From b8dc1d771fc6e6098d3a5f401383d910724024cd Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Thu, 16 May 2019 17:58:38 -0700 Subject: [PATCH] Delete Electoral Votes.ipynb --- Electoral Votes.ipynb | 604 ------------------------------------------ 1 file changed, 604 deletions(-) delete mode 100644 Electoral Votes.ipynb diff --git a/Electoral Votes.ipynb b/Electoral Votes.ipynb deleted file mode 100644 index 8898610..0000000 --- a/Electoral Votes.ipynb +++ /dev/null @@ -1,604 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
Peter Norvig
\n", - "\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 52% (±4) disapproval). But do approval ratings predict election results? Surely there is a correlation—popular presidents are more likely to be re-elected. But there are three big caveats:\n", - "\n", - "1. These are approval polls, not votes. We don't know who will be on the ballot and what their approval levels will be, we don't know if there is systematic bias in the polling data, and we don't know how many people will vote for a candidate they disapprove of or against a candidate they approve of.\n", - "\n", - "2. This is today, not election day 2020. Things can change. Key economic, geopolitical, or legal events might happen.\n", - "\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 wins half the electoral votes for states where approval exactly equals disapproval).\n", - "\n", - "\n", - "# TL;DR for policy wonks\n", - "\n", - "As of 1 April 2019, Trump would expect **180 electoral votes** under these assumptions (recall that you need **270** to win). He's been below 270 every month for the last two years.\n", - "I have five ways of understanding the fluidity of the situation:\n", - "\n", - "- **Undecided**: If many voters are undecided, the net approval could change a lot. So I track the number of states for which at least 5% of voters are undecided. At the inauguration in 2017, all 51 states (including DC) had at least 5% undecided; now there are no such states. Overall 4% of voters are undecided. Most people have made up their mind.\n", - "\n", - "- **Variance**: How much are voters changing their minds from month to month in each state? I track the standard deviation, 𝝈, of the net approval for each state over the last 12 months.\n", - "\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. If all the states had maximum expected movement towards Trump he would take **259** electoral votes, and if the states all swung the other way, he would take **79** electoral votes.\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.\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", - "\n", - "\n", - "\n", - "# The details for data science nerds\n", - "\n", - "First fetch the Tracking Trump web page and cache it locally: " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " % Total % Received % Xferd Average Speed Time Time Time Current\n", - " Dload Upload Total Spent Left Speed\n", - "100 115k 0 115k 0 0 210k 0 --:--:-- --:--:-- --:--:-- 210k\n" - ] - } - ], - "source": [ - "! curl -o evs.html https://morningconsult.com/tracking-trump/" - ] - }, - { - "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/)." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import re\n", - "import ast\n", - "from collections import namedtuple\n", - "from IPython.display import display, Markdown\n", - "from statistics import stdev\n", - "\n", - "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", - " \"Arkansas\": (6, +24), \"California\": (55, -24), \"Colorado\": (9, -1), \n", - " \"Connecticut\": (7, -11), \"Delaware\": (3, -14), \"District of Columbia\": (3, -43),\n", - " \"Florida\": (29, +5), \"Georgia\": (16, +12), \"Hawaii\": (4, -36), \n", - " \"Idaho\": (4, +35), \"Illinois\": (20, -13), \"Indiana\": (11, +18), \n", - " \"Iowa\": (6, +6), \"Kansas\": (6, +23), \"Kentucky\": (8, +23), \n", - " \"Louisiana\": (8, +17), \"Maine\": (4, -5), \"Maryland\": (10, -23), \n", - " \"Massachusetts\": (11, -29), \"Michigan\": (16, -1), \"Minnesota\": (10, -2), \n", - " \"Mississippi\": (6, +15), \"Missouri\": (10, +19), \"Montana\": (3, +18), \n", - " \"Nebraska\": (5, +24), \"Nevada\": (6, +1), \"New Hampshire\": (4, +2), \n", - " \"New Jersey\": (14, -13), \"New Mexico\": (5, -7), \"New York\": (29, -22), \n", - " \"North Carolina\": (15, +5), \"North Dakota\": (3, +33), \"Ohio\": (18, +7), \n", - " \"Oklahoma\": (7, +34), \"Oregon\": (7, -9), \"Pennsylvania\": (20, +1), \n", - " \"Rhode Island\": (4, -26), \"South Carolina\": (9, +17), \"South Dakota\": (3, +31), \n", - " \"Tennessee\": (11, +28), \"Texas\": (38, +17), \"Utah\": (6, +31), \n", - " \"Vermont\": (3, -24), \"Virginia\": (13, 0), \"Washington\": (12, -12), \n", - " \"West Virginia\": (5, +30), \"Wisconsin\": (10, +1), \"Wyoming\": (3, +47)}\n", - "\n", - "net_usa = { # From https://projects.fivethirtyeight.com/trump-approval-ratings/\n", - " '1-Jan-17': +10, # a dict of {date: country-wide-net-approval}\n", - " '1-Feb-17': 0, '1-Mar-17': -6, '1-Apr-17': -13, '1-May-17': -11,\n", - " '1-Jun-17': -16, '1-Jul-17': -15, '1-Aug-17': -19, '1-Sep-17': -20,\n", - " '1-Oct-17': -17, '1-Nov-17': -19, '1-Dec-17': -18, '1-Jan-18': -18,\n", - " '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, apps, diss')\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}:\n", - ".apps (approvals) and .diss (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", - " # File format: Date headers, then [state, approval, disapproval ...]\n", - " # [[\"Demographic\",\"1-Jan-17\",\"\",\"1-Feb-17\",\"\", ... \"1-Apr-19\",\"\"],\n", - " # [\"Alabama\",\"62\",\"26\",\"65\",\"29\", ... \"61\",\"35\"], ... ] =>\n", - " # State(\"Alabama\", 9, apps={\"1-Jan-17\": 62, ...}, diss={\"1-Jan-17\": 26, ...}), ...\n", - " text = re.findall(r'\\[\\[.*?\\]\\]', open(filename).read())[0]\n", - " table = ast.literal_eval(text)\n", - " dates = table[0][1::2]\n", - " states = [State(name, *data[name],\n", - " dict(zip(dates, map(int, numbers[0::2]))),\n", - " dict(zip(dates, map(int, numbers[1::2]))))\n", - " for (name, *numbers) in table[1:]]\n", - " return dates, states, dates[-1]\n", - "\n", - "dates, states, now = parse_page()\n", - "\n", - "assert len(states) == 51 and sum(s.ev for s in states) == 538\n", - "\n", - "def EV(states, date=now, swing=0):\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", - " \"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", - "def net(state, date=now): return state.apps[date] - state.diss[date]\n", - "def undecided(state, date=now): return 100 - state.apps[date] - state.diss[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)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Current expected electoral votes, with various swings" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "180" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "EV(states)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "7" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "margin(states)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{0: 180,\n", - " 1: 180,\n", - " 2: 202.0,\n", - " 3: 224,\n", - " 4: 233.0,\n", - " 5: 242,\n", - " 6: 251.5,\n", - " 7: 271.0,\n", - " 8: 289.5,\n", - " 9: 298}" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "{swing: EV(states, now, swing)\n", - " for swing in range(10)}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We see that:\n", - "- Trump is currently leading in states with only 180 electoral votes; \n", - "- The margin is 7% (if he got 7% more popular in every state, he would win a narrow 271 vote victory).\n", - "- Swings from 0 to 9% produce electoral vote totals from 180 ro 298." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Electoral votes by month\n", - "\n", - "The following plot shows, for each month in office, the expected number of electoral votes (based on net approval) with error bars indicating a 3% swing in either direction (Why 3%? That was the [average error](https://fivethirtyeight.com/features/the-polls-are-all-right/) in national presidential polls in 2016.) Trump hasn't been above 270 since 4 months into his term, and even with the 3% swing, since 6 months in." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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n5yMmJqbYMSzfUTYDQcDHbSww+Uyaqm3bnSzMam+JBiYSPUZGREREJTl27BgaN1bv6Rs3bhzmz5+P0aNHY9asWUhPT4eVlRVyc3Oxa9cuTJkyRXXso0eP0LZtW3Tq1AkA4OLiotN4NU7sbt68qcs46oy3XM3w5aXneJKlBABkK0R8dysTn3eop+fIiIiIqKiuXbtizZo1am0vxs4NHToUn3/+OXbv3o0xY8bgwIEDyMjIwDvvvKM6duzYsXj//fdx9epV9OjRA2+++SZef/11ncXLwV1VTCoREOxpgTkXnqvaNt6SYUobC5gbaTyXhYiIqMar6Jg3fVR5MDMzK3XJVAsLCwwePBjbtm3DmDFj8OOPPyIgIEBtTF6fPn1w/fp1HD16FCdPnsSIESMwaNAgfPvttzqJt8KJ3enTp3H06FE8evQIAODk5ITevXurvV+msn3gYY7lVzOQnlcwkyY1V8QPd7IQ7Gmh58iIiIioIkaPHo2AgAAcOnQIJ0+exE8//VTsGFtbW4wcORIjR45Enz59MHbsWKxatQrGxsZaj0fjxC4zMxNjxozB0aNHIYoiLC0tAQAZGRn45ptv0KtXL2zZsgUWFkxOymNpZIBxLc2x4ppM1bb2hgzjWpnDyIDjFImIiKqL3NxcxMfHq7VJJBLVilve3t5o2bIlJk2aBAcHB/Tp00ft2C+//BLt2rVDq1atIJfL8euvv6Jp06Y6SeqACtSxmz17No4cOYJp06YhOjoasbGxiI2NRXR0NKZOnYpjx45hzpw5OgmyNprY2gLGL82XeJypQNj97NJPICIioioXHh4ODw8Pta+ibyn/+c9/Ii0tDe+++y4kEvXJkMbGxli4cCFef/119O3bFzKZDNu3b9dZvBr32P3yyy947733iiVv9vb2mDt3LhITE/HLL79g1apVWg+yNrI3lWCUmzn+G52paltzPQNvu5pydjEREVE1sG7dOqxbt67c4z766CN89NFHJe6bMWMGZsyYoe3QSqVxYqdUKtGuXbtS97dr1w779u3TSlB1xcdtLPD9nUwo/y5afTNVjqOPcxHgZFL2iURERLWcJmufF51MAXDtc40Tu969e+PIkSMYO3ZsifuPHj2K3r17ay2wuqBZPUMMcjHFLw8KX8Guvp7BxI6IiOq8ktY+fyEnJ6fYIglUQOMxdiEhIXj8+DHeeecdhIeH4+HDh4iNjcWJEycwcuRIxMXFISQkBKmpqWpfVLYpXuqTTc7F5+FCQp6eoiEiIqKaTOMeO29vbwAFhYoPHz6stu/FArgvjnlZSkpKsTYq1L6BFN0bGuPk01xV2+rrGeiix5iIiIioZtI4sfvkk090GUedNtXLQi2xOxibg6YGlrBTZugxKiIiIqppNE7sZs+ercs46jT/RsZoW98I11LyAQAigHPGHhiUfVG/gREREWmJKIqs+qChF29CK6PSa1hlZ2cjO5t117RBEARMLTLW7qqRC54LHBhKREQ1n7m5OdLS0l4pYakrRFFEWloazM3NK3V+hZYUe/DgAZYsWYJjx44hOTkZAFC/fn306dMHn376KZo1a1apIAgY2NQUTS89x4MMBQBAKRjgvLE7AnKu6zkyIiKiV2NoaAhLS0s8f/68/IM18Pz5c9SrV08r16qOLC0tYWhY4VVfAVQgsbt8+TIGDRqErKws+Pv7o3nz5hBFEffu3cOuXbtw4MAB7N27Fx06dKhUIHWdoYGAj9tY4JOIdFXbRWlzvJFzG6bI12NkREREr87Q0BBWVlZauVZCQgKcnJy0cq3aRuPE7rPPPoOZmRmOHTuGFi1aqO2Ljo7GwIEDERISgkOHDmk9yLriXTdzLL6cgaQcJQAgTzDCRWNXvJEbrefIiIiIqCbQeIzdtWvXMH78+GJJHQB4eHhg/PjxuHr1qlaDq2tMDQVMbKX+Tv28tAXyKz8UkoiIiOoQjTMGe3v7Mt/3GhkZwd7evtKBrFixAtbW1pg5c6aqTRRFLF68GC1btoSjoyMCAwNx69YttfPS0tIwYcIEODs7w9nZGRMmTEBaWlql49C3ca0sYG5YOGso08AEV6RN9RcQERER1RgaJ3bBwcHYvHkznjx5Umzf48ePsWnTJgQHB1cqiAsXLmDr1q3w9PRUa1+zZg3Wrl2LJUuW4Pjx47Czs8OQIUOQkVFY323cuHG4du0adu3ahdDQUFy7dg0TJ06sVBzVgY2xAd73MFNrO2bSFvefy/UUEREREdUUGo+xk8vlsLS0ROfOndG/f380a9YMgiDg7t27OHDgAFxdXSGXy/HNN9+ozhEEAZMnTy7zuunp6Rg/fjz+85//YOnSpap2URSxbt06TJ06FYMGDQIArFu3Du7u7ggNDUVQUBCio6Nx7NgxHDp0CD4+PgCAVatWoV+/foiJiYG7u3uFHkZ18WFrC2z48zkUggQAkCsYYfSJFBwJtIOpIWsAERERUck0TuzmzJmj+vOuXbuK7b9x44baMYBmid2LxK179+5qid3Dhw8RHx+Pnj17qtpMTU3RtWtXREZGIigoCFFRUbCwsFAldQDw2muvwdzcHJGRkTU2sWtiYYg+OddwyLRwhvH1lHx8FpmGNX42eoyMiIiIqjONE7tLly5p/eZbt27FvXv3sGHDhmL74uPjAQB2dnZq7XZ2dnj69CmAgunOtra2apWsBUFAgwYNkJCQUOp9Y2JitBF+uV7lPj55f+GhoR1uGTVRtW29k4VmYioCHRR6i6smqO2frzrjs9cfPnv94HPXn7r67MvrtNI4sdOk+HBFCgbGxMRgwYIF+O233yCVSks9rujyI0WXJClpeZLyli2pqp68V7mPAGBQ1gXEW1ghRWKpal9yzwQBnnZobWOkl7iqu5r8Cr6m47PXHz57/eBz1x8++9JVrqzxS+RyOQ4fPoydO3fiyJEjqt608kRFRSE5ORm+vr6qNoVCgXPnzuG///0vzp8/D6CgV65Jk8Jeq6SkJFUvnr29PZKSktQSOVEUkZycXKynT9siIiIQGRlZ5jGrV68u1ubj46P2mctiAjnezorAJotekP893i5bIeL9Eyk4PsAOlkYsg0JERESFKp3YnT9/Hjt37sSePXuQmpoKS0tL9O3bV+PzAwMDi61SMXnyZDRv3hzTp0+Hm5sbHBwccOLECXTs2BEAkJOTg4iICCxYsAAA4O3tDZlMhqioKNU4u6ioKGRmZqqNu9MFX1/fUhM0bf4m4ahMxz+yL2GfWZfC66fLMfVsGjZ1t+GCykRERKRSocQuJiYGO3bswK5du/Do0SMAwIABAxAUFAQ/Pz8YGWn+etDa2hrW1tZqbWZmZrCxsUHr1q0BFJRYWbFiBdzd3eHm5obly5fD3Nwcw4cPB1BQGLl3796YNm0a1qxZA1EUMW3aNPTt27dWddF2zH8AS/fu+F9Mlqpt9/1s+DpIMa6Vhc7uq0mvZEkq0itJRERE2lNuYpeYmIjQ0FDs3LkTV69ehaWlJfr374/XXnsN//rXvzBixAj4+/vrJLgpU6YgOzsbM2fORFpaGjp16oSwsDBYWhaOOdu4cSNmzZqFoUOHAgD69eunNru2tlj2mhUuJ+XhZmphPbuQqHR0bCBFR7vSxyi+ipJ6JYu+Xp46dapO7k1EREQVV2ZiN3z4cJw8eRKGhoYICAjA9OnT0bdvX0ilUsTGxmo9mAMHDqhtC4KAkJAQhISElHqOjY0NvvvuO63HUt2YGRrghx710ePXRGTkiwCAfCXwfngKTg20h40xx9sRERHVdWVmA7///jsaN26MHTt2YOvWrRgwYECZM1hJt9ysjPCfInXsHskUmHQ6FUpR1FNUREREVF2Umdh98sknEAQBgwcPRseOHfHll18WW6uVqtbgZqaY2Mpcre3woxx8fV2mp4iIiIiouigzsZs9ezYuX76MgwcPwt/fH5s3b4afnx+6du2Kb7/9ljMy9eT/ulihs536RJUFl57jzLNcPUVERERE1YFGA7Nee+01rFy5EtHR0fjhhx/g5uaG77//HqIoYuHChVi2bBmuX7+u61jpb1KJgC3+9WFjXJhYK0VgbHgK4rNebVUKIiIiqrkqNOLeyMgI/fv3xw8//IDo6GisXr0aNjY2WLx4Mbp37462bdvqKk4qwsnCEN91q6/WFp+txLiTKVAoOd6OiIioLqr0VEorKyu8//77OHDgAK5evYovvvgC5ubm5Z9IWtOniQlmtLVUazv9LA+LL2foKSIiIiLSJ63UyHBycsInn3yCiIgIbVyOKiCkgyXecFSfqbz8WgaOPMrRU0RERESkLyx+VsNJDARs6l4fjqbqf5UTT6fgkUxeyllERERUGzGxqwUczCTY7F8fkpcmKafmiggKT0GeguPtiIiI6gomdrWEn6Mx5nSsp9Z2MTEfcy6k6ykiIiIiqmpM7GqRf3lZ4E0nE7W2DbcyccOwiZ4iIiIioqrExK4WMRAErH/DBs4WErX2vWadkWRgoaeoiIiIqKpUOLG7ePEiVq5ciZCQENy9excAkJWVhStXrkAm47JW+mZtbICtPepD+tLfbJ5ghJ1mvsiDpPQTiYiIqMbTOLHLz8/HBx98gICAAPzf//0fNmzYgMePHwOAaj3ZDRs26CxQ0lyHBlIs9rFSa0uQWOOgaQc9RURERERVQePE7quvvsL+/fuxePFinD9/HqJYONvS1NQUgwcPxm+//aaTIKnixniYY7irqVrbFWkzfG/eHXsfZEPO1SmIiIhqHY0Tu507d+KDDz7AxIkTYWdnV2x/ixYtcP/+fa0GR5UnCAJWd7VGCytDtfYHhvZ4/0QK2u2Kx/KrGUjM5tqyREREtYXGiV18fHyZa8EaGxsjMzNTK0GRdlgYFYy3MxKLFyqOy1Jg4aXn8Nz5DBNOpeBiYp5aLywRERHVPBondg4ODnj48GGp+y9fvgxnZ2etBEXa08rGCGNkx+EiTyxxf54S2Hk3G733J6LHr4n4X0wmsuVM8IiIiGoijRO7AQMGYMuWLbh3756qTRAKljo4evQoduzYgSFDhmg/QnplDZXpCMoMR3DGYXTKvQszQ6HE464k52PymTR47nyGf19Mx8MMLklGRERUk2ic2M2aNQsNGzZEt27dMGnSJAiCgDVr1uDNN9/EiBEj4OXlhalTp+oyVnpFDsrnGJBzCTffdsQibyu4WpZc/iQlV4nV12XosDse7/6ejPAnOXxNS0REVANonNhZWVnh2LFj+Pjjj/H48WMYGRnh1KlTSE5OxsyZM3HgwAGYmpqWfyHSO2tjA3zoaYGLwxwQ2scWfZsYo6Q+PKUIHIzNweDDyfD+JQEbbsqQA8MSjiQiIqLqoEI/pU1NTTFr1izMmjVLV/FQFTIQBPRuYoLeTUzwIEOOzbcz8eOdTKTlFe+di0mXY1ZkOqT1+qNd3kN45/0FO2WGHqImIiKi0mjcY7d9+3ZkZPAHeW3V1NIQ/9fFCjdHOOJrP2t41Tcq8bg8wQgXjN2wziIAdwwdqzhKIiIiKovGiV1wcDBatGiB0aNHY+/evcjJydFlXKQnZoYGGN3CHKcG2uHQPxpgWDNTlDTXQikY4LRxq6oPkIiIiEqlcWJ35MgRvP/++7h48SI++OADuLu7Y8KECTh06BDy8/N1GSPpgSAIeM3BGJv96+PPtx0R0sESjqbq3y5PJDbIVXBSBRERUXWh8Ri7Ll26oEuXLli8eDHOnj2LsLAw/Prrr9i1axesrKzQv39/DBs2DD169NBlvFSOiIgIREZGlnnM6tWri7X5+PjA19e3xOMdzSSY1b4epre1RPMtf+G5gRkAQCFIcC05H13spa8eOBEREb2yCk9xFAQBr7/+Ol5//XUsW7YMJ0+eRFhYGPbu3Yuff/4ZycnJuoiTNOTr61tqgvaqjAwEOMmTcUNqpmqLSsxjYkdERFRNaPwqtiQpKSn466+/cPfuXS4nVkc0Uagn7hcS8vQUCRERERVV4R67tLQ07Nu3D7t378bZs2ehUCjQrl07zJ8/H0OHDtVFjFSNODGxIyIiqrY0Tuy2b9+OsLAwhIeHIz8/Hx4eHvj0008xfPhwuLq66jJGqkYcFamQiAoohIJVK+KyFIjLVKCxecmrWBAREVHV0TixCw4OhrOzMz766COj5f9mAAAgAElEQVQMHToUbdq00WVcVE0ZQkQjRSoeGTZQtV1IyEPjZlx1hIiISN80TuyOHj2Kzp076zIWqiGaKJLVEruoxFwMZmJHRESkdxpPnmBSRy84yTnOjoiIqDoqtcduxYoVEAQB06ZNgyAIWLFiRbkXEwQB06dP1+jGGzduxJYtW/Do0SMAQMuWLTFjxgz07dsXQMGr359//lntnM6dO+PYsWOq7dzcXMyePRu7d+9GTk4OunXrhhUrVqBx48YaxUCVU3Rm7NXkfOQqRBhLSliigoiIiKpMqYndwoULIQgCPvroI0ilUixcuLDci1UksWvUqBHmz5+P5s2bQ6lU4ueff8aoUaMQHh6uGr/n7++PDRs2qM6RStXrpYWEhODgwYPYvHkzbGxs8MUXX2DEiBE4efIkJBIO5teVemIOrJSZSDcwBwDkKYGryXnwtjfWc2RERER1W6mJXVJSEgCoEqQX29oSGBiotj1nzhxs3rwZFy5cUCV2xsbGcHBwKPH89PR0/Pjjj1i7dq1qtYsNGzbAy8sL4eHh6NWrl1bj1SVdrBaha03kyUiXmqu2oxKY2BEREelbqYld0R4vXfaAKRQK7NmzB5mZmfD29la1R0REwM3NDVZWVvDz88OcOXNgZ2cHALhy5Qry8/PRs2dP1fFNmjSBh4cHIiMja1Rip8vVInTFSZGMG3BWbV9I5Dg7IiIifdN4VqydnR3Wr1+PYcOGlbh/z549GD9+PBITEzW++Y0bNxAQEICcnByYm5tj27Zt8PT0BAD07t0bAwYMgIuLC2JjY7Fw4UIMHDgQ4eHhMDY2RkJCAiQSCWxtbYvFmZCQUOZ9Y2JiNI6xsqriHvpUdJxdxJMsxMSk6CkadbX92VdnfPb6w2evH3zu+lNXn727u3uZ+zVO7ORyOZRKZaX3l8Td3R2nT59Geno69u3bh+DgYOzfvx+tW7dWSyA9PT3Rvn17eHl54fDhwxg4cGCp1xRFEYJQ9iD+8h7Kq4qJidH5PfTNUZEGQ1EB+d+FihPyDGDasBmaWFR4MROtqgvPvrris9cfPnv94HPXHz770lVordiyEqZLly7BysqqQjeXSqVwdXVFhw4dMG/ePHh5eeHbb78t8diGDRuiUaNGuHfvHgDA3t4eCoUCycnqPUdJSUmq17WkO4YQ0VCRqtbG17FERET6VWb3yoYNG/Ddd9+ptmfPno3FixcXOy49PR0pKSl46623XikYpVKJvLySk4Pk5GQ8ffpUNZmiffv2MDIywokTJ1T3jYuLQ3R0NHx8fF4pDtKMU9FCxQl5GNLMTI8RERER1W1lJnZWVlZwcnICANy7dw/169eHvb292jGCIMDMzAwdOnTApEmTNL7xv//9bwQEBKBx48aQyWQIDQ3FmTNnsHPnTshkMnz11VcYOHAgHBwcEBsbiwULFsDOzg79+/dXxfbee+9h7ty5sLOzU5U78fT0hL+/fwUfA5WkvNm6TeTJwEsTYfddi4VDxPd6na1LRERUl5WZ2I0cORIjR44EAPTr1w+zZs3SWtIUHx+PCRMmICEhAfXq1YOnpydCQ0PRq1cvZGdn4+bNm9i+fTvS09Ph4OCAN954A1u2bIGlpaXqGosWLYJEIkFQUJCqQPH69etZw05Lyput+yxLgZ07nqm2E6S2mBQ0BSaGLFRMRESkDxqPdP/tt9+0euN169aVus/U1BRhYWHlXsPExATLli3DsmXLtBkaacjRTAInCwkeyRQAgPy/CxX7OLCeHRERkT6Umtg9ffoUQMGkhZe3y/PieKobvO2keCTLVm1HJTKxIyIi0pdSE7vWrVtDEAQ8e/YMUqlUtV2elJTqUcuMqkYXeyl23y9M7C4kcGYsERGRvpSa2K1ZswYAYGRkpLZN9DJvO/X1ey8k5mlUS5CIiIi0r9TEbvTo0WVuEwFAm/pGMJEAOQXD7PA0S4m4TIXeCxUTERHVRRUqUFySxMRE/PXXX9qIhWogqURAe9vivXZERERU9TRO7LZu3YoPP/xQre3TTz+Fh4cHvL294e/vj9TU1FLOptqsi716YhfFcXZERER6oXFi9/3338PExES1febMGWzcuBFDhgzB559/jpiYGCxfvlwnQVL11qWEcXZERERU9TQeCPXgwQO8++67qu1ffvkFjRo1wsaNG2FgYACZTIa9e/fiyy+/1EmgVH15F+mxu5qcjxy5yELFREREVUzjHru8vDxIpYU/wE+cOIHevXvDwKDgEs2bN8ezZ89KO51qsReFil94UaiYiIiIqpbGiZ2LiwtOnz4NALhy5Qru37+Pnj17qvYnJCTAwsJC+xFSjVC07EkUX8cSERFVOY1fxX7wwQeYNWsW7ty5g8ePH6Nhw4YICAhQ7Y+KioKHh4dOgqTqj4WKiYiI9E/jxG7ChAkwMjLC4cOH0bJlS0ydOhWmpqYAgNTUVMTFxWHcuHE6C5SqNxYqJiIi0r8KVZENCgpCUFBQsXYbGxucOXNGa0FRzVNSoeLHmQo4sVAxERFRlanUT93o6GjExsYCAJydnfkKliCVCOjQQIqI+MJXsBcS8pjYERERVaEK/dQ9dOgQQkJC8PDhQ7X2pk2bYvHixejbt69Wg6OapYudemIXlZiHoa5meoyIiIiobtE4sTt+/DhGjRqFhg0bIiQkBB4eHhBFEXfu3MHWrVsxatQohIaGwt/fX4fhUnVWdAUKTqAgIiKqWhondkuXLkWLFi1w+PBh1KtXT23fxIkTERAQgCVLljCxq8OKTqC4lsJCxURERFVJ4zp2169fx6hRo4oldQBQr149/POf/8S1a9e0GhzVLA5mEjgXKVR8hYWKiYiIqozGiZ1EIkFOTk6p+7OzsyGRSErdT3VD0eXF+DqWiIio6mic2Pn6+mLjxo148OBBsX0PHjzA5s2b4evrq83YqAbqwhUoiIiI9EbjMXZz587Fm2++CR8fH/Tv3x9ubm4AgJiYGBw8eBBSqRTz5s3TWaBUM5TUY8dCxURERFVD48TO09MTx44dw/z583Hw4EHVa1kTExP06tULc+fOZT07Qpv6RjCVCMhWiACAZ9lKPMpUwJn17IiIiHSuQj9tPTw88NNPP0GhUCA+Ph4AYG9vD0ND/tCmAkYGAto3MCpWqJiJHRERke5pNMbuypUr2Lt3L06dOoW8vDxIJBI0atQIjRo1YlJHxRQtexLFCRRERERVosysLCMjA++88w7OnTunamvUqBF27dqFVq1a6Tw4qpmKFSrmBAoiIqIqUWaP3ZIlS3D27Fm8+eabWLRoEcaNG4fExETMmDGjquKjGqjoBIpryfnIlot6ioaIiKjuKLPHbv/+/QgMDMS2bdtUbS1btsSMGTOQkJAAe3t7nQdINY+9qQQuFhI8lCkAAHKxoFCxr4OxniMjIiKq3crssXvy5Al69+6t1ta7d2+IoojY2FidBkY1GwsVExERVb0yE7v8/HyYm5urtVlYWAAAMjMzdRcV1XjFChUzsSMiItK5cqe0ZmVlITU1VbX94s8ymUyt/QUbGxsthkc1VbEeu0QWKiYiItK1chO7adOmYdq0acXa33vvvRKPT0lJefWoqMbzLFKoOD5biViZAi6WLI9DRESkK2X+lP3kk0+qKg6qZYwMBHRoYIRzLxcqTsxjYkdERKRDZf6UnT17dlXFQbVQFzupWmIXlZCH4a5meoyIiIiodtNo5QmiymChYiIioqqlt8Ru48aN6Nq1K5ycnODk5IQ+ffrg8OHDqv2iKGLx4sVo2bIlHB0dERgYiFu3bqldIy0tDRMmTICzszOcnZ0xYcIEpKWlVfVHoVIUnUBxnYWKiYiIdEpviV2jRo0wf/58nDx5EidOnEC3bt0watQo/PnnnwCANWvWYO3atViyZAmOHz8OOzs7DBkyBBkZGaprjBs3DteuXcOuXbsQGhqKa9euYeLEifr6SFTEi0LFL8hF4HISe+2IiIh0RW+JXWBgIPr06QNXV1e4ublhzpw5sLCwwIULFyCKItatW4epU6di0KBBaN26NdatWweZTIbQ0FAAQHR0NI4dO4bVq1fDx8cH3t7eWLVqFQ4fPoyYmBh9fSwqoqSyJ0RERKQb1WKKokKhwJ49e5CZmQlvb288fPgQ8fHx6Nmzp+oYU1NTdO3aFZGRkQgKCkJUVBQsLCzg4+OjOua1116Dubk5IiMj4e7uXur9qiLxY3JZoCkMARQmdz9H3IL4+zmNz3d3d0eLFi0qdE8+e/3hs9cfPnv94HPXn7r67MvKb4AKJHZPnz6FjY0NTExMStyfm5uLlJQUNGzYUOPgbty4gYCAAOTk5MDc3Bzbtm2Dp6cnIiMjAQB2dnZqx9vZ2eHp06cAgISEBNja2qoVvBUEAQ0aNEBCQkKZ9y3vobyqmJgYnd+jpgi0ycOye4mq7ZR6TpgyfgoEQcDq1avVjp06deor34/PXn/47PWHz14/+Nz1h8++dBq/ivX09MSvv/5a6v4DBw7A09OzQjd3d3fH6dOncezYMYwdOxbBwcG4efOman/RVQqKrlxQ0ioGXN2genlRqPiFhGwlHsoUeoyIiIio9tI4sRPFsmczyuXyCidUUqkUrq6u6NChA+bNmwcvLy98++23cHBwAIBiPW9JSUmqXjx7e3skJSWpxSWKIpKTk4v19JH+vChU/LILXDeWiIhIJyo0eaK0xC0zMxPHjx9HgwYNXikYpVKJvLw8uLi4wMHBASdOnFDty8nJQUREhGpMnbe3N2QyGaKiolTHREVFITMzU23cHelf0QkUUZxAQUREpBNljrFbunQpli1bBqAgqZs0aRKCg4OLHadQFLxaGz9+vMY3/ve//42AgAA0btxYNdv1zJkz2LlzJwRBQHBwMFasWAF3d3e4ublh+fLlMDc3x/DhwwEAHh4e6N27N6ZNm4Y1a9ZAFEVMmzYNffv25Xv3aqaLnXpid5GJHRERkU6Umdh5eXnhnXfegSiK2LZtG3x9fdGsWTO1YwRBgJmZGTp06IBhw4ZpfOP4+HhMmDABCQkJqFevHjw9PREaGopevXoBAKZMmYLs7GzMnDkTaWlp6NSpE8LCwmBpaam6xsaNGzFr1iwMHToUANCvXz8sXbpU4xioarBQMRERUdUoM7Hr168f+vXrB6Bg1uv48ePRpUsXrdx43bp1Ze4XBAEhISEICQkp9RgbGxt89913WomHdMfOVIKmlhI8yCjo2WWhYiIiIt3QuNwJEyh6Fd52UjzIyFZts1AxERGR9lVo8sTjx48xffp0dO7cGS4uLjh79iwAIDk5GZ9++imuXr2qkyCp5utSdAIFZ8YSERFpncaJXUxMDLp3747Q0FA0btwYGRkZkMvlAABbW1ucO3cOmzdv1lmgVLMVnUBxITEPHGVHRESkXRq/ip03bx5MTU1x9uxZGBkZwc3NTW1/3759sW/fPq0HSLVDm/pGMDMUkPX3pImEbCXSBDPYiFl6joyIiKj20LjH7syZMxg/fjwcHR1LrGfn7OyMJ0+eaDU4qj0MSyhU/MjQVk/REBER1U4aJ3ZyuVyt1EhRqampkEgkWgmKaifvIq9jH0uY2BEREWmTxoldy5YtcebMmRL3iaKIAwcOoF27dloLjGqfohMoHjGxIyIi0iqNE7vg4GD88ssvWLlyJdLT0wEUJHT37t3DhAkT8Mcff2Dy5Mk6C5RqvqITKOIl1sgDe3mJiIi0RePJE2+99RYePHiARYsW4csvvwQADBs2DKIowsDAAPPnz8ebb76ps0Cp5rMzlaCZpQT3/y5UrBQM8ERig6aKJD1HRkREVDtonNgBwMyZM/H2229j3759uHv3LpRKJZo1a4bBgwcXW2qMqCRd7KW4/1Kh4scSWyZ2REREWlKhxA4AXFxc8PHHH+siFqoDvO2k2Hm3MLF7ZGgLsFYxERGRVlRo5QmiV1V0AsVjiS0LFRMREWlJmT12nTp1qtDFBEHAxYsXXykgqt08bYxgbigg8+9CxZkGJkgVzFFfzNRzZERERDVfmYldkyZNSixGXNSzZ88QHR2t0bFUt70oVHzmWeH718eGtqifz8SOiIjoVZWZ2O3du7fMk58+fYpVq1bh/PnzMDQ0xLvvvqvV4Kh28raXqiV2jyS2aJsfq8eIiIiIaocKT54ACnroVq1ahR9++AFyuRwjR47EjBkz4OLiou34qBYqWs/uMZcWIyIi0ooKJXZM6EgbOhdJ7J4ZWLFQMRERkRYIaWlp5U5KZEJXMVabrPUdQrXnZvAb7grOqu1wxfvoDk68ISKimit9XJq+Qyi7x44JHemKr3hFLbGLENqju8jEjoiI6FWUmdh16NABubm58PLywvTp0+Hi4oLU1FSkpqaWek779u21HiTVPr64im0YqNpeIbyPf4in0BZ39BgVERFRzVbmq1gbG5vCA8spZSKKIgRBQEpKivaiq6H4KrZ8d+CClgb7IQqFNbLri2k4phyLDritx8iIiIgqp9q/il2zZk1VxVGrXOx+Ae7u7voOo1pzAOC7/necM26paksRrNHTNAx7+jZA+wbS0k8uwerVq9W2p06dWunYtHmtuiQmJobf93rCZ68ffO76w2dfujITu9GjR1dVHFQH9cm5DgUMEGncQtWWlidi4OEk/BLQAJ3sKpbcERER1XWVqmNHpA0CgDdzrsIAIiKMPVTtz/NEDDmchNAAW3jbG+svQKqTIiIiEBkZWeHzfHx84Ovrq4OIiIg0x8SO9EoAEJBzDQaiiLMmha9ln+eLGHo4GbsCbOHrwOSOqo6vr2+xBI2v54mopjAo/xAi3RIA9M69jhntLNXaZXIRw48k48yzXP0ERkREVMMwsaNqQQAwu2M9fNZePbnLlIt460gyTj5hckdERFQeJnZUrXzWoR6+6KCe3GUrRIw4loTjcTl6ioqIiKhmYGJH1c7M9vXw70711NpyFMA7vyfj2GMmd0RERKWp8OSJrKwsxMXFITU1FaJYvLaxj4+PVgKjum1qW0tIDIA5F56r2nIVwLu/J+OHnvXxppOpHqMjIiKqnjRO7NLT0/HZZ59h9+7dkMvlxfZz5QnSto/bWMJQEBASla5qy1MC7x1Pwff+9RHowuSOiIjoZRondtOmTcPevXsxZswYvP7667C25rJZpHvBnhYwNABmni9M7vKVwPsnUrDZvz4GNWVyR6RPrPtHVL1onNgdPXoUEyZMwOLFi3UZD1Ex41tZwFAQMC2icA0+uQiMCU/Bpu42GNLMTI/REdVtrPtHVL1oPHnC0NBQq+uyrVy5Ej169ICTkxOaN2+OESNG4ObNm2rHBAcHw9raWu2rd+/easfk5uZi5syZcHV1RaNGjTBy5EjExcVpLU6qHoJamuNrP2sIL7UpRGDsyVTsupult7iIiIiqE4177AYOHIjff/8dY8aM0cqNz5w5g7Fjx6Jjx44QRRGLFi3C4MGDERkZCRsbG9Vx/v7+2LBhg2pbKlVfPzQkJAQHDx7E5s2bYWNjgy+++AIjRozAyZMnIZFItBIrvTpNXtcU/S0fUH9dM7qFOQwFYPKZNLyYtqMUgYmnUzHIyBnt8mO1HTYRVZG68EpXm5+xLjwvqpxSE7vU1FS17WnTpiEoKAgfffQRxo4dCycnpxITp5eTsrKEhYWpbW/YsAHOzs44f/48+vXrp2o3NjaGg4NDiddIT0/Hjz/+iLVr16JHjx6q63h5eSE8PBy9evXSKBbSvZJe11TGu+7mMDQQMOl0KpR/Z3dKEfjF1BtKGKBD/oNXvgcRVb268EpXm5+xLjwvqpxSEztXV1cIgqDWJooirly5gp9++qnUC1Z2VqxMJoNSqSw2KSMiIgJubm6wsrKCn58f5syZAzs7OwDAlStXkJ+fj549e6qOb9KkCTw8PBAZGcnErpZ6u7kZDAVg/KlUKF503QkC9pp1gTJLQKf8+3qNj4iISF9KTew++eSTqowDn332Gby8vODt7a1q6927NwYMGAAXFxfExsZi4cKFGDhwIMLDw2FsbIyEhARIJBLY2tqqXcvOzg4JCQml3ismJkZnn6Mq71GXeQH40kOCL6KlUIiFv4D8atYZ5pm5Wn3+/LvUXG19VumCKe4b2qOxIgV2yoxq+TmrY0wvaCu26vgZtRFTkoEFHkls0UyeyP+7KqC2f77SlDffodTEbvbs2VoPpjSff/45zp8/j0OHDqm93h02bJjqz56enmjfvj28vLxw+PBhDBw4sNTrvaipVxptTgIpSUxMjM7vQYC7O9CkUTaCwlOQryxs/92kDda5uZX5PVCx+/DvUhO19fv+kUyO7yx6I9PABAaiEkGZJ6rd56zuz15bsVW3z6iN5/5YYoPvzXtALkhgICphlmmFaW0tYWTw6v9/VbfnpU3V/XtenzSeFTtlyhT88ccfpe6/fPkypkyZUuEAQkJCsHv3buzbtw9NmzYt89iGDRuiUaNGuHfvHgDA3t4eCoUCycnJasclJSWpXtdS7dbfxRQ/9KgPQSzM7BIlVjgXn6fHqKg2WXE1A5kGJgAApWCA4yZt9BwR1SZHTNpBLhR0aCgFAyy6nIEevybiShL/D6PK0Tix++GHH1QJVUnu37+PH3/8sUI3nzVrFkJDQ7Fv3z60aNGi3OOTk5Px9OlT1WSK9u3bw8jICCdOnFAdExcXh+joaC5tVof0czaFh/yJWtt/b2fqKRqqTZ5mKfDTX+rldO4bOuBOWr6eIqLa5OyzXMQaFu+E+DMlH732J2LBH+nIkRdfupOoLBonduVJSUmBsbGxxsfPmDEDP/30EzZt2gRra2vEx8cjPj4eMpkMQMFkitmzZyMqKgoPHz7E6dOnMXLkSNjZ2aF///4AACsrK7z33nuYO3cuwsPDcfXqVUycOBGenp7w9/fX1kejGqBL3l217X0Ps5GQrdBTNFRbrLshQ56yePsm/uJAWrD8akap+xQisPKaDN32JSAqIbcKo6Karsw6dhERETh37pxq++DBg4iNLV4rLC0tDaGhofD09NT4xps2bQIADBo0SK191qxZCAkJgUQiwc2bN7F9+3akp6fDwcEBb7zxBrZs2QJLS0vV8YsWLYJEIkFQUBBycnLQrVs3rF+/njXs6phm8gTUV2QgRVLwvZGvBLbFZGF6W8tyziQqWVqustSe35//ysKcTvVgaaS1342pjrmYmIcTT9QTtub5z3DXyFGt7U66HH0PJGFSa3PM7lgP5vyeo3KUmdidPHkSS5YsAQAIgoA9e/Zgz549JR7brFkzLFq0SOMbp6Wllbnf1NS0WK27kpiYmGDZsmVYtmyZxvem2scAQOe8uzhi2l7VtiU6E1PaWECihUHIVPdsvp0JWSmvwTLyRey8m4WxLS2qOCqqLZYV6a1zkSfgvazTaDlsIqafS8OTrMKuYhHAupuZ+O1RDr72s0G3hpq/HaO6p8zEbvLkyRg9ejREUUSbNm2wdOlSBAYGqh0jCALMzMxgZWWl00CJytM+/wGOm3ipBiI/kilwLC4XfZ1M9BwZ6Zq2q/Bny0WsuylTa6unzMJzg8J1iTfdysQYD3Otzb6muuNqch4OP8pRa+uWcwsA8KaTKXyHGGPuhXRsvaM+vvNBhgIDDyUhyMMM8ztboZ6UvXdUXJmJnaWlpeq156VLl2Bvbw9zc/MqCYyooszEfHjmP8JVaVNV239vy5jY1QHarsL/v5hMJOUU9phIxXyMyjyN9RZ9IAoFP0xvpclxNj4PrzvWvN4TLkelXyuvqffWNZEnw1VRWHvVSmqANX42GNLMFP86m4ZYmfp44S3RWTjyKBer/azRpwn/fyN1Gq8V26xZM13GQaQVXfLuqiV2Rx7n4kGGHE0tNf5WpzpOrhTx9Z/qvXVd8u7CQfkcLeVPcMuoiap94y1ZjUzsuByV/txOy8e+B0V663JvoqR+X/9GJjg32B4L/niOjbcy8fLAgLgsBd46mox33MywyNsKNsbsvaMCpf60GzJkCARBwK5duyCRSDBkyJByLyYIgkbj4oh0pbEiBY6KVDyTFKxZLALYGp2JeZ05VIA0E3Y/W62HRGoAvJZbUOHeO/cvtcRu/8McPMlUoJE5J2uRZlZezVBL0BwVqXCXPyv1eAsjAyx9zRpDmpni4zNp+Ou5XG3/z39l4fe4HKzwtS7lClTXlJriZ2dnIzs7G6IoqrZzcnLK/MrOzq6ywIlKIgDokqte+uTHmCzkKlgLisoniiJWF3lN9q6bGSzFgh6WpopE2CnSVfsUIvD9HZY+Ic3cey5H6H31n5Pdcm6V2FtXlK+DMU4PsseUNhYoOh8sIVuJ946nYKfpa5AJNa8HmbSr1B67Q4cOlblNVF155cfipLQLnucVJHNJOUr8+jAbw13NyjmT6rojj3NxM62wR8RAAP7lZYl9Fwq2BRS8lj1o2lF1zNboTMxoawmphJMoqGyrrmVA+dLvmC2tDdEyPU7j800NBczvYoVBTU0x+UwqbqWp997dlDrhvqE9+uVchlf+I22FTTUMBx5RrSOFAiObm+G7W4U9KZtvZzKxo3Ktvq7eWze4qSlc66n/N9ku7yFO1+uEjPyCn9Dx2Ursf5iNoTX8+0sEkAcJjKDQXuV6PamOk0NiZXL8XGQVk0/aWiLuYcWv1dFOivCB9lhxLQMrr2bg5ao82QbGCDN7DbfzGuMjpaiVNWepZtE4sevXrx/8/Pzg6+sLb29vtSLBRNXNmJbmaoldRHwebqbmo7WNkR6jouosIj4XEUXWGJ7iVbxOnTHkGNncDBtfKl688XZmjU7sknMU2GreHQ8M7WGizENTRQLMbsrQrZExPKwMa1xJl+o4OeTr6zK1BMzVUoIhzUzxTSWvZywR8HmHehjgYoqPzqTiarL6Mnc3pU7Ycz8bbzWvud+XVDkaJ3ZSqRTr16/HihUrIJFI0KZNG3Tt2hW+vr7o2rUrbG1tdRknUYW0tDaCn6MUZ58V/qD+7+1MLK8hA4yrY49DbZr7cRcAACAASURBVFd0bF2vxsZoZyst8dixrczVEruI+Dz8mZKPNvVr3i8OGflKDD+ajAeG9gCAHAMpbhs0waeRBWMJ7U0N0K2hserLxUJS4xK9V6GNf4tPsxT4MUZ9LOb0dpYw1EJvmld9I/ze3w7/+VOGhRdToRAKJ/Lse8jEri7SOLHbu3cvFAoFLl26hHPnzuHcuXP46aefsG7dOgiCAHd3d/j5+WHlypW6jJdIY+NamqsldjvuZuHfnevBogYsyVMdexxqsz9T8nH4sfryTlO9Sn8r0dLaCG84SnH6pe+vTbdkWO1no7MYdSFHLuLdY8m4nJRf6jEJ2UqE3stG6L2CQf9OFhJVkveGo3GtnxGsjX+L//kzA7kvlaJrYi7BCC0mXIYGAqa1tcSDk/uw1cJf1X48Lhc5chEmhnUnEacyZsWWRCKRoEuXLpgyZQp27NiBu3fv4ttvv4Wbmxvu3LmD77//XkdhElVcoLMp7E0Lv8Uz8kXsusuZ21Tc10XG1nWxM8LrjiX31r0wvpX6a9qd97KRlqss5ejqR64UMfZkilpyqolHMgX+F5OFiadS0XrnM3QJi8cnEWnY+yAbyTmK8i9QxyTlKLDltvrYumltLXQy9s1FkQhLZeH/cZlyESef5pZxBtVGFZo8oVAocPnyZVWPXWRkJNLT0+Ho6IihQ4fyFRBVK1KJgNHu5lj+0iu2zdGZ+MDDTCevkvj6tGZ6kCHH7iIlKKZ6WZb7PfIPZxM0NpMgLqsgmcmSi/jpryx86Fn9149ViiL+dTYNB2LVC+U2lifjzZwriJU0ANy8cS4+D5mlrJf7Qky6HDHpcmz++9W0p40hLE3aoak8AfWVmTAV85CrEGFcR2cNf3tDhuyXyi01NDPAKDfdrOBkAMAjPw4Xjd1UbQdis7n6Th2jcWI3aNAgXLx4EdnZ2WjatCm6du2KhQsXws/PD02bNtVhiESV976HGVZeLywx8GdKPi4k5sHbXvu1nvj6tGZa+6cML5c59LAyRD/n8n8QGhoI+MDDDF9efukXh9syTGptDoO/k0JtJvvaupYoivgiKh0/FZmhaadIx6isMzAT8+CkSMHUgH8gXyniUmIeTj3NxamnuYhKzFN7pViSG6lywLgFzhu3ULWt+OEJzAwF2EgNYG0swMbYoPBLWvhna7V2QTVLt6amhGm5Smy8pT627uM2ljp9NdpS/kQtsTv0KAdKUVR9T1Ltp3Fid+rUKRgYGGDIkCEYMmQIunbtivr16+syNqJX5mRhiIAmJjj00oLbm29n6iSxo5onMbv4oPYpXhYa/xB838McS69mIP/vN7B3nysQ/iQXPRsXJIbaTPa1da3lVzOw7qb6Z3a2kGD4k1MwE9VfyxoZCPBxMIaPgzFmti8Ykxf1d6J3+mku/kjMQzkdeipZchFZcgXisso/VsVqKCSiAiZiPozFfOzemwALIwHmhgLMjQSYGxrA3EiAhaEAcyMDVbtqW/VnAc8FE0hFOYyggARVU7B8/U2ZqiwOADQwMcD7LXQ7maGpPBFSMR95QsFEnoRsJf6fvfsOa+ru4gD+vUnIYIaNqIAKCiLuhdZNUUHUuq21iqu1Q2urVWq3LVi1VarW1qrt+7bWKmrV2jpftVZEXFVsceBAxMFeCQmQ5L5/UAOXmUBCApzP8/i0uffm5nBJwrm/cX4XjXQzS8yTzondnj17tF2wc+bMQXFxMTp06IB+/fpp/7Vo0cKYsRJSJ7N9rTiJ3S/3FIjsrYajuGkP+ia1+zpRBmWFQe361Dt0kfAxxkuinVgAAN9el2sTO3Oz5bqM08IIlM563TfcCQe2Kat5VhmxgNFOnABKZ9SeSytr0UvIKjF4yqRm+JAzfMghRnZ29ZM8amUbBgBgWA18VY8wq1gDW6HxJlLlF2vwdSJ3zeFX/a1hZeTJWwJo4FPyBP8IW2u3/Z6ipMSuGdE5sRs6dCiGDh0KACguLsbFixcRFxeHuLg4xMTEQCaTwcvLC5cvXzZasITUxbCWpSUa7v+7/mexBtieVIgFNcx6JE1ffrGGU7IEKP3Dq+8KEnN9rTiJ3ZFUJe4XqOBpY17132PuFGLJuTzONlshg73BTpWKMOvKxoKHZ1uJ8Wyr0kQ2p0iDM0+KsP5QPB7z7VHICKFghCjii2Euq/qxDA/XLVph/NFM7A52gp2RkrttN+TILS77oaVCBrN9jTO2riJf1UNOYvdbihIf0nrZzUadPs1CoRCOjo5wcHCAVCqFlZUVCgoKkJycbODwCKk/HsNglq8VPriYr9323U05Xuuke5cbaXq+vynXLjsHAA4iHl6sQzdZbxchAhwscO3f1iQNW/r+Mqc/pEceKDH/zxxOa5qEz2BXkKNBa+/Zi3gI85TgjvIKZ/vChQtRUMIip0iDnCINcos1//5/2bYc7TYNcv/9b0ZhCacumyFdyCjBc0cysTfYCVKRYZM7eYkGG/7htta93NHaqC2E5XmXPAGP1UDDlL5e6QSXEvjYNa46i+Y4RtXQ5zIGnRO7hIQExMbG4uzZszh37hyysrLAsixat26NQYMGabtjCTFH03ws8enlfBT/OxbqXoEaJx8VYZiZdpkR41KqWGys8If3pY5WdeomYxgGc/2ssCA2V7vtv7cKsayrrVnUDzv7pAgzTmZxxsJZ8IAfhjqgr2vDdM8xDANbIQNbIQ+eejSUr123DiXgQ8lYoJgRYMLzL0KmYiEvYSEv0ZT9v6r0sVz19P8r7tcgLVcGJWMBFVP2Z+9yZgnGHMnEvuFOsDdgcvefW4XIVJaVvrGxYPByx4abLS1BCbxUGbhr4ard9nuKEgsDGldiZ45jVA19LmPQObEbNGgQAKBDhw4YNWqUNpFr2bKl0YIjxFCcxHyM9ZJgV7kus6035JTYmZAp73p33ilEmqLsD6+VgMHcenSTTWgrwXsX8pD3bwtgdpEGvyQrMNXbtFX/r2YVY8rxLM44QgbANwPsEdTK/N/7DErXfhayaoAFujrVXFuwJuvWrYMSAmy3GoAHAift9qtZJRh9OBP7hjsaZNytUsVi/d/ccYxz/awM3ipYmw6qh1UkdjT8pDnQObH773//i/79+9NMWNJozfK14iR2hx8okSpToZW1eY2Fai5Mdder1rCIrlCQeEYHSzjU44+6pYCHF3ysOK2AW67LTJrY3c4rwfijWcgv4Q5u+yJQ2qjXta0PMVR4Qf4ntls9gxSBs3b7tewShB3OxP7hTnCW1C+5++l2IR4Xlt00SPiMSWob+pY8wiFJd+3j8+nFSFeo4VLPn4+YP53/ooWFhRkzDkKMro+LEB3tBUjMUQEoHQv1n1uFWN7d1sSRGZe5jwdpaAfuK3C3oKwJy4IHvOpf/5aM2b7cxO5SZgkuZxSju3PdW5nqKlWmwtgjWZzuQAB4v4ctwhtoAL+5Ev2b3J3uMAVnyq26kZijQtjhTBwY4VTn5KdEw2JthZuGcF9LOJlgBr4dq0AXRwtczSod+8mi9Gb2xfbN+/ffHFBTBWk2GIbBHF9rvBlXfiyUHG93tTHK8j7mwtzHgzQklmWxNoE7tm5SO0u0NMB6p21tBQhqKcLxh2VLOH17Q45NDZzYZSnVGHc0C6lybiXh1ztZY1GA+a+K0RCEUGPXs46Yejybs+TWjVwVRh0qTe7cLPV/T+y8U4gHsrLrLuKXFiQ2lRAPsTaxA0pnx1Ji1/SZ/2rohBjQxHYSWJcb0J6m0OC3+7XX7yJNw8lHRUgoVwuNAbCwk+GSnTl+3D+ae+8VNuj6qfnFGkw4loVbeSrO9uk+lvi4p61RltJrrCwFPPwc5Iih7twJJLfySpO7R3L9fm9qDYsvrnJb66b7WKFFHRJEQwnxkHAen3qkhKyk8axnTOqGEjvSrNhY8DC5wrinrTdk1RxNmpq1Cdw/vKM8xWgvNdxMwWdbiuFhXfaHvEgN/Jikz1ILdVcCHp7/Xxb+yuQW8Q3zFGNtPykldVWQCBj8NMwRz7bkJne381UIPZSBVJmqmmdWtvcet4tfwAALTNxC2sleUOn9eKJcizJpmiixI83OrA7cVpU/nxTjVm49KtqTRuFiRjH+fMJdMusNA88S5PMYzKkwhm3rDTnUGuNW51WDwW7LvpwxYwAwqIUIWwY5QNCEhxrUl1jA4MdhjhjRmjtL+F6BGqGHMnG/oPbkTsOy+LzCTcMUb0t4mHhiFsMwCKmw7vHvKYpqjiZNBSV2pNnxd7BAXxfuuKdtN+XVHE2aioqtdQNbiNDDCOPfXvCxRPmx8ikyNY6mGq+7XwPggKQnblpwS0/1dLbA9mEOEOm5kkZzJOIz+O8QB4RWSILuy0qTu+RakruD95W4kVt2DI8B3uxsHqVFKnbHHklVQmXkGw1iWjondunp6bh69SpnW1JSEhYvXow5c+bg0KFDBg+OEGOZVaFV5afbhZDT2JMmK4Nng99SuMmVsSYSOIj5GNeG292/5YZxbhxYAEfEXXBV6MXZ7icVYFeQI6yNvC5pUyLkM/h+iANGe3KTu1S5GqG/Z+JuftXJHQtgTYWxdRPaSOq8TJuh9XMVQiosS+5ziljEpRXX8AzS2On8qV+6dCmWLVumfZyTk4OQkBB8//33OHz4MKZNm4ajR48aJUhCDG2MlwSO5QqG5hez2HOveXRR5BdrcNnCC/skPREv9EZzSGdjRR04j7s4WmCwu/FWXZhbYRLF/x4W4U6e7uO1dKFhWZwU+SNe1J6z3cOaj73DnepVl6+5suAx2DrYAePacFu5HhaqEXooA7fzKg/ZSBK4cSbkAMCbXcyjtQ4ABDwGw1tTd2xzovMtxYULFzBz5kzt4507dyInJwenTp1C+/btMXr0aERHRyM4ONgYcRJiUCI+g+ntLbHuWtnEiW035E22FICGZfHn4yJsv12IX5OVUFj2AgBcAaBkLPCmjudpjDXx8hgJEiw8Odve7Gxj1MkE3ZyE6OlsgYsZZX/wt96UIbK31CDnv5ZdgrfO5uK8uCNnu4uEh33DnUw6E7Oxs+Ax2DzQHgIGnILmjws1CD2UiV9HlK1awQL4Q8T9HYz2FMPXgBNyDCHEQ4Kdd8p+lt9TlIjszdKEmiZK58QuMzMTbm5u2sdHjhxBYGAgOnXqBACYMGECIiMjDR8hIUYys4MVoq/JtAujX8kqLSjblNzNV+Gn24X4+XZhpbpmT50W+eFOngrt7Gr/OmiMNfHiRO21i6EDQDtbPkZ5GH85rTm+1riYkaN9/GNSIZZ3q18x7PxiDaL+ysc31+WoOExKzBZjb3Ars+kCbMwEPAabBtiDz2Ow43bZrOY0RWlyN5FnCxdNPu7xXfBQ4Mh57mIzaq17alhLEUT80lmxQOnYwcQcFfwdShPQxnjDRqqn8zeAVCpFeno6AEChUCA+Ph5LlizR7mcYBkVFNI2aNB5eNqUFZY+Vm/6/9aYcPiaMyRDyizXYl6zAjtuFOo2lUTN8LDmXiz3Bjk3uDr6QEeKSsC1n28IAG/AbYJboWC8Jlp/PQ1ZRaWd3fjGL3