Time_line_aggregates.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homed/content/anaconda3/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import findspark\n",
    "findspark.init()\n",
    "\n",
    "from pyspark import SparkContext, SparkConf\n",
    "from pyspark.sql import SparkSession\n",
    "from pyspark.sql.types import IntegerType, StringType, BooleanType, TimestampType, FloatType, StructField, StructType, LongType\n",
    "from pyspark.sql.functions import col, when\n",
    "from pyspark.sql.functions import udf, array\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import statsmodels.api as sm\n",
    "from patsy import dmatrices, dmatrix\n",
    "from IPython.display import display\n",
    "\n",
    "from datetime import datetime\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sparkConf = SparkConf()\n",
    "sparkConf.setAppName(\"Sentiment Metadata\")\n",
    "sparkConf.setMaster(\"local[20]\")\n",
    "sparkConf.setAll([\n",
    "    (\"spark.local.dir\", \"./tmp\",)\n",
    "])\n",
    "\n",
    "sc = SparkContext(conf=sparkConf)\n",
    "spark = SparkSession(sc)\n",
    "\n",
    "sns.set_context('poster')\n",
    "sns.set_style('ticks')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "(tid, len_hashtags, len_urls, len_user_mentions, created_at, is_reply, is_quoted, is_retweet) \n",
    "        + (uid, user_since, is_verified, has_url, followers_count, friends_count, statuses_count)\n",
    "        + (scores[\"compound\"], scores[\"neg\"], scores[\"neu\"], scores[\"pos\"])\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "schema = StructType([\n",
    "    StructField(\"tid\", LongType(), True),\n",
    "    StructField(\"len_hashtags\", IntegerType(), True),\n",
    "    StructField(\"len_urls\", IntegerType(), True),\n",
    "    StructField(\"len_user_mentions\", IntegerType(), True),\n",
    "    StructField(\"created_at\", StringType(), True),\n",
    "    StructField(\"is_reply\", BooleanType(), True),\n",
    "    StructField(\"is_quoted\", BooleanType(), True),\n",
    "    StructField(\"is_retweet\", BooleanType(), True),\n",
    "    StructField(\"uid\", LongType(), True),\n",
    "    StructField(\"user_since\", StringType(), True),\n",
    "    StructField(\"is_verified\", BooleanType(), True),\n",
    "    StructField(\"has_url\", BooleanType(), True),\n",
    "    StructField(\"followers_count\", IntegerType(), True),\n",
    "    StructField(\"friends_count\", IntegerType(), True),\n",
    "    StructField(\"statuses_count\", IntegerType(), True),\n",
    "    StructField(\"compound_score\", FloatType(), True),\n",
    "    StructField(\"negative\", FloatType(), True),\n",
    "    StructField(\"neutral\", FloatType(), True),\n",
    "    StructField(\"positive\", FloatType(), True),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8 ms, sys: 0 ns, total: 8 ms\n",
      "Wall time: 5.18 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df = spark.read.csv(\"output/metadata/part-*\", sep=\"\\t\", header=False, schema=schema)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Row(tid=911509105344466945, len_hashtags=1, len_urls=2, len_user_mentions=1, created_at='Sat Sep 23 08:34:40 +0000 2017', is_reply=True, is_quoted=False, is_retweet=False, uid=492532196, user_since='Tue Feb 14 20:42:15 +0000 2012', is_verified=True, has_url=True, followers_count=85484, friends_count=0, statuses_count=7554314, compound_score=0.0, negative=0.0, neutral=1.0, positive=0.0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def parse_twitter_datetime(tweet_datetime):\n",
    "    ts = datetime.strptime(tweet_datetime,'%a %b %d %H:%M:%S +0000 %Y')\n",
    "    return ts\n",
    "\n",
    "def get_user_age(tweet_created, user_created):\n",
    "    tweet_created = parse_twitter_datetime(tweet_created)\n",
    "    user_created = parse_twitter_datetime(user_created)\n",
    "    time_delta = tweet_created - user_created\n",
    "    return time_delta.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(1726, 30880)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = parse_twitter_datetime('Sat Sep 23 08:34:40 +0000 2017') - datetime.strptime(\"Jan 1 2013\", \"%b %d %Y\")\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "user_age_udf = udf(get_user_age, IntegerType())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Row(tid=911509105344466945, len_hashtags=1, len_urls=2, len_user_mentions=1, created_at='Sat Sep 23 08:34:40 +0000 2017', is_reply=True, is_quoted=False, is_retweet=False, uid=492532196, user_since='Tue Feb 14 20:42:15 +0000 2012', is_verified=True, has_url=True, followers_count=85484, friends_count=0, statuses_count=7554314, compound_score=0.0, negative=0.0, neutral=1.0, positive=0.0, user_age=2047)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.withColumn(\"user_age\", user_age_udf(\"created_at\", \"user_since\")).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "SENTIMENT_KEYS=[\"positive\", \"neutral\", \"negative\",]\n",
    "SENTIMENT_COLORS=[\"b\", \"k\", \"r\",]\n",
    "def get_sentiment_label(x):\n",
    "    max_sentiment, score = max([(k, x[i]) for i, k in enumerate(SENTIMENT_KEYS)], key=lambda t: t[1])\n",
    "    return max_sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "get_sentiment_udf = udf(get_sentiment_label, StringType())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Row(tid=911509105344466945, len_hashtags=1, len_urls=2, len_user_mentions=1, created_at='Sat Sep 23 08:34:40 +0000 2017', is_reply=True, is_quoted=False, is_retweet=False, uid=492532196, user_since='Tue Feb 14 20:42:15 +0000 2012', is_verified=True, has_url=True, followers_count=85484, friends_count=0, statuses_count=7554314, compound_score=0.0, negative=0.0, neutral=1.0, positive=0.0, sentiment='neutral')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.withColumn(\"sentiment\", get_sentiment_udf(array(SENTIMENT_KEYS))).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_sentiment = df.withColumn(\n",
    "    \"user_age\", user_age_udf(\"created_at\", \"user_since\")\n",
    ").withColumn(\"sentiment\", get_sentiment_udf(array(SENTIMENT_KEYS)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_log_odds_categorical(df_sentiment, grouping, title, ax, color=\"0.6\"):\n",
    "    df_sentiment_counts = df_sentiment.groupby(\"sentiment\", grouping).count().toPandas()\n",
    "    df_t = df_sentiment_counts.pivot(\"sentiment\", grouping, \"count\")\n",
    "    (df_t / df_t.sum()).apply(lambda x: np.log10(x/(1-x))).assign(\n",
    "        beta=lambda x: x[True] - x[False]\n",
    "    )[\"beta\"].plot(kind=\"barh\",\n",
    "                   xerr=(1/df_t + 1/(df_t.sum() - df_t)).sum(axis=1).apply(lambda x: np.sqrt(x)),\n",
    "                   color=color, ax=ax)\n",
    "\n",
    "    ax.axvline(x=0, linestyle=\"--\", color=\"0.8\", lw=0.5)\n",
    "    ax.set_title(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "has_url User has URL\n",
      "is_verified User is verified\n",
      "is_reply Tweet is reply\n",
      "is_quoted Tweet is quote\n",
      "CPU times: user 1.04 s, sys: 52 ms, total: 1.09 s\n",
      "Wall time: 1min 16s\n"
     ]
    },
    {
     "data": {
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iuqGVlJSgqanJvK/L27dvsXDhQrHTaQ4cOBBbt25l3ldXV4PNZsPLyws3b94U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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3540dfb550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "with sns.axes_style(rc={\"font.family\": \"monospace\"}):\n",
    "    fig, ax = plt.subplots(2,2, sharey=True, figsize=(7,4))\n",
    "    for i, (grouping, title, axi) in enumerate(zip(\n",
    "        [\"has_url\", \"is_verified\", \"is_reply\", \"is_quoted\"],\n",
    "        [\"User has URL\", \"User is verified\", \"Tweet is reply\", \"Tweet is quote\"],\n",
    "        ax.flatten()\n",
    "    )):\n",
    "        print(grouping, title)\n",
    "        plot_log_odds_categorical(df_sentiment, grouping, title, axi, color=[\"r\", \"k\", \"b\"])\n",
    "        if i%2 == 1:\n",
    "            axi.get_yaxis().set_visible(False)\n",
    "\n",
    "    sns.despine(offset=10)\n",
    "    fig.tight_layout()\n",
    "    plt.savefig(\"figures/Cat_odds_timeline.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## User meta data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_log_round_udf(round_digits=1, add_1=False):\n",
    "    def get_log_round(x):\n",
    "        if add_1:\n",
    "            x = x + 1\n",
    "        result = np.round(np.log10(x), round_digits).item()\n",
    "        return result\n",
    "    return udf(get_log_round, FloatType())\n",
    "\n",
    "def get_log_digitized_udf(bins, add_1=False):\n",
    "    bin_values = ((bins[1:] + bins[:-1])/2)\n",
    "    def get_log_round(x):\n",
    "        if add_1:\n",
    "            x = x + 1\n",
    "        result = min(np.log10(x), bin_values[-1])\n",
    "        result = np.digitize(result, bins).item()\n",
    "        result = float(bin_values[result])\n",
    "        return result\n",
    "    return udf(get_log_round, FloatType())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Row(tid=911509105344466945, len_hashtags=1, len_urls=2, len_user_mentions=1, created_at='Sat Sep 23 08:34:40 +0000 2017', is_reply=True, is_quoted=False, is_retweet=False, uid=492532196, user_since='Tue Feb 14 20:42:15 +0000 2012', is_verified=True, has_url=True, followers_count=85484, friends_count=0, statuses_count=7554314, compound_score=0.0, negative=0.0, neutral=1.0, positive=0.0, user_age=2047, sentiment='neutral', value_count=-inf)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sentiment.withColumn(\n",
    "        \"value_count\", get_log_round_udf(add_1=False)(grouping)\n",
    "    ).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Row(tid=911509105344466945, len_hashtags=1, len_urls=2, len_user_mentions=1, created_at='Sat Sep 23 08:34:40 +0000 2017', is_reply=True, is_quoted=False, is_retweet=False, uid=492532196, user_since='Tue Feb 14 20:42:15 +0000 2012', is_verified=True, has_url=True, followers_count=85484, friends_count=0, statuses_count=7554314, compound_score=0.0, negative=0.0, neutral=1.0, positive=0.0, user_age=2047, sentiment='neutral', value_count=5.5)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sentiment.withColumn(\n",
    "        \"value_count\", get_log_digitized_udf(np.arange(-1.25,7.25,0.5), add_1=False)(\"followers_count\")\n",
    "    ).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_log_odds_numerical(\n",
    "    df_sentiment, grouping, title, ax, value_fn, add_ylabel=True,\n",
    "):\n",
    "    df_t = df_sentiment.withColumn(\n",
    "        \"value_count\", \n",
    "        #get_log_round_udf(**kwargs)(grouping)\n",
    "        value_fn(grouping)\n",
    "    ).groupby(\"sentiment\", \"value_count\").count().toPandas()\n",
    "    \n",
    "    df_pivot = df_t.pivot(\"value_count\", \"sentiment\", \"count\")\n",
    "    \n",
    "    df_tt = df_pivot.divide(\n",
    "        df_pivot.sum(axis=1),\n",
    "        axis=0\n",
    "    ).apply(\n",
    "        lambda x: np.log(x/(1-x))).apply(\n",
    "        lambda x: x-x.iloc[x.shape[0]//2])\n",
    "    for axi, k, color in zip(ax.flatten(), SENTIMENT_KEYS, SENTIMENT_COLORS):\n",
    "        df_tt[k].plot(color=color, marker=\"o\", ax=axi, linestyle=\"none\", alpha=0.7, ms=5)\n",
    "        axi.axhline(y=0, linestyle=\"--\", color=\"0.8\", lw=0.5)\n",
    "        axi.axvline(x=df_tt[k].index[df_tt[k].shape[0]//2], linestyle=\"--\", color=\"0.8\", lw=0.5)\n",
    "        axi.set_xlabel(title)\n",
    "        if add_ylabel:\n",
    "            axi.set_ylabel(k)\n",
    "        fit_model_plot(df_pivot, k, color, axi)\n",
    "        \n",
    "        \n",
    "def fit_model_plot(df_t, sentiment, color, axi):\n",
    "    df_tt = df_t.assign(\n",
    "        success=lambda x: x[sentiment], \n",
    "        failure=lambda x: x[[k for k in SENTIMENT_KEYS if k!=sentiment]].sum(axis=1)\n",
    "    )[[\"success\", \"failure\"]].fillna(0.).reset_index()\n",
    "    model = sm.GLM(\n",
    "        df_tt[[\"success\", \"failure\"]],\n",
    "        dmatrix(\"value_count\", data=df_tt[[\"value_count\"]], return_type=\"dataframe\"),\n",
    "        family=sm.families.Binomial())\n",
    "    res = model.fit()\n",
    "    print(sentiment)\n",
    "    display(res.summary2())\n",
    "    y = res.predict(dmatrix(\"value_count\",\n",
    "                    data=df_tt[[\"value_count\"]], return_type=\"dataframe\"))\n",
    "    y = np.log(y/(1-y))\n",
    "    y = y - y.iloc[y.shape[0]//2]\n",
    "    axi.plot(df_tt.value_count, y, color=color, linewidth=1, linestyle=\"--\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "statuses_count statuses\n",
      "positive\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>2564.0560</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>2381.1441</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>  <td>-1280.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:34</td>       <td>LL-Null:</td>      <td>-2988.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>            <td>15</td>              <td>Deviance:</td>     <td>2416.3</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>   <td>0.0899</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>              <td>13</td>               <td>Scale:</td>       <td>1.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>             <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th>  <th>Std.Err.</th>     <th>z</th>      <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>-2.8042</td>  <td>0.0068</td>  <td>-410.2711</td> <td>0.0000</td> <td>-2.8176</td> <td>-2.7908</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>-0.0871</td>  <td>0.0015</td>  <td>-58.6883</td>  <td>0.0000</td> <td>-0.0900</td> <td>-0.0842</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Generalized linear model\n",
       "====================================================================\n",
       "Model:              GLM                    AIC:            2564.0560\n",
       "Link Function:      logit                  BIC:            2381.1441\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -1280.0  \n",
       "Date:               2018-05-10 01:34       LL-Null:        -2988.2  \n",
       "No. Observations:   15                     Deviance:       2416.3   \n",
       "Df Model:           1                      Pearson chi2:   0.0899   \n",
       "Df Residuals:       13                     Scale:          1.0000   \n",
       "Method:             IRLS                                            \n",
       "---------------------------------------------------------------------\n",
       "                Coef.   Std.Err.      z      P>|z|    [0.025   0.975]\n",
       "---------------------------------------------------------------------\n",
       "Intercept      -2.8042    0.0068  -410.2711  0.0000  -2.8176  -2.7908\n",
       "value_count    -0.0871    0.0015   -58.6883  0.0000  -0.0900  -0.0842\n",
       "====================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neutral\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>5570.4285</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>5383.8703</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>  <td>-2783.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:34</td>       <td>LL-Null:</td>      <td>-2829.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>            <td>15</td>              <td>Deviance:</td>     <td>5419.1</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>    <td>0.147</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>              <td>13</td>               <td>Scale:</td>       <td>1.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>             <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th> <th>Std.Err.</th>     <th>z</th>     <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>2.8037</td>  <td>0.0060</td>  <td>469.8803</td> <td>0.0000</td> <td>2.7920</td> <td>2.8154</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>0.0124</td>  <td>0.0013</td>   <td>9.6540</td>  <td>0.0000</td> <td>0.0099</td> <td>0.0149</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Generalized linear model\n",
       "====================================================================\n",
       "Model:              GLM                    AIC:            5570.4285\n",
       "Link Function:      logit                  BIC:            5383.8703\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -2783.2  \n",
       "Date:               2018-05-10 01:34       LL-Null:        -2829.8  \n",
       "No. Observations:   15                     Deviance:       5419.1   \n",
       "Df Model:           1                      Pearson chi2:   0.147    \n",
       "Df Residuals:       13                     Scale:          1.0000   \n",
       "Method:             IRLS                                            \n",
       "----------------------------------------------------------------------\n",
       "               Coef.    Std.Err.      z       P>|z|    [0.025   0.975]\n",
       "----------------------------------------------------------------------\n",
       "Intercept      2.8037     0.0060   469.8803   0.0000   2.7920   2.8154\n",
       "value_count    0.0124     0.0013     9.6540   0.0000   0.0099   0.0149\n",
       "====================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negative\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>4268.5960</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>4103.4725</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>  <td>-2132.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:34</td>       <td>LL-Null:</td>      <td>-5040.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>            <td>15</td>              <td>Deviance:</td>     <td>4138.7</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>   <td>0.0631</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>              <td>13</td>               <td>Scale:</td>       <td>1.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>             <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th>  <th>Std.Err.</th>     <th>z</th>      <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>-5.0452</td>  <td>0.0117</td>  <td>-431.3197</td> <td>0.0000</td> <td>-5.0682</td> <td>-5.0223</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>0.1861</td>   <td>0.0025</td>   <td>75.5083</td>  <td>0.0000</td> <td>0.1812</td>  <td>0.1909</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Generalized linear model\n",
       "====================================================================\n",
       "Model:              GLM                    AIC:            4268.5960\n",
       "Link Function:      logit                  BIC:            4103.4725\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -2132.3  \n",
       "Date:               2018-05-10 01:34       LL-Null:        -5040.2  \n",
       "No. Observations:   15                     Deviance:       4138.7   \n",
       "Df Model:           1                      Pearson chi2:   0.0631   \n",
       "Df Residuals:       13                     Scale:          1.0000   \n",
       "Method:             IRLS                                            \n",
       "---------------------------------------------------------------------\n",
       "                Coef.   Std.Err.      z      P>|z|    [0.025   0.975]\n",
       "---------------------------------------------------------------------\n",
       "Intercept      -5.0452    0.0117  -431.3197  0.0000  -5.0682  -5.0223\n",
       "value_count     0.1861    0.0025    75.5083  0.0000   0.1812   0.1909\n",
       "====================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "followers_count followers\n",
      "positive\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>2539.2270</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>2321.3313</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>  <td>-1267.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:34</td>       <td>LL-Null:</td>      <td>-1336.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>            <td>17</td>              <td>Deviance:</td>     <td>2363.8</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>   <td>0.0599</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>              <td>15</td>               <td>Scale:</td>       <td>1.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>             <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th>  <th>Std.Err.</th>     <th>z</th>      <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>-3.1440</td>  <td>0.0051</td>  <td>-622.3577</td> <td>0.0000</td> <td>-3.1539</td> <td>-3.1341</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>-0.0172</td>  <td>0.0015</td>  <td>-11.7349</td>  <td>0.0000</td> <td>-0.0201</td> <td>-0.0144</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Generalized linear model\n",
