diff --git a/data_raw/columns.csv b/data_raw/columns.csv
index 78723fe0..752f819a 100644
--- a/data_raw/columns.csv
+++ b/data_raw/columns.csv
@@ -128,3 +128,4 @@ Intermediary Admin Expenses Calc
 PRIMARY_FUNDING_RECIPIENT_TYPE
 TRIBAL AFFILIATION
 PROJECT PARTNERS
+ej_category
diff --git a/data_raw/data_types.csv b/data_raw/data_types.csv
index 76c8d7fd..62fa25fb 100644
--- a/data_raw/data_types.csv
+++ b/data_raw/data_types.csv
@@ -127,3 +127,4 @@ Intermediary Admin Expenses Calc,int64
 PRIMARY_FUNDING_RECIPIENT_TYPE,object
 TRIBAL AFFILIATION,object
 PROJECT PARTNERS,object
+ej_category,object
diff --git a/initial_view/overview_hypotesting_20241031.ipynb b/initial_view/overview_hypotesting_20241031.ipynb
index a8115c1d..4fc9dd98 100644
--- a/initial_view/overview_hypotesting_20241031.ipynb
+++ b/initial_view/overview_hypotesting_20241031.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,39 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: pandas in /home/dadams/Repos/california_equity_git/.venv/lib/python3.12/site-packages (2.2.3)\n",
-      "Requirement already satisfied: numpy in /home/dadams/Repos/california_equity_git/.venv/lib/python3.12/site-packages (2.1.2)\n",
-      "Requirement already satisfied: matplotlib in /home/dadams/Repos/california_equity_git/.venv/lib/python3.12/site-packages (3.9.2)\n",
-      "Requirement already satisfied: seaborn in /home/dadams/Repos/california_equity_git/.venv/lib/python3.12/site-packages (0.13.2)\n",
-      "Requirement already satisfied: python-dateutil>=2.8.2 in /home/dadams/Repos/california_equity_git/.venv/lib/python3.12/site-packages (from pandas) (2.9.0.post0)\n",
-      "Requirement already satisfied: pytz>=2020.1 in /home/dadams/Repos/california_equity_git/.venv/lib/python3.12/site-packages (from pandas) (2024.2)\n",
-      "Requirement already satisfied: tzdata>=2022.7 in /home/dadams/Repos/california_equity_git/.venv/lib/python3.12/site-packages (from pandas) (2024.2)\n",
-      "Requirement already satisfied: contourpy>=1.0.1 in /home/dadams/Repos/california_equity_git/.venv/lib/python3.12/site-packages (from matplotlib) (1.3.0)\n",
-      "Requirement already satisfied: cycler>=0.10 in /home/dadams/Repos/california_equity_git/.venv/lib/python3.12/site-packages (from matplotlib) (0.12.1)\n",
-      "Requirement already satisfied: fonttools>=4.22.0 in /home/dadams/Repos/california_equity_git/.venv/lib/python3.12/site-packages (from matplotlib) (4.54.1)\n",
-      "Requirement already satisfied: kiwisolver>=1.3.1 in /home/dadams/Repos/california_equity_git/.venv/lib/python3.12/site-packages (from matplotlib) (1.4.7)\n",
-      "Requirement already satisfied: packaging>=20.0 in /home/dadams/Repos/california_equity_git/.venv/lib/python3.12/site-packages (from matplotlib) (24.1)\n",
-      "Requirement already satisfied: pillow>=8 in /home/dadams/Repos/california_equity_git/.venv/lib/python3.12/site-packages (from matplotlib) (11.0.0)\n",
-      "Requirement already satisfied: pyparsing>=2.3.1 in /home/dadams/Repos/california_equity_git/.venv/lib/python3.12/site-packages (from matplotlib) (3.2.0)\n",
-      "Requirement already satisfied: six>=1.5 in /home/dadams/Repos/california_equity_git/.venv/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pip install pandas numpy matplotlib seaborn\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -59,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -70,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -80,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -93,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -107,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -456,7 +424,7 @@
        "[8 rows x 82 columns]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -468,7 +436,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -520,82 +488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Columns containing 'County':\n",
-      "['County']\n",
-      "\n",
-      "Program Types:\n",
-      "Program Name\n",
-      "Low Carbon Transportation                                                    105965\n",
-      "Funding Agricultural Replacement Measures for Emission Reductions Program      8554\n",
-      "Low-Income Weatherization Program                                              6468\n",
-      "Community Air Protection                                                       5514\n",
-      "Water-Energy Efficiency                                                        5111\n",
-      "Climate Smart Agriculture                                                      1688\n",
-      "Low Carbon Transit Operations Program                                          1003\n",
-      "Transformative Climate Communities                                              973\n",
-      "Urban and Community Forestry Program                                            967\n",
-      "Woodsmoke Reduction Program                                                     959\n",
-      "Name: count, dtype: int64\n",
-      "\n",
-      "Project Types:\n",
-      "Project Type\n",
-      "Voucher                                                2808\n",
-      "Voucher Incentive                                      1730\n",
-      "Air Filtration (Air Filtration)                        1570\n",
-      "Water Use and Energy Efficiency                        1217\n",
-      "Off-Road Equipment - Mobile Agricultural (Tractors)     891\n",
-      "                                                       ... \n",
-      "Procurement Requirement                                   1\n",
-      "Education and outreach and enforcement/inspections        1\n",
-      "Personnel, education and outreach, and procurement        1\n",
-      "Education & Outreach and Planning                         1\n",
-      "Equipment, Education and Outreach                         1\n",
-      "Name: count, Length: 1007, dtype: int64\n",
-      "\n",
-      "Funding Recipient Types:\n",
-      "PRIMARY_FUNDING_RECIPIENT_TYPE\n",
-      "Business                            423\n",
-      "Government Agency                   285\n",
-      "Nonprofit                           113\n",
-      "Educational institution              21\n",
-      "California Native American Tribe      4\n",
-      "Government agency                     2\n",
-      "Name: count, dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Let's look at the actual column names in the merged dataset\n",
-    "print(\"Columns containing 'County':\")\n",
-    "print([col for col in data.columns if 'County' in col])\n",
-    "\n",
-    "# Then modify our analysis using the correct county column name:\n",
-    "geographic_analysis = data.groupby(['County', 'project_category']).agg({\n",
-    "    'Total Project Cost': 'sum',\n",
-    "    'Total Project GHGReductions': 'sum',\n",
-    "    'Total GGRFDisadvantaged Community Funding': 'sum'\n",
-    "}).reset_index()\n",
-    "\n",
-    "# Rest of the analysis:\n",
-    "# 1. First look at program distributions\n",
-    "print(\"\\nProgram Types:\")\n",
-    "print(data['Program Name'].value_counts().head(10))\n",
-    "print(\"\\nProject Types:\")\n",
-    "print(data['Project Type'].value_counts())\n",
-    "print(\"\\nFunding Recipient Types:\")\n",
-    "print(data['PRIMARY_FUNDING_RECIPIENT_TYPE'].value_counts())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -813,24 +706,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_738944/3534785368.py:18: FutureWarning: \n",
+      "/tmp/ipykernel_182042/3534785368.py:18: FutureWarning: \n",
       "\n",
       "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
