{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3a45304a",
   "metadata": {},
   "source": [
    "# 03 결과 도출 — 3장 표·그림, 이전 판 대비 변화, 검증\n",
    "\n",
    "`02_분석.ipynb`의 산출물로 3장의 표와 그림을 만들고, 이전 판(2026-10-08 패키지가 재현한 공표 산출물, `data/reference_prev/`)과 비교한다.\n",
    "마지막 셀은 계산 결과를 정비판 기록 산출물(`data/reference/`, 2026-10 정비 실행에서 동결)과 `config.PUBLISHED`의 핵심 수치에 대조한다. 허용오차는 기록 파일의 반올림 자릿수(소수 셋째·넷째·여섯째 자리)에 맞춘다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4c117029",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T00:09:08.656000Z",
     "iopub.status.busy": "2026-10-08T00:09:08.656000Z",
     "iopub.status.idle": "2026-10-08T00:09:09.562342Z",
     "shell.execute_reply": "2026-10-08T00:09:09.561838Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.12.10 · pandas 2.2.3 · numpy 2.4.6 · scipy 1.18.0 · matplotlib 3.11.0\n",
      "  실행 방식   ONLINE=0 (ECOS·FRED 재수집) · FULL=0 (Dynare 재추정)\n",
      "  ECOS 키     없음 · FRED 키 없음  (ONLINE=0이면 쓰지 않는다)\n",
      "  Octave      있음 · Dynare 있음  (FULL=0이면 쓰지 않는다)\n",
      "  BOKDPM_data_2025.m               있음      23,440 bytes\n",
      "  bokdpm_forecast_2026_2035.csv    있음      56,135 bytes\n",
      "  bokdpm_posterior_draws.csv.gz    있음   5,426,716 bytes\n",
      "  bokdpm_posterior_mode.csv        있음       2,909 bytes\n",
      "  bokdpm_smoothed_2000_2025.csv    있음     142,828 bytes\n",
      "  cpi_kor_1999_2000.csv            있음         146 bytes\n",
      "  ecos_labor_force.csv             있음       2,620 bytes\n",
      "  keis_labor_supply_2024_2034.csv  있음         353 bytes\n",
      "  kosis_생산연령인구.csv                 있음         735 bytes\n"
     ]
    }
   ],
   "source": [
    "import os, time\n",
    "import numpy as np, pandas as pd\n",
    "import config as C\n",
    "import bokdpm_macro as B\n",
    "pd.set_option(\"display.float_format\", lambda v: f\"{v:,.4f}\")\n",
    "pd.set_option(\"display.width\", 160)\n",
    "env = C.setup()\n",
    "import matplotlib.pyplot as plt\n",
    "import koreanize_matplotlib  # noqa: F401  (한글 글꼴)\n",
    "post = pd.read_csv(os.path.join(C.OUTPUT, \"posterior_summary.csv\"), encoding=\"utf-8-sig\")\n",
    "ann = pd.read_csv(os.path.join(C.OUTPUT, \"baseline_annual.csv\"), index_col=0, encoding=\"utf-8-sig\")\n",
    "ann_prev = pd.read_csv(C.REF_PREV[\"baseline_annual\"], index_col=0, encoding=\"utf-8-sig\")\n",
    "fc = B.read_exact(os.path.join(C.OUTPUT, \"baseline_forecast_70.csv\"), index_col=0)\n",
    "fc_prev = B.read_exact(C.REF_PREV[\"forecast_70\"], index_col=0)\n",
    "sm = B.read_exact(os.path.join(C.INTERIM, \"bokdpm_smoothed_2000_2025.csv\") if C.FULL else C.SMOOTHED, index_col=0)\n",
    "mac = pd.read_csv(os.path.join(C.OUTPUT, \"dsge_macro_paths.csv\"), encoding=\"utf-8-sig\")\n",
    "ex = pd.read_csv(os.path.join(C.OUTPUT, \"dsge_exog_paths.csv\"), encoding=\"utf-8-sig\")\n",
    "lev = pd.read_csv(os.path.join(C.OUTPUT, \"exog_levels_2000_2035.csv\"), encoding=\"utf-8-sig\")\n",
    "lev_prev = B.read_levels_xlsx(C.REF_PREV[\"exog_levels_xlsx\"])\n",
    "hist = pd.read_csv(os.path.join(C.OUTPUT, \"exog_hist_v2.csv\"), encoding=\"utf-8-sig\")\n",
    "scen = pd.read_csv(os.path.join(C.OUTPUT, \"exog_scen_v2.csv\"), encoding=\"utf-8-sig\")\n",
    "reach = pd.read_csv(os.path.join(C.OUTPUT, \"lever_reach.csv\"), index_col=0, encoding=\"utf-8-sig\")\n",
    "pop = pd.read_csv(C.POP, encoding=\"utf-8-sig\")\n",
    "lab = pd.read_csv(C.LABOR, index_col=0, encoding=\"utf-8-sig\")\n",
    "CLR = {\"S1\": \"#333333\", \"S2\": \"#c0392b\", \"S3\": \"#1f4e79\", \"S4\": \"#2e7d32\", \"S5\": \"#b8892f\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0c6cc91",
   "metadata": {},
   "source": [
    "## 표 0. 정상상태 정비 — 이전 값·새 값·근거"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "af46c01a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T00:09:09.563341Z",
     "iopub.status.busy": "2026-10-08T00:09:09.563341Z",
     "iopub.status.idle": "2026-10-08T00:09:09.572023Z",
     "shell.execute_reply": "2026-10-08T00:09:09.572023Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>의미</th>\n",
       "      <th>이전 값</th>\n",
       "      <th>새 값</th>\n",
       "      <th>근거</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>파라미터</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>growth_kor_ss</th>\n",
       "      <td>한국 잠재성장률(%)</td>\n",
       "      <td>4.2925</td>\n",
       "      <td>1.8000</td>\n",
       "      <td>한국은행(2024.12) 「우리 경제의 잠재성장률과 향후 전망」: 2024–2026...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pietar_kor_ss</th>\n",
       "      <td>한국 물가목표(CPI, %)</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>한국은행 물가안정목표 2%(2016년 이후)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>def_kor_ss</th>\n",
       "      <td>GDP 디플레이터 상승률(%)</td>\n",
       "      <td>2.1943</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>물가목표와 같게(최근 10년 평균 2.08%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rr_kor_bar_ss</th>\n",
       "      <td>한국 중립 실질 정책금리(%)</td>\n",
       "      <td>1.5000</td>\n",
       "      <td>0.5500</td>\n",
       "      <td>한국은행(2024.11) BOK 경제연구 「한국의 중립금리 추정」: 명목 1.8–3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tprem_kor_ss</th>\n",
       "      <td>CD(91일) − 콜금리(%p)</td>\n",
       "      <td>0.3900</td>\n",
       "      <td>0.1800</td>\n",
       "      <td>최근 10년 평균</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tprem_tb_kor_ss</th>\n",
       "      <td>국고채(3년) − 콜금리(%p)</td>\n",
       "      <td>0.7645</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>최근 10년 평균</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tprem_cb_kor_ss</th>\n",
       "      <td>회사채(3년, AA−) − 국고채(3년)(%p)</td>\n",
       "      <td>0.7419</td>\n",
       "      <td>0.6700</td>\n",
       "      <td>최근 10년 평균</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>house_kor_ss</th>\n",
       "      <td>주택가격 상승률(%)</td>\n",
       "      <td>4.2000</td>\n",
       "      <td>2.3300</td>\n",
       "      <td>최근 10년 평균</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ge_kor_ss</th>\n",
       "      <td>통합재정 총지출 증가율(%)</td>\n",
       "      <td>7.5885</td>\n",
       "      <td>6.5700</td>\n",
       "      <td>최근 10년 평균</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>growth_us_ss</th>\n",
       "      <td>미국 잠재성장률(%)</td>\n",
       "      <td>2.3000</td>\n",
       "      <td>1.8000</td>\n",
       "      <td>미 의회예산처(CBO, 2025.1) 잠재GDP 증가율 2027–2035년 1.8%...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pietar_us_ss</th>\n",
       "      <td>미국 물가목표(CPI, %)</td>\n",
       "      <td>2.4982</td>\n",
       "      <td>2.3000</td>\n",
       "      <td>CBO 소비자물가(CPI-U) 장기 전망 2.3%(PCE 목표 2%에 CPI 괴리를...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rr_us_bar_ss</th>\n",
       "      <td>미국 중립 실질 정책금리(%)</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>FOMC 경제전망(2026.9) 장기 연방기금금리 중간값 3.2% − 미국 CPI ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oil_g_us_ss</th>\n",
       "      <td>유가 상승률(달러 기준, %)</td>\n",
       "      <td>9.8763</td>\n",
       "      <td>2.3000</td>\n",
       "      <td>장기 실질유가 불변(= 미국 CPI 목표 2.3%)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         의미   이전 값    새 값                                                 근거\n",
       "파라미터                                                                                                        \n",
       "growth_kor_ss                   한국 잠재성장률(%) 4.2925 1.8000  한국은행(2024.12) 「우리 경제의 잠재성장률과 향후 전망」: 2024–2026...\n",
       "pietar_kor_ss               한국 물가목표(CPI, %) 3.0000 2.0000                           한국은행 물가안정목표 2%(2016년 이후)\n",
       "def_kor_ss                 GDP 디플레이터 상승률(%) 2.1943 2.0000                          물가목표와 같게(최근 10년 평균 2.08%)\n",
       "rr_kor_bar_ss              한국 중립 실질 정책금리(%) 1.5000 0.5500  한국은행(2024.11) BOK 경제연구 「한국의 중립금리 추정」: 명목 1.8–3...\n",
       "tprem_kor_ss              CD(91일) − 콜금리(%p) 0.3900 0.1800                                          최근 10년 평균\n",
       "tprem_tb_kor_ss           국고채(3년) − 콜금리(%p) 0.7645 0.3000                                          최근 10년 평균\n",
       "tprem_cb_kor_ss  회사채(3년, AA−) − 국고채(3년)(%p) 0.7419 0.6700                                          최근 10년 평균\n",
       "house_kor_ss                    주택가격 상승률(%) 4.2000 2.3300                                          최근 10년 평균\n",
       "ge_kor_ss                   통합재정 총지출 증가율(%) 7.5885 6.5700                                          최근 10년 평균\n",
       "growth_us_ss                    미국 잠재성장률(%) 2.3000 1.8000  미 의회예산처(CBO, 2025.1) 잠재GDP 증가율 2027–2035년 1.8%...\n",
       "pietar_us_ss                미국 물가목표(CPI, %) 2.4982 2.3000  CBO 소비자물가(CPI-U) 장기 전망 2.3%(PCE 목표 2%에 CPI 괴리를...\n",
       "rr_us_bar_ss               미국 중립 실질 정책금리(%) 2.0000 0.9000  FOMC 경제전망(2026.9) 장기 연방기금금리 중간값 3.2% − 미국 CPI ...\n",
       "oil_g_us_ss                유가 상승률(달러 기준, %) 9.8763 2.3000                       장기 실질유가 불변(= 미국 CPI 목표 2.3%)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t0 = pd.read_csv(os.path.join(C.OUTPUT, \"steady_state_revision.csv\"), index_col=0, encoding=\"utf-8-sig\")\n",
    "t0.to_csv(os.path.join(C.OUTPUT, \"table0_steady_state.csv\"), encoding=\"utf-8-sig\")\n",
    "t0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41fa5a95",
   "metadata": {},
   "source": [
    "## 표 1. 재추정 행태계수 20개 — 사전분포와 사후분포"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dedff637",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T00:09:09.573026Z",
     "iopub.status.busy": "2026-10-08T00:09:09.573026Z",
     "iopub.status.idle": "2026-10-08T00:09:09.613592Z",
     "shell.execute_reply": "2026-10-08T00:09:09.613592Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_e015d\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_e015d_level0_col0\" class=\"col_heading level0 col0\" >의미</th>\n",
       "      <th id=\"T_e015d_level0_col1\" class=\"col_heading level0 col1\" >사전분포</th>\n",
       "      <th id=\"T_e015d_level0_col2\" class=\"col_heading level0 col2\" >사전평균</th>\n",
       "      <th id=\"T_e015d_level0_col3\" class=\"col_heading level0 col3\" >사전표준편차</th>\n",
       "      <th id=\"T_e015d_level0_col4\" class=\"col_heading level0 col4\" >사후평균</th>\n",
       "      <th id=\"T_e015d_level0_col5\" class=\"col_heading level0 col5\" >표준편차</th>\n",
       "      <th id=\"T_e015d_level0_col6\" class=\"col_heading level0 col6\" >HPD_low</th>\n",
       "      <th id=\"T_e015d_level0_col7\" class=\"col_heading level0 col7\" >HPD_high</th>\n",
       "      <th id=\"T_e015d_level0_col8\" class=\"col_heading level0 col8\" >이전 판 사후평균</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >파라미터</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_e015d_level0_row0\" class=\"row_heading level0 row0\" >gamma_kor1</th>\n",
       "      <td id=\"T_e015d_row0_col0\" class=\"data row0 col0\" >한국 테일러 준칙 — 금리 평활</td>\n",
       "      <td id=\"T_e015d_row0_col1\" class=\"data row0 col1\" >beta</td>\n",
       "      <td id=\"T_e015d_row0_col2\" class=\"data row0 col2\" >0.50</td>\n",
       "      <td id=\"T_e015d_row0_col3\" class=\"data row0 col3\" >0.15</td>\n",
       "      <td id=\"T_e015d_row0_col4\" class=\"data row0 col4\" >0.841</td>\n",
       "      <td id=\"T_e015d_row0_col5\" class=\"data row0 col5\" >0.021</td>\n",
       "      <td id=\"T_e015d_row0_col6\" class=\"data row0 col6\" >0.808</td>\n",
       "      <td id=\"T_e015d_row0_col7\" class=\"data row0 col7\" >0.876</td>\n",
       "      <td id=\"T_e015d_row0_col8\" class=\"data row0 col8\" >0.883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e015d_level0_row1\" class=\"row_heading level0 row1\" >gamma_kor3</th>\n",
       "      <td id=\"T_e015d_row1_col0\" class=\"data row1 col0\" >한국 테일러 준칙 — 산출갭 반응</td>\n",
       "      <td id=\"T_e015d_row1_col1\" class=\"data row1 col1\" >gamma</td>\n",
       "      <td id=\"T_e015d_row1_col2\" class=\"data row1 col2\" >0.50</td>\n",
       "      <td id=\"T_e015d_row1_col3\" class=\"data row1 col3\" >0.20</td>\n",
       "      <td id=\"T_e015d_row1_col4\" class=\"data row1 col4\" >0.466</td>\n",
       "      <td id=\"T_e015d_row1_col5\" class=\"data row1 col5\" >0.118</td>\n",
       "      <td id=\"T_e015d_row1_col6\" class=\"data row1 col6\" >0.269</td>\n",
       "      <td id=\"T_e015d_row1_col7\" class=\"data row1 col7\" >0.654</td>\n",
       "      <td id=\"T_e015d_row1_col8\" class=\"data row1 col8\" >0.574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e015d_level0_row2\" class=\"row_heading level0 row2\" >beta_kor1</th>\n",
       "      <td id=\"T_e015d_row2_col0\" class=\"data row2 col0\" >한국 IS — 산출갭 지속성</td>\n",
       "      <td id=\"T_e015d_row2_col1\" class=\"data row2 col1\" >beta</td>\n",
       "      <td id=\"T_e015d_row2_col2\" class=\"data row2 col2\" >0.50</td>\n",
       "      <td id=\"T_e015d_row2_col3\" class=\"data row2 col3\" >0.15</td>\n",
       "      <td id=\"T_e015d_row2_col4\" class=\"data row2 col4\" >0.211</td>\n",
       "      <td id=\"T_e015d_row2_col5\" class=\"data row2 col5\" >0.059</td>\n",
       "      <td id=\"T_e015d_row2_col6\" class=\"data row2 col6\" >0.113</td>\n",
       "      <td id=\"T_e015d_row2_col7\" class=\"data row2 col7\" >0.306</td>\n",
       "      <td id=\"T_e015d_row2_col8\" class=\"data row2 col8\" >0.203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e015d_level0_row3\" class=\"row_heading level0 row3\" >beta_kor2</th>\n",
       "      <td id=\"T_e015d_row3_col0\" class=\"data row3 col0\" >한국 IS — 산출갭 기대(선행항)</td>\n",
       "      <td id=\"T_e015d_row3_col1\" class=\"data row3 col1\" >beta</td>\n",
       "      <td id=\"T_e015d_row3_col2\" class=\"data row3 col2\" >0.10</td>\n",
       "      <td id=\"T_e015d_row3_col3\" class=\"data row3 col3\" >0.07</td>\n",
       "      <td id=\"T_e015d_row3_col4\" class=\"data row3 col4\" >0.021</td>\n",
       "      <td id=\"T_e015d_row3_col5\" class=\"data row3 col5\" >0.015</td>\n",
       "      <td id=\"T_e015d_row3_col6\" class=\"data row3 col6\" >0.001</td>\n",
       "      <td id=\"T_e015d_row3_col7\" class=\"data row3 col7\" >0.043</td>\n",
       "      <td id=\"T_e015d_row3_col8\" class=\"data row3 col8\" >0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e015d_level0_row4\" class=\"row_heading level0 row4\" >beta_kor3</th>\n",
       "      <td id=\"T_e015d_row4_col0\" class=\"data row4 col0\" >한국 IS — 실질 회사채금리갭 반응</td>\n",
       "      <td id=\"T_e015d_row4_col1\" class=\"data row4 col1\" >gamma</td>\n",
       "      <td id=\"T_e015d_row4_col2\" class=\"data row4 col2\" >0.10</td>\n",
       "      <td id=\"T_e015d_row4_col3\" class=\"data row4 col3\" >0.05</td>\n",
       "      <td id=\"T_e015d_row4_col4\" class=\"data row4 col4\" >0.081</td>\n",
       "      <td id=\"T_e015d_row4_col5\" class=\"data row4 col5\" >0.041</td>\n",
       "      <td id=\"T_e015d_row4_col6\" class=\"data row4 col6\" >0.018</td>\n",
       "      <td id=\"T_e015d_row4_col7\" class=\"data row4 col7\" >0.142</td>\n",
       "      <td id=\"T_e015d_row4_col8\" class=\"data row4 col8\" >0.086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e015d_level0_row5\" class=\"row_heading level0 row5\" >lambda_kor1</th>\n",
       "      <td id=\"T_e015d_row5_col0\" class=\"data row5 col0\" >한국 필립스 — 기대물가 가중</td>\n",
       "      <td id=\"T_e015d_row5_col1\" class=\"data row5 col1\" >beta</td>\n",
       "      <td id=\"T_e015d_row5_col2\" class=\"data row5 col2\" >0.55</td>\n",
       "      <td id=\"T_e015d_row5_col3\" class=\"data row5 col3\" >0.15</td>\n",
       "      <td id=\"T_e015d_row5_col4\" class=\"data row5 col4\" >0.306</td>\n",
       "      <td id=\"T_e015d_row5_col5\" class=\"data row5 col5\" >0.114</td>\n",
       "      <td id=\"T_e015d_row5_col6\" class=\"data row5 col6\" >0.128</td>\n",
       "      <td id=\"T_e015d_row5_col7\" class=\"data row5 col7\" >0.493</td>\n",
       "      <td id=\"T_e015d_row5_col8\" class=\"data row5 col8\" >0.321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e015d_level0_row6\" class=\"row_heading level0 row6\" >lambda_kor2</th>\n",
       "      <td id=\"T_e015d_row6_col0\" class=\"data row6 col0\" >한국 필립스 — 산출갭 기울기</td>\n",
       "      <td id=\"T_e015d_row6_col1\" class=\"data row6 col1\" >gamma</td>\n",
       "      <td id=\"T_e015d_row6_col2\" class=\"data row6 col2\" >0.15</td>\n",
       "      <td id=\"T_e015d_row6_col3\" class=\"data row6 col3\" >0.08</td>\n",
       "      <td id=\"T_e015d_row6_col4\" class=\"data row6 col4\" >0.083</td>\n",
       "      <td id=\"T_e015d_row6_col5\" class=\"data row6 col5\" >0.041</td>\n",
       "      <td id=\"T_e015d_row6_col6\" class=\"data row6 col6\" >0.021</td>\n",
       "      <td id=\"T_e015d_row6_col7\" class=\"data row6 col7\" >0.143</td>\n",
       "      <td id=\"T_e015d_row6_col8\" class=\"data row6 col8\" >0.092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e015d_level0_row7\" class=\"row_heading level0 row7\" >lambda_kor3</th>\n",
       "      <td id=\"T_e015d_row7_col0\" class=\"data row7 col0\" >한국 필립스 — 물가목표 가중</td>\n",
       "      <td id=\"T_e015d_row7_col1\" class=\"data row7 col1\" >beta</td>\n",
       "      <td id=\"T_e015d_row7_col2\" class=\"data row7 col2\" >0.65</td>\n",
       "      <td id=\"T_e015d_row7_col3\" class=\"data row7 col3\" >0.15</td>\n",
       "      <td id=\"T_e015d_row7_col4\" class=\"data row7 col4\" >0.766</td>\n",
       "      <td id=\"T_e015d_row7_col5\" class=\"data row7 col5\" >0.028</td>\n",
       "      <td id=\"T_e015d_row7_col6\" class=\"data row7 col6\" >0.725</td>\n",
       "      <td id=\"T_e015d_row7_col7\" class=\"data row7 col7\" >0.813</td>\n",
       "      <td id=\"T_e015d_row7_col8\" class=\"data row7 col8\" >0.753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e015d_level0_row8\" class=\"row_heading level0 row8\" >alpha_kor1</th>\n",
       "      <td id=\"T_e015d_row8_col0\" class=\"data row8 col0\" >한국 오쿤 — 실업갭 지속성</td>\n",
       "      <td id=\"T_e015d_row8_col1\" class=\"data row8 col1\" >beta</td>\n",
       "      <td id=\"T_e015d_row8_col2\" class=\"data row8 col2\" >0.80</td>\n",
       "      <td id=\"T_e015d_row8_col3\" class=\"data row8 col3\" >0.10</td>\n",
       "      <td id=\"T_e015d_row8_col4\" class=\"data row8 col4\" >0.579</td>\n",
       "      <td id=\"T_e015d_row8_col5\" class=\"data row8 col5\" >0.097</td>\n",
       "      <td id=\"T_e015d_row8_col6\" class=\"data row8 col6\" >0.421</td>\n",
       "      <td id=\"T_e015d_row8_col7\" class=\"data row8 col7\" >0.738</td>\n",
       "      <td id=\"T_e015d_row8_col8\" class=\"data row8 col8\" >0.611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e015d_level0_row9\" class=\"row_heading level0 row9\" >alpha_kor2</th>\n",
       "      <td id=\"T_e015d_row9_col0\" class=\"data row9 col0\" >한국 오쿤 — 산출갭 계수</td>\n",
       "      <td id=\"T_e015d_row9_col1\" class=\"data row9 col1\" >gamma</td>\n",
       "      <td id=\"T_e015d_row9_col2\" class=\"data row9 col2\" >0.30</td>\n",
       "      <td id=\"T_e015d_row9_col3\" class=\"data row9 col3\" >0.12</td>\n",
       "      <td id=\"T_e015d_row9_col4\" class=\"data row9 col4\" >0.070</td>\n",
       "      <td id=\"T_e015d_row9_col5\" class=\"data row9 col5\" >0.015</td>\n",
       "      <td id=\"T_e015d_row9_col6\" class=\"data row9 col6\" >0.046</td>\n",
       "      <td id=\"T_e015d_row9_col7\" class=\"data row9 col7\" >0.094</td>\n",
       "      <td id=\"T_e015d_row9_col8\" class=\"data row9 col8\" >0.072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e015d_level0_row10\" class=\"row_heading level0 row10\" >gamma_us1</th>\n",
       "      <td id=\"T_e015d_row10_col0\" class=\"data row10 col0\" >미국 테일러 준칙 — 금리 평활</td>\n",
       "      <td id=\"T_e015d_row10_col1\" class=\"data row10 col1\" >beta</td>\n",
       "      <td id=\"T_e015d_row10_col2\" class=\"data row10 col2\" >0.50</td>\n",
       "      <td id=\"T_e015d_row10_col3\" class=\"data row10 col3\" >0.15</td>\n",
       "      <td id=\"T_e015d_row10_col4\" class=\"data row10 col4\" >0.887</td>\n",
       "      <td id=\"T_e015d_row10_col5\" class=\"data row10 col5\" >0.017</td>\n",
       "      <td id=\"T_e015d_row10_col6\" class=\"data row10 col6\" >0.860</td>\n",
       "      <td id=\"T_e015d_row10_col7\" class=\"data row10 col7\" >0.917</td>\n",
       "      <td id=\"T_e015d_row10_col8\" class=\"data row10 col8\" >0.916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e015d_level0_row11\" class=\"row_heading level0 row11\" >gamma_us2</th>\n",
       "      <td id=\"T_e015d_row11_col0\" class=\"data row11 col0\" >미국 테일러 준칙 — 기대물가 반응</td>\n",
       "      <td id=\"T_e015d_row11_col1\" class=\"data row11 col1\" >gamma</td>\n",
       "      <td id=\"T_e015d_row11_col2\" class=\"data row11 col2\" >1.50</td>\n",
       "      <td id=\"T_e015d_row11_col3\" class=\"data row11 col3\" >0.25</td>\n",
       "      <td id=\"T_e015d_row11_col4\" class=\"data row11 col4\" >1.721</td>\n",
       "      <td id=\"T_e015d_row11_col5\" class=\"data row11 col5\" >0.273</td>\n",
       "      <td id=\"T_e015d_row11_col6\" class=\"data row11 col6\" >1.263</td>\n",
       "      <td id=\"T_e015d_row11_col7\" class=\"data row11 col7\" >2.153</td>\n",
       "      <td id=\"T_e015d_row11_col8\" class=\"data row11 col8\" >1.677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e015d_level0_row12\" class=\"row_heading level0 row12\" >gamma_us3</th>\n",
       "      <td id=\"T_e015d_row12_col0\" class=\"data row12 col0\" >미국 테일러 준칙 — 산출갭 반응</td>\n",
       "      <td id=\"T_e015d_row12_col1\" class=\"data row12 col1\" >gamma</td>\n",
       "      <td id=\"T_e015d_row12_col2\" class=\"data row12 col2\" >0.20</td>\n",
       "      <td id=\"T_e015d_row12_col3\" class=\"data row12 col3\" >0.10</td>\n",
       "      <td id=\"T_e015d_row12_col4\" class=\"data row12 col4\" >0.462</td>\n",
       "      <td id=\"T_e015d_row12_col5\" class=\"data row12 col5\" >0.154</td>\n",
       "      <td id=\"T_e015d_row12_col6\" class=\"data row12 col6\" >0.208</td>\n",
       "      <td id=\"T_e015d_row12_col7\" class=\"data row12 col7\" >0.707</td>\n",
       "      <td id=\"T_e015d_row12_col8\" class=\"data row12 col8\" >0.349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e015d_level0_row13\" class=\"row_heading level0 row13\" >beta_us1</th>\n",
       "      <td id=\"T_e015d_row13_col0\" class=\"data row13 col0\" >미국 IS — 산출갭 지속성</td>\n",
       "      <td id=\"T_e015d_row13_col1\" class=\"data row13 col1\" >beta</td>\n",
       "      <td id=\"T_e015d_row13_col2\" class=\"data row13 col2\" >0.70</td>\n",
       "      <td id=\"T_e015d_row13_col3\" class=\"data row13 col3\" >0.12</td>\n",
       "      <td id=\"T_e015d_row13_col4\" class=\"data row13 col4\" >0.452</td>\n",
       "      <td id=\"T_e015d_row13_col5\" class=\"data row13 col5\" >0.058</td>\n",
       "      <td id=\"T_e015d_row13_col6\" class=\"data row13 col6\" >0.359</td>\n",
       "      <td id=\"T_e015d_row13_col7\" class=\"data row13 col7\" >0.547</td>\n",
       "      <td id=\"T_e015d_row13_col8\" class=\"data row13 col8\" >0.446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e015d_level0_row14\" class=\"row_heading level0 row14\" >beta_us2</th>\n",
       "      <td id=\"T_e015d_row14_col0\" class=\"data row14 col0\" >미국 IS — 산출갭 기대(선행항)</td>\n",
       "      <td id=\"T_e015d_row14_col1\" class=\"data row14 col1\" >beta</td>\n",
       "      <td id=\"T_e015d_row14_col2\" class=\"data row14 col2\" >0.15</td>\n",
       "      <td id=\"T_e015d_row14_col3\" class=\"data row14 col3\" >0.08</td>\n",
       "      <td id=\"T_e015d_row14_col4\" class=\"data row14 col4\" >0.039</td>\n",
       "      <td id=\"T_e015d_row14_col5\" class=\"data row14 col5\" >0.021</td>\n",
       "      <td id=\"T_e015d_row14_col6\" class=\"data row14 col6\" >0.006</td>\n",
       "      <td id=\"T_e015d_row14_col7\" class=\"data row14 col7\" >0.070</td>\n",
       "      <td id=\"T_e015d_row14_col8\" class=\"data row14 col8\" >0.041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e015d_level0_row15\" class=\"row_heading level0 row15\" >lambda_us1</th>\n",
       "      <td id=\"T_e015d_row15_col0\" class=\"data row15 col0\" >미국 필립스 — 기대물가 가중</td>\n",
       "      <td id=\"T_e015d_row15_col1\" class=\"data row15 col1\" >beta</td>\n",
       "      <td id=\"T_e015d_row15_col2\" class=\"data row15 col2\" >0.45</td>\n",
       "      <td id=\"T_e015d_row15_col3\" class=\"data row15 col3\" >0.15</td>\n",
       "      <td id=\"T_e015d_row15_col4\" class=\"data row15 col4\" >0.378</td>\n",
       "      <td id=\"T_e015d_row15_col5\" class=\"data row15 col5\" >0.098</td>\n",
       "      <td id=\"T_e015d_row15_col6\" class=\"data row15 col6\" >0.219</td>\n",
       "      <td id=\"T_e015d_row15_col7\" class=\"data row15 col7\" >0.543</td>\n",
       "      <td id=\"T_e015d_row15_col8\" class=\"data row15 col8\" >0.340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e015d_level0_row16\" class=\"row_heading level0 row16\" >lambda_us2</th>\n",
       "      <td id=\"T_e015d_row16_col0\" class=\"data row16 col0\" >미국 필립스 — 산출갭 기울기</td>\n",
       "      <td id=\"T_e015d_row16_col1\" class=\"data row16 col1\" >gamma</td>\n",
       "      <td id=\"T_e015d_row16_col2\" class=\"data row16 col2\" >0.05</td>\n",
       "      <td id=\"T_e015d_row16_col3\" class=\"data row16 col3\" >0.03</td>\n",
       "      <td id=\"T_e015d_row16_col4\" class=\"data row16 col4\" >0.090</td>\n",
       "      <td id=\"T_e015d_row16_col5\" class=\"data row16 col5\" >0.024</td>\n",
       "      <td id=\"T_e015d_row16_col6\" class=\"data row16 col6\" >0.049</td>\n",
       "      <td id=\"T_e015d_row16_col7\" class=\"data row16 col7\" >0.128</td>\n",
       "      <td id=\"T_e015d_row16_col8\" class=\"data row16 col8\" >0.077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e015d_level0_row17\" class=\"row_heading level0 row17\" >lambda_us3</th>\n",
       "      <td id=\"T_e015d_row17_col0\" class=\"data row17 col0\" >미국 필립스 — 물가목표 가중</td>\n",
       "      <td id=\"T_e015d_row17_col1\" class=\"data row17 col1\" >beta</td>\n",
       "      <td id=\"T_e015d_row17_col2\" class=\"data row17 col2\" >0.50</td>\n",
       "      <td id=\"T_e015d_row17_col3\" class=\"data row17 col3\" >0.15</td>\n",
       "      <td id=\"T_e015d_row17_col4\" class=\"data row17 col4\" >0.431</td>\n",
       "      <td id=\"T_e015d_row17_col5\" class=\"data row17 col5\" >0.037</td>\n",
       "      <td id=\"T_e015d_row17_col6\" class=\"data row17 col6\" >0.368</td>\n",
       "      <td id=\"T_e015d_row17_col7\" class=\"data row17 col7\" >0.490</td>\n",
       "      <td id=\"T_e015d_row17_col8\" class=\"data row17 col8\" >0.439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e015d_level0_row18\" class=\"row_heading level0 row18\" >rho_oil1</th>\n",
       "      <td id=\"T_e015d_row18_col0\" class=\"data row18 col0\" >유가 상승률 — 정상상태 회귀 가중</td>\n",
       "      <td id=\"T_e015d_row18_col1\" class=\"data row18 col1\" >beta</td>\n",
       "      <td id=\"T_e015d_row18_col2\" class=\"data row18 col2\" >0.90</td>\n",
       "      <td id=\"T_e015d_row18_col3\" class=\"data row18 col3\" >0.05</td>\n",
       "      <td id=\"T_e015d_row18_col4\" class=\"data row18 col4\" >0.880</td>\n",
       "      <td id=\"T_e015d_row18_col5\" class=\"data row18 col5\" >0.049</td>\n",
       "      <td id=\"T_e015d_row18_col6\" class=\"data row18 col6\" >0.798</td>\n",
       "      <td id=\"T_e015d_row18_col7\" class=\"data row18 col7\" >0.958</td>\n",
       "      <td id=\"T_e015d_row18_col8\" class=\"data row18 col8\" >0.881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e015d_level0_row19\" class=\"row_heading level0 row19\" >gamma_kor2</th>\n",
       "      <td id=\"T_e015d_row19_col0\" class=\"data row19 col0\" >한국 테일러 준칙 — 기대물가 반응</td>\n",
       "      <td id=\"T_e015d_row19_col1\" class=\"data row19 col1\" >gamma</td>\n",
       "      <td id=\"T_e015d_row19_col2\" class=\"data row19 col2\" >1.50</td>\n",
       "      <td id=\"T_e015d_row19_col3\" class=\"data row19 col3\" >0.10</td>\n",
       "      <td id=\"T_e015d_row19_col4\" class=\"data row19 col4\" >1.539</td>\n",
       "      <td id=\"T_e015d_row19_col5\" class=\"data row19 col5\" >0.101</td>\n",
       "      <td id=\"T_e015d_row19_col6\" class=\"data row19 col6\" >1.368</td>\n",
       "      <td id=\"T_e015d_row19_col7\" class=\"data row19 col7\" >1.702</td>\n",
       "      <td id=\"T_e015d_row19_col8\" class=\"data row19 col8\" >1.595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2543810f920>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1 = post[post[\"구분\"] == \"parameters\"].set_index(\"파라미터\").drop(columns=\"구분\")\n",
    "t1.insert(0, \"의미\", [B.PARAM_LABEL.get(n, \"\") for n in t1.index])\n",
    "t1[\"이전 판 사후평균\"] = pd.read_csv(C.REF_PREV[\"estimation_results\"]).set_index(\"파라미터\")[\"사후평균\"].reindex(t1.index)\n",
    "t1.to_csv(os.path.join(C.OUTPUT, \"table1_posterior_params.csv\"), encoding=\"utf-8-sig\")\n",
    "t1.style.format({\"사전평균\": \"{:.2f}\", \"사전표준편차\": \"{:.2f}\", \"사후평균\": \"{:.3f}\", \"표준편차\": \"{:.3f}\",\n",
    "                 \"HPD_low\": \"{:.3f}\", \"HPD_high\": \"{:.3f}\", \"이전 판 사후평균\": \"{:.3f}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "440c309e",
   "metadata": {},
   "source": [
    "## 표 2. 구조충격 표준편차 34개 — 사전평균과 사후분포"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "048cdbed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T00:09:09.614595Z",
     "iopub.status.busy": "2026-10-08T00:09:09.614595Z",
     "iopub.status.idle": "2026-10-08T00:09:09.620692Z",
     "shell.execute_reply": "2026-10-08T00:09:09.620692Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_69230\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_69230_level0_col0\" class=\"col_heading level0 col0\" >사전평균</th>\n",
       "      <th id=\"T_69230_level0_col1\" class=\"col_heading level0 col1\" >사후평균</th>\n",
       "      <th id=\"T_69230_level0_col2\" class=\"col_heading level0 col2\" >표준편차</th>\n",
       "      <th id=\"T_69230_level0_col3\" class=\"col_heading level0 col3\" >HPD_low</th>\n",
       "      <th id=\"T_69230_level0_col4\" class=\"col_heading level0 col4\" >HPD_high</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >파라미터</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row0\" class=\"row_heading level0 row0\" >RES_RR_kor_BAR</th>\n",
       "      <td id=\"T_69230_row0_col0\" class=\"data row0 col0\" >0.200</td>\n",
       "      <td id=\"T_69230_row0_col1\" class=\"data row0 col1\" >1.142</td>\n",
       "      <td id=\"T_69230_row0_col2\" class=\"data row0 col2\" >0.157</td>\n",
       "      <td id=\"T_69230_row0_col3\" class=\"data row0 col3\" >0.892</td>\n",
       "      <td id=\"T_69230_row0_col4\" class=\"data row0 col4\" >1.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row1\" class=\"row_heading level0 row1\" >RES_RS_kor</th>\n",
       "      <td id=\"T_69230_row1_col0\" class=\"data row1 col0\" >0.300</td>\n",
       "      <td id=\"T_69230_row1_col1\" class=\"data row1 col1\" >0.117</td>\n",
       "      <td id=\"T_69230_row1_col2\" class=\"data row1 col2\" >0.022</td>\n",
       "      <td id=\"T_69230_row1_col3\" class=\"data row1 col3\" >0.083</td>\n",
       "      <td id=\"T_69230_row1_col4\" class=\"data row1 col4\" >0.151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row2\" class=\"row_heading level0 row2\" >RES_UNR_kor_GAP</th>\n",
       "      <td id=\"T_69230_row2_col0\" class=\"data row2 col0\" >0.200</td>\n",
       "      <td id=\"T_69230_row2_col1\" class=\"data row2 col1\" >0.138</td>\n",
       "      <td id=\"T_69230_row2_col2\" class=\"data row2 col2\" >0.020</td>\n",
       "      <td id=\"T_69230_row2_col3\" class=\"data row2 col3\" >0.104</td>\n",
       "      <td id=\"T_69230_row2_col4\" class=\"data row2 col4\" >0.172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row3\" class=\"row_heading level0 row3\" >RES_UNR_kor_BAR</th>\n",
       "      <td id=\"T_69230_row3_col0\" class=\"data row3 col0\" >0.100</td>\n",
       "      <td id=\"T_69230_row3_col1\" class=\"data row3 col1\" >0.083</td>\n",
       "      <td id=\"T_69230_row3_col2\" class=\"data row3 col2\" >0.031</td>\n",
       "      <td id=\"T_69230_row3_col3\" class=\"data row3 col3\" >0.036</td>\n",
       "      <td id=\"T_69230_row3_col4\" class=\"data row3 col4\" >0.130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row4\" class=\"row_heading level0 row4\" >RES_UNR_G_kor</th>\n",
       "      <td id=\"T_69230_row4_col0\" class=\"data row4 col0\" >0.100</td>\n",
       "      <td id=\"T_69230_row4_col1\" class=\"data row4 col1\" >0.056</td>\n",
       "      <td id=\"T_69230_row4_col2\" class=\"data row4 col2\" >0.014</td>\n",
       "      <td id=\"T_69230_row4_col3\" class=\"data row4 col3\" >0.034</td>\n",
       "      <td id=\"T_69230_row4_col4\" class=\"data row4 col4\" >0.076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row5\" class=\"row_heading level0 row5\" >RES_Y_kor</th>\n",
       "      <td id=\"T_69230_row5_col0\" class=\"data row5 col0\" >0.500</td>\n",
       "      <td id=\"T_69230_row5_col1\" class=\"data row5 col1\" >1.835</td>\n",
       "      <td id=\"T_69230_row5_col2\" class=\"data row5 col2\" >0.143</td>\n",
       "      <td id=\"T_69230_row5_col3\" class=\"data row5 col3\" >1.582</td>\n",
       "      <td id=\"T_69230_row5_col4\" class=\"data row5 col4\" >2.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row6\" class=\"row_heading level0 row6\" >RES_LGDP_kor_BAR</th>\n",
       "      <td id=\"T_69230_row6_col0\" class=\"data row6 col0\" >0.400</td>\n",
       "      <td id=\"T_69230_row6_col1\" class=\"data row6 col1\" >0.059</td>\n",
       "      <td id=\"T_69230_row6_col2\" class=\"data row6 col2\" >0.005</td>\n",
       "      <td id=\"T_69230_row6_col3\" class=\"data row6 col3\" >0.051</td>\n",
       "      <td id=\"T_69230_row6_col4\" class=\"data row6 col4\" >0.068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row7\" class=\"row_heading level0 row7\" >RES_G_kor</th>\n",
       "      <td id=\"T_69230_row7_col0\" class=\"data row7 col0\" >0.200</td>\n",
       "      <td id=\"T_69230_row7_col1\" class=\"data row7 col1\" >0.245</td>\n",
       "      <td id=\"T_69230_row7_col2\" class=\"data row7 col2\" >0.021</td>\n",
       "      <td id=\"T_69230_row7_col3\" class=\"data row7 col3\" >0.210</td>\n",
       "      <td id=\"T_69230_row7_col4\" class=\"data row7 col4\" >0.278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row8\" class=\"row_heading level0 row8\" >RES_BLT_kor</th>\n",
       "      <td id=\"T_69230_row8_col0\" class=\"data row8 col0\" >10.000</td>\n",
       "      <td id=\"T_69230_row8_col1\" class=\"data row8 col1\" >2.447</td>\n",
       "      <td id=\"T_69230_row8_col2\" class=\"data row8 col2\" >0.192</td>\n",
       "      <td id=\"T_69230_row8_col3\" class=\"data row8 col3\" >2.150</td>\n",
       "      <td id=\"T_69230_row8_col4\" class=\"data row8 col4\" >2.764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row9\" class=\"row_heading level0 row9\" >RES_BLT_kor_BAR</th>\n",
       "      <td id=\"T_69230_row9_col0\" class=\"data row9 col0\" >5.000</td>\n",
       "      <td id=\"T_69230_row9_col1\" class=\"data row9 col1\" >3.716</td>\n",
       "      <td id=\"T_69230_row9_col2\" class=\"data row9 col2\" >1.314</td>\n",
