{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from scipy import stats\n",
    "import statsmodels.stats.multitest as multi\n",
    "from sklearn.decomposition import PCA\n",
    "import pickle\n",
    "\n",
    "sns.set_style('white')\n",
    "%matplotlib inline\n",
    "\n",
    "mpl.rcParams['xtick.labelsize'] = 16\n",
    "mpl.rcParams['ytick.labelsize'] = 12\n",
    "mpl.rcParams['font.weight'] = 'bold'\n",
    "\n",
    "mpl.rcParams['pdf.fonttype'] = 42\n",
    "mpl.rcParams['ps.fonttype'] = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load in processed data from Cedars_Data_Processing.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rel_sort_list = pickle.load(open('pickle_files/rel_sort_lod_list.pkl', 'rb'))\n",
    "df_abs_sort_list = pickle.load(open('pickle_files/abs_sort_lod_list.pkl', 'rb'))\n",
    "\n",
    "df_pseudo_rel_sort_list = pickle.load(open('pickle_files/pseudo_rel_sort_lod_list.pkl', 'rb'))\n",
    "df_pseudo_abs_sort_list = pickle.load(open('pickle_files/pseudo_abs_sort_lod_list.pkl', 'rb'))\n",
    "\n",
    "df_col_names_list = pickle.load(open('pickle_files/col_names_lod_list.pkl', 'rb'))\n",
    "df_metadata = pickle.load(open('pickle_files/metadata_all.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Color palette\n",
    "pal = sns.cubehelix_palette(20, rot=-.25, light=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_important_features(PC1, PC2, components_, columns):\n",
    "    \"\"\"\n",
    "    This function will return the most \"important\" \n",
    "    features so we can determine which have the most\n",
    "    effect on PCA\n",
    "    \"\"\"\n",
    "    num_columns = len(columns)\n",
    "\n",
    "    # Scale the principal components by the max value in\n",
    "    # the transformed set belonging to that component\n",
    "    xvector = components_[0] * max(PC1)\n",
    "    yvector = components_[1] * max(PC2)\n",
    "\n",
    "    # Sort each column by it's length.\n",
    "    important_features = { columns[i] : (xvector[i], yvector[i], np.sqrt(xvector[i]**2 + yvector[i]**2)) for i in range(num_columns) }\n",
    "    \n",
    "    return important_features\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA on log transformed values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform log transform on pseudocount values\n",
    "\n",
    "df_pseudo_abs_log_list = [None]*6\n",
    "\n",
    "for i in range(6):\n",
    "    df_pseudo_abs_log_list[i] = np.log10(df_pseudo_abs_sort_list[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "      <th>PC5</th>\n",
       "      <th>PC6</th>\n",
       "      <th>PC7</th>\n",
       "      <th>PC8</th>\n",
       "      <th>PC9</th>\n",
       "      <th>PC10</th>\n",
       "      <th>...</th>\n",
       "      <th>Prevotella</th>\n",
       "      <th>disruptor count</th>\n",
       "      <th>disruptor sum</th>\n",
       "      <th>disruptor percent</th>\n",
       "      <th>Lacto sum</th>\n",
       "      <th>Log IL8</th>\n",
       "      <th>all symptom count (50th)</th>\n",
       "      <th>observed_ASV</th>\n",
       "      <th>Shannon_Diversity</th>\n",
       "      <th>Evenness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.309357</td>\n",
       "      <td>-2.207728</td>\n",
       "      <td>-1.367284</td>\n",
       "      <td>-0.673843</td>\n",
       "      <td>1.373674</td>\n",
       "      <td>-1.200488</td>\n",
       "      <td>1.303062</td>\n",
       "      <td>0.836120</td>\n",
       "      <td>-0.185666</td>\n",
       "      <td>-0.586185</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.064750e-04</td>\n",
       "      <td>1.782660</td>\n",
       "      <td>0.595574</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67</td>\n",
       "      <td>2.649995</td>\n",
       "      <td>0.630247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.305143</td>\n",
       "      <td>-2.375203</td>\n",
       "      <td>4.171164</td>\n",
       "      <td>2.663148</td>\n",
       "      <td>1.211241</td>\n",
       "      <td>-1.595946</td>\n",
       "      <td>0.944059</td>\n",
       "      <td>-1.162879</td>\n",
       "      <td>0.408286</td>\n",
       "      <td>-0.280888</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.604938e-04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.745345</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28</td>\n",
       "      <td>2.372844</td>\n",
       "      <td>0.712094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.635186</td>\n",
       "      <td>4.608403</td>\n",
       "      <td>-0.263369</td>\n",
       "      <td>-1.829603</td>\n",
       "      <td>2.000261</td>\n",
       "      <td>0.657961</td>\n",
       "      <td>0.552824</td>\n",
       "      <td>0.475250</td>\n",
       "      <td>-0.540894</td>\n",
       "      <td>1.476518</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6.075084</td>\n",
       "      <td>9.781659e+01</td>\n",
       "      <td>3.499769</td>\n",
       "      <td>1.866092</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0.264972</td>\n",
       "      <td>0.103305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.649551</td>\n",
       "      <td>3.381998</td>\n",
       "      <td>-0.959204</td>\n",
       "      <td>-2.162824</td>\n",
       "      <td>2.542481</td>\n",
       "      <td>-0.737162</td>\n",
       "      <td>-0.641858</td>\n",
       "      <td>0.537998</td>\n",
       "      <td>1.165477</td>\n",
       "      <td>1.803064</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6.848472</td>\n",
       "      <td>6.695237e+01</td>\n",
       "      <td>6.123787</td>\n",
       "      <td>1.652414</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18</td>\n",
       "      <td>1.474521</td>\n",
       "      <td>0.510149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.946728</td>\n",
       "      <td>-0.298982</td>\n",
       "      <td>-1.413457</td>\n",
       "      <td>0.234601</td>\n",
       "      <td>-0.266377</td>\n",
       "      <td>-0.773524</td>\n",
       "      <td>-0.656356</td>\n",
       "      <td>-0.309244</td>\n",
       "      <td>-0.346123</td>\n",
       "      <td>-0.497327</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.736910</td>\n",
       "      <td>7.505051e-02</td>\n",
       "      <td>3.234272</td>\n",
       "      <td>1.171636</td>\n",
       "      <td>4.0</td>\n",
       "      <td>62</td>\n",
       "      <td>2.230434</td>\n",
       "      <td>0.540432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2.802839</td>\n",
       "      <td>-0.671422</td>\n",
       "      <td>0.143438</td>\n",
       "      <td>-1.252455</td>\n",
       "      <td>-0.748153</td>\n",
       "      <td>0.266413</td>\n",
       "      <td>0.634768</td>\n",
       "      <td>0.364633</td>\n",
       "      <td>2.123027</td>\n",
       "      <td>2.807976</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.400000e-04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>30</td>\n",
       "      <td>2.227956</td>\n",
       "      <td>0.655050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>-2.532180</td>\n",
       "      <td>0.363125</td>\n",
       "      <td>-0.284730</td>\n",
       "      <td>1.232586</td>\n",
       "      <td>0.476035</td>\n",
       "      <td>0.005479</td>\n",
       "      <td>-0.598495</td>\n",
       "      <td>0.051585</td>\n",
       "      <td>-0.467293</td>\n",
       "      <td>0.309490</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.860262e-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>39</td>\n",
       "      <td>1.667908</td>\n",
       "      <td>0.455270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>-2.141835</td>\n",
       "      <td>-0.860352</td>\n",
       "      <td>-1.577201</td>\n",
       "      <td>-0.019433</td>\n",
       "      <td>0.021337</td>\n",
       "      <td>0.341237</td>\n",
       "      <td>-0.019915</td>\n",
       "      <td>-0.305078</td>\n",
       "      <td>-0.298536</td>\n",
       "      <td>0.218310</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.366197e-05</td>\n",
       "      <td>3.330958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>62</td>\n",
       "      <td>2.728353</td>\n",
       "      <td>0.661077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>1.374000</td>\n",
       "      <td>2.314311</td>\n",
       "      <td>-1.760103</td>\n",
       "      <td>-0.408404</td>\n",
       "      <td>0.869878</td>\n",
       "      <td>-2.712318</td>\n",
       "      <td>-1.932925</td>\n",
       "      <td>-0.011212</td>\n",
       "      <td>0.083271</td>\n",
       "      <td>-0.972462</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.187285e-04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>36</td>\n",
       "      <td>2.073629</td>\n",
       "      <td>0.578657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>-7.390179</td>\n",
       "      <td>3.850819</td>\n",
       "      <td>2.337280</td>\n",
       "      <td>1.831659</td>\n",
       "      <td>0.187810</td>\n",
       "      <td>-1.290594</td>\n",
       "      <td>-0.460629</td>\n",
       "      <td>-0.649413</td>\n",
       "      <td>0.654366</td>\n",
       "      <td>0.680011</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.082616e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>38</td>\n",
       "      <td>1.231268</td>\n",
       "      <td>0.338485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          PC1       PC2       PC3       PC4       PC5       PC6       PC7  \\\n",
       "0    1.309357 -2.207728 -1.367284 -0.673843  1.373674 -1.200488  1.303062   \n",
       "1    4.305143 -2.375203  4.171164  2.663148  1.211241 -1.595946  0.944059   \n",
       "2    3.635186  4.608403 -0.263369 -1.829603  2.000261  0.657961  0.552824   \n",
       "3   -0.649551  3.381998 -0.959204 -2.162824  2.542481 -0.737162 -0.641858   \n",
       "4    0.946728 -0.298982 -1.413457  0.234601 -0.266377 -0.773524 -0.656356   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "245  2.802839 -0.671422  0.143438 -1.252455 -0.748153  0.266413  0.634768   \n",
       "246 -2.532180  0.363125 -0.284730  1.232586  0.476035  0.005479 -0.598495   \n",
       "247 -2.141835 -0.860352 -1.577201 -0.019433  0.021337  0.341237 -0.019915   \n",
       "248  1.374000  2.314311 -1.760103 -0.408404  0.869878 -2.712318 -1.932925   \n",
       "249 -7.390179  3.850819  2.337280  1.831659  0.187810 -1.290594 -0.460629   \n",
       "\n",
       "          PC8       PC9      PC10  ...  Prevotella disruptor count  \\\n",
       "0    0.836120 -0.185666 -0.586185  ...           0               0   \n",
       "1   -1.162879  0.408286 -0.280888  ...           0               0   \n",
       "2    0.475250 -0.540894  1.476518  ...           0               2   \n",
       "3    0.537998  1.165477  1.803064  ...           0               2   \n",
       "4   -0.309244 -0.346123 -0.497327  ...           0               0   \n",
       "..        ...       ...       ...  ...         ...             ...   \n",
       "245  0.364633  2.123027  2.807976  ...           1               0   \n",
       "246  0.051585 -0.467293  0.309490  ...           0               0   \n",
       "247 -0.305078 -0.298536  0.218310  ...           1               0   \n",
       "248 -0.011212  0.083271 -0.972462  ...           0               0   \n",
       "249 -0.649413  0.654366  0.680011  ...           0               0   \n",
       "\n",
       "     disruptor sum  disruptor percent  Lacto sum   Log IL8  \\\n",
       "0         0.000000       4.064750e-04   1.782660  0.595574   \n",
       "1         0.000000       8.604938e-04   0.000000  1.745345   \n",
       "2         6.075084       9.781659e+01   3.499769  1.866092   \n",
       "3         6.848472       6.695237e+01   6.123787  1.652414   \n",
       "4         2.736910       7.505051e-02   3.234272  1.171636   \n",
       "..             ...                ...        ...       ...   \n",
       "245       0.000000       1.400000e-04   0.000000       NaN   \n",
       "246       0.000000       7.860262e-06   0.000000       NaN   \n",
       "247       0.000000       2.366197e-05   3.330958       NaN   \n",
       "248       0.000000       1.187285e-04   0.000000       NaN   \n",
       "249       0.000000       2.082616e-07   0.000000       NaN   \n",
       "\n",
       "    all symptom count (50th)  observed_ASV  Shannon_Diversity  Evenness  \n",
       "0                        0.0            67           2.649995  0.630247  \n",
       "1                        3.0            28           2.372844  0.712094  \n",
       "2                        6.0            13           0.264972  0.103305  \n",
       "3                        2.0            18           1.474521  0.510149  \n",
       "4                        4.0            62           2.230434  0.540432  \n",
       "..                       ...           ...                ...       ...  \n",
       "245                      6.0            30           2.227956  0.655050  \n",
       "246                      6.0            39           1.667908  0.455270  \n",
       "247                      6.0            62           2.728353  0.661077  \n",
       "248                      6.0            36           2.073629  0.578657  \n",
       "249                      6.0            38           1.231268  0.338485  \n",
       "\n",
       "[250 rows x 101 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate top 10 principal components for each of the taxonomic levels\n",
    "pca_list = [PCA(n_components=10), PCA(n_components=10), PCA(n_components=10), PCA(n_components=10), PCA(n_components=10), PCA(n_components=10)]\n",
    "df_pca_list = [None]*6\n",
    "for i in range(6):\n",
    "    df_pca_list[i] = pd.DataFrame(pca_list[i].fit_transform(df_pseudo_abs_log_list[i]), columns=['PC1', 'PC2', 'PC3', 'PC4', 'PC5', \n",
    "                                                                                                 'PC6', 'PC7', 'PC8', 'PC9', 'PC10'])\n",
    "    df_pca_list[i]['Sample'] = df_pseudo_abs_log_list[i].index.tolist()\n",
    "    \n",