Xfr1gXGsiz23lNg+fk8PFFU7jwXsiV4Xn4GnRym1ytmUobPY7DxGSkEDPBDuZI1GUoNvrcajBflf+APsR/nOcNbi9HZseFXGqmNtQUPg1qIcDS17LvutxSFNrFrjDdspHo6j7Hr3bs3tm3bhkOHDuHdd9+FUqnEyJEjtftv377NadEjpDGoOIli710FFDCvbhRdaFgWpx4pMe+PbHT4+QkWxObWmNRZarg3YSceFWF/ctMr1Bwv9EYJU3b/6ibhYXK7hlknVSxg8GJ77mt9e0MOfecjJuWVYOyRLMz+I6fKpK5DyUPMLzgKD3VWPaIlVeExDKL7SxHegft7LOSJsM16CO4LXDjbl5hha91ToRVmx/6e0vQ+76SUzondhx9+CB6Ph+effx7btm3DnDlz4OvrCwBQq9U4cOAA+vfvb7RACTGG4FZitCq3nJRCzeJKhRmG5uxOngqfXMpH55g0jD2ShV13FVCoq04dJHwGk9pKsG+4IxYXHICXKp2zP+J8Lgqa0MzgIvBxXujN2faqv3WDlv8I97VC+cbBv7NL8IDvWP0TyilUabDiUh767UvnLHv1lIc1H1PlZzC18Czs2YYpgtwc8RgGXwRKMbfCTWAxw70BHOIuQk8TrAusqxGtxSj/zr+SVYKHeq6uQRoHnbti27Zti4sXLyIxMRG2trZo27ase0MulyMyMhJdunQxSpCEGAufxyC8gxVWXM7XbrsobIe+xUkw105JJQT4x6I1RvyWgXPptXe1BroKMdXbEmO9JLAVlt7LXQEQqriMTdbB2vFnjwtLx28ZaoC/qV0WtoWCVzbz1U7IYKZvw06O8bAWYERrMad15LzQGx6KmlvXDqUo8HZ8Hmfd0acseMDCTjZ4s4s1Nm94bPCYSWUMw2BVXzvwecDXiVWXrjHHsXXluVry0dPZAhfKTeg5lKLAHD9aP7ip0avIkVAoRNeuXTlJHQDY2tpizJgx8PLy0vlcX3zxBYYMGYLWrVujXbt2mDx5MhITEznHsCyLqKgo+Pr6ws3NDaGhobh+/TrnmNzcXMybNw8eHh7w8PDAvHnzkJubC0J0Nb29JcqX+8ri2+Ae36X6J5hIGs8WeyS9scY2DL9a9qwxqWtlxcfiLja4NM4Vh0Kc8WJ7K21S95SzpgD9im5ytn2TKMe17Ma/CkdygQpnK5QBmetnDRsT1HWr2NKTaNEKBUzVpVbuF6gw9XgWpv4vu8qkblALEc6OdcG7PWxhKaAadQ2JYRhE9bbDa/6VE6FAVyH6uxmvfI6hVOyOrVjbkTQNek2fKigowNatW3H69GlkZGRg3bp16NGjB3JycrB9+3aEhIRUSvqqc+bMGcyePRvdu3cHy7KIjIzE2LFjER8fD3t7ewBAdHQ0Nm7ciI0bN8LHxwerVq3Cc889hwsXLsDGpvTuaM6cOUhNTUVMTAwYhsGCBQvw0ksvYefOnXpeCtJcuUj4CPOUYG+5OnYXRO3QtjC9hmc1rPsFKmy1Hlqp+6c8CZ9BmJcY07wtMaCFCDwdJkIMLLqOa0IP5PFKkw81CyyOy8WhECednm9uZCUarE0owIZ/ZCjilY2LErAqvNzRNKVsBrmL4G0rwO1/C9xqGB4uC9tiUFHZTWqRmsWGv2VYc7Wgyq50NwkPkb3t8FwbidlNcNFlRmXFgfhA5RmVhjqPMTEMgxW9bPHXpYuIFZcOReKxGrzbvX6znRtKiIcYH14q650486QIecUa2AnpJqEp0Tmxe/LkCUJCQpCSkgIvLy/cvXsXMllpDTB7e3ts27YNqampWLlypU7n27t3L+fxN998Aw8PD5w7dw4jR44Ey7LYtGkT3njjDYwZMwYAsGnTJvj4+GD37t0IDw/HzZs3cfz4cRw+fBh9+vQBAKxduxYjR45EUlISfHwa+/xG0lBm+VpxErsbAnfkM2LYsuZxR/vRpfxqk7pAVyGe97bEmHJdrboSQo0QxV/YYfWMdlt8ejG2JxVieiOq6ceypTNOP7iYh0eFlccJdi++ByexZxXPND4ew2C2rxUizudpt10UtsUzRTfAB4s/Himx+FwekqooYMxjgJf8rBDRzVbv321NDJlEVTWjsi4MdR5jYxgGQUXX4KHOxF2BC9qXPEZ/t+dNHZZO2kstODcZJRrgeKoS49s2zIQi0jB0Tuzef/995OTk4OTJk3B3d4e3N3dQcmhoKI4fP17nQGQyGTQaDaTS0vE99+/fR1paGoYOHao9RiKRoF+/foiPj0d4eDjOnz8Pa2trbVIHAH379oWVlRXi4+OrTeySkpLqHKeuGuI1SPX0vf4uLNDGUox7haV/PFmGh8vCNhhcdN2gv8u6nCshn4e997h11+w0ckzwFCLURYXWkkIAuUi7D6TVIaYOqscY6KDC6eyyr4N343Pgq3qEutRZbejrdVPGYM1dIa7kV12Ut40qDUHKa0hKalnlfmPFVV4fHiDhSaDQlLa2FfAscVHYFg/4TvjwSNXj7QJs1FjarhgdrAuRdj9Dp9+trnE5OTkhNDRU1/Dr9Brmrj4/B4PSz00H1eN6n6siY58r0MYCt/PLPtg7/8lAZ3XtY3XN9ffemK69odTWaKVzYve///0PL730EgICApCdnV1pf5s2bfDw4UP9I/zXsmXLEBAQgN69ewMA0tJKv8acnZ05xzk7O+Px49IPU3p6OhwdubW3GIaBk5OTtuZeVYzdkkethaZXl+s/XyXD2/FlrSqXhG0xoOiGQX+X+p6LZVnM/y0DQNm4Nzd1DubJjuPNIMPVlVo/tCX6/pKu7QbMUzH4IccJX/a31/tcDXW9spRqfHI5H9/fLKyyhIi1RoFnlQkIKEkBrwHjqs6UnBx8d7Ns9uohSfcqj3MQ8fBRT1tM87HUuzucvnd0Z+r3g6nO9YJtEX54mKl9fC7PAp5tPSCsZba4ub63GtO1byg6t+3L5fIa69QVFhZCra7b1Ol33nkH586dww8//AA+n3vXXXE8CcuylRK5iioeQ4guJntbwlJQ9r4p4FnilqCFCSMC9t5TcJalAoDhiqv6zXrSgaeNAG935c7q+++tQpxPN7+i4yoNi82JMnTfk4bvqkjqLHjAGwHWeL3gMLr8m9SZgzm+tc8+nNHeEhfHuWB6e6tGOcaRmL+ezkI4i8utk13C4swT8/uck7rT+Tuvbdu2+Ouvv6rdf+LECXTs2LHa/dWJiIjAnj17cODAAc6sWldXVwCo1PKWmZmpbcVzcXFBZmYmWLbsq51lWWRlZVVq6SOkNnZCHia25c4aOyHuhKJq6sIZm1LFcgY6A4BvyUO0UWcY5fVe9bdG+wrLir0ZlwdVxbWrTOiPR0UYuD8db8fnIa+4clzDW4lwbqwrPuxpBxEqj1kzJX8HCwS6Vl3nrLODBY6FOiO6vz0cxLTOKzEePo/BiNbcoR1UrLhp0Tmxe/HFF7Fjxw7s3r1bm0gxDAOlUokPP/wQp06dQnh4uF4vvnTpUuzevRsHDhxA+/bc0gSenp5wdXXFyZMntduUSiXi4uK0Y+p69+4NmUyG8+fPa485f/485HI5Z9wdIbqqWNMpg2+HzxMKTBLL14kyTskLHqtBkDLBaK8n5DNYE8itYfd3dgk2X6+6bldDSpGpMONkFsYcyURibuWErZ0tHzuDHLHzWSed1rw1lVcrlMoQsSX4rI8dToQ5o5eL+Ra3JU1LiEfFxE7BaSBpLgoZC+yW9MF66+E4LuoEWRMp0K7zN+DLL7+Mf/75B3PnzoWtbenU7rlz5yI7OxsqlQpz5szB88/rPjNo8eLF2LlzJ3788UdIpVLtmDorKytYW1uDYRjMnz8fn3/+OXx8fODt7Y01a9bAysoKEyZMAAB06NABQUFBWLRoEaKjo8GyLBYtWoThw4eb7XgAYt4CHCzQrfge/hK20W774moBxnhKtOsqNoQMhbpSQtmr+DacNDKjvu7AFiJMaifBrjtlM4QjL+djrJcE7lYN35JUqNIg+poM0dcKoKxipIe1gMHbXW3wckfrWscImYNRnhI8q7iKvy1ao5U6GwOLruOlji+bOizSzAx2F8NSwKBQVZrMPSrU4EpWCbo5NZ+bC3mJBtstB+ChoHQlmDN8WwTuS8fnfaUIrtCi2djodWu7fv16TJkyBfv27cOdO3eg0WjQpk0bjBs3DgMGDNDrhbds2QIA2lImTy1duhQREREAgIULF0KhUGDJkiXIzc1Fjx49sHfvXm0NOwD49ttvsXTpUowbNw4AMHLkSKxatUqvWAgpL1hxFUkCN8h4pd2yKhZ4PTYHx0KdG2TxeACI+qsABSVld9BSIYNB+Yk1PMNwVvS0w+EHSuT/29UpU7F490Ietg12aJDXBwAWQKKgJXrvTUdqNcseTWknwYc97eBm2bi6LvsX30L/4lumDoM0YxIBg6HuIhws1wX7W4qy2SR2JRoWM09ma5O6px7I1Jh0PAvj20gQ1ccOLpLG9d3ylN59Fv379zfImrC6rA7BMAwiIiK0iV5V7O3tsXnz5nrHQ8hTEpQgVHEZO63K3ueXM0uwKVGG1zoZf9mg6zkl+P4Wt/vz7a62KD7WMCtCuFry8V53Wyw5VzZDeO89Bab7KDGkpfHvZNN4tjgk6YZkgQtQRVLX1dECq/raobeL+Vf6J8RchXiIOYnd7ymKRlNouT40LIvXzuTg2MPqJ4zsuafA8YdKrOhlhxfqMDvd1HQeY+fs7Iw9e/ZUu3/fvn00YYE0GX6qR/ArSeVs+/RyAe7lG39A/nsX8lB+vkJbGz7mNPAap7M6WKGrI7frefG5XChVxhuHo1CxOCzugq+tny1N6ipwEvOwvr8UJ8KcKakjpJ6GtxajfAdEYo4KyQXmNeHIGD64mI+d5YaaAICtphBMhTGGecUsFsTmYtShTNzKbVzLLOqc2KlUKmg01Q8srG0/IY1NiOIyxGxZ4U6FmsXCs7lGHWR8PFWJ4xXuJD/uZdfg48f4PAZfBEpR/lXv5Kvx5d/GmUiSkFWMIb+m45yoPViG+7UkYIBX/K1wcZwrlQEhxEAcxfxKs7Sb+tqx668VYP3f3HHK9moZ5sqOY67sODpXMY76bFoxntmfjs+u5JusQoK+9CrxVFNtuMuXL8POzq7eARFiLmzYIgxXXOFsO/24CD8kFVbzjPpRaVi8dyGPs62/mxChHqYZyNvdWYhZFVoKP08wbKulWsMi+loBhh3MwI0qZrsOdhfhzFgXRPaWQioyl4p0hDQNIR7c8k6/pyiqObLx23G7EO9d5JaPstIo8ULhadiwRXDX5OJEmDNW9LLl1DMFgGJN6bjnAfvTcbYR1Pyr8Zvym2++QY8ePdCjRw8AwLvvvqt9XP6ft7c3vv76azz77LMNEjQhDaVryX0Mced2+717IQ+PC+tWjLsmP9wqxPVyyQ0D4NNediYttv1ed1s4lStm7DmhaAAAIABJREFUWqQGlsYbptUyRabC6COZ+OBiPipWGbDTyDFZHotfgh3hW5d1zQghtap40xiXVozsqqafN3JHHijx2pkczjYbCwbT5H/CUVM2nlnAY/B6JxvEjXVBUMvKwz1u5akQcigTb8TmILfIfHsoa0zs7Ozs0Lp1a7Ru3RoA4ODgoH389J+Hhwf69OmDd955B59//nmDBE1IQ2EArOsn5dzB5RezWBxn2C7Z/GINPv2Lezc5xdsSXU08S00q4mFFL25L/NHUIvx6v35dNrvuFOKZfemIfVJ5jcquxfcwv+Ao/FSPaAUZQozIy0aAjtKyOZQaFjj8oGl1x55PL8LMk9ko34sq5AE/DnWEu6bqSZyeNgLEPOuIrYPsOat0PPX9rUL0+SUN++6ZZ/2/GmfFTpkyBVOmTAFQWkZk6dKlGDx4cEPERYjZ8LQR4L3utog4X9ZN+luKEgfuKzHGS1LDM3W3NqEAmcqyO0AJn8F7ZjJDbUo7CX64JcfZtLIkLCI+D0NbimBtoV/3aG6RBm/F5WLPvcpdPvYiBkHZsfBX1X3NaUKIfkI8JUjMLRs7+3uKEs/7NOxkLWO5kVuCSceytGtgA6U365sHOmCQuwjVr6VVOvRsfFtLDG0pxvsX8ioNwUlTaDDzVDaGtxZjTV/zGoam87fyoUOHKKkjzdY8Pyv0cq4wSzQuFzkGaI6/X6DCV4ncAb0LAqxNUhC4KgzD4PNAKcoPO3lYqMaqK/pNpDj9uAj996VXmdQNcRchdowrJXWENLCK3bEnHhVBYcTZ7w0lVabC+CNZyK2w9ODqvnYY20b3G3J7EQ/rn7HHwZFO8Lat3BZ25IESfX9JR5zQB+bSOavX7bZGo8GOHTsQHh6OYcOGYdiwYQgPD8fPP/9MM2JJk8bnMfiyvz3KN1BlKDV453xe9U/S0YrL+SgqN6zFTcLDgk61LxjfkPzsLSoth/XVPzIk5tReBqBIzeLd83kYczgTDyuMTRTxgajedtgT7Gg2iSwhzUlXRwu4W5Z9sRWqWPzxuHF3x2Yr1Rh/NKvS983bXW0qLRupq2fcRDgzxgVvd7VBxY4KuYrFEUlXbLEahsc8adUnaEA6J3YFBQUYOXIkXn31VRw7dgwKhQIKhQLHjh3DK6+8gpCQEMhkxl3uiBBT8rO3wOIu3ALFO24X4n8P6/4leCG9GLvvcluw3uthCys9uzgbwpKuNmhVLvlSscBbcbmo6d4+MacEQ39Nx4Z/ZJWO6+RggVNhLpjvb00lTAgxEYZhMLLC7Njf6jmG1pTkJRpMPp6Fm3ncWfbhHSwR0bV+BebFAgbvdLPFn2Nc0LeKtZ0fCRyw2XoYnvBM2zWr81+PTz/9FBcuXMDHH3+MO3fu4OzZszh79izu3LmDFStW4MKFC/j000+NGSshJrcowIYz2BgA3jibW6fFo1mWxfIKLX4BDhaY6m1ZrxiNxdqCh5V9uF9YcWnFuGrhWelYDcviq39kGPJrOv7J4X7BMgAWdrLG/0Y5w8+eZrwSYmoVu2MPP1CaTbeiPko0LMJPZeNCBrcnIcxTjDV9pQabjOUrtcDvIU5YGyiFrZB7Tm9VGlw19e/JqQ+dE7tff/0VM2fOxGuvvQaRqGwasEgkwquvvooZM2bgwIEDRgmSEHMh5DNY/4w9p2L7A5kaKy7lV/+kauxLVuB8BndW6Ce97My69SrUQ4zhrbhlAI6KO6OQKUvQHsnVGHc0C++cz+N0MQNAKys+Doxwwke97CBq4KLLhJCqPeMmgq1F2ecxQ6lBKt+xhmeYHw3L4vUzOTiayq0z94ybEN8OdDD4Ot88hkG4rxXOP+eKsf9OorNgVQhVXIapv9l0TuwyMzPh7+9f7X5/f39kZmYaJChCzFkPZyHmd+SO09h8XY74NN0LVypVLD6oUCxzZGsxBrmb91JZDMPgs75SiMsNhyvkiXFCFAAA+EfQCv32peHUo8rXYmJbCc6MccGAFub9MxLS3Aj5DIJacVvtblq4myiauvngYj5+rrBUWCcHC2wf5gixwHiplpslH98PccBU+RmEKP6ClDVOAXt96JzYtWrVCn/88Ue1+//44w+0atXKIEERYu7e6WYDL5uy7IYFsCA2V+clZ765LkOKrKw5S8AAH/cyj/ImtfGyEeCtztyxKheFbfGTZX/EWAVWmoVmK2SwZZA9vh3kQKtHEGKmQip0x94QtDRRJPqraqkwT2s+9jzrCDthw3zndFA9RreS5AZ5rdro/BNPmzYNv/76K15//XXcunULarUaarUat