{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3cc941d1",
   "metadata": {},
   "source": [
    "## Debarcode 12/14/2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7a4174ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import os\n",
    "import sys\n",
    "import glob\n",
    "import pandas as pd\n",
    "import xml.etree.ElementTree as et\n",
    "import datetime\n",
    "from imageio import volread as imread\n",
    "\n",
    "import tifffile\n",
    "\n",
    "# from pystackreg import StackReg --> don't run this, run this with imlab environment\n",
    "from skimage.filters import threshold_otsu\n",
    "\n",
    "import seaborn as sns\n",
    "#from pystackreg.util import to_uint16\n",
    "\n",
    "from skimage import measure\n",
    "from scipy import stats\n",
    "import umap\n",
    "#from umap.umap_ import UMAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4c687cc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load Feature Data\n",
    "MP_CP_DIR = 'mp_cp_output'\n",
    "MP_DIR = 'mp_score_max'\n",
    "\n",
    "DATA_DIR = 'max_clean'\n",
    "Compartment = 'Soma'\n",
    "DATA_TYPE = 'max_clean'\n",
    "META_DIR = 'metadata'\n",
    "\n",
    "_allFOVs = sorted(glob.glob(f'{MP_DIR}/*'))\n",
    "allFOVs = [x.split('F')[-1][:3] for x in _allFOVs]\n",
    "allFOVs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e25654fb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>channel_number</th>\n",
       "      <th>cycle_number</th>\n",
       "      <th>marker_name</th>\n",
       "      <th>Filter</th>\n",
       "      <th>excitation_wavelength</th>\n",
       "      <th>emission_wavelength</th>\n",
       "      <th>Bandwidth</th>\n",
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       "  </thead>\n",
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       "      <td>561</td>\n",
       "      <td>594</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "      <td>Cy5</td>\n",
       "      <td>637</td>\n",
       "      <td>698</td>\n",
       "      <td>77</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>DNA_1</td>\n",
       "      <td>DAPI</td>\n",
       "      <td>405</td>\n",
       "      <td>445</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
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       "      <td>488</td>\n",
       "      <td>521</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>VSVG</td>\n",
       "      <td>RFP</td>\n",
       "      <td>561</td>\n",
       "      <td>594</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>FLAG</td>\n",
       "      <td>Cy5</td>\n",
       "      <td>637</td>\n",
       "      <td>698</td>\n",
       "      <td>77</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>DNA_2</td>\n",
       "      <td>DAPI</td>\n",
       "      <td>405</td>\n",
       "      <td>445</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>488</td>\n",
       "      <td>521</td>\n",
       "      <td>38</td>\n",
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       "      <td>9</td>\n",
       "    </tr>\n",
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       "      <th>10</th>\n",
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       "      <td>43</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>Cy5</td>\n",
       "      <td>637</td>\n",
       "      <td>698</td>\n",
       "      <td>77</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
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       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>DNA_3</td>\n",
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       "      <td>405</td>\n",
       "      <td>445</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>Ollas</td>\n",
       "      <td>GFP</td>\n",
       "      <td>488</td>\n",
       "      <td>521</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>GFAP</td>\n",
       "      <td>RFP</td>\n",
       "      <td>561</td>\n",
       "      <td>594</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>NeuN</td>\n",
       "      <td>Cy5</td>\n",
       "      <td>637</td>\n",
       "      <td>698</td>\n",
       "      <td>77</td>\n",
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       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>DNA_4</td>\n",
       "      <td>DAPI</td>\n",
       "      <td>405</td>\n",
       "      <td>445</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>pRPS6</td>\n",
       "      <td>GFP</td>\n",
       "      <td>488</td>\n",
       "      <td>521</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>RANGAP1</td>\n",
       "      <td>RFP</td>\n",
       "      <td>561</td>\n",
       "      <td>594</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>NFKB</td>\n",
       "      <td>Cy5</td>\n",
       "      <td>637</td>\n",
       "      <td>698</td>\n",
       "      <td>77</td>\n",
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       "    <tr>\n",
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       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>DNA_5</td>\n",
       "      <td>DAPI</td>\n",
       "      <td>405</td>\n",
       "      <td>445</td>\n",
       "      <td>46</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
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       "      <td>TOM20</td>\n",
       "      <td>GFP</td>\n",
       "      <td>488</td>\n",
       "      <td>521</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
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       "      <td>594</td>\n",
       "      <td>43</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
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       "      <td>637</td>\n",
       "      <td>698</td>\n",
       "      <td>77</td>\n",
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       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>DNA_6</td>\n",
       "      <td>DAPI</td>\n",
       "      <td>405</td>\n",
       "      <td>445</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>TDP43</td>\n",
       "      <td>GFP</td>\n",
       "      <td>488</td>\n",
       "      <td>521</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>Blank_561_6</td>\n",
       "      <td>RFP</td>\n",
       "      <td>561</td>\n",
       "      <td>594</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>G3BP1</td>\n",
       "      <td>Cy5</td>\n",
       "      <td>637</td>\n",
       "      <td>698</td>\n",
       "      <td>77</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>7</td>\n",
       "      <td>DNA_7</td>\n",
       "      <td>DAPI</td>\n",
       "      <td>405</td>\n",
       "      <td>445</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>7</td>\n",
       "      <td>GM130</td>\n",
       "      <td>GFP</td>\n",
       "      <td>488</td>\n",
       "      <td>521</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "      <td>Calnexin</td>\n",
       "      <td>RFP</td>\n",
       "      <td>561</td>\n",
       "      <td>594</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>7</td>\n",
       "      <td>Golgin97</td>\n",
       "      <td>Cy5</td>\n",
       "      <td>637</td>\n",
       "      <td>698</td>\n",
       "      <td>77</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>8</td>\n",
       "      <td>DNA_8</td>\n",
       "      <td>DAPI</td>\n",
       "      <td>405</td>\n",
       "      <td>445</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>SYTO</td>\n",
       "      <td>GFP</td>\n",
       "      <td>514</td>\n",
       "      <td>538</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>9</td>\n",
       "      <td>DNA_9</td>\n",
       "      <td>DAPI</td>\n",
       "      <td>405</td>\n",
       "      <td>445</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>9</td>\n",
       "      <td>ER</td>\n",
       "      <td>GFP</td>\n",
       "      <td>488</td>\n",
       "      <td>521</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>9</td>\n",
       "      <td>AGP</td>\n",
       "      <td>RFP</td>\n",
       "      <td>561</td>\n",
       "      <td>594</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>9</td>\n",
       "      <td>Catalase</td>\n",
       "      <td>Cy5</td>\n",
       "      <td>637</td>\n",
       "      <td>698</td>\n",
       "      <td>77</td>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    channel_number  cycle_number  marker_name Filter  excitation_wavelength  \\\n",
       "0                1             0        DNA_0   DAPI                    405   \n",
       "1                2             0  Blank_488_0    GFP                    488   \n",
       "2                3             0  Blank_561_0    RFP                    561   \n",
       "3                4             0  Blank_637_0    Cy5                    637   \n",
       "4                5             1        DNA_1   DAPI                    405   \n",
       "5                6             1          NWS    GFP                    488   \n",
       "6                7             1         VSVG    RFP                    561   \n",
       "7                8             1         FLAG    Cy5                    637   \n",
       "8                9             2        DNA_2   DAPI                    405   \n",
       "9               10             2          HSV    GFP                    488   \n",
       "10              11             2            C    RFP                    561   \n",
       "11              12             2            S    Cy5                    637   \n",
       "12              13             3        DNA_3   DAPI                    405   \n",
       "13              14             3        Ollas    GFP                    488   \n",
       "14              15             3         GFAP    RFP                    561   \n",
       "15              16             3         NeuN    Cy5                    637   \n",
       "16              17             4        DNA_4   DAPI                    405   \n",
       "17              18             4        pRPS6    GFP                    488   \n",
       "18              19             4      RANGAP1    RFP                    561   \n",
       "19              20             4         NFKB    Cy5                    637   \n",
       "20              21             5        DNA_5   DAPI                    405   \n",
       "21              22             5        TOM20    GFP                    488   \n",
       "22              23             5        LAMP1    RFP                    561   \n",
       "23              24             5         4HNE    Cy5                    637   \n",
       "24              25             6        DNA_6   DAPI                    405   \n",
       "25              26             6        TDP43    GFP                    488   \n",
       "26              27             6  Blank_561_6    RFP                    561   \n",
       "27              28             6        G3BP1    Cy5                    637   \n",
       "28              29             7        DNA_7   DAPI                    405   \n",
       "29              30             7        GM130    GFP                    488   \n",
       "30              31             7     Calnexin    RFP                    561   \n",
       "31              32             7     Golgin97    Cy5                    637   \n",
       "32              33             8        DNA_8   DAPI                    405   \n",
       "33              34             8         SYTO    GFP                    514   \n",
       "34              35             9        DNA_9   DAPI                    405   \n",
       "35              36             9           ER    GFP                    488   \n",
       "36              37             9          AGP    RFP                    561   \n",
       "37              38             9     Catalase    Cy5                    637   \n",
       "\n",
       "    emission_wavelength  Bandwidth  ch_index  Split_Num  \n",
       "0                   445         46         0          0  \n",
       "1                   521         38         1          1  \n",
       "2                   594         43         2          2  \n",
       "3                   698         77         3          3  \n",
       "4                   445         46         0          4  \n",
       "5                   521         38         1          5  \n",
       "6                   594         43         2          6  \n",
       "7                   698         77         3          7  \n",
       "8                   445         46         0          8  \n",
       "9                   521         38         1          9  \n",
       "10                  594         43         2         10  \n",
       "11                  698         77         3         11  \n",
       "12                  445         46         0         12  \n",
       "13                  521         38         1         13  \n",
       "14                  594         43         2         14  \n",
       "15                  698         77         3         15  \n",
       "16                  445         46         0         16  \n",
       "17                  521         38         1         17  \n",
       "18                  594         43         2         18  \n",
       "19                  698         77         3         19  \n",
       "20                  445         46         0         20  \n",
       "21                  521         38         1         21  \n",
       "22                  594         43         2         22  \n",
       "23                  698         77         3         23  \n",
       "24                  445         46         0         24  \n",
       "25                  521         38         1         25  \n",
       "26                  594         43         2         26  \n",
       "27                  698         77         3         27  \n",
       "28                  445         46         0         28  \n",
       "29                  521         38         1         29  \n",
       "30                  594         43         2         30  \n",
       "31                  698         77         3         31  \n",
       "32                  445         46         0         32  \n",
       "33                  538         38         1         33  \n",
       "34                  445         46         0         34  \n",
       "35                  521         38         1         35  \n",
       "36                  594         43         2         36  \n",
       "37                  698         77         3         37  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load metadata - load full codebook as well\n",
    "full_codebook = pd.read_csv(f'../{META_DIR}/full_codebook.csv',sep=',', index_col=0) # this is \"legal\" codebook\n",
    "Procode_gRNA = pd.read_csv(f'../{META_DIR}/PROCODE_gRNA.csv',sep=',')\n",
    "legal_codes = sorted(list(set(Procode_gRNA['ProCode ID'].to_list())))\n",
    "codebook = full_codebook[legal_codes]\n",
    "#AllProcodes = pd.read_csv('AllProcodes.csv', sep='.')\n",
    "sns.heatmap(codebook, linewidths = 0.3)\n",
    "markers = pd.read_csv(f'../{META_DIR}/markers.csv')\n",
    "markers.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "b249c4a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# mp_cp_output measures coloc, intensity, intensity distribution across 7 channels + nuclei\n",
    "# max_clean contains normalized background subtracted epitope channels --> use this to calculate X_norm\n",
    "# mp_score_max : use this to calculate P, logit, entropy, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4c6d6205",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cell_label = 1\n",
    "# coord_data = AllFeat\n",
    "def get_cell_coords(cell_label, coord_data):\n",
    "    \n",
    "    x2 = coord_data.loc[cell_label, 'AreaShape_BoundingBoxMaximum_X']\n",
    "    y2 = coord_data.loc[cell_label, 'AreaShape_BoundingBoxMaximum_Y']\n",
    "    x1 = coord_data.loc[cell_label, 'AreaShape_BoundingBoxMinimum_X']\n",
    "    y1 = coord_data.loc[cell_label, 'AreaShape_BoundingBoxMinimum_Y']\n",
    "    \n",
    "    return round(x1),round(x2),round(y1),round(y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "29319770",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ImageNumber</th>\n",
       "      <th>ObjectNumber</th>\n",
       "      <th>FileName_max_clean</th>\n",
       "      <th>PathName_max_clean</th>\n",
       "      <th>Children_Cytoplasm_Count</th>\n",
       "      <th>Correlation_Correlation_C_DNA</th>\n",
       "      <th>Correlation_Correlation_C_FLAG</th>\n",
       "      <th>Correlation_Correlation_C_HSV</th>\n",
       "      <th>Correlation_Correlation_C_NWS</th>\n",
       "      <th>Correlation_Correlation_C_Ollas</th>\n",
       "      <th>...</th>\n",
       "      <th>RadialDistribution_RadialCV_Ollas_3of4</th>\n",
       "      <th>RadialDistribution_RadialCV_Ollas_4of4</th>\n",
       "      <th>RadialDistribution_RadialCV_S_1of4</th>\n",
       "      <th>RadialDistribution_RadialCV_S_2of4</th>\n",
       "      <th>RadialDistribution_RadialCV_S_3of4</th>\n",
       "      <th>RadialDistribution_RadialCV_S_4of4</th>\n",
       "      <th>RadialDistribution_RadialCV_VSVG_1of4</th>\n",
       "      <th>RadialDistribution_RadialCV_VSVG_2of4</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>-0.405568</td>\n",
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       "      <td>1.212362</td>\n",
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       "      <td>0.758903</td>\n",
       "      <td>1.124459</td>\n",
       "      <td>0.309291</td>\n",
       "      <td>0.368628</td>\n",
       "      <td>0.819462</td>\n",
       "      <td>1.144822</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>-0.120659</td>\n",
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       "      <td>0.108147</td>\n",
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       "      <td>0.605029</td>\n",
       "      <td>0.370029</td>\n",
       "      <td>0.497995</td>\n",
       "      <td>0.401728</td>\n",
       "      <td>0.364964</td>\n",
       "      <td>0.318315</td>\n",
       "      <td>0.627694</td>\n",
       "      <td>0.566441</td>\n",
       "      <td>0.769759</td>\n",
       "      <td>0.943922</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>F000_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/101222_D10_Coverslip1_Process...</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.043058</td>\n",
       "      <td>0.207603</td>\n",
       "      <td>0.194595</td>\n",
       "      <td>0.193065</td>\n",
       "      <td>0.173620</td>\n",
       "      <td>...</td>\n",
       "      <td>0.252914</td>\n",
       "      <td>0.569577</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.324172</td>\n",
       "      <td>2.446358</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.855921</td>\n",
       "      <td>1.268671</td>\n",
       "      <td>1.450964</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>F000_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/101222_D10_Coverslip1_Process...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.032741</td>\n",
       "      <td>0.102508</td>\n",
       "      <td>0.114925</td>\n",
       "      <td>0.351646</td>\n",
       "      <td>0.135021</td>\n",
       "      <td>...</td>\n",
       "      <td>0.631931</td>\n",
       "      <td>0.913233</td>\n",
       "      <td>0.169474</td>\n",
       "      <td>0.356332</td>\n",
       "      <td>0.522433</td>\n",
       "      <td>0.730840</td>\n",
       "      <td>0.242420</td>\n",
       "      <td>0.501220</td>\n",
       "      <td>1.100836</td>\n",
       "      <td>1.040675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>F000_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/101222_D10_Coverslip1_Process...</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.256089</td>\n",
       "      <td>0.230455</td>\n",
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       "      <td>0.485189</td>\n",
       "      <td>-0.006212</td>\n",
       "      <td>...</td>\n",
       "      <td>0.774487</td>\n",
       "      <td>0.511029</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.039550</td>\n",
       "      <td>1.155751</td>\n",
       "      <td>1.047892</td>\n",
       "      <td>0.079289</td>\n",
       "      <td>0.091116</td>\n",
       "      <td>0.104703</td>\n",
       "      <td>0.218402</td>\n",
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       "      <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>133695</th>\n",
       "      <td>225</td>\n",
       "      <td>258</td>\n",
       "      <td>F224_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/101222_D10_Coverslip1_Process...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.016898</td>\n",
       "      <td>0.946519</td>\n",
       "      <td>0.955163</td>\n",
       "      <td>-0.045599</td>\n",
       "      <td>-0.034391</td>\n",
       "      <td>...</td>\n",
       "      <td>0.243910</td>\n",
       "      <td>0.365390</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.096991</td>\n",
       "      <td>1.612524</td>\n",
       "      <td>0.836004</td>\n",
       "      <td>0.597099</td>\n",
       "      <td>0.537393</td>\n",
       "      <td>0.890798</td>\n",
       "      <td>0.737002</td>\n",
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       "    <tr>\n",
       "      <th>133696</th>\n",
       "      <td>225</td>\n",
       "      <td>259</td>\n",
       "      <td>F224_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/101222_D10_Coverslip1_Process...</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.118881</td>\n",
       "      <td>0.333881</td>\n",
       "      <td>0.735675</td>\n",
       "      <td>0.133157</td>\n",
       "      <td>0.714265</td>\n",
       "      <td>...</td>\n",
       "      <td>1.094172</td>\n",
       "      <td>1.108408</td>\n",
       "      <td>0.603334</td>\n",
       "      <td>0.314654</td>\n",
       "      <td>0.680284</td>\n",
       "      <td>1.457429</td>\n",
       "      <td>0.200216</td>\n",
       "      <td>0.107832</td>\n",
       "      <td>0.389603</td>\n",
       "      <td>0.697496</td>\n",
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       "    <tr>\n",
       "      <th>133697</th>\n",
       "      <td>225</td>\n",
       "      <td>260</td>\n",
       "      <td>F224_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/101222_D10_Coverslip1_Process...</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.058207</td>\n",
       "      <td>-0.021145</td>\n",
       "      <td>-0.112881</td>\n",
       "      <td>-0.116101</td>\n",
       "      <td>-0.113942</td>\n",
       "      <td>...</td>\n",
       "      <td>1.190447</td>\n",
       "      <td>1.224097</td>\n",
       "      <td>0.980566</td>\n",
       "      <td>1.063520</td>\n",
       "      <td>1.301983</td>\n",
       "      <td>1.526587</td>\n",
       "      <td>2.645751</td>\n",
       "      <td>1.242633</td>\n",
       "      <td>1.452134</td>\n",
       "      <td>0.919034</td>\n",
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       "    <tr>\n",
       "      <th>133698</th>\n",
       "      <td>225</td>\n",
       "      <td>261</td>\n",
       "      <td>F224_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/101222_D10_Coverslip1_Process...</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.068751</td>\n",
       "      <td>0.338446</td>\n",
       "      <td>0.076893</td>\n",
       "      <td>0.002022</td>\n",
       "      <td>0.339401</td>\n",
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       "      <td>0.263019</td>\n",
       "      <td>0.577645</td>\n",
       "      <td>0.919920</td>\n",
       "      <td>0.704140</td>\n",
       "      <td>0.612588</td>\n",
       "      <td>1.229488</td>\n",
       "      <td>0.120038</td>\n",
       "      <td>0.191206</td>\n",
       "      <td>0.338359</td>\n",
       "      <td>0.523559</td>\n",
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       "    <tr>\n",
       "      <th>133699</th>\n",
       "      <td>225</td>\n",
       "      <td>262</td>\n",
       "      <td>F224_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/101222_D10_Coverslip1_Process...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.207050</td>\n",
       "      <td>-0.142933</td>\n",
       "      <td>0.979791</td>\n",
       "      <td>-0.173542</td>\n",
       "      <td>-0.168577</td>\n",
       "      <td>...</td>\n",
       "      <td>0.928728</td>\n",
       "      <td>0.557939</td>\n",
       "      <td>1.446617</td>\n",
       "      <td>1.325720</td>\n",
       "      <td>1.080990</td>\n",
       "      <td>1.125394</td>\n",
       "      <td>0.122647</td>\n",
       "      <td>0.252892</td>\n",
       "      <td>0.273385</td>\n",
       "      <td>0.544454</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133700 rows × 522 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ImageNumber  ObjectNumber  FileName_max_clean  \\\n",
       "0                 1             1  F000_max_clean.tif   \n",
       "1                 1             2  F000_max_clean.tif   \n",
       "2                 1             3  F000_max_clean.tif   \n",
       "3                 1             4  F000_max_clean.tif   \n",
       "4                 1             5  F000_max_clean.tif   \n",
       "...             ...           ...                 ...   \n",
       "133695          225           258  F224_max_clean.tif   \n",
       "133696          225           259  F224_max_clean.tif   \n",
       "133697          225           260  F224_max_clean.tif   \n",
       "133698          225           261  F224_max_clean.tif   \n",
       "133699          225           262  F224_max_clean.tif   \n",
       "\n",
       "                                       PathName_max_clean  \\\n",
       "0       /mnt/disks/store/101222_D10_Coverslip1_Process...   \n",
       "1       /mnt/disks/store/101222_D10_Coverslip1_Process...   \n",
       "2       /mnt/disks/store/101222_D10_Coverslip1_Process...   \n",
       "3       /mnt/disks/store/101222_D10_Coverslip1_Process...   \n",
       "4       /mnt/disks/store/101222_D10_Coverslip1_Process...   \n",
       "...                                                   ...   \n",
       "133695  /mnt/disks/store/101222_D10_Coverslip1_Process...   \n",
       "133696  /mnt/disks/store/101222_D10_Coverslip1_Process...   \n",
       "133697  /mnt/disks/store/101222_D10_Coverslip1_Process...   \n",
       "133698  /mnt/disks/store/101222_D10_Coverslip1_Process...   \n",
       "133699  /mnt/disks/store/101222_D10_Coverslip1_Process...   \n",
       "\n",
       "        Children_Cytoplasm_Count  Correlation_Correlation_C_DNA  \\\n",
       "0                              1                      -0.405568   \n",
       "1                              1                      -0.120659   \n",
       "2                              1                      -0.043058   \n",
       "3                              1                       0.032741   \n",
       "4                              1                      -0.256089   \n",
       "...                          ...                            ...   \n",
       "133695                         1                       0.016898   \n",
       "133696                         1                      -0.118881   \n",
       "133697                         1                      -0.058207   \n",
       "133698                         1                      -0.068751   \n",
       "133699                         1                       0.207050   \n",
       "\n",
       "        Correlation_Correlation_C_FLAG  Correlation_Correlation_C_HSV  \\\n",
       "0                             0.579144                      -0.029948   \n",
       "1                            -0.053589                       0.108147   \n",
       "2                             0.207603                       0.194595   \n",
       "3                             0.102508                       0.114925   \n",
       "4                             0.230455                      -0.008437   \n",
       "...                                ...                            ...   \n",
       "133695                        0.946519                       0.955163   \n",
       "133696                        0.333881                       0.735675   \n",
       "133697                       -0.021145                      -0.112881   \n",
       "133698                        0.338446                       0.076893   \n",
       "133699                       -0.142933                       0.979791   \n",
       "\n",
       "        Correlation_Correlation_C_NWS  Correlation_Correlation_C_Ollas  ...  \\\n",
       "0                            0.891888                         0.002846  ...   \n",
       "1                           -0.019662                        -0.006271  ...   \n",
       "2                            0.193065                         0.173620  ...   \n",
       "3                            0.351646                         0.135021  ...   \n",
       "4                            0.485189                        -0.006212  ...   \n",
       "...                               ...                              ...  ...   \n",
       "133695                      -0.045599                        -0.034391  ...   \n",
       "133696                       0.133157                         0.714265  ...   \n",
       "133697                      -0.116101                        -0.113942  ...   \n",
       "133698                       0.002022                         0.339401  ...   \n",
       "133699                      -0.173542                        -0.168577  ...   \n",
       "\n",
       "        RadialDistribution_RadialCV_Ollas_3of4  \\\n",
       "0                                     0.391393   \n",
       "1                                     0.605029   \n",
       "2                                     0.252914   \n",
       "3                                     0.631931   \n",
       "4                                     0.774487   \n",
       "...                                        ...   \n",
       "133695                                0.243910   \n",
       "133696                                1.094172   \n",
       "133697                                1.190447   \n",
       "133698                                0.263019   \n",
       "133699                                0.928728   \n",
       "\n",
       "        RadialDistribution_RadialCV_Ollas_4of4  \\\n",
       "0                                     0.781718   \n",
       "1                                     0.370029   \n",
       "2                                     0.569577   \n",
       "3                                     0.913233   \n",
       "4                                     0.511029   \n",
       "...                                        ...   \n",
       "133695                                0.365390   \n",
       "133696                                1.108408   \n",
       "133697                                1.224097   \n",
       "133698                                0.577645   \n",
       "133699                                0.557939   \n",
       "\n",
       "        RadialDistribution_RadialCV_S_1of4  \\\n",
       "0                                 1.212362   \n",
       "1                                 0.497995   \n",
       "2                                 0.000000   \n",
       "3                                 0.169474   \n",
       "4                                 0.000000   \n",
       "...                                    ...   \n",
       "133695                            0.000000   \n",
       "133696                            0.603334   \n",
       "133697                            0.980566   \n",
       "133698                            0.919920   \n",
       "133699                            1.446617   \n",
       "\n",
       "        RadialDistribution_RadialCV_S_2of4  \\\n",
       "0                                 1.064993   \n",
       "1                                 0.401728   \n",
       "2                                 0.000000   \n",
       "3                                 0.356332   \n",
       "4                                 2.039550   \n",
       "...                                    ...   \n",
       "133695                            1.096991   \n",
       "133696                            0.314654   \n",
       "133697                            1.063520   \n",
       "133698                            0.704140   \n",
       "133699                            1.325720   \n",
       "\n",
       "        RadialDistribution_RadialCV_S_3of4  \\\n",
       "0                                 0.758903   \n",
       "1                                 0.364964   \n",
       "2                                 2.324172   \n",
       "3                                 0.522433   \n",
       "4                                 1.155751   \n",
       "...                                    ...   \n",
       "133695                            1.612524   \n",
       "133696                            0.680284   \n",
       "133697                            1.301983   \n",
       "133698                            0.612588   \n",
       "133699                            1.080990   \n",
       "\n",
       "        RadialDistribution_RadialCV_S_4of4  \\\n",
       "0                                 1.124459   \n",
       "1                                 0.318315   \n",
       "2                                 2.446358   \n",
       "3                                 0.730840   \n",
       "4                                 1.047892   \n",
       "...                                    ...   \n",
       "133695                            0.836004   \n",
       "133696                            1.457429   \n",
       "133697                            1.526587   \n",
       "133698                            1.229488   \n",
       "133699                            1.125394   \n",
       "\n",
       "        RadialDistribution_RadialCV_VSVG_1of4  \\\n",
       "0                                    0.309291   \n",
       "1                                    0.627694   \n",
       "2                                    0.000000   \n",
       "3                                    0.242420   \n",
       "4                                    0.079289   \n",
       "...                                       ...   \n",
       "133695                               0.597099   \n",
       "133696                               0.200216   \n",
       "133697                               2.645751   \n",
       "133698                               0.120038   \n",
       "133699                               0.122647   \n",
       "\n",
       "        RadialDistribution_RadialCV_VSVG_2of4  \\\n",
       "0                                    0.368628   \n",
       "1                                    0.566441   \n",
       "2                                    1.855921   \n",
       "3                                    0.501220   \n",
       "4                                    0.091116   \n",
       "...                                       ...   \n",
       "133695                               0.537393   \n",
       "133696                               0.107832   \n",
       "133697                               1.242633   \n",
       "133698                               0.191206   \n",
       "133699                               0.252892   \n",
       "\n",
       "        RadialDistribution_RadialCV_VSVG_3of4  \\\n",
       "0                                    0.819462   \n",
       "1                                    0.769759   \n",
       "2                                    1.268671   \n",
       "3                                    1.100836   \n",
       "4                                    0.104703   \n",
       "...                                       ...   \n",
       "133695                               0.890798   \n",
       "133696                               0.389603   \n",
       "133697                               1.452134   \n",
       "133698                               0.338359   \n",
       "133699                               0.273385   \n",
       "\n",
       "        RadialDistribution_RadialCV_VSVG_4of4  \n",
       "0                                    1.144822  \n",
       "1                                    0.943922  \n",
       "2                                    1.450964  \n",
       "3                                    1.040675  \n",
       "4                                    0.218402  \n",
       "...                                       ...  \n",
       "133695                               0.737002  \n",
       "133696                               0.697496  \n",
       "133697                               0.919034  \n",
       "133698                               0.523559  \n",
       "133699                               0.544454  \n",
       "\n",
       "[133700 rows x 522 columns]"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpcp_df = pd.read_csv(f'{MP_CP_DIR}/MPFeat_SYTO_.csv', sep=',')\n",
    "mpcp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "6c07919f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ImageNumber</th>\n",
       "      <th>ObjectNumber</th>\n",
       "      <th>FileName_Cyto</th>\n",
       "      <th>FileName_Nuclei</th>\n",
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       "      <th>AreaShape_Solidity</th>\n",
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       "      <th>Location_Center_Y</th>\n",
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       "      <td>F000_max_clean_Soma.tiff</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>F000_max_clean_Cyto.tiff</td>\n",
       "      <td>F000_max_clean_Nuclei.tiff</td>\n",
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       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>529</td>\n",
       "      <td>728</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>24.665766</td>\n",
       "      <td>25.501090</td>\n",
       "      <td>-86.568274</td>\n",
       "      <td>87.012193</td>\n",
       "      <td>0.958333</td>\n",
       "      <td>1728.088847</td>\n",
       "      <td>33.882798</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>F000_max_clean_Cyto.tiff</td>\n",
       "      <td>F000_max_clean_Nuclei.tiff</td>\n",
       "      <td>F000_max_clean_Soma.tiff</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>996</td>\n",
       "      <td>2220</td>\n",
       "      <td>...</td>\n",
       "      <td>3.605551</td>\n",
       "      <td>35.198990</td>\n",
       "      <td>32.656906</td>\n",
       "      <td>73.725863</td>\n",
       "      <td>174.888348</td>\n",
       "      <td>0.684536</td>\n",
       "      <td>1229.575301</td>\n",
       "      <td>42.458835</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>F000_max_clean_Cyto.tiff</td>\n",
       "      <td>F000_max_clean_Nuclei.tiff</td>\n",
       "      <td>F000_max_clean_Soma.tiff</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>480</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>24.067315</td>\n",
       "      <td>-38.092519</td>\n",
       "      <td>80.769553</td>\n",
       "      <td>0.967742</td>\n",
       "      <td>41.410417</td>\n",
       "      <td>43.433333</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>F000_max_clean_Cyto.tiff</td>\n",
       "      <td>F000_max_clean_Nuclei.tiff</td>\n",
       "      <td>F000_max_clean_Soma.tiff</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>998</td>\n",
       "      <td>1440</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>29.346811</td>\n",
       "      <td>30.157882</td>\n",
       "      <td>-0.908166</td>\n",
       "      <td>126.053824</td>\n",
       "      <td>0.945076</td>\n",
       "      <td>1542.640281</td>\n",
       "      <td>45.852705</td>\n",
       "      <td>0</td>\n",
       "      <td>5</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>133695</th>\n",
       "      <td>225</td>\n",
       "      <td>258</td>\n",
       "      <td>F224_max_clean_Cyto.tiff</td>\n",
       "      <td>F224_max_clean_Nuclei.tiff</td>\n",
       "      <td>F224_max_clean_Soma.tiff</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>1298</td>\n",
       "      <td>1960</td>\n",
       "      <td>...</td>\n",
       "      <td>5.521010</td>\n",
       "      <td>31.243303</td>\n",
       "      <td>30.573337</td>\n",
       "      <td>54.753466</td>\n",
       "      <td>146.367532</td>\n",
       "      <td>0.957227</td>\n",
       "      <td>1763.477658</td>\n",
       "      <td>1954.303544</td>\n",
       "      <td>0</td>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133696</th>\n",
       "      <td>225</td>\n",
       "      <td>259</td>\n",
       "      <td>F224_max_clean_Cyto.tiff</td>\n",
       "      <td>F224_max_clean_Nuclei.tiff</td>\n",
       "      <td>F224_max_clean_Soma.tiff</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>1049</td>\n",
       "      <td>1600</td>\n",
       "      <td>...</td>\n",
       "      <td>4.472136</td>\n",
       "      <td>30.661697</td>\n",
       "      <td>29.361916</td>\n",
       "      <td>-77.674563</td>\n",
       "      <td>147.095454</td>\n",
       "      <td>0.877090</td>\n",
       "      <td>1595.878932</td>\n",
       "      <td>1963.214490</td>\n",
       "      <td>0</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133697</th>\n",
       "      <td>225</td>\n",
       "      <td>260</td>\n",
       "      <td>F224_max_clean_Cyto.tiff</td>\n",
       "      <td>F224_max_clean_Nuclei.tiff</td>\n",
       "      <td>F224_max_clean_Soma.tiff</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>1064</td>\n",
       "      <td>1665</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>30.530467</td>\n",
       "      <td>31.309183</td>\n",
       "      <td>57.955243</td>\n",
       "      <td>132.710678</td>\n",
       "      <td>0.958559</td>\n",
       "      <td>1415.094925</td>\n",
       "      <td>1982.528195</td>\n",
       "      <td>0</td>\n",
       "      <td>260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133698</th>\n",
       "      <td>225</td>\n",
       "      <td>261</td>\n",
       "      <td>F224_max_clean_Cyto.tiff</td>\n",
       "      <td>F224_max_clean_Nuclei.tiff</td>\n",
       "      <td>F224_max_clean_Soma.tiff</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>662</td>\n",
       "      <td>870</td>\n",
       "      <td>...</td>\n",
       "      <td>4.472136</td>\n",
       "      <td>26.870058</td>\n",
       "      <td>27.749116</td>\n",
       "      <td>-50.909529</td>\n",
       "      <td>94.669048</td>\n",
       "      <td>0.969253</td>\n",
       "      <td>1855.261329</td>\n",
       "      <td>1989.951662</td>\n",
       "      <td>0</td>\n",
       "      <td>261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133699</th>\n",
       "      <td>225</td>\n",
       "      <td>262</td>\n",
       "      <td>F224_max_clean_Cyto.tiff</td>\n",
       "      <td>F224_max_clean_Nuclei.tiff</td>\n",
       "      <td>F224_max_clean_Soma.tiff</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>1515</td>\n",
       "      <td>2703</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>46.510214</td>\n",
       "      <td>40.948147</td>\n",
       "      <td>16.990738</td>\n",
       "      <td>198.202056</td>\n",
       "      <td>0.806709</td>\n",
       "      <td>1229.413861</td>\n",
       "      <td>1977.320132</td>\n",
       "      <td>0</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133700 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ImageNumber  ObjectNumber             FileName_Cyto  \\\n",
       "0                 1             1  F000_max_clean_Cyto.tiff   \n",
       "1                 1             2  F000_max_clean_Cyto.tiff   \n",
       "2                 1             3  F000_max_clean_Cyto.tiff   \n",
       "3                 1             4  F000_max_clean_Cyto.tiff   \n",
       "4                 1             5  F000_max_clean_Cyto.tiff   \n",
       "...             ...           ...                       ...   \n",
       "133695          225           258  F224_max_clean_Cyto.tiff   \n",
       "133696          225           259  F224_max_clean_Cyto.tiff   \n",
       "133697          225           260  F224_max_clean_Cyto.tiff   \n",
       "133698          225           261  F224_max_clean_Cyto.tiff   \n",
       "133699          225           262  F224_max_clean_Cyto.tiff   \n",
       "\n",
       "                   FileName_Nuclei             FileName_Soma  \\\n",
       "0       F000_max_clean_Nuclei.tiff  F000_max_clean_Soma.tiff   \n",
       "1       F000_max_clean_Nuclei.tiff  F000_max_clean_Soma.tiff   \n",
       "2       F000_max_clean_Nuclei.tiff  F000_max_clean_Soma.tiff   \n",
       "3       F000_max_clean_Nuclei.tiff  F000_max_clean_Soma.tiff   \n",
       "4       F000_max_clean_Nuclei.tiff  F000_max_clean_Soma.tiff   \n",
       "...                            ...                       ...   \n",
       "133695  F224_max_clean_Nuclei.tiff  F224_max_clean_Soma.tiff   \n",
       "133696  F224_max_clean_Nuclei.tiff  F224_max_clean_Soma.tiff   \n",
       "133697  F224_max_clean_Nuclei.tiff  F224_max_clean_Soma.tiff   \n",
       "133698  F224_max_clean_Nuclei.tiff  F224_max_clean_Soma.tiff   \n",
       "133699  F224_max_clean_Nuclei.tiff  F224_max_clean_Soma.tiff   \n",
       "\n",
       "                                            PathName_Cyto  \\\n",
       "0       /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "1       /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "2       /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "3       /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "4       /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "...                                                   ...   \n",
       "133695  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "133696  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "133697  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "133698  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "133699  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "\n",
       "                                          PathName_Nuclei  \\\n",
       "0       /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "1       /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "2       /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "3       /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "4       /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "...                                                   ...   \n",
       "133695  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "133696  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "133697  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "133698  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "133699  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "\n",
       "                                            PathName_Soma  AreaShape_Area  \\\n",
       "0       /home/imaging/Desktop/Brian/NAS/Brian/101222_D...             630   \n",
       "1       /home/imaging/Desktop/Brian/NAS/Brian/101222_D...             529   \n",
       "2       /home/imaging/Desktop/Brian/NAS/Brian/101222_D...             996   \n",
       "3       /home/imaging/Desktop/Brian/NAS/Brian/101222_D...             480   \n",
       "4       /home/imaging/Desktop/Brian/NAS/Brian/101222_D...             998   \n",
       "...                                                   ...             ...   \n",
       "133695  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...            1298   \n",
       "133696  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...            1049   \n",
       "133697  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...            1064   \n",
       "133698  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...             662   \n",
       "133699  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...            1515   \n",
       "\n",
       "        AreaShape_BoundingBoxArea  ...  AreaShape_MedianRadius  \\\n",
       "0                             784  ...                4.123106   \n",
       "1                             728  ...                4.000000   \n",
       "2                            2220  ...                3.605551   \n",
       "3                             600  ...                4.000000   \n",
       "4                            1440  ...                5.000000   \n",
       "...                           ...  ...                     ...   \n",
       "133695                       1960  ...                5.521010   \n",
       "133696                       1600  ...                4.472136   \n",
       "133697                       1665  ...                5.000000   \n",
       "133698                        870  ...                4.472136   \n",
       "133699                       2703  ...                5.000000   \n",
       "\n",
       "        AreaShape_MinFeretDiameter  AreaShape_MinorAxisLength  \\\n",
       "0                        26.563132                  27.014496   \n",
       "1                        24.665766                  25.501090   \n",
       "2                        35.198990                  32.656906   \n",
       "3                        23.000000                  24.067315   \n",
       "4                        29.346811                  30.157882   \n",
       "...                            ...                        ...   \n",
       "133695                   31.243303                  30.573337   \n",
       "133696                   30.661697                  29.361916   \n",
       "133697                   30.530467                  31.309183   \n",
       "133698                   26.870058                  27.749116   \n",
       "133699                   46.510214                  40.948147   \n",
       "\n",
       "        AreaShape_Orientation  AreaShape_Perimeter  AreaShape_Solidity  \\\n",
       "0                   57.617647            96.840620            0.954545   \n",
       "1                  -86.568274            87.012193            0.958333   \n",
       "2                   73.725863           174.888348            0.684536   \n",
       "3                  -38.092519            80.769553            0.967742   \n",
       "4                   -0.908166           126.053824            0.945076   \n",
       "...                       ...                  ...                 ...   \n",
       "133695              54.753466           146.367532            0.957227   \n",
       "133696             -77.674563           147.095454            0.877090   \n",
       "133697              57.955243           132.710678            0.958559   \n",
       "133698             -50.909529            94.669048            0.969253   \n",
       "133699              16.990738           198.202056            0.806709   \n",
       "\n",
       "        Location_Center_X  Location_Center_Y  Location_Center_Z  \\\n",
       "0             1519.874603          16.922222                  0   \n",
       "1             1728.088847          33.882798                  0   \n",
       "2             1229.575301          42.458835                  0   \n",
       "3               41.410417          43.433333                  0   \n",
       "4             1542.640281          45.852705                  0   \n",
       "...                   ...                ...                ...   \n",
       "133695        1763.477658        1954.303544                  0   \n",
       "133696        1595.878932        1963.214490                  0   \n",
       "133697        1415.094925        1982.528195                  0   \n",
       "133698        1855.261329        1989.951662                  0   \n",
       "133699        1229.413861        1977.320132                  0   \n",
       "\n",
       "        Number_Object_Number  \n",
       "0                          1  \n",
       "1                          2  \n",
       "2                          3  \n",
       "3                          4  \n",
       "4                          5  \n",
       "...                      ...  \n",
       "133695                   258  \n",
       "133696                   259  \n",
       "133697                   260  \n",
       "133698                   261  \n",
       "133699                   262  \n",
       "\n",
       "[133700 rows x 37 columns]"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg_df = pd.read_csv(f'seg/SegFeatSoma_.csv', sep=',')\n",
    "seg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "7f90a766",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22, 2015, 2018)"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fov = '125'\n",
    "mpscore = imread(f'{MP_DIR}/F{fov}_mp_score_max.tif')\n",
    "mpscore.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "563e7be8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/s7/fg8686z944s5332vtll29z5h0000gn/T/ipykernel_3520/2368406064.py:2: MatplotlibDeprecationWarning: You are modifying the state of a globally registered colormap. This has been deprecated since 3.3 and in 3.6, you will not be able to modify a registered colormap in-place. To remove this warning, you can make a copy of the colormap first. cmap = mpl.cm.get_cmap(\"tab20\").copy()\n",
      "  cm.set_bad('k')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x312190ac0>"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = plt.get_cmap('tab20')\n",
    "cm.set_bad('k')\n",
    "plt.figure(figsize=(12,12))\n",
    "plt.gca().matshow(np.where(mpscore.max(0)==0, np.nan, mpscore.argmax(0)),cmap=cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aff69811",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Calculate_PCA_Loadings(X):\n",
    "    pca = PCA(random_state=1)\n",
    "    PCs = pca.fit_transform(X)\n",
    "    exp_var_pca = pca.explained_variance_ratio_ # only visualize top 10 PCs\n",
    "    \n",
    "    ratio = exp_var_pca[0]/(exp_var_pca[1]+1e-15) # PC1/PC2\n",
    "    if math.isnan(ratio): \n",
    "        ratio = 1\n",
    "    #\n",
    "    return exp_var_pca[0], exp_var_pca[1], exp_var_pca[2], ratio # %var of PC1, PC2, PC3, ratio "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "eac9e0e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "225"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(allFOVs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "410292f4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading... mp_cp_output/F000_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "280\n",
      ".................................................\n",
      "Reading... mp_cp_output/F001_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "580\n",
      ".................................................\n",
      "Reading... mp_cp_output/F002_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "833\n",
      ".................................................\n",
      "Reading... mp_cp_output/F003_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1196\n",
      ".................................................\n",
      "Reading... mp_cp_output/F004_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1594\n",
      ".................................................\n",
      "Reading... mp_cp_output/F005_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1922\n",
      ".................................................\n",
      "Reading... mp_cp_output/F006_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2254\n",
      ".................................................\n",
      "Reading... mp_cp_output/F007_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2522\n",
      ".................................................\n",
      "Reading... mp_cp_output/F008_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2843\n",
      ".................................................\n",
      "Reading... mp_cp_output/F009_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3086\n",
      ".................................................\n",
      "Reading... mp_cp_output/F010_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3418\n",
      ".................................................\n",
      "Reading... mp_cp_output/F011_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3726\n",
      ".................................................\n",
      "Reading... mp_cp_output/F012_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4039\n",
      ".................................................\n",
      "Reading... mp_cp_output/F013_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4392\n",
      ".................................................\n",
      "Reading... mp_cp_output/F014_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4773\n",
      ".................................................\n",
      "Reading... mp_cp_output/F015_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5225\n",
      ".................................................\n",
      "Reading... mp_cp_output/F016_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5608\n",
      ".................................................\n",
      "Reading... mp_cp_output/F017_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5979\n",
      ".................................................\n",
      "Reading... mp_cp_output/F018_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6299\n",
      ".................................................\n",
      "Reading... mp_cp_output/F019_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6631\n",
      ".................................................\n",
      "Reading... mp_cp_output/F020_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6928\n",
      ".................................................\n",
      "Reading... mp_cp_output/F021_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7349\n",
      ".................................................\n",
      "Reading... mp_cp_output/F022_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7664\n",
      ".................................................\n",
      "Reading... mp_cp_output/F023_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8070\n",
      ".................................................\n",
      "Reading... mp_cp_output/F024_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8549\n",
      ".................................................\n",
      "Reading... mp_cp_output/F025_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8928\n",
      ".................................................\n",
      "Reading... mp_cp_output/F026_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9324\n",
      ".................................................\n",
      "Reading... mp_cp_output/F027_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9784\n",
      ".................................................\n",
      "Reading... mp_cp_output/F028_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10288\n",
      ".................................................\n",
      "Reading... mp_cp_output/F029_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10716\n",
      ".................................................\n",
      "Reading... mp_cp_output/F030_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11296\n",
      ".................................................\n",
      "Reading... mp_cp_output/F031_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11824\n",
      ".................................................\n",
      "Reading... mp_cp_output/F032_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12332\n",
      ".................................................\n",
      "Reading... mp_cp_output/F033_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12743\n",
      ".................................................\n",
      "Reading... mp_cp_output/F034_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13058\n",
      ".................................................\n",
      "Reading... mp_cp_output/F035_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13479\n",
      ".................................................\n",
      "Reading... mp_cp_output/F036_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13926\n",
      ".................................................\n",
      "Reading... mp_cp_output/F037_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14457\n",
      ".................................................\n",
      "Reading... mp_cp_output/F038_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15148\n",
      ".................................................\n",
      "Reading... mp_cp_output/F039_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15863\n",
      ".................................................\n",
      "Reading... mp_cp_output/F040_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16630\n",
      ".................................................\n",
      "Reading... mp_cp_output/F041_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17461\n",
      ".................................................\n",
      "Reading... mp_cp_output/F042_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18215\n",
      ".................................................\n",
      "Reading... mp_cp_output/F043_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18755\n",
      ".................................................\n",
      "Reading... mp_cp_output/F044_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19281\n",
      ".................................................\n",
      "Reading... mp_cp_output/F045_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19948\n",
      ".................................................\n",
      "Reading... mp_cp_output/F046_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20625\n",
      ".................................................\n",
      "Reading... mp_cp_output/F047_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21382\n",
      ".................................................\n",
      "Reading... mp_cp_output/F048_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22322\n",
      ".................................................\n",
      "Reading... mp_cp_output/F049_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23463\n",
      ".................................................\n",
      "Reading... mp_cp_output/F050_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24016\n",
      ".................................................\n",
      "Reading... mp_cp_output/F051_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24753\n",
      ".................................................\n",
      "Reading... mp_cp_output/F052_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25729\n",
      ".................................................\n",
      "Reading... mp_cp_output/F053_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26802\n",
      ".................................................\n",
      "Reading... mp_cp_output/F054_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27630\n",
      ".................................................\n",
      "Reading... mp_cp_output/F055_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28717\n",
      ".................................................\n",
      "Reading... mp_cp_output/F056_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29878\n",
      ".................................................\n",
      "Reading... mp_cp_output/F057_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30895\n",
      ".................................................\n",
      "Reading... mp_cp_output/F058_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31691\n",
      ".................................................\n",
      "Reading... mp_cp_output/F059_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32326\n",
      ".................................................\n",
      "Reading... mp_cp_output/F060_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33051\n",
      ".................................................\n",
      "Reading... mp_cp_output/F061_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33835\n",
      ".................................................\n",
      "Reading... mp_cp_output/F062_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34944\n",
      ".................................................\n",
      "Reading... mp_cp_output/F063_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36268\n",
      ".................................................\n",
      "Reading... mp_cp_output/F064_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37595\n",
      ".................................................\n",
      "Reading... mp_cp_output/F065_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39227\n",
      ".................................................\n",
      "Reading... mp_cp_output/F066_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40991\n",
      ".................................................\n",
      "Reading... mp_cp_output/F067_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42526\n",
      ".................................................\n",
      "Reading... mp_cp_output/F068_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43786\n",
      ".................................................\n",
      "Reading... mp_cp_output/F069_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44781\n",
      ".................................................\n",
      "Reading... mp_cp_output/F070_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46064\n",
      ".................................................\n",
      "Reading... mp_cp_output/F071_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47699\n",
      ".................................................\n",
      "Reading... mp_cp_output/F072_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49487\n",
      ".................................................\n",
      "Reading... mp_cp_output/F073_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "51281\n",
      ".................................................\n",
      "Reading... mp_cp_output/F074_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "52993\n",
      ".................................................\n",
      "Reading... mp_cp_output/F075_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54343\n",
      ".................................................\n",
      "Reading... mp_cp_output/F076_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55930\n",
      ".................................................\n",
      "Reading... mp_cp_output/F077_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57646\n",
      ".................................................\n",
      "Reading... mp_cp_output/F078_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "59305\n",
      ".................................................\n",
      "Reading... mp_cp_output/F079_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60947\n",
      ".................................................\n",
      "Reading... mp_cp_output/F080_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62181\n",
      ".................................................\n",
      "Reading... mp_cp_output/F081_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63466\n",
      ".................................................\n",
      "Reading... mp_cp_output/F082_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64641\n",
      ".................................................\n",
      "Reading... mp_cp_output/F083_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65748\n",
      ".................................................\n",
      "Reading... mp_cp_output/F084_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "66835\n",
      ".................................................\n",
      "Reading... mp_cp_output/F085_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "67750\n",
      ".................................................\n",
      "Reading... mp_cp_output/F086_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "68571\n",
      ".................................................\n",
      "Reading... mp_cp_output/F087_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69421\n",
      ".................................................\n",
      "Reading... mp_cp_output/F088_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70239\n",
      ".................................................\n",
      "Reading... mp_cp_output/F089_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "71236\n",
      ".................................................\n",
      "Reading... mp_cp_output/F090_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "71869\n",
      ".................................................\n",
      "Reading... mp_cp_output/F091_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "72417\n",
      ".................................................\n",
      "Reading... mp_cp_output/F092_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "73136\n",
      ".................................................\n",
      "Reading... mp_cp_output/F093_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "73901\n",
      ".................................................\n",
      "Reading... mp_cp_output/F094_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "74645\n",
      ".................................................\n",
      "Reading... mp_cp_output/F095_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75315\n",
      ".................................................\n",
      "Reading... mp_cp_output/F096_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75914\n",
      ".................................................\n",
      "Reading... mp_cp_output/F097_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "76488\n",
      ".................................................\n",
      "Reading... mp_cp_output/F098_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "76976\n",
      ".................................................\n",
      "Reading... mp_cp_output/F099_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "77557\n",
      ".................................................\n",
      "Reading... mp_cp_output/F100_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78192\n",
      ".................................................\n",
      "Reading... mp_cp_output/F101_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78891\n",
      ".................................................\n",
      "Reading... mp_cp_output/F102_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79636\n",
      ".................................................\n",
      "Reading... mp_cp_output/F103_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80538\n",
      ".................................................\n",
      "Reading... mp_cp_output/F104_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "81598\n",
      ".................................................\n",
      "Reading... mp_cp_output/F105_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "82507\n",
      ".................................................\n",
      "Reading... mp_cp_output/F106_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "83284\n",
      ".................................................\n",
      "Reading... mp_cp_output/F107_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "84082\n",
      ".................................................\n",
      "Reading... mp_cp_output/F108_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "84689\n",
      ".................................................\n",
      "Reading... mp_cp_output/F109_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "85285\n",
      ".................................................\n",
      "Reading... mp_cp_output/F110_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "85880\n",
      ".................................................\n",
      "Reading... mp_cp_output/F111_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86470\n",
      ".................................................\n",
      "Reading... mp_cp_output/F112_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86979\n",
      ".................................................\n",
      "Reading... mp_cp_output/F113_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "87741\n",
      ".................................................\n",
      "Reading... mp_cp_output/F114_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "88586\n",
      ".................................................\n",
      "Reading... mp_cp_output/F115_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "89311\n",
      ".................................................\n",
      "Reading... mp_cp_output/F116_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90023\n",
      ".................................................\n",
      "Reading... mp_cp_output/F117_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90698\n",
      ".................................................\n",
      "Reading... mp_cp_output/F118_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91284\n",
      ".................................................\n",
      "Reading... mp_cp_output/F119_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91758\n",
      ".................................................\n",
      "Reading... mp_cp_output/F120_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "92245\n",
      ".................................................\n",
      "Reading... mp_cp_output/F121_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "92758\n",
      ".................................................\n",
      "Reading... mp_cp_output/F122_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93298\n",
      ".................................................\n",
      "Reading... mp_cp_output/F123_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93952\n",
      ".................................................\n",
      "Reading... mp_cp_output/F124_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "94669\n",
      ".................................................\n",