    "    #Merge with metadata\n",
    "    df_pca_list[i] = df_pca_list[i].merge(df_metadata, left_on='Sample', right_index=True)\n",
    "df_pca_list[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1 Loading</th>\n",
       "      <th>PC2 Loading</th>\n",
       "      <th>Magnitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ASV255</th>\n",
       "      <td>-3.311846</td>\n",
       "      <td>3.295403</td>\n",
       "      <td>4.672044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV247</th>\n",
       "      <td>-3.434600</td>\n",
       "      <td>2.924376</td>\n",
       "      <td>4.510926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV71</th>\n",
       "      <td>-2.068507</td>\n",
       "      <td>2.586753</td>\n",
       "      <td>3.312101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV81</th>\n",
       "      <td>-3.177959</td>\n",
       "      <td>1.795362</td>\n",
       "      <td>3.650033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV70</th>\n",
       "      <td>-3.236772</td>\n",
       "      <td>1.239488</td>\n",
       "      <td>3.465981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV219</th>\n",
       "      <td>-2.854642</td>\n",
       "      <td>0.968210</td>\n",
       "      <td>3.014367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV63</th>\n",
       "      <td>-2.647063</td>\n",
       "      <td>0.960566</td>\n",
       "      <td>2.815960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV142</th>\n",
       "      <td>-3.036108</td>\n",
       "      <td>0.928167</td>\n",
       "      <td>3.174814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV267</th>\n",
       "      <td>-2.299128</td>\n",
       "      <td>0.865654</td>\n",
       "      <td>2.456694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV2</th>\n",
       "      <td>-3.126966</td>\n",
       "      <td>0.853748</td>\n",
       "      <td>3.241420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PC1 Loading  PC2 Loading  Magnitude\n",
       "ASV255    -3.311846     3.295403   4.672044\n",
       "ASV247    -3.434600     2.924376   4.510926\n",
       "ASV71     -2.068507     2.586753   3.312101\n",
       "ASV81     -3.177959     1.795362   3.650033\n",
       "ASV70     -3.236772     1.239488   3.465981\n",
       "ASV219    -2.854642     0.968210   3.014367\n",
       "ASV63     -2.647063     0.960566   2.815960\n",
       "ASV142    -3.036108     0.928167   3.174814\n",
       "ASV267    -2.299128     0.865654   2.456694\n",
       "ASV2      -3.126966     0.853748   3.241420"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features_list = [None]*6\n",
    "\n",
    "# Calculate the feature loadings for the first two principal components\n",
    "for i in range(6):\n",
    "    df_features_list[i] = pd.DataFrame.from_dict(data=get_important_features(np.array(df_pca_list[i]['PC1']), \n",
    "                                                                             np.array(df_pca_list[i]['PC2']), \n",
    "                                                                             pca_list[i].components_[0:2], \n",
    "                                                                             df_pseudo_abs_log_list[i].columns.values), \n",
    "                                                 orient='index', columns=['PC1 Loading', 'PC2 Loading', 'Magnitude'])\n",
    "\n",
    "df_features_list[4].sort_values('PC2 Loading', ascending=False)[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the feature loadings for PC2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['ytick.labelsize'] = 16\n",
    "sns.set_style(\"ticks\", {\"xtick.major.size\": 8, \"ytick.major.size\": 8})\n",
    "\n",
    "fig = plt.figure(figsize=(7,6))\n",
    "ax1 = fig.add_subplot(111)\n",
    "        \n",
    "_df = df_features_list[4].sort_values('PC2 Loading', ascending=True)\n",
    "clrs = [pal[9] if (x > _df['PC2 Loading'].tolist()[-6]) else pal[0] if (x < _df['PC2 Loading'].tolist()[5]) else 'grey' for x in _df['PC2 Loading'].tolist()]\n",
    "\n",
    "sns.barplot(ax=ax1, x=_df.index, y='PC2 Loading', data=_df, palette=clrs, edgecolor=clrs)\n",
    "ax1.set_xticklabels('')\n",
    "ax1.set_ylabel('PC2 Loading', fontsize=16, fontweight='bold')\n",
    "ax1.set_xlabel('')\n",
    "ax1.set_ylim(-4,4)\n",
    "ax1.set_xticks([])\n",
    "        \n",
    "leg1 = ax1.legend(pd.DataFrame(_df.head(n=5)).merge(df_col_names_list[4], left_index=True, right_index=True)['label'],\n",
    "                                                             loc='upper right', fontsize=14, bbox_to_anchor=(0.6, 0.4))\n",
    "leg2 = ax1.legend(pd.DataFrame(_df.tail(n=5).iloc[::-1]).merge(df_col_names_list[4], left_index=True, right_index=True)['label'],\n",
    "                  loc='upper right', fontsize=14, bbox_to_anchor=(0.9, 1))\n",
    "\n",
    "ax1.add_artist(leg1)\n",
    "leg2.legendHandles[0].set_color(pal[9])\n",
    "leg2.legendHandles[1].set_color(pal[9])\n",
    "leg2.legendHandles[2].set_color(pal[9])\n",
    "leg2.legendHandles[3].set_color(pal[9])\n",
    "leg2.legendHandles[4].set_color(pal[9])\n",
    "\n",
    "leg1.legendHandles[0].set_color(pal[0])\n",
    "leg1.legendHandles[1].set_color(pal[0])\n",
    "leg1.legendHandles[2].set_color(pal[0])\n",
    "leg1.legendHandles[3].set_color(pal[0])\n",
    "leg1.legendHandles[4].set_color(pal[0])\n",
    "\n",
    "#fig.savefig('PC2_feature_genus.png', dpi=200, bbox_inches='tight')\n",
    "#fig.savefig('PC2_feature_genus.pdf', bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlate metadata categories with PC space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "continuous_var = ['Copies/mL', 'bloating_vas', 'excess_gas_vas', 'incomplete_evac_vas', \n",
    "            'constipation_vas', 'diarrhea_vas', 'urgency_vas', 'IFNY', 'GM-CSF', 'IL13', 'IL4',\n",
    "            'IL12P70', 'IL5', 'IL6', 'IL10', 'TNFA', 'IL8', 'IL2', 'IL1B', 'MCP1', 'Age (years)', 'weight (lbs)', 'anaerobic_percent',\n",
    "            'bloating>50th', 'excess_gas>50th', 'incomplete_evac>50th', \n",
    "            'constipation>50th', 'diarrhea>50th', 'urgency>50th', 'Shannon_Diversity', 'Evenness']\n",
    "\n",
    "df_pca_corr = pd.DataFrame(columns=['PC1_corr', 'PC2_corr', 'Magnitude', 'PC1_scaled', 'PC2_scaled', 'Magnitude_scaled', 'PC1_pvalue', 'PC2_pvalue'])\n",
    "\n",
    "level=4\n",
    "\n",
    "for var in continuous_var:\n",
    "    pc1_corr, pc1_pvalue = stats.spearmanr(df_pca_list[level]['PC1'], df_pca_list[level][var], nan_policy='omit')\n",
    "    pc2_corr, pc2_pvalue = stats.spearmanr(df_pca_list[level]['PC2'], df_pca_list[level][var], nan_policy='omit')\n",
    "    \n",
    "    # Calculate scaled correlations to overlay on PC plot\n",
    "    if pc1_corr>0:\n",
    "        pc1_scaled = pc1_corr*max(df_pca_list[level]['PC1'])\n",
    "    else:\n",
    "        pc1_scaled = pc1_corr*min(df_pca_list[level]['PC1'])\n",
    "    \n",
    "    if pc2_corr>0:\n",
    "        pc2_scaled = pc2_corr*max(df_pca_list[level]['PC2'])\n",
    "    else:\n",
    "        pc2_scaled = -1*pc2_corr*min(df_pca_list[level]['PC2'])\n",
    "    \n",
    "    df_pca_corr.loc[var] = [pc1_corr, pc2_corr, np.sqrt(pc1_corr**2+pc2_corr**2), pc1_scaled, pc2_scaled, np.sqrt(pc1_scaled**2+pc2_scaled**2), pc1_pvalue, pc2_pvalue]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1_corr</th>\n",
       "      <th>PC2_corr</th>\n",
       "      <th>Magnitude</th>\n",
       "      <th>PC1_scaled</th>\n",
       "      <th>PC2_scaled</th>\n",
       "      <th>Magnitude_scaled</th>\n",
       "      <th>PC1_pvalue</th>\n",
       "      <th>PC2_pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Copies/mL</th>\n",
       "      <td>-0.989226</td>\n",
       "      <td>0.099248</td>\n",
       "      <td>0.994192</td>\n",
       "      <td>49.936833</td>\n",
       "      <td>1.686726</td>\n",
       "      <td>49.965311</td>\n",
       "      <td>5.790664e-209</td>\n",
       "      <td>1.175257e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shannon_Diversity</th>\n",
       "      <td>-0.196508</td>\n",
       "      <td>-0.742184</td>\n",
       "      <td>0.767758</td>\n",
       "      <td>9.919861</td>\n",
       "      <td>-6.026755</td>\n",
       "      <td>11.607127</td>\n",
       "      <td>1.796350e-03</td>\n",
       "      <td>5.376552e-45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anaerobic_percent</th>\n",
       "      <td>-0.042023</td>\n",
       "      <td>-0.631271</td>\n",
       "      <td>0.632668</td>\n",
       "      <td>2.121349</td>\n",
       "      <td>-5.126112</td>\n",
       "      <td>5.547715</td>\n",
       "      <td>5.083536e-01</td>\n",
       "      <td>3.360484e-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Evenness</th>\n",
       "      <td>0.136393</td>\n",
       "      <td>-0.420946</td>\n",
       "      <td>0.442491</td>\n",
       "      <td>7.115889</td>\n",
       "      <td>-3.418205</td>\n",
       "      <td>7.894302</td>\n",
       "      <td>3.109591e-02</td>\n",
       "      <td>3.707915e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>incomplete_evac_vas</th>\n",
       "      <td>-0.088175</td>\n",
       "      <td>0.233936</td>\n",
       "      <td>0.250002</td>\n",
       "      <td>4.451163</td>\n",
       "      <td>3.975775</td>\n",
       "      <td>5.968219</td>\n",
       "      <td>1.760864e-01</td>\n",
       "      <td>2.802566e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bloating_vas</th>\n",
       "      <td>0.041244</td>\n",
       "      <td>0.242641</td>\n",
       "      <td>0.246121</td>\n",
       "      <td>2.151786</td>\n",
       "      <td>4.123718</td>\n",
       "      <td>4.651369</td>\n",
       "      <td>5.274796e-01</td>\n",
       "      <td>1.618223e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>incomplete_evac&gt;50th</th>\n",
       "      <td>-0.101217</td>\n",
       "      <td>0.220209</td>\n",
       "      <td>0.242358</td>\n",
       "      <td>5.109527</td>\n",
       "      <td>3.742490</td>\n",
       "      <td>6.333522</td>\n",
       "      <td>1.201900e-01</td>\n",
       "      <td>6.397800e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IL8</th>\n",
       "      <td>-0.026677</td>\n",
       "      <td>0.238113</td>\n",
       "      <td>0.239603</td>\n",
       "      <td>1.346677</td>\n",
       "      <td>4.046767</td>\n",
       "      <td>4.264958</td>\n",
       "      <td>6.860584e-01</td>\n",
       "      <td>2.521893e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>constipation_vas</th>\n",
       "      <td>-0.005425</td>\n",
       "      <td>0.225991</td>\n",
       "      <td>0.226056</td>\n",
       "      <td>0.273883</td>\n",
       "      <td>3.840745</td>\n",
       "      <td>3.850498</td>\n",
       "      <td>9.337853e-01</td>\n",
       "      <td>4.546238e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IL1B</th>\n",
       "      <td>-0.156344</td>\n",
       "      <td>-0.142324</td>\n",
       "      <td>0.211422</td>\n",
       "      <td>7.892342</td>\n",
       "      <td>-1.155711</td>\n",
       "      <td>7.976511</td>\n",
       "      <td>1.716442e-02</td>\n",
       "      <td>3.022388e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>urgency&gt;50th</th>\n",
       "      <td>-0.108924</td>\n",
       "      <td>0.158390</td>\n",
       "      <td>0.192229</td>\n",
       "      <td>5.498560</td>\n",
       "      <td>2.691859</td>\n",
       "      <td>6.122113</td>\n",
       "      <td>9.433161e-02</td>\n",
       "      <td>1.464997e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>urgency_vas</th>\n",
       "      <td>-0.099456</td>\n",
       "      <td>0.155428</td>\n",
       "      <td>0.184525</td>\n",
       "      <td>5.020629</td>\n",
       "      <td>2.641521</td>\n",
       "      <td>5.673125</td>\n",
       "      <td>1.268098e-01</td>\n",
       "      <td>1.663377e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age (years)</th>\n",
       "      <td>-0.116094</td>\n",
       "      <td>0.137340</td>\n",
       "      <td>0.179834</td>\n",
       "      <td>5.860504</td>\n",
       "      <td>2.334116</td>\n",
       "      <td>6.308217</td>\n",
       "      <td>6.686098e-02</td>\n",
       "      <td>2.993460e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>constipation&gt;50th</th>\n",
       "      <td>0.062295</td>\n",
       "      <td>0.168012</td>\n",
       "      <td>0.179189</td>\n",
       "      <td>3.250052</td>\n",
       "      <td>2.855383</td>\n",
       "      <td>4.326205</td>\n",
       "      <td>3.396392e-01</td>\n",
       "      <td>9.561990e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TNFA</th>\n",
       "      <td>-0.084517</td>\n",
       "      <td>0.157271</td>\n",
       "      <td>0.178542</td>\n",
       "      <td>4.266463</td>\n",
       "      <td>2.672835</td>\n",
       "      <td>5.034556</td>\n",
       "      <td>1.996104e-01</td>\n",
       "      <td>1.650855e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>excess_gas_vas</th>\n",
       "      <td>-0.067272</td>\n",
       "      <td>0.133094</td>\n",
       "      <td>0.149129</td>\n",
       "      <td>3.395927</td>\n",
       "      <td>2.261956</td>\n",
       "      <td>4.080289</td>\n",
       "      <td>3.023868e-01</td>\n",
       "      <td>4.063408e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>excess_gas&gt;50th</th>\n",
       "      <td>-0.057110</td>\n",
       "      <td>0.137410</td>\n",
       "      <td>0.148805</td>\n",
       "      <td>2.882958</td>\n",
       "      <td>2.335294</td>\n",
       "      <td>3.710127</td>\n",
       "      <td>3.814300e-01</td>\n",
       "      <td>3.449413e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diarrhea_vas</th>\n",
       "      <td>-0.069598</td>\n",
       "      <td>0.130203</td>\n",
       "      <td>0.147637</td>\n",
       "      <td>3.513377</td>\n",
       "      <td>2.212816</td>\n",
       "      <td>4.152153</td>\n",
       "      <td>2.859344e-01</td>\n",
       "      <td>4.524360e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IL2</th>\n",