       "====================================================================\n",
       "Model:              GLM                    AIC:            2539.2270\n",
       "Link Function:      logit                  BIC:            2321.3313\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -1267.6  \n",
       "Date:               2018-05-10 01:34       LL-Null:        -1336.6  \n",
       "No. Observations:   17                     Deviance:       2363.8   \n",
       "Df Model:           1                      Pearson chi2:   0.0599   \n",
       "Df Residuals:       15                     Scale:          1.0000   \n",
       "Method:             IRLS                                            \n",
       "---------------------------------------------------------------------\n",
       "                Coef.   Std.Err.      z      P>|z|    [0.025   0.975]\n",
       "---------------------------------------------------------------------\n",
       "Intercept      -3.1440    0.0051  -622.3577  0.0000  -3.1539  -3.1341\n",
       "value_count    -0.0172    0.0015   -11.7349  0.0000  -0.0201  -0.0144\n",
       "====================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neutral\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>8072.5803</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>7850.4976</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>  <td>-4034.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:34</td>       <td>LL-Null:</td>      <td>-4228.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>            <td>17</td>              <td>Deviance:</td>     <td>7893.0</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>   <td>0.0964</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>              <td>15</td>               <td>Scale:</td>       <td>1.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>             <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th> <th>Std.Err.</th>     <th>z</th>     <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>2.7775</td>  <td>0.0043</td>  <td>642.1251</td> <td>0.0000</td> <td>2.7691</td> <td>2.7860</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>0.0248</td>  <td>0.0013</td>   <td>19.6718</td> <td>0.0000</td> <td>0.0223</td> <td>0.0272</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Generalized linear model\n",
       "====================================================================\n",
       "Model:              GLM                    AIC:            8072.5803\n",
       "Link Function:      logit                  BIC:            7850.4976\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -4034.3  \n",
       "Date:               2018-05-10 01:34       LL-Null:        -4228.1  \n",
       "No. Observations:   17                     Deviance:       7893.0   \n",
       "Df Model:           1                      Pearson chi2:   0.0964   \n",
       "Df Residuals:       15                     Scale:          1.0000   \n",
       "Method:             IRLS                                            \n",
       "----------------------------------------------------------------------\n",
       "               Coef.    Std.Err.      z       P>|z|    [0.025   0.975]\n",
       "----------------------------------------------------------------------\n",
       "Intercept      2.7775     0.0043   642.1251   0.0000   2.7691   2.7860\n",
       "value_count    0.0248     0.0013    19.6718   0.0000   0.0223   0.0272\n",
       "====================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negative\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>9376.3001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>9182.1032</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>  <td>-4686.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:34</td>       <td>LL-Null:</td>      <td>-4846.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>            <td>17</td>              <td>Deviance:</td>     <td>9224.6</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>   <td>0.0590</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>              <td>15</td>               <td>Scale:</td>       <td>1.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>             <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th>  <th>Std.Err.</th>     <th>z</th>      <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>-4.0416</td>  <td>0.0080</td>  <td>-503.1590</td> <td>0.0000</td> <td>-4.0573</td> <td>-4.0258</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>-0.0419</td>  <td>0.0023</td>  <td>-17.8884</td>  <td>0.0000</td> <td>-0.0465</td> <td>-0.0373</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Generalized linear model\n",
       "====================================================================\n",
       "Model:              GLM                    AIC:            9376.3001\n",
       "Link Function:      logit                  BIC:            9182.1032\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -4686.2  \n",
       "Date:               2018-05-10 01:34       LL-Null:        -4846.6  \n",
       "No. Observations:   17                     Deviance:       9224.6   \n",
       "Df Model:           1                      Pearson chi2:   0.0590   \n",
       "Df Residuals:       15                     Scale:          1.0000   \n",
       "Method:             IRLS                                            \n",
       "---------------------------------------------------------------------\n",
       "                Coef.   Std.Err.      z      P>|z|    [0.025   0.975]\n",
       "---------------------------------------------------------------------\n",
       "Intercept      -4.0416    0.0080  -503.1590  0.0000  -4.0573  -4.0258\n",
       "value_count    -0.0419    0.0023   -17.8884  0.0000  -0.0465  -0.0373\n",
       "====================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "friends_count friends\n",
      "positive\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>11740.5279</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>11567.5463</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>   <td>-5868.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:35</td>       <td>LL-Null:</td>       <td>-5869.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>            <td>14</td>              <td>Deviance:</td>      <td>11599.</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>     <td>0.109</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>              <td>12</td>               <td>Scale:</td>        <td>1.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>              <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th>  <th>Std.Err.</th>     <th>z</th>      <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>-3.1939</td>  <td>0.0056</td>  <td>-575.0058</td> <td>0.0000</td> <td>-3.2048</td> <td>-3.1830</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>-0.0025</td>  <td>0.0017</td>   <td>-1.4497</td>  <td>0.1472</td> <td>-0.0058</td> <td>0.0009</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Results: Generalized linear model\n",
       "=====================================================================\n",
       "Model:              GLM                    AIC:            11740.5279\n",
       "Link Function:      logit                  BIC:            11567.5463\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -5868.3   \n",
       "Date:               2018-05-10 01:35       LL-Null:        -5869.3   \n",
       "No. Observations:   14                     Deviance:       11599.    \n",
       "Df Model:           1                      Pearson chi2:   0.109     \n",
       "Df Residuals:       12                     Scale:          1.0000    \n",
       "Method:             IRLS                                             \n",
       "----------------------------------------------------------------------\n",
       "                 Coef.   Std.Err.      z      P>|z|    [0.025   0.975]\n",
       "----------------------------------------------------------------------\n",
       "Intercept       -3.1939    0.0056  -575.0058  0.0000  -3.2048  -3.1830\n",
       "value_count     -0.0025    0.0017    -1.4497  0.1472  -0.0058   0.0009\n",
       "=====================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neutral\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>22236.2023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>22059.4167</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>   <td>-11116.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:35</td>       <td>LL-Null:</td>       <td>-11143.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>            <td>14</td>              <td>Deviance:</td>      <td>22091.</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>     <td>0.161</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>              <td>12</td>               <td>Scale:</td>        <td>1.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>              <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th> <th>Std.Err.</th>     <th>z</th>     <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>2.8264</td>  <td>0.0047</td>  <td>595.4896</td> <td>0.0000</td> <td>2.8171</td> <td>2.8357</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>0.0108</td>  <td>0.0015</td>   <td>7.3494</td>  <td>0.0000</td> <td>0.0079</td> <td>0.0136</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Results: Generalized linear model\n",
       "=====================================================================\n",
       "Model:              GLM                    AIC:            22236.2023\n",
       "Link Function:      logit                  BIC:            22059.4167\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -11116.   \n",
       "Date:               2018-05-10 01:35       LL-Null:        -11143.   \n",
       "No. Observations:   14                     Deviance:       22091.    \n",
       "Df Model:           1                      Pearson chi2:   0.161     \n",
       "Df Residuals:       12                     Scale:          1.0000    \n",
       "Method:             IRLS                                             \n",
       "-----------------------------------------------------------------------\n",
       "                Coef.    Std.Err.      z       P>|z|    [0.025   0.975]\n",
       "-----------------------------------------------------------------------\n",
       "Intercept       2.8264     0.0047   595.4896   0.0000   2.8171   2.8357\n",
       "value_count     0.0108     0.0015     7.3494   0.0000   0.0079   0.0136\n",
       "=====================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negative\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>11728.4928</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>11569.1678</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>   <td>-5862.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:35</td>       <td>LL-Null:</td>       <td>-5926.5</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>            <td>14</td>              <td>Deviance:</td>      <td>11601.</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>    <td>0.0527</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>              <td>12</td>               <td>Scale:</td>        <td>1.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>              <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th>  <th>Std.Err.</th>     <th>z</th>      <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>-4.0845</td>  <td>0.0088</td>  <td>-465.9906</td> <td>0.0000</td> <td>-4.1016</td> <td>-4.0673</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>-0.0308</td>  <td>0.0027</td>  <td>-11.3569</td>  <td>0.0000</td> <td>-0.0361</td> <td>-0.0255</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Results: Generalized linear model\n",
       "=====================================================================\n",
       "Model:              GLM                    AIC:            11728.4928\n",
       "Link Function:      logit                  BIC:            11569.1678\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -5862.2   \n",
       "Date:               2018-05-10 01:35       LL-Null:        -5926.5   \n",
       "No. Observations:   14                     Deviance:       11601.    \n",
       "Df Model:           1                      Pearson chi2:   0.0527    \n",
       "Df Residuals:       12                     Scale:          1.0000    \n",
       "Method:             IRLS                                             \n",
       "----------------------------------------------------------------------\n",
       "                 Coef.   Std.Err.      z      P>|z|    [0.025   0.975]\n",
       "----------------------------------------------------------------------\n",
       "Intercept       -4.0845    0.0088  -465.9906  0.0000  -4.1016  -4.0673\n",
       "value_count     -0.0308    0.0027   -11.3569  0.0000  -0.0361  -0.0255\n",
       "=====================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user_age user age\n",
      "positive\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>291.0285</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>184.2689</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>  <td>-143.51</td>\n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:38</td>       <td>LL-Null:</td>      <td>-2008.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>             <td>8</td>              <td>Deviance:</td>     <td>196.75</td> \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>   <td>0.00216</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>               <td>6</td>               <td>Scale:</td>       <td>1.0000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>             <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th>  <th>Std.Err.</th>     <th>z</th>      <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>-2.5616</td>  <td>0.0102</td>  <td>-250.0054</td> <td>0.0000</td> <td>-2.5816</td> <td>-2.5415</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>-0.1721</td>  <td>0.0027</td>  <td>-62.6597</td>  <td>0.0000</td> <td>-0.1775</td> <td>-0.1667</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Generalized linear model\n",
       "===================================================================\n",
       "Model:              GLM                    AIC:            291.0285\n",
       "Link Function:      logit                  BIC:            184.2689\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -143.51 \n",
       "Date:               2018-05-10 01:38       LL-Null:        -2008.2 \n",
       "No. Observations:   8                      Deviance:       196.75  \n",
       "Df Model:           1                      Pearson chi2:   0.00216 \n",
       "Df Residuals:       6                      Scale:          1.0000  \n",
       "Method:             IRLS                                           \n",
       "--------------------------------------------------------------------\n",
       "               Coef.   Std.Err.      z      P>|z|    [0.025   0.975]\n",
       "--------------------------------------------------------------------\n",
       "Intercept     -2.5616    0.0102  -250.0054  0.0000  -2.5816  -2.5415\n",
       "value_count   -0.1721    0.0027   -62.6597  0.0000  -0.1775  -0.1667\n",
       "===================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neutral\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>642.4966</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>533.6242</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>  <td>-319.25</td>\n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:38</td>       <td>LL-Null:</td>      <td>-2260.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>             <td>8</td>              <td>Deviance:</td>     <td>546.10</td> \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>   <td>0.00367</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>               <td>6</td>               <td>Scale:</td>       <td>1.0000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>             <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th> <th>Std.Err.</th>     <th>z</th>     <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>2.2963</td>  <td>0.0089</td>  <td>258.2359</td> <td>0.0000</td> <td>2.2788</td> <td>2.3137</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>0.1516</td>  <td>0.0024</td>   <td>63.6662</td> <td>0.0000</td> <td>0.1470</td> <td>0.1563</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Generalized linear model\n",
       "===================================================================\n",
       "Model:              GLM                    AIC:            642.4966\n",
       "Link Function:      logit                  BIC:            533.6242\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -319.25 \n",
       "Date:               2018-05-10 01:38       LL-Null:        -2260.5 \n",
       "No. Observations:   8                      Deviance:       546.10  \n",
       "Df Model:           1                      Pearson chi2:   0.00367 \n",
       "Df Residuals:       6                      Scale:          1.0000  \n",
       "Method:             IRLS                                           \n",
       "---------------------------------------------------------------------\n",
       "              Coef.    Std.Err.      z       P>|z|    [0.025   0.975]\n",
       "---------------------------------------------------------------------\n",
       "Intercept     2.2963     0.0089   258.2359   0.0000   2.2788   2.3137\n",
       "value_count   0.1516     0.0024    63.6662   0.0000   0.1470   0.1563\n",
       "===================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negative\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>659.0535</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>561.1092</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>  <td>-327.53</td>\n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:38</td>       <td>LL-Null:</td>      <td>-496.08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>             <td>8</td>              <td>Deviance:</td>     <td>573.59</td> \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>   <td>0.00233</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>               <td>6</td>               <td>Scale:</td>       <td>1.0000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>             <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th>  <th>Std.Err.</th>     <th>z</th>      <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>-3.8672</td>  <td>0.0170</td>  <td>-227.5890</td> <td>0.0000</td> <td>-3.9005</td> <td>-3.8339</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>-0.0845</td>  <td>0.0045</td>  <td>-18.6027</td>  <td>0.0000</td> <td>-0.0934</td> <td>-0.0756</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Generalized linear model\n",
       "===================================================================\n",
       "Model:              GLM                    AIC:            659.0535\n",
       "Link Function:      logit                  BIC:            561.1092\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -327.53 \n",
       "Date:               2018-05-10 01:38       LL-Null:        -496.08 \n",
       "No. Observations:   8                      Deviance:       573.59  \n",
       "Df Model:           1                      Pearson chi2:   0.00233 \n",
       "Df Residuals:       6                      Scale:          1.0000  \n",
       "Method:             IRLS                                           \n",
       "--------------------------------------------------------------------\n",
       "               Coef.   Std.Err.      z      P>|z|    [0.025   0.975]\n",
       "--------------------------------------------------------------------\n",
       "Intercept     -3.8672    0.0170  -227.5890  0.0000  -3.9005  -3.8339\n",
       "value_count   -0.0845    0.0045   -18.6027  0.0000  -0.0934  -0.0756\n",
       "===================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.94 s, sys: 72 ms, total: 4.01 s\n",
      "Wall time: 4min 41s\n"
     ]
    },
    {
     "data": {
      "image/png": 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Hp9PpyMrK4rbbbmPPnj0ApKamAs7VibK8TtnGPgrW7F/evhUtm3H9kSOhJfpFuLnBb7+Z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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3540b84ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "with sns.axes_style(rc={\"font.family\": \"monospace\"}):\n",
    "    fig, ax = plt.subplots(3,4, sharex=\"col\", figsize=(12,6))\n",
    "    for i, (grouping, title, add_1, axi) in enumerate(zip(\n",
    "        [\"statuses_count\", \"followers_count\", \"friends_count\", \"user_age\"],\n",
    "        [\"statuses\", \"followers\", \"friends\", \"user age\"],\n",
    "        [False, True, True, True],\n",
    "        ax.T\n",
    "    )):\n",
    "        print(grouping, title)\n",
    "        add_ylabel=True\n",
    "        if i > 0:\n",
    "            add_ylabel=False\n",
    "        plot_log_odds_numerical(\n",
    "            df_sentiment,\n",
    "            grouping,\n",
    "            \"Order of\\n{}\".format(title),\n",
    "            axi, \n",
    "            value_fn=get_log_digitized_udf(\n",
    "                add_1=add_1,\n",
    "                bins=np.arange(-1.25,9.5,0.5)\n",
    "            ),\n",
    "            add_ylabel=add_ylabel)\n",
    "        #if i > 0:\n",
    "        #    for axii in axi:\n",
    "        #        axii.get_yaxis().set_visible(False)\n",
    "\n",
    "    sns.despine(offset=10)\n",
    "    fig.tight_layout(h_pad=0., w_pad=0.01)\n",
    "    plt.savefig(\"figures/User_odds_timeline.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Row(tid=911509105344466945, len_hashtags=1, len_urls=2, len_user_mentions=1, created_at='Sat Sep 23 08:34:40 +0000 2017', is_reply=True, is_quoted=False, is_retweet=False, uid=492532196, user_since='Tue Feb 14 20:42:15 +0000 2012', is_verified=True, has_url=True, followers_count=85484, friends_count=0, statuses_count=7554314, compound_score=0.0, negative=0.0, neutral=1.0, positive=0.0, user_age=2047, sentiment='neutral', statuses_friends=7554314)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sentiment.withColumn(\"statuses_friends\", sum([col(\"statuses_count\"), col(\"friends_count\")])).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_ratio_udf(round_digits=1, add_1=False):\n",
    "    def get_log_round(x, y):\n",
    "        if add_1:\n",
    "            x = x + 1\n",
    "            y = y + 1\n",
    "        result = (np.log10(x)/(np.log10(y) + 1))\n",
    "        result = np.round(result, round_digits).item()\n",
    "        return result\n",
    "    return udf(get_log_round, FloatType())\n",
    "\n",
    "def get_ratio_digitized_udf(bins, add_1=False):\n",
    "    bin_values = ((bins[1:] + bins[:-1])/2)\n",
    "    def get_log_round(x,y):\n",
    "        if add_1:\n",
    "            x = x + 1\n",
    "            y = y + 1\n",
    "        result = min(np.log10(x)/(np.log10(y) + 1), bin_values[-1])\n",
    "        result = np.digitize(result, bins).item()\n",
    "        result = float(bin_values[result])\n",
    "        return result\n",
    "    return udf(get_log_round, FloatType())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_log_odds_ratio(df_sentiment, grouping, title, ax, value_fn, add_ylabel=True):\n",
    "    df_t = df_sentiment.withColumn(\n",
    "        \"value_count\", value_fn(col(grouping[0]), col(grouping[1]))\n",
    "    ).groupby(\"sentiment\", \"value_count\").count().toPandas()\n",
    "    \n",
    "    df_pivot = df_t.pivot(\"value_count\", \"sentiment\", \"count\")\n",
    "    #print(df_pivot.columns)\n",
    "    #print(df_pivot.index)\n",
    "    df_tt = df_pivot.divide(\n",
    "        df_pivot.sum(axis=1),\n",
    "        axis=0\n",
    "    ).apply(\n",
    "        lambda x: np.log(x/(1-x))).apply(\n",
    "        lambda x: x-x.iloc[x.shape[0]//2])\n",
    "    for axi, k, color in zip(ax.flatten(), SENTIMENT_KEYS, SENTIMENT_COLORS):\n",
    "        df_tt[k].plot(color=color, marker=\"o\", ax=axi, linestyle=\"none\", alpha=0.7, ms=5)\n",
    "        axi.axhline(y=0, linestyle=\"--\", color=\"0.8\", lw=0.5)\n",
    "        axi.axvline(x=df_tt[k].index[df_tt[k].shape[0]//2], linestyle=\"--\", color=\"0.8\", lw=0.5)\n",
    "        axi.set_xlabel(title)\n",
    "        if add_ylabel:\n",
    "            axi.set_ylabel(k)\n",
    "        fit_model_plot(df_pivot, k, color, axi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('statuses_count', 'followers_count') statuses/followers\n",
      "positive\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>1412.0326</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>1290.8127</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>  <td>-704.02</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:39</td>       <td>LL-Null:</td>      <td>-3482.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>            <td>12</td>              <td>Deviance:</td>     <td>1315.7</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>    <td>0.104</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>              <td>10</td>               <td>Scale:</td>       <td>1.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>             <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th>  <th>Std.Err.</th>     <th>z</th>      <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>-2.6201</td>  <td>0.0081</td>  <td>-321.6414</td> <td>0.0000</td> <td>-2.6361</td> <td>-2.6041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>-0.3761</td>  <td>0.0052</td>  <td>-71.6994</td>  <td>0.0000</td> <td>-0.3864</td> <td>-0.3658</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Generalized linear model\n",
       "====================================================================\n",
       "Model:              GLM                    AIC:            1412.0326\n",
       "Link Function:      logit                  BIC:            1290.8127\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -704.02  \n",
       "Date:               2018-05-10 01:39       LL-Null:        -3482.0  \n",
       "No. Observations:   12                     Deviance:       1315.7   \n",
       "Df Model:           1                      Pearson chi2:   0.104    \n",
       "Df Residuals:       10                     Scale:          1.0000   \n",
       "Method:             IRLS                                            \n",
       "---------------------------------------------------------------------\n",
       "                Coef.   Std.Err.      z      P>|z|    [0.025   0.975]\n",
       "---------------------------------------------------------------------\n",
       "Intercept      -2.6201    0.0081  -321.6414  0.0000  -2.6361  -2.6041\n",
       "value_count    -0.3761    0.0052   -71.6994  0.0000  -0.3864  -0.3658\n",
       "====================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neutral\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>4115.1108</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>3990.9447</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>  <td>-2055.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:39</td>       <td>LL-Null:</td>      <td>-3867.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>            <td>12</td>              <td>Deviance:</td>     <td>4015.8</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>    <td>0.175</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>              <td>10</td>               <td>Scale:</td>       <td>1.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>             <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th> <th>Std.Err.</th>     <th>z</th>     <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>2.4682</td>  <td>0.0068</td>  <td>365.6310</td> <td>0.0000</td> <td>2.4550</td> <td>2.4815</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>0.2532</td>  <td>0.0043</td>   <td>58.5103</td> <td>0.0000</td> <td>0.2447</td> <td>0.2617</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Generalized linear model\n",
       "====================================================================\n",
       "Model:              GLM                    AIC:            4115.1108\n",
       "Link Function:      logit                  BIC:            3990.9447\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -2055.6  \n",
       "Date:               2018-05-10 01:39       LL-Null:        -3867.8  \n",
       "No. Observations:   12                     Deviance:       4015.8   \n",
       "Df Model:           1                      Pearson chi2:   0.175    \n",
       "Df Residuals:       10                     Scale:          1.0000   \n",
       "Method:             IRLS                                            \n",
       "----------------------------------------------------------------------\n",
       "               Coef.    Std.Err.      z       P>|z|    [0.025   0.975]\n",
       "----------------------------------------------------------------------\n",
       "Intercept      2.4682     0.0068   365.6310   0.0000   2.4550   2.4815\n",
       "value_count    0.2532     0.0043    58.5103   0.0000   0.2447   0.2617\n",
       "====================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negative\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>6225.2900</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>6117.6244</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>  <td>-3110.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:39</td>       <td>LL-Null:</td>      <td>-3122.7</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>            <td>12</td>              <td>Deviance:</td>     <td>6142.5</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>   <td>0.0986</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>              <td>10</td>               <td>Scale:</td>       <td>1.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>             <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th>  <th>Std.Err.</th>     <th>z</th>      <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>-4.2376</td>  <td>0.0114</td>  <td>-371.1188</td> <td>0.0000</td> <td>-4.2600</td> <td>-4.2152</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>0.0357</td>   <td>0.0072</td>   <td>4.9293</td>   <td>0.0000</td> <td>0.0215</td>  <td>0.0498</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Generalized linear model\n",
       "====================================================================\n",
       "Model:              GLM                    AIC:            6225.2900\n",
       "Link Function:      logit                  BIC:            6117.6244\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -3110.6  \n",
       "Date:               2018-05-10 01:39       LL-Null:        -3122.7  \n",
       "No. Observations:   12                     Deviance:       6142.5   \n",
       "Df Model:           1                      Pearson chi2:   0.0986   \n",
       "Df Residuals:       10                     Scale:          1.0000   \n",
       "Method:             IRLS                                            \n",
       "---------------------------------------------------------------------\n",
       "                Coef.   Std.Err.      z      P>|z|    [0.025   0.975]\n",
       "---------------------------------------------------------------------\n",
       "Intercept      -4.2376    0.0114  -371.1188  0.0000  -4.2600  -4.2152\n",
       "value_count     0.0357    0.0072     4.9293  0.0000   0.0215   0.0498\n",
       "====================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('statuses_count', 'friends_count') statuses/friends\n",
      "positive\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>2863.0183</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>2701.5644</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>  <td>-1429.5</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:39</td>       <td>LL-Null:</td>      <td>-4370.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>            <td>15</td>              <td>Deviance:</td>     <td>2736.8</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>   <td>0.0332</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>              <td>13</td>               <td>Scale:</td>       <td>1.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>             <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th>  <th>Std.Err.</th>     <th>z</th>      <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>-2.8224</td>  <td>0.0054</td>  <td>-527.1284</td> <td>0.0000</td> <td>-2.8329</td> <td>-2.8119</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>-0.2333</td>  <td>0.0032</td>  <td>-71.8354</td>  <td>0.0000</td> <td>-0.2397</td> <td>-0.2269</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Generalized linear model\n",
       "====================================================================\n",
       "Model:              GLM                    AIC:            2863.0183\n",
       "Link Function:      logit                  BIC:            2701.5644\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -1429.5  \n",
       "Date:               2018-05-10 01:39       LL-Null:        -4370.1  \n",
       "No. Observations:   15                     Deviance:       2736.8   \n",
       "Df Model:           1                      Pearson chi2:   0.0332   \n",
       "Df Residuals:       13                     Scale:          1.0000   \n",
       "Method:             IRLS                                            \n",
       "---------------------------------------------------------------------\n",
       "                Coef.   Std.Err.      z      P>|z|    [0.025   0.975]\n",
       "---------------------------------------------------------------------\n",
       "Intercept      -2.8224    0.0054  -527.1284  0.0000  -2.8329  -2.8119\n",
       "value_count    -0.2333    0.0032   -71.8354  0.0000  -0.2397  -0.2269\n",
       "====================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neutral\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>7494.9492</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>7327.3884</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>  <td>-3745.5</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:39</td>       <td>LL-Null:</td>      <td>-5685.4</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>            <td>15</td>              <td>Deviance:</td>     <td>7362.6</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>   <td>0.0800</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>              <td>13</td>               <td>Scale:</td>       <td>1.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>             <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th> <th>Std.Err.</th>     <th>z</th>     <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>2.6074</td>  <td>0.0043</td>  <td>601.3531</td> <td>0.0000</td> <td>2.5989</td> <td>2.6159</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>0.1552</td>  <td>0.0026</td>   <td>59.5438</td> <td>0.0000</td> <td>0.1501</td> <td>0.1603</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Generalized linear model\n",
       "====================================================================\n",
       "Model:              GLM                    AIC:            7494.9492\n",
       "Link Function:      logit                  BIC:            7327.3884\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -3745.5  \n",
       "Date:               2018-05-10 01:39       LL-Null:        -5685.4  \n",
       "No. Observations:   15                     Deviance:       7362.6   \n",
       "Df Model:           1                      Pearson chi2:   0.0800   \n",
       "Df Residuals:       13                     Scale:          1.0000   \n",
       "Method:             IRLS                                            \n",
       "----------------------------------------------------------------------\n",
       "               Coef.    Std.Err.      z       P>|z|    [0.025   0.975]\n",
       "----------------------------------------------------------------------\n",
       "Intercept      2.6074     0.0043   601.3531   0.0000   2.5989   2.6159\n",
       "value_count    0.1552     0.0026    59.5438   0.0000   0.1501   0.1603\n",
       "====================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negative\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>8073.1753</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>7923.0713</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>  <td>-4034.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:39</td>       <td>LL-Null:</td>      <td>-4040.7</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>            <td>15</td>              <td>Deviance:</td>     <td>7958.3</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>   <td>0.0793</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>              <td>13</td>               <td>Scale:</td>       <td>1.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>             <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th>  <th>Std.Err.</th>     <th>z</th>      <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>-4.2062</td>  <td>0.0071</td>  <td>-593.1505</td> <td>0.0000</td> <td>-4.2201</td> <td>-4.1923</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>0.0147</td>   <td>0.0042</td>   <td>3.5238</td>   <td>0.0004</td> <td>0.0065</td>  <td>0.0228</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Generalized linear model\n",
       "====================================================================\n",
       "Model:              GLM                    AIC:            8073.1753\n",
       "Link Function:      logit                  BIC:            7923.0713\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -4034.6  \n",
       "Date:               2018-05-10 01:39       LL-Null:        -4040.7  \n",
       "No. Observations:   15                     Deviance:       7958.3   \n",
       "Df Model:           1                      Pearson chi2:   0.0793   \n",
       "Df Residuals:       13                     Scale:          1.0000   \n",
       "Method:             IRLS                                            \n",
       "---------------------------------------------------------------------\n",
       "                Coef.   Std.Err.      z      P>|z|    [0.025   0.975]\n",
       "---------------------------------------------------------------------\n",
       "Intercept      -4.2062    0.0071  -593.1505  0.0000  -4.2201  -4.1923\n",
       "value_count     0.0147    0.0042     3.5238  0.0004   0.0065   0.0228\n",
       "====================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('followers_count', 'friends_count') followers/friends\n",
      "positive\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>3302.1088</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>3166.6105</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>  <td>-1649.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:40</td>       <td>LL-Null:</td>      <td>-1829.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>            <td>13</td>              <td>Deviance:</td>     <td>3194.8</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>    <td>0.135</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>              <td>11</td>               <td>Scale:</td>       <td>1.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>             <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th>  <th>Std.Err.</th>     <th>z</th>      <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>-3.1158</td>  <td>0.0047</td>  <td>-665.0274</td> <td>0.0000</td> <td>-3.1249</td> <td>-3.1066</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>-0.0668</td>  <td>0.0035</td>  <td>-18.8843</td>  <td>0.0000</td> <td>-0.0737</td> <td>-0.0599</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Generalized linear model\n",
       "====================================================================\n",
       "Model:              GLM                    AIC:            3302.1088\n",
       "Link Function:      logit                  BIC:            3166.6105\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -1649.1  \n",
       "Date:               2018-05-10 01:40       LL-Null:        -1829.8  \n",
       "No. Observations:   13                     Deviance:       3194.8   \n",
       "Df Model:           1                      Pearson chi2:   0.135    \n",
       "Df Residuals:       11                     Scale:          1.0000   \n",
       "Method:             IRLS                                            \n",
       "---------------------------------------------------------------------\n",
       "                Coef.   Std.Err.      z      P>|z|    [0.025   0.975]\n",
       "---------------------------------------------------------------------\n",
       "Intercept      -3.1158    0.0047  -665.0274  0.0000  -3.1249  -3.1066\n",
       "value_count    -0.0668    0.0035   -18.8843  0.0000  -0.0737  -0.0599\n",
       "====================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neutral\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>6079.0130</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>5939.4714</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>  <td>-3037.5</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:40</td>       <td>LL-Null:</td>      <td>-3221.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>            <td>13</td>              <td>Deviance:</td>     <td>5967.7</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>    <td>0.201</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>              <td>11</td>               <td>Scale:</td>       <td>1.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>             <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th> <th>Std.Err.</th>     <th>z</th>     <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>2.7864</td>  <td>0.0040</td>  <td>695.9438</td> <td>0.0000</td> <td>2.7786</td> <td>2.7943</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>0.0576</td>  <td>0.0030</td>   <td>19.0632</td> <td>0.0000</td> <td>0.0516</td> <td>0.0635</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Generalized linear model\n",
       "====================================================================\n",
       "Model:              GLM                    AIC:            6079.0130\n",
       "Link Function:      logit                  BIC:            5939.4714\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -3037.5  \n",
       "Date:               2018-05-10 01:40       LL-Null:        -3221.3  \n",
       "No. Observations:   13                     Deviance:       5967.7   \n",
       "Df Model:           1                      Pearson chi2:   0.201    \n",
       "Df Residuals:       11                     Scale:          1.0000   \n",
       "Method:             IRLS                                            \n",
       "----------------------------------------------------------------------\n",
       "               Coef.    Std.Err.      z       P>|z|    [0.025   0.975]\n",
       "----------------------------------------------------------------------\n",
       "Intercept      2.7864     0.0040   695.9438   0.0000   2.7786   2.7943\n",
       "value_count    0.0576     0.0030    19.0632   0.0000   0.0516   0.0635\n",
       "====================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negative\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>3356.1634</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>3233.7631</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>  <td>-1676.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:40</td>       <td>LL-Null:</td>      <td>-1690.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>            <td>13</td>              <td>Deviance:</td>     <td>3262.0</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>   <td>0.0664</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>              <td>11</td>               <td>Scale:</td>       <td>1.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>             <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th>  <th>Std.Err.</th>     <th>z</th>      <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>-4.1434</td>  <td>0.0074</td>  <td>-559.6027</td> <td>0.0000</td> <td>-4.1580</td> <td>-4.1289</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>-0.0299</td>  <td>0.0056</td>   <td>-5.3714</td>  <td>0.0000</td> <td>-0.0409</td> <td>-0.0190</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Generalized linear model\n",
       "====================================================================\n",
       "Model:              GLM                    AIC:            3356.1634\n",
       "Link Function:      logit                  BIC:            3233.7631\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -1676.1  \n",
       "Date:               2018-05-10 01:40       LL-Null:        -1690.6  \n",
       "No. Observations:   13                     Deviance:       3262.0   \n",
       "Df Model:           1                      Pearson chi2:   0.0664   \n",
       "Df Residuals:       11                     Scale:          1.0000   \n",
       "Method:             IRLS                                            \n",
       "---------------------------------------------------------------------\n",
       "                Coef.   Std.Err.      z      P>|z|    [0.025   0.975]\n",
       "---------------------------------------------------------------------\n",
       "Intercept      -4.1434    0.0074  -559.6027  0.0000  -4.1580  -4.1289\n",
       "value_count    -0.0299    0.0056    -5.3714  0.0000  -0.0409  -0.0190\n",
       "====================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.96 s, sys: 36 ms, total: 3 s\n",
      "Wall time: 2min 20s\n"
     ]
    },
    {
     "data": {
      "image/png": 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sMGDAAPW24OBg2NnZITk52ewKe7qeC66uQFgYewHswWfLFjbq9+BB1t2hspL1//vzT9Y8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\nEUIIIYRkMAp7hBBCCCEZ7BdfQVNxxK0DsAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3540d19668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "with sns.axes_style(rc={\"font.family\": \"monospace\"}):\n",
    "    fig, ax = plt.subplots(3,3, sharex=\"col\", figsize=(9,6))\n",
    "    for i, (grouping, title, add_1, axi) in enumerate(zip(\n",
    "        [\n",
    "            (\"statuses_count\", \"followers_count\"),\n",
    "            (\"statuses_count\", \"friends_count\"),\n",
    "            (\"followers_count\", \"friends_count\"),\n",
    "        ],\n",
    "        [\n",
    "            \"statuses/followers\",\n",
    "            \"statuses/friends\",\n",
    "            \"followers/friends\"\n",
    "        ],\n",
    "        [True, True, True],\n",
    "        ax.T\n",
    "    )):\n",
    "        print(grouping, title)\n",
    "        add_ylabel=True\n",
    "        if i > 0:\n",
    "            add_ylabel=False\n",
    "        plot_log_odds_ratio(\n",
    "            df_sentiment,\n",
    "            grouping,\n",
    "            \"Order of\\n{}\".format(title),\n",
    "            axi, \n",
    "            get_ratio_digitized_udf(bins=np.arange(-1.25,9.5,0.5), add_1=add_1),\n",
    "            add_ylabel=add_ylabel\n",
    "        )\n",
    "\n",
    "    sns.despine(offset=10)\n",
    "    fig.tight_layout(h_pad=0., w_pad=0.01)\n",
    "    plt.savefig(\"figures/User_ratio_odds_timeline.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tweet numeric metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Row(tid=911509105344466945, len_hashtags=1, len_urls=2, len_user_mentions=1, created_at='Sat Sep 23 08:34:40 +0000 2017', is_reply=True, is_quoted=False, is_retweet=False, uid=492532196, user_since='Tue Feb 14 20:42:15 +0000 2012', is_verified=True, has_url=True, followers_count=85484, friends_count=0, statuses_count=7554314, compound_score=0.0, negative=0.0, neutral=1.0, positive=0.0, user_age=2047, sentiment='neutral')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sentiment.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Row(tid=902885983921852416, len_hashtags=0, len_urls=0, len_user_mentions=50, created_at='Wed Aug 30 13:29:28 +0000 2017', is_reply=True, is_quoted=False, is_retweet=False, uid=1315713750, user_since='Sat Mar 30 04:09:04 +0000 2013', is_verified=False, has_url=False, followers_count=201, friends_count=223, statuses_count=1422, compound_score=0.5514000058174133, negative=0.0, neutral=0.9399999976158142, positive=0.05999999865889549, user_age=1614, sentiment='neutral')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sentiment[df_sentiment[\"len_user_mentions\"] == 50].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sentiment</th>\n",
       "      <th>negative</th>\n",
       "      <th>neutral</th>\n",
       "      <th>positive</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value_count</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>179330.0</td>\n",
       "      <td>7548118.0</td>\n",
       "      <td>340921.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>115978.0</td>\n",
       "      <td>9320034.0</td>\n",
       "      <td>393154.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10546.0</td>\n",
       "      <td>1656487.0</td>\n",
       "      <td>54013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1312.0</td>\n",
       "      <td>482424.0</td>\n",
       "      <td>8721.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>240.0</td>\n",
       "      <td>161887.0</td>\n",
       "      <td>1855.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>55.0</td>\n",
       "      <td>68054.0</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12.0</td>\n",
       "      <td>33507.0</td>\n",
       "      <td>172.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.0</td>\n",
       "      <td>18651.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.0</td>\n",
       "      <td>12552.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>11872.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0</td>\n",
       "      <td>11058.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5686.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2060.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0</td>\n",
       "      <td>994.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.0</td>\n",
       "      <td>574.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.0</td>\n",
       "      <td>432.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.0</td>\n",
       "      <td>268.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sentiment    negative    neutral  positive\n",
       "value_count                               \n",
       "0            179330.0  7548118.0  340921.0\n",
       "1            115978.0  9320034.0  393154.0\n",
       "2             10546.0  1656487.0   54013.0\n",
       "3              1312.0   482424.0    8721.0\n",
       "4               240.0   161887.0    1855.0\n",
       "5                55.0    68054.0     500.0\n",
       "6                12.0    33507.0     172.0\n",
       "7                 4.0    18651.0      68.0\n",
       "8                 5.0    12552.0      24.0\n",
       "9                 0.0    11872.0      17.0\n",
       "10                0.0    11058.0      15.0\n",
       "11                0.0     5686.0       1.0\n",
       "12                0.0     2060.0       1.0\n",
       "13                0.0      994.0       0.0\n",
       "14                0.0      574.0       0.0\n",
       "15                0.0      520.0       0.0\n",
       "16                0.0      432.0       0.0\n",
       "17                0.0      268.0       0.0\n",
       "18                0.0      234.0       0.0\n",
       "19                0.0      181.0       0.0\n",
       "20                0.0      230.0       0.0\n",
       "21                0.0      129.0       0.0\n",
       "22                0.0      188.0       0.0\n",
       "23                0.0       86.0       0.0\n",
       "24                0.0      121.0       0.0\n",
       "25                0.0       47.0       0.0\n",
       "26                0.0       37.0       0.0\n",
       "27                0.0       36.0       0.0\n",
       "28                0.0      106.0       0.0\n",
       "29                0.0       48.0       0.0\n",
       "30                0.0       97.0       0.0\n",
       "31                0.0       23.0       0.0\n",
       "32                0.0       52.0       0.0\n",
       "33                0.0       19.0       0.0\n",
       "34                0.0       39.0       0.0\n",
       "35                0.0       13.0       0.0\n",
       "36                0.0       30.0       0.0\n",
       "37                0.0        4.0       0.0\n",
       "38                0.0       41.0       0.0\n",
       "39                0.0       29.0       0.0\n",
       "40                0.0       37.0       0.0\n",
       "41                0.0       19.0       0.0\n",
       "42                0.0       31.0       0.0\n",
       "43                0.0       12.0       0.0\n",
       "44                0.0       64.0       0.0\n",
       "45                0.0       51.0       0.0\n",
       "46                0.0      136.0       0.0\n",
       "47                0.0      229.0       0.0\n",
       "48                0.0      270.0       0.0\n",
       "49                0.0      679.0       0.0\n",
       "50                0.0     2003.0       0.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pivot = df_sentiment.withColumn(\n",
    "    \"value_count\",\n",
    "    col(\"len_user_mentions\")\n",
    ").groupby(\"sentiment\", \"value_count\").count().toPandas().pivot(\n",
    "    \"value_count\",\n",
    "    \"sentiment\",\n",
    "    \"count\"\n",
    ").fillna(0.)\n",
    "df_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_log_odds_ratio_tweet(\n",
    "    df_sentiment, grouping, title, ax,\n",
    "    add_ylabel=True, clip=None\n",
    "):\n",
    "    if clip and len(clip) == 2:\n",
    "        df_sentiment = df_sentiment.withColumn(\n",
    "            grouping,\n",
    "            when(\n",
    "                df_sentiment[grouping] < clip[0], clip[0]\n",
    "            ).otherwise(\n",
    "                when(\n",
    "                    df_sentiment[grouping] > clip[1], clip[1]\n",
    "                ).otherwise(df_sentiment[grouping])\n",
    "            )\n",
    "        )\n",
    "    df_t = df_sentiment.withColumn(\n",
    "        \"value_count\", col(grouping)\n",
    "    ).groupby(\"sentiment\", \"value_count\").count().toPandas()\n",
    "    \n",
    "    df_pivot = df_t.pivot(\"value_count\", \"sentiment\", \"count\").fillna(0.)\n",
    "    df_tt = (df_pivot + 1).divide(\n",
    "        df_pivot.sum(axis=1) + 3,\n",
    "        axis=0\n",
    "    ).apply(\n",
    "        lambda x: np.log(x/(1-x))\n",
    "    ).apply(\n",
    "        lambda x: x-x.iloc[x.shape[0]//2]\n",
    "    )\n",
    "    \"\"\"\n",
    "    df_tt = df_pivot.divide(\n",
    "        df_pivot.sum(axis=1),\n",
    "        axis=0\n",
    "    ).apply(\n",
    "        lambda x: np.log(x/(1-x))).apply(\n",
    "        lambda x: x-x.iloc[x.shape[0]//2])\n",
    "    \"\"\"\n",
    "    for axi, k, color in zip(ax.flatten(), SENTIMENT_KEYS, SENTIMENT_COLORS):\n",
    "        df_tt[k].plot(color=color, marker=\"o\", ax=axi, linestyle=\"none\", alpha=0.7, ms=5)\n",
    "        axi.axhline(y=0, linestyle=\"--\", color=\"0.8\", lw=0.5)\n",
    "        axi.axvline(x=df_tt[k].index[df_tt[k].shape[0]//2], linestyle=\"--\", color=\"0.8\", lw=0.5)\n",
    "        axi.set_xlabel(title)\n",
    "        if add_ylabel:\n",
    "            axi.set_ylabel(k)\n",
    "        fit_model_plot(df_pivot, k, color, axi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len_user_mentions mentions\n",
      "positive\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>7166.3383</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>7043.1318</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>  <td>-3581.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:41</td>       <td>LL-Null:</td>      <td>-13218.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>            <td>11</td>              <td>Deviance:</td>     <td>7064.7</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>   <td>0.0443</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>               <td>9</td>               <td>Scale:</td>       <td>1.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>             <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th>  <th>Std.Err.</th>      <th>z</th>      <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>-3.0601</td>  <td>0.0015</td>  <td>-2020.0379</td> <td>0.0000</td> <td>-3.0631</td> <td>-3.0571</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>-0.1915</td>  <td>0.0015</td>   <td>-130.4677</td> <td>0.0000</td> <td>-0.1944</td> <td>-0.1886</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Generalized linear model\n",
       "====================================================================\n",
       "Model:              GLM                    AIC:            7166.3383\n",
       "Link Function:      logit                  BIC:            7043.1318\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -3581.2  \n",
       "Date:               2018-05-10 01:41       LL-Null:        -13218.  \n",
       "No. Observations:   11                     Deviance:       7064.7   \n",
       "Df Model:           1                      Pearson chi2:   0.0443   \n",
       "Df Residuals:       9                      Scale:          1.0000   \n",
       "Method:             IRLS                                            \n",
       "---------------------------------------------------------------------\n",
       "               Coef.   Std.Err.      z       P>|z|    [0.025   0.975]\n",
       "---------------------------------------------------------------------\n",
       "Intercept     -3.0601    0.0015  -2020.0379  0.0000  -3.0631  -3.0571\n",
       "value_count   -0.1915    0.0015   -130.4677  0.0000  -0.1944  -0.1886\n",
       "====================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neutral\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>3675.7058</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>3551.0094</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>  <td>-1835.9</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:41</td>       <td>LL-Null:</td>      <td>-31526.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>            <td>11</td>              <td>Deviance:</td>     <td>3572.6</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>   <td>0.0206</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>               <td>9</td>               <td>Scale:</td>       <td>1.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>             <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th> <th>Std.Err.</th>     <th>z</th>      <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>2.6443</td>  <td>0.0013</td>  <td>2042.9572</td> <td>0.0000</td> <td>2.6417</td> <td>2.6468</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>0.3055</td>  <td>0.0014</td>  <td>224.8131</td>  <td>0.0000</td> <td>0.3028</td> <td>0.3081</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Generalized linear model\n",
       "====================================================================\n",
       "Model:              GLM                    AIC:            3675.7058\n",
       "Link Function:      logit                  BIC:            3551.0094\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -1835.9  \n",
       "Date:               2018-05-10 01:41       LL-Null:        -31526.  \n",
       "No. Observations:   11                     Deviance:       3572.6   \n",
       "Df Model:           1                      Pearson chi2:   0.0206   \n",
       "Df Residuals:       9                      Scale:          1.0000   \n",
       "Method:             IRLS                                            \n",
       "----------------------------------------------------------------------\n",
       "              Coef.    Std.Err.       z       P>|z|    [0.025   0.975]\n",
       "----------------------------------------------------------------------\n",
       "Intercept     2.6443     0.0013   2042.9572   0.0000   2.6417   2.6468\n",
       "value_count   0.3055     0.0014    224.8131   0.0000   0.3028   0.3081\n",
       "====================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negative\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>142.6293</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>        <td>45.2916</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>  <td>-69.315</td>\n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:41</td>       <td>LL-Null:</td>      <td>-29844.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>            <td>11</td>              <td>Deviance:</td>     <td>66.873</td> \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>  <td>0.000863</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>               <td>9</td>               <td>Scale:</td>       <td>1.0000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>             <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th>  <th>Std.Err.</th>      <th>z</th>      <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>-3.7805</td>  <td>0.0023</td>  <td>-1646.0688</td> <td>0.0000</td> <td>-3.7850</td> <td>-3.7760</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>-0.6551</td>  <td>0.0030</td>   <td>-218.0291</td> <td>0.0000</td> <td>-0.6610</td> <td>-0.6492</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Generalized linear model\n",
       "===================================================================\n",
       "Model:              GLM                    AIC:            142.6293\n",
       "Link Function:      logit                  BIC:            45.2916 \n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -69.315 \n",
       "Date:               2018-05-10 01:41       LL-Null:        -29844. \n",
       "No. Observations:   11                     Deviance:       66.873  \n",
       "Df Model:           1                      Pearson chi2:   0.000863\n",
       "Df Residuals:       9                      Scale:          1.0000  \n",
       "Method:             IRLS                                           \n",
       "--------------------------------------------------------------------\n",
