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maxberggren/voting-corr.ipynb

Created August 15, 2016 16:51
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voting-corr.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sweden have a long tradition of keeping statistics of different KPI:s by municipality. Let's see what KPI:s correlate with different voting patterns. And while we're at it, demonstrate some Pandas method chaining – and do a Bayesian analysis of the Pearson product-moment correlation coefficient often denoted as Persons `r`.\n",
"\n",
"Note that the conclusions might mainly be of intrest of Swedes, and there will probably slip in some Swedish. Quick wordlist: `kommun = municipality, brott = crime, gravid = pregnant`.\n",
"\n",
"**TLDR;** Sweden Democrats (SD) shows high correlatons with mothers smoking while pregnant, unemployment and motorcycles. Also the table is a the bottom if you're not interested in code.\n",
"\n",
"And as usual, the Jupyter Notebook (former Ipython notebook) is here in a repo together with the data."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/max/anaconda/lib/python2.7/site-packages/pandas/computation/__init__.py:19: UserWarning: The installed version of numexpr 2.4.4 is not supported in pandas and will be not be used\n",
"\n",
" UserWarning)\n"
]
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"%matplotlib inline\n",
"\n",
"from matplotlib import rcParams\n",
"rcParams['font.family'] = 'monospace'\n",
"rcParams['font.sans-serif'] = ['Courier']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Starting with the data from the Swedish election 2014, we can read it directly from an url with the very nice Pandas method `read_html`.\n",
"\n",
"Encapsulating the DataFrame `votes` with parentheseses makes method chaining possible. Every method in fact returns a DataFrame which makes every preprocessing step just another step in the chain."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Percent votes in Swedish election 2014"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>SD</th>\n",
" <th>M</th>\n",
" <th>C</th>\n",
" <th>FP</th>\n",
" <th>KD</th>\n",
" <th>S</th>\n",
" <th>V</th>\n",
" <th>MP</th>\n",
" <th>FI</th>\n",
" <th>ÖVR</th>\n",
" <th>BLANK</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Kommun</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Ale</th>\n",
" <td>10.64</td>\n",
" <td>21.08</td>\n",
" <td>4.95</td>\n",
" <td>3.28</td>\n",
" <td>3.50</td>\n",
" <td>33.59</td>\n",
" <td>6.25</td>\n",
" <td>5.70</td>\n",
" <td>0.18</td>\n",
" <td>10.83</td>\n",
" <td>1.39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alingsås</th>\n",
" <td>7.48</td>\n",
" <td>21.58</td>\n",
" <td>6.17</td>\n",
" <td>10.30</td>\n",
" <td>6.44</td>\n",
" <td>29.19</td>\n",
" <td>6.85</td>\n",
" <td>10.81</td>\n",
" <td>0.30</td>\n",
" <td>0.89</td>\n",
" <td>1.21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alvesta</th>\n",
" <td>14.44</td>\n",
" <td>19.70</td>\n",
" <td>14.89</td>\n",
" <td>1.74</td>\n",
" <td>3.12</td>\n",
" <td>28.77</td>\n",
" <td>4.08</td>\n",
" <td>3.49</td>\n",
" <td>0.20</td>\n",
" <td>9.56</td>\n",
" <td>1.98</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" SD M C FP KD S V MP FI ÖVR \\\n",
"Kommun \n",
"Ale 10.64 21.08 4.95 3.28 3.50 33.59 6.25 5.70 0.18 10.83 \n",
"Alingsås 7.48 21.58 6.17 10.30 6.44 29.19 6.85 10.81 0.30 0.89 \n",
"Alvesta 14.44 19.70 14.89 1.74 3.12 28.77 4.08 3.49 0.20 9.56 \n",
"\n",
" BLANK \n",
"Kommun \n",
"Ale 1.39 \n",
"Alingsås 1.21 \n",
"Alvesta 1.98 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"votes = (pd.read_html('http://www.val.se/val/val2014/slutresultat/K/rike/index.html')[1]\n",
" .rename(columns={u'Område': 'Kommun'})\n",
" [['Kommun', 'SD', 'M', 'C', 'FP', 'KD', 'S', 'V', 'MP', 'FI', u'ÖVR', 'BLANK']]\n",
" .set_index('Kommun')\n",
" .apply(lambda x: x.str.replace(\",\", \".\")\n",
" .str.replace(\"%\", \"\").astype(float))\n",
")\n",
"partier = votes.columns\n",
"votes.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's read some migration patterns straight of the web. I.e. what municipalitys that had a positive net growth in terms of people living there."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Net inhabitant growth"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Befolkningsökning, 2012-2015, procent, treårsbasis</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Kommun</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Ale</th>\n",
" <td>3.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alingsås</th>\n",
" <td>3.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alvesta</th>\n",
" <td>2.9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Befolkningsökning, 2012-2015, procent, treårsbasis\n",
"Kommun \n",
"Ale 3.7 \n",
"Alingsås 3.3 \n",
"Alvesta 2.9 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url = 'http://www.ekonomifakta.se/Fakta/Regional-statistik/Din-kommun-i-siffror/Nyckeltal-for-regioner/?var=17248'\n",
"col = u'Befolkningsökning, 2012-2015, procent, treårsbasis'\n",
"befolkningsokning = (\n",
" pd.read_html(url, thousands=' ')[0]\n",
" .assign(**{col: lambda x: x[col].str.replace(u\",\", u\".\").astype(float)})\n",
" .set_index('Kommun')\n",
")\n",
"befolkningsokning.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Rating of how good a municipality is for companies"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Företagsklimat, ranking 1-290, 2015, ordningstal</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Kommun</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Ale</th>\n",
" <td>43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alingsås</th>\n",
" <td>185</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alvesta</th>\n",
" <td>221</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Företagsklimat, ranking 1-290, 2015, ordningstal\n",
"Kommun \n",
"Ale 43\n",
"Alingsås 185\n",
"Alvesta 221"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url = 'http://www.ekonomifakta.se/Fakta/Regional-statistik/Din-kommun-i-siffror/Nyckeltal-for-regioner/?var=17257'\n",
"col = u'Företagsklimat, ranking 1-290, 2015, ordningstal'\n",
"foretagsklimat = (\n",
" pd.read_html(url, thousands=' ')[0]\n",
" .assign(**{col: lambda x: x[col].astype(int)})\n",
" .set_index('Kommun')\n",
")\n",
"foretagsklimat.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Percent people with a higher education"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Andel högskoleutbildade, 2015, procent</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Kommun</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Ale</th>\n",
" <td>18.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alingsås</th>\n",
" <td>24.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alvesta</th>\n",
" <td>15.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Andel högskoleutbildade, 2015, procent\n",
"Kommun \n",
"Ale 18.9\n",
"Alingsås 24.3\n",
"Alvesta 15.0"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url = 'http://www.ekonomifakta.se/Fakta/Regional-statistik/Din-kommun-i-siffror/Nyckeltal-for-regioner/?var=17251'\n",
"col = u'Andel högskoleutbildade, 2015, procent'\n",
"hogskoleutbildning = (\n",
" pd.read_html(url, thousands=' ')[0]\n",
" .assign(**{col: lambda x: x[col].str.replace(u\",\", u\".\").astype(float)})\n",
" .set_index('Kommun')\n",
")\n",
"hogskoleutbildning.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Percent early retirees\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Andel förtidspensionärer, 2015, procent</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Kommun</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Ale</th>\n",
" <td>5.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alingsås</th>\n",
" <td>6.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alvesta</th>\n",
" <td>6.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Andel förtidspensionärer, 2015, procent\n",
"Kommun \n",
"Ale 5.5\n",
"Alingsås 6.5\n",
"Alvesta 6.0"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url = 'http://www.ekonomifakta.se/Fakta/Regional-statistik/Din-kommun-i-siffror/Nyckeltal-for-regioner/?var=17256'\n",
"col = u'Andel förtidspensionärer, 2015, procent'\n",
"fortidspensionarer = (\n",
" pd.read_html(url, thousands=' ')[0]\n",
" .assign(**{col: lambda x: x[col].str.replace(u\",\", u\".\").astype(float)})\n",
" .set_index('Kommun')\n",
")\n",
"fortidspensionarer.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Median income"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Medianinkomst, 2014, kronor</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Kommun</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Ale</th>\n",
" <td>273684</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alingsås</th>\n",
" <td>259211</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alvesta</th>\n",
" <td>241077</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Medianinkomst, 2014, kronor\n",
"Kommun \n",
"Ale 273684\n",
"Alingsås 259211\n",
"Alvesta 241077"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url = 'http://www.ekonomifakta.se/Fakta/Regional-statistik/Din-kommun-i-siffror/Nyckeltal-for-regioner/?var=17249'\n",
"col = u'Medianinkomst, 2014, kronor'\n",
"medianinkomst = (\n",
" pd.read_html(url, thousands=' ')[0]\n",
" .assign(**{col: lambda x: x[col].str.replace(u\"\\xa0\", u\"\").astype(int)})\n",
" .set_index('Kommun')\n",
")\n",
"medianinkomst.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Unemployment rate"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Öppen arbetslöshet (Arbetsförmedlingen), 2015, procent</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Kommun</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Ale</th>\n",
" <td>4.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alingsås</th>\n",
" <td>5.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alvesta</th>\n",
" <td>9.3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Öppen arbetslöshet (Arbetsförmedlingen), 2015, procent\n",
"Kommun \n",
"Ale 4.9 \n",
"Alingsås 5.5 \n",
"Alvesta 9.3 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url = 'http://www.ekonomifakta.se/Fakta/Regional-statistik/Din-kommun-i-siffror/Nyckeltal-for-regioner/?var=17255'\n",
"col = u'Öppen arbetslöshet (Arbetsförmedlingen), 2015, procent'\n",
"arbetsloshet = (\n",
" pd.read_html(url, thousands=' ')[0]\n",
" .assign(**{col: lambda x: x[col].str.replace(u\",\", u\".\").astype(float)})\n",
" .set_index('Kommun')\n",
")\n",
"arbetsloshet.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Number of crimes per 100k inhabitants"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Brott per 100k inv</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Kommun</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Ale</th>\n",
" <td>8231</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alingsås</th>\n",
" <td>10511</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alvesta</th>\n",
" <td>8300</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Brott per 100k inv\n",
"Kommun \n",
"Ale 8231\n",
"Alingsås 10511\n",
"Alvesta 8300"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"brott = (pd.read_csv(\"brott_per_1000.txt\", sep=\";\", encoding=\"latin-1\")\n",
" .rename(columns={\"Region\": \"Kommun\", \"/100 000 inv\": \"Brott per 100k inv\"})\n",
" .groupby(\"Kommun\")\n",
" .sum()\n",
" .reset_index()\n",
" [['Kommun', 'Brott per 100k inv']]\n",
" .assign(Kommun= lambda x: x['Kommun'].str.replace(\" kommun\", \"\"))\n",
" .set_index('Kommun'))\n",
"brott.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Violent crimes"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Våldsbrott per 100k inv</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Kommun</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Ale</th>\n",
" <td>823</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alingsås</th>\n",
" <td>870</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alvesta</th>\n",
" <td>967</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Våldsbrott per 100k inv\n",
"Kommun \n",
"Ale 823\n",
"Alingsås 870\n",
"Alvesta 967"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"valdsbrott = (pd.read_csv(\"vbrott_per_1000.txt\", sep=\";\", encoding=\"latin-1\")\n",
" .rename(columns={\"Region\": \"Kommun\", \"/100 000 inv\": u\"Våldsbrott per 100k inv\"})\n",
" .groupby(\"Kommun\")\n",
" .sum()\n",
" .reset_index()\n",
" [['Kommun', u'Våldsbrott per 100k inv']]\n",
" .assign(Kommun= lambda x: x['Kommun'].str.replace(\" kommun\", \"\"))\n",
" .set_index('Kommun'))\n",
"valdsbrott.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Drug crimes"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Narkotikabrott per 100k inv</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Kommun</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Ale</th>\n",
" <td>402</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alingsås</th>\n",
" <td>1487</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alvesta</th>\n",
" <td>327</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Narkotikabrott per 100k inv\n",
"Kommun \n",
"Ale 402\n",
"Alingsås 1487\n",
"Alvesta 327"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"knarkbrott = (pd.read_csv(\"knarkbrott_per_1000.txt\", sep=\";\", encoding=\"latin-1\")\n",
" .rename(columns={\"Region\": \"Kommun\", \"/100 000 inv\": u\"Narkotikabrott per 100k inv\"})\n",
" .groupby(\"Kommun\")\n",
" .sum()\n",
" .reset_index()\n",
" [['Kommun', u'Narkotikabrott per 100k inv']]\n",
" .assign(Kommun= lambda x: x['Kommun'].str.replace(\" kommun\", \"\"))\n",
" .set_index('Kommun'))\n",
"knarkbrott.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Mean age"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Medelålder, 2015, år</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Kommun</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Ale</th>\n",
" <td>39.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alingsås</th>\n",
" <td>42.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alvesta</th>\n",
" <td>42.1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Medelålder, 2015, år\n",
"Kommun \n",
"Ale 39.9\n",
"Alingsås 42.0\n",
"Alvesta 42.1"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url = 'http://www.ekonomifakta.se/Fakta/Regional-statistik/Din-kommun-i-siffror/Nyckeltal-for-regioner/?var=17247'\n",
"col = u'Medelålder, 2015, år'\n",
"medianalder = (\n",
" pd.read_html(url, thousands=' ')[0]\n",
" .assign(**{col: lambda x: x[col].str.replace(u\",\", u\".\").astype(float)})\n",
" .set_index('Kommun')\n",
")\n",
"medianalder.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Percent born in another country than Sweden"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Procent utrikesfödda</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Kommun</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Botkyrka</th>\n",
" <td>40.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Danderyd</th>\n",
" <td>15.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ekerö</th>\n",
" <td>11.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Procent utrikesfödda\n",
"Kommun \n",
"Botkyrka 40.1\n",
"Danderyd 15.3\n",
"Ekerö 11.0"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"utrikesfodda = (pd.read_excel(\"BE0101-Utrikes-fodda-kom-fland.xlsx\", header=5)\n",
" .dropna(subset=[u'Kommun'])\n",
" .assign(procent= lambda x: x[u'I procent'].astype(float))\n",
" .rename(columns={'procent': u'Procent utrikesfödda'})\n",
" [['Kommun', u'Procent utrikesfödda']]\n",
" .set_index('Kommun')\n",
")\n",
"utrikesfodda.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Percent mothers smoking while pregnant"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Rökande gravida, procent</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Kommun</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Upplands Väsby</th>\n",
" <td>3.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Vallentuna</th>\n",
" <td>2.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Österåker</th>\n",
" <td>2.9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Rökande gravida, procent\n",
"Kommun \n",
"Upplands Väsby 3.6\n",
"Vallentuna 2.3\n",
"Österåker 2.9"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rokande_grav = (\n",
" pd.read_excel(\"Lev03.xlsx\", header=3)\n",
" .reset_index()\n",
" .rename(columns={'index': 'Kommun', '2014': u'Rökande gravida, procent'})\n",
" .assign(Kommun=lambda x: x['Kommun'].str.split())\n",
" .assign(Kommun=lambda x: x['Kommun'].str[1:])\n",
" .assign(Kommun=lambda x: x['Kommun'].str.join(\" \"))\n",
" .dropna()\n",
" .set_index('Kommun')\n",
" .replace(\"..\", 0)\n",
" .assign(**{u'Rökande gravida, procent': lambda x: x[u'Rökande gravida, procent'].astype(float)})\n",
")\n",
"rokande_grav.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Rapes per 10k inhabitants"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Våldtäkter per 10k inv</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Kommun</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Upplands Väsby</th>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Vallentuna</th>\n",
" <td>1.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Österåker</th>\n",
" <td>2.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Våldtäkter per 10k inv\n",
"Kommun \n",
"Upplands Väsby 5.0\n",
"Vallentuna 1.9\n",
"Österåker 2.2"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"valdtakter = (\n",
" pd.read_excel(\"Sex03.xlsx\", header=3)\n",
" .reset_index()\n",
" .rename(columns={'index': 'Kommun', '2015': u'Våldtäkter per 10k inv'})\n",
" .assign(Kommun=lambda x: x['Kommun'].str.split())\n",
" .assign(Kommun=lambda x: x['Kommun'].str[1:])\n",
" .assign(Kommun=lambda x: x['Kommun'].str.join(\" \"))\n",
" .dropna()\n",
" .set_index('Kommun')\n",
" .assign(**{u'Våldtäkter per 10k inv': lambda x: x[u'Våldtäkter per 10k inv'].astype(float)})\n",
")\n",
"valdtakter.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Asylum seekers per 1k inhabitants"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Antal asylsökande per 1000 invånare</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Kommun</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Ljusnarsberg</th>\n",
" <td>138.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Norberg</th>\n",
" <td>113.94</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hultsfred</th>\n",
" <td>78.33</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Antal asylsökande per 1000 invånare\n",
"Kommun \n",
"Ljusnarsberg 138.26\n",
"Norberg 113.94\n",
"Hultsfred 78.33"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"asylsokande = (pd.read_excel(\"asylsdochm.xlsx\", header=1)\n",
" .dropna(subset=[u'Kommun'])\n",
" .rename(columns={u'Antal asylsökande per': u'Antal asylsökande per 1000 invånare'})\n",
" [['Kommun', u'Antal asylsökande per 1000 invånare']]\n",
" .set_index('Kommun')\n",
" .assign(**{u'Antal asylsökande per 1000 invånare': lambda x: x[u'Antal asylsökande per 1000 invånare'].astype(float)})\n",
"\n",
")\n",
"asylsokande.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Types of vehicles"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Personbilar, 2015, procent</th>\n",
" <th>Lastbilar, 2015, procent</th>\n",
" <th>Bussar, 2015, procent</th>\n",
" <th>Motorcyklar, 2015, procent</th>\n",
" <th>Mopeder, 2015, procent</th>\n",
" <th>Traktorer, 2015, procent</th>\n",
" <th>Snöskotrar, 2015, procent</th>\n",
" <th>Terränghjulingar, 2015, procent</th>\n",
" <th>Terrängskotrar1), 2015, procent</th>\n",
" <th>Släpvagnar, 2015, procent</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Kommun</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Upplands Väsby</th>\n",
" <td>0.757944</td>\n",
" <td>0.069553</td>\n",
" <td>0.000088</td>\n",
" <td>0.042546</td>\n",
" <td>0.009148</td>\n",
" <td>0.007748</td>\n",
" <td>0.006128</td>\n",
" <td>0.005077</td>\n",
" <td>0.000175</td>\n",
" <td>0.101593</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Vallentuna</th>\n",
" <td>0.672324</td>\n",
" <td>0.066290</td>\n",
" <td>0.000000</td>\n",
" <td>0.051673</td>\n",
" <td>0.009153</td>\n",
" <td>0.029813</td>\n",
" <td>0.008131</td>\n",
" <td>0.021327</td>\n",
" <td>0.000489</td>\n",
" <td>0.140801</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Österåker</th>\n",
" <td>0.692083</td>\n",
" <td>0.069446</td>\n",
" <td>0.000252</td>\n",
" <td>0.047521</td>\n",
" <td>0.013716</td>\n",
" <td>0.015516</td>\n",
" <td>0.009396</td>\n",
" <td>0.010332</td>\n",
" <td>0.000252</td>\n",
" <td>0.141484</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Personbilar, 2015, procent Lastbilar, 2015, procent \\\n",
"Kommun \n",
"Upplands Väsby 0.757944 0.069553 \n",
"Vallentuna 0.672324 0.066290 \n",
"Österåker 0.692083 0.069446 \n",
"\n",
" Bussar, 2015, procent Motorcyklar, 2015, procent \\\n",
"Kommun \n",
"Upplands Väsby 0.000088 0.042546 \n",
"Vallentuna 0.000000 0.051673 \n",
"Österåker 0.000252 0.047521 \n",
"\n",
" Mopeder, 2015, procent Traktorer, 2015, procent \\\n",
"Kommun \n",
"Upplands Väsby 0.009148 0.007748 \n",
"Vallentuna 0.009153 0.029813 \n",
"Österåker 0.013716 0.015516 \n",
"\n",
" Snöskotrar, 2015, procent Terränghjulingar, 2015, procent \\\n",
"Kommun \n",
"Upplands Väsby 0.006128 0.005077 \n",
"Vallentuna 0.008131 0.021327 \n",
"Österåker 0.009396 0.010332 \n",
"\n",
" Terrängskotrar1), 2015, procent Släpvagnar, 2015, procent \n",
"Kommun \n",
"Upplands Väsby 0.000175 0.101593 \n",
"Vallentuna 0.000489 0.140801 \n",
"Österåker 0.000252 0.141484 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fordon = (pd.read_excel(\"Fordon_lan_och_kommuner_2015.xlsx\", header=5, sheetname=2)\n",
" .dropna(subset=[u'Kommun'])\n",
" .set_index('Kommun')\n",
" .drop(['Kommun-', 'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6'], axis=1)\n",
" .apply(lambda x: x.astype(float))\n",
" .apply(lambda x: x/sum(x), axis=1) # Convert to percentages\n",
" .dropna()\n",
" .rename(index=lambda x: x.strip())\n",
" .rename(columns=lambda x: x.strip())\n",
" .rename(columns=lambda x: x + \", 2015, procent\")\n",
"\n",
")\n",
"fordon.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we now have every KPI as a DataFrame, let's put them together with `concat`."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>SD</th>\n",
" <th>M</th>\n",
" <th>C</th>\n",
" <th>FP</th>\n",
" <th>KD</th>\n",
" <th>S</th>\n",
" <th>V</th>\n",
" <th>MP</th>\n",
" <th>FI</th>\n",
" <th>ÖVR</th>\n",
" <th>...</th>\n",
" <th>Bussar, 2015, procent</th>\n",
" <th>Motorcyklar, 2015, procent</th>\n",
" <th>Mopeder, 2015, procent</th>\n",
" <th>Traktorer, 2015, procent</th>\n",
" <th>Snöskotrar, 2015, procent</th>\n",
" <th>Terränghjulingar, 2015, procent</th>\n",
" <th>Terrängskotrar1), 2015, procent</th>\n",
" <th>Släpvagnar, 2015, procent</th>\n",
" <th>Våldtäkter per 10k inv</th>\n",
" <th>Rökande gravida, procent</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Kommun</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Ale</th>\n",
" <td>10.64</td>\n",
" <td>21.08</td>\n",
" <td>4.95</td>\n",
" <td>3.28</td>\n",
" <td>3.50</td>\n",
" <td>33.59</td>\n",
" <td>6.25</td>\n",
" <td>5.70</td>\n",
" <td>0.18</td>\n",
" <td>10.83</td>\n",
" <td>...</td>\n",
" <td>0.000141</td>\n",
" <td>0.054257</td>\n",
" <td>0.016427</td>\n",
" <td>0.039285</td>\n",
" <td>0.002159</td>\n",
" <td>0.009058</td>\n",