       "      <td id=\"T_69230_row9_col3\" class=\"data row9 col3\" >1.730</td>\n",
       "      <td id=\"T_69230_row9_col4\" class=\"data row9 col4\" >5.563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row10\" class=\"row_heading level0 row10\" >RES_PIE_kor</th>\n",
       "      <td id=\"T_69230_row10_col0\" class=\"data row10 col0\" >0.300</td>\n",
       "      <td id=\"T_69230_row10_col1\" class=\"data row10 col1\" >0.447</td>\n",
       "      <td id=\"T_69230_row10_col2\" class=\"data row10 col2\" >0.031</td>\n",
       "      <td id=\"T_69230_row10_col3\" class=\"data row10 col3\" >0.394</td>\n",
       "      <td id=\"T_69230_row10_col4\" class=\"data row10 col4\" >0.495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row11\" class=\"row_heading level0 row11\" >RES_PIE_E_kor4</th>\n",
       "      <td id=\"T_69230_row11_col0\" class=\"data row11 col0\" >0.300</td>\n",
       "      <td id=\"T_69230_row11_col1\" class=\"data row11 col1\" >0.210</td>\n",
       "      <td id=\"T_69230_row11_col2\" class=\"data row11 col2\" >0.081</td>\n",
       "      <td id=\"T_69230_row11_col3\" class=\"data row11 col3\" >0.101</td>\n",
       "      <td id=\"T_69230_row11_col4\" class=\"data row11 col4\" >0.334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row12\" class=\"row_heading level0 row12\" >RES_CA_kor</th>\n",
       "      <td id=\"T_69230_row12_col0\" class=\"data row12 col0\" >2.000</td>\n",
       "      <td id=\"T_69230_row12_col1\" class=\"data row12 col1\" >4.252</td>\n",
       "      <td id=\"T_69230_row12_col2\" class=\"data row12 col2\" >0.399</td>\n",
       "      <td id=\"T_69230_row12_col3\" class=\"data row12 col3\" >3.592</td>\n",
       "      <td id=\"T_69230_row12_col4\" class=\"data row12 col4\" >4.862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row13\" class=\"row_heading level0 row13\" >RES_LS_kor_BAR</th>\n",
       "      <td id=\"T_69230_row13_col0\" class=\"data row13 col0\" >1.000</td>\n",
       "      <td id=\"T_69230_row13_col1\" class=\"data row13 col1\" >1.691</td>\n",
       "      <td id=\"T_69230_row13_col2\" class=\"data row13 col2\" >0.878</td>\n",
       "      <td id=\"T_69230_row13_col3\" class=\"data row13 col3\" >0.353</td>\n",
       "      <td id=\"T_69230_row13_col4\" class=\"data row13 col4\" >2.973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row14\" class=\"row_heading level0 row14\" >RES_S_G_kor</th>\n",
       "      <td id=\"T_69230_row14_col0\" class=\"data row14 col0\" >0.500</td>\n",
       "      <td id=\"T_69230_row14_col1\" class=\"data row14 col1\" >1.212</td>\n",
       "      <td id=\"T_69230_row14_col2\" class=\"data row14 col2\" >1.045</td>\n",
       "      <td id=\"T_69230_row14_col3\" class=\"data row14 col3\" >0.145</td>\n",
       "      <td id=\"T_69230_row14_col4\" class=\"data row14 col4\" >2.940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row15\" class=\"row_heading level0 row15\" >RES_S_kor</th>\n",
       "      <td id=\"T_69230_row15_col0\" class=\"data row15 col0\" >2.000</td>\n",
       "      <td id=\"T_69230_row15_col1\" class=\"data row15 col1\" >3.114</td>\n",
       "      <td id=\"T_69230_row15_col2\" class=\"data row15 col2\" >0.679</td>\n",
       "      <td id=\"T_69230_row15_col3\" class=\"data row15 col3\" >1.972</td>\n",
       "      <td id=\"T_69230_row15_col4\" class=\"data row15 col4\" >4.203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row16\" class=\"row_heading level0 row16\" >RES_DEF_kor</th>\n",
       "      <td id=\"T_69230_row16_col0\" class=\"data row16 col0\" >0.300</td>\n",
       "      <td id=\"T_69230_row16_col1\" class=\"data row16 col1\" >0.706</td>\n",
       "      <td id=\"T_69230_row16_col2\" class=\"data row16 col2\" >0.052</td>\n",
       "      <td id=\"T_69230_row16_col3\" class=\"data row16 col3\" >0.618</td>\n",
       "      <td id=\"T_69230_row16_col4\" class=\"data row16 col4\" >0.789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row17\" class=\"row_heading level0 row17\" >RES_RR_us_BAR</th>\n",
       "      <td id=\"T_69230_row17_col0\" class=\"data row17 col0\" >0.200</td>\n",
       "      <td id=\"T_69230_row17_col1\" class=\"data row17 col1\" >0.221</td>\n",
       "      <td id=\"T_69230_row17_col2\" class=\"data row17 col2\" >0.139</td>\n",
       "      <td id=\"T_69230_row17_col3\" class=\"data row17 col3\" >0.065</td>\n",
       "      <td id=\"T_69230_row17_col4\" class=\"data row17 col4\" >0.413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row18\" class=\"row_heading level0 row18\" >RES_RS_us</th>\n",
       "      <td id=\"T_69230_row18_col0\" class=\"data row18 col0\" >0.300</td>\n",
       "      <td id=\"T_69230_row18_col1\" class=\"data row18 col1\" >0.396</td>\n",
       "      <td id=\"T_69230_row18_col2\" class=\"data row18 col2\" >0.028</td>\n",
       "      <td id=\"T_69230_row18_col3\" class=\"data row18 col3\" >0.349</td>\n",
       "      <td id=\"T_69230_row18_col4\" class=\"data row18 col4\" >0.442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row19\" class=\"row_heading level0 row19\" >RES_Y_us</th>\n",
       "      <td id=\"T_69230_row19_col0\" class=\"data row19 col0\" >0.500</td>\n",
       "      <td id=\"T_69230_row19_col1\" class=\"data row19 col1\" >1.602</td>\n",
       "      <td id=\"T_69230_row19_col2\" class=\"data row19 col2\" >0.121</td>\n",
       "      <td id=\"T_69230_row19_col3\" class=\"data row19 col3\" >1.410</td>\n",
       "      <td id=\"T_69230_row19_col4\" class=\"data row19 col4\" >1.806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row20\" class=\"row_heading level0 row20\" >RES_LGDP_us_BAR</th>\n",
       "      <td id=\"T_69230_row20_col0\" class=\"data row20 col0\" >0.400</td>\n",
       "      <td id=\"T_69230_row20_col1\" class=\"data row20 col1\" >0.056</td>\n",
       "      <td id=\"T_69230_row20_col2\" class=\"data row20 col2\" >0.005</td>\n",
       "      <td id=\"T_69230_row20_col3\" class=\"data row20 col3\" >0.048</td>\n",
       "      <td id=\"T_69230_row20_col4\" class=\"data row20 col4\" >0.063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row21\" class=\"row_heading level0 row21\" >RES_G_us</th>\n",
       "      <td id=\"T_69230_row21_col0\" class=\"data row21 col0\" >0.200</td>\n",
       "      <td id=\"T_69230_row21_col1\" class=\"data row21 col1\" >0.167</td>\n",
       "      <td id=\"T_69230_row21_col2\" class=\"data row21 col2\" >0.016</td>\n",
       "      <td id=\"T_69230_row21_col3\" class=\"data row21 col3\" >0.142</td>\n",
       "      <td id=\"T_69230_row21_col4\" class=\"data row21 col4\" >0.193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row22\" class=\"row_heading level0 row22\" >RES_BLT_us</th>\n",
       "      <td id=\"T_69230_row22_col0\" class=\"data row22 col0\" >10.000</td>\n",
       "      <td id=\"T_69230_row22_col1\" class=\"data row22 col1\" >2.529</td>\n",
       "      <td id=\"T_69230_row22_col2\" class=\"data row22 col2\" >0.271</td>\n",
       "      <td id=\"T_69230_row22_col3\" class=\"data row22 col3\" >2.071</td>\n",
       "      <td id=\"T_69230_row22_col4\" class=\"data row22 col4\" >2.974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row23\" class=\"row_heading level0 row23\" >RES_BLT_us_BAR</th>\n",
       "      <td id=\"T_69230_row23_col0\" class=\"data row23 col0\" >5.000</td>\n",
       "      <td id=\"T_69230_row23_col1\" class=\"data row23 col1\" >5.010</td>\n",
       "      <td id=\"T_69230_row23_col2\" class=\"data row23 col2\" >1.189</td>\n",
       "      <td id=\"T_69230_row23_col3\" class=\"data row23 col3\" >3.051</td>\n",
       "      <td id=\"T_69230_row23_col4\" class=\"data row23 col4\" >6.898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row24\" class=\"row_heading level0 row24\" >RES_PIE_us</th>\n",
       "      <td id=\"T_69230_row24_col0\" class=\"data row24 col0\" >0.300</td>\n",
       "      <td id=\"T_69230_row24_col1\" class=\"data row24 col1\" >0.336</td>\n",
       "      <td id=\"T_69230_row24_col2\" class=\"data row24 col2\" >0.023</td>\n",
       "      <td id=\"T_69230_row24_col3\" class=\"data row24 col3\" >0.298</td>\n",
       "      <td id=\"T_69230_row24_col4\" class=\"data row24 col4\" >0.373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row25\" class=\"row_heading level0 row25\" >RES_CD_kor</th>\n",
       "      <td id=\"T_69230_row25_col0\" class=\"data row25 col0\" >0.300</td>\n",
       "      <td id=\"T_69230_row25_col1\" class=\"data row25 col1\" >0.508</td>\n",
       "      <td id=\"T_69230_row25_col2\" class=\"data row25 col2\" >0.037</td>\n",
       "      <td id=\"T_69230_row25_col3\" class=\"data row25 col3\" >0.447</td>\n",
       "      <td id=\"T_69230_row25_col4\" class=\"data row25 col4\" >0.568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row26\" class=\"row_heading level0 row26\" >RES_RCD_kor_BAR</th>\n",
       "      <td id=\"T_69230_row26_col0\" class=\"data row26 col0\" >0.200</td>\n",
       "      <td id=\"T_69230_row26_col1\" class=\"data row26 col1\" >0.204</td>\n",
       "      <td id=\"T_69230_row26_col2\" class=\"data row26 col2\" >0.118</td>\n",
       "      <td id=\"T_69230_row26_col3\" class=\"data row26 col3\" >0.065</td>\n",
       "      <td id=\"T_69230_row26_col4\" class=\"data row26 col4\" >0.390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row27\" class=\"row_heading level0 row27\" >RES_HOUSE_kor</th>\n",
       "      <td id=\"T_69230_row27_col0\" class=\"data row27 col0\" >1.000</td>\n",
       "      <td id=\"T_69230_row27_col1\" class=\"data row27 col1\" >1.279</td>\n",
       "      <td id=\"T_69230_row27_col2\" class=\"data row27 col2\" >0.087</td>\n",
       "      <td id=\"T_69230_row27_col3\" class=\"data row27 col3\" >1.144</td>\n",
       "      <td id=\"T_69230_row27_col4\" class=\"data row27 col4\" >1.432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row28\" class=\"row_heading level0 row28\" >RES_OIL_us</th>\n",
       "      <td id=\"T_69230_row28_col0\" class=\"data row28 col0\" >5.000</td>\n",
       "      <td id=\"T_69230_row28_col1\" class=\"data row28 col1\" >15.193</td>\n",
       "      <td id=\"T_69230_row28_col2\" class=\"data row28 col2\" >1.039</td>\n",
       "      <td id=\"T_69230_row28_col3\" class=\"data row28 col3\" >13.526</td>\n",
       "      <td id=\"T_69230_row28_col4\" class=\"data row28 col4\" >16.889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row29\" class=\"row_heading level0 row29\" >RES_RCB_kor_BAR</th>\n",
       "      <td id=\"T_69230_row29_col0\" class=\"data row29 col0\" >0.200</td>\n",
       "      <td id=\"T_69230_row29_col1\" class=\"data row29 col1\" >0.166</td>\n",
       "      <td id=\"T_69230_row29_col2\" class=\"data row29 col2\" >0.073</td>\n",
       "      <td id=\"T_69230_row29_col3\" class=\"data row29 col3\" >0.061</td>\n",
       "      <td id=\"T_69230_row29_col4\" class=\"data row29 col4\" >0.268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row30\" class=\"row_heading level0 row30\" >RES_GE_kor</th>\n",
       "      <td id=\"T_69230_row30_col0\" class=\"data row30 col0\" >10.000</td>\n",
       "      <td id=\"T_69230_row30_col1\" class=\"data row30 col1\" >12.115</td>\n",
       "      <td id=\"T_69230_row30_col2\" class=\"data row30 col2\" >0.776</td>\n",
       "      <td id=\"T_69230_row30_col3\" class=\"data row30 col3\" >10.817</td>\n",
       "      <td id=\"T_69230_row30_col4\" class=\"data row30 col4\" >13.339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row31\" class=\"row_heading level0 row31\" >RES_TB_kor</th>\n",
       "      <td id=\"T_69230_row31_col0\" class=\"data row31 col0\" >0.300</td>\n",
       "      <td id=\"T_69230_row31_col1\" class=\"data row31 col1\" >0.347</td>\n",
       "      <td id=\"T_69230_row31_col2\" class=\"data row31 col2\" >0.025</td>\n",
       "      <td id=\"T_69230_row31_col3\" class=\"data row31 col3\" >0.308</td>\n",
       "      <td id=\"T_69230_row31_col4\" class=\"data row31 col4\" >0.386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row32\" class=\"row_heading level0 row32\" >RES_RTB_kor_BAR</th>\n",
       "      <td id=\"T_69230_row32_col0\" class=\"data row32 col0\" >0.200</td>\n",
       "      <td id=\"T_69230_row32_col1\" class=\"data row32 col1\" >0.428</td>\n",
       "      <td id=\"T_69230_row32_col2\" class=\"data row32 col2\" >0.527</td>\n",
       "      <td id=\"T_69230_row32_col3\" class=\"data row32 col3\" >0.053</td>\n",
       "      <td id=\"T_69230_row32_col4\" class=\"data row32 col4\" >1.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69230_level0_row33\" class=\"row_heading level0 row33\" >RES_RES_CB_kor</th>\n",
       "      <td id=\"T_69230_row33_col0\" class=\"data row33 col0\" >0.300</td>\n",
       "      <td id=\"T_69230_row33_col1\" class=\"data row33 col1\" >0.574</td>\n",
       "      <td id=\"T_69230_row33_col2\" class=\"data row33 col2\" >0.041</td>\n",
       "      <td id=\"T_69230_row33_col3\" class=\"data row33 col3\" >0.508</td>\n",
       "      <td id=\"T_69230_row33_col4\" class=\"data row33 col4\" >0.639</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2545ae58da0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t2 = post[post[\"구분\"] == \"shocks_std\"].set_index(\"파라미터\")[[\"사전평균\", \"사후평균\", \"표준편차\", \"HPD_low\", \"HPD_high\"]]\n",
    "t2.to_csv(os.path.join(C.OUTPUT, \"table2_posterior_shocks.csv\"), encoding=\"utf-8-sig\")\n",
    "t2.style.format(\"{:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aed789f",
   "metadata": {},
   "source": [
    "## 표 3. 기준선 전망 — 연간 요약(2024–2035), 정비판과 이전 판\n",
    "\n",
    "2024–2025년은 평활값(관측변수는 실측치와 같다), 2026년 이후는 사후최빈값 모수의 무충격 전망이다. 정비판은 잠재성장률 1.8%, 물가목표 2%, 실질유가 불변으로 수렴하고, 이전 판은 2000년대 정상상태(4.29%, 3.0%, 연 9.88%)로 수렴한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f0c2857d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T00:09:09.621696Z",
     "iopub.status.busy": "2026-10-08T00:09:09.621696Z",
     "iopub.status.idle": "2026-10-08T00:09:09.629497Z",
     "shell.execute_reply": "2026-10-08T00:09:09.628993Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_dec73\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_dec73_level0_col0\" class=\"col_heading level0 col0\" colspan=\"7\">정비판</th>\n",
       "      <th id=\"T_dec73_level0_col7\" class=\"col_heading level0 col7\" colspan=\"7\">이전 판</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"blank level1\" >&nbsp;</th>\n",
       "      <th id=\"T_dec73_level1_col0\" class=\"col_heading level1 col0\" >실질GDP 성장률(%)</th>\n",
       "      <th id=\"T_dec73_level1_col1\" class=\"col_heading level1 col1\" >CPI 상승률(%)</th>\n",
       "      <th id=\"T_dec73_level1_col2\" class=\"col_heading level1 col2\" >콜금리(%)</th>\n",
       "      <th id=\"T_dec73_level1_col3\" class=\"col_heading level1 col3\" >실업률(%)</th>\n",
       "      <th id=\"T_dec73_level1_col4\" class=\"col_heading level1 col4\" >산출갭(%)</th>\n",
       "      <th id=\"T_dec73_level1_col5\" class=\"col_heading level1 col5\" >두바이유(달러/배럴)</th>\n",
       "      <th id=\"T_dec73_level1_col6\" class=\"col_heading level1 col6\" >미국 산출갭(%)</th>\n",
       "      <th id=\"T_dec73_level1_col7\" class=\"col_heading level1 col7\" >실질GDP 성장률(%)</th>\n",
       "      <th id=\"T_dec73_level1_col8\" class=\"col_heading level1 col8\" >CPI 상승률(%)</th>\n",
       "      <th id=\"T_dec73_level1_col9\" class=\"col_heading level1 col9\" >콜금리(%)</th>\n",
       "      <th id=\"T_dec73_level1_col10\" class=\"col_heading level1 col10\" >실업률(%)</th>\n",
       "      <th id=\"T_dec73_level1_col11\" class=\"col_heading level1 col11\" >산출갭(%)</th>\n",
       "      <th id=\"T_dec73_level1_col12\" class=\"col_heading level1 col12\" >두바이유(달러/배럴)</th>\n",
       "      <th id=\"T_dec73_level1_col13\" class=\"col_heading level1 col13\" >미국 산출갭(%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >연도</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "      <th class=\"blank col10\" >&nbsp;</th>\n",
       "      <th class=\"blank col11\" >&nbsp;</th>\n",
       "      <th class=\"blank col12\" >&nbsp;</th>\n",
       "      <th class=\"blank col13\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_dec73_level0_row0\" class=\"row_heading level0 row0\" >2024</th>\n",
       "      <td id=\"T_dec73_row0_col0\" class=\"data row0 col0\" >1.56</td>\n",
       "      <td id=\"T_dec73_row0_col1\" class=\"data row0 col1\" >2.32</td>\n",
       "      <td id=\"T_dec73_row0_col2\" class=\"data row0 col2\" >3.40</td>\n",
       "      <td id=\"T_dec73_row0_col3\" class=\"data row0 col3\" >2.77</td>\n",
       "      <td id=\"T_dec73_row0_col4\" class=\"data row0 col4\" >-0.01</td>\n",
       "      <td id=\"T_dec73_row0_col5\" class=\"data row0 col5\" >80.99</td>\n",
       "      <td id=\"T_dec73_row0_col6\" class=\"data row0 col6\" >1.32</td>\n",
       "      <td id=\"T_dec73_row0_col7\" class=\"data row0 col7\" >1.56</td>\n",
       "      <td id=\"T_dec73_row0_col8\" class=\"data row0 col8\" >2.32</td>\n",
       "      <td id=\"T_dec73_row0_col9\" class=\"data row0 col9\" >3.40</td>\n",
       "      <td id=\"T_dec73_row0_col10\" class=\"data row0 col10\" >2.77</td>\n",
       "      <td id=\"T_dec73_row0_col11\" class=\"data row0 col11\" >-0.01</td>\n",
       "      <td id=\"T_dec73_row0_col12\" class=\"data row0 col12\" >80.99</td>\n",
       "      <td id=\"T_dec73_row0_col13\" class=\"data row0 col13\" >1.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dec73_level0_row1\" class=\"row_heading level0 row1\" >2025</th>\n",
       "      <td id=\"T_dec73_row1_col0\" class=\"data row1 col0\" >1.83</td>\n",
       "      <td id=\"T_dec73_row1_col1\" class=\"data row1 col1\" >2.12</td>\n",
       "      <td id=\"T_dec73_row1_col2\" class=\"data row1 col2\" >2.60</td>\n",
       "      <td id=\"T_dec73_row1_col3\" class=\"data row1 col3\" >2.78</td>\n",
       "      <td id=\"T_dec73_row1_col4\" class=\"data row1 col4\" >-0.31</td>\n",
       "      <td id=\"T_dec73_row1_col5\" class=\"data row1 col5\" >69.39</td>\n",
       "      <td id=\"T_dec73_row1_col6\" class=\"data row1 col6\" >1.00</td>\n",
       "      <td id=\"T_dec73_row1_col7\" class=\"data row1 col7\" >1.83</td>\n",
       "      <td id=\"T_dec73_row1_col8\" class=\"data row1 col8\" >2.12</td>\n",
       "      <td id=\"T_dec73_row1_col9\" class=\"data row1 col9\" >2.60</td>\n",
       "      <td id=\"T_dec73_row1_col10\" class=\"data row1 col10\" >2.78</td>\n",
       "      <td id=\"T_dec73_row1_col11\" class=\"data row1 col11\" >-0.31</td>\n",
       "      <td id=\"T_dec73_row1_col12\" class=\"data row1 col12\" >69.39</td>\n",
       "      <td id=\"T_dec73_row1_col13\" class=\"data row1 col13\" >1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dec73_level0_row2\" class=\"row_heading level0 row2\" >2026</th>\n",
       "      <td id=\"T_dec73_row2_col0\" class=\"data row2 col0\" >2.19</td>\n",
       "      <td id=\"T_dec73_row2_col1\" class=\"data row2 col1\" >1.72</td>\n",
       "      <td id=\"T_dec73_row2_col2\" class=\"data row2 col2\" >2.42</td>\n",
       "      <td id=\"T_dec73_row2_col3\" class=\"data row2 col3\" >2.86</td>\n",
       "      <td id=\"T_dec73_row2_col4\" class=\"data row2 col4\" >-0.19</td>\n",
       "      <td id=\"T_dec73_row2_col5\" class=\"data row2 col5\" >63.86</td>\n",
       "      <td id=\"T_dec73_row2_col6\" class=\"data row2 col6\" >1.18</td>\n",
       "      <td id=\"T_dec73_row2_col7\" class=\"data row2 col7\" >2.39</td>\n",
       "      <td id=\"T_dec73_row2_col8\" class=\"data row2 col8\" >2.15</td>\n",
       "      <td id=\"T_dec73_row2_col9\" class=\"data row2 col9\" >2.78</td>\n",
       "      <td id=\"T_dec73_row2_col10\" class=\"data row2 col10\" >2.89</td>\n",
       "      <td id=\"T_dec73_row2_col11\" class=\"data row2 col11\" >-0.37</td>\n",
       "      <td id=\"T_dec73_row2_col12\" class=\"data row2 col12\" >66.78</td>\n",
       "      <td id=\"T_dec73_row2_col13\" class=\"data row2 col13\" >0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dec73_level0_row3\" class=\"row_heading level0 row3\" >2027</th>\n",
       "      <td id=\"T_dec73_row3_col0\" class=\"data row3 col0\" >1.73</td>\n",
       "      <td id=\"T_dec73_row3_col1\" class=\"data row3 col1\" >1.79</td>\n",
       "      <td id=\"T_dec73_row3_col2\" class=\"data row3 col2\" >2.24</td>\n",
       "      <td id=\"T_dec73_row3_col3\" class=\"data row3 col3\" >2.91</td>\n",
       "      <td id=\"T_dec73_row3_col4\" class=\"data row3 col4\" >-0.45</td>\n",
       "      <td id=\"T_dec73_row3_col5\" class=\"data row3 col5\" >65.62</td>\n",
       "      <td id=\"T_dec73_row3_col6\" class=\"data row3 col6\" >0.56</td>\n",
       "      <td id=\"T_dec73_row3_col7\" class=\"data row3 col7\" >3.15</td>\n",
       "      <td id=\"T_dec73_row3_col8\" class=\"data row3 col8\" >2.71</td>\n",
       "      <td id=\"T_dec73_row3_col9\" class=\"data row3 col9\" >3.24</td>\n",
       "      <td id=\"T_dec73_row3_col10\" class=\"data row3 col10\" >2.91</td>\n",
       "      <td id=\"T_dec73_row3_col11\" class=\"data row3 col11\" >-0.33</td>\n",
       "      <td id=\"T_dec73_row3_col12\" class=\"data row3 col12\" >74.00</td>\n",
       "      <td id=\"T_dec73_row3_col13\" class=\"data row3 col13\" >0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dec73_level0_row4\" class=\"row_heading level0 row4\" >2028</th>\n",
       "      <td id=\"T_dec73_row4_col0\" class=\"data row4 col0\" >2.24</td>\n",
       "      <td id=\"T_dec73_row4_col1\" class=\"data row4 col1\" >1.87</td>\n",
       "      <td id=\"T_dec73_row4_col2\" class=\"data row4 col2\" >2.27</td>\n",
       "      <td id=\"T_dec73_row4_col3\" class=\"data row4 col3\" >2.87</td>\n",
       "      <td id=\"T_dec73_row4_col4\" class=\"data row4 col4\" >-0.15</td>\n",
       "      <td id=\"T_dec73_row4_col5\" class=\"data row4 col5\" >67.24</td>\n",
       "      <td id=\"T_dec73_row4_col6\" class=\"data row4 col6\" >-0.03</td>\n",
       "      <td id=\"T_dec73_row4_col7\" class=\"data row4 col7\" >3.92</td>\n",
       "      <td id=\"T_dec73_row4_col8\" class=\"data row4 col8\" >2.94</td>\n",
       "      <td id=\"T_dec73_row4_col9\" class=\"data row4 col9\" >3.71</td>\n",
       "      <td id=\"T_dec73_row4_col10\" class=\"data row4 col10\" >2.84</td>\n",
       "      <td id=\"T_dec73_row4_col11\" class=\"data row4 col11\" >0.03</td>\n",
       "      <td id=\"T_dec73_row4_col12\" class=\"data row4 col12\" >81.85</td>\n",
       "      <td id=\"T_dec73_row4_col13\" class=\"data row4 col13\" >0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dec73_level0_row5\" class=\"row_heading level0 row5\" >2029</th>\n",
       "      <td id=\"T_dec73_row5_col0\" class=\"data row5 col0\" >1.90</td>\n",
       "      <td id=\"T_dec73_row5_col1\" class=\"data row5 col1\" >1.92</td>\n",
       "      <td id=\"T_dec73_row5_col2\" class=\"data row5 col2\" >2.30</td>\n",
       "      <td id=\"T_dec73_row5_col3\" class=\"data row5 col3\" >2.86</td>\n",
       "      <td id=\"T_dec73_row5_col4\" class=\"data row5 col4\" >-0.14</td>\n",
       "      <td id=\"T_dec73_row5_col5\" class=\"data row5 col5\" >68.78</td>\n",
       "      <td id=\"T_dec73_row5_col6\" class=\"data row5 col6\" >-0.08</td>\n",
       "      <td id=\"T_dec73_row5_col7\" class=\"data row5 col7\" >3.80</td>\n",
       "      <td id=\"T_dec73_row5_col8\" class=\"data row5 col8\" >3.01</td>\n",
       "      <td id=\"T_dec73_row5_col9\" class=\"data row5 col9\" >4.01</td>\n",
       "      <td id=\"T_dec73_row5_col10\" class=\"data row5 col10\" >2.83</td>\n",
       "      <td id=\"T_dec73_row5_col11\" class=\"data row5 col11\" >-0.01</td>\n",
       "      <td id=\"T_dec73_row5_col12\" class=\"data row5 col12\" >90.36</td>\n",
       "      <td id=\"T_dec73_row5_col13\" class=\"data row5 col13\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dec73_level0_row6\" class=\"row_heading level0 row6\" >2030</th>\n",
       "      <td id=\"T_dec73_row6_col0\" class=\"data row6 col0\" >1.89</td>\n",
       "      <td id=\"T_dec73_row6_col1\" class=\"data row6 col1\" >1.92</td>\n",
       "      <td id=\"T_dec73_row6_col2\" class=\"data row6 col2\" >2.33</td>\n",
       "      <td id=\"T_dec73_row6_col3\" class=\"data row6 col3\" >2.85</td>\n",
       "      <td id=\"T_dec73_row6_col4\" class=\"data row6 col4\" >-0.11</td>\n",
       "      <td id=\"T_dec73_row6_col5\" class=\"data row6 col5\" >70.36</td>\n",
       "      <td id=\"T_dec73_row6_col6\" class=\"data row6 col6\" >-0.05</td>\n",
       "      <td id=\"T_dec73_row6_col7\" class=\"data row6 col7\" >4.00</td>\n",
       "      <td id=\"T_dec73_row6_col8\" class=\"data row6 col8\" >3.00</td>\n",
       "      <td id=\"T_dec73_row6_col9\" class=\"data row6 col9\" >4.17</td>\n",
       "      <td id=\"T_dec73_row6_col10\" class=\"data row6 col10\" >2.84</td>\n",
       "      <td id=\"T_dec73_row6_col11\" class=\"data row6 col11\" >-0.03</td>\n",
       "      <td id=\"T_dec73_row6_col12\" class=\"data row6 col12\" >99.74</td>\n",
       "      <td id=\"T_dec73_row6_col13\" class=\"data row6 col13\" >-0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dec73_level0_row7\" class=\"row_heading level0 row7\" >2031</th>\n",
       "      <td id=\"T_dec73_row7_col0\" class=\"data row7 col0\" >1.86</td>\n",
       "      <td id=\"T_dec73_row7_col1\" class=\"data row7 col1\" >1.93</td>\n",
       "      <td id=\"T_dec73_row7_col2\" class=\"data row7 col2\" >2.35</td>\n",
       "      <td id=\"T_dec73_row7_col3\" class=\"data row7 col3\" >2.85</td>\n",
       "      <td id=\"T_dec73_row7_col4\" class=\"data row7 col4\" >-0.10</td>\n",
       "      <td id=\"T_dec73_row7_col5\" class=\"data row7 col5\" >71.98</td>\n",
       "      <td id=\"T_dec73_row7_col6\" class=\"data row7 col6\" >-0.03</td>\n",
       "      <td id=\"T_dec73_row7_col7\" class=\"data row7 col7\" >4.13</td>\n",
       "      <td id=\"T_dec73_row7_col8\" class=\"data row7 col8\" >2.99</td>\n",
       "      <td id=\"T_dec73_row7_col9\" class=\"data row7 col9\" >4.25</td>\n",
       "      <td id=\"T_dec73_row7_col10\" class=\"data row7 col10\" >2.84</td>\n",
       "      <td id=\"T_dec73_row7_col11\" class=\"data row7 col11\" >-0.05</td>\n",
       "      <td id=\"T_dec73_row7_col12\" class=\"data row7 col12\" >110.10</td>\n",
       "      <td id=\"T_dec73_row7_col13\" class=\"data row7 col13\" >-0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dec73_level0_row8\" class=\"row_heading level0 row8\" >2032</th>\n",
       "      <td id=\"T_dec73_row8_col0\" class=\"data row8 col0\" >1.84</td>\n",
       "      <td id=\"T_dec73_row8_col1\" class=\"data row8 col1\" >1.93</td>\n",
       "      <td id=\"T_dec73_row8_col2\" class=\"data row8 col2\" >2.36</td>\n",
       "      <td id=\"T_dec73_row8_col3\" class=\"data row8 col3\" >2.85</td>\n",
       "      <td id=\"T_dec73_row8_col4\" class=\"data row8 col4\" >-0.09</td>\n",
       "      <td id=\"T_dec73_row8_col5\" class=\"data row8 col5\" >73.65</td>\n",
       "      <td id=\"T_dec73_row8_col6\" class=\"data row8 col6\" >-0.01</td>\n",
       "      <td id=\"T_dec73_row8_col7\" class=\"data row8 col7\" >4.22</td>\n",
       "      <td id=\"T_dec73_row8_col8\" class=\"data row8 col8\" >2.98</td>\n",
       "      <td id=\"T_dec73_row8_col9\" class=\"data row8 col9\" >4.30</td>\n",
       "      <td id=\"T_dec73_row8_col10\" class=\"data row8 col10\" >2.84</td>\n",
       "      <td id=\"T_dec73_row8_col11\" class=\"data row8 col11\" >-0.06</td>\n",
       "      <td id=\"T_dec73_row8_col12\" class=\"data row8 col12\" >121.52</td>\n",
       "      <td id=\"T_dec73_row8_col13\" class=\"data row8 col13\" >-0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dec73_level0_row9\" class=\"row_heading level0 row9\" >2033</th>\n",
       "      <td id=\"T_dec73_row9_col0\" class=\"data row9 col0\" >1.83</td>\n",
       "      <td id=\"T_dec73_row9_col1\" class=\"data row9 col1\" >1.93</td>\n",
       "      <td id=\"T_dec73_row9_col2\" class=\"data row9 col2\" >2.37</td>\n",
       "      <td id=\"T_dec73_row9_col3\" class=\"data row9 col3\" >2.85</td>\n",
       "      <td id=\"T_dec73_row9_col4\" class=\"data row9 col4\" >-0.09</td>\n",
       "      <td id=\"T_dec73_row9_col5\" class=\"data row9 col5\" >75.36</td>\n",
       "      <td id=\"T_dec73_row9_col6\" class=\"data row9 col6\" >-0.01</td>\n",
       "      <td id=\"T_dec73_row9_col7\" class=\"data row9 col7\" >4.28</td>\n",
       "      <td id=\"T_dec73_row9_col8\" class=\"data row9 col8\" >2.97</td>\n",
       "      <td id=\"T_dec73_row9_col9\" class=\"data row9 col9\" >4.32</td>\n",
       "      <td id=\"T_dec73_row9_col10\" class=\"data row9 col10\" >2.84</td>\n",
       "      <td id=\"T_dec73_row9_col11\" class=\"data row9 col11\" >-0.07</td>\n",
       "      <td id=\"T_dec73_row9_col12\" class=\"data row9 col12\" >134.14</td>\n",
       "      <td id=\"T_dec73_row9_col13\" class=\"data row9 col13\" >-0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dec73_level0_row10\" class=\"row_heading level0 row10\" >2034</th>\n",
       "      <td id=\"T_dec73_row10_col0\" class=\"data row10 col0\" >1.83</td>\n",
       "      <td id=\"T_dec73_row10_col1\" class=\"data row10 col1\" >1.93</td>\n",
       "      <td id=\"T_dec73_row10_col2\" class=\"data row10 col2\" >2.38</td>\n",
       "      <td id=\"T_dec73_row10_col3\" class=\"data row10 col3\" >2.85</td>\n",
       "      <td id=\"T_dec73_row10_col4\" class=\"data row10 col4\" >-0.09</td>\n",
       "      <td id=\"T_dec73_row10_col5\" class=\"data row10 col5\" >77.11</td>\n",
       "      <td id=\"T_dec73_row10_col6\" class=\"data row10 col6\" >-0.00</td>\n",
       "      <td id=\"T_dec73_row10_col7\" class=\"data row10 col7\" >4.31</td>\n",
       "      <td id=\"T_dec73_row10_col8\" class=\"data row10 col8\" >2.97</td>\n",
       "      <td id=\"T_dec73_row10_col9\" class=\"data row10 col9\" >4.33</td>\n",
       "      <td id=\"T_dec73_row10_col10\" class=\"data row10 col10\" >2.84</td>\n",
       "      <td id=\"T_dec73_row10_col11\" class=\"data row10 col11\" >-0.07</td>\n",
       "      <td id=\"T_dec73_row10_col12\" class=\"data row10 col12\" >148.06</td>\n",
       "      <td id=\"T_dec73_row10_col13\" class=\"data row10 col13\" >-0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dec73_level0_row11\" class=\"row_heading level0 row11\" >2035</th>\n",
       "      <td id=\"T_dec73_row11_col0\" class=\"data row11 col0\" >1.82</td>\n",
       "      <td id=\"T_dec73_row11_col1\" class=\"data row11 col1\" >1.93</td>\n",
       "      <td id=\"T_dec73_row11_col2\" class=\"data row11 col2\" >2.38</td>\n",
       "      <td id=\"T_dec73_row11_col3\" class=\"data row11 col3\" >2.85</td>\n",
       "      <td id=\"T_dec73_row11_col4\" class=\"data row11 col4\" >-0.09</td>\n",
       "      <td id=\"T_dec73_row11_col5\" class=\"data row11 col5\" >78.90</td>\n",
       "      <td id=\"T_dec73_row11_col6\" class=\"data row11 col6\" >-0.00</td>\n",
       "      <td id=\"T_dec73_row11_col7\" class=\"data row11 col7\" >4.34</td>\n",
       "      <td id=\"T_dec73_row11_col8\" class=\"data row11 col8\" >2.97</td>\n",
       "      <td id=\"T_dec73_row11_col9\" class=\"data row11 col9\" >4.33</td>\n",
       "      <td id=\"T_dec73_row11_col10\" class=\"data row11 col10\" >2.85</td>\n",
       "      <td id=\"T_dec73_row11_col11\" class=\"data row11 col11\" >-0.07</td>\n",
       "      <td id=\"T_dec73_row11_col12\" class=\"data row11 col12\" >163.43</td>\n",
       "      <td id=\"T_dec73_row11_col13\" class=\"data row11 col13\" >-0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2545ad4bce0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ann.to_csv(os.path.join(C.OUTPUT, \"table3_baseline_annual.csv\"), encoding=\"utf-8-sig\")\n",
    "cols = [c for c in ann.columns if c != \"구분\"]\n",
    "t3 = pd.concat({\"정비판\": ann[cols], \"이전 판\": ann_prev[cols]}, axis=1)\n",
    "t3.to_csv(os.path.join(C.OUTPUT, \"table3b_baseline_vs_prev.csv\"), encoding=\"utf-8-sig\")\n",
    "t3.style.format(\"{:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62f0e1db",
   "metadata": {},
   "source": [
    "## 그림 1. 기준선 전망 — 주요 변수(2015–2035, 분기), 정비판과 이전 판"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "564005e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T00:09:09.630004Z",
     "iopub.status.busy": "2026-10-08T00:09:09.630004Z",
     "iopub.status.idle": "2026-10-08T00:09:10.132073Z",
     "shell.execute_reply": "2026-10-08T00:09:10.132073Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\meals\\AppData\\Local\\Temp\\ipykernel_18692\\2431658101.py:15: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from font(s) NanumGothic.\n",
      "  fig.tight_layout(); fig.savefig(os.path.join(C.OUTPUT, \"fig1_baseline.png\"), dpi=150)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\meals\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\events.py:96: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from font(s) NanumGothic.\n",
      "  func(*args, **kwargs)\n",
      "C:\\Users\\meals\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from font(s) NanumGothic.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1300x640 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "q, qp = pd.concat([sm, fc]), pd.concat([sm, fc_prev])\n",
    "t = pd.PeriodIndex(q.index, freq=\"Q\").to_timestamp(how=\"end\")\n",
    "spec = [(\"실질GDP 성장률(전년동기, %)\", lambda d: d[\"LGDP_kor\"].diff(4)), (\"CPI 상승률(전년동기, %)\", lambda d: d[\"LCPI_kor\"].diff(4)),\n",
    "        (\"콜금리(%)\", lambda d: d[\"RS_kor\"]), (\"실질금리(회사채 − CPI, %)\", lambda d: d[\"CB_kor\"] - d[\"LCPI_kor\"].diff(4)),\n",
    "        (\"산출갭(%)\", lambda d: d[\"Y_kor\"]), (\"두바이유(달러/배럴)\", lambda d: np.exp(d[\"LOIL_us\"] / 100))]\n",
    "fig, axes = plt.subplots(2, 3, figsize=(13, 6.4))\n",
    "sel = t >= pd.Timestamp(\"2015-01-01\")\n",
    "for ax, (title, f) in zip(axes.ravel(), spec):\n",
    "    ax.plot(t[sel], f(qp).values[sel], color=\"#b0b0b0\", lw=1.4, ls=\"--\", label=\"이전 판\")\n",
    "    ax.plot(t[sel], f(q).values[sel], color=\"#1f4e79\", lw=1.7, label=\"정비판\")\n",
    "    ax.axvline(pd.Timestamp(\"2025-12-31\"), color=\"grey\", ls=\":\", lw=1)\n",
    "    ax.set_title(title, loc=\"left\", fontsize=11); ax.grid(alpha=.3)\n",
    "axes[0, 0].legend(frameon=False, fontsize=9)\n",
    "fig.suptitle(\"기준선 전망(점선 오른쪽이 2026Q1 이후 전망)\", x=0.01, ha=\"left\", fontweight=\"bold\")\n",
    "fig.tight_layout(); fig.savefig(os.path.join(C.OUTPUT, \"fig1_baseline.png\"), dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69845f10",
   "metadata": {},
   "source": [
    "## 표 4. 시나리오 설계 레버 — 인구 가정과 감쇠 충격(최고치, 감쇠 분기)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "30d6d6bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T00:09:10.133074Z",
     "iopub.status.busy": "2026-10-08T00:09:10.133074Z",
     "iopub.status.idle": "2026-10-08T00:09:10.139088Z",
     "shell.execute_reply": "2026-10-08T00:09:10.139088Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>인구(생산연령인구 추계)</th>\n",
       "      <th>미국수요 yus</th>\n",
       "      <th>유가 oil</th>\n",
       "      <th>교역비용 tcost</th>\n",
       "      <th>내수 dshk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>시나리오</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>S1 기준</th>\n",
       "      <td>중위</td>\n",
       "      <td>—</td>\n",
       "      <td>—</td>\n",
       "      <td>—</td>\n",
       "      <td>—</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S2 공급제약+충격</th>\n",
       "      <td>저위</td>\n",
       "      <td>-1.2 (12분기)</td>\n",
       "      <td>-8.0 (8분기)</td>\n",
       "      <td>+1.5 (12분기)</td>\n",
       "      <td>-1.0 (12분기)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S3 제약완화</th>\n",
       "      <td>고위</td>\n",
       "      <td>+0.6 (12분기)</td>\n",
       "      <td>-3.0 (8분기)</td>\n",
       "      <td>-1.0 (12분기)</td>\n",
       "      <td>+0.5 (12분기)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S4 기술도약</th>\n",
       "      <td>중위</td>\n",
       "      <td>+0.8 (20분기)</td>\n",
       "      <td>—</td>\n",
       "      <td>—</td>\n",
       "      <td>+0.6 (28분기)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S5 지정학</th>\n",
       "      <td>중위</td>\n",
       "      <td>-0.6 (10분기)</td>\n",
       "      <td>+15.0 (8분기)</td>\n",
       "      <td>+2.0 (10분기)</td>\n",
       "      <td>-0.8 (10분기)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           인구(생산연령인구 추계)     미국수요 yus       유가 oil   교역비용 tcost      내수 dshk\n",
       "시나리오                                                                        \n",
       "S1 기준                 중위            —            —            —            —\n",
       "S2 공급제약+충격            저위  -1.2 (12분기)   -8.0 (8분기)  +1.5 (12분기)  -1.0 (12분기)\n",
       "S3 제약완화               고위  +0.6 (12분기)   -3.0 (8분기)  -1.0 (12분기)  +0.5 (12분기)\n",
       "S4 기술도약               중위  +0.8 (20분기)            —            —  +0.6 (28분기)\n",
       "S5 지정학                중위  -0.6 (10분기)  +15.0 (8분기)  +2.0 (10분기)  -0.8 (10분기)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t4 = pd.read_csv(os.path.join(C.OUTPUT, \"scenario_levers.csv\"), index_col=0, encoding=\"utf-8-sig\")\n",
    "t4.to_csv(os.path.join(C.OUTPUT, \"table4_scenario_levers.csv\"), encoding=\"utf-8-sig\")\n",
    "t4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a06ece62",
   "metadata": {},
   "source": [
    "## 그림 2. 거시 블록 경로 — 산출갭·실질금리·실질환율갭·실업갭(2026–2035)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "57882fe3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T00:09:10.140216Z",
     "iopub.status.busy": "2026-10-08T00:09:10.139088Z",
     "iopub.status.idle": "2026-10-08T00:09:10.638059Z",
     "shell.execute_reply": "2026-10-08T00:09:10.637554Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Eux6XulcyzK4onc24uDgVzCoPQwt5LLqH50U38bzoJp4X3cTzUjzSdyEiIiKiChLIGjhwoGoae/bsQZ8+fdCkSZMCBeA1pDD88OHD7yoGJsXbi1ojS8Zblpdi7zwW3cPzopt4XnQTz4tu4nkpHnNz81I6E0RERESkc4GsO7Vu3VoFsrZt24ZXX331rp9LPa3CampJUKqogSkJ/hRnfV3GY9FNPC+6iedFN/G86Cael6LTxf7EyZMn0bZtW0yfPh1vv/12We8OERERkVbpXO/L3t6eafpEREREDyE9PR3vvfceevXqhZSUFL6HREREVO7oVCBL6lzJrIfNmjUr610hIiIi0jtSS/T5559H1apVy3pXiIiIiPR/aKHMOCiF2b28vNT9kJAQhIWFqXocV69exfz581G3bl20aNEC+urc1VvYfDAI1b1cULe6Jzyc7dQQDSIiIqLSdODAARw5cgSff/45xo0bd991ORN0rqS0JKw/tRGXb16Gi6MLrM2sYGVmDSszS1jJsmnuraWZlfqZqbGpTn+IOeuobuJ50U08L7qJ56XinpfsYmy71AJZjRo1gp2dXYHHdu/ejeXLl+Orr75S91esWIE///wTRkZGqFy5Mt555x288MIL0FfxSal4YfyPCI/5bwYjJzsr1K7mibrVK6FuDU/UqV4JlVzsGdwiIiIirZFhhIMGDcI///xTpD5GRZoJWvYxLikVYVEJCItOQFhUPM6HX0JQ7EFE5QQhxyCzyNsyNjSGpQS3TC1R07UmGno1UM3d1g26QJ/Oy4PwWHQTz4tu4nnRTTwvpTcTdKkFshYuXHjXY4899phqGkOHDlWtvJjx2wYVxDIzNYaxoSGSUtMRFZeEncfOq6bhYGOpAloS2JIAlyx7uTK4RURERA/no48+UhcE5cJgUZTXmaAlM37TgbO4ERGL0Mg43AiX21ikpGUABtkwsbsFU6drMLaOyX2CAZCTZYSsZDvAMBtGxlmwsjSEgXEWUtNTkJWTVWD7mdmZiE+NV+1mfBh2XNipHvd28EKzas3QonpzNPVpDDvLghdzHxVdPS8Pg8eim3hedBPPi27ieSm9maB1atZCfXYk6Cp+X39QLY99tQee79YMl0IjEXghFKcu3kDgpVCcvhiKxJQ0xCQkY9fx86ppeLs5YPawp9DEv0oZHgURERHpm+3bt+PixYuYN29ekZ9THmeC3nvyIl797BekpRfMsDIwSYGZ+3WYOl6HoUl63uO2Jm5o5NEO7ap3xP4TIViz7yziU9MRr94HA3Rp5o/nezRBQDVnJKUnIzktCYlpSUhKS0ZUYiQOhxzFgUsHEZUYhWsx13HtyHX8c2SZel8CPPzRvHoztKjWHA0q14eZyd3vdWnRtfNSEjwW3cTzopt4XnQTz0vRFed7i4EsLUjPyMSYr5erNO5GtSqrIJachBperqr169AgLyIbcjM6N7B18QZOXAjB2WuXkJIdh9DEaDw79n+Y+FYfPPc4i90TERFR0WzcuBGbNm0qcCUzMzNTdZ5l+GBUVFS5CGrcz+GzV/DG5N9UEEtKOMiFQUOrcFxJOYJLMYHIQY5az9jIGF0CHsNTzQaiYeUG6j2S/llr/yr45PVeWLr1GH7+dx+uh8dgw/4zqkmJiNd6t0KvtvVgZvJf17l/kydV3+9C+AXsv3gQBy4ewOErR5GSnoLToWdU+3HXzzAzNkPDKg3QsnoLPBbQEd6O3mX4ThEREek/BrK04H8rdiP4yi0YGxliyvv9VGdROjYJqQkIjb2Jm7E3ERp3+/b2fWkxRrEw9AGsbm8nO80CE5ZcwJHzvTHl7adhmq+zRERERFSYyZMnY/z48QUekwCWk5MTBg8eXO6DWCfOX8erk35Gcmo6qlSywcB+9lh3ehGuh97IW8fDzh0Dm/ZHv0Z94WTtVOh2bK0s8EbfNni1VytsOngWP/27FwcCL+P0pVCMmLsUU35Zjxe6N1cXLF3sbdRzJBDm6+ar2outnkdGZgZOXj+F/RcPqGytwBunkZaZpu5Lm71xLgI8/fF4na7oUrszKjl4PrL3iYiIqLxgpKSELodGYu5fW9Xy20+0w/WkMxi7YChuxIaqmXCKwtzEDGkZ6TA0S4GF53lsCpuDAxNWY/Lzw9DOvxkLwxMREdE9STDlzroSMpGOsbExTE11e5a9kjpz+SZemvATEpLT4OluDrNqu/G/3WF570vrGq3wVLMBaOPbGkaGRkXappGRIbq1rK2alIb4afVerN55ApGxiZizeAu+XrIdvdvVx+CnOqKKR8GgmImxCRr7NFLt/cfeVRc1j4Qcxb6LB7AzeKe6oHkm9KxqEtSqU6k2utbpgq61O8PD3qNU3iMiIqLyhoGsEpCsq0++WaGGFvp4OOGJLr54duFzSM/8r/6CsDG3gae9h+qgyBVBuZX7nvaeatnB0l51bJYe+gd/7F+C1MwkJOA8Bv/5Hnwcq+PVdi+gW92uMDcpevEzIiIiqrhMTExUK8/OX72FF8b/gLjEFLg7WcGr3hkE3wpTswo+1XQABjR9ssTD+OpU88TMIQPw8cuPY9H6g/h93QEV0Ppn61Gs3XMKY17tjhe6Nb/nRUfpA3bwa6/axz0+xKnrgdh4ehM2Bm7GrfhbKmNL2qwNc1Dfux661u6CLrUfg5udbsyCSEREpIsYyCqBf7Ydw96Tl9Ty5+/0wayNs1QQy8uhEkb1GJkbvLLzgLW59QO3JanlQ7p+gHc6voX5a3/Hb3sWA+YxCIm+iE9XTMTMDbPRr2EfdVWRtRWIiIjofsaNG1fuM+KfG/8jouOT4exgjYbtwrH3UpDKupr1zAy0rNFCq68nQwmHPvMY3u3fHv/uOonZi7eoOlrjvl2lZkmc/sGTcHe6/0yFEuyq511XteFdh6ohiBsDN2Hj6c2ISIjAiWsnVZuxfqYqEC+ZWl0COsPV1kWrx0JERKTvynfRhFIUFZeIyT+uVcv9OzVCquk17LmwV90f3fMjtK/VTtVLKEoQKz+Z1WZk39exathfcEroifSoSsjJNkR8Sjx+3fs7es99Au//Nhi7zu1GVnbBKaGJiIiIRHmZta4w125F47mxPyAiJgGOtpbo08cYey/tyuuDaTuIlZ8Ue5d+3/q5g/FMlybqsZ3HzqPrB3OxcucJla1fFHJuJFglFz43jliLn17/Hs82fxrOt+t3Hb96AtPXfomuM7vj9R/fwt+HliI6KabUjouIiEiflM8eziMw+cd1iElIhoONJYY/3x4z1n2pHu8c8Bja1Gxd4u1LzYXVX3yC9t5PI+FMe6SE1oJJjq3qIO0+vweD/xiGNxa/g9XH1yAzq+A000RERETlUWhELJ4b9wNuRsXBztoCb73kg3+O/6V+9kLL51VB90fB2tIMUwc9iR/HvQRne2vEJ6ViyMy/MGjGn4iJTy7WtiSo1ahKQ3zccxQ2jlyHH15diKebDYSjlaPq9x0OOYLJq6eg84zH8c4v72PF0VWIT0kotWMjIiLSdQxkPYTdxy9g2fZjannc6z3x99HFCIu7BQtTC3zYfbjWTo6VhRm+HvUshj/TAxmRVRF5sgXMo9qhgVcTGMAAoXGhGL9iAp786imsObGWGVpERERUboVHx+P58T/g2q0Y2Fia4ZN3m+F/e+ern0km/PDHhzzyferUxA8b5w9Bj1Z11P01e06h6+C52Ho46KG2J0Mjm1RtjDG9PsbmD9dj4SvfoH/jJ2BnYaf6efsu7lclJzpO74zBi4Zh7cl1RZ5ciIiIqLxgIKuYUtMyVIF30bpeddQNsMHve39X99/p8Bbc7dy1e4IMDfHBU53wvzEvwMbSHLeuW+DAJnd80PJzdPHrDEMDQ1yJuoIx/4xD/6+ewrpTG5Cdna3VfSAiIiIq65IOz4//EZdDo2Bpboqpwx7Hgt3TVFZ6LfeamDpgcpFnJdQ2R1srLBj1LOYOfwq2VuZqyONrn/2K0QuWIzE57aG3K8fTvFozjO87FltGbcCCF+ahd4OesDKzUse9I3gnRi8di47TumDkX6Ow6fRmpGakavXYiIiIdBEDWcU07++tuBIWDTNTY3z+bl9M+XcaMrOzUM2lGp5v+WzpnCUZstjMH8tnvItqlZyRmp6Bz77dCbOYpvjnvb/Qq35PFdC6HBmCj5eMwYCvn1HFQxnQIiIiIn0Xm5CMFz/9Ceevhav+19wPn8DC/dMRmxwHFxtnzHt+NizNLMt0H6WQe9/2DbBh3hC0bVBDPbZ44yF0HzoPB09fLvH2TYxMVOmKz5+chG2jNmH2s1/i8Tq5M1qnZaZh0+ktGPnXR3hsxuOYumkGtgftQFrGwwfRiIiIdBkDWcVwNuQmFi7PLSY6+OlOCAw/gCNXjqr7Y3uPVp2M0lTDyxUrZryHx5r6qfuLNh7F7N8O4tM+47H8gyXoUa+b6khdDL+ID//+GE998yw2n9nKgBYRERHpJak99fLEn3Hm8k2YGhvhm4+ewd+nvkFI5BWYm5hh7nOztZ4NXxIeznb4dcKr+OydPrAwM1HDIJ/+5Ht88dM6dSFSG2RioE7+HTH9qSnY9tFmTBs4BR39O6h+aHJ6Mrad345hf45Ex+ldVMb+9qCdalZtIiKi8oKBrCLKysrG6AUrkJmVjVpV3PBstwaYtWGO+pmkeTf2aYRHQVLWZZjhW/3aqPurd53EG5N/g4uVB6YMmIx/3v9bXaGTgNb5Wxcw4s8P8cy3L6grc0WdSYeIiIiorCWlpOG1z37BifPXYWxkiK8+fBa7byzDwcuH1M8n9/8ctSsFQNdIH+zF7i2wds4HaFSrsup/LVyxC31GLEDwlTCtvpalqQW61e2KOc/OxLaPNmFSvwloVqUpjA2NVe0sqaE65I9h6DitM8YuG4+dwbuQkamdgBoREVFZYSCriBatP4Dj566pzsmU95/At9u/Q3RSNGzMrTGs66MtLip1sz5+uRs+GNAmb9pnmcEnOj4J1V2rqSt0S977E11qP6Z+HhwWjCF/DMdz372o6idwlkMiIiLSdR99tQyHz16BoaEB5o54GjezjuCfI8vVzwZ3HoTOAZ2gy6p6OmPJlLcw6sXHYWJshHNXw9F35Df4Z2tuNr+22ZjbqIurn/WcgC0fbsCkJz5FW9/WMDY0QmJakprp+oNFQ1Wh+HHLPsWuc7sZ1CIiIr3EQFYRhEXFYfpvG9Xyi92bw9wmCX8fWqLuf9D5fThZO6EsPN+lEb4c3B9GhobqauXA0QtxIyJW/czXrQa+fHq6CmhJ+rk4E3pW1U/oOacPftz1M2KTc9clIiIi0iUXrofj392n1PIX7/aDhWMkZm+cq+73bdgHr7V9BfrAyMgQ7w1ojxUz3oWPh5MaXjhi7lJ8/NUyNYFQabG1sFXv01cvzsPWUZswsd+naF2jlQpqJaQmYtXxfzHo9yFq+OG45RNUphaHHxIRkb5gIKsIPl24GokpaXB3ssXw5x/D56unqDTxAE9/DGjSH2XpyY4N8b9PXoC5qQkuXo/AgI++w/mrt/J+XtPdVxUE/fPdRehap4uaAScs7hbmbpqPx2f2wKSVn6shiERERES64oeVe9RtDS8X1A2wUrPzSd+riU9jjOs9RmXI65Pa1Tyxetb76NGqjrr/56bDeOKjb3E5NLLUX9vO0g79GvXB1y/NV0GtCf3Go1WNlqpPmJCagFXHVqtMrQ7TOmPU36OxIXCjGpZIRESkqxjIeoAN+8+oJia82Rsbz67D6RunVQfqk96jy2yq5/w6NfHDokmvwc7aAjej4jBwzEIcDb5aYB1/Dz/MeGoq1g5bhdfbvgp7SzukZqSpFP0BC57Gmz+9o+poZWVnldlxEBEREUXEJuCfbcfUG/FM9zoYsng4UjNSUdmpMmY+Mx0mxqU7uU5psbE0x4JRz+LTN3qpoYZnL99E7+ELsG5v4CPbBwlqPdGoL7556StsHbURn/Ydh5bVW6hMLQleSRBLglkS1Prg96FYfnQlYpJiHtn+ERERFQUDWfeRkJyKTxeuUstdmwegaV0PzN+8QN0f0ORJ1KlUG7qisX8VVYdBssZiE1Lw/LgfsO1I8F3rycw+g7sMwoYRa1XnRYYgCimcKnW0+sx9Ar/tXYT4lIQyOAoiIiKq6H5bsx/pGZlwcjDHxqs/IiIhQg2V++qFubC3tIc+kwuhr/Zuhb+/eAuVXOxVxv+70/7AxO//Vcf8KMl7+WTjfvj25QVq9sPJ/T/DYwGd1GyQMsxw57ldmLBiEjpN74rXf3wLf+z/E2Fx2i1WT0REpFOBrPT04k3zm5n5aL+8i+LL3zciLCoe1hZmmPhWb8zZNA/xKfFwsHJQRUZ1Tc3Kblg69W1Uq+SMlLQMvDn5N6zYfrzQdc1NzFXnRWpo/e+Vb9W0zdK5uh5zA1+un4WuM7vji3+n4XLE5Ud+HERERFQxpaSl47d1B9Ry1XrXERx2DsZGxpj1zAxUcaqM8qJhLW/8O2sQOjaupe7/tHovnhrzv7xap4+aBAp71e+h3uftH21RZSl61e+pCshn52TjcMgRTFs7A4/P7InnvnsJP+z8EZciLnNGbCIiKj+BrB07dsDe3h5BQUEPXHfixIlwdHSEubk5GjZsiMOHD0NXNParAmc7K3z4YlfcTLykagiI4V2HqC98XeTl6qCCWfV9vZCZlY2hs//GD6ty60wURoJXzao1VdM2/zt0JV5q9YKaiTElPQV/Hfwb/eYPwHPfvohfdv+Km7E3H+mxEBERUcWydOtRxCQkw8I6DRdic2f3+7jHKDSt2gTljYOtJX4Y+6LqZ8rMjDI7ds9h8wvNqH+ULEwt1ERBk/tPwraPNuHblxZgYNP+cL49uZGU2Ji3eQGemD8AfeY9iZnrZ+NoyDHOik1ERI+MsbY3eODAAbz77rtwcXF5YJbVjz/+iD/++AO7d+9GtWrV8Ouvv6J3794IDg6GrW3ZB4r6tKuP9o1qwszUCM8vfEE91qhKQ/Ru0Au6zNHWCn989jremboIu45fwGc/rEFUXCI+fKHrfYujejlUwohuw/Bux7fx74k1KoX8cmQIToeeUW3Wxrmo710Pj9fpgi61u8DV1uWRHhcRERGVX1lZ2XlF3n3rxeFKUg6qOFVRGeTllaGhId4f0AGN/Srjgy//QkRMAl6d9It6bNhzj8HYqGxrsZoYmaBljRaqjen5MU5eP4WtZ7epdi36Oq5GXcWve39XTeqvtqvZFu392qNV9RawNLMs030nIqLyS+sZWZ988glWrVqlAlkPsnDhQsyePRsBAQEqI+utt95C48aN1fN1hRRQ/+vQX7gQflEVdh/T6yO9mCnHysIMP4x9Cb3a1FX3v166Ax9/tRyZWQ8u5i4dj6eaDcTyD5bitzd/xgstn4Orrav62YlrJzF93Uw19PC1H95UWVtRidGlfjxERERUvm06eBYhN6NgZJqGGym5BdBfafOSTkysU9pa1KmGNbMHoWXdaur+gqXb8cL4HxEek6BTQbcGletj+ONDsXrICiwbtASDO7+Pul65MzHGJsdh1fF/MeLPD9F+2mMY9NtgLD28TNU4IyIi0umMrM2bNxdpPcnWkmGEnTt3LvB4165dsW/fPrzwQm4GVH5paWmqacTHx6vb7Oxs1R5E1pGpm4uyrsatuFv4dttCtfxc82dQ3aV6sZ5fWopyLMZGhpgzbKDK0Pp17X78tfkwwqLjMXf4QNhaWRTpdaSgvbRhXYbg+LUT2Bi4CZvPbEFUUjSOXDmq2tQ1M9R02JKpJanoxS3E+jDnRVfxWHQTz4tu4nnRTTwvxX+/SDv+t2KXuq1VLxE3MjLgYuOi6jZVFK4ONvh94muY8+cWzP97G/YHXkaPofMxf+QzeQEuXSEXdau7VlPt9XavITIhEjuCd2J70E4cuHQQaZlp2HV+j2qf3e5PdvTrgPZ+7VDDtbpeXBQmIqIKFMgqqoiICNjZ2cHU1LTA4+7u7qrGVmGmTJmiamoVtq3U1NQidTbj4uJU0ESuKhXF5A1TkZyeDGcrJzxZux/Cw8OhC4pzLO/2aQpzY2Dhqv3YcfQc+oxYgGnv9kJVD8divaaXRSW81vQVvNz4RZwKDcSOCzux+9JexKfGq1kPpX2xZhoaVKqPppWboFmVJqhkX0mrx6LreCy6iedFN/G86Cael+JJSNCdjBl9duTsFRwJugoYZiAq57R67MWWz8HUuGA/sbwzMjLEiOe7qNmoh836G5GxiXh+/A/qsXefbKez/SRnG2f0b/KkasnpKdh/cT+2Be3AruBdiEmOReCN06rN37IAlRwqoUOtdmhfqx0a+TRUwxeJiIj0IpAlsxqamNz9xSWPZWRkFPqc0aNHY/jw4QUysry9vdUwxqLU1JLOuVwBkvWL0hHYe2Efdl3crZZH9fgQPl4+0BXFPZaPX+2FujWrYNT8Zbh6KxZvTluC2cMG4rGmfg/1+h7uHujaqAsysjJx6PIhbDy9WdVLSEhNwJFrR1X7ds9CeDt4oZVvK7TxbaWytmS2xJIeiy7jsegmnhfdxPOim3heikdKI1DJ/W9lbn+rekACIjNT1Gx5EhSpqDo0qqmGGr4/YzGOBV/DjN82qmDfrKEDYW+j27WnLG8Xi5eWlZ2lylJsD9qhsrWuRF3BjZgbWLR/sWoywVBr39YqsCW3thY2Zb37RESkB8oskCXZWJKFcyd5TH5WGDMzM9XuJMGPogZAJGBS1PWXHv5H3baq0RJd63TWuTTo4hyL6N22PqpVcsFbU37HjfBYvDVlEUY81xnvD+zw0MdmZmiKNjVbqzau9xjsu7gf24N3Ys/5PQiLu4VrMddVHS1pZsZmKpil1vdtjcpO3g99LLqMx6KbeF50E8+LbuJ5Kbry8L1V1qQu1ob9ZwCDLKRaBAPpwDPNnoK1uTUqMk8Xe/w1+U1M/WUDfly9B1sPB6Pn8K/w9ajn1OzU+kDqm8lESdKktlZIZAh2BO1UfcXjV08gITUR609tUM1YrdsIHfxys7U87T3LeveJiEhHlVkgy97eHpaWlrh69SoqV66c97jMWFi9enXoghlPT8Mf+xajg//DB3p0Te1qnlj15Xt4f/piVXvhy0WbcObyTcwY3F8ViC8JE2MTtKvVVjUZJngx4hJ2n9uD3ef34NiVY6pewp4Le1Wbhhmo7OiN1r6t0LpGK1S2+i+oRURERBXHDyt3q36DR7U4JKbHqwtfz7Z4pqx3SyeYmhhj/Bs90cS/ssqqlwuRAz7+DuNe64EXe7TQu/6pj7MPfNr44OU2LyEmKUb1ESVTS/qGKekpeaUqZGKh6i7V0MS7CXo26q4KyjNoTEREZR7IEh07dlQzFA4aNEjdl07M6tWrsWDBAugCGbMvX7TljZOdNX6b+Bom/7gWP6/Zh7V7A3HpRiT+98kL8HYrXt2se5GOlRTzlCYzDiWmJqrin3vO78Xu83txK/4WrkZfw9UDf2Hxgb/Ue93Auz6aV2+GZlWbonalABgblenHk4iIiEpZdHwSlmw5KoNaYep8GclpQL9GfeBkrZ3+SHnRo3Vd+Ff1wLvT/kBQSBjGL1yNQ2evYMp7T8DasmQXIsuKg5UDejfopVpaRhoOhxzJHYIYvBPh8eHqgqi0v47+DRcbZ7Sr1Q4d/dqrfqKZiX4eMxERaccjjxRIsEpz9UjqXT3xxBMICAhA7dq18eWXX8LCwgLt2rV71LtV4ZgYG2HCW70RUM0TY79ZgaArYeg9YgEWfPgsWtevofXXk+EBjwV0Uk0+AxfCL2D3OQlq7cHxq8eRkZWBQyGHVRNWZlYqDb15tWaqSUCMV+KIiIgKJ7M6p6SkqD6WjY2N3nxn/r7uAFLTM2DvEYvYtEg1FO2l1i+W9W7ppKqezlgx/V2MX7gKf28+gtW7TuL0pVB889FzqFXFHfpMAlMqS9+3Fcb0+hhBYcHYdmY7tpzeiguRFxGREIl/Di9TzcLUQmXzyxDEtjXbFHu2bCIi0n+lFsiSOld3FkBdu3Ytvv/+eyxbtkzdb9WqFebPn48hQ4YgLCwMbdq0wcqVK/UuTVqfPdW5MWp4u+CdKYsQHpOAlyb8jDGvdsdrvVuV2nmQ7fq6+ar2atuXEZ8cj60nt+Nc9DkcunwY526dR1JaEnad262a5qpd06pN1FU4CWx5O3rxc0JERHTbJ598ovpYQmZybt26NWbPno169erp7HskAaxf1uyTy5xw8L6B6DTg8Tpd4OXw4BmPKypzMxNM/6A/mvj7YNx3K1VGfd+R32Dyu33Rv1MjlAfST/T38EMtt5pqxvBM0yzsOr8b28/uwOGQw2oI4uYzW1STwGfDyg3Qwa+9ytbyctSP2mFERFQyBjmSHqOnZNZCTdH4os5aGB4eDldXV725UvmojuVWVDzenroIx89dU/elMySdInNTk0d+LFGJ0aqjcuDSIRy8dBDXoq/f9RwPO3cV2GpStTGa+DRBJQfdKAjKz5hu4nnRTTwvuonnpXT7Io+C7NOsWbOwcOFChISEwNTUVCf7VIs3HsLoBcth4RAD08oH1WN/v7cYtdxr4lHS18/82ZCbeG/aH7gcGqXuP9u1Kca/3gNxsTF6dyxFPS/xKQlqQqFtQTvUbWJaUoHnSAZ/R78OaO/XDrU9A3TmPdDXz1hheCy6iedFN/G8FE9x+iIsQkSKm5Mt/pz8BsZ+sxJLtx7FP1uP4sK1cHw3+nm4OxU+i2RpkboYj9fpqpoIjb2JgxLUkgKglw6q9PKbcWFYdfxf1YSnvYeaETE3uNVE3SciIqqIpPM3YcIELF68GGfPnkX9+vWhi537/63YpZbda9xCdDrUjMaPOoilz/x9PLBq5vv4aP4yVe9UAoMnzl/HpNe7qIBJeWRrYYPu9bqplpGZW5ZCisVLbS2pv3oh/KJq/9v5A5ytndTshxLUkmx+c5OCI0WIiEh/MZBFeST7SmYvlJkNP/9xreoM9R7xNeYMG1gqdbOKSoJSUvhVmiQQytTNkq11JOQIDoUcUbPeSLCrYGDLU2VrNfVpzMAWERFVOOnp6UhKSkKlSpXuWVNLWv6roJoAk7QHkXXkO7ko6xZmy6EgNSzO2DIO0elX1GOvtH7pobdXEiU9lrJkZW6K+SOfRpM1VfDFz+vUTNQvf/4npn/wJLq3qgN99qDzIsMKW1RrrtpH3Uci6GYwtgfvwI7gnQgOO4fIxCj8c2S5ahLEalm9BdrXaou2vm3g+IgnE9Dnz9ideCy6iedFN/G8FE9xfkcykEV31SV4tXcr1KrihvenL0ZETAKeH/8jXuvdGqNe7KpqM5T1/lV1qaraM82fUp0CmdHm8OUjajii3MYkxyI0NhSrjklbXSCw1cSnkU4NRSQiItIWqY0lw5cCAwPV0EKZFdrZ2bnQdadMmYKJEyfe9XhERITaTlE6m5L6L9/DDzNU6uslW9Wtp+8txEl2kZsfvCwqqf1/1Ep6LLqgR7PqqOzcH58sXIdbMYl4f8afeLpTAwzq31pN8KOPintenIwd0b/2E6qFJ4Rjf8hB7A85gBM3TiI1IxXbgrarZgAD+Lv7oYVPc7T0aQ5vB+9Sr7taHj5jGjwW3cTzopt4XoonISGhyOuyRpaeehTjba+Hx2DE3KU4EHhZ3ff1dsWc4U+pjC1dPZZ7BbYKq7HVWGVr5Qa3vBy0Uzye46B1E8+LbuJ50U08L/pbI2vw4MGq4LvMXvjkk0/i119/hZWVVZEzsry9vRETE1PkGlkS9HJxcSn2d/eJc9fxxEffwtAsEbZ+e5GDHMx65ktVrLsslORYdE1kbAKGfPkn9p3OzXKr7+ulMra8XB2gb7R1XhJTE7Hv4n6VqbXr3B7Ep+ZmH2p4O3qjXc22KlurQeUGMDHS/nX+8vQZ47HoJp4X3cTzUjzSF3FwcGCNLCoZ6fT8Mel1fL9yN75ctAnnr4Wj34ffYMTzXfBm3zYwMtK9L2IJRkmhT2mSsSW/PC5FXlYBLZkR8ciVo2oootTY+vfEGtWEq62rqrGlMraqNkFlx9K/OkdERKRN8+bNUy0yMhJfffUVunfvjh07dhT6fWZmZqbaneSP7KL+oS3bLc76Gt+v2qNuPWpEIBE5qOZSVQWxyvIP/Ic9Fl3jbG+DmYP6YPmeIMz6Y4sqE9Fr+ALMHDIQXZr7Q99o47zYWtri8bpdVcvMysTxqyfUEESpqyUTCl2LvoZF+/9QzcbcBm18W6naWq19W8HWQnvB6fLyGRM8Ft3E86KbeF6Krji/Hzm0kO5LglVvP9kObRv6Yuisv3Duajim/rIeWw8HYdbQgTp/hU/+MeQPbEnG1qWIy3n1tY6EHEVUYhTC48Ox9uQ61YSLjbPK2Grs00jV2fJx9mFgi4iI9IIMJ5Ri78uWLVPF3gMCAqArrt2Kxrp9gTAwSUWKyUUgB3ilzUvl4o97XWFoaIB3+7dHkwAffDDjT4THJODNL37DW/3a4sMXu+rtUENtMDYyzs3Gr9oYIx4fhsuRISqgJdlaJ66dREJqAtad2qCa1OBqWLlBXsH4Kk6Vy3r3iYjoNgayqEgCqubOjDPjt434YdUeHDwdgu5D5mHCm73xZMeGehPkkf2s7lpNtaeaDVSBrStRV3DosgS1ZDjiETUrorT1pzaoJpysndC4SkPV8ZEAV3WXanpzzEREVPFkZWWpYYLyPadLfly9F9nZOXCpFob0nCy42bqhR93uZb1b5VLz2lWxds4HGDb7b+w6fgELV+zCkaArmD/yGXi62KOik36cZANKe63tK4hOisHuc7uxI3gX9l7Yh+T0ZNUvlDZzw2z4OFdBh1rtVWCrnnddFRQjIqKywd/AVKxZDce93hOdmtTCyLn/4GZUnKqhJTMPTX63HxxsLfWyEyPZVtIGNu2vOvxXo6/lZmyp4NZRNZ2zZG1tPL1ZNeFgaZ+XsSVDEiXji1eTiYiorEhdCRlOLwGsixcvYtKkSWjUqBFq166tMyclLjEFf206DAOjdOTYhADZwEutXoCJcdlOJFOeOdtb4+fxr2DB0u2YvXgLjgRdRc9hX2HWsIHo2LhWWe+eTnG0ckCfhr1VS89MVwGs7UE7sTN4pypJERJ5BT9H/oqf9/wKOws7tK3ZBh382qFVjZawMiu8Fh0REZUOBrKo2FrXr4H18wZj3HcrsWrnSazdG4jDZ6/gyyED0K6hr16/oxLYktRxaU82fkIFtm7E3Lg9DPGIqrUlnRkpIL/5zBbVhHRoGvk0ROPKDVHNrqoa1sHAFhERPaoglo+PjwpkmZiYqKLt/fv3x/Dhw3XqBCxafxDJqemw8wpDRnaa+u58snG/st6tClEmYvDTndDEvwoGz/wLkbGJeHXSL3hvQHsMf64zjI0q7lDDezE1NlUBKmmje47C+VsX1PBDaaeuByIuJS6v1qqJkQmaVm2iglqSreVu517Wu09EVO4xkEUPxc7aAvNGPIPHmvpj7LcrVf2Flyb8hFd6tsTHL3eDuVn5uLoqgS0vRy/VnmjUVz12Iyb09jDEo+pqnQS6pEOz7ex21YStuS0aV22kOjbNqjZBdRdmbBERUemQWQajo6N1+u1Ny8jEz//uBQyyYOZ6FWlZULUrLc30L5tbX7WqV10NNRz85Z/YH3gZXy/doS5Ezh/xDNycynbGTV3vC9Z091XtzfavIzIhEjvVEMSd2H9xP1Iz0tRQRGlf/DsNfh611BBECWz5efixFAURUSlgIItKpG+7+mjqX0UNMdx36hJ+XrMPu05cwLRBT6orf+VRJQdP1ST1XITFheUGtS4fVrcy+41M7Zw/sKUZiiiBLWlSj4E1toiIqKJYtfOEuuhl4RqKtKwkmJuY4dnmT5f1blU4rg42WDTpdcz9cwvmL9muap72GDYfc4Y/hbYN9Dur/lFxtnFWmYTSUtJTcODSITX8UAJbkYlRCLoZrNq32xeqWbHb12qLdjXbwseqfPaLiYjKAgNZVGJSMHTRpNdUAdfpv23ExesRGPDxd3i+WzOMevFxlb1VnkkKea/6PVSTYR2nL51BSEIIDl85ikOXD6uMrTuHIjpaOarC8ZqMrSpOVRjYIiKicuvQmRBIUSy7SjeQkg01fN/BSrdnPi7PQw2HP99FzWooM1JHxSXhxU9/UrNUj3iuM0xN+OdBUVmYWqjMK2mqDxh6BjuCdmJ78A41HFFmxV5y6B/VzI3N0dq3FTr6t1f1tewtWXCfiOhh8ZuKtELqQb3Rt42qkTV6wXJVTFRqYWw8cBYT3uyFHq3qVJhAjYu1M2pXC0Dvhr3U/dDYmypb69DtJjW2opOisTFwk2rqOTbOqmh8k6pN1K3U6Koo7xcREZV/0z/ojyq+KVi4dxOMDI3wYqsXynqXKjzps6mhhjP/UplZ3y3bif2nLmHeiKdRxcOpwr8/D9MXrutVR7VBnd/D9ZgbuXW1gnaqkhSpmanYcnaravJvoGHlBujg1x4d/dqrEhZERFR0DGSRVtWs7IYlU97CHxsOYdqv6xERk4D3py/GY039MOntPqhUAad79rT3yJsFR1M8/qAahigzIx5WV+siEiKx7tQG1QQDW0REVJ7I99+2i6vVcve63dR3I5U9dyc7LP7sDXz9z3bMWbwVJ85fR4+h8/H5u33xRIeGZb17es3LoRKeb/GsanFJcVh7dB2Ohh7H3gt7kZiWpPqB0r5cP0vNft3RrwM6+LdHgIc/JwwiInoABrKoVK5IvdC9Obo088fE7/9VsxpuORSkamiNeL6LKggvae0VUf7i8VJbQTr2V6OvqSt1hy7nBrYiEiIY2CIionJl9/k9aqiVeKXNS2W9O5SP9Mk+eKoTWtatjiEz/8KNiFgMm70EO4+dVxchbSzN+X6VkI2FDTr6dsDTrZ9CVnYWDoUcxnYZghi0A7fib+FC+EXV/rfzB7jYuKjZDyVTq1m1pmoGRSIiKoiBLCo1MgPO1x89h80Hz2L8d6sQGhmHz35YgxXbj2PKoCdQp5pnhX/3JbAlwwilSb0QTWArt3B8bnDrfoGt3OLxTeHt6MWhiEREpLP+ObxM3UrRa1+3GmW9O1QImaRHhhqO+Xo51uwJxPLtx3E06CrmjXwG9X059E1bTIxN0KpGS9VG9xyFoJtB2Ba0QwW1gsPOqX7f0sP/qGZpaqnWkxpcbXxbs64cEdFtDGRRqevczB8t61bDzEWb8fOavTh18Qb6jvgar/VphWHPdoa5KT+GhQW2+jd5ssiBLZkVR4JaEtyS4vGVHCoxsEVERDpj6sAvsPLYKgR4BpT1rtB9yAQ9X334LNo1PIIJ/1uNK2HR6P/Rtxj5Qle81a8Nh7yVQr/P39Nftfc6vYMbMaGqUPz2sztw5MpRJKcn500WZGhgqOpqtVfF5durviIRUUXFCAI9ElYWZhj/Rk/0a18fHy9YjjOXb+J/K3arYYefvdUHAd4Vr3ZWSQJbMgRRU0BepnqWOltrTqxVTXjYuefNitjERwJbzH4jIqKyY25ijqebPcVToCd9j6e7NEFj/8oY/OVfqs829Zf12H38AmYNHQBXR9uy3sVyS/prmrpa8Snx2HN+L7YH78Tuc7tVXS0JbkmbtWEOqjr7oL1fe3So1Q71vOuqAvJERBUFA1n0SNXz9cKqme/hx1V7MXvxZtwIj8Vrn/+Kzk188dm7T8DDmQGtoga2BtwObF2JupJbPP52jS2ZEVFmRlx9fI1qwtPeMzew5dMYjX0aM7BFRERE91XDyxXLZ7yLab9swI+r92D3iQvoNmQeZg4ZiI5NavHdK2W2FrboXq+bahmZGTh65VhutlbQDjUj9uXIEFzeHYKfd/8CB0t7tK3VVgW1WlZvAUszS54fIirXtB7IWrFiBebNm4eIiAg0adIEU6ZMgbu7e6Hrrlq1CiNGjFB/jGuYm5sjMDBQ27tFOsTYyAhvPdEW3VvVxthvV2HH0XPYfPg89r0/B8Oe64yXe7aEiTGvKhU1sOXj7KPaU00HqH9L0rE5dPnQ7aytI4hJjkVobChWHZOWO2OUzBbV2KeRCmpJcItDEYmIiOhOZibGKqO+TYPqGDl3KaLjk/HqZ7/g1d6t8PHL3dTP6dHU1WpevZlqo7qPVBMnSEBLsrVO3zit+nrSx5MmxeGbV22qsrXa1WoLN1tXniIiKne0+u2zYcMGvP7665g/fz58fX3x008/oVOnTjh+/DhMTe+ecePWrVto3bo1xo4dm/dYYetR+eTt5oifx7+M1btO4rMf/kVEbBI+/3Etlmw5ombJaV67alnvol4Gtqq5VFVNhnBIYEtmwZGg1pGQo2p2xNzA1k2E5svYcrN1U4GtJtKqNkFlR++yPhQiIiLSEZ2a+GH93MEYPmepysz6afVe7Dt5CXOGPwU/n8IvWFPp9fVquvuq9laHNxAeH4Gd53apwNaBSweRnpmOXef3qIbVQICnv5oFUepq1XKvyRqqRFQuaDWQNXv2bNWee+45db9p06Zo1aqVCnD17t37rvUzMzPh5uaGGjU4e01F/jLu1aYuald2wOKtp/DT6n0IvnILT4/5H57o0ACjX+kOVwebst5NvX5/ZXYoac+1eAbZ2dm4FHlZBbQkW+twyFE1FFGmfl57cp1qmlkRG1VphFpONdHJqCN8nKuw40NERFSBSW2sXye8goUrdmPmok0IuhKGPiMW4MMXu+L1Pq1ZCL6szoutiyo3IS05PQX7L+7H9qCdKrgVkxSDM6FnVftm23dwt3ND+1rt0b5WW1VHVbK3iIgqdCBL/kDetm0blixZUuDxfv36YevWrYUGsjIyMpiBRYqVuSnGvNIdAx9rjHHfrcLB0yFq2ufNB89i+HNd8GKP5mpIIpWMoaEharhWV02TsRUiQxFDjqiMrcO3i8fLrIgbAjdiAzZi3o6vVMenWdWmaFatqbp1s3PjqSAiIqqA/Yh3nmyHNg1qYOjMv3DhegQm/7QOWw4FYebQgajkwlqnZcnS1AKd/DuqlpWdhcDrp2/X1dqJSxGXEBZ3C38d/Fs1S1NLtPZtqbK12vi2hoOVQ5nuOxFRmQSyoqKiVH0rG5uC2TOVKlXC0aNHC31OVlYWli9fjrVrc2daa968OT799FOVpVWYtLQ01TTi4+PzgmjSHkTWkT/ci7Kuriuvx+Lr7YrFn72OlTtO4Itf1iMyNhETv/8Xf28+rIYbNvbT7amG9fG8VHGqotqAxrdnRYy6qmbEOXT5CA5eOoTo5GjV8Vl1/F/VRGXHyupKXjOZFbFqEzjqeOdHH8/LvfBYdBPPS8U9L+Xh9wpRcdWp5ol/Zw3CtN82qGGG+wMvq0LwMhN13/b1mcWtA2QWw/qV66k2pMsHuBp1DTuCd6ohiMeuHkdyejI2nd6imqGBIep711NBrfZ+7dSMiJLVT0Skqwxy8ldaL4GQkBA1jDA0NLTA4ytXrsT333+P1atzi0znl5iYiLCwsLx6Wb///rvK6pKaWhIUu9OECRMwceLEux4/d+7cXQG0e3U24+LiYGdnp/fpzxXhWBKS0/C/1fuxdNtJZN/+mPZqFYD3nmgFR1vdnI2lvJ2X2NhYJOQk4OTNUzh+/SRO3DiJhLSEu9at6uiDBl71VavjURvWZtbQJeXtvPBYdA/PS8U9LwkJCahZs6Z6HVtbW+gruTgo71NRj0Pe2/DwcLi6upaL36s8loe36/h5jJz7D25F515glpIRn7/TF/Y2Jeur8byUnrjkOOy5sBfbgnZg7/m9SExLKvBzb0evvCGIDas0gImRCc+LjuO/F93E81J6fRGtBbJklsJq1aqpDl1+Epxas2YNFi9eXKTtPPbYYxg0aBCeeOKJImVkeXt7IyYmpsidLtlPFxeXctHpqijHcubyTYz/bhWOBl9T922tzDHi+S54rmtTGBnp1rGX9/Mij52/dR4HLx/OLSB/5ai6opefXNWr5V4rt3C8T2M0rNIQNuZlG9gq7+dFX/FYdBPPS/FIX8TBwYGBLD3GPzRKLjYhGZ98sxJr9pxS992dbPHl4AFqCCLPi25/xjIyM3D0yjE1BHFH8C7ciLlR4Oc25jZo7dtKBbVkCKJcrNTVYylP56W4eCy6ieel9AJZWhtaKH9USUwsMjISzs7OeY9funQJVapUKfJ2GjVqhCtXrhT6MzMzM9XuJL94ivrLR9Jki7O+Lqsox1KneiUsnfo2lm49hqm/rFNTP3+6cDX+2nQYn77ZS+dmNyzP50Vu/Sv5q/ZymxeRkZWBs6FBagjiwcuHcPzqCaRlpuHszbOq/bZvkQps+XlIYKsxmlRtjEYqsPXoC/iX5/Oiz3gsuonnpejKw79DopKS7KuvPnwGXZr5Y9zCVQiLiscLn/6IV3u3wkcvPg5zs/8yeki3mBiboHn1ZqqN6j4SFyMuYYcUiw/ehRPXTyIhNQHrT21QTYYrNqzcAI08G6Jn4x7wcSn633hERDo7a2HLli2xadMmPPvss3mPbdmyBSNGjCjyNoKCgtCiRQtt7haVA/KHwlOdG+PxFgH48veN+H39QZWpJbMb9mhVB6Nf6QZvN8ey3s0KR1LN63nXVe2N9q8hLSMNgTdOq2ytwyFHcOLaSTUNtGbGnF/3/q4zgS0iIiLSbgC8X4cGaFrbByPmLFF1s6R+1u7jFzB7+FOqrhbp/jnUTAr0ertXEZUYjd3nd6tMrb0X9iElPUX176Qt3Pu9qqXV3q89OtRqp/qCEugiItK7QNY777yD0aNHo0OHDvDw8FDDCqV2Vrdu3R743MzMTMybNw8nT55Ejx49tLlbVI7YWVvgs3f64umuTTHp+3/V7IZr9wZi86EgvNm3Dd4b0B5WFndn7dGjYWZihsY+jVQTRQ1s+Xv4qaCWFJCXK33WZTwUkYiIiB6OzFz4x2ev44dVezDjt404fy0cT3z4DYY92xlvP9FW58pC0L05WTuib8M+qkn/TfpzUix+29kdiEiMwOXIEFzeHYKfd/8CB0t7tK3ZRhWMb1mjBazMrPjWEpF+BLL69++vCrXXqlULVlZWsLS0xLJly2Bqaqp+vn79elUr65dffskr3i7BLs3Y0datW6sMLgsLC23uFpVDclXvr8lvYt3eQHzx83pcD4/BgqXbsWTLEYx66XE82aEBh3voUWDrdOgZ1X7Z85u6mieBLQlqaQJblma6WdyfiIiICs+kf7NfW1Uja9isJQi6Eobpv23AlkNnMXPoQPh4OPFt0zOmxqaqVlbL6i3wWpNXEJsTh53ndqlsrdM3TiMmOTZvhmvJ2Jc+XAe/diqw5W7nXta7T0TljNaKveeXnJysCrB7enoWmLpVAlYyU6GmcJfMiCY1tYyMjFCpUqW8gFdRcYYdFicUqekZ+GHlHhXISk5NV4/V9/XC+Nd7orH/ox27z4J+xSOBrVPXA1VQS4JbJ6+fUoGt/CSwVdszQHWIJGurgQS2TIsX7OZ50U08L7qJ5wWl2hfRVexTsU9VWqSfNmvRZvxv5W5VT9fCzASjX+6GF7o3v+9FR/4u0k2FnZfw+AjsOrcL24N24sClg6pean4yCVCrGi3QrFrTh+rHlRZ+xnQTz0vFPS/xZVHsPT/JxJJ2Jzng/Dtkb2+vGlFJmJua4P2BHTCgUyNM/30j/tl6FCfOX0f/j79Dn3b18PFL3eDpws+ZrmZsSXBK2jsd30JqRipOXjtVILCVmZWpbqX9sOsnGBsaoU6lOuo5kunVwLs+M7aIiIh0uJ825tXueKyZH0bOXYprt2IwfuFqbDxwFtM/eJJ9tHLA1dYF/Zs8qZrU0ZJg1o7gnSpbKyoxCsFhwar9tPsXGBsZo55XXTSv1gzNqjZFXa86quA8EVFxlEogi6gsuDnZYuaQAXi5RwtM+mENDp+9glU7T2Lj/rN4+8m2eOfJdrAwK17WHz1a5ibm6mqdNCGdIQlsSVDrUMhhBF4PRGZ2Fo5fO6Ha9zt/VIGtAM8AFdSSrK0GleuzLgMRVVhyFVMuHNrYcBIN0i0yy/T6uYPxxc/rsGj9Qew+cQGPD56LT9/sjf4dGxYYxUH6y8LUAh2kALxfe5XBIaUjZAZEmd1a9eOyMnH0yjHVvtn2HcxNzNCwcsO8/p+fey0V7CIiuh/+lqByp56vF5ZMeQurd53E1F/WIzQyDnP/3Iq/Nx3Bhy92Rb/29Vk/S486Q5opoUVyegqOXz2OIyFHcfjyEQSGni6QsSVX+mQoop/U2PLJzdhqWKUBrExZcJSIyrdjx45h0KBBCAwMVMO3mjVrpuqSuri4lPWuEeWRCXkmv9sPXZsHYNT8ZbgVHa+ytNbvO40p7/eDiz0DsOWJBNUl40ra+3gXyWnJOHb1OA5eOqQCW2dvBiE1Iw37Lu5XTdiYW6OxT2M09WmC+pXrqcAWM7aI6E4MZFG5JFf1+rSrjy7N/bFw+S58u2wnbkbFYficJWoq6E9e644WdaqV9W5SMUlNhVY1WqqmCWydvHZSDUWU4JbU28rIylBFR6X9vOdXNSui1Gbwd/VDu4A2qnNka8GOMhGVL0uXLsVHH32EXr16qZmgX3nlFQwZMgR//PFHWe8a0V3aN6qJjfOHYML/VmP59uPYfPAsjpy9gsnv9kWP1nX5jpVTMnmPFIyXJuJT4lUfLjewdRgXwy8iITVRzYwoTZgZm8Hf0w/1veuhnnc91PeuCxcbBuiJKjoGsqhck6GEQ555DE91boIZv2/Esm3HcOriDTzzyfcqyDX65e6oVsm5rHeTShDYalG9uWoif40tCWxpisefvXlWtWUnlqsgZy33mmji01i1RlUaws7SjueAiPTa5MmT85Zl8hwJavXu3btM94nofuysLTB72FN4vEUAxny9AtHxyXhv+mL0bXcGE9/qDVsrc76B5ZythS06+XdUTUQmROKQ1Em9dAiHQ47iStQVVTj++NUTqml42nuooFY977oqwCX9OpkpkYgqDgayqELwcLbDrKED8VrvVvj8x7XYH3gZmw6cxbbDwXihW3MMfqYTHG05/Ky81djSzIooNbb2nd+PoFvBqkMUdDNYtd/3/aECWzXdfNUwRAlsya29JScHICL9dv36dbi733vK+7S0NNXyzxQkpKaNtAeRdWQIY1HW1XU8lrIlwwwb1aqMsd+uVAXgV+48gf2Bl/DFu30R4O3Az1gF+vfiaOWIx2t3UU3EJseqfpxcpDx5/SQCb5xBcnoyQmNvqrb+1Ia8rK0AT39VRL52pQA127WHvccD667x375u4nmpuOcluxjbNsiRvdFTnCqaU0U/DPnIbz4YhCk/r8Ol0Ej1mI2VOQYN6ICXe7VUs+s8LE4Xq5s058Xe0R5nQs+qjC1pJ66dULUZ7uTrVkMNQaxTqTb8PGrCx9lHZ6708TOmm3hedJOuTRX9KHXq1AlvvPEGnnvuuUJ/PmHCBEycOPGux8+dO1ekQvHy3soxy7GX1nv7qPBYdIP0z9YfCMLMP3cgMSVdPdatWQ2MeKaT6qfpM37GtCMrOwsh0VdwJuwszoYF4eytIITGhRa6rp25LWq61kRNV191W8vVFw6WDjwveoD/XirueUlISEDNmjWL1KdiIEtP8Y+mksvIzMIfGw5izuItiElIVo95uTrgo5ceR682dR9q9hyeF910r/OSkZmBwBun1TBEmRVR0tZleOKdJIhV3bWaqrVVy91X3dZ0r1kmtbb4GdNNPC+6qaIGsr777jv8+eef2LJlyz2Pu7CMLG9vb8TExBTpOOS9jYiIUMXky0Mgi8eiO0IjY/HxV8ux+8RFdd/dyRZfvNsPHRrXhL7iZ6z0RCfF4JRM+nPtFAJvBKoLlolpSYWu62brqrK1Am5nbfl7+CE1IZW/x3QM/71U3PMSHx8PBweHIvWpOLSQKiwTYyO83LMlnujQEAuWbsdPq/bgengMPvjyT/y4eg/GvtoDjf2rlPVuUimSWXBkVkNpb7R/TRWKP3MjN2Pr2JVjCAoLRkRCpHpcMxwxP097z7zAVi2Pmiq45Wnnofd/1BGR/tqzZw++/PJL7Nq1676/i8zMzFS7kzynqL/D5IJPcdbXZTwW3eHl6ojfJr6G39cdUNnzYVHxeO3zX9G/UyOMe60H7G0soY/4GSsdzjZO6OjfQTXNH9tXo6/dnvjnjGpBYbmzI96KD1dta9D2vOd72nmijldtNSRRAltSWF5qd+kjfsZ0E89L0RWnP8FAFlV4Ukx09MvdVK2s6b9twOpdJ3Es+Br6f/wderaug1EvPo4qHk4V/n2qCCTzSqZ6lga8qh6LSozGubBzCFYtGMFh5xESGaLS20NjQ1XbdntmHU2dhipOleHj4oOqzj7wca6ibqs4VVGz9RARlZYLFy7gpZdewrJly+5bH4tIH/7we75bM9Sp4ojZS/Zg57Hz+GfrUew4eg6fv9MX3VrWLutdJB0lfwhL30taz/o91GOZWZm4FHFZZeHnBrdO4/yt88iUvlxcqGobT2/K24aXQyUV0PL38Fe1t2SZ9VOJdAsDWUS3ebs5YP7IZ/Ba79b4/Mc1OBJ0FWv2BKrCoy92b4EPnuoIB1sGIioaJ2tHtKzRQjUNGX54MfxSXmAr+GYwzt06j6S0JFVMXpal3cndzg0+Tj55QS5NoMvV1vWhhrISEWlERkaiT58++Prrr1G/fn2+MVQueDjZ4qdxL2HZ9uP47Ic1iIxNxDtTF6Fn67pqZkNne+uy3kXSA8ZGxqjp7qvak4375U0IFHzzHA6cO4jrCTdw9mYQLoZfUMGt6zE3VNt0ekuBmRL9PPxyA1u3M7ecrHmhm6isMJBFdIeGtbyxdOrbWLc3ENN+3YArYdFqqOHSrUcwaGBHvNSzRYkKwlP5mB1RzYpTKSDvMUllvxkXprK1pF2WFhGipo6W4YkiLO6WavsvHbhre5KxJUEtlc0lt7eXbcwffR0uItIvmZmZKojVt29fVK9eXWVmCQmQV6tWjYFy0mvyOR74WGO0a+CLsd+tVLNOr9lzCntPXsSEN3uhT7v6/IxTsZmZmKkhha6mLnl1FNMz03H+1gVVZ0sCW2dDz6r7UmJCM1Pi1rPb8rbhYuOSF9TS3LrZuvHzSPQIMJBFdI9OU4/WddG5mT9+X38A8/7aitiEFHzx8zr8unY/PnyxK3q3qVsu6oKQdshnoZKDp2qtfVsV+FlCagJCIq/cDnJdyQtyXY2+qtLdJcMrN7urYA0uIVf7fJwksFVZ3Xo7esPGwAYOTo4wMzTl6SMiVXxV2pIlS1TL/122efNmVKnCeo+k/9ycbLFw9Av4d/cpfLpwFaLjkzFk1t+qJMTn7/aFu5NdWe8i6TlTY9O7LlTKxEAXIi6qoNbZ0CAV5JKsewl6RSREqLbz3K689R0s7VXmlp9HrbzhiTJUkX8zEGkXA1lE92FqYqyGGvbv2AhfS0H41XtVQfghM//Cj6v2YMyr3dG8dlW+h3RfklVV16uOavlJEEuu7knWlgS4/ru9ivD4cLVOVGKUakeuHC34y9vQGJWdvOFze4hiVRmu6FJVBbuszTnUgqgi8fDwwPnzdw9nJipvJDjbu209tKpXDRP+968KYm0+FIQDZ0JUIXjJ3OJQfdL2xEAq28rDD2ic+5hkaMnFSQls5WZuBakJglLSUxCTHIt9F/erpmFtZqUmBpLAlgS4JNAlfTcZ8khED4f/eoiKwM7aAqNf6Y4XurfAl79vxMqdJ3Di/HU8PeZ/6NLcHx+/1A3VvVz4XlLxfgEb5QajpLWt2abAz5LTknEl+mpuYOt2kOtKZO79pPQkZGbnFi6VdidJdc8f3NLU42ItLiIiKg+c7KxVXdNebeph7LcrERGTgFHzl6nA1vjXe8K3sltZ7yKV88mBfN18VevTsHe+2RKv5ga1bgbnBrhuBiE+JR6JaUnqgmT+i5IyOVANtxoqQKbJ3vJ1raGGPBLRgzGQRVTMgvBzRzyN1/u0xuSf1+FA4GVVq2HroWA1u44UhCfSBpnhMO8KYD5ZWVkICglGQk4CrkRfUUMUZaiiXBmU+ltCk+p+8PKhgts0tcydRTFfcEsKz1d29Fbp9ERERPrk8RYBaFGnqioEv3TrUew6fgFdPpiLRrUq4+kuTVRReGtLBgboUc2WKJP4+KB7vW7qsZycHJV5L8MSJbgVdDM3c0tqp8rkQDJ7ojQNI0Mj1TdTmVvuuQGuWh41WS+VqBAMZBE9hHq+Xvjz8zew+WAQpvyyDpduRKraWcu2HcMLXRth0DNdYG1pzveWtE6GTDhZOcLf1Q8tajS/K4srJEqCW5fzgluXpBZX1FWVBp+cnqxqO0jLTzpOlRwqFQhuyW01l6qwtbDlWSQiIp3Omv9yyAA15FACWheuR+Bo8FXVJn7/rwpmPdW5MZr4V+GwQ3rkfTZN/dTOtR/LezwyIfJ2YCs3c0sCXDJLYlZ2Fi6EX1RtNdbkrS99NDUk0T03sCVDE11tXPh5pgqNgSyiEnw5ybDCDo1r4s9NhzBn8RZExSXh25X7sHTHKQx+qiOe6dpU1dkielRZXDIttLTCanFdjryMkIgQXJIgl7q9rFLepeMkwS5pO4J33lVsXhPUkkwudcthikREpGPaN6qJdg19cTT4Gv7edBj/7j6JpNR0LNlyRLVqns4Y2Lkx+ndsCFdHXqShsuNs44w20mq2znssPiVBTfqTP8AlFySlj3Yj5oZqW85szVvfwcoBtdxzg1oS5Krp6gvzbGYfUsXBv7CJSsjE2AgvdGuOx+s7YuXqf3DkVCDi0wyxfOkFbNiwFAO6tkWXVk1gZmUHY1MrGBiUbKZDSVPOjIuFgYkpjMzNYWBkxHNIRa7F1b5WuwKfJSlKqglqyTBF6TRJNldobKj6uabY/OGQIwW2aWVmpdLnq6nsrdsBLpeqamYeFi8lIqKyusjY2K+yauPf6Ik1ewKxZMthHDpzBZdCIzHt1w348vdN6Ni4JgZ2boJOTWqpfhxRWbO1sEHTqk1U05BZrSU7SwJbwbcDXDJjojwekxSD/RcPqJa/7lZN95rwV0MSa6lAl69bDZibcJQIlT9aD2QFBgZi4cKFahroJk2a4P3334e5ubnW1ifSBRlpCYi7GYi40FOIDT2pljNSYlEZQGX5X35ntmDPmdvLBoYwNrOGiZkNTMxtYXz71szaGeY2bjC3cc+9tXWDmbUrDI1MkJ2ZgcRzQYg7cRTxJ48i7sQxZMRG523e0NQMhhYWMLKwhJHcmlvk3jeXx24/LsvWNjBzdoGpqzvMXFxVM7F3hIFhyQJrpN8dfkcrB9Ua+TQs8DOZeUdmT7x8u6C8ZHPJMEUpOi8ZXklpSXfVdsgLmjn+N5ui1OTS1IyQThoREdGjYGVhpoYUSrt4PUJlZf2z7ZgqDC8zHUpztrNC3/YN0DTAB7WrecDL1YHDtUhnSACqTqXaqmlosug1mVuqhQUhNjlO1d06df2UahqGBrm1u3KHJtZUAS5Ztre0L6OjItLBQNbx48fRvn17FYxq2LAhFi1ahA0bNqhW2FS4xV2fqCzkZGchMfLi7YCVBK5OISlKZorLuWtdEwsHGFu6wCAnHWnJcchIjYMhsvNvDJmp8aqlxN144GsbZpkACZlAUhYMUgCD1BwYWKp4GJAGGKQBWelpyE5PU1laxWVgbAxT59yglpmrG0yd3fKWTZxckG5giEwrS5hY2/DfZAVjYWpxe4roWgUelyCW1HHIrb91OV+gK0QFt+Tn95pN0dnaKS+oJUGuKk6VYWNgDSdnJ1UklYiIqDTIzNIfv9wNI1/ogu1HzuHvzUew9XAQIuOS8MOqPaoJGytz+Pu4I6CqBwKqeiKgmgd8vV1hxjIRpCNUQfjbWfD5i8qHxYbhQPAhhKWEqawtyeCS/lp2TjYuRVxSbe3JdXnbcbN1U308b0cvNau1NLfbt1J/i5MAUYUKZE2ZMgXjxo3DyJEj1f0XXngB9erVw65du9CuXbsSr0/0KN06vxVXjyxG3M3TyMpIvuvnBobGsHXzg51nXdh71IOdZz2Y2bir7EJXV1f1h7l8sZy7fBXf/r0O+4+fhqVxhmoNqjqiW7OqcLM1QlrsLSSGXkBK7A1kpMch2zhDUmXUa2QbZQBywcT+3n/kGxgYw9jECsZGljAyMIcRzGCYYwLDLGMYZhoCGQYq4JWTkIqMyFikh0ciKy4R8go5mZlICwtV7V4kHGFgYgITOweY2Nvfvr3d7Oxh4uCYe3v7vpGVdV42mKG5BQNg5YxkXOVmWVVBB7/2eY/LZz08IUIFuNTwRDVM8YpavhkXptaJTIxS7c5hipIKL8Me82dvqWWnKrA2t37kx0hEROWTsZEROjfzVy0iNgHLth7DlkNBOHP5JhJT0pCQlIqDp0NU++85hqjh7YoAFeByg39lZ1T3coSDrQ2MjU1gYGgEAwNpvAhPZUM+e252bmjh0yzvbxCRkJqAc2Hn1UyJucMTz+FixEV10fFW/C3V7kVqcLnaSGDLRQW95FYT5LIxt1H9MytTS1VqQmbF5gVJetQMcuSvDy2xsbHBxYsX1T8gjYkTJyIlJQVTp04t8fp3io+Ph52dHeLi4mBr++CijdnZ2QgPDy/wD/x+MjMzERUVBV0kxyL75uSk/5kMungskcFrcH3/vAKPmVi5wsrFD5bO/urWwqkGDI1Mi3wsZ0Nu4ZsVe3HgzNW8x1pVskLPM+vglf5fNlWO9IOsjGBS3RsmPp4wdLUFbEyQmRmPjKQIpCdHIjPlv6GFD8vA0BSGhqYwgBEMsg2BTADp2UBaJnJS0pGTnA5k5uQmnuWoXxa5y5JglnN3M9A8rg7i9vsBA+QYmyLbxBTZxqbIuX2bbSS3JrmPGxojx9AIOdIBNDBCtgQAZVluYYgcQwPkGMit3JcdN0K2vEeyjuySusntPOZ/+fyPya5npKfD1MwEhqqjmQPD2/1Ng9trGhjkPlst39588ckO3fErVe1A9h2P5a6T87A10jIyYGxiojed5qycTCTnpCApOxnJOclIut1kOTt/xmIhzGAGS0NLWMICVnJrYAlTAxMY5vvPyMAIRjCEgbRH8J7kfm3mqP+yb/9frnhmZGTA6PZVe3lUc1v40t2Paj4Q/32ONf/A8j+eU8gj+df/74H8axbzCJGZmQVjVTdGPz5jDzqWbt3fQ8Om/wVetam4fRFdxT6VbvVDylOfSleOJUd+TydHIT0hDGmJN5GecBPpibeQkRyB7Mx0pKenIS0tDekZacjKTEd2VgYMkQUjgxwYG2bDyCA7r+9Q6P6qDlxunwXSf4GB+k/Th8rJkkcMC/abVMvJawZymyUtGwZZ2bfXvd3/kef+12vJXc4xVB2WbLXd2/0neX2D2/sh/ad8tzn3+Fm2ep6BquOat468xu11c49JdiH3uORvJCMTk9vHefv91dyq72F5fu5uFuxd3Xbn+5h3/9F/58h3t4k6Fv1XlGORfktSThISpWUnIhVpSMuRlg7572H7DkYwhjGkP2YEY7nILksG/z0qvbS8/+Szpv6TXpzmsXzLOQbIzsqGkbGR5l9RPpr7+R+/83OT88B7D14j//27HynyuvLPOSsTRkbG5aFLBTmWbt0H6USfSmsZWdHR0eoLJ39QSlStWhUbN24s8fpCvlyk5T9QIduR9iCyjvwBUpR1hWTW9O7du0jrUvnR0jcbvRrmfkYuhQP7zhviWpQBElIleLT3dnt4tqYOSLbzRaapPfbeSMI+27bwyEqAZVYKkJmKtKwUJKSkIud4CoyOBMIgO/2u33uGBkawNJOC29Jybt/eY9lchonJcwpuIyc7HVnZ6fk2KoPxbze7QjodD00iZJkwRG5Wm1ZLqhoUYz3NRC539rj0jRxLwfip7tN8lB7q75DU202HGDzgM1YelKepYIyBQ4fWon7jtqWy+aL2KSo6CUj07NmzrHeDyjlT4xw4WUs2SQ4cNbdWubcOVpJddf/ny48tZCE3FlUshuqCmOaKX8m/AzUX5P7rsGTlbS7/pjW7+0iVp+87HouWZNxuWlI+Yovl8lgOH1pbaoGsMumqaqJnd5LHYmNjS7y+ZiiiZGzd6bvvvitygXjpcBb1qk5WVhY6dOhQpHWpfLA0BazNc3AiVSUmIR4GcPWFatpkgRyYGmYi3tBMhXgApwc8Q7I1VH7Tf7d5XZv/ujQZMID868n7F5RpACTmLqqLbZrbfMsS4FLX3W7fSkdMk6Gk2XJebtPt52nkz4PKnwhT8O/8uyNGunZBovCYlq7tZUVT2FnJ/QxWWPoafNUBWWnmmD9/fqlsOzVVxwKtRBWMsVEO/D1z0LBKDmq458DoAd18SXSKSwaikwwQkyTLBkjPzH1cWubt27z7Wbm3RtlANWSjeg5gaWSCDEMTpBgZI8XQBKmGRkgzNEaqoTHSbi+nGxoiRd0aIVOyTQyhsrsksyvv1vCO++pWjiF32VCywCTAZpCt+lmarDCpvyr9NWmynvTZ1Pr5+mT5+3b/BdrycrnUQt76t9+b/NnpGpocsNw7+Zbzfn4vd2+LyhtNvywnd0SJpqOiWc7/mHr8Xh2ZovTvHtAJMtBWf4mf2AcxNbWCLtBaIEsCSTIk8E7ymIWFRYnXF6NHj8bw4cMLBMO8vb3x9ttvF3looWRZubi46P3QQsks06Rb68vwIl0+FtmHWycXIfzkInXf1rsVKrf96K6hg9o4ltiN/yL8u7lq2axeI9x64m1ci05EWHQCbkbFq9uwqARExd9dlytXcTKlivub3ADmpiawtjCDpbkJTI0NYWNprh4zNzOBualxXjMzNYGFWs593EyaiTQjNZW1NKkrYSr3jYzUMCXT24/l/jz31ugRDH3Qhc+YtvBYcgvOp2WlIyMzXd2mZ6YhTYaFqOW7H8vIyr1C+F8qu6Swy31JYlc/UT/LHXb6X9q7saERjA2NYWRorJYlLfy/x+64NTBEfFw8nBxlCMztgY6GMtTRUM0YJNuWW7kv6+a+nm4O+ymPnzFfX1+YmpZOKqP0RT7++ONS2XZ5Ip+nNWvWQBdxOB707rzIv+2k8NOIubgZMSE7kZ2RVODnRma2MLV2V7VLTa09bt+6w9TGA6ZWLqrOaZH2IT0diUf2I37HFiQdOaBqi+aS2xQ11M7ExQ0mbu4wdfOAiWqeMHXPXZYZo9XamVm4eSsc9vYOqmeWlZ2NrKwcZOXk3sqxZmXn5D5++2cyZF3zfWRomPsdJbea76vci5H5HpNbKRiRfx3DO9eV7yPc8bPi/Z7nvxfdxPOim8rjealZsybKVSBLhggmJycjISFB1b7SuHr1Kry8vEq8vjAzM1PtTuqXchE/GLm/6Iu2vnR6PTw8oIs0mWVFrfely8r6WFRB9h1z84JY7n7dULfnZzA0MtH6sYSu+DsviOXQrBVqT5sHI7PCswlT0zMQGhGH6+ExuBERixvhMbgeHosbETGIiEnMDQiZGKvgkAoWqeCQsbrNDSblLkuTdSTQJEEpmZFH3Vqaw9rSTN23zXdf1n+YmnK6rKw/Y9rEY9FN/Pei2/9e5Pu8tP7t6/vvlEfF2NgYbm5u0NXPifQPy8t3RHk+luTYGwg9vRqhp9cgJfZa3roGRiZwrdEBnrV7w8G7IUzM/vvborhysrIQe/Qgbm1Yg8gdm5GVlPhfgMzSCs4dusClY2dY+lSHuZuHmgG6KMdiZFR++iHl+TOmr3gsuqk8nhfjIvzOexS0thdyYho1aoTdu3eje/fueY/LDITPPvtsidcnKg1S+DNoywxcPbpY3fes0wd1un2qZqDRttDlf+H8jM/UskPz1qg9de49g1hCMp2qVXJWjYiIiKgiykxLRPj5LQgNXI2Y60cL/My+Un0VvHL36woTc9sSXdRMOBuI8E1rEbF5HdKjIvN+JjM3O7VqB9euveDYqu19+25ERPRoaDWc9uKLL2Ly5Mno1KmTypzav38/9u3bh0WLFmllfSJtB7HObPgc108uU/e9GwyEf5fRauiPtoUu+xPnv/w8L4hVZ+o8GBaSXUhEREREQGzoCVzZ+wtOXd+H7Mz/Jnsyt/VQwSvP2j1h5VilRG9VenQkQpf9pQJYKdeu/PcDAwPYN2oG16494NKhC4xt9HdGUiKi8kirgSypVbVp0yb4+fmhRo0aOHz4MH766ae8+lUSpNqwYQMmTJhQpPWJSkt2diZOr5uA0NP/qvtVGj+PWp1Glko9mBv//IkLMxnEIiIiIipKH+3inu9wad/3ebU+jUws4e7X5fbQwUZauegYtWcHgiePRUZsTN5j1n4BcO3SE66du8HMRTeHwhIRkZYDWUZGRli+fDlOnDiBsLAwNG7cGM7O/w2LkoCVpaVlkdcnKg3ZWRk4+e8Y3ArepO5XbfEafNt+UPpBrBZtUGfKXGZiERERERUiNeEWTq4enTeE0MKpJqo3fwnutR6DkUnhk0EVV3ZaGi4tmIkbS/9Q903sHeDxxNNw69IDlj7VeF6IiPRAqVTqql+/vmp3cnBwUK2o6xNpW3ZmOk6sGoXwC9vV/Rpt3kO1lm+WUhBrMS7MnKyWHVu2Re0v5jCIRURERFSI8As7EbhuPDJSYtUMsnKh0abGk3Bz99BakeSkSxdw9tMPkXTxvLrv0LQlao37AmbOLjwnRER6RDdKzhM9AlkZKTi2YgSiLu9V92u2H4qqzV8pldcK/edPXJz9hVpmEIuIiIjo3pnyMnv0lcO/q/umVk6o13MyHCo3U7Mna4MUc5daWJfmz0B2epqaabDqO0Ph9cxLMNDzmcSIiCoiBrKoQshMT8bRfwYj5tphdd+v88eo0uiZUnmtmLUrEP79V/8FsWQ4oalpqbwWERERkb5KjrmGE6s/QnzYGXXfyacF6vacDDMrJzXVuzZIDazgL8Yjavc2dd+isg/8J06HTa0ArWyfiIgePQayqNzLykzDsWVDbgexDFD78XHwqv9kqbxW6D+LGcQiIiIieoCbZzfg9IZJyEpPgoGBEWq0fQ9Vm7+q1dmjYw7vR9Ck0UiPjFD33Xs/iRpDPoZRvpq9RESkfxjIonItJzsLp/79BNFXD6kgVp0ek1CpTu9Sea2bq5bi4uwpatmxVbvcmljMxCIiIiIqUOohaMsMXD+5TN03t3FHvd5T4ODVUGvvUnZGBkL+9xWuLfpRxhXC2MYWNT/6FC6dHueZICIqBxjIonJL6iGc3TwVt85tVvf9On9UakGs8I1rcW7aRLVs1bAp/D+fxSAWERGViZMnT8Lf3x8mJiY8A6RTEiMvqkl35Fa41uiA2t0nwtTCTmuvkXztCoI+HYWEoNPqvl39xvD7dCrM3T209hpERFS2WN2Qyq2Le77FteNL1LLMTFhaNbEid23D2c9Gqyt+dg2awHPUBAaxiIioTHz//fdqJuhz587xDJBOXVy8fnIF9v36vApiGRiZwO+xj9DgidlaC2LJa4StXYkjrwzIDWIZGcHnzQ9Q/6sfGcQiIipnmJFF5dLVY3/j4t7v1LJX/f6o0ea9UnmdmEP7cGbscCArCzYBdREwfT6iE5NK5bWIiIju548//sBPP/0EPz8/9Uc9kS5lyF879pe6b2nvjfp9psPW3V+rr3N98c+49NVMtWzuUQl+E6bBrm4Drb4GERHpBmZkUbkTFrQRZzfl1qpy9e2EgC5jYGBgoPXXiTt5FIEfDUZORgasatRE3VnfwtjSSuuvQ0REVBQ7duzA2rVrYWXF7yLSHdePL80LYrn7d0PLlxdrPYgVvX83Ln09Wy07d+iMxr8sZRCLiKgcY0YWlStRVw7i5JpP5PofHLwbq+KhBoZGWn+dhOAzODXiPWSnpqhpnOvNWQgTWzutTRVNRERUXN99l5uJXBRpaWmqacTHx6tb+R4ryneZrCOZNuXhe4/HUnribp7C2S3T1LJHQA/U7v6Zuriozc9Y8tUQnBn/oTwBtnUbotan02BoYqJTn01+xnQTz4tu4nmpuOcluxjbZiCLyo34sLM4tmwocrIyYONaEw2fnAMjYzOtv07S5Ys4OfQtZCUlwszdE/Xm/g+mjs5afx0iIqLSMmXKFEycmDtJSX4RERFITU0tUmczLi5OdWoNDfU7wZ/HUjoyUmJxfs0I5GRnwtyhKpwbvKU+X9o8L9IXu/rxYGQlJsDYyQUuw8YgMiYGuoafMd3E86KbeF4q7nlJSEgo8roMZFG5kBRzFUeWvo+sjGRY2FVC4wELYGJmo/XXSbl+FSeHvIHMuFiYOrug/rzvYe7GWXCIiEi/jB49GsOHDy+QkeXt7Q0XFxfY2toWqUMrmTWyfnkIZPFYtP2eZuLokrHISI6EsZk1Gvefo2pjafO85GRl4cyXk5B+4yoMTc1QZ9p82NSsBV3Ez5hu4nnRTTwvFfe8mJubF3ldBrJI76UlRuDI3+8iPTkappaOaPzUNzCzdtH+64SH4cTgN5AeGQFjO3vUm/s9LLwqa/11iIiISpuZmZlqd5LOaVE7qNKhLc76uozHol3ndy5AzLXDarluzy9g7VhF6+fl8sL5iN67Uy3XGvMZ7ALqQJfxM6abeF50E89LxTwvhsXYrv73PKhCy0hLUJlYKXE3YGRqpTKxrBy0H1xKj47CicFvIi0sFEZW1qomllXV6lp/HSIiIiJ9Fha8GSEHf1HL1Vu9Bdca7bT+GuGb1+Hqr/9Ty94vvA7Xrj20/hpERKS7GMgivZWVmaZqYiWEn4OBkQkaPjFb67PgiIz4OJwc9hZSrl6GobkF6s78Gja1ArT+OkRERET6LDHqEgLXjlfLzlVbo3qrt7X/GueCEDx5nFp2bNkWVd8erPXXICIi3cZAFumlnOwsnFw9GjHXjkiSI+r1+gJOVZpp/XUyk5JwasS7SDofDAMTE9SZNg929Rpp/XWIiIiI9FlmWhKOLx9xu16pJ+r2+kLrM0enx0Qj8KMPkJ2WqmaN9p8wDQZG2p+dmoiIdBsDWaR3ZKaEMxsnI/z8VnXfv8touNfqovXXyUpLReBHg5Bw+iRgZISAz2bCoWlLrb8OERGRNjVt2hT29vZ8U+mR9s0C132KpOjLMDQ2Q4N+s2BqYafV18