      "Reading... mp_cp_output/F125_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "94936\n",
      ".................................................\n",
      "Reading... mp_cp_output/F126_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95411\n",
      ".................................................\n",
      "Reading... mp_cp_output/F127_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95784\n",
      ".................................................\n",
      "Reading... mp_cp_output/F128_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96370\n",
      ".................................................\n",
      "Reading... mp_cp_output/F129_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96877\n",
      ".................................................\n",
      "Reading... mp_cp_output/F130_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97434\n",
      ".................................................\n",
      "Reading... mp_cp_output/F131_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97893\n",
      ".................................................\n",
      "Reading... mp_cp_output/F132_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "98279\n",
      ".................................................\n",
      "Reading... mp_cp_output/F133_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "98592\n",
      ".................................................\n",
      "Reading... mp_cp_output/F134_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99021\n",
      ".................................................\n",
      "Reading... mp_cp_output/F135_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99351\n",
      ".................................................\n",
      "Reading... mp_cp_output/F136_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99860\n",
      ".................................................\n",
      "Reading... mp_cp_output/F137_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100340\n",
      ".................................................\n",
      "Reading... mp_cp_output/F138_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100813\n",
      ".................................................\n",
      "Reading... mp_cp_output/F139_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101266\n",
      ".................................................\n",
      "Reading... mp_cp_output/F140_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101674\n",
      ".................................................\n",
      "Reading... mp_cp_output/F141_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "102053\n",
      ".................................................\n",
      "Reading... mp_cp_output/F142_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "102527\n",
      ".................................................\n",
      "Reading... mp_cp_output/F143_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "102923\n",
      ".................................................\n",
      "Reading... mp_cp_output/F144_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "103354\n",
      ".................................................\n",
      "Reading... mp_cp_output/F145_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "103769\n",
      ".................................................\n",
      "Reading... mp_cp_output/F146_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104231\n",
      ".................................................\n",
      "Reading... mp_cp_output/F147_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104643\n",
      ".................................................\n",
      "Reading... mp_cp_output/F148_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "105162\n",
      ".................................................\n",
      "Reading... mp_cp_output/F149_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "105601\n",
      ".................................................\n",
      "Reading... mp_cp_output/F150_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "105845\n",
      ".................................................\n",
      "Reading... mp_cp_output/F151_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "106320\n",
      ".................................................\n",
      "Reading... mp_cp_output/F152_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "106740\n",
      ".................................................\n",
      "Reading... mp_cp_output/F153_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "107204\n",
      ".................................................\n",
      "Reading... mp_cp_output/F154_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "107670\n",
      ".................................................\n",
      "Reading... mp_cp_output/F155_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "108004\n",
      ".................................................\n",
      "Reading... mp_cp_output/F156_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "108370\n",
      ".................................................\n",
      "Reading... mp_cp_output/F157_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "108701\n",
      ".................................................\n",
      "Reading... mp_cp_output/F158_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "109063\n",
      ".................................................\n",
      "Reading... mp_cp_output/F159_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "109371\n",
      ".................................................\n",
      "Reading... mp_cp_output/F160_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "109762\n",
      ".................................................\n",
      "Reading... mp_cp_output/F161_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "110062\n",
      ".................................................\n",
      "Reading... mp_cp_output/F162_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "110444\n",
      ".................................................\n",
      "Reading... mp_cp_output/F163_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "110815\n",
      ".................................................\n",
      "Reading... mp_cp_output/F164_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "111174\n",
      ".................................................\n",
      "Reading... mp_cp_output/F165_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "111504\n",
      ".................................................\n",
      "Reading... mp_cp_output/F166_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "111815\n",
      ".................................................\n",
      "Reading... mp_cp_output/F167_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "112156\n",
      ".................................................\n",
      "Reading... mp_cp_output/F168_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "112463\n",
      ".................................................\n",
      "Reading... mp_cp_output/F169_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "112755\n",
      ".................................................\n",
      "Reading... mp_cp_output/F170_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "113007\n",
      ".................................................\n",
      "Reading... mp_cp_output/F171_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "113319\n",
      ".................................................\n",
      "Reading... mp_cp_output/F172_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "113699\n",
      ".................................................\n",
      "Reading... mp_cp_output/F173_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "113981\n",
      ".................................................\n",
      "Reading... mp_cp_output/F174_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "114301\n",
      ".................................................\n",
      "Reading... mp_cp_output/F175_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "114749\n",
      ".................................................\n",
      "Reading... mp_cp_output/F176_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "115328\n",
      ".................................................\n",
      "Reading... mp_cp_output/F177_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "115883\n",
      ".................................................\n",
      "Reading... mp_cp_output/F178_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "116465\n",
      ".................................................\n",
      "Reading... mp_cp_output/F179_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "116981\n",
      ".................................................\n",
      "Reading... mp_cp_output/F180_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "117464\n",
      ".................................................\n",
      "Reading... mp_cp_output/F181_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "117883\n",
      ".................................................\n",
      "Reading... mp_cp_output/F182_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "118260\n",
      ".................................................\n",
      "Reading... mp_cp_output/F183_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "118637\n",
      ".................................................\n",
      "Reading... mp_cp_output/F184_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "118990\n",
      ".................................................\n",
      "Reading... mp_cp_output/F185_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "119428\n",
      ".................................................\n",
      "Reading... mp_cp_output/F186_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "119793\n",
      ".................................................\n",
      "Reading... mp_cp_output/F187_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120190\n",
      ".................................................\n",
      "Reading... mp_cp_output/F188_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120578\n",
      ".................................................\n",
      "Reading... mp_cp_output/F189_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120889\n",
      ".................................................\n",
      "Reading... mp_cp_output/F190_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "121196\n",
      ".................................................\n",
      "Reading... mp_cp_output/F191_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "121503\n",
      ".................................................\n",
      "Reading... mp_cp_output/F192_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "121827\n",
      ".................................................\n",
      "Reading... mp_cp_output/F193_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "122220\n",
      ".................................................\n",
      "Reading... mp_cp_output/F194_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "122498\n",
      ".................................................\n",
      "Reading... mp_cp_output/F195_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "122886\n",
      ".................................................\n",
      "Reading... mp_cp_output/F196_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "123185\n",
      ".................................................\n",
      "Reading... mp_cp_output/F197_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "123548\n",
      ".................................................\n",
      "Reading... mp_cp_output/F198_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "123867\n",
      ".................................................\n",
      "Reading... mp_cp_output/F199_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "124203\n",
      ".................................................\n",
      "Reading... mp_cp_output/F200_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "124693\n",
      ".................................................\n",
      "Reading... mp_cp_output/F201_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "125212\n",
      ".................................................\n",
      "Reading... mp_cp_output/F202_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "125775\n",
      ".................................................\n",
      "Reading... mp_cp_output/F203_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "126280\n",
      ".................................................\n",
      "Reading... mp_cp_output/F204_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "126789\n",
      ".................................................\n",
      "Reading... mp_cp_output/F205_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "127125\n",
      ".................................................\n",
      "Reading... mp_cp_output/F206_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "127604\n",
      ".................................................\n",
      "Reading... mp_cp_output/F207_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128037\n",
      ".................................................\n",
      "Reading... mp_cp_output/F208_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128471\n",
      ".................................................\n",
      "Reading... mp_cp_output/F209_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128881\n",
      ".................................................\n",
      "Reading... mp_cp_output/F210_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "129257\n",
      ".................................................\n",
      "Reading... mp_cp_output/F211_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "129626\n",
      ".................................................\n",
      "Reading... mp_cp_output/F212_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "130031\n",
      ".................................................\n",
      "Reading... mp_cp_output/F213_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "130353\n",
      ".................................................\n",
      "Reading... mp_cp_output/F214_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "130682\n",
      ".................................................\n",
      "Reading... mp_cp_output/F215_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "130971\n",
      ".................................................\n",
      "Reading... mp_cp_output/F216_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "131215\n",
      ".................................................\n",
      "Reading... mp_cp_output/F217_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "131507\n",
      ".................................................\n",
      "Reading... mp_cp_output/F218_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "131873\n",
      ".................................................\n",
      "Reading... mp_cp_output/F219_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132263\n",
      ".................................................\n",
      "Reading... mp_cp_output/F220_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132606\n",
      ".................................................\n",
      "Reading... mp_cp_output/F221_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132901\n",
      ".................................................\n",
      "Reading... mp_cp_output/F222_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "133138\n",
      ".................................................\n",
      "Reading... mp_cp_output/F223_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "133438\n",
      ".................................................\n",
      "Reading... mp_cp_output/F224_max_clean_Soma.tiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "133700\n",
      ".................................................\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>Barcode_Idx</th>\n",
       "      <th>SUM</th>\n",
       "      <th>Barcode</th>\n",
       "      <th>RAW</th>\n",
       "      <th>P</th>\n",
       "      <th>LOGIT</th>\n",
       "      <th>ENTROPY</th>\n",
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       "      <th>Gene</th>\n",
       "      <th>...</th>\n",
       "      <th>PC3_var</th>\n",
       "      <th>PC1/PC2</th>\n",
       "      <th>intensity_mean-0</th>\n",
       "      <th>intensity_mean-1</th>\n",
       "      <th>intensity_mean-2</th>\n",
       "      <th>intensity_mean-3</th>\n",
       "      <th>intensity_mean-4</th>\n",
       "      <th>intensity_mean-5</th>\n",
       "      <th>intensity_mean-6</th>\n",
       "      <th>Delta</th>\n",
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       "  </thead>\n",
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       "      <td>0.710910</td>\n",
       "      <td>0.864662</td>\n",
       "      <td>0.035280</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.145484</td>\n",
       "      <td>1.135724</td>\n",
       "      <td>0.015248</td>\n",
       "      <td>0.025134</td>\n",
       "      <td>0.012385</td>\n",
       "      <td>0.000820</td>\n",
       "      <td>0.005333</td>\n",
       "      <td>0.000442</td>\n",
       "      <td>0.002411</td>\n",
       "      <td>0.007052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.067428</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.054958</td>\n",
       "      <td>0.815054</td>\n",
       "      <td>1.483194</td>\n",
       "      <td>0.793739</td>\n",
       "      <td>0.043299</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.185725</td>\n",
       "      <td>1.296881</td>\n",
       "      <td>0.015638</td>\n",
       "      <td>0.025196</td>\n",
       "      <td>0.024860</td>\n",
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       "      <td>0.000203</td>\n",
       "      <td>0.006315</td>\n",
       "      <td>0.000928</td>\n",
       "      <td>0.009323</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>intensity_mean-8</td>\n",
       "      <td>0.035084</td>\n",
       "      <td>D3</td>\n",
       "      <td>0.024482</td>\n",
       "      <td>0.697820</td>\n",
       "      <td>0.836939</td>\n",
       "      <td>1.319092</td>\n",
       "      <td>0.026690</td>\n",
       "      <td>PPP2R2B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.089607</td>\n",
       "      <td>1.656365</td>\n",
       "      <td>0.000581</td>\n",
       "      <td>0.002030</td>\n",
       "      <td>0.011947</td>\n",
       "      <td>0.011055</td>\n",
       "      <td>0.001660</td>\n",
       "      <td>0.000317</td>\n",
       "      <td>0.014562</td>\n",
       "      <td>0.009024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>intensity_mean-3</td>\n",
       "      <td>0.261197</td>\n",
       "      <td>C12</td>\n",
       "      <td>0.179114</td>\n",
       "      <td>0.685744</td>\n",
       "      <td>0.780298</td>\n",
       "      <td>1.402972</td>\n",
       "      <td>0.108538</td>\n",
       "      <td>HSF1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.172751</td>\n",
       "      <td>1.909481</td>\n",
       "      <td>0.013229</td>\n",
       "      <td>0.013549</td>\n",
       "      <td>0.009692</td>\n",
       "      <td>0.039075</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.062781</td>\n",
       "      <td>0.061092</td>\n",
       "      <td>0.025526</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.272067</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.267507</td>\n",
       "      <td>0.983239</td>\n",
       "      <td>4.071811</td>\n",
       "      <td>0.117349</td>\n",
       "      <td>0.142667</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.057620</td>\n",
       "      <td>9.899952</td>\n",
       "      <td>0.074397</td>\n",
       "      <td>0.080426</td>\n",
       "      <td>0.088755</td>\n",
       "      <td>0.000502</td>\n",
       "      <td>0.002689</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.001225</td>\n",
       "      <td>0.071709</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>258</td>\n",
       "      <td>intensity_mean-13</td>\n",
       "      <td>0.022828</td>\n",
       "      <td>E4</td>\n",
       "      <td>0.019829</td>\n",
       "      <td>0.868626</td>\n",
       "      <td>1.888866</td>\n",
       "      <td>0.667654</td>\n",
       "      <td>0.014764</td>\n",
       "      <td>HRAS</td>\n",
       "      <td>...</td>\n",
       "      <td>0.153927</td>\n",
       "      <td>1.322907</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.000898</td>\n",
       "      <td>0.008159</td>\n",
       "      <td>0.009501</td>\n",
       "      <td>0.003234</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000670</td>\n",
       "      <td>0.002336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133696</th>\n",
       "      <td>259</td>\n",
       "      <td>intensity_mean-13</td>\n",
       "      <td>0.326837</td>\n",
       "      <td>E4</td>\n",
       "      <td>0.114697</td>\n",
       "      <td>0.350931</td>\n",
       "      <td>-0.614949</td>\n",
       "      <td>1.860739</td>\n",
       "      <td>0.119025</td>\n",
       "      <td>HRAS</td>\n",
       "      <td>...</td>\n",
       "      <td>0.120512</td>\n",
       "      <td>1.819898</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>0.004210</td>\n",
       "      <td>0.066644</td>\n",
       "      <td>0.071050</td>\n",
       "      <td>0.032583</td>\n",
       "      <td>0.009312</td>\n",
       "      <td>0.031562</td>\n",
       "      <td>0.001021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133697</th>\n",
       "      <td>260</td>\n",
       "      <td>intensity_mean-9</td>\n",
       "      <td>0.188926</td>\n",
       "      <td>D4</td>\n",
       "      <td>0.161259</td>\n",
       "      <td>0.853556</td>\n",
       "      <td>1.762765</td>\n",
       "      <td>0.616539</td>\n",
       "      <td>0.088308</td>\n",
       "      <td>FAN1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.172503</td>\n",
       "      <td>1.305001</td>\n",
       "      <td>0.031186</td>\n",
       "      <td>0.000526</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>0.048998</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.003363</td>\n",
       "      <td>0.064340</td>\n",
       "      <td>0.027823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133698</th>\n",
       "      <td>261</td>\n",
       "      <td>intensity_mean-19</td>\n",
       "      <td>0.003397</td>\n",
       "      <td>G8</td>\n",
       "      <td>0.002275</td>\n",
       "      <td>0.669709</td>\n",
       "      <td>0.706868</td>\n",
       "      <td>1.034844</td>\n",
       "      <td>0.021284</td>\n",
       "      <td>RAPGEF2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.151382</td>\n",
       "      <td>1.033066</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>0.007132</td>\n",
       "      <td>0.012290</td>\n",
       "      <td>0.000637</td>\n",
       "      <td>0.005305</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>0.010320</td>\n",
       "      <td>0.001827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133699</th>\n",
       "      <td>262</td>\n",
       "      <td>intensity_mean-15</td>\n",
       "      <td>1.048392</td>\n",
       "      <td>E8</td>\n",
       "      <td>0.684923</td>\n",
       "      <td>0.653308</td>\n",
       "      <td>0.633613</td>\n",
       "      <td>1.344290</td>\n",
       "      <td>0.397249</td>\n",
       "      <td>WASL</td>\n",
       "      <td>...</td>\n",
       "      <td>0.079927</td>\n",
       "      <td>3.364656</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>0.157899</td>\n",
       "      <td>0.021261</td>\n",
       "      <td>0.326146</td>\n",
       "      <td>0.141081</td>\n",
       "      <td>0.012467</td>\n",
       "      <td>0.004988</td>\n",
       "      <td>0.119821</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133700 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        label        Barcode_Idx       SUM Barcode       RAW         P  \\\n",
       "0           1   intensity_mean-0  0.057395      A2  0.038489  0.670602   \n",
       "1           2   intensity_mean-0  0.067428      A2  0.054958  0.815054   \n",
       "2           3   intensity_mean-8  0.035084      D3  0.024482  0.697820   \n",
       "3           4   intensity_mean-3  0.261197     C12  0.179114  0.685744   \n",
       "4           5   intensity_mean-0  0.272067      A2  0.267507  0.983239   \n",
       "...       ...                ...       ...     ...       ...       ...   \n",
       "133695    258  intensity_mean-13  0.022828      E4  0.019829  0.868626   \n",
       "133696    259  intensity_mean-13  0.326837      E4  0.114697  0.350931   \n",
       "133697    260   intensity_mean-9  0.188926      D4  0.161259  0.853556   \n",
       "133698    261  intensity_mean-19  0.003397      G8  0.002275  0.669709   \n",
       "133699    262  intensity_mean-15  1.048392      E8  0.684923  0.653308   \n",
       "\n",
       "           LOGIT   ENTROPY    X_NORM     Gene  ...   PC3_var   PC1/PC2  \\\n",
       "0       0.710910  0.864662  0.035280   PGGT1B  ...  0.145484  1.135724   \n",
       "1       1.483194  0.793739  0.043299   PGGT1B  ...  0.185725  1.296881   \n",
       "2       0.836939  1.319092  0.026690  PPP2R2B  ...  0.089607  1.656365   \n",
       "3       0.780298  1.402972  0.108538     HSF1  ...  0.172751  1.909481   \n",
       "4       4.071811  0.117349  0.142667   PGGT1B  ...  0.057620  9.899952   \n",
       "...          ...       ...       ...      ...  ...       ...       ...   \n",
       "133695  1.888866  0.667654  0.014764     HRAS  ...  0.153927  1.322907   \n",
       "133696 -0.614949  1.860739  0.119025     HRAS  ...  0.120512  1.819898   \n",
       "133697  1.762765  0.616539  0.088308     FAN1  ...  0.172503  1.305001   \n",
       "133698  0.706868  1.034844  0.021284  RAPGEF2  ...  0.151382  1.033066   \n",
       "133699  0.633613  1.344290  0.397249     WASL  ...  0.079927  3.364656   \n",
       "\n",
       "        intensity_mean-0  intensity_mean-1  intensity_mean-2  \\\n",
       "0               0.015248          0.025134          0.012385   \n",
       "1               0.015638          0.025196          0.024860   \n",
       "2               0.000581          0.002030          0.011947   \n",
       "3               0.013229          0.013549          0.009692   \n",
       "4               0.074397          0.080426          0.088755   \n",
       "...                  ...               ...               ...   \n",
       "133695          0.000058          0.000898          0.008159   \n",
       "133696          0.000329          0.004210          0.066644   \n",
       "133697          0.031186          0.000526          0.000544   \n",
       "133698          0.000314          0.007132          0.012290   \n",
       "133699          0.000239          0.157899          0.021261   \n",
       "\n",
       "        intensity_mean-3  intensity_mean-4  intensity_mean-5  \\\n",
       "0               0.000820          0.005333          0.000442   \n",
       "1               0.002435          0.000203          0.006315   \n",
       "2               0.011055          0.001660          0.000317   \n",
       "3               0.039075          0.004378          0.062781   \n",
       "4               0.000502          0.002689          0.000070   \n",
       "...                  ...               ...               ...   \n",
       "133695          0.009501          0.003234          0.000016   \n",
       "133696          0.071050          0.032583          0.009312   \n",
       "133697          0.048998          0.000056          0.003363   \n",
       "133698          0.000637          0.005305          0.000244   \n",
       "133699          0.326146          0.141081          0.012467   \n",
       "\n",
       "        intensity_mean-6     Delta  \n",
       "0               0.002411  0.007052  \n",
       "1               0.000928  0.009323  \n",
       "2               0.014562  0.009024  \n",
       "3               0.061092  0.025526  \n",
       "4               0.001225  0.071709  \n",
       "...                  ...       ...  \n",
       "133695          0.000670  0.002336  \n",
       "133696          0.031562  0.001021  \n",
       "133697          0.064340  0.027823  \n",
       "133698          0.010320  0.001827  \n",
       "133699          0.004988  0.119821  \n",
       "\n",
       "[133700 rows x 27 columns]"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Assign Gene ID\n",
    "procode_gene_df = pd.read_csv('../metadata/PROCODE_gRNA.csv')\n",
    "procode_to_gene = {}\n",
    "for cell in procode_gene_df.index:\n",
    "    procode = procode_gene_df.loc[cell,'ProCode ID']\n",
    "    if procode not in procode_to_gene.keys():\n",
    "        procode_to_gene[procode] = procode_gene_df.loc[cell,'Gene Target']\n",
    "        \n",
    "for fov in allFOVs:\n",
    "    print(\"Reading...\", f'{MP_CP_DIR}/F{fov}_{DATA_TYPE}_Soma.tiff')\n",
    "    soma = imread(f'{MP_CP_DIR}/F{fov}_{DATA_TYPE}_Soma.tiff') # mask image\n",
    "    \n",
    "    mpscore = imread(f'{MP_DIR}/F{fov}_mp_score_max.tif').transpose(1,2,0) # 22 barcode channels # YXC\n",
    "    seg = imread(f'{DATA_TYPE}/F{fov}_{DATA_TYPE}.tif') # 25, 2015X2018 \n",
    "    # max_clean, take first 1:8 channels to calculate epitope tag intensity\n",
    "    x_norm = np.linalg.norm(seg[1:8], axis=0, ord=2) # calculate x_norm for overall signal intensity\n",
    "    \n",
    "    # scores_df is the probs_df (Cell By MP Score)\n",
    "    scores = measure.regionprops_table(soma, mpscore, properties = ['label','intensity_mean'])\n",
    "    scores_df = pd.DataFrame(scores)\n",
    "    scores_df = scores_df.set_index('label')\n",
    "    barcode_cols = scores_df.columns.tolist() # intensity_mean-0 is first codebook column\n",
    "    \n",
    "    # create x_norm measurements\n",
    "    x_norm_measure = measure.regionprops_table(soma, x_norm, properties = ['label','intensity_mean'])\n",
    "    x_norm_df = pd.DataFrame(x_norm_measure)\n",
    "    x_norm_df = x_norm_df.set_index('label')\n",
    "    \n",
    "    # assign barcode with max mean score\n",
    "    barcoded = pd.DataFrame(index = scores_df.index) # indexed using cell labels 1,2,3...280\n",
    "    barcode_cols = scores_df.columns.tolist()\n",
    "    max_col = scores_df[barcode_cols].idxmax(axis=1) # find maximum column name intensity_mean-0, etc\n",
    "    barcoded['Barcode_Idx'] = max_col # assign intensity_mean-X \n",
    "    barcoded['SUM'] = scores_df[barcode_cols].sum(axis=1) # sum of all mpscore signal\n",
    "    \n",
    "    for cell in scores_df.index: # index is object number\n",
    "        idx = barcoded.loc[cell,'Barcode_Idx'] # intensity_mean-0\n",
    "        procode = codebook.columns.tolist()[int(idx.split('-')[-1])] # extract barcode name from intensity_mean-0\n",
    "        barcoded.loc[cell, 'Barcode'] = procode\n",
    "        barcoded.loc[cell, 'RAW'] = scores_df.loc[cell, idx]\n",
    "    \n",
    "    ## Calculate P, LOGIT\n",
    "    barcoded['P'] = barcoded['RAW'] / (barcoded['SUM']+1e-15) # normalized score\n",
    "    barcoded['LOGIT'] = np.log((barcoded['P']+1e-15)/(1-barcoded['P'] + 1e-15))\n",
    "    \n",
    "    ## Calculate Entropy ## vectorize?\n",
    "    from scipy.stats import entropy\n",
    "    p=(scores_df.values+1e-15)/(np.reshape(barcoded['SUM'].values, (-1,1))+1e-15)\n",
    "    barcoded['ENTROPY'] = entropy(p, axis=1)\n",
    "    \n",
    "    ## Calculate x_norm dataframe\n",
    "    barcoded['X_NORM'] = x_norm_df['intensity_mean']\n",
    "    \n",
    "    ## assign gene names from barcodes\n",
    "    for cell in barcoded.index:\n",
    "        barcode = barcoded.loc[cell, 'Barcode']\n",
    "        barcoded.loc[cell, 'Gene'] = procode_to_gene[barcode]\n",
    "    \n",
    "    image_num = list(set(mpcp_df[mpcp_df['FileName_max_clean'] == f'F{fov}_max_clean.tif'].ImageNumber.values))[0]\n",
    "    barcoded['ImageNumber'] = image_num\n",
    "    coord_data = seg_df[seg_df.ImageNumber == image_num]\n",
    "    coord_data = coord_data.set_index(\"ObjectNumber\")\n",
    "    \n",
    "    # Add overlap score to barcoded dataframe\n",
    "    S_cutoffs = [0.1, 0.2, 0.3] # user defined cutoff value\n",
    "    for S_cutoff in S_cutoffs:\n",
    "        mpscore_bin = mpscore > S_cutoff\n",
    "        \n",
    "        for label in barcoded.index:\n",
    "            x1,x2,y1,y2 = get_cell_coords(label,coord_data)\n",
    "            prob_ind = int(barcoded.loc[label, \"Barcode_Idx\"].split('-')[1])\n",
    "            soma_bin = soma[y1:y2,x1:x2]==label # create binary soma mask\n",
    "            overlap_score = (mpscore_bin[y1:y2,x1:x2, prob_ind]&soma_bin).sum()/soma_bin.sum()\n",
    "            barcoded.loc[label, f\"Overlap_{S_cutoff}\"] = overlap_score\n",
    "    \n",
    "    # Jaccard Index Score and PCA loadings\n",
    "    for label in barcoded.index:\n",
    "        # Jaccard Index\n",
    "        x1,x2,y1,y2 = get_cell_coords(label,coord_data)\n",
    "        soma_bin = soma[y1:y2,x1:x2]==label\n",
    "        prob_ind = int(barcoded.loc[label, \"Barcode_Idx\"].split('-')[1])\n",
    "        thres = threshold_otsu(mpscore[y1:y2,x1:x2, prob_ind])\n",
    "        mpscore_bin = mpscore[y1:y2,x1:x2, prob_ind]>thres\n",
    "        J = (mpscore_bin&soma_bin).sum()/((mpscore_bin|soma_bin).sum()+1e-15) # avoid division by zero\n",
    "        barcoded.loc[label, \"Jaccard\"] = J\n",
    "        \n",
    "        # PCA loadings\n",
    "        soma_bin = soma==label\n",
    "        score = mpscore.transpose(2,0,1) # CYX\n",
    "        mpscore_masked = np.multiply(soma_bin, score) # C Y X\n",
    "        X = mpscore_masked[:,y1:y2,x1:x2] \n",
    "        X = X.reshape(X.shape[1]*X.shape[2], X.shape[0])\n",
    "        PC1,PC2,PC3,ratio = Calculate_PCA_Loadings(X)\n",
    "        barcoded.loc[label, 'PC1_var'] = PC1\n",
    "        barcoded.loc[label, 'PC2_var'] = PC2\n",
    "        barcoded.loc[label, 'PC3_var'] = PC3\n",
    "        barcoded.loc[label, 'PC1/PC2'] = ratio\n",
    "    \n",
    "    ## Add epitope mean intensity for UMAP\n",
    "    epitopes_image = seg[1:8].transpose(1,2,0) # YXC # max_clean\n",
    "    epitope_measure = measure.regionprops_table(soma, epitopes_image, properties = ['label','intensity_mean'])\n",
    "    epitope_df = pd.DataFrame(epitope_measure)\n",
    "    epi_cols = epitope_df.columns[1:]\n",
    "    barcoded[epi_cols] = epitope_df[epi_cols].values\n",
    "    #print(barcoded.columns)\n",
    "    \n",
    "    ## Calculate Delta\n",
    "    for label in barcoded.index:\n",
    "        L = sorted(barcoded.loc[label, epi_cols].values, reverse=True) # sort in descending order\n",
    "        Delta = L[2] - L[3] # 3rd and 4th \n",
    "        barcoded.loc[label, 'Delta'] = Delta\n",
    "    \n",
    "    if image_num == 1: # if first image, set soma_df = barcoded itself\n",
    "        soma_df = barcoded.copy()\n",
    "    else:\n",
    "        soma_df = pd.concat([soma_df, barcoded], axis=0)\n",
    "    print(len(soma_df))\n",
    "    #print(soma_df)\n",
    "    print('.................................................')\n",
    "    \n",
    "soma_df = soma_df.reset_index() # becareful, missing images may be present. \n",
    "soma_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be4511a6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "5b6fc893",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(133700, 2)\n"
     ]
    },
    {
     "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>label</th>\n",
       "      <th>Barcode_Idx</th>\n",
       "      <th>SUM</th>\n",
       "      <th>Barcode</th>\n",
       "      <th>RAW</th>\n",
       "      <th>P</th>\n",
       "      <th>LOGIT</th>\n",
       "      <th>ENTROPY</th>\n",
       "      <th>X_NORM</th>\n",
       "      <th>Gene</th>\n",
       "      <th>...</th>\n",
       "      <th>intensity_mean-0</th>\n",
       "      <th>intensity_mean-1</th>\n",
       "      <th>intensity_mean-2</th>\n",
       "      <th>intensity_mean-3</th>\n",
       "      <th>intensity_mean-4</th>\n",
       "      <th>intensity_mean-5</th>\n",
       "      <th>intensity_mean-6</th>\n",
       "      <th>Delta</th>\n",
       "      <th>embedding1</th>\n",
       "      <th>embedding2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.057395</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.038489</td>\n",
       "      <td>0.670602</td>\n",
       "      <td>0.710910</td>\n",
       "      <td>0.864662</td>\n",
       "      <td>0.035280</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015248</td>\n",
       "      <td>0.025134</td>\n",
       "      <td>0.012385</td>\n",
       "      <td>0.000820</td>\n",
       "      <td>0.005333</td>\n",
       "      <td>0.000442</td>\n",
       "      <td>0.002411</td>\n",
       "      <td>0.007052</td>\n",
       "      <td>8.929145</td>\n",
       "      <td>8.983325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.067428</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.054958</td>\n",
       "      <td>0.815054</td>\n",
       "      <td>1.483194</td>\n",
       "      <td>0.793739</td>\n",
       "      <td>0.043299</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015638</td>\n",
       "      <td>0.025196</td>\n",
       "      <td>0.024860</td>\n",
       "      <td>0.002435</td>\n",
       "      <td>0.000203</td>\n",
       "      <td>0.006315</td>\n",
       "      <td>0.000928</td>\n",
       "      <td>0.009323</td>\n",
       "      <td>11.182019</td>\n",
       "      <td>8.415074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>intensity_mean-8</td>\n",
       "      <td>0.035084</td>\n",
       "      <td>D3</td>\n",
       "      <td>0.024482</td>\n",
       "      <td>0.697820</td>\n",
       "      <td>0.836939</td>\n",
       "      <td>1.319092</td>\n",
       "      <td>0.026690</td>\n",
       "      <td>PPP2R2B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000581</td>\n",
       "      <td>0.002030</td>\n",
       "      <td>0.011947</td>\n",
       "      <td>0.011055</td>\n",
       "      <td>0.001660</td>\n",
       "      <td>0.000317</td>\n",
       "      <td>0.014562</td>\n",
       "      <td>0.009024</td>\n",
       "      <td>11.592212</td>\n",
       "      <td>10.634103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>intensity_mean-3</td>\n",
       "      <td>0.261197</td>\n",
       "      <td>C12</td>\n",
       "      <td>0.179114</td>\n",
       "      <td>0.685744</td>\n",
       "      <td>0.780298</td>\n",
       "      <td>1.402972</td>\n",
       "      <td>0.108538</td>\n",
       "      <td>HSF1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013229</td>\n",
       "      <td>0.013549</td>\n",
       "      <td>0.009692</td>\n",
       "      <td>0.039075</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.062781</td>\n",
       "      <td>0.061092</td>\n",
       "      <td>0.025526</td>\n",
       "      <td>11.081718</td>\n",
       "      <td>3.503708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.272067</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.267507</td>\n",
       "      <td>0.983239</td>\n",
       "      <td>4.071811</td>\n",
       "      <td>0.117349</td>\n",
       "      <td>0.142667</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.074397</td>\n",
       "      <td>0.080426</td>\n",
       "      <td>0.088755</td>\n",
       "      <td>0.000502</td>\n",
       "      <td>0.002689</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.001225</td>\n",
       "      <td>0.071709</td>\n",
       "      <td>10.925993</td>\n",
       "      <td>1.490281</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>133695</th>\n",
       "      <td>258</td>\n",
       "      <td>intensity_mean-13</td>\n",
       "      <td>0.022828</td>\n",
       "      <td>E4</td>\n",
       "      <td>0.019829</td>\n",
       "      <td>0.868626</td>\n",
       "      <td>1.888866</td>\n",
       "      <td>0.667654</td>\n",
       "      <td>0.014764</td>\n",
       "      <td>HRAS</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.000898</td>\n",
       "      <td>0.008159</td>\n",
       "      <td>0.009501</td>\n",
       "      <td>0.003234</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000670</td>\n",
       "      <td>0.002336</td>\n",
       "      <td>10.888363</td>\n",
       "      <td>12.755734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133696</th>\n",
       "      <td>259</td>\n",
       "      <td>intensity_mean-13</td>\n",
       "      <td>0.326837</td>\n",
       "      <td>E4</td>\n",
       "      <td>0.114697</td>\n",
       "      <td>0.350931</td>\n",
       "      <td>-0.614949</td>\n",
       "      <td>1.860739</td>\n",
       "      <td>0.119025</td>\n",
       "      <td>HRAS</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>0.004210</td>\n",
       "      <td>0.066644</td>\n",
       "      <td>0.071050</td>\n",
       "      <td>0.032583</td>\n",
       "      <td>0.009312</td>\n",
       "      <td>0.031562</td>\n",
       "      <td>0.001021</td>\n",
       "      <td>12.442838</td>\n",
       "      <td>2.811264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133697</th>\n",
       "      <td>260</td>\n",
       "      <td>intensity_mean-9</td>\n",
       "      <td>0.188926</td>\n",
       "      <td>D4</td>\n",
       "      <td>0.161259</td>\n",
       "      <td>0.853556</td>\n",
       "      <td>1.762765</td>\n",
       "      <td>0.616539</td>\n",
       "      <td>0.088308</td>\n",
       "      <td>FAN1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031186</td>\n",
       "      <td>0.000526</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>0.048998</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.003363</td>\n",
       "      <td>0.064340</td>\n",
       "      <td>0.027823</td>\n",
       "      <td>10.910836</td>\n",
       "      <td>4.071362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133698</th>\n",
       "      <td>261</td>\n",
       "      <td>intensity_mean-19</td>\n",
       "      <td>0.003397</td>\n",
       "      <td>G8</td>\n",
       "      <td>0.002275</td>\n",
       "      <td>0.669709</td>\n",
       "      <td>0.706868</td>\n",
       "      <td>1.034844</td>\n",
       "      <td>0.021284</td>\n",
       "      <td>RAPGEF2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>0.007132</td>\n",
       "      <td>0.012290</td>\n",
       "      <td>0.000637</td>\n",
       "      <td>0.005305</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>0.010320</td>\n",
       "      <td>0.001827</td>\n",
       "      <td>10.747713</td>\n",
       "      <td>11.903031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133699</th>\n",
       "      <td>262</td>\n",
       "      <td>intensity_mean-15</td>\n",
       "      <td>1.048392</td>\n",
       "      <td>E8</td>\n",
       "      <td>0.684923</td>\n",
       "      <td>0.653308</td>\n",
       "      <td>0.633613</td>\n",
       "      <td>1.344290</td>\n",
       "      <td>0.397249</td>\n",
       "      <td>WASL</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>0.157899</td>\n",
       "      <td>0.021261</td>\n",
       "      <td>0.326146</td>\n",
       "      <td>0.141081</td>\n",
       "      <td>0.012467</td>\n",
       "      <td>0.004988</td>\n",
       "      <td>0.119821</td>\n",
       "      <td>10.127234</td>\n",
       "      <td>-5.068211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133700 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        label        Barcode_Idx       SUM Barcode       RAW         P  \\\n",
       "0           1   intensity_mean-0  0.057395      A2  0.038489  0.670602   \n",
       "1           2   intensity_mean-0  0.067428      A2  0.054958  0.815054   \n",
       "2           3   intensity_mean-8  0.035084      D3  0.024482  0.697820   \n",
       "3           4   intensity_mean-3  0.261197     C12  0.179114  0.685744   \n",
       "4           5   intensity_mean-0  0.272067      A2  0.267507  0.983239   \n",
       "...       ...                ...       ...     ...       ...       ...   \n",
       "133695    258  intensity_mean-13  0.022828      E4  0.019829  0.868626   \n",
       "133696    259  intensity_mean-13  0.326837      E4  0.114697  0.350931   \n",
       "133697    260   intensity_mean-9  0.188926      D4  0.161259  0.853556   \n",
       "133698    261  intensity_mean-19  0.003397      G8  0.002275  0.669709   \n",
       "133699    262  intensity_mean-15  1.048392      E8  0.684923  0.653308   \n",
       "\n",
       "           LOGIT   ENTROPY    X_NORM     Gene  ...  intensity_mean-0  \\\n",
       "0       0.710910  0.864662  0.035280   PGGT1B  ...          0.015248   \n",
       "1       1.483194  0.793739  0.043299   PGGT1B  ...          0.015638   \n",
       "2       0.836939  1.319092  0.026690  PPP2R2B  ...          0.000581   \n",
       "3       0.780298  1.402972  0.108538     HSF1  ...          0.013229   \n",
       "4       4.071811  0.117349  0.142667   PGGT1B  ...          0.074397   \n",
       "...          ...       ...       ...      ...  ...               ...   \n",
       "133695  1.888866  0.667654  0.014764     HRAS  ...          0.000058   \n",
       "133696 -0.614949  1.860739  0.119025     HRAS  ...          0.000329   \n",
       "133697  1.762765  0.616539  0.088308     FAN1  ...          0.031186   \n",
       "133698  0.706868  1.034844  0.021284  RAPGEF2  ...          0.000314   \n",
       "133699  0.633613  1.344290  0.397249     WASL  ...          0.000239   \n",
       "\n",
       "        intensity_mean-1  intensity_mean-2  intensity_mean-3  \\\n",
       "0               0.025134          0.012385          0.000820   \n",
       "1               0.025196          0.024860          0.002435   \n",
       "2               0.002030          0.011947          0.011055   \n",
       "3               0.013549          0.009692          0.039075   \n",
       "4               0.080426          0.088755          0.000502   \n",
       "...                  ...               ...               ...   \n",
       "133695          0.000898          0.008159          0.009501   \n",
       "133696          0.004210          0.066644          0.071050   \n",
       "133697          0.000526          0.000544          0.048998   \n",
       "133698          0.007132          0.012290          0.000637   \n",
       "133699          0.157899          0.021261          0.326146   \n",
       "\n",
       "        intensity_mean-4  intensity_mean-5  intensity_mean-6     Delta  \\\n",
       "0               0.005333          0.000442          0.002411  0.007052   \n",
       "1               0.000203          0.006315          0.000928  0.009323   \n",
       "2               0.001660          0.000317          0.014562  0.009024   \n",
       "3               0.004378          0.062781          0.061092  0.025526   \n",
       "4               0.002689          0.000070          0.001225  0.071709   \n",
       "...                  ...               ...               ...       ...   \n",
       "133695          0.003234          0.000016          0.000670  0.002336   \n",
       "133696          0.032583          0.009312          0.031562  0.001021   \n",
       "133697          0.000056          0.003363          0.064340  0.027823   \n",
       "133698          0.005305          0.000244          0.010320  0.001827   \n",
       "133699          0.141081          0.012467          0.004988  0.119821   \n",
       "\n",
       "        embedding1  embedding2  \n",
       "0         8.929145    8.983325  \n",
       "1        11.182019    8.415074  \n",
       "2        11.592212   10.634103  \n",
       "3        11.081718    3.503708  \n",
       "4        10.925993    1.490281  \n",
       "...            ...         ...  \n",
       "133695   10.888363   12.755734  \n",
       "133696   12.442838    2.811264  \n",
       "133697   10.910836    4.071362  \n",
       "133698   10.747713   11.903031  \n",
       "133699   10.127234   -5.068211  \n",
       "\n",
       "[133700 rows x 29 columns]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run UMAP on epitope channel intensity\n",
    "# make cell by epitope\n",
    "# run UMAP\n",
    "# run UMAP - generate cell by embedding, then merge to \"barcoded\" dataframe for visualizations\n",
    "reducer = umap.UMAP(low_memory=True)\n",
    "umap_cols = ['intensity_mean-0',\n",
    "       'intensity_mean-1', 'intensity_mean-2', 'intensity_mean-3',\n",
    "       'intensity_mean-4', 'intensity_mean-5', 'intensity_mean-6']\n",
    "umap_df = soma_df[umap_cols]\n",
    "embedding = reducer.fit_transform(umap_df.values)\n",
    "print(embedding.shape)\n",
    "\n",
    "# append embedding to epitope_df and debarcoded_df\n",
    "soma_df[['embedding1','embedding2']] = embedding\n",
    "soma_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "6bc8eabc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save soma_df\n",
    "soma_df.to_csv('Coverslip1_soma_df.csv', sep=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2687045e",
   "metadata": {},
   "source": [
    "## Check distribution of score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "fac5e688",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.609630177983025"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cutoff = np.quantile(soma_df['LOGIT'], 0.98)\n",
    "cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "70a94a3f",
   "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>label</th>\n",
       "      <th>Barcode_Idx</th>\n",
       "      <th>SUM</th>\n",
       "      <th>Barcode</th>\n",
       "      <th>RAW</th>\n",
       "      <th>P</th>\n",
       "      <th>LOGIT</th>\n",
       "      <th>ENTROPY</th>\n",
       "      <th>X_NORM</th>\n",
       "      <th>Gene</th>\n",
       "      <th>...</th>\n",
       "      <th>intensity_mean-0</th>\n",
       "      <th>intensity_mean-1</th>\n",
       "      <th>intensity_mean-2</th>\n",
       "      <th>intensity_mean-3</th>\n",
       "      <th>intensity_mean-4</th>\n",
       "      <th>intensity_mean-5</th>\n",
       "      <th>intensity_mean-6</th>\n",
       "      <th>Delta</th>\n",
       "      <th>embedding1</th>\n",
       "      <th>embedding2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.611893</td>\n",
       "      <td>4.208633e-09</td>\n",
       "      <td>0.002772</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000455</td>\n",
       "      <td>0.000365</td>\n",
       "      <td>0.000835</td>\n",
       "      <td>0.000394</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.001371</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>7.273243</td>\n",
       "      <td>17.793734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>51</td>\n",
       "      <td>intensity_mean-9</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>D4</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.215806</td>\n",
       "      <td>4.205488e-08</td>\n",
       "      <td>0.002153</td>\n",
       "      <td>FAN1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000608</td>\n",
       "      <td>0.000476</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.001194</td>\n",
       "      <td>0.000378</td>\n",
       "      <td>7.017622</td>\n",
       "      <td>18.001064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>65</td>\n",
       "      <td>intensity_mean-17</td>\n",
       "      <td>0.004004</td>\n",
       "      <td>F8</td>\n",
       "      <td>0.004004</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.014563</td>\n",
       "      <td>1.574239e-10</td>\n",
       "      <td>0.006553</td>\n",
       "      <td>NCKAP1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001987</td>\n",
       "      <td>0.002024</td>\n",
       "      <td>0.001502</td>\n",
       "      <td>0.002442</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000102</td>\n",
       "      <td>0.001431</td>\n",
       "      <td>0.000485</td>\n",
       "      <td>8.382124</td>\n",
       "      <td>16.059593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>96</td>\n",
       "      <td>intensity_mean-8</td>\n",
       "      <td>0.000173</td>\n",
       "      <td>D3</td>\n",
       "      <td>0.000173</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.874403</td>\n",
       "      <td>3.268734e-09</td>\n",
       "      <td>0.005136</td>\n",
       "      <td>PPP2R2B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000554</td>\n",
       "      <td>0.000663</td>\n",
       "      <td>0.002856</td>\n",
       "      <td>0.000827</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000622</td>\n",
       "      <td>0.001273</td>\n",
       "      <td>0.000164</td>\n",
       "      <td>8.952209</td>\n",
       "      <td>16.868021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>169</td>\n",
       "      <td>intensity_mean-8</td>\n",
       "      <td>0.000412</td>\n",
       "      <td>D3</td>\n",
       "      <td>0.000412</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.743289</td>\n",
       "      <td>1.414962e-09</td>\n",
       "      <td>0.007515</td>\n",
       "      <td>PPP2R2B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001536</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.004036</td>\n",
       "      <td>0.001148</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000077</td>\n",
       "      <td>0.002957</td>\n",
       "      <td>0.000388</td>\n",
       "      <td>9.492707</td>\n",
       "      <td>15.857043</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>133436</th>\n",
       "      <td>299</td>\n",
       "      <td>intensity_mean-15</td>\n",
       "      <td>0.003000</td>\n",
       "      <td>E8</td>\n",
       "      <td>0.003000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.726771</td>\n",
       "      <td>2.080908e-10</td>\n",
       "      <td>0.013944</td>\n",
       "      <td>WASL</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000439</td>\n",
       "      <td>0.010828</td>\n",
       "      <td>0.003024</td>\n",
       "      <td>0.002587</td>\n",
       "      <td>0.001416</td>\n",
       "      <td>0.001486</td>\n",
       "      <td>0.002270</td>\n",
       "      <td>0.000318</td>\n",
       "      <td>8.844666</td>\n",
       "      <td>12.830771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133526</th>\n",
       "      <td>89</td>\n",
       "      <td>intensity_mean-17</td>\n",
       "      <td>0.001436</td>\n",
       "      <td>F8</td>\n",
       "      <td>0.001436</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.991674</td>\n",
       "      <td>4.238936e-10</td>\n",
       "      <td>0.016180</td>\n",
       "      <td>NCKAP1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008116</td>\n",
       "      <td>0.007866</td>\n",
       "      <td>0.001556</td>\n",
       "      <td>0.007034</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.002475</td>\n",
       "      <td>0.000932</td>\n",
       "      <td>0.004558</td>\n",
       "      <td>9.126758</td>\n",
       "      <td>12.591461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133530</th>\n",
       "      <td>93</td>\n",
       "      <td>intensity_mean-9</td>\n",
       "      <td>0.000158</td>\n",
       "      <td>D4</td>\n",
       "      <td>0.000158</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.787750</td>\n",
       "      <td>3.553158e-09</td>\n",
       "      <td>0.002637</td>\n",
       "      <td>FAN1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000142</td>\n",
       "      <td>0.000992</td>\n",
       "      <td>0.000518</td>\n",
       "      <td>0.000268</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.001131</td>\n",
       "      <td>0.000251</td>\n",
       "      <td>6.989110</td>\n",
       "      <td>17.349285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133674</th>\n",
       "      <td>237</td>\n",
       "      <td>intensity_mean-18</td>\n",
       "      <td>0.003370</td>\n",
       "      <td>G12</td>\n",
       "      <td>0.003370</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.842480</td>\n",
       "      <td>1.859666e-10</td>\n",
       "      <td>0.021879</td>\n",
       "      <td>DGKE</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000222</td>\n",
       "      <td>0.017550</td>\n",
       "      <td>0.002604</td>\n",
       "      <td>0.000676</td>\n",
       "      <td>0.002182</td>\n",
       "      <td>0.002237</td>\n",
       "      <td>0.007446</td>\n",
       "      <td>0.000367</td>\n",
       "      <td>8.932912</td>\n",
       "      <td>11.426592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133692</th>\n",
       "      <td>255</td>\n",
       "      <td>intensity_mean-16</td>\n",
       "      <td>0.000165</td>\n",
       "      <td>F5</td>\n",
       "      <td>0.000165</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.827986</td>\n",
       "      <td>3.418140e-09</td>\n",
       "      <td>0.004447</td>\n",
       "      <td>ARPC3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000259</td>\n",
       "      <td>0.002149</td>\n",
       "      <td>0.001381</td>\n",
       "      <td>0.000744</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.000381</td>\n",
       "      <td>0.001087</td>\n",
       "      <td>0.000343</td>\n",
       "      <td>7.832271</td>\n",
       "      <td>16.431953</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4841 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        label        Barcode_Idx       SUM Barcode       RAW    P      LOGIT  \\\n",
       "24         25   intensity_mean-0  0.000133      A2  0.000133  1.0  25.611893   \n",
       "330        51   intensity_mean-9  0.000012      D4  0.000012  1.0  23.215806   \n",
       "344        65  intensity_mean-17  0.004004      F8  0.004004  1.0  29.014563   \n",
       "375        96   intensity_mean-8  0.000173      D3  0.000173  1.0  25.874403   \n",
       "448       169   intensity_mean-8  0.000412      D3  0.000412  1.0  26.743289   \n",
       "...       ...                ...       ...     ...       ...  ...        ...   \n",
       "133436    299  intensity_mean-15  0.003000      E8  0.003000  1.0  28.726771   \n",
       "133526     89  intensity_mean-17  0.001436      F8  0.001436  1.0  27.991674   \n",
       "133530     93   intensity_mean-9  0.000158      D4  0.000158  1.0  25.787750   \n",
       "133674    237  intensity_mean-18  0.003370     G12  0.003370  1.0  28.842480   \n",
       "133692    255  intensity_mean-16  0.000165      F5  0.000165  1.0  25.827986   \n",
       "\n",
       "             ENTROPY    X_NORM     Gene  ...  intensity_mean-0  \\\n",
       "24      4.208633e-09  0.002772   PGGT1B  ...          0.000455   \n",
       "330     4.205488e-08  0.002153     FAN1  ...          0.000098   \n",
       "344     1.574239e-10  0.006553   NCKAP1  ...          0.001987   \n",
       "375     3.268734e-09  0.005136  PPP2R2B  ...          0.000554   \n",
       "448     1.414962e-09  0.007515  PPP2R2B  ...          0.001536   \n",
       "...              ...       ...      ...  ...               ...   \n",
       "133436  2.080908e-10  0.013944     WASL  ...          0.000439   \n",
       "133526  4.238936e-10  0.016180   NCKAP1  ...          0.008116   \n",
       "133530  3.553158e-09  0.002637     FAN1  ...          0.000142   \n",
       "133674  1.859666e-10  0.021879     DGKE  ...          0.000222   \n",
       "133692  3.418140e-09  0.004447    ARPC3  ...          0.000259   \n",
       "\n",
       "        intensity_mean-1  intensity_mean-2  intensity_mean-3  \\\n",
       "24              0.000365          0.000835          0.000394   \n",
       "330             0.000050          0.000608          0.000476   \n",
       "344             0.002024          0.001502          0.002442   \n",
       "375             0.000663          0.002856          0.000827   \n",
       "448             0.000067          0.004036          0.001148   \n",
       "...                  ...               ...               ...   \n",
       "133436          0.010828          0.003024          0.002587   \n",
       "133526          0.007866          0.001556          0.007034   \n",
       "133530          0.000992          0.000518          0.000268   \n",
       "133674          0.017550          0.002604          0.000676   \n",
       "133692          0.002149          0.001381          0.000744   \n",
       "\n",
       "        intensity_mean-4  intensity_mean-5  intensity_mean-6     Delta  \\\n",
       "24              0.000019          0.000016          0.001371  0.000061   \n",
       "330             0.000009          0.000039          0.001194  0.000378   \n",
       "344             0.000009          0.000102          0.001431  0.000485   \n",
       "375             0.000015          0.000622          0.001273  0.000164   \n",
       "448             0.000019          0.000077          0.002957  0.000388   \n",
       "...                  ...               ...               ...       ...   \n",
       "133436          0.001416          0.001486          0.002270  0.000318   \n",
       "133526          0.000008          0.002475          0.000932  0.004558   \n",
       "133530          0.000007          0.000010          0.001131  0.000251   \n",
       "133674          0.002182          0.002237          0.007446  0.000367   \n",
       "133692          0.000073          0.000381          0.001087  0.000343   \n",
       "\n",
       "        embedding1  embedding2  \n",
       "24        7.273243   17.793734  \n",
       "330       7.017622   18.001064  \n",
       "344       8.382124   16.059593  \n",
       "375       8.952209   16.868021  \n",
       "448       9.492707   15.857043  \n",
       "...            ...         ...  \n",
       "133436    8.844666   12.830771  \n",
       "133526    9.126758   12.591461  \n",
       "133530    6.989110   17.349285  \n",
       "133674    8.932912   11.426592  \n",
       "133692    7.832271   16.431953  \n",
       "\n",
       "[4841 rows x 29 columns]"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soma_df[soma_df['LOGIT']>20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "b40fdbdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['label', 'Barcode_Idx', 'SUM', 'Barcode', 'RAW', 'P', 'LOGIT',\n",
       "       'ENTROPY', 'X_NORM', 'Gene', 'ImageNumber', 'Overlap_0.1',\n",
       "       'Overlap_0.2', 'Overlap_0.3', 'Jaccard', 'PC1_var', 'PC2_var',\n",
       "       'PC3_var', 'PC1/PC2', 'intensity_mean-0', 'intensity_mean-1',\n",
       "       'intensity_mean-2', 'intensity_mean-3', 'intensity_mean-4',\n",
       "       'intensity_mean-5', 'intensity_mean-6', 'Delta', 'embedding1',\n",
       "       'embedding2'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soma_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "bd3c6732",
   "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>label</th>\n",
       "      <th>Barcode_Idx</th>\n",
       "      <th>SUM</th>\n",
       "      <th>Barcode</th>\n",
       "      <th>RAW</th>\n",
       "      <th>P</th>\n",
       "      <th>LOGIT</th>\n",
       "      <th>ENTROPY</th>\n",
       "      <th>X_NORM</th>\n",
       "      <th>Gene</th>\n",
       "      <th>...</th>\n",
       "      <th>intensity_mean-0</th>\n",
       "      <th>intensity_mean-1</th>\n",
       "      <th>intensity_mean-2</th>\n",
       "      <th>intensity_mean-3</th>\n",
       "      <th>intensity_mean-4</th>\n",
       "      <th>intensity_mean-5</th>\n",
       "      <th>intensity_mean-6</th>\n",
       "      <th>Delta</th>\n",
       "      <th>embedding1</th>\n",
       "      <th>embedding2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-34.538776</td>\n",
       "      <td>3.091042</td>\n",
       "      <td>0.003000</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000490</td>\n",
       "      <td>0.000442</td>\n",
       "      <td>0.000416</td>\n",
       "      <td>0.000595</td>\n",
       "      <td>2.630356e-04</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>0.001282</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>7.139438</td>\n",
       "      <td>17.680931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>54</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-34.538776</td>\n",
       "      <td>3.091042</td>\n",
       "      <td>0.003460</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000225</td>\n",
       "      <td>0.001249</td>\n",
       "      <td>0.000735</td>\n",
       "      <td>0.000478</td>\n",
       "      <td>4.374559e-05</td>\n",
       "      <td>0.000304</td>\n",
       "      <td>0.001225</td>\n",
       "      <td>0.000257</td>\n",
       "      <td>7.217969</td>\n",
       "      <td>17.187677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>149</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-34.538776</td>\n",
       "      <td>3.091042</td>\n",
       "      <td>0.003276</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000815</td>\n",
       "      <td>0.001029</td>\n",
       "      <td>9.790461e-06</td>\n",
       "      <td>0.000074</td>\n",
       "      <td>0.001749</td>\n",
       "      <td>0.000602</td>\n",
       "      <td>7.790715</td>\n",
       "      <td>17.502989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>242</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-34.538776</td>\n",
       "      <td>3.091042</td>\n",
       "      <td>0.009985</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003902</td>\n",
       "      <td>0.002627</td>\n",
       "      <td>0.005609</td>\n",
       "      <td>0.001129</td>\n",
       "      <td>1.172883e-04</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.001881</td>\n",
       "      <td>0.000746</td>\n",
       "      <td>10.016680</td>\n",
       "      <td>14.616202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>86</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-34.538776</td>\n",
       "      <td>3.091042</td>\n",
       "      <td>0.006186</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000411</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.004002</td>\n",
       "      <td>0.000781</td>\n",
       "      <td>9.963848e-04</td>\n",
       "      <td>0.000961</td>\n",
       "      <td>0.001147</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>9.582528</td>\n",
       "      <td>16.629820</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>133601</th>\n",
       "      <td>164</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-34.538776</td>\n",
       "      <td>3.091042</td>\n",
       "      <td>0.002682</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>0.001235</td>\n",
       "      <td>0.000398</td>\n",
       "      <td>2.791363e-04</td>\n",
       "      <td>0.000315</td>\n",
       "      <td>0.000628</td>\n",
       "      <td>0.000097</td>\n",
       "      <td>7.037162</td>\n",
       "      <td>17.955889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133627</th>\n",
       "      <td>190</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-34.538776</td>\n",
       "      <td>3.091042</td>\n",
       "      <td>0.001309</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>0.000463</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000696</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>5.961690</td>\n",
       "      <td>18.530424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133630</th>\n",
       "      <td>193</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-34.538776</td>\n",
       "      <td>3.091042</td>\n",
       "      <td>0.005211</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001058</td>\n",
       "      <td>0.000146</td>\n",
       "      <td>0.000470</td>\n",
       "      <td>0.000816</td>\n",
       "      <td>6.808511e-07</td>\n",
       "      <td>0.003924</td>\n",
       "      <td>0.000617</td>\n",
       "      <td>0.000199</td>\n",
       "      <td>9.613443</td>\n",
       "      <td>16.730047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133634</th>\n",
       "      <td>197</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-34.538776</td>\n",
       "      <td>3.091042</td>\n",
       "      <td>0.001817</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000099</td>\n",
       "      <td>0.000491</td>\n",
       "      <td>0.000223</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000183</td>\n",
       "      <td>0.000938</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>6.401635</td>\n",
       "      <td>18.295460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133683</th>\n",
       "      <td>246</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-34.538776</td>\n",
       "      <td>3.091042</td>\n",
       "      <td>0.007941</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.006844</td>\n",
       "      <td>0.000635</td>\n",
       "      <td>0.000316</td>\n",
       "      <td>9.691791e-06</td>\n",
       "      <td>0.001334</td>\n",
       "      <td>0.001267</td>\n",
       "      <td>0.000632</td>\n",
       "      <td>8.395489</td>\n",
       "      <td>14.099876</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5469 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        label       Barcode_Idx  SUM Barcode  RAW    P      LOGIT   ENTROPY  \\\n",
       "6           7  intensity_mean-0  0.0      A2  0.0  0.0 -34.538776  3.091042   \n",
       "53         54  intensity_mean-0  0.0      A2  0.0  0.0 -34.538776  3.091042   \n",
       "148       149  intensity_mean-0  0.0      A2  0.0  0.0 -34.538776  3.091042   \n",
       "241       242  intensity_mean-0  0.0      A2  0.0  0.0 -34.538776  3.091042   \n",
       "365        86  intensity_mean-0  0.0      A2  0.0  0.0 -34.538776  3.091042   \n",
       "...       ...               ...  ...     ...  ...  ...        ...       ...   \n",
       "133601    164  intensity_mean-0  0.0      A2  0.0  0.0 -34.538776  3.091042   \n",
       "133627    190  intensity_mean-0  0.0      A2  0.0  0.0 -34.538776  3.091042   \n",
       "133630    193  intensity_mean-0  0.0      A2  0.0  0.0 -34.538776  3.091042   \n",
       "133634    197  intensity_mean-0  0.0      A2  0.0  0.0 -34.538776  3.091042   \n",
       "133683    246  intensity_mean-0  0.0      A2  0.0  0.0 -34.538776  3.091042   \n",
       "\n",
       "          X_NORM    Gene  ...  intensity_mean-0  intensity_mean-1  \\\n",
       "6       0.003000  PGGT1B  ...          0.000490          0.000442   \n",
       "53      0.003460  PGGT1B  ...          0.000225          0.001249   \n",
       "148     0.003276  PGGT1B  ...          0.000213          0.000050   \n",
       "241     0.009985  PGGT1B  ...          0.003902          0.002627   \n",
       "365     0.006186  PGGT1B  ...          0.000411          0.000042   \n",
       "...          ...     ...  ...               ...               ...   \n",
       "133601  0.002682  PGGT1B  ...          0.000105          0.000495   \n",
       "133627  0.001309  PGGT1B  ...          0.000056          0.000139   \n",
       "133630  0.005211  PGGT1B  ...          0.001058          0.000146   \n",
       "133634  0.001817  PGGT1B  ...          0.000245          0.000099   \n",
       "133683  0.007941  PGGT1B  ...          0.000116          0.006844   \n",
       "\n",
       "        intensity_mean-2  intensity_mean-3  intensity_mean-4  \\\n",
       "6               0.000416          0.000595      2.630356e-04   \n",
       "53              0.000735          0.000478      4.374559e-05   \n",
       "148             0.000815          0.001029      9.790461e-06   \n",
       "241             0.005609          0.001129      1.172883e-04   \n",
       "365             0.004002          0.000781      9.963848e-04   \n",
       "...                  ...               ...               ...   \n",
       "133601          0.001235          0.000398      2.791363e-04   \n",
       "133627          0.000463          0.000087      0.000000e+00   \n",
       "133630          0.000470          0.000816      6.808511e-07   \n",
       "133634          0.000491          0.000223      0.000000e+00   \n",
       "133683          0.000635          0.000316      9.691791e-06   \n",
       "\n",
       "        intensity_mean-5  intensity_mean-6     Delta  embedding1  embedding2  \n",
       "6               0.000253          0.001282  0.000049    7.139438   17.680931  \n",
       "53              0.000304          0.001225  0.000257    7.217969   17.187677  \n",
       "148             0.000074          0.001749  0.000602    7.790715   17.502989  \n",
       "241             0.000071          0.001881  0.000746   10.016680   14.616202  \n",
       "365             0.000961          0.001147  0.000036    9.582528   16.629820  \n",
       "...                  ...               ...       ...         ...         ...  \n",
       "133601          0.000315          0.000628  0.000097    7.037162   17.955889  \n",
       "133627          0.000007          0.000696  0.000051    5.961690   18.530424  \n",
       "133630          0.003924          0.000617  0.000199    9.613443   16.730047  \n",
       "133634          0.000183          0.000938  0.000022    6.401635   18.295460  \n",
       "133683          0.001334          0.001267  0.000632    8.395489   14.099876  \n",
       "\n",
       "[5469 rows x 29 columns]"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove cells that have weird PC1/PC2\n",
    "df=soma_df[soma_df.isna().any(axis=1)]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "7c46e9e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1_var</th>\n",
       "      <th>PC2_var</th>\n",
       "      <th>PC1/PC2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133324</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133347</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133394</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133398</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.649394e-12</td>\n",
