7IfEXlkhaTbwswvQbFRp5vNn8Nzxbg/yOIz36pO6KqxyESp6OKRxUYCwvvhcF/4Yz8N5GArRDYR81YQC9qXl6jzlS+Hce5ZhC+eqcg6cVwAzsISs2KY2PHEcfrq4Hkm73T9OjdEhqlPyTqcqu83O4AtdPMvIixDN1mpBlpEcPUx+3wNkp+ajrFi4b6NrvSoMeZG+5z6Qh/L7MZyvKuz1mLDsw2GYq3DrzQZ5JyM7t4gV92O1v+f07rmv2i9pNvrXvjxWL69CTMf3Q1mu4dDvI0N7TyD+bgHV92e4LrBWk6QZvhucXjvdafQg4+6fAD4xjbRERGRjVBeLiEzHpscgW/AyM3tN8fkKM3vG5rMmIiKTUl0sIjI903hJ763AVwMvLT6fBd4yhXRFRGRyqotFRKZkGkMsnunuTzezGwDc/ZSZtaaQroiITE51sYjIlEyjB7lrZingAGZ2HKgZDT+amT3HzD5nZrea2atr1v+ImX3GzG40s78zs0dOId8iInvJVtTFM2b2rmL9h83syqH1V5jZgpn92MaLISKy/abRQP4N4D3AxWb2BuCfgZ+fdOeiQn8L8K3AE4GXmtkThza7AbjG3Z8CvBt40xTyLSKyl2xFXfxK4JS7Xw38GvDGofW/CvzlxrIvIrJzbGqIhZkZ8I/Ax4FnAwa8wN1vXkcyzwBudffbijTfCTwf+Ey5gbv/fbT9hwBFhhIRKWxVXVx8fm3x+7uBN5uZubub2QuA24FzmymLiMhOsKkGclEpXu/uTwY+u8FkHgHcGX2+C3jmmO1fyYgeCjN7FfAqgFn2bTA7IiK7yxbWxdU27t4zs3ngmJktAz8JfBMwcniF6mgR2S2mMcTiE2b2lVNIZ01m9jLgGuCX6ta7+9vc/Rp3v6bJzFZkSURkp9iyurjGa4Ffc/eFcRupjhaR3WIqs1gALzOzLxIerRmhQ+MpE+5/N3B59PmyYtkAM/tG4D8DX+/uK5vKsYjI3rMVdXG5zV1m1gAOASeKY7/IzN4EHAZyM1t29zdvtDAiIttpww1kM7vC3b8EfMsm8/BR4DFmdhWh8n0J8F1Dx3oa8FvAc9z9gU0eT0Rkz9jKuhi4Dng58EHgRcD73d3ph7fGzF4LLKhxLCK72WZ6kP8/4OnufoeZ/bm7v3AjiRTj2K4F3gekwO+4+01m9jrgY+5+HWFIxRzwZ+FdFL7k7s/bRN5FRPaKrayL3w78gZndCpwkNKI3LO90SNqz4fiLi3gG2clTJK0WeacTNrKEMHldjudhVKClafHZwfNVaQIkzUb/syUkzQaeZeFYWUbe6WDFZ4B8aZmk1cKzDEus2tfvu786TnbydHGy8vBjSXX86rgzs1WalhiehTJ4loV8F8fHcyxNQ9pRmpa2+ml2e1hiYbsiq55lIb1eNywo0+z2gIxkZhbvdKs8eO7Q6+GLi/1lRRo2O0M2Px/y3WrhueO9Lkmr1S9/sW2sWlbk0/MEzzqD6+mXe9jAeSi363WxRhPIin/766t7oV8oLE3JThV5b8/ii4ukc3PRNl6ct/rjx8urz5aQLy2tOtaqstexBMiq65KdOdu/j5aLh95LS1iakqRpWFekbTPFcKNur59co9m/X4s0B+6X6PxbmuK9PMoHA/fnqjxH+9adg8FNrSqH98J5x5qkRw6Fcp6ax3MP+e11q2tV5X/4+OWxo3xW93K0TfXdGearZ66M76N4mzw6nwC+skK+tFyTaL3NNJAt+v1Rm0gHd78euH5o2Wui379xM+mLiOxhW1kXLwMvXiON124mDyIiO8FmXtLzEb+LiMjWUV0sIjJlm+lBfqqZnSH0XrSL36H/YsjBTedORETWorpYRGTKNtxAdvd0mhkREZH1U10sIjJ905gHWURERERkz1ADWUREREQkogayiIiIiEhEDWQRERERkYgayCIiIiIiETWQRUREREQim5kHWUREZOM8HwiLXIaXrcLpEsLslmFwyxDI/ZC4I8L+MhhmNqSbDYSkLcMdD6RRhU4Ox0+ajRCSutEEN9JDxZTSidF78KH6sLe9bj/kbZHHMnR2XK6qHJFk3z7ypeUqtC+eY43Z2lDLZfoeh9MtwlXHoXuTfTODoaeLc9e46Cgehd3Nuz3Suf1kZ6NwwWmKpWkIeV2eL0vAbVXo7HCOBsNnD4ekjvNQhpWOWSuEK67yX+ybHj1CPh+m945DMCezoQnjKyGUc7awUJ37tNEIYbPbbbzTqfI6cB2KbavrEOWzDL1d3RNxOONiWRWSu9vDitDmVXjk4trR6/bz226HUNb5YJh0y3Ks1RzIQzJ3kOz06XCvFqG385XlwTDZVtPHGZ9rrwk/PbxNxBrNKvR7vrjYD5FelBNCWGzvdPEyfHa8f5r2w4QPh4+OtvFePjIPZf5qw0yvoQqJHV2D+F/PkyoEPauzv4p6kEVEREREImog7zZ1/2MUERERkalRa0tEREREJKIGsoiIiIhIRA3k3WbcwHYRERER2TQ1kEVEREREImogi4iIiIhE1EAWEREREYmogSwiIiIiElEDWUREREQkogayiIiIiEiksd0ZkD0silkvIrKKJVhiALglJO022cIClqZV3eG5Y40mnmXk3R7FQixlVf1iaYpnWbUNQNJuky8tVevLNL3XHYxM6nk//UJy4ABJIyU/cxbv9qDIa35mYeCYANZqQZZhc/vJTp4q0iy2X1kePH42eA7CP0a+uAiW4LmHZY0meadDMjtDvrwycExLU/JOB0sa1bE8K85XdZwMej2s2f9T78W56D34EJamJO12yOPSEt7pDpTfc8ezTvF7ca68v011rgFrNvBuL+Sp0Rw4r7U8Hzj/ycxs//jlcs9JWi3yMwvh/Bb5TOfm8DzHrn5kWHbjzdXfm3T/vpDEvja2cA7MBvI5cOwob0mZfnEPWLtNPj9fn+9C3gnnxtIUsiwcp1hvaYq1muB5dT3z5RWwhGzhHEmz0b/2szNkZ86G8hbXIzt9mvTAAbKzZyHtn6eBspTfkV5NOTodkn37wj1Vis7r8O+eO5bkeK8XbZuF72iRZmKGr6yE+3I43Sov/fsjabWqe38gz0Pfu7q2wsB3eRKWROlm1fE9y6rl3uuSHDoYNnlg7STVgywiIiIiElEDWc4f9R6LiIjILqQGsoiIiIhIRA1kkVEGxjSJiIjIhUJ//UVEREREImogy/pdKD2rnq9+41ZERET2PP3lFxERERGJqIEsIiIiIhJRoBBZvwtt+rYLrbwiIiIXOPUgi4iIiIhE1IMsshaFzBY5L9Ijh7BmCEucP/AQ2cICycws+cpyFXLXch8IF1saCGdMCMFbhaa1pAq3DFFo5jKMcJoOhOcFSPbtw5IE73SqdPKzZ6HRCGGEe91qeXLJReRfuqs6LkDSaECjgS+cW5XXpN0mX15ZHTq3pkyeZYPhgtuz5IuLVXnLcNLJvhmI8oolJDNNrNXEV0JYas8dGg3ypeXBYxRhtpO5uRAOmRDCOV9ZpnHxcXoPPFic1CLU83AI7pnZgdDclhj50hJJuz1wnkaFES6vTxnGugoFXOxXhh+HFNu3D1ZWoAjXnLRa5MsroZyfu71aFq5TDkV5sgceCvm/+Dh+512D4bcpw34Xy3pdrNUiW+iHEPduFHZ7+EXtolxJEcI77/ZIDuwjWVmB8t7p9kLo7DSFIiR1OjcHZmQL54rw08V5WloiPXIohCgv81eEc7Y0ra5feX9YYiE8dHS+qns2Cn9dXfea8z+wrAw3Hd173umA5zQufXg4l5F8ZbnaLrn4OL64SD5/Juy/b1+RppMvLQ2GjC7DqHvev5979X9b68JMV/du7Q55P4R7Ub682+uHpSec/+xUTfjwEdSDLCIiIiISUQ+yyChlr4F6j0VERC4o6kEWEREREYns7Qbydgd42O7jy+aUgUJ2O92HIiIi66K/nCIiIiIiETWQRUREREQie7uBvN2Px7f7+NtpLz7WHy7TLiljf8qkXWCXnFMREdnb9NdIRERERCSiBvJWWU/P2F7oRSsniN/FyuACleEnArvkCYEXE+zvCrvknIqIyN62u1swIiIiIiJTpkAhW2U9PWN7pRdtl5cjDo859bJsRfjq3RDoRGG8L2jZqfkqZG0Il0v19ClfDuGSk2ajH/I4DqO7shzC2MZPSIp7Ph53ny+vkLRn8ZWVfgjnlWWSffvIFxf72y0tk87tB/pPXdKjB8kXzpF3OiH07/yZkP7yCsnMbAirXGybLy5iRfpVdorQwGVZhpVhpAGs1cY7nRC2eWW5X86lZdIDB8gWzlXL8BySpP8ZsFYr7L+vTXb2bL9ci4ukhw+zcs3VALQ+cFO/bnvkpfRuvDnkZWY2bD9/tvp9lDJ//YI2sUZCvrREcQKL5SP64DwP1zJan68skx4+DL1eFfLZGk2y06dDGPD9RZ6ynHx+Hms2qvshefoTyT/+adK5OawIUZ6mKfm5RShCYNslx8Ohv/ilKvxxFSZ8ZjYcMwofnbTbA/dHFVY6ynd5fChCM5sNbJ+vLNM4dqy6/p7nWJKQzM5grRY2E+6X3gMPYvMhnHqZZuPi4+Sn58m7PRpHj4TtTp6qrr/3uvXhs8uw3b1uCNse5bG8LkmrBWmxXadbLfcM8vLcz8yQttv44lIV3tnSNOQ7y6p0/d77SC861j9GrzeQp/Rhl9C7575qdbp/H9m5RZLZmXA5z60OKT3q74IlhtesK7+L8f7e69cZZdj62nDvY6gHWUREREQkogayiIiIiEhEDeQtZI3mdmdB1sOS3T0EoIwEuJNflhw+tzs5r2vZzXkXEZEBqtFFRERERCJ7u4G8k3p0dnNP5AWqnOZtbM//Ru+xrbgXdkMP+PD5q8vrTvoei4jIBUF/eUREREREIjuigWxmzzGzz5nZrWb26pr1X2dmnzCznpm9aOKEd1DPWZjCaPLpRXa1qMdvVbCNXcR73fBTd9128hRqu6nHdZLztxPPcZ1yvPduOv9DJqiLZ8zsXcX6D5vZlcXybzKzj5vZvxT/Pmur8y4iMk3bXpObWQq8BfhW4InAS83siUObfQl4BfDHW5s7EZELw4R18SuBU+5+NfBrwBuL5Q8Bz3X3JwMvB/5ga3ItInJ+bHsDGXgGcKu73+buHeCdwPPjDdz9i+5+I7D5rqRt6t2ZSu/xNPJ+nso/qqfYc99doY5jdb2B0xrXez7vwzJv5SwWu9Fu7Yndzed8grq4+PyO4vd3A882M3P3G9z9nmL5TUDbzGa2JNciIufBTvgL9AjgzujzXcWydTOzV5nZx8zsY13qIxeJiEitSeriaht37wHzwLGhbV4IfMLdV1XCqqNFZLfYU6Gm3f1twNsADtrRXdptKSKyO5nZlxGGXXxz3XrV0SKyW+yEBvLdwOXR58uKZefHVj7+HHoUX8Z/31bnqfwDMeHLY4waihC/4LbV05Ct5+W6Yptw3QaX7Yjp06adh+0o06hjbve53QBL0/5wot3Z9JukLi63ucvMGsAh4ASAmV0GvAf4Xnf/wiQHbDzi4eQnT4UPPWgcO0Y2f4ak1cJ7XQCs1cKXV8hXlkmPHgEgOzWPNZp4r0vSagGh7vEsI5mZJe90gOJL6zn54iLp3Bze7YU0G018ZWXg/kuaDezIIbI776mWZafmsac+jpVL9zF7/xKWhQubPHia3pfuCkPLEquOnx49Avva5LeE4lt7FlaWSffvI1tYqOqfap+VqBe9GI6WtFrV34mk3QYz8qXl/nae41lGduYslqYkRw6HxcsrJFdcBmfO0rj4eEjy9Hw43iUXMXtzGAHTW1oiPXQI8hxv9IcyWXsWEiM7eao6pzQaJJc+DL50V5Una7Wx9uzAdnmnE/LSbuPdHslsGF2TLSwU1ymvpsv0XhcsIWm1yFf65UoPHIBej3xpiWTfvpDu4iKNxzyahSccY////7mw7Fy4lvnSEumRQ+E4zZTG8YvwTpf84nA++OzteKdDnuUkhw+S33VvdZ2qIVxWfFETI5kJ5S/vkVjSbPSvSatFXm4TX89eb2A4YdKepXHkMNlDJ6q/j8nFx2FxEWu1yB94kGw+XJ/G1Y8K1+bW26p7nPYs2eMuI/ngp8kXFwfvmywL57XIU3r4MNnp03juWNL/GxWf3yq/noeXz1eygeXp/n3kS0v9/M/tp/fgCRpHj5B8xZPCZp/7ItlTria96XasbMtYQnblw0h7Gb0TJwbPwcwsvnBuoH2QnVvEEgvfB6J6M/5b4HnYt3xJvjjP4XvNqr8b5fLy/KRHD9N74MFwXyaGXRQecvnDDpN84uaw0wQPsHbCEIuPAo8xs6vMrAW8BLhum/MkInKhmaQuvo7wEh7Ai4D3u7ub2WHgvcCr3f0DW5ZjEZHzZNsbyMU4tmuB9wE3A3/q7jeZ2evM7HkAZvaVZnYX8GLgt8zspi3L4GZeFop7wSzZfO/xNHrVNvPi04h9LU3Ju73+/6rXEr/ItNU9hRt4iaruum166rrz8ZLfqBcK17rm2xlQxPP6FzyjvFua7o4X9spzuAt7v2Gyuhh4O3DMzG4FfgQop4K7FrgaeI2ZfbL4uXiLiyAiMjU7YYgF7n49cP3QstdEv3+U8LhPRETOkwnq4mVCR8Xwfq8HXn/eMygiskV2QbfMlJQ9UFvZExUfaytD6I5Ld529WwO9e+WY3EazH3657BmPes6Gt6/N307pEZwkH3F+63pbJ+mh3chxx/G8Gm8VL1v1edw1mGS7uv2mfO3KsaODh7FQPs/D+LTh/O2U+yey6nqIiMiutfP+yoiIiIiIbCM1kEVEREREIhdOA7l4GWjdL9Bs9KWbSR7Dx1OGEaaPSVqt0Y/kyxeW6tbVpbsZRZp1j79rX7KLXqqKp2WZ+iP5aT9an+BclY/6B3fzfn7WSuN8TGM26qXP6PyUw2Nq75l4+sGhITNjbdFLaNWUXa3WrpkGbtuncBQRkam5cBrIIiIiIiITuKAayPEE1hNbqwd01Lq6F6ZG7Vusq6ZKG9VjNqrXbD29aZO+MFaT5tje6zJ/43rNh7cdl81R06htpuewppxjp2uLe8RH5aMMdjJm/y1T9maXvf9F4IGx2xMm7i+DMmxLz2zNeSp7vssABLuBepBFRPaOC6qBLCIiIiKylh0xD/KWGdWLu5les03uW4ZLnTStkeNOt6AMZW/kQB6KHssqBGY5JdfQ+Or1HGfVMUrnoZwDY4mHtknas2HRykp/XPVwqOlxeRqT102HHR83Lnd4vPSo4+yQsNL98Kn9beKe7w0/+Qk7bySnG7IjQsnvIpYk5A88SHLVFQBkt94eQkI/7GJ8/gzJ3P6wYSOFpSXSo0ew2fCdtHSBpD2LZ03ohQBFnmWkBw5UY9etFcbV26WXwJkF8hMn+3VYr0uybx/mXoXXTY4dhVarHxYaSJ7waPjCXbT/ZRHPne7XPwWAmTtXwrGK0MghTyksr4B7FYLZLjoKp0+HYhy/CA4dDMe//8GQz0MH6d17XyhnYuE9lINzJEcvq85Jcugg6aGLoQg33TsRQhfbUx+Pf+qz+LkQhphWk97FB0n2z8JnbwtJHjgAMy3o9qrzlB4+jKUp2enTcOPnOfPdzwTg0J98JJyTRrMKX02akn3xTiyx/r3d6YSw1Jb0Q/8SvqdJo0G+vEK+vFKdk6Q9S7aQVd/FpN2G8t2WOFRzkpB92VU0PncnfmkIlZ26wwMnaZ49DHnYP73qCji3SLovhOEGOPuofRy6cZHFb3wSjaWw3fLzn8qh//Fp8qUleg88RKMM4ew5NBr4ykq/7ul0Bt+dAbL5M1UY5DgIlmed/vWO6pnk+EX44TlsOfw993aLe551jEveeqoKve1zs/i990Ma9ksPHADgSy98GJe+6UMhvHJx7vJHXUbztvvJEyM5HEJq5/NnsCQhOzc4tWp2+nRV/1gjfEfodUkOHSE7NV/lvQzvXYV2JoTRzjsdvNvDWq1+WOhej8bll5JdfAQrzn3vy6+mefsDcOwI1g3lzJeWadx7Eu90SJ/wWLgzhDTPnvRo+NhnsPg9puJdMM99IPR4eU/EoaTzTmf1E9qirTHqqW5V9iJUeTI7E+7FIlS3N4+SFGGnuXt1EsPUgywiIiIiErmwepDrepU2MgPBWmlu1CRp1fXCrefY6wkIUXecutk5PMfS0GNS/u96Q3lby1o9thtNM/43Sj8v/tdZCv9zrcnLqF7ycdfTEmCTvY0jZkpZ9T/sSfNUl+Y0jRjXXvdEIg46U62fZLaWbQz3vKGebhER2ZHUgywiIiIiElEDWUREREQksvcbyPEUUms9et3ItFzj0hw1zVm8e/mCHowO6jB8vM2YpHyjHrPHQwrilxNarf4UdWsca8NTdp2nl67G5Sdpt0na7f7LBNFQgIGgM3VT3MXL62yyHJam4QWTmmEH8QsRtUNAal58qH4mGVK0kanrRuyz1kttA8MrRp3ngR22L4BI+SKOiIjsfnu/gSwiIiIisg57uoFc9fIN9SoN9Bqu1Rs2SY/ZqOAb63yZbiBgwwjltDDrzs+Y5at6UUcEvyinxBk+p6t6jun3cCYzsyQzs/2kNzoNVt20ces1yb4DL7QZmPXLHW82HE571PE22HNam9bQ/p5l9fdUsW166BDpoUMD+a97SmGJ1YbTrs/4Bl+AG95n3LkbfqlwXM/2VgZiWYMXQU12S2ATEREZbef8dRERERER2QH2dAPZs2ywR8eSapLq0kDv4KgAF+PUTfVVrhruSVpPz+6IbfNisvixxxlXhrpgGeNCKUe9oJ5lYUxueU6LdZbY4LI4vyvL5CvL0+vpOw+BQmqvkyXki4vki4vje3vXChQyYl05rnmcgXt3VE9xdF6T2ZmBsdHe6fQnwK/LW/lrPJF7bUZGPHmYoAwj04nGbyetVj+wQl3vdvT9nWiqt22kHmQRkb1hTzeQRURERETWSw1kEREREZHIhRVJj5rhBCMimlma4r0JXkgas374WFWa0bGT9mwVsS1+ZO29bu3j+7pH4SMjeNW8JBg//i3TSmZmwzCICeRLS6sf+1tz1cuFnmUkUVz3Ksb6dqii/fWjslVx68tlQ4/uB6LmFWmE6xeVM57ybiOP+uv2i5ZV13XcdlG+B66NJSEGfbw9NdOmTZTNoXNRWPVC6Vov041YF9/TZZnLIRfhBdCs9tqt9zjnW919JGM0GiSPeiScPA2AtVokR4/A7AzMQzZ/BoDk4BydZz+N1t/dgJ88Ffa1BDswh2UZlPf86fmqLk2PHgkv2QLZF+4g2b8Pe9yjSB44CYAvr5DNz5M+5Qkkt3wRgJPPvorejHHxPxrcGtLpHZpl+erHceCfvwBHD5N85PPhWEcOYe7kJxf792qnA4lx+/dfxSN/8UQ49rE57PaQv96DD5GcWajK5GcWIE1J9u0L+y8tkx46SH5uEc4V5bjqkZx7/EXsv+EueidO9Ms+M0Ny7wnymRmyctvGQRr3nMIP7sPm9odjnjxF9g1PY+GyFsf++gth/16PfHERazRJDs5x+upwng7PzJCdPUs6NwfN8Lcof/AEd/zfX8mj3vz56txbq4XNzJDO7Sc7ezYsS1PSi46RPXSCxlVXkN91T9i/61h7FouHqnU6YQrNQwfx4hrnS0tgRuOuh8iuupT0vnCd6HbJL7+Emc/cCe3wsrefOIk1m+TzZ1n65qcAcOC2Rbpf/QTmbrwffyicp8a5Reb/7TMA2H/3MtmHbgr797o0LjqGn1uEIk/50lK4LrMz1X2D9+ui/Fw//41jx8L2WY61iilAV1ZYuOZyZu9fJi3uxxNPO8yl//1OfHYGmwlTQD74jKMcuekWGo97NL3PfYG0uPaX/lO4hvlXPJ5bXha2feR7nX2nZrBWC184R8kuOU564hT5wjmqtkvx9wlLqr+1jePHyE/Ph2FqRdui+jvvebjOgB08QH7PveA59/zANVx2Xbh2X3jFpTz6N2/D3LnrGw8BcMW77mThmsuZ++BtJBcdDZfzsQ+jccPteKdL/tlb+eLrvwqAR73+BrKvfhLJJ28lPRgmF7CZFrRn6d1xJxANfW3Pkp08Vb3Qb80Gtn8fvfsfCMPwiuWeZVizgUd/25Jmg+T4RWT33Q9f9uiw8M77w7orHkGS5XC2+N6t9LjjFVeFbd7AmlSbi4iIiIhE9nYD2ZKRLyCVL9MMB38oe+Bqe4NGTNs1ELShWL9qajBLsGZj1X6493v3imneqmPXTHeVtGcZVnesUTzL8NwHep2HewItTav/ccbnpOwpqc5pVN4yqMZAj2Y0/dvAi1aTis/3evcb+GhVvq3RDGWLznk1xVn047n3X0JsNMOyXreati5pt0O5m43JgmsMG/UC34hltS/EjXrCUF6T2Znqxb3aF93Ke7U8JyNMPCXduBcSa9aV99nwd7R8kdZzr+65OJjJ2Pxs80t7a77wKCIiu8LebiCLiIiIiKzTnm4gJ83GwBRS1RjcuGe5LihBsW1dWOWJp0mr6TWsxoRG41a906nyVf3UBG0op++yZChE8PAxx4zBtHK821DZhscwe+5VmeIesaoHNuqRtDQNPeO5Q+5VcJAqv61WGEOVO8m+fYNTwtWUIe65Hjjfm+gZHBjjOqK3cjATycDxq3FblvSDuZgNjPeqNe56jOoZH54KrRyPXBdAplhX9d5Hyy1Nq2ne0ouPOO4SIwAAIABJREFUD4zftTQN34ky7fiJQF2expVvXLniXvqhKQCT4p6om6Iwnp4xX1kO2/a61fmwNB0IPjMyb+PKs5EnElEvdu1mQ98NERHZvfZ0A1lEREREZL329CwW1m6HN4IL6dwcnuewstJ/Kz+eNSB6w78at1ozQ0Tc65gePDDwhisUPUmJQfzmfWL9N/RnZ6p07OAB8tPz+MpK9Taz93p48VbtQG+VJdjDL4FbFvppZqwaszwwtno4cMfiYvX2arawEMY1NxsDPaEh3X6+w3kIPc1Ju4mvhJ7wpNkg7/ZIL74ovBEM+EIX29/Gz2Skc+3qDXNLDO90wzmIe1TLnuyhntKBHs0imEQ1pnlcb3J17QZn6yjf2C7fBi7L67mTHjrYf2N+dgbv9sJb2ZdeErZ96CT50vLANUzn9odzeeBAeJM7fsowfA1qjAwpXvb+Dt135ZvI5X7V75aEN/8BThbH7fbIOx0axVvGfnah6jG24mlKtrBQpR0/YcmjJxrl+Snvh7ox+94bCpIz3OMfB5kpeo3jdK3ZoHHwopCn4i15z716QzpZXsE9h7invui5H5g9IDpWeNJSjjtn1T021lqBXwg99vnw93O4DhERkV1NPcgiIiIiIhE1kEVEREREInt6iIUvrww86s2XV0gOzpEv9YNihMfmg49JgdrACFBMGt7pVI9WszNnV0+11QuPspN2u3qUbTMz0Onivbw/KfnMLGZh6AGAlVO4LS71H/WWj+yTBpa2yO++t3+c6DF8HMxhYMhFXKY8TFlWvixYTR4+NB1bOSSherkqlDScw8XF/lCQlRWSZgM/uwDFFHbWaJLPnwkv+nV7kMePnkcEZImHExRDJIYf5RcF6L9AV6S5aiiD5+E8DA1NAUK+6gKtLC1X5ylMWB9e4rRiOEY5pGTwRbbwkl5WTuA+7kW86BF/+TJd9Yi+7nzUPqZPyTud/suRszP4QpgSMI8m8CfLSI4eJr/vAbJT81FWLNy30bVeNfymSD9+STNkfyjISLm8HO4xZjjS4Hcq6w8zajbIOxnZuUWsuB+r/T2nd8991X7VVHrRFIrey6uhFQNTMxblGh7yNHJIywSGA6WU35+qLiiOaa0mSTN8Nzi94cOJiMgOoB5kEREREZHInu5BzjudKrCGLy7iWXgRaFXPT0oRHKLooSqm+Fr1Qhn9F4KSose07E1Omo2B6dDyTieEQS33Wyqmq8qyqsct73Tw++6vjpMVIVcHpmErezbL40bTW1U9W2WPZ9yTNvyyVzVFVn96r7zbq6ZdG+59rnrcyjSLkL/JzGzV4x2Sdej18MX+y5BlGjY7QzYfejHLF7S81x14uaouIMvwS4eeJ3jWGVwPq3oKq/NS16PY6xY95sXLh0WAivjcRoXC0rTqgU3as3j0cmPYxkf29ta+zFaUaVXP8QShxMsyQFZdl+zM2f59VE4fWISaToamCizDnMa9x3H5q57g+H6JX3iLQ6TX9IrXhm+vmaZwVEjvqge6V07D2CQ9EkKbZqfmwwt9US9w3un08z98/PLYUT7rQmKPCp89dhrHoRfy4icvEJ40xE+nZA3dLjf/+GEe/8PFU7EsCyGOu13KML8A+WMup/3x28nTlAf/j2cCcMkf/Qt+5iy0muQnTlXbWqOJNRtkVz2c9L6wPD1yCH/YRdi5ZShCMGf3P0DSbnP26oMcPBFecn38tTdxtHWOmz/62Kp+t8/cwRP/aoUv/NjjSP7pU9zxricBcNX3fB7S8DSrvA8aVz6S7JJDpE+br+6ZxYe3OXDxcXxlBbvmydhnbw8Z3dcmf/Ch6ikVQHroIN7pcO6bn8yBf7oVAH/wBLPH5sK2Rb2WPuwSWF6hd+998K++nNteEJ5KPexDOWeuTDl7dcbhT4cyXfxbH2bm8/fS+thZ7njHFfwv9u48TrKrvu/+53dvVXVXT2/TPYs0i6TRioRALLLAsRMTC4jgsRGxIAhjW9g4xCHEiR0SgxcezAs/AbKAeSA2xGADtkEEvChYWAYDDsagDQltaBlJI81omb17pqeX6rr3lz/OqVu3qqtn0cz0TLe+79erXlN177nnnnPvrdNnTp0FYONP3Ytd/jySmTke/PlxbFPrVzCHl16GPbmX5qYwcDadPBCWmc6y4pdDWzuOr+qHmVmSFz0XgGTfFD4xSTIyTHPdCPbY9nD8pReS9VVJmk3yuMx2etZGmDzIgZeexfA/bAvbRkfCstPzTZL7H2XPNc8DYPQzN5NMHICRIYjnn37eetxg1baD9P1KWBa58qYmN91+E6++6lqsP/x92fG6tQw95uQVsG/fRbol5L/56OMwtIpDP3QWA1+9O2Q9dyobN0C1iu8Ny1w3LzuP9Jb7wkD/UrmQTx2CWL62vu/p2nH6d87SHKpSeSocP34H+L4JbGyUgy/aGPLz0CyVM9fz+GvWctafHKK1rPWhDf2MrBmj+Z27OLf+QgCu+t2/4xtXXQS1Kg+856LwjH5kF9m27eF+VCtYvCZ4mGbVm7NhmXWguWs309e8lOG7dsOBsCQ4G87F736IZHgwTM0avwuVTRvJd+3mml/4Jl/iZQCc99mdZLt2k44Msfmvw3epuWGMwfv2QJqy+8rNAKz5y/t55Jcv5uwvH6SyfSfnfzLcE09T5ldVSM/fjG0Nz0P4fjfD1KSt5dB378Fn58Kvz3FKVWumJMSJFRqN9lSr4WZ1TCyQNxqwZx+eOw9dF5a0XnPHMKv/eBLMmNu8mtqu8H1+z/V/xHuv/hkAfsCRqQVZRERERKRkRbcgh4U42q1HxbRoXcsst1q3ykspt1rsFtPRbzf2me2YJq7cd7eUnvBPuR9mIy7Fa6QjwyFcYjR37+ndmhWn9mqdB7KiRXzBgh9dLXvJwEAxXVkrPVbp79mC2orfy61krWWYSy1yyUBfZ4tyvHaVNWOhb2/peqWDq8gOznf0HW21wHQsHOLtacVacbaWKy63cvZckrscvuv+Wa3dCllcR89Jx1aTx2neyi2rSX/4erT6ILemxQNIK5XQGl6v46W+wR33wbr67pbSWUx51tVHt5yfoqV9vlksU160esZ7R2tpckrTj+Wdv35YlmO1akcaksFhsomJ8Ky2+qLPzS5sFe9eFKN7ir5ymCMs6mKVavGLTj493f7lozXNHKG12xvzYfxA9/Gt/teH6VNctHYfbkq3Vj/1Y9QxdVyMpyPOPClaHlmYfBERWUbUgiwiIiIiUrKyW5DLC35YUswqUSwjDO3+jVnWsRBFq19yR3Tl1rUeiwaUF1foaOmN4bv7LCZDQySVlPzAwdBS2+pTeqC9iEPRahZnKLDBVcWCCngM3+q3U7S4dl6D8I+1W+1iC6dVqqGfdn9fux8rpZbdRgNLKsW5Wq3vHX18m+3WTaBY4KQZ+8yVZ20otzQX1ylrLdjRaoXsao1upalaKRbAaLV4tq5rT+V+qLCg73Qxq0StRn5gqmjFzGdmigVl7Pyzw7a7ftBubV4V++EN1EP/QbMFLfW9FqYotwYDYRGbyUkWKPd1K8/S0LU0uqVpaBVu9ZUn9kWOM2sk1Ur73vf3FbOttO5HNjHRXuQkbV+nXnkp+h+X89FohF8kSn3PF/RPLr0PC4PkeLNZChv7qsc4E7PQV7NSXRhvuaU9th4ntVpn37Tuc5c/H+67fDQ6lpjOivN393NPWr8C7Tr6qEVE5PSjFmQRERERkRJVkEVERERESlZ0F4t09QhWjQOQdu0hm5oi6esnn5stfmq21qII3dNPlX/Gh8MMwuscbNb6XP5ZGsIAOUsSvDW1GZAfPAiVSvj5vDzYav0a8sd3FOcFSCoVqFTwqUML0prU6+Szc72n2+rKU3kwYmsavHx6ushvqxtFMtAHpbRiCUlfNQx0myst7FCpdC680hpAZQnJ4GAxOCyfmSGfm6Wybi3NXbvjRY1dHLq7nvT1d3RJscTIZ2bCgLjSdVrs5/PW/SmmhuseLEdrwFUapsmZmyumvElqNfLZuZDPBx4ttoX7lEPMT7YrDKJM1q3Ft+9YMLVc+By3NeexWq1YNAbA57u6e5TFfBVTCc43SYYGSObmwlRYcZs35sM0fLErRjo4CGZkU4dit4uY35kZ0tUjoWtOK32xG4OlaXH/Ws+HJdZxzcoLt5S7fRT3vcf1XzCYLw7IKwbCNhrgOZUNZ4ZrWZLPzRbhknVr8enpjkVekoEBcCeP09p1dHOIgxOL57nZuwtOz+n5DregSHlgX8xfPt8sdXkJ17+8OIuIiCxfakEWERERESlZ0S3I2f7JoiWqWBggtmYVy8XGZWzLi4dYmranvOpaBrkVV0s+OxcWkpiba7fMzs0uGMCUz8ySxomxW3GmY8PkU4eKgWfFVGOzc2FQWXO+PQBrerpjOixot/jlPabEAorWYQCrxenIEusY1JfPzIbBWnHC+mIAY1JaNIUwSNAbDWygHgZ2tfI1PU06Osrc5ecDUPv2ve3WubM30LwrTMfdWuAknzzYsdhJLwsGXlkVq5QW2eieXqyb5x3LgbfiTEdHIQ4Sy6amsEqVbGIitO6vimnKcvLJSaw0EXnyokvIb7+HdHAQi788pGkaJpCPLdu2fm049bbHi1bNosWzr7+YIq7VKpzU650D0WhP+1csjV2afs8bjWJS+SLs3CyV8fHi/nueY0lC0t+H1WpYX3hemrt2Y5NxoZIYZ2XdWvKJSfL5JpXWxPL79rcXySkvHlJuFW+1LDfnw68x5SkCy9PTxYF/3phvD4jNIC8tvZ7W6/j0TMe5rFbD4kI7AP7U06Rrxjvvb7NZpCk9Y33HstTpqgGyQ9Mk/WFxlOxQj4F4vX51IJYRPfa1vovl473ZWWYs+iuOiIgsS2pBFhEREREpMXc/cqhlaNjG/Ec2/xx5nBLN5+ZIR0fJJg/EPsJxqqhWy09zvliiMds/WYQpLx7iWbZoq1k6OFgsqlEsgVtarCGp1UjOWEdz+5PtfouVKnbZRcxsGKB/5wyWxX6wuydoPr6jo5+zZ1lYCnOgTvOhh8M5R0fDdF2Dgx2LWJRbuMutmHmj0TE1VqvPaj4z224lbPU3jVPktVrvfHYOWzveXrISyCfiUsxbzoKp0BrafOJJ0pGRsFjFBWeR3xFakNORYUisWOobgEqFZMMZ5I/vaJ+/VsPq/R3h8rgQh9Vq+Hyz3ToYW4Hby0i3FwDpngIsHRoq+q1CaMHMp6epXHAeUxePs+rvHgjniq2P+cxMMWVXduFZVB5+IixgcW5YMpT7Hw0t6pUqyegw+URo/c/nZkv9uVsLjvSFPs6JFc9IOjJMc+/e4tlohS/3ZW8plgMv/ZqR1PtDa/6eve3FSzZtgOlpqNXId+1utxaff264N1sfKZ5xGxpk/qw1JN+5p70ISWy97rieWUY6UlpUpDVtYo9l2Fsts70WG0lXDXQss52Oj9HcvZfK2Gryc84MwR7YRvb880nvfbRoacYSePHFpFufKK5XsWhMpYr194WW/NL1LveZXrDceuv6tX6hKY8/KP8yUc5b1/cqHR8L/ehL1yPdvInmGaMk3wvP+9/M/ent7n45sqhhG/OX2JWnOhki8iz0Nf/iEctotSCLiIiIiJSogiwiIiIiUrJiB+lZkpDv2h1+/geyrY+GAWVnrMMnD5DEAXNU0jAN1thqrD8M1LJ0Kgy8y6rFoC7PMtKhoWKqqtb0ZbZhPRyYIt+7r/gp15vzYeBX6Sf9ZHwMarUwiCj+fJxcfB48vIP63dN47sz/2PMB6Ns+F87VbHau0jc7B+7tgYdrxmBiImRj7RqIXQJ85+6QzpFhmk/FAUyJhW4ew4MkY5uKa5KMDJOOrIM4ZVdz797QneGy5+Dfvx+PP19Tq9JcN0yyqh/ufyREOTQEfTWYbxbXKR0dxdI0/CR/14MceNNLABj53C3hmlSqJKtHQ5xpSrZte8dP4jQaWL2/Y0AZxKnnKhXy2bliUFpYqa+fbKq9wlxSr7dXnSv/NJ4kZM/dQuWB7SG+DWtJ3WHXPqoHRyGPXWW2nAWHpkkH6sWguIPnDjBy1zTTL7+UykwIN3v1ZYx8+R7ymRmau/YUA93wPEzHNzfX7o7Qmi6vNGiw6OqTe0eXitbUdOUBcXhOsnYNPjqIzYauQV6v8eSPj7P+f+wvupz4YD/+1M5igFw6NATA49ecwYYPfjd0K4jXLj93E9VHdpInRjI6ErZNHsCSJAxsa3Ux8JxsYqLoNmGVOJixOU8ysrpjWrNWtxbPvUh7a+ChzzdD95nWdHjNJpXNG8jWrcbitW++4Hyqj+6C8dVYnAYvn5ml8tQ+vNEgvfjCcKLtT5Jdeh7cdh9Wnn6x1b0jro4ZzjNfPBPF89R6trpX3YtdJnoOtPO8nfeBsJpixwqU09N4dYykNaDwiYVRiIjI8qEWZBERERGRkhXbgkylQnLu2bAvtLBarUYythr6+2AytOABJMODNK58IbW/vQOPA/qwBBsaxLIsLMQAMDFZTMuVjq0uWhezhx8jWTWAXXQuya59QBjQlk1Okj7/YpKHtgGw78otNPuMdf/HYGsc0DbSz+z5FzH09w/D2CjJLQ+Gc60eCa3P+6Y7BqqRGI/+4hbOfn8YrJSND2KPhvQ1d+8hOTBV5MkPTEGahkUViNPMjQyHAU2xVTjdcjaHnrOGVXfsKAZAYQnW10fy1F7yvj6yVtjKMJUn9+PDA1hsfW/u20/2shcytanG+N+EgYM0m8XCI8nwIBPnh+s02tdHdvBgGBjYWrxl914e+60f4tyPPlhc+zA9WR/p4KpiOjlLU9I142R79lLZchb5jifD8fOO1fux6emOFuikXicZGcbjPc5nZsCMyo49ZFs2hPw8vQ/m58k3r6fvvu1QD62DvncfVq2STx5k5pWhRX/okWnmf/hiBu/aie8J16lyaJrJf3EFAKuemCX77r3h+OY8lTXjoeW9tbhG61eE/r72VG1eWpikNMisMj4ewmd58SuFz80xdflm+nfOksbnce8LR9nwl9vx/j6sL7Qg775ijNX3PkTlovNoPvAwabz3G74V4s9f/Bwe+pkQ9uy/cgb2h+ngPE7xB2Dr15Lu3U9ebMvaC+OUp4lbO04+MRkGEMbW2mJQpOfhPgM2PET+5FPgOU/+q8vZdEO4dw+/eQPn/d4jmDs7Xh5asM+6fjtTl29m8DuPkKwZC7fzwjOo3PEo3pgnv38rANve91LOfd8dZD98KcmdW0mHQ0u59dWg3k/zse20FmnBkvagzzi9oFUr2KoBmjt3hdb5uN2zDKtWilZ2CC3gydo1ZE/vhOeeFzZu3xn2nbWRJIst7QenSOaaPPbmLeHz7yAiIsuYWpBFREREREpWbgvy/Dw/+I+jPOffPRU+Z1louZyfL1rvAPILNlO//VHyNGX3vwz9Zdf/yd34gYNQq5Lv3V+EtUoVq1bItpxJ+nTYnq4ewc9Ygx2ahdiymu3cRVKvc/D8YYb3hr6pz3n7vYzVDvGDWy8sFouw+x7jkr+e4+F3XETyre/z2PWXArDlZx+ENOmY8qtyztlk60dIXzhZtJZOn1lnaN3a0N/18udh94elkRmok+/eg5VaBtORYbzR4NArn8fQt0JLnO/eS//4YAgbWybTM9bD7Fzou/yPXsAjrw0LY5zx3ZwD56QcPD9j9J6Qp3Ufv5m+B5+idttBHvt06Ou98afuxS5/HsnMHA/+/Di2KabBHV56GfbkXpqb1oRzTR4IrcdZVrR029pxfFU/zMySvOi5ACT7pvCJydCnet0I9ljoR5xeeiFZX5Wk2SSPrefpWRth8iAHXnoWw/+wLWwbHQmtyfNNkniN9lzzPEY/czPJxAEYGSr6lU4/bz1usGrbQfp+JbR2Vt7U5Kbbb+LVV12L9YcW/R2vW8vQY05eAfv2XaHvMtB89HEYWsWhHzqLga/eHbKee5iir1rF94ZfGZqXnUd6y32h9bg0pVg+dQgSC4udxH7h6dpx+nfO0hyqUnkqHD9+B/i+CWxslIMvClPPjT40S+XM9Tz+mrWc9SeHitbqQxv6GVkzRvM7d3Fu/YUAXPW7f8c3rroIalUeeM9F4Rn9yC6ybdvD/Wg9o3FZZ3LHm7PFNHHNXbuZvualDN+1uz3134Zz8bsfIhkeLJbuznbuorJpI/mu3VzzC9/kS7wMgPM+u5Ns127SkSE2/3X4LjU3jDF43x5IU3ZfuRmANX95P4/88sWc/eWDVGLL7fmffBJPU+ZXVUjP34xtDc9D+H43wy8IrV85du8JUxRWqu0FcpopCXFqxkajc2Eaz9sLthB/udkTxhc8dF1oqV5zxzCr/3gSzJjbHK5HbVeF91z/R7z36p8B4AeIiMhydlq0IJvZVWb2gJltNbN39tjfZ2bXx/03m9k5S59KEZGV7XjKYjN7V9z+gJn9s6VMt4jIiXbKFwoxsxR4EHgFsAO4FXiju99XCvM24Pnu/ktmdi3wz939DYeLV5PQi8ipcjST0J9ujqcsNrNLgM8BVwAbgK8BF7r7omtvq4wWkVNluSwUcgWw1d0fcfcG8Hng6q4wVwOfju+/CFxpZoaIiJwox1MWXw183t3n3P1RYGuMT0RkWTod+iBvBLaXPu8AXrJYGHdvmtkkMA7sKQcys7cCb40fp77mX3zgpKT41FtDV95XEOVteVLeOp19MhJykh1PWbwR+G7XsRu7T6AyekVQ3pYn5a3TEcvo06GCfMK4+yeAT5zqdJxsZnbbcvv59mgpb8uT8iZHQ2X08qe8LU/K27E7HbpYPAFsLn3exMJ1qIowZlYBRoC9S5I6EZFnh+Mpi4/mWBGRZeN0qCDfClxgZlvMrAZcC9zQFeYG4Lr4/nXA1/1Ujy4UEVlZjqcsvgG4Ns5ysQW4ALhlidItInLCnfIuFrEf29uBmwjLX33K3e81s/cCt7n7DcAngc+a2VZgH6HgfjZbyT9RKm/Lk/K2zB1PWRzDfQG4D2gC/+ZwM1g8C6zkZ0Z5W56Ut2N0yqd5ExERERE5nZwOXSxERERERE4bqiCLiIiIiJSognyKmNmnzGyXmd1T2jZmZl81s4fiv6vjdjOzj8RlXO8ysxeVjrkuhn/IzK4rbX+xmd0dj/nIUi6sYmabzewbZnafmd1rZv9upeTPzPrN7BYz+37M22/H7Vvi0rtb41K8tbj9mJfmtSMs97sEeUzN7A4z+/JKypuZbYvPzJ1mdlvctuyfSTk5VEYvz/ypjF6+eTvtymh31+sUvIB/ArwIuKe07YPAO+P7dwIfiO9fDXwFMOClwM1x+xjwSPx3dXy/Ou67JYa1eOyrljBvZwIviu+HCMvXXrIS8hfPNxjfV4GbYzq+AFwbt/8+8K/j+7cBvx/fXwtcH99fAnwf6AO2AA8TBkal8f25QC2GuWSJn81fBf4U+HL8vCLyBmwD1nRtW/bPpF4n7XlRGb0M84fK6GWbN06zMnrJbqhePR+Gc+gsfB8AzozvzwQeiO8/DryxOxzwRuDjpe0fj9vOBO4vbe8Idwry+ZfAK1Za/oAB4HuE1cb2AJW4/YeBm+L7m4Afju8rMZwB7wLeVYrrpnhccWzc3hFuCfK0Cfhb4MeBL8e0rpS8bWNh4buinkm9Tvgzcw4qo5dt/lAZvdzyto3TqIxWF4vTy3p3fyq+fxpYH9/3WgJ24xG27+ixfcnFn3ReSPhf/IrIX/x5605gF/BVwv+4J9y92SM9HUvzAuWleY8lz0vlw8B/AvL4eZyVkzcH/sbMbrew5DGskGdSlsyKe15URi+7ckxldHDSn8lTPg+y9ObubmZ+qtNxPMxsEPgS8O/d/UC5u89yzp+H+V1fYGajwJ8DzznFSTohzOwngF3ufruZvexUp+ck+FF3f8LM1gFfNbP7yzuX8zMpS28lPC8qo5cXldFL+0yqBfn0stPMzgSI/+6K2xdbxvVw2zf12L5kzKxKKHj/xN3/LG5eMfkDcPcJ4BuEn6VGLSy9252eY12a91Qu2fsjwGvMbBvwecJPeL/Lysgb7v5E/HcX4Y/mFaywZ1JOuhXzvKiMXpblmMropXwml6pviV49+9ucQ2f/tv9CZ2f0D8b3/w+dndFvidvHgEcJHdFXx/djcV93Z/RXL2G+DPgM8OGu7cs+f8BaYDS+rwPfAn4C+F90DpJ4W3z/b+gcJPGF+P65dA6SeIQwQKIS32+hPUjiuafg2XwZ7QEgyz5vwCpgqPT+H4CrVsIzqddJfW7OQWX0ssofKqOXZd44DcvoJb2henU8DJ8DngLmCX1h3kLoG/S3wEPA10o31YCPEfpR3Q1cXornF4Ct8fXzpe2XA/fEYz5KXDVxifL2o4S+RHcBd8bXq1dC/oDnA3fEvN0DvDtuPzd++bbGwqovbu+Pn7fG/eeW4vqNmP4HKI2mjdfqwbjvN07R8/ky2oXvss9bzMP34+ve1rlXwjOp10l7ZlRGL8P8oTJ6WeaN07CM1lLTIiIiIiIl6oMsIiIiIlKiCrKIiIiISIkqyCIiIiIiJaogi4iIiIiUqIIsIiIiIlKiCrKIiIiISIkqyCIiIiIiJaogy4pgZuNmdmd8PW1mT5Q+18zsH07Seetm9ndmlprZJjN7Q9xeM7P/U1r+U0TkWUtltCw3qiDLiuDue939Be7+AsJSmx9qfXb3hrv/o5N06l8A/szdM+BK4EUxPQ3C6j9vOEnnFRFZNlRGy3KjCrI8K5jZlJmdY2b3m9kfmdmDZvYnZvZyM/u2mT1kZleUwv+Mmd0SWzc+bmbpIlG/CfhLM/tR4L8Dr4vHnAv8RdwvIiKHoTJaTjeqIMuzzfnAfwOeE18/Dfwo8A7g1wHM7GJCq8KPxNaOjB6FqJnVCGvbb3P3vwduBa6OLSKPENZ8/6GTnyURkRVDZbScFtT3Rp5tHnX3uwHM7F7gb93dzexu4JwY5krgxcCtZgZQB3b1iGsNMFH6fBFwf+uDu2dm1jCzIXc/eMLndbleAAAgAElEQVRzIiKy8qiMltOCKsjybDNXep+XPue0vw8GfNrd33WEuGaAfgAzWwNMunuzK0wfMHtcKRYRefZQGS2nBXWxEFnobwn91NYBmNmYmZ3dHcjd9wOpmfUTWjaeLO83s3Fgj7vPn/wki4g8a6iMlpNOFWSRLu5+H/CbwN+Y2V3AV4EzFwn+N4T+cfcDa8zsHjNrjcb+p8Bfnez0iog8m6iMlqVg7n6q0yCybJnZi4Bfcfef7bHvz4B3uvuDS58yERFRGS3PlFqQRY6Du38P+Eb3FENx9PRfqOAVETl1VEbLM6UWZBERERGRErUgi4iIiIiUqIIsIiIiIlKiCrKIiIiISIkqyCIiIiIiJaogi4iIiIiUqIIsIiIiIlKiCrKIiIiISIkqyCIiIiIiJaogi4iIiIiUqIIsIiIiIlKiCrKIiIiISIkqyCIiIiIiJaogi4iIiIiUqIIssggz+89m9u9PdTqWGzO7xcyee6rTISIrm8roZ0Zl9NFRBXkFMrMfNbN/MLNJM9tnZt82sx86yefcZmYvP5nnWEpmthb4OeDjpW1jZvbnZnbIzB4zs58+dSk8NseS9iOFNbNvmtmsmU3F1wNdUfxX4L0nIx8iK4HK6OOnMrp3WDPrM7NPxu0HzexOM3tVVxQqo4+CKsgrjJkNA18G/n9gDNgI/DYwdwrTVDmd41vEm4Eb3X2mtO1jQANYD7wJ+L1l9L/wY0n70YR9u7sPxtdFXftuAP6pmZ1x4pIvsjKojD5h3ozK6F5hK8B24MeAEeA3gS+Y2Tml41VGHw1312sFvYDLgYnD7N8GvAu4D9gP/CHQX9q/AfgSsBt4FPjl0r7NwJ/FfXuBj8btnwVyYAaYAv5TPM+vAXcRCv4KcDHwTWACuBd4TVfaXgTcARwE/hdwPfC+Urq743sn8HAMfx/wz3vk9T/GYw4BnyQUKF+Jx3wNWL3Idfo68DOlz6sIBdKFpW2fBd5/FPdkFHg98Cngf/XYPwTsAF7WtX0T4MD48ZzjWNJ+NGHjPfzFI6Tnq8B1p/r7oJdep9tLZfSCvKqMPsFldI9j7gKu6dqmMvpI9+xUJ0CvE3xDYTgWjJ8GXtVduMQC6Z5YkI4B3y4VcAlwO/BuoAacCzwC/DMgBb4PfCh+QfuBH+2K9+Vdn++M56kDVWAr8Osx7h+PBeBFMXwNeAz4dzHsT8VC4H294ovbXk/4Y5EAb4gF7JldafhuLHA3AruA7wEvjOn/OvD/LnIddwM/VPr8QmC6K8w7gP+9yPGXEf7I/R9gktBi9Dbg7B5h3wPc1GO7Ef6Yvex4znEsaT+asIQ/oLuBPfH5WZA+4CPAfz/V3we99DrdXqiMVhl9ksvorn3rgVngOV3bVUYf4XXKE6DXSbipoRXgjwj/420Sfk5ZH/dtA36pFPbVwMPx/UuAx7viehehBeOHY4FUWeScvQrfXyh9/sfA00BS2vY54D3x/T8BngCstP/vuwrfXzhCvu8Eru5Kw5tKn78E/F7p878F/mKRuObLBUor/V1h/iXwzR7H3kBoVbg5Xt/+w6Q5BXYCr4+f1wLnlvZPAK86znMcS9qPGDY+J0NAH3Ad4Y/oeV3H/A7wqVP9XdBLr9PxpTK6Iw0qo09wGV3aXiW0wn+8xz6V0Ud4qQ/yCuTuP3D3N7v7JuBSwv/gP1wKsr30/rG4H+BsYIOZTbRehNaE9YRWgcfcvXkMSSmfZwOw3d3zrnNvLO1/wuM3t8fxCz6b2c/FAQittF4KrOk6Zmfp/UyPz4OLpH0/oRLYMkVo+SkbJlQOu/02ofCpElqJPmVmP2tm63qEvRRYB9wUP/8q8FYAM6vHNOw6znMcS9qPGNbdb3b3g+4+5+6fJrRwvbrrmCHCHw4R6aIyuoPK6BNcRse0JYSuFw3g7T3iURl9BKogr3Dufj+hpeLS0ubNpfdnAU/G99uBR919tPQacvdXx31nHWbwhR9h25PA5vilLZ/7ifj+KWCjmdki6eyIz8zOBv4n4Ys/7u6jhJ8ljRPjLuDC0ucHgYqZXVDadhmhn15nIt1vd/ffdPcXAc8j9PX6SeAHZvaZruAbgf3ufiB+vop2YftjhD8CdxznOY467ccYtkgOC6/7xYSfe0XkMFRGP2MqoxcJG+9Rqz/3Ne4+3yMeldFHoAryCmNmzzGz/2Bmm+LnzcAbCf28Wv6NmW0yszHgNwgDLQBuAQ6a2a+ZWd3MUjO7NE4/dAuhgHy/ma0ys34z+5FSnDsJ/eEWczMwDfwnM6ua2csIhcXn4/7vABnwdjOrmNnVwBWHiW8VoTDeHfP583T+gTleNxIKPwDc/RBh8Mt7Y/5/BLia8D/0Rbn70+7+h+7+LwitEL/TFWQfMGxmW8zsjYR+fpeY2Sih39uHu1p0jvkcx5L2I4U1s1Ez+2fx/lfM7E2En17/uhWHmfUDLyb8QRCREpXRJ4zK6MXD/h6hAvyT3jnLB6Ay+mipgrzyHCT0U7vZzA4RCt17gP9QCvOnwN8QBnc8DLwPwN0z4CeAFxBGR+8B/gAYift+EjgfeJzQd+4NpTj/M/Cb8ae0d3Qnyt0b8fhXxXj/B/BzsfWktf+ngLcQfvb5GcKAhp5TH7n7fcB/IxTaOwn/Q//2UV6jo/EZ4NXxJ7SWtxEGs+wi9M371+6+4H/4FuYJ9u4Xoa/hJ7uC30r4A3QnIe+vAf4R8BDhD9YHeiXuGM9xxLSb2VfM7NePImyV8Ly0Bun9W+C17v5g6Vw/SegP9yQi0k1l9ImhMrpH2Nhy/68Iz8jT1p6v/k2lc6mMPgrW2Z1IVjoz20aYoutrpzotR2JmNwO/7+5/eIrO//8Bu9z9w0cMLIV4397i7vec6rSILDcqo4/p/CqjnwGV0UdnKSbzFjkqZvZjwAOE1os3Ac+n9NP9UnP3Xz9yKOnm7i851WkQkRNPZfTKoDL66CxpFwszu8rMHjCzrWb2zh77+8zs+rj/Zosrv5jZOWY2Y2E07J1m9vtLmW5ZMhcRBg1MEH5ufJ27P3VqkySyPB1HefsKM7vdzO6O//546ZgXx+1bzewjZmHAloWlb79qZg/Ff1cvVT5lSamMlmeNJetiYWYpYfTlKwh9o24F3hj7KbXCvA14vrv/kpldS1h15w2x4P6yu5/IDv4iIivScZa3LwR2uvuTZnYpYYGEjfGYW4BfJvS9vBH4iLt/xcw+COxz9/fHyvhqd/+1JcyyiMgJtZQtyFcAW939kdjZ//OEkZdlVxPmCwT4InBlq4VCRESO2jMub939jtLgnXuBemxtPhMYdvfvxrlwPwO8tkdcny5tFxFZlpayD/JGOicR30EYydszjLs3zWwSGI/7tpjZHcAB4Dfd/VvdJzCztxIn8E5JX7zKRop9R9VS3qqLn4hWdbMTE09XfJYkeH7YGWUOH81xHr8gX2bhssVN7o6ZFdfbkvB/MM/zozv3ib5uz/Q8R9h/wq/jcbAkPLeed96XVvxWreDzzfZ2wkSkp3KAbusZKT8r3fth4fPUffxppfR/+YO+b4+7rz2FqTme8nZPKcw1wPfcfc7MNsZ4ynG2FpFYX/qp/WnC/KsLdJfRAwx33OsYpuPeHulZWVSpPG99Xzueqx7fYUsSaK80hlUqkGfhu1X+TnWFax9v4IvnZUESS2mwxDq/w4uEK/4WpCmeZUWeivSlKd7sXCvE0pR8oA+A5NBMcR6r9+Mzs0VaF3PE636C/nZamobr2p3XJOn4fnmWdVwvG+jHp2dDuDRtpyXPIUnCv9VQ3fG5xqLPWOs9AGmCN7OOMCHaxe9R9zVwd6ySFvEUYXCwdp667xfEZy/LijhCuOzo/nbFdBy2fC3np1eYJIHW85yVrkPXM95KaysPC77P1So+P98Rb6s+ABzT39HD5adje+u5aZUdrXOV8nqQ/Ucso5fLIL2ngLPcfa+ZvRj4CzN7bmnibgDc/RPAJwCGbcxfWruq2Jc3GkD4Anru0GPaQqtUQzzN0pzarTnTDz/NYTveGNYq1cXjie+Lik2WhYKhnJfSA9lKmzfnSVcNkk1NteP0vCgoj5RWS1Osr498erozTd3H9Ehr6zytc4ZNoZC2agVi3r05X6TVs4x0VVgEKTs0TVLvx+faMwIVaS6du3VskadnoivOBfvitSjO0/08WFLs7z62dc+sr498Zvbwz8Vh7kfxfHSn9XBp7447hkv6wyxH+Ux7ukurVIv96dq1NJ/eVeS79W8+N9uZzlZan0EaFmzrynv3dU5qNfL5Zvi3lY6SpK8/5GlulqSvvyOMpSlWqfY8riMdh8vP0eaxV55K+eoIFssPgK/Of+6xo4v89GVmzyVMYfXKYznO3VvTWfXa11FGvyR5BUmtFvbFMqP1bLQk1UooIyvVdjmeWFFulsvdjrI1vvcsK8qe1j3K52ZJ6gPtsjAcRDq4Cp+bK85fGRsjnzpEPjPT8Z2yvj7I8pDmViUtMaxWgywrji+e70XKgnIakv46HvPXKvta+UsHV5FNHSq24TnJ4CDZ5GRx/axWwxsNktWjNHd2LuyWDo8yd/n5ANS+fS/5bCiH0wsvIrvrB+H88TvXS+u698oDLPK3M17TxY7p2B/DpMOj0GwWf+Na5WRSH8D6QwWfLA/57q8X+UguuYT89ntIVw1iI3GRuUaD/NA0ydAgfmgaWx/qQtm2x4u/L63rXJQxlpDEinQyNERz794ina3teaPRM19JfSC8KVV280aDyug4zX37Y5j+UEHMMqxWw/rCvWvu2r2gjKyMryWfmCSfb1IZDV36m/v2F/e/XG8oPy/Fc9+cD/nqce+Svn5IY7jGfPtveilO6+sjqdfxLCObmCjiL57xUp0qXTNOtmdv8f1txeVZRmX9BppPPl0kIV01QHZomnRVuF7ZoekF13Kx8rkoG7q/R339C+pb5fy3zpXPzhXX6mv5F45YRi9lF4sn6Fx1ZxPtFXoWhLGwGtAIsNfDkrZ7Adz9dsK8kBciIiK9POPyNn7eBPw5YR7ch0vhNy0S587YBYP4b6+ld0VElo2lrCDfClxgYTWaGnAtcENXmBuA6+L71wFfj60Ra+OgE8zsXOACwgTqIiKy0PGUt6PAXwHvdPdiYYfYheKAmb00jg35OeAve8R1XWm7iMiytGQVZHdvEtZkvwn4AfAFd7/XzN5rZq+JwT4JjJvZVuBXgdbURP8EuMvM7iQMJvkld9+3VGkXEVlOjrO8fTthNbZ3l6bWXBf3vY2wcttWwi95X4nb3w+8wsweAl4eP4uILFtL2gfZ3W8kTA1U3vbu0vtZ4PU9jvsS8KWTnkARkRXiOMrb9xGXNu6x7zZgwXSbsQvclceZZBGR08aSLhQiIiIiInK6Wy6zWIiIyApT2XgmeRzhTxMq4+NkkwdIarViVLrVavjsHPncLOlYGM2f7Z9sz3DQNWq+PXI/zoLjOfn0NOngYDHdoVWqYUad8mww1Qq2eoRs+5PFtmz/JHbZRcxtGKB/5wyWhRkrkt0TNB/fEUb1l2YjSsdWw0Cd/KEwrtHq/TA3G0buT00tnMGoNKsPueO5h7y3Zleo18EszJjTEmcwyA4cxNKUZPVo2Dw7R3LWJjhwkMq6MGNDPjEZzrd+Df0/CFNbN2dmSEdGIM/xStJOU70fEiPbt7+4plQqJBvOgMd3tGdKqNWxen9HuLzRCGmp1/H5JkmccSKbmor3Ke+c6cKSBTPYpEND0GySz8yQDMRZB6anqVxwHlMXj7Pq7x4I2w6Fe5nPzJCuDlO5ZtWUyto1eGOefF24Htz/KN5okGc5yegw+Y6nivsUplhLoDXZSmJhZofE2lNilrRmUgE6Z1kp389ms2OavqTeT2X1KNmevcXMEMm6tTA9jdVq5