palladium/studies/202602_AmazonConnect/notebooks/02_costs.ipynb

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    "# 02 — Costs Analysis\n",
    "\n",
    "**Study:** Forrester TEI™ Of Amazon Connect (Feb 2026)\n",
    "\n",
    "Three cost categories, three-year horizon, 10% discount rate.\n",
    "Target risk-adjusted PV = **$22,983,076**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "46446223",
   "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": "4ec64198",
   "metadata": {},
   "outputs": [],
   "source": [
    "import config\n",
    "import seed_data\n",
    "from core.calculations import npv, risk_adjust_cost\n",
    "from core.notebook_helpers import charts, display, tables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26f1d385",
   "metadata": {},
   "source": [
    "## Costs — nominal & risk-adjusted\n",
    "\n",
    "| Ref | Cost | Initial | Y1 | Y2 | Y3 | Risk Adj |\n",
    "|---|---|---|---|---|---|---|\n",
    "| Ft | Amazon Connect usage | — | $6.5M | $8.0M | $9.8M | ↑5% |\n",
    "| Gt | Implementation & migration | $1.09M | $188K | $188K | — | ↑10% |\n",
    "| Ht | Ongoing management | — | $256K | $187K | $187K | ↑15% |\n",
    "\n",
    "Note **costs are risk-adjusted *upward*** (higher risk → higher modelled cost)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9635f334",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_032a1\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_032a1_level0_col0\" class=\"col_heading level0 col0\" >field_key</th>\n",
       "      <th id=\"T_032a1_level0_col1\" class=\"col_heading level0 col1\" >label</th>\n",
       "      <th id=\"T_032a1_level0_col2\" class=\"col_heading level0 col2\" >category</th>\n",
       "      <th id=\"T_032a1_level0_col3\" class=\"col_heading level0 col3\" >risk_adjustment</th>\n",
       "      <th id=\"T_032a1_level0_col4\" class=\"col_heading level0 col4\" >Initial</th>\n",
       "      <th id=\"T_032a1_level0_col5\" class=\"col_heading level0 col5\" >Initial (RA)</th>\n",
       "      <th id=\"T_032a1_level0_col6\" class=\"col_heading level0 col6\" >Year 1</th>\n",
       "      <th id=\"T_032a1_level0_col7\" class=\"col_heading level0 col7\" >Year 1 (RA)</th>\n",
       "      <th id=\"T_032a1_level0_col8\" class=\"col_heading level0 col8\" >Year 2</th>\n",
       "      <th id=\"T_032a1_level0_col9\" class=\"col_heading level0 col9\" >Year 2 (RA)</th>\n",
       "      <th id=\"T_032a1_level0_col10\" class=\"col_heading level0 col10\" >Year 3</th>\n",
       "      <th id=\"T_032a1_level0_col11\" class=\"col_heading level0 col11\" >Year 3 (RA)</th>\n",
       "      <th id=\"T_032a1_level0_col12\" class=\"col_heading level0 col12\" >Total</th>\n",
       "      <th id=\"T_032a1_level0_col13\" class=\"col_heading level0 col13\" >Total (RA)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_032a1_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_032a1_row0_col0\" class=\"data row0 col0\" >amazon_connect_usage</td>\n",
       "      <td id=\"T_032a1_row0_col1\" class=\"data row0 col1\" >Amazon Connect usage cost</td>\n",
       "      <td id=\"T_032a1_row0_col2\" class=\"data row0 col2\" >Subscription</td>\n",
       "      <td id=\"T_032a1_row0_col3\" class=\"data row0 col3\" >0.050000</td>\n",
       "      <td id=\"T_032a1_row0_col4\" class=\"data row0 col4\" >$0</td>\n",
       "      <td id=\"T_032a1_row0_col5\" class=\"data row0 col5\" >$0</td>\n",
       "      <td id=\"T_032a1_row0_col6\" class=\"data row0 col6\" >$6,456,448</td>\n",
       "      <td id=\"T_032a1_row0_col7\" class=\"data row0 col7\" >$6,779,270</td>\n",
       "      <td id=\"T_032a1_row0_col8\" class=\"data row0 col8\" >$7,951,164</td>\n",
       "      <td id=\"T_032a1_row0_col9\" class=\"data row0 col9\" >$8,348,722</td>\n",
       "      <td id=\"T_032a1_row0_col10\" class=\"data row0 col10\" >$9,832,961</td>\n",
       "      <td id=\"T_032a1_row0_col11\" class=\"data row0 col11\" >$10,324,609</td>\n",
       "      <td id=\"T_032a1_row0_col12\" class=\"data row0 col12\" >$24,240,573</td>\n",
       "      <td id=\"T_032a1_row0_col13\" class=\"data row0 col13\" >$25,452,602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_032a1_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_032a1_row1_col0\" class=\"data row1 col0\" >implementation_migration</td>\n",
       "      <td id=\"T_032a1_row1_col1\" class=\"data row1 col1\" >Implementation and migration cost</td>\n",
       "      <td id=\"T_032a1_row1_col2\" class=\"data row1 col2\" >Implementation</td>\n",
       "      <td id=\"T_032a1_row1_col3\" class=\"data row1 col3\" >0.100000</td>\n",
       "      <td id=\"T_032a1_row1_col4\" class=\"data row1 col4\" >$1,087,500</td>\n",
       "      <td id=\"T_032a1_row1_col5\" class=\"data row1 col5\" >$1,196,250</td>\n",
       "      <td id=\"T_032a1_row1_col6\" class=\"data row1 col6\" >$188,333</td>\n",
       "      <td id=\"T_032a1_row1_col7\" class=\"data row1 col7\" >$207,166</td>\n",
       "      <td id=\"T_032a1_row1_col8\" class=\"data row1 col8\" >$188,333</td>\n",
       "      <td id=\"T_032a1_row1_col9\" class=\"data row1 col9\" >$207,166</td>\n",
       "      <td id=\"T_032a1_row1_col10\" class=\"data row1 col10\" >$0</td>\n",
       "      <td id=\"T_032a1_row1_col11\" class=\"data row1 col11\" >$0</td>\n",
       "      <td id=\"T_032a1_row1_col12\" class=\"data row1 col12\" >$1,464,166</td>\n",
       "      <td id=\"T_032a1_row1_col13\" class=\"data row1 col13\" >$1,610,583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_032a1_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_032a1_row2_col0\" class=\"data row2 col0\" >ongoing_management</td>\n",
       "      <td id=\"T_032a1_row2_col1\" class=\"data row2 col1\" >Ongoing management</td>\n",
       "      <td id=\"T_032a1_row2_col2\" class=\"data row2 col2\" >Operations</td>\n",
       "      <td id=\"T_032a1_row2_col3\" class=\"data row2 col3\" >0.150000</td>\n",
       "      <td id=\"T_032a1_row2_col4\" class=\"data row2 col4\" >$0</td>\n",
       "      <td id=\"T_032a1_row2_col5\" class=\"data row2 col5\" >$0</td>\n",
       "      <td id=\"T_032a1_row2_col6\" class=\"data row2 col6\" >$256,200</td>\n",
       "      <td id=\"T_032a1_row2_col7\" class=\"data row2 col7\" >$294,630</td>\n",
       "      <td id=\"T_032a1_row2_col8\" class=\"data row2 col8\" >$187,200</td>\n",
       "      <td id=\"T_032a1_row2_col9\" class=\"data row2 col9\" >$215,280</td>\n",
       "      <td id=\"T_032a1_row2_col10\" class=\"data row2 col10\" >$187,200</td>\n",
       "      <td id=\"T_032a1_row2_col11\" class=\"data row2 col11\" >$215,280</td>\n",
       "      <td id=\"T_032a1_row2_col12\" class=\"data row2 col12\" >$630,600</td>\n",
       "      <td id=\"T_032a1_row2_col13\" class=\"data row2 col13\" >$725,190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = tables.costs_table(seed_data.COSTS)\n",
    "df.style.format({c: '${:,.0f}' for c in df.columns if c not in ('field_key','label','category','risk_adjustment')})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0667d1da",
   "metadata": {},
   "source": [
    "## Local validation\n",
    "\n",
    "Reproduce the **$22,983,076** Costs PV from the PDF Cash Flow Analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3e35a794",
   "metadata": {},
   "outputs": [
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       "<table id=\"T_dc982\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_dc982_level0_col0\" class=\"col_heading level0 col0\" >Cost</th>\n",
       "      <th id=\"T_dc982_level0_col1\" class=\"col_heading level0 col1\" >Initial (RA)</th>\n",
       "      <th id=\"T_dc982_level0_col2\" class=\"col_heading level0 col2\" >Y1 (RA)</th>\n",
       "      <th id=\"T_dc982_level0_col3\" class=\"col_heading level0 col3\" >Y2 (RA)</th>\n",
       "      <th id=\"T_dc982_level0_col4\" class=\"col_heading level0 col4\" >Y3 (RA)</th>\n",
       "      <th id=\"T_dc982_level0_col5\" class=\"col_heading level0 col5\" >PV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_dc982_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_dc982_row0_col0\" class=\"data row0 col0\" >Amazon Connect usage cost</td>\n",
       "      <td id=\"T_dc982_row0_col1\" class=\"data row0 col1\" >$0</td>\n",
       "      <td id=\"T_dc982_row0_col2\" class=\"data row0 col2\" >$6,779,270</td>\n",
       "      <td id=\"T_dc982_row0_col3\" class=\"data row0 col3\" >$8,348,722</td>\n",
       "      <td id=\"T_dc982_row0_col4\" class=\"data row0 col4\" >$10,324,609</td>\n",
       "      <td id=\"T_dc982_row0_col5\" class=\"data row0 col5\" >$20,819,775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dc982_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_dc982_row1_col0\" class=\"data row1 col0\" >Implementation and migration cost</td>\n",
       "      <td id=\"T_dc982_row1_col1\" class=\"data row1 col1\" >$1,196,250</td>\n",
       "      <td id=\"T_dc982_row1_col2\" class=\"data row1 col2\" >$207,166</td>\n",
       "      <td id=\"T_dc982_row1_col3\" class=\"data row1 col3\" >$207,166</td>\n",
       "      <td id=\"T_dc982_row1_col4\" class=\"data row1 col4\" >$0</td>\n",
       "      <td id=\"T_dc982_row1_col5\" class=\"data row1 col5\" >$1,555,795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dc982_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_dc982_row2_col0\" class=\"data row2 col0\" >Ongoing management</td>\n",
       "      <td id=\"T_dc982_row2_col1\" class=\"data row2 col1\" >$0</td>\n",
       "      <td id=\"T_dc982_row2_col2\" class=\"data row2 col2\" >$294,630</td>\n",
       "      <td id=\"T_dc982_row2_col3\" class=\"data row2 col3\" >$215,280</td>\n",
       "      <td id=\"T_dc982_row2_col4\" class=\"data row2 col4\" >$215,280</td>\n",
       "      <td id=\"T_dc982_row2_col5\" class=\"data row2 col5\" >$607,506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dc982_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_dc982_row3_col0\" class=\"data row3 col0\" >TOTAL</td>\n",
       "      <td id=\"T_dc982_row3_col1\" class=\"data row3 col1\" >$1,196,250</td>\n",
       "      <td id=\"T_dc982_row3_col2\" class=\"data row3 col2\" >$7,281,067</td>\n",
       "      <td id=\"T_dc982_row3_col3\" class=\"data row3 col3\" >$8,771,168</td>\n",
       "      <td id=\"T_dc982_row3_col4\" class=\"data row3 col4\" >$10,539,889</td>\n",
       "      <td id=\"T_dc982_row3_col5\" class=\"data row3 col5\" >$22,983,076</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "rows = []\n",
    "for c in seed_data.COSTS:\n",
    "    rf = c['risk_adjustment']\n",
    "    init_ra = risk_adjust_cost(c.get('initial') or 0, rf)\n",
    "    yr = [c['year_values'][str(y)] for y in (1, 2, 3)]\n",
    "    yr_ra = [risk_adjust_cost(v, rf) for v in yr]\n",
    "    pv = npv(yr_ra, config.DISCOUNT_RATE, initial=init_ra)\n",
    "    rows.append({\n",
    "        'Cost': c['label'],\n",
    "        'Initial (RA)': init_ra,\n",
    "        'Y1 (RA)': yr_ra[0],\n",
    "        'Y2 (RA)': yr_ra[1],\n",
    "        'Y3 (RA)': yr_ra[2],\n",
    "        'PV': pv,\n",
    "    })\n",
    "df_check = pd.DataFrame(rows)\n",
    "totals = df_check.drop(columns='Cost').sum()\n",
    "df_check.loc[len(df_check)] = ['TOTAL'] + totals.tolist()\n",
    "df_check.style.format({c: '${:,.0f}' for c in df_check.columns if c != 'Cost'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4109784e",
   "metadata": {},
   "outputs": [
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       "<div style=\"padding:10px 14px;border-left:4px solid #2e7d32;background:#e8f5e9;color:#1a1a1a;border-radius:4px;margin:6px 0;\">Computed Costs PV: <b>$22,983,076</b><br>Forrester target:  <b>$22,983,076</b><br>Δ = $-0</div>"
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   "source": [
    "expected_pv = 22_983_076\n",
    "computed_pv = df_check.iloc[-1]['PV']\n",
    "delta = computed_pv - expected_pv\n",
    "kind = 'success' if abs(delta) < 1_000 else 'warning'\n",
    "display.alert(\n",
    "    f'Computed Costs PV: <b>${computed_pv:,.0f}</b><br>'\n",
    "    f'Forrester target:  <b>${expected_pv:,.0f}</b><br>'\n",
    "    f'Δ = ${delta:,.0f}',\n",
    "    kind,\n",
    ")"
   ]
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   "cell_type": "markdown",
   "id": "dd1b3c04",
   "metadata": {},
   "source": [
    "## Cost mix\n",
    "\n",
    "Most of the three-year cost (~90%) is Amazon Connect *usage* (Ft) —\n",
    "consistent with the PDF's framing that consumption-based pricing dominates,\n",
    "with implementation a one-time investment."
   ]
  },
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   "cell_type": "code",
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s3yPJKvR7p99DFZxW0gchTz50u7Wb4N3bdyIpvmpJ1fnC6m5uRYHWgE+aLLdWqyF/v1tW/pS8Bzp+17YC+f1JzpiuKlPcNHdMHrwxkQAJkAAJkAAJZC0CQWv5LCaRK1X8qAj1J6lbqt7Mq2XHPVW9Jv6YWlI1aZTT0eOnmzlhf874z8y7VJfVbre0Ma6i7uUD3X9K5tdZLr5aVkWoutCO+nW6EX3vvvSIs0p1TXVPuo6pptdlCQl9uaejx085D+l8vdc++hZzxSKk4sI1OYSHe1Lr6T9ibVROX37QzywFY+U5JkJNBaBaYF3dBa3zru/qYqupmsypdU06P1bF8IHD8ef1nLJVd9zVYq0sKVZsdZtVEaeuxspE2123eaepRvMmlQrmj3dNjom9suyOpzIacVRFvC7RkVbJW188XVcVQuqWqp/Veu0f9dglvbb+5tMK1GVbhbtr0jbUmq5JH7T0f7y7cVNWq60utWOlQNoJ9HPmafxWu/6+6zg0cI8m3db0xQfPJWnF1jmtbWRJHP2c39LjFeMKW7t6JXS96XrUqXG1qcf1j6/vRFJ81eqpSZetUXd+TaHyu1WkUH5jybcCIwXy3TKVpOBPoON3b8rf35/kjkkt0YUL5sepM+fcm+Y+CZAACZAACZBAkBMIWvGp8zjVQqQBhNSqV1hu5nwlnQ+lKUdE4nU01QqoSS0hmnRe1qTRH5jgK2ol0gBD+lJr4PefvoRrKpU1+VLrj1q2XpAoj7qu5L9i9bO98JDPqtVKqEkjeHpaUzKvyzywx18cbALq6Lw2dReuXL6MKXNLz1c8tqE35Jr2S4CinXsPJriRP3P2gjmnlrKkkgpqTWrVdU/KWy3KVuRUtYJpWr1xO3JJ1Fl9SKDrnkaePmvEpwYFUsuwui7qMjVJJdegPL7yWmJcoyWnVfK3L9q+dV3VBVvXp/WU6sn4/c2n5XX5Cp2/55rc5wTqvFtN7kt/BNJOoJ8z1/4kd1s/+/MmDjPFB331i/msvPbRdxj7xWuJXLPd2xj8Rh+MEffvP6YtwKZte8xL3cTVEqzWX9fk6zuRFF9dU1WTWk715Z70fJ+etyGQ75Z7He771ufa/bjrfiDjdy2n29bnIqnfn5SMSSMDM5EACZAACZAACWQ9AomVQRCNUa0JKj5H/zZdLGV3eey53oip66LOK9Sk+d2T5XqnYtZKGhREb9C0vEaW1HmUU2cvxUSZE/bik/da2Uzdzp0UbKgVV2+41LKY1M1jhXLxETvr165iggF5a1ajX27cusfMJ3Vd91Dzq/XFU+oqQY/URfDl975Bn5eG4OcvBoprcnx7louzWjSSSleVKWGyHDxyPMH8O70WR6W8Wj8tt121olaSYEFq+VShpPNElb+63+l8zFli3VIraO3qlVPF8mz1XcWWCtojmcS9T8esSfv0wJ0drG4mereEfVL5tKB+R5Kb/O1Pcj5nye2Tt3LP9+6OHRKVWR8WvfD21/hC3MXVVd5bUouvzlvWl86BVFdvXXZGI+M+Ja63VmAvLe/rO+GL71qZW62/N/owyz2adnRMjIjRKWaetIrPQL5b3sZkfaetB23e8unxQMav31nXZP0eJPX7k9wx6YMpnQuv87uZSIAESIAESIAEshYB73dnQTBOvUlUy9Ko8TNkPufqRD1W18+HJPjMgmXrxBKSy1gsNdiOzsGykgao+WPqAiOELJc7tbTpzY8mFUjqgvpgt5vM/s698S6M1jxGT1FeTcYA/+jcMb0JriZiwZpz6q2K+nXirX9f/PCnc06qlVfrmCeL2GtSUaDJsuyaHfmj89DcrWHWOV2DVOe26pItkeL29tgLg01AJD2vLrN6Q6kM1V3QStrmrstcrGN1a8a7L6o11zVNlcifmr9ezWtcD0Otn+puqpbfDq0bmnPKXpde0Lm4OtdRl7pJzaT1q/VMl6HJDEkt4CpmNmzdDf0MuicrQqq/+dzLB7rvbzvJ+Zy590XnZP/v09F4a/Aosz6v+/mk9lVo6sMifYihEYwHf/Wr1yIa4Es/Z1ZSN1Rd17Rm1YpmHrn7XGhf3wmrDk/vGqBM00MyP1oFputLH5aphVsjcG/evjeg75antvSY5QWxSZaH0vm6VtKo4K6C1N/xe/uN8/f3J5DfC6uv+m79puqDJyYSIAESIAESIIGsRSCoLZ/q/vpEjy5QEabLpbRsUgd1Zc6WPjlXN01ddkBdaXvI2pyadB7hU7J25iP9P8aj99+MfBI19S+Z06mC8vZO1zvXANSgPK+8P1IWhG8uVosKIpaiMPmfRaYOK/ptDblR1TRk5G/Gkhcj7eiT+jbN65njvv5MlmVE1NVPLQo671Atq3rDqy6qA599wFdRc+4Gma+mc/bUytP1oYFimWlpxLWOeY6IcL2Jbt2snlkvT+dOaZTYZ2SdPz2ua3ZqBFmd6+grqUVIA37oWoi6fMiPw1816z9qdE69se8pwZH0hl3XQJ29YJVT6Fp1qmhUi4+6LPZ/6wtjoT1w6Lgs5zHFWPb6SpRV16RzOXUtU00dWsaLT7Mt9agw1+Qp2JA5kYI/jUTQ6vXQBxKe5gOnoOpkFVWXzweees8E0VG+tapWlnnNkSZ6r0ZzHffVG2a+rb/5ktUJl0L+tFNNHs4k93NmNaWCe7xE81UhqA8+kpPUkq0Wz3ueeNt8bivL8kLW2rSu9emDJV33Ul24NUpvSVmCRAWgfp/Uuq5roHpK3r4TnvLqd1uD7eSS+Ys6v9RT6tyuifmd+lvWDNaHDv5+tzzVpcd0KoL+/mnk6D4vDzHzV9dv3oUFS9clKOLv+L39xvn7+6ONJmdMs/+Lf/CiAc+YSIAESIAESIAEshaBoBafeil0uY4Gdaoal7lFKzYkuNHSuYl6A9/kssVMb5p0aZT3hv5k8mt5dXXVeV4DZNkQK7WWdS31ZtQSQ3pcb4r7PX6X081Vo96q5XWizBnTSKKaXnn6PvPu7Y/lZqrRZvWlSW9O1Zqo1pUnHuziFMBWXjG9eqxuqCx/osutqDDT5TCspMtMtJOlCjTpzejgN/tgwNtfGbdCdS3MLXMq1Yo7fvIcp9ur5rVc9pztyjEVAWrVUgvOUyLuRw19GQ/eeaNYCo8bNjoHVpMux6JBiBZLNF2rvL6P+Li/BDv6DroYvb40qWvzhwMfT+CKq8ctYanRh6uI262VNACOBmc5p9ZSt/me3uaFWcRCQpI27Ou1VvGpDxeqXn3lM2C1b71bl8Ean3Xceve3L87yVoVWBZff1SL802ev4oPPfpb5iP+Zl57SBxPtrq8v7srxwZH8zedWvcddqyvOvrnk8qedQD5nzjasRi+3pWs6atK5rq5zli+fTvSm9TjrcjmrLqGfvNXXPDB5Z8iPxtvBymd9xtXNu408nFGPCOt7qFXoZ02XYbLcda38VnnN4+k7ocfd0/K1W4z3hEa29RZcSdf9/HTEb2b92gF97vH7u2W1ZXOxblrHdIkeFdYqpPWVJ3cusyTSl6P+Mr91ms/f8fv6jfPn90fb8vf3QvNaafLMxeYBVQuJ8M1EAiRAAiRAAiSQtQiEyPzCxCFPg3SM6kKrrrZqhdQF1b0FIVKXNHUb1blzah1RAegpaQTPQ0dPmOAlGpTFWuvSNa/i07pUnFpub67n03pb29c1E9VFVpe7sFzlXNtVlzvLLbbiVaW93gy7lklqO3690aMoL3M7XefHeSp3VpbE2COMlI8uZ5GZklqo7pF1XjWC598/fuCce5cZ+qifTw38pO6vOkfWmzu2v/lSOqak2knJ50wt6SvWbsW0nz82831T2ld/yuvvxaEjJyWAziXD19vvhT91pWYef75b+r1vcdtTyCOfjVnjP03QvI5L3XlFnhs3ZG+fG3/H7+s3zp/fH+2cP2PSfDPmLhNPiS/xiKzt21/m8TKRAAmQAAmQAAlkLQJZSnxmrUvD0aQXgQ3ilqwCVIPCDH/3mfRqlu1cJqCittktfdFMLF1ffdiPXLwQUE4677eSRKxW1/lPRow3Uw2+/qi/lxLBdVgfUnXpNdBY+SeNes88dAmuEbC3JEACJEACJEACSREIerfbpAbI8ySQFAGd43ff7e2MC/Pw7ybgmUfuTKoIz6cigVUS5VjnIPuK8JuKzQVtVbp0Sd9Xhjr7ry616vqfFVK0eGc8KfPxNRiSPoBQaz8TCZAACZAACZBA1iNAy2fWu6YcUTIIaATeSTMXmpLdbm5t5ssmoxoWSQYBdf/UtSMLFciXjNLZp4gKNA1OpEsVFS1UwAQVK3V5DnCwU9h74AgWLt9g1prVaNtMJEACJEACJEACWZMAxWfWvK4cFQmQAAmQAAmQAAmQAAmQAAlkKgJJhwPNVN1lZ0iABEiABEiABEiABEiABEiABIKRAMVnMF419pkESIAESIAESIAESIAESIAEgowAxWeQXTB2lwRIgARIgARIgARIgARIgASCkQDFZzBeNfaZBEiABEiABEiABEiABEiABIKMAMVnkF0wdpcESIAESIAESIAESIAESIAEgpEAxWcwXjX2mQRIgARIgARIgARIgARIgASCjADFZ5BdMHaXBEiABEiABEiABEiABEiABIKRAMVnMF419pkESIAESIAESIAESIAESIAEgowAxWeQXTB2lwRIgARIgARIgARIgARIgASCkQDFZzBeNfaZBEiABEiABEiABEiABEiABIKMAMVnkF0wdpcESIAESIAESIAESIAESIAEgpEAxWcwXjX2mQRIgARIgARIgARIgARIgASCjADFZ5BdMHaXBEiABEiABEiABEiABEiABIKRAMVnMF419pkESIAESIAESIAESIAESIAEgowAxWeQXTB2lwRIgARIgARIgARIgARIgASCkQDFZzBeNfaZBEiABEiABEiABEiABEiABIKMAMVnkF0wdpcESIAESIAESIAESIAESIAEgpEAxWcwXjX2mQRIgARIgARIgARIgARIgASCjADFZ5BdMHaXBEiABEiABEiABEiABEiABIKRAMVnMF419pkESIAESIAESIAESIAESIAEgowAxWeQXTB2lwRIgARIgARIgARIgARIgASCkQDFZzBeNfaZBEiABEiABEiABEiABEiABIKMAMVnkF0wdpcESIAESIAESIAESIAESIAEgpEAxWcwXjX2mQRIgARIgARIgARIgARIgASCjADFZ5BdMHaXBEiABEiABEiABEiABEiABIKRQHgwdpp9JgESIIG0IuBwOOA4cwr2E8dgP3kUjlMnYD93Bo4L5+A4f/bK+/lzQGwMHHGxQFyc8x2yP6D0vwiXX9cwebxn3sPi3yPkWJ7cIcibF8ibJ+F2oQIhKFIY8gpBgfxAaGhIWg2R9ZIACZAACZAACZBAhhCg+MwQ7GyUBEggIwkYgXniKGwHdsN2cC/sB/bAduQAHCo4Tx0zYjIl/RP9iljRpPIPiHavSU46k+u286ARrYULAYVFiBYvCpQuGYIypUJQWl7FilCYXiHFLRIgARIgARIggWAiQPEZTFeLfSUBEgiYgFombXt3wrZjE2w7N8dvi+BETCJVGHDdaVXAZgdOnNKXA9t3aitXRKpaUkuXhBGjlcqHoGKFEJQvG4IcOWgpTavrwXpJgARIgARIgARSh0CIWACu3NWkTp2shQRIgAQyjID91HHEbViFuG0bLovNHSm2ZAY6mBdKzwm0SIryh4p7b9nSQOUKoagkYrTqNWIxLUYxmiKoLEwCJEACJEACJJDqBCg+Ux0pKyQBEkhPAvYzkYjbKGJTBeeGlbCL+2xGp/QWn57Gq/NHq18bgmpVQs17oYIUo5448RgJkAAJkAAJkED6EaD4TD/WbIkESCAVCKizhrrPxq74T14LYd9n/FJToebUqyIziE/30ZQqAdSpGYp6tUNwTaUQBjRyB8R9EiABEiABEiCBNCdA8ZnmiNkACZBASgk4ZH5m3PoV8YJz5UI4TsuEyEycMqP4dMWVT6Lt1qkZgnq1QlGzWghy5qRV1JUPt0mABEiABEiABNKGAMVn2nBlrSRAAikk4LDbELduBWL++wexy+YBUZdSWGP6Fc/s4tOVREQEUFeEaNNGoahVPQThYRSirny4TQIkQAIkQAIkkHoEKD5TjyVrIgESSAUCcRKVNmbBTMQumi3rbUamQo3pX0UwiU9XOrr2aKPrQtGkYbxrbkgIhagrH26TAAmknMD02TbExPhfT8d2oYzm7T8u5iSBTE+AS61k+kvEDpJA1idwKi4Ko09uxbzjOzHq7U+z/oAz6QgvXATmLrTLCygm64u2bh6K65uGIn8+itBMesnYLRIIOgLTZ9tx/oL/3b6hpYpP//MzJwmQQOYmQPGZua8Pe0cCWZrAovNH8PXxjfgtcieiHDYz1lWNGqL+8hVZetzBMLgTJ4EJk+34c6odDeuFoHWLUFx7tazpwkQCJEACJJDhBKKiY3Dq9DkUL1oQEboANBMJBAkBflqD5EKxmySQVQjEyFzOsae2Y8ixtVh3KXHgoHHNr6P4zEQX2ybPBJaudMjLhjKlbGjbKhQtGociIoLW0Ex0mdgVEiABLwRsNjva3tUPJ06dwcxxg2RN5OJecgbH4RVrt+L94WOwbdcBaPT30NAQtGxSBy89eR8qlCuZqQaxcesefDduCj5968lM1a+M7MyPv81AWFgY7r+jfUZ2I0Pb5mPsDMXPxkkg+xA4HReNj46sRqUNY/DQ3jkehafSGJ8nFpdy584+YIJopIeOAGPG2/HiW3H4e6YNFy46gqj37CoJkEB2JLB45UacPX8RxYoUxN+zFgc1gs3b9+Khfh+iVrVKmPXrJ1g+bQR+GPIyzsn4duw+kOnGduLUaSxYuj7T9SsjO6TXcMuOfRnZhQxvmwGHMvwSsAMkkLUJHIq5gEFH1+DbE5tw3h7n12C/3XYGd/7xt195M2OmYA04FCjLnDIPq2WzUNx4QyiKFKYlNFB+zE8C2ZHAc6/GBjTnc8h74Smad/7SeyMQGxuH8mVLYPaCVZj84wdO7N0eexPXN66FfxeuxpFjp9Dxhsa47abr8cnXv2LHnoPo0Koh+vfujqKFC2DP/iN47o3PcfjYSdjtdtSqWgmv9+uByhXKYPI/i/DDL9Oc9erG1RXLYNDrfeQhXRQ+/HystLEKoRLE7aY2jfFCn7uRS35Al67ejOHfTkDDulXx14yFyJsnFx65tzPu6NwqQV3WTu8XPzFCc+yXr1uHzLtaQKNjYk2dv02ea6yNx0+eQe3qlfFm/56oVL60yafjbdW0DubIeI8ej8TN7Zui3+PdkSd3ziT7EiP1D5O+Tpm9BOHhYbi9U0tjvStUIJ8EkIrFF6P+xPQ5y0z/alxbEQNkjE+9NhyHj57EtZXLmfY/GtgbVS5vWwOYt3gNxv4xGyM+ft46hDsffQPvvPgwtB7l+stf/xoX43Kli+GJHrcJw0b4ddIcfDt2Ck5FnkXuXDnRoXVDvPrM/U4X5D+n/4evRv9lrmvNqhWhbspff9QfJYoVxr6Dx/Du0B+xesMOVK9SHnfd0ga33tjc2b7rhqexaTt63X2x9tTvnDki8Mr735jqS5csah6IfDPoBdfmssU23W6zxWXmIEkg/Qkcjr2AD8XS+c3xTc75nNqL8EsxKLV6Pw41rAB7Ds8/QWOqlsOd6d9lthggAfm/HLPm2TFngd0EJrr5JhGhhShCA8TI7CRAAmlE4FJUNP79bxU+fPVxEZ8lMfLnKdi0bY8RNdrk1p37EC0/ZI8/cKtxYX394+8xa/5K9H6wC3pfVRJvf/ojZsxdhvtub28EVxcRKNfVrmJEpAqxd4eNwfefvmjEY7HCBc0ojp88jVc//BZtW9Q3+4O+/AVqfX2p772Ik3kMw7+bCBUhA/reY4Tamo07RBAVwpvP98S6TbuMS+0t7ZtJkCVZB8stbdi6G09I39yTRiZXMbt4xUb879PRMp5bUL/2tfhOxFnfV4Zg0uj3jSjT8SqTR++7WQRnLrwx6Hs0qV8D7Vs2SLIvOt45i1bj9eceRL58efDxF+NMndrWkJG/4e9/FuMRqbdGlQoixhdj/6HjuEME6jc//43nn7jbdLlUiSLuXceZsxewc++hBMfVMqiifZmI88HyIOCDVx4zolVdjg8ePm7yFpMHAs88fIcR+UdESA/8YCSqXVMed3e5AcvXbMFAuQbq2tqpbRPD9eMvxyFGHkLoq/eLg0V0VsB3nwwwbb/+8XeoU+Nqj27LnsamDyJUvHtjvXr9do/9vqVDM1QXQR0h4r3HXTeZa5Zg4Nlkx/OdXzYZPIdJAiSQ+gSOxF407rUjJJDQpctBhFxbibgQg6sW7USRHcewrkcz11PO7XmOc9hfsQKu2rPXeYwbmZeATKnCvEV2LFxqR5vrQ9G5QygK5KcIzbxXjD0jgexBYLYIT51fp3MiVcxdU6mssVKqRc1Kr4n1ssl11c3uaJmPp5bJnt1vMvttmq+DCh4Vn+VkrugdN7fC0lWbRACdMNa2jSJkNZUuUdS84uJs4hb7EZqKoOvb6zYjaCfNXIgnH+qKLje1MHkPiHhSq52KT00FC+TFkP89ZbZbNq4j1ry/oYK08eU+mRPyR0Xy6TPnjVC1jrm//yVt1bi2Ap55JP7xbfGihXDHI69DxZBV3xtiCbXGO+3fpUawqvjU5Ksv4yfPMWKuuFgONekYZy1YgcfuvxnjJ83Fw/d0Qq/uHc05q62IiDBZOzoMLRrVMscD/aNCWYV1vry5zbVTwWildtJnFa1rhdWxE6dN33ddFrFqRVZr66vPPGCy6/VR8alJr58KY7VaS+UiXsua1+wFK/GwWJ1dk1qUvY3tZbFgemPtq98l5UFDuASISi4T1/4F6zbFZ7BeOfabBDIZgXO2GCM6Pz261qPotLp7qVg+rHr0ejQdOhvFthzGiWrx7kDWeX3XmYS/3tgaL3zzo+thbmdyAnLfZSyh8xfb0U4CE+n6fHnzUIRm8svG7pFAliUweeYiY2V865NRZowxMXGYOnspXnjiHhGlicOeqPtp/P9A8UjUOnhI3EY1qTXtqYHDUFLEl8651KTut67p0xHjxc30BH775n8SCCgUJ8UlVN1h61S/2plNXWFH/DTZiEnnwcsb6s6qFkwVL+4ppxwvVDCfuP0mDtRn5T105ESCtlSAqZVVXYo9JRV1ntrSvK59OXX6LC5eijZuy4tXbHJWpXXrGNWltYG4Dqd2atagprhBt8Azr39m+nO9iFgV7RXKlTIuwCrUG9WrjopipQ4JCTViX/ugrtH1alXx2J39h46JIA7FsJETnOdVIF9SVx635GtsvljHu2977rdbE9lyl+IzW152DpoEUo+AzWGX+Zyb8eah5Tgad8lZcekVe5D3+HkcaFYZUYXyOI/rRrTsn7y2BIpvOORRfGqeMcVyQmeAULoojeBKuoD8tFl2zBdraJdOoWgjy7SEhfFKBtdVZG9JILgJqHBQd1d1lS1csIAZzA3N60Gtm0vE+uXZ8uT9d2rMhH/QSATWZ+89a6xxM+Yux/otu5yQdH/sn7Px4/BXUbhQfnO8YP68RuTu3HNIRFI1c0ytcypqVUx6Tt77UP2aCpgm4rmnuGyqRdBKaqG7FBVj5hDulLmqVjooYlTFr9Uf67j/7/FtFMiX11gw+8n817YtrktQXK29Kt404JFaQ12T9tEh9wi+klqmdU6ue9Kyaq1+7+VHxW23O1aJ9Xb4dxMwdOTveF/ccH/4ZSrefelRI061bP+3vnBWUbtaZWMRdR5w2dDAUxER4RjzxUDjNuxyKtGmzmf1Njatxxtrb/1WC7eOy/2hRaKGs/iBxI99sviAOTwSIIHUIzD99D7U2TQeT+ybn0B4agulV+3DVQt3oPmgmajz42IRmgcRKk+dNYVFxSLfkbNm29ufvbaLWNi6pbfTKT4eLebVSC//J+q5KDW/MqWIwIWLwLgJdrz1URw2bvECO0UtsDAJkAAJeCagLqUqHv73wsNGvKiAefHJe1FX5vb9LQGCAk1qJTxz7oKx9Km75x/TFjgF4O59h2WO4Uj07XmbCWx0+ux5M4dSrYfNG9bCxGnzTQAjncs4eeZiE/Qn0PY1/zOP3gl19VWXT3XfPX/hkpkX+eDT74v77AbjXrxa3FBniQtppKwBOlLmW6oA1jGnJOk4WjerK8LvN+PqqoJTl1HReaB6rlnDmhgvgY7URVkD9CgbtRTrHEwVxRu27DY8PFlZ69epYpbB0eBLx05E4vPv/zBdVUGt45g4db5Yg3OiZePaqCgWTxXuYWJVziPBmTSYkUb6XbB0nYjNnc4hqpu1itX3h/8svBc5g/xohkZ1qxlB+e7Qnww/ZThz3nJMmbXEWd7a8DU2bcMba2/91nrVVVfZ6ZxWnR+cHRMtn9nxqnPMJJBCAidPOTB2gg0rrzqDTcUjPda254ZqqDNmCZY/0Rol1x1AtT/XIOKSRDksWQB5TpyDXeaCHGjm+z/EMQ1r4Pp5CzzWbx1cHxuKt8+rq1TC9GWBKBQPS6wgz4oG+upiDmyOCzVW1WKhDjyYW9yiIuLF0aSocEyRl5ZsnzMO3XPHC2YVqs+dzYXB+aM91puwde65Ejh8FBjylQ11atpxd9cwlCxx5Ym9az5ukwAJkEBqEdCorOr+6O5e27ldUxFNv+MtcbNUK5SvXyM5LXnie/RgtxvR783P0fqOZ40rq7rPWknnGKrIUjGmL00633LuhKEmQM+zEiX3tl4DzfF6Na+RNTnvNdv6J1TcRd2T9stTqiNtfvVhP3wkwX5uuneAyaKWuaYNakhAnqtkXmoxrNu8C8+Km6omncP50Wu9ZQ5+XrPvPl6z79KWr77oXNG3Bv/gHIeuL3prh+amXg2W9Mr7I9Hz2Q/MvloF1UKskWU1Ku4DT72LWBGs4756Q9yCr3DTzDof806ZS9t7wGCTx5p/qn3LIRZKDTj0xqAfzLYyf6HPPcYi2rdnV8P6s+8nyhrUxcSSfCVAU30JCvVEjy7iYr1EROg2M8d1mYhhdWlWK/DQd56WsYxCk5v7mP6qQH9NAil5St7Gpu7A3lh767fW3/b6BiLO/0Pjzk9AI97qkjnZLXGplex2xTleEkgBgTibA//MsWPydLtEjJOZMaESZKbXH9ga51mANvp8Dk5UL43d7cTdyO5AoT0nUeBgJGwiPI/XLIOY/Ll89iZXSBi2fP0XCp4+4zXfOhGf74j4/FTEpmsqI6LSk6fnNxcisNUWivdFROp/VaMuRWCjCFEVlSo4Hz2TCwPzRSOX/N//4tmcGFUoChGy/e3FCB0CHs8rA08iZZelVpLA4PG0PCRH5xslKFH7UHli7vkGy2NBHiQBEsgSBKbPtol1zP+h6NzxHDkyx2+FWuP2HTwqwrKwWZ7E/1HAWPVUUKkoTY104eIls/yI1qeiyjWpVU3djlWM6tzT1Ewa+OjEqTNmHO4RedWKePGSPPiVPulYraTHbTI/VkWet6TBlPRJgFqrXZMyVwuhLkOTN09u11NmHqr2xdM4bRIJz3rwoFbwN0U4L5v6dYLy2mZMbGyi/ibIdHnH29i8sfbVb61S+51frOne3a899SJrHKPlM2tcR46CBNKcwLaddowZb8OhI1eaCrGHovWa5thaa8qVgy5be26oiuoTVmFvy2vMsiqnKxeDvvxNURIt969O7dFj3JXAAN7KlhOx6fJ/nbdsOOkIQYEQB/S/as1fPdyGOdFhUPvmCXsIzsn5smIx1fMqMw/YJNKe5JsveYYUTChw5TRTgAQ0KNGkaXYsX2VHj3vC5Gl96t4YBdgdZicBEkhnAh3byROoIE0qqDTYTXKSWgFTM6kQcxdjVv3xQs33w10rb6DvKpbKSuRfT0ldk/Xlnjwdc8+jwZQ8JWXujZ0GiNL1W92TBki6+YGXzbImYWKhVRdgXc/UPXlr0z2f7nsbmzfWvvqt9al1OLumsLckZdfBc9wkQAJJE4iSyY8//y7uMjJ375w8mHRPjsP5UaTuaexGYuvnxeL5UFrW9LSHh+JcueT9x3uySGE8KOHcvaWjIhjnx4QbkbgiNkzmcYbgKhGP3oxqKjzVtXatWDtziqj8XSyfbXOKS6i43Wpg1mnR4WgQIU/m5RHsLNnukScOYyRPlXAHmuewG4GqFlJfD+Jn5u/lrbs8fpnA+QuQpVkcOHsOqHJ1iAR+EPhMJEACJEACJJBCAhGylEml8qVl3en8uLbSVbLUze1o3yp+OZkUVs3iqUCAbrepAJFVkEBWJbBlux3f/2zDqcS6MsGQw8qcw+gOvyPaw7qeuU5dQHTB3HB4CGufoBIfO0snr8K1mzZ7zLFPLJPTRUwWFMunCs+FMWGoKULy5Xye/bpOydzN98RNt4iI0G3ifqvpFclbLTx+zuf4S+GYKaJTpVCrHHEy79OGl8/lxDBx61URul4ErlpJb88Vi1tyiRnPQ6LbrQcoPg4VkgfAD90n160araA+MPEUCZAACZAACQQ9AYrPoL+EHAAJpD6BmBgHJky2Y/b8eEHmTwsX7liNX/Kt9Jq10O4TiBIRGlXE+5wPb4WfEbH4vy++93Y6wfHFIj4/vZAD3xe8hPwetMzb53JAgwz1lbmbGtFW53KqYP1O5nZaS1Kek2HryAtK+eEyR1SF6q254vC4zAfVOaDbxGo6Usp9XjDxWmzaGYrPBJfE7522LUPRrUvmmd/ld8eZkQRIgARIgARIwC8CHm7N/CrHTCRAAlmUwM49dvzv47iAhKeiyDe1NsqEeRaWusxKwxHzUXH+tmRRGyeWsbgw/6aoFxFhqSnaQwxDiVtgotxWCosX1RpUqJuIyjjJq4LSSipaVXjuF6vqSrF03iZ5domVtIDkzy2vUtLGUZnvejG+KasY31NI4N8FdrwzOA579xNsClGyOAmQAAmQAAlkSgJX7rYyZffYKRIggfQioJHZps2y4aNhNhw9HnirDnF97byzqceCJ6qVwraba2N7x1rO8xHno1Fu8U5c9+1/aDJ0Fmr/vBT5D3j27z1ui8asG9s6y7puTI8Kw8qYeCGoy6Go22xpicKr1k1NE2T/FYlaq0kDDJWX+aBzZI6o5pXgvZgrVs+CYtmsetnt1mS8/OcXcbO9WZZbUTF6tQjWs5L/vJQ7KO69ZaQNy1LqWobbKSOgy7K8/2kcpvxjk4W4KUJTRpOlSYAESIAESCBzEfDPlJC5+szekAAJpDKB8xcc+G6MDes3pexm3zG/EhpdXRrLbYcT9NAh62vsa1nFeazMst24dsp6hEfH4YwEItrf/GqzDEtjWZplTa/mOCli1T39XLsyOk5zPwoRhCEYJa6xtsuWzrIiCvvlvTLfM1LOa8RaKz0l58aKqHxGXGh1KZZykv9JOaYWTde0Jy4Em8Qaquc0FRAB2kHmf/aTtT71qd1dsjYoU9oQkAj5+ONvO7btcOCxHmESZdDt4qRNs6yVBEggHQhETRorrin+Rw7Pedv9CMmReC3ndOgqmyABEkgDApzzmQZQWSUJBBOB7bvs+GaUDZHel9IMaDjh157EyKZ/ypxJz0K27NJdqDppHXa3rYYT1Uqi3g+LsOGeRoi8pgQq/7NJrKG7sPj5DojNm/BmI0zE5ebR/6D4ETGNuSWZooqTYo3MJRbMwqoM/Ug63/OSvPzNb1Wplk+Nkqtrf3pLnPPpjUzgxwvLknh9HgpD5Yp+XtjAm2AJEiCBdCRw5pGb4Tjn/384Bb79G6EFUmdtzHQcJpsiARLwQoD/m3sBw8MkkB0IqJvtoM9ST3gqs7htRXFrbHWv+Cr/sxlbu9TB7nbVcK5sYexqXx1Vpm4w+Xd1qIGYfDlRYH9i91ubiNnfO7b1WK8ue1Ja3GkDEZI63zOQ/FbD+eRX05fwtPLxPXUIRJ6GcQWfNc9zZOHUaYW1kAAJkEDgBCbPXITjJ+VHKgulOFmM+cLFS5l6RFHRMfhz+n+4eMlz0L9AO6/1RcsrPVIw8E1rDhSfaU2Y9ZNAJiSg0WxHjI4zEW3tYslL7VRiWn0UCs2RuFqZVxoeFYvTFYs5z50Vt9sCh6785728bxuPbrda4Kcy+Z3luJF9CKgb7i8T7RgxKg762WUiARIggaQILFm5ETXb9HK+Zi/wHo09qbq8nX/94++wY/dBb6cz5Phzb3yOzdv3+t22e/7J/yxC4859oHEgMms6feY8Bn74LU6dPpsqXXz29c/w8vvfpEpd7pUEI19rDO59t46n9J3iM6UEWZ4EgoxA5GkHPhpuw/JVafcfi/10Ltx2rFFiMhLx53itMmLpXI/QWBtynL2Ea2ZsxPkSV0SlLVdE4nKXj2y2XcDKJh7q9VqCJ7ISgeWr4z+7p8+k3Wc3K/HiWEggOxNoUr8Gpvz0oUEwfsRbuKHFddkCx/wla3Ey0n9R5p6/dbO6GPvFaxKgz8fckixG8uWn7sNzj3VLk1EFM1/3vqcWIAYcSi2SrIcEgoDALllG5YvvbDjj//9LyR5V2PSqqNprE7bGJXSh3ditAWqPW45W705BmAQcii6QG+seaJKwHY1yGur5P75xzeqhwdLlCfNzL9sQ0GVY3v0kDk8/Fo4KV3n+jGQbGBwoCZCAVwIqnnJExN/m6ntoaLy9pdtjb+L6xrXw78LVOHLsFDre0Bi33XQ9Pvn6V+zYcxAdWjVE/97dUbRwASxdvRnDRv6O2tUrY/qcZcYa+Mi9N6Nn95sStRsTE4th307AlNlLEC5B9m7v1BL339EehQrkc9ZTv861mDRjISLCw43YiTxzDj9PnAV1xXzgzg546J5Opl5fdX06YrzEaDiPUyIwl63Zgvq1q5i6qlepgPeHj0G09OPtT0dLoLbc6HJTC7SR/zPVgnX42EmJIG5HraqV8Hq/HqhcoYzH/Hpex/HTZ6+avmzduR/vDPkRm7btQakSRdCn5224tUNzc85XXxIBkgPaj1Xrt4m7bJTUVRS9H7gVt97oX11qkf3ihz9w5HgkKl2VOCiha3vWdfOX919yTXLlzIEnenQxrryDvhxnrrdNeCnXksUK4+PXnzBN6Ofnsftvxvwl67Bt1wG8+9Ij+Gr0Xx7H5el6pCVf/dx8MepP0/dz5y+ixrUV8eoz96Nk8SL48POx8plfJbdWIbipTWO80OduM+bTZ8+bz8uy1VtkcpMD1a4pj3cGPIxR46cn+iz16t7RFXOyt2n5TDY6FiSB4CKwZIUdH8v8zvQQnkomRNbBbL0m/j8VV1Ia+Xbd/U2wtkczrOzdCgtfvBFnyxdxZlFB2mLQDOQ45zka4m8SZfZS7tzO/NzIfgROS6ySj4bFYeWaNPAZz344OWISyFYEtu7ch9kLVuGx+27Ba88+iL+mL8TTA4eZG/JBIjCWrNyEGXOXGSZ6A792006cOXcBb/bvieaNauFjESaRp88lYqaCbc6i1Xj9uQfx3suP4t//VmH8pLkJ6jl89CTeefFhtBLrorp5ThWh2r/3XSI6O2KwiN8Tp+IDMfmq65DUoeVqXFsBQ/73JHS+4nfjppp2VPBGyP+x3W5pjeefuNsITxXCXUTgff1Rf3w7eICxaL47bIzX/GdlzNt3HzDnte4nXvoEefPkwufvP4t21zfAy+99g3XCRJOvvpgMbn9qVatk2Pz02UB0EtH/6ocjna6zvurS9rTdhnWrmTHoOH0l67r5y1vz6YMITSqo9cHEkw/djm8GvYAC+fNiz4Ejzub08/OS9EW5tm9Z37DxNi5P1yMt+Q4Z+RsmTp2Pe29vh6FvP4XSJYtiz/4jGPTlL1gsbugv9b0X/R6/C7PEBf2z7yaaMem7PmAY/u7TGPb20yhftiROijuzp747IaRwg5bPFAJkcRIIBgIaWGjC5PS/UbetKo12tSpjNnYlxCRWzciriyc8dnnPllPW4KxUDLrsyslrS2LznfUT5Dtrj8WUju3R7Y/JCY5zJ3sRkAe8+OoHG7p1caBju7DsNXiOlgRIIEUEXhPLX5Pr4gPjjf5thhGeljWzTfN1WLF2K+67vb1po2CBvPjw1cfNdpvm9TBHhMmyNZtNGddOjJ88x1g6i4uVTFNTcfudtWAFHn/gFrOv9XzyZl+zXfGq0iJM5+Cj155AhXIlzbGvf5yMleu2Sb2NkFRdt3VsYcSRFjx/4RLeHfqTqUOtdGrhVYtXCxHKVrrj5lZYumoTDh4+gdy5cmKjWDE1ecq/a99hc07/qGA5duK0WEEHolzp4mjesBZm/7cSaimsU+Nqk89bX5yVuGw8dHcnbNiyC6s3bEesWHt1Lee9B46iSKECPuuaOHUBKpcvbayMmlH78tEX41xqTrwZCG/X0jq2R+7tbCzReryBWKtV7LumT+U6tr3+yr2Jt3FdV6tKouuRVnx1jq4+7HhYrOeWhbKxfMb1+AtvfyWfl67GEq7jOHD4OH6Vz9+AvvfgUlS08RDInzcPqlQuJwK/qnOonj5LzpMp2KD4TAE8FiWBzE5Af3R+/cOOWfPSX3habK6e1QgLO+xFlMNmHXK+R1yITrCkis4BrbBgO0quPyjHc+Bc6YKQX04xoyZ0rxxTtRzSZnaGs2vcCBICv0+y46wYIe66LTRbzVEKksvDbpJApieQJ3dO6eOVeeR5cucyFj1PHdeb8UoigjZs2ZNAfGrgG428qhbVxSs2OYvmzOE5hkF8m5rtSru5pR8qBAKtS62SWs5bWi6uuU+JZVddR9VCp0ndb/1JahEsXDC/EXtWfnVBVhdeT8lXX9Qa+diAwdgnYlNFkbrwavLWF9e69h8+lkAUeWrb1zFfvF3LnRULt7oEXyeuzL5SXnFptlKg47LK6Xtq8tV5vmqpbuAiHrWNU2KlV1fsOtXjHxboMb2GI36abCL89rq7oxGnXR9+DSrYu9/aBn17dkUOL59dLZ/SRPGZUoIsTwKZlECczYHvx9iwLA0DC/kzdNuh/Oh6rh5+yZcw0qC61Tb79B8sHHCTiYBbcd42lFmxF5cK58GW2+riyHXl4QjzPDNgvuMs9leqiKt27/GnC8yTxQnMnGOXp/8O9LwnDGFhCR9UZPGhc3gkQAIpJuD/b0ZsXBx2yrzQj2G6gQAAQABJREFUzm0TxikokC8vwsPC0E/mirZNZmAjqxcprUvnurpGqh0z4R80EkHy2XvPmgd0M+Yux3qxPlrJPb91XN913qvOS1VBbFknd4tl9NrKV7lm82t7wdJ12C5zJGeP/xSFCuYzZX76faZfZatUKgdtNzWTxdu1TnWxVbfT3XsPOy3jruc9bSc1rvTiq3OL9TO4Q1ym1epupYIypjC5l9q55xAa1atmDu/aewj6kCWnzHPVa/nXD+8Z99z/lq3HJ+J2rBZxnRfqq+9W/cl593xnl5yaWIYESCDTEIiKdmD4iIwXnhaQfFNro0xYXmvXvMfkz4WTVUqg4Tfz0XzwTBTcdwob7m6Ixf074HDDil6FpxbWZ8W/dGhl6uEfElACi5Y58KUE0+JSLPw8kAAJKAEVYDGxcQaGvnuzsCVFK1bK7pSb9WMnIs18QN1v0zxh5Fyd/6dRYofKnDvNqwGENm7dYwL3JFW/+/mU1qVzQVeu22rGrqJRAw/pnFW1jGnf/pi2IIGXiHt+1/40qFNVREpOqEuwBqbRoEubtu1Fq6Z1XLP5ta39UC4Hj5wwYvaHX6b5VU4zNW9YUwL6bDft69zQ0b9N97tsoBlbNqmNkT//jTETZuJHccnWl6+U1LjSi69+bpoJp/GT5xq3cQ0+pNdaXZzVXXritPkmoNaWHfsweeZi5zXUIE5rNu4woluDbam1/sLF+Jgbvvrui0lS52j5TIoQz5NAkBG4FOXAkK9s2LXnijtPRg/BERWOzrua4tsKsxN0Zc8N1dB02Gysv7cxjtYtl+BcUjtjiuXAC5LJ09PLpMryfNYksHZj/Gf/2d5hyJWLn4yseZU5KhLwj4DOcXzk+UEmc/feb2H4O0+jXcsGRnj5+nXQWR6uMz3UnfaBJ9+FBorJny8P3pVgQuXLlojvhGS08r4hAYneGvwDbus10JwLldgGVlRYPRAa4tveo1YmfWnyVZdGK3VNVhRf69i9XduZyKYjf54Cdal8sNuN6Pfm52h9x7NGWKjLpWtyz6+C02qhWJGCeP+Vx/DaR99JVN5/TP963nUTbmwdv+RZUn1xbef6xrVNwCa9Fpp0Pz7Ft+arLnXTrS0uw8//70tTpGmDeMue1c/LFSV4C4R3iEt0/cclAq8Gfvrp939wVZkS0m5l47pqVW6uk7Uj70mNK734apfefL4nXnl/JHo++4HpoV4/tXhrEKxnJdKw9dmsV/MavPTkvSbPmXMXTX7lr9bQDq0amOjPetK97wP63GPKpPRPiDwZyjx3qCkdDcuTQDYncPGSA0NVeO7NnF/rdQ9NwXJbQteZuqMWIbJycexrlXiORcm1B1By3QHE5M+JrbfUgUbKdU2Tlu5Hy7nzXQ9liu0XSs/JFP3Irp24umII+vWhAM2u15/jztwEoiaNBaLjLSv+9DTnbfcjJIfOy0z/pFFB3xj0PeZPHG7cT9UN1V3sufcqWubdqXgpXrRQiufNJbcum80uls74Pliuk/sOHpU+FTaWTPc+u+f3dF6D1OiYrsyfdM/l375akJWhCqNAk841VYGkrqRpldS1WpfCsZIuD6MPHTRKsa/ka1zpyVf7qEGodO6qXi/rgYYe1z7qvh53TeoZcFzO6fI36qLrmpLqu2tef7cpPv0lxXwkkMkJqPAc8qVN5kVkTuGp+MKvPYmRTf+E3SXIQr4jZxB+KRanJcKtM0kEvDo/L0XhncdxqKFE75P/SHOejcK6B5s6s+hG97h8GPHJiATHMsMOxWfGXwUK0Iy/BuwBCQQ7AUt8Lpr0RbAPhf33k8DYP2aJ1XOmCSx1VNYUVTfhMZ8PxDUVy/pZA7MlReCKtE8qJ8+TAAlkWgIXLzrwiQjPvfszr/BUeHHbiuKWBtUxKeJKNMDzpRI//cwhUXAL7j2Jpc+1Q1ShPCYgUePhCV12tb6/Ii7h40IFUVAXfmQiARcCO8XtXN3PaQF1gcJNEiCBgAhUu6Y8UsvVMKCGmTnDCOi8R12ORtdyVetsC3EPVos3U+oRoPhMPZasiQQyhECUzPEMBuFpwSk5rT4Kdd2B0/YY61Ci97hcEp5eXEPCouNQbNNhVJm6HlGFE7vZRMvyLX927oCeY39PVAcPkIBTgPYVF9ycvmYHkRUJkAAJJCag60nqiyn7EFCX1Ns7tcw+A86AkSZ07M2ADrBJEiCB5BOIjXPgs5GZ3+LpOkL76Vy47Vh8sALX467b9ogwbL79OjT+7F/U+nU5TlQvhTW9mrtmcW6Pqejirus8yg0SiCegAvTzb20SZTFzewXwepEACZAACZBAdiDAOZ/Z4SpzjFmSgF3mRX75vQ1r1gffTbUj1I5Fvf7ElrhTCa5NeFQsbBJUyBEe/1wsRMKym7U+3aL7JSgkO0v+Xo2qG6+48rqfT+99zvlMb+JJt9ewXgge7xkmgS5oAU2aFnOQAAmQAAmQQNoQoNtt2nBlrSSQ5gRG/xKcwlPBhNhD0WpNM2ypNeUKJwm8Xe/7hch35KwJMGQPC4FaQPVlu/yyizC15QzH+vubwLjmXi49tk1T/C8Tic8rg+JWZiGwYo0DefPa8WD3hBGTM0v/2A8SIAESIAESyA4EKD6zw1XmGLMcgfF/2rBwafBZPF0vhG1VabSrVRmzsSv+sFg3V/Rt48wSIhFuQ2NtCJNXgnexhkKsvq5pnMQsei0iHBESLpyJBLwRmLfQLiHzga6dKUC9MeJxEkhrAoOOrMZFu/+/1S+Wug65Q3m7mtbXhfWTQHoR4Lc5vUizHRJIJQL/LrBh5hx7KtWWsdVcPasRFnbYiygJHOSeChyIRMW5W5Hn+Hns6lADR+uWc8/i3D9ui8asDu3QaeoM5zFukIAnAn/PsEMCJKNNCwpQT3x4jATSmsDHR9fgRJz/63w+WaIWxWdaXxTWTwLpSIABh9IRNpsigZQS2LjFjnETsobwVBa2Q/nR9Vy9RFhKr9yLBiPmIzp/bmzvXAuVZm9GoT0nEuVzPfBzrUquu9wmAa8Exv5ux+atWed75HWgPEECJGAI2O12HDh8HBcuXko3InHipZOe7aXbwNgQCaSQAMVnCgGyOAmkF4FDRxz4+gcJwJPQ4zS9mk+zdvJNrY2yYeIL6ZIK7zqOLV3rYcsd1+FEjTI4X6qArPuZMDiRS3azOT3kPI6XKul+mPskkIiA3IfiK/kuHTmWxb5MiUbKAySQvQnExMRi0Fe/oFGnJ3DTvQPQuHMf3PX4W9i8fW+ag5n8zyLTniOr/aed5uRSt4Hn3vg8Xa536vY6a9dG8Zm1ry9Hl0UInDvvwPBv4nDJf0+loBm5IyocnXY1SdDf6AK5UXB/JIptOYx6oxah2LajOFGtVII87jsiy/Fbx7buh7lPAh4JqAFEv1MXLlKAegTEgySQBQio8Jw4ZT4+eq03Vs74BjPGDULxogXR89kPcOr02TQdYetmdTH2i9dkyWpG2E5T0ElUPn/JWpyMTNtrnUQXeNqNAJdacQPCXRLIbATibA588rkN23dl7ZvkdQ9NwXLbYYNfgw1Vn7gaRbYfRWTl4tjZsSaiCuVJ8tJUC8uLxe9/k2S+tM7ApVbSmnDq1V+tSgj69QlDmERXZiIBEkh7AsXX/hDQnM/jdXuhWHjugDt2/ORptOveH6891wPdb23jLH8pKho33vMC7ujcCv0evwufjhiPyDPncUoEyrI1W1C/dhU891g3VK9SwZTZsfsg3h4yGuu37Eap4oVRpFAB3CX1de14vTy8isKHn4/FvwtXIVRE5k1tGuOFPncjV84cWLF2K4Z9OwE/ffaqqafbY2+iVdM6mLNwNY4ej8TN7ZtK+92RJ3dOc/7P6f/hq9F/iUfGKdSsWhFR0TH4+qP+KFGssLPv1ob2+dTpczhx6oy0swVXVyiLN/r3xE8TZmLeojW4plJZPPPInWhUr5opota/Veu34aI8wS5Voih6P3Arbr0xfu1sX+Pfs/8ItOzhYyehrsu1qlbC6/16oHKFMqZeX2zU6qzjnzJ7CcIlUv3tnVri/jvao1CBfFi6ejOGjfwd9etci0kzFiIiPNwwjzxzDj9PnCXrMtvwwJ0d8NA9nUw7vury1f/3h48x9ZUtVQz58uZGl5taoFf3jhZGvmcQAVo+Mwg8myUBfwn89qc9ywtPZVF/YVOEIl4A6Nqem+5qgP9e7YyN9zTyS3hqHVtsF7CySSPdZCIBvwhs2S4W8784/9MvWMxEAkFEYOeeg7DJg8wOLRsk6HXuXDlxfePa2LJjnzl+6OhJTBWBVOPaChjyvyeN6Ptu3FRzTgVgn1eGyHQXB4a89SRefuo+I8RORp4x5wd9+QsWr9yIl/rea4TsrAUr8dl3E825s+cvYvvuA2Zb/2zduQ8z5i5Hj7tuwpvP98KkmYuwaMUGc365iN6BH34LtZaOGvayEbFbd+5HjJcI7trnaf8uRb2a10ifn0K0CL3uvd9Crhw58PHrT6Bo4YL4/Ic/nG3XqlYJ7738qAjhgeh0Q2O8+uFIp+XX1/hVNHYRkaoi+NvBA4wV991hY0y9SbFR4Tln0Wq8/tyDpu1//1uF8ZPmmrLnhM3aTTtxWMbxzosPo5WM+2V5cKzXoX/vu0R0dsTgr3814loL+KrLV/9V8EbIGLrd0hrPP3E32jRLHGPCdIh/0pUAo92mK242RgKBEVix2o7Z87PHjXHctqK4pUF1TIrYFBgkt9xj5T+XBkuXux3lLgl4JzBrnh1VKoegQT0+j/VOiWdIILgIHD1xGqGhIShcKH+ijhcpXEDmAcaLTz15W8cWePKh202+8xcu4d2hP5ltFYWHjpzANx8/j0rlS5tjJYtPNu8qSCfNXCjluhqLmh7UoEa/TpqDAX3vMXnc/6h1ssl11c1hFY+LV2xEexHHf4n179rK5fDqMw+Yc6XFOvnxl+PciyfY1z4/0aOLOaZjmTF3Gd56oZfZz5kjAo+9MBixcXHGqvjQ3Z2wYcsurN6wXY7ZxIrpwN4DR40VVwt4G3+50sVxx82tsHTVJhw8fAIq3Ddu22Pa8MVGM4yfPMdYOotfttw2rV8DsxaswOMP3GLKFyyQF5+82ddsV7yqtAjTOeIe/QQqlIuP3fD1j5Oxct02EeKNkqzLW//Veh0aGioPFiqiRaNapi3+yXgCFJ8Zfw3YAxLwSODIUQd+GJd4CRKPmbPIwZLT6qNw152ItEcne0S/5Y7FO3nyIM/Fi8mugwWzH4EfxtpQrkwISpag+232u/occVYkUFJcZFVk6dxOdZV1TToHsFSJIq6HnNt58+SS+Arx/wcdFhdYFUmW8HRmkg11e1WLY53qVzsP165eGSN+moxosZgmldQN9Eo7J1GvVpWking9r667roGN8uTOhTibzVh+o6Iu4rEBg7FPxGZjEb7WuNWN1lNyHb8KzKcGDkNJEZBqPdVklfPN5qy4+EZj9oJVIrCvPFBWUewpWa7HkNgNVsotY1I+ev0Cqcu1/1ZdfM9cBCg+M9f1YG9IwBCIjnHgyx/i5D+w7AXEfjoXuhxriNHFFiZ74OfssZjasT26TZyU7DpYMPsR0HvNr+Q792q/cOTIQQGa/T4BHHFWI3BNxbIylzsUM+euwD1d2zqHp0Lmv6XrzbxN50EvG3VETJ45ewGRIjTdLagF8+c19e/cc8g5t3LX3kMyhzMXcsqcz0BS7WqVsXbjjkCK+J13wdJ1MnXnAGaP/1TWOI6PLP/T7zP9Kj9mwj9oVLcqPnvvWeNyq27D68WCqskXmwL58iI8LAz9endH2xbX+dWWeybrVzildWnAJ1dh7t4O99OfAH2M0p85WySBJAmMGW/DofjYO0nmzWoZwqZXRbVwz0+k/R3rT1XjgyH4m5/5SEAJHDgE/Px79vI24JUngaxKoFiRgri7yw0Y+u3vZq6lzlHcf+gY+r/1hXFH7dHtxiSHXvXqq6D1PP+/LzFZ5mi+P/xnbNq6x5TT+ZDNG9bCxGnzsUPml+oc0skzF5ugQklW7JahZZM6EhBou6lf23klFQPnqYVVA/gcFPdhtSL+8Ms0t9a972rZM+cumGixO0VY/zFtgTN6b1JsdP7q0JG/Qctp+xuFm87dDDQp55TUpXN5V67baubPpnWE40DHll3zU3xm1yvPcWdaAstW2bF4+RXXk0zb0TTqWIg9FK3WNEtR7Qvs57CvcryLUIoqYuFsR2DhUgf0O8hEAiQQ/AQG9LkHd8qcxVc/GIkGNz2Ojve9CI2CO2roy05LpkapdU06R9BKajXTZVpUuH4pkWijxB2pdMkiyCmBfTRpMB117b2t10Dc+egbUJfPl5681yp+OYRe/K7W5dqS2b/ctkbY1fmbiyUA0ejfZpg5ilpKo+Z6Su59dq3LPb8GV2ou8x01IFHLrs9giczfjE/xvXGvy3X8D4pA14i6re94FndJtF7LTVjLJ8VG57eWL1PCsKnX4VHc2/dtHDsRebltSHTgK5ydB102XMfkqy5f/dfq7u3aDhOnLsB10gcrkJRLM9zMAAJcaiUDoLNJEvBGIPK0A29+GCfzG7zlyD7H9/T4F7MR796TnFG/EhmGF7/5MTlFU1yGS62kGGGGVpBHVnX438vhcnPqequYoV1i4ySQZQgMOrIaF+1xfo/nxVLXIXdoymaJ6TzFAxIwp7C4nebPl/SyXa6dU6udWt80qRVQl2nRQDkq6qykokrFUvGihaxDAb9rZF51E9akwYjeHPwDlk39OuB6vBXQPqqwVEtuIEldVvcdPCpjK+xcFsYq7w8bnf+qAlbZ5PAy59OqL6n35NalbDVCsfZBrxNTxhJI2bc5Y/vO1kkgSxHQH/jvf7ZReF6+qlfPaoSFHfYiypE8N8gxxXLgBflPJlS4MpFAIAT04Y8GINL1P3mjEgg55iWBpAkMEDGZ3klFV/myJZLV7OMvfoKL8qOgy5ds2LpbotJeJe62NRPU5WktzgQZkthRd9CbH3gZ1SUqa5hE6NVAP7oGaGqm5PZRfwMrlCvlsSv+sNH5r2Ulam5qpOTWpaI+ueNPjX6zjoQEaPlMyIN7JJBhBP6Za8Ovf9Ddz/UCXLhzFX7Ju8r1UEDbfy07gFZz5gVUJjUy0/KZGhQzvo577ghF+9bxFo+M7w17QAIkkBEENMDO+s27zJxBXQakVZO6TgtlavVHLYi6JqbOy9S1OuvWvBq6TEhmT+nBJrMzYP8CJ0DxGTgzliCBVCdw6IgDbw+Kk0n5qV51UFcYkisO0+79HQdt55M1ju5x+TDikxHJKpuSQhSfKaGXecqGi2/QGwPCUaYU3bQyz1VhT0iABEiABIKZgO/ZvsE8MvadBIKEgLrbjpb1PCk8E18wR1Q4Ou1qkviEn0f+iriEM4WSPwfHz2aYLYsS0O/kKHG/1YAiTCRAAiRAAiRAAiknQPGZcoasgQRSRGDuf3bs3MObW28QHfMqoVFYaW+nfR6Plvmif3Tu4DMPT5KALwK79jowdyHd4X0x4jkSIAESIAES8JcAxae/pJiPBNKAgEa3nTCZN7ZJoW2wsClCEwSpT6rElfNjKha9ssMtEkgGgYnyHdXvKhMJkAAJkAAJkEDKCFB8powfS5NAigjogvZR0SmqIlsUjt1WFLfEVk/WWFfazmFLrYSRCZNVEQtlWwL6HdXvKhMJkAAJkAAJkEDKCFB8powfS5NAsgmsWmvHmvW0pvgLsOS0+igcmtPf7AnyjW2d/HmjCSriTrYloN9V/c4ykQAJkAAJkAAJJJ8AxWfy2bEkCSSbQGysA7/8QUtKIADtp3Ohy7GGgRRx5h0na2rHRnBZYycQbiSLwLgJNkTH8IFRsuCxEAmQAAmQAAkIAYpPfgxIIAMIzPjXjlORGdBwkDcZNr0qqoUXCXgUJ2zRmNWhXcDlWIAEXAlEngFmzKb105UJt0mABEiABEggEAIUn4HQYl4SSAUCGrhk6izewCYHZYg9FK3WNEtOUYypXSlZ5ViIBFwJTBfxeYrBh1yRcJsESIAESIAE/CZA8ek3KmYkgdQhMGGyDTExqVNXdqzFtqo02iJwITkD53GsdKnsiIxjTkUCMbHAhEl0mU9FpKyKBEiABEggGxGg+MxGF5tDzXgCu/bYsWQF54yl9EpcM6sxcoWEBVSNDQ78dlPbgMowMwl4IrB0pUPW5qX3gic2PEYCJEACJEACvghQfPqiw3MkkMoExv/FG9bUQGo7lB9dz9cNuKqfyuQLuAwLkIAnAr9O5HfZExceIwESIAESIAFfBCg+fdHhORJIRQIbNtuxYxetnqmFNN+UOigbFpiY3Gq7gBVNG6VWF1hPNiawa68DazdQgGbjjwCHTgIkQAIkkAwCFJ/JgMYiJJAcAn9M4Y1qcrh5K+OICkenXYGv3zm2aT1vVfI4CQRE4M+p4szt4AOlgKAxMwmQAAmQQLYmQPGZrS8/B59eBFavs2Pvft6kpjZvx7xKaBRWOqBqf8sdg4t58gRUhplJwBOB/QeBlWv5vfbEhsdIgARIgARIwBMBik9PVHiMBFKRgFpG1ELClDYEGixsKgsWh/hd+Xl7HKZ06uB3fmYkAV8E/pLvtt1OAeqLEc+RAAmQAAmQgEWA4tMiwXcSSCMCy1c7cPBwGlXOahG7rShuia0eEIkx1wZmLQ2ocmbOVgQOHwWWraL4zFYXnYMlARIgARJINgGKz2SjY0ES8I/AtFm0evpHKvm5Sk6rj8KhOf2uYIH9HPZWrux3fmYkAV8Epszk3E9ffHiOBEiABEiABCwCFJ8WCb6TQBoQ2LjFDp0XxpS2BOync6HLsYZ+N6J2ql86tPQ7PzOSgC8Cav1ct5HWT1+MeI4ESIAESIAElADFJz8HJJCGBKbNZoTbNMSboOqw6VVRLbxIgmO+dsYUjYA9xP+5or7q4jkSmP4vv+v8FJAACZAACZBAUgQoPpMixPMkkEwCeyS67ZZttIYkE1/AxULsoWi1ppnf5Q7YLmHBDa38zs+MJOCLwPadDuzaQwHqixHPkQAJkAAJkADFJz8DJJBGBKZzrmcakfVerW1VabRFJe8Z3M78XL+a2xHukkDyCdD6mXx2LEkCJEACJJA9CFB8Zo/rzFGmM4FTkQ6u/5fOzK3mrpnVGLlCwqxdn++Twi/hTBH/XXV9VsaT2Z7A6nUOHDtBb4ds/0EgABIgARIgAa8EKD69ouEJEkg+gXmL7JDlPZkygIDtUH50PV/Xr5ajHTb80bGdX3mZiQSSIqDf+bn/0fU2KU48TwIkQAIkkH0JUHxm32vPkacRgTibAwsW8wY0jfD6VW2+KXVQNiyfX3l/qljUr3zMRAL+EFi0zI7YOD558ocV85AACZAACWQ/AhSf2e+ac8RpTGCNuN6dPZfGjbB6nwQcUeHotKuxzzzWyVW2c9hcu5a1y3cSSBGB8xeAlWsoPlMEkYVJgARIgASyLAGKzyx7aTmwjCIwh253GYU+QbuOeZXRKKx0gmPedsa29k+oeivP4yTgSkDd7plIgARIgARIgAQSE6D4TMyER0gg2QQOH3Vg6w5aPZINMJULNljYVBYzTnotz3H5HYiNCE/l1llddiWgy64cOsLfgex6/TluEiABEiAB7wQoPr2z4RkSCJiAzvdiyjwEYrcVxS1x1ZPs0El7DP7p0D7JfMxAAv4S4Lxvf0kxHwmQAAmQQHYiwEf92elqc6xpSsAhoS6XrqT4TFPIyai85NT6KNx1JyLt0T5Lj6ldEZ2n+swCRORAWPnKCC1VDqEly8p7WYSVKIOQAoUQki8/QnLkBMIj8CXCEWcDYmOBCxfldcGBU6cBXYLnyDEHDh8B9h8Sa6ucZ8qaBPS34K7bQhEamrTlPWsS4KhIgARIgARIIDEBis/ETHiEBJJFYJu42p2KTFZRFkpDAvbTudDlWEOMLrbQZyszcB5Hy5RByUOHruTLkxfhNa5DeLU65hVWuSpCRFwmlXJIBn0hN1CwgG4kFiAaFXnffge274p/bRN37YuXNC9TViCgQce2bHegRtXE1z4rjI9jIAESIAESIIHkEAgRaw0npiSHHMuQgBuB0ePisGAJv05uWDLFriPUjkW9/sSWuFM++/POMeDp8ZMQ0eh6RDRti/A6Df0Smz4r9fOkitHN2xxYsdqONesdxmLqZ1Fmy6QEWjQJwUP38RlvJr087BYJkAAJkEAGEKD4zADobDLrEdB1/fq/FodLtFxl2osbVv8wvqk1xWv/2ucvh5eL1EDbQhVEcGasYLCJEF2/2YHZ8+xGkHrtNE9kagK5cwGfvheOiHBaPzP1hWLnSIAESIAE0o1Axt5hpdsw2RAJpC2B9RsdFJ5pizjFtdtWlUbbWpXwL3Y768oVEoZeRavi6RK1USN3EefxjN4ICwtBvVr6CsXBwyJC59uxeLmdc0Qz+sIE2P6lKGDdBgca1KP4DBAds5MACZAACWRRAox2m0UvLIeVvgRWrWOgofQlnrzWrpnVGCo4w2QO5qPFqmN7rfvwVYXWmUp4uo+sbOkQ9Lg7DB+8Ho6WTUMQQh3jjihT7y8XN2omEiABEiABEiCBeAJ0u+UngQRSSEBdJPsNjGOwmBRyTK/ilXrsQbeaxVA1V+H0ajJV29H1IydMsmGtWNuZMj+BnBIAedj74Qin623mv1jsIQmQAAmQQJoToNttmiNmA1mdgEYrZZTSzH+VC+QH7r8rDA3qVsn8nfXRwzKlQvD04+FYu8GOH3+14cxZH5l5KsMJRMsKP1slknHNajRZZ/jFYAdIgARIgAQynADdbjP8ErADwU5g9TpaoDL7NdSoo++8Gi7CM+v85NWV+aDvvBKO5o0pajL752+tzPtkIgESIAESIAESALLOnRivJglkEIHV6zmnK4PQJ9lsLnF5fPLRMLPcRd48WU+k5ZExPXx/OJ6SMeaWNUWZMieBNWKlZiIBEiABEiABEqD45GeABFJEYP9BB05FpqgKFk4jAmVKAa+9EI7ramf9Z2z1ZIyv9Q9H6ZJpBJPVpoiA/kYcOETrZ4ogsjAJkAAJkECWIJD178r+396dR1dx3Qke/1XV0wpIAgxiM2Aw+77Y4BhwsGMcA17ATuzGacd20k47M5kzPdMn58xk+vQsOfmjTy8z0+mkk3THjud0nJnsibE7mHhpvGDj3cQsxizGrGZHCCS9VzX3Sk1kAZKe3qu6davqW+eA9Ja6y+dXb/np3rqVijDRCVsFtmxnRMPG2MxXl7b4mkrGhg1N32hnd96Nqq9f+485mTszO33uzsLG+9/+He8VNsaFNiGAAAIImBUg+TTrTW0pE9iyndEM20J6/WJXvnSfJ1VV2UvCqlWfH3rAkxuW8NZu23G59T3eK2yLCe1BAAEEEDAvwDcU8+bUmBKBvLrEyna1iiWbPQLLb3RlzZ3qKp4Zvhim7vsf3OHJzZ/i7d2eI1Nkh1oVW79nsCGAAAIIIJBlAb6dZDn69L0sgV27A2lpLasIdg5RYPVKV1av9EIsMdlF3XGLJ7cv5y3elii2tons2kPyaUs8aAcCCCCAQDwCfDOJx51aUyDAlFt7grhcjfItv5HE88KIrLzJE23DZofANqbe2hEIWoEAAgggEJsA30pio6fipAvoC8ezxS+wcL4jq9SoJ9ulBbTNgnnZO//10hrx3st5n/H6UzsCCCCAQPwCfGOLPwa0IIECvh/Irg9IPuMO3eQJjty3JtvnePYWA30O6P3KaNKVJKC9WUX9+Ptqqj7nfUatTPkIIIAAAjYLkHzaHB3aZq3AvgMirZzvGWt8Bg0U+bJa2TXnkVT1FohczpEvf8GTwYN6eyaPRynQps77/HB/lDVQNgIIIIAAAnYLkHzaHR9aZ6nAThYOiTUyrnrn+tLnPamtJfEsNhD9lNWD93qi7djiE9j9Adf7jE+fmhFAAAEE4hbga0jcEaD+RArs3M0XyDgDp1dxHX8Fb199jYE2u40VcPvKFurz9zBdP1RPCkMAAQQQSJYA396SFS9aa4nATnXuFls8AvrcRa5hWbr9zTe4nP9ZOl/Ze+7ey3tH2YgUgAACCCCQWAGSz8SGjobHJdDSEsjBw3HVnu16PXU1lT+8iwWGyjkKXNeRe5WhtmQzL6DPF29rIwE1L0+NCCCAAAI2CJB82hAF2pAogf0HAwn47hhLzD59vSvDhnKeZ7n4jcpQW7KZF/DVjP29+3gDMS9PjQgggAACNgjw7cOGKNCGRAnokQs28wJ6ddvly3jLCkt+hbLUpmzmBT7kPcQ8OjUigAACCFghwDc5K8JAI5IkoEc+2cwL3HmrJ1WVjHqGJV+pLD97G3Nvw/LsSzkHD/Ee0hcvnosAAgggkB4Bks/0xJKeGBLYd4Avjoaof1/N8EaR+bNJPH8PEtIv85SptmUzK3CA5NMsOLUhgAACCFgjQPJpTShoSFIESD7NR2rFMn19SpLPsOUdxxFty2ZWgJFPs97UhgACCCBgjwDJpz2xoCUJEDh3LpATJxPQ0BQ1cehlIlfPJfGMKqTaVhuzmRM4ckyteJtnBoU5cWpCAAEEELBFgOTTlkjQjkQI6C+NbGYFPvVJl1HPCMn1iPKNS/koiJD4oqL1atmHuVzTRS7cgQACCCCQfgG+caQ/xvQwRIEjxxitCJGz16JyOZEF83ib6hWqzCdoY23NZk7g0Ee8l5jTpiYEEEAAAVsE+FZnSyRoRyIEjpJ8Go3T3JmO9Ktlym3U6LU1jmhrNnMCx0+SfJrTpiYEEEAAAVsESD5tiQTtSITAkaOJaGZqGrloIW9RpoJ57QKsTVnreo6fMFkbdSGAAAIIIGCHAN827IgDrUiIANNuzQVqQH+RKRMZjTMlrq3r60zVRj3HTzDyyVGAAAIIIJA9AZLP7MWcHpchcOx4GTuza58Epk12RF8KhM2MgF54SJuzmRE4xsinGWhqQQABBBCwSoDk06pw0BjbBU43MVphKkYzp/L2ZMr6fD3TJmN+3iLqn4x8Ri1M+QgggAACNgrwTcPGqNAmawVON1nbtFQ1TA94TmUUznhMp07So83Gq81khScY+cxk3Ok0AgggkHUBks+sHwH0v2iBltZA2tqKfjpPLEPg8pEi/fuRBZVBWNKuA/o7MnoU7iXh9XGnfEHkXAszKfrIxtMRQAABBBIuQPKZ8ADSfHMCTYx6GsMeQwJkzPrCisaNIfm80CSq281noyqZchFAAAEEELBTgOTTzrjQKgsFON/TXFBGX04CZE67a01jR2PfVSS6W2eboyubkhFAAAEEELBRgOTTxqjQJisFms5Y2axUNoqpn/GFdeQIkk9T+s1nmXZrypp6EEAAAQTsECD5tCMOtCIBAi2tCWhkSpo4ajgJUFyhHDY0rpqzVy/TbrMXc3qMAAIIZF2A5DPrRwD9L1qAxYaKpirribU1IlVVJJ9lIZaxc7WyrxtQRgHsWrQAyWfRVDwRAQQQQCAlAiSfKQkk3YheoJWVbqNHVjU0NBiphkp6EBjYQPLfA09oD7Ww2m1olhSEAAIIIJAMAZLPZMSJVlog0KoutcIWvUBDHYlP9Mo919BQ1/PjPBqOQMEPpxxKQQABBBBAICkCJJ9JiRTtjF2AabdmQtBQb6YeauleoLa2+8d4JDyBgrrWJxsCCCCAAAJZEiD5zFK06WtZAiSfZfEVvXNlBSOfRWNF9MSaamIQEW2XYn1GPrt4cAMBBBBAIP0CJJ/pjzE9RCBRAi7vSrHHK5eLvQmZaADTbjMRZjqJAAIIIPAxAb7mfQyDXxHoScBhMKgnntAeI/kMjbLkgjyv5F3ZsQ8CPtNu+6DFUxFAAAEE0iBA8pmGKNIHIwIOrxYjziSfRph7rITpoD3yhPagzxpmoVlSEAIIIIBAMgT4Op2MONFKCwQY+TQTBM6tNePcUy3EoCed8B7LMcIcHiYlIYAAAggkQoDkMxFhopE2CLhMuzUShqZmhoOMQPdQSQuXFepBJ7yHKirCK4uSEEAAAQQQSIIAyWcSokQbrRBgOqiZMDQ1mamHWroXIAbd24T5CAs7halJWQgggAACSRAg+UxClGijFQKVjFIYiUPTGUY+jUD3UMkp/gDQg054D1XkmE4RniYlIYAAAggkQYDkMwlRoo1WCFRz7UMjcTh12kg1VNKDwNFj/AGgB57QHmLkMzRKCkIAAQQQSIgAyWdCAkUz4xeoro6/DVlowclTIpxzGF+ktT1/ADDjzzmfZpypBQEEEEDAHgGST3tiQUssF6gh+TQWoUOHjVVFRRcIfLifUc8LSCK7yXtKZLQUjAACCCBgqQDJp6WBoVn2CVRX2demtLZo7z4SoLhi+8GH2Juy79+Pcz5NWVMPAggggIAdAiSfdsSBViRAgHM+zQWJBMic9YU17d134T3cjkqgf7+oSqZcBBBAAAEE7BQg+bQzLrTKQgG+KJoLyo5djL6Z0+5a05692HcVie4W7ynR2VIyAggggICdAiSfdsaFVlkooKfI5TwLG5bCJumRz3PnSIJMh1Zf5oZRZzPq+r2kqoppt2a0qQUBBBBAwBYBkk9bIkE7EiFQV5eIZia+kYHKO7e+R/JpOpDvbgtE27NFL9CPKbfRI1MDAggggIB1AiSf1oWEBtks0FDHSIWp+LzzLlmQKevz9Wze4p//lZ8RCzTU814SMTHFI4AAAghYKEDyaWFQaJK9AvX19rYtbS174x1ffJ8E1FRcAzXkuXkL3qa8Bw80VRP1IIAAAgggYI8Ayac9saAlCRBg5NNckE6dFtn+PsmQKfGt2wPR5mxmBAYPYuTTjDS1IIAAAgjYJEDyaVM0aIv1AoMYrTAao42vMg3UFPiGjVibstb18F5iUpu6EEAAAQRsESD5tCUStCMRAkOHMFphMlCbXler3rYw+hm1eXNzIK+/jXPUzh8vf/BA3ks+7sHvCCCAAALZECD5zEac6WVIAo0knyFJFldMS6vIi68wIlecVunP2viaL/l86fuzZ98FmHbbdzP2QAABBBBIvgDJZ/JjSA8MCgy5zGBlVNUu8JunfSkUGJWL6nDQizqtf5YEPyrf7sodMri7R7gfAQQQQACB9AqQfKY3tvQsAoGqSkcGsuJtBLLdF3n0mMgravotWzQCL78WyOEj0ZRNqZcWqFfXC66tZdrtpXW4FwEEEEAgzQIkn2mOLn2LRIDzPiNh7bHQJ54qiL4UCFu4AnrUc+26QriFUlqvAiOGkXj2isQTEEAAAQRSKUDymcqw0qkoBYY18sUxSt9LlX3gkMimN0g+L2VTzn2vKtODh8spgX1LERgxnPeQUtzYBwEEEEAg+QIkn8mPIT0wLDB6FF8cDZO3V/fjXxSkhZVvQ6NvaQ3kJ79m1DM00D4UNJKRzz5o8VQEEEAAgTQJkHymKZr0xYjA6JFGqqGSCwSOnxRZ+xQL41zAUvLNtb/x5djxkndnxzIERgwvY2d2RQABBBBAIMECJJ8JDh5Nj0dg1AhHHAY/Y8Ffp1a+PfQR02/LxT94KJDfPEMiX65jqftzzmepcuyHAAIIIJB0AZLPpEeQ9hsXqKhwZHij8WqpUAnk1SzRR39UEL1QDltpAtru//y/grp8TWn7s1d5AsOGqpVua/jrVXmK7I0AAgggkFQBks+kRo52xyrAeZ/x8W/bEciT6xm1KzUCTyg7bcgWj8AVY0g845GnVgQQQAABGwRIPm2IAm1InMCYy/kCGWfQfvmkL+/vJgHtawx27PLlV8qOLT6BcSSf8eFTMwIIIIBA7AIkn7GHgAYkUeDKK0g+44ybr/Kn7/2gIM3NjOAVG4czyuq7ykzbscUncMVYPnbj06dmBBBAAIG4BfgUjDsC1J9IAT3ttqoykU1PTaOPHBP5u38sSD5PAtpbULXRt5QVq9v2JhXt47mcyKgR0dZB6QgggAACCNgsQPJpc3Rom7UCnufIeEY/Y4+PPnfxkcdYOaenQARBIA//sMB5nj0hGXpsrJqun1PvHWwIIIAAAghkVYDkM6uRp99lC0wYx5fIshFDKGDjq4H8fC0JaHeUP3/cl5dfY3S4Ox+T90+awHuGSW/qQgABBBCwT4Dk076Y0KKECEwYzxdJW0K1dp0va9eRgF4YD22iV7dls0NgykTeM+yIBK1AAAEEEIhLgOQzLnnqTbyAXrXS8xLfjdR04OdrffnZ4ySg5wP6s18X1Igwied5j7h/VlQIU/XjDgL1I4AAAgjELkDyGXsIaEBSBSorHZnI6KdV4XviKV9+9LOC6PMcs7rpvj+mDBjxtOsI0NP0K3KMfNoVFVqDAAIIIGBagOTTtDj1pUpgxlS+TNoW0PXP+fLthwtyriV7Caju89+rvv9WGbDZJcCUW7viQWsQQAABBOIRIPmMx51aUyIwYyovIRtD+fpbgXzjr/Ny6KPsJKC6r7rPr6m+s9knMHUS7xX2RYUWIYAAAgiYFuDT0LQ49aVKYHijI5cNSlWXUtOZ/QdFvv5XeXlzc/pHAd9SfdR91X1ms09gYL3IGHWZFTYEEEAAAQSyLkDymfUjgP6XLcDoZ9mEkRVw9qzIN79XkO//U16am9M3Iqj79PAP8/K3qo+6r2x2CsyawUetnZGhVQgggAACpgVypiukPgTSJqDP+3zm+bT1Kl39efGVQH63NS/33uXJrOnpSAT0aOej/7cgJ0+lK1Zp7M2cGYx6pjGu9AkBBBBAoO8CJJ99N2MPBLoITJnkSE21yNlzXe7mhmUCOknTI4Qzp/ly562ejBiWzIRg/8Gg/ZIyb76TvpFcyw6ZUJpTXSUyaUIyj7VQACgEAQQQQACBjwmQfH4Mg18RKEVAXz5hthrZeGkTyUApfqb3eft3gbzzbl4WLXDktuWeNNQnIzE4fiKQXz1ZkOdfDtSlZEyrUV+pAjOmOZLzknGMldpH9kMAAQQQQKBYAZLPYqV4HgI9CFw1x1XJZ6GHZ/CQTQI6eduwMZCXXs3LNfMd+dR1nowcYWeCsO9AIE9v8OXFV3xpa7NJkbYUIzBvZjqmeRfTV56DAAIIIIBAbwIkn70J8TgCRQhMnexIbY1IM4u+FKFlz1Py+Y4kdMPGvEyeqJLQJa5MV+fwxj1S5fuB6Gm1Ounc+h7DnPYcMX1riZ5yO2u6nX/U6FtPeDYCCCCAAALhCJB8huNIKRkX0MnK3FmOPK9G09iSKbB1eyBbtxfa/4gwZ6Yj89Vo9hSVkJpKRNvygby7NZDX3/bbE88zzcl0pNWdAvNmO1JRQfLZKcJvCCCAAAJZFyD5zPoRQP9DE9BTb5/fyNTb0EBjKkiPXr+gzqt84eWORHTCeEcmqn/655hRjnjqDw1hbHp0c98Bke3v+7Jth16NN5CWljBKpgxbBBbOZ8qtLbGgHQgggAACdgiQfNoRB1qRAgE9StagLiZ/4mQKOkMX2gV0IvrW5qD9n76jslJkeKMjjUNEGofqn44MahCpqXGkWq14rFc9rlJTLUUNgBd8aU8m9fU3TzUF7cfF0WOBHDwcyIGDInv3B5zD2a6czv/0e8GkK8P5Q0U6hegVAggggEAWBUg+sxh1+hyJgOs6cu0CV9auU1kHWyoFWltF9uwN1D/dPaZYpzLIIXVqwTxX9HsCGwIIIIAAAgh0CjAnqNOC3xAoW2DxQl5SZSNSAAIpENB/iGJDAAEEEEAAga4CfDp29eAWAmUJXDbYkamTGO0oC5GdEUi4gD5HeMQw3gcSHkaajwACCCAQgQDJZwSoFJltgcXX8LLK9hFA77Mu8MlFvAdk/Rig/wgggAAClxbgE/LSLtyLQMkCc2Y40r9fybuzIwIIJFigboC0X3YpwV2g6QgggAACCEQmQPIZGS0FZ1Ugl3NkCaOfWQ0//c64wCJ13repa8NmnJruI4AAAggkUIDkM4FBo8n2C1y/xBWPV5f9gaKFCIQo4KjTPK/7BC/8EEkpCgEEEEAgZQJ8SqYsoHTHDoGGekeumsuCI3ZEg1YgYEZg3ixHBg/idW9Gm1oQQAABBJIoQPKZxKjR5kQILFvqJaKdNBIBBMIR+PQNfKSGI0kpCCCAAAJpFeCTMq2RpV+xC4we5cikKxkFiT0QNAABAwKTJzgydjQfqQaoqQIBBBBAIMECfFImOHg03X6BZUt5idkfJVqIQPkCjHqWb0gJCCCAAALpF+CbcfpjTA9jFJg5zZHLR8bYAKpGAIHIBUaNEJk+hY/TyKGpAAEEEEAg8QJ8WiY+hHTAZgFHLX95682c+2lzjGgbAuUKLL+R13i5huyPAAIIIJANAZLPbMSZXsYoMGeGy+hnjP5UjUCUAiOHi1w1h3O7ozSmbAQQQACB9AiQfKYnlvTEYoHbGP20ODo0DYHSBW5f4Yme4cCGAAIIIIAAAr0LkHz2bsQzEChbYLYa/Rw9quxiKAABBCwSGDvaET2zgQ0BBBBAAAEEihPgU7M4J56FQNkCjH6WTUgBCFglsGoFH6FWBYTGIIAAAghYL8Anp/UhooFpEZg13eW6n2kJJv3IvMCE8Y5Mm8xHaOYPBAAQQAABBPokwCdnn7h4MgLlCdy1ilUxyxNkbwTsELjrdj4+7YgErUAAAQQQSJIAn55JihZtTbzA6FGOfOJqFidJfCDpQKYF9Gt47Gg+PjN9ENB5BBBAAIGSBPj0LImNnRAoXWCVWh2zsqL0/dkTAQTiE6iqFFm9khkM8UWAmhFAAAEEkixA8pnk6NH2RAoMbHDkpht46SUyeDQ68wLLl7nSUM/shcwfCAAggAACCJQkwDfgktjYCYHyBD6tks9BA8srg70RQMCswGWDRJYt5WPTrDq1IYAAAgikSYBP0TRFk74kRqCq0pF77mTqXmICRkMRUAJ33+FJRY5RTw4GBBBAAAEEShUg+SxVjv0QKFNAX3pl7iy+yJbJyO4IGBGYp16rs9Vrlg0BBBBAAAEEShfgk7R0O/ZEoGyBNWokpaqq7GIoAAEEIhSoqRFZw0yFCIUpGgEEEEAgKwIkn1mJNP20UkAvXLJ6JS9DK4NDoxD4V4E7b/Wkvo5ZChwQCCCAAAIIlCvAt95yBdkfgTIFli5y5YrRfLEtk5HdEYhEYMI4R5Zcw+szElwKRQABBBDInADJZ+ZCTodtE3BdR77wObWQCdf+tC00tCfjAvp6vJ//A08ch+Qz44cC3UcAAQQQCEmA5DMkSIpBoByBYY2O3HkrL8dyDNkXgbAFPnObK8OGkniG7Up5CCCAAALZFeDbbnZjT88tE7h+sStTJvJF17Kw0JyMCkyf7MjSxVwOKaPhp9sIIIAAAhEJkHxGBEuxCPRVQE/tu3+NJ3plTTYEEIhPoF+tyH3qtciGAAIIIIAAAuEKkHyG60lpCJQlMGigI/ryK2wIIBCfwL13e6JXomZDAAEEEEAAgXAFSD7D9aQ0BMoWuOYqV65dwBffsiEpAIESBBYvdGTeLD4aS6BjFwQQQAABBHoV4BO2VyKegIB5gXvUBe1HDjdfLzUikGWBy0eKrFGvPTYEEEAAAQQQiEaA5DMaV0pFoCyBykpHHro/J1WVZRXDzgggUKRATbXIQw/k1CWPmHVQJBlPQwABBBBAoM8CJJ99JmMHBMwI6MuvfF6de8aGAALRC+jFvoZeRuIZvTQ1IIAAAghkWYDkM8vRp+/WC1w9z5Wli3iZWh+oIhvoqb8lDG8U6d+v+x1yORG18HHRm37u0CEigwd13UXff+F9XZ/BrfMCy5a6MpfzPM9z8BMBBBBAAIHIBNTXHDYEELBZ4K7Vruw/GMi2HYHNzaRtvQgsvsaRu1d5UlXVkVm+udmX7/6gIK2tnTvqZPHrX8vJD39SkA0v9RzvqiqRz33GkzkzHalWZfp+IA/+Sb69sFnTHLnns54ManBk5x5fvvNIQY4eE3HV3zH++3/KyY6dgTzyWKGz4gz/NlldW/eOW/gDT4YPAbqOAAIIIGBQgE9cg9hUhUApAjlPnf/5gCdDBpeyN/vYIDB6lMgfqmTwwKFAvv39vDy+riCzp7tyy02db8Ff/Yon3/gv6pzDnNP+r7d2/4cve7JgniPvqUTye4/mVcLq/36XpYtdOXI0kG/8TV5GDnNk7syOeq692lFTS0XWPUPiqbEa1YjxQ/d74qnXGBsCCCCAAAIIRC/Q+c0n+rqoAQEEShTo38+RrzyYE70oClvyBKZOctWooyM/+bUvr70VyC/W+rJzty9zZnS+BT/5W18e+2lnAtlTL/XI5vixrkoifflff1+Ql18L5NkXOve9cpzTPrq5c3cgx0+IjB7liJ7Oe8vNnry0KVAj6T2Vno3HamtE/p16TfWrJfHMRsTpJQIIIICADQKd33xsaA1tQACBbgVGqBGsB+/z+nQ+YLeF8UAsAi3nOqs9eFhk4MDO2++8G8imNzoTyM5HLv5t0oSOhGnaZFf+x3/OyZ9/NScrbux8O99/IFCjeo5UVqjzS/uLHD4SyPVqNLRO/f7LJwsysF5EJ6hZ3fT0Yz2boHFodg2yGnv6jQACCCAQrwDnfMbrT+0I9ElgxhRX7l4dFD1C1qfCeXJkAu9u86VQcOWP1RTP5zf6arRNZMZUR0pNfQYPciQIAjn8USD7VKI5RZ23uGqlp+4TeWK9Ly+q0c271bnC3/yLnDSfFdm6PZB/80WvfXR0ySdcWf6pjpFYPTX363+Vl6YzkXXdyoLX3Okqs85k3cpG0igEEEAAAQRSKMCnbwqDSpfSLXDDEq89eUh3L9PVuw8+FPnW9wty/GQgixa6Mm6sIwP6O+p26f08eUrk2w8X5Ff/7Mtf/l1BTqiyp03pSGeffd6Xr/55Xv73dwvyp3+Wl6mTOqbdPvGUL4tV/eufU4//1za5bLAj0yaXmgKX3vY491yxzJVPXssljOKMAXUjgAACCGRXgJHP7MaenidYYPUtnpxqCtQoWs8roia4i6lr+lubA3lrc8dCP2Mud+TP/tSV994vLn76sin6Mi0Ftbse3dQjnnPVKrd1A0ROne64r0adw6hOK/39pu/fvCVQSa7IjepSIvr8UL3V1zntqycfO67LC9RlWvROxbWjvYAE/3edGvVdtYLEM8EhpOkIIIAAAgkXIPlMeABpfnYF7r3Lk6amgrypkho2+wWmqxFGvejPFDUKuUBdv7W1NWhf9fZ8yyeoczAbGjpu6QWC9FTaHbsCaWsTueYqRx64Jyc/eCwvG9QfHF5/O5BP3yDyR/d68qSaZjt/titVlY5s2X7xOaMr1Yq6uox1T/tyrkXkIzXVdvIEV44d99tXef3gw2wcP/NmqcvPfIbJPuePN34igAACCCAQhwDJZxzq1IlACAJ69dQvqQWI/vpbhfbLbYRQJEVEKLBYjbrNm+W2X4/zg30ij/wwry6H0lnhfWvUAjjto5DSPjVXT8/9b3/RJnvVc89fC/R8mrhrTyA//qUvq1eqcxe/3PE2/tImX/S02o9v+lqgerTvp2qVXZ146u2f1aq6q1a4snB+TvS5qFvU+aBp3yarBZq+qBJ1/ZphQwABBBBAAIH4BBy1aEX6v3nE50vNCEQucPZc0J6A6oSEzV4BfWkPPU32qJruqkciw9j0SKq+/usJde7o2Y+tpNtb2Xo/veiRPm807du4MY7oa6JWV5N4pj3W9A8BBBBAwH4Bkk/7Y0QLEehVoPlsIH+jRkB3fUAC2isWT8iMgE48/0QlnjUknpmJOR1FAAEEELBbgBNg7I4PrUOgKIHamo4v2VeMZnSnKDCelHoBEs/Uh5gOIoAAAggkUIDkM4FBo8kIXErgfAI6lgT0UjzclyEBEs8MBZuuIoAAAggkSoBpt4kKF41FoHeB5uZA/ud3CrJzN1Nwe9fiGWkTGK+uofrvH2KqbdriSn8QQAABBNIhQPKZjjjSCwS6CLSoy3h8+x8LsnkrCWgXGG6kWmD6FEceesBrv+xMqjtK5xBAAAEEEEioAMlnQgNHsxHoTSBfCOThfyrIy6+RgPZmxePJF1gwzxdIl1sAAAtkSURBVJH77/Ek53Hec/KjSQ8QQAABBNIqQPKZ1sjSLwSUgL6S0o9+5stv/6Xr9R/BQSBNAjdc58rdq1xxHBLPNMWVviCAAAIIpE+A5DN9MaVHCFwk8Pi6gvxiLQnoRTDckXiBVStcWbHMS3w/6AACCCCAAAJZECD5zEKU6SMCSmDTG758X03DbWuDA4HkC+RyIg+s8eTqeSzanvxo0gMEEEAAgawIkHxmJdL0EwElsGuPL9/8h4KcPAUHAskVqBsg8m//yJNxY0g8kxtFWo4AAgggkEUBks8sRp0+Z1rg2IlA/va7edm7L9MMdD6hApePFPnKgzkZ1MD5nQkNIc1GAAEEEMiwAMlnhoNP17Mr0NIStE/Bfe0tVsLN7lGQvJ7PnenIFz6nLqVSReKZvOjRYgQQQAABBERIPjkKEMiwwLpnCvLTX/lSYC2iDB8F9nfdVbNr77jFlZuuZ2Eh+6NFCxFAAAEEEOhegOSzexseQSATAjt2+fKdhwty/GQmuksnEyZQXyfyx/d5MmE853cmLHQ0FwEEEEAAgYsESD4vIuEOBLIncLopkO89WpB3tzENN3vRt7fHkyc68uC9ntQNYJqtvVGiZQgggAACCBQvQPJZvBXPRCDVAr4fyBPrffn1k0zDTXWgE9A5R+Wat9zkykr1z3VJPBMQMpqIAAIIIIBAUQIkn0Ux8SQEsiOwe28g//BoXg4ezk6f6ak9AkOHiHxRLSo0bizTbO2JCi1BAAEEEEAgHAGSz3AcKQWBVAm0tgbyE7UQ0dMbWIkoVYG1vDPXXevKZ293paqS0U7LQ0XzEEAAAQQQKEmA5LMkNnZCIBsCm7f48shjBTnBYkTZCHhMvawbIHL/Gk9mTGW0M6YQUC0CCCCAAAJGBEg+jTBTCQLJFTh7Lmi/HMuzLzAKmtwo2tvyaxc48pnbPOnfj9FOe6NEyxBAAAEEEAhHgOQzHEdKQSD1AvqSLI/+qCD7D6a+q3TQgEDjUJF77/Jk0pWMdhrgpgoEEEAAAQSsECD5tCIMNAKBZAjkC4E8+ZQva9f5ki8ko8200i4BzxNZfqPb/q8ix2inXdGhNQgggAACCEQrQPIZrS+lI5BKgUMfBfLjXxTkzc1cFzSVAY6oU9MmO3L3ak+GN5J0RkRMsQgggAACCFgtQPJpdXhoHAJ2C2zZ5stjP1dTcQ/Y3U5aF6/AMDXF9rO3ezJzGlNs440EtSOAAAIIIBCvAMlnvP7UjkDiBXw/kOde9OWXT/jSdCbx3aEDIQr0qxW59WZXPqkuoeJ5jHaGSEtRCCCAAAIIJFKA5DORYaPRCNgn0Hw2kN887cv6Z31pabWvfbTInEBFhcjSRa6sWOZKv1qSTnPy1IQAAggggIDdAiSfdseH1iGQOIHTTWpRovW+PPO8L21tiWs+DS5DIKcWE7pOjXLqBYXq60g6y6BkVwQQQAABBFIpQPKZyrDSKQTiFzhxMpDH1aq4G17ypcDKuPEHJMIW6BVsFy/sGOkc2EDSGSE1RSOAAAIIIJBoAZLPRIePxiNgv4BOQp9SU3Gfe8GXcy32t5cWFi9QWSmyaIErN13vyuBBJJ3Fy/FMBBBAAAEEsilA8pnNuNNrBIwL6HNCdQK6/jlfTp4yXj0VhihQN0DkhiVqISF1XifndIYIS1EIIIAAAgikXIDkM+UBpnsI2CbQlg9k46ZAnt5QkL37bGsd7elJYMQwkWVLPVk435FcjpHOnqx4DAEEEEAAAQQuFiD5vNiEexBAwJDA+7t9eVYtTLTpjUDyeUOVUk2fBHI5kXmzHLnuE65MvJLrdPYJjycjgAACCCCAQBcBks8uHNxAAIE4BJrOBPLCy778i1qc6NDhOFpAnRcKDG8UWaISzmuucqV/P0Y5L/ThNgIIIIAAAgj0XYDks+9m7IEAAhEK7Nrjy8ZXA3nldV9ON0VYEUVfJNC/n8j82a4sUNNqJ4xjlPMiIO5AAAEEEEAAgbIESD7L4mNnBBCISqBQCOR3W9X5oa/68ubmQFpbo6op2+XWVIvMmenI1XNdmTLREc9jlDPbRwS9RwABBBBAIDoBks/obCkZAQRCEmhrC+TdbYG8+U5HIsqIaHmwerXaGVMdmT3dlenqZwWLB5UHyt4IIIAAAgggUJQAyWdRTDwJAQRsEfD9QHbuCeSNtwPZvMWXfQdsaZnd7RhzuSMzVaI5c5ojY0c74jiMcNodMVqHAAIIIIBA+gRIPtMXU3qEQKYETp0OZMt2/c9v/3n0WKa6321nG4eIWp3WkYnjO6bTNtSTbHaLxQMIIIAAAgggYESA5NMIM5UggIApgcNHAtmxs2N0dKe6lMuH+0V831Tt8dTjqrWB9DU4r7xCXw7FkUnqX30dyWY80aBWBBBAAAEEEOhOgOSzOxnuRwCBVAi0tgay50OVjO4OZO++QE3TDeSgupxLW1syu+d5IiOHi+hptGNGqX/q56gR6rzNCpLNZEaUViOAAAIIIJAdAZLP7MSaniKAwL8K6PNGPzoisv9gRzKqR0uPHBU5ciyQ4ydEgiBeKn06Zn2dSOMQR4YNdaRxaOfvlw0WVqSNNzzUjgACCCCAAAIlCpB8lgjHbgggkE4BfYkXnYAePd7xs+lMIE1nRJrUNUdPN3X83nxWXfpFjZzq0VN9CZi2fMfv56f36uTRVf8cNR1WT4n11L+qKhF9WZOaakeq1c/amo7bA/qrKbL1IgPVOZkN6qc+N1MnnlzyJJ3HF71CAAEEEEAgywIkn1mOPn1HAIFQBfSIqquzTjYEEEAAAQQQQACBiwRIPi8i4Q4EEEAAAQQQQAABBBBAAIGwBdRkMDYEEEAAAQQQQAABBBBAAAEEohUg+YzWl9IRQAABBBBAAAEEEEAAAQSUAMknhwECCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAAMknxwACCCCAAAIIIIAAAggggEDkAiSfkRNTAQIIIIAAAggggAACCCCAwP8HFA9xkzXwArEAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "charts.cost_breakdown_pie(seed_data.COSTS).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d15ae10",
   "metadata": {},
   "source": [
    "## Push to Athena"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03547040",
   "metadata": {},
   "outputs": [],
   "source": [
    "if config.TOOL_PUBLIC_ID:\n",
    "    from core.tei_client import TEIClient\n",
    "\n",
    "    client = TEIClient()\n",
    "    client.update_values(config.TOOL_PUBLIC_ID, seed_data.COSTS)\n",
    "    display.alert(f'Pushed {len(seed_data.COSTS)} cost rows to '\n",
    "                  f'tool <code>{config.TOOL_PUBLIC_ID}</code>.', 'success')\n",
    "else:\n",
    "    display.alert('No TOOL_PUBLIC_ID set — skipped Athena push.', 'info')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f5befbb",
   "metadata": {},
   "source": [
    "Continue with [`03_business_case.ipynb`](03_business_case.ipynb) →"
   ]
  }
 ],
 "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
}