" <td>0.000188</td>\n",
" <td>0.150005</td>\n",
" <td>2.1</td>\n",
" <td>4.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alingsås</th>\n",
" <td>7.48</td>\n",
" <td>21.58</td>\n",
" <td>6.17</td>\n",
" <td>10.30</td>\n",
" <td>6.44</td>\n",
" <td>29.19</td>\n",
" <td>6.85</td>\n",
" <td>10.81</td>\n",
" <td>0.30</td>\n",
" <td>0.89</td>\n",
" <td>...</td>\n",
" <td>0.000068</td>\n",
" <td>0.052143</td>\n",
" <td>0.009086</td>\n",
" <td>0.053940</td>\n",
" <td>0.001729</td>\n",
" <td>0.014883</td>\n",
" <td>0.000203</td>\n",
" <td>0.154902</td>\n",
" <td>4.8</td>\n",
" <td>3.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alvesta</th>\n",
" <td>14.44</td>\n",
" <td>19.70</td>\n",
" <td>14.89</td>\n",
" <td>1.74</td>\n",
" <td>3.12</td>\n",
" <td>28.77</td>\n",
" <td>4.08</td>\n",
" <td>3.49</td>\n",
" <td>0.20</td>\n",
" <td>9.56</td>\n",
" <td>...</td>\n",
" <td>0.002950</td>\n",
" <td>0.042747</td>\n",
" <td>0.014082</td>\n",
" <td>0.082378</td>\n",
" <td>0.000946</td>\n",
" <td>0.015418</td>\n",
" <td>0.000000</td>\n",
" <td>0.182122</td>\n",
" <td>2.0</td>\n",
" <td>4.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Aneby</th>\n",
" <td>9.38</td>\n",
" <td>11.19</td>\n",
" <td>20.46</td>\n",
" <td>4.06</td>\n",
" <td>14.82</td>\n",
" <td>31.30</td>\n",
" <td>2.19</td>\n",
" <td>5.75</td>\n",
" <td>0.05</td>\n",
" <td>0.80</td>\n",
" <td>...</td>\n",
" <td>0.003179</td>\n",
" <td>0.042438</td>\n",
" <td>0.011197</td>\n",
" <td>0.119712</td>\n",
" <td>0.000968</td>\n",
" <td>0.017556</td>\n",
" <td>0.000000</td>\n",
" <td>0.199198</td>\n",
" <td>1.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Arboga</th>\n",
" <td>9.75</td>\n",
" <td>20.54</td>\n",
" <td>5.53</td>\n",
" <td>7.57</td>\n",
" <td>4.08</td>\n",
" <td>39.04</td>\n",
" <td>5.77</td>\n",
" <td>5.62</td>\n",
" <td>0.14</td>\n",
" <td>1.96</td>\n",
" <td>...</td>\n",
" <td>0.001810</td>\n",
" <td>0.043954</td>\n",
" <td>0.010256</td>\n",
" <td>0.060502</td>\n",
" <td>0.006722</td>\n",
" <td>0.009997</td>\n",
" <td>0.000172</td>\n",
" <td>0.170818</td>\n",
" <td>2.2</td>\n",
" <td>13.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 35 columns</p>\n",
"</div>"
],
"text/plain": [
" SD M C FP KD S V MP FI ÖVR \\\n",
"Kommun \n",
"Ale 10.64 21.08 4.95 3.28 3.50 33.59 6.25 5.70 0.18 10.83 \n",
"Alingsås 7.48 21.58 6.17 10.30 6.44 29.19 6.85 10.81 0.30 0.89 \n",
"Alvesta 14.44 19.70 14.89 1.74 3.12 28.77 4.08 3.49 0.20 9.56 \n",
"Aneby 9.38 11.19 20.46 4.06 14.82 31.30 2.19 5.75 0.05 0.80 \n",
"Arboga 9.75 20.54 5.53 7.57 4.08 39.04 5.77 5.62 0.14 1.96 \n",
"\n",
" ... Bussar, 2015, procent \\\n",
"Kommun ... \n",
"Ale ... 0.000141 \n",
"Alingsås ... 0.000068 \n",
"Alvesta ... 0.002950 \n",
"Aneby ... 0.003179 \n",
"Arboga ... 0.001810 \n",
"\n",
" Motorcyklar, 2015, procent Mopeder, 2015, procent \\\n",
"Kommun \n",
"Ale 0.054257 0.016427 \n",
"Alingsås 0.052143 0.009086 \n",
"Alvesta 0.042747 0.014082 \n",
"Aneby 0.042438 0.011197 \n",
"Arboga 0.043954 0.010256 \n",
"\n",
" Traktorer, 2015, procent Snöskotrar, 2015, procent \\\n",
"Kommun \n",
"Ale 0.039285 0.002159 \n",
"Alingsås 0.053940 0.001729 \n",
"Alvesta 0.082378 0.000946 \n",
"Aneby 0.119712 0.000968 \n",
"Arboga 0.060502 0.006722 \n",
"\n",
" Terränghjulingar, 2015, procent Terrängskotrar1), 2015, procent \\\n",
"Kommun \n",
"Ale 0.009058 0.000188 \n",
"Alingsås 0.014883 0.000203 \n",
"Alvesta 0.015418 0.000000 \n",
"Aneby 0.017556 0.000000 \n",
"Arboga 0.009997 0.000172 \n",
"\n",
" Släpvagnar, 2015, procent Våldtäkter per 10k inv \\\n",
"Kommun \n",
"Ale 0.150005 2.1 \n",
"Alingsås 0.154902 4.8 \n",
"Alvesta 0.182122 2.0 \n",
"Aneby 0.199198 1.5 \n",
"Arboga 0.170818 2.2 \n",
"\n",
" Rökande gravida, procent \n",
"Kommun \n",
"Ale 4.4 \n",
"Alingsås 3.8 \n",
"Alvesta 4.8 \n",
"Aneby 0.0 \n",
"Arboga 13.2 \n",
"\n",
"[5 rows x 35 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"concat = pd.concat([\n",
" votes,\n",
" utrikesfodda,\n",
" medianalder, \n",
" brott, \n",
" medianinkomst, \n",
" arbetsloshet, \n",
" fortidspensionarer, \n",
" hogskoleutbildning, \n",
" befolkningsokning,\n",
" foretagsklimat, \n",
" valdsbrott,\n",
" knarkbrott,\n",
" asylsokande,\n",
" fordon,\n",
" valdtakter,\n",
" rokande_grav,\n",
" ], \n",
" axis=1,\n",
" join='inner')\n",
"concat.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pandas gives us the method `corr` that by default uses Persons `r` to show correlations between columns. Let's start of by using this simple frequentistic approach."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"corr_matrix = concat.corr()\n",
"corr = concat.corr()\n",
"\n",
"# Keep only parties as columns\n",
"corr = corr.loc[:, corr.columns.isin(partier)] \n",
"# Keep only KPIs as rows\n",
"corr = corr[~corr.index.isin(partier)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And use Seaborn:s `heatmap` to give us a nice visual representation of the correlation table."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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fqL7dOf/RWuvV2MnRTzW0V0hioqO49eLhXPGPGSgUd11xXsU+tycP/MY8/7x+\nM5/+uIotuzJ5/bMVKBSz7riG1Bo+9Q0thmhuHX01V9x0G0op7rplTGUMWdlVYgD415Sn+fSLb/Dk\n5THsolHcf+uNNa4SFqqo6GgGj7iG1ybdilKKIZffUGX/Xk/VWPKzMlg6d6qd8lpw0iWjadm6fm0R\nEx3NrddfyeU334lSirtvuq5iX2Z2zn5Z9cNTZ/Pplyvw5OVx6sXX8MCEsTWuElZr/TExjB83jitH\njUIpxZ133FFZv9uN8jvvmsqGQ3R0DDfcPI4brr0KhWL8xNur7M9yu6u8DrWVD1VMdDS33nANV4y1\nX/+7xlW+/gGvw8kz+PTzr/Hk5TPsb1dw/8Sba1wlrPb6o7j1snO5/MEp9jUwqjIhyPTkVXkNfjab\n+PT7X9iyM4OFS7+x34t3j6mxBybkGEZewGV3P2bHcO2IyhhyclHVrsSH5yxgyYqVePL3csr1d/Pg\n6MtqXCUsVE11DfiLio7m5EuuY/7fx4FSDBt5Y5X9BZ7sKh93bTerMT98Rdau7axc+j4oxcV3PkLr\nxAPXKydzVGwqnGP/hBBCNCqr9Id3m6zymGPsxKd098amiyG9B7NWbGmy+scO7Ipvp2my+qPb29+v\nUVxU1GQxtGjZksy8wiarPy2+FQClGVuaLIaYNl3xrVzcZPVHDzgTgLK1y5sshqg+QyPiOpj30/Ym\ni2HUUZ0aLZt4oEWPRrlBn1S88aDKgGQyvRBCCCGEECLiyNAvIYQQQgghIogM/bJJj4oQQgghhBAi\n4kiPihBCCCGEEBFEehJsMpleCCEOHvIHWwghIkejjc/6Z8uejfL3/sGiDQfVmDLpURFCCCGEECKC\nyBwVmyQqQghxEDEZebUXaiS6jf29Dv1uf7/JYlj9xDmsPP/0Jqt/wNufMPXrTbUXbCTjB9tfUrfL\ns7fJYmiXGMuOf4ypvWAj6fCQ/UV+Tb08cJu/PtVk9WcsmgjAiq11+V7c8BrYJZl9G1Y0Wf2unvb3\n3JjRFzRZDHrOW412bPlmepsMgRNCCCGEEEJEHOlREUKETGsdDSwE9gEjjDH7mjikg4K0mxBCiLqQ\noV826VERQtTF6cBHwCfO7yI00m5CCCFEHUmPihAHGa31i8BQY0xXrbUL2A28aowZF+LzVwFnG2O2\nBdnfC3gN6AkMMcb8XK2IC3ulExVi+ZDqDRetdXsnnkTAC9xtjPnU2TcCeBi7Z+N2Y8z7zvbHgZFA\nhjHmML+PRFBmAAAgAElEQVRjlQG/OA+/MMZMaEBoVdqtsX312RJefnY2yqW45sbxHDP4xBrLz505\nlWWffEhCYjLT5y0ISwxnHN6OW87U7LPgyffW8vm6jBrL//KfszE77Tk4P23K4rF319ar3m/z9vJa\npgcXcHmbJI6Ka1Vj+eJ9+7h1407OSY7n7JT4etUZzIbvv+C7RfNRSnH8iOvoesRxweMoyOe9p+7D\n2leGZcFRZ19Mz2NPalD9yz79hOdnP41yuRg7bgLHn1Dz8WZNm8wnH31IUnISL7yysEF1L9+awQu/\nbkYpuGFADwZ1SA1YLs9byl3LfqXMsrAsi8v7dmFolzYNqhtg8bcrmfbaB7hcitsv/wtDj+oXsFxG\ndi63TX2R/MIimsVEc+ul5zKov25w/f7OO74X91w+mH37LP4+73OW/Li5xvJHHpLOk2NPIypKsW6r\nmzFPfdig+r/7/FPefHEOLqW4ZPQtHDHwhKBlc7IyefpfD1BYkE90TDNGXDuWvkceW696P/ryO6bO\nfxOXy8Ud117CycceEbTs48+/yruffUNyYhzvzPxXveoL5KuMXF7ekoECrumRzjEpcTWWLyorY+z3\nGzi/Ywrndwp8zR4IMkfFJomKEAcfC8jXWvfHvhnfXY/nB2WMWQ8M0Fp/FmD3EvyGMIVQPuR6w8gH\n3GiMWaW17gx8A3TUWscAjwLHAi2BZUD5rPA3gQXAi9WOtdcYc2QYYtqv3RqTz+dj3uwZPDlnHl5v\nMfeNH1trojJo6CmcNOwMpjzyj7DEEO1STBjem0unfkWLmCheGDuo1kSlqMTHiClfNqhen2WxIMPD\nw13TKbEsJm3dU2uissidS/cWzRpUbyBlPh8r3pjL3x6Ygq+0hHf+c1eNiUqzVq04/67/ENO8BcUF\neSy4bzQ9jj4B5arf4Aefr5Q5M6cza+58SrxeJtw4utZE5aSTT+XU08/k0UkP1avOirr37ePZ/9vE\n02ccibdsHxOX/l/QRCU2JprJw46gRXQUud5Srn7/e07qnIarAUNfSn1lTF7wHq8+PJHiklKunjQj\naKISFeXigWsvolfn9ux0Z3PFg1P47Ol/1rvu6qKjXDww8gTOuPO/tGgWzaJJI2pNVGaMP5Nx0z/m\nR7OLpLgWDarf5/Px+vOzeGj685R4vTx65001JipRriiuvOV2OnXrSVbGbiZNGM2U/75b53pLfT6e\nmvs6Cyc/RHFJCVfd82iNicppg49m+JCB3Dv52TrXFYxvn8W8zXt48sjueMss7vtlS62JysKtbnrG\ntQxbDKJhJFER4uD0LvAXIMH5PR5Aa30ucD/QDFhqjLnd2X4ncBWwGqj4V09rfQ7wQPXyjkB3CW8C\nnYBS4AZgZk3l61qv1noIcB+QC/QGlhhjJtbeHJWMMRlAhvP7Nq11jJOkHAesNsa4nbq2aa0PM8b8\naoxZobXuEuBw4fpMK2C7heN8A1m/djVduvcgPjERgLS26Wze8Dvdeh4S9Dm9+/YnY/euhlZdoX/n\nRDbszsdTWAqUsstTRK92cazflR/0OSoMzb2hyEun5jHER0cBkBoTxdbiEroESUR2ekvJ8+1rlEQl\nY9NvJHfsQsu4BADiktvg3r6Z1E7dApZ3uaJwNbfj9hYW4IqOaVD9a1evpluPHiQmJgHQtm06G39f\nT49DegV9Tt/+h7F7184G1Quwzp1H18RYEpx2bRPbgo05BfRIar1f2SiXIspln3dBiY+YqIaPSv91\nwxZ6dmpHUrxdX7vUJMzWHeguHfYrm5IQR0qCffPaPjUZX1kZpb4yYpxrqKGO6pXOum1ZZOcXA/CH\nO48+XVJZu9UdsPzhPdrizi3kR2O/H3Oc59XXpt/W0KFrd+IS7L8HKWnpbNu0gc7dewYsH5+UTHxS\nsl22TTplPh8+n4/o6LrdMv5qNnFIlw4kOW3bLi0Fs3kbulvngOWP6N2THRmB26S+1ucX0iW2BfEx\n0RADaS1i2FxQTLfWgZO/HYVe8kp99Ayy/0CSOSo2SVSEODj9BFwORAFvAUdrrdOwk5QhxphirfVC\nrfVQYDNwLdAfe3jWrwBO+QeqlzfGLK+h3huMMTudyeGrtNavO4nBfpwb//rUezxwjDFmnda6QeNw\ntNZnAD8bY0q11unALq31aCAHuyeqXXlcQTTXWv8IFAH3GmPq+3F/Te0WtvMtl5OVRVJKKovffYvW\ncfEkJaeQk+WuMVEJt9T4FmTmebnwuM7kFZXizvOSGteixkSlWbSL18afQHFpGVM/Mvy8ue5Lr3p8\nZSRGR/FpTj6to1wkREfh8ZURKAsFWJCZw6i2ySz3FNS5rtoU5uXQKiGZNcs/pHlsHK0SkijMzYYg\niQpAaXERbz4ykbyMXQwbfWe9e1MAsrOySElJ5b1FbxIXH09ySipZWe4aE5VwyS4uIaVlM97fsJPW\nzaJJbtGM7KISeiQFLl9U6uOWJSvZmV/Evccf2qDeFAC3J5+0xHgWfvoNCa1bkpoQR6YnL2Ci4u+r\nX9ZxaNdOYUtSANISY8nI2cvI0/rj2VtMRk4hbZNigyYqHdPiyC8sYcEDfyU1oRWvLFnFix/X9Geq\nZrnZWSQmp7Dsg7eJjYsjITmZ3Gw3BElU/K36cQVdeuo6JykA7pxc0pITWfjRMuJbx5KalEBmdi46\n+OUfdjklPpKaRbN4ZzatY6JIahZNTomPYCHM27yH63u049PdOQcuyCBk6JdNEhUhDl67gAIqP/Uf\nCHQHvtFaKyDWeZwEfGWMKQHWaq231lJ+eQ11jtZan+fU2d75CTae58h61vuTMWYdgDGm3l8a4iQm\nTwDn+m83xsxx9oey+H5HY0yG1vpoYJHWuqcxxluPcGpqt7CcbyBnnmef4refLwvnYevkje/sKUnD\n+qfXWvbUhz8lu6CEvh0TmHrV0Zz172WUltVvgbRhSfanuN/nFwYt81N+Ie2bxZAa07j/FPYdOhyA\nTT99XWvZmBYtueSfs8jZ9QcfTH2QTn2PJKZ5wz7dPfevfwPgi2W1jc4Mv3N6tgfgy+01f1LeMiaa\n54Yfw7a8Qu5d/itHt0umRRiShRHDjgdgyfe13+hnevJ44uV3mHHH9Q2uN5CXlqwC4OyBNX9g0Dwm\nmmN6t+ek8fPJL/Sy5InLWfrzFrZnNuzPw8lnnw/Aj199HlJ5T3YWr86ZzoR/PN6gekecdTIAS775\nsUHHaYgz29s9RN/W0IY/ZOXToWVz0lo0rCdThJckKkIcvO7FnvPwN+exBSw2xoz0L6S1Pp+qQ5j8\nf9+vvJ8qc0qcYUrDgEHGGK/W+geqrhxYfQ6KVc96c4NsD5nWujnwOjDRGLPF2bwLO0Eol+5sC6q8\n18MY86PWeifQFTB1jKW2dmvw+a5fu4aZTzyCQnHDbXeRlJJKtjuzYr8n2+5haUz9OiXy0IX9sSx4\n+K1VuPOKaZNQeYOdGtccdy1DWLILSgBY80cuGXnFdEhuyZbMun2xYVJ0FDm+sorH5T0sgWwoKuG7\n/EJ+zC8ir6wMF5AYHcXghNg61VluzybD5/OnAYqTRt5Eq4Rk9nqyKvaX97CEdB7tOhKX0oacndto\n0y20HpB1a1bz5KP/QinFhDv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6A88BI2tKUgCMMaO01qOAo6rtigVeASYCG7XW6di9NpP8\nygwHHqrHeVVw5xeSGteKN1asIb5lc1LiWuHOL6RXLc/7xmzj0A5pDU5SANx5+aQmxPH68h+Ij21J\nSkJr3Ln5ARMVf1+v/p1DO7cPS5ICUFrgISYukYyfPyO6RSwxsQmUFHhqTFT+WPoqnc+4Evf/LW9Q\n3e4cD2nJiSz88DPi42JJTUogM9sTNFFpLDnZWSSlpPLJe2/ROi6epOQUcrLcNSYq4ZadlUVySirv\nv/0WcXFxJCenkO3OrDFRCQd3djapqcm8/s77xMfFkZKSjDsrO2iiEm7ZhcWktmrOO2u2Etc8hpRW\nzckqLKYngf90FZb4uOGtr9mRW8hDpw0IS09GVmExKa1bsOjXTcQ3b0ZKbAuy9hYFTFTKY7h+wTL+\n8Ozln8OPCUsMADlZ9nW4+N2q12FNiUq45eVkk5CcwpcfvUOr1nHEJyWTl50FQRIVf2t/+o5OPXod\n0CQFCFv7H+xkMr0Q/xu+MMYcCbQHztJa9/Pb95MxZh2AMSYPGOCULzLGeIDvsJOWcorA38YbbHsg\nW40x6535LF8CRwIDge7AN1rrlcARzuNgcdaLMWaOMeb1arFaQbYDLAJWGGPW1rdOwGuM+c4Y4wM2\nA+nGmHxgrdb6aK31EcCmhpyXvwsH9uX0w2v/BxjAnbeXp97/mnsvGBKOqitcNPQYzjimX0iD59y5\n+Tzx2mLuH3luWGMAaHPkKST3Oa7Wcjnrf6ZFSjuaJ6SEre4Rw0/hzBOPw2qU75cO3ennXsDxQ4dh\nNWEg55x/AUNOPa3uYykb6KK/nMMZpwyhqV6Ev/Ttwik929d63q2aRTP/kiG8MOJEZny9luLSsrDF\n8NfDunOq7ljr69+qWTSvjDqN+SNPZdoXqygu9YUtBoAzz7uAE05u2uvwxLP+wlEnnhLy9ZCbncWb\nz8/k0hub7jtz/tdJj4oQ/0OMMQVa68+AE4DVzubcAEVVkN/DJdjxFxtjRgZ5TqA462IXdqJWLt3Z\nVhBke7nRwONa66ONMfX9pr9Sv98tKj8kegO4ACjBnlfTIKlxsWTmFVY8Lu9hCabEV8YdL3/Mbeee\nQIfk+nRS7S8tIY5MT35lDHkFpDpDoALGUOrjtqdf445LzqJjWlK96y3YsZEtHzwPStH1rKuIiUuk\nNL+yc6+0wEOz1olBn793x0ayf/uBnPU/4SvMRykXzVonktLv+DrHkpacSEZWZd3lPSyN7fd1a5j9\n5COgFKMn3EVSSio57sohZR6nh6Ux/bZmNZMfewQUjL/jHlJSUsnyiyE7y01yauN9SV65tJQUMt1Z\nFY/dWdmkpiQ3er3lUmJb4C6snK6XVeglpVWLWp/XJak16XEt2ZyTz6FtGnbNpMa2wF1QVDUGZ1ho\njTEkx5Ee34rNWfkcml739+T6tWuY+cQjKBQ33GZfh9kH+DrcYtbyyozHUUpxyY0TSUhOITfbXbE/\nLyeb+OSaP5QoLS3h2Ucf4MLrbyE1PTyLG9SFktn0gCQqQvyvKJ9r4cLuqfiyhrIrgce01i2xh34d\nTeUwKLAThhSg+kpYwbYH0kVrfQiwHRgM/B0oAqZrrTsYY3ZorTtj90TUd+B49R6e74E+WutU7Enz\nHYwxvzqT6ffb7ve8n4AbgBe01kcZY/yTjprqrulxufeA27ETmTNCOG6N+nVuw6Y92eTsLcJb6iMj\nt6Bifsq0j74FYNxZlROKH3xtKcMH9GJQmFb7AujXrSMbd2aQk7/XjiEnr2LY19Q3lwAw/m+nVZS/\n//m3OHvgYRzfN7QeoGBad+hBv9GPVDzeV+ajyL2D0sJ89pWWUJKfU2XY1/bP7NWkOp1yMQAdT76I\njidfBMCOz9/E1bxlvZIUgP66Bxu37SAnN5/ikhL2ZOVUDPua/KJd761XXbzf86wG9jcccmhfnnzu\nlYrHPp+P7Vs3k+fxUFJSTJY7s8qwr5efnQnAFdffVC0Qq96fevfu249n5v/XL4ZStm7ZRK4nB6/X\nizuz6rCv52bNAOC6sTdXDQGrQb0g/fr0ZuPmreR4cvF6vWS43RXDvqbOtlf1Gn/DdVXrtKywdbz0\naZPI5uwCPEUleH1luAuKK+anzP72NwBuGNQbgMy9xTSPchHfohlZe4vZnruX9vHBP2AIVd/0ZDZn\n5eMp9OL1lZFZUDns6+kv7c+pbjzR7lzPLCiiWVQUCS3tGLZlF9A+oX4x9OrTl6kvVLsOt9jXoddb\nTFZmZpVhX/Ofsa/DK8dUvQ7ta7B+L0hX3Yf7ps+teFzm87Fr2xYKcj2UlHjxZGVWmZ/y9jx7IYvz\nR42pjOupf3Hs0NMP+GpfoipJVIT433CCM0clGvjMGPNhsILGmK1a6+nYN/YW8PfyieaOGcDbWuss\n4EK/+S7BtgeyGXulLQ3MMcZsA9BajwXe01pHAXuBy+tzslrrGdg9FSla623AjcaY97XWdwPfOOc1\nwfWcDaQAACAASURBVDnf0kDbHZZTZoXWegn2nJK7g9TZBXgbSAJaaq0HA/caYxZT9V/bit+NMTla\n611AdLU2rpeYqCjGDR/EqBlvopTijvNOqNjnzivEf8jzys27WLpqI1syc3hjxRqUghnXnENqfGzD\nYoiOYsKFpzPykWdRwJ2XVi7xm+nJR/kFsfL3rXz681q27Hbz+uc/oFA8fevIGntgQuWKiqbTKZew\ndu7fUSi6nF61o660wEPjdBZCTHQ0E6+5hEtvs+u+Z3Rl3ZnZHlS1eic9/SJLvv4BT14Bp1w5jgdu\nuqrGVcJCFR0dzcjRN3PPzdeiFFxzc9X1J3L+n73zDo+q6Br4bzc9IX0DoXeGrviiAiogKCiv2Mtr\nV9BIBxERe+8FpShiQRTsvBYsSBeEFywoUgek92Q3ve1mk/3+uDdhN9mSLTHhc37Pw0PuvefOOTt3\n7u6cOXNmLGaX5wHw1qsvsGHtKgry88i49hIyJt3nc6Uw7zZEcOfo8YzPGIHBYGDsJNcpNNkWc436\nmPHS8/z040ry8/K4/vJhTJgyzedKYdWJCA9n0ug7uXnUeAwYmDrhZCc4y5Jd43M//fIMVvz4E7n5\n+Vx45fU8dM8Er6uE+SI8zMjovp0ZtWgdGGDCuV2rrlmKS10+84mCEl5Y/ScGtC+Hcf26khgdGbBu\nZxvGntedOz5ejQG4e+DJGbzmolKXWj+eX8xzyzaBwQAOBxMH9iQxJipoG0Brh7eMGsfU0Vo7vGNC\nzXZItecxZ/oLbPhxFfn5eYy46hJGT77P50ph3ggLD+fy20bx4pRRYDBwTcZEl+v52RaXr4M92//k\nj/VrOHHkEGu//0pru4+9SKKPKEwoMaqICgCG+pwrqFAo/nnoHfpvpJQ96tuWUxBHydcz6k15zKXa\nQma2dZ/Wmw2R51zLzQsCnYEXPB/c1JuKvfWn39iuNwDbjoUknSkgujVN4HB2Yb3pb5GirRRnMx+u\nNxsiTS0wz6i/vAXThJcByJ37QL3ZkJTxDDKz/tqhaKxFqFb+FfoV82rLoA5pdeZNfN/mtDrpoF+8\nf7NPmz3tLeZBthzYrB+ukVJO8rcMb6iIikKhqA/UCIlCoVAoFB4whHCJaH/wsbeYO4r0xXqCKcMj\natUvhULxtyKlPOC8W7xCoVAoFApXDGGGOvlXC6r2HNM3hq7cc8yjqSEowyMqoqJQKBQKhUKhUCjA\n855jf3qQjxJC/Iq2IM4DUsq1AZThEeWoKBQKhUKhUCgUDYj6TqaXUs4FEEJc6UO0hZQyUwjRG/hC\nCFG1nJofZXhEOSoKhUJxClGZ0F6fRJ5zbb3q/+Cm3vWqvzKhvT7p1jQ0+94ESmVCe30SaWpRr/or\nE9rrkySnJbnrg8qE9vpkUIe635fnH4anPcfcUrnCp5TyVyHEUaCNv2V4QzkqCoVCcQrxxob99aZ7\ndJ82ANwX2a7ebHjetpfr3vu53vR/cttZ7L/v1nrT3+b5+QBYC3LrzYao+CT6PLW83vRveOgCALYc\nC3YP2MDp0TSRr5t2rzf9lx7T9kGx5QW9qnnARCaaKC4p9S1YR8TGaBtoXjDrp3qzYfm4c30LBYjB\nWG9p5G73HAMQQjwDIKV8QD9OBkqklKVCiDZozslBYK+nMvxFOSoKhUKhUCgUCoXC195iTXFdtbMz\nME8IUQqUAyOllCUAXsrwC+WoKBQKhUKhUCgUDYj6zFGRUn4GfObm/O3Vjv+H5qzUugx/UY6KQqFQ\nKBQKhULRgKjlUsL/71H7qCgUOkKI94QQ+/W/jUKITCFErbcBF0JsEUK0cjoeLoSYGgK7al2OEGKf\nECIlWJ0+dEwUQkQHcX8zIcRavb5+FUJc4HTtWiHELiHETiHEJU7nXxRCHBdC1JjjKoRoJIQ4IoSY\nHIRNdV5vfze7Nv7Ie1NHMH/aSPb+scGrbGlhPh89Np6FD49hwcOj2bXxx6B097x6GFO2reCeLcvo\nPGyQV9mWZ57GxF+/ZfIfS7hhwWtB6a1Ozo6N7JhzLzvenEre7t9rdU+5rZRtMyeQ+fP3Qen+8YiF\nEcv+YOTyP9hwPMenfIm9nBuW/MaivwLKN63BD8uWM/zKq7n0qmv5ca33Ofz+yAbC4C6N+XR0Xz4e\n1ZdzOphqdU9MRBhfTziX689u5VvYB+tXLWP8TVcx4eZr+HX9Wp/y778xgzuuuIjJI24ISm+z4UMZ\n9NM3nL/2a5pcMMCr7PDDm+m/9FP6L/2Ubk/cF5ReZ5YsW8ElV/2H4ddcz49r14VM1l+W/vADl106\nnCsuv4w1a3x/v/grXxuKd//CsQ8e4NgHD1Kyb7PvG4AKWylH372Hgt9/CIkNCv9RERWF4iQOoEAI\n0QNIQlv329/7q5BSLgYWB2uUn+X8HTu+TwI+AALNorQDY6SUlY7deqCFj51sFwEfAe+5Ke9B4NcA\nbank76i3v41yu52fPnuX6x+dgd1m5fPnptLu9D4e5SNj47j6/heJiIqmpDCf96fdQcczzwsomdMY\nHs5FT01l1jlXEBETTcbShez8bqVH+evmvcynI+/l4MbfiU1J8lufJxzldo6t/oROtz5Ohd3GXwuf\nIbFjL5/3nVj/FTFN2wal215RwbvbDjJjQHes5RVMXbedPunJXu/5SB6hY1JoVtIqs9t5ddZsPpw/\nD2uplZGjxjDgPPdJv/7IBkKY0cCYQR0Y8e4vREUYmX3Tv1j3l+8E8NvObcPOYwVB67fb7SyYO5vn\n3ngPm83KY5NG07vfeV7v6dP/fM4dPIRZzz0RsF5DeDhdHpzE2mHXY4yO5pzP3+XEcs8dbntxCWuG\nhHY1vTK7nVdnz+Gj997CarUyYvR4Bpx3TtCyfttRVsaMGa/xwYKFWK1WMu68g/79PTtu/srXBke5\nndz1n9Pk2odw2MvI+uJFYtqe5vO+/F+/JaJxm6B0B0p97Uzf0FCOikLhytfAZUCi/ncCaFEN4CEg\nElghpZyin58K3AZsBaqiDEKI+cBA4Csp5QSn818DLYEyYL6UcrZ+vgCYBwwFfpRSZvgox608+g6x\nQoj2wKfAtVLKPUKICcCdaB3yl6WU84UQjwLno63SsRS4EOgnpbQIISYCdwE24Hsp5f1CiMHAS2jJ\ndKuEEOXAMCmlXw6dvpRh5XKGB4UQEbqTUrWTrf4ZDgohekop/5RSbhBCtK5elhCiE5AG/OaPDW7w\nVG8D0ByhPLR5uMuklJOr1ecrUsr39Ps9PUdP8m7LD/KzcHzvTkzN2xATnwhAfGpjsg7uJa2V+9W6\njMYwjFFhAFiLCgmLiAhYd6uzTufE9l0UW7QoQu6ho6T36MzxLTtryDY/oweFmRYObtSiHcXZoVvJ\nqujoHqJNLQiPjQcgMjGVksyDxDT2PEJfajmGvbiA2PQ2QenemVNIm4RYEqO0emwcE8XevCLaJca5\nlT9cWEKerYyOSe6v+8uWrVvp0K49yUma45ee3oRdu3fTqWPHoGQDoXvzBPZmFZFXUgYlcCK/lA6N\nG/FXZqHHe1qmxJIcG8nO4/lB69+9fSut2rYnQf98piZN2L9nN23ae/58nbr1IOt4cJGt5DN6ULDz\nL2x6my4+coyELp3I37HLrbzBEPppPlu2bqNj+7ZVz7ZpkybI3X8hOnYIStZftm7dQvsOHUhO1pz1\n9PR0du3aRadOnUIiXxtsJ/YRkdqcsBjt+yAsPgWb+RCRppYe7ynLOU5FSQGRjWv89Cj+RpS7plC4\n8hvQC+gI7AIQQqShOSkDpJS9gFZCiIF6x3kkcDrwGFDVC5RS3go84qb8UXoZ/YAJQojG+vk4YCHQ\nDRgqhEj3UY5bebSOcCvgE+BmvbPdGhiHFqnoDzyhLxkI8BXwLbAH+B7oq59/DDhTSnk68LJuywrd\n9qPAQCnlGf46KdURQgwFNkkpy3DayVYIcQ0nd7L1xnPA4+iORhDUqDena/2AR6SUPYDH9Pocz8n6\nfNypPms8Fx/yNcoP8nMAUJyXQ2xSCltWfceun9cQl5hMUV6213tspSUseHAUCx4axcCbxga8NGZ8\nehoFxzI5a+R/6HHVxRScMJOQ7n6fg6RWzSjNL2TE1+8yfsNX9Mm4MSCd7rAX5RHRKAnz7yvJ3bGR\n8Lgkygq9O0LHVn9K+rlXgCO4AFtOaRkp0RF8t/8Ea45YSI6OILu0zKP8u9sOcqMI3Z4gZrOFNFMq\nn//3C5YuX4EpNZUssyVo2UBIiYvCXGjlsl7NGdSlMZZCG6mNIr3eM3ZQB95euzfolxogN9tCcqqJ\nZYu/YP3q5SSlpJJrqfslfaPSTJRmZtH6pqtpeskQrJlmopp4nvZmjIyk/5JPOOfL+aScfUZIbDBb\nsjGZTHz2xVf8sHwlqampmD21Az9k/bbDbCHNZGLR55+zbOlSUlNNmM1ZIZOvDeXFeYTFJlG49UeK\nd/9CWGwi5UXel7fOW7+IhLMuDfr7IFCMYYY6+XeqoSIqCkVNjgGFnOz89kFzQtYLIQxondF2QDLw\nk5TSBmwXQhyoRdkZQohL9bKbokUzMgGrlHIjaPkSaJ12b06AJ3kD8AXwrZRyuy7bC1irLxlYIoTY\nCPTUr2WjRY2y0aJIifr5n9GWHPxWL88ZA8E7BujO1UvAcOfztd3JVs9hkVLKQ0KIYM1xV2+V/Cal\n3KHbli+EGASscVOfK3F9LnvRnksbL/I1yg/2gzjT4/xhAPz1q+/55pHRMdz09Byyjx3iq1cepnX3\nM4iICjgViZ/f+RiA7pdf5FEmIjqK1n3PYHqviyjNL2TChq+QP6wm58CRgPVWx9RLy5HJld5nB+bt\n/p2o1HQiE1JDpntYmyYArDvq2UnccDyHFo1iaBwbFTK9lVx95RUALF+5KqSygfDV79ozHdi5sVe5\nczqYOGApIjPfGlL9Fw7XPt/GNatDWq4vDiz4HICmwy7wKrf0jMHYLNkkntaNs955jRX9LqbC5tm5\n9YdrrrgMgBWrfed6+CPrL1ddfbVW9soVdSJfGxp116aQlezZ5FWuZN9mwpPTCY//f5W6eEqiHBWF\noiYPABXAVfqxA1gipbzZWUgIcTmuHXavnXd9ms8FQF8ppVUI8Qsno5rOv0gOfEc7vclnAC8KIXpL\nWdU782Sno9q/MP38RcC5wPVo0YCQbsUthIhCW7ZwspRyv37a351szwauEkJchjb9q1wIcUxK+VGA\nZlXW25lSyl+czrsbdvNUn9V7FkY3MtXbSdC71h3fs5MV780Ag4FBt4wjLjGFotyTo6HFednEJdbu\nBzelaUsSTE3IPnqQJm39n2qRfyyT+GZNqo7jm5jIP+5+NLTghJnMHbvJP3oCgMObtpIm2gfkqBQd\n3cPhJfMAaDH0NsIbJVFWeDKJvTLC4onio3vI2/krebs2YS8uwGA0EhGXRHK3vh7v8URKdCSWElvV\ncbZVi7C4Q2YX8tPRbP53PIc8axlGg4GU6AjOb1G7pHN3pJlMZJpPRg0sFi1qEqxsbejaLIH7hnUB\nh4MXluzEUmglrdFJJyw1LhJLoc3j/d2aJ3B+58b075RGUmwkFQ4H5kIry7adqJX+3Tu28ebLz2Iw\nGLhz0lSSU01kO43G52ZbSEoNvG5rizXTTHSTk05ZdGMT1hOeIzk2i+bM5m3eRumJTGJaNqdoz/6g\nbEgzpZKVdVKn2WLB5LEd1F7WF1u3bOGpp57EYDBw/wMPkmYykZV18hlYzGZMJs+7yfsr7w7r8b3k\nrHofDAaSB96kR1BORlTLi/MIi0v0eL/txF5K9vxGyd7fqSgpAKMRY1wScZ3O9suOYDAYT73oR12g\nHBWFohqVo9pOo/QbgVlCiOZSyiN6AngpsAl4Vu90twfcTWR1/qZJAMy6k9INOM2DnDuqX/cm/xsw\nCnhXCPEv4HfgeSFEDBCF5nT8CZzHyehI9fJaSynXCiG2AdWTC/KAVLQoTBV6zssAKaX3ZZ403gMW\nSimXOZ3zuBuujoudUsqHgYeddBc4Oyl+2gMn6+0dIcS/9Olo7vBUn5U2+iMfEtLbd+bGJ1+vOi63\n28k+cpCSgjzsNiuFORaX/JR1n70LwDnXjACgMMdCeEQE0Y0SKMrNJuf4YRLS0gmEQ79spknnDsSm\nJhMRE01C8/Sq/JShT04B4IeHXwLg8K9/ktSyGdGJ8ZQVl5LeXZC971BAeuOatUeMeKrq2FFup9R8\nFHtxARV2G2UFOS75KUdXfwpAs4FaAnPTAVfTdIA2gnt87X8xRsUE5KQAiOQ4DhaUkGctw1pegaXE\nVpWf8u72gwCM6KrZcmvXltzaVZsnv2DnYWLCw4JyUgC6d+/G3r37yMnNxVpqJTMrqyrn5LXZWjuZ\nOHaMT9lA2H40n1vf3lh1HGY00CYtjsSYCKIijKTFR7nkp4w6vz0Ac1Zpsy3n/riXuT/uBWDkeW0p\ntpXX2kkB6NilGy+9vaDq2G63c/jAPvJzc7HZrGSbM13yUxa+pdXHjXeOcSnH4XAENeUn5/ctxHdq\nT2RKEsboaKLTG1flp3S+fyIAO5/VVrmLSEygvLSUCquNmBbNiG7SmJIjwa/+1r1bV/bs05+t1Upm\nprkq5+S11+cAMHHMKJ+yfuvt0YOPP/m06risrIy9e/eSk5Ojl53pkm8yc4a2uOb4CVoaZrfu3b3K\n14ao9HakX/9Y1bGj3E5Z9lHKSwpw2MsoL8p1yU/JXb8IgKR+2vhkYp8rSOyjReHyNn6FMTL6b3VS\nAIwqmR5QjopC4YzbXyUpZZYQYgywWAgRBhQBN0op9wkh3kVzWLYDewH0nIQv0aaGxQghzkGL0ixB\nm/q1Fa3z75wA7qj+t6dypJRL3Mk7/60nny8DnpRSThNCzEJzBBzAY1JKsxCiejSlUq8B+EAIkYAW\nEbinWpXMAr4UQliAq/XkeNAWCfA5x0j/HFdqf4q7dL3DpJTHhYedbHX7rwRShRAH0VYN+6Zm6S7U\nyh6dGvUGTHMnKKU8IISYSbX6dC6nWpne5OuEsPBwzrl2BJ88eTcGg4EBN45yuV6Umw1OybsFlkxW\nzHtNc7Mc0P8/GcQ0SghId4XdzvcPvcCYNZ+Dw8Hie56supaQ3ljrAOpYCwpZPOUpMpZ9iDE8nD8+\n+grz7n0B6a2OISycZgOvZff7T4ABml/gmv9iL8x1qYNQEm40MqJbK+5esw2DAUb1ODmGkV1aFpLc\nC29EhIczafxYbhlxB2Bg6uS7q66ZzWYMThZ4kw0F5RUOXl/5F2/d1hsHMH2pazK5qVFUnaYAhIeH\nc2PGWB4cNxKDwcDt41zXqsi1mGu0g7dffZGNa1dRkJ/HqGuHc8ekqT5XCquOw25n+9PTOffrBeBw\nsPWR56uuRTc2uXxTNOrQltNffZKKUhuOinL+uOcRKkqDn/oWER7OpLGjuXnkKAwGmDq5aj0WsswW\nlwR+b7JB2xERwYQJE7nt1lswGAxMudd1tX2zOcvVFh/ygWAICyep31Vkfv4sAEnn/cflenlxXp2/\nl4rAMDjqKUlIoVD8/0IIsQkYLKX0vWnE30BDsydEON7YsL/elI/u0waA+yLdrx72d/C8bS/Xvfdz\nven/5Laz2H/frfWmv83z8wGwFoRuhTR/iYpPos9Ty+tN/4aHtHyPLceCnjUZMD2aJvJ10+71pv/S\nY1sBsOXV/cIAnohMNFFcEugq9cETG6Pl0F0wK/R7/9SW5ePOrTP/Zv2A/nXSQe/345pTyidTERWF\nQhESpJShWaomRDQ0exQKhUKhUPiHclQUCoVCoVAoFIoGhOEUXEq4LlCZOgqFQqFQKBQKhaLBoSIq\nCoVCoVAoFApFA0Kt+qWhkukVCoXi1EF9YSsUCkXDoc7mZ/180aA6+b4/a8nKU2pOmXLXFAqFQqFQ\nKBQKRYNDTf1SKBSKU4hDD42sN90tn3oHgIHTf6w3G1bfPYDjeUX1pj89MY6vtx+vN/2XdtU24tx2\nLL/ebOjWNIE1Z/erN/39N64HoHTJ3HqzIfqiDMYtCum+rX4x66qeAJQW19+7EB0bx5Gc+tPfPFnb\nRLXsRGj2XgqEiCZt66xso9qZHlARFYVCoVAoFAqFQtEAUREVhUKhUCgUCoWiAWFQyfRAA3JUhBDD\ngS7AUSBWSll/MV2FQqFQKBQKhUJRrzQYR0VKuRhYXN92KBQKxanM6kNZzNt6AKPBwF0929KnWYpb\nuXxbGdPWbKPc4QAHXN+lBQNbpoXUlpK/fqXgly/BYCCh7zVEt+7p856KslKyPnqEuNMupNFpFwal\nf+Wypbwz53WMRiOjJ0yi33n9vcq/MWM6S7/7jqSUZOZ9+GlQuivZvG4lSxa+g8Fo5JJbR9P1TM+5\nHXnZZha+9BglRYWER0Ry8c0ZdDqtd1D6161axofvzMFoMHDr6In07neeV/n5b7zGj0u/IzE5henv\nfhSUbtPgQbQZlQEVDvbOmEn2uvUeZc9bv5bC3X8BkPfHH+yd/lpQugF++F0y69t1GA0w+bIBDOje\n3q1cZl4hU9/7hoISK5HhYUwcfh59ROug9Ttz/M/17Fn2MRgMdBp2C2md/+VRtqy4gE3znsZRUY4D\nB20HXEl6z8Bygn5YupRZs7V3YPLdkxjQ3/M74I+sv6xevpR333wdg9HIqPGT6Huu97LnzJzOsu+/\nIzk5mbcXBvcuLlm5hplvz8doNHDPmDsZ2O9sj7Ivvf4Wi5euJCUpkS/emxOU3mAxqg0fgQbkqAgh\nLgOeBMqBV6SUH9SzSX4hhCiQUsb7ec9E4E0pZakPuU7AJ0AHYICUcpPTtZuBGCAJ2C6l/Ka+7Kx2\nz4fAo1LK3U7nlgFGKeXgWtxf53YKIc4F3gSswA1Syp3+1mcwCCG2AP8GovHwfHW54UAXKeULdWWL\nG9saA/OllBcHUUYztM+VhFbH06SUy/Vr1wJPARXAlMp6FkK8CNwMZEopezqVVQ5s1g/XSCknBWpX\nQyOQ98sT9ooK3t6yn9mDT8daXsGU1Vs8OipxEeG8MrAH0eFh5FnLGPnDJvq3MGE0hObH0VFup2Dj\nfzFdeT+O8jIsX79cK0elcNN3RDQOvpNot5cxd/ZM5rz3PjarlUmjM3w6Kv3PH8zgIRfx7BOPBq0f\noNxu57sP5jLhhTmU2WzMeXiSV0clzBjGFXfdTdPW7cnJOsHs+8fy0NufB6zfbrezYO4snn9jPjZb\nKY9MGu3TUenTfxDnDh7KrOceD1gvgCEsjLZjR/P77XcQFhVFz9dneXVUyktL+f3W24PS6UxZeTmv\nLV7Lwsk3YC2zc8eszzw6KmFGIw9eM5iOzdI4lp3PLa9+xLIn7gqZLRXldv76YSFnjXmWijIbv779\nmFdHJTw6lt53Pk5YZBS24gLWT59Ek+59MBj9mwpUVlbGazNmsvCD97FardyRkeHR+fBH1l/s9jLe\nen0mr7+rlT15bIZPR6X/+YMZdOFFvPBkcO9imd3Oq2++y0dvvkap1caISVO9OioXDjiXYYMH8uCz\nLwelNxSonek1GoSjIoSIA94ATgeKgT+FED9IKTPr1zK/CGS960nAB4DXDoqUchfQSwix0s01fx26\nOrOzEiFEOyCxmpMSAzQHyoQQsVLK4vq2E7gReFpK+WHlib/ZQXboOj0+X/363x5tlFJmCiEsQoh/\nSSl/C7AYOzBGSrlFCNEKWA+0EEJEAM8BZ6E5hauASodwEfAR8F61soqklGcEaEdDx99265Ed2QW0\nTYgjMSoCgMaxUezNLaJdUlwN2TCDgbDwMACKyuxEhHiFmbLMfYSnNMMYo403hDVKocxymIjUFh7v\nseeeoKKkkAhT8I7K9q1badu+PUlJyQA0bpLOnt27aN+xk8d7uvXoyfFjR4PWXcnBXdtJb9WWuIQk\nAJJMjTm6fw/N2rjvMDdKSqaRbm9yWhPK7XbK7XbCwgP7qd69fSst27YnIUnTb2qSzv49u2nTvqPH\ne0S3HmQePxaQPmfiu3ejeM9e7Hl52AHriRPEdWhP0V97gi67NmzZf4wOTVNJbhQLQHpyPLuOZNGp\nec2oYWp8LKnxmlzTlATs5RWUlZcTERYWElvyDu0mrklLIuMSAIhOMlFw7ADxTd23c4MxjLBITbe9\npAhjeERAerds3UqH9u1JTtbaVHp6Ort27aJTp5rvgD+y/rJj61batGtPoh/vYtfuoXkXt2zfSYe2\nrUlOSgSgaePGyD17Ee3buZU/rVsXjh4/EbReRehoEI4KcDawtdIx0Tts/YUQvwDLgU1AN+B9KeUL\nQojW7s7r914CPAxEAiuklFP08wXAPGAosFpK6XW4RAjxNdASKEMbWZ6tnx8APAjkAZ2BZVLKyYBR\nCDEX6Assl1Le7ckeIcRg4CWgKbBKHy0eJqU8ro+u3gXYgO+llPc7mVWjJyGEmADcidbpfUVK+Z5+\n3lM5BiHEDL0efpRSZgRip7e6A64Hvqp2bgDwP92eQegd0/qoTyFEIrAa7fkOFUJM4WRExVN9Otsp\n0NpfHnA+0AxYClwI9JNSWry0w6nAbcBWtEiKM+6e73xgIPCVlHKC03nn9uz8HO8FRgC70N6NQVLK\ngwG+F4vRnLmAHBX9fc7U/z4ohIjQnZTK992s23BQCNFTSvmnlHKD/n5XJ+hetI/vDbft0Et7aALM\nBVqjORg3SSn/8qeeg3i/PJJTWkZKTCTf7D1OfEQ4ydGRWEpttKOmowJQYi9nwsrNHCss5f6zRcii