jOzMCZT4Yj7dZNVeahztR5MLZ58MQERERU/rDYO+mdywd+xPWTy9SypKxXbviU1l8jOyMDZ8YMQ9zRQ1LVDv7jpsC5XSetvw4REZG2ffPNN3xT6ZEKOfQrbp3brJYDun4CWzc/rb/GxTnTEHf8cG6/bMI0WPpU0/prEBGRfmBGFumV8As7cH7nV2rZq35/VG/9jtZfQ6ZzDpr4MaL37VL3a370KYuIEhERERUi6sohnNsxVy17NxiISnX6aP19Cl2xBKHL/lTLVd8eAqfW7XkuiIgqMK1nZEVHR2Pp0qWIiIhAkyZN8Pjjj99z3cDAQPz1118qHVlDpoIeNy63gCNRfomRF1RdLCAHDt5N4N/5YzUFqDblZGcjeMqniNi6Qd2vPuQjePQZwBNBREREdIfUhFs4uXqUdKBg51EXfp0+1Pp7FHfiCC7MnKyWXR7rBu8XX+d5ICKq4LSakXX9+nXUq1cP//77rwpoDRo0CO+999491z9w4AA2b94Mc3PzvCaBLKI7pafE4eiyYXkFRBv0nQFDIxOtvlESUL0wZypurV2h7vu8OQheT7/Ik0FERER0h+zMdBxfMRLpyTEwtXRAg35fwtDYVKvvU0bELZz9ZDhysjJhXdMftT75TOsXMYmIqIJnZH322WcYOHAgZs+ere6PGTNGBbbefvtt1K9f/671MzIy0Lp1a4wdO1abu0HlTHZ2Jk6sGoWU2GswMrFAwyfnqg6Ttl3+dg5Cl/6hlr2efxWVX9H+lNFERERE5UHQ1hmIu3kKMDBEvd7TYG7jptXtZ6Wm4Ma0CciIjYGJvSNqT50HI3MLrb4GERHpJ60GspYtW6ayrDScnJzw3HPPYeXKlYUGsjIzM5mBRQ8UvG0moq/kfq7q9pwMGxdfrb9rV35ZiGu//aCWPZ94GtXeG84rfkRERESFuBG4CteOL1HLNdsNhlOVZlrPkj8/dQLSLp2HgZExAr6YDXN3D54LIiLSbiArPj4eCQkJqFat4Awi/v7+2LlzZ6HPkYysCxcuYMaMGep+8+bN0a5du3u+Rlpammr5X1NkZ2er9iCyjnwxFmVdXVdRjuXGqRW4emSxWq7W6h241Oig9WO+sWQRQr6bp5ZdH++FasNGq/3JX7utqCrKedE3PBbdxPOim3heiv9+EVUk8beCcGZjbs0qt5qd4dPsZa2/xvU/f0XE5nVqufqw0bBv0Fjrr0FERBUskHXjxo0CUzv36NEDnp6ecHC4e7iXZGVFRUUVuh0JWiUlJang1K1btzBv3jz06tXrntNGT5kyBRMnTrzrcSksn5qaWqTOZlxcnPrj3NBQvydsrAjHkhR+Ghc3faGW7aq0gXX1PggPD9fqa8dtWYewBTPVsnWLNrB/4wNEREY+9PYqwnnRRzwW3cTzopt4XopHLuIRVRSZ6Smq3EN2ZhqsHKuiTveJWs9gjzlyEJe+nqWW7Tp3h0e/gVrdPhERVdBAlnxhSWF2DSMjI5iamqoMqztJkEp+VpjGjRurlj9QJTW19u/fjxYtWty1/ujRozF8+PACGVne3t5wcXGBra1tkTrnsu+yfnn4w7w8H0tK/E2c3TkZOdmZsHapicZ9p8HIVLt1ESK2bEDYN7n13Byat0bAF3NgeI/PalGV9/Oir3gsuonnRTfxvBRP/v4QUUUo95AccxWGxmZo0G8GjM2stLr91Fs3cXbcSCArCzb+deD65gda3T4REVXgQJZkX91ZoF2CWJK9IYGr/DMP3rx5E+7u7kXarrW1tcrIOnLkSKGBLNluYbMayh/ZRf1DW/4wL876uqy8HktWRgpOrBiB9ORoVdS90ZNzYGKu3Y5S1J7tCJ40Wv5ig12DJqg9ZQ6MtPTHSHk9L/qOx6KbeF50E89L0ZWH349ERRF+fjuun1iqlmt2GAZr5xpafeOy09JwZvRQZMRGw8TBEf6TZyFOuxOsExFROaG1bwcTExP4+fnh0KFDBR4/ePBgoYXe7yU5OZkF4CswGcYWuG4CEsKDYGBojAZ9Z8LCzlOrrxFz+ABO357KWa721ZnxFWfBISIiIrqHtMRIBK7PLe/hXLU1Kjd8WvvF3Wd+joSg0zLUAwGfz4SZa9EuhBMRUcWj1cscAwYMwNy5c/PuX7lyBWvXrkXv3r2L9Hwp/L5q1Sp06dJFm7tFeuTy/h8QFrRBLft3/hgO3o20uv24U8cR+NEg5KSnw6q6L+rO/g7GVtZafQ0iIiKicnWRcf0EZKTEwMTCAXW6T9B6XaybK5cg7N/larn6oJGwb9hUq9snIqLyRWuzFooRI0agdevW6Nq1K2rXro0lS5aoIYgeHrnT5Z47dw6HDx/Gc889p+6vXr0aBw4cUPU4rl69io0bN2Lq1KmoUqWKNneL9ET4hR04v2uBWvZu+DS8GwzQ6vYTzwXh1Ih3kZ2SAgvvKqg3938wsbXT6msQERERlSfXji9B5KXdarl2t3Ews3bR+kXGC7NyJ/dx7doTlZ56QavbJyKi8kergSypcbVv3z4sX74cYWFhWLp0aYFaV1JHQoYgalhaWqoiqVIsvnv37pg2bRoqVaqkzV0iPZEaG4IL6z6R635wrNwUfp1GanX7ySGXcHLoW8hKTICZmwfqzf0epo7OWn0NIiIiovIkMeoygrflziBYqd4TcPPtpNXtp0dF4oyUe8jMhJVvLdT8WPvZXkREVP5oNZClCU49//zzhf6sRo0aqmk89thjqlHFlp4Si8tbJyIrIxkWdpVQv890GBr9F/AsqZTrV3Fi8BuqeKipkzPqz/8B5u65WYJEREREdLfsrAyc+ncMsjNTYWnvDb9OH2r1bcrOzMCZsSOQHhkOYxtb1P5CJt7R7gzVRERUPnEqECpT2dmZOLX6Y6Qn3oSRiQUaPjlHzVSozWmcJYilOkl29qg353+w8Kqste0TERERlUcX9nyD+FtnYWBghLq9voCxqaVWt39p3gzEnTgi06TCf+J0WFTy1ur2iYio/GIgi8rU+Z1fIfrqQbVcp8fnsHHx1Wq6+snBbyAtLBRGVtaoN2ehKvBORERERPcWfe0ILu//SS1Xa/UW7D3ravXturVuFW4s/UMt+7z1ARxbtOHpICKiImMgi8pMWPBmhBz8WS271XsOrr4dtbbtjLhYnBzyJlKuXYGhhQXqzvoGNrUCtLZ9IiIiovIoIy0Bp9aMVXVL7T3roVrL17W6/YTgszg3baJadm7/GCq/9KZWt09EROUfA1lUZsVDA9d9qpadfFrBrV7hddUeRmZiAk4OewtJl87DwNQUdaZ9Bbu6DbW2fSIiIqLy6uymqUiNl5IPlqjbczIMDY21eqHxzJihyE5Pg2WVqqg1djKLuxMRUbExkEWPXGZ6Mo6vGIGs9CRY2HmqTpKBoZFWtp2VnIxTI95FYtAZGBgbq8KhDk2aa2XbREREROXZzbPrcfPMGrXs33kULB20V7cqJysLZ8d/iNSbN2BkaYXaU+bC2Mpaa9snIqKKg4EseqRycnJwet0EJEVdgqGRKRr0nQkTCzutbDsrLRWBH32A+FPHAUND+E+cAadW7bSybSIiIqLyLCX+Js5s/Fwtu9Z8DJ51+mp1+5cXzkPMoX1q2W/cZFj6VNPq9omIqOJgIIseqStHFiEseKNa9u8yBrbu/lrZbnZGBs6MGYbYIwfU7Dd+YyfDpWMXrWybiIiIqDzLycnGqTXjkJmWCDMrF9R+fJxWh/xFbNuEa7/9oJalJpZz+85a2zYREVU8DGTRIxNz7SjObZutlr3qPQmvev20st2czEyc/XQUovftUvd9PxwPt269tbJtIiIiovIu5NBviLl2WC3X6TERphb2Wtt20uWLCJ78iVp2aN4aPm8O0tq2iYioYtJe9Uai+0hLjMDxVaOQk5MFW/cA+HX+SCvvV052NoImj0Xk9k3qfvUhH8Gz30CeCyIiqpDD9w8dOoSIiAg0bNgQnp6eZb1LpAfibwXh/M75arly4+fgXLWVVifgOT16iKphau7pBf+J02FgpJ26qEREVHExI4tKXXZWBo6v/BDpSZEwsbBHg75fwsjYTCsd9vMzPkP4hn/VfZ+3PoDX0y9qYY+JiIj0S0JCAtq0aYNnnnkGs2bNQkBAABYsWFDWu0U6LisjFSf/HYOc7ExYO1dHzXaDtbZtdbFx0hikXA2BoZk5ak+ZAxNb7dRFJSKiio0ZWVTqgrfPRuyN4wAMUK/3FDVToTaCWBfnTcfNlUvUfe+X3kCVV97Wwt4SERHpn6lTp8LV1RU7d+6EkZERzpw5g7Zt26Jnz57w8fEp690jHXVh99dqAh4DIxPU7fUFjEzMtbbtq7/8D1G7t6nlmqMnwtrXT2vbJiKiio0ZWVSqbp5Zh6tH/lDLNdq+B2efllrZbsjC+bjx129qudLA51H17SFa2S4REZE+Wrx4McaPH6+CWEIysvr3749ly5aV9a6RjooNPYWQw7+r5Rpt3oOtay2tbTtq3y6EfP+VWq709Itw69pTa9smIiJiRhaVmoSICzi9YaJadqneHtVavK6V7V75+Ttc/WWhWnbv3R/Vh36s1Zl1iIiI9ElKSgquXbuGBg0aFHi8SZMmOHjwYKHPSUtLU00jPj5e3WZnZ6v2ILKOZEcXZV1dVxGPJTszHYHrPpXxf7B1C0Dlxs9r7fhTrl9F0KejJH0edg2awOfdoQ+17Yp4XvQBj0U38bzoJp6X4inO70gGsqhUZKQl4PiKEar2goW9N+r2/AwGBiVPALz66/cqG0u4du2JmqPGM4hFREQVmhR3d3R0vOv70MXFRf2sMFOmTMHEibkXm/L77rvvYG5uXuQOp6Fh+Ujur2jHkpkah6y0qgCqwTTaHQcXfKOdF8/JQUZ4GHLqt1JF3U3cPLDjm28fenMV7bzoCx6LbuJ50U08L0WXmppa5HUZyCKtkytOgWvHIznmCgyNzdGw30yYmNuWeLvX/vgJl7+do5ZdHnscfmMnc+YbIiKq8OSPY/nuLU7nefTo0Rg+fHiBjCxvb2+8/fbbsLV98He2bFuCZBIs0/c/zivascgshQd/fxE55lmo1uodVG/1llZeWz6DwRM+QsT29TAwMUG9r36Cbe16D729inZe9AWPRTfxvOgmnpfikb7Ixx9/XKR1Gcgirbt88GeEn88t7ln78XGwca1Z4m1e/+s3XPpqplp27tAFfp9OhYExP75EREROTk6IiYlBVlZWXo0sER4eDmdn50LfIDMzM9XuJH9kF/UPbckAK876uqyiHIvMJH1mw0Tk5GTB2qUmqrd8XWvHfG3xL4jYsl4t+w7/BPZ1Cw51fRgV5bzoGx6LbuJ50U08L0VXnN+P+v+blHRK1JUDOL8zd+hf5UbPwLN2yYt73lj6By7OnaaWndp1gv+k6TA0NinxdomIiMoDCwsLlU0VGBhY4PETJ06oou9E+S82JoQHw8DACHW6T4ChkXb6UzFHDuLS17PUskefAfDoO4BvOhERlRoGskhr0hIjcXL1x6pwqL1nPdTqOKLE2wxd9icuzPpCLTu16YCAz2YyiEVERHSHXr164eeff867n5CQgOXLl6NHjx58r0hJjLyAi3tzJ8vxaf4y7Ny1E+RMvXUTZ8eNBLKyYBNQFzWGj+E7TkREparUxmZt2rQJbdq0UVcJHzSe/tChQ2rceMOGDeHp6Vlau0SlKCcnG6fWjkN6cgxMLOxRv++MEl/lu7lyKc5/+bladmzZFgGfz4KhCTOxiIiI7jRq1Cg0btwYJiYmqF27Nr799lv06dMHtWrV4ptFyMnOQuC6CcjJyoCVY1VUb/W2Vt6V7LQ0nBk9FBmx0TBxcETtL2bD0NSU7zgREelfRtbkyZPRtWtXXLp06b7rydVCCXY988wzmDVrlkp/X7BgQWnsEpWyK4cXISpkn1qu030izG3cSrS9m/8uw7lpE9SyQ/PWqP3FHHaMiIiI7sHLywsHDhxQFwg3b96MV155Rc1ASKTpp8XdlKGnBmpIoZHx3fXRiks+a+dnfo6EoNOAkRECPp8JM1d3vuFERKR/GVlz587F7t27UadOnUJn0Mlv6tSpcHV1xc6dO1Vx0jNnzqBt27bo2bMnfHx8tL1rVEpk9ptzO+epZe+GT8O1RvsSbS9s3Uqcm/KpWrZv2gK1p86FYSEFaYmIiOg/0neaMWMG3xIqICn6Cs7vzr1QXKXJ87CvVF8r79DNlUsQ9u9ytVx90EjYN2zKd56IiPQzI0tmyJGaDIXNhHOnxYsXY/z48Xkz7EhGVv/+/bFs2TJt7xaVkqyMFJxcPVqlqls7V0etDsNKtL1bG/5F8Odj5TIf7Bs1Q51p82FkZq61/SUiIiKqSKUfTq+fiOzMNFjYe8O37fta2W584Im8GqauXXui0lMvaGW7REREZZKRJcMKiyIlJQXXrl1DgwYFp+Zt0qQJDh48WOhz0tLSVNOIj49Xt9nZ2ao9iKwjWWJFWVfX6cqxnN36JZKiL8PQyBR1en4BAyPTYu+T5lhubVqHc5+NUUEs2waNETBtHgxMzcr8GPXxvGgDj0U38bzoJp6XinteysPveyq/rh37GzHXj6rlOt3Gw8jk/rVriyI9KhKnxwxDTmYmrHxroebHE9T08kRERDodyIqJicGWLVvy7kswqkaNGsXahhR3d3R0vOuLz8XFRf2sMFOmTMHEiRML3VZqamqROptxcXGqU2toqN8TNurCscRd3YMbJ/5Ry+6NXkdKjh1SwsMf6lhubV6P+IVz5A4s/GrD7cMJiEpIBKTpEV04L9rCY9FNPC+6ieel4p4XqfdJpItS4m7g3I65atm7wUA4Vi750L/szAycGTsC6ZHhMLaxVTVMjcxLHhwjIiIq9UBWVFQU/vzzz7z75ubmxQ5kSYeysBpa0um8V2dz9OjRGD58eIGMLG9vbxX8srW1feBryrYlcCbrl4cgQ1keS2rCLZzZn1sXy7lqGwS0e+Ohr8ZFbNuUF8SyqVMfdWZ9C2NLK+ijsj4v2sRj0U08L7qJ56XinhfpAxHpGuljn14/SZWAMLf1QM32Q7Wy3UtfzUTciSOAgQH8J06HRSVvrWyXiIio1ANZErRaunQpSsLJyUlldmVlZeXVyNLU2HJ2di70OVJ3q7DaW9I5LWoHVTq0xVlfl5XVscgUzqfXjUdGahxMrZxQp8ekAuewOKL27kTwxI9yg1gBdVFv9ncwtrKGPuNnTDfxvOgmnpey8/777+Pdd99Vk7No1K1bV2UY3fndbGVlhV9//RWNGzeGvintz1h56E9Q+RMauBJRVw6o5dqPj4OxWckvEIZvXIsbf/+uln3eGATHFm1KvE0iIqKHUWa9LwsLC5VNFRgoUwH/58SJE6roO+muywd/QfTVQ2q5bo/PYGbl+FDbiT16EGdu11gwq14TdWZ+o/dBLCKikrhw4QJeffVV1K5dW81AN3bs2AI/Dw0NxRNPPIHp06c/cFtPPfWU2oamVatWDV98kVucWdy4cQOJiQWHb69fvx5bt25Vk67I7aZNm9R2WrZsifr1tTPTGRGVrozkSJzbNkstV6rbF85VW5V4m4kXghE8ZbxadmrTEZVffrPE2yQiItKZYu/F0atXL/z888+YPXu2ui9XgWXGw507d5blbtF9xN0MxIXdX6vlKk1efOjOUfzpkwgcNQjZ6WmwrFodnuOnqFoLREQVVXR0NB577DEMGTIEo0aNUhd87O3t835+9OhR9bP27dur7OUHWbBgAZKSkvLuHzlyRGVV3U+lSpXUcLzMzEysWLECc+bMwVtvvVWkwBkR6caQwuv75yMzPRFmVi6o1XFEibeZmRCP06OHIjstFRbeVeA3/gsYMBORiIgqSkaWDCU8duxY3n3pqP/xxx/q9pdffkHXrl3Rp08f1KpV61HuFhVRZloSTq4ejZzsTNi41kLNdh881HuXeD4Ip4a/g6zkZFh4VUbd2QthZGPH80BEFdquXbtURrLUgvT391dZVPkDWfXq1VMZUvmHAt6P1IXKn5EVFhamhg7eiwSvNm/erAJXPXr0UEP5x4wZg6+//hqdO3fG6tWr1ZBDItJdYWfXIf567pDCgK6fwMS8ZBcJc7KzcXbSaKTeuAZDcwtV3N3Y2kZLe0tERKRjGVndunW7q9ZVUFCQ6oQ3bNhQ3ffy8sKBAwfUVWPpPL/yyit4802mKuuqs1umITn2GgyNzVGv9xQYGpsWexvJVy7j5JC31NU9Mzd31Jv7PUydXaQ4WqnsMxHRvYI2MnGJtkgWk2zvXrPjSV1IY+P7f+VKkOnkyZM4c+ZMoUPs5fkP2sb99m/hwoVYtGjRPdeRbDDZ/5dfflkNaaxcubI6Fvlu/vHHH/Hhhx9i7dq1+Oabbx5qH4iodKUlRSN46wy17Ob3OFx9O5R4m1d+/g7Re3ao5VpjJsGqum+Jt0lERKSzgazPP//8rsekxoa0/OQq8YwZuV+6pLtunl2P0MBVatmv00hYO1Ur9jZSb97AySFvICM2GiaOTiqIZe7hqf7AIiJ6lCTo1LNnz0f2emvWrIGbm9t915EaVr///jt69+6tgkkSOJLhhdogtbGk7tb9srkkSCVF3eV3cv6hizIr33vvvadaamqqVvaHiLQvaMs0NRGPkZkd/DqNKvH2ZEKeKz/klpPweuZluHburoW9JCIi0vMaWaQfUuJu4MzG3MCkq28neNXvX+xtpEWE48QHryMt/JaqhVVv7v9gWdmnFPaWiEh/dezYUdXCmjBhAho1aqSKrssww5IGsaQ21v79+++bjXXx4sW8+3fOWpjf66+/jnHjxpVon4hIu8LPb0dY0Aa1XKnZOzC1dCjR9lKuX0XQxI+l6BbsGjZBtfeGaWlPiYiISo6BLLqv7OxMnPz3E2SmJcLM2hW1u41XU5kXR3pMNE4OfROpoddhZGmFurO/g3X1mnzniajMyFA/yZLS9tBC2e69hhYWlZ2dnZoERepSyYyBp06deqh9OnfunKq3FRISgi1btsDR8d4zzEomWFpaWoFjkcc8PT3x9NNPF1jXwaFkfyATkXZlpCXgzKbJatm5WlvY+5RsSGFWagpOjxmqykCYurgh4LMvYfCQw5qJiIhKA7+V6L4u7fsBsTeOAzBA3Z6TYWrxX+HhopBO0KlhbyP58kUYmpmjzowFsA24d7FhIqJHQWpNPWioX3FI8EeC/K6uroUGsh6GDH189tlnER8fD1vb4hVsllpX7dq1U8//+++/YWlped/1PTw8cPnyZUyaNAk//PCDWl8KzUsATkoAEJXUjVMrVXOs0hw1Wr/NN1SLzm2fg7TECBiZWsG/yxjEpxTvguOdvzvOTZ2ApAvnVPCq9uRZMHUsWPOWiIiorDGQRfcUc/0YLu79Ti1Xbf4qnKo0Lda7lZWSjFMj30PiubO5naEpc2HfsAnfcSKiIgTGZs6cqWbxLW4QS0hQ7dKlSw8MYOWXlJSEa9euFdhGcTNwie75+Yq5gpjrR5GVmcZAlhZFXz2E6yf+Ucu12g+FuY0b4lMefgKd0KV/IHxjbrZqjWGjYVunvtb2lYiISFsYyKJCZWWk4tTa8TLvMmzda6NGm3eL9U5lp6Uh8KPBiD91HDAygv+kGXBs0ZrvNhHRPezZswfPP/+8Wo6JiUGLFi1Ujaw7FTXAVJwgVmGGDRsGExMTni/SCoObmeo2Puw00lNii53hTXfLykjB6fWT1LKDV2N4NegvJa0eWtyJo7g4L3cCJvee/eDR7ym+7UREpJMYyKJCXdy3ECmx12BobIZ6vb6AoVHR/5jJzszAmXEjEHs4t7Cw35jP4NKhC99pIqL7aNKkCbZv3543RPFeMxb269cPPXr0KPF7eWdAzNnZGfv27VOzJxZW7F3WlVpb8nOi4jKKMQAycgATA0SFHICH/+N8E0vowp5vkSx9NSPT2zVMDZGT83AzQadFRuDM2OHIycqEda0A1Bg5lhmZRESksxjIorskRJxHyMFf1XL1lm/CyrFKkd+lnKwsBE0ag6jd29V935Fj4da9D99lIqIHMDMzK1I9KllPWklJLSypg6Xh7u6uhhbGxsYWWrheAlleXl4lfl2qmByatILhop+Q7WGAiPM7GMgqobibpxFy6De1XL3Nu8Xqq90pOyNDBbHSoyJhbGuH2l/MhpGZeUl3kYiIqNQwkEUFyJW8Mxs+Q052Jqydq8On2cvFKhB6/svPEbF5nbpf7f3h8HzyGb7DREQ6qLBZDOUxCW7JsERtFq4nsqvbAEbRRsj2yEHkxT2qz8AabA8nOysDgesnqvIPNq5+8Gn6Yok+YBfnz0D8yWMSrYb/xOkw96jEDywREek09lCpgGvHlyI29KRaDnh8XLGGFF777XvcXLlELVd+9R14P/8a310iIiKCoZkZ7BwD1DuRkRGHpKiLfFce0uWDPyMx4hwMDIxQp/sEGBo+/HXpW+tXqwLvourbg+HYnPVMiYhI9zGQRXlSE8Jxfsc8tezdYCAcKjUo8rsTvmU9Ln87Vy179B0Inzfe5ztLREREeZwbtIdBUm418sjL+/jOPITEqMu4uHehWvZp/jJs3fwe+n1MCD6Lc1MnqGWndp3g/eIbPCdERKQXGMiiPEFbpyMzPRGmVs7wbfdBkd+Z+MATCPpsjFp2aNoSNUaM4XABIiIiKsChSQsYhucGssLP5dbSpOKVfzi9fiJysjJg6VAF1Vu9/dBvX0ZcLM6MGYrs9DRYVK4Kv3FfsO9GRER6g4EsUsIv7MSt4M1q2f+xUTAxty3SO5MSeh2Boz5ATno6LKvWQMDkWTA05nTtREREVJBNrQAYx+VOVBAbegJZGal8i4rh2rG/EXvjuFqu0+1TGBk/3KQPMjHP2fEfIvXmDRhZWqL21DkwtrLmuSAiIr3BYu+EzPRknN30hXonXKq1hVutLkV6VzIT4hE48j1kxEbDxMERdb9cAGNrG76jREQ6QIppX716Vd3eSYpse3h4wNTUtMDjV65cQVZWlpq1MDk5Oa/Y+73WL0xiYqJaz8jICAkJCQVmRhRxcXEwNzcvMPOi7KO8prOz80Mfrxxrdna2Wrazs4ODg8Nd68TExKjX15Djk+MyMcm9ABMdHa32V5+L3Mt7+fvvv6Nfv36wsdGt72QDY2M4ejfGzex9yDHMRMz1o3Cu2qqsd0svpMSF4pym/EPDp+Dg3eiht3V54TzEHMod2imZWFY+1bW2n0RERI8CA1mEC7sWIDUhDEYm5vDvMrpIqeXZmRk4/clwJIdcgqGpGepM/4qz3BARaUlERASSkpIKDchERkYWKeAj63Xo0KHQQFZGRgaaNm2KFStW5D0mgSTN+hLMkkCURlpaGpo1a4aVK1eq+xIwksCRhqwrASFjY2NMmzYNderUgY+PD7766iv89ttvap3Tp0/j5ZdfxqVLl5CZmYnevXvju+++g7W1tXrslVdewa5dux7q/ZIAlOy7JpDVp08fzJuX+0d/fp999hmWLVuWd18Cbd26dcOiRYvU/ddffx0ff/wxmjdvDn01dOhQdeynTp1S50HXODZqjbAj+5DjnFsni4GsB5N/k2c2TkZWRjLMbdxQs93gh37/I7ZtwrXfflDLlV96E87tOz/0toiIiMqK/l5yJK2ICzuDK0cXq+Uabd6HhZ1nkTpU52d8jtjD+9V9v/FTYFu7Hs8IEVEJSWCpatWqKgAhgZmZM2fm/eybb76Bm5sb/Pz84OnpiTVr1tx3Wy4uLrh8+TJCQkJUO3jwYN7ykSNHEBYWVmB9Jycntb4ElQ4dOqRu8z9XgkUa586dU/unabVr18bUqVPzviPyN41nnnkGH374odqOBNnkWEaOHJn3nNTUVPVaEsS7M8MrPj7+nscZHh6ufr5161Zs375dtREjRqjA3J1mzZqVd0zS5s6di/T09Lz7EjwsLPCnLz755BN13A0bNoSusm/SHEbhuQHHiPM7y3p39MLNM2sReXmPWg7o+gmMzR5uGGDS5YsInvyJWnZo3ho+bw7S6n4SERE9KszIqsCyszNxev0kICcbtm7+qNz42SI97/ofPyFs9T9queo7Q+HSqWsp7ykRUfl34cIFvPPOOypA1bhx4wI/k0wjCdRIgKly5cpqecCAASoYVZThfpJR1b59e5w9e1bdl+F1ku1VVDLMMP8wNQmmSeBH44UXXlAZW/cSGhqq9vPpp59W92V58uTJKmj37bffqsckg0iCYm3atFFD4zR++OEHFfiSbKrCvPbaawgMDCzwmAS25H3asmXLfY9r8eLFOHr0qHpdTVBMn0lwcNKkSTqdUWZVzRemKbbIRBKS464gNeGWyjKiwqUlRSNoy3S17OHfHS7V2z3UW5WZmIDTo4cgKzkZ5p5e8J84HQb5si6JiIj0CQNZFdjVI4uREB4EGBgi4PFxMDR88MchYvsmXPp6tlp27/0kvF98/RHsKRGRduVkZiI9OlJr25NAU0ZkFNKQXWh9JVNHZ1Uf6H5k6F2DBg3uCmIJ2eaSJUvy7nfq1EllXF2/fh3VqlV74P7dmR1148YNeHl5oagki0qCJIWRoNGBAwcwZ84cFdzKX4NKw9LSErGxsWofNMPXZZv5h03WrVtXHaOVldV99z0/CTwNHz4cwcHBOHbsmAoGnjlzBo0aNcLbb7+NixcvqvepMPJa8p5LUNDCwkI99tZbb0GfDR5c9CFnEtyUpqHJepPPsmaI5v3IOnJeirLunRxqtkBy2mbAzAARl/aiUt2+KEslOZbSdnbLNGSkxsHEwh41O4584D4Wdiw52dlqdumUqyEwNDOH/+TZMLK20cnj1ZfzUlw8Ft3E86KbeF4q7nkpzrYZyKrARUMv7F6glqs0fg527gEPfE78mVMImjha/qqAfePm8P1wHKdqJiK9JEGs/f20Xxvm0j0eb7FiM8xc3e/7XMmYGjVqFH7++WdVL+p+JBCVkpKi6lAV5ubNmwWCFLIsdbE0WVSShSSBKbkvwSTJuNKsL52IO4u9Hz9+XGVwadbPn80lmVKyv19//TV+/PFHFbBq3bp1gf2RAur16tVTwaX33ntPBbukFtWbb76Zt07+jCwZRqkZGigBL1lfs++yLU0BeXmvNmzYoIJ/UhdLAnxS92rTpk0qG0uGUEqGUpUqVe4KYg0aNEjtc48ePfICZfqQkSX7+Pfff+fdb9u2LerXr1/s7UyZMgUTJ06863EZ2inDPB9EPidyXuS9K25xfOOaATA8tAnZXga4EbQNJm4tUZZKciylKe7aPtwK2qCWPZq8jdjEDCAxvNjHErXkd0Tt2qaW3d4ZhmQ7ByTrwWddV8/Lw+Cx6CaeF93E81Jxz0tCQkLZB7LkKrEUfs1fLPZOUotC1st/pVWGGxTl6jI9PHm/z26aoqa9Nrf1QI027z3wOak3QxE4ahCy01JhWaUqAibPgqFx7ixPRERUchKc2bZtG9544w38+uuvqmD3vYp1S22psWPH3rMjIUP9JBvpTpohdBp//PGHKnAudaXyr39nsff8Nbxk/XHjxqn7e/bsUcXTfX19sX79eowfP17tV2FkuKAEvWQooGRovfrqq3mBLJnFUGbZ+/PPP9V9CTBNnz49L1NIOk+rV69W9+U5UgtKgnVPPfWUahqaQJQEw6RpgnLS8ZIAnBSol32UANfGjRtVAKh///55z5d90HUSwAwKCsq7L/XJHsbo0aNVNpuGvM/e3t4qg83W1vaBz5f3VrLrZP3idmhtOnTGjTVzkO0FJN08ChdnJxgYlt0wt5IcS2nJSEtA0LJv1LJztTao2eypok3Gc8exRO/bhcg/f1E/8xz4PKoPLFoZCV2gi+flYfFYdBPPi27ieam458Xc3LxsA1lLly5VRV1PnjyJgIB7Z/rIFVHpRLm6uuY9JtNxnzhxojR2i267FbwJEZdyZ4by7zwaxqaWD6yrEPjh+8iIjoKJvSPqfPk1TGyLXluFiEjXyFA/yZLS5pd7ZGQUnJ2d7jm0sCikiPvatWvV7IAyq58UJ3/iiScKrCPZSpJddb9hcHfWhpILGFJnSr5jCwtSaAJTt27dUh0UCQjJd7PmWHKPL7LA97XUpXr22Wexbt06Ve9q4cKF990nGTI4bNgwVQdM48qVK+pWOkYSvNOQrC1pQoYsymt//vnnDwzWSdBKgmSaoYKaoJwEvySAJduU91X2tbDaYjLcUmZR1GWSXSazQZaUfBak3UnOeVE7qHLeirO+hqVXZVjkuCIBUchMT0RiRDDsPMp2hsWHPZbScmHnPKQlhsPI1AoBXcfe98LwvY4lLfQ6giflZtLbNWiC6h+M1Jnj09fzUhI8Ft3E86KbeF4q5nkxLMZ2tR7Ikg74F198oYY7PGiMo8wUJMMRpKNOj0ZGajzO3i4a6larM1xrtHtgHZkz40Yi6dJ5GJiaova0ebCo5P2I9paIqHRIvaoHDfUrDvm+M4EhzPIFf0qib9++KltIhujJsmab8h0rmU0ydK4orl27poYryvMkuCNDxiSgJFlRn376KYzz1e2SrKXOnTsXejFJZjOU7+tdu3Ivgkjw6sUXX1QBIckIktn/ZHifBOLuRwJKcpHrTjKsUY5VLoQV1Z3BOk2fQt4vydKSzpacF01QTvz7778qo0mKzxdG3pN71dQi7ZFz41SvJRLjViPHzgCRl/eVeSBLl0RdOYTrJ3In1anVfigsbIv/uyorJVkVd89MiIepixsCPv+SmfRERFRuaD2U9ssvv6gOrqZ+xf3IFeXCrgZS6Tm/cz7SkyJhbGoNv06j7ruuXME/P/sLxBzInfLZb+xk2NVtwNNDRPQIyPD8mJgYVatK7N+/Xw2pk2wtyTp6EHme1N2SAJEMqZMZDqV+lQwjPHfu3F3ZU/I7X4JBUotKAmByq2l3lgGQYXkSUHvyySfVfQn+SEaVFH2/n1WrVhXYrqbt3LlT7V9hpB6Xo6PjPbcpfY6WLVvCyclJBdqGDh2q+iADBw4sMLOixubNm9UQy8KazHSYf9gelR77Ji1gGJH7mYq8nNvPICArIwWnN0xSb4WDV2N4Nfhv6GtRqf7b1AlIungeBiYmqP3F7CJnhRIREekDrWdk/fXXX0VeNzMzEyYmJqrOhabTrqsz7OiahzmW2BsncO147qxXNdp9AFMr5/s+/8bfv+Hm8tyCslXeGATnTo+XyntX0c+LruKx6Cael/J7XiSIpMkUkoLbU6dOVfWbJGgls/I999xz+OGHH9R3n+b7z93d/Z71BC5duqTqQmmG6Gn2rXr16mooYM+ePQvsryxL5pUUTL+zRpZ8X+fPtJ45c2aBbYp27dqpJsMUNd/LRX1PNEGywtZ9+eWX7/kzCbjJUEUpVSAZYZoaQpJ5JhfWJDNLamvlf64ct7TCyDBOKS5fnPNYHn7flwWZNMbw6xxk1QBiQ08iMy0Rxma6PazzUbiw+xukxF6DobEZancbDwOD4l9zjvn3H0RsWa+WfYd/Atva9UphT4mIiPQskCXBJOnsakhHuigZWHeSq6fSKZfZd6Quh42NjRqm0K1bN52bYUfXFPdYsrMycH7dBLVs6eIPU4+2952dKSX4DK5+lfuHim3HrjDr3rfUZnOqyOdFl/FYdBPPS/k9LwcPHsT777+fV0jz8ccfV7P8ye9eKaQuWcwvvfTSXUPhevXqVej2JIglwSjJTBowYIDKNpJgmdS2kmLqUssy/+91CY5JjajFixerWWPkO1lzLFLLSoqvF+V7QCZykW1JNpl8NxflOVLbSvatuN8z8p7Iey9DJ6WOlvQr5DGZ1XHHjh1qZkbJQivqeZH+jex3cfajODPsPCpSP0z6ZrrMzNkF1tZVEJN1DTDKRtTVg3Dz7YSKLO5mIEIO/66Wa7R+F1aOBWfbLIrYowcR8ctCtezeuz88+g7Q+n4SERHpZSBLrgznnyVIptB+0FTh9+poSdNY83/27gO8yaqLA/i/e+8NhQKlUPbeIoiiCIITQUVF5BNFUbYgILJlCQq4FRygKCqgMlRQ9t6bsimF7tI9k+85t6Z2k0LSJu3/9zyBN8k786bJzXnPPff339V9qc9RvXp1kxphx9SU9Vgu7lmG9ITLsLC0RrOeU+DsU3IDNysxEZcWzpKNwLleAzSZNBOWxRTFNZSqfF5MGY/FNPG8VN7zIgGpkoJSw4YNU7eykmCOXCBaunSpyl6S7K3Q0FCVUVX4opF035PgV79+/YpkZMmxSXZY/mLvJaldu7YKmklQTKb1WUaCT5Ipps+8+cn8coxLlixRxeAlACVF3GW7PXv2VCMsSmBK3/Miy0m7oiz7UZYRdsqLdK80Bx6tOuLmte+h8bNA7MVdVTqQJRccj2+YAmg1cPVriKA2/7WP9ZURdQOn3x6j2m8uDRojZNQEo+wrERGRWQaymjZtapQaEpLq/+CDD6oCroWvOlf0CDumSN9jyUiJxcU9X6jpWm2ehatfvRLnlavW52ZPRkbkdVg5OqHh9PmwLodGelU8L+aAx2KaeF5MkymeFynw/sYbb6jbrUgA6MCBAwUKpN/OseTf1rx58/RaRoJHkgl2O+rUqZPX1bGkAKO+50VGhCwrUzrf5sajVVtcOfQdNH5SJ2snqrILu79EcnSYuuDY6MHJsLQsWxNdk5mJE2+NQFZCPKxc3dFgxntGvQhJRERUkUyu9SVXTeXqKRnOuW1LkJOZAlsnL9RpP7jUeSN+/h4xW3JHgqo3djIcAmvyVBAREZHBubVoA8uY3LpmaTevISX+SpV8lZNjzuHCrs/UdO12L8DVt36Z13Fu4SwknTwGWFmh2uiJBh2VlYiIyNSYVCBLuhZs3rwZLVq0qOhdqTSSos4i/NhqNR1y16uwtnMqcd7ks6dx/oM5atq/92Pwvb9nue0nERERVS02rm5wDQgF0nKL/cdWwawsrSYHx9dPgVaTDSevOgju8L8yr+PGb7/g+urcwXxqvzwcjo05wjQREVVu5RrIkpobUnRVRwq8SxfFkydPqkK2UhukWrVqatQjunPSTfD03/NVvQUX33qo3uThks9NaipOvj0a2qwsONYORt0R43kKiIiIyKg8WrWHVVRuICvm4q4q92pfPvgdbl4/Jp2T0bjHZFhal607YNLpEzg7b5qa9rn3AVTvX7Q0BxERUWVjtEBWSEgInJ0LDqP8559/YsSIEXn3v/zySzzyyCNqRKX3339fBbJkmGwyjOjz2xB3eY+arn/PaFhY/le4t7CwedOQduUSLG3t0HDqPFjZO/A0EBERkVG5t24Hy38DWXFX9qmi51VFakI4zm1brKZrtnoK7tWblWn5rJsJqi6WNjNTXYSsP36aqglHRERU2d1WsXd9FFe0VUZIyj9KkoxCKDcyPGkInv3nPTXtU7crvILaljjvjXVrELkhN4AomVhOwSE8JURERGR0bk1bwCrOCllaLXKyUpFw7Qg8a7auElnzJzZMRU5WOhzcqiGk82tlWz4nB6feHoOMGxFqcJ5GMxfCytFRDXBARERU2ZlUjSwynKuHf0RK3CU1+k39rv9lwRWWeukCwuZNV9M+9/aAf5/HeRqIiCqxY8eOITw8XHX137WrYFcu6fK/Zs0abN26Fenp6XmPp6WlqazqwuLi4rBu3Tr89ttvavpW8//4449YtmyZusl2bjU/VX5WDo5wq98cFv+O8xNzqWrUybp2bDXiruxV0w0fmARrW8cyLX/ps8WI35f79xs6aQYcg2obZT+JiIiqVEYWVZzMtJs4t+NjNV2zRT84eQYVO19ORrqqi6VJT4N9tUDUG/cOU9KJiCrYhQsXcODAAaSkpKB+/fro0KGDevyHH35Aamoq7OzsEBwcjLZtS860TUxMxM8//5x338rKCp07d0atWrWwcuVKNG7cWE0vWbIkb/0y/9ChQ9X92NhYTJgwQdWvlG1du3YNU6ZMQffu3fPW+c8//+Dpp59GmzZtYGlpiSFDhuCLL75QmdfFzS+2b9+Omzdvqml/f388/HBu7caS5qeqwb1VW8TsPIgcTwvESp2su19HZZaeFIUzUsMUQPUmj8C7Vu7foL5itm7Gla9zRzms8eyL8O5yn1H2k4iIyFQxkFUJnd/5CbLTE2Fj74bgTkNKnO/CB3ORcu4sLKyt0XDaPFg7FaxpRkRE5Wvs2LFYsWIFOnXqBAcHB1hbW+cFmrZt24akpCSVKSWBLhkcZf369XB0dCw2kCWBJh2Z/+rVq3jrrbdKHIzljTfewI4dO1C7dm1ERUWp5SWwtXHjRjWPPCaZVIGBgbjvvvvw9ttvq+BX+/bt1fPnzp3DY489VqCEgM7+/ftx/PjxIqMSy/pke9WrV7/DV47MmUfr9rD65UPk1LdEYuQpZKbGwdbRE5W1S+GpP2ciOyMZtk7eqH/PyDItn3rlEk5Py/07dm/THrVfqtxBPyIiouIwkFXJpMRewtVDP6hpCWLZ2LsWO1/0338i4peVarrO0JFwadC4XPeTiIgK2r17twoMyWi+hQdLEYsWLcqbljo4EjRau3Yt+vfvX2ReCTZJkEhHMp169uxZ4ksuQS4JJkn2la7GzuOPP47XX//vR7JkUklwS7K5JJAl92vUqJH3vK+vb162VWFhYWEFAmv5SXCOgayqzaVhE1il2wNZWYCNBWIu7Ua1hiW/X81Z5Jk/EXUu92+hYfe3SmynlTTC9Inxw5GTkgw7vwA0nDIXFlYlD+RDRERUWTGQVcmc+WcBtJpsOHnWQo3mfYudJy0iHGdmva2mPTt1QfV+z5bzXhIRVazsnBxExycbbH0S/ImNT0KOpZ3qZleYj4czrG/xg1OCQH5+fsUGsQqTbbi7u8PW1lavrorx8fHIzs5Wwa2jR4+qYFR+0s3v8uXLKqjk5OSUl0UVFPRf13Q3Nzd07dpVBcnEyy+/rLKvXnzxRXX8S5cuVdM6+TO4nnrqKXWTLos7d+5U80smlxyvLpuLqi5LGxu4N22NjOid0FST7oU7K2UgKzMtAaf+eldN+9W/D371upUpk0vabqkXz8HC1haNZi6AjbuHEfeWiIjIdDGQVYnEXt6D6PNb1HS9riNhaWVTZB5NVpYa5SYnOQl2vn4InTiDdbGIqMqRIFaHF2eX2/Z2ffEmArzdSp3n3nvvVVlXAwcOxMKFC1WgKj8J/nz11VcqQCS1piRA9Oijj95y28OHD1dBJF1WlASsCrO3t1eBqS5duuDZZ59FRESE6uIowamSMrJeeeUVhISE4N1330VGRgZmzZqFhx56qMT5//rrL3VsUttLukwOGzYMM2bMUNsrrusiVS0erdsh+rcd0FSTgu+7VeDGwsIClcmZzfNUt0lre1c0uHdcmZa9tvIbRG/aoKZDRk1gJj0REVVpDGRVElpNDk5vzi0c6hXUDj7BnYud7+In7yPp5DG5nI/Qd+bAxq3gDyUiIqoYEtxZvXo13nvvPTRt2lTVoBo8eHCBOlZSJys6OlplWUnQ6FY/9KWYuxSN//bbb9UIhRLQmjhxYrHzTp48Ge3atVM1sSSIIPW3ZD+Eh4cHRo4ciTfffFPd37JlCy5evKimAwICVN2umJiYvO6MdevWVUGu/N0bZV9k1EJdzS8p8C7dHXWBrMKBL6pa3Fu1g+WXWjWdmRKD5OgwuPjWQ2URfWE7Ik78pqZDu42BnbO33ssmHNqH80ty23gBfZ5AQG+OME1ERFUbA1mVRPix1UiOPgtYWKJ+t9HF/riJ3bUN4Styf1TUevFVuDdvVQF7SkRU8aSrn2RJGbRrYWwMvLy8S+xaqG8wSwq+DxgwAM8884wKFknWkrCxscGXX36ppmX0QsmeatWqFVq3bl3sur7++mssWLBAdRH84IMPVGbWxx/njmhbEgmWNWnSRHUxlOUOHjyoHpfvlEceeSRvPincvm/fvrwREaU7Yv4aWK6uRev+yP5KMEyCaXKcUt9LHtMpHPiiqsU5JBS21m7ISk6G1tkCMRd3VppAlowmfWLDVDXtVbsjqjX6L3PxVjKiI3Fy4ij541RZWHVHFj9gAxERUVXCQFYlICPfnNu2RE0HNn0ULj4hRebJiI7CGd0oN63aoeZz/13lJyKqaqRe1a26+pU1kGWlyYCvt1uxgayy0o1IKN3sJDPLzs6uwPMyUmHv3r1VhlZxgayZM2fio48+wp9//qm6KI4fPx4dO3ZU3f9KI8EryfaSDCvpbqi7KCI1tiSrS9YnXn31VfX/sWPH1PrzF30XUgurcN0rCaTJCIUffvgh4uLiVCH5p59++jZfIapsLKTuW8u2SIvahBwJZF3ahdrtBsLcSXbjyT+mIyM5Cta2zmh0/0S9u0xKOYgTb41EVnycqofVcOYCWOpRF4+IiKiyYyCrEriw+0tVc8HK1gl17xpabCPq7LuTkZUQDxt3T4ROfpej3BARmTgJBqWlpZUYOJPufS+99FKxz0sATOpo6Yq1SzaXjIgo3Qul2HtJpHuhrFvqVckohLqgnASlpL5VYcuXL1ddASWDLD8vLy+VWaYjQTnpOijrlqCXBOYk++ubb77J64pIJBfaIpf/hZw6QHz4QeRkpcHKxsGsX5jrJ39XIxWKBt3Hw8Gtmt7Lnn9/NpJOHFHlIBpMnQd7vwAj7ikREZH5YCCrFFkZSbh+ch2cvevCs4ZpdsNLuxmBy/u/VdN1OgyGnZNXkXmiNvyKuF3b1HT9CVNh5+1T7vtJRESli4yMVAEfIXWwPvvsM0yfPl0FfaSLoQSuhASEpNaUjCL4xBNPFLuu5557rshjEkCSW2mBLGORAJquppZ0RfT09CzQFVE3UiJVbVLw3XKhFtBooUUW4q4egE+du2CuUhOu4eSfuVmQ/g16oFqjXnove2PdGkT8/L2arv3ycPXaEBERUS4GskpxfP07iDq7CT7BXUw2kBW29X1ocjLVFb6gVkW7aGTGxuDcwtyhnn3v7wWvTl0rYC+JiOhWpFi6BHek25Gfn58aoVBXGP369et5z0nh9TfeeEMFsSp6VDepdfX3338jOzu7xJEYJXg2dWpufaCSFO6GSFWTQ81asPPwRVZsDDQ+uXWyzDWQJYPwHPt9AnIyU2Dv4o+G3fWvbZV0+gTOzpmipr27dkeNZ14w4p4SERGZHwaySlG98cMqkBV9YRvSkyJh7+IHU5ISdSIvXb1el+Gwsi5YQ0WEzZ+B7KRE2Hh4ou6I8RWwl0REpI9GjRqVWOxc6lvJzRBkZMLq1avD29sb3bp102sZyaB6+OGHizz+5JNPqkLy+bOr8pNuh4XrZ5Vl/VS1qEBt63ZIPf8bND5A7KXdMFcX9nyJhGuH5ajQpNc02NgXHQChOFIG4sT44dBmZsKxdjDqT5he4QFrIiIiU8NAVincvBrCxtoFWdlJCD+6GnU7DYGp0Go1uLbvEzXtHtgCfvW7F5knevMfiPknN9AVMmoCbNzcy30/iYjItEiReJ1BgwbptYwEmsaMGVPk8aZNm+Lzzz+/430qaf1U9bi3bo/ru34FGgEpsReQlngDDq7+MCc3rx/H+R25bbRabZ+HZ802ei2nzc7GybfHICPyOqycnNFo5kJYs9stERFREXc+tFIldn7+LGiO31TT146tVmnipuLGqfVIiz2rpkPvGVXkal3WzQSEzZ+upr273gfve+6vkP0kIiIi0pd7q7awkKZXhlbdj72406xevOzMNBz9bQK0mmy4+NZHSDGD8JTk4icfIGF/bhZa6Nuz4BhU24h7SkREZL4YyCqFb/eesL6sAbRAeuJ1NRS0KcjOTEXYtsVqOqBhL7gFNC4yj9TFkuGarV1cETJK/6GeiYiIiCqKjMznWCMIllG5gayYS+YVyDrz93ykxl+GpbUdmj40E5bWtnotF715I64u/1JN13zhZXh3vsfIe0pERGS+GMgqhWfHu2Fj4wbLSI26H37kJ5iC8zs+RkZSpGok1e38WpHnY3dsQdTG39R08PA3YevlXQF7SUREROXpzJkzWLRoERYvXowLFy6YdVaW1b+BrNhLe0wqI740Uee2IPzIKjVdr+sIOHsH67VcyvkwnJ4xUU17duiMWi/qn8VFRERUFTGQVdqLY2MDn3sfgNWl3MZU9LmtSE+KQkVKjDyNy/uXq2m/Zs8VKUCfnZyEs3Om5jWG/Hr0qZD9JCIiovIzf/58PPDAAzh16hSOHTuG5s2b488/c+tkmhuP1u3zMrKyM5JUzSlTl5ESixMb3lHT3rU7oWaLfnotJwPynHhrODRpabCvXgOhk9+FhSWb50RERKXhN+Ut+D3wECwjtUCaFlptDq4dX4OKIlckT2ycqvZD6i74NHikyDwXlryHzOhIWDk6IWTs2+xSSEREVAVI4OrEiRP48MMP8cknn2DBggV4++23YY7cWrSBRQZgkZAbzDq/6zNotbnTpkj27fj6ychMjYeNgwcaP/iOXu0vrUaDU1PHI+3qZVjaO6DRu+/DxtWtXPaZiIjInBk0kBUZGYkhQ4YgKCgItWrVwvDhw5Genl7qMkuWLEG9evXg4eGB7t274/Tp0zAlrk1awCEgEFZXchtQ4Ud+ViMGVoQrh35A4o2TaijnBt0nwsLSqsDz8ft34/qaH9V0nddGqToTRERExfn0009x9erVEl+cZcuWISwszKAv3oYNG9TohO+8847qBqcTFxenMoro9t17771wyjfCXdu2bXH58mWzfEltPTzhFFIf1qdy21sxF7bjxumNMFVXD/+o9lE06jEJds4+ei13eenHiNuxRU3XHz8FzsH1jLqfRERElYW1IVf21ltvITg4GH///TdycnLwwgsvYNy4cVi4cGGx869cuRKzZ8/Gt99+i5CQECxdulSlxZ88ebJAY6wiyRU13wceQurKj5FTz1IVfY+9tBvetTuW636kJ0UibOsiNV2zZX+4BTRCVNR/3Rxz0lJx9t3clHa3lm0Q0OeJct0/IiK6c1lZWfjhhx+wd+9epKSk4O6778Zzzz1XZL4vvvgCqampGDZsWInrmjt3boFgkaWlJR555BH07NlT3V+3bh2aNm2KGjVqFFn22rVrGD16NAYMGIAPPvig2PUnJydjwoQJaj+LExAQgGnTpuXdnzx5Mn799Ve1zsTERHXxSr73JQAjgaxffvkFo0aNusUrRPqStliXLl1KfD4jI0PddOScCI1Go263IvNIJpI+894O95btkLLyDCxT3JHhlIBTf82GR822sHVwN/i27uRYUmIv4szf76np6k0ehU9wV73WI/VML3/xYe5y/Z6D9709DPJaGvu8lCcei2nieTFNPC+mieelbMry3WXQQNZnn32mGso6cnX16aefLjGQJenv8pw01HWBMGl4/f7773jyySdhSt0Lryz9GJZRGmj8LHH1yE/lHsiSBlxOVirsnH0R0vnVYodsTo8Ih6WdPeqPm8L6CkREZhjEkuCOXMjp0aMHHBwcUL9+/SLzSf0j6Trm6upaaiCrcePGKttZR4p/Sxa0LpBVkp07d6rs6hUrVqgLTXJBSoJQsj/5SZBs06ZNKvu6OJ6ennnTEjD5/PPP1TLOzs7qsd69e+PFF1/E4cOH1f1z585h8ODBaNSoEUaMGFHqPlZlEmT86KOP8u7L+ezYsWCbRAKD8+bNUxlwJZk1axamTJlS5PHo6OhbZtPrGps3b95UQZP8bT+D+Tc7yWJHAqweckZWWjyObpiFmp0MH+y83WPR5GTh3Po3oclOh61LNXg0fq7ARcaSZEaE4/KUcWrasXFzOD3xjF7L6bVPxj4v5YjHYpp4XkwTz4tp4nkpm6SkpIoJZBX+wpSGq729fbHzSsbWjh07sHFjwVTxXr16Ydu2bcUGsirq6qF9YE24NGiMhEsnoPGTou9bkJYYqXfq+J2KOvcPosI2q+nQe8fC0saxwLHcPHoI137MLQBfa8gw2FULNKsrcYxUmyaeF9PE81J5z4sEhSSAsHnz5iLrzj8tQZ4ZM2aojObSticZzoWzoOUHrm4Z3f7q7q9atUoFuhwdHdX/d911lwqsyQh4bdq0QadOnVTgrGHDhnnf45KFPWjQoBL3QbduCb5I5pesW/eYZIPduHEjbx9cXFxUdziZz1DfYeXx91Le37eSKZ6/bWVlVbDMgByvBARffvnlvHNVnPHjx2PkyJEF2lTy2vv4+KggqT7HLfsi8xsjYOLZ9V5cmz0ZSM1GjVoP49LZ5Yg//ydqt3wMXkHtDLqt2z2WsG2LkBZ3DhYWVmjeZxbcAoJuuUxOaioOz58GTWoK7Hz90WTWQtWV0lCMfV7KE4/FNPG8mCaeF9PE81I2JcWOjB7IKuzjjz9WjaniyBU/abQW3ll/f38V4DK1q4cOHbsg8cvjqvio1i4bZ/d8B78m/WFskoV15o+Zato1sD20rrldCnXHkpORgasyZLNWC/v6DWHd+T6DXdUrL4xUmyaeF9PE82IYOZocxKXGG/S8JCUlIioputjvF09HD1gVqmtY3DpiYmJw6dIlFfApjmQ1SRazfHdmZmaW6fNeuvFJl3/dMnJhKD4+Pu++BC/mzJmjghny/SKPy7H069cPjz32mOruKMEr3fyS9SPr0Gcf7OzscOXKFezevRt16tTJC5xJ9pUsHxsbq7a5ZcsWVTezRYsWqIxXDw2hWrVqmDhxYonPy3M2NjYqUHWrcyK3wuR10ve1koBJWeYvC1tnF7g1bYGbh/Yj/Z+z8GzfFnFX9uLUnzPQ6YUfYWVTMEPwTpX1WOKvHsSlPcvUdJ2OL8GjerNbLiPvw7DZk5F68TwsbG3RcOYC2Ht5w9CMeV7KG4/FNPG8mCaeF9PE86K/snxv3VYga//+/epKrY40fF9//fUC80g6u3QXkIZzcSTwVFzETR4rKShVkVcP3R/pi6hlH8PykgY59S1x88KfaNztNVhYGLeRcObvechKjVENtqY9J8HB1a/AsaT8/B0yr12FhY0NGk2aCccA8yvwzki1aeJ5MU08L4YReTMSAz5+HuVlw4jf4OvmW+o80p3w+eefV0GjRYsWqQyo/KQw+59//om//vpLFWG3tbWFr2/p69SR7GcJPEm2s240NQliSNdDWYeMbnf9+vW8H9u67+jiRl6TLC1Zjywn3RylTlZxJBj3/vvv592XzK4nnngCHTp0UMEl6eq4fv16tR75Pndzc1M1neR7Xd/jMrerh8YmtdOkRINk9ekzap6pCxo4BEcP7UfC3l0IeWQaEiKOIC0hHOd2fIz6XSuu+2lWRhKO/i7vey3cqjVFnQ4v6rVc+HfLEL0ptydCyOiJcG3YxMh7SkREVDndViCrVatWSEhIyLsvV/7yk8bpK6+8ooq6Fne1T0jgSdc1MD9p3JYUlKrIq4dyxcyzXSfEHN6qAllpN68h4ep+eNVqD2O5eeMkrhz8Xk3X7fwqnNyrF3g+40IYrn3/lZoOGvQKnOvUhblipNo08byYJp6XO1femQr6fu9MmjQJ3bp1w2uvvYZmzZqpLn66wU+kFpXUPZLvXN269FmnZDlJ9z8JZuXvhpb/fSQZUDLa8H/ZZUlYvXo1WrdurbKm8pOMKllG9k/2R1fsXS5cSddA3fxSUyv//kmAToJzknUtQS7JLNNlngUGBqqi8g8++CDM7e/FVLJe5PzK+ZDyDKYUXLsTHm06wKNdJ8Tv2YGIpd8i+JUhCNv6AS7v+xYBoT3g6t+g3PcpOzMVh34argb/sbJxRNNeM2BpeevmdPz+Pbjw4QI1HfDokwh46LFy2FsiIqLKydoQ9Rnyk24Kffr0UVdepdBsSaQIrDTGIyIiVJq8jhR7rV27NkyRFH2P27kVltGAxgeq6LuxAlkaTTZObpwGaDVw9WugRios8HxWFm4snidFSuBcrwFqPPOCUfaDiKgy8nL2wh+j1hlsfapbYGwsvL28ig1syPb0JcGeAwcOqCLrMmDKmjVr8OOPP6pMJalVpa/s7Gz1XTx9+nQsX75c1aQqyeOPP65GQXzjjTfUsUhGlnTbP3XqlApqyfe+1LCUUQ91JCj21FNP5d3fvn27qsslmWWlDQojXSd1XQt1dOun2yMXF/v27atew/wD7MjrKpns+Yv+m5s6Q0fgwN6dSAk7g8C4AXDxrY+kqDM4vmEK2j/3rV5BJEMGsQ6ueg3x4Qfl1UWjHpPh6FF01M/CUi6dx8kJI+SDAi6NmqHuG7mF3omIiOj2GPTbX2plSCN36NChejVIO3furIb/zl9HS7oZSC0sU+TV+R5YOTrB6mIqND5WiAr7GxkpsbBz0v8Hir4kEysx8hRgYYmGD0wq0lC7+u0XyLh8ARZW1qj/1jRYWhfMiiMiopJZW1nDzy23q7bBurBlWKjug4bI0JF1SLd9CUBIxpNk2kjGlO77UrKXJcgk96dOnVrggpCue6BkTMkFI+lq1qTJrbswWVtbo3379qoOlgSvpJuf7lik26F0d8wfyLodEkyTWpiFyUUtCcDc6fqrKgkqvvnmm+q8F5eNZs6cQ0Lh92AfRK5bg0ufLUHD9+dhz8pBSIo6rTKzarcbWC77kZ2RggM/vYaE8EOqbdak5zQENCg4oEJxMmKicWzkK8hOSoSdrx8azVwAS1vbctlnIiKiysqggayBAweiXbt2KpClD7nyK8tIVwQZWUca6mlpabj33nthiqzsHeDdtTturF8NyxxraJCNiONrUbudYbOh0hKv49y2JWo6qOVTcPMvOOpQ0qnjuPrVp2o6cMAgONcLNej2iYio4u3atUvVwZIMaCngHRkZmfecjPZ34sQJFXhydnYusqwEMD766CNVz1LfQIZsS0YXvHz5cpEaWZLxI4Gu/F0gdTW1dHbu3Kn2K3+mlbj//vvzRiKWjO3iSDb2H3/8odd+UlEyeE5Jtcoqg1r/ew1Rf61HRuQNJG07iFqtB+DSvq9VrSy/evfqlRV1J7IzknFg1atIuHYkL4hVrVEvvUYoPD7mVWTciICVkzMaz/8Idj6Gqf9GRERUlRkskCVFaL///ntVw0rqXOhII1iuGsuVZMm+km4Fv/zyi3ruvvvuU41z6YogIzVJg1y6UJjy1UO/Hr0RuW41LC5kAiGWCD/yM2q1fd5gRd/lauqpP99FTlYa7F38UPeugkHB7OQknJw0GtrsbNgG1UbN518yyHaJiKhinTlzBnPnzlXT0q1PRglcsWKFyrbp3bt3gXlPnz6Nf/75p8SRgYXUoCoLuZD04YcfqgwvyciS4Ej+WlxSpF2nefPmCAoKKrC8fIcXR1d7i+h22fsFIPDJZ1U2+uWvPkPrFb8g8uwmVa/0xB/T0frJj41W3F4Kux/88VUkRBxVQSypiRXQ8Na13KSddnLSKCSfOQkLa2s0mvU+nIPrGWUfiYiIqhqDBbKkdodcwS0urV1XoL1nz57qlt/LL7+sbtItw5QDWDruLVrD1scPmkuRyAmxRGrCVcRd2Q+voLYGWb90V4w+v0VNN7hvPKztcov8Cnltz86egvSIcFja26PaqElMTyciqiQkcCTBIPne9PPzw9dff61G8iuOFEeXrmSGJAEzKRGwZ8+eYkctlMc//jg3YCA1tYjKU81nX8T1tauQnXgT11Z8g4Z9JuLAD68g7vIeRJz4FdUbF5/td6dBrAM/DMXN68dgYWGFJg/N1Ks7oWqvzZ2GuF3b1H0pAeHRup3B94+IiKiqMmjXwpJGKNSHOQSxhIWVFXzv74nw5UthnWSPbJd0hEvRdwMEsiR1/dRf76pp35Bu8A3pWuD5G2t/QvSmDWq67qgJsAisecfbJCIi0yB1rkrLsMpPuhN27979jrYnoyDWr1+/QNdCyayWroWFM7J0gS5jZb1Ur15ddVckKom1iyuCXngZ59+fjWs/rUC1J55Ctca9EXH8V5zePA/etTsZtGZpVnoiDvwoQazjKojVtPdM+IfeOoglriz7FDd+/UlN1375DZXNT0RERIZTfkO9VCLSIJFAlsWpFKCtlUpvz0yNg62j5x2tN2zbEmQkR8HK1gkN7it4pT3lfBjOLZiVt32/Bx9GVFTUHW2PiIiqrq5dC14sEdLVXzKk5fvF19cwhev14eDgoLZNVJpqj/bDtR+Xq8z0S58uQv03xyPmwnZkpsbj9Ka5aNYn92KgIYJY+394GYk3TsLC0hpNe8+Cf339Asc3VFH6RWo64JG+qPGsfsFpIiIi0p95pEGZGKlx4FS3Hiyva2EFO2g12bh2/Nc7WmfC9WNqpEIR0vk1VR9LJyc9DSffHg1NZgYcatZCyGhetSYiIqKqRUb7qz3kdTUd9cfvyLh8DaHdxqr7N05vQPT5rXe8jcy0m9i/8r8gVrM+s/UOYsXt3Ymzsyarac9OXRAycoLRshiJiIiqMgaybpPfAw/BQgtYhefel+6FheuD6UujycbJjdOkqgJc/RuiZovc0Z10JBMr9eJ5WNjaouHUebBydLzd3SYiIiIyWz739oBLaCM1fWHxfPiFPgDvOnep+yf/mInsjJTbXneWCmINQWLkv0Gsh+eoURH1kRx2GiffGgFtTrbav4ZT56oi70RERGR4DGTdJt/7e0kle0C6FwJIjb+C+Kv7y7weCX5d2rMMSVFnVQ2GRg+8DQtLq7zno/5Yhxu//qymg4eNgXO90NvdZSIiIiKzZmFpidqvjlLTCQf3In7PDjS8fwKsbByQnnQDYdsW39Z6s1VNrJeRFHVaBbGaPzwXfiHd9Fo2PfI6jo0aipzUFNhXC0TjeUtg5cCLjkRERMbCQNZtsvPxg3urdrBMAew0ucVFw4/kBpz0lXjjlLryp2t0BbV+Bq5+/wWq0sKv4Oycd9S0d5d7Ue2x/re7u0RERESVgkertvDseLeavrjkPdg7+SLk7mHqvpRpSIg4Wqb1SY2t83+OQ1LUmdwg1iPz4Rtyj17LZicl4vioociMiYK1qxuazP8Itp7et3FUREREpC/mPN8BKbqesH83tMcSgGbAjbN/ITTtTdg6uJe6XNrNa6qw+/WT6/Ie86nbFcGdXsm7r8nMxMlJo5GTmgo7/2qoN34q6ywQERERAajzygjE7d6OlAthiFy/FjV79sP1k+tx8/oxHFn7JtwDmkCTkwmNJguanGxoc+T/TGjU/7k39ZgmS3VH1GSnw8LKBs0fng/furlBslvRZGXhxPjhah+k/EPjOYvgGFSb54eIiMjImJF1ByRLytLOHhaXM2Bl5agaRDIMdGmj4Jz5+z1s+/yRvCCWq18DtO73KVo+thDWtg5581748D0knzkp452jwZQ5sHF1u5NdJSIiKuD111/HiRMn8u4PHToUdevWRb169dChQwc0aNAAS5cuzXv+tddew/Hjxwus46677lLLyE3mz87OVo9fu3YNAwYM4CtORuMUHAL/Xo+o6YufLVYXABv1kPIM1khPvI4bZ/5A1Ll/EHNhB+Iu70F8+EHcvH5cZV2lxF5AWsJV1RUxMyU2N4hlaYNmZQhiSWmIMzMmqu6NUmqiweTZcGvakmeciIioHDAj605ePCdneHW+B9F/rYd9ohtSnFJV98Kg1gMKZE9psjNx5dBKnN/1marBIOxdA1QafECDHrCwKBhPjNn2N6798K2arv3S63Br0vxOdpOIiMzEhQsXMGvWLOzduxcpKSl4+umnMXXqVPXcoEGDsHVrwVHZVq1ahebNi/+OePXVV7Fx48a8+/K9NHDgQEyYMEHdv3LlCpKSkvKenzJlCkaOHAmNRoPY2Fi1H87OznnPh4eHIzk5ucA2VqxYgczMTDVtY2MD63+LW6elpeHSpUsGeEWISlZr8KuqlmhmdKRqN9V87n+qW2DMhe0qu8oy72abe9/SuuD9f6dhYYVMKy/41Gqo98t98ZP31ciJIvj1sfC5R7+RDYmIiOjOMZBlgO6FEsjK2nMN6GaJlLiLSAg/BI8aLaHVanDj1EaEbVuEtJsRuS+4vSuCO/wPNVv0g6W1bbEFQ+UKn/Bo1wk1nnnhTneRiIjMQHx8PLp166YyoyRbysHBAZ6ennnPnz9/Hp9//jkCAwPzHqtVq1aJ63vnnXcwYsSIvPuffvppqaPr+vj4qJsEso4cOYIbN26o9Uu2lZD748aNU9MSDFu5cmWx6/H398eyZcvKePREt1evNLD/c7jy1ae48vXn8O/9OHzrdlG3spD3fFRUlH7zZmXh0qeLcHX5l+p+9X7PIrDfszx9RERE5YiBrDvk0bYDbNw9kZUQB0e7mkjNCMfVIz9Bo8nB2X8WqCGchVz5C2r5FOp0GAwbe9di16XNzsapyWORnXgTtl7eCJ00U43OQ0RElZ9kW4WGhmLs2LHFPi/d9iSolD+QVRpdYEpHsrPWrFlzy+V+/PFHlbklGVmS7bVhwwb1+Cuv/FfHUTK3Xnih+Ast9vb2SE9Px4EDB9T+durUCV999ZVe+0xUVjUGDML1NT8iKyEeV5Z9grojxhvtRUy9dAGnpozLLf0gJSbuuV+NKE1ERETli4GsO2RpbQPf+3rg2qoVsLyYDVSDqn91/WRuurkIaNgTIZ1fhYNb9VLXdemLD5F49JCqtRA6+V3YeuaOhkhERIal0WSr2jiGW58GmSkxSE/SwrKYCxC2Tl6qW1NpGjdujGPHjuH06dMqoFVYVlaW6r53OyQbq1mzZqVmcB06dEhlcUkXQglmvfXWWypLZfz48XBxcYGTk1PevF5ecjyWqjvi9u3b1b41atRIZWpJ8OrcuXPq/vfff6+WJTJmmYegQa/g3HszEfHzSlTv+wwcAmsadBuSyXh99Q84/8FcaDLSVf3SWoOGouazL/KCIxERUQVgIMsAfHv0VoGsrP3hsH7CHdmZuTVEPGu2Rf2uI+Dq3+CW64jftwtXvv5MTdd8/iV4tG5viF0jIqJiSBBry0cPlNtr0+WVjbB38St1nuDgYJW51LNnTwwePFhlPUl2k05OTg769++vgkuSlSXF13v37n3LbR88eBCjRo3CRx99VOI8e/bsQa9evTBmzBjVrfHmzZv4559/1H5IdtZPP/1UZBkJYkkw64svvlABNsnAkppe0i1RyL7ruiUSGVPAw31Vjay08CuqdlXDafMNtu7MuFicfXcyYrf/o+7bV6+BBu/MhmujpgbbBhEREZUNA1kG4NKgMRxq1kLalUvwtW6HnNoWqN7kEXjX7lSg6HtJMuNiVKo6tFq4NWuFWoP+675BRERVx3333acCT5MmTUKrVq3w888/o379+uq5X3/9Fampqarb3tGjR1UgS7KkpK5WSfbv34+HH35YZWRJtlX79u2LDS61adNGFWeX4u6SXSbc3d3V9hMTcwcpke+z/N9pzz77rBqZ8IcfflBF3iUra9q0acXOS2RMljY2qP3ycJycOBLRmzYisf/zBgk0xe7cquqWZsXHqfv+vR9D3TfGwcrR0QB7TURERLeLgSwDkMa63wO9cemzRUjZfAztVm24Zaq5VqNB0ukTiNu5BVF/rkdWXCysXd3QYMpsWPw76hMRERmHdPWTLClDkeBPTEwMvL29S+xaqC8JIC1atAhr167Fk08+mZfhVK1atQLdEIV0ASwpkCUZWNLVT7K8JHPLw8MDDzzwANavX4969eoVmFf2WTdCoRSdf++991QQTLK//Pz8cP/992PevHkFuiZKoO3UqVPquGXkQinyrjv2OnXqqBEVicqL9z3d4dKoKZJOHMX5RXPRaOYC2Hp639a6cjLScWHxfET89J26L+2zeuPegU9XjkxIRERkChgxMRDf+3uqQFbGjQjcPHoI7s1bFZknOyUF8ft2Im7HFsTu2qaCV3ksLVF/4gzY+fobapeIiKgEUq/qVl39yhrIsk2zgL2Lb7GBrNvRp08flfEkGVGurkUHCZHAk61t0dFvxd69ezF79mxs2rQJLVq0UI/16NFDjTr40ksvqW6DxZFsLwmMSYaWZIVJECsiIgLfffedCqrt3LkTVlZWefM/+uijqq5XcRd4pJuhdD8kKg/ynqvz6igcGfq8qje666GucKwdDPcWbeDesi3cWrSGrcd/o4CWJPnsaZya8iZSL55X993btEfoxJmw8/Eth6MgIiIifTCQZSAO1WvAtWkL1XiK2vBrXiBL6jXESuBq5xbcPLRfjUyYn1NIfXh17AKfex+Ac93c7iNERFS1SXHphQsXIiQkpNgglhRTnzx5MubOnVvs8m3btlWZXG5ubgUe/9///oe+ffuWuF2pcyWF3KdPnw5f39ygXMOGDVWXxy5duqjnO3bsmDf/kiVLVHfHwi5cuKC6GTKQReVJ2l61/jcMV5d/iZzUFBWMklvEz9+r553qhMC9ZRu4tWyjAlw2bu4FMuXDVyzDpU8/UG01Cxsb1HllBKo/OYAF3YmIiEwMA1kG5Nejd24ga/NGWDk7q8yr1MsXC8xjaWunru55dbwbnh3vhr1fgCF3gYiIzJRkOz333HNqOi4uTmVSSY0qndatWyMhIQEpKSkqA2zq1KkqwFSSwkGs/F0XSyKBs7CwMDUSoWRb6YrMr1u3DhcvXizSJTF/d8fignFE5S3ohSFqNMGks6eQcHAfEg7uxc0jB6BJS0PKhTB1kwF6ZIRop7r1VEDLpXFzhP+4HKnHDql1ONauq0o98AIjERGRaWIgy4B8uj2ghn/OSU5SV/V07Hz94Nmpi8q8cm/VFlb2DobcLBERVQItW7bEhg0bcusu+vnl1azSWb16ter65+Liop6/U8UVZJcsrBUrVmDEiBF48cUXVdBLgmpSD+uXX35RNcBud91E5UVqjbo2bKJuNQcMgiY7C8mnT6qglgS3pASEJj0NKWFn1A0/fJu3bPW+z6D20BGwsvtvxFAiIiKqxIEsGbHo448/zmuIP/LII6ohXFJjVuaTGhz5r9ra2dlhx44dMEc2rm6o9mg/XPvpO9V4UsGrTl3gFFyPDXoiIiqVvb19sSMK6gQGBhr0Ffzss89UAfjCOnXqhN9++011aZQglo+Pj/puLgsWeydTYmltA9fGzdSt5nP/gyYrC0mnjv2bsbVPZdNbuXug/puT4dWhc0XvLhEREZVnIOvll19GWloahgwZoroijBkzRjWCx44dW+z84eHhqtE+atSovMdKKlxrLuqOGI/g18fCIl8xXCIiIlNzq+wqCazdbvBMl1VG5eebb77Bli1b1Gsv5/buu+/Ggw8+yFNQDEsbG7g1baluQQOHICcrC9GxsfDwZUF3IiKiKhfImjNnjioSqyPdIoYPH15iIEsyuGrUqKHqflQmDGIRERFReZL6ZRkZGWr66tWrGDp0qCrQv2zZf6UOqHhstxEREVXhQFb+IJaQLguSoVUSCWSZewYWERERUUVr166duum89tprCA4OVqNHykVDIiIiosrCqMXely9fjv79+5f4vHQ/lDpZu3btUvelAfbmm2+WONKSXGnUXW0UiYmJ6n8ZvUlutyLzSD0ufeY1dTwW08TzYpp4XkwTz4tp4nkp++tliqS2We3atdUol0RERESo6oGsEydO4Pnnn8+7P3LkSDz99NMF5jly5AhWrVqFo0ePlrgeGWa8c+fcopqRkZEq8CVp8Pv27YONjU2R+WfNmoUpU6YUeTw6OlqN5KRPY/PmzZsqmGVpaQlzxmMxTTwvponnxTTxvJgmnpeySUpKgilas2YNrK2tVZfD4vDi4H8YvDVNPC+miefFNPG8mCaeF+NdHLytQJZc4ZPRCXVq1qxZ4PmEhAQV2Pryyy+LHREpf1fE/N0Re/XqpQJbkqXVu3fvIvOPHz9eBc3yZ2RJurxcdZTRlfR5YaQIqsxfGQJZPBbTw/NimnheTBPPi2nieSkbKYpfni5duoTp06fn3e/Xrx+6d++upr/66iv8/vvvOH78uGqbyeiTJbV3eHHwPwzemiaeF9PE82KaeF5ME8+L8S4O3lYgy9HRscQC7VL36vHHH1cjGOoaVmXRvn17nD9/vtjnZPjv4oYAl0aavoEpCf6UZX5TxmMxTTwvponnxTTxvJgmnhf9lXd7wsnJSbWVdAICAvKmZSTorl27ok6dOvj111/VhcEXXnih2PXw4uB/GLw1TTwvponnxTTxvJgmnhfjXRw0eI2swYMHo0mTJhg2bNhtLS9BrDZt2hh6t4iIiIjMnmSVS1urOJ06dVI3MWbMGDRt2hQPP/wwPD09i8zLi4MFMXhrmnheTBPPi2nieTFNPC/GuTho0MuIkyZNUulg7733XpmXlbpV0hVx9+7dePDBBw25W0RERERVipRu8Pf3x7lz5yp6V4iIiIgMymAZWdevX1c1Gxo1aoS2bdsWiED+/PPPqpbVpk2b1PSSJUvUc++++64qCC8pd1evXlWp8Bs3boSLi4uhdouIiIioyvnzzz9V2yo0NLSid4WIiIjINANZkuq+f/9+lVmVnwSyqlWrpqbvuusuNGjQIO+5gQMH4r777oOVlZUqSpq/8Ls+dNuSou/6kICZZIxJ30tzr5HFYzFNPC+miefFNPG8mCael7LRtUEKt3/KU2pqKoYPH67OXU5OjirTcOHCBfzwww96DYYj2KZi+9DU8LPINPG8mCaeF9PE82K8NpWFtiJbXncoPDxcZXoRERERVSTJfgoMDKyQbWdnZ+Prr79WDWYbGxvVNurYsWOZiqayTUVERETm0qYy60CWNNgiIiJUV0TJ/NInwieNO3lh9L1Caap4LKaJ58U08byYJp4X08TzUjbSjJJsb8k+N+dsb7ap2D40NfwsMk08L6aJ58U08bwYr01l8FELy5Mc3O1c/ZQglrkHsnR4LKaJ58U08byYJp4X08Tzoj83NzeYO7ap+J43VfwsMk08L6aJ58U08bwYvk1lvpcOiYiIiIiIiIioSmEgi4iIiIiIiIiIzEKVCmTZ2dlh8uTJ6n9zx2MxTTwvponnxTTxvJgmnhfi+8R88e/XNPG8mCaeF9PE82Ka7EwslmLWxd6JiIiIiIiIiKjqqFIZWUREREREREREZL4YyCIiIiIiIiIiIrPAQBYREREREREREZkFa5ipnJwcHDt2DBqNBo0bN4atrW2ReU6fPo3IyEg0atQI3t7eZV5eXLhwAZcuXUJmZiZat25dZD2GkJ2drfZFyL7Y2NgUmefUqVOIiopSz3t5eem9/ObNm9W+F9ayZUv4+voa/FiysrLUvlhaWqp9sbYu+hY7ceIEYmNj0aRJE3h4eJR5eXktYmJiUK9ePfj5+cFY5L1x8uRJpKWlqX21t7cvMs+5c+cQHh6O0NBQ+Pv7l3l5OZYbN26gYcOGRj8Wed0zMjLU61rcvoSFhSEiIkIdS/59kb+Bs2fPwsLCQp0v2VdnZ2e9lzc0+duVY5H3ihxLcQUHZX9lX2Rf87/Pz58/r/bzVsci9u/fr46lpOfL81iuX7+OBg0aFDgWOQ45HjkWT09PdSxOTk5lXr+hSLnFM2fO4ObNm2pbhfdFyGep3EJCQlC9evW8x69evar2U7i5ualjdXd31/t5YxyLfH8kJSWpY3F0dCwyz8WLF3H58uUix3LlyhX1dy9kH2VfZZ9LsnfvXjWffJ4Z61jkcyY5OVl9Djk4OBSZR/7GZb9lH6pVq5b3uByfLKs7FnmPubq65j0v69yxY4faho68H