27dwoIFC3Dw4EG88MILxoyVELNhZcFDdD97zrabeSqsuVp7CZAMhRqfVzhutq8VfOwaz3izBQE23Kn/DINbVdzhP+MmROwYF0xoa57jBgkhpZ5tJebM9szi2yCDV7/JBg2hqqXCnMU8/DLcCa6WzXOmfY0Fist74403cO/ePfz444/Yvn07eLzSd4BGowHLspg+fTreeOMNowVKiLkZ5C7CdB9LTuHKtQkFGOMlQacqFpN+auWVAuSXlLVq2QkZLK3nbK2GJuIzWBNoh7FHsqrcb8ED3u9ui1c7NcyM17i4OMTHx9d4zLp16ypt69OnDwIDA40VFiGNhp2Qh2fcRDhZbhjFTYE7nItvmjCqmt0SuGFFFUuFxTzriLZV1JxrLnT+yXk8HtavX4958+bhyJEjSElJAQB4eHhg+PDhCAgIMFqQhJRnTn/EV/Syw7FUJZ4oSueQqVjg9dgcHAt1hqCKwbrXc0rw3U05Z9vbXW3hIG58d5aD3cUY30ZSqeCwn1SAzYMcEFBDcmtogYGBBvvdmtP7i5CGFOIh5iR2Nyzc8YyeiZ0un5+q6Pv5ecB3wC7LwCqXCjP1UoymVmNid/36dfj5+XG2BQQEUBJHTKqmP+JJSUnw8fFpsFikIh7WBErxwols7ba/MkuwKVGG1ztVboV7/0IeNOW+iNra8DHXt/Eu3/Npbzscv5OJPF7pz9C36Bb2hQ026mBlYzNkkkhMg5LzuhnZWowl58pKdaTyHVHAiGDD6j4xrKrPT8VrXd/evQyeDX6yfAYqpiyFKb9UWHNXY2LXr18/BAQEYNKkSZgwYQLc3NwaKi5CGo1RnhKM8RJjf3JZUc/IywUIrVD087bAFccecr8gP+plB2EjLvvhZsnHXNn/cE/gAjd1Lpw1BRALhpg6LNLMNYfk3BjJaytrAbo6WuBK1r914BgGtwTu6FFyr97xGkoBI8YPVgOh4HETOH2XCmvKakzs5s+fj19++QXvvfcePvzwQwwYMACTJk1CWFgYrK3Na8kjQkxpVR8p/niUpp0RqlCzWBCbg2EovZPUADgq7sJ5Tj9XIUZVmInWGFmzRQgoeWDqMAgxCnNt/TNW8hriIS5L7FDaHWtOid1xcQDyedzJWPVZKqwpqjGxi4yMxKeffoo//vgDu3btwsGDB3Hq1Cm89dZbCAkJwaRJkzBs2DDw+Y1vfBAhhuRqyUdkbzu8ciZXu+3Mk2LYW7RBj5J7+MuiDdL53FUbInvbGawSOmn6zDXBaOrMaehHQwjxkCDyr7JZ+3cFriiCefyNv5evQoKFB2fbzPb1Xyqsqal18gTDMBg8eDAGDx6MtWvX4tChQ9i1axd+/fVX7N27Fw4ODhg3bhwmTZqEnj17NkTMhJilqd6W2H1XgRPlBh8flXSBhzoTJ8SdOMdOaSdp9gN8iX6aW4JBTMPfXgBPaz7u/1tnU83wcUdgHsOwvkgoAMuU1WRxVOdjTaA73SBXoFflPpFIhLFjx+Knn37CzZs38cUXX6B9+/bYsmULgoOD0aNHD2PFSYjZYxgGa/tJYVVu4kARY4Et1kMh55V1uUr4DN7rYdpFogkhpCoMw1QuVmwGq1DcL1Bhx23uqg4Di65XWX2guatzSWapVIqZM2fio48+wogRI8CyLO7dM59+eEJMwdNGgPd6cFeQKGK4LXOvdbJGSyvz6NoghJCKQipM/LolcIdKo9uqOsay7loBVOVCcFAXoBON7a1SnSr4JSUlISYmBrt370ZycrK2u3by5MmGjo+QRmeurxX23C3EhYySSvtcJTwsDKBBvoQ0R41lnGSgqxASTZF25qmSJ8TJR0V4tpVpJnulylT4MYnbWjeg6Dr40D3ZbCzX3hB0TuyePHmCPXv2ICYmBgkJCWBZFp06dcLHH3+MiRMnwtXV1ZhxEtJo8HkM1j9jj4H701Gs4e57t7strC1qbyhvTl9ChDQXjaUMi4DHoL3qMa4KvbTbPrqUj6HuIvBN0PUZ/bcMJeW+S6UaOTqXpOh1jsZy7Q2hxsQuPz8fBw4cQExMDGJjY6FWq9GyZUssXLgQkydPhq+vb0PFSUij4iu1wOIuNpzZZa7qXDzvrdtYleb0JUQIMT89i+9wEru/s0uw/XYhXmzfsAXVnxSq8d9b3NV6Bij1a61rbmpM7Nq3b4/i4mJYW1tj6tSpmDRpEgYMGNBQsRHSqL0RYIMD8f/gb6EHbDSFGFcYDz7P39RhEUJIrVqrs9GpOAV/C8vKi3xyOR9jvSSwFdZ5eL7evvy7AEXqsse2mkJ0KUlusNdvjGpM7IYMGYLJkydj5MiREIlomQ5C9CHkMxiviMdI5V8QsipYQFP7kwghxEwEKRNww6IlVEzpZK90hQZrEwrwQc+GmdWfrlDjuxuVx9YJTNxaZ+5DZWpM7Hbs2GH0AAhpyhgAVmyxqcMghBC9SVkF+hXdxGlxR+22jf/IMKODFbxs6jT3Ui8b/pZBoS5L4lpY8tA1L9nor1sbcx8q03DtqYQQQghpVPoX3YC1RqF9XKwBPriYZ/TXzVKqsfUGd2zdwgAb6vnQASV2hBBCCKmSCGoEKa9xtu1PViL2SVE1zzCMr/6RQV6ucJ2LhIcZDTxxo7EyflsqIYQ0U+Y+FocQXXQuuY9kx/64klVWm/Od83k4GeYMnhGW88op0mDzdW5r3YJO1pAIaJUJXVBiRwghRmLuY3EI0QUPQFQfO4z8PVO77WpWCXbcLsQ0H8O3om1KlKGgpKy1zlHEQ3gHaq3TlUm7YmNjYzFlyhT4+flBKpVi+/btnP3z58+HVCrl/AsKCuIcU1RUhCVLlqBt27Zwd3fHlClT8PDhw4b8MQghhJAmLdBVhOe8uEuNfXwpH7ISw455yyvW4OtEGWfb652sYaVDYXdSyqRXSi6Xo2PHjli5ciUkEkmVxwwePBg3b97U/ouJieHsj4iIwK+//oqtW7fi999/R0FBASZPngy1Wl3l+QghhBCivw972kJUbpnrNIUG6xJk1T+hDjYnypBfXNZaZy9iMNuPWuv0UW1XrL29PRg9+84ZhkFWVpbOxwcHByM4OBgA8Morr1R5jEgkqna5sry8PPzwww/YuHEjhgwZAgD45ptvEBAQgFOnTmHYsGF6xU8IIYSQqnnaCPCavzU+L5fMrf+nAC92sISHdf1HdhWUaLDxH26i+EpHa9hQa51eqv1NvP3223ondsYQFxcHb29v2NnZoX///njvvffg7OwMALhy5QpKSkowdOhQ7fGtWrVChw4dEB8fT4kdIYQQYkBvdLbBj0mFSFOUdsEWqYEPL+Zj22CHep97y3U5csu11tkKGczraF3v8zY31SZ2ERERDRlHlYKCghAWFgZPT0+kpKTgk08+wejRo3Hq1CmIRCKkp6eDz+fD0dGR8zxnZ2ekp6dXe96kpCRjh94gr0GqZuhrb8jzmev7wlx/RnO9XuaIrpVpNIXrfuvWrVp/jvKzt/tYeOGAZS/t4733FAixuYMutrqNt6vqtRRqIPqqBKVl3UtNdi1G+v07qP6vedO4/vry8fGpcb9Zz4odP3689v/9/f3RtWtXBAQE4MiRIxg9enS1z2NZtsbWxtouSn0lJSUZ/TVI1Yxx7Q15PnN9X5jrz2iu18vc0HeOaTSV667vz6DWsBjyawYSssvKn3z1yAbHu+tW/qSq11v/dwFyVfnaxzYWDJYP8IBUVHM3bFO4/oamd2L3+PFjXL16FXl5edBoKmfnU6dONUhgVWnRogXc3d1x9+5dAICLiwvUajWysrLg5OSkPS4zMxP9+vUzWhyEEEJIc8XnMYjsY4dRh8rKn1zOLMGuOwpM8bbU+3wKFYv1f3PH1s3zs6o1qSNV0zmxKy4uxmuvvYY9e/ZAo9GAYRiwbGlfePnWMWMmdllZWXj8+LF2MkXXrl1hYWGBkydPYuLEiQCAhw8f4ubNm+jTp4/R4iBNBxWQJYQQ/T3jJsJoTzEO3Fdqt310KQ9hnmK9S5P855Yc6YqyhiIrAYNX/GlsXV3pnNhFRkZiz549iIiIQGBgIEaNGoVNmzbBzc0NGzZsQEZGBr7++mu9Xlwmk2lb3zQaDVJTU5GQkAB7e3vY29tj5cqVGD16NFxdXZGSkoKPP/4Yzs7OGDVqFADAzs4O06dPx/vvvw9nZ2fY29tj+fLl8Pf3x+DBg/WKhTRPVECWEELq5uNedjj8QInif3Oyx4UaRP8twzvdbHU+h1LFIvpaAWfbHF8rOIr51TyD1EbntHrPnj2YPHkyFi9eDD8/PwClXaODBw9GTEwMLC0tsW3bNr1e/K+//sLAgQMxcOBAKBQKREVFYeDAgYiMjASfz0diYiKef/559OzZE/Pnz4e3tzeOHj0KGxsb7TkiIyMxatQohIeHY8SIEbCyssLPP/8MPp/eFIQQQoixeNkIKrWsrb8mQ6pMpfM5fkyS43FhWWudhM/gtU7UWlcfOrfYpaena7s3BYLSpymVpU2wDMNgzJgxWLt2LVavXq3ziw8YMAC5ubnV7t+7d2+t5xCLxVi9erVer0sIIYSQ+nuzsw22JxUiQ1manCnULD6+lI/Ng2ovf1KsZrHuGndsXbivJZwl1DBTHzq32Dk6OmqTMBsbG0gkEiQnJ2v3l5SUQC6XV/NsQgghhDQ1tkIe3u3O7XrddVeBC+nFtT53x+1CpMrLVokS8YEFnWxqeAbRhc6JXUBAAC5cuACgtIWuf//++OqrrxAXF4fY2Fhs3rwZAQEBRguUEEIIIebnBR9L+NtzOwAjzudqJ1hWpUTD4vME7ti6Ge2t4GZJrXX1pXNiN2PGDLAsq+1+XbFiBeRyOUJDQzFq1CgUFhbi008/NVqghBBCCDE/fB6DqD5SzraLGSXYfVdR7XN23ilEiqystU7IAxYGUGudIeg8xi4kJAQhISHax76+vrh8+TL+/PNP8Pl89O3bF1KptIYzEEIIIaQpGthChFAPMX5LKSt/8uHFfIR6iisdq9Kw+Pwqt7XuBR8rtLSi1jpD0CmxUyqViI6ORq9evTjrstra2iI0NNRowRFCCCGkcVjRyw5HU5Uo+XeS68NCNdb/LYOownF77ilwr6CstU7AAG90ppmwhqJTV6xYLMbatWuRmppq7HgIIYQQ0gi1tRXg5Y7cBC36mgz5TFmrnQbAmgqtdc/7WMLD2qxXOG1U9Jo88bSYMCGEEEJIRYu72MCx3FJghSoW/xOXTaz8x6I1kvLK6tzxmdKSKcRwdE6R33//fcyYMQOBgYEYPny4MWMihBBCSCNkJ+RheXdbvBlXVqP2qtALvYtvo4U6B6dFfpzjJ7WzhJdN1akILflYNzondl9++SWkUimmTp0Kd3d3eHl5QSKRcI5hGAa7du0yeJCEEEIIaRxebG+JLddlSMwta5k7LO6KvkW3kMG3027jMcBbNYytq2nJx6SkJPj4+Bgu6CZE58Tuxo0bYBgGrVq1AgCkpKRUOoZhGMNFRkgjQ3eXhBACCHgMPu1th+eOZmm3PRA4IYPPLWQ8oY0E3nYWDR1ek6dzYnft2jVjxkFIo1fT3SUhhDQnQ1qKMaK1GIcflJU/UTJC7f8zAN7qQmPrjEHnyROEEEIIIbr6pJctBNV05I31kqCDlFrrjEHv+cV//PEHjh49qu2K9fDwQHBwMAYNGmTw4AghhBDSOHnbWWBeRyt89U/ldeQXU2ud0eic2BUXF2P27Nn47bffwLIs7OzswLIs8vPzsWnTJoSGhmLbtm2wsKAMnBBCCCHA211s8d21bCh4ZWWKwzzF8HegXMFYdO6KXbVqFQ4ePIiXX34ZN27cQHJyMu7fv4+bN29i/vz5OHjwIFavXm3MWAkhhBDSiEhFPDyrTNA+FrBqLOtqW8MzSH3p3GIXExODiRMnIjIykrPdxcUFn376KTIyMrBz50688847Bg+SEFJ3NFuXEGJK3UuSIZKr8EDgCP+SB/B3eNHUITVpOid2T548Qd++favd36dPH+zfv98gQRFCDIdm6xJCTM1flQp/FS1L2hB07op1dXXFpUuXqt1/+fJluLi4GCQoQgghhBCiP50Tu3HjxmHHjh2IiopCfn6+dnt+fj5WrlyJHTt2YMKECUYJkhBCCCGE1E7nrthly5bh77//xqpVq7BmzRq4uroCANLS0qDRaBAUFIRly5YZLVBCCCGEEFIznRM7sViM3bt34/Dhw5w6diNGjMDw4cMxfPhwowVJCCGEEEJqp3eB4hEjRmDEiBHGiIUQQgghhNSDzomdg4MDvvnmG0ycOLHK/Xv37sWcOXOQnZ1tsOAIIU0XlWEhhBDD0zmxY1m2xv0ajQYMU82icIQQUgGVYSGEEMPTeVYsgBoTt4sXL0IqldY7IEIIIYQQUjc1ttht2rQJX3/9tfZxREQEVqxYUem4vLw85OfnY/LkyYaPkBBCCCGE6KTGxM7R0RE+Pj4AgJSUFLi6usLNzY1zDMMwsLS0RLdu3TBv3jzjRUoIIYQQQmpUY2I3adIkTJo0CQAwatQoLF68GIMHD26IuAghhBBCiJ50njxx8OBBY8ZBCCGEEELqSefJEz/88AOmT59e7f4XX3wRP/30k0GCIoQQQggh+tM5sdu6dat2GbGquLm5YcuWLQYJihBCCCGE6E/nxO7OnTvw9/evdr+fnx9u375tkKAIIYQQQoj+dE7sGIZBVlZWtfuzs7Oh0WgMEhQhhBBCCNGfzoldly5dEBMTA6VSWWmfQqFATEwMOnfurNeLx8bGYsqUKfDz84NUKsX27ds5+1mWRVRUFHx9feHm5obQ0FBcv36dc0xubi7mzZsHDw8PeHh4YN68ecjNzdUrDkIIIYSQpkDnxO7NN99EUlIShg8fjv379yMpKQm3b9/G/v37MXLkSCQlJeHNN9/U68Xlcjk6duyIlStXQiKRVNofHR2NjRs34rPPPsOJEyfg7OyM5557DgUFBdpj5syZg4SEBMTExGD37t1ISEjASy+9pFcchBBCCCFNgc7lToYMGYKvvvoKb7/9NsLDw7XbWZaFjY0N1q9fj6CgIL1ePDg4GMHBwQCAV155hbOPZVls2rQJb7zxBsaMGQOgdCUMHx8f7N69G+Hh4bh58yaOHz+Ow4cPo0+fPgCAtWvXahPNp8WVCSGEEEKaA50TOwCYMmUKQkNDceLECSQnJ4NlWbRp0wZDhw6FjY2NQQO7f/8+0tLSMHToUO02iUSCfv36IT4+HuHh4Th//jysra21SR0A9O3bF1ZWVoiPj6fEjhBCCCHNil6JHQDY2NhoW9CMKS0tDQDg7OzM2e7s7IzHjx8DANLT0+Ho6AiGYbT7GYaBk5MT0tPTqz13UlKSESJu+NcgVaNrb1p0/U2Drrtp0HXXn6GuWXO99rU1WumV2KnVauzZswenT59GRkYG3nvvPXTq1Am5ubk4efIkAgMDK60lW1/lkzagtIu2YiJXUcVjKjJ2Sx51A5sOXXvTo+vf8Oh9bxp03evGENeMrn31dJ48kZeXh+DgYLz00kvYv38/jh07pi1/YmNjg+XLl2Pz5s0GC+xpMeSKLW+ZmZnaVjwXFxdkZmaCZVntfpZlkZWVVamljxBCCCGkqdO5xe6jjz7CjRs3EBMTg27dusHb21u7j8/nIywsDMeOHcP7779vkMA8PT3h6uqKkydPonv37gAApVKJuLg4fPzxxwCA3r17QyaT4fz589pxdufPn4dcLueMuyOEGEZcXBzi4+