       "      <td>-0.095411</td>\n",
       "      <td>-0.109368</td>\n",
       "      <td>0.145137</td>\n",
       "      <td>4.816417</td>\n",
       "      <td>-0.888102</td>\n",
       "      <td>4.897611</td>\n",
       "      <td>1.474174e-01</td>\n",
       "      <td>9.654571e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IL10</th>\n",
       "      <td>0.015726</td>\n",
       "      <td>0.144019</td>\n",
       "      <td>0.144875</td>\n",
       "      <td>0.820446</td>\n",
       "      <td>2.447617</td>\n",
       "      <td>2.581465</td>\n",
       "      <td>8.116874e-01</td>\n",
       "      <td>2.829117e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bloating&gt;50th</th>\n",
       "      <td>0.048229</td>\n",
       "      <td>0.130379</td>\n",
       "      <td>0.139013</td>\n",
       "      <td>2.516198</td>\n",
       "      <td>2.215804</td>\n",
       "      <td>3.352766</td>\n",
       "      <td>4.599186e-01</td>\n",
       "      <td>4.495137e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IL12P70</th>\n",
       "      <td>-0.090995</td>\n",
       "      <td>-0.084292</td>\n",
       "      <td>0.124037</td>\n",
       "      <td>4.593505</td>\n",
       "      <td>-0.684476</td>\n",
       "      <td>4.644222</td>\n",
       "      <td>1.671624e-01</td>\n",
       "      <td>2.008124e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weight (lbs)</th>\n",
       "      <td>0.057008</td>\n",
       "      <td>-0.086362</td>\n",
       "      <td>0.103481</td>\n",
       "      <td>2.974211</td>\n",
       "      <td>-0.701288</td>\n",
       "      <td>3.055771</td>\n",
       "      <td>3.782545e-01</td>\n",
       "      <td>1.814740e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diarrhea&gt;50th</th>\n",
       "      <td>-0.059705</td>\n",
       "      <td>0.070067</td>\n",
       "      <td>0.092054</td>\n",
       "      <td>3.013933</td>\n",
       "      <td>1.190791</td>\n",
       "      <td>3.240645</td>\n",
       "      <td>3.601402e-01</td>\n",
       "      <td>2.826976e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCP1</th>\n",
       "      <td>-0.072612</td>\n",
       "      <td>0.048687</td>\n",
       "      <td>0.087424</td>\n",
       "      <td>3.665509</td>\n",
       "      <td>0.827447</td>\n",
       "      <td>3.757742</td>\n",
       "      <td>3.032291e-01</td>\n",
       "      <td>4.903121e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GM-CSF</th>\n",
       "      <td>-0.074467</td>\n",
       "      <td>0.035486</td>\n",
       "      <td>0.082489</td>\n",
       "      <td>3.759127</td>\n",
       "      <td>0.603084</td>\n",
       "      <td>3.807197</td>\n",
       "      <td>2.586095e-01</td>\n",
       "      <td>5.907486e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IL4</th>\n",
       "      <td>-0.069493</td>\n",
       "      <td>0.022915</td>\n",
       "      <td>0.073174</td>\n",
       "      <td>3.508079</td>\n",
       "      <td>0.389452</td>\n",
       "      <td>3.529631</td>\n",
       "      <td>2.918600e-01</td>\n",
       "      <td>7.284421e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IFNY</th>\n",
       "      <td>-0.058412</td>\n",
       "      <td>0.040748</td>\n",
       "      <td>0.071221</td>\n",
       "      <td>2.948684</td>\n",
       "      <td>0.692516</td>\n",
       "      <td>3.028913</td>\n",
       "      <td>3.758024e-01</td>\n",
       "      <td>5.368668e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IL13</th>\n",
       "      <td>-0.050233</td>\n",
       "      <td>0.038439</td>\n",
       "      <td>0.063253</td>\n",
       "      <td>2.535818</td>\n",
       "      <td>0.653280</td>\n",
       "      <td>2.618615</td>\n",
       "      <td>4.463703e-01</td>\n",
       "      <td>5.602019e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IL6</th>\n",
       "      <td>-0.062521</td>\n",
       "      <td>0.007964</td>\n",
       "      <td>0.063026</td>\n",
       "      <td>3.156111</td>\n",
       "      <td>0.135354</td>\n",
       "      <td>3.159012</td>\n",
       "      <td>3.430902e-01</td>\n",
       "      <td>9.039642e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IL5</th>\n",
       "      <td>-0.045984</td>\n",
       "      <td>0.022098</td>\n",
       "      <td>0.051018</td>\n",
       "      <td>2.321302</td>\n",
       "      <td>0.375566</td>\n",
       "      <td>2.351487</td>\n",
       "      <td>4.858080e-01</td>\n",
       "      <td>7.377636e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      PC1_corr  PC2_corr  Magnitude  PC1_scaled  PC2_scaled  \\\n",
       "Copies/mL            -0.989226  0.099248   0.994192   49.936833    1.686726   \n",
       "Shannon_Diversity    -0.196508 -0.742184   0.767758    9.919861   -6.026755   \n",
       "anaerobic_percent    -0.042023 -0.631271   0.632668    2.121349   -5.126112   \n",
       "Evenness              0.136393 -0.420946   0.442491    7.115889   -3.418205   \n",
       "incomplete_evac_vas  -0.088175  0.233936   0.250002    4.451163    3.975775   \n",
       "bloating_vas          0.041244  0.242641   0.246121    2.151786    4.123718   \n",
       "incomplete_evac>50th -0.101217  0.220209   0.242358    5.109527    3.742490   \n",
       "IL8                  -0.026677  0.238113   0.239603    1.346677    4.046767   \n",
       "constipation_vas     -0.005425  0.225991   0.226056    0.273883    3.840745   \n",
       "IL1B                 -0.156344 -0.142324   0.211422    7.892342   -1.155711   \n",
       "urgency>50th         -0.108924  0.158390   0.192229    5.498560    2.691859   \n",
       "urgency_vas          -0.099456  0.155428   0.184525    5.020629    2.641521   \n",
       "Age (years)          -0.116094  0.137340   0.179834    5.860504    2.334116   \n",
       "constipation>50th     0.062295  0.168012   0.179189    3.250052    2.855383   \n",
       "TNFA                 -0.084517  0.157271   0.178542    4.266463    2.672835   \n",
       "excess_gas_vas       -0.067272  0.133094   0.149129    3.395927    2.261956   \n",
       "excess_gas>50th      -0.057110  0.137410   0.148805    2.882958    2.335294   \n",
       "diarrhea_vas         -0.069598  0.130203   0.147637    3.513377    2.212816   \n",
       "IL2                  -0.095411 -0.109368   0.145137    4.816417   -0.888102   \n",
       "IL10                  0.015726  0.144019   0.144875    0.820446    2.447617   \n",
       "bloating>50th         0.048229  0.130379   0.139013    2.516198    2.215804   \n",
       "IL12P70              -0.090995 -0.084292   0.124037    4.593505   -0.684476   \n",
       "weight (lbs)          0.057008 -0.086362   0.103481    2.974211   -0.701288   \n",
       "diarrhea>50th        -0.059705  0.070067   0.092054    3.013933    1.190791   \n",
       "MCP1                 -0.072612  0.048687   0.087424    3.665509    0.827447   \n",
       "GM-CSF               -0.074467  0.035486   0.082489    3.759127    0.603084   \n",
       "IL4                  -0.069493  0.022915   0.073174    3.508079    0.389452   \n",
       "IFNY                 -0.058412  0.040748   0.071221    2.948684    0.692516   \n",
       "IL13                 -0.050233  0.038439   0.063253    2.535818    0.653280   \n",
       "IL6                  -0.062521  0.007964   0.063026    3.156111    0.135354   \n",
       "IL5                  -0.045984  0.022098   0.051018    2.321302    0.375566   \n",
       "\n",
       "                      Magnitude_scaled     PC1_pvalue    PC2_pvalue  \n",
       "Copies/mL                    49.965311  5.790664e-209  1.175257e-01  \n",
       "Shannon_Diversity            11.607127   1.796350e-03  5.376552e-45  \n",
       "anaerobic_percent             5.547715   5.083536e-01  3.360484e-29  \n",
       "Evenness                      7.894302   3.109591e-02  3.707915e-12  \n",
       "incomplete_evac_vas           5.968219   1.760864e-01  2.802566e-04  \n",
       "bloating_vas                  4.651369   5.274796e-01  1.618223e-04  \n",
       "incomplete_evac>50th          6.333522   1.201900e-01  6.397800e-04  \n",
       "IL8                           4.264958   6.860584e-01  2.521893e-04  \n",
       "constipation_vas              3.850498   9.337853e-01  4.546238e-04  \n",
       "IL1B                          7.976511   1.716442e-02  3.022388e-02  \n",
       "urgency>50th                  6.122113   9.433161e-02  1.464997e-02  \n",
       "urgency_vas                   5.673125   1.268098e-01  1.663377e-02  \n",
       "Age (years)                   6.308217   6.686098e-02  2.993460e-02  \n",
       "constipation>50th             4.326205   3.396392e-01  9.561990e-03  \n",
       "TNFA                          5.034556   1.996104e-01  1.650855e-02  \n",
       "excess_gas_vas                4.080289   3.023868e-01  4.063408e-02  \n",
       "excess_gas>50th               3.710127   3.814300e-01  3.449413e-02  \n",
       "diarrhea_vas                  4.152153   2.859344e-01  4.524360e-02  \n",
       "IL2                           4.897611   1.474174e-01  9.654571e-02  \n",
       "IL10                          2.581465   8.116874e-01  2.829117e-02  \n",
       "bloating>50th                 3.352766   4.599186e-01  4.495137e-02  \n",
       "IL12P70                       4.644222   1.671624e-01  2.008124e-01  \n",
       "weight (lbs)                  3.055771   3.782545e-01  1.814740e-01  \n",
       "diarrhea>50th                 3.240645   3.601402e-01  2.826976e-01  \n",
       "MCP1                          3.757742   3.032291e-01  4.903121e-01  \n",
       "GM-CSF                        3.807197   2.586095e-01  5.907486e-01  \n",
       "IL4                           3.529631   2.918600e-01  7.284421e-01  \n",
       "IFNY                          3.028913   3.758024e-01  5.368668e-01  \n",
       "IL13                          2.618615   4.463703e-01  5.602019e-01  \n",
       "IL6                           3.159012   3.430902e-01  9.039642e-01  \n",
       "IL5                           2.351487   4.858080e-01  7.377636e-01  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort by the magnitude in PC1/2 space\n",
    "df_pca_corr.sort_values('Magnitude', ascending=False, inplace=True)\n",
    "df_pca_corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PC plot colored by SIBO classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['xtick.labelsize'] = 24\n",
    "mpl.rcParams['ytick.labelsize'] = 24\n",
    "mpl.rcParams['font.weight'] = 'bold'\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "tax_level_list = ['Phylum', 'Class', 'Order', 'Family', 'Genus', 'Species']\n",
    "\n",
    "ax1 = fig.add_subplot(111)\n",
    "_df = df_pca_list[4]\n",
    "\n",
    "_df['PC1_inverse'] = _df['PC1']*-1\n",
    "\n",
    "sns.scatterplot(ax=ax1, x='PC1_inverse', y='PC2', hue='SIBO', data=_df, s=250, palette=['lightgrey', 'PeachPuff'], edgecolor='k')\n",
    "ax1.set_xticklabels([])\n",
    "ax1.set_yticklabels([])\n",
    "ax1.set_xticks([])\n",
    "ax1.set_yticks([])\n",
    "handles, labels = ax1.get_legend_handles_labels()\n",
    "\n",
    "ax1.legend(handles[1:], ['non-SIBO', 'SIBO'], loc='upper left', markerscale=2.5, fontsize=16)\n",
    "\n",
    "ax1.set_xlabel('PC1 (' + str(round(pca_list[4].explained_variance_ratio_[0]*100,2)) + '%)', fontsize=16, fontweight='bold')\n",
    "ax1.set_ylabel('PC2 (' + str(round(pca_list[4].explained_variance_ratio_[1]*100,2)) + '%)', fontsize=16, fontweight='bold')\n",
    "ax1.set_title('', fontsize=20, fontweight='bold')\n",
    "\n",
    "#fig.savefig('genus_pca_SIBO.png', dpi=200, bbox_inches='tight')\n",
    "#fig.savefig('genus_pca_SIBO.pdf', bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PC plot with top metadata correlates for PC1 and PC2 overlaid on plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(9.919860605101945, -9.026754837992854, 'Shannon\\nDiversity')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OY/+2raie/hRHIxZjV8hMHI1YjOrpT7F/21b8euI4Bd5KSMzMXN3H9vJ+C4kGsu+Pb96AIaFzrSJBiW8tazriRwBa+VodathNyuOTmWuqZZ/uY/v17YdDq6PQulyZyQyVCgCsJkFJc8SPS/9iOuJHNGjlS4gVE7P5RvnHdnF3BwA8S00pc7tLR2PQ2b8/u4H/RY6di+iIHxECBV9CrJgQmblsHrv3uInYs3QxcrOztF9LTbiLZj7tOI1Bjp2LdI/4sUFH/IguCr6EWLErcXFoKlLg0/fY7o2boM/4SdgcMgtpKckAXl/ntLYEpejlkTi0PKLMJMMYORQGMSYpKQkzg4PRpVs3dPTzQ5du3TAzOJjajoqIrvkSYsXEzMw19NhN2/pgYNBsHNmwFupSNUqKi62uBrm1HPGjxhDmQ8GXECsmZvMNY4/t3rgJxi6JQIZKhb3hS5AQfw3efu+xHoOcE5Qs/YgfNYYwL9p2JsSKidl8g8lju7i7Y3DIHFw6GsNpDHJPULLUI37UGML8aOVLiBWbGhCAQaNGczoWY6plH9PH1s2CttYa5JZ2xE/M42eEGVr5EmLFxMzMZfPY+rKgjZF7gpIlE/P4GWGOgi8hVk7MzFymj63Jgt4YHKTNgjYkLSUZe+aHyTJByRqIefyMMEfBlxArp8nM3TUvTPDAx+axazg7w760BKc3rMHuBXNw+/JFFObnQ61WozA/H7cvX8TuBXNwdfcO7N+1k5J7RCLm8TPCHF3zJcRKGGsVKGZmLpvHPnLwAFq3bq0d69GIxcjNzZVd5yJrRo0h5IGCLyEWjs1ZzV9PHBcl8Gmyfpk+tqUlKFkTMY+fEeYo+BJiwbie1RQr8FFQlT9qDCEPdM2XEAtFZzUJF9QYQh4o+BJiofic1SSVFzWGkAcKvoRYIDqrSfiwtsYQloiCLyEWSIyzmtTZpvIQ8/gZYYYSrgixQFfi4uAfMo/TfZv5tMfRiMXa/1Nnm8rJ0htDWDoKvoRYIKHOau774Qf83+wQVKtTB2q1GucO7sf9Wzfh17cfXNzdqbONlWN7RIwIh4IvIRaI71lNdWkp3v/wQzzJfIG+AVPQzKe9drWbeCMeh1ZHAXhdk9m9cZMy2dI/7NwhyQrYWNEQCgjCoiNi0qPgS4gF4nNW8/yhA0jPfIHh02fqPRvc2tcPrf9a7e4NX4I+4yehaVsfyTrb0DY4qQwo4YoQC8T1rKbq4QNc/fkkJkatYnQ2eFx4JI5tWg/Vwwfar4mZLX0uNhYDh4/Au8NHYsjCJWjl66dd3WuKhgxZuASdhn2NgcNH4FxsrCjjIERsFHwJsUBcz2oeXrMKY5aEszobPDhkDo5v2aj9mlidbahoCKlMKPgSYqHYntVMTbiH4sICTmeD1aVqZKhUAMTrbENFQ0hlQsFXJHRmkoiN9VnNRd/g46HDOT3Xe/0+x6WjMQDE6WxDRUNIZUMJVwKjZJHKxdwZuWzOanp6eKCZT3tOz9PMpz1iDx4AIE5nGyGKhlC2LrEkFHwFxLXDDLE8cppkMT2r2dHPj9fZ4KKCfADidLYRsmgIIZaAgq9AdJNFTF2zMseZSSIcuU6yTJ3V5Hs22EHx+n7xJ4/jx++3cR6nPtTgnVQ2dM1XIJQsUjlYckau5mwwF4k3rqN+8xaidbbRTAy4oAbvxBJR8BUAJYtUHpY8yeLTx/VCzGH4dPtYtM42fCcG1OCdWBoKvgIQo8NMeZQ9bX6WPsni08e1uKgQJ9d8J1pnG2rwTiobCr4CuBIXh6Y+7Tjd19SZSaVSie49e2HQqNHIcXWDf8g8DI9YCf+QechxdcOgUaPRvWcvWWxrWjspJlli49LHdevcUDgrFNi/a6do166pwTupbCj4CkCsZBEqtScvYk6ypML2bPDG/5uK6PCluHg+VvSWcuZq8E67SsQcGGU7JyQkIDY2FtevX8fTp0/x6tUr1KpVCw0aNEDbtm3x8ccfw8PDQ+yxyhbfLFJ9ySKUPS0/5sjIFeMcMZuzwScPH5asj6tmYhAwIxCfzwxGvQYNDd42LSUZMSuW8doGl9NxMVL52KjVarWhb/7222/YsGEDrv01Y9d3UxsbG9jY2ODdd9/FhAkT4Ovrq/exBgwYgJ9++kmgYcvLzOBg5Li6ceowc/vyRVRPf1rhiEj3nr3w7vCRrK4vpqUk4+ruHaJ2nKnMunTrpv2AZqswPx9HIxYj9vRpRrcvHxia+rQrExjiTx4TJDBogvvVa9fKnA02Z9s+pVLJaGIQvTySc+D94YcfEDxvPoZ/s8joeywtJRmHVyyjM/mEM0Oxz+DKd9q0aTh16hTUajVatGiBTp06oUWLFnBxcUHVqlWRm5uLjIwM3LlzBzdu3MDly5dx+fJlfPjhh1i/fr2oP4zcTA0IwKBRozkFX31nJoVI7KF+p8Lj08aPTUaulOeI5djHVcwG70qlEmMnTETSo0eYtmY97SoRszEYfG/evInJkyejf//+8PT0NPlAz58/x5EjR7Bnzx5BB2gJdJNF2K5U9SWLUKk9eRJ6kqUPXW74m9ATA82kpsTennVnJyn6GJPKxWDC1dmzZzFlyhRGgRcAnJ2dMXLkSPzyyy+CDc6SCJksYg2JPdZIioxcSz5HLGeaSU3vKdPgUKWKxR4XI9bDYPC1tdX/LbVajWPHjmHp0qUIDw/HyZMny3zfxsZG2BFaCNYdZuaHGUwWoVJ78iVmRq6lnyOWM82kJv7MGXT2/5zTY8jluBixDqxrO4eFheHQoUPa/9vY2CA+Ph5hYWGCDswSscki3b9rp8FkETGyp4kwxMzIpcsN4tCd1Dz833/x6YiRnB6HGjgQIRkMvmq1usIqtqSkBMeOHcOECRMwYMAAPH36FGPGjEFMTAwF378IkSwiVWIP4UaoSVZ5ltbZx9ztFJnSndS8TE+jXSUiCwaDb8+ePTF16lT07t1b+zU7OzsoFArcuXMHV65cwZMnT1BUVIRq1apJMlhLwidZRIrEHsKPGBm5lnK5wdLOx2omNRkqFWxs7WhXiciCweDbrFkzBAUFYf369Zg6dSp69OgBAAgMDMS3336L2NhYqNVq2NvbY9q0aZINuDIQOnuaiEfIjFxLuNwg13aKxmgmNZeOxqBByzeReCMerTlMbBPir9GuEhGMweC7Zs0aKJVKfPfdd5g+fTpatmyJ6dOnY+jQofjwww/xxx9/AADatGkDNzc3yQZcWUQvj8TA4SMYHTkB/k7s2b9rpwSjE4albFtKRe6XGyz1GJRmUpOacBefT56OY5vWcQq+Z/fuxr9jDoswQlIZGa3t7O3tjU2bNmHfvn1wdnZGQEAA/vGPf+D+/fv45JNP8Mknn1DgFYmQ2dNsiV3rlppF6Cf3zj6WegxKM6nJzc5C3K8/4+mff3I6Lpb9/DntKhHBMMp29vHxwbZt23D16lV89913GD9+PHx8fDB9+nT4+bGfQRJmxErsMUSKa3mWuG0pFTlfbrDkqms9PvkEX48fj6q16qDJ22/j7a4fYM/SxRgXHsl4V2nP0sXw9HCXYLSksjBY2/n58+eIjo7GrVu3YGdnh06dOmHKlCmoVq0aLly4gFWrVuHWrVvo2LEjpk2bhnfeecfoE1lzbWcpiF2D11hQ1MWn1q1SqWS1lZ6bnYXd88LMvm0pJba/o5ysLOyZHybI5MsYMeqXS8HQ6/r+zRs4tmk9BofMMXlcbG/4ErR5vwua16hGR7kIa4Zin8HgO378eJw/fx41atRAcXEx8vLy8OWXX+Kf//yn9jZnz57FqlWrcOfOHe01YLYDIOYnVVCkZhHMaAIGm3PEYu8OSNlUQiimXteqhw9wfMtGqEvVeK/f5xV2lS7EHIaNrQ36jJuI09u24Mfvt9G2M2GNdWOFuLg4ODo64uLFi8jMzESXLl0QFxdX5jYfffQRPvroI5w6dUr4ERPJ8LmWxzQoWvK2pdSkvtzAhKUcg9Jl6nXt3rgJxi6JQIZKhUtHYxB78ADyXmUjNzsbbbt+iAHTAuHi7k6nCIgoDAbfBg0a4N69e5g8ebL2jdOwof5ZePfu3cUZHRGdVEGRqjexI2ZnHy74HoOyt2ddTI8XNq9rF3d39Bk/Ufv/zaHB8OvbDy7u7hZ5ioBYBoPviMWLFyMoKAjnzp0DALRu3RohISGSDYxIQ6qgaGnVm4QgxFEqubT843MMKiH+GlRpaejes5feZD0xjpzxeV2/1+9zXDoag049e7EqD0oIGwaD71tvvYVffvkFWVlZsLOzoypWVkqqoGiJ25ZcWVoFKCb4VF27eCQGIxctRXFRUZkMdjF/T3xf1zFrV8Hmebpk2/qk8jG5F1SzJrPrgMQySRUULaF6kxCs9SgVn2NQNrY2cHZ7fUxHU3gjaPo0RH63SrTfE9/XtVvdepUqyY9Iz2iRDWL9NEGRCzZBUbNtyYWlNIvQrQBlKkDpVoCylGIiXNop7g1fgj7j/r6eqknWmz13rsHfU4ZKhWObNmDDrED8EBmOYltbDBnxNX799VfGY+X7uq5ZixYdRFxGy0uyMWXKFN6DIdKTqqRhZWgWIUXWuDmxbae4N3wJ+oyfBDevxmW+V69BQ7h4NkDeq5wyvyvVwwc4vnkDAKCzf398OmKkdhs68cZ1TJkdgkZR0Yy2oeVeqpMQo8G3fEtBYyj4yg+TRBapgqKcqzcJgWvWuJ29A+7eT8S773dBSUmx7Otb6x6DSk1Lw/tfDkLzdh30no8dNCukQuDV6DLgC1w6GqPNMtYUvRgSOlfvNnRr385o7duZ8TZ0ZZjsEctmMPgOGjQIBw4cQGlpKerXr081nC0I20QWvkGRabaq2M0izNmogW12re4q77Mx49HMp73FJGVpjkG94+eHxBvxOP/TQRQV5MNB4Yj6zVtoz8ca08ynPWIPHgDw+ndxbNN6RuUemTZssPbJHrF8BitcAcCpU6cwY8YMuLm54eDBg6hduzbnJ6IKV9LgUibS1cWFU0nDb79ZgNXrN2iDfFOfdmUCSPzJYxUCiBjVm8pPNpiMQ2hsKkAZW+Xp4lPKUwod/fwwPGIlp/uq1WqsmT4ZU1etw5awYPQLmCp45TO5luoklYuh2Gc04ap79+4YM2YMHj16hJUrub3JiHS4JvwAYN1BafyokQj9ZiHeHT4SQxYuQStfP23g0WSrDlm4BJ2GfY2Bw0fgXGwsgL+3La/u3oHdC+bg9uWLKMzPh1qtRmF+Pm5fvojdC+bg6u4d2L9rp8mgcy42FgOHj2A9DqExza7VXeVZelIW36QmB4UjMlQqAOBV5MUQc3YGI8QUoytfAHj16hVWrVqFKlWqYObMmayuA+uila/4+NZOViqVjEoaTpk4AaHfLORdC5pvswg5NWpguvIVa5VnDnybLTy4dQsA0OTttpz66zJt2MD0dR29PNIsgZf6Wls31rWdNapXr46wsDBRBkWEI0SZSKYlDbv37CVIVi/f6k1yyi5mkl0rxCpPTh/GfJKaLsQcxhfTA7F/xTJ8OmIkp+dnWuRFbqU6NayxGAthjnPB1REjRsDFxQVRUVFCjodwJGSZSGNBUS4NEuQyDg0mgejS0Rh09u/P6fHlWN9aiMIbRQUFkhR50be6FLIlJ1vWWoyFMGcw+B4+fNjoHa9cuYJatWppb/f5558LOzLCilRlIuXSIEEu49AwFYgyVCooL/6GlLt/4PSenXBQKFC/eUttAX9T5FrfmksG+7Z5Yeg7cTKA1wFUzMpnclxd6uZmCJXdTSyPweAbEhJi8vpuVlYWQkNDAVDwNTepykTKpUGCXMahS18g0j1S1GfCJDRv10GncEQ8Dq1+vXPUe9xEuDduYvCx5Vrfmm3hjZgVy7B0wXws+24VvLy9Ub95SyTeiOd0zddUMQy5ri7ldLnEnCr7tW6DwdfHxwc3btwAACgUCrz99ttlvn/16lU4ODigbdu24o6QMCJV7WS5NEiQyzh0lQ9E2c+fmygc4YfWf334a6pBNW3ro/ex5Vzfmkv/Ya/GjREwIxAfjhiJ2AP7OQVfY8Uw5Lq6lNvlEnOQ426EORgMvvv27cPu3bsRHR2NV69ewc7ODt988432D//mm2/C2dkZO3dSn0u2xJjxSVVOTy4NEuQyjvI0gWjcxGHAP8IAACAASURBVEl4mJqKaWvWM/rwHxceic0hszAwaLbeFbDcSx6yTWrSDdjpqSmCF8OQ6+pSbpdLpCbX3QhzMHrOd+jQoThx4gS6d++Oy5cvw9/fH2vWrEFhYaFU47MqSqUS3Xv2wqBRo5Hj6gb/kHkYHrES/iHzkOPqhkGjRqN7z16cznRODQhA/MljnMYVf/I4pk2ezOi2cmmQIJdx6OPt7Y1q1atjzJJwVh/+g0Pm4PiWjXq/z+ZvZE6aZL3Y06cRd+kSYk+fxoply/ROKjUBe/umTdj5zTxWDRtiVixD9PJIvd8XYnUplitxcWjq047TfZv5tMfVa9cEHpF0rL3xCFsmuxrVrVsXq1atwvr16+Hi4oK1a9eib9++UozNqohdDEI34YcNtuX0pAryljIOffh8+KtL1dojSRrWXvLw008/xY4tm8sUw9DtbLR6WgA2zArEsU0bcO96nMliGEKsLsUix8slUuGzG2GNGLcU/Oijj3D8+HEMHz4cqampYo7J6kg14+PS8s3YCkIfqYK8pYxDHz4f/u/1+xyXjsZo/8/lb2SJNNvQZzesRfiIwfhx5TI0efttjF60FFNXrcPoRUvR+K23cGr7NtSuWROuLi4GH0vOq0upWnjKjZx3I8yFVT/fqlWrIiwsDD/++COmTJmCkSNHijQs6yLVjE+qcnpSBHlLGkd5fD/8UxPuAah8JQ/TMzLwMicHY75dhomRUWjt27nM7pC333uY/N1afDBuotHdITmvLuV8uURMct6NMBdWwVejdevW2uD79OlTPH78WOhxWQ2pZ3xC107WRy41c+UyjvL4fvi/epHJ+29kaYTcHZLz6lLOl0vEJOfdCHPhXOFKY+DAgXj27Blu374txHisjjmyG6Uop8fleIkY5DIOXXwzsRW2NmYreWguQmYnS5X5z0VlbXUo590Ic+EdfNVqNUz0ZqjUzFkMgm/tZFPkUjNX6nGYOirG98O/X9++lSrw8tkdevj4Md5q1w6169TR/g341Jw2dnZYKGL3tZYjuR4NNCfewffHH39EcXGxEGOxSpVhxid2kJfLOJgWB5gVOAPzlobL9sNfbvjsDn00eCge/ve/+HTEyDJ/Awd1qWxXl1yqgln6dX8570aYC+fge/jwYTg5OaFHjx5CjkcU5ixjRjM+63AuNhYTp07DF7PDTBYHCAwJRfWqTrL98JcbPrtDzdt1wPmfDlb4G+xbvBD7ly7GqPBIWa4u5Xi5RExy340wB04JV8Dr2s/h4eFCjkVwYha1YMpasxuTkpIwMzgYXbp1Q0c/P3Tp1g0zg4Ot8khATEwMhowchRHfRjBOBsrIeI4D4Utkl4ktR3x3h4oKyiZX1WvQEGOXR6G4sADbQ4NllYynS3O5ZP+2raie/hRHIxZjV8hMHI1YjOrpT7F/21b8euK4xQdeQN5HA83F4Mq3VatWJu/85MkTtGrVCjY2NrJLuJJLGTNrm/FVtrqs52JjMW7yFIxftoJVMtCguQvw65rvsGteWKXZWuSK7+6Qg6Li/arWqInB8/+JsxvW4uruHbJeXcrlso3YKuO1bmMMBl9bW1uUlJQYvKONjY020UpuCVdyKqpuTdmNcpnQSEWpVGLspADUa9SIUzKQjaMjohZ+g+XR35n88I+KCMeRY8cw95tvKl2HF77XA+s3b6H3e/UaNESxvT02rVsLAGZNCiSV81q3MTZqA5Hz7t27mD9/Pm7evAmFQoEJEyagX79+AF4H208++QR169bFvn37AACenp5Gn2jAgAH46aefBB6+ft179sK7w0eyDnZXd+8Qpai6UqlkPePbMz9MVtd62P4MudlZ2D0vzKJ7kHbv2Qt5Ckd4d36fU9ed25cvonr6U6xYtkybd3D12rUyH/49PvkEkVHR2p2Epj7tyuwkxJ88ZlU7CfokJSVh0KjRGLJwCev7bg4NxhfTA+Hspr8nsu7fgMiDUqlkdK07enmkbD7/+DAU+wyufFu2bIl9+/Zhz549iIqKwurVq3HlypUynY3s7OxMBl2pybFllzXM+OTaJUYsmtdR1vPnaMajOIDmqJi+rcXKtpNgCJ/dIRtbG4OBFxCvdzPhTi5HFM3NaLazjY0Nhg4diu7du2PRokU4deoU/P39MXbsWKnGx5pcW3ZZcnajHCc0YtO8jv69Z6coR8XkdGlEDrhcD9wbvgSDZoUYvZ2lHNerjNhe6zbnqRUxMMp2rlevHlavXo1169bB2dkZ69evF3tcnMm5jJmlZjdWxrqsmteRg0IhSqlCS+7wIkamO9tSoVtCZ6HP+Elw82ps9LZ0XM/yyeHUihhYnfPt1q0bfH19ERUVhdOnT6NevXpijYszSyhqYWnZjeas0mUumtdR/eYtkXgjntM1X0NHxSx1J0HsTHcmu0MXYg7DxtYGg2aFmAy8gLyP6xHTrPnSjMHge//+fTRt2rTC16tWrYo5c+Zgzpw5eu+XnJyMhg0NX9MUGxW1EJ4UExq5bSlpXkd+ffvh0OooTsHX0FExuV4aMYbNh+Anvfugroszun/8Meu/n77rgVkvs/DkWRre6vIhBkwLhIu74Wu85cnxuB5hxtovzRjcdu7duzeGDx+OQ4cOITMz0+iDZGZm4uTJk5g4cSJ69uwp+CDZsNaiFuZkZ2fPa+vV3t7wBotct5Q0ryPNB72QxQHkfGlEH7Ydh6at3YDsgkKkFIPz30+zOxR7+jRuxF3FW6298W6v3qwCrxyP6xHmLPnSDBMGPxVDQkKwevVqxMXFwdbWFs2aNUOLFi3g6uoKR0dHZGVlIT09HUlJSUhISIBarUb16tUxa9YsKcdfgbUVtSjPHCtEG7yemLT27cz6vgnx1wADx8DlvKWk+zrqPW4i9ixdjHEsShX+FLkUB/fs0ft9S7g0oovLh+CQ0Lk4smEtxi6JEOTvRwUaKhdLvTTDhsHgO3LkSPj7+2P79u04cOAA7t69i7t37wIoW2ADANzd3fHll19i8ODBqFOnjvijNsKailroMna9LfHGdXT/vD+yn2fAw90dH33wgaDBuKSkGBdiDnMKvhdjDqMqSit8ne+WktiTEN3XkXvjJugzfhI2h8zC4JA5Jo+KfT8vDHu+32Ywcc6SLo3w+RBUl6qRoVIJsiVoDcf1CHOWeGmGLYNFNsr773//i+vXryMtLQ3Z2dmoUaMGPDw80KFDB7Rs2RI2NjZG7y9lkQ1rKGqhy9gKUVdaSjL2LF2Mt7t8gCd3bgtWnKGjnx8K7OzRL2Aq6wnNkfVroSgpQtylS2W+x7UQytkNa1GtenVJilKUfx2pHj7A8S0boS5V471+n1dIBvrt8E9IT0nG5rVr4e/vb/BxZwYHI8fVTbs7k6FS4dLRGKQm3P2rXKIC9Zu3hF/ffhW2WaUuGlF+rGzcvnwRD27dQp/xEwEIU8imshVoEIrccipM6dKtm3aBwVZhfj6ORixG7OnTIoyMPdZFNsp766238NZbbwk6KDEkJSVh6/btsLMBVk2eiNFLwi16lsx2hTg+Yjk2h8zCwKDZsLO3F2S71snJCR98NYz11uve8CUYMC0QV/btKvM9rqup7OfP8TA1FWOWhEuyTV1+teXeuAnGLonQBsvYgwdQVJAPB4UjnN3dUfTyJfbv3m3yOTVb2rXfeAPHN28AAHT2749PR4zU2c2Ix6HVUQCA3uMmwr1xEwDSXxrhm+kee/CA9v9CbAlSgQZ2LLUWu6VdmuGCdz9fuSj/Ihu+dDkyVI8Rs241iouK0GXAl2jRvoNFFLXQxeV62+CQOdrrbUJkAHbq2BE5L16w2nrdG74EfcZPQnbm8wpJbFy2lFQPH+DYpvWYtma9pJmPho6/9B43oczrqFphATb/uJ/R68jLywsleXnYF/Eths1doHci0drXD63/mkhofpc1nJ0lvzQidMchobYELe24njnIOafCFEu6NMOVxQVffdsnHm5uuKW8XaHXqnvjJhi3NFK7UvnP/r3IfPIEDRs0wEcfdJXNLNnQllB/f3/e19tc3N15l3nUrNSGLFyCgUGzcWTDWoNbr+XPYe5eMKfCSo3Laur45g0YEjrXLOUthV5tKZVKvMrNxYTIlYwmEuPCI7ExOAj2pSU4orOSNEaobUahOw5Z6rlvS2Ppx3T4NtuwhFMrFhN8DW2fJN/5Az+ujMSkFdEGX2Qu7u7a606agv+jv/7a7IHX1JbQ0DFj8NnocZwe+71+n+PS0Rj0GT+R93Zf+eQjQ1uv9Zu3KHMO01ASG9vVVIZKBQBmz3wUarU1I2gW/hEyh9VEYmjYXJzesMbkylrobUahOw5ZypagpbP0WuzWfmoFsJDga2z75Ncd2zBi/kKLe5Ex2RI6d3A/mrXjNoMrf72N73Zf+aMeuhMafYwd9WC7mrp0NAad/ftzGrfcMh/5ZA+X2jsYnUiIsc3I50PwQsxhfDE9sMzXLGVLkCs5JDZZwzEdaz21ostkbefMzEzcunUL6enper9/8+ZNXL16VfCBaRg74C/EisgcmBQtyFCp8PzJE2ydF4rV0wKwYVYgjm3aoP2ZTSl/vY1vcQa2tXf3zA8zmMTGthBKasJdXp2FpC5KYYxYdbLZFsLQbDOaKn6h+yHIhqGOQ5ayJciWnIrFWEst9ujlkTi0PAK52VmMbq+Z8EcvjxR5ZMIwuPItLCzEN998g0OHDmm/1rVrV8ydOxcNGvz95p42bRqePXuG27dvizJAY9snlroiMvYzqR4+0GbA9guYgubtOpjMgNWn/PU2Ibb7hOrMxHY1VVRQYDWZj2LVyRZzm3FW4AyMmzIVE6NW8e44ZClbgmzILbHJWmqxW/vZboPBd/369RXOJp07dw7Xrl3DmjVr4Ovrq/06w6PCrJnaPklNuItPR4zk9NjmepEZ+5nu37yBY5vWY0joXMYZsE3b+uh9nvLX24Ta7hMi+YjtlpKms5A1ZD6KcYRCrG1G3evHPh93x6bZQRgSOpdxpnv5xgeWtCXIlFwSm3S3u+/evWc1k1VLbsVqisHge+LECdjY2CA6OhoffPAB4uLisHDhQqSkpGDixIlYt24dOndmX/GIDVPbJ5a4IjL0M2mO0jA5R6vJgNWc59W3Ai5/vU3o7T6+yUdsygW+7izErbyl3LY5xThCIUY1IH2ruVa+ftpM987+/dC8XQfGHYestdyjuROb9CXYbZ0XajWTVcB6z3YbDL5paWmoWbMmevToAQB4//33sXfvXowcORKJiYkICAjAypUrRR2cqe0TS1wRGfqZuByl0T3Pq0vf9Ta5bfex2VJq0bEjftn2L07BV24/txhHKITeZjS0miuf6X569y68eJYGZ3d3eLVuY7DjkCVuCTJh7sQmQ9vdYrTBlANrO9ttMOGqfv36yMrKwuHDh1FYWAgAcHV1xY4dO9C0aVPk5+dj8uTJePbsmWiDM7VFp3mRcWGuF5m+n4lP4pjmPK+G5npbn3F/ZyKLud3Hp7G6Zkvp6u4d2L1gDm5fvojC/Hyo1WoU5ufj9uWL2L1gDhJ+/RludeoI2lnIXKYGBCD+5DFO940/eRzTJk+u8HWht7JNreY0me7T1rzeqXGsVg2PEhPw5OEDvX+/q7t3YP+unbIp4CAUcyY2GUuw8+vbDxePHDJwT+MMvcaI8AyufL/66issWrQIoaGhOHjwIHbufL1d5OzsjJ07d2LcuHFQKpVQq9Um6zpzZWqLToxeq2LT9zPxSRzTPc+r73qbWNt9Qp0nZbqlxKVetxy3OcU4QiHkVjbb1Zzuanj7/FDcOh6D4uJii98SZCL2twsY+A23vBG+OSfGJki6bTCt9ZiONTAYfIcMGYInT55g165dqFevXpnvOTs7Y8+ePYiIiMD+/ftRUlIiyuBMbdFZ4otM38/EN3Hs153bsTk0uML1NrG2+8TI7jS1pWRNmY9Ct8cTciub62rOxd0dn40ZL2nTB3M6FxuLh0lJZsk5YTJB4tIGU46TVWtmcNvZxsYGM2fOxO+//46QkIpHBhQKBebPn4+zZ88iPDxclMEx2aLTvMgs5SyYvp+Jb+JYzosX6DshAGMWh8PZzV3U7T6xzpMywXSbWu7bnEKemQaE3cq+EheHplZyplosmveAi4cHCvPzTd9BDz45J0wmSLptMIV4jRHhmaxwpVarYWdnV+HrmmSBunXrol+/fqIMjskWHdteq3JYET1/+gRrpk+GWq2Gg0KBvFeveG0b1qxeDRe2b5EkA9Dc2Z3Wkvlo6ghF/Jl/48KhAyguKISLizPmfvONwUpJXl5eUNjYCLIDxOX6sbYl4r27yHiUii7dusm6XR1fmvfA7yeOmyWxiWmCXdO2PiZrsVvqMR1rYDT4njhxAgsXLsRXX32FwMC/j60UFhZi4MCBqFevHpYtWybqH43JFp3ui6y0pASd/fvLroOR7jXSbiPHoplOH9p9keFIiL8Gb7/3WD9u4o3r6Ne3ryRbfebO7tRlDZmP+iYSGenpePEyC7Xq1sVnYyaUeZ3ou5aueV1lvnqFHf9cYLTGuS5D24xsrh/rFoQp3xJRzu3q+NB9D5gr54TNBElfLfaM1BS8+WZLi5qsWiODwffatWsICgpCaWkprly5UuZ7ly9fRlZWFrKysjB8+HD8+OOPaNLEcLUlPphe63Nv3AT+Eyfjx28X4emVizi7/V/Izn4FG1sbVHVyQrcPP8Tc0FCzvNBMXSPtPXYCDq2O4hR82b6J+dSeFfo8qRzq4MqBZiKheZ2M+jaC0bX0CaNGYuO277Wvq/s3b/DeAWJ6/dhUQRgu1/0t4fWg+x4wV84JlwQ7TYa63BrNV2Y2agPlqSZMmIBz586hU6dOCA8Ph4eHR5nvX79+HfPnz8f9+/fRt29fLDOxChkwYECFillsKJVKk1VOqkCNkuJilNo7oF3PPmhabtUQf/KY5DNxplm6W8KC0S9gKus38dXdOxht55bPTubyu+nSrZs2q5kt3Te9EGOxNmyzuXOzs/Dd5An4KjgUjdu8rf266uEDHN+yUe82473r13Dt+BG41qiB6OWReneAkpKStO0jDVE9fID9yyMYJ/NoOokZqupkSa+H8u8Btr+LnKws7JkfxmsHbmZwMHJc3Tgl2N2+fLHSJMXJhaHYZzD4vvvuu8jOzsb58+fh4uKi90GTk5Px6aefwsPDA2fOnOE0ALY0s+Or166Vudb3TocOWBSxTO/qUldaSjIOr1gmWePort0+RtexE0wGVTHfxMZW3rpM/W46+vlheAS3wipqtRq7QmZiRUSEIGOxNt179sK7w0eynnzpK7IC6FyHTbinbfno7O4O2xeZOP+fs7zGIuREUajXplT0vQc0uwBMdhy2zgnBvh3bef0MTCZIhmj6a9NxIukYin0Gt53z8vJQvXp1g4EXABo2bIgaNWoY7HgkBn3X+tisGqRqHK1UKjEhYDKevnzJ6ENKrMQxIWvP8j1Pqi4tlUUdXLnhcy1dU2SlfGUpQy0fdy+YY/Lau7E8CyF7K8ulLjIb+t4DTBKbLsQcBqCGh9sbvCcPYpwVt4Qtf2tjcOXbo0cPJCcn48iRI2jevLneO9+/fx+9e/eGp6cnTpu4hiDUylcfrqsGplu2bGlm83UaNoJ35/dZJWRotg2LCgrQZcCXZRLHEuKv4drxo3CpXt3gtmF5Qv5u+G53nd2+DQPnzJfN30ku+P5eH9y6ZbS3cvnbM9l21LyGy+dZHNu0AU3ebsspyaj8c8vtfcuEqb+Vvh2H+s1bwK9vPzz986FgW75cis7o2ymzpC1/S8V65fvxxx9j69atCAwMRFRUVIUAnJCQgP/7v/+DjY0NPv74Y+FHzJCcMnCBsrP5Hf9cwLoPrSY78UlSEv41NwTObm4oLiyEfZUqqF6rNkoKC6FWqxl1khL6d8Onsfrlnw6gpqsr77FY4wydb23m2IMHWN2eSWUlQ0ehUu8J00lMbu9bpky9BwztOADAzxvWCFZVT4iiM3JrhVjZGAy+EyZMwM8//4zExET4+/vDw8MDb7zxBtRqNZ4+fQqVSgW1Wg03NzdMnMhs1i0GMTq68KF7DpZP8Yw3GjVCTWdnTFoeXeF7TN8MQv9u+Gx35WVlwW/kGM5jWfDPf+Kx6gnvcpZyxLc2c1EB80IPbCor6T0K9ShVkKpOcnvfMiXGli9XfNrtWeKWv7UxGHxr1aqFnTt3YubMmbhx4wYePXqER48elbmNt7c3oqKi4OzsLPpADZFT4+jys3m+XZccFPrvx/TNoPu7+Xs77O5fj61A/eYt4de3n/Z6oe5tCvPzkfsiEwDKrCq5lkasXbs258pJdnb2OHX2Pxi9JNwqZ+h8r6Ubep0Yuj3bykq6eRZdunUTpI60nN63bLF9D6Qm3MOeRd/A08MDHf38BN2t4Vp0xtzFcuTk4cOHuHv3Lj777DNJn9dokQ1PT0/s27cPN2/exO+//w6VSoWSkhLUq1cPnTp1QqdOnaQap0FiNCfnqvxsnm9rr/rNWxj8PpM3Q15eHjJUjw0WQki8EY9Dq6OQn5sDWzt7VFEoGBVL4LLdNXP2bE5/J9XDBzj5/b8wdc16q52hs6nNXH4SVVJcDHuHKnqTrvTh281LqDrScnrfssX0PaB6+ACH16xCcWEB+gRMQTOf9qLt1rApOmOpW/5iuXDhAubPn4+tW7fiww8/lOx5TZaXBIC2bduibdu2Yo+FEzGak3NVfjbPpwLOhZjD+GJ6oNHbmHozFBYW4ofIcAwNm6d3xdja1w8KJyccWb8Gw+aGsFpVst3u4vp3Or55A4ay7HNsaTN0JtfSjVWTSrxxHYdWRwF4XevcvbHhgjd8u3nxue6v+9xCvW/NlQNg6j1w/tABXP35JMbIcLfGUrf8xWJjY4PS0lIEBgZi7969ePPNNyV5XqPB99y5c9ixYwdUKhXq16+PUaNGwc+P/ZtOTGI0J+eq/GyeTwUcG1sbOLuZXskYejMolUq8zH6FiVGrDAYu1cMHOLZpPSZEruS0qmSz3cXl7yTksRY5M3Ud0VQ1qda+ndHat3OZlpJN2/pUeBwhrjsKdc2T7/u2caNG6N6zl1lzAAy9B9SlpUjPfIFpAu/WCDXRsOQtfzHY2tqiatWqsLGxwbhx43Dw4MEKnfxEeV5D3/j5558xadIkXLx4EQ8ePEBsbCzGjh2L8+fPiz4oNsRoTs6VZjavi0vXpb3hS9BnHLMkNkOdZGYEzcLwbxYZffMf37wBQziuKjU0212xp08j7tIlxJ4+jRXLllX4MODyd+LT55hvs3KpRS+PxKHlERVeJ5oJ0rjwSEZdpMaFR+LYpvVQPXxQ5ntCdvMyNFZD9D03n/ft+X17cOX6dbw7fCSGLFyCVr5+2kmvZlU5ZOESdBr2NQYOH4FzsbGcnoep8u8BZ2cXk+89XfreV7qUSiW69+yFQaNGI8fVDf4h8zA8YiX8Q+Yhx9UNg0aNRveevRh3DrPkLX8xaPrR16pVCy9fvsSECRMk+RkNBt+NGzeitLQUjRs3xtixY9GoUSOUlJRgw4YNog+KDd2ZOBtiZB9qZvO62Lb22hI6C33GT9L25DVF35uByTUdIVaVbHD5O6Um3GV9VEvD0trbGWozyGWCNDhkDo5v2aj9mtAt44Roicj1fau8dAEvX7zAiCUV61+XJ3RLSyaEuJ6q61xsLAYOHyHoREPfIoEpoS/VyYGdnR1sbV+HQmdnZ9y7dw/BwcEoLS0V9XkNBt+kpCTY2dlh+/btCAoKwubNmwEAiYmJog6ICyFm4kIwNJvXrYCzOTRYbx/aVVMn4adV0Rg0K0TvlqEh+t4MTK7pmGNVyfbvVFDJZuia64g75oViY/BM/H7iGNRqbhMkdUkpfj9xTLT+xkL0Vub0vl39HUYtXirYqlJoQlxP1RCrd7a+RQJTQl+qkwPNylfzb1dXV5w5cwYrV3IrpcuUweBbUFCA2rVro27dugBel5KsXbs2cnJyRB0QF0I3J+fK2GxeUzxjwLRAPLh1C1vnhWHN9MnYOi8M/7t4AcVFhZgYuZLxildD35uBSUN0c6wq2f6dXj1/Xulm6NWqVYNHIy/8IzAIV3/5GZ39ufXK7uz/OR78Fov927bi1xPHRWmjqbnmuX/bVlRPf4qjEYuxK2QmjkYsRvX0pyafm+3r4fvZM+Hh5SXZbg0XTN57hpR/X/E5DmSMnC7VmZNarUZGRgaePXtWJgDb2trCxcUF//rXv3DhwgXRnt9gwlVpaSkcHBzKfE2hUODly5eiDYYPPgfOhWTqDGD5Cjg5WVnYOWc2alWvwen59GWvMrmmw6cACJ9VJZu/U5/PPpNNMp1UNCsnF3d32FdxQPN23H6G5u074M6pk5IknPHprczm9fC+nx+qtGjF6XmkytIV6nqqmMeB5FQoRGyFhYV48uQJVCoVHj16hD///BMJCQm4e/cunjx5goKCAhQWFqJmzbKf1SUlJVAoFKhfv75oYzOa7ZyXl4fDhw9r/5//1ypE92san3/+ucBDY4/rgXOhx8D2HOyG1auweGm4YG8GJsc4+BYA4bOqZPp30nRv4XusxZLoZqKaa4IkNaavhy7dusF/4BBOzyFVlq5QR6jEPg7EtVjO/l07OY1JLGq1Gi9fvoRKpcLjx4/x6NEjJCQkIDExEbdv30ZBQQEKCgpQVFQEtVoNW1tbKBQKKBQK1KtXDy1btoSNjQ1+++23Co8ZFhYm6kTDaPDNyspCaGhoha+X/5qNjY0sgq8Gn5m4ELiswl1dXAR7MzA5xsG3AIgQq0pTf6fKNEPX0F05mWOCZM7a2aZeD5aQpSvU0UexjwMJURtaKsXFxXj69CkeP36Mx48fIzk5GQkJCbh37x5SU1O1q9fi4mIAgIODAxQKBapUqYI2bdqgadOmaN68ORo0aAB3d3d4enrC2dlZu9V85MiRMtvLz58/R/v27TF48GBRfy6DwdfDw0PUJ7Z2bFfhQr4ZmBRC4FMAiSyn4wAAIABJREFURMpVpbXM0JnSXTlJOUEq391GjrWz5VRQxxChipBIMdGQy6U6AMjOzsbjx4+128OJiYm4f/8+bt++jZycHG2A1axeq1SpAoVCAWdnZ7z55pto2rQpmjZtCg8PD3h4eMDd3R1OTk6MnluT6Qy83t21t7fH0qVLy3xdDAaD75kzZ0R94sqCzSpcqDcDkxUjnwIgUq4qLWmGLgTdlZNUEyRL6W4jp4I6hgi1WyPVREOqS3UlJSVIS0vTbg9rVq+JiYl4+PAhCgsLUVBQUGb1qgmwLVq0QLNmzdCiRQs0aNBAG2BdXV0FCZCax1Cr1cjKysLChQtFvdarwai8JJGOUG8GJitGTQGQceGRsl5VymmGLqakpCRkZ2fjyA8/4N97dsJBoUD280xRJ0iW1N1GqFWl2ITYrZF6oiHEpbrc3Fzt6jU1NRUPHz5EYmIi7ty5g8zMTG2AVavVsLGx0QbXWrVq4c0330SzZs0qrF6rV6/Oa0xMaLafMzIy4Ovriy+++EL05wQAGzWTxrACMNRQmIjHUEN0Xfdv3sCR9WswdM58xqtKc3YM0kxKrl67VmZSYsn9fI01NL/y80mcP/Qjpq/ZwKtpuiFCN7QX+5qx0OMVC5P3nmZs+t5XmmTDIQuXsH7u3Qvm4Mfvtwm+O6VWq/Hs2TPt6jUlJUW7ek1MTNRuDRcVFQEA7O3ttQG2YcOG2hVso0aNtAG2bt26sLc37xrwl19+weTJk1G7dm0cO3YMbm5ugj6+odhHK18rxnTF6OHignNbNqAQNrJfVZo7mU5oprZ83/+8P9wbN8bGWf/HaoLE5O8k5HEWqa4ZC7GqlCKpjO9ujbmSDfPz86FSqbTXXjWr17t37+LZs2fa1WtpaWmZ1auTkxPatm2LZs2aoVmzZvD09IS7uzs8PDxQo0aNMudo5UbTWGHBggWCB16jz0sr38qByYrRGleV5mbsgz4nJ4dxIFE9fICjG9chL/sVPhk2HM3bddB+kN+7fg1xRw+jbq1aiF4eyXiCNDM4GDmubpy2Nm9fvojq6U+xYtkyoxMIXWkpyTi8Yhnva8ZcV5XGdhju34hH/MljoiSVcX1fKZVK1hMNU7searUaz58/165eU1NTtSvXO3fuaFevhYWFAF6XXtRkDnt6empXr15eXtrV6xtvvFGhJoQlefDgAY4ePYpp06aJMkkwFPso+BIiAiYf9I+SktB70mS09u3M+HHvXY/D4TWrUMXJCTmZmXBwsEePjz/B/LlzWE+QunTrpl2hslWYn4+jEYuxftUqVgEiNzsLu+eF8b5mrFQqGa0qNZMRqScIQuEy0fDz9cWTJ0+0R3MMFZYoKSmBjY1NmaM5rVu31q5e69evr1291q5dW9arVz7E3gmh4EuIRNh80BtrAWjImmkB+ODdTpgbGsrrw6Gjnx+GR3CrX6tWq7ErZCbq1K5j1muwTFaVbFeQmgnCyvCl+OXf/zbLmWdd5ScaTdu2Q0lRETKeqHDnymXcPn8OKCrCmy1a4NGjR0YLS7zxxhva1WuTJk20wdXNzQ0KhUKyn0kOpNoJoWu+hEiAbfbwuPBIbA6ZhYFBs+HeuAmj5+g2ZBiqpz/lHQD4HmextbERrQQiU0xyANjWSH6Zno5iO3tMmR2CboOHmeXMc/nCEn17foa4uDj8sn4Nnj17hpK/juTY29ujevXqcHR0xP379xkXlrAkI0eOxPbt2xnd9oMPPsB//vMfk7fTN0FOTbiHtOQ/0f7j7pyO1w0bNgy7d+8GAPz3v/9FmzZtjN6egi8hAuJSDH9wyBwc2bAWY5dEMLqPUKUS+R5nsbW1g4+IJRCFwDap7P7NGzi2aT2GhM41eea5e5++qFmtKurWq8dpRcylsERVJye08/HhXViiMis/Qc7PycGRDetw6VgMPvxyENp/3F17WzGP11HwJUQgfLKH1aVqZKhU2uInxghVKpHPudkze3bhZfozfMGjg48UtZYXL12KEnsHbJgViKKCAjgoFKjfvCX8+var8LtWPXyAY5vWMzr3Xq9BQ0xdsx6bQ2ah01fDkPPiRYUVsZCFJTSrV6EKS1iSadOmlSlffObMGaxevRoAMGjQIHz11Vfa77m6upp8vPIT5D//uI2LRw4ZvL1utyghj6tR8DUzc9bSJcLiUwz/vX6f49LRmDIdrwwRqlQin+MsimrVUBOQba1lzfW8JJUK3YYMQzOf9tqt48Qb8Ti0OgrA60Izmu3+45s3YEjoXFa7Fv8IDMLhdavx2dej0bLLh0i+cxvvde0KT3d3lJaUGCwsUbNmTXTs2NFshSUsSfv27dG+fXvt/1+8eKH995tvvsmqr4CY3aLYouBrJpZQS5eww7cYfuzBA4xuK2SpRC7nZveGL8GgWSE4vHaVLGst617P66Nn67i1rx9a/7V1rEl4q13vDQCo8KGsVqvx6kUmsjIy8DI9HS/SnuLZo1Skp6Yi/VEqiouK8OJZGrYtmANbWzvY2tvBxt4B9+7dQ9Vq1aBwdESNGjXwVps2GDNqFDp27CiLwhKVwdGjR7F27Vpcv34d2dnZaNy4MWrWrg3vz/6eIB/fshGndv59PfnsD3tx9oe9GDZnPjp++hkA4I/fL+PsD3uRcu8OmjZtCmdnZ7Rr1w4zZ85Ejx49OI+PXgFmYCm1dAk7fIvhFxXkM7qtkKUS2dbO1gQrN6/GsuiMVR6XhLeNwTNR5w03NHnbBzfOnkHGk8d4lpqKZ8nJePXyBYqLilBcVAT1X4Ul7BzsYe/gAAeFIxo2bYYGb76JgpxcvNWlK66d+gX2Varg/f8LQvN2HcpMqBevjKIJtURmzpyJlSvLZvL/8ccfAIAHKanwatMGtVzrmnycm7H/wffz50D3UFB6ejpOnTqF06dP4/Tp0/jwww85jZGCr8QsqZYuYYdv9rCDwvT90lKSUQVqQUsHmqrGlBB/DRePxMDG1gaDZoXAzasxAHl2xtKX8KZWq5GblYWsjHS8zEjHi7S016vXR6lIS05GQW4uUu7ewYNbNwEAtnZ2sHdwgL2DA2rXrYu6DRrC1bM+nN3cUMu1Lmq5uKKGszPs/lq9FubnY0NQIH4/cYxRohZNqMV14MABbeCtXbs2goOD8cYbb2D79u2IjY3Fs9QU7Fq8EJOj16DDJz1gZ++An7dtAQC0eb8LOn3WCw1btgLw+lKEWq2GompVfDZyDG79ehK9evTA2rVrUVpail27dlHwtRRcsmHFuNhPhMc3e7h+8xZGb5OTlYXdS/4Je7Ua52JjBf3w1tfQ4+7de1Db2qBt148wYFpghQQlOXXGKiwsxNWrV/H46VM8fnAfyosXkJaagmcpycjKyEBJURGKi4tQWvLX6tX+9erVzsEBDVu1QklJMT74YhA8mzdHTRcX1HJ1hVN1ZmURHRQKvHiWhqAt22hCLQORkZEAXpeNPHnyJHx9fQEAX3/9NWrXqYNX2dlIiL+OP/+4jUatWqNxm7e0963rWR9vd/kAAFBaWorBs+fgUWICatethxYdOuL5HSXWrFmDPXv2IDMzE48fP+Y8Tgq+EpLTxX45+e233xAUEoo/U5JRUlwCO3s7NGrQEMvDl+L999839/AY45M9fP6ng/jy/4IMfl+z5es/aQrcmzQR7cNbc242KSkJH/Togc/GTDC6spWqM5ZarcbLly+1mcOPHj3SZg7fvn0bBQUFUKlUKAaQGr0Stra22uBao04d1GvYCK4ennDx8EBNF1fUcnVFTWcX2FepAgD438ULuH8zHi07vsNqXMDrXQtnd3eaUMtAbm4urly5AgBo27atNvACr0tl+vr64t+nTgF4faysUSvDpVhtbW3RuM1baNzmLTxJSsKxTevxIj0dzZo1Q2ZmJgBom0hwQcFXQnyyYaU6FykEphncMTExmDwjEE61aqHbkGH4okxG6nWMmjIVeS9fYm10FPr162e+H4ghPtnDGarHOPhdFN7r93mFLd+zP+yFY7VqZbZ8xf7wXr1uHWwdqqCZiaNE7o2boM/4SdgcMguDQ+YwLoFYtWpVzAwOLvMa6di+PQb94x+ws7MrUxYxISEBqamp2nOvukdzNGUR27Rpg1d5efAd8CVcPT1Ry8UVtVzromrNmoxWry3ad8C/d+9g90v6y73r1+DV2nhBhfLYTqjpVAQzGRkZ2n97enpW+P5XgwZpg29uVpbJx3tw6yb2r1yGJw8fAgAUCgXeeecdqFQq5Obmgk+BSAq+EuKbDSvFuUg+2GRwnz5zBt+uWIGx3y7Te42stW9ntPbtjLSUZEyYPh1/Jidj2tSpZvrJmOOaPTx60bdwUDji0tEYxB48gKKCfDgoHOHepAlKS0srFOAQezfkSlwcbO3sGF2/btrWBwODZuPIhrVQl6orTCB0ay3PDZ6F0DlzkZmdDY9WreFUvyHynz5F0sMH+O//lFi/di2gVqNmzZrawKpQKODs7Kzt+dqkSRO9hSU6+vmhS39uvVgdFAq8ev6c033P//QjBgXNZn0/JhNqOhXBTr169bT/fvToUYXvayZuAFC9dm2jj5Xz8iU2hQQhPycHrp710cjTExfPx6JKlSpwd3fnfVSOgq+E+GbDinkuki82Gdx9+g9AbkEBZqzbyOga2fS1G/Ht5Alo1LCh7FfAfLKHAVQ456tWq7Fm+mS99xdzN0TzWmWaQObeuAnGLonAs0epiD2wH79s34YXT5/A1cUFtWrUQJ3atZFw5w98PXIkHGvUANTA49TUMtdeG7RoCVdPTzhWqwbluf9gfmgI/P39GReW4JvwVlJSwmnXwt7BAc5upoujlGdqQk2nIthTKBRo164d4uPjcfPmTVy5cgWdOnUC8Poa7pYtW7S39WzeHABgY/v3zojuSvbetTjk5+QAAGxLS7Bj21ZUqVIFjx49QlpaGu+xUvCVEN8PB825SLltQbHN4HZyccHQqTNYXSMbsyQCUwIDZR98gYrZwz49eqF5+w5lVoIXYg5XyB7Wx1gWtJi7IU5OTqj5hrveo0SF+fl4mZ6OrIx0vHj2DBmqx0h/lIpHiQnIfPoERQUFKC0pAdRASUEBCuvUQebz53jy9Cm83+sCN6/GcPWsj1quf197VZQ789v1iy8RPS8Mfn5+ZVYzxvBJeEu4fg1N3nqb9fXrLWGzMXrRt6yfDzA+oaZTEdxNmTIFY8aMgVqtxmeffabNdt6xYwfi4uIAAJ06dcLZ7dtQy7Uuqui8v+5dv4YrP5+Ae+MmcKxWTfv1ovx8xMbG4uzZs4iKikJpaSkAoKCggPM4KfhKiG82bONGjdC9Zy/ZbUGxyeDOUKlgZ2fHKelMUaMWfvvtN4tIwtLNHv5q2HCc2r0DdnZ2cFA4on7zFnqzh/UxlgUt1m6IWq1Gm1at8GdeIX7Z/i+9hSWKi4pQUlIMqAGo1cjPzYGNjS3qNWyIhq1aw62RF2o4OyMrPR0P4q4g7fFjjFjwT8btE7kkJfFJeLt4NAZfTA9E5tOnjK9f7w1fAqdq1TitegHjhUboVAR3o0ePxu+//45NmzYhMzMToaGhZb7v7e2No0eP4tmzZwicFYyM7GzY2dujpLgYjxMTsGfpYnTq2RvFr7LhVLUq8nJzkZmZiUmTJmkfw8HBAUVFRUhNTeU8Tgq+EuKVDbtvD9SlJfgiOExWW1BsM7gvHY1BZ//+nJ6r25ChCAoJxeXfznO6vzl4eXlh366dGDRqNIYsXML6/hdiDuOL6YF6v8enSlR+fj5UKpW2qP/Dhw+1DdXT09ORk5MD1dOnUKvVOLl1MxwcHcsUlnD1rI+69evj1cuXuPrLCYyPWA7P5i30Jjd1/cdAbbBSOFVl3D6R7XVtPglvNrY2cHZzh7ObOwYGzcYPkeGoYm+PLgO/qnD9WnfXIu7XXwQvNEKnIvjbuHEj+vTpg/Xr1+Pq1avIzs5GkyZNMGjQIMycORPVq1dHvXr1tBPkyVOn4j9nzyIvNxdVqlSBM0qxfsd2qNVqzJo1C7GxsSgoKEDz5s0xatQoZGRkYMGCBUhKSsL//vc/kx2M9KF+vhLr3rMX6/6nyksXcHzjekxZvU7ShuVMzAwORo6rG+MJxYZZgRi9aCnnBu5R40bh8Z9JrO9rblz+7mkpyUa7Hd2+fBHV05+yuuY7ffp0/PzzzwBen40tLCwE8PoYhibBycPDAy1atMC/z5yBVydf/O+38xg+/xvUa9BIW1gCeN2IYP/yCMbbtLnZWazbJ7L9Gdn27s3JysKW0FkVtv8L8/OxbORQ1KhRA7a2trCzs0Pmi5do3vEdfDx4mHbXIkOlwqHVURj7Lfvr7rsXzMGP32+rcN6Z7XtKF5fXBBGXodhXudpjyED08kgcWh6B3GzTae7AX+ciV3+HUYuXctqCEtuVuDg0ZdHZpqiggFfSmW62oiXh8nffG74EfcYZbrQQf/I4pk3Wn4xlyIEDB6BSqeDj44OhQ4diyZIl2L59O06ePIkrV67gf//7H06dOoW1a9fipwMHkPrfW+g9fhL2L1+GDFXZggJcGhEMDpmD41s2mrzt/7N33lFRnG0Uv0uvKlVQEVTsvUbAFttnBSTGFluMDawEKaLY6CCKDVEQC2CNBXvDROxKURNMFEWCBUXBQi+y3x846+wyuztlQYjzOyfnGHZ2ZrbNM+9T7s3JysLJ7eG4fOgAYvbtQ9+BA+Hi5oaMjAyZzyMa3mK8PJH9LFPmttnPMhG51FWs4Y1AVV0d9Y0bwnFTOAb/PKtSZMGyBYQ5b3E2fDMe3LyO0uJi6JuYoKJCiDfPn8l9TZLHliY0wvQ3RcaySzfcSUpi9VyemoVPO9cwTLthD/l5o5GFRa1NQTHt4FZVV+fUdFZXBem5dkFTbcNGJcrf3x8hISHYtm0bGsgZtSCf8+DJU8VGifRNGgGoakQgD3n2iVlP03EqIhwAYG07BkOnTmfc10A0vA0dOQp6jRrDxn6MzNQx1XtMNLpJlnSOhQRhhbsbbicl4USgDwoLC6FRUYHoVV6Yu36jQoRG/stTETxfqJtXsjqOPC1d8lxkHysrqLVqy+o4NSHMwbSDm4sYf1pKEpqaMbvY1ybYaihLwlYlCgDGjh2LkJAQ7Nq1C4sXL2Z0zpoqKrD8zgppKcn451YYRs1xlPt8KqTZJzIxspfX19C+fXtMGPcjXimpIf3+fbHZaToNb1SNbkRXsffnkg75d0WMBdG5sSKERtq1o1ZXUtRUBE/thq/5fmWIsaE7SUkoLCyElpYWenbvLhob6jtwoKirmSmlxcU4EeiDhPj4ajjzSpjWp7jUyDYtcMLusM2wsbFh/NzahuTnXlEhxKvXrzFm8a9o1a2H1OeRL95sG+p++eUXpKSkIDExkZExO/mcHz5Kw5Idu1l/L6O8PDE3+IvrDJv6sby+hoyMjCqNbjlZWbhxIg7P0x5+Xt2qo0nL1rAabScWjCOWuuGHRc6UnczZzzJxJ3ZPla7i1NRUWjfUoWuDpQZegK/5/teQFvv4le9XhtDSlUZtT0Ex7eDmIsZfkvdBIYG3NsxJU33uxMX7TtxRmRfvgzHRMi/e8li2bBnGjBmD69evMxrbIp9zDysrhdonsqkfyxutIXc/l5eVSU1nP76bgqOb1gOo1KpWVlERdT9TIa2kQ2VOQb6hPrgzitb3i8tUBFO3qNrwW/hW4YNvLae2p6DYjHeMnDUXMT6rMSd4He0a2Y5l7ti+caPM7eRdSGqDVJ+sc1TUxVsezZs3h4aGBlxdXXHjxg1W+1CkfWJOVhYAdvVjeX0NoWuDMcrBAVBRw0/LvKRImVqh3ed0dqyfD0qLizBt5RqZx5ZV0pF3Qy0PLiNTdPsAasNv4VuH73au5RDCHGyoLsNySZh28tYzMERRQQFCnWbT6kjdOH8OPF1cYGtrS7lNamoqhgwfgfE/z0CBoQlsPbwwJXAdbD28UGBogvE/z8B3ffrAduyP+G7KdExa7Yu2vb+s3Ih64qTVvug1eRrGTZmKywkJjN6DjIwMuLi5oe/AgZUawxLduXTOccjwEUhNTRVdvBPi45F44wYS4uMREhSk0JXIypUrUVhYiKzPgY8pXL+X5Hoql9lvIgjKQlVNHbOD1soNZMZmTTEnaC1UVFXlCuZXd1cxq6mIkCCErg2Wu+3lhASMmzK12n4L1cGqVasgEAggEAiwa9eur306lOzatUt0jqGhoXK354NvLWeBkxNSzpxk9Vw2oyhsYDPeMX6JO0bNcUKkpzs2LnBE6o1rKC0uhlAoRGlxMVJvXMOmBU7Y47UU2zdulGqqQPdCMthxASqUlPGR5HpCBVmqLzU1Ve5rlxdU7SdMhJmFBQaPHFWrLnZDhw6FkpIS1q9fz+r5XL6XV44chrXtF5nQ52kP5bonSUNeEFy8xBXjGKazp3itkjsOVd0lHaa/qb0rPGU2cRGQZSvp3Iww+S1UB8RNbdTu3aK/7T94UO7IWV2ATzvXcmoiBaUIyF2xT1++xPcTf0LLrvL1jDv16Ycn9+7hTFQEjm3eWClZWCFES0tL7NqySWZNkqn+7eygtbREHuhK9ckSvs/Jeolrx4+iuLQEAg1NLAwJrVUavSoqKhg9ejSOHz8OHx8fqH32taULV/vE30JDYG07Bq26dec8+y0ZBIm0fcKVK3hXWIxRLNLZJYVF+C00BG9fPKdszKqJkg6TqQi6fQB1RbZSMi3euk8/PPsccEt06v0n0uJ88K0DsLGpYzuKwgWiZjlzzhwkX7yAK0cO0xrvaNG5M+Zv2AyAWbcmmwvJRI9lMlWjCOTVE2UFfvLITFzYJoxf4lErL3bz589HXFwcTp8+DXt7e8bP52qfGL8vBnFhmyD89EkhfQ2SF2xdi+bo1pVZ2YWYM1ZWUUa773rDsmt3ysasNr1610hJR5F9AHVFtpLqpjbj7y8rb9MWlvhu/qI67+bEp53rANWVgqouli9dik9FhZgbvA4LNoZhbvA6jJo9l5aRAN1UOZcLCSHyIA9Z9URpgT/raTpObt+KWQHBUFKuvLflcrGrToyNjaGjowM/P3auPMT3co+nO2M1KQNTU4z71RXGZk3R5rveeMyxr4Gq/JCV/oRROvvJvbs4uDYQdk4LMDd4PdpZ2YiVB9r1tsJMvyDYOs7H1aOH8V0P6SNhikYRfQCbwsLQdfgoVsenU1tXBIpMi586dQrDhw+HsbExNDQ00LZtWyxfvhwfPnyosu2LFy8wb948WFpaVjp61auHDh06YPXq1ZTORWFhYWjfvj00NDTQqlUrbN26lfFr5YNvHYFIQd2J3YPYlctE8nZEjfTBzeuIXbkMd2L34GBM9Fe9EywoKMCLjAyFSu5JwuVCQog8yENaPVFW4CePzFR3I5EiWLduHUpKSpCWlsbq+f379UOv7t1wIDgAEUvdKL+XEUvdcDx8C8a7elQxVbCxs4ewQojrx4+yOn7KmVMYNmQI5QWbSTqbfNNE56K/cPNWrAkI/Gq1UDbUBdlKLmlxMu7u7hg1ahTOnj2LN2/eoKSkBP/88w98fX3Rs2dPMTeid+/eYcCAAQgLC8OTJ09QXFyMvLw8pKamYtWqVZg5c6bYvp2dnTFv3jw8ePBA9NtxcnLC2rVrGb1WPvjWIYgU1MGdUdB5+xonAn0Q4+GCE4E+0Hn7Ggd3RuH86VNfbcVLsHiJK8YsXIy9/j6MujV3eXnS6tYEuF9Inqc9krudtKYaaYFfcmSmOhuJFEWvXr2goqKClStXst5HekYG5gStg8NCZ6Tfv48oL09sXjQPUV6eSL9/Hw4LnTHTN5BSscuySzeRZjTbm7WgdespL9iElCkduMwZ1xVqu2aAItLiAHDs2DEEfS5b1atXD76+voiKisL3338PAEhLS8OkSZNEz4+MjMTjx48BAD/99BP279+PTZs2oWHDhgCAAwcOoKysDACQkpIi6mRWVlaGi4sLdu3aheHDhzO+EeNrvnUQrnOE1QnxA2rX2xrqmlqMvFHrN2hAu4mF64VEUuSBCmlNNbcTE2Hr4VXl75IrXUU3ElUHAoEAM2fORHh4OPLz86Gjo8N4H8RnYWBqWkUyUh7EZ2HntAQxvmswJyiEUV/D+sAALPfzp7xg05UyJW6alJRVcHJ7uFz1K4K6ZuFX2zUDFJEWDwkKQnDwlxv406dPi4R5pk2bBmtra9y6dQtXrlzB9evXYW1tjXHjxsHc3ByPHz/G0qVLRbaYmZmZCA4ORllZGd68eYNGjRrh0KFDon2vXLkSXl6V14GpU6eiW7duuHv3Lu1z5oMvj0Ih/4BadO6CcUvcxQT5ZQnc577Koq1FrUiRB2mkJSdRNtVIC/zP0x5i6NTpov/naiJRUxq9kydPRnh4OPbv318lxUYHeZ+FLElHXT09FHz4gKjlS6Gqro6tLoswxWs1bX3k4ydPSr1gW422w9FN6+UG3wvRu1GUn4+jm9bLVb+S7JKvCf10QDFKVMRsNhvlrJrQDJB2U0sHyy7dcCLQByUlJSLhmA4dOogp4ikpKWHWrFm4desWACAhIQHW1tYwNzeHubk53r17h1OnTuHmzZu4evUqrl27JnousfIlr27Hjh0r+rdAIICDgwMffHm+HpI/INNmzTHTN1B0AZYlcK9vYooTgT60jsP1QiIpmk/FpX0xiD9etTYsLdhIrnS5mEjUlEAKAOjq6qJ169bYunUrfvnlF9GdP12kfRayHIqIoFaYl4/iwkLMW78RRk3MkPU0XerN2qPkJCSeOAaj+vVFozXLV62SesGmI2X65N5dPP3rPmb6BcpVvyKcpsh1a+KiX10oUomqJmUr2aCItHhubq5IIKVx48ZVtiP/LTc3FwDw8eNHzJs3D/v37xdZlrZs2RItW7bE33//DQCifRYUFIieb2BgILZvY2NjRufMB18ehSLtB0QnJckk1crlQnIt7hh+WOQsc5vsZ5ko+vCBsvlLWrCRXOnSXXlRURMXOzJr1qzB+PHC/jqQAAAgAElEQVTjkZSUhB4Mu3ipPgt5DkXkoBbjuwYfc3Jg1MRM5s2aiYU5Ct6/R8z2baK+BnkX7JGz5mKvvw+lYQPRZLVoSzitGexZAcFV5sRV1dXx6FEaUlNTFT5vKmuOnKnLE1D7NQMUkRY3MjKCQCCAUCjEixcvqmxH/puRkREAYMGCBYiJiYGysjI2btyICRMmwMjICF5eXvDxEb+xql+/vujfb968EQu42dnZjM6Zb7jiESFPIpEOxA+IDUxSreQLCROyn2XKFM0HKuuJ0d4rMWTg95SPS1N3Ila6BOSVF9NzrEmBFABo06YN1NTUsGTJEsbPlfwsmHYOzw1eh5PbtyLrabro78TNGnlczX7eIvzo6SXW5CTv+2barDlGzXZEhIdrlXEoNk1WEz2WialflZWUQNfISOHKZNWlREUlW5mTlYWT28MR7uqMTQudEO7qjJPbw5GTlcVItpIripDSVVFREd08/vXXX2La5RUVFYiMjBT9f//+/QEAR49Wdto3adIECxYsEAXllJSq59Kly5esB9mpqKKiQqweTAd+5fuNQq4hffjwATk5uVBRV0OfMT9ySm3VZF2JjchDrO8aTPRYRvl4TlYW4vfF4OGd24BQiL8fpcHFza1KXU3aCoJqpStr5SXtHOkKpCjakcbDwwNr1qzB27dvYWhoyOi55M+CbVBjI35C5/tG1Xugb9IIALsZbGJO3MDUFI/vJsOiXQcMnDhJocpk1aVERcxmOy12hs34iaKRO6qywOHQELx9lolAX58amaCgm83682oCcl+Jz+n/+fsl/Ghvh7Nnz2L+/PmYNm0aAGDEiBFwc3ODiYkJYmJicPPmTQDA4MGD0atXLwCVHdF5eXn4999/MX/+fFhbW+PYsWM4derL+0jM+k6aNAlr1qxBeXk5vL29UVxcjDZt2iAmJgZ//vkno9ervGrVqlWMnsGSAwcOYPz48TVxKB4ZpKam4qdp0xF7+DAMOneD1djx6DnKFj2Hj4R+o8ZIvHAW9y7/DtPmLdDAyAhGTczQ8ftBaGDRDN6uS9ChbVtYyFiRtW7ZEpvWBqPj94MYn9v5iHCE+PmiQYMGtLY3NjZGh7Zt4e/uBrP2HaBNSglJkv0sEztXeEJFVRXpf96Hpo426hsaQUlZGc8e/oM9a1biz6sJ+G74SNjOnYeBEyahtU1fvP6Yj01rg3HwwAF079pFlGbq3rULvF2XoK1NH6iqqwMAtHR1kRx/AWat20C7XuW56OrpwaiJGfYH+qF5p85yz/E3vzXYEroePWWkfqk+w27DR8Kyt43U86VDixYtEBkZiYKCAtGqgC7EZ7F68SIoqamj3w8/Mnq+dv36SLp4ARbtO0BLV1fmtiqamrh7+Q/8b8gQ2t83XT09dBs0BBbtO+DPKwm4EL0LgyZNltnYJQ1NHW38eSUBrbr3wLEtm/C/adNRz8AQZu07ICpkLab89BPjfZLJyMhA7OHD6G3HbEZcu3593LlwDkMG9Jf5G7IwN4eOhgbCQ0Iwwd0TfceMhVETMyirVK7FlFVUYNTEDN2HDEXrXt8hav06ub97RdCgQQMcPHAAes2ai34/BI/vJotWxdmZmXhyN0Xsv7wP73Hr1i0YGhrC3d0db968wZ07d1BcXIz4+HjExcXh6dOnAIC2bdsiLi5O1Nn//v17JHzOWty5cwdHjhzBgwcPoKqqioqKCgCAvb09mjdvDn19fairq+PixYuoqKjAlStXcPToUTx58gQjR44UzcsPGzYMvXv3BiA99vHB9xvickICps+Zi8GO89HbbgzlD67boCFo0qo1DgRVjm/om5gAqPxht7XpgwB3N/Tv00fqRV3WD0gW2c8ykfv3AyyY58ToNVmYm2NAnz6IClmLOxfOQUVTUxRUy0pK8M+dW9gX4Icn91IwdrELBk2aLLoAX/7tIP44sA/3Lv+OqStW4/vxk2Bs1rTKe0J18yEt8Js2b4H9Qf7o1K+/KCjrm5jAvF17nNy+FUkXL4gF/rKSEvx9+xbOhG/B+4f/YEf4VpmBl85nyORmiYyKigr++ecfnD17FjNnzoSSErOqlIW5OVJSktHMpi+MWAQ1dS0t7PX3RuqNa3iVkQGDRo0pA3F9QyP8fugAfp42jfH3TUtXF62698Dd3+MxdMp00XvHhPqGRrj820E0bdsWj5LuwOZzkKQb/OTh7ecHg87dGK/KAfEbE2mkpqZisbsHZgavQwMj2TdndH/3ioLqphYQD76y6N27N4YNG4aRI0eiZ8+e+PDhA96/f49Pnz6hZcuWcHR0xM6dO8Wapfr27QstLS1kZmaisLAQ5ubmsLe3x7Zt2xAREQEA0NPTw7BhwwAAffr0QYsWLZCWloYPHz7A0tISPj4+mDFjhkjpik7wFQjleWcpCAcHB7EcOU/NkpqayihFW5j3kdKEIPtZJu7E7pGZ2mJ6rIKPH7F3hSdnk3giDbv/4CGoaetAQ0cHTVq2QmPLlkg4fLDKvHHW03QcXBtIOyVcmPcRsV6eYqnF1NTUKsL3mQ//xsltlc1GkiurL2M3j1CUn4fCDx8wcEB/BPj4yE0Vs/kMJc9XHs+fP8fIkSOxdu1aDJFxAZdG34EDRWULppQWFyPKyxMzvP3w+G6KSPVKcsRHKBQixsMFiZ/reWy+b+tnz8Dy/b8xPkfi+BsXOAJCoZhJCMBMm1waXN/DE4E+SIiPl7rNkOEj8N2U6YybruT97hUF0Whm7+JGe+SsNms7S4t9fM33G0FRJgR0hAXIdSUmPyCudSVCfGTGtEprvskrVoler0mzZlVGWLioGhEXIbLw/WofH4SGbUZZeTlUNTWxe5UX6hkaoo+9g8jhSVdPD807dcKHF89gZqCP0LXB0NLSolW7ZfoZFuUXoH5TcwwdOQqmpia0asJNmjSBpqYmli9fzir4KkL8RN6ID1VjnqaaGjbOd8QMH39a3zczsyacOms/5uRgkseyKqpdihg9qk4lqrpgrlAdbk61ET74fgMowoSArPBDR1jga/6AqIK/5AjLxb0xgFCosIvQv5mZuJ2cgp8l5kWJ4xEOTwIlJRTk5mJdYAA6duiARS5LaM1wamtr0/4MJedr7Z0WMGqgCwgIwKJFi/Dvv/8y7rgmuo/z3r2TKqwhzWCDSvyEasSH3JhHHscpLyuTOiOclpKE+NhoGNWvh4Mx0dixaxfrxsBHyUlo3aNnFZ1qQDHKZNWpRKUoFanqRpFuTrUVPu38DeDi5oYCQxNWF5oHN68j/f59sRldOqktMsQP6E5SktgPiG1XLl2oUsLExXh/sD+6Dx6K9lY28nckgWRqkVVK/9eFUFPXwFiPZTIDavazTBwLCUKHNq2h16WH3M9Q1nwt1X6pZkM/ffqEnj17omfPnti2TbapvCTTZszAteQUaOrowNp2DCy7dBXroJWWSgaov2vk8yWyMLErl+HQrp3Iz8+nfN/JqX2yoEvXgQNxevNGHIjeA21tbYz/eQYmrfZl9PoAIGKpG35Y5Ew5rsb0t0EF19+rrLR3dae0earCp52/YbjKtiUcFq+NMb27/1pa1LLunt+/eImWDL1eCSRTi0zTwR/evkWFsgqm+QfREneY7O2HTfMdMXHAYNHfqSQb9RuaIPPhP3Bat4H2fqnGY5SVlTFx4kTExsaK0uB0uJyQgNtJyZjg7slKLUqW+AmRhXmUnCiagR4yfATl+y5L0IVcNmArOCFrTlwRymTVqURV280VviV4kY1vAEWbENSk7rAioPJC1dNroJCLEJuU/qmIcExevpJRrflnH3+cj96FrKfpiPR0w9FN69G8U2fM8PbHgo1hmOHtj/Y2fVBP3wB7/X3EBCtk7VeaM88vv/wCoVCIY8eO0TpHQhRiKoVMoyREKpksrEFH/MTa1g7HQtchdG2wQmqXVIITsij4+LHypmGWdKU2un7UsuAiICNPnKWmRHB45MMH328Arj84yTpcTeoOVxeKuggxraFJ2g7SxdisKQrev8f+IH/YOS3ATL8gtOttJWb23t7KBrMD18LWcT4Org3Ek3vUIu9kRaMDwQG4n/oXZjs6iimZ6evrw9TUFCEhIaBTmWLb0HcqchutoAYALbt2R8OGDdGuXTuF1C6J3oAYL88q6leSZD/LRORSV4ya7UhpjUhsoyhlMjY3BnSUqBShIsWjGPjg+w3A9QcnaUKgiLt7LihCBlNRFyGmvsKStoN0yXqajvKyUswJCmG1siT2QbVqdonYBUEzS4z/eQaGDB8hkigMDAxEWVmZXMlCLqvQT2Xl2OqySGZQI1BVV4eSUqXpg6KM4YnGwDuxexC7chke3LyO0uJiCIVClBYXI/XGNUQsdcPx8C0Y7+pB2WQFVAa/mNVeyMl5y/o7SYbpjcHeFZ60JgakSaPS4Wv/7v9r8DXfbwBFmhB8Dd1hgtro8MI0pS9pO0iXUxHhmLpiDetRMflGB9Zo19taTKS/X9++UFVVhYeHB06erHrBJmrpx46fQJlQiHBXZ7kdzZLY2DvgwY1rUoMaGXLGQZG1S2m9ARUVQrx6/RpjFv+KVt2kC59kP8tE1PKl6DVsBPqM+YH1d1KS6pgYqO3mCt8SfPD9BuDygyPX4ZjoDiua2urwwnQsRNJ2kA5cUtXCCiH+vn0LZ3dG0jKql2zEWrx4MYKDg/H+/XuRapPkTdDcjWG0/W8ladWtO64ePUzr9ZAzDtUxjkPVGEh0zN+JO0oR/JJwMSYaahqamL7aR2zlzuY7SUX79u2xPWwLfPz9cWHHdsQVFUFYIYSurg4GDhjAauSGjSb61/rd/5fhR42+EdioAEUudRUp+HxNNZnqUnbiqsSVkZGBCZOn4PX791BWUaE1y7pp4TzMCQqpEjRkGc7fOBGH5p06s7ImvHrsCC7ti4VjSCgrRaNjvx2CtbU1pk2bBmdnZ5k3QZLPp+polkQoFGLzonlYsDFM7O9U70fh+3eI3bEDffr0qdZxHCokx+WUlJTw/MVLTF6xCo0t5XtDs1Ebk7zJaUEa23pyNwUpZ06yXlX/11SkajP8qNE3DlPVqb0Bvhg+/RfkvspC/M7Ir6omUxMOL0wuQkKhEEOGj8DH4mJ0oZhllbbye3LvLj7m5uDx3WS0620NgJ7h/Ot//0WnfsyMDojj3Th5HCYWzVh3BWdnZ6NHjx6Ijo7G4MGDRTZ3bPxvqQKqafMWIN/9y3w/UpLg4rUC9TQ04Oq8GF7+ATVmDC+5Kh4yfASGzltE+32l+50kUHSmR5JvRUWqNsOvfL8xZAlPPL6bjCsH9+PD27doUL8eDAwNa0QMQxYZGRn4Yco0TPUNYPzc3cvccSR6Dy3NZDoXodC1wXjz9i2rlR+hIz3W2QVnoiIx0y+IkSAGnVUkGeJ45u3aoVX3XqxWzcQKcb6jI+zt7aGmqYn/LfyV8Qr6cGiISCS/qvBGMi7t2wsNbS106jcAN07E0RYI0VRTw7CFzjWuUczlOxm7cpncVHFNaHiTqQ4RHEXbXdZlpMU+Pvh+o3wt1SmmuLi5Id/AGO1YKFGlXr8G3dxs2ulFee8JF3OKUxHhsHNaAKMmZoj0dIO1rT0uRO9mZOpAZXQhjUhPN9g5LcDhDesww9ufs6JRly5d8Dj9KXxPnmW8nw3z5mLEzFkyRU2yn2Ui0tMddk4L0N7KWu4+C/M+YvdSN5SVlWHm2vXVbuBBDibp6U+hpqMDyy7dGDWXAfRS3rXd+EAW1Zkqr6vwaWceMb6W6hRTrl6/gbEr1rB6bstu3XHYewXt7eW9J2xnWY9sDIWKqoroYjpy1lzs8HTHnOB11WI4T27QYtPgRUDuCm5uaYlnr7PxIect6hsYMtrP4MlT8PDOHZnB19isKRaHbUOEhyv0TUzk3mBo6dbDD+6eiA/fjBgvz2oz8JDVYc+0uQyQb7xQF4wPpFHdqfL/GvycL0+t5tWrV5yCR1bWK4WcB5eLYnlpKTr2+XKRUdPQhKGZGSejC1mQZ4lV1dUVIiaS/fYtNLS0cPngAcb7adm1O56nPZK7HVl4gw7GZk1RoaKK9QH+Uud0H9y8jtiVy3Andg8OxkQzuthfTkjAuClT8d2U6Zi02hdtJURN2vW2wky/ILmiJmTkSTQqQjzka0AonE329qM1h0501MubIf8vwwdfnlpNaVkp4+AhUnBashh5eXmcBQ8AbhfFAeMn4tW/X45940Qc+jn8yGpfNnb2uHEiTuY2z9MewvKzAEWTlq3xWAFiIsUlJejQpx/+SbyF8rIyRvuhkiiVBt0bDIKuw0fi3MWLOH/6FA7ujILO29c4EeiDGA8XnAj0gc7b1zi4MwrnT59ivOJlEkyoRE2okCfRqCjxkJqGS1PktwoffHlqNaoqKrSDh6SC0y++gVh56ChsPbxQYGhSRcGJCVwuiq26dcdfV6/g5PZw5GRliQVHplh26SZ3FUlONVuNthM5CTGFrGikqamJ3iNHQVghxIObNxjth0qiVBZ0bjAIyAGHSsM7JCiIVRqWi1ymLORJNNZF4wNFpMq/Rfjgy1OraWhsjGtx8oPHk3t3cXBtoFTd47a9rTBptS96TZ6GcVOm4nJCAqPz4HpR1NXXR/NOnXF003q8ffGc076K8vNkbiMQCETZAqIZiKtIf68ePfA6IwPqmpq4ELOb0b6oJEplQecGg0BWwGErQ6oI/2tp3DkRJ1OisS4aH9TVVPnXhg++PLWafn37orggX2bwyHqajpPbt2JWQHC11Zu4XhTVNDRFNcIGRsac9vUxJwfb3Fwo65sRS91QmJ+Px3eTRc8ZOWsu9vr7cBLpJzSB7ecvQnlpKaNgfi3uGKxt7WhvzyRNTRVwUlNTMWT4CIz/eQYKDE1g6+GFKYHraGdAuAQTWav27GeZePPiuUyJxrpofFBXU+VfGz748tRqFjg5QVNNTWbwOBURjklLl1drvUmR5hQW7TuKBUcmPEpOQuf+/THWeQnS799HlJcnNi+ahygvT6Tfvw+Hhc74xScA149/sQI0bdYco2Y7IsLDlZZIf/Ry9ypdwYQcp5auLpSVlXF6RwSt86VjFSgJkzS1ZMCR1yRFJwPCNZhQrdoLPn5EpKc7tHR0Za6866LxQV1MldcG+ODLU6uxsLCAiYEB+oz5gTJ4cNE9ZlJv4nJRlFz5VdZh6fnkVtnXsSPo2HcASSmq+LMUZSvRzClVqrlF5y4Yt8Qdx8O3IGKpG+WqeZubC/Z4LcXG4GAcP3mySrrW1XkxjoUEodvgoXj971OUyLlo0rUKlIRJmpoccBTVcato/2vCjrDXsBHQadhQZpq1Or18q4u6mCqvDfBzvjy1HkII3tZxHo6Hb4GwQggbO3tYdumGGyeOwdrWntV+iXoTnXlnRZlTAOJ1WKb7ysl6id/3x0qVogQq08xEqpks4mHarDlm+gaKZB4TDv/2OXhroKG5OQreZMO8qTmW+/lTukZ5+QdAR0sT9y+eR3lpGZIunpf63mc/y8SOZR4Y5+Im1ypQEkknLVnvBzngKEqGlKtpg6r6F/Wua3HHIFASYLyrB/RNTLFjuYfcNKs04wNp+t9dvh+IM1s2fjXjAyIrxEbq81v2COaDL0+tR1KDWVlFVRQ83j5/hqFTf2a1X3mCB5KwcYPZF+CL8a4eVR6jCo7y9hXp6Q77eQurSEUSM6ftPgsYEFKURKp5oscyMQEKA1NTjJr9ZTWa/SwTsSuXQ11LCwNmO8oVSDgc6IcPOW9xclsY6hsZoWXX7mJynBdjo6GmrgFdfX3UMzCQ+9rI0E1TSzrtcGmSep6djZ5WVhCiMvCWlZbh8d0UVpKcj5KTkPsqC1FenmjSshUcFjqLbraEQiHKS0vlplklv++fystl6H8n4+jG9TDQ1UENiRVWQVH2nN8avLwkT52BSoN5m9uvVRxx6CIUChHj4YLEG/RHZ5i4wcjTY35y7y5ObAvDpKXL5e4rcqkb7OYtpC29SEhRAsDJ7VtRnJ+PgRMno2U38UB57dhRfMx9i7LiEizcEk5b6jLK9Vd8Ki6CVZ8+ePbiBR4+fAS9Ro1g1qoNLLt0xbndUZWZiq1bGN1gbJg3B3bzFqB9b+mvU9Jph427FJnUG9fw+G4K7Bzno7S4GHcvxeP22VOYz+J7FbHUDT8scqa8eSgtLsaO5R4w1tVBQny8/PNKTcX0mbPw/NUr/OIbQEvv+mupRtVlSczqhpeX5KnzUJme5+XkKNzXVRZ03GDIqUZZKVeiDrvVZREMTEzx/cSfqqwiU86cQta/GbCfv4j2SkxSinKmXxBW/zgGD25ex5Wjh0Wp5iYtW8Fh0a84umk97JwWMErXjlu2AhsdZ+FZRgYSEhLQw8oKUwLXibZRVVfH8a1bYGPvQLn6loS4WRn80xTcOBGH63HHRKUFaU47Yu5So+1g2bU7K+nHll2748qRSk9hNQ0N9BoxEvevXlZIiYHM47vJ0K5fHz270jPHAIDCkhIs3LyVsQ9zTesm8x7BzOGDL0+dg6zB7OLmVuP1JsmbgPWzpkOzgR7UNDSrpBrlYdioMTq0b489O3aIbijIpg7rvNfAxWsF4xQoeeZUV08Pxubm+PFX1yrbcWlY09bTR1JyMq5cuQKBQEnsJoi4sTgVuQ1KKio4EBwADS1tWNvZi91gPEpOwrWjh/E26yV+8fGHiUUz9Bo2QlTfPB+9GxVFRTA3b4qe3buLHIHk6QhTpeGlZSComqTYlAWklRgIrsUdg3LFJ9odydVlpVkdsLXn/JatCvngy1On4VpvWue9Bi5ubpyszzQ1tVDGcvVN3ABIM3VwcXPjPHParGNHNG3dlnKbSh1odg1ro+Y4Yl+gH2zt7aGurSPmUwxUbfBK/+s+4sI2Q1hRASVlZdQzMIB52/boNOB7PPv7gViWgKhLx65chkO7dop18ZK7mtn4CktCNdpEHs+iu2ofNdtRaqYj+1kmystK0cjQkFZHcl00WOA9gpnBB1+eOg2XLuSsfzPg4rWCsrN3/M8zpFqfSTrdzN0YxtrpRl7Dye3ERNh6eNF+XWQsu3RDwuHfkPU0XWr38JP7dzF06nRW+2/doxdU1dVR8DEPvwQE49zunWLBl0CywUuSjfPnIv/jR+RkZYllDKSNz7CVfpTmCCVttIk8nlX0MQ+DfposFkzSUpJw/Xic3BJDwcePiPXzgUrFJ4TSbC5ShGrU13AtoyoNkTM58ryMvyX44MtT52FTb9qxzB12TtSdw7KszxSZ7qQzm8l15jQvNwdCAAdDgsTGU6xG26G0uAgf3r7htH8lJWXoGRuj4GOlAAqbmyBNXV0M+mkKDoeGQElZCSNnzUU9A0PKmqAipB8lSwKyRpuI1fut0ydx7/gx/H3uNAoLC1FRIcTLrCz88OsStOrWQ+bri/VdA6VP5dgREUF7tcf1potJF391UFcsS78mfPDlqfMwrTdFeLiitKQEl/bFQl1TCy06d6bcVrKJBYDC0p10G07YzpxmPU3HiW1boaqhgaFTpsGyS7cqq/PszEzoNzTh1LCmoqYKTV1dnN4RgYnunqzrpCYWzdDeyqays9vTHaVFRfjf0CFVGuIUIf0oOWZFZ7Sp68DBeH7npliXcmpqKubMn4/zu6IwYMJPaEXqJE9LSULC4UP4kJ2NZmZmiNwWzijNyqtG/ffhgy/PfwJ59aZHyUm4cuQQVFRV8YtvAPRNTPH4bjJO79iGDzlv0cSyJYoLC6usDg1MTUVNLEKhUCHpTiYNJ2wEDJ7cu4uT27di0tLlUlfnDc0tcDg0BI1aWHKaaTVr3QbWo+1xeMM6qGtqcq6TGps1xeKwbdjuvgQVxo2qpP8VkYYnoNMkRUAV0Nq3b4+rv/+OjIwM+AUGYtvuHcjLz4dAIICWpiYGDhiA5UuXskqzchX6+FZVo+oSfPDlqRMQNSRZjVGS9aaoJYvx6vVrGDZuDIt2HTDOxV0s5diutzXa9bau1DP2XolRsxzRumcvytrt69xcqKqpU6Y7pSkPWY22+5zurMCrjAzkvnrJuOGEaUMZ2WRC1k3CjRNx6DPmBzQ0t8DRTetZBd9Le2Mw3tUdRmZNoaSsjAsxuzF+iYeoTkpWImMyiqWlWw+Tli4X3bSQ0/+Kkn6k0yRFRlZAs7CwwPatW1mdkzR41aj/Pnzw5anVSDY30WmMsrCwQD8bG8QePAi3qN20UsSOIRsQ4eGKeoaGMG3WvErtVlVDA71HjRZ7XtbTdBnKQ1+Cd5tevXHAdxXG2Nkxbjhh2lBG12TiedpD0fkC7Gq1H3Nz0NDcAgDQffBQJJ4/i9KSErEu598P7MXRzRtRT1//y2wxjVEsco2WnP7X/Pz+sl0RfvpUjoilbrTmsMnUdEDjVaP++/DBl6fWIq+5SVZj1LzFzhjrvASX9u2lXJFKXvylpYiJ2u2GeXOh00BP9Hc6qV0ieO/190E9HR3WDSh0G8qYzOxW2hxWBjC2Upe6+nqiQNh75CjcOXcGf165jO6DhwKo7HLW0NLGGAYCIWTINVpihvWQ7xrWK8JHyUnIf/cOU5avlhr8pWUxsh8/wvED+xkfky1cuvi/lsECDzN4VyOeWgkXh5ro6GiUlJXh8qEDaN6pM2Z4+2PBxjDM8PYXGdpHeroh62l6lf1QmaFr6dbDL74BOB+9CwBz/+DZgWuRnZPLyD+YDNFQFuPlKdMSkMnMrqq6usiJhqnlYITHEmhq66BFp654/NlmUUu3HuobGuGPA/vFNIafpz2EJQt7vpysLKSlJOPe5d+xaaETwl2dcfvMaSirqyHx5HHG+wOAe+dOo7VlS1R8Kq/yWNbTdER6uuHopvUU35lOKCopwcy5jqw/QzaErg3G0bWBnHyYeWov/MqXp1bCZpaz/9TpGG5rh/zCAtTXN0R5WRnS799DQ3MLGJia0hoFouqIBT4H5k8VyMnKYuUfPMM3gJPyEB0Bgz+v/EF7ZrdJy9ZijVbkmVZ5tdqe/xuGd6yjY7wAACAASURBVK+zYTXaTqxebDvXCdE+q/HqaTpMm7cAIL7CpoN4Kt8ew3+eKZbKf572EP8+SWO9IlwfHFQliyA/i/GlN0Ayw1JdZGRkIGr3bqirqiBk1gzoNWwIi/YdpepV86pRdQ8++PLUOpjOchIX7E+fPmHY7LloSUPjV9ookGRHLBlrO3tc2hcLgJt/MFuRAXkCBiZGxrQDnWTgBGRbDpJrtZLmAUQgNG3eAsoqKjixbStmB64F8GWFTee86KbyU29cQ6SnGxZt2cZYR7hdu3ZVHIPoNKgBNaOfLNnjMNE7QPRdTktJwqF1QVBSVsGo2XNh2KgxrxpVh+GDL0+tg8ksJ5Paq+RKl6rOS6XzS9Cya3ec3LYVo+fS0+aVRFHKQ9IEDPoOHEg70MnyFJalSCU5FytZLx7w43jE74tFYV4etHR1q6ywpUG3SxsA2lvZABBgw7w5+MU3kLGOMDmL8OeDVPziF8RYP9lp4SL06N5NriwpnS59Ank9Du2tbESz0FHLPGBsoI/BgwZhnfcaHD1+HHPmz2ctkcpT8/A1X55ax+3ERLSgUSdkWnudFRCMk9u3itV6Jeu8VDq/BKrq6igtKWZVwwQqV9XyjNS5QIyn0IUInExqivsCfDFq1pfATNSLt7osQvazTHTqNwACgQC3Tp8EULnCvn78qNx9M03lt7eyhq3jfOxc5o7NC53w1/VrKC0uhlAoRGlxMVJvXMPWJYsRtcwDutpaMJTwFW7fvj22h21B46YWrLIYL97m4JWSGmw9vDAlcB1sPbxQYGiC8T/PwJDhIxAXF4chw0dg/M8zUGBoInU7oobMtMdh4ZZwFBQW4dbt23DxWkHrGDy1C37ly/NVkLUioDvLyab2StXRTK7zStP5BT6PqpR/qrXKQ0zHUxRlHtCicxfUr1cfV3dGoOhTBXT19XHn3Bn0GzsOunp6KMzLl1mjldalLWt+2sDUFO2tbBAfGw0rW3tk/PUnrkrYJRJz3dJqtZvCwtBjlC2t90qSQT9NRvr9+6LvArn7PvHCOcxZtAgz/YJod+n7+Acw6nHISk9HuUCAAbOdGE8C8NQO+ODLU6PQmdt9mZWFzH/+RtM21E48ADcrPEmNX3KdV5bO7+O7ydDV1am1ykNsxlNadO6CwZOnVnoKN2qMAeMmVpFJ/P3APnx4+wYaWtq4ELMbf9+6Kdb4k/0sE6aGhqJ6tI+fH/bt3Ytt8+egoYkJ+nTvisOBfpjqF0gZXCq7tMeI/p/u/PTIWXMx6KcpePrnnzKNG6TVahWplkU+92vHjtCqRxPnNXfBQqioUwu4UEFkfOYEr6sVdWoedvDBl6fGYDK3G+29CvbzFko1JZC8YDNBsqOZqPPK0/lNOXMKw4YMYS3HWBNCDWxMJi7G7MEMb3+8fPIYpyLCcbggH1q69VBaUgJVNTX0HzseXQcOogyAAydORvyO7SKNagsLC0Ru344/792DQCAQaSETnz2V9jYh+AEwr+EPm/4Lnqc9kvs6qbxuFaWWRYZNNsbBbSkOBAfQPjabY3wtn18e6fA1X54agWlNyzEktEp9lgzb+VGgctVCvmCXlZRAWVW1Sj2TDDGq4unujrtnTrI6bsqZU7SN1NlCdyYYqHxNkUtdMWq2I8xat8F3I0bBfWc0HEM2oLSkBMOmz4Bb1B58N2KkWHq1XW8rzPQLgq3jfBwI9sfsn6dX6bJdtWoVioqK8PLlSwBfmpzuxO5B7MpleHDzuliNVk1Dg1UN/8yuHSj48I7We0PuOAe+6Cezgao3gEs2RkNLu8p8ORVcjkF+7TxfHz748tQIbD1YT0Vuo3yc6fwoGclVS1pKEt69fi1V55csXkBO7TKhJpWH5AW6BzevI2KpG46Hb8F4V48q2QVjs6Zw3rodV48elnrzQ2y3aMs2bN+5q0pTz5AhQ6CkpIR169aJ/kaMSh3cGQWdt6+xz2spfCb+iI85OSgtLma1opvksQzFhUW03xui4xxg3qBGhqo3gFM2xn4MbpyIk7sdl2OQXzvP14cPvjzVDhcP1opPnyhXBGSFJqZIrlri98Zg7OJfKVPc2c8ysXeFp9ioSl1QHpIMdKGzZ2CD0xxEeXki/f59OCx0xkzfQKnaxvJufsjbESlNMioqKrCzs8PFixdRUlIi9hgxKpV86yYuXzgPvXr1kBx/AcCXFV1OVhZObg9HuKuzSOHq5PbwKt8FY7OmqG9oSGvVCIh3nC9wckIKyyzGtbhjsLa1E/ubIrMx0uB6jOrstudhBl/z5al2uHiwWtuOQfy+GIz71VXs73TnR6kgr1qyn2Wi8MMHfCovR2lxsZiikzTxAqb+wYpQHmIyL0rGwsICC5yccPVOIv43d76oe3ivv7dY9zAAys7iksIiSgN6MpICIsS5Xr1+HY8ePUKXHj0wYvhwynMlbhL6DRmK4bPmMmq2IoRRvh8/kVKVjApyxzkX/WSq3gBFZmOkwfUYvM9v7YEPvjzVDpeu0lbduuP0ti2IXblMTFKxx9BhOLZlA6vgS3Q0EyvSPRHbcfbChSqKUbIciOjIPSpCeYiNq5MkK9esQc67dzi6aT1FQEvGXn9v5OXmot/Y8VWC3fO0h4havhSTl68QBTsqug4fidU+Pnj+4qXoXMeu8MYbl0XILy0VzZ1KnisRqAvy86CsrIyDawMZC6a07NodV44cpvV+Snacs2lQk+YBzETNi+q8pM2XK/IYvM9v7YEPvjzVDteuUlMTExzcGVVFUvFTfuX8qJKyisx5UDLEqqW8rAyHP6eT+/frhyFDhjA+N3lyj0ztAyXh4upE3seF3//ADN8AubrF+wJ8YdqsGVp07kJLB5uMZZduCN4YitlBIWLHsZ+/CPuD/GHQuDEmrfYVnavbooWI2X9AFKjVtLRwdtcO2jKPZGlQE4tmtFaNQNWOc6ZZDFkewFyyMWnJSXJtFrkeg/f5rV3wwZen2iG6SrncrVNJKsbFxWHWvPkwNjeHjZ2D3BRlwcePiPZeifr16uNO7B6FaeFKk3vkArk7nO0sJ7GPBZu3Mg5o5FWurMcIVNXVoaOvXyXAN23TFsrKyjgTFYlpK9eIztV93hzYOS1Au97WAIAjm0IxyZudYMpUr9W0Vo0AtdctnSxGfGwMNOvpyvQAptLLpkviqePIe5eLwryPMt8DLsfgfX5rF3zDFU+1w7WrlOpu/XJCAjxXr8HsoBDM8g9Gu95WUsdhDq4NROKFc9i8wBFW3brhxMEDOH/6VK0WoWfTHS7Z+KTIDnM63efqmlVTmgKBAFaj7fD636co+VxvrLRoDMT1z929OVlZaGBszFowJeXSRamqZGRkdZxLNqidCPRBjIcLTgT6QOfta5g0qA87x/lSAy8grpfNhOxnmTDU1UVkWJjcETEDU1OUlZTW6m57HnrwwZen2uHSVUo1G8t0ZnhWQDAuxUYjfEMoDuzdW+vF5jMyMvA2L4/TLCeXDnMqT2N5j8mS5ew+ZCgAIOniecp93TgRh+/HT2R0ngQ2dva4/NvBKp3HktDtOCeyGAnx8Ui8cQMJ8fEICQrCtrAttDrc2ehlE+dFZ0QsduUy6Guo47cA31rdbc8jHz748lQ7ip6NZbOim+7th83hssdm6JKRkQEXNzd816cvzC1bwtTcHE1btEBPKyu4uLlxFjJY7eODnqNlBxNpELOcXDrMCQUwJo9Rjd4QaGhpw9jMHNdPxEEoFFbZV+X4TDdW52rZpRvKSkqkqpIB1ONiTKErXmLarDls7B2wYd4cWiInkuclbwV+cGcUrl9JwLbNm2gLqXB97TzVA1/z5akR2HSVEh6sZLis6Lj66RKdx7n5BegxajTGrlgjVmO+FncUcecv4Pwfl2FiYCCzA1kW5y/GY/G4n1ido2WXbjgR6AOhUKhw3WJpj8mT5QSA4b/Mwq6Vy/Hs4T8izW5iX1zHZwTCiird8IrsOCeQVhvOy83F1bgjePrnfRS8fw9VFRVYde+Bq1ERKKqoYHVe8voIaqrbnqf64IMvT42gqNlYLis6Ln668jqPJTuDTXr0YuUmk5GRgbLycoXMclbHzKnkY7JGb8g0bGoOFTU1HNu8EQs3bxXbF9fxmdatW2PPjh3V0nEuCbnDfeWaNQgN2wzN+g0waNJPGP7zTLExsJdnTkKtogKlj/7GiXOnFX5e1d1tz1O98MGXp8ZQxN06VyeaE4E+jJ/HtPOY6Ay2dZzH2E1mU1gYtBs04NwdTtZMZrMPad3D5Meyn2VixzIPOCxYLLMRiWDwT1NwducO5L9/B50GeqJ9NWnZivP4THV0nMvi38xMJN69h5/9AuWOgR0LCapWS7+afu08ioGv+fLUKHRqWrI6kbnODLNR+GHbNRy/L5ZSelEWtxMT0axjJzxm2R2elpyEnt27K1y3mOBRchJysl4gaMZU/BYagmYdO+HTp3Ja+23f2xoCgQBXjx4RO47VaDtcP36U1blyMasgavd9Bw5EDysr9B04kFbNnmnDHzEGxpva85DhV748XwW2d+uKmBlmAteuYWUVVUa15qKiInxv/wPrWc5L+2IQf7yysWn8zzPQloMCGBU3TsRhpm8gcrJe4vrxY3j/+jXi98WK5nVloaKmhlbde+DPqwkYMmWa6DhErZiNzCOb8RmuqmFcxsB4Sz8eAn7ly1OnqI6ZYVkoomvY8jsrTJg8hdYKS1NTE7p6egDYzYsWffgAc3NzTh3m0pqniMcamlugXW9rzPQLgsMiZ+S/fyc2RiSLAeMmoqKiAnfOnxE7DpcRHSZcTkjAuClT8d2U6Zi02hdtJebD2/a2wqTVvug1eRrGTZmKywkJYs9XRMMfDw/AB1+eOoaiZ4blcTsxES1YusjoNNDDvcu/4/6VBHQebQdbDy9MCVwHWw8vkdbxkOEjxNKRvXr0wN1L8dBpoI9IT3dGwSjaeyWGDPxe9Dc27kvSPI2lPWZs1hSLNofj7K4oPP3rvtxjNDAygoqqKo5sDBXbl2mz5hg12xERHq7VNj6jiHSxIhr+eHgAPvjy1DFq2k+XbY35yb27OLIpFLMD12KWfxDaWdnIXWGlpqbiTmISbp89hU79+sNhwWLawShyqSt0tLSxeuVK0d/pzqaS90GlWyzrMaAyrTrLPwixft60jpP//h106jeo8r626NwF45a443j4FkQsdZMqMkFIgzJtYFKEahiXmzHe0o+HDF/z5alzKGpmmA5sasxZT9NxcvtWzKZpEjDZ2w+z5s3Hp/JyTPRaJbYqq2doiOPhWyCsEMLGzl6sO/xRchIu7Y1BaXER+jj8gJz796rcXNDpML+0NwZ5796hj8OPMGvdBkKhUPTYtbhjECgJZGoaE6/DqIkZfgsNgaqaOqxt7dCya3fKTvaQgAA4OTnh+NYtmL5avPvctFnzzzXlLMTvi8HRjaEwMjSAgYEBp/EZRc2Hf42GP57/JgIhWXKmGnFwcMCRI0dq4lA83wDE3C2TmWE2ox4ubm7INzBGOysb2s+J9HSDndMCxs1Dx7ZsxOyAtZSPEzKMz9MeoaSoEB9zctC6ew/0Gzseua9eIj42GiZ6eti2ZbPUsSZiHvROUpJoHrRNq1aoqKjAvfv3kf70KSBQgrKKChoYG8O8bXtKZyhpPLh5Hen378NqtB1ORYbjw/NnaNCggWjulOzn6+rqivBt29C6S1d0H2UrdeQsdG2wQgQiXNzcUGBowqoB7cHN69B5+xohQUHoO3CgqEGLKaXFxTgR6IOE+HjGz+Wpu0iLffzKl6dOUlMKP2NsbfHz/AW0gy+he8xmhSUQKEk1rjcwNRUzi89+lonj4VtgYmEBEwsLkS2gLGEPcoc50fF775+H6Dp8FMb9MEHM4/f68WN4lZGO0uIi2q+BUKwyMDWFcnk54s+elZrm9/T0xPnz5zHZYQyevXxZ7QIRipoPJxr+2ARx3tKPhwwffHnqLDWh8HP0+HEIQX8M5saJOFjbjmF1LKI7mhxkpUE2JiCCtTRrQUnkq3WJe/zK8vElQyhW0amv6+npoXHjxoiJicGdO3cgEAjk7p8LikoXL3ByYj3CxVv68ZDhgy9Pnac6FX5uJybCzmkB9vr70DJ6f572EEOnTmd1LGmayl9Szg8/q0Kpo0nL1ujUt1+VYC1vppStWpc0H18yZSUlUFZVpV1fDwgIwJQpU/Dnn3+iU6dOcrfngqLmw8kNfzUxk8zz34XvdubhkUFRURGatmlLewymKD9fYZrKWU/TEenphqOb1qN5p86Y4e2PBRvDMMPbH807dcb9Kwm4d/l3ZD1NF9uPrJlSRXr8SpKWkoT8N29ojwB17NgRqqqq8PCQrQutCBQ5H85mhIu39OORhA++PP8p2EoGSoNYMdEZg4lY6obCvDyUFlObEsiDrJv85N5dHFwbCDunBZjpF4R2EmIQ7XpbYZZ/EGYFBOPg2kA8uXdXbF9UM6XV4fFL5vd9sdgdGUG7sU0gEGDx4sV48eIF3r9/z+icmKLI+XCmI1y8pR8PFXzamUdhELXX24mJKCoqgqamJnr16CHW5VpdcJUMlAa5wYY8BnPjRNxnO7xikTmAw0Jn3DgRx8kkoEnLVqJRJTppbmmpYSoTCUWodUmrR2c/y0SzRo0wePBgRvt1cHDA2rVrsWPHDri4uLA6NzooOl3MW/rxcIUPvjyckRX4HqckYbDdGBR+eIchAwdi9YoVCg/E8hqIyA4zTG3+qBpsJDuPyViNtmOty0xoHR/ZuB6Tli5nnBo+Hr4FM30DAVSmsP/9N1NMU5prxy+5Hk2uQ5cUFSE/Nxejhg1j7JespaWFnj17IjY2Fs7Ozrh58yZ69uwJVVVVVucpC0XPh/OWfjxc4IMvDyfkds5a2aCdlQ2yn2Ui2nsV7jj8gMYNG7I2mpeEaQMRnW5gMkxXTETnMZsVlkBJAGLqnktq2MDUFGUlJVDS1BRb8efm5nKuR2c9TcepiHAAgLXtGAydOp1zhmHZsmWws7PDuXPn4OXlhT179lTLSlFRntKS8JZ+PGzga748rGGqlesYEorisjI0GzCQUrSeDYqQDJQH0wabgRMnI9LTjVFDTqzvGoyaNVcho0pAZQrbsnNXkYSl7dgf8Tr7Dad6dFlpCQ7IqEPLMyWQhoWFBTQ0NLB69Wrk5ubiwYMHrM6RDkS6+E7sHsSuXKZwCUseHrrwwZeHNWw7Z2+cPK4Qj9Oacphh2mATv2M7PF1caG+/x9MNgk/lqGdggOdpD2H5WTs4JysLJ7eHI9zVGZsWOiHc1Rknt4dLbXyy7NINz9MeAahMYVvb2oleL9TU0abXd6x9gm+dPYXigkLMDgiuFg/b1atXo7CwEEVFRbh9+zarc6QLV09pHh5FwKedeVjBtXO2KL+As8epIhxm6KYL2TTYDBo4kNb2R/bvw5u3b+G02BlFJSXIyXopNbX7+G4Kjm5aD6DSho88e0sWuSDb9f1z+xYgFGLgxMns69FHfsMs/6Bq87AdPHgwlJWVoampiaQaMh/g08U8XxM++PKwQlGds0yM5iVRlGQgXZg22DDd/mD0HgwYPAQHggPwk6eXFPUpK7T73DwmqT5FiFzsC/DFeNcvs7PHw7dg1Oy5rOvRj5IToaunz9mUQBYqKiro06cPrly5glevXuHDhw+oX78+o+Px8NQl+ODLwwpFdc4yXYGS+VoOM0xXTEy219LVxezAtaxGjNJSkvDu9WtMIDkQ5WRloay0FJZdugH4YlpPZ4wJqKxH7w/0x1jnJbTOXxI6ny9xcxK7dy9KSkogAOA4bx78fHz4bmGe/yx8zZeHFVwDH6HkxMXjlBDAYANZMrC2sHiJK6as8matPhW/NwZjF/8qpsN840Qc1DU1RZ8VU9P6iKWuUFISiOrQTJH1+aampmLI8BEY//MM5OsbQ71efZi0aAltA0O8LC7D+J9nYMjwEZz6Anh4aiv8ypeHFVy1cgklJy4r0P+SwwzXGvqj5ERo1auH1j16iT3+PO0htOvVE/usyGpdVD7BZB/fHxY6I3rNCoVnGCRH1PLe5UJYUQGBQABVNXWUFBdh0mpfVrPZPDx1AT748rCCa+Br0rIVAG4r0P+Sw8ymsDB0YVlDt7a1w/5AP4xbUlUjuaykBM06dKqiukVHrcvA1BR/Xb8GHW0dhZgSEFDNZmvXb4B2VtZ4cPMGNLR18OLJYwiFQlaz2Tw8dQE++PKwgkvgI5ScAG4r0P+SwwyXGnrLrt2hpVsPj1OS0aan+MpXVV0dPYYOw8ntYWjX20qqQ9KPv7pS+gj/sT8Wg74foNAMA9WImpKSEkbPnQft+g1w59wZVFRU4MPbt2hgZCTqnB46chQmjPuxRuRKeXiqGz748rCCS+Ajj8FwXYEqWjLwa8G1hq6mqSma8SXTpGVr5L7KQnFhAbYuWQxVNTXa40vZzzKhVF4GT3d3hWUYZKXXlZSUMGjSZGjXr49L+2Lx+t8MNDAyAlCZXtdr1BivlNRY63Tz8NQm+IYrHtawsVbbF+CLUbMqdZEVsQL9rzjMcG0eU9PQFLMjJLAabYeLMbtRWlyCsYtdpDokzfQLgq3jfJFDUsHHj4havhRbQkPFbrSYQPX5yhtREwgEsBpliwluS2HWurXYYzb2Y5D9/BkrFS0entoGv/LlYQ1TrVxiLtXEoplCV6B1yWFGmvNT29at8TglCe2sbBjv8/HdZJg2b46s9PQqj5UWF6Eg7yMWbQ6nPb60zfVXlJYUo4mJicilSFEZBrrpdXLHNgF5RI2vBfPUdfjgy8MJcuB78+EDeoy2R6tu3Sk7Z8d/nj9lIlpPl9ruMCPP8vB2whXkXbvOKvheizuGTv36QVm56s/5VEQ4ZvoGMhpf+mnZCmx3+xW7d0SK/q4oU4K8j3kKGVEjzpWrShoPz9eCD748nCEHvtU+PgjdtgVlZeXQ1tNDsw4d4bBgMXT19fH4bjLid0ZW6wq0NkoG0rU83OqymHUN/fbp0+g1YgRKi4tFNz4ply6iOD+f1fhSE4tmVbqUuWQYiJuPV2+yaXdOC4VCJJ4/izvnzuLlkzQU5uUBADbMn4suA76HjZ2DSEVr//79mDhxIgBg0aJFCA0NZfSaeXhqGoFQSJiYVS8ODg44cuRITRyKpxZArEDvJCWJrUC/tU7V1NRU2unarKfpOBAUgNlB8hWugMrUbuRSVwyeMg0Pz51Bj+7dxN7v0pJSdLYdw0rL+cHN69B5+1rqjQyTz5d883Hr9Ck079RZ7jlVVFRgz+oVuPvHJanbWLTvAMeQDXhyLwXvku/g0IEDAPjgy1O7kBb7+JUvT7VQG1egXwMmzk+mzZpj9FwnbHP9FT8tW0Grhj548jRc37+XMpPQd+BATspU2xY5Sb1Zovv5Ss70Wo22o2XukHzxvCjwGpk1hfVoWyRdvIBW3Xsg8dxZfMzNQUbqX7i0LxaDJk1G9P5YVq+Th+drwXc78/BUE2xUq1p07oIJ7p7Y7r4E4a7OlH6zEUvdcDx8CwZPmYbrB/ZKrZ1zHV8qqRBylniUvPkgmzvI4q9rV0X/nh0QjPbWfdDA2Bi2c+fhZ28/0WN/Xk2oPFeWneI8PF8LPvjy8FQTbJ2fTJs1h8PCxSjOycHZHdsR5rIImxY6IcrLE2kpyejUtx80VVSQ8cclmYbvXMeXdBrocRrrkXbzQZg7yBpRKy0uEv075VI89vr7iEbUmnXoiIkeyzDe1QODJk5GWUkJ1CVuMm7evIn+/ftDS0sLjRo1grOzM/Lz88W2EQqF2Lp1K3r06AF9fX1oaGigcePGmDhxIh4+fCi2rYWFBQQCAQYPHoycnBzMnj0bxsbG0NLSwvfff49bt26JbT99+nQIBAKoqKjg06dP8Pf3R4sWLaChoYGOHTtiz549lK/72rVrGDt2LBo1agR1dXU0b94cCxcuRJaEh/Mff/wBgUAAgUCAmJgYXL58Gf3794e2tjYaNmyIefPm4eNHeiOAPF8HPvjy8FQTtxMT0YJD2tfMvCkunzsHh+HD0NjQANpKQHn2K5hWlNEyfCckQNlAlgAlj/UwWQFLu/mgY+7QrGNn0b9P79iO3FevcOPkcaTeuIaSwkJ8N3wkrEbZovuQoXh8NxltWrUSbX/t2jX0798fCQkJKCoqQlZWFkJDQ+Ho6Ch2DFdXVzg5OSEpKQnv3r1DSUkJXr58if3798PGxgbZ2dlVzuvdu3ewsbFBREQE3rx5g6KiIvzxxx8YOHAgXr9+TflaJk6cCE9PT6Snp6OkpAR//fUXpk2bhpMnT4ptt3nzZvTr1w+HDx9GVlYWSktL8fTpU2zatAldu3bF/fv3Kfd/4sQJDBo0CAkJCSgsLER2djbCwsIwY8YMyu15agd88OXhqSYUYXlI1FYT4uOReOMGEuLjERIURKtpbYGTE1LOnJS7HRXX4o7B2tZO9P/ksR66yLr5IJs7RCx1Q+qNa2LpdSOzJlDT1BRt/zHnLS4fOoAID1cssxuBnSuWISv9CYBKFS0He3vRtomJibC2tkZ0dDSWL18u+vuBAweQm5sLAMjKysKGDRsAAK1atcKOHTsQExOD//3vfwCAnJwcnD59usp5JycnIycnB2vXrsWOHTvQsGFDAEBhYSGio6vOrH/69AlHjhzBkiVLsHfvXvTu3Vv0WHh4uNg5Ozs7o6KiAurq6vDw8MCuXbvg4OAAAHj9+jUcHBxQWlpa5RgHDx6EjY0NoqOjsWTJF+vHo0ePUt5A8NQO+IYrHp5qgqvzE1fLQ0VJgBIQYz0ZGRm0gr+8mw9Jc4djWzZBW7ceNHR00KRlK8wP3YLLh/Yj5fd4VHz6JHpeeWkp7l3+HQ9uXIPDol/RQFMTJiYmosfNzMxw7tw5qKmpAQBSUlJw6tQplJWV4cmTJ9DX10e9evVw5swZ3L17F//73//QsWNHAEDXrl1Fgh0vX76kPO/o6GgMGzYMAKCsrIzp06cDAJ48eUK5/ZIlLAQ/9AAAGYtJREFUSxAQEAAAsLa2Fr135O3Xr1+P8vJyAEBUVBQmTZoEAJg2bRrGjRuHQ4cO4cmTJ/jtt99EjxE0bdoUFy5cgJqaGiZPnozExET88ccfqKiowNOnT2FsbCz1M+D5evArXx6eaiAjIwNlpWUI+3UhNi10QrirM05uD0eORO1OGoqyPOQqASpJ1+EjsSksjNa+6NacDUxNMWr2XMzw9oNASYCpK1Zh1Oy5aNqmDaZ4rcLKQ0cxzsUNnfsPgJauruh5ZaWlOLx+LYL8fMX2169fP1HgBSAmb1lUVFlL1tbWxuDBg7F48WIUFRVhw4YNmDBhAvr37/9l/2VlVc5VIBBg0KBBov9v06aN6N/FUl7rkCFDxM5F4/MNCXn733//HQCgq6srmlcmmDNnjujfCRR1d8nXS+eceL4+/MqXh0eBkJWsutiOgWWXrnINDKhQhOVhRkYGonbvhrqqCkJmzYBew4awaN8RVqPtKB2MJCVAqbDs0g0nAn1oHZ+p7SS5FjzRYxl0GjTAp/Jy1DcwhLWtPaxt7VHx6RNSb1zDXn9fFOXnoby8HO/evYNAIBDtR19fX2y/qqqqon9XVFSI/h0ZGYnly5eLarV6enro2LGjKMBRSSBoa2uL7U9dXV30b2mSCQ0aNBD7f3V1dRR/TrET5OTkAAAaNWok9loAoHHjxqJ/E2lzefuXd048Xx8++PLwKAh5SlbteluhXW8rsSBHpWHM1XBCUspyoneA6AYgLSUJh9YFQUlZBaNmz4Vho8aUEqDSUFVXx6NHaUhNTZWrp8zGdrJF5y743/Rf4Dd5AgDAqIkZXHfsFlPRun/hHIwMDZGZX6l4lZ2dLaq9ApXuSPK4dOkSZs2aBQCws7ODt7c3OnTogPT0dFhaWkp9nrKyMu3XwuQ5xsbGeP78OV6+fAmhUCgWgF+8eCH6t9Fnlyeu58Tz9eGDLw+PAqAyiJcGYWAQ4eGKcUvcxVbAXA0n5N0AtLeyQXsrG2Q/y8SOZe7Q0NJBy27d4bDQmXI1LElZSQl0jYwwbspUhIWulzrmBLCvOes1bAh1dXWUlJTgzfNniHb/FWoaGiIVrX2REZg8eTIyM54CAJo1a4bCwkLa+weAY8eOif69ePFiUc03JYVddzhXrKyscOjQIeTl5eHAgQOYMGGC6LHt27eL/k1Oi/PUbfiaLw+PAmCiZAVUdg9P9FiGU5HbRH/janlIvgGQF+yMzZpi0ZZtUFJWQvf/t3fnUVVXawPHvweZ5IITAqWoiZUpKoIKV+MmYtmCqIRQTK85teR9RVPERCWFaxgOWCytpZbmTSAJBxxCMzUBMyeGoosTXjUFxSFEQZDxvH/wnl8cGTwMotTzWeusdfhNe3taq+e393723q+M0CnwQuVY9DO9++g89aghY867Pl6Bn5+fcuxCZibDnJ35nylTeLZ7dyZMmMCxY8eAynXFBw0apNOzq2rT5o//TrNnzyYqKooPP/yQKVOmKMeLi4vr/dyGmjZtmtLanThxIkFBQWzatAlvb29iY2OByrFcTfazaPmk5StEIzVkJSuoDIDq8gqO7/mW88ePNnrDiYa+AOxa+xnvLlmm0z1Hdu7grZn+Ou8o1NDdkF4cMoSLFy8SFxfHvXv3CA8Pr3Z9+/bt2bx5c7UxUl2MHTuW8PBwiouLSUtLY/z48UBlQlWrVq0oLy8nKyur3s9tKBcXF8LCwliwYAHFxcV89NFHWuc7derE9u3b0deX/2X/WUjLV4hGauhKVgBD3hjJhR+TdFo0oy6NegGoUOuUhf3gFKSqU4/qotkN6WT0JqKDg2pcMjM6OIiT0ZuUFbv09fXZtm0bUVFRjBgxAgsLC/T19Wnbti39+vVj7ty5nDlzRukurq/evXuzf/9+/vGPf2BmZoalpSXDhg1j7969Sjbz3r17lek/zSEwMJCkpCRGjx7N008/jaGhId27d2fmzJmkpaXRq1evZquLePRkVyMhGukfrq7K/rz1VXL/PruXhZJ08GCj6hAwdy73Oj5Vr+QmjVPHfuJCejoeU2ueXgR/7KD0YELWw3Y/epDsdiX+amRXIyEekcauZJWXl9foOpxITuaNeQsbdO+z/R1I2ra11vN1TUGqz9QjkN2uhNCQ4CtEIzV2Jauc6zdITEqqM3P4YRr7AnAr6wqnjv3Es/0dtKb1PGwKkmYZTCFE/UjwFaKR6ruYRFXnf07F9sUX8ZvlzzeRmx46d7Y2jX0BaGdpxYX0dJK2baW0+D4GRsZYP/f8Q6cgNcUymEL8FUnwFaKRGrKYhIYme7istPShmcN1acwLQGZaCm07dqxzzLc2TbUMphB/NZLtLEQjVV1Moj6qZg/rmjlcm8bsYJQSv5uy/18tqr7S9sbzXpU5uUII3UjwFaIJNMUGBvXZtOBBjXkBMDc1xaJduwbd25hlMIX4K5PgK0QT0CwmsXr6/9a6QbzGjSuXWT///WrZw8/2d+BkSkqD69CQF4CdK5cTEb6iUfc2tZCQEFQqVbVP69atsba2xsvLi8OHD2vdc+nSJeW6kVX29m0JEhISlLrPmjWr2vm4uDhlRS/x5yHBV4gmMvSll2jfxkzZIL6mxSS+mD+XXWs/w+f9edU2VWhs5rDmBSBq4QKdXgCqLmXZmHuby/3798nOziYuLo6hQ4cSERHRbGU/DufPn2fEiBF4eXmRk5PzuKsjmpgkXAnRhDqYm/PGvIXk377N0d0765U93BSZw5rVpPzfn0teURH2bq9Vmz6Utje+xqUsG3PvozJjxgxcXV0pKyvj7t27JCQkEBUVhVqtJiAgADs7O4YNG4alpSVxcXEAPK3jOtVPij59+ih179Gjh3I8KiqK/fv3P65qiUdMgq8QTahq1nF9s4ebKnPY1taW7/fEK6tJ7V4WqrWaVOzGL2tdTaox9z4KDg4OWt3IkydPZvDgwUybNo2KigoWLlzIjz/+iImJSYvrbtbo2LFji627aDjpdhaiCTUm67ipM4c1q0klHTxI8tGjJB08yMrly3UKno2591Hz9fWlS5fKNayPHDnClStXahzznT59unLsu+++03pGYWEhZmZmqFQq7O3tleMlJSWEhYXxwgsvYGRkhKWlJWPGjOH06dNa91cdp92wYQMTJkzAxMQEc3Nztm3bBsC2bdtwcXGhffv26OvrY25uzvDhw9mzZ0+tz9KM+bq4uPCvf/1LucbT0xOVSkVKSgpGRkaoVCp69uxZ7bfx8/NTnvXguLh4skjwFaIJNSbrWDKHdaOnp4ezs7Py9/Hjx2u8bvLkycr3zZs3a53btWsXBQUFAEyYMAGAsrIyPDw8WLBgAWfPnqWkpISbN2/yzTff4OjoyNGjR2ssJyQkhE2bNlFUVERubi4DBgxgw4YNeHt7k5iYSF5eHuXl5eTm5vLDDz/w+uuva+0nXB/m5ua4u7sDcO7cOX799VflnFqtVp7btWtXrd9IPHkk+ArRxJ6kzOE/q06dOinfr169WuM1Dg4O9OvXD6jMGL5//75yLjo6GgB9fX3GjRsHwKpVq5Qx1jfeeIPIyEhWrlyJlZUVBQUFTJgwgYqKimrlZGVlMXr0aCIjI5k/fz7PPPMMK1ZU/re0srIiIiKC2NhYFi1ahKGhIRUVFQQGBlLXnjahoaH4+Pgof8+bN4+4uDgsLS155513lOOaVjbAsWPHlN9i7NixDdpqUTQfGfMVook1dA/b5swcbulat26tfM/Pr32BkEmTJuHv709+fj7x8fG89dZb5Obmsm/fPgDc3d2xsLAA4IsvvgDgueeeIy4uDj29yraJjY0Nnp6eZGZmkpiYyLBhw7TKsLKyIjo6WmuvXU2runPnznh6etK1a1dGjRqFk5MTarWa3r171xkcnZ2dOXDggPK3k5OT0p3u4eGBubk5v//+O1u3biUkJARAa+cczQuFeHJJy1eIR6Ahe9gK3RUVFSnf68oQHzduHAYGBsAfXc+xsbGUlpYCMHHiRKAyWJ45cwaAzMxMWrVqpYydenp6Ks+rab7t4MGDq21yP2bMGABSU1Pp1q0btra2+Pr6UlBQwNChQ+nevfomFboyMDBQnp+RkcHZs2cBlIzpfv360adPnwY/XzQPCb5CPCKazOHYjV9ieus6u5eFEjUvgN3LQjG9dZ3YjV/y/Z54afE2QNV5r0899VSt11lYWODh4QFAfHw8+fn5ShA2NzdXzum6rWNNXdwdO3asdmzp0qWEhIQorepTp07x+eef4+Pjg7W1NevXr9epvNpU7XreunUrv/zyC//9738BafW2FNLtLMQjJnvYNr0TJ04o3//+97/Xee2kSZOUMd/o6GiOHDkCVI6LalrFZmZmyvV9+/Zl8eLFNT6rpoS4ql3gGvr6+gQHBxMUFMSxY8dISEjg8OHDHDp0iDt37uDr64uTkxN9+/Z9+D+2Bo6OjvTq1YvTp0+zbds2SkpKAFCpVIwdO7ZBzxTNS1q+QogWJT4+nszMTABefPHFh3bhurm5Ka3joKAgysvLgT+6nAHatm2rPCc7OxsXFxdGjhzJyJEjKS8v58CBA1y7dq3GVq5mbFgjJyeHRYsW4e3tTVhYGM7OznzwwQfs27eP0NBQACoqKmrN0q7puTUlZ40fPx6AtLQ0Zbx66NChWFtb1/lc8WSQlq8Q4omVmppKu3btALh37x4nT55k3bp1QGUrr+pc2Nro6+vzz3/+k/DwcHJzc4HK1q2Dg4PWdZMmTWLRokXk5ubi6uqKn58fhYWFBAcHc/v2bQwNDXn99dcfWl779u1Zu3YtN2/eZMeOHdy9e5eBAwdy48YN1q5dq1z3sJeGqmPZMTExXL9+HW9vb+UFYPz48XzwwQdUVFRw7do1QLqcWxR1M/H09GyuooQQLVhwcLAaqPOjUqnUK1asUO65ePGicu7NN9+s9syMjAyt+8PDw6tdU1hYqB48eHCtZa5evVq59tChQ8rxmTNnVntWfHy82sDAoNZnVa1jbc86cOBAtfuOHDmiVc7w4cOVc0ZGRurbt2/X78cWj1xtsU+6nYUQLYKhoSFdunTBx8eHH3/8kTlz5uh8b+/evXF0dAT+aAk/qHXr1hw8eJAPP/wQW1tbWrduTYcOHXBxceHbb79l+vTpOpfn7u7OyZMnGTduHDY2NhgZGWFqasqgQYOIiIhg69atD32Gq6srCxYsoFu3bhgbG2Nra4uRkZHWNaNHj9YqU9NLIJ58KrW6jpneTcjLy0trHpoQQojGmTx5Mhs3bgRgy5YteHt7P+YaiQfVFvtkzFcIIVqQlJQUrl27xokTJ/jqq6+AyulOuoxHiyeHBF8hhGhB9u3bR1BQkNaxkJCQal3S4skmwVcIIVqQXr16YW5uTlFRET169MDPzw9fX9/HXS1RTxJ8hRCiBfH09NRa8lK0TJLtLIQQQjSzZmv5Zmdn4+Xl1VzFCSGEEI9ddnZ2jcebbaqREEIIISpJt7MQQgjRzCT4CiGEEM1Mgq8QdXB1daVnz57Kp1evXtjb2zNy5Ej27t1b7fq8vDyWL1/Oq6++St++fXF0dGTq1KkkJyfXWkZwcDA9e/Zk9erVOtXpwIED9OzZk4SEBLZv365Vvwc/WVlZWvd5eXnRr18/hg8fzpdffvnQsuLj4/Hy8sLe3h5XV1cWL15Mfn6+cr60tJQ1a9bw6quvYmdnxyuvvEJERATFxcXKNYmJiYwYMYIBAwYQEBBAYWGhVhleXl5a+9MC3LlzBzs7O5YuXarTbyJESyPBVwgdtG3bFisrK9q1a8f9+/c5ffo0/v7+HDp0SLkmKysLLy8vNmzYwKVLlzAyMuLu3bskJiYyfvx49uzZU+25+/fvZ8uWLTrXo6SkhCVLltC5c2deeuklWrdujZWVldZHszetiYkJpqamAMraxBkZGRgaGpKVlcWyZcv4+uuvay0rJiaG2bNnk5GRQatWrbh69SrR0dH4+fkpW9xFREQQERHBpUuXMDY25vLly6xZs4YlS5YAUFhYyPvvv89vv/1GaWkp3377LZs2bdL692dkZPDee+9V+73d3NyIjIzk3LlzOv8+QrQUEnyF0MG8efNISkri6NGjJCQkYGNjg1qtJjIyUrlmzpw5ZGdnY2Njw86dO0lOTubw4cM4OjpSUVFBcHAwRUVFAOTn5/Pxxx8zc+ZMZX9ZXezcuZOrV6/y5ptvoqenh5ubG0lJSconISGBHj16ALBkyRLatWtHWVkZYWFhqNVqAgMDSU5OJigoCJVKxffff19rWZo9Yv38/EhOTuabb75BT0+P48eP8+uvvyr1gcogfPz4cSIiIgD47rvvALh06RJ37txhzJgxyovKL7/8AlTuUbtq1SqcnZ0ZOHBgtfI9PT0pKytT6iHEn4kEXyHqycrKCldXVwCuXr0KwM8//0xaWhoAn3zyCS+88AIAFhYWLF26FHt7e8aMGcO9e/cAWL16NevWreOpp56iS5cuOpcdExMDwMsvv1zj+c2bN/Pzzz/j4uKCu7u7Urdbt25p7eYzduxY0tPT+fe//13jc0pKSrC3t8fR0ZGRI0cCYGdnp+yao+nOLikp0bpP0yLW7Dmr2RD+3r175OXlAZW7CgHs2bOHc+fOMXPmzBrrMHDgQNq1a8fevXu5fft2XT+LEC2OBF8h6uny5cvs378fgM6dOwNw/Phx5W9N4NXo3LkzMTExBAQEKEHJwMCAUaNGsXXrVp5++mmdyv3999/JyMjA1NQUW1vbaueLiopYtWoVKpWKuXPnKsfPnz8PQIcOHfjiiy8YOHAgLi4uWt2/DzI0NCQ8PJzIyEi6du0KwIULF5Qg2KlTJwDefvttAGbNmoWTkxP+/v506tSJjz76CAAbGxssLCzYvXu38jIwaNAgysvL+fTTTxk2bBj9+vWrsQ6tWrXC3t6e0tJSjh49qtNvJERLIcFXCB0sXbqUl156CScnJ1555RV+++039PT0mDRpEgDXrl0DwNLSUqfnzZ49m9DQUDp06KBzHdLS0lCr1Tz33HM1nt+xYwd5eXm4uLgoXc9QmbwEcPPmTVatWoVarebmzZusWLFC5/HmwsJCAgMDUavVPPvss0rA9PPzY8CAAQBKy1alUmFgYABUBvFPP/0UR0dHevToweTJk3n77bfZtWsXFy9eZMaMGQBUVFRQUVFRrdznn38egNTUVJ3qKURLIcFXCB3cuXOH69evk5+fT5s2bbC3t2ft2rU4OzsD1Bg46tKqVat61+H69evAH126D4qKigLAx8dH67imbmq1moULF5KSkqK0jNesWfPQcgsLC/H19SU9PR19fX0WL16sdCcHBQWRkpLCnDlzSElJwd/fn+zsbGbNmqWU279/fyIjI9mzZw+BgYGoVCo+++wzXn75ZWxtbVm5ciUODg44ODjw8ccfa5Vtbm4OwI0bN3T6jYRoKST4CqGDsLAwzp49y6lTpzh58iQxMTEMHTpUOa9p8ebk5NR4f2JiYrXx0frSTPExNjaudu7SpUucP38eExMThgwZonVOk/0MKEu8jho1Cqhc+q7q1KEHFRUVMXXqVE6cOIGenh5LlixRWrrXr19n9+7dmJmZMWXKFExNTfH19cXU1JTLly+TmZlZ4zO3b99OVlYWM2bMIDk5mc8//xw3Nzfc3d1Zt26d1rQsExMTAAoKCh76+wjRkkjwFaIJDBo0CKjsfj579qzWudTUVKZOncqQIUO4cOFCg8vQBNG7d+9WO/fTTz8BlUlKD+7r2r17d+W7Jtu6asu7tLS0xvIqKirw9/fn5MmT6OnpERoaqiRfQWWymVqtRq1Wo1KpgMouZ833qnN9NUpKSlizZg1ubm707NmT//znPwB4eHjw2muvASjHAGVOcJs2bWqsoxAtlQRfIZqAk5OTkgQVEBCgJDldvnyZhQsXApWZzzY2Ng0uQ5PcdevWrWrnNJnWvXv3rnZuwIAB/O1vfwNgw4YNQOW8X80zaxt33rhxozI9aP78+bz11lta562trVGpVBQUFBAbGwtUTj3Kz8/H0NBQa9xZIzY2lpycHKZPn651/Nq1a0q3uiZ4Q2WSGaAkfQnxZyH7+QrRRD755BPGjx9PZmYmr732GmZmZkqXromJCcuXL2/U8+3t7dHT0+PKlStUVFQo467wx3hwTcHd2NiY9957j7CwMDZs2EBMTIwy5UmT8JSTk8Po0aOBygCpyYzWWL9+PevXr1f+nj9/Pm5ubnh7e7NlyxYWLVrE8uXLle7hSZMmKQFfo7i4mLVr1+Lh4aEEZnt7ewAWLVqkXNe/f3/l+5kzZwBqzYgWoqWS4CtEE+nWrRtxcXGsWbOGH374gRs3btCpUyccHByYNm1ajS3B+mjbti39+/cnNTWVU6dO0adPH+WcpoWomYf7oIkTJ2JsbMzGjRuVhUB8fX2VbuSysjIlgJeVlZGenq41t1ZzTkPTfR0SEkKPHj2IjY0lKyuLzp074+Pjw9SpU6vV4euvvyY3Nxc/Pz/lmJ2dHbNnz+arr74C4J133sHOzg6oTBBLT0+vcRxbiJZOthQUogXZsWMHgYGBBAQE1Bjg/kzS09MZNWoUXl5ehIWFPe7qCNGkZMxXiBbE3d0da2trdu3a9bir8sjt3r0bfX193n333cddFSGanARfIVoQQ0NDAgMDyczM/FOv+lRQUMD27dvx8fFpdHe9EE8i6XYWQgghmpm0fIUQQohmJsFXCCGEaGYSfIUQQohmJsFXCCGEaGYSfIUQQohmJsFXCCGEaGb/Bwhk3aiYPsHLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['xtick.labelsize'] = 24\n",
    "mpl.rcParams['ytick.labelsize'] = 24\n",
    "mpl.rcParams['font.weight'] = 'bold'\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "tax_level_list = ['Phylum', 'Class', 'Order', 'Family', 'Genus', 'Species']\n",
    "\n",
    "ax1 = fig.add_subplot(111)\n",
    "_df = df_pca_list[4]\n",
    "\n",
    "colors = ['lightgrey'] + sns.color_palette('Set3', 12)\n",
    "\n",
    "sns.scatterplot(ax=ax1, x='PC1_inverse', y='PC2', data=_df, s=250, color=pal[0], edgecolor='k')\n",
    "handles, labels = ax1.get_legend_handles_labels()\n",
    "ax1.legend(handles[1:], ['no SIBO', 'SIBO'], loc='upper left', markerscale=2.5, fontsize=16)\n",
    "\n",
    "ax1.get_xaxis().set_ticks([])\n",
    "ax1.get_yaxis().set_ticks([])\n",
    "ax1.set_xlabel('PC1 (' + str(round(pca_list[4].explained_variance_ratio_[0]*100,2)) + '%)', fontsize=16, fontweight='bold')\n",
    "ax1.set_ylabel('PC2 (' + str(round(pca_list[4].explained_variance_ratio_[1]*100,2)) + '%)', fontsize=16, fontweight='bold')\n",
    "ax1.set_title('', fontsize=20, fontweight='bold')\n",
    "ax1.get_legend().remove()\n",
    "ax1.arrow(0, 0, df_pca_corr.iloc[0]['PC1_scaled'], df_pca_corr.iloc[0]['PC2_scaled'], color='k', width=0.05, head_width=1, alpha=0.75)\n",
    "ax1.arrow(0, 0, df_pca_corr.iloc[1]['PC1_scaled'], df_pca_corr.iloc[1]['PC2_scaled'], color='k', width=0.05, head_width=0.6, alpha=0.75)\n",
    "\n",
    "ax1.text(df_pca_corr.iloc[0]['PC1_scaled']-6.5, df_pca_corr.iloc[0]['PC2_scaled']-3, 'Total\\nLoad', color='k', fontsize=20)\n",
    "ax1.text(df_pca_corr.iloc[1]['PC1_scaled'], df_pca_corr.iloc[1]['PC2_scaled']-3, 'Shannon\\nDiversity', color='k', fontsize=20)\n",
    "#fig.savefig('genus_pca.png', dpi=200, bbox_inches='tight')\n",
    "#fig.savefig('genus_pca.pdf', bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation of symptoms with PC1 and PC2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "continuous_var = ['bloating_vas', 'excess_gas_vas', 'incomplete_evac_vas', \n",
    "            'constipation_vas', 'diarrhea_vas', 'urgency_vas']\n",
    "\n",
    "df_pca_corr = pd.DataFrame(columns=['PC1_corr', 'PC2_corr', 'Magnitude', 'PC1_scaled', 'PC2_scaled', 'Magnitude_scaled', 'PC1_pvalue', 'PC2_pvalue'])\n",
    "\n",
    "level=4\n",
    "\n",
    "for var in continuous_var:\n",
    "    pc1_corr, pc1_pvalue = stats.spearmanr(df_pca_list[level]['PC1'], df_pca_list[level][var], nan_policy='omit')\n",
    "    pc2_corr, pc2_pvalue = stats.spearmanr(df_pca_list[level]['PC2'], df_pca_list[level][var], nan_policy='omit')\n",
    "    \n",
    "    if pc1_corr>0:\n",
    "        pc1_scaled = pc1_corr*max(df_pca_list[level]['PC1'])\n",
    "    else:\n",
    "        pc1_scaled = -1*pc1_corr*min(df_pca_list[level]['PC1'])\n",
    "    \n",
    "    if pc2_corr>0:\n",
    "        pc2_scaled = pc2_corr*max(df_pca_list[level]['PC2'])\n",
    "    else:\n",
    "        pc2_scaled = -1*pc2_corr*min(df_pca_list[level]['PC2'])\n",
    "    \n",
    "    df_pca_corr.loc[var] = [pc1_corr, pc2_corr, np.sqrt(pc1_corr**2+pc2_corr**2), pc1_scaled, pc2_scaled, np.sqrt(pc1_scaled**2+pc2_scaled**2), pc1_pvalue, pc2_pvalue]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>PC2_corr</th>\n",
       "      <th>PC2_pvalue</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bloating_vas</td>\n",
       "      <td>0.242641</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>lightblue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>incomplete_evac_vas</td>\n",
       "      <td>0.233936</td>\n",
       "      <td>0.000280</td>\n",
       "      <td>lightblue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>constipation_vas</td>\n",
       "      <td>0.225991</td>\n",
       "      <td>0.000455</td>\n",
       "      <td>lightblue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>urgency_vas</td>\n",
       "      <td>0.155428</td>\n",
       "      <td>0.016634</td>\n",
       "      <td>lightgrey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>excess_gas_vas</td>\n",
       "      <td>0.133094</td>\n",
       "      <td>0.040634</td>\n",
       "      <td>lightgrey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>diarrhea_vas</td>\n",
       "      <td>0.130203</td>\n",
       "      <td>0.045244</td>\n",
       "      <td>lightgrey</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 index  PC2_corr  PC2_pvalue      color\n",
       "0         bloating_vas  0.242641    0.000162  lightblue\n",
       "1  incomplete_evac_vas  0.233936    0.000280  lightblue\n",
       "2     constipation_vas  0.225991    0.000455  lightblue\n",
       "3          urgency_vas  0.155428    0.016634  lightgrey\n",
       "4       excess_gas_vas  0.133094    0.040634  lightgrey\n",
       "5         diarrhea_vas  0.130203    0.045244  lightgrey"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pca_corr_PC2_symptom = df_pca_corr.sort_values('PC2_pvalue')[['PC2_corr', 'PC2_pvalue']].reset_index()\n",
    "df_pca_corr_PC2_symptom['color'] = np.where(df_pca_corr_PC2_symptom['PC2_corr']>0.16, 'lightblue', 'lightgrey')\n",
    "df_pca_corr_PC2_symptom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.1, 0.28)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6,5))\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "sns.barplot(x='PC2_corr', y='index', data=df_pca_corr_PC2_symptom, palette=[pal[0]]*3+['lightgrey']*3, edgecolor='k')\n",
    "ax1.set_ylabel('')\n",
    "ax1.set_xlabel('Spearman Correlation Coefficient', fontsize=16, fontweight='bold')\n",
    "ax1.set_yticklabels(['Bloating', 'Incomplete Evac.', 'Constipation', 'Urgency', 'Excess Gas', 'Diarrhea'], fontsize=16, fontweight='bold')\n",
    "ax1.axvline(0.16, ls='--', color='red')\n",
    "ax1.set_xlim(0.1, 0.28)\n",
    "\n",
    "\n",
    "#fig.savefig('symptom_correlation.png', bbox_inches='tight', dpi=200)\n",
    "#fig.savefig('symptom_correlation.pdf', bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation of cytokines with PC1 and PC2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "continuous_var = ['IL2','IL4','IL5','IL6','IL8','IL10','IL12P70','IL13','IL1B','TNFA','IFNY','GM-CSF', 'MCP1']\n",
    "\n",
    "df_pca_corr = pd.DataFrame(columns=['PC1_corr', 'PC2_corr', 'Magnitude', 'PC1_scaled', 'PC2_scaled', 'Magnitude_scaled', 'PC1_pvalue', 'PC2_pvalue'])\n",
    "\n",
    "level=4\n",
    "\n",
    "for var in continuous_var:\n",
    "    pc1_corr, pc1_pvalue = stats.spearmanr(df_pca_list[level]['PC1'], df_pca_list[level][var], nan_policy='omit')\n",
    "    pc2_corr, pc2_pvalue = stats.spearmanr(df_pca_list[level]['PC2'], df_pca_list[level][var], nan_policy='omit')\n",
    "    \n",
    "    if pc1_corr>0:\n",
    "        pc1_scaled = pc1_corr*max(df_pca_list[level]['PC1'])\n",
    "    else:\n",
    "        pc1_scaled = -1*pc1_corr*min(df_pca_list[level]['PC1'])\n",
    "    \n",
    "    if pc2_corr>0:\n",
    "        pc2_scaled = pc2_corr*max(df_pca_list[level]['PC2'])\n",
    "    else:\n",
    "        pc2_scaled = -1*pc2_corr*min(df_pca_list[level]['PC2'])\n",
    "    \n",
    "    df_pca_corr.loc[var] = [pc1_corr, pc2_corr, np.sqrt(pc1_corr**2+pc2_corr**2), pc1_scaled, pc2_scaled, np.sqrt(pc1_scaled**2+pc2_scaled**2), pc1_pvalue, pc2_pvalue]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>PC2_corr</th>\n",
       "      <th>PC2_pvalue</th>\n",
       "      <th>abs PC2_corr</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IL8</td>\n",
       "      <td>0.238113</td>\n",
       "      <td>0.000252</td>\n",
       "      <td>0.238113</td>\n",
       "      <td>lightblue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TNFA</td>\n",
       "      <td>0.157271</td>\n",
       "      <td>0.016509</td>\n",
       "      <td>0.157271</td>\n",
       "      <td>lightgrey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IL10</td>\n",
       "      <td>0.144019</td>\n",
       "      <td>0.028291</td>\n",
       "      <td>0.144019</td>\n",
       "      <td>lightgrey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IL1B</td>\n",
       "      <td>-0.142324</td>\n",
       "      <td>0.030224</td>\n",
       "      <td>0.142324</td>\n",
       "      <td>lightgrey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IL2</td>\n",
       "      <td>-0.109368</td>\n",
       "      <td>0.096546</td>\n",
       "      <td>0.109368</td>\n",
       "      <td>lightgrey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>IL12P70</td>\n",
       "      <td>-0.084292</td>\n",
       "      <td>0.200812</td>\n",
       "      <td>0.084292</td>\n",
       "      <td>lightgrey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MCP1</td>\n",
       "      <td>0.048687</td>\n",
       "      <td>0.490312</td>\n",
       "      <td>0.048687</td>\n",
       "      <td>lightgrey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>IFNY</td>\n",
       "      <td>0.040748</td>\n",
       "      <td>0.536867</td>\n",
       "      <td>0.040748</td>\n",
       "      <td>lightgrey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>IL13</td>\n",
       "      <td>0.038439</td>\n",
       "      <td>0.560202</td>\n",
       "      <td>0.038439</td>\n",
       "      <td>lightgrey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GM-CSF</td>\n",
       "      <td>0.035486</td>\n",
       "      <td>0.590749</td>\n",
       "      <td>0.035486</td>\n",
       "      <td>lightgrey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>IL4</td>\n",
       "      <td>0.022915</td>\n",
       "      <td>0.728442</td>\n",
       "      <td>0.022915</td>\n",
       "      <td>lightgrey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>IL5</td>\n",
       "      <td>0.022098</td>\n",
       "      <td>0.737764</td>\n",
       "      <td>0.022098</td>\n",
       "      <td>lightgrey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>IL6</td>\n",
       "      <td>0.007964</td>\n",
       "      <td>0.903964</td>\n",
       "      <td>0.007964</td>\n",
       "      <td>lightgrey</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      index  PC2_corr  PC2_pvalue  abs PC2_corr      color\n",
       "0       IL8  0.238113    0.000252      0.238113  lightblue\n",
       "1      TNFA  0.157271    0.016509      0.157271  lightgrey\n",
       "2      IL10  0.144019    0.028291      0.144019  lightgrey\n",
       "3      IL1B -0.142324    0.030224      0.142324  lightgrey\n",
       "4       IL2 -0.109368    0.096546      0.109368  lightgrey\n",
       "5   IL12P70 -0.084292    0.200812      0.084292  lightgrey\n",
       "6      MCP1  0.048687    0.490312      0.048687  lightgrey\n",
       "7      IFNY  0.040748    0.536867      0.040748  lightgrey\n",
       "8      IL13  0.038439    0.560202      0.038439  lightgrey\n",
       "9    GM-CSF  0.035486    0.590749      0.035486  lightgrey\n",
       "10      IL4  0.022915    0.728442      0.022915  lightgrey\n",
       "11      IL5  0.022098    0.737764      0.022098  lightgrey\n",
       "12      IL6  0.007964    0.903964      0.007964  lightgrey"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pca_corr_PC2_cytokine = df_pca_corr.sort_values('PC2_pvalue')[['PC2_corr', 'PC2_pvalue']].reset_index()\n",
    "df_pca_corr_PC2_cytokine['abs PC2_corr'] = abs(df_pca_corr_PC2_cytokine['PC2_corr'])\n",
    "df_pca_corr_PC2_cytokine['color'] = np.where(df_pca_corr_PC2_cytokine['PC2_corr']>0.16, 'lightblue', 'lightgrey')\n",
    "df_pca_corr_PC2_cytokine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.rcParams['ytick.labelsize'] = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.28, 0.28)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.patches import Patch\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "fig = plt.figure(figsize=(6,5))\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "sns.barplot(x='PC2_corr', y='index', data=df_pca_corr_PC2_cytokine, palette=[pal[0]]+['lightgrey']*12, edgecolor='k')\n",
    "ax1.set_ylabel('')\n",
    "ax1.set_xlabel('Spearman Correlation Coefficient', fontsize=16, fontweight='bold')\n",
    "ax1.axvline(0.16, ls='--', color='red')\n",
    "ax1.axvline(-0.16, ls='--', color='red')\n",
    "ax1.set_xlim(-0.28, 0.28)\n",
    "\n",
    "#fig.savefig('cytokine_correlation.png', bbox_inches='tight', dpi=200)\n",
    "#fig.savefig('cytokine_correlation.pdf', bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate Benjamini-Hochberg corrected significance threshold for correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "pvalues = df_pca_corr_PC2_cytokine['PC2_pvalue'].tolist()+df_pca_corr_PC2_symptom['PC2_pvalue'].tolist()\n",
    "coeffs = df_pca_corr_PC2_cytokine['PC2_corr'].tolist()+df_pca_corr_PC2_symptom['PC2_corr'].tolist()\n",
    "\n",
    "qvalues = multi.multipletests(pvalues, method='fdr_bh')[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.25, 0.25)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=coeffs, y=-np.log10(qvalues))\n",
    "\n",
    "# q-value equal to 5% FDR\n",
    "plt.axhline(-np.log10(0.05))\n",
    "plt.axvline(0.16)\n",
    "plt.axvline(-0.16)\n",
    "plt.xlim(-0.25,0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PC plot colored by SIBO classification with marker shape determined by Lactobacillus presence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pca_list[4]['Lacto-noSIBO'] = np.where((df_pca_list[4]['SIBO']==0) & (df_pca_list[4]['Lacto']==1), 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['xtick.labelsize'] = 24\n",
    "mpl.rcParams['ytick.labelsize'] = 24\n",
    "mpl.rcParams['font.weight'] = 'bold'\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "tax_level_list = ['Phylum', 'Class', 'Order', 'Family', 'Genus', 'Species']\n",
    "\n",
    "ax1 = fig.add_subplot(111)\n",
    "_df = df_pca_list[4]\n",
    "\n",
    "_df['PC1_inverse'] = _df['PC1']*-1\n",
    "\n",
    "sns.scatterplot(ax=ax1, x='PC1_inverse', y='PC2', hue='SIBO', style='Lacto-noSIBO', data=_df, s=250, palette=['lightgrey', 'PeachPuff'], edgecolor='k')\n",
    "ax1.set_xticklabels([])\n",
    "ax1.set_yticklabels([])\n",
    "ax1.set_xticks([])\n",
    "ax1.set_yticks([])\n",
    "handles, labels = ax1.get_legend_handles_labels()\n",
    "\n",
    "ax1.legend(handles[1:3]+handles[4:], ['non-SIBO', 'SIBO'], loc='upper left', markerscale=2.5, fontsize=16)\n",
    "\n",
    "ax1.set_xlabel('PC1 (' + str(round(pca_list[4].explained_variance_ratio_[0]*100,2)) + '%)', fontsize=16, fontweight='bold')\n",
    "ax1.set_ylabel('PC2 (' + str(round(pca_list[4].explained_variance_ratio_[1]*100,2)) + '%)', fontsize=16, fontweight='bold')\n",
    "ax1.set_title('', fontsize=20, fontweight='bold')\n",
    "\n",
    "#fig.savefig('genus_pca_SIBO_Lacto.png', dpi=200, bbox_inches='tight')\n",
    "#fig.savefig('genus_pca_SIBO_Lacto.pdf', bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}