++9995iP7sN8Tkm+5OamqrOS3HHIn8T8n6pX78+AgIC8h6X952cUyF/+3Je8h9LfrK8rCc9PV29ZvnPb2XFNtV/2KYyDrapcrFNxTaVPtimysU2FdtUpdKaoT///FNbq1YtbaNGjbRNmjTR1qlTR3vy5Mm859PT07U9e/bU+vj4aNu2bat1cXHRfvzxx3ovL44dO6Zt3bq1NigoSHvvvfdqH3jgAe0///xj8GPZuHGj2kbjxo3V/gQHB2vPnDmT93xaWpratq+vb96xfPbZZ3ov37t3b7V8/pufn5922bJlBj+WX3/9VVujRg1t06ZNtQ0aNNDWq1dPe/78+bznk5OTtd26ddP6+/tr27Rpo3V1ddV+/fXXei8fERGhlqtbt662c+fOavlRo0ZpjWHXrl3a+vXrq1vLli211apV0+7Zsyfv+ZycHO1TTz2l9fT01LZr106dl5kzZ+q9fEJCgrZr167a6tWrazt06KCO5f333zfKsezYsUMbEhKiDQ0N1bZo0UIbGBio3b9/f97z2dnZ2ieffLLAscyZMyfv+RUrVuS9d5o1a6Z1c3PTfvTRR3ovb0jbtm1T51/eH82bN1fvl0OHDuU9n5WVpX388ce1Xl5eefsyf/78vOe/+eabvGOR95m7u3uBvycdeV9aWFgY5W9eR9Ytf68NGzZUr6v8HR85ciTv+czMTO0jjzyi9fb2zjuW/O8R+RvWHYt8jnl4eGi//PJLvddvSIcPH1b7IOdG/kbl8+rvv/8uMM9LL72k3jvt27dX7/dx48blPffbb7/lHUurVq20zs7O2nfffVfv5w3p4MGD6rNU/mbkO0A+L7ds2ZL3vEaj0b744ovqvaM7lgkTJuQ9v3bt2rx9lb99OW9z584tdls7d+5U5+3ll182yrHI37mcf92xyGfv9u3bCxzL888/X+BYJk+enPf8L7/8kncs8tkhx/Lee+/lPS+vi7w/83+/9OjRQxsdHW3wY9m7d6/6DJPvBXkPBAQEqM/Z/J/JAwYMUK+nHIvs67Rp0/KeX7VqVYFjKe4z9+LFi+pzWT6v5btK5pXXoLJjm4ptKrap9Mc2FdtUbFPpj20qtqmqGbFNZZaBrCVLlqg/DJ2JEyeqF0hn1qxZ6r4EtHRBKflhe+7cOb2Wj4qKUi96/iCLsSxatEj9CNSRH3f3339/3n1piHfv3l2bkZGh7ssPUQkYXLp0Sa/lC5P1SPDk2rVrBj+WhQsXao8fP553f/jw4do+ffrk3Zcfew899JAKNuh+ZMmPDglQ6bP8gw8+qJ0+fXrefVlOfpwbI9ggQYL8P/gWLFigAh86EvyQH4aJiYnq/oULF9QPK12A6FbLDx06VDto0KC810KWlwDT6dOnDX4sEtyQH8w68qNafmDrSFBKAiUSaBTydyI/3vMHiPKTH4/yI1F+9N3O8nfi888/LxAQlL91CfDqyN9Dx44dtSkpKer+2bNnVVBFPgNKapBKUOTKlSt5j/30009qnXJMhYMxhvTJJ5+oH+f5/9Zl3/O/Z+THRWpqqrov7w0JzhcOuuts3bpVHYvu7+lW6zeklStXqh/DOkuXLtXWrFkz7/6PP/6oAiqxsbHqvuyjXEzYtGlTseuT8yWfc/k/p8vy/J2QwO3mzZsL/K3LxQ6d7777Tl04iIuLU/fDw8PVsZb0OSSf2fI5d/To0QKPy3tU3mMS9B0yZIjWGL799tsC+yV/qxIIyh/YlQa5BNaF/B3I51D+z6785G9agpEnTpxQ9//6668Cn9HGJN/H8h7P/7cu76n87zkJUN28eVPdl+9I+R7fvXt3seuTz2oJZuku/Mj5kECsBOokwFeVsE3FNpVgm0o/bFOxTSXYptIP21RsU2mM2KYyy0BWYfJjVX7U6MiPjPwNXvHKK6+UeAW/8PIS2DLWFfJbkR+p8sNbR65A5w9CiP/973/aefPm6bV8YcuXL9c+9thj2vIgPzLz/5iVH66Ff3g+++yz2sWLF+u1vGR8nDp1qsA8Tz/9tPoxZmzyo9XS0lJlHwm5av/zzz8XmGfSpEna0aNH67W8ZEYUzlqYOnWquhmbBGqtra3zPljuuusulUWS3/jx4wtkzBQmwdUNGzbc9vKGIg1vOzu7vPuSibFu3boC84wZM0b9TZfknnvuUT/IdcaOHasCLnLlwJiBrMIkiODo6Jh3XwKlf/zxR4F5RowYoX3nnXdKXIcEvkoKqBRevzHJ+1ze77pgj2SW5c8WE5IpN3jw4BLXIRmPEoi53ecNRYL/kp2nC1pLML7wRY7Zs2eX+p3Rt29f7ffff1/gsVdffVXtv3x+GSuQVZgERSURWxcclewpaWTmN2PGDO1rr71W4joeffRRld0k5DPgiSee0FYEOR9yXnQXee677z7tDz/8UGCeKVOmqAsiJZGM5dWrV6tp+R6S9ymxTZUf21TGwTZVQWxTGR7bVAWxTWV4bFNVfJuqUhR7l3okUg9DV+dBami0a9euwDxt27bF8ePHb7m8WLduHV588UVER0dj27ZtqgZSecm/L1LXRurjtGnT5raPpbAlS5bglVdeQXnIvy9SS0VqFrVo0eK2j6Vfv36YM2dOXj0WqS0jt969exv1OHT7EhwcDCsrK3Vf6niV9T2mW17qREiNL6lrlJ/UpilpeUOSfZG6PlLL5naOReoGSf0YqR1zO8sb+ljy1xYq674kJiaqWkdSb0dn9uzZRc5NeSj8fpd9LsuxJCQkqFoCUsdIn/Ubk9Qi8vHxyasNVdbzIrXcjh49qmr53c7zhiTfJ1JrSVcrrazHInWW5Ln8n31//fWXqnv2zDPPoDzJe6BmzZp5taXKeixS70/W0bx587zvKF09OfmulM+18iL7UatWrbz6lmU9FqmzJe/TZs2aFfjel78jORapY1NVsU1V/GtRHLapbv89xjZVLrapjINtqv+wTWW89xjbVBXbpjLbYu86EhSYNWsWxo8fr+7HxcWpIsOFi7dLkWQpln6r5YUULP7nn3+wePFi1KhRQ91/6KGH8OmnnxqlgK2OBOFkX6ZMmaLuy48CKbBbeJslHUvh5Qs7cuSICs5JIV5jkx84EhBYsGCBui/bLa5QvhzL33//fcvlxVtvvaVu3bp1Q6dOnVQhe/lDKa2QsqFMnz4db775Zt7rLO8z+aGuz3kpvLwUspf31e7du9GxY8e8eeR44uPjYWwzZszI2xcZCECCOYXPTeFjkQ+ggwcPqr+FlStX4u2331aBN32XL49jkR/ZEjQoPIBA4X2RINyhQ4fUB64cy7Rp04xanF4fEpydOXNm3rFIw1Y+mwoXoy58LFLw/fDhw3nHIn//hd+Xxa3f2OT9PmbMGPVe1/393+rvRS4Y7NmzR52f77//HoMHD1YFufV93pjHMnbs2LzArz7HIsXCJcgug0HIvg4dOjQv4CrF8GV9GzZsQHnTHYuOPsciBd8lUCXvNTmWN954Q/0AFTLAhbxXJ06cqAbwkM+H0aNHq8+H8jgW3ftZ3t/yfXmrY5GC7/mPRfZVgmFC9l2Co8OGDUNgYKB6n0lgbPny5cUW+6+s2Kb6D9tUxsM2FdtUxsQ2FdtU5YFtqksV3qYy+0DWu+++q66WP/zww3mP5R9BKf9juh8ipS0v80lUUU6MnAQZ5Usa6vfff78KZMkPEmP+KK9duzZ69ep1W8dS3PKFrxwOGTKk2GUNbfLkySoqmz9oVpZjKW55CR7JCH+SGSGjCkkjU/5IJLvImOS8S5Bk4MCB6r78OP+3W65ex1J4eTFhwgT0799fBVAlCCQ/aiUgIZF9Y/rwww/V6/bss8+q+7K/+pyXrVu34ptvvlFZdZKtpMvG0nd5Y/jggw/UCJ1PP/10qdst/JgEqVesWIFr166pH775s7EqigRsJQD/5JNP5j2mz+sqxyJfHhLkkaCCjNCq7/qN5YcfflBfal999VWRfS/tWPbt24ePP/5YZSrJ526rVq0KzH+r543hu+++U1luhT/3b3UsEnD75JNP1L7KCKH5s7EkEDRu3DijjBpbmq+//lqN4ivfAYX3vbRjkYD7Z599po5F3kO6bCwhI/nmD8jJ+7Bz587qQoMxL5h8+eWX6iqfXO3L/zl0q2PZuXOnWlY+xyRInP9Y5Hte/p7kO8XFxUVdwX7sscdUG2Hq1KmoKtim+g/bVMbBNhXbVMbGNhXbVMbGNhVMo02lNWMyyp3Uw9IVqtWN9CX1f3SF3nWk+PYzzzxzy+WFra1tgaLjugK/Dz/8sNZYpIq/FAPXFaoVcgxyLLqC4PkLXstIU7daPj85Ril4HxMTozU2qQUjxcR1xb+F7JfUMipc8E360BauD1Pc8rKcFEbOXxMnMjJSjV4lhaSNRTdK3vXr1ws8LsWbCxfM142sps/yQuoyScF3qTkjhYs//fTTIufVkKR2koxeJq9bflLwuPBj0se5V69exa5HiidLkf34+PjbWt4QpEi4jAhZuM6Y1IDSFRTXkXo+Jf3tSrF3qd1W3N9NedXIkpFHpRZe/v2W97v8vRTeL/nbkFEZiyPvtdq1a+fVcipt/cYide1kH3QDAejIY7oC4TpSUFz+posjn7/yHrt69eptPW8I+/btU0XeL1++XOBxGSkz/8iwur+tTp06lVjsXfZV6rnJ+0lq/a1fvz7vJjXcZJRdqTelq/dkaDJAghxL4ddLBqjIPzKs7nOpS5cuJRZ7l2O5ceNGiduSWpTGrI8nNSNlNM7Cn78yEELhcyWvqYw6XJwDBw6oY5GagULqMeavlSfkfJX0Hq2M2Kb6D9tUxsE2VVFsUxkW21RFsU1lWGxTmU6bymxrZEk3J7myvXbt2gJdyyRDQ7o9SL2M/CQiGBoaesvlhXT7kivP+ck8xsowkUyDUaNGqX3J35VIsg6CgoKK1PgofCwlLZ/fsmXL0LNnT3h5ecGYJDor3UxkX/K/hrJfknkkWW6lHUtJy0vXNuk6kr+mjGQ0vPPOOyq7xhikPtlzzz2Hn3/+WWW75Cd1iGTfSzuW0pYXkrHwxRdfqOdfe+01/PHHH6qmizFI17MXXngBv/zyS5FMEOmedatjyU9SQyWLadeuXbe1vCH6pEu3stWrVxfp0ljWfZGunVL/RDJoKoJ8Tr388stYs2ZNgbpc8lkj+1yWY7nrrrtQp04dlU16q/Ubg3Sn69u3r/p71KUWl+XvJT/JLOvatWux3Y71ef5OSXc6yZiUbLfCWZJlPZamTZuqcyNXpkT16tWxcOHCvNvGjRvVeZJpY9SYkhpwTz31FH788UeV3n0nxyJX29q3b68yNEsiKeOSgWoMkpou3wGrVq1SXZvv5FikvppklG3fvr1CvvdNDdtUbFOxTaU/tqnYpmKbSn9sU7FNZbQ2ldYMybDackVWIqLFkVGKXn/99bz7aWlp6iq6RAv1WV5G0pJRqPIbNmyYGs3J0C5cuKCulMtQ4MWR0aNGjhxZYISE6tWrq6v8+iyvI9krkn1iTJKlIJkXun0r7nV966238u4nJSVp/fz8tKdPn77l8pLN4O7uXiSjbMGCBWokDkOTiLJksRQeNU5n+vTpasREnZycHJXdJ1eC9Fm+sF27dqmMucIZRoYg2RP16tVTWUzFkVHwnnvuuQIjzjVo0KDE+SVTTrKYdH8/ZV3+Tsj7QLLKShqZT0YnlCw3HclmlGPfsmVLsfPLezD/Z0N5ZmSFh4erbD25Ql0cyWp56aWXCmSbyudWSX/Hkr1VrVq1vL+fW63fkCTjUzJCf/rpp2Kfl8xLyTrKr2PHjtqVK1cWO79kJjVr1kz7+++/39bzd0IyDSVrSjfySmELFy7U9unTp0i2aEnHLpm1+T8bCjPmqIWShdeoUSOVLVocGf02/yi2ciyS5bpmzZpi55fv0tL+tuVz8O677y7xvN4J+eyXbUsWW0mZYE8++WSBfWnevHmJ7xH5Ls3/2SCfHTIqaH7ynS/f/ZUd21RsU+mwTXVrbFOxTWVsbFOxTSXYpro1C/kHZkSKgMtVYbkaX3i0OnlM6pFI7Ru52vrSSy+p2iRSq0T6Z8pVXH2Wl/oukhkjo/vJ8nLVX7JlpIaRbuQqQ5D+olIHSupvSbaUjkQrZV8kI0vqs8hVY6nRIlf2P/roI5VVJZkC+iyvGyFLMrak2LuxSF0x2T+pNda9e/cC+yLF2SVTTrKq5HUdPny4ypxZtGiRytyQbDF9lpe6Mj/99BNGjBih6ppJFo3UWpAaLYbOZJJMHcluGTBgQJHHJbtMirLLa//EE0+omjDffvutquElBdtln2+1vIyEJlcoJHNBstDkOCQ7S9ZnSPLnLRlU8npLVkZ+UsdG/i6kSLMcizwv+yf9vqVvs7xvhByTFHWXIsDyfpQ6W/J6f/755+r5Wy1vKLJ9GcFTtlW41pNkvMjfphSvlr9ved0lc2Tp0qVq33V1fDZt2qQ+A3THIgM6yLJSe6mwHj16qPec/C0ZmtQpkxpPcjyPP/54gefk/SSZIVLPSI5FaqvJ6y3vD/Hbb7+p/+X1lVpxcizyXpJjueeee9T/+qzfkB588EH12Vm4/pJsXz6vUlJS1Gey7J+8rvJ3LH8DUn9JBrOQrBgZ2VTer9LXXv4eJHNJPrOlJt2tnjck+fyRq0aS9Zef/B3JQAKyH/K9IJ+7Mq9kOklRd/k7llFJJVtJPs9kX+W7SL5/pHahFOMv7kqUfHbI8RX3HrxT8npLloVkY+YnfxsykIgM1CDvMRnMRD5n5XtF/i5kf+R13bJli/qMkmOR+leyj1K0XrLu5FikeLpkJgipoSV10WSglfXr1+eN8Goo8r6VrCnJcs2vQ4cO6nxJEX35bHjkkUfU36zso9RUlEw4ORb5XwaDkGOR7EH5LpXMUqn9J+TvTZaXrMIuXbqoYv1SlFQylSp6MAhjYpuKbSq2qfTHNhXbVGxTlQ3bVGxTdTFim8rsAlnSWJUuH8XttvxgkK54Qn5YSLE/+dEjjXb5opYGtr7LS3c+CbRI41wau7J8caOB3QkJfEg3icL7Ij8QpLiurhuIdFGTbieyLxIokECQBHb0XV6WleN69NFHYSzyg0F+LBW3LxKo0r1p5cfr+++/r340yA+T119/Xf2Q1Xf533//XXUpk2OXH1Tyw7lwN6Y7JYEB+WEn/xc2Z84cFXAT8sNu3rx56sec/LCV0RokQKDP8vLelC5F0g1Hur7IsRujaL0EOiRgW9z7ff78+XnFweWHnRyLBEQk4CDHohtVQn4Yyg9AOU/SnUeOrU+fPgXWVdryhiKBW9mX4o5F3uO6LkSyD3JsuiCwBHEdHBzUchIolcCW7ljkOOR4iiOjf/br188oXSQl0CGNoeKORT53dO8F6Rb23nvvqddXglkjR45UASMJVMlrIT9C5VgkqCPHpgto67t+Q5FtSbCquEEbJNAgZPS4uXPnqgsF8r6T0fN03bolOCHPS/BDuuFKkEgCCrog1a2eNyTd+70wKU6pC5jL55cci3zPyPeDHIuuW7d8JktwV/ZVAu4PPPCAClCXlE4tQaMTJ04UCQIagvzty3ukuCLWumL58r0if7vSbU8+m2S0SQlwC/mulKLqumORIKSca92xSFBVAqdyX/6epLEiA0kYozuevLeL+0yVwqG6AqPyfS/nRf5uJCglx6K7+CTvFwmwy7HIvkrwVb4T8++r7u9NPkPkb0TKDxh7AI6KxjYV21RsU+mPbSq2qQpjm6p0bFOxTXXZiG0qswtkERERERERERFR1WS2xd6JiIiIiIiIiKhqYSCLiIiIiIiIiIjMAgNZRERERERERERkFhjIIiIiIiIiIiIis8BAFhERERERERERmQUGsoiIiIiIiIiIyCwwkEVERERERERERGaBgSwiIiIiIiIiIjILDGQREREREREREZFZYCCLiIiIiIiIiIjMAgNZRERERERERERkFhjIIiIiIiIiIiIis8BAFhERERERERERmQUGsoiIiIiIiIiIyCwwkEVERERERERERGahygWyrl27hpSUlApZPi0tDVevXr3lfBqNBjdu3MDZs2dx5coVpKam4k4kJycjMjJSTWu1Wpw/f77EeWVb0dHRMCTZdkZGhkHXSURERObdpoqIiCjz8hXdptEX2z5ERETGU+kCWZmZmSr4U5L+/fvj22+/LfBYTk4OTpw4gePHjxe4nTlzpkgAprjldST4lH/58PDwAs///PPP6NOnT4n7tnHjRvTs2ROenp5o1aoVHn74YXTp0gXe3t5o0qQJJk+ejPT09CLLSSOw8L6HhYWpgJj44osv8PLLL6tpCY41bty4xH0YMmRIXgMxPwnAlRaMKu610rl06RJef/31EpclIiKiytGmyi8xMRGXL1/Oa48U1q9fvyLLm3qbRl9s+xARERlPpQtkbdmyBc2aNSvxebl6J7f8pJEljbHCtxYtWuB///vfLZfX+eCDDwosP378eL2X/eOPP/DEE0/g8ccfVw0sucp56tQpXLx4UTUEP/74Y/z555944YUXiiy7devWIvvetGnTvO3n325p+/Ddd9/B1dW1QKPw0KFDqFWrFjp27KgCanPmzCmy3ObNm/HQQw/BwsKi2PW2a9dONUx//fXXYp8nIiKiytGm0i3XunVr1KxZE3fffbe6QDdu3DhkZWXdcnlTb9Poi20fIiIi47FGJXPhwgUEBgaWaZk6derg2LFjBR5LSkpSWVD33nvvLZeXtHbpNvj000+rW35yJVEEBweXuo59+/aphtqLL75Y5Dlra2t06tQJgwcPxrvvvlvk+QcffFDd8pOMLnd3d5Tlqutbb72F7du3F3hctvnaa69h9OjR6uqiNGh79Oih9lWXzTZ8+HDMnz8ftra2Ja5/4sSJeOSRR9CrVy9YWla6+CkREVGlczttqqNHj6J3795YsmQJBgwYoAJCclHu2WefVbfvv/++1OXNoU2jL7Z9iIiIjKPSBbLkCpyQK3S3ezVNGjISUAoJCVGNrluRFPfr16+XOs+nn35a6vPPPPOMyrqSxpuk2terVw/Ozs7q6qVkaMkVyqVLl2L27Nm33J9t27bhyJEjqitj/sCcBNWkMVmcZcuWqYZi9erV8x67efMmDh48iL///lvdl6uY3bt3xz///JPX6Pvkk0/g5+dXapdJERoaqtb9008/oW/fvrc8BiIiIjK/NtV7772nMszzt59q166NFStWICgoSGWW69oa+tQANcU2jb7Y9iEiIjKOShXIku55u3btgq+vLxYtWnRbdZmkiOhTTz2F3377TTV49Mkeki5/+RtXUo9BrhxK2nrhK5slkQaVdCX84YcfVKPq888/V/tiZ2enjkdS1Hfs2IGGDRuWui/STVICYWPHjlUNxvwZX5KeL1cpS2qs6mpO6MTHx6vjd3FxyXvMw8NDPS4SEhIwdepUbNq0CfqQhq1ciWUgi4iIqHK2qaReqHTdK0yCSpLlNHToUDg4OKjHSgpEmXqbRvZb9kfWJ8crATnpRikZ9IWx7UNERGR4laaP1549e1SjZu7cuSrrZ9KkSfj666/LtI7ff/8djRo1Ug0sWV5S4/fv36/XspK+3qZNG9WwkdoKzZs3V8EpybLKT4q164qXStBLiqjq7kuae9u2bVXKuywnBVClqOmsWbNUtzwpdKqbV4rTS+ZYfj/++KOqSSHdIadPn17guW7duqnl1q5dW2TfJWC2c+dO3HPPPQUer1atmmqkSZHV/AVQa9Sooaal+LwEpeQ104fslzQQC+83ERERVY42lbRjZPCawgXe161bBysrKxw4cCCvLSO1SEtiym0aCdRJG02yvuR4pfyDv7+/arMVxrYPERGR4Zl9RpY0lKTI+oQJEzBt2rS8K4aSUSWF0yVbasaMGepKWUkkC2rBggUqkCRd95577jn1uASipAEi9RTkaqCbm1uJ63jssccwb948lUqvS7+XxpJkUkkWlRQ7zV9YXsj+SgH0vXv35q1HsrmkO2H+mhQS8JIrftLVUUe2sXr1alXfSxpT77//vsr4mjlzZpGrkPpcWZRujHLVNT+5cio1v1566SV1lVK6N0otsUcffRSnT59W2VWSRSbkimZcXJzqPlBSFpvsvzQwo6KiEBAQUKZ9JCIiItNvU0lRdmnbyEW9YcOGqawnyTSXGp/Sda+0WlfShdEc2jSynyNHjlRlI6T9l380RamvKsEtHbZ9iIiIDM/sA1lSm0quFkoKvFwR0+ncubOqqSANKrn6p2t0NWjQoEAQRRoj33zzDZ5//nlVt0G68ukMGjRINUZkHRERESqQVXh5nfr162PNmjXw8fFRz0vwSbKPZP350+FlvsOHD+fdlwZUfjKqT3h4eIHhqCVgJVlausLx+Ul2k8wrqfcyzLSM0FNcOr8uCCYp9S1btizwvASWCjf4dKSRJg1aabDJ1Uzp3iiNUmkMShFTGYno7bffVjUx5PWRhqI0eIu7oinBN+luyUAWERFR5WtTCclqly6JixcvVl0SJSgk80ltqsIZWFJDKv/yEkgzlzaNZG/pglhCaquuX78eCxcuVPXAdNj2ISIiMjwLbUnjFpuhlJSUW9ZbkKt0kmll6OUTExNVd0AJXsnQzdJok7R2qQUhwSshjTPJ2sofyNKRK38SLJMGkGRlSXdCHVmfLCcNS9m2vb19sfuWnZ2Nc+fOqYZhSSTbS45Risnn7xYpWWil1fAq3AVTMtSkUbt79248+eSTakhrKZAqDUGpXSHdCoojDUZp4Ja2j0RERGTebSoZzfnatWuoW7duictLeQXJ0CouYGXKbRrpRvjhhx8WCGQJCd599tlnal35se1DRERkWGafkZWf1EQYMWJEqfNIdz+5UiYp8oZYPiYmRnX7Ez179lS3rl27qnT89u3bq0aWZFLpCpuWZNWqVSqdX0fX/VBHGnnSzVEahFu2bCl2HVLroXHjxioLrLRhse+66y7VwMzfIJPgmT7keEaNGqX2VYqaSuBO6ndJg0/873//U90z5apq4XT8jIwMVUxVNy8RERFVzjaVZDtJuYXSRnWWEZsHDBigsq/MrU0jQbzCpK1XeCRGtn2IiIgMr1IFsmQYZWmESHCppIaPNJrkeUMtv3z5cnX1LT9pRElmVX6SOi/dF0siNRsefPBBNTx0SeSKoIxmWBLdPhfXBVFHrm4WPjapsyVZXrKcNBpLI1cbJaX//vvvV/ely0D+2mFyZVWuokrXysI1xaQWmGxLrkwSERFR5W1TCd3FvNKyvkpat6m3aaTmaWHnz59X28+PbR8iIiLDq1SBLN0IOJJiriu4XphcQZMC7IZa/o033lA33ZXJ4ODgEoudS5HUkkbDkfpaUhOitBR8Ge1HUt5vpXA2V+FREwuT/ZVG3F9//VVqo08aq1KsVdL2daQovRRM1ZHXQGpWFFcYXxrEDzzwwC33n4iIiMy7TSXk4lxpbZJbdV005TbNsmXLVMZW/swrubj55ptvFpiPbR8iIiLDq1SBLLk6JsU7T5w4USHLy+iE+/fvR7NmzUq8uim3kq4qStdBqdVwp0q7ein7J2n4hb3yyitqdMbhw4eXuKzUipBuAvlHT5TgmxRGlVEf27Rpo4qoDhw4sMiykpYvQ3f/8ssvt3VMREREVH7utE0kvLy8Sm2TFNceMYc2ja6ovHSplFqoEsSaM2cOnJycVNF3HbZ9iIiIjKNSBbIkY0nqJMhwyiVdPRRyZa1GjRoGX17I0M2yntLIFb/Cw09LbQa5Olhag03IfsnVURsbmyLP6TLBZESh/KMv5nfy5MliM8buvvtuNVqPFJbv1q1bkeelxoXsW+FAm7wOsozUkJCRjmTZSZMmFVl+7dq1akjqkoJ8REREZDrutE0ky0jGlNSxKilTXbrylbRuU27TCBngR7oNTpkyRR2nBNQk8JV/X9n2ISIiMo5KNWqhXB2TFHT5vzRt27bFl19+afDlX3jhBdXgkitwpZFhnwcNGlTgMWlQSYq61GEojTT4pDC8biTEwrUm+vTpo/a/pNMqDT4pQi9DUBd2+vRpVXD1n3/+KbXRWlZSX0Iald999x2CgoIMtl4iIiIyjjttE0mwqG/fvmqQl5JIBpO0R4q7yGWqbRpdLVRpi5WWUca2DxERkfFUqkAW3TkpJi+FSou7gnm7fv31VyQnJ+Opp54y2DqJiIiIyrtNI6RMhAzqU1LdU8G2DxERkfEwkEVERERERERERGah+KIFREREREREREREJoaBLCIiIiIiIiIiMgsMZBERERERERERkVmwhhmT0QEjIiLg4uJi8BFpiIiIiG5FxsyREYerVaumRtEzV2xTERERkbm0qcw6kCVBrBo1alT0bhAREVEVd/XqVQQGBsJcsU1FRERE5tKmMutAlmRi6Q7U1dVVr6uN0dHR8PHxMeurpoLHYpp4XkwTz4tp4nkxTTwvZZOYmKguqunaJOaKbSq2D00NP4tME8+LaeJ5MU08L8ZrU5l1IEvXnVCCWPoGstLT09W8lSGQxWMxPTwvponnxTTxvJgmnpfbY+4lDtimYvvQ1PCzyDTxvJgmnhfTxPNivDaVeUdziIiIiIiIiIioymAgi4iIiIiIiIiIzAIDWUREREREREREZBYYyCIiIiIiIiIiIrPAQBYREREREREREZkFBrKIiIiIiIiIiMgsMJBFRERERERERERmgYGsKkSr0VT0LhARERFVSVqttqJ3gYiIqFKwrugdoNuTePwI4nZuRZqVJTRp6chJS829paYgJ/Xf6bzHcv+XBpRPl/tQ59VRsA+oxpeeiIiIyMCk3ZVy8RxSLoQh5cI5pJyX/8OQnZQIlwaN4d6iDdxatIZbk+awcnDk609ERFRGDGSZoYhfViJs7rTbWjZ680bEbv8HNZ59ETUGDIKVnb3B94+IiIiostNkZSHtyqW8QJUKWl0IQ3pEeInLJB49pG746lNYWFnDObQR3CWoJbemLWDt5Fyux0BERGSOGMgyM9d+XI5zC2apaZuA6nCqEQRrR0d1RU/dHJ3+/V/uO/w37eiEjKhIXPz4fWREXsflLz7Ejd9XI/i10fC+pzssLCwq+tCIiIiITF7c7h24+PFCFcDS5mSXOJ99tUA41akLpzoh6mbl5ISbRw/i5qH9SDp1Qi2bdOKIul399gvA0hIu9RuqoJYKbjVtCWsX13I9NiIiInPAQJYZufrdV7iwaK6a9mh/F7yHvwX/wEBYWupf6sy7y724unwprn7zBTJuRODkxJFwb9kWwSPGwTm4nhH3noiIiMh8SYmGK19/hkufLpI7eY/beHjCKbjev0GrumrasVYwrJ2ciqzDq1MX9b+UfJAyEQmH9iPh0D4knTwGbVYWkk4dV7fwFcsACwu4NmqKoBeHwrNdp3I9ViIiIlPGQJaZuPLNF7j40QI17XVXV4ROnYeYhIQyr8fK3gG1XhwK/56P4MKS+aqrYcLBvTgwsC+qP9oPQYNfhY2rmxGOgIiIiG4neFLWrGmNRlOmi1x0a9kpyTgzfQJitmxS992atVIBJglc2Xp6lfkllIx5jzYd1E3kZKQj8fhR3Dy0DwmH96sglzYzU/1/bMQQNZ/UOHWuF8rTRUREVR5bOWbg8tKP84JY3l3vQ8MZC2Bpa3tH65Ri7w2nz0ezxV/CKTgEyMnBtVUrsLdfL0Ss/gHanBwD7T0RERGV1aVLl9CvXz94enrC3d0dAwcORHJycqnLzJgxA76+vrC1tUWbNm1w8OBBvvAGkHr5Ig4NfioviFW97zNouuhzeLRud1tBrOJIzVKPVm1Ra/CraL54Ke76Y7dqo0kGvojftwsHXuiL01PHI/16hEG2SUREZK4YyDLxq7AXP1uMS58tVvd97n0ADabOhaWNjcG2Id0KWy39EXVHTVB1GLJvJiBszlQcHNQPN4+wAUxERFQRZs+ejS5duuD8+fM4e/YsIiIiMGrUqBLnX7Zsmbr99ddfSExMxAsvvICHHnoISUlJ5brflU3M1s04+GJ/FcyytLVD6NuzUHfEeFhaG64tVhy5YClttKbvfYym738G53oNVHfGyA2/Yu9TD+H8kvnISrxp1H0gIiIyVQxkmXIQ6+OFuLL0Y3Xf9/5eaDB5tlEaThbW1qj++FNou/J3VHu0nyo2mhx2GodfeQ6nJo9Vw0UTERFR+fnoo48wdOhQlZElWVazZs3CH3/8UeL8n3zyCRYsWICmTZvC0dFRLdu8eXOsXbuWp+02aDUadTHxxLjXkZOaAjv/amj+yTfw69G73F9P6VbY8suVCJ38rtoP6XIYvnwp9vZ9UNVP1WRmlvs+ERERVSTWyD2y9xgAANdDSURBVDLRINaFxfMQ/t1X6r5fz4dRf/xUWFhZGXW7Nu4eCBkzCQEP91UjI948cgBRf65TVyGbLvxUPU9ERETlLyEhAa6uxY9gl52djX379uH+++8v8HiPHj2wc+dOPPPMM0WWycjIUDcdyeLS1deS263IPNJe0WdeU1f4WOQCnnThi9+1Td13b9UOoVPmqHZQRR6vT/ee8Lr7XkT8/D2ufvWp2k8ZBOjaquWo9b9h8LnvQUgJ+sp6XswZj8U08byYJp6XqnteNGVYNwNZJkbeHOcXvotrPy5X9/17P456b06GRTkWbZVCos0+XIbI9Wtx9t3JSD57CodffQHNPvgctl7e5bYfRERElGvx4sV45ZVXin05oqOjVZBLamPl5+/vj61btxa7jGR4TZkypdh1paen69XYvHnzpmq3mHth+fzHknX1Mq7NnoysG7l1qDweeRI+z7yI+MwsICoKpsD23gdRq20nxP78HRJ+X42M6xE4M3U8Ln37JbyfHYysmnUq3XnhsZgOnhfTxPNimnheyqYs5RAYyDKxNPZz82cg4peV6r5085PaVeUZxNKREZL8ez6shpQ+OX44Ui+ew+Ghz6PpB5/D3i+g3PeHiIioqvrpp59U8fcXX3yx2OczMzNhZ2dX5HEbGxv1XHHGjx+PkSNHFsjIqlGjBnx8fErM/CrcOJe2gsxfGYIMciwWxw7iyruToUlPh6W9PeqNn6bqk5okX18EjJmE9AGDcPnzJYja+BsyLoTh2pQ34dSmA2pNmgk7AxWiryiV8T3GYzEtPC+mieel6p4Xe3t7vedlIMuEglhnZ0/BjV9/yhsRJ3j4uDIPuW1oXh06o/H8j3B87KtIu3oZh195Hs0WfQGH6jUqdL+IiIiqghMnTmD06NH4888/VWCqOBJ4ksyVwuQxNze3YpeRwFdxwS9pnOrbQJU2SlnmN1Xa7GxEf/MZ4lf/oO7bV6+BRu++D+fgejB1jtVroMHkdxHY/3lcWDIfCft3I2XfLhz539NoOOM9uDZsAnNWWd5jgsdimnheTBPPS9U8L5ZlWK/5fytUAtqcHJyZOSkviBX41PMmEcTSkeGgpUaWlZMzMm5EqGBW6qULFb1bRERElVpkZCQee+wxLF26FHXr1i1xPg8PDzg4OODq1asFHpfRDoODg8thT807iCUF3XVBLM8OndHyi+/NIoiVn0v9Bmp0wwbT58PS0REZkdfVoD3X16xSXfOIiIgqEwayTEDY3GmIXLdGTdd4bjDqvDbaZIJYOm5NWqhMLGtXN2TGROHw0IFIPnemoneLiIioUkpJSUHv3r0xceJEdO3a9Zbzd+nSpcAIhRK8+O233/RatioL/+FbxO/erqZrPP8SGs9ZDBvX4rPYTJ20Hb27dkfQnA/hWDsY2qwsnJ39Ds7Oehs5Gbeue0ZERFSpA1nHjh1DUFAQzp07V+S5L774Qg39HBAQoBpg58+fL3VdZZ2/skk5H4bra1ep6ZovvIzaQ94wuSCWjktoIzRfsgw2nl7ISojDkVdfQOLJYxW9W0RERJWKBKH69++vRiHs27evKr4ut/yjDBYm9a6mTp2qirvHxsbirbfeUl0RJcBFxUu/fg2XPl+ipj16P45a/3vtliNEazU5iDjxOw6sehWHV4/G6c3zcXn/ckSGbUbijVPITEuo8Awo22qBaP7pcvh276nu3/jtFxwe8izSIsIrdL+IiIgMpcw1siRN/cknn1TFQwuPavPzzz9j0qRJWLZsGUJCQlQq/H333YeTJ0+qlPfCyjp/ZXT1u6/U/05166HW4FdNNoil4xQcooJZR14fjMzoSBx9fTCazP8Ibs1aVvSuERERVQrXrl3DX3/9pepizZs3L+9xaSOcOnUKtWrVwrp16/DZZ5/hl19+Uc/dddddWLhwIYYMGYIbN26o+2vWrDH5dkVFkWBT2Lzp0KSnwc4vAN79B95ifg0iz27Gue0fIiW29PIKVjYOsHcNgINrAOxd/f/9PwBOnrXg6t+wXM6JlYMjQt+ZDZdGTXFh0Tw1AvXBF55Uj0n9UyIioioVyBo4cCC++eYbvPzyy0WeW7RoEd5//311BVHIlcHt27erxtbjjz9+x/NXNhnRUYj64zc1XeOpgXo3bHKy0nH54EokRF+BRf3O8KrZBtZ2TigvjkG10fyjr1QQKz0iHEdHDEHjOYvg0bp9ue0DERFRZRUYGIi0tLRS5+nZs6e65ffUU0+pG91a9KYNiNu1TU3LCNHZJVxAlYBXzIVtCNv2IZKiTv/7qAX8Qx+AraM70hJvID0xQv2fnZ6ons3JSlPBruICXp4126B+15Fw9W9g9NMk7crAJwfAJbQhTk4chcyYaBwfPRRBLw5F0MAhFTIqNhERUYUEsv755x/Y2toWeTwnJycvCJXfQw89hC1bthQJTJV1/sro2qrlqsiorY8ffO57UK9l4q8dxvF1k5Eaf1ndjz75EywsreFerSm8arVXN7naZ2lp3AEpHSRt/cPczKy0K5dwbPRQNJqxAF6d2IWBiIiITFdW4k2cW/iumva59wF4drwbUVFRRQJYcVf2ImzbEtyMOJr3uF/97qjb6WU4exctop+dkYL0pOtIu3kd6Yk3kJYo/1/P+z89KRJxV/Zh19dPo1qjXgi5exjsXfyMfrxuTVui1dIfcXLSaNw8vB+XP1+CpBNHETr5XbOtB0ZERFVbmaMdxQWxRExMDJycnIp0CZTaV7t3777j+YXUhshfHyIxMffKl0ajUbdbkXmkYaLPvMaWnZqC67/kjpBTve/TgJVVqfslV/cknf3KgRXSvIKllS0cvOohNfYMtDlZiA8/qG4yj7Wds7ri5xnUHl5B7eDgXsMoaew23r5oungpjo14Cannw3Bi/BsqZV0KjZaFKZ2XO8VjMU08L6aJ58U08byU/fUi83Lxo4XIiouFlbML6g4fX+T5+PBDKoAVf3V/3mM+dbuqAJarX2iJ65XseGe7unD2Ln6EydjL+3D2n/eQGHkKESd+w40zf6FWm2dRu+1Ao2fW23p5o+kHn6ljD//uK5WNdnBQPzScsUCNeEhERGRODJa2IynwxdW1kseKS48v6/xi1qxZmDJlSpHHo6Oji9TrKqmxefPmTRU0sazgdOr4335GdnISLB0cYdWhS5ErgfklRx7H1Z3vITMpQt139A5F9Q4jkAFX1HKyQ2r0SSRfP4Sk6weRHn8R2RnJiAr7W92ErbMfnANawqVaS7gGtlNBMEOq9va7CJ86Hunnz+LU22PgP2ws3Lrcp/fypnRe7hSPxTTxvJgmnhfTxPNSNklJSUY6E2QMCYcP4PqaH9V0naEjVYBHF4y8eeMkLuz4CDEXd+TN71WrA+p2Hgr3gCZ3vG2voDZo/9xyXD+5DmFbF6kMrQu7PkP40Z9Rt9MrqN70EaNm1Fta2yB42Bi4NmqK0zMmqvIQh4cMQMjYt+Hf82GjbZeIiMjQDPZt6eLiUmxjTrKm5Lk7nV+MHz9ejcqTf94aNWrAx8cHrq6ut9xHaahIZpLMX5EBE+lOeGndajUd0OcJBNSuU+x8OZlpCNu2CFcPrczNwrK2Q3CnoQhq9TS0sFABPHUsgbWAFrl1MjJSYhF3eQ9iL+9B3OXdyEiORmZyJOLC1qubq38jtHj0fdg6eRrugHx94bNkKU6MeQ2Jxw7hxgez4eHrB+8u9+q1uKmcF0PgsZgmnhfTxPNimnheysbe3t5IZ4IMTZOZibA5uRdEXZu2QECf3DIWyTHncfHv95B4dVfevB6BrRDS+VV41DDsYDYWFpao1ugh+NW7F5f2f4uLu5ciMyUWJ/+YjisHv0O9riPgXbuTUQvC+3R7AI516uLk+OFIvXwRZ6ZPQHLYaRXkYt0sIiKqUoEsLy8vWFlZqZFy/P398x4/f/48ateufcfzCzs7O3UrTIIf+gZApGFQlvmNIWrrJmTciFDdCQP7PVvsvkgNheMbpiAtIXeoZPfqzdH4wSlw8gwq8EOj8LE4uPigeuOH1E0ynKTQaOyl3eoWc2kXEm+cwN7vBqLVE0vy1mUItq5uaLrwExwbNVTVXzgzdTwcPvoKLqGNzOa8GAqPxTTxvJgmnhfTxPOiv8rwvVVVXF3+JVIvXYCFtTXqvfmOCtokRZ3Fnm+fgyY7N7PfLaCJCmB5BrUzajBJRjYM7vA/BDZ9DOe2f6SysiSgdnDVa6reqRSEd/GtZ7TtO9UKRovPv8eZmZMQ8/cfuLbyG2jS0lR2FoNZRERk6gza+pKhnjds2FDgMbnfuXNng8xfGUhw6eqKpWra994esPcPKPB8dmYqTv45E/u+/58KYlla26N+t9Fo+9QXZQ48SQNMipEGtX4GLZ9YhJaPfwArWye13j3Ln1eF4w091HOjd9+HQ83a0GSk4/jY15Aeed2g2yAiIiIqK8k8urzsEzVd49nBcKodrNpcR9a+qYJYts7+aP7oQrQb8LUKJBkziJWfnZMXGj0wEZ1e+AHede5Sj8nFx53L+uH4+ilITyq59MSdsnZyQsPp8xHY/3l1//raVTgzYyK0OTlG2yYREZHJBbKGDRuGt99+G2fOnFEBm4ULFyIhIQHdu3c3yPyVgWQrJZ8+qaYDn8ptOOhId8AdXz6Oq4dyi8B7BLZExxd+QK3WA2BhaXXH2/au3VEFxOycfJCVloD9K4cg8uwmGJKMftNk3hJYu7nnDvM89jVkp6QYdBtERERE+pI25tm5U6HNyoJDzVoIeu5/6vFTf85EStxFVbqh1j1vwyf47nILYBUmBeJbPbEYrZ/8GM4+komlxbVjv2D7F48i+vw2o21XjrfOsNGo8dxgdT9y/VqcmjIOmuwso22TiIiowgJZ0i1Qbvn16NFD1bDq0KGDKtq+fPlyrFmzJm++devW4YknntB7/sro6opl6n/3Vu3gUr+hmpbi7Cc2TleBJRme2crGHqH3jUObpz6Hk0dNg25fRttp9+zXKlNLk52Bw6tH4/L+5QbdhkNgTTSa9T4sbGyQEnYGpyaP4dU9IiIiqhCRv6/GzYP71HTImLdhaWeHa8fXqpEDRf17RsPBo/h6peVNssE6Pv8dGj/4jrrwmJOZgkM/D8fVw6uMGsyqPeQNBA1+Vd2P/ms9Tk0aDU0Wg1lERFTJAll///03GjQoOlzv8OHDERcXp0ah27dvH0JD/xumuGfPnli5cqXe81c2KZfOI27HFjUd+PRA9b9Gk4293w1G+JHcBopHjdbo+MIqBLXsrwqCGoODawDaPr1UbUuu+J3ePBenN82FVmu4IcTdm7dC/fFT1XTczq04v3iewdZNREREpI/MuFicXzRXTfv3egQerdoiOfaCysZSj4U+gOpNHzOpF1Oy8Ks3eQQdX1gJ92pNodXmqGLwZ/5ZaNC2WoFtWlig1qBXUPuVEep+zJZNODH+DWgyMoyyPSIiojtx25ESR0fHUp8vrii7KCnbqqT5K5Pw775S/zvWDoZn+9w6CBHHf0VS1GlYWFihQffxaNP/Uzi6Bxp9X2zsXdG674cIaJg72uHlA8txZO1Y5GQbrsHi16M3ar7wspqWIqLXfvreYOsmIiIiupXzH8xBdlIibNw9UOe10cjJSsORNWORk5UOB/caaPTApArrTngrto6eaN3vU/jVv0/dv7R3marpZci2WmE1n30RwW+8mXchUkpE5KSnGW17REREt4ND7ZSTzNgYRG74VU3XeGqgajRJQ+T8jo/VY9Wa9EHNFv2MloVVHEtrWzTpNR212w9S9yPP/KW6N2amJRhsG7UGvwqf+x5U0+cWzkLc7u0GWzcRERFRSeL27EDUH7+r6eDXx8LGzV1loCfHnIOFlQ2a95kDaztnk34BpdxEsz5zUKttbl3VyDN/Yv/Kl5CZGm+0bcqI2iFjJqnp+H27cGzUK8hJTTXa9oiIiMqKgaxycm3VClVk1NbLG77391KPSVH39KRIWFrZom6n3Myl8iaBs3p3v46G3SdILjsSrh3G3uUDkZoQbqD1WyB0wnS4Nm4G5OTg5MRRSDkfZpB1ExERERVHsojC5k5T0x5tOsD3gYdw/eR6hB/9WT1Wv+tIuPoXLZFhiqStVr/rCJW5n9tWO6JGn06Jv2K0bVZ7tB/qvzVNNo6bh/bj6IiXkJ2cZLTtERERlQUDWeUgJy0VET/ndqur3vcZWNraqgLvF3Z/oR6r2bI/7F38UJFqtOiLFo8uUFf+UuIuYc+3z+Hm9eMGWbcUVW307gewD6iOnNQUHBs9VGWoERERERnD5S8/RnpEOCxt7VR2UWr8FZzYmBvY8g3pptpe5kYy91s8ulC11eR4pK0Wf+2w0bbn/9CjCH17FmBpicRjh3H09cHISrxptO0RERHpi4GscnDj99WqPoOlgwMCHnlSPXZp3zfISkuAla0Tard7AabAt24XtOn/uarJkJkah33fD0bUua0GWbetpxcaz/sQVk7OyIi8juNvDkNORrpB1k1ERESkk3zuDK5+lztKdNCgV2Dr75dbBzQrFfauAWpEQFOti3UrvnXvRpunvoStk7dqR+7//iXcOP2H0bbn98BDaDh1LiysrJF0+gSODnsRmfFxRtseERGRPhjIMjJtTg7Cv/9aTQc89BhsXN1UkEgCWaJ22+dh6+gBU+EW0BjtBnwNR48gVQj10C/DERX2t0HW7VQ7GA2nz5eK/0g6eQxnpk2AVmOc0XeIiIioara7zr77jipn4BQcgsCnn8fZfxYgKeoMLCyt0azPbDXgjTlz82+I9s9+A2fvYGhyMlWQ7uKeZdBqtUbZnk+3B9Bw5gJY2NggOew0jrw2iJn1RERUoRjIMjIZvlhS2yUtu3q/Z9VjF3Z9oa4KSgArqNUzMDUyamK7AV/BrVpTQKvB0d8mICnaMHWtPNt1QsioCWo6evNGXPpssUHWS0RERBTxy0p1sUxqO9UbNwVR57fgysHc8g4hdw+Du7RtKgEH1wC0fWYZPIPaqftntyzEqT9nQqPJNsr2vDvfg8azF6mumqkXz+HwqwORER1llG0RERHdCgNZRiRXxq6uWKqmfe7pDodqgUhLvI4rh39Qj9Xp8D9Y2znBFNk6uKPl4x+ooakl6Hbo5+EGGyGn2iNPIrB/7ug7V776FJHr1xhkvURERFS1C7zrLpBVe6w/rKt74cSGd9R9nzqdUatN7gXFysLGzgWtnliMao37qPtXD/+II7+MRE5WmlG259n+LjSetwSW9g5Iu3IJx0YOUaUziIiIyhsDWUaUePRQ7lVBGcr4qYHq//M7PoE2J0vVaKjR7AmYMhXMemyBquOVdvMaDq8ZA01OlkHWXefVkfC66x41HTZ7ClJPHDHIeomIiKhqiv77j9yapLZ2qDloCI7++qYaXMfO2ReNe05Vo/9VNpZWNmj84BTUvWuouh9zcTvO/zEWWRnGGWHQo3V7NJm3BBa2tmoU6uPjXocmI8Mo2yIiIipJ5ftGNyFXV+QWGnVr3hquDZsgOfYCrh1fqx6r2+llWFrbwtQ5e9dF04dmyuDPiL+6H6c3zzXIei2srNDgndlwDgmFNjsb12a/g/Tr1wyybiIiIqp6rq/9Sf3v0+1+XDz0Ve7oyxaWaNb7XZOqR2poUrg+uONLaNJrhqoDlhYbhiOrRyEn2zgBJveWbdFg8mzVffPmof04NXW8qk1GRERUXhjIMpLUyxcRuz23SHrg07nZWOe2fahqTjl51UG1Rg/BXMhohlJXQlw99AOuHvrRIOu1cnRE47mLYevlA01yEk5NGg1NZqZB1k1ERERVR8ql80g8clBN27Wvi8v7cwfVqXvXK/Co0RJVQbVGvdCk138XH4/9LoPqGCfAJCUz6o58S03H/P0Hzr0/22jF5omIiApjIMtIwld+LUWy4FCzNrw63q2uCkae/Us9F9L5NVhYWpnVu7F2uxfg36CHmj61aTbiruw3yHrtfP0ROnWOKoaffPoEzr8/2yDrJSIioqrjxq8/q//t6tbAxdO5o0V71WqPOu1fRFXiV/8+VG/7ipqOPPOXyqQ3VoCp+uNPoeZz/1PTEatW4Oo3XxhlO0RERIUxkGUEmXGxiFyX24WwxlPPw8LSEmFbF6n7bgGN4RuSWxvK3NLWG/d4B65+DaHVZOPwmtFITTBMV0C3Zq3gM2Bw3mhDkRt/M8h6iYiIqPKTbO7I9Wsh4ZqsVlbISr8JWyev3K52lbAu1q14h/ZB7X8DeDJi48U9XxptW7WGvA6/no+o6YsfL8SNdRzAh4iIjK/qfbuXg4ifv4cmMwM2Hp7w69EbsZf3IvbyHvVcyN2vq6CQObKysUeLxxbA1skbWWkJOPTLcGRnphpk3R4P94XX3feq6bOzpyDlwjmDrJeIiMicZWRkYMKECYiJiSl1voMHD2LYsGF47bXX8m6jRo1CVRCzbTOyEuKh9bZGaka4eqxJr+mwc/JCVRXcaSiqN8kNMMnF1GvHVhtlO9KmrTduMjw7dFb3z8x6G7G7thllW0RERDoMZBlh6GcJZInqTzytRnUJ2/qBuu8V1A5eQW1hzuxd/NDi0fdgYWWD5OgwHPt9IrRajWEaQm9NhUNgTWjS03DirRHITkkxyD4TERGZo6ysLPTt2xdz587FjRs3Sp330KFDOHbsGEJDQ/Nu9evXR1Vwfe0q9b91Oz/1v3v1ZvCu1QFVmbSrGj4wET7Bd6v7JzZMQ/T5rUbZlqW1DRpOnw+XBo2BnBycnDASif+O2k1ERGQMDGQZmKS2y1VBSzt7VHu0H6LC/s4dNUdlY+UWTDd37tWaotEDk9R0VNhmnN/xsUHWa+3sgoYz3lPDZqdduYizs95m4VAiIqqynn32WbRq1QpNmzbVK+jVpk2bAhlZL730Eiq7tIhwJOzbDa09kGYbrR6r2fKpit4tk2BpaY1mfWbDrVpTaLU5OLxmLBIijBNgsnJwRON5H8KhRpC6IHl89KtIvXrZKNsiIiJiIMvAIlb/oP737/UIrF1dEbZtsbrvV+8+VR+rsqjeuA9qtXlOTZ/f+SlunP7DIOt1DglFyJjcIFn05o249uNyg6yXiIjI3AwaNAiTJ0/Wa14JZNna2qKqFnlHEzeVIW7n5AO/ermlCkjKQjig5WPvw8mzFjTZ6Tj40zCkxF4yyktj6+GJJu99DBtPL2QlxOHYiCHIjCu9SywREdHtsL6tpahYGdFRSAk7o6b9HuyDiJO/IyX2AmBhibqdX610r1q9Lm8gOeY8Yi7uwLF1b8PRoyZc/ULveL0SBLx59KBqnF5YNE+lqrs1aW6QfSYiIjIX999/v97zZmdn4/jx46qelmjXrh369OlTau0tuekkJiaq/zUajbrdiswjo+HpM6+xaLOzceP31dBaANmBGki19+rNHgcsrMq0X6ZwLIZS3LFY27uhxeOLsXfFQGSmxGD/j6+gzdPLYO/sY/Dt2wVUV5lZR199AekR4Tg28hU0WfwlrB2dyryuyn5ezBWPxTTxvJgmnpeyKctnJANZBhS/d2fui+rqBqe6ITi09M287CVnr9qobCwsrdC09yzs+fY5pMRdwqGfh6P9c8sNUly17si3kHz6JJLDTuPUpFFoufRHdaWPiIiIiurevTtsbGzUdGRkJN58801899136lacWbNmYcqUKUUej46ORnp6ul6NzZs3b6of55aWFZPgn7x3JzJjoqCpYYkcbRosLK1hX70LoqKiyrQeUzgWQyn5WKxRq9tUnNswBumJ17Fv5cuo+8A8WNmWPcB0S+5eCBgzGeEzJyD57CkcGfMaAt+aDot/35/6qhrnxfzwWEwTz4tp4nkpm6SkpIoJZO3Zs0elthcuNtmoUSO4u7sXmT8uLg6nTp0qUAfJzs5O1XgwR3G7t6v/Pdp2RPjxX1RDQYqiB3cagsrKxt4VLR57H7u/eQbpSTdwePUotOn/GSytytZYKczKzh4NZy7AgReeREZUJE69MxZN3/sEFlZWBtt3IiKiyqJx48bqpjNx4kRVW2vr1q24++7cgt/5jR8/HiNHjiyQkVWjRg34+PjA1dVVr8a5tPFk/or6YR69bVPuRFPZ32T4h96P6kFlL3BvCsdiKKUei68vXBwX4uCqoUiPv4hr22eixROLYWVtZ/gduf9BOEGDM1PHI/XIAcR/sRj1J86ARRle3ypzXswMj8U08byYJp6XsrG3ty//QJacpHHjxhUJZJ0+fRoLFy7EgAEDiiyzZs0alQJfp06dvMekvsPmzZthbrQ5OXkZWW5t2uDsro/UdM0W/eDgGoDKzMkzCM36zMGBVa8h4dphnPxjJhr1eFt9Yd8Jh+o1EDppJk68OUwVcr289CPUGvyawfabiIiospILgz179sSRI0eKDWTJ83IrTH5k6/tDW77nyzK/IWVERyJu1zZo3IEsm2T1WM1WT932vlTksRhaacfiHdQGTR+aiSNrxiI+/ABOrJ+EZr1nqyx7Q/Pv0RtZcbG4sHgeov/4HXY+vgh+dVSZ1lFVzou54bGYJp4X08Tzor+yfD5aGnKjf//9N7Zv3553k/sSVevWrVuxy0jQq3///gWWMccglkg6dRzZSbn1JZIdriEzNR5WNo6o034QqgLv2h1Rv+sINX3t2C+4dmyNYdbb+R7UGPCimr689JO8rDciIiIqXUJCApydnSvly3Tjt9VyFRXaho7qvltAE7gHNKno3TIL/vW7o0H3cWo68sxfOLVpjtFGia7x9EAE9n9eTYcvX4qIX1YaZTtERFS1GPWSwKpVq9C2bVtUq1atxEBWcVcDzZEuwOIYGoLw4z+p6Vptn4OtY9Wp6xTUegACGvZU06c3vWuwUXFqvzQMbi1aA1otTr3zJtKvRxhkvURERJWVZGL9+uuvZSoYby60Gg1u/PYztHZAlm+2eqxmy/4VvVtmRXoM1OkwWE1fPbQSF/csNdq26rw2Cj739lDT5xbMws0jB422LSIiqhqMWux9yZIlpQ4breuGeOzYMfV/vXr1Sg1smfIIO7pAlqaJA7Izk2Hj4I6arZ4x2ugkpjoCQui945Bw7SjSbobjyK/j0PbpZbC0tr2zY7G0ROg7c3Bw0JPIio3BiYkj0WzJMlia4DDjpnpebgePxTTxvJgmnpeqe14q6vP+6NGj2LlzJ15++WV1f+XKldi2bZvanytXrmDfvn346KOPUL16dVQ28ft3I/36NeTUlxIGGtg6ean6WFQ2de96FRnJMbh2bDXCti6Ci09d+AQX7YZ6p6QuVv0J05B25ZIaxOfEhBFo9eVK2Pn685QREZFpBbKkgSWj5tx3330lziNFRX/66SfV8JJ5pfj7hx9+iH79+pnVCDs5STdV10JJyk7SXlaPeTd8AnEJKQDkVrVGQAjsNAZh60ciKeo0jv4xB9Vav2SQY/Ef/hauTh6N5FPHcWLOVPi99DpMjSmfl7LisZgmnhfTxPNSdc9LWUbYuV1STzQoKKjAY56enqhd+78RkWvWrInQ0FBYWVmhV69e+Prrr9U8ldGNtT9BawFo6klR2CzUaPbEHQ8yUxVJ3ZaG909AakI44q/ux9Hf3kL7Z5er2qeGZmXvgEbvfoCDL/ZTdbNOjB+O5h9+BctK0jODiIgqSSBLsrGGDBlSasHvxx9/XN10du/erRpf0h0xf+PM1EfYiT52UHV7swiwR052buAqpE1f2Dn7oEqOgODrC23Sqzi37QNEn/wZgQ3ugXftTnd+LL73wfr6G7j44QIkbFgLv7Yd4Ht/L5gSkz4vZcRjMU08L6aJ56XqnpeyjLBzux599NEijwUGBqqbTocOHdStssuMj0PM1k3QVLOAxioLFpbWqNH8iYreLbMlAUAZsGf310+r0acP/TIC7Qd8A2s7J4Nvyz6gGhpMm4+jw/+nLgCfnTsV9SdMv+PBgYiIqOoxSiBLrn5KptWZM2fKtFz79u3x8MMP459//ik2kGWqI+zE78kdrdC6qaRIh8MtoDEcXP1QlUdAqNN+IOKu7EHc5T04sX4yOr7wI+ycvO74WGo8MwiJx48gdutmhM2dCteGTeBYsxZMiSmfl7LisZgmnhfTxPNSNc9LZfisNyeR69dAm52NnHpSXkADv/r3GfXCYVVg5+SJ5o/Mx94VLyAl9gKOrZuE5o/Mg4WF4d/bHq3aInjYGJxf+C4i162BS/2GqN73GYNvh4iIKjejtL6++uorPPjgg/DyKjlwURLJrEpOzh1G2RxId4X4PdtVt8JM19xsLN+6XVHVSeOnSc9pqlZYZmqcahRptRqD/CAJnTAd9gHVoUlLw8lJo6DJVzeNiIiIKidpc11f+zM0boDGLbdNEdTyqYrerUrBLaARGj4wSU1HhW3Ghd1fGG1bErjy6/mwmj73wRwkHNxrtG0REVHlZJRAlhQX1RUfLYu0tDRs2LBBdS00FynnziAzNgZaZyAzK1495hvCQJawd/FF4wenqunYiztxed+3BnnNrV1c0WDaPFhYWSMl7AzOL5prkPUSERGR6ZLR7tKuXER2cG7z1dW/IdyqNa3o3ao0qjfujZr/BgbPbfsQ0ee3GWU7clGy3pi34dKgMZCTg5MTRyH9xnWjbIuIiCongweyNm3aBBsbG3Tq1KnYQJWMpKNz/vx5bN++HVu3bsW3336Le+65B61atUK7du1gLuJ271D/WzXIzT5zcA+Ek1dwBe+V6fCte3deo+js1g9w88ZJg6xXuhTWHjpCTUf8/D2iN/9hkPUSERGRabqxdhW0toCmhpW6L+0L1lcyrPr3jIRHYCvJf8PR38YjJS53ECNDkyLvjWYthI2HJ7IS4nFi3OvISU8zyraIiKjyMXggS4Z7njQpNzW5MCnmPn/+/Lz769atw7hx49T8GzduxBtvvIHly5fDnMTt2a7+1wRa53UrZKOqoHpdh8PZpx60mmwc/XUcsjNTDfLaB/Z/Dp6duqjpM7PeRtq1qwZZLxEREZmWrMSb6qJVTi0LwEILW0cPBIQ+UNG7VTmLvz88F/YufsjOSFbF37MzjDMCt52vPxrNXKgy7JPPnsLZd99R3UeJiIjKPZAlgam+ffsW+5xkXL3//vt594cNG6YysrZs2YJvvvkGTz1lXnUOslNSkHjkELR2QIY2Tj3G+lhFWVnboVnvWbC0tkdq/BWc+utdg7z+ql7WxBmw8/VDTkoyTr09BpqsLIOsm4iIiExH1B+/IycrAzl1crOxAps9AUtrKfhOxir+bmllq4q/H18/2WgBJrdmLVF35Pi8cxz+/ddG2Q4REVUuHGrnDkhxSm1ONjTVbVQKto29G9wDmxvu7FQizt7BCO02Wk1HHF+L66c2GGS9Nm7uaDBlLmBlpYZyvvjxQoOsl4iIiEypyPsqaAIsoLUHLCytUaP5ExW9W5WajMDd8P4Jajry7F+4uOdLo20r4JEn4d/7cTV9Ycl8xO/bZbRtERFR5cBA1h2I353brdCynpv63ye4Mywtc7sYUlGBzR6Hb7171fTJjdORmnDNYFfzag1+VU2Hf/cVYnf8w5efiIiokpALVSnnziK7Tm6z1S+km+r6RsZVvcnDqNmyv5oO27oY0Rdy272GJhn2IaMmwKVRM0CjwclJo1kugoiISsVA1h1cHYzbvR1aKyDTIUk95lM3t14TldxQafTA27l1FzKTVRFRTY5hugLWfHYwPNp0UNOnp01ARtQNngYiIqJK4Mban6BxA7TeFup+zVbmVYrCnNW/ZxQ8AlvmFn//dTxS4v8btMmQLG1t0WjmAth6+yA78WZu8fc0w9RUJSKiyoeBrNuUdvUy0q9fg8bHAlptDiysbOBdq6Nhz04lZOvghiYPzZR+AbgZcRTnd35ikPVaWFoidPIs2Hh6qQbQycljoc3ONsi6iYiIqGLkpKYi6q91yPk3G8vFNxTu1VnGobyLv9s5+yI7IwmHpfi7gQbtKczOxze3+LuNDVLOh+HsLOPV5iIiIvPGQNZtkmwspY6T+s8rqC2s7XKnqXSeNVohuMNgNX1h1xeIu7LfIC+Zrac3GrwzW1K/kHjkIC598SFPBRERkRmL2rQe2dmpyAnMbbIGterP0aHLmZ2TF5o/mlv8PTnmPI6vf9toASbXxs1UN0MRs3kj4n5ZaZTtEBGReWMg6zbF79kB+QrP8dGo+77BXQ15Xiq9Oh1f+veKqhbH101EdnqiQdbr0bo9ag4coqavfP0Z4vbuNMh6iYiIqPxdX/sTcoIsACvAxsED/g0e5GmoAO4BTdDw/rfUdOQZKf6+1GjbCujzBKo9llubK2b5F4jfv9to2yIiIvPEQNZt0GRkIOHgPmg9AY1FpnqM9bHK+MaztEbTh2bC2s4ZGclRuLprgcGu7tV64WW4NW8thcxweso4ZMbGGGS9REREVH6Sz59F4smjyK6d21wNbPYorKzteAoqSPUmj6BGiyfVdNjWRYi5aLyLhcFvvAnXJi1UW+7M1PFsyxERUQEMZN2GhCMHoMlIR041K3Xf1b8R7F18b2dVVZqDWzVV/F0kXt2Fa8d+Mch6Layt0WDKbFi7uSMrPg6npoyDNifHIOsmIiKi8nFjzSpoAiwARwtYWFihRvPcIApVnNBuY+Ae2CK3+PtvbyEt0TiD61ja2CB0ymxYubgiKy6WbTkiIiqAgazbEL97R+5ETXv1n29ddiu8Xf6h96Na4z5q+szmeUiJuwxDsPPxQ+jEGWo6Yf9uXPnmc4Osl4iIiIxPq9EgavNGZP9b5N035B44uPrzpTeB4u/N+8yBrZMXstIScGTtWIONQF2Yna8//IeN/a8t9zXbckRElIuBrNsQt2c7NM5Ajm2Guu8bwkDWnajfbQxsnQOgyU7Hsd8nGKxB5NWpCwKfHqimL32+BAmHDxhkvURERGRcSadPICM7FlpvC3W/Zqun+JKbCDtnHzR96N28EajP/LPAaNtybt0e1Z96Xk1f+mIJEg7tM9q2iIjIfDCQVUbpN64j9eJ5aPwt8rrHOXvXNca5qTKsbZ1Q864xuQ2i68dxYddnBlt37SFvwKVRU0CjwanJY5GVEG+wdRMREZFxxO7Ygpx/s7GcferBI7AlX2oT4hXUBiF3DVXTVw6swI0zfxptW7WGvA6XRs3+bcu9icz4OKNti4iIzAMDWbcxWqHQ1rBV//vU7cphoA3AybcharcbpKYv7PoCCdeOGKzGQsOpc2Hl7ILM6EicnvaW6q5AREREpit2+9/IqfZvNlaLJ9nWMkG12w+Cd5271PTx9e8YrDxEYZbW0pabA2sXV2TGRLEtR0REDGTdTrdCrS2Q45pbPNy37j18GxlInQ7/g6t/Q2i1OTj6+0RkZ6QYZL32AdUROmGamo7btQ1Xv/3CIOslIiIiw0uPvI7EhLOArQSyLOBXrxtfZhNkYWGJJr1mwN41ADmZKTi8ZjRystKMsi1py9V/K7ctF797O64uX2qU7RARkXlgRlYZaLKzEL9vN3KkW6EFYG3nAo/A5sY7O1WwgGjTh2bBysYeaQlXcXrzPIOt27vLfaje7zk1ffHTRayxQEREZKLidmzJHa0QUF0KbR09K3qXqAS2Dm5o/vBcWFhaIzk6DCf/nGW018q7y72o/uQANX3x0w9w8+hBnhcioiqKgawySDpxDDkpydAE5L5sPsGdVfCFDMfJMwj17xmtpq8d+wWRZzcbbN11ho74r8bC22OQGRdjsHUTERGRYcTs+Ac5/wayfOsx893UuQU0Rmi33LZbxPG1CD+62mjbqjN0JJxDGwI5Obm1T28mGG1bRERkuhjIKoO43duhtQQ0fv8OBV2XoxUaQ2Czx+ETfLeaPrFxKjKSow1XL2vaPFi7uiEzNkYVDNXm5HYRJSIiMlXXr1+HRs/6jikpKbh27Zre85uanLRUxJ3bAzjkBrL8QhjIMgc1WvSDf2gPNX3qr1lIjDxtlO1Y2tqi4dR5sHJyRkbkDZyZMRFardYo2yIiItPFQFYZ62NpfC0AS61Kofau3dF4Z6YKs7CwQKMek2Hr6IGstARVQNRQjRR7/wCEvp2b9p5wYA8uL/3IIOslIiIyhtWrV6NmzZo4fbr0wEBOTg5eeeUVeHl5oWnTpmqZP/803khyxiIlHLK9cy8yOXuHwMGtekXvEunbdntgEpw8a0OTnYHDa8YgKyPJKK+dQ2BN1B8/RU3Hbv8H4d9/zXNERFTFMJClp8y4WCSfPgmN1McC4BnUFtZ2zsY8N1WanZMXGvV4R03HXNyBq4dWGmzdXh3vRo3nBqvpy0s/Qdy/I1ESERGZko0bN2LKlCkICgq6ZYbV4sWLsX//foSHhyM2NhYffvghnn76acTEmFc3+th89bH86t9b0btDZWBt54Tmj8zNq3V6fN1ko2VL+XR7ANUe7aemL364AIknj/FcERFVIQxk6Sl+707IV3HOv/WxfOt2MeZ5oX9f48BmT6jX4sw/C5Ace8Fgr0vtwa/BrXlrQKvF6SnjkBEdydeciIhMymeffYb169fD3d39lvMuW7YMs2fPhre3t7rfp08fdOnSRWV0mQutRoPoQ5uhddV1K+RohebG2bsuGt4/SU1HhW3G5f3fGm1bwa+PhVNIfWhzsnFq0mhkJyUabVtERGRarA25sqNHj+K7774rcPXFzs5OXU0siVwp/OGHHxAdHY3WrVujV69eMNn6WB5yQLn3WR+rfNS/ZxTiruxFavwVHP31LbR/9huDFNi3sLZGgylzcGDgE8iKj8PJt8eg2aIvYGnN4v1ERGQaVq1apdd8mZmZOHbsmApc5XfPPffgwIEDGDw4Nws5v4yMDHXTSUzMDQJI5pc+9bVkHmnvGbIWV9Kp48iwl+LdVrB39oOjV3C51PoyxrFUFFM4Fv8GDyIu/CCuHfkJZ7e8D1f/RnCv3tzwx2Jjg9Apc3H4xf5Iv34Np2dMQoMZ76lujqbGFM6LofBYTBPPi2nieSmbsnxGGjSQtW/fPmzduhW9e/fOe8zW1rbE+a9cuYL27durW0hICEaOHIk1a9bg008/haldIZSMrJxqudlYrn4NYe/iV9G7VSVY2zqgyUMzsPfbgUiKOo1z2z9CvS6vG2Tddj6+Kph19I3/IfHIQVz6ZBHqvDrSIOsmIiIqL3Ix0MPDA1ZWVgUe9/Pzw6ZNm4pdZtasWcVeaJR1paen69XYvHnzpvpxbmlpmAT/mD/X52W+O9foqPalPBjjWCqKqRyLZ+PnEXf1CNLizuHwmrEI6bUYNg63ziws87HYO8J3yBu4vnAWYrduwtmvPoNHz0dgakzlvBgCj8U08byYJp6XsklKSqqYQFZWVhY6dOiAcePG6TX/tGnTVP2GefPmqfuyXJMmTXDo0CG0aNECpiL5zElkJcRD0zK3gejDboXlyj2gCYI7DcG57R/i4p6l8K7TCZ41Whlk3R6t2yPoxaG4/PkSXF3+JVybtYT3XRyNkoiIzIcUei8cxBLyg1meK8748ePVBcT8GVk1atSAj48PXF1d9WqcS+aLzG+oH+ZXju+Ctl5uNk2tZr3g4euL8mCMY6kopnQsbo8twO5vnkZWagxu7F2Alo8vhoVl0ffpnR6L7xNPQXv+DG78+jOiv/oE1TvcBef6DWFKTOm83Ckei2nieTFNPC9lY29vXzGBrOzsbNWVUF+//PKLKkyqI1cTJbC1du1akwpkSTFwjRPyajb4hjDQUd5qtx+EmAvbkRBxFMd+n4iOL/wAGzsXg6w76PmXVEZW/L5dODPtLTgv+xH2ARwliYiIzIPU0EpISFDZHvm7VcXHx5dYX0vaa8W12eRHtr4/tGVbZZm/NBlRN5CUel51K7S2dYFnYAtYlOMPfkMeS0UzlWNx8qyBJj2n4dAvwxF3eQ8u7v4cde96xSjHUnfEeCSdOIaUC2E4+dZwtPh0hcq8NyWmcl4MgcdimnheTBPPi/7K8vlo8Iyss2fPqnR10a5dO3TrVnyhTrnyl5ycjFq1ahV4PDQ0FFu2bCl2mYqq5yD1sXQj6Ni7BsDJq26F93Gvev1tLdGo5zTs/uoppCdex6k/30XjntMMswMWFqj39kwceqEfMmOicGLiKDT78CtY2pS9XlbVOy/mgcdimnheTBPPS9lfr4omGVQSsLpw4QKCg4PzHj916hTq1asHcxC7c+t/oxXW61amzB0yXXLxt1bbgbi0dxnO7/wUrn6h8A25x+DbsbJ3QMPp83HwpWeQEXkDx8e8imZLlsHaycng2yIioopn0EBW165d8wJNkZGRGDRoELp3765G3SksLi5OZWAV5uXlpZ4zlXoOOSnJSDx+BDkd/63ZUK1NudVsKE3V7G9rh4DWQxC+awGun/wdNl5N4VHbcKNH+g0fj6tvj0LyqeM4MW86/F58tczrqJrnxfTxWEwTz4tp4nkxXj0HY7rvvvtUcfg333wzL0teRixcvnw5zEH0jr+g8fk3873evRW9O2RAIXe/hpvXjyH+6gEc+XU82vT7BO7Vmxn8NXasVQeNZi7EsZFDkHz2FE5OGoUmcxarAX6IiKhyMegnu3QHzN8lcPr06WjatCl27dqlamflJ0XgZZSdwiQQVlKB+Iqo5xDzzxForTXQeuW+VDWbPAivcqrZUJqq2t/Wx2cAMmMOIyrsb0TsXYQa9TvA0T3QMDviey+shryOSx8tRMLvv8C/fSf43HN/mVZRVc+LqeOxmCaeF9PE82K8eg6GPk8pKSlwccntZj9mzBiVBS/tokaNGmHBggUICgpS2fGmLic9DXHh+wAf6XplC6+gthW9S2RAlpbWaP7Ie9i74gWkxF7AwZ9eR7tnvoKTV8FeGYbg0bod6r81Daenjkf87u0ImzcdIW9ONsmRDImI6PYZ9RKFk5MTHnroIRw8eLBIIEt+HEsgSjKp8jcCIyIiEBAQYDL1HOL37ITGzwKwAKztnOFVs7XJ/Kivqv1tG/WYjMQbJ5GeFIljv41Du6eXwdK65NExy6LmM4OQePQQ4nZsQdisyXCt3xAOgTXLtI6qel5MHY/FNPG8mCaeF/2Vx+djnTp14OzsXOCxP/74AytWrMDXX3+t7jdr1kxlZM2cORM3btzAXXfdhZ9++gnmIH7/HmT7SFF6S3jX7ggrm4oJDpLx2Dq4oVXfJdjz7XPISI7G/h9fQfsBX8PO2cfg2/Lr0RvpNyJw6dNFuL52FeyrVUfN5/5n8O0QEVHFMXrrS+pgFXe10sbGBg0aNMC+ffsKPL5nzx7VGDMF0qUqfs925Pxbs8G7zl2wtCp73SQyLFsHdzTt/S4sLKxUQOvMloUGW7cUlg2dNBN2fgHISU3BiQkjkJNx626rRERExvLDDz8UqSnao0ePvCCWzj333IM///wTx44dw0cffVRioXdTE7t9U+5FQwlChHav6N0hI3FwDUCrJ5bA2tZZ1Ts9sOo1ZGckG2VbNZ9/Cf69H1fTFz9+H5EbfzPKdoiIqBIGss6cOaNGIJQ6WcXp27cvFi78Lwhx8eJFbNiwAb1794YpSL14HukxkdD4/luzoa7hi1PS7fEIbIG6nXNrWF05sAKRZzcb7KW0cXVTBUOlpkJK2BmEzZmqgppERERkWFqNBpGn/gZspK1lCZ/gznyJKzEX33po/ugCWFhaIynqDA6tHgVNTpZRskpDxkyER/u71P0zMyYi/sBeg2+HiIgqQSBLioqOGzcOY8eORf/+/dG5c2fMmzcPNWvmds06ffp0gauHUu9KRtiRmg7Dhg1Dp06dMHnyZPj7+8MUxO3ZkVt41NpCfeF61+lY0btE+dRuNxDetTup6ePrJyPt5jWDvT6ujZoi+I3cgrmR69fi2g/f8rUnIiIysOQzJ5HpmDsKtbt/U9jY37rmKZk3r6A2aNJrupqOu7xHteG0WsOP/mlpbYOG0+bDOSQU2uxsnBj/BlIunDP4doiIyMwDWZLCLjepf/Xoo4/iyJEjeP755/OelyLuuqKkuhpaUgh+yJAhqFu3rsreGj58OExFZmw0NNVyh3/2rNkaNnb/7TtVPAsLSzTpNU3VV8jOSMKRtW8a9Kpetcf6w/+hR9X0+cXzEL9/t8HWTUREREDM9r/zSjgENHmQL0kVEdCgB+rfM0pNXz+5Dme3vG+U7Vg7OaHxvA9h5+ePnOQkHBv1CjJiKn70cSIiMqFAVteuXVVGloyc069fvyJF26VYqQS48pP6WTLvG2+8gdatW8OU1Hl1JKwaeKlp37pdK3p3qBi2jp6qXhYsLHHz+nGDNoRUWvroSXBp1AzIycHJSaORFhHO80BERGQgkYf+AOx1JRzY1qpKarV5FkGtn1XTl/Z+hcv7lxtlO3Y+vmgy/2NYObsgI/I6jo8eipzUVKNsi4iIyof5D0VmRDevn0Bmaqya9qnbpaJ3h0rgWaMV6t71ipq+vP9bRIX9Y7DXytLWFo1mLoCtlzeybybgxLjXkZPGxg8REdGdyoiORHLWZTXt5FYb9i5+fFGrmPr3jIB/aA81fXrzPNw4vdEo23GqUxeNZi5U9U+Tz57CyUmjVHdDIiIyTwxklSLq3N/qfxffUDXSCpmuOu1fhFet9mr62Pq3kXYzwqBX8hpK48fGBinnzuLMzEks/k5ERHSHYrZvgSavW2FPvp5VtUxEz6nwrNlGSv/j6O8TEXdlv1G25dG6Heq/NU1Nx+3ahrD5M9ieIyIyUwxklcLJIwju1ZrCN4SjFZpHvayZsHPyQXZ6Io78Os6g9bLcmjRHyKgJajp600Zc/eYLg62biIioKorcsx5a59xAll/9eyt6d6iCWFrbovmj78HZpx60OVk49MtwJEWHGWVbfj16o9ZLw9T09TU/sj1HRGSmGMgqRfUmD6PdgK8R3PGl8jsjdNvsnKRe1szcelkRRxG2dZFBX82APk+oAvDi4ifvI3bXNoOun4iIqKrISU9DQvRhNW1n7wMnz9oVvUtUgWRApVZPLIa9awCyM5Jx4MdXkZ54wyjbqvn8S/Dv/biavvjxQkT+8btRtkNERMbDQJaeRb/JPEhqet1OL6vpS/u+RtS5rQZdf/Abb8KtWStAq8WpyWORejW3tgcRERHpL+HAHmT7atS0f8MH2NYi2Lv4olXfJbC2d0VGchQO/vSaGpXa0NRgPmMmwqP9Xer+mekTeHGSiMjMMJBFlbJelmdQOzV9fN0kpBnwip6ljQ0azngPdr5+ahjnE28OQ3ZKssHWT0REVBVEbl8PrUfuhUL/hvdX9O6QiXD2qoOWj70PS2s7pMRewKW/pyA70/CD7Fha26DhtPlwDglVRd+Pj30NN35fbfDtEBGRcTCQRZWOhaUVmj40E7ZOXshKv4mjv75p0HpZtp5eaPTuB7C0tUPqpQs4PfUtaDW5V5WJiIiodFqtFtEXcrvnW1s5wS2gMV8yyuMR2AJNe89SpSJSoo7jwMqXkJESZ/BXyNrJCU0WfgqXhk2AnBycmTERl5d9wgLwRERmgIEsqpTsnLzQ9KFZEtZCwrUjCNu2xKDrdwlthHrj3lHTsds24/LSjw26fiIiosoq+ewpZLrmZjP7BHdRA7YQ5ecX0g2NH5wKWFghMfIk9ix/HinxVwz+Itl6eKLZoi/g2amLun/p00UImztVZWkREZHpYsuBKi2voLZ5hfov7V2G6PPbDD7yTWD/59X05S8+RMyWTQZdPxERUWUUtXUDNF653QqrNXuooneHTFRAw56oc+9UWNk4IC3hKvYufx43rx83+HasHBzReNb7alAfcX31jzjx1gg1IAEREZkmBrKoUpNAlhSAF8cMXC9L1Bk6Au6t26vp09PGI+XieYOun4iIqLK5ceIPwNIClrDJ+44mKo5LtVZo3e8z2Dp6IjM1Hvu+H4zoC9sN/mJZWFsj5M3JCBr8qrofu/1vHBk2GFkJ8TwxREQmiIEsqhr1shw9kZWWgEM/v66GdTbY+q2t0XDaPNhXC0ROaipOjn9DFYEnIiKiojKio5CKCDXtEdASllY2fJmoVK7+DdFuwNdw9KiJnKx0HPrpDVw7ttoooxnWGvQK6o2fAlhZIenEERx6+VmkRYTzDBERmRgGsqjSs3P2QbM+c2BhaY2kqLM4vGaMQYu/27i5o9G778PS3gHp4VdwfcFM1lYgIiIqRsz2TdD45nYrrN7qEb5GpBdH90C0e2aZGhhAq83B8fXv4PzOz4xSmD2g9+NoPHuRatelXbmEQy89g6QzJ3mmiIhMCANZVCV41myNxj2nqunYS7tw8o8ZBm38ONetj9CJ09V0yqF9OPfeTI56Q0REVEjEgbWAtQWgtfx/e+cB19T1xfEfkLD3lqnIUsSBe8+6d2vVWrVba7cd/u22u7WtHba1tVttrbbuvfdERBEVAWXI3nuT/+fckJAwFDRIAufr5/r24913X5Lzzj33d+DQfhDfH6bBUHQ9DTN08BoolqOOfYcrez+CrLJC43fRrt8gdFn+K6TWtijLzMCFZx5B5unj3FoMwzBaAjuymFaDS8ex8Bn0vJinkPToEz9p9PwOw0bB4/EFYj55y7+I/UWzmRIZhmEYRpepKClGTu5VMW9p7QuJkVlzXxKjY0gMTdB16jK4Bk4Ry/Gh6xG6+RUx5FDTWHYMRLefVsPY1V3IR1x65Rkk79ys8b/DMAzDNB52ZDGtina9H4VbF3lWmujjPyDh0haNnt/jkXmwGiXPwBT76wok/LdWo+dnGIZhGF0l6+wJVDhUinmXbhOb+3IYHUVfX4KA0W8rM1OnRh5E8Lp5KC3K1vjfMnHzQLcfV8PCPwCyinJEvP8G4v5smiGNDMMwTMNhRxbTqiAhzw73/U8Zlh6+6z1kxJzS6PmdnngOdoOHi+WoLz9E2oE9Gjs/wzAMw+gqCSc2AkY0rBBwDhjV3JfD6DBkb3kPWICOI9+kzD7ITriAM2seRVGOPJGAJjG0tUOX5b/Btq/cdryx4mtEffEhKss1p7fKMAzDNA52ZDGtsiev88RPYenUEbLKcpzf9LIQgdcUegYG8H/7E1h16wHIZLiyZBGyzp3R2PkZhmEYRtegCJbMxLNi3tTYFUZmds19SUwLwL3rA+g2+UvoS4xQkHkDp1fPRW7yFY3/HQNTUyEA7zxhqlhO3LAW55+chYLoSI3/LYZhGOb2sCOLaZVIDE0RdP83MLFyQUVpAc79+yyK81I0dn59IyNh8Jj5+EFWVobwRc8hL0LzhhXDMAzD6AL0G1hqXSTmnTgai9Egjj5D0HP6T5AaW6GkIA2nVs9G9MmVqKws1+h91pNI4Pu/JWj71HOAgQHyIy7j3GMPIu7PnzlbNcMwzD2GHVlMq8XI3B5BDyyHxNgSJfmpwplVVpKnsfNLzC0Q+MUKGLdxRUVhAcJeno+ihHiNnZ9hGIZp2Vy4cAELFizAtGnT8Omnn6KoSO4IqovDhw9jwoQJGD9+vLLcf//90BYSj/wHmOqJedfuk5v7cpgWhrVrF/Se9QfM7LxEtH3U0e9wetUc5KdHaXxIo+cj84RulmlbL9FZeWPFVzj/9BwUxt7Q6N9iGIZh7pEjq7i4GEuXLsXw4cMxYsQILF++HJWVclHPuti+fTsCAwPRqVMnZenevbsmL4lhbom5nReCpnwFPQMp8tMicWHTK6is0JzmgZG9AwK/+kmZvvnii0+hNDOdW4VhGIa5JSEhIRg4cCDs7e0xceJEHDhwQDin6rOrIiMjoa+vj/nz5yvLvHnztOYuF1YmiKmhvjXMbDya+3KYFoiZXVv0nfs32vV+TOhm5aZcxok/ZuL66d80Hp1FGQ27/7YebrMeJe8W8sIv4tzcB3Bz7Z+Q3eLdh2EYhtEMEmiQp59+WvRULFq0CBUVFXjppZeQk5ODN954o879ExMT0bVrV7G/AkNDQ01eEsPcFhv3IASO/QAXty5CRuxphO9+D53GvCeeZU1g6u6JwC++R+izj6I4IR5hLy8QoqESM047zjAMw9TNJ598giVLlghbipgxY4awmY4cOYIhQ4bU2r+srAx+fn7C2aWN2PceisJzKXD0Htrcl8K0YAwkRvAd/DwcfYbi0o63UJAZg8jDXyP12gF0GvsezO3aaVRGov0zL8N+0DBEfPAmiuJjEf3NZ0g/vB9+b7wvMh4yDMMwOhCR9dVXX+HXX3/FyJEjMWbMGHz//fdYs2ZNvfuXl5fDxcVFLSLL19dXk5fEMA2iTYdR8B38ophPvLQV0cd/0Oids+jQCQEffy30FUhTIXzxC6gsLeXWYRiGYepkx44dePjhh5XLUqkU06dPx65du+q1qbS5M9Cj23QMeGIjvAc+3dyXwrQCrF0C0XfuWrTtOYcGBCInKQwnf5+OmDMUMVWh0b9lFdgN3f/4F67TZonlnAvnEDznfiT89zdHZzEMw+hCRJaVlZXasrm5OUpKSurdn3oPyTBjGG2gba+5KMpNRPz5dYg+8ROMLV3g1llzOh62vfrB/82PcOXd15AdfApX338dHZZ8Bj19lqpjGIZhqsnMzBRZ/hwcHNR/p9q2xe7du+u1qY4dO4ZZs+Qv07179xbDC+tzbpF9pmqj5ebmiikNXbyVLIQC2oeusSH7qqFn0Phjmpg7rosWwnWpRs/AED6DX4SD9xCE73oXhVlxiDj0JZKv7UfAmCUaHeKqZ2gErxcWwXbQcER+9BaKkxIQ9cWHSDu0F76L34OhozM/Y1oIf160E26X1tsulY04t0YdWTVZtWqV0qCqCxp+uG3bNhw8eFBpdNEwRDs7O+0yurQQrkvT4Df0VRTlJiM9+ggu734fhmb2sG/XT2PtYj9iNLwy03H9m8+Qtn8XpDa2wvDR1DBGTcLPmHbC7aKdcLtoJ9pmdDUUkmWo2TlI0DraVhckCK+Iak9JScHff/+N//77T9hYpJ1Vk48//lgMXaxJWlqa0DxtSL3pWuj+1nV+XYLr0sLbReqC9mO+QdL5P5B+ZRNyEi+I6Kw2QY/C3n8i9PQ0+Py6esD98xVI/fNH5OzehpxzZ3Bu9lTYz50HWY++/HnRMvizr51wu7TedsnLa3jiNT0ZXUkTcO7cOUyZMgVhYWF1GmME3Yj4+Hil0bV69WqcPXtWCJzW1YP47rvv1ml0Xbt2DRYWFg2++XQ9LcXo4rponoqyYkTveQ1FGdegLzFBu+Hvw9ypk0bbJW31z8jcsFbM2896DHb3PwRtg58x7YTbRTvhdmm97UJGFzmQ6O9YWlpq5JxkE5HeVXZ2ttp6ck5t3LgR69ata1Dde/bsiY8++gijRo1qUOegu7s7srKyGlQPOj85vShqrCXYVFyX1tEuWfHnEL5rCYpybopla7cgBIx+B6bW7ho5v9rfOnsS1z5+G6WpKWLZtEt3+L60GBbeui2jwp8X7YTbRTvhdmkcZIvY2Ng0yKZqkoisjIwMEYn1559/1uvEImibYjvpY1G2QxIw3blzJyZNmlRr/8WLF2PhwoW1jC76gWuo0UWRLy3F6OK6NB12D36PM389Igyd6/sWI2DU22jTcZzG2sXhpcWILClByvaNSF/zK6ycnOEydQa0CX7GtBNuF+2E26X1touxsbHGz0nXS1FRZOeo2jdxcXFwdXVt0DmovmRTXb16tU5HlpGRkSh1HdfQe0X3tjH7azNcl9bRLnaePdHv0fWIPPI14kLWIvtmCE79MR2ePWajbc/ZkBprxhkt/lbv/ui5ehOiv/4Uyds3ofDCOYQ+Og1Ooyeg7RPPwLhNwz7L2gh/XrQTbhfthNul4TTmu17jjizq3aNILHI41ZVV53ZQ7+GNGzfq3MZGlzr8oWg6jC3s0euhXxDy33PIS70mMt8UZd9E+/7zbzsMsKHt4rfoHZRlZyLz+GFEf/mRyGhI2W9IEF5b4GdMO+F20U64XVpnuzTFeemcQUFBOHr0KMaNq+5EoeVbSTbUJCkpSZyHYZhqJIYm6DDif3D0GY7wXe+gKCcR10+uRNy5v+HZ4yF49nhYYw4tibkF/N74AHZDRyLym89QGheDlJ1bkLp3B1ymTIfH3KdgaFu3pArDMAxTPxq1vmiU4iOPPIK+ffviqaeeuqNzREZGNri3kWGaEmMLJ/Sa+RscvAaK5egTPyJs+5uoLNdMtkFyWHX84AvYD7lPLCf8swphryxAeZ5c+41hGIZpvcyZMwcffvihUq/qxIkTOHXqFMaPH9/grId79+4VWaQZhqkvOutf+Ax8FhJjS5SX5otkP0d+HIuo4ytQVtJwrZbbYdtnANp+8SP83vpIRGLJysuRsH4NTk8bjZifl6O8IJ+biGEYprkcWYsWLRLOrE8++eSOwv+///57BAcHY+zYsZq8LIa5YyRGZug6dRk8gmaK5aTL2xG8bj5Ki9R1S+4UAyNj4czyfEyejjzrzAmEPPkQCuNiuNUYhmFaMU8++aTo2CP9raFDh4rIrN9++02pCXr8+HG89dZbyv3JhiInF9lQJNfwwgsvYMOGDbC1tW3GWjCMdiMxNIVX3ycweN4OeA94BhIjC5SX5CP6+AocWTFWOLZoWRPoGRjAcdR49Fy7Dd4LXxcJfyqLihD76wqcfmA0bq79E5W3yPbOMAzDVKOxMUyJiYlYunQpfHx8EBgYqBbSv337dnh4eIieQRIoXblypdj2wQcfYO3atcKJlZCQgK5du2Lfvn0wMzPT1GUxzF2jry9BhxGLYGrjjqsHPkfWzRCcXjUbQQ8sh5mt512fX09fX2glmHl54+r7b6AoLgYhT8xEx/c/h23v/tyCDMMwrRADAwOsX78e4eHhSE5ORrdu3dScUgEBAWo6pCNGjBC2Fh1HUxKLl2jRUHWG0WYkRuZo3+9JeHSfgdjgvxAbvArlJXmIOvY9YoJXC/0sz6CHRAfn3aIvlcL1gYfgPHYybv6zCvF//YbynGxEf/MZbv7zJ9o+/ozQ0dImqQmGYRhtQ2PfkE5OTrh06ZKIyFKFHFlubm5injSzyBBTMH/+fEyePFkYXSTabm5urqnLYRiN49n9IZhYueHi1kUozI7H6dVz0HXKl7B1766R8zsMGwVjV3eEL3oOJakpCHv5abR/7lW4PvjwbXW5GIZhmJYJOayo1MTa2loUBRS5RYVhmDtHamQB7/7z4Nl9JmKD14hSXpyLqKPfIfbsarTtNQce3WZoxKFlYGoKz0fnwWXKg4hb9TMS/vsbJSnJiPjoLeHcajfvBdgNGsY2IMMwTFMOLSRnFBlaFM6uWmidQghVKpXC3t5eeQzN0z4dOnRgJxajEzh6D0Kvh36DkbkDyopzEPzPPCSGb9fY+S38OiLol39g2akLjbcVmW6uffIOKsvKNPY3GIZhGIZhmPohsXfvAU9j0Pwd8Or7FAwMzYTdF3nkWxz5aRyun/oVpYWZGrmFUmsb0XHZ65/tcJ4wlYYCoDDmOsIXv4Dghyfj5rrVKMvN4eZiGIZRQffzJTPMPcbSyR99Zq+GhaMfZJXlCNv+BqKO/VArGvFOMbSzR5flv8Fp7CSxnLx1Ay48/zhKMzM0cn6GYRiGYRimYQ4tn4ELMGgeObSegIHUFGVF2Yg88g0OfT8SoZteQdr1Y5BVVtz17TR2agO/xe+hx+qNykRAhTeiEf3VJzg1cRiuvv86csLOa8zeZBiG0WXYkcUwd5rR8KGaGQ3f0FhGQ31DQ5Gu2evZV2h8LnIvhAjdrPzIq9xeDMMwDMMw9xBDEyuR3ZAitNr1eQxSE2vRmZlybR9C/n1WZDqMPPodCrNv3vXfMmvbHgEfLUOP1ZvgOm0WJBaWqCwtQcrOLQidNxvnZk8VGQ85yzXDMK0ZdmQxzF1kuuk29St4dFdkNNyBc+vno7xYM+HfpIvl/tAj6LT0OxiYmaMkORHn589G+uF93GYMwzAMwzD3GEMTa/gOeh5Dnt6DLhM/g127fmSxoTgvBddPrsTRn8bj7NqnhOxERVnxXf0tSgLk/dJi9Nm8H35vfgjLwK5ifcH1SEQt+xgnKUrrgzeRExbKUVoMw7Q62JHFMHeBnr4BOgxfBP/hi2gB2QmhiNzxIjJiTmnsvtr1G4RuK9cIIXhK0xy++EXE/vYjGy0MwzAMwzDNgL7EEM7+I9Fj2vcYNH8nvAcsgImVi9iWGXdGROkf+v4+XN77EXKTr9zV3zIwNoHz2Eno9uNqdF+1QWQ8NDC3QGVJMVJ2bELovIdxbs5UJPz7F0dpMQzTamBHFsNoAMpuEzT1KxhITVCan4SQfxfg/KaXUZSTqJH7S2HmQb+shXWPPmI5ZuW3OP/kQ8i5GKKR8zMMwzAMwzCNx8TSGe37PYWBT21Dj+k/wrnDaOgbGKK8JA/x59fh5J8zcerPmUi7vBFFuUl3dYvN2/vCe+Hr6LvlgJCgEMmBKEorOhJRX34korQuLXoeyds3oSw7i5uTYZgWi6S5L4BhWgoO7Qeh9+w1uLjzA+QnhSD12n6kXz8Orz6PoW2vuTCQGN3V+aWWVgj88gdc/2ap6HXLuxyG0PlzYD90JLyefhEmbh4aqwvDMAzDMAzTcPT09GHn2VuUsuJcITlx8+Im5KVeRV5qhCiJwT/C0qkDHH2GimJu7y2kJO4oSmvcZFHyoyKQtPlfpOzaioqCfGQcPSAKZT+06hwE+0HDYDdwKExc3bk5GYZpMbAji2E0iJltW3iN+BB6ueGIOPgFinOTEHXseyRc2gL/Ya/B0XvQXZ1fXyIVPXGOo8Yj+tulyL14HukH9wiDxfWBWfB45Cnh8GIYhmEYhmGaL9uhR9AMUXJTruLmxY1IuroX5UWZyE25IgrZhybWbnD0Hgonn6Gwdu0iJCsai7m3H3xefgNeC15CxokjyDh6UEwr8vOQExosSvQ3n8GsvQ/sBg2H/cChMPfreEcONIZhGG2BHVkMo2HIMHD0GQYHr/64fuo3xJz5HUXZN3F+w/Miy6Hf8FdhZnN30VOWAZ3R9Yc/kX5oH65//yWKE+Jxc+0fSN6xCZ6PzofL1BnQl0o1VieGYRiGYRim8Vg6+QstVZtOc2FcmYa0qMNIjTyIgswbwj6MDV4litTEBo7eg0WkFkV1GUiNG/V3DExM4Th8tCiVZWXCgZV+RB6dVZKaIoYfUon7bQWMnJxhN2CoiNay6tZDdJQyDMPoEuzIYpgmgvSyfAYugGunCbh64HOkRR9G2vWjSI89hXa95qJd78chMTS5K4eZw9D7YDdgCBL/+xuxv61AeW4Oor/+VCx7PbNQ9LxxjxvDMAzDMEzzDz20ahMIG9cu8B38PPIzbiA16pBwauUkXkRZURYSwjaJQk4su3b9Yd+2L2zcg2Bm265R9hx1Ztr07CsKRfLnR1xGOkVqHdkvnFklKcnCVqRCmbGtOneDVbeesO7WA+Z+HdixxTCM1sOOLIZpYkxt3BF0/9dIiz6CK/uXoig7HtdP/ozES9vgN+wVOPnenbOJjBW3GXPgNHaSyGaY+N9fKLoZJ7IbWnXtgfbPvQKLDp00WieGYRiGYRjmzjG3ayeKV+9HUZKfhtSqSK2MuDOoKCsWWqtUCENTG9i4dYeNO5UgWDj4CMdYQyAb08I/QJR2Tz6LooR4Mfww/egB5FwIEbpamSePikLom5jAKpAcWz1gHdQTFv6dOMqfYRitgx1ZDHMPxeBtPXsj9uwqRJ/8GcV5ybiw+RURPu498BnRS3c3Di3SxvJ+4TW43j8D179fhvRDe0VYecjjM+A4chzazX8Rxs5tNFonhmEYhmEY5u4wMneAe9cHRCkvyUf6jeNIjTqCrPhgFOeloLQwCynX9olCSIwsYOMWBFuFY8vJH/r6DXutI9F36gClQpkNs86dRs75YGSfP4vCG9GoLCpC1pkTohD6RsawDOwqorWog9ScO0cZhtEC2JHFMPcQylzo1fcJtAkYh4iDXyIlYi8yYk+LQkLxLp0moE3HcSKV851C2QsDPlometlIEJ6yG6bu2Y60A7th23cQnEaNg23/wTAwapz2AsMwDMMwDNO0SIzM4ew/ShSZTIainERk3TyHrPhzyIwPEZH95SV5csmK6MPiGAOpKazdusLGrRusnANg4egPIzPb2/4tqbWNUleLKM3KRE7oOeScP4vs0GAURF1DZUkxsoNPiULoGRrC2NsP+YHdYNmhE8w7BAjnmJ5+wyLEGIZhNAE7shimGTCxbIOuk5YKB1bkkeXISQpDQWYMIo98K5ZtPXsJbS1Hn+F3rKNl1SUI3X5ag7R9u3B9xVcoSU5UpmQ2MDWD/ZARcLxvLGy694aehL8KGIZhGIZhtAmK1De1dhXFtdNEsY4itLLiQ5B1MwSZ8edQkHEdFWWFyLhxQhQFRuaOQmienFqWzv6wdPSHsWWbW0b/G9rYCv1VKkRZTrZwbJFTi6K28iOvQlZaiqLLYUigUnUc6WxZ+HUUTi3FMEZjFzfWaWUYpsngt1eGaUZoWKHd7N7Iz7guNLMSw7ehJD8VmbGnRTGQfggnvxFwCZgIW4/uDdZDUEC9Y44jxwqnVebJI0jZvR0ZJw6jorAAKTs2iyK1tZP3xo0aL7S0WByeYRiGYRhGOzG2cEKbjmNEIUoKMoVTi6K2shMuID8tCpUVpcKeTKMSfUR5rNTYSgxDtHT0g6VTBzFPmbT19A3q/FtSK2vYDx4uClGel4us0HNIOXsKlfExyLsajvKcbKGzlR1yRhQFEgtL4dASzi2/AJh5+8KkjSt3njIMoxHYkcUwWoC5nZfIYOMz8Blkxp1FwqWtSLm2HxVlRUi8tFUU6kVzCRgHl4AJMLP1bNT59Q0NYT94hCjl+XlIP7QPKXu3I/vcGZRlZiBh/RpRjF3dhZ6W08hxMHZv3N9gGIZhGIZh7i00hNDZb4QoRGVFGQoybiA39SpyU64gLyUCuSlXRdRWWXGOsrNUgb7EWNiVJHEhil27qnlPkYFbFXJO2fUfjAqfDnB0dBSdnyXJScKhlXf1kpjmX70sHF7C6XX2pCgK9KRSmHq0hWlbL5i2bQ+zdu1h6ukFE3dPYasyDMM0FHZkMYwWQT1idm37iFJe8jpSru0VTi3SRSjOTRLZDqmQMLytZ09Yt+kMK5fODdJBUCAxt4Dz+CmilKSnIW3/LqGhlXflEooT4hH32wpRzH07wLhnXxj1HSA0EAxMTJu07gzDMAzDMMzdoW8ghYWjryiK4YgyWSUKs+KFQytPOLjkpawoC5XlxchLjRClJtSJKndukWOLHFztYGLjIbS7CHJkGbdxEUUxHJG2kT0pd27JS37EFRG1JSsrQ0F0pChqGBgInS25g8sLZm3JwdVOdLCS84xHCzAMUxN2ZDGMliIxMoNr4GRRCrMTkHR5u3BqkcgnaWpRUWBi7QZrF7lTy7pNoDBeyJC5HUb2DnCbPluUwrgYpO7dIZxaRfGxyL92RZT0Nb8KA8O8vS8sAgJhGdAFlgGdRe8ZC3syDMMwDMNoNyRNIY+68kSbDqOUDicafpiXek3otKqW0oIMsQ91olLJiKmOqlJEcUVbybW7TKxcYGLpAhOat3SBsZWLcEBR8iHHEWOUf6s0LQUFMddFZsTCWJpeR0FMtBiaiIoKFMXFiJJx5IDa3yL9LWMXVxi3oeIGE5oXy27CgcYdrQzTOmFHFsPoAGQotO/3FLz6PonshFCkRh1GTuJF5CRfFj1pRdk3RUm6vEPsry8xgqVzR+HUEs4tl84wtnC89d/waIu2jy+A52NPI/9qOFL2bEf66RMoibshDAyFYytp47pq7YOAzsKpRcWiYyCkllb35H4wDMMwDMMwd46IprJwEsWh/UC1bWXFudWOrQya3kBBZiwKs+IgqywXtmdBRrQodSExNBcOLeHksmojd3BZOMHI1RF2fsPgYjYd+hJDZabEwphoFFY5uQqqnFyl6aliO0VyFURGiFIXUhtbISwvHF3OLjBycIShPRUHGFVN9aW379xlGEa3aBJHVnFxMXJycpRjpzW9P8O0VujzQamVqSh0EEjUMzvxArITw4RzqzA7HpXlJci+eV4UBUZmDjCxcYOpVVXvmZi6ih40Y3NHpdAn/Q0SfTej7DPTU2Fnbo6Ca5eRe+kici9fRO6lC0JXS2gfnDomigIT0j3w9KoKM1f0nsnnaUgjwzAMwzAMo91IjS1FJygVVYT+VlY8kmLDYYQCFOcloSgnEUU5CSjKSRJDFYny0nzkp10Tpd6/YWIDI3MHGJs7iAyLNErAuK0PLC36i/USfTNU5BSgNDkFxYk3UZyUgOLEBBQnyecrCgvFecqyMkXJC79Y/9+ytoGhg6PSsUWOLvp7Ujt7FOkZoLiyHEZ29jAwMtbYPWQYRoccWZWVlXjhhRewcuVKGBsbw8rKCr/99huGDRumkf0ZhlGHhg9aOncQxSNohlhXWpiJ7MRLyEm6iOyEi8hJuiQEPksK0kRRdW4p0NOXwNjSucq55SKcXUaWLigqN4SZQTuY+nvDqmt34ewSoejJScgNv4Dc8IvCcMiLuCx0DxRh4XVBEVyip0w4tuTh4MLR5dQGUmtrSKys2YBgGIZhGIbRYruTNLMsy01FAIK+vno27fLSwirHViKKchNRnJOIwpwEMS3OT6sasijX1yKnF5VbObsIiZE5DE1sILW3gaG7DYxNu8DCZDAk+sZAcSVk+aWoyC5EeXoOylMzUZaWidL0NOHcUlCWnSVKfVFdcVVTA1NTSG3sYGhjqzKVFzFvay+mEksrSC0soW9kdNf3lGEYLXBkfffddzh+/DhiY2Ph5OSEDRs2YPr06YiIiICtre1d788wzO0xNLWFo/cgUQhZZQXyM64LDYTqXjMqiSjOTYZMViHCxBXDE2tSHTSuJ3ropCZWMDSxFj1pUh8rmHbuDkvDIajMK0Z5SibKM3NRllllSCSnoiI7D6gAyqoy2ORHXq332vWNjOXGgZWVfGppLVI/SywtxZSWaT05xQxMTGBgbAJ9mpqYinnKhsNRnQzDtCSuXbuGtLQ0BAQEwNraWuP7MwzDaAqJoSksHLxFqQuK6KIO1+L8VJTkpQmNrpL8NPlyPi2noTgvFeUlecpjykvyRUF2/O0vgFQ0nPQhNbKAxMgTBhJTGEAKvUoD6JXrASWVkBWWoTK/WER7VWTmojK3ECgH9MqB8rJClCcUCrH6hqBnaCgcWhJFMbeQT8lWrZqXb7cQWl8GpmaQ0NTMHBJTM2HDst3KMFrgyKJoqqVLlwqnFDF16lSsWbMGGzduxOOPP37X+99rysvLkZEhFzvUNiiaja6NomNq9oboGlyXe4ElDOx7wNweMFdZS06u0sI0lOYlozQ/BaX51dOSvGSUF2Uqe85oSmmbqZBGwi2xqCpe6l8zemRMyPSFYwtllUBJOVBaAVTSg0BCCKXQq0wDqJQBSAOQAuhVyuTHKParBPRkVfM0VcxDD/pSI7nugtRQPi81hJ7UCOXQRyw5vmjZQCJ0xITjy0Aqn9L+EvkxehJDYZzoS6QkOAY9AwMheK9vUD2vpzZvIB+aqa8vF8CneYOqeT29Gtv01eYVQzrFfAOGVvPnRTvhdtHudqHOMUMdS+1eVFQk7KLg4GB4eHggKioKy5cvx+zZszWyP8MwTHNEdCl0udCm/v0qyopQUpCBUoraKsxCKZWibDEtq5oqtxVlyR1dCmSVSnu1Tsgsta4qnooV6ujpSaEPCfQq9IWDi2xWWXG5cIIJ+5UcXxVkm5ahrCIDqMiAXmaV3Vqh2EYvk2TDyqdyO5csVdUboi93bpmaVjm6zCExMxPrRFHttDU2qbFsKpb1q9brGRmhIj8flVZW0DM2ZgcZ0+LRmCOrrKwMFy5cwODBg9XW0zBBMqpqOqYauz9RUlIiioLc3FyloUrldtA+5PhpyL4E9WhOmDChQfsyTEvNcmMsBUwNATMjmspgStOqefk6WpZVTQFDibzUhQxlkNEvuKSqmNBaTTtiy6pKQa0thbc7pAFQxumqrNOQicpU+dJU5hW+PxmZK7S/4ljxv1718cr/9GrsQzPydcpllb+heqzinGrHqu1Tfe5adbllTauvsz7q2izqfLfUOHGd9dJlhBdW3mwy1doob13NdbL6z1FrfT37qyCr7281iuo66AIXXKdh5tzXmuTcDbUpGgt19NG5ExIShBPu3LlzGDlypLCTXF1d73p/hmEYbcVAagJTazdRGgJFegkHV1GOiOYisXrFtKw4D+U0LameinVV2ytKa9uLMlkZKsgwJBPVsKqYAbDTgN2q2jkrnF6FQGUhUJEu76glJ1gRAPLN0bJKJ67Yn45XXafYp+qcEVWdu/r6UtEhKy/yTl5FZ6++1BgGhkYqU+rcNayar+rMlUqhL6bUIWxYNS9fpyeRQk8iEcs01av6O2Ke9qlap7ZPVQcwZzxntM6RlZqaKno8JRL1U1K01f79++96f+Ljjz/GkiVLaq3/8ccfhcZWQyAjr6ERTBUVFRgyZEiD9mWY1kZpVRGynvSjST7maj8zBSKJd1wxrZoX0UliuWqqsh/9p3gnVgQm6dW1TuEqUVlWWazaphF3SvNSVwV0vlKM1qApb6COeBVLbxbi22+/bZJzU8KapmD16tWiKCLJunfvjokTJ+K///7D888/f9f732s4yv3ewNGh2gm3y73CApBScYGBBUAx78a3aZf09DTYWJpCVlGMyrJCoStbUVpYPV9WIJ9XWUdJlSpp//ISyCpK5cvlxaismpdVqBjENdHXU/OF1e6s05SxV6linKtErN2K4ionWs0RD7Kq/rO6RkNUzetR72c929SOl1v/olCHuXxKy1WjGGidnr7oTL2qbwA9vapRDXr6ym1iWrVNPvpBMVUZDUHnU8yL/RXHqZxDMWqi6iVEHCNePhRTmhjQFSrXi2tVPZe4Dvm8Pp1L8beqtlF7FhQWItPcAvoGVedVnq+q7mKV4rqr5sWzUnUeugIxpXMq7pFB1amq7o/qcY1Aj55H5b2S109xL+XXp7j/1KT6yMrKgpWFOUzMVMf4NA8ac2SR08eAhtnUgJxGtO1u9ycWL16MhQsXqkVkubu7Y968ebC0tLztNdKXFUVZOTg4NMiZpc1GF0WW0bXZ2dnpfOgo10U326WkrBypWflIycxDSmY+krPykJqZh5yCYuQWlCC3oBh5hfJpcSnFVN979PVkMJbqwUiiD4meDFKpPiT6ejCUjwgU8xJ9GgWoB4lYFiMClVMDPT1hbyh+d4TtQeuhsiycZjJhk+ijUv6jQkN+6RdbhhpT+X7CTKHjqkKexLyK6aKIr1KsU92PzlVeXgYpdQLQb19VaFaNWC25g09xXD2xU6qIv6HazjK6RrUQsVpnqD/WqzZ1x4xRXSogoZuvtp/qZd4+HqvOb8AaK+U2VSXKUIbyqn9leirzqutRhgq9un+HmhR6ToS5VGXgCZOqer76n7yC1VP1dar/q05V/6++PVXzKmFatEYmo2e5+ndS/SyqqJ6rPura59ZH1b21cb91ir0rK2UYPnwB+gwYi6aAbJH//e9/GneOXb9+HT169FBb37t3b4SEhNz1/gRHuTMMwzQVBsKulBpA2JxSidyWpGW5vSmrnq/aj2xP+bxMaZPSVGmXqtmpMuUyFanKfsJOrSqNVp8RTg1x+bW4ncXXeAccnbEZbK2mCaK+LfJ8ni2Dm1dnY8wjLzV7lLvGHFkkKJqdnS1eflVfeslrV5fYaGP3J4yMjESpCTmlGhplRX+roftTr2abNrcYwN2MKCLL6soYomtwXbST7LwCXI1LQ1xuBpLSc0RJTMtGYnoOEtKykZ7dwN6dOj6DVubGsDIzgbWFKSzNjGFiZAgTI6koxoZSGFfNK9YbGSqWpcplQ4kBpFIDSCUGMJRIxFRRjKTyZQP6Fa96xigKtKV8Xrgudd+X9PwM3My6iYSsRCRkJYhyMysBSdlJyCjIRGk5xRA2FPXnRF9PH+bG5jA3MoOZkRnMjarmxTpzmBmaorKsEnbWdjA2NIaRxAjGUqOqqXzZUGIon5cawbhqmYpEXwKJgQQGCr20ZoafscbRFN8p1OlGnQg1z03fYfT5v9v9CY5yZxiGuVeodP2pdQ7XnidNernaxS32U/bU6JEkmFyDq+Z+NWbVRzOoX0r1svooB+WVq+1Xo+O1nhES8susfb7q46pdY3Vur/q79W6rUYe6z1Wj07bGuercVof0gm6HjGie0oRcRGlBlLvGHFkUEWVlZYUbN27Ay0soPAuuXr0KHx+fu96fYZimgZzJFEkVfiMR4depJIlpfErD+g7IoeRsbwUXUaxhZ20Ga3NTWJobi6m1hYnSaWVlbgILUyOddyYxzQM5omLSYxGfGS8cVKoOq4TsxAY7qsgpZW1qDVszG9iZ26lNbaumNqY2sCAnlbE5zIzMYSK9tXBqS3L+MK0DjnKvhiPDtRNuF+2kJbVLWXk5EpJSYWxqjuKychSXlKOotAzFJWUoUp0vLa+a0nI5imlK68rKUVIqL4p1NGJBTEvLUVreFBFHauqp0Ab0FYEiYlpVqtbRqAcRSKKnJ+blASV6yvUGde2rr4eK8nIRVCIfAaEYfqiYV5UpoaFwVbHoVSMpxCDBqnWoWiffv3o/xTnofHKHWfV+1cuKeXmwmnyIoUyM2BCjKZT7Vc3TKA6F15CCdUhpTVaCwqJcSI0NUIlyob1WUVmGSpl8DECFrKpUlqFcVobyylKxTPOVVesrKsvl87JysY2OrZSVqzkU7/STqHqsmqRLjaK6bsHIdzGqz3g0d5S7RrMWDh8+XGgxvPrqq2KZhghu2rQJq1at0sj+DMPcHfSyHZucqeawunw9Eek5tYUuFdhZmsHFwRouDlZi6krz5LQS66xhb2XGL+6Mxo3kxOwkRKZEIjIlSlliM2JRUVlxWyeVs5UTXKxd4GbrBldrF7jYuMDB3B625jawNbODtamV1kQ/MUxdkIYoRajX1PVURF7d7f4ER7lXw5Hh2gm3i3aiTe1CzqWc/CJk5xciO79IzAtpCyFvUVWq5kn2oua6opIGZvppIgz09WEoNYChVCIvNMpAsSyRqG2Tjziof5tUoo/S4mJYW1kqRyWIqb4+JAb6YpmmEgP5NrGuahtJPEjIkaRYX7WfRHVZv/o4uSOq2nGlaYemNnQOUudoblEuckTJqZrPQV5RPvJL8lBQUiAvpYUoKKZ1VcslhSgokS+XV6hIq9STRPPO0at/ix7JqFDUvxQSA6lyXlo1lS8bitEAUgNp9VS/ell1Xlq1TKW4qBhdOvRosnZpzHk16sgih9SIESPg6emJgIAAfPnll3Bzc0OfPn2ULyeFhYUwMzNr0P4Mw9z9D8GFyATsP3sFp8Nv4MqNZOQX1S1ASUP6/Ns6I8CrDQK8XNChrTOsjfXg6e7a7IYK03LJLc5DXEw8otOuC2dVFJXUaGEM1Ac5olytXeFq6yocVQqHlZutK5wtnSGVSO9pHRhG05CdRNIGV65cEfaRgrCwMPj7+9/1/gzDMNpGYXEp0rLyhHRFWjZNC4RjSumoypNPxXLVPEU9aRpy0Jgay+UtTI0NYWJsCFMjqXxeuU4KE0NDGBtJlFIYRoYSpSQG2dRiqpDEkJIsRpXzSeGEkkiUEhgtxfmjrZSVlyGzMAuZBZnIzM+snhZmI7coR+6sKlQ4q3KRW5yLolJSvdc8JDVhYmgMU0NTGEtNYGJoIqL+aVnMVy3TVOwrNRaSFXKZCvlUrJNWSVVULdM8OajIBiaHU1NESyqfMStHaAMadWR169YNa9euxUcffYTk5GQMGDAAGzZsUG7ftWuXyKizZs2aBu3PMMydGQLHLkRh35krOBAcUaeWFQ3xUzisAtq5iPl2rvai96XmlxXDaIrswmxcTryCywlXcDnpCsITLiM5J7ne/elH3MfRGz5O3vB2kk+p0LBAhmnpjBkzRthMpGVFUEfgxo0bsXv3bo3szzAM09RUVFQKp1RyRi7SsuQOqmpnVb58WrVcUNwYHcu6HVCku0rFgoppVRHzRrBUzlfvY25shOKifLi5OIv15LSiqChdHzLZGqAAmazCbKTkpiAlJxWpuSnIUDipCrKQkZ8hprScV5x3V3+LngdLY0tYmVjCwtiiSnaCNFNNhfyEQj9VdZkcU7SfidQEhbmF8HBxh6mRKY8I0FZHFnHfffeJUp+RRaWh+zMM0zCSM3Kw/+xV7DtzFccvRqO0TL2HqmO7Nhja3Q+dfdyE04qGB/KPNNOU5Bbl4UriFYQnXq6aXhFaVvUNB2xr7yl3VlU5rqjQ8EDuVWRaK4sWLULPnj1hYWEhoqy+++47DBo0CJ06dRLbCwoKkJiYqNQVvd3+DMMwmqSsvALpOblIpoRAGTnCWUWJgcgmpcRAyem5SM3KQ0UjspARZJ/aWprCzspM6Ksq9FZpaqXUXTWp1mGtmpqbGDXatlVGmDhYs72hRVC7ZBZmIi0hHWn5aUjJTUVKTorSaUXT1Ly0Ribxqe4ktTOzha2ZLWzMbGBlaiUcVMJRZWoJSxP5slXVlJbJMXWn9qh4xipShaOLbVotd2QxDHNveiFI32qfcF5dwaXoRLXt1JvUt3N7jOjlj+E9/IWWFcM0FcVlxSLKKizhUlXE1WXEZcbXuS+Ntfdz8kVH1w7wd/aHk7Ejevh2h4mRCTcQw6jQrl07HDlyBMuWLcPJkyeFrujChQuV24ODg7Ft2zYsXbq0QfszDMM01tbMzC0Q2qqxSRmIScpAXHImYhIzEJ+SiYzcQrFPQyFnk72VORxsLGBvbQ4Haws42JjL56vWOVqbw9bKTG2EANMyoWcnLS8dcRlxiMuMQ2xGvHw+Iw43M2+iuLxuKZS6oCgpR0tHFT1UuaPK1pym1cvkuDI1ZHuzpcCOLIbRIVIzc/HnjlP490CI6PlShXquhvXwx/Ce/hjY1RtmJkbNdp1Myxdivxh/ERfiL+JifBgikiNQXocIOwmDUpRVR5cO6OjSEQGuHeHt2F4ITKr2hNK4foZhatOxY0esXLmyzlszePBgURq6P8MwTF2/6SmZuWrOqtikTMQm0zQDeYW3dyZQFJSDtTna2FvB2c4SbeysRDbrNnaWVVMrONpaCN0opnVG6EenRiO2yklFTqs4clplxjdIh4p0UZ0snYSjiqaU0Mepat7JyhGOFo5iyB7T+mBHFsPoAFdikvDz5uPYcuSCCOVW4OvhiOE9O2BErw7o6uOmUdFIhiFKykpElJVwXN0ME1PqQavLkG3v4IUA1wAEuModV75OPuykYhiGYRgtcFil5+TjWmwKIuJSxPRaXCquxaXUmwRIFXJGebaxhaezHTycbWBpbAD/9h4i4t/RxkJkx2NaN/SMJeUkIyIpAhHJ13A1OQIRSdeQmK0+aqQmlEXaxboNPGw94GHnDg87D7jbuMFUZoIO7Tqwk4qpF3ZkMYwW/yAcCrmGXzYfF+LtCshgmDuuLyYM7AwPZ9tmvUam5UHCmCGx53E+9jxC4y4IQ0QtfXAVliaW6OwWiM7ugeji3hmdXAOEqCXDMAzDMM0HZfWLjE9FRGyycFRFCKdVCjJzC28plO7qaC0cVW3JYdXGTjnv7mQrsu8p4Ox4DGUBvJ5+o9ppVTWtT1SdOjvbWDkLJ5WnnYea04qyTtfMNq14xigjH8PUBzuyGEbLKC4tw6ZDofhly3FhiCjo0K4NnpjYXziwDKX80WU0A4lnnosNwbmY8wiJDcH1tBt1GiBeDl7o4k6Oq87CcdXWzpNFKxmGYRimGSkqKcXFqAScj4hHyNU4hEUlCOH1+pAY6KO9mwN8PZzg5+EEX08n+Lg7ws3RhqOqmHohsfXqTs6LiE6LrrOTk6Bsfb7OPvBz9oN/G18xbe/oxU4pRuPw2zDDaAkZOflYtfM0Vu04hYycAuV6yjb45OQB6BvoxZkGmbuO8ovPvCkcVyExITgXe77OTIKU0YWcVd08uoqIq0C3TkJIk2EYhmGY5vsNv5maJRxWIRFxCLkaL6Qnyisq6+yAomgqclgpip+nE9q2sePOUOa2z1lsRqxwXMlLaL1Zp0m3ys/ZF/7OfvCrclq52bhyRydzT2BHFsM0M1HxqSL66r+D51FaJu/doIir+4d2w+MT+8Pb3bG5L5HRYWMkJj0GZ28EIzgmRERc1aVvRcMEgzy7idK9bZAwSCi7IMMwDMMwzUNxSRkuRt1ESFW0FTmv0rPz63RaUXRVN393dPP1QMd2ziLqysRInliFYW4FRVZdS4lECEXmx1HUVSgyCzJr7Ucdml09uohOTkre4+vsKzICMkxzwW8qDNNMUErjz/7cjbV7g9UyD84e2wcPj+4t0hAzTGMdV9RrduZGMM7eOCscWHU5ruzM7YTDqrtnELq37Yb2Du2594xhGIZhmvk3/HpCOg6ei8Chc9dwOvyGWoIfBdYWJsJhFeTvjiA/D3TxcYe5KWf/ZRrXyXk88iRORJ3E+bhQFJbW1k9zsHBQdnJSoazT+vqcVIrRHtiRxTD3mIqKSqzdexafrdojBDkJbzcHPDFpACYP6crpiZlGa1yR4+rMjbMIvhGMxOykWvtQquLubbujBzmv2naHh607D1NlGIZhGC2IujoVfgOHgiNw4FwE4pLVI2H09fXEsEByWHXzkzuuvFzt+TecaRSFpUU4c/0sjkcex7HIE3VmEvS080SQZ1el48rVxpWfM0arYUcWw9xDQq/F4+0ftwhhTsLGwhSL5ozCgyO6cy8H0+CsgoejjiLi9DWcjQlGXEZcnRFXPdv1QC8qXj3hZuPGxgjDMAzDaAGkc6WIujp+IVok+VGFsgcO6+6HoT380KtjO462Yu4o6upGegyOXTuO45EnhDZqWYX6c+Zi3Qb9ffqhj1dvdPPsKmxHhtEl2JHFMPeArNxCfLZKPoyQflxIz2DmyJ549eGRsLE05TZg6qWkrESEfZ+MPo2TUacQkRxRax8rEyv0aNcdvdr1FA4sL4d27LhiGIZhGC2A7L5zV+Ow6WAwzly5qZaRWpFJsGfHtiK5z7AefkLfiuxEhmkMhSWFOHHjJC6dvoyT0SdrRehLDaRCVmKAT38M8OmHtvZt+TljdBp2ZDFME1JZScMIg4UTKztPPoywi48b3p83EZ193PjeM3UavJEpUTgZfUo4rihjTEl5ido+plIT4bjq2a6niLjydfLhiD6GYRiG0SIS07Kx8VAo/j1wDjcSM9S2OdhYYGh3X+G8GtDVGxamxs12nYzu2ovRaddx/Jp8uCDZi+WV8qRRCmh4IDmtKPKKbEZTQ5Nmu16G0TTsyGKYJuJC5E0xjJCmCnHORXNGYzoPI2RqkJaXhlPRZ4Tz6nT0aaTnqxu8BvoGCHTthD7te6O3Vy84GTqijXMbdl4xDKNT5BTmwEhqBGMpv7QzLZOiklLsOXUZ/x4IwbEL0cLZoKBjWyeM7tcJw3r4o2M7/g1nGk9+cT5OXz8jhgsejzqB5JyUWlFXPdp2l0dd+fYTulcc3ce0VNiRxTAahgTcv/5vs9owwhkje+C1h0fxMEJGQDoFoXEX5NoFUSdEBFZNSJCdHFd9vfuI4YKU9lgR5Zeaqj4sgWEYRttZe3odlu//Ho8OmIPHBz3W3JfDMBqDbL2Qq3HCebXt2EXkFVZHUbexs8LUod0wZUhXmEsr4ejoyJ1QTKOeLbIRj0XKta5C40JRXqmeydLd1g39ffqjX/s+8DTzgIerBz9jTKuAHVkMo8Efm/X7z+Gj33Yip6BYrOvs7Yr35k1EV193vs+tnPS8dOG0OnrtmBgymF9SoLadHFUUbUWOK3Jgudm4Ntu1MgzDNMV3YF5xHn458jumdJ8CWzMbvslMixw6aGQoweg+AXhgeBD6BbaHgYE+d0IxjYq6OhV9GkernFcUta+KkcRIdHD28+krIq887TzEeu7oZFob7MhiGA2QmpmL/323EQeC5ULcVuYmeG32SMy4r6cwYJjWBxkU4YmXheOKIq9oXhWK1KPhggN8+6Nv+z4IcO0ohhAyDMO0RNwMe0GvcjUKSguw4uBPeH38oua+JIa5o9/2vWeuYvXOU7WGDgb5eQjn1bj+gcIOZJiGEpsRh6MRx3Dk2lGRYbC8Ql3rysPOQ2hdkeOKBNt5eDbDsCOLYe6aLUcu4K0ft4ghhcTYPv547+kpsLeWDwVjWg+5RXkiU4xwXkWeQFZBltp2SxNL9PfuiwG+A8TUhiMSGIZpJYRcSURhohdM3K7g3+D/8FCf6SJrFsPoAhUVldhx4hKWrz+IiNhqXSJnO0sxdPD+oUEi2yDDNISy8jKExJ3HkYhjwmaMzYitFXXVq10Pub3o0xfutjyyg2FqwhFZDHOHZOUW4s0Vm7H9eJhYtrcyw4dPT0aXdnawtTTj+9pKSMlJwYGrh3Dg8kHRi1ZRQ7vAz9lX9KAN9B2AQLdOkBjw1y7DMK2PZ6cNwbp9Z1FRHAcYF+Crvd/iq5lfNPdlMcwtKa+owObDF/Ddv4dwPSFduX5Un46YNbo3+neWDx1kmNuRkZ+JY5Fyx9WJqFMoqCEx4WzlhEG+A4W9SEMHTTjDIMPcEn6jYpg74EDwVSxavhFpWXlieUzfAHzw9CTYWJiyEHcrCQEnx9X+KwcQdvOS2jYyPPp49RKGCDmwnKycmu06GYZhtAVHW0vMHdcPv+xPglm78zh45RBCYs4jqG235r40hqlFaVk5Nhw8j+//O4y45EyxTl9fDxMGdMYz04bA14N/25lbQ8NOo1KjcejqYVFIYkJ1KKq+nj46uwdikO8ADPIbCG9Hb84wyDDN6ciibFqHDx8WH8ShQ4fCzs6u3n2jo6Oxa9cutQ+1kZERnnzySU1fFsNohLzCYnzwyw78sy9YLFuaGeO9pyZi0uAu4pkn7QSm5UHfURHJ13DgykHsv3xAGCaq2JnbYaj/EAzrMET0ohlKDJvtWhmGYbSV+VMHY82u0yjPj4HEPAtf7F6G1U/9wS9vjNZQXFqG9fvOYcWGI0hIyxbrDPT1RdbBBQ8MgZerfXNfIqPFkLYVZaU+KJxXh3AzK6FOiQnq7Ozv0w/WptbNdq0Mo+to1JH1yy+/4M0330Tfvn1RUVGBp556SjiqevXqVef+hw4dwk8//YSBAwcq1xka8gsgo52cDLuOV775FwmpcsNmUDcffPbcVDjbWTX3pTFNADklL94ME46r/VcOIqGGMeJi7YLhHYdieIdhokeNhdoZhmFujY2lKR6fNADLN6fB3PcULiWEY/elvRgdOJJvHdOsFJWUYs2uM/hp41GkVkXbSyUGQrx9wf2D4e5kyy3E1ElhaRFORJ0UjisaNphdmKO23dPOE0P9B2Ow3yBhL7LEBMNooSPLysoKFy9ehIODXOxw2bJlePXVV0WEVl2Ul5fjvvvuw+eff67Jy2AYjVJcUoZPV+3Gb1tPiGVTY0O8/sgYzBrdi3uRW2DkFQ0V3Bm2C3vD9yEtr1oPg2jv2B7DOwzF8I7DhPYVReExDMMwDeeJSQPwx/aTKMlyhqFNMr7Zt1xEs3IkK9McFBSV4M8dp/Dz5mPIyJFrFhlKJZg5sgfmTRkEFweOmGFqk5GfgcMRR3DwymGcvn4GJeUlym1kG3Z2C8QQ/8HCgdXOoR3fQobRdkfWAw88oLZMTqqlS5fWu39ZWRlHYDFaTei1eCz8ar1S4LNnR098/vwD8GxT/5BZRvegoYI7L+7CrrDdtcLAO7kGCMfVsA5D0dbes9mukWEYpiVgYWoshhh++lcmpFapItp17el1mNP/4ea+NKaVse/MFbz94xYkpssjaEyMpHh4TG88OWmA0HRjmJr6qKTtR+XCzYtq0jjkiO/j1RtDOwwWgu32FjwElWF0Wuz91KlT9Q4rVERkpaSkYNWqVWK5Z8+e8Pf3r3f/kpISURTk5uYqhwA1RJuI9qEvnYbqGBWVFuO/4I0IcA1AN8/O0CYaWxdtRhvrQllqlq8/hO/WH0ZFZSUMJQZ4edZ9eGxCP5Gdpr5r1ca63CktvS708rTr0h7sDtuDyNQotf0DXDpidOAojOg4DM5WzmrnaW5aervoKlyX1tsuLeH5vdfMHdcHv2w5jrx0Dxg5xmDlkV8wqdsEWJnyUH2m6UnJyMW7K7di58lwpQPr0Qn98PjE/rCzMucmYAT023E58YpwXJHExPW062p3xsrECoP8BmCI/xD0a98HpkamfOcYRtsdWSTovm7dOuUyaVx16dJFbZ+CggJ8/PHHWLt2bb3nCQoKQlRUFE6fPi0cWi+88AKef/55vPvuu3XuT+dbsmRJrfVpaWkoLi5ukLGZk5Mjvpj09W+fKnf+6tdwIzccbmZ++GXul9AmGlsXbUbb6kLaCO/8shvnIxPFsp+HA955dCS8XOyQkaE+1Ezb63I3tMS6ZBZk4tj14zgYeRhXUq6q7eNh446hPkMw2GcQXK1c5CtL5N932kRLbBeui3bB7dI48vLkejpNAemMfvPNN8LO6dGjB9577z2lfENNtm/fjv/973+1EuicO3cO2oaJkSGenTYE7/ySCUPbBOQW5Qpn1iujFzb3pTEtGPpuIx2sz1btRl6hvGN8eE9/LHlqAtwcbZr78hgtoKyiDCGx53GgKvIqJTellj4qDYWmBD9dPbqw3hXD6Jojq6ioCFevVr8EBgQE1NqHHFJTpkwRUVb1MWjQIFEU0Atjt27dMHnyZHTt2rXW/osXL8bChQvVIrLc3d2FUWdpadmgHzAat0z7N+QF0N2ku3Bk3SyIQEpxOgI9OkJbaGxdtBltqsv+s1fx2rcbkJVXKK5p3pSBeGnmcCH4qWt1uVtaSl3yi/Ox7/IBbA3ZhtCEC6iUVUdPULTV6E4jRfSVr5OPTmhetZR2Ibgu2gm3S+MwNjZuknY4cOAAZs6ciS+++AI+Pj747bffMGLECAQHB0MqldbaPzExUdhOixYt0okEOjNG9hTC2mkpXjBxjRDDC2f0ehButm7NfWlMC+RqTDIWf78R5yPixbKjjQXefXI8xvTrpBO//UzTUVxWLLJSU6bBI9eOCse6KmQfkrzE0A5DWB+VYXTdkeXp6Ynly5fXu/2rr75CXFwcfvzxx0ad19HRUTixTp48Wacji3oWqdSEXuYa+kJHP1YN3f+thx7F8I83Qd84Fx9s+hrrX1wJbaIxddF2mrsupWXl+OSP3fh163GxbG9lhmULH8TArj46VxdNoqt1KSsvw7HI49hxcScORxxVE+G0MbPByIARGBM4Gl3cO+tc3XS5XeqC66KdcLs0nKb6HJID68svv8Sjjz6qjH7v3bs39u7di7Fjx9Yp1+Di4oJOnTpBFzCSSvDCjGF4bXkmjOzjUGZUhG/2fYfPHvy4uS+NaWHZCL9ee0CIuZdXyDuCKFnPa7NHwdKsaZzQjPaTV5wn7MN94ftFxsGaYu3dPLqKqCuKvmLnOsO0Eo2sTZs24ddff8XRo0chkUjuqCdYW17OHG0s0ddtDE6n/4NrGSG4lnwdvs5ezX1ZjIaJScrAc0vXIixaLvI9sKs3vnhxmuitY3QH+u4IjbuAbRd3iIyDqj1qpoam6Nu2D6b2mow+7XtzKDjDMFr/fbZv3z78/fffauunTp0q1tflyKIEOnVFamkzU4d2w4r/jiA+yRembS9g96U9mN1vFgLddMMZx2g3R85H4s0VmxGXnCmW/Tyd8NGCKeju79Hcl8Y0AzmFOSLqat/l/TgZfQrlFeXKbYYGhsI+pKirwX6DYGduy23EMK3JkUVaV6+88ooIh7eyarxgZ3JyMrZs2YIXX3wR2sK7M5/EqM+3Qd+oAO/9uwyrn/22uS+J0SCbj1zAG99vQn5RCQz09bFw1gg8PXWQ1jhTmdsTmRIlIq8o62BSTrJyvUTfAP19+mNs5zEY6DMAedm5IuqT25ZhGG0nIyNDRKDXlE1wc3NDaGhoncdUVFRg27ZtOHjwoFim6K033ngDdnZ2WpFApy709fTw4szheP6LNJQXWEFiloMvdn2FXx798Z4O9+JkDdrJnbZLenY+PvhtB7YcuSiWjQwleP7BoXhi0gAhFdEcCRr4GWseMvMzcfCqXKz97I2zKK+sUG4zkZpgoG9/9HDtgdHdRsLCxEKnk3jwM6adcLs0jsZ89iSaFDudMGECJk6cKJxRCsgQmTt3LszNzZGQkIDLly/jvvvuE9sOHz6MsLAwccE0FPGvv/4S2lp+fn7QFlwcbNDdaQTOZ29GWMopxKUnwMPetbkvi9FAqPm7P23DP/uCxbKrgzW+eXk6unfw5HurAyTnJGPnxd3CgXUtJVJtG4lvjus8BvcFjBDDCAn6jsmDuuYBwzCMNkB2EGlhKXjttdeEfijZTTWhdfn5+XWe57HHHlPaV5RAZ/Xq1Rg8eDBCQkLq1Mq61wl06qOHtyN83BxwPdEP5j5ncD7uPDaf3oJ+Xn1xr+AEB9pJY9uF9tt6/DKW/3cMuVVi7r06uOO1WUPh5mCNrMwMNBf8jN07Mgoycfz6CZHcJyzxkpo2qjxCvzcGtO+P7u5BkOpLxTNWkFOAorwi6DL8jGkn3C5Nl0BHY44s6gmcPn26+BFRFYInRxb1+JHxlZ6ejsjISKWhlZmZKfY1MDCAh4cH9uzZo5XaDu89tADjvtoNfcNivPfvV/h5/tLmviTmLoiITcazS9ciMl6ejW5k74747LmpsLbgtLnaTEFJgQgH3xq6HcEx59Qyc3k5tMO4zmMxuvMouNmwo5lhGN2BhNxVMzyTzhXZVHU5rGidmZlZneehSHhFNDzZUsOHD8eQIUOwc+dOTJo0qdkT6NyK1+aMxpMfpaMs2xFS61T8fvZPjOs5DlIDjStg1AknONBOGtMuWbmFWLR8A/adlb+D2Fma4Y3HxmDSoC5aIebOz1jTkp6Xjj3h+4SdSDITMlTbiFYmVhjiNwjDOw5Hb6+eMJRUO/a5XbQTbpfW2y7GjUigozELwdraGt9+e+thd126dBFFAWU1pKLteDjbI9BuCMLzduFswmGk5qbB0bLu9NeM9kKOj7/3nMWSn7ehpLQchhIDvPnYWMwe20crjBymNhWVFThz/Sy2XtiO/ZcPiMwyChwsHDAmcBTGdhkDf2c/bkOGYXQSMtpqduLR7xU5s2iIoerQwBs3boiEOw2FMkfTMXVxrxPo3IoRvTqgq687LsYUQGqdjtiMOGwM2YQZvR/EvYITHGgnDWmXs5dj8Pzn/yApI0csPzAsCG88OhY2ltrVQcnPmGbJLcrD/isHsOvibpy5cVYt8ooi8inT4H0dh6NHu+6QGtSvH8jtop1wu7TOdtFvxHnvTVdXC+C9h57D1O8OQE9SiiXrvsF3T7zf3JfENILcgmIs/m4jth8PE8teLvb49tUZCPBy4fuohUSnXseW0G3YfmEH0vLSlOtNDE0wouMwTOgyXhgmBvoGzXqdDMMwTWUoksbV/v378eCD1c4c0iB97rnnGnweioLv1auXTtT31Yfvw6y341GS7iayGK44+CPGdRkDC2NOvMLUHx3ww39H8OVf+1BRWQkLUyN88uxUjOsfyLeshVJUWiSyDe4K24VjkSdQVlFWKys1SUsEeXZjG5FhWjjsyGog3q7O8LXqh8iCQzgesxfZBa/C2uz2ofdM8xN+PRELPv0LsVVZa6YO6Yb350+EmUntnmim+cgsyMLOsF3YFrodlxOvqL/QteuF8V3HYXiHoTA10q4eVoZhmKbgqaeewrvvvothw4bB3t4e69evx7Vr1+rMWFjXC/6KFSsQHByMNWvW6EQD9e/ijb6BXjh1uRQmdknIKszGb0f/wPP3Pdvcl8ZoIalZeVi4bD2OXYgSy529XbH81ZnwcOZscy0NcladjDqFnWG7hXA7ObMUmBuZicir0YGj0NurF2elZphWBDuyGsF7M17AjJ+OAQZleP/fb/HF3DearmUYjbBuXzDe+nGLGEpoYiTF+/MniZBzRjsoLS/FkYijYujgsWvH1LLJkO7VhK7jhXC7k5VTs14nwzDMvWbGjBk4d+4c2rdvD1tbW5SXl+Pff/9VDgfcu3cv1q1bh5UrV4rlDz74QGhtkROLkut07doV+/btq1dTSxt55eGRuH/RdRQmt4Vxm0isPvkXHuz1AJytnJv70hgt4lhoFF5ctk5kJyQoG+Frs0fCUMqvNS0F+h4LiT2PHWG7sC98P3KK5MNGCSOJEQb5DcDowNEY6NMfRlLumGaY1gh/4zeCjp4eaGfWCzHFJ7A/cjsKil+CmTFHh2gjxSVlePunLVi375xY9nK1x4r/zYKvBztEmhvSfrmSdBWbz2/Fzou71IwTa1MrjAkcLRxYHV06sO4VwzCtmqVLl+KNN94QWlmkjSWRVJttJOTerVs35fL8+fMxefJkkUCHRNvrynqo7XT398CwHn44cK4CZk6JKCkvwHf7f8D7U2tnV2RaH+UVFfjq7/347t/DwpawtjDBFy9Mw/Ce/s19aYyGiEmPEUl9tl3YjuScFOV6kpLo49UbYzqPwlD/ITA31r3vN4ZhNAs7shrJOw8+j0d+Pw3ol+CjDSvw4UPVGX8Y7SAmKQNPf/oXrtxIEsvjBwTik2emwtyUe2yak4z8TOy4uBObz29BZIp8KAAhMZBgsN8gTOgyDgN8+kMqqV+Qk2EYprVByXSo1EQqlYohhwpoXnVZV3l51n04EByBvJteMPUIExG7M/vMEJ0bTOslMS0bL3z5D85ejhXLvTq2xdcvT0cbe3mmTkZ3yS7Mxu5Le7Dl/DZcSghX20ZaV5TYZ0TACNia2TTbNTIMo32wI6uRBHn7wM24KxJKz2HH5Y14u/QZGBmyg0Rb2HUyHK9+8y/yCksgrcpKOIezEjarrsHRa8ew5fxWMVUdOtihjT8mdpuAsZ1Hw9q09ksawzAM0/qgJCzj+nfC9uMySFwSUC7JxDsbl2DNvD9hKDFs7stjmoH9Z6/i1W//Q3ZekYjUfnbaELwwYxgkBpzwRVcpKy/DscjjwlF9OOIIyivKlds87Twxoes4jOsyFi7WbZr1OhmG0V7YkXUHvHX/C5i3Zi4q9QuxdPOveHPa05pvGaZRlJVXYOmqPfhp01Gx7GJvheWvzUSQnwffyWbgWnKkiLzafnEnsgqy1DLKjOs8FpO6TYCvsw+3DcMwDFOLlx4agZ0nw5F13Q9WfmdwLSUSKw7+xMLvrYzSsnJ8te4I1u4PFcv21ub4euGDIjEAo3vQcFBK5kNZqSnrYHZhtbSEpYklRncaKaQlAt06sbQEwzC3hR1Zd0DfDgFwkgYgteISNl5Yh/9NfZKzZDQjKRm5ePbzv5Xh5oO6+eCrhQ/C1lJ3BG5bSmj4jou7hAPralKEcr1E3wADfQeI6KuBPgN46CDDMAxzS7zdHDFlSDf8dyAE0ryOKDEPw2/H/sAQ/8Ho7B7Id68VEBWfihe/XIdL1xPF8sCu3vjypWlwsLZo7ktjGklaXprQvdoaug3X026o2YcDfAcI59Ug3wEccckwTKNgR9YdsmjSc1j43zyU6+fi622r8fKkR+70VMxdcOJiNJ7/fC3ScwpE782LM4bhuQeHQl9fn+/rPcoqc+r6aWwM2YyDVw6JoYQKfJy8ManbRIztPAZ25pwOm2EYhmk49Hu+5cgFpEY7w6dvDlIL4/Dmhnfwz9NrYGJowreyBdsVq3aexke/7xQZpw309bDwoRF4+v7BbNvpEBWVFTgRdRL/ndsoslPTsgLSuyPn1ejAUax7xTDMHcOOrDtkRNcesNvki8zKa/j77Bq8NGEO/8DeY0Pnh/+O4Iu/9qKyUgZbS1Mh+jmwKw9XuxckZCUK3SuKvkrKSVYLDaesg5ODJgoNLHIuMgzDMExjcXeyxdxxffHz5mOIu9gWFn5JiM2IxTf7lmPR2Ff5hrZAkjNy8Oo3G3A0NFIse7axw1tzhmFYny5sY+sISdlJ2BSyBZvOb1bLOmhvbofxXccL7Stvx/bNeo0Mw7QM2JF1F7w89hm8se0FlOlnYsWu9VgwdrrmWoapl+y8Qrz89b9C/JMgHazlr86AiwMLhjclJWUlOHDloDBQTt84I7QOCHJW9fbqhclBkzDMfwiMpJz8gGEYhrl7XhEZDK/iekI6nAu7ItvoLP46tRZD/Yegl1dPvsUtiO3Hw/D695uQk18klh8a1QuvPzIa+bnZzX1pzG2gaHyKutpwbhOOR51Q2of6evro790XU3tMERITUgPOSs0wjOZgR9ZdML7XAHy2vR1yZDfwx4k/MH/0NO4xamJCr8XjmaV/IyFVbtg8OqEfFs8dDUMpP8pNBeldbQ7dih0XdyK3KFe5vo2Vsxg6SNpXrjYuTfb3GYZhmNaJsZEUX744Dfcv+hGxV23g26cdUopu4J1NS7B+wVqYG5s39yUydwk5rt5duRUbD1ULun/23FQM6+Evou/zq80ORsuIy4jH5tAt2Hx+KzLyM5Trna2cMCVosojOd7ZybtZrZBim5cJv/3fJcyPn4YPd/0Oxfgr+PLgdjwyfoJmWYdSg3p3ft50UmgmUodDcxAifPjcV4/qz6GtTkFOYg+0XduK/sxsQlR6tXE+9acM7DsPkbhNFFBZrkTEMwzBNSVdfdzwzbQi++ecArp/3gG2nJCRmJ+GL3cvwzqS3+ObrMCfDruPlr9YjMV2evW5k7474+JnJsLNiB6W2UlxWjP2XD2LdqfUITbigJtw+yG8Q7u8+BX29+8BA36BZr5NhmJYPO7Lukmn978Oy3d+iAAn46dDP7MhqAnILivG/5Ruw48QlsdyhXRt8/9pMtHOxb4o/12opryjHyehTQvvq4NXDasLtfs6+YujguM5jYGVq1azXyTAMw7QuKIkLDTG8FJ0ISXZnlJifEcOYaIjhIL+BzX15TCMpLi3DF2v24ufNx0VHpZmxId55cgKmDQ9ibU0thNooPPEyNodswc6w3cgrzlNuc7d1E9FXk7pNgL0F2+UMw9w72JGlAZ4a/DiWHX4PBXo38c/RvZg+8D5NnJYBEH49Ec989jdikuQhyzPu64F3n5wghhswmuFG2g0RFr7twg6RIlmBhbEFhngPwsx+MxDg1pFvN8MwDNMsSCUGYojh+IXfITHaGj69vZFaHIUlm9/Hf8+ug7Upa2TqCldikvDSl+txNVaeKKZnR0/RtiTuz2gXGfmZ2H5hh0jsE5VaHZ1vaGCIfu36CPuQtOo4Op9hmOaAHVkaYO6wifjh0A8o1kvD8r0/sSNLQ70/a/cG452ftqK0rBwmRlJ8MH8S7h8WpInTt3qoN233pb2id+3izTDl/SBhzr7t+2BS0AQM8hmInKwcODo6tvr7xTAMwzQvvh5OePXh+/DhbzsRdc4VTl2TkJ6fgU+2f4ZPpn3EzaPlVFRUigyUFIlVWl4hnJMvzRyBeVMGwsBAv7kvj6mCovGPR57ApvNbcDTiKMorK5T3JsClo9C9GhlwH4rzioV9yE4shmGaC3ZkaQDK2ja331z8ePJz5OI6tpw+jom9+2vi1K2SgqISvLlis1L409vNAd8vekgYscydQ6KpZ28EC+PkwJUDKC4rUW7ztPPEpG7jRWpkJ0tH5f4MwzAMoy08NqE/9p65gjPhMahM7QTYnBZDnYZ1GIqRnTgaXluJupmKxd9txNnLsWLZx90RyxY+iE5enChGW4hOvS4iryg6X1W43cbMBuO7jBXJfXycvJX2ITmyGIZhmhN2ZGmIeSMfxG/Hf0GpfhaW7fieHVl3SGRcCp7+9C9E3ZQPcZs8uCs+fHoSzEyMNNVUrY74zHhsOb8NW0O3ISlHHspPmBmZYVSn+4Rx0sW9M+tSMAzDMFoNRe588cIDGP38N0iNs4KPkx9SSyPw4baPEeTZjTV6tAxKzvPjhiNCqJ+isBTOyNfmjISxIUtENDe5RRSdv0c4sMJuynVoCRJqH+DTX0RfDfQZAKmE24phGO2DHVkawsDAADN6zcKfwcuRURmBnWdPY0zP3po6fatg46HzeHPFFhSVlMFQKsG7T47HzJE92cFyBxSWFGLv5X3YHLIV52JD1Lb1atcTE7tNENkHTQ1NNNV8DMMwDNPkkJbSW4+Pw/++24io823Qplsisgtz8P7Wj/DVzC/YZtASLkbexGvLN+BqjLwDzcvFHh8/OwW9A9o196W1aioqK3Aq+jS2hG7DgSsHUVpeqtzm5dBOdG5SBBYLtzMM06ocWYcOHcKnn34q9I0UGBkZYfPmzfUeExISghUrViAtLQ09evTASy+9BFNTU+giz499GH+d+QPl+nl4ff0SdPdeC0cby+a+LK2nuKQMH63ajy3HwsWyp7Mtvlv0EIecNxL63J2LCcHm0K3YG74PRaVFym0u1i4io8yEruPhasOh/AzDMIzuMv2+Hthz+jIOBEeg+GZHwPE0Dl09jC2hW8WLONN8FBaXYtnf+/DLluOorJTBQF8f86cOwnPTh3IUVjMn9iHnFQ0dTM1NVa43p+j8wFGY3G0iAt06sSOYYZjW6ciKiooSjqv58+cr1xkaGta7f3BwMIYNG4ZXXnkFQ4cOxapVq7B3714cOHBAJ8UDKfR28fhFeH/Hm6g0TsGsz9/EjiXLhKAlUzcRscl44ct1yh67MX0D8Olz98PSzJhvWQNJzE7ClvNbxdDBm1kJyvXGUmOMDBghoq+6ewbp5GeKYRiGYerSJv3kmSkY+fzXyEwC2rfpgPSKK/hsx+ci6riNdRu+ac3AsdAoLP5+I+JTssRyYHtXfPrcVHRsx+3RnEMHyUZUTexDn58+Xr1FB+fQDkOEvcgwDNOqHVllZWXw9fXF6NGjG7T/J598gvfffx8vvPCCWJ42bRq6du2Kw4cPC8eWLvJAnzE4cPkQjsfsQ4reSSz68Q98+cxjzX1ZWgcJRf669QQ++3O30E2QGOjj9UfG4NEJ/bg3qAFQtNX+KweFcXLmxlm1KEjSCaEe6fsChgsdLIZhGIZpaTjaWuLDpyfjmc/+RnRoG7j1SEZeSRbe3fQ+fpiznDtv7iHZeYUim+T6/efEMulfLXxoBB6b2A8SA+7M1Zahg8rEPl3GwcmKEygxDKPbaNSRVV5efssILFXoxXvnzp1iWKHyYiQSTJ8+Hbt27dJZRxbx+cPvYtRnocgtTceeG6vwz75OmD6iV3NfltaQkJaNV77+FyfDrotlL1d7vDVnOAb3CmQn1m0+Mxfjw4QoJ/Ww5ZcUKLc5WzmJYYMTu06Ah5170zciwzAMwzQz4/oHYvegcGw5chE51/2h73oSp66fxvrgfzG914PNfXmtwi7ZceIS3vlpK9Kz88W6fp298PGCKfBsY9fcl9eqiEqNxrbQ7dh+cWedQwcndZ2Azu5sZzMM03LQeETWkSNHhDOK6N27N5555hkx3LAmWVlZwmlhb2+vtt7T0xO7d++u8/wlJSWiKMjNzVVG91C5HbQP/eg2ZN+7wVhihG9nf4a5vzwOA9NcLPnvC/h5fI7O3q4a+xv3qi6ahK538+ELeHvlVuQXyttx7rg+eGXWCOTn5uhUXe5lu6TlpWPbhe1i6OCN9BjleiOJEYZ3GCocWDSUQjF0UFN/Wxefsfrgumgn3C7aCbdL4+8X03y899REnL4Ug5R0oJ1LADL1wvHl7q/R2S0QHVw6cNM0EckZOXjrxy3Ye/qKWCZJiDcfG4tpw7tzp+Q9Ij0vHTsu7hK6VxHJEcr1PHSQYZjWwB05siIiIpTDAYl58+ZhypQpePDBB9GxY0exLiUlBX/99Rc2btwohgrW1OfJycmBpWVtIXQrKyuxrS4+/vhjLFmypNZ6EoovLi5ukLFJ56aX86bWC3I2csKDXR/EutB/ILGPwhOff4U/X3kGtpaaEbK/l3XRBDkFxfhszQHsPxcllh2szfDm3BHo3dETeTnZOlWXe9EuZRVlOBVzBnuu7kVw3DlUyqpflDo4+WOk/wgM9h6kHDqYnp4OTaNrz9it4LpoJ9wu2gm3S+PIy8tropZgGoK1hanQYXpkye+4cckZHj1SkFOSjvl/PoOfH/0JPk7efCM1SFl5BdbsOo0v1uxFXlWn5Nh+nfDuUxPgaGPB97qJKSwtwsErB4XzioYQqtqHlHWQMg6O6zIWzlbO3BYMw7Ro7siR1aZNG7z44ovK5Q4d5D1ebm5uoiiYPXu2iMras2dPLd0sykxYWFhY69y0rr6shYsXL8bChQvVIrLc3d3h4OBQp1OsLuOceilo/3vxYv7KhBdxPiEUkWkRKLY5jbd/88KaJU9pRPz9XtflbjgaGonXvt2AlEy5sT+ufye8P2+iMD51rS63427rEpF8Tehe7bi4E9lF1Q5de3M7oWlA0VdkqNwLuF20E24X7YTbpfW2i7ExCyU3N0OCfDFrdC+s2XUGqeGBcO58AVmFmZj3xwL8+thKtLX3bO5LbBEcDrmG93/ZjqibaWKZHFfvz5+EUX3kndhM0+lenb0RLKLz918+iMLS6vcnWzNbjAkchfFdx6FDG3+OhmMYptVwR44scho1RNCdjMbBgweLCK6a+5NRSVFU5IxSdULFxcWpOcNUoSGKdQ1TpL/TUAOVDNrG7H83GOkb4vOZH+OB72YARkUIz9iNj//wwLtPTtDI+e9lXe6EopJSfPLHLvyx/ZRYtjAzxgfzJmLioC61fmi1vS6NobF1yS7MFqHhpH11Nak6NFxiIMFgv0Eiq0x/bxJM1ehI4AbRmttFm+G6aCfcLq2zXe7Fd8r69esxduxYmJmZ3Vbi4eDBgyJSvXv37vD390drgRLGHA2NQlxyJkwzhwK2B5CRn4Gnfn8avz6+Em42mpN3aG1cT0jHB79ux4FguY1ioK+P2WN746WZI2BlbtLcl9diiUyJxLbQHdgRtktN94qkJSjbIEVf9W3fp1nsQ4ZhmOamyb/5EhISEBQUVKfhR0YWaWqNHz9euZ6WKZKrpUC9gK+NWYgPt30CQ7sErD6yWaQjvn9Y7XvSkrgYeRMvfbUe0VW9diT++fnzD8DFwbq5L00rKK8ox8noU8J5dejqETGUUIGvk4/IOji2yxjYmtk063UyDMMwzctrr72GpUuX4tKlSwgICKh3P9Iepc5Dctx5e3sLCYiXX35ZRLO3BsxMjPDli9MwbfFPuByRj+5dRqLceAdSclOEM+u3x1ZyprZGkpNfhG/+OYA/tp9EeYV8CNvgIF+89dhYeLs7NkUztnqSc5Kx8+JuEZl/LSVSeT/oc92zbQ+M7zoWwzsMg7mxeau/VwzDtG6a1JG1ZcsW7Nu3D99//32d2+fOnYsPPvgAw4cPh4mJCY4ePYozZ87gn3/+QUtiWs8HhLPieNQJmLiFY/GP/8DH3RGdfeqOPNNlyisq8MN/h/H12gPC6DGUSrBozig8Or5vi4iEuVti0mOwKWSLCA8nEXcFliaWGNt5DCZ3mwB/Dg1nGIZhALz//vuIiYlB165dhV7hrfjwww8RGBiI1atXi5feGzduoFevXnjggQfg4+PTKu5njw6eePfJ8SKL3rkL2fDxHoIKmwNIyErAkxSZ9dhPsLdQTzLE1KaiohJ/7zmLL//ai8zcwuoM04+Pw9DufnzLNExuUS72hu8T0fnnYkPUPuvtHduLyKuxnUez7hXDMExTObKWL1+Obdu2CU0KGiIostRt3gwbG3lUybFjx7Bjxw589NFHYvmJJ54QIfDUc+jl5YXw8HCsWrUK5uYtq5eBDMolU97G/cunIwc5kDhfwFMfr8a2L5+FvXXLqeu5q3FYsnIrLkYliOWO7drgq4UPwtfDCa2ZvOI87Lm0F5vPb8WF+IvK9fp6+iIkfFLQBAzxGwwjae1hswzDMEzrheQUKHFOnz59brsvdQLu2rVLOXS/Xbt2IgkPJd2hqK7WwtxxfWFjYYqXv/4XkVGlcHbtAyPnY4jNiBWaWT8/+iNsONq5Xk5cjMZ7P2/H1dhkZTbCF2cOx+wxfTSi8crIKSkrwdFrx7D94k4xVY3Md7R0FLpX1MHp5+zLulcMwzBN7cgiHSxyShkYGMDDw0M5r6Bz586ws7NTLlOEzt9//y00tJKTk0WPI2UtbIk4WDjg7Ulv4uW1r0JqlYaM3MtY8NlfWPPe4zpvGFAK5k//3I2Nh0LFMhnR86cOwkszh4uIrNYIOXNJmHPT+S04cOUAisvkmX0ITztPTOo2HuO7joeTJYfmMwzDMHXTUAdUQUGByBZdc+gh2VWnTsl1KmtSUlIiigLSLFX8flG5HbQPdVg2ZN97zfgBgSJL9PxP/kJyAmBd2hMSt9OISo3G038+ix/nfA8LEwudqEtjudO6xCZl4OM/dmHP6StiWV9fDw+N7IUXZw6DraVcm+1e35+W1i4k2n46+gx2XtqN/ZcPIL8kX7nd3MgMwzsOx7jOYxDk2Q0G+vJ3A6r/7aIx7zUtrV24LtoHt0vrbZfKRpxbo14GclxRqQ8Sda8ru6Cfn58oLZ0RHYdhYtfx2BK6DSYuVxF8zRYf/rZDY+Lv95rikjL8vPkYvvv3EIpK5D1JQX4kZj++RQ6bbAg3M29i7Zl1OBB5CEk5Scr1ZkZmGNXpPqF91cW9M/euMQzDMIL09HRs2rRJeTf69u17Sy2s+s5ha2tba729vb0Qfq+Ljz/+GEuWLKm1nvanZDwNMTZzcnKEUauN0gHezhb44eWpeOnbLUhPA0zKu8LYMwRXkq5i3u8L8NGE92FqaKoTdWkMja0L6WCt2h2Cfw6cR1m5/AWih787XnpwENq72qG8uACpxQVoDlpCu9C1R6RG4HDUURy6dhiZRVnKbVJ9CXp69sQw36Ho7dkThhJDsT4jPQPaTEtoFwVcF+2E26X1tkteXl6D922d4TLNyGtjX0FwzDkkZifBxCMMv28z1jnxd3p4d50Mx4e/7cTNVPkPsrOdJRbPHV1nRsLWoG2w+9JeoXsVGndBbVvPdj2E82p4x2EwNeTMPgzDMExto001asrT07PRjiyKfq+rF7OiokItMl4VEoFfuHBh9W9Zbi7c3d1FVum6Oh1rQn+Pfu9pf219mXV0dMSGz1zwyHt/4HoCUCHrDFPPC7iSchUf7P0Y3876GiaGxjpRl4bS0LqkZeXhly3HsXrXGRQWl4p1ns62IvvjiF7+WmHL6Wq7kJ0cdvMS9l7eh32XDwgBd1W6ewYJzasRHYcLjVRdQ1fbpS64LtoJt0vrbRdjY+MG78uOrHuMhbEFPpj6Hh7/9SlIzLJh5HgDi7/fpDPi71dikrBk5TacunRDLBsZSjBv8kDMv38wTI3lPUmtgbLyMhyNPIZtF3bgSMRRNW0DJwtHTA6ahIlBEzjdN8MwDHNLSMvq559/vqu7RJFXlLWwvLwcEkm1aUfDDcmZU5/+FpWakHHaUAOVDNrG7N8ceDjb4b9P5uOxD/7A+QhAhgCYel4Sotov//Mqvpm1DBJ9iU7UpaHcqi4Jadn4aeMRrN0bjJLScrGONMWevn8w5o7vCyMtk4TQlXahFzxyXu0JJ+fVPiTnpKht79CmA/p69sYDfe6Hq60LdB1daZeGwHXRTrhdWme76DfivNr1a9VK6N42CHMHzMHvx/6AsXM08vLshfj7Z8/dj0HdtDOzUGZuAb5Ys1dksamslI/VH9e/E/43dwzcneRi/i0d6mG7GB8mIq8oAiunKEe5zcLYHPcFjMDYwDFwNXGBs5Nzi/hxZxiGYbQf6sEkh9iFCxfQvXt35frz58+jS5cuaO3YWJrir/cfx3NL12LfWaBQvxKm7uE4GX0Kr677Hz6b9glaOjFJGfjh38PYcIiGEFaIdY42FnhqykA8NKpXq+qM1KTz6uLNMJFxcG/4fqTkqjuvAlw6CtvwvoDhcLF2QWpqKhytWRuVYRhGE7Ajq5l4Zth8nIg8gWspkTBvewnJV80x593fMKCLNxbNGYVAb1doA2TsrNp5Cl/9vR+5BXLNjA7t2uCdJ8ahTycvtAbiM+Ox/cJObL+wA3GZ8cr1En0DDPAdINIiD/IdKLIOklFDhgrDMAzD3EsmTpyIlStXKh1ZmZmZQnvr9ddf54YAYGJkiBWLZ+GtFVvw9x6gSK8CJm5XcejqYby54S28OPD5FnmfrsWl4Lv1h7D12EVlR6Sro7VIyjNteHcYG0qb+xJ1ChJspwzU+8L3Y+/l/UjNTa3XeeVmWz3SoiUIozMMw2gT7MhqJkjQ8aMH3sfMFbNRZpiP9l3SEB3qjGMXonDs5ShMGNgZr8y6D55tqrM83msh992nL+Pbfw4g6qZcKJYyAL3y8EhMH9EDBgYtO9ooqyBL9LCRAys0Xl33KtCtE8Z3GSfE2zmFN8MwDNMcULbna9euYdCgQcoMh+TEeuqpp4TG1i+//ILZs2fDy6t1dDo1BImBAT5aMBlOdpaigw76lTBxuSaGg5WXluPDB9+HqZFcAF7XCYtOwPf/HsbuU5eV67xc7PH0A4MxeXBXnc+YfS8pLS/FmetnceDKQRy8ehiZBZlq2wNcAzAyYARGkPPKRjs6ohmGYVo67MhqRnycfPD8iGfxxe5lSJddwJxZHXH+tDHCohKx9ehFIag+a1QvPPvgUNhbmzf59VBvEWlfbTwUip0nLiG/SJ6WW2Kgjzlj++KFGcNgZd5yBcsLSgqEgbLr4i4x3KC8Uh56T7jauIqUyOO6jEVbe89mvU6GYRimdTFt2jQ4OTmprUtMTMS5c+eUjizSwgoODhZ6W1FRUXjjjTcwffr0Zrpi7db3eHHGcDjZWuKNHzahWL9CyDxQtuGZKx7GB/e/JzqsdBGy406GXce3/+zHqfBY5Xp/T2c8M20Ixvbr1OI7IjVpEx69dlw4r45FHhfLqnRSOq9GwNVG9zWvGIZhdA12ZDUzD/d9CGdunMXRa8ew+dLfGN9vHB6fdD+WrTmI2ORM/L79JNbvPyc0DJ6YNABmJrWFWe+WqPhUoZmw+fAFIQKqwFAqwcjeHYTB5+3u2GJ72Y5HnsDOsN04HHEYxWVy5x1hZWIlwsPHdRmDbh5dtSKDD8MwDNP6WLRoUa11QUFBoqhCzi5yYDG3Z+bInnCwNsezS/9GUYUExm0iEZMRizkrH8XjAx/FvCFPQiqR6oR+56XoRGw5egHbjoYhKaNav7OLjxuenTYUw3v6sW5nA8jIzxS24IHLB3Hq+hm1RD4G+gbo0bY7hnUYiqEdhsDJsmXaxQzDMLoCO7KaGRIEXzbzc3yyfSn+Df5PCInf9LiJ9Z99ht3Ho/D12v1IzynAsr/3Y9WOU3hhxnDMGNkTBvp351RJz84XRs/Gg6Ei/FyVXgFtcf/QIIzp1wmWZg1PgalL+gbBMeewK2y3EOfMK85TbjOWGmNYhyEYEzgafdv30QkjlmEYhmGYxjOiVwesef8JPPHBn8i5Zg8TjzBITHOx8sgv2HlxH76Y+TH82/hp5a2Nvpkm7LitRy7iemK62rbufm54fsYIkUCIO+FuTVxGvMg+vf/KAYTGXUClrFrLylhqhH7e/YRdSFqoVqZWTdSaDMMwTGNhR5YWIDWQ4s0Ji+Hl0Baf71omfkjn/vKoSAk9degrWLn5GFZuPCocWm/9uAW/bDku9LO6ezs0Wvdq75kr2HjoPA6HRKJCRXjSy9UeU4d0w6TBXVtkFkLqsQxPvIydF3dj96XdSMurNvokBhL09+6HMZ1HY7DfIJgattzhkwzDMAzDVNPd3wP7lr+IXzYdwoYjzsjKDYWR03XczI7F9O8fxlCvifjwoVdhZtT8HXuJadlCemLL0YsIv56oto0S8Uwa1AXj+gVAijIx1JSdWLUpKSsRnZk0XPBY5AnEZcSpbbc0scRgv4Ei8oo6NE3YJmQYhtFK2JGlJZCxMavvQ/C088Rr6xcjMTsRc39+DJ9O+0gM7Zs1uheWrzuINbvOiBTKz36+FpamRpBKJWrOGvm0ahnqy0UlZSgtK1fuT+LtJCo/dWgQOnu7tjiDh+7H5cQrQsR1X/g+3Myqjjyjunb3DMLYzqMxouNw7mVjGIZhmFaKjaUp5ozugRdnjcLeM1fxw7YtiCnfAwOTfBy8sQn93z6MyR0fxzOTxsPB2uKeXltGTj52nLgkIq/OXI5R29a2jR0mDuyMiYO6KCUgOHtybcj+Ox55XGhenb1xVk1GgnCydMIQ/8EY3mEogtp2Ex3MDMMwjHbDjiwtY4Bvf/z55G94fs1LSMhKENOXR72EWX1nYslTE/HohP74fPUebDsWhtxC+iFW/zG+HaR7dV8vf0wZ0g2Dg3xbXNYaReTV3kv7RFpkuoeqdHTpIIYNjgocyfoGDMMwDMOoZTUc1z9QlJCIWXj3v6WIKToFGGVhQ+SX+Oe17RjdYSIem9Afgd6uGrdf0nPycS02BRFxKcrpxcgEtQh6EqmnTkhyYAW2wE5ITemfhsSeF44rcmDdSFd3AEr0DdDVoysG+PTHAN9+8Hb05vvIMAyjY7AjSwvxdmyP1U/9gYV/v4LzcaFYuusLXE+/gcXjXhO9b8tfnYnnHhyK85ejYW1tBT09fSjsGD3IZ5TLVTM0IT2ubr7uLS7zoMJ5tefSXqF5RdFsqvg5+4nMMvd1GgFPO49mu06GYRiGYXSDIL+22PL6dzgUfgpvbXwHuaXpMHS+gj3xydi0+ASCvPzR2ccNNhamIqJLTMW8mXLe2KjuyJ6c/CJci0tBRGwKIuPlU1rOzC2sc39rCxOhWzpxYBf06tiWMw/WYQdeT7uB09fP4FT0aZFEqai0SG0fBwt7uePKpz96t+8FC+N7G1nHMAzDaBZ2ZGkptmY2+OmRH/D+lg+xJXQb/gvegPiMeHw+/VMxDM7H3RFWRvJ02+Sgam2ILD0J4dgbvq9O5xWJswrnVcB98LBzb7brZBiGYRhGdxkS0Ae7fTZh2e5vsO7sOkjMs2DhewJhiRk4d9VNdCHWh4mRVM25RZ2KkfGpSM7IrfcYipRv7+oAX09H+Hk4I6C9C/oFeomIeqaa5JzkKsfVGTFcUFX7VJFlsIt7Zwzw6YcBvgPg68TC9wzDMC0J/lXUYgwlhnhvyrto59AO3+xbLnqYHl75CL6d9RU8bN1brUDn4YijOBJxBEk5yWrbO7Txx33CeTWCnVcMwzAMw2gESgLzxoRFGBEwFO9seg9J2Ukwcb8MW49kWFb4A/nuyMmpRFZeoZoWKWmTFpXkIDE9p9Y5KWK+bRtb+Ho4wc/DCb6e8mlbF/sWJ/ugCXIKc3D2RrDceXX9TC2RdsLFug16e/VCP+++6NO+DyxNOOqKYRimpcKOLC2HDJ3HBj6CtvaeWPzvm+KH++Gf5mLpg5+gnXlbtHTS8tKExsGRa0dFuHjNUHHSvJI7r4bDvRU69xiGYRiGuTeQk+TfBWvx+a4vsTFkM4pkmSjSPwGpjRQj+g/D1O5TENCms3BoZecViqGCNJ+VWyCmZeUV8HZzhK+Ho5jWN/SQAbIKsnAxPgxHrxzDpeRwXE2OUCY1UmBtaoVeXr1Eu/T26gk3GzfWumIYhmklsCNLR6A0wL8/8QteWLMQKbkpeGbV83ii36OYO3guzIxN0VKgbDtkrBwRUVdHhfZVTYHOIM8gDPIbiKH+g+FmS2H9DMMwDMMwTY+5sTnenfw2Zvd7GBvObcLW0G3IKcrBzrDdolDEPDm0JnYbj07tNSsI35LF2a8mRSDsZhjCbl4SRTXTtAJjqbHIOE0aV+S8ouGCrVFeg2EYhmFHlk5BQ+fWzPsDL/z1MsITwvHj8Z+xJvhvjO08BlO7T0YHlw7QRQpLCnHixklcPBWGY5HHa+kcUI/bAJ8BGOQ3QISLs0AnwzAMwzDNSXtHL7w6ZiGeH/EMDlw5iP/ObRRD3+Iy4/HV3m+wfP93GOI/BPf3mII+Xr3Z4VIFRVXRPVI4rKhEJEegvKJ6SKYCQwND+Dh4o79fP/Rt3weBbp0glXAUG8MwDMOOLJ3DwcIBvz72E77YtQybz29FfkkB1p39VxQSOCeH1pjAMVqtC5CSk4LzcRcQGheKC/EXEZF0DRWyilqZGwf7DRKRV2S4kGgnwzAMwzCMNmEkNcKYzqNFic2IE1FaW85vRWZBJvZd3i+Ki7ULpgRNwqSgiXCydERrgeQgYtJjEJV6HdfTrgt7LyzhEnKL6ha7JxkNsvmodHLtBG+H9sjKzGq1iY0YhmGYezS0sKKiotb4ddJ4MjAwaPAxt9ufkYdWLx63CNM7T0Nwcgg2hWzGlaSrIiz7o22f4otdXwndKHJqBXl2a1a9AOphi0yJRGj8RYRWOa+Sc1Jq7SfVl6CnV0/hvBroOwCuNi7Ncr0MwzAMwzB3gqedB14a+TyeHfY0Dkccwb/BG3Dq+mmRWfm7Az+I4mrjKiLsO7r4i0h60vq0NrXW6RteWOWwik6NRnTqdVHIcZWQnVjrvUCBjak1OlU5reSOqwBYmljWkptgGIZhmCZ1ZNGPjbW1NYqKimqtX7VqFWbNmlXrGFo/d+5ctV4Wc3NzZGdna+qyWjTmRuZ4sOcDmNH7QVxJvCKER3dc3Im84nxsu7BdFE87T9ELSFoNduZ2TXo91NYZBRm4lhxZ5bS6IHreagq0ExbG5ujs1hldPDqji1tnOBs5wcPVg3vcGIZhGIbRaWj424iA4aIkZCViY8gmbArZIhLYJGQliEKRWgraWDkrnVpUaN7WzAbaAjmj8orzREdkUk6SiKxPyE4SjqvraTeEo64+h5ViNAENxfR29EYntwDhuHK1dmFhdoZhGKb5HVnkjMrLy6u1vmPHjujcuXOdxxQXF+OFF17AsmXLNHUZrRYyeqi8NPIF7Lt8ABvPbcK52BDEZsQqtRoo0snX2RdWJpawMrWGtYmV6P2inkDSoSLH2K1Ct4vLiuVGTHYSknKSkZyTrDZP28oqyuo81t3WDV3cu6Crh7y0d/BS/i1ygKWmpjbZvWEYhmEYhmkOKML82eELMH/IU4hIviYi6C8nXhEdkJEpUcJuIjuKCmltKXCydBJRW+0d28PKxApWppawNLYU85amNJUv09DGu42cJ/uOnGxkxyXnpghHlbDxcpLl87kpdXZK1sTR0lHYd+S0ouLlIC/aLHfBMAzD6CZNmrXwwIEDsLW1RWBgYJ3by8rKYGR0dz/AjDomhiaY0HWcKDHpsSJKizLqZORn4ODVw6LUh76evjA2rEysq5xdVpAYSJCak4rEnCSRCrkh0DEd23RQOq26uHeGvYU9NxXDMAzDMK0Sso0CXDuKoqCsvAxRadHCqSV3bl3FtZRIkcWPMlRTuZXdRhhLjWBJji6y20ysREKc8rJy6BnoobSiVJyrpKwEJeUl8nlRisWUlisq1TVKb4eZkZlwslEUWTuHdnKnFTmsHNtxMh6GYRimZTiyvv/+e8yfP7/e7eTIkkobnn2kpKREFAW5ubnKiJ6GjKOnfSj0uSWMuW9IXSgF9AsjnsWCofNx7Nox7L9yUIiPZhfmiFTRuUU5QixeeU5ZpdhG5VZQFhlnK2c4W8kNmTbWbarm2yjXG0oMa13v3dRFV+C6aCfcLtoJt4t2wu3S+PvFMHc6BJH0sqhM7T5FrKMILRqup4jaupmZgNziXOQU5Qq7LbcoT9hrCorLSlBclorU3LuPbCfbzdnSCU5WTsKWk9t0zmrrOHM0wzAMo9OOrPLy6jS5JM5eU1A8ISEBx48fx5o1a+o9Bx3322+/4euvvxaGYK9evcQwwy5dutS5/8cff4wlS5bUWp+WliaGKd4O+hs5OTnCaaLr2U8aW5cAu44IGFDdC6gaUp5Xkie0D3JL8oSxRBpb8mkeyirLYG9mD0cLBziaO4op9fhR9FadVADZmY3TOGvN7aLNcF20E24X7YTbpfW2S12yCgxzp0gNpPBz9hWFNE7reqapE5LsNMr+lyM6J6udXFmFWcjPz4e1pbVIDkTOKYraMpQYVU0NYSQxqi5V60hHlQTYmzNBEMMwDMM0qSPr7Nmz6Nu3r3L5888/x4svvqi2z08//YSHH374lkMHn332WVGIjIwMrF69GmPGjMGlS5fEkMSaLF68GAsXLlSLyHJ3d4eDgwMsLdUzndQF/fjTDzTt3xKcDFwX7YPbRTvhdtFOuF20E26XxmFsbNxELcEwtSH7lWQghO6UjWut7QrdUUdHR523dRmGYRhGo46snj17qkVk1TVk8Oeff8bhw7ce16+KnZ2dEH6nY/bs2YMZM2bU2oecYnU5xuiHuqE/1uT8acz+2gzXRTvhdtFOuF20E24X7YTbpeG0BHuCYRiGYRhGl2gS62vDhg0ICAiAt7d3o49t3749kpOTm+KyGIZhGIZhGIZhGIZhGB1GvzlE3m/FqVOn4Ofnp/FrYhiGYRiGYRiGYRiGYXQbjTuywsPDER0djYkTJ952XxrHT0MUS0tLERUVhXnz5qGgoAAjR47U9GUxDMMwDMMwDMMwDMMwOo7GHVkrV67EggULIJHUlt/asWMHJk+erFx+5ZVXhEiqhYWFEHmn+QMHDohshgzDMAzDMK2d0NBQuLq6ig6/W/HPP/8Ie8rc3FxZnJyc7tl1MgzDMAzDaLXY+6346quv6t02duxYURR8+eWXojAMwzAMwzDqXL16FTNnzoRMJkNxcfEtbw9lcp4zZw4+/fRT5TruGGQYhmEYpiXCqXYYhmEYhmG0kMcffxxr1qyBi4vLbfeljNGq0VhUTExM7sl1MgzDMAzD6HREFsMwDMMwDHP3HDp0CFKptEH7kuaooaFhg89dUlIiimpEl0K/lMrtoH0oUqwh+2o7XBfthNtFO+F20U64XbQTbpfG0RibQqcdWWRAqRpfDbkxeXl5QotLX1+3g9G4LtoJt4t2wu2inXC7aCfcLo1DYYMobBJN0lAnlqLdVq1ahV9//VUs9+7dWwwz9PHxqXP/jz/+GEuWLKm1/saNGyKaqyF/j+pOpSXYVFwX7YPbRTvhdtFOuF20E26XxpGfn99gm0pP1hSW1z3i5s2bcHd3b+7LYBiGYRimlRMfHw83N7dGH3fu3DkMHjxYufzJJ5/g2WefVdunR48e+P3339GpU6d6z1NRUYGioiIxn5KSgtWrVwunVlhYGCwtLW8bkZWQkICOHTs2+voZhmEYhmHutU2l044s8nAmJiaKLD16enq33Z9628jxRTemLqNOl+C6aCfcLtoJt4t2wu2inXC7NA4yoyjam3Ss7iQyiWyZwsJC5TJFjdfM/NwQR1ZdTJgwQQjAT5s2rUHXwTYV24faBH8XaSfcLtoJt4t2wu3SdDaVTg8tpMrdSe8nObF03ZGlgOuinXC7aCfcLtoJt4t2wu3ScKysrO7KlmnIUL47wd/fX0RaNfQ62KZi+1Ab4e8i7YTbRTvhdtFOuF00b1PptqgBwzAMwzAMUyfnz5+Hl5cX3x2GYRiGYVoU7MhiGIZhGIZpQaSnp+O1114TQwXHjBnT3JfDMAzDMAyjUVqVI8vIyAjvvPOOmOo6XBfthNtFO+F20U64XbQTbhftw8DAoJZu1o4dOzB16lTl8iuvvCKGKJqamqJDhw5C8P3gwYONynzYGPg50U64XbQTbhfthNtFO+F20U6MtMyXotNi7wzDMAzDMC0dyi5Yl+FIJpwi2U15eTmKi4uF08vExKQZrpJhGIZhGObewI4shmEYhmEYhmEYhmEYRidoVUMLGYZhGIZhGIZhGIZhGN2FHVkMwzAMwzAMwzAMwzCMTsCOLIZhGIZhGIZhGIZhGEYn0ElHVkVFBVauXInJkydj4sSJ+Pnnn1FZWam2z4kTJzBjxgwMHToUCxcuRGZmZqOOJ7Zv3445c+Zg0KBB6NOnDw4cOKDxupA4648//ohJkyaJ8uuvvwrxVlWOHTuG6dOni7pQVqKsrKwGH6+49ppl165dGq9LWVkZvv/+e3FP6d7++eeftfY5dOgQpk2bhmHDhmHRokXIzc1t1PHUJnQvhg8fjueeew7R0dFoCugerl69Gg888ADGjRuHb775RtxrVc6fP4/Zs2djyJAhePbZZ0Wa88YcT88T1WXw4MGiLpRhqqnqQvfy/vvvx/jx47F8+fJa1xISEoKHH35YPGN0LcnJycpt9PmgZ6Zv374YO3Ys3nvvPWRnZzf4eE1Cn9Pff/9dZOqaMGGCeF7o86xKcHAwZs2aJdrl+eefV7uv9FlRrcsHH3yAnJycWn+HPmP0HF66dKlJ6qGoy2+//aasy4oVK2rV5cyZM3jooYfEfX3xxReRlpam3EZ1V9SFnrEPP/xQ7fPUkPNrkn///Vd8544ZMwZLly4V4tSqXLlyBY8++qhol3nz5iEmJkbtWMV306hRo/DGG28gNTW1wds1zbp168Rnk56RL774AqWlpWrbw8PD8cgjj4i6zJ8/H7Gxscpta9euVV7r6NGj8eabb6q1myokzE1t8+WXXzZZXeh6HnzwQVGXZcuW1apLWFgY5s6dK+ry9NNPIz4+XrltzZo1anV56623kJ6ernYf6PlT/W2h5aZoG/oe++uvv8TvBz3vX3/9tfjNUOXChQviN5vqsmDBAiQkJCi30Xeg4hrpGaWsOxkZGbX+zuHDh/H444+Lc9C+69evR0uHbSq2qdimajhsU7FNxTZV42CbSg7bVH00b1PJdJBnnnlGNmXKFNn69etl//77r8zHx0f2wQcfKLefPn1aZmFhIfvyyy9lO3bskD388MOyLl26yEpKShp0PLFgwQJZYGCg7Ndff5UdPXpUdvLkSVlmZqbG6zJv3jzZ/fffL66Drqd9+/ayTz75RLn9xIkToi5fffWVqMvMmTNlQUFBstLS0gYdf+7cOXHtqoXqtWfPHo3XZe7cubLp06fLNmzYIFu7dq3M09NTXLeCgwcPyiwtLWXffvutbPv27bIHHnhA1qdPH1l5eXmDjv/hhx9kfn5+stWrV8v2798ve+mll2ROTk6ypKQkjdflnXfekY0cOVL2999/y7Zs2SKenxdffFG5/erVq6Iu9Nzs3LlTNn/+fFm7du1kubm5DTp+165d4tqpTrt37xbHd+zYUdmumuSNN96QjR49WtzTzZs3i/Z/5ZVXlNvDw8PFM/bRRx+Jujz55JMyb29vWX5+vtienJysfHY2btwoGzdunCwgIEB5rbc7XpMsWrRINnbsWNk///wj27Rpk7iO//3vf8rtFy9eFNdCnwG6lscff1w8MwUFBWI7PSuKutBzNmbMGFnnzp1lZWVlynPk5eXJ+vbtK7OzsxPPbFPx8ssvy8aPHy9bt26duK8dOnSQvfnmm8rt58+fF3X57LPPRF0eeeQRsU9RUZHYnpiYqKzLf//9Jxs1apSsW7duys/T7c6vSZYtWyYbOHCg+GzSZ5s+13S9CuLi4mQ2Njbi79Ozv3DhQlmbNm1kaWlpYnt6erqyLlu3bhXfA/T5V3yebrddkyxdulQ2ePBg2Zo1a2Tbtm2T9ezZU/bEE08ot8fExMisra1lb7/9tqgLfa5dXV1lGRkZYjvVSXGt9Nmn7zn6bqjr80DfYdRu9D3eFHz88ceyoUOHKuvSvXt38V2jIDo6WmZlZSV79913RV2ee+45mbu7uywrK0tsT01NVdaFvjumTp0qPtuKzxN9Pvr166f2+3Lq1ClZRUWFxuvy/vvvy0aMGCH766+/xDNAz/qzzz6r3B4ZGSm+k9977z1RF/qdp2ckJydHbE9JSVGry+TJk2W+vr7KzxNBx9Jv6IoVK2RHjhwR+9JxLR22qdimYpuq4bBNxTYV21QNh20qtqlWNKFNpZOOrJo34fDhw8L4VPDggw+qOUAqKyvFixUZrw05ngx+eulrihfxmtS8FjIm6MVbAb04LF++XK0uPXr0EC+LDTm+JteuXZN5eXk1yYtGzWuhayQHjgJyQPz888/KZboGcqrQNTfkeHL0kGNOFXoZoZe0pq4LOWvo5VXV0VnTKUAOnl9++aVBx9ML5d69e9X2IYckOb40Tc1ruXDhgnDSKHjqqafEi6wq9HL9xx9/1Hk+ajf6fNDn5k6O12Rd6Hkgh6CCxx57rJZTevjw4fU+I+T0oZfZ48eP12pHqkNTOrJq1oUc8OQQUTBnzhw1pzRBDhZy4tUFOePos03nacj5NQk5POi7SUFCQoJMKpUqOw9ef/11NacDMWvWLNkXX3xR7zn79+8vHHR3uv1u6qJKbGyszMjISOnsfO2119Sc0gQ51r7++ut6z9m7d2/l748C+vzQ99eqVauazJFVsy7kuDI1NVV+/5NDUdWprfge+u677+o8X83fH+oQoU6he0HN5zkiIkJmbm6ufO6ef/55Nac2MWnSJNmPP/5Y5/nouK5du4qOBOLs2bPi86FwrrYm2KZim0oB21SN/7ywTVUN21SagW0qtqmampQWYlPp5NBCR0dHtWU7OzsUFBQol2nYHA0LUaCnpyeGI+zZs6dBx//yyy9i2JuZmRmamttdy+7du++qLjWh4UhPPvkk9PU13/S3uhYaurBv3z4xxEUBXQMNvWtoXdq2bYukpCS1oVM0DKZdu3b3pC5FRUXKoVk1nzGC6naruiiOp+u+evWqGC6mysiRI8WwlntVF8UQ1NvVpSbUbg4ODpBIJHd0vKbrUlhYqFxu7LUYGBjA3t5eWRfF8MPHHnsM2vY9dru6UB1U69LY74a7gZ4H+m5SYG1tLZ4vxfDCO3lGnJyc1Nqlsdvvpi6q2NjYiM+tYkieJuqSn5+Pl19+GT/88AOakpp1sbW1FfVQDMlrbF2ojem5UtSFzmNoaIh7QV3PM9VFMUz6butCw/JpWDR9hlobbFOxTUWwTXXnnxe2qeSwTaUZ2KZim6qpcWwhNpVOOrJqQroZiptNLwj0g+Ls7Ky2j4eHh5qGUX3HK3RpunXrhldffVXoBpDjh/Rd7gWq10I6RPQCVfMhaExdVKEXftp+L17Sa14LacSYmprCwsLijuvy6aefijbZu3ev0DVSaKCRJktTQ9dCTjf6kSbomt3d3RtVF8Xx5AiidlXVM1K0t6o+TVOh0JmhLx1yNpBzsKF1oS85cvTQC2zv3r0bfXxT1EXhHCUjnPSwGlOX7777TrRHjx49lOtdXV3RHKg+73R/SYvIzc2tQXUhhxHpsJmYmIjvrtudv6n5559/hJaV4vNOWkUNbRf6EaVrJf07cu42dntTaEyRJhN9f91JXUgrLy4uDiNGjFCuJ91GcmTV/J1qav7++2+hpWhkZHRHdfnjjz+E/p3CCU/rQkNDhfYe/Va+9NJLTaaPVxN6BqZMmQKpVNroutDni4ws0sIjHUnF7z5pYr399tuiLqQbRnp7rRG2qeq+FzVhm+runjG2qdimakrYppLDNlXTwTaVFthUMh3nypUrMhcXF6Hjo9DAIS2WmpBmBg0xut3xhEQikQ0YMEBobO3bt0/ojNBQrEuXLjVpXWj4GV2LYjhIfHy8zMHBodZ+NPSRhj3d7viarFy5UjZjxgzZvYD0fdzc3JRaK6RfQtorNSFtLwpVvN3xqsNxTExMRBvTEFJVbaOm4ubNm+K+Uh0I+pv00ak5PDM4OFhtKGR9xyvC92l4kuIcNOSnbdu2dT6jmoSGSdG1XL9+XSyTPoyenl6t/UjnhoY/KiAtL9KjomFJw4YNU7ZLQ49vCm7cuCF0lqhOCm0rGs5Wk2PHjomhXQpoqC7VhZ4j0txRaOjUpKmHFqoSFRUl6kKfeYLuL11fTQ4dOiSG1CmgIdQ05Jb2peutTzOq5vmbEtKzos96aGiocp2ZmZlSQ0r1mkjDSAENq+3UqZPQBSNdKtXv5IZsbwrou5S+h8LCwpTraJhhzWeGNPNUh6eTVhhdK4Vp07OnGsJNWoekm6WgKYcWqkL3i8K8L1++rFxnYGCg1LtSQL9zqsPTf//9d1EXakPSw1INE6fnTaE7Rb9LpLVEf6Mp9CRVIX04+jsUCq8IaafvZMVQVgX0DNJnXQENGaZlqgv9xqs+k/S92KtXLzE0mX73aago/e4rhlC3FtimqoZtqqaBbSq2qZoatqnYpmpq2KaSaYVNpdOOLHpxJccBiT4roBcMetGoCWmpTJw48bbHk0FML+Y1tYpI2LcpXzboukkvil4GFNADQY6DmpBGDmln3e74mpA4Lr0INzX0EkMaSqqC8qSZY2trW2tf0lF66KGHbns8Qfpa9CJFThKq7+LFi4VeS32OO01AL0b04VRoXykgh0lNpwF9OOn6GnI83Q8Sx3Z2dhaOCHqhf/XVV4WDq6koLi4WIuaq2lX0vOvr68sKCwvV9iXdskGDBqldLwn1kWA8aZ0phLwberymIQcafUGqal+RNgN9dmsK5tN4bXK+qRrRVBf6jJMIvqqQd3M4ssiRQM+xqvYVPTd0XxXC7QpIm4iSCNSsCwlg03pVIe9bnb+poOule0qdAKqQQ54E32vqitCzr4A+x+R0pKQV06ZNk02YMEFNd+t22zUNOazvu+8+2TfffKO2npzo5EipqdVGSQMUkONKca2kOaXQkaLvNvrNUf3OuheOLKoLfQa+//57tfXkFKz5/UlaapRQRNVYo4QnlDSAvp/o3t+K2bNnC/H/poI+36QV99NPP6mtJ2duTQcaad/R94QC6uiiutBngewB1Y4d+i6ued10v+rqaGmpsE1VDdtUTQPbVHLYpmo62KaSwzZV08E2lfbYVDrryKIXJhJlpkxpNaEsTDVfNEhUWFVs+FbH04sK/cjUjOiqKwpKUx8Ievmj7GQ1oR79mi8an376qZrY8K2OVzXoyTnU1NBLBr0w1Xz5o/ttaGhYy/mzZMkSNTG5+o4no5I8uTVfVCiTFQnSNRX0UkaCyDWh6AvKkKcKOYhqRrzVd7wCermnF3oy7ihjFglJNwX0wk8ZL+s6PznRVKM0CHK8UbbPuqAoMnppDwkJuaPjNVEXisYjAfGaUJQGJTRQhYQJVTPo1awL9SbUbMt75ciiv09OaXKU14RE7BWRcwpIhLs+xxt9xvz9/dXa4lbnbwroO5YyRdaEovPI4aYKiZ/T91Z9ULa9W2VXvd32u4WcgpTIoCbkiKJssjU7SihDZH2Q0/rAgQPiO4LalaK0FIW+SxwdHUVCkqaKmKNnhhJU1ISe/TNnzqitI4PkVg51cobXbEtVKHqTIrOaikcffbTO73yKIlN8JykgR7dq9FvN7xFycinakn4fayYToYiuuqJsWyJsU7FNRbBN1TDYpmKb6l7ANlXdsE2lOR7VcZtKZzWySECsTZs2WLx4ca1tpN1z8OBBtXWHDh0S6xtyfGBgoNBfUaUuHSBNsWDBAnh6egr9p5r06tXrtnW51fGqIu/z589HU/PEE0+gc+fO4v6qQtpQpEN0u7rUdzxpR5EQHQkvqxIUFIRr1641SV3eeustIY69dOnSO3rGbnW8AnqmqL6k00T6M2PGjEFT8Prrr4tx8p988skd1UUVulYSr1boeTX2+LvltddeE8/TBx98oJG6kDjhvdAmqwvSSSLtpSVLltx1XeiekECoal1udX5N88UXXyAiIqJOAfM7eUZIP+pW7XK77XcD6fHFxMQIDTVN1mXChAnYvHkzvvrqK2WZPXs2+vfvL+abQjPrww8/FL9fpKGmybrUB903+m1tCug5zsjIwLJly+66LqQRqPo9dq9/97UNtqnYpiLYpmoYbFOxTdXUsE3FNhXBNtVtkOkglJKeevLr00eiHmUasqKI3qGxmTS0hcLmG3I8RXCQBovieIqaIc9kU2hl0NhRigyrOYRI1ftJ+iQKTSIaJkW99wpdk9sdT1BEF0WZ1dSb0jRvvvmmGEJTUztKNUqHhqwoorK2bNkixtDSkLfbHU+RWu3atRNjbBXbqX2GDBki7oGmoWulyIOaQ+ZUh855eHgoI/8ozSjdYwqzbMjxqtAQUhquozpkTJPQ80y6SjQcry7omSJ9LoXmEA3dpLrUNWSTPO4UVULtphgL3Zjj7xaKSKIhi4pnpiY09M7Ly0upSUSRiDSklXSb6qrLr7/+KqK4srOz73lEFj3LFFVUU9dHNWLJx8dHqUlEUTBUl7o+x1QX0sAjbSrF5+t259ckNISOelnq0xujzwd9b5GumUJ7hupCOhZ1sXHjRhG5pNA/a+z2u4GGz6p+T9WEnm8KmVb8bdLPoroo6lYTitaia6VhoHXRlEMLSauLhpXm5+fXuZ2GP9JnWTHsk3rL6LNbcxioAhpeSHWvGfGsgH5r6V6o6gFqCtLqot6+mppeCuj3mfTMFPeZehLriq5WQEOLqS6K7wr6vNHvvGJ/+tyRHUB1bumwTcU2FcE2VcNgm4ptqqaGbSq2qQi2qW6Pzjmy6IeWdHDIuaM6PIMKCWYroDA5MmJJN4qGpCmGoDTkeHKU0FAkEi8jh5ilpaVs6dKlGq8LvejTtdA1ql4HDTGJiYlR7kfDNBR1sbe3F06UxhxPwycfe+wxWVNCQ2LIL0ovszWvRfHSQy/bNMSFXnTo/tOLrWKISkOOJxFaerGgl0Ia2kZDSJ9++mmNv6hT+9MwSBqmVfMZUQ2TfPfdd8U10LVYW1uLH56GHv/hhx+KZTqWdGrmzp2rdLRqEnIAkp4XhXjWvBZVMW5yIlId6HroWduwYYNyGzmOaH+69yQYTi/5JGyvyq2O1xTkiKNEDHXVRVWMm7TTVK+FXlAVzx/plSnqQi+x9JJPiQXqoikdWeRYIKFt+n6pWRfVoYGkm6aoC31uFBp45Lgm56SiLvSZoJd8GqbamPNrCvqO9fb2rvW3VDX5SDeLPi/0GacpOREV0Nh62p/qQM4Ium7VjoPbbdckdG3kQKxZF3L6KKBh3Kp1+fPPP5XbqGNB9Vpp+B5pCNzK2VSXtpkmoN8wX1/fWnUhcXYFlMxEURd61lR15+gzoKgLOXzpt0Z1WCVpsynOSY59ciLv3bu3SepC2pfkaKpZF9WhkTQsSrUu5JRUQIkdFHUh5x3tQ05JVV555RWhS0ltRt/LixYtkrV02KZim4ptqobDNhXbVGxTNQ62qdimCmhCm0qkG4MOUVFRIdI31nXZ3bt3V6biJlJSUkQqcF9fX5GWvrHHU9puOt7Hx0eZRl6T0FCvc+fO1boWGvJA1yKRSJTr6DqoPn5+fjA2Nm7U8TT0joZQWFlZoakoLS3F+fPn67wWGlJIw55U72taWhr8/f2VaeAbczylWc/MzETbtm2bpF0UaUMrKytrre/SpYvyWSLS09Nx8+ZNeHt7w9zcvMHHU6gotSkN+/Ly8lI7VtOcPn26zue9Zl2oTeje0vNuZmamXE/tUlJSIp4nFxcXUeqivuM1BdWB7mtddenatavyc3GrawkJCRHP2u3qQtAwI1dXV7Xzagp6Ns6ePVtnXbp166b8XBCpqaniM0PfY/S8KKDPPqW8pbrQdaoO52rM+TWB4lpqEhAQoPYZpdS8sbGx4pm3tLRUrg8LCxPDcOlzTkPX3NzcxGe/ods1Cf0+0HdrTTp16qT2OaXvoLi4uFp1uXjxIgoLC8W1UptQ29zqWnNzc5GXlyf20zT0DNBvXk1oGJ3q54KG69EQu/bt26u114ULF1BUVFRvXei4yMhIsY4+S03ZLvV9p9LQbNXPhaIuNb+TQ0NDUVxcLOpC11rf/abvDjq+Xbt2tYayt0TYpmKbim2qxsE2FdtUqrBNdWvYpmKbKr4JbSqdc2QxDMMwDMMwDMMwDMMwrROdFXtnGIZhGIZhGIZhGIZhWhfsyGIYhmEYhmEYhmEYhmF0AnZkMQzDMAzDMAzDMAzDMDoBO7IYhmEYhmEYhmEYhmEYnYAdWQzDMAzDMAzDMAzDMIxOwI4shmEYhmEYhmEYhmEYRidgRxbDMAzDMAzDMAzDMAyjE7Aji2EYhmEYhmEYhmEYhtEJ2JHFMAzDMAzDMAzDMAzD6ATsyGIYhmEYhmEYhmEYhmGgC/wf2Q3dLb7CLuMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x660 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tt = pd.PeriodIndex(B.quarters(\"2026Q1\", B.H), freq=\"Q\").to_timestamp(how=\"end\")\n",
    "fig, axes = plt.subplots(2, 2, figsize=(12, 6.6))\n",
    "for ax, (c, title) in zip(axes.ravel(), [(\"산출갭Y\", \"산출갭(%)\"), (\"실질금리RR\", \"실질금리(%)\"),\n",
    "                                          (\"실질환율갭S\", \"실질환율갭(%)\"), (\"실업갭UNR\", \"실업갭(%p)\")]):\n",
    "    for s in B.SCENARIOS:\n",
    "        ax.plot(tt, mac[mac[\"시나리오\"] == s][c].values, color=CLR[s], lw=1.6, label=f\"{s} {B.SCN_KO[s]}\")\n",
    "    ax.axhline(0, color=\"grey\", lw=.6); ax.set_title(title, loc=\"left\"); ax.grid(alpha=.3)\n",
    "axes[0, 0].legend(frameon=False, fontsize=9)\n",
    "fig.tight_layout(); fig.savefig(os.path.join(C.OUTPUT, \"fig2_macro_paths.png\"), dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff1df94e",
   "metadata": {},
   "source": [
    "## 표 5. 산업 외생 6종 — 2035년 4분기 수준(정비판, 괄호 안 이전 판 S1)\n",
    "\n",
    "유가는 두바이유 가격(달러/배럴)으로, 실질환율은 로그 수준(100·ln)으로 표시한다. 정비판에서 취업자 수는 인구 레버에 따라 S2·S3가 S1과 영구적으로 다르고, 유가는 유가 증가율 편차를 누적하므로 수준 차이가 남는다. 나머지 네 외생은 충격이 감쇠하여 2035년에 시나리오 간 차이가 거의 사라진다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "32bb9d4e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T00:09:10.638557Z",
     "iopub.status.busy": "2026-10-08T00:09:10.638557Z",
     "iopub.status.idle": "2026-10-08T00:09:10.646573Z",
     "shell.execute_reply": "2026-10-08T00:09:10.646573Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_42f5c\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_42f5c_level0_col0\" class=\"col_heading level0 col0\" >두바이유(달러/배럴)</th>\n",
       "      <th id=\"T_42f5c_level0_col1\" class=\"col_heading level0 col1\" >세계수요(%)</th>\n",
       "      <th id=\"T_42f5c_level0_col2\" class=\"col_heading level0 col2\" >실질금리(%)</th>\n",
       "      <th id=\"T_42f5c_level0_col3\" class=\"col_heading level0 col3\" >실질환율(100·ln)</th>\n",
       "      <th id=\"T_42f5c_level0_col4\" class=\"col_heading level0 col4\" >취업자 수(천 명)</th>\n",
       "      <th id=\"T_42f5c_level0_col5\" class=\"col_heading level0 col5\" >국내 경기(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_42f5c_level0_row0\" class=\"row_heading level0 row0\" >S1 기준</th>\n",
       "      <td id=\"T_42f5c_row0_col0\" class=\"data row0 col0\" >79.59</td>\n",
       "      <td id=\"T_42f5c_row0_col1\" class=\"data row0 col1\" >-0.00</td>\n",
       "      <td id=\"T_42f5c_row0_col2\" class=\"data row0 col2\" >1.31</td>\n",
       "      <td id=\"T_42f5c_row0_col3\" class=\"data row0 col3\" >833.46</td>\n",
       "      <td id=\"T_42f5c_row0_col4\" class=\"data row0 col4\" >28,762.75</td>\n",
       "      <td id=\"T_42f5c_row0_col5\" class=\"data row0 col5\" >-0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42f5c_level0_row1\" class=\"row_heading level0 row1\" >S2 공급제약+충격</th>\n",
       "      <td id=\"T_42f5c_row1_col0\" class=\"data row1 col0\" >66.48</td>\n",
       "      <td id=\"T_42f5c_row1_col1\" class=\"data row1 col1\" >-0.00</td>\n",
       "      <td id=\"T_42f5c_row1_col2\" class=\"data row1 col2\" >1.31</td>\n",
       "      <td id=\"T_42f5c_row1_col3\" class=\"data row1 col3\" >833.46</td>\n",
       "      <td id=\"T_42f5c_row1_col4\" class=\"data row1 col4\" >28,267.54</td>\n",
       "      <td id=\"T_42f5c_row1_col5\" class=\"data row1 col5\" >-0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42f5c_level0_row2\" class=\"row_heading level0 row2\" >S3 제약완화</th>\n",
       "      <td id=\"T_42f5c_row2_col0\" class=\"data row2 col0\" >74.39</td>\n",
       "      <td id=\"T_42f5c_row2_col1\" class=\"data row2 col1\" >-0.00</td>\n",
       "      <td id=\"T_42f5c_row2_col2\" class=\"data row2 col2\" >1.31</td>\n",
       "      <td id=\"T_42f5c_row2_col3\" class=\"data row2 col3\" >833.46</td>\n",
       "      <td id=\"T_42f5c_row2_col4\" class=\"data row2 col4\" >29,249.70</td>\n",
       "      <td id=\"T_42f5c_row2_col5\" class=\"data row2 col5\" >-0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42f5c_level0_row3\" class=\"row_heading level0 row3\" >S4 기술도약</th>\n",
       "      <td id=\"T_42f5c_row3_col0\" class=\"data row3 col0\" >79.59</td>\n",
       "      <td id=\"T_42f5c_row3_col1\" class=\"data row3 col1\" >-0.00</td>\n",
       "      <td id=\"T_42f5c_row3_col2\" class=\"data row3 col2\" >1.31</td>\n",
       "      <td id=\"T_42f5c_row3_col3\" class=\"data row3 col3\" >833.46</td>\n",
       "      <td id=\"T_42f5c_row3_col4\" class=\"data row3 col4\" >28,767.78</td>\n",
       "      <td id=\"T_42f5c_row3_col5\" class=\"data row3 col5\" >-0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42f5c_level0_row4\" class=\"row_heading level0 row4\" >S5 지정학</th>\n",
       "      <td id=\"T_42f5c_row4_col0\" class=\"data row4 col0\" >111.53</td>\n",
       "      <td id=\"T_42f5c_row4_col1\" class=\"data row4 col1\" >-0.00</td>\n",
       "      <td id=\"T_42f5c_row4_col2\" class=\"data row4 col2\" >1.31</td>\n",
       "      <td id=\"T_42f5c_row4_col3\" class=\"data row4 col3\" >833.46</td>\n",
       "      <td id=\"T_42f5c_row4_col4\" class=\"data row4 col4\" >28,762.45</td>\n",
       "      <td id=\"T_42f5c_row4_col5\" class=\"data row4 col5\" >-0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42f5c_level0_row5\" class=\"row_heading level0 row5\" >이전 판 S1</th>\n",
       "      <td id=\"T_42f5c_row5_col0\" class=\"data row5 col0\" >169.53</td>\n",
       "      <td id=\"T_42f5c_row5_col1\" class=\"data row5 col1\" >-0.00</td>\n",
       "      <td id=\"T_42f5c_row5_col2\" class=\"data row5 col2\" >2.78</td>\n",
       "      <td id=\"T_42f5c_row5_col3\" class=\"data row5 col3\" >824.93</td>\n",
       "      <td id=\"T_42f5c_row5_col4\" class=\"data row5 col4\" >31,464.06</td>\n",
       "      <td id=\"T_42f5c_row5_col5\" class=\"data row5 col5\" >-0.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2545d355f10>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def lvl35(d):\n",
    "    x = d[d[\"분기\"] == \"2035Q4\"].set_index(\"시나리오\")[B.EXOG_COLS].copy()\n",
    "    x[\"유가\"] = np.exp(x[\"유가\"] / 100)\n",
    "    return x.rename(columns={\"유가\": \"두바이유(달러/배럴)\", \"세계수요\": \"세계수요(%)\", \"실질금리\": \"실질금리(%)\",\n",
    "                             \"실질환율\": \"실질환율(100·ln)\", \"고용_취업자\": \"취업자 수(천 명)\", \"우리경기\": \"국내 경기(%)\"})\n",
    "t5 = lvl35(lev)\n",
    "t5.loc[\"이전 판 S1\"] = lvl35(lev_prev).loc[\"S1\"]\n",
    "t5.index = [f\"{s} {B.SCN_KO[s]}\" if s in B.SCN_KO else s for s in t5.index]\n",
    "t5.to_csv(os.path.join(C.OUTPUT, \"table5_exog_2035.csv\"), encoding=\"utf-8-sig\")\n",
    "t5.style.format(\"{:,.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e60ebb4",
   "metadata": {},
   "source": [
    "## 그림 3. 산업 외생 6종 수준 경로(2000–2035, S1–S5와 이전 판 S1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c70b56a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T00:09:10.647576Z",
     "iopub.status.busy": "2026-10-08T00:09:10.647576Z",
     "iopub.status.idle": "2026-10-08T00:09:11.308331Z",
     "shell.execute_reply": "2026-10-08T00:09:11.308331Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x700 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ta = pd.PeriodIndex(B.quarters(\"2000Q1\", 144), freq=\"Q\").to_timestamp(how=\"end\")\n",
    "fig, axes = plt.subplots(2, 3, figsize=(14, 7))\n",
    "for ax, c in zip(axes.ravel(), B.EXOG_COLS):\n",
    "    yp = lev_prev[lev_prev[\"시나리오\"] == \"S1\"][c].values\n",
    "    ax.plot(ta[104:], yp[104:], color=\"#b0b0b0\", lw=1.3, ls=\"--\", label=\"이전 판 S1\")\n",
    "    for s in B.SCENARIOS:\n",
    "        y = lev[lev[\"시나리오\"] == s][c].values\n",
    "        ax.plot(ta[104:], y[104:], color=CLR[s], lw=1.8 if s == \"S1\" else 1.2, label=f\"{s} {B.SCN_KO[s]}\")\n",
    "    ax.plot(ta[:105], lev[lev[\"시나리오\"] == \"S1\"][c].values[:105], color=\"#777777\", lw=1.1)\n",
    "    ax.axvline(pd.Timestamp(\"2025-12-31\"), color=\"grey\", ls=\":\", lw=1)\n",
    "    ax.set_title(f\"{B.EXOG_LABEL[c]} — {B.EXOG_DEF[c][1]} 투입\", loc=\"left\", fontsize=11); ax.grid(alpha=.3)\n",
    "axes[0, 0].legend(frameon=False, fontsize=8)\n",
    "fig.suptitle(\"산업 외생 6종(수준): 회색 실선 = 과거 공통, 색 = 시나리오(2026Q1 이후), 회색 점선 = 이전 판 S1\", x=0.01, ha=\"left\", fontweight=\"bold\")\n",
    "fig.tight_layout(); fig.savefig(os.path.join(C.OUTPUT, \"fig3_exog_levels.png\"), dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4a0d40b",
   "metadata": {},
   "source": [
    "## 설계 일관성 점검 — 레버가 외생 6종에 전달되는가\n",
    "\n",
    "이전 판에서 확인한 세 가지 차이(인구 레버 미전달, 교역비용 레버 미전달, S3 세계수요 누락)가 정비판에서 해소되었는지 확인한다.\n",
    "\n",
    "1. **인구 레버.** 외생 취업자 수의 2035Q4 시나리오 차이가 인구 레버의 크기와 같은 방향·크기여야 한다(첫 표).\n",
    "2. **S3의 미국수요 레버.** 세계수요 외생에 더한 편차가 모든 시나리오에서 설계 레버 `yus`와 같아야 한다(둘째 표).\n",
    "3. **교역비용 레버.** 레버를 껐을 때 외생 6종이 달라져야 한다(셋째 표의 `tcost` 행)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "77e6c810",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T00:09:11.309334Z",
     "iopub.status.busy": "2026-10-08T00:09:11.309334Z",
     "iopub.status.idle": "2026-10-08T00:09:11.323219Z",
     "shell.execute_reply": "2026-10-08T00:09:11.323219Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_08a4e\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_08a4e_level0_col0\" class=\"col_heading level0 col0\" >인구 가정</th>\n",
       "      <th id=\"T_08a4e_level0_col1\" class=\"col_heading level0 col1\" >생산연령인구 2035, 중위 대비(%)</th>\n",
       "      <th id=\"T_08a4e_level0_col2\" class=\"col_heading level0 col2\" >인구 레버 2035Q4(100·ln)</th>\n",
       "      <th id=\"T_08a4e_level0_col3\" class=\"col_heading level0 col3\" >거시 블록 10년 누적 고용, S1 대비(%p)</th>\n",
       "      <th id=\"T_08a4e_level0_col4\" class=\"col_heading level0 col4\" >외생 취업자 2035Q4, S1 대비(%)</th>\n",
       "      <th id=\"T_08a4e_level0_col5\" class=\"col_heading level0 col5\" >이전 판 외생 취업자 2035Q4, S1 대비(%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >시나리오</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_08a4e_level0_row0\" class=\"row_heading level0 row0\" >S1 기준</th>\n",
       "      <td id=\"T_08a4e_row0_col0\" class=\"data row0 col0\" >중위</td>\n",
       "      <td id=\"T_08a4e_row0_col1\" class=\"data row0 col1\" >+0.000</td>\n",
       "      <td id=\"T_08a4e_row0_col2\" class=\"data row0 col2\" >+0.000</td>\n",
       "      <td id=\"T_08a4e_row0_col3\" class=\"data row0 col3\" >+0.000</td>\n",
       "      <td id=\"T_08a4e_row0_col4\" class=\"data row0 col4\" >+0.000</td>\n",
       "      <td id=\"T_08a4e_row0_col5\" class=\"data row0 col5\" >+0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_08a4e_level0_row1\" class=\"row_heading level0 row1\" >S2 공급제약+충격</th>\n",
       "      <td id=\"T_08a4e_row1_col0\" class=\"data row1 col0\" >저위</td>\n",
       "      <td id=\"T_08a4e_row1_col1\" class=\"data row1 col1\" >-2.067</td>\n",
       "      <td id=\"T_08a4e_row1_col2\" class=\"data row1 col2\" >-1.735</td>\n",
       "      <td id=\"T_08a4e_row1_col3\" class=\"data row1 col3\" >-1.756</td>\n",
       "      <td id=\"T_08a4e_row1_col4\" class=\"data row1 col4\" >-1.722</td>\n",
       "      <td id=\"T_08a4e_row1_col5\" class=\"data row1 col5\" >-0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_08a4e_level0_row2\" class=\"row_heading level0 row2\" >S3 제약완화</th>\n",
       "      <td id=\"T_08a4e_row2_col0\" class=\"data row2 col0\" >고위</td>\n",
       "      <td id=\"T_08a4e_row2_col1\" class=\"data row2 col1\" >+2.052</td>\n",
       "      <td id=\"T_08a4e_row2_col2\" class=\"data row2 col2\" >+1.678</td>\n",
       "      <td id=\"T_08a4e_row2_col3\" class=\"data row2 col3\" >+1.661</td>\n",
       "      <td id=\"T_08a4e_row2_col4\" class=\"data row2 col4\" >+1.693</td>\n",
       "      <td id=\"T_08a4e_row2_col5\" class=\"data row2 col5\" >+0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_08a4e_level0_row3\" class=\"row_heading level0 row3\" >S4 기술도약</th>\n",
       "      <td id=\"T_08a4e_row3_col0\" class=\"data row3 col0\" >중위</td>\n",
       "      <td id=\"T_08a4e_row3_col1\" class=\"data row3 col1\" >+0.000</td>\n",
       "      <td id=\"T_08a4e_row3_col2\" class=\"data row3 col2\" >+0.000</td>\n",
       "      <td id=\"T_08a4e_row3_col3\" class=\"data row3 col3\" >-0.002</td>\n",
       "      <td id=\"T_08a4e_row3_col4\" class=\"data row3 col4\" >+0.017</td>\n",
       "      <td id=\"T_08a4e_row3_col5\" class=\"data row3 col5\" >+0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_08a4e_level0_row4\" class=\"row_heading level0 row4\" >S5 지정학</th>\n",
       "      <td id=\"T_08a4e_row4_col0\" class=\"data row4 col0\" >중위</td>\n",
       "      <td id=\"T_08a4e_row4_col1\" class=\"data row4 col1\" >+0.000</td>\n",
       "      <td id=\"T_08a4e_row4_col2\" class=\"data row4 col2\" >+0.000</td>\n",
       "      <td id=\"T_08a4e_row4_col3\" class=\"data row4 col3\" >-0.018</td>\n",
       "      <td id=\"T_08a4e_row4_col4\" class=\"data row4 col4\" >-0.001</td>\n",
       "      <td id=\"T_08a4e_row4_col5\" class=\"data row4 col5\" >-0.001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2545b05efc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>레버 yus 2026Q1</th>\n",
       "      <th>세계수요 편차 2026Q1</th>\n",
       "      <th>이전 판 편차 2026Q1</th>\n",
       "      <th>40분기 최대 차이</th>\n",
       "      <th>일치</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>시나리오</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>S1 기준</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S2 공급제약+충격</th>\n",
       "      <td>-1.2000</td>\n",
       "      <td>-1.2000</td>\n",
       "      <td>-1.2000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S3 제약완화</th>\n",
       "      <td>0.6000</td>\n",
       "      <td>0.6000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S4 기술도약</th>\n",
       "      <td>0.8000</td>\n",
       "      <td>0.8000</td>\n",
       "      <td>0.8000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S5 지정학</th>\n",
       "      <td>-0.6000</td>\n",
       "      <td>-0.6000</td>\n",
       "      <td>-0.6000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            레버 yus 2026Q1  세계수요 편차 2026Q1  이전 판 편차 2026Q1  40분기 최대 차이    일치\n",
       "시나리오                                                                       \n",
       "S1 기준              0.0000          0.0000          0.0000      0.0000  True\n",
       "S2 공급제약+충격        -1.2000         -1.2000         -1.2000      0.0000  True\n",
       "S3 제약완화            0.6000          0.6000          0.0000      0.0000  True\n",
       "S4 기술도약            0.8000          0.8000          0.8000      0.0000  True\n",
       "S5 지정학            -0.6000         -0.6000         -0.6000      0.0000  True"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_a46fd\">\n",
       "  <caption>레버를 끌 때 외생 6종의 최대 변화폭(2026–2035)</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_a46fd_level0_col0\" class=\"col_heading level0 col0\" >유가</th>\n",
       "      <th id=\"T_a46fd_level0_col1\" class=\"col_heading level0 col1\" >세계수요</th>\n",
       "      <th id=\"T_a46fd_level0_col2\" class=\"col_heading level0 col2\" >실질금리</th>\n",
       "      <th id=\"T_a46fd_level0_col3\" class=\"col_heading level0 col3\" >실질환율</th>\n",
       "      <th id=\"T_a46fd_level0_col4\" class=\"col_heading level0 col4\" >취업자 수</th>\n",
       "      <th id=\"T_a46fd_level0_col5\" class=\"col_heading level0 col5\" >국내 경기</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >끈 레버</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_a46fd_level0_row0\" class=\"row_heading level0 row0\" >인구</th>\n",
       "      <td id=\"T_a46fd_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
       "      <td id=\"T_a46fd_row0_col1\" class=\"data row0 col1\" >0.000</td>\n",
       "      <td id=\"T_a46fd_row0_col2\" class=\"data row0 col2\" >0.000</td>\n",
       "      <td id=\"T_a46fd_row0_col3\" class=\"data row0 col3\" >0.000</td>\n",
       "      <td id=\"T_a46fd_row0_col4\" class=\"data row0 col4\" >494.622</td>\n",
       "      <td id=\"T_a46fd_row0_col5\" class=\"data row0 col5\" >0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a46fd_level0_row1\" class=\"row_heading level0 row1\" >yus</th>\n",
       "      <td id=\"T_a46fd_row1_col0\" class=\"data row1 col0\" >0.000</td>\n",
       "      <td id=\"T_a46fd_row1_col1\" class=\"data row1 col1\" >1.200</td>\n",
       "      <td id=\"T_a46fd_row1_col2\" class=\"data row1 col2\" >0.959</td>\n",
       "      <td id=\"T_a46fd_row1_col3\" class=\"data row1 col3\" >2.503</td>\n",
       "      <td id=\"T_a46fd_row1_col4\" class=\"data row1 col4\" >138.569</td>\n",
       "      <td id=\"T_a46fd_row1_col5\" class=\"data row1 col5\" >0.539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a46fd_level0_row2\" class=\"row_heading level0 row2\" >oil</th>\n",
       "      <td id=\"T_a46fd_row2_col0\" class=\"data row2 col0\" >33.750</td>\n",
       "      <td id=\"T_a46fd_row2_col1\" class=\"data row2 col1\" >0.000</td>\n",
       "      <td id=\"T_a46fd_row2_col2\" class=\"data row2 col2\" >0.366</td>\n",
       "      <td id=\"T_a46fd_row2_col3\" class=\"data row2 col3\" >0.862</td>\n",
       "      <td id=\"T_a46fd_row2_col4\" class=\"data row2 col4\" >50.137</td>\n",
       "      <td id=\"T_a46fd_row2_col5\" class=\"data row2 col5\" >0.216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a46fd_level0_row3\" class=\"row_heading level0 row3\" >tcost</th>\n",
       "      <td id=\"T_a46fd_row3_col0\" class=\"data row3 col0\" >0.000</td>\n",
       "      <td id=\"T_a46fd_row3_col1\" class=\"data row3 col1\" >0.000</td>\n",
       "      <td id=\"T_a46fd_row3_col2\" class=\"data row3 col2\" >1.145</td>\n",
       "      <td id=\"T_a46fd_row3_col3\" class=\"data row3 col3\" >2.902</td>\n",
       "      <td id=\"T_a46fd_row3_col4\" class=\"data row3 col4\" >162.872</td>\n",
       "      <td id=\"T_a46fd_row3_col5\" class=\"data row3 col5\" >0.663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a46fd_level0_row4\" class=\"row_heading level0 row4\" >dshk</th>\n",
       "      <td id=\"T_a46fd_row4_col0\" class=\"data row4 col0\" >0.000</td>\n",
       "      <td id=\"T_a46fd_row4_col1\" class=\"data row4 col1\" >0.000</td>\n",
       "      <td id=\"T_a46fd_row4_col2\" class=\"data row4 col2\" >2.664</td>\n",
       "      <td id=\"T_a46fd_row4_col3\" class=\"data row4 col3\" >6.954</td>\n",
       "      <td id=\"T_a46fd_row4_col4\" class=\"data row4 col4\" >384.914</td>\n",
       "      <td id=\"T_a46fd_row4_col5\" class=\"data row4 col5\" >1.496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2545afbd310>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt = B.population_transmission(pop, ex, lev, lev_prev)\n",
    "display(pt.style.format(\"{:+.3f}\", subset=pd.IndexSlice[:, [c for c in pt.columns if c != \"인구 가정\"]]))\n",
    "dc = B.design_consistency()\n",
    "assert dc[\"일치\"].all()\n",
    "display(dc)\n",
    "reach.rename(columns=B.EXOG_LABEL).style.set_caption(\"레버를 끌 때 외생 6종의 최대 변화폭(2026–2035)\").format(\"{:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6271194",
   "metadata": {},
   "source": [
    "## 이전 판 대비 변화 — 외생 6종(투입형)\n",
    "\n",
    "산업모형에 들어가는 투입형 외생을 이전 판과 비교한다. 과거 구간에서는 실질금리 한 열만 달라지고(수준 → 1차 차분), 나머지 다섯 외생은 같아야 한다(관측자료가 같다. 평활값의 수치 오차 때문에 소수 넷째 자리 한 단위까지는 같은 값으로 본다). 미래 구간은 기준선·레버·노동공급이 모두 바뀌었다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "44161d8b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T00:09:11.324224Z",
     "iopub.status.busy": "2026-10-08T00:09:11.324224Z",
     "iopub.status.idle": "2026-10-08T00:09:11.335238Z",
     "shell.execute_reply": "2026-10-08T00:09:11.335238Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_03295\">\n",
       "  <caption>과거 2000Q1–2025Q4(투입형)</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_03295_level0_col0\" class=\"col_heading level0 col0\" >최대 절대차</th>\n",
       "      <th id=\"T_03295_level0_col1\" class=\"col_heading level0 col1\" >평균 절대차</th>\n",
       "      <th id=\"T_03295_level0_col2\" class=\"col_heading level0 col2\" >최대차 위치</th>\n",
       "      <th id=\"T_03295_level0_col3\" class=\"col_heading level0 col3\" >새 값</th>\n",
       "      <th id=\"T_03295_level0_col4\" class=\"col_heading level0 col4\" >이전 값</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >항목</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_03295_level0_row0\" class=\"row_heading level0 row0\" >유가</th>\n",
       "      <td id=\"T_03295_row0_col0\" class=\"data row0 col0\" >0.0001</td>\n",
       "      <td id=\"T_03295_row0_col1\" class=\"data row0 col1\" >0.0000</td>\n",
       "      <td id=\"T_03295_row0_col2\" class=\"data row0 col2\" >2023Q1</td>\n",
       "      <td id=\"T_03295_row0_col3\" class=\"data row0 col3\" >-4.9567</td>\n",
       "      <td id=\"T_03295_row0_col4\" class=\"data row0 col4\" >-4.9568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_03295_level0_row1\" class=\"row_heading level0 row1\" >세계수요</th>\n",
       "      <td id=\"T_03295_row1_col0\" class=\"data row1 col0\" >0.0000</td>\n",
       "      <td id=\"T_03295_row1_col1\" class=\"data row1 col1\" >0.0000</td>\n",
       "      <td id=\"T_03295_row1_col2\" class=\"data row1 col2\" >2000Q1</td>\n",
       "      <td id=\"T_03295_row1_col3\" class=\"data row1 col3\" >1.6680</td>\n",
       "      <td id=\"T_03295_row1_col4\" class=\"data row1 col4\" >1.6680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_03295_level0_row2\" class=\"row_heading level0 row2\" >실질금리</th>\n",
       "      <td id=\"T_03295_row2_col0\" class=\"data row2 col0\" >8.2946</td>\n",
       "      <td id=\"T_03295_row2_col1\" class=\"data row2 col1\" >2.0087</td>\n",
       "      <td id=\"T_03295_row2_col2\" class=\"data row2 col2\" >2000Q3</td>\n",
       "      <td id=\"T_03295_row2_col3\" class=\"data row2 col3\" >-2.1900</td>\n",
       "      <td id=\"T_03295_row2_col4\" class=\"data row2 col4\" >6.1046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_03295_level0_row3\" class=\"row_heading level0 row3\" >실질환율</th>\n",
       "      <td id=\"T_03295_row3_col0\" class=\"data row3 col0\" >0.0000</td>\n",
       "      <td id=\"T_03295_row3_col1\" class=\"data row3 col1\" >0.0000</td>\n",
       "      <td id=\"T_03295_row3_col2\" class=\"data row3 col2\" >2000Q1</td>\n",
       "      <td id=\"T_03295_row3_col3\" class=\"data row3 col3\" >0.8177</td>\n",
       "      <td id=\"T_03295_row3_col4\" class=\"data row3 col4\" >0.8177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_03295_level0_row4\" class=\"row_heading level0 row4\" >취업자 수</th>\n",
       "      <td id=\"T_03295_row4_col0\" class=\"data row4 col0\" >0.0000</td>\n",
       "      <td id=\"T_03295_row4_col1\" class=\"data row4 col1\" >0.0000</td>\n",
       "      <td id=\"T_03295_row4_col2\" class=\"data row4 col2\" >2000Q1</td>\n",
       "      <td id=\"T_03295_row4_col3\" class=\"data row4 col3\" >0.8011</td>\n",
       "      <td id=\"T_03295_row4_col4\" class=\"data row4 col4\" >0.8011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_03295_level0_row5\" class=\"row_heading level0 row5\" >국내 경기</th>\n",
       "      <td id=\"T_03295_row5_col0\" class=\"data row5 col0\" >0.0000</td>\n",
       "      <td id=\"T_03295_row5_col1\" class=\"data row5 col1\" >0.0000</td>\n",
       "      <td id=\"T_03295_row5_col2\" class=\"data row5 col2\" >2000Q1</td>\n",
       "      <td id=\"T_03295_row5_col3\" class=\"data row5 col3\" >-0.8543</td>\n",
       "      <td id=\"T_03295_row5_col4\" class=\"data row5 col4\" >-0.8543</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2545d36ad50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_2efbe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_2efbe_level0_col0\" class=\"col_heading level0 col0\" >정비판 S1 40분기 평균</th>\n",
       "      <th id=\"T_2efbe_level0_col1\" class=\"col_heading level0 col1\" >이전 판 S1 40분기 평균</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_2efbe_level0_row0\" class=\"row_heading level0 row0\" >유가(1차 차분)</th>\n",
       "      <td id=\"T_2efbe_row0_col0\" class=\"data row0 col0\" >0.561</td>\n",
       "      <td id=\"T_2efbe_row0_col1\" class=\"data row0 col1\" >2.451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2efbe_level0_row1\" class=\"row_heading level0 row1\" >세계수요(수준)</th>\n",
       "      <td id=\"T_2efbe_row1_col0\" class=\"data row1 col0\" >0.153</td>\n",
       "      <td id=\"T_2efbe_row1_col1\" class=\"data row1 col1\" >0.177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2efbe_level0_row2\" class=\"row_heading level0 row2\" >실질금리(1차 차분)</th>\n",
       "      <td id=\"T_2efbe_row2_col0\" class=\"data row2 col0\" >0.009</td>\n",
       "      <td id=\"T_2efbe_row2_col1\" class=\"data row2 col1\" >2.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2efbe_level0_row3\" class=\"row_heading level0 row3\" >실질환율(1차 차분)</th>\n",
       "      <td id=\"T_2efbe_row3_col0\" class=\"data row3 col0\" >0.104</td>\n",
       "      <td id=\"T_2efbe_row3_col1\" class=\"data row3 col1\" >-0.109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2efbe_level0_row4\" class=\"row_heading level0 row4\" >취업자 수(로그 차분(×100))</th>\n",
       "      <td id=\"T_2efbe_row4_col0\" class=\"data row4 col0\" >-0.003</td>\n",
       "      <td id=\"T_2efbe_row4_col1\" class=\"data row4 col1\" >0.221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2efbe_level0_row5\" class=\"row_heading level0 row5\" >국내 경기(수준)</th>\n",
       "      <td id=\"T_2efbe_row5_col0\" class=\"data row5 col0\" >-0.150</td>\n",
       "      <td id=\"T_2efbe_row5_col1\" class=\"data row5 col1\" >-0.103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2545d5b72c0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hp = pd.read_csv(C.REF_PREV[\"exog_hist_v2\"], encoding=\"utf-8-sig\"); spv = pd.read_csv(C.REF_PREV[\"exog_scen_v2\"], encoding=\"utf-8-sig\")\n",
    "ch = B.compare_versions(hist, hp, [\"분기\"], B.EXOG_COLS)\n",
    "# 소수 넷째 자리 반올림 값이고 평활값이 수치 오차(10⁻⁹) 수준에서 다를 수 있으므로 마지막 자리 한 단위(10⁻⁴)까지 같은 값으로 본다\n",
    "assert (ch.drop(index=\"실질금리\")[\"최대 절대차\"] <= 1.0001e-4).all(), \"실질금리 밖의 과거 외생이 이전 판과 다르다\"\n",
    "display(ch.rename(index=B.EXOG_LABEL).style.set_caption(\"과거 2000Q1–2025Q4(투입형)\").format({c: \"{:.4f}\" for c in [\"최대 절대차\", \"평균 절대차\", \"새 값\", \"이전 값\"]}))\n",
    "s1n, s1p = scen[scen[\"시나리오\"] == \"S1\"], spv[spv[\"시나리오\"] == \"S1\"]\n",
    "cmp = pd.DataFrame({\"정비판 S1 40분기 평균\": s1n[B.EXOG_COLS].mean(), \"이전 판 S1 40분기 평균\": s1p[B.EXOG_COLS].mean()})\n",
    "cmp.index = [f\"{B.EXOG_LABEL[c]}({B.EXOG_DEF[c][1]})\" for c in cmp.index]\n",
    "cmp.to_csv(os.path.join(C.OUTPUT, \"table6_exog_vs_prev.csv\"), encoding=\"utf-8-sig\")\n",
    "cmp.style.format(\"{:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8eadf9b",
   "metadata": {},
   "source": [
    "## 검증 — 정비판 기록 산출물과 핵심 수치\n",
    "\n",
    "계산 결과가 2026-10 정비 실행에서 동결한 기록 산출물(`data/reference/`)과 반올림 범위 안에서 같아야 통과한다. 표 단위 비교는 최대 절대차를 `계산` 열에 적고 `기록` 열을 0으로 둔다. 허용오차는 사후분포 요약·기준선 전망 5×10⁻⁷(소수 여섯째 자리), 거시 블록 경로 5×10⁻⁴(셋째 자리), 외생 6종 5×10⁻⁵(넷째 자리), 핵심 수치는 표기 자릿수의 절반이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a31c8f45",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T00:09:11.336239Z",
     "iopub.status.busy": "2026-10-08T00:09:11.336239Z",
     "iopub.status.idle": "2026-10-08T00:09:11.390293Z",
     "shell.execute_reply": "2026-10-08T00:09:11.390293Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>항목</th>\n",
       "      <th>계산</th>\n",
       "      <th>기록</th>\n",
       "      <th>허용오차</th>\n",
       "      <th>통과</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>관측자료 SHA-256(앞 12자리)</td>\n",
       "      <td>79c7586814e9</td>\n",
       "      <td>79c7586814e9</td>\n",
       "      <td>일치</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>관측자료 분기 수</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>관측변수 수</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>평활값 − 관측자료(최대절대차)</td>\n",
       "      <td>2.88287e-09</td>\n",
       "      <td>0</td>\n",
       "      <td>1e-06</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>방정식 수</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>구조충격 수</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>추정 행태계수 수</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>정상상태 growth_kor_ss</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1e-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>정상상태 pietar_kor_ss</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1e-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>정상상태 def_kor_ss</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1e-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>정상상태 rr_kor_bar_ss</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>1e-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>정상상태 tprem_kor_ss</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>1e-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>정상상태 tprem_tb_kor_ss</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1e-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>정상상태 tprem_cb_kor_ss</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1e-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>정상상태 house_kor_ss</td>\n",
       "      <td>2.33</td>\n",
       "      <td>2.33</td>\n",
       "      <td>1e-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>정상상태 ge_kor_ss</td>\n",
       "      <td>6.57</td>\n",
       "      <td>6.57</td>\n",
       "      <td>1e-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>정상상태 growth_us_ss</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1e-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>정상상태 pietar_us_ss</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1e-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>정상상태 rr_us_bar_ss</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1e-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>정상상태 oil_g_us_ss</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1e-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>사후 사후평균 54개(최대절대차)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-07</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>사후 표준편차 54개(최대절대차)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-07</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>사후 HPD_low 54개(최대절대차)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-07</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>사후 HPD_high 54개(최대절대차)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-07</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>사후평균 gamma_kor1</td>\n",
       "      <td>0.841458</td>\n",
       "      <td>0.841458</td>\n",
       "      <td>5e-07</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>사후평균 gamma_kor2</td>\n",
       "      <td>1.53946</td>\n",
       "      <td>1.53946</td>\n",
       "      <td>5e-07</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>사후평균 gamma_kor3</td>\n",
       "      <td>0.466217</td>\n",
       "      <td>0.466217</td>\n",
       "      <td>5e-07</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>사후평균 beta_kor1</td>\n",
       "      <td>0.211223</td>\n",
       "      <td>0.211223</td>\n",
       "      <td>5e-07</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>사후평균 lambda_kor3</td>\n",
       "      <td>0.765561</td>\n",
       "      <td>0.765561</td>\n",
       "      <td>5e-07</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>사후평균 alpha_kor2</td>\n",
       "      <td>0.0702645</td>\n",
       "      <td>0.070265</td>\n",
       "      <td>5e-07</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>사후평균 rho_oil1</td>\n",
       "      <td>0.879517</td>\n",
       "      <td>0.879517</td>\n",
       "      <td>5e-07</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>사후평균 RES_OIL_us</td>\n",
       "      <td>15.1934</td>\n",
       "      <td>15.1934</td>\n",
       "      <td>5e-07</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>사후평균 RES_Y_kor</td>\n",
       "      <td>1.83497</td>\n",
       "      <td>1.83497</td>\n",
       "      <td>5e-07</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>기준선 전망 11변수 × 40분기(최대절대차)</td>\n",
       "      <td>4.96344e-07</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-07</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>기준선 실질GDP 성장률 2030(%)</td>\n",
       "      <td>1.89091</td>\n",
       "      <td>1.89</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>기준선 CPI 상승률 2030(%)</td>\n",
       "      <td>1.92217</td>\n",
       "      <td>1.92</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>기준선 실질GDP 성장률 2035(%)</td>\n",
       "      <td>1.82258</td>\n",
       "      <td>1.82</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>기준선 CPI 상승률 2035(%)</td>\n",
       "      <td>1.9342</td>\n",
       "      <td>1.93</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>기준선 두바이유 2035(달러, 연평균)</td>\n",
       "      <td>78.9038</td>\n",
       "      <td>78.9</td>\n",
       "      <td>0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>거시경로 6열 × 200행(최대절대차)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>거시 블록 성장경로 5열 × 200행(최대절대차)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>10년 누적 고용 증가율 S1(%)</td>\n",
       "      <td>-0.169</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>10년 누적 고용 증가율 S2(%)</td>\n",
       "      <td>-1.925</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>10년 누적 고용 증가율 S3(%)</td>\n",
       "      <td>1.492</td>\n",
       "      <td>1.49</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>10년 누적 고용 증가율 S4(%)</td>\n",
       "      <td>-0.171</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>10년 누적 고용 증가율 S5(%)</td>\n",
       "      <td>-0.187</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>S4 2030Q1 산출갭(%)</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>수준 외생 유가 S1–S5(최대절대차)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>수준 외생 세계수요 S1–S5(최대절대차)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>수준 외생 실질금리 S1–S5(최대절대차)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>수준 외생 실질환율 S1–S5(최대절대차)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>수준 외생 취업자 수 S1–S5(최대절대차)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>수준 외생 국내 경기 S1–S5(최대절대차)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>취업자 수 구성값 2025Q4(천 명)</td>\n",
       "      <td>28800.8</td>\n",
       "      <td>28801</td>\n",
       "      <td>0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>관측 취업자 2025Q4(천 명)</td>\n",
       "      <td>28792</td>\n",
       "      <td>28792</td>\n",
       "      <td>0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>취업자 수 2035Q4 S1(천 명)</td>\n",
       "      <td>28762.7</td>\n",
       "      <td>28762.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>취업자 수 2035Q4 S2/S1 − 1(%)</td>\n",
       "      <td>-1.72172</td>\n",
       "      <td>-1.72</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>두바이유 2035Q4 S1(달러)</td>\n",
       "      <td>79.5853</td>\n",
       "      <td>79.6</td>\n",
       "      <td>0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>실질금리 2035Q4 S1(%)</td>\n",
       "      <td>1.3132</td>\n",
       "      <td>1.31</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>exog_hist_v2 유가(104분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>exog_hist_v2 세계수요(104분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>exog_hist_v2 실질금리(104분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>exog_hist_v2 실질환율(104분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>exog_hist_v2 취업자 수(104분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>exog_hist_v2 국내 경기(104분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>exog_scen_v2 S1 유가(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>exog_scen_v2 S1 세계수요(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>exog_scen_v2 S1 실질금리(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>exog_scen_v2 S1 실질환율(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>exog_scen_v2 S1 취업자 수(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>exog_scen_v2 S1 국내 경기(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>exog_scen_v2 S2 유가(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>exog_scen_v2 S2 세계수요(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>exog_scen_v2 S2 실질금리(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>exog_scen_v2 S2 실질환율(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>exog_scen_v2 S2 취업자 수(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>exog_scen_v2 S2 국내 경기(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>exog_scen_v2 S3 유가(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>exog_scen_v2 S3 세계수요(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>exog_scen_v2 S3 실질금리(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>exog_scen_v2 S3 실질환율(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>exog_scen_v2 S3 취업자 수(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>exog_scen_v2 S3 국내 경기(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>exog_scen_v2 S4 유가(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>exog_scen_v2 S4 세계수요(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>exog_scen_v2 S4 실질금리(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>exog_scen_v2 S4 실질환율(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>exog_scen_v2 S4 취업자 수(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>exog_scen_v2 S4 국내 경기(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>exog_scen_v2 S5 유가(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>exog_scen_v2 S5 세계수요(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>exog_scen_v2 S5 실질금리(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>exog_scen_v2 S5 실질환율(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>exog_scen_v2 S5 취업자 수(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>exog_scen_v2 S5 국내 경기(40분기)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>레버 전달 인구(최대 변화폭 &gt; 0)</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>일치</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>레버 전달 yus(최대 변화폭 &gt; 0)</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>일치</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>레버 전달 oil(최대 변화폭 &gt; 0)</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>일치</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>레버 전달 tcost(최대 변화폭 &gt; 0)</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>일치</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>레버 전달 dshk(최대 변화폭 &gt; 0)</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>일치</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             항목            계산            기록    허용오차    통과\n",
       "0          관측자료 SHA-256(앞 12자리)  79c7586814e9  79c7586814e9      일치  True\n",
       "1                     관측자료 분기 수           104           104       0  True\n",
       "2                        관측변수 수            20            20       0  True\n",
       "3             평활값 − 관측자료(최대절대차)   2.88287e-09             0   1e-06  True\n",
       "4                         방정식 수            70            70       0  True\n",
       "5                        구조충격 수            34            34       0  True\n",
       "6                     추정 행태계수 수            20            20       0  True\n",
       "7            정상상태 growth_kor_ss           1.8           1.8   1e-09  True\n",
       "8            정상상태 pietar_kor_ss             2             2   1e-09  True\n",
       "9               정상상태 def_kor_ss             2             2   1e-09  True\n",
       "10           정상상태 rr_kor_bar_ss          0.55          0.55   1e-09  True\n",
       "11            정상상태 tprem_kor_ss          0.18          0.18   1e-09  True\n",
       "12         정상상태 tprem_tb_kor_ss           0.3           0.3   1e-09  True\n",
       "13         정상상태 tprem_cb_kor_ss          0.67          0.67   1e-09  True\n",
       "14            정상상태 house_kor_ss          2.33          2.33   1e-09  True\n",
       "15               정상상태 ge_kor_ss          6.57          6.57   1e-09  True\n",
       "16            정상상태 growth_us_ss           1.8           1.8   1e-09  True\n",
       "17            정상상태 pietar_us_ss           2.3           2.3   1e-09  True\n",
       "18            정상상태 rr_us_bar_ss           0.9           0.9   1e-09  True\n",
       "19             정상상태 oil_g_us_ss           2.3           2.3   1e-09  True\n",
       "20           사후 사후평균 54개(최대절대차)             0             0   5e-07  True\n",
       "21           사후 표준편차 54개(최대절대차)             0             0   5e-07  True\n",
       "22        사후 HPD_low 54개(최대절대차)             0             0   5e-07  True\n",
       "23       사후 HPD_high 54개(최대절대차)             0             0   5e-07  True\n",
       "24              사후평균 gamma_kor1      0.841458      0.841458   5e-07  True\n",
       "25              사후평균 gamma_kor2       1.53946       1.53946   5e-07  True\n",
       "26              사후평균 gamma_kor3      0.466217      0.466217   5e-07  True\n",
       "27               사후평균 beta_kor1      0.211223      0.211223   5e-07  True\n",
       "28             사후평균 lambda_kor3      0.765561      0.765561   5e-07  True\n",
       "29              사후평균 alpha_kor2     0.0702645      0.070265   5e-07  True\n",
       "30                사후평균 rho_oil1      0.879517      0.879517   5e-07  True\n",
       "31              사후평균 RES_OIL_us       15.1934       15.1934   5e-07  True\n",
       "32               사후평균 RES_Y_kor       1.83497       1.83497   5e-07  True\n",
       "33    기준선 전망 11변수 × 40분기(최대절대차)   4.96344e-07             0   5e-07  True\n",
       "34        기준선 실질GDP 성장률 2030(%)       1.89091          1.89   0.005  True\n",
       "35          기준선 CPI 상승률 2030(%)       1.92217          1.92   0.005  True\n",
       "36        기준선 실질GDP 성장률 2035(%)       1.82258          1.82   0.005  True\n",
       "37          기준선 CPI 상승률 2035(%)        1.9342          1.93   0.005  True\n",
       "38       기준선 두바이유 2035(달러, 연평균)       78.9038          78.9    0.05  True\n",
       "39        거시경로 6열 × 200행(최대절대차)             0             0  0.0005  True\n",
       "40  거시 블록 성장경로 5열 × 200행(최대절대차)             0             0  0.0005  True\n",
       "41          10년 누적 고용 증가율 S1(%)        -0.169         -0.17   0.005  True\n",
       "42          10년 누적 고용 증가율 S2(%)        -1.925         -1.93   0.005  True\n",
       "43          10년 누적 고용 증가율 S3(%)         1.492          1.49   0.005  True\n",
       "44          10년 누적 고용 증가율 S4(%)        -0.171         -0.17   0.005  True\n",
       "45          10년 누적 고용 증가율 S5(%)        -0.187         -0.19   0.005  True\n",
       "46             S4 2030Q1 산출갭(%)         0.463          0.46   0.005  True\n",
       "47        수준 외생 유가 S1–S5(최대절대차)             0             0   5e-05  True\n",
       "48      수준 외생 세계수요 S1–S5(최대절대차)             0             0   5e-05  True\n",
       "49      수준 외생 실질금리 S1–S5(최대절대차)             0             0   5e-05  True\n",
       "50      수준 외생 실질환율 S1–S5(최대절대차)             0             0   5e-05  True\n",
       "51     수준 외생 취업자 수 S1–S5(최대절대차)             0             0   5e-05  True\n",
       "52     수준 외생 국내 경기 S1–S5(최대절대차)             0             0   5e-05  True\n",
       "53        취업자 수 구성값 2025Q4(천 명)       28800.8         28801     0.5  True\n",
       "54           관측 취업자 2025Q4(천 명)         28792         28792     0.5  True\n",
       "55         취업자 수 2035Q4 S1(천 명)       28762.7       28762.7     0.5  True\n",
       "56    취업자 수 2035Q4 S2/S1 − 1(%)      -1.72172         -1.72   0.005  True\n",
       "57           두바이유 2035Q4 S1(달러)       79.5853          79.6    0.05  True\n",
       "58            실질금리 2035Q4 S1(%)        1.3132          1.31   0.005  True\n",
       "59       exog_hist_v2 유가(104분기)             0             0   5e-05  True\n",
       "60     exog_hist_v2 세계수요(104분기)             0             0   5e-05  True\n",
       "61     exog_hist_v2 실질금리(104분기)             0             0   5e-05  True\n",
       "62     exog_hist_v2 실질환율(104분기)             0             0   5e-05  True\n",
       "63    exog_hist_v2 취업자 수(104분기)             0             0   5e-05  True\n",
       "64    exog_hist_v2 국내 경기(104분기)             0             0   5e-05  True\n",
       "65     exog_scen_v2 S1 유가(40분기)             0             0   5e-05  True\n",
       "66   exog_scen_v2 S1 세계수요(40분기)             0             0   5e-05  True\n",
       "67   exog_scen_v2 S1 실질금리(40분기)             0             0   5e-05  True\n",
       "68   exog_scen_v2 S1 실질환율(40분기)             0             0   5e-05  True\n",
       "69  exog_scen_v2 S1 취업자 수(40분기)             0             0   5e-05  True\n",
       "70  exog_scen_v2 S1 국내 경기(40분기)             0             0   5e-05  True\n",
       "71     exog_scen_v2 S2 유가(40분기)             0             0   5e-05  True\n",
       "72   exog_scen_v2 S2 세계수요(40분기)             0             0   5e-05  True\n",
       "73   exog_scen_v2 S2 실질금리(40분기)             0             0   5e-05  True\n",
       "74   exog_scen_v2 S2 실질환율(40분기)             0             0   5e-05  True\n",
       "75  exog_scen_v2 S2 취업자 수(40분기)             0             0   5e-05  True\n",
       "76  exog_scen_v2 S2 국내 경기(40분기)             0             0   5e-05  True\n",
       "77     exog_scen_v2 S3 유가(40분기)             0             0   5e-05  True\n",
       "78   exog_scen_v2 S3 세계수요(40분기)             0             0   5e-05  True\n",
       "79   exog_scen_v2 S3 실질금리(40분기)             0             0   5e-05  True\n",
       "80   exog_scen_v2 S3 실질환율(40분기)             0             0   5e-05  True\n",
       "81  exog_scen_v2 S3 취업자 수(40분기)             0             0   5e-05  True\n",
       "82  exog_scen_v2 S3 국내 경기(40분기)             0             0   5e-05  True\n",
       "83     exog_scen_v2 S4 유가(40분기)             0             0   5e-05  True\n",
       "84   exog_scen_v2 S4 세계수요(40분기)             0             0   5e-05  True\n",
       "85   exog_scen_v2 S4 실질금리(40분기)             0             0   5e-05  True\n",
       "86   exog_scen_v2 S4 실질환율(40분기)             0             0   5e-05  True\n",
       "87  exog_scen_v2 S4 취업자 수(40분기)             0             0   5e-05  True\n",
       "88  exog_scen_v2 S4 국내 경기(40분기)             0             0   5e-05  True\n",
       "89     exog_scen_v2 S5 유가(40분기)             0             0   5e-05  True\n",
       "90   exog_scen_v2 S5 세계수요(40분기)             0             0   5e-05  True\n",
       "91   exog_scen_v2 S5 실질금리(40분기)             0             0   5e-05  True\n",
       "92   exog_scen_v2 S5 실질환율(40분기)             0             0   5e-05  True\n",
       "93  exog_scen_v2 S5 취업자 수(40분기)             0             0   5e-05  True\n",
       "94  exog_scen_v2 S5 국내 경기(40분기)             0             0   5e-05  True\n",
       "95         레버 전달 인구(최대 변화폭 > 0)          True          True      일치  True\n",
       "96        레버 전달 yus(최대 변화폭 > 0)          True          True      일치  True\n",
       "97        레버 전달 oil(최대 변화폭 > 0)          True          True      일치  True\n",
       "98      레버 전달 tcost(최대 변화폭 > 0)          True          True      일치  True\n",
       "99       레버 전달 dshk(최대 변화폭 > 0)          True          True      일치  True"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "검증 통과: 100개 항목에서 정상상태·사후분포 요약·기준선·거시경로·외생 6종(S1–S5)·레버 전달을 재현했다.\n",
      "참고(통과 조건 아님): 파일 단위로도 기록본과 바이트가 같은 산출물 5/5개 — exog_hist_v2.csv, exog_scen_v2.csv, dsge_macro_paths.csv, dsge_exog_paths.csv, exog_levels_2000_2035.csv\n"
     ]
    }
   ],
   "source": [
    "P = C.PUBLISHED\n",
    "checks = []\n",
    "def add(item, calc, pub, tol):\n",
    "    checks.append({\"항목\": item, \"계산\": calc, \"기록\": pub, \"허용오차\": tol})\n",
    "\n",
    "# (1) 입력·모형\n",
    "obs = B.read_dynare_data(C.DATAFILE)\n",
    "add(\"관측자료 SHA-256(앞 12자리)\", B.sha256(C.DATAFILE)[:12], P[\"관측자료_SHA256\"][:12], \"일치\")\n",
    "add(\"관측자료 분기 수\", obs.shape[0], P[\"관측자료_분기\"], 0)\n",
    "add(\"관측변수 수\", obs.shape[1], P[\"관측변수\"], 0)\n",
    "add(\"평활값 − 관측자료(최대절대차)\", float(max((sm[v] - obs[v]).abs().max() for v in B.VAROBS)), 0.0, 1e-6)\n",
    "mod = B.parse_mod(C.MODFILE)\n",
    "add(\"방정식 수\", mod[\"방정식\"], P[\"방정식\"], 0)\n",
    "add(\"구조충격 수\", mod[\"외생충격\"], P[\"구조충격\"], 0)\n",
    "add(\"추정 행태계수 수\", int((mod[\"사전분포\"][\"구분\"] == \"parameters\").sum()), P[\"추정계수\"], 0)\n",
    "ss = B.calibrated_values(C.MODFILE, [r[0] for r in B.SS_REVISION])\n",
    "for n, _, new, _, _ in B.SS_REVISION:\n",
    "    add(f\"정상상태 {n}\", ss[n], new, 1e-9)\n",
    "\n",
    "# (2) 사후분포 요약 54개 × 4통계\n",
    "pub = pd.read_csv(C.REF[\"estimation_results\"], encoding=\"utf-8-sig\")\n",
    "m = post.merge(pub, on=[\"구분\", \"파라미터\"], suffixes=(\"\", \"_기록\"), how=\"outer\", indicator=True)\n",
    "assert (m[\"_merge\"] == \"both\").all() and len(m) == 54\n",
    "for c in [\"사후평균\", \"표준편차\", \"HPD_low\", \"HPD_high\"]:\n",
    "    add(f\"사후 {c} 54개(최대절대차)\", float((m[c] - m[c + \"_기록\"]).abs().max()), 0.0, 5e-7)\n",
    "pm = post.set_index(\"파라미터\")[\"사후평균\"]\n",
    "for k in [\"gamma_kor1\", \"gamma_kor2\", \"gamma_kor3\", \"beta_kor1\", \"lambda_kor3\", \"alpha_kor2\", \"rho_oil1\", \"RES_OIL_us\", \"RES_Y_kor\"]:\n",
    "    add(f\"사후평균 {k}\", pm[k], P[k], 5e-7)\n",
    "\n",
    "# (3) 기준선 전망과 연간 요약\n",
    "bf = pd.read_csv(C.REF[\"baseline_forecast\"], encoding=\"utf-8-sig\")\n",
    "add(\"기준선 전망 11변수 × 40분기(최대절대차)\", float(max(np.abs(fc[c].values - bf[c].values).max() for c in bf.columns[1:])), 0.0, 5e-7)\n",
    "for y in (2030, 2035):\n",
    "    add(f\"기준선 실질GDP 성장률 {y}(%)\", ann.loc[y, \"실질GDP 성장률(%)\"], P[f\"GDP성장_{y}\"], 0.005)\n",
    "    add(f\"기준선 CPI 상승률 {y}(%)\", ann.loc[y, \"CPI 상승률(%)\"], P[f\"CPI_{y}\"], 0.005)\n",
    "add(\"기준선 두바이유 2035(달러, 연평균)\", ann.loc[2035, \"두바이유(달러/배럴)\"], P[\"두바이유_2035\"], 0.05)\n",
    "\n",
    "# (4) 거시 블록\n",
    "rmac = pd.read_csv(C.REF[\"dsge_macro_paths\"], encoding=\"utf-8-sig\"); rex = pd.read_csv(C.REF[\"dsge_exog_paths\"], encoding=\"utf-8-sig\")\n",
    "add(\"거시경로 6열 × 200행(최대절대차)\", float(B.max_abs_diff(mac, rmac, [\"시나리오\", \"분기\"], list(rmac.columns[2:])).max()), 0.0, 5e-4)\n",
    "add(\"거시 블록 성장경로 5열 × 200행(최대절대차)\", float(B.max_abs_diff(ex, rex, [\"시나리오\", \"분기\"], list(rex.columns[2:])).max()), 0.0, 5e-4)\n",
    "cum = ex.groupby(\"시나리오\")[\"lab_g\"].sum()\n",
    "for s in B.SCENARIOS:\n",
    "    add(f\"10년 누적 고용 증가율 {s}(%)\", cum[s], P[f\"누적고용_{s}\"], 0.005)\n",
    "add(\"S4 2030Q1 산출갭(%)\", mac[(mac[\"시나리오\"] == \"S4\") & (mac[\"분기\"] == \"2030Q1\")][\"산출갭Y\"].iloc[0], P[\"산출갭2030_S4\"], 0.005)\n",
    "\n",
    "# (5) 수준 외생 — 외생별, 5개 시나리오 × 144분기\n",
    "rlev = pd.read_csv(C.REF[\"exog_levels\"], encoding=\"utf-8-sig\")\n",
    "dl = B.max_abs_diff(lev, rlev, [\"시나리오\", \"분기\"], B.EXOG_COLS)\n",
    "for c in B.EXOG_COLS:\n",
    "    add(f\"수준 외생 {B.EXOG_LABEL[c]} S1–S5(최대절대차)\", float(dl[c]), 0.0, 5e-5)\n",
    "emp = lev[(lev[\"시나리오\"] == \"S1\") & (lev[\"분기\"] == \"2025Q4\")][\"고용_취업자\"].iloc[0]\n",
    "add(\"취업자 수 구성값 2025Q4(천 명)\", emp, P[\"고용구성_2025Q4\"], 0.5)\n",
    "add(\"관측 취업자 2025Q4(천 명)\", float(lab.loc[\"2025Q4\", \"취업자\"]), P[\"관측취업자_2025Q4\"], 0.5)\n",
    "e35 = lev[lev[\"분기\"] == \"2035Q4\"].set_index(\"시나리오\")\n",
    "add(\"취업자 수 2035Q4 S1(천 명)\", e35.loc[\"S1\", \"고용_취업자\"], P[\"취업자_2035_S1\"], 0.5)\n",
    "add(\"취업자 수 2035Q4 S2/S1 − 1(%)\", 100 * (e35.loc[\"S2\", \"고용_취업자\"] / e35.loc[\"S1\", \"고용_취업자\"] - 1), P[\"취업자_2035_S2대비\"], 0.005)\n",
    "add(\"두바이유 2035Q4 S1(달러)\", float(np.exp(e35.loc[\"S1\", \"유가\"] / 100)), P[\"유가_2035Q4_S1\"], 0.05)\n",
    "add(\"실질금리 2035Q4 S1(%)\", e35.loc[\"S1\", \"실질금리\"], P[\"실질금리_2035Q4_S1\"], 0.005)\n",
    "\n",
    "# (6) 산업모형 투입 외생 — 과거(외생별), 미래(시나리오 × 외생별)\n",
    "rh = pd.read_csv(C.REF[\"exog_hist_v2\"], encoding=\"utf-8-sig\"); rs = pd.read_csv(C.REF[\"exog_scen_v2\"], encoding=\"utf-8-sig\")\n",
    "dh = B.max_abs_diff(hist, rh, [\"분기\"], B.EXOG_COLS)\n",
    "for c in B.EXOG_COLS:\n",
    "    add(f\"exog_hist_v2 {B.EXOG_LABEL[c]}(104분기)\", float(dh[c]), 0.0, 5e-5)\n",
    "for s in B.SCENARIOS:\n",
    "    ds = B.max_abs_diff(scen[scen[\"시나리오\"] == s], rs[rs[\"시나리오\"] == s], [\"시나리오\", \"분기\"], B.EXOG_COLS)\n",
    "    for c in B.EXOG_COLS:\n",
    "        add(f\"exog_scen_v2 {s} {B.EXOG_LABEL[c]}(40분기)\", float(ds[c]), 0.0, 5e-5)\n",
    "\n",
    "# (7) 레버 전달\n",
    "for lever in reach.index:\n",
    "    add(f\"레버 전달 {lever}(최대 변화폭 > 0)\", bool((reach.loc[lever] > 0).any()), True, \"일치\")\n",
    "\n",
    "def passed(r):\n",
    "    if r[\"허용오차\"] == \"일치\":\n",
    "        return r[\"계산\"] == r[\"기록\"]\n",
    "    return abs(float(r[\"계산\"]) - float(r[\"기록\"])) <= float(r[\"허용오차\"])\n",
    "res = pd.DataFrame(checks)\n",
    "res[\"통과\"] = res.apply(passed, axis=1)\n",
    "res.to_csv(os.path.join(C.OUTPUT, \"verification.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "show = res.copy()\n",
    "for c in [\"계산\", \"기록\", \"허용오차\"]:\n",
    "    show[c] = show[c].map(lambda v: v if isinstance(v, (str, bool)) else f\"{v:.6g}\")\n",
    "with pd.option_context(\"display.max_rows\", 200):\n",
    "    display(show)\n",
    "assert res[\"통과\"].all(), \"기록 산출물과 계산 결과가 다르다 — 위 표에서 통과=False 항목을 확인한다\"\n",
    "print(f\"검증 통과: {len(res)}개 항목에서 정상상태·사후분포 요약·기준선·거시경로·외생 6종(S1–S5)·레버 전달을 재현했다.\")\n",
    "same = [f for f in (\"exog_hist_v2.csv\", \"exog_scen_v2.csv\", \"dsge_macro_paths.csv\", \"dsge_exog_paths.csv\", \"exog_levels_2000_2035.csv\")\n",
    "        if B.sha256(os.path.join(C.OUTPUT, f)) == B.sha256(os.path.join(C.REFERENCE, f))]\n",
    "print(f\"참고(통과 조건 아님): 파일 단위로도 기록본과 바이트가 같은 산출물 {len(same)}/5개 — {', '.join(same)}\")"
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