\nKQDlxfmExSZSvH0NhqhYjLGJVBTngRdHJX/jf0k85zqKd64LWn+2xUKqycTXXywiPj6BlFQTFrPZ\na+co1BTkZhOfnMqGpV8TExdPfHIKBTkW8OCoOCN//5nm7ToG7KQA5GRbSE41sXTxf2kUn0BySio5\nFrNXRyVURKamYrNYSL/8Muz5+dgsFiJTTR4dFWNkJL3ee4cKq5V9b7xJ/h+b3crVFktBMaaERny+\n/k8SYqIwxceRlV/k1lFxZt2O/XRp2ThkTgqArSCXqPhkDv+8jIiYRkQ1SsJakOPRUQGwW0v40g7P\nMAAAIABJREFU5c2HKLacoMd1E/yOpgBYzBZMaSY+X7SIhIQETKkmssxmt86HP7L+kp2tvYvffLmI\nRvq7mG35e95Fc3YOaakpfPb1dyTEN8KUmozZku3RUWlIqGR6jYZSC+lAltNxln4OoC3aD39v4A4h\nREtP54UQafq5AVLKXkArIcRAXT4OWIjWQblICFFZvidG6WX0AyboU2Eq6Qc8IqXsATymn4sFPtXP\n9RZCnOPJHinlCv34KDBQSnmGU+fkMeBMKeXpgNfYo97xGoc2Mt0feFwIYfJRjnM9DBVCpAdopzfO\nBX6tdm4o2sj5aqD6dKK/tT6llHm6/GK0aUdn6E6Kt/p0trOnk51fAd8Ce4Dvgb6e7NTLH4kWOXwM\n8PlNKaW8FXjEzSV3z7ElWqe6FzAVaAMQxHvxM+B9nkgtEUIMBTZJKcvQ3u1jQogMIcQ1wHG0zro3\nooQQvwoh1gohgrHJ0/cJVGuH+vMaj/v2MBP4Vm9XFwC5/tZzEO+XTy5pl86AliZ8BSZjwsN4a8gZ\nvHHh6by5eR+l9vJQqHchtmt/Ytr39mlL6YE/CU9qQlgj91PVAuXSK67i/AsuxBFQkDY09BlyKaed\ncz44amdDfo6Fb957nSvvmhwS/UOGX0m/gRfgqKX+UHL8y68wr1wFPpzgjcMv5/fbRrJn+gy6PPEY\nhojAogjVubpfT4b0ErV6/ub8Il756kceuOaCkOiuTouzLqRJj761aonhUTH0nfAyfcY9z67v3qfc\nZg1Y79VXXcWQCy+s1fP3R9ZfLrn8KgYOrpuyfXHNpcMYen7/etGtCI6GElGpjnNLOqBPjUEIsRY4\nA/jDw/kKtM7feiGEAa1z0A6tc2yTUm7U5fehdZa8dQoyhBCXoo3kNkUbNa+civablHIHgJSyctct\ne+X8e2AZWkcoxYs96GVX//b+GZgnhPgW+MKLfaB1SNdKKUuAEiHERqAnsNJLOVanetir10PLAOz0\nRmug+tyBoWijxxXA09WunQr1WcNOIQRANpCg/5+o/+vjwc5k4CcppQ3YLoQ44MMeb7h7jm10+0sB\n6VS+J3tW4/29OAq0CsJG9HLT0Z79cOfzUl/ZTwhxZS2KaaFPR+sNfCGE6CClDOSX2/l74ye0741D\n+rXqz3cQWj6Mu/YwELhBly0ECoWWSxRIPfv7fnkkJToSS8nJaskuLSM1OtLnfS3jY2kSF8WB/GJE\nil+pYVXYTuwjb80CMEDieTcSFptIeXFe1fWK4jyMsYke7y87sY/SvZuw7t9MRUkBGIyExSYS0/Gs\nWunfsW0rLz37NAaDgbunTiPVZMKcdXL8qzLCUpcc3LWDRXNewmAwcEXG3cQnp5Kfba66Xhlh8Ya9\nzMaCFx9l+O1jSWniy393ZfeObcx5+RkwGMiYdB/JqSZyzCfrIFePsPwd2MxmIp3qOzI1BZvF7FG+\nLCcHgMKdO7FmmYlu2pSSgwcD1m9KiCMrr7Dq2JxfTFqC+8gigM1uZ8q8xUy5fAAtUj2309qQd2g3\nO76cCwYDnS+9g6iEZKwFOSd16RGW2hCX1pyYpDQKMw+T2MJ3JM4ZU5qJLKd3wGyxkObhHfBH1hc7\ntm1l+vNPAwYmTZ1Gaqrru5hjsZDyN7VDU2oKmZbsqmNzdg6m1NAOhtQVKpleo6E4KscB54hFY05O\nN3F+UrX5e4mU8mY3OmxOfzvwEk3Sp4FcAPSVUlr1KWjO8nnu73Sh0tnyZI8nLkKLSFyPNprrvOSL\nu6EAT/XgqZyyavdXfi5/7fRFlS36qHU7NIfDADQXQrSXUlbOAaiv+vRqNzU7j+7sdDjZ5gAq5wvU\nsFMIcbmP8v0Z6vH0HEP5Xhj8tKkGQogo4DNgspRyv376GJrjX0k6NR1bFyqnhUopfxVCHEVzymQA\nJnn75nf3fD3Vp6d6Cfr7Jxg6pzTiQH4JedYyrOUVmEusVfkp72zZD8DIHm0AMJdYiQwzkhAZQXap\njcMFJaTHVZ+NWHsim7Ql7ZqHq44d5XbsOUepKCnAUV5GeVGuS35K/kZt3CDh7CsAiD/rMuLPugyA\ngl8XY4iIrrWTAtClW3feWXBylSq7vYwD+/aRm5uDzWrFnJnpMtVk7uszAcgYM96lHIej1oGPGrTq\n1IW7X3mn6rjcbufEoQMU5edSZrORZzG75Kd8v0Bbhf/imzKqzn0y41l69b+QTqef6bf+jl268fLb\nC6uO7XY7hw7sIz83F5utFIs5y2Xa14K3ZgNw051jXQtyOIIeeS7Ytp3Ytm0JT0wkLCqKyLS0qmlf\nbUZrM0z3v/EmAOHx8ZRbrThsNqKaphOVZsJ6IrjgYvfW6ew9biGnsBhrmZ3MvMKqaV8zFq8FYMLw\nk8HZhxcuYdi/utC3c5ug9AIktuxIn/EvVh1XlNspyjyMrSifijIb1vxsl2lfu3/Q0iQ7Dr0BgNL8\nbMLCI4iIjcdakEOR5RgxKY3xl+7durF37z5ycnKwWq1kZmZWTeWaMVNr/xPGj/cp6y9dunVn7vvV\n3sX9+8jL1co2Z7m+i2+/odlyx2jXdxFHkD9AQI8ugj37D5KTm0ep1UZmlqVq2terc+cBMCnDdREH\nhyMEihUho6E4KhuBrvr0qmK00coH0BJtWwshOqKNep6DNmXG4OF8CTBTCNFcSnlEaAm8pXonxx/X\nNAEw605KN+C0WtwTrs85X4Xm5NwH/OXGHquUsjJTKw9IRRuNr6S1lHKtEGIbsLOaDjNa9KNyVajf\ngeeFlqgehdYJ/9NHOe7qYUMAdnpjP1oUqjITbijwlpRyPIAQYrp+7nUvZfwd9Vkdb/XpCXej4e7q\nsxTtuT2rd97bo0WenKn+fKvr8XYMmnNfaX9rTkZD3NpTi/eiGVBjSFMI8Sja9KZBXu6t5D1goZRy\nmdO5n9HedxPaO95cSulczy51KoRIBkqklKVCiDbV7fLTHnffG57w1h5WoeUCzRVCxOqfI9B69vf9\n8ki40cgdPdowYeVmDBgYc/rJ2YXZpTYX2cxiK9N/+6vKG73rtLZVSfihwBAWTvzZV2L+8nnAQEK/\n61yuVxTXZnwicMLDI8gYO56xI2/HYDAwfvIUl+sWsxlDtelIr774HGtWrSQ/L49rhg/j7qnTfK4U\n5o2w8HCG3ZzBrGljMRgMXDrStSOWn2PB4NQ09u3YwpYNa8k6eogNS7/GYDAw4qHnSfARhfFEeHg4\nN2eM4/5xIzEYYMQ416lkOZaadfDWqy+wYe0qCvLzyLj2EjIm3edzpTB3OMrL2Tf7dU5/+01wONjj\ntNxwpMnk4g3GtG6NePhBKmxWHBUV7Hr6WSqsNnfF1pqIMG2Z4Vte/RiDAaZeMbDqWlZ+kctMtN/3\nHmHF5r/Yn5nD5+s3YzAYmH3XlZi8RGD8wRgWTsehN/LLnIfAAJ0uuc3luq0gx2VqXGmumR1fvknl\nWFGni28hMtb/SGdERAQTJ4znltu0d2DqlJPvQFa19u9NNljCwyO4c8x4xt95OxgMjLnb97v42kvP\n8dNq7V38z2XDmHjvNJ8rhbkjIjycu++6nZvGTMZggPvGn0zDNFuya0xJfHr6bJavWUdufgEXXH0z\nD00e53WVsLrEEOIFTk5VGoSjIqUsEkKMQkt0LUdb1jZTnyO+D22FIAHM1ZNy3Z4HEEKMBhYLIcKA\nIrRkYHD1j335ykvQpn5tRevc1iaZuAht1P5VtETc//mwB2AW8KUQwgJcjZab84EQIgFtxPWeajqm\no01jehQt6faAEGImWsfPATwmpTTr0048lVOjHqSUWf7YKX2vxrYWrVNXWW9DgI+drq9Ey6Xw5qj8\nHfXp0g481acXGx1u/nmsTynlPiHEu2iOyHZgb7XyXJ4vWuf4S7QpYzFCiHOAB6SUS3D/HA8JbQGC\n3/Ty96E5ct6er7f34iy0Z1mdloDPbGfd3iu1P8VdevmVixxMQ1sFzIG26lXlPbP0e1KFEAeBMYBF\nr5dStO+Hkfp0LL/s0XH7veEOH+1hIpqTMgYtWnKD1JLpA6lnf98vrwxoadLzU1y590zX0dGuqQm8\nNaRuF1KLad9bz0+pSdL5t3m8L773cI/X/OH8Cy7k/Avc78Vy/yM1l9+ddO80Jt07LSS6KzntnPO1\n/BQ3XDf+fpfjtl168Nxny93KBkq/gRfQb6D7nIvx02ou/XrnpKncOWlqSHSbV67S8lOqsetJ19m/\nBVu38ut114dEpzNDegmG9BI1zj9540Uux73aNefXV+p2xfMmPfrSpEdft9e6Xe0a0Upq1Ym+E0Kz\nPO6QCy9kyIU134EnH6/Z/j3JhoKBgy9k4GD3Zd/3cE1bJk6ZxsQpoXkXh57fn6Hn13Rynrq/epcA\nHrx7LA/ePbbG+frAqJLpATA05MQi3SH5Rk9u9Xle0TAQQrQFXpdB7MGhCAwhRCMpZaEQIhUt56JN\nEGV9CLwsqy1PLITYBAyWUua4v/Pvpbb2/D/53nAcemhkvSlv+ZQ2rWng9B/rzYbVdw/geF5RvelP\nT4zj6+0hWfMgIC7tqq13se1Yvg/JuqNb0wTWnB3YBoShoP9GbT+W0iVz682G6IsyGLfIV8C97ph1\nlbYnUWlx/b0L0bFxHMmpP/3Nk7WIV9mJffVmQ0STtnUW9th+y/A66aB3fX/xKRWqaRARFR94elAN\n18P6h6NHDrKFEB2l014qir+F6UKIs9Dej4mBFqJPw0ys7qQAyAa2n4mf9qjvDYVCoVA0eNSGjxoN\n2lGRUh5AW2WnVucVDQcp5Y2+pRShRkp5Z4jKyQT+HYqyGgrqe0OhUCgUilOLBu2oKBQKhUKhUCgU\n/zTUho8aDTpHRaFQKBQuqC9shUKhaDjU2fysnXdcXiff953f/vKUmlOmIioKhUJxClH2y9f1pjvi\nzEsBSB5Sc7Wov4ucpY+TO/eBetOflPEMk7/aWm/6X7msOwB5RSU+JOuOxLgYvm9Tm1X764aL928G\n6j+Zvtl1b9Sb/qOfjAbqf1GF3MLietOf1CgWgO23hGaVwEDo+v7iOivbYFQRFVCOikKhUCgUCoVC\n0aBQyxNrqFpQKBQKhUKhUCgUDQ4VUVEoFAqFQqFQKBoQKpleQzkq/0CEEMsAo5RysJ/3bQH+7Wk3\nbyFEJ+AToAMwQEq5qZblngu8CVjRdvjeKYS4GYgBkoDtUspvalHOROBNKWVptfPDgS5SyhcC+Vx/\nF77srGUZ16LVGUCxlHJBSIwLzJZmaO0hCe3ZTpNSLtevXQs8BVQAUyqfrxDiReBmIFNK2dOprHJg\ns364RkpZt9tI/414areBsmTDZmZ+vgSjwcA9N1zCwF5d3cpl5uQxZeZCCopLiIgI5+7rLqZv905u\nZQPl8v7dePC2QVQ4HDwydyk/bNzlVf5fnZvz6sRLCQszsmP/CUY+83lAepfLw8xZtw0jMGFAT85t\n39StXF6JjYmLfqK8ogIHcNvZnblAtAhIpyeObl7Hrh8+AoOBLv++lSZde3uUtRUX8PPbT+KoqACH\ng/aDrqTZaecEpX/Z0h+Y8/psjEYjEyZN5rz+NXfoDkbeG+n/HkKne8bhcFSw8+lXyFq5xqPsRXs2\nkb9Dax85P//GjideDFhvJT/8Lpn17TqMBph82QAGdG/vVi4zr5Cp731DQYmVyPAwJg4/jz6iddD6\nnRnepz1TrzsLh8PBEwv+x/JNB7zK9+rQmBcyBhBuNLLzkIXRry0PSv+6Vcv48J05GA0Gbh09kd79\nzvMqP/+N1/hx6XckJqcw/d2PgtINsHzpUua8MRujwciESXdzro925a+8N9ZZClh42IwRuLVVGmcm\nN/IqX1JewbjN+7i0aQqXNU0OWK8iNChH5R+GECIGaA6UCSFipZT+ZMJ5XYFCSrkL6CWEWOmnWTcC\nT0spP3Qq6wM/ywCYBHwAuHT4pJSLAW8Zbw1iJaVa2FmbMj4NkTmhwA6MkVJuEUK0AtYDLYQQEcBz\nwFlozugqoNIRXQR8BLxXrayihrbRZAhx224DocxezquffMdHT0yg1FbGiKfneHRUwoxhPHT7FXRq\n2ZRj5hxuenw2K2Y+FKwJVYSHGXl05AUMHv8WMVHhfP3CbT4dlTfuvZKxL33BLzs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FWodVbjqKyRUm7WbCsVQgzCc39birrKNUyTLQfKtVwib/Vmc+vPu1D7c83Kiaf+EBaOH3QhAFt/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JSDhwFKi+E3H/z3/8XcAC4H0RcQAj3eYIBU1ERARj0qdw1+RxWCwwbmrddRDyHXaw\n1I2dMufMZuOXaykuKmT8NVeSfuudPlcKq4/IyEimT5vKjTffgsVi4Y5Zs2r25drtWNzsetMGi1Hm\nNG686SajzNtv92G/fm1IPkREcOs/RnP9tHuxWCzcNemWWh/yCv4QtT40/3lWffUtBUXFXHLdRP41\n/R9eV4jyx/6MKRncOC4dsHDHzJqfGHa7o07Te9M2BJGRkUydNo2xNxt1fNusunVsr6dNvOkDJSIi\nkoyp00gfezMWLMyYWbePOex23J8j+NL7S1P3gRofJo7lhoxbsVgs3DmtdtlhuyPvD9eA/zy9gNXr\nNlBQVMylI27gnzOneF0lzF8i3NoUi4WZ9fQBd1986YPyISKSiZOnkjHOuNZMq6cfuPdDX/oTwcmY\nptUYWJoid1mj0Zy8mIHTgIac/B0K/vpjTo7/WCnV58R41ihUbT8c9HShkOmVaqypUHrsaJP50KJl\nK0pLSnwLG8t+TAwVv2xtMvvhHYzu6ywKegAuZKLjkzlyrOnaILZlDAB5xceazIfkuJbNoh+U5+xr\nMh8iW3emuAn7QZzZD3IKm+561DqhVaPMLwT4/kB+o9ygn9MxqdF8bgz0iIpGowmI5vY+kwD90U9m\nNBqNRtPsqdADCYAOVDQazf8RlFL7qV21S6PRaDQaTTNHByoajUaj0Wg0Gk0zQs9RMdCBikaj0Wg0\nGo1G04w4GZcSbgz0ZHqNRqM5edAXbI1Go2k+NNrE9PXZjka53l/Y1aon02s0Go2mcfj89L80me1L\ntnwHwKFHJvtQNh5t717IHR9tazL7s4ekUZZ3sMnsRyUb70otK7T7UDaiDwk2thwM+X2wQXN6uwQA\nZn6Q1WQ+PDW0N2Ub3moy+1F9RwGw33GkyXzoZI1l4/6mW33u3E7GyysP5DVdHXRMjm20snXql4F+\nM71Go9FoNBqNRqNpdugRFY1Go9FoNBqNphmhlyc20CMqGo1Go9FoNBqNptmhR1Q0Gh+IyFKgv1Kq\ns4iEAYeBN5RS03wcNx14VilVegLcrLa5FzhLKZXntq0n8CbQHeinlNp8gnxpZ9pNBJzAXUqp1ea+\nUcBDQCUwSyn1sbn9cWAMkKOUOt2trArgJ/Prl0qpGSfiHE4EDd1PWg+6lK5TJlJVVcnuJ+fh+PIr\nj9qLf/yGIzt3AVCw6Ud2zX66IVxg7d7fWfLjHixAxjk9OL9DSr26Imc5t6/8kYqqSqqq4IbTu3Bx\nlzYN4gPAb//bwI4Vr2OxhJE25CZSe53tUVt2rJhvMv8fVZWVVFFFz0tGcMoZfYO2vWL1WhZkvoDF\nYmHW1Az6XXBeg2j9tr9qDQsWP4clzMKsaVPod6HncwlEGwxfr13FGy8sxmIJ48aMaZx13oVe9S8v\nnseXKz8lISmZJ5e8HrL9gz9tYOdny8Bi4bQrbqKNj37w3fMPUlVZCVVVdLtkOO3+FFx9rPhuKwve\nW2O066jL6XeG1KvLyS/i9sVvUXSshKiICGaMHMR5ad2CsumJdWtWsjRzERaLhfSpMzi370Ve9ZkL\n5rBmxackJiXx7Ctvhmz/23WreWdpJmEWC9emT+WMcy/wqM135PLMf+7j2JFiIiKjGDUug7Q/hz43\nb93qlbyYuYiw6jq4wEcdzJ/DarMOMl8NvQ4CpVIPqAA6UNFo/KEKKBaRPhg33Yf9PG4G8ApwwgIV\n6lkVSim1EzhTRD4/gX4AuIBJSqmtItIR+BpoLyKRwKPAX4AYYC3wsXnMO8AyYOlxZR0N8A30JxMN\n1k8sEeF0mzGZH66/mbDoaM5csshroFJRUsL3f78xVLN1cFVWkrlpN4uuPIeyikpmrNjkMVBpFRnB\n3L+dRYuIcApLy7jp/W/o17k1YZbQF6WprHCx/dNXuWj6Y1SWl7Fh0b+8BiqRLVrSd9JDRERFU3a0\nmDWzp9Lu9POwhAWeeFDucjFn0XMsW7IIp9PJ2Mm3egw+AtEGZH/hYpYtfc4oM2Oqx+AjEG0wuFwu\nXntuIY8sWkqZ08kDt2b4DFTOvehiLrhkEAsf+38h26+scPHzf1/lgqmPUVFexsZn7/caqES2aMl5\nEx8k3OwH656YRts+gfeDclcFc95exbL7JuAsdzF29gseA5Xw8DDuHXMlPduncshRwA0PP8eaJ28P\nyJ43XK5ylixawPznX8LpdHLHlAk+A5UL+w/g4oGX8cRDDzSAfRfLlyzi/vlLKHM6efSOyV4DlfCw\ncG6cOosOXbrjyDnMgzPSmfP6hyH6UM7zixawYIlRB7dPnuAzULng4gFcPOgyHn/wgZBsB0uFjlQA\nHahoNP7yITAUSDA/xwOIyDRgPEaA8JRSaqmIDACeANoCa83RgMFKqcP16c1y+gH3AoXAqcAqpdRM\nEWkDZAKdMG5kbzD3X6uUusE89gEgXyk1F3OpRBHpBrwFjFJK7THP4Q93fyLyIdABKAdeUkot9OZP\nIBWmlMoBcszPB0Qk0gxS/gpkKaXspq0DInK6UmqLUmqjiHSqp7iQ71zNclcDm4E04GWl1Gxzn6f6\n99Ref2gXpdRuEbkSuA+IAtYopWaZ+mLgReAy4Aul1ARv/STYc4zv05sju/dQXmCsyOQ8/DutenTn\n6K7d9R/QAAHB8WzPLaJLUisSW0QB0KZVC/bkFdMtOe4P2vAwC+Fh4QAcKXMRGURQ4In8A7uIT+1A\ndKt4AGISbRQe3EdCu8716i1h4UREGb6UlxwlLCIyaNtbt+2gR9cuJCUaq1O1TW2D2r0H6f7Hp+SB\naP22n7WNHt26kJSYaJTZpg1q126kR/eQtMGwa0cWHbp0Iz7BKN/Wpg379+yiU7ceHo/p2asPuYcP\nNYj9ggO7iGvTkSi3flB0aB/xbTvXq7eEhRNe3Q9Kg+8HW7N/pUf71iTFtQKgbXIC6pfDSIfUP2it\n8bFY443Vo9paE3FVVFDuqiAyIjwo28fz87YsOnftRkJiEgApbVLJ3r2Lrt09t8Fpvfvw+6GGaYPs\nn7dxSueuxJl9wJqSyoHs3XTsWn8fi09KJj7JWNHL2jqVCpcLl8tFRETwt6zH10HrVN910KsB60AT\nPDpQ0Wj8YxMwGggH3gXONm98pwKnA9HATyLysVJqDcYIRjZGylg+1NwoTwH+dJy+ep3R84FzlFI7\nRCTe3DYf+EQplSkisUAL4L/AHBGJUUqVAKOA6kdDVUBH4HlgjFuQ4omJSqmDIhIBbBOR5WaA4cmf\noBCRy4DNSqlyEUkFDolIOpCPMULVFtjipYhoEfkBKAHuUUqtD9KVLsAVwAFgi4gsU0r9Yu6rc75e\n2tdOPe0iIikYQUo/pVSpiLwlIv2VUl8ArYDXgJnAHhFJ9dRPQiHKZqUs1067EcMoLyrGaXcQnWLz\nGKiERUVx9rKXqHQ62TPvGQo3/y9UF8grcWKNieYj9Stx0ZEkx0ThKCnD0233sXIXUz79gYPFx7j3\nwt4NMpoC4CzKJzo+iX0bVxIZE0t0fBLO4nygs8djXM4S1i+4h6P2w5w1ekZQoykAdkceNmsyy9//\niPi4OKzJSdjtjnqDj0C0Adm32Vj+3gdGmVarUWY9wUcg2mAoyHOQlGxj1UfvERsXR2KSlXyH3Wug\n0pA4iwtoEZ/EgY0riWwZS3RcIs6ifPAQqIDRD75eeA/HHL9zxnXTg+oH9qJibAlxLP/ie+JbxWBN\niMVeWFxvoOLOhqxdnNaxXYMFKQB5DgfJVhufvP8ucfFxJFut5Nlzvd6kNySFeQ4Sk62s/eR9WsXF\nkZCcTGGeHTwEKu5s/WEjnbpLSEEKGHVgNesgNi6OpGQrjhNYB8Gglyc20IGKRuM/h4Aj1D7dPxNj\nvkQJUCIi32Lc1FanWFmoOxJwJrDei36TUmoHgFKqyNzWH7je3HbEtI+IfAAME5HdwB63YMcCvIdx\nE73dj3NKF5GrzOPaAu0wR0E8+BMwZmDyBDDEfbtSKtPcP9yPYtorpXJE5GzgPRHprpRyBuHOfjMV\nDhH5CvgzUB2o1DlfEbkEz+3bn+PaRUSGAF2Br0XEghGcdAW+AMqUUt+advcCqdSmEB7fT0Lm4Dvv\nA9D60v5edRsGXkl5Xj5xvU6jz5zZfHPFcKrKyxvEhyHSHoAv9+d41bWMjOCFoedyoPAod63+H+ec\nYqVFA96kdT53EACHtm70qY2IjuHi256mOOc3vl3yH1J6nkFEVHTQtq8ZZnT5Net8x9WBaP22f/VQ\no8wv1jWoNhgGDrkagG/Xf9Eo5fuio9kPDvvZDy6a+TRHcn7j+xcfJqXnGYQH2Q+u6X8OAGs2+b4c\n2wuLeeLNFcyfdkNQtnxxxTDjUvvVF2sbpXxfXHzFMAB++Mq/PlaQ5+CNzPnM+PfjDeZDTR2sa5o6\n0ASODlQ0Gv+5B2Py9wjzexV1bzD9udn0pq/vDWqeHqm8DDwM7DI/u5MOPC4iZyulfvBUlpnudClw\nnlLKKSLfU3clwJDf6CYi0cByYKZSap+5+RBGQFRNqrnNI9WjPEqpH0TkIMZjcRWES97aqL7z9dRe\nntplhVJqTD3by447tlFWXCzLtRPdunY+SJTVijPX84sBy/OMQZzi7Ttw5uYSc0pbju07EJIP1pho\n7MdqY8i8kjKsMVE+j+uY0IrU2BbsKzjKqbbAB/DyD+zip7cXgwVOH55OdHwSpYW1g1TO4gKi45L8\nKiuu9SnEJKVQ/PsvJHUIfGQhxZpMrt1R890YtbCGrPXbvs1Krlu72x0Oz/YD0PrDrh3byHzqESwW\nC/+YcQdJyTbyHbk1+wvyHCRZbUGX74uCA7vY+u6zAPS+2uwHRbUvJSwtLiA63r9+EOvWDxID7Acp\nCXHkFhTXfLcXHcGW8Mf0x2rKyl3c9syb3H7t32if4p9/nvh5exZzH3sYiwWmzrobq82Gw17