       "      <td>6.059161e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133530</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1297 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        PC1_var       PC2_var       PC1/PC2\n",
       "24          1.0  0.000000e+00  1.000000e+15\n",
       "330         1.0  0.000000e+00  1.000000e+15\n",
       "375         1.0  0.000000e+00  1.000000e+15\n",
       "518         1.0  0.000000e+00  1.000000e+15\n",
       "519         1.0  0.000000e+00  1.000000e+15\n",
       "...         ...           ...           ...\n",
       "133324      1.0  0.000000e+00  1.000000e+15\n",
       "133347      1.0  0.000000e+00  1.000000e+15\n",
       "133394      1.0  0.000000e+00  1.000000e+15\n",
       "133398      1.0  1.649394e-12  6.059161e+11\n",
       "133530      1.0  0.000000e+00  1.000000e+15\n",
       "\n",
       "[1297 rows x 3 columns]"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=soma_df[soma_df['PC1/PC2']>1000]\n",
    "df[['PC1_var','PC2_var','PC1/PC2']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "f9e2df87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>Barcode_Idx</th>\n",
       "      <th>SUM</th>\n",
       "      <th>Barcode</th>\n",
       "      <th>RAW</th>\n",
       "      <th>P</th>\n",
       "      <th>LOGIT</th>\n",
       "      <th>ENTROPY</th>\n",
       "      <th>X_NORM</th>\n",
       "      <th>Gene</th>\n",
       "      <th>...</th>\n",
       "      <th>intensity_mean-0</th>\n",
       "      <th>intensity_mean-1</th>\n",
       "      <th>intensity_mean-2</th>\n",
       "      <th>intensity_mean-3</th>\n",
       "      <th>intensity_mean-4</th>\n",
       "      <th>intensity_mean-5</th>\n",
       "      <th>intensity_mean-6</th>\n",
       "      <th>Delta</th>\n",
       "      <th>embedding1</th>\n",
       "      <th>embedding2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.057395</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.038489</td>\n",
       "      <td>0.670602</td>\n",
       "      <td>0.710910</td>\n",
       "      <td>0.864662</td>\n",
       "      <td>0.035280</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015248</td>\n",
       "      <td>0.025134</td>\n",
       "      <td>0.012385</td>\n",
       "      <td>0.000820</td>\n",
       "      <td>0.005333</td>\n",
       "      <td>0.000442</td>\n",
       "      <td>0.002411</td>\n",
       "      <td>0.007052</td>\n",
       "      <td>8.929145</td>\n",
       "      <td>8.983325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.067428</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.054958</td>\n",
       "      <td>0.815054</td>\n",
       "      <td>1.483194</td>\n",
       "      <td>0.793739</td>\n",
       "      <td>0.043299</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015638</td>\n",
       "      <td>0.025196</td>\n",
       "      <td>0.024860</td>\n",
       "      <td>0.002435</td>\n",
       "      <td>0.000203</td>\n",
       "      <td>0.006315</td>\n",
       "      <td>0.000928</td>\n",
       "      <td>0.009323</td>\n",
       "      <td>11.182019</td>\n",
       "      <td>8.415074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>intensity_mean-8</td>\n",
       "      <td>0.035084</td>\n",
       "      <td>D3</td>\n",
       "      <td>0.024482</td>\n",
       "      <td>0.697820</td>\n",
       "      <td>0.836939</td>\n",
       "      <td>1.319092</td>\n",
       "      <td>0.026690</td>\n",
       "      <td>PPP2R2B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000581</td>\n",
       "      <td>0.002030</td>\n",
       "      <td>0.011947</td>\n",
       "      <td>0.011055</td>\n",
       "      <td>0.001660</td>\n",
       "      <td>0.000317</td>\n",
       "      <td>0.014562</td>\n",
       "      <td>0.009024</td>\n",
       "      <td>11.592212</td>\n",
       "      <td>10.634103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>intensity_mean-3</td>\n",
       "      <td>0.261197</td>\n",
       "      <td>C12</td>\n",
       "      <td>0.179114</td>\n",
       "      <td>0.685744</td>\n",
       "      <td>0.780298</td>\n",
       "      <td>1.402972</td>\n",
       "      <td>0.108538</td>\n",
       "      <td>HSF1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013229</td>\n",
       "      <td>0.013549</td>\n",
       "      <td>0.009692</td>\n",
       "      <td>0.039075</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.062781</td>\n",
       "      <td>0.061092</td>\n",
       "      <td>0.025526</td>\n",
       "      <td>11.081718</td>\n",
       "      <td>3.503708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.272067</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.267507</td>\n",
       "      <td>0.983239</td>\n",
       "      <td>4.071811</td>\n",
       "      <td>0.117349</td>\n",
       "      <td>0.142667</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.074397</td>\n",
       "      <td>0.080426</td>\n",
       "      <td>0.088755</td>\n",
       "      <td>0.000502</td>\n",
       "      <td>0.002689</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.001225</td>\n",
       "      <td>0.071709</td>\n",
       "      <td>10.925993</td>\n",
       "      <td>1.490281</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>133695</th>\n",
       "      <td>258</td>\n",
       "      <td>intensity_mean-13</td>\n",
       "      <td>0.022828</td>\n",
       "      <td>E4</td>\n",
       "      <td>0.019829</td>\n",
       "      <td>0.868626</td>\n",
       "      <td>1.888866</td>\n",
       "      <td>0.667654</td>\n",
       "      <td>0.014764</td>\n",
       "      <td>HRAS</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.000898</td>\n",
       "      <td>0.008159</td>\n",
       "      <td>0.009501</td>\n",
       "      <td>0.003234</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000670</td>\n",
       "      <td>0.002336</td>\n",
       "      <td>10.888363</td>\n",
       "      <td>12.755734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133696</th>\n",
       "      <td>259</td>\n",
       "      <td>intensity_mean-13</td>\n",
       "      <td>0.326837</td>\n",
       "      <td>E4</td>\n",
       "      <td>0.114697</td>\n",
       "      <td>0.350931</td>\n",
       "      <td>-0.614949</td>\n",
       "      <td>1.860739</td>\n",
       "      <td>0.119025</td>\n",
       "      <td>HRAS</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>0.004210</td>\n",
       "      <td>0.066644</td>\n",
       "      <td>0.071050</td>\n",
       "      <td>0.032583</td>\n",
       "      <td>0.009312</td>\n",
       "      <td>0.031562</td>\n",
       "      <td>0.001021</td>\n",
       "      <td>12.442838</td>\n",
       "      <td>2.811264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133697</th>\n",
       "      <td>260</td>\n",
       "      <td>intensity_mean-9</td>\n",
       "      <td>0.188926</td>\n",
       "      <td>D4</td>\n",
       "      <td>0.161259</td>\n",
       "      <td>0.853556</td>\n",
       "      <td>1.762765</td>\n",
       "      <td>0.616539</td>\n",
       "      <td>0.088308</td>\n",
       "      <td>FAN1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031186</td>\n",
       "      <td>0.000526</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>0.048998</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.003363</td>\n",
       "      <td>0.064340</td>\n",
       "      <td>0.027823</td>\n",
       "      <td>10.910836</td>\n",
       "      <td>4.071362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133698</th>\n",
       "      <td>261</td>\n",
       "      <td>intensity_mean-19</td>\n",
       "      <td>0.003397</td>\n",
       "      <td>G8</td>\n",
       "      <td>0.002275</td>\n",
       "      <td>0.669709</td>\n",
       "      <td>0.706868</td>\n",
       "      <td>1.034844</td>\n",
       "      <td>0.021284</td>\n",
       "      <td>RAPGEF2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>0.007132</td>\n",
       "      <td>0.012290</td>\n",
       "      <td>0.000637</td>\n",
       "      <td>0.005305</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>0.010320</td>\n",
       "      <td>0.001827</td>\n",
       "      <td>10.747713</td>\n",
       "      <td>11.903031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133699</th>\n",
       "      <td>262</td>\n",
       "      <td>intensity_mean-15</td>\n",
       "      <td>1.048392</td>\n",
       "      <td>E8</td>\n",
       "      <td>0.684923</td>\n",
       "      <td>0.653308</td>\n",
       "      <td>0.633613</td>\n",
       "      <td>1.344290</td>\n",
       "      <td>0.397249</td>\n",
       "      <td>WASL</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>0.157899</td>\n",
       "      <td>0.021261</td>\n",
       "      <td>0.326146</td>\n",
       "      <td>0.141081</td>\n",
       "      <td>0.012467</td>\n",
       "      <td>0.004988</td>\n",
       "      <td>0.119821</td>\n",
       "      <td>10.127234</td>\n",
       "      <td>-5.068211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>132371 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        label        Barcode_Idx       SUM Barcode       RAW         P  \\\n",
       "0           1   intensity_mean-0  0.057395      A2  0.038489  0.670602   \n",
       "1           2   intensity_mean-0  0.067428      A2  0.054958  0.815054   \n",
       "2           3   intensity_mean-8  0.035084      D3  0.024482  0.697820   \n",
       "3           4   intensity_mean-3  0.261197     C12  0.179114  0.685744   \n",
       "4           5   intensity_mean-0  0.272067      A2  0.267507  0.983239   \n",
       "...       ...                ...       ...     ...       ...       ...   \n",
       "133695    258  intensity_mean-13  0.022828      E4  0.019829  0.868626   \n",
       "133696    259  intensity_mean-13  0.326837      E4  0.114697  0.350931   \n",
       "133697    260   intensity_mean-9  0.188926      D4  0.161259  0.853556   \n",
       "133698    261  intensity_mean-19  0.003397      G8  0.002275  0.669709   \n",
       "133699    262  intensity_mean-15  1.048392      E8  0.684923  0.653308   \n",
       "\n",
       "           LOGIT   ENTROPY    X_NORM     Gene  ...  intensity_mean-0  \\\n",
       "0       0.710910  0.864662  0.035280   PGGT1B  ...          0.015248   \n",
       "1       1.483194  0.793739  0.043299   PGGT1B  ...          0.015638   \n",
       "2       0.836939  1.319092  0.026690  PPP2R2B  ...          0.000581   \n",
       "3       0.780298  1.402972  0.108538     HSF1  ...          0.013229   \n",
       "4       4.071811  0.117349  0.142667   PGGT1B  ...          0.074397   \n",
       "...          ...       ...       ...      ...  ...               ...   \n",
       "133695  1.888866  0.667654  0.014764     HRAS  ...          0.000058   \n",
       "133696 -0.614949  1.860739  0.119025     HRAS  ...          0.000329   \n",
       "133697  1.762765  0.616539  0.088308     FAN1  ...          0.031186   \n",
       "133698  0.706868  1.034844  0.021284  RAPGEF2  ...          0.000314   \n",
       "133699  0.633613  1.344290  0.397249     WASL  ...          0.000239   \n",
       "\n",
       "        intensity_mean-1  intensity_mean-2  intensity_mean-3  \\\n",
       "0               0.025134          0.012385          0.000820   \n",
       "1               0.025196          0.024860          0.002435   \n",
       "2               0.002030          0.011947          0.011055   \n",
       "3               0.013549          0.009692          0.039075   \n",
       "4               0.080426          0.088755          0.000502   \n",
       "...                  ...               ...               ...   \n",
       "133695          0.000898          0.008159          0.009501   \n",
       "133696          0.004210          0.066644          0.071050   \n",
       "133697          0.000526          0.000544          0.048998   \n",
       "133698          0.007132          0.012290          0.000637   \n",
       "133699          0.157899          0.021261          0.326146   \n",
       "\n",
       "        intensity_mean-4  intensity_mean-5  intensity_mean-6     Delta  \\\n",
       "0               0.005333          0.000442          0.002411  0.007052   \n",
       "1               0.000203          0.006315          0.000928  0.009323   \n",
       "2               0.001660          0.000317          0.014562  0.009024   \n",
       "3               0.004378          0.062781          0.061092  0.025526   \n",
       "4               0.002689          0.000070          0.001225  0.071709   \n",
       "...                  ...               ...               ...       ...   \n",
       "133695          0.003234          0.000016          0.000670  0.002336   \n",
       "133696          0.032583          0.009312          0.031562  0.001021   \n",
       "133697          0.000056          0.003363          0.064340  0.027823   \n",
       "133698          0.005305          0.000244          0.010320  0.001827   \n",
       "133699          0.141081          0.012467          0.004988  0.119821   \n",
       "\n",
       "        embedding1  embedding2  \n",
       "0         8.929145    8.983325  \n",
       "1        11.182019    8.415074  \n",
       "2        11.592212   10.634103  \n",
       "3        11.081718    3.503708  \n",
       "4        10.925993    1.490281  \n",
       "...            ...         ...  \n",
       "133695   10.888363   12.755734  \n",
       "133696   12.442838    2.811264  \n",
       "133697   10.910836    4.071362  \n",
       "133698   10.747713   11.903031  \n",
       "133699   10.127234   -5.068211  \n",
       "\n",
       "[132371 rows x 29 columns]"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soma_df[soma_df['PC1/PC2']<30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "7beb514d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.791677598107693"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6356ce7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs = plt.subplots(3,5,figsize=(20,10))\n",
    "axs=axs.ravel()\n",
    "cols_to_plot = ['RAW','P','LOGIT','ENTROPY', 'X_NORM', 'Overlap_0.1','Overlap_0.2','Overlap_0.3',\n",
    "               'PC1_var','PC2_var','PC3_var','PC1/PC2','Jaccard']\n",
    "for i in range(len(axs)-2):\n",
    "    col = cols_to_plot[i]\n",
    "    if col == 'LOGIT':\n",
    "        \n",
    "        #df = soma_df[~soma_df['LOGIT'].isna()]\n",
    "        #cutoff = np.quantile(soma_df[col], 0.999)\n",
    "        axs[i].hist(soma_df[abs(soma_df[col])<10][col], bins=100)\n",
    "        #axs[i].hist(barcoded[col],bins=25)\n",
    "        axs[i].set_title(col)\n",
    "    elif col == 'PC1/PC2':\n",
    "        cutoff = np.quantile(soma_df[col], 0.99)\n",
    "        axs[i].hist(soma_df[soma_df[col]<30][col], bins = 100)\n",
    "        axs[i].set_title(col)\n",
    "    else:\n",
    "        axs[i].hist(soma_df[col], bins = 100)\n",
    "        axs[i].set_title(col)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "5e11963a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x45e5dfca0>"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot UMAP of 5 images - embedding based on epitope signals\n",
    "fit, ax = plt.subplots(figsize=(10,10))\n",
    "sns.scatterplot(data = soma_df, x='embedding1', y='embedding2', hue = 'Barcode', ax=ax, s=0.3)\n",
    "ax.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "12a3fee2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot by 0) OverlapScore, 1) P, 2) RAW 3) LOGIT 4) ENTROPY\n",
    "cols_to_plot = ['Overlap_0.1','Overlap_0.2','Overlap_0.3','Delta','RAW','LOGIT',\n",
    "                'ENTROPY','X_NORM','Jaccard','PC1/PC2']\n",
    "fig,axs = plt.subplots(2,5, figsize=(20,10))\n",
    "axs = axs.ravel()\n",
    "df = soma_df[~soma_df['LOGIT'].isna()]\n",
    "##cutoff_logit = np.quantile(df['LOGIT'], 0.99)\n",
    "#cutoff_raw = np.quantile(df['RAW'], 0.01)\n",
    "# df_to_plot = df[~(df.RAW < cutoff_raw) & (df.LOGIT < cutoff_logit)]\n",
    "df_to_plot = df[abs(df.LOGIT) < 10]\n",
    "df_to_plot = df_to_plot[df_to_plot['PC1/PC2'] < 30]\n",
    "for i in range(len(cols_to_plot)):\n",
    "    col = cols_to_plot[i]\n",
    "    if col == 'Delta':\n",
    "        plot=axs[i].scatter(df_to_plot.embedding1,df_to_plot.embedding2, c=df_to_plot[col], \n",
    "                            vmax = 0.1, s=0.01)\n",
    "    elif col == 'PC1/PC2':\n",
    "        plot =axs[i].scatter(df_to_plot.embedding1,df_to_plot.embedding2, c=df_to_plot[col],\n",
    "                            vmax=10, s = 0.01)\n",
    "    else:\n",
    "        plot=axs[i].scatter(df_to_plot.embedding1,df_to_plot.embedding2, c=df_to_plot[col],s=0.01)\n",
    "    fig.colorbar(plot, ax=axs[i])\n",
    "   \n",
    "    axs[i].set_title(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7374bea6",
   "metadata": {},
   "source": [
    "## Export single cell images "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "f3b3e3bf",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_num=1\n",
    "barcoded = soma_df[soma_df.ImageNumber == image_num]\n",
    "barcoded.label.values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "763abb0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Plot by single-cell ##\n",
    "def plot_single_cells(image_num, soma_df, seg_df, interval, print_flag): # interval=100\n",
    "    #image_num = 5\n",
    "    epi_cols = [f'intensity_mean-{k}' for k in range(7)] # to define scores_df\n",
    "    coord_data = seg_df[seg_df.ImageNumber == image_num]\n",
    "\n",
    "    barcoded = soma_df[soma_df.ImageNumber == image_num]\n",
    "\n",
    "    fov = coord_data.FileName_Cyto.values[0].split('F')[-1][:3] # 004\n",
    "    soma = imread(f'{MP_CP_DIR}/F{fov}_max_clean_Soma.tiff')\n",
    "    mpscore = imread(f'{MP_DIR}/F{fov}_mp_score_max.tif').transpose(1,2,0) # YXC\n",
    "    seg = imread(f'max_clean/F{fov}_max_clean.tif')      \n",
    "    scores = measure.regionprops_table(soma, mpscore, properties = ['label','intensity_mean'])\n",
    "    scores_df = pd.DataFrame(scores)\n",
    "    scores_df = scores_df.set_index('label')\n",
    "\n",
    "    labels = barcoded.label.values.tolist() # cell numbers 1,2,3,...280\n",
    "    \n",
    "    import math\n",
    "    image_sets = range(math.ceil(len(labels)/interval))\n",
    "    \n",
    "    for image_set in image_sets: # 0,1,2,3\n",
    "        labels_subsets=labels[image_set*interval:(image_set+1)*interval]\n",
    "        if print_flag:\n",
    "            print(labels_subsets)\n",
    "        # plot intervals\n",
    "        fig, axs = plt.subplots(interval,10, figsize = (30,3*interval))\n",
    "        for i in range(len(labels_subsets)):\n",
    "            label = labels_subsets[i]\n",
    "            cell_id = coord_data[coord_data['ObjectNumber']==label].index[0]\n",
    "            x1,x2,y1,y2 = get_cell_coords(cell_id,coord_data)\n",
    "            im_to_show = soma == label\n",
    "            axs[i][0].imshow(im_to_show[y1-1:y2+1, x1-1:x2+1]) # plot cell \n",
    "            axs[i][0].set_title(barcoded.loc[cell_id, 'Barcode']+ \"...Label \" + str(label))\n",
    "            axs[i][0].axis('off')\n",
    "            # top 2 barcodes and 7 channels\n",
    "            top_2_idx = np.argsort(scores_df.loc[label].values)[-2:] # increasing orders\n",
    "            for j in range(1,3): # 1 2 \n",
    "                idx = top_2_idx[j-1] # 0 1\n",
    "                axs[i][j].imshow(mpscore[y1-1:y2+1, x1-1:x2+1, idx]) \n",
    "                axs[i][j].set_title(codebook.columns.tolist()[idx])\n",
    "                axs[i][j].axis('off')\n",
    "                \n",
    "            for j in range(3,10): # 3, 4, 5, 6, 7, 8, 9\n",
    "                axs[i][j].imshow(seg[j-2][y1-1:y2+1, x1-1:x2+1],\n",
    "                                vmin = np.quantile(seg[j-2],0.05), vmax = np.quantile(seg[j-2], 0.95))\n",
    "                axs[i][j].set_title(codebook.index.tolist()[j-3])\n",
    "                axs[i][j].axis('off')\n",
    "        fig.savefig(f'F{fov}_{image_num}_{image_set}.png')\n",
    "        plt.close()\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "90b9dfe8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13\n",
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100]\n",
      "[101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200]\n",
      "[201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300]\n",
      "[301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313]\n",
      "63\n",
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100]\n",
      "[101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200]\n",
      "[201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300]\n",
      "[301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400]\n",
      "[401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500]\n",
      "[501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600]\n",
      "[601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700]\n",
      "[701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800]\n",
      "[801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900]\n",
      "[901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000]\n",
      "[1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100]\n",
      "[1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109]\n",
      "113\n",
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100]\n",
      "[101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200]\n",
      "[201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400]\n",
      "[401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500]\n",
      "[501, 502, 503, 504, 505, 506, 507, 508, 509]\n",
      "163\n",
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100]\n",
      "[101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200]\n",
      "[201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300]\n",
      "[301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382]\n",
      "213\n",
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100]\n",
      "[101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200]\n",
      "[201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300]\n",
      "[301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400]\n",
      "[401, 402, 403, 404, 405]\n"
     ]
    }
   ],
   "source": [
    "# select representative fovs\n",
    "selFOVs = ['012','062','112','162','212']\n",
    "image_nums = []\n",
    "for fov in selFOVs:\n",
    "    if fov in allFOVs:\n",
    "        image_num = list(set(mpcp_df[mpcp_df['FileName_max_clean'] == f'F{fov}_max_clean.tif'].ImageNumber.values))[0]\n",
    "        image_nums.append(image_num)\n",
    "\n",
    "for image_num in image_nums:\n",
    "    print(image_num)\n",
    "    plot_single_cells(image_num, soma_df, seg_df,100, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "50774fe6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13 313\n",
      "63 1109\n",
      "113 509\n",
      "163 382\n",
      "213 405\n"
     ]
    }
   ],
   "source": [
    "for image_num in image_nums:\n",
    "    print(image_num, len(soma_df[soma_df.ImageNumber==image_num]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "6e651c91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A=soma_df[soma_df.ImageNumber.isin(image_nums)].index\n",
    "B=mpcp_df[mpcp_df.ImageNumber.isin(image_nums)].index\n",
    "np.array_equal(A,B)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ec6de46",
   "metadata": {},
   "source": [
    "## Load ground truth "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f01a197a",
   "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>label</th>\n",
       "      <th>Barcode_Idx</th>\n",
       "      <th>SUM</th>\n",
       "      <th>Barcode</th>\n",
       "      <th>RAW</th>\n",
       "      <th>P</th>\n",
       "      <th>LOGIT</th>\n",
       "      <th>ENTROPY</th>\n",
       "      <th>X_NORM</th>\n",
       "      <th>Gene</th>\n",
       "      <th>...</th>\n",
       "      <th>intensity_mean-0</th>\n",
       "      <th>intensity_mean-1</th>\n",
       "      <th>intensity_mean-2</th>\n",
       "      <th>intensity_mean-3</th>\n",
       "      <th>intensity_mean-4</th>\n",
       "      <th>intensity_mean-5</th>\n",
       "      <th>intensity_mean-6</th>\n",
       "      <th>Delta</th>\n",
       "      <th>embedding1</th>\n",
       "      <th>embedding2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.057395</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.038489</td>\n",
       "      <td>0.670602</td>\n",
       "      <td>0.710910</td>\n",
       "      <td>0.864662</td>\n",
       "      <td>0.035280</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015248</td>\n",
       "      <td>0.025134</td>\n",
       "      <td>0.012385</td>\n",
       "      <td>0.000820</td>\n",
       "      <td>0.005333</td>\n",
       "      <td>0.000442</td>\n",
       "      <td>0.002411</td>\n",
       "      <td>0.007052</td>\n",
       "      <td>8.929145</td>\n",
       "      <td>8.983325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.067428</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.054958</td>\n",
       "      <td>0.815054</td>\n",
       "      <td>1.483194</td>\n",
       "      <td>0.793739</td>\n",
       "      <td>0.043299</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015638</td>\n",
       "      <td>0.025196</td>\n",
       "      <td>0.024860</td>\n",
       "      <td>0.002435</td>\n",
       "      <td>0.000203</td>\n",
       "      <td>0.006315</td>\n",
       "      <td>0.000928</td>\n",
       "      <td>0.009323</td>\n",
       "      <td>11.182019</td>\n",
       "      <td>8.415074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>intensity_mean-8</td>\n",
       "      <td>0.035084</td>\n",
       "      <td>D3</td>\n",
       "      <td>0.024482</td>\n",
       "      <td>0.697820</td>\n",
       "      <td>0.836939</td>\n",
       "      <td>1.319092</td>\n",
       "      <td>0.026690</td>\n",
       "      <td>PPP2R2B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000581</td>\n",
       "      <td>0.002030</td>\n",
       "      <td>0.011947</td>\n",
       "      <td>0.011055</td>\n",
       "      <td>0.001660</td>\n",
       "      <td>0.000317</td>\n",
       "      <td>0.014562</td>\n",
       "      <td>0.009024</td>\n",
       "      <td>11.592212</td>\n",
       "      <td>10.634103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>intensity_mean-3</td>\n",
       "      <td>0.261197</td>\n",
       "      <td>C12</td>\n",
       "      <td>0.179114</td>\n",
       "      <td>0.685744</td>\n",
       "      <td>0.780298</td>\n",
       "      <td>1.402972</td>\n",
       "      <td>0.108538</td>\n",
       "      <td>HSF1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013229</td>\n",
       "      <td>0.013549</td>\n",
       "      <td>0.009692</td>\n",
       "      <td>0.039075</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.062781</td>\n",
       "      <td>0.061092</td>\n",
       "      <td>0.025526</td>\n",
       "      <td>11.081718</td>\n",
       "      <td>3.503708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.272067</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.267507</td>\n",
       "      <td>0.983239</td>\n",
       "      <td>4.071811</td>\n",
       "      <td>0.117349</td>\n",
       "      <td>0.142667</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.074397</td>\n",
       "      <td>0.080426</td>\n",
       "      <td>0.088755</td>\n",
       "      <td>0.000502</td>\n",
       "      <td>0.002689</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.001225</td>\n",
       "      <td>0.071709</td>\n",
       "      <td>10.925993</td>\n",
       "      <td>1.490282</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>133695</th>\n",
       "      <td>258</td>\n",
       "      <td>intensity_mean-13</td>\n",
       "      <td>0.022828</td>\n",
       "      <td>E4</td>\n",
       "      <td>0.019829</td>\n",
       "      <td>0.868626</td>\n",
       "      <td>1.888866</td>\n",
       "      <td>0.667654</td>\n",
       "      <td>0.014764</td>\n",
       "      <td>HRAS</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.000898</td>\n",
       "      <td>0.008159</td>\n",
       "      <td>0.009501</td>\n",
       "      <td>0.003234</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000670</td>\n",
       "      <td>0.002336</td>\n",
       "      <td>10.888363</td>\n",
       "      <td>12.755734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133696</th>\n",
       "      <td>259</td>\n",
       "      <td>intensity_mean-13</td>\n",
       "      <td>0.326837</td>\n",
       "      <td>E4</td>\n",
       "      <td>0.114697</td>\n",
       "      <td>0.350931</td>\n",
       "      <td>-0.614949</td>\n",
       "      <td>1.860739</td>\n",
       "      <td>0.119025</td>\n",
       "      <td>HRAS</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>0.004210</td>\n",
       "      <td>0.066644</td>\n",
       "      <td>0.071050</td>\n",
       "      <td>0.032583</td>\n",
       "      <td>0.009312</td>\n",
       "      <td>0.031562</td>\n",
       "      <td>0.001021</td>\n",
       "      <td>12.442838</td>\n",
       "      <td>2.811264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133697</th>\n",
       "      <td>260</td>\n",
       "      <td>intensity_mean-9</td>\n",
       "      <td>0.188926</td>\n",
       "      <td>D4</td>\n",
       "      <td>0.161259</td>\n",
       "      <td>0.853556</td>\n",
       "      <td>1.762765</td>\n",
       "      <td>0.616539</td>\n",
       "      <td>0.088308</td>\n",
       "      <td>FAN1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031186</td>\n",
       "      <td>0.000526</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>0.048998</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.003363</td>\n",
       "      <td>0.064340</td>\n",
       "      <td>0.027823</td>\n",
       "      <td>10.910836</td>\n",
       "      <td>4.071362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133698</th>\n",
       "      <td>261</td>\n",
       "      <td>intensity_mean-19</td>\n",
       "      <td>0.003397</td>\n",
       "      <td>G8</td>\n",
       "      <td>0.002275</td>\n",
       "      <td>0.669709</td>\n",
       "      <td>0.706868</td>\n",
       "      <td>1.034844</td>\n",
       "      <td>0.021284</td>\n",
       "      <td>RAPGEF2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>0.007132</td>\n",
       "      <td>0.012290</td>\n",
       "      <td>0.000637</td>\n",
       "      <td>0.005305</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>0.010320</td>\n",
       "      <td>0.001827</td>\n",
       "      <td>10.747713</td>\n",
       "      <td>11.903031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133699</th>\n",
       "      <td>262</td>\n",
       "      <td>intensity_mean-15</td>\n",
       "      <td>1.048392</td>\n",
       "      <td>E8</td>\n",
       "      <td>0.684923</td>\n",
       "      <td>0.653308</td>\n",
       "      <td>0.633613</td>\n",
       "      <td>1.344290</td>\n",
       "      <td>0.397249</td>\n",
       "      <td>WASL</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>0.157899</td>\n",
       "      <td>0.021261</td>\n",
       "      <td>0.326146</td>\n",
       "      <td>0.141081</td>\n",
       "      <td>0.012467</td>\n",
       "      <td>0.004988</td>\n",
       "      <td>0.119821</td>\n",
       "      <td>10.127234</td>\n",
       "      <td>-5.068211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133700 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        label        Barcode_Idx       SUM Barcode       RAW         P  \\\n",
       "0           1   intensity_mean-0  0.057395      A2  0.038489  0.670602   \n",
       "1           2   intensity_mean-0  0.067428      A2  0.054958  0.815054   \n",
       "2           3   intensity_mean-8  0.035084      D3  0.024482  0.697820   \n",
       "3           4   intensity_mean-3  0.261197     C12  0.179114  0.685744   \n",
       "4           5   intensity_mean-0  0.272067      A2  0.267507  0.983239   \n",
       "...       ...                ...       ...     ...       ...       ...   \n",
       "133695    258  intensity_mean-13  0.022828      E4  0.019829  0.868626   \n",
       "133696    259  intensity_mean-13  0.326837      E4  0.114697  0.350931   \n",
       "133697    260   intensity_mean-9  0.188926      D4  0.161259  0.853556   \n",
       "133698    261  intensity_mean-19  0.003397      G8  0.002275  0.669709   \n",
       "133699    262  intensity_mean-15  1.048392      E8  0.684923  0.653308   \n",
       "\n",
       "           LOGIT   ENTROPY    X_NORM     Gene  ...  intensity_mean-0  \\\n",
       "0       0.710910  0.864662  0.035280   PGGT1B  ...          0.015248   \n",
       "1       1.483194  0.793739  0.043299   PGGT1B  ...          0.015638   \n",
       "2       0.836939  1.319092  0.026690  PPP2R2B  ...          0.000581   \n",
       "3       0.780298  1.402972  0.108538     HSF1  ...          0.013229   \n",
       "4       4.071811  0.117349  0.142667   PGGT1B  ...          0.074397   \n",
       "...          ...       ...       ...      ...  ...               ...   \n",
       "133695  1.888866  0.667654  0.014764     HRAS  ...          0.000058   \n",
       "133696 -0.614949  1.860739  0.119025     HRAS  ...          0.000329   \n",
       "133697  1.762765  0.616539  0.088308     FAN1  ...          0.031186   \n",
       "133698  0.706868  1.034844  0.021284  RAPGEF2  ...          0.000314   \n",
       "133699  0.633613  1.344290  0.397249     WASL  ...          0.000239   \n",
       "\n",
       "        intensity_mean-1  intensity_mean-2  intensity_mean-3  \\\n",
       "0               0.025134          0.012385          0.000820   \n",
       "1               0.025196          0.024860          0.002435   \n",
       "2               0.002030          0.011947          0.011055   \n",
       "3               0.013549          0.009692          0.039075   \n",
       "4               0.080426          0.088755          0.000502   \n",
       "...                  ...               ...               ...   \n",
       "133695          0.000898          0.008159          0.009501   \n",
       "133696          0.004210          0.066644          0.071050   \n",
       "133697          0.000526          0.000544          0.048998   \n",
       "133698          0.007132          0.012290          0.000637   \n",
       "133699          0.157899          0.021261          0.326146   \n",
       "\n",
       "        intensity_mean-4  intensity_mean-5  intensity_mean-6     Delta  \\\n",
       "0               0.005333          0.000442          0.002411  0.007052   \n",
       "1               0.000203          0.006315          0.000928  0.009323   \n",
       "2               0.001660          0.000317          0.014562  0.009024   \n",
       "3               0.004378          0.062781          0.061092  0.025526   \n",
       "4               0.002689          0.000070          0.001225  0.071709   \n",
       "...                  ...               ...               ...       ...   \n",
       "133695          0.003234          0.000016          0.000670  0.002336   \n",
       "133696          0.032583          0.009312          0.031562  0.001021   \n",
       "133697          0.000056          0.003363          0.064340  0.027823   \n",
       "133698          0.005305          0.000244          0.010320  0.001827   \n",
       "133699          0.141081          0.012467          0.004988  0.119821   \n",
       "\n",
       "        embedding1  embedding2  \n",
       "0         8.929145    8.983325  \n",
       "1        11.182019    8.415074  \n",
       "2        11.592212   10.634103  \n",
       "3        11.081718    3.503708  \n",
       "4        10.925993    1.490282  \n",
       "...            ...         ...  \n",
       "133695   10.888363   12.755734  \n",
       "133696   12.442838    2.811264  \n",
       "133697   10.910836    4.071362  \n",
       "133698   10.747713   11.903031  \n",
       "133699   10.127234   -5.068211  \n",
       "\n",
       "[133700 rows x 29 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soma_df = pd.read_csv('Coverslip1_soma_df.csv', sep=',', index_col=0)\n",
    "soma_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e59ea116",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n"
     ]
    },
    {
     "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>label</th>\n",
       "      <th>Barcode_Idx</th>\n",
       "      <th>SUM</th>\n",
       "      <th>Barcode</th>\n",
       "      <th>RAW</th>\n",
       "      <th>P</th>\n",
       "      <th>LOGIT</th>\n",
       "      <th>ENTROPY</th>\n",
       "      <th>X_NORM</th>\n",
       "      <th>Gene</th>\n",
       "      <th>...</th>\n",
       "      <th>intensity_mean-1</th>\n",
       "      <th>intensity_mean-2</th>\n",
       "      <th>intensity_mean-3</th>\n",
       "      <th>intensity_mean-4</th>\n",
       "      <th>intensity_mean-5</th>\n",
       "      <th>intensity_mean-6</th>\n",
       "      <th>Delta</th>\n",
       "      <th>embedding1</th>\n",
       "      <th>embedding2</th>\n",
       "      <th>True_Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.057395</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.038489</td>\n",
       "      <td>0.670602</td>\n",
       "      <td>0.710910</td>\n",
       "      <td>0.864662</td>\n",
       "      <td>0.035280</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025134</td>\n",
       "      <td>0.012385</td>\n",
       "      <td>0.000820</td>\n",
       "      <td>0.005333</td>\n",
       "      <td>0.000442</td>\n",
       "      <td>0.002411</td>\n",
       "      <td>0.007052</td>\n",
       "      <td>8.929145</td>\n",
       "      <td>8.983325</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.067428</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.054958</td>\n",
       "      <td>0.815054</td>\n",
       "      <td>1.483194</td>\n",
       "      <td>0.793739</td>\n",
       "      <td>0.043299</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025196</td>\n",
       "      <td>0.024860</td>\n",
       "      <td>0.002435</td>\n",
       "      <td>0.000203</td>\n",
       "      <td>0.006315</td>\n",
       "      <td>0.000928</td>\n",
       "      <td>0.009323</td>\n",
       "      <td>11.182019</td>\n",
       "      <td>8.415074</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>intensity_mean-8</td>\n",
       "      <td>0.035084</td>\n",
       "      <td>D3</td>\n",
       "      <td>0.024482</td>\n",
       "      <td>0.697820</td>\n",
       "      <td>0.836939</td>\n",
       "      <td>1.319092</td>\n",
       "      <td>0.026690</td>\n",
       "      <td>PPP2R2B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002030</td>\n",
       "      <td>0.011947</td>\n",
       "      <td>0.011055</td>\n",
       "      <td>0.001660</td>\n",
       "      <td>0.000317</td>\n",
       "      <td>0.014562</td>\n",
       "      <td>0.009024</td>\n",
       "      <td>11.592212</td>\n",
       "      <td>10.634103</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>intensity_mean-3</td>\n",
       "      <td>0.261197</td>\n",
       "      <td>C12</td>\n",
       "      <td>0.179114</td>\n",
       "      <td>0.685744</td>\n",
       "      <td>0.780298</td>\n",
       "      <td>1.402972</td>\n",
       "      <td>0.108538</td>\n",
       "      <td>HSF1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013549</td>\n",
       "      <td>0.009692</td>\n",
       "      <td>0.039075</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.062781</td>\n",
       "      <td>0.061092</td>\n",
       "      <td>0.025526</td>\n",
       "      <td>11.081718</td>\n",
       "      <td>3.503708</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.272067</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.267507</td>\n",
       "      <td>0.983239</td>\n",
       "      <td>4.071811</td>\n",
       "      <td>0.117349</td>\n",
       "      <td>0.142667</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.080426</td>\n",
       "      <td>0.088755</td>\n",
       "      <td>0.000502</td>\n",
       "      <td>0.002689</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.001225</td>\n",
       "      <td>0.071709</td>\n",
       "      <td>10.925993</td>\n",
       "      <td>1.490282</td>\n",
       "      <td>0</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>133695</th>\n",
       "      <td>258</td>\n",
       "      <td>intensity_mean-13</td>\n",
       "      <td>0.022828</td>\n",
       "      <td>E4</td>\n",
       "      <td>0.019829</td>\n",
       "      <td>0.868626</td>\n",
       "      <td>1.888866</td>\n",
       "      <td>0.667654</td>\n",
       "      <td>0.014764</td>\n",
       "      <td>HRAS</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000898</td>\n",
       "      <td>0.008159</td>\n",
       "      <td>0.009501</td>\n",
       "      <td>0.003234</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000670</td>\n",
       "      <td>0.002336</td>\n",
       "      <td>10.888363</td>\n",
       "      <td>12.755734</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133696</th>\n",
       "      <td>259</td>\n",
       "      <td>intensity_mean-13</td>\n",
       "      <td>0.326837</td>\n",
       "      <td>E4</td>\n",
       "      <td>0.114697</td>\n",
       "      <td>0.350931</td>\n",
       "      <td>-0.614949</td>\n",
       "      <td>1.860739</td>\n",
       "      <td>0.119025</td>\n",
       "      <td>HRAS</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004210</td>\n",
       "      <td>0.066644</td>\n",
       "      <td>0.071050</td>\n",
       "      <td>0.032583</td>\n",
       "      <td>0.009312</td>\n",
       "      <td>0.031562</td>\n",
       "      <td>0.001021</td>\n",
       "      <td>12.442838</td>\n",
       "      <td>2.811264</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133697</th>\n",
       "      <td>260</td>\n",
       "      <td>intensity_mean-9</td>\n",
       "      <td>0.188926</td>\n",
       "      <td>D4</td>\n",
       "      <td>0.161259</td>\n",
       "      <td>0.853556</td>\n",
       "      <td>1.762765</td>\n",
       "      <td>0.616539</td>\n",
       "      <td>0.088308</td>\n",
       "      <td>FAN1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000526</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>0.048998</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.003363</td>\n",
       "      <td>0.064340</td>\n",
       "      <td>0.027823</td>\n",
       "      <td>10.910836</td>\n",
       "      <td>4.071362</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133698</th>\n",
       "      <td>261</td>\n",
       "      <td>intensity_mean-19</td>\n",
       "      <td>0.003397</td>\n",
       "      <td>G8</td>\n",
       "      <td>0.002275</td>\n",
       "      <td>0.669709</td>\n",
       "      <td>0.706868</td>\n",
       "      <td>1.034844</td>\n",
       "      <td>0.021284</td>\n",
       "      <td>RAPGEF2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007132</td>\n",
       "      <td>0.012290</td>\n",
       "      <td>0.000637</td>\n",
       "      <td>0.005305</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>0.010320</td>\n",
       "      <td>0.001827</td>\n",
       "      <td>10.747713</td>\n",
       "      <td>11.903031</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133699</th>\n",
       "      <td>262</td>\n",
       "      <td>intensity_mean-15</td>\n",
       "      <td>1.048392</td>\n",
       "      <td>E8</td>\n",
       "      <td>0.684923</td>\n",
       "      <td>0.653308</td>\n",
       "      <td>0.633613</td>\n",
       "      <td>1.344290</td>\n",
       "      <td>0.397249</td>\n",
       "      <td>WASL</td>\n",
       "      <td>...</td>\n",
       "      <td>0.157899</td>\n",
       "      <td>0.021261</td>\n",
       "      <td>0.326146</td>\n",
       "      <td>0.141081</td>\n",
       "      <td>0.012467</td>\n",
       "      <td>0.004988</td>\n",
       "      <td>0.119821</td>\n",
       "      <td>10.127234</td>\n",
       "      <td>-5.068211</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133700 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        label        Barcode_Idx       SUM Barcode       RAW         P  \\\n",
       "0           1   intensity_mean-0  0.057395      A2  0.038489  0.670602   \n",
       "1           2   intensity_mean-0  0.067428      A2  0.054958  0.815054   \n",
       "2           3   intensity_mean-8  0.035084      D3  0.024482  0.697820   \n",
       "3           4   intensity_mean-3  0.261197     C12  0.179114  0.685744   \n",
       "4           5   intensity_mean-0  0.272067      A2  0.267507  0.983239   \n",
       "...       ...                ...       ...     ...       ...       ...   \n",
       "133695    258  intensity_mean-13  0.022828      E4  0.019829  0.868626   \n",
       "133696    259  intensity_mean-13  0.326837      E4  0.114697  0.350931   \n",
       "133697    260   intensity_mean-9  0.188926      D4  0.161259  0.853556   \n",
       "133698    261  intensity_mean-19  0.003397      G8  0.002275  0.669709   \n",
       "133699    262  intensity_mean-15  1.048392      E8  0.684923  0.653308   \n",
       "\n",
       "           LOGIT   ENTROPY    X_NORM     Gene  ...  intensity_mean-1  \\\n",
       "0       0.710910  0.864662  0.035280   PGGT1B  ...          0.025134   \n",
       "1       1.483194  0.793739  0.043299   PGGT1B  ...          0.025196   \n",
       "2       0.836939  1.319092  0.026690  PPP2R2B  ...          0.002030   \n",
       "3       0.780298  1.402972  0.108538     HSF1  ...          0.013549   \n",
       "4       4.071811  0.117349  0.142667   PGGT1B  ...          0.080426   \n",
       "...          ...       ...       ...      ...  ...               ...   \n",
       "133695  1.888866  0.667654  0.014764     HRAS  ...          0.000898   \n",
       "133696 -0.614949  1.860739  0.119025     HRAS  ...          0.004210   \n",
       "133697  1.762765  0.616539  0.088308     FAN1  ...          0.000526   \n",
       "133698  0.706868  1.034844  0.021284  RAPGEF2  ...          0.007132   \n",
       "133699  0.633613  1.344290  0.397249     WASL  ...          0.157899   \n",
       "\n",
       "        intensity_mean-2  intensity_mean-3  intensity_mean-4  \\\n",
       "0               0.012385          0.000820          0.005333   \n",
       "1               0.024860          0.002435          0.000203   \n",
       "2               0.011947          0.011055          0.001660   \n",
       "3               0.009692          0.039075          0.004378   \n",
       "4               0.088755          0.000502          0.002689   \n",
       "...                  ...               ...               ...   \n",
       "133695          0.008159          0.009501          0.003234   \n",
       "133696          0.066644          0.071050          0.032583   \n",
       "133697          0.000544          0.048998          0.000056   \n",
       "133698          0.012290          0.000637          0.005305   \n",
       "133699          0.021261          0.326146          0.141081   \n",
       "\n",
       "        intensity_mean-5  intensity_mean-6     Delta  embedding1  embedding2  \\\n",
       "0               0.000442          0.002411  0.007052    8.929145    8.983325   \n",
       "1               0.006315          0.000928  0.009323   11.182019    8.415074   \n",
       "2               0.000317          0.014562  0.009024   11.592212   10.634103   \n",
       "3               0.062781          0.061092  0.025526   11.081718    3.503708   \n",
       "4               0.000070          0.001225  0.071709   10.925993    1.490282   \n",
       "...                  ...               ...       ...         ...         ...   \n",
       "133695          0.000016          0.000670  0.002336   10.888363   12.755734   \n",
       "133696          0.009312          0.031562  0.001021   12.442838    2.811264   \n",
       "133697          0.003363          0.064340  0.027823   10.910836    4.071362   \n",
       "133698          0.000244          0.010320  0.001827   10.747713   11.903031   \n",
       "133699          0.012467          0.004988  0.119821   10.127234   -5.068211   \n",
       "\n",
       "        True_Label  \n",
       "0                0  \n",
       "1                0  \n",
       "2                0  \n",
       "3                0  \n",
       "4                0  \n",
       "...            ...  \n",
       "133695           0  \n",
       "133696           0  \n",
       "133697           0  \n",
       "133698           0  \n",
       "133699           0  \n",
       "\n",
       "[133700 rows x 30 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_labels = {}\n",
    "image_num_labelled = [13, 213]\n",
    "true_labels[13] = pd.read_csv('Coverslip1_GroundTruth_13.csv', sep=',', header=None)\n",
    "true_labels[213] = pd.read_csv('Coverslip1_GroundTruth_213.csv', sep=',', header = None)\n",
    "soma_df['True_Label'] = 0\n",
    "for image_num in image_num_labelled:\n",
    "    N_cells = true_labels[image_num][0].values.shape[0]\n",
    "    labels = true_labels[image_num][0].values.reshape((N_cells,))\n",
    "    idx = soma_df[soma_df.ImageNumber==image_num].index\n",
    "    print(len(idx) == len(labels))\n",
    "    soma_df.loc[idx,'True_Label'] = labels\n",
    "soma_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0fa7d06b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removing 0 Cells\n"
     ]
    },
    {
     "data": {
      "image/png": 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2eIWZ3y9iy4kv1w7vZ++1JHLLc2LJuxYrFIKFC6GgQARd/DU6MeG93qyswvcpFIIlS2TyderU2H2Ag4XQoVi/fD7IyJB6/H4YPFi2l/R9xYsorxgqiqLEWyKBVlbij3HtCQQSly9tXYX+rmHkBm/cKF9UJXgSGHuEhQlOT0+3c+bocj1Fqc6EQvDFF3DuuaXrYI0xc6216ZXfsspF+ydFqZ4Eg8CWLTzQ4lX+xflAW3p3zSH7m5IF3tHQP1X3vsmJIO/f3m0QW790662xbf6kCIE9OQTrNCSQYgq5LWZni+DYuBHee08Cf82aBf37i7h64QU4/XQ480w51513wiOPQFqa1JGdDfn5Iqpefhm6Rw0unTrBsmXQuTOEwyJsfD5xrQuFRJjddhtMnAgDB8aEy9SpMfHhvZbU1IMtfk5YekVFMFhYuDgR1rNn4XN4353Y8tYRCBR9j4v6Tsr6HcZvd20+lOOVoimub1ILnqIoFU5eHjz3XPWPoqkoytGPG0xvS2rInNQh1EdSXp7aXt0zDxfx1hsXcMK5IjpLWDAo1qvcXNmWmytl3P+Syy+XbY88AldeGeGRVm+yvnE6f2v1JmtWRfjiC3jjDdmfkwPvvAO9e0ssnaeeknoeewx++1sxnMyaJYJs0iQRevffD199Bc8+C+npsHQppKTAu+9Cnz5w990iyO66C4YMgeRkEXfOY8UJwFAIPvpIRF4iq1QgAHfcIXU5nDUNYm59XgKBwoLNWatSU2MWpvj3RNYod6z3PRFlFVtFlY8XmRV1PqV41IKnKEqFk5cH118Pr71W+B9YURwNM+Sg/ZOiVEeciBg/HvL2RPjTvXv5fmsqw4YZRo8u+fijoX+qir7J6xo4YYJYwNLSYPNmeOUViQ5/8snyP2LDBtnesiWsXg179kCrVjBnDrRvL66FdeuKKAuH5ZW/bz8XbnmOpXRgMw3Ib9KOdh1qMG2a1NmyJTRvLukON2yAtWvhooukTfPny/u994qwyM4WATd+vIi2/v2lDmc181q74q2NcLB7o98v/wcTiazi7peKHKUsFNc36U9JUZRKYe/ekssoiqJUJs6NrU0b+Owz6Ns3iZS6tWFr9R9MG2MCwBSgJjJe+9Ba+2djTEvgfeA4YC5whbW2oOpaGrOyuQm9UEju97ZtIuTmz4cHHhCXyU8/lXVgxojQO/lkcTGsU0cE1THHwM6dUk+TJvDBByLU9uyR7ywpSf5u08ZPXV9d/u+Hq5h22vWEH+zEkCHw5ZdiVUtJkXVgzgXS6/J4zjny7j4PHix1n3uufPZa2hyJrF6JfkNuW2kmNxMdpygVgf6cFEWpcDZvFteUzZvhlFOqujWKovyc6dpVXOt+/3sYOTJC51NyWLmyIc2aVfsUCT8BA621ecaYGkh+zjHAXcBT1tr3jTEvAdcBL1ZFA4NBEXaffSaukRdfLNa1LVvgP/+R/wFenOVs3brYtu3b5X33bhFmxx0Hw4eLiLv4YrHktWgh/1P69YutYUtLMwSSryKwZzgX1WsIxuD3ixunoyhXxKI+q8hSjhb0p6woSoXTtCnccIO8K4qiVAUuauLgwfDMM0AkQt15mXy+qxFQn205huocisDKGhq3krlG9GWBgcBl0e1vAw9RyQIvPhLktm2wZo1Y4L75RgRdbi5Mn150HTVrwk8/yd+//CXUqyf13XOP1J2bKy6caWkxq5vXxfFXv0okwJKgVqNCg1kVaYqiAk9RlEoiJaWqW6Aoys+dggKx9qSmQvC7HC7Y9SabOItZtKeOLweo3oFWjDE+xA3zFOAF4Fsg11rrwpZsBJoUcewIYARAs2bNDrkNTiinp4ulbtUqeOklsbiVhkGD4E9/gh9+gGbNoH59scg5Sht+XoWbopQefVwURalwcnNhzBhxizqU3DGKoijlJS9PXAIXLpQQ+F9/05CXA3eyIBgVO6kNq7aBpcBaGwY6G2PSgE+A1mU49hXgFZAgK4fahlBIxNlFF8GUKSWXv+UWuPBCOPZYEWWtWxcOza8oSuWjj5qiKBVKKARz58bCNyuKohxu8vLg+edFbKSmirBISbFc/uDJbHy2Jt9tjuU7OxKw1uYaYyYBPYE0Y4w/asVrCmyqjHPm5sp9fOcd+OMfiy53ww0i6I4/Xu5p06bFBx9RFKXyqb7O54qiHJH4/XDGGRLOWvPgKYpyuAmFJCBHQYGsCfP7JSF297+fw3t/mEvTzRMAqPN/f4dIpIpbWzTGmAZRyx3GmBTgTGA5MAm4OFrsKuCzijxvKATr10tEyQ4dihZ3d94pCbVfeQWGDZPE3y1aqJBTlOqAPoaKolQooZDM4g4cqBY8RVGqBp8PbrtNElmHQhDakkPqrMn0oTvTSANg+g8ByYbdqNquwzseeDu6Di8J+MBa+6UxZhnwvjHmr8B84PWKOmEoBO+9By+/LMnBc3ML709Kkvymw4ZB48YVdVZFUSoaFXiKolQYLudUhw4wdizcf7+uwVMUpWqYNUuseOPGweJFDbmxe38iM5JIQmaeTq1roGH1XYdnrV0EdEmwfS3QrTLOmZcnAi4cloTjXlwicO3TFaX6owJPUZQKw++XtXe5uZLEVlEU5XDhQvgD9O8v/VFWliTXHjjQML7tl0z+ewFzFtWA/ZB73p3aUcURCEDduhIt08t998E//lE1bVIUpeyowFMUpULx+2WQcNppOtOrKMrhwYXyD4fltXgx3H67WJ0CAZl06nJ6Eg2aBNg/N4Kkk1PiWbLkYHF3/vkq7hTlSEMFnqIoFU4oJIlvvTPqiqIoFYXrW1xAD79fEpo7+vWDr7+GZctgxAj49a/BWuiaHqEpS9lIW+p89jREbpeFZQqQOLLop58e7lYoilJetFdTFKXCycuDlSs1iqaiKBWPs9ZlZkaFXiQCW7bg99kDgi8zUyxRp50momXkSAnn/9tf52DZAUAwd4cEWVEOsGhR4c/FpUdQFKX6ogJPUZQKJzUVWrXSKJqKolQ8zlo3eDAi7gYMINSkOWRkECqIEArBqlXw6KOSbDsUErfNMWPghQ8bEk1zTrMTAtU6yEpVsGtX4c9//WvVtENRlPKhAk9RlAonFJKJcXXRVBSlMpn06S6C2XOYFO4j75/uwu8Xi93y5dCjh+TE++orGDQIvvnGsP74vgBsPPcPGmQljrFjq7oFiqJUBCrwFEWpcNavlxDb69dXdUsURTnacOlYAAZcmEagTzoDfNnyfmEaADNnQpcuMHu2rMc79VQReK1awY8/JgE+UlNV3MWzbFlVt0BRlIpABZ6iKBWC11rXtKl4PjVtWnXtURTl6MSlY/H7wV/DwKRJ+Dd9B1lZ8hnJfzdqlIi81FS48UYJ9T9rlgRcAY3ym4i9e6u6BYqiVAQaRVNRlHLjZtQHDJDPeXmwc6cGWVEUpXLwe0YvoUgS/kaN5O/oRNOQISLyXnoJ2rWTv5s0gfx8+PxzKfP994e50UcAa9fG/m7ZsuraoShK+VALnqIo5cbNqIMIvdRUaNYscchtRVGUisJNLgWDseian34qf6emwq23SoCVTz+FjRslp1v9+hEgTMOGmguvOLxiT1GUIwsVeIqiVAh+f0zobdsG69bB5s1V1x5jzBvGmK3GmCVF7M8wxuwyxiyIvh707DvLGLPSGLPGGPP7w9dqRVFKi7PW9e0LU6fK3507w8svSw48l+Tc54OhQ2X9Xd6eCBs2bAIg7b2/SxRO5QAdOhR+VxTlyEQFnqIoFYrfD40bw0knyXsV8hZwVgllplprO0dffwEwxviAF4BhQFvgUmNM20ptqaIoZSIUkoiPLuqj8yBYsADefBNSUiR65rhxMG+eWPaOPx5efj6EL5oHb9UPEc2DF8e77xZ+VxTlyEQFnqIoFYabUc/Lg02bqnYNnrV2CkRHcmWjG7DGWrvWWlsAvA+cX6GNUxSl1HgDOMWnXgmHISsr9rmgAJYsgf79Zd+QIRJc5V//guxsyN1XgzBNAGhcX/PgxXPKKXDTTfKuKMqRiwo8RVEqBLcWJhSCvNwQ+flh8nZXe/ennsaYhcaYMcaYdtFtTYANnjIbo9sSYowZYYyZY4yZk6PWAEWpULz9ine9nd8vbpfDh0vCcxd0JTkZMjKkzKefyiTTjBmy//nnYdw4Q+PG9QBodN89mgcvjlAI9u3THKaKcqSjAk9RlHITCnlClxNiW5fBgGVbp4HVeaQwD2hure0EPAd8eiiVWGtfsdamW2vTGzRoUJHtU5SfPYVSIvihb+8IUz/fSWi/PSDqvBE1Bw+W91deEfE3fbqsvbvsMnjiXxFWzNiJzyfBVdatP7zXciSQmgovvCDviqIcuajAUxSlXHhn2P1+YMUKWrOYBnxHaxbDihVV3cSEWGt3W2vzon+PBmoYY+oDm4ATPUWbRrcpilLJJJoPOiDgIhECwwYw4LLj8Q/OIG935CDrnit/111i4VuwAN56C578V4SeX9xPjxHtMJskm3fjTzTISiI0P6CiHPmowFMUpVx4Z9gBaNuWYM0G5JNKsGYDaFs945MYYxobI/5ZxphuSH+4HZgNtDLGtDTGJAOXAJ9XXUsV5eeBd7IoITk5MH06/vBPBLPn8Pw/99KzZ6zvGTAgliohFILZs2HECDj1VLjtlgLylq0lk35sR8xTy35Ag6zEUeJ3oCjKEYEKPEVRyo0bYIVCQFISgVWLOLbeMQS+XQpJVdPNGGPeA2YApxljNhpjrjPG3GSMuSla5GJgiTFmIfAscIkVQsCtwFhgOfCBtXZpVVyDovycOGiyKJ6GDaFXL/D7CfRJ59b7UklNLSzqsrIkmXl2NnTpAi++KGkSuveuSfjUtnzGuRyDRH9qWj9Zg6zEUeJ3oCjKEYE+woqiVAhu5ndA/wj+Sy4gsONR/L+8H7K/rBKRZ629tIT9zwPPF7FvNDC6MtqlKErReIXFAbdvhzHSyeTkEKrXkBlZhr59pUw4LO8ZGSLyevSQoqtXixPBGemGOfwZ/5Igx62uybZVcOIDGmQlESruFOXIRy14iqJUCAdmfnfmsH7G93xLazbOWKcuUIqilJkiXQWTkqBRI/w1zIEE56GQWOlCIVk/1qcPTJsmxX/xC9mXkQE1aiQx8OxarF3nA3zs2q3iTlGUoxMVeIqilBs3CPP7IVinIfNaXgKE8Z/RQV2gFEUpM0W5CnoFnyvjRF1WlqRHmDxZ8uGBCLsvvoAJEyRlwrffwv79ESDMihX2MF2NoijK4UUFnqIo5SI+T9XUyRE6rvsKMLBglfhOKYqilJFE4s71NcFgLGpmMChr7rxdTUEBzJ8v4u+JJ+TzZ5/B5ZdFaMxiAJI+ekmjaCqKclRSqQLPGHOWMWalMWaNMeb3CfY3M8ZMMsbMN8YsMsYMr8z2KIpS8RyUp6r+cuZyGmAIhfdW2zQJiqIcXioiMmPfvvI+dWrs76wssdQNGiSCrn9/6Xasha+/hlmzJK/buHGw8ptd7KcWAO2D09WFXFGUo5JKE3jGGB/wAjAMaAtcaoyJj5f+JyRCXRckFPmoymqPoiiVh3em3d+xLZZjAAO+Y6ttmgRFUQ4fhxJ+31s2FIKxY0XMgYg7l68tHI56D0TX46Wmwu23y2vVKujeHZYulZx4HFuHGmwBYC2toH79irg8RVGUakVlWvC6AWustWuttQXA+8D5cWUsUCf697HAD5XYHkVRDgdJSfSZ/Qzgxz/vmypLk6AoSvUh0Zq64sReMCipD4JBeUEsWIpLh5CbGyufnR2z6AWDMGqUWO5GjoS0NLjpJliyBAq27SYV8eXcQQ3Ytq0Cr7JiMcacGPVyWmaMWWqMuT26vZ4xZrwxZnX0vW5Vt1VRlOpFZY68mgAbPJ83Rrd5eQi43BizEQlJ/ttEFRljRhhj5hhj5uSoO4WiVFvcLH0g1U+NGj4CtVTcKYoixIs7r0Uv3lqXlSXr5saNgyeflG2DB0sd48bBjh1wxx0i5gYNiu3LzJQ67rpLXDVnzhTL39SpkvD844l1yKEpAK35obpb8ELA3dbatkAP4JaoJ9TvgQnW2lbAhOhnRVGUA1T16OtS4C1rbVNgOPBvY8xBbbLWvmKtTbfWpjdo0OCwN1JRlMTEz8B7Z+n376+aNimKUr1IZKnz9hXxYs/vF8E2ZAgMHw633RZb4xsMwrx5kt/uX/+Sbc5tMy9P3DXd5xkzJLpm796waBEkJ0OzuvuA7QBE+KlaW/CstT9aa+dF/94DLEcmys8H3o4Wexu4oEoaqChKtaUyBd4m4ETP56bRbV6uAz4AsNbOAAJAtZ5OUxRFKGpNjd8v610g9q4oys+T4tbeOXHn93MgYbkXt6YuOzvmrjl5MtxzD9x3n6y1y8oSUZeXJ2vuevcWN06QOrOz5fzt2onYm7WsFruiFrytnFjdLXgHMMa0ALoAs4BG1tofo7s2A42KOEa9nxTlZ0plCrzZQCtjTEtjTDISROXzuDLfA4MAjDFtEIGnvZCiHAG4Gfh4gkFoFB1uNEo47FAU5edEonx2AKGCCJM+3kkw3x4Qcw5vjrvBg+UFEijF1TV1qoi5oUNl28kny/YxYyQ1AoiomzMHFi8WY11SaD/HsRWA01hRrS14DmNMKvARcIe1drd3n7XWIvEMDkK9nxTl50ulCTxrbQi4FRiLuBV8YK1daoz5izHmvGixu4EbjDELgfeAq6OdlaIo1RyX9y4zMzYwCwbh6adjA7DU1CprnqIoVYyz3iUkEsF/5gAGXHY8gbMyGNA/cpAIdJ+de2YgAHffHYuY2bNnLJLmjBniyjllingOXHddzEK4bh00awaXXAK169VkD8cD8ONxXaBhw8q5+ArCGFMDEXf/Z639OLp5izHm+Oj+4yGqWBVFUaIkmFOrOKy1o5HgKd5tD3r+Xgb0rsw2KIpS8ThhF5/DPBCQwAdLlsTKKYry8yRR5MwD5OTA9On4wyGYHs1H1+Rgk79z4XTvAM8/D9dfL+6XffpIv+O8CXw+2ffqq9C+vWw75RQ4+2zJibd/v6HDGQ2YO9fiv/5uMKZyLr4CMMYY4HVgubX2Sc+uz4GrgH9E3z+rguYpilKNqeogK4qiHIG4IAhDh8bcoyA2CJs5s2rbpyhK9SChuAOxnPXqBX4/oZ59mbS04QGvAIebSAoGY+v4AgG49VZxu8zLE4+BsWNj5QHmzhVx5/PJ6667xJvgvPNcm5IAHw0aVF9xF6U3cAUw0BizIPoajgi7M40xq4HB0c+KoigHqFQLnqIoRy/xAzfnjuVyUSmKohSJMdJh5OTgb9iQAWERW5MmHWz1c0FYHIGArL3LyhKPgVBIROCMGZIywVn8QFIqTJkiOfFCIdi6VSJqgrhuVmestdlAUSp00OFsi6IoRxZqwVMUpULwBkVo00a25eVVbZsURam+hCJJEokp6iYZnzrB749FxMzKEmteXp6IQO++22+Hf/4TunaNrcnLyoolSO/WDb7/XvLgde0KkXAECFOrli75VxTl6EQFnqIoCSnr+jk3IAsG4bvvZNsREoFcUZRKoLg+xOt26U2l4MSdc82cOlXKDx4MPXqIlc5Z87KypPwzz0B6uriGB4NyfEGBRNKcO1fW6j36qJRPMhEaFywGIPmdxyASqdR7oCiKUhWowFMU5SCKy13lLeONnjlpUmxA1rC+zJB//53OkCvKz5Hi+hBvFEznipkoGEu8Re+ll6BDh5iVziU1DwQkKXqfPuKSmZUFp58OSUlwzTXwxReQlgZ//jNsWB1kK2kA1N2yWIK7KIqiHGWowFMU5SDiB1bxuBl27yx7377RaHb9I/j+cJMUvOcGnSFXlJ8hRSUv9+6bMUP6j8zMg/cPHhxLjwDSt1x/Pfzxj+Km6fdLgKf0dBF0EybAV1+Jxa5zZ3jgAWjbVoKxnHuupLt7+GHoO7gm9dgIwDpOUTcDRVGOSlTgKYqSECfuEs3CuwGYG4R5Z9+DG3JYtLQGAL4lS3SGXFF+hriJn6K8AFxqg4QJ0EOFX4769cUd0+XXDAbh3nvFdbN7d/j8c5lPSk2Fp56SgCsffww7dogl79hj4ZUXw9RlBwDJ7D4iEp0riqKUFY2iqShKkRSXx8q7ViYjI+Zy9cx7DWnT2sAKCLdvX+0TCSuKUrE4F8yiLHgOt89NFLljx46NWeKSkwvvd+LOpUw4+2zZN20anHWWWOsmT5Yy+fnixjl+PPzqV1LX55/VoFmgDgShyYl1tX9SFOWoRC14ivIzpCwBVIoboHnLOCHYoaNh7TXPAlBr1KvVOpGwoigVjzeibnG4fig+JcKgQXDGGbKubvDgg49xa37z8mD5cklgvmsXvPmm7Pf55PjkZHjsMfjxR/jvf0Uc/uGPhp0tJUrLposf0P5JUZSjEhV4ivIzozQBVMqCd3Z96lQZlPXoJYmE/TV08KQoP0cSrd91n+MjZyY6dtCgmED0RtvMzJQ1d127wuzZcPPNsGyZiLpGjaRMejrcc4/sT0uD3/5WyvXvL0FYli2X/qmgQPsnRVGOTlTgKcrPjOLcLsuCG6B563WBVnJzZZsub1GUnyfxAs59dtY3SNwPBYPiopmVFUubMGCA7HNrf/v0EfHWt69Y5Tp3FlfNCy6QNXlZWdC8ObRvL66b4bC8srPhwguhRQupb9++Sr8NiqIoVYKuwVOUnyGHKu7cgMvV4R2guXU3AwZASopsc+tlqgJjzBvAOcBWa237BPt/A9wHGGAPcLO1dmF03/rotjAQstamH652K8rRQHz/4P0cvzbPa8XLypJ3l8TcMWlSTOi5XHh+v4jBcFi2Dx0qlryzzhL3TJ8POnWC3/xGgq80aSIWvU2bIoDlhBOSkMdfURTl6EIFnqIopcLNwMcP2rwMGCDl1q+Xz40bH9YmxvMW8DzwThH71wH9rbU7jTHDgFeA7p79A6y1aoNUlEMkUV4770SQ3y8WOyfqXGReKBzAKV4Ueo/Nz4fFi2HePMl7F4nIMf36SZnJk+HGG0UEdu0KN94YIbJ/FdCKbc8+B3+5VQ5UFEU5ilCBpyhKQrzWOkhssfOus5k0SQZhU6dKgIOqjl1grZ1ijGlRzP7pno8zgaaV3ihF+Zni+ov4HJtTp8ZEHMS2Z2VBQYHktwOxxnlz4wWDsm/FCllj56J2PvOMfDYGzj8/Zsn7/HNYsADO77+LKVMkjUvent2SxqVRo6q4JYqiKJWGTlspinIQxeW/8+7Pyyu8nsbltgKwVgZhRwjXAWM8ny0wzhgz1xgzorgDjTEjjDFzjDFzcjTnn6IcRHH9iTfapivj1toNHy5ul0OHFhaBoZCIu0GD4I47xBV82TJ5v+8+SYlwzjmwZIlY7pKTxT3zf/+DCXPTAElunlq7jqZJUBTlqEQFnqIoBxE/y55of9++hdfCeGfg0+pEqOELUf84e3gbfggYYwYgAu8+z+Y+1trTgWHALcaYfkUdb619xVqbbq1Nb9CgQSW3VlGOLLyBUpzlLT74isNbxtufOGud95iFC2X7jBlS5u675T0QiAVfAbHeDRsmUTS7doXHHjM0O7E2AIFrf1v1rgaKoiiVgAo8RTnKOdR0CE7cJZp5d0mGvbPvrkxwX4RPBj/G/rBl43lXyKKYaooxpiPwGnC+tXa7226t3RR93wp8AnSrmhYqSvWlqDQI3s+ZmYXdub0J0INBePpp8QTIzJRj8vJi25yXwFNPwTffFK7/tttg5kzo2TMmCEePhi++kBQJ/frB7bdLwKfcXBgxQrTcq6/Cj5slTUKtWiruFEU5OlGBpyhHMaXJeVfcvkQpFbx1xrtsBoMw4eNdbF2XByTxw4ItssalGmKMaQZ8DFxhrV3l2X6MMaa2+xsYAiypmlYqSvWkqDQIpelP3KRQICAuln6/rLcLBiWVwa23igVuwAB5v/NOSYXgROG//iXHFxTA+PGy5m7jRli0SI75xS9k/0svQevWYuW7/nr461/h++8lkqZrj6IoytGIdm+KchRTUs67RJExE9VRUp3eXFW+49K4qMs6/jG/gFPPqFtla1yMMe8BGUB9Y8xG4M9ADQBr7UvAg8BxwCgjblouHUIj4JPoNj/wrrX268N+AYpSjSkuDYK3jAuM4t3mcJNEY8fC/Pmx7U4AeutevlxcLUHW1YVCsrYuIwM6dICHHpL9gQAMHAhffQW7dsFVV0k03xYt4IMPYItnzmnVKhRFUY5KVOApylFOcbPUh5r0PNEgzW3r0dOw5On/QH9L3nP/rbI1LtbaS0vYfz1wfYLta4FOldUuRTlaSDT5U1IZr7umi7w7dKgETIHYWjvvcYEAjBwp76EQdOki7pkZGfL50Ufh978XwTZhgqROePlllxYBhgyBL7+EMWPgkkvkWIA6dSrsViiKolQr1EVTUX7mlMdNyeuWFQrJepk774TxE2SNy959usZFUX5OlOQO7nXvdmlVQMSb64vGjYtF4M3NlX5l1CjZFgqJYMvIkGNSU+Fvf4N//ENcNs84QyJovvMO9Ogha/BCIVi3TiazXM49EAugoijK0YgKPEVRDhmva6YbuD33nAysFEU5uvCKt0RCrrigTHCwx4AL1OR1xezfXyJkTpgA27bBL38JX38Np54q9Tz5JIwbGyHrk52E9ltCIahfX0RdOAyXXw4ffwxvvCGWvLlzJehKs2ZgiLB04X4kCwrs21ex90dRFKW6oAJPUZSDiURksYotOs2Bd9Dm94s71N13y/bly2XfsccehrYqilLpxFvrXVAlL/ECzlvW2194A7PEp2JJTYV77xW3zbQ0cc0cPFj6FL8fbrs1wvAnBpNxeVOCA4YyaWLkwHq8QYPg2muljrZtYe1aCdhy7rkwflyEgnc/oPHWGUAOEFELnqIoRy0q8BRFKUwkIqO0pk3FDyqa5sAN1tzf8TP1aWnwzDMyK794sWzbteuwtlxRlErCK9687pXx+eziI+vCwaLPiUP3PnZs9PjoxFKgpj1wnnPPFcF2+ulSJvurXeRNnc+ESF+enXYGXZvnEArB7NnS/6xZIykSzjlHxN2CBdC9O+zbEWTKttOowzagFmBp2qT65+lUFEU5FFTgKYpSmJwcmD5dRlPTp0NODsGg5Kny5rRKlAg9EJDXBRfI54KCKrkCRVEqgfjAJ94+wPUN3rJeQejFbXfu3QsXQqggQihj8EETS66uQYMkhUJejTReaPYPujOHET0XMmVFQyZPFgHYtavkx5s2TVw8MzOlO/vDH2Dd5hSuPXEcbVkLiOlx3/P/rNZ5OhVFUQ4VFXiKohReM9OwIfTqJaOqXr0I1WvI1Kky5vKGPI9PhO4GeXl50KiRlKlb93BfiaIolU28ta4oXB+RyPLvrHt+P9x1F/h35jBpWg1CIUto2izIySlUPhCQPiglxXDV1BsYdd86njvzK+YvMPTuLZa+4cOlPp9Pgq18/LEkP3/wQfjf/wzz+t5LyuW/oQHrANi2Na/a5ulUFEUpDyrwFOVnzkHulsbIho0bISsLfw1zIDlxSTnxCgrETerbb+Xzli2H7TIURTkMJHLP9ua784q5UEjcL50LpjfRubPeZWZG+4+GDRnQez+hpJpMajOSUL2GCXPthcPw1jtJjLynNvfea7j9dnHPDAbl9fzzEuTpm2/gpJPk+BEj4M03YehZSZzatzE5tAQgkFavyvJ0lhZjzBvGmK3GmCWebfWMMeONMauj7zqVpihKIVTgKcrPnIS58JKSxAwXzWFXUi49N3gbPhxuuQU+/1z25edXXrsVRal84iNiFpc7M359XaKom/E5NCFaPssQ+jqTqe9tpO83T+CvYQ6kRXDBXPx+SWLerp14j0+eLO6YHTrAs89KtM2bbpI1ewUFsgbv+++hXt0IKxbsY8XyMKn7c+nSRfRQ6NI7qixPZxl4CzgrbtvvgQnW2lbAhOhnRVGUA2iic0VRihysFSfsEiUsDiRHSN2bw6kn1wcgJZAEVPsBlKIoCXDPdlGCLlG5vn0Lr88bOlT2T5168Jo9gD59ZG2d5LVLYsAv6uL3i6v3P/8p+svnk7V1qaki6hy9e0s9b7wBV14J990n0TS7dRNLXn4+tG0TYdlzE2F3DidnTuTdyBDmMwyoRShU/fsma+0UY0yLuM3nAxnRv98GsoD7Dl+rFEWp7qgFT1GUg4hfWxe/L2HC4inRIAnNmzPo0UFAhEYPX6FBDBTlCCWRta6oXHfO5dJF1vS6VSZytRw8WESdc7v0egIEgzBxogi7m2+WlAfPPitJz8NhqSMclnasWCGpFOrXF5fMXr1EMDZsCF9+CeHtuzh592xyaMiySHMihEhhPWCP5CBQjay1P0b/3gw0SlTIGDPCGDPHGDMnR9caKsrPChV4inKUkSgBcVlJlMDc1e0+e2fjAwHoe1oOoWmzGBsewDTaAEkEF6zUIAaKcgRT0rpbF2E30T4oHFAlvm/KypJol25fZqbUl5UlAu722yVRuc8HP/0Ezz0n253I8/nEDTM1VY5PSpL6li6Fv/9dyizZlEbHVvvpx1Q6+dbgx5BPXSCJQKCCblIVYq21uMztB+97xVqbbq1Nb9CgwWFumaIoh0JFjOFABZ6iHPHEr3NJNLt+KCSaeYdY/ivv+YJByFrWkAmtbyGMn33RCeW8tmdU+yAGiqIkxpvIPD6oitvmxFj8Pu/nAQMSJzwfPFjSH/TvX/i4Hj0kdYLbduaZkvz8jDNgQP8IC2fuASzYCFM+28noryzjx8tavAULoHVrmDEDWrSA774zrL7kT9ww82ZS//s6oYzzSEs7ATA0bVoht6kq2GKMOR4g+r61itujKEoFUJFjOBV4inIEk2jQVNJ6mbISn6A4Pr+Vi4o3+ExD/xn/YMj610j6w5/koEdHHQlBDBRFiSMYhKef5kCgk3iXbedOOXiwrLOL73PiByhTp0LPngfnzpwwAZ58UrZlZIhgnDRJRKMrN3kyTJkCBT9F8J93FjWf+Ac9HzuHIf8aAldfScH9f2Thggjp6SLsdu+Gxx+HNm3g17+Ga69N4uGXGnFicx/rt9Ti5JOlT0pOrow7d1j4HLgq+vdVwGdV2BZFUSqQvn0rZgynAk9RjmASCbqKFHdFnSvR+UIheH5UEqG6Dej12UgAUv50va7BU5QjkEAA7rgjlh7F67LtjZLp+oOiPAlcmZ49RXzl5hY+dtAgscy5zxkZIu4iEYmQ6Sx84TB89kGQ0LTZXM2r3DfrIvKyFwARfMuX0ub4XcyfLwFW6taFs88W182+feH//k/Of/fdcv5Fi6SdP/xwOO/ooWGMeQ+YAZxmjNlojLkO+AdwpjFmNTA4+llRlCOIRPENMjNlkksteIqiVJqgK825vC5cgYCsh5n8+S4mLG0MwObFW3QNnqJUIaUZKBRVxrtGzT373jx23nV4Tvh58915XbhnzICuXeGll+TdG1Slf3/Jnzl6tGxbsQK6dJEomX6/HHvmmXD+r1LIbDOS17iRpk0N09tcD/goaN2eBevTyMuDmjXFxfO++2DZMrEcXnUVrF4Nv/kNnHce7N9/8PVVV6y1l1prj7fW1rDWNrXWvm6t3W6tHWStbWWtHWyt3VHV7VQUpfQUlU80I0MiC6sFT1GUQ6I8s0Pe3FVPPCHhzB1+P/iOS6Nvm20ANO7QSNfgKUoVUdJ6jviIuIn2e/92wVSgsOXeuV9mZUlSczfxM3Vq4bQJaWkyCeQSk48dK3UGAhJQxeeTeu+4Q9wnR42Scs610+c3LP3VI9y65FbuX3Y1Q+b8nYFrXmXRhX8le5rh88+l/JVXSrtOOw3mzYOnnoK33xYB6cU7QaUoVU1FBddQqj8u+nh8QKpx48S9vCL6pkoVeMaYs4wxK40xa4wxCRNxGmN+ZYxZZoxZaox5tzLboyhHK2X5x1CeRbzeGXmQoAbZ2bFkxFOnQo+ehlFNnwdg5Y2v6xo8RakiSpOUHBKXie8nQqHCwVS8DBgg7pAZGfLZuRg5a59rC8TcJv1+EXTp6fK3K5eVJX8PGSK578aPl3x4Y8bI+Tt2SmLW+kZ8PdYwYVISNGpEl9MNffrAqafCLbfAccdJXStXQvv24rb55z9Dq1YiHJOiI58TTijjDVWUSqIig2v8nDjUcUx5ji/rORLtc+OlROUqalVLpQk8Y4wPeAEYBrQFLjXGtI0r0wq4H+htrW0H3FFZ7VGUo5Wy/mMoayCWeBeCvn1lEJaVJYMwN6hzs/WpqTBgYBLg4/QzVNwpSlVS1HNe1JraRPtdtMx4vCIRRJgNHRoLluLKxL+PGyfvPXpIdMy8vNh6vMGDpZwTiQsWSIqEcFhcLvv2FdfNTz6BXbskX15KirhkpqfDnDmwTRwIaNtWBN28edLOtWvFiugGULt3l/YuKtWdwzFoL+685T1nZQRIOxTKOllclZRl7JMo1VNpvRcqsl3edXbxFjy/H/r1i01AlZfKtOB1A9ZYa9daawuA94Hz48rcALxgrd0JYK3VUL+KUkYO5R9DcWVLSrvgIucNHix/u1movn3BnxQhtGkL+wskLdPOnWW8GEVRDhsl9Rne/d5omd6ovd5BijfoivvsLP6ZmfI5Lw8++khEXiAAF14YS27uRGEwKOWmTRM3y40bZV1Khw6yhi8YlBQIS5dK+f79ZWIJpJxbu7d4sUTSXLcONm0Sofj88xxIj1BRAymlfJRXKHj/T3kDAB3KOUoql+j/Y1HnLOu54931DjdlFUzumS5N2bK0obTbSzv28V6X3x9bA1zU8YciHOMpql7v+Mm7BtgJzpkzpc+r7mvwmgAbPJ83Rrd5ORU41RgzzRgz0xhzVqKKjDEjjDFzjDFzcjRgg6IcRFGdQVn/SSTq2Lyz+PEdpRvgTZgA48ZGGNvl90xocR29PrwOiFC79iFfkqIo1QCvlc71A94ZcDfBE983uAGM6z9c2oPZsyWgyvDhsn3gQOk/JkwQi5xb2/vppxJdMzVVAqMsWCCBVtq1gxdfhI4dwe+P0O+0LcyYbsnNhfffl30dOsi5Vq6EG2+EE08UUffXv4r4278/AoQJhRLmB1cqgeIG7qUdTLsgPvF4g/p403F4j/MuLYg/fyLrTlFtzcw8OJhQIqFQVJ2lEZ7xgvFQ/o+Xtlx82YqyIsZff1H3Pv6zu7/x24v6XkrTVu/3k5cXi6Zb1PGHIhzjtznctXgFcXzkYe8xnTvDG28Ujm1wqFT1/JUfaAVkAJcCrxpj0uILWWtfsdamW2vTGzRocHhbqChHKIfi0+8Nh55oVhRiic69s/gAvj276L/8JXwUsHKxJCJesaLCLkdRlHJQnll072AnfvDj+gvvIDve4g+y3s5Z+tPSYgOeiRNl/xlnwGuvyfa774aLLoK5c0UYpqaKZS4QEJF3xhkw9MwI3T78PYG2J9HzL0OZkBlh82a45hqJwjl1Kpx1FjRuDL/9LTRrBo8+CoYItbbMAWDVP1/XNC6HgfhJAS9ukjDRMd53J/y9QXziBZ8TdzNmxAbIwaBMHnTtKvu9A+e8PPkNegfe3gnNRITDYn0eOzZWxgUf8rbVe73eOr3/kxNZ/kIFEbI+2UnfPjbhMd7zJKK0bofOqu7up/d/fKLjEn1vzounuDa479c7EZTo2t17OHxwmoDiLG1FtTcedy8DAfkdzZ5dfgFdVJoq93v25hKNPz7+e3LHzJghuTudR0J5qEyBtwk40fO5aXSbl43A59ba/dbadcAqRPApilIKiuuYyuPT7xVzbr2d64DcrLy3kxs6FIb+Oo3Uvl3ISJrG9416AUnofIyiVD3FDRITzaLHr1GJJ37g6S3/xBMSrTJ+Jr5PH3j2WRkcu0F3erqskevfX0TfjTfKwCY1Fc49V6xtPp/snzwZvvhCgq6EwzBz7C56L3uVrEhvJk4LsGTWXnr1gvr1JZgKyJq7c86Rc86ZA8ccA/PnhdmPRItpGP5W07hUEIkGsQ7voNebTsOVi4++6nIlOhESDMYirfbvL+VzcyXa4BdfFB4kOxe8Z5+VtZgTJsh3n5kp2594Qo4NBsVdt08fESpe4Vecxa9378TBhtyERV5ebD26E6Xuep3Yc39nZcVZGyMRQoOGwG8uI3TmUIhEDvo/7r0nRd3rRP/3vW7SztLZp4/smzAhQVvijkvkjlma9b0QGzNA7L5616C5ewcylhg8OPFEUbxALcoVt6jt7jpSU4sW3UXV4doTvy9RDlD3e3YRgZ0rpltf7A1uFQrJb9j9pt99Fx55JLaOuDxUpsCbDbQyxrQ0xiQDlwCfx5X5FLHeYYypj7hsrq3ENinKUUNxrg+O0s54xR/jOr9AQAImZGTIZ+9A7sknY+f2+8FfwxAaP4nQqrVMOekOwLBDszMpSpUTP+Dy9h3xfYh3XV1xk0SunNcN0w3Chw+XPmPq1NgAEWDECBF0oRB8842sp0tPl0i8EyZIsBTvwP/ZZ2VwHgpBQYEES5k3T/b3OCuN2e2vISNpGv16FdCpVypnnCGDpNdflzx67drJOry//10ibM6eDSNv8XMMPgDqN66jaVwqAK+lwuvx4bXWOKtMz54iuDMz5bsKhcQtzVnGtm2DO++U7w8gPz82+M3OFqFfUCDrM089FZYsEVE1dqwc/9hjUneLFvL76t1bIqt++aW0Y9UqcREOBuH66+U3u22bWE22bZNjJ0yIpf1w1q1QSM7xzDPyG+zdO2ahcv8fw2FpoxNKkydLoJ/x42XS48knpa3uXoXDYsF2/1ODG3KYOt1Hup3B89NOJ7ghNvngyowbJ9fv7md8Dko42FXUiY5wWMq55zo1NTo5Gw2M5CJie4/NmhQho80WBg+yCetN9HcivBb/kmIAuPHF6NGFXRy9HkWJXHHdeeKDlzgGD47lmEvokRCJwJYt+H32oP7SiUPvNbh77hXOEBPOIL8v70RZvJfUl1/Cxx9LtN85c+R7yMsru1UxEZUm8Ky1IeBWYCywHPjAWrvUGPMXY8x50WJjge3GmGXAJOBea+32ymqTohxNxLs+lERxrhtuv7dut81Z8CA24+T3Q6dOB/8jmTQ5idBxjRg0SKJnNolfdXuYMca8YYzZaoxZUsR+Y4x5NprKZZEx5nTPvquMMaujr6sOX6sVpeKJdyNyKQzi+xDvYLy4viIUkoGwGzC6gdeMGVLG1e33y4D08cdlcNyjh+y7917JiTdrlgyI+veXKJretnToIGvtpkwRS97pp0u/4/PBtOmGntP+Re68tTw1aAzzFxjmzpU1eHXqwMsvi7i77TZ48EG53n794KvRhpod0gHYcdHvNY3LIeId0Pv98l2CDHa//FKsZSC/ic8+E6E2Zgx8/bUMZJs2le/mz3+GSy6RgFxTp8pv78wzYfp0Kb90qQx+n39eJgOGDoVevWKBdW64QcReQYFsb90a5s+H5cvlHFOmyO/t6afFunvhhWIpfuYZOf8HH8Crr8K118ZSfRQUxH63X34J//qXXMfQoXKszydWF2cNnDBBrrV3b/ktT5wog3SfT353y5fLb++uu+Qct94q7/37S1udq2jWsob07GmZn9Sdm3otINCs4QEB8cUX0oaFC2Xdqjd6tTcHZSJLVDAo4qZ/f5k0cc+swwkeZ5k8UEckAr/7HZx0Ev7BGQfcmb2Cx2vhy8s7OKiSty1OIDlR7J515+4ZCsn3FArByJFy/7ImRQh+FxNd7pq7dj1YkDoxHu/i6bWuPv/8wa69fj+ECiIwYAChJs0hIwN/UqTQ/sGDY0LereXzul96XV4nTy587njXX9eeceNg0SKJRv7GG/I7//ZbeRYqQuAdgvNW6bHWjgZGx2170PO3Be6KvhRFKSPe2fOSKG42Pi9PBmbeTsh1ZM6twM1YPf20uB0MHXpw/T17SgfasX0EsGCTgCodQL0FPA+8U8T+YYhbeCugO/Ai0N0YUw/4M5AOWGCuMeZzF/FXUY403DPtSOQ+5Z3pdwLO54uttfG6d/XsKZaMcFgGKG4SKCOjcDknrMKhCAum7SUzM5WUFOkTwmGpo6BALDH33hubRHL1JSdLGwYNku0TJ8oAd/Jk+HpcEi+/3IhrrpEyixdD8+awerWMRSMReOEFcdU8/ng5l98foWvbHBYtbkggoOLuUHCDaJeWIj8/9j21bg3XXSfrJzt3lv8tzz0n/zOWL4caNWTbb34jAqugQNJfzJ4Nn38ukU6POUYsXykpIv7eeEOsa08+KULs+uvh5pvl9/PMM3KeefPknMbI9pNOgrfekn2zZklbBwwQwZiSIm18+WVp0y23yHPghMOnn8rkRDgsAqxjRxETffrIBMIVV8gxr78uAnLuXJl4eOABWff52WdyPXffLa7H114r0RH79JFBvc8X+/8ZDksdQ4YAGOj/NX1zcpi6siEDwga/X9p+993S/k6d5DmYMUOeQffsxqcmcc93z54i6kaOlGvs1Cn2PTqrpGuLzyffzezZ0cmZ7TlkLHuRqZGeDJiWTWhDDoHmjYCYIHNjBDe507Nn7Lq8OCu8wzsBBLHxxq23yr0GGDQgQmjIWUydYRjQez9kZuJPTjqwdKSgINaGvDz5LbjUKO6cjoIC+Q5uuinmjdS1q3w/wSBM/XwXfbPnkBXpx+BpU/Dn5BA8ttEBCxzIOd1a4OzsmFDv00c+u8krd25vH+i2ZWZKWwYOjInqggLYv18mGvblydhp5owkWrQoX/9U1UFWFEUphtK6VJa2fCJx59YiOHeHRD7m3sHgHXfEPntdJkIh6exuGhHhtcvHAbDyxj9XaRADa+0UoDhH0fOBd6wwE0gzxhwPDAXGW2t3REXdeOCsym+xolQ8JVnvIeZCNHiwiCm/XwZ8btbaDU7c2pnUVHGl84m3IxkZscmgf/1LxGHfvnLcSy9GGPDEcO58shkr736F3j0jDBokrpx33CEWm/btY/3K1KkyWEpJkYGvS88wfrxYMECsJYMHw7//LVE2AU45RdawnHuuDN4iEUmF0KwZrF8P3btF+PWmR5n334VAhNB+DbBSVpz43rVL3HHbtxdB0L+/DHRff10G2gUF8OGH8Oab0L27WCWys0WwuUmDggL4299g82ZxnYxEJEDOkiXwyitS17XXyoD944/lN3rHHVI+J0e+688/hy1b5DdzyiliYatRQ0T9ZZeJAF28WERUZqa47Z52mgT0ue46+W2MGiVt7tJF/scNHx4TEGvWyHuPHvJ/7ttvRZC+/bYE8HEi4623ZO3U2WeLEI1ExLK4caNMXOTkyOTEggVyTDAonzt2lPsRDIr4e/LpJILHNqJrNxncB4NyDRdcIPc/HBbB1qWL/N/OyxNXRpBnMBgU0ZabG7OitWkjE7MTJshzk5UlxwWDImCdSOrRQ+rs0kXKZC5qiL93d/omzSDUsw9Pv9vwgJXO/Q5Gjy4sOCdPjlk0vVZeZ63yegrEB14Buf8HXCl35jBjhqFvJIvQtFlM+nQXoZCU6dMnauHLik1Q33KL9BmDBkldbi1wKCS/oT59YsFVunQR0ezWafYcloa/d3cwPujVi82Rhjz9dOH7WFAQc+8tKIi5X2ZnyyTHM8/IdY4ZI78B5+rr7X/T08U9/auvpI0+n/zmb7wRbrguwvw35wP5dH7yl+UeO1WqBU9RlEPHdQqltdCVtbzDiTY381RScBY3+zVgQGy2/YknZKZ00CAI7MmhbWg64ziTJt/NkP9sjRqVvkGHl6LSuZQmzQsgaVyAEQDNmjWrnFYqyiHgtcSX9Ey7WWaIzTo7weZwbmGur3Dv48bJAMpZ+jp2lAG/E4Y3/SKH2X+y9GUft3x/HzM/GALNW9Cnr+H552Nr5c48s7DreUZG7By5uWJtueEGGRi/9x5YK4N1N2A//XSJsHnuuTIIe+45EQmZmZJCIW9HkNHrzmE1Ev0ptCsHqLZ9U7XC/ZZCIVnfNmGCiJdPP5WB9IgR8h39979iMduwQQa3q1fL/4t//1smBX73OxFww4eL22FurtT3/fdi/duxQ4RTcrIM2lu2FDGVnCy/k0WLJEH9v/8t499QCN55R+r873/lfCedJJY6ZyEJhURshkIi/h58UH4zt9wibX3oIahXT34vbduKCFu/Xtr400+ylm/pUqhZUyYb+vUT8da9u4i9m2+W3+lzz4kFsU0bOf6KK+CPf4QGDeQePfqoHDtpkvyG586V851xBgeegxEj4Kmn5Dp+8Qu5Z61by/uzz0q7O3SQ8910kwjXDz6Q6wyHpe3Wiotrly4iXkeMkO+wd29pd36+/M/u0sWlG5F74/ZNny4WpkDAwKBMpn66i77npnFr2BSyvPXpI8JxyBD5rgMBESxuDZoLNuP3y/fnJoedZatr15jlz/U/mZmxgEy3/bahWO6mGya1uZG+56YBsWOckHMeBS5mQCAAebsjLJmxl9v+kEogYA70Tc76B7Jec9YsEVzhkGW4tQw2E8ndP5Crr7a89LJh5ky5r36/XGevXvL34sUxK5zrF30++X6nTJHxUO/eInjD4Vg6mGnTJH3LlCnS/l69ZLLk7rtheM9dbA0lAzXJnbO63GMnFXiKUk0paxTM8kTNTLRQuTTncbNpd98dm9HLz2/INzUHw09QcGq7oz6IgbX2FeAVgPT0dE2spVQL4id8StMveCdunEu2G6S5WXmvy6bfL4OUCRNigxw3Gz9xogxsliyBTh0bMrCnZcK0wfhSkukzshP+3t1g7Di6dEmid2855quvxEJy112FQ+cHg7GB6vTpMoj65hsZRGVkyLZ33hF3zO++k+sIh8Ut8/nnZSDdqBEsXplCqqnFCfZHfqARyfWO7r6pInAeGpMny4D1kUfETdFaST/x/vviFjtihAyW9+yR77BxY1lvl5cnbos7dohY2rgRjj1WyublyaB57FhZH7dvnwi47dvld5afL2IuPV3cHl3ajJQUEU3bt0uOQ/dd16ghlpXXXpPfQdu2IiBTUuT/1MyZIiq2bpV0Gq+9JsdPmiTt7ddPzu/W2xUUiHUwN1fK1a0rgta5A0+ZIq51d90laT2slXoXLZJr/stfREwuXCjXe911IvrWrpVjdu0Sy+SiRSK2kpOlnaecIvfd5xPxO3CgrB2sUUMEZYcOYiHq3Fnu38CB0uZnnpH71aOHPEu//a08q2PHyuSIu6dOLA8ZUnhJRu/eco8LCuT77t8fAoEkeg6vy7jxcnw4HHsfOFDa7dbO3XGHiDO3Bq1379hkkfMEGDtW+oyePWPr7Zzw8wZta98eJk8xDM3MxL8zh561GuKvYQ6s43NCLhSKiTbXtj69IszofQ+3LX2N1OldYNIk/P6kg9b7gVxjfj6Et+8iN3sxaYSZMwtem53DkhWNDvRrIBNZH30k9/n226WPmzNH7sGZZ0oZt17YRf7dsUME3cCBsm3mTPl+mzeX9Z1jxsh15+RAbq80jmEtkMSqU86kRznHTirwFKUaU1axVlT5+PU38fvKavmLH/z5/d6wy4asEX2Y/pwl/MBT1T2IQVHpXDYRjfDr2Z512FqlKOUkUeRMt937d1Hl459v567pyublFc7V5F2/50hOlkHmzJkGRn+N793v6DOyE9m2BwXZNUn+bBf9z61LdrYMKseMEfdOENE4b564gQYCMuADGRD36ydWDyc8V62CbukRkgv28uQTqaTWNkycKJaZm28Wy14oBD/+aNh93Mns3h2GgiRq1arWfVOV4lznxo2TQf/u3bBpk4iq/HyoXVsC56SmHpyU2Vr44QexZLnIlMnJIoYWL46Vq11bBOGJJ8ogeMcOKXPiieJaGwrJ/smTpey+fWIZPPlkCaDiIqvu3CkD5iQT4aL+OSxb1pAffzQsXy7zi6tWye952za4+GIRHn6/nGvfPjnvV1+JxaygQAbxa9eKEMzLg1q1xDp3881iyatfXwbk69fLxMHu3XJc8+ZisRsyRH6zP/0Ef/iDWCGtlQmIRx4Rt1AX+bNJE7nWjAy5lx9/LBbLSy+VOt95R+5jcrIIOhFdIuIWLowJ8FGjpN4HHoArr5R9L78sKUNef11eaWnyPA4bJvfbTdQ884xYBlNSxCV29uxYYKQOHeTcc+fKsbNmieXyxRflO+zXT97DYXGhTk6O9QHTpkl7QfqGHj3EJTQcjrlnT5wo5Zx1sX9/uSZn6SQpibxjGh1YR+j1InBrQXv0KDzBNPnTXQxa9jJBm0Ro2ixCG3LIO6YRs2ZJmf795ftesULuz/Ll8NNPaYyq8wXv7T6X/NO6MG9TwwP1zZghfc3AgdEULTOj57MR7J69zJ2Tis9nDgjM/v2lbfn5sq6uUSOZ7MjIkDWQ+fki7mbNkt9FvXry23z0UcN2fxcIWeYMe5wryzl2MhLn5MghPT3dzpkzp6qboShHDKURcEUJwOK2T5oUSyrrXDVdUIaNG8WV6j//kcX0JWGMmWutTS/blZUOY0wL4EtrbfsE+85Gov0OR4KsPGut7RYNsjIXcFE15wFnWGuLTfyg/ZNSnfC61Xlnvp2LUkmJil0aBLcNYsEJ7rxTXMmmTZNBT3JybJbee24nBmfMgJ49LIGzBxGaNosJbW6h/4zHSK1t2LZNZsLT02UQOnasDJLGj5dBZ0aGCA2QwdHQoWLB2L9fBklvvBbhzYw3CW/YSOfW+wn/+S/Mm5+EMeLetnq1HFunjqyVWrNG2nfnnVJPSVRm/3S4KEvf5H4v4bBYmdaskfvk1iHt31+6c9apI0Js2TIRRS63lzExi9uePSKyvKk6GjcWy9kxx4ho+v57KeeOrVkTTjhB1uIZI5a6nJwILbZMY3V+Q+rVCLI5rQMnnJDEBReIWMvMFAtcKCQTkV9+KcKse3epe948sazceqsM/EHEx9atcg3r18vvftcuae+LL4rgOPZYsd6tXSvitUMHeXcWzU8/ld/0nj1itUtLi1nTnLjo1Enqv/JKuP9+cR19+mkRuvn54nZ51VUiVL/7Tr6XnBzZtmaN3J+zz5a1qG+/Lc+kteISOmeO3PctW2Sd1wsviGBculSe2S5doFs3EYitW4vwOekkKfvyyyI+77xT7l9KSmxip3dveU5XrYq5Ri9YEFsH6PfL/XNr3yZPlmtOT/e6gMYiiC5bJtfVtq247p54okwoPPmklHfBeDp0kHqGDJHxxq5d0uaLL5bvzOeDhQssV3x1Idd9cxPXnTadRRc8zJw5hmuvlWsoKJDrveoq6c+GDZNr6tg+QmNfDo+92ZDmLQz16kmdvXrJffQGR/EnRWDwYHKnLmJqmxvom/U3pk5LOuAq+9prIu5mzZLfVlISrF0bocVxe/HXTeWGGwwvvyzX1KKFXOPKlbHf+b33wj//WfIzVlzfpBY8RTmCKc4y5/AnRRjQNge/ryFFRbQsKviKc69ItD8+BxbE/O/vu08+5+eX/loqA2PMe4glrr4xZiMSGbMGgLX2JSTK73BgDbAPuCa6b4cx5hEknyfAX0oSd4pSVXj7Aa+o807sxEe9g9KvyfOWddsvvDAm/iQCYMxlCmJ9hwtCIIM8g+/+TPqcmgPLG5I9zdCjh/QXDz8sg6g+fWSWv3t3GRymR4cuziowe7b0Mx07yv5bbpFof/4Na7mZl3hqxZ18++8gP+bWwhjJldaliwwWP/tMgn5ceqkMqA/Fnf3ngPuNbNwowT0WLRLB4KxwxeHyHEYiIqDixR3ELHy1asn/iLp1Cwu8zZvl/aefpFw4LGJv716xyOXkyEC9Zk0RFuKSa/mBNpzMclbtb8NPOZY9e+R3deyxUDs1wg+bwuTt9dOkiUwqnHGGWAQbNZIB+E8/wdNPRfhpX5jadf20bGm46CKxLEci8ptu0kTcQ59+WgRsTo4Iqfx8EaPr14uIy8kRa+fVV0vZ4cPFetejhwjG7dtFKM6bJ9azX/1KROPOnXDPPSIm8vPl/p16qliAfvxRxNT338s1ffWVtLtJExGKAweKdWnYMLl/L74oYnXNGnEhfP55OdeaNfJMtG8vlr4ZM2KTNGdFQ4m99JKUTU2V73P58tiawvPOk+9rzBj4xz/kuR81SgRvMCgiefhwObZHD3n+e/eWcp99Fot46sRwJCLPMYjr9QUXyLXffrsIpPnzY/vdOsN+/eTvhQtFkHbpIkF3RoyAfv0Mz/Ex3bruJdx9KDXXGN56Kxb11+eTCYI6deD88+W6e/WCe+9N4vHHG3HSyeJJ8NFHYkFdsEDqffXV6PFJEZJ//I70qQt5gZvYs/wYZj6Uh79uHYyR67rySplMumlEhCUz99KybS2mPTuXE/ZNYG3a6czpfCa33JJ0YN1m797ye1i2TKJozppV/gjkGkVTUY5QShMZj4jkdvG3aCpTa56oTMUd5xZBe8MYO5cdbzQ972DSLboGmfUE+SdUlVhrL7XWHm+trWGtbWqtfd1a+1JU3BGNnnmLtfZka20Ha+0cz7FvWGtPib7erLqrUJTCxOd4cv2A9+9Ea3KzsgrnoiqqTkei/sVZ5YYPl88uAqBLau6O8QpEJ858PujTN4ns1Y3on2FIT5dB4GOPyWDIhVFv104GjC5S42efxawjn3wis93hsAzyXnwRLruzIdd0X4wfw+zaZ9KiTQrXXSdC4q23ZE3Y3/8ulov//EcG4RATEuXBGHNr+WupfriB+sKFch+hcJj7oigoODj437Ztif8X7Nsnde+Imzpznmn794vg2bdPxB2IJSopSQbnxx0XW28JSVgs22hAXXaRlpbEKafIMW3bRti1aQf7dm0hEtrLySdFiERksmDtWhE411wDBQUR1n/3E8GcDZywYQpTpkR47DGxALtI0e5efP+9/J4iEWl/fr6IqVmz5Pf55z/L7/3DD2ORE30++U1/9JFs27BBrH9PPCGeLkuWiBWnWTMRc3XqyPWDWDX375f2WhuL3nn66bFcep9/LpbRSEQmS6ZOlYmNZs3k2P37RTBee6389ocNEwvZrl1yDqkjwof/3seyZZYvvpBtL70kgujVV+V93jyJcvr734t4y8wUEeqiVu7dKxbSXbtkTVpenrh7btwoVsFvvxW3VZ9PRGT79uImes89ci87d5Z74tZ9tmsnfdeSJbHrnjRJrn3ePLHEPfywiOTXXota5+9K4u6HavP114a2bWWSweeLWRFffTWa265fhIItO/H7LGefLffMlxThlCb7WLzI0rq1WHbnzpXfSDgUgT/8gc439+ClGrdxPOuZSXemvrGC5s0inHJKzDoaDkWYOPBhgk8/w5oH3qTNvmkkEaFZ7lxmTdjLU0+JBbR5c+kH162LANJZzpoypdxRNNVFU1GqmNJY4Q752C1bZLrQFdy4ERo1Sui2GV9XMFh4Rt4NDL2R9Zy4GztWZtxHjZLO+aefpLP94gs455ySr+NocIEC7Z+UiiV+AsVtK+7Zde5RRbleQmJx5+r07vf2AfHHOFHXtWts9j4tTQZzkydLIAW3PsYbeADEdaugQMTZzTeL1cDlt5sxQ2bjnbv3hx+KtTA5WQakGzeKFW7tWhk0vvKKuLodWycCubvYZdI4Ns3Qr5/sb9hQyl91lUTXPO88sUaAfP7885K/h+L6J2PMPGvt6Yn2VSfK2jdt3ix9+ZYtldemshHNrUpxlo1YmRNPNGzf7oRNGAhh2I6lLpAM+BIcHwYKgAI6MY9vU/qQl1+DGjWknqQkcSutVy8mwCKR2BrEWrXEsrh/v4jUunXFUpeaKuWTk8USaa245dWqJeImO1uEXEZGzCp37LFiKWzTRoK8bNkiExN5eXLs+vVS77ZtIpKSk0XMde0q393mzTEL++7d0q7hw0V8tGolz1e/fmKdGjZMrmPd2ggbvpxPo9C3ULsB+3v25/TTkzj7bLHc1akj5/ruO7j9txH+cF8BIVOTE04wZGTIs3j99bKO8JxzRGSefba04fHHxdJ27rkiMr/7Tq53yhQZU0yYIMLr7bfFBTspSaxqPp/0CUlJYkH8v/+Te+JcW3v0EPH2v/+JZfLSS+GRv0Q4r98uBl2UxlNPG7p0kXPk5oqr99/+JmOWhx6K8NPYiWzJ9dH3hA10+uflLF0GvP02327y89eun/Cnkz4kFErinHPEYkn+PoZ8MYK1nEJT1jOZQewgQC4NSercnRrHJPProbtIOSENduby6b1j2Ul9zmQ0z3AfrVhBg7QwG5sPYPduQygkExjbt7vfnyMbu7l1iVE01UVTUaoph5rawFHiMQ0bEurZF/+MqdJbRqMyJQrA4G2Hs+B519jFCzt3XCgks5PffCN/h8Oxf3huZlBRlLLhXQfndZVOZJnzPo8u0IlLXhxfzglAr2jzumR6hZ6z4mdlFU5Z4A1zHgjIgGvaNBF1kyeLRaF3b7FqPPecDEiTk0XELVokA0w38EtOjkXdCwSkzqefFkvGU0/FyrggLC4CYH5+YTeycCSJgkBd1q6Ium36xY3zq69k5n36dBlcN20K338fYe9eS4sW5XeDOhoJBmWgXfXizgk2gCAQQAbBSSR2QItt27AhfnsES6NonUU5ryVh2IvlWBbSD/KlXFK0eCQik5c//iifd++OHXn88bJv+3YRZrm5IuD27BHB537DBQUi/hYskN/6Rx/JMXXryrO3d6/8nrdvF0+Yb7+VfYGA1GWtbDvuOBE/kYgImxo1REDVri3ipWNH2Z6SArVrRzjhuCA/bEohOdnwn/+IRXDFCpmYPe44ce1cv6aAraFjKeB4du6px9ZJ9kCgkHbtYu6kixZFuDRjE23tLHJrnET+mZ35v/9LOnA9f/yjPHONG4vF/bzzJO3B1VeLKPv2W7HGzZwpQ5JNm6T9L78sY4cVK0QIduki9/add+Q3OWWK1HniiSJsP/hA+pp27eQ5DwZhwfwIP4yZz3/e+5Y5D/+E76rfMGM6vP9mkNlLUmjY0LB3r0R/Xbu8AJNbgxCw6gc/STP24qsBV296mOcYwVuzO1HvpC0s+KEx33xjiESgQZMUXvTdiQnnsa9GY3qlLmDdzgYEqUP+nhrUWTiPh6bVI0SIpm3r8QMDOYGVPMfvqdvyOHz1u7J8W03WLoxwTC3Yu8/9Pg3G82sHOLAI8BAplQXPGNMKeBRoizxhAFhrTyrX2Q8BnSFXjjbKY8ErTd2TJkYY0C4HGkqY4dK2w83Qu/wy3mANTuy5waA3oenEibL96aerR5CVw4n2T0pFksiCVxIuslxGRmFh6CZjnn5aXO/cxE2itXvxkTfHjo2lR/Cex4Vqd7iw63l5HMgf1amTzLwff7xY+oJBce16/vnC5/O2uU0bWZd38cVSx+DBMkDcv18scW+/LaIwPV1EyIUXyvmXLpVB7ooVsXV67dvL2qddu2R7Tk6Egt1bCNKQBxr/k79sui82gi+CEix4IWT97kG7EC/wOsVWfpgobd8UCskg/dJLY+6IVYNzV7OItW07cJxnfyILXEn1lWQBLEu50lOrllhpSjpH7doyceGeu5o1RTQmJR3srXfMMSJ0XEAYEJFUo0bhc9WtGya0cwt7qE8dckhKO57cXPm9+/1yjn37RGjt3GkJFPxILXaymZOpU6cm4bAhLU2evbw8Kdu18z5qLxhDGB97qcuxfbsyZ1Et6tWTCKc5OeJSXauWfN61K5Ytae5ceSZ//BGOqxchd3uIBnXDfL8lQH7QULeunCsUiqXDSE6We5CWJn3E22+LBbB3bwnKYq30Exs3wsolBaxfv5sA+bRiHb/5Uzs+fXItQ/e9zaYGvfm286/Zuy+J6dPh2GMtHQumMH/fSXQNrOXql/uRH4SP7pjC/PyTuZVn+Q+XUdOXxPaG7RjUfR8LVqeyZGmElo3zOfHUWqxfDyYc4vsfalC/bgjfjpVspgXHsYlw7ZNot286q8JNqMdeaNWelasNx7Ge7Rwf/YZ8JP4tZ2Oz/RzIIVMEFWHBexMJTvAUMAAJRKDr9xSlAqjMhf5+PwwYmAQ0OiDMSnM+l9/OmxQ0PulxKCTiz1kV3D+lcFg6cSj/GhdjzAXAdGvt1vLVpChHHl4Lm6MksedN9uvW0Hot9HfccXBwpPiJG+/5g0GxzLl6XH/Qo4dYIlxUuQkTYiJt9uxY2PJgUNbm3nKLWCZmz5YABoGArNlZuFCi9Lk29+wpLmsXXyztv+ceadfIkbKObuRIEYrhsFhA3HVMnCji7/XXZdB7wQVS94cfyvnfeUcGy2cP3MNXn24lSEPyNm8tdzJhYLG1tkt5KqhuLF1a1eIOYgLI4RV3hzL8LO0xJZXzWhVd+eKF4MHizlkhnWqTAb6LoOj46afoEXHirmlTcen0ijuIrbPznkvW8B0HhNnNsZArbXeCqWZN+a7FWmsooDF7TSOwSezZY7BWrIopKdKO1FSYvSCFNLqSSwN87OaUrSnk5ckzmZsr5Z3r6vr1IvC+/162nXyyuJ8uXBihYOuPBDHsyYmQXnMVM00GmzfHrKYpKSIU09LktWCBTEy1bi3nWr1aLIG7dknZk0+G9p1rsHp9LVqwlOV05KlXa3HsvgIe4hGa56xm/QRLkk/asHevYevJfQmtCzO/ZmMaj4Xp0w1bTV/addjJ64uvYRP1qRE2FPy4nQ8/TWI/IWqShG/zChZvPoGdnMDw4TXIC1pCBZYcTgaCbOckjvNFWBFOYzuNycEPq/cDNaLiLjn6HRVQ5GTFxx+XKPCKo7S/+BRr7QTE4vedtfYh4OxDPquiKIeN0gq6RIEa3GDRG3XPReP75z/Fr94tPh87NuqjTizyXePG5W7+5cB8Y8xqY8zbxpgRxpj25a5VUY5A4p/Tosq4gCfu3e+Pib2i1tQlqt9Z6caNi9WbkSGvadMkL1YgIK+hQ2NpEty5xo0TF83bb4/lP+vbV9wlnTt3mzYizrKyZECYnS3icdgwGdQNGyZCcNw4ce989llZw+PKBAJy/KxZYvVr0UKsGAC//rVEYZw4USabrroiwoZxa7AHBuQ1ZQGPcgC/v7DrYdXhhqc7E2yvKrdaJ+5CcdvKEgzDK1xdPWULprFxYyzoTMnnArHlWCDlwLkjEXFz3LQpXoAmEbY+wERFvgjS/HzLTz+55ReGXJoCyYRpyMqV5sDSjJ07Cwfjccs0nEh17plgySWVIMewj9pk/dSfYDAmnCORWICZmjU5YNnLy5O1fdbKfXCC9pRTRKSOG2+AAPsa9aRpqzQ2bklmNa1IYQ/f0hlDhMaNLbm5EcL7w6xYYcn/aT/7dv3Ae+/m8v13YfLzDbMX1+N7TiFMMkkUEKEWfnIxhAmRzxpOZydpQIjRo/fi276emnvWIoKtNpDE9txkttMecXysQUzIJRPD+3ccV15Z9L5SUFqB95MxJglYbYy51RhzIZBarjMrinLYcMKsuLDo3oiZXrcuFy1z0qTYYDEQgNtuiw2kXH6kRYvkc8vm8k+h4KfyTQNbay+21jYBzgTGAh2Bt40xOcaY0eWqXFGOMLxr5TIzE4s8V8ZZ8JzIc89xXl7sWYbCqROcZQ+iSYaR5zy+3lBI1r5MmSLtcHWNGydibMIEOY/PJ9H10tJirtyhkKzLycuTgZ4vOuZJTxe3y7w8EXHjxsnfCxZIlM358+HyyyXq3KJFseTlX34pfU/r1iLiVqwQd9DXXxer4bXXSn3DhsFbb4Xx7fuRPZwAwD58heP3Hxr/K28F1Ylt2ySiYdVjoq+6yFDVubJVpLjzuoGWBq9gSrS9NOfz4uqpLHOpG+JboCaxe1pavNbKeCHrvhNDWe+jRE1NQmTEMUQ4BgjjI6/QOZwFc8uWCPPnF64/HBaRt3OnlJszxyt8k9i0JZnVq+V6C6jPHpoAllDEsmFDiBpsI3IgqElNfqIplgARC9a6c0kb99EI8FNAgBC1CXNcdF+t6HsKW2nOZk6msDRy7XX3vIyW5xdeKFv5OEp7ttuRK7kNOAOZVS+ftFQU5bBSnCUvPsx5omPdYNENKtPSxH0KZPDoclL5kiJ8/nsJubnr4YfLHeoXwFq7Hkk2Ph9YAGxFpiMV5WdFcc9xvOCLd8UMBmXtW9eusefdCUWvO2heXux5BxFUrkxurljE7rhDovJlZMjzHwyKGAuHY4mE27UTAeas/I8/HnuvX19cM4cNk2NGjYrN/I8cKe/Tpskam9tuk/5l3TqJ/Hf11fJ65BG5nrlzZX+3buLalZoqAVh++1txxZo9WywVx9T2s9M0xcd2ALYltYgtDjp0jjfGPFvUq7yVH27mzavqFnjxioh4yirOEh1flHgprj1l2V7U+RJRkSIvFm4/JuqKCkxTHPFtStRG73Xtp/TWSBP38hEmNcE5vPWHy1B/Ivw4K9p+0ijsGhkhZk1z4U4KPPtqUHDAtdJ7XFKCv93vtTTfaVFr8JDAeOWgtN92C2ttnrV2o7X2GmvtRUCzcp1ZUY5Cis1JVwWUpj3xwVS863a8SZG9s/te3LqbQYNkFq3fqVtoG5wFwLHfzy1XKDZjzB+MMV8YY2YC9yO96/NAR2ttgtYoypFNaZ7ZeIu817Xam4vOlfVa6O64QwRQ/Bo8R26upDjJy4uVcdHs8vJEbL3/fuG6w2Gp44wzRLCde66ItLfeEpdJb5CXp54SUebSKeTmijjr0AH69o7w0X/2MXWqRO874wypY9UqEYsdOoiFcOxYEXN33in91hlnxBI1JyWJ2+c//iFr75KTpf3r1kF6umFj3Y4kJ7cAoMWd18eSrh06c4C5xbwqBWPMWcaYlcaYNcaY31dUvdnZFVVTZRIvzg5F6JVGvMSTyAJWWpfRkuovq6tncfXYuL8P1fKZSCJYYvc8/r77KNv34BWd1rMt/nwOb5zJ0n7viYR1IrHrXRPpSPbs89YngvSQLXMHcMcX8b0714hDpLStur+U2xTlZ0upEo8fRlzghPj2eCNexqdD8FrynOtV/HV51+hkZcmsuxsohsMwZVZN9kU7rjwC5R1AXQmcAHwN/B/wrrV2vhUfCkU5qiipD/Fu94omZ3V3LpRFiTdXzr37/bHIla5caiqMGCGTNa5/6N1b3gMBcXf85S8L152XJ95E/fvHzhsISLS8++6LRde9914RZZ06SZnp0yXK7hlnQHKNCP4Lh+P77AM6PHoRr78WIRQSS11SkgT+2L0b/vIXicJ58slyjmOOEffOMWMk/UJSkkTzHDJE+rT8fOmnateW3Hnt2yeRX1AT8BEKld/dz1r7dnGvcp8gAcYYH/ACMAyJbn6pMaZtRdR9ZAi8oixJZRFIiQb4pT3OR9ldRktzvoqw4hV1bw6FRIK2JEtkWcWO14qXSCwXJcRKa31N1NaS02wUjxOXrt3Fpd4oDkuxIrVdu0OoM0axLTLGDDPGPAc0iXM7eIvCq0wV5WdPovxU1QHvAC9+lh+kzW5G3zs4DIdjkfH69gUiEfzbtzAgwx64RjdD7gaC/ftDfkpdujTPByCl5UnlilBnrW2NrL+bA2QAnxhjvjHGvGqMueaQK1aUakhxfUi8+PMKO2d197pZuiAp7hjvGjrvObyfXbkLLigciXP27MJBWnw+cbMcO1YscKO/inDjBVvAWjIzxcr25ZeSRDwYlLL/+58IwczMmBtgerqIyUAAyN1FaPocLuATFsyGK87dxcsvy5o6FzZ99WqxzrVqJX3WtGkiAN94QyyHLoH5mDHw6aeSqmHWLHjtNZlnKiiQ/HoNGki55GLiG5QWY0wfY8yVns8fGmMmRl8Dy3+GhHQD1lhr11prC4D3gfMrouJ16yqilsqmqKFrWQRS2SJhlh+vWHLnS3QdicRKWaxViSiPcCyLcDnU++hEcyLBk+i+ldb6Wp77UZrriBeaFcz06eU6vKRv7gfExSBIYZeDz4Gh5TqzohyFVCdxl2h2PtEgLz4Uu9s2dKi8Bg8Gf1KEzDN+R6hJc/yDMwgVRBg7VmZ7vQPBYBD++U9D3k2PAJD853+U2wXKWrvDWvsl8CDiOfA/JF3La+WqWFGqIYksb267V5h5A654n3F37NixscmZktIseFMpuHomTCicLsUFV5k6VZaG+P2SJHn6tAjnzvkTwfZdeL7Dy6SfHsHnE/H0z3/KWt1774Xzzxcr3//+J8tyg0EOlHv+ecjzp3Fn/XfII5VPjrse33FphEJiqdu4UayB3brBiy9K9PCzzxbr3KhR8O23ETo22sKc2ZZTT4UvvpAADAUFUn/v3hKgpWFDCcjg4qqUP74KAA8jE1CO04B7gYeA31XIGQ6mCeBN5b0xuq0Q0ajDc4wxc3JyckpV8fr1FdK+OMq7Xi6eooKFlMUC423L4coJEb+mMNE1FLcG7VCsVe685aGk44tbK1laShvQJVF7yir4bTH7Sjr2MNKyZbkOL3Y4aq1dCCw0xvzHWqsWO0WpxiQauCWanS8pt1Z8OYDgdznR5EghQtNmQU4OPl+jA7mu3MCvSxf473/h6qulwz02rXzXZIw5D+gF9AbaAUuB6cDd0XdFOerw5q1L9Ow64qPeeo/3+WKTL25bpsQ+OpDqxLsvFIoJRhclc9Ag+ezW2YK4c0+eLAFMMjNh2ay9nLJ8DdfyKq98dxOzxv2a/hfUZeJECXRy+ukSjGXwYGlTp04ivJ57TpKkn3mmWN/EXfIsls/ty9mdU0muaVizRgTHkCGyDq9NGxGJPp8IvvnzYX9BhLNm/5Un3m3C692+gL4fs3ZtEs2awXvvSWL3RYtg1KgIxyQFadIkhWDQEAxWRHwVAOpYa5d5Pq+21s4FMMY8WiFnOESsta8Ar4AkOi/NMdddJxFIK474gXtRlquy4uo4lMTkh7L+rjyUJ3l6WdqaaB1ZRVBcmyvK+lmW63QCv6R7WtT98P52qjG/+EW5Di/JRXOxMWYRMM8Ysyj+Va4zK4pSYRS3dsclKy5qv9dtM/5vtz9rWUN69LTg8zOpzUho2PDAIPHJJ+W9Sxe47DJZ99K+vRxbyknj4rgayEFmwhtba/taa++z1n5GxYwSFKXKSWSti09SHm+h877HizuXr84JOFcmI4NCz617zkePhieeKOy63a5d4bW1Y8ZIuoKvvxZhFQrBV1/ByN+l8os+O3iFm1jU9jfMXp0GSLRL1w8EgyK0fD45/5o1sbZnZ8cCrqTWTuL622tT7zhDnz6ShuXRR0XknXpqzAXz+uvFMvfTT/DNzAKGrX6Wx7mbWd/4OffsEMOGQZMmIjDvvBM2bYywddoKFk39kVVL8wgGZWBXQRa8NO8Ha613VFauDOrFsAk40fO5aXRbuan4NeSVLaYOxXp0qOvvDoVDidbppSxtLcqyWZrzlmRlTdSOikxbUdbvpDTnLy4oTllTRpRESe0t67l6ldslrKQWnQOcW8xLUZRqQElrd6ZOPdhVy+3zpkAA+Tsrq3CAloL9hlHDRxP69jsGzHsCfw3prLKzZVYdxBXr6qvFtStQU/5ZfP99+f6ZW2t/Ya19wlo7w1pbYIxJM8ZcZ4yZgKRMUJQjGmdZ8wZMc8+sd+2cN9WBKx+fz87hXCq99bk6Qerq1ClWZ3KyJCN37pggYsqdv39/EWcdO0rZW2+V5/255yCtrmHcPeN48FcryX/wUdp3MIwfL7nUFi0ScZibK/UMHCjH3XWXuHkOGyZWvHvvFZF3xhnwpz/JthkzxM3StXHNGhGMbdrA22+Lu6YxcOPImvh7pTOdfhSc2oGde2pwySURWtfdQv3jLL/8JbzwXJgTWUU3vuFeHsYNYmvVqpCvcIUx5uz4jcaYc4CVFXKGg5kNtDLGtDTGJAOXIEtnyk2fPhVRi5fDKaZKS6J1XZVFSQK3pPtT1rY64VNSO7yURoRW9j2rrPqLC4rj3QcSWiREbC1gUfV46/BG1EyKHu8ijBrPMSVdk3d/EiedVKO0F1gkxtrSDcCMMc2BVtbaTGNMCuC31u4pdwvKSHp6up0zZ07JBRVFOUAi982i3MDcPjg4DLtbh+P25eZKoAM3Mx8KwbatEc5sPo8VdOGPjR7nrz/8TkLbFYMxZq61Nr2IfSlIAIHLgC5AbeACYIq1tlr5WGj/pJQVt17OPUPe6JhekebKZmaKaBo6tPDkzdSpkt9u5kwp69KbgDzn8YFYgsGDXTSzsmLHep/3zExxqxw4UKJh+nyx9jpyc2MxAXw+Wffm90uglTfekHQH9evH6nTC9Pnn4ZJL4J13JA3C7NlyHQUFklcvEJD8mn6/tGPRIgnMMm0a/O53ElEzZ2uE0O693Pb7VMaMsfC3R0hdNZ//1r2RM/85lDlzDJNfX07L0FLanBDkzeDl7NhhGDBArqckSuifTgG+QlzGXRa5MxDX8nOstatKPkPZMcYMB55GRo9vWGv/Vlz50vZN69eXe+lPAsrjonik49IKOBLdg8q4P6U5ryNRUOoicrNVc+rWleTn8bh+NSlJvBMWL5b+65tvZC1vWhrk50cI74+Q/5OhZg1LzRRDi+N/YtHKFFJSDPkSOy5aFk46CTZvlj7qxx9lXyAQocUJBWzNrUkoZKhZU/rrXbvg+ONlgqx5c1lXbC1s2CDvPp/0eeHoV3H//fD3v5d8vcX1TaWy/xljbgBGAPWAkxF3gJeAQaU5XlGUiiORWCuJROUTBW1ItNbHCTo3MBs7Vj737y8uUDfdJBHtgkFxs8rbsIsa7AWg/paF4qd5iJE0jTHvAn2BccBzwEQkelzWIVWoKNUI97wNjYYsc8+gd62c91l0bpbub+8z3LOniKVbb40JNzeB46zyTriBCELvmrupU8V64471isuMDLHMgwxQMjJirt8zZsi5p0+XQcqgQbJ99myJdrlyJVx6qQiyNWvEUjhxorh5duki7pZz5ohVz++PDXQWLRKLHkjd4bAIvv374eWXZa3YiSfClCnw1FNJZGfXJmsypIZy6bvqOfKowX93XsQbLxfQpGWAG/7Rmg/fO4ml9WvCbBnotm5d/u/QWrvGGNMR+A2yThhgCnCTtbZ8iayKP+9oYHRF19uihVhv//1vee/USdxyCwpk8Pzjj5I8/p574IMPpM/fv19+L36/fNerVsFnn0lqij17IBJJwlr57bhk9iCW2vXrZXKgtCQny+9q6dLiy9WsKS68VU9p1osVPwGalCSBiQ7lvLUCYYIFfiIRQ1KSWL2diKhTR6zYmzcnAduBGkAyzZoFsBY2bZLzHnecuEjv2SPf37ffwmmnwbJl4kbdoIH8m9+/X/qfN98UF+k9e+R7yM0V6/vatVKmbVsRRwsXyvl37ZLfgbXyG2vRQiz6tWtLucxMqS8nJ1bfccfJa+5cya35979LmR07JAen6zM2b5Z+rmZNaesll8hvs1cvqefyy6WvysuDJk2SqFEjicWLo67dm6BGjVpcVleucc4c+OEH+f395jfw0UcSBMrnE6H47bfQqlUSgUCANm3k/GvXyjPx8MNw3nnSFne+FSvk+nxRLb1kCTRrJp5Rf/lLWb/vgyntMPEWJCzvLABr7WpjTMUsT1YUpdTEW93KS7yQi687frDpCIelzK23SmfUpw/861/iPrXguzT2kwpA3gntyxvJoC2wE1gOLLfWho0x1SC8laKUj6KeZZeU3P3tLQ8xK5tbY+dEYWqqPI+pqYldO10dzuLnxJ17vvv0iUXFdWIQZEJn0CB55idPjgVeCYXEDfOqq2D8eJkRv+su2TdqlKzHvegiuPFGEYfGyBo+kAGNS/E0dap8dikYBg4UQfHttxLIZfJkDsyct20r51m7VgTijTdCSorsmztXtjdpksa7ae+xMzfMP7q8w1+bXM7gwTBhQhLH1Aswdx7s2yfHxFtIDxVr7U/GmEnA1uimZZUp7iqTUEisCvfeK9/v3XeLKIfYbw3kd1azpvT7bvuoUdCjB/zxjzJg/+9/5fcB8p3WqydBd5yV+YILZJD9zDPynaSmwjXXyL5t2+CTT2K/5Y4dZZ7w8cfFPXfAABF7mzfLoPukk+CKK+Cvf5XAPTt2yPnGjxfhMHiwDPr79ZPf9/nni2j55BMpu26dTEz86lfS9osuEitK//7y28rJgVNOEWEzfrz8drZvl7YlJYkAyMiQiYzrrpPnqksXObZBgyTGjRPB07u3iOZdu8QSNG6cTJCcfbb81t9/X0TDpk2wd68EKNqzRyY8fvELuebdu+UaX39d2pScLGJh61ZpkzEy+dGpUxIpKUnMmyfX17KlCK0FC+Ra09Kk7CmnGGoF6hHavoszBgVY861YqxYskEmQGjXkeWzfXu7H11/LczdxImzZIveqSxexpv/xj9KeDRvg3HPle5o2TSaxgkE5JjlZXn//uwijbt1ke69ecl+zs0UEbdwo37ebMCookPN7+6HHH4eLL5ZrcRNYaWkxD4G8PPkOXI5Ol5/T/dbdRFUwKG3p1y92b8aNk3IDB0qb/vnP2HMQCMi9cf3rL38ZezZc+9xzEQiI6HX9df368hu88MKDXen9fhGoFTG+K5WLpjFmlrW2uzFmvrW2izHGD8yz1nYsfxPKhrpAKT93DsWCV1Q9kHgQWdy2YFAGbC5K34QJ0nk++aQMwJYtg4KfIvzt75YXnk9i5C0lu5yU4ALVGrgU+DWwDQlD3t5au6UMl3tY0P5JKQulfZaDwZjFzSv0nAhzLphTp8pgccaMmICLd7t2Vjdv6gPnrjl6tESs9EbNfPJJuO02GbRA7FkfOVLKff45NG0qA/cLLpAyrp7cXBlc5efLwP/VV2WwPXKkbD/jDNnWvn0sncG0aVL+ww9lxvuFF2RmPilJBr4dO8YGd6NGyYD7009loJqRIYPvU06OMLRbDi+815BZswznny/HjxkjA1VnDfn732UQXxIl9E91kJQtZwALENNJZySl1HXW2t0ln6HyKUvf5B34FieC43+/wWDxbsBQWCR6yzm853PBd/r3jw2Oc3Njv1u3z9UVCMgxbr+7BldvXp6Iyx49REy6/aGQ/G7CYTjnnFj53NyYd4qrz9Xj98u7Sww/eHCsrHsHEQk+n6QUgdh5vffMO1Hjbb+buEnksu3a7r2HkyfHXKO934M73n12/7OhsBiJ/36Kiq7t7UsmTpRn3d0X7/3yXlei9C/effFlvNef6PfjyublFe7PElHafja+XFHnLGs95WlTSRTbN5VS4P0TyAWuBH4LjERmqP5Y/uaVDR1AKcqh4/2nl5Ul/9AGDSr8T7g4C6G3M3b/mDIzZfbfdf6bN8tM3IYN8NBD8Oc/l9yu4jqpuHJnIGvxfglstNb2KsPlVzraPykVjXsmXfATZ4GbMEFmxeMHZG6/K3tgjW1BhLEf7MJ3XBp9+poDg6JQSFw7b7pJBNPIkfJcewWl9xwAf/ubzOrfdlts7V5ysgz0/H4ReMnJMuh1A8+CAnFxikRiQV6WLhXrkN8vA0Un+nr1kgAuv/ylWAI/+ECSo/t84sbUvr1YNLp0kcH6yy+Ly9R774lLaLduMts+dCgsXRIhxVdAj/41adjQ8MIL8N13ch1XXCFr/0qiBIH3FrAe+ItbE2yMMcADwCnW2isTHXe4OVL7puIGwocySC7qmESTm6Wtr7jjDrXeQ+FQhEx5hUZFCZXyUB3aUFUU1zeVNpzR75FQ5YuBGxG/7z9VTPMURSmO8oat9g7+XNQ9t94GYgmNi3Pr8h4fCsXcaoJBqef556VMMCgDsdq15bPzLa8orLVzrbV3I2uBv67Y2hWlehA/y+0CpDgLnVur5sW5WsYLQb8fUVWDB8MVl9Pnb0MJJEcOiMTsbHHtdK5Bo0bJ+b1BWTIzpaxbh3f66WKtnzRJ3N4WLxYBN2GC9A3JybH+JTtbrBc+nwiyDh1ke3p6rM+ZMUNcoyIREXdTp4rr2d/+Js2eNw++/DJ2bxYuFDe6ggIJtBIMypqVK64Qq9+sWeJaOHdOhEarp3DG7KfZ/+93+fijyIH1RyDrXSqA3tbah7wBn6zwF6BnhZzhZ0xxA/dDGdQXdUz82vOy1FdSGw+X+CjteeJdwg/HOSuT6tCG6kipBF604/oUGGmtvdha+6otbfhNRVEOGa+oKu/xTry55OepqWK98/mKPo9bg+dcQfr2lb/vuUcGYJMny/Y77pDyzz8v/uqXXy6fjz32kC8dEPcnY8z9xpjnjTFDjHArsApxiVKUakVFTMjEpz9wKRP69hXBFArFAp04nMAaM0YsaoVclnJyYPp0wlgmTqvJ6Pd2AfLs9+ghRX77W3F3OvVUsaZlZcWuZfDgwn2FE5cLFoi1rEMHEXW7dsnarfbtRbQFg+Ju+eyz0j6fTyxsoZAIM5cPr2tXuZa1a8Wl7cMPxdW7RQtxyVy+XNYPde8u1zRypEwwDRki/VCXLrLm6KGHxCvhxx+lfzulaZBde8IkEea7LUmE8wto0ACMkTDmuTsrPQjvzy1kpKIo1YSSEp0bY8xDxphtSD6XlcaYHGPMg4eneYry8yaRRa08x8e/BwIyeHOiDw4WegUFMthzbp1+vyz2Pjua+cktRA4EYhH85kcz1G0p/yq5fyNr7hYD1wOTEPfMC62155e7dkWpQMo6IZOonJtImTq18MSKC64SDouQcwLM1ZGcLAJo+fJYNMwDa1nqNYRevfBhCLfpwKLv0wAp99JLcvzAgbIezT27Tvi5xOeBQGyNWzgsr0hE1sgtWyaWt7VrJcjCO++IaHOumW7N27Bhco6775Y1c0OHygTS7beLuGzRQoTZM8+IcGzbVgRrmzYSlOWBByTU+P33S+S8Z56RlAmpqeLyedFFcq/q1ZMAHye1S+HGU6eQzkKOb2jJza/Jnj1hUqwsnMod9c/yK3KYbox5MOqWeQBjzAPAjPJWriiKciiUZMG7E+gNdLXW1rPW1gO6A72NMXdWeusURal0F4p4dw1vXjwoHBbdRc90a2169xZ3qXHjxDUrO1sGpS4Pzcknl6/twEnW2quttS8jgVbaAkOttQvKXbOiVDDFuTjH46zjifYHAuKK6U127uofOlQCJGRkFBaUGRmyxs2toQuFJALm2LEweoyBzEwGrX+DYXP/xh13mgMBEO64Q943bJCIdN27i7vkSy/JM+3tD4JBsa4VFIiQvP12EYIjR0rUuVat4N135bn3+8V6t3SpWPiWLZPIiIGA1HPKKRLZLxyWaHvffCNumy5gQ36+HON1RR00SPqYu+8Wy96yZSIwd+wQIdi1q6z5bdVKvAmOOcbAH//M7OteZ/Jxl3LMMYacH0Lk0xSA+myRSsrHb4EOwBpjzEfR17dAJ+DW8lauKIpyKJQk8K4ALrXWrnMbrLVrgcuRgCuKolQi5Z9cLl398a6c3s/eXFzORWvcOBkMTpsGN98sg7TnnpOBVm5ubMxUUq6iUrDf/WGtDSOBVY7I8OPKz4N4cVeSRS+RW3QoJELGrafz+wsnQX/2WXkGs7JE1DhrXn6+5IXzCsfu3WUSJnd3EuMWNmLceMPkyfKcPvlk7Lx33SUizecTi9jVV4v4cyHEMzPlWpo1i4V6T0sTsfnKK1Lf6tVw7bUSCGX8eOkHWreWkPSXXSbvK1aIoGvdWtxCV62KWQtd//LooxJdEyRYypIlIjhXrxbXzquvlms1RvZ99ZWIv9RUScWwaZOsyWvRPMLH7waZvTyV088wnHQSnNAkgkUW3+0gmuCqHFhrd1trfwkMAd6KvoZYay8GrilX5YqiKIdISQKvhrV2W/xGa20OkhFRUX52VLbo8p6nPOvviqs3vv5ErpzOTSyR2EtOlkSg/fuL1cDnkwAHb7wBL74o0e9ABoDlpJMxZnf0tQfo6P42xpQYftwYc5YxZqUxZo0x5vcJ9j9ljFkQfa0yxuR69oU9+z4v95UoPwviA6QU5WLt94vVzT1j7ljvMx//jIJYt+66S/JjZWSIoCsokNxUixbJZEt6eqz8rFliUXvuOZmQycuTMpmZst4uFBKh51yw8/Nl24wZktIgL0/amB6N0/btt7IG1+XSCgSk/lmzRLTNnClr6wYMEMve+PEi/v79b7G8NW4s1ruUFBFkbdrAH/4Qy3Xn1h6++aa4adavL+08/nixzK1eLakR0tPF9bNGDXn/7DNx13QeBklJEVY88C6rxq7gtOUf8vnnEa6/HvoNDJDE9wB0PXG/NKgCsNZ+a639Ivr6Nrr5rgqpXFEUpYyUJPAKDnGfohyVJBJdlSX4yrv+LlG7ShJ1Xty6vECg8BogNzANBGQQ2KePCLylS+Gtt8R1yq3bcTlsDhVrrc9aWyf6qm2t9Xv+rlPcscYYH/ACMAxx7bzUGNM2rv47rbWdrbWdgeeAjz27890+a+155bsS5WgkkfXNPV+lCY/unjHvM+ieuYwMeb5c1Mz44xwLF8r6t5Ur4ZZbRLzde68IpUGDxNq2dq2IqpkzJRplx46SO27hQmln+/Yi+Fq3lrx2waA80+edB6+9JsLqtdekLS1bSl033CBWxm3b4IsvJL1BcrIINReCLS1NrHEzZkj7hgyRPuHii2P3a/BgOY/PJ8c++aTUO326RM/cuFHE4aBBsvawZ09ZyzdypAi7li0l/57PJwL344/Funle313cvfluhjGavjs/4dTmBdxwA3zyiSFCSwDGtP+LmAErDw2yoihKlVDS0LFTEbPkBigm/aWiHJ3Ei6KS8sZVxPkOhaLaVZKoK+r88W5nLhGyi9Q3aJAMHOfNkwHm+vVSdttB9v/DSjdgTdStHGPM+8D5QFGLbi4FSpG1T1ESP2Pu+XKRL+P7Clcmfl1dIrwTK143ae+5+/aVlAVpaWIdAxFQjz8uf48bJ6LLrbM791w5dtYs+dvnE6tet25wySViLWvVSo5NThbhCPDww/DII7JObuNG+fzqqxIE5cUXxYI4Y4Y8+199JYnJJ02Sz4sWSZ2RSCzB8pw58MMPYgW0VgKoLF0qycjbt+dAKoPVq2VN3WWXSUTe88+X83z7LdStK+6imzaJoN2/X9o3dqxs9x2XRnab69m4vAWnngz9Btdk124Ru1u3ytx2ixaH/v2XEo02rihKlVCsBS9u9rxO3Ey6umgqP0uKE0zVhaLaFZ8Q1OsKFl/O++7WALnP8ZH8gkGZOQ+HZZa+TWsJQ17DV+lhyIujCbDB83ljdNtBGGOaAy2BiZ7NAWPMHGPMTGPMBUWdxBgzIlpuTk5OTgU0WzkSKO7Z9yYbh9gaNpdypCjX67y8WIqEeC+BRK6fgUAs2bnLk5eeLtavxx8XAdWnj+ybMEHeZ82S5zQlJRbAJBAQS9/554slbuZMqeeVV0ToPf64BFUZOBDOOkvcJ1eujLlmt20r7cnIEMGWnCznmD8f9u6VtXjNm0sfceutIsIeeECSq69fHxN3t98ux7p7+tBD0v5vv5Vce336ADbCjRdsYedOy6ZNYsHLzoY6dUQMDh8u550x0xB+8BEaXXM+T9f4IytXGb7+Wtpcq5bUH6mA7sm5iyd47QFOKP8ZFEVRyk5pE50rilIEpYmYVxYqyuUzkbhLtL7HDTjz8g7ePnZsrHwwCE8/LZ+HDpUBoTeSH8igdsyXYb79RuIyJf/l5sO3aLF8XAJ8GA3k4mhurU0HLgOeNsYkjAlqrX3FWpturU1vUM6ADUr1pKgJkKImdpzwc8/R1KnyrHhTksQfGwxK5McOHUSMjR4tFrg+fSRCZGZm7Bn1ntv73rOnWNRArFojRsjfY8aICJoYnb4YMkSsdikpYlGbNk3cNtdFw6n16CGTNyNGiOBKS4sJ1q+/FhH3r3/J55EjJe/lzJlyzC9/KXX36SPl5swRkdexo7h2/uIX4gb673/Lmt2zzhKR2LWrXN/cuXJ/X3gBLrwQHnxQtrdsCS++GOGjEV/jT+9Iw8WZ/OqXEW68UQTi3Lmy9m/ZMnEBnTAB8vYmsSG3Npu3GG65RdqzaBHs2ydtL6iAhSbOXbyIifBqNvWnKMrPBRV4ilJBVERQlLLUUdSgszjiXcacFaBnT4nMl5cXsw5AbI2OC7rg8twFgzLb/tlnsTx4v/iFvE/4JJcefAlAMrslbF7VsAk40fO5aXRbIi4B3vNusNZuir6vBbKALhXfRKW6U1zgk6LKQmErm3svyuU5FJL9N90Er78ua9EKCkSMBIMiiDp3FgHogpAkIhAQF8eBA6XOK66Av/1NLHqzZok759Ch8pxfeWUsGIuz6I0cKWLpo4/gr3+VZ7ugQOoaM0bO0bKlnGPmTBFnr7wiqRNat5a1eC4YyxtvSDCYq66Se7JsmYg8l+h8wwZZe/fJJ1LP+vXiNjp5Mlx6qSQuHz5comEmJ4uwvOG8HC7a/jIhLFtzDWecmMMjj8BvfiPibds2acuqVVJnaqrk6zz1VFlD2KSJCG2IAGECAfWgVBTl6KRSBV5JEew85S4yxlhjTHpltkdRKpNE6/PKW0dRxA8yi3P7cvszMwt/fvzxWHk3MMzOjrlx+v0yQHMBHQYMkAGTG4yee664bYXDMVev6dPh94/WZRGiEPNJE/+tqmE20MoY09IYk4yIuIOiYRpjWgN18SQlNsbUNcbUjP5dH8kHWu6EWcqRR6J1q8XluvPmjYsXdIncLr2um6mpMTfFlBRZO5eWJkm9GzeOraU7kMA8VFh4hkKxFAWpqWIl69ZNXBKvuUYSkAeDYlUbMUKe43HjYmve/H5ZDzdhglgbfT5xjczNFctgbq48zosXw6hRIs7eflsseO++K9f+4IMiCl1Eyzp1RKjdfruIy2AQrrtOXEHvuktinJxzjqzJ++1v5TqfeELEX7dukrT8u+9k7d+0NQ0Jt+7AKG6F1LrM3dCQ+++X61yxQq77pZckXUuXLrImeNUqsQ7Wrw/bt8PkyRFATHiB/z5ZMX6aiqIo1YxKcx/wRLA7E1n7MtsY87m1dllcudrA7cCsymqLolQmRQ3kKjv4incg6db8JDpXKCQDtnjig8e55OXePHguYIQL6BAMygz70KEyQ37WWTGL3uOPy8Bu1qwkOLE9bIC8370gi2uqAGttyBhzKzAW8AFvWGuXGmP+Asyx1jqxdwnwvrXWO53fBnjZGBNBJsL+Ed93KT8f4p+rogKeuMAqiZ5/56rprONuv1iUYlFqMzJE4GVkyLFjx8r2oUPlPTMzJsgKCkQIZmTIM967t+SlHDUKzjxT1uANGxazILrJGbcub8wYWa/WsaPUN2qU5LFbsECs9d98I5bDQEByz734ooitf/xDhN6qVVLnvHki6KZPlz5h2TLZtmmTCDZjxEOgUyfpDho0EIviG2/IMQsXigWvY0eZaBo5Uto3apS4dZ5yiuuvDPzlL7Tfuoufvk1j0WLD51/IOcNhcTd97DERkN26iXXxmGOk7u3bpY6rL97FYy9In7R2RxhycqBRo/L9QBRFUaoZlTnyOhDBzlpbALgIdvE8AjwGaPJipVpQFstbIvctOPTgK2Vx0UwUWr0ofD6JdOnNpXXPPbFBX2ZmbIDpBKtXMPbtKzP9//ynDPry8mTANnlyrK0dOkj5k0+GHTt9gI8kX9V6gVtrR1trT7XWnmyt/Vt024MecYe19iFr7e/jjpture1gre0UfX/9cLddOTJwz4p3bZ1zf/ZO+GRlxZ6p+GibIBYzl37ErdWLt/hlZYk4691bxMySJdHAI4hg+u1vpYxzqwyHRVi+8II8v5Mny9q+J5+UZ3jBArHYJSeLILztNrHiGyNlfT7o10+Els8nAmz4cHHPXL1actgZI+ebOjXm6nnppbB5M/z+9/DjjxIkZdUqaW+3brJ+r1UruQ9Ll8r72WdLpMzbbhN38CeflGiYa9fCfffF2jB3XhILv6/Lj5sNN98skTCPPRauv17adfrp0p4//UncUDt3FrfX886TXHpfZ6fh0vhmnLgXGjas/B+JoijKYaYyR18lRrAzxpwOnGit/aq4ijRKnXK4KOs6unhLmvfY4sRdcUEb3Ax/Se2Mb0dRuflcBMx4AegGkG6/syQ4d7GsrJjrp1v306WLuFb5/TIQGzRI1uE9/rgM7k4/Xep1Y6YjI76Kohw6TqS5Z8kbjMjtd8T3CVlZsv516tSYhc/rdj11qjxjLlJmQYG4Id55Zyx/nVvb97vfSTLzPn1EsLkE6DNnwo03yrq8oUNj6/OyssQ70VkDJ08WF8xPPpFJmtGjRUiCCKVly0QQLl8uLpodOxaOxDlvnojGOXNkLeG998rawYcfFjHn88l6vDPPlCicLVuKAFy1Ck46SYRc795i0fvmG7n2r74S4RYIiIVy9mwpFwpJVE537d26wcsvi9g0RvJxLl4MP/0kbbrmGsn7t2oVHHOMRdbgwZytx8eS9imKohxFVNn0ujEmCXgSuLukshqlTjlcxAu20h5TlmNLE7ShOJGZqLw38qX376KOT1SPG2R6y0AsAAuIu1evXrImaOJEmaV/5RVxoVqyRNa6LF0aC0O+O1EWTUU5ioh/7gMBWSvnXJfdMxafy87h8t3NmBGLVOks6u6zq9vnE4H01FNy3OLFImDGjpXncdIkEU0jRojLtc8nQU9eekms796JmuRkscCNHSvPdH4+3H+/BDdJSYFnnpHyv/iFiMcrrhB3x1tuEfF21lnSlh49ZHIoEhFBdsUVMsHz739LXe++K+caMkTatXGjBFpZuVICuRgjbWjXTq4pHJZJpBEjZF92tkwiPf+8iLtevaSOpKRYmpbJk8V19K9/FbH36qvSvvfeE7HYvLmsZWzQAPJyC0hiLQCRn7aLi6aiKMpRRmUKvJIi2NUG2gNZxpj1QA/gcw20olQ1iaxxxRE/Q+9cHos6Nn5AmCiIQ1Hr6RyJEpi7NUChUCxBsgu84K7F+7fXWuiOz84WC8CM6RF6tNxC1iSL3y9uTkOGxNy9Tj1VrAMPPCDrXQYMkBDlL74oodbdzP/xx5d8/xTlSCTRc++eLWcdnzo1ZqFLZHV3a+HiI20OHiwvEFfF3NxYMJTJk8UqFwrJczllSswKN2yYWA8XLIilHJg+XdbPGSORKJ98UsoOHCiBWJ54Qs7rEqTfc49Ewwzui/DKE3u45mrL+eeLULv6alk399VXUs/EieIqOn26RNEMhyXoSpMm4jK6ahX86lcSYCUlRfqMv/wFTjhBxFogINa9li1jKVfmzBFB2LeviLhwOJbOoHFjcbds1kzcUQsKRFgWFEi5l1+WPuhPf5J70707NG0q9/TUU6X+5atrEommp2vR0K8umoqiHJVUpsArNoKdtXaXtba+tbaFtbYFMBM4z1o7pxLbpCil4lCjWcbvK07kxYst73GJBoTe88Vvg5glwB3rBn0Qu5ZEVsb4NgSSI/R9ZAgz21xN/t1/hEiE5GQp8+STsH+/DNYGD5acV8OHy6CvWTO44QaJovfKK1LX3r3F3z9FORJJ9NwXFW0zNbXwc+ms7Hl5hdMeeJ9J96z6/eKGOWmSiLZ+/cTNMiNDLH69eokY6tcv1o5AQATPCy9IIJSCAnGJbNtWLHodOnDgeV6wQCaCnn1WzvPaa7Ld2giTBz5Ewb+eJPnRPzH0zAidO0swlGuvlfQNbdvG1vjNmSNRLE+MTummpkqwl6uuEtH52GNibbzxRrEkdu8ubpnffCMC77vvRJh+8olcV9u28Ic/iOWtoEBcMxs2FDE5erRYJcePF0tdfr5MLg0bJvfriiukzpQUOc8f/wh79shkU24u3HCDAeoBMK317w6ONqUoinIUUGkCz1obAm5FItgtBz5wEeyMMedV1nkVpaIoTYCURELQ75cBWFZW6S158ZQmDLvb7nXHdCIvO1sGX/HWOyfkvELRW9/gweDfmUNg5mT62MksWW4gJ4fBg+WY5GRxPxsyRAZdQ4bIcaedJjPyV18trlBuexUF0FSUCsfr2lyUO3b8Mxsv9oJBePRRsZBlZ8fySkJhd07vOryBA0WYtW0rljuQY7p0kcAj6emSqHzBArHyZWXJMR07yvPq84m1y/09bJgIpdmzZR2tzydr7N54A3btEqvf327LIXnlIjqwgM9WnMakT3cxcKD0KdnZ8P33ct6FC8VdtHVrcbHcskUE5HXXwfnnS2qC9HRxBV2/XtbxvfCCrNezVto4YYJY/E47TQK3XHmltCEcFqvbZ5/B++9L33TOOSIeP/lE1ve5YC5XXCEun6tXi5CEmDC87TYJwpKcLBZAiUqaBPjo0EHFnaIoRyeVOvwqTQQ7T9kMtd4pRyJFratxblbFCUXvANHrQunFK+K8+7zRLp11wLmHuVDrbn9WlgxsnOCMdwMtdN6GDQn26E/AF+H0dvvxn9CQUEgGZjfdJGHVvVEAb7lFzvXoozJQmzFDBnQgblKKcqQT/wzGT5TEW+vjt7vARZMny36fT9wznStnKCRCp0sX2T92rFibbrpJLFUuOqVz6QwGRdytWCHukd27S2qBgQNlTZw7B8ia2FBIhI+zuM2YIa6jfj98/LHU1ayZWNROOw0eeL4hs44/n97MonmTCL76aYBYy1xE3nfekQmdb7+VcyQni6Vu4UK44AIRZZmZcOGFYqF7/HERf+vXS7vXrRPLYt++sqZu2TIRimvXSltPOknKnn++BGrJyRGrZ2amtOPHH+X6GjWSetatkz5p/HjYtzfC/Cl7SEqyNGwoVs8lS8Tb4IILwCU6//57DbCiKMrRibFHWASp9PR0O2eO6kDl8BPvSlkRuGiVGRmFc2TFn8c7oPQOLF14dq+7pRNezsXLHe/eXfAHlzsv/rzBIDz5RIS7Ls+Bhg0JpBhCIRl0DhoUq8Pvl0HoRRfJuph//1tcNP1+cd3MyhLLgQvQUhzGmLnW2iN+/a32T0cv3iiZ3oi08c+eN++d+9vlshs0KLY9Xuw99phYxh57TKxpI0aItfwf/xDR1L27rGtzETWdq2coJO6QxohIW79e1rR16yZBUIJBOa5TJ5j9TYTFM/Zy2x9SmTnLkJEhFrIlS2RS5vPP4eKLpfyG7yOc3Hgv63NSeeJJQ3a2uJRefbVYCps2lXPMnSvCbflyqWPxYnGtzMqSNg0fLoKuY0c5z69/LWvyXnhBrP333iuWtUBA3EQHDZL74vfLPZ0zR9bZde4srqHhsLxWrxaRt3mz1PHjj7BvH6xcGSEyeQp5+8K8lv4iM0d+wJivk+jeXUTgp59E2LhpD5DKAN5nYvjSEl0Njob+SfsmRTn6KK5vUgcqRSkFRUWuLMvx8eWduAuHC7t8JRJlXlcv9+7EmRswum2hUOEw7V684didBS/+vH4/dOqcBI0aMTXbHKh/0CAZ2P3rXzJgzcsTd6n/+z+ZwS8okKALt90GCxbIDPmOHUfWBJKixONdF5dofaz32XNl3LPtLOZOtIRCYi2bOFE+DxoUS2HSo4cEPFmwQKxxqalw7rkihHJyZKJk1iypIzdXzpWaKvX4fBK8pF07EUNr18rz+dVXclx2Ntx/f4T8P8i6urxh5xMORcjLE8tg+/Zigfv1r8WlMhCAs89J4v6/1eaFUSLu8vJkHVxysuSeq1Ejdg8KCiQlwaefyvk3bBCR9/jj4joZCok4W7NGJoIuuwweeUSsbY8/Lvfuuuvgyy+lf7nrLrl3ffvK+rkNGySy5vr1cm9btRJLY9Omco+mTxc3zfHjYeWSEN/sa8UW6vOHORfx2X+DfPeduJJ+8w2c1DwI5AOQyg/VNoqmMeaXxpilxphIfPA5Y8z9xpg1xpiVxpihVdVGRVGqLyrwlCOGsgiqiq4zfr1cWaNsZmYWXo/nBooZGbEZ+fj1O84lzLl3xdcZH1QlFJJBpd8fC9PuPT8cnJDZWRYc7jxDhxauPy8vVub00yVK5tNPS8CDBQvEutCpkwzg/v7XCCfmSvbmel89LSM+RTkCiX/OE/UD7t1NnowdG7PKO8s8iAhz+SOHD489Y67eQYNEsBUUSGTMMWNk37RpYtFr1UpcOLdtEyH2xRci9KZNE5Hz1lty7uHDZZ3d55+L4GrXTsTR+f12wcoV5OPj6lkjyVm9izvvFHfHRYvk5dbP5eXJZE5urrTh2WclAEvz5iIIf/c7eeb79BHR9dprcu3GiFhs107cLkHW2vl8krvvkUdiUTKPO07E3eTJcs1vvy37u3eX+ufPF/fRDz6Q/uydd2Tt4PjxksKhZUsReX37iptm/foSAbRd5xq0ND/SmhWEazcg6ZgUfv1rSZFQty7k5qUAdQEI0rg6R9FcAvwCmOLdaIxpiwStawecBYwyxvgOf/MURanOqMBTqj3x4f0P5fiitpc1qbn375JSGXjLxq/HcwNF5+ro8EbUcwPJcFjW53gHk95w7IkCtcQnNXfHeS1+waC4fnkDOjz5ZGHR5/fLWp1nnpEBn88XC6/epYv87fJshcMyQJw1bhc/IOON1NxV1XaGXFFKoqhASN58k/FBV7zr6yA2qdKnjwg/55rp6nETORMmyAtEdC1dKoLmpptkneuxx8oz9vrr8howQJ7LXbtEHHbuLHngVq+WV4sWIvwGDRJB1TkjjS/qXcNGmvNS11c4tkUaLVrEUpo0bw7/+Y9YwurWleiTV10lIu+EE6Rc9+5yfa7PmDtXRNnxx4uVLSND1tKFw+Iu+sorMr9jjFzb+++Li2dysrhU3nuvCN1wWNwrX35ZrI2XXCIid/lyeOghWee3bp2IyyFD5L5MmiRislMnSeHwyCNSjzGGbU1Pp+U1wxn22CDA8MEHkpNz3TrYvdsA8oU2vvw31TaKprV2ubV2ZYJd5wPvW2t/stauA9YA3Q5v6xRFqe6owFP+n73rDoviWt/v7C6wwFJFRQErFrChYMdubImm92pMMYkxvd20m2KSm15MLOnJTS/2jgVBQUFBinRQQGlKXdhl2Z35/fFyMivBknYtv/meh4fdKWfOnJlz9nu/9yvntLSX7fGPukaeDMSdaSmE9to8VSmD9qQtQye2OYvVSlbMGeQZDFRm9PoTz3MGl87tiNpZzn0VANPZrdNsZqKFe+9VFVGjka5RglEUcXcGA2NmhGIlFNQJE+hq9t57dJXKy2stzOzrCzd4AQBKjYPPZQu5JpqcVpzZOjEn3niDn9vWsxS17RISOMfefJPARiQ2GTVKBUiC6Rs+XL1WZCSNJjk5wE03kRnLzuacnDaNwMjhYHbInTvZ7tKldO384QcCqqIiNUFJejrnZ0ICcNfdEsa/NB32WVci/clfAEgYOJClBnJzCe46d6bx5q67GC9nt9Mt9PLLWQR9xgyybQ88wPszm8msBQWxja+/5vqybh0B58MP83///ryn225j+w4H7T7jxtG7YOVK7s/N5T0++ijr2aWmcl1JTeU18/LIDKalsW8HDjCzaM+edL/8/HMC28ce18HFzwvffichOBiwt8hQWmzo0UNBcJAMgAu37pel56OHQRCAEqfvpa3bfieSJN0lSVKyJEnJVZqhTRNN/l/J35wyQhNN/l45mWuksIr/0fOd5c8kTRHXnzTp720X4P04u1YCqmI4caLapgBnbQFvTMyJyVoECBUJVMS4jR6tZtFr245zynZATVcuxOGg4jd2LF3DLBYqZocOMYtmXBywP0WC2XsQUK+gx2Pzz1kLuSaaCDndnBVxq5MmnZgExDnhkZhnsbE0fhiNTCwyZQo/jx5NdmriRB6n1xP8fPABWSiLha6XP/zANj//nEmK4uLY/syZBHmTJ9OwMm0aSycAwI030vXz1VcJdA4cYMmCOXPI/I0YQeNRcYkORl8PDB9BkGgy0XXSbmf829tvszTBvn3s24gRnPOZmWTPRL+7duU1dDqCST8/gtO0NLpnmkw83mplSQO9nuBw2TK2MXAg7/WLL+jCOWsWmb/qarpd5uSQvbvhBrqqVlSQ/duwgeuPw8HrPfccAe3o0VxmcnLY98JCXv+mmwDZLuPn90tggQtyi5tQ49kdgBWACd6WIiLNzp3/xrfpzEWSpBgAge3selpRlFV/tX1FUZYDWA4wycpfbU8TTTQ5f0Rj8DQ556U918g/yp61lT/r8tleUpK/o932+ioAmnOmTFEcWaQ4F/F1ztdzBnfjxqntjh5NxiA+nttFAWbhrili/axWMg9ms8ooCAbR1ZVKZ2IiFVJXVypkr75KhTE1lcpbt26sM1VdrYE7Tc5tOdmcbRtfJ+aK0aiy3GK/c4KVffsI8pzj9qzWE2tTxsXRtfmzzzhfJk8mKKyrU+P0RBITh4OxaN9/z/lrtRJIlZYyg21aGuff1VcTMAngNXo0wd348ezT0KHc9p//EDympXF+JyaSpbvkEt5X794sa2CxEDzt20fmbNgwrgOjR9Ogk5NDUPbKK1wrJk1inwoKyGKOHUtGsWdPXlMkhBk4kHF4y5aR0Zs7l/u6dWP/f/mFba9ZQzDXrx8TOT3zjMrwORx0y3zxRbZvNBJYlpTwPgoLidm2bgW2bbbiQbyKZ/AsOqMEPYLtAFwBAHsx+qx6GCiKMlVRlIHt/J0K3B0BEOL0Pbh1myaaaKLJb6IBPE3OOzlZ3NkfkZMlS2grJ3PtPJN2/yjIO1lCBwHu1q+nkjh/PhWitpn8oqPbz5IpYnwEEyjcNUVSFrOZ/y0W9btw/RJMxPTpJyaGGDWKVv1Jk4AnnmDhYlFDKy2NiiHw++Qwmmhyrkl78bTtxdc5M+sCtJnNwOuvq26Yws1ZlENITeUxIqHSyJEEf8OHM4nIwoVk5uLjmSRk40ael5LCef7++wQ0DzxAV8v+/em62KULAY6Ih42OZrtNTUx0FBFBQDZmjGqMycnh9/37ydrNm8dYvlGjCJji4nithx4ik5edzTi53FyyY0uX8l5iYxmv5+LC/jz+OEHdxx/TPbJ3b8bWbd9O101XV7L9Dgfve8IE1VC0YwfjBPfu5TPo0YOgVTCJBw7Qg3LPHo6xw0FWcc0aAsIvvxRxd4C/P13Ok5KA0FDAzY1jGz3JHYt1/8IOXIRALwd6hhkBuAEAul19+fnoYbAawHWSJLlJktQTQB8Ae89ynzTRRJNzTDSAp8l5KScDWWcKqpzryZ3Kgv9nWb4/e25b4OpcUysjg8pYYuKJRc9F3J2z26Vg/YTy2hYUi745HFTYoqLo0hQRQUUuMlJ1F3Uuji4SscTE8NydO1VmMSGB8TkiGQJwPoa3aPL/TYSho+1cdZ4vbQ1BYh5s2cI5M3Kkyq7FxtKV8IMP6EK5axfZM7sdeOQRpvQXJQBMJrZts3EObdvG7WFhvE5BAdt4/HHWuhNM1/ffA7ffTnauVy91HtrtBDaRkaxv9957agmE8HAmSvrhB4LAf/2LBhlxTzYbma8PP2Q/FIWgqaiIhcYjIghYf/mF87p3b/azvJwupnfOkxHRvQaZmQq6dOE9f/QRmcXVqxkPOCxCxjsvNiA7W0F2NmP1Jk1iP15+mZkuLRYCugHhMr74TwWGD6dnYe/ebDM+ntfPymKfS0p4/dGjCQLFmpOfzzH49jsJtk7BUKbMgXHWFKxdqwK6pqa/+WX6G0WSpMslSSoFMBrAOkmSNgGAoiiZAH4EcBDARgD3KYriOHlLmmiiyf9H0QCeJue9tJdd8nTHC1arbQIXZ/mzSVjO9Fxndss5mUx7/Y2Pp7VfuDkBqvulkEmTuN+Z9XPOnAmcWC5BJHCx2ehKJVzGBgyg5Vtcy2aj4mQ2sx+33859Dz9MkPf221S25s0jsLNYmModYOFmTTQ5n+Rkc1GsFyJWdeFCAqfx4+luaLOpxwombOpUbn/kEW5/7z3Or9WrOZ/EHE1NpVs0QHbMbuexNhtdJCMj6Sa5cCFZvs8/Z5HwmTPJ7r3zDnDFFTQCTZnCZCfduwMtLUyOUlXFubpmDcsTXHmlWuZAlEoAyKaFhfE4ALjmGiY8KSjgd4MBCAlhTFyfPgS4/foBliYZTw7biIR7lmH9Ozk4fEiGwUCAlZ1NEHrnPBlLxn6JNUty0LJtG1psMiQJOHKE68eMGcye+frrwE8/yrB//jWSR92D/Q98jtQUGSEhXGOSktieTkdg17Mn+7ZtGxnHlhaOrc3Gv86dgREjdOjYzQM//yzBw0NNsmLatPyctUIpirJCUZRgRVHcFEXprCjKdKd9ixRF6a0oSj9FUTaczX5qookm56ZoSVY0Oa/FOcHBmQIy5+NOd/xfcQM9lYiMmYIla89ltC1rII4T6dZF8hOHgwqlYPKcY/OmTlVjFseNU2OAnM/X66k4CqVVXEeAQpuNx2zbxnM+/pjuVSYTtw8dqh7jcNB1KziY8Tiurv/M+Gmiyd8lzllmhbTnvi1q24ntwmXTZCLIEtumT1eB24cfssSBsxQWAhddRHZr0CCCqoULCdLGj6cb5Rdf8NirrlLPKy4moMvJYR8WLOCx339P9ur++3mcXs92Z8zg959/Jlh84AFea9YsZslcuJAGme7dOW/XrSM7GBnJ+StJjLHT6zk+Bw4Ad9/N+f/CC3SP7NqVc3/LaitGH/8ZR9AD4Y59mBLZGbfd5ofrr+c6NH06kBRThwGlG2DBURyq7YHKYitefNUDzz9Ptq2oiKDxqaeAOSOr8EHEYVhggHS0FLnpVhwu9sDNN3O8o6LoXgqQadzb6qDo70/AmJkJeHnxGVx5Jdeho0d5fy2NLaivPw6gM3zth4hWu3T50++PJppoosm5KBqDp8l5LW2B0ZkCspMd/3cVUz8dm9i2GLmzODMFIj5OZN6z29VYuunT1XiWiRNVpbK9+xEZ/kSx5alTeb7ICAgwbXtsLK9fXs7U62vX0govANxFF9FqnpjItl1dCRwzMsg0WCy0xiuyDMCBHj20xG2anPvSNkvvybwC2sbpCYNJbKyamAigK+S2bXSLFOe+8QaB1vz5BCL9+tFt0W5XkxvddBMB1MCBZP2mTCG7V1LC+DRXVyZASUhgfbgffyTLZrPRJTQ9nSUHkpJUhtDHh5ksCwuZrGX7dn6+5RaWRrjpJgK9iy5i+6NGqQwdQDfGxx5je4Kp/OILoLGR/Vq/HvAPcsfHxscQjoNYMvob+Pf2xWWXEdzV1hI87s31RUrX2RiJFFzeNxdX3OiO3Fwyj6GhvN6AAfQSsPt3QoH/KKzHxZg7PBX9hrjjX//iGpSUxBp9FgvXnIwMtp+YSGBqtQJPPsncKZdcQuPWqlWMSdTpgIoaPUSh81qYzscYPE000UST04oG8DQ57+XvYtmcgdXJ5I9k7jxVGYXt29tP6uCcIMVuZ5IF4frVXj1AUVsLICBbs0bt48SJdK18660TEz20zQB64ABdrYRL02uvUem79VayAO+8QyVxyhSeN3myyhaOGsWYIrudmfUsFqC62gGfo3EAANfv3zlnXaA00aSttM3S65xgxTk5kd1OEJeWxvMmTCB42LaNc3j/fs45h4OuzBYLwd7gwTx+7NjW4uMRdGe++WZunzePIMdm41z+4AOCsQkTmKDE3R249FICHZOJ7tJFRYxZk2Wyby+9RLBz9dXc//PPPPaWW8j+DRrEa82YQTfNF17g8Zs3s4/797P8wLZtbLO4mMzX2LFk58PDCcbKygik+vUD/PwkrMrsB58vlsCwbh1+/lnCsmU0Bu3fT6axZy8Jm/xvRN1rH2Nj1L8RHS1Br1fH4v77CfbmzAES90jofsdUzHp/Dj6d/AuysiVcdx3XpoICrkWSxPWvsJAAccQIjk9YGMHwjBnqMwgLY1KalhagpUUPPY4CAGoRctZKJGiiiSaa/JMiKcr5ZWGPiopSkpOTz3Y3NLnA5FT19ZwTsji7g55KREa9tm2c7Hvb6zgnV3Gut+XsfulwUEFbvpzuVgIQPvywWt9OKKPOiVcEANy+na5d4r5cXdXsfzExVCIzM+mCOWGCmllTUaiMvfMOlawhQ9gXhwOwNcvY9eAP+KqmLyyIwK1YhC/K7z6tEiVJ0j5FUaJOParnvmjr0/knbecq8Pu5aDarczIujmBr1y6CE5Eo5dgxAq2xY8lqbdzIEgKJicyKqdMRaHz8MYGYhwfn7SefMPYsLY1lElauZMxcTQ3w00+Mt9u0ibFy3t6t86wVAE6fzti8AQNoeBF1K0XcbWUl3Trz8ggyExPVYuuenmQTP/yQ/XI46ML51luc42Kei9hAT0+Cxpdf5vfAQPb5s8+YAdRuZzzuww+rADgri/eSmcnjAwN53/n5wGWXEZCtWAE0NcoI79aIh541YUes9FstuyuuIIOp06mMol5P1k6WCW4nTeL61b072c1JkwhOL7uM45ebywQtdruaFbS5WQagYNIkHbZtOz2DdyGsT9rapIkmF56cam3SGDxNzgv5q66Tpzu/vVTobTNsnmkJBBFfJ2LY2nPXbA8gtuc2KuLhnO/BYFBdNvfsoXIWH8/tkZE8RyRgiYs78d6cWcrRo9nPmBgqUWPH8tikJFq/3d3pRjplCpXVBx6gQigKI0sSv0+apNam+vgjGw7XGGCBLwCgHN3Oap0pTTQ5lbSdq0KcDTq1tcxg+corZMSHDlWZ9e3bCb6OHaPxw2JRkw69+CLBT3Q0zxk2jPFvt95KVmzwYILD+fMJwMLDOc/69gW++kqt+3bnnXSlzMvjNdPTyYo1NxNIyjLn3mef0RAzbBjn44EDQHCQDNTUIC9Pwd69nIqJifwT9ePCwnjsZ5/xXnU67hs6lOOydy9dMWtrCfi6d6d7aHS0Wqh940Zg0SL29403yOrPmUNXUz8/ArsuXQj27HYmiBElGqZNlYHt25DzwWq8HfE5kvbKkGWWWBg5UmX33N0J+g4coMEKIHArLFSf29GjZOqys8lU1tezzl5DA8FudbWMoI5WADRsBwf/wy+YJppooslZEg3gaXLOy18tHn4mrpdA+y6TwO9j/ATDdar4ugULTjznj2TjbK9dZ0VUZPGbPp1/s2cT8CUkUMETSVQAgri4OLWNuLgTtw0eTIu4ojDVutWqZuMUIBIgAyHLvIeNGwny+valcrdoERVPV1egc4gb3Hz80BeVAICop27WYlw0OWfFaKQRoz2jjXOJkUsuIds2ZQqLdEdEcF96Otm9pUtZE27qVAIjnY5zprmZdevS09nmzp0EfxUVBDebN5MJDAsjkOvalW2npXEux8XxmvPnk/UCCDZLStjfnj3pIrl6NV0wa2vp7nn99cDwKBnha17G2ofXY3ba8xg7RsabbxJo9e/PuLRFi8gYAkxcsmwZAdz777Ovzz7L9t56iyD29ts57+++m/ddWMiMnUVFXBcmTGBbNhvXkoMHCbQmTuT5EyYQoP38M9nAnTuBDaussNcfQw8Uoe/RGJTkWdHSQuD85JMEzbGxZPquuILrTFQU166wMBq5mpt5HRcXMpJeXiwHcckljM976il6F9xgX4zCUstvz7j46+80F3JNNNHkghQti6Ym57z8lXIFokxA20LGf/aabTNQtt0HqO6Tej2t3MKF60z76+wGKphDZ0W0PTdRoYxu3apuE8XNhRumAHcmE13MEhKAadPUe925U3XxBNSaWW+9RcVp9mwee/AgLd/CJeznn+kidsklgM0mISVkEjLeSQQAFL+9DHh5PjVeTTQ5y9KeO6bBoJYPEbGlgr2PjSVYWbuWwOzRR1XDx9atajzbggUEatu3M1nJRRdxu6srmXGA8662lnPm7rvJlKelce7s2sUYMsG+JSWRZTMYgIsvZgzs3Xczc6Uw4lRVEfBkZvLv3XcZn5aTQ2BUlGdHfn5f5KEPXst5GvmOx7B3rxfsdoIxN9b6Rs+edLO22dh+eDgNOJs2MQPov//NtaVrV/5/8kk1c29YGMsm7NvHuLfiYuKlffsI5Jqb+fnXX+lqunQpr3PzzYwlfOopIHqKO5ZuiEBv2yHkd52CF193h8GFbNwzz3ANMpvZ1s03czyrqpgtMySEzBzA52Iy8a+xEaiuJvPZqRPBc0dfG36tmAAJjVDgBUCCHbVsTIvD00QTTS4w0QCeJueF/NlEKqdLdvJH2xUZKNv2yRmYif1WK7B48cmzZbbXj7ZuoM5gTrQhMmI6K6uiDb1eVUAFoIuNVcFmQgLB3dKlJ7KMu3YR7AnABzBl+w8/8JwFC2gZB8hk7NxJt1CjkcphVhZdyOx24OABC8pas9SZm+vPahpySZJmAHgPgB7AJ4qivNZm/20A3gBwpHXTYkVRPmnddyuAZ1q3v6woypf/k05r8o9I29IkYv44z2m7ncAtKYk2iUGDGMMm5mB8PAHKtGkEOePGMQZ25ky6Ez76qGrc0euZXGT7doKd224j2zVoEK+3axfdNXfvJoAMCqIb4vjxbCMlhS6I33wD3HiDjOQtdbA1+yI2VkJwMIGcIMfDwnhsbi6BVq9ewOWXuyDlGQfyy4GEvreiX6QJmZkEcJ068bpBQQRKrq7s7/XXM47tpps4p+fPZ3zd3Lk8TpbJ8HXvTndTIRYLwVTPnmTzDAb2Iz+f99utG11W587lmO3ezTHu1Ak4XCyh/yV9Yeh4H5q9TLhtLu8vKIjxfkFBdFmNiGCSlLg43u977zHb7/r13Ld8OV1C//UvPq+SEoK9YcNolAsIdEPDQU/oFCMckAHo0dXgormQa6KJJhekaGZ1TS54ORm4c3azbK+8QHtunQJInaoouvgzmX4P7tqWMmgvLbtzzJ0z8+h83qZNjPWprT2xYPvUqbye0aj2R4A+o5GALymJgM1oVDN3Hjhw4rjExHD/5ZdT+bNaqUxu20ZmYcwYMnlz5zIxREsLz7XZgOpaPZrgDQAIQMNZc9GUJEkP4EMAMwGEA7hekqTwdg79QVGUiNY/Ae78ATwPYCSAEQCelyTJ73/UdU3+gJzMVbrtPDMa1ffeaiUz3Tb2TrhEPvkkmWzx7icl8bzoaMa/1dby/+7ddFsE6Cr45ps8Z+9ezpN33uG5jY2cK0FBnCM7d5I4uvVWuh527kzGKjyc+0SfCwuB666VseOpjdh/1weQv/oSqSkyjh5l6QKdjtlru3YFvvuODN7VVzOmFpCQ3vd6fLMpGPnX/hupqRJkmaxcXR0zT3p4kCGMiKCrZ2wsmfgnniBb1qcP+xkYSJDl4UGDjizT5XL5cvY/K4v3EhvLft96K9nAnBwmg0lIoEulw8H6fx9/zD4cOcI+v/YfHTw7e2HAAAk6Hcd59Gh6CkREqARbaCjj9664gt/nziWYdXengclqpT1pyBAeM2MGmTxJAkpKJVz9eC90CfTCbzF4pjr6p2uiiSaaXGCiATxNzkn5u+rRnUnbJwNzbZm/9o47XT+dwZ3VSmVn06YTE6a0ZeysVrokvf022QQRd+ec7GXKFODee6ksbd5MZUhk3HTum3MNvZgYKmDCTdM5+crChTxm8WIqV6L0gsnExC3z5jHGZ9Ys7t+zhwphaiqPdXGh0ldWBkSONsKIGgDAUfQ5m+5PIwDkK4pSqCiKDcD3AC49w3OnA9iiKEq1oig1ALYAmPEP9VOTPymnMsSI7cJoYbWqAMRgIHBoe6yYD8JI43AwznTo0BO3LVlCxqy6mmDo118JYoxGMnBHjpBlkmWySlFR/Fu3jnFuVVXAhg1kl0JCCFAee4yMWVwcryGSqhRlNOKdY7fgcbyB0Ud+RWhAHVxc6Kq4ciWB4aFDBJqPP06XRX9/Gl06ddLh4LHOCB8goaSEgFGMlU5H5i07mwaejAyydfffT6byuecI9mbNImN3//2qQemHHxift3Qpmbq9e2lcuvhiumTGxhIAzpkDvP463bmPHmUil4ICMoT+/gRgeXk8p2dPrjcREcws2r8/wbHDQfZv2zZisZEj2a7JRHA5dCjjAhWFbQ0dyue9fz+B3dSpPLZzZ+Dd93Soa5AAENnX19Zx0dJEE000ucBEA3ianHPyV5OqnEnbwImuls5g7lRK48mOa/u5vetu3kyFRBRDdt4n+jFuHEFZfLya1EHU5BKumSKezmDgMXo969gtWKAWNxYF0i0WKmRbtxKYORxsWxwHEHCKLJwPPshtCxeyv9HRQIC/jFvn1OCrLxWYzbyWxUKlMD2d1nKDgczF5MlAcbEEK0iU+d90y9lMshIEoMTpe2nrtrZypSRJaZIk/SxJUsgfPBeSJN0lSVKyJEnJVVVVf0e/NTlDOZkLdnvbBVMtAJ5IYgKote9EwfGYGM6Z8eMJ5D76iIDFYCAoCw2lW+UXXzBeLSeHwGXFCrb/+usEFQD36fUELD16MNYuK4uAR5IIBm02MoqzZ9PtcMAAAq/GRiAh3QTryDFI0o3B4BE6/HetL7y8CJYUheULUlM5J11c+D83ly6MXbrQqDRlCrNbKgpB2eTJBHg7dvCYvn0J/o4eJSCNiCBQzMoioBo0iMft2MFYuBEjWG8vKYn32KkTXU7Hj+dYubsDHTuyL08+ydjeWbPI+On1dOMcP57Gp1mzaER6+22yeiYTi7xnZnIcvvySbGifPozx278fKC1lTGJcHN0///tfgul+/TgG48cTTIu/ggKOp90ONDTqALgCAA7BnyhdE0000eQCEw3gaXLOyemSqvwV4CdAlGjbmRVre4wAVkD76dRFP9t+bg8cGgyM23n7bSobAgg6tytigqZOPdHVUvTFbD6xxMHixWxXZNJcvJjHr1lD5S4qigDszTeZ+Q+gcjVqFK/pzCRGR5/oetqzJ90wE3bLsE+ZBtOCW2D/5FPEbJHx008EikYjkxe4u/NaffoQ/MkOGQCt4gX//eVcz1K3BkAPRVEGgyzdH46zUxRluaIoUYqiRHXs2PFv76Amp5bTrROirEhSEg0XwkVTxKOK+NbhwwlEhGHD4SDQW72awOOhh8hg6fV0T+7Vi3F1b7/NeTB1KoHdZZdx/tlsnBv33MO5++67BCphYdw3YgTnzbRpvPbTT/NajzxCd0uHg0DG31/C8mkrEbb3K3w1awXCB0hYtYo18QACPDc3xur98AMJqfvvJ1AbNkxl8nfuJBOXlQV8/z3XjLvuIuBbuZLHBQby3vR69unIEQLTL74AmpoIprZvp6vpkiVqKYeQEF57xgy6Rqans7bfjz9yLENDuQx8+y1BZng4Y+dmzyawjI/n/WRkcP+4cWRFd+4EXn2VzFxYGEHqsGHA889z25IlBICXXkrAmp5OQLd/v1ruISSEANbLSzgTSAC4JvXHUaJCTTTRRJMLTDSAp8k5KadS2v4KuyfYr7Z17doe4xzDBqjxOwkJv4/bawsS23PtFNc1mRjbIzL3CdbMbv997TzhViZi55YupdIVF3diPJG4lslDhvVwBTIzFNx3n+p6OXo0a9gZjVTcjEa1eLnBQGUzPp7X2rSJ7lXh4QSFQ4OqYEiIxyxswONHHkZ03yr07ElQefvtzIy3fz/7tXkzrfs5mXaIpcUFZfRHOztyBECI0/dgqMlUAACKohxXFKW59esnACLP9FxN/vdyunl/MoOMMJSYTGq5kOHDyTaZzdy2axfdnvV6NXbVZmObrq4EYnv20PBRWkq35auuortgUhKByv33Mx4O4Pm33875tHEjWbPhw8m2ffcdE5Ds2sV+fv8968L5+rLeXnAwzwcIUvr00WHxT53Rr7+E8ePpivnf//K4Sy4hcBk6lICyc2feZ48eBFWVlWQFly/nfdbUsN96PQHcpEkEiA4HGcXrriOQHTaMbpT//jdB1y23AFdeSZDIGD+206MHx+faa9kng4ExeKWlvJ/t2xkX99lnbMPFRU1E07cv3ToLCoAOHejqmZVF4BYYyDH/4AOCzv79GbtXWkrGUKdjdlEvL4JBm41jJct8vp6eMmorrTiQqqBzZ653vXsDLpIDIgYvG12JUDXRRBNNLjDRAJ4m55WcacmEU9Woa1uCoO15bQGbaMtkUtk/58QmYltb8Nk2dk4wceJ8cYzIzjd/PrcJ9zCbjZZ3q5XXXrBA7QOggk2rFdi+TYZ1wjTE97kNg79/CpBlLFhAC/iECaor1dSpqntaQgKV24MHVQZPr2dsT34+FapH/tMJ6/s+CDO8sC3sXjz0aifk5pIlFDWydDo1C2efPoDeqAeTVgKu8DubWeqSAPSRJKmnJEmuAK4DsNr5AEmSnNN7zgGQ1fp5E4BpkiT5tSZXmda6TZOzJKcz7jjvF2VFjEZ1jggWfMcOFez17UsAtmED48Dsds4Bu51zJCuLAGrQIAI4i4VtBgbSRXDjRoIVi4VzxmQiQIuM5Jx76CGCj5AQzpOjR8m43XILAd/u3QQz/ftz36RJBFOFhZxLXbrQLTExkcxUdTXbraggWDl0iP0VoO7JJ3k/337LttesIZP300+c35WVvJfvv6fBKDeXjN9DDwHHj7M2XkoKgd6gQTzfZmMyk8OHCeD692ccodHI7du28d5tNvb1scdo9FEUspl9+5Kt1OnYhy5d1HHr1Ysgct48tnvttexHVhaf08KFXGfq6th+QgLZw7IyAtWaGoLRZquM5MQWbItxwMfdiv79HAjJ24abU+6DnJqCskMWrF+vIDkZGD/ZAIA1IoyQaM3SRBNNNLnARAN4mpx30l5WSWdxVvTaZskT5zsnNHE+71TxeIKFs1pVJRE40ZVz+HDuq62lZd4Z2AFs226nO2VUFP/X1lLZ2blTTYoyfbqa0GTHDiqmsbF0axJFzIVLaFwcMLp3FYyJsYhWYjEr5134tlRh8WKCLpOJLJ6475gYAsrRo6k43n23er8TJ9LqPm8eU7rPuljCsI2v4MOnijF47avo20/CjBlU9l59lXW8+vWje9bmzcDoUTKWus0D0AAAcJ15w1mLwVMUxQ5gAQjMsgD8qChKpiRJL0qSNKf1sIWSJGVKknQAwEIAt7WeWw3gJRAkJgF4sXWbJmdR2jPuCENKW+ONAHebNtFgIkqGOBzqMbm5jDFzdaWev3SpOi+tVgKX9evp+jdvHv9brWSNbruN82XlSm4PCgJ++YWsncVCVspmo1FlwAACp549yabffz/B1Oefk7kbNIg14X76iedIEmPYfv2V4CYlhd+//prgSJb5PS6OrGJFBdDQwPMFe3bVVSTPy8oItMrLycpVVnKdioqiS+WRI3QL7duX4Gz3brpZ5ucTHLq5kSl77DG6sH7xBUFXXBw/T5lCoLZqFQFofT3ZwoYGjm9oKMc7IIDAsKwMePZpGfr6GiQnKwgPV6/7008c16uvJujcs4frzPLlXMcEsJ45k/c/ZgwQHS1jZOn38KvKxOOrx8EjdSdeO3QlBjTsQi5CYbIWoTzzCLqU78PQCBm7dukgVJ+q4Qu1Gp2aaKLJBSnayqbJeSmnsuYLRa+t22PbY5zj7NqWTRDXaFv+QIA6Uejc+VqbNlEBslgInESdOec4OkBlFwICyNp98glZM1dXKoziWrW1aqHlLVvUa4qsmM59SijoBPPISXgXD8E+Ohro1AlGo8pavPOOyl5ER6ttWCx0gXr1VSqqa9fyup99psYKPfGUDt3CvPDCixJuvpng8LbbeO2kJLaRl8c2+/tX4aG0+9AHhziG5mN/4un+faIoynpFUfoqitJbUZRFrdueUxRldevnpxRFGaAoyhBFUSYpipLtdO5niqKEtv59frbuQZMTkyM5iyh3IOJJ22PSAXXuAAQkYl4uXEgQMXYswc7gwQQS8+fz/zvvELwUFHB9EO5/11xDIwfA/SK5h05HEDJ8uOry+NVXPH/xYgKsjz4i0PH3p3vjq68SwL38MucbQCC4dy8TGAHMiGm18pwpU4Abb2S/jUZex24nk5aRQTZ9wgQCOn9/9llk+Fy8mPXhbDYen55Ogr2sjDF8vXuTQfvhB7JxRUVkGvV6jumiRbzexRezZl5pKe8xLIxJTN5/ny6dM2fy+OPHGSOXkMBtPXsCIcEyXhi3GY575mNEzleIj5Oh0xGQRkXxftetI4uXmkpjV2oqjUk2G+MGN27kGD/8MDC0Rx2MFUUYh634WrkUUdiLrDQFvXsq+BVXIhMR8EAj9GhCQZ6jNbMxvQx8fDUVSBNNNLkwRVvdNDnv5FTxc0KEAneqIuMCeLVtWzB77blq7thBpsrh4GehRO7YQaUqMhK46CI1SYpoq21/xX9fXyqTvr6q++T69czYd/31BE9jxjCj3PjxBIGA2q4Ar5MmS7Cv3gjl8SeBzVsASToh8Ysg0SwWgsUNG5h8BWDGvDvvpJL50Ue8/r33sj8bNzKxwuWXUxEtKmL2vM8+I4DNzqaV/u67qTDe+0InDPE7hDyEAgCaoBUR1uSvi3NyJGfgZjCQfZo+/cR9grE2GAjeAL6fzkAP4PF79xKYiOywmzcT2P3nPzSwDB5Mdu7zzwmABMALDlYZ/fXrea2nnyZb9txzBDfdu3PezptHMLlvH2P0ZJkujKtW8Rpr13L9iIoic1ZURNaqsZHzUpZZg66wkP197TXOOU9Pgrq77uIY9O1LNq+sTGUCJYnFv1n8nGNTVcXxLCsjyy9i6LZvZ/8qK8lmlpURPFssPH7qVDJ09fV0Aw0IIDuq03Eds1gYH9i7N9fBrCz2S6fjvb7yCtDDtw69qvfAAQfkslJ8utwGi4Wgd+NGGryaW6Ni8/L4zA4epMuqiwuT2lx+Od1A33sP+HmrL3a5z8QKXIyPMR+1MMHSdyg8Fz2LJ18MQDfXOvihFqHex+DuaUBtrfr8xXU00UQTTS400QCeJueVnCpGrj05GbgT5+3YQQUGUAGWqIXVVqEU2fhEOQBxvNhvNKqxbUJE2YOYmN/fg9VKtuyjj06MyVuxgu3efTdZheRkuniZTCpwHDeOitWiRcwsZzYDHy3V4Z7HvGBwkU5I1pKQQDAG0Aq+fz+VpmHDeK96PV2thg5lXzZsUBO8dOtG0Gq3E9QNH07W7t57GTPz0ktURuPjyQxGRUk4GHolAIa2+fpoRYQ1+evi7B7dtjSJAHKCRV+/noBNzK/Fi08EdiKu1WolkBs0iAAJYBbLESP4zt94I10kDxwg0LnjDs790lK6HZpMBDTJyQQxwcF0bayrI1P10UcEeffcwznbsSPn6Ysv0n2xtJRZNzt35nEHDhDA9ezJ/r75JsHZ99/z/JwcGn0kiXFsEycSONXVcX04coTumSEhXCfy8wmMMjKAF14gUycYweBggriKChqPmpo4Vi0taizuxIkExw4HXU9ffpkGHS8vMnx2O41P77zDvh04wPtKTOQ1hg3jGrF8OdeWa68lMCus8UX/vsAGzEFLYHeMGe+Gq69mPcGZM3lvl1xCF029nte8/Xb2UyS3GTyY9zNwIKDXSxh4QwSuujkIU6ebkDV8PlYMeh4//qxHsdkfQ2+LgD18JH5uuhydA090F/fx+WffW0000USTsyUawNPkvJIzrVkn5FTgTwA2ZxFuYO0plAAVrehoFcSJ40Q7CQm0kIu2DAa17IEzeycA2uLFdG8CCKqMRipMRiMz4hmNKqh0vicBJt3cqEAaDFR2du+mgvvWW1TYRLbAXbvIFNx3H5U8d3eC1KQkMgwCAH77LS36Bw5QwSooUO8lLIwKqijPMH++6tY1eTIt8MHBgLlBAcDMdEfX/vefKWioyf8raWt4cd7mvBbo9XyfH36Y3z/6iODnoikyJg+owLSLFEycyLlnt6sVPJYsIQjp1o3Giro6xpkdOUL3y6uu4nVzcgig5sxR5+WoUQRmW7cybnbTJs7Dzz5Tywk8/DBj7I4cUY05DQ0EikuWcI6mpanxdI2NBFuSxHXh+ut5ze+/JxhMSGDcWlMTYwjnzeMcf+op9icmhgxhYyOBaGkpQdrixYxzkyQCyNtvZ3v3309WzuEgEPXzo4unTkcWraGBYFeWeextt5FNq6piqYOVK9m+nx/dTiMimKAlI4P99vDgs9HpgI0bJdgeexZjXpyNA/1vRN++EnJyWCZhyBC2+dlnXL9CQ1X3+YQEsouffqqWg9DpeM1fftWhuMYLYQP0GD3BiIumSXBx4f1v3aZDTZMRdruEjAyAJRKYSfP48f/RC6yJJppo8j8WDeBpct5J25p17ZUlEP/PpKSCSEfeNt6uLZMn2nQGcQD3i3IF48ap2S2dz2srRiOt/o8+SgUJUGNpEhPJorUVs5nKo4gVFLFCu3cz1nD8eN7LtGkEcklJ7OeuXXTXcjh4bEYGlUJATRW/fj1BncPBdp94gsxgWBgVxrQ07luyhG5ZVisBnc1GZc7fn+5ocXGAj94MwA8AEIAq0giaaPIXRWSf3bGD76twfRZs3tatNHJ8+CG/x8eTaZ45Xca20U/hzdDF2Bz5FI5Vyti3j+e7uPD9PniQGSiLijhvNm/m/Lz1Vs63mhqCoW7dOD8//ZSGmKQkXvP669VU/seOEdAkJ3P+9O/Pa73yipql9pZbWLvtrbeYUOSWW8gYDh9OoLV2Le8hIIDflywhKOvZk+uKonC+BgYS4EyaRBfMpCS6LWZkcO4LhqpzZ4KjoiL2TZY5f7OzCd727pFx3fQalB0l6OncmcA2PJz9b2wkWI2IoDHn0CGOzbXX8pjiYvbpyBG2+eSTXBs6diRwFq6lkkSD0PoNOiz70Q9hYRJyc7m2DR1Kd+8bb2T/7rqLLF1BAZnVTp0IHt94g66owcG8RlwcXdztdrbvcHDbggUEgdXV3N+rF+DtLcPQmgAKkDFk8Dldo1MTTTTR5E+LBvA0OS+lbRxOe6DuTAqmx8WpJQLa7hPtOmfJFCAuNpZga9MmNRZPMGuCndu6lVbomBgeJ7L9ObclXEhFZkyDgQpnVhYVQeFGarOprmaiv1Yr3Z8sFrosCdcqgKBu6FA1CYOo4zV5oowh3WrwwQcK7r6bmTtvuYWAMiRETXJgtzM5QmgoldnBg8lYuLjQDS0+njExxcVse2C4jMJcG2w2Bc89bgbAG9NBYYErTTT5iyLmwqhRBDdbt6oMe20tAcRTT/GdtVo5L3buBCmhgwcRhgwcyzqC226yITSUxo577yUo0uvpDv3mm5x3orj2xo2cz9u3c55MmULAFRnJEiEFBZyDTz9N1i89ncaO0lImIsnJIeABOL969lRZr2PHWChcGF8+/JCsXceOBJddu5KhOnyYRpdDhwhWdu0i0AoMZLtWK4HRrl08v7SUbXzzDY8rKCAYGjmS58ybx3aLijg1ExNlHFqXjsKnP0Jw3HcAZHTvTmB3zz2MNxwzhn/Z2UzKlJnJ9cFg4DjIMq917bUElTNnckzLy7mWWa0EzbLM9i67jEaoY8cIRv/7XwKyuDi6lur1dJ395BORjEZGn8AG9OqpIDuba9vll3NcQkO5Brm4cCyPHiXovuEGejnMmcNYxTlzgKNHFdjR+Ns7VV1y1mp0aqKJJpr8o6IBPE3OO2mb8bJtQhRn5qw9cNcWrJ1QT641Nk603x5IdK4XN3063S+B38cG2mxk1kaN4vetW09k4MSfwUDQNmUKjxMxbrt2USkaNYoK4aOP0noeH6/ew733UoEbM4bbhavo7t10CzObqfxMmQIYdDISxj6KcfP64J7Ns9Grp4wxY6gsT5rEpAphYVRSt2+nhf7QIYK7SZPY5qOPUmlauZLXuewyYPRIGf8eHwP3vATsXn8cDz4fAOAYAKAGJhV1aqLJXxDnepT338+5ZzRS2V++nCzaBx/wdVu4kPMhKQkwe3SCI3wgUjEMX3reh6WfumHWLCY82b2b87RLF7KCS5YQbO3aRffH7Gw1HjUzk0z2d9/RNfrZZ8lqjRhBMOnuTkNJSwvBxZIl7PPVV9MN0m4nizRiBFmsAweY5GPPHsaryTKzRT79NK//zTfsQ14eWbKaGs5FHx8VyBQUkFGsqSHDNW0agU5mJtsuLWU/vbz42dubwCwvj6zW6tVA72AbulhzAMhA5WE4rDYkJgLPP09Qm5jIvhmNdHcdMIBjdPw42zx0SM3a6eHBuEIfH4LK0lKga6CMmmNWTJ2q/FY8fv16guegIPbpoYf4//bbCfQmTOCYuLsDmzbJyPt4O1qWfIAfnkzC9dfJv2U/NZv5LHr35n2nphK0fvYZmcm1a7mG1tXRrdPTQwcd3EEXTUD20JJAaaKJJhemaABPk/NahGuWc0IUZ8atvePbxtQ515Nrz62yPZA4ahRdG53BorN75/TpzCInWLrp0/k3ZYoKCGNi+Gc2MynE5s2qa6fJRCUqKUkt9WAwqHW8zGZuF6nek5PVRBJ2O5WeOXPUxCwGA4CqKgzN+AoP4T/4MDEKQ7rX/abgensz/u7nn2kR37uXgO+ee2iN37SJMThmM/Dxx2QCUlKoUMWuroPt+DHMxWfQowY+PjI6oQIA0IIu9PfSRJM/KG1LlohYV6uV76bZzM8ffshX7OKL8VuGxJAQJhNqbgY+WiLB9uwiRCxZgOHzRyL1gITt28lijRnDdzgxkYwZQDfJtWvpVijma79+BDJVVXS1zMrivNm/n/PFYiHwGTeOwG3dOhpL+vRhJt9p0ziHnn2WIPHyy9m+pycTHL3/Pq8lEh3ZbAQsXboQLCUmkskqK+NYrF7NOS6AoSTRCHT4MOMFhwyhC+i8eTwnJ4d/nTvTmFRWxrWkb18gqJcbDD5+sMENh7yHo97iBp2Obcyerca/9enDOW+zESAGB5ON+/JLuq76+hJw7tzJMVAUlkSIf2kbRia+jWEbX8Lu3TLWrSOAy82l8emJJ4Aff6T3QE4OWc3+/Qmuu3UDZk9uhLu5DN2RB7mxEetW2BAWxjXq00/JVppMZG8jIzkWssyxCQ1lv/Pz6ULu6ibB3dMHLJOgQ0XF2anRqYkmmmjyT4sG8DT5S/JP5M84XZsicYk4NiXlxH1tEy+0Pbe97JjAibF0zklRnEWwezt30g0IOJHtM5vVY53LNIi2RC08u/3E5Csii59w7QTI1g0Zwr9t23htvZ7KUVLSiXX2oqO5fccOMhcPP0x2DVBj99bs7YTd4XfgUqzDsPBmTL3KF3o9geGKFVTU+vThObm5VIpEHb0NG5hFLzWVTIleT6UvLw+o1/kiz20YIpAOKwLg6WtEnRszYXhPmnPWCp1rcn6JMyPf1hDjXG8yIYEZLZcu5bGyTHB35ZU0dKSnq0XD//UvskLpGTo4vP1w5Ij023rxwAPqnC0sJEhTFIKpRx4hQ5Wfz1i1qVPJmK1fT+NObS3njShrEBhIcBYfT3ApMkjm5BD4fPstwWRICNm5uDiycY2NbGPfPgKWQ4f42ceHgG7PHvb1ttsIVioryXpddhnP7d6d55SUkO2rriYrmZnJeDWbjUxZRQXvrWtXtl9WxrGj67gEx4Qp2D98Iaa/NgWzZ0vo2JEAc+VKsmgAx+nHH+lW2djI9nx8yJi5uhIY5+XxGfToQdfLwb0bodRVwAAHkJcFnd2G4GC23b8/19Aff2S84OefE5ALg9uePXRpjZpgQlAn4FMswKAO5Ygc4wZ3d45rUxPX4tpaehysW8dnUFXFcdm2jSyiycQx6dIF8PLSgQCPiVg00UQTTS5E0QCeJn9azjSJycnO/TNtOsewidIDQ4f+3oWyPSVRnC+yYzq7SraNpWt7jvgTMXsimYmzmM1UGp3j7pzdKYWSeuwY2TfR5muv0WoPEKTp9WqR8+xslRXYuJEMhmAdjUZeZ+VKthEbqxYxNxp5j2vWEGQyPkiC46VXMDV7CRz/fgU74ySMHctzLr9cdT3NzGRfZsygErt5M9OWG41sZ948Knj+/rTiHzosYcaCvsh/eyu6hvqiuERCczOVqPJyDdxpcnoRc7W2Vp0bwlDjPP/ef59gwNeXBg6jUTU2iMRHisLz9uxhQo5HHqHBwuFglsuoKIK59eu5PyODLFpiIhmjH35g28ePs53Fi1knUrhxf/89483q6rgtOZkM39tv00By220Eg3v3sg+zZ7PkSXY2XRivv57H19YSeLm7E6SWlNCV0sOD1+3cmfFtJSXsQ34+789gIFj8z38INkeMIJu3ahVdQ8PCyGR9+y3vy2RishadjqCnspL9cXPjtXv2BBRFh4AgI2J3Srj7bgKvHj3YTmMj3T+zswkuY2P5vamJ+ydP5vo0ZAhZyOJiAr0XXwR2HfBAd5RDBwWZiEDUSBccOsR+KQqfQ3Y2gen8+QR+X37JhDVjxpAV3bRJguutN2D8bX2R0f86HD4sQVEI4goLuX517877mDCB/erTh4B41Ci6atrtXJ8PHqQrqRARx3guiiRJb0iSlC1JUpokSSskSfJ12veUJEn5kiTlSJI0/Sx2UxNNNDlHRQN4mpyRtAe4TseWnWrbyUCcc5uCDXNOoCLcGp1Bk3ORY6GEvfXWiUqis7S3TdR9AlTFT9TKEslUxLkiMYrZfGKJBJOJGexEaYPp0/nfmS2sraUCeNttqjvn8OFk3GbNYhtRUYx1i4zk/4AAKpgrV/KazjX1Ro0ig5mXxyQK8fEEZOvXM8sfAPz73wRjCxcCrm46bM/siJ+/sWDPHgWLFlGJHDWK/YiOJmDu1YtK8e7dBI+Njeyjzca2UlMZ91NeTkv8yFE67C/yg6eJypdIRe7hodXB0+T0Iph1Z2Ya+P2cHzKE+zZtIjsDMA7V1ZVz09VVNfiIAuddupC5WrWKf4MGETAtXUoAceutBBXjxtHFUZIIgB5+mGCrvJzzafx4gr7aWromBgYSoA0cyHi7YcM4P7KyyEQNGsT5nJLCJCI2G9muoiL2qbiYLpJGI8FOz55k9YYM4X6A889kIrDq2lVNsvTllwRHogadzcbrr11L4NOjBwGovz9dGPv04T01NPB6VVV0y54wgZ/LyrhedezIxCbC/TQsjGvjpEkEdtddR6BosZBF3LmTANnLi0AtP5/3XlBA10uH1YHeyERP5GAPIgFLE+x2rm2yDHz1Fc/r3JlraefOBOr33cf3IDubru4DB+ngFeiFq6+R4OpKUHvXXexX//4Es1lZBNYWC8f14EEaq8xmPjNRXsNZnIuen4OyBcBARVEGg3VnngIASZLCAVwHYACAGQA+kiRJC3TWRBNNTpB/FOBJkjSj1cKUL0nSk+3sf1iSpIOtFqqtkiR1/yf7o8mfk7aArG0GS+D39eJOdd7pslsKcCfYMJH4xLmmnNGoujcKACVYM4BJCcQ+IW376OzmKdoUbpCiIPDWrVSqbLYT79dsJvgaNIjbnN00335bzfDnXEvPaqVF/4svCNpE8obp06nECeYxOZnW6+XLVdC6dy+VwLg4tZxCTAzbdTjUfo0axf5Pnsz4OYAAb9Qo3t/Y0TImvTUL/Va9hrlbrsSRUhkff0y3sbo6JjuoqmK68759aeWfOZMucBdfzH4OHEiFdc0asi0lJbSWr1xJ5VVRZACVHNt9G9RiY5r8v5b21g9ncS5JIpIRATSSTJxI48WECdzvcHBObNhAcFVVxXfYYuG+tWsJPhobCbAOH+Z1KyqALVsIimbP5jxzd+f7XlHBdSMkhKDi8GHO05QUsnuDBxM0+vqqKfxnzQLmziWoaWjgOrJ3L+fl6tXsw/r1BCrFxVxHvLzoLeDtzT7fdRf7P3Ysa8ndfTcBmiwTePn7E7xt2UIA/M03vN66dWSr6uo453Ny6J4qMm4GBvKe8/LY/x9+oEuloqhuqbt3cwwqKujSumoV29DpyFL6+HBMfHzYp8OHyQCOHk3gGxpK1jAjg2OdmUkPheef5/m9+rngI93j+AJ3oMXFGw2yCWlpZDBdXXmv335LIO3ry4LsGRkE1/fdx/4sWwZ8/TXHddkyJrEZOZL3tWgRrzd2LPt56aVA//4yUpJsCA1VEBhIkNq1K5+Fm9uJ79y5DPAURdmsKIqYLYkAgls/Xwrge0VRmhVFKQKQD2DE2eijJppocu7KPwbwWi1KHwKYCSAcwPWtlidnSQEQ1Wqh+hnA6/9UfzT58+IMyE7l9tg2QcmpzmuvLIFzm4IN8/VluyJxijNoaxtHJ1wn7XZab51jegTYGj6cDJcAZGvWEJAJF57Fi6lEzpqluiyOGUPFRbRjt9NS/fLLBGtWqwq6DAYqJ1OmqG5jzolckpMZA2O3U9l7440TXdBELa8lS+guFhvLfk2bRuVm9myVsRSFloW71kcfMc27zQZs2ypj+VsNuGiqgnff5bUXLQLuv8uK7buNCEcavk4ahKY6K+x2poX/+WcqfYsX05KelUVFb+BA9is3l/czZAgZhNpa3uM11xDoffUVGQofdxsAPiQFx6m9avL/WsT8P5khyPn9FzGo0dH8L1ynHQ6CPIDvvMigOXcus2f260dgl5hIoNStGxOazJ1LxT8sjMyYwUCXvs8+oxuyxUJGqWtXzp2ffmJMal0d26uuZnKUn3+mQWfePPZh2zaygF9+SbaqoIBrlpsbr221EgAJQCpJNIYUFXENGDiQ3597jv1591325d57CQpLSwnwJIntHT1KMOruzn4mJPD4kBCVmfPxIQDbt4/gMSeH+7p141yWZQKirCzO27lzydpdcglB47hxXAPq69XSLZdcwvWvtpb3dPgwjTkHDvDYyEi6nb7yCsFnQwPH4ccfgfHjJfiP7ouhc6OgGz4cu3ZLmDqVY3TkCPty//28F+G++uGHZDMNBq7/VivHOj6eIHPGDD7v776jEevYMXoT5OYCCbtllMXlY0De50jeUYMWmwxPTxqrAI6Bs7T9fg7L7QBEddQgACVO+0pbt/1OJEm6S5KkZEmSkqu0dVgTTf5fyT+5vI0AkK8oSqGiKDYA34OWp99EUZTtiqI0tX51tlBpco6JM6hqr2yAs+vimZ4npK0blnM6dEAFSO2BQqEsiuQLEyfyvAceUBk5wagNH06l58ABKo9WK4Hg7bezvYQEugUJNg3gccnJdAPatk0Fg488wvizBx9U+7t1K+9BgNDRo9UYPNHe0KEEkHY7Xb50OvX+rVa6Rfn6UsH096cCd//9BKViHMV9C/fUadN4Xr9+BKTjxsqY9uY03PtaNxhmXYRd8fJv/X7jA3fMjDbDHTJuHp6OA1nu2LuXCtu11/KeZs0iY1BSQteutWuZQfPyywn+zGYqvRdfzPF46imyEmvWUAnUG1UzeT26kerQ5P+1OK8Rbd26nee+Xk/jiDCQCEOIYO+io7lt+XK6CF55JdvZtInvrqjVaLNxDRg2jKxaTAyB2YwZZIUOHyagKy+n/SE5mdvr6ggCv/qKjNHFF9Nl2sOD2zduJFh8/HHOhdJSMlC5uQRI4eFkz4qK+Np7exNIXnUVQdwjj3Cfzcb5Mn8+76GggP2+7DIWLpckjlVQEFnEnTv5OTKS4C0ggKUBfHz42c+PwG/+fI5tly4Eo5060eiyYgX3jx3LfoaFkQl74QWCsbIysoFbtvDzgQN8BmPGcFxycujOqteTmQsN5bVvvpmFyRsbOfefeYbfExLY1x07gOHDdRg71Qu33irBy4us4Y8/chzefpv9uOkmguDduwmkAY79LbdwDF94gW7t113HPh4+TCA8bBjHprmZoPTHHxQENGciCZPwHa5ES6MV8fFqLcKwsBPfy7MN8CRJipEkKaOdv0udjnkagB3AN3+0fUVRliuKEqUoSlTHjh3/zq5rookm57icxEnub5H2rEwjT3H8PKgWKk3OYWkPpJ3M3fJ0xwjw4xyrFhfXPoh0ZuvEec4MofPnhARVobz3Xv6Pi6PSIty8Fi+m0vfJJwReU6aoIFUongAVvORk9lHUoRPunVYrlbZFi6hoCSZw1y61bIFoT1jEN24kaLrkEl7X+R6HDKECk5lJlzDBYowZw/pSjz+u1rtzBo7JyTx+0CBg6VuNGBDviUzMx4D4g5j0SRXq6jtj5UogIVHCRes2I/WlRoz/lwnXvMaYluRkKoDh4XSbSkqii5e7O+Px8vJoLVcUKlRNTVSqZsxgf3bsoKLapQvQvbuC/SlE580AT9Iyaf6/l5PVphRzCVDXAUBlvQG6GG/ZQuZp4UIq6itXcm4vW8Z5vHUrWbAHH+R7vGcP59OuXQRUTz9No0VVFeeqAA2rVrFWXWYm2xw8mACntJTzrq6OLowpKQRItbVcM6qraYRpbCQwaWkhuCstZZ/LyggiH3uMoMxg4Nzq14/bDx5k37t2pZvju+/yGrJMVu7IEfbzeCsJbjZzW1gYwUxdHUFfSgrB4e23E2yWltIwdPPNBDBGI9tpaOD+0FAym4rC8ySJ97x3L0HywIG8J5uNbFp6Oq+t09H41Ls3jWWyzHFavpzPxGLhGKxbp3pT/Pgj2bqWFtXwNXky+xwcrGYSdXfnda66imxpWBjX6y5dCBSrq7luXn89Qbm7O/sm3Mazs1kWZvt2Hfo5rOhZuQ7LTM+ipskdwcFcq3x9+Uycxd//b3ix/4IoijL1VPslSboNwCUApiiKIgKajwAIcTosuHWbJppooslvck44KEiSdBOAKABvnGS/5mZwAYqz25ZwwTxZLTpnS79IfCIATlsmzxn4iZpZgLotIYHfH3yQis+AASeCO0CN9xs1isrcqFFqSQIhZjPdwV54ga6aZjOZtgceOLFcguhTdDTZNldXuneaTGTxEhK4z2jkfnd3MokjRhA8pqRQed29W40ZEYlk1qzhtRwOKlSXXAI8+IwJM6MbcQc+Q2rIHGw/2AmZmVRSHQ4AOh0cHl6wOyQMH85ta9aQcRw4kApbXR3vxWLh/Vx9Nfu6dCmt7mVlVPCWLWPMzCWX0EUrMxPYuFEBUA0A0MOsuWj+PxSr9fTHtHXbjonhey6SGomESQcO8LgDBxivZjTSVXn+fM6lzZv5vbqabE9+Pg0Uoq7j3r1kumfOJOPT1MS14Kuv6NrXvz/j04ThIi2N77aXF+fBp5+SyfbxISh74AGCBaORx0sSv//4I1m6ceMIakTikfBwgiJvb7JpTU1k/ACCsqNHeQ2DgeeVlXEdFCzj1VerNf/MZq4LJhMZsGPHuIYFBTEeccgQgilXV47Bf//LdcPXl8fZbGw3N5f97tWL681HHxG8NjZyTYuMpBHq2DECzOBgsnM//KDWw1u9muvRBx+w3R9+IMvfqRPHCWD7997Lfbt20Wh0550EVgEBBOH//jef39y5NFDddBONcJ07s4/u7mQNAwLozeDlxfsT5RuCgwkER4wABg6UsD/kWvzkvhA7lAkoLnLg5psU9O3LvqoAj0mgjh07d5NASZI0A8DjAOY4eToBwGoA10mS5CZJUk8AfQDsPRt91EQTTc5dkVSj0N/csCSNBvBvRVGmt35/CgAURXm1zXFTAXwAYIKiKJWnazcqKkpJTk7+B3qsydmQtglbnEGb2Nb2WLud4OaRR1SrsNWqMmSCERDbgROBWW0tlZipU7n//fcZN+ecsMUZOArXTr1eBWJWKy3uAwaoCkh6OkGSSJQSHU23TldXfl68mErpW2+p1vy9e09kBQ0G9m/nTrpdtrTQyn3ppVS2Pv+cTFpyMq+5dKnKHFx+OdvYtQuwNMlIT2yEVW/CQw9Lv92byUQl8a23qDw5HGw/MpKZMffto2U/KortlJXR4i/Swr//PtmFnBxuv/xythETw/OjogBZVvDzz8cB+CEE8SiWx5+WwZMkaZ+iKFF/9P0510Rbn9S5IWpAnu5Ywdg7b4uN5WebTa1/FxfHuQQwycZbbxGolJfz/fXyIrB44w0aKXbs4HFbthBQBAYSJPzyC8He+vUEfIMG8Tp793KOl5cTaA0YQOPLgAEEUD//zH64uzMW7OhRAsddu/g5JISMtqKw/6Wl6vzatYvM3aBBnNuVlTK8TTI8THoEBkrw9iYwio0l6AwKYntVVfyflkYGraGBTPnu3SroCwkhaGxoIDCtr+fauGoV4+vKyrjGZGcT3MkyGa3p0znfc3I4TtnZ7P+2bQRNRiP7bzRy3Pv14/k6HY09GzdyWnt6su979zIxSkYGQeDXX7P/fn78XljIz5WVBOF9+3L8TSaOa1kZ6xZeey2fuZsb1xiTievqlVeSwYuP5xo5aBDHS8Qs1taSoe3UCWixyajMOIIeOIhcRMC1c0fYbLpWZlIGQL1nDH7FLseVp/XVPBvrkyRJ+QDcABxv3ZSoKMr81n1Pg3F5dgAPKopyWu8nbW3SRJMLT061Nv2TDF4SgD6SJPWUJMkVTOu7uk3HhgJYBlqoTgvuNDl7crLMd3+HbN+ufnbOiOlcDkHsE4BPFCkWDKDIqjlxotqmqKkl+i+SpHz0ERU6u11VEEQmTLOZ55rNJzKBej3By+LFbCM+norfzJkEbx9/DAwdIiN+TQ32JCqIjlaTK0RHU0kR8TEuLrRsf/ghEzqIBBIAr/vhh/y+YAHdu/Lzecz+/ex3XBz/p6bymDFjqOwC7J/FAhzM0qHnIC8k7pFwzz0EsABB4kcf0dJ/++1s98cf6ar23XeMBZIgY2DHCnh7K7j8cip2ABW0hx6i0uXnR0VOrye4tFj4OT+fdasA+j7ZOp8e3GlyYYnReGpwV1t78oy6VitB1dixBG0rVqi1HAFuN5s5r+PjadwIDqYR5vrrCfjee4/vo4sL392AALZ75AiNMLNm0UDSoQPBUGIiz7NYmD1TZKVcsYLzJT6eoMxiIQvn5cV7qKoioOzYkeDD35/9SUoi6Lr6arpU5+VxLAoLybJ5e8swogF9zNthqiqAt5eMxEQCstpata5bWhpBYkEB8Ud9PccqN5fHNTWxPyJWT5LYR19fzs1evegRYDYTvCUk8H50OoKynTuZ2fOKK7geXHklWf6hQzkGZWVsy9OT23ft4r01NxO8pabynhYu5DlGIxm5PXsIIqOjacQ6fJjg7/rrCRRDQmQserQGr76i4MgRju+6dby/r78mU7p3LwH6Sy/R/f3TT7l+rl5NgNixI9dfgONUV8c1qXNnbjPX2mFHC/ZjFHxQiQ5+NtTUcN100TsgAF4YMs5ZDwNFUUIVRQlRFCWi9W++075FiqL0VhSl35mAO0000eT/n/xjAK81ve8CAJsAZAH4UVGUTEmSXpQkaU7rYW8AMAH4SZKkVEmSVp+kOU3Oopyqbt3Jjj/T/SdL2iLAmnPRckBNmiIs/CLDpmDq4uLU7aKmFkAL79tv8/PDD6tFylNSqLgcO8Yf/9hYKosimYhg+EaNotV8/nwqMhERVAKFKLKMPZOfRNQNfeB44inE7pB/S06yZQvb+uADtn/XXVR6hg1j9kqRTMZup/X87rv5felSXuuBB/hd1LoSmTMnTqQSNGECFaIDB4A77qAVfuFCKkFz55JBeP11PsNbbmG8j8MBPPusjMLMJlw0lcpOaCgwdYoM21ff4tqJxej230UYOVzG559TgSouJsg9coSK1AMPkM3IyaGi5+PD4xoaAC4tenTqpIG7C13am+/tgTu7ncDk2msJ2pwzatrtav3JQYM45z78kIArN5eAZ/hwHr9iBefhmDGM1Xr2WSr+L7zANlesIJCqqqLL5+zZnKv+/rxOYiLZoehoggqTiWBl+nQaOWSZxwvDRq9eZOVEPbmmJmalFDGz/v50TT50iO6ioh5cTAzn/7FjnG8tLVyXKstkyGhBKoajwWHB4QI7vLzoZti5MwHUnj2c3y0tBDTl5Zz3wvhktbJPrq4qKzZmDO/L1ZXXO3qU65irK0HawIG8R5HlE2C2yv37CS4PHmSfb7+dLqUdOjDGzd+fc9zfn6Bq+nT2oWNHtnvddUwkM3s2QaPRyLVu6lT2e/BgjqfNBri4yLj0wMs4+K/lmDO5FgczZYSFqSzfwIFcs/R6glujkS60oqxCbS0Zzi5dVDfe9eu5T5LUEhCy3gXdpBqE4gA6uMjoFKgmfmpxGABwXSpGqJYEShNNNLkg5R+NwVMUZb2iKH1bLU2LWrc9pyjK6tbPUxVF6exkoZpz6hY1ORtyurp1znKyNOjOn9vuP1mh9B07+GP99tvqj7nRSPAiSgWIbJVbt56YaEXU1DIaVdfNhQtVxXPHDh734IN0T7rhBoITgMfMn0/L8rZtVDoAsm3bt9PF68YbCQzXriVoW3hdFUZmfoblmAfHwWxMCKuCycRCxMKtbNgwgjHBvI0ezbiW8nIqZGYzFUxRe2/AAJVl/M9/gFWrZEwKr8DIEQqeeIJK5Kuv8vy6On7/8EOWSoiJoVJ09ChdOgMCODbz5pG9yM2R8VLuFei17m0ULfoa46JlzJ0LfPO5DYayQ5iAGKwv7I0lbzYiMpIA7pFH6IJ16BCV2Pfe4zhWVFAhjI6mkuntrT7Df8gDXJNzRM7U+COOM5kYjzV7Nuff8OHqPLfZVKbprrvINLm7c/71789srRUVfN/F9QoKyHIfOMC5FRjI/8IolJlJsJibS5BSXU3gcPAg50FDA+eGhwff55IStlFaSnD10EM0xtTVEUCI7JjOCUpSUsiY1dWxby4uNHYUFfFcUbrF1ZUukfWNegSgDkOwD1UIxZFKl98AXmkp779bN97rgAEcq/HjOZ9DQghKRWxa165qce+YGM4/i0WtG9q1K6/f3ExAtmkTY9UaGznmfn6MEezXj4B43DjgySd5D3Y7gfOWLdw+cybHMzubwK6kRHWxFPF08+fzWbW00K182zYynu+8Q9a1o3sjLsr/EF6w4Ak8h9mTG5GbS1BfVkZAXFgoDFky+nVrwuzZCjZuJMs3aRL7W1bG8Z04kWtPfj63NTTwPo8fV2DRuSIPQ6DIChx254VI/Wy67EbNw0ATTTS5IOWcSLKiybkv7SU9aSsiTmb48BNB28kSoLQ91/k4QC3gLerKCTZPJEkRrpxbtlBxEvWzRDICweZNnEjlymjk9h07VBfNhARuHzmSCsPIkWxn1y41g6Vg+66+moyCycTkIq6udA+yWIDtBzthebdFuB1fwH1AKIzdOmHiRLo4ORwEaVOmsB2bjZbuTz7htqee4vUEsNu/n3+i7tfYscCwoTI+KJwF04DuSJ78GJ59Wsabb7IG17/+xbZeeomZAhcsYLzQVVfRHe2TTwgc33mHbaakAHdeWoWUvQry0QMFR/QID6rDl18C1Y1umBuVCnc4kG0YivwKE/r3Z/bCRYto/a+v5z306sX7qq5W2Zlt2wSoYxKDykoN4V3IIubzmR4n4kABMi/vv6/Wn3Q4COosFs7n++8nOElPJ6gIDOR7PGQI32kBtpKTqeAfP875lpJCFrxjR+DWW8m2+/lxX4cOXE/8/QloJIkgpqaGxwQG0oDh4cF+/fwzQZ7DQZAVFkbQFh9P0JSdrSZaEe7Xx4+zTx07qsxdly5qyQOdTkKZ1BP7MREmbyNcXSXU16vFyQMCeLzNxnPXrCGADQjgfRYXq26WmZkElyLbbWoq5/zRo+xfQAD7l5ND9t3Li8CsuZn3v3s3QaS/P+P0nnyS+6+4gv3Jz2fb0dEExSLrb0gI6+g99xzH86qreK/btxNo3Xorjxk9miDzhRdak7PkmbAxdAEy0Q+fGB7D3AUmXHklPRBuv53urEFBgItBRnBVClxiV8P9jecR1FWGXs+2e/dm/N6tt5IdLShgzKXRyL6mpgJTxzSi2uEOoAVmRz3SDoiUxmr8HQDYV35JylYTTTTR5AITDeBdYPJH3Cj/jMulOK89q71g05YuVV0mnRVAcY5zEhXnNkaP5n/n2DvBvgk3zLg4HifaiI4m0BIg0Golq/X227QKiz6IhCqLF1PR0etPVDqjo8ka7N5NxUqISBH+wAO0jou4uu+/p5X59tt5fVdXCXcl3YnUb7MxKu4N2B3Sb312OPh37BhZAoDK4vz5TFLyxhtU2gAqQTod+6fX0zV0504gZVcjDAm7YXa4oC7jEJK31uHSSxl/5OLCe/r8c/V+Z8ygu+Y771BZ/OQT3se+fbzH+NxOMPeJRAW6omcXBe5dfDFoEPDUUxKynvoRw5bch4eX9EOvXhIyM6nA2mxU/lxdqUAWF9Ots3t3jtOvv/L6DQ0ygCIAgGflTk2B+n8gp2PthVitJ87viAi+qwsW0B3wsssIJGw2vqcA37GhQ2m8+OYbvu+33UZA4+ZGcNK5M5X8vDyCotWrCXw+/JDva10dDQ+lpXQn9PYm09fQQJAYEsK5NG6cyma5uRFEClfMdevoChkZybk/axbnQEMD76dvXzJjnp7sd1UVjSEA++xwqCn+g4J0MBj0cHeXYLPxfs1mAlBX19Y5n0IAOWQIz12/nmMRFkbQNmcO+x0UxP3duhGsiRp6IsOmry9Zu6++Iog7coQMI0DPgOXL2efYWIJZNzeupwMHcs1TFBqPPvmE4HvrVoLXuDi6fr//Pu+zooLj0LEj18dx49RyCtnZHI/jxyWkzHwaYfddggffDMGXX0n47DPe4yOPMD7vh6+s2JfYjEHN8ahEZ1TnlOJIgRXR0WxLljnmH35IUO7lxbhiEY/Yrx9wpMYEwBV2eCIXI1HXICyKCpwBnhW152wMniaaaKLJX5EzcLrT5HwRAbxO505pt9NVR68nu+Vc++1kzJozSJs06ffXEOeaTCdPsOBcp277djV+btw4xq6kpanJFEQR77bnjhtH0BcVRcUkLEwFa1u3UgEBCLwef5wASq+nYpOQQMVQJBITfd6xg0rbsmVUHiIiqPyJgukGA7Pm7dhB5WzaNAI0q5VW5FtvpbKXmKiD2cUP739AcDlpEhmt1FTGutx8MxkIk4lJUbKyaI2eO5f9mTKFSu62bVSsdu/mODkcgOJhwuo+D2Ndbi+UePRD162+WLyY5334IZW/Tz6hArlwId2cAgIItjMyCBQBsg+ffw74+kp4PeUZGLbbMPheN9hsEtato8K3bJkONpsfrFYCuuPHOd46HRXpjAwqY199RcbhwAEqx+XlQqFVAFCDlFBNBUpkP9DkvJe260RbVr69NUPMcb2e77NwWYyOJgOfns45Y7XyGDHPLRa69k2cyO1mM0uSZGfzeKOR+375hSCwZ0/uMxqp8PfsSbDXpw9BQefOZJSsVgIovZ4Aadcu9r+wkMe0tBBQvvIKQWVVFUFRZiYBkJ8fWXaArP7+/XQDdTgIsFxdCdoUhe2KunaNjSe6SB4/zrHo0oXgtKCAx+t0BC6KwmP79yeAS0sjaMrJYZybiwuPCwwk0//GG2zT05PjsWEDXbQ/+ICAMDubINXLi7FzS5cSCB48SOPXY4/xnsaPJ1u2bh375udHVrS2luNYVaWWfOnTh2uvKHdZVcUxFpmHa2rIDJaVsa1163S49lovfPcdwfWtt3JNmjhBxuiY55GSY8SNQ9OwEHfjMIKwGA+gl80NH37IsTh6lLF808Y0oqrahOhoCelpMqQWG0aMccOhwxJcXSX07xeM1atkjBipx5YYCS4uQEuLDoDjt3fXW++lxeBpookmF6RoDN4FJH8kVk6k/BcsU3usXFuXyrafnY9ztso7A0YBJkXRcHGMiI9z7u+AASrb5nwN55pYABWn5cvJuIn4NoDKVXo6AZqvL8HduHFUimJjCQY//pj3PWGCWnuvro7bBwxQ3cK2bFGzZdrtBGFpabxGbS0txvv2EVzl51OZiYpif0JDqfBt2sS+hodTIausVOMI582jQtTQwLpQ3bqpjIVez77V1REc2mxAWJiEwpuew6LE6Xg/fgS6d5eweTMz39XW8rp33MHzMjLIDn7yCTNkpqeTcRg4kMrYnj1kCIYP1+GL74xwd5dw4ACV2p07eb2MDCaraG4mGI2MZN937eK2zZupRKalkcWYPBkoKZHRUO/AgDAA8AMAmOFLpKnJBSHOtSud5WSATxhl7HbOl5EjeUx8POcLwPczNJQGgnffJVjZvJnukSYTgcf+/QR7Q4dSyS8o4ByKiCBYGTmS82bzZr6jBw4QVIWHc26VlpJhEsmADh+m8aKmhsf26sX32dOTromSRNfp0lKyZc6JSZqbVWYuJIRtennxXAFCnb0AQkII/lxdiSXq6zlnLBYCJxcXbtPrOa4eHiqoamoii2i1ck0qKuL/Dh1otBk9mvdTWsqadwCNUAYDr+nryzXkllvIdOp0/F9Wxnvv3Jnn2+10wc7PJ2CeOJHrhYhb7taN61FRkRrXN2wY/66+mtcdOZJr7COPqEXi4+LY11WraGD77DO2e+gQjWkOBz9PmQJ4yI0Yk/MF9mAEvksZhI9wF6KwHz6oRs9OTWhsZH91Ohkjir5F2XfrcHBVDlJSHCjZewSeKRuh/Pg9XF1lTJoE5OXr0KGjAS12xtj5+gJMrqKqPbor5mkxeJpooskFKRrAu8DkTMCdAFEmk8qiAe1b4duz1p9KBKhrqwhOnKgCwU2baPEViQDsdtUlUcTRCbFaqRiuWkV2aetWgol77yVumDCBipPdTqtzZCTvIz6eysOePWQFIyMZqyYUL5Et02plPac77+T1RXzeihVU1MxmunzOm6dmsXzqKTKEY8eymPKgQVRuli6lEpSZyXZXrlRZgieeoHX8iy8YU7NkCRXZ48dpvX72WfZryxb2df9+pgafPZtW+Kws4KabdXj0jc6Yc6mEnTt5/IABVKh++on36+pKNq+8nPF5d93F69psTAhz663cb7NRqfzkEwLLvDwqlZmZahbBTp2ofN5yCxNjFBdzHBobqSwHBcmQrVbU1iooKZbRA4UwKuU4nHUMADVgdzSQItDkghAB2JwNQycTsQ5s3cr3uqCA88vhILh76CHOvfBwzpNly5gg6PvvmS7f4eA7uWAB2bikJB43bRoNFfHx7EduLhnk9HT2SWRlvOIKgptu3VRgExxMVqq0lO92//4Eb4WFapFxg4HMX00N50FFhVpwXWTO9WcVEJSU8NzychpwnLGCOLaoiPtycjgVMjNplAH4v6WF1xClUkRNOE9PrhF+flyHFIXj4OHB6xQWEpiKeMGMDLXIeV0dgajDQdB22WUEvp06EUx17UrWbt8+Zrn08+MaM2QIx0Ekalm2jP339qYxKi+Pa67DwXNTU+mampfH46ur+Vx79+Z6PHMm+x0QwDUyPJz3NWwYDVLjxtG122oF9hw0YWvvuyFDj10es/BxpxehgwJXd0/0HWqCry+f5biIRtxd9jyq0RGK0owAt1p0QSESMAxbqvrjyCEbtmzhM6mq4v0DAuABIsMvoIe3twbuNNFEkwtTNBfN/6cigJozi3a68gWnamviRH4WTN3EiaqblZCtW6lciHIAO3bwuK1bqTQA3C5EMGjh4VSKBg+mkhAbS2Vr4UJahaOiaPlvaSHY2rWL4Ov999U4vE8+oYvSjBnsb0IC06WPGkW3JpOJrpslJTxv4kT27+BBtj93LpWZpCS2sXcvlRxZ5n0mJ1PpXL2aSqTBQLdNV1cqnlOnUlG65x7e11dfUblbsoRuU6NGUUlbvZpt3X8/9z/5JIFjaiqVW0WhsubmRlevZcuonMmyWiNq7lyORUkJY/7mzSNDefw4x/O999j2nDm8z/37yYSkpAA33URFVJFlWBrsuH+BCxY+wDih8nIqlX36AFkHHSiPz0cPRxrC0B3bt0agJ7JhRX94oRp5GAIAKIHPWXWBkiRpBoD3QI3uE0VRXmuz/2EAd4AFg6sA3K4oyuHWfQ4A6a2HFmtZftWak85zWzDuQoQhx+HgfNfrqeiPHs33Ly2NnwUjNnUq/zc3cz707Uulf/BgGl6iogiAfvmFIHHaNLpNLlnCueXiQkDh4cF1RNRj++EHNeOj2UwFv76ef/3702XSZGLfPTwIijp04P/KSjJhOh3BVFMT2/HwUDNxinqWOTknxhyLkgB6vQrO/PwI2tzcuK93b94TwDnt56eGgkkS53N+Pttwa83w7+3NeSsMQwDHuaSEa05EBOPPNm4kS5abq8Ye3ngjrzdwIEFmdTXPCwlh/w4dIpjev59Ab/NmMnC+vjQ4JSZyn5sbDXQAQbu3N9edG2+kV0N9Pa83fryaiOnf/yagi4xk/woLedzq1bxHWea2IUMkZEX9C9f51GHjHl9sLoiAT5ANN811w+4ECZMmsZ2CfBOWdn4BAypSEe86G3ofX/RHIQrRCyORhIoOg2G18j7NZrbv5sZn2lZOU99cE0000eS8FQ3gaXJCXNykSaoboTOb195nISL5yaRJVNZENs1x41TlzW4nKBs7lsqCiP+z2wkshEK0bx8ZOlHeQADHSy7hj/WyZYwFGTv2xJi8BQvUuEKbjecPGqRm4hw6lMqmyKQJ8Id/5EgyWOPHU6F7/XW16PmqVWTvRC06AdyGDqWF292d7GBSktpP4aKVm8u/gQNVJiMri+BKuF/efTeZClGnLjubzEFREZWTuDgCvfR0jt0DDxCY9u9PJe6TTwjmcnJ4r9XVdD3buJGJTx5/nCC4oIDKX+/eZPUiIsj4iRil/ft5XwMH8r7Ly2UMz/8GXcoqseiKcRh/fRRi43SorhbFlmUoO2JR7tCjDv7QoRktFgcOYBS8cQQl6AWxtAR0Gn/WXKAkSdID+BDARQBKASRJkrRaUZSDToelAIhSFKVJkqR7ALwO4NrWfRZFUSL+l30+F6S9OQ5wnr/7rhpjK+Z62zInIvlRfDy/R0dzm0j0M3gwwZlI+gNwnhYVqanvfXwISIYN4zsvSXTjFEW+jx7l9QcNItg6fJjg7uBBlXWTJH42mQherFaCrK5dua+hgfuzszkXrFbV9VKsgcL9WbhyA+p66eXFuecsffpwvnp4EACK83v0UOvXiTXC1ZXHiaRKQo4f57bQUNXFsrGRjCJAUNSpk+oq6uXFe8nOZtzy+PEcx5AQjmNaGl0b6+u5bo0YwbWgoYFgUa/n2rhlC9s3mbi+JiRwfayr41g8/zzB29KlPP/BhTIOJDQiv8yE6moJkZHAlEkyclIaUdFowjvvSJg5k4ze2rU0iq1cyXsG2J9hwwhuN23iPR86pIOXlx+6dAEmTpRw4IARJaUE3ikpBGkjohR8WHERAlGGYe7pSMnpjgpMQzDycQBhyIsFhgzjGOt0fF6NjRwvisikqUNoqMbgaaKJJhemaPar80DONNvlXxHB2NntVOKERbpt+YKYGHWfcNHasYPujqIdYeEXrp9i28KFKhgSZQ8SEgjOXF0J7CIi1Lg3h4PX2LGD/5cvJyB57z1myXz9df6Ii6QNq1bxe2qqmr1yzRrumz6dfbBayQCGh1PJ27yZLmHbt7Mf+/bRFTQ9nUzBF1+oNfYANY37r7+yfXGvY/QeowABAABJREFUooSDw0GXy379yEQIgGm304p87bVU/O6+mwyCcCW1WKi8RkSQ7Xj1VYK7K6/kvrw83ltiIl3M7HYql66u7FN9PTPdBQdT+cvOZht2O8c9JUWtSXXppVTu3n2XMTF+flSQY2OpNN95VR2CynZiD8bj/YabMCWqDrMvkfHzsgqEhSk4VmKFzlwFM7wwECkoRw+YOrjBH7VQYEI3VPz2XlmazmoGzREA8hVFKVQUxQbgewCXOh+gKMp2RVGaWr8mAgj+H/fxnJJT1bUzGgnunMHfjh0nxtr+8gvdLGNjybxt3cpMruvWManHoUN83zt25HxNTeWcM5sJJB54gG7HdXUEJXv38v2+5hoCnMZGzoGrruK109NpqGlpIQtUU8M5JZID6fVqlsvLLuNcOnZMBWb796sgUDBlAOdyba1abw4gyBBjBNCgApwIztLTCZp8fXkPdjvZv4QE9kNkyTx2jCCppITXsVhOjCeWZc7p/fvpNinG3MNDBXfHjvHcESN4jV69CMK+/14tMF5WpgJGkdHy118Z32ixcL3x8aGRa84cXstiUWsQxsfzc3Y2+1JQwPuorpZhf+MVbP2yAMmbK9FQL6OmRkbh69/C9NW7ePnw1bhoigxZJrgD2E5QENfwbt14nQ4d+A4EB3O99PGhISwnh/c6ZgzHKTWVz2LsWOCn72zwRD2CUYT1dcMROagZAbpGyJBQhp5otuuQm6teUwDz1pGFmkVThl7SMvxqookmF6ZoAO8clzMtJNzeee19PpUIICaUOOckK0IcDrUw8Ztv8r/FQrdGkQwFOBH4OPdDtOUc92c0qvEn7u4EgAYDmThRJ2vXLsa9+fjQ6rtwIUGa2L95M5WXjAz+j4mh4vWf/6h9sFrVTJtZWTw/K4sKhwBighmcO5fXuukmntu7N6973308R1GoMG3YAGxYL2Pl5zW49x4FdXWMN0lLI3hauZLtx8cTuL3+OhXeigpa6MeNo8W9tJTM3MyZ7HdgIBXWNWuoCLe0EKB16cJ4vaAgWvKFlbqoiIre9u28x+PH2XaXLnTz7NCB15w5kwBv/34q49OmMcPe1VcT3BUUAInZvngVL2Mk4pDc+2ZU271g//K/+Ndl6XCJ3YIXX3ODwacDvFEP2S8YUpcg9Oihg71LLyAgBE1d+4Mej4DeHH9ixon/rQQBKHH6Xtq67WQyD8AGp+9GSZKSJUlKlCTpspOdJEnSXa3HJVed5ynXT+WaLdYD54RKzmKzEcjddhvd8ZYuJat2551k7B57jMzbr7/SxbJPH76/TU1cO4YM4VqQm8u5odMR0EVG0sU4LY3nBwQwoUhhIRkdATx27qTRRBT4NhoJhoTr5dKlXBtsNrVyR0sLwVLnzmpyKGfCWVFUgCDcItuKWLsEGCwv558QUZIAIABtbqaRJzVV1IzkNWw2MolCSku5LStLHW9vb4JHLy81fjA/n/sPHmR7oaG83+3bCcxcXAgGg1rf/IsvZl/79eO+Q4fYztGjHEujkR4DtbXsQ3w81z6R5GXYMGDs4EYUFygYhAw8gefga2jErOg6JJd1xU6MxZPJVyIpwQZJYqzdHXfwuQQG8n9NDftRUEAgWlrKNfbmm8nKTptGwCpiOAcNAm64gX1pbHHDPd3WYDy2wRN65B71QsiEUNh7jYLsHwRJktC5M93ef/+eKhBrEwCY7Of3fNVEE000OZloLprnqAhl6o/EwglxdpkEfl864WQuWM77RCydcLGMieFnwbwJq71I9jFokFrIW/yoCjdN4ep0zTVMSrJnDxU/wXpt3qxmi7zkkhMTr9x7L483m+nitWABwd6ePWp9udRUJgCZMYMKiFAom5vJXBkMBHOurqprlLBQDxlCpuHTT6nUDBtGxfPJJ+ni9MYbVP5mziR41OupFEoSM2gWFcnoHvc1hh9dhc6BV+KLw9cjKEgHV1cqpcItSadjLMrSpcDLL7Nvu3YRBCYmUuHs3p3jsXSpGjezdi2vN22qjC8/teH6m9ywcKGE48fp+rZ9O5m6jz8mQ7FpE2tY6fV01Zw6lRb9Rx9lVtHNmznGVVUEnjExBHgtLRxHT0/Wquo6uCOOdJiPp3Pc0HhvJUbAHQGoxPjqrYj5dTgcEyYDh2zY1eyGDi4SSksBk0mHvDxXuEgWiKWlFiA9IHxYz1GRJOkmAFEAJjht7q4oyhFJknoB2CZJUrqiKAVtz1UUZTmA5QAQFRWltN1/vsnJwJ0od+C8TcTfCeONzca5d/gw32fh6jhsGJm6jz4iw33wIOfujTdyHlgsfHfz8gg4XFx4jHBRNBjU+nL19TRc9O1LILd9O7c7HJyvjz1GMJOURFDWpYtqCOnUifuKivjONzZyviclsQ3h7vhnJCiIIAlQE594evJepk7lXPPxIfhTFBUYCtHr1XXJYuExskww5ONDECjAo4cHx0ZROObCDdFgUEsvCCPSsWNcw1at4rHdu/P8f/+bscBDhnAdLy7m81UU1ei1Zg3BYm4ugbjJxNjiSRNN2BVrw43ZL+I+r2/QkGDC4WJglDcg1VfCxT8AI8e5Qafj+ubmxrXY25ugvKxMTSRzqEhGWKgV33/nDkknwWQis/nf/wJdusi4+bIaLPnKCxXlLujRU8Kdd0q45poH0Owio1d3O44fB0pLdRg+3BW5a3jPhw6pz9rXl98bGwHatFW7dmCYViJBE000uTBFA3jnoLStZ/dHwJ1z/Js4ry24E2DtZDXvnGPn2mtbsG5PPsntW7eqyopIrjBhAhMpiL6YTEz2YTQS9AAEhCKr3l13sb2pU1WFb98+Kj1jx9Itc/Bgni8Kmr/zDt253N0J4EwmAs2kJFq1KyoIlgIDeWxoKIHQffcxLsRuZ7u//kqlJyJCvdeyMvY7KYnW5G3b6EZksVD5uOQSHifXN6L0qIR7kICfyq+ArpsNL71kxNdfqyyFw0GF6dNPmX0vJITW8C++IFBNS6Mil5REJVccs3UrXZkMehmHXv4SHcvcYKwowK1PPo2MTB0OH+b47NlDZbi5uTWWUSdDX1+H0lJfHDokYdiw1iycN1F5y8+nK9w771D5S0tjOzfcwBjHwEDAbtdB8vCAyWjDIMSjEX7QowYv4jnUvOSDoCAd3NyMcHenMu9wCDZCRotiBWAC0FoNzxkV/G/lCIAQp+/BrdtOEEmSpgJ4GsAERVF+i9RRFOVI6/9CSZJ2ABgK4HcA7/+DiHhXUXfOamX5ggcf5DzasIHsT1gYvws3zbffVt+Njh3VmDvhUrlhA5mk3Fwq4a6uakKMoCC66pWWck4I5iovj+9waSn7ER5O4FBQwDWkoUEFbB4efMfr6wlQ6utPTLYhSWxPiNIGngcGch1pu709KS1Vx0rE+TY28m//foIswQaK9dJk4rEC1InsmiIGWhT2dnYzFEyhzUaA6+nJa3h5kd3MyeGYHjpEQ06vXmqtuSNHOObNzXQT9/Xl/ffsyba9velOmp7O9TYiguN60UVk/H/8keOxYoWEYx1fQMeeT8Gl2R2ujRJGjgSuvnoi3JvrYPP0hV4vYdcurnvBwfzdqK9n/y+9lGPaJ1TGD88cQGWZhFr4o/vwYFRX6/DSS4DN6sD9hXfi5VdvwGwko/d+BT8ceQIJCUCf5nS4tZTgcH5/dHI1o8ZjCFav1qFfPzWZTVkZx85qJUglwJPAfEuMwTN5aTF4mmiiyYUpmovmOSp/lLUDTnSpdC403jYJwsnEOQW6EGEdj4pS3S6djzcYCLhcXdVsmqI4sShFsHUrlZiCVtV4xAiVCYyOpjJhNPJH32hUwZ0I8jcamdhEFEveupUWZ4eDyszQodxus7Ef48YRqCxeTFfO2lrV0g3wvJ07CXD69iVYE+BOr2cbn37KbJZLl1Ixvegibs/IYFsWCxmyYeNNeCpyLZIxGj26KBg32Q0ZGWzr7rvZ3sCBdPlMSSHgu+8+lk146y2yHSEh7EdUFIGfwUArfXNzK/vYtQ6DyzagO4rwScFYbF7NIMiyMgLAAQOo8La0ABJkfHxvChIf+BRSyn4EdZGxdy9LRPTowXHdsoVsalmZmoW0d28C78BA9vuBBwiWJ09zwdV9svASnkBXVMIMT/R3pMFolBEcrCarCAykAjV+TCPcYYGIc2kGzmadqSQAfSRJ6ilJkiuA6wCsdj5AkqShAJYBmKMoSqXTdj9JktxaPwcAGAvAOTnLBS1t1wph3BExsfHxqtvd5s2cK3l5BFZr13K+p6WprLTIWLt8Od8TV1cagZKTVRdEX19+FiULqJCr2StF8iLhnihigQ8f5toikpo4S1OTytS1B9JEwg8hApgJEWzb6cT5Fbfbf//Ki+LgFgu/i3g/cT2LhWMl2vLz4/01Nckw6FsAKJAkrlOKQlYvJITsZFER1xUvL46rycT1wMVFrS2amsrrm80Ef1dfrY5X584EbUOG0Nj14ou8dlERr2W18nqDBqngvrkZeP11HRSjB46WSfD35/HJ+3So0/lhwwYJGRmMI16xgsBu82ae36UL73HtWuDHr23ws+ZjLLbjGnyLOZPrkJoK3HWXjC2bmnBt3ksoRC9kIgxvHr4G6QcUGHVNCLfGogKB6I0MDLbthMWiwMUF6NBBhl5qgcWi/OZ2f/x4W/daUSZBOiHuUhNNNNHkQhJJOZNfr3NIoqKilOTk5LPdjX9M2rJ3f9d57WXJPFVbzmUTzGYmB/nyS9UCP2ECAZzDQZcdQHW5fPNNKn++vgRjyckEEWPHcptw79yxg9vfe48uXAD7t3gxFTiAbpcbN/JzejpdmO67T+3/xo0EXVYr2xJxQBdfzHpvkyfTst69OxWMDh0Y8xYdDVx/PS3eoaG09JaUAK+8Ajz9NBMOjBxJoCjLtHw/8wyzXvbqxf8REVSs3nxDRngvKwaPcMd9C2i1XrGCfY+JIauwejXrcn38MYHe8uW8nz596HY6ZAit8kOHMuYoPp7HDh0KpKcpGJbzDaTKEiR7XoSb345Edo6EDRsI2ry96Y7k6QnYaxuQvXQjquGPwUjFwdH3YUCEEb160dq/YgWVSYeDSo+w9Is6WDU1dOc8cIDg/JVXAD8/GUX5DvijGDpI6IEcJEnT0CFAjw4d6Pq5dSvHqLxcgc5xHC1wA+ABPeJhL+9PLfIUIknSPkVRok550J8QSZJmAXgX1Og+UxRlkSRJLwJIVhRltSRJMQAGAShrPaVYUZQ5kiSNAYGfDGqE7yqK8unprnchrE8nW0tEUiWRrOj992lAiY0lw1Rfr6bYv+girhWSxOOzsvh+V1Zy/tTW8vjAQL5z7u58j48e5eeWFhogALI/zjFtYl2SpPbBl48PAV17Bi03NzWB05mIn5+avfJMJSBALf0YHk52/UylUye1lt+ePTKAFgBWuMIGF88O6NlT95sBacAAlo/R6dSYQi8vjl+nTjRMZWTwGTY1MWFTYyNdNQMDOe+vvprGLouFa15MDI1GRiPbP36cz2XXLj63OXO4lolsvn5+nNrHj7Pt3FyeO2sW+7RiBQ16w4ZxPb7xRuC772S4wop6qzt0koyKvYWohQdGe+Wi+w0TsXKVBF9PGy4t+Dc2YQaK0QXNMKI3KlEZOAyBflaUZNWgCYAbgEjXPFT2Gw+7TUFuTiOMaEIjTPD2dkd9ve6Esa2tPTEkeNMmemicTv6p9el/KRfC2qSJJpqcKKdamzSAdw7KqWLk/sx5bQHbqVw0nY8TSp7ZzB9Ck0lVjPR6KgXp6Yx5EQla7HbGnGVl0Q3SOV5v+3bGsgFqspO0NCppfftSORg+nKBM9O3XX8mm3Xwzrz9qFEGXwUCXz/37qUDExtJ6Ldw/+/VjHwYO5D18+SUVnPnzCdyeeopK4zffELx+/DHPHzmSLEReHsHT+vUEapGRBI42G79v2MCxqKsjQExMJHDs2BFoqJdRkmuF7OaOjp2k39wj8/LoTjlgAK3pM2dSyaqro4V9xAgCvc8+4zFbt9JKHxoKpKfLGDO4ETfPN+GnnyU0NvL+GhoINN2NMsK61EHfwQfL5ydhsCUOlT6DMOa5afj4E+k3kN6rF/DddxyfXbs47jodrerFxXwmOTm8vosL77dHD2DlSgWmyjzIsKBZF4AGj67w8pIwfjyVzIEDOT5NTYDD4QBQA8APQDwUe/SJqQbbkQtBgQIunPXJeS0QhpsNGzi3P/yQc339eirHx45xru/dy/dIUWhMsNn4l5tLg1BurspM+/vzfWtpIUCwWglOBKA4epTXHTPmxNqYzjJ0KI0lwkXxj8rJAGJbcQZszuLm5px6/+8RZ7AGOBCIHNTDF01whUHyg4eX/rdSDgCfQ4cOBC6+vhxvPz/2V7hm33uvWrJi3z72eehQGrQCAvgMOnXi+uRwcFyam7kWX3YZn3llJa/Tty+NZT/8QIAXH881qEsXTvFLL6XR7bbbuC4kJqrPKSwMqKmW4bNtBbbXD8LsDslogQ7/PT4F7rDC1L0rXFz18PEB+vdTsPO7EvSRk3AAo3EMndG9uw4+PhLc3BS4pe5CWktPuAKweXeBLOvg621D6dEm+OA4uuI4shCJ8HA9XF25vgoZPJiMZUUF7zk09PTP5UJYny6UtUkTTTRR5VRrkxaDdw7KmYC79sCcs0LmbL12tsa3dWFybm/7drUY8cSJdHW0WumuN3u2WqRc1JLbvp0KwZYttFI/8AD3ubryx1woZjYbf+B37+ZnART1egKJkSOZGbJrV7U0wp49ZPzWrycwiYlhTE5yMhXFGTMI+kaMICArLGQbP/3E8z7/nArP2rW0Yj/0EJWbefOYIvyFFwhOr7ySKdePHgXGRcvo27EOffv44vvvmYlNpyMDOH8+lZWyMgb/+/pyLF95hVZq4cK2+H0ZD16cixut72KV553I7DoUkk6HlSvJWPj5cSyOHOE1RdbOG26gwutwEOQJ5Tc5meMZEKCD3eiFV1/jvYuEKIsWAa+9JqNh7Q7sagRu7JmIyDuegJsjHJJkwu4ECb178/m5upJdGTKEwHjsWL4XiYl8llOn0rULoIKXmUllMyQE6NZNgs+gUNRW2GDXu6HpkAQ/PwLr0FA1s2L37nwWgJPvW2UlNUBNzisR8boTJ6qxspddxn1WK9eAVas4x8aNI2vucHC+2e18xzw9yVKLkh5ubpw7wl27uZnvucMhEvzwnfPx4fwqKlIBQlsR204N7tSaZ4y/UuVMbZvtgTvR979bZJnj5OICNDbqUI4geKEOQEcY3HTo1o39KS/nuOblqdPL25vjVVVFVr1zZ64jiQkycjPt8O3oguZmCb6+HLOLLuLYb95MY9O2bVyf9HquBfX19HT49FM1i2ZWFteryZPV5+vhwTkv1nSTie9AQQENXopCQ1ZODuBucECpH4x78TaCj9fDDiAHLgAkZNTPht5Vj5Ejgf37JURfG4xjpQGwp7nDS+azO35cho/JhnT9aAwa0IymFneUl0toaAAqj7nAFRIs8IavWxlcZMDRZEXqQRcAQM+eOhQVSSgqIij9o/HtmmiiiSbnk2gxeOeZOLNhbUshiO21tVTMRCZMAe7MZuCRR+jK2N4P2+jR/JG3WMge7djB4wQ4EIWOa2vpnmWzkT0aN47gLj5eLeQLqFkuDxygkvbxx1TcJkyggmCxEBx99RVruBkM3PbAA2QDrFaCi44dgddeo9LT1ETQk5FBi/+QIbyniy9mG507k/W78UaCposuoqIRG8v+hoQQHO3ezfg6Hx9asa+7VsaUrP9g0ROVkP/zMh5/VIanJ2NIAgPpcvrDD+z77bcTwA4ZQuv44cMEgJMmAS88Y8NY62qswRW4snE5Zo5vxNy57O8VV9ACHhJCS7eIoVm/ngyjxUJlqqiI28PCyLT16aPGLI4bx+OSk3k/33wDBJoaUdWox1H44b2iWUjY2QIzvLB9uwSbjdfU6+laJepw2WxkZAICqCBOncrYu4EDyfBVVfGeBg2iC1dtLZCWpkNJpRHHjkmYNo3AVySQqKtjvwoLAQ83B+g8BQSg5WzG4GnyJ0QAO+f1ZdQoGlaGDmXilJdeYgmS/fsJLETc1rp1nFsJCXQVTErieZmZXB/0er57zc1U/PV6ApbjxzmPRGr+48e5dhw+rCYvOZmMGnWyPTIABwBL698/VfNMXOfPecOItVinU4ueNzYC7u4S3Ny8YHftCsAAq1VCcbHqrlpcTMDs5UXD06FD/DxiBA1ksbHAhHEymrbGoay4Dh4HdqJjRxk6HQHOvn1k8UNDud57eKhMYE4OSywI5u+669RMpIrCdcFmo6HKw4Nrhd3O9TYhgcau/fu5dgrDlSQB8+7WIQD1+AL34GU8g8WujyMB45GOUdC7GuDuznN1Ohkrf1VQWe+OHj0lREUB7u4yJjSshD0nG5K1BkmpRmRmShg9muu4TichcrQX9G7+SHcZjU4th1B/6Ag6oASeqIC5qAw9ussICmp1Z7f/b2rMaqKJJpqcDdEA3jkoJ/vRcQZ2zrXpxHaAIO2jj2h5FW6YQoEwGpnYIyXlxGQCQqHbskWNsZsyRc1sLzK8xcTwf1wcgUdqKhW4hx+mcmezUamIjCSoczioEAL8vGgRAcS2bbzW2rU87/bb2WZREfsVEsJSB++/r7oNJiYynsXNjdZkg4GgIiuLbfj4EOSNGsWYvooKMlQuLswQl5DAMZozh1bqI0eozGRn02WnqsSK7w+Ngh4NWJPfB//93Ib169nHmBiCrFGj6KqalsY+mUwEoxMnsoRDSQkwbJQbBvZsQX8cxCcuDyExw4SVK9n32Fjez7RparH35cs5bg6HmtL76FGOjyQRFB4+zLGLjeX/d9+lwiVJVMDqZROK0B+N8EG0Tw7MLW4oLeX96/W08ouSDVu30mofEMCxufJKUZBZxrybrIjZomDFCiqKa9awv0YjmbnGRqB/PxnzrqtBs1VBnz5kXUWR5YED+Qyaml1+e7eOweW08XeanDsilF6RoEKwdzt38l187jkm6Bk7lnPhoYc4Jzt04JyorOQcTknhHAwJISvV1ERDzeHDBBcuBhkebi2IjFR+Sw4iMkW2jQ8+XWnBxMST7VFA1s4dJxa4/jtFBlDf+t+OtiDSuQbeyUSs97LMOW3QywgMsEFRFOj1Onj5GtC/v4RevQiM3dwIqgcP5lz29+f5Oh3BWmYmj7nuOqC00IrDFh+4ohm1dj3GD2tE165cR/R6NTNpQAB/OxYs4No+ejQNQxERdN984w2uAe+9pxZqz85WY6jj4xnP5+tL8DR4MP+XlgKFhTK6drBiyGAH4t+ORxrCUIjuaOnWF4c9wtAMfxxHF/j5SejWDSgvl1GTcQguzUVoTM/H8EgHyopt6OBlwf76XpiIzYjGDgwfZIWvL387bDau0ZKkw+ixeoyPaoY7zBiCfeiBw+iAKuhgQb/uVjQ08Dfh4osJQDXRRBNNLkTRAN45JicrbC5cL0WWS+fjnGvlGY1qwWDn7JkCxJlM/PFevFjNQme3E7y4ulJhc3dXi4Lv2EHAtnYtLbKAGqR+6BCVismTaTG2WKjcffABj0lKYnpum41g5qWXWBpg504Cv5kzmcAgJoZswLBhPLeggODi8GG6Xaan0y0yMZEgavZsnr9sGROuPPqomnDg55+Be+4hWHrnHcaD1NWRvQoNZcKTUaM4DrfcQoUkNhZolN3R1deBTjDD5hEIvYcbunYlECwsJOAKC1MLia9YwXaTk1lnzsODyldjk4RXWp7C+i73Ycwt/SBJEjw9eZ/JyTJ8XRrw368VlJerBdPr6gikXn2VSp5I5V5QQDAXEEBQ+u67vK6/P98DQYzt2SPhg286otm7Gwa9cA3GjZNQUcH3xN9fTQM/a5bKBNbWUuEeORLo1VPGnk/SULTvCPL3VaLFJuOyy6gADh5MRQ4ALrlYxr64evznLTsS15SiuVlGeTnZxqoqKn179gBUpBms6QmcuS+cJv+4tLeuCLFaORc3bybAE+7YUVE0kMyfT6X9nnuEUYDMenEx5/hnnzEbq8i+mJMDVFer9dgkSSj9MposTdBXZ2Pv3kZUV8snJL4Q6xLgHI/2Z0QHAi4LyCj/Ez93SmvbEgAZrvoTs7c4lzcQng0uLvidiG0Oh4wGczPKj9lgtTajpUVGZSXn4JEjHPOWFhqMsrK4LtTVcbyDggjWTCYmSnn9dWDXPnd4GiSMwwakIxLJ2Sbo9UwwVVHB5yVJqjv4W2/x+XXowPVbMHd79/JdeOwxsnfLl/Naw4eT8c/PBzZtkrHoxRbU1CgoKCAI7dzRga75cWjKyoG0Jw6FjZ5wQRNkAK7FGaiptcGEYwjqqqCkRMahXBvsLTKOoBMAD3jJR+H6y7doLMjDriQJ1QhALkKRgRE4kOMOm42/V7NnM1b42mv5Ls680h02gx+isRMu0GMQsuGra8GsK9zxzDMErzfffOpkY5poookm57NoSVbOQWkbX9c2q53ZzB/x9o5zjr8TxyYkEBBs3QpMn64eK7LhvfkmwcmwYfyhFCBNJChJSiKgsttpqd23jyAxPJxtb9xIi/Czz1LZLyyk4tC7N92zJEkto3D55fwLCqJLYHY2lY3bb+e5vXrxvNdfZ/kCT08ek5PD8+fPJyATyRXuvJPf+/VTiwMnJxO47N1Lq/KePQSaCxfSvaljR7pcxsbiN/CVmwvcv0BGcVYjtieZ8MYbEm67jeMM0OKbns7zVq5kLNL27exbZCQt5iIVt5cX78FkIkAuLwfuu1fGyhfTEGzNhL+/CzZ4XIXOnXUYNozt9u3LvtxyC+MIs7LUhAhXXsm+GgxUlKuqVFB11VVUsOrref3RozlWPbrLSD/gQPdeBlx5pYTYWLV/UVFUyOx2uo5272KFvH8PAlCOUdiNyttexvdrvdCtG8d09mwCbG93G/oXfIaVuBZesMDdvyN8OrigvLxtcWjhriYBiIdyFrNo/q/lXF6f2q4jVqta1xIguIuOJsMukilNmkTDQnU153VdHedlcjKNLQcP0mjzzDOcz6mpNKbY7Xx39HoaFdatoxGkqQng+2Fr/WudYDh1Ep4/LyePwft7RAEzXepa/xtPc53f90fUwqPQpdSECtjggx69TMgtNMLTk+u1KHxuNHK9kCSOsdGoeitUVnI+FhVx3Q0MlNFideCmWw2IjWW82iWX0BhWVsZENl5efB+OHmWbpaXcpteTuUtI4Bory+o6Ex/PvkRGAnl5Mvyr0lGCIAAOdOwWALtdwrGjdQhGCSoRABMsCEAZauGFanRFd+TDAjd0QykqQi9CTr4DnqiHKxQEIR++aEQFumI04vAlbgNgQVg/L1Qdc8G8O/TYt09CcDD7XFLCMRw6lO/qd98BK1fIWPahHTZZj0fvqMfaXb4oLKR7uYcHvS8CAs7sKV8I69O5vDZpookmf05OtTZpDN45KG3r1gnmTihlgn1rDwQK63dMDFmad97hj7HBwB9rYaUXbRmNBE3Dh1MZsNsZlxEWxuN37eK+3bsZ61ZVRSatb18WJ1+1imnMk5OBKZNlDAqugSQpaGwkSHQ4+ONrtfLvu+9YHDkwkPv69OH1S0vVlN8FBdx/5AgBUmUllcWQEN5jUxOVjCuvJDg9dIiugps2sf+jRwP9+8rw92hC2VEFEyYwqYos06pcXAx8+y3vzWSi66GfH/DFlzoUVXnBZJLw8ccErI8+yr4MGkSA2NhIa/cHH5D9mz2bipTDQWWhQwe6p5aVEYQ1NfF/wk4rjlsljME2HKkGWqwO1NTwPtzcqABv3Ei3odpaWuAbGxnjl53N9t3dCURLSzkeotSEXk/81NhIYKvXy+ic9CuUyhJYE/fhl19k5OWRMa2rIwBMS2O/fXyAS692A1w8UQtPfCD9C5mHTQgPZ5sOBxX+hgbAv5MLcgyT0Q2l8IMF/p0MyMvj+Py+npSTknumWpQm/6g4ryMiKdC4cb+P5XU41PjYmBjOma1bydD26kWXPLOZACAigopyUxMZ/q++UksmtLTwvRDMP8EdAOjgAivoOmnFP/szJGqeCVb57zZoSmCuMglnBu4cMKCm9TPpSYI7EcfH81vgDV/Uo/AwJ5aLC9dk4VJotXLtMJnIzLu40Bi0dy/nar9+dK+94QYmF7E5XJCRwbZvu40eGZ6efIZFRXzuOh3fDQ8Put0HBHDNrKpSy0vU1amGgdtv53qTmQnU1yjIRw/MwvfohArUFR9F2VEFMhwYgkSY0Ag3NKIMXWCBCa5oQS4GIgwZyMJglFcaEIzD6I4i1MGEI+iFXRgNC7zg4d8B4ciCB9zg6WXEvfcZ8OGHEgoLCTw9PRn7t20bY5ijo/kbdbxah4WPuCJiqB6zb/HDvHkS1qzhWn/ffXSrd2aLNdFEE00uJNEA3jkqVqvqgikUecG6Pfjg711LhPImjhPFvBVFBYLR0WrtOquVLFltLX/oxo9Xs2QKMDF+vNrOiBFU0L7/ngxQbi5j5AwGgr1d8TI+v38fUh9YgqZNO1BeJmPaNLKCF1+sWljXruV9lZYyyUlqKgHZ7Nl0renVi2CxUycqM0YjXWkuvZRKhqLw87PP0iWnXz8qN1lZ/KFPSgKsTTI+u38vKjfvge3L/yIpiYkFundnG3o9+19fTzDm50dlKSpKBVw5OVRcMjKo6OTltbo87eI9eXkRuB04QAtyTg7HauBAKkFmM+PeunYlKzdusjuOohu+xm1IwARETzCgrg6Ye6uMrAw7Zs9W4O1N0LhpE8d15Eiyha6uVJabm8l2ennRSl1VRfDr4UFQPGoUt/cMtGJb7VDUwQWV8EN4SCMURcYP39rwztvKb8lx5s0js7l2rYSywGFIwExcPb8TEhIkJCURpI4dSzdPT09gb5KEgNGh8OzbB8XohfJyZtI8epT7TyonS0Ooyf9UBKhzdusGyNBZrXx/3nmHICElhRliGxvVBEcREXTftFg4n2NjyeCZTPy+YQMBgXONubo6tZ6dKhJa4AMCLw/8M8yaszjH36nA6u8TtXD2ycTVFRCsth2eILBlnzw9ZLigGoANOlSjSxd3uHv7os61O4xudgAK6uoIZkRilbAwrku9ehGAe3lxLR84kOv00KEE1ikpXMOOHePaUFxM9r5LFz7b6mquVaKUwNGjbGvnTq6FZWUq25+TQ7d5s5nune+9RyOciwvg31EHB1xQgR44jgA0wR09utngBSt2YzICpCZ4ogWN8EUtXFEHT9jhgY3SjXAL6Y7uPQ0YaKpCMMrhgA6RU7piQrQbqj17oSDqOtQFDoWpkzeCgiSEh3PdGjSI4zBjBr0g0tNVJlOvp+EhJIT9TE9n7HOPHjTaBQS0/zuqiSaaaHKhiJYk+BwUZ+t625IHwMl/lJwZPRFrNW0a/8fEqIqXSM3f0sK2FyxQs0ympFA5GDNGzcKZnk5F7fhx4PnnGe8WH0/Fr2tXKn0ZKXb0aiyEHTpkNAThEs9GDBrkhbVryRwpCi3Fnp5k/FpayCIlJJAJdHVVf6AjI2mB3b+fP+KbN1NxzMqi+1BaGs/v0oXKS8eOBEVmM5VPo8GKYy1GeKEZVeV1+OlrBZ9/Abz8MkGrKPHg5sZze/ZUM1M2NnJ/v35kJ+vqCEABKsGiUHCXLrQYC3dZi4WumNu3Eyj27899aWlkGP39JUy62AfdO0TA97AJmZkSZsyQ8cWt2zCktg5Dd+YixvYEevXSwWbjdUpKCN5sNsZVBgYCP/5IoNq1K13hRo9mv7t0IWj28wMuu84ddYk1GG/Zi2qPAahs6oGepXHo0bQfL142Bq9tGo743Tq88goVoW7dAA8PHS69DFi1mm137kzmsLxcLYoeHAzU1+uQV2iEJIk4PhkGyYGqasFiAGrsU6t06vQXZoMmf0Wc3bidY3Wdt99xB+ezYIXr62mIGTyYyvIdd9AIFBREkGexECgMG0b35+PHOS/bS4Zy8lpx/0vbYlvW7n8flsAYQx0AKzxxDI0IgBiDxiYFgBcAGQp0rYBYDwMa0GxT4I0mWPSBmHu7DjExHOeOHbm+5ORwrfLw4JqRnc016+OP1Wc5Zw7nalwcn9WKFdzeuzfXjcpKZh7u359AsLiYwE3UBa2qIpAXMYTNzQSSGRlAhw4y6qodqLcYYDC4odQeCj2aYYMvCouNALrC5CHD7qGH1SpDZz6GgShELbpA19MbJi8JVVX8Xeg3bzyG96lDnywTosdJiIz0QHw8YDJJqKvTwdWVfbr4Yq65I0fynvR6ejpcdJEaMwrwmIkT1d9S8btpMKiuyppoookmF6poDN45KEIRc/5BmjjxRADXnmuJOG7rVtWlSihz0dGMvxs5koWKzWZaZh96SI3/MJv54202M2uacLEcNIiKg5sbmbY776QSMXUq23vrLeCxf7nA098Xg5GBe7utQEq+Cfv2EeCkplLHP3SIimBZGdu6/XZea906WpIBKh8uLgSR9fUEhcnJZNPMZioqPj5kGPv1o3uSw8F+eHoSrOzd7456dEABQpHhMg6dA3V47DECpJ07WZagtpYKUXo6mQeRRKK+nmOfm0v3UQEiq6vJVIq4xY4deX59PX4rRSASk3h6UkEKC1MTz9TVARMn6rAn0wt79kqorAS2bHAgpbYT9mEA1hX2xYDeVri4cLzXrOE91tfzu07Hcayr43NNT6flfscOgt/evcn+de8OuLhKuPTfw7DS7W4kuoxHZroDhU2+KEZXpFtDcMXlDixeDEy7SEbf7lZ08FfQvTvBvQDY2dlU4ENDee8eHjQaREQAvr4yFMUBwIGqKjuk6iwAjVBBnQQV7I3XyiScJRFu22bzie7ewjvAaqVhZNkyPleA7/z+/XwPbDayHx98QMPB668TCKamci4fOaKuUScL5f4nasX9cWn7M3e2fvYkAG5oRBec6M6pgyuOAXBAgQ8GDtTBwyhjBBIgQYEXquDjaf8t4y7AdbGkhGxbXh6fiZ8fWXlfXwIhj9ZSlAcP8nnV1vI5SZIayys8ANzcOM/r6zn3RZH67GxR11I1EJpMZPA8PWUU7z0Cz5ocOBpr4esrIWR8X5j13UD3WwkPPaSDXwcDmpsleHjoMWBER9z1ymAMv7oPnnhSwowZ9Er49FO6VI6/1A8dO0lwOOhGecklZOjKy9WSNwYDf8sCAvh7NGsWj0tIYP+mTROMKd9P599S4ERDx7kskiS9JElSmiRJqZIkbZYkqWvrdkmSpPclScpv3T/sbPdVE000OfdES7Jyjkl7iVOA3xcrX7pUdTERYE+ct2YNlbq332YsRV4e9911Fy3x/fsTnG3fzvi6r75i6vM77lBjyfR6ukLOns1zf/6ZQGjAADJHZjPjzbp0UROqpKfJ2LfbhuJKN5jNEkaNYl/9/BiMHxFBC/KMGcAvvxC0TZ1KsHL0KBWTQ4fI+OXnU2l54AHGy9XXEzg1NjJJS0IC3YXc3AjcGhrUhAAeHkBZmQxXnQ0e3m7w8pJ+c29NSFBrOfn5kdHz9uY5b77J9kStuT596ILZ0sJ+RUbyGvX1dAXV6QjqqqupCHl58Tx3dx6bk8O/Sy5RC4gfPUoXKmY5VeA4ehBVCIY7gG5h3sjKknDLLWpfc3MJXo8e5T26u1NBq65m//38eN34eBkmDxmzL9UjJUVCSAjBdadOQKdOCmq27ce1jk+wBI+gxNAbHToosFVUYSJW41dci65dTais1MFk4n0ZjWTy0tIIuK1WAs2iIhnhjXHYYx8AGZ7ogRzUwAt16N76xv5eazqTJeZCSGIAnDvrk1hHzGa6YA4dqiZY2rSJLL5YO6xWrifCwLNhAw1F8+dT+S4u5jsmkvQI8fFRs0R27kyl/9yVfzrZyqlFTTBDJry5mYYaRREJs2R4m+yoPMai35KkYIx+J3LtIWiCF2wuAejUSfoto+7x41xbRbIVkYxKsLCKwrVCp+Nz9/XlcxXMa3U11/pjx9gXV1caxzp0oPeEhwfbEH329FS/d+jAZ6/YbAgvXYlgHEIKRqK+92j07u+KnByuo4WF/L3IzaVBbPp0vncmE37LfjlyJA0IkZHcZjLRGGk08n309eX1RbKw0aPVxFdtxfm3s+3v6F+Vs7E+SZLkrShKfevnhQDCFUWZL0nSLAD3A5gFYCSA9xRFGXm69s6VtUkTTTT5++RUa5MG8P4HcqY/NqKUgahfZ7WSoZk6Vd0PUGG74w7++NnttKwrCl1TJkwg+xYRwR9xf3/V8pqZSdACEHhERHCfzUb3R5uNCt399xMYzJrFH+k+fZgC3dub5+7fTyXFz48xDenpBEBGI2NCHA6CIEniD3tNDa3NAwcy8+awYSyZYDIRTB4/TgWypISWaUniNUtLeYyiUAERbJivLxXK3FyOUb9+BLEuLgSaR47QnSwvj30WcXUipbjFQoVCWKZ9fXmdwkKCOZ2O7Yrsn25uahF2i4X7O3Zku0Yjx6CggNuqqtQxBXhNgPfg4kLF2dOTbkMFBYBeJ2P0cCtC+7sjJ1eC2UyXUS8vMptmM+8hOJgAr08fWt1ra9m/Tp2Aa6+WsfelFUir64JShKJj7wD06aNDURFBWl0dcOCAjH6hNrgY3WCzScjKciAAh+GNepQiGN7+PlAkF3h58T4qKui6u349+ypJHPOyIw744RAa4A0b9ABcoYMVMnygJpsAnBVqRTm9Qq0BvL9PnLNlAgR0Eyao2RpffZXvuV7Pd1C46n3zDd/pigq+7/7+/NzYyLUiNfVs3tW5Jn8MMEoS57/d/vvSDx06qMYkb2+uHYMGAU1NMnKzHDC4GTB5soSyMq5HBw9yfR0+nAAtN5drkqgnWFbGa4lnLIx2x47xOqJsisEgau/J6OBtR1WtC1xcaAzz8eFa5+rK96NvX/4WVVezb0OGAAkJChY2v4rdRV0xsXcp3F98GoCEX38l4zZsGA2MS5bQm8HfnyV12rJpZvPvt7Unfzdo+yNyttcnSZKeAtBNUZR7JElaBmCHoijfte7LATBRUZTfRbs6y7mwNmmiiSZ/r2hZNM+inKqundjm/FmAMbudbpYWi/r9rbf4ef58xr6sX8/t4eFkuiZM4P7ISLUsgjg/OprgKzOTDJ/dzh/qn3+mYtHQQIXg668JRjp0YPrzmBiybWYzFYTjx5lw5eKL6Tq5Zg0VgA4daB1OSaECUllJRUJRVCAZG8s/YYH29OTxaWkEEbW1BGayTABqsxHAhISoWeOam6m8yDKVGoDsVvfuvL+CAl6rupr3c/y4WqOuvJznGQwsQVBTo1qDc3IIhtzdeayXF/vi7a0yZmIsJYntiqyjR44QlLm7q65NooiwXs/zwsN5vz/9xJp6BQV0OZoyVYcGqwdKSiWEhrLPDQ08zmSi65KojafTse3ychk9g5ug1ytwOIDXFjmwvm40iwdDh1FDGtGvHwH/3r1U7vr00eFgjhH5+UyT7u2tQ1epAROxDaH6I6hrMCA8nPeTlAQ88QTPLS5mv+12GdXH7PAwOtCAAAxFEphYwg0BaMCJiq5zUoudf7WYmSZ/UJwTqNjtZE0SElR36NxcZsUtLeXc2b+fdShzcvhuyzLfwWPHCO4AzlEhmseteL9FiQTZaXv7mToNBhVQARxDf3+uEZJE1qylBXj5RRk6xQ69zoGKo3bUNxpQXS1h/35mAj56lO107EivgIMH+YxtNhpyKiu5Xvr50TAl1jeR56iggP9NJl5flmV4OcrQUFMPRTHDbpehKIzbi47mujV6NPs3bBh/W+64g4a4sDAJgW8+iTdSZ6Ji7tOYM0fCNdfQS+SRR5ikp0cPJsTq2JHgTrhRivGw23+/7WRyrrtU/hMiSdIiSZJKANwI4LnWzUEASpwOK23d1t75d0mSlCxJUnJVe0GymmiiyQUr/w+XzP+ttOfvL5g5gIyO82cRyA4QqKSm0mVRuMhZrczkaLGQObNY6DIpjjUY1ELnPXtSSS8tBX74gezSDTcweYLZzB/ew4d57N69qrIh6p5ddhldtywWgpiBAwl4IiJY6HbyZPY9JoYgQoAjgK48Nhv7FBTEmI+kJAK6q65SU3origpuGxqolEgS/9zcqGD2709AJqS0lH9irBob1Tpsnp4qkBXSuTMVEkDtn7e3WkdK7BeWbUB1o6qq+n0ckQDhokCwnx/ZP/GMSkq43WDgGOh07M+AARzv5maCScEUZmSwTZEdr6aGY1laKuPxRx2w2QwwmyVIEpm4ppo6FJWXooPBjjH3DEFysgHu0CMQ5Qj2qEfs3mh0KFCLmffpQ0XQ3Z3PPiMDGDJEQkXFIMQ0hqH8mAtabBJycmi5B1jQeMAAAtnx42RsWlcPubkRHmiGHX7I9pwENOoB2FGJjm3e+jYKblXVaevgafLXpK17mtVKQ0JqKufYwoX8/vPPfPdqaliyJDqa60ReHkGAcCUGaFQQ4ozRzzOnj39AZKhxpizBoEcLXFEHC1wAGOHp4YLGJh28vFTjWUsLzxbxYeHhBGyHD7e2Kst4+u6j6IsMZO7oCwda0AUGVBl6oq6OSZEqK/nsdLrf200URY1HrqwkiHf+3QkK4lS022n0MhiA0G52LCh+Hnq0IB/h6PfyXTAE+KGwkF4Z995Ll/hnn+XvxPbtNExdfz3PNxp1MBg64+F+KgsnyseIaxuNaq2+tr+F50s83D8lkiTFAAhsZ9fTiqKsUhTlaQBPtzJ4CwA8/0faVxRlOYDlABm8v9pfTTTR5PwRzUXzfyyihtC4cfxuNKoMnog7ED+UouD4zTcDzz3H2DWRNQ1QFYX6ev64f/UVMHcurbT33SPju2V1+PQXX/TuLaFvX1p2f/2V1tmVK4FrryVDJNp85hm6bwn2KSOD1/DzIxhKTKSSUFlJRUJReKyfH125hg6lsiLL3F5ZSSBlNBLUtLRwn0hScjqDooiVCwhQjxVttX1tR4wgeOzQgUzdqaS9zH7u7ipDBxDgBQRQQXZ15XWdFV6ASokYB2cR8Ta+vgRZHh5su0MHHtuvH8fW3Z0g6tgxWro3bSI4PXoU0EkyamubcCfewwrcDLN7MNzcdDDoWuBanYcGmBCMYhS5jYarmx7+/jKa6h3QuxnQq5eEvXs53i4uwEcfMTGOycR3ID9fTbGekkLgnpamFn0PDGQ/1fT2DgBm+KAGejgQiR3YgtvgijrY4NO6n7FDFAUqqxEPRT59opWz7QL1d8nZWJ/aumSuX8/31m5n3O2yZTT6VFfTvbtfP74HO3YQDCQk0MCh1qk7F+TsxsydWhQAwiVDFDg3wAUVaEEnAM1gkhFaoIQxp7RUbcFopNHr2DHn9coB3ncT9GhpnUU2AJ0B6GEw0NiVn891VLiBi/W6pYXrzIwZnPfV1ZzXYv285hquWYcO8R2IiADGRSv4csInkEqKsXBMEkw7NgCS9JuRwGT6vXEyLu6Pg7Kz6V75d8jZXp8kSeoGYL2iKAM1F01NNNFEiBaDd46IUMREceGEBP5QirIIQ4dSGQ8Pp/Kv1/MH/fnnVeUtJ4dsUWkps1BmZtIy7+FB8DVjBvDrrzKwayd2msPRW1+Gu98ZhIJCHfbu5XHZ2fyhnjFDLXpeW6tm0+zQgW6gy5fzeJ2O4Ob4cYKCgAC6dep0gKurDMXhQL3ZOf6Kbj3C9SYoiAwgQD1fUU7c1p6cPL26Ku1ZsZ23iWudWlgXy9VVD5vtzBVJb28qVIGBVJhEwoDt20lWeXurjGBICPvU0kKlrKmJ/bTb1bgcg4GKYHExGTed3YqMXAsAd0zHN8gOuhlHK13R0qJAh0ZEIQGVUi+0dO2FI0ckjB3Lfgg3TqHsTZtGV1qdTo0DOn6cCt6BA3SvbWqS4SLZ4ObphsxMCZ07A3W1MposCnp1t6Giyg2NTVZQkXUDlVt351HH75VwUQxNgqKc3hP8bCtQf5f8r9entgmWRM3MqCi6aOv1ZL8bG8mYV1WReW9sVDPWCvZbxOidfXF28QX4bp1r0QQyTO52NFpET/VQC5U3A/AEIKFrV4552/qAQhgrJ+5XgjvKYYG32AsJLVDgCYOBiZMCAlTjWEsLn9e999KYNm8e3wOTic9+zRrgySe5Dg0axN+OsWOZSVjEwhkMrBuKqioYu3U6I//b8x2s/Rk5S0lW+iiKktf6+X4AExRFuUqSpItBNk8kWXlfUZQRp2vvfNadNNFEk/ZFA3j/YznVD6Bg6958k1kwDQb+GIvyBVYrlfGHHuK+t96iEp6ZSYZnwQL+sC9ZomaM9PEhQJwwgaAqI8OBTjiEBrjDBUDPcH+4eBhRWcm4i7AwKnfCpcfbm9sLC1VWTiR5MRrVgPuGhraASQbT4+uhRyMMbh3Q3Mx6RcK6fKbirFy2D8z+qkW/vfNlANUAfJy2n1qRFBlBncXLSx0rYTkXQFMwgy4uZMpSUtR2RMY7Hx+yZ3V1BIdVVYCXl4L64uO4Gh/je8xHx16+qKiQ0NgIDBsmI+2AArtDh5AQJmbx8CBgFmPn60slPjBQRkmJgp49dTh+XEJkJJCbK6OuRoa5SY/gYAWlpY0A3OCPY/DtGYiKSqCxsQmAEUYUQ4I3LPBzumMLCPa8WsdL1L0T4yvikwAgHopjnBoweRLRAN4fF6uV64iicL2IjVUZnI0bGcfZtSvnYkwM34cjR9S55uw9cG5JO0gI+na2/VH566wg2XnRTjUI5HSg0cMID1cHmmyu6NhR+i1T5e/Pd/ruLqPJ0oxuyEewrhoYEQ13QzOyi9ww77p6BPX1hcEgQacjaF+4kDFw48fzN0CWgaefFu6SvzciMkPnibUP/z8CtL8qZwng/QKgH/jiHgYwX1GUI5IkSQAWA5gBoAnAXEVRTrvonA+6kyaaaPLH5FRrk7bM/03irCQ5lzRo+2MqsoYJV8UdO+hGYzaTubHZmM76vfeYtnrnTtV1cOxYFiA+fpwxEN98w4LkohD57t10z+zbV4fAoqM40BIEMzogp9AN7u68RnGxmqJaAJXqarUOHUBWSrgTiTTq7YsCwB2dcACVCIWjWYEk/XFwB5zIHJwc3FWA7kp/1KIvgJxX63cXqEDEBzo0QYYeJ7JS7UtbcAeQjczOVmNsAJVFFPfS0qKCO+D/2Lvu+KaOrHvGko0MBgyYanrvYHrvPaRnk7BppJMQQnqyySab8m162UBCet9NIw1CCb13G0w3vXcMBlxkWdJ8fxxN3rOQwRRjW7nn9zNIr8y7M++90T1z7tzJ3f6pqZait2cPwzUPHVKo0qg8ppx4DPWrRGBVsh9ABCIiFHbujIAzEvD5gePH/XDAh4MHnShbVsHtBnxeP7Iy/HCVUDi1Zz+Actixg3U7fkxh375sOHEcDjhRqWxJHNqbjRhsRyaq4NgO0/glAOTAjaqIxHHQjygVaDMXSuI4MlEGVjimD6ctcG5w6BDTNAouCkyf4nIBjz3GbSbL69ixJAILFvCdmjCBz+Dx49aSBuZdy/u9LmwEP0f5fdftyl8wibPvCy5bIb/kj+TuOIBYAIEMJjgFoBwABzI97Ozt4eeRkfzLzOTctD/+YJREr15Au3on8Mcz03E1fkFZfza6f9oYrlqVkZYGrFtX7s/1T02Ux48/cvDG6eS8OODsa7zZF7q3/y8o2tBaX5vHdg1g5CU2RyAQFDNIV3+esI+GmrAogEsadO9uEbmFC5HrR9rrZRjm3/8OvPMOVbN+/ZgExSxC278//3/rLYbf7NvHJBmHDpEA+P2cQ3fsGDB7No/1elmWxwPs26ewK6IrsgMOS5RSSEsjcTtwgGWdCYbcGZvzRgSAVBxGU/BRiiigBAxmzktl2/dzPb806MQ5YSlMJHl+RIKEJm9H0r7eYCgEz+EziIqyVFl7OOn+/dbnyEg64Katd+zgOWlpEShRAjh1MBXRyEAsvDjgr4ucnAiUKgW43X6kp3tAdcOLEyeoctTHemz31UR2ThRKIAqlcBKR2I001ETy6mgA2YgE4IEfK9dHAyiB4ygFwAPAi4gIJxr712IPyuIUasCHEiiNo/BElEC2PxJADpxIAxAHLtjsh6VihOhSJO3iRYNZSqVbN2t+lNfLvqZdOypGr77KwZ/Spfm+n/09zg8u5Zw4k8DkXK4XHNYZTOLs5C4Clkqog8r3IibGgfT0vPoCDT7jx1ENe5GOSiiHLJwsF4cRIxR+/pmRFSVKMKJi0CDgiy/4/t90E0Pujx5lfxITAzgdsbhlymdwLZ0PdOkCZ9NKgOK+KlVyk7Jg4mYndnYIgRMIBAKB/BScB+xhMIbYmcVZzUhr+/ZU4erWBaZP5zFmzlPlykyakpVFZ2DNGipAJUtyrtu6dXTONm1iWvOaNblOXenSJHBK8Uc8M5N/8fEcnTdOXEYGUKFCxJ9z2AzxsJJmEGebo3Z2p1ABKI+Cd/wiwEfVAyAK5z4fx4QRmsfdEBGjBBpyl7f9ZyJ3KSl57zOJWZTywxmh4XBGIDtorl/16laWT/u1IiOBrCyNLOQAqIYS2AJA/zlnyiK+JqmDF4ADu1Eh4N5mIBvlUBkbsRsNbVeMhsJhuFAOzIKfgwi44YIbkUhFlqMmNvhbIQLHASj4EYlTqA74TZs5cRJNA2WZjIIalpJhvgcgGTQvKrKyuNblww8zbHvxYi51UKqUtaal12u97xeP3JnPeSnoF5MEGhXPELKzlXemQZ9QaqADucmdD1WwBAfRHj5vBGJiuCTAvn0Mob71Vr6Xe/dEoOnUsXBt24he7dPxUYdvMPLJqoBSqFKF674Z4mXmuP3739Z3gO+7BYWY+X9Q8quUew5cMFET4iYQCASC/EJ+MvJAXvMUzHaT9KRfP/7wL1xIkjdvHufTzZzJOWuffcZMdj16kKiNHMnkIX/7Gx2z1autDIvp6dZC2sYpW7GCYZrZ2XTgTHhVaqplUyiCYd+fFy6O2nYmsnWxHD4zom/I3ZnKCnVNK505YW7sxZQbz1RXP3y+HABueD0lA7ZY7WYnd0rx/vt8JslMBKhcepGGBrnOa9I4AhtTDDk9CaAMAB88uZROH3ajceAYzqsDIuF11kXlqgoZe4CSSEMmnPADyEIZeHLowPv/JO9mnlFwvYx6aOZHmXl6Qe3q84l3eoGwr5mZlcW17H75hYmQUlKYeGPbNv5/+PDFvnrwe2IU8DOFQBoSeL59QH5JpUFwWKc3sC0bZXAMJxEPQCG+qhelyzrhSFmDa/EFfPF1cN3EB+G+8w4cSE5HnTYuxP70HZyRDIV0u63EJQDg9So433oaOHIEzmqV8K9slUtJi4s73bK8lDbL9AgZBBEIBALBRYV4XSFgTzsePDndbPd6qcz16cM5FR4Ps1kuXsywyV27OO3oww+BTz7hfAm3m87Z8eNMmmLCOCtVytsps8/5urgZ7i4G+QouI69EG/lx0M4Gc+6ZRvXz6xSmw5o/Y471wpqbd66gTTHYh3RUD3Fd0yZ220MrqCbxjQVDbs2ratX92HESVwey4UMMSLiOgaGTDgBpIOkjAatRowz27GF5Hq/Gnj1+ANnIRCkAMXD/qQieBFAy0B5Wcgva6wfgDtjjw9mSX0QBjAlu2fKMxwksBA8upaezbwH4bPz6K8O+J0ygwnTihDW/7uKTOyD03Mrg9ysUCQw17y2/yVJClZcbDgcjIlJTga5dFUrHKBzclYkaVfy488EYfHftD7j76Iuo0qkejn78O2JKK8TERLFt/S0Rk/U0UKkSnJEKSPoa3gMkbXYVLSYm9zV5XyKAeBKys5I3gUAgEAgKAULwQsDpZJp/IHc4ptnu9XL0fNIkrmH01Vc89uab6XR5PFw8eOpUqngrV3J+XM2aTIoBaLjdFkGhU5ZfwpVXNsjzna9iHK9zIXpnmu8S6jtw/mrZuZDGvJzCYHuDvLY/QzjP10aqGSR3oewwZZcBM1ByAcOzKahMoW7ON3XIBOtdEocO0W5nCRd82YaYxQWOPwwTLlm2DHDiJOdvWvAFbEmHC9nwwoWYUkBaRkTAziMAKv5ZN8te47gDJIBnRnlkARXqn/U4ARG8pp3XC7z2GlPdV65MpX/2bCZWOnIkd3Kk80eodyx40CYU7NtDJUYJ1Q9YAxxnRgRK4Cg6YDl6xW9Ghw8eQlQJ7qlZk+HrbduSgLndVNuACHi9MX8moGm36wa4TvUBKlVCldPmgRpl3HyNgDNeVDSBQCAQhAf+kgTPhDsFR42ZkfO0NM6b6NKFjlbnzhZxS0nhvLqvv2YK8nHjSOKOH+d8ObP2WXo658cZ8gcAu3cHZxsMDmMCQmd4g21fMMFBiG1nc6BCOWz5PfdMDt+ZcD7KmF3Kyg9pzCv7Xn7tPV+F0Vw3rzmCpl1NuGP+iLQ9K6dFrEqC7WKFyUVEmBBUxodFYR88iIdpixMnTVrTqD9tUcoJraMAVIGZXuhwRQAZOWCWwHKw5vjZQ0pNggpfoL4BrxtZAdty190HB6UWQb5gwr9N4qbWrTmPMzubafHNM3FxiB2Qd98T6n0Lhv1eh0qMYkKHNQAPmiAFrS5vgUxVDv36kaCWKsVkJG43s8cePcq6166t4PVUQGxOO7hqDqHKZkPjxme3zlUyAigppE0gEAgEfz0UKMFTSg0C8C7oIX6qtX41aH8JAF8DaAsgFcANWuudF+v6zETI0V2TwdI4Th4PwysBK/xy3jw6G88+SwK3cyczoLVqBXz0ETOjRUUxgmf//tOzJk6ebH0+fQ6cfZTcQAf9H+qcs4VBBSM/ZCav0fX8nJvXMeoM+84nDDTUWlihyg22IVT2vbzqG1zW+Yaq5meO4IWEpwafb5QVfs7KioD9GfGgJCyy6QeVNh8Yvsl5dVx8POrPc4DDSE2tDHYJ5aBwCBplcHrYqgIzkmrb+cDpqiiRjihODCskXEgfpJT6B4A7wcZ7UGs9rSBttfdRM2YAycnAo4+ePeuthfMJuz7XwZp08L67ADCxSP/+JKF9+0YgOpprajocQNmyCvXrxMB79VVwrkpCbNfmcP08DV6flZAKyD3QVru2/VpBKptAIBAIBIJ8ocAInlLKAeB9AP0B7AWwQik1UWttd1fuBHBca11fKXUjgNcA3HAxru92c924lSs5/Sczk9kls7LofGRnM+QsIyPvMn76if///rt96/k4UXmpXvlx+s8nDOpssBOhc7Enr2uZtjjbAsXnEoZ6NpwLkQomfsFhaBcDF0rgzgZ7HUzYpCFxdmhY6ls2rAQzTpDcHQWVPkMSFai+kYS5XBFwuzU0ysEKwQyuW/AghZEaT1fqYpFFWaYQkkhcSB+klGoK4EYAzQBUAzBTKdVQa52fkYd84+BBhhmmpQHPP8++5vzm0Z1rUhKDCPD+ZYHPRjpKIA0NkYXWNw+A+/vP0NK7GFHV66LFq3cjoU9luKIVjh7lwJlJ9583HEDihFxZIp2Bx0ry7ggEAoFAUDAoyJ/YDgC2aq23A4BS6nsAVwKwO1dXAng+8PknAO8ppVRgIc8LwlNPkdwBXIbg4uB8nai8yJ1dickL+QmDChWWeDbYk5bkl+yEqofdqT+TLefSdme6/edLyoLbsTgieJ5gcDiovY0VGD5pz3aoAVSARQwdICk3oZsqsESDQs0aJbB7T36eC5MAxq5OW8/AAUTTsS8cnHcfFNj+vdY6G8AOpdTWQHlLLpZxF3d5QPtzkRn4v/RpR1WsCNSowTnCffoA5csrNG/qRBWHGzF16wJaw3XKlrL/qzuBI1eclsKfc97yCckSKRAUK5xtCSWBQFD0UZAELx6APa3DXgAd8zpGa+1VSp0APdCj9oOUUvcAuAcAatasma+Lv/vuedl8FuQnRDIUQhGf4PlzeZ0XKjmAHXmFJeYX56I8nY1MnsmWc2m7vJTJi6m4FVdYbdysWQTWr7c53TEepKVHgg5+tO1Yc588YAZMeyipuQ9WmGdsLOD1netzEWwjyy1fvsfFZjLnggvpg+IBLA06Nz7URc6nf7rYTVK6dARu1O+hW/p09OmoEDdnAlzR+T3bHgqpcs9bE3ImEPylYPomIXkCQfFGsQiS0Vp/DOBjAGjXrl2+uhytC8KvjAAw3/a9Rz7PM472EQCVkJukBJfZHece7ndxwwMjIzk/xuHgnMPMTM6rKVNGIe24QsnII9CRleBwKMTGUhE4epRhZqVKRcDnY+IZpYCmTYFDh4CoqAhg8U+Ixj5kojoqXXktjh7lor8xMbxeZiav5/EoxJZViFFHcNJfCR6PQvXqgN8PREczoU1UFP/8fi4Qf/Qo16A6fJhJcA4eZLY9n4/zLU+coC1bt1LFqFiRiSpOnOD1Y2K4Hl18PJCQwEWijx0DqlRhWR4Pjylb1jrH46ENAEN/fT7u9/lo58GDQPPmXMuwZk1eu1Ejy6YaNVjv7dtzi1ypqZz3mZbGZD1xcSwrK4uL3btcEYiNpW2JiQyTy8gA2rYpgeU97kbddTNxsFUPxH33FdzZClu20K5qVSLhOpUGb/kyOJqqAiF2XMfO62XK/fh4EjynE1i3jnOiDh60QvHMvNa4OB6TmAi0a8dlQUqUYPv7fBHIyuJC3PlZj7G4oyD7p59/Bq65Jj8lKsA/Ejhy/Wlqm0AgEOQXpm8ScicQFG8UJMHbB6CG7Xv1wLZQx+xVSjkBlAUTHVwUXPwOSgH+7rnmk+QfeSUMuJAyCwPnm/hAAf5rz6GehZtgoXXri1dW8+b8v2/f879WXlkDe/Wyf1MYsPpj4MgR1Le1sVX+mds0+Bqm7NyJL3Kjfv3c/9tx6615n3eJcCF9UH7OvSBc9P5J1DaBQHARIOROICj+KMjMECsANFBK1VFKRYEJCyYGHTMRwG2Bz9cBmH0x5t8VKIwTdTGJWEGUWRTxV6lnYULa2I4L6YMmArhRKVVCKVUHQAMAyy+R3QKBQCAQCATnjQJT8ALzWR4AMA3M5vC51nq9UupFAIla64kAPgPwTSCBwTHQARMIBIILxoX0QYHjfgQTsngBjLzYGTQFAoFAIBAICgIFOgdPaz0FwJSgbc/ZPrsB/K0gbRAIBH9dXEgfpLX+N4B/F6iBAoFAIBAIBBcZBb14l0AgEAgEAoFAIBAILhGE4AkEAoFAIBAIBAJBmEAInkAgEAgEAoFAIBCECYTgCQQCgUAgEAgEAkGYQAieQCAQCAQCgUAgEIQJVFFfdi4YSqkjAHbl8/A4AEcL0JyCgNh8aVDcbC5u9gLnZnMtrXXFgjTmUkD6pyKH4mYvIDZfKvyl+ifpm4okipvNxc1eIPxtzrNvKnYE71yglErUWrcrbDvOBWLzpUFxs7m42QsUT5svJYpj+xQ3m4ubvYDYfKlQHG2+VCiObSM2FzyKm73AX9tmCdEUCAQCgUAgEAgEgjCBEDyBQCAQCAQCgUAgCBOEO8H7uLANOA+IzZcGxc3m4mYvUDxtvpQoju1T3GwubvYCYvOlQnG0+VKhOLaN2FzwKG72An9hm8N6Dp5AIBAIBAKBQCAQ/JUQ7gqeQCAQCAQCgUAgEPxlIARPIBAIBAKBQCAQCMIEYUHwlFKDlFKblFJblVJPhdhfQin1Q2D/MqVU7UIwM9ims9n8iFJqg1JqjVJqllKqVmHYGWTTGW22HXetUkorpQo1NW1+7FVKXR9o5/VKqW8vtY0h7Dnbc1FTKTVHKbUq8GwMKQw7bfZ8rpQ6rJRal8d+pZQaE6jPGqVUm0ttY2GjuPVP0jddGkj/VPCQ/unMKG59U8CmYtU/Sd90aSB9UwhorYv1HwAHgG0A6gKIArAaQNOgY+4H8GHg840AfigGNvcGUDLw+b7iYHPguNIA5gNYCqBdUbYXQAMAqwCUC3yvVNTbGJx8e1/gc1MAOwvZ5h4A2gBYl8f+IQCmAlAAOgFYVpj2FtF7WmT6J+mbio7N0j9dFJulf7qw+1lk+qZzsLnI9E/SNxUpm/9yfVM4KHgdAGzVWm/XWnsAfA/gyqBjrgTwVeDzTwD6KqXUJbQxGGe1WWs9R2udGfi6FED1S2xjMPLTzgDwEoDXALgvpXEhkB977wbwvtb6OABorQ9fYhuDkR+bNYAygc9lAey/hPadBq31fADHznDIlQC+1sRSALFKqaqXxroigeLWP0nfdGkg/dMlgPRPZ0Rx65uA4tc/Sd90aSB9UwiEA8GLB7DH9n1vYFvIY7TWXgAnAFS4JNaFRn5stuNOkMkXJs5qc0BCrqG1nnwpDcsD+WnjhgAaKqUWKaWWKqUGXTLrQiM/Nj8P4Gal1F4AUwCMujSmnTfO9VkPNxS3/kn6pksD6Z+KBv7K/VNx65ty2RNAUe+fpG+6NJC+KQScF9UcwUWHUupmAO0A9CxsW84EpVQEgLcBDC9kU84FTjDUoBc4yjdfKdVCa51WmEadBcMAfKm1fksp1RnAN0qp5lprf2EbJvhrQfqmAof0TwLBeaI49E/SN11S/OX6pnBQ8PYBqGH7Xj2wLeQxSiknKM+mXhLrQiM/NkMp1Q/AMwCu0FpnXyLb8sLZbC4NoDmAuUqpnWDM8MRCnDCcnzbeC2Ci1jpHa70DwGaw0yos5MfmOwH8CABa6yUAXADiLol154d8PethjOLWP0nfdGkg/VPRwF+5fypufVMuewIo6v2T9E2XBtI3hUJBTB68lH/gSMJ2AHVgTa5sFnTMSOSeKPxjMbA5AZw02qCw2zi/NgcdPxeFm2QlP208CMBXgc9xoBxeoYjbPBXA8MDnJmAcuSrkZ6M28p4ofBlyTxReXpi2FtF7WmT6J+mbio7N0j9dNLulfzr/+1lk+qZzsLnI9E/SNxUpm/9yfVOhVewiN9IQcARhG4BnAtteBEdvADL18QC2AlgOoG4xsHkmgEMAkgN/E4u6zUHHFoWO6mxtrMDwiA0A1gK4sai3MZj9aVGgA0sGMKCQ7f0OwAEAOeCo3p0ARgAYYWvj9wP1WVvYz0QRvadFqn+Svqlo2Cz900WxV/qnC7ufRapvyqfNRap/kr6pyNj8l+ubVKAggUAgEAgEAoFAIBAUc4TDHDyBQCAQCAQCgUAgEEAInkAgEAgEAoFAIBCEDYTgCQQCgUAgEAgEAkGYQAieQCAQCAQCgUAgEIQJhOAJBAKBQCAQCAQCQZhACJ5AIBAIBAKBQCAQhAmE4AnyDaVUdaXUBKXUFqXUNqXUu0qpqItQbm2l1LqLYaOtzLZKqbVKqa1KqTFKKRXimMZKqSVKqWyl1GMX8/oCgeDSQfomgUBQVCH9k6AwIARPkC8EXvJfAPymtW4AoCGAGAD/vsBynRfBvFD4AMDdABoE/gaFOOYYgAcBvFlANggEggKG9E0CgaCoQvonQWFBCJ4gv+gDwK21/gIAtNY+AA8DuEMptVwp1cwcqJSaq5Rqp5QqpZT6PLB/lVLqysD+4UqpiUqp2QBm2S8SGJFaoJRaGfjrEtjeSyk1Xyk1WSm1SSn1oVIq5POrlKoKoIzWeqnWWgP4GsBVwcdprQ9rrVcAyLkI7SMQCAoH0jcJBIKiCumfBIUCIXiC/KIZgCT7Bq31SQC7AUwGcD3wZwdRVWudCOAZALO11h0A9AbwhlKqVOD0NgCu01r3DLrOYQD9tdZtANwAYIxtXwcAowA0BVAPwDV52BoPYK/t+97ANoFAEH6QvkkgEBRVSP8kKBQIwRNcDMwFcF3g8/UAfgp8HgDgKaVUcuAYF4CagX0ztNbHQpQVCeATpdRaAOPBDslgudZ6e2AE7DsA3S5iHQQCQfhhLqRvEggERRNzIf2ToIBQUDG8gvDDBlgdEQBAKVUG7HRWAEhVSrUER45GmEMAXKu13hR0XkcAGXlc52EAhwC0Agcg3LZ9OujY4O8G+wBUt32vHtgmEAjCD9I3CQSCogrpnwSFAlHwBPnFLAAllVK3AoBSygHgLQBfaq0zAfwA4AkAZbXWawLnTAMwymRhUkol5OM6ZQEc0Fr7AdwCwGHb10EpVScQP34DgIWhCtBaHwBwUinVKXDtWwFMOLfqCgSCYgLpmwQCQVGF9E+CQoEQPEG+EJhwezWAvymltgDYDI4QPR045CcANwL40XbaS2DYwBql1PrA97NhHIDblFKrATRG7tGqFQDeA7ARwA4Av56hnPsBfApgK4BtAKYCgFJqhFJqROBzFaXUXgCPAPinUmpvYGRNIBAUE0jfJBAIiiqkfxIUFhSfPYGgaEMp1QvAY1rroYVsikAgEPwJ6ZsEAkFRhfRPf12IgicQCAQCgUAgEAgEYQJR8ATFGkqpZQBKBG2+RWu9tjDsEQgEAkD6JoFAUHQh/VP4QwieQCAQCAQCgUAgEIQJJETzLwSllFZK1S9sOwQCgUAgEAgEAkHBQAheIUIpNVwptVYplamUOqiU+kApFVvYdl0IlFIPB+pyUin1uVIqOATAHBellPpJKbUzQDx7XQLbhiulfEqp9KC/aoH9O5VSh5VSpWzn3KWUmquUqhl0jlZKZdi+d1dKfamU8gS+H1NKzVBKNQ6yobpS6n9KqdTA+cuVUkODjrGXvU8p9bZSqpRSaotS6ragY59TSi0KpD8WCIoMAu9Tvzz2dVFKzVZKnVJKnVBK/a6Uahp0TOnAs78z8D7sDvQZHW3HaKVUfaXUh7Z30aOUyrF9n1rAdcwK6hveC+wbHrDviaBz9iqlep3NZqVU7cD5ZttOpdRTIWw44++IUup5W9lpSqnFSqnOgb5to72PVkpVCPSBgwqqzQSC4oy/YL92MODbxAQdExNsh1KqasD2yrZtz+Sx7Y+Csl9AiFNYSFBKPQrgNQCPg+uXdAJQC8AMpVTURb7WJVnQXik1EMBTAPqCdakL4IUznLIQwM0ADha8dX9iidY6Juhvv22/A8Do4JO01rvt5wQ2t7JtWxDY9npgfzy4QOhnpgylVHmwzh4AzQDEAXgHwLdKqVwLoZqywbb8O7iuzZ0A3jQdpVKqCYBHAdwZWPtGICjyUEp1BjAdXF+pGoA6AFYDWKSUqhs4pgSA2QBaABgKoAyAJgC+BzA4uEyt9Qjbu/kygB9s7+Zpx19kXB7Unzxg23cMwBNKqdIXYHNs4JjrADyrlOpvdpzD78gPgTIqgn3QL2DftA/Ac7bj/gNgitZanC+B4BwQrv0agNYAEgD8I2j/tQCyAfRXSlUJ2HsAXF6hh+24HgBSQmybXzBmCwyE4BUCFNcLeQHAKK31H1rrHK31TgDXA6gN4GalVLXACEp523kJSqmjSqnIwPc7AiOwx5VS05RStWzHaqXUSMV1V7aEsOEypdQqRaVtj1Lqeds+M3J8j1Jqv1LqgFLqsXxU7TYAn2mt12utj4NrtwwPdaDW2qO1/o/WeiEA31na6walVGLQtoeVUhMDn4copTYERs325dPWvPAGgMfUBSqpWusscF2b1rbNDwNIBwnZQa11ltb6OwD/BvCWUlzUNKicFAALADTXWs8PlPle4NhPAbwSOEYgKC54HcDXWut3tdantNbHtNb/BLAUwPOBY24BUB3AVVrrdVprn9Y6Q2v9k9b6+dDF5h+BfnOo7btTKXVEKdVGKeVSSv1XUWVPU0qtsI8+nyM2AlgCrhd1QdBaJwJYj0Cfkp/fkRBl5AD4CkAVABUA3AXgfqVU68AAXV+wnxIIBOeGsOzXtNYHwYXXWwftug3AhwDWIHdfMx8BMqe4qHsbAO8GbesMIXgFDiF4hYMuAFzgKOqf0FqnA5gCoH9AVVoCjpIY/B3AT1rrHKXUleBCmdeAo7ILAHwXdJ2rAHQE0BSnIwPArQBiAVwG4D6l1FVBx/QG0ADAAABPqjzCEmxoBo5YGawGUFkpVeEs550NvwNopJRqYNv2dwDfBj5/BuBerXVpAM3BEbLzRSKAuQAuhCRCMcxzGDiaZdAfwM8h1LYfAdQE0DBEOU0BdAewKrDpSQDtAfwMZsB640LsFAguJZRSJcH+b3yI3T+C7wgA9AMwTWudEeK4i4HvwPfTYCCAo1rrlaDjUhZADZAEjQCQdQHXehbAQ/bBuvOBUqoT2L+ZPuWsvyMhyigBDrrt0VofDRDC5wB8Djpr9wcG5wQCQT4Rzv2aUqo6qC5utW2rBaAXgP8F/m61nfInwQOVv40AZgVtiwSw/JxqJjhnCMErHMSBL503xL4Dgf0ACcwwAAgoNjfCIjUjQPVmY6CclwG0tqt4gf3HAmpSLmit52qt12qt/VrrNWDH0DPosBcCo0trAXyB3B1HKMQAOGH7bj6fFp50LtBaZ4JhD6YtGgBoDGBi4JAcAE2VUmW01scDnVle6BQYvTJ/20Ic8xyAUUqpiudh7mNKqTQApwB0A0fsDOLA+xuMA7b9BiuVUsdBcvsp2P7GeRsJ4GpQCTyj+ikQFDGUB3938noPzDsQB1vodkBhSgtEHGy6CHZ8C+CKgGMGcMDIDJDlgA5Q/cAIe5LW+uQZyvotqE+5275Ta50MYAY4OHM+OKqUygIH/MYB+C2wPb+/IwBwfaBf2gOgLdh/GLwH1jlZa/0bBALBuSJc+7VTYJ9xGMC/bPtuAbBGa70BDC9tppRKCOybB6B5IAqqO4AFWustACrati3VWnsusK6Cs0AIXuHgKIA4FXpuXNXAfoAqTWelVFVw9MMPKnUA51m8a5wKcK6HAud+GezJywClVEel1JyAfH8CJIxxQYfZz98FxpWfCelgTLmB+XzqLOflB3+SXbDT+i1A/ACqnEMA7FJKzVOMhc8LS7XWsba/esEHaK3XAZgEzic8V7yptY4FQ6SyADSy7TsK3t9gVLXtN2ijtS6nta6ntf5nkOq3Puh/gaC44DjYj+X1Hph3INV+jNY6OfBeXYPT1246Z2itt4Ijy5cHnKErYA2efQOGJH0fCFF/3YTF54GrgvqUT0Ic8xwYJXE+oZ5x4ODZo+CoubElv78jAPBjwLZKWus+Wusks0NrrcG2kP5EIDg/hGu/Vhrscxojt394K6jcQWu9DyR1twW+7wTn9nYH/Vbjsy62bZPwzEsAIXiFgyXg5NRr7BsVsxQNBuVsBEJlpgO4ASQ13wd+jAGSr3uDHItorfViW5FnWuTwW1ABq6G1LguG5wTPAath+1wTwH6cGesBtLJ9bwXgkNY69Szn5QczwBGg1iDRM50WtNYrtNZXAqgEjm7/eBGu9y8AdyM3Yc43tNa7wWQt7yqlogObZwK4Rp2e8fJ68H5uPk9bBYJigUBo0hIAfwux+3oE+r7A/wOULaNtAcCEM10JYEPAOUJgLtsLWuumYNjVUOQOQTpnBObJ/gLgmfM836e1fhuAG8D9gc35+h0RCAQFi3Du17TW8wB8CeBNgJlCwak7/1DMsHkQnAr0d9tgkwnT7AwSO4BErwcY2SQE7xJACF4hQGt9ApwcP1YpNUgpFamUqg0Sk73gSIvBt+BLeB1spAYkZP9QSjUDAKVUWaVUqM4lL5QGcExr7VZKdQAJZDCeVUqVDFzjdgA/nKXMrwHcqZRqGpDi/wl2DCGhlCqhlHIFvkYFJgGflmgE+DM5wHhwzll5kPCZ5RZuUkqVDRxzEhxJuyAEOsUfADx4AWXMAEnxPYFN74Ax8J8ppaoE6jsMdPoet5F3gSBcEBl4zs2fE1TGb1NKPaiYMrycUur/QGfAZN39Ggxt+lUp1Vwp5Qj0Fe0uom3fg/OL74Otb1VK9VZKtQgkAzgJhjZdjCy1L4D9aOwFlPEqmJXTdY6/IwKB4OLhr9av/QfMltkKVOpmgLkdWgf+mgOIhpUJdD7ot+63hYEuDGwrC5JhQQFDCF4hQWv9Opgk5U3wZVsGqjh9tdbZtkMngqMlB7XWq23n/wqmx/5eKXUSwDqESLN7BtwP4MVAjPVzCK16zQMn1s4CQw+nn6VOf4CZpOYA2A2Gdf4Zt62UWq+Uusl2yiYwjDEeDB3IAkNP88K34CTl8UHzTm4BsDPQDiMA3BTq5AA6q9PXwWufx7EvArjQkbY3QIesREDJ7AYmRtgAhms8AuAWrfXZyLNAUBwxBXyvzd/zmplzB4LK0wGwn0gA0C0wVwNaazeY5GkDgMlgH7kJTDB0/cUwTDOl9xJwNNv+/lUB8FPgmhvBfvBMZOn3oP7k1zyutyNQzoX0KZPBcLC7A2Xm93dEIBBcPPwV+jV7mUdAcvpcwM6xmpnAzZ/p28w6vfPAiKqFtmKSQRKYZJteIyhAKBENBMEIjALvABCZxwR+gUAgEAgEAoFAUAQhCp5AIBAIBAKBQCAQhAmE4AnOCUqpqSFCHNOVUk8Xtm0CgUAgEAgEAsFfHRKiKRAIBAKBQCAQCARhAlHwBAKBQCAQCAQCgSBMEGqB1CKNuLg4Xbt27cI2QyAQXEQkJSUd1VpXLGw7LhTSPwkE4Ydw6J+kbxIIwg9n6puKHcGrXbs2EhMTC9sMgUBwEaGU2lXYNlwMSP8kEIQfwqF/kr5JIAg/nKlvkhBNgUAgEAgEAoFAIAgTCMETCAQCgUAgEAgEgjCBEDyBQCAQCAQCgUAgCBMIwRMIBAKBQCAQCASCMEGxS7IiEAgEAoFAUJyhlPocwFAAh7XWzUPsfxzATYGvTgBNAFTUWh9TSu0EcAqAD4BXa93u0lgtEAiKC0TBEwgEAoFAILi0+BLAoLx2aq3f0Fq31lq3BvAPAPO01sdsh/QO7BdyJxAIToMQPIFAIBAIBIJLCK31fADHznogMQzAdwVojkAgCDMIwRMIBAKBQCAoglBKlQSVvp9tmzWA6UqpJKXUPWc49x6lVKJSKvHIkSMFbapAIChCCOs5eEoBWhe2FQKBQHA6pH8SCAT5wOUAFgWFZ3bTWu9TSlUCMEMplRJQBHNBa/0xgI8BoF27dtLb/AXg9QJOm2d/9CgQEwMcPAhsTvEjef4R+ErGoWbZo4hvXgnNWyjExuY+RxAeCNtbqpT1vzhRAoGgKEH6J4FAkE/ciKDwTK31vsD/h5VSvwLoAOA0gif4a8HrBebMAXr35ve0NH5u1w748ks/KPzGBY6OA+BHnToK//d/EbjiCpI8l+t0kigongjbEE3jNInzJBAIihqkfxIIBGeDUqosgJ4AJti2lVJKlTafAQwAsK5wLBQUJTidFrmbM4cELyeHn0nu7PAByEKdKm6sXQu89hrw5ptAejqP93ovqemCAkBYc3RxngQCQVGF9E8CwV8XSqnvAPQCEKeU2gvgXwAiAUBr/WHgsKsBTNdaZ9hOrQzgV8UwACeAb7XWf1wquwVFG04nyVn79sCoUcBddwFvv+0HkAmgZOAoDwCgLA4j018LrVsDAwdSvXO5SBJFwSv+kFsoEAgEAoFAcAmhtR6Wj2O+BJdTsG/bDqBVwVglKO4wYZqdOwO1awNHjgDN6rtx6EAO/PABcACIAuBFJsogLiYbffuWxIoVlvon5C48UGAhmkopl1JquVJqtVJqvVLqhRDHlFBK/aCU2qqUWqaUql1Q9ggEAoGB9E8CgUAgCDeYMM2YGGDkSH6v3SgafpQEoAD4AfhQCntRDlm49iYXYmNFtQtHFOQcvGwAfbTWrQC0BjBIKdUp6Jg7ARzXWtcH8A6A1wrQHoFAIDCQ/kkgEAgEYQenk3PpPv6Y3+vVU6BqFxH4cyADNeCHB70+vAbOCP9p5E7m4BV/FBjB00R64Gtk4C941smVAL4KfP4JQF+lTH45gUAgKBhI/yQQCASCcITXC8ybB9SvDzRoACxaBBhixz8/gJPIRFksWO6C98CR086XRCvFHwWaRVMp5VBKJQM4DGCG1npZ0CHxAPYAgNbaC+AEgAohypHFOgUCwUWF9E8CgUAgCEd4PMBvvwG//w7Ex5PoWXCiDDxoivXo3N4DZ7VKp50vIZvFHwVK8LTWPq11awDVAXRQSjU/z3I+1lq301q3q1ix4kW1USAQ/DUh/ZNAIBAIwg1eLzB4MHDVVcD+/cCMGcCWLfYjFKIrV0Hzv7XBfWV+RtoJletcLqsgKO64JOvgaa3TAMwBMCho1z4ANQBAKeUEUBZA6qWwSSAQCADpnwQCgUAQHnC7gf/8h0RtwACgenUqeCa5ipmJUL58BDbuLo0PPlSIibHON0laRL0r/ijILJoVlVKxgc/RAPoDSAk6bCKA2wKfrwMwW2tZHUogEBQspH8SCAQCQbjB5QJGjOAcvGXLgEGDgGy3H7E4CJK7DAB+HD4MnDwJLFkCTJtGYmgg5C48UJAKXlUAc5RSawCsAOe4TFJKvaiUuiJwzGcAKiiltgJ4BMBTBWiPQCAQGEj/JBAIBIKwgtcLLF3Kzx07AuvXA8cOe+FHKkjwqOJVrgz07Qts3cr5enPnSlKVcEOB8XSt9RoACSG2P2f77Abwt4Ky4a8Ir7dgRl9MuQVV/qVGuNRDcH6Q/klwLkhLA2JjC9sKgUAgODOcTqBfP/o4EyYwg2bqiUicQn1UwgYcRlMAEdi1C9AauO8+IC7OOlcQPrgkc/AElwYFldrWlOt2h0fqXEkBLBAI8ou0NODGG/m/QCAQFFUYn8YMxicmAocOAYOHKFStVgKH0RyAEyVLKrRpwwQsH38sA97hCiF4YYQLmRx7JrJjynW5itbk2/MlaDKJWCAQ5BcxMcB//ysKnkAgKLqwD1ybdfBKlyaBW7UKyMmJQGysA4BGlNODpjVOolFDjQcfpG9nL0cGv8MDQvDCDOdL7s6maJlyiwopCmXzuXRKl6oe0lEKBMUXXi8wcyaTFdiTEAgEAkFRgn3g2ukEBg4E7rgDeOIJoFw5IDsbSEvzA/Di1MmTWPHtKmR++R2cEf4/yzD93cyZ4ruEA4Tg/YWQ1wubX0XrbC/8+ZKtc70OcLrNhRV2eabr2UNbBQJB8YPTCfTqBZw4AUyfLk6PQCAourD7cE4n59bdcAOwfDkzZjLJSiZicBLbUB8/H+qAX788kSu0s18//hWVwXzB+UMI3l8EZyNA+SF3ZxrVCQ4POF+ydaZzDVGyd0Z2+89EUvNjizkm+Nj81jkYTifQvTuwYIE4hgJBcUV6OvDyywx5kvdYIBAUdXi9nDM8aRIwZQpQu7bZEwGgJE6gChpiPa6qtBzbUmPhduf2q4TchQeE4P1FYMhG8ItrJycX4rwEhwec7xy3vM41i3emp+dNqs5E7s5GOPNKJHOmc42tQN7H5DVvURxFgaD4QGtgxQpJtCIQCIo2vF7gt9+AkSOp2kVFATt3+hFb2hM4wonoEk6sjOiL7CuGYcR9CvPmSVhmOEII3iXA+Uxavdgvmtt9upLk9XKBy5kzLWKTVzihke7PRNqCFbVQyG/4ZTBcLuChh5jw4GykKlR5Z1P38kokc7Zzz0ZoQ9l3oQqnQCC4dDB9TqtWwCefSMi1QCAouvB6gXXrgGrVgD/+AE6d8qOE9xjSTikAJHlZ2VFo286BlBSF2bMBn4+h6KLchReE4BUwzmfS6sWeT+b1ktyFUvAcDr7YLtfZwwkv9OUPrte51s9kerKTqvwiLwXNblOoRDJnSx8cKlzUvs8QaHtdz5UQ2vfJ8g4CwaVFWhowcSLf49tvz51xTiAQCIoSXC7gsceALl2Aa68FOjfPQCmcQBlsAKDAeXhU9ipXBtauJcGTKKPwgxC8Asb5TFo9nxDHMyU4satToWxzuXiOIXlmDRXgwkar87LDlB8cankuBNjgTHPg8irTHo55prY+27p/+SFchkAHl58XuTvbPMmLkQxHIBDkH7GxwKBBgN8v75ZAICj6cLmAqEg/utY/hHotS+EkyiMbNQBkwbj9J04AgweT6Dkcuc+XweTwgBC8S4DzmbR6LqMp55vgxE6sZs7k/La5cy1ik57OeW9nInl5katQal2wSmYUOHuIaH4yddqPCxWuGUqdC1bQ7GplXvMS81I97eWcLXzzbMQ+v8qe/ZgzIb/3XzpugSB/MPN+t28HRo/md4FAICiy8PuR9dhzuLndGnz3eCISOpdBNsoiNrYMqOJxkXOHAxg1Chgy5NyS1gmKB4TgFWHkJ9EHcOYEJ15vaPIUHDrq8+HPWGxTRkwM570Z5S+YKJ0p/NRuR3o6j5k2LXedjI32a9rVw7zq2r07P8+Zc/q17HU9UxIUu1oZqq2B/GXlzE8HmFd4bqj7mp/yztY+Z2vHMz1PwduECAr+6vB6gawsfl63TubgCQSCog3n8SMYtHkM2mIZHN5TcPizATjg9ao/1boyZbi+54cfhvbthNwVfwjBu8TIr8MciqiEIiMG9n32zzNnArNmna5EBYeO9u1Lqb5v39yKoyF3hqQZpc+U0a1b3iqVIVzvvQd06pS7DUIRDHO8IUT21L32cxcs4Gf7HDxDaIzqFtwWwfU3xwarnsFtHcrOixW+cL6huGcjZ3aiG8oZzeu6oVRXCdMQ/NUREwPUqMH34MQJeR8EAkHRhrtMJTi7dkAp5GBojSRs2xWN6GggM5MD6gAQHQ2MHw/ceCN9AeN3ye9++EAI3kVCftWQs83pChUeaCcqhgCdSZUzTr3TyflfDsfp5NDsNzaZY0MlEEhLI0nr1o1/773Haxjydqa627NfmpGjYIJhtnu9DBH1+Vj2m2+ervp5vbnJmr0N7OXas4POnGmFntrrfrZlHc5EwIK359UGZwvTPJ/Q3bzImb2DXrAA6Nw576Q5eRHyc8kgejbID4QgHJCeDuzYwc+pqRKiKRAIii7cbuA/7yq4J0xH3bcfwqeRjyMjUyErC4gIePwlSwKRkUCJEqGTykl4ZnhACN5FQKi5XnmF350pe6JRyACSGJP8xF72rFmAx5N3WR4PjzHHu1wWwQguyxCq7t35efr03Pu8XjozH34IjBhBkmYP2zTkLZgUut1cXNNO8oKJjv3/fv1yfzYqYkIC0LOn1T7TpgFvvGHZCZDEhFI07dlBe/XiXzDZCVY0Q903o4bZ62ZCOO337kz3P7gDPRcVNxTO1vHaQ2zPtaPOTzIYO/IK6ZRRQEG4IDYW6NrV+j55sjzXAoGgaMLlAh54AFi0JAIbD5ZDjlfhppsYoQX4Afjg92scPMhsm99+y/7M7iMJuQsPCMG7CAg118u+LfhYIHS8c69eJDj2NetCzamyK2H2Mrxe7vP5TicY9rLMPjuBmDoV+PlnKxTz999J0hYuZGdhyGYw6TDkzmw3RPGXX6yR7vzMV7OTgblzSVJ79gTmzSOxA0j8Hn6YHZUhfK++SqXPPs8PyE1qFyw4s/IVTPrsCqFZYN2EixplLNSEZFOPvLJzGhJvyO+ZnET7wu75gZ0om+/2/wsCZwrplFFAQbggPR1YutT6PmWKzMMTXDiUUp8rpQ4rpdblsb+XUuqEUio58Pecbd8gpdQmpdRWpdRTl85qQXGAy0X/6fHHgYYNgQ8+ADweP7xeDeA4nO5UAH6UKgXcc481YC+/2eEFIXgXCXmF+p1tDpf5nJ5O8mDCHu0kwqhICxaQ5AwcmJvwud0kNnPncqQ5Kip3MpLgcM65c1n+vHlWCv/oaODddzla3a0bsGoVjzX7336bpO/116mi2R2co0eBt95iHZxOYMAAkpMVK3KHlNrrH9wm9iQp3bpZYaUmXtyQK6NImg7M5bLUxV69LHJr2t6UnZeSZk/+Yg9vDA4xNfe3e3dgyZLT7bfvt5Pz4OM6dWKihrQ0az5jqPYwo3D2a+VHNbCHaZ4NZwqtzc/5oUI67eRYfigE4QCXC6hZ0/oeEyPPtuCi4EsAg85yzAKtdevA34sAoJRyAHgfwGAATQEMU0o1LVBLBcUGbjd9tTff5Pdu3cweDSAbQCloeBBX1oOvv6bvZp/WIwgfCMHLA2eaJ5df2JWdvEI17aTBhGWaOWvmPPucPKOk2VW4WbN4bLduHGnu1YvfZ83iaLMJwzQEyuPh/uTk3PPEYmNJPJxOksQ+fazwygcfBC6/HHjiCY4KuVwsKz2d3+vXJ2E0RNWMBnm9zEBnDxs18+PS03OTz/R0kpo//rA6JZP4xZ710nRCJlw0MdHqoNasoYII5FY+7WqYIXamzRyO3MldQmWhNDaebZTLvt9O5E35S5dyxOyjj2jPW2+xMzYk2H5Ne4hlqPDavJBXeGSwamxXcYOfqfwuSG9vp2ByLBCEA5xOoFw56/vWrfKMCy4cWuv5AI6dx6kdAGzVWm/XWnsAfA/gyotqnKBYwkzjWbUKyMkBXnkF+PhjszcCQAkAQBSy4FUl0LIlsHu39GfhCiF4IXAmBzlUYpOz7Q+1lIA9VNNOCkzCE/u5gLVQuX1tOrta0rVraOXEPhfNJC5JTua+0aMZgmmUt7Q0ZlRyuxmbHRNj2TBvHs8xhC89nUqe0wmMHUtV0ePhcZ07k1Smp1P5S0zMrcQ5HFSy3n0XmDTJSgbicjHkcsMGYMYMho1262aRiLlzQytdHg9JndNJwhcVlVsxdLmAu+4iiQ5WEhcupC0zZrAccw37WoAmLDP43gF5Z6k0/xuCP3euFYIbG8u2HzqU8wyjooDWrXlMXs+JeQYA63kKvrYh6uZ5stcz+Nm0q432eYXmOQu17EQo2EmhhGUKwhFuNweODHbtkhBNwSVDZ6XUaqXUVKVUs8C2eAB7bMfsDWw7DUqpe5RSiUqpxCNHjhS0rYJChtNJX+yJJ7i+XUwMB83r1AGqVlWIr6YAROE46qJVK4Vy5YCrr5bf7HCFELwQOFtWRYNQTrNRrcz+BQssxz6YGATPWXI6c4dmer0WMTIkZ8kShiQuXGhlhvT5SJamTrVCKp3O3OGcpowmTajGLVnC7x4PSZjbTeLx/ffsFOyJU4wiaIjPlClcM09rq67vvEMiZxLA+HzA/PlU9jp2ZNgmQJs7deK17r2X6pVRzdxuks8WLXj+zz+TeE2bdjoxs3/3eDhiZchyt260x8x1c7uBTz8F2rfn9QHrfmRlsfxffgG6dGFdZ83icQkJbKeEBGsuoJ3o2Ofn5YVQiVxmzuT9A7jAaNeuwDPPsF3s8+js9bWv+dev3+lJY0LNczTtZp4xE7JrYCdydkXZlGNXjkOFm5rj8lo0XkYFBeGA9HTg8GHr+4kTHAwTCAoYKwHU0lq3AjAWwG/nWoDW+mOtdTutdbuKFStebPsERRTz5tHnufxy4L//BZQCjh0D9u3n/krlPPB6NZo1AwYNksibcIUQvDxwtoQgRrEIXl/NrrAZx9moXgaGGBin257QY8wYi/yZ0EvAevk6d7bmm/XqRQLSpw/QsiWwdq11rD1czh4C+cknPMaEe/bpk/saLpdVv/R0i1iY7JzTp5PI9ekDPPIIicoff5CQ+f3W3Lm+fblt6lSgRw9LDcrKspZZiI0FrroKWLTIqnu3buyUhg7lcb17W4uwG6I5bZpFSr1ejq43C4xtGsWzSZPcyWhMts9gwrx2Lf+GDmW9DDp1YgfZogXDKU1Yqz1E0ukk2TZhs3nBkDJzn+0EDeD9NPMfgdAqcPCaf3mFgtrPWbnSutezZvEvlPJn7nGoeYXm+T2Tkhe8aLwpS7JoCsIBVaowssGOd94RFU9QsNBan9Rapwc+TwEQqZSKA7APQA3bodUD2wQCuN30v+rWtSKxYmKAnBw/gAwAWTh5/BRiZvyCDev9EnkTxhCCd46wKxbp6bnDBt1ua0kB4/SHemnsiTtMiJwpo1Ur67PDYS0TYLIvGmVq7lzLAY+JAfr3B9q2ze1Y28PtAB43YgTPM3PiYmJI1LxeqmkmAcv06QznbN+e527cSKWpTx+Tbpdld+oEbN5s1dkQRqeTNl15JbebSb8eD8moaZv+/a12adIkdzjmzJkkeVlZwOrVVrsMHEibDWk0cwbNfL8ZM4D16622MwTGqHeARdIeeYT1vfJKS/00bfXAAyR/o0eTdPbsyTKmTePflCnAuHFWBk9Diu0kMFS4rj0s19hnV0ztxM2eTTM482lwKKhpD9O2rVpZxzgc7PR79cr9zNpV6TMtPm9X8oIRau6d/GgIwgVeL9eMsqNyZXm2BQULpVQVpZQKfO4A+mupAFYAaKCUqqOUigJwI4CJhWepoKggPZ3+itvN3/y2benDORyA3+8H5+BFwgNg4qnOOHUgI08/VVD8Ibf1POBykRQsXGgpQma7UYqMM5/XItfmGDNfzZTTtStJzYgRlsrTvbsVumdCHQ0BMOUsXMhzTUhhcGidmQMWFUXFbd06iwDNng0kJQFbtpCwmNBQramu9ezJsM6FC1nGQw8xBMDYfP/9TB6SkMDvM2dyn89HYmaycjZrBqSkAHfeSbXOnOfzWTb5fCRXrVsDn38ODB/O7aNGWWGNpi2mTaPqNnIk22H2bCvEMyHBIk2GaBgC5/WSwP70E+0YOJDHzZmTO4wxJoado5mLuGRJ7nBVl8u6Hx4P1coJE6gGGhJs7pm5Hz4f74X9ubATupkzrWvMmcN9DzzAunfrxnLsxMntzr1Uxdtvk9j17WuRXhMmbJ4V+zNrEIqQGcJmT/Bjv5793FAKnvxoCMIBLhfQNChH4eLFfBdMMiyB4FyhlPoOQC8AcUqpvQD+BSASALTWHwK4DsB9SikvgCwAN2qtNQCvUuoBANMAOAB8rrVeXwhVEBQxmGggt5v5DTIy6JscOwYACvw3A/WRgsOoj13HYvDHH/RZgn/XBcUfouAFIT8hZXZn3z7HDcj9koRypO1lmOQlvXrRITfnjxhBJRA4fX6TUd+MM28UHZ/PIlkff8z/zRw9QyCNc79uHdC8OQnRG29w2333AY0b85gHHqASNmqURTgBbjPKoc/HjmPFCu7r149ExpDAvn05p23rVuD990kW+/YlyfvkE67NMn8+SUuPHlTcRowANm0iufvyS+COOxg/3qkTO65u3fgZoB0dO7LcxYstwtm6tZUB1D53zZ6kZcEC7r/6araZaUMzf9Lh4P9pacCjj1pJaAwBmjXL6kSNLQ4H1cg33+SSEx4P7THJGQxBGjgwt1IYPIfOEECjwJpwTPNs2El7sLoHUJEcONBa4N0ohfbn0oSumuvmtSB7cOKVvOYcGoXPnpFTwjMF4YTo6Nzf27QRh0hwYdBaD9NaV9VaR2qtq2utP9Nafxggd9Bav6e1bqa1bqW17qS1Xmw7d4rWuqHWup7W+t+FVwtBUYPTyWklr7xCH2fzZmYBjlARABQ0orEP9fHNhEpo0kRh9WoOeMvvdfhBxthtMI7pmULLjCNsHPbg7XZH2e5I248DLEXJECY7WejXz1pcHLCyTHbvzvNXrybxMuGcZs6bUfKaNAGWLbMIZr9+FsFxOHKv65aVxWOSkqjSOZ1AfDwweTLDMlu1YgZKgCTsgQdOr9877wD/+EfuOs6ezc9XXUXbFi+25pxpTbJm5gP6fJwEbLJLLllC5a5KFV7PkLekJIswejxU7x54gOe53TyuZ0+GaM6YQafMWgPGandz7y6/3Jr717AhJxu/9x7ra+Y/vvaaNUpv6t23L9vfKLj2tvf5uG/cOCqUgwfzHKO8ud2snwmLnDaNdYmKIjEbONBa+mLgQOv+GtXSPAfmf6MYm212NTD4u30OoD28004aQ4V9mntqV6jt93ru3NPVRfucQYGguKNKldzfd+0SlVogEBQ9OJ2MYlq3jv+nplLJ82sFCr4R0CUr4+VXFbp04aB4KF9VUPwhCp4NZ5s3ZAigSZ1vV1/MdjPHyp5Yw37clClWdkOzaLfXSzWuZ08rPM8scO12kzh07kynf+FCrqO2YgUVrDFjOL9t1iwSnG7dqCQZRdAsV2CySzoctPHtt7lA+ZQpVjjlnDkMlXS5gH//m0Txl194jdmzqfCZhCg+H6/z6KMs07RJp060IymJ5HH9em7v0cNKAtOsGUMp77uPql9UFOtk7kFCArNKGrt9PqqBo0Zx4rCZc9e8OROhmDYCeM7KlSTBnTpxu7knpm3tapPLxWtPmMDvDzxgZQP1eKxQLICKniFfS5cC7dpZat+SJRaZdLlI7lassAiQIefvvpt7yQWPh7ba1d7ZsxnSahKb2BchNc9n9+7W4IB53owCaf9uT8JiXw7B/kyb+aT2dfbM9ezPd14Orc8Xen3A4KyjAkFxhNvN7MJ2bNwoz7ZAIChaML/RffqQuL31Fj+npQFMohoBwIGaNRU8Hg62R0fT95TBqvCDELwgnO0hty9KHqyCGAXH7iAHZ7FctSp3chZzTETgTphEIIYQzJ1rLT3gcFjz2dq3J/m4/36SHTOfbvJkErL0dGt9tQcfJJlbsgRo1IjkqlEjyvgvvEDlqkcPixg1agR8/TU/Dx3KsL8BAywi98cfwK+/8lqGzHz6KYnZokWc2NuiBa9vsns6nSwnKsoKw/z0U6tdHnuMpPL//o/1+ve/eU5aGonSww+zLSIiuL1dO+DZZ6m2xcSQKLdtCzz5hB86/RQeGq0RG8v2MksfzJrF8rp3Z0iCWXDd6QRq12b7mQylUVG8l4mJPPboUeDaaxnX7vPx+p9+ahFa8zyYbKNLl1rzNLOyrPvctCk/G9IZHc26DxliPU9RUZaCa+ZomkEBe+gkYIX6miQvb79tLT1hf6aD17ezDzrYn2kgNzGzK9bA6YlegNzZSu3n2RPjCATFFU4nB7sAPwAfAI2dO+XZFggERQd2oeGtt4APPmB008KFQGYmkJpq9V9bttAvS0kBOnSwBAVBeEEIXj4RajFowHopjBpnMi3asxkaZWXsWKpgW7aQGBiHeOHC3KGPhgCa0DfjQJs5Vd2705affsqt7nTtykQf119vkY/lyy0lqkED4JprGObYti0XFH/6aeDll0kQcnKoikVHc/7b8uXsBGbOJMFJTAQmTmR5L79M1Wv8eIaDDh9Oexctoqq3ejWJzaBBJHuzZ9PWAQNIOJOTrdDPjRsZLjlyJEM109PZOT3/PEMCmzdnGGi/fuyMjOp0xRVWAhSvF/j8Mz9e23IFnno3HrFX9UL6ST/ee4/lZWXx/+uvJ8lLTmZ79epFm7W2bASogEZHk6Ca+9ulC23w+VhnkznUrME3fboVPtuvnzVvcN06a/mMlStzDwCY4+zEyNg0bRptMsqgWabChJiaRc379bPKuf9+3le7mmyuZf63/xCY5Trs6t+yZdZnQ+jsAw929doePmrIpslses89Mk9JUPzhdAKtWvgB6MAWP44f9+PgwcK0SiAQCCzYB3LbtQPuvhsoUQIoVQoA/PD7TwLwIxKpuON2P3r0AE6dIrmzr78sCB8UGMFTStVQSs1RSm1QSq1XSo0OcUwvpdQJpVRy4O+5grLnQmAIhZkLZ99uVzRMqKDJqGgP13Q6OTH/sstIWkz4nkl8smgRHfh27TjHzChPJkRx+nSqR0bJiYoiefrkEyv5ictFBeyHH0g+YmIozzdoQIVuzRrO6/q//2OGytRUS9Hx+YBt21jWunVWspemTUnibrqJ69z16cO6eb3A3r1MBnPsGG3+/nvOZbv6ambK/OILq20WLABuuMGqw4kTDFecPdtas275cl5j0yaqiA4HlS2AbTNuHMNS//iDNrVvb63tBgANK53A8qURmKe7wL0wETN+OoHhw3ner7/y3KpVWbcWLXjvDDFu3dq6b2++yfJ69sytTrVpw/8NYVu6lO3QrJnVhq1a8V6a58Pl4jaXiyTepFu3L2hukpekpVlJU3w+2rtmjTXHsmfP3Gsd2hUz+/IOo0db2UnNsfZnFiBRM8tmLFnCa48aBRw8COzenXtwArDKCrXmHWCp1OZZ/f13Pufp6Xm9VYWHcOqbBAUPrxfYueZE0FaNo0cLxRyBQCAICeNrdu1K35D5DoxyVwpOHEcFHMcnn2ps2AD8/e/8jRcFLzxRkAqeF8CjWuumADoBGKmUahriuAVa69aBvxcL0B4aFeIhDt5mn4dkYNaba98+99wks4h0ejoTdHTqRBK0cWPuBB9OJx30JUssJ9mEYBrnHaDS1qIFjzdhnCZj5W+/kRi9/z5f4LJlOUpjkonMmsWXun59a8HytDQrZb/HA+zbB3z2GZWyTz/l9wcfJJlISaFCN2wYwzw9HpKZevWAe++lMrd/P+1dvx64+WaGTEZFkaQpRfIFkJjcfruVyGX/fuCrr2jnRx+RzG3bRkVu40YSly5dSHDvu8+yOSeHNk2cyM5owQKWXa0a7fd4WKbXC6QcjIWncUsATqR16I1x38Zi4UKqWtddx/Ouvtq6fz/9REIzdixVuNWrWV6DBtw/Ywbt8nhY/tq1JGP33cfMVJ06WWvupadTOevd25qXZ19U3BDw++/nM5CeboWIer0kQ7GxVDXHjSNBW7DAUhAXLOD5owNUxD7n05RhBhyMqmaSvxg7vF5rDT8zN3PpUj7TiYkcMPjvf4FXX82d/t0knDHhtEbBs78jZuTQLM/QubO1OHwRRJHsmwRFE04nUK1JbNDWCHGIBAJBkYE9CmjRIiZWSUz048hP81AdKXDhCLyIRTpcACKQmckBbbM8lEn2J/1a+KDACJ7W+oDWemXg8ykAGwHEF9T18oNQ6duDtxkn2O40T5vGffa0+fYU9atX8+V44AG+WGa+GcBzzeLXRgoH6GDPmmXN2TPr2JkU/4C1b8MGqnn/+Q9J3b330pn2eEjozBy9vn2puG3YwPOaNGGo5bBhtMvh4ILeTidHdpKSSKAMMXzoIRK2jz4iaZk0iQTgnnuA8uV5zdtv5/Ft2wJ79pDkdO3KsuvWJYEaNIi2b9zIuXQTJwK1apE0LFxIMuly0ZaYGF63USMSh+RkEuUVKyw16IUXqPZ9/jlDHO+4w5qn5vORZM2bB4y4TyHm7RfRNeUzLH/qd3zxpUJUFEMODfFyOGhD9+5UCz/7jCS5WTMqbevWMbFMWhrtv+MOkjlzz0eP5r1r3pztuGEDSZlZj8/MBwR4X+bNs+ZOTp1KxbNFC5LK9HQSWhNGapYbqF/fsnXAAGsgwSjEc+fyOTKJXcz1nE5rzp4ZPJg+nXaYsGHACk2dPZvke+lSfr/ySrbD8uW53xGHg+dMn85nsH17bp858/RQULM8w8KFwKFDRfPHoij2TYKiDYdTwawjxZ9NhSNHCtEggUAgCMAM5JrIm549gdKlgc9ePgJ3xnEcQVW4URGARpaqCoCD5EePWv6jmbsnydHCB5dkDp5SqjaABADLQuzurJRarZSaqpRqlsf59yilEpVSiUcu4Fc1VJbMUNscjtyLYicn0zlescJKm28I3apVVGXMXKOkJGtNkf/8h8TKzHNq396Sws0aej175na0TebHSZOs5Bx33sm17RYs4LYxY3gNh4NhdYDlwK9fz/9nzCBpa94c+OYbnufxMET00UdJZjZsIMlJTrYWNFeKIXqXX866jhrFMjt14roqH37IjsGQxB07mABl/Hhg+3baYsLy6tShIhkfzzaaOZPz9kaPZkKWqChe47ffLDXuvvtIUnfvJnEaNIghpenpJKMeD+fnLVrEzum336ysl4mJQLfuEZi/qTJ++FH9SRJ9Pl67a1dL7YyLY/uMHMl78sknvCcjR7JuCxeS3K1YAdx6K4lgq1YksD4fbXe5eHxyMq+/aJGllk2fzjbo2dNaSH3tWs7BTEpiKOvvv7N9hg8HHn+cbfjTT2zvNm2stfweeIDt9u67vA9mvp1J7GNX6cySEmYZjqgokkRDujweKsBpadYafd26sS4mkc+aNbk7eDMnELAyqXq9FoE1BNJ+jsfDrF1FPa7/QvumQBkXpX8SFF1wvh0z0BmiF/yeCAQCQWHDTA1p2RJApUrY6miH6tgF9ltO+HQGymIPqldIR8WKGlFRlgAxevTpazsLii8KnOAppWIA/AzgIa31yaDdKwHU0lq3AjAWwG+hytBaf6y1bqe1bleRuV7PG6Ee3GDCZ5JXAPz/0Uet8MzERDrHc+ZYWQ5XrOC22bNJToyD/dBDPOehh/gCffghic2sWZZD/McfVEwAOu8rVnA+2K+/0tmfMIHn3XEHnW+fj/+3aWMRwLVree7SpQwv3LuX123alKM49euTII0dSzVtzhw6J6NG0bkfPZr1SEpieQMGkKA8+ywVJoDkYelSkrVdu0g2hw+n8paTwzDOK66gwnbDDSQu06ZREdy7l+Rk9WqgZk2Wt2YNbdy+ncrWTTexXu+/b4VF/utfJKFdunDZhE2beK7fz7/nn+dfly4kO4ao9OlDxbB7dxK6Nm3YhlOmsD4LF1qJQMaNo6JmHqukJKqOa9Ywscu775L83Xsv1dNu3VhOx468f0uXclufPuwYu3WzlonwePhMGIW2dWtmJTXr3V17Ldu6ShWS561b2YajRnH9vIULWR+7cmsWljfPqgkftcOkPHY6+VwY8tavn5XM5ssvOf8uKsoiiQDnPJp1EoMzdvbpw/Y0ynJUFFXjIUOsJUHS06mA/vYbQ36DM24WJVyMvgm4uP2ToGgiIeH0bRUqiCMkEAgKH/ZkZ2b6xsqVwJixCr3+Xh2HSrYCCZ4P5bAbg/EbohdMQer0ZWjf1v+n0GHEC0F4oEAJnlIqEnSg/qe1/iV4v9b6pNY6PfB5CoBIpVRcQdp0NhiJ2+6Ymu/jxvHFGTeOhMiMerRvT/KUlETnNznZUnBMuOSSJXSoFy/m9l69WOYvv1hz7ObMsYjWFVdQLYqKolK2cCGvvWULcNttwJNPMpNlejqJXEwMnfkdO5hI5IMPSJRuuYVK0cSJDDlMTOQ8PmNH27YkmCYk8KqrGI45YAAJ1j//ySQq8+axrHnzSJ5OnGB9brmFJLJ5c9pQogRw440kUldeSVXq3/9muN5tt3FU6f33acvKlQy/rFkT2LnTSuTRtCnn9731FonczJlU8ho1oorWsSNVyfh44NtvWYeHH6YaOGUK29WQ0E6deK0PP+SyDu+8Q9sNwT51iplEtSbxXr6chK5ZM9blnntIuExGzKQkdqSLF1tzL2fPJhE0IZR9+9Ie8yy43bzfAwaQbHm9DMv0+bjP7Wa5rVuz/k89xQEDQ97M0g2rVuVOcOJ2s/x58yzF2ahqR4/yvpslMuzr2MXGWgvE9+1rrQ9ojjFrBwKWuu1w0HYzV3ThQisJkEF6Oonqxx+TwDZuXHSzaBbHvklQeEhLO33b5MlFewBDIBD8dWD8gu7dgfnzgZMnOZA7Z24E0jOdoLsfgeNogu9xI3KgcV3WJ/jk3QwJywxTFGQWTQXgMwAbtdZv53FMlcBxUEp1CNiTWlA2BSNUchUzr+jtt3OvO+Z00lm+6ir+73KRQHTvTkf54YcZZle2LD8bh3jkSL5sJg3typUMFXQ6GepWowYJzcqVJHsmg6LDQbWkd2+GLa5aRYfi5pupjDz7LNUggNvT0qgm3X8/wyz9fiA7m4lN6tQBXn+dSlBCAsnUHXeQoNx6KwmPUR4B2vDGG9bcsPHjOS/P4aBC2KQJiebNNzNRyksvcZ5a794s7+OPSZR++422vfQSVbannybBbNgQOHyY1/znP7mtSRPWt3lzKnqdO7P9du5keFTnzsxAuXEjlcAFC2j/HXew/a+5hkqdz0fyVquWlZ20fXsS7csuY/2Sk0n8vF5eu2pVqpxRUZyPt3s379Ejj5BcjR1LMj1jhrUOYFYWj9m4kcrWo49amTTNMzNpEu0bN451mj6d98nrJflZsYLkcdIk2tW7N9tgwAC2YWIizzOdrz2U2OslkWvRgqTL5bJCdI8do+1ZWRwoSEy0MsCaRCnz5pEMm/UB7eWbZRfsMN/NgvL2EOZp01jW5Mnc17EjiWNRnYNXHPomQdFC9eqnb9uzp2g+3wKB4K8He8SNw8Hf4caN6RcRCmb+cEVkojp24xOMgjuiJLp2pX8gA1bhhYJU8LoCuAVAH1uq8SFKqRFKqRGBY64DsE4ptRrAGAA3aq11XgVeTIRKuAJYE05btco9ImJUOONYA7kXjV6yJHd4p8mkOGcO8OOPdH5nzyYBmD+f53fsSDKxbh0J00svMZGISdySnk5l6P33eXz16pwLtmcPiVyDBlR+XnqJTvyxY1Z2xpo1GRpZrx4J4fPP08mfNYuhleXLk7x8/DH3//YbQ0J//ZU2livH8Mn9+1nu229bYYJ//EFit2UL9332GTuHadOA//2PIZ8dOrAtv/mGxHf2bGbs3LkT6N+f5axdSyL23XckIj16WJkfx45l2115JUnXV19RTRs92lILH36Ybb5gAe9F//5s9/XrzcLEtOH330mQHQ52eEuXWgrV3XdzuYg6dbh/6VKGRz79NOvTpw8VwmbNLJXziius5QrM0glOJwmcz8e6OhwknTExJHkAyeaoUVS5PB4+J4MHk9R17GjNc4uK4iDCE09Yi8uPGcPOd8oUK/mPx8PrzZtnqYd33UX1ctAg3iP7GnlGdTYZUk3yFPscuuDkQoa8Aaxjt25WhlOzRIjHQzI5aRLbNzLSUoiLKIp03yQoeogLod326JE726xAIBAUFuzr4PXty4Hidu2saLLoaKBkSbr8lbELDmgAPjSvcfLPzO72NW4FxR8FmUVzodZaaa1b2lKNT9Faf6i1/jBwzHta62Za61Za605a6wJxC0M9sE5n7nA349SuW8f/TWKMmTP5gsydaxG95OTcaem9Xos8mUyIJtwuKoplmblk7dtboXmLFzPzZMOGDK00ZGDjRipw69dTAWzfnmSmUyee9913DHXctInq2yefALVrU3UbM4ZkZft2bvN4SKp69CBJ6tWL69O1bUvnPjHRCs2sVYthoTExJCJvv81Uu+XLM9HJokX8a9CAqfSbNWOd77yT5Xftym3ly/PzsWO8zpo1nIvXpQvrOnu2tZbcsmVUtV59ldecNYvH+nwkDWvX8vOcOcBjj1FJ+/VXS0m76SbW9513SHQGDCB5nDaNpPvQIdbjyitZVlQUyWdWFpW1K65gSORHH9G2SZNI6gYNstb769GD8+NatmQZXbuyTefMITEz933dOh4bFcW69u7NOj30EMMl9u8nGY+I4L0dNYrHdu1Kop2VxfMmTbJUY4D32ZDE8eNZ96lTSe7NfEWA/ycn83pXXMGMp2a9RbebgwVeLwcH+ven8rdoEe9Pt27WkhZG1fP5aJtJJmEGMsyx3buzzZOT+Qy98w6fnbZtaUtRpUNFqW8SFH14vdYghx2ffspBNIFAICgKMNOJZs3i77Hfz4H5mBj+tmdnk+QpRyyOohq+b/sayteNxdy5oRMOCoo3LkkWzcJEXkqd15t7PS97GKZ90UeTidDMU5o3z8q0aNLVz5pF0rJsGcP4GjRg+OKkSXR2t2yhw7t4MQnA+vUkJz4f1a0qVUhaYmJINCIiqCzt2kU7HA6e27kzbfn6a37etYtKn9vNbd99x3ljDRtSvalfnzaOHEnStXYtyeOGDSSMr7wC/Pwz15fr0oVhoh07cn90NBXDp54iOfL7revVrk2SmJJC0pWSQnXtqaeolJ04QSWsShWGYa5ezXXo/vUvkpoJE6yEKV4vQ1Czsxly2rcvjzVkauFCHtu5M0nJqlVst+ho2j98OOfXpaSwrQGGh5q0/t9+SwL82mssx+dj+86YAVx/Pa//5ptUHrdto6K2ZAnb/NVXqbYuXcrEMU8/zXu5YgUJMUClcdky3oP777cSmzz+OLeNHUvSvG0bO9y4OKrDu3Zx/yefWKQrJYWjb2++yfvsdJJsNm1KZ9LMAzIDES1a8D5GR5P02Z/T114jKWzRwnrOW7Tgc/fOOxwMmD2bJC8hIXeIZ9euvA8OB21ISLA6/c6dc8/R69uXgxkbNvD7xo0ckPjjDxJ8GQ0UFHc4nYyECIbW8nwLBIKiA6+Xfk/Hjpw2s3Ah/aL0dPoeHDhX2BLZAhVuvhK/XP4DevZSf065EIQXwv6WhhqVMMkmeve2junenZ9N+vm5c/kymMyIAImcyWJokk0Y9OlDEpeURKKUkMARk08+YdhaSgr3x8SQ/H3xBUlT585WmOPIkTyvQweSjFq1mCxl2DASlWXLSPTMQuCvv05V5uRJOtYNG5I49OpFpadWLdqwaxcJXHIylSmASx3s3w9kZpLoNGlC4rFsGQlFjx7sFDZvJpk4epS2JCYyjPSOOzjX75ZbSO48HoaF1qpFpfC223iNqVM5327UKEsBio9nOabN27fnAttZWSRrDRqwPo0aWWT3iy9IUsePt8Jk//EPjk4tXMj78e67PGfjRo6sP/QQ22nPHtpmljd48knWZ+RI1mPAAJ5j5j4uX05S5/ORmN1+O69VqxZDRGNiSIjMfEmfjwMDtWrxWm++yXZ+8knaZMia08l5kCMCQYBxcSSWiYkkhc2aWeG/WVlW2KWZ+/jVVyRmLhefgXHjSL47dSLRe+MNnrtwIecEer28hll/sEsXhvu2bs22N2GUJk3ywoXWMgj2ZCp9+/LzzJmsq8nSOXcun7XBg2njsmV8FhYvZv0zM4tukhWB4FxQuXLo7fJ8CwSCogK3m2JBhw70B0uX5sA/4MfRoxpABLRWiIuLgCuuNBo15nnmt9xMdxGyFx5QxW1aSbt27XRiYuJ5n2/UOkPu5sw5ndCZUdnp0+kUf/ghFaUBA7jdrInXvj2JgMdDgjd7thUul5REkvHSS1RF2ralmnPffSQMN91ERWnbNipV5cox4co115CIRUZa669NmUIHfuhQEqb160kk+venynTkCG1p1Yrz0/r3t0JFzVpnfr9FMNevpxN+zTUklyb80iTzaNiQDvvOnazX+vUM+SxRgm2Uk0Mi0Lw5z//iCxK0Vaton2mnzz8HSpUCSpYk+Tl4kISjVCnL+V+0iElOqlal0vfZZyQV69eTgPbvz3mKTZvSpt69mV0zK4uq0fr1TNO/fj3tOnWKZOjKK1nf3bupXK1axfrUq8d7uXkzyemaNVYYo8NhhdaaturShW2VlUUi5HDw/tx/P8NhlSJ5HTuWiumaNazvww/z2erfn6TNEKaXX7aW22jXjvWZM4fPQNOmvMdpaZx/d8UVnD9n5ua1asXPU6ZQQTQLoTudnGdolE1DVM0C62ZtPIMePahMJiXxuTRlmNE/U459fTzASs5iL9fjIcEDeO/Ns5+VxbLmzAk9fykYSqkkrXW7sx9ZtHGh/ZOgaCI5OfRSCQsWWAOAgvBFOPRP0jf9NXD0KP2LgwcZRfb7737keDKg4QcQjZo1nIirGIHatTlFqHt3/qabaUlC7ooXztQ3hX2IZjDsip79c69efMjdbipj6en8UX/vPZIZkx0RoAqSkEBiYH7cjbMbHc2yunVjCOG331rroS1eTBXkrruoRrVty3XJmjcnmXM46MQvXcoX7+mnqTI9+SSJyqxZJDK7d5N4ORwkLzfeSEd8yxYmShk2jM71J59w7lhKCrfv3UsSUbcuicuaNbRt2TKm061YkZ1D48b8q1aNpG/fPoZlVqvGTuDQIZKrDz4gGevcmSTnl18YagkwRK9VKxLRnj3p9Ldrx/DMKlVIuHJyeE2fj+SrQgUSpRde4L4+fUgsmzYl8Vq5kvakpLBepUsznPOll6jy9ejBekRGkiw+8wzrA1Ad+89/SO7WrbOSz3TowPaPiGBbGiJkkifMnUtbo6L4rPTowdCsBQv4f+vWlqKWksK5lPZMVCaxjmmT7dt5nZ07qRS+8Qb3N2rENk1P54BC//68P4sWMWSybVs+Bz//zPDLyy9n+5jMV927k+S1bZt7TT6T9bJHDz4vhtz5fBaZBazlJezE0Kzh5/WSdC5ZQjXPlNu2Le+nmato5ksa5bVWLVE4BOGBvAYpJMmKQCAoKvB66a+0bk2f9IYbAI9HoxbWwYEcAGnYvUfjxAlmH1+xgj6QWdxcyF144S9F8AxBM2qFwcyZFskDSB4WL2bY3VNPcfvatZy7ZNadS0ykEmNeiK5deYxR6jp2JMl48EE631u3knh98w33mUWjo6JIng4fprrzzTckPc2b8+WbM4cK1f33U/HTmorLjBnWEgSdOgHHjwMvvsj/mzalc16hAtWVli2B555jmT4ficOLL9L2lBQSnrg4kqHy5Xmd33+nw1+nDpdk2LOH11y5kirZ0aOsR+3avOb111tZRFNS2CZt27LjOHiQ2SZ37qRSt2OHpTjWqcO5fiaT5e7dbIMSJUho69alGnTddWzPrCyLOLz2mqUypaTQ7jfeIHkbPZoK2MKF7OjeeIMEa+1a1mvDBpLYxx+3kub4fFwvMCmJBLhLF5Z5550kPW+9xWdl5EiSvU6deG9ff52k3+ulord9OwlTcrKl6M6ezfupNev31lskert2WRktmza1iGNMDAlx164cZBg3jvfz4EFrLb0lSzjQ8O67PG/IEB73n/9Y4Z29epGUGaV5/nwrKYoZuUtP50DEu+/ynkZF8fwff7QWLTfhwwDLzcpiO735Jp9jgD8qPh/rvmEDybEh2AJBcUZejo/p6wQCgaCwYaYbrV3L3+x77wWUisBONIYPpQHEAojA367zY9kSD268QSMpSZZHCFf8ZQiePdlK8Gejzi1YwBfk9ddJopYu5fZPPyXBuu8+ZjsE6HyPGUP1wh4GOW8enehJk6jQHD3Kc5o2JXG75Rau/TZ7NgnOmjUMRbz+eo68bNtGUvTOOwzz27CBnxcsoMq3fz+vZebS/fOfJF2VK5MUlCvHa3brRhVl5kyqVzfcwHlx27aRXK1YQQIWGcntN97IBck//JBKUfXqJDspKSSR9etTVTpxgvPGTEbQBg1IuFJSaNv//kcVr08fXsvpJAncs4e25OTw2tu2kTisWMFwycqVmYxk/nyGJ27dym0zZ7Jd//tfXttks7vsMpLs9evZOWVk8JyBA1mvjz5i2XfeSfVp9GgSy/r1qUhmZTEcdPBgEpM9e0hsnn2WRPSdd/h3001MYNOyJcv/4QcSyzFj8OfaMUpxDmOnTqxD/fqc09e0KcscMIDttW8fr9eiBdtlxw7ev02baKshgw8/TIVuyBCSucce40BBaiqvCVgJflatYiceHc26JyRw7qFZk884pp068ZgePdi+PXqQDGZlWfMk77zTmg+4bBnnSk6YQAK4b5+VXbNTJ9bL56N9vXpZc/yiomh7zZp8tmREUBDOKF++sC0QCAQCwuSXaNGCUVt16wJt2yoAZcGUG5EANJaNXYA6G3/Frpe+QvopP955R0heOOIvQ/CcTmuxcROa6XZbc++cThKWqVOtDIjmvAceoMO7bBmTcpislo0a0WF/6CFr7buBAxku+McfdIzvvpvEzCwUHRVFFatNG4Zg+nxUWO67j/PZtOYx6el0mrduJWFYupSEpG9fJtvYto1r2x08SCJZrRpDJrdtY+jiDz9Y5HXVKn6/7jomZImJIbHIyqJq9sILVHCuvppE4uhREporr6ST36+ftUxDr15UoGbMIEn+v//j8TNnMuFJdjZt/d//aNP27fwcH0/ba9cm2YyMJPFp25aEau9eKlVdupCk1KnDdrj9dipLJpX/yy/z+KQk1uuuu6wwyoYNOb+vVy+WO3o0w0lr1WJdGjUikXvxRV7P7WZbGLuyslifgQPZMW7bRsJbrRpJ3uWXc3tiIgmM00kid8st/N8kjWnalGppVBRJm9fLZ2fgQJa5ahXresMNJIs1a1KN+/ln3sv582lbWhrVOJMs5aOPGHZqEsCY5TmWLSNJnTOHKidgrX2Tns4BizFjqFi+/z7Lnj+fZNvn47Fff81reDxWkpU2bfjslS9Pgm1I3dKl3O/xsFyzePqYMVaW0wMHCvyVFggKHfv2FbYFAoFAYK1j+/vv9GE3bKCfk5QEABGoXdsBLnausT6zOiLgR9aBQ4jKPJFr3WdB+OAvcUvNOmDvvWepG+npdHZHjqTD6nZT0ahXz3Leu3WzsgtlZbGs99+n42vC+QYO5PndulkLcA8ZQoe/eXMSvJUr+ZJlZJDwbNlCBfCKK1imx0NCkpLCazscVFv69+f/Zp5cx45MwgHQJrN0wsaNJDZmDbqvv6YDXro0idK335JYmLlTW7eSUC5eTMXP4yHhuuMOqkpXXEGiWaUKCcdvv1Hy372baltsLJcn+PRTOv0bNlBZMtklq1UjSZs6ldfv04ftmJlJVfHHH0kgv/uOShzA+/HPf5LovPsuSdeCBZxT9/TTbNOoKJKPDRv417SplXXy3nsZS/7FFySDfj/v+/btVBmnTiXpfvZZ0+ExPHL0aEuF27CBquHatax369Zst23bqJgOGEA7Dh4k0bzuOrbZI4/weZg0iaGXZcuyPVq1YhitCcHcsIEjaw4Hj//2Wy4dsWYNn4e9e2l3ejrnb+7axSQ6S5aQPJr5bP36kfw9+CDtWr+eZdWpw+fIrNV4//18Nhs14vPw2GO0f9EiXr9NGw50rF3Lezp3Lm01C5lv2sT7aObtrVljZec0+++9l983biSB7NOH70jXrhwEkDTygnBAenro7YsWcYBHnCOBQFCYMFNkAPoLU6fSn6hWjfkYTJ6D1NQIHEV5tMNSjC9xHx7rH4sqVaUPC0eEvYJnRjXmzaMS53QyUceYMSQIMTF8KVwuOszR0Txmxgyek5VFZ3rCBDq/ERF0ivv1o2JkFof+4w8SPqNmNWlCkjBiBEd569RhCOOAAdZ6bybMcuJElr10KckXwBDJadNYxuTJJG8my+Ebb1D9efVVoFIlOt6XX85jx42jWrZvH8ucNYtz/1JTSThLliRhmjKFalREBFWaXr1I2HbuJDGsUYP2Vq1KlXLnTtpVqpSVTKZcOZ4DWIk7TJiqx0MCkJ3NcNTYWNppknscOUJ7PviA5GPwYCp9M2eS2LZsyXl4jzxCojNlCtWhyZPZbt9+SzKenGzNm/vnP0mevV7asW0br9OgAUe10tOp+P30E+9DgwbW0g+1avEcgCQ2Lo5tWL06iddbb3Ew4JVXGLIaH89jk5N5L+vXJ7EbNYqkG7DCMydN4r1dsoQK25o1JET161MN7dCB7fr226yzWei+YkUODrRowc56yhSrHsuWsR3WrmVbXXEFBy86d+a9yMoigW/enAQdYHtFR5N8mUybgLXeXevWrI/BI4+wDubeJiRwIOTBB3lP772X5HjMGCqtU6fyvVm71lpLUH40BOGA6tVDh2OaJVAEAoGgsGH6omXL6DskJ3OQ3IBChYIfZTGr0yuoN6gRViSqS2+o4JIg7AmeUeIcDivle9++VDr69+d3t9ta6HzIEMrbUVFUzNaupSIxeDCd4xYtrMWvH32U5xin+LLLGFI3bpy1llpWFtWeiRPpWP/wAx3799+3FjuvWpVEpE0bOuFG8fF6+ZLGxJA0/fEHyUDfvnQ4Pv+c5KRaNTrjiYlUFA1J3b6dqpHTSQd82zZe79prOcIzZQpJ3KZNVG9KlWKoYuXKDOVs2JDE8tgxktx69UgYdu7kWitHj7K+qak8tmRJlv3ooyQo3brRFr+foZKPPEKV5/BhExvOpRpiY/nZ52Pdje1t25Kc/Pe/JGWNG/Ne+f1s0127SEoef5zqYtWqXD6geXOqZ9HRnP8YE0OF7oUXrOUitm4lGfP7mTTmvfeooG7ZwnoNHMhnoE0bltuoEclaVBTnKnbowHvevDmJaePGJFfz5lH93LOH19m+nfdr/352tO3bs02bNKFdzZtTVY2IIKl84gmqio0akZwfOcLw2B9/JLH78UdrmQiAJM7no4r31lsk8ydO8B6ZdQUPHOA1br+dz9WcOWxXj4efAZb96KO0t0MHzj8EeLx5Hnv0sIil+bxypTVQMmAAyevx45zXeeqUtTi7QFCc4XYzAiEYZsBFIBAIigq6duXvfpMm9CUMrD4sAktWlkS1ePWnrykIP4T92KPJNtirlzW64XJZIYCdO9O5Hz7cWuTRIDGRasW0adYaaAYme2N6ujVvrksXOtpjxpDIdetGUvTNN3SK584lIdmwgf+vXk3neeVKEr7nn2dZy5eTWFSvzpC3116zlKzZs0koc3IYilm/Psnatm1UlZYsIUlr3pxKVHy8pRJt3kznf+xYOujDhjEL4sGD7AQOHiQRGjCAxKBdOypKAImYx0NC98ADXHC8fn0qkxs3co5bdDSVtOxsEqsyZTinzes1i20yBPXyy1nW//7HcufPJyHTmuSvfn2qUg4H7bn1VpadkEDVMjKSdezQgfW56Sa2x4EDJJo//MAQ1H//m/MWMzO575tveHyFCiQyXq+VCOaXX3gvR4+mKrp7N+9d9+4k5/HxbL+ICLaNUrxvWtOulBQqeCastlUrZicdNYrKXEQESdCqVby2z8ftLhfvR4sWvP7ixXzutOY9j4kh2YyJsZKbGMTEUEXr04cDE/Pns5wBA7ivd2+e27Qpr3fHHTx+4EC2rVm7sXVrtsEVV1gDISowqNezJ+2sX5/lp6dTqYuOZtsYUv7OO2wDrdm+n3/OcmJjL+77LBAUBmJi2CctX557uxlMEwjOFUqpzwEMBXBYa908xP6bADwJTpw6BeA+rfXqwL6dgW0+AN7ivkaf4OLAJBBMSGBUUnq6NRgM+AFwsXOHQ+H66+kHmuWWZIHz8EPY306TNtakljdLJLhc1gM9YgSd9hEjrLl6JpzT62VY4BVXUH1ZudIKyQT4vU0bko0FCzjP7pVXSOjMgtH33EPHuVYtKmO//cZyu3dnWbt2kfwdPEgys2kT/9avZ9gdQCKyciVVnTvuIOn8/XcSp7vuIsFJTSUR2bCB6mFsLFW5jh2pMO3YQUL7668sMymJdhw5QvJSvjwVrP37+eIfOMA6HD1KIrdjBx2dtWvp8APMvJiUxOtv3kyF6ZFHOP+renWqi926UYHLzGSoa0QEFa4WLXi8IQkAFR8TKtm8Oefjud1+zJ/lRZkKkShbVqF3bxKhtm2pSvXqRYWwa1d2bjk57Ng++ID1cTqpKBmC3rYt67BoEW0ZPJgK11tvkQAtXMj7sHo1E5scOEDS6nCQyE6ZwvYwy06sXcsOtUMHhs6WLk1iW64cSZnTSWXTEKd//YuJch59lO3o8/E5eOghkl+zHIFZimDJEmY5HT6c9YiJoW0jRnBg4LPP+LwmJDDWfvNm2vbee1QLt2/nsevWWUlS+vSx5vT5fCT6hqwuXcrjFy7kvbv/ftpq1iUcMICq7gcfkEgnJlJFNXNd3W7a6XZTKa1evWDebYHgUsHr5eCT3UkCFLZsYZ9VpUphWicopvgSwHsAvs5j/w4APbXWx5VSgwF8DKCjbX9vrfXRgjVRUJxgkgm+8w4H03/6ib6W1W/5AaSjVs3SiI2NwJo19GlMAkJBeCHsQzTN8gcmDNP+v3mgY2PpIC9das3Vc7lIpgCSO5+PDvaJE1R7fvqJqe2XLuW2Rx7hucuXM9RuzRoqHz/9xM+TJ1Ml272bRGfVKipkmzeTGLz2GomJmX+1Ywcd/ooVLYff6aSCtmQJ7Rs6lASmQQMqOE88QaJUvTpJ45o1JD4NGnDu3sGDdMzN6M6xY7SpbFkSna5dGdoXE0NS16cPrxkbS/sWL6baU7MmHf9Fi6g+xcRYYazVqlG9ufxybtu920qmUrIkR8EbNyYZbdaMpLl9e9YjIYEKmt9PhfCuuwC/z4/kzxNRZfNkVFg/F+Vi/Zg3j+0ybx7DAaOjee0vviBJve02EvBFi0jE6tQhCfvsM86ra9OGyqTPx3YAeH8aN2Zb5OSQ6K1cyfZs356q4datJI6RkSRQAI8BqKy5XCR0J0/Slvh4Ph8tW5Lwm6Qv//oX293h4DWV4nMwZAi3DR5shdC2bs3nbu5cksedOzkw0aQJyd/o0Ra5u/122jhiBJ+Nhx6iojh8OO/h6NFsF7MIqgnLcDhIYP1+1qNTJ74zWVl83pxOqqFz5zKhxIYN/F6lCsM02wXGjufM4XswZgzbwLxbAkE4wFXCOEkAHSU/mjYVcic4P2it5wM4dob9i7XWxwNflwKQoTJBvlC7Nv2uiAj6XRa58wJwoGWjDCxdSuHg6FEOBssyCeGHsOfsZkkE+/+dO9OB7d7dInkxMVaWSZeLo7JJSXRyTfilw0FH3WS5NAudf/ghk5Js305n98ABZli8805rnTdDEp1OhgCOG0fnOCODjrHJrrhihbVg9I4dTPCxeTNVtldeoTNfqpSVwGLCBP7vcLC8evVIGCIiOHesVSuW07EjHfqqVanUfPopP8+Zw+u9/TaTgVSpYimW331HFeqyy1iex0NieuwYiZRSDNurUIEk9YEH2I5/+xvDTStUYBhmXJylhqalWWsAzpzJTqVECZKh5cvZhps3U9nq0AG447IjeLLfTiSjLWqc3IuKHjfSdUnMmEGyU726tXzD8eMknCa0s2NHqy0//5yqXGampdia7KJeL693550kglddRbWrSRMStS5dqCROmkTi+e67VB4zMkicBwygrYsWMWuq08lRs6uushQ+o+b27ctEJA4Hly9YuZL1HzHCWiqhXz+SqTp1GE45bRrJ186dDPkEeN+vvJLqWffu7KATEljnpCReD+AzMXkyFTizPt2SJbyuSbaSkMB269yZ9TBz9F54gWWYsl95he346KMkqNu2sR4PPURbmzSx3oMGDXInbBEIijOcTqBRzRNI2VTGtlVj0yYO+sTEFJppgr8G7gQw1fZdA5iulNIAPtJaf1w4ZgmKEtxu+nI5OfR3PvzQJO6LAKN5AcCLqXPKwuPhoPEXX9CvMBE9gvBB2BM8E5YJWCRjyRISqrlzrWyC3bpxe7t2fEkef5wK1po1VGA++IAOrNvNOUiPPkrF5JtvSGzS05mGvlUrzj3r1Ill+Hx09Lt2pRNdowad68WLOYeuYkXaZua6tWlDhahBA6pNgwbRkd6wgcpI6dI8p0kTJt8wZMFk4fR6gZtvJiH0+Xit8uVZt44dSa5KlaLCUq8e65CczPXVvF4mxmjenMpfbCyVthkzqMzNm0eSkpnJ+kREkKRs2EBlqHNn2vzppySKcXFUEo8dI5k8fJiZKAcOpJpWpQrJyBVXkFCkpVnE5PBh3q8vJlfCsZL1UCNzL1rFHUCKJxptWpB0Z2ezXo8/zsyiEyeSQK1dyzDW9eupgsXHs9x166i+lizJ+3bffVQZFyxg2/zzn7w/JjNedDRVxOhokrd584C//52ktX591u3ee0ly332XJOyNN6yQXIDPj9tNcti3L5+5bt0YSpmSQts7dGA7mPmfbjfDaN99l53uhg28V9nZtNeEhjocfM4WLeL+GTPYtka17t+fobZ16/KZzcpieGrnzlSWp01jmy1fzuegf38SuHr1SO5eeon/9+5tdf7Nm/Nz2bK8zuefsw18PqrPrVrR3rVrebwkoBCECxylY8FRcIMI+Hx0oFq3LhybBOEPpVRvkOB1s23uprXep5SqBGCGUioloAgGn3sPgHsAoGbNmpfEXkHhwSxyDvD3vmJF+pbZ2Qp09x1QyoUSJRSuvZa/9Q6HDFCFK8Ka4JkJp/Y1xMyC5g8/zDjlmBhrUfHWrTmS8ckndNSXLSO569ePTvmnn5JIVavG89u04bytPXv4Mm3ZQmn8scfoNGtNhSk5mcrMxo0kHtddR9JQtSrJTe/elmqyYwdtPHaMpGP7di7MPWIEVZLBg0kMmjShtP7ZZ6yP38+Qx6lTub9cOZIzgHaUKsVwwAEDSB4GD6bS+Oab3B4fT1KXkEB73W5uL1+eo0Hr1rEdV69m0o6NG9m+d91lraVnVK/LLmObKMUkNStWMPGJUQVnzmQdc3JIRN97j/ehZ08SRJ+PnZLXC+zYqeBISECbBhnYfTwGd12rEBVFZbNBA5LEu+6irenprPPNN/OeTZvGto2OpvrVoAEVrGuvtcInb7qJ5KtpU7b9nj3W2nxGgWrdmnUwIY2//0518qOPeG+GDCEJN+pYly6sz5gxJMOHDvG477/nAMLixbxnANvCJHFJTye5fP997nO5eB/uu4/fH33UWp6hdm12zPPns+137GBmUa+XiprPZ5HA3btJyJ59lsrq8uV8li67jITsxRdZZnQ0R/2efprn/eMfwJNP8lrt2lllzp5NO2+6iQSvSRPa1ro12/HUKaqhzz1Hki8QhAOcTgWzULCZgwfIIIag4KCUagngUwCDtdapZrvWel/g/8NKqV8BdABwGsELKHsfA0C7du108H5B+CE6mr/Xr7zCCB+nk4PDQAQiIzktJTubvknv3vRV7PPv7KKIoHgjrOfgmQQrCxbkTgPrctG5jYnh9nHj+CM9bx7JUXo6X4yuXUkC3nmH6tvw4QzJu/12qiB3303Hv0IFkq3atUlQFi6k+nf11VTjtCbZqVmTL12tWlR2ata0EqKsWkVn/sQJkrCKFamWbNpEYtCmDRWz338nIXn5ZRKL6GiGCk6dSsLWqhVJYYkSVMQqV+Z8rFatSFZ//pkK44YNJJiNG5Owdu/OEMdp03j9iAiSLzNvr21bEpWICLZPbCxJQOnSJIH793O/282yV67k+evXM+Syc2eSw7JlqX498wxJRoUKJBEPPsiyoqJIRgcP5nXr1QM+/DACbXqWxo4dCgkJJIJ167J90tN5btOmVFKPHKHC9uab3Pf111THDh1iOzZrxpAEo7q98AKTiAwcyHvYvz/vze+/0+5t21hOUhLJpNNJ226/nYQW4HNUrx5JesuWJFJjx7LtExPZzhs2kNiPGWMtC/GPf/DePPccy5kwgfehVSva5vVS/b3+ej6bV1zB+3riBO91+/a8Bw4H2/SDD0iW33yT4ZhGRRw1ikTr2WdJLtu04f0wa9r168drjR9PNfD119kGX3zBAQqPhwMYP/7IP4+H78bevXwO160jWf/lF5K+v/+dda9aVebgCcIHjLKIAOCAIXeApBgXFAyUUjUB/ALgFq31Ztv2Ukqp0uYzgAEA1hWOlYKihm7d+DsfFcUInowMa5+JONi3j4PZaWm5+y8jikifFh4Ia4IHkMx1787PXi+d9Vmz6JSaRCv33EOiNHAgiVJsLF+EtDQ6rYMHU/Uwyyu8+CL3f/MNnezjx6nMJSSQrKxZw+v4fCSMXi8JQIMGdLT/+IPX2biRBOLECW5bsoQkYPNmkps1a0iwBg2y5r4dPEiFpEYNEjOASlOZMiRmNWuSVLRqRac8K4vEJjWV5+3axTJiY0kUDxzgi/7FFyzLhJIePUqilZnJ0aAmTdgxdOhAErN9O4md00lyUbMmwwWPHWPnkplJApacTIWvUSMSutRUkr6bbyaZOHaM6tN337HetWuzraKi2K4ZGSRu69bx2sOGUdFauJBhgFpzLt2UKcxMWaUKE8KsXMn2r1KF5Hr4cCqix4+THK5ZQ3K5axfVxTlz2J4+H+/1kCGs18CBvEfXXstjPv2UaummTSQ6Q4ey3tOnU107eJBJVIYNY/x7+fJWCO22bSRx69ez7c2SAyNGcL95PidO5DPqclEJq1KFdg0cyPs+bhyTmXz+Oetk1sHbvp37vV7aY5bg8HpJzCtWpL3mvLg4fi5ThmStWjVe2+m0lqlo3pzPCMDrREby85w53Fe2LJ+de+5hXYYMIeGdMIH3WiZuC8IFJrtvMEwGYIHgXKCU+g7AEgCNlFJ7lVJ3KqVGKKVGBA55DkAFAOOUUslKqcTA9soAFiqlVgNYDmCy1vqPS14BQZGC18uIoHnz6EdkZ1PciIigfwtYfVi7dvRvb7mFooPbbSl3slxC+CDsb6PXa8218/moIo0axR/lefP4f3Iy45bfeovqRlQUj121io79tGkM2wQswtaqFQna6NEkKlFRJCF//7ulJvn9DNtMTWXo2/TpVKl27qRTv3s3X76oKJKacuVIqmbNosO8cyfLuOsuOs2//UYSOXQoiVHDhiQqLVqwXj/8QOWkVSs629nZdNxvuokq4fz5VOz27SNRTU215gDWrEnStHIlSWtaGo+rUYNO/eTJtKFhQxKpjAx2AlWqcNvKlVR7rr2WpDg1lc5/27ZUOQ8etJKfpKYyjHH7dpLO22+3Oqc6daguHTvGTmr3bpJLj4cqWK9ebDODjh1JyB57jMc2bUqC2rQp2+DzzxmyaM6pW5fKa3IyyeS11/JeRUWRSP/xBwlbt24kjVu20GateY127bjw+ubNtKdUKd73GjXYWQ4bBjz1FJ+fPn1o3/r1tN/nYxuZ5C/vv8/QRrPP4WDI7XPPMZSyb192zM89R9Lq85EId+nCe7B/P5/t1atJyOrWpfI3cyZDjAFrLt/IkVxo/a67aI/Z9tVXJN9vvMFnvEQJ/ihERVHZTE5mJk2Xi7b07s0fg4ce4vaPPqJdUVG0bc8e3i9DgCV8TRAOcLv5exAK8owLzgda62Fn2X8XgLtCbN8OoFVB2SUonnA6GY2TlsbInPr1rWk61lp4PG7LFvZpffvSH3Y6OWgr5C68EPYKntNJUtC3LxWuNm24LSqKapUJY+vUyVInevbksVFRJHDNmtH5ffBBEoW33+Z+pejkrltHUrJrFwnZU09xX4MGPO7WW0lmatakstW/P8mIIXArV/Kl/OgjXnvwYBIIpUgYIyIspSg7m8pV+fJ07HNy+L1CBV5n6FASqxMnrEya771HEpSeTse7ZEmSOaVIQg4dIsHas4dEc9MmEtmsLJYdF0cbd+9miF5aGutVvz4zgv7wA+v2v/+RzJUowWt7vVa20X37qBrWrk0VLTmZimWJElTFtm8n+d26leVOn87205rkcsECnufzkdjk5JCwjhnDMhwOtuemTVSilGLdP/qIIbFa83pbtnCOWXo6SU2VKiQ+33/P+3j11STI//qXFaagNT+vXUsymJZG8nLsGNXcNm3YduPHs7Ps25fXOXCA7Qtw3wcfsP327CFJ27OH5TocbG+Hg6Rx+XLWb+pU1u/++/kMR0dTKT54kCS6alVuj4xku5Qvz9DXN97goMPTT/sxoP1xLFumsWgRyX1yMkM9t20jybvtNtZ7xQranpBgrcv3/vsktMuWMYGLz8d2++QTEtpKlXheRATbwOslSS5ThgMNANtKICjuMEugWPCDWek0TpwoHJsEAoEgGIsX0zeYNYtRT3aYRIOxsezPTp3iILHXK2vhhSPCnuB5vXzQzZp2PXuS3NgXfG7RgnO17riDTva8edZ8uCefpLOrFNPSjx5NQvCf/5B8zZxphdeZcLTPP2cWyGHDSJ6mT6dD/cQTnHf1+OMkPLVr05E3C4U/9RTJQdmyVEPmzydRKFmSpLJOHZKESpUsgtahA89NS6PCt3Yt7bz7btoVF8eMmRUrkrBedhmJQkICCcp333Fkeu9eEt6EBM77qlOHit1335F8aU0itXw5j50xgzZ8/z3t2LSJ569fz7rt3UtC1awZiUPbtiQx339P4nPllexgDh8mGUlJYfkzZzJMdc0akh2AoYB16pDMrFvHOh07xs5pxAiS5m+/5bVNJtDx4zl37aqrqKy1a8f69+rFMjduZPv06MFwx8GDSVa6dmX7DxpEImTWydOaxNIsdH74MMvctInHjhvH0NG5c/nXowdDKl0uqmsNG7Jt//tf2rhmDRdDX7aMdXrwQSpqJvtpw4a095ZbeO2UFN7b3r1p0zPPkBC7XJY6DPD5+eQTYOJEP55LuQ63PlkVr6fdBEeEH1FRfOaGDmWbGlW3SRMSv1mz+AytWkVCP38+Q0mzsnjff/iBoa633EIV0izSrhSf6YkTScabNycZB4TgCcIDXi/fTcIP+3p46SfziN0UCASCSwgzKB0Vxc8HDlgiBuCH18tBqYoVSfKee45+hRlMlikV4YW/DF/3eOjAejx05r1eK7OmUUY+/5yOtc9HQrVhAx3ilBQ66QBJz403MoRu3To67CkpTGyxfDmPmzuXhOOdd+iQT5tGopeWxhBAh4POP8D5S8OGUcnbvJlqzPLlzNK5ZAlVwerVeazDwXl/qal05Js25Ys8dy7L++orEsmcHJ5XsyYd97g4Xj86mvPESpViOGqFCpwzlZpKAqUUVaUDB6jsTJnC+Oz586nYfPABO4L69dk21aqxg0hOJgmtVInHbdxIlXLqVJKI335j+Xv20IaoKO7LzCTx1Jpz/kxIYmoqycuqVbxPTZpYKlW5clQu//Y32nXkCNvlxhupjtWrx7avX5/EsUEDEqshQ0jYTSbLJk2oPDVtyvK2buVxK1ZwJOvBB3m9gwdpY/v2DEscM4YKX/36vG7btswqaebZ1axJEnTddbRvwwbW14ThRkWRAHXpQuVy+HCeN306n63OndkGt93GQQefj9eIiuI133qLpPvFF2n/5Mm8Ty+8QDVy7lyGf9avcAIvfTsUd2Mrhq56Bx3fc+O2e0ti40a23YwZQJsEP1YvzECn/jFo0YJJI+rW5bM3dy7b6MsvGS4cEUHCC3COp8fDbZs2cdHzu++2ssZ+9hlDXDduLMg3WiC4dHC5OED3+++ARe6IA9uOgNOiBAKBoHBgpiOZdXl37aI/o7UZkDJ/HqxdWwJxcRH48kv6Fr170y+WtfDCC2Gt4JlJo4Y4dOzI5A9paSRwZu6EUSjuv5+O+ubNdGqjoujMN2tGFezDD6kM3XMPX4hVq6hOnThBxapePUvtczpJunbssBbtfu45Khs//kiVZe1aqky//w68+iqd9Y4dSRi/+orO89GjtCEzk4pZTg6P2bqVoZcrV5K0jRtHwtagAV/sQ4fo8EdHkxy1bs0Qy0mTSKBq1aKtH31EQrd2LdWz0qXpvI8dS/K0aRND+wAm/KhZk+0zdy73Dx9OghoRwdC8n34iYdq+nSqXyRJ67BivsXcv69KkiUUoS5WiMhYVxU4mM5N216pF4rhnDwnwrl0kPMePk6j160fV7+mn2al17Ej1tUoV3quqVXn8778ze+gPPwDXXGMRI7Nw+4IFVFHXr6etM2aQ2NWuzWQwx46xrebPt+qSmUmy/vLLVlIbj4cqX2wsM1Z+/z3rqzXnQb73Hp/JyZMZRnHzzXwOTpzgHLumTdnOy5bxGatenW1/6BCfwZQUfv7pJ9bx4YfZTv36kfi73VRF+/cHYqrHomu9fXgeb+CR6l9j1fpILFmi4fNRxR5xrx+jFl2DpA8WIeHVq+GI8MPhYDu73bxWr15MUGTCTDduZDuNH882bdCAbfePf/AZf/xxEvlvv+WfebcEgnCAmc8SjDRP3KU1RCAQCPJAejqnmPz+OzBwgB9AZmCPBnAKQA7atXKjYUP6hvffTyJowjcF4YOwJXj2dK8uF53guDgqbUuWkCiNGUMCNWECHVeAJOO+++hAX3MNHd1ffiHhueEGEpC2bXnsffdRsVu7lmTkzjupzNx1FxWlpUutxZ8bNSLZ2LmTTnpODp38L7+knTVrkhSNH08nu0wZOvyNGnGuVLlyVnbOmjVJRiIjrTlvl11GglGpEv/fs4fEY8cOkoCGDVlGkyYkuNnZJALt2nHf2rUkUzNmWIpV5cpst5IleW5sLImTUSF37iTZe+wxzk1bvJjtUL48y7ztNtpfowado2eeYZ3KlOF5Bw5QBVy0iCrff/9LYnDPPWyrY8dIMJxOa6mJevVo1969PH7+fKqg/fuT9HbtyuOef56K7KlTXMB89mwqdOPH87gqVUh4t22zMkympLBd163j94gI1jE2lnVeuZJldOjAY0qUYLk1ajCUd906jvJ//DHb54cfrAQt69YxBPfuu7mvZUve+3r1eH8aNyaBNyR+2DCStnXrGGY6ZAjv1eDBrM+yZXwGPR6G8darR3XvgQeYWGX5CoUFnZ7Bbf9ph3vi5+Km1Dex7stEeLIZquk4dQJrlvtxM77A18tbYPmcDCQk8N24/37ex+ef532cN4/PZP36JPYvvED7+vWj4tipk5WY6JNPgCZN/GjZOBOARo0al+qNFwgKFlwM+PRwzFUrTopjJBAIChVOJyNnXnuN/lSfXn4kL8sO7PWCS7tEAXAgZasL27bRH0xKsjLNyxIJ4YWwJXgm3StgKXluNx3jtWs5l+6RRxhe+MYbVB+WLqXiMHYsiVS/fnSq//Y3Hr9wIRWwBQvo0L/0EpWhn3+mYzxjBgnX7NkkJ9u3k0BkZFBhe/ZZOufvvEOHuEIFErKaNfm5fXu+oP36kVyYNUy++44k4uabqZr98gvnAB4+TKLkdvO6cXEkqz4f5xVmZVGR27ePpKF0aRKOrCxeMymJhLRUKZKmV16hCta4MdukXDm2SdmybMPjx3lsmzYkQ7GxJIpjx1oK4smTtMOEpU6ezHOSk/m5ZEkSu+XLGQ5YuTLJVKdOXLvtwAGSmnXrqI6uWMF2KFmSndCOHVQVtaadERG8ZqVKVN0++IB2dO3Kes6eTZWyb19mzFSKxyYmklzWqkXSWaMGyVuzZiSlQ4bwHinFTrBnT97PpCQSHa2p4nXrRsJ69dVs0xEjgFMn/ejV5BDat9N49VXauGoV78u997LOn33Ga2/cSIL3wgtUPv1+to3W1p/HQ3U1KopEtmpV2jlzJtu1cWOW0aoVVePp01nOS/8XgbKxEfhwWRs8iTdxR/pbiPGeQIcOQMrBWGQ1ao3daIAhCduwbF0Mli0j8TSL2v/f//H5+vVXksjISGb93LaNz80TT1BVvfNODpTs2gXcdacfB+ZvRmrSagC+vHPLCwTFDFwO4fS1og+nlbnktggEAkEwnE76Iz9858eqKXuRk3YIHJRyoA7WogzcqIbdKHl0BzZt8iM9nX6S0ylLJIQjwpbgGcycyTlK6ekkZj17kvwYBSQri3OhvF6GpGVkUOF57TUqZwYmRbwhF2XLklDccAOJW/fuJEV3301nv1YthrA99hjP69ePTvzixVSoYmKo5DidDIEbOpTOf4kSVpKS99+nM/344yRbZcrQkS5RgvPBmjQheTp6lETL7SZRycjgOaVLM/yvRQsqURs3MuwvJoa2VanCa1x1FUMc77yTTvySJVSb1q61yt+1i4Rs+3aSo/h42nngAAmWCak0YZNVqzLEsXFj2hAVxeufOsVrly5NInP8OOvepAnJW6NGLNeQ05IlqRqWLk1ivG8f6zpiBNvm1ltZ1549rbULf/yRxCwhgTaYuYQDB5JsrVlDAr5pE/f9978sd+hQkrR33qH616ABiVRWFpXFiAiqk7Nnc37c6NEMQV23jiGwX34J7Njhx6aP52JB83uQ9dQ/8cE4P3bssEbFKlcmifL7eZ7PR4J5zz0cKChb1kqqUqkS933yCcMyzZIQAweyPc0cxbVrrWUWHA7WdedOKptt+sQiq3o9zEYfTCk/HCciYvHJJ8Bddys4Xnoe18wchdtz/ocbhyn07Uti/NxzLPuLL6jgXXYZ22LyZOudAazsouvW8dm95RZg/5YMNMhJhh8+AArHdxyBQBAO4JIyp/9k7t6RJckJBAJBocPlYmTZyT0n8Kj/RWh4wEEphR1oiUyk4wRiUBqHcfK4D198Qd/Cvg6eIHwQ1gTP6aQytGYNw8zat+cL4PNxNNakfm/QgOFtR4+S1D37rJXoondvOt5jx3JeXPXqJDANGzLscNQoOuW7dvGYyZNJHKpXt8Ikr72WxGvTJqo0t99OwpmZSZI4YQLDRX//ndtKlCDZPHLEWmpg7lyS0saNSf4qVWII6ddfc95XWhrtcDh4zDPPkEQ4HKz/4cMkiFFRVN62bycJSE2lInjFFfz8ww9UbXJySFTLlKGtdeqQfJmQy9KlWUavXrSrSRO29YwZJEtpady+ZAnbweOx1vo7cIAEsGRJqm0bN1qq46lTtPmKK2j3+PEMiZ04kYSqQQPO+crIIMGYPp1kqG5ddlIPPMC2+ekn1jUykiS1Tx8S2oMHqTB268b6N25MhdTl4r378EOG2MbH8/q//cZBgpwcknWnk/bOmcPw0HbtSIy/+IIOYOVyHqSd8iMBy7E6pQR2bXLjiiuorm3ezOdi0ybWY8sW1uGrr0gQX3yR14+I4L1zuWhn165sO6+X7fnll1bm1mbN2L4+H4n066+z7Rs25L0a94HClLK3Yvldn8PdYyA2b1aoXZskdex7EXj+g8rwehW2bCGxnTTJGnz429+YDGfLFr4LdevyPowfz4RFL73EbWYu49/+BixMjkHXmrvgARdYdMRVuvQvvkBQAGCIpjptu1c7JKxJIBAUOtxuRlMt2RiLqDp1EAEnAJNGU8GLGshANNIdlVCthhORkfS1zDp40o+FFwqM4Cmlaiil5iilNiil1iulRoc4Rimlxiiltiql1iil2lxMG7xezu964AEqPO+/T+d3wgSqEMnJJFw+H511l4uhl3FxJDR33EFyY9ZVa9uWYZo338wf+7Zt6dhu2EBCsHIlU+a3aUM1r149ll+zJglgmTJUOfbtYyZFsyZdmTJMwGEWrm7ViuRi6FDub9WKpCotjWpJrVokAK+8wgQjERFUWQyZmz2b88tatGBdrrzSSlxi1pBbvZqkp3t3EpP//IehkDVrWglNBgwgobj2WtZv4UJL4Vu7lsRiyRKWER9PstmqFW0aNIhq5r/+RRIwdCjPNQvFlyjBMMiWLamKLV1KdSo1le1i6tChA+u3ezeJRrduJGJ+PzNVHjjAe/zdd1THliwh+fT7+d3r5f0xxLFhQ9q2eTOPO3qUamhsLO0cMIDtcfnlbIPLL6cq2bEjwxO9XoZJzp1LcnvgAENAt27lfUk9VQJj2nyKRHSCiq+BOk2j0acP1UYz72/HDs71S0uj8lW/PgcgHA4+V06nlTzmhRd4H1u25OCD10uC16MHw1cdDoZSOhxsn+7dSYRr1ybhf/BBoHefCDjKlMbWrQrXXsvw2VWrSMi7d6eCm5DAOo4dy3odO0bb3G7+AIweTbUU4LM0fjztmzyZbe73czDE71cYX/lx+KrUAhCB9IzTHeLCRlHomwTFD9aSH7l/NiMjXTLyLRAIigTq1we271CY0vQpZJeIRxROBvaY6RLl4KlcF1WqKHz7LYUCl0vCM8MRBXk7vQAe1VqvVEqVBpCklJqhtd5gO2YwgAaBv44APgj8f3EM8NIJBui8tmjBB/nKK7ntvvtIWnr1onpmFkC//34SlrZtqd4kJdGBHjmSzqxZbmDFCpKY7t2paNx3H/ePH0+yV68ey7z7bs4NO3nSWsC6Th0SkD17SDomTCBZuP12hgNWqcIyqlWjQ26Uufr1qeD5/dwXF0eCunIlnfr4eJKE1FRer1EjKlj165NkxMSQtDoc3H/yJG1MTqb9UVG0a9Uqpr9v25YLmDdqxO1r1vD48uXNnBQSsqwstktEBMnf/Pm0u359HlOiBMnUyJFU144cIcm87jreJxNmmpBA4tmqFUnn8uW0pXJlqkurV7POJ06Q2GlN8rNyJRW5UqVYt7p1WbbPR9VsxgyeU68e79V115FUXXaZRTq3bKGyOGEClbnERHZ6yclsh9RUEv2hQ2njV1+RHGVlUYHr2ROIiFDY0O5b/PGbGz0HRWPXLoWkJCspyXXXMbzUzANdvZrXffBBKqJeL0ms10viVqMG6+PzUTncvJn3PiaGhGv9ej671arxvsTGcn3GOXOYmXXGDJLlBx6g2jdhAhXBefOYIOXVV/k8TpjAe3P0KInboUN8X/bvJ7G98UZev25d6/0aNowhyjNmUJm96y7eg5SUCBw4zHzL5hkpYij0vklQ/BD3Z7JMFfjTACKQk6OQnm4UPoFAILj08HoZ0bRpE9Cvjx8/PbwQp7JbAzAdlxlsdSAtzUrat24dfQ5ZIiH8UGAKntb6gNZ6ZeDzKQAbAcQHHXYlgK81sRRArFKq6sWywemkUtaxIx3o9HRmQwSYOOLVVxmC53aTHJlJpn6/lahixgw6+x06UKn5/HOSk82b6cwOHkxH/D//4Vywjz4iaZs3j4pL3bpWCKPfTzXJ7yeBq1CBc9D8fjrwrVuTFLnd+HPeVmoqFZO0NJKnrVupRu3ZYzkUNWtaC2Cnp9M5d7up/u3fT+e9VCme9+uvJA233UYiuns3r+PzMURv40Zewyh927czoca2bVQeY2O5tIOZq1WuHInW/v0ki+vWsdzsbJ6fmUnSadaii4/ntvLlSdqmTmXYZGYmQytPnWJ7Xncd27lfP7b5vn1s688+I8n6+WeSkUqV2FEBJHKXX06ieuqURaarVOG8vF69SJZSUvj98GES2tGjWb+kJJLZY8d4P828QbPMwbXXktyNHUvSs3mzlb0yOZn3LjERWL0mAk/8qyRmzFC45x4meImLo2I2fDhDgBMT2W6tWvE6MTEkYB99xLDfNm3Yhj4fyWBSEtujXj3ek0aNWJdmzUhKV6zgczRyJNs2Pp73c+JEEu///pd1iYtj3StX5rP13HP8QTh5koS+f3+SsttuowJowmrnzCFBNu/Url3AV1/6Ub6UG316a8QH3uzISNa3TBHOO1EU+iZB8UPu8KUI0GEya0wJBAJB4SIqiiLBnD/cqJ61ClHIAEM0jXpHl19r+k7PP8/f9bfeYj4CCdEML1ySOXhKqdoAEgAsC9oVD2CP7ftenO5oQSl1j1IqUSmVeORI/pM2uN1UfdxukoLVq0l2evWywux69KC6tmgRyRxAhW/oUDri//43RzacTipnjRtzvtWaNXRkb7yRxCwigoQiNpYvT/nyJFUrVjAm+uRJKj07d1Lxmz6dDnudOiQjSUkkKKVL04bsbDrJe/Yw0YbTSRK1ZAkd9TJlqFpt2ULymJjIMEeThMXhYJ127KAdEydy3leZMlR9Zs2iHU2a8PgyZejM16pFsuL3U1ksVYoq5oEDtOnUKRKDQ4dIzMqXp92ZmZzjGBVFEtG1K8mmmac2fjyVuxo12EYA59GVKEGiC7Bz2baNylFODknOgw+S4FWuTPKwciXJbL16tP3ll9k2SpGIDhvGNurYkfdba9Zp7FjWKTqaBKpdO2tu38yZvH8An4dGjXhOw4a8zyVLkvD07897Wa0a27lrVyajqV2b4YxTp7IOW7fy2Vm7lqG333/Pdujfn+R1zx7WZ9w4zuu79VaSrE6d2Mbz5tGW1FSSuZtvJqHy+2lXZCRJ2Kuvsq4uF+uzZw/Vuqws2jF1KslZtWpsjy1bOMfv2mtJYm+9lc/0L7/w2D17mBmzWTOe17IlPzdowHacOJH/A0B8NT+WfJgMrFqCqF+/Rd06fkyaZK0HWaIEj2NiiqKLC+2bAmWcV/8kKF7IrdDZiZ0fWzdLtliBQFB4MDkn/vtfoGqdaGxw9UJ1HAGQDrr6HJRyODgVpUYNP2pVOoWBAzQSEugzCsELLxQ4wVNKxQD4GcBDWuuTZzs+FLTWH2ut22mt21U8B48xJoZKzOrVJGqdOnFunMtFYvLbb/y/WTM66+3bc/+kSXT409OZrGT7dpKNV16hApeTQ9K1ciUVk+hoOrLHj9MBr1qVzvnevZyjFBVFp/2rr0gWoqM5euJ2k2RdfjnPX7WKhPD4cZZ79908d8sWKjmlSvEFbNCAyqDLRWf+1VdJHKtUIflp1YrzpipUsBKvPPIICZnLRSc9O9tSF7t1s5QgpWhXXBxHdEqUsJx8t9tK9X/qFBWwhQtZh5YtSQzLlGEI4vLlbIv+/VmH5ct5jiEQsbFs36pVWafsbLZL8+a8b40akZxefjnJcNu2VJFef51/WpMgr1pFte/gQd6b665jW950E0MXK1cm6enQgeXXrk0yNXMmFbg9e0j6K1aknf/8J8u9914SoOHDaafXy1GuqlV53JEjLPf550lUvV6Ge8bHU3Vs2JDtEhfH5CkHD/L4ypU5IPDzz1RC/X7eg4wMKr6bNtGevn1JKrdto2pZuTLv4759JMn9+pFIzZ3Le92xI2375RcSMfOcLF5sre3YpAnbv0oVPudffsnn/LXXmLjmmmu4r107hm8mJzODp1kcPS2Nx0yZAkRkZqBadgraYSkOHfLhmtYbsH+fxpo1HDA5dIjv4KZN5/PGXxpcjL4JOP/+SVC8kJvg5VbtTu0RYi8QCAoPZkrSAw8AbdspxA9piWb96wLIHTvudAITJ/qRMXMB2o+9Dc7LB2LIID969mSmeSF54YMCJXhKqUjQgfqf1vqXEIfsA1DD9r16YNtFg9NJlePZZ/nwf/ABidqKFXR+v/vOWn/sgw+ovgwYQMf3s8+YkKN6dTrJLhczBZo5cDExVpp4pUisUlNJbIYPZxbKu++m4rZrF8mDWYS8UycSqzp1aI/Xy/lqDgcVRLOY+MMPk3jGx9PhHjKECtzWrSQlXi/Lio2lffXrU+lbuJCqWmYmFbEFC6j8REaStKWkMDyzfHmST4+H9TxxgkSgRg3Wef16tpXLxXo1b+7Hc0+64XRqrFhBG9av53U6dyYJ9PtJgMqXZ/1++YVkpGFD1n3uXLaHw2HNYatcme136hTPPXKE9jRoYGVCrVqVbZmRQSJ46hTnKy5aRCJZrhzbZ/9+3tcffyQxdLupXA0ZQnK5dy/rs2MHSeqOHXxGGje2lsaIiKB9ZcuSKFeoQNWrc2cSOaWomj75JEnP3XeTbGZmUrHLyKBiOWYMO9zy5Tlvb+VK1u3660n66tVj/Tdvpg0OB/D223xuK1bkkgh+P9useXPalZhIghoTQ3Vv2DC2wfLlJLD9+/PzZZdRIbz6ai6avn077Z8wgaR11y5e/7PP+DysW8fnwOOh4nn0KJ+RlBTew5f/z4/HH3ajfHmNll1j0KiqB4dRHRVxDLffnA2dlIRGjfx/qndAkZ2DVyT6JkHxgrUUgu+0fcf8QuwFAkHhwb728+rVQK3aEVi/rRSAbABZYNSBH04nEF/Vi30ZZdEcSXhvURu49xz5M2Ge0ykkL1xQkFk0FYDPAGzUWr+dx2ETAdwayFjXCcAJrfWBi2WD10vy0qEDHdann2Z4nsNBdSQpiX9ZWXSchw2z5rk1bkwVLSqKCtKOHUwosXs3y9m7l9d46ikSl7p1SUI6d7bWSfvtN6okJoNjQgIVkgMHSC7Hj6fydtNNJHDTpnGeWUICVcK33qI6V7481ZOYGJbfowevb5Yb2L6dilabNlStdu+2FL6jR6lImiyLOTl0ujt0IEk5epRk0MwJ27SJbVCrFkP7TOKWl14CtPZj3YJUJK09hY1rsxBXwY/4eBKqPXuo2nzxBRWphAQSiNWreR/atGGY6ief8Lq1apE8TZvG73fcQfLWrh2JXf/+tO3UKSpvsbEkiy1bMhQzPZ3tk57OcpYtYzbM8uV57cmTSaw++shSVJs35/WqVSOB7d6dhDYriwStdGm2W9euVMGSkkgumzShomcWhd+yhQru6NG0PTqa9dOa7fDWW1T2nn+eBC01lcRu61aS+hdeIMm67TaqeVOnss6dO5PwOp0k9k2bkty3aMF6HT9OJbpbNyp7L7/MMNArryQ5jo/n/wCfj0mTeFxUFAcmlCKZi4tjdtNnnuFz7feT+GZn0841a3gvpk1jG6anA3+7zo8frvkedRd+jInj9mDNGo3Ribfi/TkJaI8lKIN0aH8Gsk554ffz/QE4P7SooSj0TYLihypVgNatQodibnriNb5IAoFAUEhwu+kjTJ1KPzE9zQfOwYuGCSvPyADc2ZGoWuoEvsetGNElGa6aldC7N31Gr1eWTAgXFKSC1xXALQD6KKWSA39DlFIjlFIjAsdMAbAdwFYAnwC4vyAMGTCAD+68eVSycnKovOzbRxJm0vePHEniAwAff8yXpGdPkpEPPiCh8vnoIDdrRnXI5+Ofy0XFZd8+hs+1bEnCUKUKy7zlFpKnn3/mS5iZyXl+99xDB3z1apKIOXMYPleyJB32jAw63Q0bkqSNHUsS16gRic3f/sby/v53qlalSzMcr1w5ErUKFUhajh1jeVFRJCJmvb327UkeMjJIFI2KtmwZFaYKFeikjxoFHNjrQ3/8gHLIxgD8gPXr/Fi8mCTQ76eS9/nnJB3HjzNc88YbSSyUos0ffURbUlNJ2oYMoXr3yy8kdA0akAhOn067IiNJeC6/nApX5cpUpvbupUq6fz/nt5UqRVK0axdDDqtWZdufOEHS2KULCXVGBknP0aOse+PGbNuyZXmd2rWBN99k+2zdykQlixaR/ERFkQguX842XLKEz9KkSSR2LheJ6pNPcq7d//7H56tyZZJLp5MK6+rVnH/3zTfWmoxz57LMjRtZ927d+Gx8/z0J5T33UCn8/nve3zZtWL833+TzNX06n78NG/hXvTrPu+kmkvebbqKC6PWy7bKzef369UmI09NJ6Hbu5L3++98ZWlqjBsOJVy7MwEeH+mIBeqAadiBn7yG8/4HC9H3NsKbaZTiJGLQufxi33xWJPn14DwE+Q0UQRaZvEhQfpKcD27eEXtHce3A3pXmBQCAoBHi9zCOxfDnFiFtuAdp3doJuvh9mHl6ZMkAJl8JqV3dcNe0BxMydipmzrOWMjBIoSyYUfxTYLdRaL0SoVWFzH6MBjCwoGwzS0uiM+wKRNU2b0mm/7TY623/8Qef34EGG6H3/PUPmPv2UzvfRoyQldevSod+0ic71vn0M8ZsyheQsJYWObWQky+/Xj/OjHn6YL15mJp1xr5fnTZ5Mh/74cZJHr5cEZOBAEtEKFWjzhg1UkUqW5PUjIkggf/2VakuJEgyjrFKFZX3+ubUkhAmZTEkheWvRguStdGmSks2baa/XSxK2ezfJYWQkr3HyJPedOgU0auzE3lOd0SFzCXY4OmLo5Q4sWMj9FSsyxM+ECSYnU3HbtYt1mDDBUg7btGF5Jmtko0a8R7GxbMvly0m60tJIsFet4sThGjUYUjhnDm08dowksFMnKp/DhpGklirF8+++m/Po2ral+rl4Metbp461nMRtt5HoJCeTyMfE8DoREbz23Lkkql99RaKWns5naNAghkl27Mhzb76ZSt7nn5Ogv/Ya28TvJ7Hs0IHP3TvvkMgNHMjnrX59SwF+7DFrsfU2bazkK40bA2XL+NE07ggSV1TCe+9xonRWFp8NrWnbr7+y3MwMP9555gju/kclrFypkJDAY6Kjec+rV+fztG0br9enDwcH+vRhVk2nE3BE+BHtPoHkTbGoWVNhxYoYdCuThEYnlwFwYu/3e1G9oh+/9rwJVa6+GdXXuPHL+jbIeEGhTh0Sb6BoEryi1DcJig9cLqB2/WisWXO6UucuX58jIQKBQFAIcDrpB3k89KFKlgRSUhSo4HFJF0Dh5En6jIMHR+CtbyrjjZY83+u1SJ2Qu/DAJcmiWZg4cYIq0rffMstkUhKVmA4dqNBMnEj1pm1bZtd88EGqZDVqkDxs3kyi0LEjsw+aBcinTKES4vPR6b/sMpKs//2PZDI+nufedBNfpoYNmb62fXsSjTJlSIQqVyZZ6dSJNpQqRTJ24IA1f6lSJX42CVOqV6c91apRZSxZkkR13jwSNJ+Pzv+BA1SCSpTgtcqXZ/bMjh1JvHbsIAE0yQNuvZUO+YkTJCdmLb3582njps0KlQck4KUpPVCzXxMcPqJw+DDJ4qlTJEVxcbzOqVNU3UyG0dKlSXYHDybR8HppS+PGdJzKlSOpadGCoQW33ELys2uXlcL/1Cnes5wcbuvWje2ZmUkS3aoVyxk0iIT6hx9IuG67jXVKTua1MjI4J3PTJv6ZycleL5+N117jff76a7ZHWhpJ/oYNXCOxbFmeN2QI79vf/862N2rc+PEkOJdfzjDO4cP53EVHU93r14/t8dxzVD0BDhbMmMGQ3sxMkkeAxGzKZD+OPPZvXNVtL34fsw3r1/lx8iSfXZ+P5LBJE6qWM2b40SPxZUy5fBxGZf0bMaXojDZvzucqPp62fP0161u9OkN0X3qJgwNuNzDqAT+WjvoGa+4bi20fTkezpn60aKGQ2aIHdna/C4loiz2Ix44jUdi9xYPpMyJwylMSrVopuFx87tq0of1ly16Cl1wguARwu4Ht20IviXCy3zCOkAkEAkEhweWiL+rx0Jfctg2gm+8AoBAVxeNSU+nDdOlCH6ldO0mwEo4Ia4Ln9ZJkdezIh94oVh4PFaoWLUi8hg+nY9+gAZUmk7EwO5vlOBx08j/4gD/yv/3GuWY1apCUXX45yzLEMSODpGzVKjreUVF0widMoIJToQIJxalTVvbN9esZHlerFgkkQJLWuTOJS6lSdJaPH7fIUc2arEt8PNWsli1ZZsOGtP3RR0lq9+zhC7x9OwmBWT7A1M/tph2ffMLrVKjANti3jwpj6dIkVTVqAF26RmDEs5Uxe7bCvn30aXbtsoiNSeLRoAHJ7sGD1sLjCxeSPGVmkogZUnjoEMnzhg1MCJKWxhDZuDi2SUYGcOedvMbu3Zyf16oVCdHSpVShoqNJqmrV4jX37CHRWLmS7Va+PK+dk0NS+d13lsLYpQvt2b37/9k77/CoyvR/f87MpFeSEBLSQ+i99yodRcXeu2td17W7rq66u/auuzZs2FcRBREQ6b0TOiSUEJKQ3uvMnN8fN8eDlf3tV9DgPNc1VzIzp7zlnDPP/X6e93kp8733AmE1NWRhfeMN+mTCBJKjOBwkUHn1VSDyvffsayYpCSXR6aSPPB7+j4nh78CBtP+ECUDVqlWAbGoqfREfjxo8dqw9bzMhskahWZt1q55URtNaFR5q1MKFnCstjWvqhRcYxPjm/SK1yNooj0wtzk5Up5giff45IbANDVyfb7zBNeNwAGJ//jPhySNGoDyfOqRCIXl7dImmKqV8neZ+Xq/Fi6VTT3Wo1B0ld2CkKhWh1Bi3eg8K+Bbihg3xqnObet1zt6mgID6zflB85rOTwVyOHw/RrK07qX9KfeYznzUDq69HcPjiC37jQ0K++70Ffikp+IuLFzOwv26dnWDFZyePGab54yOSv1Xr06ePuW7duv9qW7cbQNi0CWe5ooJwzNatgYrx4wGAtWvZPjYWp3fECBSUDRtwxENDcf4HDUK1O3AAIAsOJtSyRw8c9I0bmSc1Zw5wFBXFeWpruZnS07mRunRhflRYGKMotbWEJ3q9OO3t25OopaaGMrdowYhLfT0wkJPDuUNCuJm7dwcUPB5ALS2NOicmMi2ktJSRnaYm4K26GiDdsIGHQEEBwFtWRht07AggWZL9/v1AclQU5TVNVMGgIBS15GTaITycbR0OwOef/6S9evSg7tu20XbW4uddunCc//wH6LKWTujalbpabePnR7keeYSQyRUrKFtICGB19tmA6KpVlGPoUBS4GTPY709/QhW85hrKaGWPTElBzXrqKQDy6qsJyx09GjDJyqL9LGVSoj+9XtrC6QRkraQ2DgflPnyYcNlrryWZSWEhYJiTA2TX1tK+b71FmRcsYPuAANrb6wV2y8tRMt9/39SA3W+pW/5Xqs7oqm963ae//tVQZCQhruHh0ksvAZ0zvzC1999f6ZHiq7W8w5XactbDqqw0tGEDbZGejuq5fj3lqa6mHXNzOdenn0pvvmGq/MzzdPP6axTbwqsHl4/V8y8Y2r6d69g0vdqTWa/OvYO0fAUhH7GxXq1aXK2z9Y72Bg5UVlhPFRU59PHHzBM9lhmGsd40zT7/3VPgt2v/P88nnzUvKy+XOnQwdfjwD0M0Bw6QVqx0nvhC+eyE2MnwfPI9m05uq65m3d3LL8cPtSKrVq6U7HU7HQoJMdS2LQJC587SxRd55V9boVPOilRgkC8KobnZzz2bTtphR7cblWbsWOKRPR4c+/HjAZp77kHNW78e6DnnHMBq2DCcZsNAAVu+HKDo1Alg8Hq8GtG9TA6Hqbg4zrNnD4DSvTsq2KFDKDUtWqAexcejLu3axXGWLgVAWrQAcm64ATDq0YOyzp0LKPj5UbZBg+xkI4bBjdnYCHiGhwNcYWGodC4XUFJRAVA98QRO+ZlnAiXV1Zy3vh7HvqaGc4SHAzVBQYDf4cMAX3Ex323cyDFLS6lzQQFAFhNDHSMjgdOICKDnzjtpz7o6zrt6NcdITwdm8vJo2/37qV/XrpSnvh4I/vprgCcgADCZNImQxldfBcAHDCC5SWQk/bRuHaBUWsoyAxdcwPn796cdrryS+px6KnPdnE4efPv3A6KmyfXRoQPt6O9Pdsr+/dn31FOBQsOgzsnJtEfXrpStSxfKnp8PYB44gMqbkgLcLV0KuCUncw1ccw0ZUl98kYfw118DXBddRPtu3Ei7790rnX++oZ5PX6bPJr6laXH36ZRTDD30EGGpu3bZa99cdZUUF2/o1BfGa+3UbWq892FVVxs6fBglMC2N62vDBpTnv/2NclrhvgsXogS+8KKhC+o/VNApgzXh6bF6+x1DS5YQYhsUJK1Z49DKTcFKSTWUmsr1W1vWqHbapHzFy1tfpbpq4otXr/417n6f+eyXt9BQqVs3Qz82fXP3jtqjllHwmc985rMTZ243/sx11/GcioxEnduyRbLhrk5StWpqvN8m/J04wauhT56q4ZemaFG/O+Ru9GUCPpnspAa8/ftJSX///agyt9yCY9/UxJyuW24hm2FREVBXVYWCcc01OPqTJqHMud2AkkyvVv97jRbfP1vX1z6qV/7t1f33o3i9/74NMNdfjwKTlwdwtWljz5GbMwdYa2wktLC2ljIZBuWIi8PZbtcOiMnIwIGPiiKF/pQpfOf1Amvh4fwfEoKqV1OD096zJ+1w++2oRSUlKDSNjYxEp6ay/ZVXcu5WrciaOGkSYXyhoQBnUBBQYprU6Q9/AHBSU/XtZN1ly4DG8nLKExXF/L4tWwgBXLgQIGxo4FwuF/WsqSExS3IyStZ11wGbSUlkcqyp4UFVWkrY586dtMnu3ah+vXsTilBXRx+lpVHGdu2AnzFj+H7mTMr2+uvA+ujRLCReU4PS2LEj2wYE0N6NjbTp1q2A44YNhDV27Mg5XS7mJf7jHwDW669z3cyfD2gvWUJ7ZWfzd8EClNWMDFQ+r5drYfx4kqJ06AD09epFvd1u2iAwkHL/619S+w4OOcOCFRtLgpWCAsp5550AdUkJ+3z4oVRW7tC67BZ65RVD8+czmLF9OzDavz/9kJmJGtrURPjmpEmUb9Uqrp+hwxwKaREsf39DnTpRpunT7UXW77uPa8aCvuzcAJWrlZZrtPz9DVXXsRieL++Ez04mO3qNx6OtTftQBQae2LL4zGc+85mETzJ0KL/f9fUMzDc0WEnhTEmNkvwl+UkyVVeHb7V5eY1uWXmevjaHybN1hy8T8ElmJy3gBQZKd9zB/Lj6eqBr5kxUlQcesMPp/vlPnNzgYD6LjGTu1J49OMalpYS09e8vFRe61alxtRrk1L/3j9czf6/RkiUkzmjViuOVlAAOhYUA5imnAHctWwIIw4fjENfVATuZmWRonDgRwGzdGoDZvp2FugsLKV9cHIrVww9Tth49AMO8PACkqoqbunNn6lxdTYhmeTn/r1rFGmjjxuGUz5nDtv/5DwAYFASIffMNoYmmyX5BQZQzKooQxhkzOGdYGHBqhaKuWgWA1dTgBA0fTh/4+wNtQUHAr58fZZUoX8+ezAG76ipiwRcv5niLFtE21gLyOTkooVu3sn9uLvt7PNRx/XqOGR4O8MTFUZfu3YGn0aNRwxITUan+9S/a9T//ob55eUBMZSXz7mpqaN+nnuK7sjLUqM2b6aORI1lHrqkJtbZtW5K5zJ+PopiSAth36ACMzZvHcbdto23z8zl+p0604ZAhnCsoiHYbMgToXLmSdfmefhog7NCBY1xwAeW+/HLO1bs3yV4uvphwzUsuQVmbNIm2uOoqju31UsbSUrYbONAOS+7cmbDR+Hh9O78yM5PrduxY2vaVV1CD33kHqN24kWu5d29DrrS2alSoWp46TJbKUV5+wm55n/nsuFt0tITD9F3LzjZ8CQp85jOf/Wp29Py51q2ZBoM5BNxJkluSQ1lZDOhm7gtVbEunxhhLNG5onVytfSOyJ5P9LOAZhtGsJxW43ahxjz2GmtTYCKzt2YPzvGQJjnpNDdCwbx+AZhiE5CUmcpysLEZDnnzaT6tcY9RSRfLIX/V+oaqsBHzq6ghrzM1lrphhkJFz2jQc5sRE4HHWLBSmTp1QvWJjcby/+orzb98ONJSUsM/EiYQCbt+Ocx4ZieI1ZQpKmmHwf69eONNr15LhMy/Pzo4ZGcm54uJQDfftY7+vvgLAoqKYw7ZxI21UWAiEWItg5+ezzfvv2+W15iHGxVGOv/6VerVuba8J1707x7KSvJSXc7zUVAaKWrQA0O68E/iuqyOs0uEAVlu1opxhYYTO7tzJufPzgZGdO6mflXzGNFEy9+/nu5QUVLTCQvplxgzavUsX+szhANpMkzbdt4/2CQxkv4ULUXlTUgCgOXMA1yVLqMeAAdSxQwegfNEi2uPdd1myoKmJdjr/fM779ttA0nXXcR1mZgJUBQUAbNu2qLTt2vGwPnCA6zcqioGIqCjA7bPPGJC4+24GLqwMntu22YMU06bZWVhvuw0wXbGC/m7Xjm2uvJJru2dPQosbGwG+Ll3oqzvvpF4jR7JvRQXguXs3gwR//CMAev/93F9hYQ61bedUZqYdwnY8Fbzm/nzyWfMyt5v79Ic/mw6VlPgGM3zmM5/9euZy4acsWoQvVVtrfWOFlTslhUgyFBvLAP555xna0u1C1W/Zw4i4LxPwSWXHUvDWG4Yx8ISU5DhYeTkXe3y87YR7PMDDG2+wGHVWFv/HxPAD3r0733s8gIm1flp+vnTrnw3FD++gPf2u0cjrOmn9ekNlZdxUSUnATV0dsGAYjPbGxtoJS7ZuJVvhmjWE9lVW2slP6ur4PyQEeAkIANrefhsnvrGRY1rJRfbtA0KdTqDH4bAXNrfS9ZeVAVZFRewTFEQ71NdTri5dGOWx5t9VVLBAZkYG7TBmDOUqL8exiYqy1T2v11bjBg7kXLm5QFtSEgrQhg1sm5LCdhMnsk96OsDj709bZGbSxp07U8dFiziex4PqlprK54mJnCMzE/g56yz6tnVrILCsDMhuaEB1Wr2aRCf19faC3cuW2SGVI0fSfsnJKKJPPw1gxcVxrWRnc6y0NIAsJob9BgygjKmpAPurrwLWGzeSaXXIEI5z773A93/+Q3t9+in998orbG+aKMOvvsp1duWVzMV77jkezg0NhFzu3AlUrl7NNXTGGfSn08mxR41C5cvKQp279lpg8e23eej/9a/048yZ9O955/H9jBmUx+mUMjd79eYr9Wrf3tSePfT1s894Nb5/mS680NTQoXyWmUmo5jnnoLh260a45qmn0ie7dx9ZR8/pleRRQcFxTeLUrJ9PPmte5nJZ2W6/7wRxjfsUPJ/9/5hhGG8YhlFoGMbWn/jeMAzjecMwsgzDyDQMo9dR311mGMaeI6/LTlypffZbNrcbPyE3F7/ANmupBFOSR0VFpvLz8Tuuucah1ftbaf43viiEk82OBXh/kPScYRivGYbR4kQU6Je0yEjiklevJsRMAiTefRcn2e3GiX7mGWBnzRqcZI+HLJSFhQBRRgY/7m63dDDXoc69AtW2raH8fJQUCae3RQuUjdRUVJI5c9jn8GFUmoQEkm2kpaFU+fsTMpmXB2QMHAgAWUs4rFiB852fD5zExgIiWVnAwr/+BdDt2weEjR7N++7dgUa3m7+JiRxTAiLDwgCg4mLqWFEBBLpcwM+WLZRp1iw7i2dwMJA2YoSdirepCXVq4UJgqHNnYDY7G8A7eBCwqawEUjZtAmy++YbPRo8mjPHWW+1wWGt+YO/e7B8bi9L4ySdky7TmRCYlAT2FhQDjsGF2spCCAvbLzub/f/2Lek+fjgp5xhnASUgI7XDgAArWM89Q7rQ0QmotIH3sMfqisJA+crno0z17aNsRIwgHTk9Haa2uRjGbPh3Ay8iwwzEfeoh+6NePc+fkoLZZo23nnUcZrYXCvV4gNjMTldXqKyvRTq9eQOGLL9IuzzxDiG52Nu1x/fUkU9m8mbDKJ56gLt98QxuMGCF16+rV/q93q/uq5+T/yIPyuL3q3Mmrv+05W3Pv/krb3t0gh8OrkBDOt2MH/Th2LNfme++hOA8axP126JBXLs9BSZLrrcf07YzuX96a9fPJZ83LXC6ecSQtONpwmnxz8Hz2/2lvSRr/M99PkNT2yOtaSf+WJMMwoiQ9IKm/pH6SHvA9/3zmdjPff98+/KqKCikoiIFWnlFeSVWSGuXyFqljR6+2bWMa0bhx+GO+ZRJOLvtZwDNNc7V4iGyQtM4wjBePjCg9bxjG8yekhP8HCwxkXlJiIopLmzaA1KWXEsbX2Mj7ggJgKyYGyIiIAEaefx4nvL4ep3/wYI67YAEg9MwzbBcfj3O/bx+Oc34+DvqePTjuPXsCJKWlOAjl5ahNl10GuMTH4wN/+SUqyf797GepjVYyll69OEZyMiM09fWodps3U86EBMCjqIg5Wl4v4Bofj1pUXk6d16wBFK36NjXZ664xKZdj9OjB/yUlwHJREaARHMzcrh49SFKSlkYbrlrFsfz87HNKtjoZEwPkpKXx+aJFnG/2bDKennUWD6XychTO2FjaYPRoYCUvzw7jtBTDPn04j2HQFk1NhDVWVjKC5XbT32eeSf/dfTdA98YbJG758ENgLjCQeXKWMtmiBeXr2hVoLCqi7Js30x5z56LQ5eUBnRs2cD1s3kx/9erF6Jg1Z87joa+vvJLvd+2i3m43CuecOcyvLC0FmA4f5loZPBhojIyk32bPJnHOjBnM2bPmR1oLxrtcALfbzf6BgbR3796cIySE63f4cPbr0d2rIS2369TGjxWkRq3aHaz1qxp19eQirV9raLfaK7I2W10TK/Tkk6iTw4fTfosX0xYbNqAOV1aiDAa6PApVuSTJ7/Ce4zZxu7k/n3zW/Ixn14+p0h5fiKbP/r/MNM0lkkp/ZpPTJb1jYqskRRqGES9pnKSvTdMsNU2zTNLX+nlQ9NnvxEaN4hnV0CBt3uRVXV3NkW+sTJpNaqksmQrQoq/dWraMiKH6eh/cnYz23yRZiZLUV1KRpPXfe/2mzeXCIT18GGd2/nxS00tkR5w6lVT9p51GmGJqKqCyZw8O68MPo4yEhxN69s03QFREBDD05JOA17RpwEibNiS26NsXyBs1ijDA7GxC2ZKSAMnOndnvo4+4EWtrmQcYGIg6d/Ag2yQl4eBHRKCOfP45iTbcbuoTFYUa16MHjv+jjwICHg/JN9q2pRyjR6M+1dfja3u9dsKPigoUvxUr+BsWBsgYBmpWQADHKS8HWnJycORXrgRcW7Zku9JS6hIcDABUV/O+spJyZmdTx4MH7Wybp54KuPn52WsPtmnD/rm5tME55wBGTif7Pv00x8nOpvzWEgNLlgCfW7cCmhs3cu68PBS0Z5+lj957j7Y6cIByXXQRYLh/P3A4YQJ19/enXUNC+D4xkTrl5dHGTz1lA/MHH9Dnt91GCGhBAcfyeNjv4EEA2uPhnBkZtGlDA/0SFIQCOGsW5fJ4aNu9eznm889Tbj8/+v7xxznGnXcSTmplcJ00CShdvhyAfO01yilRj+RkwNTfn8GIinKvPrvpa511Wq3OcnwpU4YWB5yq5/8doNX7YuXfob3+pKcV1CJKb3wWqWefBTLXraONBgwArs8/3w5/Xr9eavC4VKJUSVJZi87HO5Vms30++az5mTWv+YfmUm7uiSyJz34HliDp4FHvc4989lOf/8AMw7jWMIx1hmGsK/JlSDxpze1mYLe+Hn+spESqr/WKsMxvt5IUrCKlqYWKVVXvp7POQgRYvNgXYn4y2s8yu2EY10m6Q9ITkq4ym9mq6PX1hOf96U+8JFSM558H+u6+m5vBWr6gbVtgbccOQGHSJIBq4kRC5wwDx3r9eiDwiy8IlbSWJ/jkE/ZLT8d5T08HiIKCAKLgYNSThgYgob4eQLJuyl69KFfr1ryvrOQza/6ey0UW0JAQPouOxuG+/nqgpVUrgCc/n3DGyEigZ+lS6vH11yhS69YBC23bAkqJiUdS3WcDMzU1QODhw3aik/p6IHPzZt57PMBEZCSq2vDhAMSWLXYGyvHjAVarrrt387d1a1SphQsBlz59aIeiIsrx/PO0+a5dlLmhAbDeuBEoluiP1q2BwgEDUN/Cw4HCZcuA65AQO/TUWnsvNBR1MSmJ+nXtSgimtdSElRjF47HrOWgQ8H3GGVwr27fT9nfeST9ERnLOHj0As4oKFN59+1C3OnUidPG++xhdS0jguFb/bt8OYO7aBdTFxVHXyEjawukkNHTOHK6p227juOnpwFWfPnao8Pr1hE/26kW4sP+R5FljxzJA0a2brQJ/M7tefy/5g17UNbrL+5DOvqedbmmTqnXrDT33nHTTbX9XqLtCkwIjtW2boZwc+nbHDtTWb77hx6GsjLKffjrhtNHRhurrwtTYZMr/6luP28Tt5v588tnJZA41NPzaZfCZz75rpmm+KulViYXOf+Xi+Ow4mcuFj7B4MX7JBRdIEVFOVdVaXe6V1CCSrEjVweka0MPQqlX4Ef36/Vol99nxtGMpeEMkDTBN8+Xm6DxVV3PB33qrrTQsW4aDvHgxjnaPHih3Dz5I2NuhQ4S7tW7Ntjt3kqQiORl16cABbqbevVH3li9Hgdu8Gac6LQ1lqVs36eWXATuJGyg4mBuvvByn/eqrKWNFBcCwahVAUl7OMZqaKOfWrXZGTyvE0zRxqkNCOEbHjpSjthZVyd+fv06nvaB5jx4c8/TTKUtdHergggVAT20tCpOlxrVrB4hs305bvfMOYJSYyPHy8ih3TAwJPEpLAccvvwSe5s2j/fbtA4x792ZJicOHUQZ37aKdevZEgYuMBPDuu4/Q1EsuATot1a+iwoazgweBvc6dOf/kyZRn2DDCKxcsACCHDLHn0t1/P+2SmorilpFBWe69l2vCzw/Ab9mSdtu5E8C0ktc88QR9EBND2+7cSfu4XCitmZn022effXeJgoMHAduhQ+0MoYWFJFeJjqZdHn6Yv8nJdpbQ/fvpVyuevksX2m35ctr6uuvsJT4yMuinjAyU6bw8QOyLL0j8Mm8e5XO5aOelS6UR44K0qcPFClOjzmmzVi99karZXxn66ivCTb9Z4NCHc1vo668NXXUViuuTT6IIDxiA2jtxgld9O1bpwb+Zio3l+g8MlBqbmNTt9R7XrFzN+vnks+ZnZKb9sZ9N89vwdp/57BeyQ5KSjnqfeOSzn/rcZ79Tc7vxFVesIOO2JEVGGgoICJR0WDyzrEnCTqWmGgoKYnA7MBB/52gFz6fmnRx2LMB7UdJ8wzCqDcNYaRhGpxNRqF/CrGxCU6bgjF55JfA0cSKOc2Agjra18PTmzTjbjY045NHRwI8FTKmpOLfLlgFGmzbhQPfpw75bt+JA796Nk+7vDww2NHCOr7+2579NnMgxvvySG6tLF0AgMRFlpL7eXr7g/PM5d3Iy5Swp4X1GBrBTWkpon2EAXbGxHKtLF0Iu09JQtjZu5ObftIlXeDhhkFYGz23bAKPGRiAiLY02OHQIKIiOpk3r62kX00Tdczppyz59OE5CAtAWH88SAx072qn79+4F9lJSULW8XtonNxeACQ8HTIKCgNJly5inGBREwhLDAF569wbMhgyxQ0579ACKzjoLGLSWbPjXv8ha+fHHZJVs2RKFa9YsymAlw1m/HjUxLo6+PPVUjte+PSNcn3/OyJi1IHhgIMevrKTNZsygrJddZs+jnDaNPgkLI2R28GCOdc01zAnMzmb7xkYyXzY20tbJyahi779PyOj8+XxmrfE3fTpKWY8e1MfhkOZ85VXloSo9/LCpOXNoq1tvJVNscjLH7tCB47tchA5/Pd/U+lPuUJd/3aKYR/6iCRMM3X030HrLLfbSFtdfT/kef5zBjvR0FGx/f6+WPzhX454drtX/+FrZ2V61a4dqa9lxnpfUbJ9PPmt+5nbzfLfTjh9tXhUdPm7JhHz2+7QvJF16JJvmAEkVpmnmS5oraaxhGC2OJFcZe+Qzn/1OzeUiWUr//gzA9umDv3J+myEAAQAASURBVEVUQcsjWxGu2aoVz7J9+/BjbrmFCJ+lS/ncCvf0QV7zt/8G8G6XFC3paUnPHPcS/YJWVISDu2ULP8zt26Okbd9OeKbbjfNbUsLNUVCAQ24YfObvTxjijh3cKGvW4CiHhXGTREQAITEx0rnncqzKSo4xYgQg09gIpIWGAke7duEct2mD8jZuHDCVn48znHRkXM7hoAzPPgtkZWez/44dwENDA+UND5fSUr3audMjr9dUWRnhiZmZdrihtQ5fnz58VlNDe7RrR6hj376Uc9cugLa+HtAyDBSxTp2AMKeTY23bBhyXlVEu0+RY9fXUPz+f7TZsAJjcbkJZJcJYV6ywgTAlheUYJMDaSuCxejXbTZ9ul3PQINro1Vd5eO3ZQ11qalhPrqYG5SwuDkjdtIn6Sjz0brwRcIuLo/22b6ed9uwBFrds4YHXsiX9cuqpXAOLFwO406czz7GxEajcswflMDiY/pdQaE1TuuIK2vymm6hTmzYooGPGAKyNjUDtpZei3IaG2mESXbpwHTz6KG3w6KMon5Mn0//p6VyvISHSH/4gdezo1eQtD2rAW1fq1Pr39ckn3m/nJj74IBlK33yT+g4ZwoN95w6vkpa8L8dLz6nu2edVV2dq717KnZDAGnfr1lGvv/yFMt1wA8DpcnGtHDrgVWrJAk3R56oorVBDeb0WL6Zvo6KoixUiepysWT+ffNa8zOViwOqnrHT/4RNXGJ81ezMM4wNJKyW1Nwwj1zCMqwzDuO5I6LkkzZa0V1KWpNck3SBJpmmWSnpY0tojr4eOfOaz37G5XPgQzz3HlJ2wMOm7Lr4pyaHDh5k+M2AAPsKzz+KjWRnCrQFgK3O8z5qvHStvjtM0za+P/P8fwzDuOd4F+iUtKAhVqqICVWHbNi5mlwulq7wcReS990hyUVKC+jRoEMpP+/Y473feCawcPMhNUVyMk52Wxjpk+fnAZPfugFBAAMCRlMQxPv4YR3fHDvaJj0c1GjkSJee667gZhw4lYUdFBef182NEJi4OkJo8mZC7Fi2APklKTvLq0Kp9ClKQTEl5eXHassWh1FS2u/lm6hIUBJRYdUpJob779tmq45AhhB0mJODAr1/P55ZKNmYMIzthYbSf1wvElJQARuPHA0NWZk4/P+mqq4CjWbPYrlMnQOyrr3gYzZsHzIaHoxytXYtK9txzqHF1dZRl3z5UwJQUoKxHD84VFkbbhIQATcHBAHdyMseV6J/p0wmLLCn2KjygXjWeIKWkGPL3p02//JLr4fXXUd6eeALgO/dcAL24GFDq1QvYCQpCwVuzhnmP770HKI8cybXy+uu0z8qVtMuf/kSdnE5CS/fsATizs4HikhJG0iorqW/79iT/6dSJkNX4eOo3bx6hve+8Q/2uuUbavaFGW3f7yaGuCjq8XxdefUDTFqcqM9NQYiKgFh2N8vj22/RJ7s4aufL3qq226andd6ndh/VKahescePo43HjGEgICgLWFizgOP7+9JvDIXXp4dS0RbcoxCxReoxb6+uCFBlphyVL9tzA42TN+vnks+ZnOE3Sj2XS3O9LsuKz/w8zTfOCY3xvSrrxJ757Q9Ibx6NcPmt+5nYT6TNiBAOxn3zC77W9wLl55C+RB598gl+0fTuRRV9+iY98yy0MNltwt3ChDXs+a352LAUvwjCMKdZLpOk9+v1v1izJunt3FIUHH8TB37QJp/P22wGGv/xF+vOfAYiQEADjo48InduyBQAsKkIts9ZCi47GCc/OxmkuK+NGWb+eMLi8PJzjrVuBlqoq9hsyhHXQrBC66mqWcSgoACpXrOBY8fE41W3a4PjPnMn2u3dz/DFj+O4Pf5Bqq9wqV5Aa5K9aRSnQ3/vtsgShoXbWxDZt7GUXDh4EQhoaSAITEoJTXlwM3DU28llCAgrgoEH2nLvycpRLh4M6W22zeTOv4GB7TbeqKumRRxhN8npR4vbuZXuPB+ANDOSzvn0ZHY+MBIiteX5OJ+fq2xeFcPNm5hAOHIj69Ze/cFwrM6dh2Alw0tM5Zv/+qHIyvSpbs0eBiz9X0IYlevFFr9ato2yFhdR/wwbaasYM5uy9/DKfP/wwAL5vH4lPamupe6tW9LPbjZrbpg3g53RKL7wg/eMf9N/ChbT1zJkofKecgkKYmkr7nnaa3V5OJwMBjY2ER95+uz2S1qYN8xW9Xq7RadOkdr1C5UhMklcOHVS6gv5xjxK2z5PX41VaGmGtBQUct0WLI4umbwuVt3WqRmiFwgMaNf6MIG3YwOCEabJNly5cP6mpOLZWrpTUVAYhamoMxXRrrcrIzvJMOV+lZabap1QpMNB2fo/zvKRm+3zyWfO0yEjph+vgYdUffXY813z0mc985rOfteJi5txXVBwdPcN8+KPDyvPyGDx+7TV8tl278CdXrrR9jaOVPJ81TzsW4C2WdNpRr6Pfn3p8i/Z/M2sOXuvWONWvv46T360b8HX99Tj/Y8YAANbi2unp7PvhhzjWGRmE0B08CED4+QEZERH2vL3kZIBo504UnfJyFKziYuBl5Egc5HXruImGD0eZKinhFRVFWaKi+DwpCagpKgL4HA7q4nAAXGvWACLPPSc1mX6KVq0SdEjJylN8glN9+xKS+PHHqJY1NdzwDQ122dLScFYsZUjCsQ8PB1oGDGC77t2R8evr2aZzZ1SjuDgeIOedh9pkze2rqkLdO+UU1Mg2bQCLq66i3U85hWyUPXuijLlcQMXnn1POSy6hH7p1A9Ruvpm2nj+f/d1u6lBVBXz85z+oXvv22VlAw8Mpa5cu1MlaKH5c/wq1827UMH2t/NpQDelRo9xc2rh7dwCmspI+DgkBqNu2BfRHjuTBmZlpq5qpqQB3mza09969zPe780766euvgcEVK4D/9HQUSD8/5hC63bTjli20QUwMSnJ9PXC4ciUQO3UqI2y5uYQcW/MLhw+n77ZvN+S87BJ1+ftFaqUDkjy6r/RmrVnWqLlzqRtARl0zMqRXXzUUcOXFWvp8plKn9NfIUYYGD6b9JkzgfIMHc510787I4OjRKOCzZgG/Doc0fLhDDz7sUvt2pir25Gn6bEP19eVicVWP6uuPa+6TZvt88lnzNJydH7+maxsruLF95jOf+ewEmsvFb/TatQzsNzYSSfVD80ryqGNHU4MH47P88Y/4LDExPwQ6H9w1bzOaW/K5Pn36mOvQno9pxcWE2rVqxYW8eDGqx+efczNMm8ZcvO3bGcHYvx9oCQ4GIqZMIVzw/PNRPhwORj5cLpzekhJ7jbzwcL7ftYv9k5M5f3Awf/39ceythbE9Hm7C9HTO6/HYCktiIgAXHg70NTSwX3o6MLFhA/u63XzeuZNX/mpUcVWA8vONbxOluN3I9Z99xv+pqcBEx472UgmhoYThlZbai13ecw9AdeAA5T54EDgOCQEKzz5beuklwK5zZ+bXRUQAp/7+lLeqCjiwwk337aNubjcPkuxs4MjrRdmKjwc++valLQYMQP2Kj+fcZ59NWv7ERNSovDz+tm4NoFvz+gICKG+rVihjn30GMJumlHPA1PaZexRlHlKrUK/Srx4lj8dQ9+60wzvvsO9VVzH3MiAAALv9dr6/5hp7Dl3Xrii9s2YBXddcQ/Kc8nLaZccOO8xh40auodtuI1z2yiu5PqdO5fuEBOoSHc1AxB13UOYLLiC09Prr6fvx44G+UaM4fnY210y7dgwcPPesqTefKlRLFSg1pEzB44YrNdVQhw60Q0QEkBYU6NXODTVq2yNUc74ydeslRXrm3VjFxhpauRKl0ornX7OG0I2MDEJmn3uOhDG33EKY8MCB9Pn61U1KLZir/UpWgVqLQcAWuib0X3q14kY7XvYnzDCM9aZp9vkfHgm/Kfv/eT75rPlZcbHUsqWpH1PxBug1rcw/g4eGz04qOxmeT75n08lv5eX4Ilu28KwqKZHsRc4xQ7XqqH2qaN1Ff7nPoaIiIC8yEn/EB3XNy37u2XQsBU+GYXQxDONta7HMI/93/eWL+ctafT1qw+efo6p88w2LWm/cCJS88w7zjOrqcH4LCnDut25FPbrzThzvu+9GdbFCESsqCCv0eGxFzesF7KKi7DXfRo1COYqLY/vkZCTxVq0ApcJCzr19O+qexyNdfDEq4I4dAFJFhZ2Axevl5luzhmNERgItXq+0b79DgRGBKi83FB4O9Fhzn155BeWnqAiQtNag8/PjGKNGAS9t2tjp9ufPB7SuvhrAOucczmUtHP7MM8BIWRnO/dCh/G8YgJ/LRdvl5VHvmBjayunku+pqoC0mhjJER9NOPXsSxlpYSIhl797AYufOwERVFdukpHCMP/6R43fvDgRt3Uo5JUIMV63is7Vr6aeiYkPhvTJUnjFYve8YpWXLDM2ciao4fDj1v+IKkrh4vZTlm28Ioy0u5rheL/WfMgVYSkkhTLR3b9ohIIB+LS+nzh07ouT178/+1dUkTLnrLhTgtDTCJMLCmB83ejTXwsMPM8/wq6/oo4wMBijGjOFa69KFh/fYscDW2rVSt+6GLryppQaek651gcM1cqShAwe41quqGJ3bv9+rTbe/o6ann1HNg/9Q1Ncf6NaJW1U76xtVVXi1dSv93KkT27dvjyhRXIzyWlTE+a65hjpVVNDXrkCX1mmwIlUmh5wKPbIOb3V1JTsdJ2uuzyefNWf78aU/XDoS/uCz340ZhnGGYRixv3Y5fOYzCV/uhReIHAoMlCzFzh6Q8siUob1qLafcmjePyJ4XX8Q3sbJn+pKrnBz2s4BnGMbpkj4ToU9XHnktljT9yHe/WQsMBFCio3H6PR7UlkGDAKwuXVCVnngCiMjPxznv3BlHf80a0sLn5qIONTQAApbitXMnTrCE8+zxoJAkJOCsz5rFXz8/zh8djdNvrcnW0GAvjZCTgwozfz7b1dai4DQ1MV+voYEQw8ZGlMilS1ENCws5Z3U1IZzdulGeykr2sRKrxMcDUk4noNS1K457SAgQZJrsm5ZGWWprGQF66imOtXs3YJaYiNNfUwPEbtjAvp99xvENg/8dDnupBWsdvClTgDprfp7TSfm3b+e4Bw/y3SuvAC7l5fTDhAl2kpULL0R13bsXmN67F5h6+23Kac0vHD+e+u3Zw/ft2tHfr7wipaU5lN7WX88+a2jvXrJLZmZS1+3bAUHm2dBWV19NuadOpY9fe43jPvMMqpm1PmKnTihq48YxEPDGG9TZ4WDdus8+I6PmvHkcr3Vr6uTxAJLvvsv11r8/oacdOnDttWr13aivvn1RX8eM4UG+aBEQ+t57XHNe06Fdh8LUf4ChM87gev73v8mGevCg1C62QocKDJUpUPdlX6a5xZ1VrEg1VJZqZ2aj7rsPKH79dUKbv/qKui1eTP8kJnJN+fuj+rZqZWUWNJTQJkIRffvK7YhQ9ZFlmoJDIrh4j4M15+eTz04+q2g/0p6o6rPfi10saaNhGHuODC5daxhGl1+7UD77/ZnbzW/2NdeQp4BEfKYkt2xX3y1DteqkbXIG+OnwYfyjP/0Jn3nkSLaaP98HeSeDHUvBe0jSGNM03zBNM/PI6w1JY45895u1+nrWEbvsMtYDO+00YOiDD3BWLcUuIAD/MyoKRSg/n2QZn33GxX7NNShEQUFAR6dOhKwlJ+Pot24N4EkoZBUVvBobCUksKcERzs0Fmtau5XsJcDp0iHOvXIm6GBUFsGVlUYfaWs6Zl0fZkpPtRSmt8L7ISMCgpgbgiowEIuPjUZgkYOnwYT5bswYnvrwcCEtMJBSwqsqO3d61i3MdPsx5O3RAiNm1i33KyjjuzJkkC8nNpa7791OHsjKUyJYtOWbPnrxqa8lEOWoUvlBiIudNTQV4qqoo8+WXU87AQEIDLWXs4EHOM3Ei88QOHQI2Xn+dY5gmoBURwfmTkkjyEhsLdCUksO855zC/bu5c+mPvXtSwrl3pc5eL9vnoI47ZqRPgWFsL0DQ12RA/eTJ1MQz62LqGtm6lvZ5/niQ/55yDymdlXE1JAcwKCwG3iy9mMOHQIa7PoCCu19hY+jwpifK8+SbX8I03MkjQuzfnbtGCeZCSPXc0LY0Q0uuv50EemhCpv/aZqcNKUqqrWJNit+g/ukDt45pUVhfw7UDARRfxt76ePnU4CB2NjgYud+ygnjExtG9BgVdxUfVauT5IgYEOSayTEHj5jcfT6W22zyefNU+z0oj/2E9ncaUvNPP3ZqZpnm2aZoJ45syV1E3S24ZhFBmGMfvXLZ3Pfm9WV4df6efHi+eU31FbuGUqWBs0VIWFhmpqGDS31DufnVx2rGhbl2ma+7//oWma+w3D8PuR7X8zFhjIXKdzzuH9qafizMfGAgChoagamzbhlF92Gc5wbCyO9OjRONcff8z8r9Wr+WEvKUGx2bkTp/mttzjHunU4/4YB9BUV8T4yEse6pATF7MABzm0tsF5bCyS0a4djX1fHMQID+d40AUmHw4Y6j4fv3W5Up5gYkqk4HMBUYyOK3Lp1QEtTE/BTXo7617o15yktRb2KimIuldcLmFVVAULWWoDh4UDW8uUoUBKwM38+c8O2b+dvXBywU1eH8jh8OKpaTQ0Pj4kTvMraWq/p04PUuTNzBZOTAay4OIAuNJS2TU+3YXfqVJYrePttAKmigr5JSQGi6upQkmpracfqarJghofT3ikplHX5cntOY4cO1KOq0qsNi2u0c2eo4uIMPfssgJeXR3kCAoCaDRvo/65dyYS6ezfHmz0biCstlZ5+mpEwC3omTqQvJk+mftXVgN8f/mBfox4PwPTNNySs6dfPmucD8LduTR3atKEs6enst3Ur8xS9Xub25ed7tX2zW6eM81NAoKF9+wC83Fxes2ej+A0fbuiDtI+1u8KtpHQ/ubp010eeM7R/f6jGpBjKy+Ne2LuXsufnU1crXDQ6GvDzeFArt22TYqK9+lurJ/X12jZKVHeVu9JlOcAFBcf1Nm+2zyefNV9LSZGys384dz0/3/DNYfmd2pFnTqCkoCMv63+f+eyEmb8/vu6CBdZguCF+i63nVciRv6bSUk2Fhhq65BL84CFD7GfX6NG+59jJYMdS8NyGYSR//0PDMFIkNQsBt7QUsPr3v0nasXu3DU5WUo8uXXCWQ0OZX9S6Nenx+/YFErKyUG1GjiQ5i2miPH38sa14tWwJSIWEAC0SsDh+PMcNDrZVmMJCoK5zZ0ZZLFXKUglNk6QcTif/19cDNa1a2dksExNRoPz8UIPS0gC+8HCAp6KCes6cCWBZ65iNHImC9vHHlCE+Hie8rIy2qa7mHNbyCRddhILzwQeomkVFbOPvT5s88QTz1qqrUeCmTmVh7sWLUczq6oDI1au9Wv+XD5Wy7GXFevJUV+f9VgksLgY8k5OBmFGjgJuaGiCqXz9CI++/H8AsKZGuvRYQnjIFtS8nR/rnP6nLvn30xfDhtPeVV9I+F14IKHXvTrlkerXw1d3aO3WWUnMWyenwqk0blF+rjzMy6J85cwCtSZP4vrQU9e2KKwC4Fi2kJ5+kz/72Nx6OW7YAWXv2sO7Mm2/y+Xvv0U5Dh9JuHo+dxTUigrmjo0YxIHHDDfTzgQOA6euvE04qMcAgSRvWe1W5MVvX77leI7+5Vxdd4NXhw/T/3//OdXPgAAr15s3SxIkOXf9Hf+XmGnJ7HDrvqjDt2mWoRw/aav9+ruv6eu6F3r2B+AceoJwJCfTZ5s20884tbm0/EKgVGqZ6OdQ2tf4E3d3N//nks+Zj9fXMVfnpTJqeb5/PPvt9mGEY9xqGMdMwjFWS7pHkL+lFSd1M0xz565bOZ78nc7uJrLnkEsSCkpKjow2cspdKcKu1snVp9WNKSPAqIgK4+/4SCb4QzeZvxwK8ByTNNwzjcsMwuh55XSFpnqT7j3/x/nezEpKMHIniER0NjAQF2RkhY2IAicJCHPCVKwmBa2piu/XrccADAvj76KMoGxERKDIWfG3fjtObkwOgbduG09+6NU5wYyM3TLduHNvfH6e+vp55fcHBdnp+Pz9GiN96izIMGkTZ+/dHoRo9GmixwiXDwji2lYHTgrbQUM4VGIgKI9mgsmsXZUxJYZuMDM4bE8PctcBA4MaaX+d0Uv9x4zjO+PG07YIFZHp85hlU0V69UMgMww4Z3bOH8MaEmEZ9WdhFczVZd+kB3XRRhZKSeBAFBABMHTsCJbt327B67bWAUFAQCtnZZ1P/wEDqOn8+EHv77ShMiYns99BDlLmiAtCaMQPQWb+eOn74oXTNORW62v2s/OWWo7pQm9czB83fH8Xq6qtRRbOzUXv/+U+uGwtmzzkHaPvsM8r36KPA22efcX7TpO0uuYRw0oYGwKi0FGU3L48Bgw8/5BxTp3LsxYtRjD/4gGvL6eTVpg3Xbvv2tMG775I59JyJNZrg/kT7labSHbm65CK3mpoA+jVrUDU7dOBeqKiw52Lecgvt8corXH8bN5JcqLqaPpgxg/7u0UM680wWXJ8+nbqffjrl79JFKq/10zxNUZQKVKgW2rjVHrg+zsuCNdvnk8+anwUG8pyx5l7/0Kp9TtHvzy6V1FrSHEnvSXrfNM2Npml6ft1i+ez3aO3aMbBOsjmv3G6PvjsYZUpqUp7iNf1Af43uXSF/f3xIK3u2ZC9y7huwat72s4BnmuYMSedIGiXprSOvkZLOPfLdb9rq6pCeExPtbIuDBuHc7t1LpsX27fnBXrMGJ3fyZFSj5GSc0zFjcL537kQFLC8H2Pz8ADgrnHLLFt6npQEsO3cCfLGxAENqKn+TkgCwXr1wpOfPx9lvamK+29ixnDcuDpjYvBkwy8qiTp9/DuiZJmBoKXXl5Rx/yhT+X7WKm7WxESjYvx/AKCmh7M89h6NeW0s5CwsBv+JiOywwLIzyDhiAMrZ1K+2yd689/+rwYaBOApCzsoDdbt1QntasQYkaOCJAh5ShQFXL1SZNT06N1MKFwFhBAQCydCnn79CB8ng8AIXDAdgaBkB0552AVUEByl5iInXyevk7fjzltJKtGAbtOWcO/XHJJbTNs29H6indo4FaotKgDHXrHaDPP+c6mTNH3y4YP3gw5bn/fso4bx5gGxUFHPv7c6433+ShuHAhKtyqVWQV3bqVMMnUVNbt+/xz+s7t5u/556O0JSUBf+++S39ZcwE7duS7l17i2pk1C9i8+GIGCIZOCJWzVWvN0QTtaD1G4yb5afhw4M/j4Tyvv05b3XQTYaarVwN8LVrQjl27cn1YIZ3jx1Pev/+dMuTno7Zefjl/58+n7KYpJSUZiu/ZWpWhHWUYkYqNNWRl73K5fkzp+GWsuT+ffNb8zOWyMur+2E9noNxNx+9699lvz0zT7CDm362TNELSZ4ZhrDEM47Ujg00+89lxN7cbv2TNGsun9EpqkJ1B0xpvcEhyKFxVOjd1pRZtilRdHfsuWvRd1W7oUPwd36BV87VjRtmaprlZjFJ9xwzDSDZNM+e4lOoXsro6nNecHBzRBQsAosREHNZzzgHaTjsNx/qzzwDCykqk7uuuQ9kbN459776b7Wpr+ZGvqgJywsPtLI5FRcjd9fXMwdq1C8e5sJB9o6L4v7qacjQ0ACBVVbzq63Gw6+txoB0OlBt/f0AzKgplzO1GpQsNpQz796Mivfkm8Lhiha1qlZUxhy0nB+ioqbHneV1wAXVu25Zjv/ce7RYdTRvt3w/ExMXp2/X1srIoc+fOHLt/f6DS6aS8VubSrCwgIyaGhbW7dAvUy/+I157SexX8T0NxcbbCt2MH/RAWRpskJTHvz1IfCwooX0oKc8R27ADmbrqJB9GLLwJ/Bw8Cp1YymcZG+iA0FDjJzGT/665jPtq6kCTtafu0lB2q3FxDvXuzbY8ePPTy81H+3G5g6ssvAcmXX6au6emc0+m0Fh2nPgsW0N5vv01yGaeTeY7XXMMgwrhxQGRVFQrewoVcN48/zrVhQWhjI8ssDB3KtRgYSGbRnBzKdMcd0hNPGGp89hKZC2vUd1QvDR9uqLzUq7/dU6/c3CAlJRkKDUWVW7oUaJswgfmAWVnMCfzwQ66Vrl1pe6vvpk2j/YqK6NP//EcaNMirGdO9cvk79emnhlq1khITHTLlkGFIJSX2ujvF/3lf8l5wzHXw/ldrzs8nnzU/q67mOUyok6Hvjo7XqfrU8xSzcvpxu9599tsz0zRLJc0yDGOOpN6Shkn6g8jq++avWTaf/X6srg7xIiZGWrfGFFDXKCn0yBZeAXiB6twnUoFX3KO0Q4b8/fEzxo61YW7hQgaAv7/wuc+al/036+ANNAzjbGutF8MwuhmG8b6k5ce9dP8Hq69nLbXsbDu7ZNu2OP5BQTjwlZUAw+7dzDPq0we4qKlBwZg921bk+vQBPqyEKNYcpb59UbhGjcLZjogAAEJCUAJ37OBlmpx39GjOnZAAJO3aZatekZEk84iIABrDw9knO5tjnHKKnQQlJAQgMAwc/8ZGzlFRgSrj54dyVllpzxO0ErAYBok5SkuZN7ZnD2Dy9dfUtaYGiA0K4ua21vbbtctei87jIaQvPBwgra62560dOID607Il2y9ZQjsOGOjQn//ZSmvXGYqIsEHVWkC9pATF7YMP2P6NN1CaVq4kHHDfPsImExLoj0svpU327CGU0zQBkaFDga6GBsAzLg6l0Vo3sGVLMlvm5kqSQ0s3hmnMWEOHD3PNWAlEkpPx0yIiUGgbGwHt664DHufMoX3uvx/F1kqCs20bGS6ffpr17oYN49yrVwP911yDCpqWBiSfeipgd+AA7zMyOO9zzwHYo0bx0P773+m3ujr64tlnuU7r66XMLQ4dKAnTpk2G/nizV+f13qWWX7yg1svek7+fV4GBnK9fP9pv2zZgt0MHfhQGDWI9v6Qkro9586jTtm203aWXMocvOtqrIYv+ocfKrtDfQv+u9u28atuWdr/xRs7RJqlRErEdDhUd73XwmuXzyWfN00JDGdT5cXNKa9Yf1+vdZ78tMwxjsmEYjxqGsVRSoaQnJcVIuk2SL62qz06Iud34ibffzu+199vsmaFHbWUNRjm0fU+w/v4PQ23a4C/NnMmg/7PPciwL7Hxw17ztWOvgPSHpDUlnSfrSMIy/i/ktqyW1Pca+bxiGUWgYxtaf+H6EYRgVhmFsOvL6RefMhIbiNPfoAUjs3w+8lJXhqK9fbyt6nToRCmkl6OjUCcc2PZ1RjQ8+QBXz9wcYPB5AzzQBgnffZf7Um29yk2zYAJRYKlmnTjjtpglIrVsHXE2ejLMdG4uq19TE34EDgYvycttXiI1FQrfWtwsIAOCsRCstWvC/y8XnYWHAnbUg+86dvDIyCDuNieHVtSvO/fLlvN++nXKWlHCOgADqsW8fYJeXxzYuF7B8wQW0S2QkZc/P5wHxxhsAy/nnk7hj1y4gdedOe1297t2Bk/p69s/KQg2MjgYeW7YEfpYvB0RKS1HfIiIoS1AQfebxAHnTp/Pd4sUAmsMBqJ5xBm29cSNKVWkpEDNyJMAzaBAKYHAwZVu3jjBQPz8yWy5eDNzk5ZFAJT+f/ceNA/ZvvJHrxQK0mBjKvG8f8xP/9S/OcfrpLNnx1luUuWNHyrd8Ocrgxx/TP4MHA101NbaCt3o17fzEEwD9nXcCnZbaV1fHg/3GG6Wn7y7Sp/UTlK8EbcyP00VjimSa1KdHD66vwYNRFSdOpC8CAijT++9T/osuok5ZWVwP33zDq2dGjbZlOxSqKoVkb1egq1Ft21KXF17gmqt1B8jKceJQ9PFcB6/ZPp981jytvp5nMPb9cMxgFXcZdNyud5/9Ju1ySUWS7pQUZ5rmUNM07zJN83P9FwPoPvPZL2GBgQxyP/64tY6yIZfj6OyZ37XRoxmM37YNf+2004g2uu46juUDu5PDjtWNkyT1NE2z3jCMFpIOSuryY6nJf8TeEtmk3vmZbZaapnnqf1PQ/1+rrwe46upwulNSSILh9aIydO/OmnRuN46/0wkwWGn0U1NRn954g/+//BLn2JpPdf75gF94ON+vWgUUSXbq/44dUViWLsVZr64mdDI2FnB76y32iY62lz5obCRUzs8P6PF6ASdLmYqLI5TUUl2sJQ727CHUsn17gMzrJbRwxw5AZO9eoNc0qcvXX6NyJSTweXg44DZuHOePjeWYZ54JuLRsCRy3acPn0dG009dfAwGNjahp8+fTjg4Hdf7yS+CuUycrvbhdNo+HY+TkAC9RUcwVKy8nA+YXXwDm773HyFJ4OJDV0ADkWFAbFweMOxy0UWYm9WzVijZcupRzVFYCgC4X6uf06bTtKacAaoGBHDcyEiizFla/917Kt3o1gGet0xcSArB2787xU1LYzulkjtzGjcCbnx/n3LWL66NdOwYUZsyQMjd79f4LRfp8WawkQ04n5UhKQiW8/HKO9+ab1HHuXP5WVpLB098fBfmyywDPuXOladNidXmbq1SYHafesYWaOitWq1czt/KLL4DTv/4VaD14EPiVOE+/fkBkfDzw17Ur23TuzHW3rzhU+/zPlrvRocDWcerZN0BZWfR79+5AbU6OISlMkqninhcfz3Xwmu3zyWfN00JDCQVfu1bCf/9uFqFdd36gPr7Fzn83ZprmlKPfG4YRKQacLpTUUSRg8ZnPjruFhurbaJpFi7xyqFas1OE8soU93vDFF/h+1lrCL75oZ3z32cljxxphqjdNs16STNMsk7Tnv3SeZJrmEkml/7fi/e/mcuGU1tQACPPnk+J+1CjgxJpLZ4VaxsezGHVlJUCzejWQZmU+tJSM2FiO/fLLnMdSc7p0sdeCO3QICMzLAyJqazlubCzOvTUXLzSUYw0fzvn9/KRXXwVkLOUsMJAb0VJYcnOBkxYt7DXgKiuBkoQEgNXh4CY/dIhRmjVrqGNeHvsnJVGm8HAcla1bUW3Ky1E2DQM1a9AgRngOHbLbokULwvXef99OpRsZCQCuW+uVTLdCQ00FB9vr/hkGdZk5kz4JDgaqVq0COkaPJrz16aftNfUKC6Wbb0aVeust+um66wCa009nxMkwKN+AASimXboAZP37A0Tr11PfsWMBv7Zt7TmP1dXSY49RB4+HYy5dypzMF16g3f7wBxS9PXukd97BsWvThn5/7z0A7sILASA/P45bXw/0rVtH+O769ahyvXsDZK+8Atw5HFJAgFd3O/+p8smX67JvzlH//l7168dAQteuXC979wLE/foBu1Y99u0DGGfMQPn9299QJGfPljweQ/rLX3Tl073V9Z8XaO9eQ2Fh9NWuXfTJI4/QTpMnU/+vvqJNevSgTm+/DdB/+CH9MXQofeLvb2joFW21d8wtqjrjcuUXGGrZ0qu4qEZt22bKNK2kO6Rm7tHjuDq7zfb55LPmad9dEPiH13Zj4wktjs9+A2YYRpBhGOcbhvGFpC2SnpL0sKTEX7dkPvs9WWAgPlO3blJokFdOVYhnlPVQsp9XTU32Os2WCLFsmT0Hz5dY5eSwYyl46UceWpalHf3eNM3J/8fzDzQMY7OkPEm3m6a57cc2MgzjWknXSlKytcjcMczlwim25qulpqLOREfjiPfuDaxkZtqLhD/4ICrEhg2ob127osAYBsBkZY+sq7NVFH9/9m/dGhAYNAgH3+FA1enQge8TE1GvSkrsuWCWcrVqFSDRqhWp6GtqcLoltmnVinKvWUOdystxvpOT+Zubyw1bUsIyAmFhzHtLTKQs1lqA9fU48Fu2oLJNmYIzPm0aABcYSHni4zl3XR1w0tAAHGZlSVddhdOfkwPUbN9O+VKSvYrbs0T1ddUqy0nSLr8uMgynWrbkQXLKKTxERowAQqzkLfn5OExW2w4ZAoCUl/PAOe88+uDwYcIxHQ4gZf16QDE9nayU/fqx72OP0d4Sat2IEfwNCkKdLC4GhO+8kyUxhgyhPH37kkBk4UL6YtcuroXu3WmTtm1p00cf5dqaPRvwzMqi3a3EL7t2AfEDBlDu8HAGFxITaU+Ph2OOGiUtnlEh/4/XqYfW6OJ176p9pxo5nWHavJn2sRLWsEA5ULd3L7CVmMg5//Y3rlU/P/ra6UT1+2qOQw0NLb5N7jNiBOUcPZrrZcIE2qSiglDlIUOA2Kgo2rmwkH7dt4+5gLt302633ir9+c8O7doVrOj9XvXrWKmZn9QrWgU6LWivlvidrqJie9woL++/fhb8L9Zsn08+a54WGMizYN06O5HQ0Zazo0hSqxNeLp/9OnZkvu9QERr+gqQFkrJM01z0a5bLZ78/q69ngLqpSapvcsqtBPGM8tf3tZyICAa1AwLwb6wlkqxBeyvJii9Us3nbsbrv9O+9f+oXPPcGSSmmaVYbhjFR0gz9xLwZ0zRflfSqJPXp0+e/ykPtdgMB/v72/KTkZJzjBQtQYpKTUbAcDmAtMJBtDQMImzcPQLQWQS8sPLJAtnCoa2txiHNzAaioKH78O3cGUGbMQLXq2BFlr7raPra/P+AUG8s5eve2Q0WtbJSxsWyflsY2FRUoVUOGUKbiYm7QkBBALyCAeVLR0YDcypUc58wzAQM/P5z3tDTaY+9eynjlldKnn3Kuc88lHX9FBWBw8cW0w/79OPyrV1OPb76hLZ1O1KpOyTXaVudUpoYryiyUGivVKjlCkkNjx3IutxuIaN8eMNu6lYdRVhblCgnh+Dk5jEJlZ6MWhoYCxJa6OHEi7Xz66cwB7N6dB1F2Nu28eDFtPnEiAOrxcM633qKdHQ5GukpL2eacc6jnJZeQECUpibKuXcvL6aQvkpN5gO7Zw/Vz7rm08cSJbLNmDeGybdrQhvv2AVG33IJCaRh2aOoTT0j+fpHySzxVYw4t0Iu9p+qCzeM1eBxlv+EGwPCZZ1DTrDa68krme1pqblIS1/OIEaiq11wDPO7ebYcDR0fzQB82jH4tKABG6+oIu2zdGuUhNtYOXY2Ppx8MAzjv25dy3XsvSmnXLl6tf3uLFu0JUWdtVY2ilFsXqZETarR2S5j27OE+se6X42TN9vnks+ZpLpc0aYJXH3zw491cVO+bf/c7s06SyiTtkLTDNE2PYRi+Z4DPTri53fhmWVmS221l+ZWAu+9GG1RW4jOuXo0vYU1dcbv564O7k8OOFaJ5hWmai3/q9X85sWmalaZpVh/5f7YkP8MwYv4vxzzaXC4c0/h4XpmZOJtlZbzfvRuF6LTTgIdDh1BbHA5Ar7DQDsksLLTXimvXDlWpro5to6LYJj0dEJkzB6Vn2za2GT4cOPrqK0ZYYmNRniZPtsNEc3IAIIfDDo/80594X16OomiaONZeLzfk+vUoMitXAhENDahYPXty3JkzUSiDgjh/URHb7tuH+mMtyG4YhBumpNA2s2ZRzqAgPsvKAgxbteLhUVfHQyAmhv/9/Tnn0o2hkoLURps1VrOUoFxdf1GFwsNJDmMt9j50KABkQUt1NW1y//0AW3U1mSc9Htpg2TIUuro61CC3m7mGnTsDIq1bU5+MDGDMMACRyEjpqafo023b6JuXXgJuDh2izGPHooJ17IiaFxwMLO3bR9ilYdC/LVrwf0EBYOlw0EbTpzOX7euvgeD+/VG4AgPZZvJkwh/uuov2O/dcQHDDBlTTxiZDbf9xpea8ckC77/uP7r7HUK9eZDZ94QXaoEMHro/Ro1Fni4ulGdO9+uqtwzpriqkhQwC63FzCUseOJZ6+e3cmXZ92GlDn8dAHTzxBOK3DgVIaHMx106EDWTS/+ALFzt+fffLy6P+6Wq/2ZlZp4gRTs2dLB/fUK7ZxjwJVrTpFKl/xMpwOzfw6VDk59n3odH7/zvxFrdk+n3zWPM3tlvZuqfjJ7ysrffPvfk9mmmYPSeeKScfzDcNYJinMMAyfjOuzE2ZuN/Pp5szBT5Ekp5NpEjbcWevhmYqJwScdPJjIqt278Wnmz7chz2fN344FeN2O14kNw4gzDGajG4bR70hZSn7JcxQXE45omji6qakoEVVVqD1r1uDYWksWFRczspGQAAC0bo1zfOAAABEfj+O/fTvhhP7+djZJK6V+t26EyjU1EeK5dy/H8PPjHOnp3EivvQYkWc5wWRnOcG0trw8+ANqSk4ETj8de+628HNBMTrYXwk5LY/tRozjexIm8r64GOAoKUHs6dQJ2W7UCPv70J+pphfIVFlIPa7mImTOBqwsuAMx27wYQ4o4kgN6yhbYcNsxQXsue2qM++kLnaJ/aasnGSJ15JseLjyd7Z3ExZT10iDIFBnIew0DBPHiQMoeFSXPneDWkc5latjS1YgV9VVKCEvfeezzQysqAsy+/JPFNYCBQvWEDwFhWxgOrosJWvrxee6H7iROBtNpaQLFTJzJ3duwIUFdU2NkmJ09mpGvMGOresSPt73QCU9acyu7duX78/fmsXz9g+YorqPtddwGft94qzfrSoU++aSGPlzX4HnoINa6xEbXz1FNR3lat4vPdu72aOuptTR3wb22940317O7VBx9Q/qlTAbilS7m2MzPpr759KeOppwJwEREstzBmjD2g0aYN+3bvTtuuXs15U1Ol5CSv3vrjOm19/kv95/6Nqqnx6lBpkAr9MxSpCoUHGwpMTFZu0jAFBxtHsgzaPybH0Zr188lnzc9cLmnQ2Mif/H7Hdu9Pfuezk9NM09xpmuYDRxY9v0UkblprGMaKX7loPvsdmb8/Pp2/P++taT7Y0SHlXhUXeZTSul6NjaamTSP667813/y85mPH4vRgwzB66sdmk0syTXPDT+1oGMYHkkZIijEMI1fSA2JhDpmm+bKksyVdbxiGW1KdpPNN0/xFvcHISDu8MTcXFevxx1E5Nm0CFlatAj6shcbDw4G3jh0Bl44dcZbXrcPhHzQICLHWxCspASL27wcGcnIYFVmyhDDKqiqcAj8/AGLFCspkhXtmZHCMxkac7bAw4NBas66wENUoOxvoshYQdziAvLg4FK7AQF5ffMHNumgRdQkOttXJ9u1pl/JyynT0frW1HLugAPBMSgK0evcGnhYtou4tW6JwGQaKVWkp8/LWrpWCgw3FRDrUuUOESqoD5PYYmjYNNbCqCmiIiuLYU6einh04wAPj448Br969CUuMj/MoaP9WzVyWpR0apaDICKWmOtShg50opbYW2LvvPtowK4twxBUryIJ5331Adk0NQF1UZK9heOAAn990E/Xr2ZP6NDZSvylTUNzGjKEP5syh/3r14pz791On9etR+M44gzmczz9PO3bsyHy8rl154F5xBW1+661cTx4Pc/Ryc7lO0tJI6hIRwfYHDrDWn9PJdlYyoMK8RqUcytdFekfTcy/Siq8qdNllLb7N3rl5MyGnH39MPxoGcLlsGYMXJSW0wznn0Cbp6aih33xjg+ucOfTT228Dh59+2Khu1Qt1SIly1FcpMaVR1dWBKg3uphat3Cp2+KmszFBjI+Xnx4QMXk0LP5a85xyvhZ+b9fPJZ83TKn5GpasoKpAvceLv10zTXC9pvWEYd0u659cuj89+PxYUJF1/PVNffmimWJsWOotTscr2SO6Cfco6f5TatOH3ecgQW72zlDwL6Hzz85qfHauLEsS8lh/7RTMljfqpHU3TvODnDmya5osiTflxMbebkMTISP5ftw7H2TAIw7PUluxsHPWEBMAqIgIVx1qqwOOxQzCbmjiGFc5YVgaQBQaiiOXn41Tv3QtMWRkUIyIAoZoalJLGRv6X2M9aILu0lOOffz6g4HLxmbXsgcPB3LqiIiCnqspWngYNwkl3OgGSjAy+b2iwy1dWxty70aM5xu7d9tIJ3iMDz61bA43t2gFtGzag3hkGoFhYiKozaBDAsX49UBnb0iu/g1nq4d2i/au6qiYuQ3V1hoYMAbiys4Gow4dJdrN6NaDXrRtt3akT5a+tlSaf5pU56zPN9IxQtlqoj5ar3ZjhWrsjTMOHc/7wcOYWzpvHQ6mx0V7c3OkEEq3JwyUl9GF+Pqrnrl1k5HzvPa6Pfv34rKnJXu7gueeYN7dw4ZFR+0H0iZWMZc4c+vDDD1EK09MJZ+zVi7Js2QLc9enD/Mb77qOPFy+mTT/6iPlsEtfLJZcw784CvD59uGZnz2bfadPY7+LLAjRnWz9dVDFGb/d6Sn/6T6S6dGG/669n0GDuXI63YwcA2aEDgP7uuyRgeeghYPTKK1kGxOkknHnXLrafNAlAffNNBiqSkwPkapWopMOHFBPj0mIvw30xMQ4NGeKv1au5BvftYy7jE4+bMo88Mrwq5WJrdVwilprt88lnzdfIEvvj5sjZLnnjjteAhs9+Y2YYRrikG8Wz6AtJXx95f5ukzF+xaD77HZnLhc9www34Wn5++DO2cmdICpRUJSlIRYqQV17FV23UiE5dJaOVnnnGHoQODMT3GToU30HCb/TNz2tedqxfoSzTNEeZpjnyR14/6Tz9FszlAigKCoCKiAgc11NPxdEOCeH7fftQqvbuxQ9dvhy1o3dvsi6GhwNZubkARG4ux2xoAOYOHbLnx9XW4ix37sw5Wra01b7ISDuTZnm5Xc6cnCPhnYZXDq9bfn6mZswgpDQ42F6bz9+f82VmUp/gYOCosREF6cABbuhNmyh7TQ3KUEkJ4HHwIOe79lrmpGVnA3eHDvF/UBDfl5ZyzP/8h/f79gG5oaG0UWws4BAbCygnJABI8WE1qvE6JXlVJ0Md0+tVXU04q6UkWktIdOgAbJqmdPXV0llnUfY//pGyr13l1rzqAUrUHoWqSZs0WKUNoXrzTZSzLVtoy+3bAaDoaNqxpgaVauhQjnfoEEpcWhp98dBD9HvvXl5V5lWpTbqpt96ijvX1HNs06b8JE1C50tJ4rVxJlqnJk7m29u6l3Xv2ZA5eQQEgW1aGMrpsGSB7772Ed44ZY4dM3Hwz0Dx2LED9yCMAVU4OdfP3B7iyslCDb7qJfq+vl/buM3TG86P0xycytOvOD3ThhYa+/JJ+v/12QmpHjrSzkk6cSP0CAoBoK2vrli1ck1On0vezZ1O2Rx7hXBMnAscvvCBVVxtannSB5qX/SYsiz1V4uKGaGqB3925AcedO2mnOHMnhlCQg0FDU8Vz4udk+n3zWfC0jQ/qp0ONd8uOHxGe/F5smqb1YHuFqSQslnSPpTNM0v58Eymc+Oy7mdtvRYS1aIAR8PywTC5Pkkltl8qqFVmiAFm2NltOJ73Xzzfg6En5EYCBgZ8Gd5IO75mQnbVe53ahEo0cDMCtWkECkqYkL9PLLAS+nE2c3NNReyNta827LFsCkoQEoa9kSlaOxkX1LS4EuCwyjonCm27blWJKdMXP/fkDOUvz8/Gwlb+sWr5zF+QpWkfbtSFFkqwgtWeJQUhLnPfts1JeICCCyuhpnf/ZswDI1FZWoZUvUJysxhjXZ1ipPcTHrsBUVAWUNDcCXFW7aty8x3JmZgFJUFGGUs2YBMhJKlb8/57/iClSemhpp2fpQuVUmP0mdg/PUFJChyko7o2hICIrUoEHMkfvoIz4/91xUr/vuk/78Z/qtQ2c/RVat1Dcl3dXBmav6XmmKb23onXdQwNq1Q02rqaE8Tz5J2zgcPOA++QSYSkhg9GnlSumii6irn8urUzP/Ic8XO7U74kodChupmBiHDhyg7ZqaUCStOPa8PObM/eEPnNvfH7CNj+f6soC6SxdCPd99F7HqhRdYUsHKNPrVVyhw6ekAdteuZOQsKCCk4owz6NfFi6Ubb+T/DRtwJl98kfN7vVzLbrdDBw+2UP5MwihHjeL6a2iwl1JwOIC0iAge9l4vqvLcudRhxAiumQceYMTOSt4TGkpf79jBZ6+9xtITt97qUGCYvw4eJFOnlaUzIgIFNS2Nz4qKvCIMxJTkp2LFcBP4Fn/22Uli27dLPxEVLClS7siYk/eH1Wfft3TTNLtKkmEYr0vKl5Rsrc/pM5+dKPP3J3Ln5Zel2tqj4c5S8CSSrnhkKFamyhSvAg1osVNbvV00YgS+6fcVOh/QNV87loJ35wkpxXGw6mrp2WdJVMH8MNQsa2HHt9+W/vlPlBank+2bmuw16Kxwv969gaWOHQG65GRuAmt9vbAwFCQJKEpMRNHp3JkbIz2d7QMCSKEfEkJoYno6+zY0SCFBbkUrS26Fq14unT2+Qm43Co8ElCUn20sNZGQAWGFhKFRr1wISXbqg2gwaZCttnTvjtMfFUadDh/guJOSI8hYP+LhcqJtTp9rz9vLygNDMTGDG5ULZKyoCft94A8hbvVqKjDQ0/pJEOU+ZpH0Jw3TwoKEzzwTirEyWERGois88Y8PEaafRro8/Tjuefbbk528ou91ZCkxMVUH6MHXt5tCsWewzbBjA4nQCLEVFtHlMDOeprCRUoXdvYDwhgfo6HMyd27LRrWVZEZqmy5VasVr3Xl+hzp2B3127gDcJWPb3R9F67z177cR33qEtrYxTZ5yBorpzJyGjAwdSto8+4pp66SVAbcIE9u/Rgz7x92cgISaG8m7eDLRGRKDE/etfqHsuF9sMGsS+GRkot48+yn533QWEPf44UNm+PYDWuTP9367dEVg1vQqsK9M555jq1Iky+/kBgdOnM3Bw4AAK4I4dqKzWYvfPP08bJibSPzNnAnc9e7LN1Vdz7cXESPyYeGX9oBgq4qI8PtZsn08+a56WlUVI9k9buVzlxSeqOD779a3J+sc0TY+k3P8W7gzDGG8Yxi7DMLKOzNn7/vfPGIax6chrt2EY5Ud95znquy9+iYr4rPmay8VA6/r1ktvtVbC7UD9c4Nzx7ftY5aiHdmmvOujlxZ00YMB3Qe6/WfDcl2zlt2/HArx7DcNY8BOvb05ICf9Hc7mshA84suPGoTI4HISoeTzAzZln4qTW1wMcRUXse9FFONr19UCJ240TW1nJ/4MG4Tjn5eF0WypVTg4wV1Ojbxf5tmBx1SqgrFs3VLf8fMoTEOynfequEJXplui39f4XkYqKQrkJCqI8mzdTlqgonIywMM63YgXznjwee+H1mTOle+4BngYMoDwtWuCgW4t2b9kCtDz7LPO2CgsBhbw8lJghQ2izmTMBnNhYwlM9HkDz8GHgb/VqoKZ3b2nhIoeaXMGKiDBUUEAykfx8lK2kJEJkP/iA/igpoU6nn06I3/DhHO+NNyhP9+4O+Yf4K7KFIY+H7S0VdM0a4CI/n76or+f8cXHUqUsXyn/bbYBvjx6ohBdeKHkcfpqhc5WhLToY2Ve51ZG69VbKUFVF24wahYq3ciWKF+vKoFZGR3Mdbd7M9bJ6NdfCmjVcaz170u49egBXkZEIWAcOUNc9e1DsMjIoq2HQ7hLqcUmJnYHVynq6dCltsmMHAPfaa4BjTg7HvuYa2tbKoOXxEF65YAFgeSjXq1X/Wq+ZN81Sn39O1u5dHqWmcg0OGULbHDxI2b/8ks979eI4H31E38fH0/7BwWTbDAxEAVy8mGtxyRLK3buXQy1UJmL9pSiVQvDHx5rt88lnzdPi4rg3fvqns7OKHb618H5H1t0wjMojrypJ3az/DcOo/KmdDMNwSnpJ0gSxlt4FhmF0Onob0zRvNU2zx5GlGF6QNP2or+us70zTnPyL18pnzcrcboSFVaukpkZTlQoSC5xbqTQdOhr0Glt0UurIDDUmd9b5Fzi0fDkRYdbA9dy5/L9w4Y+DnJVsxQd5v207lvh6+498NkCMnBf+8sX5Zc3fH1XHNAlhi43F0a6uZm7TwYOoNqGhwJJh4MiaJgAzfz6ZMYODOVZwMCDndqNqJSfbmQtbtACSAgNtUMzNBarCwtjX4wFuPv0UaEtNxWkuLzfkdIYrrH1nvV/US0GBhrKzAQSHg22bmlARrTXvrKQnZWXc2GeeSVmtTJuXXgrwRUYClJs2US5/f0CqtpabuKYGsImJQcVKTqasa9bQTlOmSH/7G8cKDgYkioupy759wOCWLcDL6NEoWWefLb3+ur222l/+Qjs/+yzQOHcu7TZ0KIuAh4fTF+HhgPVHHwFaY8fabX3xxZS9uJhtnnmG+XADB6KQ+flRntBQG1KmT2d9vb/+Vfr73+nbuDhDb70RqxeevE5eV5C8pqEFC6hP3760X1QUYZYNDZT5T38C8qw+eOcdW9FNTARuuncndGvnToB60iT6/d13UUXPPZfyXHqp9OqrKH+dOlHeDh3oE39/QmIfeIB6BQUBam3bsiD96adzffXty99nnpGefprj9+4NBH71FQMLh3K9SoiqV7++QcrfU6OLal5UhCr0xsYecue9pcZzrtC6DQ717s294PUSMhodDThfcIFUXOxV17QaOZ2hWrzYUHIyAwpr1nBNdu7Mdb56tT1H9MABQ01qJSvmv0phdvrWX96a9fPJZ83PQkNJwNS370+FaEaovsEXjvx7MdM0/9eVPvuJOcR7JckwjA8lnS5p+09sf4HI9Oszn/3ArClJO3ZIffs5tGJF4JFvnPqxhc7LypyasTBes2fjb3Xvju8ydizTNxYt+vmEKr5kK83DflbBM01zvfWSFCrpMfGguc40zb4nooD/FysvB0ySkgCDigqc0DPOAI7278eJ79wZp7imhnlYXi/gcvgwjnR1Nc52ixY40kFBOOSWspOaaidDsRYADwhAQbTWYHM4OKfHA9w0NnLunj05R3i4QxFRgaqqQv1q395en+6SSzhHSgplaGiwHezkZM41dy6gsX07znZICCpQdDSgMX489aiuRsZ3OGibjz6i3lbiFq+XuXJRUZT7ww/Zt7iYMm3cSHvm5BDSmZFBnXbtskd9PvmE7cPCmN9VWAjIDRzI+SdOpJ0yMvTtPL3ERPrs669R03JzAbaNG2nDwEAeOtaSFlOm0L+1tYwkNTRwPoeD8lx5JaD51VdkzMzJob+jo6WcXIeS2wfL5TKUkUE45IMPoqy9/z5tXFdHW3TsCKzddhsj96Gh/F29mgdhv36Ur6CA+jgcjPLv3m0D3M6dQGnLlsDbDTeQWCY8nLbYvp0wyDZt7AQy112HEpmdTTt/8gnqXkUF52rTxh61W7YM2PzmG0I2Dx/26pT1j+jcry5R73kPyh0YrI9Cr1d/rVY3ZSrj8Ddav7pRpsl1OH48iu+4cXbWzcWLvGq//RNtmbZacR8/oagor0yTAYXISDK9jhhBXVNSaIempqOn2hGlFKQ6GuM4WHN/PvmseVpg4M99m6PQQN+wts+OaQmSDh71PvfIZz8wwzBSJKVJWnDUx4GGYawzDGOVYRhn/NRJDMO49sh264p8yX9OagsNZf5dRoahIyv+6Ltw9921afv1w7/Ys4fB+dtv59nmctlJVY5eJuH75oO7374Zx1rayTCMcZLuk9Qg6R+maS48EQX7KevTp4+5bt26Y25XXIzSsX8/7x0OlK3+/e25bDU1qCM9eqD6xMXhVP/nPzjn1qLjdXU44mvXsm9ICIASH49zHhwM9Hi9QN3Gjdw8QUGE+lnrqwUEcEOVltpq0d69qE+FhRy3pgbAcLk4XkkJ30+axPudO/nbrh3hlPHxAM2ePSgv9fXAoJX9celS6u108t7KOLlzJ8CRm8vffv3s0M9NmyirtQj8pk1AhwXKNTXUu00b9vvyS3vtvLw8HhK5uYDY2LGoaJ99BrwsW8a2w4cDVAcOsF/r1sDJRx/RZxZkh4XZS0f4+9tht9a6eUVF9tICpknbzZsHbL30Eurf5s3Ufc8e1KdBgywQ9Kp3eoWq/SK1bZshp5N2vvVWYHTnTjJpWnMRV62i3wYMoI3vvpsw2rffpi/9/FCK4+JQ23buZJ7cmjW0RVAQdZs0iYdxXR2fDRmibzOOSiiuVgKX5cuB1WnTUGk//RQ18NNPOXbbtrTjqlWUKzhYqjlcpcypK3SBpukDXaJz/tFP/i3Dtf+BN+XOz9eB6H6q7T1EZmCwysoMXXgh18Tu3ajcwcFSq8AyvfiPYmUoU1nqpvZDE5RTGCyPB4irqaG/du60lxSpq7Mgz6OCglpJwUrXF8puOu2YvwaGYaw3TbPP/+/zoLk+n3zWfG3dOqlv36OTGBxtTdoxI0sdTu9yoovls+No/+vz6WeOd7ak8aZpXn3k/SWS+pumedOPbHuXpETTNG8+6rME0zQPGYaRLsDvFNM0s3/unL5n08ltxcXSww+Ts6Gh4fvffv95ZcjhcCgkhEHsuXPtJcWOXufOt+7db99+7tn0swqeYRhrJb0i6UMR9lRhGEYv6/XLF/WXs8hIVBIJhSghAYjYvBmYqq0l/XxZGYASHg4AZGYSspaTw2dxcYDEokU444MGARLWHLLISABr3Dic+MpKFJeSEhzfyEhgx4Kqbt2Ah/37AShrTb2WLXH0AwP5vL4e4LNUoFmzUKw8Ho4ZG0t59+8HPioqbMAJDwcqlixh38hIu11WrybhiduNotjUBLzNm8cDIieHMvTvT1tYSmBqKjDRvj37DR4MdG3cCPS0a2dn3Tx8mPaNikLlefpp3p9zDkqYn58NZmFhdmjsbbdRbsMgCc2ECYRHnnUW877eegvnqnNnQPz001FUAwOpR10dKtb48ahsN94IlDc0oMw1NZEQpGNHqWsXr5zvT1Pm9S/o67u+Vof2Xq1cCYCPGsWSCm+8ASA3NtpLa1gLpD/9NID78MNcHwcPUqbYWFul3b8fhbZTJyBszx6O89RT9JcVHvHHPwLBjY30wdVX074OB9dFUBBtd+gQEBcVxUidnx/9axhAqDUYERAdqlf7vqJcpWtK2nq98G6kZs12qv7CK9X7lRt0f+qrunjexZqy80FFhHv1yCNct4bBQ3zJEunN6ZGKUoUi1aCxkZsU1jJI7dvb8zorKpiTuGgRda+vp08HD5ZKik1Z0d9hqjxuaeOb8/PJZ83XUlMlw86t8T3z06HgTj/xnc989q0dkpR01PvEI5/9mJ0v6YOjPzBN89CRv3slLZLU85cvos+ai7nd+CrLl9vLMWHfVe1sM9WhA4PE1dX4HdbC5kfDnC8Us3nbsZKs1EjaJ+lssaDw0a8nj2/R/m9mKXQS4YOHDgE6EyagYqWmkvBj3DhCNq2EGMnJ9shFaSnA43SyfVYWx+rZk+9NE2hobCSDYV0d369YAWjFxLDvoUN2cg6Xi+0zMkhQER+P896+PZCVlISSYy2OXlsLOA4YQHy1lYRj1y7AYccOHPKKCsoYGWmnwb/gAsL5oqMBU6tuL79MuyQn2zCVksJ+f/gDZVq0iHrecw8qY1QUoDR3Lupeq1ZkcuzVi/htlwtQXLMGmA4NBU6WLaNsX31F0pKPPkJZS0+nva68EigqKKC9oqMpy8GD1HPYMNS/li05z5Il9OEFF1D3TZv4bs8ee4H58nKgtFcvlC1LsZ0/n/I98YS0YWmN/A7t1QR9ooaSEm1dXaPSUqBu1SpAMTOTsnTpQnuvXs25duzgoffVV7Tz9dcD5RMmMC+wrIxyW6GP27fT5gEBhGhOmkT/nnUWqlybNpynTx/m9112mb1cQYcOtN9LL1GGq65inb2pU7m+/f3p25deon2XL5dWrjS06Y5P1P7FW5U9+R4VFhqo2Qccqqhy6Pz1d6laftq926FOKTW6+25Ae8wYoDQ/X2rZ0pC7Sy+NfGiClsWdrbg4wlnPPZc2T0+XDh3y6uABj26/zdS4cdSppERqchuywkJqFXw8l0hots8nnzVfc7lYbuWnLGvvsX5WfeYzrZXU1jCMNMMw/AXE/SAbpmEYHSS1kLTyqM9aGIYRcOT/GEmD9dNz93z2OzCXC59n7178DMxassgyi/zckhzKzkZ8uPBCpoTMnMlArQ/mTh471hy8ET+xiPBvfiHhwECcYQknuHVrhIQvjjxCd+4EKrKzUfC2bmUU4+BBHPumJgCrshKQ27kTJ9zj4f+GBiDu8GEAICAAJScpCSgrLMTZbd8exS4nh9C3w4cBti1bgJk9ewC9mhpurPJyVBKnkzIEBbGdBYbBwRzb4UBBHDgQ1aRVK0AyN5fyJiSQDKCujvJ++CF1Ly1lXTOPB6c+JoYyVVYCfDt3sn99Pe3x8cc47p9/Dvg4HCRHWbHCbpcpUwjva2qi3N262Wu2WfMaJdSv3r0Jbdy7FwVu2jTaafx4jl1aynGqqwl9LCiw1w3MywPUiov5fN8+gCMvj/O1aWNnlvzqK7giLo59n3+ecluL3nfsG6pze+/QJM3XdvXWwbJQjRmDctm3L+3Trx/tXldH+/TogYqXlQUMJSdT73vv5fzz5nEdvfUWiV9276a8WVkoiVu3otalp9Me48fTv506UcZ16+jLgQOZF9jYCOAtWgTEFRUBuMnJfLdgAQ/1FSt4SD/2GLHzN94oDRnq0JbcFjJl6PTTacvt2yVnVKT+1PYr+Uvytk6RQkOVk0MZZ8+mPJMm0W433ODQ+AtayDQNbdzICOHVV7OGZEqyVxGl+3Rz7V+07Pm1WrTQq7Aw+i0qSrJGDJN1GCo+Dtacn08+a77mckmp6T89Ea+u5qfhz2c+kyTTNN2SbpI0V9IOSR+bprnNMIyHDMM4Oivm+ZI+NL87l6ajpHWGYWwWC6s/apqmD/B+x2b5GQ89dPTPrSmSrFhm/e+v4GBDfn52/obzzsOnWbTou3PufNkym7cdK0TzzqP+P+d73/3zeBXql7K4OMCntpawxchIHNjUVJQWhwOnu7DQTgqyfz/bx8Vx0Q8ezD6BgTY4BQQAJzU1qF3l5YDd888DWY2NAIG1iHp+vp0RcccOXgkJJN6IiACyBg5ESbRip+vrAR0LnA4cYD5baSk329at9vp9mZkAT8uWOPqXXAJwNDQwR6xDB/Zp25Y6FxcDvatWAbSVlXxWWIiC1NBgJwc5dAiQ6tzZVvu2b0e16t4duHrgAcqblweILFtGW59zDkB68CDq5Y4dgNeBA2y7ahVhstaSEwEBqFabNtGmAwYAgLt20dbWPLeMDCAmM5PjFRby+eDB9N1DD6GM+ftT3qQk6eabmbcWF0cmy3ffNRT68Yd64K9+6nZWW7VrZyg4mDZdswZF7IUXON7777MQ+7ZtqI1ZWVwPN9xAu7RtS5t/8glwt2gR18Att3CdnX025fnLXwDc994DuIuLAcG5c1EynU6Od911QPOXX1L3f/6Tfu7Zk/7p3BkovO02EuZ068ZnjzzC33HjgPuFC+3QW4eD0NMdOw3tmvJXfX7aW/JefKlcLkOdOqG+lpbSTtXVtNMHH5Cp0zQpv9PJdZybKzVV1WufJ0Zv6koF1OeqtsqjwEDgLjW+QdYk7ywlU4DjYM39+eSz5mkuFwr3Ty12npflS2bhs2ObaZqzTdNsZ5pmG9M0/3Hks/tN0/ziqG3+Zprm3d/bb4Vpml1N0+x+5O/UE112n/32bPRofMjgYOsTh+zQTPeR905JhmpreY5ZOR4WL8aH+254py9Es7nbsWJJzj/q/3u+9934X7gsv6jV15Ma//Bh3m/fDixVVXED5OQAHSUlOLP9+5Ncg1T6QENODmDhdLJPejoKzqhRANXAgTi/27YBhrffzo1gGABVr17Mx0tL4xh1dUBaYiLnff99IGrzZmDFAqOVK+2ELdb8skOHAKSmJsrSpQvwevgw8JqQAKBu3Uq9Fy8GZO+4g32jonDyrSxJrVsDcVOmcM6oKIBqyhTOMXEibbJpE3WuqAC6Vq2yk4BcdRV1W7UKiC0vJ0FNWRnHLykBBrKyOLfTiQqWkABExMYCj9nZgNr48dSlVy++q6qi7JddRt/t3UtZQ0OZr/iHP9Du3bsDSB9/DMxmZlJu07QVvcZG4Mrfn/P16CHdcZdDM5e20OTTDd1zD4pdz56Us18/INblor5vvcXDs7wcCHK5aOu33yZc9OGHeTheeCEQNH068yanTgXiJPreysLpdKJkZmTwYH7nHWBt0CC7vqecggIcF0fbDB7Mq7FR+sc/uJ7+9S8g8LXXUCgl2nbvXvpg7Fggs1s36nTggNSjp0PnXBqsvn0NdejANbh/P9fb9dcD3U6HV+6aeq1bZ6qmBvUuNpYfA69XcgQHKVQNitFh5Ti6KTLGpW3b2G57dpCsHxaXItjx+FizfT75rPma283ADz+fP/wJ3Z3nWwfPZz7z2YkxS2Wrryc3QFaW9Y0pexDKX98fkKqpITqsoIABbCv/zvcVPAvufCpe87NjAZ7xE///2PvflLlczKuzLCwMJScxEUVp0yZAJDqa1/LlrAPmcOBgd+0KfFRWkpTDCvc7cIBYZSvcMC8Pxzky0k7b36ED51i6FNVozRpAyOMBPsLC2O7QITtRxsaNnNtK2hIaCmhFR+Oc+/nxshJcrFtH+S2lr7ycepaW8n91NY6/FVLo50edIyNx5KuqAA0LGuvqaIN584DblSs5hgRMHjiAoulyAaLz5vF3zRrKeMcdKGzt2hFKuGkT0FBTA3i4XPyfkQHgjRoFnNbXc76GBsAlPZ33gwdT5uhoO4y2UyfK1NRE2y5bxrGXLGGe3oABhLyuXk1IZFISD6UtW4AYh4MyLV/OMSTAyHlUFMOwYYBRQQFz9pYv5//+/WnbxkY+f+opwLJfPwAuIQGwGzwYBS0pibJceinnnDMHVa1LF/r2wQcJKf3iC3tpiIYGrr0vvuCcTz3FPMesLBKaeDwsah7XyqsH/1SmiRNMxcbSZo89BtC/+ioDDRMm8HlAALA3YQIhGE4nGUxPPZX6zpvH9dqhAyDYpo10+21erXl5jS7dcL38t65Tz57eb9dtHDzIK2ddlRITpcBWMapL6i8ztY3q641v15CsrzdkhYMYGcOO5xy8Zvt88lnztdBQ7mvsh5dZdaUvRNNnPvPZiTFLZZPwlfi5/X7WTPt/f/8jn5iMvbZvj8gwZAiDykuX4jcdHZ75c4ue++y3a8cCvB+/Qn78/W/K3G4cbMsSEwEba45aSAgKWEAAYW0OBxd7RQWgFxYGqISF4Vjn5+Psx8ejMnXrxgXfpQtKVatWbOfvj7Mu2YujJyQAHe3bAznFxahBHTrg1Cck8Lm1Nt/BgyhMpgns7NtHOODBgwBVYyNhgVFRQOdLL1GfqirK1qIFx7TmbdXXU+/UVB4GDQ28P3CAbQMDAVq3m/0aG+3QU2u5Ba8X2L30UsBryBD2TUnh3LNnczyrzg0N1PP88zlORgYA0bMnwOFwsO+BA0BcbS0q2Msvc7ynnmIu2Jw5vF+0iDDBZ5/l+Na5rbT9F1wAvH7yCTB22WWEQh44wHUwZAh9nZJCKOumTdR15UpGr/72NxTAV18Fhp54goel2w30vfqqvS7eaafRT/37U4+ZM7mGrLBQCeVQ4nt/f/pz3z72aWhAgbvmGqBw1Cj6JSeH+X/p6ZT7+uvpz/feo382b5auudqr7n8/QxG3XKbXBr+h1BSvFiygTvv3037t29uhlG43/fn224DkqFGoftaAwCOPUD6vl1DRtDTJ8DbqD/XParzm6Nrap3TumAqdeaa0ebNXqZ/9U2teXaMlb+1Rt25STGt/hUcYcjiA5u7dcYCtcJDGxuPKWc32+eSz5m0ZGT/9nf/Cd7ihfOYzn/nsBBshmj/9c9jYiA9n5YRIScGHWbAAP2TgQHsNPAscly61B+p91nzsWIDX3TCMSsMwqiR1O/K/9b7rCSjf/2xuN+AVH49q0bIlIOB0otB4PDjaI0agkERGAkjFxUBRfj7bHz5M6FyLFgCiYdiJP5qagLDsbBSjxEQc6xtu4HiBgQBgfj5lMgw+q6pin/XrgRd/f1tFMk3Ok5UFxLVrx3effAJ8Dh9ux0o3NnIz3nknCpzbzbENA7WrtBRY6NgRuGnXjn1KSvg+KQnwLC2l3EOHokj17QtMFBWxbUEBABUcTJnKy+2MnAMHoiQaBtDbrp29vETnzoRsDh+OvzN7Nue/+WbAtV07O0NnTQ0Pk9xcwmlTUpjnOGG8VyuWNio/39SrrxJGe/HFwFFKijRjBsrd1q3MGbvhBmCsQwd7Lb577yUZi5Wt3+UCsEtKULWmT6fNzzjdqysnHda0d0wtXw4k1tWxT3Q0SzZMm4by6fUy59LtJpx1zhy227SJ/jnjDADtm2+Yi/fhh9TvjTcAvYQE5l4++ijw9fzz9NXtt9P+DgeQ5+fHtlu2AE//frBIm1d7NErz1fXgLA3qVKGHH7bXWJw2jTYaN472OXSIfunTh5DRt98mpPjmm5lf9/jj1L+hAfWuVy/pqj8EaGi/ep2mLzQj8mp9uTxSfftKXdNqtHRvip7QHyV3taqKG9WzJwMHZ53FvbNmDdewlZ7Z7T6unNVsn08+a77mdnOvYT8EuXI1HLelQXzmM5/57GizlLbqan7vy8qkH7r23x1otXzXbdvwh599lukcbje+SH29HZ5pgV7gT+eV8tlv1I6VRdNpmma4aZphpmm6jvxvvfc7UYX8XywwEMe/rAww2bQJZz8tDYixknrMn0/4XlUVTnWfI8sF7t4NjNTW4gwnJuJoP/ooat3MmTjqh46sXJOUxDEKCgAyK9zQCtts147/m5oI7dy2jblOSUl8fsEFnCM4mHJt2QKgVFRwrLo6nO+aGuqVmwsY5edzY/v5sV2rVgDbkiX6NulFZSXnX7AAaGjVCjALDeWc69fz+eefo2KZppUJERCzwht37mSu2IgRtNvs2ahCcXHsM2oUoYSWOnrWWWRdXLzYnjv49tuEqUqc47zz7EQgubn019ChtNndd3m19d2NunDn7Zoz9aBCQ7zfOldffAE8TppEW375JeesqUFJs0IoY2Op6+7dnLOxkfZ68UWSlDz3HIB8xWVeBT10r9YNuF5dd7yvtWu92rKFhCfWeoJ33UU9XnuN486ZQwbLgQPp6+HDOf9NN9GWS5YwsHDllQDi4sUooC4X8Lt4MQ/R8nIANCiIzyTmF/r787BdtYr9nE7pwZdjtSrxLFUrTBsTT1ddYKT+9jeA89FHUQHr6+kfh4P28ffnun7sMVTd0aPpd8PQt+rf5s20zYwZ0ttvG/ryok8V2qWdxvxjlBJae3X1BRX67Otg7fHrqk91gcbHb1NYqEdbt5qqqyMUOTzcGkSx1t6RGvOWHDc1ozk/n3zWfM3l4rn9wzAobKs6Hs95pz7zmc989q25XPhMixcfnc/s+5Ezpr4/GGVlBV+5koHob77Bd+jene/nz7dDMn3KXfO0k7bbqqtRJurreaWkoMZ164ZTby09INlZNiXm1J16KuDi7486s2AB4ZHV1SQWaWoC9lwunHLTBAAscBk6FEVv7FiAKDoaxau8nLLU1qLAvfoqytGwYZRr0SLOWVyMelVaCsx07IjatHUrKktWFsfYsoUyhYaiouzbx3chIfZyDBbg9u8PIKxaxbGysijLpEnsay0EfvrpzCmLjqaMRUWooJmZqJiXX0544d1347e/+CKw8vXXwOHgwbRhSYm9BEJkJOcaOJDtKipQ29xu1KbgYPonOJjMm2+/jeq1YXm9omt3a50GKEilqiiKlSMo8NsF02NjAdzKSsrdsyfqX04OcwvfeosyXHIJYB8ZSV926ECY5ZYttO/zz0vbVlSoccdurVAvrS2IV/kst7p29deCBcB1cTFlvOsu6c9/Bk7HjAGO//1vwH7OHBRRCXXss88A69WrCZ8cM4Z5kWPGEDbauTOwmJZGSOiDD5KRtKmJflq9mu3atLHCHqVTTzWkSZfrmSXnaOXWUHXwGBo9mnYNDeXafekle4kGt5v28Hrpw4YGQjQ7d6Z9AgPtbKbTpqHsnneOV+f13qkKd4C+vHO92tesV1d51aDBcvQZqP2tMjQw80W9sbRaf0p8VXPib1FOjkP9+3MvLFtmO70emVxErVod1/vdZz47kdarl/RTUcBVEg9Pn1fkM5/57ASYy4VfZa39/OP23edV7974jC1b4hv07Ilv8Mc/Htei+uwE2rFCNJutRUZKr7/+3TXYrLloYWE4+Xv3ory0bAloxcbi4GZn44QHBABKJSXcQDt3sm2XLoBYVRUgYQkUISGA4t69HP/VV7nhrrqK89bWAmleL9sGBeF8f/QREvmwYbbStW4dAOL1co7Dh9ln4kR8B0suDw2lrGVl9mhLUhLQEBZmH2/uXHvu4ZVXUpcOHTj/7t32/LmOHYGXmBiOZYV+tmmDCjpoEMfbu1d6913me23dyvkPHiRZTGMjsv+kSZTfNAGgnTtxjNq2RdmzUvredBPtVFiIGjhqFICc2jFIgS2idKceU3yIW2ddECDDAIamTmX5gNxc6dprAdijs3W2aYOKl51NG1lK6ZgxZArNz0eFlZjjVuqNVGXbHlqv/ro4faUmn+GnwYMpd3Ex7T9iBApXZCT1vf56gDY3FzizFg3t0wcIXLiQY/frR5mysmivt9+mDaz5g1OmcJ1u2cI8xbQ02nzQILa/8UZ7yQ1J8ngdyi4MU8uWhrZvBxqTkxkgCAri+nzwQVRdiWvcyqTZogUZS198kbItX871Z63ld+ONUofoIt3tflhn62OV1kj18mihRqtcoVJ9tSJCTeUd9GqwFuvT3JGKDKpX586ogEzwtrMLumT41AyfnXTGHNaf+fncufMElcRnPvPZ792qq/k9Dwo6+tOjn08W3Nkq3hdfMPi9YgV+3ogRTHEJDGTO3YgR+L2+xCrN105awHO7AZe0NJxOhwNJ+tAhwtOqqnitW4dSVlHBxPn27QHB2bMBu6YmHF9rkfGzzuL4ERHAnKX89evHcXv3BlK2bAEQm5pwqGtrAaCwMG6c/Hwb2txuHHwrKUx1NWBhAWZlJecOCWEuXtu2gFh4uP39jh3AWqtWwMD+/YCVtZj20KGM0rjdlCcwECDZsQMwSE7G+X/1VcIF27Sx1nsC/LZuxalZtoxzFBYSNpiXB5y1awc8NTQADU4nCT/CwoAeC7Qfeoj6HzwIyHXowHyxli1R+BITqePWrdIZZxhqfekY3T1otbpc2ltfzjbUtSt9+Npr0iuvAFqffIJaay2l0LYtdb7xRqDDNKlv69a048iRlH/cONIKt2kjvfa6oTdb3aeI0YOVe969Sks3NG0aZXrrLYDp8GHKV15uraVHvQ8fBn4vv5ywUT8/IHr4cD4bOZL3XbtSbisb5+efM6Cwdy9Q5nQCjG434RLXX08dXn+dtuvUifNs304dzzjDXvrBAu1evVAO//Y3FGSXi+t+9262u+EGrk+Xi+u6ro4+3LWLMr3+ulQdHKuoTsm6TlOVoENyyqv22qMMZSsqIUwfzQrVlpDBmqwvFebvUXVTkDp14h7bt0/y97fDQ7xRQ49nFk2f+eyEm9uNcv+ziVo7dTpRxfGZz3z2O7fQUPw2a1kwQO7okEzrWWXKmj6Rm4uPNmAAvuMzzxDd5nbbc+7c7u+GavqsedlJC3guFwpITQ0w5vWiLsXF4WB36YIj7nSibCQnM49oyxagIz2dsL+CAhzXYcPY/t13cb5zc3Gm6+oAo/btCdvcto25Vy4XN1tTE6BYUwNcJCcTntixI2Xq04fkHju3uVVQYKqkhPIkJNhLM3i9NpR6vQColR7f6UQp6tMH6EhMxJFv1Qrw83i4OWfP5m/HjgCSaQKdxcWU0e1m0HnAAOBr7Voi6xISAIWwMEaIPv+ccjQ1kVBjyBCAraSEuXQHD9qKYfv2tMPy5YRc5uai3FnJUqylHO65B7h1uWjzSy7huwsukFavcejCywLVooWhZ58FzFq1oh2sBdQvuQTQSknh+9mzaaPnnqM9rrkGyDn7bKDZelh99RXbzJ7NMR95xKHuA4KVkmropZeou2mS3TMoCKj66CNgdPBgYGb3bq6J0FCUvQEDOFdQED7evHn0+VlnUSeXi/l1oaFcW8OG8f2ePVxLt99OOw0fzvX60kv04dCh9OuBAyievXsDekOGMEd0/nzaYvlye0mOrVsBfQsaGxsJwZgxg0XcMzLo88REFMeBAzn27K8M5f3xUf1h4FZFxzq1TgPkCG6hkClnaPLpDmW0NbQlfKi2Xvqiokf3UkODoUWLaMP8fCk6yiupUZJklC71ZRT02UllLhcRA5jzR7aI+zZLrc985jOfnQgLD7cir7yS6mWB3E+ZYeCzBAXhM3TujO9pJVbxWfO3kxbwJJzaggIu3IgInOL4eABv8WIu4oAAHOmoKBzezZvZz9+fOVCNjfinixYBLiUlHNtKTJGaCmw99RRwJ3GTTJ4MnBkGcNfYCFTGxqJyxcRwzu3bvVr5eY6CKnfIcXCvwsK82rqV82RkAGFWmKY1h6+6GtWlrg7oqKwk/O/QIcrQvTtz5M46izq0bAl4WIuojxkDHG7eDMRYWSwTE4GLujo7hLSmhuP94x/SX//KQ2TMGKCvqgpIMk1gLiyMUaRXXgEw/vxn2jE2FpDq1MnOKHrgAH3idNoLeaemAnsBAUD1iBFs63QCIn//O0A4bhzx4mlp0hVXcCyvl3YfNMhWYidNspIhoML17m2HSHbvzv45OYDh8OFk52zZkrDO9HTg7eBBlD+nk/Jedx1hsv/4BwrkmjWMdFnhDF26MIdx+nT+rljBvDorW6qlkvXuTVkXLiTU9Lzz+KxtW461cyfZNCWOuW4ddTaPRFo0NpJY5o9/5JqIi6MvZswApidPZoBh4kSgb+VK9mnfnnJcfz1Q6fGwLERUFIMbWVnSX/4iPf6kQ4FhQcrtc77MXoPU/69jlZLq1L59XH+m6dCa7WHKyjJUXQ1EHj5MW9XXeWQ7vm5fRkGfnXRmzYnFvq/k1Su0znfN+8xnPjsx5nbjx5L125QUoB8ffJKs51VDA8JAXh6D1Zs34w8cbS6X7dv4rPnZSQ14oaFAhZUKdv16nHpLPTEMbohOnYAbPz+gxVJmwsJw7NPS2K6kBEd48GAgIi6O7RISgJL6elQMPz99C2luN8cwTRzs/fsBxe3bAc2YFo1qpUMqVqLStFV9utQrNpZj7NhB6GhTEyrhzTcDPpYzHhMD3LRubS8LEBFBso/167lxq6uBnKIiHPpDh4BbC1Tr6ripS0rsZSUsIIiOtsM2r7/eVhVXrQLYMjOBhU2bOHdNDcrfH//Icf72N/Y/cIAyhIaykOagQYQRfvABELp8OeV44QXKa9Vp3z7KtnEj8HD33dRv+3bAavVq2nnLFso0axYw5DhyVQcFcd6dO1Hv7r+feXdnnUWMeXo6/WgYtEt8PMsZ7NpFnwcFAYnWaP2oUcDlokVcL+PGAc6LFqHUzZqFwrlqFQA8eDDncrmYJzhxvFdDu5Rpxmemtm/nuhg5kqyfb7zB8Vq3Jgz25Zfp37597SyY997L9889x7633IL69+KL9PWoUYRt1tfT1w8+SB08Ho61fTt9ZeV/6NCBNpw3j/DRxkZCSl95hWuhqEjK2utQUGSg/vlPQytWUEY/PwC5VSvAOCyM9mzR4siyGRWsgSdJMSq3J3T6zGcniX0X8L6fbCVU1YG+a95nPvPZiTG3m8FZAO/7bv3R7w1JDqWlkul65kxTLVsyELxvH76UtUSC9bIWPvdZ87OTGvD270dFaWjAIbYyWVoA4PHgkMbHk0WyvByIyc4GRAoL7fl3Vmjfrl2oUFFRHLdTJ2CouBh1Ky+P7awpGB07osL5+6Pm5OQAE2FhgEWr+AANij6keB1QlV+qCiuDVFPDdwkJqCvJyZTx5Zf5v6QEcNmwASe8e3fOGxcHPLpcQEdJCYpNQYEdAti7t72Ae0wMwNTQgNrWsiUK0N69nLu6GuUpJQWFqbKSuh86RH27dCHlflIS7dW3L+c4/XSAJj8fSNy/H5iqqEDZWrqUMg8aBLzefLOdECQ5GdVs8GASz/j5AXimCRAahg13UVE81MrKKIv1EMrJob6VlWw3axYQM3QoKuHevSh6y5ejAIaGMtdtwADOY2VH/cMfgCKPByD98kvUutNPJwzztdco/8aNtJVkK7r79zPy1a8f11t2tlcLh9wnz+VXaNtpd6kg36umJkIw+/ZlqYa77qI/PB7KN2sW+2/bRt28Xo61YgVtsHMndTVN6vb++1yLkycDvnfdxZzAzZspc/v2lKuigsyhQUGEr06YwNzIyy6jvzp1ki66yF478eyzAb/evfnbvz/9kpXFD0J5Odeex4PSiBGWeUAxR+du9pnPTgorKDj63fd/RlO1c0XhCSyNz3zms9+7uVxWPrPvDziZYsDVKZ5VXu3b71aY9muI32IVFnq1axe5CG6+mQHmuXOZ9iExCO1T8JqnnbSAl5uLKlFUBHRs24bDf/gwwObxcDM0NgJPDgeOfvv2OMt79+Jsh4biUFdVAUQOB7A2dy7HCAqy51Y5nexfXY3THxwMCLRujePfqRPhosnJQFLr1lJevqHd3c9WRXgXebt0U2WlIacTYNq9m/NYaf+Dg3Hca2tRngYPxvn+5huc7IICwvjatrXbYfduQhc7dOC7UaOAttGjUVwCAnDsMzNpM4nz5OdL559vL7D9/PPUsboa4Lv0UhbJXr0aUCsvl+64g/qFhqIgdukCiFx9NQ+LzZuBhKgo2iozk77Zvh1YTU0Fdm6+mXZ97DEA7LrryJoZGMi8x/h41KPRo4Fxt9teQ8/plB55BACZNw9Qad+efv/gA4CstJRMnHV1HDc5mVDJwkLOP2UK9Zg7F6jr358Q0lmzaOOgIK6btDT6saqKjFSNjbRV+/aA/YoVdubLG88uknPHFkketdn2qe77C6G4S5eies6bx5Ia27YBaO3bkzzmpZeo77JlfH7bbbTn3r3U/bTTuCaXLUPJGzWKspx5Jlw1ejTXT2YmfTBkCH+vvZbrISuL6zkhAeUxPl5atdKrYG+VnE5Tqaksf9GrF21SXU2dDh3i/ujWjb63MsKeeaYUEMCPiCSFSL4smj47qSwri98W236YbKW49IQVx2c+89nv3FwuBmOZkvJjgPf994biVaSkqo1qqqxXUxPPtMBAfIZx4/jrm4/XvO2kBTyXCwdUQvUxTRxRyzGNicFZbdMGRzw8nBDDrVsJEWxowHHu2hWYsKDDSndvhU1GRABMTU041CtWoKZt28bnS5eiDAUHA5tlZTjC3bpxM/XuLe3e7VBFlUtFRca3c6x27gRSvF7C/JKS7DX9AgJQ57Zvt2X0+HjALj8fBczhAL4aGsgUGRhIeN7jj+PQv/IKwFtcTJKQiy4CzG65BXWvVStCOSMjqU/fvkDSkCEAV/fuhDMOGYK6VVZGtkxrYfjJk1HtTJPz+ftL990H3A0YQCjhiBFA5IYNwGBODgrfvHkAbGEhMPHll4RXVlWx7ciRlDsri3MYBsc/5xzafcUKPk9NpewdO7JUxYUX0n/z5jGHLiUFRfb111Hj7riD/vd4gKu33wbiVqwAlAcN4jjWEgxz5lCWzZuBxcxMQNww+NuvH9eF0ykFJsfK07GrMtVDqzIuV0Y7p846CwX50CGgMy+PxDKhodQtIYF+f/ZZ4G/6dJKkSPb6eKedBrwnJgLEjY20QUQEoZytWjEHMDCQ9v/kE66Vr7+mD6w1Gd1uVMPx47wa8vRkdf/XVYrfOk99envVpw/9kJNDGzsc3CNxcfT/vfcC2IMGMdjgdhuSkGSLNcyXRdNnJ5VlZPCs+K5996c06/5XfMmFfOYzn50Qc7nwZ1iH9+dCNK33pvYqWR/qCm3LDtLq1SQZfPppfIGfArujQzV9YZu/fTupAc8K+zOPGsAoLweOWrQgVG3mTJzTsWPxQ63Mk0VFAI210LU1Pywx0Xbci4sBDtPkR99K9e9220lK2rRBMVm2DOBo3ZpyFBfj2E+fTnnCwwGaxETKtmMHjn5gIBBSU4OzfvrpQFJ0NGWdPZvzWOvHmSbnCQ9HeerZk+Pk51O/iRPZZvJkQhBjYznun/6EOrR8OdtlZNht98UX1KuiAhVr40YeBEuWUM9u3QDYdu2AzrlzUaUyM1EZFy2i7SIigIONG2nXYcMApl69KOsf/0i/5ecDRQMG0EZjxgA7OTmA5Pbt1HfvXtrR4SA5i5UF9YsvKH9gIHXs2hVIfOkl2tHjoTzr1qHeXXop9du3D8Vw5Eggun9/Fg7fvh2wXrKEto+MBIqffZY+v+IK6t+nj70QugRErV3Lg9ftMeR85O+6bNVNmtP7PsXHGwoM5ByPPEK7T59O+w4bRujkmjWodKNHU+/WrVHexoyhDN26Uf+nngKQX3gBaB48GGA86yyuveuvpw6rVrFcQ8+egOC4cZR582ba/fLLpV3ra/SnVedph9rrzIqpclZVyOMBZuvrueaGDiXhzZlnApP+/vT9nj0MIuDXMg8vJcUHdz47+ezaa7//yXevc/+cbb7kQj7zmc9OmPn7W3ODDdnPI8dR/zPvjr9OyRGr4OgIte9g6uJJZQoKNHXDDT8EOwvk3G4inSxRwfrfZ79dO2kBz1qg2jKXCzCTUC4KC4GXqioc/vJy/q+rw/mPibGzIsbHs21AAM7r3r1AWEwMfxsbcXwLCuxQzLAw4GfDBiDllFOY/7R2Ld9feSV/PR5UmMREnPbSUsCyfXvO2bcv5+vRg3MsX04ZKipQ/1q0YN/CQunJJwHDkhLKsn49QNKvH1Dg8QBdGRm8ZsygnOXl1DE3l/9TU3HYzzoLkAwJAZqCgoA0a95WXBxOfmYmbdGzJ8fMykIla9+e8oaGAlNr13Leiy8GMJcto45JSYRKbtpE/5gm4HDNNdRj40ba0cp2uWQJIFNeTv9IwN3FF9NHLVvaa7e88grQ1749EFtUxPc332zPXVywAEisqABkXnoJEO7WjXltGRkc95lnKPctt9BnTz5Jvfz9eT9+vL1IqMdDO1xxBQD7yCMs+TB1Vit5PIZOO42+2rIFAD50CAi7/HLabNUqILdXL7JaXn45D9TbbgMc4+LY/r77UBn9/WmPCy5AyduzB6gdPVqaNg2Qf/JJ2nL6dBTMc87huly7lrYKCpIc4aFKiJdu1KuqzuiqGYsj5fVyHXXvzjGsQYcZM2i7Bx6gXcvLAd3wcOuHxPw2mY/PfHYyWWDgz3+fGTbUF5rsM5/57ISYtXazvTyLlejsaLizlI5GSaVye70qLfUqYe3Hyv3LC3p23ySFBnu/A25Hg5zLZc/HO/p/n/127bgBnmEYbxiGUWgYxtaf+N4wDON5wzCyDMPINAyj1y95/vr67743TRSRjAzmYlVU8Fl4OIBhJTVp3ZqbZOBAwhYTElBPJk0CgkaOZPukJIArIoJXZCTA9emnOOZt29phooYBbBkGcNGtGyGBKSncODt2AH8pKUBB584cv66O8p16KiGb9fWodK1bA2O5uYTYnXEGqldoKKB1xhkojGeeCWhJrBUXE8P5160DAurrAZFduwC6OXPs8MBPP2UbwwAivV7+NwxCFzt04JyXXw6EzZ4N2Hz6KTBqqUwdOgAOd98NzDQ0ENrZrx8AOWwY+zQ1oV7deac9H2z+fMq8fj1tlpcHyGZkUM5u3diuvJx2sLKVbt6MOnXjjUCJYXC+008HhgoKUPUCAgCgSZMo89df0+979xKS6XSyX1AQACURmnj66UBN377032efkT1zwQJb1e3VC7jLzgZ+rEXRb7yRv0FB7H/99dQzKgqI/te/6PfRo1EJTjmF/oqIADrHjQPeOnZEnUxL43hr1wKC//43MLt3L9eVx0M/f/IJamX//szVu+km9ne7yeDpdLJtehtD86Iv0kcP7NS9ZfcpN5c5ob17c30sWcLx5s5lX0tVTk/n2LW1XoVW7D1y13nl7/fbDFP7tZ9PPmveFhn5/Uya373Oc4fc6AtN9pnPfHZCLDAQ3zQy8qe2OHoenr+kQDnUIMN0q6DYoT1KV/3KTVr0eYWGDrW3/D7IHQ10Prj77dvxVPDekjT+Z76fIKntkde1kv79S548IwNgOtoseHEcVevAQKAjLw+nuKzMTlqxZw9ZCMvLGcWw5nc1NQER+/fz/+HDtoLn9aKg7drF98XFQEp0NI6+nx/Hqqgg42Hr1nyWnIyT7vVynLg4e628WbMAm4kTKWdmJkpfXBxq4+LFAOEVVzBobClz+/cDLkVFLCzeqhWg2Ls3AOL1EvYYGkp5unQBOvz8AIwLLyRz5403AgBRUYBGVpYNQJGRQOdpp9kJWwYPBoSamjjnhx/yXgI+LrqINfUqKwGG1q2Bjp49CZmdNIkyWglGXC4AKzkZwHE62e7wYY49ZgxQNWcOsPHqq/TdHXcAS35+1GvRIuC5qAjwWrmSMMsvvqDMcXHA5rhxwM/OnXZSnSuv5LiXX877qCj++vmheEqA1q23Mo+vogLgshKp/POflG3NGo6ZmUkfxMRIjz6Kmjt5snTDDVxns2ezrt0LLxCqaql5ixcDfjfeCDQaBsfbv59rYfly5tJNnAisbtsGIA8aZC+Z8PXXqHtTp3Kdv/IK+33yCffAyJEOHaptoWHDDF17rQ23jY02pEdGct76eiA7Lo7+93OZKlKQpDJJUljg4V/ytv4l7S39is8nnzV/S0w8+t13ExmUHPaFZ/rMZz47MVZdjS9kZfM+trkUq/3yU5WGaaXccuiu6Kkq9UbK7bYjoHzWvO24AZ5pmksk/VwusdMlvWNiqyRFGoYR/0udPzAQYLAsNRUHVQK2hgzB6S0q4rvcXJzfrl1RuW68kfdW1sewMECkVSu+b2oCHg4e5Ebo2ZNQuBYtgBiXC4Bp3x4VKzubsEKPh/dW6KMVitnYiJPcsaOdudMwcKhDQzne3Lk46y1aAF9XXKFvQ/0aGlDW8vN5XXwx509Pp6xlZTaMFBaiMA4fzvyzlBTmCgYEAE1hYahSK1YAY1aGzqgoyjFlCiBz0UWUffFi6nLWWSTcuP56wG3FCsDOAp8rrwTkNm+mvFu3Ut8HHgByDIO23LMH5ey222inq67ivGvWkMQjORk4NU1CDv38UI8mTSJs8eGHUfSSkzlHy5bMjXv+eWDliito9549OX9TE22bn89yEHPnAlbp6fbi5C+/zLGefRbo79WLMNXLL+c6SkpiqYHaWtp3zhyusU6dOM7w4Xba4auuApIvuQSgfO01rosdO6hj585cLw88wPUnUa9TTkFVfeMNYPOmm5i32KsX/WnNb+zbFyB8/XXKFxREtlWHg7afO5dzf/ghGTIl2qGkhBDPwkL2f/xx1OOmJgYfTJO6LFxoz91cvpxrKzube8FrOtSoKB3Jn6mm/C2/yV+KX/v55LPmbaGhDMb8lO3csMuXZMVnPvPZCbHAQOncc3/ukfN9V9+laFUqRuXarzj5jx2nEf8crzlzDc2Zg8/jdjOA65tr13zt15yDlyDp4FHvc4989gMzDONawzDWGYaxrui/nLheX2+n/TcMlImoKECkoQGH1VJi6usBn7Q0nHivF9VswQLeX345N1BcnD1vLDGROXuhoaiFn36KAlJdzc1RUYHTfvfdvA8M5EYpKLAXOQ8L4/+yMrYvLuZGys/nb2oqyszIkWyTlISaExVFtqR330WNGTYMUA0NBYyio3G8q6oAwvx8AGnLFuZImaYdGvrUU+zbvj1OfWoqwGKt42et7ff226iBw4dTn6eekt57z1aQ0tMBiv37qdtDD1HWkBCOt24d76uqbBiYO5e2fvNN2qGoiDo2NlI2f3/AMzSUfS+/nGQ4EmUdN45+WrWKtvZ49O0SE5s32xlSH3+cOXdDhxKyuWcPIJWZyVw0Pz/qFxXFXLjkZI751VeAkNMJ+I8bh+J58CDq4Icf8lnXrvTl/ffbyVncbuDpiiuozxlnAI0VFQDdqaey3fDhtKu1BMSgQWx7wQUsFF9XR1kuuwyoW7aMvrrySq5bK/PnVVdRr/nzJa/Hq13rq5SYaOrf/5YaG7wKqi/TpEmmBgwAUk89lb4yDKDWyqr58ccAf2Mjx/z734FRC6gDA+mDSZMAwnvuoe+TktjPMAwlxHgkVUmSyhRA5zY/O67PJ581f0v40asBK5d8SVZ85jOfHXdzuxn43rXruwkFv2vfT7ziUrliNVRzVRrVXUNOi1LuIUP3329H7MybR9RT376+cMzmas0iyYppmq+aptnHNM0+LVu2/K/2cbmAE/bnVVBAFsXKSj4/eNBOiFJTY69bFxJir19WVoYyl5WFM9y5Mw6tNck+Lw+QjIxEnTIMe1H1qChUqOpqICg+nnljoaGca/x4yhkSgkM9fjzH83op4/DhfLfryGBwURGqSX4+KsvatTjbOTn2UgTR0YQWHjrEOaKiKGt+PmrQmjWAan4+DnpYGMfasYN9LCe+f3/K1qMHYX6XXUY5LrpIevZZr5YtqNfFF5m68Ubm+p15pg0HAwYAPlu2cK6MDOD3tddswK6spM2sMKe0NKDmpZcIaUxNZZ+nn6aePXuyVMAbbwAjrVsTJjp6NGGjy5cDimlpKJxPP40D9swzHP+xx1AizzsPteuGG2jfyEjO1aUL9bzqKkIr337bnmv36acA9bx5KKbvvAMMnXEGamZoKKrs++9Tnzvv5HjXXGOvGXfrrcCevz+QVl9PW7/0EiGmd9xBHe+6i+vpmmu4XpxOjvXmm6i2mzbRLhdeSLbOCRP4PiaG0Ny77vRqyJz7dMXTnfTVbfO0dIlHjfc9qPXXvqg3b1yrulqvVqzgwZ2WRt9s346Ct3Il13eXLpTz3HNpe4cDFdRadH79esJaV60iVLZtW94nJwPEV1/aIClMEqlW9F/es83V/pfnk8+av3k8x9ggJuaElMNnPvPZ79dcLqaYFBfj+/zQrKRnhki8Ykry6pDSNcP/OnW7fZxefdVQZCQRWB4P026sTOYvv/zDnBZHm0/d++3ar8nlhyQlHfU+8chnv4i5XIDYggW8DwvjwvV6UVxyclBbdu4EcJKTAZuVK+0F0A0DZSI5mWPk5gJgTicp9YODgYv8fHvh9MhIQKG0lPeGwbYREWzTsydOf1ERitcllzBnqkULFJg+fYCfoCBgbOhQHGmXi3JnZ+NQr11L/Vq2lIICvdqyydRllzr06XRDLVsCUWlpAOBzz+G4W+v9de6MAx8SwjkiI4GMb76h7F27UubyclSuVq1QbIqKpJ07vLq46knNONBXX9/t1rmvnKJrrnHo7ruBSbcbZWjqVKBn40aUy1GjqMO115JRcsoUyrBxI/tt3cpDqqjITupSVATQff450HfFFbRLXR1A+sUXtEVFBSDZti1z15KTAeu//AUwu+02+i07m/DR0FDCZUeNQlVbs4a+6d8fkPJ6AfqzzpIefJC+v/degCwzE2XP66W8OTnAW3o60Pn554BXr160qWQ7gp9/bi+w/tZbXBOdOtmZqv79b1S6deu4drp1Q4n87DPA8bXX+MzrtcNe3W6yg372GedIia5R3x07NVWXK6o0U6eOa6fJ9z8jf43U3to0OavaqjGghbZsAd7792cOXk0N/d67N0pkQwNtEBsLvDud3BO7dlEGPz8AOzeXfv3znwHfykqpfUaEpBpJ/mqlSjq4+dlxfT75rPnbMee7FBT8vMznM5/5zGe/gAUG4tPMmPH9b44ehfp+/Gad3N5g7dxpaORI/IuYGObuBwbymx8ain/xU1mDLd/Fl1Hzt2m/poL3haRLj2SrGyCpwjTN/F/q4NYSBxIgV1WFgx4cDLykpQF30dFs29SEwxoRQQhcSAgXdVISYFRdbWeS7NEDB/ujj+ylA264wY5ZtpKOVFbiOF9+ORDY1ASo5OXh88bF4RSPHg2opaZS1qAglBpr8cmCAuZanXsu5woI4CZsbJTWrvWq5NOFOr/2OW16dqHuv8+r4GDKsm4dqlrPnqTTDwgAFHbsoBzLluHAjx0L+FhJWEpLAZnISEZwkpJot7o6KW9HhZ44cL48ciixcoPWLarRm28SirpmDe2dmspctKVLgQFrNGj/fh4gjzzCAyEwkLY57zwUrDlzON9VVxFSW1rKfLfKSubozZwJVEVEEHLZqZO+zfDYuTMAmJVFGOZZZ7FdXR3LAqSmeKW6Wi1YYOqjj6jvCy9Q3qlTKc8dd/C66ir6uaLCnleYlkYSlOBg3hsGqqHHg1rZti3nue464HvLFuqzeDFlHDECdXPlSvr69dcBaGtC84QJKIAxMex7+un2wvNnnkmd9u8n/PSGGyinywUAT54MgCclSblloRrQ360qhegtXadV+SlyDe6nUx3zNaXDDvnHRqqxkWt80yaS3Tgc0tVXo+Y9+CBladuWY7pc1HHbNiDSSurTqRNt3qsX101sLK+mJmn/zhodCVJTvY6MmjQ/O67PJ581f7MG/n7SSn9uiqfPfOYzn/3fze0mumjbNn5/bfupCXmN337ndpv6z3/4re/aFX/Cil4bPZq/P7ckjG+5hN+2HbduMQzjA0kjJMUYhpEr6QFJfpJkmubLkmZLmigpS1KtpCt+6TJYSVUaGqwyAQvx8ag5ISFAT8+ewFDnzjirLhcqUmgoju6WLSiADgfwdeAAN9Pjj9thju+8ww1y4ABzs8LDgb0pU+yFz4uL2b9dO8JDo6I434oVdjr74GCOGRQEUMbHA0tvvslgsMeDo9+7N2F1CeE12rDSoZYyNauyp2ZeYCohke+cTuoSH48DbymTCxfaafHbt0clCg0FFKZNAwB79WLNM7eb9dL69EFJbGyMVL+wzWpftULZYf1VtjVUA8KAGgmnx+WijMOGkbRFok7nnMOD6JRTKMfKlfRDu3aAmGlSbiu0VaJNp09HITt0CDUyN5ft09Jo78OHgaf33gO2LGDu3Zu69erp1YK/zFeb8rXq3r5Rnnsf0OtTHZo0CdjftAmAevFFwOWrr4CciAhCJp1OzhcURHs99hhqXf/+LGuwYweK1lNPePXZezUKDQnVpZcaeu89YPHFF6mvBaSDB9PeV18NUH/yCQlZ1q8HoKw4+g0b2M9awHTyaV7NmlahZ96M1MRJhgICSLSyaBH19POTHnvc0Ib1M3RfRpEaXgxVSYkh95fz5M4v0o73Y9Wu3tDrrwPevXtTPwnl84UXUOLGjCFs08pcOmcO4axffMG21iLyLhfZOr/6CvjbvJn7qUahkoIkSXXyl4YO+aVv7f+z/RaeTz5r3uZ0/ty3GSqIjlfciSqMz3zms9+tNTbaawAXFlqf/tSEvCOT7I7oO+PHM61kxAgidAYP5lu3m0H6YwGcD+5+u3Y8s2heYJpmvGmafqZpJpqmOdU0zZePOE86kp3uRtM025im2dU0zXW/5PktBcsywwBw2rRhXp1hABI5OTj47dsDUBs2AF81Nah+W7YAey4Xap+1fpq/PyqXvz8AOXq0najkwgtxnOPjcdpnzQJGTjkFOCksBEoCAwHOESNQX/LygMWWLVFuUlMBj169AIpevaiDtSh369bS4dpQlTvi1U67NSokU0lJDvn728s2OBz8HT6cc0dHA0vDhgENhw5xzksu4f+aGhssp04lI2VCAm15yy3Szp2GSjoOU+oD12tv8ij16m3I4yHE0uOhDh07UscDBzj/c88Bi6tWAW5vvsn2VuReSgrtfuGFQJNE21iZTq01CvPz7YQ4a9fSD2efTSKQ7t1tZfaee4D111+nLIHuGhnlheqiTQratVmn9S/S1VcDbDffDGQNGEB5MzNtuBs2DPXRSiqzciVt9sADnH/xYsrbq5fkcXt1y7C1Knn6ZT3xh926526vLrqIfbKygP7PPwfgXnyRvwcP2ou5v/QSffrVV7SDtTbhzJm0x6ABXunBBzTvzzM1vmSabrvVq+7duS43b6aeDQ208X8+cUitWikqylDbtlJ1rUPPf9RKXboamjyZbKLffENIRnU1GU8l6i1R16VLOe7Bg6iKOTn0Xfv2AJ4VwmzN3XQ46Ctg3Sli/aXw+EHH8oR/Ffu1n08+a/5mOzY/NlIepfq6n8x44DOfSZIMwxhvGMauI+tt3v0j319uGEaRYRibjryuPuq7ywzD2HPkddmJLbnPfivmdjMga+VmsO3n3XuHauVyOWSa+CcREfZA98yZDBwPHfpdgPPNt2te1iySrPwv5nIBVJaZpq3cRUXhiJaXE0WTmgrYREQAElZ2weBg5oGVlgJ7dXWENVqK0dj/1951h0V1vN0zu8uyKCr23hUFu6KggiAg1qgxMdWYYmKviWm/lC+9WKLGGqOJ6T3G3kClKCgoqNi7YgUVIsq67O79/jg73pWoMYlYcM7z8LB768zce2ffc89bokgohOADsWIFicnGjTSas7Lohtm4MQ3hmBjuk5vLZadO0Q3y4EGWNejTh+1wOHienBw98+T584xXq1xZd/3ctg0YPlwAvr74tMo0VH+oA+r7CpQpQ2KybRv7JGuh1ajB/nfqRHfGqVPpLlmxIpORlC/P9uTksE6a00kXvosX6Rbo58e3PX/+aUDM5tLw9RWX4wGrVOFfnTrcpnVrkoIaNagOPfggifIvv1ANHDKEroe1a+sFzePiODaypl3dujy/zcaJ66efeM0eeICqXt26vCbPPksykp9PFa9CBZJIWWS9QYA36lZ1YDuaYmPV+7FoYwX8/jsJDkA1Kz2dxM3Dg6Td4WDtukce4eTZoQOJTNOmvL5OJ1UrT0+qhqMezUTlC3tQAhfxivUNTHo1E19/rSfP2beP25nNJJ8WC91ufXyowknF8pVXeN78fLZ98mS+QZs18QLSdplRGwfx6vFRsJzPxObNQPQqJ5B7HjnZGn7/nX3u04fHt1ioIM6axXFyOHhdf/mF53vyScbfxcZyuZ8fXTTi43lPDxlChW7tWrovBwVxfA4d4vhWqcJj7tvHflauTGKan+8AwAyCOSfWq18FhSIJvajw1YicB7Lue0SVSlC4JoQQRgDTwZqb/gAeFUL4X2XTnzRNa+76m+PatwzodRAIoA2A/xNCXDXFhkLRhsXC3/zFiwuucc+c6Q4NgAYnSsPLS6BKFb64BpgHoGFD2ilhYX8ld6pkwt2FIk3w2rShQQpQedi0iQ+DpvFzTg7JXp063Pbnn6nilCpFYnfwIH+fS5QgycnPp2Lz559UfX7/nUSqbl0+DCdP8i2KrJlXrhyPERjINyPe3lSIHniA8XVZWTzf8eN0jUxPp2F9330kYhYLDXAvLxKl8+e5zM+PbbdYGA8WEmJAk2YeiI0T2LiRxGrJEqp1NWty2z599Hi6I0fYf5mBUgi2MSWFqtlvvzEu7umnSbYqVuQ+OTk04GvXpgpasSLH7NgxkiWZTyA2liSmSxe6uK5eTeXT15cK3MaNTBAydy7FHRnv1bkzM2ReukQy9/DDJCJCsG3z5/ON0qJFJInnz9Pls3VrHq93b/bP359xkl26kNh8+61Ag3efgDZkNHa06o8lSwSMRhKfcuVIugMCmFzE6WSb2rZlv9q2ZdFzWeB7zBi6KrZsyXtk0CDG4iXur4D7G+5BG6SiZ/tsWGpUuFzrr0cPxkBaLCSiNhvJ/pIlDGB2OEhcX3uNJLN7dxLqHTtIruLigH6DvWGuXgUtkAZT+9ZYtbUCDux3Am++Ab/JA7DuzWXo0c2Jjz7SXZNHjKDrsK8vn4fNm3nPygk6OpqEetMm3se9e7O/gwdTjduwgfdep068bkOH8lkJDub1N5l4ncuV470cG0tXXL/a5wCUAuCaYO7OMgkKCteFbvxc7WdUIGfnQd1HXUHhr2gDYJ+maQc0TbMB+BGsv3kj6AxglaZpZzVNOwdgFYAuhdROhTsYdjvtyAcf1O1dwomrv3xygknQmJPh6FF6nPXtSzsvMhLo3MkJ05lTWLNau2wvqHi7uw9FluBZrSQJdjsN9vPnqTC0bcv/vXqRiHl78zd4+3YSlGbNGC/3/vtUAE+dIgFr1oz7HjlCF7WXXiKZ+Ppr3vBLl9JIvniR546PJ7nIzWXK/pIlaWg7nTTYmzTh+YoVI2E8c4aEzMuLZCkggMdp357HPHOGJO/oUZ6vQQOSyIwMko8KFXRXuY4dqTo2aMBsjSxtQBdBPz+6pH71FcnJjBkkq3XrUqXy8OCyHTtIRGfNYrsAEqvmzak2Pv00Cd+4cSQuc+bwrc/Bg5T7J0+myjh1KpU7IZhBcv9+viGSLrNDhnAsvv2W7erenWP74IMkaLt3sx2BgRzvn37iuTt0oPoYEsIxS0igyjRqFFU8m41jv28flSqzpwGadwkULy4wdiz78803JFay3ovBQJdJgAri/feTdMnMUp07M9Nply68lrLchs0GbEsXwP+9Dcz9EktGrMD4CQJ2O5Xa9evpIrtgAV8S7NrF/uzdS+K3ezcJ7BtvkIhHRFBJa9CALwQ2bABee13Af9zTMM/7ArGvrIDZLDDltUx02T0VpfAnJpx5GuGtcvDWW9znww957+fl8R4fNIj3dd++HPNVq0jQixVjopu1azkWGRkcz2bNSKjT0tjH7dtJVJcu5bWuWZNEf+9eEupKlXg/enkBGoqDZeOAfKDIl0lQuNdxtbfkwG7U5ESnoHB13GitzQeEEFuFEL8KIWRm3xvaV9XoLPowmWhfNGvmnmTlWuQOYPiEN8qUNsBq5Ytqmfyua1fAYnbC1KkjTLWqoeN7ETAZZEIWRe7uNhRZgmexUAHSNBKM48dpkEv1ISODRC0vjwZ37dokUVIBO3uWxqwMH9q0iSSibFmqdD/9xN9us5nH8vYmwZAxftJl0WIhadq7l2pZ8eJU+kqX5nHq1SMROndOV1OCg+kGV706yYfcf98+vqnZv5+G95IlJB3lyrG9UVEkLVYrSd7BgyQt335LxalGDbazWTMql7Vq8aHNyuIYjB/P/nz5JUmLjw9d9JYvJ6Fat45/Viv7GhFB0leqFPDDDyQioaGcJEaP5vI6dUg46tVjGyTRuXSJ7fnySz1uMSaGbqjLlrEdvr5UE81mEq3ff9cJ06JFdEl47jkSQqORRPi550hojUZg7Fie12zm9kYj+/HCCyTutWuTbCcmct1777E98fF8o5WXx2u/ejXH5/BhLo+MJMkJC6MqZjSy/MOOnQbkWUrjt98E9u7lfeRw0IV19Wqe89lnSUItFiqXX3zBdmzbRqK3dSvHQdN4bWXGzNq1ge49DOjQqzQ6dxGIiADW76uAWP/ByEMxJDR4Din7ffB//8dxiIvjOCYncxy//prH6tGDcXqVK7M9jRoBkeFO1ChzHjNnaqhWTb/nHQ6S7w4deA+cOcP76fPPXQldPub9EBHBe2jPHt43J8+ZAdQE4DJ9K1S4dQ++gsItgnRrIv76U7rPpy3foigo/HssAlBL07SmoEr31T/ZWdXoLPqw2/nC9ocfGDJC/F38rwFnzwlkZNAe9PCgvWk2A2sX5CA3PhWw22FKjAcyMy+7Z16vHp7CzcPNcoMtsgQPIEEpWZLKhMlExWn2bBrqCQncJjeXN210NA3VypXpnhYXxxteZjQsVozHycykmnHuHAlSXh7JWrduNMKLF2ec1okTPHalSnq2zJwcbluiBA3hV17hcTp0oHuc1UoFZPNm3Z2xc2eSIR8ftiUri+3x8qLa07o123T6NIneM8/wQdyzh651UpU8dYoGe0YGj5efz++enmxH6dJ0P9y9m+pls2Zsc3Iy+/Dhh4wZO3mSbpnx8TT6K1dmu5KTafBMm0aitGULyYG/P9vw2GPs04kTVOc8PUm6q1WjWli6NImT00lV8OhRqo9Hj1LdGj2aZSJOnyYBmTaN58/N5bU1GvXi8b16kXzVrs2xcjhIJv396UbYty+v8eHDvFYbNvA6fvEFz//881S17ruP29epQ7Jst7OPlSqx3oyPD4/jcJAkSvdOPz8mMpk5k9dJCC4fMIBlCMaNIznatIn32a5dLPKekcFj7NgBDBvGe/KJJ9hHi4X/x4zRJ9ktWwWaLf0IO8Z+hW0Pv4tGjQS6d+c4VK7M6z9nDs9Vty77mZtL0uzhwXtr61YnXm21HPU/HYz1UzfAnu+Ew8HrV6eOXsNv5072/9IlJmHJyiKRr1qVLxzKldPrF5bw0nM1e8Gp37QKCkUIV97Wf1XqrFaj/gOioPBX/G2tTU3Tzmia5soDjjkAWt3ovgr3BkwmehidPatnjL+2aW8A5yonTCYNjRvTjj1yRK+PHNTFB5/WGA+roRjQrh3sZSrAZKK3VHy8isErbNzMWMciS/Dsdio9xYvzs8VC4zMri0TD15dqUV4eH5AyZXjznjnD7cqUIfnQNH7299eTb7RurUvVJUpwn99/p1F87hwN61q1qHaUKsUHr3hxkieZVMNsplIXEUGje/t2Klw9e9JoXreOpGDpUpKio0dJLsaM4YMoC2UvWkSiYrfz+I8/TgJWvjxVv6ZN2fdSpeimKpOEhISQsJYqxTix997j2Njt3NfDgwpTvXp0J/Tx4f49enD7OnXY119/5XgIwTHy86Phn5pKEtu4MclG377s4969JD0GA9vw0090RQwO5vlbtSI5mz+f+9aoQbIzZAjJep06JMBvvknlc8IEqpwdOtAVUwgSkOnTeY1//50xc5J8XbrENjduzOPu2kWXz27d9LpuycnsE8B7aORIKnQyw6fVSqXtwAG6Nqak8L5o2VK/RtK10cuL+27fTsLYqxfP3bIlx8tk4jh/8AHJ32uvcQxMJu777LPs7+DBJISyeLqs8/jVNwYMfbkEhgwR2LvHCS/rOYwZraF+fbrHbt3KTKidO/N6xMdzTJs3Z/u1Py8gKutrRGIVKuXtw/EDVmRm8h6xWhknmZvL/tWtS2W5fHm9EH1wMMltRgaPZ7cD+ZonAFq/l2BQCp5CkUTDhpxPdVz5c7rGWoUPiYLC1ZEMoL4QorYQwgzgEbD+5mUIISq7fe0JYKfr8woAUUKI0q7kKlGuZQr3GOx22mctW7ovvVpyJyNkghXul4fKlZ14/316/aSnu47nEPD75DmYjhyAdflaRMeIy+6ZBbNqurdB4ebgZsY6FmmCl5xMcmQ00kgFaDSXLEliZzbTjdJup8J14QLVmQ0buO/WrSQf/v5UjqQb5+nTJCgXLvB4ubl0ozMYSPh+/pnulE4n3UJr1+Y+9evTSHc6+blnT6o1depQzdqwget37SJ52rWLhnXJkrSR8/JI9EqVIjmR6k/x4lRQWrRgvNqgQex3rVokfdu2sZ1ly9I9slIlHmfsWMBocOLd4aeQlqphwACeLy1NJxDNmtF4l/1p357K3JIlVLe6dmWCGqORJKx+fRI1TaML5fz5VJReekmP2crP57ESEhjj5+XFfn/4IeMYz5yhorlzJ91O581j3xct0mPkTp0ioTMYuO7jjxkzt24dicaTT+pqWvfujE90OLjsiSeoyK1dyz5GR/PYCxZwDLdu5ZjFxTEW8umneW0cDi6PjSWhnDiRZPTAAR63UyeOj8lEMjVpEvefOpXjZ7VyuxUrSCAPHuSY9e7NNr77LrdxOBgHGRhIYpaaSrfNvDyO+0cf8Z4LCaG75/r1wIwZTjT88S2YB/SDqXdX1K/nxL59VI3/9z+638oXGseP6wXTj5z1xoJSz+AzDIVvVTv+70MvzJ3Ltnt780djxw5en/h4FmP38uI1OnmSbiGy7Ee1amxTtWoCAF3TLiBExSEpFEmYTJwvdFx5n+9CJU7MCgpXgaZpdgDDQWK2E8DPmqZtF0K8I4To6dpspBBiuxBiC4CRAJ5y7XsWwLsgSUwG8I5rmcI9BKn25Oby5Stxvcy97h4FAsuWaihb1hV7Z+Hv/ZgxQGqaAbnFK2JtrLhcozk6mjZTQTKnsmvefNysWMciGzJpsTDWav58KlwSQtAgLl2aBmnbtjRSq1Rh4ohNm2hYnzpFkpSdTaJRujSN5RIl+Pn8eaogMivm+fMkYmXLct+TJ/UsmrVrk6hUr86acPPm8Y1J5cpUf/LzqTZJpaRyZRKr+HgSiK1b6b6YmsqHOD+f56tRg23Zto3Eaft2xnpFRlJy/+UXPVHLoUNUtZ5+mu35dLITb758EWui8zF/8ZuY5zESQU/7YedOA06eJMGYOJGKXffubFfjxiTFffuSwO3bRyLctStJ46ZNvDHr1dMzMg4cSJK8cycVzogI3aWyalWqP7t2UT2TiWvy89n3Fi1IZmV6/1OneO1MJipbP//Mt+hGI69F/fokfGYz48SSkqhA+fhwGUAyXaoU2/HGG2zz228zW+mRI9x+2DCeQ5YasFpJ3nx8SHbLlSMBOnCA4+PrS2KzahXvtxo1qLj99hvvg6VL6WL6+eckdCEhnCiNRrYjLk4vobB+Pds5cCCvgezX4sU8p83GsfzoI947P/7I7R05F2DevRXtEYdJ68dgjcOG1u0tMJuZ5EcWLE1KIpFOSeGxet8vsLVOJIY93AybjlZAufICkZG8bx0OKrkGA6/PCy/wOIcOcfmDD5KkGwyMd42L4z5//ukE4ADfGMYDzhBupKBQhJCdzZc1Ohx/3SgrS8XhKVwTmqYtBbC0wLI33T6/CuDVa+z7BYAvCrWBCnc0pNpjtUpv8OslVwGo6egE0ORhQN26JG99+9JbqE8f2ijJybQ/Ad0WNZn+Sj5Uds07F0Xa6vLx0Wvh+fjwTyp5xYvz/8WLJH316pE0+fuT+B0+TNJ37hxdNP38qGjYbIwF69qV3jeaxmWrVlGty8ricfLzSSaCgkgSLl2iQtSxI4lB5co8r9PJ3//atanqnDtHI1nG8MXHU3GS5CgggMa52UwlpW9fnmPkSBKwxx7jvqdOsf1Nm5IEVqnCGLPAQCAo0Im0R97D6ehkVMRhHERtBOcvQ2yMHcWKUS386CMSw7w8ujTu20dXxx9/ZPIOLy8SnSpVGAsn492kH3d6OpWf2bM5biYT1bd27agedunCcZg7V0/O0bUrSc/48RybU6dIqqdM4fpPP+V5TSYSLn9/ktotW6g6rlhBd02bjeM0dy5J1SefkCQnJzNebNMmxqbJUgDr1/N6hYTwOk+fTpI3bhyNuPHjGZf39dfc5rffeG6pco0YwWtiNNLV0sODE+X773Mc/fx018xJk6jGLVoE9O/Pc//wA9+AdeyIy0XjTSaOQZs2JKQ9enDZoUOM5QsI4LU0mdgPeHsDfv7wNuZjTLuNaN/REyYTr5nVymthsXC/b78lcTQamfTG5GHAxsMV0SpAYMoUJ04cvIjWLR3wzD8Pg0FDw4Yk2AD3mTKF47FxI6+/EHxG3n6b7Tl25HIgACrjokoVr1BkIRMxXRPKPVlBQaEQYTLxRTLDQq6XORO4sjaeBTVrCmRn076T8fZt29IuCgnRX3J37Mj/ksQVVOsUubszUWQJnt1ONev0ad6YRiMVtmLFaAtnZJBozJ/PG1tmMLTZdKNYCN64YWEkBrVrU6mKi6Pac+ECCd3PP/PYUVEkjJUq8Tj79/PYxYuTFJ04wQexbFmSwKNH6aYZGUn1SsZHXbzIuL1OnUggDx4kSXA4WH7AYqFqJMM7HniAboMHDpDM7N7N9nt761k8g4K4bZcuQFqyDX/s9YUGA3yQjRQEwVmhCgxmD1SqxHi0vDwSqtmzSQ6mTaPCmZXFWm29ejH+ymIhkW3WjMRkyxa2JymJZPWdd0isNI3Er317jkF+Po81axYVwq++4vgfPsx2N2zIvsii9KdP03XAZiPh/ewzfUIaNEivs/fOOyRiOTlUyQ4eZOKZhg1JjmTtOk2j+jRjBo+zeTNJnbc3Y818ffUELEOGUIDy9iaJOXCA9wjAfTdsIGEC2P9Ro3i/+fjwes2ezc8hIRwXh4PH+fFHkjNfX+4bF8cxGjKE5xoyhPu2asVrsG4d+/LGGzxGixYkjJMmAXv3CtjefI8DuHgZWrUSCAri2H79NfsM8HhPPUXim5vLRDD+/jze7M+c0L74Eh/9VhfJvo/huU8awPnNNzAanLDZSJ6Tk3lf7NvHa5iZyXFauJDnaNUKOHjYDIA1JJwwKBdNhSIJHx++eLk2DLDnqyQrCgoKhQe7nfYB56KCJr1MquKATv4MIOETOHiQtujgwbQ58vJoi0pPInl8k0kndcol8+5BkSV4UtnIzKR6ZrUy9ignh4pISAjjqB54gOTp0iXe4JrGDI/Vq5OoeXqSqHl58bilS/NNyYEDVOzq1iW5e+89EoiAAD5sfn485+HDJIMNGvC827aRLDRvTnK3cSMN8Nde4z41avB4DgeVrcqVqZC98AKNf39/lkJo3JhxUPPm0W/69Gn2qXZtKn2jRvF8djsVST8/Er+4OODhfp4w+JRHJZxA5ZJOnKnbFifbPYrgYIEDB+juWaMG9/niCxKKqVNp3Pv4kOB99pmeHdJg4P8qVahMDR1Kdc9ioaIlXVDnzydJqViRiqcsIXD4MGvrBQbS7c9i0RPjhIQAjzzCPvTrR4I3YAAnJYeD12bmTCpMfn68Zq1asR0TJnD8Zs6kClm/PmvkyXjFLl04lkuXUk2TE9yXXzJZTRdX2ViLhURt+3Zu07Qpxyc+nuvPnuWkKN/my/pzEydy3OLi6ALh48Pz793La/7ss2zLmDF68pRWrdjepUs5yTocJO+aRrXNx4fnNhrZviZNqJpWqwZsSzcg27MiRo4SSE3lW7dSpUgKU1J4/GbNXGKfH+MvtdzzMBo1hIQAA3tnotXxRViHIEyxPYNJGIqlxxohNyMHBgNdQ+vV4z3boAGJ/Ysv8r4qV4734549gFMzAGDF1WxYlIuaQpGEyaR7iFwdfsg9qJKsKCgoFC5sNuko467QSXdMSexk6IQOGRJ04gRtZbNZr21steqxd9nZ/C/JXseOt6ZfCv8NRZbgATSGH36YN2teHpUsPz8qb9u2UTGScVJ5eTT6TSaSh5o1STw0jUbypUs0ZIsVo3G7axcNXF9fLnvzTcad7d1LUpiby4euZk1K3keP8uGQStCyZTxeu3YkMZs2kTSWLcs2VKzIvzZtSJzef58PWO/eJG9nzlDdKleOJKl8eRIQg4FvV8xmKi0XLtAg372bbenYEVi+QuCkX0fUGdAd3T/qCP8mnqhVS+DECRLYunV5jBo1mFQDoOHeogUNfJuNRLlhQ46FwcBzfP899zUYqEpu3cpyAt98Q4JhtzPWbNIkEuhz56h4PfwwzwdwrLKyGHP23nt0PQ0MpCvrgQMkGWXLkuAYjRz/ESM4Pvv2cSIym9nOl18medqzh/vWqEGCOWECydOuXTxutWq8bhMncsKz2xkDJ8swjB7NiS4hQVd8pUtCmzZUtjp10guEv/AC+yUVragojgdAAjpqFIn6hg1s74wZPFdyMj/XqsV9oqLYtz17SOLkOYXgSwS7nedJTdVVUpOJxM9k0u/BOXP432SighobC6Rvc8L8f/9D/0+aYNPILzFyhBOjP6yAvAZNAQhUwDm0wlZEVdqOo7k+6NGDCmfJkricjGfpUibq+eADvjzRNF6f4sU1uEqcowygUsUrFElYrXSv1nGVn9MyZW5VcxQUFO5R5OXxZTchFbpr/e7qMXg9e3KK6t2bL/LXraO9PGIEX06vXEk7Ytq0K19gKxWvcKHq4N0AcnNp+Naty7cUTifVNJkQRbo4ahrJgiw2Xq4ct7NaqZQdOUJC1bIl1buOHUkK1qzhQ5WZSYN72TIeq3x5bl+3Lo3t5GQa2Hl5TCwiBElbo0ZUniIjadTn59PQL16chv/FiyRJTZvyuMWK8U2L2Uxi2qgR99u6leSlfHkmgTl+nEb97Nnsq9NJQpaczMQrW7YATqcBh8+WwJfzBGw29jUtjdtaLMy8GBREEnvokK4gZmQw3X/p0iRIn39O4njwIPeNjGQs4erVJCY//MD1Hh4c4+xs7nPsGBWpw4c5sVSsyPF//HGqYc8+y/G2WqmUeZqdGPPYKYwcoaFBA70Yd//+VLz+9z/29eWX+XAYjWzv5s0koh07si0LFvCarFrF5a1acbtGjXi8qCi6K1SrRhdULy8SwnLlSNhMJm4vUxJv3Mj+mc16Lb033uAYbNx45f24YoWemGHbNt5r69aRTBqNvNcaNqQqm51NF1PpIirvL4AkeORIvfSH2cwXBUOG8J7cuZP32VNP8RzDhzM7qdVKAhkaCrzyTCba7PwSQzADOHEMvUJz0KmTwO6H3oJj2lzUf7UvwtOmotiAJ1C9usDSpYxNTEnh/bN0Kdv6yCMcH6ORYzhhAtCy8UVIn/9MQMXgKRRJWCyMx9ZR0BXZlRFJQUFBoRDhcFCouBLXInj68uxsICvLiS1rzyEoUEOzZvydt9tp43ToQEFi+HA9Y7zMpnmtkgmAIn7/BaoO3g1AlkkYMIDui1lZVHXKl6cid+QI3RkrVyZxql6dKtvx47RHs7NJCs6do0rjcFBls1qp/nh68th16vCzLA5dqhQfiJAQkof160kADAaSynr1SIays2nQ9+lDMnTwIPezWrldnz5U3y5coOKydy/Vn9q1ec4HHiCJ3LiRREqWhMjLY+HyoCASv3r1+BDu2kWiePo0lbaICCpy27dT3cvI4BhUqECS2bQpydFnn1H1DA7mOeQkUq0a47v27HZic/x5nD6t4eGHSd6OHGHJgv/7PxLTvDz+2e3Aq6+yD7Vq0fYJC2Ob9u9n8o+PPyZpmT2b+5w7B2ze5ET12G9hb9wUnwd9jvx8J+rWpZoms3T26kWlr2dPjmvjxiRF7drx+gUFcX1UFFWn0qV5jQICSD4BkvCYGF7DPn2Y4TMxkZPZypVcNnMmSWzHjpzwOnTg8Xbu5Hjn5bHfsnSEw8H9c3N5rcaPZ9xhvXpsn6xNt3kzv0dFsa0bN+pxkyYTXXrT0jiGoaEkhnY7+zRmDEne9OmM8bPZeM4mTUjEVqzgeVevpuJpMgH2MhWwyf9JfIOn8XK7dYh4wAfffw/Uq2/ArlOlUd/XiIS9FZF7QVwm6cOGkcQdPMiXGt268b48dYovM958kyR4U5ongAwAQAk4VQyeQpGFzXa9tRWRdqrsrWqKgoLCPQovL1eytcu4XqkE3exft86Jmtv+wIIXV0DErYbZw4kBAygQBAbSnggLozdccDBtl+Bg2kAWy9WPrtS9/4abmZW0yOa+kQWkAwJIrjSNZMnTk6QBICGy22m0v/wySculS1Qm1q2jQb19O7eXZM3Liz/qVatSrWjXjhJ3t2504fPw4FvdNWuozgUE0NhPTCQ52rdPdxedMYPk8sABtsvDg+6KgwZRoSlViqQzNJTkMi+PbQ0K4oNXowaPLwt4HzjAPq9cSYJmNLIPo0eTUJpMej256tXpkjhyJI30iAief88e9vnRR6mqbd9Og375ciYw2bmTfatYEXj9dSceFD8j+1sbGpfzRMkSfVGypAF16pCofvghlaxHH2WfOnYkUdm7l+SyaVOeTypD27ezbZ07k0B07sy+P/moDZVPGDETz+HpI+/iycT+OHLEgg8+YP+6dGGSDy8vxsPNm8fJSQi9HMH27bzegwZRvXv1Vap+O3YwPu+rr3jNMzLYHpOJfV6/nveIpyevdXw8t2/QgLGGn37KMRw7lscNCWGWyYQETowpKfr1at6c6psQzBi6YQNdJuPi6NppNpPkBwbyOkVH8z6dMUOPo1u+nMu2btWTzMikOwsXsq/3389xlzUNAS5v04bE2WgEtm4VsN//EXKGvop9Z3wwVAgEBlKdXbGC91mLFiTfzzxDF9SNGzmOdepwO6ORfZClvubP5/24e7cBQD0AQAnk0qdWQaEI4spC53/F2ayrlE5QUFBQuEmw2+kRdPGi+9JrqXcy6QrhYbBjSU5zlMV5lMs5i42rL8BoKoHBg/WEcCaTnrRv9Oirl0pwhyqb8N9xs8auyCp4AFO5nzmjZ6DMzqZapWksKu3hQbXs9dd1901PTxr1DRvSxa1mTRqymZkkbv7+dHfMyaGhLjNlGo0kY4mJNMhPniTxK1OGBn+TJnTL7NqV52zcmG9FcnJooBcvTrKzYQPw1lt012zYkITk2DHWr7twgURuyxaSmSNH9HplO3bQPbNjRx47OZkPZbduJDwrV/L4MTE0xGWx7s8/p1E+ZQoN+9q1OU7PPMMHukULvs3Jz6c6NmkSz2E0AkcOOhCUtQiv4S38kdUWm2IvoGpVjtnXX5OoREcDP/1Et0dPTxLEXbs4IZnN7P/QoSxpkJ9P4nTkCF0158whUcrJ80REnUNogu3wadcIQ0d4om5dxvbJIvbjxpF4REeTjPv66qpqx44kZ++9x8yV4eEkmV270vW0UiX+HzGC5M5iodoVFsbxt1iYUGfaNB6zZ0+22WJhmz/9lGOdmqoXiF+wgNsHBOjkzmwmSZLxd+3b65Oo0cjrYbfz/4IFdMMFeK6NG3mPJiezTy1asB9GI4nl8uXs8+TJVDN79OD5zGYef+hQkr2RI/V+N25iwJL1pWG1Cqxdy+fBYqEyGxjI6//++3zRMHMmkJPthD37PI4e1bBzJ49XrRonfSFI5C9eBNo1PgcZzH0e3spNTaHI4u9+iOOWH7w1DVFQULgnYTLRpsjPd196tWyazJypwwlrvoZsVMBxlEW+qTQ27PDG/PlcGxbGl+x2O+0Kq5XLpTrnrtCpsgl3JorsZXjkEVy+UePi+D87W18/bhwJk7c3Y7cMBhIEm42KRGIil1utVFeys2lgV6pEste6NW/+qVOp5hUvziyP8fF0BxWC+2zbRnfAU6doaB85QrKzbx8N57g4qm41apAgnD9PEpefT2N5/nwSD6mUWK0kqX5+VBA//phucxUrMvvjBx/wfLm5JHwmE9WpVatIvJYuJdELDyfZq1WLytb58yQcy5aReK5fz+XHj5N8Pf88/2Ji9PILr75ugmVKXfjsXoNHy6+FoewTqFSCcX79+ump9FetorrodPJ8p0/TRTEnh0QqIoKkOy2NBNPp5PIuXUighw0TWL/uVVTpcgE7KnhjaKSAVzGqXEYj29KgAWPsIiM5zl99xWNKt4X27UnEqlZlJssWLRj/V6sWx6NlSxLPzp1Jhn/8kWMeEECy2b07FVlJwnfu5H0hY/HWrmV/ly4l+Z40ie2KjaWq+PLLVExjYrhNt2689u3acd8tW0jejh0j2e7bl/3w8uJxZJIUgOft2lVPmiILx7drx+sm69t5efHFwLPPcj8vL70UQ1gY/99/Pz/HxPAlBKArlg0bMqZw6FAgMdGJJaNXos65jZgQmIKpNf7Ahg0G9OhBQlmsGEmz0wmczy8OgL5rXsijX7SCQhFE27bXX7/uYN1b0xAFBYV7FrLUlw6ZTVNDQdVOhwYjziMSvyEZ3fHIyzVQqYFAp070ZjIadVdMqdytWUMvJUCv3Wu30+5Vqt2dhyKr4MmshddCXh6N0fx8EgmbjWSmWjW9Vt2hQyRuQlDdGDaM5O7sWRLAX35hzNTevSQxJUqQCAQF6TFkjRvTWA4I4M1/8SIN+ObNSSiCg6n8xcWxDU4nz3/iBJd37szj2+006uvVIwmrWJFEsUEDkpnPPmO/qlcnQTMY2I5LlxgTdv/9fCDvu49ve0qVIuELDycZzssjwYqPp0vqu+/q8WuHDrFd27cz7rBDByqH0dECU0q/hUcCD6DB208gMVEgLY0KWvHiPP8bbzC2zm6nW+HmzSQdfn4cM6eThKVUKY7dhx+yjTVrkjD9+SdJ8YcfGWAuUwL+jQTWr2fbduxgny0WjqnTSRVy1SrgySd5foOBRHjNGo79oUMkTwYDtz90iG1r25akLy6O/Rs3Tle/9u9nGYv0dJ7XYiFpMhrputi1K8lT1650k9y4kSQQINFeuZKEzmIhQezWjZPxhg1s95YtvE+Cgvjdx4dj0KMHrz9AQvnCC/yTsZqLFjH4efVqXsf163kNbTYuCwjgNZNkW7p0Bgezz2lpeozgjBl8wbB4MZXHhg3ZvwMHSFLNtgu4/9wcBGEDfDauhcl6AQDv1ZAQEl0hgBrVnTiVZQTA9WdQnG9FFBSKILy9r78+bZuyeBQUFAoPJpNu1+mQ5RGuRe4AwAAv2LAJndC42AEsiiuN33+nbWuz6XaCLFkF6AXP5WeANuP1Eq4o3D4UWYLXuPGV32VafQm7nUZsy5aMuzIYSCYcDtqjMiV8sWIkdCVKcJkkO04nVa+DB6mgdehAY7dGDb3mWMeOfEhq1uRDULcuvzdqRNdRi4UGQv/+VN0cDhr3RiOTnaSlkQTMm0dXzzVreN4dO6gOVqxIYtGqFV36unVjv8xmuuvFxrIdfn5se0YGCUeDBly+eTMf5E8/JenJyeExBwwgmUlLI3FYu5YkuEIFkrQJE6iEVa8OZOcYUK6SBfv2C9SoQfLWtCnJR+vWJKcffECS9t13Oulavpzq2csvs+9+fjxfz55MCHL0KInnggV00zSZSCDCwqh0rV9PktyuHQlUQgLHYvx41tIrVYrEunlznq91a66vVYvq3KFDJO29enH9zJkksykpVEU//5zumTImbv9+Ep/4eOC553hNkpNJJgEqaZKkms3cZ9UqkrdJk7jcYmHduD59mBilaVPepy1bsm3h4bz+EjExV97PJhPPGRDAa+Zw8N7q0EGfXEeOJOnctInqcm4ux1Jm6nQ4SP5WrdILpAYF8VqOGMFtBg8msTOb6Zrh7Q2MeMUbvYKzEWVYC7QLwohXvdG+PdXYt97ieWrVcqLyoom4ePoUpIumqXgHlWRFocji/Pnrr7dDqdcKCgqFi6ws2mZEwdp310q4IpCLSshCTRwsGwJ/f4HsbL7oTkujjTB+PF+aW616KSuZRAVQ8XZ3OorsZXE3lAEatrIgd926dM/MyqIiV6oU4+tiYqg8lSzJh6V0aRKUKlVohFepAvz+O2/wkyfpBrd/P5WhbdsYg7R2Ld0M+/dnhs6tW2n83ncfz2+xML7N6aRL4aFD3L9xYxrT773Hh8tspprYqBEJZ6lSJJmHDlERa9FCrw2XmqqXUfjtNz5806frpQn27+dft25MlPLhh/zscJDIVa3KMSpViiSjTx9+z83lw92tG91Ie/ZkG2Rm0ccfJ7k0Gqkwvf46ydPOnRzXpCS698XG8q3Q/PkkY3v3sn+A/jbIy4vEZPFiXpevviKxeOABnkMSPKuVfRkwgP2ePJl9N5vZns2bddfG5s1JCAG6mT75JMdu0CD202TifZKdTcIUG0vCFBPDsd+4keRpzRqOd8OGVPq2baMqlprKP5uNily9elTohg0jMU9JYb/tdl21lXF3OTkkr/Xq0fV13TqePzRUL/S+ZQu/r17Nfkk3zA0b+LYuMpJtWbNGT6hjs+kF0CMjea4RI6hMpqWxDQC3Dwjg5/R0kle7nXULO3akS8aqVXrx9xkzBZqMWYm8R3KwINYH9aYJNG7MazJ5Mp+fLesu4OsjD6M5UhCLcAD6dVZQKKp4+mm6x18dmQCUgq2goFB4kPFxRMEEK9erQ2tA6dK0ewE9mRtAO6BrV362WK4kcgVJXXQ07Q2TSS+GrnD7UWQVvGPH/rrMaOSNd/o0DeE//ySJOXuWpKV0aRq0x49z3aFDumvl2rU8Zt26NGpzcqgalSrFGLKcHJYfMBrp7tayJcnD4cM6QVm4kEb8F19Q4apcmURBZtL88EMaxM2bk0w0acJ9ZFwaQKLVurVu0GdlsU0xMfybOZNtHTCAapzdTgNdkjMh6La3YAFdRGVxzEOH6M45fjzw9ttMrrF7N9uYnk6l7YEHqDwFBXESSEl2wnzhHLwsGqxWugvWrMnjPfmkPhZ5eRzTI0dIfjw9SZYPHeKxc3I43vv3U1GTStqoUdzHaCRhzcnhf5uNWTBlDF6jRjzfkCFU2P74g8fYupUqVkoKt/3qK7pWenmxXTNmcGJMSeEx/viD1/DNN9nGvDySp19/5X2ydCldJOvXJ0ls3ZrEb+9eEhzpA79xI++JMWNI6Ly9uV9gINMMyxIOffqQ3Hl7c3uHg2rf0qU8ztChVxY3t9n0gqPbtnHdiy/ynomK4uS8eDHvmUWLuJ+cdI1G3ledOpHcduvGlw733aeXY5g+nS6hSUm8b37/neR3+3YSUYfTgIXxpZFxTODiRd4/w4fzWD/8ACxc7Y1a5rOwwAEgHQBQ8lycfvMqKBRBfPGF+7eCP6m71f2voKBQaLBaaffpuFqClWtDCNq5v/1G22LrVtqeSUlcv3btlaTtagROhn+oEgl3FooswatY8crvZrPuS5ydTTVNqkL33Uej3WjkTV6qFBW+Bx+kOjRkCP+fPaunsxeCro67dtFw/v57XXWbMIHujEeO0Og/coTnGzWKatyePVyenU3FS/ovV69Osla+PPc5coSJVex2KkEbN+p1zI4eJTls0oQJXBo10othV6rEh7JpU5IZf3/ue/IkY8mOHiWRk2SsTh3aIMOHk5gePEiikJfHbatUIQk5dYoEqlEj4Il+Thh+/Q7pQ6ag9bhesHg68fTTJG0GA/vYti1J46JFJKm+viTRXbqQmHbvTtVt4kSOma8v23PiBJW3nj1ZNqJXLxK4NWvYppo1OVbe3nSXXLmSk9LMmZyoZO2/Fi2oqqamkjDXr89xdDg4Nv7+3C4sjOT188+Z8TM9Xc9M2qIF237wIO+T2rV53n79uF2nTsBT/Z0YPSQPz913EqNGapfvtZQU3hurV/Naz5lDt9Mvv+S5u3ShcicJXYcOJIUA+zRpEq9nu3Ykgl2inGhd7xzCQjU4nboLrsNBAvrtt3SXLFOGY5qczOWffkpyaTZTlfvkE/4omExcP3o0Yw5lKuSwMD4nNWrwHA0b8n4NCOA1CQyku++OHRyz8HC6+J4+raGW+SCS0QaAH587nFaFzhWKPKQa/td4l2qw7j16i1ujoKBwr8Bup2ihQxT4fP0QibNndY+eUqVoJ0VG8k8KBO7nuloWTRn+pFw27ywUWYLn43Nl6I/JRBk6J4dGbG4uk1hUq0Zjdts2kjWLhS6TZ87QxfHcOd68Zcrwhl6/nupYWBgJYLFijCf74QeSqWbNdHXP15cGe/XqdHX86ScqLQsXUsXx9+fDUKoUSd6JE2zX779TESpenDF7cXFsh91O4iSTw6xYoZOHpCQSzgYN+IAeOEAXv7Jl2ZfBgxl/JguVh4WxvcePs6RBZqZeCLtmTbajXj0StI8+0lWdrl2ZtGVY30xEn/BHVRzEzA0BcJ6/AE0jcapZkwRg+XKqch4eJGIjRlANkiUIVq1iXN7YsVTkOnYksbnvPl6vnTtJXux2XpuRI6msHTnCY7ZpQ9fFjz4iMX37bRI9WS4gKYmq6KxZJGgLF/L6bdnCMUhNJYFatky/RwAqec2a8fq88QYJkIyda9WK7pbLl/Maxq51Ir3bi5iztiY2t34WnwXMRlAbJxIT9RqFycnc/5FHSMJ8fan6Wa1U5VJTqaiOHs1lZjPJnsNBYj5yJDBpkhOx7V5B+yfqID70NXiYnAgM5FimpLCPTz3FFw1BQbz/27blGPj6cpvQULbH35/7Wa1c/9FHVCNNJpI16TZrt3PMbDa+8Ojfn8cxGvlSwNeXhHfGDJL1ujXsOJbrg/6YA8ALAJCJ8ioGT6HIo3Tpa63J5UOsoKCgUAiwWNxfMAFXxtxpYDz8tdw0nQAc2LFDQ7FitAPHjaMAkJXFl8E//aS7gJpMV2bRlGqe9BSS2yjcGSiyBK9cOZIpifx8kjGApK5VK6pTTqeuVrVpQ/Xp3DmKDqtX09j+/ns9M+XZs7rr2oULJIPly/MNSmws3Th37KCB3qgRb/ZTp2gc79jBhCMvvUTjXQi2ISGB+zocbKOvL0nBrl00/g0GGuceHlTTAD58lSvT+J4/n0SkeHFcVna++YZufw8+yOOuWaPXXOvenYQ2N5dtEILLZFKOxo2p3hw8qMco9u1LJS0mhn3/cmkFVLacx2p0wl6fIFRr6I2JE7m/xUKytXw5296qFQne5MkkK92789jduzO5SsmS7NO8eSTd3buzrVWr8tqsWkUlbP9+9k8IqkpTppCcTprEouVRURzzPXuouApBJatWLRKs++8nwW3cmBPiwYO87r/8wj6NG8fta9dmGyIiSGQAPdFLp04kkz17sr2OMzkI2j4XyQiEAwKjjr4I77xMtG5N4mM00j2zVy+Sz0aNeOxnn2X7jUYqu15ebP/69SS/3t68H9u04XFefSYTETunIw5tsXmnGQN7Z17O1Pn88yTJ3t7sm1QFExL4gkCqciYTr7/DwbbY7bx/EhJIrs1mfXLOziaRDg1lBk4h+Azt389xk0mMli+nupeYCNRt6IGLxjL4DMMAnAEA1EDeX+V0BYUihueeu9aaRkjLrnArm6KgoHAPQXor6bgambtashWZjMWB5GQnTpxw4tdfSdYaNWIm+IEDdU8nQC+JAFyp1ClSd2eiyBI8u53kSyI/X5exz56lwtWwIcmLtzc/79lDYli9Ol0sZSmFo0dJCo1GEr2yZWkYz5lDw3nbNt0wP3OGKpq3N2Oi5s3j249KlYBXXiGxaNKEJLJiRW5/9izP06ULHyxZNqxdO+47dCiN70aNSGYkMWvVivFiZ86wH488wrbabIwba9GCf/v381zlXWKK2cw3zmvWcFlAgO4KuHgxVbRnnqEC1KED48iKF6dhHxZGV8gdOwSsrTug4/vdUX9oJxw5IlC/Ps8lBOMDjUaqSdOnU3k7dIj9SU9nopC8PNaWy82lG2e/fnqNvYYN6Vr4wgskJFOnkmiYzUDv3iREe/fSpdVs1ssa7N5N9e2553S1Mjqax+zVi8cxm5kQpkcPkiqDgfdLgwYcv1Gj2M7p06lizpzJNlqtOunv1YukKuohH8T5DcLv6IMUtEJuYAcsTamANWt4vKgoPZW6zcYxWb2ax46Lo+Ip1+/cyeyXACfMDh30OnimKhVgah8IGyw4UDYQifsrIDeX265bx/ZNm0YFLiKCbQwOvvLt2rJlvC67dlHRtVi436xZ7P+YMfyfm8uXEFWqcDLv25dumdWqsQ+nT/Pamc10Ca1ThyLF+vUCmRWaoH7DkgDKAACOl41SCp5CkUffvn/N3EwInKsdfKubo6CgcI8gN5dkTMe1zHp34ue84rsFh9D/vhwMGUL7CZoT2vnz8CmlYeRI3asH0EOKFKm781FkCZ7MkChRpgzJTMWKJErdulFRO32ayUeys2nUVq5MwvT441TTjh7VXQT//JPEIT+fSl1+PkmSnx9t2D59SBwPHSKBWbWKJGzXLhKLmTOpiKWnk5CtW0eXR6uVKtayZXxQk5Ko4G3fTrJptTJByPHjdL88epSkZ+lSnq9VK7oVynpmnp4kJufOkRzWqUM1JiuLx0xn/gt060YC0bo13fvatGEf7r+f5woIoPr1ww9UeerVI1mSRbf79TMgPr00hgwRaNOG8XLPP8+ELF26MFHL5s1sm6cnXTc//5yG0AcfMAFNtWp6gO6XX5I4ZGdzO6eTx5BKVe/eJCGlSpG0+PnpSV9MJpLFevW47ddfk8SlpOjkbO1aTlQhIVTpvOhFiPvu47gbjYzFM5l0krdnD6/5zp1Uq2RmS4vF9eclEJ70Iabu6IIWs0dibJ1F2LBRYMMGkmWrlX8LF+plJ6KiOEaDBlHptFq5vHVrKpWSZE2fzr7FxADRMQLWxdEwzp2LB2d3Qes2AkuXUk3My2MdxGef1Sdd6QIrVVWrlX0YMULP8pmby2VPPsmxWbKE8aZWK1XG0FC64z75JO+FTpFOHDtswycTNfz8M6/TG2/oz0OVKkD9+gbAwwyAb1fKn5mvIq4V7gn4+V19+caN18tip6CgoPDvYbHw5byOa8XdSXP/SnIH5EFDOew86YNWrYDx451YMCIGgybUg71TZ8yY7rxsx0RH60lXFO58FFmCZ7XqWQ0BGrOhoVQdjhwhSQKYNfDcOZK9zZv5PSuLBrmUvitVIsEpV4439+bNVPFq1aKbptNJFahlSx5TCK77+GOSyrw8EpxXX6XrZOvWfCg7dKCr3A8/UCHx9CSh696d5NTbm8rITz+RcEZEsE+ZmTyGPK/VSqJ07BiJw6VLJFWffMJtunZlHxs3JrmrWJHqV3AwCc2uXVTQPvmEsXMyacagQezLU08xAUtkJF03ZYkGb28a91JB2rqVboerVrHvn31G8ubtTZXz6adJrH79lS6OFSvy+NHRLKw+YgQJ1IwZPMdrr3H5iRN6oo+1a6+8zps28bqmplLNPHyYbbl0iSUhGjfmWCxfzuuwbBmP37gxiero0XpcmtnMsbLb2b9hwzi+Pj5sm5eXngRFTnDZ2cC0GQagcmV0ebQMHnpYYMwYXs9evTg2FgsVxrZteU/FxHDZQw+R+JtMdHEMD2dbxo/nGDZpomfADAoCVq81YFtGabRuI/DVV7wec+fymg4YQBX5ww+5v9XKe/rJJ9nf2FiqshuSnJg67jw2btSwdCkVV5lcaOtW3kcxMRzr8HB+f+EFZkyN/d8KhO6chHqju6J3Lyc2b+Y5qlYFGvg6MfChc9i2TcPenQ4A2wEARlj1ivQKCkUY7h4j7li1MP3WNkRBQeGeQW4u7Zu/hyR97uTOAcAbl1AS27YJrF4N+HjZUOVcMjaiFWLXeaBxtZzLrplhYbQ7FAoPN5M8FyrBE0J0EULsFkLsE0K8cpX1TwkhMoUQaa6/Z2/WuU0mullK2GxMSnL0qO4qeO4ciU52NlWh9u35oFSrRuIWGkpS0KwZ1TyZTahGDZ0kli1L8ufpyfiyS5cY/7VpEw3kFStI7B5/1ImVf1zE57M1bNzI9pw4QULy4oskWP/7HwlaairPcfEiFbGLFxl/FxfHGDAPD8bpnT1LQvrsszrR1DQqdrVqsW0TJlAdlLFwxYvrpRG++ILKTMOGJBsLFtCtcOJEKn9vv62rjqmpbEtSEsnQ/v0ktEJw2dy5OjEKCdHP17s3CVLfviTGX39NFfLtt6mUNm3KCePtt0ksGjfmOEZGknC9/76uNPr7k8wEBpI4AbxG3t4khG+/TTfV779n9sz77+e5e/fmsaRS6O/P4xmNJJzBwfr63FwSojp1eFxZe0+ez24n8Vu0SK+f98gjJGqrVpG0p6ToLpI2G485dy7bmptLEiuzWFosnDhDQvg5KorjZjbzrVxCAq9RXBzb5+/P6zZgAFXpUaPoKrpxI9e3akUy6uPDdenpwNDBToQ2PIUv5joQ+lEXvDqxEsZEd8Xu3U7UqsX79IEHeB+2akUy7u/PvsbEsC3mizkIP/st4hGB39aXxeez7ChThvfplClOWL/8AeXfHYIOWgz8G9gB1NcfvrJlb9ZjfdNwO+cmhaKJhg2vvvzEcfW6W0FB4ebDbqf99eCDV9q710+q4g4jAAPKlBE4fJhhR+WreeJ02ZZoh40IbZ+PqId8sGoVX1DLGr2qFELh4GaPbaERPCGEEcB0AF0B+AN4VAjhf5VNf9I0rbnrb87NOr/JRLIiYbHQqPbxoeKQnU0ykp5OF81ixagknTnD5efP0zjPyGAsV58+dNHMyqIR/9hjVHsGDNDTzF66RAM+Korrzp+nO2gxixPTxuzByuhLKP7DBFSt6oTBQJe20FAqWy1bksw89RSXy/p8mzaRzDkcTNayfTsJ25o1/D9oEElfejrJSJkybIunJ/v6zDOcAMqXZ1Bs587s45AhNM7796e6lJKiF/DOyKCr4IkTJJwffkh1sHZtqnJLllDJ2ryZhPLIESqKI0bwu8mkFxnfvp1xgy1bklQNGsR9a9cmidy5k2SxShW6MC5YQNfTSZN43fr04XimpnIM6tZxIn7hOQS20eDhwXNZrSRJTiev82OPkYzm5uqlBNq1o5DkcHB8OnQgcWnWjO6gNhv7M3UqxyUwkMdq2VInV4GBJH333cdjLl1K9XP2bO6Tmsptg4J43FGjgJ9/5n3VpAkf2pdf5pjHxuoZqEJC+Dk6msft1o33xezZepmD5GS2OzKSYyzLPchaNbI4abt2JKOyJIjtkhNxwf+DybcOGn/9MrAuESbnJZgS16NVnRw0a0ZCK+vqmc08j8NBNTYvj33s0NMHlRpVxUsYh8eDT2HJcg/068f79Y3hOfA+vRcHUReTs5/GkPtPAjgAADiJSnoO5TsEt3tuUiiamDCB80BBHMi8cOsbo6CgUORhMtHe+Pln91IJ16u7eTXiJ3D2LMWG7GzgrbcEJm6NQvzcvZjSZQWycwSmTaNXm7S1ZBze1aCI37/HzS4zUZgKXhsA+zRNO6Bpmg3AjwB6FeL5roDdTjJmcPXQauXnP/8kccrJoVJXrRqXJyTQRa1mTbodenmRnISHUx1JTCSBOHdOT9iyfj3JTu/eFCkuXeKblFKlGLflcHC94XwOfnH0xFt4FT9nhKNJ7RycPk0VqHp1Ji/p1Ytui8WLM5ajbVuSsfx8/k9PJ2mqXJkPYfXqJEkAj9OtG9edO0cC4O+vl4TYt48udy+/rMdKff89Y7weeEDPNrpuHdUpmbHTZqPBL+Ph6tblRDJoENcvWkT3VQ8P/q1bx7FatEgm3dBLNthsHA+zmW+JZHZHs5lEKCiIMYDjxpGIyUmiY0eqSPHxwPz5TswZmoxvByzFsvbvwt+PvuExMdz27bcZEyYLmR8+TIK9eDH3HziQXGPrVhJkgIlWXnyRMYOtW5Nsf/cd2wLo5Qry8ujOmZXFY0hF+IMPSKxXruQbfFms/IUXGMfYrZteiNzHR69Pt2mTHp+3eDH7nZuru6CaTLqS2aIF2xYerhdNl1k3ZQIVk4ltnDGDYy/Hz3EmB1t3GmF3asCePZhQZTz+QE9MqjYBbaJ84O1NFbV5cx6jWzcScKmKRkay394lBCI2foQy338G68JV2JwqkL7NCUfWOZSvXwovt1+PofgM0yt9gJ8SawJoDQCojQw9a9Cdg9s6NykUXbi/VLwCqti5goJCIcBioe1HFIyvc4cTVy+C7oRROFC8OF+ajxgBjJ9oQNLu0hg2XMDHhy9yV6ygXdGx45VJV9yh1L07C4VJ8KoCcK/wmuFaVhAPCCG2CiF+FUJUv9qBhBADhRApQoiUzMzMGzq5tzdd12ScGkADtkoVEpXSpalMZGUxFuzBB7mthwfVNx8fGuiyfl6xYvzz8aFSExREYnbxIgnDkSOMd1q/nmrIq6+SNLRpA1gtPhjh/S02oAMsZhM+/cIHxYoxpm/5crZj5UpczkJ55AjPm59PRWj7dhrgZjPfEJcrx3Z6epKklSlDw75aNdrShw5x/eefU628cIGkpXdvJiEZOZLjEB5OUpafz3M6ncATTzDLY1ISFa28PJLbsmVJCmvX5sO7ZAnHcPt24L33qMZ1786MjHv2cBz8/fm5Wzf2Ly2Nk0OjRvoE8OKLHFMp8iQnk6A+9xzHcelSqnzPPQdMfT0TX114EL7Yhzl72yFp9QWMH882pqUxlvHdd3k9vvmGZP3bb1nnzWikOgeQoEqlSrZDlqnYupUkLzGRyuWECXrtt6pVqaZt3sz9p0zhtQgMJHErU4Zq6pgxVF0HDqTa5y5gWSzc39+fLp3LljFGUJLfsDCSvgkTuH7qVF1xXL2ak6x7Zkz5pmfkSF73kSN570ll06uyD4a32wSL0Ymo4ItoOf05GOfOhd/E5zBnrkD79iSLDgeJsnT5iOjoRIcGpzB+vHb5vHanAUFdS2PGTIG8i07Yv/wWC4cvRt4rb8D+x1IsnrQXP5v7ITszH8AuAIAd4EN2Z+GmzU3Av5ufFIomZBz2XyBT+iooKCjcRFitfHFPXC+hk4YrE7AYXMvy0VxLgOHPI9i/zwkh9FJLWVm03cqUoY1jsVxfXVKFzv8b7hoXzRvEIgC1NE1rCmAVgK+utpGmabM1TQvQNC2g/D9QA2QWTYOB5Mxm0+uoFSum1zV74AESnEcfZUxebCzXRUVR+bJYSOYyMvTEFydPcj8vLyplNWuSnJQvT9KzahWVqLZtgbWxAs0HBODhmV0x/LOmaN1aIDubx5O2b8eONOofe4xGfVAQ2x0WRgN/0yYa3hs3kgReuMAHsH9/9ue++xg/5XRSSZw/n5k3AZLDH36gUrRvH8lXgwZUaPLzGRMWEkKSFBbGdY8/TsKRn0/jf9Mmbr9rF0lOaCg/jxvHOL2uXamgvfUWiW67djz/gQNs29SpbJ/VyvMPGUL1S0K6BTocvAZz59LFdckSxhju3QvM/K0Cvqz+FgZhDtpVyYBWzBsHD5LsvPqqThYPHyYR3b+fqpp0tUxMJBn84guSsvbtSdKWLdPj82RSGDluzZvr10GSQl9f/jeZdDfGtDRes8RE7tusGe+DNWt08iTLGnh6UsHcvp3jPmsW296tGx/s2Fiqap0inBjx0ClEddKwbh3PYbPpx1qzhv8nT2Yfn3+e/TeZeG9MngwEtRVIen0pVsw7AfvSVQiPNMKrSmlEdhJo0YL7hYZeWVQemhPWTt0Q3XAIFk/cgaaNnXA4mIQnLo5k3utSDpofX4BqOIy0XRaMfzcPC+NKw89PoFQFTwCVAABmlCwYHHC34IbmJuDfz08KRQ80bAr+rPogt9fjSsVTuAI3EAf8vBBih+slU4wQoqbbOodbfPDCW9tyhTsFdjt/k3VCcD2T3uD2X0BX+2xIRUs0wnZUrmjHhQucx8LDmUV7wwbaSykpDCP5O/KhyN2/x93konkMgPtb72quZZehadoZTdMuub7OAdDqZp3cvSCjrIlXsiRVubQ0EqWaNUl6xo+nqjJ+vJ6FMSuL6tPbb9MN8f77SRa8vZn0YudOErs+ffR4O5kA5eJF2rQVKjCb5JAhwOlMA5qFlYbZLJCZyfg2u51qVn6+Tt40jUSxenUW4A4L4/ECA6kGPvssFcgSJUh+Xn+d6mNmJona3r0kVVWq6AUqjxwhsatRg39btrAvu3ZR0WvThusff5ykw8+PJKNWLboM3n8/x6JuXbb30CGO04wZ7N+KFSRUTZvSnfT770mMXniBCqOU8zdvpjvm8uUc0xYtdKWpWTOS6bAwKpmPP85r1akTLrsRDhkq4Oz/NOaN3YF67zyJEycEevX6q7tAt24kz88+S9IydChJaNu2XNe/P0mqdEl1j8kzm3kuLy9e17AwXlNJEocN4xsth4NtHzaMYyMTobRuzXp0ubl64pPwcN11E2BG0rQ0XgMZZzdrlh5vl5cHhIc5sSTwDUxvOAWI6oTOnZx46SW2yeHg2Mug58GDeVz3cXA4eK28vYHgEAMcJUtj4icCsbEkoklJJLiydMSYMWxnQgIQUCMTs9Y3RxhW43+X/g+7NuQgKkqvx2gyAcayPkDDhshADTir1ETDAG+cPMkXIceOCQDlAAB5TXrfiXXwbuvcpFB0Ua4c8NcU5b44mbKfk7SCAm44DjgVQIDrJdOvAMa5rctziw/ueUsarXDHwWTS7TfieiUS5PKCbpxe8EYmbMbyiOjkgbw8Jo579VXav5rGl/JBQbrnkIQke3a7csu8E1GYXDsZQH0hRG3QeHoEwGPuGwghKmuadsL1tSeAnTezAV5eVEsuucw0o5Fk68svSRxkgXB/fyYXGTuWhn7ZsnxghKDLW/XqfHsRGkqyVL48CdDYsXTje/hh+kDv26cnzqhShcpQixZMHPLnn1T8QkNJBv39SYpOndLjsYYMIUnw9CQ58PMjeVq5kqrYgQMkP3/+SXXKaqXrZb9+bKvTyX3CwvQkHDVqMCNkXBwN/mXLSC6tVhKo6tVJdN97j4Rh9mz2rUYNkqR33iFRHj+ebgBlyjBuTdbT8/WlK+qWLVQsDQaeZ9s23fUvLY3/mzTR68t9/jmvgdXKRDWnT9OtEmDfXn+dE8uSJSRQhw9zXbPmBgQHl8CLL5LsdO7MPqWmcgykq2P//lTqLl3iuDRqREU2Lo5qpqbRhRIg8TQa9SyXPXqQ+AMkUjJBSno629ynD9+yrFzJ+6VZM47RH39wsm3UiPfLk09ybGRJDIDHSU6mgicLjU+fTqLo48M2zZ4NBNfPxNxdwfga/WBKvADTuUyYKlYEQJIq4yb/+IPj7nQCr7yiZ7lKT2e77HbeC+3a6X1cvZrjFBenu6lKF1WHA9hwsAL6Be5AanIgOgflAl18ADB28qmnSALbBwusG/8u7s/IQdphH9x3n4DNxnGrVtWJnTsPAagFx7ZowBmp+0nfGbjtc5NC0cTo0Zwrjx93X2rGd6Wfw//dnUq2QuHgchwwAAghZBzw5Zoymqatcds+CUC/W9pChbsGF66bx0mAhE7G4BV04xSwlK2Gw8baKHdCoFo1vqRfvZr2afXqtPOmTGEeB6uVL46lO2FIiJ47oCABVPhnkElsbpaKV2hWl6ZpdgDDAawAjaOfNU3bLoR4Rwgh3ziNFEJsF0JsATASwFM3sw02G/+ksmG30/Du0oVKV+XKJAA5OSxyXqoUMwVdvEiVKiuL8XdCcMA9PFgawWDQ3TZtNpKFHj34kB09ygdi3Tq9vtqOHVS/ihfXlTtZeLxaNe5fujQNfbudRrRUHQ0GKlHlypFo/PQTFaL69dnm/HzK5iEhJIq1atEAr1iRyU4WL6Y6FBbG/k2cyMyaMlX/xo007tu0ITFatYqkqkkTxqrNnMn6cuHh7MuSJRzLFi34v359Kk6LF+uJVGw2jlmbNvzeoAHHeds2fSJo1Yo3sN3ObVu2JPGZNYskymQieTMaqRzdfz+PtWULJ5devXgNo6N199ERI0hSBw5kOQYZj9azJ7ddv14vCVC7Nier8eNJhJo1IxHy8dEVRoB97tSJilezZlxmNvO8P/3E8QwP5zYTJ5JUtm3L67Z8Of+WLqWLal4er03r1rorp7c3x1oSs9RUtrucfwX83G4KvEU+VjQYCWvJCpffkMlxW7mSLw/692d5CQmpyEVFcVtJ3AMD+V1mJJWEz/3Nm9EI5F4QeKPhH2iy+Wt8Er4MI0eJy/flF1+wH9HRQGiYAaG9S2PDBoGMDE72RiPQvOY5APRHteAofyXuINwJc5NC0UX79n9dduxCSU70CgrEjcYBSwwAsMztu8UV95skhOh9rZ1UfHDRh8lEjy4dBecZzW1ZQTdxfv/zghl2u8Dp07TDTp3iy2ejkS+tpGtmVhZf0ufm6u6EFguJnSJ3/w3S6/B6GUr/KQr1tbqmaUs1TfPVNK2upmnvu5a9qWnaQtfnVzVNa6RpWjNN0zpqmrbrZp1b+hADJDIASc/u3TS4MzNpHBcvTuO9TRsOcGAgM1HWqUPDuHx5qiEHD5IgHDpE4lWnDm/yF1+k+pSSwoQeW7fyHDVqME6vZk3uX7Mm2xESQgPZ6SQRLFGCZMffn6SiYkXdAD93jkRw0CCqaampbE9KCknTgQN6OYK1a5kddOBAkkmLhcSmaVOqZXFxbP/MmSQVAwdS+WvfnoSlTx/ut28fVbvt2/USAz176rWqa9Sge6rNRpIyfjyPuXAhyZTRSIXUZiOp+e03tjc5me6gv/xC5S0zkwRl3TqSt+LFSXyeeookuWZN/q9dm9vYbBxbiR49SOocDtpNISG6yrppk+5CCfCaSVdMi4XJZl5+mUlhTEYnWtc4hQHPaPDz4wPm48N7Ydky3iN2OxW3qCiOiay9N3EiyandTtJtsZBobdpE99SHHqKK2aED1dn9++nKmZysFz2XWLtWz1BlMgErVgqYli3FJy8cw8YHP8bKVeJyFqvcXL5Na9eORHfmTCp5K1fqrhIJCfy+dCmP5+vL7axWjpuXF/sRG8tjhYayf1FRfBEwfoIB3nUromkzgfvvZxvnzeN9ExLClxHLl7Ov/frxR2D4cI57+m4PAJ4AAAOcd6KL5m2dmxSKNsqUAQr+tEbbaioXTYV/BSFEPwABAMa7La6paVoA6HkwWQhR92r7qvjgog+TiTkl/gusVmZnr1ePtq3MOO/nx/rMkZEkdt9+y7AcKZpIG8Y94ZvCv4M7Yb5px7x5h7rzYLXS+Pf2plF86pSeibJKFb6NqFyZ5G7/frpOms1Um/btYwydpydVNpuNxGzgQLrtmc0kZz/+yNi+atWoZnl4UOWYMYPGbrFiJHL791P23rSJ2wMkHHXrkni1bMkHy8OD5/HwoGrXtCnjsoKDWaIgNJQG+YgRNLS3bqWLYWAgCZGMOxSCN0rVqky2kZdHQmgwkDyVL8/9O3em0rdrF2+wevUYVLt3LxXJI0dIBtq0IVFs0oTH37aN68LDSWSrVaOCM2YMCURYGNtSsyZVy3nz9GyZXbsyUUiPHiQoUkF65BEe56OP2K927bjdzz/r161ZM7oOREXxmu7axX7Fx3MbgKTVZuN2ABVLmTgF4PnWrQP27nFiRHQP+HywGs/VH4v0be8grKPhclyg2cw2xsXxuo0ZQ3fTiAj2D+B1AkhwLBY9jg1gP+12jn+jRrx3UlM5PomJev07WRR97VoeNyaG27Vvb0CTdiUuK5qSwMXGkuCnpHC/mTOZ0IbxP9w2LIwEVZb6MJtJ8pKSdGVPjlVEBL9HR3O/oCCOj6ytN2oUx2/4cLa7dWs+P/KN3ZIlTA708st8cZF3sQRkDF4ZmPnWQkHhHgHnmivfol9E6bs12ZBC4eBv44ABQAgRCeA1AKFuMcHQNO2Y6/8BIcRaAC0A7C/MBivcmfD2ZpjLK5fT9LD0wY3BAMCAYsVoy6Sm0mZ2OEjutm+nXZaRwa2HD6eds2aNypZZGLjZ43lHBcbcbPj4UBmS2QtlyYTMTBKgSpVIGg4epGoRFkbSUrkyCcKPP9I2feQRxlQUK0aXRpuNBvvevVwm3fmMRhKnFSuYXbJ3b5IMPz/Gl73yCsnU0KF06Tt0iCqI1coYs8OHSa6+/ZbnrFKFauDQoSQZ5cuz7V9+yXMnJJBwHT9O2+HPP5npUNb8y8vjA1uxIvtbvTpJ23vvkUyMGkWFZ8kSLq9fn59TUtjH5GQSg4kTOYk89xyJ7/btHMtatdj/evX49mf6dJIQm43Hz81lYfTHH2dWzPffJ9ELCyNx2raNbU1IIEHNyaE6Oncuj7F+PSea7GyWP/D05D4pKZxwJkwgKX7oIZKp0FD2f9Ik3VV0wwbGSkoXUlk3LzQUaFE7BylJGuCwo/feCYhsmnnZVTI+nuTH25tE89Ah7vvCC3wI167l/yFD2G+rVVfOGjfm2K5cyftCxiTGx5PczZ5NkrRsGdsbFHTlG7HOnUkY16zhWMuHfutWnQx7eXEcfXw46fqUdGLlT+cQvUq7HAfq5cV1JhPHY+dOPfbPbue1Hz6c18lu5/j88gvVyNBQqp4vvECyJ/3uQ0L4/7PP2I7ly+lW6+GhJ7O5eFFDOZetYYVJuaYp3FPo3RsoSPBOod4dqWQr3DZcjgMWQpjBOOArsmEKIVoA+AxAT03TTrstLy2E8HR9LgegPdxi9xTuLchwDR3/fJ65eJG//23a0Bbz8KDtlJ9PO+yRR2hHrVp1paeRSqxyc3Gzx7PIE7xWbrnvzGadCJ05w9/bzZup2CUm0s3twQepCpnNNLyLFydh8/bm9rm5NJL79tVr01WrRnWweXN+jo2larNjB0mZl5de9uDkSSpSmzeTmF26RIXL4aCb5qxZJJjHjlEh6dmTbqDHjpEg1a1LgtakCVW8YcOoan3/PdUy+fZFElCZNh9gO7p1YxstFpJMWbQ7OlpPw69pfMCrViWJsljoJjp7NlWoFi243uEg0bPbSTofeICE5LPPgN9/5/kWLGAbZHbPKVPYVoeD36OjSUS3biWp8fUlITabudzLi4lkKlViGQAPD/bt66/pvnnokC5re3szfq15c/bLbOZkNWECv2/eTEKyebOrqPdjPogMvgQYTTC1D4S1ZAVYrXrcWlIS22mx0C3htdfcCoi7SgqsX0+C++mnJEF5eVQ6IyPpmhkby3EeOVKPQWzcmOeQBHfdOh4rMpLHlu4OqakkYHKZLGvQuTMJGEDS2DbQidh2ryDtqYkIeLc7TAYnTCbep0lJeiH4Zs30SXntWqrGTiddaVeuJMGeMIH3kd1OxTI3l0R/2TLeu8uW8XnIzeV6h4Nup/v28fN33wFavg3SwL2EXOWapnBPITgYCGxR8J7fp8okKFzGDcYBjwfgDeCXAuUQ/ACkuOKD1wD4SNM0RfDuUdjtBZOs/NN5Jh+AA/mXHFixQsPvv/PF+XPP8SX+O+/QlpWJ3WQeBatVFTW/mSiMIvFFmuABVH0AvQCtdMOUxq7ZTNe2cuW4zerVXFe3LslOjRokLT4+JF5lylA9WrGCRKh8eZK7Z56hIrJqFVXAZctIIqOjuczDg39z5lDpGDyYhnSdOlTYvLzodjd/Pl3mbDYSusmTSVw0jcRh5kyqeZmZJEsPPURlrkEDxtH5+tKdEKAil5VFEnnuHAnRr7+SLDVowHYBzAI6dizb5+dHdenBB6lCbt7M89WoQXIXG0uy1K8fCci+fXQNrVGD8XYjRvD/OFdC5+Rkkp9KlbjfL79wrLy8OMaATjL37OH227bxTdKSJfy+ejWPa7WSsEVEcAyLFSNZkg+FfECMRv6Fh/MvLY2ERNP0TKomEwAhYF8ejTXfn0DWr2sxeIjAuHFUDBMTScpl/TcfH13JlA+g1XolKZZ9sdtJJFes0JPYACTlu3axTd7eJKzt25Osxcdf+YDb7bpqOHky2y9Jnd3OMY2J4TFfG2tFq+1foQnSMC2xFaxHMy9nzwzr4ERE41MwGthAWczcZiNJ/uQTxk5GRbFNdevqrq4OB9vu58djPfIIX07k5PA+fvddxjGGhXFMd+7kS4ynn/O8/PxVFB7KNU3hnkNAu4Juye2QFn36qtsq3Ju4gTjgSE3TKhYsh6Bp2npN05q44oObaJo293b2Q+H2IjeXtoKOf+oxYwRgg92ZBf/cOLQNcsLTky/pK1Sg91liop6HITiY/6+WEESRvX8Pk+nmJlgBijjBk282PDzo6giQyPn6kvgcOcKbdPduqlo7dtCgPnCABKpCBbpN+vuTkDRsSEIRHs5jGwz8y82lD3S9etx26FASgp07gaefJpmyWEjEZs92Yvem8+gYpqFbN6p3xYoxU+XBg8ATT5AE+fuTZPXtS/fDqlV5zpMnmT2zalUqQ+HhJJJ16+pESwga8Lt3sw+nTjFlf5kyVDTHjWMfq1XTx0oWU3/6afY1OZkupRs3kmwdPUoC+/77JHxvv822+vszSUqpUtzOYqGq9fLL7EdeHhW3rCwWPG/SRCego0bpRMzLi2Tnu+84Ht7eJB+tW7Mf775L985WrahoxsaS5HTuzAQvMkj4t990Mr96Nf8aNyaZ8XdVGZo5k31ZtAhYGW1A66jSSNkk0Ls3r926dTzvunXcXqqUycm6i2deHtfXq8fvw4Zx227dgDfeoNumtzfVxPbtqcx27EgiLd0xvb1JVqXro8WiJ18xmTiumzYxi+rUqRyPFSu47/PPc99t24CPp3ghrdETaItkOKrXvkyoQto7gc5RMNWtCeNrLwOaE3l5PLasa7dpE/smFV0/P4633c4XBunpvEaeniRxU6fqz8K8eXxJIOMi8/MZM1qylEBe8TYAgD/DnlauaQr3HEymgve8AXtHvK9UPAUFhZsGu13Pnk78m/lFA2AGUApZecCfWTZUqUJbYMcO2qAmk+4ZJXMMFEwIUhgK1L0Eu82J+IXnYM+/eSEtRZrg+fiQkOTn86arXJlubiEhfCCaNGGcW24uXQibNiUB3LiRhKZDBxKh9HQqL48+yv0OHmTGSplc5Nw53uibN7OG3nffkWz4+VGRMxqBt94Cjhx24rcpGSj/yUiMbbESCxc44XSStHz3Hd0UTSa6Ox4+TKO6fn3GlJUvz7ZWq0ZSmJvLbTIzmagkMJAkLjWVx9u9m6qSrFN36BBJyapVVALz8zlGGzeSFO3cyVi5b7/lOCQnc8z8/TmOvXszUUxUFPsq3TxbtKCK4+VFQ3/NGhK97t1JfnfvJgmbPJkk0Gxm+7ZvJzGaP5/nCQ0lOSpXjq5/CQkkih07cvty5ejCWa4cyd7ixVxvMrEWocnExDb5+eyfw8Ex2ryZfz/8QLWzY0cSxHbt9Hi+uDi+lTKbSfg3beIyh4OkR9aXk29XbDaOV/v2JM2TJ3O9rPEH8HtEBK+9TFCSnMx1MhumrHki/8vsmLm5dIOQLrQ+PmzzSy/pap/JpLfJp7SA7f1xmPn8PhwIeRrWSwLR0cDKX3IwPiEQVocBoTtmAdk5WLSI9+batezjunXMhJqby+O98QaJ35w5HBODga6jLVvyPvP25rWLjKQKvXEjE6y8957+EqV5c8DpNAAwwt9fkTuFew80fK689xP3mJS7soKCwk2DyUT7SNYJ/ufqHaDXxjNhNxph90HPyxm4P/6Y9pbNRk+d8HD+d8+a6V6+SSVe+ZdwOmHq1BFtH6kJU2TYTXsRWKQJXnY2lSSARjJA17kLF2jc/vorXTYNBioS+flUwpo3Z5xeXBwJ3ZNPUgUbNIhxWOvX093x+HHe3EYjSVRgIA3g8HCSlBMn+OB5e7NmWp1KF/Ci820cQ21cOJONrBM21K5NcmS1kjQ88gjPW7cuFcZ9+0iWUlKomv3f/1EuX7CABvXbb5NQbdlClejcOQomBgOJmsHA+KpGjUgYnn+exNNk4j2UlEQiWLcuyeylSzxX69ZUcHbtYh/Dwmi0jB3L8Tl4kORs2zaOa/v2bMOPPzIxh9lMYjpmDEmEzO5pszGW76mnuE3PniSFH39MlW/iRG4XFETysGYNyUdMDMcoOprn6dOHJG38eJIfq5UEaPp0uhx27EgyO3gwz+ntrSdJyc7mWA0cyPEwm/XraDaT4MjC4AkJ3G/FCr2UgdHIfsn6LxYLx3PKFP4fN44vFnJz+XLAbuf5O3bksbZs4br4eI6zxaK/DWveXCeRFgvHQZI5gMpl27b8LGPp7HagW3cDXn2/BKZPF/D2ZrvC+/igsb8TseiIWP/B6PqoD6ZM4UuAsDC+wJBlHpYv59harVz/7LMcfym+yesM8L6SZTfkD0zZslRoU1KYJEe6ecoERwoK9xKeegooaGwtQzflrqygoHBTIT27/j00AAIVyuQDptKoVp3CQs2a/H1fv54CyKRJ9CKS2bwB3TZyJ3kK/wKZmbAmpGCaNhjWhJSb9iKwSBM8mfYfoFGek0MFav9+jl/VqowNa9KEcXX5+boq5nRSnUlIYFZBTSOJe/hhZoBMTaWhe/o0lcHatanQLFxIMrJ9O2PK8vNJhLy8gIOnvLG61MM4j2LILlYHn870RJs2JGjNmlGdKlNGTwYSHEwSGRhIY7xjR34/f55xe+fPs22dOrFvBgPdOb28gKZNnLCdOofatTQcP0717vnnGdc2dy4f4KZNqcSFhtLFTsaSORx8oMPDaeAvX0517LnnaPQbjRxbTaObZmwsyYGmUeXp2ZPHmDGDBC0nh6pYhw68Fg0akIilpZFo5ObyGFOnchxXreKkceAA182dS8V0+nQm9OjcmX3esIEKY69eJKp2u57d0WLh8UwmkrnFi/n5/ffZlrw8Ll+3jqRq1iyOQ2goXT/HjiVpDQrid4dDL40gs1nK2Ljx4/WYQ4uFBPHAAU6MI0de+aZrxgwSy+RkkrtZs3TVT2bQtNtJDLOySEhllsuVK9nn1at1MuZw6EHPdjvHQRYlTVgnYP74fYTu+wLG8R8DQlx2B5WlIkwm3ge7dunj0KKFXiJh+HCSU5msKD6e5Bfg/0GDOE4hIbz/li3jc+VwcBtJ9BQU7iU0bAi0bHnlz+suhCt3ZQUFhZuKm+MSqeH0WRMs9sM4tyMDJ0860bQp4+9q1mS4iCz1JF+Iy3CVlBTllvmfUaECrEEhGC5mwRIccNNeBBZZvm230xDOyaHRrWl0U0xMJCmrUYMkbcwYErEyZfRaedI1smlTGsGenhzvqVO5ncFARbB+fZKd/v2BDz4gSdu4keRk0SL+yNvtugtmiRICFTtEYsvpDmjZxhMffCjg4UEV7quvqMwcOMC2tm9PNSwgQHd/9PTk59Gj2cczZ0j+Wrak+mc0kvDs3uVE7hvv4Z19D6F68QMoG9ECZrMBvXrpWR63bKHh/uKLJBFPP01/ayH4IMfFkWA0b85zXbpEsrtrF1WxoCDGsr3zDonv9u1U9WRm0bQ0ktu8PJIrTeP4yiQyiYl8y71yJQlkQADHa9cukgODgWTvvvtIbJOT6UI6YwaJakICj71jB6+hrHPXvDnPM3MmE7Ns2MBYsS1bSD527GC8nMNBAmu1UlEcOlRPoBIYyHNYLHpSkvBwvbxAo0bcdvRoEqQxY3ivxcaybz4+jJ+U9ebca8Y0bsz1MuZOljGQ2wAcm6FDSdY+/lhvl9HIcYqK0u/vdu30dk2bxmtjsejqImCAyVQRwRVJBGX5iJ07ST4jIvTnJSGB+ycn8+XC6tUkzC+8wPtRZvKcPp3H+P57PktDh3K81q4FSpfmS5JGjTj2NWoU7nOuoHCnolEjgc2b3ZckAE7XmxAFBQWF/wi7nS/tif/q1ueEFRZ0wTIs2fXU5VJL06ZRCOjbl3beiBG6LREayv9KuftvyM4R6Ou1FEPn5OC+J3xgukkvAovsL41MJDFtGtWuS5do9J44QQKycycN5x9+oGErC3LbbCSC6ekkaT4+VIhKltSTdxiNVP727iU5GTaMCmCxYlQDq1UjUQOoBv7yC4uie3gAPe4zILijBY0aC9SoQeLwf/9HojFqFGPmEhLYLqeTx83MpD90/fokTkuWkBB26UJyqmk0pi9e5Lo9W60w79uBB/Abnr0wERbNii1b9OyS5crRiJep7jVNrwG4b59etPyPP/jwtm5Nm8Ro1BN+zJtHcvjBB4yta9GCyp23N1XEESO4j9nM8evdm+esVYtjM2AAXUwXLiTBLVaMfRk5kgRr1CgS7Ohoqko//EAXTBnnFRbG9gAko1IFCw3VVbE1a0iWNm8mQTGbeVyZPRUggfHz00mUjAe0WDiJtW9PUiVJlN3Oe0fWmluzRr/XAL3+n5eX/lZLEjdAf/slY+/cA5alX7t015R1/ORxOndmnCJAlfP770kQJ07kNoMH84WEJKXyeJK8hYWR0Hl5cZyla6mM6QOudBeNitIVxNGjeT/MmEEiPmIEY++++EJXtCMj+ZLizz/1eEPloqlwr+LixYJLGuHkNhWDp6CgcPNQrhzg5eXEv4u/k3ACMMKOckhDAPKsBhQvTpt19Ggn7MdOISiIx589my/4JZSXzn+Htzfw7HMGdH2sNEweN8/Lo8gSPGlEZ2ezhtzZszSoZW27Nm1Yt61kSW6/fj3Vr9q1aagWL06id+4clbPmzfm/fHmqQ716kZQEB/MBe/hhrq9YkccCmDXzzz9JgJ57jgrUoUN0G508mQpNejrJYkoKjfPu3elCWqoUSVydOlRM8vJIbi5cIOFr1YrbXLhAktm8OcngxYuAZvHC1so9UAKXEBmYi659vHDoEI3txYt5fiHY3w4ddNUnPJwP9IoVJKV16lD5kjFuNhsVytq1GStotzMpR6tWJFv79vGzLAQuSa7ZTFJhNpNQtm1LZU6SirQ0um0KoS+Ljyf5S01lOx96iNdryhQ+DCaTXvLA4SDJWbGCy0ePBtq1daKj/ymYzRpsNj0jJsBjBAfzOC+8wLYAJNHjxlE1W7aM/Y2L43nca75IcgSwL5IgRUTwuNOnU6mbPFn3T1+zhseIjNTr7F3vrZd0u5T3ssye6b6udm0qhaNGUW2Li2Ob3SGJr4TJpLchOprLJKELDtYJ6tKlOlGcM4f18bp04QuM7du5rE0bBmBbrVxutVK1XbcOqFbVCcABs1kVOVe4N/HYY8CVP7GlMWehisFTUFC4OTCZGBKRl/dvk6tIMmF0fTbieLGmqFVL4MIFYMMGJ/ISUmBv3gJtFryGMaOcl/MEOBz6i2Ol4P032O0UOG42iizBk0b0H39QgQJIXpYvp2tjaird9UaOZAbATp1ISLy96Wrn5UUDunhxqigxMVSCTpxg3Nns2SRUsbE0eH/5hesMBpKx/Hyqhg0aUCGz2ah87dzJz2XK0I0tJUUnMElJJCwtW9K11MeHBOrll5nBs2ZNIC3Niab1z8O7uIaGDRlPJotxV6xIwli5ssCxsMfQN3oYXqq9AIsXCxw9ynZWr071ZcgQPpxPP00C07cv1c6QELYvMZFjNmoUCdDRo4w9O36cbQoPZ+xZeLgel1evHjNOZmfT1bJ3b5IGLy9uFxHBcQ8MZN9efpkkyGjkdWnYkEQI4PV48EESwkOHSMx379brxdntHLuZM7l906ZU3tauBSxmJ0I/6gLvRjUxckVXmD2ciIigAmaxkIxIguYeH2c08n5o3ZpktEMHLgsN5Xi0bs32rl6tBxfLpCfuCU8aNKDC2agR95X3oiyGvmIFP8vkNVcrGBoZSQWtc2d+T0vTVbnISI6PhwdJl8XCcXU42O6ICG4na+jZ7Tp5k/F5MTGAw+4ETp2CyajBetGJ2D/OIe+ihokTObbS9WL4cMCnpBMJi86haxcNL73ElwKVKpEMr1jBdj70EPerX9+J8keZVcc2a7xKDa9wT6JPH6BkCccVyxb8YbvG1goKCgr/HA0bAiVK/FNTXrj+mO3a/e/iRQ2HDjkwepSGNi2sqOA4iKkYhLSdJsQvzrlcH9dopF3mXipB4d/BZKL33s0mykWW4AF6enp5A+bnM0bowgWqFHv2uGqhrSRxOHuWpOf8ebpU7tlD9S8nh2oFQJWuSxca102a8OEyGHRD3OmkSpaeztprAQEkfFu2UM2rWJHLmzYlOTt9mgUlp00jwfP21uOWpFKzfz/bPHWqEyVS1+Lsl3+g1k/vYfhwJ3r10jMeZWeTYJUsCYSFGfDe5xVRvYZA3bqMIczLIxn73/9IuN5+m38OB431IUP44O7eTQK7Zw/b4OXFINuGDUkMqlfn92bN2DabTa9l16OHHp8G0K0yIIAEScZwvfACSdvq1XTxa9JEL7C9ebOeoMPLi9du4ECWjqhXj9kcJcFr0YLKZadO+j42G5B7MBOT1rXGIkcnrFnvibSECwB00iPdJHNzSXikWiXvmW7dSJKSknTy3LEj1zVqpLuGyni21av12niffsrjDx9OV9WEBJ7DZOJ4WSy8V55/nstiYriPVPTcyZ7MPGqxcMzcSal0M5WfAwI4DpLEytp28jokJpLkybdtRgNJMGrWhD00AjGtX4btiacQMqE7PExODB1Kgrl2LZAQ74Q9Igq2x/oDUZ2wOsaJF1/k/ZaSwnFZsYKK66JFwPixmSgJ5m2ukZmsUsMr3LPwq7Hjiu8HNq//q8yuoKCg8C9gt9PePH9eEjaAZv3fmfbXUvycAPJRDIcw8ckt2JjqieOmBjiC2ng0YAfMFXwux965h8Mo/Dfk5jJc6GaHtBRpggfQ+PT15edixWigly9PVU26FeblkSQFBlLdK1+ehOnCBRrxxYpRkTIYSPzatiWJSkqi0VymDC/OyZM6WSpXjvFmFy/Sve3MGRIjmc3T4SDJqVOHxx80iIqRvz+N5VOn9Isti087bHZ45Z7Eu3gV5n3bkXfOhi++YLzT+PF6QpZ9+xiLd+wYFbAhQ0he09KAfv2Y9XPqVGanrFaN5Mxk4lg5nVSgtm0j6dq0SbdHduzgdgMHUumTRGb3bj7k27ezX7KkwY4dPJfM3Gi10n21UiWWnsjLo+ozfDiPGRzM8ZHSf0AAxy4+ni6PXl4cn9xcpuyVpM5q5USzZAn7GL21Ahr7O2EEAH9/NA/2vrzd5Mk87sqVPEZQEPvRvj3JkSRMFgu3k2TQnRyGhvK8MqbRaCQh7NqVhLNDB14TqbbJUgoFjy+zZsqELlIFlAlY3GvKFHyzI0tWSNInVUaAx5FKnSSKbdtyTKXKGOafiYT1RkQ7OsCakALbjj3YisbYkCgw9MFMzJvH7SIjuS3WJyId/rAmbEJU80yMH8/rGhzMvleqRFU2NxcY/GYFaIaKbEytBio1vMI9i4YNy1/x/SwMug+/goKCwn+A3Q63RE5SjXMne9fCtUx/DQZcgBeAY84SKOHlRNuBTfFuQmcMK/ELqlYTWLCAL7XlC2Sl4N0cyNrUNxNFnuDZ7SQ6AMlWUhIVOiFooObn021y82aSrQsXuE/x4nSzA7htlSo0ZIsV43HS0+myt2QJMxkOHEiCd/gwyVH79rz5q1al++OlS8y2Wbw4CaePD11DGzcmMfjtN5LCVatozJcpwzczS5bQNbJZM+C+Xh6oV/kSPscg/FpqIM7neyIggC6kbdpQDZs1i8SpZk2qhSYT+yHdVAE9ra2vL0mrtzfj2wASvNRU2iCLFjGeKi2NbTCbSSTLlOG28+fz+KNG8ZjPPkuyl5vLZBz+/uyv1cqYtk8+4X5DhnA8AWYKnTaNhOiVV3gMLy8Sh/XrdRInVbotW3hsX18Sq/R0tuG55xi/OHIksGevQMia9xG+bw68JnyADqHiMrGx2Uh4HA6OvSQp7m+ipLr34oskbDIubcoU7h8bSxIbEUGVKyJCJ2wA7zFZq+5qkrskXu5wryUjt3F3H5WEzR2SFEZHk7y1bq0nVQHY5k8/5XJZGy842BUzWKUCIoMvIcywDrH+g2H298UozEBE8CWU86+A4cN5TLsdiN9dAab2gRiK2fi0+sdYtrkCZs7k/TF5MsciN5eK3htvAA89LIAOnQAAtpHvqtTwCvcshr9cCVdmtwvCrvIht6s5CgoKRQyentdaI8leQRiusZzrnHDiAjxRxZKLzt0MyDpmRYatAsqVFxg1Srd7AEXubhasVr3u4M1EkSd4FouuuAAkMM2bk6hlZlLZ+vRTkpZHHiHxKFuWRGLePMZRXLxIEWL1arop7tlDcnDmDMlJYCCJ4WuvkTw++ijJzfffc9uwMLpFxsWRUBoMeimATZuoJpYsyXjBhg15sVu2pF2cl8fYQKsVCOkgMDjhSRzo8QL2V+uITp0EIiKohu3YwfNK8nrsGPsqU/OPGUPSuX8/FcrGjbnPoUM6sVm8mAZ9YiKJ6n33MdbqhReoZg0ZwnGx2ah81amj11KTcXv+/jyWvz8J1/PP0+UwJYXKYIcOHJcHHuBE0bAhS0gYjcBHH5GwAfqN3rKlrqLt3k0y+fzzTDDTtSv7FRdHhXX1ahKZgQOBmZ8ZELurIoLaCqxbp8eTBQTwuDJezWrlPoMHs/3SRdJkYlbQjRvZN5NJT8gSEaG7acbEkADKMTSbeQ7pmindQOVx5Xf3dStX6m/hwsKujJWTkES3IKRKKEmqLKAeHEyi6een1wgE2MeQEMDkIWBaGw3TkQMwjv8YoYkfI+n7A1g2ZhXsDnHFRNO2nWvbfbvQasZz6NpV4MUXOf6yXuCnn/I6fPgh752dO/k20eypyJ3CvYuA1gI1qhvdlpjwxVfGa26voKCgcKOwWJjF/doo6Iop1T0mQbv6+jIwelXCiVKNseqbkzAs+BZR4Rcx4Gknnn2W5bAKFjhX+PfIzaVtOWGCXu7rZqHIEzyrleSicmV+dzpJcACSp4sXSSxsNpKF556jG2aPHvxr1IgXID+fRCw1lTFzDgfJ2xtv0CBfvJgZN195hclXli/X5fPKlVkWoGNHxu7VrUvS9tVXVKP27qX64XAwzqxmTapYQlBd7NiRSomvL/DNdwZ0f6AYxo4VCAmhO+ZLL/GNyrZtjB/r3ZvbenjwuPPn68lITCa9GLos/B0fz/7XqsW2GQwkZ15eJFUxMSSis2fTvfT335kgpXFjvY6aVNccDio7qalUurKzSSoOHiR5s1hI0CIjeTyjkfGIHTpQKQwK0pOEmM1sa/v2JEcDBvC4ktTIem9GIyc5o1FPvtKsGYl3XBzbJ0sfhIdzu127mPFz2jS2MTnZRXxMelbJrl11YrV27ZWlBFhjjuulKma18vOcOXq8W2ysTi5DQkj8goP5JzNQdetGtdC9fp2MlZNqnySUVuuVCqBU+mTco7e3Hi8YFkZCKtsK6H0DwAtdsSIiOwlYihkQ0Kk0FiwUyM7WSWJMDJXLrLMGfPJdRYSGCVjMTpjOnEJ8HH8ckpJIqmUJjjFjOHaA3m4FhXsVfv7urlPAggUqs6yCgsLNwZUxcO7E7WpvhTXXNprb9gWToBlwIc+EU6cAk3YG51ESHbAC779jw9atehjRxo2qDNLNgN1OzlAYamiRJ3gWC937LjDPBpo0obuhwUC3xpIlSQZ69KCqt2IFb+DUVGDBAsbWmUwkT7LGXYsWVMpq1mR2ybg4Kn8HDlAhs9t5zmeeocvh6dM8t8PB9ZUrUyXMyOCxPTxIwFq21DNxms2sMXf4MIlWvXqsBVerFt02d++mAW6zsXC5pye3GTSIxOHJJ3nszz7j+Zo25ff69amg5eaSsH7+Od0kFy4k+W3YkMRS1r1r0IDErHlzFrT28qLxfv/9ev0TmaBk9GgSw2nT6LbarBnbsXEj+yLdRO12EqqBA3kso5FjKGPrZAyfJCaTJpHgTp/OY8jkI3Jis9muDGuJjCRhnDGDZK99e46JrDsXFcW4v23b9MLektzJLJ4S6ekkhbItMgFKbi7bmZamZ8mcNk0v0yBLOTgcVxYTlxmoYmP5gsE9Js99opaEMTqa5DIsjPt/8gmTwqxY8dfsm+4ThIznk8dcsUIvteBODqVSGB1NV9+uXfk/JIR96NyZqumGDS6y63Qit0MXmGpVQ8i7nQCnEw6H7k5rtTK+U5bIyMm51pOpoHBvYPhwwN2o2rMnTmWWVVBQuCnQVbSCxO1qMOCvqt21XjgZUNwgYIMJVq+aaNTcE4cP8zf+t98ojkybppKs3AwUVt6tIk/wTCaSjnLlSFpkFkarleSsa1eqZPfdp0vPGRl0X2zXju52hw+zCPjZsyRY3bqRNAnBUgoDBtDg372biUYSEliXbfJkHq9WLSpIq1fzWG++SSPdz4/q1s6dXPbLLyQK5cuzrXFxJGEAiZYsaQDwXLGxVPBsNhKw4cOBb75h4fTvvmOGTKeT7pqBgSSBXl7AU0/RAB88mP2bMoVjdOgQ3QUDAvRyDrt367bIypU05JcupdK2fTv753BwXKOj6aon3WLT06lS/vEHYw6NRiqdkyeTLM+Zw/3ds1IaDGyrLCNgMlHdGjFCjzHr2JGq2+TJ/L9tG/eXZRLsdqpKTZroRcoLujgmJZHE+PhwzCU5ck9sApB0u2evlMliXniB7XzpJa53z9gqyZrdzra5l0YIDSVpCg3lPeYeLzd5Mv/b7brS5p6kxWSi+hkVpY+ZJKbynnYnbTYbCaG8RsBf4/kcDsBkcCLM7xTat9Pg5aVPNlarrg5GRFDxXPVrDoavexhZ9uJYllAcaxfkXOECbTLxRUNGBr9LtVxB4V5Fjx6AEe6ZZIORsEhlllVQUPjv0AnWjXgGXG2ba9EAgZ3ORthZ6T6UC2mII0doC6alUYgwm/Uaygr/HhkZtMcLQw0t8gQPoNHZqBHJWLFiJHL5+VTR0tNJkl5/nTFlJhMNWaeThGvXLqp2MgPmoUPAt9+SGGzbRmKVlMR1ISGMy2vQgOUYAgNJtBITuW1oKJW+ihWpAvr7c7nRyJpv//sfCdCePYz5mzKFBvKOHSRaL71Epa18ecYPBgayzfv20T1y+XISnJUrdfXx0CG2f948Ft5NT9cN/s8/pwtpYiKXy/IMnp4kr15eJFaBgdx+5kxua7NR9WrYkH0PD2dyEy8vqnwmE8nn2bO4nI1xxAhegx072OZNm0g0k5I4Lh06kDAOG8axbdxYLxBusbA0RZs2ukvliy8yI2hqKklPt25XTjSyVpwMBpb3gSzyLd0mrVYSr+zsK9U7mX1SkhdZqHztWp5H1oqTx7Db9aQk7vXnRo1iHyMiOEbSx1oWWZeJUhISOFnGxpKUSQLmnmhFxgZaLHqsnlTpli5lm+S1NZl4XSSxk5k+3dU7gOUS7BFRiKlMswv8AAAqhUlEQVT1DKY1/xy2S05s28b78JNP9EnHYuEYd+rrgwltf0M8wjC/3EAEdfG57CYrj7t1K9/uAbwnFBTudbQLLnfF9wEvlrvGlgoKCgo3Dj1u63rmvIy9K0jwrpdwhcg+eR6DVnZFicQViAx34sgRvuAfMYL2l4rD+/fIzqan32OPUYS62SjyBE+m0G/QQC/iLVGtGgmHtzddNR94gAP+448kL+XLM5lHZiYHv107HkMqVg4Hj12hAvD44ywCvmcPFavMTKpdvXoxk6bMkOl0kuA9+CDb4OFBwtOhA8nWJ58AmtOJDWsuYu4cDadPk5heuECS2aULiZDTyeOfP0+3zypVSPBatmRM34svkpj16kUVq3p1KnppaXSXrFOH8YYtW1LBSkqieubrq8evtWtHEiMzRT71FNv56KO6C19AAPsVF8exTUrimOflMXOmJM1OJ7N81qvHMcrNJcHMyaGyGR3Nfqxfr7syrl7N8+Tmkry8+CJdXr29qThu2cK2ywnOZNILekvitnKl7kIqt5ExbVIhbNxYV/Tct5Mxc8CVyp7drp9Tkjmrlf2Xxxw+nGRNkkmA5Dg3V0+2IgmpfAPnXiNPEj9J7KSq5078wsLYXxmvOGgQ/kK2DIYr+zttGidn6eYa2TQTlqRYdMYKjM0Yg97tMvH88+zf0KE6aZXt/HSqQMzIxTDO+xL3f9YN3iWYkCU0VE9as2eP3idF8BQUgC8+ygKg//js2funMowUFBT+E+x24Oef5bfrETUNV1fv/i4JmgYbgKcxG5azu7Hmj3PYuVODpjH0RnoQKfw77NpFsaRXLxWD969gsdDYlkWhq1fnmweAhr/DQWXp0CESjjNnWNOrdWsmTTl3jqTGw4Mka/JkKlN2OxW4Ro1I7FatojFdpw5ru4WEkNT88Qdj9bp147E7d6Z7Z+PGNNz79qUbpsywuWe3E82yotFl0bP45f6vkXHUiT/+0N0xw8KAn36iOpOXR1LXpw+Jkc3GcgzLlulkZft2Esevv2ZiFIsFePVVGuFjx/INjEwO0q4dE2Ts3k21bMYMniM7m2P2zTeMm+vShe6VzZszLu7XX7md0UjykJCgl1Q4dEiPBRw5koR59Ghu+8QTJKM2G7e//36SSbudRHTTJp57+nSSlz59SDxiYri9rJcnY9WsVj2pCaAre6GheuIW9yyTa9dyu27dOK6AfhzgSkInv4eFuZGjSD25iclEwrlype4CuWWLTq4kYUxMpHorfdetVn4OCNCJqCR3MktV27Z6qYbx4/Xsm8uW6W6qQUG6y2tkJP/PmMH7MymJ7S6YiMVkYrkEtGsHk0nAEhwAU5UKlxO0SNdWSTRjY/m2aclSA9p0Lo2u3cRlBTMhgW2IiWFpjI4d2ZeyZf/zI6ygcNejXlA5VIJ7/buSl18eKSgoKPwb2O16fol/juub/8WKcZvKOAUHzIhGD4xYFYlGJ6PRuJFTJVC7CZCkzsencI5f5AkewEG8dInZJPPySIZ8fPTCggsXUnE7fZqKUnIyyZQscF6uHInI1q10qwsLYyFygL6z0h2tTBm6fx4+TAO8RAkew2Dgdvn5JGQZGSRSTzwBfPwx2yNdHZGdgz5nP4MZVhw+ZkQlHyvGjSMpqFVLT54yaBDJ0hdfkLzJsgWbN1OFS0khSWrUiIpchQokcydOkEQdOMDJoX9/KnONGtGlEWBfExI4ZjYbVcqqVan2zZ3LbQYPJgEbNoxqpFRqZPF3o/GvKtBnn+kq16xZJJpPPcV9w8O5vXRXbNKEfzNnUn3dtElXoYxGbh8eznO5w2hkUpVp03RiIpU897pw7pAJSWJieH/IOD53RVCSNotFJ89SDZTxcWPGcGyDgngPDR2qu1PK+zAkhNdm+HDd3XL4cCYx2bxZd4mU2UBXrqSSKZU/TdPbZTbzmstSEc8+SwIpS12MHMmMqtItVeKKRDJCAGvWwH4oA/botbA7xBXjYbPpimhaGtvbsyfJZEwMyWVMDF96rFhB18433+S9r6Cg4EJWFt7ArCsWubuPKygoKPxTWCzMzUD828RNVy+ZQLtW4BD8kOdZFReh4R28gfxzWVi1yIpmzfiyW3ki/HssXsz/MhHjzUaRJ3jSUH/9dca4+fgwo2S1aiRePj5U7MqXp7ujlxdJgqxP5+vLz8WLU83Yu5fKTPnyNHgdDqp7Vqteb6xKFRKqhg3pAnrpEl04K1cmyRKCBnBiIlW1Jk2o9I0fD0z71gc5dZtjJ5qiu+9eeJXxwtatVNCaNqUK+MUXPN7UqSRN3buTFNWty4Qq3bvrpGL7drZRCJLXo0dJFjIzdaVLCBryy5dT9alblyTFw0NPyuLhwb8DB5jFMy6OEr3MuBgVRaVTJgSRCT7WrCFpiosjOQVI5H78kUqnrP8miZAsI2A287uHB48ZEcFjyvWrV/P448eTYEjFKSyM13T4cI6vyUTVUSq4jRuzLZ9+yuPY7fr+ERHsrxw7STaDg3WSJOPwoqP1uD2pqMXGkpDJbJ9JSSQ9MTFXSWxi0l0vZbbKESP0WnayZl94OADNiR0bzsNo0PD881yXmEhlslQpyvvh4SSEknxKF1pJ7GQMYXy8vs1l10ubASvSKmLFSnE502Zw8JXjYbczmdC6dUwaIevfTZ9OcjdrFq9lRARfAuzZwx+Nw4dUSngFBVSogKEdTgPQZbv8fAcSFpzUM2kpKCgo/APY7fxNJv7pPFKQ2F2tZAIAGHDhkhmX4INodESpskZ0us8LX39N7yLlovnvsHIly6y9+27hvewr0gRPZgxcsYL/N2+msSrjxCpXJsk7eZIuiwCVKpOJrpkbN5IsCEES+OefNOL37ycZrFGDyVQMBhrCgweTEG7ZQvK3aBFJXmIi17VvTyWuXz8Sjq1bqYBt2ULD2MMD8C4h8G2V12Af9gI6rX0Lb78tsHAhScGhQyRJ771HcjR3Lkleq1bsgxCMJTSbacj7+9NNdNcu7h8fT/XlwAEqMI0bU/3r108vtj10KFXKr7+mO1+bNlRkDAaSvo8/Zv9btuR5AwNp7P/+OxOKyIQjssSCw0FitnEjyexPP/GthY8PFUOzmeMiywXIVPtbt5JIDR6sX68lS0h6ZMzZyJFsQ0SEnrlSqk3e3px87HbW25Oxat268U/GuS1bRtIN8LvcTrpqSjdQeT/Fx+uxbzNmkMhJEti5M4/9wgvsnySmERF6mQXpFiqzgbpDElOpeHbuDHgXc6LbxEi89EkldJsYAe9inIAlgYuIuLIkg9UKjBsHxEQ7YT18CvZ87YraftI1c9EivdTC2rW8RjKhjIxfBNhXq5VK4OjRvI4SkrR7efEaAnwZsnKlE412fgUAML47RqWEV1BwKeU/z2p4xeKQ3nth7xCunhEFBYV/DJklnu6SN8Oc/2usHm1cgXxzGWgoiRWmvti9R2Do0JtfmPtewuzZ/F+pUuGdo0gTPJk1URrBkyfzsyz+7HTqWS0DArhPRgaVGR8f7n/+PGPTHniAClV2NklQ6dKUVb29+QLWbidRyMigelenDl0X69bVE6FMmkR3zOXLaYx7e1OBqVCB2zz5JNWuJ580oEm7EhgyVGDsWLp8btlCI9zLiyTOYKAytWABCVOLFtxOxlilpJAkmUzAkCEknpUrU7nLy2MtvGHDuF+pUlQ0NY2KnN1Ot06ARLB7d7149ZYtzPjz+us8jo8PC8V7e5PcmEx6bOP27TT4d+wgcXn7bcbZpafrBdADAqj+yHg0m42qZL9+dKmVboIWC9W6lBQSEvfyB+7KmnQpzMrS3TQbN9ZJm/u9YbWyHIR0l8zOZuZSd2IXGnqli2NIiF4KoVkzvQ2SlEm3S4mYGJJn9/uxc2d9vVQApZon1U9JBO0nMmFKjIfFeRGmxHjYT2Rero0nzyfJo81G0uWwO9H+w26IqfUMljZ/FdaLzsvupLLfCxZwe5OJxF2WbJBusPL6y3aEhnI8X32V3xcvZkzmSy/xnhg0SN/+s/czsS+nDADAe88W3nQKCvc6DAb0HVQR5UtluS1sh7kJPrrPv4KCgsINwm6nNxO9sWSmzH+Cq21/pZJ37hxtZZvNgBIljKhVS1zOdq7wL+B0on+fU/jtNw3FiukJFwsDRZrgAbrBHRnJG9Lbm9kYvb1p6B47RiUpN5eKXGAg4+ZataLBv38/sxT9+itVvvx8GrdS1ZNJQjIyqE5duqQrg1u2UD5/9FGee+BAKmhnz9LmvXCBquBLL1E5/Ppr+j3LBCKbN7P+3p9/si27d7NEQ4sWJHqenlSgTp5kO7dvJ6GQMWYeHnqGSIAKYPXqVFvsdl2NMRq5rSwn8eKL7H9SEomq2Uy16uRJEtQvviBR3r2b7Zozh2Rv506Oo9HIMR06lMf196ci9OCD7Jefnx4LJ+PR7HYSYIDEMzWVpHP7dh575UpuGxREcjd2LEnK/PlsgywG3q0bt5k9m+qfVPsk3FXd2Fi9dMGiRVRD/f15v0REUJWTxE3uJxVCk4lETdb8k2UL3DNOAuzv1q2A3eaE/dgpmIza5QLlgJ54xT2Dp3RPBYA12ysgu00E7EZPJkOpUgGRkbrbpHS9lEXXO3QAXn36FCyJcXAA2LjDCzG/5yA7+8o4wsmT+aJD1gwcNoz/AwLodinVvLw83sPymQF4rq1bmRE1LIzK7bff8l7z9QUyrBUQXHk/gHxUa1mRbzAUFBQAAKfPVYC7q+ZgfINdO5wkecpdU0FB4QZhMtGbSsc/NemvNd9cffn587QNN29WBc7/FZxOTK79Dr6ZXw6AE30fdBYqUS7yBE9CugBGRjJpymuvUTFr355G7MCBJD/9+lFp2r2bhK9sWRqyp07xpr5wgQqU3U6S17gxY9s0jWSmZk2Skho1SML69KErZblydG9ctIgGfKtWNJjLlmVyjNhYxvnt2kUi07Ur29i5M8nQwoUkehkZPO/mzfxcpQqJ0oED7F/t2jTUzWYa3g4HiWblyiSBhw4xcUnDhlQmvbxIxgIDGWP1/feM59q0iX1bsoTHv3SJ47V3L0mglxdjBzds4Hezmcb97NnMrrl6NTDncyda1DqHXj01/O9/VP3Cw7ntgQNXFuBOSCDxk7FyaWkcz6FDSawdDrZnxgyec8IExp59+inHKy9PV9mSkqicmkw68ZPFvC2e2uX4MqNRz1i5cydj4O67TydMsbFXqmmRkSR98fF6nJzMgukeaOxeOqFbN2Ds806YukRiTY3+yA7uimlTnZd9100mupICuoon3URNJqB1G4Ex9Zdi6dwTl5OgSLVy7Vrum5DA6ycEsC7BCfR/Amu1EHRAHCw1KiOwsw+mTdMT3KxYQdJmt5M4Oxy6ErlhA11rAY5BaqqeSfOTT7gsIoIurg0a8J62WvV7ondv4MIFgS8wGoAHrNN+YMMUFBQIIaDZg6GTPC/4RXril0r9+WblxAlF9BQUFP4WdjsTut18/JUaVKpEW1gKCO4vtBVuDGHBmRhz5BXXtwTMG1e43k2FSvCEEF2EELuFEPuEEK9cZb2nEOIn1/oNQohahdkeQCcBMlX8o49yWUIChQajkdkqg4KAV16holO8OLcpVoyETdb8OnWKSl358iRfmkbXzXPnSBgMBuDLL3VCExLCRCkbNpAkHj5Ml7dnnuH6Dz8kATp+nElIVq+mojd1Kvc7fZoGdK9e3GboULprDhpE8jR7Nj97eZHc5eZSbTt3jirboEE8FkD1a+pUPrCSaL32GrNqHjzIfadO5fKkJL0G3QMP0MDfsYP96dyZZDQ3l33o35/lJhx2J4Yv74aOA+rA0iUM3sWcCAujWin3mzVLT5wSFMR2R0Swfa1aMf6vXDm6hzocDEgdOJDbxMdzgvH2JiHauVN3QWzenIW/V61y1T10OrG0+f+wtvZTyA3pjGlT6X4gXRtNJlyu/Wa10k3TZuN5oqJcBNF13xQsc2Ay0S1WqsQAP8uMllYrgEy6WbZ1xiM5ScPgPpmXz7ViBZXLZctI1mJiSKSWLtXVtqnTDOj2eGlACCxdSlIbFKS3y+EgyR07FohokglTYgIADd7GfIyN7Qmf0gItWlwZyCtJW3o6yaFUIkNDuWz1ar78CAjgi4DkZKqdstxEUhLv+9hYqtOyBuLy5bzvw8IMAIzYuevOJHd34tykcA/BaIR23Bc6ySuNh7AUIv5dfFdlAB9uFZdX5PFf5iEhxKuu5buFEJ0L7qtQ9GEy8QW9Hhbyd3PG9X6P5bqrFz+XpbC8vGhLFMzOrXBthIXxPXdsYgUAHgASoHV4s9C9mwqN4AkhjACmA+gKwB/Ao0II/wKbDQBwTtO0egAmAfi4sNpTEDLhxkMPkWg8+SQN59WreTEee4wGbPHiwHffkUyVKUMF7/HHqdQ1a0YCFhVFElexItcbjXQvPHeOhOz110kk+/UjGXn/fRrNDz9Md7iMDKBSJSei55+Hb30N+fnMrFOiBA3rzZupXo0Zw/1276bSt38/lbW+fanSLVvGGLecHBKHgQOpJFWoQMKXnMyJICCArpeVKvEhfeABkhZN4/omTThGGzZw8ti1iwRu+XIa//Hxep01u50ukpMmsX/p6WwrsnOwYr03RmECcuNTYT+RifXrqTACJDJ+fjy+THwD6MW4w8M5rtLF1tub5/DxYVsWLiTJkUrX0KF6nbt58zjGXbvyuthPZCJ9p0CQlgBLUhxGP5Z5eUKMjiaZkp8TEuiuKMs+yHIB8k1V27ZUtaT7p3R7lOpbdDTbMGsWx3T8eGD8VxWQG9gRiYYQtA4SSD5c4XJGy9BQKoc7d/L4stC5LBo+caLeR4D31sCBVOAWL6YCFxGhk7343axrF2mMg6l9IFCx4mV3UpNJP6dUSocOJfmXMYuA7vIRF8dJ/bPP+NZO7i9r7cnY1o8+An7/zYmUdVaEhWpYvFivFViYAcT/Fnf63KRwj6BSJf7Iu7lrAsHoh0VoHdcfi7/NREIC59+sLM4r6o35DcJ5FZfXqy27jfgv85Bru0cANALQBcAM1/EU7jH4+NCDhrheMXOD219ByOVGXIsEtm1LcaF7d700lMKVEOLqf7Gxl7cAIKCdbEjjspC9m4RWSJOdEKItgLc0Tevs+v4qAGia9qHbNitc2yQKIUwATgIor12nUQEBAVpKSsoNtuHa68qWpdJQogSN+VKl+L9WLRqtJ0/y88GDJCVZWXThzMigmpabyx/eXr0Yj+TjQ8O/cmUatf7+jLXLyKA75J49PL6vLw3zqlX1OmaJ653YNmMNPs15CsvqDEbbX17FnC8McDiotNStS/KYmkoD38+PBOehh/jQTZpEo7xMGbarY0ca523aUGnr0IF9lin5T56kmyZAl8tPPuHy4cMZI1iqFEnAwoW6K+bPP3M8ZCKV1au5f1SUXmPuoYf0LJrRqzTseGEu/I8uRWS7i/BeuwzRMeKy+2FWFomr0UjyUdDF0dubZEkW7ZYkZ8UKum8++STVPek66HBQ9TMaqUjNmaMXIIemwdqxMxLWGwH/RohMHQ+TB28OWRtPlh+Q4wTomTlXrmQ/Y2L0Yu7JyVQi5TJJsFasoPI1eTLPP2UKybVPSSesRzMRv7sCQjqIy8eWZQtk7TqZ5VK2YelSfcyl6yhA4vX770D9+lRXZabPsDDAYnYCmZnILVYB06aLywqjjCWUx4+P5/msVp67bVu9FqF039yyhUl6NmxgPwMCqBSPHEmFNC0NcDqdGBbdEwlJZnj510OD+R/h/gcMSE9n+7t2vZFnVWzSNC3g77f87yisuQn4Z/OTggKcTuD0aXwdPB5P7+/pev8e7Fqpv0lv0IAvjB59lC+/5Auqu9HIknOQ/Gy18iXh888Dv/xyM87gBBCPCrChFY6j6UtPMJnUH0NRbkciGrarDMuqpTCZDTc8foUxP/2XeQjAK+7bum93rfP9U9vpDuHBCn+D3FwKB1u2ACR47ire1dU4bqP9zTbEww9zznnoIT3BXFGF+9wEcF6y22nHyDJf/wWlSvGYNxPXm5sK8+ehKoCjbt8zAAReaxtN0+xCiBwAZQG4pxmDEGIggIEAUKNGjRs6+d8R4zNn+P+/Jvj7+ut/vo+7AU9oKAE/vIcX8NWBAajY3YGKVQzYto1JXby9SfIOHODnWrVIHD08qBRJl7r//Y+k8/PPGWsXGMht8vIYS1ezJs+7eDFdSQMDSTJTUkgEa9akCjh1Ktu4bx/3nTGDx/z2WxJHs1lX1+x2EtymTflDLcmWEAKj054BMu/DCx9XwJRLApGR3H7cOBLLXr1IIGVhbZtNJ7GjR3NkcnP5uU8fEp3OnfVC5g4HCcfIkVS0QkNJdHx8cAWpWbNWoOPq5YjMzAQqVLhM7gCOpyyZAOh9aNZMz3a5bRsnOKORE6lMimKx6Nu4X9N160i4pVrq7Q3AYIClZkV0rKpPILLwuSRakty5H08miJGETLpIAkCXLlcmEZL7AQbYy1ZE4hqSdjkO0dEcM6OR28vzSXdl93NLhVteZ9n3hASXQgvGmjZpApgv5KDch9HoDTvsuzyxdtOrGD26NN5//69F5e8Q3LS5Cfh385OCAgC+ZaxUCf33jEf/zEygXDk8PyQLpy9WwCOPCPj48EVWuXL6HPXpp3wGzea7z01KejvIEjFLl/I39LffbuZZNADBOA1gGYBl46QhOx2AE63Wr0fX1y4gKLLEZc+G24T/Mg9VBZBUYN+qBU/wX2wnRfLuDlgswP/9H8tnbd4sM2lquD5xu7rzXrFitAeeeIK2SatWnHvcvYiKKtznJplV/f77aaseO/bfj387nqXCVPAeBNBF07RnXd+fABCoadpwt23SXdtkuL7vd23zFyNKorAUPB8fPii+vvzhPH2a6prBwAuflaW7LprNJCM2Gx+AvDy6RdarR9Xu5Eke588/GSN34QLPJ1UbGX8F8Ca6ZNXQ4PVeKJccg0Mtu8Jn4S+weAlkuUbBZGL7srP1NyjyONJF0tub28g3onI9oG8vH1CZIEQa/vI/oKt8gN5Gq5XHdj+G+8PurrC5Z1aSBklu7pX1UqTK5/4W2v38cl/5NiU3968uAfI88rzuylTBiehqy64H9/7I7+7tud7x3In73217I+0r+Lb7n/Sj4PYF2/ZP4N53uf/l+0HTYIoMY4rVdu1gj14LCIHsbP443AhusYJXKHMToBQ8hcLHtebhuwWFr+BpAOJ0Be/l/lTw5g/RFbzoZTB5iNut4P3reQjAWwCSNE371rV8LoBlmqb9eq3zKQWv6EL+Lo8fz8zi10u8Ur8+RQJ/f76A7d5dtx1v1GYpqvivCt6TTzJE6Fbidil4xwBUd/tezbXsattkuNwPSgE4c7MacPdMUAII/wPIzETDChUuM9OC6VOvlU7VfXnBOmwS7gTrauuv9kDL7Qr+v9Z219qmYDHM653fvR3uCXGud85r7V9w3Y2i4LELukNd73gF193IuW/0eP+0H/+mLX93LPdj6OPEIs6QCqnr/r1RcncbcNvnJgWFf4u73UWq4Jzm7c2/n3++WWcQgDPENR9F6m9635lxeY66QzL7/pd56Eb2/de4e2wnBUB/pl59Va9V+2+Pca+SO+CvfZe2df/+/LvbUJhZNJMB1BdC1BZCmMGA4IUFtlkIQPLiBwGs/rsYlyILg4GBdnfGD4+Cwj/D3XX/qrlJQaEo42rz0Z03R/2XeWghgEdcWTZrA6gPYOMtareCgsJdgELj6i5/8eEAVoCpeb7QNG27EOIdACmapi0EMBfAN0KIfQDOghOcgoKCQqFBzU0KCgq3G/9lHnJt9zOAHQDsAIZpmua4LR1RUFC4I1FoMXiFBRXjoqBQ9HArY/AKE2p+UlAoeigK85OamxQUih6uNzcVaqFzBQUFBQUFBQUFBQUFhVsHRfAUFBQUFBQUFBQUFBSKCBTBU1BQUFBQUFBQUFBQKCJQBE9BQUFBQUFBQUFBQaGIQBE8BQUFBQUFBQUFBQWFIoK7LoumECITwOEb3LwcgKxCbE5hQLX51uBua/Pd1l7gn7W5pqZp5QuzMbcC/3B++i+4E+4H1QbVhjvl/IXdhrt+flJzk2qDakORbMM156a7juD9EwghUu621MaqzbcGd1ub77b2Andnm+8W3Aljq9qg2nCnnP9OaYPCnXEdVBtUG1QblIumgoKCgoKCgoKCgoJCkYEieAoKCgoKCgoKCgoKCkUERZ3gzb7dDfgXUG2+Nbjb2ny3tRe4O9t8t+BOGFvVBkK14fafH7gz2qBwZ1wH1QZCtYG4J9tQpGPwFBQUFBQUFBQUFBQU7iUUdQVPQUFBQUFBQUFBQUHhnoEieAoKCgoKCgoKCgoKCkUERYLgCSG6CCF2CyH2CSFeucp6TyHET671G4QQtW5DMwu26e/a/LwQYocQYqsQIkYIUfN2tLNAm67bZrftHhBCaEKI252W9m/bK4R4yDXO24UQ39/qNl6lPX93X9QQQqwRQqS67o1ut6Odbu35QghxWgiRfo31Qgjxqas/W4UQLW91G4sKhBDvusYwTQixUghRxbX8lo2xEGK8EGKX6zzzhRA+butedbVhtxCicyG2oa/reXUWnGNuVRtc57qh+fAmn/Mvz5sQoowQYpUQYq/rf+lCbkN11xwk581Rt7odQgiLEGKjEGKLqw1vu5bXdv3G73P95psLqw0KV0LNT5fPc9vnp9sxN7nOe1vnJzU3FYCmaXf1HwAjgP0A6gAwA9gCwL/ANkMBzHJ9fgTAT3dBmzsCKOb6PORuaLNruxIA4gAkAQi4k9sLoD6AVAClXd8r3OljDAbqDnF99gdw6Da3uQOAlgDSr7G+G4BlAASAIAAbbmd77+Y/ACXdPo90m9Nu2RgDiAJgcn3+GMDHrs/+rvvVE0Bt131sLKQ2+AFoAGCt+xxzi9twQ/NhIZz3L88bgHEAXnF9fkVek0JsQ2UALV2fSwDY4xr7W9YO173u7frsAWCD697/GcAjruWz5Fyp/gr/T81Pl9twW+en2zU3uc59W+cnNTdd+VcUFLw2APZpmnZA0zQbgB8B9CqwTS8AX7k+/wogQgghbmEbC+Jv26xp2hpN0y66viYBqHaL21gQNzLOAPAuOLFab2XjroIbae9zAKZrmnYOADRNO32L21gQN9JmDUBJ1+dSAI7fwvb9BZqmxQE4e51NegH4WiOSAPgIISrfmtYVLWia9qfb1+LgvQDcwjHWNG2lpml211f3eakXgB81TbukadpBAPvA+7kw2rBT07TdV1l1y9qAG58Pbyqu8by5/759BaB3IbfhhKZpm12fzwPYCaDqrWyH617PdX31cP1pAMLB3/hCb4PClVDz0+U23O756bbMTcDtn5/U3HQligLBqwrgqNv3DNeyq27jevhzAJS9Ja27Om6kze4YAL4Bu5342za7XC+qa5q25FY27Bq4kTH2BeArhFgnhEgSQnS5Za27Om6kzW8B6CeEyACwFMCIW9O0f41/eq8rXAdCiPeFEEcBPA7gTdfi2zXGz0Cfl+6E63wr23An9FeioqZpJ1yfTwKoeKtOLBju0AJ8S31L2yGEMAoh0gCcBrAKVC2y3Qx8NdfcYqj56bq4VW24E/rqjtsyP6m5qWgQvCINIUQ/AAEAxt/utlwPQggDgE8AvHC72/IPYALdNMMAPArgc3ef/TsUjwKYp2laNdD15RvX2CsUAQghooUQ6Vf56wUAmqa9pmladQDfARh+O9rg2uY1AHZXO25LGxT+Co3+P7ek9pEQwhvAbwBGF1Bvbkk7NE1zaJrWHFRp2gBoWJjnU1Dz0z9pg8JfcavmJzU3EabbcdKbjGMAqrt9r+ZadrVtMoQQJtC17cytad5VcSNthhAiEsBrAEI1Tbt0i9p2Lfxdm0sAaAxgrcv7tRKAhUKInpqmpdyyVuq4kTHOAOMB8gEcFELsAQlf8q1p4l9wI20eAKALAGialiiEsAAoB74puhNxQ/e6AqFpWuQNbvodqOD+H27yGP9dG4QQTwHoASDC9WOJW92Ga+BW3mt30n19SghRWdO0Ey7Xt0KfC4QQHqAB9Z2mab/frnYAgKZp2UKINQDagu5/JtebcjXX3GSo+enG2nAN3Ko5406am4BbPC+ouUlHUXjznwygvitDjRlMorKwwDYLATzp+vwggNVuD/7twN+2WQjRAsBnAHreAbFhwN+0WdO0HE3TymmaVkvTtFqg//vtInfAjd0Xf4DqHYQQ5UCXzQO3sI0FcSNtPgIgAgCEEH4ALAAyb2kr/xkWAugviCAAOW6uEgr/AEKI+m5fewHY5fp8y8bY5cb8EvhsX3RbtRDAI4IZi2uDL0o2FkYbroNb2YYbeVZvFdx/354EsKAwTyb4Bm8ugJ2apn1yO9ohhCgvvS2EEF4AOoHxNmvA3/hCb4PClVDz09/iVrXhTpqbgFs7L6i5yR3aLcisU9h/oKvaHtDP9TXXsnfAhxygEfwLGNS6EUCdu6DN0QBOAUhz/S2809tcYNu1uI1ZNG9wjAXoVroDwDa4Mhzd4W32B7AOzIyVBiDqNrf3BwAnAOSDiugAAIMBDHYb4+mu/my73ffE3fwHvpVMB7AVwCIAVW/1GLvm0KNu89Ist3WvudqwG0DXQmzD/a577ZJrjlxxq9vgOtdfntVbcA9c7XkrCyAGwF7X70aZQm5DMOjitNXtPuh2K9sBoCmYAXmr65l407W8Dvgbvw/8zfe8FddF/an5ye08t31+uh1zk+u8t3V+UnPTlX/CdWIFBQUFBQUFBQUFBQWFuxxFwUVTQUFBQUFBQUFBQUFBAYrgKSgoKCgoKCgoKCgoFBkogqegoKCgoKCgoKCgoFBEoAiegoKCgoKCgoKCgoJCEYEieAoKCgoKCgoKCgoKCkUEiuApKCgoKNyzEELkXmVZKSHE10KIfUKI/a7PpdzW1xdCLHat2ySEWCOE6OBa95QQYpoQ4jUhRJrrz+H2eeSt7J+CgsLdCTU3KfwXKIKncMMQQlQTQiwQQux1TR5TXIU0/+txawkh0m9GG92O2UoIsc01CX7qKoBZcJuGQohEIcQlIcTYm3l+BQWFuxpzARzQNK2epml1ARwEMAcAhBAWAEsAzNY0ra6maa0AjADrHF2Gpmnva5rWXNO05gDy5GdN0z69pT1RUFAoSlBzk8INQRE8hRuCiyD9DuAPTdPqA/AF4A3g/f94XNNNaN7VMBPAcwDqu/66XGWbswBGAphQSG1QUFC4yyCEqAegFYB33Ra/AyBACFEXwOMAEjVNWyhXapqWrmnavFvaUAUFhXsKam5S+CdQBE/hRhEOwKpp2pcAoGmaA8AYAM8IITYKIRrJDYUQa4UQAUKI4kKIL1zrU4UQvVzrnxJCLBRCrAYQ434Sl5oXL4TY7Ppr51oeJoSIE0IsEULsFkLMEkJc9f4VQlQGUFLTtCRN0zQAXwPoXXA7TdNOa5qWDCD/JoyPgoJC0YA/gDTXHAfg8nyXBqCR62/z7WmagoLCPQw1NyncMBTBU7hRNAKwyX2Bpml/AjgCugQ8BFwmV5U1TUsB8BqA1ZqmtQHQEcB4IURx1+4tATyoaVpogfOcBtBJ07SWAB4G4O4y0AZ0N/AHUBdAn2u0tSqADLfvGa5lCgoKCjcVQoj5Qoh0IcTvt7stCgoKChJqbrq3oQiews3AWgAPuj4/BOBX1+coAK8IIdJc21gA1HCtW6Vp2tmrHMsDwOdCiG0AfgHJnMRGTdMOuN5Y/QAg+Cb2QUFBQQEAdgBo7u4h4Prc3LVuO/iCCgCgadr9AJ4CUOaWtlJBQeFeg5qbFG4YiuAp3Ch2gL7flyGEKAkStmQAZ4QQTUHV7Se5CYAH3AJ4a2iattO17sI1zjMGwCkAzQAEAHBP4qIV2Lbgd4ljAKq5fa/mWqagoKBwXWiatg9AKoDX3Ra/DmCza933ANoLIXq6rS92C5uooKBwD0LNTQr/BIrgKdwoYgAUE0L0BwAhhBHARADzNE27CJK6lwCU0jRtq2ufFQBGyAyWQogWN3CeUgBOaJrmBPAEAKPbujZCiNquN1YPA0i42gE0TTsB4E8hRJDr3P0BLPhn3VVQULhHUEwIkeH29zyAAQB8XdmC94NJpQYAgKZpeQB6ABgshDgghEgEjaz3blcHFBQUiiTU3KTwryGYg0JB4e8hhKgOYAaAhuDLgaUAxmqadkkIURFUyd7VNO1t1/ZeACYDaOfa/qCmaT2EEE8BCNA0bbhru1oAFmua1lgIUR/Ab6A6txzAME3TvIUQYWC2qPMA6gFYA2Coiwhera0BAOYB8AKwDMAITdM0IcRgANA0bZYQohKAFAAlATgB5ALwd8UWKigoKCgoKCgoKNx1UARP4a6Ai+CN1TStx21uioKCgoKCgoKCgsIdC+WiqaCgoKCgoKCgoKCgUESgFDyFuxpCiA0APAssfkLTtG23oz0KCgoKCgoKCgoKtxOK4CkoKCgoKCgoKCgoKBQRKBdNBQUFBQUFBQUFBQWFIgJF8BQUFBQUFBQUFBQUFIoIFMFTUFBQUFBQUFBQUFAoIlAET0FBQUFBQUFBQUFBoYjg/wHU50PAVe8g+AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot partially labelled \n",
    "# scatter plot, same as above, except outlier 94.\n",
    "# P vs X_NORM, RAW vs X_NORM, LOGIT vs X_NORM, ENTROPY vs X_NORM\n",
    "fig, axs = plt.subplots(2,3,figsize=(15,10))\n",
    "axs = axs.ravel()\n",
    "col_to_plot = ['Delta','RAW','LOGIT','ENTROPY']\n",
    "\n",
    "\n",
    "#df_to_plot = soma_df[abs(soma_df.LOGIT) < 10]\n",
    "df_to_plot=soma_df.copy()\n",
    "print(f\"Removing {len(soma_df) - len(df_to_plot)} Cells\")\n",
    "\n",
    "col_to_plot2 = ['ENTROPY','RAW']\n",
    "for i in range(len(axs)):\n",
    "    if i < 4:\n",
    "        col = col_to_plot[i]\n",
    "        df1 = df_to_plot[df_to_plot['True_Label'] == 1]\n",
    "        axs[i].scatter(df1['Overlap_0.1'], df1[col], s=5, color='r')\n",
    "        df2 = df_to_plot[df_to_plot['True_Label'] == 0]\n",
    "        axs[i].scatter(df2['Overlap_0.1'], df2[col], s=0.01, color='b')\n",
    "        axs[i].set_xlabel('Overlap_0.1')\n",
    "        axs[i].set_ylabel(col)\n",
    "        axs[i].set_title(f'Overlap_0.1 vs {col}')\n",
    "    else:\n",
    "        \n",
    "        df1 = df_to_plot[df_to_plot['True_Label'] == 1]\n",
    "        axs[i].scatter(df1['LOGIT'], df1[col_to_plot2[i-4]], s=5, color='r')\n",
    "        df2 = df_to_plot[df_to_plot['True_Label'] == 0]\n",
    "        axs[i].scatter(df2['LOGIT'], df2[col_to_plot2[i-4]], s=.01, color='b')\n",
    "        \n",
    "        #axs[i].scatter(df_to_plot['LOGIT'], df_to_plot[col_to_plot2[i-4]], s=1)\n",
    "        axs[i].set_xlabel('LOGIT')\n",
    "        axs[i].set_ylabel(col_to_plot2[i-4])\n",
    "        axs[i].set_title(f'LOGIT vs {col_to_plot2[i-4]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f795a04f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removing 10353 Cells\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot partially labelled \n",
    "# scatter plot, same as above, except outlier 94.\n",
    "# P vs X_NORM, RAW vs X_NORM, LOGIT vs X_NORM, ENTROPY vs X_NORM\n",
    "fig, axs = plt.subplots(2,3,figsize=(15,10))\n",
    "axs = axs.ravel()\n",
    "col_to_plot = ['Delta','RAW','LOGIT','ENTROPY']\n",
    "\n",
    "\n",
    "df_to_plot = soma_df[(abs(soma_df.LOGIT) < 10)&(soma_df['PC1/PC2']<30)]\n",
    "#df_to_plot=soma_df.copy()\n",
    "print(f\"Removing {len(soma_df) - len(df_to_plot)} Cells\")\n",
    "\n",
    "col_to_plot2 = ['ENTROPY','RAW']\n",
    "for i in range(len(axs)):\n",
    "    if i < 4:\n",
    "        col = col_to_plot[i]\n",
    "        df1 = df_to_plot[df_to_plot['True_Label'] == 1]\n",
    "        axs[i].scatter(df1['Overlap_0.1'], df1[col], s=5, color='r')\n",
    "        df2 = df_to_plot[df_to_plot['True_Label'] == 0]\n",
    "        axs[i].scatter(df2['Overlap_0.1'], df2[col], s=0.01, color='b')\n",
    "        axs[i].set_xlabel('Overlap_0.1')\n",
    "        axs[i].set_ylabel(col)\n",
    "        axs[i].set_title(f'Overlap_0.1 vs {col}')\n",
    "    else:\n",
    "        \n",
    "        df1 = df_to_plot[df_to_plot['True_Label'] == 1]\n",
    "        axs[i].scatter(df1['LOGIT'], df1[col_to_plot2[i-4]], s=5, color='r')\n",
    "        df2 = df_to_plot[df_to_plot['True_Label'] == 0]\n",
    "        axs[i].scatter(df2['LOGIT'], df2[col_to_plot2[i-4]], s=.01, color='b')\n",
    "        \n",
    "        #axs[i].scatter(df_to_plot['LOGIT'], df_to_plot[col_to_plot2[i-4]], s=1)\n",
    "        axs[i].set_xlabel('LOGIT')\n",
    "        axs[i].set_ylabel(col_to_plot2[i-4])\n",
    "        axs[i].set_title(f'LOGIT vs {col_to_plot2[i-4]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d665d8d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['label', 'Barcode_Idx', 'SUM', 'Barcode', 'RAW', 'P', 'LOGIT',\n",
       "       'ENTROPY', 'X_NORM', 'Gene', 'ImageNumber', 'Overlap_0.1',\n",
       "       'Overlap_0.2', 'Overlap_0.3', 'Jaccard', 'PC1_var', 'PC2_var',\n",
       "       'PC3_var', 'PC1/PC2', 'intensity_mean-0', 'intensity_mean-1',\n",
       "       'intensity_mean-2', 'intensity_mean-3', 'intensity_mean-4',\n",
       "       'intensity_mean-5', 'intensity_mean-6', 'Delta', 'embedding1',\n",
       "       'embedding2', 'True_Label'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soma_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "e04c1b0a",
   "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>ImageNumber</th>\n",
       "      <th>ObjectNumber</th>\n",
       "      <th>FileName_max_clean</th>\n",
       "      <th>PathName_max_clean</th>\n",
       "      <th>Children_Cytoplasm_Count</th>\n",
       "      <th>Correlation_Correlation_C_DNA</th>\n",
       "      <th>Correlation_Correlation_C_FLAG</th>\n",
       "      <th>Correlation_Correlation_C_HSV</th>\n",
       "      <th>Correlation_Correlation_C_NWS</th>\n",
       "      <th>Correlation_Correlation_C_Ollas</th>\n",
       "      <th>...</th>\n",
       "      <th>RadialDistribution_RadialCV_Ollas_3of4</th>\n",
       "      <th>RadialDistribution_RadialCV_Ollas_4of4</th>\n",
       "      <th>RadialDistribution_RadialCV_S_1of4</th>\n",
       "      <th>RadialDistribution_RadialCV_S_2of4</th>\n",
       "      <th>RadialDistribution_RadialCV_S_3of4</th>\n",
       "      <th>RadialDistribution_RadialCV_S_4of4</th>\n",
       "      <th>RadialDistribution_RadialCV_VSVG_1of4</th>\n",
       "      <th>RadialDistribution_RadialCV_VSVG_2of4</th>\n",
       "      <th>RadialDistribution_RadialCV_VSVG_3of4</th>\n",
       "      <th>RadialDistribution_RadialCV_VSVG_4of4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>F000_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/101222_D10_Coverslip1_Process...</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.405568</td>\n",
       "      <td>0.579144</td>\n",
       "      <td>-0.029948</td>\n",
       "      <td>0.891888</td>\n",
       "      <td>0.002846</td>\n",
       "      <td>...</td>\n",
       "      <td>0.391393</td>\n",
       "      <td>0.781718</td>\n",
       "      <td>1.212362</td>\n",
       "      <td>1.064993</td>\n",
       "      <td>0.758903</td>\n",
       "      <td>1.124459</td>\n",
       "      <td>0.309291</td>\n",
       "      <td>0.368628</td>\n",
       "      <td>0.819462</td>\n",
       "      <td>1.144822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>F000_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/101222_D10_Coverslip1_Process...</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.120659</td>\n",
       "      <td>-0.053589</td>\n",
       "      <td>0.108147</td>\n",
       "      <td>-0.019662</td>\n",
       "      <td>-0.006271</td>\n",
       "      <td>...</td>\n",
       "      <td>0.605029</td>\n",
       "      <td>0.370029</td>\n",
       "      <td>0.497995</td>\n",
       "      <td>0.401728</td>\n",
       "      <td>0.364964</td>\n",
       "      <td>0.318315</td>\n",
       "      <td>0.627694</td>\n",
       "      <td>0.566441</td>\n",
       "      <td>0.769759</td>\n",
       "      <td>0.943922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>F000_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/101222_D10_Coverslip1_Process...</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.043058</td>\n",
       "      <td>0.207603</td>\n",
       "      <td>0.194595</td>\n",
       "      <td>0.193065</td>\n",
       "      <td>0.173620</td>\n",
       "      <td>...</td>\n",
       "      <td>0.252914</td>\n",
       "      <td>0.569577</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.324172</td>\n",
       "      <td>2.446358</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.855921</td>\n",
       "      <td>1.268671</td>\n",
       "      <td>1.450964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>F000_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/101222_D10_Coverslip1_Process...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.032741</td>\n",
       "      <td>0.102508</td>\n",
       "      <td>0.114925</td>\n",
       "      <td>0.351646</td>\n",
       "      <td>0.135021</td>\n",
       "      <td>...</td>\n",
       "      <td>0.631931</td>\n",
       "      <td>0.913233</td>\n",
       "      <td>0.169474</td>\n",
       "      <td>0.356332</td>\n",
       "      <td>0.522433</td>\n",
       "      <td>0.730840</td>\n",
       "      <td>0.242420</td>\n",
       "      <td>0.501220</td>\n",
       "      <td>1.100836</td>\n",
       "      <td>1.040675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>F000_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/101222_D10_Coverslip1_Process...</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.256089</td>\n",
       "      <td>0.230455</td>\n",
       "      <td>-0.008437</td>\n",
       "      <td>0.485189</td>\n",
       "      <td>-0.006212</td>\n",
       "      <td>...</td>\n",
       "      <td>0.774487</td>\n",
       "      <td>0.511029</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.039550</td>\n",
       "      <td>1.155751</td>\n",
       "      <td>1.047892</td>\n",
       "      <td>0.079289</td>\n",
       "      <td>0.091116</td>\n",
       "      <td>0.104703</td>\n",
       "      <td>0.218402</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>133695</th>\n",
       "      <td>225</td>\n",
       "      <td>258</td>\n",
       "      <td>F224_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/101222_D10_Coverslip1_Process...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.016898</td>\n",
       "      <td>0.946519</td>\n",
       "      <td>0.955163</td>\n",
       "      <td>-0.045599</td>\n",
       "      <td>-0.034391</td>\n",
       "      <td>...</td>\n",
       "      <td>0.243910</td>\n",
       "      <td>0.365390</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.096991</td>\n",
       "      <td>1.612524</td>\n",
       "      <td>0.836004</td>\n",
       "      <td>0.597099</td>\n",
       "      <td>0.537393</td>\n",
       "      <td>0.890798</td>\n",
       "      <td>0.737002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133696</th>\n",
       "      <td>225</td>\n",
       "      <td>259</td>\n",
       "      <td>F224_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/101222_D10_Coverslip1_Process...</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.118881</td>\n",
       "      <td>0.333881</td>\n",
       "      <td>0.735675</td>\n",
       "      <td>0.133157</td>\n",
       "      <td>0.714265</td>\n",
       "      <td>...</td>\n",
       "      <td>1.094172</td>\n",
       "      <td>1.108408</td>\n",
       "      <td>0.603334</td>\n",
       "      <td>0.314654</td>\n",
       "      <td>0.680284</td>\n",
       "      <td>1.457429</td>\n",
       "      <td>0.200216</td>\n",
       "      <td>0.107832</td>\n",
       "      <td>0.389603</td>\n",
       "      <td>0.697496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133697</th>\n",
       "      <td>225</td>\n",
       "      <td>260</td>\n",
       "      <td>F224_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/101222_D10_Coverslip1_Process...</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.058207</td>\n",
       "      <td>-0.021145</td>\n",
       "      <td>-0.112881</td>\n",
       "      <td>-0.116101</td>\n",
       "      <td>-0.113942</td>\n",
       "      <td>...</td>\n",
       "      <td>1.190447</td>\n",
       "      <td>1.224097</td>\n",
       "      <td>0.980566</td>\n",
       "      <td>1.063520</td>\n",
       "      <td>1.301983</td>\n",
       "      <td>1.526587</td>\n",
       "      <td>2.645751</td>\n",
       "      <td>1.242633</td>\n",
       "      <td>1.452134</td>\n",
       "      <td>0.919034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133698</th>\n",
       "      <td>225</td>\n",
       "      <td>261</td>\n",
       "      <td>F224_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/101222_D10_Coverslip1_Process...</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.068751</td>\n",
       "      <td>0.338446</td>\n",
       "      <td>0.076893</td>\n",
       "      <td>0.002022</td>\n",
       "      <td>0.339401</td>\n",
       "      <td>...</td>\n",
       "      <td>0.263019</td>\n",
       "      <td>0.577645</td>\n",
       "      <td>0.919920</td>\n",
       "      <td>0.704140</td>\n",
       "      <td>0.612588</td>\n",
       "      <td>1.229488</td>\n",
       "      <td>0.120038</td>\n",
       "      <td>0.191206</td>\n",
       "      <td>0.338359</td>\n",
       "      <td>0.523559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133699</th>\n",
       "      <td>225</td>\n",
       "      <td>262</td>\n",
       "      <td>F224_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/101222_D10_Coverslip1_Process...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.207050</td>\n",
       "      <td>-0.142933</td>\n",
       "      <td>0.979791</td>\n",
       "      <td>-0.173542</td>\n",
       "      <td>-0.168577</td>\n",
       "      <td>...</td>\n",
       "      <td>0.928728</td>\n",
       "      <td>0.557939</td>\n",
       "      <td>1.446617</td>\n",
       "      <td>1.325720</td>\n",
       "      <td>1.080990</td>\n",
       "      <td>1.125394</td>\n",
       "      <td>0.122647</td>\n",
       "      <td>0.252892</td>\n",
       "      <td>0.273385</td>\n",
       "      <td>0.544454</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133700 rows × 522 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ImageNumber  ObjectNumber  FileName_max_clean  \\\n",
       "0                 1             1  F000_max_clean.tif   \n",
       "1                 1             2  F000_max_clean.tif   \n",
       "2                 1             3  F000_max_clean.tif   \n",
       "3                 1             4  F000_max_clean.tif   \n",
       "4                 1             5  F000_max_clean.tif   \n",
       "...             ...           ...                 ...   \n",
       "133695          225           258  F224_max_clean.tif   \n",
       "133696          225           259  F224_max_clean.tif   \n",
       "133697          225           260  F224_max_clean.tif   \n",
       "133698          225           261  F224_max_clean.tif   \n",
       "133699          225           262  F224_max_clean.tif   \n",
       "\n",
       "                                       PathName_max_clean  \\\n",
       "0       /mnt/disks/store/101222_D10_Coverslip1_Process...   \n",
       "1       /mnt/disks/store/101222_D10_Coverslip1_Process...   \n",
       "2       /mnt/disks/store/101222_D10_Coverslip1_Process...   \n",
       "3       /mnt/disks/store/101222_D10_Coverslip1_Process...   \n",
       "4       /mnt/disks/store/101222_D10_Coverslip1_Process...   \n",
       "...                                                   ...   \n",
       "133695  /mnt/disks/store/101222_D10_Coverslip1_Process...   \n",
       "133696  /mnt/disks/store/101222_D10_Coverslip1_Process...   \n",
       "133697  /mnt/disks/store/101222_D10_Coverslip1_Process...   \n",
       "133698  /mnt/disks/store/101222_D10_Coverslip1_Process...   \n",
       "133699  /mnt/disks/store/101222_D10_Coverslip1_Process...   \n",
       "\n",
       "        Children_Cytoplasm_Count  Correlation_Correlation_C_DNA  \\\n",
       "0                              1                      -0.405568   \n",
       "1                              1                      -0.120659   \n",
       "2                              1                      -0.043058   \n",
       "3                              1                       0.032741   \n",
       "4                              1                      -0.256089   \n",
       "...                          ...                            ...   \n",
       "133695                         1                       0.016898   \n",
       "133696                         1                      -0.118881   \n",
       "133697                         1                      -0.058207   \n",
       "133698                         1                      -0.068751   \n",
       "133699                         1                       0.207050   \n",
       "\n",
       "        Correlation_Correlation_C_FLAG  Correlation_Correlation_C_HSV  \\\n",
       "0                             0.579144                      -0.029948   \n",
       "1                            -0.053589                       0.108147   \n",
       "2                             0.207603                       0.194595   \n",
       "3                             0.102508                       0.114925   \n",
       "4                             0.230455                      -0.008437   \n",
       "...                                ...                            ...   \n",
       "133695                        0.946519                       0.955163   \n",
       "133696                        0.333881                       0.735675   \n",
       "133697                       -0.021145                      -0.112881   \n",
       "133698                        0.338446                       0.076893   \n",
       "133699                       -0.142933                       0.979791   \n",
       "\n",
       "        Correlation_Correlation_C_NWS  Correlation_Correlation_C_Ollas  ...  \\\n",
       "0                            0.891888                         0.002846  ...   \n",
       "1                           -0.019662                        -0.006271  ...   \n",
       "2                            0.193065                         0.173620  ...   \n",
       "3                            0.351646                         0.135021  ...   \n",
       "4                            0.485189                        -0.006212  ...   \n",
       "...                               ...                              ...  ...   \n",
       "133695                      -0.045599                        -0.034391  ...   \n",
       "133696                       0.133157                         0.714265  ...   \n",
       "133697                      -0.116101                        -0.113942  ...   \n",
       "133698                       0.002022                         0.339401  ...   \n",
       "133699                      -0.173542                        -0.168577  ...   \n",
       "\n",
       "        RadialDistribution_RadialCV_Ollas_3of4  \\\n",
       "0                                     0.391393   \n",
       "1                                     0.605029   \n",
       "2                                     0.252914   \n",
       "3                                     0.631931   \n",
       "4                                     0.774487   \n",
       "...                                        ...   \n",
       "133695                                0.243910   \n",
       "133696                                1.094172   \n",
       "133697                                1.190447   \n",
       "133698                                0.263019   \n",
       "133699                                0.928728   \n",
       "\n",
       "        RadialDistribution_RadialCV_Ollas_4of4  \\\n",
       "0                                     0.781718   \n",
       "1                                     0.370029   \n",
       "2                                     0.569577   \n",
       "3                                     0.913233   \n",
       "4                                     0.511029   \n",
       "...                                        ...   \n",
       "133695                                0.365390   \n",
       "133696                                1.108408   \n",
       "133697                                1.224097   \n",
       "133698                                0.577645   \n",
       "133699                                0.557939   \n",
       "\n",
       "        RadialDistribution_RadialCV_S_1of4  \\\n",
       "0                                 1.212362   \n",
       "1                                 0.497995   \n",
       "2                                 0.000000   \n",
       "3                                 0.169474   \n",
       "4                                 0.000000   \n",
       "...                                    ...   \n",
       "133695                            0.000000   \n",
       "133696                            0.603334   \n",
       "133697                            0.980566   \n",
       "133698                            0.919920   \n",
       "133699                            1.446617   \n",
       "\n",
       "        RadialDistribution_RadialCV_S_2of4  \\\n",
       "0                                 1.064993   \n",
       "1                                 0.401728   \n",
       "2                                 0.000000   \n",
       "3                                 0.356332   \n",
       "4                                 2.039550   \n",
       "...                                    ...   \n",
       "133695                            1.096991   \n",
       "133696                            0.314654   \n",
       "133697                            1.063520   \n",
       "133698                            0.704140   \n",
       "133699                            1.325720   \n",
       "\n",
       "        RadialDistribution_RadialCV_S_3of4  \\\n",
       "0                                 0.758903   \n",
       "1                                 0.364964   \n",
       "2                                 2.324172   \n",
       "3                                 0.522433   \n",
       "4                                 1.155751   \n",
       "...                                    ...   \n",
       "133695                            1.612524   \n",
       "133696                            0.680284   \n",
       "133697                            1.301983   \n",
       "133698                            0.612588   \n",
       "133699                            1.080990   \n",
       "\n",
       "        RadialDistribution_RadialCV_S_4of4  \\\n",
       "0                                 1.124459   \n",
       "1                                 0.318315   \n",
       "2                                 2.446358   \n",
       "3                                 0.730840   \n",
       "4                                 1.047892   \n",
       "...                                    ...   \n",
       "133695                            0.836004   \n",
       "133696                            1.457429   \n",
       "133697                            1.526587   \n",
       "133698                            1.229488   \n",
       "133699                            1.125394   \n",
       "\n",
       "        RadialDistribution_RadialCV_VSVG_1of4  \\\n",
       "0                                    0.309291   \n",
       "1                                    0.627694   \n",
       "2                                    0.000000   \n",
       "3                                    0.242420   \n",
       "4                                    0.079289   \n",
       "...                                       ...   \n",
       "133695                               0.597099   \n",
       "133696                               0.200216   \n",
       "133697                               2.645751   \n",
       "133698                               0.120038   \n",
       "133699                               0.122647   \n",
       "\n",
       "        RadialDistribution_RadialCV_VSVG_2of4  \\\n",
       "0                                    0.368628   \n",
       "1                                    0.566441   \n",
       "2                                    1.855921   \n",
       "3                                    0.501220   \n",
       "4                                    0.091116   \n",
       "...                                       ...   \n",
       "133695                               0.537393   \n",
       "133696                               0.107832   \n",
       "133697                               1.242633   \n",
       "133698                               0.191206   \n",
       "133699                               0.252892   \n",
       "\n",
       "        RadialDistribution_RadialCV_VSVG_3of4  \\\n",
       "0                                    0.819462   \n",
       "1                                    0.769759   \n",
       "2                                    1.268671   \n",
       "3                                    1.100836   \n",
       "4                                    0.104703   \n",
       "...                                       ...   \n",
       "133695                               0.890798   \n",
       "133696                               0.389603   \n",
       "133697                               1.452134   \n",
       "133698                               0.338359   \n",
       "133699                               0.273385   \n",
       "\n",
       "        RadialDistribution_RadialCV_VSVG_4of4  \n",
       "0                                    1.144822  \n",
       "1                                    0.943922  \n",
       "2                                    1.450964  \n",
       "3                                    1.040675  \n",
       "4                                    0.218402  \n",
       "...                                       ...  \n",
       "133695                               0.737002  \n",
       "133696                               0.697496  \n",
       "133697                               0.919034  \n",
       "133698                               0.523559  \n",
       "133699                               0.544454  \n",
       "\n",
       "[133700 rows x 522 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpcp_df = pd.read_csv(f'{MP_CP_DIR}/MPFeat_SYTO_.csv', sep=',')\n",
    "mpcp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "b598c4e1",
   "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>label</th>\n",
       "      <th>Barcode_Idx</th>\n",
       "      <th>SUM</th>\n",
       "      <th>Barcode</th>\n",
       "      <th>RAW</th>\n",
       "      <th>P</th>\n",
       "      <th>LOGIT</th>\n",
       "      <th>ENTROPY</th>\n",
       "      <th>X_NORM</th>\n",
       "      <th>Gene</th>\n",
       "      <th>...</th>\n",
       "      <th>intensity_mean-4</th>\n",
       "      <th>intensity_mean-5</th>\n",
       "      <th>intensity_mean-6</th>\n",
       "      <th>Delta</th>\n",
       "      <th>embedding1</th>\n",
       "      <th>embedding2</th>\n",
       "      <th>True_Label</th>\n",
       "      <th>ObjectNumber</th>\n",
       "      <th>FileName_max_clean</th>\n",
       "      <th>PathName_max_clean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.057395</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.038489</td>\n",
       "      <td>0.670602</td>\n",
       "      <td>0.710910</td>\n",
       "      <td>0.864662</td>\n",
       "      <td>0.035280</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005333</td>\n",
       "      <td>0.000442</td>\n",
       "      <td>0.002411</td>\n",
       "      <td>0.007052</td>\n",
       "      <td>8.929145</td>\n",
       "      <td>8.983325</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>F000_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/101222_D10_Coverslip1_Process...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.067428</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.054958</td>\n",
       "      <td>0.815054</td>\n",
       "      <td>1.483194</td>\n",
       "      <td>0.793739</td>\n",
       "      <td>0.043299</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000203</td>\n",
       "      <td>0.006315</td>\n",
       "      <td>0.000928</td>\n",
       "      <td>0.009323</td>\n",
       "      <td>11.182019</td>\n",
       "      <td>8.415074</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>F000_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/101222_D10_Coverslip1_Process...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>intensity_mean-8</td>\n",
       "      <td>0.035084</td>\n",
       "      <td>D3</td>\n",
       "      <td>0.024482</td>\n",
       "      <td>0.697820</td>\n",
       "      <td>0.836939</td>\n",
       "      <td>1.319092</td>\n",
       "      <td>0.026690</td>\n",
       "      <td>PPP2R2B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001660</td>\n",
       "      <td>0.000317</td>\n",
       "      <td>0.014562</td>\n",
       "      <td>0.009024</td>\n",
       "      <td>11.592212</td>\n",
       "      <td>10.634103</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>F000_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/101222_D10_Coverslip1_Process...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>intensity_mean-3</td>\n",
       "      <td>0.261197</td>\n",
       "      <td>C12</td>\n",
       "      <td>0.179114</td>\n",
       "      <td>0.685744</td>\n",
       "      <td>0.780298</td>\n",
       "      <td>1.402972</td>\n",
       "      <td>0.108538</td>\n",
       "      <td>HSF1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.062781</td>\n",
       "      <td>0.061092</td>\n",
       "      <td>0.025526</td>\n",
       "      <td>11.081718</td>\n",
       "      <td>3.503708</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>F000_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/101222_D10_Coverslip1_Process...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.272067</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.267507</td>\n",
       "      <td>0.983239</td>\n",
       "      <td>4.071811</td>\n",
       "      <td>0.117349</td>\n",
       "      <td>0.142667</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002689</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.001225</td>\n",
       "      <td>0.071709</td>\n",
       "      <td>10.925993</td>\n",
       "      <td>1.490282</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>F000_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/101222_D10_Coverslip1_Process...</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>133695</th>\n",
       "      <td>258</td>\n",
       "      <td>intensity_mean-13</td>\n",
       "      <td>0.022828</td>\n",
       "      <td>E4</td>\n",
       "      <td>0.019829</td>\n",
       "      <td>0.868626</td>\n",
       "      <td>1.888866</td>\n",
       "      <td>0.667654</td>\n",
       "      <td>0.014764</td>\n",
       "      <td>HRAS</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003234</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000670</td>\n",
       "      <td>0.002336</td>\n",
       "      <td>10.888363</td>\n",
       "      <td>12.755734</td>\n",
       "      <td>0</td>\n",
       "      <td>258</td>\n",
       "      <td>F224_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/101222_D10_Coverslip1_Process...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133696</th>\n",
       "      <td>259</td>\n",
       "      <td>intensity_mean-13</td>\n",
       "      <td>0.326837</td>\n",
       "      <td>E4</td>\n",
       "      <td>0.114697</td>\n",
       "      <td>0.350931</td>\n",
       "      <td>-0.614949</td>\n",
       "      <td>1.860739</td>\n",
       "      <td>0.119025</td>\n",
       "      <td>HRAS</td>\n",
       "      <td>...</td>\n",
       "      <td>0.032583</td>\n",
       "      <td>0.009312</td>\n",
       "      <td>0.031562</td>\n",
       "      <td>0.001021</td>\n",
       "      <td>12.442838</td>\n",
       "      <td>2.811264</td>\n",
       "      <td>0</td>\n",
       "      <td>259</td>\n",
       "      <td>F224_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/101222_D10_Coverslip1_Process...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133697</th>\n",
       "      <td>260</td>\n",
       "      <td>intensity_mean-9</td>\n",
       "      <td>0.188926</td>\n",
       "      <td>D4</td>\n",
       "      <td>0.161259</td>\n",
       "      <td>0.853556</td>\n",
       "      <td>1.762765</td>\n",
       "      <td>0.616539</td>\n",
       "      <td>0.088308</td>\n",
       "      <td>FAN1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.003363</td>\n",
       "      <td>0.064340</td>\n",
       "      <td>0.027823</td>\n",
       "      <td>10.910836</td>\n",
       "      <td>4.071362</td>\n",
       "      <td>0</td>\n",
       "      <td>260</td>\n",
       "      <td>F224_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/101222_D10_Coverslip1_Process...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133698</th>\n",
       "      <td>261</td>\n",
       "      <td>intensity_mean-19</td>\n",
       "      <td>0.003397</td>\n",
       "      <td>G8</td>\n",
       "      <td>0.002275</td>\n",
       "      <td>0.669709</td>\n",
       "      <td>0.706868</td>\n",
       "      <td>1.034844</td>\n",
       "      <td>0.021284</td>\n",
       "      <td>RAPGEF2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005305</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>0.010320</td>\n",
       "      <td>0.001827</td>\n",
       "      <td>10.747713</td>\n",
       "      <td>11.903031</td>\n",
       "      <td>0</td>\n",
       "      <td>261</td>\n",
       "      <td>F224_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/101222_D10_Coverslip1_Process...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133699</th>\n",
       "      <td>262</td>\n",
       "      <td>intensity_mean-15</td>\n",
       "      <td>1.048392</td>\n",
       "      <td>E8</td>\n",
       "      <td>0.684923</td>\n",
       "      <td>0.653308</td>\n",
       "      <td>0.633613</td>\n",
       "      <td>1.344290</td>\n",
       "      <td>0.397249</td>\n",
       "      <td>WASL</td>\n",
       "      <td>...</td>\n",
       "      <td>0.141081</td>\n",
       "      <td>0.012467</td>\n",
       "      <td>0.004988</td>\n",
       "      <td>0.119821</td>\n",
       "      <td>10.127234</td>\n",
       "      <td>-5.068211</td>\n",
       "      <td>0</td>\n",
       "      <td>262</td>\n",
       "      <td>F224_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/101222_D10_Coverslip1_Process...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133700 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        label        Barcode_Idx       SUM Barcode       RAW         P  \\\n",
       "0           1   intensity_mean-0  0.057395      A2  0.038489  0.670602   \n",
       "1           2   intensity_mean-0  0.067428      A2  0.054958  0.815054   \n",
       "2           3   intensity_mean-8  0.035084      D3  0.024482  0.697820   \n",
       "3           4   intensity_mean-3  0.261197     C12  0.179114  0.685744   \n",
       "4           5   intensity_mean-0  0.272067      A2  0.267507  0.983239   \n",
       "...       ...                ...       ...     ...       ...       ...   \n",
       "133695    258  intensity_mean-13  0.022828      E4  0.019829  0.868626   \n",
       "133696    259  intensity_mean-13  0.326837      E4  0.114697  0.350931   \n",
       "133697    260   intensity_mean-9  0.188926      D4  0.161259  0.853556   \n",
       "133698    261  intensity_mean-19  0.003397      G8  0.002275  0.669709   \n",
       "133699    262  intensity_mean-15  1.048392      E8  0.684923  0.653308   \n",
       "\n",
       "           LOGIT   ENTROPY    X_NORM     Gene  ...  intensity_mean-4  \\\n",
       "0       0.710910  0.864662  0.035280   PGGT1B  ...          0.005333   \n",
       "1       1.483194  0.793739  0.043299   PGGT1B  ...          0.000203   \n",
       "2       0.836939  1.319092  0.026690  PPP2R2B  ...          0.001660   \n",
       "3       0.780298  1.402972  0.108538     HSF1  ...          0.004378   \n",
       "4       4.071811  0.117349  0.142667   PGGT1B  ...          0.002689   \n",
       "...          ...       ...       ...      ...  ...               ...   \n",
       "133695  1.888866  0.667654  0.014764     HRAS  ...          0.003234   \n",
       "133696 -0.614949  1.860739  0.119025     HRAS  ...          0.032583   \n",
       "133697  1.762765  0.616539  0.088308     FAN1  ...          0.000056   \n",
       "133698  0.706868  1.034844  0.021284  RAPGEF2  ...          0.005305   \n",
       "133699  0.633613  1.344290  0.397249     WASL  ...          0.141081   \n",
       "\n",
       "        intensity_mean-5  intensity_mean-6     Delta  embedding1  embedding2  \\\n",
       "0               0.000442          0.002411  0.007052    8.929145    8.983325   \n",
       "1               0.006315          0.000928  0.009323   11.182019    8.415074   \n",
       "2               0.000317          0.014562  0.009024   11.592212   10.634103   \n",
       "3               0.062781          0.061092  0.025526   11.081718    3.503708   \n",
       "4               0.000070          0.001225  0.071709   10.925993    1.490282   \n",
       "...                  ...               ...       ...         ...         ...   \n",
       "133695          0.000016          0.000670  0.002336   10.888363   12.755734   \n",
       "133696          0.009312          0.031562  0.001021   12.442838    2.811264   \n",
       "133697          0.003363          0.064340  0.027823   10.910836    4.071362   \n",
       "133698          0.000244          0.010320  0.001827   10.747713   11.903031   \n",
       "133699          0.012467          0.004988  0.119821   10.127234   -5.068211   \n",
       "\n",
       "        True_Label  ObjectNumber  FileName_max_clean  \\\n",
       "0                0             1  F000_max_clean.tif   \n",
       "1                0             2  F000_max_clean.tif   \n",
       "2                0             3  F000_max_clean.tif   \n",
       "3                0             4  F000_max_clean.tif   \n",
       "4                0             5  F000_max_clean.tif   \n",
       "...            ...           ...                 ...   \n",
       "133695           0           258  F224_max_clean.tif   \n",
       "133696           0           259  F224_max_clean.tif   \n",
       "133697           0           260  F224_max_clean.tif   \n",
       "133698           0           261  F224_max_clean.tif   \n",
       "133699           0           262  F224_max_clean.tif   \n",
       "\n",
       "                                       PathName_max_clean  \n",
       "0       /mnt/disks/store/101222_D10_Coverslip1_Process...  \n",
       "1       /mnt/disks/store/101222_D10_Coverslip1_Process...  \n",
       "2       /mnt/disks/store/101222_D10_Coverslip1_Process...  \n",
       "3       /mnt/disks/store/101222_D10_Coverslip1_Process...  \n",
       "4       /mnt/disks/store/101222_D10_Coverslip1_Process...  \n",
       "...                                                   ...  \n",
       "133695  /mnt/disks/store/101222_D10_Coverslip1_Process...  \n",
       "133696  /mnt/disks/store/101222_D10_Coverslip1_Process...  \n",
       "133697  /mnt/disks/store/101222_D10_Coverslip1_Process...  \n",
       "133698  /mnt/disks/store/101222_D10_Coverslip1_Process...  \n",
       "133699  /mnt/disks/store/101222_D10_Coverslip1_Process...  \n",
       "\n",
       "[133700 rows x 33 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = mpcp_df.columns[:4]\n",
    "soma_df[cols] = mpcp_df[cols]\n",
    "soma_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "e4b1677d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removing 10353 Cells\n"
     ]
    },
    {
     "data": {
      "image/png": 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IIc0glzOpx5cupXFHCEkHzO5LCOkgkjTwlgE45Fkfc7Z5+QiAt4nIGIDvwMSUz6GuuVwIIYQQQoC2SrJCCCHVaHUWzesBfEFVlwN4DYB/EZE5ZeJcLoQQQgipCdsGpqdNJl83fHxsDNi+ndEFMeF9xISQ9JCkgTcOYIVnfbmzzcsNAG4FAFXdDeD5ALoTLBMhhBBC2p2gMXe5nMnoq0qrJAY4rJGQ9JKkgbcHwIUi8kInje82ALf5jjkIM38LROQVMAYeYzAJIYQQUj9hY+5olVSmBpcchzUSkl4SM/BUtQjgvQC+D+ARmGyZD4nIR0Xk9c5h7wfwmyKyH8BXALxLlV1qhBBCCGmAsDF3tErCCTN+Q4w+DmskJL0kOtG5qn4HJnmKd9uHPf8/DGBTkmUghBBCSIcRNrG5a5UMD9Mq8RNk/C5ZYow+93kNDZlQV7Tj3PGEtA+tTrJCCCGEEBI/7pg7r+UhAtxxB7B3r7FOaJWUCHLJVfF4Bj1iQkjroYFHCCGEkM7AtoGrrgJWrzaeKY7BKxGUaZRxmIRkkkRDNAkhhBBCYsO2G4sJDPJI9fbGX86s4rrkXBiHSUgmoQePEEIIIeknjgyY9EjVDuMwCckc9OARQgghJP3E4X2jR4oQ0gHQg0cIIYSQ9BOX940eKUJIm0MPHiEkfhodJ0MIIX7ofSOEkEjQg0cIiZc4xskQQkgQ9L4RQkhVaOARQuKlyrxJhBDSUmwbmJ4GVFtdEkIISQQaeISQeElJljoR+ZyIHBaRB0P2bxGR4yKyz1k+7Nl3tYg8JiKPi8gHmldqQkiiMMKgLmgTE5ItaOARQuIlaLLc1vAFAFdXOWanqq5ylo8CgIjkAXwSwKsBrARwvYisTLSkhJDmwAiDmqFNTEj2oIFHCImfFIyTUdW7AByr49T1AB5X1SdU9RSAmwFcE2vhCCGtISURBlmCNjEh2YMGHiGkk+kXkf0i8l0RucjZtgzAIc8xY862QETkRhEZEZGRGWo+hKSb9EQYNISIPCkio054+UiS96JNTEj24DQJhJBO5QEA56vqMyLyGgDfAnBhrRdR1U8D+DQArF27liNUCGkmnT0ly1ZVPRLrFQOeJ2enICR70INHCOlIVPWEqj7j/P8dAPNEpBvAOIAVnkOXO9sIIc0gakaPegaHcUBZOBWeTQqi7gkhNUADjxDSkYjIUhGjrojIehh5eBTAHgAXisgLReQ0ANsA3Na6khLSQdRigNUzOKx9BpQpgB+IyP0icmPQATWHjyf0bJiBk5DmQwOPENKWiMhXAOwG8DIRGRORG0TkJhG5yTnkzQAeFJH9AP4BwDY1FAG8F8D3ATwC4FZVfagVv4GQjqMWI6OewWHtM6Bss6qugcn2+x4RGfQfoKqfVtW1qrp2yZIl1a+YwLOhw5SQ1sAxeISQtkRVr6+y/xMAPhGy7zsAvpNEuQghFXCNjOHh6kZGPYPD2mRAmaqOO38Pi8g3YbL/3tXQRRN4NkH2em9vw5clhFSBHjxCCCGEpINas1zWMzgs4wPKRGS+iCx0/wfwKgAPxnLxmJ9N+zhMCckW9OARQuKnszPbEUIawTUykiL78qkXwDedIcRdAL6sqt9rbZGCaROHKSGZgx48Qki8cNAFISSttIF8UtUnVPUyZ7lIVf+y1WWqRMYdpoRkEhp4hJB4aZ8sdYSQdoPyiRDSAdDAI4TECwddEELSSnc3MH+++X/+fLNOCCFRyci8HzTwCCHxUmuSBEIIaRZHjgDPPGP+f+YZs04IIVHIUIg3DTxCSPxw0AUhJI10dwMLFpj/FyygB490LhnxRKWKDIV408AjhBBCSGdw5Ajw9NPm/6efpgePdCYZ8kSligwNQeE0CYQQQgjpDLq7gYULgePHzV968Egnwhno6yND837Qg0dIJ8LQDEJIUqRZvhw5Ajz7rPn/2WfpwSOdSYY8UakjI0NQaOAR0mkwNIMQkhRply9UbAlhMrQOgCGahHQaDM0ghCRF2uVLhkKsCEkU1xNF2hJ68AjpNNiDTQhJiizIl4yEWHUqaY7wJSQr0INHSKfBHmxCSFJQvpAGcCN8h4dN/8DQkLHHCSG1wWZDSCfCHmxCSFKkXb7QRVSdFj2jDE0zRkiqoYFHCCGEkM4g7Ulg0kALn1EWInwJyQI08AghjeHt6WXPOCEkzdBFVJ0WPiMmdyQkHmjgEULqx9/Tu2ULe8YJIemFLqLqtPgZpT3Cl5AswCQrhJD68ff0qgKWlc706IQQojp3oSVRDhPlEJJ56MEjhNSPv6eXPeOEkDQzMwPs3m06onbvZohmGHSjEZJp6MEjhNSPv6dXlb2+hJD04nZKuXn42REViG1TlBOSZejBI4Q0hren1/1flclWCCHpg1k8qtIOiUaZ74t0OjTwCCHx0g7aASGkfWH4YUX8Q6sPHzbbs2I08RNECA08QkjcpCQNuYh8TkQOi8iDIft/TUQOiMioiAyLyGWefU862/eJyEjzSk0ISYysWCgREZG8iOwVkf+M65q2bR5Pf7+xf4tF4Nprzd+sGE0p+QQR0lJo4BFC4iU9aci/AODqCvt/AuAKVb0EwF8A+LRv/1ZVXaWqaxMqHyGkWbSnW+d3ATwS18XcR7RiBXDqlHF0AiYXzaOPZsdoSs8niJDWQQOPEBIvKRnjoqp3AThWYf+wqj7lrN4DYHlTCkYIaT5t5tYRkeUAXgvgM3Fd0/uIRkaA9etLRtLKldkxmlLyCSKkpSRq4InI1SLymIg8LiIfCDnmWhF5WEQeEpEvJ1keQkiTyN4YlxsAfNezrgB+ICL3i8iNlU4UkRtFZERERmYyrjQS0ra0n1vn/wL4IwChrshaZZP3EW3aBOzcWTKScrlsGU3Z+wQREi+JTZMgInkAnwTwSgBjAPaIyG2q+rDnmAsBfBDAJlV9SkQyL3EJIdlCRLbCGHibPZs3q+q4I5NuF5FHHY/gHFT103DCO9euXdseg3sIaRGJpedvo8m7ReR1AA6r6v0isiXsuFplkwhwxx0mHHPlypKR5OJfJ4SklyQ9eOsBPK6qT6jqKQA3A7jGd8xvAvikGyalqocTLA8hpF7aLDmBi4hcChPidI2qHnW3q+q48/cwgG/CyDNCSIIkPkyufdw6mwC8XkSehNGtrhSRf230orYNXHUVsGqV8eBZVqNXJIS0iiQNvGUADnnWx5xtXl4K4KUisktE7hGRwIQIDIEipIW0Z3ICiMh5AL4B4O2q+iPP9vkistD9H8CrAARm4iSExEebDZNLDFX9oKouV9ULAGwDcKeqvq3R687MALt2GcPunnuAgYG2EfeEdBytTrLSBeBCAFsAXA/gn0XkDP9BqvppVV2rqmuXLFnS3BIS0ulkVOsSka8A2A3gZSIyJiI3iMhNInKTc8iHAZwN4B990yH0ArhbRPYDuA/At1X1e03/AYS0Of7AgPYbJpctenqAdetK63v2hIv7RoI62jQghJBUkaSBNw5ghWd9ubPNyxiA21S1oKo/AfAjGIOPEJIWMqp1qer1qnqOqs5zers/q6qfUtVPOft/Q1XPdKZCmJ0OwQkrv8xZLlLVv2ztLyGk/QgKDGD2w9pR1e2q+ro4riUC3H23mQMvTNzbNjA5WX9QR5sGhBCSOpI08PYAuFBEXigip8GEEdzmO+ZbMN47iEg3TMjmEwmWiRBSK9S6CCExExYY0D7D5LJJPm+MvCBx750nb+fO+oI6MhoQQkjmSMzAU9UigPcC+D7MRJy3qupDIvJREXm9c9j3ARwVkYcBDAH4Q2+iA0JISqDWRQiJkUiBAXHG8jEuMDJh4t41zizLPMZ6gjoyGhBCSOZIdAyeqn5HVV+qqi92w5xU9cOqepvzv6rq76vqSlW9RFVvTrI8hBBCCGk9VQMD4ozlY1xgLHiNs4EB4NCh2oM6GBBCSHNodZIVQkg7wt5yQkgVKgYGxBnLx7jAyFQS3V7jbMcOYOnS+gw0BoQQkjw08Agh8cLeckI6iyQ6dOKM5fNfS5WdTwHMEd1F571a1uz7pXFGSDaggUcIiRf2lhPSOSTVoRNnLJ97rYMHjWG3YgU7nwIoF92KmYE3mfd69tnAsmV8ZoRkCBp4hJB44Sh6QjqHGDt05jgC43QX5XJm2b2bnU8hlInudQX07Pm2eVbHjxsvHp8ZIZmBBh4hJF44ip6QziGmDp3EI7tt21iOlSZ563DKRPfd8yCbnPe6eLGZP4HPjJDMQAOPEBI/HKhBSGcQU4dOYpHd3pm5V6ww5Tt4kJ1PIcyK7pznvR49CoyPN/bMmHiLkKZCA48QQggh9RNDh04ikd1hM3PncjTuouC+13y+pvc7x5Zj4i1Cmg4NPEKyCHtDCSFtRCKR3XHMzE1q+twE2nJMvEVI06GBR0jWYG8oIaQd8FkOsUd2xzEzd4dT6+cm0Jar4p5lfyUh8UMDj5Cswd5QQkjWiWo5NKL9xzUzdwdT6+cm0Jar4J5lfyUhyUADj5CswWkICCFZJ4rlEIf2z4RPNeO1qWv93ITaciHvgf2VhCQDDTxCsganISCEZJ0olgO1/6ZTLAKbN5ds6qkp4M47a/vc1GJT12pAMpyTkGjQwCMki7BXmhCSZaJ0VDFaoanYthmq6M4Fv3OnSUB65ZXAkiXJfG5q6a9kOCch0aGBRwghhJDmU62jitEKTWVmBtizp7SuahKQJu08jdpfWatDl94+0snQwCOEEEJIOmG0QtPo6QE2bTLT3vX1mVDNNDlPa3HoZtbbR6s0GTrwudLAI4TETwcKU0IIyTKuw3R83HjIduxIl/O0FoduJodvZtYqjZEkdIcOfa408Agh8dKhwpQQUifN7hBiB1QoXodpGp2nUcuUyeGbmbRKYyQp3aFDnysNPEJIvExPm9H57ij96elWl4gQklaa3SHUJh1QIvJ8EblPRPaLyEMi8udxXbsd7N9MDt/MpFUaI0kZYh36XGngEULiRaSkGahm5MtKCGkJze5db5/e/JMArlTVywCsAnC1iPQ1etGK9m+TLL+4bpNGD2RFMmmVxkhShliWnmuMbYwGHiEkXnp7Ta7tfN787e1tdYkIIWml2b3rbdKbr4ZnnNV5ztKwVhhm/9pFG9Ob3gRdlqzns00crPWTOas0RpI0xLLwXGOu/DTwCCHlNNqDJGKE8/i4GaWfZoFKCGktze5dz1JvfhVEJC8i+wAcBnC7qt7r23+jiIyIyMhMRE9lkP1r28DWwSKW3/NVbLFuh71rd2Kez6oO1naIHyXhZMEQS4qYowto4BFCSsTVg5QSIS0inxORwyLyYMh+EZF/EJHHReSAiKzx7HuniPzYWd7ZvFITkhGyGkuXEvnUKKpqqeoqAMsBrBeRi337P62qa1V17ZIlSyJdUwS44w5g715jB4s4eueeeShiHoaxCTPrXpOY57Oig7Xj3XukrYk5uoAGHiGkRPuMT3H5AoCrK+x/NYALneVGAP8EACJyFoA/A7ABwHoAfyYiZyZaUkKyRBaU7Q7x9qjqzwAMobKsi4RtA1ddBaxebV6vbbt6p6CrS7GxX9Gz65uJGccVHazt930ipETM0QU08AghJdpkfIqLqt4F4FiFQ64B8CVnPMs9AM4QkXMA/DJMyNMxVX0KwO2IQXkipG3wK9vT0+kyprJggDaAiCwRkTOc/18A4JUAHm30ukE2VEnvFGzfdRokl6znM9TB2mbfp0h0SCcFcYgxuoAGHiGkRFw9SNn5KC0DcMizPuZsC9s+h3rGuRCSebzKdn8/sG1buoyp9vf2nANgSEQOANgD0yH1n41eNMyGSkVUaxuNn4xEm3dSkGTpanUBCCEpw/2S14v7URoeNhrC0JC5Zpuiqp8G8GkAWLt2beotWkJiwVW2Z2ZMR86KFeXGVD0yxLbNuT09jSvvrqXiyqE28/ao6gEAq+O+rve1xvEaYqfO71OcVatpBHVSMCs1iUj7al2EkNaQrZ7zcQArPOvLnW1h2wkhLq6y3dvbeOhc3N6KTvP2xEglb51tA9OTNnSq+REa/sCQqIEimXWEdWJIKokNGniEkHjJ1kfpNgDvcLJp9gE4rqqTAL4P4FUicqaTXOVVzjZCmkomop3jMKaS6BhKRVxhtigWgQcfDDaCjKGkWL7MxpZzHoN9xdamWUt+I61YjG60ZavP0QM7KUgD0MAjhMRLij5KIvIVALsBvExExkTkBhG5SURucg75DoAnADwO4J8B/A4AqOoxAH8BM7ZlD4CPOtsIaRqZ8jw0akxlq2OoLSkWge5u4JJLgLPOMuteZg0l7cIw+jG963FMP3x0TudDLZ0SUY/1G2mPPhrdaMt01cpKJ0UmeqI6Cxp4hJD4SclHSVWvV9VzVHWeqi5X1c+q6qdU9VPOflXV96jqi1X1ElUd8Zz7OVV9ibN8vnW/gnQqmfU81EOKOobiQETe2+oy1MrDDwPHj5v/jx83615mDSUpYiN247oF/4nlq7tnOx9sG5icjN4pUUsHht9IW7kyutHWZlUrfWSqJ6pzoIFHCCGEpJBMex5QR6d+SjqGYuLdrS5ArfjnQu/uLl83k6AL9u7L4eb9r8Dun1+GYlFmZ8nYutXk2tm5M1qnRC0dGP4J2HO52oy29qpaKaOjeqKyAw08QgghJIVk2fOQ6k59hpMFsnQpsGmT+V8EuP768vc2Own65Tlc994lzuTnpvNBxOj2lmUea5ROiVo6MIImYKfRlhKy3hPVpkSaJkFELgTwVwBWAni+u11VX5RQuQghhJCOp9FZS1pFajO8F4vAwACwZ4+xZpKbxuVSETkRsF1gosMXJXHTRhABvvpV4Lzzgt+b953u3g0cPGgeXXe32dffb7b39wO33lrd+KplSobU1ieSgbk1OpOoUu3zAP4JQBHAVgBfAvCvSRWKEEIAQETeICLsDiQErXE81XvPVHTqB+XVHxwE7rnHuJp27UoynGxUVRcFLAvTaNy5LF0a/t7873TpUhPWeeWVJjRTxBh9O3aYfVH0/KheuFTUJxJOu7lT28DLH9XAe4Gq3gFAVPWnqvoRAK9NrliEEAIAeBuAvSLyYxH5oojcKCIXt7pQhDSbVoQ8NnLPloeXBhV+ZsZ47lzWraOl4EPUxtDN0xg7pHPem/+dqppELLt2lTxruVx979qrTwfp1i2vT6RzSHV8eXSiGngnRSQH4Mci8l4ReSOABQmWixBCoKpvVtVlAF4JMw/dpQC+KCIzIvKd1paOkObRijwGjd6zpZ36QYX3uoH6+41lklzhvprUhRPDtmFvuRIzK9ag59otEJ2r2LrvVNXowKtXAwsWAPl8fHPcb9kSrFs3Wp/awClDmkGzhG1QhEGMFTSqgfe7AE4H8N8AXA7Tq/6OWEpACCFVUNUnATwAYC+AfQAOA3hBC4tESFNpRYha6D2zoCkHFd7rBtq1K6mxdy7niMg/hC1J3rhe7OkZbN35USy3nsSWnX8Be3quYuu++sOHSzrws88C+/bFN8d9Erp1TU6ZLNRvkhzNELb+Clksxu41jJRkBcAFqroHwDMAfh0AROQtAO5tuASEEBKCiPwxgH4ASwA8BuAeAJ8AcKOqWq0sGyHNpBV5DALv6Somw8NG+UkuSUljhD2w5mWtGal+SLqYkR4My9lmInPZiBnJoxel6NbubjPezn31blKVjRuBiy5qfI5797qqpevGpVtHTtLSxPrtPldv9QzaRppMM4Stv0I++mjsWYSiGngfxNxwg6BthBASJ+8A8CyA/wAwDOBeVT3e2iIR0hrqtk0a0Brn3DNL6QxbmIJUVb/Ykhs3QE+voH9zHsPDiv6NefT0Spm9s3YtMDJSevVuFs1GdWC/Pq0av27tNyJDDccY63elZhdkRwLZ6DvpCJKWHf4KuXJlxAoanYpVR0ReLSIfB7DMF17wBZiMmoQQkhiq+nKY8XcjALYA+KaI3Cci/ywiv97SwhHSAE2LAos7YQDTGUZCRDaLyDs8618TkTud5cpWli0MVUBEZhfX0HLtnT17TF4abxbNesbEBdV97/i6JMZuRk7SUmv9DmnI1ZpdkB3J+cI7CH+FzOXM+sGDwC23xHKLan0DEwDuB/Cc89ddbgPwy7GUgBCSGO0wlEBVj6nqfwL4MEqRA1sBfKalBSOkTpqapC1urbGWdIatEkDpEHx/jvIwzZcB+EMAHwHwR60oUDWq5aXZtAnYuTN6Jsug19DKBIWRDMda63fIj6nW7ILsSPaddBhBFXLbNjPnSAyNo6KBp6r7VfULAF6sql/0LN9Q1acaujMhJFFamum3WAQefLDhm4rI60Xkr0VkJ0xilb8D0A3g/QCWNl5QQppPU3vqk9Aao2jKrRJA6UlxvkhVH/as/1hV71fVuwAsbFWhKlEtL8327SZbZhTvWthr8Nf96ekUzu0Y1YVYoSFXa3be53rnnSZpDcCpIDqa6WnTg1Ismr/T0w1drlqI5qiIHADwgIgc8C8N3ZkQkiiVlMhEO7iLRTMa/5JLgLPOMuv18y4AMzA93ktVdUBV/4eq/juiZwEmJFU0tae+VROINWrF1iuk0hPndoZ3RVXf5FlN5aBF1bkLUF/IZNhr8M9UsW1bduZ2nEOFhhyl2eVypYni3fIA7TVfOKkBkVKjM/HSDV2umoL0OgC/UmEhhKSUsG9P4h3cjz4KHHfyoBw/btbrRFXfpKr/W1V3q+opETlDRG4QkTtgpkwgJHM03eZqxYR0jVixjQip9MS5PSoir/VvFJHXwWQETh2uUWZZZhYJ16tUD2GvwVv3b701e3M7llGlIUdpdk3tj0hH6DIJo7cXGBgwbvKBgYaTvFQL0fypuzibLnT+PwzgWLWLi8jVIvKYiDwuIh+ocNyvioiKyNqaSk8ICSXs25P4B2XlSmDxYvP/4sVmvQFE5AUisk1EbgMwCuB/A/gLAMsbLCkhLaOlk4DHQFVdsRErthEh1SqP5Vx+D8DHROTzIvI+Z/kCgI85+1JHdzcwf77537aBa6+da1tHtREqvQa37vf2pmhux3ppsCE3rT8iPaHLJAwR4Ic/NMvQUOIePOee8psAvgbg/zmblgP4VpVz8gA+CeDVAFYCuF5E5mh6IrIQZiJ1zqlHSMwEfXsS/6DkcsCRI8DoKHDsWEN5nkXkywB+BJNJ8+MALgDwlKpuV1V+oQhpAZF1xXqV30aFVAqsZ1V9HMClAHbCyK0LANwF4FJV/VHLClaBI0fMpOUuu3d7bGvbhj05ja1bNbKN4L4G1WCjsJm2uGuYAnXcM0HPV9OeQXpClzubSnXp1CnTy7J1K3D22Y0Ob4k8huU9ADYBOAEAqvpjANUk7noAj6vqE6p6CsDNAK4JOO4vAPwNTKZOQkjCNOWD0tUFXHxxHJP4rATwFIBHADziTG7O+BJCWkjiumJ6vHANoaonAQx5lu2q2rCuIyIrRGRIRB4WkYdE5HcbvSbg2tUKQCGiJdvasehnVqzB8E6rpvderTOgGba4vwxADfdsguerKf0R6Qld7lwq1SXbNu/l6afNeoPDW4DoBt5Jx0gDAIhIF6orWcsAHPKsjznbZhGRNQBWqOq3K11IRG4UkRERGZlhrwMhDZOCDu5IqOoqANfCZJ37oYjcDWChiKQySQEhsdDisTLVbt8UXTErQioEEVkkIrcC+CGAXwfwbhgZ9lURWdTg5YsA3q+qKwH0AXhPUIRUrYja2I6tmMitwMT6N2L7nbZ5/I5F32NNYKMOo6tLI7/3NDiO6i2DbQPTDx+F7go+OVND2tqk06Rh6nlpcb3oShVxZgbYt6+0ns8DL395Q7eLauDtEJE/BvACEXklzDxU/9HIjUUkBxOL/v5qx6rqp1V1raquXbJkSSO3JYRkDFV9VFX/zJn0/HcBfAnAHhEZbnHRCImfFo+ViXL7VOuK6dG6/wHAwzC5C37VyaL5YphxxJ9o5MKqOqmqDzj/Pw0T4bCs8lkRmJlBbvcunGOPY+n934YcKU99KV1dGBr4MMYORX/vSXYGRH3V9ZRhth2s7saWBXtg5+eVnZzJIW3VOk3S03aSoZ6XFueLrlQRe3qAdevK79tgb0hUA+8DMKnKRwH8FoDvAPiTKueMA1jhWV/ubHNZCOBiANtF5EmYXqjbmGiFEBKGM4/U+2EUpe+1ujyExE6LXR5Rb59KB1u6tO5NqvoR71hhNXwUQH9cNxGRCwCsRkAeg5qjnyKkvsztGELvUon83pPqDKjlVddThlI7EAw/exmmHxjH9C3boRDf/jYY0mbbwORkmtpOMtTz0uJ80ZUqogjw9a+X1pswTYJzH7Vhkqr8jqq+WVX/WbWqib8HwIUi8kIROQ3ANgC3ea55XFW7VfUCVb0AwD0AXq+qI/X8EEJICBnulXPCnD4oIp8QkVeJ4b0wiVcub3X5CImdKu6GpJtzpofqRFHGXGV2aqqVMjEWM0dEFgD4OoD/rqon/Ptrjn4KmwgPaMiiDzu1kbpcq95da/HL24HguvcuwfIVMmv7ZLqdeHEt5RUrSpNsZ95iDaGel5bUiw6q/Oec07xpEhxl6iMicgRm3pbHRGRGRD5c7cKqWgTwXgDfhwkfuFVVHxKRj4rI6xsqNSEkGunq0a6HfwHwMpjogd+ASVTwFgBvVNWgpE2EZJsKvbzNaM6pDr+sRjVlzLbNgzv3XLMkKxOHReTDIuVPUET+FMDuRi8uIvNgjLt/U9VvNHo9AEap373bTIRXlkIzfhqty5H07gYsSG87uOUW8zi8tk+m24kX7+SHqm1gsVagnpcW54t2K/2yZSZLpr/yi5h7jI8DO3Y0XKmkkiNORH4fZpqDG1X1J862FwH4JwDfU9X/09Dd62Dt2rU6MkInHyGRmJ42QqRYNIJ7bKzhXqEkEJH7VXVOeLaIjKrqJc7/eQCTAM6LIxNdElA+kSSptzm7wzl6ejKsiFbD/ZHd3Sbff9CPnZw0hp1LxIcYJp+qnLMIwGcBrAGwz9m8CsBeADeo6vFarue7tgD4IoBjqvrfo5wTSTapGoVzeNgo+TFYLmF1L45PU8V67SrT7m8ZGqo7q3PQY1GNt021rI16f1x/v5l9PnWx1zWSVoHnrfQuDepllWRTtdr+dgDXu8YdAKjqEwDeBuAddZWGENI8sh9HUnD/caZIGKvFuBORq0XkMRF5XEQ+ELD//4jIPmf5kYj8zLPP8uy7zX8uIc2moWQRDXj9Uh/l7f2RV14JLFkSrNj5t61dm5hMVNUTqvoWAK8C8AVneZWqvhkmq2YjbILRz670yKjXNHjNmrwVUepEpboXx6epYthljGOn/I9FNV5PeqQ2mlQj9P64HTuApUvTZRTVSpqjltxKn88DixcnrpdV8+A9qKoX17ovSdhDTkiNpLU3y0MFD54FwJ16VwC8AMDPnf9VVUPTjTseP3eS9DGYccHXq+rDIce/D8BqVX23s/6Mqi6o5XdQPpGkqbU5V/OUVLtejI6Q5IjqDlIFrrgC2LXLZKwbHo70Y+rx4FW53kFVPS+u60UhqmyKUr+i1onJSTO0y7Iq171KTte6qcUbWWOjijswpur16m2EGfj2x06aopaCnn/Mlb4RD96pOvcRQtJCKtPdRUNV86q6yFkWqmqX5/9qc0mtB/C4qj7hzON5M4BK4/auB/CVuMpOSCXq7ZBvLFlEeWdxlM7uTGQLjOoOcse4TEyYQVWts1RTKYyjOj+i5rLZtq10jf7+ua8llzPO1iuvTMDhEtUbWYfHp1qbqrVdV62+9TTCNHuykiQtUUthz98V4Pl8/JmHfFSTbpeJyImA5WkAlzR8d0JIe5KOmK5lAA551scQMleUiJwP4IUA7vRsfr6TYvweEXlDYqUkHUdU3SuOZlRJz42iN5brS4oebXm7nkstiRDS0eGVsgdoiGpHRNGh3Wu5eTs++cnG7lkXUd51HQUIq2712lRVq289RksmemYSIC3Zb1JglFc08Hy954t8PenzGrozIaQ9yWbP4TYAX3PG+bmc74Q+vBXA/xWRFwedWPNcU6TjieoBiasZhem5UfTGWX3poI3tugWyIqXtOh2G2ywi8nSFDvJzq16gBdTiCK2mQ5trKbq6FPPnK1avNtVmcrK8fyAxh0sNs6Db/ZswnT8X2h+9AEHVrRGbqmL1rcdoSYsnqxWkQRakwChPWyQ9ISTrpKfncBzACs/6cmdbENvgC89U1XHn7xMAtsNMJjyHmueaIh1PLR6QJJtRVL0xlwN6czOQ3alo1/XTxMgCN4w8pIO8K/EC1EGcjlBRG0O6FXvtVXjmuAXLMtOsrVgxNzN87A6XGnpHbBVslSEslzFske2wtf4CRGnXdVfBGowW2wamDwv0zhR4slpFq6OIUmCU08AjhMRLenoO9wC4UEReKCKnwRhxc7JhisjLAZwJz9xUInKmiDzP+b8bJmtdYHIWQmolugck+WYUWW9MT7uuj2xGFqQaV4e2rABdemYGud27cJF9AJswjK68QtUc6+8fqNfhEqrD19A7Yg4VFIuC4WFpqN9CBLjjDmDvXtO+g4ZX1VsFo9orZfe4Mgd7SXq82k0jLW291opdrQLVevuGziaEED8piYFX1SKA9wL4PoBHANyqqg+JyEdF5PWeQ7cBuFnLUwq/AsCIiOyHmVz9r8OybxJSD1U9IOloRpEK1OrO8kikJ7Igtdi2STJ67rnmbyW92KtDn322mbu5TJd2OgSkqwtDAx/GoUPAwEB8/QOl+yu2bDoF26ov7jPOfgvbBq66Cli92pTN//zqrYK12Cus5qj8ENIsrNwXvWpVLIZpxWkS0gjTkBPSfsSdhrxVUD6RTiMT0ygADU3i3Q7yKYpsOnQIOM8zecPBgyakMohIczb70sTHmbXf3F9RLAq6UMBY31vQu+sbpcpXw83iKle1DP3VqmAck8InMFd99gh7CGkXVj/9KXDBBaX1J58Ezj+/4imNTJNASGZJc0cNIaR9Sbvs8ZevkfK21GNQS8FT5xJNHz/+MVBK8Km4997wR+v1fC1ebLK+z/GA+dzUjYRiTk4CU1Ol8vT0ABvXFdCFAjZiF3r2fLvuuM+4cnJU8wZWqoJxTQrPao7wh5B296a/A6bBzmIaeKQtSUsINiGks0i77PGXr1hsrLwtG5pXz4NOQ3a9FPPyl5Y/w7e8RUMfrVeHPnoUGB9PxqCwbfN6zz3XLG55RIChnfMw1vcWbM+/ErKp9eNCoxhXYVWwku1Rq9HGao7ghxCDsLKLNqYfnIHaCfTeXXNN5fUaoYFH2pK0d9QQQtqTtMsef/kefbSx8rbMY5D2B51BenMzWITjKHnxpOKjrTZn8xzqcBW7rxkwp3nLk8sLend9AzKeHndVvcZVNduDRlsMNCis7KKNrd0HsPySM7DlrP2wizH33nV1AU8/DXzsY8DJk2a9AWjgkbYk6wnfCCHZpKWyJ4IC7S/fypWNl7clyieFfOwcRg+exmIAbkilxvZo7aKN6U1vgi6rzVXsvmbA1K9qYaAuxSLw4IPRbpOGkGqGVjaJBoTVzKNHMXz8IhQxD8PHL8LMo0fjLVuxaLIV/f7vA0uWlA9wrQMaeKQtobAkhLSCarInMWUyYsiiv3y5XEZlJYV87CgE3mr5wAMSy6O1bWDrYBHL7/kqtli3w961O7LHVcS83okJs0QpT7EIdHcDl1wCnHVWZT05DSHVrkwQib+jJA3Ga7vQs7IbGxc/ZMZ9Ln4IPSu7473Bww8DJ06Y/0+cMOsNQAOPtC0MaSCENAO/EhUmexJVJmsIWfSXL7OyMrMFTycmoaD7LCW2RzszAwzvmWc8H9iE6bWvxbT2QLW6AeJmlly61CzVymPbwN13A8ePm/Xjx00YcsWytTDSN0mZkMi1O9hilJxg6MilGBv9GbYfuwySa6BxBD3Hs88uP2bJkvqvDxp4hJA4qSUuhpA2IDVzVLVbyGKcimQHK6W10NsLDA6aKjQ4GJ6Kv1ZM1RR0dSk29imuO+0bWL5CcMUVldtOrQZKsQhs3gz80i+ZcYGAyfC5cmXw8bZtqkR/f+uaTZIyIfZr12MxNqvtNek+ua4cei9e0rhx53+Otg289a2lHoyBAdOj0UhZGzqbEEJcaomLIaRNqEWJqssGi6q4tFPIYpyuhzTE4GUEEeCOO4C9e01VCqtC3ioZpXqWriv48tdOw/CwzLYXb9uZni6/Vi1tq