       "\n",
       "  sns.barplot(data=plot_data,\n",
-      "/tmp/ipykernel_738944/3534785368.py:28: FutureWarning: \n",
+      "/tmp/ipykernel_182042/3534785368.py:28: FutureWarning: \n",
       "\n",
       "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
       "\n",
       "  sns.barplot(data=plot_data[mask],\n",
-      "/tmp/ipykernel_738944/3534785368.py:38: FutureWarning: \n",
+      "/tmp/ipykernel_182042/3534785368.py:38: FutureWarning: \n",
       "\n",
       "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
       "\n",
@@ -930,7 +823,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -1112,7 +1005,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -1126,7 +1019,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1134,9 +1027,20 @@
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_182042/2919179271.py:33: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.boxplot(data=data[data['dac_funding_ratio'].notna()],\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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",
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zXy9z+nkPGjSobv1M7du3T+/evest++rrfG4acpzdunXLjjvuWO96CEOHDs03vvGNbLHFFnPdfpcuXRr83vB1j/3rmNfv6bwMGTIkTz/9dF0Pd9xxR6ZPn97gaWoAABY2IToAQBMbM2ZMJk6cWC/onTnqcs8998yKK65Yd/v973+fDz74IE888USS/4ZbSfLqq68u0B6LRnsWXUCxaMRxqVRqsp4W5n5mfsEwePDgnHjiibn11ltzzz335JJLLqmrqampydZbb50HHnggJ510Uu655548/PDDdRfonNMFU5uD1157La1atZpryH388cfnrbfeyjnnnJP27dvnlFNOySqrrPK1Rtd/nbnuG+LrviYXhIaOrJ9fQ4YMybvvvptnnnkmkyZNyn333Zd99tknlZVz/3g2aNCgTJw4cb6/aPmqhf1+sPfee6dNmzZ174u33npr1ltvva/1hRsAwMIkRAcAaGIzL6Q3ePDgJP+dBuTee+/NXnvtlTvuuGO229JLL10XJq200kpZeeWVc++999aNKF4QZo4cnTBhQr3l8zM6tV+/fnn77bdnW/6vf/2r0duc0z6S/17odFYzZsyYrxHN22+/fTbddNP86le/qrtI5quvvpq33norF1xwQU466aTstNNO2WqrrbLMMsvM9vyGTkHRu3fvdOjQYY4/kzfffDOVlZXp27dvo49j9OjReeKJJ/Ktb31rntN+DBw4MCeeeGIeeuihvPbaa6murs4FF1xQt74pp9V45513ZgtY33rrrSSp+wuCr/OabGhvM18vc/p5/+tf/6pb31QacpxJsu2226Z3794ZOnRo7r777kyZMqVBo7B33HHHJP8Nnefl6xx79+7dZ/u5J/P3fjC3f6MePXpk++23z9ChQ/Of//wnTz/9tFHoAECzJkQHAGhCjz76aH75y19mwIABdXOB33333Zk8eXKOPvro7L777rPddthhhwwfPjzTpk1Lkpxxxhn57LPP8v3vfz8zZsyYbR8PPfRQ7r///vnqc+DAgUmSv/3tb3XLJk+enJtuuqnR2/zud7+bZ599Ns8//3zdsrFjx9ab+3h+rbfeeunZs2euvfbaej+boUOHztf83kly0kkn5bPPPsu1116b5P9G284aipZKpXqj1Wfq2LFjktkD4K9q1apVttlmm9x77731Qv9PPvkkw4YNyyabbFI3pc/XNX78+Oyzzz6pqanJL37xi8K6KVOmZOrUqfWWDRw4MJ07d657DSb/PaZ5HU9Dffjhh7n77rvrHldVVeXmm2/O2muvnaWWWqquh6Rhr8mG9rbeeuulT58+ueqqq+od24MPPph//vOf2X777Rt7SHPUkONM/jv90D777JM//OEPufHGG7PGGmtkzTXXnOf2d99996yxxho5++yz8/e//3229ZMmTar7t/86xz5w4MC8+eabGTt2bN2yl19+OU8//fTX+wHMYl6/EwcccEDeeOON/OQnP0mrVq2y9957N3pfAAALWut5lwAAMCcPPvhg3nzzzcyYMSOffPJJHn300Tz88MPp169f7rvvvrqL+Q0dOjQ9e/bMRhttNMftfO9738u1116bBx54ILvuumv22muvvPrqqzn77LMzcuTI7LPPPunXr18+++yz/PnPf85f//rXevMpN8Y222yT5ZZbLoceemhdiHX99dend+/eGT16dKO2+dOf/jS33HJLtt122xx33HHp2LFjrrnmmvTr1y+vvPLKfPU7U9u2bXP66afnmGOOyRZbbJE999wzo0aNyo033piBAwfO1+jp7bbbLquvvnouvPDCHH300Rk0aFAGDhyYH//4x/nggw/SpUuXDB8+fI5h/brrrpskOfbYYzN48OC5hoJnnXVWHn744WyyySY56qij0rp161x99dWZNm1afv3rXzeo17feeiu33nprSqVSqqqq8vLLL+eOO+7IF198kQsvvDDbbrvtXJ+75ZZbZs8998yqq66a1q1b5+67784nn3xSr+d11103V155Zc4666yssMIK6dOnzzzn7C6y0kor5dBDD80LL7yQJZdcMtdff30++eST3HDDDXU1X+c12dDe2rRpk/POOy8HH3xwNt100+yzzz755JNPcskll6R///454YQTGnU883OcMw0ZMiSXXnppHnvssZx33nkN2n6bNm1y1113Zauttsp3vvOd7Lnnntl4443Tpk2bvP766xk2bFi6d++es88++2sd+yGHHJILL7wwgwcPzqGHHppPP/00V111VVZbbbVGX+h2Xr8T22+/fXr27Jk77rgj2223Xfr06dOo/QAALBQlAAC+lhtuuKGUpO7Wtm3b0lJLLVXaeuutS5dcckmpqqqqrvaTTz4ptW7dunTAAQcUbm/KlCmlDh06lHbZZZd6y//617+Wdtppp1KfPn1KrVu3LvXu3bu04447lu6999559tivX7/S9ttvP9eaF198sbThhhuW2rZtW1puueVKF154Yd2xvffee/Pc1qabblradNNN6y175ZVXSptuummpffv2pW984xulX/7yl6Xrrrtutm1+9bmPPfZYKUnpjjvuqLe99957r5SkdMMNN9Rbfumll5b69etXateuXWmDDTYoPf3006V11123tO222871mOd2PKVSqXTjjTfW298bb7xR2mqrrUqdOnUq9erVq3TYYYeVXn755dl6mjFjRumYY44p9e7du1RRUVGa9TQ7Sem0006rt58RI0aUBg8eXOrUqVOpQ4cOpc0337z0zDPPzLP3mdubeausrCx169attM4665SOO+640uuvvz5b/Vd/huPGjSsdffTRpUGDBpU6duxY6tq1a2nDDTcs/eEPf6j3vI8//ri0/fbblzp37lxKUvfvNfM18sILL8y2r7m9fv7yl7+U1lxzzVK7du1KgwYNmu3fulRq+GuyqLeZr6PHHnus3nZ///vfl9ZZZ51Su3btSj169Cjtt99+pTFjxtSrOfDAA0sdO3acrafTTjut1JCPTV/nOGdabbXVSpWVlbP1Mi+ff/556dRTTy2tscYapQ4dOpTat29fWn311Us/+9nPSh999FG92oYce6lUKt16662l5ZdfvtS2bdvS2muvXfrLX/5SOvDAA0v9+vWrq5n5Wjr//PNne/5XX+dz+52Y6aijjiolKQ0bNuxrHT8AwMJWUSo18VWaAABgIautrU3v3r2z66671k3HAs3dOuuskx49euSvf/1ruVspixNOOCHXXXddPv7443To0KHc7QAAFDInOgAAi5SpU6fOdvHGm2++OePHj89mm21Wnqbga/rHP/6Rl156KUOGDCl3K2UxderU3Hrrrdltt90E6ABAs2ckOgAAi5THH388J5xwQvbYY4/07NkzI0aMyHXXXZdVVlklL774Ytq2bVvuFqHQa6+9lhdffDEXXHBBxo0bl3fffbfu+gmLg08//TSPPPJI7rzzztxzzz0ZMWJE1l577XK3BQAwVy4sCgDAIqV///7p27dvLr300owfPz49evTIkCFDcu655wrQafbuvPPOnHnmmVl55ZVz2223LVYBepK88cYb2W+//dKnT59ceumlAnQAYJFgJDoAAAAAABQwJzoAAAAAABQQogMAAAAAQIFFek702trafPjhh+ncuXMqKirK3Q4AAAAAAIuIUqmUSZMmZZlllkllZfF480U6RP/www/Tt2/fcrcBAAAAAMAi6v3338+yyy5buH6RDtE7d+6c5L8H2aVLlzJ3AwAAAADAoqKqqip9+/aty5mLLNIh+swpXLp06SJEBwAAAADga5vXVOEuLAoAAAAAAAWE6AAAAAAAUECIDgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAAAAUECIDgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAAAAUECIDgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAAAAUECIDgAAAAAABYToAAAAAABQoHW5G4CFrbq6OsOHD8+YMWOy7LLLZrfddkvbtm3L3RYAAAAA0AwJ0VmsXHbZZRk2bFhqamrqll166aXZd999c8wxx5SxMwAAAACgORKis9i47LLLcsstt6RHjx454ogjsskmm+Spp57KVVddlVtuuSVJBOkAAAAAQD0VpVKpVO4mGquqqipdu3bNxIkT06VLl3K3QzNWXV2dTTfdNF27ds3999+f1q3/7/ujGTNmZIcddsjEiRPzxBNPmNoFAAAAABYDDc2XXViUxcLw4cNTU1OTI444ol6AniStW7fO4YcfnpqamgwfPrxMHQIAAAAAzZEQncXCmDFjkiSbbLLJHNfPXD6zDgAAAAAgEaKzmFh22WWTJE899dQc189cPrMOAAAAACARorOY2G233dKqVatcddVVmTFjRr11M2bMyNVXX51WrVplt912K1OHAAAAAEBzJERnsdC2bdvsu+++GT9+fHbYYYfcfffdGTt2bO6+++7ssMMOGT9+fPbdd18XFQUAAAAA6qkolUqlcjfRWA29eirMdNlll2XYsGGpqampW9aqVavsu+++OeaYY8rYGQAAAACwMDU0Xxais9iprq7O8OHDM2bMmCy77LLZbbfdjEAHAAAAgMVMQ/Pl1guxJ2gW2rZtm3322afcbQAAAAAAiwBzogMAAAAAQAEhOgAAAAAAFBCiAwAAAABAASE6AAAAAAAUEKIDAAAAAEABIToAAAAAABQQogMAAAAAQAEhOgAAAAAAFBCiAwAAAABAASE6AAAAAAAUEKIDAAAAAEABIToAAAAAABQQogMAAAAAQAEhOgAAAAAAFBCiAwAAAABAASE6AAAAAAAUEKIDAAAAAEABIToAAAAAABQQogMAAAAAQAEhOgAAAAAAFBCiAwAAAABAgbKG6DU1NTnllFMyYMCALLHEEhk4cGB++ctfplQqlbMtAAAAAABIkrQ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lFuQuAAAAAABggWlUiL7LLrvMcaqWioqKtG/fPiussEL23XffrLzyyvPdIAAAAAAAlEujpnPp2rVrHn300YwYMSIVFRWpqKjIyJEj8+ijj2bGjBn5/e9/n7XWWitPP/10U/cLAAAAAAALTaNGoi+11FLZd99989vf/jaVlf/N4Wtra3Pcccelc+fOuf3223PEEUfkpJNOylNPPdWkDQMAAAAAwMJSUWrE1T979+6dp59+OiuttFK95W+99VY22mijjBs3Lq+++mq+/e1vZ8KECU3V62yqqqrStWvXTJw4MV26dFlg+wEAAAAAoGVpaL7cqOlcZsyYkTfffHO25W+++WZqamqSJO3bt5/jvOkAAAAAALCoaNR0LgcccEAOPfTQ/PznP8/666+fJHnhhRfyq1/9KkOGDEmSPPHEE1lttdWarlMAAAAAAFjIGhWiX3TRRVlyySXz61//Op988kmSZMkll8wJJ5yQk046KUmyzTbbZNttt226TgEAAAAAYCFr1Jzos6qqqkqSssxJbk50AAAAAAAao6H5cqNGos9KeA0AAAAAQEvVqAuLfvLJJznggAOyzDLLpHXr1mnVqlW9GwAAAAAAtASNGol+0EEHZfTo0TnllFOy9NJLp6Kioqn7AgAAAACAsmtUiP7UU0/lySefzNprr93E7QAAAAAAQPPRqOlc+vbtm/m8HikAAAAAADR7jQrRL7744px88skZNWpUE7cDAAAAAADNR6Omc9lrr70yZcqUDBw4MB06dEibNm3qrR8/fnyTNAcAAAAAAOXUqBD94osvbuI2AAAAAACg+WlUiH7ggQc2dR8AAAAAANDsNDhEr6qqSpcuXeruz83MOgAAAAAAWJQ1OETv3r17Pvroo/Tp0yfdunVLRUXFbDWlUikVFRWpqalp0iYBAAAAAKAcGhyiP/roo+nRo0eS5LHHHltgDQEAAAAAQHNRUSqVSuVuorGqqqrStWvXTJw40RQyAAAAAAA0WEPz5QaPRH/llVcavPM111yzwbUAAAAAANBcNThEX3vttVNRUVE37/ncmBMdAAAAAICWoLKhhe+9917efffdvPfeexk+fHgGDBiQK664IiNHjszIkSNzxRVXZODAgRk+fPiC7BcAAAAAABaaBo9E79evX939PfbYI5deemm++93v1i1bc80107dv35xyyinZeeedm7RJAAAAAAAohwaPRJ/Vq6++mgEDBsy2fMCAAXnjjTfmuykAAAAAAGgOGhWir7LKKjnnnHNSXV1dt6y6ujrnnHNOVllllSZrDgAAAAAAyqnB07nM6qqrrsqOO+6YZZddNmuuuWaS5JVXXklFRUX++Mc/NmmDAAAAAABQLhWlUqnUmCdOnjw5Q4cOzZtvvpnkv6PT991333Ts2LFJG5ybqqqqdO3aNRMnTkyXLl0W2n4BAAAAAFi0NTRfbtRI9CTp2LFjfvCDHzT26QAAAAAA0Ow1OkR/++2389hjj+XTTz9NbW1tvXWnnnrqfDcGAAAAAADl1qgQ/dprr82RRx6ZXr16ZamllkpFRUXduoqKCiE6AAAAAAAtQmVjnnTWWWfl7LPPzscff5yXXnopI0eOrLuNGDGi0c2ce+65qaioyPHHH9/obQAAAAAAQFNpVIj++eefZ4899mjSRl544YVcffXVWXPNNZt0uwAAAAAA0FiNCtH32GOPPPTQQ03WxBdffJH99tsv1157bbp3795k2wUAAAAAgPnRqDnRV1hhhZxyyil59tlns8Yaa6RNmzb11h977LFfa3tHH310tt9++2y11VY566yzGtMSAAAAAAA0uUaF6Ndcc006deqUJ554Ik888US9dRUVFV8rRL/99tszYsSIvPDCC/OsnTZtWqZNm1b3uKqqquFNAwAAAADA19SoEP29995rkp2///77Oe644/Lwww+nffv286w/55xzcsYZZzTJvgEAAAAAYF4qSqVSqVw7v+eee7LLLrukVatWdctqampSUVGRysrKTJs2rd66OY1E79u3byZOnJguXbos1N4BAAAAAFh0VVVVpWvXrvPMlxs1Ev2QQw6Z6/rrr7++QdvZcsst8+qrr9ZbdvDBB2fQoEE56aST6gXoSdKuXbu0a9fu6zULAAAAAACN1KgQ/fPPP6/3ePr06XnttdcyYcKEbLHFFg3eTufOnbP66qvXW9axY8f07NlztuUAAAAAALCwNSpEv/vuu2dbVltbmyOPPDIDBw6c76YAAAAAAKA5aNI50f/1r39ls802y0cffdRUm5yrhs5ZAwAAAAAAs2povlzZlDv997//nRkzZjTlJgEAAAAAoGwaNZ3Lj370o3qPS6VSPvroozzwwAM58MADm6QxAAAAAAAot0aF6CNGjEhFRUXd48rKyvTu3TsXXHBBDjnkkCZrDgAAAAAAyqnBIfp9992X7bbbLm3atMnjjz++AFsCAAAAAIDmocFzou+yyy6ZMGFCkqRVq1b59NNPF1RPAAAAAADQLDQ4RO/du3eeffbZJP+dA33W6VwAAAAAAKAlavB0LkcccUR22mmnVFRUpKKiIksttVRhbU1NTZM0BwAAAAAA5dTgEP3000/P3nvvnXfeeSff+973csMNN6Rbt24LsDUAAAAAACivBofoSTJo0KAMGjQop512WvbYY4906NBhrvVPP/101ltvvbRr126+mgQAAAAAgHKoKJVKpQW18S5duuSll17K8ssvv0C2X1VVla5du2bixInp0qXLAtkHAAAAAAAtT0Pz5QZfWLQxFmA+DwAAAAAAC9wCDdEBAAAAAGBRJkQHAAAAAIACQnQAAAAAACiwQEP0ioqKBbl5AAAAAABYoFxYFAAAAAAACrRuzJPee++9zJgxIyuuuGK95W+//XbatGmT/v37J0kmTZo03w0CAAAAAEC5NGok+kEHHZRnnnlmtuXPPfdcDjrooPntCQAAAAAAmoVGhegjR47MxhtvPNvyb37zm3nppZfmtycAAAAAAGgWGhWiV1RUzHGqlokTJ6ampma+mwIAAAAAgOagUSH6d77znZxzzjn1AvOampqcc8452WSTTZqsOQAAAAAAKKdGXVj0vPPOy3e+852svPLK+fa3v50kefLJJ1NVVZVHH320SRsEAAAAAIByadRI9FVXXTWvvPJK9txzz3z66aeZNGlShgwZkjfffDOrr756U/cIAAAAAABlUVEqlUrlbqKxqqqq0rVr10ycODFdunQpdzsAAAAAACwiGpovN2o6l5mmTJmS0aNHp7q6ut7yNddcc342CwAAAAAAzUKjQvSxY8fm4IMPzoMPPjjH9bNecBQAAAAAABZVjZoT/fjjj8+ECRPy3HPPZYkllsif//zn3HTTTVlxxRVz3333NXWPAAAAAABQFo0aif7oo4/m3nvvzXrrrZfKysr069cvW2+9dbp06ZJzzjkn22+/fVP3CQAAAAAAC12jRqJPnjw5ffr0SZJ07949Y8eOTZKsscYaGTFiRNN1BwAAAAAAZdSoEH3llVfOv/71ryTJWmutlauvvjoffPBBrrrqqiy99NJN2iAAAAAAAJRLo6ZzOe644/LRRx8lSU477bRsu+22ufXWW9O2bdvcdNNNTdogAAAAAACUS0WpVCrN70amTJmSN998M8stt1x69erVFH01SFVVVbp27ZqJEyemS5cuC22/AAAAAAAs2hqaLzd4JPqPfvSjBu/8wgsvbHAtAAAAAAA0Vw0O0UeOHFnv8YgRIzJjxoysvPLKSZK33norrVq1yrrrrtu0HQIAAAAAQJk0OER/7LHH6u5feOGF6dy5c2666aZ07949SfL555/n4IMPzre//e2m7xIAAAAAAMqgUXOif+Mb38hDDz2U1VZbrd7y1157Ldtss00+/PDDJmtwbsyJDgAAAABAYzQ0X65s7MbHjh072/KxY8dm0qRJjdkkAAAAAAA0O40K0XfZZZccfPDBueuuuzJmzJiMGTMmw4cPz6GHHppdd921qXsEAAAAAICyaPCc6LO66qqr8uMf/zj77rtvpk+f/t8NtW6dQw89NOeff36TNggAAAAAAOXSqDnRZ5o8eXL+/e9/J0kGDhyYjh07NlljDWFOdAAAAAAAGqOh+XKjRqLP1LFjx6y55przswkAAAAAAGi2GjUnOgAAAAAALA6E6AAAAAAAUECIDgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAAAAUECIDgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAAAAUECIDgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAAAAUECIDgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAAAAUECIDgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAAAAUECIDgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAAAAUECIDgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAAAAUECIDgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAAAAUECIDgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAAAAUECIDgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAAAAUECIDgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAAAAUECIDgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAAAAUECIDgAAAAAABYToAAAAAABQQIgOAAAAAAAFWpe7AZqfqVOnZtSoUeVugybUv3//tG/fvtxtAAAAAMAiR4jObEaNGpUhQ4aUuw2a0M0335xBgwaVuw0AAAAAWOQI0ZlN//79c/PNN5e7jQVq1KhROfXUU3PmmWemf//+5W5ngVscjhEAAAAAFgQhOrNp3779YjNquX///ovNsQIAAAAAX58LiwIAAAAAQAEhOgAAAAAAFBCiAwAAAABAASE6AAAAAAAUEKIDAAAAAEABIToAAAAAABQQogMAAAAAQIGyhujnnHNO1l9//XTu3Dl9+vTJzjvvnH/961/lbAkAAAAAAOqUNUR/4okncvTRR+fZZ5/Nww8/nOnTp2ebbbbJ5MmTy9kWAAAAAAAkSVqXc+d//vOf6z2+8cYb06dPn7z44ov5zne+U6auAAAAAADgv8oaon/VxIkTkyQ9evSY4/pp06Zl2rRpdY+rqqoWSl8AAAAAACyems2FRWtra3P88cdn4403zuqrrz7HmnPOOSddu3atu/Xt23chdwkAAAAAwOKk2YToRx99dF577bXcfvvthTU/+9nPMnHixLrb+++/vxA7BAAAAABgcdMspnP54Q9/mPvvvz9/+9vfsuyyyxbWtWvXLu3atVuInQEAAAAAsDgra4heKpVyzDHH5O67787jjz+eAQMGlLMdAAAAAACop6wh+tFHH51hw4bl3nvvTefOnfPxxx8nSbp27ZollliinK0BAAAAAEB550S/8sorM3HixGy22WZZeuml626///3vy9kWAAAAAAAkaQbTuQAAAMDiYOrUqRk1alS526AJ9e/fP+3bty93GwAsYM3iwqIAAADQ0o0aNSpDhgwpdxs0oZtvvjmDBg0qdxsALGBCdAAAAFgI+vfvn5tvvrncbSxQo0aNyqmnnpozzzwz/fv3L3c7C9zicIwACNEBAABgoWjfvv1iM2q5f//+i82xAtDylfXCogAAAAAA0JwJ0QEAAAAAoIAQHQAAAAAACgjRAQAAAACggBAdAAAAAAAKCNEBAAAAAKCAEB0AAAAAAAoI0QEAAAAAoIAQHQAAAAAACgjRAQAAAACggBAdAAAAAAAKCNEBAAAAAKCAEB0AAAAAAAoI0QEAAAAAoIAQHQAAAAAACgjRAQAAAACggBAdAAAAAAAKCNEBAAAAAKCAEB0AAAAAAAoI0QEAAAAAoIAQHQAAAAAACgjRAQAAAACggBAdAAAAAAAKCNEBAAAAAKCAEB0AAAAAAAoI0QEAAAAAoIAQHQAAAAAACgjRAQAAAACggBAdAAAAAAAKCNEBAAAAAKCAEB0AAAAAAAoI0QEAAAAAoIAQHQAAAAAACgjRAQAAAACggBAdAAAAAAAKCNEBAAAAAKCAEB0AAAAAAAoI0QEAAAAAoIAQHQAAAAAACgjRAQAAAACggBAdAAAAAAAKCNEBAAAAAKCAEB0AAAAAAAoI0QEAAAAAoIAQHQAAAAAACgjRAQAAAACggBAdAAAAAAAKCNEBAAAAAKCAEB0AAAAAAAoI0QEAAAAAoIAQHQAAAAAACgjRAQAAAACggBAdAAAAAAAKCNEBAAAAAKCAEB0AAAAAAAq0LncDAABA09tggw1mW/b888+XoRMAgAXHOQ8Lg5HoAADQwszpw+TclgMALIqc87CwCNEBAKAFmdeHRh8qAYCWwDkPC5MQHQAAWoivflh8/vnn625zqwMAWJQ09FzGOQ9NxZzojfDGG29k9OjR5W6D+fDhhx8mSZ555pmMGjWqvM0w35Zbbrmsuuqq5W5jgfr4448zYcKEcrexQE2bNi0fffRRudugiSy99NJp165dudtYYLp165alllqq3G3AXH01OH/++ed9kKTZ81lr0eezVsuyOHzWAmgIIfrX9PHHH+eQQw5NbW1NuVuhCVx11VXlboEmUFnZKvfcc3eLDbQ+/vjj7L77HqmunlbuVoD/r23bdrnzzjta7PsOQDl8/PHHOeTQQ1Nb47NWS+CzVstQ2apV7rm75X7WAmgoIfrXNGHChNTW1qRj2+VTWblEuduBxV5t7ZeZXP1uJkyY0GJP7CZMmJDq6mlp3W+dVLTvVO52FphSbU1S/WW526CptF0iFZWtyt3FAlGa+kWq/zOyRb/vAJTDhAkTUltTk/YbLZ+Kru3L3Q4s9koTp2bqMy37sxZAQwnRG6lt615p06pLuduAxd70mqpMrn633G0sFBXtO6WyQ7dytwGLvdpyNwANtMEGG+SEE07ImDFjsuyyy+aiiy4qd0vQIK0H9EzrJX3WgnKb8UlV8szi8VkLYF6E6AAA0EJ8dd7zouD8q/OlAwAAxSrL3QAAANB0DjjggPlaDwDQ3C2xRMOmWG5oHcyLEB0AAFqI6urqDBs2LD169Jjj+h49emTYsGGprq5eyJ0BAMCiS4gOAAAtxPDhw1NTU5PNNtssSy+9dL11Sy+9dDbbbLPU1NRk+PDhZeoQAGD+tWrVqknrYF6E6AAA0EKMGTMmSXLXXXdl/Pjx9daNHz8+d911V706AIBFUc+ePZu0DuZFiA4AAC3EMsssU3d//fXXz3XXXZfHH3881113XdZff/051gEALGpWXnnlJq2DeRGiAwBACzFgwIAk//3T5bPPPjvV1dV58sknU11dnbPPPrvuT5pn1gEALIpWWGGFJq2DeWld7gYAAICm8corryRJampqsummm861bqONNlpYbQEANKl///vfTVoH82IkOgAAAACwyJg6dWqT1sG8GIkOAAAtxDrrrFN3v7KyMj169EhtbW0qKyszfvz41NbWzlYHALCo6dGjR5PWwbwI0QEAoIWYMWNG3f3a2tqMGzdunnUAAIuaTp06NWkdzIvpXAAAoIW47bbbmrQOAKA5+vTTT5u0DuZFiA4AAC3EhAkTmrQOAKA5Gj9+fJPWwbwI0QEAoIWorq6uu9+qVat662Z9PGsdAMCi5ssvv2zSOpgXIToAALQQNTU1c7w/r3UAAIuS6dOnN2kdzIsQHQAAWoiGXjDUhUUBgEVZmzZtmrQO5kWIDgAALcTSSy/dpHUAAM1RQ6emM4UdTUWIDgAALURlZcNO7xtaBwDQHLVr165J62BenD0DAEAL0bFjxyatAwBojtq3b9+kdTAvQnQAAGghVl111SatAwBojrp3796kdTAvQnQAAGghPvzwwyatAwBojpZaaqkmrYN5EaIDAEALMWLEiCatAwBojoxEZ2ETogMAQAsxefLkJq0DAGiOevTo0aR1MC9CdAAAaCFqamqatA4AoDnq1atXk9bBvAjRAQCghTASHQBYHNTW1iZJ2rZtm4qKinrrKioq0rZt23p1ML9al7sBAACgaTT0g6IPlADAomzkyJFJkurq6nTv3j29evVKdXV12rZtm3HjxuXzzz+vq9twww3L2SothBAdAABaiFKp1KR1AADN0cxzmfbt2+fzzz+vC81nat++faZOneqchyZjOhcAAGghKisbdnrf0DoAgOaoS5cuSZKpU6fOcf3M5TPrYH45ewYAgBaiTZs2TVoHANAcderUqUnrYF6E6AAA0EJMnz69SesAAJqjRx99tEnrYF6E6AAA0EK4sCgAsDiYeWHRZPZp6lq1ajXHOpgfLiwKAAAtRKdOnTJp0qQG1QEALKpm/au6rl27Zrvttsuyyy6bMWPG5MEHH6y70Ki/vqOpCNEBAKCFuOGGG7L77rs3qA4AYFHVvXv3jBs3LknSunXrDBs2rG5dnz596tVBUzCdCwAAtBBjx45t0joAgOZol112qbv/1fOaTz/9dI51MD+E6AAA0ELMHJHVVHUAAM3Rsssu26R1MC9CdAAAaCF69OhR73GrVq1SUVFR7wJbc6oDAFiUNPRcxjkPTUWIDgAALUR1dXXd/R49eqSmpialUik1NTX1PkTOWgcAsKh56623mrQO5kWIDgAALcRtt91Wd3/8+PH11s36eNY6AIBFzYgRI+rud+rUKVtuuWV22GGHbLnllunUqdMc62B+tC53AwAAQNOYNGlSk9YBADRHr7/+epKksrIyX375Zf7617/WrWvVqlUqKytTW1tbVwfzS4gOAAAtRLdu3Zq0DgCgOZo+fXqSpLa2drZ1NTU1s9XB/DKdCwAAtBBjx45t0joAgObIwAEWNiE6AAC0EF+dB31+6wAAmqPvfve7TVoH8yJEBwCAFmKJJZZo0joAgOZo1KhRTVoH8yJEBwCAFqJdu3ZNWgcA0By9+eabTVoH8yJEBwCAFmLMmDFNWgcA0Bx98MEHTVoH8yJEBwCAFqK6urpJ6wAAmqPa2tq6+xUVFfXWzfp41jqYH0J0AAAAAGCRVCqV5voYmkLrcjcAAAAAM9WOn5IZ5W4CSO34KeVuAQq1b98+U6dObVAdNAUhOgAAAGXXrVu3tG3XLlMefL3crQD/X9t27dKtW7dytwGz6du3b95+++0G1UFTEKIDAABQdksttVTuvOOOTJgwodytMB9GjRqVU089NWeeeWb69+9f7naYT926dctSSy1V7jZgNv369WtQiN6vX7+F0A2LAyE6AAAAzcJSSy0lsGsh+vfvn0GDBpW7DaCFatWqVZPWwby4sCgAAAAAsMho6MVDXWSUpiJEBwAAAAAWGZWVDYs0G1oH8+KVBAAAAAAsMjp16tSkdTAvQnQAAGghKioqmrQOAKA5uuuuu5q0DuZFiA4AAC1E586dm7QOAKA5qq2trbvftm3beuvatWs3xzqYH0J0AABoIfbcc88mrQMAaO6qq6vrPZ42bVqZOqElE6IDAEAL0bdv3yatAwBojtq3b9+kdTAvQnQAAGgh+vTp06R1AADN0U033VTvcdu2bTNgwIDZpnb5ah00lhAdAABaiDXWWCOtWrVKZeWcT/MrKyvTqlWrrLHGGgu5MwCApjNu3Lh6j6urq/Pee+/NNrXLV+ugsYToAADQQrz66qupqakpvIhWbW1tampq8uqrry7kzgAAms6LL77YpHUwL0J0AABoIcaMGdOkdQAAzdGMGTOatA7mpVmE6Jdffnn69++f9u3bZ8MNN8zzzz9f7pYAAGCRc8899zRpHQBAczR58uR6j/v375+DDjoo/fv3n2sdNFbZQ/Tf//73+dGPfpTTTjstI0aMyFprrZXBgwfn008/LXdrAACwSHnnnXeatA4AoDmaPn163f2KioqMGjUqN954Y0aNGpWKioo51sH8KHuIfuGFF+awww7LwQcfnFVXXTVXXXVVOnTokOuvv77crQEAwCJl1j9Zbt26dYYMGZLhw4dnyJAhad269RzrAAAWNU899VTd/VlD868+nrUO5kdZQ/Tq6uq8+OKL2WqrreqWVVZWZquttsrf//73MnYGAACLnlmD8m7duuXmm2/Obrvtlptvvjndu3efYx0AwKJm1hHmvXr1qreud+/ec6yD+VHWs+dx48alpqYmSy65ZL3lSy65ZN58883Z6qdNm5Zp06bVPa6qqlrgPRapqW25cyqVSjWpKU0tdxs0oVYV7VNR0arcbSwQLfl38atqJ36a0tQvyt3GAlOqrUmmT5t3IYuGNu1SUdky33dK06aUu4WF5q233sq7775b7jYWmMmTJ7foaU3GjRuXXr16pXfv3hk7dmzGjh1bb/15551Xps4WjBVWWCEdO3YsdxsL1PLLL5+VVlqp3G0wH6ZOnZpRo0aVu40FaubxtfTjnGnm9d1YdLX0852kZZ7zzPpXdRMnTqw7D5g8eXLef//9enXOeRYtzfV8Z5EagnLOOefkjDPOKGsP3bp1S9u27VI19fWy9gH8n7Zt26Vbt27lbmOB6datWyorW6Xm43+VuxXg/6usbNWi33dmuuCCCzJy5Mhyt8F8GDduXMaNGzfb8mnTpmX48OFl6Ij5sc466+Tqq68udxvMh1GjRmXIkCHlbmOhOPXUU8vdwkJx8803Z9CgQeVug/ngfGfRN23atMIvCb788kvnPIuY5nq+U1EqlUrl2nl1dXU6dOiQO++8MzvvvHPd8gMPPDATJkzIvffeW69+TiPR+/btm4kTJ6ZLly4Lq+18/PHHmTBhwkLb38I2bdq0fPTRR+Vugya09NJLp127duVuY4Hp1q1bllpqqXK3sUC98cYbGT16dLnbWKCqq6vnGPSwaOrVq1fatm1b7jYWmOWWWy6rrrpqudtY4Fr6yKyWOCrrX//6V1577bV51q2++upZeeWVF0JHC09LH5WVNN+RWTTc4jASfXFjJPqir6Wf7yQt85zn008/zZNPPln3uH379unatWsmTpyYqVP/b3aFb3/72+nTp085WlxgWvo5z8I+36mqqqp77cwtXy5riJ4kG264YTbYYINcdtllSZLa2tost9xy+eEPf5iTTz55rs9t6EECAMDioLq6Optsssk865566qkW/UUXANCy1dTUZNddd824cePmOO95mzZt0rt37wwfPjytWrXMaSZpGg3Nl8t6YdEk+dGPfpRrr702N910U/75z3/myCOPzOTJk3PwwQeXuzUAAFiktG3bNgcccMBcaw444AABOgCwSGvVqlWOP/74zJgxIxtuuGGWWWaZdO7cOcsss0w23HDDzJgxI8cdd5wAnSZT9jnR99prr4wdOzannnpqPv7446y99tr585//PNvFRgEAgHk75phjkiRDhw5NbW1t3fJWrVpl3333rVsPALAo23zzzXPuuefm4osvrpuWeNKkSUmSc889N5tvvnk526OFKft0LvPDdC4AADBn1dXVGT58eMaMGZNll102u+22mxHoAECLU1NTk5deeinjxo1Lr169svbaaxuBToMtMnOizw8hOgAAAAAAjbHIzIkOAAAAAADNlRAdAAAAAAAKCNEBAAAAAKCAEB0AAAAAAAoI0QEAAAAAoIAQHQAAAAAACgjRAQAAAACggBAdAAAAAAAKCNEBAAAAAKCAEB0AAAAAAAoI0QEAAAAAoIAQHQAAAAAACgjRAQAAAACggBAdAAAAAAAKCNEBAAAAAKCAEB0AAAAAAAoI0QEAAAAAoIAQHQAAAAAACgjRAQAAAACggBAdAAAAAAAKCNEBAAAAAKBA63I3MD9KpVKSpKqqqsydAAAAAACwKJmZK8/MmYss0iH6pEmTkiR9+/YtcycAAAAAACyKJk2alK5duxauryjNK2Zvxmpra/Phhx+mc+fOqaioKHc7LEKqqqrSt2/fvP/+++nSpUu52wEWE957gIXN+w6wsHnfAcrBew+NVSqVMmnSpCyzzDKprCye+XyRHoleWVmZZZddttxtsAjr0qWLN1dgofPeAyxs3neAhc37DlAO3ntojLmNQJ/JhUUBAAAAAKCAEB0AAAAAAAoI0VkstWvXLqeddlratWtX7laAxYj3HmBh874DLGzed4By8N7DgrZIX1gUAAAAAAAWJCPRAQAAAACggBAdAAAAAAAKCNEBAAAAAKCAEB0AAAAAAAoI0QEAAAAAoIAQHZpYbW1t3f2qqqoydgIAAADA/CqVSuVugTJrXe4GoCWpra1NZeV/v5u65JJLMmHChOy7775ZccUVy9wZsKiZ9f0EoNzm9p5UKpVSUVGxkDsCAFjwZp7nONdBiA5NaOaHy5/+9Ke54YYbcskll6RDhw5l7gpY1MwaVr366qsZP358Bg4cmK5du6Zz585l7g5Y3Mz6nnTdddfl+eefT7t27bLGGmvksMMO86ESaDI1NTVp1apVudsASPJ/AfpTTz2VP/3pTymVSll11VVzwAEHlLs1ykCIDk3s5ptvzrBhw/LII49krbXWSpJMmzYtH3/8cfr165fEiC2gWKlUqgurfv7zn2f48OGZNm1aunfvnjXXXDP/+7//669bgIVq1kECN954YwYPHpwPP/ww11xzTZ588slcf/31ad26tfMb4GuZ+Z4xfvz4lEqldOvWTYAONCsVFRW56667cuihh2brrbfOjBkzcu+99+aVV17J+eefX+72WMj8nTg0sdGjR2ettdbKWmutlbfffjuXX355/ud//ieDBw/OmWeemSQ+YAKFZr4/XHzxxbn++utz9dVXZ9SoUVl//fVz//3356OPPipzh8Di6O9//3uGDh2aO++8M7fcckvuueeePPjgg3nggQdy1FFHJXF+AzTczAD93nvvzdZbb52NNtooK664Yi6//PKMHj263O0BJEmee+65nHDCCTn33HPzhz/8IWeeeWY+/fTTXHzxxfn+979f7vZYyIToMB/mdGGJDh06ZMyYMTnooIOy++6758knn8xOO+2UAw88MJdffnnefvvtMnQKLCpKpVKmT5+eJ598Mj/96U+z2Wab5YEHHsjtt9+ec845J9/5zncyderUTJo0qdytAouRcePGpW3btllnnXWSJK1atcrmm2+e66+/PnfeeWeefPLJMncILEoqKiryyCOPZK+99soee+yRX//619l1111z8cUX59JLL837779f7hYB8tZbb2XbbbfN4YcfntGjR+d73/tedtxxx1x44YW55ZZb8pOf/KTcLbIQmc4FGmnW+UHHjx+fqVOnZplllsn3v//9VFVV5dlnn81RRx2VLbbYIiuuuGIef/zx3HfffenUqVOZOweam1mnQJj53y+//DIbb7xx/vrXv2bvvffOb37zm/zgBz9IdXV1brnllvTv3z9bb711OdsGWqg5XUS0X79+GTduXJ588sl897vfrVu++uqrp3379pkwYcJC7hJYVJVKpZRKpdxyyy3Zd999c/LJJydJdtpppwwYMCAXXHBBVllllRx66KEutA6UxZ/+9KdMmDAh+++/f1ZeeeVMnz49hx56aDbddNPccMMN+eCDD/Kb3/wmF1xwQSZPnpwrrrii3C2zEPi/ETTCrHMWn3HGGdljjz3qLi5x33335fTTT88f//jHHH744VlhhRUyZcqUXHjhhenevXuWXHLJMncPNCezBugzR121adMmHTp0yF577ZVddtklv/3tb3P44YcnST7//PPcdttt+fe//122noGWa9ZznCuuuCKvvPJKkuQb3/hGttpqq1x77bX1Rp137949PXv2TE1NTVn6BRY9FRUVqayszOTJk+vOgaqrq5MkRx99dLbbbrtccMEF9d6PABaW5557Lvvtt19mzJiRioqKbLDBBhk9enQ++eST/OAHP0jy389rm2yySa6//vqceOKJZe6YhcX/keBrmDl9y8yTvdNOOy2XXXZZjj/++PzpT3/Kxx9/nJNOOilvvfVW2rRpky+++CI333xzdt1114wePTr33ntvKisrU1tbW87DAJqJ2trauveT2267LUceeWT+9re/JUkuuuiiLLnkkll66aVz4IEHZsqUKRk/fnwOPvjgVFdX57DDDitn60ALNOt70tixY3P22WfnkEMOyRtvvJGePXvmiCOOyMSJE/OLX/wiF154Ye65557svffeadOmTXbccccydw8savr27ZuHHnooU6dOTdu2beuC9HXXXTcdOnTItGnTytwhsLh577338uijj+aHP/xhhgwZUpcBtW3bNh9++GEefPDBTJ06NRdffHFGjRqVHXbYIQMHDixz1ywspnOBBpo5WnTmnxS+//77efjhhzN06NAMHjw4jz76aJ555plcdtllWWmllVJTU5NWrVrlnXfeyUorrZT7778/rVu3zowZM9K6tV89WNzN+ufJf/vb3/LQQw/lmWeeyUUXXZT27dtngw02yKmnnpojjjgi/fv3T69evdKmTZtUV1fn2WefTatWrereZwCawsz3pJ/97Gf55z//mb59++all17KLrvskrvvvjuDBw9O+/btc9ddd+Wss87KiiuumF69euW5557zngQUmvk5atSoUamtrU3Hjh2z5JJL5vTTT8/DDz+czTbbLI8++mg6dOiQJHnxxRfTuXNnA4+AhaZUKuXjjz/OJptskkmTJuXQQw9NkroMqE+fPjnxxBNz/vnn59Zbb82kSZPy0EMPpVevXmXunIWpojSnKyMC9Zx88smZMmVKLr744roPmB9//HE233zzPProo3n++eez//775/zzz88RRxyRqVOnZtiwYRk8eHB69+6dNm3apKKiwodLYDYnnnhi7r777uy+++757LPPcuedd2arrbbKySefnPXXXz8TJkzI1VdfnTZt2qRPnz7ZZ5990qpVK1/IAQvElVdemZNPPjkPP/xw+vTpk88++yw//OEP88knn+T+++/PqquumiSZMGFCSqVSunXrloqKCu9JwFwNHz48J598ciZMmJANN9wwe+65Z4YMGZLnn38+3//+9zNu3Lh885vfzIwZM/LYY4/lqaeeylprrVXutoHFzB133JFjjjkmK6ywQq688sqsscYadesmTJiQd999N//+97+z4YYbZrnllitjp5SDEB3mYcqUKTnxxBPz0ksvZauttsoZZ5yRysrKjB49Ottvv30GDx6c66+/PmeddVaOOuqoJMnLL7+cU045Jccdd1y23HLLJPXnPQZIkr///e/ZeeedM3z48GyyySZJknvvvTennXZa+vXrl5///OfZcMMNZ3ueL+SABeWEE07IRx99lNtvv71u2SeffJLvfve7qa6uzu23357VVlut3nNc+A+Ym3fffTfbbLNNTjzxxHTv3j33339//vnPf+awww7LEUcckSlTpuRXv/pVxo0blyWWWCKHH354Bg0aVO62gcXUHXfckeOPPz4777xzjjvuuKy00krlbolmwnARmItSqZQOHTrk3HPPzbnnnpuHH344pVIpZ555ZpZbbrnsu++++cUvfpGjjjqqLkCfPHlyfvGLX2T69OnZbLPN6rYlQAe+qm3btqmsrEybNm3qlu20004plUrZfffd06ZNmxx//PF1AfvML+ME6MCCMnHixLz++ut1j2fMmJEll1wyRx11VA477LDsv//+uf/++/ONb3yj7gs9ATpQ5KWXXsrvf//77LDDDjniiCNSUVGR9ddfPxdffHGuvvrqTJ8+Pcccc0zOOuuscrcKLEZmfq76xz/+kbfffjuTJk3Kd7/73XzjG9/IHnvskerq6px00kmpqKjIcccdlxVXXLHcLdMMCNFhLmbOf9W1a9ecfPLJqa2tzSOPPJLa2tqcddZZ+dnPfpYPPvggV1xxRaZPn57p06dn1KhRGTt2bEaMGJFWrVoZnQUk+b8Tta/+VUpNTU1Gjx6dDTfcMNOnT0+bNm2y8847Z9VVV82bb76ZG264If369Uvfvn19GQc0maLzk8MPPzyPPvpofvnLX+aUU06pm6JlqaWWypFHHpkRI0Zkzz33zNNPP+0LPaBQqVRKVVVVfvWrX+XRRx/Nt771rbrzmIEDB+b4449Pktxyyy2ZPHlyTj755DJ2CyxOZn4eu+uuu3LYYYdlzTXXzBtvvJE777wz+++/fw444IDst99+SZL//d//zRdffJH//d//zQorrFDmzik3yR4UmHkhm5kfMLt27ZqTTjopm222WR555JGceuqpKZVK+e1vf5srrrgiEyZMyNSpU7Pppptm5MiRadOmTWbMmCFAB1JbW1v3wXHChAmZMWNGkmTdddfNHnvske9///t59tln60akjx8/Puuss04OO+ywDB8+PM8991zZegdanlkD9OHDh+fss8/ORRddlMceeywbbrhh3WjzmdeE+c9//pOrrroqXbp0yS9/+cu89dZbefbZZ8t8FEBzNHO22JkDkX7yk59kyy23zPPPP5/bbrutrm7gwIE54YQTstpqq+XBBx/M559/Xq6WgcVETU1Nkv8OlnziiSdy5JFH5te//nUee+yxPPzww3nsscdyxRVX5He/+11KpVL222+//O///m9eeOGFdOrUqczd0xyYEx3mYNYPl//4xz9SXV2ddu3aZd11183kyZNz9tln55FHHsk222yTM888M5WVlZk6dWrat29ftw1zFgNfdfbZZ+fuu+9Oz549s/HGG+fUU09Nkuy9996555578tOf/jRdu3bNgw8+mKlTp+app57KN7/5zayxxhq59tpry9w90NL89Kc/zW233ZYNN9wwFRUVeeihh3LVVVdl2223zeWXX57rrrsu48aNS8+ePdOlS5e88sorGTlyZHbdddc88MADdRcZBUj+b3TnQw89lD/+8Y8588wz071797z88ss5++yz88knn+Too4/OnnvuWfec9957L0sssUSWWmqpMnYOtGSXXnppNtpoo6y33nqpqalJbW1tfvOb3+TTTz/NRRddlHfffTdbb711vvnNb2bcuHF599138/Of/zwHHnhgKisrU1VVlS5dupT7MGgGDJGFryiVSnUB+s9//vPstddeOeKII7L55pvnBz/4QcaPH5+f/exn2WKLLepGpNfW1tYL0JMI0IG6v2hJkmuuuSYXXnhh9t133/Tu3TvDhw/PAQcckCS5/fbb8/Of/zyPPvpohg4dmk6dOuWvf/1rkqR169ZZeeWVy9I/0LLM+p50991357bbbssf/vCH3Hnnnfnud7+bL7/8MlOnTk337t3z05/+NC+//HKuvfbaXH/99Rk5cmSSZOjQoendu3d69+5drsMAmqmKiooMHz48e+21VyorKzN69OgkyVprrZWf/OQnWXLJJXPZZZflzjvvrHvOgAEDBOjAAjNu3Lj88Y9/zHbbbZeXX3657louO+20U37wgx/kiy++yAEHHJDNNtssQ4cOzRVXXJGxY8fmwgsvzI033pgk6dy5c3kPgmZDiA5fMXPKhYsvvjjXXXddhg4dmldeeSXHHHNMbr755rz//vvp3LlzXZA+bNiwXHfddWXuGmiOZn4h98gjj2TChAm57rrr8qMf/ShXX311jj/++LzwwgvZd999kySnnnpq7r333jz33HO555570qZNm5xyyil59913s9NOO5XzMIBF3EUXXZQk9aaYe/vtt/Otb30r3/rWt3LXXXfluOOOy29/+9scfPDBqaqqyuuvv54uXbpk7733zhZbbJFXXnklxx57bH73u9/l2muvFaIDsxkxYkR+8IMf5De/+U0uueSSrLXWWkmSL774Iuuvv37OOOOMLLvssjnzzDNzzz33lLdZYLHQq1evXHjhhdl0002z7bbb1l27boUVVsgqq6ySkSNHZtKkSfnxj3+c5L/Taq633npZe+21s9VWWyWJ61JRR4gOBV588cX8+Mc/zje/+c3ceeedueKKK3LRRRdlo402ypdfflk3x99PfvKTHHLIIeVuF2im/v73v+f73/9+zjvvvHTt2jVJ0rFjx+y55545+eST89JLL9VduKZnz55p06ZN3n777Rx99NH53e9+lwceeMDV4IFGu+222/LUU0/VzQM6U4cOHbLUUkvlnnvuyYEHHpjzzz8/P/jBD5IkDz/8cO666656cxT/5z//qZtmamYwBjCrt99+O2uttVYOPfTQjB8/Prfddlu23377/M///E/OO++8rLLKKjn++OOz3nrrZZ111il3u0ALNfMv76ZNm5YkWWONNXLeeedl4403zvbbb58RI0akbdu2SZLJkydn8uTJ+fe//51SqZQHHnggyy+/fK688sost9xyZTsGmidzokP+b/6+maZMmZKNN944Z599dnr16pUtt9wy559/fo444ohMnz495513XjbaaKNsscUWdc8xBzowJ5988kl+97vf5dJLL82OO+6Y3/3ud3XrJk+enOHDh+eEE07Isccem9NOOy1JUlVVlREjRqRfv34ZMGBAuVoHWoCqqqp06tQplZWV+ctf/pLBgwcnSe68884cdNBBmTZtWi677LIcccQRSf47YnT33XfPyiuvnEsuuaTetqZMmZIOHTos9GMAmq9ZP0c98MAD2XHHHXPuuefmnnvuSa9evbLsssumc+fO+e1vf5u///3vWXPNNWe7lhRAU5l5fbt33nknv/3tbzNx4sQMHjw4e++9d1577bWcfvrpefrpp/OnP/0p66yzTt5///3svffeGTduXNq0aZMPP/wwf/3rX33RxxwJ0WEWn332WXr27Jkk+cUvfpHf//73GTNmTK655poMGTIkyX//vGf33XfPjjvumBNOOKGc7QLNzKwXJZ7V2LFjc9111+XGG2/MjjvumPPPP79u3RdffJG//e1vGTx4sC/igAXm2WefzW677ZaddtopV1xxRZLk9NNPz5lnnpnf/e53WWONNdK6deucdNJJGTt2bF544YW0bt06Mz8q+FNmYFYzw/OJEyemXbt2qaioSLt27XL++edn6NCh2WSTTXLIIYfk/7V3p3FVlXv/xz+bUZxwQkVFDTEFywGw9M4Uh5xLVNC0csy8U3EAFfKkiYYdNYcsc8gjhGiSYuKEIHrSnHKijqaJpoZDgKYImkwb/g/8sw8e4py8j7lBvu8nyVprL3+7V6/Vtb5c1+9yd3cHwN3dnUWLFtGhQ4ciE5hERB6FgnexkydP0qNHD/r27YuHhwcDBw7Ezs4OwBSk79+/n61bt9K6dWsuXLhAXFwcmZmZ9OrVS6uApVgK0aVMKxx4ffzxx6xdu5YVK1bQokULDh06RFBQEBkZGWzbto06deqQmprKsGHDSEtL45tvvlHgJSImhZ8nsbGxXL16lcqVK/Piiy9Sq1YtkpOTCQ0NJSIigl69ejFv3rwi99CKFhH5s6SmphIWFmYKt5YuXQrApEmTiIqKIi0tDVdXVypVqkRMTAzW1tZ6JonI7yoIwbdv386CBQu4e/cu2dnZzJ07l65duxZZtTJt2jQ2btzIvn37tImoiPypLl68SIcOHRg0aBBz5841HS88pikcpG/btg1PT09zlSuljEJ0KbMKB1779u3jxIkT+Pv74+3tzdy5c2ncuLEpVD916hQuLi7k5uZiaWnJwYMH9XIpIiaFZ1QFBQWxYcMG7OzsqFGjBpmZmURGRtKgQQOSk5P5/PPPWbt2LW3atGHlypVmrlxEnkSnTp3i/PnzxMfH4+DgwCuvvEKrVq3IyMhg5cqVrF69mo4dO/LJJ5+Yri/Y78XFxQULCwtyc3OxsrIy8zcRkZJq+/bt+Pr6MmPGDDp27MiiRYvYvHkzBw4cwN3dHYPBwLZt2/jqq6/YsmULcXFxao8gIn+agvexefPmsWfPHsLDw6lZs+bvXgPwww8/MHPmTKKjozl27BjNmzc3R9lSymhkLGVWQYAeFBREeHg4fn5+vPnmm0RFRTFq1ChWr17Na6+9Rtu2bfn73//OrVu3cHJywsfHB0tLS71ciohJwWBs4cKFrFmzhk2bNvH8888zf/58AgMD6dKlCzt37qRRo0YMHTqU9PR0fv75Zy1nFpFH7osvvmDBggWmNgvp6el88MEHzJw5k/HjxzN69GgAQkNDGTt2LEuXLuWZZ5554B55eXka44jI78rLyyM7O5vPPvuMqVOnEhQUxJUrVzh+/DhDhw7Fw8MDuL+hX0pKCmlpaezduxc3NzczVy4iT7KCd6qvv/4aOzu7IgE6/DNEz8rKws3NjdmzZ1O+fHnt0SB/mGaiS5l24sQJunXrxhdffEGXLl0AOHv2LB06dMDV1ZXly5fTpEmTIp/TDHQRgQdXtKSlpTFy5Eh8fX159dVX2bFjBwMHDsTPz4+vv/6amzdvEhcXR/369bl58yZVq1bFYDAoSBeRR2blypUEBATw0Ucf4eXlhbOzM2fPnmXFihV89NFHzJgxg/fee4+bN28SGhrKmjVraN68OeHh4eYuXURKuILxyr1797Czs8PNzY2wsDCaNGmCm5sbvXv3ZsWKFQD87W9/w8fHB3t7e+7cuUPFihXNXL2IlAXZ2dl4e3tTo0YNwsPDyc7OxsbGpsh17777Lq1bt6ZPnz5kZWVha2trhmqlNCq6+5lIGWI0GrG2tsbJyQmAnJwcmjRpQkxMDN9++y3Tp0/nhx9+KPI5Begikp+fbwrQN2zYgJWVFRMmTKBNmzYkJCTw9ttvM2/ePObMmYO3tzeJiYk0a9aMK1euUK1aNQXoIvJIrVq1ivHjx7NmzRpGjBhBw4YNAXj66adZuHAhkydP5v333yc2NpZq1aoxfPhwvL29yc/PJy8vz7zFi0iJZzAY2LhxI0OHDgWgRYsWrFixgubNm9OnTx8+/vhjANLT09m4cSPr1q0DUIAuIo+NjY0Nbm5uREVFceXKFWxsbDAajQ9cc+XKFX788UeqVKkCoABdHopCdCkzfm/RRd26dbl9+za7du0CwNramry8PBo0aECjRo2Ijo4mKCjI9ODVwg0RgQf76c2ZM4cpU6Zw6dIl2rdvT8OGDdm3bx8tW7Zk2LBhANSrV49XX30Vf39/HB0dTfdRgC4ij8KlS5d466236N+/P97e3sD9tnWFn1WTJ0+mefPmLFiwgOzsbKpVq0ZAQADh4eFYWFgoSBeRIu7cuWP687lz5wgODqZz584YjUbatWtHfHw89erV49NPPzXN9vzrX//KxYsX6dGjh7nKFpEyqCCrGTx4MLVq1aJXr16kpqYWmQC5atUqrl69+rsdB0T+E4XoUibk5eWZXiJTU1MxGo3k5uZSp04dpk6dyty5c4mIiADuv3SWK1eO9u3bExcXx549e1iyZAmgwEtE7it4Fpw+fZpTp06xcuXKB3oK//rrrxw+fJjMzExycnLYsGEDjRo1Ijg4GEtLyyIzIkRE/hsNGzZk/vz5REVFsXjxYtPxwuMWBwcHPD09uXHjhulFs1KlSqZVMQUra0REAI4fP06LFi24c+cO33//PaGhobi7uzNs2DAsLS0ZPnw4PXv25O7du/Tt25eZM2cyePBgli1bxvr1602rYUREHoeCMU/Lli2ZPHkyN27coF27dsTGxpKYmMiePXt4++23Wbx4McuXL6d27dpmrlhKI+0YJGVCwYvhrFmziImJITs7m1GjRuHj48O4ceP49ddf8fPz49ixYzRo0ICtW7dy+/ZtPv74Y9q0aUNiYqKZv4GImNu/tl4JDw8nODgYGxsbpk2bBvyzR/orr7xCfHw8jRs3pk6dOuTm5rJhwwbTfdQSSkQetYCAACwsLAgICMBgMDBhwgTTuYLn1927d3FzcyuydFmTBESksO+//56OHTsyfPhwKlasyHvvvUd8fDxNmzY1PT/Kly/P/PnziYiIIDY2ln379vH0009z8OBBXF1dzfwNRKQsKpgUMHr0aCpXrsyyZcvo0aMH1tbWNGrUiOrVq/PNN9/w7LPPmrtUKaW0sag80Qpv+rdq1SqCgoKYM2cOcXFxXLhwgbZt2xIcHIydnR3r1q1j4cKFVK9enerVq7Nx40asra3p1KkT7du3Z+bMmepfLFKGpaSkkJ2dza1bt3jqqaewtLTE19eXmJgYlixZwpgxY0zPm7y8PE6cOMH+/fsxGo1MmDABKysrbUosIn+6RYsWMXnyZBYtWsT48eNNx69evcqQIUPo27cv48aNM2OFIlKS/eMf/6Bt27ZMnDiRkJAQALKysnjjjTdISEhg3Lhx+Pn5/e7qFb0ricjjUtzzpnAGBLBv3z7S09NxcXGhVq1aVK1a9XGWKU8YhehSJnz77besXbsWLy8v+vXrB8DChQuJjIzEw8ODv/zlL9StW5fffvsNOzs708M4MDCQiIgI9u7di4uLizm/goiY0bp161i+fDnnz58nOTmZevXqMXLkSCZNmoSPjw+//vors2bNokePHsW2RFCALiKPS0GQvmDBAiZOnAhA7969ycjIYM+ePXoWicjvunz5Mu7u7nTq1InIyEjT8c8//5zdu3eTkZHBjRs3GD58OCNGjAAgNzcXKystcBeRx6O48FzvWvI46P928sSLj4/n7bff5s6dO3Tr1s103N/fH4PBwJdffklISAgBAQE0atQIgISEBCIiIoiMjGTbtm0K0EXKsNDQUMaMGcOCBQto2rQp1tbWrF69mtmzZ3Px4kXCw8N54403mD17NgaDge7du5s26SscqGtQJyKPy6RJk4D7m4laWloSFxfHuXPnOHXqlGlfBj2TRORfGY1GnnrqKTIzMzlw4AAvvPACH3zwAe+//z6HDh2ibt26jB49mrCwMCwsLBg2bJgCdBF5bAoC9P3797N9+3by8vJwc3Nj6NChGt/IY6GZ6FImvPvuu6xcuZKXX36ZefPmUb16ddO5jz76iI8//pjRo0czZcoUAO7evcv+/ftp2rQpDRo0MFfZImJmCQkJ+Pr6MmfOHAYMGGA6/uuvv/Lll18yYcIExo0bx9y5c+natStGo5GJEyfSt29fLWcWkUfuX385V9yxAosXL8bf3x8XFxd++OEHrK2tNWtURP6tc+fOMX78eGxsbKhVqxbR0dGsWbOGrl27ApCcnMyECRM4c+YMgYGBvPbaa2auWETKkk2bNjFy5Ei6du1Kbm4uZ86coXv37ixcuBDQjHT5cylElyfKzz//jIODA+XLlwcgJycHa2trAKZNm0ZMTIypF2i1atVMn4uMjMTHxwdLS0v18hMRky1btjBjxgx27tyJg4PDA8+ItLQ0FixYwMKFCzl69Ci1atWibdu2dO7cmWXLlpm7dBF5whQOy5OSkihfvjzVqlXDwsLi3wbj27Zto3v37lhZWSlAF5E/JDExkXHjxrF//35mz55NQEAA8M/WLdeuXeOdd95h1qxZmnAkIo/NkSNH8PX1Zdq0aYwePZoffvgBLy8v0tLSeP311wkNDQXUZkr+PArR5YmxZcsWvL298fPzo2rVqsycObPIbyEnT57M3//+d/r06WO6rjD91lJECgsODmbZsmUkJycDRXvwJSYm0qxZMz777DOGDRtGeno6FSpU0HNERP4006dPZ926dVSoUIGmTZsSERGBjY3Nf3xh1AuliDyMn376iTFjxmBpacm0adNo164d8M9JSnpvEpHHofCzJjQ0lG+//Zbly5eTlJSEl5cXHTp0wNPTE39/f8aOHWuakS7yZ/j9tZ8ipdBPP/2EhYUFHTp0IDo6mm7dujFjxgySkpJM13z44Yd07NiRrVu3EhISQkZGxgP30EBQRApzdXUlIyODuLg4gCKrVJydnalduzaZmZkAVK5c2dSPT0TkUcjLyzP9OSoqipUrVxISEsKgQYP46aefcHd3Jzs72zTTvDgK0EXkYTRq1IhPPvmE/Px83n//fQ4cOABgWuWr9yYR+TMUjHsKshpLS0sSEhK4ffs2Q4cOZeTIkeTk5DBy5Ejat29PaGgo/fr1w9HRkcWLFzN69Ghzli9POIXo8sQYP348Xbp0ISsri6NHj9K5c2du3LjBM888Q3BwMDt27ADuB+nt2rXj+vXrVKxY0cxVi0hJ5unpibW1NStXrnzgF3IFIXlSUhI1atTg6aeffuBzerEUkUeloIXL+vXrSUtLY+7cubz66qsEBQWxYsUKLC0tadWqFVlZWf8xSBcReRiNGzdmyZIlWFtbM3nyZA4fPmzukkTkCWdhYcG1a9cYNGgQMTExREdH4+HhwenTp7GwsKB169ZcvnyZlJQU3nrrLeD+RIEXXniB0NBQAgMDzfwN5EmmEF2eCPn5+eTn59OyZUtiYmKwsrJi6tSprFixgqysLLZs2cLrr7/OoEGDiIiIYNGiRYSFhWEwGFBHIxEpjrOzM8uWLWPbtm288847nDhxArgfkv/222+MHz+eypUr4+XlZd5CReSJdv78eaZOncqoUaPIyckB7q+M8fDw4G9/+xs2NjZ4enqSmZmpGeci8kg1btyY+fPnU69ePerUqWPuckSkDEhJSaFcuXJMmTKFV199lbVr19K2bVvTLHUbGxuSk5PZvn07v/32G4sWLeLixYv06tULZ2dnM1cvTzL1RJcnSmJiIh4eHmzduhUvLy9atGhBtWrVWLVqFampqYwdO5YmTZqwbt06U4CuTURF5N/Jzc0lLCyMsWPH4uDgQIsWLahSpQpJSUlkZGRw9OhR9QYVkT9VVlYWO3fuZPr06VSoUIFDhw6ZzuXn55OQkEDv3r3p2LEja9euNWOlIvKkys7OxsbGxtxliEgZERYWxogRI3BxcWHhwoX07t0buL8iODc3l8WLFzNv3jwqV67M3bt3iY2NpVWrVmauWp50CtHliZGXl4eFhQXTp0/n6tWr7N+/n5o1a7J582Zq1KgBQHp6OpUqVVKALiIP7bvvvuOzzz7jzJkz1K9fH1dXVwICAkztEzT7U0QehYLxzL+6d+8eu3fvZtKkSTRs2JBdu3aZzuXn55OYmIiLi4t+mSciIiKlVsHEpO3bt5OUlMSBAwf4+eef8fPzY8CAAabr0tPTuXDhAhcvXsTDw4P69eubsWopKxSiyxNn06ZN+Pj40LlzZ3bs2GHa/KbwS2lxL6giIg9LM9BF5FEpPD5Zv349Z8+excLCAm9vb5599lkyMzOJj49n8uTJNGjQgNjY2CL30DNJRERESpuCSY63bt2iatWqpp8PHTrE4sWLuXbtGhMmTMDHxweAXbt28dxzz2Fvb2/myqUsUYguT6Thw4eTkpJCdHS0KUQXEflvaQWLiDwOgYGBrF+/niZNmmBnZ8fevXvZunUrL774oilIDwwMxNbW1rRXg4iIiEhptmXLFmbNmkX58uV56qmn+Oyzz7CxseHQoUN89NFHXL16lf79+3P79m2Cg4NJSkqiXr165i5byhBNxZVSo2ATiT9yTdeuXUlNTeX777//s8sSkTJEAbqI/NmWL1/OunXriIqKIi4ujoEDB5Kens5LL73Ejh07KFeuHF26dCE4OJimTZv+ofGRiIiISElUMK83ISGBgQMH0qtXLzw9PTl58iTu7u5kZWXRtm1b/P39cXNzY8WKFWzYsIGjR48qQJfHTjPRpVQovLx58eLF5Obm4u/vX2xLluzsbBwcHBg7dixz5sx5nKWKiIiI/GFZWVnY2toCcPv2bWbPnk2zZs0YPnw427ZtY/DgwcycOZPvvvuOqKgotm/fjpeX1wOb/KlNnYiIiJRWJ06cME2CDAwMxGg0kpCQwMiRI8nOziYhIYFy5cpx/fp108pgBwcHc5ctZZBG21IqFLwYTp06lQ8//BCj0UhKSsrvXms0GrGxsSE2NpZZs2Y9zjJFRERE/rC4uDiWLFnCkSNHALC3t+f111+nQ4cOnD17lkmTJjFnzhz8/f3p168f9+7do1OnThw8eNAUoAMK0EVERKRUunHjBq+//jo9e/bk9u3bAFhaWuLh4cHq1auxsbGhdevWZGZm4uDgQM2aNRWgi9loxC2lxqpVqwgLC2Pbtm0EBgbi6OiI0WjEaDRSeEFFwWZabdq0wcrKitzcXHOVLCIiIvK7QkNDGTFiBBcvXnygVVTLli1xdnbm1KlT1KxZk9deew2AqlWr8tZbb/HJJ5/w3HPPmatsERERkUemcuXKvP/++7i7uz+wYbrBYMDd3Z3Q0FDS09Px8vIyX5Ei/5+VuQsQ+SPy8/NJTExkwIABtGzZkjNnzvDNN9+wdOlS7O3tGTVqFIMHDzYF6IVZWek/cxERESk51q9fz7hx4wgNDaV79+5Urly5yDW3b9/m0KFDXLt2jby8PBYsWEDt2rUZM2YMALm5uRrjiIiISKlmY2NDjx49sLGxYcKECbz00kvs2rULuB+kt2rViujo6N8dK4k8buqJLiVSQZ+rgn8CvPPOO3z44YfMnTuXiIgInJyccHd35/jx41y8eJGDBw9SqVIlM1cuIiIiUrzr168zYMAAfHx8GDt2rOn4nTt3OH36NEajkbZt2wLQq1cvYmJicHFxwdbWlhMnTmBtbf3A+EhERESkNCgYvxw/fpyEhAQMBgMvvPACTZs2JTMzk/j4eCZPnkz9+vWJi4szd7kiRShElxKn8OZYOTk5WFtbm86NGjWKI0eOMHToULp160azZs04fPgwEydOZNOmTdSpU8dcZYuIiIj8R9evX8fLy4uQkBC8vb0BWLZsGXv27CEqKgpHR0caN27M119/TW5uLlu3biU3N5d+/fphaWmpGegiIiJS6hQE6Js2bcLPzw9HR0cqVKjA6dOn+eqrr2jXrp0pSA8KCqJ8+fKmPWNESgqF6FKiFA7QlyxZwt69e8nPz6dRo0bMnz8fgIyMDNOM89zcXHr37o2trS2bN2/WrCwREREp0a5fv467uzvdu3dn0KBBfPrppyQmJtKuXTv69u3L7du3mTJlCqNGjWLatGkPfNZoNP5u6zoRERGRku6bb76hX79+zJkzh1GjRnHs2DGee+45ypUrx8aNG+nZsyeZmZns2LGDv/71r2zcuJH69eubu2wRE4XoUiK98847rFq1ijfffJPr168TGxuLg4MDO3fupGbNmqSnp7N582YiIiJITU3l6NGjWFtbPxDCi4iIiJREu3fvpn///lSvXp1KlSqxcOFCWrRoQfXq1bl16xadOnXilVdeITg42NylioiIiDyUglymcD5z79495s6dS35+PsHBwVy9epX/+Z//oXPnzhiNRiIjI9m5cydeXl5kZWWRk5NDxYoVzfxNRB6ktaBS4pw+fZrIyEgiIiLo1q0bABcuXKB///54e3tz8OBB7t69y+XLl6lTpw47duzAyspKy5tFRESkVOjcuTPnzp3jzp07PPXUU0XOV6pUibp165qhMhEREZH/u4Lg/NKlS8TFxeHu7o6npyd2dna88sorZGVlkZGRQf/+/enevTsrVqzgwIEDrFmzhk6dOrFz5066du2Kra2tub+KSBFKHKXESUtL4/bt27i6ugL3e2c5OzsTFhZGz5492bBhA76+vowfP56KFStiMBgwGo0K0EVERKTUcHBwwMHB4YFj169fZ/jw4WRnZzNy5EgzVSYiIiLy8AoC9JMnT+Lj40OzZs2oV6+e6by7uzsAR44cwWg0MmnSJACqVKmCr68vDRo0wMnJySy1i/wRSh2lxHF1dcXOzo5NmzYxceJEU5/zevXqYWdnx40bNwBMfdHz8/PVH1RERERKrRs3brBq1Sr2799PamoqBw4cwNLSUj3QRUREpNSwsLDgxx9/pEOHDowePRo/Pz/q1KlT5LqUlBSOHz9Obm4uAOvXr+fOnTvMnDmT8uXLP+6yRf4whehidoX7ZOXn52Nra8vLL7/M1q1bcXR0ZODAgQCUL1+eKlWqYGNj88DntZmoiIiIlGZXrlzhwIEDuLi4sHnzZrWpExERkVInMzOTGTNmMHjwYD744APT8ZycHFJSUrh79y5NmjTh5ZdfpmfPnjRv3pzWrVtz+vRp9u/frwBdSjxtLCpmsXv3bg4dOsS7774LUGRD0DNnzvCXv/yFpKQkWrVqhYeHB19++SU3btwgISFBs7JERETkiZKWloa9vb2pTZ3GOiIiIlKa5Obm0qlTJwYMGMC4ceMAiI2NZefOnaxevZrq1avj7OxMfHw89+7dY82aNfz222/06tWLxo0bm7l6kf9MIbo8dllZWYwfP55Dhw7xxhtvMGXKFOCfQXp+fj4Gg4Hz58+zefNmIiIisLe3x9HRkTVr1mBtba2XSxEREXkiFYyDREREREqT9PR0nn/+eV588UUCAgLYtGkTn3/+Oc888wzt27enYsWKhISE0K9fP+bPn2/uckUemkJ0MYtr164xb948Dh8+TN++fQkMDATuB+kGg8H08pibm2sKywsf0/JmEREREREREZGSY8+ePXTr1o26dety8+ZN5s+fT+fOnXFxcSEnJ4fevXvj6OhIWFiYuUsVeWhKIsUs6tSpQ1BQECEhIXz11VcABAYGYmFhYQrSU1JS8Pf3p3fv3gwaNAi4PztLAbqIiIiIiIiISMnSqVMnLly4QGpqKg0aNKBGjRqmc5aWltjb2+Pk5ETBfF6tvpPSRDPRxaySk5MJCQnh6NGjeHt7ExQUBMAvv/yCr68vqampnD59WsG5iIiIiIiIiEgplJ2dzezZs1m9ejVff/21eqBLqaQQXcyucJDev39/RowYga+vLykpKXz33XfqgS4iIiIiIiIiUgpFRERw9OhRIiMjiYmJoVWrVuYuSeT/RCG6lAjJycnMmTOHI0eO8OOPP1KnTh2+//57rK2t1QNdRERERERERKSUOXv2LP/7v/9L1apVCQkJwdXV1dwlifyfKUSXEiM5OZnAwECuX79OdHS0AnQRERERERERkVIsNTUVW1tb7O3tzV2KyH9FIbqUKLdu3cLe3h4LCwsF6CIiIiIiIiIiImJ2CtGlRMrLy8PCwsLcZYiIiIiIiIiIiEgZpxBdRERERERERERERKQYmuorIiIiIiIiIiIiIlIMhegiIiIiIiIiIiIiIsVQiC4iIiIiIiIiIiIiUgyF6CIiIiIiIiIiIiIixVCILiIiIiIiIiIiIiJSDIXoIiIiIiIiIiIiIiLFUIguIiIiIiIiIiIiIlIMhegiIiIiIiVAcnIyfn5+ODs7Y2tri5OTEy+//DK7d+9+rHUYDAY2b978WP9OEREREZGSzMrcBYiIiIiIlHWXLl3ihRdeoEqVKsyfP59nn32WnJwcYmNjGTt2LD/++KO5SxQRERERKbM0E11ERERExMzGjBmDwWDgyJEj9O/fn6effppmzZrh7+/P4cOHAUhKSqJPnz5UrFiRypUrM2DAAFJSUkz3GDZsGN7e3g/cd+LEiXh5eZl+9vLyYvz48UydOpVq1apRu3ZtZs6caTrfsGFDAPr27YvBYKBhw4ZcunQJCwsLjh079sC9Fy9eTIMGDcjLy3uk/y5EREREREoahegiIiIiImZ08+ZNdu7cydixY6lQoUKR81WqVCEvL48+ffpw8+ZN9u7dy65du7hw4QIDBw586L/v888/p0KFCnz77bfMmzePWbNmsWvXLgCOHj0KQGhoKL/88gtHjx6lYcOGdOnShdDQ0AfuExoayrBhw7Cw0CuFiIiIiDzZNOIVERERETGj8+fPk5+fT9OmTYu9Zvfu3Zw8eZJ169bh4eHB888/T3h4OHv37jUF339U8+bNee+992jcuDFDhgzB09PT1HfdwcEBuB/c165d2/Tzm2++yRdffEFWVhYAJ06c4OTJkwwfPvz/8pVFREREREoVhegiIiIiImaUn5//H685c+YMTk5OODk5mY65ublRpUoVzpw581B/X/PmzR/42dHRkdTU1H/7GW9vbywtLfnqq68ACAsLo2PHjqb2LyIiIiIiTzKF6CIiIiIiZtS4cWMMBsN/vXmohYVFkUA+JyenyHXW1tYP/GwwGP5jX3MbGxuGDBlCaGgo2dnZrFu3jhEjRvxX9YqIiIiIlBYK0UVEREREzKhatWp069aNpUuXcvfu3SLn09LScHV15fLly1y+fNl0/PTp06SlpeHm5gbcb8Xyyy+/PPDZ77777qHrsba2xmg0Fjn+5ptvEh8fz6effkpubi79+vV76HuLiIiIiJRGCtFFRERERMxs6dKlGI1GnnvuOaKiojh37hxnzpxhyZIltG3bli5duvDss8/y2muvceLECY4cOcKQIUPo0KEDnp6eAHTq1Iljx44RHh7OuXPneO+99zh16tRD19KwYUN2795NcnIyt27dMh13dXWlTZs2BAYGMmjQIOzs7B7Z9xcRERERKckUoouIiIiImJmzszMnTpygY8eOBAQE8Mwzz/DSSy+xe/duli1bhsFgIDo6mqpVq9K+fXu6dOmCs7MzkZGRpnt069aN6dOnM3XqVFq3bk1GRgZDhgx56FoWLFjArl27cHJyolWrVg+cGzlyJNnZ2WrlIiIiIiJliiH/j+xkJCIiIiIiZd7s2bPZsGED//jHP8xdioiIiIjIY6OZ6CIiIiIi8m/duXOHU6dO8cknn+Dn52fuckREREREHiuF6CIiIiIi8m+NGzcODw8PvLy81MpFRERERMoctXMRERERERERERERESmGZqKLiIiIiIiIiIiIiBRDIbqIiIiIiIiIiIiISDEUoouIiIiIiIiIiIiIFEMhuoiIiIiIiIiIiIhIMRSii4iIiIiIiIiIiIgUQyG6iIiIiIiIiIiIiEgxFKKLiIiIiIiIiIiIiBRDIbqIiIiIiIiIiIiISDEUoouIiIiIiIiIiIiIFOP/AW13P415eW60AAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 1500x800 with 1 Axes>"
       ]
@@ -1146,22 +1050,43 @@
     }
    ],
    "source": [
-    "# Create a stacked bar chart for project distribution across top counties\n",
+    "# Define a more distinct color palette that groups similar categories\n",
+    "colors = {\n",
+    "    'Agricultural Projects': '#228B22',      # Forest Green\n",
+    "    'Air Quality Projects': '#87CEEB',       # Sky Blue\n",
+    "    'Business - Small': '#DDA0DD',          # Plum\n",
+    "    'Community Projects': '#FF7F50',        # Coral\n",
+    "    'Individual - Home Energy': '#FFD700',   # Gold\n",
+    "    'Individual - Vehicle': '#DAA520',       # Goldenrod\n",
+    "    'Institutional - Education': '#4B0082',  # Indigo\n",
+    "    'Institutional - Government': '#800000', # Maroon\n",
+    "    'Institutional - Large Business': '#4682B4', # Steel Blue\n",
+    "    'Institutional - Nonprofit': '#2E8B57',  # Sea Green\n",
+    "    'Other': '#808080',                     # Grey\n",
+    "    'Transportation - Other': '#FF4500',     # Orange Red\n",
