{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "be617771",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T18:02:13.379282Z",
     "start_time": "2021-06-07T18:02:13.377450Z"
    },
    "code_folding": []
   },
   "source": [
    "# The Pyleoclim User Interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b991b26-5523-4cb0-8b8b-72de80903644",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4748faa2",
   "metadata": {},
   "source": [
    "## Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2481a572-c427-4f50-8a94-3aa429e48dda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[2021-06-15_10:29:04]: start //home/wenchang/wython/wystart.py\n",
      "[imported]: os.path, sys, os, datetime, glob\n",
      "[imported]: xarray(0.18.2) as xr, numpy(1.20.3) as np, pandas(1.2.4) as pd, matplotlib(3.4.2) as mpl\n",
      "[imported]: import matplotlib.pyplot as plt\n",
      "\n",
      "**wython plot settings (//home/wenchang/wython/wyconfig.py)**\n",
      "[config]: plt.rcParams['figure.dpi'] = 128.0\n",
      "[config]: plt.rcParams['figure.figsize'] = [6.4, 6.4*9/16]\n",
      "[config]: plt.rcParams['figure.constrained_layout.use'] = True\n",
      "    plt.tight_layout() is NOT needed if plt.rcParams['figure.constrained_layout.use'] = True\n",
      "[registered colormaps]: tc and tc_r\n",
      "[registered colormaps]: parula and parula_r\n",
      "[imported]: import misc.colormaps\n",
      "[config]: xr.set_options(cmap_sequential=\"parula\")\n",
      "[shortcut functions]:\n",
      "    constrained_layout_on():  plt.rcParams['figure.constrained_layout.use'] = True\n",
      "    constrained_layout_off(): plt.rcParams['figure.constrained_layout.use'] = False\n",
      "[iPython config]: InlineBackend.figure_format ='retina'\n",
      "[imported]: from matplotlib.pyplot import plot, figure, close, show\n",
      "[executed]: plt.ion()\n",
      "\n",
      "[2021-06-15_10:29:04]: **done**; **0** seconds from \"start //home/wenchang/wython/wystart.py\"\n"
     ]
    }
   ],
   "source": [
    "run -im wystart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9a636e17",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T18:03:59.950522Z",
     "start_time": "2021-06-07T18:03:27.069291Z"
    }
   },
   "outputs": [],
   "source": [
    "import pyleoclim as pyleo\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "27df6385",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T18:04:01.880758Z",
     "start_time": "2021-06-07T18:04:01.723104Z"
    }
   },
   "outputs": [],
   "source": [
    "data=pd.read_csv(\n",
    "   'https://raw.githubusercontent.com/LinkedEarth/Pyleoclim_util/Development/example_data/soi_data.csv',skiprows=0, header=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "49463e75",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T18:04:23.758526Z",
     "start_time": "2021-06-07T18:04:23.700479Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Year</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>195101</td>\n",
       "      <td>1951.000000</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>195102</td>\n",
       "      <td>1951.083333</td>\n",
       "      <td>0.9</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>195103</td>\n",
       "      <td>1951.166667</td>\n",
       "      <td>-0.1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>195104</td>\n",
       "      <td>1951.250000</td>\n",
       "      <td>-0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>195105</td>\n",
       "      <td>1951.333333</td>\n",
       "      <td>-0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>823</th>\n",
       "      <td>201908</td>\n",
       "      <td>2019.583333</td>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>201909</td>\n",
       "      <td>2019.666667</td>\n",
       "      <td>-1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>201910</td>\n",
       "      <td>2019.750000</td>\n",
       "      <td>-0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826</th>\n",
       "      <td>201911</td>\n",
       "      <td>2019.833333</td>\n",
       "      <td>-0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>827</th>\n",
       "      <td>201912</td>\n",
       "      <td>2019.916667</td>\n",
       "      <td>-0.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>828 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date         Year  Value\n",
       "0    195101  1951.000000    1.5\n",
       "1    195102  1951.083333    0.9\n",
       "2    195103  1951.166667   -0.1\n",
       "3    195104  1951.250000   -0.3\n",
       "4    195105  1951.333333   -0.7\n",
       "..      ...          ...    ...\n",
       "823  201908  2019.583333   -0.1\n",
       "824  201909  2019.666667   -1.2\n",
       "825  201910  2019.750000   -0.4\n",
       "826  201911  2019.833333   -0.8\n",
       "827  201912  2019.916667   -0.6\n",
       "\n",
       "[828 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "47cc97da",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T18:05:23.821715Z",
     "start_time": "2021-06-07T18:05:23.818188Z"
    }
   },
   "outputs": [],
   "source": [
    "time=data.iloc[:,1]\n",
    "value=data.iloc[:,2]\n",
    "ts=pyleo.Series(\n",
    "    time=time, value=value,\n",
    "    time_name='Year (CE)', value_name='SOI', label='Southern Oscillation Index'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b9acfb9c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T18:05:29.619000Z",
     "start_time": "2021-06-07T18:05:29.615896Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pyleoclim.core.ui.Series at 0x2b07373c4d90>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aee6848",
   "metadata": {},
   "source": [
    "### correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b316c357",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T18:13:39.617469Z",
     "start_time": "2021-06-07T18:13:39.395228Z"
    }
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('https://raw.githubusercontent.com/LinkedEarth/Pyleoclim_util/Development/example_data/wtc_test_data_nino.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9f8b8a2d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T18:13:58.097384Z",
     "start_time": "2021-06-07T18:13:58.088827Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t</th>\n",
       "      <th>air</th>\n",
       "      <th>nino</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1871.00</td>\n",
       "      <td>87.36090</td>\n",
       "      <td>-0.358250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1871.08</td>\n",
       "      <td>-21.83460</td>\n",
       "      <td>-0.292458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1871.17</td>\n",
       "      <td>-5.52632</td>\n",
       "      <td>-0.143583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1871.25</td>\n",
       "      <td>75.73680</td>\n",
       "      <td>-0.149625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1871.33</td>\n",
       "      <td>105.82000</td>\n",
       "      <td>-0.274250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1591</th>\n",
       "      <td>2003.58</td>\n",
       "      <td>-206.03800</td>\n",
       "      <td>0.238497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1592</th>\n",
       "      <td>2003.67</td>\n",
       "      <td>103.90200</td>\n",
       "      <td>0.411449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1593</th>\n",
       "      <td>2003.75</td>\n",
       "      <td>218.24100</td>\n",
       "      <td>0.592756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1594</th>\n",
       "      <td>2003.83</td>\n",
       "      <td>-154.66200</td>\n",
       "      <td>0.664131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1595</th>\n",
       "      <td>2003.92</td>\n",
       "      <td>15.04510</td>\n",
       "      <td>0.604324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1596 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            t        air      nino\n",
       "0     1871.00   87.36090 -0.358250\n",
       "1     1871.08  -21.83460 -0.292458\n",
       "2     1871.17   -5.52632 -0.143583\n",
       "3     1871.25   75.73680 -0.149625\n",
       "4     1871.33  105.82000 -0.274250\n",
       "...       ...        ...       ...\n",
       "1591  2003.58 -206.03800  0.238497\n",
       "1592  2003.67  103.90200  0.411449\n",
       "1593  2003.75  218.24100  0.592756\n",
       "1594  2003.83 -154.66200  0.664131\n",
       "1595  2003.92   15.04510  0.604324\n",
       "\n",
       "[1596 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "16f437bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T18:14:42.916881Z",
     "start_time": "2021-06-07T18:14:42.913251Z"
    }
   },
   "outputs": [],
   "source": [
    "t = data.iloc[:, 0]\n",
    "air = data.iloc[:, 1]\n",
    "nino = data.iloc[:, 2]\n",
    "ts_nino = pyleo.Series(time=t, value=nino)\n",
    "ts_air = pyleo.Series(time=t, value=air)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e29ca1b2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T18:16:49.975970Z",
     "start_time": "2021-06-07T18:16:49.962236Z"
    }
   },
   "outputs": [],
   "source": [
    "corr_res = ts_nino.correlation(ts_air, settings={'nsim': 20})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3762f0ff",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T18:17:01.442706Z",
     "start_time": "2021-06-07T18:17:01.440248Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'r': -0.15239413332839047, 'signif': True, 'p': 0.0}\n"
     ]
    }
   ],
   "source": [
    "print(corr_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e73ecfa7-f9ec-401f-8e1d-0e2ea009daad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_res['p']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e414bba3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T18:18:16.882964Z",
     "start_time": "2021-06-07T18:18:16.714551Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'r': -0.15239413332839047, 'signif': True, 'p': 6.597552697594002e-08}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_nino.correlation(ts_air, settings={'nsim': 20, 'method': 'ttest'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0614a4f6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T18:19:07.655171Z",
     "start_time": "2021-06-07T18:19:07.641852Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'r': -0.15239413332839047, 'signif': True, 'p': 2.2215868046822725e-31}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_nino.correlation(ts_air, settings={'nsim': 20, 'method': 'isopersistent'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d9c4f404-ec6e-41b6-8d6b-4f41d60736af",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr, pandas as pd, xaddon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5059a54d-c2d7-4de7-8cde-be0e80561933",
   "metadata": {},
   "outputs": [
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       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (dim_0: 1596)\n",
       "Coordinates:\n",
       "  * dim_0    (dim_0) int64 0 1 2 3 4 5 6 ... 1589 1590 1591 1592 1593 1594 1595\n",
       "Data variables:\n",
       "    t        (dim_0) float64 1.871e+03 1.871e+03 ... 2.004e+03 2.004e+03\n",
       "    air      (dim_0) float64 87.36 -21.83 -5.526 75.74 ... 218.2 -154.7 15.05\n",
       "    nino     (dim_0) float64 -0.3583 -0.2925 -0.1436 ... 0.5928 0.6641 0.6043</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-dbfaab99-f298-4ede-b871-dcdbbf5898f7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dbfaab99-f298-4ede-b871-dcdbbf5898f7' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>dim_0</span>: 1596</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c177ebb1-7c9e-4292-8581-5bcbfb95554f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c177ebb1-7c9e-4292-8581-5bcbfb95554f' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dim_0</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1592 1593 1594 1595</div><input id='attrs-fc4819eb-9294-4c29-8038-62546f3198ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fc4819eb-9294-4c29-8038-62546f3198ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86a3061f-036d-4ac2-8aba-685a5e7bfd12' class='xr-var-data-in' type='checkbox'><label for='data-86a3061f-036d-4ac2-8aba-685a5e7bfd12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 1593, 1594, 1595])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0c53a210-eaa8-466f-acdd-0295a7e92c28' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0c53a210-eaa8-466f-acdd-0295a7e92c28' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>t</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.871e+03 1.871e+03 ... 2.004e+03</div><input id='attrs-880719a1-7fc0-4636-8697-5ce853304837' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-880719a1-7fc0-4636-8697-5ce853304837' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ee8dce8-3477-42aa-bbf1-75e98babbf9c' class='xr-var-data-in' type='checkbox'><label for='data-7ee8dce8-3477-42aa-bbf1-75e98babbf9c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1871.  , 1871.08, 1871.17, ..., 2003.75, 2003.83, 2003.92])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>air</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>87.36 -21.83 ... -154.7 15.05</div><input id='attrs-df2724b2-7f96-4718-87f0-9b9145891f91' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-df2724b2-7f96-4718-87f0-9b9145891f91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2aab53d-32ca-4745-8258-d97709e71d56' class='xr-var-data-in' type='checkbox'><label for='data-c2aab53d-32ca-4745-8258-d97709e71d56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  87.3609 ,  -21.8346 ,   -5.52632, ...,  218.241  , -154.662  ,\n",
       "         15.0451 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nino</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.3583 -0.2925 ... 0.6641 0.6043</div><input id='attrs-0e45bb8a-a9ad-4852-a621-4df729fe303e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0e45bb8a-a9ad-4852-a621-4df729fe303e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41a880cf-5cbe-4736-9147-4db04d6ed563' class='xr-var-data-in' type='checkbox'><label for='data-41a880cf-5cbe-4736-9147-4db04d6ed563' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-0.35825  , -0.2924584, -0.1435833, ...,  0.5927562,  0.6641306,\n",
       "        0.6043244])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-88dfcece-17ee-471c-97c4-0121cd72e672' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-88dfcece-17ee-471c-97c4-0121cd72e672' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (dim_0: 1596)\n",
       "Coordinates:\n",
       "  * dim_0    (dim_0) int64 0 1 2 3 4 5 6 ... 1589 1590 1591 1592 1593 1594 1595\n",
       "Data variables:\n",
       "    t        (dim_0) float64 1.871e+03 1.871e+03 ... 2.004e+03 2.004e+03\n",
       "    air      (dim_0) float64 87.36 -21.83 -5.526 75.74 ... 218.2 -154.7 15.05\n",
       "    nino     (dim_0) float64 -0.3583 -0.2925 -0.1436 ... 0.5928 0.6641 0.6043"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.Dataset(data)\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "424e449d-2bad-4608-8c88-87ed3ae4a8b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='t'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 819.2x460.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 475,
       "width": 834
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d492bec8-5ec5-4e60-8668-2125828628ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
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       "\n",
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       "\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (dim_0: 1596)\n",
       "Coordinates:\n",
       "  * dim_0      (dim_0) int64 0 1 2 3 4 5 6 ... 1590 1591 1592 1593 1594 1595\n",
       "Data variables:\n",
       "    slope      float64 -46.06\n",
       "    intercept  float64 3.6\n",
       "    r          float64 -0.1524\n",
       "    p          float64 9.408e-10\n",
       "    stderr     float64 7.482\n",
       "    predicted  (dim_0) float64 20.1 17.07 10.21 10.49 ... -23.7 -26.99 -24.23\n",
       "    Nef        int32 1217\n",
       "    pe         float64 9.187e-08</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4cc5b3ee-f9c0-40de-b424-1b516d4c8da9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4cc5b3ee-f9c0-40de-b424-1b516d4c8da9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>dim_0</span>: 1596</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-4e2c1c0b-4876-48f2-a78b-c271e5fa65c2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4e2c1c0b-4876-48f2-a78b-c271e5fa65c2' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dim_0</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1592 1593 1594 1595</div><input id='attrs-f76d2bbf-3f2c-4427-8684-a0cdfafd01b7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f76d2bbf-3f2c-4427-8684-a0cdfafd01b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf29b2a1-f6c6-4c2f-a390-96feb605bfa4' class='xr-var-data-in' type='checkbox'><label for='data-bf29b2a1-f6c6-4c2f-a390-96feb605bfa4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 1593, 1594, 1595])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-562c8dc9-5199-420a-bf50-6fa660b5f4b8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-562c8dc9-5199-420a-bf50-6fa660b5f4b8' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>slope</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-46.06</div><input id='attrs-fddec91a-7b80-4dcb-95d3-9cfe89cb7774' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fddec91a-7b80-4dcb-95d3-9cfe89cb7774' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38c93f30-920a-4dca-835e-70203dd21d3d' class='xr-var-data-in' type='checkbox'><label for='data-38c93f30-920a-4dca-835e-70203dd21d3d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>slope of the linear regression</dd></dl></div><div class='xr-var-data'><pre>array(-46.06003419)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>intercept</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.6</div><input id='attrs-f495f2d3-6903-46ca-b416-37cb7ed01800' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f495f2d3-6903-46ca-b416-37cb7ed01800' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5de1326-8396-4b7b-8da3-fe222ba99a8e' class='xr-var-data-in' type='checkbox'><label for='data-c5de1326-8396-4b7b-8da3-fe222ba99a8e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>intercept of the linear regression</dd></dl></div><div class='xr-var-data'><pre>array(3.60029489)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>r</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.1524</div><input id='attrs-94dfc6df-c078-4a8d-8be6-bcbb31c71df0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94dfc6df-c078-4a8d-8be6-bcbb31c71df0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c19d8e1b-b8a6-4ec7-9d60-9730d878c8ee' class='xr-var-data-in' type='checkbox'><label for='data-c19d8e1b-b8a6-4ec7-9d60-9730d878c8ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>correlation coefficient</dd></dl></div><div class='xr-var-data'><pre>array(-0.15239413)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.408e-10</div><input id='attrs-5c90658b-039c-486f-ae5a-c4162d48fa89' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c90658b-039c-486f-ae5a-c4162d48fa89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b82fddb8-d326-4fe0-b802-b0b07cd23698' class='xr-var-data-in' type='checkbox'><label for='data-b82fddb8-d326-4fe0-b802-b0b07cd23698' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>p-value</dd></dl></div><div class='xr-var-data'><pre>array(9.40792838e-10)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stderr</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.482</div><input id='attrs-64a6c307-ddf9-4238-a889-4d0ed781f98b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64a6c307-ddf9-4238-a889-4d0ed781f98b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-beaadd68-9eba-43af-9fb2-39124e457046' class='xr-var-data-in' type='checkbox'><label for='data-beaadd68-9eba-43af-9fb2-39124e457046' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>standard error of the estimated slope</dd></dl></div><div class='xr-var-data'><pre>array(7.481856)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>predicted</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>20.1 17.07 10.21 ... -26.99 -24.23</div><input id='attrs-50fec4f2-8961-478a-9a46-2013f2bc6bc3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-50fec4f2-8961-478a-9a46-2013f2bc6bc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e562114-6e89-4dc3-9a12-c6b792824271' class='xr-var-data-in' type='checkbox'><label for='data-2e562114-6e89-4dc3-9a12-c6b792824271' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>predicted values by the linear regression model</dd></dl></div><div class='xr-var-data'><pre>array([ 20.10130214,  17.0709388 ,  10.2137466 , ..., -23.70207595,\n",
       "       -26.98958325, -24.23490763])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Nef</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1217</div><input id='attrs-d74115aa-feee-41a8-b730-bef31f71172d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d74115aa-feee-41a8-b730-bef31f71172d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eed0c8b4-914b-45fa-ad87-3b77d99afcf0' class='xr-var-data-in' type='checkbox'><label for='data-eed0c8b4-914b-45fa-ad87-3b77d99afcf0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>effective sample size</dd></dl></div><div class='xr-var-data'><pre>array(1217, dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pe</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.187e-08</div><input id='attrs-171d0576-6cfc-4ddb-88d9-d7f152af0087' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-171d0576-6cfc-4ddb-88d9-d7f152af0087' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6485258-c7e2-4c36-871f-93e671442bfd' class='xr-var-data-in' type='checkbox'><label for='data-e6485258-c7e2-4c36-871f-93e671442bfd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>p-value taking into account effective sample size</dd></dl></div><div class='xr-var-data'><pre>array(9.18669389e-08)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-60cd738e-306a-44ca-b1f6-80db97ac8732' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-60cd738e-306a-44ca-b1f6-80db97ac8732' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (dim_0: 1596)\n",
       "Coordinates:\n",
       "  * dim_0      (dim_0) int64 0 1 2 3 4 5 6 ... 1590 1591 1592 1593 1594 1595\n",
       "Data variables:\n",
       "    slope      float64 -46.06\n",
       "    intercept  float64 3.6\n",
       "    r          float64 -0.1524\n",
       "    p          float64 9.408e-10\n",
       "    stderr     float64 7.482\n",
       "    predicted  (dim_0) float64 20.1 17.07 10.21 10.49 ... -23.7 -26.99 -24.23\n",
       "    Nef        int32 1217\n",
       "    pe         float64 9.187e-08"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rg = ds.air.linregress.on(ds.nino, ess_on=True)\n",
    "rg"
   ]
  },
  {
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   "execution_count": 28,
   "id": "7dcc7ed4-64d6-471f-b2e1-532d7cfadd10",
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       "Dimensions:  (year: 1596)\n",
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       "    t        (year) float64 1.871e+03 1.871e+03 ... 2.004e+03 2.004e+03\n",
       "    air      (year) float64 87.36 -21.83 -5.526 75.74 ... 218.2 -154.7 15.05\n",
       "    nino     (year) float64 -0.3583 -0.2925 -0.1436 ... 0.5928 0.6641 0.6043</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-685376da-4440-49ca-b0e9-82531caecfe8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-685376da-4440-49ca-b0e9-82531caecfe8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>year</span>: 1596</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-babda40e-27d3-4d42-a171-9206fe7b20b0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-babda40e-27d3-4d42-a171-9206fe7b20b0' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-04db67e6-2e33-4c77-b22b-b0608a469dfb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-04db67e6-2e33-4c77-b22b-b0608a469dfb' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>t</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.871e+03 1.871e+03 ... 2.004e+03</div><input id='attrs-452e59bb-887d-4133-9572-da0d1989dfac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-452e59bb-887d-4133-9572-da0d1989dfac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12fdb1d4-82a5-4608-8c61-812997e66975' class='xr-var-data-in' type='checkbox'><label for='data-12fdb1d4-82a5-4608-8c61-812997e66975' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1871.  , 1871.08, 1871.17, ..., 2003.75, 2003.83, 2003.92])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>air</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>87.36 -21.83 ... -154.7 15.05</div><input id='attrs-3301e30a-179a-43a6-bc98-72bc6d71c2b7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3301e30a-179a-43a6-bc98-72bc6d71c2b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbf47c33-76c6-4d77-ac05-260281311ecc' class='xr-var-data-in' type='checkbox'><label for='data-dbf47c33-76c6-4d77-ac05-260281311ecc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  87.3609 ,  -21.8346 ,   -5.52632, ...,  218.241  , -154.662  ,\n",
       "         15.0451 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nino</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.3583 -0.2925 ... 0.6641 0.6043</div><input id='attrs-0bccbe41-e7e3-46ba-8ad9-cd99b486b9ba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0bccbe41-e7e3-46ba-8ad9-cd99b486b9ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab2b62de-2783-4a8e-a266-1573b804fcc4' class='xr-var-data-in' type='checkbox'><label for='data-ab2b62de-2783-4a8e-a266-1573b804fcc4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-0.35825  , -0.2924584, -0.1435833, ...,  0.5927562,  0.6641306,\n",
       "        0.6043244])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c35b2eae-4e93-404b-9587-696044ef14c5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c35b2eae-4e93-404b-9587-696044ef14c5' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (year: 1596)\n",
       "Dimensions without coordinates: year\n",
       "Data variables:\n",
       "    t        (year) float64 1.871e+03 1.871e+03 ... 2.004e+03 2.004e+03\n",
       "    air      (year) float64 87.36 -21.83 -5.526 75.74 ... 218.2 -154.7 15.05\n",
       "    nino     (year) float64 -0.3583 -0.2925 -0.1436 ... 0.5928 0.6641 0.6043"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = ds.rename(dim_0='year')\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d82bbd2c-a73d-4845-bf8a-858f0f68bd23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9429213491464299"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r1x = ds.nino.isel(year=slice(0,-1)).linregress.on(ds.nino.isel(year=slice(1,None))).r.item()\n",
    "r1x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8ca15d91-523a-4330-a288-7d411781074e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14268516038230056"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r1y = ds.air.isel(year=slice(0,-1)).linregress.on(ds.air.isel(year=slice(1,None))).r.item()\n",
    "r1y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "163d2898-c720-43be-949b-b6b4b51c39a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1217.4728727806178"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Nef = ds.year.size*(1 - r1x*r1y)/(1 + r1x*r1y)\n",
    "Nef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "28b4baa8-96cc-4a27-8a0f-8b9e2b14c4fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1215"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dof = int(Nef) - 2\n",
    "dof"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c257ab1b-e6d9-4410-9d24-cea84c6deea1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import t as stu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "980526e2-30e7-4d43-8ff2-dc7d7e73617c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.186693894871496e-08"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2*stu.cdf(rg.r.item()*np.sqrt(dof/(1 - rg.r**2)), dof)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0e529372-5725-4cce-be69-2a57d883fd2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1596"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N = ds.year.size\n",
    "N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "176f749b-934c-45cd-887f-809db62490c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46.8868834048888"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Nx = N*(1-r1x)/(1+r1x)\n",
    "Nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f7ce6725-bf52-45d0-a60a-1f776c01ae34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1197.4203669294823"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ny = N*(1-r1y)/(1+r1y)\n",
    "Ny"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "dd0add54-48cd-441f-92a5-2d3dfb440342",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "236.94579365513496"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Nef2 = (Nx*Ny)**0.5\n",
    "Nef2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0849fce7-5852-48bd-90e6-a9153037b5ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "234"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dof2 = int(Nef2) -2 \n",
    "dof2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e0a8bf43-16de-4a65-bcb2-5d830fd959de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2581462447830205e-06"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2*stu.cdf(rg.r.item()*np.sqrt(1000/(1 - rg.r**2)), 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e7bc7c2a-bbc9-4dc5-9d7e-c1c939836a3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "!ln -s /tigress/wenchang/analysis/LMTC/fig_lines_tc_all_stde.py ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3ace78c4-9df9-4ecb-bd4b-29aca413198f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ECS_TCR.csv                \u001b[0m\u001b[38;5;34mfig_violinplot.py\u001b[0m*  \u001b[38;5;27mrainyseed\u001b[0m/     Untitled.ipynb\n",
      "\u001b[38;5;51mfig_lines_tc_all_stde.py\u001b[0m@  \u001b[38;5;27mFLOR1860CTL\u001b[0m/        \u001b[38;5;27mtc_composite\u001b[0m/\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a1011c21-7e7c-426b-93cb-89398eb2d2e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from _fig_lines_tc_all_stde import ds_liz, ds_lmr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e8b3a478-f643-4764-b5be-133aabae4e66",
   "metadata": {},
   "outputs": [
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       "\n",
       ".xr-array-preview,\n",
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       "  padding: 0 5px !important;\n",
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       "\n",
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       "  display: none;\n",
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       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
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       "  display: inline-block;\n",
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       "\n",
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       "\n",
       ".xr-dim-list li {\n",
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       "\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
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       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:         (year: 1516)\n",
       "Coordinates:\n",
       "  * year            (year) int64 500 501 502 503 504 ... 2012 2013 2014 2015\n",
       "Data variables:\n",
       "    normTCcounts    (year) float64 0.01712 0.01706 0.017 ... 0.01272 0.01105\n",
       "    nopost1700Vieq  (year) float64 0.01712 0.01706 0.017 ... 0.01386 0.01202\n",
       "    smoothlower     (year) float64 0.01361 0.01357 0.01352 ... 0.008644 0.006937\n",
       "    smoothupper     (year) float64 0.02063 0.02056 0.02049 ... 0.0168 0.01517</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d8b5afbb-d3b8-4515-bcea-3a18f07629c5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d8b5afbb-d3b8-4515-bcea-3a18f07629c5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>year</span>: 1516</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a73752b3-c294-4ce1-81c0-a592043ccb5e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a73752b3-c294-4ce1-81c0-a592043ccb5e' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>500 501 502 503 ... 2013 2014 2015</div><input id='attrs-c5a2d802-9f81-4f88-9aa6-3ae55f7d979d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c5a2d802-9f81-4f88-9aa6-3ae55f7d979d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb7a005e-1fd0-4001-a79d-e936693fe83e' class='xr-var-data-in' type='checkbox'><label for='data-bb7a005e-1fd0-4001-a79d-e936693fe83e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 500,  501,  502, ..., 2013, 2014, 2015])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d42568fb-fc64-4345-b719-51506fd5ae1a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d42568fb-fc64-4345-b719-51506fd5ae1a' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>normTCcounts</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.01712 0.01706 ... 0.01272 0.01105</div><input id='attrs-58062e3f-16a4-4333-b3f6-cbec261d72c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-58062e3f-16a4-4333-b3f6-cbec261d72c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f080d0b-b49f-4009-a754-d588906941c6' class='xr-var-data-in' type='checkbox'><label for='data-9f080d0b-b49f-4009-a754-d588906941c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.01711906, 0.01706153, 0.01700273, ..., 0.01439542, 0.01272127,\n",
       "       0.01105489])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nopost1700Vieq</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.01712 0.01706 ... 0.01386 0.01202</div><input id='attrs-4c0806e3-9828-469e-9390-21f176928a43' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4c0806e3-9828-469e-9390-21f176928a43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec8b9cc0-fa07-4497-a7bf-dcdf63254761' class='xr-var-data-in' type='checkbox'><label for='data-ec8b9cc0-fa07-4497-a7bf-dcdf63254761' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.01711906, 0.01706153, 0.01700273, ..., 0.01570766, 0.0138603 ,\n",
       "       0.01201801])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smoothlower</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.01361 0.01357 ... 0.006937</div><input id='attrs-dabddda3-a04c-44b5-a753-79045c2dd687' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dabddda3-a04c-44b5-a753-79045c2dd687' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4598ffd8-ef68-4902-bb3a-970204b5ba14' class='xr-var-data-in' type='checkbox'><label for='data-4598ffd8-ef68-4902-bb3a-970204b5ba14' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.01360814, 0.01356591, 0.01351728, ..., 0.0102868 , 0.00864391,\n",
       "       0.00693715])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smoothupper</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.02063 0.02056 ... 0.0168 0.01517</div><input id='attrs-c5fdfbbb-154f-4d42-a0d4-20b57e878409' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c5fdfbbb-154f-4d42-a0d4-20b57e878409' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84ac982e-a291-4076-a76e-59d0c6df67c8' class='xr-var-data-in' type='checkbox'><label for='data-84ac982e-a291-4076-a76e-59d0c6df67c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.02062998, 0.02055714, 0.02048818, ..., 0.01850404, 0.01679863,\n",
       "       0.01517264])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-181d2b59-af38-4606-9532-1b50b5e76f51' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-181d2b59-af38-4606-9532-1b50b5e76f51' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:         (year: 1516)\n",
       "Coordinates:\n",
       "  * year            (year) int64 500 501 502 503 504 ... 2012 2013 2014 2015\n",
       "Data variables:\n",
       "    normTCcounts    (year) float64 0.01712 0.01706 0.017 ... 0.01272 0.01105\n",
       "    nopost1700Vieq  (year) float64 0.01712 0.01706 0.017 ... 0.01386 0.01202\n",
       "    smoothlower     (year) float64 0.01361 0.01357 0.01352 ... 0.008644 0.006937\n",
       "    smoothupper     (year) float64 0.02063 0.02056 0.02049 ... 0.0168 0.01517"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_liz"
   ]
  },
  {
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   "execution_count": 64,
   "id": "184ba72d-c0f0-4a0c-92cb-3e70991d91f8",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (mcrun: 20, year: 2001)\n",
       "Coordinates:\n",
       "  * year     (year) int32 0 1 2 3 4 5 6 7 ... 1994 1995 1996 1997 1998 1999 2000\n",
       "Dimensions without coordinates: mcrun\n",
       "Data variables:\n",
       "    HU       (year, mcrun) float32 5.234 6.442 6.044 5.527 ... 5.552 5.026 5.276\n",
       "    MH       (year, mcrun) float32 0.8484 1.135 0.9549 ... 0.9613 0.8497 0.8651\n",
       "    MHnoCO2  (year, mcrun) float32 0.7718 1.032 0.8687 ... 0.977 0.8636 0.8792\n",
       "    TS       (year, mcrun) float32 7.701 8.858 8.373 7.981 ... 8.213 7.7 7.892\n",
       "    PDI      (year, mcrun) float32 1.764 2.267 1.97 1.875 ... 2.13 1.912 1.951\n",
       "    MDR      (year, mcrun) float32 -0.03904 -0.04728 -0.1791 ... 0.1807 0.1124\n",
       "    TROP     (year, mcrun) float32 -0.05526 -0.04614 -0.146 ... 0.2754 0.1624\n",
       "    co2      (year) float32 277.5 277.1 277.2 277.2 ... 363.3 365.9 367.8 369.1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f8342c6d-0e8b-4b2c-9009-4caf09933105' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f8342c6d-0e8b-4b2c-9009-4caf09933105' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>mcrun</span>: 20</li><li><span class='xr-has-index'>year</span>: 2001</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2cd794a9-8955-421b-a00a-1ff25d841276' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2cd794a9-8955-421b-a00a-1ff25d841276' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1997 1998 1999 2000</div><input id='attrs-44be39a7-a26f-42e3-b0d3-4023a5f197ee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44be39a7-a26f-42e3-b0d3-4023a5f197ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7ad3e20-302d-458a-86ee-b901bfabb980' class='xr-var-data-in' type='checkbox'><label for='data-a7ad3e20-302d-458a-86ee-b901bfabb980' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 1998, 1999, 2000], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-315c2170-b953-4141-ba9f-fe50bdd5d40d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-315c2170-b953-4141-ba9f-fe50bdd5d40d' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>HU</span></div><div class='xr-var-dims'>(year, mcrun)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-31fdb793-4203-4b9f-a5de-b34c432e8e59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31fdb793-4203-4b9f-a5de-b34c432e8e59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23cbaf01-33da-45e5-8f59-efd8fbd5d083' class='xr-var-data-in' type='checkbox'><label for='data-23cbaf01-33da-45e5-8f59-efd8fbd5d083' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>HU#: EXP(1.707 + 1.388*MDRa - 1.521*TROPa)</dd><dt><span>yearsRef :</span></dt><dd>1979-2000</dd></dl></div><div class='xr-var-data'><pre>array([[5.233572, 6.442211, 6.044354, ..., 6.006451, 5.781193, 5.859242],\n",
       "       [5.150266, 6.517063, 6.253347, ..., 6.111382, 5.597956, 6.046769],\n",
       "       [5.273979, 6.42114 , 6.063237, ..., 6.156029, 5.663002, 5.857195],\n",
       "       ...,\n",
       "       [8.607759, 5.735194, 6.309206, ..., 5.426043, 7.792073, 6.349372],\n",
       "       [5.951543, 4.817696, 4.865606, ..., 5.416904, 5.246526, 5.486865],\n",
       "       [5.562082, 5.253688, 5.364417, ..., 5.551528, 5.025664, 5.275593]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MH</span></div><div class='xr-var-dims'>(year, mcrun)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3e3aee0d-0ad9-4d34-8f31-875a0131076b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e3aee0d-0ad9-4d34-8f31-875a0131076b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-107a284c-5cb3-4e8e-b4ea-83dca76869eb' class='xr-var-data-in' type='checkbox'><label for='data-107a284c-5cb3-4e8e-b4ea-83dca76869eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>MH#: EXP(-0.01678 + 2.19472*MDRa - 1.79117*TROPa - 0.38783*logco2a)</dd><dt><span>yearsRef :</span></dt><dd>1979-2000</dd></dl></div><div class='xr-var-data'><pre>array([[0.848376, 1.134567, 0.954869, ..., 1.034443, 0.954215, 1.024907],\n",
       "       [0.824386, 1.162915, 1.042043, ..., 1.056151, 0.915041, 1.063733],\n",
       "       [0.870265, 1.138879, 0.997377, ..., 1.070725, 0.929367, 1.000787],\n",
       "       ...,\n",
       "       [2.123141, 1.202116, 1.369071, ..., 1.021327, 1.726748, 1.324007],\n",
       "       [1.049104, 0.79278 , 0.799286, ..., 0.923721, 0.886139, 0.9061  ],\n",
       "       [0.991126, 0.916043, 0.935973, ..., 0.961338, 0.849738, 0.865122]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MHnoCO2</span></div><div class='xr-var-dims'>(year, mcrun)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1d6118d4-ad69-4275-a760-d658529875c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d6118d4-ad69-4275-a760-d658529875c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b33a40fe-4816-4586-8194-e9a684de66aa' class='xr-var-data-in' type='checkbox'><label for='data-b33a40fe-4816-4586-8194-e9a684de66aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>MH#: EXP(-0.01678 + 2.19472*MDRa - 1.79117*TROPa)</dd><dt><span>yearsRef :</span></dt><dd>1979-2000</dd></dl></div><div class='xr-var-data'><pre>array([[0.771821, 1.032187, 0.868704, ..., 0.941097, 0.868109, 0.932423],\n",
       "       [0.749663, 1.057508, 0.947592, ..., 0.960421, 0.832101, 0.967316],\n",
       "       [0.79141 , 1.035684, 0.907003, ..., 0.973705, 0.845156, 0.910105],\n",
       "       ...,\n",
       "       [2.150444, 1.217575, 1.386677, ..., 1.034461, 1.748953, 1.341034],\n",
       "       [1.064745, 0.8046  , 0.811203, ..., 0.937493, 0.89935 , 0.919609],\n",
       "       [1.007259, 0.930954, 0.951208, ..., 0.976986, 0.863569, 0.879204]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TS</span></div><div class='xr-var-dims'>(year, mcrun)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f1bb75e6-55c9-467b-b015-2862ba1f24ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1bb75e6-55c9-467b-b015-2862ba1f24ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e8b9585-0ba1-415a-b4b9-3b2c1b322b0c' class='xr-var-data-in' type='checkbox'><label for='data-5e8b9585-0ba1-415a-b4b9-3b2c1b322b0c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>TS#: EXP(2.10356 + 0.97612*MDRa - 0.97102*TROPa)</dd><dt><span>yearsRef :</span></dt><dd>1979-2000</dd></dl></div><div class='xr-var-data'><pre>array([[ 7.700802,  8.858367,  8.373077, ...,  8.45753 ,  8.206414,  8.35135 ],\n",
       "       [ 7.609796,  8.939194,  8.62148 , ...,  8.551465,  8.03367 ,  8.520521],\n",
       "       [ 7.758632,  8.850148,  8.443143, ...,  8.598428,  8.095691,  8.317599],\n",
       "       ...,\n",
       "       [11.35288 ,  8.633804,  9.20207 , ...,  8.203881, 10.501794,  9.178846],\n",
       "       [ 8.591447,  7.469404,  7.512539, ...,  8.069045,  7.901129,  8.090614],\n",
       "       [ 8.2602  ,  7.949969,  8.052602, ...,  8.212787,  7.699562,  7.89198 ]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PDI</span></div><div class='xr-var-dims'>(year, mcrun)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c84ae3ce-f135-45b0-95ff-ed43a097399d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c84ae3ce-f135-45b0-95ff-ed43a097399d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26b49d9d-df1e-4a41-8f3e-ff33ffdd9ef2' class='xr-var-data-in' type='checkbox'><label for='data-26b49d9d-df1e-4a41-8f3e-ff33ffdd9ef2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>PDI: EXP(0.76 + 1.87*MDRa - 1.58*TROPa)</dd><dt><span>yearsRef :</span></dt><dd>1979-2000</dd></dl></div><div class='xr-var-data'><pre>array([[1.764139, 2.267454, 1.969975, ..., 2.092435, 1.956825, 2.070539],\n",
       "       [1.721461, 2.313551, 2.115247, ..., 2.130306, 1.886035, 2.13883 ],\n",
       "       [1.800178, 2.272291, 2.0369  , ..., 2.154965, 1.911669, 2.032453],\n",
       "       ...,\n",
       "       [4.136564, 2.531439, 2.833231, ..., 2.217835, 3.483728, 2.764105],\n",
       "       [2.296548, 1.800329, 1.814099, ..., 2.056627, 1.983548, 2.030182],\n",
       "       [2.180596, 2.036992, 2.076778, ..., 2.129763, 1.911505, 1.95099 ]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MDR</span></div><div class='xr-var-dims'>(year, mcrun)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f1fa2039-77c2-4f7a-ab1f-0020b28199ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1fa2039-77c2-4f7a-ab1f-0020b28199ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72c78b69-58ec-4d1d-8bd6-412e938fffd7' class='xr-var-data-in' type='checkbox'><label for='data-72c78b69-58ec-4d1d-8bd6-412e938fffd7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>var_desc :</span></dt><dd>Sea surface water temperature</dd><dt><span>long_name :</span></dt><dd>MDR Sea Surface Water Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_surface_water_temperature</dd><dt><span>units :</span></dt><dd>degK</dd><dt><span>level_desc :</span></dt><dd>surface</dd><dt><span>statistic :</span></dt><dd>Ensemble Mean</dd><dt><span>GRIB_id :</span></dt><dd></dd><dt><span>GRIB_name :</span></dt><dd>WTMP</dd><dt><span>valid_range :</span></dt><dd>[-20.  20.]</dd><dt><span>dataset :</span></dt><dd>NOAA Last Millennium Reanalysis version 1.1 Annual Averages</dd><dt><span>actual_range :</span></dt><dd>[-3.8174858  3.9329534]</dd></dl></div><div class='xr-var-data'><pre>array([[-0.039044, -0.047281, -0.179137, ..., -0.067344, -0.104362, -0.003234],\n",
       "       [-0.057311, -0.028303, -0.095429, ..., -0.06747 , -0.112276, -0.003878],\n",
       "       [-0.010471, -0.034356, -0.108677, ..., -0.05825 , -0.108774, -0.045748],\n",
       "       ...,\n",
       "       [ 0.74415 ,  0.491981,  0.565559, ...,  0.315151,  0.518547,  0.476624],\n",
       "       [ 0.265052,  0.118918,  0.154655, ...,  0.142988,  0.162764,  0.110104],\n",
       "       [ 0.308246,  0.197182,  0.233706, ...,  0.165041,  0.180703,  0.112441]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TROP</span></div><div class='xr-var-dims'>(year, mcrun)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b5b801bd-6f1a-4193-a4fb-4c045eb0a72e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5b801bd-6f1a-4193-a4fb-4c045eb0a72e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67b176d3-3b49-4054-9153-04538aad35e1' class='xr-var-data-in' type='checkbox'><label for='data-67b176d3-3b49-4054-9153-04538aad35e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>var_desc :</span></dt><dd>Sea surface water temperature</dd><dt><span>long_name :</span></dt><dd>TROP Sea Surface Water Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_surface_water_temperature</dd><dt><span>units :</span></dt><dd>degK</dd><dt><span>level_desc :</span></dt><dd>surface</dd><dt><span>statistic :</span></dt><dd>Ensemble Mean</dd><dt><span>GRIB_id :</span></dt><dd></dd><dt><span>GRIB_name :</span></dt><dd>WTMP</dd><dt><span>valid_range :</span></dt><dd>[-20.  20.]</dd><dt><span>dataset :</span></dt><dd>NOAA Last Millennium Reanalysis version 1.1 Annual Averages</dd><dt><span>actual_range :</span></dt><dd>[-3.8174858  3.9329534]</dd></dl></div><div class='xr-var-data'><pre>array([[-0.055265, -0.046137, -0.146011, ..., -0.048392, -0.076769, -0.012129],\n",
       "       [-0.061385, -0.036412, -0.091972, ..., -0.059893, -0.062815, -0.033429],\n",
       "       [-0.034247, -0.032187, -0.083764, ..., -0.056264, -0.067214, -0.050696],\n",
       "       ...,\n",
       "       [ 0.332312,  0.522402,  0.505372, ...,  0.367471,  0.295421,  0.372951],\n",
       "       [ 0.137719,  0.296573,  0.301219, ...,  0.211471,  0.230801,  0.134469],\n",
       "       [ 0.221633,  0.311034,  0.309191, ...,  0.215456,  0.275448,  0.162418]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>co2</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-962395f6-b8c4-4d7f-815e-a09396c1acde' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-962395f6-b8c4-4d7f-815e-a09396c1acde' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70d8e440-629e-48c7-a6b8-d819f3be7d46' class='xr-var-data-in' type='checkbox'><label for='data-70d8e440-629e-48c7-a6b8-d819f3be7d46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>global mean CO2 concentration</dd><dt><span>cell_methods :</span></dt><dd>time: mean area: mean</dd><dt><span>units :</span></dt><dd>ppm</dd></dl></div><div class='xr-var-data'><pre>array([277.454  , 277.137  , 277.16   , ..., 365.93295, 367.84497, 369.12497],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-95ed0e6a-8a62-4cc4-a93c-386de262321d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-95ed0e6a-8a62-4cc4-a93c-386de262321d' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (mcrun: 20, year: 2001)\n",
       "Coordinates:\n",
       "  * year     (year) int32 0 1 2 3 4 5 6 7 ... 1994 1995 1996 1997 1998 1999 2000\n",
       "Dimensions without coordinates: mcrun\n",
       "Data variables:\n",
       "    HU       (year, mcrun) float32 ...\n",
       "    MH       (year, mcrun) float32 ...\n",
       "    MHnoCO2  (year, mcrun) float32 ...\n",
       "    TS       (year, mcrun) float32 ...\n",
       "    PDI      (year, mcrun) float32 ...\n",
       "    MDR      (year, mcrun) float32 ...\n",
       "    TROP     (year, mcrun) float32 ...\n",
       "    co2      (year) float32 ..."
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_lmr"
   ]
  },
  {
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   "execution_count": 65,
   "id": "e3167dea-5103-42ae-9a76-583918b9de85",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;HU&#x27; (year: 1001)&gt;\n",
       "array([5.9251113, 5.7018557, 5.58632  , ..., 5.843549 , 5.6326075,\n",
       "       5.0078006], dtype=float32)\n",
       "Coordinates:\n",
       "  * year     (year) int32 850 851 852 853 854 855 ... 1846 1847 1848 1849 1850</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'HU'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>year</span>: 1001</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0d12be17-26f8-47d6-a132-701c3e91800b' class='xr-array-in' type='checkbox' checked><label for='section-0d12be17-26f8-47d6-a132-701c3e91800b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>5.925 5.702 5.586 5.602 5.527 5.761 ... 5.409 5.268 5.844 5.633 5.008</span></div><div class='xr-array-data'><pre>array([5.9251113, 5.7018557, 5.58632  , ..., 5.843549 , 5.6326075,\n",
       "       5.0078006], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-7603bac9-afee-4d97-809c-b8cf84bc0933' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7603bac9-afee-4d97-809c-b8cf84bc0933' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>850 851 852 853 ... 1848 1849 1850</div><input id='attrs-90b95666-1c1c-46b9-8e08-0f6d35f1632c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-90b95666-1c1c-46b9-8e08-0f6d35f1632c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9b27656-2b76-4853-a354-25d54b7c30c1' class='xr-var-data-in' type='checkbox'><label for='data-b9b27656-2b76-4853-a354-25d54b7c30c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 850,  851,  852, ..., 1848, 1849, 1850], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7a985cd9-dbef-41e7-a6d4-2a4bad741665' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7a985cd9-dbef-41e7-a6d4-2a4bad741665' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'HU' (year: 1001)>\n",
       "array([5.9251113, 5.7018557, 5.58632  , ..., 5.843549 , 5.6326075,\n",
       "       5.0078006], dtype=float32)\n",
       "Coordinates:\n",
       "  * year     (year) int32 850 851 852 853 854 855 ... 1846 1847 1848 1849 1850"
      ]
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     "execution_count": 65,
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   "source": [
    "x = ds_lmr.HU.sel(year=slice(850,1850)).mean('mcrun')\n",
    "x"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;normTCcounts&#x27; (year: 1001)&gt;\n",
       "array([0.0183048 , 0.01841119, 0.01853156, ..., 0.02311869, 0.02252923,\n",
       "       0.02197403])\n",
       "Coordinates:\n",
       "  * year     (year) int64 850 851 852 853 854 855 ... 1846 1847 1848 1849 1850</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'normTCcounts'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>year</span>: 1001</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d4bd0a9c-8775-41f2-a6ff-6fdd2de92fd3' class='xr-array-in' type='checkbox' checked><label for='section-d4bd0a9c-8775-41f2-a6ff-6fdd2de92fd3' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0183 0.01841 0.01853 0.01867 ... 0.02374 0.02312 0.02253 0.02197</span></div><div class='xr-array-data'><pre>array([0.0183048 , 0.01841119, 0.01853156, ..., 0.02311869, 0.02252923,\n",
       "       0.02197403])</pre></div></div></li><li class='xr-section-item'><input id='section-fbec01a7-6c91-4a39-96a6-a4297c842291' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fbec01a7-6c91-4a39-96a6-a4297c842291' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>850 851 852 853 ... 1848 1849 1850</div><input id='attrs-1a5fdc20-ff41-4d8a-94fc-0061b3019c14' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1a5fdc20-ff41-4d8a-94fc-0061b3019c14' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5dd48d7-28a8-418d-a218-4b7e2b8ac34e' class='xr-var-data-in' type='checkbox'><label for='data-a5dd48d7-28a8-418d-a218-4b7e2b8ac34e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 850,  851,  852, ..., 1848, 1849, 1850])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b4e3097a-2688-4295-8cec-860eec08c39f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b4e3097a-2688-4295-8cec-860eec08c39f' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'normTCcounts' (year: 1001)>\n",
       "array([0.0183048 , 0.01841119, 0.01853156, ..., 0.02311869, 0.02252923,\n",
       "       0.02197403])\n",
       "Coordinates:\n",
       "  * year     (year) int64 850 851 852 853 854 855 ... 1846 1847 1848 1849 1850"
      ]
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     "execution_count": 66,
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   "source": [
    "y = ds_liz.normTCcounts.sel(year=slice(850,1850))\n",
    "y"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (year: 1001)\n",
       "Coordinates:\n",
       "  * year       (year) int32 850 851 852 853 854 855 ... 1846 1847 1848 1849 1850\n",
       "Data variables:\n",
       "    slope      float64 0.004538\n",
       "    intercept  float64 0.00153\n",
       "    r          float64 0.31\n",
       "    p          float64 9.793e-24\n",
       "    stderr     float64 0.0004404\n",
       "    predicted  (year) float64 0.02842 0.02741 0.02688 ... 0.02709 0.02426</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b5c80322-c286-4f94-adf0-a4aa067c44eb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b5c80322-c286-4f94-adf0-a4aa067c44eb' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>year</span>: 1001</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f11999ab-fc36-484d-abb9-dbe6878f3377' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f11999ab-fc36-484d-abb9-dbe6878f3377' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>850 851 852 853 ... 1848 1849 1850</div><input id='attrs-46877aee-62ba-41aa-b74e-90b14a77da24' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-46877aee-62ba-41aa-b74e-90b14a77da24' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1f24b42-c5db-4c1d-9db2-65b61cf95036' class='xr-var-data-in' type='checkbox'><label for='data-d1f24b42-c5db-4c1d-9db2-65b61cf95036' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 850,  851,  852, ..., 1848, 1849, 1850], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1161925b-006d-46cf-831b-76fd4b4d2636' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1161925b-006d-46cf-831b-76fd4b4d2636' class='xr-section-summary' >Data variables: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>slope</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.004538</div><input id='attrs-aa62f4b4-bfb7-4a44-8d9e-93d0fb9fde44' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa62f4b4-bfb7-4a44-8d9e-93d0fb9fde44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d99662b-1f69-45a2-bb26-720e8e87eaf6' class='xr-var-data-in' type='checkbox'><label for='data-6d99662b-1f69-45a2-bb26-720e8e87eaf6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>slope of the linear regression</dd></dl></div><div class='xr-var-data'><pre>array(0.0045384)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>intercept</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.00153</div><input id='attrs-475fed98-ab54-42f0-aadb-994cda4961cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-475fed98-ab54-42f0-aadb-994cda4961cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f15b62f2-cb6a-479e-8755-a06a7fc5d436' class='xr-var-data-in' type='checkbox'><label for='data-f15b62f2-cb6a-479e-8755-a06a7fc5d436' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>intercept of the linear regression</dd></dl></div><div class='xr-var-data'><pre>array(0.0015299)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>r</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.31</div><input id='attrs-4fdfc3f7-4d54-4e3f-b960-208210cbdc48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4fdfc3f7-4d54-4e3f-b960-208210cbdc48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61e2f5b9-fb76-461d-bd94-e3d6bc6f0892' class='xr-var-data-in' type='checkbox'><label for='data-61e2f5b9-fb76-461d-bd94-e3d6bc6f0892' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>correlation coefficient</dd></dl></div><div class='xr-var-data'><pre>array(0.30998597)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.793e-24</div><input id='attrs-a3a82869-4197-4171-bb1d-6ee9d7c67dc8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3a82869-4197-4171-bb1d-6ee9d7c67dc8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b415bc0-dd8e-4508-9f0b-2c3cccdb42e7' class='xr-var-data-in' type='checkbox'><label for='data-1b415bc0-dd8e-4508-9f0b-2c3cccdb42e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>p-value</dd></dl></div><div class='xr-var-data'><pre>array(9.79348518e-24)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stderr</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0004404</div><input id='attrs-fa130ac8-b4e2-468e-b94d-ab79bbe6bd78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa130ac8-b4e2-468e-b94d-ab79bbe6bd78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82cae90a-c1d1-47cb-86ba-6574ad201730' class='xr-var-data-in' type='checkbox'><label for='data-82cae90a-c1d1-47cb-86ba-6574ad201730' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>standard error of the estimated slope</dd></dl></div><div class='xr-var-data'><pre>array(0.00044039)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>predicted</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.02842 0.02741 ... 0.02709 0.02426</div><input id='attrs-225cab27-ea1d-408b-849f-da20a4813dd1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-225cab27-ea1d-408b-849f-da20a4813dd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09cf273c-b12a-4ea0-bd71-2b754bab6b33' class='xr-var-data-in' type='checkbox'><label for='data-09cf273c-b12a-4ea0-bd71-2b754bab6b33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>predicted values by the linear regression model</dd></dl></div><div class='xr-var-data'><pre>array([0.02842043, 0.02740721, 0.02688286, ..., 0.02805027, 0.02709293,\n",
       "       0.02425731])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-642f992b-3a9b-4e3b-979c-a58373d25a2a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-642f992b-3a9b-4e3b-979c-a58373d25a2a' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (year: 1001)\n",
       "Coordinates:\n",
       "  * year       (year) int32 850 851 852 853 854 855 ... 1846 1847 1848 1849 1850\n",
       "Data variables:\n",
       "    slope      float64 0.004538\n",
       "    intercept  float64 0.00153\n",
       "    r          float64 0.31\n",
       "    p          float64 9.793e-24\n",
       "    stderr     float64 0.0004404\n",
       "    predicted  (year) float64 0.02842 0.02741 0.02688 ... 0.02709 0.02426"
      ]
     },
     "execution_count": 67,
     "metadata": {},
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    }
   ],
   "source": [
    "y.linregress.on(x)"
   ]
  },
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (year: 1001)\n",
       "Dimensions without coordinates: year\n",
       "Data variables:\n",
       "    slope      float64 0.004538\n",
       "    intercept  float64 0.00153\n",
       "    r          float64 0.31\n",
       "    p          float64 9.793e-24\n",
       "    stderr     float64 0.0004404\n",
       "    predicted  (year) float64 0.02842 0.02741 0.02688 ... 0.02709 0.02426</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e242f1a9-1b1c-46a0-b320-c579bb475f55' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e242f1a9-1b1c-46a0-b320-c579bb475f55' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>year</span>: 1001</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-14a8a0c4-7c28-4018-b82d-50880f8c1a4b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-14a8a0c4-7c28-4018-b82d-50880f8c1a4b' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-b03deabd-cbbc-4c1b-9de4-b3b513fc8322' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b03deabd-cbbc-4c1b-9de4-b3b513fc8322' class='xr-section-summary' >Data variables: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>slope</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.004538</div><input id='attrs-e183102a-151a-4b49-b686-74b539d149cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e183102a-151a-4b49-b686-74b539d149cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11bd860d-a449-4635-b67c-1f9c322dd5a5' class='xr-var-data-in' type='checkbox'><label for='data-11bd860d-a449-4635-b67c-1f9c322dd5a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>slope of the linear regression</dd></dl></div><div class='xr-var-data'><pre>array(0.0045384)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>intercept</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.00153</div><input id='attrs-b08aa118-dd9b-4113-b5b7-0ef68cff42bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b08aa118-dd9b-4113-b5b7-0ef68cff42bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d30557a-41b1-48d4-943d-a5093b6cea69' class='xr-var-data-in' type='checkbox'><label for='data-1d30557a-41b1-48d4-943d-a5093b6cea69' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>intercept of the linear regression</dd></dl></div><div class='xr-var-data'><pre>array(0.0015299)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>r</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.31</div><input id='attrs-d42c9a99-c2c3-4412-b240-4eb796139f37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d42c9a99-c2c3-4412-b240-4eb796139f37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-510addc5-1136-4aaa-a7e5-83d0b26f3e35' class='xr-var-data-in' type='checkbox'><label for='data-510addc5-1136-4aaa-a7e5-83d0b26f3e35' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>correlation coefficient</dd></dl></div><div class='xr-var-data'><pre>array(0.30998597)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.793e-24</div><input id='attrs-3765a97f-6df1-41b9-94df-95142c51214b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3765a97f-6df1-41b9-94df-95142c51214b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ccdc4a2-2377-452f-8708-ac5350e1239c' class='xr-var-data-in' type='checkbox'><label for='data-2ccdc4a2-2377-452f-8708-ac5350e1239c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>p-value</dd></dl></div><div class='xr-var-data'><pre>array(9.79348518e-24)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stderr</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0004404</div><input id='attrs-b942d9ce-522e-4170-a5b5-996bddb0bc6c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b942d9ce-522e-4170-a5b5-996bddb0bc6c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fda005ad-eb66-45f9-9ef6-db84cf425611' class='xr-var-data-in' type='checkbox'><label for='data-fda005ad-eb66-45f9-9ef6-db84cf425611' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>standard error of the estimated slope</dd></dl></div><div class='xr-var-data'><pre>array(0.00044039)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>predicted</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.02842 0.02741 ... 0.02709 0.02426</div><input id='attrs-9518cace-d2cc-40d1-b27d-411de94cb973' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9518cace-d2cc-40d1-b27d-411de94cb973' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ee8cc69-1384-454a-9c52-3bf30ea9d0c7' class='xr-var-data-in' type='checkbox'><label for='data-7ee8cc69-1384-454a-9c52-3bf30ea9d0c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>predicted values by the linear regression model</dd></dl></div><div class='xr-var-data'><pre>array([0.02842043, 0.02740721, 0.02688286, ..., 0.02805027, 0.02709293,\n",
       "       0.02425731])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-743afe15-7188-4930-9909-23ff4dce79b0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-743afe15-7188-4930-9909-23ff4dce79b0' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (year: 1001)\n",
       "Dimensions without coordinates: year\n",
       "Data variables:\n",
       "    slope      float64 0.004538\n",
       "    intercept  float64 0.00153\n",
       "    r          float64 0.31\n",
       "    p          float64 9.793e-24\n",
       "    stderr     float64 0.0004404\n",
       "    predicted  (year) float64 0.02842 0.02741 0.02688 ... 0.02709 0.02426"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rg = y.linregress.on(x)\n",
    "rg"
   ]
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  {
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   "execution_count": 86,
   "id": "6a35d347-2b7f-4e62-bcf3-f77759f3295a",
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   "outputs": [
    {
     "data": {
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     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "r = rg.r.item()\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "7424103b-3643-4d21-9801-fefd1dbd6995",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;HU&#x27; (year: 1001)&gt;\n",
       "array([5.9251113, 5.7018557, 5.58632  , ..., 5.843549 , 5.6326075,\n",
       "       5.0078006], dtype=float32)\n",
       "Dimensions without coordinates: year</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'HU'</div><ul class='xr-dim-list'><li><span>year</span>: 1001</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-44b2e43c-f6e9-46fe-885f-8acb1ad510a8' class='xr-array-in' type='checkbox' checked><label for='section-44b2e43c-f6e9-46fe-885f-8acb1ad510a8' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>5.925 5.702 5.586 5.602 5.527 5.761 ... 5.409 5.268 5.844 5.633 5.008</span></div><div class='xr-array-data'><pre>array([5.9251113, 5.7018557, 5.58632  , ..., 5.843549 , 5.6326075,\n",
       "       5.0078006], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-a20de326-8e14-4b5c-9d35-39c18963f2be' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a20de326-8e14-4b5c-9d35-39c18963f2be' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-e550e6a5-2021-492b-8a1d-c2a8e714a815' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e550e6a5-2021-492b-8a1d-c2a8e714a815' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
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       "<xarray.DataArray 'HU' (year: 1001)>\n",
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       "       5.0078006], dtype=float32)\n",
       "Dimensions without coordinates: year"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;HU&#x27; (year: 1001)&gt;\n",
       "array([5.9251113, 5.7018557, 5.58632  , ..., 5.843549 , 5.6326075,\n",
       "       5.0078006], dtype=float32)\n",
       "Dimensions without coordinates: year</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'HU'</div><ul class='xr-dim-list'><li><span>year</span>: 1001</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-cf212c2e-199c-4074-90bd-edb2f7412ccc' class='xr-array-in' type='checkbox' checked><label for='section-cf212c2e-199c-4074-90bd-edb2f7412ccc' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>5.925 5.702 5.586 5.602 5.527 5.761 ... 5.409 5.268 5.844 5.633 5.008</span></div><div class='xr-array-data'><pre>array([5.9251113, 5.7018557, 5.58632  , ..., 5.843549 , 5.6326075,\n",
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    "x = x.drop('year')\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;normTCcounts&#x27; (year: 1001)&gt;\n",
       "array([0.0183048 , 0.01841119, 0.01853156, ..., 0.02311869, 0.02252923,\n",
       "       0.02197403])\n",
       "Dimensions without coordinates: year</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'normTCcounts'</div><ul class='xr-dim-list'><li><span>year</span>: 1001</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ff2419ab-61a7-4d3b-8e53-6362fd746276' class='xr-array-in' type='checkbox' checked><label for='section-ff2419ab-61a7-4d3b-8e53-6362fd746276' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0183 0.01841 0.01853 0.01867 ... 0.02374 0.02312 0.02253 0.02197</span></div><div class='xr-array-data'><pre>array([0.0183048 , 0.01841119, 0.01853156, ..., 0.02311869, 0.02252923,\n",
       "       0.02197403])</pre></div></div></li><li class='xr-section-item'><input id='section-eaed3500-9f68-4b41-9294-b845d66b8e4e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-eaed3500-9f68-4b41-9294-b845d66b8e4e' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-78d8facf-d809-4be8-8965-117b2ee1e2c4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-78d8facf-d809-4be8-8965-117b2ee1e2c4' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'normTCcounts' (year: 1001)>\n",
       "array([0.0183048 , 0.01841119, 0.01853156, ..., 0.02311869, 0.02252923,\n",
       "       0.02197403])\n",
       "Dimensions without coordinates: year"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = y.drop('year')\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
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   "id": "52438df3-4505-45a9-8bbd-0414d581eefb",
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    {
     "data": {
      "text/plain": [
       "0.5207979359029661"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r1x = x[0:-1].linregress.on(x[1:]).r.item()\n",
    "r1x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "129bd4c3-2253-4765-85b7-7a4a75d98167",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9988739391137444"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r1y = y[0:-1].linregress.on(y[1:]).r.item()\n",
    "r1y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "7a6f14e9-f493-4d21-9df1-0c9fcde612e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N=1001\n",
      "int(Nef)=315\n",
      "dof=313\n",
      "tvalue=5.768354276081179\n",
      "pvalue=1.9224222060511453e-08\n"
     ]
    }
   ],
   "source": [
    "N = y.size\n",
    "print(f'{N=}')\n",
    "Nef = N*(1-r1x*r1y)/(1+r1x*r1y)\n",
    "print(f'{int(Nef)=}')\n",
    "dof = int(Nef) - 2\n",
    "tvalue = r*np.sqrt(dof/(1-r**2))\n",
    "print(f'{dof=}')\n",
    "print(f'{tvalue=}')\n",
    "pvalue = 2*stu.cdf(-np.abs(tvalue), dof)\n",
    "print(f'{pvalue=}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "cb4dca5e-a585-48a3-9190-d3ffd7f2ade2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xfilter"
   ]
  },
  {
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   "execution_count": 97,
   "id": "d3caaff5-e3da-4713-a82d-60192d1f74b3",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;HU&#x27; (year: 1001)&gt;\n",
       "array([5.72471602, 5.7225005 , 5.72102525, ..., 5.45894145, 5.45703961,\n",
       "       5.45519645])\n",
       "Dimensions without coordinates: year</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'HU'</div><ul class='xr-dim-list'><li><span>year</span>: 1001</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1ab276f8-d525-4010-ba82-77e318aff0c6' class='xr-array-in' type='checkbox' checked><label for='section-1ab276f8-d525-4010-ba82-77e318aff0c6' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>5.725 5.723 5.721 5.72 5.72 5.72 ... 5.463 5.461 5.459 5.457 5.455</span></div><div class='xr-array-data'><pre>array([5.72471602, 5.7225005 , 5.72102525, ..., 5.45894145, 5.45703961,\n",
       "       5.45519645])</pre></div></div></li><li class='xr-section-item'><input id='section-b48f63da-b1cb-49df-8232-4ec23ff4fb09' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b48f63da-b1cb-49df-8232-4ec23ff4fb09' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-cec451a8-a802-4425-9a9b-66b9aba76f3d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cec451a8-a802-4425-9a9b-66b9aba76f3d' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'HU' (year: 1001)>\n",
       "array([5.72471602, 5.7225005 , 5.72102525, ..., 5.45894145, 5.45703961,\n",
       "       5.45519645])\n",
       "Dimensions without coordinates: year"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xlp = x.filter.lowpass(1/40, dim='year', padtype='even')\n",
    "xlp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "51c15b0f-e99c-4fb6-b9cc-944ca359f8f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "years = range(850,1851)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "586be4b9-baeb-4062-94be-78386d0a9c65",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (year: 1001)\n",
       "Coordinates:\n",
       "  * year       (year) int64 850 851 852 853 854 855 ... 1846 1847 1848 1849 1850\n",
       "Data variables:\n",
       "    slope      float64 0.01175\n",
       "    intercept  float64 -0.03948\n",
       "    r          float64 0.4995\n",
       "    p          float64 2.787e-64\n",
       "    stderr     float64 0.000645\n",
       "    predicted  (year) float64 0.02781 0.02778 0.02777 ... 0.02466 0.02464\n",
       "    Nef        int32 1\n",
       "    pe         float64 nan</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4fa4f146-db11-484f-9b4f-f79538a497ad' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4fa4f146-db11-484f-9b4f-f79538a497ad' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>year</span>: 1001</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-bee8dc71-e4ba-4a93-999c-61cdef480d64' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bee8dc71-e4ba-4a93-999c-61cdef480d64' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>850 851 852 853 ... 1848 1849 1850</div><input id='attrs-65ad5374-89a7-4c53-a08f-61814461fbca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-65ad5374-89a7-4c53-a08f-61814461fbca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8847bb7d-e0af-41ce-ba5c-23855e88e391' class='xr-var-data-in' type='checkbox'><label for='data-8847bb7d-e0af-41ce-ba5c-23855e88e391' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 850,  851,  852, ..., 1848, 1849, 1850])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c7d446d0-c7dc-4109-a186-b939f3c08fcf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c7d446d0-c7dc-4109-a186-b939f3c08fcf' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>slope</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.01175</div><input id='attrs-1ee0c7be-520b-41ad-b231-27d5134e5a79' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ee0c7be-520b-41ad-b231-27d5134e5a79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4969db72-1cde-4ba6-a909-e401947b67c2' class='xr-var-data-in' type='checkbox'><label for='data-4969db72-1cde-4ba6-a909-e401947b67c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>slope of the linear regression</dd></dl></div><div class='xr-var-data'><pre>array(0.01175475)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>intercept</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.03948</div><input id='attrs-bc581f2b-d2fc-4604-bb07-d5fc1558b4c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bc581f2b-d2fc-4604-bb07-d5fc1558b4c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8cf5a6c-efee-451e-a3bf-d2c28cc9e2e5' class='xr-var-data-in' type='checkbox'><label for='data-e8cf5a6c-efee-451e-a3bf-d2c28cc9e2e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>intercept of the linear regression</dd></dl></div><div class='xr-var-data'><pre>array(-0.03948217)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>r</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4995</div><input id='attrs-f49733d0-2785-4fc1-8352-e0b1b1c7db21' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f49733d0-2785-4fc1-8352-e0b1b1c7db21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2be65f77-4250-43be-bdd3-11e4a202853a' class='xr-var-data-in' type='checkbox'><label for='data-2be65f77-4250-43be-bdd3-11e4a202853a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>correlation coefficient</dd></dl></div><div class='xr-var-data'><pre>array(0.4994817)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.787e-64</div><input id='attrs-d947d22b-488d-4814-a117-8c1729099d5a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d947d22b-488d-4814-a117-8c1729099d5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7cb594c5-bdd3-413c-bdc1-2f0b86a81f0d' class='xr-var-data-in' type='checkbox'><label for='data-7cb594c5-bdd3-413c-bdc1-2f0b86a81f0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>p-value</dd></dl></div><div class='xr-var-data'><pre>array(2.78687362e-64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stderr</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.000645</div><input id='attrs-6c3147e5-b081-445a-8535-85f443385a47' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c3147e5-b081-445a-8535-85f443385a47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84ad8a52-f653-411b-a293-ee5448324782' class='xr-var-data-in' type='checkbox'><label for='data-84ad8a52-f653-411b-a293-ee5448324782' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>standard error of the estimated slope</dd></dl></div><div class='xr-var-data'><pre>array(0.00064505)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>predicted</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.02781 0.02778 ... 0.02466 0.02464</div><input id='attrs-1b2e0e20-f047-4817-90c7-c89763d920b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b2e0e20-f047-4817-90c7-c89763d920b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fdfe0dc-c85c-400e-bafe-977bf744862b' class='xr-var-data-in' type='checkbox'><label for='data-2fdfe0dc-c85c-400e-bafe-977bf744862b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>predicted values by the linear regression model</dd></dl></div><div class='xr-var-data'><pre>array([0.02781046, 0.02778441, 0.02776707, ..., 0.02468634, 0.02466399,\n",
       "       0.02464232])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Nef</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-d8f64da7-687a-4987-b28d-a1dc7329b352' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8f64da7-687a-4987-b28d-a1dc7329b352' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35bf5193-4276-48b6-88b1-da095ce760b2' class='xr-var-data-in' type='checkbox'><label for='data-35bf5193-4276-48b6-88b1-da095ce760b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>effective sample size</dd></dl></div><div class='xr-var-data'><pre>array(1, dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pe</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan</div><input id='attrs-10cefc14-458e-4caf-a8a5-bd91fec3bd1c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10cefc14-458e-4caf-a8a5-bd91fec3bd1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7da6a869-7439-4af3-ae5b-cd1951181a90' class='xr-var-data-in' type='checkbox'><label for='data-7da6a869-7439-4af3-ae5b-cd1951181a90' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>p-value taking into account effective sample size</dd></dl></div><div class='xr-var-data'><pre>array(nan)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9959433f-83d2-486d-afa9-270604a50743' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9959433f-83d2-486d-afa9-270604a50743' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (year: 1001)\n",
       "Coordinates:\n",
       "  * year       (year) int64 850 851 852 853 854 855 ... 1846 1847 1848 1849 1850\n",
       "Data variables:\n",
       "    slope      float64 0.01175\n",
       "    intercept  float64 -0.03948\n",
       "    r          float64 0.4995\n",
       "    p          float64 2.787e-64\n",
       "    stderr     float64 0.000645\n",
       "    predicted  (year) float64 0.02781 0.02778 0.02777 ... 0.02466 0.02464\n",
       "    Nef        int32 1\n",
       "    pe         float64 nan"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rg = y.assign_coords(year=years).linregress.on(xlp.assign_coords(year=years), ess_on=True)\n",
    "rg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "61247c29-65b6-48c9-8b19-fd1c5e454282",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9980695422751903"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r1xlp = xlp[0:-1].linregress.on(xlp[1:]).r.item()\n",
    "r1xlp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "6ee70b72-cbe0-422a-b070-1744c0d659d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N=1001\n",
      "int(Nef)=1\n",
      "dof=-1\n",
      "tvalue=nan\n",
      "pvalue=nan\n"
     ]
    }
   ],
   "source": [
    "N = y.size\n",
    "print(f'{N=}')\n",
    "Nef = N*(1-r1xlp*r1y)/(1+r1xlp*r1y)\n",
    "print(f'{int(Nef)=}')\n",
    "dof = int(Nef) - 2\n",
    "tvalue = r*np.sqrt(dof/(1-r**2))\n",
    "print(f'{dof=}')\n",
    "print(f'{tvalue=}')\n",
    "pvalue = 2*stu.cdf(-np.abs(tvalue), dof)\n",
    "print(f'{pvalue=}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "fbe3c611-4e67-457c-83ff-8fd67e09b15b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0030543447981288274"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1-r1xlp*r1y)#/(1+r1xlp*r1y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "d584a6c1-30a5-4267-97f1-1e5d62e5fbda",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ess_in_corr(x, y):\n",
    "    '''effective sample size in correlation between x and y.\n",
    "    Equation 30 in https://doi.org/10.1175/1520-0442(1999)012%3C1990:TENOSD%3E2.0.CO;2\n",
    "    '''\n",
    "    N = x.size\n",
    "    assert N == y.size\n",
    "\n",
    "    s = 1\n",
    "    for tao in range(1, N//2+1):\n",
    "        rx = np.corrcoef(x[:-tao], x[tao:])[0,1]\n",
    "        ry = np.corrcoef(y[:-tao], y[tao:])[0,1]\n",
    "#         rx = corrcoef(x[:-tao], x[tao:])\n",
    "#         ry = corrcoef(y[:-tao], y[tao:])\n",
    "        if rx < 0 or ry < 0:\n",
    "            break\n",
    "        s+= 2*(1 - tao/N)*rx*ry\n",
    "#         if tao == 1:\n",
    "#             print(N*(1-rx*ry)/(1+rx*ry))\n",
    "\n",
    "    return N/s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "75909c65-c7f4-467e-81bc-ca57f0714a73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29.2 ms ± 156 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit ess_in_corr(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "a044a63c-905f-44ac-aa5c-949c603834a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from numba import jit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "bf6dd5e1-8bed-48f1-849c-2723ceda1369",
   "metadata": {},
   "outputs": [],
   "source": [
    "@jit(nopython=True)\n",
    "def ess_in_corr_jit(x, y):\n",
    "    '''effective sample size in correlation between x and y.\n",
    "    Equation 30 in https://doi.org/10.1175/1520-0442(1999)012%3C1990:TENOSD%3E2.0.CO;2\n",
    "    '''\n",
    "    N = x.size\n",
    "    assert N == y.size\n",
    "\n",
    "    s = 1\n",
    "    for tao in range(1, N//2+1):\n",
    "        print(s)\n",
    "        rx = np.corrcoef(x[:-tao], x[tao:])[0,1]\n",
    "        ry = np.corrcoef(y[:-tao], y[tao:])[0,1]\n",
    "#         rx = corrcoef(x[:-tao], x[tao:])\n",
    "#         ry = corrcoef(y[:-tao], y[tao:])\n",
    "        if rx < 0 or ry < 0:\n",
    "            break\n",
    "        s+= 2*(1 - tao/N)*rx*ry\n",
    "#         if tao == 1:\n",
    "#             print(N*(1-rx*ry)/(1+rx*ry))\n",
    "\n",
    "    return N/s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "2244b6da-fc92-41af-a1fd-236989a6ba15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "2.039383587847558\n",
      "2.9426389790848626\n",
      "3.7712126687425696\n",
      "4.575663978795239\n",
      "5.343928896394412\n",
      "6.027237029446691\n",
      "6.654824599287426\n",
      "7.26143889048188\n",
      "7.853696409848666\n",
      "8.379690007080928\n",
      "8.88010870680205\n",
      "9.332252680161817\n",
      "9.72191240907359\n",
      "10.051889045317356\n",
      "10.415445780834006\n",
      "10.736860662765206\n",
      "11.02058569990529\n",
      "11.246259238214597\n",
      "11.427521871651054\n",
      "11.624827083281831\n",
      "11.784052428491975\n",
      "11.909175225280299\n",
      "12.05205725174591\n",
      "12.189877523936165\n",
      "12.316114084646694\n",
      "12.399961883994738\n",
      "12.458408959514118\n",
      "12.544049192446423\n",
      "12.62402115162868\n",
      "12.702383215195331\n",
      "12.76964551475168\n",
      "12.81259383587365\n",
      "12.894039813139202\n",
      "12.965097114477745\n",
      "13.044563379721124\n",
      "13.115598843061203\n",
      "13.156556018185528\n",
      "13.216024523522961\n",
      "13.283227090267378\n",
      "13.330674254682393\n",
      "13.356408372478995\n",
      "13.368108479550168\n",
      "13.404966196578695\n",
      "13.425532937150422\n",
      "13.449840120566293\n",
      "13.460120818416808\n",
      "13.476498227708841\n",
      "13.498854875576333\n",
      "13.508151393725111\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "74.1034039983436"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ess_in_corr_jit(x.values,y.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "fcdc86bc-189a-4787-9f67-1347791d6ebf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "aeefb63a-c2ce-42b0-944e-a3ce9ed4cab8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from numba import guvectorize, float32, float64, int64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "054193fe-0e0b-4c53-a11b-93fe90c50ea1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://github.com/numba/numba/issues/2935\n",
    "#@guvectorize([(float64[:], float64[:], int64[:])], '(n),(n)->()')\n",
    "@guvectorize(['f4[:], f4[:], i8[:]', \n",
    "             'f4[:], f8[:], i8[:]',\n",
    "             'f8[:], f4[:], i8[:]',\n",
    "             'f8[:], f8[:], i8[:]'], '(n),(n)->()', nopython=True)\n",
    "def ess_in_corr_guv(x, y, res):\n",
    "    '''effective sample size in correlation between x and y.\n",
    "    Equation 30 in https://doi.org/10.1175/1520-0442(1999)012%3C1990:TENOSD%3E2.0.CO;2\n",
    "    '''\n",
    "    N = x.size\n",
    "    assert N == y.size\n",
    "    \n",
    "    N = N\n",
    "\n",
    "    s = 1.\n",
    "    for tao in range(1, N//2+1):\n",
    "        rx = np.corrcoef(x[:-tao], x[tao:])[0,1]\n",
    "        ry = np.corrcoef(y[:-tao], y[tao:])[0,1]\n",
    "        if rx < 0 or ry < 0:\n",
    "            break\n",
    "        s+= 2*(1 - tao/N)*rx*ry\n",
    "\n",
    "    res[0] =  int(N/s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "8cb92aac-199d-4ca0-84ef-3c8c5d16b098",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.85 ms ± 16.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit ess_in_corr_guv(x.values, y.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "80068c23-e24a-4b79-9418-3648727f355e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74.1034039983436"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ess_in_corr_jit(x.values,y.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "863c048c-8ef3-4b99-9116-69e45b0eff2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74.1034039983436"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ess_in_corr(x.values,y.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "cf2bbd8d-13cb-46a0-bcc4-7b7e66b08775",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(float(30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "32b29720-f35c-4d96-b877-a30a161eed3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Set the current `.rcParams`.  *group* is the grouping for the rc, e.g.,\n",
       "for ``lines.linewidth`` the group is ``lines``, for\n",
       "``axes.facecolor``, the group is ``axes``, and so on.  Group may\n",
       "also be a list or tuple of group names, e.g., (*xtick*, *ytick*).\n",
       "*kwargs* is a dictionary attribute name/value pairs, e.g.,::\n",
       "\n",
       "  rc('lines', linewidth=2, color='r')\n",
       "\n",
       "sets the current `.rcParams` and is equivalent to::\n",
       "\n",
       "  rcParams['lines.linewidth'] = 2\n",
       "  rcParams['lines.color'] = 'r'\n",
       "\n",
       "The following aliases are available to save typing for interactive users:\n",
       "\n",
       "=====   =================\n",
       "Alias   Property\n",
       "=====   =================\n",
       "'lw'    'linewidth'\n",
       "'ls'    'linestyle'\n",
       "'c'     'color'\n",
       "'fc'    'facecolor'\n",
       "'ec'    'edgecolor'\n",
       "'mew'   'markeredgewidth'\n",
       "'aa'    'antialiased'\n",
       "=====   =================\n",
       "\n",
       "Thus you could abbreviate the above call as::\n",
       "\n",
       "      rc('lines', lw=2, c='r')\n",
       "\n",
       "Note you can use python's kwargs dictionary facility to store\n",
       "dictionaries of default parameters.  e.g., you can customize the\n",
       "font rc as follows::\n",
       "\n",
       "  font = {'family' : 'monospace',\n",
       "          'weight' : 'bold',\n",
       "          'size'   : 'larger'}\n",
       "  rc('font', **font)  # pass in the font dict as kwargs\n",
       "\n",
       "This enables you to easily switch between several configurations.  Use\n",
       "``matplotlib.style.use('default')`` or :func:`~matplotlib.rcdefaults` to\n",
       "restore the default `.rcParams` after changes.\n",
       "\n",
       "Notes\n",
       "-----\n",
       "Similar functionality is available by using the normal dict interface, i.e.\n",
       "``rcParams.update({\"lines.linewidth\": 2, ...})`` (but ``rcParams.update``\n",
       "does not support abbreviations or grouping).\n",
       "\u001b[0;31mFile:\u001b[0m      /tigress/wenchang/miniconda3/lib/python3.9/site-packages/matplotlib/pyplot.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rc?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "246c6d3b",
   "metadata": {},
   "source": [
    "### spectral"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e77c38d8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T18:25:49.747807Z",
     "start_time": "2021-06-07T18:25:49.567380Z"
    }
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('https://raw.githubusercontent.com/LinkedEarth/Pyleoclim_util/Development/example_data/soi_data.csv',skiprows=0,header=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "35437232",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T18:26:28.739075Z",
     "start_time": "2021-06-07T18:26:28.734805Z"
    }
   },
   "outputs": [],
   "source": [
    "time = data.iloc[:,1]\n",
    "value = data.iloc[:,2]\n",
    "ts = pyleo.Series(time=time, value=value, time_name='Year C.E', value_name='SOI', label='SOI')\n",
    "ts_std = ts.standardize()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b8e2629a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T18:27:13.514605Z",
     "start_time": "2021-06-07T18:27:12.229623Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Performing spectral analysis on surrogates: 100%|██████████| 20/20 [00:00<00:00, 151.08it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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1xPKNGjVSWvfs2TOlngU2NjbyuXPnanUXfXZ00fvo008/Vbp7Xlft0UUPtfT0dLmTk5NYZuHChfK0tDS1dcXExMjXr18v//nnn9Wuz20PtcyeSkOHDpXHxMQolUtKSlJ63WxsbDT2LoqIiJA7OjqKZZs0aSJ//vy5yvOcN2+e0n6hRc8Zbeiqh9qOHTtUesPEx8crlYmPj5d/9tlnSuV2796ttj7FXiuZPTq8vb3lQUFBSuXi4uLkbdq0Ecu2adNG3qNHDzkA+fjx41Ve98ePH8vLlCkjlp87d67G56TYQ83MzExuZGQkX7x4sUqPQ39/f/lHH30kli1ZsqQ8MjIyNy+fWhkZGfIKFSoo9RoaOnSo/Nq1a/nq5ZSeni5v3ry5WK+lpaV81apVao+d1NRU+YEDB+Q9e/ZUW9eiRYvkGzZskEdFRWnc34ULF+SVKlUS97dlyxaNZRWPw8xep19++aU8ISFBqVxaWpr4Pjx48ECp10/Xrl1VXv+kpCT5mDFjVI6h/PZQ++6778Ryzs7O8h07dqjtkbpr1y65vb29WHbHjh0a69SW4nFkZmYmd3Nzk1+6dEmpjFQqlU+dOlUs5+DgIJ87d64cgLx169Y5Hk+tW7fWuP/w8HC5i4uLWPbTTz+Vh4SEqJSLiIiQ9+rVSyxXu3ZtuVQqzfE5aUPxf4SZmZlcIpHIPT095deuXVMpu3PnTqUejJs2bdJY79ixY8VyxsbG8qVLl6q8rwEBAfIGDRqI5ezs7FTO3Zn8/f2VPnft2rWTh4eHK5V5+/atfMiQIUrnPG3/VxIREREVBgbUiIiIiCjPchNQO3TokNKFwhkzZqiUmThxosb0VAMGDJAvXrxYfuHCBXlKSorWbdRFsCTz4mvmhcX8XMjXdUDt/v374vrmzZvnuV1yee4DapkXkDV59+6dUqq6v//+W205xYvd5cqVU0ljp2jatGlK+zeUgJpMJlMK/PTp00fj5yQjI0MMeAGQe3p6qg1AZE0DV716dXlycrLaOoODg1VSfn7++eca27tt2zaxXLVq1TSWUwyoAZD/9NNPGstGRkbKXV1dxbLTpk3TWDY39uzZozalopOTk7xLly7y2bNnyw8ePCh/8+aN1nWuWbNGKWh14cIFnbQ1Oy9evBBTImaX1lbxOAQgHzFiRI519+3bVyzfsGFDeWpqqsay/fr1U6o/PwG1Fy9eiDdC2Nrayh8/fpxtO0+fPq30ec5v6kfF52FpaSl/9OiR2nIymUxevXp1pfI1a9aUv3v3Tm15xeNJIpHIw8LC1JYbPny41u+TVCpVCtRpOh/mJ6CWeVyEhoZqLK94vu3UqZPaMs+ePVNKGbly5UqN9b1580Zevnx5seywYcPUllO8waJ27doaz2UymUzepUsXrT6jRERERIXNCEREREREBWz//v347LPPxHkzMzN88cUXKuWWLFmC8ePHqyx/8eIFduzYgWnTpsHb2xv29vb45JNPcODAgQJtdybF9JQymQwJCQmFsl9tZKYZA4BSpUoV6r7NzMywdOlSjestLCzw6aefivM3btxQKZORkYFNmzaJ83PnzoWDg4PGOufMmZPten05ceIEXrx4AUB4XX755ReNKf0kEgl+/fVXMdVaYGAgTp48meM+fvrpJ1haWqpdV7ZsWXh5eYnz5ubmWLhwoca6evXqBXNzcwDAkydPtPpMV6xYEV999ZXG9aVKlRJTzQFCqk+pVJpjvTnp1asXNmzYoPLcY2JicOTIEcyfPx/du3eHk5MTmjZtitWrV2tMz5hpyZIl4vRXX32Fli1b5rudOSlfvryYxvbGjRtKx64mFhYWWLRoUbZl3rx5g/3794vzixYtgpmZmcbyS5YsgZGRbi4FrFixQkwjO336dFSrVi3b8m3btkXHjh0BAI8fP8a///6rk3YAwJgxY5TSKCoyMjJCv379lJYtXLgQFhYWassrHk9yuRy3bt1SKRMVFYW//voLgPA/IqfUrcbGxkrpajO31bWZM2eiTJkyGtcPHz5cnFZ3TgaANWvWiOl369Spg3Hjxmmsz9HRET///LM4v23bNsTHxyuViY2NxZ49e8T5xYsXazyXGRkZYcWKFVqlRCUiIiIqbAyoEREREZFOHDlyBOPGjVN6fPbZZ6hatSp69uyJuLg4seyyZctQtmxZlTqMjY2xcuVKXL9+HX379tV4sTM1NRUHDx5Ejx490KJFCwQHBxfU0wIA2NjYKM0bUkCtXLly4vSZM2fw+PHjQtt3ixYtULp06WzL1KtXT5x++fKlyvpHjx4hKioKAGBqaprt+DuAMCZZz549c93WgnbmzBlxunPnznB1dc22vJubGzp16iTOnz17NtvylpaW6Ny5c7ZlatWqJU57e3tnG2C1sLCAp6cnACFgoO69yWrQoEEwMcl+GO6BAweKgbro6Gg8ePAgx3q1MXToUAQEBGDChAkoWbKk2jJyuRzXrl3Dl19+iSpVquDcuXNqywUFBcHf31+czy5YkFvBwcHYvXs3fvzxR0yfPh3jx49XOidmBl3lcjnu3r2bY30dOnTIcXy7y5cvIz09HYAwdlnr1q2zLV+2bFl4e3tr94RycOTIEXF64MCBWm3Ttm1bcdrPz08n7QCAPn36ZLte8fjI7fGU+b4pOnXqFFJTUwEA3bp1U/k/oU6TJk1gZWUFQLfPXVHWwGFW1apVE4NZMTExSExMVCmjeD4bNmxYjsGtXr16oUSJEgCE/89XrlxRWn/58mXxtXJxcUH79u2zra9SpUpKNwgQERERGYrsfw0REREREWnpxo0bGu92z2Rra4sVK1Zg2LBh2ZZr1KgRdu3ahYSEBFy8eBGXLl3CrVu3cOvWLURHRyuVvXTpEpo2bYobN27Azc0t389DnawBNDs7uwLZT164u7vDy8sLly9fxtu3b9GwYUMMHjwYvXr1QosWLWBra1tg+65du3aOZZycnMTprL0WAODOnTvidPXq1bVqb6NGjbBx40at2lhYFHvaNG/eXKttmjdvjoMHDwIAbt++nW3ZKlWqiD3aNFEMvNSoUSPH/SuW16a3VNOmTXMsY2Njg1q1aok9ev7991/UrVs3x+204e7ujhUrVmDJkiW4fv06Ll68iBs3buD27dsqAY9Xr16hffv2OHz4MDp06KC07urVq+J05cqV4e7unu+2XblyBTNmzMDFixchl8u12ibruUydBg0a5FhG8Rhq1KiRVj17GjVqpDHgqK2YmBgEBASI88uWLdNq348ePRKnX716la82KKpZs2a26xU/71WqVMkxOJzT8aEYNAoICNA6MJv5GsXGxiIpKQnW1tZabacNe3t7tTerZN2/o6Mj3r17B0A4LysGA+VyudJnSpvzmampKRo3boxjx44BEM5nijcMZP2MatNDsmnTprh06VKO5YiIiIgKEwNqRERERFRgbGxs4OTkhDp16qBdu3bw8fHJVbo+W1tbdOnSBV26dBGXPXz4EH///TdWrVol9noLDw/H2LFjCywFpGIgyNTUtECDVHmxfv16tGnTBuHh4UhOTsaaNWuwZs0aGBsbo3bt2vD29kbnzp3Rrl27HC8i54ZiKkxNFINAmb1oFCkGFbQNbBRU4DQ/MnvZAYCHh4dW25QvX16czim4os1rrfje5ra8uvcmK8XekNkpW7asGFBTfF2AnHuDVa5cGRMnTsy2jImJCby8vJR6sISFhWHfvn1YtmwZnj17BgCQSqXw8fHB8+fPxV5BAPD69WtxumLFilo9p+ysX78eI0aM0DqQlkmbnq7apHHV1zEUHh6uNL969epc1xEbG5vvdmTK6TOv6+MjLCxMnNbmhhJ1YmNjdR5Q00Z25+X4+HilZbo4nymeB3JzHiEiIiIyNAyoEREREZFOzJkzB3Pnzi3w/dSsWRMLFizAmDFj0L59ezHF4cGDB/HixQtUqFBB5/tUTA/n4uKi8/rzq2rVqrh79y4WLlyITZs24c2bNwCE8d7u3LmDO3fuYOXKlShTpgzmzp2LkSNH6mS/uhjjRjHdmGLQIzu6vACtK4rPQ9v2KZbLKbiS29e6IMYfysv7k/V5/frrr9lu26pVqxwDauqUKVMGX3zxBT7//HP4+Phg586dAITg2Y4dO5R6xSq2SZs0fdl5/PgxRo8eLQbT6tSpgxEjRqBZs2bw8PCAra2tUupaX19fcczAzDGqsqNpnClF+jqG1PU4zS1djLGXKTefeV0cH4b2/AHdn5MB3ZzPist5noiIiIhjqBERERFRkeTm5oa1a9cqLbt48WKB7OvatWvitDZp73RJm4vugNCTZenSpYiIiMDFixfxww8/oHPnzkrpKcPCwjBq1ChMmDChoJqba4oXTZOTk7XaJikpqaCak2eKgRlt26dYztB6PaqTl/ensJ+Xubk51q1bp5SuL+t5QbFN6saPyo1ly5aJQZEuXbrg5s2bGD9+PBo2bIhSpUqpjANZEOMv6usYUtyvg4MD5HJ5rh+Glro1NxSf//Lly/P0/BV7dRmKrEFmXZzPFOssyud5IiIiIgbUiIiIiKjI8vLyUkpxlTUFmS7cvHkTL1++FOdbtmyZr/oUU21p0zsht70gTE1N0aJFC8ycORNHjhxBdHQ0jh07hlatWollfvnllzylJysIJUuWFKdDQkK02iY0NLSgmpNniqn5goODtdomKChInFZ8HQyVts9LsVzW55VTgCG/43oBwsV7xeM063lBsZdp1rHXcuv06dPi9IIFC3Ic507xPdcVfR1Diq9jXFycSnrP4k7x+T99+lSPLdEte3t7pc+xLs5neTk/6nJ8PSIiIiJdYUCNiIiIiIo0c3NztdO6smTJEnHazMwMvXv3zld9infux8TE5Fj+/v37+dqfqakpOnbsiBMnTqB27dri8oMHD+arXl2pW7euOP348WOtegwZSjBQUb169cTpy5cva7XNpUuXxOn69evrvE26duXKlRzLJCYm4sGDB+K8vp6XYs+wrOcFxV6mAQEBWgeh1FEcR6tmzZrZlo2Pj8e9e/fyvC9NFI+hmzdvajWWmy6OIVdXV6XxsE6cOJHvOouSJk2aiNPHjx/XY0t0SyKRKH2mtDmfSaVSXL9+XZzPetwr1nfjxg2tel5fvXo158YSERERFTIG1IiIiIioyAoLC1PqFaF4cVcXdu7cib///luc9/Hxgbu7e77qVBzj7e7duzle/M4cCyq/zMzM0L59e3H+9evXOqk3v2rWrCn2XkhPT8euXbuyLZ+UlIR9+/YVQstyp23btuL0kSNHEBkZmW35iIgIHDt2TO32hmr79u059qrcvn070tLSAAi9VGrVqlUYTVNx584dcTrrecHDwwPVq1cX53Ma1y07Rkbvf1LnlMpu7dq1SE9Pz/O+NPHy8hJ7FIWFheXYy+/Vq1c6S4/btWtXcToz7eGHomPHjjAxEYalf/bsGQ4dOqSTehWDwQXxedGG4vlo06ZNOb6vBw4cEG8QsbCwQLNmzZTWe3l5wczMDIDwv+fkyZPZ1vfs2TOtb0wgIiIiKkwMqBERERGRQdi4cSN2796dqwuys2bNEsubmprqNCixadMm+Pr6ivMuLi6YM2dOvuutXr262EstPDw8214dhw8fxuHDh7OtLzY2Vutx1hRTbSmm4NInIyMj+Pj4iPNz585FXFycxvLz5s3Ldr2+dOjQQQyWpqamYtKkSRrLyuVyTJgwQQw8eXp6ol27doXRzHx5/vy5Uo/NrKKjo5WOEV9fXzHgkFfXrl3D//73v1yNp7Rp0yYEBASI8506dVIpM2XKFHF6yZIleQ4wVaxYUZzev3+/xnJPnz7FvHnz8rSPnDg5OeGTTz4R56dPny5+ttSZOnUqZDKZTvb91VdfwdjYGIDQOy43zzEiIkInbdAXNzc3fPbZZ+L8mDFjtE6lmZGRoTFFppOTkzitr/S2I0eOFIPFt2/fxp9//qmxbHx8PKZPny7Of/rpp0qpmAHA0dFRqXf3tGnT8O7dO7X1yeVyTJw48YMKzhIREVHRwYAaERERERkEf39/9OvXD9WrV8fChQuzHdvo5cuXGDhwIDZs2CAuGzt2LBwcHPLVhpSUFBw+fBjt27eHr6+veMHPwsIC+/bty3fvNAAwMTFBv379xPmRI0fi0aNHSmXkcjm2bNmC/v3755jGcv/+/ahcuTIWL16s8TVLSUnB8uXL8c8//4jLunTpko9noVtfffWV+N4FBwejU6dOKs9FKpViwYIFWLx4cYGk9swvIyMj/PTTT+L89u3bMXLkSJUUlgkJCfj888+VeuL9/PPPSj2dDJWZmRlmzpyJJUuWqARxAwIC0L59e3G8MicnJ0ybNi3f+4yNjcW0adNQvnx5TJo0CTdv3tQYQH779i0WLFiAESNGiMvq1auHjh07qpT19fWFl5cXAKEXUKdOnbB69Wq1PYLS0tJw8OBB9OrVS2Vdt27dxOkpU6aoTf13+vRptG7dGgkJCbC2ts75SefBnDlzxF5qN2/eRO/evVUCNsnJyRg7dix27typs2PI09MTs2bNEufnzZsHX19fjWk0ZTIZTp06BR8fnyKR5jQnP/74I1xdXQEIwa9GjRph9+7dGj+joaGhWLFiBapVq4YdO3aoLaOYmldXPZRzy9PTE6NHjxbnx40bh19//VXleQUGBqJDhw4IDAwEANjZ2eG7775TW+fs2bPFz939+/fRo0cPlaBqQkIChg4diiNHjog92oiIiIgMSf5uFyQiIiIi0rEnT55g5syZmDlzJlxdXVG3bl2UKlUKVlZWiIuLw6NHj3D//n2lu9e9vLzwww8/5Fj3mzdvMG7cOKVlycnJiIuLQ0hICO7cuaNyQb169erYunWrTi/+fvfdd9ixYweSkpLw6tUr1K1bF61atULFihXx9u1bXL58GcHBwTA2NsYff/yhFCBQ5/nz55g+fTqmT5+OcuXKoU6dOnB2doZcLkdERASuXr2K2NhYsfzgwYPFYIIhcHV1xe+//45PP/0Ucrkc165dQ5UqVeDt7S2+JufPn8fr169hamqK77//XgzW6DoQFRYWpjTeT04aNmyItWvXAgD69++PCxcuiCkE165dix07dqBNmzZwcXFBZGQkzpw5g4SEBHH7SZMmoU+fPjp9DgVl0aJFmDRpEqZOnYqlS5fC29sbNjY2ePr0Kfz8/MReT8bGxlizZg2cnZ11tu/o6GisWLECK1asgJ2dHerXr48yZcrAzs4OycnJCAwMxM2bN5Gamipu4+Ligr/++kvsQaXIxMQEO3bsQNu2bfH06VMkJyfjyy+/xLfffosWLVrA1dUV6enpCAoKwq1bt/D27VuVXjcAMHnyZKxbtw5RUVGIjY1Fp06dUL9+fdSoUQMSiQS3b9/Gw4cPAQgpAp2dnbFlyxadvS6ZateujR9++EHsKXT48GF4eHigTZs2cHNzQ0xMDM6cOYO4uDg4ODhg8uTJYm/C/B5Dc+bMwcuXL7Fp0yYAQg/BrVu3ol69eqhWrRpsbGzw9u1bBAUF4e7du2KQWbEnVlHl6uqK/fv3o0uXLoiOjkZ4eDj69esHZ2dnNGnSBC4uLsjIyEBMTAwePHiA58+f59jzqk+fPmI62BkzZuDYsWOoWbOmUhD022+/haOjY4E+t//973+4efMmbty4AalUinHjxuGnn35CixYtYGNjg8DAQFy4cEE87k1MTLBu3TqltMaKqlevjkWLFmHixIkAgJMnT6J8+fJo27Yt3N3dERUVhTNnzuDt27dwcHDAxIkTC6xXJxEREVFeMaBGRERERAahWbNmqFGjhlJvrfDwcLHHizomJib48ssv8cMPP2jV8yMhIUHr8ZIqV66MUaNG4csvv4SlpaVW22irfPny2L17N/r06YPk5GSkp6fj1KlTSmXs7OywYcOGHAN5NjY2kEgk4kXa4OBgpdSOioyMjDBmzBgsX75cJ89DlwYMGICUlBR88cUXSE5OhlQqxZkzZ3DmzBmxjJ2dHTZv3gwrKytxWWb6TF1JT0/H3bt3tS6ftVfkqlWrULp0aXz//fdITU1FQkICDhw4oLKdhYUFZs+ejW+++Sa/TS40PXr0gKWlJcaPH4+wsDCl8QUz2dnZYc2aNWp7c+VFhQoV0KpVK6WA3du3b3McJ6xz58749ddfNV7cBwB3d3dcvXoVI0aMwN69ewEAcXFxGsfCsrGxUVnm7OyM/fv345NPPkF0dDQAIUXe7du3lcr17NkTGzduFIMJBWHatGmQSqWYPXs2pFIp3r17hyNHjiiVcXV1xe7du8UgH5D/Y0gikWDjxo1o2LAhZs+ejdjYWMhkMty8eRM3b97UuE3z5s3ztV9D0ahRI9y8eROff/45Tp8+DQCIjIzEwYMHNW7j4uKCypUrq13n6+uLbdu24ezZs5DL5Th79izOnj2rVGbcuHEFHlCzsrLCmTNn8Pnnn4s95UJCQtQe966urli3bh06d+6cbZ0TJkyAVCrFN998g7S0NKSmpuLo0aNKZVxcXLB79248e/ZMd0+GiIiISEcYUCMiIiIig9CjRw/06NEDgYGBOHfuHC5fvozHjx/jxYsXiIuLg1QqhY2NDZydnVGrVi20bNkSAwYMENNt5YWRkRFsbW1hZ2cHR0dH1KhRAw0aNICXlxeaNWsGiUSiw2eorFOnTvD398f//vc/HD9+HK9evYKxsTHKlSuH7t27Y+zYsShXrhxevnyZbT19+/YVx2K7dOkS7t69i+fPn4vjjNnb26NKlSpo0aIFfHx8UKNGjQJ7Tvk1dOhQtGrVCitXrsSRI0fw6tUrmJqaomzZsujevTvGjBmDcuXKKaVKy2+az4Iwa9YsDBkyBGvXrsXx48fFz7CDgwMqVqyIjh07YsSIEShXrpy+m5pro0aNQosWLfD777/j1KlTCAkJgVwuh4eHB7p164bx48fDzc1NZ/urWrUqzp07h+joaJw7dw5+fn64f/8+nj17hpiYGKSkpMDKygqOjo6oVq0aGjdujP79+yulzctOiRIlsGfPHty4cQPbtm3DuXPnEBISgtjYWFhaWsLd3R1169ZFp06d0LdvX7V1NGvWDA8fPsTy5ctx8OBBPH/+HIAQZGjQoAE+++wzdO/eXWevSXa++eYbdOvWDatWrcKpU6cQFhYGKysrlC9fHn369MGoUaNQsmRJpTHjdHUMjRs3DkOHDsWWLVtw8uRJ3L17F1FRUUhJSYGtrS3c3d1Rs2ZNtG7dGl26dEHZsmV1sl9D4OHhgVOnTuHKlSvYtWsXLly4gFevXiE2NhYmJiZwcnJC5cqV0bBhQ3To0AGtW7fWOL6giYkJjh8/jvXr1+Off/7B/fv38ebNm2zHxSsoNjY22LFjByZNmoQtW7bg3LlzCAsLw7t371CyZEnUqlUL3bp1w/Dhw7VOZzplyhR06tQJq1atwokTJxAaGgorKyt4eHigV69eGD16NJydnRlQIyIiIoMkkXOkVyIiIiIiKkK+/fZb/PjjjwCAhQsXYsaMGXpuEVHRMnjwYGzbtg2AMN7fwIED9dwiIiIiIiLDZ/gjXxMREREREf1HLpdj9+7d4nyjRo302BqioicpKUkpFSSPISIiIiIi7TCgRkRERERERcbKlSsREBAAQEip16pVKz23iKhomTVrlpgStlGjRvD09NRvg4iIiIiIiggG1IiIiIiISO8uX76MUaNG4e7du2rXJyYmYu7cuZgyZYq4bPLkyRrHISL60OzevRvTpk3TOPZUdHQ0vvjiCyxfvlxcNm3atEJqHRERERFR0ccx1IiIiIiISO/OnTuHNm3aAAA8PDxQt25dODs7QyaT4dWrV7h8+TKSkpLE8t7e3jhz5gyMjY311WQig7Jx40YMGzYMAFClShXUrl0bTk5OSE1NxYsXL3Dt2jWkpqaK5QcPHoytW7fqq7lEREREREUOb+ckIiIiIiKDEhQUhKCgII3rBw4ciHXr1jGYRqRBQECAmBo1K2NjY3z55ZdYunRpIbeKiIiIiKhoYw81IiIiIiLSu4yMDJw/fx5HjhzB9evXER4ejujoaCQkJMDe3h7u7u7w9vbGkCFD0KhRI303l8jgpKWl4eTJkzh27Bhu376N169fIzo6GsnJyXB0dET58uXRunVrDB8+HFWrVtV3c4mIiIiIihwG1IiIiIiIiIiIiIiIiIiyYaTvBlDhun37NhYtWoR+/fqhfPnykEgkkEgkePnypb6bRkREREREREREREREZJA4htoHZv78+di/f7++m0FERERERERERERERFRkMKD2gWnWrBlq166Nhg0bokGDBmjatClCQ0P13SwiIiIiIiIiIiIiIiKDxYDaB+brr7/WdxOIiIiIiIiIiIiIiIiKFI6hRkRERERERERERERERJQNBtQKiEwmw4MHD7Bx40aMHz8ezZo1g5WVFSQSCSQSCXx9ffNU7/79+9G3b194eHjAwsICzs7OaNasGRYtWoT4+HjdPgkiIiIiIiIiIiIiIiJiyseC0r9/f+zZs0dn9SUkJGDQoEE4dOiQ0vKoqChERUXh6tWr+OWXX7Bjxw54eXnpbL9EREREREREREREREQfOvZQKyAymUxpvkSJEqhcuXKe6pJKpejTp48YTHNxccGsWbOwbds2rFq1Cs2bNwcAhISEoGvXrnj48GH+Gq9jDRs2hLu7Oxo2bKjvphAREREREREREREREeUae6gVkMaNG6N69epo0KABGjRogAoVKmDjxo0YNmxYrutas2YNTp48CQCoUaMGzpw5AxcXF3H9l19+ialTp2LJkiWIi4vD6NGj4efnp7Pnkl8REREIDQ3VdzOIiIiIiIiIiIiIiIjyhAG1AjJz5kyd1COTyTB//nxxfsuWLUrBtEw///wzTp8+jTt37uDSpUs4fvw4OnbsqJM2EBERERERERERERERfciY8tHAnT9/HhEREQCAVq1aoX79+mrLGRsbY8KECeL89u3bC6V9uSGXy/XdBCLSIC4uDm/evEFcXJy+m0JEavAYJaLiguczIsPH45TIsPEYJaLioiiex9hDzcAdO3ZMnO7SpUu2ZRXXHz16tMDalFdZx5UjIsMREREBqVQKExMTODg46Ls5RJQFj1EiKi54PiMyfDxOiQwbj1EiKi4iIiKK3HmMPdQM3P3798XpRo0aZVvWxcUFZcuWBQBERkYiKiqqQNtGRERERERERERERET0IWAPNQP35MkTcbpChQo5lq9QoQJevXolbluqVKkCaxsABAcHIzg4ONsyaWlpBdoGIiIiIiIiIiIiIiKigsSAmoFTzCNasmTJHMs7OTmp3TbT4cOHsWDBAnE+sxdbr169YG5uDgDo2rUrvvvuO63at379esybN0+rsnK5HP7+/tmWsbS0hIeHh9KyoKAgvHv3Lsf6S5YsqfQayWQyPH36VKu2eXh4wNLSUpx/+/YtwsLCctzOyMgIVapUUVoWERGhVf5XW1tbuLm5KS179uwZpFJpjtuWLl1aqTtsamoqXrx4keN2AODp6QlTU1Nx/s2bN4iMjMxxO3Nzc5Wg7qtXr5CUlJTjtiVKlICzs7PSspw+C5nc3d1hY2MjzicmJiIkJESrbatVq6Y0HxkZiTdv3uS4nbW1tdjbM9OLFy+Qmpqa47bOzs4oUaKEOJ+eno7AwECt2luhQgXxOASEYzhzDMXsmJiYoFKlSkrLQkNDkZCQkOO2Dg4OKF26tNKygIAAZGRk5LhtmTJlYGdnJ86/e/cOQUFBOW4HAJUrV4axsbE4Hx0djejo6By34zmC54isPpRzhLr3necIVTxH8ByR1YdyjgCKzveIzM9o1jI8R6jiOYLniKwK6xyR+TmRSqV49uwZv0fwHKGE5wj9nyMUj1HF5/IhfI/IxHMEzxGKeI4wrO8RinI6R2jzmTI0DKgZuMTERHHawsIix/KKJ1h1/7yioqJw7do1leV37twRp7Me0LqU00Gibr1UKtXq4FI3Rpu2B6VcLleZ12ZbIyPVrKkymSxf7dVm26xfLrRtr6a6tNlW8ctGpvw+V23k9b3R1I7Cfm8yt9VG1ueq7XujTn6fqzZfYPXx3vAcwXNEVh/SOUJdXTxHqLZN3TJD/RzyHMFzRFYf0veIrHiOUMVzBM8RWenjHKGuDL9HqN+HNvWpw3MEzxFZy2WV3XujuPxD+h7BcwTPEYp4jjDc7xHqaPtcDRUDah8YX19f+Pr66m3/JibZf+TUrTcxMclxO0D9yVOb7QBAIpGozGuzrbp/TsbGxgXe3qz71ba9murSZlt1ZQrjueb1vdHUjvy0V90/razUfSby+lz1+d5o8wVWH+8NzxE8R2T1oZwj1H3Z5DlCFc8RPEdk9aGcI4Ci8z1C049nniNU8RzBc0RWhXWOUDxO+T2C54ic9stzhHZ0eY7QdIx+CN8jMvEcwXNETm37kM8RmhjiOaIoBtYk8qzhRSowGzduxLBhwwAAQ4cOxcaNG3PcpkSJEoiNjQUg9DhT7EaqTu/evbF3714AwMGDB9GtW7f8NToH2oyh1rt3b0RFRcHFxUWrrqBEVPj8/f0hlUphYmJSoL1UiShveIwSUXHB8xmR4eNxSmTYeIwSUXHh7+9f5M5j7KFm4BwcHMSAWnR0dI4BtZiYGKVtC1q5cuVQrly5bMuYmZkVeDuIiIiIiIiIiIiIiIgKimo/PzIoVatWFae1GcBRsYzitkRERERERERERERERJQ3DKgZuNq1a4vTN27cyLbs69ev8erVKwCAs7MzSpUqVaBtI6LiIzPvd15zPRNRweIxSkTFBc9nRIaPxymRYeMxSkTFRVE8jxW9Fn9gOnXqhMWLFwMAjh49iunTp2sse+TIEXG6S5cuBd623CqKBwjRh6JSpUr6bgIRZYPHKBEVFzyfERk+HqdEho3HKBEVF0XxfMYeagauVatWKF26NADg3LlzuH37ttpyMpkMK1euFOcHDhxYKO0jIiIiIiIiIiIiIiIq7hhQM3DGxsaYPXu2OO/j44PIyEiVcjNmzMCdO3cAAM2bN0fHjh0Lq4lERERERERERERERETFmkQul8v13Yji6MWLF1i3bp3Ssnv37uHgwYMAgDp16qB79+5K6+vXr4/evXur1CWVStGlSxecPHkSAFC6dGmMHDkSNWrUwJs3b7B9+3b4+fkBAOzt7XHp0iXUrFmzIJ5Wnri7uyM0NBRubm4ICQnRd3OIiIiIiIiIiIiIiIhyhQG1AnLu3Dm0adMmV9sMHToUGzduVLsuISEBgwYNwqFDhzRu7+7ujh07dsDLyytX+y1omQG10qVLIzw8XN/NISI1QkNDIZPJYGxsDDc3N303h4iy4DFKRMUFz2dEho/HKZFh4zFKRMVFZiecosRE3w0g7dja2uLgwYPYv38/Nm/ejBs3biAyMhK2trbw9PRE7969MXr0aNjb2+u7qRoxdktkuBISEiCVSmFiwn8LRIaIxygRFRc8nxEZPh6nRIaNxygRFRcJCQn6bkKu8cxbQFq3bl0gAaQePXqgR48eOq+XiIiIiIiIiIiIiIiI1DPSdwOIiIiIiIiIiIiIiIiIDBkDakRERERERERERERERETZYECNiIiIiIiIiIiIiIiIKBsMqBERERERERERERERERFlgwE1IiIiIiIiIiIiIiIiomwwoEZERERERERERERERESUDQbUiIiIiIiIiIiIiIiIiLJhou8G0IfDyIjxWyJD5eDgAJlMBmNjY303hYjU4DFKOQoIAHx8AFNT4MABwNFR3y0iUovnMyLDx+OUyLDxGCWi4sLBwUHfTcg1iVwul+u7EVS8ubu7IzQ0FG5ubggJCdF3c4iIiIiKn/h4oGlTwN8fmD8f+O47fbeIiIiIiIiIqFhhlyEiIiIioqLu7VshmAYAmzcDvGeOiIiIKM/u3LmDKVOmoGnTpihVqhTMzMxgaWkJV1dXNGvWDCNGjMAff/yBwMBAreuUSqXYs2cPRowYgVq1aon1uri4oG7duhg3bhyOHj0Kbfs+zJ07FxKJBBKJBHPnzs3jMyUiotxgykciIiIioqImIAAwNwc8PIT5smWBjz8GTp8Gnj0DLl8GmjfXbxuJiIiIipikpCSMHTsWW7ZsUVmXnp6OiIgIRERE4OrVq1i3bh0A4MiRI+jcuXO29e7fvx/Tpk3D06dPVdZFRkYiMjISd+/exa+//or69etj+fLlaNmypW6eFBER6QwDakRERERERcnu3cDw4UDVqoCfnxBYA4ChQ4WAGiD0UmNAjYiIiEhrUqkUnTt3xsWLFwEAxsbG6NatG1q2bAk3NzdIJBJER0fj/v37uHDhAh4/fgwAkMlk2dY7f/58zJkzR5x3c3NDz549Ubt2bTg6OiI6Ohq3b9/G/v37xem2bdvit99+w4gRIwruCRMRUa4xoEaFRiqV6rsJRKRBQEAApFIpTExMUKVKFX03h4iy4DFKAID0dODrr4Fly4T5mzeBRYvej5fWqxdgbQ0kJQE7dgDLlwOWlnprLpE6PJ8RGT4ep/Sh+u2338RgWrly5XD48GHUqlVLY/mnT59i7dq1cHBw0Fhm8eLFYjDNyMgI8+bNw/Tp02FmZqZSdvny5fj222+xcuVKSKVSjBw5Eg4ODujbt69SucxjNCYmJg/PkojIcAQEBBS57xocQ42IiJCRkSE+iMjw8BglhIYCrVu/D6YBwKBBwOTJ7+dtbIDMCy7x8cDBg4XaRCJt8HxGZPh4nNKHSjHN42+//ZZtMA0AKleujJ9//hktWrRQu/7GjRuYOXOmOL9p0ybMmjVLbTANAGxsbLBixQp8//334rLPP/8cwcHBSuUyj09tx1ojIjJURfG7BgNqRERERESG7NQpoF49YVw0ADAzA1avBrZuFYJoinx83k9v2lR4bSQiIiIq4vz9/cXpNm3a5Lu+2bNni9mahg8fjs8++0yr7WbOnIm2bdsCAN6+fYuffvop320hIiLdYECNiIiIiMgQZWQA338PdOgAREUJy8qVE8ZNGzsWkEhUt2ndGihbVpg+fhyIiCi05hIREREVZYpDlURlfvfKoydPnuDYsWMAhLHY5s6dq/W2EokECxYsEOc3bNiA+Pj4fLWHiIh0gwE1IiIiIiJDI5MBn3wijI+Wmc6nc2fg9m2gUSPN2xkZAUOGvK/j778Lvq1ERERExUClSpXE6eXLl+errhMnTojTH3/8Mcpm3vCkJS8vL1SuXBkAkJKSIo7tRkRE+mWi7wYQEREREVEWxsZAzZrA4cNCT7T584GZM4WAWU6GDgXu3xfSP3bvXvBtJSIiogIz4597ePI6Qd/N0LuqLrb4qU+dAt3HoEGD8M033wAAli1bBn9/f3z++edo27YtHB0dc1XXpUuXxGkvL688tcfLywtPnz4FAPj5+aFbt255qoeIiHSHATUiIiIiIkP0ww/A8+fA6NFAu3bab1elCnDgQMG1i4iIiArNk9cJ+Dc4Tt/N+CBMmjQJhw8fhp+fHwDg6NGjOHr0KCQSCTw9PdGgQQM0adIELVq0QMOGDSFRl377PyEhIeJ01apV89SeatWqidOhoaF5qoOIiHSLKR+JiIiIiPQtKQk4c0Z5mYkJsGtX7oJpRERERJQnFhYWOHXqFGbMmAFra2txuVwux7Nnz7Bjxw5MmTIFjRs3RoUKFbB8+XKkpaWprSsmJkacdnBwyFN7FLdTrI+IiPSHATUiIiIiIn3y9wcaNwa6dBHGSCMiIiIivTA3N8fChQsRGhqKDRs2YPDgwahYsaJKuaCgIEyePBne3t45Bruy68mWHXnmOLpERGQwGFAjIiIiItKXnTuBRo2AR4+A1FRg2DAgI0M3daenA/v3A336AMeP66ZOIiIiog+Avb09fH19sXXrVgQGBuLNmzc4ceIEZsyYgbJly4rlrl27hkGDBqls7+TkJE7HxsbmqQ3x8fFq6yMiIv3hGGpUaIyNjfXdBCLSoEyZMpDL5Xm+c46IChaP0WIoLQ2YNg1YufL9spo1hQCbkY7ueTt+HOjZU5g2MQE6dtRNvUT5wPMZkeHjcWpYqrrY6rsJBkHfr4OjoyPat2+P9u3bY968efjqq6+watUqAMCJEydw+fJleHl5ieXd3d3F6YCAgDzt88mTJ2rryzxGbW352SCioq1MmTL6bkKuMaBGhYZfxokMl52dnb6bQETZ4DFazLx6BfTvD1y9+n7ZkCHAb78BCuN15FvHjkDJkkB0tNBTLTYWcHTUXf1EecDzGZHh43FqWH7qU0ffTaAszMzMsHLlSly8eBF3794FAJw8eVIpoNa8eXPs2LEDAHDp0qU87efy5cvidIsWLcTpzGPU3Nw8T/USERmKovidgykfiYiIiIgKS2SkMF5aZjDNzAz4/Xdg0ybdBtMAwNQUyExBlJoK7Nql2/qJiIiIPlASiQTt27cX58PCwpTWd+jQQZw+ffo0QkJCclX/5cuXxZ5tlpaWaNmyZT5aS0REusKAGhERERFRYVm1CoiIEKbLlwcuXwZGjwYKqif/0KHvpzdtKph9EBEREX2A0tPTxWkbGxuldVWrVkWnTp0AADKZDHPmzNG6XrlcjtmzZ4vzw4YNK5K9OIiIiiMG1KjQyOVyfTeBiDR49+4dkpOT8e7dO303hYjU4DFaTKSmCr3RAMDYGLhwAWjQoGD3Wa+eMDYbIATvnj0r2P0R5YDnMyLDx+OUPlSvX7/Wumx6ejqOHj0qzteqVUulzPz582FiIoy2s379evz1119a1f3jjz/i9OnTAIR0aF9//bXS+sxjVDGgR0RUFBXF7xoMqFGhkclk+m4CEWkQFBSE58+fIygoSN9NISI1eIwWExIJsHgxUL8+0LcvULZs4exTsZfa5s0Fv0+ibPB8RmT4eJzSh6phw4bw9fXFlStXsi2XmJiIYcOGiSkZ7ezs0KNHD5VyjRo1wo8//ijO+/j44IcffkBaWprGeidNmoRZs2aJy9atW4dy5coplcs8RuPj47V+bkREhqgoftcw0XcDiIiIiIg+CGZmQnDLxwdITi68/Q4eDMyYAWRkAFu2AHPnAka8r46IiIhIUXp6OjZt2oRNmzbBw8MDrVq1Qt26deHs7Axzc3PExMTg33//xZ49exAVFQVAGEvtl19+QYkSJdTWOW3aNCQlJWHevHnIyMjArFmz8Ntvv6FXr16oXbs2HB0dERMTg1u3bmHfvn2Ijo4GAJiYmOC3335D3759C+35ExFRzhhQIyIiIiLKIk2agVOPX+NNUhqaeTrBs5RNzhtpSyIBrK11V19OypQB2rcHjh8HXr4ELl4EWrUqvP0TERERFQEfffQRTp48CblcjqCgIGzevBmbs+ndX7p0aaxcuRL9+vXLtt65c+eibt26mDZtGp49e4bQ0FCsWrVKY/l69eph+fLl8Pb2zvNzISKigsGAGhERERGRgjRpBgb8eQX/BscBACxMjbDq0/poV8NFvw3LDx8fIaAGAKdPM6BGRERElMXx48cRFhaGEydO4NKlS3jw4AFevHiBuLg4yGQy2NjYwM3NDXXq1EGXLl3Qq1cvWGt5k1TPnj3RrVs37N+/H4cPH8bVq1cRGRmJt2/fwsHBAaVLl0aLFi3QvXt3dOrUCRKJpICfLRER5YVELpfL9d0IKt7c3d0RGhoKFxcXRERE6Ls5RKSGv78/pFIpTExMUK1aNX03h4iy4DFauH4/H4ifjvorLbMxN8HRiS1RtoRV7isMCwOmTgXGjweaNhV6qBW25GRg/nxg0CCgTp3C3z/Rf3g+IzJ8PE6JDBuPUSIqLvz9/YvceYyDJxARERER/UeWIceWK6oDIyemSjFt911kZOThXrTffwe2bwe8vIBs0vsUKCsr4KefGEwjIiIiIiIiyiMG1IiIiIiI/nPuSSRC496pXXf1+RtsuPwydxWmpgJ//CFMGxsDvXvnr4FEREREREREpBcMqBERERER/WfLVdXeaYoWHfPHs8gE7SvcuROIjBSm+/QB3Nzy0TodkssBqVTfrSAiIiIiIiIqMhhQIyIiIiICEBSThPMBUdmWSZVmYMrOu0iXZeRcoVwOrFjxfn7ChHy2UAdiYoDFi4HatYGlS/XdGiIiIiIiIqIigwE1IiIiIiIA264FQ67FEGn3QuKx+mxgzgWvXgVu3RKm69UTxlDTt7g4YPp04OFDYNMmaPWEiYiIiIiIiAgSuZy/oqlgubu7IzQ0FG5ubggJCdF3c4hIDZlMJk4bGxvrsSVEpA6P0YKXki5D04WnEZecrlV5EyMJ9n7RHLXd7TUXGjQI2L5dmN6wAfD1zX9DdaFlS8DPT5i+eRNo0EC/7aEPCs9nRIaPxymRYeMxSkTFhUwmK3LnMfZQIyIiGBsbiw8iMjw8Rgve4XvhaoNpHWu6wLOUtcpyaYYck3feQUq6TGUdACAsDNi1S5guWRIYOFCXzc0fH5/305s26a8d9EHi+YzI8PE4JTJsPEaJqLgoiucxBtSIiIiI6IO35WqQ2uUjW1bE0v51YWwkUVn3LDIR/zv+RH2Fv/8OSKXC9KhRgIWFrpqaf/37A+bmwvT27UBamn7bQ0RERERERFQEMKBGRERERB+0B6HxuPMqTmV5tdK2aODhiI/KOuDLNpXUbrvu0gtcfR6juuLqVeGvsTEwdqwOW6sD9vZAz57CdHQ0cPSoXptDREREREREVBQwoEaFJiMjQ99NICINoqOj8fr1a0RHR+u7KUSkBo/RgrVVQ++0Ic08IJEIPdPGt62EWm52KmXkcmDqrrtITJUqrzh+HDh3Dli0CHB313WT82/o0PfTmzfrrx30weH5jMjw8TglMmw8RomouCiK5zEG1KjQMKBGZLiio6MRFRVVJP+REX0IeIwWnPh36dh3J1RluY25CXrWdRPnTY2NsKx/XZiZqH59Dol9h+8PPVJeKJEArVoBU6bovM060b49ULq0MH3wIBCjppcdUQHg+YzI8PE4JTJsPEaJqLgoiucxBtSIiIiI6IP1z60QpKSr3vTTp74brM1NlJZVdrHF9I5V1dbz941XOOP/ukDaWCBMTIDBg4Xp9HRgxw79toeIiIiIiIjIwDGgRkREREQfJLlcrjHd4+CmHmqXD29eAY0rlFC7bvru+4h7+ERn7StwPj7CXwsLIDJSv20hIiIiIiIiMnAMqBERERHRB+lyYAyeRyepLG9SoQSquNiq3cbISIIl/T6CtZmxyjpJRDhsPqoFedOmwL59um6u7tWpI/RMCw8H5s7Vd2uIiIiIiIiIDBoDakRERET0QdpyRX3vtCHN1PdOy1S2hBW+61ZDZfngO0dhIpNCcu0acOOGTtpY4Pr3Bxwc9N0KIiIiIiIiIoPHgBoRERERfXDC49/h5GPVMc9K2ZqjQ43SOW4/oFFZtK3mLM6bSdMx+M5RAIBUYoTIwcN011giIiIiIiIi0jsG1IiIdEQqy8ClZ9H44fAjfL37Hg7cDUO6LEPfzSIiyh2pFDh/Hli5EggM1HdrCsz2668gy5CrLP+0UVmYmeT8FVkikeCn3rXhYGUKAOjyxA+lkuIAAMeqNse0q28gl6vWb9DevQMiIvTdCiIiIiIiIiKDZKLvBhARFWXpsgxcfR6DI/fDcfzha7xJShPX7bj5CmXsLTC8RQUMbFwONuY85RJREdCmDeDnJ0wvXgwEBACWlvptk46lyzKw/XqwynJjIwk+bVJO63qc7SzwQ8/a+HLbbfjeOigu39igG24GRGHb9WAMbpJ9+kiDEBUFfPMNsGsX0LMnsGmTvltEREREREREZHDYQ42IKJfSpBk49yQSX+++h0Y/nMKQddex/forpWBaprD4FHx/+DGaLTyNn4/5I/Jtih5aTESkRkwM8M8/qsu9vd9Ph4QAa9YUXpsKyYmHrxGVkKqyvF11Z7ja5y542LWOK8Zbx6BueAAA4IGLJ266CeOr/XD4MYJikvLf4IJmayt8Ft6+Ff4mJuq7RUREREREREQGhwE1KjQSiUTfTSDKszRpBs76R2Lqrrto9MMp+G64gR03XyEuOV2r7RNSpPjtXCBa/HwW03ffxbPIhAJuce5YWlqKDyIyPDo7RoOChFSObdsCLi5A377As2fKZfr0AT755P38zz8DKcXrZoAtV1+qXT6kafk81Tf+wVFxelP9bsB/33mS02T4auddtaklDYqFBTBggDCdlATs2aPf9lCxxu8cRIaPxymRYeMxSkTFRVE8jzGgRoXG2NhY300gypVUqQynHr3GlJ130OD7kxi28QZ23wpB/DvtgmjqpMkysPNmCNotvYDPN97AtecxBjHGjoeHBzw9PeHhUQRSkxF9gPJ8jMrlwP37wIIFQP36QPnywMSJwNmzgEwmlNm/X3mb+vWFZT17CvNhYcC6dfl9Cgbj6esEXH3+RmV5xZLW8PJ0yn2F4eEw2yv09IuxtMOBGq2UVt8MisWai8/z1NZC5ePzfnrzZv21g4o9fucgMnw8TokMW9Zj9Pnz57C0tIREIsHBgwdz2Dr/GjduDIlEAl9f3wLfF+lH69atIZFIPvjOEYb6Ovj6+ortevnyZb7qOnfunFjX3LlzddK+3CiK3zUYUCMiUpCSLsPxhxGY9Pe/aLDgFEZsvok9t0ORkCLV+b5O+0diwJ9X0XP1ZRy9H274PRiIqGiZNQuoVAmoUweYPRv491/l9Z6ewNSpQLt26rf/7rv30z/9BKSqpkgsirZeDVK7fHBTDxgZ5eGH0h9/AOnCjRb3u/RHqomZSpGlJwLgH/E293UXpmbNhM8LAJw5A7x6pd/2EBERERmIpUuXihecJRIJ/v77b623ffnypdK2WR+2trYoX748unfvjl9//RXx8fG5bt/EiRORkpICb29vdO/eXW0ZmUyGBw8eYOPGjRg/fjyaNWsGKysrsR25CY4tWrQIALB582Zcvnw51+3VlevXr2PUqFGoXr067OzsYGlpiYoVK2LgwIE4dOiQVnVs3Lgx2/cn6yOngEN4eDi+/PJLlC9fHubm5nBxcUG/fv1w584drdpz6dIlGBkZwdTUFHfv3tVqG23s27cPc+fOxdy5cxEXF6ezeok+RCb6bgARkb6lpMtw7kkkDt+PwJnHr5GUJstTPSVtzNCxZml0qe2Kd2ky/HnhOa6/VO0FkdXdV3EY+9dtlHeywuctK6JfA3dYmLJHJxHlgkwGZO0Jfu8e8DxLz6gGDYSeZ716ATVqiKkJ1apfH+jeHTh4UBhLbeNGYPRoXbe8UCWlSvHP7VCV5RamRuhb3z1vlX78sRCsPHYMjX/+FuX+eYHgN8lKRdJkGZi84y72f9kcZiYGej+bRCL0Ups9W+jZuHUr8M03+m4VERERkd6tX79eaX7dunUYOHCgTupOTExEYmIigoKCcOjQIcyfPx8bN25E586dtdr+3LlzYvBo9uzZGsv1798fe3SU1rt169Zo0aIF/Pz8MH36dPj5+emkXm29e/cOX3zxBTZu3Kiy7sWLF3jx4gV27NiBrl27Ytu2bbCzsyuUdj19+hStWrVCeHi4uCwyMhK7d+/GgQMHsHv3bo0BTwBIS0vDqFGjIJfL8dVXX+Gjjz7SWdv27duHTZs2ARB6Nzk4OOisbqIPDQNqRPRBysiQ49Tj19h/Nwxn/SORnMcgWilbc3T6L4jWuEIJGCv0bmhXwwX/BsfizwvPcexhBHLK7PgyJhnf7XuAZScD4NPMAz7NyqOEtWpPhyJBLgdevAASEwEjI/UPY2PA1BQoU0Z528REQCpVXz5z2sC62xPpzZ07wA8/AJcvC8ecmcI5o1cv4MgRoFUrIYjWowdQrlzu6v/uOyGgBgA//ggMG6a8jyJm351QJKaq9jju8ZEb7K1M81Zpy5bCIyoKVqVKYWl/O/T744rKOf9x+FusPP0UUztWzdt+CsOQIUJADQA2bQJmzOD5loiIiD5oV69excOHD5WWnT59Gi9fvkT58uVzVVepUqXw559/Ki17+/Yt7t27h7/++gsRERGIjIxEr169cOnSJTRo0CDHOr/99lsAQIMGDfDxxx9rLCeTKV/zKFGiBJycnPD06dNcPYdMX3/9Nfz8/HDp0iUcO3YMnTp1ylM9uSWTydCvXz8cPnwYAGBhYQEfHx94eXnB3Nwc/v7+2LBhA4KDg3H48GF0794dJ06cgLm5eY51jx8/Hm3bts22TLVq1TSu8/X1RXh4OCQSCUaOHIkWLVogMDAQS5YsQWJiIoYMGYLnz5+jRIkSarf/6aef8OjRI3h6emLOnDk5tpeI9IMBNSo0Wf95E+lLRHwKxm+/jRsvY/O0vYudOTrXckXnWqXRsLxyEC2reuUc8dtnDfAyOglr/Z5j180QpEozsq3/TVIalp96it/PB6Jfg7IY0bICPJys89RWbQUFBUEqlcLExEQ3+Yu//hpYvDjncm5uQs8XRcOHA7t2Zb+dqanQk2LtWuXlPXoAKSmApSVgZSX8zXwoznfuDFSp8n67xETgwQPA3FwIFmQ+FOfNzQETE15cJr1QOUYTEoTAx8qVQMZ/55SzZ4GOHd9v1L+/cExo+MGmlUaNhOPl6FEgKQl4/BjQ4Z2ShUkul2PLFfXpHoc008F5r1QpAEDD8iUwyrsi/jivOm7a6nPP0La6M+qXc8z//gpC+fJCAPb8eeDJE+DGDaBxY323iooZnX/nICKd43FK9N46hbGEhw0bhg0bNkAul2PDhg2YN29eruqysrJCz8xxirP47rvv0KVLF1y+fBmpqamYOXMmjh8/rrZs5jH677//iikXx44dm+2+GzdujOrVq6NBgwZo0KABKlSogI0bN2LYsGG5eg6ZunTpgnLlyiE4OBiLFy8utIDaunXrxGBa6dKlcebMGVSvXl2pzPTp09GnTx8cO3YMFy5cwLJlyzBjxowc665fv77G9ycnd+7cEd+LH3/8UWl/7dq1Q8uWLREfH4+tW7diwoQJKts/efIEP/74IwDgjz/+gKWlZZ7aQVTUBAUFFbnvGgyoUaGR59Q9h6gQXA6MxoTt/yI6MS1X25W2s0Dn2qXRtbYr6pdzzPU4O+VLWuP7nrUxuV0VbL4ShM1XXiI2OT3bbVLSM7DlahD+uhaEzrVcMcq7Ij4q65Cr/Wrr3bt34o/mHL15A9y8CVy/LlxszXy4ub0vU6eOdjs2UpP6LCP7gCMAYbwidYGtM2eE4FhOSpdWDqg9eCCMH6SNuDjA3v79/IoVwK+/KgfiFINxFhaAhwdQu7bQu4coD8Rj1NhYCDhPmgSEhb0v4OQEvH6tvJG1tfDIr3nzgDZtgLFjARub/NenJ7eCYuEfkaCyvG5ZB9Rys1ezRd5NaV8F5/yj8OS18v4y5MBXO+/iyISWsDQz0NS+Q4cKATVA6KXGgBrpWK6+cxCRXvA4JRIkJSVhx44dAIAKFSpgxYoV2LVrFxITE7FhwwbMmTMHRup+0+aBvb09fvvtNzHN35kzZ5CQkABbW1uVspnH6IYNGwAIvbT69++fbf0zZ87USTszGRkZYfDgwVi4cCHOnDkDf3//bHtv6cry5cvF6dWrV6sE0wAhcLlt2zZUqFAB8fHx+PHHHzFu3DjYFOBvmWvXrgEAJBIJxo8fr7SuRYsWaNiwIW7evImrV6+qBNTkcjlGjx6N1NRU+Pj4ZNvTkKi4effunb6bkGv8dkREH4SMDDl+vxCI/x1/ggwtY7tuDpboXKs0utRxRV13h1wH0dRxsjHH5PZVMKaVJ3bdeoW1F1XH2lFpuxw4fD8ch++Ho0mFEvimS3XULaDAmoqkJGFsoBs33gfQAgNVy2UNqDVrJoy95OoqBMjUPWQy9T1n6tYV9qtYLut2qalAhQqq22r7jzjr3V65+QeeNVVEZCSgTZqMunVVA2rz5wsBusqVgUqVhL9ly6qOhUUEwOzVK5RZuBC4ePH9QgsLIS3j1KkFl4qxUSPhUcRtuaqhd1rTPN4Nt3ChcNz27Cn0mlVgbmKMpQM+Qs9fLyFdpvxP50V0En46+hjzetTK234LWp8+wJdfCjceeHrquzVEREREerNz504kJAg3SA0ZMgS2trbo06cPNm3ahFevXuHkyZPoqJghIp/q1KkDR0dHxMbGQiqV4vnz5xrH0UpISMDp06cBAB06dFAbeCtoffv2xcKFCwEAmzZtEqcLSnh4OB4/fgwAcHR0RI8ePTSWzVy/efNmJCQk4ODBg/j0008LrG3R0dEAhLSe1mpuaqxYsSJu3rwpllO0bt06nD9/HiVLlsTSpUt12i5fX19x7LRMFdRcS2nVqhXOnTuXbV179+7F2rVrcefOHURHR6NEiRJo1qwZJk6ciFatWmncTrEn5IYNG+Dr64vbt2/jzz//xJkzZxAeHi4GqX19fZW2jY+Px9q1a3H06FE8evQIMTExsLa2RoUKFdChQweMGzcOborXotSIiIjAH3/8gRMnTuDJkyeIj4+HtbU1nJ2dUbp0abRs2RLt27eHt7e3VgHyvL4Oiu7cuYO1a9fi7NmzCA0NRUpKCpydndGwYUP06dMHn376qc6C9cePH8fvv/+Oq1evIjY2VtzPmDFj0KFDB53s40PDgBoRFXvxyen4atcdnHocmWNZd0dLdK3tis61XfGRuz0kBZTez9LMGD7NymNwEw8cexCBPy8E4m5IfI7bXXvxBv3/uILfBtfHx9VddNuorL1IZTIhIJag2qNDiZ0dkPVLoacncOBA3toxa1betgOA5GQhOKb4yLosORnImou+TBlg4kQgLU14pKa+n876yHLhHObmQmBQsYw6lSurLvvrLyAgQHmZmRlQsaJQPjPQ9vHHyj3q6MOSmgqn1avh9OefMEpNfb+8Sxdg1Sr1wWVSEp2YiiP3w1WWO1iZomsd19xXGB4OzJkj9Jb96CPhxoMs/y9qlrHHpHZVsPj4E5XNN10JQvsapdGicsnc77ug2dkJz6dyZfW9iImIiIg+EIrpHn18fAAAQ4cOFQMU69at02lADYDSWF8pKSkay128eBGp//020PYivq7Vq1cP9vb2iI+Px969ews8oBaiMFxE5cqVcww4KPaYO3ToUIEG1DKDaHFxccjIyFBpW2YgLWuw7fXr15g+fToAYNmyZXByciqwNuZVSkoKhgwZgt27dystj4iIwN69e8X3Xpu0mgCwaNEizJw5M8ehgXbt2oUxY8bgzZs3SsvT0tIQGxuL27dvY/ny5fj9998xdOhQtXUcPXoUAwYMEAPjmeLj4xEfH4+nT5/i4sWL+PHHHxEVFYWSJTX/PtPF6yCTyTBlyhT88ssvKpncXr16hVevXmHv3r343//+h/3796NcbsdAV5CRkYHRo0djbZahUhT3M2HCBPTq1SvP+/hQMaBGRMXaw7B4jN16O9teYKXtLNCznhu61nZFLTe7AguiqWNsJEHXOq7oUrs0rr14gz8vPMcZ/+wDf2nSDHy16y5OTPKGs51F/hoQFASsXg2PY8eQ5uqKcIX0CTA2FtIU/pcHHIDQG6Zevfc9Vho1MqyLrpnpFu1zmb6talVA8bnnxuzZwiOTXC5cZM8MriUkAM+fq6bKk0qBFy9U60tLA/z9hUemP/9UDqhFRAhj1Hl6Co+KFYW0kgXVQ4n068EDlFy1CpLML9zu7kKq0V699DOmX1iYMPZhEUoFuPPmK5WeYgDQv2FZWJjmoUfoH38IxzkgjDGn4X0Y7V0Rpx6/xr/BcSrrpu2+i2OTvGFvaaq6ob5VrarvFhARERHp1ZMnT3Dp0iUAQso+z/967rdu3Rrly5fHy5cvsX//fkRHR2d7ET43oqKi8FohjXt24wpdVvid3rRpU53sP7ckEgkaN26MkydP4smTJ3j58iXKly9fYPtTDEBoc91Gsfy9e/dyLL969Wr8/PPPePXqFTIyMlCyZEnUrVsXnTt3xtChQ2FlZaVx25o1awIQgj0nTpxQGlMuMjJSTAlZo0YNpe0mTJiA2NhYtG/fHp999lmObcytCRMmoGfPnli5ciXOnj0LQBijzdnZWalcdp/hzz//HLt370bNmjUxaNAgeHp6IikpCYcPH8aePXsACClFvby84O3tnW17du7ciaNHj8LGxgY+Pj5o3LgxzMzM8PjxY5QuXVost2bNGowePRpyuRwmJibo1q0b2rZti9KlSyMpKQmXLl3CX3/9hXfv3sHX1xdmZmYqAdOwsDD0798fif8NC9KqVSt07doVpUuXhrm5OaKjo/HgwQOcPn0aAVlvdC6g12HEiBHYuHEjAMDExAQDBw5EmzZtYGVlhUePHmH9+vUIDQ3FnTt34OXlhdu3b6u8V9qaPHmyGEwzNjbGoEGD0KZNG5ibm+POnTtYt24dVq5cqRSoJu0woEZExdbOG6/w3f4HSJVqHpOrXXUXLOn/kd4vaEokEjSt6ISmFZ0Q8DoBay48x747oWovAANAXHI6vtlzH2uHNsxbAPDePWDRIuDvvwGZDJYATMLCVHup9e4N1KjxPnhWq5ZqDy1SJpG8D+wBQu81dT+EjI2FQNvTp+8fz54JfwMDAcW7EbP2bnv4EMiaCsLISEgXmRlgy/zbqxffs6KuQQPEDRgAh127EDtkCEqsXAnoIaUL0tOBKVOANWuAcuWAx4+LRHpSWYYcf10NVrtucJM83PGXlgb8/rswbWwsjC2ngYmxEZb0+whdVl5ESrry/6Lw+BTMO/gQS/vXzX0biIiIiKhAKfZOU+z9IpFIMGTIECxYsABpaWnYunUrJk2apJN9/vDDD2IQqGLFinBx0ZyVJjNAZGpqivr16+tk/3nRrFkznDx5EgBw9erVAg2oubq+zywREBAAuVye7fUQxSDJkydPcix/48YNpfnMnjwHDx7EnDlzsH79enTr1k3ttt7e3ihdujQiIiIwcuRIrF27Fs2bN0dgYCC+/PJLJCUlAQAGDBggbnPkyBHs3LkTVlZW+D3z94WO1a9fH/Xr18e+ffvEZR06dMjV+7Rt2zZMnjwZ//vf/5R63g0fPhyLFi3C119/DblcjsWLF+cYUDt69CiqVKmCkydPaux9de/ePYwfPx5yuRxly5bFwYMHVVKf+vr6YurUqWjXrh1CQkIwevRodOzYESUUhhXZtm2bGEz75ZdfMG7cOI3tun79eo5j7OX3ddizZ48YTHNwcMDx48fROMtNqtOmTUOPHj3EVJCjR4/G3r17s22XOpcuXcIvv/wCQBhT8OjRo0ptGjRoECZPnoy2bduKwUDSnoF0KSAi0p2UdBmm776L6f/c0xhMM5IAX3eqhj+HNNB7MC2rKi62WNzvI/h93RZjWnnC1lz9vQ+n/SOx61Yu7iSRy4Hz54U0cR99JKQbVOhin2FtDaP/vmyIvvpKuHg+apTQM42BGd2RSISeRm3aCK/v4sXA3r3AgwfCGHLBwcDp00JPmDp1lLd99ky1vowMocfhmTPA2rXAN98APj6qAY+//hLWrV0LnD0rbJNDqgUqRDIZsG2b0INRQdTkyQjcsQORX3+tn2AaIBz/Dx4IKVGfPgX+G6Dd0J17EonQONVxEltVKQUPJ9XxDXK0axeQeedwr15CcDEbFUvZ4JvOqoOlA8Ce26E49ei12nUGIzAQuHpV360gIiIiKjRSqRSbN28GAFhYWKBfv35K6xUDbIqBt7xISEjApUuXMHjwYKxYsUJc/s0332gM/qSlpSEoSBgfuHz58rCwyGfmmnyoXv3991xteoHlR9myZcVee7GxsTiQzTAT8fHxSkGk9PR0JCerz1xkbGyMFi1a4JtvvsGGDRuwa9curFmzBmPHjhUDNFFRUfjkk0+wfft2tXWYm5tjxYoVkEgkCAkJQadOnWBra4u6deuKPR0nTJiAOv/9tk9KSsIXX3wBAJg7dy4qVqyYuxejEHl7e2PJkiVqU2xOnToV7u7uAIBTp05BmuV3bFYSiQR///13tqkM586di9TUVBgbG2P//v0axxGsWrUqNmzYAEA4jtasWaO0/pnCdZPhw4dn267GjRvneBzl93VQTIn622+/qQTTAMDW1ha7du0Sewzu27cPjx49yrZd6ixZskQMzi9cuFBtgM/V1RU7duyAcRG4SdbQMKBGRMVKcEwyeq++jJ03NQeaStqY468RTTG2tSeMjPSQLk1LLnYWmNG5Gi5/0xYdaqi/M23+wUcIidWczhKAcIF+zx6gaVOgdWvg6NH365ycgLlz8dTPD08PHUKGvi7Uk7LM3mZt2wrBNoW7rAAAffsKwbY1a4AZM4D+/YVx4RwdlctVqKCajvOff4CffgJGjhTqL18esLISUrx17iwEUf8b6JkK2e3bQLNmwODBwOrVSqsy7OyQYghp+BTTmy5YUCSCsVuuBqldPqSp5hQ62frvTj8AwPjxWm0ypKkHWlRSn0Zl1Vk1AXJDkJgItGghjOP45Zf6bg0RERFRoTl48KCYerFnz56wzzKkgKenJ1q0aAEAePDgAa5fv65VvUFBQZBIJEoPOzs7tGjRAtu2bRPLTZ8+HSNGjNBYT3h4ODIyhJuHS2T9rVjIFMf8evnyZYHvb/To0eL0F198gSdPVMcrfvfuHQYPHoz4eOVx6t++fatStkWLFnj58qU4jpavry/69u2LESNGYPXq1QgKCsKgQYMACCkkhw8fjuBg9dkv+vfvjx07dsDNzU1pubW1NebPn4/lCsNMzJo1C0FBQahXrx4mT54sLt+6dSu8vLxga2sLGxsbNGvWTAzu6svkyZM1BneNjIzQpk0bAMIYY4GBgdnW1aJFC9SrV0/j+ri4OOzfvx8A0L59+2zLAkC7du1QpkwZAMDx48eV1imOV3fr1q1s69FGfl6H4OBg3Lx5EwBQoUIFpZ6KWTk5OWHMmDHifG57kKWmpuLw4cMAADs7O4waNUpj2Tp16qBDhw65qp+Y8pGIipFTj15j8s47SEjRfEdMo/KOWDWoPlzyO/ZYIbK1MMXPfergdvAFRCemKq1LTJVi+u572Pp5E83BwbQ0YMwYICrq/TIPDyFwMnw4YG0Nmb+/So8YMmBOTkIwrG1b1XWxsUIqSU1fZJ8/V12WlgYEBAiPY8eAZcuASZNU00pSwXj7FvjuO2DVKqGnISDMDx2a+/EAC1rr1kDLlsDFi8I4f7t3A9n8GNC3oJgknA+IUlnu5mCJNtXykIv+2jXhAQg9fVu21GozIyMJFvWtg47LL6j8j7rzKg4xialwsjHXsLWe2Ni8Tz17+7bQO7FWLf22iYiI6EO3dKl239Hr1wey9t755BPhf3pOpkwRHpkSEoDq6nvbq9i/X7jRL9OhQ8Jv0ZzY2CiPIQ0A06YB27ertqcQaEr3qMjX1xd+fn4AgPXr16vtbZJbVatWxZYtW9CoUaNsyykGivQdUFPc/5s3bwp8f5MmTcLu3btx+/ZthIWFoX79+vDx8UGzZs1gbm4Of39/bNy4ES9fvoSLiwvevn2Ld++EbBXqehZVqlQp2/3Z2Nhgy5YteP36NU6fPo2UlBT8/PPP+PXXX9WW79evH/r06YM7d+4gIiIC9vb2qF+/PiwtLcUyN2/exC+//AJjY2OsWbMGJibC5fnx48dj1apV4n4lEgmuXr2Kq1ev4saNG2IKv8Lm5eWV7XrFAGJsbGy2ZVvm8Pvp0qVLYrDY1tZWqZehJpmpGrP25OrQoQOW/ne+7N27N77++mv06dMHFSpUyLFOdfLzOlxVyPjRoUOHHIdu6dSpE77//nuVbbVx9+5dpKWlAQCaN2+eY8+7jz/+GEcVb7ynHDGgRkRFnlSWgaUnA7D6XPZ3woxoUQFfd64GU+Oi1znX0doMP/WujRGbb6qsuxwYg63XguDTrLywIC3t/fhdAGBpCUyYIFygr1MHmD5d6NHE9I3Fk6Oj8CNW8Yeson/+EdL1ZQbdAgPfT2emwJDLVcdtI92Ty4GdO4HJk4Hw8PfLa9QAfvvN8IJpgJCqdPZsoH17YX7BAqBfP9WekAZi27VglaEhAWBQk3IwzksP5ay903IxhmUZB0tM/Lgyvj+s2gPU71k0etR1U7OVng0dCmTezbl5szD2JhEREenP27dAaGjO5cqWVV0WFaXdtll78cjl2m0HCL9FFb17p9226jKlxMYK26rpVVSQwsLCcOzYMQBCSrT2md97s+jfvz8mTJiA5ORkbN++HUuXLoWVlVW2dZcqVQp//vmnOJ+cnIwXL17gr7/+wuPHj/HkyRMsXrwYW7duhZnib/os0hReZzs7u9w8PZ1T3H9m4KogWVpa4siRIxgwYADOnz+P5ORk/P777ypjkJUtWxb//PMPmjZtKi5zzJrRRUtGRkaYP38+Tp8+DQA4dOiQxoBaZnlN49pJpVKMHDkSMpkMkydPRoP/frcfOHBADKatWLECX/6XIWLVqlWYNGkSVq1ahQ4dOqB79+55eg75kZl+UBNz8/c3BqYojgWvRmZaRE0Ueznu2rULu3btyrmB/8ka0O3YsSN8fHywefNmREdHY9q0aZg2bRrKlSuH5s2bo1WrVujSpQvKqjtfqpGf1yFc4fd+lSpVctxXVYXsNGFhYVq1T135ylpc18kpqEyqGFCjQqPuThCi/IpKSMWE7f/iyvMYjWVszE2wqG8ddKntqrFMUdCuhgv6NXBXO27awiP+aG0rRbnNfwIbNgD//iuMz5Xpiy+EAEunTmovAJcsWRIymYy5kz8Enp7CIyu5HIiIALZuFe4EzZpj/OVLICZGc6COcufZMyGN3okT75dZWgrBqilTlIPiMLBj9OOPAS8v4PJl4OFDIaVs3776bpWKlHQZdtx8pbLc1FiCAY20+9GkJCJCCIACQhrW/1K/5Eb7Gi5qA2rnA6IMM6A2cKDweZRKhXPDwoWq4zIS5ZJBnc+ISC0epwbMzg5w0+I7Q6lS6pdps23WAI1Eot12gMp3WFhaarftfz1MlDg6CtsWcsBo48aNkP2X1nzw4MEajwNbW1v06tULf/31F96+fYvdu3fDx8cn27qtrKzQs2dPleUzZ87ElClTsHz5cuzatQvW1tbi2FDqODu/z7SgLo1hYVLcv2IvrILk4uKCs2fP4uDBg9i6dSuuXbuGyMhImJqawtPTE3369MGECRMQExMj9nZydHTMNkiZk6ZNm8LS0hLv3r1DcHAwkpOTcwygqrN06VLcuXMHHh4eWLBggbh82bJlAITeSxMmTBCXT5w4EYcOHcKpU6ewfPlyvQTUdHk9N6fPSFxcXJ7rTk9PV1m2adMmfPzxx1i2bBnu3LkDQEi/GBwcjO3bt0MikaBz585YunSpUhBLnfy8DgkJCeK0YipKTWwUzomK22ojMTFRnNbmM6pNewpSToFKQ8SAGhUaBtRI126+fIMvt93G67epGstUdbHFb5/VR8VSar6gF0Hfda+BS8+iERb//m4Xz5hXGHVtD1wXngVk/6USW74c+N//3m9YooQwPpYGRfEfGOmYRAK4ugqpVaZOVQ28zp0LbNoEdO0qBH10kNLkg/XDD0LPrlSFc1e3bkLvp/Ll1W5iUMdoZi+1Tp2E+QULgN69Da6X2uF74YhLVv1R1aW2K0rmJb3iqVNA5o+0UaOEC0S55OFkjXIlrBD8Rnnsy4tPoyGXy3NM/VHoSpUSjvn9+4VelLt2CUE2onwwqPMZEanF49SA5Sf9YdYUkNqytQVCNI9Rnq1u3fK+7eLFwqMQyeVyrF+/Xpz/3//+h/8p/q7Oxrp163IMqGkikUiwZMkS3Lx5E35+fti4cSM++eQT9OrVS215T4UbJAsjzWJ2FPdfmOknJRIJPvnkE3zyyScayyimsctvSk4jIyM4OjqKvfDi4uJyHVB78eIF5s2bBwD47bffxECGVCrFlStXAAB91dyo2K9fP5w6dQqXL1+GVCoVU0QWR4qBpOXLl2PixIn5rtPHxwc+Pj4IDg7GxYsXceXKFZw9exaPHj2CXC7HkSNHcPHiRVy6dAm1a9fO9/7UsVXohZuUlJRjecWgmK26HrzZUHwNk5OTsympfXsKUlH8zmFYVz6IiLQgl8uxzu8FBv55NdtgWq96btj7pVexCaYBgJ2FKRb1/QgAUC/UH3/s+R4n136BAfdPwjQzmGZmBrU5zoi0lfWifmCg0DsFAA4fBpo0EYIply8XftuKg6io98G0smWBvXuFCxwagmkGqUOH90HVe/fyfoGmAG25GqR2+ZCmHnmr8LPPgCdPhBS6Y8fmuV3eVVR/MEQlpOJxeO7uPCw0/6WbAQB8+61qKiciIiKiYuL8+fMI1DQWdQ4uXLiAp0+f5nnfRkZG+OWXX8QbrKZNm6aU2lFRuXLlxJvW9R1Qi4l5ny2ovIH9nlEMqHl7e+erroyMDKXX2sHBIdd1jBkzBsnJyRg4cCA6K9zwHB0djdT/fh+qS4mYmZIwJSVF6fUujhSf/4MHD3Rad7ly5TB48GCsWrUKDx8+xMOHD9GqVSsAQi+wmTNn6nR/ilxd32fMCggIyLG8YpkyZcrkal+KY7k9e/Ysx/LalCFlxTekTUTFUmKqFF/vvofD98M1ljEzNsJ33WvgsyblDO9u//ySy9Ei4BrOHZqD8g9vKa16a26N7fW7oN0fP8KzNnMgkw65uwOrVgnp3oKDhWXHjwuPjz8G5swBchhcmBTMny8E0QYMEHp6qUtxY+gkEuF979pVmP/zT0BN+hp9uR8Sjzuv4lSWVyttiwYeeRs7AQBQpQqwYkXetwfgXbkUtl4NVll+4WkUapTR7xgYarVrJxznp08L4y3+/rsQVCQiIiIqZtatWydO9+rVC3Xq1Mlxm+vXr4uBm/Xr12PhwoV53n/dunXRq1cv7NmzB4GBgVizZo04lpYiU1NTVKtWDY8ePcLLly+RkpICCwuLPO83Px4/fp/O/KOPPtJLG9SJjo7G7t27AQAmJiYYOnRovuq7cuWKOC6Wu7t7rnunbd26FSdOnECJEiWwIpvfE+p6Cyn2VsrrNS7FrGFyA74Bu1WrVpBIJJDL5Th06BDS0tLylaozOzVq1MCePXtQqlQpZGRk4OLFiwWyHwBKY/mdOHEix+wkmeM4Zt1WG3Xq1IG5uTlSU1Ph5+eX4/khc2xA0h57qBFRkRHwOgGfrPLLNpjm5mCJXWOaYUhTj+IXTAOAHTuAbt2UgmkRNiXwQ+vh8Bq7AQu9h2LiuXCkyzJyVa1MJhMfRCrMzYExY4CnT4E1a5R7Up0+DXh7A23aAGfPsnekOhlZjkc7O8DfH1i0SOtgmkEeo507Az16CKkq9+zRd2uUbNXUO62Z/v83NPN0gomRahsuBETpoTVakEiEz2qm+fOB+Hj9tYeKPIM8nxGREh6n9CGKj4/HP//8AwAwNjbG6tWrMXfu3Bwfy5cvF+vYtGlTvo+bb7/9VpxeuHChGMRRJJPJ0KRJEwDCuFG3b9/O1z7z4+rVq+J0ZpsMweTJk8Xg1GeffabUaye3MjIyMHv2bHG+W7duudo+JiYGkydPBgAsXrxYaQw8QEh5Z24upKR/+PChyvaZy8zNzfOcVlMxDaC+U/xlp2TJkuj6302bERERWLJkSYHur0SJErD7b5xGdWOw6Uq5cuXQqFEjAELqz52ZY3OrERsbi99//x2AEEDt06dPrvZlbm6OLl26ABDGOFy7dq3Gsg8ePMAJxXHd9aAoftdgQI0KjVQq1XcTqAjb928oeqy6hOdRmv/xt6pSCofGt8BHZR0Kr2GFrWdPoHRpAMA7z8qY3nkCvEevw5omvZFoLtwh9SD0LX49m7su20+fPsXjx4/zlaKCPgBmZsCIEUBAALB+PaCQux/nzgnjSmUNHn3obtwA6tYVUgUqyuX4WwZ5jEokwL59wLhxgJ7uiFUnPjkd+++Gqiy3MTdBz7p5+CEdGwvo8Eu+rYUp6qvpJXfzZSyS0wz0u1L9+sCgQcJ0TIwwlhpRHhnk+YyIlPA4pQ/Rtm3bxPGxOnTogNL//e7OSZUqVcQeJOHh4Thy5Ei+2lG/fn0xHWBoaCj++OMPlTJPnz5FzZo1xflr167la595JZfLcf36dQBA1apVCy3lo5+fn8aL8CkpKRg/fjy2/jdkgaurK5YuXaq27JUrV/Dnn3+qDVpmSkpKgo+PD86cOQNACFZ8/fXXuWrvlClTEB0djdatW2P48OEq601MTMTP0Jo1a5QCXomJiVizZg0AwMvLK8/jp1WoUEGc1mcAVhvff/+9GGCcNWsWVqxYkW2vuvj4eCxfvhynTp1SWj5v3jwcP34cGdlco9i2bRvi4uIAAPXq1ct/47PxzTffiNNjx47FjRs3VMokJiaif//+iIoSbrbs2bMnqlevnut9TZ06VbyRdMaMGfDz81Mp8/r1awwYMEDvAa2i+F2DKR+JyKClSmX44fBjbL6ivrcBIFzTnfRxFYxvWwlGau76L7LS0oCbNwEvr/fLLCyApUsBKytYdu8Ox2NPkHbhucqmq848w8fVXFDb3b4QG0wfDFNTYNgwYMgQYPt24PvvhSDbrFmAsbG+W2c4goKA7t2B16+BZs2ACxeAWrX03apib/ftEKSkq/5o6lPfDdbmefjqO2ECcPGiMJbYF18A/w0enh+tqpTC9RfK412kyTJw7fkbtKnmrGErPfv+e2E8xfnzhTH0iD5Ujx8Dw4cD1asLvbb5f4+IqFhQTPfo4+OTq219fHzEnlrr1q1D9+7d89WWWbNmiWkkf/rpJ4waNQqWWW7Ia9GiBSwsLJCSkoIzZ86IPaA0efHihdJzBIB79+6J0//++y9mzZqltL5+/fro3bu3xjrv3r0rBiN69eqV7f4Vs0S8ePEiX8G3MWPGICYmBl27dkX9+vVRqlQpJCUl4cGDB9i5cydevXoFAHBycsKhQ4fg6Kg+5fvr168xevRofPXVV2jfvj0aNGiAsmXLwtraGvHx8bh9+zb+/vtvcdwyiUSCtWvX5qrtp0+fxubNm2FhYaE2OJpp/PjxOH/+PEJDQ+Ht7Y0vv/wSEokEq1atQlhYGABg4sSJWu83q3bt2onT06dPR2RkJKpWrQpTU1MAQk+txpljZOvZRx99hLVr12Lo0KHIyMjApEmTsHr1avTq1QvVq1eHtbU1EhISEBgYiOvXr+P8+fNIS0vDli1blOo5e/Ys5s6dC2dnZ3Ts2BF169aFq6srJBIJwsPDcfToUaUgXEGOoQYIx4ivry82btyI2NhYeHl5YdCgQWjdujWsrKzw6NEjrF+/HiEhIQCEsdAye6rllpeXF8aPH4+VK1ciKSkJrVu3xuDBg9GmTRuYm5vjzp07WLt2Ld68eYPevXtjj4FlnDF0DKgRkcGSyjIwavMtnM8mDZaDlSlWDKyHVlVKFWLLCsGlS0Jvn+fPgQcPlHsCffqpODm5fRWc8Y/E08hEpc2lGXJ8tesODoxrAQtTXuihAmJiIgTVBg0SxgTr0UN5/f37wOefA999B3TrJkS/PxTx8cL4Yq9fC/O1agGVK+u3TYUhJARwc9Pbe52RIdeY7vGzph65rzAiQki1m54O/PST0BtPB1pWLonFx5+oLD8fEGW4AbUKFQCFlD5EH6SMDOH/3q1bwvHQvTuQwwVEIiIyfHfv3sWtW8KwCvb29uiZy7GBBw4ciMmTJyM1NRWHDx/G69ev4eLikuf2eHl5oU2bNjh79iwiIiLw66+/YurUqUplrK2t0b59exw8eBDHjx9HVFQUSpXSfF0kKCgIP/zwg8b19+7dUwqwAcDQoUOzDahljlEG5D4ImV8RERFYt26dSpAwk5eXF9auXatV757ExETs3bsXe/fu1VimdOnSWLt2rZiOUBspKSkYM2YMACFIWqVKFY1l+/Tpg5EjR2LNmjW4ffs2Pv/8c6X1Y8eORY+sv7dzoXbt2vjss8+wdetWvH79GtOmTVNa36pVK5w7dy7P9evaZ599BhcXFwwfPhwhISEICAjAzz//rLG8ubk5SpYsqbQsc9y4yMhIbNmyRSXglsna2hq//PKLmCaxIK1duxa2trZYtWoVpFIpNm/ejM2bN6uU++ijj7B//36V9KC5sWzZMiQlJWHdunWQyWRq9zVx4kT07NmTAbVcYspHIjJYPx7xzzaY9lFZBxye0LJ4BdPi4oSxqlq0AB49AlJSsr2Aa2FqjKX968JYTc+8gNeJWHYqoAAbS/QfY2Ogb1+h55qi778XUh5+8gnQsCHw77/6aV9hS08H+vUDMvPfV64sBBz/S1tRLL14IaQDrVAB0GMO9suBMXgRrZoauGnFEqjsYpv7Cv/8U3g/AWDkyFyn6tSkVhl7lLBWHVz7wlMDHUeNiAQbNgjBtExqLoAQEVHRoxiU6devHyxymc7c0dFR7JUmlUqxadOmfLdJsbfYokWL1I579el/N9ump6dj+/bt+d5nbmRkZIhpFdu0aZNt4Co5OVmcNjMzE8esyqvVq1fjm2++QYsWLVCuXDlYWlrC1tYWlStXho+PDw4cOAA/P78cg2nt2rXD/v378c033+Djjz9G1apV4eTkBBMTE9jZ2aFSpUro378/Nm3ahBcvXuQqmAYIKQefPXuGWrVqYfr06TmW/+OPP7BhwwY0adIEVlZWsLKyQpMmTbBx40asXr06V/tWZ9OmTVizZg3atm0LZ2dnsXeaoWrfvj0CAwOxceNG9OvXDxUqVICNjQ1MTEzg6OiIevXqwdfXF5s3b0ZERAQ6deqktP2hQ4dw/PhxzJgxA97e3ihTpgzMzMxgamqKUqVKoWXLlpg3bx4CAgIwbNiwQnlOxsbGWLlyJW7fvo2xY8eievXqsLW1hbm5Odzd3dGjRw9s3boVt2/fhodHHm4IVWBkZIS1a9fi6NGj+OSTT+Ds7AwzMzO4u7ujV69eOHbsmNIYkKQ9iTy7JKREOuDu7o7Q0FC4uLggIiJC382hImLP7RBM2XlX4/ohTT0wq1t1mJsUk95Xcjnwzz/A+PFCj4hMDRoI6YRyyOW87GQAVpxWzTsskQC7xzRDA4/sB6719/eHVCqFiYkJqlWrlqenQKQkJQVo3hxQzM9uZgYsWwaMHVt8e6vJ5ULv0syBf52chF4MlSrlq1qDP0Z37QL69xemmzUTetnq4T0eveUmjj98rbL810H10bWOa+4qS0sDPDyEc7KRkRA0LFdORy0FJmz/Fwfuhqks9/u6DdwdrXS2nwIjlwNXriinJSbSgsGfzzRJTQXKl1f+nmZqCoSHC+d6omKkyB6nRB8IxWN05MiR8PPzQ7169Qp1bKwjR46IAaYjR46I476pc+zYMXH9hAkTsGLFikJpIxEZPn9//yL3XYM91IjI4NwLicOMPffVrrM0NcaKgXWxoGet4hNMCw4WevD06/f+Io21NbB8OXDtWo7BNAAY17YSapZRvctLLge+2nkXyWlSHTeaKAcWFsIYgAcOAHXqCMvS0oRxqPr3F1IiFkeLFr0PppmZAfv35zuYpiguRYarz2Ow5cpLzN7/AAP/vIKPl5yDz/rruB+ix9e0Tx+gRg1h+soV4PTpQm9CePw7nHykGkwrZWuODjXzkHJn//735+SePXUaTAMAbw29qy8EROt0PwXixg3A21sImp89q+/WEBUOc3PgyBEhi0Bmz4X0dCEtLBERkZ58//33AIQx0A4fPlxo+81Mv+fl5ZVtMA0ATp48CQCwtbVVGaeNiKioYUCNiAxKdGIqRm+5hTRphso6cxMj/D2qKXrUddNDywqATAasWCFchD506P3ybt2EdI8TJ2o90L2psRGW9q8LM2PV0/rLmGT8fNRfV60m0p5EIowvc/268HnOtHs3UL++EHArTnbtAmbMeD+/caMQcMiD6MRUXAmMweYrL/HdvgeYdjQUg3e9wsC/X2Lgn1fx3f6H2HwlCFefv0FgVBIuBEThs3XXEJWQqpvnkltGRsJYeZnmzRMi+oVo+7VgZKjZ5aeNysJUzbkxR4qDho8dm/eGaeBduaTa5ReLQtrHx48BPz9hevp0YVwpog9BvXrAhQvKNw1oGI+DiIioMLRq1QrdunUDAMyePRuFkYjswoULuHDhAiQSCRYtWpRj+VOnTgEApkyZku04b0RERQEDakRkMNJlGfjir9sIj09Ru/7nPnXwUVmHwm1UQUpNFQJqmbnQS5cWLsgfOJCnnhBVS9tiSgf1g9xuuhKES8+KQK8HKp7MzYUel3v3Ag4OwrLnz4VUcWvW6LNluvPsGTBkyPv5778H/hvTQBO5XI6ohFRcDozGpssvMWvfffT/4wrqLziJht+fwqdrrmL2/ofYcjUI91+nID41+6BF/Lt0bL0apItnkzf9+gGZqRr8/IBCHNQ6XZaB7TdeqSw3NpLg0yZ56Fn27Nn7C+aenkDbtvlsoSpnOwtUK606rpvfs2hIZQYeoBo8GPjoI2H65k1g5079toeoMEkkwv+vzN7XV68CARyzloiI9GfFihWwsLDA7du3sXfv3gLf37Rp0wAAPj4+aJ7DDYSRkZG4f/8+SpUqha+++qrA20ZEVNBM9N0AIqJM3x96hOsv3qhdN6JFBfSsV0x6pmWysgJ+/x3o1AkYPRpYuPB9sCGPRrasiBMPI3A7OE5l3fTd93B0UkvYWRj2wLNUjPXsCdStCwwYIPRaS08HnJ313Srd8PQE5s8Hvv4a8PUFZs5UWp2RIceNl2/wOPwtnkYm4unrRDyNTEBscrpOm7H/TigmtasMiT7GqDM2BmbNAj77TJifPx9o06ZQdn3i4Wu1vfPaVXeGq71l7iv888/306NGCT3wCkCrKqXgH5GgtCwhRYq7IXE5jn2pV8bGwM8/C/+/AOHz3quXEDwnKm4CA4UbnUyzfH/y8RF6JXfpIqQ0JiIi0pOKFSvi3bt3hba/a9euaV3W2dkZGcxmQETFCHuoEZFB2HnzFTZdUd+zonklJ8zoXLQGqFTr2DGh14OiDh0Af3/gt9/yHUwDhN4YS/rXhYWp6uk9NO4dvj/0KN/7IMqX8uWBixeBKVOAyZOBHj303SLdkEiE1HenTgmpAhUCWq/eJOPjpecx4M+rmHvwEf66FozrL9/oPJgGCCle7+pzLLWBA4Eq//WUPXdOSI1WCLZcfal2+ZCm5XNfWWoqsGGDMG1qKgRIC0jLyupT3pwvCuOodegAtGsnTL94IdwgQrkS+TYFX+++h96rL2Hhkcd4lybTd5Moq3fvhM95vXqqY0OOGAGEhQnjLdaqpZ/2ERERERFRoWJAjQqNsZZjQdGH59/gWMza+0DtOndHS/zyaX2Y5GX8G0Px+jUwaBDQubPQEy1rTvMq6tM05lWFktb4pnN1tet23gzB6cevVZZ7eHigYsWK8PDw0GlbiNQyMwOWLBEeiuRy4MiRQh97S6c+/lh4fv+Ry+WYtOMOXkQnFVoT9t8JLbR9qTA2Br799v38/PkFvsunrxNw9blq7+aKJa3h5emU+wqTkoDevQEbG+FvAfaibFjeUe0NEBcCisA4ahIJoDhmxoIFQFyc3ppT1LxJSkOv1Zex4+Yr3A6Owx8XnmP4xhuQqRsIsJgpUt85liwBXr4EHj4EfvxR+f+TvT3AcWComCpSxynRB4jHKBEVF0XxPFaEr1BTUaOX9FNk8CITUjBm6y2kqRkvxsLUCH8OaYgS1mZqtiwC5HJg3TqgenVg+3Zh2ZkzQsCggA1p6qHxQvKMPfcRm6ScmsjS0hJWVlawtMxDajSivMr6f2H9eqBrVyE15Bv16V8NhlwOjBsHHD+ebbGrz9/gVlBsvndXxt4C3lVKYUSLCvi5T23s+cILN75tBxtz1ezdB++G63cMrkGDhBSYgNCj48qVAt2dpnHjBjf1gJFRHr57lCgh9DIMCwP+9798ti57FqbGaFpR9Vx9LyQOcclFIIVcvXrvU3zGxAhpIClHcrkc03ffQ2iccmqmK89jsN7vRf4qf/dO6PluwIrMd45Xr4R03IBws8CKFar/t4iKqSJznBJ9oHiMElFxURTPYwyokf506ABMmgScPQtIpfpuDelBmjQDX2y9jddvVce9AYBFfT9CjTJ2hdwqHfH3B1q3FtIBxf53Mb1ECSGNWJcuBb57IyMJFvWto/Zie1RCKmYfeFjgbSDKlchIYMIEYfrAAeFCfQEHYvLl+++BX38VAoBr12os9seFwFxVW8beAq3+C5wt6lMHe7/wwv25HXD5m4+xeXhjzOpWAwMalUP9co4oZWuOTrVKq9QRnZiKy4ExuX5KOmNiIvRSq1hRuKmgYcMC21VSqhT/3FbtkWdhaoS+9d3zV7mtLeCezzq04K0m7WOGHPB7VgTSPgJCz7TMXpnLlwtBCMrWX9eCcUpNb3EA+N+JJwiMSsy+ArkciIgQ0qr+/ruQPrdzZ6BCBcDaGqhdWxijkvJn+nQgOVmY/vLL7NM6pqUJqR9TUgqnbUREREREpBeqV1qJCoO/P3DypPBYsUIINHTrJoyl07GjcDGAir15Bx/ipoaeG6O9K+KTj8oUcot0IDlZSIG1cKHyAPWffQYsXVqoqYHcHa0wu1sNTP/nnsq6g3fD0LGmC7rVKYKvMRVPzs7Arl2Aj4/Q0yU4GPD2FlJsffUVYGRA9wD99Rcwe7YwLZMBjo5qiz0Of4tzT9Sn7nNzsEQlZxtUcbFBZRdbVHa2QSVnG9hamOaqKT3qlsHuWyEqy/fdCYV3FT2mIhsyRHiYFOxXzVOPXyMxVfWmnB4fucHeKnevpb5oep8uBkQXjXN0+fLA+PFCaryUFCGoMG6cvltV8GJjgYAAYfxTR0fhr1nOPeqfRSbg+8OaxzNNlWZg+u572Dm6GYyz9rD09xfG9PP3B+KzGStRKgWePweqVtXqqZAaFy4Af/8tTJcsCcydq7ns1q3CTYIxMcDOnUC/foXRQiIiIiIi0gMG1KjQyBXHHPj3X+EiW2bPtDdvgM2bhYeFBdC+vRBc6969QMcuIf3Zfj0Yf10LVruuZeWSmN6pWiG3SAeOHQNGjgRCFC5uV6wo3D3evr1emtSvoTuOPYzAGf9IlXXf7XuAxhVKwNnWAm/fvoVcLodEIoGdXRHtFUhFX5cuwJ07wMCBwKVLwv+I6dOB8+eBjRuFi5r6dvEiMHz4+/lFi4A+fdQWXXPhudrlU9pXwYSPK+dqt5qOUS/Pkihla46oBOWevscfRCCllwwWpnoav7SAA2mZ7oWoDyp81jQPedhTU4W0o59+KgRHColnKWu4OViqpP+78DRKfM8N3syZwOPHQs9ELy99t0Z3QkOFAPrevcCpU8o3fF26JHxPVWRlJQTXMgNsjo7ve6dLJEiVyjB++x24h7+E47u3eGthg3hzG0iNjeERGw7PmBB4vgmBZ8wrJP4YBfvZM4XvFZns7YFr1zS3184OqFZNCKQZ0k0IWRj8dw6Z7H2PaUC4sUPDjRMAAFdXIZgGCL9lGFCjYsDgj1OiDxyPUSIqLt6+fVvkzmMMqFGhkclk72c+/VRITXP0qHAn85EjQEKCsC4lBTh4UHhYWAgpbezt9dNoKhC3gmIxe/8DtevKlbDCL5/WU70ruyiwsXkfTDM2BqZOFXqxWFnprUkSiQQ/9a6N9ssuIP6dcvqn2OR0zNzzAGt8GiAsLAxSqRQmJiZF7h8ZFTPu7kIas9mz349dc/iwkAJy+3agRQv9te3pU2F8t8zep6NGCce5GqFx73DgbpjKcktTY/g0y32wR9MxamwkQfc6ZbD+kvK4S0lpMpx6/NqwejjJZMK5UYceh79VWeZkbYZabnk4j+3dC3zxhdAjcvly4f0tBBKJBN5VSmL7deVUieHxKXgWmYjKLraF0o58KVFCOE6Lg4QEYM8eYMsWYdzTzBvC9u8XxgfMFKumh31ysvAIVUhDam8v3BAAYPGxJ3gc/hY/X9+LAfdP5tiU+Jt3YK8QT0Pp0kKgzsHhfeCsWrX306VLq47xFRUlBHkmTABMDaPXpsF/51izBrh7V5iuX1/5Jgp1WrcW/neFhAg3V0VG8oZAKvIM/jgl+sDxGCWi4iIsLKzInccM99ZFKv4cHITA2t9/Cz/2jx4FRo8W7vLM1LWrajAtcywDKpJev03B2K23kC6Tq6yzMjPGnz4N4GCVc8okg5CRoTzfooVwV3LXrsCDB8BPP+k1mJbJ2c4CC3qqH/fj1OPXascfItIrExOhR8CxY+97pYWECBct//lHP22KjhZ60L15I8x36ACsWqV68fo/6/1eQJqhep4b2Liszs9xPeupD5rt+1c1oKcXjx4JgYi+fXVarVwuVxtQq+5ql7deXX/8Ifx99w6oUiWfrcudlmrGUQOA8wHqU4aSjkmlwPHjQnrm0qWFtIqnT78PpgHA7dvK21SpIgSphgwR0pY3bw7UqAGUKQMoDqz9X8+mCwFRWOsnBL7tU3MYIw1Asqk5rvhHQKZ4HpFIgNevgRcvhO/Ny5cDY8YI50ZXV9Xz0S+/AGXLCoH/ffu0fjk+aG/eCD0tM61cmfONAMbGwODBwrRU+j5VJBERERERFTvsoUaGwdwc6NRJeKxeDdy4Idwd3LWrcrnUVMDTE2jSRLhzvGNHnd/tTgUnVSrDmK23EJklNVmm//X7CNVKF4G7EhIThWDZhQtCTxrFtEpbtgifZwPTvY4rjj+IwOH74Srr5h14iNWfuMHR8JpNH7qOHYUUkIMGCcebq6tw4biwpaQIPdOePRPma9cWxnvT0NsjPjkd26+rprQ1NpLg8xYVdN682m72qFDSGi+ik5SWnw+IRFxymn5vUpDJhEBkUJAw/++/Qm9DHYhMSEVscrrK8mql89Cj68kT4XwOCIGSVq3y17hcau5ZEkYSIGsM9sLTaIxoWbFQ26ITGRnAoUNCoMmAUw8CEMZ++9//hIwIWXl6CgGzzz4TphU1aSI8NElNFXqxvXuHmMRUfLXrrrjqWBUvBNuXhn1KItwkqajhYIpDCRYIdHJHYAl3BDq5I8LWCXKJEWZffonhiucNLcZpE1WvLrQDEAJDTEWYs/Pngbf/BeoHDxYCpdoYMgT4+WdhessW5ZSRRERERERUbBj4L1z6IBkZCRcofv4Z8PZWXrdvn3DBY/9+IdhWsSKwYIFyah0ySHK5HHP2P8S/wXFq13/R2hNdaruqXWcwZDJhfJ3KlYEffhDGUtq6VbmMAQbTACGl2IKetVDSRvVCXEKqFEv9IpXHOSQyFG5uQk+R774T7vp3cir8NowaJYyXBAi9Vw4dEsYq0mDrtSAkp8lUlnev4wp3R933WpVIJOhRV7WXWrpMjiP31QQJCpOxsTAOXqYFC3RW9SM1vdMAoYdarq1Z83561CiNPQ8Lir2VKeqWdVBZfu15DFLSVT9LBu3KFaBhQ2Es3B079N2anCUmKgfTHB2FXl+XLwtpXufMUQ2macPcHChdGvLy5TF99z2lcQ731WyDH9t+jtmfTIbziUMocf4UAuf8hE0NusOvQj2E25WCXCL8TFt03B8vswTLtfbxx0JQDQD8/ISANmWvVy/g3j3h85sZINNGzZpCekgAuHlTGFOQiIiIiIiKHQbUqGhJSxMurmYKDhbG2SlXTvjhe/iwEPQgg/PXtWD8feOV2nWtq5bCVx2qFnKLcuncOeEC4eefv7/wZmoKhKv2+DJUJazNsLB3HbXr/g1/h6NPc05BRaQXJibA/PmqPQVevxZSc509K4xZU1AGDACsrYUUrocOCf9zNEhJl2FDlvHMMo3yzsNFeS31rOumdvm+OwZww8nw4UIaPEAYp+zePZ1Uqy7dI5CHgFpKijjGFczMgKFD89ewPPKuopr2MVWagesv3uihNfmQmPg+cDNz5vseUvqUmCiMI9a+vRAk+z979x0eRdX9Afy7Nb33BgmphN47KF1BQOyAKCpiA9vPXl7bq+JrwQZWRBARRJSqVOm9t4QESCAJ6b1n2++PS0iZu8km2Z3Z3ZzP8+Rh9s6WG5KZ3cy555z6ZsxgP/fbb2fVETIzgUWLgEGDzBJY/eXgFWxP5J+fXpvQGTHXe+S9dEscOngLA+5VGj1eXH0aek4J2WbJZA0zpb78suXP0R517swW8YXwz6tG3X9/3fayZWadEiGEEEIIIcQ6UECN2Jb77wdSU4F16xqWEdLr68YiIlhZG2I1jqQW4K1157j7wn2c8fm9vaCQi5sNYLJLl4CpU4Gbb2al52rdfjvrC/TSS5JNrTXGxAfgjt6h3H0/HivEtRJh+TRCrJJOxy6Ev/8+MHIkEBAA+PuzY3XuXOCbb1hGRmFh219rwgSWkfr770CfPk3edc3xDOSV1QjGh8f4IT7YciVtw31d0IOT4XQ4pQAZRZUWe12TODo2PFeaKUstMbNUMKaUyxDp79KyJ1qzBsjPZ9t33lnXt09kvIAaAOxJtrE+amPGsB6DAPvMtnChNPPQ6YAtW9h5IiCABUq3bRNmlkdGsl6+a9aw93YzZponZZfivY38TKVRcf64f2DHG7ed1UrMv4O/6OVwagGWHkht3SRmzKjrR/zrr+x7JZZx3311peh/+UXYa5cQQgghhBBi8yigRmyPUgncdhuwfj27UPOf/wCh9QIEaWk2lTVk7zKLK/H4L8eh5aysdlEr8N3MvvBw4vchklRxMfDCC2yV8p9/1o336sWyYdasAaKipJtfG7x5WzyCPBwF49U6AxYfy5dgRoS0wr59rBRkfbm5LJv0q6+Axx8Hhg0DvL1Zf6T6dDqgVBiMaVKvXqwXWBN0egO+33OZu++x4ZbvgzW5h7DsIwCsO3nN4q/drNmzWblMAFi9Gjh7ts1PyctQi/J3hYOyhb1Vv/uubvvRR9s4q9brEerJfT/cnZQnwWzaaP78uuyu994DiorEe+1r19j7d1gY68O4fDlQUVG3v7Z8a31NlHBtrSqNDvNWnEC1VhhU8XV1wPw7u0PWKANuUKQPZg7qKLg/AMz/5wKu5Lei9KOrK8uuB1i2YP3ypoSpqAA+/7zt2ZQBAex3DmB/j+za1fa5EUIIIYQQQqwKBdSIbQsLA956C0hJYQG2225jAbdHHml4v4wMFni7elWSabZXVRodHlt2DHll/AsUn9zd80apI6tz6RLwySeA5nrGVmAg65925Ahw002STq2tPJxURlfB771SbrSMGiFWZfhw1qPm229ZSbPaLDWeTo2CWefOsQvoHTuy7LMXXwR+/hk4doxdWE1IYEG5FvYV3Ho+CymcXkfdQjwwKNLyvd8m9ggCL9l3rTWUfXRyathL7e232/R0VRodLnP+r1tc7jExse6id1ycsHeriBRyGYZGCbPjLmSXIqu4SoIZtUHPniwzCgAKCoAPP7T8a+r1LDO1c2cWRK+/uMrLC5gzh2Wtbt1q+bkA+OifC0jM4gfuP7m7B3xd+ZlwL42PQ6iXk2C8UqNrfenHJ5+sC3AuXFj32YYw8+cDzzzD+qDt39+255o5k5W4ffFFIDzcHLMjhBBCCCGEWBEKqBH7oFSyco/r1rELKI2bx//0E+u/ExHB7rd+PfVaszCDwYDX/zqLU+nF3P3zRkZhfNdAkWfVAr17s/JQjo6sR1NSEjBrVl0pHxs3PMYP0wbw+0At2JYk8mwIaaXYWJZR9PnnLFstK6suS+3rr1mW2vDhQPdGAeRz10vQXr0KbNoE/O9/wIMPsj6Jrq6srOPcuexLqzVpKgaDAYt28bPT5ozoJMhEsQR/N0cM4QRkErNKkZhlBYHyOXNYWU6AZan9+2+rnyo5uww6TmChc1ALF2nUz9Z59FGz9Mxqi2HR/HKTu22t7CPASnvWlk9csMDyi5oWLWLHfMn133WVCpgyBfjjD/bZ8JtvWB9GEX7GOy/kYLGRXooPDYnACCPlPQHAxUGJj4wsejmUUoBfDl1p+YQ6dWKLzgC2yKx+5n17l5oKfPQR275yhWU1t8Udd7Df9fnz2d8dhBBCCCGEELtCATVifxr3PjEYgCVL2LZeD2zcCEyaxC7EfvFFy8t+EZMsPXAFq4+lc/eNivPHM6NjRJ5RExIS2OrtxhfOP/yQZS+89x7gZqWZdG3w7OgYOKmEAcLN57Jx7ho/EEqI1fP1BUaMAJ54gmVi7NolLM/q5AQMGsQv82YwAJXXe47t2wdUmZYZdDilAKfSigTjHbydMb5L2xcPyOXyG19NmdIzhDu+1hrKPjo7A//9b93tuXNbvbglwUiAMC6whRlqL7/MAqrdurHMEokZ66O2O8kGA2odO7LsUYCV0nvzTcu+3qxZdQGMWbPqAkdTp5q1L1pz8sqq8X+/n+buiwt0w4vjY5t9jsFRvpgxkL/o5cO/E3E1v4K7r0m1PwsA+PHHlj/ejEw9n4ni//6v7jz/9NMsU7UtlEq7WXxF2jerOk4JIQJ0jBJC7IUtnsdkBkML6xkR0kKhoaHIyMhASEgI0tP5ARaLS09nWWo//CBcIe3uznpLzJ1LK0nN5ODlfMz44RC3b1onPxf89eQQuDtaQd+0vDyWubhwIbuou3AhW93ejnywKQHf7hZm1YyJD8D3M/tKMCNCRGQwsIvuZ8+yrLXafxMSWBBuwwYghB+gauyhJUewIzFHMP7u5C64f1C4mSduXGmVBn3f2ybo2xTi6YQ9L94MOa8mpJj0emDgQNZT68sv6/oNtdDb68/hp32pgvEjr42Gn5t4wRNLGP3pLlzMKWsw5umswrHXx0Ah9c+vpQoLWdWAwkKWGXbiBNCjh3meOztbWOZ19262OGbkSPO8RgsZDAY8tOQI/r0gDIA6KOXYMHcook0sdV1WrcW4z3Yjo6hSsG9gJ2/8+sjAlh3PBgMLLo4ezYLHdrhQqMW2b2f/HwD7XUpKskg/PUIIIYQQQoj9sL0QICGtERoKvPEGcPkyu0A6ZkzdvpIS4LPP6i6ekjbJKKrEk8uPc4Nprg5KfHd/X+mDacePsyBqWBi7oFubIfH99y3umWTrHh3eCc5q4UrqreezccZIuU5C7IZMxt4fxo8Hnn+eLbw4fJhlLp84YXIw7UJWKTeY5u2ixp19wsw96ya5OaowOl7YSy6jqBJHrxSKOhcuuZyV4DtzptXBNADcXo++rg42H0wDgOHRwiy1ogoNzmTY4DnZy4uVTa61Y0fbn7O4mGWgduoEXLzYcN/w4ZIF0wCWnc8LpgHA6xPjTQ6mAewz00d38ks/HrxcgOWHW1hCUyZjGXtPPknBNID1kXv66brbH35o/mBaejor/Vi/nx8hhBBCCCHEplFAjbQvCgUwYQKwZQu7mPfII3VlgFxdWZkw0mpVGh0eW3YM+eU13P2f3dMTUf6uIs/qupoa4NdfgcGDWX+kxYvrSvw4OwNvvw3s3St5/xyx+bg64IHB4dx91EuNENN8x8nyBIAHBoXDiROwtjTjZR8zRJ6JEWFhbSrBZzAYkJglLNfc4v5pVmp4DL+P2h5bLPsIsADOAw+wIPWzz7btuf78E4iPZ/3SKiqAxx6zmoUwF7JK8d9NCdx9ozsHYIaRvqVNGRLla7Tf6QebEpBW0IrSj4RZtKiun2b//uYv+bp4MdChAysru2KFeZ9bLHo9kJ/PMvf272c9qH/6Cfj4Y+CVV4DZs1mWHyGEEEIIIe2IUuoJECKZrl1ZRtL77wPffceCbY1X7L72Guu1M2cO4Ge8gTxhFzhfXXPG6Ar6Z0fHYAwna0IUe/YAd93FykPV5+EBPPgg8MILJmei2KNHh3XC0v2pKK9p2Mtoe2IOTqUVoUeYpzQTI8QGZBZXcgNVTioFZg7qKMGMgBExfvB0VqGoQtNgfOOZTPznti5QK61sPZXBwC7aNu6BakRWSZXgewOAzkEtyC556y1WdvLRR1mAxooMiPCBWilHTaOynbuTczF3VLREs2oDR8e6Xratde0a8NRTLKBWy8UFmDiR/f5IvBimSqPDvBUnBD8zAPBzc8D8O7pB1so5vnJLHHZdyBWUfqyo0eGlP05j+SMDWv3c0GpZz6/2Jje3YU+/L75g2bPmNHRoXbB36VLguefM+/wtpdUCBQWs3Hl+fsN/AwJY38H6BgwAjhxpPmAdHQ2MGmW5eRNCCCGEEGJlrOyKCrFner3wIoNV8PNjgbOXX244npcHfPopKxXZoQNbhXn2rDRztAE/7UvFmhP87Iex8QGYOzJK5BnVExfHLpzW6tYN+OYbVopnwYJ2HUwDAC8XNe7swQ8Yf0ZZaoQ0afHeFG6J23v6hcHLRW2218nKykJGRgaysrKava9aKcet3YIE40UVGuy2tiync+fYxdhRo9gFXxPwyj0CLchQq6wEPv+cffXpw0o/WxEntQIDIrwF48evFqGkShhItGt6PXu/7ty5YTDt1lvZ784zz5g/ENIKH/6diAvZwqxJAPj07h7wcW19Rqabowof3tGNu2//pXz82tLSjwBw+jT7XBsXx0ofiqwl5zOLeO01VjoUYAurBgww/2vExNQ976lT7P9cKl9/zSpxBAQAXbqw0qhTp7LfgVdeYT2mG5PLTcv+zM83/3yJVZD8OCWENImOUUKIvbDF85j0f4GSdsNqA2rG7NzJygQCrDTgDz+wQMyYMcDGjewiDwEAnL9Wgg/+5pc5ivJ3xSd394BcLsLq8bIyVsJnwYKG435+wIwZLEtt1y52YWPOHHZxgQAAJkY5wkkl/BntvJCL41etoO8SIVaouFKDXw8JL2Yr5DI8PDTCrK9VVFSEwsJCFNVfHNCEyT2CueN/WUvZR4BdrH34YeDff9nF5kWLTHpYQiY/cGFyhtrq1XWLLO66y/x9k8xgWLQwW0+nN2D/RTu4eK3Vsoy12rLLxpw/zy78P/54XdDT35+Vz9uwAegoTQZoY/8m5mDJ/lTuvkeGRmAYpydeSw2L9sN9/fn9GN/fmID0whaWfvzPf9jn2kuXWE9DkbX0fGZWBgMQGAio1awyxQcfWO617r+/bnvZMsu9TlOOH2eB5+pq4/fJyxOOdesG9OvHeozOmMH6zb37LrBwIbByJSv1ePIk8OKLDR+Xmkp/I9kJSY9TQkiz6BglhNgLWzyPtcMaH4SY6M47WbP7r75iFx1qL+Zs28a+YmLYH5cPPMDKDrVT1Vodnlt1EhqdcBWrm6MS393fB26OKstOIjGR/YH/88/s5+TpyVbd1v+5fP+95CWhrJmbgwKT49zx2xlhyc4F25Kx9KH+EsyKEOu2/NAVQalUAJjQLQhh3s4SzKhOv3BvBHs44lpxw6DFtoRslFVr4epgBR8BZTLgs89Yb0uAZYTffTfLomgCL0NNpZChk6+JiyS+/bZue84cU2crquExfnh/U6JgfHdyLsZ3DZRgRmaybx8Lol64wMrPGSuDp9ezz2EJ9RbrPPQQ8L//Ad7C7D2p5JZW44XVp7j74oPc8cL4WLO91qu3dsauC7mCY7q8RoeX/ziDZQ/3N73047x5wF9/se0vvwTuvdds87R6MhnwzjssM+3sWRZcs5R77mF9AzUa1sf3ww9ZiXmxVFWx3nC12b+DBrG/X3x9AR+fun+DhBnN+O67lr9ecjIwZAgwejQLmqvNl6VNCCGEEEKItaAMNUKaEhEBfPIJKw34+edAp051+5KSgCefBG66SbLpWYMF25KRmCXMFpDJgM/v7YlOfhbKAtPpgLVrWcZg587sglBt0LOoCNiyRTgh0qQpcW5wUQnfFnYn5eLYlQIJZkSI9arS6PDTvlTuvkeHd+KOi0kul2FST2E52yqNHlvOWVFJhUGD2IVtgJVge+WVZh/CC6hF+buZ1hvu3DkW1AFY6bPaYJ6ViQ1wQ4C7sEzg7qRcGEwpw2atXF3Z5ycAeO89oNBIBrRczj53AUBUFLBjB/Djj1YVTDMYDHhh9SnkldUI9jmq5Pjivp5wUJoveOLmqMIHd3Tn7tt7MQ+/HUkz/cluuon1EgaA/fuBo0fbPkFb06kTMGmSZV/D15eVJwVYH8AdOyz7eo3V1LBMMwDo2ZNV31iyBPj4Y3aunT2blX4cNMg8rzVhAutPt2IF+76trJwuIYQQQggh5kABNUJM4ebGVvMmJbEVvfWDaPXLuQDA3r3AxImsjNTMmWz1+7PPAq++ykqlfPwxy6bilX5JS2OrsVNTgZwcoLTU5J4yUjh2pQDf7rrE3ffYiEiMjGs6y6BVcnPZCt9OnYApU1i2YC0nJ3Zx4ORJ4Pbbzf/ads7VQYHbu3hw9322NVnk2RBi3f46kYHcUmEJrWHRvugawj+OxDall7Gyj9dEnkkz5s8HPK7/n/30E3DggNG7Vml0SMkrF4yb3D+tftbFo49a7WILmUzGLRWYXljJ/f5tRo8edZ+bCgvZ+znAFsKkpze875gxwKpVrBzozTeLOk1TLNmfip0X+D0J35gYjyh/E38nW2BEjB/u6csv/fjfjQnIKKo07YlkMva5ttaXX5phdoRr5sy67aVLxX1td3cW3Fq5kr22JTPG1Gr2N46jI7u9fTswYgRggz0xCCGEEEIIaYoV1PshxIYoFMDkyezr5EkWGJs1q+F9rlxhPdaa4+kpDMa9+SZbOdqYry8LINV+DRwI3HZbK78J86io0eL5Vaeg5yyUjwt0wzOjo83/ogYDa/CektJwPDKSZQs++CDg5WX+121Hbo/3wLrEUpRUNQzk7r2Yh8MpBegfYT3ZAYRIRa834Lvdl7n75gyPFHk2xsUFuiM2wA0XshtmEe9NzkVuaTX83IQZUJLw92cLTmov8D/1FHD4MLc02oWsUu77Trwp/dMqK+suaDs6Ct+DrczwGD+sPpYuGN+TnGe57G8xvPsuu8BfXQ3tggU4q/RCj5++gCwujl2Erx/kvOsu6ebZhITMEnzwt7AkJwCMiQ/AtP4dLPbar03sjN3JuchsVPqxrFqLl/84jaUPmVj6cfp04KWXWGDzt9+Ajz5qttyqTXvnHbZI7eWXAWcRS/JOmMA+mxYWAmvWsF6RYvfwvftucV5n0iR2DN92GyvpevIky37bvJmVmiSEEEIIIcQOUIYaIa3Vsydb6e7WaAVypYmrg3l/zFcYaSqfl8cuLv72G/D++8Dy5cL7PPgg8MQTbHXomjXsj1gLllr58O9EpOYL56tSyPDZPSaWOdJo2Ir0I0eAdetYX5u33gIee4wFLfv3B65erbu/TMZ61tVuT5gA/P03yxx89lkKppmBi1qB2cP45eo+25ok8mwIsU5bE7JxmZMl1CXYHUOifCSYkXGTOVlqegOw8bSVZak9/jjQ/Xo5u+PHWd9LjsQs/vtaXKAJAbVVq1gmFMB6G1n5e8bQKF9uAt3uJH5WlM3o0AGp0x8BAChratDz/Vcgy8wE/v2Xv6jIylRpdJi34gRqtHrBPn83B8y/o7vpvcxawd1RhfenduPu25Och1VHTSz96OzMsvoBVq6vNT2zbMXFi8B//8uCuT16sO9XLA4OdQGtigr2Gd3SSoWl2EUzeDArq9vhelA5NZX1VTt0SLo5EUIIIYQQYkaUoUaIuc2cyUoRVlSw4FrtV+PbSs7hd/PN7AJH/fuXl7OgU3o6y9ACGvZyA9iK2+XL+eUhG2e3PfggEF0ve6yqir2OTMZ6lsjlxrflLAa/JzkXSw9c4X77z46JQWc3Obt4kZnJvmJiWACyVm4uEB/PAoXNSUur+6McYCW6ysrYxdfG/w/ELB4cEo4f96WgqELTYPzA5XwcvJyPgZ2sK2BAiJgMBgO+MVLqds6ISIteSG+NST2C8dE/FwTjf528hgeHREgwIyOUSuCrr4Dhw9ntV18F7ryTvYfVk5DJv1BsUsnHb7+t254zp7UzFY23ixrdQjxwOr24wfiBy/mo0epN6xlnhTQ6PR4KGo0/HJfCq6ru53ltyEgEjxol4cxM8/6mBCTnlHH3fXp3T3i7WLCs3nU3x/rjrj6h+J2TwfjehgQMi/ZDsKdT809UuxBLr2eZUy+9ZNmygFJ5/vm6INqdd4r/Pc6cCSQmsqzYyZMt+1pbtgDTprEqGmJlpjUWF8dK995yCyvZmpcHjBzJFjVMmCDNnAghhBBCCDETCqgRYm5qteACoMkee4x98VRXs1Wely8DHTs23JeWZrzXWl5eXYYbAIwb1zCgtnSpaRcWw8KAq1dRXKnBC7+fBgB8uXY+xiYfgEEmhwEs8Ob4BYRZei++2DCg5uUF5Oc3/5oymfB+QUHA//7X/GNJq7k5qjB7WCf8b7PwIvynW5Ow8tGBVhc0IEQsR68U4sTVIsF4qJcTbu0aKP6EmhHq5Yz+4d44nFrQYPxkWhFS88oR7usi0cw4hg0DZswAfvmFlUf76y/gkUca3OV8pjBDzd/NAT6uzZSvLChgJZkBoGtXVjrZBgyP9hME1CpqdDh6pQCDI1v5WUNiOxJzcFmrwvs3z8L//v4Cuc6eeHv0o6iZeie+62C5UonmsD0h2+iCokeHd8LQaPF+Jq9PjMee5DxklTQs/VharcXLa87g51n9mn+v7tiRLQJbs4YtgPrjD+C++yw3aVPV1LDPp66u/C83N8DFhb84rbF//mFVEAAgOBh47TXLzp1n8GBg507Lv05hIfDQQ+yz8z33sN6U48ZZ/nV5goOB3bvZ79fOnWyh4JQpLLAYaT2lkQkhhBBCCGkpCqgRYiscHIDYWPbVWHg4kJPDgm2XLrF/6381ld1m4DSj4bl+UebtdeduXLxR6bVw0BkJ5NXXuCG5Ugn07s3+DQpiX4GBddu1X/7+pl0sIWb3wOBw/LDnMgobZakdTinAgUv5GBxlmxdyCWmrb41kp80e1glKhXVmDE3qGSwIqAHA2pPX8LQl+l22xUcfsfet//4XuOmmBrsMBgMSOQG1zqb0T/P2ZgG1DRvY+4qNLAoYHuOHr/69KBjfnZRnswG134+yrKrfu4/F7ojeKHTyQI1SBY/UQuj1Bsjl1vmzySmtwgurT3P3dQl2x/+N5Xw+syAPJxU+mNoNs5YcEezbnZSL34+m4+5+Yc0/0bx5LKAWGQmoVBaYaSsUF9eVo2yKoyMrL9i7d93Yzp3A55/XBd82b67b99FH4vcvE9O8eUBGBtseMwYYO1ba+Xh4sIDmzJksO+2ddyiYRgghhBBCbB5dqSaioYwWC5LJAD8/9jVggHB/bXbbpUtsxWh9YWGsJItez4Jrej1/OyAA/5zNxJoTGTcemuYRgHP+nSCDAUFuang5Xj+l+Po2DIzVv9BR6+hR833/pM3c3Nyg0+mgULDed64OSjw6PBLz/0kU3PezbUkYFOlDxzRpd5KzS7EtIUcw7uWswl19Qy362o2P0ZaY0C0Ib607B62+4QKKtacyMG9UlHUdy0FB7AI5x7XiKpRUCRdxxJlS7hFggbQpU9owOfH16uAJVwclyqobft+7k3Lx8i1xEs2q9XJKq/DvhbpjKNutLihYXKlBQlYJugR7SDG1Jun1Bvzf76dRUC7sveWokuPze3tJUoLz5jh/3NknFKs5pR/f3XAew2J8EeTRTOnH4cOBbdtY2XG5ON9Dg/PZsmWs/Hj9AFoZv6SmQFUV4NTo+7t4kWW3NjZ4MCuFaK/++INl9wIskLV4sXUsHHBwAFasYOUnp06VejakBdryuYMQYnl0jBJC7IWbm4l/z1sRCqgR0dAbvYSaym679Vb21Yzc0mq8umB3g7H/jmSluIZG+WLpQ/0BK11VTpoXEhIiGJs5qCO+33NZcAHxSGoh9l3MF7W0FSHW4Lvdl7njMweFw1lt2Y9UvGPUVF4uatwU6ycIBl7OLcfZjBJ0C7W+AAZPwjVhdhoAxJuSoWajVAo5Bkf6YMv57Abj5zNLkFtaDT+3ZkpdWpk/j2dApzeeGX/wcoFVBtR+2p+K3Um53H1vTuyCKH/psp7emBCPPcm5yC6pbjBeWq3FK2vO4KcHmyn9KJMBIveuu3E+27GDlSjUatnCr/feY/Px8gK++44F1pr78mj0+8ILxjk7sx6NUgeY9Hpg1y4WRJw3r2E59LbIzm5Yvv3LL4FQyy7yaBG5HLjjDuH4zp2s/K6jo+hTIs1ry+cOQojl0TFKCLEXtng+o4AaIaRZBoMBr/55hrsy281RiY/u7G61JZpI67k4KPHYiE54f5MwS+3TrRcwJIqy1Ej7kVVchb9OZgjGHVVyzBzUkfMI6zK5Zwg3u+6vkxnWHVDT64Hly4EJE5DAKfcImFDyMSuLlRW2UcNj/AQBNQDYezEXt/eyoovmzTAYDPidk0lV34FL+Xh4aIRIMzJNcaUGn24R9hQFgHFdAnBffxPKKlqQhzMr/fjQEmHm/84LuVh9LB139ZV2jlxnz7KMpdoewEVFdfs8PU0r+cjz2GOsf1j9oFunToA1/KG+dCkwaxbb9vAwT0DNYAAefbSu5/Dtt7NelNZuyxZgwgRg0CBg7VoWRCWEEEIIIcQGWGezD0KIVfnjeAa2ci7mAcDbk7og2LOZckLEZs0Y2BG+rmrB+PGrRdidnCfBjAiRxk/7UqDRCTNr7u4bBh9X688SGt05AC5qYab4+lPXmswYklRSEjB0KOu/88YbSMwqFdxFrZCjk6+L8ec4dYpdSJ8wgWWG2KARMX7c8d1JtnUOPpFWhIs5TZfyO5ySb3W/j7uSclFeoxOMB7g74MOp3a1iYcnIuABM7c0PGL2z4TyyiqtMf7Lz54GNG800MyOuXWPVEYqL2e2JE1nfM3P8Xzo6stKx0dFAr17AsGHWEUwDgMmTAfX1z1S//loXTGyLn38G1q1j235+wLffSp+J15zKSuCBB9j3v2cP+xmlpUk9K0IIIYQQQkxCATVCSJMyiirx9rpz3H3jugTg9l5WcpGCWISzWonHRvAbyH+2NQkGg3Vd+CTEEkqqNFh+6KpgXC4DHhnaSYIZtZyTWoFxXYRZWjml1Th4OV+CGZnA2Rk4fZptf/MNNEePCe4SHeAKpaKJj7Pffcey3DZtAs7x38usXZi3M8J9nAXje5Jzobey4FNTfj/a/AXzkiqt0UxEqewzsnjkk7t6wstFuOBEKv+Z2AX+nBKgpVVavP7X2eafQKdjPXW7dAEefhioEVYlMIvSUhZAqw2g9OkD/PYb63Fo77y8gNtuY9s5OSxLqy2uXgWefrru9nffsaCatXNyAtavB/z92e1z51iPOxs9RxNCCCGEkPaFAmpENFpzrMIkotLrDXhx9SmUVgt/dj4uarx/ezerWJlN2u7ixYtITEzExYsXBfumD+jI7dNzMq0IOy/we8oQYk9+PXQVZZzz4K3dgtCBE+iwhKaOUVNNNrIA4q8TwlKWViE0FHjzTbat1+PR3z+DzKBvcJcmyz2WlwO//MK2nZ2B6dMtNFHLG87JUssrq8F5Kws+GVNZo8P6U5km3deaArwGgwF7LwoDaiGeThgS5SPBjIzzcFbh/du7cfdtS8jG3uayyhUKwPV6L7jsbOD33808Q7CMpLvvBk6cAABoQkKADRsAlyayTO3NzJl128uWte25Ll8GVKq6550ypW3PJ6a+fYH9+4HI64u20tNZRvKePdLOi9xgjs8dhBDLoWOUEGIvbPE8RgE1QohRSw+kYt9F/oWtD6Z2s4kyZ8Q0Wq32xldjTmoFHjeWpbaNstSIfavW6rB4bwp335zh/OPCEpo6Rk01JNKHW8L1n7NZqNIIS9pZhWeeAWJjAQB9MxIw9ey/DXY3GVD77Teg5HrA6b77WM8iGzU82kjZx2TbWNTw99lMblCax5oCaqn5FcgoqhSMD4v2tcoFRaPjjVcOeG/j+ebLac6bV7f95ZdmnBlYr68nngD++QcAoHNzQ9q339p0f8NWGT8e8LkejP3rr7qyl61x002sF93DD7OSmbYmMpIF1fr2ZbeLioAxY4A1aySdFmHM8bmDEGI5dIwSQuyFLZ7HKKBGCOG6nFuGD/9J5O67o3coxnJKhxH7NW1AB24pqdPpxdiRmCPBjAgRx9oT15BTWi0YHxLlg26hthWgUSrkmNg9WDBeWq213uNYrW5wYf/lnT/BvaquD1fnQDfjj/3uu7rtOXMsMTvRDIr0gUohDODsTrKNgNoqI+UefTglEw+lFFhNH7W9RgKWQ6J8RZ6J6f5zWzy8nFWC8cSsUqw80kzZzaFDgZ492fahQ+zLXD7+GPj+ewCAXqXClc8/R02keIsSrIZazQL8AFBVBfzxR9ueLzAQ+OEHwNOzzVOThL8/8O+/wLhx7HZ1NXDnncDChdLOixBCCCGEECMooEYIEdDq9Hhu1SlUafSCfcEejvjPpHgJZkWk5KhS4Mmbo7j7KEuN2Cu93oBvd1/i7hMzO82cJvcUBtQAYO1JKy37CABjxuDcoDEAAL+KIjyz99cbu4xmqJ08CRw+zLZ79qzLgLBRLg5K9OnoJRg/dqUQ5SZmfknlan4FDl4uEIzHBbrhzr6hgvHSKi3OX7OOUpa8co+AdQfUPJ3VeGZ0DHffp1svoLRKY/zBMhkwd27dbXNmqY0adSMbLev991HRr5/5ntvW3H9/3fbSpS17rD1+3nJ1ZT3VasthGgzAu+8ClcLsUEIIIYQQQqRGATVCiMC3uy/jZFoRd9//7uoBd0fhymdi/+7pF4ZAd0fB+NmMEmw9ny3BjAixrO2JObiUWy4Y7xzkjmHR1ntBvSk9wzzRkdP37d/EXBRXNHGhXWJfTXwMlUqWJTvz+AbE5qYi0N0RXpwMJwDAt9/Wbc+ZwwIFNo7XR02jM1hViUSe1cf4WVF39Q3DoE78PmTW8D1pdXrsvyScR5dgd3gb+72zEtMGdECkn7AvWV5ZDb7+l79I4Ib77qsrSbhqFZBpWu+7ZvXuzTLefvgBJRMnmuc5bVW/fjdK2WLXLuDKFdMf++GH7GeUL/0xYlYqFbBkCfDyy+z2okWAk5OkUyKEEEIIIYSHAmqEkAbOXSvGgm1J3H0PDg636lXZxLJYlpqxXmrJ0FtJiS5CzOXbXfwLz4+N6GSV/ZNMIZPJMLmnsMdSjU6Pv8+a6cK5mRkMBuytdsFXg+4GACgNeryzZRE6B7ryH1BWBixfzrZdXIBp00SaqWUZ7aNmxWUfdXoDVh9LF4yrFDJM6RmMvuHeUMiFx5I1BNTOZBSjtEqY/TfUBoLpKoUcr0/gVxNYvDcFV/MrjD/YyQl49FG2rdE0DE63VYcOrN9XeyeTNcxS+/NP0x536hTwn/+w/pA9e7JznT2RyYAPPgC2bQOmTGm4LycHKBBmuhJCCCGEECI2CqgRQm6o1urw3MpT0OiEgZFOvi54aXycBLMi1uTufmEI9hBmqSVklmDL+SwJZkSIZRxNLcDRK4WC8RBPJ9zaLUiCGZmPsbKPf1lp2cf0wkqUVmvxff+pSPFi//epXsHo6iPs6wgAWLECKC1l29OmAe5GykLamPggd27Psd3J/LKE1mDfxTxcK64SjI+KC4CPqwNcHZToFiLsRXg4pQBanbDstJj2Gvl/HWojC4tuivXjZtLW6PT48J+Eph/8+OOAQsG2v/mG9bVqqawsFvzR6Vr+2PZgxgwWuNyzB3j66ebvX13NSiJqNHWPdzWyqMDWjRrV8LbBwP6v4uPb3nOOEEIIIYSQNqKAGiHkhs+2JuNCdqlgXC4DPr67B5zUCglmRayJg1KBJ0fye6ktoCw1Yke+3X2ZO/7IsAioFLb98SnSz5UbxDiUUoDMYuvrWZOQyfpp1ShVePGWp3H7jI/x0q1PIzrcn/+AsDBgyBC2XZtpYwfkchk3QJKSV460giYyjiT0Oyc7DQDu7lfXO21QpLDsY2m1Fuczpe2jxuufplbK0S/cW4LZtJxMJsPrE+LBSQDEpjNZOJzSRLZPWBgwdSrbzs4G/vqrZS9eXg5MnAi88w5w++3sNmmoY0eW/Td0qGklad9+Gzh9mm137w689ZZFp2dVfv8dWLuW/S7eeSf7yqJFXIQQQgghRBq2fUWIEGI2x64U4Lvd/PJmj98Uid4dvESeEbFWd/UJQ4insK9FYlYp/jlHFziI7buYU8btC+jprMI9/cIkmJH58bLUDAZg/alrEsymaQmZdQs9joR1xYkQli0dH+TGf8D48cDevcCFC0DfvmJMUTS8PmoAsMsKyz4WVdRgM+c9wd/NoUH5yoFW2EetvFqL41eFGar9wr3gqLKdxUWxgW6YNqADd9+7G843vQhm3jxg5EgWTLvzTtNfVKsF7r0XOHaM3T51CiiRNjhq8w4cAObPZ9sqFbB0KeBgJEPXHg0dCkyeXHf7jz9YttrPP7M3LkIIIYQQQkREATVCCMqrtXhu1Snwrqt0DnLH06NixJ8UsVpqpRxzjWapJVGWGrF5P+zhZ6fNHNgRzmqlyLOxjEk9grmZK3+dsL6AWmKW8GK8g1KOcB+Xph8YY3/vXcNsqI/aulPXUKMVlm2c2jsUynpZnn07ekHJ+WU8cEm6gNrh1AJu+euhUfz/f2v27OgYuDkKz1tnMorx54kmyrwOHQps384CGQoTg4gGAwvEbdjAbnt4AJs2AUG2XSZXUuXlwAMPAPrrx9JbbwE9ekg6JdEFB7M+c7/9BvhdPwYLC4EHHwRuvRW4elXS6RFCCCGEkPaFAmpENApT/xgnovvg7wRc4TSoVyvk+OyeHlAr6VRh7wIDAxEcHIzAwECT7n9Hn1CEeQuz1JKyy7DxTKa5p0eIaHJKqrDmuPAis4NSjpmDw8Wf0HUtPUab4+/uiMGRwvKB5zNLkMwp/SulBE7pv5gANyhhYP2d7r673WQp+Lk5ID5I2BNu/6V8aCTuOdbY70f55R7v6hva4LaLgxLdQ4UlSI+kFkrWR83W+6fV5+PqYHQRzEebE1FRozXfi338MbBoEdtWqYA1a4AuXQR3M/f5zKYVFrLz2JAhwPr1wv2vvAIkJ7PtAQOAF18Ud37WQiYD7rkHOH8emD69bvyff9jv2MKFdUFHYhZ0nBJi3egYJYTYC1s8j9FVciIamSn9AYjodifl4peD/JWdz46JQVyg8MIdsT+enp7w9vaGp6enSfdXKeSYOzKau+/z7cnQUZYasVE/7U9FDeci/l19Q+HrKl2JrZYeo6aYxCn7CABrT1pPllp5tRZXOP3BOge5sYurjz/O+uusXs2yaf791+6Da7yyj2XVWpxMKxJ/Mkacv1aCMxnFgvG+Hb0Q6ecqGOeVfSyr1uLcNWlKBe7j9E/zdFahS7BtfiZ6YHA4Ovo4C8azS6rxzS5+Ri5XU8fWypUNgz0//shKRnJY4nxmsw4cYOex/ftZKcf6tm8HvvySbTs5sRKHSvvIkm41X1/gl19Y8DEkhI2VlQFPPsnO/8Rs6DglxLrRMUoIsRe2eB6jgBoh7VhxhQYvrj7N3denoxceHd5J5BkRWzK1Vwj3At3FnDJsOG09F+QJMVVplQa/HLwiGJfLgEeG2t/5cHzXQG4G8tpTGTBYSVDqQnYp9xp+5yB3YObMuoHnnmNfI0cCcXFAQYF4kxTZ8Bh+lpQ1lX38/Vgad7xxdlotY33UDkjQRy2ntAqJWcIszSGRvpDz6qTaAAelAq/cEsfd993uS8gsrjT+YL0e+Ptv4JZbWLlBnj17Gh6P774L3H9/6yfcnowdC/j7s+1161jGWq3Vq+u2P/wQiI0Vd27WbOJE4Nw54NFH2e077gBGjZJ2ToQQQgghpF2ggBoh7dhb688hq6RKMO6kUuCTu3pAYaMXjog4lJSlRuzMr4euorRKWP7slq5BCPdtpl+XDXJ3VGF0Z3/BeFpBJY5fLeQ8Qny8co/A9YDabbexi/wAkJ4OnL6+QMTdHfD2FmmG4uvT0QtOKmEZbWsJqNVo9fiL05vLSaXAhO78rMi+4fw+agclCKjtv8h/zaHRtlfusb5xXQIxIEJ4XFRp9PjonwvGH5iVBUyaxErrLVoEVFc33H/hAuuzVlPDbj/8MPDaa2acuZ1TKoFp09h2TQ3LuK21cCGweDEwZQrw1FOSTM+qeXgA334L7NgBfPWVcH9qquhTIoQQQggh9o8CaoS0U3+fyTTajP7VCZ3t8uIxMa66uhpVVVWobnyhrBlTegYjgvO7cjm3HOtO8X+/CLFGOSVV+GrHRe4+a8jWbe0x2pzJPUO443+dsI4sU6MBtUB31lPniy8AtbrhztqMBTvloFRgUKQwo+t0RjEKymskmFFD2xOyUVihEYxP6B4EVwd+uTpntRI9wjwF40dSCkTvo7bHjvqn1SeTyfDGxHjwKrD/eSLDeMnQ4GCW/QMAubmstGN9np5A1PUebWPHsqBbM2XeLXU+s1n1s/mWLavblsmAWbOAP/8E5PRnu1E33ww07r2xejUQHQ385z91wV7SInScEmLd6BglhNgLWzyP0SdzIhqt1oxNz0mb5JZW49U/z3D3DYv2xYwBHUSeEZFaSkoKLl68iJSUlBY9TqmQY96oKO6+z7cli34hlJDWem9jAkqrhe9Tgzr5cC/0i621x2hzbor1g7ujMMix8UwmNFZw/CZkCkvvBXs4wsNZxW5ERQEvvFC309UVuO8+kWYnneGcbCmDAdjL6f0ltlVHjZR77MMv91hrYCdh9lR5jY7bi81SDAYDt39aRx9nhHkLSxzbmq4hHrizN//n8N6G88ZLvc6bV7f9xRcNe6kFBLDeVc89x7KrVKpm52Gp85nN6tULiI9n23v3Apdb0NeOCBUUsJ5qWi3wzjtA797A4cNSz8rm0HFKiHWjY5QQYi9s8TxGATVC2hmDwYBX1pzhrh53c1Tiozu7Q9bMymJC6pvUIwSd/IRZaqn5FfjrpHVkuRDSlH0X87DuFP93dd4ofllTe+GgVODWbkGC8YLyGuw1kqkjFr3egAucXladg9wbDrz6KtC1K9t+/nkWVLNzw2P8uONSl33MLqnCLs4cwn2c0Z9TbrC+QZ34GWAHL4vXD+9Sbhm3FPYQG89Oq++FcbFwVgtLhh69UoiNZzL5Dxo0COjTh20fOwYcONBwv4sL8MknrNwqaTmZrGEPutoSkKR1XFxYprLy+mKRc+fY7/D//R9QUSHNnAwGoLQUuHpVuG/1ahYAvOceYPRooGdPICwMcHJigVYKsBJCCCGEWBV+3RVCiN36/Wg6tiVkc/e9M7kLgjycRJ4RsXUKuQxPj4rG07+dFOz7ckcyJvcMhkpB6zeIdarW6vDGX2e5+yb3DOaW1rM3k3uG4Lcjwqyiv05m4OY4YY81saQXVqKMkzUYF+TWcMDZmV3gT0mpC6zZuQhfF4R6OSG9sLLB+J7kXBgMBskWxvxxPB289pl39Q1rdk69O3pCpZBBo2v4BAcv5+PxmyLNOU2jjAWRh9lRQM3f3RGPj4jEJ1uTBPs+2JSI0Z0D4Ni4R59MxrLUHniA3R4yBCgsZOUeiXlMnw68/DLbPnQIePFF4KOPpJ2TrXJwAN59l5Uqffhh4PhxQK9nQd8VK4CICJZJOW5c3f95rXnzWJ9AlYp9qdXCbbWaPTY2tu5xqamsNGd+PpCXJ/w3Lw/QaNhjq6oalkXdtYv1y+NJSADuvhvYv19Y3pgQQgghhEiCAmqEtCNHUgvwupELx7d0DcQUI710CGnOxO7B+HLHRVzMKWswfiW/An8ez8Dd/cIkmhkhTftu12VczisXjLs5KvHahM4SzEh8AyK8EejuKMjM2XIuG+XVWrgY6XtlaeeN9U9rnKEGsKy0bt0sPCPrIZPJMDzGD78eapjtkF1SjaTsMsQGuhl5pOUYDAb8fjRdMC6XAVN7N//5wlmtRI9QTxy9Uthg/EhqATQ6vSgLM3glM2Uy2F1gffbwTlhx+CquFTc85jOKKvHj3hQ8eTOnlPM997Dyqjk57HZEBHDqFNCByoSbRWgoMGoUsH07u92/v7TzsQc9e7Lg5McfA2+9xQJl166xLwAIDxc+ZulSoNiEMrO//towoHbxIit72pyaGqC8vGEmtU+j84tKBfj6smy64mKWFfryy8Cnnzb//IQQQgghxOIoZYCQduJSbhlmLz2KGk5PHF9XNd6b0pVKPZJWq81S4/liR7JV9GIipLGr+RX46t+L3H0vjIuFv5ujyDOShlwuw6SewYLxSo3OaEazGBJaElBrh4ZHW1fZx2NXCpHCCU4Pi/YzOfudF7iqEKmPmkan55aX7B7iAU9n+8oMcVQp8NItcdx9C/+9iJxSYdlLODgAc+bU3S4qAvbsscwE26tvvgEmTwY+/xy4806pZ2MflEoWjDp1ChgzBpDXu/zB6/enEZbE52qcLeZrJIvVwQEICQF69ABGjmTZZjU1De/z0EPAkSOstGNJSV3gb+fOutdZuBBI4/enJIQQQggh4qIMNULagbyyajz402EUcfqmAcAHU7vDx9VB5FkRezOhWxC+3JGMpOyGWWrphZX460QG7upLWWrEehgMBvxn3VlUa4XB3m4hHpg+oKMEs5LO5J7B+G63sE/LXycyMFmi7OXELGFAzVElR7iPsGdjezQ4ygcKuQy6RjUWdyfnYvbwTqLPZ9VR/sXeu1tw7h/YyQdf7hAGuQ9ezkfvDl6tnpspTqUVcUuM2lP/tPom9QjGT/tScTKtqMF4eY0On25Jwod3dBc+6IknWNAnNxd45x1WppCYT1QU8NdfUs/CPsXGAlu2sF5mOh0LnPEWEh49ygJeGg37MrbdOIOwUyeWtebryzLOav91dua/Tn0dOvAzPXv2ZGUqv/gCWLmS9VUjhBBCCCGSo4AaIXauskaHh38+irSCSu7+R4ZGYEx8gMizIvZILpfhmdExeGL5ccG+daeuUUCNWJXN57Lx7wVhJo9MBrw3pSsU8vaVsRsf5I5of1ckNyrbujs5D/ll1ZIsukjILBWMxQa4tbufjTHujir0ChOWSDyUUoDKGh2c1AojjzS/8motNpzOFIx7OqswOt70Pny9O3hBrZALsukPXMrHEzdxyhCaEa/cIwAMjbbPgJpMJsMbE+Nxx6L9gn0rj6Zh5qBwxAc3ygYNDATOnGH9oLp0EWmmhJiRTMay1pRGLoN0bmWpZ3d34L77Wj8vY558Epg1C3ChhSSEEEIIIdaCSj4SYsd0egPm/XYCpxqtPq51S9dAvHpr++gRRMQxvksgYgJcBeMHL+ejpMrEMjqEWFh5tRZvrz/H3Td9QAf0CPMUd0JWQCaTYUovYSaaTm/AxjPCQImllVZpcLWgQjBO5R4bGh4jLPtYo9XjUEq+qPPYdCYTFTU6wfiUniFwUJoe2HNSK9CTc/wdTS20eOngvcnCgJqjSo4+HS2bGSelPh29MKmHsNyrwQC8u+E8DAaD8EEBARRMI0QsMhkF0wghhBBCrAwF1AixY+9uOI+t5/n9b3p38MRn9/SEnFb6EzOSy2Xc8nAanQE7OdlAhEjh8+3JyCwW9gjydVXjhXH8vkLtAe/COgCsPXlN5JkASdnC7DSAAmqN8QJqALA7iZ9tZSm/H03njt/VN7TFzzWwk7dgrFKjw+n0ohY/l6lKqzQ4wVl81D/Cp0UBQVv00i1xcFAK/yQ8cDnf6GdIQohENBrWE+7ff6WeCSGEEEJIu0UBNULs1I97U7Bkfyp3X7iPM76f2ReOKvu+SESkYayE6JZzWSLPhBChC1ml+HFvCnffaxM6w8NJJfKMrEeYtzM3G+fYlUKkcbLFLOk8p9wjQAG1xrqFeMDTWfg7uztZvAUMKXnlOJxaIBjvEuyOLsEeLX6+gZE+3PGDl4WvYS6HLhcIetEBwNAo/lzsSYinE2YP4/fce39TAmo4fSYJIRIoKACGDwfmz2f9C3NypJ4RIYQQQki7RAE1IhqlsVr1xOz+PpOJ9zae5+7zclZhyaz+kvTDIdYrMjISsbGxiIyMbPNzRfu7ItzHWTC+80IuqrXCkmCEiEWvN+D1v85wL5wP7OSNKZzsSmthzmO0KVN68rPU1p8WN0stIbOEOx4b6CbqPKydQi7D0Chhj6+LOWW4VsTvnWpuq4+lccfv6tPy7DSgro9aYwcvW66MpdH+aVH8DEB78/hNkfBzE34uTM2vwNIDqWZ/PbHOZ4TYFU9PwPV6WfXMTODBBwG95QLedJwSYt3oGCWE2AtbPI9RQI0QO3PsSiGeWXkSvLYXaqUcPzzQF+G+VIufNKRSqW58tZVMJsPYLoGC8bJqrUUzDAhpzurj6TiSWigYVylkeG9KV8hk1lsC15zHaFMmdA+GglMKeMMpcfuo8QJqIZ5O7TqD0Jjh0fygjxhldnV6A1YfE5Z7VCvk3PK/pnBUKdCzg6dg/GhqocWypXgBNR8XNeLaSQDXxUGJF8bGcvd9sT0ZheU1Zn09sc5nhNgVuRxYtgzw92e3//4b+PRTi70cHaeEWDc6Rgkh9sIWz2MUUCPEjqTmlWP20qOo5lxwksmAz+/piT4dhb1JCDE3KvtIrE1heQ0+2JTA3Td7WCdE+bePC+fN8XZRYwgn4+l8Zgku55aJMge93oALWcKSj1TukW9YjPDnBQAfbEqwaN8xgJWWzC6pFoyP6RIALxd1q593UCdhqUVL9VHLKq7CxRzh7/aQKN921Wf2jj6hiOccYyVVWizYliTBjAghAoGBwC+/1N1+5RXg0CHp5kMIIYQQ0g5RQI0QO1FQXoMHfzqMAiOriF+7tTNu6RYk8qxIe9W7gxd8OBdTt57Php5Tbo8QS/tocyIKKzSC8RBPJ8wdGS3BjKzXxO7894oNp8XJUrtaUIGKGmF52M5BFPTkCfJwQkyAq2C8tFqLGT8cwtmMYou99u9HzVvusdZATkANsEzZR+PlHvmBSnulkMvwxsR47r5fDl3FxRx+X0NCiMjGjAFefplta7XAvfcCRUWSTokQQgghpD2hgBoRjd6CNd7buyqNDrOXHkVqfgV3/4ODw/Hw0AiRZ0VsSUFBAfLy8lBQYJ6SjAq5DKM7C7PUckqrcdqCF3cJ4Tl+tRArDvMv/L89qQuc1AqRZ9Ry5j5GmzIuPhAqBafso0h91Iz1T6MMNeMeHMx/jy+p0mLGj4eM/p+2RUF5DbaezxaMB7o7YpiRMpSm6tXBE2ql8M+UAxYIqO0zElAbEt2+AmoAMCjSB+O6CN+7dXoD3tvIz/BtDTHPZ4TYpXfeAQYNYtupqcDs2eDW+28DOk4JsW50jBJC7IUtnscooEZEQwE1y9DrDXh25UkcuyLsCwSw0ntvTIy36t5ARHo5OTnIyspCTk6O2Z5zLOeiHEBlH4m4tDo9XvvzLHff6M4BGG2kPKm1scQxaoyHs4obEEnKLkNStuWzVCig1nL39AvjBkIAoKhCgxk/HEKymX92a09mQKMTXsC9s08otw9fSziqFOjN6aN27EohqrXC7MXWMhgM3Ay1Tr4uCPF0Mtvr2JJXbunMDajvvJCLXUnm6csn5vmMELukUgErVgCenuz26tXAd9+Z9SXoOCXEutExSgixF7Z4HqOAGiE27oO/E/D3WX6AokeoB764t1ebL2wR0hpDonzhpBJm/mzhZDQQYik/H7jCDdA4qRR4axK/vBlpouzjKctnqSVw+qc5qRTo6O1s8de2VQq5DF/e1xujO/tz9+eX1+C+7w/hkhn74K06ms4dv7ON5R5r8co+Vmn0OJ1uviznpOwy5JYKe8ANbYfZabXCfV3w4OBw7r73NpyHVkcL5AixCh07Aj/+WHd7+XKAFrASQgghhFgcBdQIsWE/70/F93tSuPvCvJ3wwwP9bKKUGbFPjioFRsQIs1wu5pThshkv6hJiTFZxFT7dcoG7b96oaIR6UYDGmDHxAdySextOZ8Jg5rJSjfECoLGBbpDT4pAmqZVyfD29N/e8CwB5ZdWY9v1BpOaVt/m1zmYUc39O/SO8Ee7r0ubnB4z3UTtwyXxlH/ck8zOuhrSz/mmNPTUyGt6cPqjJOWVYcYRfPpcQIoGpU4GnngKeew7Ytg2Q0+UdQgghhBBLo09chNioreez8fb6c9x9Hk4qLJnVH35uDiLPipCGjJV95PXdIcTc3t14HuU1wvJw0f6u1FeyGW6OKtzECcxczivHeQv046pVUqVBemGlYJzKPZrGQanAt/f3wVAjAaHskmrc9/1BpBXwe66a6vej/KDK3X3D2vS89fUM84QDJ6h70Ix91Hj90+Qy1kusPfNwUuHZ0dHcfZ9tTUJxpUbkGRFCjPriC+CTTwC1MAhOCCGEEELMjwJqhNigU2lFmLviOPScJAG1Qo7vZ/ZFpJ+r+BMjpJGRcf7ckqNU9pFY2u6kXGw8ncnd9+6UrtzsK9LQxB7B3PENRv5fzeECp9wjAMQHuVnsNe2No0qB72f2xcBO3tz9mcVVuPe7g0gvbF1QrUqjw18nhaU/XdQK3NotsFXPycP6qHkJxs3VR61Gq8ehFGED7B5hnnB3VLX5+W3dff07INpf+FmyoLwGX/97UYIZEUK4eH2yLZxJTgghhBDSntHVJEJsTFpBBR7++QiqNPwa+Z/c3QP9I/gX0QgRm6ezGv3Dhb+Px68WcvvWEGIOVRod3lx7lrtvaq8Qo6XkSEOj4vzhqOKVfbxmsbKPvDKCAGWotZSTWoEfH+iHfuHCgBQAZBRVYtr3h5BZLMwGbM62hGxuhtLE7sFwVitb/HxN4R2r1Vo9Tl4tavNzn7haiApOBuuwdl7usZZSIcdrEzpz9/20L8UspUMJIRZw5gwwcCCQnCz1TAghhBBC7BIF1AixIUUVNXjgp8PIK6vh7n9pfBxuM5JRQIhUeGUfDQZgewJlqRHL+GbXJaTmC7Nv3B2VeNXIBWIi5OKgxKg44fGbVlCJ0+nFFnlNYwG12EDKUGspFwclfprVH707eHL3Xy2owLTvDyG7pKpFz7vqaDp3/O5+oS2dYrOMlV48eFmYWdZSeznlHgHqn1bfTbH+uClWWPpVozPgg78TJJgRIaRJO3YA/fsDhw8D994LVNPiNUIIIYQQc6OAGiE2olqrw6PLjuFyLn9F8PQBHfDYiE4iz4qQ5o2J5/dRo7KPxBJS88qxcOcl7r4Xx8fB15V6S7bExO5B3PENp4Ul/8zhfKaw5GOYtxPcqARfq7g6KLHkof7oEerB3Z+SV45p3x80OWP4WlEl9iTnCsY7+blwyzO2VY8wD4v1UeMF1JzVCvSywPdhy16f0JlbunnzuWwcuGS+fnaEEDPo3x/o0IFtHz8OvPSStPMhhBBCCLFDFFAjxAbo9Qb83++ncZjT6wNgfarentQFMl4NfUIkFurljHhOuba9F/NQXq2VYEbEXhkMBry57hxqtMKSuD1CPXBf/w4SzMq23RznDxe1QjC+8XQm9LxGnm2g0xuQxOmh1jmQyj22hbujCksfGoCuIfz/x0u55Zj+w0HklzUfVFtzPJ3bmueuPmEW+QzioFSgL6ds5bGrhajStL6PWnGlBqfSigTjAyK8qb9iI1H+bpg+gH/uXLiTeqkRYlVcXYGVKwGH64uHPv8cWLdO2jkRQgghhNgZ+ouRiIaCPa33vy0XsP4UPxuga4g7vryvF5QKOpxJ6zk4ONz4sgRe2ccarR67k4SZDoS01qYzWdzfKbkMeG9KN26Wha2w9DFqjKNKgdGcLNNrxVU4wQlItMWV/HJUcoIk1D+t7TycVVj20ADEGSmdmZRdhuk/HEJhOb+kNMAW9/DKPSrkMtzRO8Rsc21sYISw7GONVo+Tbfj9O3g5H7x48NBoYXlDAjwzOgZujsL+eIcuF7QqsC7V+YyQdqFnT+CTT+puz5oFpKW1+GnoOCXEutExSgixF7Z4HqMr8EQ0CoVwhTtp3vJDV7DISPmyEE8nLH6gH1wchBc5CGmJiIgIREdHIyIiwiLPPzY+kDtOZR+JuZRVa/HOhnPcffcP7IhuRkre2QpLH6NNmdid35vT3GUfEzjlHgGgcxD1TzMHLxc1lj8yADEBrtz9iVmlmPHjIRRXaLj7D6cW4GqBsDfhiBg/+Ls7mnWu9Q002ket9eUG9ybz+6cNpf5pXN4uakwf0FEwXqPTI9eEzMbGpDyfEdIuPPEEcPvtbLugAJg2DdC2rCoEHaeEWDc6Rgkh9sIWz2MUUCPEiv2bmIM3/jrL3efmqMRPs/pZ9CIWIebSOcgNIZ5OgvEdiTnQ6ITl+Qhpqc+2JiG7RHhh18/NAc+Pi5VgRvZjeIwvNztl0xnzln1MzCrhjlOGmvn4uDpg+SMDEennwt1/7loJZi4+hJIqYVDtd052GgDc3TfUrHNsrEeoJxxVwj9Z2tK/ax+nf5qfm4PRYCMBovz5/zdpnCArIURiMhnw4491/dT27gXeflvaORFCCCGE2AkKqBFipQ6nFODJX49zSxKpFDJ8e38fxATQqn1iG2QyGbfsY3GlBkeM9AYkxFTnr5Vgyf5U7r7XJ3SGu6NK3AnZGQelgptlml1SjSOp5jt+EzKFATUXtQJhXs5mew3CAkcrZg9EhC8/qHYqvRgPLj6Msno9LkurNNh0JlNwX28XNUbGCc/t5qRWytG3o7dg/ERaUav6qGUUVeJyXrlgfGiUL5Unb0Kol3BRDACkF1aKPBNCiEm8vIAVK4DaKjH//S+wY4e0cyKEEEIIsQMUUCPECv1y8AqmfX8QFTX8C0Uf3dkdgyOpLBGxLVT2kViCXm/A63+dgY6z+mBIlA8m9eCXKyQtM7FHEHd8w2lhkKW1eCUfYwPdILfh3nfWyt/dEb/OHoAO3vxg5fGrRXjopyOoqGFBtY2nM7n97ab0DIFaafk/JwZ2EgbUarR6nLha1OLn2kflHlvFeECNMtQIsVqDBwPvvce25XIgMVHa+RBCCCGE2AEKqBHR6HQtX0Xc3tRo9XhlzRm8/tdZaI2U0Xp+TAxu72XZ8kqk/UlLS0NqairSWtG03FT9wr3g6SzMFNpyLgsGg/nKxpH2ZdXRNBznXFRXK+R4Z3JXu8k4EeMYbcrQKF/u8fv32UxozVC2tbhCg4wiYaYLlXu0nCAPJ/w6ewC3HC/AeqY9tOQIKmt0+P2YkXKP/cT5PDLISB+1A63oo7aHU+4RAIZQQK1Jge6OUHCC263JUJP6fEZIu/Lii8BDDwG7d7Peaiai45QQ60bHKCHEXtjieYwCakQ0dMG8aTmlVbjv+4NYcfiq0fvc0zcMT42MEnFWpL0oLy9HWVkZysuFZbDMRamQY2Scv2D8WnEVzl3j904ipCkF5TX48B/+aus5Izoh0s9++iGJcYw2RaWQY3wXYZZpXlkNDpmhbCv1T5NGqJczVsweiCAPfj/Wg5cLcO/3B3HsSqFgX/dQD8QFivPz6RbiCSeVQjB+sIUBNb3egP2cgFq0vysCjfwfEEapkHN/T1oTUJP6fEZIuyKXs35qgwe36GF0nBJi3egYJYTYC1s8jwk7zBNCRHcyrQiPLTuGrJIqo/cZ3yUQ791uP9kWpH0aGx+INcczBONbzmeja4iHBDMilpaYVYLvdl9GRmEl3J1U8HFRw7vRl4+LA7xcVPBxcYCTWnjR3JgP/05AUYVGMB7m7YQnb6bFB+Y2sXswfjsiXD224fS1Nmf38PqnARRQE0MHHxZUu+e7A8guqRbsP5VWxH3cXX3Ey5ZXK+XoG+6FPY3KNZ68yvqoOXKCbTwJWSXIL68RjFN2mmlCvZwEATQq+UiIjSooALyF5XQJIYQQQkjTKKBGiMRWH0vHq3+eQY3WeMmseSOj8MzoGOojQ2ze8BhfOCjlqG70+77lXBaeGxMj0ayIpRxNLcCMHw+hSmN6SUAnlaJRsE0Nr3rbteN5ZTVYdZRfhu6dSV1NvsBOTDewkzd8XNSCgMTfZ7PwzuSuUClaX/iA1z8NYD3UiOWF+7rg19kDcc+3B5FXJgyqNaZWyjGpR4gIM6szsJOPIKBWo9Pj+JVCDDYxILbPSLnHYdEUUDNFmJczDqJhRmpGUSV0egO3HCQhxArp9cA77wALFwKHDgEREVLPiBBCCCHEplBAjRCJaHR6/HdjApbsTzV6H2e1Ap/e3QPjuwaJNzFCLMhZrcSwaF9sS8hpMJ6YVYq0ggqEeTtLNDNibjklVXh8+fEWBdMAoFKjQ0ZRJbeflinGdQnAzZzSoqTtlAo5bukWiF8ONixNXFShwb6LebgptvX/7wmcko8dfZzh6kAfVcUS6eeKFbMH4N7vDnKzuOob3yUQHpyeepY0sBO/j9rBy/kmB9QaB+QAQCmXYYCR5yYNhXoJ36M1OgNySqsQ5MHvxUcIsTL/+x/w9ttse9IkYP9+wI0WrxBCCCGEmIp6qBEigYLyGsz88XCTwbQO3s7484khFEwjdmdsvLAPE8DKPhL7UKPV44nlx5Fb2nymizk5qxX4z21dRH3N9mZCt2Du+IbTma1+Tp3egAtZwgy1ziL15yJ1ogPcsHz2AHg1Eyy7q6945R5rdQ/1gDOnJOzBy6b18KvS6HAkVXjfXh08KXBrolAvftCsNX3UCCESmTMHiI1l22fPAtOnAzqdtHMihBBCCLEhFFAjRGTnrhXjti/34sDlfKP3GRbti3VPDaFSV8QujersD15lqC3nssSfDLGI/248j6NXCkV/3WdGRyPYk7IkLKl/hDf83BwE45vPZaFa27oLcil55YIysAD1T5NKXKA7lj08AO6O/CBTiKcTBkeKXyJRpZCjb7iw38+JtEJU1jT/u3f8SiE3Y5b6p5nOeECN+qgRYjM8PYF169i/ALB+PfD661LOyLgDB4CJE4HXXgMKxf9cSQghhBDCQwE1QkS0/tQ13LFof5OlzB4d3gk/PdgPns5qEWdGiHh8XB3Qp6OXYPxIagEKmykzRqzfH8fS8fOBK9x9bg5KqNvQZ6spsQFumDWE+oBYmkIuw4Ruwszp0iot9iTx+1M1JyFTWO4RAOKCaFGJVLqGeOCXRwbAjZO5dV//MMn6ZQ3sJAyoaXQGHL/a/IXWvdQ/rc1CjZRlTi+gDDVCbEpMDLBqFaC4nvX74YfA8uXSzqmxXbuA0aOBjRuB998HIiOBjz8GqqqknhkhhBBC2jmqb0KICHR6A/63+QK+2XXJ6H0clHLMv6M7pvQKEXFmhEhjbHwgjqQ2vACqNwDbE3NwZx/xS4kR8zibUYxX/zzD3dfB2xnrnxoKdyclymt0KCirQX55NQorapBfVoOC8oZf+df/LSyvQWm1tsnX9XVVY8G9PaGyULCONDSxexC3ZPGG09cwOj6gxc+XyOmfBgDxlKEmqe6hnlj6cH88ufw4rhWzC5j9w70xe3gnyeY0qIk+as1lmvECaq4OSnQP9TTH1NqFQHdHKOUyaPWGBuNU8pEQGzRmDPDZZ8C8eez2ww8DUVHAgAHSzgtgwbRbbwUq6mW/FhYCL7wAfPklsHcvEBYm3fwIIYQQ0q5RQI0QCyuu1ODp305g54Vco/cJ9nDEdzP7omuIh4gzI0Q6Y+ID8N9NCYLxLeeyKKBmowrLa/DYL8e4pfscVXJ8e38feFzvy+TqoISrgxIdfPjZDo1Va3UoLNfUC7ZV3wi2BXo44aZYPyr1KKLeHbwQ5OGIzOKGq8S3ns9GlUYHR5Wwz1VTEjKF/dPcHJRGy8sR8fTq4IVtz4/AoZQCOKkUGBDhDZlMmuw0gGXOuagVKG9U4vHAJeNltAGgqKIGZzKKBeMDO/lQIL4FFHIZgj2dcLWgYYnHNCr5SIhteuop1kftu++A6mpgyhTgyBEgVMLP4o2DabfeCvj5AUuXAgYDEBQk7fwIIYQQ0u5RQI2IRi5vfxcskrNL8eiyY0jJKzd6n/4R3lg4vTd8XYU9aQgRi7e3N3Q6HRSKll0Ib61wXxfEBLgiKbuswfju5FxU1ujgpBZnHsQ8dHoD5v12wmiWwvw7urepH5aDUoFADwUCPRxb/Ry2TuxjtCny62Uff9ib0mC8vEaHnRdyML6rsCRkU3glH+OC3CQN3JA6zmolbo71l3oaAOr6qO1KarhI6VR6ESpqtHBW8/+02X8pHwaDcHxoFD/jjRgX6iUMqLU0Q82azmeEtGsyGcv4SkwEdu8GsrKARx4B/vlHmuP04sWGwbQJE4A//gAcHIBnnwVefhl45RU278aPi4oSb56EWAF6LyWE2Atvb2FZf2vX/iIcRDLtLaC25VwWpny9r8lg2sxBHbH8kQEUTCOS8/f3R1BQEPz9xbtoOjY+UDBWpdEb7XNDrNenWy9gTzL/5/bQkAhM7kmlbNtKimO0KRN7BHPH15/ObNHzFFXUCDLdACAukMo9Er5BkcIgmEZnwPErRUYfY+x9ZWi0n7mm1W7wMkevFVVCp+dELI2wtvOZPSut0uDVP89gzKe78MTyY0groGxC0ohazYJW4eFAly7AokUAJDpOIyOBOXPY9q231gXTAKBHD+Dvv4Hhwxs+ZudOIDoamDEDSE0Vb66ESIzeSwkh9sIWz2PtK8JBiAj0egMWbEvCo8uOCUoS1VIr5Jh/Rze8M7krlRoi7dbYLvxeS1vOZYk8E9IWm89l4et/+f0h+0d445Vb40SeERFDj1APhHkLL6zvSMhBRU3TPe/q45V7BNCmjEZi3wYa6aN24LLxxRh7OQH/QHdHRPq5mG1e7UWol7BUr1ZvQHaJMDBOpPf4L8fx66GrSM4pw6YzWbjzm/2o1vL/PiHtmK8vsHUrsH8/EBEh3TxkMuCTT4AffmgYTDPGYABefJFtL18OxMYCzz0H5DddBpgQQgghpC3oSj4hZlRWrcVjvxzDgm3JRu/j7+aAFY8OxD39Oog4M0KsT7cQDwS6C0v4bU/MadFKdyKdS7lleH7VKe6+AHcHfD2tNy0asFMymQwTugmz1Co1OmxPyDH5eXjlHgGgc5Bbq+dG7FvXYHe4OghLOx68XMC9/9X8CkGJQgAYGu1LZUVbwVhvw5aWfSSWd62oUpCdmV1Sjd1JVAmAcERFAe4SLGapahSMl8mAhx8GHE0o863TAdOnAz7XF1rU1ACffcYy3ebPByrpvEQIIYQQ86OrXISYSWpeOW7/eh+2nM82ep+eYZ5YP3co+nT0EnFmhFgnmUyGMfHCLLWC8hocu1IowYxIS5RVazFn2TGUVQuzkVQKGRbN6AM/Nypna88mduf3Sttw+prJz8ELqMlkQGwgBdQIn1IhR79w4eeoU2lF3OxIo+Ueo3zNPrf2gJehBgDphVRK0Npcyef/TFLyyrjjhDRQUQE8+CBw8qTlXmPvXhbIO3iwdY9XKoGnnwYuXWK91WqDcMXFrN9aTAzw008s8EYIIYQQYiYUUCOi0WpNLwFla3ZeyMGkr/YiOcf4H6h39w3FyjkDEcDJyCFEaomJiTh79iwSExNFfV0q+2ibDAYDXvj9FC4aOee9NakLeneghQPmJNUx2pQuwe6I8BWWzPv3Qi5KqzQmPUdCljCgFu7jAme1MAOJkFq8so9avQFHU4WLMfYZCagNoYBaq5gjQ02s89mxKwV446+zeHPtWZy/xs+GtWd5ZdXc8ewS/jghN2Rno6pfP+Dnn6G59VYg2/iC0Vbbtw+45RYgIwMYOxY4c6b1z+XhAbz/PnDxIstuq+3dnp4OPPQQ0LMnUMDPYibEVlnj3waEENIatngeo4AaIW1QrdXhg00JmLXkCEqq+AFDpVyGdyZ3wfw7usNBqRB5hoRYtwERPnBzFF4433I+GwYDlX20Vt/uvoy/z/KDnnf1CcW0/lTStj2QyWTcLLUarR7bEpq/+KbV6ZGULQzKUrlH0hxjfdQOXm7YN0enN2DfJWFALS7QjTJoWynA3REqhbBUZhqnrKaU9iTn4p5vD2LZwStYeuAK7vpmP85mFEs9LVEZC6hlUb870hwPDxiU7PO5KjMTmDoVqDZjIHbfPmD8eKDs+meAwYOB6Oi2P29ICOu/dvo0MHFi3XhAAOBFC70IIYQQYh4UUCOklS5klWLK1/vx7e7LMHbd39tFjV8eGYCZg8KpTwchHGqlHDfH+gvGrxZUcC+0E+ntTc7DR//wVxB1C/HAu1O60vmuHZnYXdhHDQA2nMps9rEpeeWo0eoF450DJejhQmxKl2B3uHH6qB1oFFA7f60ERRXCbEkq99h6CrkMwZ7CLDVr66H2/Z4UaOv1Yy2v0eGNtWfb1WKd/LIa7nh2MQXUSDMcHZH+5ZfQ+F//jL5/P/DYYzD6R29L7N/fMJg2dizw11+m9UwzVZcuwPr1wM6dwIABrJ9a/c+mBgOQlma+1yOEEEJIu0IBNUJaSK834Ic9l3HbV3u5vV9qdQl2x7qnhhhdRU0IYajso+1IL6zA3BXHoedcT/FyVmHRjN5wVFEmbnsSG+iGaH9Xwfju5FwUcwIZ9Z038h4aF0QBNdI0pUKOfhHegvHT6cUor9fXcc/FXO7jh0RTQK0teGUf04usK0PtdHqRYOzE1SLsSMwRfzISMVrysZQCaqR5On9/XPniC+hrA11LlgCffda2Jz1woGEwbcwY8wfT6hsxgr1mnz4NxzduBCIjgS++sMzrEkIIIcSuUUCNkBbILK7E/YsP4b2NCdxV9bUm9QjG6scGG23cTgipMyLGD2qF8O1oy3kL9GsgrVal0eGxX46hkBMkkcuAL+/rTee8doqXpabRGbD5fNNB8cSsUu44lXwkphjYSRhQ0+kNOHqlro8ar3+aWiHHAE4wjpgu1FN4rs8sqoJWZ/yzsZiKKmq4mYkA8PGWJOh5q0LsUFM91NpTph5pvaouXZD5/vt1Ay+8AGza1LonO3AAGDcOKL3+3j9mDLB2LeDE78toNo2rJuh0wMsvAxoN8OyzbevdZquSk4Hly9n/hV4PHDsG5PH7jRJCCCFEiAJqhJho3alrGPfZbuy7mG/0PmqFHG9MjMfn9/aEk5qyNAgxhZujCoMihZmcZzKKca3IukpItVcGgwGv/3UWZzP4GUUvjo/DUMr4aLcm9hD2UQOADaebLvvIy/J2d1QihFNOjpDGBnXin3MOXGKf06o0OhxJLRTs793RE85qYblIYjpehppWb0B2qRl7LLVBSl650X0JmSXYdLb5krT2IM9IyccarZ67OIYQntJbbgHefJPd0OuB++4DEhJa9iQHDzYMpo0eLU4wjUejAXr2ZNt6PTBvnnlKWdqSt98GZswAlEpAoQD69gVWr5Z6VoQQQojNoIAaIc0ortTg6d9OYN6KEyip0hq9X1ygG9Y+NQQPD42g/kGEtJCxso/bEihLzRosP3QVq4+lc/fd0jUQc4Z3EnlGxJpE+rmiM6dM476LeSgo51/QBfgBtbggd3oPJSaJN9JH7eD1PmpHUgu41QSof1rbhXrzL4KnF1hH2cfUfOMBNQD4dEuS1WTTWZKxDDUAyC6hso+kBf7zH+COO9h2SQlw221AvvFFpgIJCdYRTANYeckffgA6Xf/sunMn8Mcf0sxFComJwIoVwvENG8SfCyGEEGKjKKBGSBP2X8rDLQt2Y+3Ja0bvI5MBs4dF4K8nh3AvKBJCmjems7E+ahRQk9qxK4V4e/057r4of1f8764eFAAhmNhdmKWm0xvwz1l+2ceC8hpklwgv9nYOpHKPxDQKuQz9OaUbz2QUo6xai73J/PJVQ6P9LD01uxdmpLxvWqF1ZJWn5DUd2LucV441JzJEmo00DAZDkwG1LAqokZaQy4GffwZ69GC3Y2NZdpOpZs0Cvv++rsyjs8Qlwh0dG/aDe/55oMI6FgRY3DvvsMw8APjgAyD4etnu7dvbz/8BIYQQ0kYUUCOEo1qrw383nsf0Hw7hWrHxPziDPRyx/JEBeG1CPBxVVOKRkNbyd3dErw6egvGDl/NRXElliaSSW1qNJ5Yfg0YnLIXj6qDEt/f3gSsnQ4S0P7yAGgBsOM1fkJLIyU4DQAtTSIvwygXr9AYcSS3AXk7/NHdHJbqFeIgxNbtmrF9meqF1XIxNbaLkY63PtyWjWqsTYTbSKK/RoUpjPAsvu4m/bwjhcnEB1q0D3nqL/evRwnPpI48A//wjfTCt1m23sTKUAHD1KvDRR9LORwwJCcBvv7FtX1/gqaeAiRPZ7aoqYMcO6eZGCCGE2BAKqBHSSGJWCSZ/tQ/f70lpspz6lJ7B+PuZ4RgcSaWDCDGHMfHCLDWt3oCdF3IkmA3R6PR48tfj3CwiAPjk7h6I9HMVeVbEWnX0ceEGKg5ezkcup6/SeQqoETMY2EkYUAOATaczce6a8HdscKQvFHLKqG0rfzcHqBTC/8d0K8lQa67kIwBkFFVi5ZE0EWYjjfwmstMAGH1vJ6RJHTqw8o+KZhaSHj4MrFwpHJdb0eUnmQxYsKAu027+fODKFUmnZHHvvFPXL+6FFwBX17qAGkBlHwkhhBATWdEnGmLvFM198JaYXm/A97svY9KX+5CYVWr0fu6OSnx5Xy8suLcXPJxUIs6QEMsJDQ1Fx44dERoaKtkcxsYHcsep7KM0PtiUiMMpBdx9T90chXFd+D8vYhnWcIw2h5elpjcA/5zNFIwnZArfZ+UyICaASj4S03UOcoe7ozBL1lg5vyHRtAjKHORyGUI8hf2PTM1Qs+T5zGAwIMWEDDUA+HLHRVTW2GeWWlPlHgEq+UiaZ/JxmpEBHDhQd/vIEWDsWGDaNODXXy07ybaKiwPmzWPbVVXA//2ftPOxpHPn6oKcfn7Ak0+y7VGjWAlMgAXUmlpRTKyKLfxtQAghprDF8xgF1IhorLnHzrWiSkz/4RD+uykBNU00KR8c6YPNzw7HbT2CRZwdIZbn6uoKNzc3uLpKl3EU5e+KTn4ugvGdF3LsuiyTNVp7MgOL96Vw9w2P8cOzY2JEnhGxhmO0OROMlH1cf5oXUBNmD4X7usBJbd2Lb4h1YX3U+GUfeYZFUUDNXHhlH03NULPk+aygvAalVVqT7ptbWo2lB1LNPgdrkFta0+T+HAqokWaYdJwePgz068eynC5dYsG0MWOA4mLWp2vxYusP0Lz5JuDvz7bXr7ffLLV33637Wbz4IivhCbASnDffzLYzMoBTp6SZH2kxW/jbgBBCTGGL5zEKqJF2b+3JDIxbsBsHLucbvY9aKcfrEzrjl4cHIMhDuCKXEGIevLKP5TU67L9k/Pgk5pWQWYKX/jjN3Rfm7YQv7u1JJdMIV6iXM7cX4pHUAmTXu3ir0elxMadMcD8q90haY2Anb5PuF+LphI4+VtK7xw6Eegk/D2cWV0HbxMI0MRgr9zjeSFb1ol2XUFplf71a88spQ42IYP58IDMTKCgAbrmlLpgGADfdBKxdy0orWjMPD+DDD4HbbwfOnwc6dpR6RuZ37hywahXb9vcHHn+84X4q+0gIIYS0CAXUSLtVXKHB3BUn8PRvJ5tcyRoX6Ib1Tw3FI8M6QU4XkQmxKCr7KK3iCg3mLDuGKo3wgqiDUo5F0/vA01ktwcyIrZjYXZjBbTAAG+tlqV3OLedmg8dTQI20wqBIfh+1xoZF+1p1tQRbE+YtDE7q9AZkFksbqEnJ45edfHBIOPp09BKMF1Vo8MMefka2LctrJkMtmwJqxBwWLwY6d2bbycl1wbQRI1hgxkVYecIqPfggsGYN0KmT1DOxjJ076wKb9bPTak2YULdNATVCCCGkWRRQI6IxWFG5h/0X8zD+891Yf+qa0fvIZMCc4Z2w9qkhiA2kni7EvpWVlaG0tBRlZcKsETH1CvOEr6uDYHxbQjb0Rkp4EfPQ6w14euUJXC3gX4z8YGo3dA3xEHlWpJa1HKPNmdAtiLsYfcPpuvdbXrlHgC1gIaSlOge6m9TTdgiVezQrXoYaYFrZR0uez1KN9E+L8HXB/42N5e77cW8KCsubDkDZmuZ6qOWV1UAjcTYhsW4mHaceHsC6dYBXvWD1iBHAxo22E0wDrD+Lrq2efJJlqT36qDA7DWBZed26AT4+rK+cjkrt2wJb+duAEEKaY4vnMQqoEdHorOCDWZVGh3c3nMe0Hw41uYI2xNMJK2YPxCu3doaDkvq5EPuXnp6OK1euID09XdJ5yOUyjIn3F4znllbjZHqR+BNqRxZsT8bOC7ncfQ8M6oipvW2vUaw9sZZjtDmBHo7o11FYgu/41SJkFLEL7QlZ/IAalXwkrSGXy9A/ovmyjxRQMy/jATX+oowG97Hg+SyFU/LRWa2Av5sDBkX6YFi08PegrFqLb3ZdMvtcpNRcyUcAyClt/j6k/TL5OI2KYkG1uDjgrrtsL5jGU17Ovid7EhcHfPst65nGs2kTkJ0NLFkCKOj6hy2wlb8NCCGkObZ4HqOAGmk3TqUVYdJXe/Hj3qbLutzeKwR/PzMMAzuZVkKIEGJeVPZRfBeySvHljmTuvr4dvfDahHiRZ0Rs2cQeQdzxjdez1BIySwX7PJxUCPJwtOi8iP0a1Mxnti7B7vB2oXK15hTqxb8oa0qGmiXxMtQ6+rjcKPf5vJEstZ8PpCLHjsogNlfyEQCyJC7PSezI0KFAQgLr02XrwbQ//2TBpylTgBMnpJ6NeEJDKZBGCCGEmIgCasTuVWl0+GBTAm5fuA9J2cbTSD2cVPhqWi98dk9PuDs2XzqIEGIZgyJ94KIW/kG35XyWBLNpH1YeSQOvKq+fmwMWTu8NtZI+LhDT3dI1CLyWoxuu91HjlXzsHORG/a1IqzW3CGooJyuJtI2fqwPUCuF7g5QBNYPBwA2oRfjWBf96hnliTHyA4D5VGj2++veiRecnpuZKPgKwqwAiIWZz6RKQns4awM6dC+4HZFuxdSug0Ug9C0IIIcTu0BUyYteOpBbgls/34Nvdl9FU+6WhUb7Y/MxwTOweLN7kCCFcjioFbooVln28nFuOizm2V1vZ2hkMBmxPFGb/KeUyLJreG/7ulDVEWsbPzYEb4DidXozjVwuRyykzFhdI5R5J68UFusHT2fhiqKFU7tHs5HIZQjhlH00p+WgpuWXVKK8RlpgP92mYMfP82Bhuy6QVh68izUgfUVtjSkAtiwJqhAjNmwfExLDtffuAFSuknU9rnToFjB3LvpdVq1r22JISICfHMvMihBBC7AAF1IhdKq/W4q1153D3tweQYqQ5OQColXK8OTEeSx/qj0AqNUWI1eCtHgeAreep7KO5Xcotw5V84QXEid2D0De8+b5EhPAYW6Dy8eYL3PF46p9G2kAul2GAkT5qaqUc/ehcZhG8PmpSZqil5vGDYeG+DQNqcYHuuI1zjtLoDPhiO7/8sS2p1upQUqVt9n4UUCOEQ60GPv+87vaLLwJlNrig7+232b+pqUCWiVU+kpOB0aMBX1/ggw8sNjVCCCHE1lFAjdidfRfzMG7BbizZn9pkhYb4IHdsmDsUDw2NgJxXm4oQIpmbY/2h5ByXVPbR/LYl8FegjurMD2oSYorxXQOh4BzD+y/lc+/fmQJqpI2MlX3sF+4FRxX1hbEEXh+1zOJKaHR6CWbD758GABG+wp5Oz46J4Z6j/jiejku5NnjxvJ78sub7pwFATknzWWyEtEvjxwMTJ7LtjAzbCy6dPMl6wQFAUBAwe7Zpj/PzA3buZGUi16+37XKXhBBCiAVRQI3YjZIqDV5ZcwbTfzjU5OpYlUKG58fEYO1TQxAT4CbiDAkhpvJwVnEvjp5MK6KeH2a2gxNQU8plGB7jJ8FsiL3wdlFjiIll9uQyIDrA1cIzIvbOWEBtaBSdyyyFl6GmNwBZxdK8T6fm8wNqjUs+AizIdmfvUMG43gB8ujXJ7HMTk6kBNal+ToTYhM8+Y9lqAPDxx6y3mq2ozU4DgFdeAZyE52ouT09g2DC2fekSkGTb50JCCCHEUiigRuzCv4k5GPfZbqw4fLXJ+/UI9cCGucMwd1Q0VJxG6oQQ68Er+2gwGM+oIi1XWF6Do1cKBOP9wr3h4WS8HxEhppjYPcik+3Xyc6UMItJmcYFu6NPRq8GYs1qB23uFSDQj+8cLqAFAmkR91HgBNVcHJXxd1dz7zxsdDTXn74GNpzNx7lqx2ecnFlP6pwFAdikF1AgxKioKeO45tl1TU7dt7U6cAP76i20HB5uenVarNjMPADZsMNu0CCGEEHtCEQVi04oqavDcqpOYteQIMptYZemglOPVW+Pwx+ODERtIWWmE2AJjfdSo7KP57EzKgZ5TzWVUZ3/xJ0Pszrj4QKgUzZdUpnKPxBxkMhk+vqsH+l/vpRbi6YQv7u1FPXItiFfyEZCuj1oKp4dauK8zZDL+eSjE0wnTBnTg7vt0i+1mZuSaGlCjDDVCmvbaaywoBQDr1gGbN0s7H1O89Vbd9iuvAI4tfA+kgBohhBDSLAqotUM1NTX44IMP0KVLFzg5OcHPzw9Tp07F8ePHpZ5ai/xzNgujP92NNcczmrxfv3Av/P30MDw6PBJKykojxGYEezqhW4iHYHz/xXyUVWslmJH92U7904gFeTirMDy6+XJ7nYNooQsxjwhfF6yaMwipH07A3pduxmgjCzOIeYQZyVCTIqBmMBhwhZOhxiv3WN+TN0fBiZMhuz0xB8evFpptfmIyteRjeY0OpVUaC8+GEBvm6gp89BHbdnZm/dSs2bFjLPAHACEhwCOPtPw5YmJYdh4A7NkDFBWZbXqEEEKIvVBKPQEirpqaGowbNw47d+6Ev78/brvtNmRmZuLPP//Ehg0bsH79eowbN84ir61UmufXLa+sGv9Zdw4bT2c2eT9ntQIvjY/D/QM7Qs5pOk4IqRMXFyf1FLjGxgfgTEbDsks1Oj12XcjFBBPLyRE+jU6PXUm5gvFOfi6I8G36AiQRn7Ueo82Z2CMI2xObLtPaOZAy1Ij5GctKIubj5+YAB6Uc1Vp9g/H0gqZLPlrifJZTWo2KGp1gvLmAmp+bAx4cEo5FO4X9kT7efAG/zh5otjmKxdSSjwCQXVINN0cq8UyEbPVzh9lNmwZcvAg8/DAQKuy7aFXq90579dWWZ6cBgEzGstQWLAB0OpaVd889ZpsiMR86Rgkh9sIWz2eUrtPOzJ8/Hzt37kS/fv2QnJyMVatWYc+ePfj111+h0WgwY8YMlJaWSj1NLoPBgLUnMzDm013NBtOGRPlg8zPD8cDgcAqmEWLDxnShso+WciSlAKVVwky/0ZSdRsxodOcAqJVNf9ykko+E2CaZTIYQTpaaFBlqKXnC7DQACDdhgcic4Z3g5iBc+Lf/Uj72X8xr89zE1rKAGpV9JKRJMhnwn/9YfzAtLQ345x+2HRrKAoCtRWUfCSGEkCZRQK0d0Wq1WLBgAQBg4cKFcHevu4B133334dZbb0VeXh4WL14s0QyNyy6pwuylx/D0bydRWGG8NImbgxIfTu2GXx4egDBvfl8HQojtiA1wQwfOsbwjMQcanZ7zCGIqY1lDI+OofxoxHzdHFW6ONV720ctZhQB3BxFnRAgxJ14ftfTCpjPULCHVSEAtwrf5vwc8ndWYPbwTd9//tlyAwcBpNmrFTC35CABZ1EeNEPsQFgYkJbEyj2++CTi04bPVsGGA2/Vy3Js2sUw1QgghhNxAAbV2ZN++fSgoKEBERAT69u0r2H/P9VT+tWvXij01owwGA1YdTcPoT3dhW0J2k/e9OdYPW54bjnv7d6AyP4TYCZlMhrGcHjilVVoculwgwYzsg8FgwHbOOdXDSYW+Hb0kmBGxZxO7Bxvd1znInd6zCbFhoZwMtaySKtRoxV30ksLpnwY0X/Kx1kNDI+DtohaMn7haZLTfqLXiZaiFePL73WWXUkCNkBYpLgb+7/+Aw4elnolQeDjw/ffA7Nltex61Ghg7tu45M5uuDkQIIYS0NxRQsxCdToezZ89iyZIlmDt3LgYNGgRnZ2fIZDLIZDI8+OCDrXretWvX4s4770THjh3h6OgIf39/DBo0CB999BGKi4ubfOzJkycBAH369OHu7927NwDg1KlTrZpbc/T6lv1hnVFUiQd+OoIXV5/mliWr5eGkwmf39MDiB/shyIP/xyIhpGk5OTnIzMxETo71XTQawwmoAVT2sS0u5ZYjNV+YQXBTrB+UCvpoYI2s+Rhtzsg4fziq+L9XcdQ/jRCbxguo6Q1NZz5Z4nzGy1Bzc1Ryg2Q8rg5KPHFTJHffx1suQK+3nSw1XkDNWGndbMpQI0bY8ucOizl/HoiJAT75BJg3D2jh9Q2b8v77QHo6cOyY9Ze7bKfoGCWE2AtbPI8Ji8UTs7j77ruxZs0asz1faWkppk2bhg2Naljn5uYiNzcXBw8exJdffomVK1di8ODB3Oe4cuUKACDUyAei2vGCggKUlZXB1dXVbPMHTA+olVdr8cvBK/hiezLKOc3F6xvfJRDvTOkCf7dWNNwlhNxQUFAArVYLpVIJf3/rKvnXp6MXvF3UKChvWMJo6/lsvD2pC2W3tAIvOw0ARlH/NKtlzcdoc1wclBgVF4CNZ4QrnDsHuUkwI0KIufBKPgKs7GMHH/4+S5zPUvOEi0QifF1a9BlhxsCO+H7PZWSXNAxIJWaVYuOZTNzWw3i2rbXQ6Q2Cz0sAEOThCF9XNfIalYPMoh5qxAhb/txhMdHRgK8vkJMDHDoELFsGPPCAtHPKyAC8vQEnMy8sjokx7/NZK4MB2L4dCAwEunaVejYtQscoIcReFBQU2Nx5jJahW4iuUZ1pb29vREdHt+q5tFot7rjjjhvBtICAALz++uv49ddf8dVXX2HIkCEAgPT0dEyYMAHnzp3jPk9ZWRkAwMWFX/qkfgCttLS0VXNti+JKDb7Ynowh83fgg78Tmwym+bqqsXB6b3xzfx8KphFi55QKOUZx+nplFlfhbEaJBDOyfbz+aQq5DCOijfe6IqQtbusRxB3vGeYp7kQIIWbFy1ADgDQR+6jp9Qakcko+mlrusZajSoG5I/l/r322NQlaG+jdWlhRA14yna+rA/dvpsbBQ0JIE1Qq4PPP626/9BJQIvHfIrNnA5GRwBdfADWm908k1330ETBmDDBgAHDhgtSzIYQQYiMooGYh/fv3x8svv4zff/8dly9fRn5+Pl599dVWPdf333+PrVu3AgDi4+Nx6tQpvPvuu7jvvvvw5JNPYu/evXj++ecBAEVFRZgzZw73eWobaltbNkd+WTU++icRQz7cgU+3JqGoQtPk/af0DMaWZ0fg1m78i3OEEPtjrOzj7uRckWdi7gZx5QABAABJREFU+4oqanDsSqFgvF+4FzycVRLMiLQHY+ID0auDZ4Oxm2L9EB1AGWqE2LIwoxlqlaLNIaukCtWcnm3hvi0LqAHA3X3DEOYtDBJezivHmhMZrZqfmHjlHgHAx1WNQA9eQI0y1AhpkdGjgdtvZ9vZ2cC770o3l4MHgb//Zj3OPv3Ucq9jMAASLLi2uJQU4OWX2XZFBfDll9LOhxBCiM2QrOTjtWvXsGrVKuzduxdpaWkoLCyETqfDpUuXGtyvoqICqampAFiWV2BgoASzbbnWBs8a0+l0eOedd27cXrZsGQIChBeW58+fj+3bt+PkyZPYt28fNm/ejHHjxjW4j5sbu2hVm6nWWP3x2vtaUlZxFb7bfRm/Hr6CKk3zKz4D3B3w3yndMNrIhXVCiP0aFu0HtUKOmkarwxMyKUOtpXZeyIWOs3x9NJV7JBakkMvww8y++Hb3ZSRll6JLsLvRTBBCiO3wdVXDQSkXBLTEDKjx+qcBQIQvP9jXFLVSjmdGxeD534U9pT/flozJPYPhoFS0+HnFkl/Gz1DxdXVAgLswoJZTWg293gC53LoWXBJi1T75BNi0CaiuBhYsAB55BIiNFX8eb71Vt/3664DatJ6RJquoAF59FdiwgZW7/Ptv8z6/1ObNa3i7qEiSaRBCCLE9ogfUqqqq8H//93/44YcfoNHUZSIZDAZu5pTBYMCwYcNQVFSErl274tQp4R839mzXrl3IysoCAIwYMQK9e/fm3k+hUGDevHl46KGHAAArVqwQBNQ6duwIgJWG5Kkd9/b2Nnv/tPqu5ldg0a5L+ONYuuDiuDH39A3DqxM6w8OJsicIaY+c1ApE+bvifKMAWmKWHa6WtDBeuUcAGMkpq0mIOfm4OuDVWztLPQ1CiBnJZDKEejnhUm7DoFa6iCUfUzjlHoGWl3ysNaVXCBbtuoSLOQ0XIWYUVWLlkTTMHBTequcVg7EMNT83NQLcHQTjOr0BeeXVVEKfkJaIiABefJFlp2m1wDPPsACbmJWADhwANm9m2+Hhlunl5uQE/PEHkJ7OvsrLASPtQ2zOunUsUFjr9GmgWzfp5kMIIcSmiFrysaSkBIMHD8aiRYtQU1MDg8Fw48sYFxcXPProozAYDDh79ixOnz4t4oyl988//9zYvvXWW5u8b/39f3NWD/Xs2RMAcOzYMe7jjx8/DgDo0aNHS6dpEp3egOdWnsTNn+zEisNXTQqmDYjwxorZAzH/zu4UTCOknYsLEmbOpuSVo0pjvN8iaUij02PnBWFArZOvCzr5WW4hBSGEEPsVyin7aB0Zaq278KuQy/D8mBjuvi93XERlE32epZZbyg+o+bo6IJCToQYA2cXUR42QFnv5ZSAsjG3/8w+wcaO4r984O01lgWslMhkwYQLbrq4Gtm83/2tIoaKiYXbaihUUTCOEENIiogbUZsyYgZMnT8JgMMDf3x8ffPABDh06hBkzZjT5uGnTpt3Y5gWK7NmZM2dubPfr16/J+wYEBCDs+oe6nJwc5OY27C00ZMgQeHt7IyUlBUePHhU8fuXKlQCAyZMnt3XaXIWVOqw5kcEtNdbYiBg//P7YIKycMwiDIn0sMh9CiG2JCxQG1HR6g2AFOTHuSGoBSqu0gvFRnSk7jRBCSOuEegl7jmWVVKGG09fMElLyhNlwns4qeDq3vvzZ+K6B6BriLhjPLa3GzwdSW/28lpZnpOSjj5GSjwD1USOkVZydgY8/rrv9zDMs6CSG/fuBLVvYdqdOwMyZlnutiRPrtutndNmy998Hrlxh26NGAffcI+18CCGE2BzRSj7++++/2LBhA2QyGeLj47Ft27YbvcA8PDyafGy3bt3g7++P3Nxc7N+/X4zpWo0LFy7c2I6IiGj2/hEREUhLS7vxWD8/vxv7lEolnnnmGbz55pt44oknsG3bNri7sz8Uf/vtN2zatAm+vr43ykaa4urVq7h69WqT96mp4f9hxzOuSwCeujka3UKb/p0ghLQ/cYHCC1sAcCGrFF1D6Jxhih0Jxso9Uv80QgghrcPLUDMYgMziSnRsZdnFlkjllHxsbbnHWjKZDM+PjcWsn44I9n2z6xKmDegAd0frq56Rzyn56KiSw0WtMBpQy6KAGiGtc9ddwMKFwMmTwFNPAXKR1qv/5z9125bKTqs1ciTg6AhUVbGAmsEgbmlLc0tKAv73P7atUgFff93w+9FogGPHgIEDpZkfIYQQmyBaQO2XX34BwP44Wb58+Y1gmql69uyJLVu2IDEx0RLTs1pF9Rqj+vr6Nnt/H5+6bK4iTlPVl156CTt27MDOnTsRHR2NESNGICsrC3v27IFKpcKyZcvg5ibMAjFm8eLFePvtt02+P49cBoyIcMU93TzROdgTHRsF065cuYLKyubLxvj6+jb4P9LpdEhOTjZpDh07doSTU93q2pKSEly7dq35ucvliIlpWBImKyuL+3/fmJubG0JCQhqMXbx4EVqtMHukscDAQHh6et64XV1djZSUlGYfBwCRkZFQ1fvQXVBQgJwc/kX2+hwcHARB3bS0NJSX88vs1Oft7Q1//4YZMKYey6GhoQ16+pWVlRntA9hYXFxcg9s5OTkoKCho9nEuLi43sj1rpaSkoNqEVYf+/v7w9va+cVuj0eDSpUsmzTciIgIODnX9LYqKim70UGyKUqlEVFRUg7GMjAyUljbfX8zT0xOBgYENxpKSkqDXN7+qPDg4+EZQHgAqKytxpXa1XzOio6OhUChu3M7Ly0NeXl6Tj1FU8I+Nw0np6OLSfJYanSNS8Pdp4bHjqpbDrToHiYksq5nOEdZ5juD93OkcIeTk5HSjZ2wt+hwhRJ8j7O8cAUj3OSLMW5ihBgD7Tl1AZbAw2Fb7O9r4PNKac4ROb8DVfGGGmrdKy/0dack5ItBgQLy/I87nNAw4FVVo8OOeFMwbGWl154jUbOHvp6+rA2QyGQI9+AG1c5czkOjZ8PuncwSdI2qPU61Wi4sXL9LnCCOfI1SvvQa9szN0Pj7wLS62+OcIp2PH0HHbNgBATVgYUvv3R+MCteb+HBE6YABcd+0CMjORsmYNqrt0sd1zhMEA9O8P7N2LvFmzkGcwANfPFV5Ll8L7xx+hLCjAxR07oLu+ON1azxH1j9H65ztb/Bxhz+eI+uhvDRs4R9RDnyOELHWOMOV3ytqIFlDbs2cPZDIZ+vXrh+7du7f48bUn2OzsbHNPzaqVldVdJHZ0bL5ZdP0TLO/NS61WY/Pmzfj444/xyy+/YN26dXB1dcXkyZPx5ptvonfv3uaZuAmUcmBkhCvu7OqOYDd2suQdRFqt1qSDS6cT9jMw9aBs3MfPYDCY9Fg5ZxWaTqdr03xNeWzjDxemztfYc5ny2PofNmq19Xs1RWt/NsbmIfbPpvaxpmj8vZr6s+Fp6ffq4uICnU4HhUKB0tJSkz7Aiv2zcVcZ4O6gQEl1w5/Ppbwqq/09tKZzRGp+JTJKNIL79gl2BPQ6GKvMRecI/v0aE+Mc4eTkBLW6roSZmOeI+rRarVWeI2rnxhuz1t9DazpH0OeI5ln7OUKqzxGhXvxssMziamj9jZddbPy3TWt+NpnFldy+zEGuCqPnA96Ysde9v4cHXtkqzOD6cW8K7h8YZnXniELO4iMfV3bhw8tZBZUc0DT678orrxE8H50j6BzR3PPT54jr+4KCau8kyu+h97ff3tjOefRRaDnZYub+PSwePpwF1AA479iB8thY2z1HxMYCu3cDK1YgOyoKhnqPlefmQnX9or37ypXIfewxALZxjqg/boufIxqzp3NE49cw5fl46G8N+hzR+H6NWfPnCB5Tv1drJVpArTZiGR8f36rH1waKKiqEKxBJy6jVarz66qt49dVXpXl9hQzjo91wZ1dP+Ls2LE+gVAp/JZVKJXe8Md7J05THASxzsvFtUx7Le3NSKBQWn2/j1zV1vsaey5TH8u4jxvfa2p+NsXm0Zb68N63GeL8Trf1exfzZ1F/dYuqKMCl+NpE+jjhxreEKo5TCaqv9PbSmc8TRTP5qpYEdXJt8XjpH8O/XmBjniNDQ0AYrwqT82VjrOYI+R9DniMba0zlCqs8RoR78DLXcSn2Tz9G4+kZrfjapnP5pABDq6WD0fMAbM/a6vUJc0Tu4BMevNVx5Xlatxbe7U3BHpHWdI4qrhedmP1f1jdf0cVYhq6zh4poCzs+JzhF0jmju+elzBP81UFMD7NwJ9OgB+Pqa/fcw6733oPnhBzgfPIiyKVNEOW4qb74ZePddAID7nj0onDvXts8RMhkwbRoUjbJPiu+9F76LF0Om18N79WoUPvYYoFTSOYLOEYK58cas9b2K/tagzxGN2do5wlrJDI3Dixbi4uKCqqoqPPzww/juu+8a7Js7dy6+/vpryGQyoz/8e++9F6tWrYK3t7dJabjWaMmSJZg1axYA4IEHHsCSJUuafYy3tzcKCwsBsIyz+mmkPFOnTsWff/4JAFi/fj0m1m8iawGm9FCbOnUqcnNzoXTzwTu/7cHDwyLg79Z8th0hhDT21rpzWLI/VTB+/I0x8HYxvgqeAPd8ewCHUhqWD1DIZTj++hh4OFtfHxhCCCG2wWAwoPOb/6CqUerTlJ7BWHBvL4u+9rKDV/DGX2cF4+ueGoLuoZ5meY1TaUWY/PU+wbijSo7dL9wMfyO9ycRmMBgQ+/o/goy9e/uF4cM7WIWYOxftx9ErhQ32xwa4YfOzw0WbJyF2Kz8feOMNYNEidrtDB6BfP1ZisF8/oE8foF55ujbRagExL0T27AmcOsW2MzOBRiX6rJ6pvd8mTwbWrWPbf/wBTJ1q2XkRQgixSaK9A/v5+SEtLQ1paWmtevyJEycAQFBb1955enreCKjl5eU1G1DLz89v8FhL69ChAzp06NDkfWrLU/m5OeKVWztbfE6EEPvVOYjf4zExqwSDI325+whQXKERXEADgL4dvSiYRgghpE1kMhlCvZxxMaeswXh6YfP9RNoqNY/fFyPcl1+GsjV6hHlibHwAtpxv2HqgSqPHV/9exDuTu5rttdqipErLLX/p41q34CiAE/zLLhWWtCSEtML06cDmzXW3r15lX3/8wW7LZEBcHAuu/d//Ad26tf61xF7VP3EiC6i5ugJnz9pWQO3cOWDaNGDBAuDmm5u+7xNP1AXUFi6kgBohhBAuYZ6fhXTv3h0GgwEHDhwwqVljfUePHkVycjJkMhkGDhxooRlap9jY2BvbpjRwrH+f+o+1BnITFgQRQkhTYgP5qzoTM5tveNye7UzKgU4vTEgf3TlAgtkQQgixN6FewrKPUgXUfFzUcHc072KR58fGcpMbVhy+irQC62hJkF/GL+3s61pXKpgXUCuq0KBK03yJIEJIM/74A1i1igXLRowAXBoF9g0GICEBWLoUqGoUyE5MBH76iQV/eFWbpG59MmsWsGULkJcHjB4t7VxawmBgQbLTp4GRI4HVq5u+/5gxQFQU296+nf1cCCGEkEZEC6jdeuutAFjZws8//9zkx2m1WsybN+/G7QkTJph9btasW71VS0eOHGnyvtnZ2TcyAP39/eHn52fRubWUKbVcCSHSSElJQXJyskmBeynFBLhyL2hdyKKAWlO2J+Rwx0d29hd5JqS1bOUYJYS0T2FezoKx7NIqVGuFn//NeT5LyRcG1MyZnVYrNtANk3oEC8Y1OgPWnsww++u1Rl5ZDXe8fkAt0MOBe5+cEn4wjrRf9LmjFVxcgLvuAv73P9ZHrbiYZXP99BPw+ONA376ASsW+undv+Ni1a4GHHgK6dgU8PYGbbgJeeAH4/Xdg5UpWPvLDD4GyMs4LiyAykgWbHPjnEKu1fDmwezfbjoxkmXZNkcvZz6pWbflOK0THKCHEXtjieUy0gNrMmTNvlGt866238NtvvzX7mLy8PEycOBEHDx6ETCZDdHQ0br/9dktP1aqMHz/+xvbff//d5H03bdp0Y7s2gGlNRGrXRwhpherq6htf1sxZrURHb+FFu8SsEglmYxs0Oj12XhAG1CJ8XRDp13QZYWI9bOUYJYS0T7wMNYMBuFYkLCdorvOZVqfnZoeF+5g/oAYAz46OgYJTciPBShb15BnJUGuu5CMAZJVQ2UfSEH3uMAOFAujSBXjwQVY+8MgRoLQUOH5cGJg6fLhuu6wM2LUL+Phj4O67gXvvZf3ZXnmFBd6IaYqKWLZgrS+/BBxN6Hk5axbgdP09bckSoJxfWlhqdIwSQuyFLZ7HRAuoOTs738hM02g0mD59OsaNG4fFixcjI6NuVd/Zs2exZs0azJkzBxEREdi6dSsAQKFQ4NtvvxVrulZjxIgRNwKRO3fuxPHjx7n30+l0+OKLL27cvvfee0WZHyGEiC2OU/YxKbuMW9KQAEdTC1FSpRWMj4qj7DRCCCHmEcrJUAOA9ELLlSm7VlQFjU743h/uw59LW4X7uiDaX7gQJavYOoJRxko++jVT8hGggBohonFwYFlojb3wAjB/PnDHHSwbjSc2lgXXrIEtLJZ+4w0g+3rvy9tvB265xbTHeXkB993HtktKWJabvUtNZZmVqalSz4QQQmyCqJ1M77rrLqSnp+OFF16AXq/Htm3bsG3bNgCsmTUA9OjR48b9azOaFAoFFi5ciBEjRog5XaugUCjw5ptv4oknngDAMv127NgBf/+GF0JffvllnDx5EgAwZMgQjBs3TuypEkKIKGID3fDPuawGY5UaHa4WVCDCAmWebN32hGzuOJV7JIQQYi68DDXAsn3UeOUeAcuUfKwV6OGIxEYZadYSUMs1peSjkYBaDgXUCJHWwIHsq1Z2NstoO3yY/VtSAixYwLLepPTzz8CffwKXLrG+ZLxa/Nbg+HGWFQgAzs7s/64lnnyS9cSbNQsYNcrs07MqZ88CN9/M+uMtXMhuN+7/RwghpAFRA2oA8Oyzz6J79+54/PHHcfHixRvjtQG1xmUBo6KisGjRIoyysTexlJQU/Pjjjw3GTp8+fWP7xIkTeP311xvs7927N6ZOnSp4rtmzZ+PPP//E1q1bce7cOfTo0QOzZ89GfHw8CgoKsGLFCuzduxcA4OHh0S4z+Qgh7UfnIDfu+IWsEgqocexIFJZ7dHNUol+4twSzIYQQYo+MB9Qsl6GWmscPqFnys0CQhzAglV1SBb3eADmnHKSYeCUfFXIZPJxUN24bzVCzkqAgIeS6gADW76u5nl9iW7YM2L6dbSckAPHx0s6HR68HnniC/QuwTDVjWX/G9O4NZGbWlX60VwkJLGCYl8dup6YC770HfPCBpNMihBBrJ3pADQBGjRqFCxcuYP369di0aRMOHDiAa9euobi4GC4uLggICMCAAQMwceJE3HHHHZDLRatMaTZXrlzBf//7X6P7T58+3SDABgAPPPAAN6CmVCrxxx9/YNq0adiwYQOysrLw7rvvCu4XGhqKlStXokuXLm3/BgghxErFcko+AkBCZinGdw0SeTbW7XJuGS5zLjjeFOsPlcL23lsJIYRYJ28XNZxUClRqdA3GLZqhZiSgZtEMNXfhxVWt3oC88mr4u5nQm8eCeCUffVzUDQJ9TmoF3B2VglLQVPKREGKSiRPrAmobNlhnQG3xYuDQIbYdFwc891zrnsfeg2lJScDIkUBOo8WXH38MzJjB+v8RQgjhkiSgBrCMtEmTJmHSpElSTcGmuLm5Yf369Vi7di2WLl2KI0eOICcnB25uboiMjMTUqVMxZ84ceHh4SD1VQgixqA7eznBUyVGl0TcYv9CoBBMBticIs9MA6p9GCCHEvGQyGcK8nZCUXdZgPK3AghlqnJKPvq4OcHWw3J+4vAw1gGV4SR1Qy+OUfKxf7rFWoIcjSqoa/pxySmyvGTwhRAITJwLPPsu2N2wAXnxR2vk0VlMDvPlm3e2vvwbUaunmY81WrwayrrdR6N0bGD6clcbUaoHHHwd27bLekp6EECIxyQJq9u6mm24SlK80h8mTJ2Py5Mlmf15CCLEVCrkMsQFuOJVe3GA8MatEohlZr+2Jwv5pCrkMN8X6STAbQggh9izUy1kQULNkhhqv5GOEr7PFXg9gwSiezOIqdA+16Es3i1fy0cdVeCE5wN1R8HOiDDVCiEmiooCYGJbdtH8/UFAAeFtRGXm1Gtixg/VA8/dnGVhtlZfHst68vIDZs9v+fNbilVeA8nJg40Zg61bWa279etYfb88eYPlylqlGCCFEgOo9EUIIsTmxgcI+alcKKlBRo+Xcu30qrtDgSGqhYLxPRy94OtNKTUIIIebF66OWU1qNqkZlIM1Bo9MjjROsC/exbC/VpjLUpJbPyVDz42So8fqoZZVUWWQxKCHEDtX2ddPpgM2bpZ0LT1wcsG0b8OOPbX+uwkKgY0fgpZeAd99l2Vv2QiZj/dL27WNBUUdHYOFCQKFgZTJpIT8hhBhFATVCCCE2J47TR81ggGDFdXu2MykHOr3w4tjozlTukRBCiPnxAmoAcK3I/Flq6YWV3Pc4S/ZPA4CAJjLUpFSl0aGsWnih19eNF1ATjtVo9Siu1FhkboQQO1MbUANY2UdrJJOxjKu28vKqy3JLS2PZXLYqPR04eLDhmEwGuNR73xw7lmWoffIJ4CZcwEoIIYQxa8nHq1evmvPpjOrQoYMor0PMSy6n+C0h1srf3x96vd5mjtM4ToYaAFzIKkHPME9xJ2OldiTy+6eNjAsQeSbEHGztGCWEtD+hXvyLl+mFlejk53rjtjnOZ7xyjwAQYeGAmpuDEi5qBcprGmbdZRVbrrSlKXJL+T3QfFyEGemBnAw1gGWpUQY7qUWfO4hRQ4cC7u5ASQnw998sa0spcTeZI0eAbt1YlpW5PfFEXeDw66+tJnOrRcfotWssMHjtGrBpE+uXZkzHjuabJCGEmMDf3/YWfZv1XS88PBwyCzetlMlk0NpTmnU7Qh/GCbFe3tZU+94EvJKPAJCYVSryTKyTVqfHzgu5gvFwH2dE+ln2YiOxDFs7Rgkh7Y+xDLXGfdTMcT5LMRJQs3TJR5lMhkAPR1zKbfj6Umeo5ZcLyz0CgK+JJR8BILukGnGBZp0WsWH0uYMYpVIB48cDq1axkogHDgDDhkk3n+xsYMwYwNcX+OorNjdzGjcO6NQJuHyZ9RpLSmJ95CRm8jGanQ2MGgUkJ7Pbc+cCJ04Apl6fu3oVCApiP3dCCLEAW/zMYZEIh8FgsOgXIYSQ9s3H1QF+nDJGiZkUUAOAo1cKuaWbRnUOsPjCF0IIIe2TsQy1tMIKs79War6RgJqvGUp8NSPIQxg4zCqRNqCWZyRDjV/y0UhAzQr6wBFCbET9so/79kk3DwB44QWguJiVKly50vzPL5cDjz9ed3vRIvO/hqXk5rLMtMREdjs8HFi/3rRgmkYDzJ/PetItWGDJWRJCiM0xa4Zahw4dTLpQd+3aNWi12hvBMZlMBg8PD7i4uKC8vBzFxcUN9qlUKgQFBZlzqoQQQmxcXKCboMRRYlYJDAZDuw8abU/I5o6PirO9VHpCCCG2wctZxS2H2DhDzRx4GWoB7g5wVlu+7Fggp49aZnGVpJ8/8spaUPLRSB84qYOChBAbcsstwGefARMmANHR0s1j925g2TK27enJAkCWMGsW8MYbQFUV8NNPwHvvNew9Zo3y84HRo4Hz59ntDh2Af/9l/5ri3Dng1VcBvR546y3g7rupHCQhhFxn1gy11NRUpKSkGP1KSkrC7bffDo1GA5lMhvvvvx9bt25FUVERCgoKkJaWhoKCAhQVFWHr1q2YPn06ZDIZNBoNpk6diuTkZKSkpJhzyoQQQgBoNJobX7aC10etsEJjtI9Ie7Kd0z/NzVGJfhG2l0pPGFs8Rgkh7YtMJuNmqaU3ylAzx/nsSr4w683S5R5rBXECUjVaPYoqpDs/Gyv5yMvm93V1gJwT98u244CaXm/Av4k5WHU0DTl2/H2aE33uIE3y9QWeeUbaYJpGw/qb1Xr/fcBSfXh8fIB772XbxcXAihWWeZ0WaPIYLSxkZTBPn2a3Q0KAHTtYhpqpevYEnnySbVdUAPPmtXXKhBDCZYufNURtavXUU0/h888/h5+fH/bs2YOff/4Zo0aNgptbw4uibm5uGDVqFJYtW4bdu3fDx8cHCxYswOP106yJzaHed4RYr0uXLuHChQu4dOmS1FMxWVygO3e8vfdRS8krx+Vc4cr9ETF+UCmol6WtssVjlBDS/vD6qDXOUGvr+axGqxcE6QAgwlecgJqxDC8p+6gZW0zkzclQU8hl3ECbvQbUdHoDHvjpMGYtOYIXV5/GsI/+xd7kPKmnZfXocwexel98wbKoAKBvX+DRRy37erXBJQD4+mtA4nY0Ro/R4mLW9+3ECXY7KIhlpkVGtvxF3n2XPR4A1q0D1q5t26QJIYTDFj9riHZlbdu2bfj+++8BACtWrMCgQYNMetzgwYOxYsUKGAwGLF68GJs3b7bkNAkhhNiIWE6GGsDKPrZnRss9dqZyj4QQQiyLF1DLLa1GlUbHuXfrpBVWQM+5jhkuUkCNl6EGAFkl5i9taSpeyUdPZ5XRhTSBnD5q9lry8Z+zWdhTL4BWrdXj1T/PQM/7JSKE2IaMDFaGEABkMmDhQkChsOxr9u0L9OvHtpOTgStXLPt6raHTsTKcR46w2wEBLDOttZmEHh4N+6fNnQuUlbV5moQQYutEC6j98MMPAICePXti5MiRLXrsqFGj0KtXLwDAjz/+aPa5EUIIsT1R/q5QcGoWtfcMte0JwnKPchlwUwwF1AghhFgWr+QjAGQUmS/YlMrpnwaIV/Ix0F0YNASkzVDLLxOWfPR1FWah1QrgBNSyS+yzZPbOC8LPRVcLKnAirUj8yRBib44fB157jQWaxAy0PPdc3es9+mhdoMvS3ngD+PJL4Nq1lpVPFItCAcyZA8jlgJ8fsH07EBfXtue86y5g7Fi2nZYGvP122+dJCCE2TrSA2pEjRyCTydCzZ89WPb5Hjx4wGAw4duyYeSdGCCHEJjmqFNzyTomZ7TegVlypwZHUAsF4347e8OKUfSKEEELMiZehBgBpBcISja2VYiSgJlbJR6MZahIG1HgZar6uxt/3eQG1vLJqaHR6s87LGpzP5Fcu2HI+S+SZEGKHfviB9S47ehTYtk2c19y2DVi1im37+rLXF8tttwFPPQW481sPWIX772c93rZtA7p0afvzyWSsxKXD9UUan30GnDnT9uclhBAbJlpALTMzE0DrG83pdLoGz0MIIYTwyj5ezCmD1g4vCJliV1IutJwSRlTukRBCiBjCvPkZao37qLVFaj4/oNbRh//a5ubprIKDUvhntJQZaryAmk8TGWq8PnAGg/FebLaqRqtHcjY/a2bLuWwYJO6BRIjNmzixbnvDBnFe8+LFuuDORx8B3t7ivK614p3H7r4b6N7dfK8RFcUyEQFWVvKxxwB9+/x7mxBCABEDam5u7KLn4cOHW/X4gwcPAgBcXV3NNidCCCG2rTMnoFaj0xtdvW7vdlD/NEIIIRIylqFm1oBanjDbLdjDEY4qC/fPuU4mk3EDUlJlqGl0ehRWCBet+rWw5CMAZNtZH7VLuWWoMbLIKiWvHMk51AuIkDa5+WbA6fp5f+NGcYIsjz0GnDsHvPgi8MADln+9phQXswCTRGTV1QidMwdYssTyL/bii0BMDNvOyADS0y3/moQQYqVEC6h17doVBoMBycnJ+O2331r02F9//RXJycmQyWTo2rWrhWZICCHE1sQG8stttMc+alqdHv9eyBWMd/RxRqQfLUYhhBBieR5OKrg6KAXj6YWWLfkYLlK5x1qBnIBUZrH5goYtUVgu7J8GNFfykR9ss7eA2rlr/HKPtTafpbKPhLSJkxMwejTbzspiPdXEEBkJzJ/PeoVJISmJ9SoLDgY2bZJkCrKaGnR49lm47tkDPPQQ8NNPln1BBwdg0SLgpZdYQLNDB8u+HiGEWDHR3n3uu+++G9sPP/ywyUG1FStWYPbs2TduT58+3exzI4QQYpviOBlqAJCY1fQFFHt07EohiiuFK9RHxQVAJpNJMCNCCCHtjUwm42apmStDrUqjwzVO4ErsgBqvj1pmcZUkJQRzOeUegWZKPhrNULOvko/nmwmobTnPz+wnhLSAGGUfDQag2orOT8nJwHffARUVrL+Y2GpqEPzss3Dbs4fddnauyx6zpJEjgQ8/BFzEfc8lhBBrI1pA7aGHHkLv3r1hMBhQWVmJ6dOnY8iQIfjmm29w4sQJ5Ofno7KyEvn5+Thx4gQWLVqEIUOGYMaMGaisrIRMJkOvXr0wa9YssaZMCCHEyoV6OXFXwl9ohxlq2xNzuONU7pEQQoiYLBlQSyuo4LaLCRepf1qtQA/h91hRo0NptVbUeQBAXpmxDDXjATV/IwG1LDvLUDufWdzk/jMZxcgokiazkBC7MWFC3ba5A2qFhcBXXwHdurEeXhXmy3Zuk/HjgfBwtr15M+vrJhaNBrjvPrjt2AEA0Ds6snKbQ4aINwdCCGnnRAuoyeVyrFu3DjH1Vk0cPHgQTz75JPr27Qt/f3+4urrC398fffv2xVNPPXWjbxoAREZGYu3atZBLldJNCCHE6shkMsRystTaY8nH7Zz+aW4OSvQLb+eNugkhhIgq1EsY3Morq0aVpu19Zoz1SA33kT5DDQCyJeijlm8kQ62pko/ujko4cXrOSTF/SzEYDM1mqAHAlnNU9pGQNgkJAXr1YtvHjgHXrrXt+QwGYPduYOZMVlJx7lxWYvCTT4D4eCAzs+1zbiuFAnj88brbixaJ87paLXD//cCaNQAAvYMD0hctAkaMEOf1G7t0if0/1PAXdhBCiL0SNToVHByMAwcOYObMmTAYDCZ/3X///Th48CBCQkLEnC4xM6VSmEVCCLEOERERiIqKQkREhNRTaTFeQC29sBKlVcLyh/YqNa8cl3KFFxmHx/pBraSFKPbAlo9RQkj7wstQA+r6qLXlfJaazw+oRYjdQ81IQC1TgoBUntGAmvEMNZlMxv0eskvtJ6CWXliJkqrmMwY3U0CNiz53kBapX/axtT3FcnOBjz8GOndmAaJly4CqeuekgQNZUC0oqG1zNZeHHmJ9xQBg8WLLZ8+lpLDMuJUrAQAGtRra1asRUK+9jqiWLQO6dgW++Yb9XAghpJVs8bOG6FfZvLy8sGTJEiQmJuKFF15A3759oVY3XD2nVqvRt29fvPDCC0hISMDPP/8Mb29aYU8IIZbi4OAAR0dHODgYv/hirTob6aOWlN1+stS2cbLTAGBUHJV7tBe2fIwSQtoXXoYaAKRdL/vYlvNZSp7wgqVMBoR5i1vy0ViGWpYkAbWWl3wEAH834X4p5m8p5zNN66d7OKUAheWUXdEYfe4gLdLWgNorr7BMtxdeAC5cqBv38gKefho4cwY4cAC44462z9VcfH2Be+5h20VFwG+/We61tm5lZS+3b2e3VSrI/vwT6okTpTtGu3aty0x75x3g8mVp5kEIsXm2+FlDspShmJgYzJ8//8bt4uJilJWVwdXVFR4eHlJNixBCiI2JDXTnjidklqJPx/axGGMHp3+aXAbcHEsBNUIIIeIynqHW9l5VqZySj8EeTnDklC+0JGvPUHNRK+Ckbvr/hJuhVsLPdrNFppR7BAC9gS1MuqtvmIVnRIgd69uXlWYcORIYPbrljw8MZL3Bat10EzB7NjB1KuDIP99ahSeeAJYuZdtffw3MmsVWeZhb796AiwtQXg6EhQFLlrD/ayn16gXMmwcsWMAyCefOZT30LPH9E0KIlbGaOlAeHh4ICQmhYBohhJAW4ZV8BIAL7aSPWkmVBodTCgTjfTp6wcvFeP8UQgghxBLCjGSo1ZZ8bAteyUexyz0CgK+LA5Ry4UXDrJK2Bw1bipeh5svJPmss0F14kbqsWouy6ubLJNqCc5yAGu9nBgCbz/Ez/QmRml5vwNqTGfhxbwrOpBdLPR3j5HLgiy+AKVMAV1f+fbRaYP16YNIk4NSphvvuvx/o0AF48UUgKQn4919g2jTrDqYBQP/+QJ8+bPv4ceDwYcu8jo8PC9g99hhw9qz0wbRa77zDMgsBlpn455/SzocQQkRiNQE1Yv8MBoPUUyCEGFFUVISCggIUFRVJPZUW83BSIZizyjoxy7SVybZu14VcaPXC8+uozgESzIZYii0fo4SQ9sXdSQk3B2EhlNoMtdaezyprdNwMsHBfccs9AoBcLkMAJyAlSYZaqTCrzMeEBTX+nPkDQHaJfZR9TOCUfOwe6oHYAOFCrD3JuaiosY9AornQ5w7pGQwGzFx8GE//dhLvbjiPKQv3YeWRq1JPq+VSU4E33sD/s3ff4XGUV9vA79mqXlddtlVtWS7YgGkGbINpoYROAiG0EHhJwkdIQtobkhBSCLxJIKRDQklIQuglYJpNMcWAC26yLcuyrN67Vtvm+2NYq8wZacu03T2/69JleVbaHa81o93nzLkP5s2TimnPPw/85S9TvyYnR5oRdtddQHW1IbsZEUEAvvKVib//7nfR3+euXcBnPgN0TCv0X3wx8Ic/ABlSOospjtH0dKlDLejmm4GhxLiolTGmnlh8rcEFNaYbv99v9C4wxhS0t7ejtbUV7e2xOZid6lKrax9KiEL+6zw/LSHE+jHKGEscgiCghIh9DBbUIj2fHeyVd6cBQFmu/h1qAB2ZaMQMsp4ReUFttvlpAN2hBgAdcTBHrX/Ug5Z+ebdgbXEGzlgkv+Bo3BfAW3u79Ni1mMGvO4z35t4uvFPfffjv/oCIX7+6Dz5/wMC9CpHHA/znP8DppwMVFcCddwKtrRO3v/kmMP19miVGlycvu0ya9VZeLkVfRsrrBX72MylK8aWXphbqCKY5Ri+6CDjrLOnzlhbghz80dn8YYzHH8PNYBGL0NxZjjDE2oaZIPkdtyO1DaxwsCs3E5w9gA7EANDcnBVX5CnErjDHGmMbm5Mi7xpp7o4t8pOanAcZEPgJ0QU3vDrVAQERPpJGPmfTXdAzF/munXUR3GgAsKs7E6YsKyds49pGZzXsNPbJt7YNuNEV5LtVUby9w772A0wlceinw6qsThTOrFfjsZ6U5W1u3xs+srZQU4L33gPp6qUMrEtu2AccdB3z/+1IxEgC2b5eeT7MTBOD++yfiOe+9F9iyxdh9YowxjelWULNarap82Gzy+BDGGGOJrUZxjlp8xz5ubupH/6hXtv3UhfkQ4uVNKmOMsZhTSnSo9Yx4oorVa+yhF5HLDCqoFREdXgNjXl2jAwfGvGTssyuUyMd0ukOtfUDe8RZrdhHz0wCgtigDi4ozUJIl//l8fXcHvLHQ+cMSxoEu+iICqvvSNP7+d+CWW6ZuKy8HfvpToKkJeOYZ4OyzpeJaPFmwILIOO49H6ug6+mhpBhsg3c9tt0lFx5wcVXdTMxUVUqwnAAQC0qy3BEiKYYwlLt0KasHYLVEUo/5gjDHGJqsplHeoAcDutvjOcFeOe+T5aYwxxoxTmk3PNWvpi3whmOpQswjAHIXH0hrVoQboG/tIxT0CoXWoUTPggPiYoUYV1CyCFBEuCAJOJ2IfB90+fNAQA90gLGE0KHTlNkdxHtXc5z8PzJkD2O1SFOJrr0mdW9/7HlBcbPTemctHHwFHHQXccQfg+/RCjEWLgPffl2bJJcsL/6b2zW8CNTVSce2OO+KnA5Exxgi6Rj5GUgwTBIGvsmeMMTajirxU2K3y3xV72uO8oFbXKduW7rThmPIYuZqRMcZYXKI61IDoFoIPEIvLpdkpcNiMmWJQlEn/G/UsqHUNyeMegdBmqDlsFuQSnWxxUVAjIh8r89KQZJe6Ys5QjH2MvRkeLD75AyIO9ih0qJm5oJaXJxXQhoaAf/0LOPXU2J2NFqk9e4A//nHmr/nFL4BjjwV27JD+brMBt98OfPwxsGKF9vuoBYcDeO456d90xhlG7w1jjGlKt99sgUAgpA+/34/e3l68+eabuOmmm2C32+F0OvHQQw8dvp0xxhibzG61oDJPPjOsLo4jHw/2jKC+c1i2/eT5eYYtLjLGGGPATAW1yGf/NBKLy0bFPQIzdKjpWJDqHqY71KhCGSWf6FLTc/+14Pb6sY94fbSoeCLN4Oh52chOscu+5pVd7QgQEZqM6a25bxReP/2zaOrIR0AqrDhnL+rHpWuvlbq0broJ2L9f+evKyqRoRABYvhz48EPgxz+O/eetujr2OusYYywCpltxEwQBWVlZOOmkk3D//ffjvffeQ1paGq655hr86U9/Mnr3GGOMmRQ1R62hawQeX3zOw3htt7w7DQBOqcnXeU8YY4yxqZQiHyPtUBv1+NAxKC8elecaE/cIAEUKBbU2PSMfFQpqoUQ+AkBhhvzrOonnOZbs6xiGnyiK1U4qqNmsFqxdKI997Bgcx7bmfi13j7GQKMU9AtFdmMA0Nn++9KcoztyldtllwKWXAnfeCXzwAbBsmS67pztRBDroEQWMMRbLTFdQm2758uV4+OGHIYoibrnlFtTV1Rm9S4wxxkyopkg+R80XELG/S36Vcjx4o07+5sQiAGu4oMYYY8xgmcl2pCfZZNsPRbgQ3NhNf5+RHWp56U5YiMkEekY+dg9HHvkI0HPUOgbdMd2ltattgNxeW5Q55e9KsY+v7OLFX2a8hi7lgpqpIx8T3XXXSR16APDXvwJjY8CGDcBtt039OkGQIjG//31p3lw82rsXOO004OSTgfHYvlCDMcamM31BDQDOOuss1NTUwOPx4A9/+IPRu8MYY8yEFhAdakB8xj4Our34oKFXtv3IudnICTHmiTHGGNPSHKJLLdIONSruETC2oGa3WpBHdILp2aFGRT46rBZkEMVMClVQ8wVE9IzQhbpYsKuVft03uUMNAE6sdiHFYZV9Hc9RY2ZwoFv5gsD2QTe8/vhM4Ih5eXlS5xkA9PZKBaU1a4C77waeeWbq1wrEFRnx5IYbgNdflwprZ50F/Oc/gDu2I4UZYywoJgpqAHD00UdDFEW88sorRu8KY4zFHZvNdvgjVi0slHeoAUBd+5DOe6K9t/Z2wUdcPX7KQu5Oi1fxcIwyxhILNUetuW8sovPZAYX4s/Jc4wpqAFCYKf83tg/q1z1CdajlpjkghLhQqzQHriOG56jtJApqRZlJsguOkuxWrF6QJ/vahq4R1HfG32vHcPHrDmPN1KEWEPXthGVhuummic83bpz4/B//UPVhTH+M3nsvYP30ooX166VCY1GR9Px88IEUB8kYY4B5z2MziJk9TkqSXuw3NzcbvCcsUrF4gDCWKKqqqozehagVZDiRmWzHwJh3yva6tvhbFHldYX4aNQ+ExYd4OEYZY4mFmqPWO+JB0ZwypDrDe1/QSBTUbBaBLNrpqTDDiW3Ttukb+SjvUAs17hGQXjtROgbdWFySSd5mZoGAiN1t8oJaLRELDgCn1xbiv9vlHWnrdnagKp9OPkgU/LrDWEoXEQQ1941hTo5xMyTZDI47Dli+HNiyRfp7Sgrwi18AX/mKqg9j+mN06VLgsceAb34TOHRI2tbfD/zhD9JHTQ1w1VVSTGae/OIGxljiMP35jBAzHWrB2WkWS8zsMmOMMR0JgkDGPu6Jsw41f0DE+j3ygtqcnGRU56cZsEeMMcaYnFKxq6U//A4uKvJxTk4KbFZj3xsWER1q3cMejPv8ujw+VVDLTQs9+pmKfASkSLlY1NQ7ihGP/LmfHvcYtKYmHzZiEN4rHPvIDDTq8c0aHdsc4TxKpgNBAH71K2DuXOCcc4Dt24GvfQ1IxLXMSy8FGhuB114DrrwSSJ70O7OuDvjud4HubsN2jzHGIhUTZ/SNGzdi48aNEAQB5eXlRu8OY4wxk1pIFNTaB93oH43dWSDTbW7qQ/+oV7b91JqCkCOeGGOMMa0pFdQiWQg+0C3/nrJc47szlCITOwflhS4t9BCRj+F0qBUqFNQ6dNp/te0iutMAYJFCQS0z2Y7jK3Nl27c1D6BtQL/oTsYmm607DYjswgSmo9WrgYMHgeefByoqjN4bY1kswKmnAo88ArS3Aw8+CJx8snTbMccACxdO/fqXXpKiMjkSkjFmYqYuqI2OjuIvf/kLzjvvPIifnkzPPfdcg/eKMcaYWS1IgDlqr+3uILefyvPTGGOMmQgV+QgAh3rDWwgecnvJTqwyl7Hz0wBpNhdltu4SNYyM+zDmlXdjhVNQy05xwG6VX4zTEaPzmXa2DpDba4uU4yvPWFRIbn9lJ/16izGthVRQ6+OCGotBGRnAtdcCb74J7N8P/Pa3U28XReCWW4ATTwQWLADuvBNoajJkVxljbCa6DbU65ZRTQv5an8+Hnp4e1NfXw+fzHd7ucrlw6623arF7TAd+vz7RJ4yx8LW0tMDv98NqtaKkpMTo3YlYTRE976KubRDHVcivQI5FbxDz09KcNhxbHh//PkaLl2OUMZY4SnPoDrW6Q51oarKEfD472EN3tJXlGl9QU+rw0qO7iSoyAoArjMhHi0VAfnqSrNulYyg2C2q7WuUdaulO24yz9k6rLcD/PrNDtn3dznZcdUKZmrsXU/h1h3EaumYvqDVzQS3hxfwxWlEh7957/31g717p8337gB/8ALj9duCUU6R5axdeCKQa/7ufMaaulpaWmDuP6VZQ27BhQ9hRVOKkFt/i4mI8//zzyM7OVnvXmE5EbtlmzLSGhobg8/lgs+n2a0ET8wvogtqejvjoUGvqGcW+zmHZ9pPnu+CwmbrpnEUpXo5RxljiyEiyIzPZjoGxqTHFh3pHMTg4GPL5TKlbwxwdanShpkOHGWTKBbXQO9QAKbZyekGtPUY71KjIx4VFGbAQc9KCCjKSsHxuFrY09U/Z/sGBXvSPepCVEnqBMp7w6w7jcOQjC0VcHqNLl0rRkA89BLzxhrRNFIHXX5c+broJuOQS4OqrpS62RJxLx1gcGhqKvfU6Xc8+oiiG9SEIAmpqanDHHXdg165dWL58uZ67yxhjLMakOW2YmyOPmNrdFnu/oCnvN/SQ20+pKdB5TxhjjLHZUZ1BHcM+4iuVNSosLpeboEMtP4MuXukR+dhNzE8Dwi+oFRD/Bj0KgmrrHh4nZ7/VKsxPm4yKffQHRLxOpAIwprWGLvnFc9O1DYzBH+ALllmcSU0FrrxSKp41NgI/+QlQWTlx+/Aw8Le/AWedBYzMXnhmjDGt6HYpw/r160P+WrvdjvT0dJSVlSE9ne42YIwxxigLCtPR1Ds1HmpvxxACAXHGK5Rjwb5OujB48nyXznvCGGOMza40Oxk7p8XwdQx7Fb6adqBHvmhmtwoozqLjFvWUZLciN9WBnpGpxS09OryUOtRyw4h8BKQOren6Rr0Y9/nhtFkj2jcjUHGPQOgFtV+8VCfbvm5nOy46qjTqfWMsVKIooiGEDjWvX0TnkFuxS5axmDdvHvC//wt8//vAu+9KXWuPPw4MDgIXXQTwWjFjzEC6FdRWrVql10MxxhhLYAsL0/HqrqmD5Ec9fhzqG8U8E1zNHo16Iu4xO8WO/HTjFxUZY4yx6Uqz5V3jg+MBjHoDyAjxnSjVoTYnJwU2qzmingozk2QFNV061IbU6VBTmgPXOTiOOUTXv1lRcY8AUFs0e0Gt3JWK6vw0Waz2W/u6MObxI9kRO4VFFtu6hz0YcofWxdvSN8YFNRb/BAFYuVL6uPde4JlngJoao/eKMZbgzPEuhDHGGFPJgkJ64aSuPfZjH+uJCJiq/DQD9oQxxhibHRX5CACdYcQ+NvaMyraZIe4xqChTXpDSo0OtZ0TeoSYIQE5q9B1qANAeY7GPVIeazSKguiC010lU7KPbG8Bb+7qi3jfGQhXK/LQgnqPGEk5KCnD55cCRR07dvmED0NxsyC4xxhKTbgW1pqYmNDU1ob+/P6LvHxgYOHwfjDHGmJKaIjr+oS7G56i5vX4098nfOFfmcUGNMcaYOc0hOtQAoGMktILawJgXvSPyTqwyl3kKalRBqnPIDZ8/oOnjUpGPOSkOWMOMt1YqqMXaHLWdrQOybdUF6SHHVlIFNUCKfWRML6HMTwui3hcwlnCefBI44wzpo4eeN84YY2rTraBWVlaG8vJy3H777RF9/5133ony8nJUVFSovGeMMcbiSVluKpw2+a+3PR10FFCs2N81DJGYPc4daowxxsyqNCe6DrWDxPw0wFwFNapDLSACXQozztRCRT6GG/cIAAUZ9Pfo0WWnllGPj5w7FUrcY9DikgyUZMl/Xl/f3QmvxsVRxoLC6VDjghpLeB4P8N3vSn/u2gWcfTYwHHpRmjHGIhVTkY+iKEKkVhMZY4yxT1ktAuYXyLvUYj3ykZqfBgCVXFBjjDFmUlSBAgi9Q01pcdlMkY+FCjOMtJ6j1k1EPrrSw4t7BKQZcJRY6lDb0z5EXnRUWxx6QU0QBJxWWyDbPjDmxaYDvdHsHmMh298lP+dlJtvJc2lznzwOl7GE4nAAL78MFH7aYfzBB8CFFwLj2l7QwhhjMVVQY4wxxkKxoFBeUGvsHoHb6zdgb9RBvcEGgCqOfGSMMWZS6Ul2ZKXYZds7h0P7fdzYTS8Yl7noKEkjUB1qgPYdXt1D8gXD3NTwO9RSHDakJ9lk2zsGY2dBcicxPw0Ir0MNAE5fJC+oAcArHPvIdHKgW34BXbkrFSXEPEqeocYYgIoKYN06ICtL+vurrwJf/CLgj933/Ywx84uZgtr4p1cYOBzhX3XHzMFiiZkfN8YSTlZWFrKzs5EVfCEa42qIglpABPZ1xG4ExH6iQy3ZblW8+p/Fl3g7RhljiaOUWAjucYshnc8aichHh82CYoWuMCModXhp2aE27vNj0C3v8osk8hGg56i1x1CH2q42hYJaGB1qAHBMWQ6yiQLwK7s6Ei4ph1936M/nD6CpV34RQUVeKkqJ1/stfWMJ93PJJvAxOsnSpcALLwDJnx4njz8OfOUrIFuXGWOmE4vnsZipcOzcuRMAkJOTY/CesEhxQY0x8yosLERJSQkKC+mB7LGmppBeQNndHrtz1KjIx4q8VFgsggF7w/QWb8coYyxxlGbJu8nah30hnc+oyMd5OSmm+t1XSBSjAKB9QLvukd4R+fw0ILLIR4D+N3TGUkGN6FArzU5GZrK8ODYTm9WCUxfKu9TaBtz4pHkg4v2LRfy6Q3/NfWPw+uUFgApXKnlhwrgvgO5h+lzA4h8fo9OsXAk88QRg+7Tj+k9/An7wA2P3iTEWklg8j5m6wuH3+9HU1IR77rkHGzZsgCAIWLJkidG7xRhjzOSoyEdAmrERi3z+ALmoWMXz0xhjjJncnBz5QvDAmBeDbu+s30t1qJW5zDM/DQBSnTZkEJGJ7RpGJnYPKRTUIoh8BJQ71GKh+8UfEFFHXDAVbtxj0BmL6EWddRz7yDSmODPSlUZGPgIc+8jYFJ/5DPDwwxN//+lPgV//2rj9YYzFLU0KalarVfYBAKIo4ne/+x15O/XhcDhQXl6Ob3/724dfzF900UVa7DJjjLE4kpfuhCtNfpU2teASCw71jcHjD8i28/w0xhhjZleaTc87a+mbeSG4f9SD/lF50a3cZAU1ACgiIii17FDrHqaLdZF2qBVkyAtxbm8Ag2PyWEmzOdA9DLdX/hppUXFmRPd3UrULyXarbPsruzoiuj/GQrW/i46mr8hLRQnR6QvMfh5lLOFcfjlw333S504nUF5u7P7s2wdccw1w5ZXAcOyOn2CMTSW/lE4FoihCEATyirZornJbs2YNrr322mh2jTHGWIJYUJiO7vqeKdtitUONinsEuEONMcaY+VFRZYAUb7Zwhi4ipW6NslzzFdQKM5Owp2PqawwtZ6gpFtQinKGmNAeuY8iNTGKmmJnsJOIegfDnpwUl2a1YNT8PL0/rSKvvHMb+rmFU8sVMTCMznfOoIi8ANPfJZ66x0NV3DmPdznY4bRZ8/pi5SHVqskTK9Pa1rwEjI8BxxwGrVxuzD4ODwJ13Ar/5DeD99OKg448HbrrJmP2JhCgCgnkithkzE81+W6gRD5GUlITc3FwsWbIEF110Ea6++urD3W4s9vh85r/CkbFEtXfvXvh8PthsNsyfP9/o3VFFTWEGNk4rqHUPe9A1NI689MgWnIzCBTUWj8coYywxKHWobd5zEKfVyudVBVFxjwBQ5qLvz0hFREGqY9CNQEDUZN6b0tyk3AgLalTkIwC0D7gxv4CO0TaLXW3qFtQA4IzFBbKCGiDFPt60uiri+40l/LpDfw1d8nNeSVYykh1WFGXRxyhHPkburb1duP6RjzDukzpc/7axEf+9+STTX0QQxMfoLL7zHWMeNxAAHnoI+N73gI5Jnc1/+ANwww3G7FOouruBlBQpKnPDBqCwEHjySaP3iiWAvXv3xtx5TJOCWiAgj1ywWCwQBAFf+cpXcF+w/ZYxxpgpBAKBwx/xYqY5avFQULNaBMwz4VX6TBvxeIwyxhKD0uyfdoU5YEEHuunOCzNGPlIFKa9fRM+IR5PXHEodarmpkUY+KhTUBrXrslPLLqJDLTPZjmKFrrtQnLKgADaLAF9g6kXC63Z2JExBjV936I/qUAue75w2K/LTnegcmnrsc+Rj5P6wYf/hYhogFSf//VETvnxypYF7FTo+RiPw+99LXWLLl2tz/+++C9x8M/DxxxPbnE7gW98CvvAF83d7XXAB0N4O1NdLf8/MBPx+gBtbmMZi8TymyQw1JbEw1Jgxxlh8WFhIX5kci3PU6omZCvNyU+Cw6fprnDHGGAtbmtOGbOKK/47hmdMrGonF5SS7BQXpkRdKtEJ1qAFSh5cWeoiCWnqSDUkKsXCzKVQoqHWavKAmiiJZUFtUnAEhioXLzBQ7jqvIlW3fdqhfs/9TlthGxn1kAbsib+ICAio+t5kLahERRREfN/XJtn/YKN/G4oAoArffDnzlK8CZZ0pzzdTU3AxccQWwcuXUYtrFFwO7dwM/+QmQZvJkmT17gHfemSimAcDAALB1q2G7xJiZ6bYSt379eqxfvx4333yzXg/JGGMsgVUXpIFKWaqLsTlqoihiP9GhVsUzPBhjjMWIOTnymMaOYe+M30NFPpblpmoSoRgtpRlkbQPaLHZTkY+Rzk+TvtdBvmYye4da59A4ekbkz0XtDLP5QnXGIjqO9NVd8ihIxqKlND9tckduCRGf29I/xheuR2Bo3AePT94RwQXKOOXxAG+8IX3e2QmcfjrQ2qre/d94I/DYYxN/X7oUWL8e+M9/gPLyqV8rilLXl9n87W8Tn1dO6tLcsEH3XWEsFuhWUFu1ahVWrVqFqqrEiEhgjDFmrCS7FWVELFSsdah1DI5jeFx+FX8lz09jjDEWI6jOipk61ERRJBeYy0wadVyUqRBrqVFBiop8dKVFFvcIADarhSzItQ/Q0ZJmQXWnAdHNTws6rbaQ3L5uZwe5nbFoNCgU1ComXUBXkiU/zwyP+zAwNvPFCUyua4g+t7X00VHDLMY5ncDzzwNLlkh/b2yUimq9verc/89/DlgsQG6uNCvt44+B1aunfs3AAHD//VLcpNnGIPl8wMMPS5/bbMCDD07ctn69MfvEmMlxVhRjjLG4VUPMUdvXMQx/IHau5KTmpwHcocYYYyx2lBKdFcOegOJCcO+IB0NuecGNulDGDJQ71LQqqKnboQbQ/4bOIXN3qO1sHSC3q1FQK8xMwrI5WbLt7zf0YGCUCxhMXQe6FApqrpkjHwHuqopEt0JBbdDtw6Cbj++4lJ0NrFs30TG2cydw9tnACH3sKdq6VZqVNtmSJcC//y1FSd54o1SUmq6tDfja14Bt24AHHpA61czipZek2WkAcN55wMknA/n50t/fflsquDHGpuCCGmOMsbi1oEC+oDLuC5AxUmZV30lHVFZxhxpjjLEYobQQ3KKwEKz0e7osV16YM4OMJBtSHPL5ZVrM2/IHRPSOyBeDc6PoUAOAfGI2ndnnhe1qk3eoOWwWVKp00dEZi+Rdar6AiDf2cJcaU9eBbvkFdA6bBcWTutJKlM6j/VxQCxd1UUKQ0u8lFgeKioBXXwUKPo30ff994KKLpEjI2XR2Al/+MnDkkcC11wLeaYXXiy+WinZKamqAE0+UPt+1S3pss/jrXyc+v/ZaQBAmOuwGB4EtWwzZLcbMjCibR+6UU045/LkgCHj99dfJ26Ix/X4ZY4wxJTVF8g41AKhrG1JtsUVr+xWuWOXIR8YYY7FCubNilOwmOtBNx26ZtUNNEAQUZiahYdrvbC0KUn2jHlCN9tF3qMm/v3t4HD5/ADarOa/DpSIfFxSkw67S/p6+qAB3vVwn275uRwcuWF6qymMwBtCRj2W5KbBOGm44hzvUVNM1Q/dtS98YFqowh5GZVGWl1Km2apUUw7huHfDFLwL/+AdglV8YA49Himr88Y+l4hIA7NkjzRz78pfDe+wvfQl45x3p8wceAI4/Prp/ixo6OoAXXpA+LyoCzjhD+nz1auDxx6XPN2wAVqwwYu8YMy1VC2obNmyAIAgQRRGCIJC3RYO6X8YYY0wJFfkIAHvaB3H20iKd9yYyVORjUWYS0pyq/gpnjDHGNENFPgLKC8GNCvOEyk1aUAOk382ygpoGM9R6FDoroi6oZcg71AKi1MmhFGlppOFxHxp75IXXWhUXwivz0lCVnyZ7Lfbm3i64vX4k2YnFV8bCJIoiGflY4Zp68VwxMUMN4I6qSMzUodbMc9Ti3xFHSEWk008HxsakuMa5c4Ff/nLq1/33v8DXvw7s3TuxLT0duP124Oqrw3/ciy8Gbr5ZKsz9+9/Ab34j3Z+R/v73iUjHq6+eiKucPANu/XrgW9/Se88YMzXVLzUTZ8iBFUUxqg/GGGMsHHOyU8gIpt3tdIyiGdV3yQtqHPfIGGMslpQoLAQfUli4PEBEPqY4rMhPj65opKXCDPm/sW1gTPX3sd3D9OyfaAtq+URBDdCmKKiG3UTcI6DO/LTJzlhUINs25vXjrb1dqj4OS1xdw+MYGpfPKCrPm3oBQYrDhpxUebRrSz8XgMKldB4FOEIzYZx4IvDEE1IBqahI6lILqqsDPvMZacZasJgmCMB110lz0r75TcARQcxyaipw+eXS5yMjUlHNSKIIPPjgxN+vuWbi85qaiWjMjRsBv1/ffWPM5FS9vH39+vUR3cYSg5Vqn2aMmUJxcXFcdgFbLALmF6Rj66H+Kdv3xEhBbWDMiy5iaHasxFUy9cTrMcoYSwypThtyUx3oGZnaFaDUoXaQKKjNy0019TmwiOjicnsDGBjzIisluvlmkykX1KJ7DKpDDQA6TFpQo+IeAWCR6gW1Qvxu/X7Z9ld2deB0YsZavODXHfqhutMAoILoyC3NTkZviOdRpiweCmp8jKrgM5+RYg2XLQPKy6VtDz0EXH/9RNcWAKxcCdx7L3DUUdE/5pe+BPzxj9LnDzwg/d0oo6PAcccBTU3SbLjq6onbBAH4+c+BjAwpHpPXc5mGiouLjd6FsKlaUFu1alVEt7HEwL/oGTOvjIz4zYlfWCQvqDX1jmJ43Gf62EQq7hHgDrVEFM/HKGMsMZRmJ4dUUBNFEY3EDLVyFx0baRZKsYhtA26VC2oaRT4q7H+sFdRqVJ59tKQkE0WZSWibNg/v9d0dpp4vFy1+3aEfan4aAFTkyQtqJVnJ+KR5YMq2WCkAmQl1wWJQrBQo+RhVyQUXTP37ypVSMQkASkuBu+8GLrtsYlu0jjxSKuBt3Qp88AGwfTuwZIk69x2u1FTgr3+VioXt7fLbJ3esMaahWDyfxeerP8YYY+xTCwroXPK9HebvUtvPBTXGGGNxgpqjRs2q6R72YJiIPyvLNe/8NEC5w6t9QN2ClGKHWpRxmAXp+uy/Wna2Dci2leWmqH6xlCAIOL1WHvvYN+rFpsZeVR+LJaYDijMj5a/3qfjc/lEvec5kymaaocYz6RJcdTXwne8AP/whsGcP8LnPqVdMA6T7mtyVNjly0Sjp6VO70xhjs+KCGmOMsbimdKVyXZv5C2rU/DSAC2qMMcZiT2m2fCF4yO3DwJh3yrZGIu4RAMqI+DMzmalDTU3dRGeF02ZBKjEzNhwZyTYk2eXLAx2Dyp0cRvH6A9jbLn+NtKg4U5PHO0Mh2vGVnR2aPB5LLA3E6/2sFDs5L406jwJcBAqHKIromiHysWfEgzEPz4tKaHfcAfzoR0CKRp3xl18OJCUBdjvgNudFK4yxmXFBjelG7YHcjDH1jI2NYXR0FGNj8fdmrKaQ7lDb005HBZkJFfmYlWJHLvEGm8W3eD5GGWOJQWkheHqXmnK3hrkLatQMNQBoH1D3vE11qLnSnFHH6wuCQHbZmTHycX/XMDz+gGx7rcrz04KOKc9BVopdtv2Vne1x+x6XX3foh4p8VDrflRCdvgDQ0i/v9mW0oXEfPD75+WOyWHg++RiNYdnZwFNPAa2tE/PU9Pbcc8BQCBcYd3UB//wncMMNUkwlYxqIxfMYF9SYbvx+vsqHMbM6ePAgGhoacPDgQaN3RXVZKQ5ygWh3ewx0qBEFtaq8NJ5JmYDi+RhljCUGKvIRAA71Tn0T3ahQUDN75GNOqgMOYp6W2h1q0+fQAdHHPQblE6+X2k1YUFOan1ar8vy0IJvVglNr5LGPrQNu7Ggx/wVagHRx6+/W1+OUezbg7PvexnPbWmf8en7doQ+vP4CmHnnxpoKIewToyEcgduZ+mcFM89OCYuH5VPMY7RvxYOuhfgy6vbN/MVPHWWcBLpcxj71vH/DZzwKFhVIn3kyeflrqqPvzn4GXX9Zl91jiicXXGqoGjN9xxx1q3p2i22+/XZfHYYwxFh8WFKbLFoT2tA9BFEXTFqfcXj8OEbNlOO6RMcZYLJqTE1qHGhX5mOa0wZVm7u5sQRBQmJmEpt6p/x61C1JU5KNLpc71WOlQ26lUUNOoQw0ATl9UgCc3N8u2r9vZjiWl2kRNqum+1+vx69f2Hv77zf/cgoPdI/jaqTw3x0jNfWPwBeRdjhV5Sh1qHPkYLeocOl1Lf+I8n89ta8V3nvwEox4/UhxWfGVNFf5nVSUsFnO+R2Yq+NvfpD9HR4G0WdYWVq+e+HzDBmm+HGNM3YLaj370I10WJrmgxhhjLBw1Rel4c2/XlG0DY160D7pRlEm/MTVaQ9cIqBShyjwuqDHGGIs9JVl0h9r0ToAD3fKLScpcKaa9AGYyqqCmZoeaKIroHiY61NLU6VCj5sANuX0Y9fiQ4lB16SAqVIeaK82BfJU69SgnV+chyW6B2zs1Km7dznZ884wFmj2uGg71juJ36+tl2//vVanAxkU14xzopuclVyhEPmYm25GeZMOQ2zdle3MCFYCiRZ1Dp4uFDjU1DIx68f2ntmP005lxox4/7l63Bx8f7MOvL12GTCLqlmnA7Za6xpYs0f6xfD7g4Yelz61W4MorZ/766mqguFiKp3znHcDrlWa/MZbgVI98FEVR0w/GGGMsXEpz1OpMHPtYTwwoB7hDjTHGWGxKdljJLrPJC5eiKOIg0aFm9rjHIGqOWoeKBbWhcR85O8yVrk6HmlJBqmNw9o4OvYiiiF1t8oLawqIMTYuuyQ4rVs3Pk23f1zmMBoXXbGbxi5fryJ8bQCqq/fb1fTrvEQtq6FKYGanQoQbQ8bmJUgBSQ9fQ7OfkROn4e3NfF4bGfbLtb9R14uzfvo3tzQMG7FUCEUXg1lulgtWZZ0rFLq298opUHAOAc84BCuRxxlMIwkSX2sgI8NFHmu4eY7FC1cvMfvjDH6p5d4wxxpgqFhTQEUB72oewZkG+znsTmv3E/DSAC2qMMcZiV0l2iqw7YHLkY+fQ+OEr5ScrV+jWMBuyw2vchyG3F+lJ0V/RrRRVlpuqXYcaALQPuE3zf9A64MbAmHzOj5Zxj0FnLCrEup0dsu2v7OrAjavM+frs44O9ePGTthm/hjvVjNNAzIwUhJkvIijJSsbuaUXlRCkAqSGUDrVEiXzc2tSveFtz3xgu+uO7+NG5i/D5Y+bERJd4zBEEoKEB6OuTPtatA84+W9vHfPDBic+vuy6071m9GnjsMenzDRuA449Xe68YizlcUGOMMRb3KvNTYbMIshkFdcQVzmZBdagl2S2Kw8gZY4wxsyvNTsa2Q/1TtrX0jR2eaXqAWFwGYqhDjZhBBkhzyFQpqCksBLtUijosmGH/zWJnC90xUVukfUHtlJp8WC0C/NNeT67b2Y4bV1Vq/vjhCgRE3PHC7pC+lotqxqC6G4szk5Fktyp+TykxR617eBxur3/G72OS7uHZO26nz/aMV1sO9c14u8cXwPee3o6PD/bhzvMXI9nBP1+qu+464Nlnpc8feEDbglpXF/Dcc9LnhYXAWWeF9n1r1kx8vn498N3vqr9vjMUY1SMfGWOMMbNx2qzkcG8zRz5SHWoVrjQeEM0YYyxmUQvBQ+O+wx1HjUoFNZN0R82mUGEuq1pz1HoUFoKpKM1IFMZAQY2KewSARcWZmj92VooDx1XkyLZvaeo31XMU9PwnrbIC9kw4/lF/1EUE1HuWyajzKJA4XVXRCqWg1jk0jnGfvFs6nnh8Aewk5lFSntzcjAt+v1HxohcWhbPOAoqKpM+ffx5ob9fusf7+94lYyS9+EbCF2GNTWQmUlEifb9wIeGbv8mQs3nFBjTHGWEKoKZRfuby/axhehZkSRvIHRDIChuMeGWOMxbI5xOwfYGL+zwFifhoQ25GPgHoFNaWFYFeaOh1q+Rn0/bSbqFi0i1gATrJbdPsZOWNRIbn91V3yKEgjub1+3PVSHXnbSdUuxe/jopp+hsd95HzCill+lpXSKjj2MTRdCtG5k4ki0NZvnvOeFna3DcLjC/19cF37EM777Tt4ecfMEbIsTDYbcM010ud+P/Dww9o8jihOjXsMPmYoBGGiS210FPjwQ3X3jbEYxAU1xhhjCWFBYbpsm9cvKg4DN9Kh3lHyDQ4X1BhjjMUypc6KYLwW1aGWkWRDdkr0cYl6KJphBpkaupQiH1UqqDltVvK5NlP3FdVRUVOYAatOHfyn1RaQ29ft1LCrIAIPvN2AVuLnrjQ7GX/54tG466Ilit/7f6/uxWPbZo6CY9FT6sityJv59X4Jd6hFJZQZakD8P59bmuhjfPWCPMXvGRr34ca/b8ZPX9xlyotSY9a11058/sADUvFLbR9+COzcKX2+ciVQUxPe969ePfH5hg1q7RVjMUvVGWrhCgQC2LFjB7Zs2YKuri4MDQ0hPT0dLpcLRx55JBYvXgyLhWt+jDHGorewSF5QA4C69kGy2GakeiLuEeCCGmOMsdhWOkuHWmO3fG5NuSsVghAbcceuNCc5Y0vLyEerRUBWsnoFx4KMJPSNeqdso7pojDAw6iUXuWuLtZ+fFlSUmYwjSjOxrXnqLLf39vdgYMyLTBX/LyLVOeTG7zfsJ2/7zlk1SLJbcdmKuQCAbz+5nfy6R7b0IhDw4wvLlbvZWHT2E/PTgNk7cpXPo4kx9ysaoiiiK4TIRyD+O/62EnGwggD89vPL8e7+Hnzz8W0YGveR3/uXtw9g66F+3H/5kYqzN1kYKiuBU04B3ngDqK8H3noLWLVK3ccoKwN+/nPgr3+dWsAL1Zo10j6tXq3tnDfGYoQhBbWenh7cfffd+Nvf/obu7m7Fr3O5XLjmmmvwzW9+Ey4Xv5CLdbZQ83kZY7qrro7/AeQLiMhHQIqv+KzO+zKbeoU32FxQS1yJcIwyxuKfcofaGAIBEY1E5GOszE8DpOJWQbpT1hnUPqDOwiwV+ZiT6lB1vmphZpJsxqxaHXbRUp6fpl9BDQBOX1QoK6j5AiLW13Xi/OUluu4L5Vev7MWoRz7/6ci5WTh7SdHhv89WVPv7tgHk5eWH3cjAQqM0j2q2glp2ih3JdivGvFP/j+O9AKSGoXFfyDGHZi9QRvveYAtRUKvOT0N6kh1nLCrEgq+l48a/f6w4c/zDxj6cfd87+O3nl+P4ytyo9oUBuO46qaAGSF1qahfU8vOB73wH+Pa3pWjJcFVUcGca00wsrnXo3v71wgsvoLa2FnfffTe6urogiqLiR1dXF+6++27U1tbiueee03tXGWMsYVit1sMf8ao4MwnpSfLCfp3C4oyRqA41q0VAWW7sLCoydSXCMcoYi39JdisZT9jcN4r2QTfGiYXOWPvdR81RU2+GmjyqTK24x6CCdPn+dw65EQhoEEEVJqWCWm2RvgU1pTlq//qwyfDnaVfrIP790SHyth+cUyvr9rxsxdwZ4x9//do+3P8Gz1TTAhU777BZFGekBQmCQMY+NnNBbVahzE8LajZ55GM07w16Rzw42CMvGC6bk3X48zJXKp75ykpcclSp4v10D4/jigfexx827Df83BfzLrwQyM6WPn/iCaC/X5vHEQRpbhtjJhKLaxy6FtSefPJJXHTRReju7ob4aSas0+nEUUcdhfPPPx9XXHEFzj//fBx55JFwOByHv6+7uxsXX3wxnnzyST13lzHGWBwRBAE1RLTjHoWr7oxEFdTm5aTAYeMYZMYYY7GN6lI71DumOE9otm4NsynKlP/72lWaQUZFPrrSHMRXRq6AKAh6/SL6RkObO6Slna0Dsm0WQZqhpqeq/DRU5sl/Lt9v6MUf3qSjFvUgiiLufHEXOX7ns8uKsXxuNvl9sxXV7nllLxfVNEB1qJXnpobUcUqdR+N95pcausMoqMVzx9/WQ/T8tOnniCS7FXdfcgTuumiJ4vvQgAjc9XIdvvzoxxgY85Jfw0KQlAR84QtAcTHwjW9E1kXGGNONbitzbW1t+NKXvgSv1wtRFFFUVIS//OUv6O7uxocffoinnnoKjz76KJ566il89NFH6OnpwZ/+9CcUFhZCEAT4fD586UtfQltbm167zBhjLM5QCy6tA24MjJrnxb8oithPFNQqOe6RMcZYHJiTI5//09w3igNE3CMAzMul5wWZFdWh1j/qhdsb/eKYHh1qhQrzcNQqCkZjV6u8Q60iLw3JDv2vbD57aTG5/f9e2YMPGnp03hvJG3WdeHe//LGdNgtuO3Pm3EYuqulLFEWyoFZBFGopVBdbx6A75DjDREWdQwEgyS5fGo3njr+tTf3k9skdapNdtmIunvqfEzAnR7l78rXdHTj3t+9gR4v8wgcWojvvBA4elP7MVSlGs6EBuO02oK5OnfsLBIAdO4CnnlLn/hiLUboV1O6//34MDAxAEAQsW7YM27Ztw3XXXYfUVPoFQ2pqKq6//nps27YNS5cuBQAMDg7i/vvv12uXmcoCAX5xx5hZdXd3o6OjY8a5lvFgAdGhBgB7OszTpdY5NE4OgOb5aYktUY5Rxlj8y3XK22dGPH7FBb7Y61BTKEhFGfvo9voxTLw+UL1DLYMu0HUOht7ZoYVxn5/s4Nc77jHo2pVlyE+XP1cBEfjaP7eQ8+605PUH8NP/7iZv+9JJ5bPGCAJcVNNT19A4eTyHer6jIh8DonnmHZpV1xD9/CwtyZJtax90w+c37xpWNO8NqPlpKQ4r5hfQ75UBYHFJJl746klYuzBf8Wuaekdx4R/exb8/bAp7nxiAjAz14xj/+lfg7ruBhQuBhx+O/v5WrACWLAGuuAIYN/Z1AYsfsbjGoVtB7fnnnwcg5WL++9//hsvlCun7XC4X/v3vfx/O0wzeD4s9XFBjzLy6u7vR1dUVk7/IwkFFPgJAXbt55qhRi0UAUJnHBbVElijHKGMs/qVb6a7wd+rl57esFDuyUtQtGGmN6lADop+jplSgUX2Gmkk71PZ1DMNHzOipLTamoJaV4sBvP78cVDpf59A4bvnXVvh1nCn0j/cPkjO5XGlO/M/qqpDvh4tq+mhQiLitCPH1fmk23bnb3C+fi8UmKHWoLZubJdvmD4joCCMiUm+RvjcIBERsJQpqS0szYZ0lbjQzxY4/X3k0bjtzAXnuAwCPL4BvP7kd3/rPNlU6s1kU/H7goYekz61W4PTTo7/PxYulP91uYNOm6O+PMXBBbUYHDx6EIAhYuXIlqqurw/re+fPn48QTT4Qoijh48KBGe8gYYyzezVcsqJmnQ02poMYdaowxxuJBQZqd3E4VnMpyY6s7DZihQ20wuvgwpYXgXLUjHzXqsIsWFfcIGNehBgDHVuTiG6cvIG97p74b979Rr8t+DIx68ZvX6SLXN0+fjzRneB0Pl62Yi1tOyFO8nYtq0aOKn0AYHWoKHYfxPPdLDdSFCQ6rRfGiy+be+CtQNnSPYMgt745UmrE4ncUi4KbVVfj7l46dsUP6Px8344Lfv6s4H5XNoqkJ+MlPgNEofgZffRVoaZE+/8xngKKi6PdrzZqJzzdsiP7+GItRuhXUBEG6fCHcYlpQ8PuC98MYY4yFKyPJTr4BrWszT4fa/i6lDrXYW1RkjDHGpitIC31xP9biHgHlDq+oO9QUOiXUjnzMSXHAbpW/5+5UiErTyy6F12pGdagF/c+qSqxeQBeffvP6XrxLdF6q7b439qGfmAdcU5iOS46eE9F9njk/Azcfl6N4OxfVonOgO7rX+3OIyEcgvud+qYEqqOWmOcjZngDQ0h9/z+eWpj5yu9L8NCUnVLrw4s0nYUWZciFud9sgzr3/Hbyysz2s+054v/oVUFYG3H478OSTkd/Pgw9OfH7ttVHvFgBg9eqJz9evV+c+GYtBuhXUSktLAQDDw/QLh9kEvy94P4wxxlgkFhbJr0Dc2zEMUdQvlmcmVIdaYUYS0pPoK/oZY4yxWJKfGnpBLRY71PLTk0BdAxpth1fPiD6RjxaLgPx0eVHQjB1qBRlO1f/94bJYBPzq0mVkZ6IoAjf/ays6NYzLPNA9gkfeayRv+9+za2eNcJvJ6VXp3KmmEapDLTuMiFtXmhMOq3w5Lx4LQGrqIi5MyEt3JlTHHxX3CADLwyyoAdIFJI9dfxyuP6lc8WuG3D58+dGP8a9NPFctZMcdJ/0CAYAHHojsPrq7gWeflT7PzwfOPludfSsrkz4A4L33pOhHxhKQbgW1M844A6Io4q233gr7e4PfJwgC1q5dq8HeMcYYSxQLiEiP4XGfaa7opApqHPfIGGMsXjhtFmQnWUP62jIX3TVgZg6bhSzyRD9DjY581KKglJ8hv8/2QeNmCQUCItmhZmTc42Q5qQ7cf/ly2IjiVffwOG7+1xb4/NrME//FS7vh9csvCju1Jh8nVoc2t34mZ87P4JlqGjhAxOCFOj8NkAq5xVnyIq4ZCkCt/WNo7htFQMcZgqGizqOuNCcKMpLI49cs7w/VtKWpX7atJCsZ+Qrd1bOxWy34/tm1+OMXjpwxXvYnL+zCuI9nqoXk+OOBhQulz996C9i7N/z7+Mc/AO+nnctf/CJgV/Hi3GCXGs9RYwlMt4LaDTfcAKfTiba2Nvz6178O63t/85vfoLW1FQ6HAzfeeKNGe8gYYywR1BTSiy9mmKM26Paik7hykgtqjDHG4kmosY+xGPkI0HPUou3wojorACmuTG2FxMKqll1WsznUN4rhcfnMH6PjHic7al4ObjuTnqf2fkMv7lWYcRaN9/b3YN3ODtl2m0XA985eqNrjXLZiLhfVVOT1B9BEzOYK93xXmi2/4KC537iZXz5/ALc+vhUn/OINnHjXelz1t01kxKJRRFFEF7E/rjQHrBYBRVSBMs46/kY9PuzpkL/nDTfukXLm4iI899WVivPoRjx+7G2PLLEs4QgCcN11E3+fHN0YClHUJu4xiGMfGdOvoDZ//nz89re/hSiKuO2223DPPfcgEJj5Kq1AIIC7774b3/rWtwAA9913H2pqavTYXcYYY3FK6UX+nnbj56hR3WkAUMkFNcYYY3GkIC3UDrXYLKhRBaloO9R6RuSdFVkpdtiJ2LdoUXPgekY8hnUXUHGPALCoOFPnPZnZ9SdVYO3CfPK2+9fX4829Xao9ViAg4s4Xd5G3feG4eagMo9spFFxUU8+h3lH4iO6tijDnJVMxhW39bvgN6gx7anMLntrccvjvb+/rxp/fajBkXyiDbh88PvkaZLDLl3o+m/uMK1BqYXvzAPnzsXxulir3X5GXhqdvWonzjigmbzd6FmdMufLKia6yhx6a6DYLxccfA9u3S59P7nZTy+SC2oYN6t43YzFCt4JaU1MTTj/9dNx7772wWq349re/jQULFuCHP/whXnrpJezcuRMHDhzAzp078dJLL+H222/HggUL8J3vfAd2ux333nsvzjjjDDQ1Nc34wRhjjM2k3JVKzhzYbYIONaWCWpXKiyKMMcaYkUKZo5ab6kBGjM4PpTrUuofHycXcUHUTHWq5qep3pwF0QQ0AOg2KfaTiHgHzRD4GCYKAey45glyYF0Xg6//eirYBdTpentrSgp1EoTEjyYb/d2q1Ko8xXShFtd+tr9fkseMJNT8NACrCvICgJFv+c+YLiOgwqJv0ma0tsm0v7WgzYE9oSt1yeelSQY3q+Gvtd5syujJSSvPT1OhQC0p2WHHDqgrytg4Do4NjTn4+8NnPSp93dgIvvBD69z722MTnanenAcC8eUD5p3PzeI4aS1ChT4SOUllZGYRJ05lFUcT+/ftx5513Kn6P+OkQRo/Hg1tuuQW33HLLjI8hCAJ8PnkUBGOMMRZks1pQlZ8mW5zZY4KC2n6lghp3qDHGGIsjoUQ+xmp3GgAUZsoXugGgY9CNOTmRzYWjFoO1mJ8GAIWZ9P12DkW+/9GgCkdpThvmGrAvs8lKceB3VxyJS/74rmy2We+IBzf/cwv+ef1xsEXRWTjq8eHudXXkbTefWo1sjQqtgFRUA4BvP7mdvP3udXtwXEUujpqXrdk+xDpqfhoAlLvCe71fShTUACmmsJgo6mpJFEXsaBmQbW8fkApSFmI+md6oixKAmTvUPP4AuofHI54vZjZUQc1mEbC4RN1uX6WLMowq9sasL30JeOIJ6fMHHgAuuCC07/vFL4ATTwQefRS49FJt9m3NGmBwEFi1CujrA4qKtHkcxkxKtw61oGCRTBAECIIAURQVP4JfE/y+UD6YeU0uqDLGzCU5OfnwRyKoKZLHPh7oHoHba+ygZKpDLTPZDpcG81FYbEm0Y5QxFr+Sk5MxJ2f2YllZbuwW1KgONSC6xUQq8tGVrk1BTWkxtH3AoA41oqC2sCjdFIv0lGVzsvDds+iIrQ8b+3DPK3ujuv8/vdlAdnqU5abgi8eXRXXfQTO97pitU+2Vne2q7EO8aiAKaoIAzMsNr0BMFYAAoKVP/7lfzX1jGHTLL273+kXy3GWE7mF6Pw4X1BQKlIcMeD5DEcl7gy1N/bJtC4sykGQPLYY5VDkpDtiI8zNHPoZp7VpgrnQRA15+GTh0KLTvcziACy8Enn4ayNCok/vXv5Y65558kotpLGqxuMahW4fa3LlzuaCS4KxWdX9JM8bUM2/ePKN3QVfUHDV/QER957DqV+iFo75LXlCryk/j358s4Y5Rxlj8mjdvHvwpw8BLMy8MlbvM130UqkKFglqkc9R8/gD6RomCms6Rj0Z0F/QMj6OdeFyzxT1Od83KMmw60IuXieLSH9/cj2PKs3FKTUHY99s+4Maf3tpP3vadsxbCYVPnmunZXnfM1Kl2sCe+5k6prYF4vV+SlRx2UUOpAGTE3C+qizSoY9B9OFbRSF0KxZyJyEfljj8zdlyG+96gbWCMPJeqNT9tMotFQH66E63Tfudx5GOYrFYpsvFHPwICAeCRR4Dvf9/ovZJoVahjCSkW1zp0K6g1Njbq9VCMMcbYjGoK6ReAde1DhhXU3F4/DvXK3wDz/DTGGGPxRmkheLJYjnxU6lBrj7Cg1jviARXGolnko4kKaorz04rNvZgnCAJ+eclS7GobRBPx+u7Wx7fhxZtPUuwyUvLLdXVwe+Wz+I4tz8EZi8Iv0EXjshVz8ej7B7GjZer/UUu/OTt6zIKKfKyI4PV+YUYSrBYB/mkzvox4/ne2yuMeg9oH3IZesBik1KGW9+l5tDSLvojDiI4/LWwlutMAdeenTZafkUQU1LhDLWzXXAO88QZw3XXAxRcbvTeMsU/pHvnIGGOMGY3qUAOAPe3KV1dq7UD3CKiZ1zw/jTHGWLxx2qwoyJi5GBTLkY9KHV6Rdqh1EfPTAO0iH1OdNqQ75dfeUt0NWqPiHgGgtsj4BfrZZCTZ8fsrjoSDmJfWP+rFVx/bDI9PXhxT8klzP57a3CLbLgjAD86pNSTRgCpCGNEhFSuG3F50ErO8KiK4gMBmtZDF72YDCkAzdagZcd6gUHMoHVYLMpKlc11hZhKoQyhefp6p+WmAdgU16nc8d6hFYO5c4M03gS9+EUiZpXP/pz8Fzj8feO45wOvVZfcAAG430N+v3+MxZgJcUGOMMZZw8tKdyE6xy7bXtQ8ZsDcSan4aAFTmx+6CImOMMaakNHvmhaFY7lBLslvJ1xntg5EtdPcodFbkahT5CAD55GKoOTrUbBYB1QWxccHR4pJM/ODcWvK2LU39+OXLdSHdjyiKuPPF3eRtFy4vNawDiIrJ6xv1YmRcPk+LAY3ddHGmIi+y8x31/BvRUbWjZeYONTOgCmq5aY7DhWiHjS5QxkvHJTU/LTPZjnKNftdSF5b0jIzD6w/9IgIWBr8f+POfgWefBS64QJpvprXt24E1a4CsLOBXv9L+8RgzES6oMd34/X6jd4ExpuDgwYPYv38/Dh48aPSu6EIQBDL20YwFtao8upuOJZZEO0YZY/EreD7LdigvquWlO5FGdEjFksJM+UJ3pB1q1EIwoF2HGkDPgTOiu4DqUKvKTwt73pSRvnDsXJyztIi87YF3DmAdMWdtunU727HpQK9se7Ldim+dsSDqfZwu1NcdSvGt8VKEUFtDN/16P9KiBvX8t/SPQaQyYjXSOeQmu+6CzNKh1kXs4/TZblQEq1kjH8N5b+DzB7CdKHoum5OlWWcrVVATReXfZyxKb7wBNDVJn595JlBSov1jZmcDGzYA4+PSn4xFKBbXOLigxnSj54s6xlh4xsbGDn8kigVE7GPX0Dh6DHqRv58YUO60WUKaM8PiXyIeo4yx+BQ8l7mSlRfxymM47jGImqMWaaeG0gJknkYz1AB6MbR9wK3re7oxj598fVRbZO75adMJgoCfX7hEsWjyzf9sI+foBo37/Pj5S3Qn2w2rKsjiZ7RCfd2h1GkaLzF5amvoks9PAyKboQYApUQBaNwXUJwXpoWZ4h4B88zNop6T6XMoqfddzX36FihDFc57gz0dQxjzyi9wXz43S4M9k+QrXPDBsY8RCgSA9euBK64A/vAH+e1//evE59deq88+lZYCVVXS5x98AIzyeZ9FJhbXOAy97G9kZASNjY0YHByEN4x815NPPlnDvWKMMZYIFhYpzVEbwglV2i1QKaE61Cry0mC16D8PgzHGGNNaQZo8EjGozDXLnJAYQBU5OofG4fMHYCNmas1EMfIxTbvIR6qgNub1Y2jch4wk5f87Ne3pGCLny9YWx1ZBDQDSk+z43eVH4oLfb8T4tLlpQ24fvvLYZvznxuPhtMk77x597yAO9sgXKgsynPjyyRWa7XMoqI4ewLxdPUZr6JYX1JLsFhQpzF2czUwFzendV1pRmnMYFGlnrppEUSRnUbqmnUOpCM0xrx99o17kaBixqzUq7hHQbn4aoDxLtNMkBdaYs28fcMop0ufbtgE33ojDQ/96e4Gnn5Y+d7mAc8/Vb79Wrwbq6wGPB3jvPeDUU/V7bMYMpHtBbWBgAPfddx/+9a9/Yc+ePWFf6SEIAnw+zuNmjDEWnQVE5CMgxT6eUOXSdV/8AZF8g12VHxvzQRhjjLFwFaQpvxWN5flpQdQCuT8gonvYE3ZHEbUQnOKwIsWh3dt5apYQAHQMuHUrqCkt1MdiQQ2Q9vvH5y3Cd57aLrvtk+YB/OzF3fjxZxdP2d474sG9r+8j7++2M2o0/RkIhVKSQjMX1EgHiMjHstxUWCK8gG6myM3lc7Mjus9w7WxVnp8GSOcMow26ffD45DHD8shHukDZ0jcW0wW1rYf6ye1GFNQ6ZogHZTNYsABYuRLYuBHYuVPqCDvuOOm2xx6TYhcB4MorAYeOP6tr1gAPPCB9vmEDF9RYwtA18vHdd9/FggUL8KMf/Qh1dXUIBAIQRTHsD8YYYyxa8wvSQEXG17XPfJWlFpr7Rsk3eVURxr8wxhhjZlc4Q4daPEQ+KhXN2gbCLzSEElWmtoIM+v71nIe0q41eqI+1yMfJLlsxBxcsp2fbPPzeQbz4SduUbfe+thdDbvkFxYtLMhTvR0+ZyXZkJMmLelxQkxNFEQeIyMeKvMjPd0odgno+/ztaZn7vNDTuw8i4sRfFK86hnHYepTrUgNiPMN3S1CfbVuFKRVaKll3O9O8Q7lCLwpe+NPF5sIgFGBP3GLRq1cTnPEeNJRDdLmdqamrCmWeeieHhiStynE4nKisrkZ2dDZsttodOM8YYiy0pDhvm5aSgcVqEzp72Id33hYp7BLhDjTHGWPxypdogCAB1veS8OCioFWXSC7ORzBOi5rtOjypTm2J3gY7zb6jZTCVZyZouAmtNEATcef5ifNLcj/1EceXbT36CRcUZKHOlor5zGH//oIm8nx+cXRtxV5PaSrJTMNg29f+quZ8LatN1Do1jxCOfY1Xhivz1flFWEnke1Styc2DMi6YZ5v8FtQ+6UWnghYLdCl1RocxQA6SOv1g1MOYlzzXLNJyfBkjFdofNIrto1Cwz9WLSJZcAN98MDA0B//oX8OtfS3GLW7ZIt69YASxePPN9qK2kBKiuliIpg3PUUmI/tpux2ehWxfrZz36G4eFhCIKAoqIi3HPPPbjgggvgdOo/p4YxxhgDgJrCDHlBrWMI/oCo6+wyLqgxxhhLNA6rgIL0JLLjKV5nqAGRzROiuityNe5QU9p/vRZD/QERdW3yi5wWxnB3WlCq04Y/fOEonHf/O3B7py42D4/7cNM/NuOpm07Az/67G35iiNyZiwpxbEWuXrs7q9LsZOyeVlBrifGOHi00EEUNACiPIuLWabMiP90pK3TrVQCabX5aUPuAwQU1hTmUsoKaCTr+1LZNIe5xuYZxj4B08UBBhhOHeqc+d3pelBF3UlOByy8H/vQnYGQEePxxYOvWiduvu86Y/VqzRiqoeb3Au+8Ca9casx9a8XoBuz5R1yx26Bb5uG7dOgBSV9obb7yBz33uc1xMY4wxZqgFhemybW5vIKQrLdVEFdQsQnwsKDLGGGNKqHitggyn4XOh1KBUkGoPs6AWCIjoMSDy0ZXmJKOx9SqoNfaMYMwr7+ZZFKPz06abX5COO89fQt62q20QX3xwE96o65TdZrcK+M5ZNVrvXlio47h72AM38f+XyBqI+WlAdJGPAF0E0iuicLb5aUHhnvfU1jVEP/70GWpJdit5bo3lgpry/DTtZ+wVpMt/D3KHWpSmxz7W10ufJyUBn/ucMfu0evXE5/EW++jzSQXC735X+pyxT+lWUGtvb4cgCDj11FMxf/58vR6WMcYYU7SwSF5QA4AdLaG9OVRLfZf8Dfa83FQ4bVZd94MxxhjT00nVebJtJxPbYlGa04Z0YrZUuB1qg24vfESXUp7GkY92q4VcWNZrYZyKewSA2jgpqAHAxUeV4pKjSsnbNjX2ktuvOr4MZVF0NGkhHrt6tEDNTwOi61ADgNJs+QV4LX1jEKk8XZUpHafT6Tl7kaLUoZZHnOOo2MdYjnykCmpOmwU1Cu+D1URFB3cqxG+yEB11FLB0qfT5++8Dd98NbN8uFdcyM43Zp8kFtf37jdkHNfh8wFVXARs3Tmy7/XbgrbeAX/xC6sRrbjZu/5ip6FZQy82VIglKSowfnMsYY4wBUuQjZf0e+RXBWhFFkexQMzIWhTHGGNPDF46bi/kFE7/vijOT8D+rKw3cI3UVEouJ4RakqLhHQPvIR4Def726C5Si5GrjIPJxsjs+uxgLCkJb2M5OseNrp1ZrvEfhowo6gH5dUrGioVteUMtJdUQ9E5AqAI14/BgY80Z1v6GIlQ416jzqsFqQkSy/6KHUwI4/tYmiiC1NfbLtS0oyYbdqvxycnyH/PdU74sG4j7tXIyYIU7vUHnxQmpt2xRXG7VNREfD001Kx6Z//NG4/onXrrcAjjwCnnAL85z/SNpcLsH16nnjnHWDZMuC//zVsF5l56FZQC3aldXbqt0jJzMVi0e3HjTEWJpfLhby8PLhcLqN3RVfzclPIq2rX13XC5w8Q36G+rqFxDLnl8QE8P41NlqjHKGMs/kw+n+WmOfH8107E7684Evd+bhleuXUVKuLoghIq9rFtMLxOh66h0Gb/aKGAWAzVa/7NrjZ5QS0jyUbGC8ayZIcVv7viSKQ4Zk8luGXtfGQm6zPHJZzXHUr/J7Hc1aOFA0RBrUKFbkOl51/rDsExj19xDvR0xneoUXMoHRCIXFvq+Rxy+zDo1r5AGY5QjtGm3lH0jcr3e/ncLA33bALVoQZI739ZFK64AgiOUNq8GdChG3VW558PxHIDzX33Ab/9rfR5IAB82hSEW28F3n4bmDtX+ntPD3D22cC3vy3NVmOqiMU1Dt0qHFdddRVEUcSbb76JsTF+YZWIuKDGmHm5XC4UFBTE5C+yaAiCgNNqC2Tb+0a9+Pig/Go+LSi9Ea2Mcp4Ciy+JeowyxuLP9POZ02bFZ5YU4bPLSpDmjP3ZaZMVEQW1joFxBIgIRyU9I/TCo0vjyEeAXgztGh6HP4z9jxTVoVZbnEEugMe6qvw0/PxCep5aUGVeKi4/dq5OexTe6w6jCjqxxOOjZzRHG/cIGBe5Wdc+iFBPBUbPzaIKONPnpwVRHX+AFKNpJqEco1ua+sntesxPA+iLMgD9LsyIWzk5UvHnvfekmWVx+HtRVy+8AHz96xN///OfpS61oOOOA7ZsAc47b2LbL38pRV02Nem2m/EsFtc4dKtwfP7zn8f8+fPR39+P733ve3o9LGOMMTajtQvlBTUAeG13hy6PT81PA7hDjTHGGIt1hZnyhVmPP4DeUbrrjNKtcCW/UZGP/oCoGEOpls5BN/kYtUUGzYfRwWeXlcxYMPv+2Qt1iWiLRGaynSyGc0FtwqG+UbIQrUZHrlEdgkrz07JS5F2Uxkc+ys+5Sl2+8TQTkJqfBujYoZZOd6h1GlxgjQvXXy8VeriYFp0tW4DPfU7qSgOA730PuOYa+dfl5ADPPAP86lcTEZDvvgssXy4V5FjC0e0VmcPhwPPPP4+ioiLcd999uOGGG9DT06PXwzPGGGOkY8pzkE4sAry6q0OXYd77lTrUuKDGGGOMxTSqQw0Ib3GZWggGgDxdIh/p/de622QnEfcISB1q8ez2c2rJGXEnVrmwZkG+AXsUGkEQyCJES4zOndJCQ5c87hFQq0PNmBl2SvPTVs3Pk23rGh6HV6c4/elEUUQXUaBX6vJVmgkYiz/P1Py0/HSn4u8mteUb9DuEGeT114Gbb5aKTMOhxcEaqqUFOOccYOTT8/NllwE/+Yny1wuC1Mn2zjvAvHnStt5e4JVXtN9XZjq6ZmpUV1dj8+bNuOGGG/DAAw/g0Ucfxcknn4za2lpkZmaGHN9w++23a7ynjDGWWPz+icHAVuvsMxziicNmweqafDy/rXXK9saeUezvGkZVfmiD4iNFdagVZDiRkaTPjAwWGxL5GGWMxZdEOp9RM9QAoG3AjcUloXVbUZGPdquAjGTt38oXzFAQXFqq3eNScY8AsCjOC2pJdiv++IWjcPXfNqHh03lbJVnJ+OXFS3WPugz3OC3NTsaejqEp22Kxo0crB7q1i3hPdliRm+pAz8jU4rvWEYVUh9qcnGTML5C/dxJFKXaxWKH7S0uDbh88PnkxL+zIR5PNBJztGHV7/eQsymVzsnQ7nyhGPvIMtfj0xBPAH/8ofb5xI3DGGcbuz0yGh4FzzwVaP10DOu444G9/A0IZVXTssVJn27XXAocOAXffre2+JgC/3x9z7wl0D6nft28fBgcHIYoi3G43Xn31Vbz66qth3QcX1GKTz+czehcYYwr27dsHn88Hm82Gmpoao3dHd2sXygtqAPDqrk7tC2pEhxrHPbLpEv0YZYzFj0Q6nyl3qIW+MNs1JO9Qy0116rIgSkU+Atp3F1AFNYfVgkoV4vHMbm5uCp796kq8t78HXr+IVQvyDJktGO5xSsUOdg6NY9znh9MWW4tkWqA61CyC9P+thpLsZFlBTcuCptcfQF3bkGz74uJMxfNG+6DbkIKaUkStUuRjmtOGzGQ7Bsa8U7abrUA82zG6s3UQXr88bWX5XH3mpwHSc5nisGLU45+ynTvU4tSaNRMFtfXrzVtQE0XgiiukohgAlJcDzz4LJIdxfsrOBp56CujvB5zTziWtrUBxsWq7mwj27dsXc+8JdA3hfvDBB7F69Wps2LDh8BsAURTD+mCMMcbUtnpBPmwW+cKU1nPUBt1ecihzVQIsGDHGGGPxriiDXpxpD2MxkVoMdqXTUWVqU+wuIF67qInqqqguSIPDZs4ZYmpLT7Lj9EWFOHtpkSHFtEgodfW09vPCOYDDHYeTlWanqFZspAqaWnZU1XcOw0NEOC4qzlDszDVqjprSHEqlghqg//OpBaX5acvmZOm2D4IgkNHBnRr/DmEGWbVq4vMNGwzbjVkJAvD5z0uFsMxM4MUXgfwIYpUFQSqsTbZzJ1BdDXzjG4An9Hm5LPbo9ups48aN+PKXv3y4KGaz2XDCCSdg8eLFyM7Ohs0WGy8UGWOMxZ/MZDuOrcjBxvqpsz03N/Wha2hcMRIkWkrz07hDjTHGGIt9Gck2JNutGPNOvTq/LYyFZSrycaaFYDVlJtvhtFkwPi0uLZyCYLiGx31o7JEXH6jZYsw8lOZONfeNqjInLNYdIApqaj4v1Ay7gTEvhtxepGsQI7+jhZ6ftqgkU3H2omEFNYU5lDOdR0uykmWRllpHaKqNKqhZBGBpaWhxw2rJT3fKfv65Qy1OFRQAtbXArl3ARx8BQ0NAurZpPxH73OeAuXOB8XFg4UJ17nNsDLj0UmB0FPjVr6RZa//+N1BWps79M1PRrYp1zz33QBRFCIKA1atX4+GHH0ZpqYbB64wxxlgY1i4skBXURBFYX9eJS1fM0eQxqbhHAKjkghpjjDEW8wRBQFFmkqw7JZyF5W6FyEc9BLsLmnpHp2zXcjF0T/sgqGCaeJ+fFuuogg4Qe0UILQy5vegiuqQqVJifFqT4/PePoaZQ/YIaNT8NkI7TFAe9zGhUEaVriH7cmS6YpArEPSMejHp8iv8+s9nS1CfbNr8gHak6d71SBVYuqEVPFEXsbB1Ekt2KyrxU3edsKlq9Wiqo+f1SQemss4zeI2UnnKDu/SUlAf/zPxPdaZs2AcuXS7PZzj9f3cdihtMtM2Hjxo0AgJycHDzzzDNcTGOMMWYqaxcWkNtf1TD2sb6LO9QYY4yxeEYtJoZaUBsZ98m62wD9Ih8Beo6alouhSgv1tcX6dlWw8FAReYD55k4ZgepOA4AKFTvUlDoEtSpoUnMO89OdyE9PQprThnSiaKNlZ+tMlDrU8mbqUFOMMI2Nn+euoXHy2NNzfloQFR086PZhzCP/3cZC0zfiwXn3b8Q5v30Ha3/1Jq5/5GO4idcKhli9euJzM8U+bt0KPPSQto8hCMBXvwq8+y5QUSFt6+8HLrgAuOUWjoCMM7oV1AYHByEIAk499VSkm7XlkzHGWMKak5OCmkL576e393Vp9oKfinzMSLLN+AaPMcYYY7GjiJgn1DbgDmk+eE8EC8FqKyD2X8voNmqhHgBqingNwcxyUh1ItsvngTX3jRJfnVgauhQKairOTFYqAGkx9ysQELGzVR75OLmLlDpvhBN1qyZqDqXDakFGsnKnllLH36EYKRArzU9bruP8tCClCNBOhc5BNrufvLgL2yfFrr62uwP/+KDJwD2axIxz1FpagHPOAa65Bvjud4GAfP6jqo46Cti8Gbjkkolt994LnHgicOCAto/NdKNbQa2wsBAAkJnJV5Yxxhgzp9Nq5V1qbm8AG+u7NXk8KvKxKj/NPJENjDHGGItKIbGwPOb1Y3DMN+v3dhELwQCQm6Zfh1oBEYumZXfBrjZ5QW1uTgoyNJgDxdQjCAJZ1NGioBNrpke+Bqk6Q03HDsGDvaMYIY7/RZO6SPXubJ0JFbfpSnPM+H5LqeMyViJMtx6Sxz0CwPK5WfruCJSjNTsG6d9vbGb9ox48v61Vtp3aZoj8fGDRIunzjz8GBumLZHQzPAyce65UVAOkIp8enWKZmdL8tN/9DnB8+prtww+lCMgXXtD+8ZnmdCuoLVu2DKIo4gBXYxljjJmUUuzjaxrEPrq9ftlMEoDjHhljjLF4QnWoAUDb4OwLs1RnBQC4dOxQowqCgDaL4z5/AHXtQ7LtPD8tNlBFCI58BBqIiPcku4UsOkUqI8mOjCR5x5UWBaAdLfLuNABYXDJxnFLnjfYQO3PVRp1HXTPMTwNmKKjFSIF4S1O/bFu604ZKFbsiQ6XUocZz1CLz/LZWeP3y42hHywBGPbNfqKOLNWukP4Nz1Izi9wOXXw5s2SL9vawMePZZadaZHgQBuOkm4P33gaoqadvQEJDBr2nigW4FtauvvhoA8Pbbb6MlWBlmjDHGTGRJSSaZ8/7a7k4EAuq+AWzsGQF1l1xQY4wxxuJHYSa9MBtK/JlS5KOeBTWlxVAt5iHt7xqBxyePYqot4sWnWEDF5HUMusn/00RCzVArd6XBYlE3kaKEmKOmReSm0pzD2TrUxn0B9I96Vd+f2VAz1GY7h2Ym25HqoCJMzV9Q8wdEfNIsL3ounZOp+s9cKLigpq4nN9Pr6b6AiK1EIdUQF10kRSuuWzc1AlJv3/gG8Pzz0ueZmcCLL0oddHpbvlzq1rvsMuCOO4CTT556+4MPAq+8IhUAWczQraB2/vnn48ILL8T4+DiuvPJKjI9zey9jjDFzsVgEnEp0qXUPj2Nrc7+qj0XFPQIw5MpBxhhjjGlDqUMtlDlkSh1qukY+6rgYuquN7nyp5Q61mFBKFHQCorYz98xOFEWyoFahYtxjENVVpUVHFTU/LSPJNuXxqRlqgDaF+JmIokhG57pmOYcKgkD+PLfEwEzA/V3DGB6Xdyotn5NtwN4A+QrdgJ1EFCeb2f6uYcX5eACwqbFXv52ZyerVwM9+Bpx+OpCq/rkuJL/7nTS3DABsNuDJJ4HaWmP2BZC60v75T6nQONnYGHDrrcAZZwDz5km319UZs48sLLoV1ADgkUcewWWXXYYNGzbg6KOPxosvvgg/V2AThtUqv8KHMWYO8+bNQ0VFBebNm2f0rhjuNKXYx13qxj4qFdS4Q41R+BhljMWLRDufKUUmhtKhRhXUBAHISdGvoKYUS6dJQS2Ezhemj0iOU6WYPC26pGJFx+A4Rol5YxV56i8yUx2C3cMeuL3qrbmJokh2qC0qzpwyk0zpvKF3QW3Q7SM7JJXmek1m9pmASsfoliZ6ftqyOVk67JVcqtOGdKc8jpQ71ML31ObmGW//0CwFNaP997/AzTdP/P2PfwROPdW4/QkSBMAyrQzz3HMTc+ZaWoBf/AJYuBA49ljg978HehPj/zQW3xPIz2oaOeWUUw5/7nA4sHPnTpx33nlITk7G/PnzkZmZOeNQ0CBBEPD6669ruatMI6H8/zLGjJGcTL8BTkTHV+YixWGVvfl9dVcHbjuzRrXH2d8lv1rVYbOQV0MyxscoYyxeJNr5LCfFAYfVAo9/6qJuR4QFtZwUB2xW/a6LzSeisAGgfUD97gJqoT4n1UHGcTNtRXKcUgUIAGg2URFCb9T8NAAo16lDDZBiCtW6YK990I3eEXmE4uT5aYByZ24o5z01RTOHko4wHce4zw+nzfiLxZWOUaUOpmVzs7TbmVnkZzgx1DW1a44LauEJBEQ8rRD3GLT5YD+8/gDsOr5GMJ1t26RoxcCnr7m+/W3guuuM3aeZnH8+8MQTwMMPS4XAYNPRpk3Sx9e/Dpx7LnDVVcDZZ8sLcnEiFt8b6FZQ27Bhw5SCiiAIEEURo6Oj2LZtW0j3IYoiF2UYY4xpKsluxcnVeXh5Z/uU7fs6h9HYPYIyld4AUx1qFa5UWA3ItmeMMcaYNiwWAQWZThzqnVpUaAthMZGa/aNn3CMgvS7KSrHLZh91DKm7GCqKIna1yQtqtUUZvAYQI2Yq6CSqBiLuEQAqNIh4V3r+W/rVK6jtaAmti1QpKjaUzlw1dSvECoZSUFN6Ptv63aq9H9TCFmKO1pycZF1nb05XkJEku5i0c5AjH8PxfkMPWmc5fsa8fuxsHTSsG3EKUQTq64H166XYxyuu0OdxfT4gLQ0YHgYuvliKnjQzp1OaOXfRRUBnJ/DYY1JxbetW6XaPR4qr/OgjqaDGTEPX0qYoilM+lLYrfTDGGGN6WFurEPu4W53YR39AJK9Y5bhHxhhjLP4UZcgXZtsHZi8yUN0VRiyKUvFtaneatA24ZUU7gOenxZK8NCecNvkSU0sCF9So+WmANh1qJVl0yoWazz81Pw0AFk07TnNTHbBb5YVwvbuSqIsSgBA71GYoUJrV8LgPezuGZNuNmp8WRBVYuUMtPE/O0p0W9JFZYh97e4H584EbbgB+/Wv9Hveoo4APPgCuuQZ45JHY6ujKzwduuQXYskXqtLv1VqDg03Wpq66S/1seewxob5fdDdOHbh1q69ev1+uhmElxUZQx8xocHDzcBZyRwQsXp9TkwyJIg9Qne3VXB750UkXU99/SN4ZxIs+fC2pMCR+jjLF4kYjnswIi/iykGWpEd4URBbWCjCTUtU9dpFW7Q+3lHfSi0PSFeqaPSI5TQRBQkpUs68pK5Blq1AV0rjQHMpPtqj+WYuSmis8/1aGWZLfIOu4sFgH56Umy4pPeM9S6FM5TIc1QIyIfAfP8PFPH6CfN/bL3r4Bx89OCqOjgEY8fw+M+pBHz1dhUI+M+vLSjLaSv3XSgV5X1iqjl5gJLlwKffCIViPr7gawsfR577lzgr3/V57G0snQp8H//B9x1F7BunfT3yRoapK4/qxU44wyp4HbeeUAS3R1sdoODgzH3nkC3M9eqVav0eihmUn6/esNwGWPqam1thc/ng81mi7lfZFrISXXg6Hk52DTtCq+PDvahb8SD7NTo4pbqu+RXDgJcUGPK+BhljMWLRDyfUfOEhty+GRcTPb4ABt0+2Xa9Ix8BkDPMOgbHVRvJcKB7BL9cV0feVluUGD8jZhPpcVqSTRXUzNvRozWqQ02L7jQAyE6xk3Og1eyo2kV0qC0syiAj6wsziYKa7jPU6A61vJAiH7Xv+IsGdYyacX4aABSkK8zUG3QjTYP403izbme77LhW8tHBPvOMS1q9WiqoBQLA229Ls8C08Mwz0n1bjZ9tqDqbjY56fOQR6U+/X5q99t//AhkZwMknSx+rVgFHHil9fwxobW2NufcEMdT7CHg8Hjz++ONG7wZjjLEEsLY2X7bNHxCxYW9n1PdNzU8DuKDGGGOMxSMqMhGYeXG5ZyTy2T9qo/bf4wugj4hoDJfPH8Ctj2+F20t37vNro9hCzZ1qH3TD55f//8Y7jy+AQ0TxpcKlzc90sENwOrUKQL0jHnKG0+Jp89OCqPOG3h1qVGyuw2pBRvLsi8yuNAcZYdps4sjHrcT8NIfVYninr9JMPY59DM2Tm5vJ7cdX5Mq29Y54sJ/ojDXEmjUTn2/YoP7919VJkZIXXCB9DJvk362HK64Avvc9YM6ciW2Dg8ALLwC33QYceyyQnQ188YvG7WOci4mC2nvvvYcbb7wRhYWF+PznP2/07jDGGEsAaxfSc9Re3RX9HDWqoGYRtLtilTHGGGPGoTrUgJkLat1DkXdWqI2KrATUWQz901sN2EIsAgPAD8+tNcdV9ixkVFePPyDqXkgxg6beUfiJ/L3yPO1e71Oxj2p1CIY6Py2IKqL0j3rh9uqXXNRFxuY6QjqvCIKg6fOpNlEUsYXoUFtYnAGnzdjOHarLGQA6B+kLR9iE1v4xvLu/R7a9pjAdV51QRn7PpgN9Gu9ViE4+GQgea2qNgfL7geeeA04/HVi4EPjzn6Xtzz8PvPSSOo8RC6qrgZ/+FGhsBF57DbjySiAvb+rXDA8DHuK15AMPSAXOMXOey2KFaXv/mpqa8Oijj+KRRx5BfX09AJinbZUxxljcq8hLQ2VeKvZ3TY1qeXNPF8Z9/qjemFAFtbk5KYa/2WGMMcaY+goVClJtA8qLGd0KHWqGRD4qxHW1D7qxMIpIxh0tA/j1q3vJ2754/DycVJ1H3sbMi+pQA6QihFKEXryi5qcBQIWGF9BRz3/HkBseXwAOotsqHNT8NABYXEJ3qCldSNAx6Ma8XH0uIqQ61FwhzE8LKslKRsO094JmiXycrnXATRYQlxs8Pw3gDrVoPL2lBSIxF+/CI0uwoiyb/J4PG3tx+bFzNd6zEOTkAEccAWzdKn309UldU5Ho6QEefBD4/e+Bgwen3paSAvz4x8All0S7x7HHYgFOPVX6EEVg927grbeAN9+UPqaP3xodBW66CfB6AYcDOOYY6WtWrQKOPx5I41SAUJmqoDYyMoInnngCjzzyCN58802In541xElnD1uM5H8yxhiLfWtrC7D/zYYp20Y8frzf0ItV8yNb5BFFkSyocaQRY4wxFp+KMukiw8wdaiaKfFRaGI9iHpLb68c3Ht8GH9XB40rFd86qifi+mXGoyEHAvEUILVHz0wCgQssOtSx50VIUpeJ9tEUsqkPNZhFQXUC/h1HqbG0b0LOgJu/OCOccOlOEqc1qrsCvLU10V9Jyg+enAUCeQhGzgzvUZiSKIp4i4h4tAnD+shLkpjnJC4A3HeiVfY9hVq+WimmiKM1RO++88L5fFIEvfxn4+98B97TXHJWVwFe+Alx9deSFungiCEBtrfRx443Sc+ef1hH83ntSMQ2QutfeeUf6+OlPpXlrRx0lFddOPlkq0iXR53FmksjHN954A1dddRUKCwtx7bXXYsOGDQgEAhBF8XAx7ZhjjsF9992H5mY6O5YxxhhT22mKsY/tEd9n1/A4Bt0+2fZKLqgxxhhjcSkv3QmrRZ600jbD1fnUQjAQXneFWvIV4rqiWQz99at7sadjSLbdIgD/d+kRSHHwhbSxSKkLzawxeVqa3tkESD/fc3P07VAD1Clo7mqVd6hVF6QrJmwozY7UqytJFEV0UR1qYXT5xlKEKTU/DQCWzzG+0JBktyIrxS7b3jFkvufRTLY1D8iKZQBwUnUe8j89vo4pz5Hd3tI/hlazzPpbvXri80hiHwUB6O2dWkz7zGeA//4X2LsX+PrXuZimRBCkItlkRxwBPPIIcN11QFXV1Nt8PuCDD4Bf/hI45xxpJttkL78M/O1vwLPPSsXRnTuB1lZ5oTMC9paWqO9Db4a9St27dy8eeeQRPProo4eLZOK0Ptbq6mpcccUVuOKKK1BZWWnEbjLGGEtgy+dmIzfVgZ6RqYtar+3qxE8+G1kMMdWdBgBVeVxQY4wxxuKR1SIgP92JtmkdXTN1qPUQC8EAkJuqf+SjK9UJm0WQdZNFuqi86UAv/vx2A3nbTaurcORcXhyLVfnpTtitArz+qT8rLf2jBu2RcagOtTk5KVFHL86EmvkFAM1RLq4Pj/vQQPx7FivMTwMimx2ppkG3Dx5fQLZdqVuKMlPHpdkiTKn5aTmpDszJof8NeitIT0L/qHfKtk4TFibNhOpOA4CLjio9/PmKshz8c9Mh2dd82NiLzy4r0WzfQhacoyaK0tyumbS0AA89BHzrW1IcYdBXvwq8/jpw7bVSXOH0QhALncslzVu78krp762tUyMid++WttfWAvn5U7/3/vuBF1+k7zc5WYr4zM4GvvhF6f9wsgcfBDIyJr4mOxvYs0cq0r38Mir37AGZbWpiuhbU+vv78a9//QuPPPIIPvjgAwDyIpogCBBFEUcffTQ2bdqk5+4xxhhjU1gtAk6pycd/Pp76YrZ90I2drYOKMwNmsl+hoMYdaowxxlj8KsxMCqugRs3+SXfakGTXf96q5dOCYOu0/Y2k02R43Idv/GcruW5SW5SBm0+tjnQ3mQlYLAKKs5JxsGdqAc1MHWoNXcPY3jKAhUUZmF+Qrt3jdMtf85drOD8NAEoVCkDRPv+72+j5aYtmKKgpdbZOPw9qhTqHAuFFPioWKPvGcGxEe6UNjy+AHS3ySM7lc7IiugBUC/kZTllXMkc+Khv3+fHctlbZ9nSnDafXTqTorCiTd6gB0oUrpiioZWdLXWqpqcApp0hFk8k/k8EoyPvvB556SooorKwEPve5ia9ZvVoqtqXqExWbUIqLpec6+Hx3dkr/H9OjIgFpBp6SsTHp/6ilBejunnqb1wt86Uvq7bNJaF5QCwQCeOmll/Dwww/j+eefh8cjXeU/uZDmdDpxzjnn4Morr8T5558PQRB4VhpjjDFTWFtbICuoAcAruzoiK6gRsQ0Az1BjjDHG4llRZhK2TNs2U4cXOfvHgLjHoPyMJFUKaj99cRcO9coX9x1WC3592TJNu3eYPkqzzVtQ+/v7B3H7szsQbLa8ZW01blk7X/XHGRjzksdwhUvb1/uuNCccNousMyvayEeqWANgxvdCTpsVOakO9E5L+tAr8lGNOZSKEZpmidP7VF37IMaJbrxlc7L03xkFBUQEaOeQG6IYWepLvFtf1yXr6AOAs5cWTbmwpjQ7GUXEBTsfNppojtobb8i3jYwA//iHVEjbvn3qbX/4w9SCmiBwMU0v+fnARRfRt/3gB0BjoxTBOfmjr2/i854eqQttsv7+mR/TasXosmUwV8/v7DSrWn3yySd4+OGH8dhjj6GzsxOAvBtt5cqVuPLKK3HppZciKytLq11hjDE2C4vFcviDTXVStQtOm0X2JuW1XR249bTw34BTkY/56U5kJMlz5RkL4mOUMRYvEvV8Ri0m9o544Pb6ya4zqrsinNk/aqPmIYW7MP5GXQcZTQUA3zxjPhYUatctxMITzXFKxeS1DYzBHxDJWYJ6GRjz4qcv7sbk5NLfvLYPS0oycarC3ORINRLxiABQnqftorDFIqAkK1kWNxlt5OZOYn6aIAALi5Q71ADpvDe9oKbX/DFqfhoQXuRjfnoSGXerxky6aE0+RrcScY8AsGxulq77NJMComPR7Q1g0O1DZjK/D57uSYW4xwuPLJ3yd0EQsKIsR9bNtrdjGH0jHmQbEBM9o/p64Pe/B/76V2BgWqE+Px+44Qbpg5nPmWeG9nWBacX95GQp8nF6IS4nBzj9dODUU9Hc0QH1L23RluoFtd/85jd4+OGH8cknnwCQF9GqqqrwhS98AVdeeSXKy8vVfnhmYtx1yJh5zZ8fa7++9JPisOHEKhder+ucsn1X2yBa+scUs/WVUAU17k5js+FjlDEWLxL1fKY0T6hj0I15ufJFdqq7JTfVuA61QmL/u4c98PoDsFtnL7r0jnhw2xPbyduOKcvBdSdWRL2PTD3RHKfUbCmvX0TnkBtFmcbNc9p8sA9jXnmM1fee3o5XynJUXdSn4h4BoFLjyEcAZEEt2g5BqkOt3JWKVOfMazxFmUmyuMgOvSIfVehQs1oEFGUlybpqm00wE3DyMfrHf2+V3S4IwBEm71ADpDlqXFCbqnfEg/XT1h4AYE5OMlaUyWeMriiXF9QA4KODfTitVt2LBaLyl78AX/6yfPvxx0tz0i66CHAa9zqHqWT6hThpadL8uxnMzww/+cloql8WeOutt+KTTz6BKIqHi2k5OTm48cYbsXHjRuzduxe33347F9MYY4zFjLUKL0Rf29UR1v0Mub3kVZlcUGOMMcbiW6FCIYGaJ+QPiOgdITrU0o270lxpHlKnwqL1ZKIo4n+f2U523aU6rLjnkiMM7Vxi6lKKyTM69nHvtPlNQR2D4/j5f3er+lgHFCLete5QA+jnv33ADX+AGFwYgnGfn7wgcFHx7AugVBGlY2g84n0JB3VRAgDkhVFQA4DSLHmB2AwdapNtITrUKvPSTJWAkp+udFEJz1Gb7rmtLbKuSAC4cHkpGY95jMIcNVPFPgLSDLXg/judwDXXAB99BLz7LnD55VxMYzFF05yN5ORk3HfffWhvb8fvf/97HH/88Vo+HGOMMaaJUxfmk9tf2x1eQY3npzHGGGOJSalDrZ0oqPWPekCtN4fTWaE2KvIRoPd/uue2teK/29vJ235wTi3m5sba5Aw2E6X0BqOLEPuIolDQvz48hI313ao91n4i8jHZblU8jtREPf++gBjx7LK97cPk4v7i4pnjHgH6vOEPiOhRiGNUE1XAd1gtyEgOLzmphChQtva7EdChKBiKvhGPrCMRAJabqDsNoCMfAf1m6sWSp7a0kNsvmhb3GFSdn0Z2+W06YLKCWmWlFOd4111AS4sU+3jUUUbvFWMR0aygJggC3G43vv71r+Mzn/kMHn30UYyM0AuJjDHGmJnlpyeRQ53fb+jBoFs+LFgJdXUnAFTlcUGNMcYYi2dKC+lUh5pSZ0WuCQtqnbMshrYNjOEHz+wgbzulJh+XrZgT9b4xcynNoQukzX3GxuTNVFADgO889QlGPT5VHovqUCt3pZLdJWqjCkBA5B2CO1rlcY9AaB1qihcS6FBE6SK6Z11pjrD/D6gCpccfUJzRpretzf3kdjPNTwOUIx87hrigNtm+jiF80iw/5laUZStefGKxCGQU5I6WAdXOaar5wx+A224DcnON3pMZDYx6Ud85hOFxkz1/zDRUL6hdeumlcDqdhyMf/X4/Xn/9dVx99dUoKCjAF77wBbz88ssITB9Sx+Ie/58zZl7t7e1oaWlBezt99TADmT/u9Yt4c09XyPehWFDjDjU2Cz5GGWPxIlHPZ0qLie0D8kVuqrMCAPLSjIx8DH9hPBAQcdsTn2DQLV+Qyk6x4xcXLdGlwMDCF81xWpDuJCM8W/qN61ATRRH1CpGPQYd6x3D3uj1RP1YgIJIdQxU6xD0C9Aw7AGiJcO7XTsWC2uwdagVhdOaqjTqPutLDvyjBrBGmwWP0nV3N5O3L58gLLEbKU3juOznycYonN4fXnRZ0NBH76AuI2NrUr8ZuJZRntrTgxLvewNpfvYWTf7keb9SFl0rEwheL7wlUL6j961//Qnt7O/70pz9h5cqVAHC4uDY6Oop//vOfOPvss1FSUoJvfOMb2Lx5s9q7wEyKC2qMmVd/fz/6+vrQ399v9K6YltJA33BiH6mCWnqSTfENBmNBfIwyxuJFop7PHDYLGdlId6jRC4yGRj5G0Gny9w8O4u19dIzeTy9YojhThxkvmuPUZrWQnUlGFiBaB9wY8fhn/bqH3m3Exweji0nrGHJjzCt/rAqXPgU1xQ613gg71FoG5Y+RlYzs1NkL/IpRsTp0qFGdvpGcQ5WeTyMLxMDEMbqVmJ+WbLdifoG5Lti0Wy1wEReFcOTjBH9AxDNE3KPTZsFnlhbN+L0rFOaobTLbHDWT6xh043tPb8fQp51pvSMe3PbEJ9ypprFYfE+gSeRjRkYGrr/+erz99tuor6/H//7v/6KsrAzARHGto6MDv/nNb7BixQosWrQId911Fw4dOqTF7jDGGGNRq85Pw1wiwmZ9XSe8/tAuGNjfJS+oVeWn8dXZjDHGWAKgigzUwrIZIx/TnDakOeWzh5S6Cxq6hvGz/+4mb7tgeQk+s2TmxUEW26iuHiMLavtm6U4LEkXgtic+gZsoiIWqQWFmcoVOEe8F6U7YVOoQ9PkDqGuXF9RqQ+hOA6KbvRgNURTJSMa8CM6hcxQ6/oyOMAWkf+eebvm/c0lpJmxWzSb8RIy6iIILahPe3d9NviY4fVEhMpLkM9ImW1KSiSS7/P/8Qy6oheW/29swOu3ii+5hD97eG3oqEUsMmp9hKyoqcMcdd6ChoQHr16/HVVddhfT0dAATxbW6ujp873vfQ3l5uda7wxhjjEVEEASsXSjvUht0+/BhCAN/x31+HOyRv8Hm+WmMMcZYYqC6vKiFZeUONeMiHwEgP0O+GE3tv88fwK2Pb4PbK7/gqDAjCT86b5Em+8fMoyRLXoRo6R9DICAasDfKseuU/V0j+O0b+yJ+rAYi7hGQZqjpwWa1kOeaSApqDd0j5HG8OIT5aQCQkWxDst0q2651h9qg2wePT77frvTwz6GFmUkg6pNoMTjyEQBah3wY9sj/nctNNj8tqID4HdLBkY+HPaUQ93jhkSWzfq/DZiFnvm8+2B/yxb8MWK8wzmPjfrrbniUuXS9ZWLVqFf72t7+hvb0djzzyCNauXQuLxXK4sBYIBCAIAkRRxObNm3HxxRfj6aefhsdDX6HHGGOM6Ukp9vHVEGIfG7tHQa0h8Pw0xhhjLDFQHWpdw+Oyxa4eoqDmtFnIDjE9Ud0mHUPyhfE/vrmfjCEDgLsvWYrM5JmvtGexj+pQ8/gCisVire0lOtQEAbhlbTX59X98swE7WujZYbNpIBIpAKBcpxlqgBTJOF0kHYLRzE8DpAsSQ72QQE1qxubarRZyBqbRM9QAoI7oTgOA5URhxQyo57FzyA1RNKbQbibD4z68vEM+Ryov3YmTqlwh3ccxROzjmNePna3yLlMmN+rx4f2GHvK2d+vp7SxxGdIDnJycjC984Qt45ZVXcPDgQfz0pz9FTU0NAKlrTRAEeL1ePP3007j44otRUFCA66+/HuvXrzdidxljjDEAwNFl2eQi0Gu7O2Z9I6B0ZSwX1BhjjLHEQC0siyLQOTR1UVRp9o/REdHUYmjHtIXxHS0D+M1rdHfPVcfPw0nVeZrsGzMXqqAGAIcMKkLsI16Hz81JwVfXVGFJibzbyh8QcdsTn0TU2XGA6FBzpTlnjWxTUykRUxhJhyA1Pw0AFhPPmRKqK0nrDrXuIXXnUFI/z0bPUAOAvUoFtbnZOu9JaPKJ3yFev4i+Ua8Be2MuL21vI2cvnr+sOOT4zhXl9By1UNJ0GPDe/h6ysxWQunXbBow/5pl5GB6qW1JSgu9+97vYtWsX3n//fdx4443Izs4+3LUmiiIGBgbw17/+FWvXrsWcOXOM3mXGGGMJym61YM0C+ULQod4x7JllNoNSQa2SIx8ZY4yxhKA8T2jqIg3VXWF03CNAF9RGPH4MuaXFULfXj1sf3wofsWhf4UrFd85aqPk+MnMoUSioGVGEEEUR9R3y1+HV+emwWS345cVLyZlju9oG8ac394f9eFRBrUKnuMcg6vn3+ALoHgmvQ5DqUMtNdZBFMiVFmfJ9mV6IVxs1Pw2Qun0iQXX8tfSNGd5ZVdctv/iiKDOJPFebgdLPDc9RA57c3Exuv/DI0pDv48i52bAS57JNPEctJOv3dM54+0buUmOTGF5Qm+yYY47B73//e7S1teGJJ57AueeeC5tNirUIFtdaW1sN3kvGGGOJ7LTaQnL7a7tmjn3cT8S/OGwWzMmhB10zxhhjLL5QHWoA0DZtcblHoUPNaIWKi6HS4vWvXt2LvUThwiIA/3fpEUh2yGcpsfg0h+iQAoDmvlGd90T6+Rwa98m2VxdIF7UtLMrATWuqyO+97/V67JvlornJxn1+HOqV/xsrdIx7BIBSogAEhBdTKIoiGRVXW5wRVrfsbIV4LajdoUYVKMe8fvSOGDeexu0LoLFP/vhmnZ8GAAXp9O/ARC+oNfeN4v0GedGrtigDC4tCi1cFgFSnjYxj/aix17D5lbFCFEWsr6PnpwVtrOc5amyCqQpqQXa7HRdeeCGeffZZtLa24le/+hWWLVtm9G4xxhhjOHm+C3ar/E3kq7tnvqKJ6lCrcKWSV5ExxhhjLP5QnRrA1HlCoiiS3RW5Ju1QA6TF0A8aevCXtxvI27+ypsq0EWRMG4WZSaBe4rYYEPlIzU8DgOpJsetfXVOF+QXy1AiPP4DbnvwE/hAXow/10jOTy3XuUFOK3Azn+T/UO4Yht7wQGU7cI6BciNdyjhoVmwsAeRFHPtIFYiNjH+t7xuEnftaWmXR+GqD8O6Rz0JjZimbx9OYWcvuFR5aEfV8riDlqfaNe8uJeNqG+c3jW43ljfbfhXanMPExZUJvM5XLhlltuwebNm7F161Z8/etfN3qXGGOMJbD0JDuOq8iVbd92qF/x6rpAQERDt/xFbCXPT2OMMcYShlLk4+QOtaFxHznDwwwdagUKHXb7u4bxjf9sA7XOtKg4A187pVrjPWNmY7dayJ/3cDqk1ELNTwOkyMcgh82CX158BFkE3NLUj4febQzpsfZ3yeMeAaBC54h3NSI3qbhHAGQHzEyUOnO1nKNGxeY6rBZkJNsiuj8q8hEwpkActKcrtuanARz5SBFFEU9tkRfUrBYBn12mTkEN4NjH2cwW9whI8265MMmCTF9Qm2zp0qW45557jN4NFiGjh2gzxpSlp6cjIyMD6enps38xw+m1BeT21xW61Fr6x+D2yhfHqnh+GgsRH6OMsXiRyOezZIcVWSl22fbJnRpqR5WpSakgePe6PWShxGGz4NeXLYPDFlPLDgzqHKdUV48RkY/1nfIONUEAqqZd2LZsThauO7GcvI+719XhYA9dLJuMmp8G6N+hVpSZDGr5JZznf4dCQW1xcZgdaiF05qqtiziPutIcEa9JKRUojSgQB+3v98u2WS1C2P8/espNc5JF646hxC2obW7qJ88bq+bnRTTzb0UZXVD9qLEv7PtKJLPFPQbxHDVtxOJ7An5ly3RjtXJmPmNmVVJSgrlz56KkJPyroBLRqQvpgtpru+k5alTcIyB/I8+YEj5GGWPxItHPZ1RRqm1gYlG2R2EmjxkiH/PSneQiPRULBwDfOn0B5hfE3iIJU+c4pYoQLf1jukdm7SPm+pVmJ5Mz/W49bQHKcuWFQLc3gO88uX3WfW8guhesFgFzdZ6Z7LBZyHlV4XRUUfPT0py2sP8tSoV4bSMfiYJaBMWJIMUONQMjH+u65M9fTWG6qWdVWi0CWSTqSODIx6c2N5PbLzqyNKL7y01zopKY2bjpAHeoKRlye/FhiB18PEdNG7H4noALaowxxliYirOSybiTd+q7MUIMPeeCGmOMMcYAoIiIPwulQy3S2T9qslstyE0NbT+OKc/BtQrdPiwxUHO83N6AYtFYC6IokjPUJsc9TpbssOIXFy0lb3uvoQf/3HRoxsejOk3mZCcb0qVJFTTD6aja0SIvqNUWZ8AS5vxnV5qD7ErSNvJR/jMWTZdvkt1Kfr9RHWodg260EgXJ5XOz9N+ZMFFz1DoTNPLR7fXj+W2tsu0ZSTacujA/4vs9plwe+9jSP4ZWAwvAZraxvhu+EOdkvtfQE/JMTRbfuKDGGGOMReA0IvbR4wvg7X3yq5aogppF0D/+hTHGGGPGouLPOobGDy/QUJ0VQHTdFWoqzJx9P1IdVvzfJUfAGubCO4svZpg71TU0jkGig7K6QPmituMqcvGF4+aSt/3sv7undJRO10DMUNN7floQVdAMtUOwc9BNnovCnZ8GADarRaErSZsiiiiK6CL2PdqLEugCpf4RpoA014+ybI5556cF5ROdk4naofZGXSd5fjrniGIk2SPvNFSaoxZqF1aiUYp7XLMgT7ZtyO3D9hY6DpclFi6oMcYYYxFYG0bsYz0R/zInJyWqF8qMMcYYiz1Uh5o/IKLn0wVgqrMCAHJTjY98BEDGyE13+7m1mKNzxB0zH2qGGqBvV88+hZQIpQ61oG+fWYNi4lgdHvfh+0/vIItSA6NesvvOqAvoqILmqMeP/lHvrN9LxT0CwKII53NRFxJo1aE26PbB45PPrnalR3cOVSpQGmHroX5ye2x0qMkLm13D4wnZ9aMc9xhd/J1SQY1jH+VEUcT6PZ2y7cWZSbhmJd1lz7GPDOCCGtORz0dn6zPGjFdfX4+6ujrU19cbvSsxY1FxBvlG+426zilvCERRJDvUqgy6WpXFJj5GGWPxItHPZ4XEawcAaPs0vovqCrFaBGSnmKSgprD/QafW5OPSo+fotDdMK2ocp1QBAtC3q2cfEfcIANWzxK6nJ9nxswuXkLe9UdeJZ7fKY9oauuniXQUxz0gPVEcVEFpBc4dCB8bikvA71ACgkCiiaDVDTbHLN8oOtVKiQDnk9mFgbPYCpdq2NPXJtmUk2VCea/70E6pDzR8Q0TOSWF1q3cPj2LBH3hlVlpuCI+dG12lYmp1MXrzDHWpyu9oG0UlEba+uyceKshw4rPKyybv7uaCmtlh8T8AFNcYYY/D5fIc/WGgEQcBaIvaxd8SDzZPe5HQPe8g3Wjw/jYWDj1HGWLxI9PNZITE/Bpi5oJaT6gh7bpFWlPYfALJT7Pj5RUsgCObYVxY5NY7Toiz6Z0XPrp69UcwxXr0gHxcqdIr8+PmdsmOVmp8GGNehptQh2NI/e0GT6lBz2CyojPCCQOq80T3sITvJoqU0hzLqgppCgVLPCFMA8PkDZOTcsrnZpvk9MROqQw0AOhMs9vG5ra3k3K4LjyyN+neoIAhkl9rejmH06TjDMhZQRU0AWLMgH8kOK9n1+VFjH9xev8Z7llhi8T0BF9QYY4yxCCnGPu6aiH2kutMAoJILaowxxljCoa4aB4D2T+cy9RCRj2aJewSUF0MB4GcXLCG7D1hictqs5M+LnpGP9R3y1+ElWclIddpC+v7bz6klCzF9o1788LmdU7YpFdQiLUJFS2mGXUgdaq3ygs3CwnTYiW6NUFCRjwDQOaR+lxo1Pw0AOcctHEodf3rHPu7tGMaoR76Yv2xOlq77EakChYsytJqpZ1ZPKsQ9XrA8urjHoBXldOzjRwfl3Y2JbH2dPO7RYbXghMpcAMDKKpfs9nFfAJv5eUx4XFBjjDHGInRsRQ7SiDfkr04uqBHz0wDuUGOMMcYSkWLk46Byh1q0C8FqmqcQKXbB8hKctaRI571hZkd1SekV+SiKIvZ2yiMfqwtCfw2eleLATz67iLztxU/a8PKO9sN/b+iSF9RSHVbkG3T8RlpQGxj1kl9TG+H8NAAozKSfAy1iH7XqUCvJUpoJqF+EKTDD/LQYKajlK1yU0ZFAHWp17YNkF+ix5TmqzR89RmGOGsc+TugfnZosFHRsRc7hiy6oghoAvMNz1BIeF9QYY4yxCDltVqxakCfb3tA9gv2fFtL2K3Wo8Qw1xhhjLOGkJ9nJi3HaD0c+yjvUol0IVtPR87JlC/UlWcn40Xl00YElNqqo09I3BlGUR52prXvYg/5Reez6bPPTpjtrSRHOWlxI3vaDZ3dg4NPHaCA61MrzUg2LQE12WOFKk3e3ztZRtZPoTgMin58GKHcltWvQlUSdQwEgL9qCmkkiH7ceojtjuEMtdjy1uYXcftGRpao9RnV+GjKT7bLtmw5wQS3orX3dIFI3sXpB/uHPjyjNJF+zbdzfo+WusRjABTXGGGMsCqfNEvu4n+hQy0t3ki9wGWOMMRb/qC61tgE33F4/hsflcyTMFPlos1rwz+uPw6r5eXClOXFqTT7+c+Px/LqGkai5UyMeP1noUts+ojsNAKrz08O+rx9/dhH5M941NI6fvLgLgYCIA93y1/zlLmMvoFMqaM6E6pwBgEXRdKgpFdS06FAjunwdVgsykkOL+VSS5rQhK0X+M6B35OOWpn7ZtrLcFGSb6PfETHJSHLARs960iP80I58/gKe3yAtqSXYLzlpCF+4jYbEIWFGWLdu+o2UAo57Ym1elhQ1E3CMArJl0wbTNasGxRHzm9uZ+DIxp/3uMmRcX1BhjjLEorF6QByvxpiAY+0jNUKvi7jTGGGMsYVFz1NoH3ORCMAC4TBT5CABzc1Pw8LXH4KP/XYsHr16BYoVoOcaMnDulNMc4nMjHoPz0JNx+Ti152xMfN+Pxjw7B7Q3Ibit30RGpeqGe/9kiCqkONatFQE1h+IXIIKWoWy26krqIyEdXmkOVTkGqQKnnTMBBt5ccJ7B8rrxwYlYWi0DGoCZK5OM79d3kz+gZiwqRnqTuhSkriNhHX0DEVqIom2gCAREb9nbJts/LTZGdt08gYh8DIvBBA3epJTIuqDHGGGNRyEpxkBnlHzf1oalnFG3ElZc8P40xxhhLXFS3RvuAm1xkA8wV+chYOKgZaoA+c6f2dag7x/jCI0uwmoh6B4Dbn91Jbq/MM7agRj3/g24fBt3KnRU7iA61qrw0JNmtEe9HisOGjCR5hxj1Pila1IUJal2UQHVc6tmh9smhAVBpqbES9xiUT/wOTJTIRz3iHoNWEJ1VALCJ56jhk5YB9I7I42HXLMiXFd9XVuWS9/Euxz4mNC6oMcYYY1FaWyuPfRRF4C9vN5BfzwU1xhhjLHFRHWoefwD7FDpqcok5SIzFAqoAAejT1bO3Qx75WJSZFHEXiCAI+NkFS8h5Oh6/vDsNMEGHmkL3qFLs46jHhwaiA2pRceTz04KoLjUtiihazqEsyZIXKHtHPLpF6CnNT1s+N0uXx1dLQUZidqgNur1Yt7Ndtr0gw4mVRBdUtBYXZyLJLl/2/5ALalivEPdIXTSxoCCdnEf5Tn236vvFYgcX1BhjjLEoKc1Re/yjQ+R2Lqgxxhhjiaswk17k3tkij1oDgDzuUGMxSqmgo0dBjYp8rC6IPLYQAIqzkvGds2pC/nqjC2pKBU2lgtrutiEEiA6oRSWRz08Los577SoX1ERRJDt91TqHKkaY6hT7SM1Ps1sE1BRGX/DUUwHRodYzMg6vQmE6Xry0vQ3jPvm/8fzlJeQIiWg5bBYsnyOPA918sD/un+vZbNgjL6gl2S04rkLejSYIAk6olBc86zuHE6azkslxQY3pxmqNPCKAMaatwsJCFBcXo7BQvUG4iWRubgoWEG/QqRfMABfUWPj4GGWMxQs+n9EdagCwXaGgxpGPTG9qHadJdiv586t1Qa1neBw9RJxXtQqvwS8/Zi6OVYhSmywv3an6TKRwhTvDbhcxPw1QqUON6koaGIdIZRhGaNDtI7sFXenqdPkqdlzqEPsoiiK2HuqXba8tSoPDFltLu1RBTRTpuM548uTH+sU9BlGxj2NeP3YS0a6JomtoHNua5ee6EypditG2yrGP3KWmhlh8TxBbZ10W09QYAssY00ZWVhZycnKQlZVl9K7ErLW1+SF9XbrTRg5iZmwmfIwyxuIFn8/oxUQA2NVGL3DlpHLkI9OXmscpVYTQeoYa1Z0GqFNQs1gE3HXRUjJKbbIKg7vTgJk6BOnnf0cLfQ6qVaWgRkfdUnOMIqVUkFEv8tG4DrXmvjGySHx0ufpRgVpTei8cz7GPTT2j5OyyJSWZmB9l5+xMqFnvAPDhgcSNfXxrbxe5fY3CjEwAZIcaAGys5zlqaojF9wRcUGOMMcZUsFYh9nG6yvw0vsCAMcYYS2BKHWpur7yzIjPZHnPdB4xNRnVJKXVIqWWvUkGtQJ2UiDJXKr5x2oIZv6Yiz/iCWnqSHZnJ8i45ped/Z5u8a2NebgoyVOi0K1A476kZ+9hNxD0C6hXUjJwJuLmJnp+2bE6W5o+tNqWLSuI5Pu/pLXR32oVHlmj6uMvnZpFxklRxL1GsJ+IeAWD1AuULpOfkpGBujnyG4rv13ap22bLYwa/MGWOMMRUcUZqFvBA6zzjukTHGGEtsWSl2OEMskrnSuDuNxTaqCDHk9mFgzKvZY9Z3DJHbq/LV6wS59sRyHDFDMaPCZY7X/FRXFdVR5fEFsKdd/rwtLo5+fhqgfCGBmkWULoUOtVDeo4UiM9mONKdNtl3rAjEAMu4RkAomsUapoNYZpwU1URTx1JZm2XabRcB5RxRr+tipThsZ2fpRYy8C1MDEOOfzB8gOtar8NMwhCmaTUbGPrQNuHOgeUW3/WOzgghpjjDGMj4/D7XZjfDx+Yxa0ZrEIWLtw9thHLqixSPAxyhiLF3w+k6LwlRaXp8vl+WnMAGoep6UGxOTtIzrUCjKcZLdWpKwWAXdfvBR2K508UW6CyEeA7hCkOqr2dQ7B65cvsKsR9wgoF1HaBmKnQ00QBIUCpbYRpgCwpalfts2V6oArOfaWdQuIeXpA/EY+fnywDwd75D8jqxfk6/I7fgUR+9g36sX+LrqTN55tOdSPQbdPtn2muMeglVUKsY/7OfYxWrH4niD2zrwsZvl88pMWY8wcDhw4gPr6ehw4cMDoXYlpocQ+VuVxQY2Fj49Rxli84POZpDDEgloeF9SYAdQ8Tkuz6av+tZyjRhXUqlXsTguaX5COr66plm23CMCSUnU6u6JFdQj2jHgw5vFP2bazlZ6fRnW3RIKaoQYAHWoW1IbpeWxqnkfpmYDadqi5vX7sIv5/qrKtaGxs1PSxtaAUZRyvkY9PbpZ3pwHAxUdpG/cYRBXUgMSMfVxfR8c9rpkh7jHo+Ap5hxogxT6y6MTiewIuqDHGGGMqWVnlQrLdOuPXVHKHGmOMMZbwijLprp3pOPKRxTq95071j3rQRXQqaZUS8T+rK3FcxdQF60uPnqPYkaU3qqMKkMcU7myRz08DgEUqRT7mpDrgsMqXIFWdoUZEPjqsFmQky2MaI0V1/HUOjWPc5ye+Wh1v7u2Cxy+fsTnfFZsXXAiCQHapdSp0GMYyt9ePFz5pk23PTLZjTc3sRRw1rCjLJrd/eCABC2p75HGPqQ4rjlYoOk6Wm+bEwiL5BQbvNfQkZHxmouOCGmOMMaaSJLsVJ1XTUQCA9IZujsKiAmOMMcYSR6gdampFlTFmFKoAAWg3d4rqTgOkbjItOGwWPHTNMbj9nFpcdvQc/OLCJfjZBUs0eaxIKBc0p3YIUh1qBRlO1eaPCYKAgkz5fakZ+UgVUl1pDggCHcsZCaUCZWu/dt1Vz2xpIbcvKTBH0TYSBenyfY/HDrVXd3VgiIgYPO+IYjhtM1+Iq5bcNCcq8+QRtB829uny+GbRPuDG7jb5ee7EahfZMUlZWSnvUusf9WIXcb8svnFBjTHGGFPR2lrl2MdyVypsxJWZjDHGGEssPEONJYoUhw05qfJOS60iH/d10AW16gLtUiKS7FZce2I57rp4KT53zFxYLOoVcKKlFLk5uaDpD4jkgvBilbrTgqjYRzWLKFSHmkulgmCQ4vOpUcflwJgXrxMxdQVpNtS4YreDmergjMcOtacU4h4vPFKfuMegY8rlHVgt/WOaXdhgRhv2RB73GKQ0R+0djn1MOLyqxxhjjKno1Jp8KF0EqVXUDGOMMcZii9I8oek48pHFA6qrR6vIx70dQ+T26gR9Ha7UUTX5+W/sGcGoRx5ZqNb8tCCqiNKu8Qw1tbt8lTsutSkQv7yjDR6fPO5xTUWaqp13essnIh97RzyaRmfqxe3144OGHvxufT3e2icvtFTkpWLZnCxd90lpjloixT6uVyiorQ6joHZMeQ5sxAUTG7mglnDUCxJmjDHGGHLTnDhqbjY+OiiPUOD5aYwxxhgDwpihpnJ3BWNGKM1OxvZpM7q06oyoJyIf89KdyEpJzOJ0VoodKQ6rrGA2uaNqh9L8tBJ1O9SoztxBtw+jHh9SHNEtT4qiSEY+5qldUAuhQKmmZ7a0kttPqdAmwlQvSjMGu4bGFbsAzapraBwfH+zFR419+OhgH3a2DsDrV56pddGRpboXQ5UKapsae3H+cn275Yzg8QXwDlHcXFiUEXIENwCkOm1YPjdLFpf5YWMvxn1+3WI8mfG4oMYYY4ypbG1tAVlQ4w41xhhjjAEgZwlRXKlcUGOxj5rj1T/qxfC4D2lOdZel9nXKO9QStTsNkGaXlWYnY++0KMzJBc1dxPw0QJ8ONUDqUqvIi+7/aNDtg8cv7+RypatbSHWlOeC0WTA+rWtMi8jHtoExvH+gR7Z9cUkG5mY54PPJZ3PFigKiQw0AOgbNXVALBETUdw1/WjzrxccH+3CwJ/TuREGAIQWs0uxkFGUmyWYWftSYGB1qHzX2YoTowl2zIC/s+zqh0iUrqLm9AWxp6sdxFfIZayw+cUGNMcYYU9lnFhfh/17ZM+XKNIfVguMq6CvDGGOMMZZYXKlO2CwCfAHlq9gB9ReDGTOCUldPS98YFhSq12kzMOZFx6C8SymRC2qA9PxPL6hNnmG3kyioZSbbFf/fIqXUCdI+GH1BjZqfBqgf+SgIAkqyk9HQNTJle7MGHZfPbW2FSPyKOH9ZCQB5vGUsKUinfxY6VZyppwa314+th/rx8cE+fNTYi81N/RgY80Z8fysrXaofV6EQBAErynLw3LapHY97O4bRN+JBNjHnMp4oxT2uqQk97jFoZZUL976+T7b93fpuLqglEC6oMcYYYyqbm5uCr582H798eQ8A6Uq0/7e2GvkKbxwYY4wxllgsFgEFGUkzxt6lOKxRx6AxZgZKHSfNfaOqFtTqie40AKgqiO14vGhRz3/n0Dg8vgDsVgE7WuWRj4tLMlSPpaMiHwGgQ4UiSjcR9whIcZ9qK8mSF9S06FB7Zqs87tEiAOceUYy+1kbVH09P+Qrdimr8LERjenzjjpaBWS98CVWS3YLbz61V5b4isaJcXlADgI8O9uG02gID9kg/6/d0ybZlJNmwPIJZdsvmZCHZbsWYd2rH28b9Pbg10h1kMYdfnTPGGGMauGl1FU6pyUdd2xCq8tOwWOUZBIwxxhiLbUWZMxfUctPi+4pxljhKc/SZO7WvQz4/DQDmJ3qHGhG5KYpSpKDNakH/qLzjZlGx+u9dlCIfp8fQRaJLpw41gC5Qtg+64fMHYLNaVHmMPe1D2N0m7xw8odKFgowk9NGj1WKGYuSjQmFUa2/t7cLPX6ojn/NoOKwWLC7JwLEVufji8fNCnp+qhWMU5qh92Ngb1wW1Q72j5GzNk+fnRXS8OmwWHFuRgw3TinRbD/VjyO1FepI94n1lsYMLakw3Nhv/uDFmVpWVlUbvQlyqKcxATaG6swdYYuJjlDEWL/h8NkEp/ixIi4VgxkKh9nGqGPmockzePmLRFACqE7xDTen5b+4bw/A4PYdL7flpABTTOjpUKKgpdahpU1CTP5/+gIj2Qbdq87+e2dpCbg/O34r136VpThtSHFaMTptrZUSHWn3nEK762yYyXjNc2Sl2HDUvG0fNy8HRZdlYUpKJJLs1+jtWQXV+GjKT7bLIyk0H4nuO2galuMcF4cc9Bq2sdMkKav6AiE0HenHqwvgtTmolFs9nXOFgjDEGu52vomHMzPgYZYzFCz6fTVCKPwvighozitrHaXqSnVzInTzHSw1UQS031YGcOJ8PNBuqAARIMYVKs7+06FBz2CxwpTnQPTx1/le7GpGPw/RMsTwNzqMzFSjVKKgFAiKe3SIvqDltFpyxSFqsj/XfpYIgxR4f6J4andlJzEDU2kPvNkZcTKtwpeKoedk4ukwqolXmpaoelaoWi0XAirJsvLZ7aoFpR8sARj2+uI2YpuIeAWDVgryI7/OEKnpW2sb6Hi6oRSAWz2fxebQwxhhjjDHGGGMmVjhL9JOLIx9ZHCnJSpYV1NSeO7WvQz5DrSrB4x4BOvIRAJr7x7CzRT4/LcVhRbkrVZN9KcxMIgpq0RdRuonIR4fVgoxk9Zc9ZypQquHDxl60El17a2sL4ipOLj/dKSuoGdGhtr1ZfgxQHFYLlpRm4uh52Z92oWUjN8YufFlRliMrqPkCIrY29eOEKpdBe6Udt9ePd/d3y7YfUZoZ1UVLCwszkJPqQO/I1HMZ9VgsPnFBjTHGGGOMMcYY0xl3qLFEUpqdjF3T5hOpOUNtyO0lZ3HNT/C4RwBwpTrhsFng8QWmbG/uG8XOVvnMqIVFGbBatOmyKcxIwo6WqY/ZPhD9z0EXEfnoSnNo0i2kVKBUK8L0ma30gLQLlpWocv9mQc3U07ug5g+I2EMU4oFgfKMU3Xj0vGwsNlF8Y6RWlNNz1DY19sZlQe39hh64vQHZ9tVRxD0CUrff8RW5eHF725Ttde1D6BoaR146v36Ld1xQY7oJBOQnMcaYOfT29iIQCMBisSAnh36RxRgzDh+jjLF4weezCTxDjZmVFscpFYXXM+JRLWqsXnF+GneoWSwCSrOS0TCtG+iT5gEybnGxBvPTgqgiStfQOHz+AGxWS8T3S3WouTRa1M5PT4LdKsDrn5oTqEaE6bjPjxc/kRfUslLsOHn+RERdPPwuLciQ//8Mun0Y8/iR7NCncHWwZ4QsuNywqgLfObPGtPGNkVpcnIkku0X2b/6wMT7nqE2fcxa0pia6ghoArKxyyQpqgNSl9tk4K35rrbe3N+bOY5H/tmIsTFxQY8y8Ojs70d7ejs5OemArY8xYfIwyxuIFn88mzNahlsuRj8wgWhynSl09rSp19VDz0wCOfAyinn+lIqQW89OCqPNeQFSegRYq6vu1uijBahFQRET2qtGhtmFPFwbdPtn2c5YWwWGbWMKNh9+lVHEVADqH9OtSq2unu9OWz8mOu2IaIM0xXD4nW7Z988F+eP3xtWYriiLeqJMfH7mpDiwtif4ct1Jhjtq79T1R33eiicXzGBfUGGOMMcYYY4wxneWlOTFTqhp3qLF4ojR36pBKsY/U/DSAIx+DSrJmntk4Wa3OHWoA0BZF7KMoimTkY56G51Dq+VQjwvSZLS3k9vPjsOMlX+FnoUOFmXqh2t0mjzwFgIVF8XveoGIfx7x+Mv41lh3oHkFTr7xrdNX8PFhUiLSdm5NCngc28hy1hMAFNcYYY4wxxhhjTGc2q2XGORtcUGPxRKmgptYcNapDLTvFjtxU7vQElJ//6exWQdMipFLUbTSzswbdPniI7hpXunb/99Tz2dbvRiAgEl8dmkG3F68THTWl2ck4ap68qyjWFSj8/tNzjtruNnkhPtVhxRwiojZeHFNGR+t9eCC+Yh/XK8Q9rlYh7hEABEEgu9Sa+8bQ1BN9/CszNy6oMcYYY4wxxhhjBigkYsOCXBz5yOJIaRa9QN2iWoeavKBWnZ8el7FtkVCK3JxufkH6lGhBtRUqdCW1D0ReRKHmpwHaXpRAPZ8efwBdCvsSipe3t8PjkxcGz19WEpc/x0rdinoW1Ora5V1ZCwrTVelgMqvlc7NgJf59m+JsjtqGPfLitEUATq52qfYYK6vo++IutfjHBTXGGGOMMcYYY8wARQoLinargMxku857w5h2MpJtSHfaZNub+6K/kn9k3EfOr6oq4PlpQaUhdtws1nB+GqDcodYeRcxfNxH3CGDGDuBoKUVoRvPz/LRS3OPy4ojv08zyM+j/n06F/0+1Dbq9ZIdsTZF2kadmkOq0YTER6/pRY29UHZZmMjLuwwcN8gLhkXOzkZWi3sVKJ1TSBbV36rmgFu+4oMYYY4wxxhhjjBlAaXE5N9UZlx0JLHEJgkB29VCFsHDVE3GPADA/nwtqQaHOUFtUom0xIT3JjlSHVba9PYoZakpdYVp2qCkVKCONMG0bGMP7B3pk2xeXZKAqPz7neaU4bEhPkhfZ9epQ29NOz11cWBifz/dkK4jYx75RL/Z30efSWPPu/h4yBnaNSnGPQXnpTiwgInLf298TN8VJRuOCGmOMMcYYY4wxZoAipYIaxz2yOETNnVJjhho1Pw0AqjWcBRZrCjKSYAshxm6Rxh1qAFBAnPfaoyiiKHWoaVtQowuUkRaIn9vaCpFYfz9/WUlE9xcrqNhHvQpqdW3yuEcAWBjnHWoAsKKcnqMWL7GP64m4RwBYvSBP9cc6gZij1jviQZ1CwZbFBy6oMcYYY4wxxhhjBlDqUNNyIZgxo1BdPV1D43B7/VHd775OeuGymjvUDrNaBBRl0eebIEEAFhZpX4Sk5qh1RBP5OOwht+dpeB4tzEwCVZ+MtED8zNZW2TZBAM49Ij7jHoMKiNjHzih+FsKxW6HgMT9BO9QA4MMD2hTUfP4AdrQMoEuHOE9RFLGhTl5Qy093olaDYulKhdjHd3mOWlzjghpjjDHGGGOMMWaAoky6y4ELaiweKcUOtkYZ+7ivQ96hlpFk03SGViyaLfaxwpWKFIc8gk9t1IUEbQNjEKkWrRBQi/QOqwUZydr9W+xWC1kYbImgoLanfQi7iW6plZUusoMrnuSnm6tDrTQ7GRlJ8T+/NCfVgSrigoMPG/tUfRxRFPH4h4dw3M9fxzm/fQfH/fx13LNuT8THeij2dgyjdUD+M7RmQb4mUdrHVuTASlTXN/IctbjGBTWmG54BwJh5OZ3Owx+MMfPhY5QxFi/4fDaVUuSjiyMfmYG0Ok6VYvKijX2kOtTmF6TzGsQ0JVn03K+gxSXaxz0CdIea2xvA4JgvovvrJmaoudIcmv//qzUT8JmtLeT2zy5T7k6Ll9+l+USH2ojHj+HxyH4WQhUIiGQkX01h/Mc9Bq0oy5Zta+kfU2WuJQA0dA3j8395H7c9+cnhLlJ/QMT96+vx4+d3aVZUU4p7XFOjftwjIM2FPKJUfu784EAvPD75HDcmF4vnMe0vPWHsU1arfPAsY8wcysvLjd4FxtgM+BhljMULPp9NVZiZBFeaU7YgvIRYnGFML1odp1QBAoiuoDbq8ZHfX13AcY/TKRU0gxYV61NMUIq6bR90IzMl/O4gsqCmQ3diaXaKrKOnuW8UoiiGXMwLBEQ8R8Q9Om0WnLm4UPH74uV3aQHRoQZIXWppedodw4f6RjHqkUfN6hF5ahYrynLwz02HZNs/PNCLkuWRz+7z+AL481v7cd8b9YoFpYfebUSS3Ypvn7lA9cL3eiLu0WYRsLKKjmZUw8oqFzY39U/ZNurxY1tzv2K8JpsQi+cz7lBjjDHGGGOMMcYMYLdacMPJFVO2lbtScVptgUF7xJh2qBlqANDSPxrxfTZ0jYBqdKjKT5yF8VApFTSDFhcb16EGSAW1SFAz1LScnxZERWi6vQH0jtAz3SgfNvaSHUFrawuQngDRg0qRllrHPu5uo+enLdRgxpZZKRV6NjVGPkdtc1Mfzv3tO7jnlb2zdmf98c39uPf1fRE/FmXQ7cVHB+WxlSvKcjQ9nk5QmKPGsY/xizvUGGOMMcYYY4wxg1x/cgUq81Px1t5uZKc48KWTyuG0cboHiz/ZKXakOKyyzpBoOtT2dtAL4/O5Q02mdJYZarVGd6gNhP9zIIoiOUNNjzmUSgXKlv4x5Ib4+M8Q3WkAcP6yyDuEYkkBEfkIAJ2D8v9TNdW1y+enAUBNYeIU4kuzk1GUmYS2afPGPjwQfkFtyO3F3ev24NH3D5IXOCj5zWv7kGS34sZVlWE/JuWdfd3wB+Q7oFXcY9CR87KQZLfA7Z1aRHy3vge3rNX0oZlBuEONMcYYY4wxxhgz0Ck1BfjReYvw/9ZWI9XJ172y+CQIAtnV0xJFQW1f5zC5vZo71GSUOgSl25KRlaLP7EbFDrWB8Isog24fPH55J4wrXft/S7QzAcd9fvx3e5tse1aKHavma1sAMAujOtTqiA61JLsF83JTNX1cMxEEgexS29c5jL4wuixf2dmO0371Fh55L7xiWtAvXqrDQxsPhP+NBCruEQDWLMhX5f6VOG1W8rnccqgPox5t5wEyY3BBLcFs3rwZv/zlL3HJJZegrKwMgiBAEAQ0NjZq/th+vzyfmDFmDocOHUJjYyMOHZJnaDPGjMfHKGMsXvD5jDHz0/I4pYoQ0XSo7euQF9TSnTbFzpdEVpiZBKVxRXrNTwOA3DQnrBb5jkQS+UjNTwN06lBT6PgLtUC8YU8XBsa8su1nLymCwzbzcm28/C7NU5h116Fxh9puokNtQUE6+XMZz1aU07GPH4YQ+9gx6Mb//P1jfPnRj2c8dgUBuPqEMtx10RLF88+Pnt+Ff25qCmmflQQCIjbs7ZJtL8lKRlW+9h3LVOyj1y9iUwQdf4kmFs9jfOlbgrnjjjvw7LPPGvLYYiSXKjDGdDEyMgKfzwebjX8tMGZGfIwyxuIFn88YMz8tj1OqS6pjyA2PLzBrEYFS3ynvNKkqSIOgtHKbwBw2Cwoz5BFvgH7z0wDAahFQkO5E67T9iCTysZuIewSUCzVqKlYoqDX3hTYT8NmtLeT2C5bPHvcYL79Lk+xWZKXY0T86tbDYMaRdh9rIuA8He+T/R4k0Py3oGIU5ah829uL0RYXkbYGAiMc2NeGul+owND5z91VNYTp+cdFSLJuTBQCwCAK+9cQn5Nd+7+ntSLJbcMHy0tD/AZPsahsk41/X1OTp8vvgxCoX7iK2b6zvxmqNO+Ri3cjIiNG7ELbYPvOysB1//PFYsmQJjj76aBx11FE47rjj0NJC/xJnjDHGGGOMMcYYUws1d0oUgbaBsbDj1txePw72yhfG53Pco6KSrGSyoLaoRN9iQkFmkrygFkFXUpeBHWpJdivy0p2yRfyW/tkLg4NuL17bLY+nK81OxlHzslXbx1hQkJ4kK6h1ahj5uEdh7mIizU8Lqs5PQ2ayXdYpuamxj/z6fR1D+O5T2/HRQfr2IKfNgv+3thrXn1QBu3XiQolLjp4Dty+AHzyzQ/Y9ogh84/FtcFitOHtpUdj/FqPiHoNqizPI53JjfY8uj8/0xQW1BPPtb3/b6F1gjDHGGGOMMcZYAppp7lS4BbX9XcPkzJ7qAu3jvWJVSXYyuRi+SMcONYCeoxbJ3CylDjU9CmqAVKCcXlALJcL05e3t8Pjks98+u6w44bor8zOcsiKXlpGP1Pw0AKhJwA41i0XAirJsWXF3Z8sARj0+pDikssG4z4/frd+PP2yoh9c/c/rYyqpc/PT8JShz0efzK4+bh3GvH3e+uFt2W0AE/t+/tsBhs+C02oKw/i3r98gLag6bBcdX5oZ1P5GyWgQcX5GLl3e2T9m+q20QvSMe5KTqM6OS6YNnqDHGGGOMMcYYY4wxzSnNnQo1Jm+y+k75/DQAuszLiVVHzpV3P1W4UpGvQ0TiZIWZ8oJa74gHbq8/rPvpHvaQ2/WIfATojstQZqg9oxD3eP6y2eMe402BQnFVq7Exu9vk89OAxOxQA4AVROyjLyBia1M/AGDTgV6cde/buO/1fTMW07JS7LjnkiPw9+uOVSymBX3ppAp864wF5G2+gIiv/GMz3iTmoSnpHfFgy6F+2fbjKnIPFwX1sLKKLt69t5+71OINF9Sm8fv92LFjBx566CF87Wtfw/HHH4+UlBQIggBBEHD11VdHdL/PPvssLr74YsybNw9JSUnIz8/H8ccfj1/+8pcYGBhQ9x/BGGOMMcYYY4wxZjLUDDUgtCLEdPs66IJadUFiLoyH4sIjS6YUNQUB+Ppp83XviqI61ACgM8zOJGpmksNqQUaSPovoVMfl0LhPFvs2WfuAG+81yBfYFxVnJOTPbkGGvPg57gtgcGzm+VyRqmuXF9SKMpOQlZKYHUQryuk5aq/t7sR3n9qOS//0Hhq6Zp5xdf6yYrx+6ypcfFRpyOeSr6ypwtdOqSJv8/gD+PIjH4VciHp7XxfZrbxmQV5I36+WE6pc5PaN+7t13Q+mPY58nObSSy/FU089pdr9DQ0N4fLLL8cLL7wwZXtXVxe6urrw/vvv47e//S3+/e9/44QTTlDtcRljjDHGGGOMMcbMxJXmgNNmwfi0uLtQYvKm20vMQkp1WFFMdD8xSXqSHS/fchIeff8gBsa8OHtJEZaWZum+H1SHGgC0D7oxN5cuulK6iRlqrjSHbgXCUoWOy5a+MWQm28nbntvWQi7+X7A88brTALpDDQA6htzITKGfw0iJokhGPi5MwLjHoMXFmUiyW+D2Tj0n/3XjgVm/tzQ7GT+9YAlWzY+scHXrafPh9vrx/9u78/ioyrP/499Zsu9kJ2EnEBaXorKqYF1QcEFcK8qmVJ+KrdXWautS9fHX1qd9tLZqW1HRupTHWtwFUcAKiMUKLsgSZA9kA0ISyDaT+f1BGZPMmckkmZkzM/m8Xy9ezDlz5sw9ydz3ZOaa67qe+sjzvhodLbr+uXX66/WjdUo/46DfcWb3TztuYFaS8tPiPfpUrt5GQC3akKHWjtPZNr28V69eKioq6tK5HA6HLrvsMncwLTc3V3fffbdeeukl/fGPf9SECRMkSXv37tXUqVO1cePG7g0eAAAAAAAgTFksFsMyeXurOx9QMyr5ODg3pcf1oOqslPgY/WDSYN11wTBTgmmS9yBKWSf7qBkG1EJYvtLouSz5LmG6eP0+j30Wi3TRSb0DNq5IkpPiJaDWhZ56HSmtrldto2fmW08t9ygd6zP2nT6epWB9sVktuvHMgXrvx2d2OZgmHXs9+PmUYbpubD/D6482OTX7mXX6Ym+113M4W1yG5SEHZCV1WHoy0CwWi8YP8sxS23XgaJfKGiN8kaHWzujRozVs2DCdcsopOuWUUzRgwAAtXLhQc+bM6fS5nnrqKS1btkySNHz4cC1fvly5ud82Vbz55pv1k5/8RL/73e9UXV2tG2+8UatWrTI819y5c7VmzZpO3f+ll16qX/3qV50eNwAAAAAAQDAUZiR6lBDrbMnHRodTOw94liEron9aRMj3lqF2uHPPA6MeatnJoQuoeS1h6iVAvLW81rCH1/hBmV6DjNHOqOSjJJV3svynPzYZZKdJUnEPzlCTjpV9NCpDamRkQap+Pf1EjSxIC8h9WywW3X/xCDU6nPq/T/d6XF/b6NB1T/9Lf/v+WMNMws/3VuvQUc8Sq5NCXO7xuAmDM/XqZ56PY822A7ryNP+zbzvj63012n3wqFpcrv/8O5aN2eJyqaVFanG55HLJfd2x7W8vHz/e6Jis5DhNGprjNau4pyKg1s7Pf/7zgJzH6XTqgQcecG//9a9/bRNMO+43v/mNPvjgA23YsEGrV6/W0qVLNXnyZI/jdu/erS1btnRqDPv37+/8wAEAAAAAAIKkwKBM3v7D9Wp2tijG5l8hpe2VR9RiUDZvSC4BtUjgNUPtsP9BFJfLZdhDLSuEATWj57LkPUD82vpSw/3TTu6Z5R4l78+FitrAZ6htNghmStKwHpyhJkmj+/suqShJCTE23X7eEM0e3192P9dpf1mtFv1q+olqdLTo9Q2eGZyH65t17YJPtOjGcRrc7ksTK8Ok3ONxE3z0UbvytD4Bva+Kmgb97NUvtGKLZ4ZeIKUlxOiv1482LaM5HFHyMUg+/PBDlZWVSZImTpyoUaNGGR5ns9n0wx/+0L398ssvGx73/vvv/yda7P+/hQsXBvxxdYfVytMNCFe9evVSZmamevXq+A8pAKHHHAUQLVjPgPAX7HlaaFAmr8UllR32/wP0EoNyj5JUlNOzPxiPFPExNqUb9MfqTJm/mgaHmpwtHvuzUmK7NbbOSIqzK8PgcRj1BGxpcRkGC+LsVp0/Mq9T9xtNr6XZXkp0VgQhQ21zmWeGWqzdqgEhLg0Ybr7TN102q/dSuROHZOu9H5+pG84YGPBg2nE2q0W/u+IknT/CeC4cONKkGQvWale7zGSjYFJCjE2jB5gzN3JT4zUo2/P5tHrbAbmMmid20ZKvyjT50X8GPZgmHQtoPrJsa9DOH4nrGBlqQbJkyRL35SlTpvg8tvX17777btDGZDYCakD4yskx59s7APzDHAUQLVjPgPAX7HlqFFCTjgUh+vTyryTWtnLj0m3tsxcQvvJS41XdrlRbZ3qoGfVPk0KboSYd66PWvuScUcnHT3cdMtx/zvBcpcR7BuV8iabX0hibVVnJsR7lO4PRQ21TmWeG2pDc5KAFiSJFUpxd54/I09tftq10lpkUq3svGq6LT+odkt6UdptVj33vO7rphX9ruUHmWXlNo6556hMtunGsCjMSVVHboC9LD3scN2FwluJjbEEfrzenD87SN+3KGlfVNWpreZ2GdjMb8kijQw+8+bUWfbqnW+fprE92HFRLi0tWH4HXrorE9axnrxhB9OWXX7ovn3baaT6Pzc3NVZ8+x9I+KyoqVFkZ/OgyAAAAAABAqHkLqHnrO2XEKEMtIcbmtQQfwo9RT57OZClWGZR7lLxnPAWL0XPO6Lm8mHKPXuWkeD4XAh1Qq29yameVZ9/F4rye3T/tuLsvHKZRfdMlHcuavPq0Pvrg9om65OSCkATTjou1W/XEjFE63UvpxNLqes1Y8InKDjfoQy/ZWWcVm9M/7bjx3so+bqvq1nnX7z6kKY99FPJgmiQdbXJqXyd7XEYzMtSCpHW/swEDBnR4/IABA7Rnzx73bbOzzZ38/tq9e7d2797t85imJs8msQAAAAAAoOcpzDDOQtt76Kjf59hqkKFWlJsclG/PIzjyDHpnldc0+J0FURkmGWpGz+eDR5p0tMmhxNhjH7s2OVr0TrvsH0lKT4zRxCGR8flfMOWmxunrdj+e8gCXfNxaXmvYd7G4h/dPOy4/LUGv/td4HTjSpPgYm5LjzAsZxMfY9JeZp2j2M+v0r50HPa7fdeCoZixYaxiUl6RJJvVPO27swExZLfJ4vq35pkpzT+84RtCew9mix1d8o8eWl8hp9CQOkZKKOq+v3z0NAbUgqa6udl/OyjKOTLeWmZlpeNtAe/vtt/Xggw+6t49nw1166aWKizv2R8fUqVN1zz33+HW+Z555Rvfff79fxzocDm3evNnnMQkJCerXr1+bfbt27VJ9fcdR8KysrDY/a6fTqZKSEr/G1q9fPyUkfPutopqaGu3b51nbuj2r1aohQ4a02VdWVubX7zAlJUUFBW2/ibRt2zY5HI4Ob5uXl6f09HT3dmNjo3bs2NHh7SRp0KBBion5tpzAwYMHVVFh3MSztbi4OI/g8J49e3TkiOc3fNrr1auXRwpvR8+F4woLC5Wc/G3Zjrq6Ou3du9ev2xYXF7fZrqio0MGDni/G7SUlJbmzRo/bsWOHGhs7/oMuJyenTf3f5uZmffPNN36Nd8CAAe55KB1bC473YvTFbrdr8ODBbfaVlpaqtta4DEpr6enpystrW6N669atamnxrEXfXu/evZWa+u23uerr67Vr164ObydJRUVFstm+TcGvqqpSVVXH39ZhjWCNaI81gjWiNdYI1oj2WCNYI1pjjWCNaI81wpw1wuF0KsYqNbdbKjbuLNPmzcd2+lojmp0u7TDINMmJc2rz5s2sERGyRuQaBNQcLS598vlGZSS0/bjSaI0o2VNueB+1FaXa3PRt5kqw14iYJs8ygpJUeqheRbnHgjUrt1TocH2zxzET+iRo+7a2vYl64hph9Fwor6nXpk2bvGZHdfbviBVbjX9Pw/JTWSMMmL1GJMba9cyc03T1n1brq/2eGcnfVB7xKKsoSUNzU9xZo2b9HbF/714NzozT1qq2c3bNtipt/HqTu1+dP39H7K9t1sP/LNemSt/z/8wBKbq4OEVWi0VWi2SxSFZZjv3/n22LLMrK6qWM9HT3cU6nQ3t275bVYpFFx47be7hZP1vq+fzaXFqts1oFKwO5RrT/WYQ7AmpBUlf37WSPjzeOmLfWemH050WnqyorK/XJJ5947N+wYYP7crCexC6Xq8OF1+h6h8Ph14LtdDr9Op+3sbXf9ue2Rn3hnE5nt8brz23bf0jh73i9ncuf27Z+Q3Jcdx+rP7r6u/E2jlD/bo7f1h/tH6u/vxsjnX2smzdvlsPhkN1uV0tLi18fhJnxu2GNYI1oryetESUlJbLb7e7X6VCuEa05HA7WiHZYI1gj2h/XXrT/HdGav2vE3r17NXz4cPc2a4Qn1gjWiPaiaY3ISrJrf23bc5TXNrnP6+t3s7u6yTDTpDDVbjgu1ghP4bBG5HvJLimvaVRKu5ZiRmvEgSPG1ZBSYtv+7IP9u8lMMO6ms7dVQO21DcblHs/sl+Bxvp64RuQYBlePZfqlxRv3wursvPnmgHEJyeK8FDmOVLNGGBzXXqjXiOQ4ux67bKhuePELbT/kGZA2MqlVuUcz/444MdczoHa0uUWbyo+qONs4i7b1eF0ulz7YfkR/XndQ9Q7vWWlJsTb98uIROim13q8KcXkZ8crK+jYA2NzcrKMVbYPWCb3shhl22yrbBjZDsUaEKwJqPczs2bM1e/Zs0+7fbvf9lDO63m63d3g7yfgPLH9uJ8njGy8Wi8Wv2xq9ONlstqCPt/39+jteb+fy57ZGx4TisXb1d+NtHN0Zr9GLbHtGz4muPlYzfzf+fBBmxu+GNYI1or2eskYY/bHJGuGJNYI1or2eskZIkfN3hLc3z6wRnlgjWCPaC9Ua0XqeBmuNyEuO8QioVRxxus/r63dTWmf8wXj/XvHdGi9rRGjXiFwvAbVDjZ5jMTr/oXrPORNjldISYtr8TIP9u+mdavzh+N7/9FGraWjW+5s8M3Vzk+06IT9J1nb34c8a4W2ORurfEblefoaHm6TMZOPzdHbe7DrsGZDJSYlTZnKcqhpYI4yOa8+MvyPSE2P1q8kFumNJqXZVdxxUa51BZebfEacUJOnvGz2zIr+oaNLI/CSv47Db7appcOqxj6u0apfv7LpRfdP16FXfUd/MRO3YscOv9+j+rBF2u5SfEqPSmrY/723tMgIDtUZEYmDN4mofSoWHhQsXas6cOZKkWbNmaeHChR3eplevXjp06JCkYxlnrdM/jUyfPl2LFy+WJL355pu68MILuzfoEPGnh9r06dNVWVmp3Nxcv1JBAYRe6wy1SEu1BnoC5iiAaMF6BoS/UMzTn/39Cy36dE+bfXarRVv++wJ3OSxvHlm2Vb//wLNU2j9/epb6ZtLfJVJs2l+jC37/kcf+B6eN1HVj+xncoq3rF67TB5vbBqp6p8VrzV1nB2yM/jh8tFknPfCex/6bJg7SnRcU6//W7dEdr37hcf3NZw3STyd3bX5F22vp+1+X64bnP/XYv3DOaQHph+VyuXTyA8s8ym6eOSRbz88d3e3zI/gqaht09Z/XartBud/jUuLs+uzecxVjM84aDaWGZqdOvP89NTnaBrnGDuylv31/nNfbrSqp0u2vbPDZQ9BmtehHZxfpB5MGyR6kxzrv+U+17Ou2ZXWT4+z68pfneS3D2lWbN2+OuHWMDLUgSU9PdwfUqqqqOgyoHThwoM1tI0Xfvn3Vt29fn8fExsaGaDQAAAAAACDcFWYkeOxztLhUXtOg3ume17W2rcKzn058jFUFBudE+PJa8vGwcQZie1V1nh84Z6UYZzoFU2qCXclxdtU1ts2y2HvoqCTv5R6nnVxguL8nMuqhJkkVPoIKnVFW02DYw25YXkpAzo/gy0mJ14vzxujKP3+sPQeNe9adMSQrLIJpkhQfY9Op/TK05psDbfZ/tqta9U1OJcS2zYpraHbqf5Zu0dOrfPea65+ZqEeuOlnf6ZsR8DG3VpST7BFQq2t0qKymQflpvNaGx7MsCg0dOtR92Z/Gi62PaX1bAAAAAACAaFLYy/gDub2HjD8oba2kwrPv/KDs5A4z2xBe0hJiFGf3/Fhyv98BNc9+QdnJoQ+oWSwWwwBxaXW9yg436OPtBzyuG9E71d1fDd5LPpbX+Pdc6Mjm/Z5rhiQNy08NyPkRGvlpCXrphrFeg/GThnQ/mzGQJgzO8tjX5GzRp7sOttm3paxW0x5f3WEw7erT+ujtH54R9GCaJBXlGicGlZR7fqGlJyKgFiQnnHCC+/K6det8HlteXq49e46VOsjJyVF2drbP4wEAAAAAACJVQbpxacbS6qM+b9fsbNEOg5JfQwhORByLxaI8gw/G/QmiuFwuVdYaZKiZEFCTpAKDrMrSQ/V64/NSGTXaITutrczkOBnFw8trAxNQ21Tm2ctKkorzWTciTZ9eiXpp3lhlt8tGzU+L19QT800albHxgzIN96/edizI3tLi0jOrduiiP67S5jLjoK8kZSTG6E/XnqJfX3aikuJCU2ywKMd4bpQ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      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 395,
       "width": 874
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "psd_ls = ts_std.spectral(method='lomb_scargle')\n",
    "psd_ls_signif = psd_ls.signif_test(number=20) #in practice, need more AR1 simulations\n",
    "fig, ax = psd_ls_signif.plot(title='PSD using Lomb-Scargle method')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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