bBvl5\ndpJt9c8bawj2/LyNpXMfw2KxcOPU20mwWilw1PaxwnwHCcne+0B5WRkLH7qX6yZMJ6VtO6/a+vh5\nWxZzquvg9rtJth5XBw471kasg4agQg+oADpQ0Wj8pnpUQaRmQuT/gNkiEoORGnQ2ddOXCgErUP1f\n8kfgMS/6+lgLjAUyRaQl0FIpZVdK/WTOkxDgzuOO2QRMBF4QkbOUUtWPyO0Y81GqV/2KB+xmkJKG\nkZLmExG5HyO96RI/5EuB15RSq9y2fQf0EhEbxmT6U5RS7vVQZ4RBRJKAEjOdqjNGkHPAbX8g/nQS\nkR4Yoyh9gQe8aL211/HtEgNsBOaLyClKqd/MBQRKzSDLV4Dk3k+CpihrG626diEyMYGw6GiiW7eu\nSfvqOi0DgOx5iwCIiIuj0umksqyMFu3aEp2SQumh30N1gdNS4tlfeJSC0jLKKiqxH3PWzE95bpPh\ny/izjJs++zEnUeFhxEdH4jjm5JfCY7SNbRGU3aSOPeg/88ma75UVLopzfsF5tIjK8jJKCh115qds\n//RVAHoNNp5elxTmER4ZSVTLOEqL8jmSe5BWycGtQNY77TT27NtPfkEhTqeTnFx7TSrX3EXPATA9\nY7xPbbD0TuvFnr17yS8oMMrMsdekcs19ZrFhf9JEn9pg6HFaGo8/92rNd5fLxa/791JUWECZ00me\nPadO2tfrzz0DwPXjJ9Upp4oqqoJIfUns2IMLZzxR872ywsWR33+l7GgRFeVlOIvy6sxP+fm/rwFw\n6t9GA1BamEdYdT8ozudo7kFaBtEPendpz56DOeQXH8VZ7iInv6gm7WvuO8blcPqIgTX6fy55lyvO\nPZ3z00JPuTu1V28WvVS7WprLVc6BvdkUFuTjdDqx59ZNeXph8QIAxk6cUqecYNug26lpPLio9vmZ\ny+XitwP7KDb7QL49t878lOUvGNeka8Zm1Gx77omHOO+SQfQ+K7jVvk5N683il+vWwf59nutgySKj\nDsZlNEwdaBoOHahoNL6p9yqllNovIgswbryrgAfcUrAAFgDvi4gDGGnq53vR18d0jJvhSRhP5a/H\nCDjAmCvT87gUqCrTt40isgp4ELjL3Pc08KJ5Yz8YWIGR+pUF/IwR4PhDB2CDL5GI9AWGGx9lgulb\n9aICd2GsAlaFsepV9TELzGOsInIAmAQ4TL9LgQpgnJmOFZA/JnsxVhwTIFMp5XH4wEd7/aFdzMn0\nGcBHIhIOHMWY1wR1+9Dx/en4fuI9V8oLVa4Kds9ZyFkvL6GKKnbNfqpmX7TNVucfbssunTntwfuo\ndJZRVVnBz/c/RKUzmGy6ukSEhZF+VnemfPoDFmDKX3rW7HOUOOtEbL8fKeXJb3ZgwaiUSef0JKGF\n79EXfwgLj6DX4DGsn383WCz0GTq2zv7SonwsbvNhSgpyjREZk95Dbiaqlecn4N6IjIhgRsZ4xqRP\nwWKxcMeMyTX7ch15derAmzZYIiMimDE5gzHjJmKxwB0za1dSz7U76py3N21DEBERwejxk/nnlHFg\nsXDz5LprcuTn2bEcF8c/P/dxvlu/luKiQib+fQjjZ9zhc6UwT4SFR3Dq4Bv4euE9gIVeQ26ps9+Y\nt+TeD+xsfWeRsdBEVRWnXXlTUP0gMiKcGSMHMebh57AAd1w3uGZfbkFxnTb4cdd+Vm/ezr7Ddpav\n+x4LFp65dYzXEZhAiIiIZNykqcyYMBaLxULG9Nvq7M+z2+v4A7Dgycf4at3nFBcWMvrqwUyddZfP\nlcI8249g1LgMHpyRjsViYXRG3dXlC/Icddb12LntJzZt+ILDv+5n7SfvY7FYmPnQUyQmBz/SFxER\nybiMqUxPN+tgxnF14PhjP5z/RG0dXD9sMNNm3eVzpbCGRM9RMbDoSFGjOTkRkU+AJ5VSJ3TZYRHZ\nDAxoiMnfDYG//piT4z9WSvU5MZ41ClWfnx76+wSC5ZIt3wFw6JHQb6aDpe3dC7njo21NZn/2kDTK\n8g42mf2oZCMNpqzQ1zOORvQhwcaWgyFnhgbN6e2MFdJmfpDVZD48NbQ3ZRveajL7UX1HAbDfcaTJ\nfOhkjWXj/pAHgoPm3E7GymAH8pquDjomxzb80okm72UdapQb9Kt7t200nxsDPaKi0ZxkiEgy8A2w\n4UQHKQDN7X0mAfqjn8xoNBqNRnOSoAMVjeYkw3zrfP1vDtN4RCm1H2PVLo1Go9FomjU69cugUVae\n0Wg0Go1Go9FoNJpQ0CMqGo1Go9FoNBpNM0KXwM+PAAAgAElEQVQvT2ygJ9NrNBrNyYO+YGs0Gk3z\nodEmpr+15WCjXO9Hnd5OT6bXaDQajUaj0Wg0waHnqBjoQEWj0WhOIly/Nt3SvBHt0wDIvvX6JvOh\n69Ov8+H2w01m/6peqZStf6PJ7EddeC1Aky+N+8//7mgy+w/97TQAXvjB42uQGp2xZ3dsFv0gt+hY\nk/mQEt+yqZcGBiCn8GiT+dA6oVWjlV1ZqQMV0JPpNRqNRqPRaDQaTTNEj6hoNBqNRqPRaDTNCD2Z\n3kCPqGg0Go1Go9FoNJpmhx5R0WiaGBFZCvRXSnUWkTDgMPCGUmpaI9nbClyhlGrUBG8RaQe8CSQC\nTuAupdRqc98o4CGgEpillPrY3P44MAbIUUqd7lZWBfCT+fVLpdSMxvT9RCIi04FnlVKlDVHeii82\nMO+F1wkLC2PWhJvof97ZHrVPPPsSH65ahzUxgfeef7ohzAPw5aE8XlYHsVjgH6e2569tEr3qS1wV\njF+3jeFd2jC8a5sG8+OnDZ+z4rUlWMLCuPKmDHqdc75HbWGendeeeICSo0eIiIzib2PS6fknz3Xn\nixXfZ7Hg/c+xWCzMumYQ/f5U/ztacwqKuP3Z5RQdKyUqIoIZIy7lvF7dgrZbY/+7rSx4b41hf9Tl\n9DvDg/38Im5f/BZFx0oM+yMHcV5a6Pbd+eXHr8j69HUsFgunD72ZdmnneNSWHS3my8X/pqqygqqq\nKk4bOJIOZ14Qkv0dG9exfvmLWCxhXHx9Ot3/fK5HbXG+nQ/n/4fSo0eIiIyk37X/oHPvPwdlt6n7\ngDtrVq3kucULCbOEMXn6rfS98KIG1fti3eqVvJi5iDCLhfSpMzj3Au/lZc6fw+oVn5KYlETmq2+G\nZLuaz1et5LnFzxAWFsakaTN8nlOg+oZGT6Y30IGKRtP0VAHFItIH46a+sWcKn6irnwuYpJTaKiId\nga+B9iISCTwK/AWIAdYCH5vHvAMsA5YeV9ZRpVRwdwvNnxnAK0DIgUq5y8XTz7/KGwsfo9RZxi23\n/ctroDLwwnMZfPEF3Dt7Qaima3BVVvHiz78xp+9plFVUcue3O30GKm/sPkyPhJYN5gNAhcvFp69k\nMm32YsrLylh83wyvgUp4WDhXT7iVtp26kZ/7Owvvnsw/n387KNvlrgrmvLOaZfeOx1nuYuzjSz3e\npIaHhXHv6Cvp2b4NhxwF3PDoEtY8fltQduvYf3sVy+6bYNif/YLHQCU8PIx7x1xJz/aphv2Hn2PN\nk7eHZN+dygoXWz9+hQG3Pk5FuZMvFt7nNVCJjGlJ/6n/ISIqGufRIj57ZCrt/3Q+lrDgEkAqXC7W\nvfE8Nz64AFeZk2X/ud1roBIWFs6gm6eR0rELRfYcXnlgOpMXLAvYblP3AXdcrnIWL5jHcy+9grPU\nybSMdK833YHq/bH//KIFLFjyEk6nk9snT/AZqFxw8QAuHnQZjz/4QNB2j/dh8cL5ZC59GafTyXR/\n6iAA/f8FPD1g9KD19qAyoAePOlDRaJoHHwJDgQTzczyAiEwDxmMEF08ppZaKSCdgNbAZSANeVkrN\nNvVXAvcBUcAapdQsc/sdwM1AFtCi2qgXfT/gXqAQOBVYpZSaGcgJKaVygBzz8wERiTSDlL8CWUop\nu2nrgIicrpTaopTaaJ7f8YS87ruPeqv3fOurf1PfBsgEOmEEGDcopXZ7qc9i4EXgMuALpdQEERkA\nPAG0BdaaF+/BSqmgA9UtO3bRvXMHkhLiAWjb2obasw/p1rle/Z96CQd/zwnWXL38XHCUTnExJEQZ\n/15ax0Sxt+gYXeLrD0R+PVJKYVk53Rs4UDmwczupHbvQKt4IkhJtrTm4bw/tOtf/pDo2MYnYxCQA\nklLaUOFyUeFyER4R+L/JrXt/pccprUmKM1YEamtNQP1yGOmQ+getNT4Wa3ysqUvE5aqg3FVBZER4\nwHZr7Gf/So/2bvaTA7BfEbp9d/L27yQ+tSPRsUafbJmUQsHBfSS261yv3hIWTkSUYbu85ChhQdS/\nO4f2/ExKh860jEsAIN7ampwD2bTu2LVefauEJFolGP0g3taayorg+kFT9wF3tmVl0bVbNxLN/t0m\nNZXdu3bSvUfPBtH74udtWXTu2o0Es7zWqalk795F1+49PB7Tq3cffj90KCh79bH9+HNq4/2cAtU3\nBhXNaETFxwPG+qj3QaW5L6AHjzpQ0WiaB5uA0UA48C5wtnljPRU4HYgGfhKR6gtDF+AK4ACwRUSW\nYdww3wf0U0qVishbItIf2AuMA/oA3YEtACKSUp9eKfWFaeN84Byl1A4RiQ/l5ETkMmCzUqpcRFKB\nQyKSDuRjjCC1rfbLA9Ei8gNQAtyjlFofpCt/qDel1C/mvjrn66n+zQBrPvCJUipTRGKBFj7qsxXw\nGjAT2CMiqUqpNcCZIpKNkfqXH+Q51WDPyyclOYm3Pl5JQlwstuQkcvPyPQYqjUG+s5zk6Eg+PZBL\nXGQESVER5DnL6eJB/6L6jYm9OrDyV3uD+lFckEdckpWNKz8kplUccUnJFOc7wEOg4o768TtO6doj\nqCAFwF54BFtCLMvX/UB8qxis8bHYC48gHbwftyFrN6d1ahvyDaq9qBhbQhzLv/jesJ8Qi72wuN6b\n5Lr2d3Fax3YNdoMMUFpUQEx8Etlff0Zky1haxCVSWpgPHgIVAJezhM/n3MURx2H+esOtQY+mABwp\nyKNVopX/ff4JLVrF0iohiSMFDo+BijvZW76nTefg+kFT9wF38hx2rLYUPnj3HeLi40m2WnHY7R5v\nugPV+7bvwGq18cn77xIbF0dSshWHPddroNLQOBwOrDYbH773DnFx8SRbbV7PKVB9Y9DMlif2+ICx\nPrGnB5VKqXICfPCoAxWNpvlwCDhC7Y/4TIxh0RKgRES+xbhp3gPsV0rtBBCR9cCfMYZjuwJfi4gF\n4+a4K5AEfKWUKgO2i8h+s/xzPei/MPdvUkrtAFBKFQV7UmZg8gQwxH27UirT3D/cj2LaK6VyRORs\n4D0R6a6Ucgbhjnu9fYVRb9WBSp3zFZFLqL/+Pwf6A9eb2iPAEREZguf6LFNKfWva3QukUpviZ6GB\n32486spBAKxav7Ehiw2IwR1TANhw2HP89e3vBbRv1YKUmKhG8+PcQVcBkLXxS7/0RfkOPl76DLfc\n80jItq/pZ6Tdrdm83afWXljME8s/Y/6UhntHzTX9jRSrNZv8tP/mCuZPu6HB7LvT9fzLAPjtJ999\nMiI6hkF3zuX/s3fe4VEV6+P/nM0mIaSQTYEQeh2qgB1QQCwoKtbrvRZEQVFQqg316uXaC4pSFLGA\ngiKC93oRFaWJCFJUVOpEaUEp2U022U3bkuzvj3OS7IbsJtkkBr+/+TxPnid75j3zvmdmdnfeed+Z\ndZ74g41vPkmLbv0wR0XXSX/foZcDkLH92xrJ5+fmsP79+Vx33xN10tvYY8Cfq669DoAN69c2iHx1\nXH61/lH/7Yb19VJfOIy4xnimr9c1iPz/YcJZYAQCFyqNS7VaeFSOikJx6vAIurNxnfHaR+AEtib/\nr5JSjvSvVAhxdW3k/ciridGhEEJEA8uAqVLKQ8blY0C6n1iacS0oxuoMUsrvhRBHgfaADMOkUA5B\nVc8brN2CLXUFa093pXsb5MTF1GQLWdkVjoEtJ5fUJEtDqApKUnQk2S5P+euyCEtVyLwCNh23811W\nLnluLxFAUpNIhqQn1VpvZsZePp43A03TuGbsFOItyThyKqI0ZRGWUHg9bha/+C+uvP0eklq0rLUN\nZaQ2i8ea6yx/Xba6Hgy3x8t98z7igRsupXVq3fvrJP2OfFKaxYfW/9pSHvjHZXXWn3M4gx8+eh00\njdOvv4smCRaKHDnl5cVOO02a1UxHfItWxFpScRw/QlLbzjW65+j+fXz59itomsbFt00gLjGJfHt2\neXlBrp24xOrHwf9mPcnQW+4msXl446Axx8Ce3bt48Zmn0DSNqQ89THJKKjartbw8x4gWBKO28pXZ\nt3sXrzz/DJoGEx542IhGVNRnNyI2Dcme3buY8ezToGnc9+A0klNSatkGtZNvCBrreGLjgJc7CPye\n04DNtVxg9F+oHOF3uVYLj8pRUShOEcqiFkKUb7j8CXhBCBGDnnp0JvrqRSzQTgjRBT0aMBCYjr46\nMVsI0UpK+YeRF1qMvifjWcNp6IS+rwJgSxXyLinliVB2CiH+hZ7eNLQGj7UQeF9Kudrv2jaghxAi\nBT3XtVWl8HFAhEEIYQGKjHSq9uhOTqZfeW3sqardgrEDeL6K9gc9P3c0MF8I0dR4jqras9hwsqpz\nkJKBnBAyNaJ3ty4cOHwEe56DYpebLFt2edrXK28tBmDyHYEr5j4f+OrxfAWRGEtmfhF5bi/uklKy\niz3l+1MWyj8AuE20AuDWrq24tav+//u/HiUmIiIsJwWgbdfuTHn57fLXJV4vJ44cpsCRi8ftJi/b\nFrA/5YvF8wG47Jax5deWznqWfoMupmvf4Ju9a0KvDq3Yf8yK3VmAy+MlK7ci7erV/6wBYNK1F5XL\n/3PBf7n8nNMYUE+nbfXq0Jr9R7Mq9NsdFfo/1t+Kk667uEL/2//h8nNPY0DPmjkDoUhq15WLH6g4\nQa60xIvj+BFc+Q5KPC6K8nIC9qfsXLkIgN5X6P59UV4OEeZIomLjKXbYcVqPEpvcvMb60zt14/Zn\n5pW/LvF6yf7jMIXOPLxuF067LSDta8NSfcwM/vuY8mufv/EiPQYMpUPvM2r38H405hjo0bMXC97/\nsPy11+vh0MED5ObacRW7sFqzAlKY3pg7G4C77plg3N8zpHx1dOvZi3nvfRCg//ChA+Tl2nG5XNis\ngWlfb7+uH+YxZty9AfX48OELc59Gj569eGdxxSEIehsc1J/J5cKaVakNXjPaYLx/GwSX/7+MlPJV\n4FX/a0KIgcA0v0vVLjBWWqg86Fd/rRYelaOiUDQ+VX4SSykPCyHmoE/sfcB0KaVNCBGLvu/kOUAA\n88uOGhZCjAM+FUJEAAXAzVLKg0KId9Adlj3AAaN+a1XyNbC3DbCpOiHjg+1a/V9xl/EMw6WUx4UQ\n09A31/nQT70qu2eOcU+yECITGA9kAwuEEMVACTDGSMeqlT0GVbZbVRjtP5tK7W8UT0J3UsajR0tu\nMjbTB2tP/z6u3N9zgE+EENnA9WUf4uEQaTYz5c6R3DzhYTRNY9o9o8vLrDl2NC3QX3pq1pus+XYL\nuQ4nF944lscmjg15SlhNMJs0RovW3Ld5H2gwtkdFUn5Osaeek9yCE2E2M3zkWOZMuwdN0xgxZkJA\nucOejeZnzMG9O9m5ZSPWo0fY8tUKNE1j9D+fJ6GaKExVRJojmHzdxYx87m004MG/X1peZs11BvTD\njl8zWfPjXg4dz2bZhu/RNI3XJt0cMgJSI/3XX8LIZ97U9d84PIT+w6z5cQ+HjttYtmE7GhqvTRlZ\nJ/3+mCLM9L7yVta9qs9x+l4zOqC82GHHf1AU2q38sPQ10DTw+ehz1e1Ex4a/RS7CbGbwP8awePok\nNE3jwpHjAsrzc3MC2uN3uYuM7ZvIOfY7P637DE3TuP6Bp4lLrJ0D3dhjwB+zOZK7753I3WNuQ0Nj\n0tT7A8qzbTa9vWsoH47+MeMmMGnsaDRNY9zkwBPNcrJtAe9FgNkznufbDetw5uVx09XDmXj/tGpP\nCqvOhrvvmcC4MbejaRoTq2gDrXIbhJD/MzjFjicOucAohHgGQEr5iN89C6m0UFndwmNVaOF6qwqF\nonEwNnmvlFL2biT9PwIX1sfm7/qgpvY0drvVEz7v77sbTbm5dU8ADkxpmBz6mtBx5ges2NPQJ3gH\nZ0SPNNwbP6xesIGIOv8fALg3fdR4Ngy8gX9+sbfR9D91WXcA3vm+QX8KKiSjz2x7SowDq6Ow0WxI\nTWhKZk5+o+lvm6Sn0mXlFTSaDc2bxTbY0suc7w42yAT93v4dwrJZCPE34Gn0xbYpUsrP/coWAD4p\n5Wjj9UD0/Zy70VcifMBw9ANtFqBne5QAD0spvwqlV0VUFIq/Jo22wnCq/Z5JLe1RKzMKhUKhUNQS\nKeUy9FSuqspur/R6E3rKdGWOo/8EQI1RjopC8RdDSnkY/fQpRS1Q7aZQKBSKvwolp9bxxI1Gg5w8\no1AoFAqFQqFQKBR1QUVUFAqFQqFQKBSKUwgVUdFRm+kVCoXir4P6wFYoFIpThwbbTP/yxv0N8nk/\n9fxOf9LZi/WDiqgoFAqFQqFQKBSnECqioqMcFYVCofgLUXrg+0bTbeqo/8bK3xduazQblt52NnOa\nieoFG4h78yQPftp4R0S/cKV+RHTJnq8bzYaIHkMoOfxz4+lv1weg0Y+p7jPt8+oFG4ifn9N/G+en\nP3IbzYa+rRIpLiqqXrCBaBITA8De20dUI9lwdF+wosHqVo6KjtpMr1AoFAqFQqFQKE45VERFoVAo\nFAqFQqE4hVARFR0VUVEoFAqFQqFQKBSnHCqiolDUM0KId9F/WDANKAGygC1SyvFh1HUQOENKmVNF\n2STgDSllcR1NbhCEEOnAUiARcAHTpJRrjLIbgKeAUuB+KeVK4/qLwEggS0p5ml9dJUBZUvw3UsrJ\nf9qDNDD13Y9ffLOFV99bhknTeOCOm7jgnNODyr741gesWPctSc0S+N/rz9WH+gDse7dyfMNy0DTS\nh95Isy79qr2nxF3MvjceJPWcy2h+9mVh6e18zWWc+89J+Ep9bPrn8xz68uugsi3OOI0LXn0SzRxB\n9p4Mvho9NSydwfjjp03sXfUBmmai55WjSOtxZlBZd6GT7+Y/ga+0FB8+ug69jlZ9B4al94tN3zPr\n/f9hMmk8MOp6hpwV/LdOX1y4nBUbtpLcLJ5PXnk8LH1V2rBhM7MWLtXH4tiRDDn3jOA2zF/EirXf\nkJzYjE/emFFvNgD8vGkdq95/G81k4opR4+hx1oCgsnk5Nt6fMZ2ignzMkVFcNnIsXfsE77Oacknv\nNO69RFDq8/Hy5/v4Zl9WSPkfn7mMjGMOAH44mMOLK/fWSf93X69h6Tvz0DQTt9w9kTP6nxdSfvG8\nWXyz+guaWZJ48a33w9b75ZdfMmfuXEwmE1OnTmXwoEH1IlsbvrU5+CDThqZp3NYulbOS4kLKF5WU\ncs+Og1yVbuGq9KR6sSEcVERFRzkqCkU9I6UcBSCEeBzIl1K+XIfqQn1STQYWAaekowJ4gfFSyp1C\niLbAZqC1ECISeA44G4gB1gMrjXs+BpYACyvVVSClDD7j/mtTb/3o8Xp5ecGHfPTKkxS73dz20NMh\nHZWLB57F8CH9eeSlN+qq+iR8JV6Ofb2UrqP+TanXzW/vP1MjR+XE5v8R07JD2HpNZjP9p9/Hsgv+\nhjkmmqtXvhfSUbnojRdYO24ax7f/RBNLYth6q6K0xMuezxczaNLzlHrcbHr98ZCOSmSTpgwc/xTm\nqGjcBU7WvjCB9NP6o5lql/zg8ZYwc9F/WfrCw/o4eOzlkI7KJf1P5/Lzz+aR2QtrpSe0DV5mvv0B\nS2c/Q7HLzW0P/juko3LJ+edw+QUDeWTGa/VmA0CJ18vni+Yz8YV5eNxu5j02OaSjEmGK4Jq7ptCy\nXSfs1hPMffge/vnW8jrZYDZpTLq0GzfP3UR0ZARv3XlOtY5KkdvLP2ZvqpPeMrxeLx+8OZenX1uA\n2+Xiianjq3VUzh50AQOGXsLrLzwZtl6Px8Ors2bx/uLFuFwu7rjzzqDOR21ka4O31MeiwzZePK0t\nrlIfj+0+Uq2jsvz3bDrHNqmzbkX9oBwVhaLhOOmsciHEYOBRIA/oBqyWUk4VQqwA2gAe4F0p5Vz/\nOoQQnYCPgBuA9sAMoCWw3og2DJdSHhdCTATuRHdwXpZSLqxG75XAP4EoYK2U8v5Q8rV5eCllFno0\nCSllphAi0nBSzgF2SSlthq5MIcRpUspfpJRbhBDtatKWtcWodw3wI9ATeE9K+YJRFqx9grVnC2A+\n0A7dwbhFSvmbEOIK4DFObk8nsAAYBnwtpbxLCHEhQfox3Gf8Zd9+urRrg6VZPAAtmycjD2YiOrSt\nUr5v9y78ccIarrqQFBzdT5OU1pib6rZENUumKCuTmOZV2wJQnH0Mb6GTpmntw9bb4sw+5Oz5leIc\nOwD5vx8juacge7c8SbZ5v14UWbM5vv0nXb+9fk9Qsmf+SkJaG6JjEwCISUwh7+ghmqW3r1JeM0Vg\njooAwFNUgMkcGZbeXzIO0qVtOpYEfUKWnpKEPPQ7on3rKuX7iI78kZUdlq6gNuz7jS7t22Bppj97\nemoK8sBhRMeq3t7Qp3vXBhmLmRl7SGvbgdgE3QlNTGnO0UP7SW/fqUr5uEQLcYkWACypLSjxeinx\neokwhz9d6t0mkd9OOMkt9AAejucV0SUtnl+PO4Peo2n191MXv+3dRZv2nUhoprdBSvMWHN7/K+06\ndQl6T9cevbEeP1YnvTt37aJz585YLHp7pqWlkZGRQdeuXeskWxsy8oto2zSKhEi9/1KjIjlUUEz7\nII7IH0Vu8jwldIqLrpPe+kBFVHSUo6JQ/PkMAM6SUu4VQiQY1+6WUh4VQpiB3UKIZcZE3we0Bd4C\nRkop9wP7gX5CiAPAECmlHcon4vcCfYBo4GchxMoyh6CyXiFEKrqTMlhKWSyE+EgIMURK+XUIO8NC\nCDEM+FFK6RFCpAHHhBBjATtwHH2y/kuIKqKFEN8DRcAjUsqNYZrSAbgcyAR+EUIskVIeMcoqt087\nYAJ6Gl/l9pwNfCalnC+EiAOaGO35GFW3ZyzwPjAV2C+ESJNSrqWKfqwLNnsuqUmJfPT5OhLiY0mx\nNMOakxvUUWlIvAV5RMYlYtuxDnOTWMyxiXjyc0M6Kse+/ohWF99Czs8bwtbbtEUKBSey6HnbDRTn\nOig8YSU2LbVKRyWudUvcznyuXP4mMalJ7HlvObveXhK27sq4HHaiEywc2vIVkTFxRCdYcDnt6GsN\nVeN1FbFxziMU2I5zxs2Tax1NAbDl5pFiacZHX31DQmwsKZYErPa8oI5KQ2DLySUlycJHn63Rx2JS\nItYce1BHpaFw5uYQb0lmy1criImNJ96ShNOeDUEcFX/kjm206tilTk4KQHJ8NDani+vOboOjyIPN\n6SY1PjqkoxIVYeKDewfi8pQw60vJjkPhfzzk5mSTmJzMmpX/JTYugcSkZOw52SEdlfog22YjJSWF\n5cuXk5CQQEpyMlabrUrnozaytSHXXYIlysyXx3OJM0eQGBWB3V1C+9iq5RcdtjKmQ3PWZuXVSW99\noBwVHbWZXqH48/lBSrkXQErpMK6NFUL8CGxHn7SnG9c14L/oe1z2VKpHIzDS0A/YKKUsklLmAlvR\nJ9nB9J4LdAQ2CyF2AH2N16HsrDWGYzIDGOd/XUo5X0q5jJpFS1pLKc8EpgAfCCHCXe46LKXMMPaD\nfAv450VVft5+6PthqmrPIcA7hmy+4byEak+3lHKrlNILHETfv1RG5X6sMzcMH8ql55+D7xT4nkvp\nN5TE7udQnTF5v+4gOjmNqITketG7e+FH7P9kFYRYmTZHR5N2Tj/WTXiU/15+K33GjSK+bat60e9P\n+3MvoVWfAdW2gW5TDBfcN5PBU2aw+9N38bpdYeu94ZJBXDrwjEYdBzdcfhGXDuqPr5EH47mXjKDP\nwAtq1AcADns2Kxe+xrV31d+epY+3HWH1zuM1erNf8uw6bpqziRdX7uW5f/QlMqLu07WLrriG/kMu\n/NP74vrrr+eSSy4JmcccjmxtGJaWyMCU+JAy23PyaRUTRWp0eJFMRcOgIioKxZ9PwFKNkXZ0EdBf\nSukSQmwncBFhLPCiEOJMKWV1v/anBfn/JL0Gq6SUI2tiZzgYDsUyYKqU8pBx+RgVjhjok/aQOQZG\ndAkp5fdCiKPoS9InL5FXT6g5QlXPG6w9g32PBmtPd6V7G2SRKDUpkazsipXXsgjLn0HB0f38vmoB\nAK2H3YY5LhFPfoUtZRGWYBQe3U/evu/Jy/gRb6ETzWQiMjYRS8/+tbPjuJXYtOblr5s2T6XgeNUp\nRYVWG/Z9+yk4pu8XsP60C0uXjjgz/6iVzjLsmb/y8/J5oMFp144lOsFCcV5FG7icuUTHW2pUV3zz\nVsRYUnGeOIKlTeda2ZFqRNLKsOXmkWppVqs66kpqciLW7IozQGz2PFKTavbsdSEzYy8fz5uBpmlc\nM3YK8ZZkHDm28vKyCEsovB43i1/8F1fefg9JLVrW2oZerZvx2LW98Png6U92Y3O6SE2oWFtJiY/G\n6gztgOYU6B8Ze/7Iw+pwkW6J4bCtoEb6f9u7mzdnPgdojJn8AInJKdhtFW2Qa8/GklQ/CwKhSElJ\nwWqteO/ZbDZSU1LqLFsbLFER5Li95a/tbi8WI72yMhn5xXyX7WRrTj4OTwkmDSyRZgal1imhIGxU\nREVHOSoKReOTANgMJ6UneuqWPz8AdwPvCCHOkFJ6jOt5QDJQNhvYATwvhIhBT1U6k9DpVFuA2UKI\nVlLKP4wN7y4p5YlQxgoh/oWe3jS0Bs+2EHhfSrna79o2oIcQIgV9M30rKaW/nQERBiGEBSgy0qna\nozs5mWHa004I0QU4AgwEpoeQDdWe64HRwHwhRFPjOapqz2LDyarOQfLvx7DpLTqxP/MP7HlOit1u\nTmTby9O+Zi5cCsCU2/5+0n2+eli/jE3vhBj9VEWdJV6KbUfxFjop9brxOO0BaV9Hv/4IgPQhNwDQ\ncvD1tBx8PQDHN/4HU3RMrZ0UgKwffiGpW2eaJFkwx0QTl968PO3r3MenALDliZkAnPhxJ3GtWxLV\nLB5vYTHJPbriOHQkaN3VYWnbhSFTXyp/XVrixZl1BFeBg1KPm6K87ID9KXs+XwxAj+G3AFCUl0NE\nZCRRTeMpdtjJtx4lNqlFre3o3aUD+48cw+7IN8ZBbnna18zF/wVgyi3XVLrLV68r7b1FZ2MsOih2\nuTlhyylP+5r5zge6DaNvqmRC3W1o29Kvm58AACAASURBVLU7U15+u/x1idfLiSOHKXDk4nG7ycu2\nBexP+WLxfAAuu2Vs+bWls56l36CL6dr3rLBs2PV7Hn+fVbER3mzS6Ng8jsSmkURHRtA8oUlA2teE\nYXp60+wvMwCIjzHj8pTi9paSbokhNSGa47k1/wX4zt178vz8ReWvvV4vfxw+iCMvF7fLhd1mDUj7\nWvKWfoDBjXcEHkzpq+OY6NWrFwcOHMBut+NyucjKyipP5Zo1axYAEydOrFa2LnSJi+FIoRuHx4ur\n1EeO21u+P2XRYd0xGtkuFYCb26Zwc1vdOfrwiI2YCFOjOSmKCpSjolA0PqvQU792AfvQHZMyfADG\nJvPVwJPANKNsDvCJECIbuF5KeVgIMRvdEfAB0/32p5yElNIqhBgHfCqEiAAKgJtrYG8boNrjaIQQ\nA4Fr9X/FXYZNZZv+p6GfAuZDP/Wq7J45xj3JQohMYDyQDSwQQhSjH/c8Rkrp/61dI3sMDqKfOCaA\n+VLKzGCC1bTnJHQnZTx6tOQmYzN9sPb0/7av/M1fuR9DHwcUgkizmamj/8GN901HQ+PhsRXBHWtO\nLlolf+nJ1xayetN2ch35DL11Io/dc1vIU8JqgxZhJn3IDfz63hOgQauLAoeWNz83ZFpWuJR6vWye\n/hLXr16Czwcbpz1TXhbbIjUg88fjLODbh5/hmpXvYYqIQC77lNz9h+rNFlOEmR7DR7Jx9sOgafS+\nanRAebHDHrBpuijXqkdkDHpdeRtRsaHTVaoi0hzBlJHXctO059E0jWljbigvs9rzThoHT81fwuot\nO8h1FjD0zmk8PvamkKeE1cwGM1PG3MRNkx9D02DauFEVNuTkntT1T815m9XfbiPX6WTozeN4fMId\nIU8JqykRZjPDR45lzrR70DSNEWMmBJQ77NkB7XFw7052btmI9egRtny1Ak3TGP3P50moJgoTCm+p\nj1dXSd4bNwAfPl5YGZjFmxrfJGCxoENqHE9cfxoubymlpT6mf7wTl7c0bP1ms5kb77yHxyfcAWiM\nGj8loNyebTtp8/47r77Itm+/xunIY/w/RjBm0oPVnhRWmcjISCZNnMito0ahaRoPPvBAeZnVFqgz\nlGxdMJs0bm2XyrSd+kf96A4V0Va729sQH0H1hoqo6GiNnTeqUCj+Whh7aS6sj83f9UFN7TE2x6+U\nUvb+cyxrEHylB6rL/ms4TB31o3X/vnBbo9mw9LazmdNMNJr+e/MkD366u9H0v3BlTwBK9nzdaDZE\n9BhCyeGfqxdsKP3t9KDzij1hH5BXZ0b0SKPPtM8bTf/Pzw0H4Kc/6vekutrQt1UixUU1j/TUN01i\nYgDYe/uIRrOh+4IVDebqPPjp7gaZoL9wZc9T2D07GRVRUSgUteJU+z2TWtqjVmYUCoVCccrjVREV\nQDkqCoXi/xOklIcJPAVNoVAoFIpTEpX6paOOJ1YoFAqFQqFQKBSnHCqiolAoFAqFQqFQnEKoiIqO\n2kyvUCgUfx3UB7ZCoVCcOjTYxvR7P/6lQT7v51x3mtpMr1AoFIqGYeBz6xpN96Zp+k/V9H9mbaPZ\n8N0jF+J8b3qj6Y+/dTr7TjgaTX+3FvrvOsisxrNBNE/AseDxRtOfcPsTAMix1zaaDWL+f06J099+\nz8lvNBtaJ8VRtHJuo+mPueIeADxbP2k0GyLPubrB6i5RgQRA7VFRKBQKhUKhUCgUpyAqoqJQKBQK\nhUKhUJxCqD0qOspRUSgUCoVCoVAoTiGUo6KjHBWFog4IIR4Gbkbf5LxHSvn3auRHAjFAoiG/shr5\ng8AZUsqcWtg0CXhDSllc03saAiFEOrAU/VldwDQp5Rqj7AbgKaAUuL+sHYQQLwIjgSwp5Wl+dZUA\nZT+F/Y2UcvKf9iANTEP119BuzblzUEd8Ph9z1v3G5v3Z1d4TExnBkrHnsmRbJku3H6mzDRd2b87Y\nQZ3w4WP22l/Z9FvNbFh697l8sDWTD7eFZ8PqPZm8vuEXNA0mX9iP87u0qlIur8jFhCVf4y0tBR/c\nPrAnF/doG5bOYHy7bjXvvzUPzaRx+/hJnDXg/JDyC157la+/+pxmiUnMWrikXvQvflPXP3r8JM4a\nWI3+ua+y3tA/+9266V+9N5N53+xE0zQmDe3L+Z3Tq5TLK3IxYekGSkp9+IDb+3fn4u5174dvs/JY\nfCgLDRjdKY2zkuNDyheVlDBu229c3TqZq9uk1Fm/P3/8tIm9qz5A00z0vHIUaT3ODCrrLnTy3fwn\n8JWW4sNH16HX0arvwDrp/3rNV7wz/3VMmsZdEybT/7xBIeXfmP0Kq1d9TqLFwluLl4at98ufMpj7\nxRZMmsbUEecxqEeHKuWy8vJ5aNEqnEUuoswRTLx8AOd2rZ/34qqtPzN7+VeYTBr3/eNyhvTrXrUN\ndgf3z30fZ2ExkeYIptxwGf17dakXGxThoxwVhSJMhBBnApcDfaSUJUKIPtXdI6VcVEs14SypTAYW\nAY3qqABeYLyUcqcQoi2wGWgthIgEngPORnfa1gNlDtvHwBJgYaW6Cmr5C/R/Jeq9vyJMGncP6cSd\n735PtNnE7Jv61chRGTWgHfuO188m7QiTxrghnRmzcDvRZhNzbz6dTb99V+19tw1sz95jzrD1ektK\nmbP+J969fRjFXi93L14X1FGJi45k/sgLaRJpJrfQxd/e+IwLu7fBpNXPoTher5f33pjDjDfexeUq\n5p+Tx1XrqAwYPJRBFw7j1Wf/XS/63503h5fm6/ofnTSuWkel/5ChDLpoGK88Uzf93pJS5nz9C++O\nuphibwnjPlgf1FGJi45k/s1D9X4ocnHDm19wYbe69YO31Me7B0/w0ukdcZX4ePTnQ9U6Kh8dttE5\nPiZsncEoLfGy5/PFDJr0PKUeN5tefzykoxLZpCkDxz+FOSoad4GTtS9MIP20/mim8LYVe70e3np9\nDnPffheXy8V999xVraNy/gUXcsElw3jhyelh6QTwlJQw67PNLJ70d1weL3e+/p+gjorZZOKR64bQ\npWUKx+xORs3+iK8eHxO27nIbvCW88tEqlky/l2K3h9HPzg/qqEREmPjnqKvp2qYlx2x2bnnyNda+\n+midbQgXFVHRUY6KQhE+7QCrlLIEQEpZtuKPEMIJLACGARuklGON6xOBO9EdkJellAuN65OAuwA3\n8IWU8mGjKs0o7wR8BNwgpdxfVT1CiAuBGUBLYL0RhRgupTwuhBgMPArkAd2A1VLKqUKIFUAbwAO8\nK6Wca+irUr42jSOlzAKyjP8zhRCRhpNyDrBLSmkzdGUKIU6TUv4ipdwihGhXRXV1njka9a4BfgR6\nAu9JKV8wyoK1T7D+agHMRx8DxcAtUsrfhBBXAI8BUcBaKeX9hrz/ePhaSnlXqP6q67P2TE/goDWf\nvCIPACccLjqlxrLfWhD0njaWGBKbRiGPh+8kVLbhgJ8Nxx3FdEqNY781+ClFbZJiSGwaWScbdh3N\npmNqIolNo4Fo0hKa8usJO11aWE6SjTCZiDAmf/kuN1EREWHrrYqMPbto26ETCYmJAKQ2T+PQ/l9p\n3yn4Kq3o2Zus48fqTX+7jn76W6Rx8Ldf6dA5uP5u9aR/19FsOqU2M/oBvR+ycunSPPEk2YB+KPYQ\nGVH3c34ynIW0i21CQqQZIiG1SSQH84vpENekSvk/Cl04PF46BymvC/bMX0lIa0N0rH5iW0xiCnlH\nD9EsvX2V8popAnOUPhY9RQWYzJF10r939y7ad+xEs0T9PdA8LY39v/1KpxDjoEev3pw4VrdxsOvw\nCTqnJWOJ052/NEs8GUdtdE0/OVqVFN+UpPimALS0xOMtKcVTUkJkHd+TO/dn0rl1CyzxsXrdyYnI\nzGOIti1Pkk1OiCM5IU6XS7HgKSnF4y0h0ly/nwuK2qEcFYUifNYAzwohtgIrgLf9JpmxwPvAVGC/\nECINiAYmAKcZ//8shFhpTNinA62llAVCCP9PcR/QFngLGGk4Ke2Ae4E+lepZC/QTQhwAhkgp7ZXs\nHQCcJaXcK4RIMK7dLaU8KoQwA7uFEMsMByOYfFgIIYYBP0opPUZbHBNCjAXswHH0yfovIaqIFkJ8\nDxQBj0gpN4ZpSgf0KFgm8IsQYomUsiy/KOB5jXYO1l+zgc+klPOFEHFAEyFEKrqTMlhKWSyE+EgI\nMURK+TVVjIca9FfYJMVGYct3M6JPOs5iD9n5LpLjokM6KuOGdOKVNb9y+Wknf4GHQ3JcNNkFbq7q\nm46z2EtOgZuUuCj2W4PfM/6Czsz8KoMr+lS98l4TbPlFpMQ14T8//kZCTBTJcTHY8ovp0qJq+UK3\nh9HvruZ3ez5PXTWg3qIpAPacbJKSU/hyxX+Ii0/AkpxMTrYtpKNSn9izs7Ekp7CqTH9SMvZsW0hH\npb7ILijW++Gn/SQ0iSI5rgm2/KIqHRXQ+2HMorX8npvPk1eeW+d+sLu9WKLMrDqaQ1xkBJYoM3a3\nl6rX8+Hdgye4s1NL1hyvt7dhOS6HnegEC4e2fEVkTBzRCRZcTjvQPug9XlcRG+c8QoHtOGfcPDns\naApATrY+Dld+8h/i4+NJSkomx2YN6ajUBzZnASkJsSz/bhcJTaNJjm+KzVFQpaPiz+Z9h+neunmd\nnRQAW56T1MR4lq3fSkJsDCnN4rHlOqp0VPzZ9IukR/v0RnVSSkpLG033qYQ6nlihCBMpZR7QC3ga\n6Ap87zehd0kpt0opvcABIA3oh76/okhKmQtsRZ8EA2wDFgghRqFHVcrQgP8CW6SUe4xr/YCNQeop\nu6eqb/kfpJR7DdvL8nvGCiF+BLajOwvp1cjXGsMxmQGM878upZwvpVwWxNbKtJZSnglMAT4QQkSH\nac5hKWWGsR/kW8A/nazy84bqryHAO4ZsvuG8nAt0BDYLIXYAfY3XAG6/8XAQfTyUEay/6syKn4+y\nXlrRqpn0DeiUTGZOIVlOV73b8L+fjrJuX1a1cgM7J5OZXX82XHt6Zy7q3pbqftS4aVQkH945nMVj\nLuWVtTso9njrRb8/w0Zcy8ALLqrWlobi0hHXcl4j6b+2bycu6taG6lQ3jYpkyZhLWXTbJby67ud6\n64dL05M4L7VZyGffnu2kVUw0qU3qFrmojvbnXkKrPgOotjEAc3QMF9w3k8FTZrD703fxuuv+vrji\n6msZfOHFf/qvxl7fvxeX9OlSo0Rmm6OAlz/9lkeuHVKvNvztgnMYdvZp+GpghC3XyYwPP+efo66p\nVxsU4aEiKgpFHZBSutGjKSuMNKpzga/QU6n8KVsU8J8x+v9/KXAecCP6Kr5/AvNY4EUhxJlSyu+r\nqScUef4vjHSni4D+UkqXEGI7gYsXAfLhYDgUy4CpUspDxuVjBDpEaca1oJRFeaSU3wshjqIvRcow\nTArVVlU9b7B2DvZtt0pKObKK6/7Op496XiTq3jKeBy/ths8HL30lyc53kxJf4cslx0aRnR98otMz\nPYHBojnndUklsWkkpaU+svPdrNl7osY29GiZwEOXdcOHjxdXSbLzXaTERVXYEBeNLd8d9P6e6c0Y\nIppzftdAG1bvqbkNAClxMdicReWvy1b2q6N9cgItm8VywJpHj/TkWuksI2PPbl576Rk0NO6e+hBJ\nySnkZFeEkMoiLA1Fxp7dzJ1h6L/vISzJKeTYKvTn5ugRlj+DlLgmWJ0V2670fqh+/0f75ATSmjXl\ngM1Bj5ZJYeu3REWS46r4GM41IixVIR2FfGdzsC3bSZ7HiwkNS3Qkg5s3C0u3PfNXfl4+DzQ47dqx\nRCdYKM6riNS4nLlEx5+cilgV8c1bEWNJxXniCJY2nWt0z77du5j5/DOgwaQHHiY5OYVsv3GQk20j\nKSW1dg8VBikJsVgdFameZRGWYLi9Xh547wvuG3E+rZLDa/uTbEhMIMtekUpqy3OSkhg8QcDt8TJ1\nzmIeuOlyWqeGP/7qA7VHRUc5KgpFmAgh2gNmY29CDPrk+XejuKoJ8Q7geUM2Gt0ZKUt3aiel3CiE\n2A3sq3TfD8DdwDtCiDOqqQf0CXcyUN1JYQmAzXBSeqKnklWLEOJf6OlNQ2sgvhB4X0q52u/aNqCH\nkeIWA7SSUvrbHxBhEEJYgCIjnao9upOTGaY97YQQXYAjwED0lLtghGrn9cBoYL4QoqnxHFuA2UKI\nVlLKP4wDBIoNJ6s6B6km/RWUvcec3L5ge/nrCJNGh+RYmsVEEm02kRIfmPZ11yA90PPGNwcAeHPj\nQd7ceBCA2we2p9BdUisnBWDPMQej3tkWYEP7lAobUuOiA/an3D24EwDzNuwHYP43B5hv2DP6vA4U\nur21dlIAeqUnc8DmILfQRbHXi9VZVL4/Ze56fRvZPRfoQ93qLCTKHEGzmGhs+UVkZjtJT4yrtc4y\nuvboyStvv1/+2uv1cuTQQRy5ubhcxeRYrQFpX4vm67/qPXLsPQH1+Hy+Gq26V6X/1XeC68+2WgPS\nvt57Q9d/611V6K/junvPlskczM4z+qEEq7OwPO1r7gb9bXTPYD1AaXUWEWU2VfRDjpP0xOAT2prQ\nNT6GI8a+E1eJj2y3t3x/ynsH9HF1a0c9H/CWDi24pYP+/5JDWcREmMJ2UgAsbbswZOpL5a9LS7w4\ns47gKnBQ6nFTlJcdsD9lz+eLAegx/BYAivJyiIiMJKppPMUOO/nWo8QmBcldrIJuPXvxxnsflL/2\nej0cPnSAvFw7LpcLmzUw7eut1+cAcMe4ewPq8RHeOCyjV9sWHDiRgz2/CJfHS1ZeRdrXrM83AzBx\n+IBy+ceXrGH46YL+ov5O3uvdsQ37j57A7iyg2O0hK6ci7euVZasAmPy3S8vlH33zIy4f0I8BvbrW\nmw3hohwVHeWoKBThEwMsNCaqGvpm9LL0LP9PGB+AlPKwEGI2+kTdB0yXUtqEEBqwyEgbMwH3VXHv\nFiHEauBJKeU0IcScyvX43TMH+EQIkQ1c77fnpDKr0FO/dqE7Rz/U8LnbAJuqExJCDASu1f8Vdxm2\nlm3un4Z+CpgP/dSrsnvmGPckCyEygfFANnpaXDFQAoyRUhb5qaqRPQYH0U8cE8B8KWVmMMFg/WUU\nT0J3UsajR0tuMhzWccCnQogIoAD96GqoYjz4UdP+qjElpT5e//o35o08A3w+Zq35NaA8OS66RikQ\ndbZh/X7m33omPny8siajkg1RQe6sG+YIExOG9mH0u7pvPPXiiuw+W35RgOxxRyFPf74NDQ0fPiZf\n1K9883e92GI2c+td9/LQ+DGgwZgJgedR5GTbTkrLe2PmC3z3zXqcjjzGXH8Fd099qNqTwkLqv/te\nHhw3Bk2DOyYG6rdn26CS/nkzX2DLhvU4HHmMvu4Kxk19qNqTwqrUHWHi3iF9GLNoDWgaUy/qV15m\nyy9C8/PdjzsKeGbV92gYHwhD+5IYU7d+MJs0bu3Yggd3HEQD7uhUkW1pd3sbKNmyakwRZnoMH8nG\n2Q+DptH7qtEB5cUOe8A4KMq16hEZg15X3kZUbOgTy0JhNkdy57gJTBg7Gk3TuGfyfQHlOdm2gP4A\nmDXjeb7dsA5HXh43Xj2cifdPq/aksMpERkQw8fKBjJq9DE2DB66uuN/mKAjQuePgUdbu/I1DVjvL\nv9OPtJ5zx4iQEZga2WAcM3zLE6+hafDQLVdW2JDrwH8g/JhxiDXf7+LQMSvL1m1F0+D1+0aTkhh+\n2yvqjtZYObMKheKvibGn5cL63PxdF2pqj7E5fqWUsvefY1mD4Bv43LpGU75pmh606v/M2kaz4btH\nLsT53vRG0x9/63T2naifI5zDoVsLPW1FZjWeDaJ5Ao4Fjzea/oTbnwBAjr220WwQ8//Dg5/ubjT9\nL1zZE4Dfc4KfotfQtE6Ko2jl3EbTH3OFHgn0bP2k0WyIPOfqBnN5r3tna4NM0D8efc6f6KbXHRVR\nUSgUteJU+z2TWtqjVmYUCoVCofiLoBwVhULx/wVSysMEno6mUCgUCsUpidqjoqOOJ1YoFAqFQqFQ\nKBSnHCqiolAoFAqFQqFQnEKoiIqO2kyvUCgUfx3UB7ZCoVCcOjTYxvTh8zY3yOf953cP+Ettplep\nXwqFQqFQKBQKheKUQ6V+KRQKxV+Icct/bjTdr1+v/1Bi9ykrGs2GvTNH8GXXxjt4bljGj3y882ij\n6b+udzoAhUXF1Ug2HE1jmlD48YzG03/d/QAUf/56o9nQZPg4znt+faPp//ahCwBYWcsfZq1Pruje\nAu+OVY2m39xP/6FG68wpjWZD6pSZDVa3Sv3SUREVhUKhUCgUCoVCccqhIioKhUKhUCgUCsUphIqo\n6KiIikKhUCgUCoVCoTjlUBEVxV8GIcS76D/YlwaUAFnAFinl+HrUcQOQaLwslFIuDrMep5QyPkhZ\nS+BVKeUN1dTRFVgKdAYGSyl/rFR+JdBdSvlCODY2NEKIdHT7EwEXME1KucYouwF4CigF7pdSrjSu\nvwiMBLKklKf51VUClG3O+EZKOflPe5AGRggxCXhDSlmvmw5O7NzMgdVL0TSNzpeNJKXbGUFlPYX5\n/LTwaXylJeDz0W7INbToPaDONlzaN51Jl3Wj1OfjxRV7+HpP6Hz6XS9diTyaB8D3+7N59pPdYelt\ncdnFdJk8Hp+vlIznXsH69cagspfs3Y5zXwYA9u072PdM/e69+GXzelYveQdN0xh+6zi6ndk/qKwj\nx8aSl5+guDAfszmSYbeMpfNpwfutJnz15ZfMnTsHk8nElKlTGTRocL3Kh6zrl/3MXf09Jk1jyvBz\nGNStXZVyWY4Cpi1Zi7PYTaQ5gonDzubczq3C1lvGlzsymPPFZkyaxtQR5zO4Z8eq9efl8+C7n+Ms\ndhEVEcGkK87jXNG2zvr9GdotlTvP70ipz8fc9fvZvD+72ntiIiP44M5z+HD7EZZuP1In/T99u44v\nPngLTTMx4rbx9Dgr+Ps7L8fGohenU1TgxBwZxeW33kXXPmeGpXfVdzuYtfQzTCaN+2++iiFn9KpS\nLisnj/teXYizsIioSDNTbryS/r1FWDors/a3Y7y5XWLSNO7p342B7VpUKecodjP1s+2UlPrw4WNk\nv85c2LllvdgQDj4VUQGUo6L4CyGlHAUghHgcyJdSvtwAOj6qp6qCfsJIKY8BIZ0UQy4D6CeEWBek\n/FPg07AtbHi8wHgp5U4hRFtgM9BaCBEJPAecDcQA64GVxj0fA0uAhZXqKpBSNt4O6oZlMrAIqDdH\npbTEy/4vP+DMcc9Q6nGz4+1/h3RUzE2acvod04mIisZT6GTLK1No3vNcNFP4QXezSWPqFd25YeZG\nmkSaWDh+YLWOSpHby3UvfRO2TgDNbKbr/RPYcv2tRERHc9ai+SEdlZKiIr675uY66Qxat9fLl4vf\nZPxzr+Nxu3hr+pSQjoopIoKr7pxMWruO5FpPMO/Re5k2f1nY+j0eD7Nmvcqixe/jcrkYe+cdIR2P\n2sqH1F1Syqwvt7No/NW4PF7GvvVZUEfFbDLx8FXn0SUtiWO5+dw2bwVfTrspLL0V+kt4deW3vD/l\nRlweL3e8tjyooxJhMvHo9UPpkp7CMbuDW1/9iNXT76iT/sD6Ne4e3Ik73/uBaLOJWTf2rZGjcmv/\nduw77qiz/hKvl88WvcGkF9/A43Lx+mOTQzoqJlME1901hZbtO2G3nmD2tPE8/vbHtdbr8ZYwc8mn\nfPjUVIrdHm5/ck5QRyUiwsRjY/5G17bpHLXlcMvjr7DutSdqrbMy3pJS5m3dx5vXDsRVUsLEFVuD\nOiqxUZHMGXEuTSIjyCt2c/OH33BBpzRMWuOc5luqHBVAOSqKvyYnfWoIIa4AHgOigLVSyvuN64OB\nR4E8oBuwWko5NcT1FUAbwAO8K6Wca9TjBBYAw4ANUsqxxvUHgNFABtATGCqlzAQ0IcSsKuRfAC4H\nSqWUvf3sL4/ACCHWA/f5RVCqet53gSHA/6SUE/3rqY2dIZ63yvYJ0ScnIaXMQo96YeiKNJyUc4Bd\nUkqboStTCHGalPIXKeUWIURVs5k6f1MY9a4BfkRvg/fKolEhxsNE4E50x/NlKeVCQ74FMB9oh+5g\n3CKl/C3EOPTvl6+llHcJIS4EZgAtgfVG1Gi4lPJ4XZ/VceQ34lq0ISo2AYDoxBTyjx8mLq3qiaJm\nMhERFQ2Ap6gAkzmyriZwWjsLvx5zklvgBuBYbiFdWyaQcSz4xEurh58kaNanF/kZ+/HYc/EARUeP\nEye6kC9/Daq1oTjy615atO1AbEIzABJTWnDs0H5atu9UpXxcMwtxzSy6bGoLSkpKKPF6iTCH91W9\na9dOOnXujMWi15mWlkZGRgZdu3atF/mQuo9k0amFBUtsE72uxFgyjmXTtWXySbJJcTEkxcUA0DIx\nDm9JKZ6SUiIjwneUdx4+TueWKViMetMS48k4aqVreupJssnxTUmOb6rrtyQY+kuIjIgIW78/PVsm\ncMBaQF6RB4ATDhedUmPZby0Iek8bSwyJTSORx/PrrP9wxh7S2nYgLkFPFrCkNufoof2kBxmH8YkW\n4hMthmwLSrzesMbhL78donObllgS4gBomWJBHv4D0e7kaFlys3iSm+lJCOkpSXhLSvB4S4g0160P\ndmfl0jEpnsSYKABaxMXwW7aDzskJJ8lGmDQiTLq+fJeHqDqMP0X9oXpB8ZdHCJGKPjkcLKXsB7QV\nQgzxExkAPG44BtOruX63UccAYKIQorlxPRZ4H32CO0wIkSaEaIM+ie0HPAi096v7JHkAKeWDwHBO\njrjUaunEiC49XkVRbe0M9rwQvN1qjRBiGPCjlNKDnrp3TAgxVgjxN+A4+mQ9FNFCiO+FEBuFEOfX\nwZQO6GPlTOAOo23KCHhew7GZgB75GQT8WwiRYsjOBj6TUvYFLgJyqxmH/v1yqRAiTUq51pA7CgyR\nUp5eH04KgDs/l6h4C39sW0PWzu+IjkvE5bSHvMfrKmbrrPvZNvsBul5xe52iKQApCdFYHcX8rX87\nhvVpic3hIjUhOuQ9kWYTy6YOzBuuHgAAIABJREFUYtGEgZzRMSksvdEpybisNlr//VpaXHoRbpuN\n6NSUoPKmqCjO/XgxZ3/wNoln9gtLZzCcuTnEW5LYtvpTdm7+mrhEC87c6lfSATJ+2karDl3CdlIA\nbLZsUlNS+Hj5clZ/9RXJySnYbNZ6kw+p21lIanxTPt62l9U7D5Ac1xSbs7D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P4igqAoeD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\np1atn1B6Q7au73g+S7b+1LTPJohBDvCe935r4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"text/plain": [
"<matplotlib.figure.Figure at 0x1123ad7d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig1 = plt.figure(figsize=(8, 8))\n",
"ax = sns.heatmap(\n",
" corr, \n",
" annot=True, fmt='.1f', linewidths=.5\n",
")\n",
"\n",
"ax.text(-1., 1.141, \n",
" u'Korrelation med andel riksdagsröster',\n",
" verticalalignment='bottom', horizontalalignment='left',\n",
" transform=ax.transAxes,\n",
" color='#000000', fontsize=14)\n",
"\n",
"ax.text(-1., -0.181, \n",
" u'Källa: BRÅ/SCB/Val.se/Ekonomifakta.se – Korrelationen är på kommunnivå och Pearson-korr har använts',\n",
" verticalalignment='bottom', horizontalalignment='left',\n",
" transform=ax.transAxes,\n",
" color='#666666', fontsize=8)\n",
"\n",
"plt.savefig('SD_korr.pdf', bbox_inches='tight')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First of, a Persons `r` correlation ranges from 0 to 1. Zero meaning no correlation and one full correlation. Depending on textbook, somewhere over 0.4 will be a correlations to take seriously. But let's get back to that in a moment.\n",
"\n",
"Some interesting correlations are though:\n",
"\n",
" - The right-wing populist party the Sweden Democrats (SD in the figure) \n",
" - Correlations with municipailtys having large porportions **motorcycles**,\n",
" - and women **smoking while pregnant**.\n",
" - Also high unemployment rate shows some correlation.\n",
" - Right wing Moderaterna (M)\n",
" - High percentage mopeds\n",
" - High percentage cars\n",
" - High education\n",
" - High income\n",
" - The Greens Miljöpartiet (MP)\n",
" - Pretty much the same as Moderaterna. Only not that high income.\n",
" - Centerpartiet (C) historically a party for the farmers\n",
" - High correlation with tractors"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So let's go into detail what a correlation of 0.5 actually is. Is it a fully linear correlation between smoking while pregnant and votage on xenophobic parties? Let's plot the municipalitys with these KPI:s on the axis."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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uPhoeHmZb9wn56ZuBjCdaeyalOyAUUwSKKbo0xpXSmFpj91HaVHO+Qu6+uQOg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\n/aLvBDgcZK4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11087f410>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def corrplot_SD(kpi):\n",
" (concat.replace(0, np.nan).dropna()\n",
" .plot(kind='scatter', \n",
" x=kpi, \n",
" y=u'SD'))\n",
"\n",
"corrplot_SD(u'Rökande gravida, procent')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you ask me, that looks like a correlation. But that's also what I want to see since the Sweden Democrats is pretty much as far away from my personal political opinions you can go. I'd pretty much take any chance to misscredit them. So let's go further down the rabbit hole, but first plot the other KPIs."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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NJY2mZDGFJLWKpZ6Ga7Ixr7rgHN5z8TUM0c6MllaOWLai6mST5lViImmlZDGF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\nJ3tQJAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1123e4150>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"corrplot_SD(u'Motorcyklar, 2015, procent')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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enJUmTGQtzkpyPT1WJCvUUpBqtCZVQIkiJVm9ZkFEttNqwgFNj01JHFM7RSRZ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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x111d00850>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"corrplot_SD(u'Öppen arbetslöshet (Arbetsförmedlingen), 2015, procent')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are now going to calculate the `r` coefficient in a Bayesian fashion with Monte Carlo Markov Chains (MCMC). That will give us a distribution over `r` instead of a single number which allows us to give a upper and lower bound of the correlation. This closely follows the method described by [Philipp Singer here](http://www.philippsinger.info/?p=581).\n",
"\n",
"You should read his blog post if you want more details. And in more general, Monte Carlo simulations is a really appealing way to solve problems. Whenever I get the chance, I recommend [Bayesian methods for hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers). The topics covered there should really be in any data scientist tool belt."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pymc as pymc\n",
"from pymc import Normal, Uniform, MvNormal, Exponential\n",
"from numpy.linalg import inv, det\n",
"from numpy import log, pi, dot\n",
"import numpy as np\n",
"from scipy.special import gammaln\n",
"\n",
"def _model(data):\n",
" mu = Normal('mu', 0, 0.000001, size=2)\n",
" sigma = Uniform('sigma', 0, 1000, size=2)\n",
" rho = Uniform('r', -1, 1)\n",
" \n",
" @pymc.deterministic\n",
" def precision(sigma=sigma, rho=rho):\n",
" ss1 = float(sigma[0] * sigma[0])\n",
" ss2 = float(sigma[1] * sigma[1])\n",
" rss = float(rho * sigma[0] * sigma[1])\n",
" return inv(np.mat([[ss1, rss], \n",
" [rss, ss2]]))\n",
" \n",
" mult_n = MvNormal('mult_n', mu=mu, tau=precision, value=data.T, observed=True)\n",
" \n",
" return locals()\n",
" \n",
"def analyze(kpi, party='SD'):\n",
" freq_corr = corr.loc[kpi, party]\n",
" print \"Standard Persons r correlations gave {}\".format(freq_corr)\n",
" data = np.array([concat[party].values, concat[kpi].values])\n",
" model = pymc.MCMC(_model(data)) \n",
" model.sample(10000, 5000) \n",
" \n",
" print \"\\nMCMC gives an lower and upper bound for this of {} and {}\".format(\n",
" *model.stats()['r']['95% HPD interval']\n",
" )\n",
" return model.trace(\"r\")[:]"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Standard Persons r correlations gave 0.483367702644\n",
" [-----------------100%-----------------] 10000 of 10000 complete in 23.2 sec\n",
"MCMC gives an lower and upper bound for this of 0.369437362648 and 0.586077745399\n",
"[ 0.73810054 0.73810054 0.74991356 ..., 0.55073747 0.55073747\n",
" 0.55073747]\n"
]
},
{
"data": {
"text/plain": [
"array([ 0.36943736, 0.58607775])"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"analyze(u'Rökande gravida, procent')"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"This tells us that the correlation is statistically sound. No `p`-values, just a interval that the true `r` should be in with 95 % probability. Even if it's *just* the lower bound 0.38 it's a correlation worth mentioning. But of course, it does not tell us if **correlation imply causation**. "
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SD\n",
"Standard Persons r correlations gave 0.483367702644\n",
" [-----------------100%-----------------] 10000 of 10000 complete in 23.8 sec\n",
"MCMC gives an lower and upper bound for this of 0.394215609081 and 0.578209252562\n",
"[ 0.44203507 0.41925566 0.43545534 ..., 0.40907752 0.43013007\n",
" 0.41994856]\n",
"M\n",
"Standard Persons r correlations gave -0.0468800765707\n",
" [-----------------100%-----------------] 10000 of 10000 complete in 24.8 sec\n",
"MCMC gives an lower and upper bound for this of -0.164567359884 and 0.0701012283692\n",
"[-0.02626155 -0.05461335 -0.01020018 ..., -0.01172495 -0.14345404\n",
" -0.14345404]\n",
"C\n",
"Standard Persons r correlations gave -0.0578358696783\n",
" [-----------------100%-----------------] 10000 of 10000 complete in 24.9 sec\n",
"MCMC gives an lower and upper bound for this of -0.167457887351 and 0.0527373274497\n",
"[-0.06658982 -0.13355477 -0.14867998 ..., -0.03385024 -0.08950976\n",
" -0.06186684]\n",
"FP\n",
"Standard Persons r correlations gave -0.0596452035277\n",
" [-----------------100%-----------------] 10000 of 10000 complete in 24.2 sec\n",
"MCMC gives an lower and upper bound for this of -0.182832841261 and 0.0545973956103\n",
"[-0.21019278 -0.26648326 -0.26648326 ..., 0.01765948 -0.0640007 -0.0640007 ]\n",
"KD\n",
"Standard Persons r correlations gave -0.149681056299\n",
" [-----------------100%-----------------] 10000 of 10000 complete in 23.5 sec\n",
"MCMC gives an lower and upper bound for this of -0.370144773704 and 0.0776821426528\n",
"[-0.50458061 -0.49034921 -0.51042208 ..., -0.15108604 -0.15108604\n",
" -0.15108604]\n",
"S\n",
"Standard Persons r correlations gave 0.105515970794\n",
" [-----------------100%-----------------] 10000 of 10000 complete in 24.5 sec\n",
"MCMC gives an lower and upper bound for this of -0.0489966752856 and 0.272048964136\n",
"[ 0.29756003 0.33990415 0.26781572 ..., 0.08536111 0.08536111\n",
" 0.08536111]\n",
"V\n",
"Standard Persons r correlations gave -0.0817404939717\n",
" [-----------------100%-----------------] 10000 of 10000 complete in 23.6 sec\n",
"MCMC gives an lower and upper bound for this of -0.236186068414 and 0.0475714192344\n",
"[ 0.06306357 0.11163593 0.11163593 ..., -0.02596263 -0.15566182\n",
" -0.05848548]\n",
"MP\n",
"Standard Persons r correlations gave -0.11899287591\n",
" [-----------------100%-----------------] 10000 of 10000 complete in 23.5 sec\n",
"MCMC gives an lower and upper bound for this of -0.234708662769 and 0.00486413429649\n",
"[-0.13288671 -0.13288671 -0.13288671 ..., -0.11058089 -0.11058089\n",
" -0.13005886]\n"
]
}
],
"source": [
"traces = []\n",
"for party in ['SD', 'M', 'C', 'FP', 'KD', 'S', 'V', 'MP']:\n",
" print party\n",
" traces.append(analyze(u'Rökande gravida, procent', party))"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dfs = []\n",
"for party, trace in zip(partier, traces):\n",
" df = pd.DataFrame({'source': party, 'trace': trace})\n",
" dfs.append(df)\n",
" \n",
"alles = pd.concat(dfs)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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ASJIyVXYVU0R8AHgHcA54pLOMqSSpT1RyBhER1wJvAV6RUroauLOKPCRJG6uqi+mFwGxK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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x112e98410>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = sns.boxplot(x=\"trace\", y=\"source\", data=alles, color=\"c\")\n",
"sns.despine(trim=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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