+    "    'Water-Energy Projects': '#00CED1'       # Turquoise\n",
+    "}\n",
+    "\n",
     "plt.figure(figsize=(15, 8))\n",
-    "county_category_pcts.loc[top_counties].plot(kind='bar', stacked=True)\n",
+    "county_category_pcts.loc[top_counties].plot(kind='bar', \n",
+    "                                          stacked=True,\n",
+    "                                          color=[colors[x] for x in county_category_pcts.columns])\n",
     "plt.title('Distribution of Project Funding by Category Across Top Counties')\n",
     "plt.xlabel('County')\n",
     "plt.ylabel('Percentage of Total Funding')\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+    "plt.legend(bbox_to_anchor=(1.05, 1), \n",
+    "          loc='upper left',\n",
+    "          title='Project Categories')\n",
     "plt.tight_layout()\n",
     "plt.show()\n",
     "\n",
     "# Create a visualization for DAC funding ratio by county\n",
     "plt.figure(figsize=(15, 8))\n",
-    "sns.boxplot(data=data[data['dac_funding_ratio'].notna()], \n",
-    "            x='County', \n",
+    "sns.boxplot(data=data[data['dac_funding_ratio'].notna()],\n",
+    "            x='County',\n",
     "            y='dac_funding_ratio',\n",
-    "            order=top_counties)\n",
+    "            order=top_counties,\n",
+    "            palette='viridis')\n",
     "plt.title('DAC Funding Ratio Distribution by County')\n",
     "plt.xticks(rotation=45)\n",
     "plt.tight_layout()\n",
@@ -1170,92 +1095,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Environmental Justice Analysis by Category:\n",
-      "                          Total Project Cost                              \\\n",
-      "                                       count           sum          mean   \n",
-      "ej_category                                                                \n",
-      "Individual - Solar/Energy               7427     320008647  4.308720e+04   \n",
-      "Individual - Vehicle                    4538    1286418141  2.834769e+05   \n",
-      "Large Projects                         10009  127060954384  1.269467e+07   \n",
-      "Other                                 119455    1537986491  1.287503e+04   \n",
-      "\n",
-      "                          dac_funding_ratio          \\\n",
-      "                                       mean  median   \n",
-      "ej_category                                           \n",
-      "Individual - Solar/Energy            0.6504  0.7405   \n",
-      "Individual - Vehicle                 0.1660  0.0245   \n",
-      "Large Projects                       0.2761  0.0000   \n",
-      "Other                                0.4129  0.0000   \n",
-      "\n",
-      "                          Is Benefit Disadvantaged Communities  \\\n",
-      "                                                          mean   \n",
-      "ej_category                                                      \n",
-      "Individual - Solar/Energy                               0.7824   \n",
-      "Individual - Vehicle                                    0.3455   \n",
-      "Large Projects                                          0.0664   \n",
-      "Other                                                   0.3275   \n",
-      "\n",
-      "                          Is Low Income Communities  \\\n",
-      "                                               mean   \n",
-      "ej_category                                           \n",
-      "Individual - Solar/Energy                    0.9338   \n",
-      "Individual - Vehicle                         0.4125   \n",
-      "Large Projects                               0.5602   \n",
-      "Other                                        0.5708   \n",
-      "\n",
-      "                          Total Project GHGReductions  \n",
-      "                                                  sum  \n",
-      "ej_category                                            \n",
-      "Individual - Solar/Energy                      693914  \n",
-      "Individual - Vehicle                           993992  \n",
-      "Large Projects                              103527784  \n",
-      "Other                                         3938822  \n",
-      "\n",
-      "Percentage of Funding to Disadvantaged and Low-Income Communities:\n",
-      "                           DAC_percentage  LowIncome_percentage\n",
-      "ej_category                                                    \n",
-      "Individual - Solar/Energy           41.46                  4.97\n",
-      "Individual - Vehicle                 5.13                  2.01\n",
-      "Large Projects                       0.91                  1.47\n",
-      "Other                               10.11                 16.38\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Impact Analysis:\n",
-      "                           Total Project GHGReductions  Total Project Cost  \\\n",
-      "ej_category                                                                  \n",
-      "Individual - Solar/Energy                        93.43            43087.20   \n",
-      "Individual - Vehicle                            219.04           283476.89   \n",
-      "Large Projects                                10343.47         12694670.24   \n",
-      "Other                                            32.97            12875.03   \n",
-      "\n",
-      "                           GHG_reduction_per_dollar  \n",
-      "ej_category                                          \n",
-      "Individual - Solar/Energy                    0.0022  \n",
-      "Individual - Vehicle                         0.0008  \n",
-      "Large Projects                               0.0008  \n",
-      "Other                                        0.0026  \n"
+     "ename": "KeyError",
+     "evalue": "\"Column(s) ['dac_funding_ratio'] do not exist\"",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[18], line 22\u001b[0m\n\u001b[1;32m     19\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mej_category\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mapply(classify_for_ej_analysis, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m     21\u001b[0m \u001b[38;5;66;03m# Analyze environmental justice metrics by category\u001b[39;00m\n\u001b[0;32m---> 22\u001b[0m ej_analysis \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mej_category\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\n\u001b[1;32m     23\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mTotal Project Cost\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcount\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msum\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmean\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     24\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdac_funding_ratio\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmean\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmedian\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     25\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mIs Benefit Disadvantaged Communities\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmean\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     26\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mIs Low Income Communities\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmean\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     27\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mTotal Project GHGReductions\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msum\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\n\u001b[1;32m     28\u001b[0m \u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mround(\u001b[38;5;241m4\u001b[39m)\n\u001b[1;32m     30\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEnvironmental Justice Analysis by Category:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     31\u001b[0m \u001b[38;5;28mprint\u001b[39m(ej_analysis)\n",
+      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/core/groupby/generic.py:1432\u001b[0m, in \u001b[0;36mDataFrameGroupBy.aggregate\u001b[0;34m(self, func, engine, engine_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1429\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mengine_kwargs\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m engine_kwargs\n\u001b[1;32m   1431\u001b[0m op \u001b[38;5;241m=\u001b[39m GroupByApply(\u001b[38;5;28mself\u001b[39m, func, args\u001b[38;5;241m=\u001b[39margs, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m-> 1432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1433\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_dict_like(func) \u001b[38;5;129;01mand\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1434\u001b[0m     \u001b[38;5;66;03m# GH #52849\u001b[39;00m\n\u001b[1;32m   1435\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mas_index \u001b[38;5;129;01mand\u001b[39;00m is_list_like(func):\n",
+      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/core/apply.py:190\u001b[0m, in \u001b[0;36mApply.agg\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_str()\n\u001b[1;32m    189\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_dict_like(func):\n\u001b[0;32m--> 190\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg_dict_like\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    191\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_list_like(func):\n\u001b[1;32m    192\u001b[0m     \u001b[38;5;66;03m# we require a list, but not a 'str'\u001b[39;00m\n\u001b[1;32m    193\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39magg_list_like()\n",
+      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/core/apply.py:423\u001b[0m, in \u001b[0;36mApply.agg_dict_like\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    415\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21magg_dict_like\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m Series:\n\u001b[1;32m    416\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    417\u001b[0m \u001b[38;5;124;03m    Compute aggregation in the case of a dict-like argument.\u001b[39;00m\n\u001b[1;32m    418\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    421\u001b[0m \u001b[38;5;124;03m    Result of aggregation.\u001b[39;00m\n\u001b[1;32m    422\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 423\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg_or_apply_dict_like\u001b[49m\u001b[43m(\u001b[49m\u001b[43mop_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43magg\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/core/apply.py:1608\u001b[0m, in \u001b[0;36mGroupByApply.agg_or_apply_dict_like\u001b[0;34m(self, op_name)\u001b[0m\n\u001b[1;32m   1603\u001b[0m     kwargs\u001b[38;5;241m.\u001b[39mupdate({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mengine\u001b[39m\u001b[38;5;124m\"\u001b[39m: engine, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mengine_kwargs\u001b[39m\u001b[38;5;124m\"\u001b[39m: engine_kwargs})\n\u001b[1;32m   1605\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m com\u001b[38;5;241m.\u001b[39mtemp_setattr(\n\u001b[1;32m   1606\u001b[0m     obj, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas_index\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m, condition\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mhasattr\u001b[39m(obj, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas_index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1607\u001b[0m ):\n\u001b[0;32m-> 1608\u001b[0m     result_index, result_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute_dict_like\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1609\u001b[0m \u001b[43m        \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mselected_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mselection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m   1610\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1611\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwrap_results_dict_like(selected_obj, result_index, result_data)\n\u001b[1;32m   1612\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
+      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/core/apply.py:462\u001b[0m, in \u001b[0;36mApply.compute_dict_like\u001b[0;34m(self, op_name, selected_obj, selection, kwargs)\u001b[0m\n\u001b[1;32m    460\u001b[0m is_groupby \u001b[38;5;241m=\u001b[39m \u001b[38;5;28misinstance\u001b[39m(obj, (DataFrameGroupBy, SeriesGroupBy))\n\u001b[1;32m    461\u001b[0m func \u001b[38;5;241m=\u001b[39m cast(AggFuncTypeDict, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc)\n\u001b[0;32m--> 462\u001b[0m func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormalize_dictlike_arg\u001b[49m\u001b[43m(\u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mselected_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    464\u001b[0m is_non_unique_col \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    465\u001b[0m     selected_obj\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m    466\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m selected_obj\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnunique() \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mlen\u001b[39m(selected_obj\u001b[38;5;241m.\u001b[39mcolumns)\n\u001b[1;32m    467\u001b[0m )\n\u001b[1;32m    469\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m selected_obj\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m    470\u001b[0m     \u001b[38;5;66;03m# key only used for output\u001b[39;00m\n",
+      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/core/apply.py:663\u001b[0m, in \u001b[0;36mApply.normalize_dictlike_arg\u001b[0;34m(self, how, obj, func)\u001b[0m\n\u001b[1;32m    661\u001b[0m     cols \u001b[38;5;241m=\u001b[39m Index(\u001b[38;5;28mlist\u001b[39m(func\u001b[38;5;241m.\u001b[39mkeys()))\u001b[38;5;241m.\u001b[39mdifference(obj\u001b[38;5;241m.\u001b[39mcolumns, sort\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m    662\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(cols) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 663\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mColumn(s) \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(cols)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m do not exist\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    665\u001b[0m aggregator_types \u001b[38;5;241m=\u001b[39m (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m, \u001b[38;5;28mdict\u001b[39m)\n\u001b[1;32m    667\u001b[0m \u001b[38;5;66;03m# if we have a dict of any non-scalars\u001b[39;00m\n\u001b[1;32m    668\u001b[0m \u001b[38;5;66;03m# eg. {'A' : ['mean']}, normalize all to\u001b[39;00m\n\u001b[1;32m    669\u001b[0m \u001b[38;5;66;03m# be list-likes\u001b[39;00m\n\u001b[1;32m    670\u001b[0m \u001b[38;5;66;03m# Cannot use func.values() because arg may be a Series\u001b[39;00m\n",
+      "\u001b[0;31mKeyError\u001b[0m: \"Column(s) ['dac_funding_ratio'] do not exist\""
      ]
     }
    ],
@@ -1384,7 +1241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1642,7 +1499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {