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   "cell_type": "markdown",
   "id": "4173501f",
   "metadata": {},
   "source": [
    "# 03 — Business Case\n",
    "\n",
    "Combine the benefits and costs into the consolidated TEI summary,\n",
    "render the Cash Flow chart, and run scenario analysis. This notebook\n",
    "should reproduce the headline numbers from the PDF Financial Summary:\n",
    "\n",
    "* **NPV $78.7M • ROI 342% • Payback <6 months**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3cc4b453",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "from pathlib import Path\n",
    "\n",
    "ROOT = Path.cwd().resolve()\n",
    "while ROOT != ROOT.parent and not (ROOT / 'core').is_dir():\n",
    "    ROOT = ROOT.parent\n",
    "if str(ROOT) not in sys.path:\n",
    "    sys.path.insert(0, str(ROOT))\n",
    "STUDY = ROOT / 'studies' / '202602_AmazonConnect'\n",
    "if str(STUDY) not in sys.path:\n",
    "    sys.path.insert(0, str(STUDY))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "62c56628",
   "metadata": {},
   "outputs": [],
   "source": [
    "import config\n",
    "import seed_data\n",
    "from core.export import build_report_data\n",
    "from core.export.report_data import _compute_summary\n",
    "from core.notebook_helpers import charts, display, tables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11fdc7c3",
   "metadata": {},
   "source": [
    "## Local summary (no Athena round-trip)\n",
    "\n",
    "Compute the moderate-case TEI summary directly from `seed_data` so the\n",
    "notebook produces results even before the Athena tool is provisioned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7d295b06",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div><div style=\"font-size:1.1em;font-weight:600;margin-bottom:6px;color:#444;\">Forrester composite — moderate case</div><div style=\"display:flex;flex-wrap:wrap;align-items:stretch;\"><div style=\"flex:1;min-width:140px;padding:14px 18px;margin:4px;border-radius:8px;background:#f7f9fc;border:1px solid #e3e8ee;\"><div style=\"font-size:0.78em;color:#6b7480;text-transform:uppercase;letter-spacing:0.04em;\">NPV</div><div style=\"font-size:1.6em;font-weight:600;color:#1a2540;margin-top:4px;\">$78.7M</div></div><div style=\"flex:1;min-width:140px;padding:14px 18px;margin:4px;border-radius:8px;background:#f7f9fc;border:1px solid #e3e8ee;\"><div style=\"font-size:0.78em;color:#6b7480;text-transform:uppercase;letter-spacing:0.04em;\">ROI</div><div style=\"font-size:1.6em;font-weight:600;color:#1a2540;margin-top:4px;\">342%</div></div><div style=\"flex:1;min-width:140px;padding:14px 18px;margin:4px;border-radius:8px;background:#f7f9fc;border:1px solid #e3e8ee;\"><div style=\"font-size:0.78em;color:#6b7480;text-transform:uppercase;letter-spacing:0.04em;\">Payback</div><div style=\"font-size:1.6em;font-weight:600;color:#1a2540;margin-top:4px;\"><6 months (0.7)</div></div><div style=\"flex:1;min-width:140px;padding:14px 18px;margin:4px;border-radius:8px;background:#f7f9fc;border:1px solid #e3e8ee;\"><div style=\"font-size:0.78em;color:#6b7480;text-transform:uppercase;letter-spacing:0.04em;\">Benefits PV</div><div style=\"font-size:1.6em;font-weight:600;color:#1a2540;margin-top:4px;\">$101.7M</div></div><div style=\"flex:1;min-width:140px;padding:14px 18px;margin:4px;border-radius:8px;background:#f7f9fc;border:1px solid #e3e8ee;\"><div style=\"font-size:0.78em;color:#6b7480;text-transform:uppercase;letter-spacing:0.04em;\">Costs PV</div><div style=\"font-size:1.6em;font-weight:600;color:#1a2540;margin-top:4px;\">$23.0M</div></div></div></div>"
      ],
      "text/plain": [
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   "source": [
    "summary = _compute_summary(\n",
    "    seed_data.BENEFITS,\n",
    "    seed_data.COSTS,\n",
    "    config.DISCOUNT_RATE,\n",
    "    config.ANALYSIS_YEARS,\n",
    ")\n",
    "# `_compute_summary` returns roi_pct; expose it as `roi` for kpi_cards.\n",
    "summary['roi'] = summary.get('roi_pct')\n",
    "display.kpi_cards(summary, title='Forrester composite — moderate case')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c3fc75fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_cfb03\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_cfb03_level0_col0\" class=\"col_heading level0 col0\" >Year</th>\n",
       "      <th id=\"T_cfb03_level0_col1\" class=\"col_heading level0 col1\" >Benefits</th>\n",
       "      <th id=\"T_cfb03_level0_col2\" class=\"col_heading level0 col2\" >Costs</th>\n",
       "      <th id=\"T_cfb03_level0_col3\" class=\"col_heading level0 col3\" >Net</th>\n",
       "      <th id=\"T_cfb03_level0_col4\" class=\"col_heading level0 col4\" >Cumulative</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_cfb03_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_cfb03_row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "      <td id=\"T_cfb03_row0_col1\" class=\"data row0 col1\" >$27,279,019</td>\n",
       "      <td id=\"T_cfb03_row0_col2\" class=\"data row0 col2\" >$7,281,067</td>\n",
       "      <td id=\"T_cfb03_row0_col3\" class=\"data row0 col3\" >$19,997,952</td>\n",
       "      <td id=\"T_cfb03_row0_col4\" class=\"data row0 col4\" >$18,801,702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cfb03_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_cfb03_row1_col0\" class=\"data row1 col0\" >2</td>\n",
       "      <td id=\"T_cfb03_row1_col1\" class=\"data row1 col1\" >$40,333,658</td>\n",
       "      <td id=\"T_cfb03_row1_col2\" class=\"data row1 col2\" >$8,771,168</td>\n",
       "      <td id=\"T_cfb03_row1_col3\" class=\"data row1 col3\" >$31,562,490</td>\n",
       "      <td id=\"T_cfb03_row1_col4\" class=\"data row1 col4\" >$50,364,192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cfb03_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_cfb03_row2_col0\" class=\"data row2 col0\" >3</td>\n",
       "      <td id=\"T_cfb03_row2_col1\" class=\"data row2 col1\" >$57,983,494</td>\n",
       "      <td id=\"T_cfb03_row2_col2\" class=\"data row2 col2\" >$10,539,889</td>\n",
       "      <td id=\"T_cfb03_row2_col3\" class=\"data row2 col3\" >$47,443,605</td>\n",
       "      <td id=\"T_cfb03_row2_col4\" class=\"data row2 col4\" >$97,807,797</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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       "<pandas.io.formats.style.Styler at 0x10cf32f00>"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_cash = tables.cashflow_table(summary)\n",
    "df_cash.style.format({c: '${:,.0f}' for c in df_cash.columns if c != 'Year'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd58e4c8",
   "metadata": {},
   "source": [
    "## Cash flow chart\n",
    "\n",
    "Mirrors the chart on PDF page 25: stacked benefits/costs by year +\n",
    "cumulative-net line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5d293439",
   "metadata": {},
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G6nFPRUeTamDr2IhIpwxvytksd9x8jf1KQ56Row3d0EinN3/y+ZfywKPP2MM6GnTCeWfa0NARTjrq6+taE2jrUgWuI0gd53WzH9dNgfLytsrFZn3PN2d8JIcetL+MOvowR1Xnq7d90NBX14DUEbavv/OhnHv2GOnSuZO9Tmb2Wvm/5//jvKa+6Wd+DzTQbMizyzajUhNMUFj3OcTFxtS6dmM+NOb5aDu6pIJOe9ed4t2VDRs32Wei52KidxyV3cf8bv9ilmJwFMeyCjoSlYIAAggggAACCCCAAAIIIIAAAk0nQKDpYq/hoo7keu+jz93uZKwj+94wgZBukNPNhI+6kY6GejdNvkJOOelYiY7sLbkmiBpx0HEuVxU7evL9N16wm+JoMKgB30yz4/e/rrpRfv3mM2fduiGY68ZCzkq7+MYRFmqgV7foeoZaenTvZkdAHnPUoXYat04312nzOmJN70/XCP3NjMjTYPDCc8+se5lanx0bypxu1mzcd+Retc79+Mvv9vq6+7muXego9Tmon26WpM9qhtmJfviQQXYa+9T7HrPPxnGNuq86zV93sP/ux1/sOo86cu94MwrVUXSEqzfPyFFfw8fbb7rGBsm6PupPZjTgy6/PkGdfeNVOKdep7HVLp04d7fqpOZu32NGMdc+7ftalAC67+Dyz3uPNNkB3F2h62wddduCjt1+S/345125kdPvdD8u/n3rQNqe7zq9Y+JNr086Rog15dj17dBO9r7pFQ8PGFB1961oa+nz0u5G9etp70d8Td8UxelPPbdy04z8PdUdiOuq4Cz/dXZ9jCCCAAAIIIIAAAggggAACCCCwewQ8D2vbPe0166ued/ZY0em275hp0LphTN3y3IuviQY0ur6kjkTTDXJ0Y5qrLp1gQywN4n7btmGII4zUkEt3QNcNTnRk3mFm2rZuLKQjBnWtRd2IZE+W4WbtSe3nW+9+bNf/dLS9bv0GuwahjnbU9TW16AY+OsLtaTOCT0f0JQ7ob6aHD7VrRz717Ev23InHHeW4hNtX3VBGraZcf5XZ3fu0Wj83Xnu5/Y5jWrPbC9Q5+IdZv1DLFf+6wE7H1jU5K8w08z/+WmCPV9s/d/zjFLNDtU7B12f75Zzv5LSTj5NQswamloY+Iw22dSdsHfmnlvH9+siFZmTmPXfU7Ji+YtXqHTtgjqitlvp2QrcVtv2ROKCffZezuWb6ueu5hvRBNz7SZzrRLDOw94hhNkTWDae0aP91arjrj26uo6Uhz05HHOt6lXO/qx2O7uzZ6hICWhwbJul7/V38x4yCdpSGPB/HP3f6Xd0QapVZ51WDVg3pHT+33fWwjB53kVRVVonjnwfdvVx/jxxFd2z//qdfHR/ta3pmln1N2PZcap3kAwIIIIAAAggggAACCCCAAAII7DEBRmi6UGvgdddt18uNt98rF152nV0PUNex9DNTv+d+96NdX1JDqdtvmmS/FRnZU1auSjMjFl81oVacLFycIq+9/b49pwGmhika9OgUVQ2/dIfzXj272w2DUsz6fYMHJtZat8+lK7vtrU4n1g2AdOr7uPMvtwGjhqr/fvkNu/P0/VNvtiGXdkDXzNSdyXVDn5OOP9oe17Ur9zXrC/706zyJiuxlg1lPnU1OXW6/qxu2OMI817oaBuvmPrrpi2604k2J6t3LVtMdxjWMKzCbAL0383Oz8/pye1ynleuan3WLjmYcY9Z3fO2tmudzjtnd3VEa+ow08BuUNMCO5u1oRnoefsiBUlxS4hwhqqMe3ZX9zbqb6q7BmN73zoqO6NSi4Vrd0pg+aLCsm/acYDbFufPeR80aoiPcPhdtq6HPbtLlF8n/zOjmK6+7XS4366vqKEbd0KruTuF172P4sEH20IOPPSvrTMCv9/Xiq2+bdV7LzCjhYHvO2+ejv6v6u6RT6PV37hqzCZf+Ppx94VVmtOu50tss//C9+RcVc7790Y6s1TBc/3kYc8oJouurnjfxGjn15FFSZJZJeNn8fpWXbw84tSOOIFoDYgoCCCCAAAIIIIAAAggggAACCDSdACM069jrzuQfvDndrKHZ34YcU6Y+aKcq62Y5Ohrxk3f/I46gSUfk6dqEjz89Xa6YfJvoKC9dF1M3a/nrn0VmavO3dsObh++93YaFOtV3olkX8annXjYjwwbJC0/XTPv196/ZDKZOV8Q/wN8EPL55RK5rG9439Sa5+rILZblZg/LG2+6VaWZnc7MPjTz92D1yugl3HEVHMB59RM1uzgftv4/jsBx8YM37E8wu0fWVD2bOsqcdu167q6sb6mjRUNIbh+OOOVz0GS1YtNSGxLfe9ZAdefnUI3fb6+jO1o7ies96bPy40+wpHR3ruu6nbkq0s2dU95qPPzjV7myvIz4vu+YWuz7oKjP1/NYbrjabKx3qqF7rVQNiDewWLFxa67inDxqw69Tz+X/8Y0cGO+o57svbPjjq6/c1wLvx2stsyH773Y84LrnDa0OfnYbT773xbxNk9raBov5e6dqy15pNs7QE+NeM+qzbkG7MdMlF4yU9I0vueuAJueehJ+1GWhq6O373vX0+Okq2qLhYnjSjh+ebEbu6vusHb02XXuZfQjzy5L/luil3238udaSqY9Mj7c9D99xmlzxYuCRZbp32kNz70FM2kD36iENsd/22/fO50Gw+pTvbE2jWfYp8RgABBBBAAAEEEEAAAQQQQGDPCviZKZqeZunu2Z40w9Z0U5SUZStsiNHXbBjkummLo7s6TVWn2oaGhtgprXpcpwPraC7dzdoRJukxXaNSR/J1M7tBa1DV1EUf/br1G+1GNbomaEsqumnOltw8O+I1NDTUdl2XA9CNbzzZ6o7tEy6dLA/cNcWOTK17v415RvlmhKijXR19q1O36yuTbrjTruP567efSft27eqr6vW5hvbB6ws3smKueS5b8wtsKKijaF3LK2+8a0LLp+y/GHAdpVpQWCjZ2eslNjbKuRSA6/f0fWOej+MaapRv+uRYTsFx3PW1qqpK0syGYN27d93h2WzclCMHH32aXHTBWTa0dv0e7xFAAAEEEEAAAQQQQAABBBBAYM8KEGjuWW9aawIBnVJfbf4z4dLr7KhU3YhJdyhvirJoSYpZv/FCmWp2dr/YjBRsSyUza60dgamjnfUZ6GZFLaU8ZkZhv26WK/jxq5nOEdotpe/0EwEEEEAAAQQQQAABBBBAAIHWJuCb+cytTYX7aVUCd5tpzEP2Pdqu0znJrKvYVGGmog4dnGTXZn3lzfdqbcrUqsA93My/rrrJrkN7+CEHtKgws8SMqn5rxkdyqdl13rHchIdb5DACCCCAAAIIIIAAAggggAACCOwBAUZo7gFkmmhagZ9+mWfXVBwyKFGONTvUN3XZvCVXfjR9Ou7ow8xSBTXT5Zu6T3ui/V9++8OGycOH1mwEtCfa9EUbOoVeNxM61qyNqhsJURBAAAEEEEAAAQQQQAABBBBAoGkFCDSb1p/WEUAAAQQQQAABBBBAAAEEEEAAAQQQQKABAkw5bwAWVRFAAAEEEEAAAQQQQAABBBBAAAEEEECgaQUINJvWn9YRQAABBBBAAAEEEEAAAQQQQAABBBBAoAECBJoNwKIqAggggAACCCCAAAIIIIAAAggggAACCDStAIFm0/rTOgIIIIAAAggggAACCCCAAAIIIIAAAgg0QIBAswFYVEUAAQQQQAABBBBAAAEEEEAAAQQQQACBphUg0Gxaf1pHAAEEEEAAAQQQQAABBBBAAAEEEEAAgQYIEGg2AIuqCCCAAAIIIIAAAggggAACCCCAAAIIINC0AgSaTetP6wgggAACCCCAAAIIIIAAAggggAACCCDQAAECzQZgURUBBBBAAAEEEEAAAQQQQAABBBBAAAEEmlaAQLNp/WkdAQQQQAABBBBAAAEEEEAAAQQQQAABBBogQKDZACyqIoAAAggggAACCCCAAAIIIIAAAggggEDTChBoNq0/rSOAAAIIIIAAAggggAACCCCAAAIIIIBAAwQINBuARVUEEEAAAQQQQAABBBBAAAEEEEAAAQQQaFoBAs2m9ad1BBBAAAEEEEAAAQQQQAABBBBAAAEEEGiAAIFmA7CoigACCCCAAAIIIIAAAggggAACCCCAAAJNK0Cg2bT+tI4AAggggAACCCCAAAIIIIAAAggggAACDRAg0GwAFlURQAABBBBAAAEEEEAAAQQQQAABBBBAoGkFCDSb1p/WEUAAAQQQQAABBBBAAAEEEEAAAQQQQKABAgSaDcCiKgIIIIAAAggggAACCCCAAAIIIIAAAgg0rQCBZtP60zoCCCCAAAIIIIAAAggggAACCCCAAAIINECAQLMBWFRFAAEEEEAAAQQQQAABBBBAAAEEEEAAgaYVINBsWn9aRwABBBBAAAEEEEAAAQQQQAABBBBAAIEGCBBoNgCLqggggAACCCCAAAIIIIAAAggggAACCCDQtAIEmk3rT+sIIIAAAggggAACCCCAAAIIIIAAAggg0AABAs0GYFEVAQQQQAABBBBAAAEEEEAAAQQQQAABBJpWILBpm2+9rWfnFLfem+POEEAAAQQQQAABBBBAAAEEEEAAAQQQ8FIgsmuYlzW9q8YITe+cqIUAAggggAACCCCAAAIIIIAAAggggAACzUCAQLMZPAS6gAACCCCAAAIIIIAAAggggAACCCCAAALeCRBoeudELQQQQAABBBBAAAEEEEAAAQQQQAABBBBoBgIEms3gIdAFBBBAAAEEEEAAAQQQQAABBBBAAAEEEPBOgEDTOydqIYAAAggggAACCCCAAAIIIIAAAggggEAzEGh0oPnSa+80g+7TBQQQQAABBBBAAAEEEEAAAQQQQAABBBBoSwKBDb3ZlGUr5OXXZsjnX8yR7378Vc4/Z6wcf8wRzstUV1fL5JunyXlnj5F9997LeXyWqT9r9lwJCPCX00cfL8cceag9V1hYJK+8+Z7M++NviYuNlssuPk9ioiOd33O8qa9e6vKV8taMmZKWkSn77TNCLp94ngQG1tyap3Yd13W8eqrX2HYd1+UVAQQQQAABBBBAAAEEEEAAAQQQQAABBHwn0KARmhpWXjn5Nons3VOGDk6SU04cJdfdcpdkZa+zPfriq2/khlvvkU//+5XkbMl19nLOtz/K5FumSe9ePWR/Ezhedf0dNgzVCnfc+4i88c6HcuzRh0lJSamMO/8y0RCxbvFUb0tunky4dLKkrlgpY045QT4zbd9xz6P26/W163r9+uo1pl3Xa/MeAQQQQAABBBBAAAEEEEAAAQQQQAABBHwn0KARmmvXbZBVa9LtCMtFS1LkrLGjpXu3LlJeXm579OXcH6SgoFD8/WvnpK++9b5MGD9O7pwy2db7dd6fMuP9T2TE8CHy8WezZeaMl2WkeV9+RoUkjTxc5nz3k5x60ijnXeZtzfdYT8NPPz8/ef+NF2x9HZl5/ZS7Zappy1O7Rxx6oPPa+sZTvfr6V1+74eFhta7PBwQQQAABBBBAAAEEEEAAAQQQQAABBBDwjUCDAk0dmTl4YIJMunGq5BcUiAaNRx1+sLMnTz18l30/eJ+jnMf0TVpapg0/HQf7xMbIXBNapqVn2kPDzGhPLUFBgRIV2UvWpGXYz44/6qtXWFQkw4cMclSVPnExUmYC1ux16z2266y87U1j+ldfu/H9+ohf3Ub4jAACCCCAAAIIIIAAAggggAACCCCAAAK7LNCgQFNbe/bx++TJZ1+SL77+VvY99EQ53Uzzfvje2zx2pKqqSrLWrpOIDh2cdTQY1UAwIzNbdDSjY71LrRBlzhUUFjrr6pv66ul094iI9s76Gohqyc8v8Nius7J509j+eWrXMV2+Z5dQ12Z4jwACCCCAAAIIIIAAAggggAACCCCAAAI+EGhwoNm3T6w8/di9csEl18qJxx0lt057yKyleawcfOC+bruj08+Dg4Nli8uamiWlpRIbEyVhYaFSVFQs+jk0JMR+v9iso6kjOF1LffU2b86VHPPjKMXFJfZtXzNS01O7jrr62tj+eWo3ztyXlnWba/phP/AHAggggAACCCCAAAIIIIAAAggg0MwEdAnBdWszpV37DtKlS7dm1ju605oEIrv6dnnG2otd7kRK174864IrbK2AgAA5+4xT5bCD95fZX39X7zd11GW6GY3pKKvNOpwJ8f3s5kJ6LDNrrT2loyXTM7LMub6OqvZVR3RqcVevtzmnIzgdRa+t63p26tTRjvZ0166jruO1Mf2rr13HdXlFAAEEEEAAAQQQQAABBBBAAAEEmqPAqpXL5NH775DpzzwmTzw4Td5/+1WprKxsjl2lTwjsINCgQDM6srf8/sff8vn/vrYXWrk6TdaYdTAHDKgdQNZtRXcwf/fDT2Xjphz55vufZdbsubL3iKGSlBAv0VG9Zfp/3rLTzB9/erodrZlojuuozedefN1uQlRfvVFHHSbLVqySr7/5QTKysuWp51+WvfcaZrvgqV09qTux63e0eKrX2HbtRfkDAQQQQAABBBBAAAEEEEAAAQQQaIYCOjJzxusvSVFhgbN3C//5Q3796TvnZ94g0JwFGjTlPCY6Ui6feL5cc9NUqa6ulh9/+V0O3G9vOffM02vdo9l03GyKs31bnGsuv1jmzf9b9j3sJFtv7KknmmnqNbuY6/T1CWb6+gcfz5LQ0BD7bwUiOrSX3Nw8efSpf0v/fnHSb9s0d3f1hg0ZKNdccbFcOukW26e42Gh57okHbDv1tfveR59Jty6d5VgTiNZXz1P/6mu3FgYfEEAAAQQQQAABBBBAAAEEEEAAgSYW0FmxmelrJGXpIlm04E8pLi7aoUcrl6fIIYcfvcNxDiDQ3AT8TDBZ3dBO6e7mE6+8UR6973bRNTW9Lbpbebt24dKta5daXykvr5CVq9fYa4WY9TYdRdfnvGD8WBmYOMAe8lRPT+blbZWNOZtFdxivW9y1+9Mv8+S3+X/Jjdde7qzurp6ebEy72TnFzuvyBgEEEEAAAQQQQAABBBBAAAEEENjTAmVlpbJiWYoNMTXIdB2R6a4ve+29n5xx9gR3pziGwC4J+HoNzQaN0HT0vGNEB7n2yosbFGbqd3X0pLsSFBRop5+7ntPdwiN79ah13F09x3c6dowQ/XFX3LU7/68Fcu5ZY2pVd1dPKzS23VoX5wMCCCCAAAIIIIAAAggggAACCCCwmwW25uXaADN5yUJZtWKZWRezwm2LfmZ6resYt8DAIDn4MEZnusXiYLMTaNQIzWZ3F82wQ4zQbIYPhS4hgAACCCCAAAIIIIAAAggg0AoF1mZlSLIZgZliQsxs895Tad8hQhIHDpGkQUOlX/8BMv/3X0Snmbdr396Gmb0j3Q9E83Q9jiPgrYCvR2gSaHor38B6BJoNBKM6AggggAACCCCAAAIIIIAAAgh4JVBRUW5HX+o0cv3RUZmeSs9ekZI02GzMbELM6Jg+oiMzKQjsaQFfB5qNmnK+p2+a9hBAAAEEEEAAAQQQQAABBBBAAIG2LFBYkC+pKUtsgLkiNVl0fUx3JSAgQPr0izcB5jAbZHbu3NVdNY4h0KIFCDRb9OOj8wgggAACCCCAAAIIIIAAAggg0FoFNm5YJ8lLFkmqGYWZnraq1pqXrvccFhYuCUmDbYCZkDhYQkJDXU/zHoFWJ0Cg2eoeKTeEAAIIIIAAAggggAACCCCAAAItUaCyslLS16wyozAX2iBzc85Gj7fRtVv3mlGYZip5XN/+4u/v77EuJxBobQIEmq3tiXI/CCCAAAIIIIAAAggggAACCCDQYgRKiotl+bKlZkMfMxIzZbHoZ3dF176Mjeu3bT3MYdK9R0931TiGQJsQINBsE4+Zm0QAAQQQQAABBBBAAAEEEEAAgeYisHnzJjuNXDf0Wb1yuVRVVbntWnBIiMQnDJSBZj3MxIGDJbxde7f1OIhAWxMg0GxrT5z7RQABBBBAAAEEEEAAAQQQQACBPSpQXV0tmRlr7ChMDTHXr8v22H7HTp1NeDlEBg4eJn37J0hgINGNRyxOtFkB/qlos4+eG0cAAQQQQAABBBBAAAEEEEAAgd0lUFZWJiuXp5gQc6GkJC8W3aXcU4mMjjXrYQ41IzGHSu+oGE/VOI4AAtsECDT5VUAAAQQQQAABBBBAAAEEEEAAAQR8ILA1L1dSTXipozA1zKyoqHB71cDAIOkXn2BHYWqQ2SGio9t6HEQAAfcCBJruXTiKAAIIIIAAAggggAACCCCAAAII7FRgbXamDTB1JGZWZrrH+u3ad6iZSm4CzP5mXczg4GCPdTmBAAL1CxBo1u/DWQQQQAABBBBAAAEEEEAAAQQQQMApoKMudSOflKVmKrkZiZmXu8V5ru6bHj1710wlN+thRsf2Ed2pnIIAArsuQKC564ZcAQEEEEAAAQQQQAABBBBAAAEEWrFAUWGBpKYssZv6LF+2VMpKS93erb+/v/TpN8AZYnbu0tVtPQ4igMCuCRBo7pof30YAAQQQQAABBBBAAAEEEEAAgVYosHHD+pqp5GYkZvqaVaI7lbsroWFhkpA02IaYCYmDRT9TEEBg9woQaO5eX66OAAIIIIAAAggggAACCCCAAAItQKCqqkrS1qyUVDONPNmsh5mzaaPHXnfp2t0GmEmDh0pcn/4SEBDgsS4nEEDA9wIEmr435YoIIIAAAggggAACCCCAAAIIINACBEpKimVFarIdiam7kxcXF7ntta59GWPWwEwya2EmDRomPXr2cluPgwggsGcECDT3jDOtIIAAAggggAACCCCAAAIIIIBAMxDYsiVn2yjMRWZzn2WiIzPdleDgEIlPSLIjMRPNzuTt2rV3V41jCCDQBAIEmk2ATpMIIIAAAggggAACCCCAAAIIILBnBHTty6zMdLOhz0JJNuthrl+b7bHhjh07iYaXSeanX3yCBAYGeazLCQQQaDoBAs2ms6dlBBBAAAEEEEAAAQQQQAABBBDYDQLl5WWycnnqtk19FklB/laPrfSOipGB20LMyOhYj/U4gQACzUeAQLP5PAt6ggACCCCAAAIIIIAAAggggAACjRTI35onug5mitnUZ8WyFKmoKHd7pcDAQOnbP0EG2vUwh0qEGZVJQQCBliVAoNmynhe9RQABBBBAAAEEEEAAAQQQQACBbQLr1maZqeSLbIiZmbHGo4uuf5kwcIgdiRmfOFB0fUwKAgi0XAECzZb77Og5AggggAACCCCAAAIIIIAAAm1KoKKiQtasWmECzIU2yMzN3ezx/rv36GVHYeqamLpDub+/v8e6nEAAgZYlQKDZsp4XvUUAAQQQQAABBBBAAAEEEECgTQkUFRXKspQlNsBcnrpUSktL3N6/BpZxffubUZjDJMlMJ+/StZvbehxEAIGWL0Cg2fKfIXeAAAIIIIAAAggggAACCCCAQKsS2LRxQ80oTLMeZtrqlaI7lbsroaFhMiBxkAkwh0pC0mAJCwt3V41jCCDQygQINFvZA+V2EEAAAQQQQAABBBBAAAEEEGhpAlVVVZKetmrbepgLRQNNT0VHXiaa9TB1FGafvvESEBDgqSrHEUCglQoQaLbSB8ttIYAAAggggAACCCCAAAIIINCcBUpLSsxu5MmSbNbDTE1eIsVmarm74ufnJ9FmDcwksxam/vTsFemuGscQQKANCRBotqGHza0igAACCCCAAAIIIIAAAggg0JQCuVs2m/BysSQvWSirVy6TyspKt90JCg6W+AFJdhRmkhmN2a59B7f1OIgAAm1TwOeBZmlZmbz7wacy4dxxbVOUu0YAAQQQQAABBBBAAAEEEEAAASuga19mZ6ab9TAXmZGYi2RddqZHmQ4RHZ2jMPsPSJTAwCCPdTmBAAJtW8CngeasL+bI2+/NlAWLk2VpynK55KLxEt+vj/03LrO//k4+N+eDgwLlpOOPkeOOOdzKFxYWyStvvifz/vhb4mKj5bKLz5OY6B2Hj9dXL3X5SnlrxkxJy8iU/fYZIZdPPM/8F1/NrWmfZs2ea9bU8JfTRx8vxxx5qNsn7qleY9t12wgHEUAAAQQQQAABBBBAAAEEEGjlAuXl5bJqRaodhalBZkH+Vo933DsyuibENOthRkXHeqzHCQQQQMBVwN/1w668X5K8TKZMfUDOPWuMDB86SKrNf26+4357yf99+Y1Mve8xOfyQ/WXI4CS5+obb5edf59tzd9z7iLzxzody7NGHSUlJqYw7/zLRELFu8VRvS26eTLh0sqSuWCljTjlBPvvvV3LHPY/ar8/59keZfMs06d2rh+xvgs6rrr9Dvvvx17qXlvrqNabdHRrgAAIIIIAAAggggAACCCCAAAKtWEBDyz/n/SJvvTZdHph2k7z5yr/lj99/3iHMDAgItLuSjz79LLnp9vvkqutulaOPO5kwsxX/bnBrCOwOAZ+N0Pz5t/kSHRUphx96oHw193uZNuU6mT3nO6moqDCff5D99x0h54w7zd7Dp7O+Eq2v4ebHn82WmTNelpHDh0j5GRWSNPJwmfPdT3LqSaOc95u3Nd9jPQ0/dYHg9994wdbXkZnXT7lbpk6ZLK++9b5MGD9O7jTvtfw670+Z8f4ncoTpo2vxVG+E6ZOn/tXXbnh4mOvleY8AAggggAACCCCAAAIIIIBAqxNYvy67Ziq5WQ8zM32Nx/sLb9deEpMG25GY8YkDJSQk1GNdTiCAAALeCPgs0Bx11GHywKPPyK3THrQjLENDQ2TsqSfaPowyoy9vNP/m5annXpatJpxMWbZC7p16k6SlZ9rzw0ywqSXITEePiuwla9Iy7GfHH/XVKywqkuFDBjmqSp+4GCkzw9uz162XtLRMOWvs6O3nYmNkrglL6xZP9Rrbrk6zpyCAAAIIIIAAAggggAACCCDQmgR0A581q5bbEDNlySLZsiXH4+11695TBg7WXcmHSUxcX/H399kEUY9tcgIBBNqOgM8CTQ0SX5v+pLzw8pvy2/y/ZP8jRstdt10vJ59wjF0TM9iEldNfecuO2OzSuaNEdGgvqctWio5mdKx3qexRvXtKQWFhrSeQkZntsV5W9jqJiGjvrK+BqJb8/ALJWmvOddi+E1qkubYGoK6lqqrKY73GtOuYLt+jU4hrM7xHAAEEEEAAAQQQQAABBBBAoMUJFJq/ny9evEgWLlhgXhdLcXGx23vQwDI+Pl6GD99Lhu81XHr06Om2HgcRQAABXwj4LNDUzuhU7r1HDJNLJ90sPXt0l7sffMKujXnXA0/Y488/+YDo4sDjL54kL736jt0YqKioWEpKSyU0pCYALDbraPYxIyldS1hYqHiqt3lzruSYH0cpLi6xb/uagDU4ONj8G6Pt57Sd2JgoR1X7qv+l66leY9qN23b9nK1ltdrhAwIIIIAAAggggAACCCCAAAItQWDTpg2iIzCXmqnkaatXig4EcldCQkOdU8mTBg6RsPBwZzX+Tuyk4A0CCBiBnp19u9SEzwLNh594Xtq3bycXjD9DunftInfffoOMOOg4+WfBElmSnGo+32hHWYqEmZ3GD5Ev53wvF51/pn2omVlr7W7o+l+S6RlZkhDft9bD1pGVWtzVW7t+gyxakuKsv3pNunTv1kU6depoR3umm9GdjqLnEuL7OT46X3VUqLt6jW1XL1xZVe28Pm8QQAABBBBAAAEEEEAAAQQQaK4C+nfxDLMGZooJMFOWLpSNG9Z77Grnzl0lcZBOJR8qffsPkICAAGdd/h7spOANAgjsZgGfLWLRMaKDfPDxLFm5ao3t8g8//Wb/LU6/vrHSv28f+fx/X8n6DRtl46Ycu6u4rquZlBBvNhLqLdP/85adZv7409PtaM1Ec1xHUz734uuyyoSQ9dXTtTuXrVglX3/zg2RkZctTz78se+81zPZBd05/98NPbZvffP+zzJo914wUHWrPfWp2Q9fvaPFUr7Ht2ovyBwIIIIAAAggggAACCCCAAALNVKC0tESWLPpHPnrvTXno7iny0nOPy4/ffe02zIyO6SPHHD9arr7+Nrnhtnvk5NPGSXxCUq0ws5neJt1CAIFWKuBXbYov7i03N08uvPx6+WfhErvruE7jnnzVRLniXxfIn/8skrvNtPPFS1PNf+H5yyEH7if3T7tFdATkXwsWy4RLrpX8gkLRjYSeeHCanHjcUaLX28uM8Hzh6Yfk+GOO8FhP+/7EMy/KMy+8KnorcbHR8sGb06VH9652c6LzJk6Sv02ftOgmRY8/ONW+H3/x1dKtS2d5+rF7663nqX96EU/t6rnsHPfriug5CgIIIIAAAggggAACCCCAAAJ7WiAvd0vNhj5LF8mqFcuksrLCbReCgoKk/4AkSRo8THQqefsOEW7rcRABBBDwViCya5i3Vb2q57NA09HasuWr5L5H/k9efOZhE1DWnh+fvXa9Wa8ySLqZKemupby8QlauXiN9+8RKiAlCHeXWaQ+ZKexjZWDiAHvIUz09mZe3VTbmbLZT1x3fd7zqbuXt2oXXavenX+bZzYtuvPZyRzW763rdenqyMe0SaDpZeYMAAggggAACCCCAAAIIINBEAtmZ6ZJsAswU87M2K8NjLzpEdJREE17qVHINMzXUpCCAAAK+Emj2gWZFRYX88fdCOWDfkbt0z7pb+CtvvCtXX36RHfG5Sxdz8+Unn31Jzj7jVOndq4ebs7t+iEBz1w25AgIIIIAAAggggAACCCCAQMMEdCPe1SuXSbJdD3OR5G/N83iBXr2jakZhmhAzKjp2t/zd22PjnEAAgTYl0OwDzTb1NOq5WQLNenA4hQACCCCAAAIIIIAAAggg4DOBgoJ8WZa82I7EXLEsWcrLytxeWzfw0Y18kgaZqeQmxOzUufbsSbdf4iACCCDgAwFfB5o+2+XcB/fGJRBAAAEEEEAAAQQQQAABBBBAwAuBDevX2lGYqWYque5Q7ml7jLDwdpKYNNiMxBwqAxIGSUidpeG8aIoqCCCAQLMTINBsdo+EDiGAAAIIIIAAAggggAACCCBQW6CyslLSVq80ozAXSoqZTr5lc07tCi6funXvYUdg6kjM2D79xN/f3+UsbxFAAIGWL0Cg2fKfIXeAAAIIIIAAAggggAACCCDQCgWKi4tkeepSE2AukmUpS6SkpNjtXfr5+Ulcn/6SaKaRDzQ7k2ugSUEAAQRaswCBZmt+utwbAggggAACCCCAAAIIIIBAixLYnLPJ7kiuozDXrF4hVVVVbvsfEhIqAxIH2vUwEwYOlnAztZyCAAIItBUBAs228qS5TwQQQAABBBBAAAEEEEAAgWYnoIFlplkDM8Wshak/ujamp9KpU5dtozCHms19EkQ3+aEggAACbVGAQLMtPnXuGQEEEEAAAQQQQAABBBBAoMkEyspKZcWyFLsWZqrZnbywsMBjX6Ji4ux6mAPNepi9IqM81uMEAggg0JYECDTb0tPmXhFAAAEEEEAAAQQQQAABBJpEYGternMU5qoVqVJRUeG2H4GBQdJ/QOK2TX2GSoeIjm7rcRABBBBoywIEmm356XPvCCCAAAIIIIAAAggggAACu00gOyvDuR6mvvdU2neIkMSBQ2yIGZ+QJEFBwZ6qchwBBBBAwAgQaPJrgAACCCCAAAIIIIAAAggggIAPBCoqymXVimXOkZg6KtNT6dkrUpIGm13JzVRynVauO5VTEEAAAQS8EyDQ9M6JWggggAACCCCAAAIIIIAAAgjsIKDrX+o6mLqhz4rUZNH1Md0V3cCnT794uyu5BpmdO3d1V41jCCCAAAJeCBBoeoFEFQQQQAABBBBAAAEEEEAAAQQcAhs3rJPkJbor+ULJSFst1dXVjlO1XsPCwiVh4GA7lXxA4iAJDQ2rdZ4PCCCAAAKNEyDQbJwb30IAAQQQQAABBBBAAAEEEGgjApWVlZK+ZpUNMDXI3Jyz0eOdd+3Ww7mhT1zf/uLv7++xLicQQAABBBonQKDZODe+hQACCCCAAAIIIIAAAggg0IoFSoqLZVnqUkk1U8lTUxaLfnZXdO3L2Lh+dj3MpEFDpXuPXu6qcQwBBBBAwIcCBJo+xORSCCCAAAIIIIAAAggggAACLVdg8+ZNNsDU9TBXr1wuVVVVbm8mOCRE4hMG2g19Es2U8vB27d3W4yACCCCAwO4RINDcPa5cFQEEEEAAAQQQQAABBBBAoJkL6NqXmRlrJMWuh7lI1q/L9tjjjp06S+LAITJw8DDp2z9BAgP567RHLE4ggAACu1mA/wbezcBcHgEEEEAAAQQQQAABBBBAoPkIlJWVycrlKSbEXCgpZnfywoJ8j52LjI41ozCH2jUxe0fFeKzHCQQQQACBPStAoLlnvWkNAQQQQAABBBBAAAEEEEBgDwvkb80zG/roruSLbJhZUVHhtgc66rJffKIdhamjMSM6dnJbj4MIIIAAAk0rQKDZtP60jgACCCCAAAIIIIAAAgggsBsE1mZn1ozCNCFmVma6xxbate9QM5XcjMTsb9bFDA4O9liXEwgggAACzUOAQLN5PAd6gQACCCCAAAIIIIAAAgggsAsCOupSN/JJWWqmkpsQMy93i8er9ejZ204j1/Uwo2P7iO5UTkEAAQQQaDkCBJot51nRUwQQQAABBBBAAAEEEEAAAReBosICSU1ZYjf1Wb5sqZSVlrqc3f7W399f+vQbYEPMpMFDpUuXbttP8g4BBBBAoMUJEGi2uEdGhxFAAAEEEEAAAQQQQACBtiuwccP6bethLpT0NatEdyp3V0LDwiQhabAJMYdJQuIg0c8UBBBAAIHWIUCg2TqeI3eBAAIIIIAAAggggAACCLRKgaqqKklbs1JSzTTy5CWLJGfTBo/32aVrd+cozLg+/SUgIMBjXU4ggAACCLRcAQLNlvvs6DkCCCCAAAIIIIAAAggg0CoFSkqKZUVqsiSb9TCXJS+R4uIit/epa1/GxPWtCTHNSMwePXu5rcdBBBBAAIHWJUCg2bqeJ3eDAAIIIIAAAggggAACCLRIgS1bcpyjMNesWi6VlZVu7yM4OETiE5JsiJlodiZv166923ocRAABBBBovQIEmq332XJnCCCAAAIIIIAAAggggECzFdC1L7My0ux6mDoSc/3abI99jejYadsozKHSLz5BAgODPNblBAIIIIBA6xcg0Gz9z5g7RAABBBBAAAEEEEAAAQSahUB5eZmsXJ66bVOfRVKQv9Vjv3pHxchAMwIzafAwiTTvKQgggAACCDgECDQdErwigAACCCCAAAIIIIAAAgj4XCB/a56kJi+2IeaKZSlSUVHuto3AwEDp2z9BBpoAM8kEmToqk4IAAggggIA7AZ8HmqVlZfLuB5/KhHPHuWuPYwgggAACCCCAAAIIIIAAAq1cYN3aLEkxO5LrVHKdVu6p6PqXiQOH2AAzPnGg6PqYFAQQQAABBHYm4NNAc9YXc+Tt92bKgsXJsjRluVxy0XiJ79fH9uHXeX/K+zM/l7S0TDlt9PFy5tjREhoSIoWFRfLKm+/JvD/+lrjYaLns4vMkJjpyh37XVy91+Up5a8ZMScvIlP32GSGXTzzPrKlSc2vap1mz50pAgL+cbto95shDd7i2HvBUr7Htum2EgwgggAACCCCAAAIIIIBAKxTQDXxWr1xuRmEutEFmbu5mj3fZvUcvOwpTN/SJie0j/v7+HutyAgEEEEAAAXcCPvtfjiXJy2TK1Afk3LPGyPChg6Ta/OfmO+63bS5fuVr+deVNEh4WJueePUZefPVteeOdD+25O+59xL4/9ujDpKSkVMadf5kNOet21lO9Lbl5MuHSyZK6YqWMOeUE+ey/X8kd9zxqvz7n2x9l8i3TpHevHrK/CTqvuv4O+e7HX+teWuqr15h2d2iAAwgggAACCCCAAAIIIIBAKxMoKiqUf/6cJ++++R95YNrN8tpLz8hvP38vdcNMDSz79h8gJ44eK9dPuVuuvelOGXXiqRLXpx9hZiv7neB2EEAAgT0l4LMRmj//Nl+ioyLl8EMPlK/mfi/Tplwns+d8Z9ZHqZAXX3lbRh1zmNw/7RZ7X/36xErO5i2StzVfPv5stsyc8bKMHD5Eys+okKSRh8uc736SU08a5TSor56OoPTz85P333jB1teRmfo/klOnTJZX33pfJowfJ3ea91p0lOiM9z+RI0wfXYuneiNMnzz1r752w8PDXC/PewQQQAABBBBAAAEEEECgVQhs2rihZhTm0kWStnql6E7l7kpoaJgMSBxkNvQZKglJgyUsLNxdNY4hgAACCCDQKAGfBZqjjjpMHnj0Gbl12oN2hGVoaIiMPfVE2ymdCj58yCCZeOWNUlBQKGPM8dNGHyepy1ba88MGJ9nXoKBAiYrsJWvSMmrdTFp6psd6hUVF9tqOL/SJi5Gy8nLJXrfeTm8/y0xtd5Q+sTEy14SldYtOg3dXr7Ht6jT7zh2C6zbDZwQQQAABBBBAAAEEEECgRQlUVVXJqlUrZdGCBbJo4QJZv36dx/537dZNhg4bLsPMT/yABLPsV4DHupxAAAEEEEBgVwR8FmhqkPja9CflhZfflN/m/yX7HzFa7rrtejn5hGMkLT3LrJH5j1ww/gw7mnLa/Y/ZPrdvFy46mtGx3qUejOrdUwoKC2vdU0Zmtsd6WdnrJCKivbO+BqJa8vMLJGutOdehg/NcpLm2BqCuRf8H2lO9xrSrIze1FJdUuDbDewQQQAABBBBAAAEEEECgRQiUlJTIstSlsmTxQkleslh0arm7ojPlYuP6yOAhw2SQ2Zm8V+/teyGUlZuRm+X8ncidG8cQQACBtigQFuzbf8nls0BTH4ZO5d57xDC5dNLN0rNHd7n7wSdE18bUBaKPO/pwueeOG+0za2dCzJmf/k8uM5v3FBUVS0lpqd0gSE8Wm3U0dSSlawkLC/VYb/PmXDN9PddZvbi4xL7vawLW4OBg2bJl+zltJzYmyllX3+h6Lp7qNabduG3XLymvqtUOHxBAAAEEEEAAAQQQQACB5iqQu2WzpCYvNgHmQrO5zzL7dzh3fQ0yf8eKH5BkppIPs7uTt2+/fQAJfwdyJ8YxBBBAAIHdIeCzQPPhJ56X9u3b2VGY3bt2kbtvv0FGHHSc+1ja/AAAQABJREFULFi4VHr17FFr5/Ju5nxu3lbREZNaMrPW2t3QdbRkekaWJMT3rXWv9dVbu36DLFqS4qy/ek26dO/WRTp16mhHe6ab0Z2OoucS4vs5PjpfdVSou3qNbdd5Yd4ggAACCCCAAAIIIIAAAs1QQNe+zM5Ml2SzFmaK+VmXnemxlx0iOkqS2ZFcf/rFJ0pQUJDHupxAAAEEEEBgTwj4LNDsGNFB3v3oMzn4gH1sv3/46TfRgLJvnxg56vCD5IOPZ8nY006UDu3by0yzEZBORU9KiDcbCfWW6f95S6bddp38+6U37GjNRHNcR1P+5/V35YRRR9ZbLyQkRJ594VX5+psfJCkxXp56/mXZe69htg86OvTdDz+V8WeeZkPPWbPnyn1Tb7LnPjW7oYebkZ/HmrU/PdWrr3/1tbsnHhxtIIAAAggggAACCCCAAAINESg3ew2sWpFqR2HqaMz8rXkev947MromxDQjMSOjYuzSYR4rc6LVCqzPWyvlVeWt9v64MQTakoCf+ElEWEfpEBrRKm7bz/ybOffb0jXw9nJz8+TCy6+XfxYusf9jp9O4J181Ua741wWy1axnedk1t8ivv/9przrQBI+vv/iU9OjeTf5asFgmXHKt5JvNgnQjoScenCYnHneU6PX2MiM8X3j6ITn+mCM81tMLPvHMi/KMCTX1VuJio+WDN6eba3e1mxOdN3GS/G36pEU3KXr8wan2/fiLr5ZuXTrL04/dW289T/3Ti3hqV89l5xTrCwUBBBBAAAEEEEAAAQQQaDKBgvytNVPJzSjMlcuSRUNNdyUgINCMvkxwjsTs2Kmzu2oca2MCM35/Xb5cOquN3TW3i0DrFAj0D5QpJ9wl8T0Sm+QGI7uG+bRdnwWajl4tW75K7nvk/+TFZx42AWWo47B9zcjKlqrKKruOpS4g7SjlZrHolavXmNGcsRJiglBHuXXaQ2YK+1gZmDjAHvJUT0/mmSnsG3M226nrju87XnW38nZmAyKd6u4oP/0yz25edOO1lzsOmc2LdqynJxvTLoGmk5U3CCCAAAIIIIAAAgggsAcF1q/LttPIdT3MrIw0O/DDXfPh4e3sOpi6HmZ8QpKEhNT++5u773CsbQk8/+2T8vnCj9rWTXO3CLRSAR2Zef9pT8iAnklNcoe+DjR9NuXcodGvb6xceemEHcJMPR8TtX3XO0d9fQ0KCrTTyl2P6W7hkb161Drurp7jOx07Roj+uCs6arNumf/XAjn3rDG1DrurpxUa226ti/MBAQQQQAABBBBAAAEEENgNAroJ65pVy22ImbJkkdkYNcdjK92695SBg3U9zGESE9fXbpLqsTInEEAAAQQQaKYCPg80AwMD5YB9R+7y7eqIyklXXLzL1/F0geuuvsTTKY4jgAACCCCAAAIIIIAAAs1aoLioSJalLDEh5kJZlrpUSktK3PbX399f4vr0l0SzoY8GmV279XBbj4MIIIAAAgi0JAGfB5ot6ebpKwIIIIAAAggggAACCCDQUgRyNm1wjsJMW7PSbsLqru8hZumvhMRBNsRMTBoiYeHh7qpxDAEEEEAAgRYrQKDZYh8dHUcAAQQQQAABBBBAAIHWLFBVVSUZaatrQkwzEnPjhvUeb7dz5642wEwyIzH79h8gAQEBHutyAgEEEEAAgZYuQKDZ0p8g/UcAAQQQQAABBBBAAIFWI1BaWiIrlqXYEDM1ebEUFRZ4vLfomD6SZKaRDzSb+vTs5X6/Ao9f5gQCCCCAAAItWIBAswU/PLqOAAIIIIAAAggggAACLV8gL3fLtlGYi2TVimVSWVnh9qaCgoKk/4AkE2IOk6SBQ6R9B/eborr9MgcRQAABBBBoRQIEmq3oYXIrCCCAAAIIIIAAAggg0DIEsjPTJXnpIklZslDWZmd67LSGljqNXH80zNRQk4IAAggggEBbFyDQbOu/Adw/AggggAACCCCAAAII7HaBiopyO/oy2QSYKSbIzN+a57HNXr2jakZhmhAzKjpW/Pz8PNblBAIIIIAAAm1RgECzLT517hkBBBBAAAEEEEAAAQR2u0BhQb6kmHUwNcBcsSxZysvK3LapG/joRj5Jg8xUchNidurcxW09DiKAAAIIIIBAjQCBJr8JCCCAAAIIIIAAAggggEAjBNZmZUhBQYFEx8ZJWFi4vcKG9WtFR2GmmhAzI32NVFdXu71yWHg7SUwabDf1GZAwSEJCQ93W4yACCCCAAAII7ChAoLmjCUcQQAABBBBAAAEEEEAAAY8C5eXlMuONl2RZyhJbJ9CsaxmfkCQb1q2VzTmbPH6vW/ce29bDHCaxffqJv7+/x7qcQAABBBBAAAHPAgSanm04gwACCCCAAAIIIIAAAgjUEigqLJDZ//3EGWbqyQoTcKYsWVSrnn7QtS/j+vS3ozB1Knm37j13qMMBBBBAAAEEEGi4AIFmw834BgIIIIAAAggggAACCLQBgeKiIrsDeVZmmuiu5Fnmp74RmEoSEhIqAxIH2vUwEwYOlnAztZyCAAIIIIAAAr4VIND0rSdXQwABBBBAAAEEEEAAgRYoUFJc7BJeZpjwMk1yNm1s0J2ccc4EGTJspAQG8tesBsFRGQEEEEAAgQYK8L+0DQSjOgIIIIAAAggggAACCLRsgZKSYlmXneUcdamb92zO8S681LCyc9fuJuzcIFWVlU6IffY/WPYauZ/zM28QQAABBBBAYPcJEGjuPluujAACCCCAAAIIIIAAAk0sUFpaUhNeZpkp4xk6bTxNNm5Y71WvAgICpUev3hIVHWt/Is1rr95REhAQIJs2bpDffv5OCgvypX/CQBm5zwFeXZNKCCCAAAIIILDrAgSau27IFRBAAAEEEEAAAQQQQKAZCJSVldYJL9NNeLlOqqurd9o73XG8R89t4WVMnGh42Tsy2oaX7r6sO5affNqZ7k5xDAEEEEAAAQR2swCB5m4G5vIIIIAAAggggAACCCDge4Hy8jJZvzbbbtSTlZEmWWYE5oZ1a70OL7v36FUz8tKGlzEmvIxh7UvfPyauiAACCCCAwG4RINDcLaxcFAEEEEAAAQQQQAABBHwlUF5ebsJK1/Ayw4SZWV6Hl92693ROGdfp472jYiQoKMhX3eM6CCCAAAIIILCHBQg09zA4zSGAAAIIIIAAAggggIBngYoKDS/X1oy8zNQ1L9NteFlVVeX5S9vO+Pn5iU4Fj4qumTKu4WVktIaXwTv9LhUQQAABBBBAoOUIEGi2nGdFTxFAAAEEEEAAAQQQaFUCFRUVsnF97fByXXameBtedu3WfVt4GeMMMYODCS9b1S8JN4MAAggggIAbAQJNNygcQgABBBBAAAEEEEAAAd8KVFZWmvBynd1lXEdd6o+Gl3rcm6LhZWSU2W08Rncc1xGYMRISEurNV6mDAAIIIIAAAq1MgECzlT1QbgcBBBBAAAEEEEAAgaYW0JBy08b1NdPGMzS8TJO1WRpeVnjVtS5du9UKL3XqeEgo4aVXeFRCAAEEEECgDQgQaLaBh8wtIoAAAggggAACCCCwuwR0erhreJltwstsE17qWpjelM6du5rRljUjL3UEZrTZdTw0LMybr1IHAQQQQAABBNqoAIFmG33w3DYCCCCAAAIIIIAAAg0V0PAyZ9OGmpGXZsp49rYf3YXcm9KxU2czXVzDSzNlXMPL2DgJCwv35qvUQQABBBBAAAEEnAIEmk4K3iCAAAIIIIAAAggggIBDoLq62oSXG+10cQ0uHetelpeVOarU+xrRsVNNeLltvcvo2D4SHt6u3u9wEgEEEEAAAQQQ8EaAQNMbJeoggAACCCCAAAIIINCKBTS83JyzyYaWOmXcEV6WlZZ6ddcdIjra8NJOHTcjMDW8bNeuvVffpRICCCCAAAIIINBQAQLNhopRHwEEEEAAAQQQQACBFi6wefMmybab9ThGXqZJaUmJV3fVvkPEDuFl+/YdvPoulRBAAAEEEEAAAV8I+DzQLDVTUN794FOZcO44X/SPayCAAAIIIIAAAggggMAuCGzZklM7vMxIk5KSYq+uqKMs7XqXuu6ljrw0a1/qaEwKAggggAACCCDQlAI+DTRnfTFH3n5vpixYnCxLU5bLJReNl/h+fWrd3ytvvCvJqSvk0fvvsMcLC4vklTffk3l//C1xsdFy2cXnSUx0ZK3v6If66qUuXylvzZgpaRmZst8+I+TyiedJYGDNrWmfZs2eKwEB/nL66OPlmCMP3eHaesBTvca267YRDiKAAAIIIIAAAgggsBsFcnO3mPDSTBnPMiMvdQSmeV9cXORVixpe9o6KMQGmhpdxNrzUdTApCCCAAAIIIIBAcxPwWaC5JHmZTJn6gDx0z20ifh9LtfnPzXfcLzPfecl5zwtN0HnPQ0/JyOFDnMfuuPcR+emX+TLpiotk4aJkGXf+ZTJ31ntmzZ3aux16qldmdlSccOlkiY2JkvHjTpPnX3pDMrPWmn7cKnO+/VEm3zJNzj/nDOkbFyNXXX+HTH/6ITni0AOd7eub+uo1pt1aF+cDAggggAACCCCAAAK7QWBrXq5zrUsNLvWnqKjQq5Z0cx4bXjp2HI+Okc6du3r1XSohgAACCCCAAAJNLeCzQPPn3+ZLdFSkHG7Cwq/mfi/Tplwns+d8JxUVFXa0ZFFRsUy+eZocedhBkpe31d533tZ8+fiz2TJzxss25Cw/o0KSRh4uc777SU49aZTTpr56OoLSz89P3n/jBVtfR2ZeP+VumTplsrz61vsyYfw4udO81/LrvD9lxvuf7BBoeqo3wgSvnvpXX7vh4WG2Pf5AAAEEEEAAAQQQQMAXAvlb83YILwsLC7y6dFhY+Pbw0gSYkWYEZpcu3bz6LpUQQAABBBBAAIHmKOCzQHPUUYfJA48+I7dOe9BODw8NDZGxp57ovOe7H3xSDthvpJ2CrtO7taSlZ9rXYYOT7GtQUKBERfaSNWkZ9rPjj/rqFRYVyfAhgxxVpY8ZiamjNrPXrZe0tEw5a+zo7ediY2SuCUvrFk/1GtuuTrPvEOYz2rrd5TMCCCCAAAIIIIBAMxdYsylD/kn/R6qrqxrc07LiUsnflCf5OeZHXzflSllxmVfXCQwOlPZdIqRDt47Soav5Ma9hEe2c310na2XdurUi65yHeIMAAl4IRHaKln37jhR/fz8vareOKpVV1dKGbrd1PDTuAoGdCAQG+reavMpnqZsGia9Nf1JeePlN+W3+X7L/EaPlrtuul5NPOMaO1Pzp13ny5advy/sffe7kzcjMFh3N6FjvUk9E9e4pBYW1p8rUVy8re51ERLR3XlMDUS35+QWStdac67B9x8VIc20NQF1LVVWVx3qNaVdHbmqpdm2E9wgggAACCCCAAAJtSmBr8VaZ/sMzkl9SMzPJ080HVgZKeHmYhJeFSbuycPMaLsFVQZ6q1zpe6VcpRUHFUhRcJIXB5jWoSEqDtgWfuaaq/qys9RU+IIBAIwVOHT5W9jaBZtuJM/k7bSN/VfgaAs1eoLXkVT4LNPWJ6dqUe48YJpdOull69ugudz/4hBx80L5yy50PyJljTpY//looy1eukVwz5fz7n36TsLBQs85PsZSUlkpoSIh96MUlpdLHjKR0LfXV27w5V3LMj6MUF5fYt7pmZnBwsGzZsv2ctqNrbboWf39/j/Ua027ctusXFFe4NsN7BBBAAAEEEEAAgTYkUFG5418XAisDtoWXJrg0IaYGmMGVwV6pbA8vtwWYJsgsDSw1a9d79XUqIYDALgqYwYpSXFq5i1dpeV/X+6YggEDrEaioqJKmyqsiwr37F7beavss0Hz4ieelfft2csH4M6R71y5y9+03yIiDjpM//1okXTp1lDnf/Gh/tpqRkwUFhXLX/Y/Lc08+YPupm/joNG0dLZmekSUJ8X1r9V9HVmpxV2/t+g2yaEmKs/7qNenSvVsX6WTa1NGe6WYUqKPouYT4fo6PzldP9RrbrvPCvEEAAQQQQAABBBBokwKl5l+yhxeFSnhBiLQrrxl5GdKA8LI4qKRm5OW2EZglhJdt8veIm0YAAQQQQAAB9wI+CzQ7RnSQdz/6TA4+YB/b0g9mBKYGlMOHDpRvv/jA2fob73won3xesxGQHoyO6i3T//OWTLvtOvm32aFcR1EmJsTb1/+8/q6cMOpISTKfPdULMSM7n33hVfn6mx8kKTFennr+Zdl7r2G2vWOPPkze/fBTGX/maTb0nDV7rtw39SZ77tP/fiXhZoTosWbtT0/1GtuubYA/EEAAAQQQQAABBNqEQLFZ0ig7O0OyM9PNTuPmJzNNtmzOkWjpvdP7r/Krqpk27jJ1vCTQzDhi5OVO7aiAAAIIIIAAAm1XwGeB5tlnnGLXyjzt7Il21/Ev5/4gt1x/pRkt2bVe3acfu1cmXHKtfPDxLNGNhJ54cJpZ97K95ObmyaNP/Vv694uTfn1ixVO9YUMGyjVXXGymud9iFl2vlrjYaHnuiZqRn9dcfrHMm/+37HvYSbYPuknRKSeOsu/fM+Frty6dbaBZX73GtFvvDXMSAQQQQAABBBBAoMUKlBQX1wovs7PSJWfTRq/up0qqREdeFpo1L3XdS13/Uj8TXnrFRyUEEEAAAQQQQMAp4GdCQJ+uirFs+Sq575H/kxefedgElKHOhup7U15eIStXr5G+JrgMMeteOsqt0x4yU9jHysDEAfaQp3p6Ms+sy7kxZ7Oduu74vuNVdytv1y5cupmp8I7y0y/z7OZFN157ueOQ3XW9bj092Zh2s3OKndflDQIIIIAAAggggEDLEygtKZG12Zl2xGWWGX2pIzA3bdzg1Y34B/hLUWCx5AfmOzfsIbz0io5KCDRLgdHDxsqVR17XLPu2Ozv1/LdPyucLP9qdTXBtBBDYQwIdQiPk/tOekAE9k/ZQi7WbiewaVvvALn7y2QhNRz/69Y2VKy+d4HWYqd8LCgq008od19BX3S08slePWsfd1XN8p2PHCNEfd0VHbdYt8/9aIOeeNabWYXf1tEJj2611cT4ggAACCCCAAAIINFuB0tISWZedZcLLminjGl5u3LDeq/4GBARIj16REhUd6/zJ98+XOz+/aae7nHvVAJUQQAABBBBAAAEEagn4PNAMDAyUA/YdWauRxnzQkZKTzFTy3VWuu/qS3XVprosAAggggAACCCDQjAXKyspMeKkjL2tGXerrxg3r7PJFO+u2v7//DuFlz95Rov8f2LUsX79900rX47xHAAEEEEAAAQQQ2HWB2v/Pa9evxxUQQAABBBBAAAEEEGg2AuXlJrxca0Zems167KY9JrzcsH6t1+Fl9569zKjLOOfIy16RGl4GNZv7oyMIIIAAAggggEBbFCDQbItPnXtGAAEEEEAAAQRaoUB5ebmsX5dtwsu0WuFlVVXVTu/Wz89PuvfQ8NJMG4/RqeNx0isy2iw9RHi5UzwqIIAAAggggAACe1iAQHMPg9McAggggAACCCCAwK4LVFRU2PDSMepSQ0wNM70NL7t17+kMLyNNeBkZpeHl9s0pd72HXAEBBBBAAAEEEEBgdwkQaO4uWa6LAAIIIIAAAggg4BOBysrKHcJLnUbubXjZtVsP55TxSDMCMzI6RoKDQ3zSNy6CAAIIIIAAAgggsOcFCDT3vDktIoAAAggggAACCHgQ0PBS17h0jrw0a17q7uOVlRUevlH7cNdu3e10cQ0udfq4hpchIaG1K/EJAQQQQAABBBBAoEULEGi26MdH5xFAAAEEEEAAgZYroCMsdXdxnS6uO43rj+4+rtPJvSldunbbFlrWbNqj4WVoaJg3X6UOAggggAACCCCAQAsWINBswQ+PriOAAAIIIIAAAi1FQMPLTRvX293GszJrNu3JztLwstyrW+jcuavdrMcx8lI37QkNI7z0Co9KCCCAAAIIIIBAKxMg0GxlD5TbQQABBBBAAAEEmlpAw8ucTRtqwsusdMnOMD/mVXch96Z06tRFIu1O4zXTxjW8DAsP9+ar1EEAAQQQQAABBBBoAwIEmm3gIXOLCCCAAAIIIIDA7hKorq52hpcaWmbZ8DJDyspKvWqyY8dOJrw0U8ajTHi5LcQMb9feq+9SCQEEEEAAAQQQQKBtChBots3nzl0jgAACCCCAAAINFtDwcnPORrvWpd20x4SXWSbELCv1LrzsENHRudt4lIaYZtOedu07NLgffAEBBBBAAAEEEECgbQsQaLbt58/dI4AAAggggAACHgU252zattu42bRnW3hZWlLisb7rifYdInYIL/UYBQEEEEAAAQQQQACBXRUg0NxVQb6PAAIIIIAAAgi0AoEtm3O2hZe623jNruMlxcVe3ZkGlZFRMdt2HI+VaDP6UkdjUhBAAAEEEEAAAQQQ2B0CBJq7Q5VrIoAAAggggAACzVggd8tml/BS171Mk+LiIq963M6sb+nYadzx2rFTZ6++SyUEEEAAAQQQQAABBHwhQKDpC0WugQACCCCAAAIINFOBrXm5NrDMytSRlzXhZVFRoVe9DQ9vZ8JLHXkZ5wwxO3Xu4tV3qYQAAggggAACCCCAwO4SINDcXbJcFwEEEEAAAQQQ2MMC+VvznKGlI7wsLCzwqhdhYeE2vIx07jYeJ527dPXqu1RCAAEEEEAAAQQQQGBPChBo7klt2kIAAQQQQAABBHwkoOFldlZGrQCzIH+rV1cPDQ2za17aKeMxsXYEZpeu3bz6LpUQQAABBBBAAAEEEGhqAQLNpn4CtI8AAgggsINAYWmhVFdX7XCcAwi0VYHCggJZl51lfjJlXVaWrM3KFG/Dy+CQEOnVO0p6RZmfyGjzEyWdu3YVPz+/WpwFJfm1PvvyQ3BgsAQHhvjyklwLAQQQQAABBBBAoA0LEGi24YfPrSOAAALNVWBO8hcy1/xQEGiLAn4VIv5F/hJQ5CcBhfrqL/7ltcNHTy7V/tVSGWZ+2lVJZXiVVIVXS1VIiWzyy5PFxUtFVppv6s8eLAH+gXLVkddLfI/EPdgqTSGAAAIIIIAAAgi0ZgECzdb8dLk3BBBAoIUKZG3JkOUbUlto7+k2At4LBFQFSHhZmLQrC5fw8jDzPlxCKoO9ukClX6UUB5VIYXCRFAWZn+BiKQksFXFknyYYFe9moHvVXmMrdQiNMCOuqxv7db6HAAIIIIAAAggggMAOAgSaO5BwAAEEEEAAAQQQ8L1AQJW/DSw1uLQBpgkyQyq9m4Zd5VdlQstiZ3CpIWat8NL33eWKCCCAAAIIIIAAAgg0WwECzWb7aOgYAggggAACCLRUAQ0vw3TkZbkZeWledeRlqLfhpVRJsRltWagBpo6+NO+LA0u2j7xsqSj0GwEEEEAAAQQQQAABHwkQaPoIkssggAACCCCAQNsU8NeRl9umi7fT8NKEmCEVwWbmt2Put2cXs8qlnTauwWWhCS516rhOI/fiq54vyhkEEEAAAQQQQAABBFq5AIFmK3/A3B4CCCCAAAII+E5ge3hZE1xqgPn/7d0HfBTFF8Dxl54QSELovYiKIF1UsAsKFgREAQFBQZAi1QIoIkpRQVFRERUbKoiKivoXRbD3rgiKCtJBICQhCaTnP2/CHpeQhJDcJeTyGz/H3e3tzu589+7cvHszE5IecozBy+zApQYwk00WZtbR456eawA1IYAAAggggAACCCDgAwIENH3gJNIEBBBAAAEEEPC8gH+mn4Rp5qXJuLSZl9ptvJDByyzJMpmWGrg0XcedbuMm8zLLj8lxPH+mqBEBBBBAAAEEEECgvAkQ0CxvZ5z2IoAAAggggMARAn4mTdKOdenWdTw0PbRQmZfZwcvkHBP2aLdxgpdHMLMAAQQQQAABBBBAAAGPCBDQ9AgjlSCAAAIIIIBAWRHQ4GVYWqhrpnHNwNTnhRnzUoOXyWaCHjvepWZe2ol7tNs4mZdl5fxznAgggAACCCCAAAJlX8DjAc2U1FR55bXlMqj/1WVfhxYggAACCCCAQJkWcIKXmn0ZbrqM6+Q92o288MHLFDvTuDNhj844TvCyTL8lOHgEEEAAAQQQQAABHxDwaEDz3RWr5OWlb8ivv/8h6/78W4Ze30+aNG5omV5cskxWf/KFhAQHS49uXaTT+edIcHCQJCUdkGdfXCrf/fCzNKhfV24cPEDq1a19BG1B663/e4O8tOQN2bx1m5x+WhsZPmSABAZmN02P6d33V0tAgL/07NZVOl9wzhF164L81ivqfvPcCQsRQAABBBBAwGsCmiQZaoKVzkzjGsTUzEt/89/RimZepgSmuGYa18ClZl9m+mcebVNeRwABBBBAAAEEEEAAgRIWOPoVfiEPaO0ff8mkqbOkf58rpVWLZubPgiy5bcpMu/XLS9+UmXPmyYknNJJzzjpDps54QJ5+frF9bcr02bJo8etyUadzJTk5Ra6+9kYb5My92/zWi42Ll0HDxsn6fzbIlVdcIm//b6VMuWeO3XzVx5/LuIl3Sa2a1eUME+gcNWGKfPL517mrloLWK8p+j9gBCxBAAAEEEEDAswImeBmWGipVkqKlfmwdafrfidJ6ewtptvskaRBXT6olVZFw05U8r2BmdrfxFNkXFitbI3fI+mr/yC+1f5e1NdfLpugtsrvSXkkMSSKY6dkzRm0IIIAAAggggAACCHhMwGMZml9+873UrVNbzjung6xc/ancNWm8vL/qE0lPT5dPv/haruvfWybdPMoe+NbtO+Std9+XAX2vlDfffl/eWLJQ2rY6VdKuSpembc+TVSaTs/tlF7saGb8/Id/1NIPSz89PXl20wK6vmZkTJt0tUyeNk+deelUG9bta7jSPtXz93Y+y5NW35HxzjO4lv/XamGPK7/gK2m+FCmHu1fMYAQQQQAABBIojoMFLM0GPnbTHdBsP127jJvsyr2Bl7t3YzMuAVNttXLMuk4LMuJfmnszL3FI8RwABBBBAAAEEEECg7Ah4LKB58YXnyqw5j8rku+61GZahoSHSq/ulVuKmG6+X6tWq2MdZWVnyy69rpU3L5rJ5yza7rGXzpvY+KChQ6tSuKZs2b7XPnX8KWi/pwAFpdWozZ1Vp2KCepKalyY5d/8nmzdukT69uh1+rX892e3ctOPQgv/WKul/tZh8a7LHk19yHy3MEEEDApwUyM7PMD1U+3UQaZwQ0u7JSSiVJ90+T2Arxh8el1G7j6SE5JuzRQGZhgpcKmxKgY16awKVrwp4DkkG38VJ/zwX4+5W7ayN/LgVL/X3HASDgSQG9NgkO9BN/831WXgrXZOXlTNPO8iTgS9dkHgtoaiDx+ScfkgULX5Rvvv9Jzji/m0y7fYJcfklnaXnqKfb9sWdvjEy8c5b8tvYPkzU51gQ0t4tmMzrjXepKdWrVkMSkpBzvp63bduS73vYduyQioqJrfQ2IaklISJTtO81rlSq5Xqtt6tYAqHvJzMzMd72i7FczN7WEBXuM1v1weYwAAgj4vEBGRqbNvPf5hpbjBlZLrCr14mq7JuapE58mcWHxhybsCZWArIBC6aQ4mZdmrEsbwDSBzAz/jEJty0olKxAQYGaWL2fXRoFm/HYKAgj4joD2CgwNCrBzM/hOqwpuCddkBfvwKgJlUcCXrsk8GnXTrtzt2rSUYaNvkxrVq8nd9861Y2PqREA6duWYW+60kwS9t2yRNGpYX3bviZEDBw5KckqKhIaE2PfCQTOOZkOTSelewsJC811v3744iTE3pxw8mGwfNjIB1mCz39jYw6/pfurXq+Osau/9zc/n+a1XlP02OFR/bGJqjv3wBAEEEECg8AKaEUDxDQGdqCfYZFyGZASZ+2CTfRkq1U1A032W8eDMIKmeVLXABqea4GWSCVwe0MzLQ13HMwIIXhaIdhy9mJqWKeXt2kjbTEEAAd8R0GuT/QfTfadBhWwJ12SFhGI1BMqIQGlek4WFeHZ4Ro8FNO+fO18qVgyXgf2ukmpVouXuO26WNh27yK+/rbNZkcPHTpZJE0bKdQN6uzJvNGNSy7btO22gU7Mlt2zdLic1aZTjrVDQejv/2y1r1v7pWv/fTVukWtVoiYqKtNmeW0x2p1P0tZOaNHaeuu41KzSv9Yq6X1fFPEAAAQQQQMDHBfyy/CQoI1BCTLAyOMMELtNN4NLeB5vHIRKUeeyXGqmmG7oGLt27jacTvPTxdxLNQwABBBBAAAEEEECg8AIe6wsTGVFJXnvzXdmwcZPd+2dffCMaoGzUsJ489+JS6XhGO+l45mny1z8bZf3fG0SDi01PamImEqolTz7zku1m/uC8J2225slmuWZTPv7UC7LxKOvp2J1a54cffSY62dDD8xdKu9Yt7THozOmvvL5ctKv7R59+Ke++v9pkkLawry03s6HrNlryW6+g4ytov7ZS/kEAAQQQQMAXBEyGpQYsw1MqSHRSlNTaX0Ma7KsnJ+05QU7deYq0MTOLt9jVTE7a20QaxtaTWgk1pcqBylIxNfyYgpmJwUnyT5WN8muttbKm9jrZUHWT7IrYLfvDEoRgpi+8kWgDAggggAACCCCAAAKeEzj2tIl89t33qivsrOY9+g6xGZgfrP5MJpqMzGpVq8gvv62V/WZMy48/+8q1dd3ateSLVW/KvAemy6ChY20wVCcSmnvvXWbcy4oSFxcvcx5+Qk5o3EAam+7p+a2n43OOGTHYdHOfKDrhUIP6deXxubPsfsYMHyzfff+ztD/3MvtcJym64tLs2dOXLntbqkZXlotMQLSg9YqyX1cjeYAAAggggMDxLmACloGZASarMjujMthkWIYcyrDMXhaco3v4sTYnUzIlLSBdUgNTzYQ9qfY+NC1Eog9WdlWlE/lsqLLJBC7LX1c+FwIPEEAAAQQQQAABBBBAoNACHgtoahfvt155Rv76e6PMmP2IPPXo/RIaGmoP5LdvV+V7QG1bnSo/ffmBbPh3kx1XU8fb1KL1XXN1D3HGpMxvPV13wuhhMmRgX9kTs892XddlWsLDK8ib5ph0tnJ9XNV0hXfKyBsG2smLjrZeUfbr7IN7BBBAAAEEjgeBgEwzXrTpEh5igpZ2PEvbPdwELnVsS/O4sDOI59WWLMmSNP+cAcsUE7zUMS+dexMRPaLsTtlrZjmvaGY5T5d9FeIkk5nIjzBiAQIIIIAAAggggAACCOQt4LGAplN940b1ZeSwQa5gprO8oPugoEDb/dx9HZ0tvHbN6jmW57Wes01kZIToLa+iWZu5y/c//Sr9+1yZY3Fe6+kKRd1vjsp5ggACCCCAgJcE/DP9DmdYmsl37HiWhybi0aBlYWcNz+vwNGCZbmYOdwUo3TItNeMyJTBF8gpY5lWX+7KkEDNGprlREEAAAQQQQAABBBBAAIFjFfB4QDMwMFDObN/2WI/jiPU1o3K06UrurTL+pqHeqpp6EUAAAQQQ8KiABiyDNFBpMyw10zLEZFo6GZYhEpgVUKz9ZfhluLIpNavSyazMfpwiWTpVOQUBBBBAAAEEEEAAAQQQOE4EPB7QPE7axWEggAACCCBQZgRyzhSuY1m6zRhuMiyDM4OK1ZZMv0zX+JXOOJY2WHkow5Lu3sXiZWMEEEAAAQQQQAABBBAoYQECmiUMzu4QQAABBMqhgM4UnhmYPY6lHcvyUIalzbTU4GVQsSfeSQ1MO9wt3G38yuSgFMkwXcYpCCCAAAIIIIAAAggggICvCBDQ9JUzSTsQQAABBEpPIMdM4Trxjlu38EMzhvsVZaDJQy3ScSxTA0zAMtf4lc7zNBPMpCCAAAIIIIAAAggggAAC5UWAgGZ5OdO0EwEEEECgWAJHzhQe5JqIp7RmCi9Wg9gYAQQQQAABBBBAAAEEECijAgQ0y+iJ47ARQAABBDwrcHimcO0C7oxjmX1f3JnC9UjT/dPtOJbuE+44GZZFnSncswLUhgACCCCAAAIIIIAAAgiUDQECmmXjPHGUCCCAAALFFNCJd+zM4K6Zwg91DTeT7miGZWBW8f6XmHum8NRD41gyU3gxTxybI4AAAggggAACCCCAAAK5BIr311uuyniKAAIIIIBAaQn46cQ7ZnKd7PErc2ZYBntipnAxM4WbMSydrEp7r89N4DI5MEWYKby0zjz7RQABBBBAAAEEEEAAgfImQECzvJ1x2osAAgiUVYFcM4U73cJD7Ezh2eNZFmfinUwTsHRNvHMoUOl0D2em8LL6puG4EUAAAQQQQAABBBBAwBcFCGj64lmlTQgggEAZFUhKTJDY2H1ycGeC1NhfTUJc3cOzu4UXJ2DJTOFl9E3BYSOAAAIIIIAAAggggAACuQQIaOYC4SkCCCCAgPcEkg8elNh9e80txgQuzU3vD932xeyR9PR0187rSm3X48I80IClnXjHZFemmG7g7l3CnUxL8StMTayDAAIIIIAAAggggAACCCBwPAsQ0Dyezw7HhgACCJQxgdTUVIlzBSo1cLnP3LIDmDEmYJmaklKsFjkzhTvjWOYc0zJFzLw/FAQQQAABBBBAAAEEEEAAAR8XIKDp4yeY5iGAAAKeFEhLS5P4uFibVZk7cKkBy4MHkoq1u5CQUKkcXUXiMuNkc9ImO+GOBi2zbxqwNANpUhBAAAEEEEAAAQQQQAABBMq1AAHNcn36aTwCCCCQUyAjI0P2749zdQOPc+sSrl3E98fH5dzgGJ8FB4dIZFRlG7TUwKW9Vc6+j65aTUJDw2yN8z9+SL787atjrJ3VEUAAAQQQQAABBBBAAAEEyoMAAc3ycJZpIwIIIHBIIDMzUxIT9mcHLF1dw2PECVzGxe2TrKyiZ0EGBgZKRGSugKVb4LJipQjOBQIIIIAAAggggAACCCCAAALFEiCgWSw+NkYAAQSOP4GkpMRDGZY5x7DULuL7YvaKBjWLWvz9/SUiIkqioqNNdmXVHBmWmm0ZERklfn4MZFlUX7ZDAAEEEEAAAQQQQAABBBA4ugABzaMbsQYCCCBwXAm4ZgqPPTzhzuGZwveamcLTiny8GowMr1hJok1wMsrJrHQLXEZVjhYNalIQQAABBBBAAAEEEEAAAQQQKC0BApqlJc9+EUAAgXwEUlKSXRPvOIFKO1O4CWDu27tH9PXilAoVwm2wUoOWmlUZdWgMSyfjUruNUxBAAAEEEEAAAQQQQAABBBA4XgX4q/V4PTMcFwII+KyAZlDGxcaaW4xr8h33wKV2GS9OcWYKdzIsswOXVU3gMlqiq1ST4ODg4lTPtggggAACCCCAAAIIIIAAAgiUqgABzVLlZ+cIIOCLAu4zhTuT7diA5aEAZnFnCg8yAckoO1N49hiWmmHpZFtqwDI0LHumcF+0pU0IIIAAAggggAACCCCAAAIIENDkPYAAAggco4DOAp6wP15i3cawdA9cFnem8ICAQIm0AUu3iXe0e/ihruHMFH6MJ4zVEUAAAQQQQAABBBBAAAEEfEqAgKZPnU4agwACnhJwZgrXQOU+c9MxLOPcApiahVnUopPqVIqIzJ4hXCfcMV3BnfErtZt4JDOFF5WW7RBAAAEEEEAAAQQQQAABBMqBAAHNcnCSaSICCBwpYGcKd41hudcELN1nDN8raWlFnylc96ZZlDrhzuHb4cAlM4UfeT5YggACCCCAAAIIIIAAAggggEBhBQhoFlaK9RBAoEwJpKamHMqodJt4xwlgxuyV5OSDxWpPmJkpXIOVOnbl4VnCDwcwAwODilU/GyOAAAIIIIAAAggggAACCCCAQN4CBDTzdmEpAggc5wI6U3h8XGwes4SbAKYJXCYlJhSrBTpTeFT04a7g7oHL6CpVzUzhIcWqn40RQAABBBBAAAEEEEAAAQQQQKBoAgQ0i+bGVggg4GWBzMxMiY+PlTj3ruBOhqUZ07LYM4UHBZnMyuyApXNvg5aadWkClmFhFbzcQqpHAAEEEEAAAQQQQAABBBBAAIGiCBDQLIoa2yCAQLEFdKbwxIT9hyfcyRW41OxLDWoWtQQEBByaKVy7gZvxK23X8GjTRTz7MTOFF1WW7RBAAAEEEEAAAQQQQAABBBAoXQECmoX0f/r5xTL0un6FXJvVEEBABXSmcJ0lPPbQTOFxrgzL7El4MjLSiwzl5+cnEWY2cDvpTmVn7Mqqrm7iEWYWcZ1NnIIAAggggAACCCCAAAIIIIAAAr4lQEDzKOfzz7/+kYXPL5F3VqySTz7/Wq69ppd07Xy+3Wr93xvkpSVvyOat2+T009rI8CEDJDAQ0qOQ8rIPCbjPFK6By302eLnXNRmPTsxTnHLETOFugcvIqMqiWZgUBBBAAAEEEEAAAQQQQAABBBAoXwJE3wo439olduS42+XySzpLi+ZN5YpLL5bxE6dJi3dekQoVwmTQsHFSv14d6Xd1D5n/9CLZtn2n3HfP5AJq5CUEypZAamrqoeCkZlTGmMdO0FKzLs1M4Qc9M1N4ZVeg0sm0zJ45PMiMc0lBAAEEEEAAAQQQQAABBBBAAAEE3AUIaLpr5Hq8c9du2bhpi/Ts1lXWrP1T+vTqJtWqRktaWpqsWPmNaJfXVxctsFtpZuaESXfL1EnjbLAzV1U8ReC4FEhPTzczhe87cqbwQ13DPT1TeO7Apc4kTkEAAQQQQAABBBBAAAEEEEAAAQSORYCAZgFatWvVkOannCSjb5kqCYmJEr8/QS487yy7xeLX3pJWpzZzbd2wQT1JNYHOHbv+kyaNG0pURTLLXDg88LjA9m3bJMFMqNOgYSMzG3dYvvXrpDpxZnKdmL17JSYmxtzMvX2cfR8fHy+aiVzUohmU0VWqSBUzK7i9VXV/XFXCw8OLWjXblWOBjIwsCQ+tIJVCI8qxAk1HwHcEAv0DJSQooNxdG4XsCxBtO99lvvNepiXlW8Bem4QFmiGP/MoNBNdk5eZU09ByIuBr12QENI/yxn3swRny0GNPy4oPP5b251wqPa+4RO6ffrvtXh4RUdG1dZ3aNe3jpKQD9r5CSOnQ7l6/Ufb/+KNIZtGDVK5G8eC4E0g3Aco31vws/8TssccWEhAoXU4+RaJM8Ccu+aDEm1vcwQOH7s3zlORiBSz9deKd0FBbf5QJnEaGhpnH5hZWwT6uGBKS0yguSURvGzZLvHlFb5TiCQTUriP1z+tgLp7LzwRHaemZ0iuktXSrVbl4eGyNAALHhYCfmaCtYkqglNa1UWkh1DJtfrzWQMky/++mIIBA2RcIDqkvocEBEhTINVnZP5u0AIHyKeBr12SlE3UrQ++dRg3ry7wHpsvAoWPl0i4XyuS77jNjaV4kYSbIE7MvztWSgweT7eMGZkxNLbEJqfa+pP9JMccU88iDkrmfUFJJ25fE/n6pXVv+adDAtasUM0v42+vWuJ4f6wM/k51ZwYyTWSklRSolJ2ffuz0ON6/l9xu0jp5ZvBE0j/Voy+f6la7qI3HtTjMztud3JnzPJdP8IJP62SeS8PpS32scLUKgHAr4R0RKxMPzS+3aqLTI/ROSJXH+41yTldYJYL8IeFhAr8n2n96RazIPu1IdAgiUnEBpX5NVrhTs0cYS0CyA8+vvfpSHH1soSxc9YWdT7ntVdzN25sfy/oefSC3THV3H1XTKv2asTR1fMyoq0i46mJrhvFSi95kVKkrUDSOKlZVXogfMzo5JYM8fJngZH3tM24SbbuGRZqxK180E453HEcEhEmAyZyjHr0CQGVYgxWQslrdSjJEQyhsV7UWgTAikmx8qSuvaqLSAtM0UBBDwHQG9NuGazHfOJy1BoLwKlOY1maf73xHQLOBdXLd2Lfn2h5/lnfc+tGtt+HezbNqyTTpdcLa0btFcHlvwnHz40WfS9OQm8vD8hdKudcsCaiuZl4Lq1hO9UXxTIPrVNNn0/ddHNK56jVpSrXpNqRx9eJZwfRxlZg9npvAjuFiAAAIIIIAAAggggAACCCCAAAJlWICAZgEnr17d2jJ8yLUy5tapNuPx86++lQ6nt5P+vXvajM0xIwbLsNET7WsN6teVx+fOKqA2XkKg+AJnn9dZfv/tJ0k13cKd0q59B+nZe4DzlHsEEEAAAQQQQAABBBBAAAEEEEDApwUIaB7l9E66eZSMGDpQhoy8RebMuEN0TE2nTBg9TIYM7Ct7YvbZmc2d5dwj4C2B6jVqysixE+XrLz6RpMQEOeHEptLOjOVDQQABBBBAAAEEEEAAAQQQQAABBMqLAAHNQpzpyIhKMnbk4BzBTGezyMgI0RsFgZISqFqthnTr2aekdsd+EEAAAQQQQAABBBBAAAEEEEAAgeNKgNlACnk6zul4RiHXZDUEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8JYAAU1vyVIvAggggAACCCCAAAIIIIAAAggggAACCHhcgICmx0mpEAEEEEAAAQQQQAABBBBAAAEEEEAAAQS8JUBA01uy1IsAAggggAACCCCAAAIIIIAAAggggAACHhcgoOlxUipEAAEEEEAAAQQQQAABBBBAAAEEEEAAAW8JEND0liz1IoAAAggggAACCCCAAAIIIIAAAggggIDHBQhoepyUChFAAAEEEEAAAQQQQAABBBBAAAEEEEDAWwKB3qqYehFAAAEEECiqQHjnrhLSolVRN2c7BBA4jgT8goLFPyLyODoiDgUBBBBAAAEEEECgrAsQ0CzrZ5DjRwABBHxQIFSDmQQ0ffDM0iQEEEAAAQQQQAABBBBAoPgCdDkvviE1IIAAAggggAACCCCAAAIIIIAAAggggEAJCZChWULQ7AYBBBBAAAEEEECg/AgEVI6WqnfNEsnKKj+NpqUI+LCAf3S0D7eOpiGAAAJlT4CAZtk7ZxwxAggggAACCCCAwHEuEFijpuiNggACCCCAAAIIIOB5Abqce96UGhFAAAEEEEAAAQQQQAABBBBAAAEEEEDASwJkaHoJlmoRQAABBBBAAAEEEEAAAQQQKMsCwSecKCFt2pXlJnDsCCBwSCAgIkr8goN9xsMvyxSfac1x1JAdMQePo6PhUBBAAAEEEEAAAQQQQAABBBBAAAEEECgdgdpVwjy6Y7qce5STyhBAAAEEEEAAAQQQQAABBBBAAAEEEEDAmwIENL2pS90IIIAAAggggAACCCCAAAIIIIAAAggg4FEBApoe5aQyBBBAAAEEEEAAAQQQQAABBBBAAAEEEPCmAAFNb+pSNwIIIIAAAggggAACCCCAAAIIIIAAAgh4VICApkc5qQwBBBBAAAEEEEAAAQQQQAABBBBAAAEEvClAQNObutSNAAIIIIAAAggggAACCCCAAAIIIIAAAh4VIKDpUU4qQwABBBBAAAEEEEAAAQQQQAABBBBAAAFvChDQ9KYudSOAAAIIIIAAAggggAACCCCAAAIIIICARwUIaHqUk8oQQAABBBBAAAEEEEAAAQQQQAABBBBAwJsCBDS9qUvdCCCAAAIIIIAAAggggAACCCCAAAIIIOBRAQKaHuWkMgQQQAABBBBAAAEEEEAAAQQQQAABBBDwpoBfline3AF1I4AAAggggAACCCCAAAIIIIAAAggggAACnhIgQ9NTktSDAAIIIIAAAggggAACCCCAAAIIIIAAAl4XIKDpdWJ2gAACCCCAAAIIIIAAAggggAACCCCAAAKeEiCg6SlJ6kEAAQQQQAABBBBAAAEEEEAAAQQQQAABrwsQ0PQ6MTtA4OgCHS68QhYtfr3AFT/69Es57ZxL7Trr/94g733wkX3svrygClp1uFi++vbHglbhNQQQQKDQAnfeM8d+J8XvT3Btk5GRIZf1GiTXXDfStczTD15e+qY8+sSznq6W+hBAoBwKfP3dj9Kw2Zny4Uef5Wj9owuek6Ztz5PtO3blWO6JJz/9+ruMvXWq9Bk4Qp5+frHs3hPjiWqpAwEEyrFASV+Tbd2+QybeOct+j90/d77o36YUBEpDgIBmaaizTwRyCWRmZsnR5udq3bK5PDZ3ht3y2+9/lieffck+dl+eq9ocT7MyM8XsJMcyniCAAAJFFbhl3HC76YPznnRVocHGv/7ZINOn3uZa5qkHa//4ywYyZ86ZJ39v+NdT1VIPAgiUY4EOp7eTHt26yt2zHpLk5GQrsW37Tnnsyedl7MghUqd2TY/q7Ny12/zgM0r0h6A+va6Qjz75Qq4fPt6j+6AyBBAofwIlfU3W7/qbZOOmzXL1lZeLBjeHjZ4omfq3JgWBEhYgoFnC4OwOgaMJ9Ow7RJ5/6VXp0r2/nHlBN3lk/jN2k383bZEFC1+Un80v+48/9YL9JWzcxGniLNeVUlPT5K6ZD8rZF/WUzpf3lVkPPHq03fE6AgggUCSByIhKMnXyOHnplTdEg40x+2LlgUcWyPAhA+WERg3kiYWL5PxLrrY3/R5zfrTRX/FvNBe+bc/qKr0HDpf3V31i9//Lb2vNH/YT7Heefn/lLvr6r7//IVGREblf4jkCCCBQZIE7J46VhKQkc221yNZxz30PScP6dWXodf0kNi5ebrp5irQ/9zK58pob5POvvnXt5533PhS9Zmt/7qUyfOxk0UCoFs3unPPwEzL0plvlnnsfcq2vD7778RepVjVann/yIbmy+yUy+ZbR9vuTH2lyMPEEAQSOUaAkr8k2bd4qW7ftkIWPPyBX9bhMRg+/XjZv2SYbNm4+xqNmdQSKL0BAs/iG1ICARwXWrf/HXgyPGzVELrrgXHnosadFf9HXX/N/M3/MNzaBgksuvkBq16whNwzq61quB7Foyeuy6uPP5c6J48xF8k3ywsuvyxdffefR46MyBBBAwBG44tKL5ewO7eXO6XNk1pxHJTq6stw0/DpZ8tpymW9+eLmu/9UycfwIee7FpfLMolfsZpOn3iuZWZny5Lz7pH3bVnLnPbMlJTVVEhOT5OPPvpI33l4hA/pe6ezCdd+/T09z8TzH7s+1kAcIIIBAMQWqmO+t22++SZ585kX7g/LK1Z/JrLsnSWBgoIyacIds2rxNZt41Uc7ueLoMHnGzbNm63X5n6Q/IXS+6QBY8cp/5QWef/bFZD2XHzv/s4/T0DOnS+fwcR3dam5Z2fWeh/kitP9I0qFfXWcQ9AgggUCSBkroma9ignvzz2xcSGhpihzPTLueazd7khIZFOm42QqA4AoHF2ZhtEUDAOwK33zraBC0vtBfCr77xjmz89/AvXvoLXOOG9W2m5qnNmoqOoemUtq1bSJtWp0rL5qfI5q3bpHq1KrLuz7/tRbizDvcIIICAJwVmmO7lF13RT376ZY0sfvYxCQkONlmby6T3ld3kugG97a7+/MuM+/v+avMjzDUyyCzTbp4VKoTK3pjsIMAetzHknpn/gDRp3NCTh0hdCCCAQIECvXt1k2VvvyfTZs21P6i0M9dTmoX01Tc/yLLFT4s+v7jTufLeyo/kQ/PDcY/Lu5gM9fH2XsfAbGSuy9b9+ZdrHyc1aSzPLZjreu480D/69abjDT/93GJ5YN4CGX/TMAkODnJW4R4BBBAoskBJXZPpDz7bzHfk0FG3StKBA9LV/HiTkpJigpyhRT52NkSgKAIENIuixjYIeFmgXp3adg/+/v7mj/4wmwlQmF2GmV/K7ph2v/yyZp1UqlRRMtLTC7MZ6yCAAAJFFqhfr479o/6fjZuk45mn2Xo2b9luf0xxsjJ1oa6nJflgslzR+zrZ9d8eqV2rhl3m/BNhfrAhmOlocI8AAiUl4OfnJzfdeL0M/GGs7T6p+91kulBq6dVvqL13/tm7d5+Eh1eQ39f+KVNNdvpBM/Zm5agoqVWzurOKtDOZmPmVfbFxMmjYODvh0Lw50+XSLhfmtyrLEUAAgWMSKMlrMs3UXPPdKvvDz4AbxpghhD6114PHdMCsjEAxBQhoFhOQzRHwhoC/v1+Rqp0xe56EmKDm5yvfsBkA3fsMLlI9bIQAAggci4B2O9KbUyIiKsqNQ/rLkIHX2EWaianDZuzZGyNTps+WcaOG2u7ocfH75azOPZzNRIMKFAQQQKA0BEJDsr/DnHv9gUXLyuWLXZMD6Q83VatEywfmD/zYT5gAABrKSURBVPeXX31TXnrmUWndopkdUuN/JgvdKfl9len34IAho219Ly2cJ5GMCeyQcY8AAh4S8PY1mQ5vNv/pRfKGyV7X5BsdjqOBGXdYs9opCJS0AGNolrQ4+0PAAwL6P4/95qLYmWTDqTImJlZandrMXijrwPVr/1jPjHMODvcIIFBiAmee3tb+wa8TauhtvJnAbPGrb8n+hERJS0uX88/pYLpYBsvCF5bYY8r9XVZiB8qOEEAAgXwETjbdxnV8y1deXy4BAf52qJ/e1w63M/vqOJq1alS3Q/zohGjL3nrPdCM/+gy/76/82I7BOWqYyVLfvcdO8KgTpSWbrpoUBBBAwBsCnr4m0yHOdKLGF15+TQ6aXjcrPvzYTgrU7dKLvHH41IlAgQIENAvk4UUESkbA/Zd898e6d81YcrKWnHv9H8nO/3ZLDzO7prOO3g+9vp+8sPg1aXF6J5k2c65casbhfHj+Qtm6fYdWZNflHwQQQMDTAs53k1PvxPEj7Y8p53bpJXrTLpk3jxlmZz+/rGsn8901WFp1uEi066X+qj9+0t3Z33VOBUe5z72/o6zOywgggEChBfzMj8ZatFv57BlTZOmyd6TlGRfJtUPH2rGBz+l4hvTqfqmkpqZJ6w4XywWX9LbDbehM5ToBmhb94Tmv8pOZBCgx6YC9fuvSvb84t7//+Tev1VmGAAIIHLNA7mskT1+TRVeOkuvNeOg6MVrz9hfavznHjBhsr/GO+WDZAIFiCviZrIisYtbB5gggUAoCSeaCWItecLsXXa7ZAs54df+ZDIDq1aq6gqLu6/IYAQQQ8JZAZmam/Gu6H+llRu5xMXUW4HAzPrB2t9SgQEJiouhMwxQEEEDgeBPQDKQNZnJGvZbSyRadot9tulxnKA8KCpR4M4SGZp6HhTEphmPEPQIIHB8C3rgm0+GE9G9OnQQtdxD1+Gg1R1EeBAholoezTBsRQAABBBBAAAEEEEAAAQQQQAABBBDwEYG8+0P4SONoBgIIIIAAAggggAACCCCAAAIIIIAAAgj4lgABTd86n7QGAQQQQAABBBBAAAEEEEAAAQQQQAABnxYgoOnTp5fGIYAAAggggAACCCC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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "charts.cashflow_chart(\n",
    "    summary['yearly_breakdown'],\n",
    "    initial_cost=summary.get('initial_costs', 0),\n",
    ").show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b4b7b39",
   "metadata": {},
   "source": [
    "## Waterfall: Benefits PV → Costs PV → NPV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "04012e0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "decreasing": {
          "marker": {
           "color": "#C62828"
          }
         },
         "increasing": {
          "marker": {
           "color": "#2E7D32"
          }
         },
         "measure": [
          "relative",
          "relative",
          "total"
         ],
         "text": [
          "$101.7M",
          "$-23.0M",
          "$78.7M"
         ],
         "textposition": "outside",
         "totals": {
          "marker": {
           "color": "#1565C0"
          }
         },
         "type": "waterfall",
         "x": [
          "Benefits PV",
          "Costs PV",
          "NPV"
         ],
         "y": [
          101696567.55071375,
          -22983075.77535687,
          78713491.77535687
         ]
        }
       ],
       "layout": {
        "font": {
         "color": "#1F2937",
         "family": "Helvetica, Arial, sans-serif"
        },
        "legend": {
         "font": {
          "color": "#1F2937",
          "family": "Helvetica, Arial, sans-serif"
         }
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
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              "#46039f"
             ],
             [
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              "#7201a8"
             ],
             [
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              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
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             ],
             [
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             ],
             [
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             ],
             [
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              "#fdca26"
             ],
             [
              1,
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             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
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              "#7201a8"
             ],
             [
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              "#9c179e"
             ],
             [
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             ],
             [
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             ],
             [
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             ],
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             ],
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             ],
             [
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            ],
            "type": "heatmap"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
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              "#0d0887"
             ],
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              "#46039f"
             ],
             [
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             ],
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             ],
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             ],
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             ],
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             ],
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            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "charts.waterfall([\n",
    "    ('Benefits PV', summary['total_benefits_pv']),\n",
    "    ('Costs PV', -summary['total_costs_pv']),\n",
    "    ('NPV', summary['npv']),\n",
    "]).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b48db610",
   "metadata": {},
   "source": [
    "## Scenario analysis\n",
    "\n",
    "Apply the default Palladium multipliers (see `core.calculations.SCENARIOS`):\n",
    "\n",
    "* **Conservative** — 80% adoption, +10pp risk on benefits / -10pp on costs\n",
    "* **Moderate** — base case (= the published Forrester study)\n",
    "* **Aggressive** — 115% adoption, -5pp risk on benefits / +5pp on costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1fb9aa20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_406e6\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_406e6_level0_col0\" class=\"col_heading level0 col0\" >Scenario</th>\n",
       "      <th id=\"T_406e6_level0_col1\" class=\"col_heading level0 col1\" >Benefits PV</th>\n",
       "      <th id=\"T_406e6_level0_col2\" class=\"col_heading level0 col2\" >Costs PV</th>\n",
       "      <th id=\"T_406e6_level0_col3\" class=\"col_heading level0 col3\" >NPV</th>\n",
       "      <th id=\"T_406e6_level0_col4\" class=\"col_heading level0 col4\" >ROI %</th>\n",
       "      <th id=\"T_406e6_level0_col5\" class=\"col_heading level0 col5\" >Payback (mo)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_406e6_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_406e6_row0_col0\" class=\"data row0 col0\" >conservative</td>\n",
       "      <td id=\"T_406e6_row0_col1\" class=\"data row0 col1\" >$71,672,868</td>\n",
       "      <td id=\"T_406e6_row0_col2\" class=\"data row0 col2\" >$17,437,916</td>\n",
       "      <td id=\"T_406e6_row0_col3\" class=\"data row0 col3\" >$54,234,951</td>\n",
       "      <td id=\"T_406e6_row0_col4\" class=\"data row0 col4\" >311%</td>\n",
       "      <td id=\"T_406e6_row0_col5\" class=\"data row0 col5\" >0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_406e6_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_406e6_row1_col0\" class=\"data row1 col0\" >moderate</td>\n",
       "      <td id=\"T_406e6_row1_col1\" class=\"data row1 col1\" >$101,696,568</td>\n",
       "      <td id=\"T_406e6_row1_col2\" class=\"data row1 col2\" >$22,983,076</td>\n",
       "      <td id=\"T_406e6_row1_col3\" class=\"data row1 col3\" >$78,713,492</td>\n",
       "      <td id=\"T_406e6_row1_col4\" class=\"data row1 col4\" >342%</td>\n",
       "      <td id=\"T_406e6_row1_col5\" class=\"data row1 col5\" >0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_406e6_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_406e6_row2_col0\" class=\"data row2 col0\" >aggressive</td>\n",
       "      <td id=\"T_406e6_row2_col1\" class=\"data row2 col1\" >$123,911,705</td>\n",
       "      <td id=\"T_406e6_row2_col2\" class=\"data row2 col2\" >$27,682,369</td>\n",
       "      <td id=\"T_406e6_row2_col3\" class=\"data row2 col3\" >$96,229,337</td>\n",
       "      <td id=\"T_406e6_row2_col4\" class=\"data row2 col4\" >348%</td>\n",
       "      <td id=\"T_406e6_row2_col5\" class=\"data row2 col5\" >0.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x10f9d5ee0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from core.calculations import apply_scenario\n",
    "import pandas as pd\n",
    "\n",
    "scenario_summaries = {}\n",
    "for name in ('conservative', 'moderate', 'aggressive'):\n",
    "    sb = apply_scenario(seed_data.BENEFITS, name, table='benefits')\n",
    "    sc = apply_scenario(seed_data.COSTS, name, table='costs')\n",
    "    scenario_summaries[name] = _compute_summary(sb, sc, config.DISCOUNT_RATE, config.ANALYSIS_YEARS)\n",
    "\n",
    "scen_df = pd.DataFrame([\n",
    "    {\n",
    "        'Scenario': k,\n",
    "        'Benefits PV': v['total_benefits_pv'],\n",
    "        'Costs PV':   v['total_costs_pv'],\n",
    "        'NPV':        v['npv'],\n",
    "        'ROI %':      v['roi_pct'],\n",
    "        'Payback (mo)': round(v['payback_months'], 1) if v['payback_months'] is not None else None,\n",
    "    }\n",
    "    for k, v in scenario_summaries.items()\n",
    "])\n",
    "scen_df.style.format({\n",
    "    'Benefits PV': '${:,.0f}', 'Costs PV': '${:,.0f}', 'NPV': '${:,.0f}', 'ROI %': '{:,.0f}%'\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0ff81b9d",
   "metadata": {},
   "outputs": [
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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "charts.scenario_comparison(scenario_summaries).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "270745bf",
   "metadata": {},
   "source": [
    "## Cross-check vs Athena (optional)\n",
    "\n",
    "When `TOOL_PUBLIC_ID` is set, ask Athena to recalculate the summary on\n",
    "the server side and confirm it matches our local computation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c8239dbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"padding:10px 14px;border-left:4px solid #0277bd;background:#e1f5fe;color:#1a1a1a;border-radius:4px;margin:6px 0;\">Set TOOL_PUBLIC_ID to compare Athena vs local.</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if config.TOOL_PUBLIC_ID:\n",
    "    from core.tei_client import TEIClient\n",
    "\n",
    "    client = TEIClient()\n",
    "    client.calculate(config.TOOL_PUBLIC_ID)\n",
    "    server_summary = client.get_summary(config.TOOL_PUBLIC_ID)\n",
    "    display.kpi_cards(server_summary, title='Athena server-side summary')\n",
    "else:\n",
    "    display.alert('Set TOOL_PUBLIC_ID to compare Athena vs local.', 'info')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "794848f5",
   "metadata": {},
   "source": [
    "Continue with [`04_export.ipynb`](04_export.ipynb) →"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ac0231c-5b57-4544-a464-148d2e74332c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}