Lt2k02G+1M5/9xwb7Y+Ujzj1PvJLtpE8p17iplOOma+qlSLNKWjo2QTE2FmqaQ1RV6ycKaf+Lx7cx6fyzq2p6sGOmZCSgZX0dy9l8rYapIXhx+N7IFtZM8/n/TeR7HWLFOWkJ1zBmkzo7l3b+c16OvHpw51zLaRHZrGEmvPVNKavaM8c4XnxcwU5Rm6ihk5uma5KM9uA5COjdLctTs8l4lha8JswX7GKMn3wqwtlL56i1ELsoiIiIhIiSrIIiIiIiIlqiCLiIiIiJSogiwiIiIiUqIKsoiIiIhIiSrIIiIiIiIlqiCLiIiIiJSogiwiIiIiUqIKsoiIiIhIiSrIIiIiIiIlqiCLiIiIiJRUTnUCTiar18kPTRef08FBPM9hbg5vLUNeWtPbS2uLF2uel9YVL+JtrR0OpMND5Iem28cCVqmG49O0vX59YsUxrXXqLU2x4SHyiUl8bq5Yf96bTby1JrqV/g9jCXbmenhoqh1nVlpbPubDSuftOJ6wtn06OAgQ1kK3hKRaaa9xXsTbTne4DgmeO0m9is+FRcyTaoV8vkm6bg0er7NPzWOr6viBjHSwDnENdksMb8yHa1BaQx3PO+9Bc759L1pp95ykViOfb7aPWUxx79L2KeK69cnwID51KFyHmF/PnXRkmGzyQHFvfL6JpSnphvUh7J595DOzHfcwHVwVruXQENnBgx1pXXAPemjlc+GOvOP5amfLsEq1OK54bwnJ2OoQaF8873yTvNGgsmYsRHlwiqRWC3HXakC891ESt+F5uC4xDa3r03oeuvNkaYo3885nrHVvWv/GfZ5lJLVaka9WvFatUBleE9K0b3+xL4lpT2bncM+h9HxiCXmjUaS7eHZL96B4bjMWPGOHdbhwcXtSr5N3fz+7yxAREVnW1IIsIiIiIlKiCrKIiIiISIkqyCIiIiIiJaogi4iIiIiUqIIsIiIiIlKiCrKIiIiISIkqyCIiIiIiJaogi4iIiIiUqIIsIiIiIlKiCrKIiIiISIkqyCIiIiIiJaogi4iIiIiUqIIsIiIiIlKiCrKIyApkZleZ2QNmttXM3tljf5+ZXR/332xm58Tt42b2DTObMrOPlsIPmdmdpdceM/tw3PdmM9td2veLS5VPEZGToXKqEyAiIieWmaXAx4BXADuAW83sBne/rxTsLcB+dz/fzK4FPgC8AZgFfgu4NL4AcPeDwAtK57gd+LNSfNe7+9tPUpZERJaUWpBFRFaeK4Ct7v6IuzeAzwNXd4W5Gvh0fP9F4EozM3c/5O5/T6go92RmFwLrgG+d+KSLiJx6qiCLiKw8G4Htpc874raeYdy9CUwC40cZ/7WEFmMvbbvGzO4ysy+a2eZeB5nZW83sNjO7bZ65ozyViMjSUwVZRESO1bXA50qf/zdwjrs/H/gq7ZbpDu7+CXe/3N0vr9K3BMkUEXlmVEEWEVl5ngDKrbib4raeYcysAowAe48UsZldBlTc/fbWNnff6+6tJuE/AF78zJMuInLqqYIsIrLy3ApcYGZbzKxGaPG9oSvMDcB18f3rgK93dZlYzBvpbD3GzM4sfXwN8INnlGoRkdOEZrEQEVlh3L1pZm8HbgJS4FPufq+ZvRe4zd1vAD4JfNbMtgL7CJVoAMxsGzAM1MzstcArSzNg/Avg1V2n/GUzew3QjHG9+aRlTkRkCaiCLCKyArn7jcCNXdveXXo/C7x+kWPPOUy85/bY9i7gXc80rSIip5sVXUH22TnwPHywhHx2jmR4kHymPXuRJYbnsadJKyzgWe84k1qNvNEAC8dkBw52HAfgzRyr1UjqdbKpqXCevj5ozOPNnHxmJsTV14+Z4Y35EKbeHyKYngnxe17EbUkFS2vkTzzVPk/e/jXUEivS7Fm24PgQPsEqVfLZ0FXQKlUA8vlmO540xbMMq1Tx5jyWpnFPiDOfniYZGAjxzc2RVCv4wSmoVoo488kDeO74fBPyvJ0mFl5US9OOfGBJzEvWESZmIOyPnz3LivSWMhmugyUdcQIhXdnCNPjMbHGd8plw7T3LsKlDRT47riuAGVhCFsN0PwPl87bSjeck9Xr7PD0syE8hJW80irxbfx8+NY9nGfm+/WFbrQZZRjI2Sv70LrL9k6WkWHhuS/e64xpBEb/n3pGG1vejO13enC/iDuF6/Drf8Z3KijBJtULeyMgOTWPxeSyO95zmk08XxyXVSvsaxvN5Mw/5gY7noZWvjucnPsvPVPm7BRTfn6IsiOe0WpWkGr4bTDzj04mIyGlAfZBFREREREpUQRYRERERKVEFWURERESkRBVkEREREZESVZBFREREREpUQRYRERERKVEFWURERESkRBVkEREREZESVZBFREREREpUQRYRERERKVEFWURERESkRBVkEREREZGSJa0gm9lVZvaAmW01s3f22N9nZtfH/Teb2Tld+88ysykze8dSpVlEREREnl2WrIJsZinwMeBVwCXAG83skq5gbwH2u/v5wIeAD3Tt/+/AV052WkVERETk2WspW5CvALa6+yPu3gA+D1zdFeZq4NPx/ReBK83MAMzstcCjwL1LlF4REREReRZaygryRmB76fOOuK1nGHdvApPAuJkNAr8G/PbhTmBmbzWz28zstnnmTljCRUREROTZY7kM0nsP8CF3nzpcIHf/hLtf7u6XV+lbmpSJiIiIyIpSWcJzPQFsLn3eFLf1CrPDzCrACLAXeAnwOjP7IDAK5GY26+4fPfnJFhEREZFnk6WsIN8KXGBmWwgV4WuBn+4KcwNwHfAd4HXA193dgX/cCmBm7wGmVDkWERERkZNhySrI7t40s7cDNwEp8Cl3v9fM3gvc5u43AJ8EPmtmW4F9hEq0iIiIiMiSWcoWZNz9RuDGrm3vLr2fBV5/hDjec1ISJyIiIiLCEleQl1reaJDU+wHw6Wk8g2zffpJajbzRCIEswVLAczwPYxYtTeNnB88XxAmQVCvtz5aQVCt4loVzZRl5o4HFzwD5zCxJrYZnGZZYcaw/vbM4T7ZvIgT2PLwsKc5fnLevv4jTEsOzkAfPspDueH48x9I0xF2K09JaO875JpZYCBeT6lkW4mvOhw2tOOebQEbS14835os0eO7QbOLT0+1tMQ7r7yObnAzprtXw3PHmPEmt1s5/DFtWbIvp9DzBs0bnftr57tZxHVrhmvNYpQqEbVapFvuLZ6GdKSxNyfbHtNf78elp0sHBUhiP1633+cvbi8+WkM/MLDjXgrz3YgmQFfclO3Cw/RzNxhlbZmawNCVJ07Avxm19ccDqfLMdXSn/rTg7npfWPWk9R818wfbWPVqQ5tK963UNOoNakQ9vhnNhVdLVIyGf+yfx3EN6YzrzRqOd/u7zt85dSmfxLJfCFN+dbl3phs7nqBwmL11PAJ+bI5+Z7RGp9GJJQr5rN8mWswDItj5KPj1NesY6fPIAyeCqELCSwswM6dhqrD+Uf5ZOhe9lVoVmuA+eZaRDQ3iWkdRqWK0awm5YDwemyPfuK55vb86TDAxg7sV3Mhkfg1qNdNUA2VQYD55cfB48vIP63dN47sz/2PMB6Ns+F87VbBbHW5rC7By4F2WcrRmDiVCuV9augZHhcP6du0M6R4ZpPvV0yGdiJLUayfAgydim4pokI8OkI+sgPlvNvXuxNMUuew7+/fvxQ7HsrVVprhsmWdUP9z8Sohwagr5a+O7H65SOjobybWIC7nqQA296CQAjn7slXJNKlWT1aIgzTcm2bY/fl/g9aDSwen8oz0plp+dOUqmQz84VZZKlKUm9n2wqK743Sb0OWdZRHlhikCRkz91C5YHt+Ia14fTusGsf1YOjkIfj0y1nwaFp0oE6hFlgOXjuACN3TTP98kupzIRws1dfxsiX7yGfmaG5aw+VsdWthEKlgs/NtcueHn+LsskDRZlY/q63/haFsqpdJiRr1+Cjg9hsKG+8XuPJHx9n/f/YT9IfymAf7Mef2glpOC4dGgLg8WvOYMMHvxv+vsZrl5+7ieojO8kTIxkN5WE+eQBLErJDWbueAGQTE0UZa5VYR2jOk4ysLv6OQfg7nM/Nhu9BTHtSrYS/xfNNrFZr//1sNqls3kC2bjUWr33zBedTfXQXjK/G5mN5PDNL5al9eKNBevGFsP3JkKZLz4Pb7sNa9zpep9Y1DX+P43cxPhPlelneaHSW5XF7x7NY5nk77wMDIW/9feFZjPUTr46RrBkPYbpHwPWwXGaxEBERERFZEqogi4iIiIiUqIIsIiIiIlKiCrKIiIiISIkqyCIiIiIiJaogi4isQGZ2lZk9YGZbzeydPfb3mdn1cf/NZnZO3D5uZt8wsykz+2jXMd+Mcd4ZX+sOF5eIyHKlCrKIyApjZinwMeBVwCXAG83skq5gbwH2u/v5wIeAD8Tts8BvAe9YJPo3ufsL4mvXEeISEVmWVEEWEVl5rgC2uvsj7t4APg9c3RXmauDT8f0XgSvNzNz9kLv/PaGifLR6xvXMky8icmqpgiwisvJsBLaXPu+I23qGcfcmMAmMH0Xcfxi7V/xWqRJ8VHGZ2VvN7DYzu63hWlRFRE5fqiCLiMjRepO7Pw/4x/H1s8dysLt/wt0vd/fLa9Z/5ANERE4RVZBFRFaeJ4DNpc+bWLi4ahHGzCrACLD3cJG6+xPx34PAnxK6cjyjuERETmeqIIuIrDy3AheY2RYzqwHXAjd0hbkBuC6+fx3wdXf3xSI0s4qZrYnvq8BPAPc8k7hERE53lVOdABERObHcvWlmbwduAlLgU+5+r5m9F7jN3W8APgl81sy2AvsIlWgAzGwbMAzUzOy1wCuBx4CbYuU4Bb4G/M94yKJxiYgsR6ogi4isQO5+I3Bj17Z3l97PAq9f5NhzFon2xYuEXzQuEZHlSF0sRERERERKVEEWERERESlRBVlEREREpEQVZBERERGRElWQRURERERKVEEWERERESlRBVlEREREpEQVZBERERGRkpW9UIjneGO++GiJ4Rl43l4B1ZvzWJriWUZSq4VtWQaWANmiUefzza54s3Bc2ICladxbisPz+E84f1KtkDcaWKUKbqQjwyFcYjR37ynCd2SpOR/TRmUS1WUAACAASURBVJHGpFYjbzQ68lXkoyQZGCCfmcUSK9JjlX7yRqPrJHkRv5fyiefhmjbb1zQZ6Ou4xq1rV1kzhs/MdlyvdHAV2cH54nyWpliaks8329fLEvBwPVvbPMviNcrDvYr5LO5T13UqwnfdP6tVi7QX19Fz0rHV5JMHQphKtbhuSX/4evjcHADZ1FRx7dNKBW/Ok9TreKNRpLXjPsSwxX0opTOp1UK40rbu/LSex3y+iVVjWlpZiveO5nw7vfU6+cwM5HlH3JblWK3akYZkcJhsYiI8q5WwL5+bLb4L7YvW9X/o8rUuPScdn3s8txCubVLvD+eang7PV5ZhMZ8A1heeJ5+dW3h8vMZWqXY8g91hvJkvmoZW+nzxr/aiWt+b8j3oiDNPSOJ9YmHyRURkGVELsoiIiIhIiSrIIiIiIiIlqiCLiIiIiJSogiwiIiIiUqIKsoiIiIhIiSrIIiIiIiIlqiCLiIiIiJSogiwiIiIiUqIKsoiIiIhIiSrIIiIiIiIlqiCLiIiIiJSogiwiIiIiUqIKsoiIiIhIiSrIIiIiIiIlqiCLiIiIiJSogiwiIiIiUqIKsoiIiIhIiSrIIiIiIiIlqiCLiKxAZnaVmT1gZlvN7J099veZ2fVx/81mdk7cPm5m3zCzKTP7aCn8gJn9lZndb2b3mtn7S/vebGa7zezO+PrFpcijiMjJogqyiMgKY2Yp8DHgVcAlwBvN7JKuYG8B9rv7+cCHgA/E7bPAbwHv6BH1f3X35wAvBH7EzF5V2ne9u78gvv7gBGZHRGTJqYIsIrLyXAFsdfdH3L0BfB64uivM1cCn4/svAleambn7IXf/e0JFueDu0+7+jfi+AXwP2HQyMyEicqqogiwisvJsBLaXPu+I23qGcfcmMAmMH03kZjYK/CTwt6XN15jZXWb2RTPbvMhxbzWz28zstobP9goiInJaWNkVZEuwxLDEwBKSej1sTtPihSVYpQpAPt8kn2/iWQaeh1c5ujRtf4j7k3odzzI8y9pxAt6cD/GUwufzzY74kqEhKuvXYdUKWAKJQWLkB6Y6zmlpSlKvk9RqJCPD7bTF9OVzs+B5O69d1wBLsDQln56OSXE8d6xSJW80SPr7FuQzqdU64mzF67l35JNmE6tWsFoNq9WKOJq795DPzIR01+vgOd6Y7ziP507eaIR9MU3l69a6rkBxjfJGg65I6MnzIu8ASV9/5/ktCfevViM/MFWkP5+bJenvw2pV7IJzsAvOCWmI8aSrBkhXDWAD9fDcmBXpLO5369yle5TUauGatu5HvV7kdcGxUd5okDca4dq3wsQ4LU1DGuMzjCXks3NgCdnUIZJqhaSvn6SvH+vvI5s6VDyvSb1ONjFBOjQU4kuT8IrXvPs6erN93Yp8AMnAQGfY0vXufu+5x7iaeLPZsa917ZOBAXxurnhW8+np9vUoXZdWepLS89bzvpfT0qXju3w0uuOM5y/Kkko1XN+R4fAdXcHMrAJ8DviIuz8SN/9v4Bx3fz7wVdot0x3c/RPufrm7X16z/qVJsIjIM3DMFWQzWxX7t4mIyEl0HOXtE0C5FXdT3NYzTKz0jgB7jyLuTwAPufuHWxvcfa+7z8WPfwC8+BmkWUTktHHECrKZJWb203H08i7gfuApM7vPzP6LmZ1/8pMpIrLyncDy9lbgAjPbYmY14Frghq4wNwDXxfevA77u7n6E9L2PUJH+913bzyx9fA3wg6NMp4jIaalyFGG+AXwNeBdwj3v4rdPMxoB/CnzAzP7c3f/45CVTRORZ4YSUt+7eNLO3AzcBKfApd7/XzN4L3ObuNwCfBD5rZluBfYRKNPF824BhoGZmrwVeCRwAfoNQaf+emQF8NM5Y8ctm9hqgGeN684m4GCIip8rRVJBf7u7z3RvdfR/wJeBLZlY9mpOZ2VXA7xIK7D9w9/d37e8DPkP4eW4v8AZ332ZmVxB+1gMw4D3u/udHc04RkWXkhJW37n4jcGPXtneX3s8Cr1/k2HMWidZ6bXT3dxEq9SIiK8IRu1j0KqyfSZjjnJfzHuByd38BcBXw8dhnTkRkxThR5a2IiByfo65kmtlHgOcADnwf+FN3v/MYzlXMyxnja83LeV8pzNXAe+L7LwIfjfNyTpfC9Mc0iIisSCegvBURkeNwLLNY3Af8F0IXiV3AH8c+bkfruOblNLOXmNm9wN3AL8X9HcpzbM4z171bRGS5ON7yVkREjsNRtyC7+++XPt5oZh8ljJT+6AlPVe/z3ww818wuBj5tZl+JfejKYT5B7Ks8bGP/t707j7PjrO98//lVnT6n91WLtVqSZbyvCGMyzL0MZjFcBhPMYiYQs0yce4NvSDLJjJmZ+Pr64lzghpDwYgmemMEwTGzHQNALHIwxJCzGi2wZ2ZIlq63FkiVraUmt3rtP1e/+8VSfrm61sFpSd6u7v+/X67xU9dRTVb+nqs7Tj+o8VY/uMovIjDTd9a2IyFw34X68Zva/A6uBBsJTzSdqIu/l3H2893K6+3Nm1g1cDKybWPQiIjPHKdS3IiJyCk5mJL0HCO+4XAr8vxNY76Tfy5mtUwAws7MJffN2nETsIiIzycnWtyIicgom8pDePwC3uvtzwF1m9t+B9cD3T2T9U3wv5+uBW8xsCEiBP3D3gycau4jITHKq9e2MUCgQrTobDh0BCMONt7ZAdQk6IekMN8yjxnoGr7mC4sPr8UOHw7oWYQ31WJLA8LDhRzrD8ORA3NoC4T3NJC/sJKqrxc5bRbT/EADeP0DS2Ul86QVEW3cAcOialZRLxoKfGbSH7ZSbqulffR4Nv3gBWpuJHn8+7KulCXMnPdRbGfI8HRyEyNj+71dy9qfDD59JWz22PcRXPnCQ6Gh3pUx+tBviuDJke9rXT9zUSNrTCz1ZOVaeTc/586hbv5tyR8dI2Uslor0dpKUSyXDeQiOFPYfxxlqsvi7s89BhkjdcQffSIm0/eiGsXy6T9vZihSqixnqOrA7HqblUIunqIq6vh6rwJsH0QAc7//w1rPri85Vjb8UiVioR19eRdHWFtDgmntdGcrCDwsrlpLv3hPWHHKupxnp7R4axHxwMQ943NeLZOU77+sCMwu6DJCsXE78czhNDQ6TLFlLatAtqwtDk3nEIq6oi7eyi7y2XAtCwrZeh111A/YZ9+MFwnAo9vXS+7yoA6l7qJ3l0Y1i/PERhXhve0wtZTGlfXzgv1aXKdUM2Zk5ULJL2jMRfaGsL+ZMUK4bj5AMDdK9ZRvW+fuLseuy4opnF39uFV5ewUgmAA1e10rJxK4XzzqG85QXi7Nwv/nk4h+mrz2frB0Pes3/g1B4uYcUi3t3DMFs4n7jjMGl3D5CMXBNxDBaF6xAozG8jPdKJRYYVsvM5kPVK9TScZ8AaG0j37AVP2fP7a1i6Npy7Fz68mHO+sg1zZ/ebmgBYfu8uutcso/5X24jmtYbT+aqzKKzfjg8OkW5uZ8enrgZg1afWk7zuYqKn24kbG8K+SkWoqaa8cxehGZjFXlNNcugwUSmcY6sqYHW1lPftB08r6Z4kWFUB7x95xiyqKhDNn0fy8j646JyQuGtfWLZ8CVGSQlf2vRsos/PDK0OeO3hFE+li8U3gXgtvh38SqCc0Vk/Yyb6X092/me1fRGQuOOX6VkRETt5EHtJbC6w1s0uBywndMx74zWuJiMhEqb4VEZler9hAzt5DXHkjhLtvADb8pjwiIjJxqm9FRM4MJ/KQ3k/N7P80s+X5RDMrmtkbzexuRh6sExGRk6f6VkTkDHAiXSyuBT4K/L2ZrQSOADWExvWPgL929/WTF6KIyJyh+lZE5Azwig3k7MG5LwNfNrMqYB7Q5+5HJjs4EZG5RPWtiMiZYUIDhbj7ELB3kmIREZGM6lsRkelzMgOFiIiIiIjMWmogi4iIiIjknHADOXuyumUygxEREdW3IiLTbSJ3kBcCT5jZfWZ2bTbCk4iInH6qb0VEptEJN5Dd/b8C5wJ3AR8GtprZX5jZOZMU2ymLW5qIWpqJWpoBSLq7iUrVpAP9YTz5YpGoWAxjrNvoQ2GFKiyOKx9gZCx5i8LybHxzi+OwndTx1Cv586LaWuK6WqKqQhgbPTLSri7Srm5IErw8hCcJniREC+dV1htOs0IBK5XCmOwWjYo3qqkBiyp5RwpxbJkgjC0/XO6oppq0r2+knBaFMmRjx1e2aRFRqZqopnpUXgoF0v6ByscKVeBpyF9fT1RfR1RfB4Rx4AsL5o8E5ClRsXhMnFGpeiQ9G2M+7evDqgqjY8r2c4zhcek9DZ/csRzJYqEctbVhu6lD6kTFImk2zrtv2Y5v2R6OV011GA++WAXFKpL9B0P8C+Yfc50MXxOjrpFikXRwsBKTDw2NPk/5T5ZWOU+pY7W1RNWlUWk+OAS5csZ1tcTZsfbUK8fJ+/qIW5rCdpMEkgQrVJH29mbHtp+0rz/Emh27SuzDxzKTDg6SDg5W1hs+j6M+Fo0+9tm/niSV793w8sKihaQ9vaQ9vaE82XUyfDzjRWcRNdaPOu9WKoXjmeXLH0dPHTw95nyMNep7Mrx6dq7GXyEd9V0ASIfKo67FuL6e5HAnyeHO42/nBM3E+lZEZDaZUB/kbPSml7NPGWgB7jezz05CbCIic5bqWxGR6XPCr3kzs08AvwscBP4O+DN3HzKzCNgK/MfJCVFEZG5RfSsiMr0m8h7kVuDd7r4zn+juqZm94/SGJSIyp6m+FRGZRhPpYrECqHSuM7MWM/sagLs/d5rjEhGZy1ag+lZEZNpMpIF8aX64U3c/DFxx+kMSEZnzVN+KiEyjiTSQo/x7Oc2slQkOVS0iIidE9a2IyDSaSIX7OeBXZvYP2fx7gTtOf0giInOe6lsRkWl0wg1kd/+Gma0D3pglvdvdN01OWCIic5fqWxGR6TWhn+yyClqVtIjIJFN9KyIyfSbyHuQScD3h6erKeu5+++kPS0Rk7jod9a2ZXQv8DRADf+funx5nH98AXg10AO939x1m1gbcD7wG+Lq735xb59XA14Ea4AHgE+7uWR/pe7N4dwDvyx4sFBGZkSbykN73gOsIIzr15D4iInJ6nVJ9a2Yx8CXgbcCFwAfM7MIx2T4GHHb31cDngc9k6f3AnwN/Os6mvwL8HmEY7HOBa7P0W4CH3f1c4OFsXkRkxppIF4ul7n7tK2cTEZFTdKr17VVAu7tvAzCzewgN7nyXjeuA27Lp+4Evmpm5ew/wCzNbnd+gmS0CGt390Wz+G8C7gH/KtvWGLOvdwD8D/+kU4hcRmVYTuYP8iJldMmmRiIjIsFOtb5cAu3Lzu7O0cfO4e5kwMEnbK2xz93G2udDd92bTLwMLx9uAmd1kZuvMbN1g0nsi5RARmRYTuYP8euAjZrYNGAAMcHe/dFIiExGZu2ZsfZv1SfbjLLsTuBOgqXTWuHlERM4EE2kgv23SohARkbxTrW9fApbl5pdmaePl2W1mBaCJ8LDeb9rm0uNsc5+ZLXL3vVlXjP2nEryIyHSbSBeLF4F/Ddzo7jsB5zg/o4mIyCk51fr2CeBcM1tpZkXgBmDtmDxrgRuz6fcAP3H3497VzbpQHDWzq83MgN8lPEw4dls35tJFRGakiTSQvwy8DvhANt9FeEpaREROr1Oqb7M+xTcDDwLPAfe5+0Yzu93M3plluwtoM7N24E/IvXnCzHYAfwV82Mx2596A8QfA3wHtwAuEB/QAPg282cy2Am/K5kVEZqyJdLF4rbtfaWbrAdz9cHZnQkRETq9Trm/d/QHCu4rzabfmpvsJQ1iPt+6K46SvAy4eJ70DuGYi8YmInMkmcgd5KHu3pgOY2XwgnZSoRETmNtW3IiLTaCIN5C8A3wUWmtkdwC+Av5iUqERE5jbVtyIi0+iEu1i4+7fM7ElGfkZ7l7s/NzlhiYjMXapvRUSm1wk3kM3s1jFJ7zUz3P320xyTiMicpvpWRGR6TeQhvZ7cdDXwDsLT0Wes5HAnUTE812KR4QngKVhE2j8AQFRVAE+JikXSwcGQN45JB/qxOMbT3FuPLKpsa1jaP0BUU40PDFT2lQ70E9XWkvaOjBSV9vUT19cBVLYZtzaSdveQDg5ihSrSzqNh+/0DRKVqvDxUyZv29mLF0c/oWKEKT5JKWcaKaqorMVixBh8cxCIjHegfKWdfP3FDA0l3TyUNTyGKRuYBKxbD+rU1JF1dI+Xq7SVubmZgTRiVtvjLjXiShIVnL6a8IVwiUak65O/sqkwfz3B8IwWtwgoRaV8f2QHM0o/TQ8jTcC5zy9OBfuLmZiiXAUi6u7FCFcmRI0S1tVhdFlOSknZ2YlWFyvUQXXkh6ZPPEtfXYzU1AMRxTNrTC319RDU12ML5Ydc7XgzXTZJUjkNUqibp7gaLwvUGRDU1o64PCNdiPu7h/QP44CCYjc470E+hra1y/j1NsSgiqi5hxSJWCtdLef8BrDMJ1322zcKC+aRHOkmHyhRaW0K+Q4cr59/LQ+EQZ2UZnh6OzctDRKXqUTEOn5eoWIQ4yzc4VEn3BNLu7rCtUom4pgbv7Ru1LysWsSSpbNf3vkw8b8zgbuVyJab4rIWU97xcWRTX1ZL09BJVl8Lp7Ek4hkUj19CoZMPHWTb8Xcyv7+XRdUZUU0PaPzBy7Z+6GVffiojMJhPpYvG5/LyZ/SXhFUIiInIaqb4VEZleE3lIb6xaRo+qJCIik0P1rYjIFJpIH+RnyF45BMTAfOD/mYygRETmMtW3IiLTayJ9kN+Rmy4D+7LRmkRE5PRSfSsiMo0m0kC+fmyC5R4acve/Oh0BiYiI6lsRkek0kQbyGuA1wNps/t8CjwNbT3dQIiJznOpbEZFpNJEG8lLgSnfvAjCz24AfuPsHJyMwEZE5TPWtiMg0mshbLBYCuZeeMpiliYjI6aX6VkRkGk3kDvI3gMfN7LvZ/LuAr5/2iERERPWtiMg0mshAIXeY2T8B/zpL+oi7r5+csERE5i7VtyIi02sid5Bx96eApyYpFhERyai+FRGZPifcB9mCD5rZrdn8cjO7avJCExGZm1TfiohMr4k8pPdl4HXAB7L5LuBLpz0iERFRfSsiMo0m0sXite5+pZmtB3D3w2ZWnKS4RETmMtW3IiLTaCJ3kIfMLAYcwMzmA+mkRCUiMrepvhURmUYTaSB/AfgusMDM7gB+AfzFRHZmZtea2RYzazezW8ZZXjKze7Plj5nZiiz9zWb2pJk9k/37xonsV0Rkhjnl+lZERE7eCXWxMDMDfgY8CVwDGPAud3/uRHeU3Q35EvBmYDfwhJmtdfdNuWwfAw67+2ozuwH4DPB+4CDwb919j5ldDDwILDnRfYuIzBSno74VEZFTc0INZHd3M3vA3S8BNp/kvq4C2t19G4CZ3QNcB+QbyNcBt2XT9wNfNDMb8/7PjUCNmZXcfeAkYxEROSOdpvpWREROwUS6WDxlZq85hX0tAXbl5ndz7F3gSh53LwOdQNuYPNcDT43XODazm8xsnZmtG0JtZxGZsU61vhURkVMwobdYAB80sx1AD+FnP3f3SycjsPGY2UWEbhdvGW+5u98J3AnQaK0+VXGJiJxm017fiojMZa/YQDaz5e7+IvDWU9zXS8Cy3PzSLG28PLvNrAA0AR1ZHEsJD638rru/cIqxiIiccU5jfSsiIqfgRO4g/yNwpbvvNLNvu/v1J7mvJ4BzzWwloSF8A/DvxuRZC9wI/Ap4D/CTrD9eM/AD4BZ3/+WJ7rCwZBHpocNhpgyFtjaSzqNExSJeHgLAikW8f4B0oJ+4tQWA5HAnVqjCy0NExfDqUU8SPEmIStWkg4NAErbrKWlvL3F9PT5UDtssVOEDA2AReHgzU1RVwFqaSHbtqaQlhzuxy85jYHEt1fv6sCTc9I4OHKH84m4sjrHIKvuPW1ugtoZ0a/j/gdVUw0A/cV0tSXd32B+MrDOQ62aSOp56KHsSYo9qasCMtK9/JJ+neJKQHO3C4piopTkk9w8QLV8KR7soLJgfNnmkM+xv4Tyqn9sTDnNfH3FTE6QpXohGYqqphshIDh2uHFMKBaLFZ8GLuysxWbEGq6kelS8dHAyx1NTgQ2Wi6lI4ft3d2XlKsUJViLM8BBYRFYukAyPlihsaoFwm7esLZa+tJe3tpXDuOXRf0Ebdv2wJ++oJ5zLt6yNuaQr7qYopzJ+HDw6RLgjHg83b8cFB0iQlam4k3b23cp6wrNyW/YgRGVEplH/4GsmLqgqVdaNikXQ4T/58lst4OvKjSFRTTaGlmeRgBxbHIW3BfOjtxYpF0v0HSDrD+SmsXhXOTfu2yjVOTTXJeUuJfvUsaW/v6OsmSUaOZ5IQNzeTHDmCp45FaSW2/PGtxOspXh7CB5JR6XFdbeXYA0T1dZQPdFBobSF69cUh25YdJJeuJt64HcuuBywiWXEWcTmh3NERYsqOQ1Sqxrt7KuX3JCHp6cUiC98HwOI45M99F/E0rFseGjlfkH2vGZ03lz58fOLWZsr7D4TrMkuzeW34Wc1ET2XP0Z18D6/TVd+e+YaGeO7Pmjn/E+G7Q5JAVRUMDYGPXOvpucuoeXI7aRxz4PdeC8DCbz2DH+2CYhVpx+FKXitUYVUFkpWLiF8O6XFLE37WPKynH+rrwq727SeqqaFrdSONHeE7cf7NG2kt9vDcE6+qfCdt004u/OEAL/zpeUQ//zU77w3X6soPPQ9xhMVx5ftaWHE2ycIm4is6K/VZ76IaGhbMxwcGsDWXYJu3h0Bra0gPHMS6eyqxx02N+OAgPW+5hIaftwPgBzqobqsPebPrPD5rIfQPUN77MvzW5Wx7Vw0AZz2acnRFTNfqhOZnQ5kWfPUxSs/vpbiui513Lwdgybs3YmsuIeob4PmPtGFLsxjc4erLsD0dlJfOC/vqPMqqLz4f6qba2nBM5rfhddXQ10905UUARIe68SOdRE2NlBc0YTtDb8r44leRlKqIymXSo+E7GS9fAp1dHL16OY2P7AhpzU1451EYKhNt3s7B6y8BoPkbjxEdOQpNDZDtv/eShbhB3Y4uSn8c/u4UfqfMg08+yNuvvQGrDn83dr9nPg07nbQA9ssNxCtD+cvbX4SGOnpes5zah54JRU+dwpLFUFWFdxwK+S47h/jxTaQ9vaPrg+4eiCz87c/+dsbz26je10+5oYrC3rB+23rwQ0ew1ma6rgy9SZu39lNYtJAX3zmf5d/qAQv1R8/iaprmtVL+1QZW1VwBwLV/8y/89NrzoFjFltvOC9foF/aT7NgVzkdVAcuOCe7hb3x5pB1T3n+A3uuvpnHDATjaFfItXoU/s5WosR6yejTZt5/C0iWk+w9w/Uf/mW/zBgDO+eY+kv0HiJsaWPbD8F0qL26lftNBiGMOXBPud8773ma2/eEFnP39Lgq79rH6rnBOPI4ZqisQr16GtWe9a6uqwjmuqSHKvovlAwfx/gGsUFX5m2LlmAhCu2pwcPTfGk+JqgqVejkdHISDh/DU2XpjQ4hpfSMt/6MTzBhY1kJxf/g+33bv17n9ug8CcCJPPJ9IH2TLTa86gfzjyvoU30x4A8VzwH3uvtHMbjezd2bZ7gLazKwd+BNg+FVwNwOrgVvN7Onss+BkYxEROUOdlvoWTv61mtmyT2bpW8zsrVnaebn692kzO2pmf5Qtu83MXsote/upxC4iMt1O5A6yH2d6wtz9AeCBMWm35qb7gfeOs96ngE+dyr5FRGaA01LfnsprNc3sQsIvfBcBi4Efm9mr3H0LcHlu+y8Rur0N+7y7/+XJxiwiciY5kTvIl2V3CrqAS7Ppo2bWZWZHJztAEZE55HTVt5XXarr7IDD8Ws2864C7s+n7gWuydzBfB9zj7gPuvh1oz7aXdw3wgrvvnGD5RERmhFe8g+zu8VQEIiIy153G+na812q+9nh53L1sZsOv1VwCPDpm3bGv5LwB+PsxaTeb2e8C64D/4O6HxyzHzG4CbgKopnYi5RERmVITeQ+yiIjMcWZWBN4J/EMu+SvAOYQuGHuBz423rrvf6e5r3H1N0aonPVYRkZOlBrKIyOwzkddqMua1mq+07tsIgzXtG05w933unrh7Cvw3ju2SISIyo6iBLCIy+1Req5nd8b2B8BrNvOHXakLutZpZ+g3ZWy5WAucCj+fW+wBjuleY2aLc7G8Dz562koiITIOJjKQnIiIzQNanePi1mjHwteHXagLr3H0t4bWa38xeq3mI0Igmy3cfsAkoAx939wTAzOoIb8b4/TG7/KyZXU5488aOcZaLiMwoaiCLiMxCJ/tazWzZHcAd46T3EB7kG5v+oVONV0TkTKIuFiIiIiIiOWogi4iIiIjkqIEsIiIiIpKjBrKIiIiISI4ayCIiIiIiOWogi4iIiIjkqIEsIiIiIpKjBrKIiIiISI4ayCIiIiIiOWogi4iIiIjkqIEsIiIiIpKjBrKIiIiISI4ayCIiIiIiOYXpDmCyWBSR7j9AtHI5AEn7dtLeXuKzFuCdR4nq60LGQgx9fcStLVh1dVg37iaqqcaTKiiXAfAkIW5owJOEqFjEilUh7+KFcLSbtOMQnnrIWx4iqq3F3En7+gCI2lqhWCSuqyXp7g5pF5wDL+ym5plePHWG/tdLASjtGgj7Kpcr61scQ/8AuBMViyFtXiscORKKMX8eNDWG/e87EOJsaqS89+VQzsiIikWixnqi1qWVYxI1NRI3LYC+fgDKHR1YHGOXnY//ejPe0xvWL1ZRXtBIVFcNm7eFTTY0QKkIQ+XKcYqbm7E4JjlyBDY8z9HfeS0ATX//eDgmhSqiluawzTgm2bELiwxPkpA2OIjVVINFpIODlfPpqRMVCqT9A6T9A5VjEtVUk3Qn4GmIqaYGkiRsz6LsWjCIIpKLVlLYsitsb/F8YnfYf4iqrmZIw/rxyuXQ00tcWwNmAHStqqVpQy+9b7qYQl/I13/dZTR9/1nSvj7K+w9SaG0ZB26SygAAGP9JREFUDhQKBXxgIOwX8MHBUfEAJJ1HsTjGUycdKo+UMxkcOd/D+T0lmj8Pb67H+odCUk2RPW9sY+GXDxNVl0JafTW+dx/EYb24oQGAF68/i8WffZSoVI1nxy5dtZSqbftIIyNqbgppnUexKCLpGTmeeEpy5EiINUmwQviOUB4iamohOdxZiT0qFkkH+sP3IIs9qiqQDg7iQ2WsWAzlArxcprBsMcmCFiw79uXLV1O1fT+0tWBDoZxpXz+FvYfwwUHiC14VdrRrD8nF58C6Tdjwuc5iHT6mVqjK9jNUuSYq19PwteXpqHOCRaOvxTxPR8peWxvKVl2qXIv09uJVrUTz2sL8S8duQkREZg7dQRYRERERyVEDWUREREQkRw1kEREREZEcNZBFRERERHLUQBYRERERyVEDWUREREQkRw1kEREREZEcNZBFRERERHLUQBYRERERyVEDWUREREQkRw1kEREREZEcNZBFRERERHLUQBYRERERyVEDWUREREQkRw1kEZFZyMyuNbMtZtZuZreMs7xkZvdmyx8zsxW5ZZ/M0reY2Vtz6TvM7Bkze9rM1uXSW83sITPbmv3bMtnlExGZTGogi4jMMmYWA18C3gZcCHzAzC4ck+1jwGF3Xw18HvhMtu6FwA3ARcC1wJez7Q37N+5+ubuvyaXdAjzs7ucCD2fzIiIzlhrIIiKzz1VAu7tvc/dB4B7gujF5rgPuzqbvB64xM8vS73H3AXffDrRn2/tN8tu6G3jXaSiDiMi0UQNZRGT2WQLsys3vztLGzePuZaATaHuFdR34kZk9aWY35fIsdPe92fTLwMLxgjKzm8xsnZmtG/T+iZdKRGSKFKY7ABERmTFe7+4vmdkC4CEz2+zuP8tncHc3Mx9vZXe/E7gToClqGzePiMiZQHeQRURmn5eAZbn5pVnauHnMrAA0AR2/aV13H/53P/BdRrpe7DOzRdm2FgH7T2NZRESmnBrIIiKzzxPAuWa20syKhIfu1o7Jsxa4MZt+D/ATd/cs/YbsLRcrgXOBx82szswaAMysDngL8Ow427oR+N4klUtEZEqoi4WIyCzj7mUzuxl4EIiBr7n7RjO7HVjn7muBu4Bvmlk7cIjQiCbLdx+wCSgDH3f3xMwWAt8Nz/FRAP6nu/8w2+WngfvM7GPATuB9U1ZYEZFJMHsbyIUC0aqz4dARAKxYJGptgeoSdELSeRSAqLGewWuuoPjwevzQ4bCuRVhDPZYkEGdvNzrSSdrbC0Dc2gLhjwTJCzuJ6mqx81YR7T8EgPcPkHR2El96AdHWHQAcumYl5ZKx4GcG7WE75aZq+lefR8MvXoDWZqLHnw/7amnC3EkP9RIViwCkg4MQGdv//UrO/nRH2HdbPbY9xFc+cJDoaHelTH60G+KYqLY2rN/XT9zUSNrTCz1ZOVaeTc/586hbv5tyR8dI2Uslor0dpKUSyXDeQiOFPYfxxlqsvi7s89BhkjdcQffSIm0/eiGsXy6T9vZihSqixnqOrA7HqblUIunqIq6vh6qqENOBDnb++WtY9cXnK8feikWsVCKuryPp6gppcUw8r43kYAeFlctJd+8J6w85VlON9fbiSRL2PzhIVFND1NSIZ+c47esDMwq7D5KsXBzK8/IhGBoiXbaQ0qZdUFMdzl3HIayqirSzi763XApAw7Zehl53AfUb9uEHw3Eq9PTS+b7w63LdS/0kj24M65eHKMxrw3t6IYsp7esL56W6VLlu8ND9MioWwzkBPEkotLWF/EmKFcNx8oEButcso3pfP3F2PXZc0czi7+3Cq0tYqQTAgataadm4lcJ551De8gJxdu4X/zxsP331+Wz9YMh79g+c2sMlrFjEu3sYZgvnE3ccJq2kJeGaiGOwKFyHQGF+G+mRTiwyrJCdz4HsoStPw3kGrLGBdM9e8JQ9v7+GpWvDuXvhw4s55yvbMHd2v6kJgOX37qJ7zTLqf7WNaF5rOJ2vOovC+u344BDp5nYAdnzqalZ9aj3J6y4merqduLEh7KtUhJpqyjt3EdqEhNhrqkkOHSYqhXNsVQWsrpbyvv3gaSXdkwSrKuD9A5XjEVUViObPI3l5H1x0TkjctS8sW76EKElDWlc30UCZnR9eGebvYNq5+wPAA2PSbs1N9wPvPc66dzCmFO6+DbjsOPk7gGtOMWQRkTOGuliIiIiIiOSogSwiIiIikqMGsoiIiIhIjhrIIiIiIiI5aiCLiIiIiORMaQPZzK41sy1m1m5mt4yzvGRm92bLHzOzFVl6m5n91My6zeyLUxmziIiIiMwtU9ZANrMY+BLwNuBC4ANmduGYbB8DDrv7auDzwGey9H7gz4E/naJwRURERGSOmso7yFcB7e6+zd0HgXuA68bkuQ64O5u+H7jGzMzde9z9F4SGsoiIiIjIpJnKBvISYFdufneWNm4edy8DnUDbie7AzG4ys3Vmtm4w6T3FcEVERERkLppVD+m5+53uvsbd1xTj2ukOR0RERERmoKkcavolYFlufmmWNl6e3WZWAJqAjqkJT0REpoq7c+7HniDJpaU7dh6b8bENlLPJeX/7CMCodUZtszwEA8Djz1TWAeDAwWPypn191H7nsUq+PVdDGAj9uZFMhwbZfhVErAfg7PdtCOuOs+/yjp2wAxb/NniWVvOPI9vn0OGRuLu7QzmOHBnZQG/vsesAPLahsj2A8kt7RmYeeZpVj4zM1gJn5fJ6Lv+Sd28cSV/3DAmwKvdUTwrw6K/Dvl/cHeID6OoaXdD8/FNHRtYd9ujhSrzJM5tHtjMc/7YdIdZvd4wuZ27bzXf/aiSugX7S/SO9K0vZNZIAvDHbJvDWxZeRP3dLP7V11KaH9wtQ3tJO9Zb2UXGPOq6A/eLpcc9zOnBsT8/y3pdh78sUsljGlqkmO57DsS7+7N5R+eruf6kyHz/8JAAPXVzPcBNp9R8+Wlm3EsfgIAwOHhNLcuhwZbr224+Ojmff/mPyAJR3h/388tIiZ/HIqH2VN485jtm/Lf/95bAtYPltj4RrbUwsxR8+MfoY5q6dtK9vZLp39C/+niQk2XdkLE8SPBldAwyfk9WfeHQkXxZ7vHnk+vu/Vr6aUd/vVzCVd5CfAM41s5VmVgRuANaOybMWuDGbfg/wE3d3RERERESmyJTdQXb3spndDDwIxMDX3H2jmd0OrHP3tcBdwDfNrB04RGhEA2BmO4BGoGhm7wLe4u6bpip+EREREZkbprKLBe7+APDAmLRbc9P9wHuPs+6KSQ1ORERERIRZ9pCeiIiIiMipUgNZRERERCRHDWQRERERkRw1kEVEREREctRAFhERERHJUQNZRERERCRHDWQRERERkRw1kEVEREREcqZ0oJApNTTEc3/WzPmf2BvmkwSqqmBoCHKjV6fnLqPmye2kccyB33stAAu/9Qx+tAuKVaQdI2OWW6EKqyqQrFxE/HJIj1ua8LPmYT39UF8XdrVvP1FNDV2rG2nsaAHg/Js30lrs4bknXkVUFQ67bdrJhT8c4IU/PY/o579m570XA7DyQ89DHGFxTDoURjcvrDibZGET8RWdlXHIexfV0LBgPj4wgK25BNu8PQRaW0N64CDW3VOJPW5qxAcH6XnLJTT8vB0AP9BBdVt9yBvHId9ZC6F/IIwt/1uXs+1dNQCc9WjK0RUxXasTmp8NZVrw1ccoPb+X4roudt69HIAl796IrbmEqG+A5z/Shi3NYnCHqy/D9nRQXjov7KvzKKu++DyeJES1teGYzG/D66qhr5/oyosAiA5140c6iZoaKS9ownbuCutf/CqSUhVRuUx6NIzbHi9fAp1dHL16OY2P7AhpzU1451EYKhNlx+jg9ZfQ/I3HiI4chaYGyPbfe8lC3KBuRxelP94Tjv3vlHnwyQd5+7U3YNVFAHa/Zz4NO520APbLDcQrQ/nL21+Ehjp6XrOc2oeeCUVPncKSxVBVhXccCvkuO4f48U2kPb3gI6PVp909EBl4StoXxpeP57dRva+fckMVhb1h/bb14IeOYK3NdF25BIDmrf0UFi3kxXfOZ/m3esAMgJ7F1TTNa6X8qw2sqrkCgGv/5l/46bXnQbGKLbedF67RL+wn2bErnI/ha7S2Npy71PFyP3FrOPfl/Qfovf5qGjccgKNdIfjFq/BnthI11kPqle9CYekS0v0HuP6j/8y3eQMA53xzH8n+A8RNDSz7YfgulRe3Ur/pIMQxB65ZBsC8721m2x9ewNnf76Kwax8Aq+/ag8cxQ3UF4tXLsPZwPYTvd5mopoYo+y6WDxzE+wewQhXpQDieVo6JgLi+Hh8crKRnJ4uoqkA6OBjOx+AgHDyEp87WGxtCTOsbafkfnWDGwLJwPIr7C9x279e5/boPAvAcIiIyk+kOsoiIiIhIjhrIIiIiIiI5aiCLiIiIiOSogSwiIiIikqMGsojILGRm15rZFjNrN7NbxlleMrN7s+WPmdmK3LJPZulbzOytWdoyM/upmW0ys41m9olc/tvM7CUzezr7vH0qyigiMllm71ssRETmKDOLgS8BbwZ2A0+Y2Vp335TL9jHgsLuvNrMbgM8A7zezC4EbgIuAxcCPzexVQBn4D+7+lJk1AE+a2UO5bX7e3f9yakooIjK5dAdZRGT2uQpod/dt7j4I3ANcNybPdcDd2fT9wDVmZln6Pe4+4O7bgXbgKnff6+5PAbh7F+FtdkumoCwiIlNODWQRkdlnCbArN7+bYxuzlTzuXgY6gbYTWTfrjnEF8Fgu+WYz22BmXzOzlvGCMrObzGydma0bYmCiZRIRmTJqIIuIyAkzs3rg28AfufvRLPkrwDnA5cBe4HPjrevud7r7GndfU0VpSuIVETkZaiCLiMw+LwHLcvNLs7Rx85hZAWgCOn7TumZWRWgcf8vdvzOcwd33uXvi7inw3whdPEREZiw1kEVEZp8ngHPNbKWZFQkP3a0dk2ctcGM2/R7gJ+7uWfoN2VsuVgLnAo9n/ZPvAp5z97/Kb8jMFuVmfxt49rSXSERkCuktFiIis4y7l83sZuBBIAa+5u4bzex2YJ27ryU0dr9pZu3AIUIjmizffcAmwpsrPu7uiZm9HvgQ8IyZPZ3t6j+7+wPAZ83scsCBHcDvT1lhRUQmgRrIIiKzUNZwfWBM2q256X7gvcdZ9w7gjjFpvwDsOPk/dKrxioicSdTFQkREREQkRw1kEREREZEcNZBFRERERHLUQBYRERERyVEDWUREREQkRw1kEREREZEcNZBFRERERHLUQBYRERERyVEDWUREREQkx9x9umOYFI3W6q+1a6Y7DBGZg37s9z/p7mumO44zmepoEZkuJ1JH6w6yiIiIiEiOGsgiIiIiIjlqIIuIiIiI5KiBLCIiIiKSowayiIiIiEiOGsgiIiIiIjlqIIuIiIiI5KiBLCIiIiKSowayiIiIiEiOGsgiIiIiIjlqIIuIiIiI5KiBLCIiIiKSowayiIiIiEiOGsgiIiIiIjlqIIuIiIiI5KiBLCIiIiKSowayiIiIiEiOGsgiIiIiIjlT2kA2s2vNbIuZtZvZLeMsL5nZvdnyx8xsRW7ZJ7P0LWb21qmMW0RkppmM+vZ42zSzldk22rNtFie7fCIik2nKGshmFgNfAt4GXAh8wMwuHJPtY8Bhd18NfB74TLbuhcANwEXAtcCXs+2JiMgYk1HfvsI2PwN8PtvW4WzbIiIz1lTeQb4KaHf3be4+CNwDXDcmz3XA3dn0/cA1ZmZZ+j3uPuDu24H2bHsiInKsyahvx91mts4bs22QbfNdk1g2EZFJV5jCfS0BduXmdwOvPV4edy+bWSfQlqU/OmbdJWN3YGY3ATdls90/9vu3nJ7QzzjzgIPTHcQkUdlmJpVttLMnI5AJmKz6drxttgFH3L08Tv5RVEfPCirbzKSyjfaKdfRUNpAnnbvfCdw53XFMNjNb5+5rpjuOyaCyzUwqm5wI1dEzn8o2M6lsEzeVXSxeApbl5pdmaePmMbMC0AR0nOC6IiISTEZ9e7z0DqA528bx9iUiMqNMZQP5CeDc7GnnIuEhkLVj8qwFbsym3wP8xN09S78he+p6JXAu8PgUxS0iMtNMRn077jazdX6abYNsm9+bxLKJiEy6KetikfVxuxl4EIiBr7n7RjO7HVjn7muBu4Bvmlk7cIhQAZPluw/YBJSBj7t7MlWxn4Fm80+UKtvMpLKdQSarvh1vm9ku/xNwj5l9ClifbXsum3HXzASobDOTyjZBFv7zLyIiIiIioJH0RERERERGUQNZRERERCRHDeRpYmZfM7P9ZvZsLq3VzB4ys63Zvy1ZupnZF7JhXDeY2ZW5dW7M8m81sxtz6a82s2eydb6Qvcx/qsq2zMx+amabzGyjmX1itpTPzKrN7HEz+3VWtv87Sx93qF07jcP5TpVs1LT1Zvb92VQ2M9uRXTNPm9m6LG3GX5MyOVRHz8zyqY6euWU74+pod9dnGj7A/wJcCTybS/sscEs2fQvwmWz67cA/AQZcDTyWpbcC27J/W7LplmzZ41ley9Z92xSWbRFwZTbdADxPGJp2xpcv2199Nl0FPJbFcR9wQ5b+t8D/kU3/AfC32fQNwL3Z9IXAr4ESsBJ4gfDgU5xNrwKKWZ4Lp/ja/BPgfwLfz+ZnRdmAHcC8MWkz/prUZ9KuF9XRM7B8qI6esWXjDKujp+yE6jPuxbCC0ZXvFmBRNr0I2JJNfxX4wNh8wAeAr+bSv5qlLQI259JH5ZuGcn4PePNsKx9QCzxFGE3sIFDI0l8HPJhNPwi8LpsuZPkM+CTwydy2HszWq6ybpY/KNwVlWgo8TBg6+PtZrLOlbDs4tvKdVdekPqf9mlmB6ugZWz5UR8+0su3gDKqj1cXizLLQ3fdm0y8DC7Pp8YaNXfIK6bvHSZ9y2U86VxD+Fz8rypf9vPU0sB94iPA/7uMNtTtqOF8gP5zvRMo8Vf4a+I9Ams3/pmGEZ1rZHPiRmT1pYchjmCXXpEyZWXe9qI6ecfWY6uhg0q/JWTXU9Gzi7m5mPt1xnAozqwe+DfyRux/Nd/eZyeXz8E7Yy82sGfgucP40h3RamNk7gP3u/qSZvWG645kEr3f3l8xsAfCQmW3OL5zJ16RMvdlwvaiOnllUR0/tNak7yGeWfWa2CCD7d3+WPtGhX1/KpsemTxkzqyJUvN9y9+9kybOmfADufoQwgtjrOP5Qu6drON+p8K+Ad5rZDuAewk94f8PsKBvu/lL2737CH82rmGXXpEy6WXO9qI6ekfWY6uipvCanqm+JPuP2t1nB6P5t/x+jO6N/Npv+3xjdGf3xLL0V2E7oiN6STbdmy8Z2Rn/7FJbLgG8Afz0mfcaXD5gPNGfTNcDPgXcA/8DohyT+IJv+OKMfkrgvm76I0Q9JbCM8IFHIplcy8pDERdNwbb6BkQdAZnzZgDqgITf9CHDtbLgm9ZnU62YFqqNnVPlQHT0jy8YZWEdP6QnVZ9TF8PfAXmCI0BfmY4S+QQ8DW4Ef506qAV8i9KN6BliT285Hgfbs85Fc+hrg2WydL5KNmjhFZXs9oS/RBuDp7PP22VA+4FLCULobsv3fmqWvyr587VllVcrSq7P59mz5qty2/ksW/xZyT9Nmx+r5bNl/mabr8w2MVL4zvmxZGX6dfTYO73s2XJP6TNo1ozp6BpYP1dEzsmycgXW0hpoWEREREclRH2QRERERkRw1kEVEREREctRAFhERERHJUQNZRERERCRHDWQRERERkRw1kEVEREREctRAFhERERHJUQNZZgUzazOzp7PPy2b2Um6+aGaPTNJ+a8zsX8wsNrOlZvb+LL1oZj/LDf8pIjJnqY6WmUYNZJkV3L3D3S9398sJQ21+fnje3Qfd/bcmadcfBb7j7glwDXBlFs8gYfSf90/SfkVEZgzV0TLTqIEsc4KZdZvZCjPbbGZfN7PnzexbZvYmM/ulmW01s6ty+T9oZo9ndze+ambxcTb9O8D3zOz1wF8B78nWWQX8Y7ZcRER+A9XRcqZRA1nmmtXA54Dzs8+/A14P/CnwnwHM7ALCXYV/ld3tSBinEjWzImFs+x3u/gvgCeC67I7INsKY76+Z/CKJiMwaqqPljKC+NzLXbHf3ZwDMbCPwsLu7mT0DrMjyXAO8GnjCzABqgP3jbGsecCQ3fx6weXjG3RMzGzSzBnfvOu0lERGZfVRHyxlBDWSZawZy02luPmXk+2DA3e7+yVfYVh9QDWBm84BOdy+PyVMC+k8pYhGRuUN1tJwR1MVC5FgPE/qpLQAws1YzO3tsJnc/DMRmVk24s7Env9zM2oCD7j40+SGLiMwZqqNl0qmBLDKGu28C/ivwIzPbADwELDpO9h8R+sdtBuaZ2bNmNvw09r8BfjDZ8YqIzCWqo2UqmLtPdwwiM5aZXQn8sbt/aJxl3wFucffnpz4yERFRHS0nS3eQRU6Buz8F/HTsK4ayp6f/URWviMj0UR0tJ0t3kEVEREREcnQHWUREREQkRw1kEREREZEcNZBFRERERHLUQBYRERERyVEDWUREREQkRw1kEREREZGc/x9L681+A5SUywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import signal\n",
    "\n",
    "_f,t,sxx=signal.spectrogram(data[\"PM\"],window=\"boxcar\",nperseg=256)\n",
    "fg=plt.figure(figsize=(10,10))\n",
    "fg.set_tight_layout(True)\n",
    "\n",
    "def plot_spectrogram(ax,f,t,sxx,fmin=-1,fmax=-1):\n",
    "    ax.pcolormesh(t,f,sxx)\n",
    "    ax.set_xlabel(\"Time ($t$)\")\n",
    "    ax.set_ylabel(r\"Frequency ($\\omega$)\")\n",
    "    if fmin>=0 and fmax>=0:\n",
    "        ax.set_ylim(fmin,fmax)\n",
    "    else:\n",
    "        fmin,fmax=ax.get_ylim()\n",
    "    ax.set_title(r\"Spectrogram (${}\\leq\\omega\\leq{}$)\".format(fmin,fmax))\n",
    "    return ax\n",
    "\n",
    "(ax,ax2),(ax3,ax4)=fg.subplots(2,2)\n",
    "plot_spectrogram(ax,_f,t,sxx)\n",
    "plot_spectrogram(ax2,_f,t,sxx,0,0.1)\n",
    "plot_spectrogram(ax3,_f,t,sxx,0,0.05)\n",
    "plot_spectrogram(ax4,_f,t,sxx,0,0.02)\n",
    "\n",
    "plt.savefig(\"graphs/spectrogram.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the above comparisons, we discover that the Fourier coefficients for frequencies such that $0\\leq\\omega\\leq0.02$ are more significant than those of other frequencies, so we will only store the Fourier coefficients in that frequency interval to the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading existing features from BeijingPM_stft.csv\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stft_0</th>\n",
       "      <th>stft_1</th>\n",
       "      <th>stft_2</th>\n",
       "      <th>stft_3</th>\n",
       "      <th>stft_4</th>\n",
       "      <th>stft_5</th>\n",
       "      <th>stft_6</th>\n",
       "      <th>stft_7</th>\n",
       "      <th>stft_8</th>\n",
       "      <th>stft_9</th>\n",
       "      <th>...</th>\n",
       "      <th>stft_125</th>\n",
       "      <th>stft_126</th>\n",
       "      <th>stft_127</th>\n",
       "      <th>stft_128</th>\n",
       "      <th>stft_129</th>\n",
       "      <th>stft_130</th>\n",
       "      <th>stft_131</th>\n",
       "      <th>stft_132</th>\n",
       "      <th>stft_133</th>\n",
       "      <th>stft_134</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-29</th>\n",
       "      <td>93.976190</td>\n",
       "      <td>16.758337</td>\n",
       "      <td>24.855954</td>\n",
       "      <td>33.738768</td>\n",
       "      <td>9.682404</td>\n",
       "      <td>19.203026</td>\n",
       "      <td>8.148744</td>\n",
       "      <td>9.735022</td>\n",
       "      <td>14.879033</td>\n",
       "      <td>6.058545</td>\n",
       "      <td>...</td>\n",
       "      <td>1.197717</td>\n",
       "      <td>0.841096</td>\n",
       "      <td>0.669159</td>\n",
       "      <td>1.461749</td>\n",
       "      <td>1.248827</td>\n",
       "      <td>1.037334</td>\n",
       "      <td>1.355093</td>\n",
       "      <td>0.440955</td>\n",