       "              Coef.   Std.Err.      z       P>|z|    [0.025   0.975]\n",
       "--------------------------------------------------------------------\n",
       "Intercept    -3.7805    0.0023  -1646.0688  0.0000  -3.7850  -3.7760\n",
       "value_count  -0.6551    0.0030   -218.0291  0.0000  -0.6610  -0.6492\n",
       "===================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len_hashtags hashtags\n",
      "positive\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>909.2347</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>782.0745</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>  <td>-452.62</td>\n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:41</td>       <td>LL-Null:</td>      <td>-49571.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>            <td>16</td>              <td>Deviance:</td>     <td>820.89</td> \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>    <td>0.490</td> \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>              <td>14</td>               <td>Scale:</td>       <td>1.0000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>             <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th>  <th>Std.Err.</th>      <th>z</th>      <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>-3.0660</td>  <td>0.0012</td>  <td>-2587.1528</td> <td>0.0000</td> <td>-3.0683</td> <td>-3.0637</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>-0.7294</td>  <td>0.0031</td>   <td>-237.6086</td> <td>0.0000</td> <td>-0.7354</td> <td>-0.7233</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Generalized linear model\n",
       "===================================================================\n",
       "Model:              GLM                    AIC:            909.2347\n",
       "Link Function:      logit                  BIC:            782.0745\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -452.62 \n",
       "Date:               2018-05-10 01:41       LL-Null:        -49571. \n",
       "No. Observations:   16                     Deviance:       820.89  \n",
       "Df Model:           1                      Pearson chi2:   0.490   \n",
       "Df Residuals:       14                     Scale:          1.0000  \n",
       "Method:             IRLS                                           \n",
       "--------------------------------------------------------------------\n",
       "              Coef.   Std.Err.      z       P>|z|    [0.025   0.975]\n",
       "--------------------------------------------------------------------\n",
       "Intercept    -3.0660    0.0012  -2587.1528  0.0000  -3.0683  -3.0637\n",
       "value_count  -0.7294    0.0031   -237.6086  0.0000  -0.7354  -0.7233\n",
       "===================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neutral\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>1988.1276</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>1855.5391</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>  <td>-992.06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:41</td>       <td>LL-Null:</td>      <td>-80525.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>            <td>16</td>              <td>Deviance:</td>     <td>1894.4</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>    <td>5.57</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>              <td>14</td>               <td>Scale:</td>       <td>1.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>             <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th> <th>Std.Err.</th>     <th>z</th>      <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>2.7128</td>  <td>0.0010</td>  <td>2676.9369</td> <td>0.0000</td> <td>2.7108</td> <td>2.7147</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>0.8387</td>  <td>0.0028</td>  <td>294.6309</td>  <td>0.0000</td> <td>0.8331</td> <td>0.8443</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Generalized linear model\n",
       "====================================================================\n",
       "Model:              GLM                    AIC:            1988.1276\n",
       "Link Function:      logit                  BIC:            1855.5391\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -992.06  \n",
       "Date:               2018-05-10 01:41       LL-Null:        -80525.  \n",
       "No. Observations:   16                     Deviance:       1894.4   \n",
       "Df Model:           1                      Pearson chi2:   5.57     \n",
       "Df Residuals:       14                     Scale:          1.0000   \n",
       "Method:             IRLS                                            \n",
       "----------------------------------------------------------------------\n",
       "              Coef.    Std.Err.       z       P>|z|    [0.025   0.975]\n",
       "----------------------------------------------------------------------\n",
       "Intercept     2.7128     0.0010   2676.9369   0.0000   2.7108   2.7147\n",
       "value_count   0.8387     0.0028    294.6309   0.0000   0.8331   0.8443\n",
       "====================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negative\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>900.8935</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>787.0265</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>  <td>-448.45</td>\n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:41</td>       <td>LL-Null:</td>      <td>-31622.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>            <td>16</td>              <td>Deviance:</td>     <td>825.84</td> \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>    <td>469.</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>              <td>14</td>               <td>Scale:</td>       <td>1.0000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>             <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th>  <th>Std.Err.</th>      <th>z</th>      <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>-4.0143</td>  <td>0.0019</td>  <td>-2157.1308</td> <td>0.0000</td> <td>-4.0179</td> <td>-4.0106</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>-1.1868</td>  <td>0.0070</td>   <td>-168.4056</td> <td>0.0000</td> <td>-1.2006</td> <td>-1.1730</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Generalized linear model\n",
       "===================================================================\n",
       "Model:              GLM                    AIC:            900.8935\n",
       "Link Function:      logit                  BIC:            787.0265\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -448.45 \n",
       "Date:               2018-05-10 01:41       LL-Null:        -31622. \n",
       "No. Observations:   16                     Deviance:       825.84  \n",
       "Df Model:           1                      Pearson chi2:   469.    \n",
       "Df Residuals:       14                     Scale:          1.0000  \n",
       "Method:             IRLS                                           \n",
       "--------------------------------------------------------------------\n",
       "              Coef.   Std.Err.      z       P>|z|    [0.025   0.975]\n",
       "--------------------------------------------------------------------\n",
       "Intercept    -4.0143    0.0019  -2157.1308  0.0000  -4.0179  -4.0106\n",
       "value_count  -1.1868    0.0070   -168.4056  0.0000  -1.2006  -1.1730\n",
       "===================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len_urls URLs\n",
      "positive\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>288.9343</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>232.2086</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>  <td>-142.47</td>\n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:42</td>       <td>LL-Null:</td>      <td>-20019.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>             <td>7</td>              <td>Deviance:</td>     <td>241.94</td> \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>   <td>0.0161</td> \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>               <td>5</td>               <td>Scale:</td>       <td>1.0000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>             <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th>  <th>Std.Err.</th>      <th>z</th>      <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>-3.0683</td>  <td>0.0013</td>  <td>-2398.1650</td> <td>0.0000</td> <td>-3.0709</td> <td>-3.0658</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>-0.5236</td>  <td>0.0028</td>   <td>-189.3501</td> <td>0.0000</td> <td>-0.5291</td> <td>-0.5182</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Generalized linear model\n",
       "===================================================================\n",
       "Model:              GLM                    AIC:            288.9343\n",
       "Link Function:      logit                  BIC:            232.2086\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -142.47 \n",
       "Date:               2018-05-10 01:42       LL-Null:        -20019. \n",
       "No. Observations:   7                      Deviance:       241.94  \n",
       "Df Model:           1                      Pearson chi2:   0.0161  \n",
       "Df Residuals:       5                      Scale:          1.0000  \n",
       "Method:             IRLS                                           \n",
       "--------------------------------------------------------------------\n",
       "              Coef.   Std.Err.      z       P>|z|    [0.025   0.975]\n",
       "--------------------------------------------------------------------\n",
       "Intercept    -3.0683    0.0013  -2398.1650  0.0000  -3.0709  -3.0658\n",
       "value_count  -0.5236    0.0028   -189.3501  0.0000  -0.5291  -0.5182\n",
       "===================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neutral\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>580.7980</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>523.0356</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>  <td>-288.40</td>\n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:42</td>       <td>LL-Null:</td>      <td>-29314.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>             <td>7</td>              <td>Deviance:</td>     <td>532.77</td> \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>   <td>0.0226</td> \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>               <td>5</td>               <td>Scale:</td>       <td>1.0000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>             <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th> <th>Std.Err.</th>     <th>z</th>      <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>2.7225</td>  <td>0.0011</td>  <td>2483.8847</td> <td>0.0000</td> <td>2.7203</td> <td>2.7246</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>0.5425</td>  <td>0.0024</td>  <td>228.6426</td>  <td>0.0000</td> <td>0.5378</td> <td>0.5471</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Generalized linear model\n",
       "===================================================================\n",
       "Model:              GLM                    AIC:            580.7980\n",
       "Link Function:      logit                  BIC:            523.0356\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -288.40 \n",
       "Date:               2018-05-10 01:42       LL-Null:        -29314. \n",
       "No. Observations:   7                      Deviance:       532.77  \n",
       "Df Model:           1                      Pearson chi2:   0.0226  \n",
       "Df Residuals:       5                      Scale:          1.0000  \n",
       "Method:             IRLS                                           \n",
       "--------------------------------------------------------------------\n",
       "                 Coef.   Std.Err.      z      P>|z|   [0.025  0.975]\n",
       "--------------------------------------------------------------------\n",
       "Intercept        2.7225    0.0011  2483.8847  0.0000  2.7203  2.7246\n",
       "value_count      0.5425    0.0024   228.6426  0.0000  0.5378  0.5471\n",
       "===================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negative\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>                  <td>GLM</td>               <td>AIC:</td>       <td>450.3897</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Link Function:</td>             <td>logit</td>              <td>BIC:</td>       <td>398.4476</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td> <td>['success', 'failure']</td> <td>Log-Likelihood:</td>  <td>-223.19</td>\n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>           <td>2018-05-10 01:42</td>       <td>LL-Null:</td>      <td>-8789.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>             <td>7</td>              <td>Deviance:</td>     <td>408.18</td> \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>                 <td>1</td>            <td>Pearson chi2:</td>   <td>0.00753</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>               <td>5</td>               <td>Scale:</td>       <td>1.0000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "        <td>Method:</td>                <td>IRLS</td>                 <td></td>             <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>        <th>Coef.</th>  <th>Std.Err.</th>      <th>z</th>      <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>-4.0438</td>  <td>0.0020</td>  <td>-1995.5832</td> <td>0.0000</td> <td>-4.0478</td> <td>-4.0398</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value_count</th> <td>-0.5530</td>  <td>0.0045</td>   <td>-123.5690</td> <td>0.0000</td> <td>-0.5617</td> <td>-0.5442</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Generalized linear model\n",
       "===================================================================\n",
       "Model:              GLM                    AIC:            450.3897\n",
       "Link Function:      logit                  BIC:            398.4476\n",
       "Dependent Variable: ['success', 'failure'] Log-Likelihood: -223.19 \n",
       "Date:               2018-05-10 01:42       LL-Null:        -8789.6 \n",
       "No. Observations:   7                      Deviance:       408.18  \n",
       "Df Model:           1                      Pearson chi2:   0.00753 \n",
       "Df Residuals:       5                      Scale:          1.0000  \n",
       "Method:             IRLS                                           \n",
       "--------------------------------------------------------------------\n",
       "              Coef.   Std.Err.      z       P>|z|    [0.025   0.975]\n",
       "--------------------------------------------------------------------\n",
       "Intercept    -4.0438    0.0020  -1995.5832  0.0000  -4.0478  -4.0398\n",
       "value_count  -0.5530    0.0045   -123.5690  0.0000  -0.5617  -0.5442\n",
       "===================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.9 s, sys: 40 ms, total: 2.94 s\n",
      "Wall time: 58.9 s\n"
     ]
    },
    {
     "data": {
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64cUXX+QZjsWRyYC6ddn/z54FrlxhO5T973+soF27lv3bpYth23XWdjSL5hcRsTHVRiM2\nNmy60MSJrJjduZPtFmjqVqKUg9IRHR2NY8eOITo6GllZWcjKyiq/rmXLlmY5WitllFvC41rQbt++\nHe10K5gIdy+9xEaFADb94OFD4L332MrrqVPZopWaqO1oljmtzCXmQd/RIaEWjzVsCJSUAFu2AMuW\nAfHxbDtsU6EclI6kpCSUlZVhzpw5la7bvHkzOnfuzCEq8jSUW8LjWtBSMSteMhnrijB3LtuwITOT\nHX/lFfYGPXgw0LVr9SO3tR3Nys/Ph5OTk+EPghBOhNx1TC5n7fhiYljO/fgj4OcnXKzVoRyUjiNH\njvAOgdQA5ZbwRP0RISUlBevWreMdhsV78UWgRw/2/08/BVxcgHHj2C5lAHD7dtUrsnWjWXv3sn9r\nOkJV1c43hEiB0IvHZDI2/eDYMbbA8/JltgW2sVEOEkNdvSruLjq8UW4JT/QFbUxMDO8wSAXe3sC8\neewNNTaWHXvvPaBJEzYCe+qUcPdF2zUSqTJWayFvbzZiu20bG6U9e1aY230aykFiqBEj2HN02zaA\nOlRVRrklPFEXtETcdGsMEhLYl6srsG8fO/b118Dx47XrpakRQyNOQgxg7NZCCxcCixax1l6bNgl7\n2xVRDhJDnT7Nnqfr1rEFjgAVthVRbgnPpHNo9/69efmbb74JmUxWfvlpzp07J3gM165dw6JFi3Dx\n4kUolUoMHDgQkyZNgrW1teD3ZUm8vdl8W52sLGDlSjb3NiKCjerWFJ2SIVJlisVjb78NdOoE5OWx\n9nu5uWwRmZAoB4mhZDKgf3/2VVwMpKWx5+vEicD48UC9erwj5ItyS3gmLWhnzpwJmUyGfv36QaFQ\nlF+ubjcwIXc5UalUCAsLg5eXF2JiYnDr1i0sXboUWq0WU6dOFex+CNuJbNo04No1NscWAIYPB5yd\n2Rtxt27PbgWWlZVFffqIWavt4rFmzdi/+/ezee0bN7KFm0KhHCRCUCiARo3YgsZly4AWLVgruj59\neEfGD+WW8Exa0G7evBkAoKjQTHHcuHHw9/ev8vtPnDiB9evXC3b/8fHxUKvVWLNmDVxdXREQEID0\n9HRs2rQJ4eHh1KfPCF54gX0BbAR35072Cd3VFTh8GEhNBTw82KnZJ9XVNcklxEwJtXjs9dfZFKAR\nI4B33wWWLGEjZLVFOUiE5OMDbN7MBjmcnYGTJ9mHsOnTgeef5x2daVFuCc+kBW2nTp0qHWvRokWV\nxwEgQ+B9H5OSkuDr6/vYlnPBwcH46quvkJycjF69egl6f+RxrVsDc+awL918+P/7P7Yr0sCBwJAh\nQPfufGMkxJSE3Hmsd2/g11+BgwdZMZubCzg61j5GQoSmO7Pw/PNsQMPfH+jVi823FXraDLEcXBeF\n9erVC+7PmDBW3XSEmrp9+za8vLwAsB5wGo0Gzf7OrNu68+LEJHTt97ZtYwWthwdrHg8A//kPG2X6\nz38eYdYs/vvZE2IsQi8ec3UFQkPZNtatW/+TU4Z69OhR7W6Ak4wMYNYsYMAAVPsaou/3EeNwdWWb\n+dy6xQraevWAEyfY2Ttzb/kl1dwSM64bK3zxxRfVXu/r64vFAm7anJubC3t7e6SlpaF///7w9/fH\nmjVrABjWQiMkJKTSseLi4lrHaWlatQI++OCfy7t2sVNRe/Z4QDfFyBz27ibkScbal97NDfjhB+Cd\nd4CffmKbMiiVNb+d+vXrCx+cCeg7N1nIDTCI4RwcgAkT2P9zctj6C3t7YOZMICTEsK3XxU6quSVm\nXAvaZ2ncuDEaN24s+O3K5XI4ODhw36VDrVZDrVbD2dkZarUapaWlcHV1RWZmJuzs7GBlZYX8/Hy4\nuLggJycHGo0GLi4uyMrKKp9/8+jRI9SvXx8qlQpWVlZwcnKCSqWCvb09NBoNCgoKym/TxsYGSqUS\n2dnZUCqVKCkpQWFhYfn1crkcdevWRU5ODhwdHVFYWIji4uLy6xUKBWxtbZGbmwsnJyc8evQIJSUl\n5dfb2tpCLpfX+jEFBz+Cm1t9dOyYjl9+ccZff9li7NhSAHK88UYxfH3VaNhQWo/JGH8ne3t7Xk9d\nYkKGdkJo1w5ITmbtvQDWMsmmhq/4KpUKHh4eNQ+aM33nJgu9AQapvX79gFdfZS0gd+1iBW1KCpum\nUKcO7+iEI9XcEjNRF7RCUyqVyM/PR4MGDZCUlAQAyM7OBgCDitvdu3dXOpaamorAwEC941H+PWxi\nZ2dXfrziykddXLa2tlVeX+/v3icVE6Pi9c7OzpWOVbyvqn5GVyhVXCRX8Xrd8YoFVcXra/uYHjyo\nh4cPgfz8ulCr66FpU2DsWLagbP78OigtVeLCBcDa2hMuLuxNWuyPyRh/J2IZajOKaG8PfPwx+39Q\nECsUpkzRf8RLqvvN6zs3Wcg5zEQ4VlZsGsiAAezyp5+yAnfyZNbNwxzmhks1t8SM62907969SE1N\nfer19+/ff2av2prw8vLCrVu3Hjummzurm0tL+NPNK7xxw6l8XmGLFuz007lzQGIiW/Ty0UesFUxE\nBHDkiPnPuSKWSahRxA0b2IhXv37A/fv6/Qzvs1iG0ndusrE3wCDC+Pxz4Pvv2aLH0FB2zFiz+0w1\nr1qquSVmXAvaWbNm4ddff33q9b///jtmzZol2P0FBATg/PnzUKlU5ccSEhJga2uLjh07CnY/pHZ0\n8wo/+kiFxYsrn17V9aNetw74+WfAy4u94AGsz+Hhw7QjDTEfQm2j26wZcOwY0LEjcP68fj9T8bVS\nSnSvIXv3osrXkJp+X8Uih/DRvj3wzTfAt98CZWVA27bA++8DN28Kez+6MyJlZf+cETEGqeaWmHEt\naJ/VwaC0tFTQjRWGDh0KBwcHTJo0CSdOnMDWrVsRFxeHkSNHUg9aEdJnjmiLFsCMGWw6gkzGVnfP\nmAF4erLdaGh3QSJ1+o4i6jOyJJez7UhfeYW9UU+fXv1IF83TZioWOYQvGxuWC0ePsl62nToBf6/t\nFoSp5lVTbgmP+ySO6grW06dPw1HAyTIuLi7YuHEjAGD8+PH4/PPPMXr0aEyaNEmw+yDCMWSv6+HD\n2UKYU6dYb0MrK7YoZtw4Grkl0qTvKGJNR5aGDWOLbQICgBs3qv4e2m+eocVi4tOwIXttv3mT9THP\nzATefJNNSavN9DOhzog8C+WW8Ey+KGzPnj3Ys2dP+eXPP/8cO3bsqPR9Dx48wO3bt9G/f39B7/+F\nF17Alto2ZyQmUVBQUL5YSl9PrgjPyGBv3Dt3slGr+/fZm3dODuuFW9NV36Rmrl27hkWLFuHixYtQ\nKpUYOHAgJk2aBOuqtoYjtVLTkSVXV+C779jUnRUrgM8+q/w9huSgOXpy8RgRD0dH9lVUxHbMCw9n\n09I++wzw86v57UVFVe4qYgyUW8Iz+Qhtbm4uUlNTyxeDqVSq8su6r7S0NMjlcrzzzjuYO3euqUMk\nIlFxRzd9VTVK1awZO7V6+jRw9iwrYlevZps5hIcDCQl0KtEYVCoVwsLCUFZWhpiYGIwbNw6xsbFY\nbaxJaRbOkJElmQyIjGRv/tevs3xQq/+53pAcNEcVp30QcapTh3XDuXqVvd47O7MNGzZurNkCMn3P\niNQW5ZbwTD4+NXLkSIwcORIAGy2dPXs2Xn/9dVOHQSQgMzOzxm2qnjVKpXsNWbAACAtjq76XLWO7\n1Jw6BeTnsy1E5XLD4yZMfHw81Go11qxZA1dXVwQEBCA9PR2bNm1CeHg4zVsXWG1Hljw92alaPz+2\ng1+HDobloDky1gYYRHjW1mwKAgBcucIWks2bB0ydyj6wiWXqKuWW8LjPoSXkaWwMmA9Qk1EqLy/2\nST4hgRWwf/0FzJ3LRm7few+gnQlrJykpCb6+vo+NRAQHB6OwsBDJyckcIzNPtR1Zsrdnrb0WLmRb\nT0+fDmzcaENbwhLJeukl9vq+dy9bW1FQANy5w+bb8mbI+xupHtff6OLFi+Hr68szBCJiSgP26qzN\nKNVbb7GvO3eAgwcBOzvgiy/YC+HgwUCfPjRyWxO3b99GUFAQACA/Px92dnbl/Z51/Z9rgraaNo0h\nQ1i/5ytXgLQ0Z7i5mWZLWEN3RCPkWTp0ALZuZf/fuxf497/ZAuJp04CmTfnEZMj7G6ke14L2rbfe\n4nn3ROSys7Or3C2rOkKcGmzalG3WAAD9+7OR2g8/ZC+Af/zBjtetS8Xts+Tm5sLe3h5paWno378/\n/P39sebv/jo5OTkmj4e2mtb/MTVsqEJBgT1kMg2OHXNEWZkG6ekPjPqYEhIyIZPZonFjOVxd1di4\n0Rljxojr70SniKVv7Fj2ur56Nds579IltobC1NvqGvL+Rqon6jFvlUqFGzdu4OWXX+YdCuFADJ9g\nGzdmc6+mTmUjSE5OwNq1wPz5rEXM228DgYFU3FZHLpfDwcGh1jvj0FbTtd+WOSODPXevXvUsHwX1\n9Kz8mB488MStW0CzZmpkZlrjzBlrpKZ6olMn4z2mb7/1LF+cefu2EtbWwKxZlR9TRgawdq3T3yO5\ntoiKYsWIqf5ORPo8PIAlS1jbL2trdlaiuJj1MA8IME0MYnh/MzeinkOblJSEESNG8A6DcFJSUsI7\nhMfoTn9GRrKdltq0AT7+mI3gXrjApinQGfB/KJVK5Ofno0GDBkhKSsLixYuh/nsJPW37yIe+vWp1\nq/qdnErQqxebdvPyy8CBA0/vWVtb+s5/N9VOTsT86bpWbN0KvPYaMGrUP1vrGpvY3t/MgagLWmLZ\nCgsLeYfwVI0bA1OmsK1EnZxYf9uPPmKf/MPCxLHogDcvLy/cunXrsWO6ubO6ubTEtPTtVaubuvP+\n+4VYvBho1Yq1+EpNBbp0+Wc+opD03RHNVDs5Ectha8s230lJYR1wNBpg0CAgLg4wVt0p5vc3qTLp\nlIP09HQA/5zG0V1+muzsbKPHRMTLlH36arsgJTiYfd27B+zZwxp979oF/Pe/bFpC376AQmG8+MUo\nICAAX3zxBVQqFVxcXAAACQkJsLW1RceOHTlHZ5me3CDgWb1qn8zB8HC21eg77wDp6awTglD0nf9e\n08dAiL6srVn3G62WdbpZuhSYM4dtPDJokLD3RX1ohWfSgrZPnz6wsrLC+fPnoVAo0KdPn2q3viWW\nzZR9+nSnMYF/TmMasriscWNAt5Nyt25AWhqbljB8OPDrr6wPrlxuGcXt0KFDsWXLFkyaNAnjx4/H\n7du3ERcXh7CwMOpBy0lNu4BUlYPt27MNSvLyWKu71FTDdmQylKl2ciKWSyYDXnmFfZ0+zaa3FBWx\n592YMUD9+rW/D+pDKzyTFrQTJkyATCZ7rP9a37590bp16yq/PyUlBYcOHTJVeERk5CZcaWWM05ge\nHuzNNiqKFbaenkB8PDBxItuicfBgNqprrsWti4sLNm7ciIULF2L8+PFwdHTE6NGjERkZyTs0i1XT\nLiBPy0F7e/aVmMjOQMycCUyeDFiZYBIbbXJATEm3EDIri01JeP55YORIdnaiNvWoKd/fLIVJC9qq\n3siCg4OfulPYvn37qKC1YLoWPKZg7NOYjRqxf999F+jZE/j2W3Y666WX2Hyta9eAoCDzK25feOEF\nbNmyhXcYxEDPysFevYBffmHP66Qk9rymk27EHNWvD8TGAtHRwKefsn7l9vZs6o0h7xemfH+zFFwX\nhdnZ2dFuGeSpTNmrVN8FKUJo1IhNSzh+nM3Xun+ftZBxd2ef/O/dM959E1IT+uSglxdbHDljBitm\nr1wxQWCEcPLcc8AnnwBdu7JBkF692IY8p07V7HZ49OI2d1yryV9//bXa65s0aYIBAwaYKBoiNo6O\njia7L56nMf39WXGbns5GuBwc2Kncr776Z1qCqZt+EwI8OwefXExpb8+a1Q8bxrp+mNsZB0Iq6tYN\nuHUL2LiRPeePHAEaNGBdE551psKU72+WQtRtu9q3b4/FNFnKYllaWxNPT9bj1tmZTUXo1AlYvpwV\n27/+yhYlWNivhHD2rBx8sifs1q2sR/Nvv7EG9X/9xYreWbOAAQPYvxkZJgreAl27dg2hoaFo164d\nAgICsGrVKpTpdqsgRlG3LjBhAnD9Ottl8tNPAV9ftl6itPTpP2dp72+mIOqClli2YgvepcDNjS0e\nO3aMncIzGgDMAAAgAElEQVR96SXg6FG20Gz4cGDfPt4REkvwrBysajGlqyt7fk6dCri4sC4ftBGC\n8alUKoSFhaGsrAwxMTEYN24cYmNjsZp+4SahWxA5cyZ7zn/xBdCu3dOLWkt+fzMWURa0hYWF5TsK\nEctFffoYT0926jY4mI18de0KrFzJOypiCZ6Vg0/b3UsmY71qbWzYSNX58/+8sdNGCMYRHx8PtVqN\nNWvWICAgAKGhoQgNDcWmTZuQl5fHOzyLIZMB/fqxAYjvv2c5MHkyK3IfPvzn++j9TXhcC9qEhATM\nmzcPGo2m/Ni8efPg5+eHTp06ISIiAo8ePeIYIeEpk7bbqsTDg53eSkzkHQmxBM/KQX0WU44Ywd7k\njx8HcnJoIwRjSUpKgq+v72OFUnBwMAoLC5GcnMwxMsul2xDxvfeA338HWrRgiydnzQLWrcukKTgC\n47oo7Ouvv4a7uzus/h6r//HHH7Fjxw507doV7u7u+O677xAbG0t9Ky2UglaUEMLVs3JQn8WU06ez\nMwyHDgEtWwIREaxVnSl61lqS27dvIygoCACQn58POzu78i2mdVtO10RISEilY3Sa3DAvvQR8/TVw\n9y7wf/8HFBQAhYWO5WctaKmQMLgWtH/88QeCg4PLLycmJsLDwwP/+c9/YG1tDYVCgQMHDlBBa6Fs\nbW15h0CIRRMiB3VFr+5Ne8kSdjr266+Bhg1rffPkb7m5ubC3t0daWhr69+8Pf39/rFmzBgCfFlFq\ntRpqtRrOzs5Qq9UoLS2Fq6srMjMzYWdnBysrK+Tn58PFxQU5OTnQaDRwcXFBVlZWeY/WR48eoX79\n+lCpVLCysoKTkxNUKhXs7e2h0WhQUFBQfps2NjZQKpXIzs6GUqlESUkJCgsLy6+Xy+WoW7cucnJy\n4OjoiMLCQhQXF5dfr1AoYGtri9zcXDg5OeHRo0coKSkpv97W1hZyubxWj8nREejS5RHOnasPrbYU\nJ09qcPu2Fr17Z6Fz5zqSfEzG/DvVdCc1rgVtUVHRY82FL1y4gN69e8Pa2hoA4OPjg++++45XeISz\n3Nxc2iKVEI6MkYPTpgG5uWwl+KZNbEMRIhy5XA4HBwc4OTnV6nZ2795d6VhqaioCAwP1+nmlUgml\nUgmA9ZzXqVik6GKs+MGp4vX16tUDAHh4eFR5vbOzc6VjFe+rqp+xt7cHgMee1xWv1x3Xfd+T19f2\nMT14UA85OUDv3lkoK6uHkhLgr78awNGRbU4SEOAMmUzcjykjA1i71rPC9tP1UKeOcf5ONcH1pI+H\nhwdOnjwJAPj9999x8+ZNdOzYsfz67Oxs1KEGnBarti/IhJDaMUYOyuVsgUxcHFssBgBareB3Y3GU\nSiXy8/PRoEEDJCUlYfHixeWLq+m1VDx0886vX3dC+/bAli2sc839+2w6TseOwM6drCuIWD3Zrk8s\njTS4jtAOGDAAq1atwuXLl5GZmQknJyf06NGj/PoLFy7Ay8uLY4SEp0ePHhn0KY0QIgxj5mBgIPu6\ncAEYP54VuC1aGOWuLIKXlxdu3br12DHd3FndXFrCn24KzoMHj+DmZv/Y8UuXWGeExYtZd4TXX2fd\nQcQ2+66qdn1iwHWE9r333sOECRPg4OCAl156CTExMeVD49nZ2Th37hz69OnDM0TCUUlJCe8Q9EKN\n44m5MkUOtm0LDBkCdOkCfPON0e/ObAUEBOD8+fNQqVTlxxISEmBra/vYmU8iDlXllpUVK2JPnGDv\nJ4mJQPPmwNKlrEOIWDytXR9vXAtaKysrREZGYvfu3YiLi3ss6ZydnXHy5EmEh4dzjJDwJJU+fWI9\n/UJIbZkiB2Uydhr2p59Yz1raQMkwQ4cOhYODAyZNmoQTJ05g69atiIuLw8iRI2ktgghVl1syGfvq\n2xf44Qc2ctu8OWBAswqj0KddHw/UOIWIllT60Ir19AshtWXKHPT1BfbvZ2+SAwcC586Z7K7NgouL\nCzZu3AgAGD9+PD7//HOMHj0akyZN4hwZqYq+udW2LZtne+EC21o3JobNtf3jDyMHWA3dtIm9e9m/\n7u78YqmI6xxaQqojlbZd3t5sZLbiZULMAY8clMuBQYOAV14BZs9muyzJZCYPQ5JeeOEFbNmyhXcY\nRA81za3nnmP/Dh7MprV16cJGcOPiWM4QEYzQpqenY/78+XjllVfQrl07tGvXDq+88gqio6ORnp7O\nOzzCkVwiWSrW0y+E1BavHBw6FPjlF7bamza5IubI0Nxq0AD46CPg1i1W3MrlrKfz//5H3UK4b6ww\nbNgw5OTkoFmzZuVzaNPS0hAfH48DBw5g69ataNmyJc8wCSdqtbq8P56Y6bNbEiFSxDMHmzdni2Nk\nMmDdOuCFF9iIFCHmoLa5pVSyqTkA24lv/HjA0RH44APgzTcFClJiuI7QfvLJJwCArVu34ocffkBs\nbCxiY2Pxww8/lJ82WbFiBc8QCUe6ZsyEED5456BuqoG3NzBqFDBzJiCR5ieEVEvI3Hr3XeC331iX\nHd0ajmPHgKIiwe5CErgWtMnJyRgxYgQ6dOhQ6bqOHTsiNDQUZ86c4RAZEQNdU3BzRe2+iNiJJQcD\nA4Fff2WrvT/+mHc0hNSe0LllZQW89Rb70KfRAMuXs77OK1YAIkljo+Na0JaUlFTbTsTBwUEyvUiJ\n8EpLS3mHYFTU7ouInZhy0M0N+O9/gRkz2ChUfDzviAgxnDFzy8qKdQzZtw84c4YVtwDbctqccS1o\nW7VqhW+//Rb5+fmVrsvLy8O3336L1q1bc4iMiIFU+tAaitp9EbETWw7KZGzXpNJSYP58ICwMyMvj\nHRUhNWeK3PLzA7ZtA6Kj2da6Xl7AhAlsQZk54lrQRkRE4Pr16+jbty+io6OxYcMGbNiwAfPnz0dQ\nUBBu3LiBiIgIniESjqTSh9ZQYt1thRAdseZgmzbA2bNsJOpf/6LV3UR6TJlbMhnQsCFw5QpbONax\nIzvbYW64FrSBgYFYs2YNnJycEB8fjxUrVmDFihXYtm0b6tWrh9WrV9PWtxbMzs6OdwhGRe2+iNiJ\nOQcdHIDYWNbaS6sFtm9ncwcJkQIeuaXryHPzJtC7N3D0KNCvH1tAZg4fCrlvrBAUFISgoCDcv38f\nf/75JwDAw8MDDRs25BwZ4c3KinubZKOidl9E7KSQg+7uQFYWsGoVsHkzsHEj69VJiJgZM7cyMtia\njKtX2Zm/qKjHd/NycmL/du7MFiWPHs1yZvt2oHFjo4VldKJ4tbp37x6OHj2Ko0eP4uTJk/jll19o\nUwVS5dxqQojpSCUH69cHjh9n24T6+gJ/j40QIlrGzC19Fxzb2gLh4UBKCvB//8eK3p9+AjZtkmZ7\nPK4jtMXFxYiOjsbu3buhfWK828bGBiEhIZgzZw4UCgWnCAlPLi4uvEMgxKJJKQflcnbGY+hQ9sZ8\n9iwrcCWy4SCxMMbMrZouOLa2BkJC2P+VSmDpUmDuXGDqVGDiRMCG+7l8/XANc+XKlfj222/x2muv\n4fXXX4enpycAth3uvn37sGPHDtja2mL27Nm1vq/8/HysX78eiYmJuHfvHurUqQMfHx9MnToV3rQa\nR5RycnK47CVPCGGkmINt27J/ly0Dbt9m7b2aN+caEiGVGDO3vL3ZyGzFy/rq2hU4fJi1+9q2jRW7\nv/zCetqKrOlJJVynHOzbtw9vvvkmPvnkE/Ts2RPPP/88nn/+efTs2RMrVqzAG2+8gf8KtBQvIyMD\n3377LXr16oW1a9di4cKFyM3NxZAhQ/Dbb78Jch9EWBpa4UEIV1LOwW3b2Ght587AoUO8oyHkccbM\nLSEWHL/8MtuUQSYDvv8eaNUKmDwZuHtX+HiFwnWEtqCgAO3atXvq9e3bt0dCQoIg9+Xp6YmEhITH\nVhb6+voiICAAcXFxWEyrc0RHSqc7CTFHUs5BmYy9AffowVoWZWayOYPV7OVDiMkYM7eEXnC8YAEw\nbhzw6adsru2OHUB2NiC23em5jtD6+/sjMTGxyuu0Wi0SExPRtWtXQe7Lzs6uUpsMFxcXODs746+/\n/hLkPoiwsrKyeIdAiEUzhxz08wMaNQK2bgU6dGBb6BLCm9Ryq1EjtuOYrj1et27AG28AP//MO7J/\ncB2h7d+/PxYuXIh33333sTm0aWlp2L9/P+7cuYM5c+bgzJkzj/3cyy+/LMj937t3D1lZWQbvRhai\nm0VdQXFxcW3DIn+rW7cu7xAIsWjmlINRUWz73OBgtnPS++/zjohYMqnmlkzGvs6eBb7+Ghg+HBg1\nCpg3j3dknAvaqVOnAgBUKhXOnTsHmUwGAI91PJg2bVr5/7VaLWQyGa4KtEfokiVLYGdnhxEjRghy\ne4QQQsTr3XeBLl2AixfZZbWareomhNSMnR0wfjzw3ntAbi7w11/Aa6+xD45DhvDpjMC1oBVq3uov\nv/zyzKI0JSXlsctff/01Dh06hJUrV8K9YsfhGti9e3elY6mpqQgMDDTo9sjjHj16hHr16vEOgxCL\nZY452Lw5+zp/nr0Bb9oE0Es2MTVzyS0bG8DFhe00tmABsGQJMGcO2+Ske3cTx2Lau3vcW2+9Jcjt\ntG3bFgcOHND7+w8fPoylS5diypQp6N+/vyAxEOHVr1+fdwiEWDRzzsH27dkp0xEj2NeCBdSzlpiO\nueWWTAa8+ir7OnWK7Tj2++9sAdmECaZZQCaRdrnVs7OzQ4sWLfT63l9//RXTpk3DsGHDEBERYeTI\nSG2oVCp4eHjwDoMQi2XuOdi3L1sktno1YGXFdlaytuYdFbEE5pxbXbqwf+/cAW7cYD1sw8KA2bPZ\nrn7GIoqtb03lxo0biIiIQGBgID744APe4ZBnkMI+8uTpQkND0bp160pfly5d4h0a0ZMl5GCDBsCi\nRWzldocOrH8tIcZmCbnVtCmwcSNw4QL7wGhlxXYtu3bNOPdnFiO0+sjKysKYMWPg7OyMoUOH4sKF\nC+XXKRQKvPjiixyjI1VxcnLiHQKppRdffBEffvjhY8f0PZtC+LOkHJTLga++Ypsx/PQTsHYtYG/P\nOypiriwptxo3Zi2/ACAhAYiMZG2/Zs4EOnUS7n4spqD9448/8OeffwIAhg0b9th1jRo1wpEjR3iE\nRaqhUqnKW7kRaXJwcED79u15h0EMZGk56OfH2hFNnQrcusV2WSLEGCwtt3Tefhvo3599eIyKAo4d\nA3Jy2FSEvxtdGcxiCtrOnTtX6nRAxM2ehkcI4coSc9DBAVi/nv1/2jSgSRNg0qTav9kSUpEl5paO\nvT0bpY2MZJenTWOt9GbMAAYNMrzll8UUtER6pLyPvDFlZLBFLFevAnv38o6mepcvX4afnx+Ki4vh\n7e2NyMhI9OjRw6Dboo1MTM/Sc3DCBDYFISGBzQV0c+MdETEXlp5bFX39NXDgAGv5tWUL8N//GnY7\n5j8rmUhWQUEB7xBEafVq4PJltiJbzDp16oR58+Zh/fr1WLZsGWQyGcaNG4cTJ07wDo3oydJzsHlz\nICkJaNOGnSIlRCiWnlsVyWRsGsLx46x/rVZr2E5+NEJLRMvV1ZV3CKIk0EZ5BtN3I5NI3fmkv/Xu\n3Rv9+/dHTEwMunXrVuP7pY1MTI9ykC0WE2gPIELKUW5VzcWF/atbRFYTVNAS0crMzLTISfPP4u3N\nRmh5qelGJjp2dnYICAjAvn37jBAVMQbKQUKMg3KreoZMMaaCloiWDY/NoCUgKuqfObQ81GQjkyfJ\naGWNpFAOEmIclFvCo98oES2lUsk7BFFyd5fmKdCioiIkJSWhbdu2vEMheqIcJMQ4KLeERwUtEa3s\n7GzY2dnxDoMY4MyZM9iwYQNeffVVeHp6QqVSIS4uDvfv38eyZct4h0f0RDlIiHFQbgmPCloiWjS/\nSLrc3NxQXFyM5cuXQ61WQ6FQwNfXF1u2bKGNFiSEcpAQ46DcEp5Mq9VqeQdhTkpLS5GRkQF3d3ea\nI0OIkVG+EWI6lG9EzKigJYQQQgghkkYbKxBCCCGEEEmjgpYQQgghhEgaFbSEEEIIIUTSqKAlhBBC\nCCGSRgUtIYQQQgiRNCpoCSGEEEKIpFEjOU50/fwIqQ3qB6kfyjciFMq5Z6N8I0KpSb5RH1pOXnvt\nNVy/fp13GETiDh8+jOeee453GKJH+UaEQjn3bJRvRCg1yTf6mMmJXC6HQqHAwYMHeYciShEREbhz\n5w79fp5C9/txd3fnHYokSD3fpJ4PUo8foJyrCco3/qT+GAzJNypoOZHJZLCysqJP+k+hUCjo91MN\n3e+HTn3qR+r5JvV8kHr8AOVcTVC+8Sf1x2BIvtGiMI6cnZ15hyBq9PupHv1+akbqvy+Knz9zeAym\nIvXfldTjB6T/GGoaPxW0HLm4uPAOQdTo91M9+v3UjNR/XxQ/f+bwGExF6r8rqccPSP8x1DR+KmgJ\nIYQQQoikUZcDQgghhBAiaTRCSwghhBBCJI0KWkIIIYQQImlU0BJCCCGEEEmjgpYQQgghhEgaFbSE\nEEIIIUTSqKAlhBBCCCGSRgUtIYQQQgiRNCpoCSGEEEKIpFFBSwghhBBCJI0KWkIIIYQQImlU0BJC\nCCGEEEmjgtbErl27htDQULRr1w4BAQFYtWoVysrKeIclGrt370br1q0rfS1YsIB3aFwUFxdjxYoV\nGD58ONq3b4/WrVsjNTW10vdt3boVffv2RZs2bfDWW2/hxIkTHKIVH6nnm9TyQerPV33iDw0NrfJv\ncunSJU5Ri4fU8+3gwYMYM2YMAgIC0L59e7zxxhvYvn07NBoN79AMotFoMHDgQLRu3RrHjh3jHY5e\n8vLysGjRIvTo0QM+Pj4IDAzEV199pdfP2hg5NlKBSqVCWFgYvLy8EBMTg1u3bmHp0qXQarWYOnUq\n7/BEZd26dXBzcyu/7OrqyjEafgoLCxEfH4927dqhQ4cOSEpKqvQ9e/bswYIFCzB+/Hh07twZ8fHx\niIiIwM6dO/HCCy9wiFoczCnfpJIPUn++6hM/ALz44ov48MMPHzvWokULU4QoWuaQb5s3b4a7uztm\nz54NZ2dnHD9+HPPmzcPdu3cxffp03uHV2LZt26r8QClWpaWlGDNmDO7evYvIyEi0bNkSGRkZyMrK\n0u8GtMRk1q1bp33ppZe0Dx48KD+2ZMkSbdu2bbVqtZpjZOLx7bffalu1aqW9d+8e71BEQaPRaMvK\nyrRarVa7Y8eOKn83gYGB2vHjx5dfLioq0gYEBGinTZtm0ljFxhzyTWr5IPXnqz7xDx8+XDt8+HAe\n4YmaOeRbVlZWpWPjxo3TtmnTRltUVMQhIsNlZWVpO3XqpN24caO2VatW2qNHj/IO6Zl27dqlbd26\ntfbChQsG/TxNOTChpKQk+Pr6Pja6EhwcjMLCQiQnJ3OMjIiVTCaDldXT0/TOnTu4d+8egoKCyo8p\nFAr07t37qaNLloLyzfSk/nx9Vvzk6cwh31xcXCoda9myJYqKipCTk8MhIsMtX74cffv2hbe3N+9Q\n9Hbw4EG0adMGbdu2NejnKXNN6Pbt2/Dy8gIA5OfnQ6PRoFmzZuXXkX8MGTIE3t7e6NmzJ1atWoWS\nkhLeIYmS7nmjex7l5eWVX87Ozpbci7CQzCnfzCUfzOX5evnyZfj5+cHHxweDBw+WzPxEYzKnfKvo\nwoULcHNzQ/369XmHorezZ8/ip59+wpQpU3iHUiPXrl1D69atsWrVKrz88sto06YNxowZg1u3bun1\n81TQmlBubi7s7e2RlpaGbt26YeLEiVAqlQAgmRdyY3Nzc8OUKVOwdOlSbNiwAUFBQVi/fj1mz57N\nOzRR0j1vHBwcEBsbi44dO+Knn34qf17l5ubyDI8rc8g3c8sHc3i+durUCfPmzcP69euxbNkyyGQy\njBs3TjQL23gxh3x70qFDh3D69GmMHTtWMiP3ZWVliI6ORnh4uGjn2j/Nw4cPkZiYiOPHj2P58uX4\n5JNPcOfOHYwfPx6lpaXP/HlaFMaBXC6Hg4MDnJyceIciOt27d0f37t3LLwcEBEChUCA2NhaRkZFo\n0qQJx+jETalUom7durCzs5PsG4gxSDnfzDkfpPp8jYyMfOxy79690b9/f8TExKBbt26cohIPKedb\nRXfv3sUHH3yAnj17YuTIkbzD0VtcXBzy8vIQFhbGO5Qa02q1yM7Oxo4dO+Dp6QmAffgdPXo0EhMT\n0bdv32p/XhofOcyEUqlEfn4+GjRogKSkJCxevBhqtRoAJJ/8xhQcHAwAuHLlCudIxMfR0REAO3X7\n9ttv49y5c+jevXv580p3vSUy13yTcj6Y4/PVzs4OAQEB+O2333iHwpU55dvDhw8RHh6ORo0aYeXK\nlZDJZLxD0otKpcKaNWswceJElJSUID8/H4WFhQBYSzrd/8XKwcEBjRo1Ki9mAaBdu3YAoNe0Aypo\nTcjLy6vSH+XJOWWkMqm8mPCgm7P25By127dvw9nZWXJvJEIy13yTcj6Y6/NVyn8ToZhLvhUWFiIi\nIgIajQYbNmyAg4MD75D0dv/+feTn52PWrFnw8/ODn58fwsPDAQATJkzAoEGDOEdYverOOOmTY1TQ\nmlBAQADOnz8PlUpVfiwhIQG2trbo2LEjx8jE7fDhw5DJZGjTpg3vUESnadOmaNy4MQ4fPlx+rLi4\nGImJiQgICOAYGX/mmm9SzgdzfL4WFRUhKSnJ4JXZ5sIc8q2srAyTJ09GamoqYmNjJbUQDGD5tXXr\n1se+5syZAwCYMWMGli1bxjnC6nXp0gVpaWlIT08vP3b+/HkArNvEs1jPnz9/vrGCI49r2bIldu3a\nhV9++QUNGzbE8ePH8dlnn2HUqFHo2bMn7/BEYdSoUfjrr7+gVqtx9+5dfPPNN9i0aRPeeecdvPHG\nG7zD4+Lo0aO4du0azp07h8uXL8PLywtpaWkoKChAw4YNoVQqsX79emi1Wmi1WqxatQq//fYblixZ\nIrlFAUIyh3yTYj5I/flaXfx3795FdHQ0SkpKkJOTg4sXL2LBggW4efMmlixZ8tipUktjDvk2f/58\nHDx4EDNnzoSdnR0yMjLKvxwcHKBQKHiHWC25XA5PT8/HvgoLC7Fnzx6MGjVK9B8smjdvjp07d+LE\niRNwdXXFtWvXsGjRInh4eGDmzJnPHKWVabVarYliJWBtKRYuXIiLFy/C0dERAwcORGRkJGxsaH0e\nAHz00UdITExEVlYWSktL0aRJE7z99tsYMWKEZFaZCq1Pnz5IS0urdPytt97CkiVLALCtRL/66ivc\nv38fLVu2xPTp02mBCqSfb1LMB6k/X6uLPyIiAvPnz0dKSgrUajUUCgV8fX0RGRmJ9u3bc4hWXKSe\nb0/72wNsF7HOnTubOKLa++WXXzBixAhs2LABPXr04B3OM12+fBlLly7FpUuXYGVlBX9/f8yePVuv\nD4tU0BJCCCGEEEkT50d8QgghhBBC9EQFLSGEEEIIkTQqaAkhhBBCiKRRQUsIIYQQQiSNClpCCCGE\nECJpVNASQgghhBBJo4JWYKWlpUhNTUVpaSnvUAgxe5RvhJgO5RsRMypoBZaRkYHAwEBkZGTwDkXy\nCgsLeYdARI7yzbgoB0lFlG/CodwSHhW0RLQq7glOCDE9ykFCjINyS3hU0BLRsre35x0CIRaNcpAQ\n46DcEh4VtES0NBoN7xBEKSMjA7NmzcKAAQN4h0LMHOUgIcZBuSU8KmiJaBUUFPAOQZRWr16Ny5cv\no6ysjHcoxMxRDhJiHJRbwqOCloiWq6sr7xBE6erVq7xDIBaCchDQarXYtGkTvv76a96hEDNCuVW1\nlJQUTJw4EVOnTq3xz1JBS0QrMzOTdwii5O3tzTsEYiEsPQdzc3MxfPhwLF++HB07duQdDjEjlp5b\nVRk6dCh69OiBevXqYdq0aTX+eSpoiWjZ2NjwDkGUoqKi4OPjA2tra96hEDNn6Tk4bdo0ODo64vTp\n0/Dx8eEdDjEjlp5bAJCXl4fPPvsMkydPBgBMnjwZd+7cwcKFC9GoUaMa3x79RoloKZVK3iGIkru7\nOxYvXsw7jGfavXs3Zs2aVen4sGHDMG/ePA4RkZqyxBzUaDRYvXo13n77bcTExEChUPAOiZghS8yt\nipYtW4Zly5ahR48eiIqKAgB07ty5VrdJBS0RrezsbNjZ2fEOg9TSunXr4ObmVn6Z5o5Jh6XlYEZG\nBkaMGIGCggIMHjyYilliNJaWW1qtFomJiThw4ACWLVuGjh07Ijk5Gc2aNRPsPqigJaJl6Z9gzYW3\ntzeee+453mEQA1hSDpaUlKBHjx4YOnQo5s6dS6eEiVFZUm7t27cPc+fORUlJCSZNmgStVos+ffoI\nfj8WlbEHDx7Erl27kJKSgry8PDRp0gTDhg3D4MGDYWVF04nFpqSkhHcIhFg0S8jBoqIi7Ny5E8OG\nDcPJkydRv3593iERC2DuuZWamoq4uDj8+9//hp2dHZYvX46goCDIZDKj3adFFbSbN2+Gu7s7Zs+e\nDWdnZxw/fhzz5s3D3bt3MX36dN7hkSfQXtfmYciQIVCpVGjQoAEGDBiAiRMnQi6X1/h2QkJCKh0r\nLi4WIkTyFOaeg9evX8fQoUPx3HPPYdCgQVTMEpMx19y6ePEiFi1ahISEBAwfPhyPHj1CUFCQSe7b\nogramJgYuLi4lF/u2rUrbt68ibi4OERFRdF8KZGhuZbS5ubmhilTppSvDk9MTMT69euRnp6O5cuX\nc46O6MOcc/DixYsIDAxEdHQ0xo8fb9SRI0KeZE65VVRUhF27dmHAgAFQqVTw9/fHhg0b4OjoaNI4\nLKqgrVjM6rRs2RL/+9//kJOT89jCFcJfZmYmPD09eYdBDNS9e3d07969/HJAQAAUCgViY2MRGRmJ\nJk2a1Oj2du/eXelYamoqAgMDax0rqZo55mBubi7++OMPtGvXDidOnECrVq14h0QskDnk1oMHD/DZ\nZ5/hiy++gI+PD7p164ZevXqhV69eXOKxqIK2KhcuXICbm5tBp5roFKhxGXJamohbcHAwYmNjceXK\nleM8nXcAACAASURBVBoXtMT0zC0HT58+jaFDh+Ltt9+Gn58fFbOEGynn1tmzZ9G0aVPcu3cPf/75\nJw4dOoSXXnqJd1iWvbHCoUOHcPr0aYwdO5YWhYlQ3bp1eYdABEandaXFnHJw8+bNeO2117B06VJJ\n9HEm5k1quVVSUoIdO3YgICAAISEh+P333+Hn54cvvvhCFMUsYMEjtHfv3sUHH3yAnj17YuTIkQbd\nBp0CNa6cnBzY29vzDoMI6PDhw5DJZGjTpg3vUIgezCEHMzIyYG9vj65du+LMmTNo2rQp75AIkUxu\nZWVlobS0FIWFhfjss88wZcoUvPnmm6Jsaye+iEzg4cOHCA8PR6NGjbBy5UoaNRIpU08oJ8IaNWoU\n/P390apVK1hZWeHEiRPYsmVL+apyIn5Sz8GDBw9i9OjRiImJqXKKGCG8iD23Ll26hDVr1mDXrl1Y\ns2YNQkNDkZiYyDusallcQVtYWIiIiAhoNBps2LABDg4OvEMiT1FYWEh/Hwlr0aIFtm/fXv4Jv0mT\nJpg+fTpGjBjBOzSiJynn4MyZM7F161bEx8dzW6RCyNOIMbfKysrw8OFD1KlTBwMHDkRoaChSUlLQ\noEED3qHpxaIK2rKyMkyePBmpqamIj4+nnoMiRwvspG3u3LmYO3cu7zBILUgxB7Ozs+Hs7IyOHTti\n+vTp9DpPRElMufXw4UN89dVXWLduHYYMGYLFixcjJSVFcmevLaqgjY6OxrFjxxAdHY2srCxkZWWV\nX9eyZUvRfVqydObUp48QKZJSDmq1WsTFxWH69Ok4f/48Bg0axDskQp5KDLlVVFQEhUKBbt26oX37\n9oiPj0fnzp0BSHMBr0UVtElJSSgrK8OcOXMqXbd58+byPyQRB3Po01edjIwMrF69GlevXoW3tzei\noqLg7u7OOyxCykklB/Py8hAREYFz587h0KFD8PDw4B0SIdXilVsajQY//fQTVq9eDYVCge+++w5n\nz56Fra2tyWMRmkUVtEeOHOEdAqkBc9+5bfXq1bh8+TIA4PLly1i9ejW1EyKiIoUcLCkpgbW1NZ5/\n/nmsX79ecu2QiGUydW5ptVrIZDKEhITg9u3biIqKwtChQwHALIpZwMIKWiIt5pJkT3P16tVqLxPC\nm5hzUKPRYMWKFdi/fz+OHj2KDz/8kHdIhOjNVLl18+ZNrFu3DqdOncKJEyewfv16uLm5SXJKwbPQ\nbgJEtHJzc3mHYFTe3t7VXiaEN7HmYEZGBl599VV89913iIuLM8s3Z2LeTJFb8+fPR6dOnWBjY4P4\n+HjIZDI0aNDAbPNF1AXt+fPnMWvWLN5hEE6cnJx4h2BUUVFR8PHxgbW1NXx8fBAVFaXXzxUUFODT\nTz81cnSEiDMHtVotrl+/ji5duiAxMZE2SiCSZIzcKigowH/+8x/06dMHhYWFGDlyJO7cuYNly5ZZ\nRJ6IesrB3bt3sXfvXppXaKEePXokiZ1UDOXu7l6j53Zubm55S6KzZ88aMTJCGDHlYHFxMWbNmgVX\nV1fMmjUL3bt35x0SIQYTOre2b9+OyMhIdOrUCePGjcP8+fNx7do1i1pwLOoR2pycHNSpU4d3GIST\nkpIS3iGIgkqlQnR0NFq0aIGdO3fC0dERcXFxvMMiFkAsOXj9+nX4+/vjxo0bCA8P5x0OIbVW29zS\narU4ceIEhg0bhqysLPj5+eHEiRP473//i/Pnz+PKlSsoKysrX3BsCUw6QnvmzBm9vq+0tBS3bt3C\nl19+SXu+WzAx9OnjSavVAgACAwPh5+eHn3/+Gc8//zznqIglEUsOrl+/HmFhYXj//ffNdv4fsSy1\nya3Tp0/j/fffR05ODiIjI2Fra/vYe4OlLjg2aUEbGhqq94uRVqtF48aNMX/+fOMGRURLKj0whZae\nno7ly5fj8uXLSEhIwKlTp+hMBeGCZw6q1WpERUVhypQpWL58OZcYCDGWmuZWRkYGvvzyS4SGhsLN\nzQ3z589Hv379YGVV+US7t7d3eUtI3WVLYPI5tIMHD4avr2+13yOXy+Hh4YF27drBxkbU03yJEYm5\nZZCxLFu2DEuXLsWoUaOwadMmAKBilnDDKweTk5MxdOhQ9O7dG82bN+cSAyHGpG9upaamYvbs2di/\nfz+GDBkCGxsbNGnSBF5eXk/9maioqEqb9lgCk1eLL7/8Ml5//XVT3y2RILlczjsEvdR2x6/r16/j\nyy+/xJIlSxAcHIywsDC4ubkZMWJC9MMjB0tKSvDee+/h448/xuDBg01+/6Z27do1LFq0CBcvXoRS\nqcTAgQMxadIkWFtb1/i2Hj58iI8//hhHjhyBVqtFz549MWfOHLi4uBghclIb1eVWSUkJ9uzZgxde\neAEeHh7w8fHBp59+qvffsaYLjs2FSReFubq6WuSoGzGMWq3mHYJedDt+1XQC/u+//453330X/v7+\ncHR0RElJCdq3b0/FLBENU+ZgRkYGZsyYAZlMhuTkZIsoZlUqFcLCwlBWVoaYmBiMGzcOsbGxBi/i\niYyMxMmTJ/HRRx9h8eLFOHfuHN5///3y+fhEPKrKrfz8fCxevBjNmzdHTEwMCgoK4Obmhn//+9/0\noUQPJh2hTUpKMuXdEYlzdnbmHYJeajoB/9y5c2jdujUyMjLQrl07fPHFF3B0dDRmiIQYxFQ5+OOP\nPyIsLAxjxowBAINGJ6UoPj4earUaa9asgaurKwICApCeno5NmzYhPDwcDg4Oet9WcnIyTp8+jZiY\nGPTt2xcAGwWMiIjAyZMn4e/vb6yHQQxQMbcuXbqEkpISeHt74969e9i/fz/at2/PMTppEnXbLmLZ\npDJCq++OXz///DP69euHN954AykpKejRowdmzJhBxSwRLVPk4Pnz5zF27Fh88803+Oijjyxq3URS\nUhJ8fX0fW/EeHByMwsJCJCcn1+i2jh8/Drlcjh49epQf6969O+zs7HD8+HHBYia1k5GRgVmzZuGT\nTz7BwIEDERAQgFdffRUpKSmws7PDZ599RsWsgUTxypGdnY179+6huLi4ylMjL7/8MoeoCG+lpaW8\nQ9BLdRPwtVotSktLcfv2bQwbNgwzZszAnj17aKEXkQRj5uD169dx6dIlhISE4OrVqzUajTQXt2/f\nRlBQEAB2utnOzg7NmjUrv66mt9WoUSMoFIry99I6deqgcePGNb4tAAgJCal0rLi4uMa3Qx63dOlS\nnDt3Dq+99hpOnjyJjh074siRI1AoFLxDkzyuBW1JSQnmzZuH/fv3o6ys7KnfZyk91MjjxNID81mq\nmoCv1Wrx448/YuHChXj99dcxY8YM/PHHHxZzKpWYB2PlYFxcHKZOnYqFCxcCgEUWswDb/c/e3h5p\naWno378//P39sWbNGgBsY6Ga3pbu9xgSEoKHDx8iISEBDg4OyM3NFTz2Z1Gr1VCr1XB2doZarUZp\naSlcXV2RmZkJOzs7WFlZIT8/Hy4uLsjJyYFGo4GLiwuysrJQt25dAGw3rfr160OlUsHKygpOTk5Q\nqVSwt7eHRqNBQUFB+W3a2NhAqVQiOzsbSqUSJSUlKCwsLL9eLpejbt26yMnJgaOjIwoLC1FcXFx+\nvUKhgK2tLXJzc+Hk5IRHjx6hpKSk/HpbW1vI5XKDH1NaWhq+/PJLfPPNN2jRogXkcjkmTpyIGzdu\nIDMzU5KPydh/p5q2DORa0K5fvx579+7FgAED0LJlSyxfvhxhYWGoU6cO9uzZg3r16mH06NE8QyQc\nSbUPbWlpKbp37w61Wo05c+aUL26hYpZIjTFy8NNPP8WXX36JQ4cOoV27doLetlTJ5XI4ODjAyclJ\nkNurV68eNBpNrV5zdu/eXelYamoqAgMD9fp5pVIJpVIJALCzsys/XvH5pHu8FReLV7y+Xr16AAAP\nD48qr9fNQ614rOJ9VfUzuu1mK36Iqni97njFbWkrXl+Tx6TRaHDnzh14eXlhwoQJ8PHxwejRo3Hn\nzh2UlZXh5MmT8PHxkdRjqup6Y/6daoJrQbt//34MGDAAixcvRnZ2NpYvX44ePXqga9euGDt2LN56\n6y2oVCqeIRKOqnrCi1VZWRl27tyJmzdvYvbs2Vi3bh18fX2rbHpNiFQImYPJyclwd3dHaGgo3nvv\nPUH3sZcqpVKJ/Px8NGjQoHzRdHZ2NgDUuLhVKpVIT08HAGzZsqX8eF5eHp577jmBIib6yMvLw6ZN\nm7B27Vq0aNEC33//Pfbs2QPgnzaPDx48gI+Pj8X0iDUFru+26enp8PPzY4H8/cavm3rg4OCAQYMG\nIT4+nlt8hC+pFIPbtm3Diy++iDVr1qBDhw4AgA4dOkgmfkKeRojnsEajwSeffIJ+/frh999/R/36\n9amY/ZuXlxdu3br12DHdfFfdXNqa3FZaWtpj81zLyspw7969Gt8WMczDhw8BAO//P3v3HRbF2fUB\n+LcgCAIiCGowtohBCChgISLYECwYFf2IGkUsCUXFGhNRY2+YqK8o0WhsKLEbUowFMRZ4jWjEQhDf\nWBERFRbpCOzu98cEAtJ2l9mdmd1zXxeXsmX2LOxhzsw8z3mmTcP58+exY8cO/Prrr1UeUz5ELTQ0\nFGvXrlWoZzlflE9sGzlyJEJDQ5GRkcF1SAA4LmiNjIwqks/IyAi6urp4/vx5xf2mpqZ48eIFV+ER\njhUUFHAdQq3evHmD06dPA2Di3LZtG+Lj4zFo0CCOIyOEPWzk4JgxY3DixAkkJCRgwIABLESlOdzc\n3HDz5s0qVyJjYmJgYGCA7t27V3u8WCzGgwcParxy6e7ujtLS0iodDeLi4lBUVAR3d3fVvAECmUyG\n8+fPY8SIEXByckJZWRn27NmD48ePo0+fPhCJRDU+j8/7t/oo23td1TgtaNu3b48bN24AABo1aoT3\n338fx44dw5s3b1BSUoKTJ08KcgwlYQcfG0kXFhYiPDwc1tbW2LJlC0pLSzF16lQMGDCg1j9chAhV\nQ3IwMTERMpkMCxcuxKVLl+gsYQ3GjRsHY2NjzJw5E/Hx8YiKisL+/fvh7+9f40S5qKgoDB06FFFR\nUdXu6969O3r27Inly5fj9OnTOHv2LJYtWwYnJyf06tVLHW9Hq5R3AFmxYgVCQkIwdOhQJCUloVGj\nRnKNXebj/k1eivZeVxdOC1p3d3ecOXMGr169AgBMnToVt27dgouLC1xcXHD9+nVMnDiRyxAJhxSd\n5dsQ9V1CKT+a3rlzJy5cuIDo6GicPHlSMMvzEqIMZXKwpKQE8+fPx/Dhw5GRkQEnJyet6i2rCHNz\nc+zZswcAEBwcjG3btmHKlCmYOXOmUtsLDw+Hi4sLFi1ahAULFsDJyQnffvstHWyzKC0tDaGhoWjT\npg0yMzPx+eefIykpCYGBgQoNpVHn/o1t8vZeVzeRjMM18WQyGUpLS6Gnp1eRcGfOnMEvv/wCHR0d\neHl5YdiwYVyFp5TyWaCxsbE0EL+Bnj9/XmXWpCqFhoYiKSmp4nt7e/uKyYpbtmzBli1bcOnSJXTu\n3Jl2DjxC+aZaiuZgVlYWBg8eDCsrK+zatUswrfeIfLQ9344cOYKgoCD4+flhxowZ6NSpk9LbUuf+\njW3lE9sq917nw1hgTg+bRSJRtWbCgwYNonGIBIB6L8nUdAnlxo0b8PT0xPDhwxEfH4/3339fbfEQ\nwgeK5GB6ejpatWqF0NBQ+Pj40IEfEbw3b97g8OHDCA8Px65duzBo0CA8fvyYldUdhTzkoKbe63zA\n6ZCDy5cv4+DBgzWuDkZIVlaW2l6r/JJJcXExkpOToaurCwcHB/z555/Ys2cPFbNEK8mTg3l5efDz\n88PYsWMhEokwatQoKmaJ4F2/fh3t27fHgQMHsHz5cjg4OMDU1JS1pcrVuX/TFpwWtKtWrUJycjL9\n8SM1Kl+FRB0+++wzpKen4+LFizA3N8fixYuhp6dHE1mIVqsvB2/dugVnZ2cYGBjg1KlT9LecCFr5\nvJ2YmBjY2toiNjYWZ8+ehbe3N+ttGNW5f9MWnA45eP78OT744AMuQyBa7v79+0hLS0Pfvn3xySef\nYMKECWjZsiXXYRHCa1KpFG/evIGBgQFWr16Njz/+mOuQCFFaeno6fH19kZaWhhkzZqBbt24wMjKC\nnZ0d16ERBXB6hrZly5Z02p3UqrCwUGXbTk5OxoQJE9CrVy/89ddfEIlEmDdvHhWzLEtJSYGfnx+6\ndu0KNzc3bNq0qWLxFMJ/NeXgixcvMHToUKxfvx6mpqZITEzkXYN1QuqTmZmJtWvX4sSJE2jRogW+\n+OILPHjwAPPnz1fL+FZV7t+0FacF7fjx43H8+HHk5+dzGQbhqebNm7O+zfIDqJUrV+KDDz7AgwcP\nMH36dNZfhzBN4CdPngyJRIKIiAgEBgZi165dvGnCTer3dg6ePXsWTk5O6N69OxYuXChXg3W+ripE\ntFNRURE+/fRTdOrUCX///TdsbGzQqFEjjBgxQq3t5VSxf9N2nA45+OCDD2Bra4thw4bhk08+QceO\nHWsccN2jRw8OoiNcE4vFCrc1qa2dyNWrV7Fq1So8efIEt27doiWV1eDgwYPIy8tDeHg4LCws4Obm\nhvT0dOzbtw8BAQE1No4n/FKeg1KpFDo6Orh//z6ioqLQv39/API1WC8vegFUFL18nCFNNJdEIsEv\nv/yCkpIS+Pr6okePHli7di0sLS05i0mZ/RupG6cFrZ+fH0QiEWQyGTZu3FhtQoFMJoNIJOLNKhRE\nvZQZhF/TztPExATfffcdvvzyS0yZMoUmrqhJXFwcnJycqvQi9fLywu7du3H9+nX069ePu+C0lKL9\nI8uL2PHjx2PLli2YNm1alfttbW2r9G+uqcE6X1cVIppPJpPhP//5D8LDw9GyZUssXrwYIpEIgYGB\nXIfG+iQzwnFBS0fppC6mpqYKP+fu3buQyWR49eoVHj58iNLSUhw4cACff/55tZ7HRLUeP34MT09P\nAMxKa4aGhhVdIx4/fsxdYFpM3rOl5YXvlStXkJCQgEWLFtV4pWzWrFnVCuS3yVP0EsKmlJQUJCcn\nY9SoUZBIJDh06BBcXFzkeq66Fg1QZv9G6sZpQevj48PlyxOeE4vFsLKyUug55ubmOHHiBCQSCTp1\n6oSuXbvSakUcyc3NhZGREZ49ewZvb2+4uroiPDwcgHLLPo4aNarabSUlJQ2OU5vIe7Z08+bNuH37\nNrKzs9GjRw/k5+fXeGVDngbr8hS9gPyFBF9XKSLci4mJwYYNG5CYmIh58+YBAD7//HOFtqGuITLK\n7N9I3WiBbcJb8q6LLZFIcOLECTg7O2P69OnIzs6GVCqFnZ1drTtPoj56enowNjbm/IxEXl4e8vLy\nYGZmhry8PJSVlcHCwgKZmZkwNDSEjo4OCgoKYG5ujpycHEilUpibmyMrK6uiZ2RhYSGaN28OsVgM\nHR0dmJqaQiwWw8jICFKpFEVFRRXbbNSoEUxMTJCdnQ0TExOUlpaiuLi44n49PT00adIEOTk5aNq0\nKYqLi1FSUlJxv76+PgwMDJCbmwtTU1MUFhaitLS04n4DAwPo6ekp9J58fHxw7tw5ODo6Ij09He3a\ntUN6enqV9/TgwQNER0fDw8MDHh4ekMlkSExMRHp6ulLvSSqVYvny5VXeU3p6erX3FBMTA5FIhDZt\n2sDCwgJ79uzB1KlTq72nQ4cOwcLCAs2aNYOJiQnOnDmDsWPHquX3RAUI/+Tl5eHKlSvw8vLCtWvX\nMHbsWERHR8PAwECp7alriIy8+zciPypoCW9JpdI67y8rK8PBgwexZs0amJqaYtu2bejWrRt+/PFH\nNUVI6mJiYoKCggK0aNECcXFxAIDs7GwAyl1uO3HiRLXbyteWlzceExMTAIChoWHF7ZWLlPK4Ku8M\nK9/frFkzAKgymaPy/WZmZtVuq/xaNT2nfMdWeZJc5fvLb6+8A6x8vyLvadCgQUhJScHly5dha2uL\njz/+uOLsZsuWLbFp0yaEhYXB1dUVT548QZMmTXD37l3Y29ur/D0dP368oqXb48ePoauri9DQ0GrP\nv3DhQsXjrly5Al1dXfj7+6vt90T44eHDh9i6dSv27duHQYMGwdPTEwsXLmzwdtU1RKa+/RtRnNYV\ntCkpKVi9ejVu374NExMTjB49GjNnzoSuri7XoZG3FBUVVex4KispKYFYLIa+vj4OHTqErVu3YsCA\nATTZi2c6dOiAR48eVbmtfOwsrcDGPnkuxdc1RCApKQnR0dFISEiAgYEBNm/eDEtLS+jq6qrlSoe8\nhQSNydVeMpkMN27cQLdu3XDkyBHo6enhxo0baNeuHWuvIe8QmYaqbf9GlMfpNLuJEyfiypUrtd5/\n+/ZtTJw4kbXXo76YwvL22NeioiJs3boV1tbW+O6772Bubo6TJ0/Cw8ODilkecnNzw82bNyEWiytu\ni4mJgYGBAbp3785hZJpJnp6wNTl79izCwsLQpUsXXLp0Ce3bt68ofGfMmIG1a9eqZYzqrFmzYG9v\nD11dXdjb29daSMj7OKI5CgsLsXPnTnTp0gUTJ05Efn4+FixYgLCwMFaLWeDfg77o6GiVfvZpbgf7\nOD1Dm5CQAF9f31rvz8nJwbVr11h7PeqLKSyZmZkVl/xkMhnc3NzQpk0bHD9+nHoTC8C4ceNw4MAB\nzJw5E8HBwXj8+DH279+PyZMnU66pgKJj/0pKSrB48WL88MMP2L9/PwBUOzCsnIOqJs8EM0UeR4Tv\n2bNnsLKywt69e3Hq1Cls2rRJY05gqDO3tAWvhxzk5eWxOhSA+mIKQ/mlU11dXVy6dAlNmjTB6dOn\nERsbWzE2jvCfubk59uzZg1WrViE4OBhNmzbFlClTEBISwnVoGknRS/EbN25ESkoKbt68WevZInWu\nnEQIwJy8iI+Px+bNmxEbG4uEhAQEBwdX64EsdJRb7FP7TzQ/Px+5ubkV379+/Rrp6enVHvfq1Svs\n27cPrVu3Zu212e6LSW2EVGPz5s349ddf8ffff8PS0hJDhgwBACpmBahz5844cOAA12EImrxtquQd\n+xcVFQU7OzvMnTsXX375ZZ1nu8onnBGiam/evIGuri7OnDmDOXPmICQkBLt379bYz6Cmvi8uqb2g\n3bt3LyIiIiq+X7NmDdasWVPjY2UyGb766ivWXpvtvpgNRW2Eqr6nvLw8HDhwAM2bN0fr1q0RGBiI\n3NxcPHjwAOnp6YJ8T6r8PdGsa+0gb1/M+i7F5+XlYcaMGbh69SqOHj0q10Ij2dnZNc7+1zaVDyqi\no6O5DkejZGRkYNu2bfjuu+8QFRWFwYMHY8iQIRq/khblFvvUXtD27NkTAFOsRkREYODAgbCxsan2\nOGNjYzg7O6NLly6sx8BWX0xqI9TwNkIA8zMLCwtDVFQU/Pz8UFhYCD09Pdy7dw+PHz+u1jJICO/p\n7ftV8Xsi2oGtvpgjRozAe++9hz///FPugyE6i8SofFBB2CGVSnHv3j24urpi7NixOH/+POzs7LgO\nS20ot9jHSUFbXtRGRETAy8sLH330kVpem+2+mKRhHj58iHfeeQfJyckwNDREcnIyWrVqVWUMLc1i\nJtquIW2qpFIpoqKiMHbsWBw5ckThmdWlpaUKPV5Tqaq5vrYpLS3F8ePHER4ejokTJyIwMBCPHj3S\nyuFklFvs06pRydQXkx/u3r2LtWvX4rfffsPJkyfh5eUFLy+vivvLL52mp6fTGUmi0eQZH6tsX8wX\nL15g0qRJeP36NQYPHgxLS0uF4ysuLlb4OZro7YMKoricnBzY29ujY8eOmD9/PoYPHw6RSKSVxSxA\nuaUKnA5SSUlJUdvZWYD6YnJNJpPh/v376Nu3L2xsbHD//n24uLjU+njq00c0nTy9Y5Xpi/nq1Ss4\nOzvD2dkZly5dUqqYBYSbgxkZGQgNDcXIkSMRGhqKjIyMBj2ucu9bIr/bt2/j008/xYoVK2Bqaorf\nf/8dFy5cgI+Pj9b/LIWaW3ym2aOu3zJu3DgYGxtj5syZiI+PR1RUFPbv3w9/f3/qi6lC165dw4gR\nIxAWFgZra2ukpqZi0aJF9R6ZZ2ZmqilCQrjB9rrxJSUl+O9//wtLS0ucO3cOq1evhp6entLbE2oO\nyrvIhLyPq3xQQeQzaNAgDBkyBB06dEBwcDAAwNramuOo+EOoucVnnA45kHfRBLaa6FNfTPWSSCT4\n6KOPkJSUhC+++AJTp04FUHXiVF0asiMmRAjYXMb1wYMHGDduHNq1a4devXqxsiSsUHNQ3gMFtg8o\nyL+WLl2KHj16CPYzpGr0c2EfpwWtn5+fXCt+sPlHhvpiqpZMJkNsbCzu3buH6dOn4/PPP4ebm5tc\nLYLeVt7yihChYbt3bH3Onz+PMWPGYMmSJZgxYwZrKykJNQflPVBg84CCVOXq6sp1CLwm1NziM04L\n2pp6JkokErx48QJnzpwBAEyaNEnNURFlnT59GsuWLUNOTg6WLl2KjIwMxMTEIDw8vM6dem1ycnKo\n1yoRJLZ6x9YnPz8f+fn5cHBwQExMDBwdHZXeVk2EmoPyHiiwdUBBiKKEmlt8xmlB6+PjU+t9wcHB\n8Pf3x4sXL9QYEVGUVCpFQkICPvzwQ9y9exfz5s3DqFGjoKuri9DQULl26rVp2rSpqsImRKXUcSn7\nxo0bGDt2LAIDAzFv3jylJ37VRag5KO+BQkMPKAhRllBzi894OylMR0cHHh4eOHjwINehkBqUlZUh\nKioK9vb2mDNnDkpKSjBnzhz4+vpWzF5t6E6d2poQoXr70jXbl7K3bduGwYMHY+XKlZg3bx6r266M\ncpAQ1aDcYh9vC1qAKYDol84vZWVlAICtW7dix44d2Lx5M/773//WOEa2oTv1kpIS5QMlhEOV2zyx\nuThIdnY2ZDIZOnTogKtXr2LMmDGsbLc2lIOEb+RttcZ3lFvs43TIwdatW2u8vbi4GHfu3EFCQgI8\nPT3VHBWpSXFxMXbv3o2wsDCcPHkSISEhmD17dp3Paej4NOrTR/hG3sleqriUHRMTg0mTJiE6MUdf\n4gAAIABJREFUOhqDBw9mddu1oRwkfCPv+HS+o9xiHy8LWoBpaTFw4EAsX75cjRGRmty+fRtDhgxB\nt27dcPToUdjb28v1vIbu1DMzM2mlMMIrXOxMy8rKsHjxYhw4cACRkZGstTGUB+Ug4RtNabVGucU+\nTgva2NjYGm/X09ODubk5GjXSqpV5eSUnJwcRERHo3r07+vTpg5MnT7I+g7o+yrT6IkSV1L0zLS0t\nha6uLoyMjJCYmKiSiV91oRwkfKMprdYot9jH6Rja1q1b1/jVokULKmY5UlxcjCVLlqBjx464e/cu\n2rdvDwMDA7UXs4D8CzAQoi6qnuxVWVRUFLp06YLS0lJ89dVXai9mAcpBwj+qGp+ubpRb7ONN1fj6\n9WuIxWJYWVnRL5oDL168QGpqKrp16waJRII//viD82UKc3NzaUliwivq6Fual5eHGTNm4OrVqzh0\n6BAaN27M+mvIi3KQ8I2mtFqj3GIf510OLly4gI8++gi9evWCt7c3EhMTAQDp6enw9fXFhQsXuA1Q\nwz179gyzZs2Cra0tzp07Bx0dHaxevZrzYhYATE1NuQ6BaBF5Zk+X70yjo6Oxdu1ahRYKkYdMJsOL\nFy9gZGSEP//8k5MrI5VRDhKiGpRb7OO0oL148SKmTZsGmUwGX1/fKvdZWVlBT08PP/30E0fRabbC\nwkIAzAIWenp6+OuvvxAaGspxVFWVx0iIOpRP+JJIJBUTvtRFKpVi48aNmDZtGqytrfHtt9/yYhUh\nykHVKSgowKZNmzBixAg4OzujV69e+Oyzz5Qel71lyxbY2NhU+9q1axfLkRM2UG6xj/MuB05OTjhw\n4ACys7Nx5MiRKve7uLjg559/5ig6zXTv3j2sWbMG169fx507d/DTTz/Vue67vG2KVKG0tFQtr0MI\nwN3s6ZcvX2LSpEkQi8W8W0iGclB1MjIycPz4cYwePRo9e/ZEcXExduzYgTFjxuDQoUOws7NTaruH\nDx+u8j3NpOcnyi32cXqG9n//+x+GDh0KkUhUY1FlZWWFV69ecRCZZtq0aRPc3NxgbW2N+Ph46Ojo\n1FnMAtyetaI+fUSd1Dnhq7JDhw6ha9euuHz5Mjp06KCW15QX5aDqWFlZISYmBnPmzEHv3r3h4eGB\nbdu2oaysDPv371d6u46OjlW+WrRowWLUhC2UW+zjtKBt3LhxnSuBpaen06DpBrp+/TrGjBmDrKws\n+Pr64uHDh/jqq6/QrFkzuZ7PZc+/zMxMtb0WIeqcPV1aWooFCxbg1KlTmDlzJtauXQs9PT2VvZ6y\nKAdVx9DQEIaGhlVuMzc3h5mZGV6+fMlRVOwR+kpeqka5xT5Ohxw4Ozvj6NGjGD9+fLX7Xr9+jaNH\nj6q1ibgmSUlJwZw5c5CUlIQvvvgCTZo0QfPmzRXeDpc9/6jbBVEndc2efvjwIcaNGwcLCwvMmzdP\n5a/XEJSD6vX06VNkZWXBxsZG6W307t0bYrEY7777LsaPHw9/f/96r8TVZNSoUdVuU2S51spX9TSh\nKwHbKLfYx2lBGxISgk8++QQjRozAkCFDIBKJkJCQgFu3biEqKgr5+fmYNm0alyEKikwmw++//w5r\na2vo6+vDx8cH0dHRDWr7o442RbXh4xkrQhoqKCgI48aNw6xZs5QqNNSJclC91q1bB0NDQ0ycOFHh\n57Zr1w6hoaHo3LkziouLcfLkSaxduxavX7+ud5lyVRLqSl6qRrnFPk4L2g8++AA7duzAkiVLsH37\ndgDAtm3bADDJuWHDhgYdqWoLmUyGU6dOYdWqVRCLxdizZw969eqFgICABm+by55/eXl5MDEx4eS1\nCWFTfn4+Vq5ciUWLFuHUqVPQ1dXlOiS5UA4q5+rVq/UWpffu3avy/d69e3Hu3Dls3LhRqYm3w4cP\nr/J9v379UFJSgt27dyMgIABNmjRRaHsnTpyodltaWho8PDwU2o5QV/JSNcot9nG+sIKLiwtOnz6N\nv/76C6mpqQCYYtbOzo73Zy+4JpVKkZubC4lEgpUrV2LOnDkYPXq0YHaW9TEzM+M6BKJBRo4cqfZO\nHQBw48YNjB07Fu7u7tDV1RVUflIOKqdLly747bff5H58bGwswsLCMGfOHHh7e7MWh5eXF06fPo37\n9++jS5curG1XHrq6umq/qicklFvs47ygBQCRSAR7e3vY29tzHYogSCQSHDlyBKtXr4a3tzfCwsJw\n5coVrsNiXV5eXrVJE4Qoi4sxfa9evcKwYcOwYcMGjBs3Ti2vySbKQeUYGhqiY8eOcj02MTER8+bN\nw/jx4xEUFMRqHFyeFIqOjubstYWAcot9nBa0JSUl2LJlC86ePYv09HSUlZVVe4xIJEJycjIH0fGT\nTCZDnz59IBKJsGHDBnh5eXEdksrU9HkgpKHUMabv5cuXOHXqFPz9/XHv3j3BXlqkHFStBw8eICgo\nCB4eHli0aFG9j09NTUVpaSnatm0r1xjM2NhYNGnSBO+//z4b4RIWUW6xj9OCdtWqVThy5Ajatm2L\nAQMGKDzGR1sUFxdjz549uHLlCiIjI3HkyBFYWVlp/JAM6tNHVEHVY/rOnTsHf39/TJo0CTKZTLDF\nLEA5qEpZWVmYOnUqzMzMMG7cONy6daviPn19/RoXVpg0aRKePXuG2NhYvPvuu1XuGzFiBEaNGoX3\n3nsPZWVlOHv2LH799VfMnz+fZtTzEOUW+zgtaGNiYuDl5YXw8HAuw+C1/fv3Y8GCBXBycqo4gm/d\nujXHUalHZmYmrXIjYH5+fkhISKh2+7Fjx+Dg4KD2eNQxpu/nn3/GtGnTEBkZqfDkGT6iHFSd+/fv\n4/nz5wBQrXVl69atcf78eYW2165dO+zZswdisRgymQydOnVCWFgYRo4cyVrMhD2UW+zjtKCVSqVw\ndXXlMgReys3NxQ8//ICAgAC0atUKv/zyC5ydnbkOS+1ofJHw2dnZYenSpVVuk3dsIdtUOabv4cOH\nKCgogKenJxITE2Fpaamy11InykHVcXFxqdbpoD51Fbl0YkhYKLfYx+lKYUOHDq3xDI62ys7OxvLl\ny9GxY0fExcUhPz8fnp6eWlnMAoCODqcfT8ICY2PjaktxatrQooMHD8LFxQU3b96EoaGhxhSzAOUg\nIapCucU+Ts/Q9uvXD+vWrcPMmTPh5eUFS0vLGn/Jmr5a2MuXL9GsWTNcvnwZqamp+O9//4tOnTpx\nHRbnCgoKYGpqynUYhNQqNDQUJ06cwNmzZ+Hk5MR1OKyjHCRENSi32MdpQRsYGAgAePToEWJiYqrd\nL5PJIBKJNHalkWfPnuGbb77Bvn378Msvv2D48OHVmmNrM3Nzc65DIA2UlJQEZ2dnlJSUwNbWFiEh\nIejTp49S22roUpxsSkpKgo2NDSZMmIBFixbB2NiYkzhUjXKQENWg3GIfpwWttq/v7ODggEmTJiEp\nKUnlg8MzMjKqLWGrzubyysjJyaHZuQLWs2dPjBo1Cm3atMHLly+xd+9eBAYG4vvvv0fv3r25Dk8p\nMpkMmzdvxurVqzX2rGxllIOEqAblFvs4LWh9fHy4fHnOPXjwQG2rhWzevBlJSUkAoPbm8sqSSqVc\nh0BqIO+yniEhIVVu69+/P7y9vREREaFUQcvWUpzKKikpgY+PDzIzM3H16lW89957anldLlEOEqIa\nlFvs48VKYdpKnUvfvT1sQwjDOOiSDD8puqxnOUNDQ7i5ueHnn39WQVSq9fz5c7zzzjv49NNPMWzY\nMLma2msCykFCVINyi31U0GoJW1vbijO05d/zXVZWFvXp4yFFlvV8m9AWAyktLcVXX32Fo0ePIjk5\nWeuuKlEOEqIalFvso74RWmLWrFmwt7eHrq4u7O3tVdpcni2a1t5J27158wZxcXHo0qUL16HI5enT\np3B3d8edO3fwxx9/oHHjxlyHpHaUg4SoBuUW++gMrZZo1aoV78fMEs1x7do17Ny5E4MHD4aVlRXE\nYjH279+PFy9eYP369VyHV6/CwkIYGhpi4sSJCAoKop6RhBDCc1TQEt4qLCxEs2bNuA6DKMHS0hIl\nJSX4+uuvkZeXB319fTg5OeHAgQNwdHTkOrxa5efnY+bMmSgrK0NkZCSmTZvGdUicohwkRDUot9in\nNQVtQUEBduzYgQsXLuDp06do3Lgx7O3tMXfuXEGMJwWE2XqrIZo3b851CERJ7du3x969e7kOQyGJ\niYkYO3YsXF1d8e2333IdDi9QDhKiGpRb7NOa62gZGRk4fvw4+vXrhy1btmDVqlXIzc3FmDFjkJyc\nzHV4cilvvSWRSCpab2kysVjMdQhEC8hkMshkMjx8+BBLly7Fnj17NHahBEVRDhKiGpRb7NOaM7RW\nVlaIiYmBoaFhxW1OTk5wc3PD/v37BTG+VIittxqCxi0SVXv16hUmT56MCRMmYOzYsVyHwzuUg4So\nBuUW+7TmJ2poaFilmAWYPnBmZmZ4+fIlR1Ep5u2hEUIZKqEsWueaqFJsbCwcHR1hb2+P0aNHcx0O\nL1EOEqIalFvs05oztDV5+vQpsrKyYGNjo9Tz1b22/KxZs6qNodVkYrGY+vQRlTl48CD27t0LT09P\nrkPhLcpBQlSDcot9Wl3Qrlu3rqI1jxBoW+stIyMjrkMgGubRo0eYPn06du/eje+//57rcHiPcpAQ\n1aDcYp9GFLTyri1f2d69e3Hu3Dls3LhR6U4BDV1bfuTIkVrRrUBZtNY1YdOhQ4cwc+ZMLFy4EC1a\ntOA6HEGgHCRENSi3ale5o1N0dLTcz9OIglbRteVjY2MRFhaGOXPmwNvbW4WR1a1ytwJtOvMqr6Ki\nIpiZmXEdBtEQ33zzDU6fPg1nZ2euQxEMykFCVINyq3blHZ0UpREFrSJryycmJmLevHkYP348goKC\nVByZfDS9W4GyLCwsuA6BaJBr165BJBJxHYagUA4SohqUW7VTtibSmi4HAPDgwQMEBQXBw8MDixYt\n4jqcCprerUBZmZmZXIdANAgVs4qjHCRENSi3aqdsTaQ1BW1WVhamTp0KMzMzjBs3Drdu3cLNmzdx\n8+ZNzhZW0NXVhb29vcZ3K1BWo0YacQGBEMGiHCRENSi3ajdr1izY29tDV1dXoedpzU/0/v37eP78\nOQBg/PjxVe5r3bo1zp8/r/aYFBnsrI1MTEy4DoEQrUY5SIhqUG7VTtmOTlpT0Lq4uFTrdED4LTs7\nu9piGIQQ9aEcJEQ1KLfYpzVDDojwUNNpQrhFOUiIalBusU8kk8lkXAehScrKypCRkYFWrVrRGBlC\nVIzyjRD1oXwjfEYFLSGEEEIIETQackAIIYQQQgSNClpCCCGEECJoVNASQgghhBBBo4KWEEIIIYQI\nGhW0hBBCCCFE0KigJYQQQgghgkaN5DhS3s+PkIagfpDyoXwjbKGcqx/lG2GLIvlGfWg5MmzYMPz9\n999ch0EELjY2Fu+++y7XYfAe5RthC+Vc/SjfCFsUyTc6zOSInp4e9PX1cerUKa5D4aWgoCA8efKE\nfj61KP/5tGrViutQBEHo+Sb0fBB6/ADlnCIo37gn9PegTL5RQcsRkUgEHR0dOtKvhb6+Pv186lD+\n86FLn/IRer4JPR+EHj9AOacIyjfuCf09KJNvNCmMQ2ZmZlyHwGv086kb/XwUI/SfF8XPPU14D+oi\n9J+V0OMHhP8eFI2fCloOmZubcx0Cr9HPp27081GM0H9eFD/3NOE9qIvQf1ZCjx8Q/ntQNH4qaAkh\nhBBCiKBRlwNCCCGEECJodIaWEEIIIYQIGhW0hBBCCCFE0KigJYQQQgghgkYFLSGEEEIIETQqaAkh\nhBBCiKBRQUsIIYQQQgSNClpCCCGEECJoVNASQgghhBBBo4KWEEIIIYQIGhW0hBBCCCFE0KigJYQQ\nQgghgkYFrZqlpKTAz88PXbt2hZubGzZt2gSJRMJ1WLxx4sQJ2NjYVPtasWIF16FxoqSkBBs2bMCE\nCRPg6OgIGxsbpKWlVXtcVFQUBg4cCAcHB/j4+CA+Pp6DaPlH6PkmtHwQ+udVnvj9/Pxq/J3cuXOH\no6j5Q+j5durUKUydOhVubm5wdHTE8OHDcfjwYUilUq5DU4pUKsXo0aNhY2ODS5cucR2OXPLz87F6\n9Wr06dMH9vb28PDwwO7du+V6biMVx0YqEYvFmDx5Mjp06ICIiAg8evQIYWFhkMlkmDt3Ltfh8crW\nrVthaWlZ8b2FhQWH0XCnuLgYBw8eRNeuXdGtWzfExcVVe8yPP/6IFStWIDg4GC4uLjh48CCCgoJw\n9OhRdO7cmYOo+UGT8k0o+SD0z6s88QOAnZ0dli5dWuW2jh07qiNE3tKEfIuMjESrVq2wcOFCmJmZ\n4fLly1iyZAlSU1Mxf/58rsNT2KFDh2o8oOSrsrIyTJ06FampqQgJCYG1tTUyMjKQlZUl3wZkRG22\nbt0q++CDD2SvXr2quG3dunWyLl26yPLy8jiMjD+OHz8ue//992VPnz7lOhRekEqlMolEIpPJZLIj\nR47U+LPx8PCQBQcHV3z/5s0bmZubm2zevHlqjZVvNCHfhJYPQv+8yhP/hAkTZBMmTOAiPF7ThHzL\nysqqdltgYKDMwcFB9ubNGw4iUl5WVpasZ8+esj179sjef/992cWLF7kOqV7Hjh2T2djYyG7duqXU\n82nIgRrFxcXBycmpytkVLy8vFBcX4/r16xxGRvhKJBJBR6f2NH3y5AmePn0KT0/Pitv09fXRv3//\nWs8uaQvKN/UT+ue1vvhJ7TQh38zNzavdZm1tjTdv3iAnJ4eDiJT39ddfY+DAgbC1teU6FLmdOnUK\nDg4O6NKli1LPp8xVo8ePH6NDhw4AgIKCAkilUrRv377iPvKvMWPGwNbWFn379sWmTZtQWlrKdUi8\nVP65Kf8c5efnV3yfnZ0tuD/CbNKkfNOUfNCUz2tSUhKcnZ1hb28PX19fwYxPVCVNyrfKbt26BUtL\nSzRv3pzrUOT2559/4uzZs5gzZw7XoSgkJSUFNjY22LRpE3r06AEHBwdMnToVjx49kuv5VNCqUW5u\nLoyMjPDs2TP07t0bM2bMgImJCQAI5g+5qllaWmLOnDkICwvDzp074enpiR07dmDhwoVch8ZL5Z8b\nY2Nj7Nq1C927d8fZs2crPle5ublchscpTcg3TcsHTfi89uzZE0uWLMGOHTuwfv16iEQiBAYG8mZi\nG1c0Id/edu7cOSQkJODTTz8VzJl7iUSC5cuXIyAggLdj7Wvz+vVrXLhwAZcvX8bXX3+Nb775Bk+e\nPEFwcDDKysrqfT5NCuOAnp4ejI2NYWpqynUovOPu7g53d/eK793c3KCvr49du3YhJCQEbdu25TA6\nfjMxMUGTJk1gaGgo2B2IKgg53zQ5H4T6eQ0JCanyff/+/eHt7Y2IiAj07t2bo6j4Q8j5VllqaioW\nLVqEvn37wt/fn+tw5LZ//37k5+dj8uTJXIeiMJlMhuzsbBw5cgRWVlYAmIPfKVOm4MKFCxg4cGCd\nzxfGIYeGMDExQUFBAVq0aIG4uDisXbsWeXl5ACD45FclLy8vAMBff/3FcST807RpUwDMpduPP/4Y\nN27cgLu7e8Xnqvx+baSp+SbkfNDEz6uhoSHc3NyQnJzMdSic0qR8e/36NQICAtC6dWts3LgRIpGI\n65DkIhaLER4ejhkzZqC0tBQFBQUoLi4GwLSkK/8/XxkbG6N169YVxSwAdO3aFQDkGnZABa0adejQ\nodov5e0xZaQ6ofwx4UL5mLW3x6g9fvwYZmZmgtuRsElT803I+aCpn1ch/07Yoin5VlxcjKCgIEil\nUuzcuRPGxsZchyS3Fy9eoKCgAKGhoXB2doazszMCAgIAANOnT8f//d//cRxh3eq64iRPjlFBq0Zu\nbm64efMmxGJxxW0xMTEwMDBA9+7dOYyM32JjYyESieDg4MB1KLzTrl07tGnTBrGxsRW3lZSU4MKF\nC3Bzc+MwMu5par4JOR808fP65s0bxMXFKT0zW1NoQr5JJBLMnj0baWlp2LVrl6AmggFMfkVFRVX5\nWrx4MQDgyy+/xPr16zmOsG4ffvghnj17hvT09Irbbt68CYDpNlEf3WXLli1TVXCkKmtraxw7dgxX\nr15Fy5YtcfnyZXz77beYNGkS+vbty3V4vDBp0iS8fPkSeXl5SE1NxQ8//IB9+/Zh7NixGD58ONfh\nceLixYtISUnBjRs3kJSUhA4dOuDZs2coKipCy5YtYWJigh07dkAmk0Emk2HTpk1ITk7GunXrBDcp\ngE2akG9CzAehf17rij81NRXLly9HaWkpcnJycPv2baxYsQIPHz7EunXrqlwq1TaakG/Lli3DqVOn\nsGDBAhgaGiIjI6Piy9jYGPr6+lyHWCc9PT1YWVlV+SouLsaPP/6ISZMm8f7A4r333sPRo0cRHx8P\nCwsLpKSkYPXq1XjnnXewYMGCes/SimQymUxNsRIwbSlWrVqF27dvo2nTphg9ejRCQkLQqBHNzwOA\nlStX4sKFC8jKykJZWRnatm2Ljz/+GBMnThTMLFO2DRgwAM+ePat2u4+PD9atWweAWUp09+7dePHi\nBaytrTF//nyaoALh55sQ80Hon9e64g8KCsKyZctw79495OXlQV9fH05OTggJCYGjoyMH0fKL0POt\ntt89wKwi5uLiouaIGu7q1auYOHEidu7ciT59+nAdTr2SkpIQFhaGO3fuQEdHB66urli4cKFcB4tU\n0BJCCCGEEEHj5yE+IYQQQgghcqKClhBCCCGECBoVtIQQQgghRNCooCWEEEIIIYJGBS0hRCklJSXY\nsGEDJkyYAEdHR9jY2CAtLa3a46KiojBw4EA4ODjAx8dH69e8J4QQwj4qaAkhSikuLsbBgwfRuHFj\ndOvWrcbH/Pjjj1ixYgWGDRuGHTt2oE2bNggKCkJKSoqaoyWEEKLJqKBlWVlZGdLS0lBWVsZ1KISo\nlImJCRISErBr1y4MHjy4xsdERETAw8MDs2fPRq9evfDNN9+gWbNm+P7771mJgfKNEPWhfCN8RgUt\nyzIyMuDh4YGMjAyuQxG84uJirkMgdRCJRHU293/y5AmePn0KT0/Pitv09fXRv39/xMXFsRID5Ztq\nUQ6Syijf2EO5xT5hLN9BtJJYLNbqpSSF7vHjxwCA9u3bAwDy8/NhbGyM9u3bIzs7Gzk5OTA1NZV7\ne6NGjap2W0lJCRuhklpQDhKiGpRb7KMztIS3jIyMuA6BNEBOTg4AwNjYGLt27UL37t1x9uxZmJiY\nAAByc3O5DI/IgXKQENWg3GIfnaHl0siRgK0tMGsW0KoV19HwjlQq5ToEwhITExM0adIEhoaGFYWu\nok6cOFHttrS0NHh4eDQ0PFILykFCVINyqw4ZGcDmzcDdu0B0tNxPozO0XJJIgKQk5hdHqikqKuI6\nBNIATZs2BcAMNfj4449x48YNuLu7Iy8vr8r9aiOTqff1NADlICGqQblVh82bmdpIIlHoaVTQcql8\n/N/du9zGwVMWFhZch0AaoEOHDgD+HUtb7vHjxzAzM1No/CwrnJyAhAT1vqbAUQ4SohqUW3VQsiai\ngpZLFy8yp9ZtbbmOhJcyMzO5DoE0QLt27dCmTRvExsZW3FZSUoILFy7Azc1N/QF99RXw0UfA+vUA\nXe6TC+UgIapBuVUHJWsiKmi51LMn8PffTFH75g3X0fBOo0Y0xJvvLl68iNOnTyMpKQkAcOnSJZw+\nfRp37twBAEyfPh0xMTEIDw/HH3/8gS+++ALZ2dn49NNP1R/s6NHAtWtAfDzw6pX6X1+AKAcJUQ3K\nrTrMmgXY2wO6ugo9jX6iXLp0CcjJAcLDgUaNmCEI+vpcR8Ub5bPhCX8tX74cz549q/I9APj4+GDd\nunXw8fFBYWEhdu/ejR07dsDa2hrbt29H586duQm4bVvgp5+Y/0+aBIwZAwwZwk0sAkA5SIhqUG7V\noVUrYO1ahZ9GBS3XTE2ZS6ESCdCjBxAQAEybBohEXEfGuezsbBgaGnIdBqnD+fPn633M+PHjMX78\neDVEo6DJkwE/P8DXl/njSQeT1VAOEqIalFvsoyEHfKGrCxw7BuzdC4wYAdD4GjqCJarVty+QmAg8\neAD8+CPX0fAS5SAhqkG5xT4qaPmkUydmfN8HHwDPn2t9m6HS0lKuQyCarnlzppj9+GPg8GHmgFLL\n864yykFCVINyi31U0PKNvj5z+dPBAfjyS2DBgn/be2kZWuuaqIVIxHzZ2QFffw1MmADQKmYAKAcJ\nURXKLfbxvqA9deoUpk6dCjc3Nzg6OmL48OE4fPiwwqtspKWlwcbGptrXsGHDVBQ5C+bPB/76C+jd\nG7h/n+to1I769BG1cnBguiAYGwMzZnAdDS9QDhKiGpRb7OP9pLDIyEi0atUKCxcuhJmZGS5fvowl\nS5YgNTUV8+fPV3h7wcHB6NevX8X3BgYGLEbLMktL4OefgYgI4OhRZhJL+XJwWrBkbmZmJqysrLgO\ng2iTJk2A774DiouZ1l4//ACEhAA6vD/2VwnKQUJUg3KLfbwvaCMiImBubl7xfa9evfDw4UPs378f\ns2bNgr6CM5Pbtm0LR0dHtsNUHZHo37NFfn7A778z/dnKl8xVorWFUOjp6XEdAtFWBgbMxMyjR4GT\nJ4HISI0+eKwN5SAhqkG5xT7en3aoXMyWs7a2xps3b5CTk8NBRBzKzmb61V6+zPxfw5fMbdKkCdch\nEG327rvAhQvAhx8Crq5aufgJ5SAhqkG5xT7eF7Q1uXXrFiwtLdG8eXOFn7t+/XrY2dmhR48emDdv\nHl4JacUgBwfmy9aWKWa5ak6vJlp3wEL4p1EjYMUK4I8/gMaNgSNHtGqSJuUgIapBucU+3g85eNu5\nc+eQkJCA0NBQ6Cgwrk1fXx9+fn5wdXWFsbEx7ty5g+3btyMpKQnR0dFKNTgeNWpUtdtKVLmzmzWL\nGWagqwsMHAgEBgKffQYsXcqcTdIwTZs25ToEQhgtWjCF7A8/AOvXAwcPMm32NBzlICF+qisHAAAg\nAElEQVSqQbnFPkEVtKmpqVi0aBH69u0Lf39/hZ7bokULLF68uOL7nj17wtraGgEBAfj111/h6+vL\ndrjse3s5OIkE6NAB6NYN2L4d8PHhLjYVKC4uhrGxMddhEMLQ12d61n77LTME4cwZwNmZ66hUinKQ\nENWg3GKfUgXtixcvsHPnTly9ehXZ2dn4z3/+g+7duyMjIwORkZEYMWIEbGxsWA309evXCAgIQOvW\nrbFx40aIWFga1t3dHQYGBkhOTlbq+SdOnKh2W1paGjw8PBoamnx0dYGFC4EBA5jemba2GjUMQaVn\nuwlRhkgETJ8OeHgA1tbA7dtA+/aAhp5toRwkRDUot9in8BjaBw8eYMSIETh8+DAAICsrq2LFi1at\nWiE2NhZHjhxhNcji4mIEBQVBKpVi586drB3ViEQiVgpjzn34IZCczBSzO3YwO1kNQH36CG917syM\nrz1yhDlLe+0a1xGpBOUgIapBucU+hQvar7/+Gk2aNMGZM2ewb98+yN5aJrJ///74448/WAtQIpFg\n9uzZSEtLw65du+qcCCYWi/HgwQOIxWK5th0fH4+ioiJ07dqVrXC5U96+zNCQOXu0ZYvgl/DMzMzk\nOgRC6rZqFbBuHeDtzSybq2EoBwlRDcot9ik85ODatWsIDg6GlZUVsrOzq93fvn37irO3bFi+fDku\nXbqE5cuXIysrC1lZWRX3WVtbVzlbGxUVha1bt2LGjBkICQmpsp01a9ZAJBLB2dkZTZs2RUpKCrZv\n3w47OzsMHTqUtXg55+cH9OoFjBsHSKXMRDKBUrTHMCGc+L//A3r0APLzgcJCZtlcDelZSzlIiGpQ\nbrFP4YJWIpHU2REgNzcXurq6DQqqsri4OEgkkioTuspFRkbCxcVFru106tQJkZGROHbsGIqLi2Fh\nYYFhw4Zh9uzZmvfBsrYG4uOBsjJmKMKzZ0y7L4GtMsbrVdwIqaxdO+bfX35huo/s3g0MHsxtTCyg\nHCRENSi32KdwQWtjY4Pz589j/Pjx1e6TSqU4ffo0bG1tWQkOAM6fPy/3Y0NCQqqdmS3n6+srjE4G\nbNHXZ76ys5klc999F2jenFnCUyCrjOXm5tIsUCIsH30EmJgwV0rGjQPCwpiJZAJFOUiIalBusU/h\nMbT+/v6Ij4/HvHnzcOfOHQBAXl4ebty4gaCgINy9e1fhllpEhXr3BhITgSdPgOvX/71dAKuMmZqa\nch0CIYrr1w+4eRPo2pUpZgXcQJ1ykBDVoNxin8JnaIcOHYq0tDRs3rwZv/32GwBg1j/jNHV0dDB3\n7lwMHDiQ3ShJw1haAv7+zExsmQx4+ZJp9cVzhYWFMDIy4joMQhTXvDkwfjzw6hXQpQuzGIOfH9dR\nKYxykBDVoNxin1J9aAMCAuDt7Y0zZ84gNTUVANCuXTt4enriXQ1csUojzJ7NDDNITAQePQLu3WPO\nHPH4KLG8HRwhgmVpySzAMHYscPYssyiDiQnXUcmNcpAQ1aDcYp/SK4W1bt0aU6ZMYTMWokqVVxkr\nKgLmzQOcnICrV5mdLg9Rnz6iEbp0Ya6OlOdfWRnTw1YAKAcJUQ3KLfYpPIb27NmztMKF0BkaMmeK\nDh8GLCyYs7YSCddRVUN9+ojGMDJietaamABDhgDffMO01eM5ykFCVINyi30KF7QzZ86Eq6srvvzy\nS1y8eBFlZWWqiIuoQ48ezL9LljCLMTx9ym08b6G2JoRVI0cCoaFARga3cezcCZw4wRS2XMdSD8pB\nQlSDcot9Che0W7duRd++fXHu3DkEBQWhd+/eWLx4Ma5cuVJt1TAiACIREB0NDBoEdO8OXLzIdUQV\n9PT0uA6BaBKJ5N+WdVxq3x64dAno2RO4dYvbWOpBOUiIalBusU/hgVwDBw7EwIEDUVJSgosXL+LU\nqVP47bffcPz4cZibm8PLywve3t7o3r27KuIlqqCry5y5GjAAaNsWyMwEmjRhvjiUl5cHEwFNoCEC\nwYeWdY0aAStXMv//z3+AtDRgzZp/l7DmCcpBQlSDcot9Ss9M0NfXh6enJzw9PasUt9HR0Th8+DCS\nk5PZjJOoQ/mqaxs2ALt2AQcPMr00OWJmZsbZaxMNxuLCL6yYMAGYMgVwdWVyrlMnriOqQDmoWqdO\nncKxY8dw79495Ofno23bthg/fjx8fX2hoyP/BdS0tDR4eHhUu71Tp0749ddf2QyZsIRyi32sTLXN\nz89HZmYmMjMz8ebNGxp6IHTz5gEtWgADBzJnkYKCOAkjLy+vzmWWCVGIru6/yz7ziYUF8NNPQEQE\nMxxi61auI6pAOahakZGRaNWqFRYuXAgzMzNcvnwZS5YsQWpqKubPn6/w9oKDg9GvX7+K7zkdpzly\npGCWWecC5Rb7lC5oxWIxzpw5g9OnT+P69euQSCTo1KkTQkJC4O3tzWaMhAt+fkCvXkByMrMYQ14e\n0LSpWkOgCYeEVdHRXEdQO5EImDGD+f///scsxLBpE+c9aykHVSsiIgLm5uYV3/fq1QsPHz7E/v37\nMWvWLOgrOASlbdu2cHR0ZDtM5VQes87zZda5QLnFPoUL2oMHD1YpYtu3b4/AwEAMHToU1tbWqoiR\ncMXaGjA2BqZOZS6FjhjBjPdT09E29ekjWql1a6bAdXICDh1iJmtyhHJQtSoXs+Wsra3x+++/Iycn\nB5Y87RGuED6MWechyi32KVzQLl++HFZWVpg8eTKGDh0KOzs7VcRF+GLzZmb5TkdH5rLoo0dAXByg\nhhmamZmZsLKyUvnrEMIrRkZMa68jR5glq2/cABo35iQUykH1u3XrFiwtLdG8eXOFn7t+/XosXrwY\nRkZG6NOnDxYsWKB0UTxq1KhqtynVg55vY9Z5gnKLfQoXtIcPH0ZXDicKETUrP7q2sAD69AEePwZ0\ndIDSUpUXtTS+iKhVRgZzAHf3Lj/G/n38MTBqFJNvX30FTJ+u9ngoB9Xr3LlzSEhIQGhoqEKTwvT1\n9eHn5wdXV1cYGxvjzp072L59O5KSkhAdHc3N75GvY9Z5gnKLfQoXtFTMahlbW2YcFMC0FBo1ihlT\n6+wMzJ0LTJrEXB5VAUX+oBPSYJs3//tZ58vYv0aNmKVyASbn9uxhekarCeWg+qSmpmLRokXo27cv\n/P39FXpuixYtsHjx4orve/bsCWtrawQEBODXX3+Fr6+vwvGcOHGi2m21dVOoEZ/HrPMA5Rb7WP+J\n3rt3D1t5NEuXNNCsWYC9PXO0bW/PfN+oETOmduNGYNw44PVrlbx0QUGBSrZLSI3eHuvHl7F/5T1r\nf/gB+PRTICFBbS9NOager1+/RkBAAFq3bo2NGzdCxMJJAnd3dxgYGFALTZ6i3GKfSgraiIgItjdL\nuNKqFXOWKjqa+bf8kqe9PbNjbdUKePaMOWvLspomTBCiMm+P9ePb2L9+/YC//mKWrD55Erh/X+Uv\nqfE5mJHBLCrD4bLIxcXFCAoKglQqxc6dO2FsbMzKdkUiESuFMVENjc8tDtA5b6I8Q0Om68EHHzDD\nD1as+PfyKAtycnJY2xYh9arpagTfNG3KDPFJT2fa6h04oNKXE2wOyluolg8z4WhZZIlEgtmzZyMt\nLQ27du2qcyKYWCzGgwcPIBaL5dp2fHw8ioqKaJggTwk2t3is3jG00f+MgxkxYgREIlHF97W5ceMG\nO5ERYfn8c2DiRCAmBoiKYpbQbSCpVMpCYITIqfxqRF34MnHss8+Anj2BsWOB588BJZrwy0OwOSjv\neGiOh5ksX74cly5dwvLly5GVlYWsrKyK+6ytraucrY2KisLWrVsxY8YMhISEVNnOmjVrIBKJ4Ozs\njKZNmyIlJQXbt2+HnZ0dhg4dqrb3oxZ8ycEGEmxu8Vi9Be2CBQsgEokwdOhQ6OvrV3xf12pgdJlD\nC7VuDZw9C3zzDTO+9ssvG7xJuiRDeIdPE8e6dgWuXwcKCoAXL4CnT1nvWSvYHJS3UK086bX8+5pU\nLqJYnOwUFxcHiURSZUJXucjISLiUL0dej06dOiEyMhLHjh1DcXExLCwsMGzYMMyePVvhxRl4j085\n2ACCzS0eq7egjYyMBIAqSREYGAhXV9caHx8fH48dO3awFB4RFF3dfwvZixeZM7WbNjF9NZWQlZVF\nffoIv/Bt4piREfN14QLT5uvLL4E5c5hWXywQbA7KW6jOmlX9bF9NKhdRLDp//rzcjw0JCal2Zrac\nr6+vUp0MBIlvOagkweYWj9Vb0Pbs2bPabR07dqzxdgDI4GBQPeEhJydgyxagXTvAwQH48EOFLw01\nadJEhQESogR5CyV169ePmaT5ySdAfDxw/Dgr7fQEm4PyFqryDDMBBFs0aSS+5qCCBJtbPKbwYXy/\nfv3Qqp6ipK7hCERLNG0KdOoEtGkD3L4N3Lmj9gkXhLBO3oljXMyeb9+euTKyYAFTzKrgjKJg1Nad\nRVkCLZo0khAmbxJOKLywwvbt2+u838nJCWsFOJ6FqMDdu8C77zLja6VSZl362bOBli3lenphYSGa\nNWum4iAJUYC8Z/S4Guenp8dMFnv1Chg8mDlju2oVsyiKEigH/1H5jC/hlrw5yHOUW+xjvW1XmzZt\n4OPjw/ZmiRCVn9UQiZivd95hhiKcOSPX05VZy5wQXuB6nJ+lJXDzJpCSAvTuDbx8qdRmKAf/UfmM\nLyEsoNxiH/WhJapT+dJQly7AiRPMRLHAQODhw3qfLm+/RUJ4R95FGlQ5NMHCAvjpJ6alnrm5UkUt\nL3OQB4shENJQvMwtgVO4oI2OjkZaWlqt97948aLeXrVES9Q0jq1/f+DePeC994CdO5n/14LWuiaC\nJe84P3ka+zekgBOJgDFjmOVzx40D/P2BvDy5n85KDrJdgHK8GAIhbKD9G/sU/omGhoYiMTGx1vv/\n97//ITQ0tEFBvS0lJQV+fn7o2rUr3NzcsGnTJkgkEqW29fr1a3zxxRfo3r07unXrhrlz59KRkro1\nbsz8K5MBbm7A7t01Lp1ramqq5sAIYYm8k5LkGZrAVgH388/MGFtnZ2Y4ghxYyUG2C1Cuh3MQ+fTp\nAyxZAjx5wnUkvET7N/YpXNDW18GgrKyM1YUVxGIxJk+eDIlEgoiICAQGBmLXrl3YrOQfxZCQEFy5\ncgUrV67E2rVrcePGDUybNo06M3AhIIDpn/mf/wA19C6mAw2i8eQZmsBWAWdkBHz/PTNJTEcHePOG\nmaxZh3pzUJ6zr/LGL++ZXHmHcxBubd0KZGczB1BLlzK30X62Au3f2KfUOe+6CtaEhAQ0bdpU6YDe\ndvDgQeTl5SE8PBxubm7w8/ODn58f9u3bh/z8fIW2df36dSQkJGDp0qUYMmQIvLy8sHTpUiQmJuLK\nlSusxUwU8MEHwNWrwIQJwF9/MT00/2Gk5IIMhAiGPEMT2CzgMjKYs7NLlgCDBgEDBzKrjNWi3hyU\n5+yrvPHLeyaX2jYJQ5cuTC/ytDTg00+BkhLAzo5pK3f/PtfRcY72b+yTq23Xjz/+iB9//LHi+23b\ntuHIkSPVHvfq1Ss8fvwY3t7erAUYFxcHJycnWFhYVNzm5eWF3bt34/r16+jXr5/c27p8+TL09PTQ\np0+fitvc3d1haGiIy5cv17r6GVExQ0Pm3xcvmDZDfn6Ajg6k5d0RBLpWN2GGC61evRq3b9+GiYkJ\nRo8ejZkzZ0JXV5fr0PhBnhZE8i4SIM8a95XbiRkZAa9fM51H9u4FvLyqbbLe9eblOfsqb/zynsnV\nkLZNWsPQkOlHDjBDcL7/num8MXcus7KdRMIcnGiZenOLKEyugjY3N7fKRDCxWIyioqIqjxGJRDAy\nMsLYsWMxe/Zs1gJ8/PgxPD09AQAFBQUwNDRE+/btK+5TdFutW7eGvr4+SkpKIJPJ0LhxY7Rp00bh\nbQHAqFGjqt1WUlKi8HbIPwYMAG7cAFxdgZISFM2bB7MLFwS7Vre2Kx8u1KFDB0RERODRo0cICwuD\nTCbD3LlzuQ5PONjsfVu5SNTRYdp7rVrF5J2XF3NJuNIVuKKiIpiZmdX+mvKs2iRv/BqyAhSpg40N\n8PXXwOrVQG4u82VrC/j6Ap99xlyx0xL15hZRmFwFrb+/P/z9/QEAnTt3xsKFC/HRRx+pNLByubm5\nMDIywrNnz+Dt7Q1XV1eEh4cDAHJychTelrGxMQCmGH39+jViYmJgbGyM3Nxc1mOvT15eHvLy8mBm\nZoa8vDyUlZXBwsICmZmZMDQ0hI6ODgoKCmBubo6cnBxIpVKYm5sjKyurYtm8wsJCNG/eHGKxGDo6\nOjA1NYVYLIaRkRGkUimKiooqttmoUSOYmJggOzsbJiYmKC0tRXFxccX9enp6aNKkCXJyctC0aVMU\nFxejpKSk4n59fX0YGBggNzcXpqamKCwsRGlpacX9BgYG0NPTa9h7atIEhTNmoHlcHKQiEbItLWH4\n8iXE6enCfU8q+j3x/ZJV5eFCFhYWcHNzQ3p6Ovbt24eAgICKXCQskecMZ01FY//+zNetW0BQEHDg\nANCxIwBUuTJWI3nPvsqDzW0RftPXZ9rKAUBcHLBrF3NAtXAhMH06M767fPKwhqo3t4jCFF4pjCt6\nenowNjZmbWZgs2bNIJVKG3Tp88SJE9VuS0tLg4eHh1zPNzExgYmJCQDAsPyyOwArK6uK/5e/XwMD\ngxrvL19p5J133qnx/vIjwMq3VX6tmp5TXihVLjgq319+e+WCqvL9DX5PWVmARIJG2dkwO3wYsLaG\nlakpc4lUqO9JBb8nvmNzuBCRgzxnOOsqGrt0YYb8fPghsGwZkJaGTEtLWL16VfuwHzYv/9NQAu3U\noQNzlWDZMqCoCEhPBxwcgI8/Zs7aOjtzHaFKZGZmCurvuRAoXNCuXbsWTk5OqoilRiYmJigoKECL\nFi0QFxcHAMjOzgageNsLExMTpKenAwAOHDhQcXt+fj7effddliImDfbPTlfHwIBZhOHZM+aP2h9/\nAHSJRjDYHC4E0BCfeslzhrOuolEkAkJCmHZL//d/QKdOaNSrl3qX7iXaq1EjwMSE+bp1C9izBxg1\nClixgplXkZcHsDjhnGuNGgnmfKJgKPwTVfeyth06dMCjR4+q3Fa+MyzfOSqyrfPnz6OkpAT6/6xt\nLpFI8PTpU7i5ubERLmHDPztdk6KifyeMxcczxeydO8w4K2pKzXtsDhdig8YP8dHRQVlIyL/vqXFj\n6OTkKP6ebGwgDg6G0aNHaHrwIF4NGgTTp0+RmZ6u1UN8cnJy6Iyaurz7LvDVV8wQhLIyZglnV1fA\nx4c5a/vhh1XGegtR+VU/wh7eHyK4ublh+/btEIvFMDc3BwDExMTAwMAA3bt3r/Z4sViM7OxsmJmZ\nVTy+nLu7O7Zv347Lly9XDAuIi4tDUVER3N3dVf9miEKys7P/vezeuzczYWXOHKY5/N69QMuWnMZH\n5MPWcCEa4qPG4TCvXgGPH0PcrRssv/0W6N0bVq1aVRxICvI9sfR7Imqkq8t82doyRe2+fcDEicCG\nDcDgwUB+PrOsswBV2b8JjTwdVTig1Gmu6OhojBgxAo6OjrC1ta32ZWdnx1qA48aNg7GxMWbOnIn4\n+HhERUVh//798Pf3r3FSSVRUFIYOHYqoqKhq93Xv3h09e/bE8uXLcfr0aZw9exbLli2Dk5MTevXq\nxVrMhB3VjmBFIuDUKaBbN8DRkRmCQHjr7eFCa9euRd4/y67SKjk890+vVz1zc2DKFKZw+PNPrqMi\n2qxlS+CLL4D//Q/w9gYSE5kl1CdMAC5eFNyiDYI+Q8vT5acVPkP766+/YsGCBWjXrh3c3Nxw7tw5\n9OrVCyUlJUhMTETHjh3RtWtX1gI0NzfHnj17sGrVKgQHB6Np06aYMmUKQkJClNpeeHg41qxZg0WL\nFkEmk6Ffv35YvHgxq6ubEXaUlpZWv1FPj5lAMHAg0L49kJnJjLnS8BmxQsTmcCGiZv8M+ykVi5kz\nYOXtvMLDgc6da+xZS4haiETMWVsXF+DBA6Yrx/TpwPbtQNeuzMSyFi24jrJeNe7fhIKny08rXNBG\nRkbCxcUFe/bswevXr3Hu3DkEBASgV69euHPnDqZMmYLPP/+c1SA7d+5cZRJXXUJCQuosds3MzPD1\n11+zFRpRoeLi4trvLJ8hv2oVcPw4cPAgs6PlAk8vv3BN0eFChH8qcrD8gN/enrnkO348sHIl036J\nEK40b878vZ05k/k+NpbpaevhwYy19fTk7XyLOvdvfMfTntEK/6b//vtveHp6QkdHBzpvfVAcHBww\nZswYbNmyhbUAifaSq0/fokVM70x3dyAyUvVB1YSnl1+4puhwIcI/1XJwwABm6dzkZOp6QPijfFXJ\ngQOBx4+ZgjY0FLh3D3j1iumUwzOC7kPL0+WnFS5odXR00Pify7vlM0rFYnHF/e3atcN9WqeZsCAz\nM7P+B4lETGuvixcBU1Pm0mh+vuqDq4ynl1+4Vj5cCACCg4Oxbdu2/2/vvsOaOts/gH/DlKWCKAJV\n6gKVIaCCA4oCiq+o1FVxoCiKoFLFTa3Wan1FW7UiWotaV50ovm3VWrcFtfKzddZRO5ThJmxl5vz+\neEokBpBgwslJ7s91cUHOyp2QO+c5z3kGJkyYgA8ralOI2qsyBy0tge+/Z9OW3r4N7N5d/4ERUp1G\njYDISDb7XYcOwIULbFzbgQPZ57asjO8IAdTy/KauKob/+9//2G81uSOpcJMDW1tb3L17FwBgYGAA\nGxsbnDp1CoGBgQCAK1euSHuYEvI29PX1a79xx47s58cfgeBgNki8l1f93P5X09sv6kCR5kJE/VSb\ngyIR0KABKxwsWQL89BMQH8/asxOiToKCgLQ0YP9+Nu2upydrZ8txbFIHnih0fiO1onANbbdu3fDD\nDz+guLgYABuX9ujRowgJCUFoaCgOHTqEgIAApQdKtE/FHQCF/Pwzmy/8//6Pta1ds0b5gb1OTW+/\nEPK23piDzs5s9AM9PaB/f8H1NCdawtSUjdaRnMxGS7h0CejalXVuTEwEeOigVafzG6mRwjW0EydO\nhLe3N4qLi2FoaIjIyEjk5OTghx9+gI6ODoYPH47o6GhVxEq0TG5uruJjP96+zb6wvL1ZW6o7d1gt\nkipnZaEpO4mGqlUOmpgAW7YAT54AEglw4ADrmKOmnXEIwYgRrOY2KYmNbRsYyNrbGhgA9vb1EkKd\nzm+kRgp/4zRr1gze3t5o+O8UdHp6eli4cCFSU1Pxyy+/YMmSJTKDXRNSVw3rMs1hxe3+Bg3YqAf2\n9oCbG7vdRAhRiEI5aGUF5OQAX34JDBgAPH2qusAIeVsNGgCjRgGHDwPGxqzN7XvvAT4+bCgwFbe1\nrdP5jdSILqGJ2qrTsCav3/6fNYvNCf7xx0BYGFBYqPxACdFQCudgkyas2Y+bG/t59Eg1gRGibGPH\nsra2ISFAbCwwdCj7+8wZlTydoIftUlMK34d9+PDhG7fR1dVF48aNpaMhEFIXJSUliu9U1e3/5s3Z\n1fesWUB6On/j1RIiMHXKQX19YNky1jmzeXPWnt3VlS0nRJ0ZGLDJGlq1Ys1nrl9nTRM6dmTj2o4f\nr7SmNHXKLVIjhQu0vr6+tZpVSyQSoUOHDoiMjIS/v3+dgiPaTanj9JmaAl9/zf6ePh1o2RKIjn71\n5USTIxAi561y0NmZ/V61CvjnHzb5SevWygmMEFWpPOxiy5aAnR0QHs5qanV0gKNH2UxknTu/mnCk\nDgQ9Dq2aUvhSIyIiAk2aNIGRkRH69euH8ePHIzQ0FH369EGDBg1ga2uLyZMnY9iwYcjOzkZUVBRO\nnjypitiJhlPZOH3R0WwEhP/8hxVkAZocgZAqKCUH9+xhM4t5egJ0LiDq7vVhFx0dWZvwVavY4/R0\n1unR3f1VJUkdqHQc2seP2cQS77/Pflec5zScwjW0paWlsLS0xI4dO+QaNWdnZyM0NBQikQhLlixB\naWkpQkJCsHHjRqqlJQozUNW0mu++y9r5LVnCGv/Pnk2TIxBSBaXkoEjEpiZ97z1Ws/X8OWBoSGPW\nEvU0fbr83brKJk9mzQ9OnWJN2QB2HmnVCujRo9a1tio7vwGvKmiAVxU0WjASj8I1tAcPHsTQoUOr\n7KFnbm6OYcOGYd++fQDYwMEDBgzAvXv33j5SonVUOlqGnh4r0M6eDZw+zaZGLC9/tZ4mRyBEuTno\n6grY2LCZxTp3ZuPXEty5cwchISHo1KkTvLy8sGbNGpRX/i5SQE5ODubOnYsuXbqgc+fOmDlzpsxM\nnqQWajMLlo4O0KcPmy0PYLNTTpjAanM3bqzV06j0/KalFTQKF2iLi4tr7BiWnp4u03uvtLSUZsQg\ndZKXl1c/T+TqymqOfv0VePGCJkcg5F8qycEPP2QXk/36sdnFtJhYLMb48eNRXl6O9evXY/Lkydiy\nZQvW1rHJU1RUFC5evIilS5di+fLl+O233zBlyhRwNOGFakVEsDHPN2581flx7VpWWSKRVLmLSs9v\nr1fIaEkFjcJNDry9vbFz505YWVlh0KBBsLCwAABkZWUhKSkJu3fvlmlecOnSJdjZ2SkvYqI1GjVq\nVD9PZGHBxiLctIm19/vvf9+qsT8hmkJlORgczNrU3rjBHuflAVo4LueePXuQn5+PuLg4WFpawsvL\nCw8fPsT27dsRHh4OU1PTWh/r8uXLSE1Nxfr166XnYH19fURERODixYvo0aOHql4GAdg547332A/A\nmtXMmMEqSebMYU0VKlHp+e1NzSY0lMIF2kWLFiEjIwOxsbFYsWKFdGiu4uJicBwHR0dHLFy4EAAb\nZ83IyAijRo1SbtREK7x48aL+ZlIRiVhP1okT2Vi1H38MLFrECruEaCmV5mCrVuzn6lU2U9O2bew2\nrhZJSUmBm5ubTI/3vn374ptvvsHly5fRq1evWh8rOTkZ+vr6eK+iQAVWAWVkZITk5GQq0Na3iAhW\niE1NBSruai9YwIYAu34dL0xMYPLypWpG1NHS2SsVLtBaWloiKSkJZ8+eRUpKilkkT/MAACAASURB\nVLT5gY2NDby9veHj4yPdtkGDBlizZo3yoiVapZSH+bWho8NuGYlEbGD4nTtfXXETomXqJQddXYEd\nO4Bx49hoCJ99pjVj1t6/fx99/i3EFxYWwsjICO+++650naLHsrW1hYGBAUpKSsBxHAwNDdGiRQuF\njwUAQ4YMkVtGY6cqSCRidyIAgONYh+TZs4HcXOgPGAC8fKk1HbbqQ50nuO/Vq5dCV4+EKIq3cfoM\nDYE1a1ht0ahRwIULbDxCQrRMveWgnx9w5Qo7uevosGlH9ep8ehKMvLw8mJiYIDMzE4GBgejRowfi\n4uIAALm5uQofq6KJwpAhQ5CTk4MTJ07A1NS0/vojVJKfn4/8/HyYm5sjPz8fZWVlsLS0xPPnz2Fk\nZAQdHR0UFhbCwsICubm5kEgksLCwQFZWFoyNjQGwOwRNmjSBWCyGjo4OGjVqBLFYDBMTE0gkErx8\n+VJ6TD09PZiZmSE7OxtmZmYoLS1FUVGRdL2+vj6MjY2Rm5uLhg0boqioCCUlJdL1BgYGaNCgAfLy\n8tCoUSO8ePFCOqrT8+fP0aBBA+jr67/daxo+HC/y82F56hRKmzXDE2trNNmxA3nt2kF30CBIdHWF\n95pU+H+ysbFR6DP3Vt8YOTk5EIvFsLGxUW2PPaKVnj9/rvAHWqn69wfu3QOMjIAtW9hJ99/aE0K0\nQb3mYNOmrHa2tJSNgjB/Prug1AL6+vowNTVVWrvKxo0bQyKRQFdXt87HSEpKkluWkZEBPz+/Wu1v\nZmYGs3+HZjMyMpIur/x5qni9lcsPldc3btwYAGBtbV3lenNzc7lllZ+rqn0qmtBUbp9ceX3F8spN\nbSqvf+vX9OwZoKOD0latYHP6NNCuHSy++YY1c1u+HObjxgnvNanw/6SIOs3hdvbsWQwcOBDdu3dH\nYGAgrly5AoBNizt8+HCcPXu2LoclRIZaXCRVJF1eHuDhAezdy288hNQjXnJQXx/Yvh349FM21WhB\nQf3HUE/MzMxQWFiIZs2aISUlBcuXL0d+fj4AxTsNmZmZoeDf9+rbb7/F0aNHYWBggIKCgiqH2SQ8\nmT4dcHJCg+xswMUF2LwZSElhk464uADFxcCQIcC+fexvUmsKF2jPnTsnHQZk+PDhMutsbGygr6+P\n7777TmkBEu2lVsO9RUcDP/7IOopt3853NITUC95y0M2NDaNnYAA8eMBPDPWgVatW+Oeff2SWVbR3\nfVfBu0GtWrVCZmamTDvX8vJypKenK3wsokL/dtjSnzdPdpzbjh3Z515HB/jgAyAhAWjRgo28Q2pF\n4QJtfHw83Nzc8MMPP2DGjBly49t5enri+vXrSguQaK+Kmgq10bkzmxlm+HA2+8rly3xHRIhK8ZqD\npqZsalFHR2DWLODLL1nHGg3i5eWFq1evykx+cOLECTRo0ABdunSR214sFuOvv/6qcrIEb29vlJaW\nIjk5WbosJSUFL1++hLe3t2peAKmzanNLX58Na3fqFOu/0aULIBazJm87drBhwEiVFC7Q/vHHH+jf\nvz9EIhFEVYzVaWNjg2fPniklOKLdKtrdqBVTU8DYGEhLY21sP/+82oGzCRE6tcnBqVNZc58BAwAN\nOr+MHDkSpqam+PDDD3H+/Hns2rULO3fuxLhx46ocg3bXrl3o378/du3aJbeuS5cu8PDwwKeffopj\nx47h+PHjWLx4Mdzc3NC9e/f6eDlEAbXKrbZtgXbt2DTR06axHGjRAqiifTOpQ4HW0NBQZiaw1z18\n+FChwaAJqY7a1dBW1r8/G1/wf/9j7fwI0UBqk4OtWwPJyayN4datfEejNBYWFtj67+uJjIzEV199\nhQkTJuDDDz+s0/Hi4uLg6emJBQsWYP78+XBzc8OGDRuqrHwi/FIot/T1gcGDgaNH2Wgg3boBf//N\nhgTbtAlQlzzlmcKjHLi7uyMxMRGjR4+WW5eTk4PExER07dpVKcER7VZWVsZ3CDV7913g3Dngn3/Y\nMEMXLtCYtUSjqFUO6uu/Gq/z7Fngp5/YFLrq1Na+Dtq3b49vv/22VttGRUUhKiqq2vXm5ub4/PPP\nlRUaUaE651bFEJLl5cAnn7AC7dy5rK1tQABbp6UXMArX0EZFReHRo0cICgrC9u3bIRKJkJqaio0b\nN2LgwIEoKCjAlClTVBEr0TK8jUOrCD09dksoPR0ICWHz1NdwB4MQIVHbHHR0BK5fB7y8WE0VIQLz\n1rmlq8vuFB46BNy6xWptL19mE5XExwM5OcoJVEAULtA6OjoiISEBALBx40ZwHIevvvoKX375JYyN\njZGQkAAHBwelB0q0z/Pnz/kOofYqpvB89IjdBqJbQEQDqG0ONm0KHD7MxqkdP17jOosRzafU3LK2\nBho3Zh2XV69mw4C9+y77LZFoTX7UaWIFT09PHDt2DL///jvS0tIAAHZ2dujYsSO11SFKU9XAy2rN\n3BzYv581QzAzA27fBtq319rbP0T41DoHRSI2pufUqWwK0WXLgJgY1nGTEDWnktzS0WGjIfj5Ac+f\ns1w4fpwNOzlxIpteWl3vuihBnWcKE4lEcHJygpOTkzLjkVFYWIiEhAScPXsW6enpMDQ0hJOTE2bO\nnIkOHToofLx169YhPj5ebvncuXMRFhamjJCJEuno1GneD36JRECvXuyqOCyMXTlv2gRYWMhv+/gx\nm+rz9m2gQwd2cq4Yk5AQNSCIHNTTYwPQP3rEaqj27mXjeRKixlSeWxUF14AAdg7atImNmnDuHBvz\nVleXFYA1iMIF2pKSEqxbtw7Hjx/Hw4cPq2zYLBKJcOvWrbcO7vHjxzh48CCGDh0KDw8PFBUVISEh\nASNGjMDevXvRsWPHOh133759Mo95nV6VVKuwsFBpU0HWOx0d4MwZNn2nqysbDcHdXXabtWvZeLYA\n+7127atOL4SoAcHkoIkJ8M03rGPMf/7DeoJXmoaTEHVTb7klErG25l5erF1tw4ZsFrKPP2aVLuPH\na0yuKFyg/eyzz7B//360bNkSvr6+MDY2VkVcAFhB88SJEzJV825ubvDy8sLOnTuxvI4nf1dXV2WF\nSFTIoqpaTSExNATWrGFXyO+8A2RlsS+Til7Zt2/Lbv/6Y0J4JrgcHDmSdZRp1IgVbv38gGbN+I6K\nEDm85Fbjxux3cDCrrd20idXWXrnC8sTQkNXcCpTCBdoTJ06gb9++iIuLU0U8MqpqY2JhYQFzc3M8\nffpU5c9P+JWbm8vPXPLK1q8f+71gAXD6NLB7N+tE1qHDqxpagD0mRI0IMgcrar1+/x2YPZtNVe3v\nz29MhLyG19wSiYCuXdnPmjXsDsfXXwOffQZMmMB+7Oz4ie0tKFyglUgk6NGjhypiqZX09HRkZWW9\n1UgKPXv2hFgsxjvvvIPRo0dj3LhxderMNmTIELlllefRJm9HomkzcC1dCjRpAnh4ABs2sDazr7eh\nJUSNCDoHP/uMtWcfNw6IiwOGDuU7IkKk1Ca3TEzY78mTge7dWa2thwc7L4lErGOZQMZ6VrhA279/\nf6SmpiI4OFgV8bxRbGwsjIyMMHbsWIX3tbOzQ0xMDNq3b4+ioiIcOXIEy5cvR05ODmbMmKGCaMnb\nENztzjfR0QFmzmQn2bQ0wMoKWLiQTaVLiBoSfA76+7Ph9IyMgDt3AAMDNusYITxTy9xycQHWrWND\nf+nrA6tWAV98wS4KJ05kzRTUmMIF2l69eiE2NhYffvgh+vbti6ZNm1bZW+9Ns4VdunTpjYXSu3fv\nyjzetm0bTp48idWrV6N5HXqDDxo0SOZxr169UFJSgm+++Qbh4eEKtwdOqmI+5YyMDPj5+SkcG5GX\nlZWlmR323N3ZT3Iyu7Wzeze79UOImtGIHGzalP3+9VdgxgxWWztyJL8xEa2n1rlVUSM7axZrk755\nM9C3L2vGk5PDhqhUw6ZIChdoJ0+eDAD4559/cOLECbn1HMdBJBLh9hs6uLi4uODo0aO1ft5Tp05h\nxYoViI6ORmBgoGJB16Bv3744duwY/vzzT7i4uCjtuOTtqbLDoVrw9gb++18gMJC19Zs9W+OGUSHC\nplE5OHo0a9ozciTrBLNyJd8RES0mmNzq0IHV1H7+OTs/ff45q8UdMwaYNIl1KlMTChdo6zqywOuM\njIzQpk2bWm175coVzJo1C6NHj0ZERIRSnr8CTQRBeDV8+Ks2tQAbv5YKtYSohrs7q6n96y+gvBz4\n4w/qjElIbVSclxYtYoXZLVuA0FDg4kXg/n029BfPhXSFC7SDBw9WRRzV+uuvvxAREQE/Pz8sWLDg\njdunpaWhtLQULVu2hH4tGjKfOnUKxsbGsLe3V0a4RIlevHiBxhXDjGgyOztgxQo225GHB6u1HTiQ\n76gI0cwcNDUFOnUCbtwAfH3Z6CPTp9OMfqReCTq3WrdmM/MtW8Yeb94MJCQAI0awWlueJjZR66qg\nrKwshIWFwdzcHCNHjsS1a9dw9epVXL16tdqJG0JDQ9G/f388efJEbl1QUBC2b9+O5ORknDlzBjEx\nMTh8+DCmTp0qvKFptECTJk34DqF+GRkBX30FREWxn6IiviMiWk6jc9DZGbh0iY1XO3Ag5RupVxqV\nW8uXs86XzZuzGlyOA65dA/Ly6jWMOk99Wx/+/PNPPHr0CAAwevRomXW2trY4ffq0Qsezs7PD1q1b\nIRaLwXEc2rVrhxUrVuD9999XWsxEecRiMaw1ZAaTWvPyYu37ZswA0tOBdu34johoMY3PwdatgZQU\n4OBB1snl6VOaiIHUC43LrRYtWGG2wvbtwNatwJAhQHg44Omp8hDUukDr6ekpN9LBm9RUyK2PySCI\n8ghiHnlVMDdnXwYAq6nt2BGIiKBboqTeaUUO6uuzmZNKS9kF5bBhwKefCmbsTSJMGp9bq1cDc+ey\nc9mWLaxAm5wMODoCKhqyTMPfUSJkgphDXtWmTWMDXQ8ZwqbOBYDHj4GYGIDuLBAV06oc1NdntbVX\nr7IRSNLS+I6IaDCtyK3mzYF581j7WgDYv5/dFRkzhuWaklGBlqgtsVjMdwj8c3BgvUjbtAF27mTL\n1q5lU+aWl/MbG9F4WpeDzZoBhw+zE66+PrWrJSqjdbkFsOG+/voL6NIFOH6cLfvhB9bURwnUuskB\n0W4mFVPyaTtDQzZbC8C+BA4eZG1rNf2WFeGdVuagjg67MwKw2ZE4jk3GoI3vBVEZrcwtgE3/XjEz\nK8cBR48CISFAnz4s73x86nxoOiMStaU2c12rk06dWM3shQt8R0K0gNbn4JdfsrGhO3dmTREIURKt\nzy2A9Qv56ivgwQM2hN6NG2z5jh1AZqbCh6MCLVFbL1++5DsE9WNlxRrWd+4M6OryHQ3RcFqfg6am\nrKf2okWsmQ8hSqL1uVVZo0ZAZCSroS0vZ83soqIUPgw1OSBqy9LSku8Q1JONDXDyJN9REC1AOfiv\nUaP4joBoGMqtaujqslpbjlN4V6qhJWrr+fPnfIdAiFajHCRENSi33qAOw1RSgZaoLT09uoFACJ8o\nBwlRDcot5aMCLVFbZmZmfIdAiFajHCRENSi3lI8KtERtZWdn8x0CIVqNcpAQ1aDcUj4q0BK1ZWNj\nw3cIhGg1ykFCVINyS/lEHFeHrmSkWmVlZXj8+DGaN29ObWQIUTHKN0LqD+UbUWdUoCWEEEIIIYJG\nTQ4IIYQQQoigUYGWEEIIIYQIGhVoCSGEEEKIoFGBlhBCCCGECBoVaAkhhBBCiKBRgZYQQgghhAga\nDSTHk4rx/Ah5GzQeZO1QvhFloZx7M8o3oiyK5BuNQ8uTAQMG4N69e3yHQQTu1KlTeOedd/gOQ+1R\nvhFloZx7M8o3oiyK5BtdZvJEX18fBgYG+PHHH/kORS1FRETgwYMH9P5Uo+L9ad68Od+hCILQ803o\n+SD0+AHKOUVQvvFP6K+hLvlGBVqeiEQi6Ojo0JV+NQwMDOj9qUHF+0O3PmtH6Pkm9HwQevwA5Zwi\nKN/4J/TXUJd8o05hPDI3N+c7BLVG70/N6P1RjNDfL4qff5rwGuqL0N8roccPCP81KBo/FWh5ZGFh\nwXcIao3en5rR+6MYob9fFD//NOE11Behv1dCjx8Q/mtQNH4q0BJCCCGEEEGjUQ4IIYQQQoigUQ0t\nIYQQQggRNCrQEkIIIYQQQaMCLSGEEEIIETQq0BJCCCGEEEGjAi0hhBBCCBE0KtASQgghhBBBowIt\nIYQQQggRNCrQEkIIIYQQQaMCLSGEEEIIETQq0BJCCCGEEEGjAi0hhBBCCBE0KtDWszt37iAkJASd\nOnWCl5cX1qxZg/Lycr7DUhtJSUlwcHCQ+1myZAnfofGipKQEq1atwpgxY+Dq6goHBwdkZGTIbbdr\n1y74+/vD2dkZgwcPxvnz53mIVv0IPd+Elg9C/7zWJv6QkJAq/yc3btzgKWr1IfR8+/HHHxEWFgYv\nLy+4urpi0KBB2LdvHyQSCd+h1YlEIsHQoUPh4OCAn3/+me9waqWgoADLli3De++9BycnJ/j5+eGb\nb76p1b56Ko6NVCIWizF+/Hi0atUK69evxz///IMVK1aA4zjMnDmT7/DUSnx8PJo2bSp9bGlpyWM0\n/CkqKsKePXvQqVMndO7cGSkpKXLbHDp0CEuWLEFkZCQ8PT2xZ88eREREIDExEe3bt+chavWgSfkm\nlHwQ+ue1NvEDQMeOHfHJJ5/ILGvTpk19hKi2NCHfduzYgebNm+Ojjz6Cubk5kpOTsWjRIqSlpWHO\nnDl8h6ewvXv3VnlBqa7KysoQFhaGtLQ0REVFoW3btnj8+DGysrJqdwCO1Jv4+HjO0dGRe/bsmXRZ\nbGws5+LiwuXn5/MYmfo4ePAgZ29vz6Wnp/MdilqQSCRceXk5x3Ect3///irfGz8/Py4yMlL6uLi4\nmPPy8uJmzZpVr7GqG03IN6Hlg9A/r7WJf8yYMdyYMWP4CE+taUK+ZWVlyS2bPHky5+zszBUXF/MQ\nUd1lZWVxHh4e3NatWzl7e3vu3LlzfIf0RgcOHOAcHBy4a9eu1Wl/anJQj1JSUuDm5iZTu9K3b18U\nFRXh8uXLPEZG1JVIJIKOTvVp+uDBA6Snp6NPnz7SZQYGBujdu3e1tUvagvKt/gn98/qm+En1NCHf\nLCws5Ja1bdsWxcXFyM3N5SGiuvv888/h7++PDh068B1Krf34449wdnaGi4tLnfanzK1H9+/fR6tW\nrQAAhYWFkEgkePfdd6XryCsjRoxAhw4d4OPjgzVr1qC0tJTvkNRSxeem4nNUUFAgfZydnS24L2Fl\n0qR805R80JTP682bN+Hu7g4nJycMHz5cMO0TVUmT8q2ya9euoWnTpmjSpAnfodTar7/+iuPHjyM6\nOprvUBRy584dODg4YM2aNejatSucnZ0RFhaGf/75p1b7U4G2HuXl5cHExASZmZno2bMnpk2bBjMz\nMwAQzBe5qjVt2hTR0dFYsWIFNm3ahD59+iAhIQEfffQR36GppYrPjampKbZs2YIuXbrg+PHj0s9V\nXl4en+HxShPyTdPyQRM+rx4eHli0aBESEhKwcuVKiEQiTJ48WW06tvFFE/LtdSdPnkRqaiomTpwo\nmJr78vJyfPrppwgPD1fbtvbVycnJwdmzZ5GcnIzPP/8cX3zxBR48eIDIyEiUlZW9cX/qFMYDfX19\nmJqaolGjRnyHona8vb3h7e0tfezl5QUDAwNs2bIFUVFRaNmyJY/RqTczMzMYGxvDyMhIsCcQVRBy\nvmlyPgj18xoVFSXzuHfv3ggMDMT69evRs2dPnqJSH0LOt8rS0tKwYMEC+Pj4YNy4cXyHU2s7d+5E\nQUEBxo8fz3coCuM4DtnZ2di/fz9sbGwAsIvfCRMm4OzZs/D3969xf2FccmgIMzMzFBYWolmzZkhJ\nScHy5cuRn58PAIJPflXq27cvAOD333/nORL107BhQwDs1u0HH3yA3377Dd7e3tLPVcV6baSp+Sbk\nfNDEz6uRkRG8vLxw69YtvkPhlSblW05ODsLDw2Fra4vVq1dDJBLxHVKtiMVixMXFYdq0aSgtLUVh\nYSGKiooAsCHpKv5WV6amprC1tZUWZgGgU6dOAFCrZgdUoK1HrVq1kvunvN6mjMgTypcJHyrarL3e\nRu3+/fswNzcX3IlEmTQ134ScD5r6eRXy/0RZNCXfioqKEBERAYlEgk2bNsHU1JTvkGrtyZMnKCws\nRExMDNzd3eHu7o7w8HAAwNSpUzFs2DCeI6xZTXecapNjVKCtR15eXrh69SrEYrF02YkTJ9CgQQN0\n6dKFx8jU26lTpyASieDs7Mx3KGrHzs4OLVq0wKlTp6TLSkpKcPbsWXh5efEYGf80Nd+EnA+a+Hkt\nLi5GSkpKnXtmawpNyLfy8nLMmDEDGRkZ2LJli6A6ggEsv3bt2iXz8/HHHwMA5s2bh5UrV/IcYc26\ndeuGzMxMPHz4ULrs6tWrANhoE2+iu3jx4sWqCo7Iatu2LQ4cOIBLly7BysoKycnJ2LBhA0JDQ+Hj\n48N3eGohNDQUT58+RX5+PtLS0rB7925s374dwcHBGDRoEN/h8eLcuXO4c+cOfvvtN9y8eROtWrVC\nZmYmXr58CSsrK5iZmSEhIQEcx4HjOKxZswa3bt1CbGys4DoFKJMm5JsQ80Hon9ea4k9LS8Onn36K\n0tJS5Obm4vr161iyZAn+/vtvxMbGytwq1TaakG+LFy/Gjz/+iPnz58PIyAiPHz+W/piamsLAwIDv\nEGukr68PGxsbmZ+ioiIcOnQIoaGhan9h0bp1ayQmJuL8+fOwtLTEnTt3sGzZMlhbW2P+/PlvrKUV\ncRzH1VOsBGxYis8++wzXr19Hw4YNMXToUERFRUFPj/rnAcDSpUtx9uxZZGVloaysDC1btsQHH3yA\nsWPHCqaXqbL5+voiMzNTbvngwYMRGxsLgE0l+s033+DJkydo27Yt5syZQx1UIPx8E2I+CP3zWlP8\nERERWLx4Me7evYv8/HwYGBjAzc0NUVFRcHV15SFa9SL0fKvufw+wWcQ8PT3rOaK3d+nSJYwdOxab\nNm3Ce++9x3c4b3Tz5k2sWLECN27cgI6ODnr06IGPPvqoVheLVKAlhBBCCCGCpp6X+IQQQgghhNQS\nFWgJIYQQQoigUYGWEEIIIYQIGhVoCSGEEEKIoFGBlhBCCCGECBoVaAkhhBBCiKBRgZao1Lp16+Dg\n4ICMjAy+Q1GJFy9eYNGiRfDy8oKDg4NGv1YiDJRzhNQfyjf1QQVaDRcSEgIHBwdER0fLLP/jjz/g\n4OCAdevW8RSZZkhISMC+ffsQFBSEFStWYOXKlbCwsOA7LMIjyjnVopwjlVG+qZaQ8k0Y03eQt3bi\nxAlkZWUJbm5qdXfx4kU4ODhgzpw5fIdC1AzlnGpQzpGqUL6phpDyjWpotYCxsTEMDAxw4MABvkPR\nOM+fP0ejRo34DoOoGco51aGcI6+jfFMdIeUbFWi1gIGBAQYNGoR9+/ZBIpFUu11ISAh8fX3lljs4\nOGD+/PnSx76+voiKikJISAhcXV3x5ZdfIjExEV27dkVAQABu374td4y0tDSMGzcOLi4u8PHxwfr1\n61HVrMsXL17E2LFj4ebmBldXV4SEhODKlSs1xpuVlYWZM2fCw8MDbm5uGDVqFIqKimrz1siQSCRI\nSEhAQEAAnJyc4OXlhUWLFiE3N1dmu6SkJJm2RKmpqdLH6ty+iNQfyrnaoZwjykD5Vjuanm9UoNUS\nwcHByMzMRHJyslKOd+7cOfTu3Rv29vbYuHEj9u/fjxkzZiA3NxerV6+W237WrFmwtLTEvHnz0L59\ne8TFxcm1bfrhhx8wYcIEFBYWYvr06ZgxYwZycnIwduxY3Lx5s8o4JBIJJk2ahPz8fEybNg1TpkyB\nRCJBSUmJwq9p+fLlWLVqFezs7BATE4M+ffogMTEREyZMQFlZmXS7rl27YuXKlVi5ciXMzc3RunVr\n6WN1bl9E6hfl3JtRzhFloXx7M03PN2pDqyXat28PNzc37NmzBz4+Pm99vC5dumDChAmwsrLCzJkz\nMWbMGAQFBeHu3bs4ffq03PbdunXDqlWrAACjRo3C6NGjsXnzZowfPx5mZmYoKCjAkiVL0KlTJ+za\ntQu6uroAgGHDhqFPnz5Yt24dvv76a7njPnr0CL169cLixYulyyZOnKjw63ny5Am+/fZb9OzZE19/\n/TVEIhEAoGnTpli7di1++uknBAYGAgBatGiBFi1aAADWrl0LS0tLBAUFKfycRLNRztWMco4oE+Vb\nzbQh36iGVouMHDkS586dw8OHD9/6WBVXaI0bNwYAWFpaAgDMzc2Rk5Mjt/37778v/VskEiEwMBDF\nxcX47bffAADnz59HXl4egoKCkJubC7FYDLFYjJKSEjg6OiI1NbXaWCIjI2Uei0QiabLW1i+//AKJ\nRIKhQ4fK7Dts2DAAwIULFxQ6HiEA5VxNKOeIslG+VU8b8o0KtFrkP//5Dxo2bIh9+/a99bH09PSq\n/V1aWiq3vbW1tcxjGxsbAMDTp08BAA8ePAAALF68GN27d5f5SU5OxosXL6psM2RmZgYrK6u3fj2P\nHz8GANja2sosb9asGQwMDKTrCVEE5Vz1KOeIslG+VU8b8o2aHGgRAwMDDBkyBAcPHkRAQECt9ikv\nL1dpTIaGhjKPFy5ciNatW1e5rb6+vtyyhg0bqiQuQpSBco6Q+kP5pt2oQKtlgoODsXXrVpw8eVJu\nnb6+Pl6+fCmzrOLq8m09fPgQ9vb2Mo+BV1exdnZ2ANjVaI8ePZTynIpo3rw5ACAzMxOurq7S5U+f\nPkVJSYl0PSGKopyrGuUcUQXKt6ppQ75RkwMtY2dnhx49eiAxMVFunZWVFbKzs2WG5Dh8+LBSnve7\n776T/s1xHI4cOYLGjRvD0dERANCzZ0+YmZlhy5YtVd52yczMVEoc1enWrRt0dHRw8OBBmaFWKsY1\n5OMLiGgGyrmqUc4RVaB8q5o25BvV0GqhkSNHYtq0aXLL/f39kZSUhMjIWp1e8AAAD9JJREFUSAwZ\nMgQZGRn49ddflfKcv/zyC2bNmgV3d3ckJyfj119/xaxZs2BkZAQAMDU1xSeffIK5c+di0KBBCAoK\ngqWlJdLT05GcnAw7OzvExcUpJZaqWFlZYcyYMdixYwcmT54MHx8f/Pnnn9i7dy+cnJxqffuKkKpQ\nzsmjnCOqQvkmTxvyjQq0Wqh3796wsrLCkydPZJb7+flh7ty5+PbbbxEXFwdPT0989dVX6NWr11s/\n5xdffIGNGzciNjYW5ubmmDFjBiZNmiSzzcCBA2FlZYWEhARs27YNRUVFaN68Odzd3TF8+PC3juFN\nYmJi0LRpUxw4cAAXLlxAo0aNMHz4cMycOVPaIYCQuqCcqxrlHFEFyreqaXq+ibiqprIghBBCCCFE\nIKgNLSGEEEIIETQq0BJCCCGEEEGjAi0hhBBCCBE0KtASQgghhBBBowItIYQQQggRNCrQEkIIIYQQ\nQaMCLREUX19fhISE8B0GIQpJSkqCg4MDLl26VO/PvW7dOjg4OMjMjkQIIZqGCrSEV5cuXcK6deuQ\nl5fHdyiEkH9t27YNSUlJfIdBiNoICQmBp6en3PKKi9UzZ84gIyMDDg4O0h9HR0f4+/sjOjoaf/75\np9y+vr6+GDJkSH2ErxWEPzUEEbTU1FTEx8dj8ODBaNiw4Ru3P3bsWD1ERYh227FjB2xtbelkS0gd\n+Pj4IDAwEGVlZbh37x727duHkydPYteuXXBxceE7PI1FBVoiKAYGBnyHQAghhFSrTZs2CAoKkj7u\n3Lkzpk2bhoSEBMTHx/MYmWajJgdaquLWyIoVK+Dr64vu3bvj5MmTWLRoEVxdXTF27FiZZgC///47\nIiIi0LVrV7i4uGDYsGE4e/Zslcdct24dtm/fDl9fX7i5uWHChAl49OiRzLYVt2QqktvPz0+67PU2\nsseOHZO5jVNTG9qcnBwsWrQIXl5ecHJyQkBAABISEiCRSGS2mz9/PhwcHJCeno6JEyfCzc0Nvr6+\n2LFjR5XH3bdvHwYNGgQ3Nzd06dIFQ4cOxd69e9/4PhNSWVFRERYvXgxPT09069YNixYtQnFxsXT9\ntWvXMHfuXPTp0wcuLi7w8PBAeHg4bty4IXes8vJyfP311+jXrx86deoET09PjBo1CsePH6/yubOz\nsxEdHQ13d3d4e3tjzZo1MnlRcevUwcEBmZmZSE1Nlcm7yk0QJBIJNmzYgODgYHTr1g3Ozs4ICAhA\nfHw8SkpK5J770KFDCAgIgLOzM4YMGYIbN27AwcEB8+fPl9uWco1omp49ewJAlc0Oais9PR2zZs2C\nt7c3nJ2d4ePjg5kzZ8qdW7UZ1dBquZMnT2LChAlYv349oqOj0bt3b4SHhyMuLg779u3DpEmTcOnS\nJUyaNAnW1tYIDw+HkZERjhw5gsjISKxfvx6+vr4yxzxy5AhMTU0xfvx4PHz4ENu3b8eHH36IxMRE\n6TYrV64EAJw4cQInTpxATEwMzM3NAQCWlpYyx3NxcZFuv3z58mpfS1lZGcLCwnDr1i0EBwejbdu2\nOHfuHFatWoVnz55hwYIFcvtMmTIFXl5e6N27Nw4cOIBly5bBzs4OPj4+0m2SkpKwaNEi9OjRA8HB\nwZBIJLh9+zbOnDmD4OBgBd9xos2WL1+O9u3bY/r06bh48SL27dsHMzMzzJkzBwDLh2vXriEwMBDv\nvPMOxGIx9u/fjzFjxiApKQlt2rSRHmvDhg2Ij49Hv379MH78eLx8+RLXr19HcnIy+vbtK/fcc+fO\nRdeuXTF79mwcO3YMGzduhJWVFUaNGgUA6Nq1q0yemZubIyIiQrq/u7u79O/S0lJs2LAB/v7+CAwM\nhIGBAa5cuYL4+HhkZGQgNjZWuu2pU6cwf/58ODo6Ys6cOUhPT8fUqVOrfH8o14gmqih0NmnSpE77\nl5aWIiwsDGKxGCEhIbCxscGTJ09w+vRppKWlwdraWpnhChdHtFJ6ejpnb2/PffXVVxzHcdzChQs5\ne3t77tGjRxzHcVzfvn25mTNncuXl5Vzfvn05f39/rqCgQLp/WVkZ179/fy4wMFDumD179uSKioqk\ny2NiYmSOXVlcXBxnb2/Ppaen1yru3r17c2PGjKly3eHDhzl7e3tuw4YN0mUSiYQLDQ3lOnTowD1+\n/Fi6fN68eZy9vT23bds26bKMjAzOwcGB++ijj2SOO3XqVM7JyYkrKSmRWV5aWlqrmAk5ePAgZ29v\nz02ZMkW6TCKRcP7+/py/v7902ePHjzmJRCKzb1paGmdvb88tXbpUZvn777/P+fn5yT3X65/Lihxb\ntmyZdFlhYSHn5ubGjRs3rsp4a8ozjuO48vJymXyqMG/ePK5Dhw5cVlaWdNmQIUO4Hj16yHx/rF69\nmrO3t+fmzZsnsz/lGlFXY8aM4Tw8POSWV+T26dOnpefAxYsXc1lZWdzTp0+5//u//+NGjBjB2dvb\nc4cOHZLZt3fv3tzgwYPf+Ny3b9/m7O3tufj4eLl1lBuvUJMDLVdxxVhRO1r5cU5ODm7duoX79++j\nf//+KC4uhlgshlgsRm5uLjp37ox79+5BLBbLHLNXr14wNDSUPnZ0dAQAPHz4UKWv5cKFCwCAYcOG\nSZeJRCIMHToU5eXl+OWXX+T26devn/RvW1tbNG7cWO4WjomJCUpKSnD58mWZ5Xp6dIODKCYgIED6\nt0gkQocOHWQ+b1ZWVhCJRACAgoICiMVimJiYwMLCAvfv35c5lomJCbKysnD37l2Z5dV9Lis/t7Gx\nMVq1alXn25U6OjqwsrICwGqPcnJyIBaL0a5dO5SXlyM9PR0AawJ08+ZN9OnTByYmJtL9Bw0aVOVx\nKdeIJti9eze6d+8OLy8vjB49GoWFhVi5ciXef//9Oh2vIneuXbuGwsJCmXWUG6/QO6HldHV1AbxK\nCn19fenvkpISpKWlAQA2btyIjRs3VnmM7OxsWFhYSB83bdpUZr2RkREAduJTpcePH8PAwEDu+W1t\nbQEAT548kdvn9W2NjY3l4gwPD8cvv/yC0NBQ2NrawtXVFV5eXhg4cKD0/SKkNt70eXv+/DlWrVqF\nU6dOITc3V2bbym1tAWD69OmYOnUqBg0ahNatW6NTp07o1asXAgICpIXiypo1ayb33NnZ2XV+LYcP\nH8bmzZvxxx9/oLy8vMpYKy5ibWxsZNZXd4uUco0IVeWc69evH0aMGIGXL1/i6NGjOHnypPQ8WBct\nWrTA+PHjsXXrVnTv3h3Ozs5wc3NDUFAQ2rVrp4zwNQIVaEmtREREVDkGHyB/ctLR4a/iv6oTeU1q\nE2ubNm1w7NgxnD9/HqmpqTh37hyOHDmCI0eOYMuWLXUNlWihmj5vEokEEyZMwN9//42xY8eiU6dO\nMDMzAwDMnDlTbvuuXbvi5MmT+Pnnn3H58mWcOXMGhw4dwgcffIClS5fKba9obtTkp59+wqxZs+Ds\n7IzFixejefPm0NPTw88//4ytW7eC47ga969uPeUaUVdvOldUXm9jY4MePXoAYB2eJ06ciHnz5sHN\nzU3uora25s+fj+HDhyM5ORmpqanYtm0btm3bhs2bN6Nbt251OqamoQItqVHLli0BAIaGhtIEVSZl\nnmSbN2+O4uJiPHv2TOZLIzMzEwCkt0jrwsjICP7+/vD390dMTAxiYmJw6NAh/Pnnn2jbtu1bx07I\nH3/8gbt372LKlCmYPn26dHlJSUm1E480bNgQAwYMwIABA7BgwQJMmjQJiYmJiImJgbGxscpi/f77\n72FoaIgdO3bIPE9qaqrMdhU1s6/PUlZT8yPKNaKOzMzM5O5EAK/uPJqamla77+zZsxEUFIT169dj\n8eLFdY6hTZs2aNOmDUJDQ/HXX38hKCgIe/bsoQLtv6gNLalRx44dYWdnh927d1d5e7KisFhXFW2D\nnj179lbHASAtcB84cEC6jOM4JCUlQVdXt85J/3obYZFIhHfeeQeA6ptREO1RUcNT0Qyowq5du6o8\nkb7+udTX14e1tTU4jnvrz6WJiQmePn1a7XpdXV3o6OjIxFpQUCA3u1jjxo3h5OSE48ePo6CgQLr8\n+++/r/K4lGtEXbVt2xb5+fm4d++ezPKrV69CV1cXrVq1qnbf9u3bw8vLCwcPHqyy6dub5Ofny33+\nbW1toaenR3lRCdXQkhrp6Ohg6dKlmDRpEgYOHIhhw4bBxsYGDx8+xKVLl1BSUoKDBw/W+fidO3cG\nwIYJCg0NhampKRo3biwzm8p3330n/fvFixd4/vy5dJmJiQn8/f0BsE4vTk5OiIuLw9OnT6XDdp0/\nfx5jx46tcw1tWFgYGjdujC5duqBZs2b4+++/sWvXLtjb28PBwaGuL50QGa1bt0abNm2wZcsWlJaW\nwtraGlevXkVKSoq002Zl/fv3h7u7O1xdXWFubo6bN2/if//7H3x8fNCoUaO3iqVz587Ys2cPYmNj\n4eHhAT09PTg4OEhzqE+fPvjpp58QFhaGwMBA5OXlITExEY0aNZI7YU+ZMgVTpkzBmDFjMHjwYGRk\nZODMmTNVPi/lGlFXI0aMwJ49ezBp0iSMHj0a5ubm+PXXX3Ho0CEMHz4c5ubmch22Kps4cSJSUlKQ\nkJCAhQsXSpc/e/YMGzZsqHKfyMhIiEQiXLp0CZ988gkCAgLQpk0bcByH77//Hi9fvsTAgQOV/lqF\nigq05I08PT2xf/9+bNiwAXv37kVBQQGaNWsGZ2dnmdlQ6sLFxQUff/wxduzYgdmzZ6O8vBweHh7Y\nuXOndJu5c+fK7JOdnS1dZmtrKy3Q6unpYcuWLVi9ejWOHz+OxMREWFtbY+bMmZg0aVKdYwwODsbh\nw4exc+dOFBYWolmzZvjggw8QGRnJa3tholn09PTw9ddfIzY2Fnv37kVpaSk6d+6M7du3y4wHWyE0\nNBRnzpzB5s2bUVRUBGtra0yePBkTJ05861hmzJiB/Px8HDp0CNu2bQPHcVi+fLl0KtyBAwciLy8P\nO3bswH//+19YWVkhODgYlpaWmDdvnsyx/Pz8EBsbiw0bNuCLL75Ahw4dEBcXh8GDB8vVRlOuEXVl\nbW2N3bt3Y82aNdi5cyfy8vLQokULREdHIyws7I37d+/eHR07dkRiYiImT54s7aT59OlTrF27tsp9\nwsPDpReTvXv3RkpKCg4ePIgGDRqgTZs2WL9+vfT8RwAR96bW+4QQQogSPX36FN7e3pg0aRJmz57N\ndziEEA1Al7yEEEJUpqSkBGVlZTLLKpocuLm58RESIUQDUZMDQgghKnP//n1MmjQJ/fv3x7vvvosH\nDx7g22+/haOjo8wU04QQ8jaoQEsIIURlmjRpgk6dOuHIkSMQi8XSocZmz55NsxwRQpSG2tASQggh\nhBBBoza0hBBCCCFE0KhASwghhBBCBI0KtIQQQgghRNCoQEsIIYQQQgSNCrSEEEIIIUTQ/h9yYMcI\nfwef9wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f35408a9780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "with sns.axes_style(rc={\"font.family\": \"monospace\"}):\n",
    "    fig, ax = plt.subplots(3,3, sharex=\"col\", figsize=(10,8))\n",
    "    for i, (grouping, title, clip, axi) in enumerate(zip(\n",
    "        [\n",
    "            \"len_user_mentions\",\n",
    "            \"len_hashtags\",\n",
    "            \"len_urls\",\n",
    "        ],\n",
    "        [\n",
    "            \"mentions\",\n",
    "            \"hashtags\",\n",
    "            \"URLs\"\n",
    "        ],\n",
    "        [(0, 10), (0, 15), None],\n",
    "        ax.T\n",
    "    )):\n",
    "        print(grouping, title)\n",
    "        add_ylabel=True\n",
    "        if i > 0:\n",
    "            add_ylabel=False\n",
    "        plot_log_odds_ratio_tweet(\n",
    "            df_sentiment,\n",
    "            grouping,\n",
    "            \"Number of\\n{}\".format(title),\n",
    "            axi, clip=clip,\n",
    "            add_ylabel=add_ylabel)\n",
    "\n",
    "    sns.despine(offset=10)\n",
    "    fig.tight_layout(h_pad=0., w_pad=0.01)\n",
    "    plt.savefig(\"figures/Tweet_odds_timeline.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}