NrPGbdunWVtvXp0weBgYHGCosQQsi/dE7sfvvtN8ybNw9BQUHIzs6utL9du3bYuXOnXi8uk8lw9+5dAIBGo0FqaioSEhJgb2+P1q1bY/78+fj888/h4+MDb29vrFmzBlZWVpgwYQIAoEOHDggKCsKiRYsQHR0NlmWxaNEiDB8+nJpoCTGCwMDAahM06hohhBDT0zmxy83NRZs2bardz7IsiouL9Xrxv/76C2FhYdrHUVFRiIqKwtSpU7Fp0yYsXLgQCoUCS5YsQW5uLnr06IG9e/dyZuB+++23WLp0KcaNGwcAGDlyJFatWqVXHIQQQgghTYHOiZ2HhwcSExOr3R8bG8vpntXFgAEDalwlgmEYREREICIiotpj7O3tDTq2jxBCCCF1Q8M1TE/nxG7ixIlYt24dwsLC4OfnB6BsRuo333yDgwcPIjIy0jhREkIIIcTs1TRcgzQMnRO7RYsW4eLFixg9ejS8vb3BMAyWLVuG7OxspKWlITQ0lJbyIoQQQggxIZ0TOwsLC+zatQsxMTHYt28fGIaBSqVCly5dMG7cOEyaNKnG2nGEEEIIIcS49F55YuLEiZg4caIxYiGEEEIIIfWgc4FiQgghhBBi3qptsXv11Vf1PhnDMNiwYUO9AiKEEEIIIXVTbWJ3+vRpvcfM0Rg7QgghhBDTqTaxu3btWkPGQQghhBBC6onG2BFCCCGENBE1JnZHjx7FkydPONsKCwvBsmylY2/dukXj6wghhBBCTKjGxG7KlCn4888/tY+zs7PRqlUrnD59utKxV65cwfvvv2/4CAkhhBBCiE5qTOyqapmrahshhBBCCDE9GmNHCCGEENJEUGJHCCGEENJEUGJnBD4+PqYOodmia286dO1Nh669adB1Nx269tWrda3Y5ORkXLp0CQCQn58PAEhKSoK1tTXnuHv37hkhPEIIIYQN6tShAAAPXUlEQVQQoismNze32tkQ9vb2lVaTYFm2yhUmnm7Pzs42fJSEEEIIIaRWNbbYbdy4saHiIIQQQggh9VRjix0hhBBCCGk8aPIEIYQQQkgTQYkdIYQQQkgTQYmdAW3ZsgWdO3eGq6srBg0ahLNnz5o6pCYvKioKUqmU8699+/amDqtJio2NxZQpU+Dn5wepVIrt27dz9rMsi6ioKPj6+sLNzQ2hoaG4fv26iaJtWmq79vPnz6/0OQgKCjJRtE3LF198gSFDhqB169Zo164dJk+ejMTERM4x9N43PF2uO73vq0aJnYHs3bsXy5Ytw1tvvYXTp0+jd+/emDhxIh48eGDq0Jo8Hx8f3Lx5U/uPEmrjkMvl6NixI1auXAmJRFJpf3R0NDZu3IjPPvsMJ06cgLOzM5577jkUFBSYINqmpbZrDwCDBw/mfA5iYmIaOMqm6cyZM5g9ezaOHDmCAwcOQCAQYOzYscjJydEeQ+99w9PlugP0vq8KTZ4wkGHDhsHf3x9ffvmldlv37t0xZswYfPDBByaMrGmLiorCgQMHEBcXZ+pQmpWWLVti1apVmDZtGoDSFgtfX1/MnTsXixcvBgAoFAr4+PhgxYoVCA8PN2W4TUrFaw+UtlxkZ2dj586dJoyseZDJZPDw8MD27dsxcuRIeu83kIrXHaD3fXWoxc4AiouLceXKFQwdOpSzfejQoYiPjzdRVM1HcnIy/Pz80LlzZ8yaNQvJycmmDqnZuX//PtLS0jifAYlEgn79+tFnoIHExcXB29sbPXr0wIIFC5CRkWHqkJokmUwGjUYDqVQKgN77DaXidX+K3veV1bryBKldVlYW1Go1nJ2dOdudnZ2Rnp5uoqiah549e+Krr76Cj48PMjMzsXr1agQHB+PcuXNwcHAwdXjNRlpaGgBU+Rl4/PixKUJqVoKCghAWFgZPT0+kpKTgk08+wejRo3Hq1CmIRCJTh9ekLFu2DAEBAejduzcAeu83lIrXHaD3fXUosTMgXVfpIIbz7LPPch737NkTXbt2xU8//YTXXnvNRFE1X/QZMI3x48dr/9/f3x9du3ZFQEAAjhw5gtGjR5swsqblnXfewblz53D48GHw+XzOPnrvG091153e91WjrlgDcHR0BJ/Pr9Q6l5mZWekujhiXtbU1fH19cffuXVOH0qy4uroCAH0GzESLFi3g7u5OnwMDioiIwJ49e3DgwAF4eXlpt9N737iqu+5Vofd9KUrsDEAoFKJr1644efIkZ/vJkyfRp08fE0XVPCmVSiQlJWm/bEnD8PT0hKurK+czoFQqERcXR58BE8jKysLjx4/pc2AgS5cuxe7du3HgwIFK5ZTovW88NV33qtD7vhR/2bJlH5o6iKbAxsYGUVFRcHNzg1gsxurVq3H27Fls2LABdnZ2pg6vyXr33XchFAqh0Whw+/ZtLFmyBHfv3sXatWvpuhuYTCbDjRs3kJaWhh9++AEdO3aEra0tiouLYWdnB7VajbVr18Lb2xtqtRrLly9HWloa1q1b16zHuxhCTdeez+fj448/hrW1NVQqFa5du4bXX38darUaq1evpmtfT4sXL8bPP/+M77//Hq1atYJcLodcLgdQelPPMAy9942gtusuk8nofV8NKndiQFu2bEF0dDTS0tLg5+eHyMhI9O/f39RhNWmzZs3C2bNnkZWVBScnJ/Ts2RPLly+Hr6+vqUNrcv7880+EhYVV2j516lRs2rQJLMti5cqV+P7775Gbm4sePXpgzZo16NixowmibVpquvZffPEFpk2bhoSEBOTl5cHV1RUDBgzA8uXL0apVKxNE27RUnIX51NKlSxEREQEA9N43gtquu0KhoPd9NSixI4QQQghpImiMHSGEEEJIE0GJHSGEEEJIE0GJHSGEEEJIE0GJHSGEEEJIE0GJHSGEEEJIE0GJHSGEEEJIE0GJHSGkyYqKioJUKtUu1G4M8+fPR0BAgNHOTwgh+qDEjhCit+3bt0MqlUIqleL06dNVHjN06FBIpVL06tXLqLHIZDJERUXhzz//NOrrGENiYiKioqJw//59g563MV8TQkj9UGJHCKkzsViMmJiYStvv3LmDy5cvQywWGz0GuVyOzz77DGfOnDH6a1Xlyy+/xMWLF+v03OvXr+Ozzz5DSkqKQWMy9TUhhJgOJXaEkDoLDg7G/v37UVRUxNm+c+dOuLi4oFu3biaKrOFYWFg0m3Up1Wo1iouLTR0GIaQGlNgRQups/PjxkMlkOHz4MGf77t27MW7cOPB4lb9iNBoN1q1bhx49esDFxQV+fn5YsmQJ8vLyOMeFhoaiV69euHPnDsaPHw93d3f4+Pjgo48+gkajAQDcv38fHTp0AAB89tln2u7h+fPnc84lk8mwaNEitGnTBi1btsSMGTOQnZ3NOebKlSuYOHEi2rVrBzc3N3Tp0gUvvfSSduHx6lQ1xk4qlWLRokU4duwYBgwYAFdXV3Tv3h27d+/WHrN9+3bMnj0bABAWFqaNffv27dpjfv/9dwwbNgwtWrSAp6cnpk2bhlu3btUYjy7X5MmTJ1i4cCF8fX3h4uKC7t27Izo6GizLcs4jlUqxdu1abNmyBd27d4eLiwvi4+M5+/7zn/+gW7duaNGiBcLCwpCcnAwA2LBhAwICAuDm5obx48fjyZMnNcZNCDEMgakDIIQ0Xu7u7ujfvz9iYmIwZswYAMDFixdx9+5dTJo0CdeuXav0nLfeegvfffcdRo4ciZdffhnXr1/H1q1bcenSJRw5cgQWFhbaY/Pz8zFmzBiMGDECoaGhOH78ONauXQtPT0/MnDkTTk5OWL16NZYsWYJRo0YhLCwMANCmTRvOa86ePRuurq5Yvnw57ty5g82bN8PCwgJbtmwBAGRmZuK5556Do6MjFi5cCKlUitTUVBw6dAhyuRxWVlZ6X5sLFy7gt99+Q3h4OKZPn47//ve/mDdvHgICAtChQwf0798fc+fOxbfffou33noL7du3BwD06dMHQGlyPHfuXHTq1AnLly9Hfn4+Nm/ejODgYJw6dQpeXl5Vvm5t1yQjIwNBQUFQqVSYMWMG3NzcEBcXhw8++ACPHz/GypUrOefbtWsXZDIZZs6cCWtra7i5uWn3/fLLL1AoFJg1axZkMhmio6Mxbdo0jB8/HgcOHMArr7yCJ0+eYMOGDXjzzTfx008/6X0dCSH6ocSOEFIvEydOxOLFi5GbmwupVIqdO3eiXbt26N69e6VjExMT8d1332HSpEnYvHmzdruPjw8iIiKwY8cOvPjii9rtaWlp+PLLL7XbZs2ahWeeeQb/+c9/MHPmTFhZWWH06NFYsmQJ/P39MXny5CpjbN++Pef1WJbFt99+i88//xx2dnaIj49HTk4O9u7dy+k+fuedd+p8XW7cuIHY2Fht69nYsWPRqVMn/Pjjj1ixYgW8vLzQt29ffPvttxg8eDAGDBigfW5JSQmWL18Ob29vHD58WJtYhoaGYsiQIYiMjOT8POXVdk0++eQTFBUVITY2Fi4uLgCA8PBwuLm5YcOGDZg/fz48PT21x6ekpODSpUuchO7pZI/U1FRcvnwZUqkUAMDj8RAVFQWlUomzZ89qu6hlMhm2bduGzMxMODk51fmaEkJqR12xhJB6GTNmDBiGwf79+6FSqbBv3z5MnDixymOPHDkCAFiwYAFn+6xZs2Bra6vd/5RYLMa0adM42/r376/t7tPV0y7P8udQq9VITU0FANjY2AAADh8+jJKSEr3OXZ0BAwZokzoAcHFxgY+Pj06xX7lyBWlpaZg9ezantbBLly4YPHgwjh49yuk21RXLsti/fz+GDx8OPp+PrKws7b9hw4ZBo9EgNjaW85zQ0FBOUlfe6NGjtUkdAPTs2RMAMGHCBM64wx49eoBlWYPP/iWEVEaJHSGkXuzs7BAcHIxdu3bh5MmTyMjIqDaxS0lJAcMw8PHx4WwXiUTw9PSsNDvU3d0dfD6fs00qlSLn/+3dP0hyXRwH8K8JhVG3f4JEpaZGUJGRYwXVoA3VpIhQRERzDUFFDkI0VFxycYsU+zPWUEEUUotjfyAoyK0xhOdqgRSF7/Ci5NOjZebzvsj3Aw4e7/2dc+/05Zxzr79+ZTXGhoaGDzUAJOv09PRgaGgIy8vL0Ol0sNvt8Pl8eHp6yqqfTH0m+v3K2BP3IbE8+15zczMkSUI0Gs16TOFwGJIkYWtrC3q9PuWTWEoPh8Mp56Rb8gWA+vr6lO+CIAAA6urq/tguSVLWYyai7HAplohyZrPZMDY2BuDf2Rm9Xp91jXg8DplMltL2e6j7rnR1ErNeMpkMm5ubOD8/x9HREc7OzjA9PQ1RFBEIBJJLlj/Z53flcn7ioROr1YqRkZE/HqPT6VK+KxSKtPXSXWO+rp2IPsdgR0Q5s1gsEAQBwWDww+b799RqNeLxOEKhENra2pLtLy8vuL+/T9ln9lW/h8FcmEwmmEwmLCws4OTkBDabDX6/HzMzMz/Wx3vpxq5WqwEAd3d36O/vT/ktFAqhsrIyOQuWTV2lUglBEPD6+ore3t7vDZqI/te4FEtEOSspKYEoipidnYXVak17nNlsBgB4PJ6Udq/Xi2g0CovFknXfpaWlAHJb5pMk6cNsktFozLnuZ9KNvaOjAyqVChsbG4jFYsn26+trnJ6ewmw2Zwy06erK5XIMDw/j4OAAV1dXH86LRCI/tseQiP4bnLEjoh+RKdAltLa2Ynx8PBnk+vr6cHt7C6/Xi87OTjgcjqz7LSsrQ1NTE3Z3d2EwGFBdXQ2NRpPcyP8VOzs7WF9fx+DgIBobGxGLxbC9vQ25XJ7ce5YPRqMRRUVFWFtbQyQSgUKhgMlkglarxdLSEiYnJ2GxWGC325OvOxEE4dOndTPdE5fLhWAwiIGBAYyOjqKlpQWPj4+4ubnB/v4+Li4uoFKp8nbNRJRfDHZE9FeJogiNRgO/34/j42PU1NRgYmICTqcz5R122fB4PJifn4fT6cTz8zMcDkdWwa6rqwuXl5fY29vDw8MDysvL0d7ejpWVlbz+121tbS3cbjfcbjempqbw9vYGj8cDrVYLq9UKhUIBURSxuLiI4uJidHd3w+VyZXygISHdPVEqlQgEAlhdXcXh4SF8Ph8qKipgMBgwNzeHqqqqvF0vEeWfTJIk7mYlIiIiKgDcY0dERERUIBjsiIiIiAoEgx0RERFRgWCwIyIiIioQDHZEREREBYLBjoiIiKhAMNgRERERFQgGOyIiIqICwWBHREREVCD+AaDXx0UOIPieAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def labels(xlab, ylab): plt.xlabel(xlab); plt.ylabel(ylab); plt.grid(True); plt.legend()\n", - " \n", - "plt.rcParams[\"figure.figsize\"] = [9, 6]\n", - "plt.style.use('fivethirtyeight')\n", - " \n", - "def plot1(states, dates, swing=3):\n", - " 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.errorbar(range(N), [EV(states, date) for date in dates],\n", - " yerr=err, ecolor='grey', capsize=7, label='EVs')\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)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "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." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "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], label='Margin')\n", - " plt.plot(range(N), [net_usa[date] for date in dates], label='Country-wide Net')\n", - " labels('Months into term', 'Net popularity')\n", - " \n", - "plot2(states, dates)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Month-by-month summary\n", - "\n", - "For each month, we show the expected electoral vote total (**EVs**), the swing margin needed to get to 270 (**Margin**), the overall (popular vote) net approval across the whole country (**Country**), and then the total percentage of undecided voters and in parentheses the number of states with at least 5% undecided.\n", - "Note that the country-wide vote is not all that correlated with the state-by-state margin: recently the state-by-state margin has held at 7% while the country-wide net approval has ranged from -10% to -16%, and when the state-by-state margin jumped to 11%, the country-wide measure stayed right in the middle at 12%." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "|Month|EVs|Margin|Country|Undecided|\n", - "|-|-|-|-|-|\n", - "|Apr 2019|180|7%|-11%|4% (0)|\n", - "|Mar 2019|193|7%|-11%|4% (2)|\n", - "|Feb 2019|170|7%|-16%|4% (0)|\n", - "|Jan 2019|126|11%|-12%|4% (0)|\n", - "|Dec 2018|164|7%|-10%|5% (3)|\n", - "|Nov 2018|233|5%|-11%|4% (1)|\n", - "|Oct 2018|247|6%|-11%|4% (3)|\n", - "|Sep 2018|203|8%|-14%|4% (1)|\n", - "|Aug 2018|224|6%|-12%|4% (0)|\n", - "|Jul 2018|225|6%|-10%|4% (1)|\n", - "|Jun 2018|226|5%|-11%|4% (0)|\n", - "|May 2018|232|5%|-12%|4% (0)|\n", - "|Apr 2018|209|7%|-13%|4% (0)|\n", - "|Mar 2018|196|9%|-14%|4% (0)|\n", - "|Feb 2018|247|4%|-15%|4% (2)|\n", - "|Jan 2018|201|4%|-18%|5% (4)|\n", - "|Dec 2017|189|8%|-18%|5% (8)|\n", - "|Nov 2017|174|8%|-19%|5% (7)|\n", - "|Oct 2017|209|8%|-17%|5% (7)|\n", - "|Sep 2017|201|7%|-20%|5% (8)|\n", - "|Aug 2017|163|10%|-19%|7% (33)|\n", - "|Jul 2017|196|3%|-15%|5% (4)|\n", - "|Jun 2017|248|2%|-16%|5% (15)|\n", - "|May 2017|269|1%|-11%|5% (4)|\n", - "|Apr 2017|365|-7%|-13%|4% (4)|\n", - "|Mar 2017|374|-8%|-6%|5% (14)|\n", - "|Feb 2017|402|-8%|0%|6% (48)|\n", - "|Jan 2017|448|-10%|10%|11% (51)|" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def header(head): return head + '\\n' + '-'.join('|' * head.count('|'))\n", - "\n", - "def monthly(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", - " yield (f'|{month}|{int(EV(states, date))}|{margin(states, date)}%|{net_usa[date]}%'\n", - " 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))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# State-by-state summary\n", - "\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, dissaproval, 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", - "\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 79 (if he lost them all) to 248 + 11 = 259 (if he won them all). It would take winning all the swing states plus a three-standard deviation swing in Virgina for Trump to reach 272.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "|State|Net|Move|EV|Total|+|-|?|𝝈|\n", - "|-|-|-|-|-|-|-|-|-|\n", - "|Wyoming|+28%|8%|3|3|62%|34%|4%|3.5%|\n", - "|Alabama|+26%|8%|9|12|61%|35%|4%|3.4%|\n", - "|Louisiana|+20%|8%|8|20|58%|38%|4%|3.7%|\n", - "|Mississippi|+20%|8%|6|26|58%|38%|4%|3.8%|\n", - "|West Virginia|+20%|8%|5|31|58%|38%|4%|3.6%|\n", - "|Tennessee|+18%|7%|11|42|57%|39%|4%|3.1%|\n", - "|Idaho|+17%|4%|4|46|57%|40%|3%|1.8%|\n", - "|Kentucky|+16%|3%|8|54|56%|40%|4%|1.1%|\n", - "|Oklahoma|+11%|7%|7|61|54%|43%|3%|3.4%|\n", - "|Arkansas|+10%|6%|6|67|53%|43%|4%|2.8%|\n", - "|South Carolina|+10%|5%|9|76|53%|43%|4%|2.2%|\n", - "|South Dakota|+10%|9%|3|79|53%|43%|4%|4.3%|\n", - "|**North Dakota**|**+6%**|**6%**|3|82|51%|45%|4%|2.8%|\n", - "|**Utah**|**+5%**|**8%**|6|88|51%|46%|3%|3.6%|\n", - "|**Indiana**|**+4%**|**5%**|11|99|50%|46%|4%|2.0%|\n", - "|**Missouri**|**+4%**|**7%**|10|109|50%|46%|4%|3.0%|\n", - "|**Nebraska**|**+4%**|**6%**|5|114|50%|46%|4%|2.7%|\n", - "|**Texas**|**+4%**|**6%**|38|152|50%|46%|4%|2.6%|\n", - "|**Georgia**|**+3%**|**7%**|16|168|49%|46%|5%|3.2%|\n", - "|**Montana**|**+3%**|**7%**|3|171|50%|47%|3%|3.4%|\n", - "|**Kansas**|**+2%**|**7%**|6|177|49%|47%|4%|2.9%|\n", - "|**Alaska**|**+1%**|**11%**|3|180|48%|47%|5%|5.1%|\n", - "|**Florida**|**-2%**|**7%**|29|209|47%|49%|4%|3.3%|\n", - "|**North Carolina**|**-2%**|**5%**|15|224|47%|49%|4%|2.2%|\n", - "|**Ohio**|**-4%**|**6%**|18|242|46%|50%|4%|2.4%|\n", - "|**Nevada**|**-6%**|**7%**|6|248|45%|51%|4%|3.1%|\n", - "|Virginia|-6%|4%|13|261|45%|51%|4%|1.8%|\n", - "|Pennsylvania|-7%|4%|20|281|45%|52%|3%|1.6%|\n", - "|**Arizona**|**-8%**|**8%**|11|292|44%|52%|4%|3.7%|\n", - "|Iowa|-8%|6%|6|298|44%|52%|4%|2.6%|\n", - "|Michigan|-10%|5%|16|314|43%|53%|4%|2.2%|\n", - "|New Mexico|-12%|7%|5|319|42%|54%|4%|2.9%|\n", - "|Colorado|-13%|5%|9|328|42%|55%|3%|2.4%|\n", - "|Minnesota|-13%|5%|10|338|42%|55%|3%|2.2%|\n", - "|Wisconsin|-13%|5%|10|348|42%|55%|3%|2.4%|\n", - "|Delaware|-15%|5%|3|351|41%|56%|3%|2.0%|\n", - "|Maine|-15%|9%|4|355|41%|56%|3%|4.0%|\n", - "|New Jersey|-17%|5%|14|369|40%|57%|3%|2.4%|\n", - "|New Hampshire|-19%|8%|4|373|39%|58%|3%|3.6%|\n", - "|Illinois|-22%|3%|20|393|37%|59%|4%|1.2%|\n", - "|Oregon|-22%|5%|7|400|37%|59%|4%|2.0%|\n", - "|Rhode Island|-22%|6%|4|404|37%|59%|4%|2.8%|\n", - "|Connecticut|-23%|8%|7|411|37%|60%|3%|3.8%|\n", - "|New York|-24%|4%|29|440|36%|60%|4%|1.8%|\n", - "|Washington|-26%|5%|12|452|35%|61%|4%|2.1%|\n", - "|Massachusetts|-28%|5%|11|463|34%|62%|4%|2.2%|\n", - "|California|-29%|7%|55|518|34%|63%|3%|3.1%|\n", - "|Maryland|-30%|8%|10|528|33%|63%|4%|3.5%|\n", - "|Hawaii|-34%|9%|4|532|31%|65%|4%|4.2%|\n", - "|Vermont|-37%|10%|3|535|30%|67%|3%|4.8%|\n", - "|District of Columbia|-60%|7%|3|538|18%|78%|4%|3.1%|" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "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", - " f'|{s.apps[d]}%|{s.diss[d]}%|{undecided(s, now)}%|{𝝈(s):3.1f}%|')\n", - "\n", - "md(by_state(states))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Popularity Above Replacement President\n", - "\n", - "Fivethirtyeight is a combination sports/politics site, and it has a lot of statistics about soorts 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, 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 five states where Trump is exceeding a replacement Republican (i.e., has a positive PARP): two deep-red, deep South states, Mississippi and Lousiana, and three deep-blue coastal states, Rhode Island, Hawaii, and Massachussetts." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "|State|PARP|Lean|Net|EV|\n", - "|-|-|-|-|-|\n", - "|Mississippi|+5|+15|+20|6|\n", - "|Rhode Island|+4|-26|-22|4|\n", - "|Louisiana|+3|+17|+20|8|\n", - "|Hawaii|+2|-36|-34|4|\n", - "|Massachusetts|+1|-29|-28|11|\n", - "|Alabama|-1|+27|+26|9|\n", - "|Delaware|-1|-14|-15|3|\n", - "|New York|-2|-22|-24|29|\n", - "|New Jersey|-4|-13|-17|14|\n", - "|California|-5|-24|-29|55|\n", - "|New Mexico|-5|-7|-12|5|\n", - "|Virginia|-6|+0|-6|13|\n", - "|Florida|-7|+5|-2|29|\n", - "|Kentucky|-7|+23|+16|8|\n", - "|Maryland|-7|-23|-30|10|\n", - "|Nevada|-7|+1|-6|6|\n", - "|North Carolina|-7|+5|-2|15|\n", - "|South Carolina|-7|+17|+10|9|\n", - "|Pennsylvania|-8|+1|-7|20|\n", - "|Georgia|-9|+12|+3|16|\n", - "|Illinois|-9|-13|-22|20|\n", - "|Michigan|-9|-1|-10|16|\n", - "|Maine|-10|-5|-15|4|\n", - "|Tennessee|-10|+28|+18|11|\n", - "|West Virginia|-10|+30|+20|5|\n", - "|Minnesota|-11|-2|-13|10|\n", - "|Ohio|-11|+7|-4|18|\n", - "|Colorado|-12|-1|-13|9|\n", - "|Connecticut|-12|-11|-23|7|\n", - "|Oregon|-13|-9|-22|7|\n", - "|Texas|-13|+17|+4|38|\n", - "|Vermont|-13|-24|-37|3|\n", - "|Alaska|-14|+15|+1|3|\n", - "|Arkansas|-14|+24|+10|6|\n", - "|Indiana|-14|+18|+4|11|\n", - "|Iowa|-14|+6|-8|6|\n", - "|Washington|-14|-12|-26|12|\n", - "|Wisconsin|-14|+1|-13|10|\n", - "|Missouri|-15|+19|+4|10|\n", - "|Montana|-15|+18|+3|3|\n", - "|Arizona|-17|+9|-8|11|\n", - "|District of Columbia|-17|-43|-60|3|\n", - "|Idaho|-18|+35|+17|4|\n", - "|Wyoming|-19|+47|+28|3|\n", - "|Nebraska|-20|+24|+4|5|\n", - "|Kansas|-21|+23|+2|6|\n", - "|New Hampshire|-21|+2|-19|4|\n", - "|South Dakota|-21|+31|+10|3|\n", - "|Oklahoma|-23|+34|+11|7|\n", - "|Utah|-26|+31|+5|6|\n", - "|North Dakota|-27|+33|+6|3|" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def parp(state): return net(state) - state.lean \n", - "\n", - "def by_parp(states, d=now):\n", - " yield header('|State|PARP|Lean|Net|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", - "\n", - "md(by_parp(states))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -}