diff --git a/ipynb/Electoral Votes.ipynb b/ipynb/Electoral Votes.ipynb
index 59f276f..528dcd8 100644
--- a/ipynb/Electoral Votes.ipynb	
+++ b/ipynb/Electoral Votes.ipynb	
@@ -8,7 +8,7 @@
     "\n",
     "# Tracking Trump: Electoral Votes Edition\n",
     "\n",
-    "[538](https://projects.fivethirtyeight.com/trump-approval-ratings/) shows presidential approval ratings (currently about 42% (±4) approval and 53% (±4) disapproval). Do approval ratings predict election results? There are three big caveats:\n",
+    "[RealClearPolitics](https://www.realclearpolitics.com/epolls/other/president_trump_job_approval-6179.html) and [538](https://projects.fivethirtyeight.com/trump-approval-ratings/) shows presidential approval ratings (currently about 43% approval and 53% disapproval for a net -10%). Do approval ratings predict election results? There are three big caveats:\n",
     "\n",
     "1. These are  approval polls, not votes. \n",
     "\n",
@@ -16,8 +16,9 @@
     "\n",
     "3. These are popular votes, not electoral votes. \n",
     "\n",
-    "We can't be conclusive about the first two points, but this notebook can take the state-by-state, month-by-month approval data from Morning Consult's\n",
-    "[Tracking Trump](https://morningconsult.com/tracking-trump/) web page and compute electoral votes, under the assumption that Trump wins the electoral votes of states he has positive net approval (and for the purposes of computation we'll assign half the electoral votes for states where approval exactly equals disapproval).\n",
+    "We can't be conclusive about the first two points, but this notebook can take the \n",
+    "\n",
+    "and compute electoral votes, under the assumption that Trump wins the electoral votes of states he has positive net approval (and for the purposes of computation we'll assign half the electoral votes for states where approval exactly equals disapproval).\n",
     "\n",
     "\n",
     "# TL;DR for policy wonks\n",
@@ -36,13 +37,14 @@
     "\n",
     "- **Movement**: What's the most a state's net approval could be expected to move, due to random fluctuations (that is, assuming there is no big event that changes people's minds)? I define the maximum expected  **movement** of a state as 1/5 of the undecided voters (i.e. assume the undecided voters broke 60/40 one way or the other) plus 2 standard deviations in the net approval over the last 12 months. \n",
     "\n",
-    "- **Swing state**: I define a swing state as one whose maximum expected movement is greater than the absolute value of the net approval. There are 15 such states now. (My definition correlates well with the standard wisdom on swing states, but with a few surprises, such as North Dakota, where Trump is currently only +1% net approval. My model does not count Pennsylvania, Michigan, and Wisconsin as swing states, because Trump is currently -7%, -12% and -13% respectively, and expected movement is low at 3% to 5%.) If Trump won all the swing states he would get **253** electoral votes, and if he lost them all, **76** electoral votes.\n",
+    "- **Swing state**: I define a swing state as one whose maximum expected movement is greater than the absolute value of the net approval. There are 15 such states now. My definition correlates well with the standard wisdom on swing states, but with a few surprises: North Dakota is included because Trump is currently only +1% net approval; a typical Republican is +30%. My model says that Pennsylvania, Michigan, and Wisconsin, which are traditionally considered swing states, are currently out of reach for Trump because they have low movement (3% to 5%) and Trump is currently at -7%, -12% and -13% respectively. If Trump won all the swing states he would get **253** electoral votes, and if he lost them all, **76** electoral votes.\n",
     "\n",
-    "- **Margin**: Suppose a future event swings voters in one direction or another uniformly, across the board in all states. How much of a swing would be necessary to change the election outcome? We call that the **margin**. Today **Trump's margin is 7%:** if he got 7% more votes in all states he would be over 270 electoral votes. (This could come, for example, by convincing undecided voters to break for him at a 2% to 1% ratio, and then convincing 3% of disapproving voters to switch to approving.)\n",
+    "- **Margin**: Suppose a future event swings voters in one direction or another uniformly, across the board in all states. How much of a swing would be necessary to change the election outcome? We call that the **margin**. Today **Trump's margin is 7%:** if he got 7% more votes in all states he would be over 270 electoral votes. (This could come, for example, by convincing the 3% of undecided voters to break for him at a 2% to 1% ratio, and then convincing 3% of disapproving voters to switch to approving.)\n",
     "\n",
     "# Data and Code\n",
     "\n",
-    "First fetch the [Tracking Trump](https://morningconsult.com/tracking-trump/) web page and cache it locally: "
+    "First fetch the state-by-state, month-by-month approval data from Morning Consult's\n",
+    "[Tracking Trump](https://morningconsult.com/tracking-trump/) web page  and cache it locally: "
    ]
   },
   {
@@ -56,7 +58,7 @@
      "text": [
       "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
       "                                 Dload  Upload   Total   Spent    Left  Speed\n",
-      "100  116k    0  116k    0     0   193k      0 --:--:-- --:--:-- --:--:--  193k\n"
+      "100  116k    0  116k    0     0   234k      0 --:--:-- --:--:-- --:--:--  234k\n"
      ]
     }
    ],
@@ -68,7 +70,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now define the code. Note that `data` contains the [electoral votes by state](https://www.britannica.com/topic/United-States-Electoral-College-Votes-by-State-1787124) and the [partisan lean by state](https://github.com/fivethirtyeight/data/tree/master/partisan-lean) (how much more Republican (plus) or Democratic (minus) leaning the state is compared to the country as a whole, across multiple recent elections), and `net_usa` has the [country-wide net presidential approval](https://projects.fivethirtyeight.com/trump-approval-ratings/)."
+    "Now some imports: "
    ]
   },
   {
@@ -83,8 +85,22 @@
     "import ast\n",
     "from collections import namedtuple\n",
     "from IPython.display import display, Markdown\n",
-    "from statistics import stdev\n",
-    "\n",
+    "from statistics import stdev"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Additional data: the variable `data` contains the [electoral votes by state](https://www.britannica.com/topic/United-States-Electoral-College-Votes-by-State-1787124) and the [partisan lean by state](https://github.com/fivethirtyeight/data/tree/master/partisan-lean) (how much more Republican (plus) or Democratic (minus) leaning the state is compared to the country as a whole, across multiple recent elections). The variable `net_usa` has the [country-wide net presidential approval](https://projects.fivethirtyeight.com/trump-approval-ratings/) by month."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "data = { # From https://github.com/fivethirtyeight/data/tree/master/partisan+lean \n",
     "         # a dict of {\"state name\": (electoral_votes, partisan_lean)}\n",
     "  \"Alabama\": (9, +27),         \"Alaska\": (3, +15),          \"Arizona\": (11, +9),         \n",
@@ -113,11 +129,25 @@
     " '1-Feb-18': -15, '1-Mar-18': -14, '1-Apr-18': -13, '1-May-18': -12,\n",
     " '1-Jun-18': -11, '1-Jul-18': -10, '1-Aug-18': -12, '1-Sep-18': -14,\n",
     " '1-Oct-18': -11, '1-Nov-18': -11, '1-Dec-18': -10, '1-Jan-19': -12,\n",
-    " '1-Feb-19': -16, '1-Mar-19': -11, '1-Apr-19': -11, '1-May-19': -12}\n",
-    "\n",
-    "State = namedtuple('State', 'name, ev, lean, approvals, disapprovals')\n",
-    "State.__doc__ = '''A State has a name, the number of electoral votes (.ev),\n",
-    "the partisan lean (.lean) and two dicts of {date: percent}: .approvals and .disapprovals'''\n",
+    " '1-Feb-19': -16, '1-Mar-19': -11, '1-Apr-19': -11, '1-May-19': -12}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now the code to parse and manipulate the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class State(namedtuple('_', 'name, ev, lean, approvals, disapprovals')):\n",
+    "    '''A State has a name, the number of electoral votes (.ev), the partisan lean (.lean) \n",
+    "    and two dicts of {date: percent}: .approvals and .disapprovals'''\n",
     "\n",
     "def parse_page(filename='evs.html', data=data):\n",
     "    \"Read data from the file and return (list of dates, list of `State`s, last date).\"\n",
@@ -138,12 +168,12 @@
     "\n",
     "assert len(states) == 51 and sum(s.ev for s in states) == 538\n",
     "\n",
-    "def EV(states, date=now, swing=0):\n",
+    "def EV(states, date=now, swing=0) -> int:\n",
     "    \"Total electoral votes with net positive approval (plus half the votes for net zero).\"\n",
     "    return sum(s.ev * (1/2 if net(s, date) + swing == 0 else int(net(s, date) + swing > 0))\n",
     "               for s in states)\n",
     "\n",
-    "def margin(states, date=now):\n",
+    "def margin(states, date=now) -> int:\n",
     "    \"What's the least swing that would lead to a majority?\"\n",
     "    return next(swing for swing in range(-50, 50) if EV(states, date, swing) >= 270)\n",
     "\n",
@@ -151,9 +181,7 @@
     "def undecided(state, date=now):   return 100 - state.approvals[date] - state.disapprovals[date]\n",
     "def movement(state, date=now):    return undecided(state, date) / 5 + 2 * 𝝈(state)\n",
     "def 𝝈(state, recent=dates[-12:]): return stdev(net(state, d) for d in recent)\n",
-    "def is_swing(state):              return abs(net(state)) < movement(state)\n",
-    "\n",
-    "def md(lines): display(Markdown('\\n'.join(lines)))"
+    "def is_swing(state) -> bool:      return abs(net(state)) < movement(state)"
    ]
   },
   {
@@ -165,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -174,7 +202,7 @@
        "193.0"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -185,7 +213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -194,7 +222,7 @@
        "7"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -205,7 +233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -223,7 +251,7 @@
        " 9: 288.0}"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -254,12 +282,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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4Ed988w1u374NAJDL5Rg/fjy+++47mJmZFfkMTZNIJHB0dIShYaneZNMptRO72bNnY/Pmzfjzzz+1GU+l5WguwXsu5iplq7n0CRFRhWO96WmpfrZY+CFXyEsEcgVD9DmaUKrry8LExASOjo4qPzY2NgCAYcOG4eHDhwgNDVW55tatW7h+/bpyImRqaioCAwPh6uoKR0dHNG3aFKtXr36LllMVEBCArl27okGDBnB1dcWsWbNgYWGBS5cuAQCioqLg4eGBjh07omPHjvDw8EBUVBQAYPXq1WjcuDH8/PzUetaiRYvwzjvvwMHBAQ0bNsTo0aMB5G2+ULt2bchkeRMc79+/D2tra0yaNEl57X/+8x/069cPQN5QrLW1Na5duwYACA0NhbW1Nc6cOYMuXbrAyckJfn5+uH79usrzt27dinfeeQdOTk4YPHgwNmzYAGtr67I3XjHUTjnXrFkDGxsbvPfee3B2dka9evVgamqqUkcQBPz+++8aD7KyGOdhgV+jXiqPI+JycCkuB60cjIu5ioiISHMaN26Mli1bYtu2bfD19VWWb926FS4uLmjXrh0AYP78+bhz5w527NgBOzs7REdHIzExUSsxyeVy7N27FxkZGWjdujUAoEmTJrh37x4eP34MURRx7949NG7cGI8ePcL69esLrNBRlH379uHHH3/Ehg0b0KRJEyQkJCiTRx8fH2RlZeHatWto1aoVwsLCYGtrq5L0hoWFoWvXrsU+Y+7cufj6669Ro0YNTJ8+HYGBgYiIiIAgCLh48SK++OILzJkzB7169cK5c+cwb968sjWUGtRO7G7evAlBEODk5ITc3Fxl1vw6QRAKuZJeaWJjhC61TPDn0/xu+B9vp2GLg60eoyIiosrm5MmTqFWrlkrZJ598otzTfdiwYZg2bRpSU1NhZWWF7Oxs7Nq1C+PHj1fWf/z4Md599114eXkBAOrWrVvsM8+fP4/33ntPeSyTyZCbm6sSx6RJkzBu3Djl8e3bt9GtWzdkZWVBKpVi27Zt8PDwAAC4u7tj9uzZ6N+/PwBgzpw5cHd3x8CBAzFr1iycP38eCxYsgCiK+Oqrr9CrV69C43r8+DEcHR3RuXNnGBkZoU6dOsqJDxYWFmjatClCQ0OVid2oUaOwYsUKPH/+HNWqVcPVq1eV7VaUr776SpkkT506FT169MCzZ89Qq1YtrFu3Dp07d1a+++7q6oqrV69iy5Ytxd6zrNRO7O7cuaOVAKqacR4WKondgUdZeJgmQz3L8jteT0REFUvbtm2xcuVKlbLX35sbMGAAZsyYgd27d2PkyJE4dOgQ0tLS8MEHHyjrfPzxx/j3v/+NGzduoFOnTujRowfat29f5DObN2+u0tN14MAB7N+/H+vXr1eWvRoOfsXNzQ2hoaFITU3F/v37MXbsWBw8eBBNmjQBAIwcORIjR45U1t+xYweAvHcIvby8cOLECSgUCnTv3h3e3t6wty84OaVfv35Yu3YtmjZtis6dO8Pf3x89e/aEiYkJAKB9+/YICwvDpEmTcO7cOYwZMwZnz55V9t4ZGRkpk9uivEpGAaBGjRoAgPj4eNSqVQv//e9/0aNHD5X6Xl5e+k/sSDM61TRBExtD3EnOG89XiMDaO+n41ls7Y+1ERKRZKSNqlVzpf87GZGPgkefKd+wAwEwiYEdXW/g6mWgjPACAubl5sdt8WlhYoF+/fti2bRtGjhyJrVu3olu3bnB0dFTW6dq1K27duoUTJ07gzJkzGDx4MPr27Vvke3ZmZmYqz7Szs4OpqWmBOLKy8icOGhsbK883b94cV69exerVq/Hjjz8WuH9SUhK++eYbHDhwAJcuXYKLiwvc3d0BAC4uLrh8+TJ69uxZ4LratWvj8uXLOHPmDEJCQjBz5kwsWrQIJ0+ehFQqRfv27bFhwwb8/fffSE9PR7NmzdC+fXuEhobCzs4OrVu3hpGRUZFtCUDl/KvRy1eTU0RR1OmIptqTJ14JDQ3F7NmzMWLECIwYMQKzZ8/G2bNntRFbpSQIAsa9sc3Ytv++RMr/1rgjIqLKw9fJBB9mhMFIzPtl3kiUaT2pU9ewYcNw9epVHD16FGfOnCl09yhbW1sMGTIEa9aswQ8//IDff/8d2dnam9WrUCiQk5NT6LkZM2YgMDAQdevWhUKhQG5urvJcTk4O5PKit+s0NTVF9+7dsXDhQpw6dQp3795Vrv3q4+OD7OxsrFy5Em3atIFEIlEmdmFhYcX2UqrD3d0dV69eVSl781iT1O6xy8jIwMiRI3HixAmIoghLS0sAQFpaGn788Ud06dIFmzZtgoWFRQl3okENzDHvygvEZuYlc+kyEb/8NwNfeFrqOTIiItK0+vJ4fJgRhr3mrdDv5SX4Og3V+jOzs7MRGxurUiaRSFR2iWrdujUaNWqEMWPGwNHRscAEgW+++QZNmzZF48aNIZPJcODAAdSrV085hPmmnJwcJCcnK4/9/f3h7++vEodUKlUuFfL111+jW7duqFWrFtLT0xEcHIywsDDs3LmzwL1DQkIQGRmJVatWAQBatGiBe/fu4ciRI1AoFLh3716Rw6W//vor5HI5vLy8IJVK8ccff8DIyEjZU/jqPbudO3fi66+/VrbN06dP8ejRoxLfryvJ6NGj0aNHD3z//fcICAjAuXPncPDgwbe6Z3HU7rGbOXMmjh8/jokTJyIyMhLR0dGIjo5GZGQkJkyYgJMnT2LWrFlaC7QyMZEIGNVYNQFedycDuRpcsJiIiMqP+vJ4TEw7jPryeJ08LyQkBO7u7io/r8+AfeX//u//kJKSgg8//BASiUTlnImJCebPn4/27duje/fuSE9Px/bt24t8ZkRERIFnvvnz+iLDsbGxCAwMRKtWrdC3b19cvXoVwcHBBRLMzMxMTJ48GStWrFDGWLNmTSxbtgyTJk3Cl19+ieXLl8PJyanQuKysrLB161b07NkTbdu2xf79+7F161bUq1dPWadDhw6Qy+XK3jlTU1O0bNkSJiYmJb5fV5LWrVtj5cqVWLduHdq1a4dDhw5h/PjxBVYW0RQhJSVFrWyiXr166NOnD77//vtCz3/++ec4cOBAlV7EuDRbgSVlyeGxMxaZ8vzmX+9rg/dczDW2r2BV2J8wKioKbm5u+g6jymB76w7bWrcKa+9XM0bfVlX4u7g0srKytJbUVBRBQUE4c+YMzp8/X2Sdsn7/1B6KVSgUaNq0aZHnmzZtiv3795c6gKqquqkEH7qZY+PfGcqyH2+nY1AD3a2gTUREmqXOvt1vJnoA9+2u7L7//nv4+fnBwsICISEh2LRpk9ZGOdVO7Pz9/XH8+HF8/PHHhZ4/ceIE/P39NRZYVfBpEwv8/HcGXvXZ3UjMRdjzwl8aJSKi8s/Hx4cJGhVw7do1/PDDD3jx4gXq1q2L2bNnY+zYsVp5ltqJXVBQEIYPH44PPvgAo0ePRv369SEIAu7fv49169bh6dOn2Lx5s8qLk0DBNWson4uVIXo6m+Lwa3vGrrqdjjZ6jImIiIg0a9OmTTp7ltqJ3astPu7cuYNjx46pnHu1VsurOq9LSkp6m/gqvXEeFiqJ3dHHWXA1sICdgvvIEhERUemondh9+eWX2oyjymrraIzmdka4lpC/Hk+4cUP0ztLeGjdERERUOamd2M2cOVObcVRZrxYs/uRM/hD2DeN66Jz9F6Qi37cjItInXe8aQATkj4SWRal3nnglMzMTmZmZZX4w5etbzwy1pfnrB8kECS4bu+gxIiIikkqlSElJeat/ZIlKSxRFpKSkQCqVlun6Uu0V+/DhQ+X+aomJiQCA6tWro2vXrpg6dSrq169fpiCqOiMDAaObSDHr0gtl2UVjV7TNjoQRuNUYEZE+GBoawtLSEi9evCi5MqntxYsXqFatmr7DKNcsLS2VO3SUltpXXbt2DX379sXLly/h5+cHFxcXiKKIBw8eYNeuXTh06BD27duH5s2blymQqm5YQykWX09DWm7eb4YZBqa4ZeSMFrkP9RsYEVEVZmhoqJFFiilfXFwc6tSpo+8wKi21E7vp06fD3NwcJ0+eRMOGDVXORUZGok+fPggKCsLRo0c1HmRVYGVsgI8ammP17fwFiw+aecFakVHMVURERET51H7H7ubNmxg1alSBpA4A3N3dMWrUKNy4cUOjwVU1Y5pYwOC1d3QVggF+lXbA2Zhs/QVFREREFYbaiZ2Dg0Ox471GRkZwcHAocyBLly6FtbU1pkyZoiwTRRELFy5Eo0aNUKNGDQQEBODu3bsq16WkpCAwMBDOzs5wdnZGYGAgUlJSyhyHPjlbGKKtg7FKmVyQYPCJBCZ3REREVCK1E7uxY8di48aNePbsWYFzT548wYYNG8q8PcalS5ewZcsWeHh4qJSvXLkSq1atwqJFi3Dq1CnY29ujf//+SEtLU9b55JNPcPPmTezatQvBwcG4efMmRo8eXaY49O1sTDYuJRRc4iRTDgw+kcjkjoiIiIql9jt2MpkMlpaWaNmyJXr16qWypdihQ4fQoEEDyGQy/Pjjj8prBEHAuHHjir1vamoqRo0ahR9++AGLFy9WlouiiDVr1mDChAno27cvAGDNmjVwc3NDcHAwRowYgcjISJw8eRJHjx6Ft7c3AGD58uXo2bMnoqKi4ObmVqrG0LdxocnIlhd+LlMuYlxoMm69X0O3QREREVGFoXZiN2vWLOV/79q1q8D527dvq9QB1EvsXiVuHTt2VEnsHj16hNjYWHTu3FlZZmZmhrZt2yIiIgIjRozAxYsXYWFhoUzqAKBNmzaQSqWIiIiocIndqg42GHwiEZnygmsmCQCWt7XWfVBERERUYaid2F29qvktrrZs2YIHDx5g3bp1Bc7FxsYCAOzt7VXK7e3tERMTAyBvyrStra3KquCCIMDOzg5xcXFFPjcqKkoT4ZeotM9xArCssQG+uGWIXEH1j0YEsOOv56ibmVvotdqIp6KorJ+rvGJ76w7bWrfY3rrDti67kjqt1E7s1Fl8uDSLDkZFRWHevHk4cuQIjI2Ni6z35lYub27vUthWLyVtAaOrnryyPMcNwIULv+I3afsCyd2uGCP0buSIPvXMdBZPeVcRh9wrMra37rCtdYvtrTtsa+0q27LGr5HJZDh27Bh27tyJ48ePK3vTSnLx4kUkJibCx8dHWSaXy3H+/Hn8/PPPuHDhAoC8XrnatWsr6yQkJCh78RwcHJCQkKCSyImiiMTExAI9fZoWHh6OiIiIYuusWLGiQJm3t7fKZy5MfXk8PswIwx/mraEQBaRL8hO5z88lo5mdEZwt3vqPjoiIiCqZMmcHFy5cwM6dO7F3714kJyfD0tIS3bt3V/v6gICAArtUjBs3Di4uLpg0aRJcXV3h6OiI06dPo0WLFgCArKwshIeHY968eQCA1q1bIz09HRcvXlS+Z3fx4kVkZGSovHenDT4+PkUmaJr4baS+PB6T0g7hqcQGm6v5I/d/O4ul5oj4JCQZh/5lByMDbkxNRERE+UqV2EVFRWHHjh3YtWsXHj9+DADo3bs3RowYgXbt2sHIyEjte1lbW8PaWnUygLm5OWxsbNCkSRMAeUusLF26FG5ubnB1dcV3330HqVSKQYMGAchbGNnf3x8TJ07EypUrIYoiJk6ciO7du1eabt5a8mTM8aqGma/vIxufg4XXXmC2l3a2uVGnN7Iw6vRGEhERkfaUmNjFx8cjODgYO3fuxI0bN2BpaYlevXqhTZs2+OKLLzB48GD4+flpJbjx48cjMzMTU6ZMQUpKCry8vLBnzx5YWloq66xfvx7Tpk3DgAEDAAA9e/ZUmV1bGXzqYYGzMdk4/iR/HbvlN9PRoYYJOtUy1fjzCuuNfHNYecKECRp/LhEREb2dYhO7QYMG4cyZMzA0NES3bt0wadIkdO/eHcbGxoiOjtZ4MIcOHVI5FgQBQUFBCAoKKvIaGxsb/PTTTxqPpTwxEASs7mCDDvviEPMyb0xWBDA6NBlhfR3gYCbRb4BERERULhS788Sff/6JWrVqYceOHdiyZQt69+5d7AxW0h47Uwl+8q2O19+qi8tUYMzZZCjEguveERERUdVTbGL35ZdfQhAE9OvXDy1atMA333xTYK9W0p0OTiaY0sxSpezUs2w2YMGMAAAgAElEQVR8fytdTxERERFReVJsYjdz5kxcu3YNhw8fhp+fHzZu3Ih27dqhbdu2WL16dbFrxZF2TG1qCR9H1V7T/1x9gYtx3EeWiIioqis2sXulTZs2WLZsGSIjI/HLL7/A1dUVmzdvhiiKmD9/PpYsWYJbt25pO1YCYGggYEPH6rAxyU+q5SLw8ZlkpGQr9BgZERER6Ztaid0rRkZG6NWrF3755RdERkZixYoVsLGxwcKFC9GxY0e8++672oqTXlNLKsHq9jYqZY/T5fjiXDJEvm9HRERUZZUqsXudlZUV/v3vf+PQoUO4ceMGvvrqK0ilUk3GRsXo6WyGMU1U23v/oyxsinypp4iIiIhI38qc2L2uTp06+PLLLxEeHq6J25Ga5ra0QlNb1UWhgy6m4K+kXD1FRERERPqkkcSO9MNEIuDnjtVhYZj/vl22HBgZkoSMXL5vR0REVNUwsavgXKwMsayt6tZs/02VYWpEqp4iIiIiIn1hYlcJvO9ijqFu5iplv0a9xE0jZz1FRERERPrAxK6SWOxtBTcr1R3iDpq1QKIBJ7QQERFVFUzsKgmpkQE2+VWHyWvbxuYIRgg294GMf8xERERVQqn/xb98+TKWLVuGoKAg3L9/HwDw8uVLXL9+Henp3NpKn96pboQFra1UymIkNlhcrQ/+kdjrKSoiIiLSFbUTu9zcXAwfPhzdunXDf/7zH6xbtw5PnjwBAOV+suvWrdNaoKSeke5S9K5rqlKWIxhhm7QDzsZw2zEiIqLKTO3E7ttvv8XBgwexcOFCXLhwQWWHAzMzM/Tr1w9HjhzRSpCkPkEQ8EM7G9iZqv7RygUJBhxLwPZ7GXqKjIiIiLRN7cRu586dGD58OEaPHg17+4LDeg0bNsQ///yj0eCobG4m5SItp+A6djIRGBOaAv8DcQh5lsXtx4iIiCoZtRO72NjYYveCNTExQUYGe4PKg3GhycguZn3iywm56HcsEW33xuGX/2YgU8YEj4iIqDJQO7FzdHTEo0ePijx/7do1ODtz3bTyYFUHG5hJhBLr3U2R4YtzKfDY+Rz/uZKKZxlyHURHRERE2qJ2Yte7d29s2rQJDx48UJYJQl7ycOLECezYsQP9+/fXfIRUar5OJtjR1RZGokylvKhULylbgaU30/HurucYdSYJV+NztB8kERERaZzaid20adPg5OQEX19fjBkzBoIgYOXKlejRowcGDx4MT09PTJgwQZuxUin4Opngw4wwZXJnJMqwr7stjv3LDv3qmcGgkCxPJgK7HmSi88F4dD8Uj73/ZEKm4DAtERFRRaF2YmdlZYWTJ0/i888/x5MnT2BkZISzZ88iMTERU6ZMwaFDh2BmZqbNWKmU6svj8WFGGKwUGfgwIwy+NU3h7WiCzZ2q4/ogR3zxjgWsjAvvx4uIy8HwkCQ0C47F97fSkAkj/COxx3LLf3FNPCIionLKsOQq+czMzDBt2jRMmzZNW/GQhtWXx2Ni2uEC5c4WhpjXygpTm1li+72XWHsnA/deyArUe5Ihx+zLLyCp1hsiBCgEA/wmbY+BMdnwdTLRxUcgIiIiNandY7d9+3akpaVpMxbSAwsjA3zS2AIXBzhgV1dbdK5ZeLImFyRQCHlfl1zBEINPJHLBYyIionJG7cRu7NixaNiwIYYNG4Z9+/YhKytLm3GRjhkIArrWNsWe7nYI7+eA4Q3NYSopun6mXGRyR0REVM6ondgdP34c//73v3H58mUMHz4cbm5uCAwMxNGjR5Gbm6vNGEnHGtsYYUU7G9x5v0aR7+ABecnduNBkHUZGRERExVH7HbtWrVqhVatWWLhwIc6dO4c9e/bgwIED2LVrF6ysrNCrVy8MHDgQnTp10ma8VITw8HBEREQUW2fFihUFyry9veHj41No/eqmEmztbIvBJxKQWcgSd2YSAas62JQpXiIiItK8Uk2eAPLWrmvfvj3at2+PJUuW4MyZM9izZw/27duH33//HYmJidqIk0rg4+NTZIL2NvLWxLND3yNxEIX8Dl4jA2BHV1tOoCAiIipH1B6KLUxSUhLu3buH+/fvczuxSszXyQTv5qruOtK5pgmTOiIionKm1D12KSkp2L9/P3bv3o1z585BLpejadOmmDt3LgYMGKCNGKkceCf3CW4Y11ceP31ZzGa0REREpBdqJ3bbt2/Hnj17EBISgtzcXLi7u2Pq1KkYNGgQGjRooM0YqRyoJU9SOb6TnIuMXAWkRm/V6UtEREQapHZiN3bsWDg7O+Ozzz7DgAED8M4772gzLipnzMUcVJenIUliCQBQiMD1xFy0q8HhWCIiovJC7cTuxIkTaNmypTZjoXKuljxJmdgBwNX4HCZ2RERE5Yja42hM6qj2G8OxlxNy9BQJERERFabIHrulS5dCEARMnDgRgiBg6dKlJd5MEARMmjRJrQevX78emzZtwuPHjwEAjRo1wuTJk9G9e3cAeUO/v//+u8o1LVu2xMmTJ5XH2dnZmDlzJnbv3o2srCz4+vpi6dKlqFWrlloxUOnUkqsuZXMlngtTExERlSdFJnbz58+HIAj47LPPYGxsjPnz55d4s9IkdjVr1sTcuXPh4uIChUKB33//HUOHDkVISIjy/T0/Pz+sW7dOeY2xsbHKPYKCgnD48GFs3LgRNjY2+OqrrzB48GCcOXMGEkkx+2FRmdSQp0IiyiEX8tr2SYYcz1/KUcOcbU1ERFQeFJnYJSQkAIAyQXp1rCkBAQEqx7NmzcLGjRtx6dIlZWJnYmICR0fHQq9PTU3F1q1bsWrVKuVuF+vWrYOnpydCQkLQpUsXjcarLdrYMUJbDKFADXkKnhraKssux+egV10zncZBREREhSsysXuzx0ubPWByuRx79+5FRkYGWrdurSwPDw+Hq6srrKys0K5dO8yaNQv29vYAgOvXryM3NxedO3dW1q9duzbc3d0RERFRYRI7be0YoS215Ukqid0VJnZERETlhtqzYu3t7bF27VoMHDiw0PN79+7FqFGjEB8fr/bDb9++jW7duiErKwtSqRTbtm2Dh4cHAMDf3x+9e/dG3bp1ER0djfnz56NPnz4ICQmBiYkJ4uLiIJFIYGtrq3JPe3t7xMXFFfvcqKgotWMsK108Qx/y3rNzUx6HRqciyqr49ta2ytrW5RXbW3fY1rrF9tYdtnXZubm5FXte7cROJpNBoSh6t4GSzhfGzc0NoaGhSE1Nxf79+zF27FgcPHgQTZo0UUkgPTw80KxZM3h6euLYsWPo06dPkfcURRGCIJT4XG2KiorS+jP05c2ZsX9nGKKBSx1IDIpvc22pzG1dHrG9dYdtrVtsb91hW2tXqbYNKC5hunr1KqysrEr1cGNjYzRo0ADNmzfHnDlz4OnpidWrVxda18nJCTVr1sSDBw8AAA4ODpDL5UhMVJ2pmZCQoByuJc2zUWTAXJGtPE6XiYhMlekxIiIiInql2B67devW4aefflIez5w5EwsXLixQLzU1FUlJSXjvvffeKhiFQoGcnMLXRktMTERMTIxyMkWzZs1gZGSE06dPK5/79OlTREZGwtvb+63ioKIJyFuoOMrASVl2OT4HTWyM9BcUERERASghsbOyskKdOnUAAA8ePED16tXh4OCgUkcQBJibm6N58+YYM2aM2g/++uuv0a1bN9SqVQvp6ekIDg5GWFgYdu7cifT0dHz77bfo06cPHB0dER0djXnz5sHe3h69evVSxvbRRx9h9uzZsLe3Vy534uHhAT8/v1I2A72upJm6teSJiDLKT+w2n76KpMNX9DJTl4iIiPIVm9gNGTIEQ4YMAQD07NkT06ZN01jSFBsbi8DAQMTFxaFatWrw8PBAcHAwunTpgszMTNy5cwfbt29HamoqHB0d0aFDB2zatAmWlvlbWi1YsAASiQQjRoxQLlC8du1armH3lkqaqev5NAshx/OHwLMdG2JCvw66CI2IiIiKofbkiSNHjmj0wWvWrCnynJmZGfbs2VPiPUxNTbFkyRIsWbJEk6FRCVrYqS4UfTdFhvRcBSyMSvXKJhEREWlYkYldTEwMgLxJC68fl+RVfaq8bEwM4FJNgvsv5AAAhQhcT8xF+xomeo6MiIioaisysWvSpAkEQcDz589hbGysPC5JUlJSiXWo4vOyN8b9F5nK4yvxOUzsiIiI9KzIxG7lypUAACMjI5VjIgBoaWeMnfdVEzsiIiLSryITu2HDhhV7TFVbS3vV9+yuxOfqKRIiIiJ65a3fdo+Pj8e9e/c0EQtVIO9UN4LJa5OPn76UI+alXH8BERERkfqJ3ZYtW/Dpp5+qlE2dOhXu7u5o3bo1/Pz8kJycrPEAqXwylgh4t7rqosSXORxLRESkV2ondps3b4apqanyOCwsDOvXr0f//v0xY8YMREVF4bvvvtNKkFQ+eRUYjmViR0REpE9qr2P38OFDfPjhh8rjP/74AzVr1sT69ethYGCA9PR07Nu3D998841WAqXyJ+89uwzlMXvsiIiI9EvtHrucnBwYG+f30Jw+fRr+/v4wMMi7hYuLC54/f675CKncenMCxfWEXMgVop6iISIiIrUTu7p16yI0NBQAcP36dfzzzz/o3Lmz8nxcXBwsLCw0HyGVW3UtJLA1yf8KpctE/J0i02NEREREVZvaid3w4cOxe/du+Pr6YsCAAXByckK3bt2U5y9evAh3d3etBEnlkyAIaGmvOoHiSgKHY4mIiPRF7cQuMDAQy5YtQ82aNeHv74/g4GCYmZkBAJKTk/H06VMMGjRIa4FS+fTmBAq+Z0dERKQ/ak+eAIARI0ZgxIgRBcptbGwQFhamsaCo4njzPTsmdkRERPpTqsTulcjISERHRwMAnJ2dOQRbhbWwU03s/k6RIT1XAQujt177moiIiEqpVInd0aNHERQUhEePHqmU16tXDwsXLkT37t01GhyVf9YmBnCtZoh7L/ImTShE4FpCLjo4meg5MiIioqpH7W6VU6dOYejQocjNzUVQUBA2b96MTZs2ISgoCDk5ORg6dChCQkK0GCqVV15vTqDgcCwREZFeqN1jt3jxYjRs2BDHjh1DtWrVVM6NHj0a3bp1w6JFi+Dn56fpGKmca2lvjB33M5XHfM+OiIhIP9Tusbt16xaGDh1aIKkDgGrVquH//u//cPPmTY0GRxXDmxMornLJEyIiIr1QO7GTSCTIysoq8nxmZiYkEolGgqKKxcPGCCav/dE/e6nAswy5/gIiIiKqotRO7Hx8fLB+/Xo8fPiwwLmHDx9i48aN8PHx0WRsVEEYSwQ0rc5lT4iIiPRN7XfsZs+ejR49esDb2xu9evWCq6srACAqKgqHDx+GsbEx5syZo7VAqXzzsjfCxdeSuSvxOehTz0yPEREREVU9aid2Hh4eOHnyJObOnYvDhw8rh2VNTU3RpUsXzJ49m+vZVWF579llKI8v8z07IiIinSvVOnbu7u747bffIJfLERsbCwBwcHCAoWGZ1jmmSuTNrcWuJ+RCrhAhMRD0FBEREVHVo1ZGdv36dTx69Ag2NjZo06YNjI2NUbNmTW3HRhVIXQsJ7EwNkJClAABkyETcTZHhnepGJVxJREREmlJsYpeWloYPPvgA58+fV5bVrFkTu3btQuPGjbUeHFUcgiDAy94Yxx7nz5y+Ep/DxI6IiEiHip0Vu2jRIpw7dw49evTAggUL8MknnyA+Ph6TJ0/WVXxUgbS0U03iODOWiIhIt4rtsTt48CACAgKwbds2ZVmjRo0wefJkxMXFwcHBQesBUsXx5kLF3FqMiIhIt4rtsXv27Bn8/f1Vyvz9/SGKIqKjo7UaGFU8ze1UE7u7KTKk5Sr0FA0REVHVU2xil5ubC6lUqlJmYWEBAMjIyCjsEqrCrE0M0NAqvxNYBHAtIVd/AREREVUxJc6KffnyJZKTk5XHr/47PT1dpfwVGxsbDYZHFU0LOyP8N1WmPL4SnwNfJxM9RkRERFR1lJjYTZw4ERMnTixQ/tFHHxVaPykp6e2jogqrpb0xtt/PVB5zAgUREZHuFJvYffnll7qKgyqJwiZQiKIIQeBCxURERNpWbGI3c+ZMXcVBlYRHdSOYSoAsed7x80wFnr1UoJZUot/AiIiIqoBiJ08QlZaRgYCmtqq9dhyOJSIi0g29JXbr169H27ZtUadOHdSpUwddu3bFsWPHlOdFUcTChQvRqFEj1KhRAwEBAbh7967KPVJSUhAYGAhnZ2c4OzsjMDAQKSkpuv4o9AYve9WFirmeHRERkW7oLbGrWbMm5s6dizNnzuD06dPw9fXF0KFD8ddffwEAVq5ciVWrVmHRokU4deoU7O3t0b9/f6SlpSnv8cknn+DmzZvYtWsXgoODcfPmTYwePVpfH4n+p6Ude+yIiIj0QW+JXUBAALp27YoGDRrA1dUVs2bNgoWFBS5dugRRFLFmzRpMmDABffv2RZMmTbBmzRqkp6cjODgYABAZGYmTJ09ixYoV8Pb2RuvWrbF8+XIcO3YMUVFR+vpYBMDrjQkU1xNzIVOIeoqGiIio6ihxuRNdkMvl2Lt3LzIyMtC6dWs8evQIsbGx6Ny5s7KOmZkZ2rZti4iICIwYMQIXL16EhYUFvL29lXXatGkDqVSKiIgIuLm5Ffk8XSR+VTm5FEWgupEZknLzZsK+lImY+eNm1FCkqnW9m5sbGjZsqPbzqnJb6wPbW3fY1rrF9tYdtnXZFZffAKVI7GJiYmBjYwNTU9NCz2dnZyMpKQlOTk5qB3f79m1069YNWVlZkEql2LZtGzw8PBAREQEAsLe3V6lvb2+PmJgYAEBcXBxsbW1VltEQBAF2dnaIi4sr9rklNcrbioqK0vozyrvW0Yk4+jhLedyo+/sY7i7FihUrVOpNmDDhrZ7DttYttrfusK11i+2tO2xr7VJ7KNbDwwMHDhwo8vyhQ4fg4eFRqoe7ubkhNDQUJ0+exMcff4yxY8fizp07yvNvrn325npoha2NxjXTyoc317Pje3ZERETap3ZiJ4rFvyMlk8lKnVAZGxujQYMGaN68OebMmQNPT0+sXr0ajo6OAFCg5y0hIUHZi+fg4ICEhASVuERRRGJiYoGePtK9lpwZS0REpHOlmjxRVOKWkZGBU6dOwc7O7q2CUSgUyMnJQd26deHo6IjTp08rz2VlZSE8PFz5Tl3r1q2Rnp6OixcvKutcvHgRGRkZKu/dkX40tzPG69+Wv1NkeJGj0Fs8REREVUGx79gtXrwYS5YsAZCX1I0ZMwZjx44tUE8uz9tmYNSoUWo/+Ouvv0a3bt1Qq1Yt5WzXsLAw7Ny5E4IgYOzYsVi6dCnc3Nzg6uqK7777DlKpFIMGDQIAuLu7w9/fHxMnTsTKlSshiiImTpyI7t27c+y+HLAyNkBDK0NEpsoAACKAawm5+g2KiIiokis2sfP09MQHH3wAURSxbds2+Pj4oH79+ip1BEGAubk5mjdvjoEDB6r94NjYWAQGBiIuLg7VqlWDh4cHgoOD0aVLFwDA+PHjkZmZiSlTpiAlJQVeXl7Ys2cPLC0tlfdYv349pk2bhgEDBgAAevbsicWLF6sdA2mXl72xMrEDgCsJOdzqhIiISIuKTex69uyJnj17Asib9Tpq1Ci0atVKIw9es2ZNsecFQUBQUBCCgoKKrGNjY4OffvpJI/GQ5nnZG+G3e/nHl+Nz0Fp/4RAREVV6ai93wgSKSsvrjR0orsTnoBUAzlkmIiLSjlKNjD158gSTJk1Cy5YtUbduXZw7dw4AkJiYiKlTp+LGjRtaCZIqJo/qRjCV5B/HZiqQKpjpLyAiIqJKTu3ELioqCh07dkRwcDBq1aqFtLQ0yGR570/Z2tri/Pnz2Lhxo9YCpYrHyEBAM1vVXrunkup6ioaIiKjyUzuxmzNnDszMzHDx4kX8/PPPBda16969O8LDwzUeIFVsb+4b+9TQVk+REBERVX5qJ3ZhYWEYNWoUatSoUeh6ds7Oznj27JlGg6OK782Fip+wx46IiEhr1E7sZDKZylIjb0pOToZEIinyPFVNb/bYxUhsIOf0CSIiIq1QO7Fr1KgRwsLCCj0niiIOHTqEpk2baiwwqhzqSCVwMMv/muUKhogzqKbHiIiIiCovtRO7sWPH4o8//sCyZcuQmpoKIC+he/DgAQIDA3HlyhWMGzdOa4FSxSQIQoFlT/ieHRERkXaovY7de++9h4cPH2LBggX45ptvAAADBw6EKIowMDDA3Llz0aNHD60FShVXS3tjHHmcpTx+IqmOlnigx4iIiIgqJ7UTOwCYMmUK3n//fezfvx/379+HQqFA/fr10a9fvwJbjRG98uYECi55QkREpB2lSuwAoG7duvj888+1EQtVUs3tjCEAeLVATrxBNWTBEKaQFXcZERERlRL3ZCetq2ZsAHfr136HEAQ8Y68dERGRxhXbY+fl5VWqmwmCgMuXL79VQFQ5edkb4++U/B66J4bV0UAep8eIiIiIKp9iE7vatWsXuhjxm54/f47IyEi16lLV1NLOGL9GvVQe8z07IiIizSs2sdu3b1+xF8fExGD58uW4cOECDA0N8eGHH2o0OKo8vArsQGELsYi6REREVDalnjwB5PXQLV++HL/88gtkMhmGDBmCyZMno27dupqOjyqJJjZGMJMIyJTnpXMZBqZIFcz1HBUREVHlUqrEjgkdlZWhgYBmdkYIj81Rll01rqe/gIiIiCohISUlpcQRMSZ0pWO1wVrfIZRLk4XJWGowQnlsKObiuCIQnXBRj1ERERFpRuonKfoOofgeOyZ0pElS8aXKsUwwQi+D1Tio+JTJHRERkQYU22Pn5OSE7OxseHp6YtKkSWoldM2aNdNogBURe+wKOo3WCDBYg0zBtMA5czGTyR0REVV45aHHrtjEzsbGJr9iCUuZiKIIQRCQlJSkuegqKCZ2BdUzOI5HQq0iz9cVn+KhopsOIyIiItKs8pDYFTsUu3LlSl3FUalc7ngJbm5u+g6jXPkhJhuDTyQqZ8W+6YPmjZDaXP3/IVasWKFyPGHChDLHpsl7VXZRUVH8busI21q32N66w7bWrmITu2HDhukqDqrkfJ1MsKOrLQYeeY5coeDX7rsbafCsboSAumZ6iI6IiKhyKNM6dkRl4etkgg8zwvCbtH1ecieKwP+G+GUiMDwkCVs7V0ePOkzuSDvCw8MRERFR6uu8vb3h4+OjhYiIiDSLiR3pVH15PD7MCMNe81bwzInGOdPGyh0ochXAsFNJ+K2LLfxrF5xkQfS2fHx8CiRoHIonosrEQN8BUNVTXx6PiWmH4Z/9F75vpzrRJEcBDD2ViJBnWXqKjoiIqOJiYkd69VFDKVa0VU3usuXAByeTcDYmW09RERERVUxM7EjvhrtLsaSNlUpZplzEkJOJOPecyR0REZG6mNhRuTCqsQUWtlZN7l7KRLx/IhERsUzuiIiI1FHqyRMvX77E06dPkZycDFEsuCaZt7e3RgKjqmeshwVkChGzLr9QlmXIRAw6kYg/utuhpb2xHqMjIiIq/9RO7FJTUzF9+nTs3r0bMpmswHnuPEGa8LmnJWQiMPdKfnKXlitiwPEE7Otuh+Z2TO6IiIiKonZiN3HiROzbtw8jR45E+/btYW3NbbNIOya+a4lchYgF19KUZS9yRPQ/loB9PezQ1JbJHZG2cK0/oopN7cTuxIkTCAwMxMKFC7UZDxEAYGqzashVAEtu5Cd3KTki+h9LxIEednqMjKhy41p/RBWb2pMnDA0NNbq327Jly9CpUyfUqVMHLi4uGDx4MO7cuaNSZ+zYsbC2tlb58ff3V6mTnZ2NKVOmoEGDBqhZsyaGDBmCp0+faixO0p8ZzS0x0dNCpSwpW4G+xxIQZ2Cpp6iIiIjKL7V77Pr06YM///wTI0eO1MiDw8LC8PHHH6NFixYQRRELFixAv379EBERARsbG2U9Pz8/rFu3TnlsbKw6DBcUFITDhw9j48aNsLGxwVdffYXBgwfjzJkzkEgkGomVykadIZ03ewKA/CEdQRAw2yuv5+7H2+nK8wlZCmyR+mF4RgjsFWkFrici/avMQ7qa/GyVuZ1IP4pM7JKTk1WOJ06ciBEjRuCzzz7Dxx9/jDp16hSaOL2elBVnz549Ksfr1q2Ds7MzLly4gJ49eyrLTUxM4OjoWOg9UlNTsXXrVqxatQqdOnVS3sfT0xMhISHo0qWLWrGQdhQ2pFNagiDgP62qQSaKWHsnQ1meYWCKjdJOMIICA16W/i9FItKuyjykq8nPVpnbifSjyMSuQYMGEP63Qfsroiji+vXr+O2334q8YVlnxaanp0OhUBSYlBEeHg5XV1dYWVmhXbt2mDVrFuzt7QEA169fR25uLjp37qysX7t2bbi7uyMiIoKJXSUhCAIWtraCTAFs+Ds/ucsyMEEWgF+l7TEwJhu+Tib6C5IqtH8k9thr3gr9Xl7SdyhUgfF7ROVBkYndl19+qcs4MH36dHh6eqJ169bKMn9/f/Tu3Rt169ZFdHQ05s+fjz59+iAkJAQmJiaIi4uDRCKBra2tyr3s7e0RFxdX5LOioqK09jl0+YyqZpQtkOBojL2xql9bmWCI947HY0WTbLS0Vrz1c/hnV7zK1j5/S5ywS+oDuSDBb9L2aHPpvka+R5pQXttaU3GVt8/3NvH8I7HHNmkHyAUJftXw96i8tZMmVMbPpCslzXcoMrGbOXOmxoMpyowZM3DhwgUcPXpUZXh34MCByv/28PBAs2bN4OnpiWPHjqFPnz5F3u/VmnpF0eQkkMJERUVp/RlV1QiLLBw4ngj5G2tjZysETLprhh1dbd+6545/dkWrbN/tk0+ysF1qBvzv74tcwRCT7hpp5Hv0tspzW2sqrvL0+d6mvc/GZGOb1ARyIe/fL5lgiPF3DBHczU4j36Py1E6aUJ6/25WB2rNix48fjytXrhR5/tq1axg/fnypAwgKCsLu3buxf/9+1KtXr9i6TsOAYZgAACAASURBVE5OqFmzJh48eAAAcHBwgFwuR2Jiokq9hIQE5XAtVS6fhaUUSOpeyZSLGBeaXPhJojecjcnGB38mKpO6VzLlIgafSMTZGG5lRyU7G5ONwScSlEndKzkKoP+xBByOztRTZFRVqZ3Y/fLLL8qEqjD//PMPtm7dWqqHT5s2DcHBwdi/fz8aNmxYYv3ExETExMQoJ1M0a9YMRkZGOH36tLLO06dPERkZya3NKqlVHWxgJim8N9ZEkneeSB3jQpORW8RIGX9JIHWNC01Gprzwc3IR+OhUEk49zdJtUFSlqZ3YlSQpKQkmJup3OU+ePBm//fYbNmzYAGtra8TGxiI2Nhbp6XnLWqSnp2PmzJm4ePEiHj16hNDQUAwZMgT29vbo1asXAMDKygofffQRZs+ejZCQENy4cQOjR4+Gh4cH/Pz8NPXRqBzxdTLBjq62MBILbmvXvoaJ3ofPqOIY1tC8yHNmEoG/JJBaVrQrfhcmuQgMOJ6ISedTkF7UbxJEGlTsOnbh4eE4f/688vjw4cOIjo4uUC8lJQXBwcHw8PBQ+8EbNmwAAPTt21elfNq0aQgKCoJEIsGdO3ewfft2pKamwtHRER06dMCmTZtgaZm/OO2CBQsgkUgwYsQIZGVlwdfXF2vXruUadpWYr5MJPswIU76o/Ep4bA5ScxSwMtbY7ytUiZ1+VvRQ6/ftrPlLAqnlWUYR3XVv+DkyA6eeZWFVexu0q8HvFmlPsYndmTNnsGjRIgB5S07s3bsXe/fuLbRu/fr1sWDBArUfnJKSUux5MzOzAmvdFcbU1BRLlizBkiVL1H42VXz15fH4MCMU26S+EIW8RO6lTETwg5f4uJFFCVdTVXclPgfnY3OKPH83JVeH0VBFJVeI+P6v9ALlhqIMfRtY4I+HWVC89k7wwzQ5eh1JwKceFpjZohrMDIue5EdUVsUmduPGjcOwYcMgiiLeeecdLF68GAEBASp1BEGAubk5rKystBoo0Ztc5PHwzb6LM6b5PcWbI19ipLu02FnRVLFoY2X+H9/4x9hQlEEm5P91uPW/LzG9WTUYF/E+JxEAHIrOQlRq/mshgqiAhZiFAS8vYqXfUATGZmNsaDIepOX36okAVt1Ox4knWVjbwQYt7I0LuTNR2RWb2FlaWiqHPa9evQoHBwdIpVKdBEakjuY5/+CsSROI/0vkbiXl4npiLprb8S/LykLTK/M/TJNh3yPVmYoDXl7EPvNWyBaMAADxWQocjs5Cv/pmZYxa97g1lW6JoogVt1S3NHw3Nxr9M/MXJ/Z2NEFoXwfMvfICP93NUKn731QZuh6Kx8R3LTG1qSV/iSCNUXuv2Pr162szDqIysRYz4Sp7jigjJ2XZlsgMJnZUpDW301WGxxzkqWgse4p/chxwycRVWf5zZEaFSuy4NZVunY3JwdUE1SH7dtmRBepJjQywuI01ApzNMC4sGU9eeydPLgLf3UjD0cd5vXfvVDfSetxU+RWZ2PXv3x+CIGDXrl2QSCT/z959hzdVtn8A/54kTZqmI90t0LK6GAUEBFr23iB7OUARRXxVFF5AcKFQEGWoiCjy8hNBoYBSUaZsKGULCJQyShmleyVNmiY5vz8KaU/StEmbWe7PdfXSnHOSPD2cJvd5xn1jxIgR1b4YwzAmzYsjxJLaqm5zArtttxX4rIMX3F1oEQXhyivR4ueUYs62mJJkMADaq25xAruj6SW4VaBGUy+T73/JU2SVXm9dROlDBGgLjR7fvZ4IJ58LwPzTBdiodw1eyS1Fzz8yMe8ZT7zVkuYIk9ox+s2nUCigUCjAsqzusVKprPJHoaBEjMT2ItTpCBKXX8oyNYsdd+haJIb+lyyHXF3eXRck5iG69B4AIFBbiBB1Nuf4DTe4w2eEAMDFbBUO6q2q7lJyvdrneQp5+LqLN7b08UWgmPv1W6oFFp4rxIC/spDNc8cdvj9WeAzCHT4l2yfmMXorumfPniofE+Io+GAxKdwNX14qnxD/f8lyvBhB80FJuRINi7VXuYsmXmvuDuZReW6xdqrbuCfw0z3elFKM+c94wtWJVy9SYXrLW3WZex3FBAoRWpBj5GhD/UNckfhcAP6bVIBtt7k3oWezSnHevR8AQPu4fvGo9BJKv0NMRmMMpE54IULCCezOZZficm4pomnOCnks/nYxMhTlQZxEwGBKpAQbDpYf06L0Hg4LOyJfVdarl1uixR93FRjT1HgyY0d2h++PTZIuUDMCpw4QHGlhyK0Cw8U3M6M9cPWGea/j48rHuu4+GBKqwLuJ+cgtKb82tRXyc5YyAozbn+MQ9YuJczA5sBs4cCA6d+6MmJgYdOjQgZMkmBB7a+QhQM96Ik7S2Z+S5VgWU3VWePJ0YFkWq/VSnLwQ4QapiDsc5gItJoS5Yc3V8iHY9clypwzs/n6gxE8V8jyWMgKM2peN5TFSPB/u5lQpgRxpYcjXV4o4i2+aewvQt4EIV2v4es81FiM2SIi3T+Rj973KS489qV9MwR0xhcmBnVAoxHfffYcvv/wSfD4fLVu2RGxsLGJiYhAbGwtfX19rtpOQar0UIeEEdltuF+OTZz3hJnDcRRSO1BNRl/39oATX8svzjfEYYHrzyiepT46UcAK7xAwVrueXIkrqPL2/Rx4qMXZ/ji6oe6JUC/znRD4+PFOAviGu6BEsQo96rqgneToq9dT27+1RsQabb3IXPsyM9qh1kBwg5mNzbx80/SUduSVspcc8qV98eWxQrd6L1H0mB3Y7d+6ERqPB+fPncfLkSZw8eRKbN2/GmjVrwDAMwsPD0blzZyxfvtya7SXEqEGhrvBz5SFbWTakUahi8fsdBSaGO+5cO0fqiajLvtbrrXuukRgNPSr/+IuUuqBzkBAnHpVXpvjfdTmWdnKe3t9JB3OhqTw+AADkqVhsvaXA1ltlQ4qRXgJ0rydCj3oidAkSwbOOluWr7d/bd1dlUFUo9xrqzscIC6XEYRgGG3r6Yuz+bCgrqVJG9YuJqcz66+Xz+Xj22Wfx9ttvY8uWLbh16xa+/fZbhIWF4caNG9iwYYOVmklI9YR8BhPDuENmP90oNnI0eVr8k6PCkXTuCsb/VJNSYkok92bg11vFUKiriJQcyNdXiiArNa+tyQVqfH9Njol/56Lx5nT0/zMLcRcKkZhRgtLH445P+yrNApUW669zV0n/p6U7BDzLDWl3CxZha18/gOX++wl5oGFYYjKzFk9oNBpcuHBB12OXlJSEgoICBAUFYeTIkTQ0ROzuxQg3Tu3GU5kqXMsrRTNvyw6j0RCq89CfW9c5SFhtAuuhDcXwFRUg5/GE9gIVi9/uFDt07y8AxN8qxgdnjOdS4zGAkAGUWqOHQMMCSZkqJGWqsPRiEdwFDKSSnnjI94aW4WOTpAsGpinQP8S1RkOQzrpKd/11OQorBMx+rjxMCrf83MtuwSK0KL2Hf4Whum2xgUIK6ojJTA7shg8fjrNnz0KhUKBRo0aIjY3FZ599hs6dO6NRo0ZWbCIhpgvzckGXICGOVxhG++mGHHEdLTuMRkOozuG+TI3tejkNq+utAwARn8GkcO5Nwv+S5brAzlKBvSVvEA4/VOKN43mcbQJWDYCBmuHDhVVj+4AgdAoQ4myWCofTS3DkYQnOZqmqHLaVqVnIKqSAUTMCjP87FzwAvq48+Ih48K7w4yPiwceVB2/h4/9W2J7CD8RWSSxKLbRK11ZBokLNYo1eqpzXm7tbbf5uB9VNTmB3MacUai1r0d5BUneZHNgdPXoUPB4PI0aMwIgRIxAbGwsfHx9rto2QGpkcKeEEdr/eKsZH7bycOhcZqZnvrso5QUuElwD9Gria9NzJkRJOYHcmqzyFjqUCe0u9zj85Kjz/dy5KK/TEufCACYUnwIDVBT/dgicBAGKDRIgNEuH9Z8qGGE88KsHhh2U/NyoUta+KFmU1dbOq6v7T595N97+ljADD92SjhbcA9d0FcBcwcHdhIHFh4O7Ce/yYB4kLA4mAgYcLA4kLr+wYAYNrgmDscOtksSCxKr/cLEZmhVQ57gIGU6Os13vbQJMLsbYECl7Z75OvYnE6U4XYIOq1I9UzObDbvn27bgh26tSpUKlUiIyMRGxsrO4nODi4+hcixMqGhIrhLcpH3uPVZXklrFPnIiM1U6DS4v/0KkfMaOEOnonDh008BehRT4TDFVZab0iW40sHS6GTWqTGmP05kOnNAVzT1Rv3EzIBADOL/jL6fC8hD4NCxRgUWrYI4IFcg8MPlTjysATbbitgRthmNhbAlTw1ruSZFkxySLro/teaud7UWhZfXeGWD5sSJTFIlWNJfLAIVz/CJWFD3bY995QU2BGTmHxl9urVCwsWLMBff/2FtLQ07Nq1C6NHj8bdu3cxc+ZMtGjRAm3btrVmWwkxiauAwXi9II5KQz19fkqWo6jCnCh/Vx7GmRnc6y+i2HqrGLJSa4Y65slRajBqXzanNwkAPn3WE6Ob1OxGpr6Ej0nhEnzf3Qe/9/eFq5NkQnmS6+2o3kKZ2tqZqkBqUfkyVSEPeKOF9eu5Rqgfch7vNZLjjhB9Nao8IRQK4evrCx8fH0ilUkgkEhQVFSE1NdXCzSOkZl7Sy0V24pEKNwtKEeblPLnISM2Vall8d5UbzL/aTGL2cPygUFcEinm6ihVFpWV1iB2hXJ28VItxB3Jwq5CbG2NGC3f8p6VlEsh3q+eKrX39MGr3I5Qy5V8XYj6Djb180MLHBblKLXJLtMir8JNbUr4tt0SLBzI10uTWD4gtneuNZVms1CsfNq6pG4LdrB/tNi3NAI/VQvs4F2FygRp3CtVo7OkcBaMsOX/UEee0OjKTr5BLly7hxIkTOHnyJE6dOoWcnBywLIuQkBB0795dNxxLiCOIkrqgU4AQpzIrLqIoxsJnvezYKmIrv91R4EFxecAj5jN4pQZzolx4DJ7Xq0O8/rr96xCrtSxePpyLs1mlnO2jm4jx6bOeFn2vbsEiTJQfx2ZJF5QyAriwamzpG6Qb8jQ1yDmaXmIQIIr4wIJnPNHUSwBZKQt5KQtZqbZswUYpC3mpFnI1i6LH/y9Ts8hSaHDfSJDIAFjayXJ/4wcfluBybvk5ZgC8FW393joAEKMUoZpspAoCdNv23FNiug16Cy3BkgvMHG1Oq6MzObDr3r07ACAyMhJDhgzRBXL169e3WuMIqY2XIiWcwG7zzWIsaOsJIZ8WUdiCve6OWZY1SEg8KdwNvjUcU3wxQoLll2R4Mqh7MacUF7JV1aZMsRaWZfHOyXzsvc8dcuwWLMLqLt4mzyE0R2NNFibKjxsswjBHZQFifIUA0RxH00swcvcjqBnuVxgLYOnFIsQGiiwyB27FJe7cuqENXRFuw17/iNKHnMBu733nCeyI/Zgc2P3000/o3LkzrYQlTmN4I1fMSWJQ+Lige7ZSi7/SlHjOQpniSdXsdXd8NN2wl6U2c6IaegjQp74I+x+UB1L/S5bbLbBbfKEIP6dwE2+39HHBz718ILLiTUtjTVaVizBMfY3aBohAWZA4SX4cmyRdDIK7f3JKMXJfNn7r7wevWlTQOJOp4qyuB4B3om1bIz1SnY59aKN7fOJRCQpV2jpbGYRYhsmB3dChQ63ZDkIszk3Aw7gmbvihQrb4DTfkdTawe1rmj1RHv7duSENXNKnlvKTJkRJOYLf9tgKfPutVq8ChJtZfl2PZP9xepBB3Prb19XWaL3tLBIhPXmeS/Dh+c3sWYlaFDH55ua3z2aUYsy8H2/v7wsOlZudl5WXuee4eLEJbf9sG875aGXw1RcjhlwWUpVrg0MMSDG9UNz/DiGU4xyxMQmroxUgJJ7A7/LAEqUVqNDJSJ9SZPS3zR6pyNa8UBx6YVz7MFP1DXFHPjYeHxWVzu+RqFvG3ijG1me2GxXbdVWDWqXzONh8RDzv6+SLIBpP5HVFjTRbeLfoLKvBxKGI8EjPKe9hOZ6kwdn8O4vv6wt3M4C45vxR/pnFXoc5sZZ8h0Aj1QyTyI3WPd6cpKLAjVXKOWzxCaijaxwXt/LhzYjZS6pM66xu93rqOAUJ0CKh97i8Bj8ELegsm/pcsB8vapn7sqYwSTD2SC22FtxPzGWzp42vTOV+OSggNtvb1RccAbo9aYoYK4w/koFht3orcr/Suo9a+Luhup5JeEaXpnMf775dAo3WOusXEPiiwI3XeS3q5yH5OKdYVNid1R3qxBvG3uXPP3rRAb90TL0ZIULGi0795aoNVqdZwPb8U4w/kQFkhqwmPAdb38MazAfaZ5+eIPFx4iO/ra3Ajd/yRChMO5EKhNu1v/r5Mja23uNfRzGiPGtXFtYRQTTY8heXvnVOixdksVRXPIE87CuxInTeysRjuFfKXZSi0lOyzDvr+qoxTUquJBx+DQkwrH2aK+hI++uuVI1ufbN3e3wJGjNH7cpCv4gYlK2OlGBhKw3H6PIU8bO/nhza+3ODuSHoJnj+YA6UJwd23lVxHQxta7joyFx8s+tbnvv/e+/T5RYyjwI7Uee4uPIxuwv0S/D8rfyE7kjt8f6zwGIQ7fH97N8Vqikq1BkHWjJbu4Fu4aLp+JYrf7hQjv8TyiXfv8P2x3GMw/ifpgftybgLiec942D2PniOTinj4rb8fon24wd3fD0rw0qEclGiMB3fFjBD/l8ztrXs72sPi15G5+uvdoOxJo8COGGdyYJeZmYl//vmHsy0lJQWzZs3C1KlTsXv3bos3jhBLmaz3hXzgQQnuyWpQn9LJ3OH7Y7OkCwp4EmyWdLF4uSVH8fONYhRU6NXyEfEwIczytYF71xchxL18oYJSU1Yg3pLu8P2xSdIFhTw35PO5Q8mTI9zw39a2TbnhjLxFPPze3xfNvbmLpPbeL8GUw7lGp2KcFjaFvEKvXpCYh/FWuI7M1beBK2cawNV8Ne4W1f3PL1IzJgd2c+bMwdy5c3WP8/LyMGjQIGzYsAF79uzBpEmTsG/fPqs0kpDaauMnRKsKd/AsgI0plv1CdjR/pSmwUdJVl+n/SaH0uhbcacBgzVXuZPdXoiRwE1h+QILPY/CSXm/ZBgsuojiaXlJpbjagrLzZFzFSu831cja+rnzs7O+HKCn3XP6VpsQrlQR3KvCRJAznbHujhbtVcwOaylvEM1gYso+GY4kRJud8OHPmDCZPnqx7vGXLFuTl5eHw4cOIiIjAsGHDsGrVKvTr188a7SSk1iZHSvBuYnm6iE03ijGnjvV+sCyL449U+PKfQhxOVwEMNw3Gk0LpW/r6Gs3472z58K651EearHy4UsQvqwtrLc+Hu2HJhUI86dhJLlBz0mzU1NH0EozZl11pUMcD8HKkBAI7Dwk6G39xWXA3ZE82UgrKe7gS7irx2tE8fN+tQu47YWMoeOV/E55CxqCn354GhrhyrrM995R41YbpdojzMDmwy87ORlBQeWHlvXv3IiYmBi1btgQAjB49GosXL7Z8CwmxkNFNxFhwpgDFj7+RHxRrDHKeOav0Yg02pxTj5xQ57hRpqjy2ukLpzpQPjwVwUhjJ2Ta+qRsCxNbL6xbkxsegUFck3C3vMflfshwtavGa+SVaTDiQA2PT9bQAZp7Mt1hx+6dJoBsfCQP8MGR3Fm4Vlv9t7LijgIABogCwYJAoiuA8b2qUxKGSPvcPccWHZwt1j4+ll0BWqtXl6HO2GzJn5ujn2uTATiqVIjMzEwCgUCiQlJSE2bNn6/YzDIOSkrrxJUnqJk8hDyMbiznlmDYky9HRjm2qjVIti733lNiYUoz995UwNYOLmM9gdVfv6g90Anf5fngo4JY5nGGDWppTIiWcwG5nqgKNGCEkrHk9dyzL4vdUBeYmFXDmdumrS/9m9hDsxkfCAH8M3p2F1Ao3PltvK9BG3B6N1Fko4JX3zon4wOvNHas3LMJLgEYefF37VY+rUAxtWLYwzJluyJydo59rk29HOnTogPXr12P37t1YsGABlEolBg4cqNt/8+ZNTo8eIY5If37UvvtKFDL2S2VQEzcLSvHRmQK02PoIzx/Mxd57VQR1lcz9WtrRq0aF1x3RSRG3t25AiCsipNZP2Nu9ngiNPcp7BVVa4KJLI7NeI02mxvgDOZhyOA8ZCuMra8V8psqhc2Ka+pKynruKi18A4KKwMRLE7Tjbng+XWLXXtyYYhsEAvdWxlLaJVMbkwO7jjz8Gj8fDxIkTsX79ekydOhVRUVEAAI1Gg4SEBHTu3NlqDSXEEtr7u6B5hcnUGha4IGxsxxZVTj9FibxUi80pcgz8Kwvtd2Ri1RUZMo0EA0IeMKqxGL/398Uk+TGD4O7XW8U2q5hgTRdcGuKGSz3ONkuUDzMFjzGcf3VO2ASmJD5Ra1ms/leGmN8ysfe+4SiHp6YYfLasV8aFVVNQZ0Gh7gL8McAPDSTcoE1bYS4qA9tdR+bSD+z23VdCWwf+lollmTwU26RJE5w9exZXr16Fp6cnmjRpotsnl8uxePFitG7d2iqNJMRSGIbBS5ESzEkq0G07L2yMriXXHCap45MUJaWMAJskXZFxIBsnH6lQWFr1B3hzbwFejJBgbBMxfFzLvqguajLQU3kFh8TRuuNOZqiw444Co5rYP41DTd3h+yNB3J6zra2fC2IDbVeJYWKYGz47X6hLZpvL90AqPwBNNJlGn3MxW4W3T+bjnxzDihUuPGBmKw+4HNmO+3xf/O72LJ4rPoNuwZOs9Ss8lRp5CJAwwA+Dd2chvdgwFOcxQJpM45D1pGMDRfBwYVD0+LMgU6HFhexStPOnCiSknFnfZUKhEG3atOEEdQDg6emJ4cOHo1GjRia/1vLly9GzZ0+EhISgadOmGDduHK5evco5hmVZxMXFISoqCkFBQRg8eDCuXbvGOSY/Px/Tpk1DaGgoQkNDMW3aNOTncwtlE1LRuKZucK1ww17Ak+C2INB+DargaHqJLqgDADXDx557JUaDOg8XBlMi3XBwiD9ODA/A683ddUHdE91U1xGmV2/ygzMFkJdaPrGuLZSlBOkKluF+fPWpL7JpKhB/MR/DGnITX58VNqn0WHmpFgtOF6DXrqxKg7qYQCGODQ/A+894wgVaNNZkYWbRX2isybJK2592TTwF+KidZ6X7NCwcNi2QkM+gV31u7+0eGo4lesy6JSkqKsKPP/6Io0ePIisrCytXrkS7du2Ql5eHTZs2YdCgQQZBnzHHjx/HK6+8grZt24JlWSxevBjPPfcckpKS4O1dNkl41apVWL16NVavXo3w8HB8/vnnGDFiBM6cOQMPj7I0FVOnTsX9+/cRHx8PhmHw1ltv4bXXXsOWLVvMPBXkaSEV8TC8kRhbbil0284JmyBMnWHHVpUFLOP25+iCuqrEBArxQrgbhjcSQ+JS9f0ZA2CA8iK+FQRC+zgYelisxfJLRfignZclmm4ze9IUeP5gLtSM4fynr6/I0CXY1abDlpMjJdh+p/w6uu5SHzKlCO5seVCw754S753Kxz2Z4WplTyGDhe298GKEG3gOkp/OlBV/+hPFAcMVf5Z6HWtZdL7I6L7qVo7b04AQMXamlgdze+4pMb9t5UEqeTqZHNg9evQIgwYNQlpaGho1aoTbt29DJitLCurt7Y3169fj/v37WLJkiUmvt2PHDs7jtWvXIjQ0FKdOncLAgQPBsizWrFmDd955B8OHDwcArFmzBuHh4di2bRumTJmC5ORkHDhwAHv27EHHjmVrG1esWIGBAwciJSUF4eHhBu9LCFC2iKJiYJcsqAcZw/1CtrUZx/KgqKLcEQ9lc3+ej3BDuJd5CwT8tDJ0Ut3ASVGUbtvXV2R4PlyCxp6ON+Skj2VZ7LijwKtH8ozOY1NoYPMv4y5BQoR7CXQ50rQMDxeEjdG15DoyFRrMSyrgBH4VjWgkRlxHLwS5WWaSvqUCqcpW/NWEpV7HWlZ39ca4/TmV/s058irkvg1EYFCW6gcALueW4oFcg/oSx1rsQezH5E/0Dz/8EHl5eTh06BDq1auHsLAwzv7BgwfjwIEDNW6ITCaDVquFVCoFANy9excZGRno1auX7hixWIzY2FgkJSVhypQpOH36NNzd3XVBHQB06tQJEokESUlJRgO7lJSUGrfTVLZ4D1LO3PPtxwKNxK5IVZT1YGkZHr5xH4BxxSct9m9n7uu835iHN6+IoIFhz40Lw2JF8xJ0lBYDmUCK8WlcRnVXXsMNjwhkq8p+Z5UWeOvQAyxvbn5yXVueo9RiBp/fEuJMQdVfXK48Fu83lte6beY+f4iPACsKyuc4HRS1QDGE+DI+HUUaw3/LIJEWc5qq0MWnGEUPcmC838i89vj5+WHw4MHmNN3s93Bk5v4OwQCWN+PhrcsCTi+5K4/F8mZKBMvSUJvTYslzqv9aLT1EuFxU/vew8VwaRgVXX2LM0f6drXmO6srrVKa6TiuTA7u///4br732GqKjo5Gbm2uwv3Hjxnjw4IH5LXxs7ty5iI6ORocOHQAAGRllw2L+/tzC5f7+/khPL5svlJmZCV9fX868GoZh4Ofnp8u5Vxlr9+RRb6Ht1eR8T1UVYcGZ8oSfSp4QmyVdMMo9yCLDeea2ic0vhfaK4XVblu7Cr9ZtEkGNRZ188drRPN22Y7kCpIoD0beBeSlfLHV9V/U6xWotvvinCF9fkaG66YCWOkfVtakyb4Vq8W1aOkoej7SyDA+JrpGA3sgrjwGmN3fHvGc8dEllrdGep1VNzlM4gFOnNunmtbqwamztZ5+/f3Nea4SiCJfPlX92XSjxwNxwP5u2yRKseY7qyuvUhMmfLnK5vMo8dcXFxdBoqs54b8z777+PU6dOYePGjeDzuXfl+pOhWZY1COT06R9DSGUmhLlBoHeZ2LOe6ifnCqE/KGTpdBdjm4gNak7OTcqHqoohYFtjWRZ/3lWg42+ZWH7JMKhz4QHRqrsQsGU9FPZOCeIt4iGmmtW4rXxccHCIPxZ18DIrqCPW11iTTYLYPgAAIABJREFUhYny4/DSyjFRftwpUsv017sRO5JegmK1cy6Gqg39tFCkjMmfME2aNMGFCxeM7j948CCaN29udgPmzZuH7du3IyEhgbOqNjCwbJWifs9bdna2rhcvICAA2dnZnJxcLMsiJyfHoKePEH3/5qkNAimgvJ6qLYO7Uxkl+DONu7rNTau0+BcNwzBY2tGLM9h7q1CDNVdlFnuP2kgtKkvaO+lgbqWLDXrUE+HkcwEYpTiNSQ7yZXw0vQSJj4wPZ78c6YaDQ/3Rxo9SUjgqZ1uF3NxbwEm0rNQARx463ipea3qSFqqAJ8FmSReHWMXsKIGmyYHdiy++iF9++QXbtm3TBVIMw0CpVOLjjz/G4cOHMWXKFLPefM6cOdi2bRsSEhIQEcGt09ewYUMEBgbi0KFDum1KpRKJiYm6OXUdOnSATCbD6dOndcecPn0acrmcM++OkMrMOJYHYx1VT1bF2QLLsvioQg1IAGigzsHsoj+s8kXTxk+IyZHcHHbLLhYhvbhmPe6WUKJh8fnFQnT6LaPSpL3Bbjys7+6N3/r56haOOMqX8YxjeUZrvALA/vslEPBoBIFYDsMwGKDXa/c0pT05ml6CjZKuurmR9hxpeeIO3x+bHCTQNDmwe/311zFu3Di8+uqraNu2LQDg1VdfRUhICFatWoVXXnkFEydONPmNZ82ahc2bN2PdunWQSqXIyMhARkaGbqUtwzCYPn06Vq5ciYSEBFy9ehVvvPEGJBIJRo8eDQCIjIxEnz59MHPmTJw5cwanT5/GzJkz0b9/f4ebS0Acz+qu3jBWNciVD5utivszTYmkTG6PT1/lpUqWUFjOgraekArL30GmZvHR2YIqnmE9Bx8oEft7BhZfKIJSL7bkM2W1X0+PDMTIJm4OOcWi7DqqvF2OvLqSOLcBoYblxepCRZnqHE0vwah92ZxqIcCTkZZsuwRUv98pxk+SblA7SKBp1mSPr7/+Grt27cLYsWPRs2dPNGvWDM8//zwSEhKwbNkys9543bp1KCoqwvDhwxEZGan7+frrr3XHvP3223jjjTcwe/Zs9OzZE48ePcKOHTt0OewA4IcffkDLli0xcuRIjBo1Ci1btsTatWvNagt5OnULFmFLXz/dXK2KOgQIbTK8p9ayWHiO21sXUfoQDTXZVn1fX1e+Qe6rrbcUOJVh/Q+iJ8MVVwQN8NKhHIzcl4NbhYa9hZ0ChDgyLACLOnjBw4HnpZVdR75w0buOqMYrsabOgSJIKkwSfqTQVpr8uq55+XCu0cVUCg3w6hHDxZ3WomVZzEvKx+TDeQYJ0+0xpecJsxNYde7c2SI1YU2pDsEwDObNm4d58+YZPcbb2xvff/99rdtDnk7dgkWYJD+OjZJuuuS9AHA0XYWDD5ToVd+81aLm2pRSjBsF5QEBjwH6KC9b9T2fmBIpwYZkOf7NK3//2acKcHioP/hWGjqsWC5tm1snINVw+MjPlYdP2ntiQpjjJO2tTrdgESbKj3NWV27pa5nVlYRUxlXAoGc9EXalcZMV1+W5nGezVChUVb1IJEuhxWfnCjG7jQdERnrSLeFqXineOZGP01nG59faK9G1ybfB/v7+2L59u9H9v//+Oy1YIE6psSYLk+THwGO5vUbvJuZDobbe0Ia8VIu4C9zeuolhbgjQFhp5hmUJeAyWdpJytl3OLcVPN4qt8n5PSoHpcobpBW0MgFeiJDg7MhCTwiVOE9Q94YyrK4lz6x+iNxx7v+7Os7tTWLawqpq4DloAX1wqQveETJytIuiqKYWaxafnCtBtZ2aVQR1gv6kYJgd2arUaWq3xM1rdfkIcWVNNJl6RHwIqzFFJLdLgi3+sF2StuSrHI0X534wrH5j3jG1LA3UJEmFUY26900/PFyKvqtUANXDkoRKj9mVXWgoMAMI8+fh7iD++jJFCKnLcYdfqOMqCDvJ06Ke3gOJCdqldF0FZS45Sg9H7s5Gt5H4u8R/fjDOs4efV9Xw1+v2ZhfmnCyyWCubIwxJ0/j0DX16SweCeX29+oz2nYpj1CVrVxOXz58/Dy8u56k4SUlF9TR46qG5ytq26LMO1PMvPW8lWarDqMrfmwPTm7nYpC7TwWS+4VZirk1uixaLzlgtosxQajDuQU2WSYaUGaOtfd4eQCLGGQDc+2vlxywvur2O9dgo1i4l/5xrMw+2ivIbn5cfgpZXjRflRbO7tgyAxN6TRssDqf2Xo/Hsmjj+q+Vy3HKUG04/lYfjebNwuMgycB4a4YnTxKd08W3vn1qwysFu7di3atWuHdu3aAQAWLFige1zxJywsDN999x369u1rk0YTYi29lFcQ7Fb+Z6FmgZkn86G18GqzL/4pQlFp+Wt6ixi8He1RxTOsp76Ej/dacd97fbIcl3NrH9AefqhEl52ZBqtdKxLzGXxLK0cJqRH94djdaXUnsNOyLF47mmuQNWBMEzF6l1zh9JAPChXj1IhAPB/uZvA6d4o0GLI7G7MS81FUXRmbCliWxa83i9FhRyZ+uWk4RSVIzMNPPX2wubcPWqrvO8xUjCoDOy8vL4SEhCAkJAQA4OPjo3v85Cc0NBQdO3bE+++/jy+//NImjSbEWlyhxpKO3HlnpzJV+DnFcvPOUovU+PG6nLPtvVYedh2CfLOlOxp7lPcWalngv6fya5w+oVTL4uOzBRixNwcZCuMfpLRylJDaGRBiWIXCmnODbemDM4VIuMsNVLsECfFNF+9K00FJRTx808UbO/r5chI4P7Huuhwxv2Xi4IPqg987hWqM2JeD14/lIUdvagoDYGqUBEkjAzGskVg3mukoUzGqXBU7fvx4jB8/HgAwcOBAzJkzBz169LBFuwixm2ENXdE/xBV7KyT8/OBMAQaEuCLAWOI7M3x2vpAzLBnizserzdxr/bq1IeIziOvohfEHylMFJGaosP2OAqObGN4BVyW1SI1XDufiXLZhj5+/pgB5PAnUtHKUEIuI9nFBfTc+HjyeW1esZnH8UYnZ9Z8dzXdXZVj9L7ciTpRUgJ97+Va72rVXfVecfC4AC88W4ge9m+j7cg1G7svBpHA3LHrWcPpYqZbFN1dkWHqxsNKRhmZSAVbGStEx0HE/t0zuIti9ezcFdeSpwDAMlnXizjsrULFYcLr2CXwvZquw7baCs21BW0+rLss3Vf8Gruhbn/th9eGZAsjMGLrYdrsYXXdmGgR1DIDZrT3wumy/w5QCI6QuYBjGYDjW2atQ/HFXgXlJ3M/bIDEP8X19TR7Z8HDhYVmMFH8O9EMTD8Mb8k0pxej0WwauC+rpcmsmCZuie0ImPjlnGNSJ+MAHbT1xZFiAQwd1gJmLJ7RaLX755RdMmTIFvXv3Ru/evTFlyhT8+uuvtCKW1Cmh7gLMe4Y772zrbQUOmdCFX5WP9ZIRt/RxwZgmYiNH2xbDlPXaVcwF/LBYi+WXiow/6TFZqRYzjudh6pE8ztxBAKjnxkPCAD/Mb+sJPliHGa4gpK4wSHvixFUoTmeW4NUjuZw63u6CsikbIe5mp95F5yARjj8XgP+0dId+es5HCi1+lXTGT5JuKOBJsNv1GVzNM0xY3y1YhJPDA/Feaw8IHeAmvDomB3ZFRUUYOHAgZsyYgf3790OhUEChUGD//v144403MGjQIF05MELqgunN3dHSh7virDa57Q4+UOKwXqHuT9p7OlS+tjAvF8xowR0W/uaKDDk8idHn/JOjQo+ELGyqZB7ioFBXHB8egK7UM0eI1XQLFnHK2t2Xa3ClkgDF0d0qUGP8gVxObxmfATb09EFr35qvmncT8PDps17YN9gfUVLD4FBXNULvs9hHxMO3XaTY2d8XTb3MDyrtxeTAbtGiRThz5gwWLlyIW7du4eTJkzh58iRu3bqFTz/9FGfOnMGiRYus2VZCbErAY7AyVsqZpHunSIMvTejB0qdlWXx0lttb1z1YhF71HC/gea+1BydtgEoL7HVtY3Acy7JY868MfXdl4WYh90tExAeWdfLCpl4+8HG1fQoXQp4mYgGD7nqfJXudbDg2W6nBmP3ZyNVbqLAiVoo+Fpov2N6/rEzhrNYe1QY/PeoJcXpkACaGSxyyRnVVTA7s/vjjD0yePBlvvvkmRKLyC0gkEmHGjBl46aWXkJCQYJVGEmIv7f2FmBrF7a1adbkI1/PNSwUSf1thkD7kk/aeDvmB4eHCw0K9ScU3XOrhhqC8LE62UoPxB3Iw73SBQSb4SC8B/h4SgFebuTvk70dIXaS/OnbPPYWRIx1PsVqLCQdyDHLEzW7tgRcjjI8W1ISIz2BBW0/4i6sOf24VaODnpDelJgd22dnZaNGihdH9LVq0QHa2dQuXE2IPC9p5cnqwSrXm5bZTqll8ppfwd1RjsUPXdBzTRIxOAdz2/eHaDis8BuGkMBxdfs/E3vuGCT9finDDwaH+BkPYhBDr0q9CcS6rFDLG8UYE9Gm0LKYdycOZLO6N7/imYrz/jPVye/7Q3QfGkhzYqxSYpZgc2DVo0ABHjhwxuv/IkSNo0KCBRRpFiCPxEvIMaqomZpie2+7HZDnuycrvRF14wAftbFs6zFwMw2BpJy/OMHQR3w0FPAn2ubbmlEIDAE8hg//18Maqzt6QuDhvSTBCnFU9CR+tfctvqFgAKYJg+zXIBCzL4v3TBdill1S5e7AIX3X2tmqPf7dgEbb09dNVi3iiLuTWNPkTeNKkSfjjjz/wn//8Bzdu3IBGo4FGo8GNGzfw1ltvYdeuXXj++eet2VZC7GZYQ1f0a2CYCiRLUXVdxvwSrUG92ZcjJWjk4fgTcVv7CjElspJhEL0P2w7+QhwbFoARjc3Ld0cIsSz94dgbLo4d2H17VY6117h55ppLBfipl49NVp92CxZhovy4w5QCsxSTv13eeecd3LlzBz///DM2bdoEHq8sJtRqtWBZFi+88ALeeecdqzWUEHsqy20nxbHfMqHQlA3B5qtYLDhTgLXdfIw+b9XlIuSVlA/ZergwmN3GPqXDaqJ3fSHWJ8uN7h/bRIzVXb3hop9HwIISExORlJRU5TErV6402NaxY0fExMRYq1mEOJwBIa5YerF8cdctQSDU4EEAx0tH9q+gPrbp5Qat58bD1r6+8BLarte/sSYLE+XH8bvbs3iu+Ay6BU+y2Xtbi8mBHY/Hw9dff41p06Zh7969SEtLAwCEhoaif//+iI6OtlojydPNUb7YG3qU5bb7sMLq1i23FJgYpkT3eoarth7INVhzlZsC6O1oD6eakDs3qbDK/YkZKqsGdQAQExNjsX9HR7mWCLGG1r4uCBTzdGX8VIwLUgX+CFNnmPwapvyNVMbUv5E7fH9sc+sIBSPk5KrzcGGwpa8fGtQgV11tPcmtWVdUeQavXbuGZs2acbZFR0dTEEdsqqov9pSUFISHh9usLdNbuGPLrWL8WyFH1MyT+Tj5XKDBsUsucLOXB4l5mN7csiu8rG11V2+M25+NykacnXGCsSWDRGI7FJCbhve4CsVPN8rn/94QBJsV2FX2N6J/bms6OneH749Nki5QM9zQQ8AAP/X0QTQturKIKgO72NhYREdHY+zYsRg9ejSCgoKqOpyQOs+Fx2BlrDf6/Zmlu9u8/Ti3XcWQLZPngU03uYsr5j7j6XQLC55MMB65+xHnw7guTDAmzqMuB+SWDlr7N9AL7FzqYaDyIuydeOhoekmlQR0ArOosRc/6zl3b1pFUGdhNnz4dv/32Gz744AN8/PHH6Nq1K8aOHYuhQ4fC3d2+RcsJsZdnA4R4OUqCHysUl155uQjTeB7w15bNbzng2graCuMMEV4CPB/unIsLugWLMEl+HJslXVDKCB5PMA6ioI44HUfs+bN00NqjnggiPlDyuJc9nydBFs8TAdqqp1VY09H0Eozbn1NpUCdgUKNSYcS4Ks/m4sWLsWjRIhw5cgRbt27Frl27cPjwYbz33nsYNGgQxo4di969e4PPd545Q4RYwgdtPbHrrkI3l6VUC+wSt8Nk+WGk8f1ww6Ue5/gP23lCYOW5aNZUFycYOwJHDDTqMkea1mEtEhceugWJsP9BeZ7JZJdgBJTYL7CbcSxPt+hMn5ot2395LI0IWkq1YTLDMOjRowd69OiBFStWYPfu3di6dSv++OMP7NixAz4+Phg5ciTGjh2L9u3b26LNhNidVMTDko5emHI4T7ftrsAfF1wa4bywCefYjgFCDA51/mGGujbB2BE8DYEGsb0Boa6cwO6wqAUaqHPt1p7VXb0xcm82Kiuz7YxzdR2dWRN+RCIRnnvuOWzevBnJyclYvnw5IiIisG7dOvTr1w/t2rWzVjsJcTjPNRKjb33ucOQucVvcF/hytjlq6TBCSN2kX4VCw/CxSdIFR9MNq8XYQhtfFwgrGdijubrWUeOZ3FKpFJMnT8Ynn3yCAQMGgGVZ3Llzx5JtI8ShMQyDZTFSiCsk0tQy3E+vQaGu6BRIH1qEENsJcRegkTv3613NCDBuf45dgrv/JctRzC3wAEEdSQbsiGo0YzElJQXx8fHYtm0bUlNTdcO148aNs3T7CHFojTwEmNPGAx+fq3z+Sl0YgiWEcDn63Mij6SW4LzdMSqzQsBi3P8emAZVSzWL1v9x8niJWhfHykybP1XX08+1oTA7sHj16hO3btyM+Ph6XLl0Cy7Jo2bIlFi5ciDFjxiAw0DCPFyFPg9a+LmAAVDY1eFZiAULcBUY/ROkDixDn4+jpV2Ycy6t0PhtQFtzZcrHCpptyZFaoLS1kS/F20V9wY0tNfg1HP9+OpsrArrCwEAkJCYiPj8eJEyeg0WhQv359vP322xg3bhyioqJs1U5CHNZbJ/IrDeqA6j9E6QOLEGJpVScWh80WK6i1LL66zO2ta6+6bVZQR8xXZWAXEREBlUoFd3d3TJgwAWPHjkXXrl1t1TZCnELZh2hOpcv5acUXIcTWniQWH7E7Axq9eb/vtfKw2TDsjjsK3JWVR5d8VoNOJTds8t5PsyoDu549e2LcuHEYOHAgRCKa4EhIZco+RH0xavcjlFJ1BkKIA+gWLMLz8mP4SdINLFO+kGLrbQXeaeVh9byaWpbFyktFnG1tVKnwZJVWfd+qPC1TX6oM7H755RdbtYMQp9YtWISJVJ2BEOJAGmuyMERxDn+I2wOPUy7dKFBj441iTImybt3qPfeUuJpfvhSWxwCdVclWfc/qPC1TX5yrcCUhDuxJdQYvrRwT5ccpqCOE2F270lS0KU3lbIu7WAhZqeGqWUthWRYr9HrrRjYWw0crN/IMYkkU2BFiQU+qMzTWZNm7KYQQAgDoqfwXAra89yxTocXXV2RVPKN2jj9S4UwWd4HEO9EeVns/wkWVdwkhxExPy1wdUjd4sQrElKTgmGsz3bZvrsgwJVKCIDfL13rX763r30CElj4uOGDxdyKVocCOEELM9LTM1SF1R+eS67gmbYFsZdkQrFzNYsmFQqzsbNlV+xezVTj4kFvdYmYr6q2zJbsOxZ44cQLjx49Hs2bNIJVKsWnTJs7+6dOnQyqVcn769OnDOaakpASzZ89GkyZNUK9ePYwfPx4PHjyw5a9BCCGEODRXqDGnDTfA+imlGNfzLZtTbrleb11soJDKKtqYXQM7uVyO5s2bY8mSJRCLxZUe06NHDyQnJ+t+4uPjOfvnzZuHP/74Az/++CP++usvFBUVYdy4cdBoKsnMSAghhDylJkdK0NSzfOhVywIfn628HGJN3MgvxR93uelM3qXeOpszOhTr7e0NhjEvzw3DMMjJyTH5+H79+qFfv34AgDfeeKPSY0QikdFyZQUFBdi4cSNWr16Nnj17AgDWrl2L6OhoHD58GL179zar/YQQQkhd5cJj8FE7L7x4KFe3bc89JY4/KkGXoNr3qq26IuNU4Wnl44Le9am3ztaMBnb//e9/zQ7srCExMRFhYWHw8vJC586d8cEHH8Df3x8AcPHiRZSWlqJXr1664xs0aIDIyEgkJSVRYEcIIYRUMLShKzoGCJGUqdJt++BMAf4e4g9eLb7z78nU2HKzmLNtZit3h4gjnjZGA7t58+bZsh2V6tOnD4YOHYqGDRsiLS0Nn332GYYNG4bDhw9DJBIhMzMTfD4fvr6+nOf5+/sjMzPT6OumpKRYu+k2eQ9SxtLn2lKv54jXgKP9bo54jhwJnR/bcubzfePGjWrb/2Sldgu+L5LcyztELmSXYk3iHfTzN20KU2Xv88UtF6hZF93jUFctmpU+QFVNcubzXR1r/m7h4eFV7nfoVbGjRo3S/X+LFi3Qpk0bREdHY+/evRg2bJjR57EsW+VdQnUnpbZSUlKs/h6kjDXOtaVezxGvAUf73RzxHDkK+hyxLWc/3+a2PftgDhIqzIdb+8ANUzsGQsSvvodN/72ylRokJGYAFQZiZ7XzQVRE1dUtnPl8V8eev5vZgV16ejr++ecfFBQUQKs1zFw9YcIEizSsMsHBwahXrx5u374NAAgICIBGo0FOTg78/Px0x2VnZyM2NtZq7SCEEEKc2UftvPBXmhLqx7FYmkyDddflmNHC3ezX+u6qHApNeVBXz42H8U3dLNVUYiaTAzuVSoU333wT27dvh1arBcMwYNmyf8iKvWPWDOxycnKQnp6uW0zRpk0buLi44NChQxgzZgwA4MGDB0hOTkbHjh2t1g7ivCixLCGEAE29BJgSJcEP18rLfC27WIhJYW6QikxPmFGo0uKHa9wqFjNaekBoQs8fsQ6TA7vFixdj+/btmDdvHmJiYjBkyBCsWbMGQUFB+Oabb5CVlYXvvvvOrDeXyWS63jetVov79+/j0qVL8Pb2hre3N5YsWYJhw4YhMDAQaWlpWLhwIfz9/TFkyBAAgJeXF1544QV8+OGH8Pf3h7e3N+bPn48WLVqgR48eZrWFPB0osSwhhJSZ08YDv94sRlFpWSdNvorFl5eK8OmzXia/xoZkOQpU5b113iIGL0VQb509mRyWb9++HePGjcOsWbPQrFlZWZLg4GD06NED8fHxcHNzw/r168168wsXLqBbt27o1q0bFAoF4uLi0K1bNyxevBh8Ph9Xr17FxIkT0b59e0yfPh1hYWHYt28fPDzK8+IsXrwYQ4YMwZQpUzBgwABIJBL8+uuv4PMtXyaFEEIIqSv8XPkGNVzXXpXhbpHayDO4lGoWq//l9ta93twd7i5Uht6eTO6xy8zM1A1vCgRlT1MqyyZeMgyD4cOHY8WKFVi2bJnJb961a1fk5+cb3b9jx45qX8PV1RXLli0z630JIYQQAkxvIcGP12V4WFw2Z16lBRadL8T33X2qfe7mm8XIUJTPtXcXMJjWzPw5esSyTA6rfX19dUGYh4cHxGIxUlNTdftLS0shl8uNPJsQQgghjsZNwMP8tp6cbVtvK3AxW2XkGWXUWharLnPLh02OlMDbjPl5xDpM/heIjo7GmTNnAJT10HXu3BnffvstEhMTceLECXz//feIjo62WkMJIYQQYnnjm7qhuTd3AO+DMwW6BZKV+e2OAndl5XnvhDxgRkvqrXMEJgd2L730EliW1Q2/fvrpp5DL5Rg8eDCGDBmC4uJiLFq0yGoNJYQQQojl8XmMwYKJY49U2H+/pNLjtSyLFZe4vXUTwtwQ7EZz2x2ByXPsBg0ahEGDBukeR0VF4fz58zh27Bj4fD46deoEqVRqlUYSQgghxHp613dFz3oiHHpYHsx9dLag0lqve+8pcTW/fIEFjwHe1luEQezHpB47pVKJpUuX4uDBg5ztnp6eGDx4MAYMGEBBHSGEEOLEPmnviYrZ567lq7FJr/4rC2DFJe5K2BGNxGji6dCFrJ4qJgV2rq6uWLFiBe7fv2/t9hBCCCHEDlr5CjGuqZizbfH5QqhQPsR6l++H01nchRXvtKLeOkdi1uKJJ8mECSGEEFL3zG/rCVGFqXKPFFokiiJ0j4+JmnGO79dAhGgfF1s1j5jA5L7TDz/8EC+99BJiYmLQv39/a7aJEEIIIXYQ4i7A9ObuWHm5fLj1hCgS7VS3UciIccsliHP8TCO9dVS+0X5MDuy++uorSKVSTJgwAfXq1UOjRo0gFnO7bBmGwdatWy3eSEIIIYTYxsxWHvjpRjFySx4nLWZccFjUHMU87kKKmEAhYgINF1cAVZdvTElJQXh4uGUbTXRMDuyuX78OhmHQoEEDAEBaWprBMQxDRX/J04HuRgkhdZWXkIfZrT0w73SBbts5YROw4H7Hv0tz6xySyYHd5cuXrdkOQpxKVXejhBDi7F6JkuD7azLcKSpLQswy3Cn50T4u6FNJKhRif1T7gxBCCCEcQj6Dj9p5Gd0/M9qdRukclNmJZ44cOYJ9+/bphmJDQ0PRr18/dO/e3eKNI4QQQoh9DG/kiggvPm4UaDjbGQA+VBPWYZkc2KlUKrzyyiv4888/wbIsvLy8wLIsCgsLsWbNGgwePBjr16+HiwsteyaEEEKc3bFHKtwt0hhsZwFM+DsXW/r6olswDcc6GpND7s8//xy7du3C66+/juvXryM1NRV3795FcnIypk+fjl27dmHZsmXWbCshhBBCbGTGsTw8XhhrQKFhMeNYnm0bRExico9dfHw8xowZg8WLF3O2BwQEYNGiRcjKysKWLVvw/vvvW7yRhJDq0UpdQoglre7qjXH7c6DQsAb7xHwGq7t626FVpDomB3aPHj1Cp06djO7v2LEjdu7caZFGEULMRyt1CSGW1C1YhC19fTFq9yOUMuXhgpjP0DCsAzN5KDYwMBDnzp0zuv/8+fMICAiwSKMIIYQQYn/dgkWYKD8OF1YNAHBh1RTUOTiTA7uRI0fil19+QVxcHAoLC3XbCwsLsWTJEvzyyy8YPXq0VRpJCCGEEPtorMnCRPlxeGnlmCg/TkGdgzN5KHbu3Lm4cuUKPv/8c3zxxRcIDAwEAGRkZECr1aJPnz6YO3eu1RpKCCGEEPtorMnCzKK/7N0MYgKTAztXV1ds27bFHEyfAAAWpUlEQVQNe/bs4eSxGzBgAPr374/+/ftbrZGEEEIIIaR6ZicoHjBgAAYMGGCNthBCCCGEkFowObDz8fHB2rVrMWbMmEr379ixA1OnTkVubq7FGkcIcV6UfoUQQmzP5MCOZQ3z2FSk1WqpbhwhRIfSrxBCiO2ZNRRbVeB29uxZSKXSWjeIEEIIIcRROfpoRJWB3Zo1a/Ddd9/pHs+bNw+ffvqpwXEFBQUoLCzEuHHjLN9CQgghhBAH4eijEVUGdr6+vggPDwcApKWlITAwEEFBQZxjGIaBm5sbnnnmGUybNs16LSWEEEIIIVWqMrAbO3Ysxo4dCwAYMmQIZs2ahR49etiiXYQQQgghxEwmz7HbtWuXNdtBCCGEEEJqyeSSYhs3bsQLL7xgdP+LL76IzZs3W6RRhBBCCCHEfCYHdj/++KOujFhlgoKCsG7dOos0ihBCCCGEmM/kwO7WrVto0aKF0f3NmjXDzZs3LdIoQgghhBBiPpMDO4ZhkJOTY3R/bm4utFqtRRpFCCGEEELMZ3Jg17p1a8THx0OpVBrsUygUiI+PR6tWrcx68xMnTmD8+PFo1qwZpFIpNm3axNnPsizi4uIQFRWFoKAgDB48GNeuXeMck5+fj2nTpiE0NBShoaGYNm0a8vPzzWoHIYQQQkhdYHJg9+677yIlJQX9+/fHzp07kZKSgps3b2Lnzp0YOHAgUlJS8O6775r15nK5HM2bN8eSJUsgFosN9q9atQqrV6/G0qVLcfDgQfj7+2PEiBEoKirSHTN16lRcunQJ8fHx2LZtGy5duoTXXnvNrHYQQgghhNQFJqc76dmzJ7799lv897//xZQpU3TbWZaFh4cHvv76a/Tp08esN+/Xrx/69esHAHjjjTc4+1iWxZo1a/DOO+9g+PDhAMoqYYSHh2Pbtm2YMmUKkpOTceDAAezZswcdO3YEAKxYsUIXaD5JrkwIIYQQ8jQwq1bs+PHjMXjwYBw8eBCpqalgWRaNGzdGr1694OHhYdGG3b17FxkZGejVq5dum1gsRmxsLJKSkjBlyhScPn0a7u7uuqAOADp16gSJRIKkpCQK7AghhBDyVDErsAMADw8PXQ+aNWVkZAAA/P39Odv9/f2Rnp4OAMjMzISvry8YhtHtZxgGfn5+yMzMNPraKSkpVmix7d+DlKFzbXt0zm2DzrNt0fk2jSXOE53rmquu08qswE6j0WD79u04evQosrKy8MEHH6Bly5bIz8/HoUOHEBMTY1BLtrYqBm1A2RCtfiCnT/8YfdbuyaNhYNuhc20fdM6tj65t26Lzbbranic619Zl8uKJgoIC9OvXD6+99hp27tyJ/fv369KfeHh4YP78+fj+++8t1rAnyZD1e96ys7N1vXgBAQHIzs4Gy7K6/SzLIicnx6CnjxBCCCGkrjO5x+6TTz7B9evXER8fj2eeeQZhYWG6fXw+H0OHDsX+/fvx4YcfWqRhDRs2RGBgIA4dOoS2bdsCAJRKJRITE7Fw4UIAQIcOHSCTyXD69GndPLvTp09DLpdz5t0RQkyXmJiIpKSkKo9ZuXKlwbaOHTsiJibGWs0ihBBiApMDuz///BPTpk1Dnz59kJuba7C/adOm2LJli1lvLpPJcPv2bQCAVqvF/fv3cenSJXh7eyMkJATTp0/Hl19+ifDwcISFheGLL76ARCLB6NGjAQCRkZHo06cPZs6ciVWrVoFlWcycORP9+/enbl5CaigmJsZogEZDKIQQ4thMDuzy8/PRuHFjo/tZloVKpTLrzS9cuIChQ4fqHsfFxSEuLg4TJkzAmjVr8Pbbb0OhUGD27NnIz89Hu3btsGPHDs4K3B9++AFz5szByJEjAQADBw7E559/blY7CCGEEELqApMDu9DQUFy9etXo/hMnTnCGZ03RtWvXKqtEMAyDefPmYd68eUaP8fb2tujcPkIIIeRpRVMxnJ/Jgd2YMWOwcuVKDB06FM2aNQNQviJ17dq12LVrFxYvXmydVhJCCCHE6qqaikGcg8mB3cyZM3H27FkMGzYMYWFhYBgGc+fORW5uLjIyMjB48GAq5UUIIYQQYkcmB3YuLi7YunUr4uPj8fvvv4NhGKjVarRu3RojR47E2LFjq8wdRwghhBBCrMvsyhNjxozBmDFjrNEWQgghhBBSCyYnKCaEEEIIIY7NaI/djBkzzH4xhmHwzTff1KpBhBBCCCGkZowGdkePHjV7zhzNsSOEEEIIsR+jgd3ly5dt2Q5CCCGEEFJLNMeOEEIIIaSOqDKw27dvHx49esTZVlxcDJZlDY69ceMGza8jhBBCCLGjKgO78ePH49ixY7rHubm5aNCgAY4ePWpw7MWLF/Hhhx9avoWEEEIIIcQkVQZ2lfXMVbaNEEIIIYTYH82xI4QQQgipIyiwI4QQQgipIyiws4Lw8HB7N+GpQefatuh82w6da9ui8207dK6tq9pasampqTh37hwAoLCwEACQkpICd3d3znF37tyxQvMIIYQQQoipmPz8fKOrIby9vQ2qSbAsW2mFiSfbc3NzLd9KQgghhBBSrSp77FavXm2rdhBCCCGEkFqqsseOEEIIIYQ4D1o8QQghhBBSR1BgRwghhBBSR1BgZ0Hr1q1Dq1atEBgYiO7du+PkyZP2blKdFBcXB6lUyvmJiIiwd7PqjBMnTmD8+PFo1qwZpFIpNm3axNnPsizi4uIQFRWFoKAgDB48GNeuXbNTa51bded6+vTpBtd6nz597NRa57Z8+XL07NkTISEhaNq0KcaNG4erV69yjqFr23JMOd90fVsHBXYWsmPHDsydOxfvvfcejh49ig4dOmDMmDG4d++evZtWJ4WHhyM5OVn3Q0G05cjlcjRv3hxLliyBWCw22L9q1SqsXr0aS5cuxcGDB+Hv748RI0agqKjIDq11btWdawDo0aMH51qPj4+3cSvrhuPHj+OVV17B3r17kZCQAIFAgOeeew55eXm6Y+jathxTzjdA17c10OIJC+nduzdatGiBr776Sretbdu2GD58OD766CM7tqzuiYuLQ0JCAhITE+3dlDqvfv36+PzzzzFp0iQAZT0aUVFRePXVVzFr1iwAgEKhQHh4OD799FNMmTLFns11avrnGijr0cjNzcWWLVvs2LK6SSaTITQ0FJs2bcLAgQPp2rYy/fMN0PVtLdRjZwEqlQoXL15Er169ONt79eqFpKQkO7WqbktNTUWzZs3QqlUrvPzyy0hNTbV3k54Kd+/eRUZGBudaF4vFiI2NpWvdShITExEWFoZ27drhrbfeQlZWlr2bVCfIZDJotVpIpVIAdG1bm/75foKub8urtvIEqV5OTg40Gg38/f052/39/ZGZmWmnVtVd7du3x7fffovw8HBkZ2dj2bJl6NevH06dOgUfHx97N69Oy8jIAIBKr/X09HR7NKlO69OnD4YOHYqGDRsiLS0Nn332GYYNG4bDhw9DJBLZu3lObe7cuYiOjkaHDh0A0LVtbfrnG6Dr21oosLMgU6t0kNrp27cv53H79u3Rpk0bbN68GW+++aadWvV0oWvdNkaNGqX7/xYtWqBNmzaIjo7G3r17MWzYMDu2zLm9//77OHXqFPbs2QM+n8/ZR9e25Rk733R9WwcNxVqAr68v+Hy+Qe9cdna2wd0fsTx3d3dERUXh9u3b9m5KnRcYGAgAdK3bSXBwMOrVq0fXei3MmzcP27dvR0JCAho1aqTbTte2dRg735Wh69syKLCzAKFQiDZt2uDQoUOc7YcOHULHjh3t1Kqnh1KpREpKiu6DmVhPw4YNERgYyLnWlUolEhMT6Vq3gZycHKSnp9O1XkNz5szBtm3bkJCQYJAiia5ty6vqfFeGrm/L4M+dO/djezeiLvDw8EBcXByCgoLg6uqKZcuW4eTJk/jmm2/g5eVl7+bVKQsWLIBQKIRWq8XNmzcxe/Zs3L59GytWrKBzbQEymQzXr19HRkYGNm7ciObNm8PT0xMqlQpeXl7QaDRYsWIFwsLCoNFoMH/+fGRkZGDlypU0L8ZMVZ1rPp+PhQsXwt3dHWq1GpcvX8Z//vMfaDQaLFu2jM61mWbNmoVff/0VGzZsQIMGDSCXyyGXywGU3ZwzDEPXtgVVd75lMhld31ZC6U4saN26dVi1ahUyMjLQrFkzLF68GJ07d7Z3s+qcl19+GSdPnkROTg78/PzQvn17zJ8/H1FRUfZuWp1w7NgxDB061GD7hAkTsGbNGrAsiyVLlmDDhg3Iz89Hu3bt8MUXX6B58+Z2aK1zq+pcL1++HJMmTcKlS5dQUFCAwMBAdO3aFfPnz0eDBg3s0Frnpr8a84k5c+Zg3rx5AEDXtgVVd74VCgVd31ZCgR0hhBBCSB1Bc+wIIYQQQuoICuwIIYQQQuoICuwIIYQQQuoICuwIIYQQQuoICuwIIYQQQuoICuwIIYQQQuoICuwIIXVWXFwcpFKprsC7NUyfPh3R0dFWe31CCDEHBXaEELNt2rQJUqkUUqkUR48erfSYXr16QSqV4tlnn7VqW2QyGeLi4nDs2DGrvo81XL16FXFxcbh7965FX9eZzwkhpHYosCOE1Jirqyvi4+MNtt+6dQvnz5+Hq6ur1dsgl8uxdOlSHD9+3OrvVZmvvvoKZ8+erdFzr127hqVLlyItLc2ibbL3OSH/3969xUR1dQEc/w+UKl6GUckwYHVAGPGCUmc0miAJ1mawQaAVRmJQuQVvD1KrxAsm2tibNROwgRegoigardHiFW/Rl9EYRUlMtNHYtIREVNQBx1AUmO+h4cRTBBmQ+n1865fMA+vss/c6+4Gs7L3PjBDvjxR2Qohes1qtVFZW0tLSooofPHgQvV7PtGnT3lNm/x4fH5//m9+1bGtr4+XLl+87DSFEN6SwE0L0WlJSEi6Xi6qqKlX88OHDLFiwAC+vzv9i2tvbKSgowGKxoNfrmThxIrm5uTQ2NqraxcXFMWPGDO7fv09SUhJBQUGYTCa+/vpr2tvbAfjzzz8JDw8HYPv27cr28MqVK1V9uVwu1qxZQ0hICKNHjyYtLY2nT5+q2tTU1GCz2QgNDcVgMBAZGcny5cuVHy7vypvO2Ol0OtasWcO5c+eIjo4mICAAs9nM4cOHlTYVFRVkZWUBEB8fr+ReUVGhtDl16hRz584lMDAQo9FIamoqd+/e7TafnsxJfX09OTk5TJgwAb1ej9lsZufOnbjdblU/Op2O/Px8SktLMZvN6PV6rl69qrq2Z88epk2bRmBgIPHx8fzxxx8AFBYWMmXKFAwGA0lJSdTX13ebtxDi3fjgfScghPjfFRQURFRUFL/88guJiYkAXL9+nd9//52FCxdy69atTvesXbuWsrIyPvvsM1asWMGdO3f4+eefqa6u5syZM/j4+Chtm5qaSExMZN68ecTFxXH+/Hny8/MxGo2kp6fj7+/Pjh07yM3NZf78+cTHxwMQEhKiGjMrK4uAgADy8vK4f/8+xcXF+Pj4UFpaCkBDQwNffPEFo0aNIicnB51OR11dHadPn+bFixcMHTrU47m5du0aJ0+eJCMjgyVLllBeXs6yZcuYMmUK4eHhREVFkZ2dTUlJCWvXrmX8+PEAzJw5E/i7OM7OziYiIoK8vDyampooLi7GarVy6dIlgoOD3zju2+bk8ePHfPrpp7S2tpKWlobBYODKlSts2bKFBw8e8MMPP6j6O3ToEC6Xi/T0dIYNG4bBYFCuHT16lObmZjIzM3G5XOzcuZPU1FSSkpI4duwYq1ator6+nsLCQr766iv279/v8TwKITwjhZ0Qok9sNhvr1q3D6XSi0+k4ePAgoaGhmM3mTm1v375NWVkZCxcupLi4WImbTCY2btzIgQMHWLp0qRJ/+PAhP/30kxLLzMxk9uzZ7Nmzh/T0dIYOHUpCQgK5ublMnjyZlJSUN+Y4fvx41Xhut5uSkhLsdjt+fn5cvXqVZ8+eceTIEdX28aZNm3o9L7/99hsOh0NZPfv888+JiIhg3759bNu2jeDgYGbNmkVJSQkxMTFER0cr97569Yq8vDzCwsKoqqpSCsu4uDjmzJnDd999p3qe171tTr755htaWlpwOBzo9XoAMjIyMBgMFBYWsnLlSoxGo9K+traW6upqVUHX8bJHXV0dN27cQKfTAeDl5cX333/PX3/9xeXLl5UtapfLxa5du2hoaMDf37/XcyqEeDvZihVC9EliYiIajYbKykpaW1v59ddfsdlsb2x75swZAFavXq2KZ2ZmotVqlesdBg8eTGpqqioWFRWlbPf1VMeW5+t9tLW1UVdXB8Dw4cMBqKqq4tWrVx713ZXo6GilqAPQ6/WYTKYe5V5TU8PDhw/JyspSrRZGRkYSExPD2bNnVdumPeV2u6msrCQ2NhZvb2+ePHmifObOnUt7ezsOh0N1T1xcnKqoe11CQoJS1AFMnz4dgOTkZNW5Q4vFgtvtfudv/wohOpPCTgjRJ35+flitVg4dOsTFixd5/Phxl4VdbW0tGo0Gk8mkig8aNAij0djp7dCgoCC8vb1VMZ1Ox7NnzzzKccyYMZ36AJR+oqOjiY+PZ/v27YwbN46UlBR2796Ny+XyaJzuxuwYtye5d8xDx/bs68LDw3E6nTQ1NXmcU0NDA06nk3379hEaGqr6dGylNzQ0qO7passX4KOPPlL9rdVqARg9evQb406n0+OchRCeka1YIUSf2Ww20tLSgL9XZ0JDQz3uw+12o9FoVLF/FnW91VU/HateGo2GvXv3Ul1dTVVVFZcuXeLLL7/Ebrdz4cIFZcvyXY7ZW325v+Olk+TkZBYvXvzGNuPGjVP97evr22V/XT1jfz27EOLtpLATQvRZbGwsWq0Wh8PR6fD968aOHYvb7ebevXtEREQo8ZcvX1JbW6s6Z9ZT/ywG+8JisWCxWMjLy+PcuXPYbDbKy8tZt27dOxvjdV3lPnbsWADu3r3LJ598orp27949dDqdsgrmSb/+/v5otVpaW1uJiYnpXdJCiP9qshUrhOizQYMGYbfbWb9+PcnJyV22s1qtABQVFaniZWVlNDU1ERsb6/HYQ4YMAfq2zed0OjutJkVGRva537fpKvePP/6YgIAAdu3aRXNzsxK/desWFy9exGq1dlvQdtWvt7c3CQkJnDhxgpqamk73NTY2vrMzhkKI90NW7IQQ70R3BV2HyZMnk5GRoRRyc+bM4c6dO5SVlWE2m1m0aJHH4w4bNgyTycSRI0cICwtj5MiRGI1G5SB/T+zfv5/S0lLmz59PSEgIzc3NVFRU4O3trZw96w+RkZF4eXmRn59PY2Mjvr6+WCwWgoOD+fbbb8nOziY2NpaUlBTl6060Wu1b39btbk62bt2Kw+Fg3rx5LFmyhEmTJvH8+XNu377N8ePHuXHjBgEBAf32zEKI/iWFnRDiX2W32zEajZSXl3P27FlGjRpFVlYWmzdvVn2HnSeKiorYuHEjmzdvpqWlhUWLFnlU2EVFRXHz5k2OHj3Ko0ePGD58OFOnTuXHH3/s19+6DQwMpKCggIKCAnJycmhra6OoqIjg4GCSk5Px9fXFbrezbds2PvzwQ2bPns3WrVu7faGhQ1dz4u/vz4ULF9ixYwcnT55k9+7d+Pn5ERYWxoYNGxgxYkS/Pa8Qov9pnE6nnGYVQgghhBgA5IydEEIIIcQAIYWdEEIIIcQAIYWdEEIIIcQAIYWdEEIIIcQAIYWdEEIIIcQAIYWdEEIIIcQAIYWdEEIIIcQAIYWdEEIIIcQAIYWdEEIIIcQA8R8/FOS4o2vpiwAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 648x432 with 1 Axes>"
       ]
@@ -278,9 +306,9 @@
     "    N = len(dates)\n",
     "    err = [[EV(states, date) - EV(states, date, -swing) for date in dates],\n",
     "           [EV(states, date, swing) - EV(states, date) for date in dates]]\n",
+    "    plt.plot(range(N), [270] * N, color='darkorange', label=\"270\")\n",
     "    plt.errorbar(range(N), [EV(states, date) for date in dates], fmt='D-',\n",
     "                 yerr=err, ecolor='grey', capsize=7, label='EVs ±3% swing')\n",
-    "    plt.plot(range(N), [270] * N, color='darkorange', label=\"270\")\n",
     "    labels('Months into term', 'Electoral Votes with Net Positive Approval')\n",
     "    \n",
     "plot1(states, dates)"
@@ -292,17 +320,17 @@
    "source": [
     "# Margin and country-wide net approval by month\n",
     "\n",
-    "The next plot gives the swing margin needed to reach 270 for each month, along with the country-wide net approval. Trump has been in negative territory on all metrics since his fourth month in office. We see that the state-by-state margin roughly correlates with the country-wide net approval, but not exactly."
+    "The next plot gives the swing margin needed to reach 270 for each month, along with the country-wide net approval. Trump has been in negative territory on all metrics since his fourth month in office. He's been net -10% or worse every month since his third in office. His necessary margin has been 4% or worse every month since his seventh.  We see that the state-by-state margin roughly correlates with the country-wide net approval, but not exactly."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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r7B3mjmvjvPFykD0EOr2zahbYmFCE0L0Z+D6xEGpaWo0QQiyKTCbDiBEjsHXrVuzYsQPh4eHw8/PjnJOVlYXXX38dPXr0gL+/P1q2bImsrCw8fPiQc1737t0527du3UL37t05Q3d69uxZZ0wSiUSb4AGAt7c3ysvLkZeX15BvkRgJteSZSbBbMO4o7mi3b8pvItQ71KDXDvCR4Np470bHoHud1X1lmNbBHgvOK3AmnTvgNqdMg7f+zMMPiUVY1VuGCJqBSwhp4irHyJWWlkIqldZ6buWki5InJq41prelviZPnozZs2fD3t4e77//vt7x2bNnIzMzEytWrIC/vz8kEglGjRqlN7nC3p7bo8SybIPGZtvYcNOJymtoNJp6X4sYD7XkmUlDZtiaQ0dXEQ4+7Y7/PeWKVjqrbQDAjVwVRv6Rjakn5LhXoKrmCoQQ0vxU9o7YCiuSGXMmeEBFd6xIJIJcLsczzzyjdzwuLg6zZs3Cv/71LwQHB8PBwQEZGRl1XrdDhw6Ij4/n7Lt06ZLR4ibmRUmemehNvqhmDVu+MAyDka1scX6MFz4MdYK9jf6nuF9SSxG2PwPLL+ejqFxDS6QRQpo9Yw2naQiGYRAbG4urV69CItG/b9u2bbFr1y4kJibi8uXLmDFjBsRicZ3XnT59OlJSUrBw4UIkJyfj4MGD2LJli/aexLpQkmcmemVU5HWXUTE3qQ2Dt7o64sJzXpjQ1lbveJka+OxqAbrsTsfzR7NpiTRCSLNXOQyGj6Lyjo6OcHJyqvbYunXrUFRUhEGDBmHGjBmYPHky/P3967ymv78/fvrpJ/z+++8IDw/Hhg0b8O677wJAnV3YxPIwCoWCRtabUHJyMgIDA1GuLofvN76cdWvvvnIXrraWWzH8r8wyLDifh8vZ5bWeZ+5uippUPmtiHvS8zYeetXHl5eXB2dm5xuOGjMlrTjZs2ICVK1ciNTXV6DNl6Vkbpq73bE2oJc9MREIRAl24v6QtZVxeTcI8JYgZ4YH14TJ42tb8VqlcC5da9AghxPpt2rQJly5dQmpqKvbs2YPVq1dj0qRJVArFCtFPzIwaUhSZbwKGQVSgPS4+5wVHUc3jMUrULOacyTVjZIQQQkzh7t27mDx5Mnr37o2PP/4YM2bMqLNOHrFMVELFjHTH5VlDklfJSSzAtiFuGB+djdJqllqzFTJYH+Fi/sAIIYQY1cqVK7Fy5Uq+wyBGQC15ZqRbRsWQNWwtyQAfCXZFukO3PU8sgEWMySOEEEJIFUryzEi3u/am/CZY1rrmvQzwkWC4H3eQ7LMBtpTgEUIIIRaGkjwz8nPyg4PIQbudV5aHtKI0HiNqmJEB3PIqmaVU0ZwQQgixNJTkmZGAESDILYizz5KKIhuqlwe3oOblLCU0VtYiSQghhDR1lOSZmTUURa5LGychXCRVI/Pyy1ncyqMlzwghhBBLQkmemekleVY2+QKoWNqmhzu3Ne9ilrKGswkhhBDCB0ryzKyje0fOtjWVUXlSD50u20uU5BFCCOHR7NmzMWHChFrPmTBhAmbPnm2miPhHSZ6Z6bbkJeUkQa2ppvCcheupk+RdyKp96TNCCCGNl5mZiXfffRfdunWDp6cngoOD8fzzz+Po0aNmj8WQpMqcVq1ahY0bN5r8PrNnz4ZMJsPq1as5+8+cOQOZTAa5XF6va5nyGVKSZ2Yedh5ws3XTbpeoSnAv/x6PETVMD3cRZ/tmbjmKymmWLSGk+diduBudv+8Mly9d0Pn7ztiduNuk97t37x4GDhyI48ePY/HixYiNjcWBAwcwbNgwvPnmmya9d2OUl5unEcDZ2Rkymcws95JKpVi7di2ys7PNcr+GoiTPzBiGaRLj8lylQrR1Emq3NSxwRU6teYQQ6yX7UgbZlzJ4f+ut/XdtXy//8TIeFDwACxYPCh7g5T9eNuh1lV/19fbbb4NlWZw4cQJjxoxBYGAgOnTogFmzZuHs2bPa8x48eICoqCi0bNkSLVu2xOTJk/Ho0SPt8ZUrV6Jv376ca2/btg2+vr565+zduxfdunVDy5Yt8cILL2hbqVauXIkdO3bgyJEjkMlkkMlkOHPmDO7duweZTIY9e/Zg5MiR8Pb2xubNm+Hn54dffvmFc88TJ06gZcuWyMzMrPb7HTJkCL788kvt9ssvvwyZTIaMjAwAQHFxMTw8PBAXFwdAv1WsuLgYs2fPhq+vLwIDA7FmzRq9eyiVSixevBghISFo0aIFBg8ejGPHjtX+gwAQEREBPz8/fPrpp7Wel5iYiPHjx6Nly5Zo164dXnrpJW38NT1DY6IkjwfWvLzZk2hcHiGEmEdubi5iYmLw8ssvw8HBQe94ZQsWy7KIiopCVlYWDh48iEOHDiE9PR1RUVH1Lr5///597Nu3D1u3bsW+ffvw999/a9ewfe211zBmzBgMGjQISUlJSEpKQu/evbWvXbp0KWbOnIm4uDiMGjUKY8eOxdatWznX37p1KyIjI+Hp6Vnt/cPDwzlJT2xsLNzc3LQJ7fnz5yESidCjR49qX//hhx/i5MmT+Omnn/DLL7/g77//xrlz5zjnzJkzB7Gxsdi0aRPOnTuHSZMmYeLEibh27Vqtz0YgEGDJkiXYsmULUlJSqj0nPT0dw4cPR3BwMI4dO4YDBw6gsLAQkyZNgkajqfMZGgMleTzQnXxhjS15ANDTXXdcHiV5hBBiCnfv3gXLsmjfvn2t5508eRLXr1/Hpk2bEBoaiu7du2Pz5s24evUqTp06Va97qlQqfPPNN+jUqRPCwsIwbdo07TUcHBwglUohkUjg5eUFLy8viMVVfxNmzZqF0aNHIyAgAL6+vpg6dSqOHz+Ox48fAwAUCgV+++03TJo0qcb7h4eHIy4uDiqVCnfu3EF+fj6mTZumTfzOnj2LsLAwiEQivdcWFhbif//7H5YuXYohQ4YgJCQE69evB8NUlf9KSUnBnj17sGXLFvTv3x8BAQGYNWsWIiMj8eOPP9b5fIYNG4bevXtrE19d33//PTp16oSlS5eiQ4cO6NSpEzZu3IjLly8jPj6+zmdoDJTk8aCptOT18qSWPEIIMQdDW+GSkpLg4+ODVq1aafcFBATAx8cHiYmJ9bqnn58fnJ2dtdve3t4Gj0Hr3r273nZISAh27NgBANi9ezdkMhmGDBkCAHj++efh6+sLX19f9OnTBwDQt29flJWV4fLlyzh79iz69u2LgQMHalvyzp49i/Dw8Grvn5KSAqVSibCwMO0+BwcHdOxY1chy9epVsCyLPn36aO/t6+uLo0eP1tg6p+ujjz7CgQMHEB8fr3fs6tWrOHfuHOfalfc39PqNZWOWuxAO3VUvbituo0xVBomNda3/2tFFBIkQKPtncvDjYg0eF6nRwl5Y+wsJIcQCKV5XAABKS0shlUprPXd34m7MjZmLElWJdp+tjS3WDl2LcUHjjB5b27ZtwTAMbt26Vet5LMtyWqueVLlfIBDoJY0qlX5Be90WMoZhoNEYNsHO3t5eb9+LL76IDRs24K233sLWrVvxwgsvQCis+Huxdu1alJaWAgBsbCpSEwcHB3Tt2hVnzpxBYmIiIiIiEBYWhgcPHuDOnTu4fPkyli5dWu39DUmKNRoNGIbB8ePH9b7Xun7+lUJDQzFq1CgsXrwY77zzjt71hw0bhuXLl+u9zsPDw6DrNxa15PHAWeKMlo4ttdsqjQq3Fbd5jKhhxEIGXV2pKDIhpPkZFzQOa4euhZ+jHxgw8HP0M1mCBwAuLi4YMmQINm3ahMLCQr3jCkVFghoUFITHjx/j3r2qqg2pqalIS0tDUFBFA4O7uzsyMzM5iVBdY9CqIxaLoVYbXgJs/PjxSEtLw3fffYerV68iKipKe6xFixZo06YN2rRpA39/f+3+ynF5sbGxCA8Ph1QqRY8ePfDZZ5/VOh6vTZs2EIlEuHDhgnZfUVERbt6sGh7VpUsXsCyLjIwM7b0rv1q0aGHw97Vo0SL8+eefehM2unbtisTERPj5+eld39HREUD9n2F9UZLHE70uWytcwxYAenhwP/1Qly0hpLkYFzQO1166htzXc3HtpWsmS/AqffbZZ2BZFoMHD8aBAweQnJyMW7du4fvvv9d2Ww4aNAidOnXCrFmzcOXKFcTHx+Pll19G165dMWDAAAAViVNubi7WrFmDlJQU7cSE+vL390dCQgKSk5Mhl8vrLJXi7OyM0aNHY+HChejXrx/atm1b5z0qk7zCwkJ07dpVu2/Xrl01jscDKloBp0yZgiVLluDEiRNISEjAf/7zH05LZLt27TB+/Hj83//9H3755RekpqYiPj4eX3/9NQ4ePGjwc2jTpg2mTZuGb7/9lrN/5syZyM/Px/Tp03Hx4kWkpqbi5MmTmDdvHgoKCgDU/xnWV5NK8lauXKmdhlz5VdcgVb6EuIVwtq1xDVsA6KVXFJmSPEIIMYWAgACcOnUKgwYNwuLFi9G/f3+MGjUKv//+O7744gsAFV2q27Ztg5ubG0aMGIGRI0fC09MT27Zt03bXdujQAZ9//jl+/PFH9O/fHydPnmxQnb2pU6eiffv2GDx4MNq2bastZVKbKVOmQKlUYsqUKQbdo2/fvmAYBn379tV27UZERECtVtc4Hq/SsmXLEB4ejsmTJ2PkyJEIDg5Gv379OOesX78eUVFRWLRoEXr16oUJEyYgNjaW05poiPnz52u7mSv5+PjgyJEjEAgEGDt2LPr06YO3334bYrEYEknF8KyGPMP6YBQKRf3mVFuwlStXYt++ffj111+1+4RCIdzd3XmLKTk5GYGBgXr7f074Ga8eeVW7/XSbp/HzqJ/NGZpR3CtQoeueDO22nQ2D+1E+sBFUPybElGp61sQ06HmbDz1r48rLy+NMKNBlyJg80jD79u3D66+/jsTERNjZ2dGzNlBd79maNLmJFzY2NvDy8uI7jDo1le5afwchPKQCZJVWNIEXq1gkKFTo7Fp9EzohhJDmp7i4GPfv38eaNWswdepU2NnZ8R1Ss9DkkrzU1FQEBwdDJBKhZ8+eWLRoEQICAmo8Pzk52eQxVXcPgVoAAQTQoCI5upd/D1cTrsLOxvre+EF2YmSVVr2VDt94AKk3P+vxmuPnSarQ8zYfetbGU1mbrDaVMz2JcaxZswZfffUVwsLCMHfuXM7zpWddt/z8/GpXBqmrhb9JdddGR0ejsLAQgYGByM7OxurVq5GcnIy4uDi4urryElNt3Sw9/9sTt3OrZtUem3gMPbyrnylkydZcLcCyy/na7ahAO6wPdzF7HNSlZV70vM2HnrVxUXet5aBnbZiGdtc2qYkXkZGRGDNmDDp16oRBgwZh586d0Gg02L59O9+hVUu3y/ZG9g2eImmcnjTDlhBCCLE4TSrJ0+Xg4ICgoCDcvXuX71CqpTvD1lpXvujuLsaT0yySFCrkKw0rmEkIIYQQ02jSSV5paSmSk5MtdiJGiHvTSPKcxAJ0kFWNyWMBxGdTax4hxHIZukwYIXxrzHu1SSV5CxcuxNmzZ5GamoqLFy9i6tSpKC4urnUBZD41lRm2ANBTr16ecQs6EkKIsdjb20OhUFCiRywey7JQKBTVLhNniCY1u/bx48eYOXMm5HI53N3d0bNnT0RHR9e7qKG5tJG1gVgohlJd0eqVUZwBeYkcbrZuPEdWfz09xNiaXKzdpuXNCCGWysbGBo6OjsjPz6/2eH5+PpycnMwcVfNEz7pujo6OeoWWDdWkkrwffviB7xDqxUZgg/Yu7XE9+7p2X4I8AeEta6/ibYl66LTkXcpS1rpQNiGE8MnGxqbG2YqZmZnw8/Mzc0TNEz1r02pS3bXWSHdc3s1s61zeLFhmAzubqoQuq1SD+4X81MojhBBCCCV5vGsqM2xtBAy6uXFLqVCXLSGEEMIfSvJ4FuyuM/nCSpM8AOil02VLSR4hhBDCH0ryeKY7w/Zm9k2rnfGlPy6PZtgSQgghfKEkj2d+jn5wFDtqt/OV+Xhc+JjHiBpOt4zK1RwllGrrTFgJIYQQa0dJHs8Yhqm2Nc8atbAXooVd1VuqTA1cz6HWPEIIIYQPlORZAL2iyFY8Lk+3NY/G5RFCCCH8oCTPAui15MmtsyUPqCbJo+XNCCGEEF5QkmcBmtIM2+ppSX8GAAAgAElEQVSKIhNCCCHE/CjJswC6tfKS5ElQa6yzkHA3NxGETyxycSdfjZxS6/xeCCGEEGtGSZ4F8LDzgIedh3a7VF2KlLwUHiNqOHuRACEu3KLIl7Jp8gUhhBBibpTkWYimNS6PVr4ghBBC+EZJnoXQm2GbTePyCCGEENJwlORZiKayhi1QfRkVa13FgxBCCLFWlORZiKZUK6+9sw2cRFWzLxRKFnfyVTxGRAghhDQ/lORZiCC3IM727dzbKFOV8RRN4wgYBqF6rXk0+YIQQggxJ0ryLISTxAl+jn7abTWrxq3cWzxG1Dg93WlcHiGEEMInSvIsSIh70xmX10N3hi2tfEEIIYSYFSV5FqQpzbDVnXxxTV6OEhVNviCEEELMhZI8C9KUauV52ArRykGo3VaxwN9yas0jhBBCzIWSPAvSlLprgWpKqdDKF4QQQojZUJJnQQJdAiFkqlq/7uffR4GygMeIGoeKIhNCCCH8oSTPgkhtpGgra8vZlyhP5CmaxtNd3uwCJXmEEEKI2VCSZ2GC3ZtOUeQurmKInniHPShUI7NEzV9AhBBCSDNCSZ6F0Zt8kW29ky+kNgw6u+qUUqHWPEIIIcQsKMmzME1peTOAxuURQgghfKEkz8J0dO/I2bbmMiqA/gzbC7S8GSGEEGIWlORZmNbOrSERSrTbWcVZyCrO4jGixumlk+TFZyuh1lBRZEIIIcTUKMmzMEKBEB1cO3D2WXOXbWtHIVwlVW+zgnIWt/JUPEZECCGENA+U5FmgpjTDlmEY9HCnyReEEEKIuVGSZ4FC3HRWvrDiNWwB/ckXlOQRQgghpkdJngXSTfKsffJFL09K8gghhBBzoyTPAlXXXcuy1jtZoYc7N8lLUKhQWK7hKRpCCCGkeaAkzwL5OvjCSeyk3S5QFuBhwUMeI2ocmUSAdk422m0NC1yRUykVQgghxJQoybNADMM0waLIOpMvMqnLlhBCCDElSvIsVFNL8nTr5dG4PEIIIcS0KMmzUCHu3MkXN7Jv8BSJceiufHEpm5I8QgghxJQoybNQTa0lr6OrCFJh1XZasQaPitT8BUQIIYQ0cZTkWSjdJO9Wzi2oNNa7UoRIwKCrG3XZEkIIIeZCSZ6Fcrdzh6edp3a7TF2GFEUKjxE1nt7kC0ryCCGEEJMxOMmbNm0a/vjjD6jV1MVmLrqteVZfFJkmXxBCCCFmY3CS9+eff+KFF15Ahw4dMH/+fFy+fNmUcRHoT76w9nF5usubXZWXQ6Wx3iLPhBBCiCUzOMlLSEjA7t278dRTT2H79u0YOnQoevbsic8++wz37983ZYz1tnnzZnTp0gVeXl4YOHAgzp07x3dIDaLbkvfVxa+wO3E3T9E0np+9EJ62VW+5YhWLm7lUFJkQQggxBUahUNS7KaW4uBiHDh3Crl27cOrUKWg0GvTp0wcTJ07E6NGj4ezsbIpYDbJv3z7MmjULa9asQZ8+fbB582Zs374dcXFx8PPzM3s8zptlDX7t0jJgiU4OZAfgOwkQJar2JRZvtOBrHGSe0m5v0CzFq+wuHiMihBBCjC9vpoLvEBqW5D0pMzMTCxYswP79+8EwDCQSCYYPH445c+YgNDTUWHEabMiQIejYsSPWrl2r3RcaGorRo0dj8eLFZo+nMUmefxHwoJqfTisGSLVvRFA8WsnMxPuCN7Tb0zT7sYVdyGNEhBBCiPFZQpJnU/cp1UtNTcWuXbuwa9cu3LlzB56enhg3bhzEYjF27NiB/fv3Y8WKFXj11VeNGW+tlEolrly5gtdee42z/6mnnsL58+erfU1ycrJJY+rZiNc+rCH9vm/Fw9h6s9c42+eZLoAVfz+EEEJIdUydXwBAYGBgrcfrleQpFArs27cPO3fuxIULFyAWi/Hvf/8bK1aswJAhQyAUVlS7fe+99zBr1ix8/vnnZk3y5HI51Go1PDw8OPs9PDyQmZlZ7WvqekCNdqrhL/VngHvVJED+TMOvybeeuA6G1YBlKsbmJTBtoYAjZCjgOTJCCCHEeEyeXxjA4CQvKioKMTExUCqV6NWrF9asWYMxY8ZAJtPvjhSJRBg+fDgOHDhg1GANxTDcLIhlWb195nJx4IUG/6AXJu7GnOg5UKqrSo0wYLDwX98hL2icsUI0u6D9GUhQVBV2PvnvZAz2lTb6usnJyRbxn6q5oOdtPvSszYuet/nQszYtg5O8q1ev4rXXXsOkSZPQtm3bOs8fPHgwDh061Kjg6svNzQ1CoVCv1S47O1uvdc8ajAsah+LyYsw7Nk+7jwWLMJ8wHqNqvJ4eYk6SdzFLaZQkjxBCCCFVDC6hcv36dSxcuNCgBA8A3N3dER4e3uDAGkIsFqNbt244ceIEZ/+JEyfQu3dvs8ZiLFM7T0U/336cfTGpMTxFYxw9dYsiZ1MZFUIIIcTYDE7yXF1dsWfPnhqP79u3D66urkYJqjHmzJmD7du346effkJSUhLeffddpKenY/r06XyH1mDDAoZxto+mHuUpEuPQLYp8KUsJlqXZF4QQQogxGdxdy7JsrX+INRoNb+PenvTcc88hJycHq1evRkZGBoKDg7Fr1y74+/vzHVqDRbaOxJLYJdrt0w9Oo1RVCqmNdXZxBstsYG/DoEhV8X7KLtXgXqEaAY4NnuxNCCGEEB0Gt+QB+hMannTx4sVqJ2HwYebMmbh27RoyMzNx6tQp9O/fn++QGiXELQQtHFpot0tUJTj3yDpX8QAAoYBBd3duNeett4p4ioYQQghpmmpN8jZs2ICuXbuia9euACpKo1RuP/kVEBCA7777DsOGDavtcqSBGIZBZEAkZ9/RFOvustUdl/fltUKcTivjKRpCCCGk6am1f8zNzU07tfn+/fvw8vKCt7c35xyGYWBnZ4fu3btj1qxZpou0mYsMiMR/r/9Xux2dGo1VWMVjRI1ja8NtFVaxwITobOyMdMcAHwlPURFCCCFNR61J3vjx4zF+/HgAwIgRI/DOO+9g4MCBZgmMcA30HwiRQIRyTcVM1DuKO7iruIs2sjY8R1Z/p9PK8MXf+sWPS9TA2KPZ2BPphoEtrHO8ISGEEGIpDB6T9+uvv1KCxyNHsSP6+vbl7ItOjeYpmsaZcyYXperqj5VrgOej5YjLoK5bQgghpDFqTPIePHiABw8e6G3X9UVMR3dcXnSKdSZ56yNcYCuseRJPuQZ4+nA2Zp7KwaOiGrJBQgghhNSqxu7aLl26gGEYpKenQywWa7frkpOTY9QASZXIgEh8eOZD7fbZh2dRXF4MO5Edj1HV3wAfCXZGumFCtBwl6prL8uy5W4LD90vxemcHvNbJUW8cHyGEEEJqVmOSt27dOjAMA5FIxNkm/Ong2gF+jn54UFDRYlqqLsXZh2cxrLX1zWrWTfSkQqCnuxhnM5Sc84pVLFbEF+B/ycVY1tMZowOk9D4khBBCDFBjkhcVFVXrNjG/ylIqP1z7QbsvOjXaKpM8oCrRm3MmF+sjXDDAR4Kz6WVYcD4P13O4S509KFRj2skc9PcWY1VvGTq7imq4KiGEEEIAAydelJSUoFu3bvj2229NHQ+pQ2RrnXF5qdFWvSTYAB8Jro331pZNCfeW4NRID3zZTwY3if7bMzZdiYEHM/HGuVzIa5q9QQghhBDDkjxbW1vk5+dDLBbXfTIxqQF+AyAWVv0cUvNScTv3No8RGZ9QwGBaB3tcGuuF2SH20B2Kp2GBLUnFCN2bgQ03ClGusd4klxBCCDEVg0uoDBs2DEePWvcqC02Bvcge/X25y7QdTW2aPxeZRICVvWWIfdYTT7XQL5Ccp2Tx3l95CD+Qia+uFWDkBSmtmkEIIYT8w+Ak74033sD9+/cxbdo0nDp1Cvfv30dWVpbeFzE93VIqMakxPEViHh1kIuwd5oYdQ1zRxlGodzwpT4XFF/ORXibA+OhsSvQIIYQQ1LHixZP69OkDAEhISMDBgwdrPI9KqJjesNbD8P7p97XbsY9iUagshIPYgceoTIthGPzb3xZP+Uqx8WYhVl8tQEG5fjdtqRp47kg2tj7liqf9bXmIlBBCCLEMBid58+fPp9IVFqKtrC0CnAOQmpcKAFCqlTj94DSGtx3Ob2BmIBEymNvZERPa2mHO2VzEPNJvtVOxwMRjOZjbyR5LejpDQO9bQgghzZDBSd57771nyjhIPVSWUtl0dZN2X0xqTLNI8ip52QmRpFDVes7a60U4k67EJ72dEeapP6aPEEIIacoMHpNHLMuwAG5tvKOpR626lEpD1LU8GgDEZ5dj2G/ZmEVLpBFCCGlmDG7Jq/TXX3/hypUryMvLg0aj4RxjGAbz5883WnCkZuF+4ZAKpShVlwIAHhY8RFJOEoLcgniOzHwMXR4NAHbdLcGv90vxZhdH/KejA6S0RBohhJAmzuAkLy8vDxMnTsT58+fBsiwYhtG2HFX+m5I887G1sUWEXwSiU6O1+6JTo5tVkgfoJ3q2QgZr+8vw6/0S/JJayjm3WMVi+eV8/HSrCMt7OWNkK1oijRBCSNNlcHftkiVLEB8fj2+//Rbx8fFgWRb79u3DpUuX8OKLL6JLly64deuWKWMlOnRLqRxNaZr18upSmeh5SzTYGemGcW3t8N/Bbjj0tDs6uuh/jrlfqMaLJ3Iw6o9sveXTCCGEkKbC4CTvjz/+wIsvvojx48fDycmp4sUCAdq0aYMvv/wSPj4+eP/99+u4CjEm3SQv7nEc8svyeYqGXwN8JDjUq1S7PBoARPhIcGqUJz7vK4NrNUuknUlXYsDBTLz9pwI5tEQaIYSQJsbgJC8nJwedOnUCAIhEFYvDFxUVaY9HRkYiJqZpF+W1NK1lrdHOpZ12u1xTjlMPTvEYkeWxETCYEWSPy2O98EqwPXTnaWhYYHNiEUL3ZmDjzYol0k6nlaHzrnQqqkwIIcSqGZzkeXp6IjMzEwDg6OgIR0dHJCcna4/n5uZCrabWEHPTbc17coweqSKTCPBJn4ol0gZXs0SaQsni3fN5CN2TjnFHs/GgSI0J0XJK9AghhFgtg5O8nj17IjY2Vrs9dOhQfP3119i5cyd27NiBb775BmFhYSYJktSsuiXOmlsplfoIkomwb5gbtg9xRetqlkh7UKRB2T+TxkvULCV6hBBCrJbBSd6sWbMQGBiI0tKKGYvLli2Dq6srXn31Vfzf//0fXF1dsWrVKpMFSqrXz7cf7GzstNuPCx/jRvYNHiOyfAzDYLi/LeLGeGFpTyc41FJOhRI9Qggh1srgEip9+/ZF3759tdu+vr44f/48rl+/DqFQiPbt28PGpt5l90gjSW2kiPCLwJGUI9p90anR6OTRiceorINEyGDeP0ukhe7NQLGq+hbQEjWLOWdycW28t5kjJIQQQhquUSteMAyDzp07IyQkhBI8HumufkHj8urH206In4e6QaLfewsAsBUyWB/hYt6gCCGEkEaqMTN78OBBgy7o5+fX4GBIwwwNGMrZPv/4PBSlCsikMp4isj4DfCTYHemO0X9k48n2PLEA2BnpxinNQgghhFiDGpO8Ll26NGg1gJycnEYFROqvlXMrdHDtgKScJACAmlXj5P2TeLb9szxHZl0G+EjwtJ8Uvz+oWinjGX8pJXiEEEKsUo1J3rp162jJJysSGRCpTfKAii5bSvLqb1wbW06Sd7+QygIRQgixTjUmeVFRUeaMgzRSZOtIrLu8TrsdkxoDDauBgGnUsMtmp783t9XuirwcBeUaOIroORJCCLEu9Jeriejboi8cRA7a7YziDPyd9TePEVknLzshAp2rPvuoWeCvTCWPERFCCCENY/CU2E8++aTOcxiGwfz58xsVEGkYsVCMgf4D8dud37T7YlJj0M2zG49RWaf+XmIk56m027HpZRjiK+UxIlKX02llmHMmF+sjXBo9htKY1yKEED4ZnOTVVuiYYRiwLEtJHs+GBQzjJHnRKdF4O+xtHiOyTv29JfjxVrF2+2wateRZstNpZZgQnY0SNTDuaDYWhjqhi5uoQdf6W16O5ZfzUaYBJkTLaWY1IcSqGZzk5ebm6u3TaDS4f/8+Nm7ciPPnz2PPnj1GDY7Uj24plQvpF5BbmgsXKdV4qw/dcXmXs5UoKtfAnsblWZzTaWUYH52N0n/mx5RpgA8v5hvl2pWrnVCiRwixVo36qyUQCBAQEICVK1eiVatWWLBggbHiIg3g6+iLEPcQ7baG1eD4veM8RmSdWtgLOevaqljgQha15lmaihY8uTbBMwVa1o4QYs2M1jQRERGBI0eO1H0iMSnd1S+Oph7lKRLrptuadzadkjxLM+dMLkrU1S9FZ0yVy9oRQoi1MdpaZMnJyWBZ0//CJbUbGjAUX178Urt9LPUYlVJpgP7eEmxNrhqXF5tOLTmWZn2EC8YezUa5Rv+YAEAnVxFkEsPe94oyDa7nlKOaS9GydoQQq2VwkhcbG1vt/ry8PJw5cwabNm3Cs89S8V2+9fbpDSexE/KVFeOSskuycSXjCkK9Q3mOzLr09xZzti9lKVGiYmFrQwXCLUVfLzGcRQJkl3FTM1sh06BxdKfTyvSSRgbAzqGuNCaPEGKVDE7yRowYUe0KGCzLQigUYuzYsQaVWSGmJRKKMLjVYPyS/It239HUo5Tk1ZO/gw38HYTaFS+UGuBilhIR9MfeYvx2r7SaBK/haw0P8JHgv4Nd8cKxqqUZWQDOBrYGEkKIpTE4yTt06JDePoZhIJPJ4O/vD0dHR6MGRhpuaMBQTpIXnRKNBX1oUkx99feW4P5tbpctJXmWY2NCIWfb3obBjqGNmwk73N8WPT1EuJhVrt23724JurqJa3kVIYRYJoOTvPDwcFPGQYxoaCtuKZXLGZeRXZwNdzt3niKyTv29xdjxRJJ3lsblWYwr2Ur8mcGdDBMzwgPBLg2rj/ekV4IdcDGraqLF3pQSLO7pBAGt5U0IsTL17ocoKCjA4cOHsXHjRmzcuBGHDx9Gfr5x6lIR4/Bx8EFnj87abRYsjt07xmNE1ilcZ4btxSwlyswwm5PUbWNCEWd7oI/EKAkeAPzbXwpbYVVC97BIjQtNZGm702ll6LwrvdElYYx1HWIYet6koeqV5H3xxRcICgrC5MmTsWDBAixYsABRUVEICgrCmjVrTBWjwZ555hnIZDLO14wZM/gOixe6pVSiU6N5isR6tXIQwteuql5eqbpiAgbhV2aJGnvvFnP2vRpib7TrO4gEeNqPu4zdnpQSo12fL5V1BR8UqRtV+89Y1yGGoedNGsPg7tqvvvoKH330EcLDwzFz5ky0a9cOLMvizp072Lx5Mz7++GPY2Nhg3rx5poy3TlFRUVi0aJF2WyptnmuORraOxJoLVYn3sXvHoNaoIRQIa3kVeRLDMOjvLcauu1V/4GPTy9DPm8bl8enHpCIon5hvEeAoxLCWxv1//lwbW+xPrfq5H0gpwcowZ9gIrLPL9nRaGcYdzUblPJUSNYsxR7LR11MMD1vDfydklajxZ6YSlQ3atCqIaZ18VIrxMXLt+52eN6kvg5O8TZs2YfDgwdi3bx9nf6dOnTBq1CiMGTMGmzZt4j3Js7Ozg5eXF68xWIKe3j3hLHFGXlkeACC3NBeX0i8hrEUYz5FZl3AfCTfJy1DiHR7jae6UahbfJ3K7amcFO0Bo5OQr0lcKJxGD/PKKbCarVIPY9DIMbGF9Hxp1E7xKahY4m9H4lumKxCMbOyPdKfEwom9vFuC98/nQHSBCiR6pD4OTvJycHAwfPrzaYwzDYMSIEZwWNL7s3bsXe/fuhaenJ4YOHYp333231pm/ycnJJo/JHPeoTphrGKLTqrppf778M1yKmnZRV2M/a99SBoCtdjsuvRQJScmwoaoaAMz/3v4jU4iMkqo/bHZCFn0FaTBFGBEuYvyWWfUr8ocrGWhRxF93fUOf9Yy/pCjTmPYNW6IGpsRk4WjvElhpY6cevn5vp5cx+DpFhKPZNf95LlGzmHUiE4d6lZoxMtPh61k3BYGBgbUeNzjJ69q1KxITE2s8npCQgG7duhkemQmMGzcOfn5+8Pb2RmJiIpYuXYrr16/jwIEDNb6mrgfUWMnJySa/R02eK3+Ok+Rdzr/MWyzmYIpn3Y5l4X0zHeklFc0gpRoGBS7+CPOkT9B8vLdfTcwEUFXeJKq9A7oHtzTJvabbluK3aLl2+2SuCJva+EMsNH8W09BnnVmihuBiJlDtWh7Gladi8J9bzljVW4YeHtZdcoaP93axSoOvrxfiy78L61yuTyIEvhvsicAm0JLH59/I5sDgJG/16tUYO3Ys/Pz88NJLL8HBwQEAUFhYiM2bN+O3337D3r17jR7g8uXL8dlnn9V6zqFDhxAREYFp06Zp93Xs2BEBAQEYMmQIrly5wnsCyochrYZwtq9kXkFGUQa87Kk721AV4/Ik2Jvy5Lg8JSV5PLiYpcSl7HLOvlnBxptwoWtgCwncJALI/+nnzFOyOP64FE/72dbxSstQrNJgUowcmaXVJ3hiATCvk0O9ZiUn5Jbjq+uFnDGRT7qQVY4hv2bhhXZ2WNTDCd52NAa4LizL4kBqCT68kI+HRWqDXmMnZNDakZ4tqZvBSd5LL70EhmGwdOlSLFu2DJ6enmAYBhkZGdBoNPDy8tKbycowDOLi4hoV4OzZszF+/Phaz2nZsvpP8t27d4dQKMTdu3ebZZLnae+J7l7dEZ8Rr90XkxqDqI5RPEZlfXSTvLPpZXijCxX/Nrdvb3KLH0f6ShDobJyyKdURCRiMDrDFD0lVYwD33i2xiiRPw7J45XSuXlIsZCrG4jV06TcAiGghxYRoOUrULCpX+VPpNDxtv12Mg6kleLurI2Z3dICEh9ZPa/C3XIkF5/NwroaxkV1cRZjUzhaLLuZzltvLVVaMy/v9GQ84i2nsCKmZwUmeu7s7PDw80K5dO87+1q1bGz2oJ7m5ucHNza1Br71x4wbUanWznogRGRBJSV4j6a5jez5DCZWGtdqZltYorViNAzplTF4JcTD5fZ9rw03yDt8vRbFKAzsLH5S56EI+Dt3jjtd6qoUEr3VywNxYBdZHuDR40P4AHwl2RrphzplcrI9wgZ+9EAsv5OG3+9z7FapYLLmUj//eKsLHYc74t5+02qUxm6PsUjU+vpyP/94qhqaanll3qQCLejghqp0dhAIGHV3FiDomR0F51ck3FSpMO5GDXZFuENHvIlIDg5O83377zZRxNFpKSgp27dqFYcOGwdXVFUlJSVi4cCG6dOmCPn368B0ebyIDIvHp+U+128fuHYNKo4KNwOAffbPX3tkGHlIBsv7p9ipUsbgqL7f6cUfW5PvEIk5rUaCzDZ7yNX2XeT8vMXzsBEgrrvjZF6lYHHlQijGt7Ux+74b6PrEQ625wWz1DZDbYMtgVzmIBro33bvQ9BvhIONfZNsQNJx+X4r3zeUhQqDjnphSo8cKxHAxuIcHK3s4Ikpmu9dXSlWtYbEoowqor+chX6md3NkzFh5d3ujpC9sSayQN8JLgX5YOpJ3I4yfuJx2V4608FvuonowSaVMuyP47Wg0gkwqlTp/Dcc8+hV69eePfddzF48GD88ssvEAqb79iFUK9QuEpdtdv5ynz8lfYXjxFZn8pxeU+KpSXOzKZUxeLHJN2yKfZmWWZMwDAY05rbPbv3ruUWRj76oBTvxOVx9nnZCrAz0s3k3XqDWkhxZrQnPu3tDJlY/2dz4nEZ+h/IxLtxCih067k0A8celaL/gUy8/1detQlepK8Ef47xxMdhzpwEr5KAYbBxgAt6uHOT5J9uFeOra4V65xMC1DPJU6vV+N///ocpU6Zg4MCBGDhwIKZMmYKtW7dCrTZswKiptGzZEocPH0ZKSgoyMzMRHx+PTz75BC4uTbtkSF2EAiGGBnDXso1JjeEpGuul22VLSZ757E0pRvYTkwecRAwmtTNfS9rzOq120Y9KkVfTzAMe/S1XYsbJHE73n50Ng51D3eDnYJ6WexsBg1khDrg81gszg+z1yqmo2Yol6UL3ZuCHxCKcfFRqtOW6LG3pr8p4fr5djAkxcow9KsetPJXeee2cbLBrqBt2D3Ovc4ypnY0AO4a6wd+B23Cx5FI+9qcU1/Aq0pwZnOTl5+fjX//6F+bNm4dTp06BZVloNBqcOnUKc+fOxdNPP42CggJTxkoaSDfJ+/rS19iduJunaKyTbkvenxlKqKsbTEOMimVZbLzJbcWb3N4ODiLzdUJ0dxch4ImZjGVq4Ld7ltWa96hIjQkxchQ+0actYIDvB7qgm7v5hxW4SoX4rK8MZ0Z5IsJb//45ZRq8+acCY45WLteVjT/ulyBfqWnQ1x/3SzAhOttilv6qWIqsIp5Xz+TiyAP9enZOIgbLeznh3LOeGOZneJFtT1shdke6wVmntfTVM7k4n2EZCS6xHAZ/vFu+fDni4+OxYsUKzJgxA2JxxX/c8vJy/PDDD3j//fexfPlyfPLJJyYLljRMoZLblF+uKcdr0a8BAMYFjeMjJKsTJLOBq0SAnH+6mfLLWVzLKeflD2hz8meGEn/nVM0QZVCxwoU5MQyDsa1tsebvqv9H+1JK8EKg6cq31EdBuQYTYuTacYOVVoY549/+/M4E7ugqwsGn3XHoXikWXsjD/UJuj09lSlqiBiYeyzHKPfleEeJ0WhnGRWejrIbOLQbAlPZ2WBjqBM96LCn3pA4yEf73lBvGHs3WzrotUwMvHMtBzAgPtHaiMdekgsEfh3/99VdMnz4dr776qjbBAyrGwr3yyiuYNm0aDh06ZJIgSeN8ceELvX2l6lJ8FPsRD9FYJwHDoJ8XN6E7S122JrcxgfsB5Wk/KQIczf8HbGwbbpfticdlkJfyO0QFAFQaFtNP5OB6DrdUyqsh9maZfWwIhmEwKsAWf43xwoehTqhmuJnRVSZ65m7RO51WhvG1JHghLjY4MdIDa/u7NDjBqzTAR4Kv+sk4++RlGoyLliPHAt6bxDIY/HvfVlMAACAASURBVN9NLpcjODi4xuMhISGQy+U1Hif8eVjwsF77SfX0J1/wt8RVc/CgUIVfdcqAvMpT4hLiIkKwrCq5VLPAL6n8LinFsizmx+Uh5hE3kRnuL8XHvZx5iqpmUhsGb3V1hJvUPF3tJWoWc87kmuVelWafzkFt+VWBkjVq6/8LgfaY341bs/N2vgpRx3NQVseqGaR5MPh/m5+fH06cOFHj8RMnTsDPz88oQRHjaulYfbHomvaT6ulOvvgzowwaln6Rmsr3iUV48u9UiMwGA3z46x5/TneWLc8D3dddL+TU8AMqxg9uGuACoQXXTft2gCtsaymObCsEHEWMQV+1NYbZMMD6cFnNJxhZmZqFcy3NlLZCBusjjD8R8L1ujhjflvve/DNDif+czQVLv5+aPYP7PSZPnoylS5di9uzZmDt3rrYocnJyMtavX4/Dhw9jyZIlpoqTNMKi/oswN2YuSlTcweKzu8/mKSLr1NFFBGcxg7x/yh8olCxu5KrQ2bX51v0ylWKVRq9syishDrzWAhvbxg4fx1dNLjuXrsTjIjVa2Ju/RNMvqSX48GI+Z19LeyF+HuIGezNOSmmIymLKlatmVGroKhyVkxxKdFrQVCxwNaccA1oYPqmhoViWxX/O5uJmrv7sWaBxK4zUhWEYfN3fBQ8L1ZyVM3bfLUGAow0+CHUy+j2J9TD4t8G8efMwbdo0/Pzzz+jfvz+8vb3h7e2N8PBwbN++HdOmTcPcuXNNGStpoHFB47B26FpIhNxfMDKp+T7lNgVCAYN+XlQvzxx23ymB4olaYi4SBuPa8juJoI2TDbo/UaOMBbA/1fyzbC9mKfHKae4kBScRg92RbvCykrViKxO9yha9xiRBFddyh7Sab33RhXwcNMPPaEV8AXbr1E+s/ONqygSvkkTIYNsQNwQ6c9ttVl8twLbkohpeRZoDg5M8hmHwxRdf4Ny5c/jwww8xdepUTJ06FYsWLUJsbCw+//xzU8ZJGmlc0DjM7clNwmMfxvIUjfWienmmx7Ks3jq1U9vbW8RSYmN1umz33TVvl21qgQoTY+SccV82DPDTU64IdrGuFuXKRM/PXtjoJGiAjwS7It3hbSvAk3kuC1Ss4ZtluvGz25KLsPoqt3xYoLMNtg5xNcr3ZigXiQC7hrrBTafLeF6sAqce0++p5qre09SCg4NrnYBBLFe4bzhWY7V2O/YRJXn1Fa4z+eJcuhIsy9KSQkZ0Ok3JWRpLyAAvBVlGuZIxre2w8EJVN+ml7HKk5KvMUrJCUabB+Gg5pzA0AHzRT4ZBZuiSNAXd5dEae63EiT448agU46Ll2mXwStQsJsbIET3Cw+gzs089LsO8WAVnn9s/yVZrJxsMN3MJm9ZONtgx1BUj/6ia4atigSkn5Dj6jEezXlKuuar3R+M7d+7g22+/xQcffIAPPvgAGzduxO3bt00RGzGyXj69IBJU/SdPzUulGbb11NlVBCdRVUInL9MgUVH9OBzSMLqteCNaSc22YkNdfO2F6KtTSmdfium7A8s1wJTj+ismvNXFAVPaW0YCbCkG+0rxuU5pkaxSDSZEy426nFqiohxTTsg5aypLhMCOoa681qkL85RgY4QrZ1++ksX4aDkydQcukibP4CRPpVLhjTfeQFhYGN577z188803+Oabb7BgwQKEhYVh3rx5UKnoj50lsxPZIdQrlLOPumzrRyhg0MfM9fIsbbkmU0otUOEPndUBXjFz8eO6PN/GvLNsTz8uxZA4W5zRKdkztrUtDaqvwYvt7fFmF+77JilPhSnH5VAaobRIZoka46PlemvQboxwRZin+Qsw63q2tS2W9uS+N+4XqvHM4Sx02pVmMb9LTqeVYeQFqUUta2dp12ksgz9uLFu2DD/++CMmTpyIV155BW3btgUA3L59Gxs3bsRPP/0EmUyGpUuXmixY0njhLcNxPu28djv2USwmBE/gMSLr099bgqMPq/7jxqYr8bKJRjCcTivDuKPZKNMA46OzsSvSnZcq/ubyXUIhnvyz2cVVpNdyxrfRAbaYH5enLe9yM1eFhNxyk4yJO51WhueOyqFiucMB+niKsT7cBQIaJlCjhaFOSC1Qc1paz6QrMe+cAt+Eyxo8xKJYpcHEGLne6h1Lezrh2db8Tg560txODkjJV+HHW1UfQpLzK2KeEJ2NnTz/LqksHF2qFmDs0WxMbmeHNg1sAb2br8LW28Uo16BR1zLFdfhcfQUAGIVCYdDHmvbt26N///7YsmVLtcenTp2Kc+fOITk52agBWrvk5GQEBgbyHYbW8XvH8dz+57TbbWVtcWnaJR4jMh5zPeuLWUoM/TVLu+0hFeDWRG+jj8s7nVbGWbYIAEQC4Oehbhjiy/8YLGM/78JyDUJ2piO/vOpX0vpwGaIsZPmwJ409mo1jTxQhfrurIxYauVXtdFoZnj+aDaVOD6OPrQBnn/WEW3XTSQlHqYrFs0eyEZfJbQV9v7sj5ner+edV03tbrWEx9UQOfr3PbW2e1t4OX/RreOJoKioNiwkxcs57tZI5Zv3WpK6l35oaPp+1wd21xcXFCA8Pr/H4gAEDUFrKbwV4UrcwnzAImao/DncUd5BelM5jRNanq5sIDjZVv8yzSjVIzjPuUIXKT7nlOn/gyzXA80flWH0lv8kVOt1xu5iT4LlLBRjb2q6WV/BHtzDyvrvFRv15VLbg6iZ4AJCr1OBGDfXYCJfUhsG2Ia5o48hNiFfEF2DXnfp3sy+6mK+X4D3VQoLVfS0vwQMAGwGDl4PsUV1kfC79NiFa3mwSPIC/Zw3UI8nr168f4uLiajweFxeHfv36GSUoYjoOYgd09+rO2Ufj8upHJGDQ20u3lIpxSzTMOZNb4/JILICP4wvw3FE5EhXl1Z9kZTQsi+8SuPW8pnWwh9TG8v5wAsAz/rYQP/Hb826BGlfkxvtZzDqVg5rmCPw/e/cdFsX19QH8Oyy9V0GlWVBEsaBiQSUaSyyxY4mJGhNREzUxlp/GXqKmaTTRKJZYohGxl8SCsSCgiN0oiCJKE6nSy+7O+wevC7MFWNjO+TwPz+PcmZ29jMvu2Tv3nlMsgMrLdWkzO2MeQvrZw8aI+1qaeT1brhRIO5/kY8t/3EVBXjb62NPbFgYaXGFk/o23kPX1Qx2l374My+Ykwa4v1HGtATnm5P38888YPXo05s6di8DAQDRt2hQAEB8fj+3bt+Phw4c4cuSI0jpKFMevsR+iX0eLtsOTwjGq5Sg19kj7+DkZcW6BhKeV4FMFpvmY284cX0e8rfKYyykl8DvxBp97mmFRB0tYq6Lyu5L8m1zCGQ3V16C0KdJYG+mhn7MxzlYa1TkaX4QOCqhLmlEskPmhDCivPJYua2alj4Pv22HYuYrR0VIhMOFSeWoVD6uq51OeTyzGgpvcv0cnk/JUKZaGmv13t6WnjUR1kXdMeFD5a2lLTxuMOJ8BaXGePlO+mt7ZrGahSVJBeX1rfh3PpYrzqOvvtsZBXufOncGyLJ4+fYo//vhDNDT97haFvr4+OnfuzHkMwzBISUlRYHeJIvg5+2HT7U2ibcqXJz8/iZG8EoXly2NZFoef1ywth4AFtj8pQEh8ERb7WGBSCzPoa/CogizbxdKmDG9igoYaXr1hVBMTTpB3/EURVnW2rNNiiCI+i/GhmUgrkj6Mp865Pdqum6MRtva0wedXK0ZTckpZBFzMROgQB9jLmON4P7MUU65kQVjpg9tMn8GhvnZw1pDUPlV5l3S6fJEDd9+iDpYqfy21sdGHHgOJIK9uZe3qXiJP086jKDV+hY4YMUIj5xwQ+XVt1BV6jB6EbPkHSWxWLN4UvEEDswZq7pn26GBvCBMeI/pDTi0UIj5XgGZWdX/TP/WymFOD8h0THoPvu1ghJL5QIp1GVokQcyPfYndMAdZ1sdaqICDubRkuik0Mn+6lWWlTpBngYgwzfQYF//+1PblQgBtppejuVLtrL2RZTA/Lwq107m1f3v9/IFKAV3ejm5oiIU+ANXcqElon5AnwUWgWTn5gDxOx6QHJBQKMC80U/R8DgB4D7HrPBu0VMGqrKu8qgoiPoIWnlWK2t2r7su9pocRcYxMe6ljWrqIWcm3/TjTtPIpS40+k33//XZn9ICpkaWSJdg3a4W7aXVFbRHIEhrcYrsZeaRdDHgPfBoa4msq9ZVvXIK+Yz2LpLe5tIR97A6QXCbGlpw16NTTCJy1McfplMZbceiuRxuG/bD6GnsvAUDdjrOpspfAM/8ogPhevo70BOjlo/geomYEeBroa40ilmqXHXhTVOshbGZ2LkwncSf3+DY3wtbc5ZlxNR1DvBhTgKcDctuZIyOPjz7iKhRdR6aWYEZaN3e9VpKXJLRVizMUMpBZyI5L1vlb4wEVzUqXUVK+GRljra4n/3awIcC8kFiM+l1/r1CXy4gtZ7Izh/r1b8Fgc6OdQ57J2wf3s8GVYtuh9UhfOowiaPZmAKI1fYz/ONt2ylZ94HVtFJEXe8l8+J3DTZ4Dfe9rg4Rgn0RsFwzAY6m6CqBGOWOpjCVMpixNOvSxGl+NpWHMnFwXiX5s1yNtSIf6K465y1IZRvHfEV9meSCgCXyj/pPI9sQXY9Ih7y9rTWh97e9uid2NjnO5cTAGegjAMg43dreEvdj1PJBRh1e3yAIjPAlOuZEmsYp7hZYZALXp9igtsZY72dhXzD1mU56ZUlbOvipFUUPH+ZsJjcLxTkUJe2+9K5NX1XJp2nrqiIK+e8nMWC/Joha3c/GTUsa2t1EIBNjzgFjr/vJUZWsqoN2msz2BuOwtEj3TEmGaSIwslAuCn+3nofCwNIc/LU3woMpu7IjLVr7qdi/xKt8KcTPQwzF17Rkneb2wMK8OKIDujWCj3NQlNKsbcSG790wYmejjcz06rF9NoMgM9Bnt728LTmjuC9cvDfCyOykGfSBOEik0hGORqjDWdrVTZTYVjGEbiS9SBuELkqeiLoHjJwjHNTFDNmhdSR/QOUk91a9QNTKXsSY8zHyOzKFONPdI+He0NUXmudlKBAC/za5/8aWX0W87cH1sjPSysImHrO43MeAjqZYsLg+3hYy/5jplSKMTUa9noduINAi5mILFAUKecTe8mFr8u0avTea4kF2O32K2bKZ5mMORpz9xfIx6DD93Ey5zVvJbto6wyfHolizNPyoTH4ND7dnDVgkn92szaqDyQbmDC/Rjc8l8BioTc12AHewPs6GUDnhYuahI3ookJ53fOK2NxME65pfkA4F5GKSLF5hpP0+JRUW1B7yL1lLWxNdo4tMHD9IeitojkCHzY/EM19kq7GOsz6ORgiOuVFkGEvy6p1Ty42+mlOCS2onaxj4VcIzm+DYwQOsQBfz0rxMrbuXgjtkIzJqfi1lORgMWwcxloaKoHU/2aP0chX4jUQqEoxYeizvOO+MiKNhjd1IQzv+v0yyJs6GYNo2qC1dTC8mA7r1ICaAbATn8b+GjBnERd4Gquj0Pv22HwPxkyc7c5GOvh0Pt2MDPQjTERIx6DT1ua4ft7FXcNgp7kY2orM6WWydsuNve2V0MjeNkYIC5DaU9JQCN59ZrEvDy6ZSs38Vu2tUmKzLIsFt7k3q7zstbHpBby54nTYxhM8DBD9EhHfO1tjqpSeLEoH+V7lsuv8U+KlMBMUecBgGnXctRe0FtePZyM4GBccaFzS1mEJlVd/Se/TIixFzORXMgd+f3O1wqD3bTndrUu8HEwxJy2skeU8sqEiFVwRRt1+7SlGSrHrM9zBQhNUt7fXXqRAEfjxefeam4eTF1S4yAvMTERRUWyb0MUFRUhMTFRIZ0iqiExL48WX8hNMsiT/40yJL5IIm3Gui5Wdcp3Z2mohxWdrHBjhCO0qcSpurLC14W+HoPh7jW/ZcsXsvjsShYeZHH/z6e2MsMM+uBTi/1PZd+u1MUKI06mPIwQe81uV+ICjD2xBZwSfW7mPAxwVn/97fqgxkFeu3btcObMGZn7//nnH7Rr104hnSKqIT6S9yj9EXKKc2QcTaTp7GDIGS17mS9AYn7Nv/UXlAmxIpqbMmWQqzH8GynmDbCppT4O97OHtszf19ZqDqOacj8wzyUWS13VzLIsFt18i/NioyYDXIyx3teKcpGqyZaeNjCR8WVIW1+T1RFfgHEpuQRPlVAmsVTAYpfY3NtAL3OdmN+oDWo8Aaa6VYN8Pp/eoLSMrYktvOy98DjjMQCABYuI5AgMajZIzT3THib6DDo6GHImFIe/LsW45jX709r0KB8plfJwGepB4Sv4ejU0Qkh/e4ks7MY8YGN3a7ly0kWnl2JORA4nc76izqPupKF14dvAEM5mPFF6iEI+i3OJxRjV1JRz3NbHBdgh9oHX1tYAu/x1Y1K/tirPayb5N6LNr8nq+DgYorODAecuQtCTAvzUzVqhz3PqZRFeV5ofbKbPYEJz0yoeQRRJru/3soK4t2/fIjQ0FA4ODgrpFFEdypdXd36Otbtl+yqfj80PuSlTZniZKyUx6bvknCb/vxjAhMfgcD97jG9uBg8rgxr/jG9uhsP97JVyHm3+MNVjGIwQy5lXOUkyUL4gY0kUd9TW2YyH4H52MNeRSf3aTNrfiDa/JmtCfDTvr2eFyClRbDoV8bQpHzU3pdRAKlTllV6/fj1sbW1ha2sLhmEQGBgo2q7806RJExw5cgQjRoxQVb+JgvRw7sHZpsUX8uvRULKObU2siM7ljGQ1MNHD3HYWiuwax7sPMZf/Dyzqms3dyUiokPPUtT+aYpRYkBeaXCz6wLydXorAq9mcxSYWBgyC+9ppfI3e+kRRr21tMdTdBA1NK8KAAj6LP+MKqniEfKLTSxEtNt84kOadqlSVQwYdOnTA5MmTwbIs9uzZg169eqFZs2acYxiGgampKTp06IDhw6kslrbp3rg7Z/tB+gO8LXkLKyPtTvqpSp0dDKHPlGfJB4D4PAFSCwVVfnhHvC7BMbHJ+Ut9LGFZ1XJYBXiXhV0R5znduRgeCsoKrwva2RmgmSUPz3PLI/cyIXDmVRF6OhlhXCj3NiCPAfb2tkVrW8oEq2kU9drWBgZ6DD7zNOfU8t3xpAAzFDRnbrvYKF7fxkbwoOzHKlVlkDdgwAAMGDAAAFBSUoIpU6agU6dOKukYUQ0HUwe0tG2J2KxYAICQFeJmyk30b9JfzT3THmYGevCxN0RUOjdf3uim0uedCFkWi8Ru27WzM8AED5qnos0YhsGopqb4oVL+sT+fFuK3R/lIL+beAtvQzRp9GtPqQqJ+k1ua4sf7uSj5/7sKL/MFOJdYXOdUPqmFAhwX+yJLyY9Vr8bDBlu3bqUAT0dRvry6E69jW9Ut2wNxhbifKZYyxddKqYlIiWqI17K98aaUk4QaAL72NseklnTLimgGe2OexBdS8cTFtbE7pgCVCviguaU+3m+s+6Ojmkaue0OvXr3C7Nmz0b59e7i4uOD69esAgMzMTMydOxf37t1TSieJcknMy6PFF3KraVLk3FIhVle6NQIAI9xN0N2J3vx0gae1AVrbyL5BMsLdBMs6Vl+qjhBVmtaK+6XjWmoJ/suqfTqVEgGLP2LF0qYouaIGka7GQV5sbCz8/f1x8uRJNGvWDAUFBRAIysd37ezscOvWLezcuVNpHSXKI54U+W7aXeSV5sk4mkjTxdEQlatYPX3LR1qhZB3bn+/nccqNGfOAlZ3pQ1+XdJSRSsbTSh9be9rQBx3ROG3tDNHdkfu6DapDcuSj8YXIqDRFwdKAwXiajqIWNQ7yli9fDgsLC9y6dQtBQUESefP69++PGzduKLyDRPkczRzR3Ka5aFvAChCVEqXGHmkfCwM9tLPjTiiOSOPesn2Ry8fvYhORZ7WxoEL0OuRaagkOP5dePSEhn49b6fKXvSNEFcTnywU/L0RWseQX1eqwLIttj7mjeBM8TGFBaYLUosZXPSIiAp9//jkaNGggNV+ei4sLUlNTFdo5ojqUL6/uelRzy3bJrbec0j6NTPXwtTdNRNYlX4ZlQ9bnoi6WxyK6Y7CrMZzNKjICFAuAfVWUe5PlxptSTsk+BkBgK3qfU5caB3l8Ph9mZrInC2dnZ4PHo3xP2kqiji0tvpBbVXVsr6YU4+wrbtH6FZ2sYEbfbnVKeXks6bdjdbU8FtEN+noMporNzdsZUwC+sOpqV+K2i43iDXAxRhMlJHgnNVPjTxgvLy+EhYVJ3ceyLE6fPo327dsrrGNEtcRH8u6k3UFhmfzf4uqzro6GqJxa6kkOH5nFAvCF5fVKK+vsYICApnVLUUA0j3jVhHfqQ/UEov0mtjDjvHaTCgQSX06rkpTPx+mX3LQp0yn5sVrVOMibMWMGTp48iR9++AFZWVkAAKFQiKdPn2LKlCm4e/cuZs2apbSOEuVqbNEY7lbuou0yYRlupd5SX4e0kJWhHrzFktuGvy7F3qcFeCyWRmN9F2uq9ayj6mN5LKIbbIz0MLYZ98uneFmyquyKKUClnN/wtNaHP73u1arGQd6oUaOwfPly/Pjjj/D19RW1de3aFWfOnMGaNWvQr18/pXWUKJ/4aN715Otq6on2Es+Xd/ZVEb67w12pPLaZicwVmEQ36FrJNlJ/BIotwIhMK8W9jOoXDBXyhdjzlHurdlorc/oyq2Zy3Sj/+uuvMXr0aJw6dQrx8fEQCoVo0qQJhg4dCjc3N2X1kahID+ceOPD4gGib5uXJz8/RCFv/q3ijC37OvXVhps9geUcqGVcf6FLJNlJ/eNkYwL+hEa6mVswpDnpSgK09q/5ieiS+CNklFcN41oYMxjSjKSnqJvdsSGdnZ3zxxRfK6AtRM/HFF9Gvo1HEL4KJPv2h1lR3JyMwAGRNVZ7T1gKNzGiBEiFEc03zMuMEeUfiC7GykyUcTKS/d7Esi23/cW/rTmxhRgvLNIDW/A/s2bMHQ4YMgaurK6ytrfHy5UuJY3JychAYGAhXV1e4uroiMDAQOTk5auitdnK1dIWLhYtou1RQiujUaDX2SPvYGOnBS0bFAxdzHr5sTakECAGAkJgQeO/yhs0vNvDe5Y2QmBB1d0mnyXO9Bzgbw92iIqArFQJ7YmWXOgt7XcqZd6zHAJ+3ogUXmqDKkbx27drJdTKGYZRW2qywsBB9+vTBoEGD8O2330o95vPPP0dSUhJCQkLAMAxmz56NadOmITg4WCl90kV+zn449OSQaDs8ORw9XXqqsUfap4mFPv7L5ku0T2huChN9mp9CSEhMCGaHzkYRv3w6Q2JeImaHzgYABHgGqLNrOikkJgQzL85EiaB8dK66683TYzC1lTkWR1VkBdgVU4CvvC1gKCVFkPjijMGuxpTkXUNU+b/QvHnzGk2aTE5ORkxMjFInWL67RXz37l2p+2NjYxEaGopz586hS5cuAICNGzdi4MCBiIuLg4eHh9L6pkskgjyalyeXa6kluJAkPeXApod56O5kRJPwSb3GsiwWXlkoCvDeKeIXYVX4KgryFCytIA1fhX4lCvDeqe56f+xhirV3clHAL5988rpIiFMvizC6Kbc8WUIeH/+IpVmZ7kV3LDRFlUHe0aNHq3xwcnIyNmzYgLCwMBgaGmLChAkK7Zw8oqKiYG5uLgrwAKBr164wMzPDzZs3ZQZ5cXFxSu+bKp5DUZzLnDnbUSlR+C/mPxjytGM1qLqvdeAtY5QKpc+CKBIAgZff4HTnmued0nTqvt71iS5c69i3sfj58c/ILM6Uuj8pL0ljfk9N6UdtlQpKEZwQjF3PdqGQLz3naXXXe5CDAUJSK9JC/XInA+3EgsWN8QZgUXGMh5kQDrmvECdH+XNtv9bqVN0AVq3GU1NSUrBhwwb8+eefYFkWH330EebOnQtnZ+fqH6wkb968gZ2dHWc0kWEY2Nvb482bNzIfp+wRPm0bRWzONkej6EZIyU8BAJQIS5BrkYtujbupuWfV04RrHWRegrEXM1EkkFx6YcJjENS7ATx0ZCRPE653faHt1zqjMAOrI1Zj36N9YGUuSwJ4DA92znawNbFVYe8kafP1ZlkW516cw+LwxYjPia/yWCsjqyp/zwUNyhByrOLz81EeD2+t3dDp/1NA5ZcJcSbqNSovNfuqgy1aeNR8Pp42X2ttINfCi5SUFMybNw8+Pj7Yv38/xo8fj9u3b2Pjxo21CvDWrFkDa2vrKn9kVdmQRtrtYpZlKU+PHBiGQQ/nHpw2qmNbcxWJcLntlBCX1EelglJsubMFPnt8sPfR3ioDPADgs3xMODMBJfySKo8j0sVmxWL0idEYf2p8tQEeAOSU5OD0s9My93tYGaBvY+571vZK8+8OPStEbmnF/6mdkR5GN+HeziXqVaORPGWN3M2YMQNjxoyp8piaPkeDBg2QkZHBCepYlkVmZiYcHBzq1M/6xq+xHw7HHBZthyeFY57vPDX2SLuUB3r2ohE9CvBIfRSaEIpFVxchLlv6rThve2+kF6XjdcFrTntkciRmXpyJoA+C6At6DeUU52D9jfXYcX8HBKxAYr+VkRU+aPoBrry8grTCNM6+wHOBODP6DDo6dZR67ule5ghNrgi6j78owqrOAjia6CHoCXfF7eSWpjCmxWUapcogT9m3Ze3s7GBnZ6eQc/n6+iI/Px9RUVGieXlRUVEoKCjgzNMj1RPPl3cz5SbKBGUw4BnIeAQR925E78uwbGzpaUMBHqk3nmU/w+Jri3H+xXmp+5vbNMe6XuvQr0l5haQSfglGHh/JuWMQEhsCNys3LOm+RCV91lYCoQD7Hu3Dmsg1yCySnOeox+jhU+9P8W23b2FnUv5Z++/LfxFwIkAUDBbxizDu1DiEjg2Fm5VkUYM+jY3Q3FIfz3LLMwbwWWB3TAG6Ohri6duKLAI8BvjMkxZcaJoqg7wOHTqgrKwM3t7e+Oabb+Ds7Iy0tDSkpaXJfEzHjtK/DdTVu+d99uwZgPLVtG/fvoWLiwtsbGzQsmVL9O3bF3PmhXy4XAAAIABJREFUzMGmTZvAsizmzJmDAQMG0P1+OTWzbgZHU0fRN75CfiHupt2FbyNfNfdMu1DFA1KfvC15ix9v/ojt97ajTFgmsd/S0BILui5AYLtAzkIuI30jHPjwAPoF9+OM+v0U9RPcrdzxceuPVdJ/bXM96ToWXlmIRxmPpO7v4dwD6/3Xo41DG057H7c+2Pj+RlEKFQBIL0zHmJNjcH7MeVgbW3OO12MYBLYyw4KbFelU/ogtwK10bqmzYe4mlOhdA1UZ5JWWlv8nPnjwAJ9++mmVJ3p3mzQrK0txvatk9+7d+P7770Xb727zbtmyRbSqd8eOHfjf//6HkSNHAgAGDhyIH374QSn90WUMw8DP2Q/Hnh4TtYUnh1OQRwiRIBAKcODxAayOWI30wnSJ/QwYTGwzEUu6L4GDqfSpM9bG1ggZHoK+h/oioyhD1P71pa/hYuECf1d/pfVf27zKfYVlYctwIu6E1P0uFi5Y02sNhjYfKvN298Q2E/Ei5wU2Rm8UtcVmxWLi2Yk4MvyIRDaF8R6mWHMnF7ll5fPvMoqFuJLCnTc53YuSH2uiKoO8LVu2qKof1Vq0aBEWLVpU5TE2NjYICgpSUY90m19jsSAvKRxzOs9RY48IIZomMjkSC68uxP0396Xu79aoG9a9tw7tG7Sv9lzuVu44+OFBfHj0Q1FON76Qj0/OfIILYy/A085ToX3XNgVlBfgl+hf8Gv0rigWSaZhM9U0xp/MczOw4s0alKJf6LUXC2wQcjzsuaruWeA1fX/oaW/pt4QSIFgZ6mOBhit8fS6960cHeAJ0dtCPNVn1TZZD30UcfqaofRMOIz8u7kXIDfCEf+nqKy2IeEhOCVeGrkJSXBGcLZyzzW0aJUAnRcCExIVgWtgypBakyj3G2cMaqHqswosUIuRZP+DbyxfYB2zH578mittzSXAScCEDouFA4mjnWpesqU/m9rZF5IyzqtggjW4ys1bmOPj2K5WHLkVUs+y7ZGM8xWO63HI0tGtf4vHqMHrYO2Irk/GREpUaJ2g8+PogmVk0wv8t8zvGBrcyx7XGB1PXR073M1bpIJiQmBCvDVyI5L7lO1/vY02NYF7kOKfkpOvOZxOTk5FS9pp3UibbmAGJZFh5BHpxbJ/+O+xc+Tj4KOX9ITAi+vPglSgUV8zpM9E2wue/mWv9Raeu11lZ0vVVHU671gf8O4KtLX4EvlCzbBwDGPGN81ekrfNXpK5ga1D6VxqboTVh+fTmnzcfRB2dGn6nTeWuqLtc7JCYEsy7OkjrapmjtG7TH9+99jy6Nar+4MKMwA32D+yLhbQKnfccHOyTei/udeYNb6ZLzLY/2t8P7jY1r9fx1fW2HxIRgxoUZMl+TdVHXzyRNIFeePFJ/vJuXV5ki8+UtvraYE+ABFWV2CCGahWVZnHh6ArNDZ8v8MB3hMQJRk6KwqNuiOgdiszvOxuQ2kzltd9LuYOq5qRAIJVOEaJKV4SuVHuDpMXr4td+v+Hf8v3UK8ADA3tQeIcNCYG3EXXDx5cUvEZEcIdq+llqCB5mSAR4AfHwpC9dS1ZPbcPG1xUoJ8IDyz6SFVxaCZbV3LIyCPCKTX2NukHc96bpCzvs8+zneFEqvQpKUl6SQ5yCEKMbD9IcYcmQIJv89WWoONqB8ccUfg/+Aq6WrQp6TYRj81OcnvO/2Pqf97POzWBq2VCHPoSyqeA9jWRaftP4EeoxiPsI9bD1w4MMDnAUXpYJSTDg9Ac+yyzNafBmWjRKh9McXCVh8GZatkL7IS9ZniaJkFmdi0JFBMuedajoK8ohM4iN5kcmRdf4WnVmUiYCTsoe+nS3UVxqPEFIhozADcy7Ngf9B/2pH8ZXxd6uvp48/Bv0BL3svTvvWu1ux4/4OhT+fImQXZ0NPxscqAwYm+iZy/TCQPs9NGdfbz9kPv/X7TeL3CTgRgIzCDGzpaQMTnvT+mPAYbOlpo/A+VYcv5MsMdOW93rKuNVD+2ffewffwVehXUleQazLFzaInOqeVXSvYGNsgu7j8G1puaS4eZjys0Uo5aYr5xZhwekKV5Xbm+s6t1bkJIYpRJijDzgc7sf7GerwteVvt8Sb6Jljmt0wpfbE0ssThYYfR71A/zkKP/135H1wsXPBB0w+U8ry1tf7GegghOdxV27ldITEhmB06G0X8Is65lHW9x3iOQcLbBKyNXCtqe/H2BSacnoCTo04iuJ+dRG1udVb0CUsMg5BVzPWWdq0rY8Fi76O9OP70uNR8j5qKRvKITHqMHro37s5pC0+q3bw8ISvElxe+xI2UG1UeZ6BHVTUIUZdLCZfg96cfFl1dJDXA6+vWF6t6rIKLhQsYMHCxcFH6xHRnC2ccGnYIZgYVediErBCf/fMZ7r25p7TnlVdsVix23t8p0V6XaxTgGYDNfTer9HrP952Pj7y4mTVupt7EjPMz0MPJ4P9rc5ePeqm7ZOPRp0cl2mp7jcSvtZOZE7ztvSWOyy3NxZJrS+D3px8uvrhY676rCq2uVTJNWRVXW1vvbMW3174VbQ9qOggHhx6U+zyrw1fj51s/c9p6OveEj6MPNt3eJGrr49oHx0YeE394jWj7tdY2dL1VR9nX+nn2cywOW4xz8eek7m9m3Qxr/deiv3t/taXKOP/iPMafGs8ZuXEyc0LouFCF376U93qzLIvRJ0bj0stLojZXS1dETYyCsX7tVp2qU6mgFKNPjMa1xGuc9jmd5mB5j+W4llqisJKNtX1tl/BL4BHkgdzSXFHbsRHH0MetT536I+7ii4v49tq3Mmsw93fvj7X+a9HcprlCn1dRaCSPVEliXl5KpNTh8ars/2+/RIDXwqYF9g/Zj/Fe4zntVxOvat2cB0K0VW5JLpaFLUPX/V2lBngWhhZY3XM1Ij+JxIAmA9SaC21AkwH4/r3vOW2vC15jzMkxyC3JlfEo1biQcIET4AHA6p6rtTLAAwBDniH2Dd6HlrYtOe0bozdi78O9opKN6qzJfenlJU6AZ2dih14uvRT+PP2a9EP4x+H4rtd3sDS0lNh/IeECuu7viiXXltRoeoOqUZBHqtTGvg2sjKxE29nF2Xic8bjGj7/y6grmXOJWynAwdcDh4YdhbWwNTztPzsRqASvAqbhTde84IUQmISvEn//9iU57O2Hz7c0StWYZMPi49ce4Pek2ZnWcpTFzj6a2m4ovOnzBaXuc8Rif/v0pygTS03soW6mgFIuvLea0+TX2w9DmQ9XSH0WxNrbG4WGHJUrRffPvN/j35b9q6lWFyhWZAGC4x3CFJuuvzJBniC99vsTtybcxqc0kiUUafCEfv935DZ32dsK+R/s0Ks0PBXmkSjw9Hro17sZpq2m+vMcZjzHxzERODiNjnjH++vAvuFu5i9pGtxjNedyRp0dq32FCSJWiUqLw/qH3MfPiTKnpJ7o07IJ/x/+L3/r9hgZmDdTQw6qt7rkaQ5oN4bRdenkJ8y/PV0s+sx33d4jSjADlAfI6/3VqHfVUFDcrNxwaeohTJk3ACjDp7CT8l/Gf2vpVUFaAv5//zWkb1XKU0p/XwdQBm/puwpWPrkh8LgJAemE6ZofORp9DfRCZHKn0/tQEBXmkWrXJlye6jVJpOJ0Bg6CBQejUsBPn2JEtueVnIpMjkZyXXIceE0LEBd0LQqPfGqH/4f64m3ZXYn8j80bY+cFOnBtzDh0cO6ihhzXD0+Mh6IMg+Dhyq+/sebQHNpts4L3LGyExISrpS0ZhBr6/wb2FPLHNRLRt0FYlz68KHZ06IuiDIM7oVV5pHvz+9IPXTi+VXevKzsefRyG/ULTd2LwxujbqqrLnb9egHf4e/Td2D9wtdT7o/Tf3MTBkIKx/sVbbNXqHgjxSrR7OPTjbEckRVX5jLigrwLiT4ySSgq7quUrqLQx3K3d0dOzIaatcNJsQUntF/CJMPjsZC64s4HwwvmPMM8b8LvNxa9ItjPYcrRUjUKYGpjg09BBcLFwk9iXmJWJ26GyVfLB+F/kd54uspaEllnRfovTnVbUPm3+I1b1WS7Sn5Keo7FpXJr6qdkSLEQpLDF1TDMNgZMuRiJoYhYVdF3JGOytT1zV6h4I8Ui1vB29YGFqItjOLMhGTFSP1WIFQgM//+VwitcFnbT/DTJ+ZMp9DfKj9aKzk0nhCSM2xLIuTcSfRZV8XnIg7IfUYE30T3Jx4E4u7LeakKNEGDcwaIGR4iNQktqookfgo/RH2PtrLaZvfZb7EHDZd8WWHL2FuYC7RrupylDnFObiYwE1doopbtbKYGphiYdeFiJoYhZEtRko9Rp0lOynII9XS19OXGAqXlS9v8bXF+Cf+H05bf/f++P6976scIRjeYjjnzfpu2t0qkyYTQmR7lP4IQ48OxaSzk/Aq95XM44r5xXCzclNhzxTL085T5j5llhdjWRaLri7iZBpoZt0M09pPU9pzqhvDMCgoK5C6T5XlKM8+P8upe97EqkmtE/QrkoulC3YP2i2zcoa6SnZSkEdqRHxenrQgb/u97dh2bxunzdvBG7sG7ap21VMj80YSiZdpNI8Q+WQWZWLuv3PR62AvhCWFVXu8LpQRlPU78PR4omo9inb6+WmJ6/tdr+80ZhWyssi61k5mTirrg/jnwqiWozRqioGsa6SuvzUK8kiNiM/LC08O58zLOxd/DouuLuIc08i8EYKHBXNu9VZldEvuKlvxJfKEEOnKBGXYdncbfPb4YNeDXRK5LBkw0Ge4X7SUWR5LlZb5LZM6H4ov5OPj0x+jhF+i0Ocr5hdj6bWlnLY+rn0woMkAhT6PJpJ1rb3svKQcrXjphem4mniV06bOW7XSSLtG6vxboyCP1Ei7Bu04c3beFL4RpQ249+Yepvw9hfPBYm5gjuBhwWhk3qjGzzHUYyhnxO9J5hO5cvIRUh9dfnkZPQ/0xMKrC6UmY+3t2huRn0Ti9wG/q7Q8lqq8K0clbaQkPDkcsy/NVmhqld/v/o6XuS9F2zyGh7X+azVqNElZ3l1rW2NbTnvU6yiVJKQ+FXcKArYiB52XvRda2bVS+vPKQx2l6KqinMyBROcY8AzK82e9qkiCGZ4cDhMDE4w7OY6zak+P0cPuQbvh7SBZ968qdiZ26O3amzOp9mjsUU6yZEJIuRc5L7D42mL8Hf+31P1NrJpgrf9afNDkAzAMA087T50I6qQJ8AxAgGcASvglGH58OCdHWfCTYLhbumNRt0VVnKFmXhe8xk9RP3HaprSdUuXcQF0T4BmAoc2Hwnu3tyjPYl5pHv568pfS5ySK51Ad1UKzRvHeefd61AQ0kkdqTLzE2T/x/2DMyTF4XfCa0/7jez+if5P+tXoO8dVJR58eVUuCU0I0VV5pHlZcX4Eu+7tIDfDMDcyxssdK3PjkBgY2HVgvRpjeMdI3woEhB9DMuhmn/fub3+Ovx3/V+fyrwldxFh/YGNvg227fVvEI3WSkb4RPvT/ltAXdC5K75KU8kvOSJRIMy1rNSipQkEdqTDzIO//ivMTt1Jk+M/FZu89q/RyDmw2GEa+iHmLC2wSpiVsJqW+ErBAHHx9Epz2d8Ev0L5wVhu9M8JqA25Nv46tOX8FIX311RdXJ1sQWIcNDYGdix2mfHTobYYnVL0aR5c7rOzj4+CCnbVHXRbAxtqn1ObXZlLZTYKBnINp+nvMcoQmhSns+8dypHR07ool1E6U9n66gII/UmI+jj8yEj0B5wsxVPeuWC8jSyFJiFPBILJU5I/VTSEwIvHd5o/PZznD61QlfXPgCaYVpEsd1duqMf8f9iy39t8DRzFENPdUsTa2b4uCHBzlfGMuEZfj4zMeIzYqV+3zvUqZU5mnriSltp9S5r9rK0cwRI1qM4LRtu7tNxtF1J76qVrxSEpGOgjxSY4Y8Q7haukrd527pju0Dtisk67j4PIvjT48r9TYAIZooJCYEsy7OQmJeIgCgVCg5ctfQrCGCPgjChbEX4OPkI7G/PuvSqAu2DeAGHW9L3mLMiTFIL0yX61xHY4/iZupNTts6/3XVpobSddPbT+ds//vq31oF0dWJz4nn3NFhwEgEmEQ6CvKIXFLzU6W2lwpLYWpgqpDn6N+kPyezempBKiKSIxRybkK0QTG/GPMuz0OxoFjqfiOeEeb5zsOtSbcwxnNMvZp3J48RLUZgud9yTtvL3JcYf2o8ivhFNTpHYVkhll/nnuODph+gt1tvhfVTW/k4+cC3oS+nLehekMKfR3wUr3vj7nJlbqjPKMgjcskrzZPaLiv4qw1TA1MMajaI03YslnLmEd3HsixOPzuNLvu6SE2H8s7NiTexpPsSmBtKlpkiXF93+hoT20zktEW/jsa0c9NqdIdg8+3NSM5PFm0b6Bngu57fKbyf2kp8Re1fj/9CTnGOQp9DPGeqpuXG02QU5BG5NLZoLLVd0dm8xVdNnXx2EmWCMoU+ByGa5L+M/zDs2DB8cuYTTh42cS4WLnC3clddx7QcwzD4uffP6OPah9N+6tkpiRE6cUl5SdgUvYnTNq39NDSzaSbjEfXP0OZD0dCsoWi7kF+IPx//qbDzP854jCeZT0TbPIaHYR7DFHZ+XUdBHpHLcr/lKsnm3cetD6yNrEXbmUWZEpnOCdEFWUVZmH95Pnoe6IlrideqPFZXqlSomgHPAH8M/kOiMsOvt3/F7ge7ZT5uxfUVnNu69ib2mN9lvrK6qZUMeAYSGRWC7gVBIBTIeIR8xG/V9nbtLbFymshGQR6Ri6qyeRvyDDHUYyinjWrZEl3CF/Kx4/4OdNzbETvu75C4dchjeOjt2huNzRtrROZ8bWdlZIXgYcFwNOWuPp5/eb7U1B83Um5IrOxf2n0prIyslNpPbTS5zWTOSuZXua9w7sW5Op+XZVkcfUqrauuifi8NIrWiqmzeo1qMwr5H+0TbZ5+fRTG/GMb6xkp/bkKU6eqrq1h4dSHnNlRl77m+h3X+60Qlm+Li4uDh4aHKLuokF0sXBA8LxqCQQaIqPQJWgMlnJ+OfMf+IqvQIWSEWXlnIeWwb+zb4uPXHKu+zNrA3tcfolqNx4PEBUdu2u9swuNngOp33btpdJLxNEG0b8YzqfM76hkbyiMbq4dwDDUwbiLZzS3M5Jc8I0TYJbxPw8emPMezYMKkBXhOrJjj44UEcH3Fc42py6or2ju2xc+BOMKhYkZxflo+xJ8ciJT8FAHA26SzuvbnHedz699aDp8dTaV+1ifgCjLCkMPyX8V+dzik+ktrPvR+NpMqJgjyisXh6PAxvMZzTRqtsiTbKL83HqvBV8N3nizPPz0jsNzcwxwq/FbjxyQ0MajaIUqIo2aBmg7DOfx2nLSU/BWNPjsXrgtfYEruFs2+YxzD0cO6hyi5qnbYN2qJ74+6ctu33ttf6fEJWiONPuVUuRrccXevz1VcU5BGNJp4Y+dyLc8gvzVdTbwiRj5AV4tCTQ+i0txM23NogtRTZ+FbjET05Gl93/rreliJTh+kdpkuMPj1Mf4ju+7sjsyRT1GbEM8KqHnWr5FNfTO/ATY58+MlhZBVl1epcEckRSC2oSM1lZmBW65ro9RkFeUSj+Tb0hYuFi2i7iF+Ef+L/UWOPCKleSEwIWgS1gO0mW0w/Px2vC15LHNPJqRMujbuE3wf8DiczJzX0kqzttRYDmw7ktGUVc4OSWR1nwc3KTZXd0lqDmg7ipNMqFhRj76O9tTqX+F2bQU0HKSzhfn1CQR7RaAzDSOTME19tRYgm2XF/B6afn443hW+k7ncyc8K2AdtwYewFdHTqqOLekcp4ejzsHLgT7Ru0l3mMmyUFeDWlr6ePqe2mctp23t8JvpAv13nKBGU4+ewkp40SINcOBXlE44kvmb+UcAnZxdlq6g0h0hXzi7Hx1kYsuLwAAlZ6jrC5necielI0xrUap5A6z6TuzAzMcGjYIfAY6Ysqfrj5g4p7pN0mtpnIyaWanJ+MM88k56FW5WriVWQWVdwytzKyQh+3PlU8gshC7zJE47V1aAsPm4r0EWXCMpx+dlqNPSKkAsuyOPv8LLru74qV4SvBgpV6HAMGS/2WUikyDeRk5iSzxFlSXpKKe6PdbIxtMNZzLKdN3gUY4jlRhzYfCkOeYZ37Vh9RkEc0ntRbtpQYmWiAJ5lPMOL4CEw4PYGTz0saRZf+I4ol6/+H/t/kN60Dd0FLZEqkREoaWYr5xTj7/CynjVbV1h4FeUQriM/HCEsKQ1pBmpp6Q+q77OJszL88Hz3+7IErr65UezyVI9N8y/yWqaRkY33Qyq4V/F38OW3b79ZsNO9iwkXkluaKthuYNqD0NXVAQR7RCi1sW4iy0QPlqSlOxp2s4hGEKB5fyMfO+zvhs8cHO+7vkJh7p8foYWq7qdjYZ6PSS/8RxVJVycb6QjydytGnR5FemF7t48RX1Q5vMZySUNcBlTUjWmNUi1F4mP5QtH009igC2weqsUekPrmaeBWLrizC48zHUvf3cumFdf7r0Nq+NQDg07afqrJ7RAHelWykMnJ119+9P9yt3EXTGEoFpfjj4R9Y0GWBzMfkl+ZL1LwVz5VK5EMjeURrjGgxgrN9M/UmXuW+UlNvSH2R8DYBn5z5BMOODpMa4LlZumH/kP04OfKkKMAjpL7j6fEQ2I77JXz3g91SE4K/80/8PyjiF4m2nS2c0blhZ6X1sT6gII9oDTcrN/g29OW0iZe9IURR8kvzsTp8Nbrs6yJ1NbeZgRmWdV+GmxNv4sPmH1IpMkLETGg9AeYGFavJXxe8rnKajXgO1FEtRlGqoTrSmqu3Z88eDBkyBK6urrC2tsbLly8ljvH29oa1tTXnZ8WKFarvLFEaZa+yDYkJgfcub9j8YgPvXd4IiQlR6PmJZguJCUGbXW1g/Ys1XH93xc+3fkaJoETiuLGtxuLWpFv4xvcbGOsbq6GnhGg+KyMrjPcaz2mTlU4luzgblxIucdrEc6QS+WlNkFdYWIg+ffpg4cKFVR63YMECxMbGin7mzZunoh4SVRjRYgTnm92D9AeIy4pTyLlDYkIw8+JMJOYlggWLxLxEzA6dTYFePRESE4JZF2eJ8qJJy5vW0bEjLo69iO0DtqOReSNVd5EQrSN+yzb6dTSiU6Mljjv97DTKhGWibQ8bD7R1aKv0/uk6rQnyvvjiC3zzzTfo1q1blcdZWFjA0dFR9GNuTolHdYmjmaPEcnpFlDl7nv0cs0JnSYzaFPGLsCqcipPXB8uvL0exoFjqPkdTR2ztvxUXx12kOUKEyMHD1gP93Ptx2qSN5onflRnZYiRNgVAAJicnR3p6dg119+5d9O7dG/fv34ebG7emoLe3N0pKSlBWVobGjRtj+PDhmD17NgwNZWfKjotTzCgQUZ0Tr07gu4ffibbdzdxx2P9wrd4Q8svy8cezP3DwxUHwWen1FRkwiBocVev+Eu3Q+azs4O3KgCsw0zdTYW8I0R0RbyLw1a2vRNs8hofTfU7DwdgBAJBRnIHBlwZDiIrR8xD/ELibu6u6q1qnulXgOpVCZdq0aWjbti1sbW1x584drFixAi9fvsSvv/4q8zHKXiZPS/EV73OXz/H9f9+Lil4nFCSgxKYExjnGNb7WQlaIg48PYlX4KpmF5N9xtnCm/0MpdOm1ff/NfZn7XCxc0L6V7AL2qqBL11ob0PVWrGbNm+HXZ7/iWfYzAICAFeBy/mUs9l6MuLg4PBQ85AR43g7e6Nehn6zTETmo9XbtmjVrJBZKiP+EhYXV+HwzZ85Er1690KZNG0ycOBE///wz9u/fj6ysLCX+FkTVbIxt8L7b+5w2eRZgRKVE4f1D72PmxZnVBnh6jB5lvNdxLMti4VXpc32p4gEhdafH6GFae26psz8e/IFifvn0CPH3b8qNpzhqHcmbMWMGxowZU+Uxzs61rxvYsWNHAEB8fDxsbW1rfR6ieUa1HIXzL86Lto/GHsVHDT6q8jHJeclYGb4Sh2MOS93fyLwRfBv64kTcCVGbkBWilV0rxXSaaKSTcScRmRzJaWPAwNnCGcv8llHFA0IUYFyrcVgdvlpUsiyjKAPHnh6Dc5kzbqbe5BwrnhOV1J5agzw7OzvY2dkp7fwPH5ZXR3B0dFTacxD1GNh0IIx5xqKJ8ol5iXiY8xAt0ELi2CJ+Ebbc2YINURtQyC+U2G/MM8asTrPwdaevYWZghkEhgxCRHCHaH3Q/CJv7blbeL0PUpohfhKVhSzlt/dz7IWQ4ragmRJEsDC3wceuPsfXuVlHbtrvb4G/HrXHr29AXblZu4g8ntaQ1q2vT0tLw4MEDPHtWfk8/NjYWDx48QHZ2NgAgKioKW7ZswYMHD5CQkIDjx49j3rx5GDhwIFxcXNTZdaIEFoYWGNB0AKftQsoFzjbLsjgZdxJd9nXBmog1UgO8YR7DcHPiTSzuthhmBuUT68VrLh5+chiZRZkK/g2IJthyZwsS8xJF2/p6+viu13dVPIIQUluB7QPBoGKB3IP0B9j3fB/nGPFcqKRutCbI2717N3r16oWpU6cCAMaMGYNevXrh77//BgAYGhri+PHjGDJkCLp27Yq1a9di4sSJ2LVrlzq7TZRoVEvuvI3Q1FAIhOUF4x+lP8LQo0Mx6ewkqaXPWtu3xulRp7F38F6Jb42Dmg6Cs0XFNIFiQTH2Pdonfgqi5VLyU7AhagOnbWq7qWhhKzkaTAipO3crd3zQ9ANO29uyt6J/6zF6dKtWwbQuhYq2oVVaylPEL0KLoBbIK80Tte0ZtAdhSWH44+EfUpPZ2hrbYkn3JZjYZiL09WTPVtgcvRnLrldMuG9s3hj3Pr0HA56BYn8JLabtr+1p56ch+EmwaNvW2BZ3Jt+BtbG1GnslnbZfa21D11t5rr66imHHhknd18ulF06NOqXiHuk2rRnJI0Scib4JBjUbxGmb/Pdk7HqwSyLA4zE8TG8/HXcm38GUtlOqDPAA4JM2n8BE30S0nZyfjLPPzyqu80StolOjOQHeA1t1AAAfDklEQVQeACzutlgjAzxCdEkvl14yF7PRqlrFoyCPaDVHk+oX1fR27Y3wj8Ox/r31Nf4QtzG2wbhW4zhtsmouEu0iZIUSKVO87L0wyXuSmnpESP3BMAw6OnaUuq9yrjyiGBTkEa12LO6YzH1NrJrgr6F/4diIY/C085T73IHtuTUXI1Mice/NPbnPQzRLSEwIol9za2eu819X7eguIUQxLr+6LLVdfI4sqTsK8ohWS85Llrnvxic3MLDpwFrXP2xl1wrvub7Hadt+l0bztFl+aT5WXF/BaRvcbDD8XfylHk8IUbyU/BSp7Ul5SSruie6jII9otcqrYCtzsXCBkb5Rnc8vnqX96NOjeFNQdZUMorl+if4FqQWpom1DniHW9Fyjxh4RUv/Iet+W1U5qj4I8otWW+S3jLJAAFFuKakCTAWhi1US0XSooxZ5HexRybqJar3Jf4bfbv3HavujwBZpYN5HxCEKIMij7fZtUoCCPaLUAzwBs7rsZLhYuYMDAxcIFm/tuVlgpKj1GD1PbTeW07bq/C6WCUoWcn6jO8rDlogopANDAtAHm+s5VY48IqZ+U/b5NKtBMY6L1AjwDEOAZoLTcVhNaT8DayLXIL8sHAKQVpuFk3El6Q9IiEckROB53nNO21G8pLAwt1NQjQuo3Zb9vk3I0kkdINayMrDDeazynbdvdbWrqDZGXQCjAwivclCntG7THBK8JauoRIYSoBgV5hNRAYDtuOpXbabcRnRot42iiSQ48PoAH6Q84bev810GPobc/Qohuo3c5QmrAw9YD/dz7cdq23aPRPE2XW5KL1RGrOW0jW4xEt8bd1NQjQghRHQryCKkh8XQqJ+JOIDU/VcbRmifoXhCabW8Gm19s4L3LGyExIeruktL9FPUT0gvTRdvGPGOs7LFSjT0ihBDVoSCPkBrq49YHHjYVE4T5Qj52Pdilxh7V3JbbW7DgygJkFmWCBYvEvETMDp2t04FefE48fr/7O6dtdqfZcLF0UVOPCCFEtSjII6SG9Bg9iVJnex7uQTG/WMYjNIf4LUsAKOIXYVX4KjX0RjWWXFuCMmGZaLuReSN81ekrNfaIEEJUi4I8QuQwvtV4WBpairYzijJwNPaoGntUvUsJlzj54SrT1TJCV15dwd/xf3PaVvRYATMDMzX1iBBCVI+CPELkYG5ojo9bf8xp235vO1iWVVOPqlYmKMO3176VuV9fTx95pXkq7JHy8YV8LLq6iNPm29AXAS0pryEhpH6hII8QOQW2DwQDRrT9IP0BIlMi1dgj2XY/3I3YrFiZ+8uEZfjs78/AF/JV2Cvl2vNwD55kPuG0rfdfD4ZhZDyCEEJ0EwV5hMjJ3codHzT9gNO2/d52NfVGtqyiLKyLXMdpM9U3lTjuQsIFLLyyUGNHI+WRXZyNtZFrOW3jWo2Dj5OPmnpECCHqQ0EeIbUwvcN0zvaZZ2eQmJuopt5It+7GOuSU5Ii2LQwtcPfTu0j6IgntGrTjHLvzwU5subtF1V1UuPU31iOrOEu0bWZghuV+y9XYI0IIUR8K8giphV7OveBl5yXaFrACjUqn8jjjMXY/2M1pm+c7D45mjjA3NEfwsGA4Wzhz9i+9thSnn51WZTcVKjYrFjvv7+S0fdP5GzQ0b6imHhFCiHpRkEdILTAMI5Ecec/DPSgsK1RTjyqwLItvr30LASsQtTWxaoLp7StGH53MnBA8LBgWhhYVjwOLwHOBuP36tkr7qwgsy+Lbq9zf2dXSFV/6fKnGXhFCiHpRkEdILQV4BsDG2Ea0nVOSoxHJhf+J/wdXXl3htK3ptQZG+kacttb2rbF38F7wGJ6orYhfhHGnxiHhbYLS+xkSEwLvXd51rsAREhMCjyAPXHp5idO+uudqGOsbK6KrhBCilfTV3QFCtJWpgSkmtZmEX6J/EbVtu7cNE9tMVNtKzhJ+CZaELeG0+bv4Y1DTQVKP7+PWBxvf34jZobNFbemF6Rh7cizOjzkPa2NrpfQzJCYEMy/ORImgBACQmJeIwHOBWHl9pVzPmVOcg+T8ZLDgLhrxsPbA0OZDFdpnQgjRNhTkEVIHn7X9DL/e/lV0m/BJ5hNcS7oGfxd/tfRn+73tiM+JF23rMXpY67+2yqBzYpuJeJHzAhujN4raYrNi8cmZT3B0xFEY8gwV2sesoizMuTRHFOC9w4JFUn4SkvLrnqA5tzSXUqYQQuo9ul1LSB24WLpgSPMhnLZtd7eppS9vCt7gx6gfOW1TvKegtX3rah+71G8pRrYYyWkLSwrDV6FfKSy1Cl/IR9C9IPjs8UF+Wb5CzinLm8I3Sj0/IYRoAwryCKkj8QUY5+LPqWROm7g1kWs41SusjKywqNuiKh5RQY/Rw9b+W9GlYRdO+19P/pIIHGvjyqsr6HmgJxZcWcBJ66Is4iuHCSGkPqLbtYTUUbdG3dDWoS0epD8AUH7bccf9Hfiu13cq68P9N/ex/9F+TtvCrgthZ2JX43MY6xvj4NCD6HuoL168fSFqXxu5Fu5W7hjjOUbufr3IeYHF1xZL1JGVxohnhAW+C9CvSb8an//ii4v4IeoHzq1fE30TLPNbJndfCSFE11CQR0gdvUun8uXFinQd+x/tx6Kui2BuaK7052dZFguvLuQsPmhh0wKft/1c7nPZmdghZHgI+gX3Q3Zxtqh95sWZaGzeGH7OfjU6T15pHjZEbcCWu1tQKiiV2G9uYI7+7v0R9ToKyXnJcLZwxjK/ZQjwlK++bNsGbeFq5YpV4auQlJdU6/MQQoguoiCPEAUY1XIUll9fjoyiDADlE/8PPTmEz9vJH2jJ62TcSUQmc2vnrvVfCwOeQa3O19ymOQ58eADDjw0XBWilglJMOD0BF8dehIeth8zHClkhgp8EY2X4SrwueC31mI+8PsIyv2VwMnOqVf/EBXgGUFBHCCFS0Jw8QhTAWN8Yk70nc9q239sOIStU6vMW8YuwNGwpp62/e3/0de9bp/N2b9wdW/pxy5zllOQg4GQAMgozpD4mOjUa/YP7Y8aFGVIDvE5OnXBp3CVs7b9VYQEeIYQQ2SjII0RBPmv7GfT1KgbH47LjcPnlZaU+52+3f0NiXkXNXH09fYXNBQzwDMDibos5bQlvE/DR6Y9QxC8StaXmp2L6+enoG9wX0a+jJc7T0Kwhtg/YjgtjL6CjU0eF9I0QQkj1KMgjREEamjfEcI/hnLZt95SXTiUlPwUbb23ktAW2C6zydqq85vnOw0deH3HaolKjMOP8DBQLirEhagM67e2EQ08OSTzWiGeEuZ3n4takWxjbaiz0GHq7IYQQVaJ3XUIUqHJ9WAC4mHARnjs8lVLubGX4ShTyK2rl2pnYYUGXBQp9DoZh8Mv7v6CXSy9O+4m4E+h5ridWRaxCQVmBxOM+bP4hbk68iaV+S1Wy+IQQQogkCvIIUaBODTvB3dKd0/a64DVmhc5SaKB3K/UWgp8Ec9oWd1uslDJkhjxD7Bu8D562ntUe62XnhZMjT2L/kP1wt3JXeF8IIYTUHAV5hChYbmmuRFsxvxjzLs9DCb9EyiPkI2SFWHSVm+S4tX1rTGozqc7nlsXa2BrBw4Jl3nJlwODH3j/i2oRr8HdVT0k3QgghXBTkEaJglfPLVfa25C267u+Ks8/P1qlUWEhMiMQCh3X+68DT49X6nDXhZuVWZb+ntpvKWXhCCCFEvSjII0TBqiqp9eLtC0w4PQEjj4/Ek8wncp87vzQfK66v4LQNaTZEYs6cssj63aiMGCGEaB4K8ghRsGV+y2Cib1LlMZdfXUaPP3tgwZUFMkf+pPkl+hekFqSKtg15hljTa02t+yovab8blREjhBDNREEeIQoW4BmAzX03w8XCBQwYNDJvhN4uvSXmswlYAYLuBaHjno7YdX8X+EJ+led9lfsKv93+jdP2ZYcvVbrAQfx3c7Fwwea+m6niBCGEaCAmJyen9pODSLXi4uLg4aG4vGVENk2/1v9l/IeFVxYiLClM6n4vey+s918v89brp2c/xfG446JtR1NHRE+OhoWhhVL6Wx1Nv966hK61atH1Vh261spFI3mEqEhr+9Y4NeoU9g/ZDzdLN4n9jzMeY+jRoZh4ZiIS3iZw9oUnhXMCPABY6rdUbQEeIYQQzUdBHiEqxDBMRaLg7kthZmAmccypZ6fQZV8XrIlYg4KyAgiEAomUKe0btJeoREEIIYRUphVBXnZ2NubPn4/OnTvDyckJrVu3xjfffIOsrCzOcTk5OQgMDISrqytcXV0RGBiInJwcNfWaENmM9Y0x17ei5Je4EkEJfor6Ca13tobr7654kP6As3/9e+upTBghhJAqacWnRGpqKlJTU7Fy5UpERERg+/btiIiIwGeffcY57vPPP8eDBw8QEhKCI0eO4MGDB5g2bZqaek1I9RqZN8L2AdtxYcwF+Dj6SOzPKcmRKBvW2akzujbqqqouEkII0VJakbnUy8sLf/75p2i7adOmWLVqFcaOHYvc3FxYWloiNjYWoaGhOHfuHLp06QIA2LhxIwYOHEgTO4nG823ki9BxoTj05BBWXl+JtMI0mccm5SWpsGeEEEK0lVYEedLk5eXByMgIpqamAICoqCiYm5uLAjwA6Nq1K8zMzHDz5k2ZQV5cXJzS+6qK5yDltP1adzbojOCewdj9bDf2Pd8n9ZjXBa815vfUlH7UB3StVYuut+rQta696gawtDLIy8nJwXfffYeJEydCX7/8V3jz5g3s7OzAMIzoOIZhYG9vjzdv3sg8l7JH+GgUUXV06VpvbrUZF3dc5CQ+fsfZwlkjfk9dut6ajq61atH1Vh261sql1jl5a9asgbW1dZU/YWHcnGIFBQUYP348GjZsiFWrVnH2VQ7w3mFZVmo7IZpuVc9VVF2CEEJIral1JG/GjBkYM2ZMlcc4O1fUxMzPz0dAQHlm/eDgYBgbG4v2NWjQABkZGZygjmVZZGZmwsHBQQm9J0S53lWRWBW+Ckl5SXC2cMYyv2VUXYIQQkiNqDXIs7Ozg52dXY2OzcvLQ0BAAFiWxZEjR2Bubs7Z7+vri/z8fERFRYnm5UVFRaGgoIAzT48QbRLgGUBBHSGEkFrRijl5eXl5GDlyJPLy8nDgwAEUFhaisLAQAGBjYwNDQ0O0bNkSffv2xZw5c7Bp0yawLIs5c+ZgwIABdL+fEEIIIfWOVgR59+7dw61btwAAHTt25Ow7ffo0evbsCQDYsWMH/ve//2HkyJEAgIEDB+KHH35QbWcJIYQQQjSAVgR5PXv2rFHlChsbGwQFBamgR4QQQgghmk0rKl4QQgghhBD5UJBHCCGEEKKDKMgjhBBCCNFBFOQRQgghhOggCvIIIYQQQnQQBXmEEEIIITqIgjxCCCGEEB3E5OTksOruBCGEEEIIUSwaySOEEEII0UEU5BFCCCGE6CAK8gghhBBCdBAFeYQQQgghOoiCPEIIIYQQHURBHiGEEEKIDqIgT0l27tyJtm3bwtHREf7+/oiIiFB3l3TSunXrYG1tzflp0aKFurulM8LDwzFu3Di0atUK1tbWOHDgAGc/y7JYt24dPD094eTkhMGDB+PJkydq6q12q+5az5gxQ+K13rdvXzX1Vrtt2LABvXv3houLC5o1a4axY8fi8ePHnGPota04Nbne9PpWDgrylODYsWNYuHAh5s6di2vXrsHX1xcBAQFITExUd9d0koeHB2JjY0U/FFArTkFBAby8vLB+/XqYmJhI7N+0aRO2bNmC77//Hv/++y8cHBwwYsQI5OXlqaG32q26aw0A7733Hue1HhISouJe6obr16/js88+w/nz53Hq1Cno6+tj+PDhyM7OFh1Dr23Fqcn1Buj1rQyUDFkJ3n//fbRu3RqbN28Wtfn4+GDYsGFYvny5Gnume9atW4dTp04hMjJS3V3ReY0bN8YPP/yACRMmACgf6fD09MTUqVMxb948AEBRURE8PDywevVqfPrpp+rsrlYTv9ZA+UhHVlYWgoOD1dgz3ZSfnw9XV1ccOHAAAwcOpNe2kolfb4Be38pCI3kKVlpainv37qFPnz6c9j59+uDmzZtq6pVuS0hIQKtWrdC2bVtMmTIFCQkJ6u5SvfDy5UukpaVxXusmJibo3r07vdaVJDIyEs2bN0fHjh0xe/ZspKenq7tLOiE/Px9CoRDW1tYA6LWtbOLX+x16fSuevro7oGsyMzMhEAjg4ODAaXdwcMCbN2/U1Cvd1alTJ2zduhUeHh7IyMjAjz/+iP79++PGjRuwtbVVd/d0WlpaGgBIfa2npqaqo0s6rW/fvvjwww/h5uaGV69eYc2aNRg6dCiuXLkCIyMjdXdPqy1cuBDe3t7w9fUFQK9tZRO/3gC9vpWFgjwlYRiGs82yrEQbqbt+/fpxtjt16oT27dvj4MGDmDlzppp6Vb/Qa101Ro0aJfp369at0b59e3h7e+P8+fMYOnSoGnum3b799lvcuHED586dA4/H4+yj17biybre9PpWDrpdq2B2dnbg8XgSo3YZGRkS3wqJ4pmbm8PT0xPx8fHq7orOc3R0BAB6ratJw4YN0ahRI3qt18GiRYtw9OhRnDp1Cu7u7qJ2em0rh6zrLQ29vhWDgjwFMzQ0RPv27XH58mVO++XLl9GlSxc19ar+KC4uRlxcnOhNmiiPm5sbHB0dOa/14uJiREZG0mtdBTIzM5Gamkqv9Vr63//+hyNHjuDUqVMSaZfota14VV1vaej1rRi8hQsXrlB3J3SNhYUF1q1bBycnJxgbG+PHH39EREQEfvvtN1hZWam7ezplyZIlMDQ0hFAoxLNnzzB//nzEx8dj48aNdK0VID8/HzExMUhLS8P+/fvh5eUFS0tLlJaWwsrKCgKBABs3bkTz5s0hEAiwePFipKWl4ZdffqF5NHKq6lrzeDysWrUK5ubm4PP5ePjwIWbNmgWBQIAff/yRrrWc5s2bh0OHDmHPnj1wdnZGQUEBCgoKAJR/UWcYhl7bClTd9c7Pz6fXt5JQChUl2blzJzZt2oS0tDS0atUKa9euhZ+fn7q7pXOmTJmCiIiI/2vv/mOqqv84jj+BLwmpcMq7yw/NCwJiCpH36mpDNtQGNgQquDJGxq9R0R+RKTO6btmskIxAB2sDEiSx+WMalYWa02W35hJiuUHT0ZKxhYl5oesIBe73j8adJ37o1Xu/fLu9H9v9g8/9nM/nfT67Y6+dzzn3cvXqVTQaDcuWLcNkMrFo0aLpLs0tnDlzhuTk5HHtmZmZfPDBB9hsNrZv305DQwMWiwWDwcB7773H4sWLp6Haf7ap1vr9998nKyuLH3/8kf7+fgICAoiLi8NkMjFv3rxpqPaf7e9PdY7ZvHkzJSUlAPLZdqLbrffg4KB8vl1EQp4QQgghhBuSe/KEEEIIIdyQhDwhhBBCCDckIU8IIYQQwg1JyBNCCCGEcEMS8oQQQggh3JCEPCGEEEIINyQhTwjxr1BaWoqiKPYfn3eFwsJCoqOjXTa+EEI4QkKeEOKeNDU1oSgKiqLw9ddfT9hn1apVKIrC8uXLXVqL1WqltLSUM2fOuHQeV+jo6KC0tJRLly45ddx/8poIIe6NhDwhhFP4+Phw8ODBce1dXV20tbXh4+Pj8hquX79OWVkZ33zzjcvnmsiuXbs4d+7cXR3b2dlJWVkZ3d3dTq1putdECDF9JOQJIZwiISGB5uZmhoaGVO379+9Hq9WydOnSaarsf8fb2/tf8zubIyMj3LhxY7rLEEJMQUKeEMIp0tLSsFqttLS0qNoPHTrEM888g6fn+H83o6OjVFZWYjAY0Gq1PPzwwxQXF9Pf36/ql5SUxPLly+nq6iItLY3g4GAiIiJ48803GR0dBeDSpUtERkYCUFZWZt9CLiwsVI1ltVrZsGEDoaGhzJ07l+zsbH7//XdVn/b2doxGI2FhYQQGBhITE8MLL7xg/1H1yUx0T56iKGzYsIETJ04QFxdHQEAAer2eQ4cO2fs0NTWRn58PQHJysr32pqYme58vvviC1atXExQUhE6nIysriwsXLkxZz52sSW9vL0VFRSxatAitVoter2fnzp3YbDbVOIqiUFFRQV1dHXq9Hq1Wy9mzZ1Xv7dmzh6VLlxIUFERycjK//PILAFVVVURHRxMYGEhaWhq9vb1T1i2EcI7/THcBQgj3EBwcTGxsLAcPHiQ1NRWAc+fO8fPPP7Nu3TrOnz8/7piNGzdSX1/Pk08+yYsvvkhnZycffvghra2tHDt2DG9vb3vfgYEBUlNTWbNmDUlJSXz11VdUVFSg0+nIyclBo9GwY8cOiouLWbt2LcnJyQCEhoaq5szPzycgIACTyURXVxc1NTV4e3tTV1cHQF9fH08//TRz5syhqKgIRVHo6enhyy+/5Pr168ycOdPhtfn+++85evQoubm5rF+/nsbGRp5//nmio6OJjIwkNjaWgoICamtr2bhxIwsXLgTgscceA/4KygUFBURFRWEymRgYGKCmpoaEhAROnz5NSEjIhPPebk2uXLnCE088wfDwMNnZ2QQGBvLdd9/xxhtv8Ouvv7J9+3bVeAcOHMBqtZKTk8OsWbMIDAy0v3fkyBEGBwfJy8vDarWyc+dOsrKySEtL49NPP+Wll16it7eXqqoqXn31Vfbt2+fwOgohHCMhTwjhNEajkU2bNmGxWFAUhf379xMWFoZerx/Xt6Ojg/r6etatW0dNTY29PSIigpKSEj7++GOee+45e/vly5fZtWuXvS0vL48VK1awZ88ecnJymDlzJikpKRQXF7NkyRIyMjImrHHhwoWq+Ww2G7W1tZSXl+Pv78/Zs2e5du0ahw8fVm0xv/7663e9Lj/99BNms9l+Ve2pp54iKiqKvXv3sm3bNkJCQnj88cepra0lPj6euLg4+7E3b97EZDIRHh5OS0uLPWQmJSWxcuVK3nnnHdX53Op2a/LWW28xNDSE2WxGq9UCkJubS2BgIFVVVRQWFqLT6ez9u7u7aW1tVYW7sQdFenp6aGtrQ1EUADw9PSktLeXPP//k22+/tW9jW61Wdu/eTV9fHxqN5q7XVAhxe7JdK4RwmtTUVDw8PGhubmZ4eJhPPvkEo9E4Yd9jx44B8PLLL6va8/Ly8PPzs78/xsfHh6ysLFVbbGysfUvwTo1ti946xsjICD09PQDMnj0bgJaWFm7evOnQ2JOJi4uzBzwArVZLRETEHdXe3t7O5cuXyc/PV11FjImJIT4+nuPHj6u2Vu+UzWajubmZxMREvLy8uHr1qv21evVqRkdHMZvNqmOSkpJUAe9WKSkp9oAHsGzZMgDS09NV9ykaDAZsNpvTnyIWQownIU8I4TT+/v4kJCRw4MABTp06xZUrVyYNed3d3Xh4eBAREaFqnzFjBjqdbtxTpsHBwXh5eanaFEXh2rVrDtX40EMPjRsDsI8TFxdHcnIyZWVlLFiwgIyMDBoaGrBarQ7NM9WcY/PeSe1j6zC2hXuryMhILBYLAwMDDtfU19eHxWJh7969hIWFqV5j2+19fX2qYybbFgaYN2+e6m8/Pz8A5s6dO2G7xWJxuGYhhGNku1YI4VRGo5Hs7Gzgr6s2YWFhDo9hs9nw8PBQtf094N2tycYZuxrm4eHBRx99RGtrKy0tLZw+fZpXXnmF8vJyTp48ad/WdOacd+tejh97YCU9PZ1nn312wj4LFixQ/e3r6zvpeJOdo6vOXQhxexLyhBBOlZiYiJ+fH2azedyN+7eaP38+NpuNixcvEhUVZW+/ceMG3d3dqvvS7tTfg+G9MBgMGAwGTCYTJ06cwGg00tjYyKZNm5w2x60mq33+/PkAXLhwgVWrVqneu3jxIoqi2K+OOTKuRqPBz8+P4eFh4uPj765oIcT/NdmuFUI41YwZMygvL2fz5s2kp6dP2i8hIQGA6upqVXt9fT0DAwMkJiY6PPf9998P3NtWoMViGXeVKSYm5p7HvZ3Jan/00UcJCAhg9+7dDA4O2tvPnz/PqVOnSEhImDLcTjaul5cXKSkpfP7557S3t487rr+/32n3JAohpodcyRNCON1U4W7MkiVLyM3NtYe6lStX0tnZSX19PXq9nszMTIfnnTVrFhERERw+fJjw8HAefPBBdDqd/SGAO7Fv3z7q6upYu3YtoaGhDA4O0tTUhJeXl/1eNVeIiYnB09OTiooK+vv78fX1xWAwEBISwttvv01BQQGJiYlkZGTYv0LFz8/vtk/9TrUmW7duxWw2s2bNGtavX8/ixYv5448/6Ojo4LPPPqOtrY2AgACXnbMQwrUk5Akhpk15eTk6nY7GxkaOHz/OnDlzyM/PZ8uWLarvyHNEdXU1JSUlbNmyhaGhITIzMx0KebGxsfzwww8cOXKE3377jdmzZ/PII4/w7rvvuvS3d4OCgqisrKSyspKioiJGRkaorq4mJCSE9PR0fH19KS8vZ9u2bdx3332sWLGCrVu3TvkwxJjJ1kSj0XDy5El27NjB0aNHaWhowN/fn/DwcF577TUeeOABl52vEML1PCwWi9z9KoQQQgjhZuSePCGEEEIINyQhTwghhBDCDUnIE0IIIYRwQxLyhBBCCCHckIQ8IYQQQgg3JCFPCCGEEMINScgTQgghhHBDEvKEEEIIIdyQhDwhhBBCCDf0X+a5KIDp/YzkAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 648x432 with 1 Axes>"
       ]
@@ -315,8 +343,8 @@
     "def plot2(states, dates):\n",
     "    N = len(dates)\n",
     "    plt.plot(range(N), [0] * N, label='Net zero', color='darkorange')\n",
-    "    plt.plot(range(N), [-margin(states, date) for date in dates], 'o-', label='Margin')\n",
-    "    plt.plot(range(N), [net_usa[date] for date in dates], 'D-', label='Country-wide Net')\n",
+    "    plt.plot(range(N), [-margin(states, date) for date in dates], 'D-', label='Margin')\n",
+    "    plt.plot(range(N), [net_usa[date] for date in dates], 'go-', label='Country-wide Net')\n",
     "    labels('Months into term', 'Net popularity')\n",
     "    \n",
     "plot2(states, dates)"
@@ -334,7 +362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -381,9 +409,12 @@
     }
    ],
    "source": [
-    "def header(head): return head + '\\n' + '-'.join('|' * head.count('|'))\n",
+    "def header(head) -> str: return head + '\\n' + '-'.join('|' * head.count('|'))\n",
     "\n",
-    "def monthly(states, dates=reversed(dates)):\n",
+    "def markdown(fn) -> str: return lambda *args: display(Markdown('\\n'.join(fn(*args))))\n",
+    "\n",
+    "@markdown\n",
+    "def by_month(states, dates=reversed(dates)):\n",
     "    yield header('|Month|EVs|Margin|Country|Undecided|')\n",
     "    for date in dates:\n",
     "        month = date.replace('1-', '').replace('-', ' 20')\n",
@@ -391,7 +422,7 @@
     "               f'|{sum(s.ev * undecided(s, date) for s in states) / 538:.0f}% '\n",
     "               f'({sum(undecided(s, date) > 5 for s in states)})|')\n",
     "        \n",
-    "md(monthly(states))"
+    "by_month(states)"
    ]
   },
   {
@@ -402,14 +433,14 @@
     "\n",
     "Below is each state sorted by net approval, with the state's  maximum expected movement, and electoral vote allotment, followed by the cumulative running total of electoral votes and the percentages of  approval, disapprovalsaproval, and undecided, and finally the standard deviation of the net approval over the last 12 months. By going down the **Total** column, you can see what it takes to win. \n",
     "\n",
-    "The **bold state names**  are the **swing states**, which I define as states in which the absolute value of net approval is less than two standard deviations of the net approval over time, plus a fifth of the undecided voters. The idea is that if we are just dealing with random sampling variation, you could expect future approval to be within two standard deviations 95% of the time, and if the undecideds split 60/40, then a candidate could get a net fifth of them. So it would be very unusual for the non-bold states to flip, unless some events change perception of the candidates.\n",
+    "The **CAPITALIZED bold state names**  are the **swing states**, which I define as states in which the absolute value of net approval is less than two standard deviations of the net approval over time, plus a fifth of the undecided voters. The idea is that if we are just dealing with random sampling variation, you could expect future approval to be within two standard deviations 95% of the time, and if the undecideds split 60/40, then a candidate could get a net fifth of them. So it would be very unusual for the non-bold states to flip, unless some events change perception of the candidates.\n",
     "\n",
     "This analysis says that if we consider all and only the bold swing states to be in play, then the total electoral votes for Trump could be anywhere in the range of 76 (if he lost them all) to 253 (if he won them all). It would take winning every single one of the swing states plus a three-standard deviation swing in Pennsylvania for Trump to reach 273.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -428,21 +459,21 @@
        "|South Carolina|+10%|5%|9|63|53%|43%|4%|2.1%|\n",
        "|Oklahoma|+9%|8%|7|70|52%|43%|5%|3.5%|\n",
        "|Arkansas|+8%|7%|6|76|52%|44%|4%|3.0%|\n",
-       "|**Missouri**|**+6%**|**7%**|10|86|51%|45%|4%|3.0%|\n",
-       "|**South Dakota**|**+6%**|**10%**|3|89|51%|45%|4%|4.5%|\n",
-       "|**Indiana**|**+3%**|**5%**|11|100|49%|46%|5%|2.0%|\n",
-       "|**Montana**|**+3%**|**7%**|3|103|50%|47%|3%|3.4%|\n",
-       "|**Texas**|**+3%**|**6%**|38|141|49%|46%|5%|2.7%|\n",
-       "|**Nebraska**|**+2%**|**6%**|5|146|49%|47%|4%|2.8%|\n",
-       "|**Utah**|**+2%**|**8%**|6|152|49%|47%|4%|3.6%|\n",
-       "|**Georgia**|**+1%**|**7%**|16|168|48%|47%|5%|2.9%|\n",
-       "|**Kansas**|**+1%**|**7%**|6|174|48%|47%|5%|3.0%|\n",
-       "|**North Dakota**|**+1%**|**8%**|3|177|48%|47%|5%|3.3%|\n",
-       "|**Alaska**|**+0%**|**12%**|3|180|47%|47%|6%|5.5%|\n",
-       "|**Florida**|**+0%**|**7%**|29|209|48%|48%|4%|3.1%|\n",
-       "|**North Carolina**|**-4%**|**6%**|15|224|46%|50%|4%|2.4%|\n",
-       "|**Ohio**|**-4%**|**6%**|18|242|46%|50%|4%|2.4%|\n",
-       "|**Arizona**|**-6%**|**8%**|11|253|45%|51%|4%|3.4%|\n",
+       "|**MISSOURI**|**+6%**|**7%**|10|86|51%|45%|4%|3.0%|\n",
+       "|**SOUTH DAKOTA**|**+6%**|**10%**|3|89|51%|45%|4%|4.5%|\n",
+       "|**INDIANA**|**+3%**|**5%**|11|100|49%|46%|5%|2.0%|\n",
+       "|**MONTANA**|**+3%**|**7%**|3|103|50%|47%|3%|3.4%|\n",
+       "|**TEXAS**|**+3%**|**6%**|38|141|49%|46%|5%|2.7%|\n",
+       "|**NEBRASKA**|**+2%**|**6%**|5|146|49%|47%|4%|2.8%|\n",
+       "|**UTAH**|**+2%**|**8%**|6|152|49%|47%|4%|3.6%|\n",
+       "|**GEORGIA**|**+1%**|**7%**|16|168|48%|47%|5%|2.9%|\n",
+       "|**KANSAS**|**+1%**|**7%**|6|174|48%|47%|5%|3.0%|\n",
+       "|**NORTH DAKOTA**|**+1%**|**8%**|3|177|48%|47%|5%|3.3%|\n",
+       "|**ALASKA**|**+0%**|**12%**|3|180|47%|47%|6%|5.5%|\n",
+       "|**FLORIDA**|**+0%**|**7%**|29|209|48%|48%|4%|3.1%|\n",
+       "|**NORTH CAROLINA**|**-4%**|**6%**|15|224|46%|50%|4%|2.4%|\n",
+       "|**OHIO**|**-4%**|**6%**|18|242|46%|50%|4%|2.4%|\n",
+       "|**ARIZONA**|**-6%**|**8%**|11|253|45%|51%|4%|3.4%|\n",
        "|Virginia|-6%|4%|13|266|45%|51%|4%|1.8%|\n",
        "|Pennsylvania|-7%|3%|20|286|45%|52%|3%|1.4%|\n",
        "|Maine|-9%|8%|4|290|44%|53%|3%|3.9%|\n",
@@ -478,90 +509,93 @@
     }
    ],
    "source": [
+    "@markdown\n",
     "def by_state(states, d=now):\n",
     "    total = 0\n",
     "    yield header('|State|Net|Move|EV|Total|+|-|?|𝝈|')\n",
     "    for s in sorted(states, key=net, reverse=True):\n",
     "        total += s.ev\n",
-    "        b = '**' if is_swing(s) else ''\n",
-    "        yield (f'|{b}{s.name}{b}|{b}{net(s):+d}%{b}|{b}{movement(s):.0f}%{b}|{s.ev}|{total}'\n",
+    "        b = '**' * is_swing(s)\n",
+    "        yield (f'|{swing_name(s)}|{b}{net(s):+d}%{b}|{b}{movement(s):.0f}%{b}|{s.ev}|{total}'\n",
     "               f'|{s.approvals[d]}%|{s.disapprovals[d]}%|{undecided(s, now)}%|{𝝈(s):3.1f}%|')\n",
+    "        \n",
+    "def swing_name(s) -> str: return '**' + s.name.upper() + '**' if is_swing(s) else s.name\n",
     "\n",
-    "md(by_state(states))"
+    "by_state(states)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Popularity Above Replacement President\n",
+    "# Popularity Above Replacement President (PARP) table\n",
     "\n",
     "Fivethirtyeight is a combination sports/politics site, and it has a lot of statistics about sports players and how much better they are than the average replacement player. Given that, they [decided](https://fivethirtyeight.com/features/the-states-where-trump-is-more-and-less-popular-than-he-should-be/) to rate the president's approval versus each state's overall approval of his party (in recent elections), which is a way of rating the president's performance versus an average replacement candidate from the same party. I'll duplicate that work and keep it up to date.\n",
     "\n",
-    "There are only four states where Trump is exceeding a replacement Republican (i.e., has a positive PARP): one deep-red, deep South state, Mississippi, and three deep-blue coastal states, Rhode Island, Hawaii, and Massachussetts."
+    "There are only four states where Trump is exceeding a replacement Republican (i.e., has a positive PARP): one deep-red southern state, Mississippi, and three deep-blue coastal states, Rhode Island, Hawaii, and Massachussetts. Again, the swing states are **bold CAPITALIZED**."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/markdown": [
-       "|State|PARP|Lean|Net|EV|\n",
+       "|State|PARP|Net|Lean|EV|\n",
        "|-|-|-|-|-|\n",
-       "|Rhode Island|+6|-26|-20|4|\n",
-       "|Hawaii|+3|-36|-33|4|\n",
-       "|Massachusetts|+2|-29|-27|11|\n",
-       "|Mississippi|+2|+15|+17|6|\n",
+       "|Rhode Island|+6|-20|-26|4|\n",
+       "|Hawaii|+3|-33|-36|4|\n",
+       "|Massachusetts|+2|-27|-29|11|\n",
+       "|Mississippi|+2|+17|+15|6|\n",
        "|Alabama|+0|+27|+27|9|\n",
        "|Louisiana|+0|+17|+17|8|\n",
        "|New York|+0|-22|-22|29|\n",
-       "|Delaware|-1|-14|-15|3|\n",
-       "|California|-4|-24|-28|55|\n",
-       "|Maine|-4|-5|-9|4|\n",
-       "|Florida|-5|+5|+0|29|\n",
-       "|New Jersey|-5|-13|-18|14|\n",
-       "|Virginia|-6|+0|-6|13|\n",
-       "|Illinois|-7|-13|-20|20|\n",
-       "|South Carolina|-7|+17|+10|9|\n",
-       "|Maryland|-8|-23|-31|10|\n",
-       "|New Mexico|-8|-7|-15|5|\n",
-       "|Pennsylvania|-8|+1|-7|20|\n",
-       "|Kentucky|-9|+23|+14|8|\n",
-       "|North Carolina|-9|+5|-4|15|\n",
-       "|Vermont|-10|-24|-34|3|\n",
-       "|Georgia|-11|+12|+1|16|\n",
-       "|Michigan|-11|-1|-12|16|\n",
-       "|Ohio|-11|+7|-4|18|\n",
-       "|Nevada|-12|+1|-11|6|\n",
-       "|Tennessee|-12|+28|+16|11|\n",
-       "|West Virginia|-12|+30|+18|5|\n",
-       "|Colorado|-13|-1|-14|9|\n",
-       "|Connecticut|-13|-11|-24|7|\n",
-       "|Missouri|-13|+19|+6|10|\n",
-       "|Oregon|-13|-9|-22|7|\n",
-       "|Minnesota|-14|-2|-16|10|\n",
-       "|Texas|-14|+17|+3|38|\n",
-       "|Wisconsin|-14|+1|-13|10|\n",
-       "|Alaska|-15|+15|+0|3|\n",
-       "|Arizona|-15|+9|-6|11|\n",
-       "|Idaho|-15|+35|+20|4|\n",
-       "|Indiana|-15|+18|+3|11|\n",
-       "|Montana|-15|+18|+3|3|\n",
-       "|Arkansas|-16|+24|+8|6|\n",
-       "|Iowa|-18|+6|-12|6|\n",
-       "|Washington|-18|-12|-30|12|\n",
-       "|District of Columbia|-19|-43|-62|3|\n",
-       "|New Hampshire|-21|+2|-19|4|\n",
-       "|Kansas|-22|+23|+1|6|\n",
-       "|Nebraska|-22|+24|+2|5|\n",
-       "|Oklahoma|-25|+34|+9|7|\n",
-       "|South Dakota|-25|+31|+6|3|\n",
-       "|Wyoming|-25|+47|+22|3|\n",
-       "|Utah|-29|+31|+2|6|\n",
-       "|North Dakota|-32|+33|+1|3|"
+       "|Delaware|-1|-15|-14|3|\n",
+       "|California|-4|-28|-24|55|\n",
+       "|Maine|-4|-9|-5|4|\n",
+       "|**FLORIDA**|-5|+0|+5|29|\n",
+       "|New Jersey|-5|-18|-13|14|\n",
+       "|Virginia|-6|-6|+0|13|\n",
+       "|Illinois|-7|-20|-13|20|\n",
+       "|South Carolina|-7|+10|+17|9|\n",
+       "|Maryland|-8|-31|-23|10|\n",
+       "|New Mexico|-8|-15|-7|5|\n",
+       "|Pennsylvania|-8|-7|+1|20|\n",
+       "|Kentucky|-9|+14|+23|8|\n",
+       "|**NORTH CAROLINA**|-9|-4|+5|15|\n",
+       "|Vermont|-10|-34|-24|3|\n",
+       "|**GEORGIA**|-11|+1|+12|16|\n",
+       "|Michigan|-11|-12|-1|16|\n",
+       "|**OHIO**|-11|-4|+7|18|\n",
+       "|Nevada|-12|-11|+1|6|\n",
+       "|Tennessee|-12|+16|+28|11|\n",
+       "|West Virginia|-12|+18|+30|5|\n",
+       "|Colorado|-13|-14|-1|9|\n",
+       "|Connecticut|-13|-24|-11|7|\n",
+       "|**MISSOURI**|-13|+6|+19|10|\n",
+       "|Oregon|-13|-22|-9|7|\n",
+       "|Minnesota|-14|-16|-2|10|\n",
+       "|**TEXAS**|-14|+3|+17|38|\n",
+       "|Wisconsin|-14|-13|+1|10|\n",
+       "|**ALASKA**|-15|+0|+15|3|\n",
+       "|**ARIZONA**|-15|-6|+9|11|\n",
+       "|Idaho|-15|+20|+35|4|\n",
+       "|**INDIANA**|-15|+3|+18|11|\n",
+       "|**MONTANA**|-15|+3|+18|3|\n",
+       "|Arkansas|-16|+8|+24|6|\n",
+       "|Iowa|-18|-12|+6|6|\n",
+       "|Washington|-18|-30|-12|12|\n",
+       "|District of Columbia|-19|-62|-43|3|\n",
+       "|New Hampshire|-21|-19|+2|4|\n",
+       "|**KANSAS**|-22|+1|+23|6|\n",
+       "|**NEBRASKA**|-22|+2|+24|5|\n",
+       "|Oklahoma|-25|+9|+34|7|\n",
+       "|**SOUTH DAKOTA**|-25|+6|+31|3|\n",
+       "|Wyoming|-25|+22|+47|3|\n",
+       "|**UTAH**|-29|+2|+31|6|\n",
+       "|**NORTH DAKOTA**|-32|+1|+33|3|"
       ],
       "text/plain": [
        "<IPython.core.display.Markdown object>"
@@ -572,14 +606,15 @@
     }
    ],
    "source": [
-    "def parp(state): return net(state) - state.lean \n",
+    "def parp(state) -> int: return net(state) - state.lean \n",
     "\n",
+    "@markdown\n",
     "def by_parp(states, d=now):\n",
-    "    yield header('|State|PARP|Lean|Net|EV|')\n",
+    "    yield header('|State|PARP|Net|Lean|EV|')\n",
     "    for s in sorted(states, key=parp, reverse=True):\n",
-    "        yield (f'|{s.name}|{parp(s):+d}|{s.lean:+d}|{net(s):+d}|{s.ev}|')\n",
+    "        yield (f'|{swing_name(s)}|{parp(s):+d}|{net(s):+d}|{s.lean:+d}|{s.ev}|')\n",
     "\n",
-    "md(by_parp(states))"
+    "by_parp(states)"
    ]
   }
  ],
diff --git a/ipynb/evs.html b/ipynb/evs.html
new file mode 100644
index 0000000..c3d4596
--- /dev/null
+++ b/ipynb/evs.html
@@ -0,0 +1,1283 @@
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+	<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
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+	<title>Tracking Trump: The President’s Standing Across America</title>
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+<meta name="description" content="On a daily basis, Morning Consult is surveying over 5,000 registered voters across the United States on President Trump. Each month, we’ll update this page with the latest survey data, providing a clear picture of Trump’s approval and re-election prospects."/>
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+					<ul>
+						<li id="menu-item-0" data-index="0"><span>Approval by State</span></li>
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+					</ul>
+					<div class="primaries-sidebar__see-more">
+						<span class="primaries-sidebar__see-more_title">More political intelligence:</span>
+<ul>
+        
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+        </div>
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+    </li>
+</ul> 					</div>
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+								Go deeper							</div>
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+								Download the Latest 2020 Political Intelligence Report							</div>
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+								Featuring insights on Trump's standing as well as the state of the Democratic primary. Sign-up to always get this data first:							</div>
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+								
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+                        <div class='gform_body'><ul id='gform_fields_100' class='gform_fields top_label form_sublabel_below description_below'><li id='field_100_1'  class='gfield gfield_contains_required field_sublabel_below field_description_below gfield_visibility_visible' ><label class='gfield_label' for='input_100_1' >First Name<span class='gfield_required'>*</span></label><div class='ginput_container ginput_container_text'><input name='input_1' id='input_100_1' type='text' value='' class='medium'   tabindex='1' placeholder='First Name' aria-required="true" aria-invalid="false" /></div></li><li id='field_100_2'  class='gfield gfield_contains_required field_sublabel_below field_description_below gfield_visibility_visible' ><label class='gfield_label' for='input_100_2' >Last Name<span class='gfield_required'>*</span></label><div class='ginput_container ginput_container_text'><input name='input_2' id='input_100_2' type='text' value='' class='medium'   tabindex='2' placeholder='Last Name' aria-required="true" aria-invalid="false" /></div></li><li id='field_100_3'  class='gfield gfield_contains_required field_sublabel_below field_description_below gfield_visibility_visible' ><label class='gfield_label' for='input_100_3' >Email<span class='gfield_required'>*</span></label><div class='ginput_container ginput_container_email'>
+                            <input name='input_3' id='input_100_3' type='email' value='' class='medium' tabindex='3'  placeholder='Email' aria-required="true" aria-invalid="false" />
+                        </div></li><li id='field_100_5'  class='gfield gfield_contains_required field_sublabel_below field_description_below gfield_visibility_visible' ><label class='gfield_label' for='input_100_5' >Job Title<span class='gfield_required'>*</span></label><div class='ginput_container ginput_container_text'><input name='input_5' id='input_100_5' type='text' value='' class='medium'   tabindex='4' placeholder='Job Title' aria-required="true" aria-invalid="false" /></div></li><li id='field_100_6'  class='gfield gfield_contains_required field_sublabel_below field_description_below gfield_visibility_visible' ><label class='gfield_label' for='input_100_6' >Company<span class='gfield_required'>*</span></label><div class='ginput_container ginput_container_text'><input name='input_6' id='input_100_6' type='text' value='' class='medium'   tabindex='5' placeholder='Company/Organization' aria-required="true" aria-invalid="false" /></div></li>
+                            </ul></div>
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+                <script type='text/javascript'>jQuery(document).ready(function($){gformInitSpinner( 100, 'https://morningconsult.com/wp-content/themes/morning-consult/img/ajax-loader.png' );jQuery('#gform_ajax_frame_100').on('load',function(){var contents = jQuery(this).contents().find('*').html();var is_postback = contents.indexOf('GF_AJAX_POSTBACK') >= 0;if(!is_postback){return;}var form_content = jQuery(this).contents().find('#gform_wrapper_100');var is_confirmation = jQuery(this).contents().find('#gform_confirmation_wrapper_100').length > 0;var is_redirect = contents.indexOf('gformRedirect(){') >= 0;var is_form = form_content.length > 0 && ! is_redirect && ! is_confirmation;if(is_form){jQuery('#gform_wrapper_100').html(form_content.html());if(form_content.hasClass('gform_validation_error')){jQuery('#gform_wrapper_100').addClass('gform_validation_error');} else {jQuery('#gform_wrapper_100').removeClass('gform_validation_error');}setTimeout( function() { /* delay the scroll by 50 milliseconds to fix a bug in chrome */  }, 50 );if(window['gformInitDatepicker']) {gformInitDatepicker();}if(window['gformInitPriceFields']) {gformInitPriceFields();}var current_page = jQuery('#gform_source_page_number_100').val();gformInitSpinner( 100, 'https://morningconsult.com/wp-content/themes/morning-consult/img/ajax-loader.png' );jQuery(document).trigger('gform_page_loaded', [100, current_page]);window['gf_submitting_100'] = false;}else if(!is_redirect){var confirmation_content = jQuery(this).contents().find('.GF_AJAX_POSTBACK').html();if(!confirmation_content){confirmation_content = contents;}setTimeout(function(){jQuery('#gform_wrapper_100').replaceWith(confirmation_content);jQuery(document).trigger('gform_confirmation_loaded', [100]);window['gf_submitting_100'] = false;}, 50);}else{jQuery('#gform_100').append(contents);if(window['gformRedirect']) {gformRedirect();}}jQuery(document).trigger('gform_post_render', [100, current_page]);} );} );</script>							</div>
+						</div>
+					</div>
+				</div>
+			</div>
+			<div class="column primaries-container">
+				<section class="section section__headline">
+					<div class="headline">
+						<img class="headline__image" src="https://morningconsult.com/wp-content/themes/morning-consult/img/misc/trump.png">
+						<div class="headline__content">
+							<div class="headline__subtitle weight__bold">
+								Political Intelligence							</div>
+							<div class="headline__title h2-condensed weight__bold">
+								Tracking Trump: The President’s Standing Across America							</div>
+							<div class="headline__copy body-copy__4">
+								<p>On a daily basis, Morning Consult is surveying over 5,000 registered voters across the United States on President Trump. Each month, we’ll update this page with the latest survey data, providing a clear picture of Trump’s approval and re-election prospects.</p>
+<p>&nbsp;</p>
+<p>To receive more data on Trump and 2020, <a href="https://morningconsult.com/form/political-intel-briefing/">sign up here</a>.</p>
+							</div>
+						</div>
+					</div>
+				</section>
+				<section class="section section__map story">
+					<a name="section-0"></a>
+					<div class="headline-title">
+						Approval by State					</div>
+					<div class="headline-copy body-copy__4">
+						<p>Use the slider to track how Trump&#8217;s net approval has shifted over time.</p>
+					</div>
+					<div class="map">
+						
+<div id="mc-highcharts-map-wrapper" class="mc-highcharts-map">
+	<div id="mc-highcharts-map-container"></div>
+		<div class="mc-highcharts-map__slider">
+		<span class="mc-highcharts-map__slider__value"></span>
+		<input type="range" class="mc-highcharts-map__slider__input">
+		<div class="mc-highcharts-map__slider__desc">Slide left and right to change months</div>
+	</div>
+	</div>						<div class="ranking__expand-container">
+							<div class="ranking__social">
+	<a href="" target="_blank">
+		<i class="mc-icon mc-icon__linkedin mc-icon__sm"></i>
+	</a>
+	<a href="https://www.facebook.com/dialog/share?app_id=135331423885334&display=popup&href=https://morningconsult.com/tracking-trump/#section-1" target="_blank">
+		<i class="mc-icon mc-icon__facebook mc-icon__sm"></i>
+	</a>
+	<a href="https://twitter.com/intent/tweet?text=Explore+this+interactive+report+tracking+Trump%27s+standing+across+America%2C+via+%40MorningConsult%3A&url=https://morningconsult.com/tracking-trump/#section-1" target="_blank">
+		<i class="mc-icon mc-icon__twitter mc-icon__sm"></i>
+	</a>
+</div>						</div>
+					</div>
+				</section>
+				<section class="section section__trend story">
+					<a name="section-1"></a>
+					<div class="headline-title">
+						State Trends					</div>
+					<div class="headline-copy body-copy__4">
+						<p>Select a state to view the trend data.</p>
+					</div>
+					<div class="trendline">
+						<div class="trendline__select select2-event">
+							<select name="trendline">
+						    <option value="1">Alabama</option>
+						    <option value="2">Alaska</option>
+						    <option value="3">Arizona</option>
+						    <option value="4">Arkansas</option>
+						    <option value="5">California</option>
+						    <option value="6">Colorado</option>
+						    <option value="7">Connecticut</option>
+						    <option value="8">Delaware</option>
+						    <option value="9">District of Columbia</option>
+						    <option value="10">Florida</option>
+						    <option value="11">Georgia</option>
+						    <option value="12">Hawaii</option>
+						    <option value="13">Idaho</option>
+						    <option value="14">Illinois</option>
+						    <option value="15">Indiana</option>
+						    <option value="16">Iowa</option>
+						    <option value="17">Kansas</option>
+						    <option value="18">Kentucky</option>
+						    <option value="19">Louisiana</option>
+						    <option value="20">Maine</option>
+						    <option value="21">Maryland</option>
+						    <option value="22">Massachusetts</option>
+						    <option value="23">Michigan</option>
+						    <option value="24">Minnesota</option>
+						    <option value="25">Mississippi</option>
+						    <option value="26">Missouri</option>
+						    <option value="27">Montana</option>
+						    <option value="28">Nebraska</option>
+						    <option value="29">Nevada</option>
+						    <option value="30">New Hampshire</option>
+						    <option value="31">New Jersey</option>
+						    <option value="32">New Mexico</option>
+						    <option value="33">New York</option>
+						    <option value="34">North Carolina</option>
+						    <option value="35">North Dakota</option>
+						    <option value="36">Ohio</option>
+						    <option value="37">Oklahoma</option>
+						    <option value="38">Oregon</option>
+						    <option value="39">Pennsylvania</option>
+						    <option value="40">Rhode Island</option>
+						    <option value="41">South Carolina</option>
+						    <option value="42">South Dakota</option>
+						    <option value="43">Tennessee</option>
+						    <option value="44">Texas</option>
+						    <option value="45">Utah</option>
+						    <option value="46">Vermont</option>
+						    <option value="47">Virginia</option>
+						    <option value="48">Washington</option>
+						    <option value="49">West Virginia</option>
+						    <option value="50">Wisconsin</option>
+						    <option value="51">Wyoming</option></select>
+						  </select>
+						</div>
+
+						
+						<div class="trendline trendline__options">
+							Since Trump took office, his net approval in&nbsp;<span class="weight__bold" id="explainer-state"></span>&nbsp;has&nbsp;<span class="weight__bold" id="explainer-change"></span>&nbsp;percentage points. 
+						</div>
+						<div class="trendline__legend">
+							<span class="trendline__legend_approval">Approval</span>
+							<span class="trendline__legend_disapproval">Disapproval</span>
+						</div>
+						<div id="trendline__highchart"></div>
+					</div>
+					<div class="ranking__expand-container">
+						<div class="ranking__social">
+	<a href="" target="_blank">
+		<i class="mc-icon mc-icon__linkedin mc-icon__sm"></i>
+	</a>
+	<a href="https://www.facebook.com/dialog/share?app_id=135331423885334&display=popup&href=https://morningconsult.com/tracking-trump/#section-1" target="_blank">
+		<i class="mc-icon mc-icon__facebook mc-icon__sm"></i>
+	</a>
+	<a href="https://twitter.com/intent/tweet?text=Explore+this+interactive+report+tracking+Trump%27s+standing+across+America%2C+via+%40MorningConsult%3A&url=https://morningconsult.com/tracking-trump/#section-1" target="_blank">
+		<i class="mc-icon mc-icon__twitter mc-icon__sm"></i>
+	</a>
+</div>					</div>
+				</section>
+				<section class="section section__bars story">
+					<a name="section-3"></a>
+					<div class="headline-title">
+						Trump's Approval With GOP Primary Voters					</div>
+					<div class="headline-copy body-copy__4">
+						<p>A breakdown of which demographic groups are more or less likely to approve of Trump. Each of the below groups are sub-demographics <strong>among potential GOP primary voters:</strong></p>
+					</div>
+					<div class="headline-key">
+						<div>
+							<span class="key__positive"></span>
+							Approve
+						</div>
+						<div>
+							<span class="key__neutral"></span>
+							Don't Know, No Opinion
+						</div>
+						<div>
+							<span class="key__negative"></span>
+							Disapprove
+						</div>
+					</div>
+					<div class="ranking ranking-favorability">
+	<div class="ranking__info">
+		<div class="ranking__name">
+            <span>
+                All GOP primary voters            </span>
+		</div>
+		<div class="ranking__bar-group">
+			<span class="ranking__positive " style="--width: 85%;">
+				85%
+			</span>
+			<span class="ranking__neutral ranking__medium ranking__low" style="--width: 1%;">
+				1%
+			</span>
+			<span class="ranking__negative ranking__medium " style="--width: 13%;">
+				13%
+			</span>
+		</div>
+	</div>
+</div><div class="ranking ranking-favorability">
+	<div class="ranking__info">
+		<div class="ranking__name">
+            <span>
+                18-29 years-old            </span>
+		</div>
+		<div class="ranking__bar-group">
+			<span class="ranking__positive " style="--width: 76%;">
+				76%
+			</span>
+			<span class="ranking__neutral ranking__medium ranking__low" style="--width: 4%;">
+				4%
+			</span>
+			<span class="ranking__negative " style="--width: 21%;">
+				21%
+			</span>
+		</div>
+	</div>
+</div><div class="ranking ranking-favorability">
+	<div class="ranking__info">
+		<div class="ranking__name">
+            <span>
+                30-44 years-old            </span>
+		</div>
+		<div class="ranking__bar-group">
+			<span class="ranking__positive " style="--width: 83%;">
+				83%
+			</span>
+			<span class="ranking__neutral ranking__medium ranking__low" style="--width: 2%;">
+				2%
+			</span>
+			<span class="ranking__negative " style="--width: 15%;">
+				15%
+			</span>
+		</div>
+	</div>
+</div><div class="ranking ranking-favorability">
+	<div class="ranking__info">
+		<div class="ranking__name">
+            <span>
+                45-54 years-old            </span>
+		</div>
+		<div class="ranking__bar-group">
+			<span class="ranking__positive " style="--width: 87%;">
+				87%
+			</span>
+			<span class="ranking__neutral ranking__medium ranking__low" style="--width: 1%;">
+				1%
+			</span>
+			<span class="ranking__negative ranking__medium " style="--width: 12%;">
+				12%
+			</span>
+		</div>
+	</div>
+</div><div class="ranking ranking-favorability">
+	<div class="ranking__info">
+		<div class="ranking__name">
+            <span>
+                55-64 years-old            </span>
+		</div>
+		<div class="ranking__bar-group">
+			<span class="ranking__positive " style="--width: 88%;">
+				88%
+			</span>
+			<span class="ranking__neutral ranking__medium ranking__low" style="--width: 1%;">
+				1%
+			</span>
+			<span class="ranking__negative ranking__medium " style="--width: 12%;">
+				12%
+			</span>
+		</div>
+	</div>
+</div><div class="ranking ranking-favorability">
+	<div class="ranking__info">
+		<div class="ranking__name">
+            <span>
+                65+ years-old            </span>
+		</div>
+		<div class="ranking__bar-group">
+			<span class="ranking__positive " style="--width: 88%;">
+				88%
+			</span>
+			<span class="ranking__neutral ranking__medium ranking__low" style="--width: 0%;">
+				0%
+			</span>
+			<span class="ranking__negative ranking__medium " style="--width: 12%;">
+				12%
+			</span>
+		</div>
+	</div>
+</div><div class="ranking ranking-favorability">
+	<div class="ranking__info">
+		<div class="ranking__name">
+            <span>
+                Urban            </span>
+		</div>
+		<div class="ranking__bar-group">
+			<span class="ranking__positive " style="--width: 83%;">
+				83%
+			</span>
+			<span class="ranking__neutral ranking__medium ranking__low" style="--width: 1%;">
+				1%
+			</span>
+			<span class="ranking__negative " style="--width: 16%;">
+				16%
+			</span>
+		</div>
+	</div>
+</div><div class="ranking ranking-favorability">
+	<div class="ranking__info">
+		<div class="ranking__name">
+            <span>
+                Suburban            </span>
+		</div>
+		<div class="ranking__bar-group">
+			<span class="ranking__positive " style="--width: 84%;">
+				84%
+			</span>
+			<span class="ranking__neutral ranking__medium ranking__low" style="--width: 1%;">
+				1%
+			</span>
+			<span class="ranking__negative " style="--width: 15%;">
+				15%
+			</span>
+		</div>
+	</div>
+</div>							<div class="accordion" id="accordion-0">
+						<div class="ranking ranking-favorability">
+	<div class="ranking__info">
+		<div class="ranking__name">
+            <span>
+                Rural            </span>
+		</div>
+		<div class="ranking__bar-group">
+			<span class="ranking__positive " style="--width: 89%;">
+				89%
+			</span>
+			<span class="ranking__neutral ranking__medium ranking__low" style="--width: 1%;">
+				1%
+			</span>
+			<span class="ranking__negative ranking__medium " style="--width: 10%;">
+				10%
+			</span>
+		</div>
+	</div>
+</div><div class="ranking ranking-favorability">
+	<div class="ranking__info">
+		<div class="ranking__name">
+            <span>
+                Income: Under 50k            </span>
+		</div>
+		<div class="ranking__bar-group">
+			<span class="ranking__positive " style="--width: 86%;">
+				86%
+			</span>
+			<span class="ranking__neutral ranking__medium ranking__low" style="--width: 2%;">
+				2%
+			</span>
+			<span class="ranking__negative ranking__medium " style="--width: 12%;">
+				12%
+			</span>
+		</div>
+	</div>
+</div><div class="ranking ranking-favorability">
+	<div class="ranking__info">
+		<div class="ranking__name">
+            <span>
+                Income: 50k-100k            </span>
+		</div>
+		<div class="ranking__bar-group">
+			<span class="ranking__positive " style="--width: 86%;">
+				86%
+			</span>
+			<span class="ranking__neutral ranking__medium ranking__low" style="--width: 1%;">
+				1%
+			</span>
+			<span class="ranking__negative ranking__medium " style="--width: 13%;">
+				13%
+			</span>
+		</div>
+	</div>
+</div><div class="ranking ranking-favorability">
+	<div class="ranking__info">
+		<div class="ranking__name">
+            <span>
+                Income: 100k+            </span>
+		</div>
+		<div class="ranking__bar-group">
+			<span class="ranking__positive " style="--width: 83%;">
+				83%
+			</span>
+			<span class="ranking__neutral ranking__medium ranking__low" style="--width: 1%;">
+				1%
+			</span>
+			<span class="ranking__negative " style="--width: 17%;">
+				17%
+			</span>
+		</div>
+	</div>
+</div><div class="ranking ranking-favorability">
+	<div class="ranking__info">
+		<div class="ranking__name">
+            <span>
+                Very conservative            </span>
+		</div>
+		<div class="ranking__bar-group">
+			<span class="ranking__positive " style="--width: 96%;">
+				96%
+			</span>
+			<span class="ranking__neutral ranking__medium ranking__low" style="--width: 0%;">
+				0%
+			</span>
+			<span class="ranking__negative ranking__medium ranking__low" style="--width: 4%;">
+				4%
+			</span>
+		</div>
+	</div>
+</div><div class="ranking ranking-favorability">
+	<div class="ranking__info">
+		<div class="ranking__name">
+            <span>
+                Conservative            </span>
+		</div>
+		<div class="ranking__bar-group">
+			<span class="ranking__positive " style="--width: 91%;">
+				91%
+			</span>
+			<span class="ranking__neutral ranking__medium ranking__low" style="--width: 1%;">
+				1%
+			</span>
+			<span class="ranking__negative ranking__medium " style="--width: 8%;">
+				8%
+			</span>
+		</div>
+	</div>
+</div><div class="ranking ranking-favorability">
+	<div class="ranking__info">
+		<div class="ranking__name">
+            <span>
+                Slightly conservative            </span>
+		</div>
+		<div class="ranking__bar-group">
+			<span class="ranking__positive " style="--width: 78%;">
+				78%
+			</span>
+			<span class="ranking__neutral ranking__medium ranking__low" style="--width: 1%;">
+				1%
+			</span>
+			<span class="ranking__negative " style="--width: 21%;">
+				21%
+			</span>
+		</div>
+	</div>
+</div><div class="ranking ranking-favorability">
+	<div class="ranking__info">
+		<div class="ranking__name">
+            <span>
+                Moderate            </span>
+		</div>
+		<div class="ranking__bar-group">
+			<span class="ranking__positive " style="--width: 71%;">
+				71%
+			</span>
+			<span class="ranking__neutral ranking__medium ranking__low" style="--width: 2%;">
+				2%
+			</span>
+			<span class="ranking__negative " style="--width: 28%;">
+				28%
+			</span>
+		</div>
+	</div>
+</div><div class="ranking ranking-favorability">
+	<div class="ranking__info">
+		<div class="ranking__name">
+            <span>
+                No college            </span>
+		</div>
+		<div class="ranking__bar-group">
+			<span class="ranking__positive " style="--width: 88%;">
+				88%
+			</span>
+			<span class="ranking__neutral ranking__medium ranking__low" style="--width: 1%;">
+				1%
+			</span>
+			<span class="ranking__negative ranking__medium " style="--width: 11%;">
+				11%
+			</span>
+		</div>
+	</div>
+</div><div class="ranking ranking-favorability">
+	<div class="ranking__info">
+		<div class="ranking__name">
+            <span>
+                Bachelors degree            </span>
+		</div>
+		<div class="ranking__bar-group">
+			<span class="ranking__positive " style="--width: 83%;">
+				83%
+			</span>
+			<span class="ranking__neutral ranking__medium ranking__low" style="--width: 1%;">
+				1%
+			</span>
+			<span class="ranking__negative " style="--width: 16%;">
+				16%
+			</span>
+		</div>
+	</div>
+</div><div class="ranking ranking-favorability">
+	<div class="ranking__info">
+		<div class="ranking__name">
+            <span>
+                Post-grad            </span>
+		</div>
+		<div class="ranking__bar-group">
+			<span class="ranking__positive " style="--width: 79%;">
+				79%
+			</span>
+			<span class="ranking__neutral ranking__medium ranking__low" style="--width: 1%;">
+				1%
+			</span>
+			<span class="ranking__negative " style="--width: 20%;">
+				20%
+			</span>
+		</div>
+	</div>
+</div><div class="ranking ranking-favorability">
+	<div class="ranking__info">
+		<div class="ranking__name">
+            <span>
+                Male            </span>
+		</div>
+		<div class="ranking__bar-group">
+			<span class="ranking__positive " style="--width: 86%;">
+				86%
+			</span>
+			<span class="ranking__neutral ranking__medium ranking__low" style="--width: 1%;">
+				1%
+			</span>
+			<span class="ranking__negative ranking__medium " style="--width: 13%;">
+				13%
+			</span>
+		</div>
+	</div>
+</div><div class="ranking ranking-favorability">
+	<div class="ranking__info">
+		<div class="ranking__name">
+            <span>
+                Female            </span>
+		</div>
+		<div class="ranking__bar-group">
+			<span class="ranking__positive " style="--width: 85%;">
+				85%
+			</span>
+			<span class="ranking__neutral ranking__medium ranking__low" style="--width: 2%;">
+				2%
+			</span>
+			<span class="ranking__negative ranking__medium " style="--width: 14%;">
+				14%
+			</span>
+		</div>
+	</div>
+</div><div class="ranking ranking-favorability">
+	<div class="ranking__info">
+		<div class="ranking__name">
+            <span>
+                Extremely interested in politics            </span>
+		</div>
+		<div class="ranking__bar-group">
+			<span class="ranking__positive " style="--width: 91%;">
+				91%
+			</span>
+			<span class="ranking__neutral ranking__medium ranking__low" style="--width: 0%;">
+				0%
+			</span>
+			<span class="ranking__negative ranking__medium " style="--width: 9%;">
+				9%
+			</span>
+		</div>
+	</div>
+</div><div class="ranking ranking-favorability">
+	<div class="ranking__info">
+		<div class="ranking__name">
+            <span>
+                Very interested in politics            </span>
+		</div>
+		<div class="ranking__bar-group">
+			<span class="ranking__positive " style="--width: 88%;">
+				88%
+			</span>
+			<span class="ranking__neutral ranking__medium ranking__low" style="--width: 0%;">
+				0%
+			</span>
+			<span class="ranking__negative ranking__medium " style="--width: 12%;">
+				12%
+			</span>
+		</div>
+	</div>
+</div><div class="ranking ranking-favorability">
+	<div class="ranking__info">
+		<div class="ranking__name">
+            <span>
+                Somewhat interested in politics            </span>
+		</div>
+		<div class="ranking__bar-group">
+			<span class="ranking__positive " style="--width: 84%;">
+				84%
+			</span>
+			<span class="ranking__neutral ranking__medium ranking__low" style="--width: 1%;">
+				1%
+			</span>
+			<span class="ranking__negative " style="--width: 15%;">
+				15%
+			</span>
+		</div>
+	</div>
+</div><div class="ranking ranking-favorability">
+	<div class="ranking__info">
+		<div class="ranking__name">
+            <span>
+                Not too interested in politics            </span>
+		</div>
+		<div class="ranking__bar-group">
+			<span class="ranking__positive " style="--width: 78%;">
+				78%
+			</span>
+			<span class="ranking__neutral ranking__medium ranking__low" style="--width: 3%;">
+				3%
+			</span>
+			<span class="ranking__negative " style="--width: 19%;">
+				19%
+			</span>
+		</div>
+	</div>
+</div><div class="ranking ranking-favorability">
+	<div class="ranking__info">
+		<div class="ranking__name">
+            <span>
+                Not at all interested in politics            </span>
+		</div>
+		<div class="ranking__bar-group">
+			<span class="ranking__positive " style="--width: 74%;">
+				74%
+			</span>
+			<span class="ranking__neutral ranking__medium ranking__low" style="--width: 6%;">
+				6%
+			</span>
+			<span class="ranking__negative " style="--width: 21%;">
+				21%
+			</span>
+		</div>
+	</div>
+</div></div>
+					<div class="ranking__expand-container">
+						<div class="accordion-expand" data-accordion="accordion-0" data-text-expand="more" data-text-collapse="less">
+							<i class="accordion-toggle"></i> 
+							See&nbsp;<span>more</span> &nbsp;demographics
+						</div>
+						<div class="ranking__social">
+	<a href="" target="_blank">
+		<i class="mc-icon mc-icon__linkedin mc-icon__sm"></i>
+	</a>
+	<a href="https://www.facebook.com/dialog/share?app_id=135331423885334&display=popup&href=https://morningconsult.com/tracking-trump/#section-3" target="_blank">
+		<i class="mc-icon mc-icon__facebook mc-icon__sm"></i>
+	</a>
+	<a href="https://twitter.com/intent/tweet?text=Explore+this+interactive+report+tracking+Trump%27s+standing+across+America%2C+via+%40MorningConsult%3A&url=https://morningconsult.com/tracking-trump/#section-3" target="_blank">
+		<i class="mc-icon mc-icon__twitter mc-icon__sm"></i>
+	</a>
+</div>					</div>
+				</section>
+				<section class="section section__methodology story">
+					<div class="headline-title">
+						Methodology					</div>
+					<div class="headline-copy body-copy__4">
+						<p><em>This page was last updated on June 4, 2019. </em></p>
+<p><strong><span class="" data-color-group="turquoise">About Morning Consult Political Intelligence </span></strong></p>
+<p>On a daily basis, Morning Consult surveys over 5,000 registered voters across the United States. Along with 2020 presidential election data, Political Intelligence tracks the approval ratings for all <span class="" data-color-group="cornflower-blue">governors</span>, <span class="" data-color-group="cornflower-blue">senators</span>, House members, <span class="" data-color-group="cornflower-blue">the President</span>, and more at the national, state and congressional district level. Each week, we will release a report with the most important findings on the 2020 election. <span class="" data-color-group="cornflower-blue">Sign-up to receive that report in your inbox <a href="https://morningconsult.com/2020-democratic-primary/">here</a>. </span></p>
+<p><strong>About the state-level data:</strong></p>
+<p>Morning Consult conducted more than 2 million surveys with registered U.S. voters from Jan. 20, 2017 to May 31, 2019 to determine the approval ratings for President Donald Trump in each of the 50 states and Washington, D.C., for each month. These results use a statistical technique called multilevel regression with post-stratification (MRP) to estimate state-level public opinion from the national survey data for a specific month.</p>
+<p>In each poll, Americans indicated whether they approve or disapprove of the job performance of Trump. For each question, they could answer strongly approve, somewhat approve, somewhat disapprove, strongly disapprove, or don’t know/no opinion. The surveys also included about 30 demographic questions.</p>
+<p>Responses to the Trump approval question are modeled via multilevel regression as a function of both individual level and state-level variables. Our models use age, gender, education and race as individual-level predictor variables. For our state-level variables, we chose variables that may influence state-level vote choice such as the percent change in state gross domestic product (GDP), state unemployment rates, state median household income and state-level outcomes from the 2016 presidential election.</p>
+<p>Morning Consult obtained population parameters for registered voters from the November 2012 Current Population Survey. We applied post-stratification weights at the state level based on gender, age, educational attainment and race using the American Community Survey (ACS).</p>
+<p>Standard errors for our estimates for each state were calculated by taking 100 bootstrap samples with replacement from our full national dataset for each hypothetical matchup and then assessing this empirical distribution at the state level. The distribution of these predictions at the state level allows us to construct a predictive interval, which gives us a sense of the spread of MRP estimates. The 95 percent predictive intervals range from 1 percentage point in larger states such as California, Florida, New York, Pennsylvania and Texas, to 4 percentage points in the smaller population size of Alaska.</p>
+<p>The margin of error in each state for the last month is available <a href="https://morningconsult.com/wp-content/uploads/2019/03/Trump-MOE-2.19.csv">here</a>.</p>
+<p><strong>About the demographic approval data:</strong></p>
+<p>The &#8220;Trump&#8217;s Re-election Support With Key Groups&#8221; section has a different methodology than the above sections. This section is reported based on 60,528 interviews with registered voters who indicate they may vote in the Republican primary or caucus in their state. The interviews were collected from May 1, 2019 through May 31, 2019 and have a margin of error of +/- 1%. Each demographic group listed is based on at least 3,700 interviews over that same time frame. The margin of error for each group is either +/- 1% or +/- 2%.</p>
+					</div>
+				</section>
+			</div>
+	</main>
+<footer id="mc-footer" role="contentinfo">
+	<div class="container has-text-centered">
+		<div class="mc-footer__logo">
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Each month, we\u2019ll update this page with the latest survey data, providing a clear picture of Trump\u2019s approval and re-election prospects.<\/p>\n<p>&nbsp;<\/p>\n<p>To receive more data on Trump and 2020, <a href=\"https:\/\/morningconsult.com\/form\/political-intel-briefing\/\">sign up here<\/a>.<\/p>\n"},"map":{"menu_label":"Approval by State","title":"Approval by State","copy":"<p>Use the slider to track how Trump&#8217;s net approval has shifted over time.<\/p>\n","data":"75","social_copy":{"linkedin":"Explore this interactive report tracking Trump's standing across America, via Morning Consult:","facebook":"Explore this interactive report tracking Trump's standing across America, via Morning Consult:","twitter":"Explore this interactive report tracking Trump's standing across America, via @MorningConsult:"}},"state_trend":{"menu_label":"State Trends","title":"State Trends","copy":"<p>Select a state to view the trend data.<\/p>\n","data":"528","social_copy":{"linkedin":"Explore this interactive report tracking Trump's standing across America, via Morning Consult:","facebook":"Explore this interactive report tracking Trump's standing across America, via Morning Consult:","twitter":"Explore this interactive report tracking Trump's standing across America, via @MorningConsult:"}},"double_trend":{"menu_label":"Primary Challenge","title":"Gauging the Prospects of a Primary Challenge","copy":"<p>Below is a high-level look at Trump&#8217;s support among potential GOP primary voters.<\/p>\n","data":[{"data":"418"},{"data":"419"}],"social_copy":{"linkedin":"Explore this interactive report tracking Trump's standing across America, via Morning Consult:","facebook":"Explore this interactive report tracking Trump's standing across America, via Morning Consult:","twitter":"Explore this interactive report tracking Trump's standing across America, via @MorningConsult:"}},"bar_graph":{"menu_label":"Demographic Breakouts","title":"Trump's Approval With GOP Primary Voters","copy":"<p>A breakdown of which demographic groups are more or less likely to approve of Trump. Each of the below groups are sub-demographics <strong>among potential GOP primary voters:<\/strong><\/p>\n","data":"526","social_copy":{"linkedin":"Explore this interactive report tracking Trump's standing across America, via Morning Consult:","facebook":"Explore this interactive report tracking Trump's standing across America, via Morning Consult:","twitter":"Explore this interactive report tracking Trump's standing across America, via @MorningConsult:"}},"methodology":{"menu_label":"Methodology","title":"Methodology","copy":"<p><em>This page was last updated on June 4, 2019.\u00a0<\/em><\/p>\n<p><strong><span class=\"\" data-color-group=\"turquoise\">About Morning Consult Political Intelligence <\/span><\/strong><\/p>\n<p>On a daily basis, Morning Consult surveys over 5,000 registered voters across the United States. Along with 2020 presidential election data, Political Intelligence tracks the approval ratings for all <span class=\"\" data-color-group=\"cornflower-blue\">governors<\/span>, <span class=\"\" data-color-group=\"cornflower-blue\">senators<\/span>, House members, <span class=\"\" data-color-group=\"cornflower-blue\">the President<\/span>, and more at the national, state and congressional district level. Each week, we will release a report with the most important findings on the 2020 election. <span class=\"\" data-color-group=\"cornflower-blue\">Sign-up to receive that report in your inbox <a href=\"https:\/\/morningconsult.com\/2020-democratic-primary\/\">here<\/a>.\u00a0<\/span><\/p>\n<p><strong>About the state-level data:<\/strong><\/p>\n<p>Morning Consult conducted more than 2 million surveys with registered U.S. voters from Jan. 20, 2017 to May 31, 2019 to determine the approval ratings for President Donald Trump in each of the 50 states and Washington, D.C., for each month. These results use a statistical technique called multilevel regression with post-stratification (MRP) to estimate state-level public opinion from the national survey data for a specific month.<\/p>\n<p>In each poll, Americans indicated whether they approve or disapprove of the job performance of Trump. For each question, they could answer strongly approve, somewhat approve, somewhat disapprove, strongly disapprove, or don\u2019t know\/no opinion. The surveys also included about 30 demographic questions.<\/p>\n<p>Responses to the Trump approval question are modeled via multilevel regression as a function of both individual level and state-level variables. Our models use age, gender, education and race as individual-level predictor variables. For our state-level variables, we chose variables that may influence state-level vote choice such as the percent change in state gross domestic product (GDP), state unemployment rates, state median household income and state-level outcomes from the 2016 presidential election.<\/p>\n<p>Morning Consult obtained population parameters for registered voters from the November 2012 Current Population Survey. We applied post-stratification weights at the state level based on gender, age, educational attainment and race using the American Community Survey (ACS).<\/p>\n<p>Standard errors for our estimates for each state were calculated by taking 100 bootstrap samples with replacement from our full national dataset for each hypothetical matchup and then assessing this empirical distribution at the state level. The distribution of these predictions at the state level allows us to construct a predictive interval, which gives us a sense of the spread of MRP estimates. The 95 percent predictive intervals range from 1 percentage point in larger states such as California, Florida, New York, Pennsylvania and Texas, to 4 percentage points in the smaller population size of Alaska.<\/p>\n<p>The margin of error in each state for the last month is available <a href=\"https:\/\/morningconsult.com\/wp-content\/uploads\/2019\/03\/Trump-MOE-2.19.csv\">here<\/a>.<\/p>\n<p><strong>About the demographic approval data:<\/strong><\/p>\n<p>The &#8220;Trump&#8217;s Re-election Support With Key Groups&#8221; section has a different methodology than the above sections. This section is reported based on 60,528 interviews with registered voters who indicate they may vote in the Republican primary or caucus in their state. The interviews were collected from May 1, 2019 through May 31, 2019 and have a margin of error of +\/- 1%. Each demographic group listed is based on at least 3,700 interviews over that same time frame. The margin of error for each group is either +\/- 1% or +\/- 2%.<\/p>\n"},"form":{"subtitle":"Go deeper","title":"Download the Latest 2020 Political Intelligence Report","copy":"Featuring insights on Trump's standing as well as the state of the Democratic primary. 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