       "      <td>1.046557</td>\n",
       "      <td>0.861513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-30</th>\n",
       "      <td>90.284226</td>\n",
       "      <td>19.582026</td>\n",
       "      <td>23.528091</td>\n",
       "      <td>32.704434</td>\n",
       "      <td>12.685932</td>\n",
       "      <td>18.362866</td>\n",
       "      <td>11.384585</td>\n",
       "      <td>11.854666</td>\n",
       "      <td>13.777843</td>\n",
       "      <td>3.345975</td>\n",
       "      <td>...</td>\n",
       "      <td>1.197447</td>\n",
       "      <td>0.822349</td>\n",
       "      <td>0.820258</td>\n",
       "      <td>1.283175</td>\n",
       "      <td>1.303852</td>\n",
       "      <td>1.121831</td>\n",
       "      <td>1.312683</td>\n",
       "      <td>0.570907</td>\n",
       "      <td>1.015920</td>\n",
       "      <td>0.831692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-31</th>\n",
       "      <td>86.747024</td>\n",
       "      <td>22.865364</td>\n",
       "      <td>21.285181</td>\n",
       "      <td>34.338648</td>\n",
       "      <td>13.708756</td>\n",
       "      <td>15.143763</td>\n",
       "      <td>13.311499</td>\n",
       "      <td>9.828546</td>\n",
       "      <td>11.056826</td>\n",
       "      <td>3.931810</td>\n",
       "      <td>...</td>\n",
       "      <td>1.155313</td>\n",
       "      <td>0.847114</td>\n",
       "      <td>0.666774</td>\n",
       "      <td>1.477420</td>\n",
       "      <td>1.143638</td>\n",
       "      <td>1.189890</td>\n",
       "      <td>1.151682</td>\n",
       "      <td>0.457601</td>\n",
       "      <td>1.252122</td>\n",
       "      <td>0.593055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-01</th>\n",
       "      <td>85.319940</td>\n",
       "      <td>24.275830</td>\n",
       "      <td>20.090105</td>\n",
       "      <td>35.618984</td>\n",
       "      <td>13.301923</td>\n",
       "      <td>13.993034</td>\n",
       "      <td>12.790457</td>\n",
       "      <td>8.493307</td>\n",
       "      <td>11.888127</td>\n",
       "      <td>5.043548</td>\n",
       "      <td>...</td>\n",
       "      <td>1.150959</td>\n",
       "      <td>0.893489</td>\n",
       "      <td>0.740933</td>\n",
       "      <td>1.470793</td>\n",
       "      <td>1.162463</td>\n",
       "      <td>1.183014</td>\n",
       "      <td>1.125473</td>\n",
       "      <td>0.481120</td>\n",
       "      <td>1.245927</td>\n",
       "      <td>0.634673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-02</th>\n",
       "      <td>86.583333</td>\n",
       "      <td>23.022902</td>\n",
       "      <td>21.338873</td>\n",
       "      <td>34.444698</td>\n",
       "      <td>14.470769</td>\n",
       "      <td>13.611003</td>\n",
       "      <td>13.918821</td>\n",
       "      <td>7.924493</td>\n",
       "      <td>11.469088</td>\n",
       "      <td>6.068044</td>\n",
       "      <td>...</td>\n",
       "      <td>1.255865</td>\n",
       "      <td>0.873471</td>\n",
       "      <td>0.867190</td>\n",
       "      <td>1.403744</td>\n",
       "      <td>1.262079</td>\n",
       "      <td>1.105403</td>\n",
       "      <td>1.126400</td>\n",
       "      <td>0.448198</td>\n",
       "      <td>1.352874</td>\n",
       "      <td>0.589729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-03</th>\n",
       "      <td>87.382440</td>\n",
       "      <td>22.257658</td>\n",
       "      <td>22.109331</td>\n",
       "      <td>33.920913</td>\n",
       "      <td>15.201408</td>\n",
       "      <td>12.910137</td>\n",
       "      <td>14.167780</td>\n",
       "      <td>7.160297</td>\n",
       "      <td>12.111023</td>\n",
       "      <td>6.335689</td>\n",
       "      <td>...</td>\n",
       "      <td>1.183606</td>\n",
       "      <td>0.756186</td>\n",
       "      <td>0.748053</td>\n",
       "      <td>1.264119</td>\n",
       "      <td>1.397211</td>\n",
       "      <td>0.908167</td>\n",
       "      <td>1.330254</td>\n",
       "      <td>0.293749</td>\n",
       "      <td>1.568270</td>\n",
       "      <td>0.501072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-04</th>\n",
       "      <td>88.081845</td>\n",
       "      <td>21.638596</td>\n",
       "      <td>22.686014</td>\n",
       "      <td>33.826953</td>\n",
       "      <td>15.520841</td>\n",
       "      <td>12.256631</td>\n",
       "      <td>13.609759</td>\n",
       "      <td>7.123741</td>\n",
       "      <td>12.520976</td>\n",
       "      <td>5.503400</td>\n",
       "      <td>...</td>\n",
       "      <td>1.211888</td>\n",
       "      <td>0.865698</td>\n",
       "      <td>0.766013</td>\n",
       "      <td>1.413530</td>\n",
       "      <td>1.236022</td>\n",
       "      <td>1.060840</td>\n",
       "      <td>1.322501</td>\n",
       "      <td>0.219968</td>\n",
       "      <td>1.526478</td>\n",
       "      <td>0.318109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-05</th>\n",
       "      <td>87.501488</td>\n",
       "      <td>21.972225</td>\n",
       "      <td>22.686547</td>\n",
       "      <td>33.234033</td>\n",
       "      <td>16.154276</td>\n",
       "      <td>11.618695</td>\n",
       "      <td>13.383922</td>\n",
       "      <td>7.139839</td>\n",
       "      <td>12.704689</td>\n",
       "      <td>4.828168</td>\n",
       "      <td>...</td>\n",
       "      <td>1.357513</td>\n",
       "      <td>0.688219</td>\n",
       "      <td>0.928412</td>\n",
       "      <td>1.374144</td>\n",
       "      <td>1.236351</td>\n",
       "      <td>1.114146</td>\n",
       "      <td>1.251949</td>\n",
       "      <td>0.279845</td>\n",
       "      <td>1.470821</td>\n",
       "      <td>0.387507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-06</th>\n",
       "      <td>84.081845</td>\n",
       "      <td>23.879053</td>\n",
       "      <td>23.579565</td>\n",
       "      <td>29.883104</td>\n",
       "      <td>19.389191</td>\n",
       "      <td>8.456913</td>\n",
       "      <td>10.958005</td>\n",
       "      <td>9.698488</td>\n",
       "      <td>11.031621</td>\n",
       "      <td>6.086774</td>\n",
       "      <td>...</td>\n",
       "      <td>0.854289</td>\n",
       "      <td>0.729515</td>\n",
       "      <td>0.826604</td>\n",
       "      <td>0.880461</td>\n",
       "      <td>1.696915</td>\n",
       "      <td>1.093196</td>\n",
       "      <td>0.848682</td>\n",
       "      <td>0.685172</td>\n",
       "      <td>1.102049</td>\n",
       "      <td>0.456180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-07</th>\n",
       "      <td>83.793155</td>\n",
       "      <td>24.108669</td>\n",
       "      <td>23.600727</td>\n",
       "      <td>29.469399</td>\n",
       "      <td>19.906005</td>\n",
       "      <td>7.851199</td>\n",
       "      <td>10.511859</td>\n",
       "      <td>10.416755</td>\n",
       "      <td>10.505118</td>\n",
       "      <td>6.914431</td>\n",
       "      <td>...</td>\n",
       "      <td>1.139407</td>\n",
       "      <td>0.727978</td>\n",
       "      <td>0.694761</td>\n",
       "      <td>1.168738</td>\n",
       "      <td>1.640148</td>\n",
       "      <td>0.747971</td>\n",
       "      <td>1.017162</td>\n",
       "      <td>0.896448</td>\n",
       "      <td>0.825057</td>\n",
       "      <td>0.758805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-08</th>\n",
       "      <td>85.894345</td>\n",
       "      <td>23.802598</td>\n",
       "      <td>21.596741</td>\n",
       "      <td>29.878163</td>\n",
       "      <td>20.966184</td>\n",
       "      <td>6.190862</td>\n",
       "      <td>9.851839</td>\n",
       "      <td>12.300914</td>\n",
       "      <td>8.892153</td>\n",
       "      <td>8.726852</td>\n",
       "      <td>...</td>\n",
       "      <td>1.311558</td>\n",
       "      <td>0.689001</td>\n",
       "      <td>0.721046</td>\n",
       "      <td>1.328164</td>\n",
       "      <td>1.725027</td>\n",
       "      <td>0.633133</td>\n",
       "      <td>0.941388</td>\n",
       "      <td>1.027414</td>\n",
       "      <td>0.773840</td>\n",
       "      <td>0.627308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-09</th>\n",
       "      <td>91.645833</td>\n",
       "      <td>24.953177</td>\n",
       "      <td>15.921362</td>\n",
       "      <td>28.549830</td>\n",
       "      <td>26.382818</td>\n",
       "      <td>1.581818</td>\n",
       "      <td>4.801760</td>\n",
       "      <td>15.454109</td>\n",
       "      <td>6.867633</td>\n",
       "      <td>8.463556</td>\n",
       "      <td>...</td>\n",
       "      <td>0.827783</td>\n",
       "      <td>0.776802</td>\n",
       "      <td>0.964068</td>\n",
       "      <td>1.189379</td>\n",
       "      <td>1.261063</td>\n",
       "      <td>0.674045</td>\n",
       "      <td>1.380171</td>\n",
       "      <td>1.206162</td>\n",
       "      <td>0.457443</td>\n",
       "      <td>0.901671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-10</th>\n",
       "      <td>96.186012</td>\n",
       "      <td>27.432913</td>\n",
       "      <td>12.106706</td>\n",
       "      <td>25.870723</td>\n",
       "      <td>30.181651</td>\n",
       "      <td>5.317176</td>\n",
       "      <td>3.144461</td>\n",
       "      <td>13.935153</td>\n",
       "      <td>10.795015</td>\n",
       "      <td>4.683282</td>\n",
       "      <td>...</td>\n",
       "      <td>0.941289</td>\n",
       "      <td>0.873363</td>\n",
       "      <td>0.925597</td>\n",
       "      <td>1.161777</td>\n",
       "      <td>1.255753</td>\n",
       "      <td>0.632458</td>\n",
       "      <td>1.344943</td>\n",
       "      <td>1.225717</td>\n",
       "      <td>0.496017</td>\n",
       "      <td>0.752606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-11</th>\n",
       "      <td>95.096726</td>\n",
       "      <td>26.552753</td>\n",
       "      <td>12.481261</td>\n",
       "      <td>26.948233</td>\n",
       "      <td>30.344864</td>\n",
       "      <td>5.100455</td>\n",
       "      <td>2.230486</td>\n",
       "      <td>14.672815</td>\n",
       "      <td>11.524649</td>\n",
       "      <td>3.892052</td>\n",
       "      <td>...</td>\n",
       "      <td>1.056387</td>\n",
       "      <td>0.974641</td>\n",
       "      <td>0.869975</td>\n",
       "      <td>1.058204</td>\n",
       "      <td>1.172816</td>\n",
       "      <td>0.705727</td>\n",
       "      <td>1.485212</td>\n",
       "      <td>1.369735</td>\n",
       "      <td>0.401834</td>\n",
       "      <td>0.684046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-12</th>\n",
       "      <td>92.328869</td>\n",
       "      <td>24.173424</td>\n",
       "      <td>13.110061</td>\n",
       "      <td>29.544886</td>\n",
       "      <td>32.159501</td>\n",
       "      <td>6.898092</td>\n",
       "      <td>3.192082</td>\n",
       "      <td>14.580399</td>\n",
       "      <td>13.908946</td>\n",
       "      <td>1.991637</td>\n",
       "      <td>...</td>\n",
       "      <td>1.111436</td>\n",
       "      <td>1.170285</td>\n",
       "      <td>1.113732</td>\n",
       "      <td>1.247318</td>\n",
       "      <td>1.012386</td>\n",
       "      <td>0.506000</td>\n",
       "      <td>1.391866</td>\n",
       "      <td>1.350577</td>\n",
       "      <td>0.591583</td>\n",
       "      <td>0.567392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-13</th>\n",
       "      <td>91.508929</td>\n",
       "      <td>23.383887</td>\n",
       "      <td>12.921743</td>\n",
       "      <td>29.852644</td>\n",
       "      <td>32.898430</td>\n",
       "      <td>7.455329</td>\n",
       "      <td>3.647410</td>\n",
       "      <td>13.913051</td>\n",
       "      <td>13.250359</td>\n",
       "      <td>2.632374</td>\n",
       "      <td>...</td>\n",
       "      <td>1.096750</td>\n",
       "      <td>1.123693</td>\n",
       "      <td>0.999218</td>\n",
       "      <td>1.138352</td>\n",
       "      <td>0.920878</td>\n",
       "      <td>0.546124</td>\n",
       "      <td>1.444656</td>\n",
       "      <td>1.396160</td>\n",
       "      <td>0.689171</td>\n",
       "      <td>0.563704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-14</th>\n",
       "      <td>88.133929</td>\n",
       "      <td>20.042403</td>\n",
       "      <td>11.678633</td>\n",
       "      <td>29.880561</td>\n",
       "      <td>34.827135</td>\n",
       "      <td>8.359144</td>\n",
       "      <td>4.414248</td>\n",
       "      <td>14.212961</td>\n",
       "      <td>11.042659</td>\n",
       "      <td>3.654065</td>\n",
       "      <td>...</td>\n",
       "      <td>0.974147</td>\n",
       "      <td>1.035190</td>\n",
       "      <td>0.906366</td>\n",
       "      <td>1.096711</td>\n",
       "      <td>0.819606</td>\n",
       "      <td>0.443199</td>\n",
       "      <td>1.306165</td>\n",
       "      <td>1.235332</td>\n",
       "      <td>0.568636</td>\n",
       "      <td>0.667530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-15</th>\n",
       "      <td>82.238095</td>\n",
       "      <td>14.242026</td>\n",
       "      <td>8.404111</td>\n",
       "      <td>26.061207</td>\n",
       "      <td>32.568463</td>\n",
       "      <td>4.965724</td>\n",
       "      <td>2.590572</td>\n",
       "      <td>18.845739</td>\n",
       "      <td>16.327554</td>\n",
       "      <td>1.773203</td>\n",
       "      <td>...</td>\n",
       "      <td>0.658884</td>\n",
       "      <td>0.790949</td>\n",
       "      <td>0.648268</td>\n",
       "      <td>0.972273</td>\n",
       "      <td>0.576357</td>\n",
       "      <td>0.307105</td>\n",
       "      <td>1.090428</td>\n",
       "      <td>1.068011</td>\n",
       "      <td>0.381523</td>\n",
       "      <td>0.623728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-16</th>\n",
       "      <td>74.053571</td>\n",
       "      <td>7.473098</td>\n",
       "      <td>6.731355</td>\n",
       "      <td>18.657768</td>\n",
       "      <td>25.654860</td>\n",
       "      <td>3.335499</td>\n",
       "      <td>6.507650</td>\n",
       "      <td>17.950895</td>\n",
       "      <td>17.290797</td>\n",
       "      <td>7.192038</td>\n",
       "      <td>...</td>\n",
       "      <td>0.597512</td>\n",
       "      <td>0.567906</td>\n",
       "      <td>0.471697</td>\n",
       "      <td>0.636122</td>\n",
       "      <td>0.444667</td>\n",
       "      <td>0.563958</td>\n",
       "      <td>0.924115</td>\n",
       "      <td>0.737666</td>\n",
       "      <td>0.326834</td>\n",
       "      <td>0.781944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-17</th>\n",
       "      <td>65.308036</td>\n",
       "      <td>8.321766</td>\n",
       "      <td>10.691490</td>\n",
       "      <td>10.330952</td>\n",
       "      <td>17.732657</td>\n",
       "      <td>9.009605</td>\n",
       "      <td>10.062673</td>\n",
       "      <td>10.093091</td>\n",
       "      <td>9.772872</td>\n",
       "      <td>6.769730</td>\n",
       "      <td>...</td>\n",
       "      <td>0.244619</td>\n",
       "      <td>0.582577</td>\n",
       "      <td>0.515742</td>\n",
       "      <td>1.213995</td>\n",
       "      <td>0.489928</td>\n",
       "      <td>0.177496</td>\n",
       "      <td>0.950350</td>\n",
       "      <td>1.219184</td>\n",
       "      <td>0.301535</td>\n",
       "      <td>0.422257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-18</th>\n",
       "      <td>64.944940</td>\n",
       "      <td>8.575190</td>\n",
       "      <td>10.940515</td>\n",
       "      <td>10.018131</td>\n",
       "      <td>17.463354</td>\n",
       "      <td>9.282300</td>\n",
       "      <td>10.173240</td>\n",
       "      <td>9.702773</td>\n",
       "      <td>9.381930</td>\n",
       "      <td>6.510420</td>\n",
       "      <td>...</td>\n",
       "      <td>0.275375</td>\n",
       "      <td>0.495614</td>\n",
       "      <td>0.322811</td>\n",
       "      <td>0.753610</td>\n",
       "      <td>0.196509</td>\n",
       "      <td>0.520170</td>\n",
       "      <td>0.583858</td>\n",
       "      <td>0.817262</td>\n",
       "      <td>0.247558</td>\n",
       "      <td>0.525543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-19</th>\n",
       "      <td>66.961310</td>\n",
       "      <td>6.753456</td>\n",
       "      <td>11.245615</td>\n",
       "      <td>9.689420</td>\n",
       "      <td>16.054304</td>\n",
       "      <td>11.077789</td>\n",
       "      <td>11.836199</td>\n",
       "      <td>7.946779</td>\n",
       "      <td>7.939156</td>\n",
       "      <td>5.579418</td>\n",
       "      <td>...</td>\n",
       "      <td>0.459136</td>\n",
       "      <td>0.707998</td>\n",
       "      <td>0.477581</td>\n",
       "      <td>0.547407</td>\n",
       "      <td>0.220042</td>\n",
       "      <td>0.681309</td>\n",
       "      <td>0.427527</td>\n",
       "      <td>1.032484</td>\n",
       "      <td>0.210318</td>\n",
       "      <td>0.382376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-20</th>\n",
       "      <td>69.992560</td>\n",
       "      <td>4.006767</td>\n",
       "      <td>13.141513</td>\n",
       "      <td>8.436573</td>\n",
       "      <td>13.162865</td>\n",
       "      <td>13.573858</td>\n",
       "      <td>12.585260</td>\n",
       "      <td>7.645167</td>\n",
       "      <td>9.174667</td>\n",
       "      <td>3.455981</td>\n",
       "      <td>...</td>\n",
       "      <td>0.174927</td>\n",
       "      <td>0.652602</td>\n",
       "      <td>0.560711</td>\n",
       "      <td>0.781143</td>\n",
       "      <td>0.434131</td>\n",
       "      <td>0.822037</td>\n",
       "      <td>0.283873</td>\n",
       "      <td>1.059747</td>\n",
       "      <td>0.426079</td>\n",
       "      <td>0.182521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-21</th>\n",
       "      <td>75.623599</td>\n",
       "      <td>2.207812</td>\n",
       "      <td>18.550215</td>\n",
       "      <td>4.660043</td>\n",
       "      <td>10.479603</td>\n",
       "      <td>13.900215</td>\n",
       "      <td>8.219131</td>\n",
       "      <td>12.560890</td>\n",
       "      <td>12.221427</td>\n",
       "      <td>8.064049</td>\n",
       "      <td>...</td>\n",
       "      <td>0.784220</td>\n",
       "      <td>0.735038</td>\n",
       "      <td>0.307881</td>\n",
       "      <td>0.783930</td>\n",
       "      <td>0.288682</td>\n",
       "      <td>0.272446</td>\n",
       "      <td>0.844644</td>\n",
       "      <td>0.535204</td>\n",
       "      <td>0.652770</td>\n",
       "      <td>0.436274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-22</th>\n",
       "      <td>78.459121</td>\n",
       "      <td>4.668015</td>\n",
       "      <td>21.340834</td>\n",
       "      <td>3.052485</td>\n",
       "      <td>11.761153</td>\n",
       "      <td>12.606087</td>\n",
       "      <td>5.742172</td>\n",
       "      <td>13.430742</td>\n",
       "      <td>11.300698</td>\n",
       "      <td>8.787077</td>\n",
       "      <td>...</td>\n",
       "      <td>0.379196</td>\n",
       "      <td>0.665570</td>\n",
       "      <td>0.461078</td>\n",
       "      <td>0.544820</td>\n",
       "      <td>0.110766</td>\n",
       "      <td>0.500692</td>\n",
       "      <td>0.682101</td>\n",
       "      <td>0.727261</td>\n",
       "      <td>0.402300</td>\n",
       "      <td>0.554424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-23</th>\n",
       "      <td>79.410627</td>\n",
       "      <td>5.491364</td>\n",
       "      <td>22.136173</td>\n",
       "      <td>2.341338</td>\n",
       "      <td>12.677720</td>\n",
       "      <td>11.707114</td>\n",
       "      <td>6.014970</td>\n",
       "      <td>12.643476</td>\n",
       "      <td>10.620644</td>\n",
       "      <td>8.001760</td>\n",
       "      <td>...</td>\n",
       "      <td>0.594614</td>\n",
       "      <td>0.640189</td>\n",
       "      <td>0.275746</td>\n",
       "      <td>0.716572</td>\n",
       "      <td>0.168991</td>\n",
       "      <td>0.524247</td>\n",
       "      <td>0.551457</td>\n",
       "      <td>0.828392</td>\n",
       "      <td>0.305829</td>\n",
       "      <td>0.465871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-24</th>\n",
       "      <td>80.361607</td>\n",
       "      <td>6.255168</td>\n",
       "      <td>22.679752</td>\n",
       "      <td>1.412482</td>\n",
       "      <td>13.356730</td>\n",
       "      <td>11.282097</td>\n",
       "      <td>6.977210</td>\n",
       "      <td>12.253072</td>\n",
       "      <td>11.341310</td>\n",
       "      <td>8.534625</td>\n",
       "      <td>...</td>\n",
       "      <td>0.417842</td>\n",
       "      <td>0.740981</td>\n",
       "      <td>0.464038</td>\n",
       "      <td>0.570761</td>\n",
       "      <td>0.139811</td>\n",
       "      <td>0.366723</td>\n",
       "      <td>0.649065</td>\n",
       "      <td>0.829977</td>\n",
       "      <td>0.358726</td>\n",
       "      <td>0.395054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-25</th>\n",
       "      <td>86.148810</td>\n",
       "      <td>10.839090</td>\n",
       "      <td>23.445120</td>\n",
       "      <td>4.654828</td>\n",
       "      <td>12.213047</td>\n",
       "      <td>16.537286</td>\n",
       "      <td>7.213383</td>\n",
       "      <td>17.689178</td>\n",
       "      <td>11.782921</td>\n",
       "      <td>9.030086</td>\n",
       "      <td>...</td>\n",
       "      <td>0.612449</td>\n",
       "      <td>0.603312</td>\n",
       "      <td>0.256049</td>\n",
       "      <td>0.680285</td>\n",
       "      <td>0.190633</td>\n",
       "      <td>0.451728</td>\n",
       "      <td>0.937565</td>\n",
       "      <td>0.430178</td>\n",
       "      <td>0.778898</td>\n",
       "      <td>0.097069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-26</th>\n",
       "      <td>91.285714</td>\n",
       "      <td>15.245667</td>\n",
       "      <td>23.480535</td>\n",
       "      <td>9.570790</td>\n",
       "      <td>9.793395</td>\n",
       "      <td>20.843211</td>\n",
       "      <td>5.855503</td>\n",
       "      <td>19.165689</td>\n",
       "      <td>7.836420</td>\n",
       "      <td>4.994712</td>\n",
       "      <td>...</td>\n",
       "      <td>0.424772</td>\n",
       "      <td>0.598850</td>\n",
       "      <td>0.500345</td>\n",
       "      <td>0.475252</td>\n",
       "      <td>0.078632</td>\n",
       "      <td>0.292224</td>\n",
       "      <td>0.830138</td>\n",
       "      <td>0.608382</td>\n",
       "      <td>0.622996</td>\n",
       "      <td>0.135637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-27</th>\n",
       "      <td>92.845238</td>\n",
       "      <td>16.483135</td>\n",
       "      <td>22.976986</td>\n",
       "      <td>10.607915</td>\n",
       "      <td>8.362766</td>\n",
       "      <td>20.772469</td>\n",
       "      <td>4.430337</td>\n",
       "      <td>17.794217</td>\n",
       "      <td>7.431308</td>\n",
       "      <td>5.050084</td>\n",
       "      <td>...</td>\n",
       "      <td>0.395531</td>\n",
       "      <td>0.671582</td>\n",
       "      <td>0.600440</td>\n",
       "      <td>0.510646</td>\n",
       "      <td>0.084794</td>\n",
       "      <td>0.421535</td>\n",
       "      <td>0.799418</td>\n",
       "      <td>0.485356</td>\n",
       "      <td>0.749209</td>\n",
       "      <td>0.144067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-02</th>\n",
       "      <td>134.154762</td>\n",
       "      <td>30.041326</td>\n",
       "      <td>52.070262</td>\n",
       "      <td>34.536041</td>\n",
       "      <td>11.128420</td>\n",
       "      <td>17.902327</td>\n",
       "      <td>15.774692</td>\n",
       "      <td>9.746103</td>\n",
       "      <td>12.850393</td>\n",
       "      <td>14.273793</td>\n",
       "      <td>...</td>\n",
       "      <td>0.372934</td>\n",
       "      <td>0.427467</td>\n",
       "      <td>0.260648</td>\n",
       "      <td>1.230044</td>\n",
       "      <td>0.646624</td>\n",
       "      <td>0.924903</td>\n",
       "      <td>1.109042</td>\n",
       "      <td>0.706090</td>\n",
       "      <td>0.408758</td>\n",
       "      <td>0.280084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-03</th>\n",
       "      <td>126.982143</td>\n",
       "      <td>22.905205</td>\n",
       "      <td>51.411278</td>\n",
       "      <td>32.925981</td>\n",
       "      <td>17.007914</td>\n",
       "      <td>15.584359</td>\n",
       "      <td>9.199977</td>\n",
       "      <td>12.186828</td>\n",
       "      <td>15.526253</td>\n",
       "      <td>20.185468</td>\n",
       "      <td>...</td>\n",
       "      <td>0.714233</td>\n",
       "      <td>0.825037</td>\n",
       "      <td>0.547445</td>\n",
       "      <td>1.273422</td>\n",
       "      <td>0.574129</td>\n",
       "      <td>0.512258</td>\n",
       "      <td>0.753051</td>\n",
       "      <td>0.595506</td>\n",
       "      <td>0.078626</td>\n",
       "      <td>0.136532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-04</th>\n",
       "      <td>123.651786</td>\n",
       "      <td>19.586805</td>\n",
       "      <td>52.474624</td>\n",
       "      <td>34.206401</td>\n",
       "      <td>20.245609</td>\n",
       "      <td>17.755107</td>\n",
       "      <td>7.150746</td>\n",
       "      <td>15.353937</td>\n",
       "      <td>18.560832</td>\n",
       "      <td>21.408763</td>\n",
       "      <td>...</td>\n",
       "      <td>0.475745</td>\n",
       "      <td>0.598819</td>\n",
       "      <td>0.363767</td>\n",
       "      <td>1.300663</td>\n",
       "      <td>0.654411</td>\n",
       "      <td>0.774296</td>\n",
       "      <td>1.009644</td>\n",
       "      <td>0.465629</td>\n",
       "      <td>0.316791</td>\n",
       "      <td>0.115907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-05</th>\n",
       "      <td>124.084821</td>\n",
       "      <td>19.967393</td>\n",
       "      <td>52.060563</td>\n",
       "      <td>33.758888</td>\n",
       "      <td>20.385529</td>\n",
       "      <td>17.647670</td>\n",
       "      <td>6.693953</td>\n",
       "      <td>15.876754</td>\n",
       "      <td>19.059418</td>\n",
       "      <td>21.379768</td>\n",
       "      <td>...</td>\n",
       "      <td>0.352605</td>\n",
       "      <td>0.458329</td>\n",
       "      <td>0.273019</td>\n",
       "      <td>1.323949</td>\n",
       "      <td>0.746835</td>\n",
       "      <td>0.863417</td>\n",
       "      <td>1.060728</td>\n",
       "      <td>0.388312</td>\n",
       "      <td>0.282344</td>\n",
       "      <td>0.045705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-06</th>\n",
       "      <td>124.985119</td>\n",
       "      <td>20.729389</td>\n",
       "      <td>51.195460</td>\n",
       "      <td>32.945309</td>\n",
       "      <td>20.880073</td>\n",
       "      <td>17.801835</td>\n",
       "      <td>6.248296</td>\n",
       "      <td>16.543595</td>\n",
       "      <td>19.393522</td>\n",
       "      <td>20.896736</td>\n",
       "      <td>...</td>\n",
       "      <td>0.547282</td>\n",
       "      <td>0.604487</td>\n",
       "      <td>0.280198</td>\n",
       "      <td>1.182745</td>\n",
       "      <td>0.600657</td>\n",
       "      <td>0.835675</td>\n",
       "      <td>1.079475</td>\n",
       "      <td>0.456627</td>\n",
       "      <td>0.381050</td>\n",
       "      <td>0.176772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-07</th>\n",
       "      <td>128.750000</td>\n",
       "      <td>23.521995</td>\n",
       "      <td>47.602644</td>\n",
       "      <td>31.493829</td>\n",
       "      <td>24.519618</td>\n",
       "      <td>21.357880</td>\n",
       "      <td>9.303899</td>\n",
       "      <td>14.777651</td>\n",
       "      <td>16.111051</td>\n",
       "      <td>20.862577</td>\n",
       "      <td>...</td>\n",
       "      <td>0.473115</td>\n",
       "      <td>0.501112</td>\n",
       "      <td>0.295334</td>\n",
       "      <td>1.275986</td>\n",
       "      <td>0.573255</td>\n",
       "      <td>0.737781</td>\n",
       "      <td>1.074846</td>\n",
       "      <td>0.529451</td>\n",
       "      <td>0.539272</td>\n",
       "      <td>0.287465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-08</th>\n",
       "      <td>130.145089</td>\n",
       "      <td>24.420910</td>\n",
       "      <td>46.566254</td>\n",
       "      <td>31.911880</td>\n",
       "      <td>25.376165</td>\n",
       "      <td>22.133500</td>\n",
       "      <td>10.177101</td>\n",
       "      <td>13.623907</td>\n",
       "      <td>16.194583</td>\n",
       "      <td>21.924962</td>\n",
       "      <td>...</td>\n",
       "      <td>0.716745</td>\n",
       "      <td>0.683064</td>\n",
       "      <td>0.219430</td>\n",
       "      <td>1.158560</td>\n",
       "      <td>0.637096</td>\n",
       "      <td>0.799237</td>\n",
       "      <td>1.063220</td>\n",
       "      <td>0.521748</td>\n",
       "      <td>0.558226</td>\n",
       "      <td>0.316023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-09</th>\n",
       "      <td>135.063988</td>\n",
       "      <td>27.086259</td>\n",
       "      <td>45.018627</td>\n",
       "      <td>36.140259</td>\n",
       "      <td>24.781327</td>\n",
       "      <td>20.268601</td>\n",
       "      <td>8.480880</td>\n",
       "      <td>16.124685</td>\n",
       "      <td>20.280591</td>\n",
       "      <td>18.774420</td>\n",
       "      <td>...</td>\n",
       "      <td>0.652551</td>\n",
       "      <td>0.284089</td>\n",
       "      <td>0.370909</td>\n",
       "      <td>1.418951</td>\n",
       "      <td>0.456547</td>\n",
       "      <td>0.770751</td>\n",
       "      <td>1.263528</td>\n",
       "      <td>0.420071</td>\n",
       "      <td>0.535967</td>\n",
       "      <td>0.235880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-10</th>\n",
       "      <td>140.386161</td>\n",
       "      <td>29.679195</td>\n",
       "      <td>46.258460</td>\n",
       "      <td>41.280609</td>\n",
       "      <td>20.855361</td>\n",
       "      <td>15.247227</td>\n",
       "      <td>3.536714</td>\n",
       "      <td>20.030608</td>\n",
       "      <td>18.275193</td>\n",
       "      <td>17.812171</td>\n",
       "      <td>...</td>\n",
       "      <td>0.488560</td>\n",
       "      <td>0.616098</td>\n",
       "      <td>0.274125</td>\n",
       "      <td>1.158105</td>\n",
       "      <td>0.705216</td>\n",
       "      <td>0.693031</td>\n",
       "      <td>0.940977</td>\n",
       "      <td>0.711361</td>\n",
       "      <td>0.784742</td>\n",
       "      <td>0.512774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-11</th>\n",
       "      <td>137.932292</td>\n",
       "      <td>29.129412</td>\n",
       "      <td>44.262385</td>\n",
       "      <td>40.278494</td>\n",
       "      <td>23.228313</td>\n",
       "      <td>15.970720</td>\n",
       "      <td>4.311930</td>\n",
       "      <td>22.752294</td>\n",
       "      <td>19.670161</td>\n",
       "      <td>16.398602</td>\n",
       "      <td>...</td>\n",
       "      <td>0.404662</td>\n",
       "      <td>0.719656</td>\n",
       "      <td>0.260546</td>\n",
       "      <td>1.212159</td>\n",
       "      <td>0.718216</td>\n",
       "      <td>0.630934</td>\n",
       "      <td>1.117313</td>\n",
       "      <td>0.499538</td>\n",
       "      <td>0.537468</td>\n",
       "      <td>0.400734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-12</th>\n",
       "      <td>132.152530</td>\n",
       "      <td>29.280694</td>\n",
       "      <td>39.152155</td>\n",
       "      <td>40.516067</td>\n",
       "      <td>27.062982</td>\n",
       "      <td>15.537025</td>\n",
       "      <td>6.450583</td>\n",
       "      <td>27.437973</td>\n",
       "      <td>17.728304</td>\n",
       "      <td>20.354857</td>\n",
       "      <td>...</td>\n",
       "      <td>0.376674</td>\n",
       "      <td>0.528811</td>\n",
       "      <td>0.528363</td>\n",
       "      <td>0.961705</td>\n",
       "      <td>0.739092</td>\n",
       "      <td>0.764389</td>\n",
       "      <td>0.877766</td>\n",
       "      <td>0.710299</td>\n",
       "      <td>0.709944</td>\n",
       "      <td>0.391696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-13</th>\n",
       "      <td>128.572173</td>\n",
       "      <td>30.570392</td>\n",
       "      <td>35.601384</td>\n",
       "      <td>42.847156</td>\n",
       "      <td>27.588137</td>\n",
       "      <td>13.071341</td>\n",
       "      <td>6.441739</td>\n",
       "      <td>27.036413</td>\n",
       "      <td>14.216458</td>\n",
       "      <td>22.066582</td>\n",
       "      <td>...</td>\n",
       "      <td>0.739638</td>\n",
       "      <td>0.503804</td>\n",
       "      <td>0.954460</td>\n",
       "      <td>0.520416</td>\n",
       "      <td>1.187571</td>\n",
       "      <td>0.598044</td>\n",
       "      <td>1.069709</td>\n",
       "      <td>0.663691</td>\n",
       "      <td>0.366021</td>\n",
       "      <td>0.489011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-14</th>\n",
       "      <td>128.073661</td>\n",
       "      <td>30.787829</td>\n",
       "      <td>35.023106</td>\n",
       "      <td>43.365785</td>\n",
       "      <td>27.538320</td>\n",
       "      <td>12.271303</td>\n",
       "      <td>6.119175</td>\n",
       "      <td>26.123216</td>\n",
       "      <td>14.061350</td>\n",
       "      <td>21.087362</td>\n",
       "      <td>...</td>\n",
       "      <td>0.363476</td>\n",
       "      <td>0.540257</td>\n",
       "      <td>0.495524</td>\n",
       "      <td>1.005855</td>\n",
       "      <td>0.705208</td>\n",
       "      <td>1.002613</td>\n",
       "      <td>0.536539</td>\n",
       "      <td>1.101572</td>\n",
       "      <td>0.918391</td>\n",
       "      <td>0.345064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-15</th>\n",
       "      <td>131.204613</td>\n",
       "      <td>28.516754</td>\n",
       "      <td>37.545761</td>\n",
       "      <td>40.457619</td>\n",
       "      <td>30.703950</td>\n",
       "      <td>14.054779</td>\n",
       "      <td>9.206991</td>\n",
       "      <td>27.164054</td>\n",
       "      <td>11.168879</td>\n",
       "      <td>21.558820</td>\n",
       "      <td>...</td>\n",
       "      <td>0.655647</td>\n",
       "      <td>0.211713</td>\n",
       "      <td>0.837581</td>\n",
       "      <td>0.871307</td>\n",
       "      <td>1.227720</td>\n",
       "      <td>1.028177</td>\n",
       "      <td>1.022922</td>\n",
       "      <td>0.902981</td>\n",
       "      <td>0.404240</td>\n",
       "      <td>0.623532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-16</th>\n",
       "      <td>129.453125</td>\n",
       "      <td>30.059097</td>\n",
       "      <td>36.962700</td>\n",
       "      <td>40.930466</td>\n",
       "      <td>29.841781</td>\n",
       "      <td>15.256979</td>\n",
       "      <td>10.027544</td>\n",
       "      <td>28.711863</td>\n",
       "      <td>9.712623</td>\n",
       "      <td>22.593708</td>\n",
       "      <td>...</td>\n",
       "      <td>0.773847</td>\n",
       "      <td>0.104756</td>\n",
       "      <td>0.936279</td>\n",
       "      <td>0.738109</td>\n",
       "      <td>1.240571</td>\n",
       "      <td>0.897051</td>\n",
       "      <td>0.997410</td>\n",
       "      <td>0.831946</td>\n",
       "      <td>0.433203</td>\n",
       "      <td>0.512553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-17</th>\n",
       "      <td>127.509673</td>\n",
       "      <td>31.953182</td>\n",
       "      <td>37.360714</td>\n",
       "      <td>40.127234</td>\n",
       "      <td>30.780124</td>\n",
       "      <td>16.863948</td>\n",
       "      <td>11.604884</td>\n",
       "      <td>28.288580</td>\n",
       "      <td>10.215680</td>\n",
       "      <td>20.960004</td>\n",
       "      <td>...</td>\n",
       "      <td>0.569445</td>\n",
       "      <td>0.277429</td>\n",
       "      <td>0.812666</td>\n",
       "      <td>0.922613</td>\n",
       "      <td>1.332770</td>\n",
       "      <td>1.003865</td>\n",
       "      <td>1.153910</td>\n",
       "      <td>0.732679</td>\n",
       "      <td>0.304986</td>\n",
       "      <td>0.457334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-18</th>\n",
       "      <td>126.695685</td>\n",
       "      <td>32.766076</td>\n",
       "      <td>37.687591</td>\n",
       "      <td>39.603212</td>\n",
       "      <td>31.488194</td>\n",
       "      <td>17.437560</td>\n",
       "      <td>12.136272</td>\n",
       "      <td>27.468821</td>\n",
       "      <td>11.250274</td>\n",
       "      <td>20.229501</td>\n",
       "      <td>...</td>\n",
       "      <td>0.640552</td>\n",
       "      <td>0.242834</td>\n",
       "      <td>0.967905</td>\n",
       "      <td>0.730394</td>\n",
       "      <td>1.242901</td>\n",
       "      <td>0.902391</td>\n",
       "      <td>0.903767</td>\n",
       "      <td>0.992520</td>\n",
       "      <td>0.446803</td>\n",
       "      <td>0.753051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-19</th>\n",
       "      <td>126.901042</td>\n",
       "      <td>32.551799</td>\n",
       "      <td>37.586704</td>\n",
       "      <td>39.802782</td>\n",
       "      <td>31.171090</td>\n",
       "      <td>17.339098</td>\n",
       "      <td>12.147273</td>\n",
       "      <td>27.999615</td>\n",
       "      <td>10.788942</td>\n",
       "      <td>19.898260</td>\n",
       "      <td>...</td>\n",
       "      <td>0.723221</td>\n",
       "      <td>0.132768</td>\n",
       "      <td>0.813974</td>\n",
       "      <td>0.873979</td>\n",
       "      <td>1.240231</td>\n",
       "      <td>1.009477</td>\n",
       "      <td>1.073964</td>\n",
       "      <td>0.827756</td>\n",
       "      <td>0.398067</td>\n",
       "      <td>0.627162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-20</th>\n",
       "      <td>131.232143</td>\n",
       "      <td>28.708580</td>\n",
       "      <td>33.296120</td>\n",
       "      <td>42.911481</td>\n",
       "      <td>31.551885</td>\n",
       "      <td>20.879055</td>\n",
       "      <td>14.091589</td>\n",
       "      <td>24.140521</td>\n",
       "      <td>13.346880</td>\n",
       "      <td>23.007472</td>\n",
       "      <td>...</td>\n",
       "      <td>0.546592</td>\n",
       "      <td>0.330150</td>\n",
       "      <td>0.921072</td>\n",
       "      <td>0.741913</td>\n",
       "      <td>1.196401</td>\n",
       "      <td>1.015762</td>\n",
       "      <td>1.017411</td>\n",
       "      <td>0.866284</td>\n",
       "      <td>0.234453</td>\n",
       "      <td>0.451480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-21</th>\n",
       "      <td>137.940476</td>\n",
       "      <td>24.308717</td>\n",
       "      <td>27.590204</td>\n",
       "      <td>44.020665</td>\n",
       "      <td>37.597364</td>\n",
       "      <td>20.736209</td>\n",
       "      <td>10.062988</td>\n",
       "      <td>26.256813</td>\n",
       "      <td>8.287733</td>\n",
       "      <td>22.173338</td>\n",
       "      <td>...</td>\n",
       "      <td>1.128270</td>\n",
       "      <td>0.368187</td>\n",
       "      <td>0.689581</td>\n",
       "      <td>1.338269</td>\n",
       "      <td>1.602140</td>\n",
       "      <td>0.894847</td>\n",
       "      <td>1.053510</td>\n",
       "      <td>1.149899</td>\n",
       "      <td>0.648618</td>\n",
       "      <td>0.875022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-22</th>\n",
       "      <td>145.636905</td>\n",
       "      <td>22.409972</td>\n",
       "      <td>24.477155</td>\n",
       "      <td>41.753923</td>\n",
       "      <td>44.157560</td>\n",
       "      <td>15.621829</td>\n",
       "      <td>3.494364</td>\n",
       "      <td>32.584435</td>\n",
       "      <td>7.453187</td>\n",
       "      <td>16.587254</td>\n",
       "      <td>...</td>\n",
       "      <td>0.627085</td>\n",
       "      <td>0.535071</td>\n",
       "      <td>1.121428</td>\n",
       "      <td>0.497989</td>\n",
       "      <td>0.864779</td>\n",
       "      <td>1.392152</td>\n",
       "      <td>1.457807</td>\n",
       "      <td>0.530437</td>\n",
       "      <td>0.727575</td>\n",
       "      <td>0.896553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-23</th>\n",
       "      <td>156.494792</td>\n",
       "      <td>26.191069</td>\n",
       "      <td>28.307509</td>\n",
       "      <td>34.111600</td>\n",
       "      <td>47.788300</td>\n",
       "      <td>5.850425</td>\n",
       "      <td>11.726864</td>\n",
       "      <td>31.183561</td>\n",
       "      <td>16.357847</td>\n",
       "      <td>24.454854</td>\n",
       "      <td>...</td>\n",
       "      <td>1.373525</td>\n",
       "      <td>0.788971</td>\n",
       "      <td>0.551170</td>\n",
       "      <td>1.772049</td>\n",
       "      <td>2.110606</td>\n",
       "      <td>0.387653</td>\n",
       "      <td>0.566043</td>\n",
       "      <td>1.273484</td>\n",
       "      <td>0.532800</td>\n",
       "      <td>0.497481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-24</th>\n",
       "      <td>163.810268</td>\n",
       "      <td>31.656507</td>\n",
       "      <td>34.449939</td>\n",
       "      <td>27.295354</td>\n",
       "      <td>45.956436</td>\n",
       "      <td>8.825479</td>\n",
       "      <td>16.742656</td>\n",
       "      <td>24.841545</td>\n",
       "      <td>16.143352</td>\n",
       "      <td>26.503012</td>\n",
       "      <td>...</td>\n",
       "      <td>1.497746</td>\n",
       "      <td>0.818357</td>\n",
       "      <td>0.327276</td>\n",
       "      <td>1.600673</td>\n",
       "      <td>1.859147</td>\n",
       "      <td>0.656494</td>\n",
       "      <td>0.794054</td>\n",
       "      <td>0.984550</td>\n",
       "      <td>0.189029</td>\n",
       "      <td>0.240387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-25</th>\n",
       "      <td>166.107887</td>\n",
       "      <td>33.807209</td>\n",
       "      <td>36.717695</td>\n",
       "      <td>25.343447</td>\n",
       "      <td>43.836857</td>\n",
       "      <td>10.566806</td>\n",
       "      <td>15.476668</td>\n",
       "      <td>25.240010</td>\n",
       "      <td>14.554203</td>\n",
       "      <td>24.982590</td>\n",
       "      <td>...</td>\n",
       "      <td>1.930014</td>\n",
       "      <td>1.553821</td>\n",
       "      <td>0.884503</td>\n",
       "      <td>2.116202</td>\n",
       "      <td>2.037979</td>\n",
       "      <td>1.319523</td>\n",
       "      <td>1.423752</td>\n",
       "      <td>0.672246</td>\n",
       "      <td>0.463266</td>\n",
       "      <td>0.449035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-26</th>\n",
       "      <td>174.335565</td>\n",
       "      <td>41.917361</td>\n",
       "      <td>44.397056</td>\n",
       "      <td>22.232485</td>\n",
       "      <td>36.548128</td>\n",
       "      <td>14.377583</td>\n",
       "      <td>7.780845</td>\n",
       "      <td>32.625532</td>\n",
       "      <td>18.227219</td>\n",
       "      <td>28.714277</td>\n",
       "      <td>...</td>\n",
       "      <td>1.556675</td>\n",
       "      <td>0.875715</td>\n",
       "      <td>0.467236</td>\n",
       "      <td>1.810675</td>\n",
       "      <td>2.101424</td>\n",
       "      <td>0.803450</td>\n",
       "      <td>1.073698</td>\n",
       "      <td>0.642610</td>\n",
       "      <td>0.421580</td>\n",
       "      <td>0.461279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-27</th>\n",
       "      <td>172.685268</td>\n",
       "      <td>40.311069</td>\n",
       "      <td>43.714015</td>\n",
       "      <td>20.695408</td>\n",
       "      <td>35.636194</td>\n",
       "      <td>16.400896</td>\n",
       "      <td>6.457272</td>\n",
       "      <td>34.606796</td>\n",
       "      <td>19.656354</td>\n",
       "      <td>30.742266</td>\n",
       "      <td>...</td>\n",
       "      <td>1.559847</td>\n",
       "      <td>0.545421</td>\n",
       "      <td>0.654847</td>\n",
       "      <td>2.008651</td>\n",
       "      <td>2.452279</td>\n",
       "      <td>1.064265</td>\n",
       "      <td>1.439608</td>\n",
       "      <td>0.363168</td>\n",
       "      <td>0.537928</td>\n",
       "      <td>0.420868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-28</th>\n",
       "      <td>165.651042</td>\n",
       "      <td>33.759423</td>\n",
       "      <td>42.243528</td>\n",
       "      <td>14.834941</td>\n",
       "      <td>31.692183</td>\n",
       "      <td>23.025698</td>\n",
       "      <td>3.935790</td>\n",
       "      <td>40.856210</td>\n",
       "      <td>25.612139</td>\n",
       "      <td>36.411920</td>\n",
       "      <td>...</td>\n",
       "      <td>1.353355</td>\n",
       "      <td>0.923751</td>\n",
       "      <td>0.597435</td>\n",
       "      <td>1.879072</td>\n",
       "      <td>2.062563</td>\n",
       "      <td>0.818948</td>\n",
       "      <td>1.082039</td>\n",
       "      <td>0.592872</td>\n",
       "      <td>0.464791</td>\n",
       "      <td>0.465328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-29</th>\n",
       "      <td>157.271577</td>\n",
       "      <td>27.632796</td>\n",
       "      <td>46.037717</td>\n",
       "      <td>6.636508</td>\n",
       "      <td>23.424235</td>\n",
       "      <td>26.301017</td>\n",
       "      <td>4.572673</td>\n",
       "      <td>39.716058</td>\n",
       "      <td>23.844344</td>\n",
       "      <td>30.466516</td>\n",
       "      <td>...</td>\n",
       "      <td>1.447305</td>\n",
       "      <td>0.852250</td>\n",
       "      <td>0.617405</td>\n",
       "      <td>1.954655</td>\n",
       "      <td>2.080433</td>\n",
       "      <td>0.866000</td>\n",
       "      <td>0.983554</td>\n",
       "      <td>0.713188</td>\n",
       "      <td>0.552314</td>\n",
       "      <td>0.631779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-30</th>\n",
       "      <td>154.287946</td>\n",
       "      <td>26.022495</td>\n",
       "      <td>48.204351</td>\n",
       "      <td>4.412519</td>\n",
       "      <td>20.636380</td>\n",
       "      <td>26.429958</td>\n",
       "      <td>6.994178</td>\n",
       "      <td>37.075288</td>\n",
       "      <td>20.791710</td>\n",
       "      <td>27.068477</td>\n",
       "      <td>...</td>\n",
       "      <td>1.438696</td>\n",
       "      <td>0.644948</td>\n",
       "      <td>1.107499</td>\n",
       "      <td>2.203210</td>\n",
       "      <td>1.534497</td>\n",
       "      <td>0.447177</td>\n",
       "      <td>0.887669</td>\n",
       "      <td>0.568091</td>\n",
       "      <td>1.069909</td>\n",
       "      <td>0.734432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>157.543899</td>\n",
       "      <td>27.121062</td>\n",
       "      <td>45.102809</td>\n",
       "      <td>5.183792</td>\n",
       "      <td>21.299242</td>\n",
       "      <td>29.197139</td>\n",
       "      <td>8.576122</td>\n",
       "      <td>39.320621</td>\n",
       "      <td>22.283199</td>\n",
       "      <td>25.103560</td>\n",
       "      <td>...</td>\n",
       "      <td>1.147928</td>\n",
       "      <td>1.111131</td>\n",
       "      <td>0.732144</td>\n",
       "      <td>1.676863</td>\n",
       "      <td>1.794446</td>\n",
       "      <td>0.495443</td>\n",
       "      <td>1.247085</td>\n",
       "      <td>0.224014</td>\n",
       "      <td>0.594284</td>\n",
       "      <td>0.261649</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2163 rows × 135 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                stft_0     stft_1     stft_2     stft_3     stft_4     stft_5  \\\n",
       "date                                                                            \n",
       "2010-01-29   93.976190  16.758337  24.855954  33.738768   9.682404  19.203026   \n",
       "2010-01-30   90.284226  19.582026  23.528091  32.704434  12.685932  18.362866   \n",
       "2010-01-31   86.747024  22.865364  21.285181  34.338648  13.708756  15.143763   \n",
       "2010-02-01   85.319940  24.275830  20.090105  35.618984  13.301923  13.993034   \n",
       "2010-02-02   86.583333  23.022902  21.338873  34.444698  14.470769  13.611003   \n",
       "...                ...        ...        ...        ...        ...        ...   \n",
       "2015-12-27  172.685268  40.311069  43.714015  20.695408  35.636194  16.400896   \n",
       "2015-12-28  165.651042  33.759423  42.243528  14.834941  31.692183  23.025698   \n",
       "2015-12-29  157.271577  27.632796  46.037717   6.636508  23.424235  26.301017   \n",
       "2015-12-30  154.287946  26.022495  48.204351   4.412519  20.636380  26.429958   \n",
       "2015-12-31  157.543899  27.121062  45.102809   5.183792  21.299242  29.197139   \n",
       "\n",
       "               stft_6     stft_7     stft_8     stft_9  ...  stft_125  \\\n",
       "date                                                    ...             \n",
       "2010-01-29   8.148744   9.735022  14.879033   6.058545  ...  1.197717   \n",
       "2010-01-30  11.384585  11.854666  13.777843   3.345975  ...  1.197447   \n",
       "2010-01-31  13.311499   9.828546  11.056826   3.931810  ...  1.155313   \n",
       "2010-02-01  12.790457   8.493307  11.888127   5.043548  ...  1.150959   \n",
       "2010-02-02  13.918821   7.924493  11.469088   6.068044  ...  1.255865   \n",
       "...               ...        ...        ...        ...  ...       ...   \n",
       "2015-12-27   6.457272  34.606796  19.656354  30.742266  ...  1.559847   \n",
       "2015-12-28   3.935790  40.856210  25.612139  36.411920  ...  1.353355   \n",
       "2015-12-29   4.572673  39.716058  23.844344  30.466516  ...  1.447305   \n",
       "2015-12-30   6.994178  37.075288  20.791710  27.068477  ...  1.438696   \n",
       "2015-12-31   8.576122  39.320621  22.283199  25.103560  ...  1.147928   \n",
       "\n",
       "            stft_126  stft_127  stft_128  stft_129  stft_130  stft_131  \\\n",
       "date                                                                     \n",
       "2010-01-29  0.841096  0.669159  1.461749  1.248827  1.037334  1.355093   \n",
       "2010-01-30  0.822349  0.820258  1.283175  1.303852  1.121831  1.312683   \n",
       "2010-01-31  0.847114  0.666774  1.477420  1.143638  1.189890  1.151682   \n",
       "2010-02-01  0.893489  0.740933  1.470793  1.162463  1.183014  1.125473   \n",
       "2010-02-02  0.873471  0.867190  1.403744  1.262079  1.105403  1.126400   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2015-12-27  0.545421  0.654847  2.008651  2.452279  1.064265  1.439608   \n",
       "2015-12-28  0.923751  0.597435  1.879072  2.062563  0.818948  1.082039   \n",
       "2015-12-29  0.852250  0.617405  1.954655  2.080433  0.866000  0.983554   \n",
       "2015-12-30  0.644948  1.107499  2.203210  1.534497  0.447177  0.887669   \n",
       "2015-12-31  1.111131  0.732144  1.676863  1.794446  0.495443  1.247085   \n",
       "\n",
       "            stft_132  stft_133  stft_134  \n",
       "date                                      \n",
       "2010-01-29  0.440955  1.046557  0.861513  \n",
       "2010-01-30  0.570907  1.015920  0.831692  \n",
       "2010-01-31  0.457601  1.252122  0.593055  \n",
       "2010-02-01  0.481120  1.245927  0.634673  \n",
       "2010-02-02  0.448198  1.352874  0.589729  \n",
       "...              ...       ...       ...  \n",
       "2015-12-27  0.363168  0.537928  0.420868  \n",
       "2015-12-28  0.592872  0.464791  0.465328  \n",
       "2015-12-29  0.713188  0.552314  0.631779  \n",
       "2015-12-30  0.568091  1.069909  0.734432  \n",
       "2015-12-31  0.224014  0.594284  0.261649  \n",
       "\n",
       "[2163 rows x 135 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stft_filename=\"BeijingPM_stft.csv\"\n",
    "if os.path.exists(stft_filename):\n",
    "    print(\"Reading existing features from %s\" % stft_filename)\n",
    "    pm_stft=pd.read_csv(stft_filename)\n",
    "    pm_stft.index=pd.DatetimeIndex(pm_stft[\"date\"])\n",
    "    pm_stft.drop(\"date\",axis=1,inplace=True)\n",
    "else:\n",
    "    print(\"Generating frequency-domain features\")\n",
    "    stft_index=list(data.loc[data[\"id\"]>=stft_period,\"date\"])\n",
    "    f,t,stft=signal.stft(data[\"PM\"],window=\"boxcar\",nperseg=stft_period,\n",
    "                         noverlap=stft_period-1,boundary=None,padded=False)\n",
    "    stft_coeffs=np.abs(stft[f<=stft_upper]).T[:-1]\n",
    "\n",
    "    stft_cols=[\"stft_%d\" % m for m in range(stft_coeffs.shape[1])]\n",
    "    pm_stft=pd.DataFrame(stft_coeffs,columns=stft_cols)\n",
    "    pm_stft[\"date\"]=stft_index\n",
    "    pm_stft.index=pd.DatetimeIndex(pm_stft[\"date\"])\n",
    "    pm_stft.drop(\"date\",axis=1,inplace=True)\n",
    "    pm_stft.to_csv(stft_filename)\n",
    "\n",
    "pm_stft[::24]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature fusion\n",
    "\n",
    "In this section, we gather the original dataset, lagged features, and statistical features to form a new dataset used for our research. We put frequency-domain features into a separate file for control experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(0/2) Stripping missing values: 100%|██████████| 374/374 [00:02<00:00, 162.43it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1/2) Writing dataset to BeijingPM_dataset.csv\n",
      "(2/2) OK\n"
     ]
    }
   ],
   "source": [
    "# Combine everything except frequency-domain features\n",
    "dataset=pd.concat([data,datetime_fea,lagged,stats],axis=1)\n",
    "\n",
    "# Strip missing values from the dataset\n",
    "tqdm.pandas(desc=\"(0/2) Stripping missing values\")\n",
    "first=dataset.progress_apply(lambda s:s.first_valid_index(),axis=0).max()\n",
    "\n",
    "data_file=\"BeijingPM_dataset.csv\"\n",
    "print(\"(1/2) Writing dataset to %s\" % data_file)\n",
    "dataset_final=dataset[dataset[\"date\"]>=str(first)]\n",
    "dataset_final.drop([\"id\",\"date\"],axis=1).to_csv(data_file)\n",
    "\n",
    "print(\"(2/2) OK\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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