1gENmwAdu8GLMts+/rXTYbPoKnO3GqxYoUp38GD0ZtNnLZEkv0ysV+7VouxWW0va2086DlOTgK7dplK1dUF3HprwzKcBh4hJB4efTR6XAwhbUKic1TVqri0S8hinK6HVsfgZQjbBq68Eli1Kry6eauk3wNXLAYbPrYNXHWVue7KlaXrXn45sHataTv9/cC2beVVPWrbsm3jcXzggdK2008Hrr3W/J6g3+GvFrlcaS7saiGjcdoSSfbLxH7tWi3GZrW9rLVx/3M86yzTMNyeicsvj8fSV9VMLZdffrkSQlKIZakuXqwKmL+WFflUACOaAvnS6EL51JlYlurUlKptx3zhqSnVri7Tprq6zHonYNuqg4PmNw8ONvZgY7hWO8inKLJpYsJUNXcZH597jLdK5vPl1bOvr/SYveLfe07QsmGD6vghS7u67DlVPUrbmpoyZXGvd+mlqrlc5WYTVC0sq3xb0CcsSpNMTB6kgVp+XJztOA33iRPvcxwdLW8QIuEV0Ecl2UQPHiEkHnI54MgRYHQUOHYsOC6GkDYkMcdZu42rq4TXdVLJ9VBrfFw7ha4mjEj543nTm0zkmPuog8asudVz3Tpgz55gJ4pbjXM5YOHC0vg4l5ERRe7Nb8LG4k50SREbN+psVY/Strq7gQULzP8LFwIjI2YsXlCzcasPMLdaRHEEVWuScXv4bNu8g6mp5g0hrdjEahF2zWp7WWzj3ue4cqUZLOqiGosnkhoYISQ+urqAiy82XwgmWyFtStNydmRRcamHIK04SJGsV3tul9DVhOntNePYXO6914xRc8Mv/WPWduwoVc+77wY2bfIYPt2lRuKOwduwAfj5z01Y5sBA6XVsXFdA78i3MYQtGMudj+23HI40/s/lyBHg2WfN/7/4helfDGo2/urj/mZ3f5T+lGpNMs5oQds25Tz3XLM0Y3hZIwZqoFxsVtvLeht/6CFg//7wnok6oIFHCIkXJlshbUzTx/PXqLhkMmFkVK04a2NtEkREPicih0XkwfiuaQy1vr6Sl82yzKN+9NHgMWtu9SybT/FOG3JleSM5ehS47z5zvZER4JZbgPFx453asWseZNNG5Lry6N30EkhvsGIb1vaCDLOgZjM9Xbn6+I031eC2VKlJzilLd/0N0q3uQGxOnYrYtpl6rZ4mlrU8Jw0Tl6B1H9wFFwDve1+sHXo08Agh8cJkK6SNSbONkVklK2ooaieFrFbnCwCujvui+bxJ5jc2Zrxs7qNeubL6o581fI7MbSTeMMoFC8z555zjzG2Xi+apDmt7URzdtg1cd505V6T6b1Ctry2VlSXA0K0Ft7q7102yyruyY9UqYP782ptYmuVi7MQpaP0P7ujR2DyRNPAIIfHijSevNAkRIRmkYRvD1/Mbp8cts0pW1FDUTglZjYCq3gXgWNzXtYs2Zh6eQW+PYvv2kqGSm5nG0J0a7dEHNBJvGOWzz5qwSsBT/6W6p7pS26vm6J6ZMVMouMfeckvl39BIW6pk6NaCiHnWExNmSbLKu7/XsoBnngH23m9j+y3TEEQTTB3V91JL5agm4P0Prrs7tg8CDTxCSLww2QppYxqyMXw9v3bRjqcj2FEiepZoa5WsRqzVqKGoUY/LZKxqa7GLNracPYpll5yJK87cD9g2epd4vFBbt0arpAGNJMgAqNUR4o7lu/9+4Oaba/tt5v6Kri7Fpk1adRL3WAyWGC6Sy3k8nQn2Z3iLummT4qL3boWsiC6Y2rbvJUiO1DJ/R7UK7n1wd95p5vWIKQSDmhchnUCzlZ1cDliypI2kPCEl6h7P7+v5nX74KHbtatDj5lEiZOsWDN1ht0bJSlN8qFuWZcuMAubOL9VhiMiNIjIiIiMzESrX+IGj2HniUljows4Tl2H8wNHZOmsXLWzd+VEsP0+ivV5fIwmaIKGeebPd+fTOPdfMwxc5dFJtDOlWHLRX4JaTb6x6YiwGS4asnrKi3nIYsrt2z2PW85zMIUymRX2vUSq4bZvtPT2mYzzGEAwaeIS0O81WvNKk6BGSJjw9v3b/Jmx7X/ds8+jvr9NL4FMickdnWqNkpSk+dGbGDCSzLOCee8xgsg6UQ6r6aVVdq6prlyxZUvX4x493l63/6KluTGsPtH8jZvLnYFg2oliU2dcbtd/QTd6xe7d5Jbt3l3TaeubNdu9XUzVzTt5m/ytW3PtVbBkoVq0SUQyWqs8gC1aP8yNyYjyb0ttJ8ZYVqCTTorzXWufUOOssMwASMH+7u+dcshZo4BHS7jRb8UqTokdImvD0/M7cOoThYYGq+f7femudOmBaBr+kpRxuWdatK63v2UM5FIHBQXEi6s3UBn/+54LlKwSbCttx9k8fwMaBfNlQoSj9eJWSd9Tq4HKr2Oz0CrVUs54ezKx7DYaxCUXMw/CeeQ1XiWb0ZSYefBP0IzLkeUyURmVatfk8H34YZSEcjz1mBkAC5q87ULVOaOARkmbikO7NVrzSpOgR4qPlQ7Ocnt+eXimbJLruZpIWZSwt5XDLcvfd5TNyt5kcEpGvANgN4GUiMiYiNzR6zXwe+MUvBENDgrExwe7dRvfcfY9g4M29uOMOmX29hw8DO3ea/Tt3liYP9zMnecfe8upRz7zZs9Mr7Ag/b047F0HPXV/DxoWj6EIBG+fvN1MYNEDSfZlNCYYJ+xFZ8DwmTRwyrdJ8nqtXm5Sy+XwpVa07meSmTZwHj5C2JS7p3kzFy7bNl//OO9Oh6BHiIU3Rw27CiLVrzfxgW7caHasu47MWZSxJCzdNSmE+b4y8NpVDqnq9qp6jqvNUdbmqfjaO6552GjA4aP5/wQtK2++7rzyDu0ipCqmGP97y5B3ARRc19iqiJB0JHTp17CiGft6HMSzH9mfWmSyXDdDdbdqvq5/H3YfQlGCYBjpkW95Z1gySkGneF/vss8C+fUZGlU0muZ3z4BHStsQp3ZuheHm/qldeySQrJHW0LHo4RBM6etRM+mxZJlJncDBh4zNNFm6c+J+vuy6SHoMzIxSLxhBbsQJ4+unS9vXry3X/3l4ztDGfN3/DslKqmoyXhw5VnjzcJQ6jIbSd9/Qgt6kfvV3HIJvqt8hs23gQt2417Xf9etOnGXc1a0owTJ0dwO0qSpqC/8VedFGpYcQos2jgEZJWshbqyLF3JOW0pElV0IS85Vm3zgwVS7T5tGMb9T/fYpGaZ53YtjHW7rmn/LEtXGjCMEVKBhhg7IHx8fBQSffVnHeemWTcsiq/mmpGQ1TjzzslwsaNWmrnMUSzeH+TG6K6Z0/Dw6UCaVrwTUgHcKXn3Y6ipGn4X6xqIjKLBh4haSVNY1qikDWDlHQctTSp2MKP/JrQ4cOB5dm1qwnNpx3bqP/5PvooNc86mZ42xp2fp59WHH5oBralZXooUH1y8VpeTSWjoRaPkaiNIXsLxuxl2G5fAVHPwQ1Gs3jLKJJceKZLq6Keqz3vdhQlXhIPP/W+2ISsZRp4hKSZNI1pqUYKDVIRuVpEHhORx0XkAwH73yUiMyKyz1l+w7PvnSLyY2d5Z3NLTpIiaurzejpUA5UCb+q/YhG49tqyC7rliXn4RTApbKMN091t3J+uprlyZXtrnglSrsyqsxh+ddXjmN70JgwPa2Q91D9GzX01+bzZbtvR548u14EVM4craN7T08jdfRd67UnI3RUywNSA27aXLCmVcWCguU2pmWPeqtkczR7a38yxfk0PP03IWqaBRwhpDK/0dSc4P3y45SOvRSQP4JMAXg1gJYDrRWRlwKG3qOoqZ/mMc+5ZAP4MwAYA6wH8mYic2aSikwQJG67lra71dKiGKgUiZhBSzvncupOABdCU/pw6wrFqplkamW2b8b733WcshjvvLLeU77wzFbIoK7hTJBgUq09/1FkXjGAtZM992LiuMEcPDXrd7qvxjlHL5UxioXXrgHvvnTtZeSWjYTbsUorYWNyJnmu3hGvebhYY//914m3bW7ea3+CWsSzZS4L1vtlGRxSbo9lD+5sVcR0o/5N4t9545wSsZRp4hJD6qTT+Zd06s9461gN4XFWfUNVTAG4GcE3Ec38ZwO2qekxVnwJwO4CrEyonSYggYy7KcK16OlS9SsGuXWaKo1ldYOnSUvrrFHqVYlWimqmReXPwj4yUBkK5HU1XXsmxeDXQ2wsM9BWQRwEDuAv3/eIy9F9eMEYVdqN300swtHNemR4a9rq97cE7Rs1NLBQ2WXmY0SACDN18GGO587EdV0B2V+h5cTPAuG62sAwwAUTp8PFmEy07McF63+wxb2lx9sfyu2s0zubI/+4E3q2/vgDxW8uqmqnl8ssvV0KSxLJUp6ZUbbvVJckAU1OqXV2qgPk7OlpaB1QXLlQtFKpeBsCIxiwrALwZwGc8628H8AnfMe8CMAngAICvAVjhbP8DAH/iOe5PAfxByH1uBDACYOS8885r/JmSWLAs1cFBUx0HB0vtOqy6dnWZ/d7za5EDtm3uk8+rLl5cft+6LthE/M/F+xxqZmJCVcRcTMSs10rUZ+U+dPdhe4+P8UclIZ+avUTVnayirVN916idN8/UKto6NWGpPRn8PuY85gnz7ooFW/v7S69mctKc7r4yt4oMDNTQJCq97zk/pLb2Zlmmqvplhve2+bxqX5+nTVd8EI00ornU8tPbiYZ/d9CHIOJps9UniXcbds0a620l2UQPHiEeUpX6N82TzAQNSHAHWVx2Wem4p582I+vTy38AuEBVL4Xx0n2x1guo6qdVda2qrl2yZEnsBSTlFIvAgw9Wz7A3PT13ImZ/z+zLXw7Mn2+Onz/fjBly8XoSojRFt8d73z4ztdGcHucUj6eNdQhI1AnSwqgpm0bVmL7Uek3Tiq2C6X/6BqYemIAObUcuL+g9JwdZGlx3yx+zoue6rbCXrcCV3ftx3306O9Zu+XJznG1Xnqy8Ylurwa1kI4dp9EJRPRTZrXIrVpRkhrftunNWrl9vvI9btwZUy4TrW1o8aokS8HKCfndNqlGdLsAycR30bhvVz8KuGacCGmb5pXWhB48kScKdcNGps9epKfjLViiU9zgVCsZzBxhXRoSyIxkPXj+A73vWPwjggxWOzwM47vx/PYD/59n3/wBcX+2elE/JUiiYKuVWrUIhuOe9UFA9cEBnHcmA8SColneQTk2ZXnnA/A1q77U2xZb2tDfgJYx8qv9A/7ptG7dMPl+je8YhTiEck9c0CfnU7CWKbCoUVBctKrWZzZvNI6z2GN39xfEpncqfq5Po0S6cmn2FuVzpmv394W2ons9eUNnCrhO23Vvl3DLX5RBOsZe+Hpr6cyK+/JrrSFwC2fsw/IWYmKjvuv4HXIfsqySbWi50al2oQJEkSU0YRGoszQCilK1QMPFvEQ3ThAy8LgBPAHghgNMA7Adwke+Yczz/vxHAPc7/ZwH4CYAzneUnAM6qdk/Kp2QZHdUyo23//lLolBvy1dVlwqi6uoyymsuF2xnl7d0ODEOrVN3DFKCW6HnN6BQK6twJ06TrfQCpEcIlOsXA27u3vH0BqmNj0aqVqRq2dklBB7BDBxft1a4uWwcHVdev1zLjaWoquIrU+tkL62ucnAy+zsREqUPHu91b5QYGSuGkXlJYLZPDstSamDLvs1l9zBFffl2qUVgvQL0yylsIEVOp4nhIdVQyGniE1ECzlbPA+6X5a5JA2ZJSoAC8BsCPAPwXgA852z4K4PXO/38F4CHH+BsC8HLPue8G8Liz/HqU+1E+JYtllXvwJibKe97zeeMh8Cpxo6MRhupMWGoPBGuxYdU9dU72RjuFogi+WgYxNkLKPCGdYuDt369zDLwD+yzt6rKrvuLyqmHrxKGiTo0e1mLB1oGB0vUGBlSLxcpj3aJ+Wrwe+FxOZ8f8DQzMvY7bXsPG/kWpcvVUy5RV5eo4D2oqf652SaF5fcwhLz8oQKBh9aNR4e0thL/nol5m3eDF2MbgtVzo1LpQgcoGmRNqLaKinEnzQ4y5bO2gQCnlU1PwOofdaEC3I7Wvb65TKSySsIwQ46jSNzd1TvZGNJ+oCo//Hv7z0iirYqAd5FPUEE2vcbdpo63W5kEdxA7tkoL299mhIZt20dLB/pOO186e7TCZ6rumzECcnKzPKx5EsVjq8Fm4sBQKms+bzp9K0W9uyHaSJNkJlJh64DwoGzDvPW/H07TrsKArhdo29NvjEN6WZSrRwEDt8i8ozL3OikIDjzSV1PVsp5jUKYktoh0UKKV8agleL15Y+FdVmeQYLlZ+nskgaNlVz6nVnmpKf029N6lFEFUbg9eGtIN8iiKbnnxSywy81Ref1GJunhaQ0z4Maz5v68CArYN9riHntAunscy2n/FSo7TzXc7xpp0Ui9H04lodyvl8+dDvYrH82ErtNakqnNT3PVEdyyMLBxbtdd55g/eos8DVnl/d7y3OKKRaC+F9FgMD5gMWFlMcARp4pKnQaIlOZDmTNSUqxlS/WVoon5pPlDYURSZZBa8HIthwnHNOxKia1Hd6pTkkPAW0g3yKIpu++lX1hWja2rdwVCdyy2bD9fIoeBKo2KZdBLnHnPpkDVyhAwO25vMmaUuU3BSFgpZNsRDFoeyOu43SXqMkZImDpJpV4jqWZenU6OFIobmRqLPA1Yzyht5bq3SqsIw+9XgCtbJsarnQqXWhApV+qCvURlU5k3rt0Ecd5W0HBUopn5InpLFUa0PFYrnCGMUI9Oios56HsIQq1ap7Jjq9oqZK7ECB3g7yKYpsGhvTOWPw8nlbJw8cdhJu2CaBCoa0C6d0sP+kqQ5BH32nvkxN2mVetmphkpZljLWoQ5vcallvtHDSbTOJZtMMHSvWezRwsbDnlwmZGoT7LPyZwfwxxRFpmYEH4GoAjzlJCj4QsP/3ATwMM8nwHQDOr3ZNKlDZIAmh1iz9InV6TMyDBxL/fXVI3nZQoJTyKVnq7OhwT3MnKfaHbrlU0FFDk0KoRqvugfpN6gRNBbLWyRQz7SCfosimYlF1/nwtM/Dcib0tS3Vq0oyt84YyzxJSn711f2DALN5EJ7Mhns653sQp7rQK3rYYdxKUrHZIN0N8xHqPmAtc/t6CMyCnFnf8XtgHJ+0ePGdOqf8C8CJPivKVvmO2Ajjd+f+3AdxS7bpUoDqTZukXqdRjXEk256tYO035fVHcJT7aQYFSyqdkqbPLtpHhZVGuEVVBLLt2KwVNPYpWZrvL46Ed5FM9HrzTTw+oonXUH+8pc6YqmChvC3bRKuuQcY3LJJtLlvpa/FhWaRhXQ+XP4EOwLFN/ipuv0Kn8uSaxTyqUNh9hz9a7vc5KXkk25cKnQG+Y9QAeV9UnVPUUgJsBXOM9QFWHVPXnzuo9AJYnWB6SYWZmgOFhoFg0f2dmsn2fmhABbr4ZyDnNdffuuguW+O+zbeDKK4H77gPWrgXuvNOUn5AasW1getqomgCAnh5g40agq8v87emJdJ1aTsvlgN7euVW20jVEgKE7bIztncH2IQ2t7mXXbpWgsW1g61Zg+XJgyxazHoU6nz3JFkeOAIDOrv/856UqOj3ttEfJwV7Si+nDUmqbDnParIO37vf0AOvXe6qSzMDetRvTxbOgu4YhR2YwNASMj5v75nLmmv7mEnavamUJIqzdx0kt5anlmlu3AsuWAeecA1xxRfQmHXihWuVCPfeJ8SHkcsASzODKuz+K5daT2LLzL2BPp0Fp81Dp2XorXgLfhCQNvGUADnnWx5xtYdwA4LtBO0TkRhEZEZGRmVRo3KTZNEu/aOQ+tcqumo5fuhTYtKnhB5D4c3SFlGUBIyOuxkBITRSLwObN5pt4xRXAgQOArQIMDcE+OIbpW7ZDEU0bE3MaxsaA7dvrU+IqXsO2kbtqK3pXnwvZuiWactQqg6leJSKOh0hSz6IF3rpb+jBdcgmwbVtJR92yZa6+GqTHer9xtg1MTs7t/9MlPdi64D4sxxi2LNgDu7unTO+1beC660yVBYD+fqC7O1hndu9nWc2xV6JSq/1ULAIPPlj9OLc5uzpE3XZBMzqcEjIiZ6QHw7IRRczDsGzEjPhkaRKWdU0FjPhsk/gmhLn2Gl0AvBnAZzzrbwfwiZBj3wbjwXtetesyBKpzSSqCII6s37V61+vyxsf0ABKNxKhzQAPaIARKKZ9iwZ9kwV0WL1Y9eTKeUK1Y20C94Yv1CppGCp7VAUctph3kUxTZ9PXPHlXAns2gWfq/PB9EWYilU939zWBiYu7YO29eiXzeOyeem63RntN8/EkHN2xwM9zas0lgRkfLx8n29weXsVXUIiIKhdLcfosXm/UwgkZv1NWkmyEXEgrzNkW3dXb+RW/R0zDmppZnm6ExeP0Avu9Z/yCADwYc90sAHgHQE+W6VKA6h2YNIo6j/dcqu9p6SEvMQipLC+VT4/iTLHiXoaHG2021Nl9z9W2W0RSXsMrgWJtWk0b5hCpJ7PxLpInOT9kKWB7jzi5rf/6M7v39pWrobwbj4yXDAyhNQu41GN3stJWaT7FYmt/OPW9izNLBxXs1j1O6MP+MdnXZc4w6d9qEpvVjVGhXtYiI0dHy5zQ6Wv22mRiDl6CcDC16sxWtKGPtYqZVBl4XgCcAvBClJCsX+Y5ZDZOI5cKo16UC1Rkk3vESkMK5kfZfq+wKPb5Dla80KlD1LJRPjePNIr1+veqiRTrbm11NGYxCpW9+RblToW1aBTNvVFlWwbhp616hdJM2+YQISez8SxTZZFmql1/uNexKht7mzSUjolAwBlQ+X95OvE1kfFzLDJUNG0y1NX/LPXaVPntBHT6DfSf1pDxP1+De2bL6jbpqc1PGSgSFJeqn3bLKPXhpzBlSNxEeQqwqULFYXlGz4C2okZYYeOa+eA2AHzmC6EPOto8CeL3z/w8BTAPY5yy3VbsmFajOIFFdxtMQ7YFBx73fePuvVTDNOT4N4QQtIm0KVL0L5VM8eNtGoWB6sYOUyFqvVywaJTVsTlmv3MnnzX2rZb1sWrNleGXLSJt8ihoh5V2iyKaDB9XntXOMJxR0ctwy3qIxSw/cOaP5fPgk2G6bcK+zeXPJ4LIKlg4s2qt5FHRw0V61i1bFNm3bpr365+YbXfMOzTsTrgPGg9dUo85LAwpL0BCRsTHVAwdikiVZ6DR2ymgV7cZkaVhWSrdyBB0XF0Exyk147i0z8JJYqEB1Bg3rMtW6BD0N0ZqYSof8i/KRyIKwroO0KVD1LpRP8eE1yhqp8t558BYvLsmUiYnya7pTE7ljhbzHWhPhbbOpjrU2bf9pJ23yKWqOAwA3AhgBMHLeeedV/Z133mFpuffO/O3DsI7vO6wDA7YKTBjnotwJzeftwO+zv00cOlTqpCmMTekiPKWArYvwlJ48OFVVqTdj7nTWwOvrM4biYP9Jzeft2ekUAglpM7E2pToVFn/nUKEQc2dRFjqNPWWc6rvG492tUZb6f6u30ngvltQz8U/4GPUeDVZEGnikIVqlU9R932oNOK094dXKlQVhXSdpU6DqXSif4sFb1RcvLkXYFAqNTeHmHUtUKTRz/36fbjAZ3jbTKk5IfKRNPtWSxM5dIk10Pj6lglOOcWdpHgXtw7BuXrjX8diVvHt5nNLRoZlQr5vbJjZvLg853PtAeQjo0FD1YRLe63nH/VXVEUK+mYl8SutQWPyG8OhozJ1FWQjr9pTRznfpYP/J+mSp/7cGTSQedFy9A7krjbWbnIx2jxgqIg08UjeZtCmy7AmrwfOYGmEdw7NMmwJV70L5VDtB1SfMKPOOsYkqi7xj+rxeubD7heoGFTwBrtdvYCAjMpLURNrkU1IhmlbR1k0L92kOBd20YK9OHCrq5IHDs16V0pg8Szcv3KuTE3ag2LcKlk7sP6zjY7YODZW34717ddZYzOdtPXWqvP240XX+plbXZybkm9nMT2m18FNXNvX1xTPGOPAGKe59soq2TvVdo3belNEq2vWpE57fag1coVOTttrFgIff6DOJohRHvUcMFZEGHqmbtNoUFcmAUIvCnA9DGn9XTD0AaVOg6l0on8qpppSFVR9vVXc9eJdfXsrEF0UW+YdjTEyYxA9B2eaCmlZUhbImGVlvZ0haO6Q6hLTJpyhJ7PxLFNk0NqZl3rWxsbmRZ+OHLB3fd7hs7LpX7FsFk+GyC6d0Uf6EY8iZ9rFwoWmD/qFKlcIUCwXVqQlL7ck66n/INzOpT2nQeDpzH1sH+0+qVZx7o0LBeCXLfm/QT21D2RHl+dR6QWtiKrRult24jmdiWWqSaeUjeueq3SOGikgDj9RNGm2KSDgNfWoyuIcx7YTaTWkT1t4UZ/l83T0AaVOg6l0on0pEsf2rZbScmlI9daqUCM0brulVooJ6+733Pnmy3Pt38mR54pZG5EVkGVlvZ0gmwyjaizTKp6AkdpWWKLLpgQe0zMB74AGz3d/GKrXbqdHD2uVJfuJ67i69tOSl81Znv8HnDVPM51X7+2ztkoIOYIdObHiD8cp4ylR1jG4zxuDpXEPNvf7smDKc0qm+a+a030i5OdpUBiThQEjKKVFmjC7eq1Z+XjxKMcfgUYFqNv7e7zTZFFHIujzMjOe0WCwNsFi4sPKsrBVIowJVz0L5VNswhCjGUdA4Fa9xFxQe6T9nzRqdVTRFVBcs0NkxQYWTjQuLMhkZJjzrbdSZEQbtSzvIpyiyaf/e8iQre++3AjtPgjLRulW9cMrWxY7nzmvgeavw+HjJo755c6ldus3PbY6mU8a9hqV5nNK+y0/qqVPBY3SDmm4z9BfLMmX1j/G1bTVjynBKBzFkPD++9uuXgd7nOvt7miADGnpOdZ6chAMhKadE+SuwdWr0cCqUYhp4pCaybhypZl8naprntBGpblnl3a2z6c3iHSicpaXT5ZNXdvh76sM8bv5pEPxUagsTE6WqB5h1/zn9/eUTLvuX0aGZqsIicjPxC0+vtlbvAJvMhlG0D+0gn6LIppNPTmhponNL1172XJnxFJSo0DXuvEaZO4WCG545MFAy5ACz7jaN0tQHxnBULYVUT0yoDgzYmkfBKZe55po1wWN0/U03TJdp9LMXNGbYO1dff79H3vnGmAXdtGqnWIwyIErEQ02f8BpPDgpjjdsAT+KaaRXDNPBITWTdOFJNb2OshcR7HhuR6u65+bzx3Hm/0jEPFM7S0unyqVq4kb/KRU0LPtsWfIPmJydLxptISUH0nuN6+bxzcnknT7eKPmHhG6RXtZlU8tK5Wp8rSNtwHE0n0A7yKZIH747DOneS85JYD9MNvNvz+fJQxcnJ0nzT3s+E1yAKCrsuC+M8ZOmGNSfLPIJuyHVQ2LZLUPKkOD57/vFy3s9hYB+nr/2GNeeKeksMMiDstzek89VwctadB5FeQZNlNQ08UhPtYByp1hCj36k0Mn7OK9RzOZMBI+wrG4F2UKC0Q+WT93tWTXYEhVvWlKDEpx1ElVWuR8C13eZ4Dd0DAuKjKuov/jL5vXRhM6qTTNEO8ilSkpVDts6dB091w4bK7du/3f/N9bYh1zjzevQ2bCifhzqozY2Pa1nZxserf9+LxVJnjus5DJserRr+gJVSCGmV5CgB16lk5NRtH0QYaxgmy+rW+dx43Ygnt4PzoCJJuIyrQAOP1EyWO4z9Q2DS0GOUiufpL8Rzz5UbeKdORb+W/4tw6lTlOLsqtIMCpR0on4LaV1gIkLen23t8VAMtLHtZmHevZkK0j4rKT9A5UQcwp0IokCi0g3yKIpt+8hMtM+zcZWysdExYta1Unf0h0ydPlsbBBvUvBrU5u2jpwKK9mscpHVi0dzbZShDekEc3867XizcwYKZoGBiIllTJ66Fzp1rp7y//fHqjByoRKalKrYQoOtX6n4JkdOS8C/54fDdetwLt4jwIJex7kKASSgOP1ExWdY+gMQKt7jFKtH1HfVFBsSWXX17+FR8dre/e/q8Gx+B1BEE92t4EKN7jvOFLp07VNgajVHUDspdZTvbL8aLaAzXUQSsga2YF7SO0jPVqLGnpeSKRaAf5FEU2ffWrOse4A6IbL3PwNBxvGxodLb/+5ZdHMBinptTKz9Mp9Kid71JrbMJ0+lhzPVZeu8MNz3aTuBRPWTq4aK9JfLJor1qF6m3PH4Lq9mX6rx3FGxcpqUqthHROVet/CiKyaKrTHZdV3TISQd+DhN2WNPBITTSqe7SsAVuWSdHspiXuqil6IDESa9+1vKig2DjvIIiFCyP1wEW6NsfgtT1BPdph2eyCEhDUIlNCs5dZlloDV+ggdmgXCtqPYS1CqtdB73lS0MFBuzxUs1bhVc85bR+r1F60g3yKIpsOHtQ5xt3CheXhk5HxzRtgFUoZOS2rPPlytetblprOGKcTxxq4omSkLS430vzGmJux0w3P9k7j0IVTRp5UIawfp1IHcqXPs9fDGIsYCCmgf3PQHKB+IoumtnfH1Yn/e5Dwc6KBR2qiEd2jZR3Tzo3tfJeZZLXL9nbyN8fgDLlRYu27lhflL4RXQ1+zRnXTptpeWi0DryLQDgqUdpB88itRQ0Nz84m42HZwCnE/VsHSif2HdXLCLmu3oWGcU1M6lT+3bM6tPgyrNXBF5TroO6+ry26+fUXlKFO0g3yKIpvMOLe5i5ud1sU/ptW/b2rCUntDqdFb+XlmugBPNF/UqP7y9m8bz/uB6VAjzbYre9Zsy56diH1w8d45HsBK5fCP+avUjKN8nr3nDwxEM8CqFjBgDF7I8OJAahJNbe2OixGOwaMClRYa0T1a1jHtubGVn2dCN+JuS9XG01SwbBNp37W+KH8h6u1GDIq9bzCTTVIKFICrATwG4HEAHwjY//sAHgZwAMAdAM737LMA7HOW26Lcr1PkU1h/wWxV9I2HKxbLM+vNUQoLZnwNYKnAjI3xKiSBCQxs06Pfj2EtZfuzdWqySh10zvN68Fqin1A5ygydbuCNj5eOsazguevcfbPh1NiuFkyK26nLX6Ol+exq60sM0imqGWnVhmZYBSswvLMaUcccq0b/PLsGWC0d47WKjlr1Moqm7EADj9RMlAYeJb58zvlJSY6ke8SruSZbZdnG8TxrfXbe31rr1zqEJBQoAHkA/wXgRQBOA7AfwErfMVsBnO78/9sAbvHse6bWe7adfPLVr0oD8b3JTqyBK3Qqf64JqfIM+A+rqlOjhzU/64kzhlqZR3DCnGwV7fL7T1h68uCUrlljay5nRzfWgsbgERJCpxt43jF4Y2Pl+7yfu7LPIAo6Keeo9vWpXbRmA0bcaU2ifibDPk+VjLSk1IGkDKVarltPlFRbBAzQ6gyEBh6JnaBkJq4TJ9SZU0EyxdJ2kxQA1SRwBAmaavkUZeS1Py6lnq91CAkZeP0Avu9Z/yCAD1Y4fjWAXZ71zjbwfO3VKliRFAtrYsoZF3dKB7FDrYnK9cKyVCcnbN28cO+sB6+vz9bNm0thWfbAoAnzcsKvvZOoL15sJkLux7AWN1/BhCUkdjrLwCtNRSBigjS8nTjecGvArPuHG5nPgq0DfSfNPJPOufWOiQ8Kj4x6TiPf24aHU0UsRC3XracvuVJIbSZgUqpQaOCR2PEKGRGTithNthCaTapCmvNUtd26XJMh53l2peo31kJYXEqMGWwSMvDeDOAznvW3A/hEheM/AeBPPOtFACMA7gHwhgrn3egcN3LeeefV/QxSh0+TMAmMqisWE+PG4AJUu6SgowfCvWRlVWvA1oP3H9a+Pns2WcvEhKo9acoxhZ7ZcTf5vNeJbJfG4uTPZcISEjsdY+AdssoMvH0jBdP+nAbsT5h06aVzE6RMTISPx1Wt39iI+xtabzbJyIZjjQWOet16RmZkVvdwYVKqUGjgkYrU09PlFTL+cI7wbFIBac41BW3XH3cWJg0b6BJs+W+sF8sqz7gZ9HJjcEu22sAD8DbHkHueZ9sy5++LADwJ4MXV7tlW8smXAcCemNTBQbt8nN3EhFrjk2aagskptU4VdbDvORWYCZMXLbIDM2u6BCV39a5PTJSy53kTKG3ebDwHJounMSgHscOEhGayi5qkmU4x8CYPHFbAcr7llk6ufnXZt9AbvLFwoenYnZPEpIoB4v3E9vcbA7HqZ8SamyG7kW+oZRnPZD5v/iaS4TLBvAC1fHYzq3t4aYsY02SggUdCaaR3x3XiuILS68ELzyblSXPuEFvbrZBFKlQYBsWaJiANMymfvM/GmwO/yZmg6l2ihmgC+CUAjwDoqXCtLwB4c7V7Zl0+zWbAm/R0eHgyAFgDV5j9RVM3CshrH+7WPE7pILbrxIILS142FFXELjfWfOP3guSPd72UaMVkz7OKdllCgv5+Z149b5kJiZlOMfCKBVsX5U+YzpncCS1KcMTN6GhpAvHZSco9H9pCITxDpn8I94YNVXQQRygEZciuBa8eMDFRur83S2i4/AnJFlpJ5DgffW9oeSs8aJnUPYJI9RiX1kEDj4QSR+9Otfh4nyMgPLVyI203xFKtasD6H0CCE+dlTj4FuVcSKnxCBl4XgCcAvNCTZOUi3zGrnUQsF/q2n+l68wB0A/ixP0FL0NJS+dRgBTNtxTaZJbHDTDfgXtMvJKbMpMP92DUb0pXHKZ1Ejw5iSHM4pfNxfHbf5s1BipMZozOw2Z7tSS8UTBKHAwfK+1ry+VKih7bokSaZolMMPNO23E4ZW6f6rgn8FhaLpXnsFi82k4d7O4G8nn7/N9e2TceMa1zlcuXtfHTUp0tE9IRVEn9+PWB8vDR8XCRctng7pSpdL9Roi9nzWC+Z0z1IZGjgkVDi7t0JEiRuOEQup7poUUKx4CFaX1VlMOgBJDE6O4lTkpbaTez6S0qBAvAaAD9yjLgPOds+CuD1zv8/BDAN33QIADYCGHWMwlEAN0S5X8vkUwwDLaYmLO3K+8azub0x/npgG+XPOwfdBuzSyQUv0efQpQs8xl0uZww2bzvM50tGYZc7Xq/L9Oa7StfAQPm6+7PapkeaZIZOMfCKRfONBszf4qngb8yc7+ro4dkNE7lls+3b+831fq6KxVKItZswyY0C8kcDWcXqDb6a+IvajxtVttTSyUR5RZKEBh6pSFx2QpiQ9YdkJNKTFSJF7aLlTLBaIawjbkOpDmW75lPqVehr/a1N6vprBwVKWymfGnVrWZYWN1+hfdg1G25pA6UBKgH1oPhcQfsueUa78rb2bbB0YINpZ5dfetKTqEF1zZqA0Ke+k9qFUzqAIZNxs8vW/v5S2Je7uL3slZRFQpKmHeRTFNkUFrroZ87n1rLVGrhCJ3LLdHDR3tnQbDcDp7f9B02d6oZ9huoJVRp8PUmuwy4ZRbbUk+ikVfIq67Iy6+VPGhp4HUgrGkVYeIMr3CuN04sF/492bmzl5+lU3zVm3FAzqEPZrvmUenMlpzSdVjsoUNoK+eSOkfPPlFtj4/JObdCPXbNjb8JCoqyCpYPOxOSApevnj86GIeXzqgsWmKq5YIEzRs4J4Xaz5xULxgNo551xfZP2rKc/aB6u2VCwYmDxCUmUdpBP9cyD553g3M/c3GS25vN22bhbN/RxYqK8s8ZNzlIolK7hTeBSq54QxeBKoh837YZHij/5kch6+ZtBJdmUA2k7bBvYuhVYvhzYssWsVzp2etqI3cobq9PTA2zcCHR1mb/XXWfKsHUrcMcdwPg4cPQoMDYGbN8OiEQoS5RyuysiQG9v6cIzM8DwMHJWAb0j34Ycmanp99SN/0H09MR/Sh33cJ8HikXzdyaG51FnXSF14n3ebkNftgw491zAsoCDB0uNq4Z3MyM9GJaNKGIe9sh6HNnwWhRzp2HT6Q/g3Mu6sXEjMDFRutTMo0cxfOIiADkAOYw8+zKsu6wwWx1XrwbyefP3qquA5csVWzefwrbrFCtWAFuvFNhf+wYwNobcjiH0LhXkcqboExNmGRgw13B55hngyJEEnikhBACQ82mE/m+0/1j3c2s+LQLLEqjKrBwAyrtpXGwb2LULGBws6SmqwNBQfXqCiDk37Bx/eePAf72gcrX685jEJ7+ZZL38LSfM8kvrQg9edaI6dwJ7RxrsMnGzZ81JRjkR3t3lS9RX9bblRTShIYEnJhH8HrXbrolj8KyiHe28JAZcxtS9hjboIddG5VOQBzrAIz37vMfGyielqpQOzvdu/Je2i5YO9j1nQpkHbC0emtD+y8tDLd1kKRMTZlyM34NXOGUyXEYZb+fuq1Rt3Cy9HL9CWk07yKeoY/AWLlSn3ds6sNku87KF4U+kNj5enoFy8+aSDFm0yHjw1qypPF9eEM0amVArQeVKg/epWDQJbbIqPzl+sTqVZFPLhU6tCw286sKqoYHCDYzlqZTi3B4IlnTe8E03hKPabedMu5A/N/zEOCV7VIndxNiNusbuxVW2GNMZtoMCpfXKJ8syxpqbdcCNXfK/WO/zzufN8f44xgipJufUmYI1O35mcsM1am8e0Kn8udolhTmX9xpmzz5t6SUvP1lSBgdKU6YsWmT+btigZmyeZ7ydCeOKXm2yEApF2pt2kE/Rx+CVOnXyKGh/X3hGTH+YphuK7Z/P0jXk8nnVQ4eM6PKGbEZV3ps9MiGq7AmaXckblioSPp4xKby6VV9fcuHtzTCeKf/DoYHXRtRrYwQ1Er8hWCzq7KTC9XSZ+IXcbIrhyXCp7E/AEuW25eWuXt7YBESUr0sS3XYVfkBLU8bH2L3WDgqU1iOfggae5fOqQ0Nzu7e9z9s1Br3nuRkNVCu+mzljZfcfnh1/N4AdOiHnagGia7BnjgfPO4bGHWfn3eZV2kpeP1snNrxhdrzd5ITNXlmSKdIknwC8BcBDAGwAa6OeF30MntvmbV2NPaXMul3lUxgUi75IGmeeyqBxdG7HT19fuZ7gyg23X6oa9Xxy6v1G1qJruSLczfZr2+Y3BfW9NYskdINqQSVZGyPXDsYjDbw2olKjrZQVKqwRenvd/MK61vDCICGnqmVS2U2oEKSHhs2RF3a/2d9aoZXGKoAq/I5Z4paqVX5Ay0MYYpKQaVKgGllqlk9TU3MNtYULy3OGe1+sG5ZbsHTq0leaTJeAWhCdGnq4/DX43o1VMHMyuUqYW2cmJ2yPt85SQUEX4IQKLEfZs9Rv6M2fX15kdwky8PL5uSHa7fBhJZ1DmuQTgFcAeBmA7UkbeOtOH1XAeNzd0ErXcOvv9/RBSUGn8ueaqVM8yZbcqVMLhfJQwYGBkqwYGAifQzeIWmVHvd/IqJ9yf2DF5GQpvHzz5tJUEM2Wdc0YkRGWWC8Lcj3rxqkLDbw2IqzRVqqsUQRVo3ZJkJArw7JMpr7BUrjHxETldMXec2et0BqlR+y9WJal1thEabySXzDELVUj/IB2UJbTpEA1stQsn2x7bqilO1eA22Xue7Gltm7rYH6nFpDTwfzO4ProUPhFQfvm7zdTICzeq4WT1mydKRZV16+zZ0MtXeWuVCRbwzx5Yd69xYvLFbgs101C0iifkjDwyr1Odmn8bH7uFCZuIEFXl62D2KE2oHa+y5mWqPzzF2QIzGbULSavaNfzjYz6KQ+KhPKuu3pOUlT6bUmPyPD/du+4y7QbTC2NfooRGnhtRlCjrVRZ/Y0wyEvWqF0S5XxvGUVKzomKgsBruXo9GhGlR1C5GhJ6llU2wXNXlz1XMMQpVVvuomsOaVSg6llqkk9uPSkUzGC1XM50+VZ53+Wh0LaOfv2x2V7zwGGoBUv7FxyYNdLyOGU8eU5yI28ChPKlNsPO77Xbvz+6R56QNJNG+RTFwANwI4ARACPnnXde1d9p26VwSr8ocrd7Qy+DhnUEJf2q9BlLs6JdT061Zv6eZnqhKjkXpqaMrE/rewyiXVQrGnhtQDVBUywawQsEzxflKnPeHhZ/dqxG7ZI55/s2eBuUtxewoiDwD9KrIxbAPxC8IYE4NWV6KTGkXTilg/0nE+85y5SLrs6yplGBqmeJLJ/8FdHbGKuEHHuH7LnzkHsH07t12m3zB4ZmZjskAFv75u/XYqE0VibcuAvbp7NGnBu2tWmTMeja4YNJiJ9myycAPwTwYMByjeeY2D14qsGJU/xJVOaIpwhyP+yQdlG0XaL+njg+6802jiuVOYvvMUuqVRg08DKMq6RVMkosy0RyuSEU+XylMEw35MIui4kPMnSijnMLLXhAod3YdK+hWfGSXqnhevAGBkIfSLViNiwQnfLMTp5uBSvh7RDbXTMN/PCOM/DqrIj+IXuuQecf4+Im4TRhkrYuzD3jjKmzdPNmO3CGhXAPnpEZq1aV77/jjrkhOe3wwSTETxrlUzMMvGbRbnKj2u+JS0dIm1HVbu8xC9DAaxH1VnZ/4hP/FALeYTlhEYxBiVYmxy0dWLRXu3BK+xaOlod1BSRBKEu6MnBFJGk0+5srZM6s+dn4uw5DYgGiCM1YBGKVwqc55CRRGvjhaVSg6lkiyyfPBEWhCXsCKA3Zc9uuCYfyGn5ucoNyx3fJUHPnoIoSarl6dckzeOpUeZRAUGpwQtqRNMqnJAw8N0LA7UeNq3OSin85jeoIYV5WMpd2fz6VZFOukUnSSTi2DWzdCixfDmzZYtbnHDA9bfSokPM2bwaGhwHLMod1dQHz5wOrVgGbNpntMzPmmGIRePZZYN8+YPt2QGTuNVecL5ATJ3AQK7Dr2dXYuK6Ari5g40ZFz3XlhfVed3gYmN71OKaLZ0F3DZubBvyEst98XQ/s/k1wbgD09JT9zlwO6O0tL2co7sH5vPnb22uu6bu2v8xOMcsQAYaGgLGxuc8pMlUK39MTWLz2p2N/eI3YNnDllbDv3YPJS16FrbgTy1cAW7Yo7KLTqCwrUD6IAHffZaN/4UPoQgEb5+9HT7eN7m5gwQJzzIIFwEteArziFWVnOou5xgMPlBdpYABYs2ZuUffvN0UZGQGOHQOOHAFGR83/S5fydRPSbETkjSIyBqAfwLdF5PtxXXt6Gti507T5nTvNeqNU04VCVKHMUE/5G/lU+p8nUIMu1WFU1cPbnTDLL61Lyz14VboDIg04DXA1FU5aOjo0oxPjdlk2yr4NlnblbR3YbOuBA96wKlv7Lj85O5ambNCzbxzc1OjhkrfOSWfsDoaemFCdPHBY7bynsKOjalue6w7YunnhXs3jlG5euFcnxu3AzFdzeqUm4us6cUNV3aQNbsp3b4hkmsIV2r3XKBSOwav+Y6em1MrP00EMaQ6nSt44KehU3zVzMxn4Yx+nprSQO01HcZFaOSNc/FlsTz9dq3rnvMsDD5gU6WvXlra5OV8qtaeOreeko2gH+RQ1i6Z3cu6oc7dVkgOVvFVZH87QSPnrlZ0dGyFUB53wrCrJppYLnVqX2A28WlpZldZsdttOunI7fJyZr9YVDk7o4vwJBWxdnD+hmzc7UwkMWLpZdhrDSnbqoZ8UdOFC1bJseGterdapoolgPGU5KYqddOkFU14736WDi/fOlsueNL+3LN364r1q5eeVxXlaBZNK3T83Tik9cnnDScrAsqxSpj8REzoSlo6XCmc2aQcFSqPKJ9vWqb5rNO9JfALYughPaTE3T8ssLzehkKdhWaeKpj07Ux5YRdPhsmiRll2vFgNvwwZz+csvL9++fz/bEyHtIJ+iyKZ6vuHVjJysZtCMQivKn6aO7LTTCc+qsw28apOE1NL9UqU1T01YsxMGd0lBJ8as4Fv7at3o0OEyA2r/nTPGC3jnw57sd5YzJ03puA3YpZPoUbuvX7VQmJu+f/TwbHmt/Dzj8QoRrvm8rZN3PqxWrkun0GM8es7v8/bqeTPoeZM6xJWJM4iJibn3np1gtZJQpbXXGup47u2gQGkN8sl+7qT2veJnPkPM0vH1b5jrwfOFA3g98vm88cKPjZW3kZKhF7R97uIVa95kTYVC5FdISNvSDvIpqmwqFMwY3qjeqChGTjtm0HQTxrWi/FRtotPuz6qSbGrvMXj+ANxTp4AHHywF4kYZtOVeZ3oaWLKkYuB0j8xgow6bsTE6jKX5mbLY6NlYbZQPBFs50I3F+WcAKBbnn8HFg2eboWaDL8PG/L3IowABYFkCQAGY8j+ClViOMWy5569hP/woevZ8Gxuxy9x/XQE9K7tny5vb1I/ei7ohYsoxOWn+9vW5ZRNc+2cvx5YFe8w1F+yB3W1+X2+vGaOTzwOLF5ufv2mTidH3j2WraWxdRPzXWrcuQvx6xwdftwg+9+oUi5DeHtz1yFlYANPuDYJfKX4D1sFx2DNHMb13AtYd2zGNXmj/Rtj5eZhe+1oseUU3+vtNo7AswcteBvzkJ0E3Es+1g8nnTdt229O6daWxJKrA0aMx/WZCSOqxbeCqq4DVq40Yd8V30DiziGoRgHC9oO4x8bYNe3Ia01NaVqZmMZvXYIV5JgcPNjCmvw4a0bOyPuaxVpLQSTNDmOWX1qWmeaa8qeTyedUFC8z/ixebbqoo3UeV5qvyY5tsk1P5c9UeKJ/ws5Kz0LJUDz5p6dDXjmixYJf1OBSeHNMDuHh23rU+7Cqb02p27M6EVZa+v1hw7l2cmx3TnUdLxIRmeb1hxkvoePQmy8sYOgeOJttL4r4m7zxfVe+XstiPzPUiNXmAANqgh1yjyqf9+1UBnULPnLYM2Lp+fXlm3FxOdcMGWwc2lMKv9+zRmkIwg8IxBwY8Y1qd1+2XU5mpr4QkSDvIpyiyKUh8B+kutahFcWJZJlKquPkKHcQO7ZKCyfLd5LF7KVMvIpP1MY9kLpVkU8uFTq1LJAXKO/OvG+o0f365ljM6Wjq2kmTyThjla8leo2diQvXQIdUD+yy1JqbUKtplRsn4eLmtOX7IJAkpFuyyiYs3by41wIEB1XXrbBUUdBO268SCC9WSvA5iu+ZxShfiZ5oTWwcGjDHmGnOFU+Fz3PnDHbu6SkbewED5WLeoAqCa8VopQraW4Y81fUBSFPuRmFBNymoMK3CU+9X53NtBgdKo8unJJ9WC6CR6ZjtuFuB4mZFXaY66nNi6cGFtY+wAYygODIR30rhkrjOCkIRpB/lU7xi8IGOmWuKUZD9LtvZhuHw4SpMNrBSpFzWRVcOUhNN5Bp53UqhcTq07hnQKvWq7ms7ChdG07ELBm7lArfV9OjVhzfZ4u/PF+JMbLF5k68GD5cpVX5/qpk2l4xbkntY8TumGBQfmjJUJVu5sPfRkUXVyUgubtzgCrqB9G8qTuRQK7jxZpUY8MVESuJOT5dfdsEGdxC3GwfnTn9YuAPwZ/FzPXzXDL3BfnF+HeiZnT+DLFFmoVnOT+o9NqisuajdupbJxDF7447n9Dt2M7ZrDKd2EIZ1Arx7EOQpYs2193aqTc+RCKXmKFbCv8iJiOpmyoogQkibaQT7VO9F5kDETZuB4PxMDA0b3iEvmTEx45vpEQfsxPOvBa4Vcy2JHWFYNUxJO5xl4xeKs1WItWKSDAyb5ySC2q3XxpcYrFzZDpHfb6OishmRBdBDbtQsFvXyNpQfvL0+M4lfEhu6wyrZ1dalecok95xxBoex8v6PRe801a0wowsRYuYLnnwTdayD29ZVnnCwWjZdORHX9+tlosdnl8ss1PPtnCLZdHvbpGpresvgNG//EzFNTGtmISEKwWgVLp/quMcllYjaYIgnVsBnrw8qRZFdc1G7cGGkHBUojyqeD94yXyYF7PzeqffMPqMwabpZiVi7YZTIgXOaULwsWmKkPnnxSdWjItHtCSH20g3xqJAN5NVXJxfuZcD8VQTO91HP/wcGSrjMwYGtxfEqnJltj3GWZLBqmJJzOM/A8KeWm0FMaV4aCTqHH7PPEQloDVxjPXDEgsNxxz02g11HAjHI1D8/4lK3SksMp/enIYV23TmeNHhNSFdYjb5YXvMAuy1a5enW5IpdHQUf3WyEZ84yR5Y1O7eszvV5evdyXcV0LhZIHL8jjp6qzc/QVTtmhgsF/H3caBa+t4j2vWDT7APO3WNRQI8IrkKragHVIL8tSM70ETukghsx0ETEbMDWNGfS+jLByJN0VF6UbN0baQYHSiPLp9q/4O4eCDLdKBlz4MatWqe7bx7EVhMRJO8inZswh7B0v7+149usdtconv2oQdX4+QtqdzjPwHnhALYhOoUdPIqcLTze94YvwlI57QzXzecczt0O78rZefulJLaDLnJs/1/QQ7Z3QU5ind2JQSyFUfsUszHAzRtrevaXkJZW8fg/cPlO2bWxMdcOak+o1HPN5uywE07scOGB+vndScL9BFDQB+6lTxnPX1WWmPvAOmC6ctGbn6MujYJI89J9Uq1iu4Hv1//7+cs/d6Ohce8AvsCcm1PTGDZQbEf7y+w3JMvunzrBFUxYnYQ1Omcmmm9295X2AYVaxn2Z3xdWaP7sG2kGB0ojy6a6vTYfIgTB54l/MMfPnl9qt25nDXllC4qcd5FMzDDzV4OkDgvSOWmBoISHBdJyBZ+25XzdiuwKndB5OeBQmS3NOJsqfznux7sPFej8uLlO2Tsdx3YQhzaGg8093Q6bccCn/2Bd/L7x/u84aefPnhx1TKtvekeLseL5Fi5zkLeO25lAoOzaX0zlG3qJF5bkwwjJc+Q0xd99zz6muWWN63bxTcO2/Y2ZOWWeNIMua413zDtfyZr0sf0GW2pNTaiaFN8eWwkJttSZKRktQz12ooK8zjLD0TIzxalsJfj2iZJ0JGYPX0tCKhNNvtYMCpRHl03vecayivKi2rF1rOnMaDXsihESjHeRTsww8F69sisNAo6wjZC4tM/AAXA3gMQCPA/hAwP7nAbjF2X8vgAuqXTOKkHrittEAA8ofBhXmgXMNOf+5fkUsyGsXRUEL7rEXnCrbJ2J65ItF1f4+W3M5e9YLuHCh6smTnsydB8r17UDvmDfk0knEksuVjDlvmKbHwan799m6UDwePCeM0c53qTUxFarzFwoanMnTYyi4obGVeveCPgyhgr6Br0hTPh4NGEktT2+c0TF4jcggAB90tj8G4Jej3C+KfPrQuw+FyIvqMmTDBoZfEtJsaOA1Dg00QuKnkmxKbKJzEckD+CSAVwNYCeB6EVnpO+wGAE+p6ksA/B8AfxPHvT//w6Xeknj+uhP/SsCCkOP81/BfN2w9aGJFxen4uXPt8nIousquoQq8/OVmruj79ghWrRKIc8GnnwauuMJMMDpvHnDxxWYyR5eentLEo/39wLZt5fNOHz0KjIyY/48fN/O8P/106fyFC80EyAsWAGsuF8jCBcjngY2b8zi44Vpsz78SsmkjZqQndJ74o0eBPXsC9nkml8/t3oXenJkMPmyi1KCJUEMnrqx71tQmTYbp+e1zHlhyp8aDt1KFzi6fLhqRQc5x2wBcBGMk/qNzvYa576dLQ/YEV74f/tBU6clJYPfu8rZOCCFZwP+N7bQJtwlpNkmqCusBPK6qT6jqKQA3A7jGd8w1AL7o/P81AFeJNK5i//U/vKDC3kqXV88x9RXjpS81AmzNmnLBtX49MDkp+OjfumVzjUizBP3qEyeMMm9ZwAMPGOXe5b77gMHBcsPNxWvn3HrrXMPA1dXzeWDxYqOzL15s1vv7gWPHgH37gGefNfc+cUJgWYLd9why3/w6ZNwYUD29Eqrzh9oDATuq2WU1GV9NsdTqpAEjqeX2VQPGcwtpRAZdA+BmVT2pqj+B8eStj6NQO3acrLg/nwfmzzePeHAQuPJKYNkyYOnSrDx2QggJx7aBrVuD9RdCSDwkaeAtA3DIsz7mbAs8RlWLAI4DONt/IRG5UURGRGRkJoLrolBvictQz1/1bQvnzjtNT/vnP1++/Z//2Sho7/tvOXg9gkaXNH8vv9wo8HnHT7B4cckQ8yp2uZwxGAM9ZJ5jensR6B1zdfXxceNpGxszf8fHgV27zLEXXTTXCNy4EehZWjKgKun8oftCdqTZLouNBoykVNhX2XtJjcigKOcCqF0+nSqe7ttSkiubN5t3fPy4kSPZsaUJISQaLY9IIaQDyESwj6p+WlXXquraJUuWVD3+3ntcBUoDlnByuZJX7fTTgXxeccnpj+N0nKh6LgD09QHnnmt04IsvBhYtMtsXLTLrAHDaaSYc8mMfA557DhgYMMbT5s3Avfca5e4XvwBGR40nbccOs809bnCwZIhF8eiEGQaurp7Pl/919wcZgbV610L3Zc9QiI8GfnsnP7Y0U6t8+tGP3BdoZM155wn27gUmJoC77jIdQf72SAgh7ULLI1II6QC6Erz2OIAVnvXlzragY8ZEpAvAYgBHG73x+g2Cj/+9jff97i9w5ZXPx69ccQJnnncGfvGcYN06I0yOHQPOOAP48Y+BM880Y9lWrgRmZoxnaskS4MgRoKf7xbCmZvDosUV46csEDz5oetc3bgQee8yMV7v/fhPaeO655QbU0aPAo4+a63rHzSxYAPze75n/h4ZKYZMiRqkDSgYhYBQ+/3FB54bhGga14j2vnvMJaTGNyKAo59bFhRcC3/624PWvNx04GzfSkCOEdA5uB3IU/YUQUh+iCY1wdZSlHwG4CkYx2gPgrar6kOeY9wC4RFVvEpFtAN6kqtdWuu7atWt1ZGQkkTITQlqDiNyvqmtjvmbdMkhELgLwZZhxd+cCuAPAhapqVbon5RMh7UcS8qnZUDYR0n5Ukk2JefBUtSgi7wXwfQB5AJ9T1YdE5KMwaT1vA/BZAP8iIo8DOAaTtY4QQhqmERnkHHcrgIcBFAG8p5pxRwghhBCSBpIM0YSqfgfAd3zbPuz5/zkAb0myDISQzqURGaSqfwngLxMtICGEEEJIzGQiyQohhBBCCCGEkOrQwCOEEEIIyQAi8rci8qiIHBCRb4rIGa0uEyEkfdDAI4QQQgjJBrcDuFhVL4VJIvXBFpeHEJJCaOARQgghhGQAVf2Bqhad1XtgpnAhhJAyaOARQgghhGSPdwP4bthOEblRREZEZGRmZqaJxSKEtJrE5sFLChGZAfDTiId3AziSYHGSgGVuDlkrc9bKC9RW5vNVdUmShWkGlE+pI2vlBVjmZpFa+SQiPwSwNGDXh1T1351jPgRgLczcnVUVOcqmVJK1MmetvED7lzlUNmXOwKsFERnJ2uSkLHNzyFqZs1ZeIJtlbiZZfD5ZK3PWyguwzM0ii2V2EZF3AfgtAFep6s8TuH7mng3LnDxZKy/Q2WVOdB48QgghhBASDyJyNYA/AnBFEsYdIaQ94Bg8QgghhJBs8AkACwHcLiL7RORTrS4QISR9tLsH79OtLkAdsMzNIWtlzlp5gWyWuZlk8flkrcxZKy/AMjeLLJYZqvqSJtwmi8+GZU6erJUX6OAyt/UYPEIIIYQQQgjpJBiiSQghhBBCCCFtAg08QgghhBBCCGkT2sLAE5GrReQxEXlcRD4QsP95InKLs/9eEbmgBcX0l6lamX9fRB4WkQMicoeInN+KcvrKVLHMnuN+VURURFqamjZKeUXkWuc5PyQiX252GQPKU61enCciQyKy16kbr2lFOT3l+ZyIHBaRB0P2i4j8g/N7DojImmaXsdVkTT5RNjUHyqfkoXyqTNZkk1OmTMknyqbmQNkUgKpmegGQB/BfAF4E4DQA+wGs9B3zOwA+5fy/DcAtGSjzVgCnO///dhbK7By3EMBdAO4BsDbN5QVwIYC9AM501nvS/oxhBt/+tvP/SgBPtrjMgwDWAHgwZP9rAHwXgADoA3BvK8ub0neaGvlE2ZSeMlM+xVJmyqfG3mdqZFMNZU6NfKJsSlWZO042tYMHbz2Ax1X1CVU9BeBmANf4jrkGwBed/78G4CoRkSaW0U/VMqvqkJbmuLkHwPIml9FPlOcMAH8B4G8APNfMwgUQpby/CeCTqvoUAKjq4SaX0U+UMiuARc7/iwFMNLF8c1DVuwAcq3DINQC+pIZ7AJwhIuc0p3SpIGvyibKpOVA+NQHKp4pkTTYB2ZNPlE3NgbIpgHYw8JYBOORZH3O2BR6jqkUAxwGc3ZTSBROlzF5ugLHkW0nVMjsu5BWq+u1mFiyEKM/4pQBeKiK7ROQeMRPItpIoZf4IgLeJyBiA7wB4X3OKVje11vV2I2vyibKpOVA+pYNOlk9Zk01l5XFIu3yibGoOlE0BtPs8eJlHRN4GYC2AK1pdlkqISA7AxwC8q8VFqYUumFCDLTC9fHeJyCWq+rNWFqoK1wP4gqr+bxHpB/AvInKxqtqtLhjpLCibEofyiZA6yYJ8omxqKh0nm9rBgzcOYIVnfbmzLfAYEemCcc8ebUrpgolSZojILwH4EIDXq+rJJpUtjGplXgjgYgDbReRJmJjh21o4YDjKMx4DcJuqFlT1JwB+BCO0WkWUMt8A4FYAUNXdAJ4PoLsppauPSHW9jcmafKJsag6UT+mgk+VT1mRTWXkc0i6fKJuaA2VTEEkMHmzmAtOT8ASAF6I0uPIi3zHvQflA4VszUObVMINGL2z1M45aZt/x29HaJCtRnvHVAL7o/N8N4w4/O+Vl/i6Adzn/vwImjlxaXDcuQPhA4deifKDwfa0sa0rfaWrkE2VTespM+RRbuSmf6n+fqZFNNZQ5NfKJsilVZe442dSyHxbzQ3oNTA/CfwH4kLPtozC9N4Cx1L8K4HEA9wF4UQbK/EMA0wD2OcttaS+z79g0CKpqz1hgwiMeBjAKYFvanzFM9qddjgDbB+BVLS7vVwBMAijA9OrdAOAmADd5nvEnnd8z2uo6kdJ3mir5RNmUjjJTPsVSXsqnxt5nqmRTxDKnSj5RNqWmzB0nm8S5ECGEEEIIIYSQjNMOY/AIIYQQQgghhIAGHiGEEEIIIYS0DTTwCCGEEEIIIaRNoIFHCCGEEEIIIW0CDTxCCCGEEEIIaRNo4BFCCCGEEEJIm0ADj0RGRJaLyL+LyI9F5L9E5O9F5LQYrnuBiDwYRxk917xcREZF5HER+QcRkYBjrhGRAyKyT0RGRGRznGUghDQHyiZCSFqhfCKtgAYeiYTTyL8B4FuqeiGAlwJYAOAvG7xuVwzFC+KfAPwmgAud5eqAY+4AcJmqrgLwbgCfSagshJCEoGwihKQVyifSKmjgkahcCeA5Vf08AKiqBeD3ALxbRO4TkYvcA0Vku4isFZH5IvI5Z/9eEbnG2f8uEblNRO6EERTwnHuBiOwUkQecZaOzfYuI3CUi3xaRx0TkUyISWH9F5BwAi1T1HlVVAF8C8Ab/car6jLMfAOYDUP8xhJDUQ9lECEkrlE+kJdDAI1G5CMD93g2qegLAQQDfBnAtMCsgzlHVEQAfAnCnqq4HsBXA34rIfOf0NQDerKpX+O5zGMArVXUNgOsA/INn33oA7wOwEsCLAbwppKzLAIx51secbXMQkTeKyKPOb3h3yPUIIemFsokQklYon0hLoIFH4mA7gDc7/18L4GvO/68C8AER2ecc83wA5zn7blfVYwHXmgfgn0VkFMBXYQSSy32q+oTTA/YVAA3HfavqN1X15TC9VH/R6PUIIaliOyibCCHpZDson0hCJBXDS9qPh1ESRAAAEVkEI3T2ADgqIpfC9Bzd5B4C4FdV9THfeRsAPBtyn98DMA3gMpgOiOc8+/xhAGFhAeMAlnvWlzvbQlHVu0TkRSLSrapHKh1LCEkVlE2EkLRC+URaAj14JCp3ADhdRN4BACKSB/C/AXxBVX8O4BYAfwRgsaoecM75PoD3uVmYRGR1hPssBjCpqjaAtwPIe/atF5EXOvHj1wG4O+gCqjoJ4ISI9Dn3fgeAf/cfJyIv8ZRtDYDnATgaoYyEkPRA2UQISSuUT6Ql0MAjkXAG1L4RwFtE5McAfgTTQ/THziFfA7ANwK2e0/4CJmzggIg8hGhu/H8E8E4R2Q/g5SjvrdoD4BMAHgHwEwDfrHCd34HJ7PQ4gP8C8F0AEJGbRMTtJftVAA86YRCfBHCdZ+AwISQDUDYRQtIK5RNpFcJ3QrKAiGwB8Aeq+roWF4UQQmahbCKEpBXKp86FHjxCCCGEEEIIaRPowSOZRkTuhYn/9vJ2VR1tRXkIIQSgbCKEpBfKp/aHBh4hhBBCCCGEtAkM0ewgRERF5CWtLgchhBBCCCEkGWjgtRAReZeIjIrIz0VkSkT+SUTOaHW5GkFEfs/5LSdE5HMi4g8BcI9bKSIjIvKUs/xQRFYGHRtj2d4lIpaIPONbznX2Pykih0Vkvuec3xCR7SJynu8cFZFnPesDIvIFETnlrB8TkdtF5OW+MiwXkX8TkaPO+feJyOt8x3ivPS4iHxOR+SLyYxF5p+/YD4vILif9MSGpwWlPvxSyb6OI3CkiT4vIcRH5D3/7F5GFTt1/0mkPB0Xka2LmgnKPUSdl96c8bfGUiBQ8699N+Df+wicbPuHse5dTvj/ynTMmIluqlVlELnDOd7c9KSIfCChDxe+IiHzEc+2ficiwiPQ7su0Rr4wWkbMdGXh1Us+MkCzTgXJtytFtFviOWeAvh4ic45S917PtQyHbvpdU+YmBSmGLEJH3A/gbAH8IM39JH4DzAdwuIqfFfK+mTGgvIr8M4AMAroL5LS8C8Ochh0/ATP55FoBuALcBuLkJxdytqgt8y4Rnfx7A7/pPUtWD3nOczZd5tu10tv0vZ/8ymAlCP+teQ0TOgpl/5hSAi2B+9/8B8GURKZsI1b02zLN8K8y8NjcA+DtXUIrIKwC8H8ANztw3hKQeEekH8AOY+ZXOBfBCAPsB7BKRFznHPA/AnQAuAfA6AIsAvAJGRrzaf01VvcnTNv8ngFs8bXPO8THzKz558l7PvmMA/khEFjZQ5jOcY94M4E9F5JXujhq+I7c411gCI4O+ASObxgF82HPc/wXwHVWl8kVIDbSrXAOwCsBqAB/07f9VACcBvFJEljrlnYSZXmHQc9wggEcDtt2VTLGJCw28FiAii2AMn/ep6vdUtaCqTwK4FsAFAN4mIuc6PShnec5bLSJHRGSes/5upwf2KRH5voic7zlWReQ9YuZd+XFAGV4rInvFeNoOichHPPvcnuMbRWRCRCZF5A8i/LR3Avisqj6kqk/BzN3yrqADVfVnqvqkM3eKALAABIaPish1IjLi2/Z7InKb8/9rRORhp9dsPGJZw/hbAH8gDXpSVfUXMPParPJs/j0Az8AYZFOq+gtV/QqAvwTwv0XMxKG+6zwKYCeAi1X1Luean3CO/QyAv3KOISQr/C8AX1LVv1fVp1X1mKr+CYB7AHzEOebtAJYDeIOqPqiqlqo+q6pfU9WPBF82Oo7cfJ1nvUtEZkRkjYg8X0T+VYyX/Wcissfb+1wjjwDYDeD3Gy2zqo4AeAiOTInyHQm4RgHAFwEsBXA2gN8A8DsissrpoLsKRk4RQmqjLeWaqk7BTLy+yrfrnQA+BeAAymXNXXCMOTGTuq8B8Pe+bf2ggZc4NPBaw0YAz4fpRZ1FVZ8B8B0Ar3S8Srtheklc3grga6paEJFrYCbKfBNMr+xOAF/x3ecNADYACAp9fBbAOwCcAeC1AH5bRN7gO2YrgAsBvArA/5CQsAQPF8H0WLnsB9ArImeHnSAiP4OZ9PPjMD1UQfwHgJeJyIWebW8F8GXn/88C+C1VXQjgYpgesnoZAbAdQCNGIsSEeV4P05vl8koAXw/wtt0K4DwALw24zkoAAwD2Opv+B4B1AL4OkwHrbxspJyHNREROh5F/Xw3YfStMGwGAXwLwfVV9NuC4OPgKTPt0+WUAR1T1ARjFZTGAFTBG0E0AftHAvf4UwH/3dtbVg4j0wcg3V6ZU/Y4EXON5MJ1uh1T1iGMQfhjA52CUtd9xOucIIRFpZ7kmIsthvIuPe7adD2ALgH9zlnd4Tpk18GA8f48AuMO3bR6A+2r6ZaRmaOC1hm6YRlcM2Dfp7AeMAXM9ADgem20oGTU3wXhvHnGu8z8BrPJ68Zz9xxxvUhmqul1VR1XVVtUDMILhCt9hf+70Lo0C+DzKBUcQCwAc96y7/88JT/KU4wwYofNelIwY/zE/hwl7cJ/FhQBeDhPWCQAFACtFZJGqPuUIszD6nN4rd/mvgGM+DOB9IrKkwnXC+APHaH0awGaYHjuXbpj362fSs9/lARF5Csa4/QzM83eVt/cAeCOMJ9Cqo4yEtIqzYL47Ye3AbQPdAKbcHY6H6WdOxMFjMZTjywBe7yhmgOkwcjvICjAK0EucHvb7VfVEhWt9yydTftO7U1X3AbgdpnOmHo6IyC9gOvz+EcC3nO1RvyMAcK0jlw4BuBxGfrh8AuY371PVb4EQUivtKteehpEZhwH8mWff2wEcUNWHYcJLLxKR1c6+HQAudqKgBgDsVNUfA1ji2XaPqp5q8LeSKtDAaw1HAHRL8Ni4c5z9gPHS9IvIOTC9HzaMpw4w4yz+3lUqYMZ6CMzYL5dDYQUQkQ0iMuS474/DGIzdvsO85/8UJq68Es/AxJS7uP8/XekkpzfrUwC+JCI9IYfNGrswQutbjuEHGC/nawD8VER2iImFD+MeVT3Ds7w4oDwPAvhPmPGEtfJ3jtF6AUzv2Ms8+47AvF8/53j2u6xR1TNV9cWq+ic+r99Dvr+EZIWnYORYWDtw28BR7zGqus9pV2/C3LmbakZVH4fpWf4VRxl6PUqdZ/8CE5J0sxOi/r/csPgQ3uCTKf8ccMyHYaIk6gn17IbpPHs/TK+5W5ao3xEAuNUpW4+qXqmq97s7nDD5R0B5Qki9tKtcWwgjc16Ocv3wHTCeO6jqOIxR905n/UmYsb0DMHqrq7MOe7YxPLMJ0MBrDbthBqe+ybtRTJaiV8O4s+GEyvwAwHUwRs3NzscYMMbXb/kUixeo6rDnkpUmOfwyjAdshaouhjGw/GPAVnj+Pw8mMUolHgJwmWf9MgDTqnq0ynmAqYuno9xA9XI7TA/QKhhDzxVaUNU9qnoNgB6Y3u1bI9yvGn8G4DcrlKciqnoQJlnL34vIC5zNPwTwJpmb8fJamPf5ozrLSkgmcDpzdgN4S8Dua+HIPufvq8ST0TYB3HCmawA87ChHcMay/bmqroQJu3odykOQasYZJ/sNAB+q83xLVT8GE87+O87mSN8RQkiytLNcU9UdAL4A4O8AkykUZujOB8Vk2JyCGQr0Vk9nkxum2Q9j2AHG0BuEiWyigdcEaOC1AFU9DjM4/uMicrWIzBORC2AMkzGYnhaXL8M0wjfDY9TAGGQfFJGLAEBEFotIkHAJYyGAY6r6nIishzEg/fypiJzu3OPXAdxS5ZpfAnCDmCkQzgDwJzCCYQ4i8koxSWPyTrKAj8H0gj0SdLyTHOCrMGPOzoIx+CAip4nIr4nIYueYEzA9aQ3hCMVbAPy3Bq5xO4xRfKOz6f/AhKN+VkSWOoOer4dR+v7QY7wT0i7Mc+q5u3TBeMbfKSL/TUzK8DNF5P+DUQbcrLtfgglt+qaIXOzIiecDWBtj2W6GGV/82/DIVhHZKiKXOMkATsCENsWRpfbPYeToGQ1c469hsnI+v8bvCCEkPjpNrv1fmGyZl8F46m6Hye2wylkuBvAClDKB3gWjt054wkDvdrYthjGGScLQwGsRqvq/YJKk/B1MY7sXxotzlaqe9Bx6G0xvyZSq7vec/02Y9Ng3i8gJAA8iIM1uBX4HwEedGOsPI9jrtQNmYO0dMKGHP6jym74Hk0lqCMBBmLDO2bhtEXlIRH7NWT0DpqfpOID/AvBiAFer6nMVbvFlmEHKX/WNO3k7gCed53ATgF8LOtmhX+bOg7cu5NiPAmi0p+1vYRSy5zmezM0wiREehgnX+H0Ab1fVasYzIVnkOzChyu7yEVW9G2bw/5tglJ2fwgy83+yM1YAjB7bCtJNvw8jIx2ASDF0bR8HUpPTeDdOb7W1/SwF8zbnnIzBysJKx9B8+efLNkPv9xLlOIzLl2zAdYb/pXDPqd4QQEh+dINe815yBMU4/7JTz42oygbuLK9vceXp3wERU3e25zD4YI/B+z/AakiBCpwHx4/QC/wTAvJAB/IQQQgghhJAUQg8eIYQQQgghhLQJNPBITYjIdwNCHJ8RkT9uddkIIYQQQgjpdBiiSQghhBBCCCFtAj14hBBCCCGEENImBE2Qmmq6u7v1ggsuaHUxCCExcv/99x9R1SWtLkejUD4R0n60g3yibCKk/agkmzJn4F1wwQUYGRlpdTEIITEiIj9tdRnigPKJkPajHeQTZRMh7Ucl2cQQTUIIIYQQQghpE2jgEUIIIYQQQkibQAOPEEIIIYQQQtqEzI3BI4QQQgjJMiLyOQCvA3BYVS8O2P+HAH7NWe0C8AoAS1T1mIg8CeBpABaAoqqubU6pCSFZgR48QgghhJDm8gUAV4ftVNW/VdVVqroKwAcB7FDVY55Dtjr7adwRQuZAA48QQgghpImo6l0AjlU90HA9gK8kWBxCSJtBA48QQgghJIWIyOkwnr6vezYrgB+IyP0icmOFc28UkRERGZmZmUm6qISQFEEDrwXYNjA9Dai2uiSEkDRCGUEIcfgVALt84ZmbVXUNgFcDeI+IDAadqKqfVtW1qrp2yZJMz9PePsQt3PmxICHQwGsytg1s3QosXw5s2WLWCSHEhTKCEOJhG3zhmao67vw9DOCbANa3oFykVqIK96hGGz8WpAI08JrMzAwwPAwUi+YvoyYIIV4oIwghACAiiwFcAeDfPdvmi8hC938ArwLwYGtKSEIJMtKChLv/uFqMwIcfBnbt4seCBEIDr8n09AAbNwJdXeZvT0+rS0QISROUEYS0PyLyFQC7AbxMRMZE5AYRuUlEbvIc9kYAP1DVZz3begHcLSL7AdwH4Nuq+r3mlZxUJcxI8wv37u65x0Xp4XOvv3o1sGABkM/zY0HmwHnwmowIMDRk2mxPj1knhBAXyghC2h9VvT7CMV+AmU7Bu+0JAJclUyoSC0FGWm/vXOF++PDc41wjcHg43GjzXv/ZZ4F9+4CLLuLHgpRBD14LyOVKbZ3UB8cVk3YmlwOWLDHff9ZxQgjJEJXCMLwKYNBxrhE4NgZs3x6sKPrPq2bcUWHqSGjghcD2kF44rpi0M7YNTE6yjhNCSCaJYqRVOq6aFyDq9QEqTB0MDbwA2B7SDZNQkHbFlT0rVgA7d7KOE0JIJokaqlVvSFfU86gwdSw08AJge0g3TEJB2hVX9liWiR5gHSeEEFI3VJg6Fhp4AbA9pJtaohMIyRJe2TMwABw6xDpOCCGkTqgwdSzMohkAs9ilHzc6IQncTMV896TZ1CN7WF8JISSFpEU4J6kwkdRCD54Hb2KVpmS6TEkml5QUIxVw/CVpNbOyR23Yk9OYnlJYVnAbZX0lhJAUQuFMWgwNPIemt8WUNP6UFCM1cPwlSQW2DXvLldh67mNYfq6Fs89WLFs2t42yvhJCSAqhcCYtJjEDT0RWiMiQiDwsIg+JyO8GHLNFRI6LyD5n+XBS5alGvW2xbu9XShp/SoqRGjj+sv1Jg2yqKjdmZjAz/GMMox9F7cLx4ybxir+Nsr4SQkgKoXAmLSZJD14RwPtVdSWAPgDvEZGVAcftVNVVzvLRBMtTkXraYkPer5Q0/pQUIzVwPHJH0FLZFElu9PSgZ+NLsBG70SVFLF4M5PNz2yjrKyGEpBAKZ9JiEkuyoqqTACad/58WkUcALAPwcFL3bIR6khsEeb8ij2NNSSaXlBQjVXA8cnvTatkUSW6IQLYPYWh6BjOSR/cSwZEjwW2U9ZUQQlJInMI5asKWtCR2IS2nKWPwROQCAKsB3Buwu19E9ovId0XkopDzbxSREREZmUkwhrDWxCoNe79yOdhLejF9WFqa4KQpCWUISSGNyibnGjXJp6hyw0YOM7le9PQK8nm2UUII6UiihosxqQLxkLiBJyILAHwdwH9X1RO+3Q8AOF9VLwPwcQDfCrqGqn5aVdeq6tolS5YkWt5aaNQDz7ZISOuIQzYBtcunKHKDsoEQQgiA6MkSmFSBeEjUwBOReTAK1L+p6jf8+1X1hKo+4/z/HQDzRKQ7yTLFTSPeL7ZFQlpDq2VTNblB2UAIIQRA9LAPJlUgHpLMoikAPgvgEVX9WMgxS53jICLrnfIcTapMaYNtkZDmkwXZRNlACCEpppkTCEcNF2NiF+IhsSQrADYBeDuAURHZ52z7YwDnAYCqfgrAmwH8togUAfwCwDbVzpluOysJTjhml7QZqZdNjcgGtldCCEkQN4Z+eNj0wA0NmbCMJImasIVZt4hDklk07wZQUb1Q1U8A+ERSZUiCuJWntLfFanKMyiTJGmmVTf62VEk2hLW7VugdhBDSUTSUQp2Q5sBPfw10YuKDSmOBOvF5EJIEtbSlSsdy7B4hpONJOnySMfQkA9DAq4FOVJ4qybFOfB6EJEEtbanSsdQ7CCEdTTN6njnWjWQAGnhV8HYEtbPyFNbhVUmOtfPzIKSZeNvSunVA0GwLbhtdsiS83VHvIIR0NM3qeeYEwiTl0MCrgL8jSLVx5amZiZeiUq3DK0yOUZkkJB5EgDvuANauBe67z7RHbzv0ttGtW82xYe2OegchpGNhzzMhAGjgVSSoI6gR5SmtY9Ya6fDKojKZRiObkJkZ4N57AcsCdu40ddS7z9tGjx4tb3es04QQAvY8E+JAAw/hylHcHUFpHbPWSR1eaTWyCREpySBVs0QJD2edJoQQD1nseSYkZjrewKukHMXdEZRWQ6qTOrzSamQT0tsLDAwA+bz5u21btPBw1mlCsoeIfE5EDovIgyH7t4jIcRHZ5ywf9uy7WkQeE5HHReQDzSs1SQSGYJAE6HgDr5py5O8IaqQdptmQakWHVytkWlqNbEJEjFwYHwduvRXYvXtuePiSJcDhw+Vtppp3j3oDIankCwCurnLMTlVd5SwfBQARyQP4JIBXA1gJ4HoRWZloSUlyMASDJETHG3i1KPxxtENGDhhaJdPSbGQT4sqH3t65cimszYTVaeoNhKQXVb0LwLE6Tl0P4HFVfUJVTwG4GcA1sRaONA+GYJCE6HgDrxaFP0vt0NtzX2svfjN6/Vv5LGlkk7QTJJcqtRmvd8+yTPs9fLj8+OlpevMIyRj9IrJfRL4rIhc525YBOOQ5ZszZNgcRuVFERkRkZCbNCksnw7AikhBta+DVYqREVfiz0g69PfdXXFFbL36zev2z8izTBMPtOgt/SGbURCtnnw0sWwZce2358dddR28eIRniAQDnq+plAD4O4Fu1XkBVP62qa1V17ZKgyTVJ61EFbr4ZOHSIYUUkVtrSwEvKSPH3qnuz3KUJf09/LZ6yZnnWavGc0rBhuF0n4n/nllXKrukuLt52e/y4OXb3bqM3jI0Bt9wyd0wfISS9qOoJVX3G+f87AOaJSDeAcQArPIcud7aRrOAqNZZlhPx555keOK9Qp+JDGqQtDbwwIyWO9uJ6+1TnKtxpaY/+nv5aPGXN9KxF8ZzSsDFkKTyYxMP0dPk7f/RRY6S5xptXrlkWcPrp5edv3AgsXRo+po8Qkl5EZKmI+TqKyHoYfe0ogD0ALhSRF4rIaQC2AbitdSUlNeFVajZvDldWqfiQBmlLAy/ISIm7vfgV7gcfTE979HrHduyoLalI2pKQeJ/zrl3Aww+33oBuBQxp7Sxs23ToFoumDW7cCKxcGS7Xzj8fOHGidH4uZ7x2bvv1tus775ybiZMQ0lxE5CsAdgN4mYiMicgNInKTiNzkHPJmAA+KyH4A/wBgmxqKAN4L4PsAHgFwq6o+1IrfQOrAq9Ts2QOsWzf3w84eXRIDohn7yq9du1ZHRkaqHmfbpk309BjlZnraGF/FomlLY2OmV7viSRVQNYbc8DAwfz7w9NOlsKnQ6ydADUXOJO5z3rULWLAAePZZIweHhowS20m087sWkftVdW2ry9EoUeVTNbzyKp838mTp0jC5pigWBSIKVVMxFi0Cjh0z57q4EQbbthm51antiJBaaQf5FJdsIg3iVR43bjQ9bkeOlH/Y/cekobedpJJKsqltP+3+8L+qHpAaXXxuj/jevcAzz5jDXeOuWR6WpL34aQg5dZ/zvn3GuOvkDi1m/+wcvPJq06ZSZ9EcudZtY+Pp+9GFAjYseAgiprE++6zRGVxcWbFiBbBzZ2e3I0IIaRn+MKl8fu6HPW2hVCSTtK2B56dqe6nDJZ7LARddZBSwri5gYKC5iZCS9OKnKQTcfc4MUSSdQtTvux6egZ44AQUw7+lj2LyhMGsUetuIKyvcRC1sR4QQ0iKi9NayR5c0SFerC9BM3PYSiNtl7rrEI2o+riLWitC5OosciSDjsRkhp2G08jkT0goqyiuHGenBbjkblnZht2zEwa/nkcvPbSNeWdHfD9x6K3UHQghpKu08zoKkjo7x4FWlAZe4f76qZpGkFz+NST3YoUU6maCQ6Z5ewcaBPLq6FBsH8lh6jgS2EX/ipaVL2Y4IIaRppCksinQENPC8BFgQXqUqSMGybWBysnXtNimjhyHghKSHMN3AtFPB2Jhg+3aBiPG6P/jgXDkUdVqSVo+7JYSQlpGUEGRmTNJkaOBVwK9Ubdkyd967VicuSFIhy7rHjMoqaRcq6QbedlosAt3dwCWXAGedZda9VGoTxaIZu7dsGTuYCSEdSJJetjSGRZG2hgZeBfxKlV/BanXiAnr8w+GzIe1EVN3g0UeB48fN/8ePm3WXSm3CtoHBQeCee4w827WLHcyEkA4jSS9bWFgUe6JJQtDAq4BfqfIrWN79zc6gCdDjXwk+G9JORA2ZXrkSWLzY/L94sVl3qdQmZmbMnLsu69axg5kQ0mEk7WXzh0XF0RNNA5GEQAOvAn6l6s47zbx3Q0OmLR0+bLa1KnEBPf7hpPbZJCCMKd+JWwdEzPx3o6NmonPvJOblbULRo9OwLcX0tEkS5e7r7zcevKyGZhNCSF00O/lAoz3RDFUiFaCBVwW3w0UVuOoqYPVq057c8XhXXmmUo1YoQ0yEEk4qn00CwpjyvTOoFl7p3ZfLARdfXG7cAZ42cdDGdt0CXb4CW8/ej+XLFVu3AnfcYdrLrl1zzyWEkI6gmckHGu2JZqgSqUDHfcbr9Xb429GuXeloU1lPhJIkqXs2CQhjyvfOoFp4pSuPqo2dy+WA3twMZPcwZqwzMXz8IhSLguFh4OjRlLUXQghpZ+rtiXYVWW/oRapClUgaaFsDL2xKA7en+4orzPQGkQw920aPTmPjRkVXFzB/fqkHvb8/2TbF8Ls2IoG40dSGopJY8b9n1ZJMOOus0v+qZj3KxXryx9C/6CHk84r+fhOySUFDCCFNpNaeaK8i6w29SE2oEkkLbWnghYUzeXvBd+4EzjsvQlibczFZsRxDuhV777fxzDNmV1cXcOut4W2qUeMs6HfQ4MswCcSNpjIUlcSO+54PHjRtf8WKkkx45JGSDHPXvbhzdU5NOXLDuZgeGoNcdhlEANm/H7p8BeN8CSEkzfjDORh6QUJoSwMvLJzJ7QXP50tzRlUNa/NcLLd7Fy7qmcGmTaWe9CVLgidCDx0zU4OF5v8d09PpGm9FY7MOEogbTV0oKkmEXM4su3eXy64lS8qP8667cmjZMuCccxT9l5+CVVQgl8NMrhfDu8WEaJ64CDPWmbB37cb0w0fZpgkhJI0wbIdEpC0NvLD67/V2DAxEbB++i0lvz+w17rzTJFlxQz69xtf0dICRWaNLzv87ROIbb5WEdzGO67aETBaadCJBsm3pUiPP8nkzl93SpaXj3U4iU7UF9+6dh81nPQS7aKO7+/9v78/D4zivO1/8e6oasi2AhC0BDe6WEzmWSVniAhBrgwAZJ7aTseIlkpw7cZz4Xl/PTeZmZjLJxJNJfH/OrDf3zpJxJokn8TiZm0TymvFMtrFFgCYBUgQokIRESbbiOCTQ3QBIyqRIWUR3vef3x1vVXV1d3V29d1efz/PUA3R3LW8t76mzvefV0yFEIoyx3ufQZ7yM6e6z2HWgD0eOcPAUdkEQBKExSNqOEJBQGnjudKYnn8z9zTC0AhS4f/h0JidicvVq4YnQiXyMzDJDct5D9/cDg4NakavGceM1zipR5PyipG1Z0bGGjRY7Uag3fu92Iv3/6qp2OiWT2XTMaFTLDMB5KAmLr7wNa5eu4ehR4OxZ/fvxB38eV9GH+Zu66MrJk4w9exhTU7qPy3MtCILgQzNe/JK2IwQglAaew+OP545VcVOqf+T02QIrF5sIfWDAx4isICTnnqbh6FFgcRE4fFgrcpX2be9YxELXyPdaFDj3aLS+FR3rJkMrbLS3PW1p3AptiZ84Mgzg3nuB8XFgxw69HDkCxONAV5ezFgNQGNv6HKi/D/PzgGVpmXL1zEuIqgTGMAcTKQDIVNeMxeS5FgRByENe/EILE1oDzzdFMiBK6b66c6dWkgr1Wbc3/cSJfIMuTxGzN1CXV7D25Cw4OhA4l9pthyws6OhhpbiNM2at5BW6RoXkl18koZrU8GIGXF1lqN1oZXZhbfBHwP2lG+3XHpmuQGg03jG/ExPA00/r35izzpuTJwGAEIkAF2eu4/i1hwGi3L46dj8oEsFTW96PQSwAAIgYg4Na3shzLQiC4KFZL35JFxICEEoDTyng0Ud1nwO0AtPXF7w/rK1ppciy9N+1tcLrGoZOnVxf10ZOqai5goHpxwewazdhapqgngqWK1rLcbWOcXblSumxiMXkl9eArWZKl2IGXKF00JrIN9L3YPrwLexa/Iq+JyUMSL/2yLhnoRE4z71l5adZO8adG3f/uPtuwgPjfTh6jLB7t/7t8mVgdpZAs7rjXnthA+ciowAMGAbhS19CTlEpea4FQRBsmvHil6ihEJC6GXhEtJuIZojoEhE9R0Q/77MOEdFvEtFLRHSRiA7W4thra8CpU9nPf/RH2WIoQfqDM67F+78f5U5lkGccXAuWS13rcbXOWMTZ2eL7LFd+VZIaXsoJ5m1DX19t5dvGNQPzC3dlUtJKOeH8romMe24fmimbqsEta2Kx7OTm8/P5Ef3Xvz5/+1deAV58MdvXTp/W/ZUImY4b3W5ibIxApI3Ixx+XaZYEQRB8acaLX9KFhIDUM4KXBvALzLwXwAiAnyWivZ513g3grfbyMQC/XYsDew20l18urz8MDGQjW7GY/lyIcqcyqMbhU49xtaX2GVR+VRNRK3VNvG3wFrepVr6Ve08KXZO2HPfcmakeTZNN1eBN09YVMP2f2c3N/O23bAEeeKDws66UzkT4kz/RzzKgjcDMNEvckc+KIAhCYRr94pd0ISEgdTPwmDnBzM/Y/78C4HkAOz2rPQLgD1lzBsAbiWh7tcf2Gmj79tVGgfejnLopjgJ1/Hhre8S9On+QgjTVRNSCXG93G2ot3ypxwrWlMeelQ1M9mimbqsH73J86lX1m3VMlxGLZtMrhYf0dALz6qjbW3M86c37K5+OP56Zl9vUBawkFnuq8Z0UQBKGlkHQhISCRRhyEiO4DcACAd5TITgBXXJ9X7O8Snu0/Bu1Fx549ewIcT6cVvfACsHevVsZnZrJjpcpR4IMcy71vQCtF8/O5xoejSzvfz8y0Zr/0a6dRwg3glzEQ5Nq5CXq9gfxrXovrWM7xQ0O5N86pJlOri94CVCub7H2UJZ8qxe+5d9+u2dnsb8zZSdDd/dnZrr9fT6Xw2GM6Sjc0pKdMsCzg9GnG5b8FDJPQ16fT2+fnCWPpX8cMpmBU2skFQRCE6ulIhUUol7oXWSGiHgBfAvAPmPlmJftg5s8w8yAzD/b395dcXyng2DHgwAGt3ChV34iLe9+FnCvtkjZdSTubkTEQighasynnxoUw2lcL2QSUL5+qodhz7/7N+d9xbrnlkXMr9+zRRaSyKZ+MCKUxlj6JbY9PYaBfudKhCfM0hg1zu6QFCYIgCEKLU1cDj4i6oBWoP2LmL/ussgpgt+vzLvu7qsg1Uhgb68XHjNR6GJKfEtYuadOVtFMyBtqUQjfOr0O0i4ciIM2STfVEKTuVMpl777zyyH0riXQK59gYcOpL61gx3oxZHAGd1vc4Rx7ETERXnsnN7ZTxeIJQEUT0WSJaJ6JnC/z+v9gFnpaJaJ6IHnb99h37+/NEtNi4Vgtl0Zlj3IUWoZ5VNAnA7wN4npn/bYHVvgrgw3bFuhEAN5g5LwWqXLRSkvVGRx+d8o04KKXLizciMNEuRlCl7ZSIWnFaVs57b1yhSF27eCgC0EzZVC/0bWPs2qkwtf1FqCPTUGnl+8y5b+XwsJ4uZXYWMLdHMTCu58Nz7nGuPCDQtgG9w5BFcwWhCXwOwLuK/P43AI4w8zsA/DqAz3h+n2bm/cw8WKf2CdUQwqwXob0grpPGSUQTAE4CWAbgPNn/FMAeAGDm37EVrU9DC7lXAfw0Mxf1Rg0ODvLiYmmHlUqsYWP3QUStuFZYVlZycpadvjc3p/9n1gqPZzWh0YRwnFcl4xqbxtqafiGl0/kdoo73hojONUpRqZdsAoLLp1qjbxsjnSZEkMJl4y14fPjbmF+4y/eZS6d1MZaFBV1QJfN7Op07eLnwwfyfEUEIIfWST/YY4P/BzA+WWO9NAJ5l5p325+8AGGTmq8W2c9Ms2dSxiJwUGkAx2VTPKpqnmJmY+SHby7Sfmf+cmX+HmX/HXoeZ+WeZ+fuZ+R1BFKjARKO6coDpH3Fw0pQsK2vclTMhestGZAJQdtsbdbLt7vEqcJ3aKruxWKQuJGHapsumOpC5bZTGGE7DOnQYc2e7/J85pXDthQ0sLjIsy/W7UlBHfxBr+39YV8z063+ONywk0VxBaBM+CuAvXJ8ZwP8konN2kSdfiOhjRLRIRIsbLf3iCSEhynoR2pNWjSNUhVLA9FHCrsWvYOrwbajjs3lKqbvvxWI6Ten4ceDoUcauXYypKS5oX9TCDqmlzVTOvspueyONriKWUMsb1EWuU1vJ+XbJJRZy0LeNcPmKgT965u14+/NfgmXpe3f33UDfPSpnPoTo/h0Y676ASIQzz6Ra28D0yU9hl/UdTJ38dag1j0KoFNTUUaztOgRWDFy+LM+IINQZIpqGNvD+ievrCWY+CD1f588S0aTfto0sACV4KPIubXl9RggFoTTwsnYCYX7hLmxczVdA3H3vxAk9j9TVdYX5k5be7qSFjTV/Y6baiEwtbaZy91V22xsZfipgCbVFYK/IdWo7mykkkbpO5PGfMHDfUD9u3creu1u3GFcn36870MQEMD8PstKYuXUYK0tXM8/kGkdxEjGk0YWTiGGNcz0ROQbgqX8OBUOeEUGoI0T0EIDfA/AIM19zvmfmVfvvOoCvADjcnBYKRfF5l7aFPiOEglAaeEEjJk7fcwrC9WMDYzyPCFIY43lEyd+YqTYiU0ubqdx9ld32RoafClhCbZHiWOI6tZLNVBfvobgkm4477dzNxOEU+s7+OdbS94DPLujU9UgExvgoBvb1ZZ5JMgjO3WP7M5C9teuIYp7GkEaXnjKBWjkULQjtDRHtAfBlAD/JzN90fd9NRFuc/wH8EADfSpxCddTjtdYW+owQCkJp4JUTMUmntVN71y5g+vEonpr4JFbM+zAb+1XQgL8C497/8ePA+np5AqCWNlO5+yo7mtTo8JOPJdQWKY5tEqari/dQXJItgTft/Px5IB4Hjp/swtEtZ7ELK5jasgh14qTvczowAMRiBNPUfwcGcm/t+94PjPY8qx1gW59FtF+MeUGoFCL6EwCnAbyNiFaI6KNE9HEi+ri9yq8BuBfAf/JMhzAA4BQRXQBwFsCfMfNfNvwEQk69Xmttoc8IoaBuVTTrReBKUAEq/imlK8idOaM/RyLAymWFAUNvp5jyduHerVMtvJLqiLUsSBjCwpN5dMI5NoK6FPaqwU4bWUWznjS7Up1fP8mpsBlhrFzmjIwDUWabvr5ceTY7q793bi3AGMYZfAXvwzbzGmhVqsIJnUEY5FOzZVO7Uc8imKLPCLWiKVU0m0pA18vGhi4T7jA0BES36QiSYsrbhXe3a2uVh9r9ph9zaiCUmxJQq/S/Vs6ya6UUx3amLt5DcUk2HafvEuXLFcsCurv1F93dQN+jRzNCTKVVRqZNTACnT+v1T5/OyoLBzKuDsIghGKYJGpf7LAhCeKnna030GaERhNPAC5jkHI3qCJ5pAiMjwKlT2Q7ntwvvd0S1EQCO4bhzJ3Dvvc3JdJMsu86gLpmkbZKeGlYK9V3n+z17gBs39He3bgFXT38rI8Q2XriWkWkL2eF5GBsDHnsM2L1bfx4Z0XJyfNJEdOUZuc+CIIQLj4e7otdaK3vJhY4jnAZeNAo1Oo41cwd4tLDl5XTg1VVtsLnTK/28N97vBgZqo9e6iyPcuNGcwbdBbGKRXeGgLt5DcUk2jUJ9d2MDmJvLFl0xTVuWjd2fEWLRvX0ZmTY+Dpy0h+c9+aSO4qXTOoX9y1/WcnJ2lkDb5D4LghAiCnjJynqtiZdcaDFCaeApJkzTDHbRCqZoFooL985CHZgIeOopYGlJ/11f1997DTpvJc5KjB/HcDRNoLe3SETQa2HV0OIqlY5QiewSg1AQ6k+hvtvXB/T06P9NU8srIgIfn4G6vIK1J2cBoqxMO65gXl3DQJQxMACMjmr59tBDep8DAwCxdGpBENqEoEpILUpbBt2HKEZCgwilgaf7Gen57Oapor6aTutKdPv3a0Vp505t2AD5BmG5xo+3f7sjideu+UcEVVphbfz94J32QdLpmnqLnDZcvqy9917KlX/VXhOhRa5JSzRCKEahVKKrV4Hbt/X/lpXtu2sbBqYfH8Cu3YQjR/TtjfYp0NFsh2VLZcYdP/MM8KY3AatXFPjIlBaGR45omWQ/GvKYCILQUpSjhNRiwF2QfUiUT2ggoTTwolHtfTZN/bfcvqoUMDmpU5OctEnLKmzYlGP8FOrfTiTQNAsYkJNp7DrzBUxZX4OaOw288EJdJlN5/HE97sYre4LKLkfJq8U16WRa4pq0RCOEIBgG0N+fO2VLocwAomzfPHlSj9GbiqW1XLE77Nqla5iby+7/lVeAN7+FMHXqU1CWgjp5CtOxFHbtAo4ckcdEEIQWoxwlpBbjyIPsQybBExpIKA08ZicdCUilylc4vNU1t2xxjV/xMWzKcf7o/s12/+ZA/XtjA5hf6NITDGMcG0PvAfburXmJp2Kyp5Ts8toC99yjq+8Vu25BjtuptMQ1aYlGCEHws8ULZQYMDGQNPyL79i50YWPw3YBpQo2M4Sr68o5hWYR5jOMS9upJzxfvyjwa8pgIgtBoimYOlBuVq8U48lL7kIrTQgMJpYHn1kvPnNHlv4sZeV4h4e6Do6PA9evAlSv+qYtAMMeNc4z+exXGui/oyYK7LyDaV9r61O3Rc1iNjTKic1/RgqTGlQtLyZ5isstrC0xOAouLwOHDejL4Ys0TmZdPS1yTlmiEEIRCtrg7M8CJ8AFZ0RGLuW7vXd+FgoHp5f+AAweBrVu9R2G8wdzEfizh0a1/ibHR7LbymAiC0Eh8E0zcylwtqzvXKgddKk4LjYSZ22o5dOgQl0Ip5tFRZt0bmU2TOZn0X9eymCcnmSMR/deyst8nk3pfhdYJinv72MgdXqFdnECUlRkp3DCffTjtqSeVHkep7DmOjOhrDujPQU6xnOM26lo0m5Y4zwY1AsAit4B8qXYJIp/qgbv/TU7m365ici4eZ05cXGdlRjiJKEewmZGbZ88yG4buy4bBbBjK7teK4/FcGdn0Z1UQ6kQY5FOzZFO9SCa1PMvoGfEqFbVCVKsACkIdKSabQhnBI9JjSzJzN40X9iqX8nwTVV8cyb39yTNdeDP/Dd6HP4UaHYfqiwZyDDWqCn2lx8ma09mS6+V49IMet5OGhWWuSTMrF8r0B21BKcdwMRn2+OPA7oN9mOpZQJ/xMsZ6n0MkwhgaAj72sdx59e6+OzthepR1xU15NARBaDR5CSZUpyEFMlRBaFNCaeAB2rCbm9PjT44fzy0+4KZkFppSiPIaxsY4bx2lgLWEAifXoCzG1FSmwJxvgRLTBACChQjOYAQTqRlMH6VQGCsbG3reLMvSabFPPFGfLISOk7WdZNEKFaOU7gvRqH9/c8u5oSGdrgm4+xNh/vbDuHphFU9tPIzBQcLZs8D5884eGG94vcKtW1qI3rph4eqeg8DUFFRaySMqCEJDyXNqDdRpSIEMVRDalNAaeI7C09cHHD3qr3wopQ2/48cLGCO2ck27d2GGp7FyWWXW0T8xdu5QGNv+bcRHP4CTJxmWpaOHa2vZ3binIDh4MPMtFhYpNMaKVwZu21Y68FNJWnuQ+fpCVa694yxaoVyC+ACceT0HB4GzZ/V6V67ovjIy4vQnQnRfP65dJywu5u8n9ZqFXvMWTJMxjjn0WQkkvvFNPPeNa/KICoLQcHISTKoZ31ZMcQjTuLnQKUhCMUJp4CkFTE0xdu5kTIwz5ubylQ+3UnT0qPZo5/Vbl3JtnJ7DgLGRWWdjQ0cInWjcIwv/FM7mTgVPLx/6kPaIb9nS2OIE1fbpINuXKwMrDUwVO04og13iPRRKENQHcO2aLnzkOKH27AEefhi4eFFXDZ6Z0f3JmWZGwwAYBEYaXbhtvR7nP/8tHMc0pnEcO7CKh471obtbHlFBEJpMJUMKgigOYRiqEEoFSShGKA28tYSyo2mEM08zBgdZV6Ac2kS0X1spgZSiIsp1NKpTnbQCRLiA/Ri2PeGxmJYFbpzjKQW8+iqwtAScOFF/x1C1fdq9/fi4Vg4LUY4MrCYwVeg47RrsKmpAh8l7KNSFoD6AXMMty82bwKFDup870ys8+SQQiWjZBgDduK0r//Y+h32P3I+rw38H8xiHfoUQbt3SMs39iGaea0u8xoIgtCjtqjiUS6ecp5AhlAYeXb+WjaYB+PJvr2Nl8H2YPdsNmp7S4+oKKEVuZVsxYe2PnwI/s5R1bzv7JeDUKcLoiFaExidNnDpFeXp4ZnqE/uzxxseBffv0OvV2DFXbp93bnz5desqJoNQjMNWOwa5ABnireg8l3aMlCOoDcAw3PRbYDUOp3Hk5o1Hg4b0pOA6sW+jBQ2/fxPGrD4NMA9G5r2BsxI7uUa5MA5wsCmDnTsaRN56H2iFeY0EQWpB2VBwqoVPOU8gQSgNvYF8fYlsvIoIUYlsvYvs2xsDin4GsrJXjjElZctlubmX7yBFgeoqxazcw9dB1qKmjecqJaQLfOElYWiLMzBBMM1cPd+9velofr5aBmHQaePbZ4jpT2X3ao7RnI5WahYXaOH7cSmmxIjiV7rNdgl1t61STdI+WIqgPYNs2bYyZpp6j8vwzChNbLsBECoNveA7996rMrT13sQsEBcfIO//i3bh6TR+ATAOzJyOIX7iK+Crn9eG1NWSyKE7e2o+kulfntLfNAy4IQkfQjopDJXTKeQoZQmngkUGYufYQVpa/i9mXHwZtG8izcpQCjh0DDhzIpiZ5le35eSDNEcxjFBvz38pTTvz24ca7v2vX8pWwSoMg6bQuIPOOdwD33KM/+16Lcvq0j9JezpQT5WIYOrJZqAhOpftsxWBXIdrWqda2lmln48iD1VVd7fbhHRt46tYI9uM8Fl95G6Yn00gkdJ8HCGynYAKMw4cp+3wqBePYNLYf2oHoY9M4epRz+jARkO2ChPfjS1CDh9voARcEoWNoN8WhUjrlPAUAITXwAEDBwAb6YSnC2jqBj+daOX76qVfZHhsHIpTGGE4jOnZ/nnJSSscNUvGx0iDICy8AN27o/2/c0J8LEaRPKwWsXboGnss/IfeUE7PHFWi9dml57ms4NwdcutRZGX+BDfBWS4dsW8tUcMuD9D1RjL5hCecwiDS6ML/QhWvX3Gtr4+7QQd0/M8+nq+NuzH0T83PZPry+rvc/PAI40b9FDGPjK6dEsRAEQRCEBhBKA88d3Xr96/XcdFNHDaj+rJXT16dLhptmVj91K9u6AAphZdXAbOJtoBOzOcqJUlrX9uq4bj28msmHS7F3L9Dbq//v7dWfnXYFsQNyxho6huYBPdmxMrvylHbDAAb6FehobdPy3HME9vToaGinZfyVNMBbMR1S0j1aknL6fyIBjE8Qnnn1ATiGXDpN+Lmf02NtnQqaPbiJM2+YhgGV3X9/tuNGe17FqHUSgIJSjMce08f/xjcIBw8ChqHHKEe3hfJ1IwiCIAgtRyjfuJcuZaNblqUX7xQJR4/qkuGHD+sxYI5+6la2DQMY2G7oFE+PcTc9DezerRWZy5e1jsucr4cXU94LTT4cBMMArl4FlpeB69f156B2gHe9tbXcyY43zq/W3iItgGMnnD8P3L4tGX++tGo6pKR7tBTl9v89e/SceNlkSv339Gngt37LKcZCeA1349qZb0GtbWT3P01If20Ga19fhrr1KjZhAiAw67k919aAH/xB4OJFwvAw4fhxksdEEITw0WrZNfWiU84zRITSwPMaSoahDam+Pv18rq9n9eWzZ4Hnny/+zHqjXZcuITO33unTev9E5evh3smH/cbxFSMSAR58UB8fCH5873pE7kiknuy4pEVaw7Q8w9AV+CTjrwDNSIcUYd52ePv12pr/LXSv5yY7Pyfjwf41jHtS1DcompF7p04Bw6MGdhx7AIffcAELOAwnCjg0pMWHc4yFBe2MEgRBCBWtmF1TDzrlPENGKA28bduAiQkGEaOnR5fxdkfXHntMzwcVJC0wr7LmtF6/pyc3vRMAon0KY0Obes69gHq4e/JhP6OsHD27nPmw3OsNDATMtqtjWl7YM/6qspcafXFEmLcl7n49Ogo8/rj/LXSnRTtTJmzZoo2wlcsKszwFY/dOzNwZx8plzqSo9/UTenr0+kTAM88AShGeuf0A7u42QcQYHgZOfWkNA1GuODtBEAShLWjV7Jpa0ynnGTJCaeARK5ygaVyk/XjttpUxnhzv8/y8ng+qVFqgN1qXqayZ1tudP+/St5UenzZztkfPuXdcBdLDvUoZc9YI8OrZ6bQ2EizL31goZz4s93rM2SIzJdtcx7S8QGPR2jCqVBN7qZHpkCLM2xJ3v/785wvfQndatPM4fe97wMsvAwPGBqxTp/Gs9QDw9GkMPHoEFO2HYsLzzwO3bun1LcvdBwm3bhsgAl73/AVg927Q9BSe+prKyU5IJNqu6wqCIBSmU4qNdcp5hoyiBh4R5U2J2xbYCmqfSmKU52GajJ6erHIxOqqfz76+ws+so5QfOAB0d2v9emQktzCLe2Jf55iGldJz7l0NphQ7ytbly/r/3buzRoC3wuTkpDYS7r3XLhwzlW8sBLUDnPX8xg22JG0WVXLbom1nL7WJMG9b+VRHnH49kD8zTN563rRoZiD1pij6jGt4B5bxJryM1TNXYCX12Lv9+4G772YQ0tiKGzCQBlHWYlOKMHdzHzasNwHz87j2zWuZ7ISTJ3NlmyB0OkT0WSJaJ6JnC/xORPSbRPQSEV0kooOu336KiL5lLz/VuFYLOYQ99cihU84zZJSK4J0jotGGtKSGqL4opnvOYjdWQFu3YnFBR9yYtTLzxBO6yIq3SIr7mXUr5Tdv6u+efda/MAuAqpRiw9BLsWkbhob0WJZ0WheQKZTSWS5tY3xU0dBGB/68tmgxR0JL0j7CvC3lUyMIcgvdziVmLQ9HRgk3rB4AhJvoxZv5bzDxAT32zrKAV14BDuA8HsYFENjTpxhDW15E1LwOjI0hurcvkwrKXDuZJQgh4XMA3lXk93cDeKu9fAzAbwMAEd0D4JMAhgEcBvBJInpTXVsaRmqlGNQqu6bVM5SkqFrbUcrA+98B/Aci+s/tJEA2rupqkGl0Yf7Vh/Hxv0dIp7PFRNzGlLtIihv3OBUi3fdu3ChSNKBKpdjPPnTvcm4u+3tvb/74v0ppk2BNxQ1tRuDPa4tevdou9pKLcoR5815MbSmf6o1zO4hK30KnQNOpU/p5feYZYMsWXSwFACxEsLBA2L/f2YJwAftxGmOw0JWzr0OHCHPX94JW9YPOoEzK+datPl231RUaQagjzPwNANeLrPIIgD9kzRkAbySi7QB+GMDXmPk6M78M4GsobigKXlotI6jV2iOEgqIGHjM/De0legbAIhF92k4Z+E0i+s2GtLAC+vqAu+/WWk06TXj6af09kR5719+fPweeF7dxFYvpdR3DanCwQNGAKjwcpexDZh15vHJFF2ZZLTCTQa2P2zJU2NC6RChLKKZ+tmhNnF+tqBA38cXUrvKpnpR7O5TSDgj3I3X//QBA6O4mRCLA+Dhw5oxObY9EgLGYibERtlPf9QN9+DDjv392A2RkrcqNDe1AU0pnUCwt2V2X7Un4RKERhGLsBHDF9XnF/q7Q93kQ0ceIaJGIFjckdJ6lmGLQjPds26RSCe1EkCIr9wAYArAB4JxnaUmuXs0WA3DT06PHrxWaA8+LYeiKnDMz2qDa2NDbLC6WP6VBELxGgKOs7dyp271nj64AGsQzX81xW5YKGlrzCGUADbqQLVrVe6NVPXzNfzG1nXyqJ+XcDvc4461bs98vLem/t28DTz+tn+FIREf5VlaAEycIT528C4cPE157DRgZZrzuhQvY8/AbMXXPBai0fjbdfW983B6zzK5JRE+ehEpbWJt7CbwuCo0g1Bpm/gwzDzLzYL+Uss1SSDFo1nu2bVKphLaCmQsuAD4O4K/tv1RsXZ9tPwtgHcCzBX6fAnADwHl7+bUg+z106BCXIp1m3rLFSQ7KLqbJfOECcySiP0cizMlkyd1lSCbzt7Us/Vep4Pup5HjOUm6b/ahnm1uRmp6v30MQsA2Tk3qTyUn9uRHHrTtK5Z5YhRcZwCKXIV+4jeVTPSl1O9x9wf1ImSazYeTLzOPH849hWczLy+5tFUewqR9NbHJyeT2zXjzOnEi42uE6aAoGj2CeTaR4clKV3ycEoUFUIp+CLADuKyKDfhfAh1yfXwSwHcCHAPxuofUKLc2WTS2Hn2LgFYqJRHPbIwglKCabSkXwJgCMMPPv2Dsqh8+hdF74SWbeby+fKnP/BVlf18UAvIyNAT/7s8gZj1eOo8TrZOnrq6+zxz0OsLe3Ns6dVg0E1ZOaRigr9LT5RVbKiui1qoevuTm+bSmf6kmx21Go+I9p6iJOQ0P5+/vkJ3NlhLOP/ft1dWHncRzb+hwiSGGs9zlE9/Zl1nOyDpy7o/qiWBv8EVhGFya3XMAZjMBCBHNzJFlJgpDLVwF82K6mOQLgBjMnAPwVgB8iojfZY49/yP5OKAc/xSAa1bnogBZ2jz3WOCWpbVKphLahkOVn60sjAC4AuAXgNIC9xdb32f4+FPeQ/49y9scBvVCrq/meaNNkvnixeudMKqW9146zpeqgSgmvjfNzOl0b506rBoLaBt+wRGm8kZV0uoKIXog9fKgsgteW8qlZ+PX9VIp5dFR/jsWYDx/OlZteGeHeBxHzzFMWW/EkW5tpTi6vs7KUaz1l70Nlsh1iMR3xGz54J/M7wDwyEsrHWggJlcinUguAPwGQAJCCHkf3UehshI/bvxOA34LOUlgGMOja9mcAvGQvPx3keM2WTW3z+orHRUkS2oZisqlUBO/TAP4xgHsB/FsA/65M+7EUo0R0gYj+goj2FVqp3IHCfg4XZwyIe0zIwEB5jVUKOHZMj1mZnq5B+fsA4TTHqWOatXHutGogqC0oFJYIgDeycvVqkbFShUJ74uHz0pbyqVn49f1r17LTr5w+rR8td0Epr4zQDm4GoF8gR48Rpne8CBw7hoG99+oiKwCifQpj3Rd0VK/7AqJ9Cmtrej48yyI8/cxdGBzURVxGR3WVYHmshU6CmT/EzNuZuYuZdzHz77PORvgd+3dm5p9l5u9n5ncw86Jr288y8/328l+adxbBaKvMoW3bREkSwkEhy08bhljyfH6m2Po+29+Hwh7yrQB67P/fA+BbQfYZxAt1/nyuFxpQvHp+nZMJVVUkrOZj8JoUTmsbT1qjKXVhani/Co6VqnqwXnuCyiJ4S57PbSGfmomVsnIibe7ncGQkKzOJFF84vpFZL9M10hbHh3+MTXvMHcBsYpOXjYdYJZLZ9RJJThl38TL2sWXovpJI6KifE/1bXRU5JLQHlcinVluaKZvaLnNIlCShTSgmm0pF8HqJ6P3OAj0Pi/tzNYblTWa+Zf//5wC6iKivmn063HNPzpEw+Prn8KH9z2PXDgtHjzL6+4t7iwsFUPr69LgTQP/t66syqNKkcJoEgnwI4mKs4f0qOFaq+VUp24m2lE/NQqUVNmLvR3T/DtD0FKBUznP45S9n12VmRH/wYdD0FFRaZbtGLI3owp9hDHMAFABGD27jAJ/DkUej2fUejeJoz9M4gCVMb1lA+h7dVyYmdPeJxYDt20UOCULYUUrrUm0VFBMlSQgBpQy8EwD+jmtxf/7Rag5MRNuIdO8hosN2W65Vs0+H656pQ//1a/8n5jGKNEeyOnMBK66Ynu+efuHWLZ/JzgOQc9i2mYSuAwhiWNX4fvm+Q8o1IltxbrzG0ZbyqRkoBUxPprHrzBcwZX0Nau505hl3nsNt25w5PxkxnMKAigPz89h44Vq2ayx04er+H8Ss8U7Ehz+A8+cUbpu9tmyl7HqnCfO3H0YaXZi79TAmR1PYvZtBBFy+LOJOEDoBR5/avVu/oqTv+9DZ73ChjkSK/cjMP13pjonoT6ALFfQR0QqATwLosvf7OwA+CODvEVEawPcAPG6HG6vGqxO/fWgrxhZOY57GMDZmItpnS535ea1Ez8xoLQdePZ+xsQ4MbKPMfsfHs5uV64VSvoc1yh8M2ESU0tcoGm1RIV1pAx3DqtTNrff9cozIIOfg/0DVr20tRrvKp2awsaGNszQI8xjHxtB7MOB5xom08rWxDkQf/VXQae1kiO7ts7sG6/F0F74GGh7CwIkv4rFjJtIWADBGB1MwXteF+XnC2BjATDh9mjH4huewcO5t+tjzDAMMWm9lISIIQi1YW9Pjay1Lj/E1jDbp8rVWdArtr8Pf4UKdKZS76SwAHgTwBwAW7eUPALyj1Hb1WoLkkSvlVGuzxzeldaW3ZELplOoiCeF6TIriCKV4EidYxXLHQVWTmt2oPPQKCz3m7sDnJFt+eFi1DWy3vPu2G9hQGFQ4xqUd5VMzyI61Uzw5eicztq4gnr5gWazH7pnZ5y25vM6myZlxziOY59TEFCfjFiuV3cYyIjyJGY5gkydHXuPN8SleNh5iazymBVW79DehYykknwD8GICo32+ttjRaNjmvY2fcbSzWOl296Ku+1opOsf2F6B0uNIdiulMp5ekRAN+CLsn7kL38jP3dI8W2rdcSVEjd+Z7FM1+6yumUTw8uMRuwFU9y0tzBqsadrkZzQhfFKUXuFDIoWz4VEUYtL4tavoE1phEPVIOoxMBrZ/nUDMrxX/iu63rerNgRTsSVXZhFZSc5N3fk9jt7G8vs4uTII7x5OcG9eJkBxb14mVPGXS3qLRKELEUMvC8CWLVlzh8A+BiAB/3WbfbSaNnkfR03cs7wYpS032qtRxTbX4je4UJzqMbAuwDgPp/v7wNwodi29VqCCKnUHYt7zZtaiTBv8p3vWTnKimUxJ+MWp1ddUT0Hy9KSKBbL63RVB3gsTySxFvv0kEyyy6tegXwqGd1sYVnU8g2sA60WdaywPRUaeG0pn1odtwI0Osqc3sxOxmlNTPIKdvJw9zKbpuKJCebhYcUmUhxzMh7c8jJusVrNphMsX1QZgxBQvIx9NSpJLAj1o5R8smXOT0BP3XIOwAaAPy+2TaOXRsumVn0dl7Tfat3wUvsTuSdUQTHZVCrZN8LM3/FJ6/wO7PEqrcgL89dxw+oBQLhh9WB0OJ0pmpJO20VU9hi4d+8Adu6ibDEV94hgVzUAxYREIr/4ijM21rICjJG1923s2YWBx6ZArAoWdKlmzK0zlAzQp1D2WMEiRT5aviZMyzewOBXd9xLVvmo6frvUzho/2VFbyqd6UMv7vLaWHYd8+jRj4t5LUDt3Q01MYurUp7ALl/H07X2wLMKpU0AqZU+eNzyM5H/8gjbdFDA9zdi1U2Fq57egHn0cYMbefYTeXgBg9Jq3sNd4EWp0HGtWH3iqXSbKEoRcbJnzDIAlAOcBrAN4QxOb1HRa9XVcso5ZrRtean+lKnZKERahUgpZfpz1kO/x+f7NAC4W27ZeS6AI3qb2KAOKTaSZSGW8NcvLWe9NXpTLx7XjeLNNM5tLHonooSOOU6a3Nzver2Cmkc++vV8tLzOn09Wnf9djDF47O5naoe36OVN6jNSkqknGWk2HEgTZWRU5Oag8gtd28qnW1PI+u1O83fPcJRHlpLnDlqvObyojE/Vi6XF2W85x/EqaI5Fs6uay8RBvXkny8jLznTta1lkpndGQee5xgi1QZ6RXC21FIfkE4J8C+O8AzkCnaP49AAcAmH7rN3MJQ3ZBrWgHnYCZ26DwgdBsiulOpSJ4nwTwdSL6CBG9w15+GsD/BPBrNbQza8q167ZHGQSYBg4epIy3Zu/erPemt1evlvHi+Lh2nKqalqVVmEgEGB3VUyTMzWkv940b+vei05b57Nv9VXc3sH+/nicqyDRoxZw6hqHnmNq2rULnk8ej1PjATO1oStsr8LhtrCnMn7SQThPmT1rYWKu+oTWdUi/IzpwHmkiv9+ij9b7gbSmfak0t7/Pamq5250DEGNv6HKLmdUTH7sfQsAnAea7J9Yjrf9Lowvwr7wC99+9gbBSIUBrduI2H1Dm84b4o3vEORn8/44EHACNiYMMYwPw86eeexrBhbm+TibIEAQDwYQA7APwlgD8C8MfMvMTMVnOb1aEEfPe2zTR3Mi+uUA2FLD9nAfAwgD+Ezis/Z///cKnt6rWUW0Vz61Zmw2AeGdHRMeas9yad9vHieFw77vTpWIx5dTU/cueO4BX1CNn7ttIqcwjL0t5sZ9ycaepxL8XSvxvt1KlH7ZJGedAaXnelwpujEkmexAkdAcEJVonqG1rToQTFdua+mSsr2Ye5jAuOyqtotp18qjW1us+WxTwxwZmI3Nat9j5jiq24vr/pNPPwcP46IwfvZCtlYoYVGWytxHn560k2jeyYO+fvhSXdL5z9GQZzLKb0c9/ybnWh0ygmnwDcAz3v5r8EcBzAWQD/GcBPF9qmGUvoI3iVKkY1VkZqurtWHcgotAxFZVOhH0ot8EmNasQSREg5ioo7fSgSUZxMVJZy6F7XazDMzDCnUoX35T1OVgYpnhy5w1Za5fVhX8PTRa2NllLXotYyppEGqtvYb0iZ5kpvjlJsxY7o6q2x2gnymr5s/HbmvZmOheCUcA144EoNvEJLK8unelCL+xyPZ+Wl8/h6H2PLYo6vWLx6fp0TcZWVVZZia2KSk4jyJgxePvCTnBqb5AQGOIYZJjtl3jHw4hfW7XRQp+iK4sOHtQNNdBih1Qgin6DnFR4G8IsAXgJgldqmkUvoDbxy373OWJYaKiN10W3aJp9UaAZVGXgARqEn/Y3anx8C8McArpTath5LECG1suJWVBQTcue0q6YTuo2dUmPv/I4Tj3NmTCDB4vjwj2Xa5NuHfX6o1uBy7zLotailjGlkVK3hKezV3JxyL3IrCH7vzXSHous8Bo/bVD61KolErlPMuZW9vdrpZFnMkzGLI7Y8tWJHcjuUZXHqcpx7t1j2+OdNNrHJk5jlVWzniZ5n2MQmT/QscSKuOLFq5Y3pA2SoidB6FJJPAN4L4F8DOAnguv3330BP4dLvt02zlnaWTYEo591bqLhClcpIp83UJDSfig08AL8B4HkAfwJgAcA/B5AE8PMAXl9s23otQYTUxYucozTMYCpnTrtqO6E3rdIpkOKVJ37HSSSyigxgccLYkSnm4ja6kkk9QXsh66RS3d5r8MTj9RVIfu1sWNaBZXFyeT1b7KFRArcRhlerDL723kyf6UWCUImB167yqVVRiu157XIX09SPczKujTvvnHfux315mfMMNhObnBj6O2xdWeX4+TW7qArz5MgdHscJBiyXTBTFSGg9ihh4XwbwC7aj6S7PbwN+2zRraWfZVJKi4258cCtnjtCpgTIiGZVCo6nGwLvkKEoA3gTgFnzmnWrkEqiKZsodSFB8Z2wqJ/Ut2wkVT47eYWWV3wuDRPLS6dzxdOm0NvAmJnLnjbLSKmecX0ZAjN5hy+yqqdbjV+iwXgKpoA3iMx9gzbEPrswIT/Yu2dUpQyRwW8lV6OudKO9CV2jgtaV8amXSaW3kGYaWa7rvKt68kuSLx9c5htnsOLuRUbbSKicFOpXS22UNPL2M9yxlJjvPOlwUj3RfZAOb3IPvMmAxQe+v4grAglAHgsonAG8E8FEATwGIB9mmUUu7y6ZCWCmLkyOPsDLLcHZ6iyvUUOC0QmKN0DlUY+A94/m8VGz9RixBhJROg+SMc+Yd71Bsmrnl5x2hkDa00qHSwSMgbmeRN5LnHqviZACMjDBvbubKkwtLVqZwgVtXN0233q44OfJITa0vPw9TvQSSrw3SqMiT6+CW2cXJ5fVwCdyQuQorNPDaUj61OjnO8LjFm+NT3IuXGVC81bzJl7GLEwffzelNiy9cyMpZgPncOeZnnmH+4hdyo3KGPdVC2uji0UN32JlE3TSzUb4LB36KV1dUThBYUjWFVqBEkZU3AHgcwFcBXAHwXQBTAIxC2zRjCYNs8mJZ2hHuOJ0ssyu4s1MsMSEEVGPgfdcWWs6S87nYtvVaggip1dVcpcObasTMOrXI7MpWfhu9E0iZ8BpuqZS/nu03x51fRoBl5TuTcvaXrr0QapRc87VBGhV5CpkB5EsrvKBq1IYKDby2lE9tRTLJy8ZDOQVSDj10hyMR5YrU5S/db7B4C76bieBNdJ/jtNHFk71LbJrKIzuzmRRaPGQjfJKqKbQCRVI0/9g26n4fwDsBmAD+xm/dZi+hk03MufICm9ohHsZ3vSAUoJjuFEFxHvF8/n9LrN8SGAVm99u/3zW9UjSKjaH3YP7MuJ67aYGxtqa3jUZd86Mopecesb/c2NDz31kWcOYMMDkJfOMbwLVruds5U4LNz+fOvzc3p3fpntZkYACYmckexrKAF17Q25AzYUuNr0+Nd+kLUe55ESH/wtRrvivfg4eMRt3IQjiTDDr3cmamcOerD20pn1oRj5jLEo1i79gb0XvqBm6gF1u2ABcu3ZWZ/7MQt79HMNCDAziHzz78H/HgV/8Vnv/2Kubf2QfLIiwu6uM98QRARBgYuAtEQLRPYaz7IuZv7MNY93OI9j0ElJyuVRCaxl4AL0OPBX6emS0i4ia3qWPQ6gRhfp4xNsSInvpKON/1glABpA3AAj8SfY6ZP9K45pRmcHCQFxcXi66TSgFveIM2lBy2bAFeftme/9yGLYWpWBrzC10YHSUQeXRV5CuwTAbGxrRxB+hJyldW/PVsr9KkFLC+Djz2WHaXs7O58qj5OnMDKKhNCm3F2pqeQT6dLt4RAkBE55h5sMxt2lI+tRolZY5SSCc28ML1KN6+l3D0qF63uxt45RWgpwe4dUv/vXnTu3dGBBa6cQuvoAdbek3cvk0YHUW+vDUArK1B7dyNDetNiJrXQauVP1OCUCuKySciegDAhwA8BuAqgLcBeJCZ1xrYxJK0o2wKQq3UibqqJfXauehSHU8x2VTKdHioDu2pOy++mGvcfelLwHe/m2vcAQCZBmZO3YWVFcLnP6+VDXdkDRsbeV8SAadOAaOjWqctFoRyAixOvzMMYNs2rcysrOQbd/A/ZNuilLYB8nwI3gsjtCdONLZUR6gfbSmfWo2SMscwYGwfQH+UYBhZ+XXtGhCPA1evAhcu6M8rK/r/yUktbwlAGhHcQC8UIrh1C1hagr+8BYBoFMb4KAYi10HjTXmmBKEsmPkFZv4kMz8AXcH3DwEsENF8k5vWEdRCnXCcXLt2AVNT+nPNqNfO69poIQyUMvDuJqIDRHTQb2lICyvggQeyxpxpAj/6o1qB8AtWOsJhYMBHV7UVWGV2YW3wR8D90cw+T50qbKSVophA8urMfX0FjKSC1lNrILKnA3DSYCvtCNXTlvKp1Shlp3v7MqDll2kC996rjbn9+4Fjx4Dt24GHHso+FrFJIEJp9OIGTKQzMi0a1U4y09R/M8ckAp56SluBMzPiBBLaCmY+x8y/AOD7Afxls9sjBKOujvV67dwZL5RO67/OfltcNxQaRykDbyf0uBa/5f+pb9Mq59q17LOtlFZKShkavroqEdRTM5geuoVdi1/B1DTV3VBxt+P4ceDoUZ+2N8N6KlNotFMksqPkYa1PtrnR2LaUT61GKTu9UF9WSht3Z87ojAm3jpHNViCsrBq4tnoHV1ZNEBF27wbGxxi8uQkiBpHePpEAknEFPnoMOHBAyzivbOuoziq0OkS0lYg+QUSfJqIfIs3PAfgmgEPNbp/goYD8qFUyiu/u65Xp0ten8+IB/bevTzzrQi6Fqq/YY/OWiv3ejCVIJSingKJpMh886D+NQVDcUy4Q6c+plK6g6Tf3XS0pWHCyRpUoAxdArGBqg3YpYtkq84U3hBY+WVRWRXOp3G3qvYSxUl2hvuydK3hkJL+fu2VM7vqK9QTn9nx4I9nvJ3CCLVBGtmX2kW7d51cIN4XkE4D/BuBzAP53AJ8HMAvgBID9fus3cwmjbGKunR5TbUHooruvR8VrPz2wlebHFRpCMd0pbOU7AGSzfIYGGRcvMLq7GaZZmfOEKOuNcVQQt9f61Cng0qX6OJQLOn5q4BEqy9FTQTiuKdl7FXj32ynSWDWNPFmJtISGQn05m2bJGDm4iblT7FswatcuYHxcp3MODQEAQ4/OAyJIYejhFM6ezRwNpzCBNWOHTo+3GNPTrOVULA01d7pDOqvQJnwfM3+EmX8XutDKXgA/zMznm9usEOLzTqmlHlNtMkrR3dcj08VPD2z+uHihhShl4P1SQ1pRB9YSCmeeBtIW4ZWbwDOLqiJDY2AAiMX0WJFYTHfarDKiK8kdOFCfaHhBI6kG1lNZun6FQqOh2XsVpiZ0jDxUSr8YvdWB0mng2Wf131oZZI1LE2lb+dRu+PVlZoDAIMvCXc+cAaanodIq8xi5Zczp08DwMGP2j1YxcvcyTKQQw0lcGf5xnHq6C4cPu49GwLlzADM29hzC/ElLy6mFLmwMvacDOqvQRqScf5jZArDCzK8F2ZCI3kVELxLRS0T0yz6//zsiOm8v3ySi77p+s1y/fbUWJ9LSFHinBNVjlALWOAoerd/LvuG6hJ8e2Pxx8UIrUSi0pyN/mAFwvMDyVLFt67UESTOwLObhA3dyJuddPb9eSfQzs794nHliIpuOBDAfOJBN38yZRL0NKDuFshUm1S5GFakJrX5qVePNHYnH9cmmUpyZrdo0mQ2jNqlvFdwLVJai2ZbyqV3x9hPvJMNxYydPjt7JPGbpNPPoqFtmKh7BHE9ghk1scgyznLqS4GRSP4rDw9l1YyN32DK7WAE8iRmOYJMne5dYjU/oZzUWkxRNoWEUkk8ALAA37eUVAGnX/zf9trG3MwH8NYDvA3AXgAsA9hZZ/+8D+Kzr861C6xZaWkk2lf3OTSbZMrs4iSgrM/tOCaLH5L7+FFvx+r3sQ69LCC1HMd2pVATvHwP4Rc/yJegKUVsrMSgbwcYGsHixC04aEMAwon0V788w9HL6dPY70wTuuisb8Oju1mNc60Id0t3KdvS0+tQGVbjPvKcWuuxCr5vTMPTJvvBCdrZqy9InXovUt8a5MttSPrUjfg70zG2mNMZwGjQ0iPmFrsxjdvUqcPIkcPAg4KRlLmIIpzEOC12Yxzgmf3wAu3Yxjk1u4ktf5Ez149OLOlpHpokZHMMKdmH21hD49BmsWfeC509LiqbQdJjZZOat9rKFmSOu/4vJoMMAXmLmbzPzJoAnADxSZP0PAfiTWra9WVSS4KH6opjuOYtdWMFUzwJUn36nBNFjcl9/hA2jMj0miF7Q6mqS0FkUNfBYl/w9x8znAPQA+DfQgubjzDzUiAZWQjQKjI8TtFLBmJggDGyrrsc5ygygO+/QELCwkP391Ve1QlNz6pju1khhVHejqUapCaEsQlXI4Nq7F+jt1f+bpn4gamGQNajUfbvKp3bELxVKdzldJXM28TYMzH8FY2OU85iZpk5pHx1xDME5jGEeEaQxNGJiYRFIpwnzpwnmB9+H8XFGJAKMjhL4C18EP7MEY3ICA5Hr4LFxTPcs5Cl5gtCG7ARwxfV5xf4uDyJ6M4C3QGcmOLyeiBaJ6AwR/VihgxDRx+z1FjdaxCFSyVDwjauE+dsPI40uzN9+GBtXs++UUnpMLfyNVekFofMYC21DodCeswD4YQAnAXwdwHSp9eu9BE0zuHOH+etfZz5/vnaZPJbFvLLCfPGiTj+KxThTXTNopchKUhMaVRWpXukFLVy8MY/QFqEqdHNTKeblZf23Vje/ghuOClI0uY3lUzthWcyJRLBUKEc+em+5ZTHHVyxOnE9yeiXByYTSj8noHZ1+iRlWZoSteJLjcebJmGITKR7BPG+OHeHkhSQn4iqbEhpR4embQstTqXwqtAD4IIDfc33+SQCfLrDuPwHwHz3f7bT/fh+A7wD4/lLHbBXZVEmF7Wqrcler21SsF7ST8iO0JcVkU9EIHhEtAPhd6PSBXwJwox0mEk6ngf5+4Ad/ENi/nzE1tgll5XpPKnWq/MRP6JSjo0f1PHXxuF6OHwfW14vvr9LUhLXBHwGb9U13q2fkqp0qVYa26EohN2ckAjz4oP5bq3Bug254u8qndsKRC7t3a9l2+bJ/gNw93+jDD/tPYff4TxjYPTiAoz+xDf1RggGFmS9ex8rwB3Hc+CGsD/0IuD+Ka9eAU3OAhQjOYAT3zv837DoUxfveTxgZcSKEFJ6+KXQiqwB2uz7vsr/z43F40jOZedX++23oqRkO1L6J9aGSZJtqE3QKRvkCKoIV6wXtpPwIoaPUGLzbAP4G2tvUNhMJv/ACcPOm84kw/zRhY+J9GY2jlDFTqM97++q1a8D27bqz+05IjuLbl+rrSgHTR0lPsn74NtTx2bqlu9VTDmXLqeu/rayYSRGqCnF3msZZyW0pn9oJbyVMZ/iml7U1PdE5oB8BrwzJky9rWggbb96N/rtu4OjhV7Bz4Su4t49w4IAe0+yM23sFW5FOE86c0dt/5zvAk0/W/9wFoY4sAHgrEb2FiO6CNuLyqmES0QMA3gTgtOu7NxHR6+z/+wCMA7jUkFbXiEqGh9R8SEkZXu2K9YLQeoyFdqDUGLwpZp4usBxtVCPLZe9eoKcHcMbgjWEO0YU/y2gcxYyZYn2+r0+PvfP2Vb/9+RmJ5fb17H4J8wt35eSd15p6yiHmbAVf97yCDaGCUK0MlC4Tb6dhboiV3K7yqZ0IIheUAh5/PCsrifJn4ejry+5naAjoR1Zobsx/C/OLd8GyCDdu6G2+9z3CoYMME2lswU1oWa7HPX/wgzqiGJoxskLHwcxpAD8H4K8APA/g88z8HBF9ioje61r1cQBP2KlYDm8HsEhEF6ArCf9rZm66gdd2Q83K9GpXpBeIx1hoJoVyN2158kuu/3/c89u/LLZtvZYgeeSpFPPWrcyA4i3mLd6ku3ISt4vlcxfKtXZSqU2TeWREj8Fz8O4vnc5+Hh3NXbecXPBq887LpV5j8Bo9ri1zHmnJf28INbjBqGyahLaUT+1GKbngvv2mqcfgeWfh6O1l/t73tOw0TbtceewIcyTCKjbJExPOlDZ6icWY06tJTpo7OAXiEZzSY/KGFZtm42SJIDBXJp9abam1bHLLhZYZamZZbMWTnEyo0npMoxUsQagDxWRTqRTNx13/f8Lz27uqNS7rRTZFk/CKdTdePL6a4z0p5lQp5LF2nD2WBSwu5lbM9O7v6tXctKaJiaynuRwvUKOdP/WKXDUySyEnmBRLQ82dlvz3etO8NJS2lE9hw337x8f1kE7vLBw3bgBnzmjZaVl2ufInjwMrK+CZWaTTWaETiQCf/zxgbo9i4PCbEQFjDjFcoTcjghQsKzdKKAhCY/EmbayttcBQM6Wgpo5ieseL2LXDwtQUF4/wE0E9NYO1pTh4Zlaia0LoKGXgUYH//T63DD/wA+5PhC1v6UNyjcCu1AG3MeNOLShkVJXSYd37i0Z1GpLDwkLlAi8M6YKNNFTdWRdzZ7tw6eEP1b1ATU1puzwXNDMNpS3lUzsRZJiK3+1XCnjTm5CZ1840tfGXI0O3aeG2cZVyppwZHNTbJ9cI/I2TwMgIDNOAMTyEM+e6Msf89Kc9DW23fiMIbYo3u9FxuDR1qNnGhk73xijSHClpaCoFTB8zsOtAP6amSdK9hdBRysDjAv/7fW4ZvvnN3M/33Qfs2FG4oIpXgfEzqsrRYYn0BL8jI1nFxivwOk0fca4pc33P2zHETRPo6SEcuPi5uheoqRntPAlfczwRbSmfWh23bAo6TMXrMJue1nLXsvTvzMD164WdZ+Pjus8OD2slcedOW2YfM6FOzgGrq4jO6Xn2TFMXYTl40O4m6TbuN4LQhngd3gMDLTDULBpFdOx+jOG0nnOzhKG5saYwP8+S4COEllIG3sNEdJOIXgHwkP2/8/kdDWhfRTzwQNZz7MCsq7ytr+d+X84423J0WNPUx1tdzRd4zdDjW8GgbMR5O4b4+fPA7duNKVBTM6Skcrm0pXxqZbx91F0cJahn3v0YE2lZODGht/WToczAE09o5fBP/1SntTvfz88DG9f0RgzCH/8x8NBDwCuv6P2fPAmsXbom/UYQGoifw7vp2UZEoNkZzMTfhpW4idlZKtwWpRB9bBpj6ZO2MchVRx1bQccSBDelqmiazLyVmbcwc8T+3/nc1ahGlsu1a/6dTCngscdyDYt6Dh8qJPCq1ePLFSStEhhqlP1iGMC+fS2QMlIuYSip3MC3XLvKp1bG20evXi3fM+9+jGOx4ts6smnPHi2b+/v1tkDuOLtsVJCxtJR9tpgB6q/AChUEoSqabtD5YRgwtg9gYFsR4w4ANjZAp+cxgymsGG/G7JPrVZ1HEB1LDECh0ZSK4FUMEX2WiNaJ6NkCvxMR/SYRvUREF2s5MXE0qj3GjoJx/rz+P+MRdhkWzRg+VI0eH9RYqyTNqt400n5py+rEbdloF63iSQhAM+VTK+PXR8tV5LyP8bZthbf1MyhnZ4F4XC9ON9DrsasYi54CZ2JCH5CPt3G/EQShsdiCzoiYGBi/HzRQgTLiUrKy8kn/9epYbfRqFEJE3Qw8AJ9D8Up27wbwVnv5GIDfrtWBiYCvfU0vMzM6pSdogZSgVOONqUaPD2Ks1SLNytlPLT1OYakKWlfastE2reJJCMbn0CT51MpU0keVAhIJIJnMyoqgj3Ehg3L79lzDMBoFhh5OwZn8nGDh3P+8hlTKlnNHDaj+Nu03giCUppYKSbXKiEfJit6Txlj3BUSQwlj3BUT7ci249no1CmGhbgYeM38DwPUiqzwC4A/tqRzOAHgjEW2vxbHTaa0QTE8D994L3LmjF6WyMy1VQyXeGK9sMgydjrS+Xl57gkTBapFmVS+PUzX2i6Q4NJEgF9/9cA4N6Qe8RWmmfGp1yumjjpzYuVMbZUeOlCcrsjPf2ZUzk/mPmFLAelLhG396HSM9zwFQYBiY+sC9ePppXcjl1CnGs7NXkUywyAdBCBv1UEiqUUY8Sha9+AJmbh3GCnZh9tYQ6GquBReG0RdC+1HPCF4pdgK44vq8Yn+XBxF9jIgWiWhxI4Drwzv/0vAw8PTTWiYUi3o5+mspXXZtTRdQKeWNcfZjWfmyqVJ5RQTMPKWwsrSB2Rn2lU21SLNqNY+TpDg0kaAXnwh46ild5/7pp3VpRKeMYvtRN/kUJhw54cjK+Xkt84I6YjY2dFEVbaTpsXjuR0wpYHpKYdcOhWO7X8Tn3/arMAwCYOCVV7Lpmnfzq3j42D268uaREvNfCYLQXrSaQuJVsvbuhTE+ioHIddB4vgXX7qMvhPakmQZeYJj5M8w8yMyD/QGiAnv3Alu36v97eoBLl7K/DQ35T1ng1l+npgrrskoBjz+e/X501N8b497nxES+bFpb0xXgMpXg1gJeDKVgHJvGwIEdoGmfBqI2wqTVPE4tId87NYRY7OJ7r8m1a3riR6X0zNaxWOit8XLlU5hw5IQjY0ZHtXwM6ojp69My2TT1PryP2FpC4eRJQhoRnEQMP37uF8H2Pt2Vkm/xGwAYYFBL6H+CINSQVlNIvEqWYZRUumo6+qJTdRGhLJpp4K0C2O36vMv+riY8/LBWAPbvz8qF0RHG3JfXQJ4psrz6azFDwu2xjkSA3/ot/+O797mwoJUYt2wiyvZNZ4L1QAS0dKoVJq3mcWq6fO/kEGKhi+93Tfr6dKdzWFhoV227rvIpLDhyYnVVj8P7wheCO2KUAo4eBc6e1UFfpzCW+xGj69fAtrxmAIsYAkPPhWdZjhx3hJMCQfmOgREEoY1xKyTHj5c/tqUeeJWsRo2f72RdRCiLZhp4XwXwYbta3QiAG8ycqMWOddoPw7KAM2dYz7F0WWGuawrGnvxO4dVfixkSfX16kl1Ay5cDB/z7mHuf4+M6Suc2lgYGdHDDNPXfgQH/c8lz1DTQ0mmleh9NNzhbIoTYJApdfO81WVvTGvvSUja80t2tO037UTf5FDbcRVEGBoKLJ+fxsSzg3DngySfzH7GBt9+DmDEPEylM4BsY3/ocIhHG+BhjrPsCnGqaBhS+g12IYztmb+ePgREEoc2xCxeo6WNY23kQfGQqVMZN4KBcJ+siQnkwc10WAH8CIAEgBT1+5aMAPg7g4/bvBOC3APw1gGUAg0H2e+jQIS6FSls82bvEEWzyZO8Sq7TFnEwyRyJ6PH8koj+7sOxVlMr930syyWya7tIAenfLy/b6ro2L7cd7zEK/T07q/U9O6s/OD1Y8ycmEKritUGOUyr0ZcuHzr0kike1jzmKaeX3NDwCLXCdZ5Lc0Uz6FnVJyzSFQl0om2TIinESUFcDW4RGOr1icuLjO5/EQA8op0cIH8TRbZpf0T6HmNFo+1WMJg2yy4kmexAmt2+EEW/HS75Z2oKCu54foIoKLYrKJ9O/tw+DgIC8uLhZfaW0N6R178IJ6K/YaL8KIr2hX8pEj2uMxNgacOJFxEyulnSBO6mQxmHXEbm5Oj++7fVsHKW7dAsbHGTM8DeP0nD7GzIz2OlXI2pqOwqfT2iO+sqI95E6E3jmVKg/TmZRz06vZpkWp2am4dwTozjE/7+4UgUKuRHSOmQeraElLEEg+dSh+z5xKK2y8cA3RvX0gg/LX7WfQxLiuxAJAGRFMD9/G/EIXRtUc5tQIFEzoqRPSSMx8CwOTbwMMI0zdVWgyYZBPYZBNa0nGrh0W0hxBhNJYiZsY2FZZ524l+VBI1ytIKzVeaCrFZFMozQLVF8WxLU/jAJYw0b0E695odqCbs9iGbbnpzO4xJ9eu6Wy0W7d0mtH8PLAx/62ahc77+vTYFMPQf536DRKhr5JKc9hbKWe1Cmqawu++Jk4VzaUlPTfH6mprDOAUmo7vM1egYFTOutMEdeIkMDICmCY2Dv8I5he6kE4TTqthKHtePIBxuPsFWEffieTY+2GllAxTEYSQER0gjMVMRCKMsZiJ6EDlxl3D5EOA3MuyRt60inEnhV5anlAaeBtXCXO3HkYaXTj9yj5MxAhqzd8qCmQseR5kR6c1TWDfPh2kyHTMsfurGh/nnlrh6FFdo6K7G1hc1AJJqRYoOFKEtujzHW4h1+30lQKOHdMDU48dE61ayOB+5ubmdI2EQg+i9+tLL5rgU3PA6iqic1+xZR/jDcYmABOAwvDdz+Ku29ewky9j+9NfxvhI2rUPxsaljRYXSoIglEI72AkrK4TZWarYvmmYCuCeKHRsrOC0QYFrDFRqmdZaMZNCL21BKA28e+4BspXVCGfPAhvkbxWVNJZKPMi5HZNAs5VXAnEfKhbTipBlAa+8kiuIallwpJb9vm36fCtbyA2gbqfvfmuePKkfhHJnvhZCSTSqp1AA9OPw2GM606KUTO7u1kVZp44aUP0DIDCeUkfxcPocXlF3AzBgwMJnXv27mMM49CvNwNPPdGFwUBuCY90XEH14e1EFSxCE9qAWiTTlvAOr0pE2NrKKXIlpgwKdVyWWaT0Usw53krcNhQbnteoSZKDw8jLn1Hk4dCi3AIqVVjlFAIoWBfAUZ7HiyUAFBCrBWwdmZETXqOjtrc942rIG9gagRB2b1iJoJYiQUpfTdw/+dnfAeLzkpghBEQMOSSGDehGP+8iHAg+iZWk57hS0yqyfTHLS3MERbGaKqwAWx7Ys8fBhlVN0ZXVFcXJ5nZXhqoo1Olq9oBM6jjDIJ5FNuQR5B1atIymlFTl3Rb5qFKNKCqzUQzGTQi8tQzHZFMoI3gMPZKu0G4Yen0+kP6j+AUwfpRxnRlHPicvVo0bHMf14NM8RUo6Hp9i6Xq/SqVPZsX71mB6g1k6YtgqMhWQ8XaXU5fSd0LJ3IH+QjiG0HeV6tqPR7BQzmdkzCjyIhuGT/h7VO+kbfSu6cRvIzGdqYP7Vh/HFLxF6egCAsRU30P/YUZ3OMTSU3XH7zssoCEINcYueQrKsah2JSCtyo6O1UYzKTN9SCljjKHi0xopZ0+etEoIQSgNvYyObiaOUrvfg/i1nfMelEgqK60He+PwM5ucpp7OXE/0uuq5SoPU1zBxnXL6s54Ryj/Wrhy1Sa4NM+ryQp7ATSYnXEFJJ1s/Vq7ogFaD/uuWyH77yhAhXP38ct8xe6DR8/Zzdfbcej/O97wEA4Ra6cWTuX2LXmw1M3TUHdXBQC9KW9zwJgtBIismymuhIpqmNvFopRgG9s5nz2k2YolmoyzVWzDrcSd4OhFLzcgr6ef8HCozvmCqhoNgPcnSA8jp7OR6eguu6Jcz0NB5/nLF7d/3HsdXDIJM+L2BgQI81iET036L1noV2pBLPdjSajciNjwdTlvzkSXSbgfFxymRpAHqc8gc+AIyPARFK4zAWsUBDSKcJ86cUNi7EgcOHgePHRTgJQgNpmcJrBRqSK8sYG+vZ32umI9VCMSrzQuaeF2HDEMWs0wilgeenXzp9A9AdNm96gwAKil9nL8fDU3BdV0/cmP9WQ8euikEm1BwJ5YaeSjzb5TwWxXQZZz9LS7nfLy4CTzxJWFk1MBf/vmw5dZ5H1Irr9MxSYUNBEGpGyxReK9IQLcsYEUpjLH0S0Udzf28JHcnd/vHxQMWi2mrIjFAXQmnguafjmpnRSoK7bwMFxncEwC8DLajSUnBdV0+Mjt0vnVLI0jLuT0HIUqkNX0hZcj/mQZRCwwAefBCYmMh+Nz4ObNum928YwMxxYOUKMBv7VZAjUPv6pD8JQoNomWKLRRpCBMw8sY4V482YxRHQ6RasCulu/+nTWvCVsJbFzyqE0sDT03ExDhxgTE8z1tby+3YtH/5yPDy+67oawzOzeOIJwpUr0ik7npZxf5ZJu7ZbKItaeba9j4ufvPaDCDhxQsvwixe1CCXO7syIjWPg3jToySeAK1d0eubRo/JcCi0DEb2LiF4kopeI6Jd9fv8IEW0Q0Xl7+V9dv/0UEX3LXn6qsS0PRqVRpJr7NUs0xNgWxcD4/VlHUKmGehtYb0dsNFpRsaiWiD4KTSOUBt7GmsL8SUuPvzhpgVj59u2KH/56dGZXhc89e/Q8UeJk7nBaxv1ZJmtreh48Zz48JzdaEHzwPuZE5SmFP/ETwMGD2q5Ta/a8U46n+957gd27tUBdX2/P/iSEEiIyAfwWgHcD2AvgQ0S012fVJ5l5v738nr3tPQA+CWAYwGEAnySiNzWo6YGpxJHudfgkEjXQhUo1pJyGptM6XWDnTt3AdDqnwSqtylMPg+iTRPpdOjKii7YEHcQsdDShNPCitIExnkcEKYzxPAaMDTz1FHDuHPDEExXu1OmEllW36ES76vNCnahnEn09PY5E2f0yi/tQAFD4kfM+5gMDxXUt937yZCb3AXffnV35lVeyA63dluPQENDfX/dzFoQiHAbwEjN/m5k3ATwB4JGA2/4wgK8x83VmfhnA1wC8q07trIpyHenuPn3yJHwLzlX0+irVEJ/f846jFDA5qScttyztTHrhhUyD1dxpTE+mA6uHKq2wNv5+8M4AG5imPt7qqqR3CYEIpYFHA1E8NfFJLBmDmJn4VXB/FMeO6YqZO3YAR46UaZe5XUoTEyWtsEp152r1eRmuFTLqlURf7xRKp8qRaUoVzZBSrqwp9sj5PebFxuq599PXp6eYMk39tw9XsfbqFuQ1y7Ecn3oKGBwEzp61Q36Spik0jZ0Arrg+r9jfefkAEV0koi8S0e4yt20K1egibj2IOb8QXqnppmqlBPkeZ2NDp0c6DA0Be/dmGrwx9B7ML3QFctIrBW0MnvkCpqyvQc2dLu3VL9NaFp3QQ4ddkFAaeIoJx4zjOGCcx7Qxi7V1wvx89p56O17Je+52KS0s6E5dwAqrRneuVJ9XSqcxTE3VTmdvqX7QUo1pMPVIoq93qJhIj3c6f148jSGkEhlX6pEL+ph797O+np0KJ5UCph6LYpf6W0zhBJQ9Rx5ME+l//2k8+yygNq7pcpuO1ri21rmyRWgH/juA+5j5Iego3R+UszERfYyIFolocaNBKUHV+g8dPejKlWw1dLeqFWi6Kc+BK1EhfI8TjUKNjmPN3AEeGdURNcPIKG7Rua9gbIwCOek3NqCNQXRhHuPYGHpPTbN0ZCi8h068IMzcVsuhQ4e4FMkks2EwA/pvIsE8OclMpL+LxZiV0utalv4tEtF/Lctnh0oxT06yZXZxcuQRVqk0czLJVlpxMql/tix93ERC7wvQf5PJks2tCnf7tfiq/riBrkmjaKnGhAT7ec5cU6cz1IoK7hmARW4B+VLtEkQ+tTvJZPkyrtxHzpGnaS1qM+t79+OWt+7FNBUnDr+XORLh1JY3cS9eZkBx71bFqYkpvVEsJrJFCEQ95BOAUQB/5fr8CQCfKLK+CeCG/f+HAPyu67ffBfChYsdrlGyqRD4UwpEDbnlRUJYUOHAqxTw6Wn439zuOfrUpjkQUT04q3335tbn4/hXHRu7w6oriRCJXn6zm1RyPM5tm43TRlqeWD2YLUUw2NV0hKncJIqQ2N7MPtmnqz5alH/hEIldpCHrPrZTFk6N37I6thYbT+d16gldncHfQWnRaL+72A4oNQ3Espqo6RjKZe/0KXpMC51PsPMu+BiHtlE2nHg+jQ9AHyIUYeO1Dpf6BoI+c2z/Q26uddCMjWm5796OU/s1r4BExx2KK409d4vN4iAGVkZEzX09zejXJyYtrrEyRLUJp6mTgRQB8G8BbANwF4AKAfZ51trv+fx+AM/b/9wD4GwBvspe/AXBPseM1SjbV23/IXECWOAc2TeZDh5jTabYs5pERlen/5XZz73FqrY44emks5pZb7uun2IqX/552ZKhfUKNjacSD2QQ6zsBbXs592S8vZ3/zBhfS6WD33Nuxl5ezn00z97d4PF/41CsQlZVpinvNmxzBJk9uXWIrVfkB0mmtWAH6r6NYuSl0PsXOs6JrENJOGWrSaeatW/UDtHWr/wPkQQy89qLe/gG/qNzoqL/MWF3NX9dRlkxTcQ/dtBW87NK71fbCmyfZAhUWdILA9ZNPAN4D4JsA/hrAr9jffQrAe+3//xWA52zjbwbAA65tfwbAS/by06WO1UjZ5Ccfaur4LcSdO8xbtmSUl+TlO2wilXHujI5U5/yuhzri9ofm6ZOU4qS5o2yl0auvJhLVtzMU1PPF1SQ6zsCzrFwDxd0v/DwwQe65t2O7jZViUbtixw1EgMZZFvPyzAZHsKn3j01OLq8HPEBlbS20TrFt63kNhBYiHs/VtOPxkpuIgSc4uGWto6sVkxlKZT3gjk8h10BUnG8AqqysRDRwpFnoTMIgn5opm8p2/Jbxzs9Z1ePdV8dneBIn2MQmj2BeR8NqcC61VEfc8is3gqd4EidYVRAuFL9451BMNoWyyIph6MH3MzN6IOvGRnZwrV+lyiAD/L0FUFzjanHiROniKBVVyAw4KNQwgH2T92Ks9zk9NUTvc4ju7QtwAH+CtLXQOsW2LfcaZAZGk8zW6UurFp/xtqfV2ie0NG5Ze/26ro5ZTGYQabkbj+tiU1evAs88owseRyLA1h4GoACwvSj04oaWlebTiBrXZF4pQagjxYos5f22Fkzv8S0u98BeoLdXr9DbC5qMYSb2a1g178N87JdhbIvmbF/J67PWdc/c8ised+mTV4DZ2K8Gn3zds896FOAW2oxCll+rLkEjeI73YutW7Zz1phFW44GpZOxZRcctM+RlpSxOLq+zsqp31wRpa0Vj8AK2UWqrlKCVL1AikU3+JwqUH4IQeMi5yV7yZlLPIHs5+/bKfsNgnhhXvNL9A3wF23gYZ9jEJk9ghuMY4LTRxcnjz7GKJ8TNLRQkDPKpmbKpWEQp77eEv97jlgNFi8ulUjqSV0Tha7XXZ0EZJ9lLQgmKyaZQRvDW1vQEmek0cPNm/jwq1Xhg/IJqQacpKPu4RUJeft4nI2Jg4MF+kFG9uyZIWwutU3BbpWAcm8bAgR2g6amiZWpl0vcStPIFcubBi0RkHrwOoJ7Vp5XSj3Y0GkxuurvFzZt6+zNPA5HXbsEE4yyGYKELc5iEgoGjPWex69gPYGrHN6GOTOuJh1swKC4I7UyxiFLebwP5eo9XxqytZfs5kZ4HM6MiRSLAgw9qRQQADAOqfwBr65Tp1yVfnw3MjikqP2XeO6EKQmngEXkMH6OyicP98AqG4fsLegAAQChJREFUtTXdOffsyRqVNdO3C0jFtp3OowyjpNpJ30NPoy9QOW8OyQ/pKIJ261pOjl5on+5u0dvrUvzG7gcZZmYCdAbh+tfPY/72w0hzBPMYxdrcS3ri4XaTq4LQBhSzVXJ+83l/eGUMUbafx2LFXzVZOcKYGt+Esrj467NcBatKq6pWvtq21QuFuhFKA+/ee7MdnQj427+tnZ7pFQxERTxJtcBHKrZy8KYoZRglYiOUoJEXqJI3Rz0maBdakiDduh6To/vt090trl0DVleB2VkCH58Bzp/HxATBNLVSuO//+nGMWd/QY/FwGjQ0qCcebje5Kghhw/P+8MqYgYHc19+2bYVfNVqOMNJpwvxpwsbE+0CsCr8+y1GwamBV1cpX27Z6oVA3QmngPf981pnCrAfqB9EzgzhivHr1wEBwT1KtaNvoVplGidgIJWjUBZI3h1CEIN26kkeolJwrtE+nW5im/ssMTB8zsPtgP9JpwuXLwInPr8M4M48ZTGPFeDNmL96DgfmvYGyM2k+uCkLI8ZMxvq8/HyUuGgXGhlK2I2cO0YU/AzY2Cr8+y1GwavBurJWvtmF6oeSBtg2hNPD6+4t/9qOcdCCirGDwds5inqRa0crRrZJ9X6y29qOSN4e8BDqKUt261CPk97iUknNBH0u3DnbmDPCBDwDcrzc2IiYGJt4K64F9eO4S4amnfI4nz7IgNB3D0Lrc+npuV8x0T8tfiSMCZk52YWXkxzFrvhM0XuIdVo6CVUIIBRUdtVCLGqIXSh5oWxFKAy8aBbZu1SWxt27lQPpoJelADtV2zkr0h1a0k6TvhxS/N4f7ofU+wErpB2DnTuDIEXkQhKLKR6WyNahCE40Cg4PZzwsLwMbV7Mbpr8+ir5/wjncAffcy7l2eBactrCUUOB6gepYgCDXH77XilhPptC5ul/kuloaaO+2rxBkmYWDuy6DVgNaPj+Dx1dOKCKFm6EMV6YXlKKCSzdNWhNLAW08q3LwJAISbN/XnQjjPdn9/ZelA1RImo6jpfV887fXD/eZwP7RHjuQ/wE4ZW8vSf9fWmt16oQUopHxUIzeCKDTMQFdX9vPoqF1Y3Z5f89LzhBs39G83bgKX3vlzmL7rFHbtVJja+S2ok6dEoRGEBuKnF3nlRCwG7N7tKm630IWNofcUVuKqqEhZSaXLputDQShXAW3b8UGdSSgNPLp+DXDXS7t+zXc997M9PQ3/9Bx7PcsC7r5bf+7uBvoqn0c8h0BCoAaGS6Fd1NImamrf93PvibFXH7wPrfcBdnKXgdz/BcGHStI3y8F5XAGti6XTWjGcmgI2N4GPfcxZk7EVN9CPDcxhDGmOYA6j2EC0DtWzBEHwQyng0iVgbi73teKWE0NDOhJvWVouaNlBiJ76ck1yFItNy1CrMcQtQblWaCuPDxLyCKWBd+/b+mBCAWCYULj3bf7WmPfZvnYt3xHjdPQ3vxl2VBC4dQu4erU2bS0pBGoQ4iu0i1pHD5va9903c24OmJwMR1i0FfE+tN4HWObBE8qg0vTNoPT1AT09+v/ubq0YOmJibAx4+unsug/gOfThKnpwGwCjB7fRN/42UWgEoQE4/f3AAd1n3X4VYoWZJ9awcoVx6hQwPp59xVy5YndPszZjV4pNyxDUWKuVPlTXxKRKrNBWHB8k+FNoBvRWXQ4dOlRyZvflZWZAsR1c5+Xl/HUsizmRYI7FmCMR5slJZqXy10sm9e8AMxGzaRZet1JSKd1my/L5MZlky+ziJKKszIhuUJm4zyHi2oX7e9PU16NtUUrfmEiEeWTE/4SF2mFZ+roqlfu/3+8BALDILSBfql2CyCchOIVkVzG8j553H8PD+WLCeV+Y2ORl7OMINvX62OTVpbVyHmUhhIRBPtVbNpUp8n3x9tXlZXt/lpV9v09OMltWLV47BXGrE5OTzOk0czyudaRGygGf067PQUTAtS3FZFMoI3h79wK9vdq70NtL2Ls393fHS7R7t3ZCXL5c2MPidnDUYxoEpYBjx7THano630Ot+qKY7jmLXVjBVM8CVF/5cf5cJw0jytodFI3q8ShOOx57rI2DXW53meOab+nciCZQS1eg24vn59FzBk3U4lhCx1Kug9kv4ufsg0h75O+6S8t8t5jYsgUwkMY4TmPv+D0Y61kGQSENE2+P9WHnTsbUFLevfBSEOlJJpN3vdeTt7/v22cO+1zawNvcSuEgqYS0zktzqxPHjwNGjwJ49Wkcq9Uqr5Wu2IeP4JCIXXgpZfq26BPVC3bnDPDOjPS9eyvUK19PBUaot+ndl/64qDkZZFnMybrGK5bqD4vGQBrvEK5VLQ1yBNqkUc2+vfqh6e/XnEiAEHnJugJe8EynUlf2+LyRP43GdpeD93tlHOm3Lx0SSeXOT4933s2lH8ZzoXoRSnIzXsd8ILUsY5FM9ZVMlOlWh15G3X+t1FUcoxZM4wVbsCFtplbd9JdH+Wp6bZWk5484Kq/Y1640kijojeCkmm0IZwVMKeOc79XL0aL4np1yvcD0dHKXaon+n7CDiCoNRhgEMGBvg+dNYS98DntPuoG3bQhrsCoNXqpVdgcXadukSsmUJbwAvvFDdsYSOplCA2M9bX0iebtuWHbPj/j4zKTopDBgboIEo1PMvgm7fxDjmEEEKvbgBEymM8Ryi1Iql8AShuZSrUxV7HXn7u16XkOYI5iMxbHx+BhvrjPl5LliEpZa6TJD9OvJozx5XVU/PeVXyOpeaJkJVFLL8WnUJ4oUKEvVqpQBPqbaUGu4U+DhpxZO9SxzBJk/2LrGVVoGOH3j/LXRN255SEbdyL3YtXYGl3K+Tk2yHPpi3bg3kxkQIPORcZy+5kKWYV72ciJ83s8GKHeHJmMURbHIMM7yCbbyKAU4gymrLVuZ0WuRcBxIG+dTsMXju38t5HbnXjcWYE6sWWxOTPIkTOqo3qTLbF8zcqrLT+kUVC43z9avV0MgEmpogQq5tKCab6ipQALwLwIsAXgLwyz6/fwTABoDz9vK/ltpnECGV3rS417zJgOJe8yanN1u9NwWjWiFRq3TPerRN8FBKg63kYtdKaBdrm7dyTzweaJeNVqDqIZu4AUqUoKmFvyLbjZRO/QJx0tyRkZEmNnkUc9ohhhm2zC624kmRcx2IGHjV4ffKKscgTKWYL1zI7a8pGJw0d+i0avYfGVBoaEqtz8Urj7zFWOqVPloXRJlrK5pi4AEwAfw1gO8DcBeACwD2etb5CIBPl7PfQBG85fXM+AkTm5xcXs/+2MaeiWqFRD3zudtKgLUDxW5Wsy92sbY5v5mmLlMY8OXQSAWqXrKJm6xEdRrVivKcbkQprSzGJvV4H1PxCE7lVNNMjjzCyYSqm5NMaF3EwKuOasboxWK68q1h6OiYu7+63z+6enp28RqEFqgm78tC5+Kthl5pxLIiaqnXNlu/qAdtrPeXophsqucYvMMAXmLmbzPzJoAnADxSx+NliO7tw9jW5/S4ia3PIbrXngev1hO/NZhqc8zrmc/tbVtfX53nGa/r5DAtQLGb1ewZVIu1jQh46ing8GFgcdG/NGzzaZpsEmpHqWG2pURETjeKmYiuPAM6MYuZGcLKFcbclh/BmD0Ob6xnGdETX0BUJTHWfUF/130B0T4VelEkCJXg7hflvLKcic6dMXonT+q5KpVyTWpu91fMzuov19aw9+2M3l69j95eoL/f2QdhnsawYW6vyfvS71y81dDT6VxVk7mOY+lqrdc2W7+oNW2u91dFIcuv2gXABwH8nuvzT8LjEYf2kicAXATwRQC7C+zrYwAWASzu2bOnpEVrWcyxmGLTVDwZU9kggsszYZldnFxebzuDvlbj8eqBuyJdXSP8kkJQ3s1v9INSgQcQjY3g1Uw2cQXySag/XhERj/s//kW7xsoKW2TqOUgNk3l0lNk0OQWTl7GPLSOSSdl0AtbpzRYTykJNaKR8qtfSyAheJSmZ7u1MU6dZmmY2agfoSJ6T/uiXfpm6Y+koWkpXxJ2cVPZPSqdy1qhflpprc3m5BkGwoO/tekTcWk25rIYwRiRdFJNNza6i+d8B3MfMDwH4GoA/8FuJmT/DzIPMPNjf319yp2trwKlTBMsinDxFWFuzf7A9E8rs0nPLHeirmUHveKssK+u1qodn1/FaM9fYKVGDxjptu3q1znO3NGRymBYnaJXQZnivwuEBDCSbgPLlk1B/3CLi5Ek95+mRI0AikSviinajHTtgxMYxELkOOjwELCxAWQpH8XXsxxKm7n4aa6ofc3Na7p85wzj4xr9GevvuzvMUC4ILv1d0kFeWs51lAbduAUtLev7hSETP2Ts/ryviZvSfPYSpk78OlbaA+XlEXt7Ag3sVjGPToN27MMPTWLmsMDtLoG0FDl6B7uM9F+8rb+9e/1dg4EOV896ux/s2DFXIHcKhj1RGIcuv2gXAKIC/cn3+BIBPFFnfBHCj1H6DeKFWV7MeH0B/zmBZnFxed42jKG8evEKV2BxvleN1mpys7XwoXmrqlKhxRKzu+eYyOUxwmuW98g5IKAEaG8Gri2ziBnvJhcK4RYT7XVC2iLMstuLJTKQgbuxkwLL3Z/HK4CM8MqJcx1C8Bd/llHFX6DzFnUwj5VO9lkbKJu8rOp12zTdZJDCkt9PjXJ3qmCXnu4RnPJ5PplbB49ZQ9ylVabOsQ7XSZM1hIMTXp5hsqqcSFQHwbQBvQbaQwT7POttd/78PwJlS+w0ipBKJ3Jd6IqG/d+6xt6MFSRlwBvu6t0ul9P4SCX9Fwm9i3VpRUxunDkZA3ftTiDtsTWmEMVzVm0zTYAOvLrKJG6xECcWxLC2bY7HcVK9yRFzOoxxTfP6pDSaojDF3EE/z974d57179Wfn++WDHxbZFCLEwCsf75ANJ+3S+1rIeX1YFluxI5mCR4XeHRlD0J78PHHg3axSafePbJldPNm7xKapfI/LzA11gAY5VOZaWG3gxBYdrCVoioGnj4v3APgmdMW6X7G/+xSA99r//ysAz9kK1gyAB0rtM9A0CWk9/ZZTLjedztc5HeOsnPRm08ztoCMj+YZfoQheI/TqipGIWLippyD2M+ZafAwe10k2cROUKKE0jqFXiYjzzm9lGMw9d1s5xtyWHotNU7GBFDtT81ib3om4hHZGDLzKcfchtwPcz9luxYO/O6x4kuPGTo5hRk9lMnona7x5MrW8x83QQN2n1KHyrkWqhQ0oqYPQMjTNwKvHEkRIxeO5wiQer9xR451k0/nfHm+f2Z9zDHcqQDG9uuWcH0UaVM58NUKH4dexKnhphkGB4iYqUUJpKpFT/qmeiu/GrRwjz5lKYQaTbBnZF4zIxnBQD/mE0nNx/iMAl6ALPT0F4M2u3yxk5+j8apDjNUs2uWfOcUfSnGxKR48yTeZkovC7I68vKcXJkUeyU5l4pi0pdtwcGthJix2qrAifKvRFgwh54ZJ2ophsanaRlbrgHRdKVPk4S3dF+BMnsv+fOgWMj2f3t22bHpNqmtmxqYXGqTa07kXQUb0FGluqrZ1cgVaAf8eq53wcglAhldQNcB7lK1eAkZHMt/ge3Y2ebgbAAAgAo9t8DTGahzGh+4HIRqEQRGQC+C0A7wawF8CHiGivZ7UlAIOsCz19EcD/7frte8y8317e25BGV4jTh1ZXgWvXgMuXgSef1L/19QE9Pfr/nh6gr9//3aH7EmPXLsbUFOu+RIToqS9jbJQRiTDGxihHr/Me1+91pBSwtmGAowNQTHWZ7sStghWTQaV01Dx5km6igOnkwiXtRCHLr1WXIF4opbLjLmKxwoNeHSp1glS6XcOcHzUIo5dqaygdOeJ2L49So8sDAIngCS1OOq3T8p0pEZzIgxPBM7HJ8QPv4eRq2lvrQUcXEiJP2pVayyeUX+jpAIA51+db5R6zGbKp1PDseDyY/pBcSXEEKc5Mch63Ch6jnLYVqq9QUcahT0PKVcHKivAtrxe+eI3QYURPagmKyabQRvBmZ7Xn5sSJrLfEz3tSjZe10kqy1To/ApfarcF0AqXaGjpHjrjdy8fdEeT6CSHFNIG5Of1emZvLZnD0bmGYSGEUc3j/0q9g5x4DU1M6OjE2xohQGmPpk4g+OiX9QXDYCeCK6/OK/V0hPgrgL1yfX09Ei0R0hoh+rNBGRPQxe73FjQZPJ+T3KvCqJEQB9AelEP1ADGM4hQhSGON5RCl7LpXqYd62OP/PzQHr6zU4WZ9jlLoFQSN8o6MA39sHHi0wF0Mj3sFhmkohrBSy/Fp1qbUXym+CykY4JGrhdSrpEarRAOKOGoMXypBkA6nw+kEieEKbkakSuJLgOAZ4BHPZaJ6pC7tY8aSuCCjypK2ptXwC8EEAv+f6/JMAPl1g3b8L4AyA17m+22n//T4A3wHw/aWO2WjZFHR4trvapq8eYQ/Us0CcRJTTw2OcTCh/faMMZcQ9Rm94mHligjPFlHJ0q2IZKs7/7lLqrn4eSAUro82WpaOe2X0qXZim4BwSInPCTjHZFMoIXjlE+xTGhjYRiTC6u4H9+ytzepQ7V2atvE5FPUI1GgtVqq2hcuSELiTZYOT6CR2CI/fMHQMwhoexiCE44/EsC3j0UQDRKAbG7webXVgb/BHwvX1lT6oshJJVALtdn3fZ3+VARD8I4Fegq/vecb5n5lX777cBzEKncDaUUjpP0OHZhgH09wNHj+YHnZQC1jgKHhuHYRD6D+zG0btOYdduytfTyohcKaWjdF//OnD4MHDunNapIhF9PhndKp3WofqdO/U+0+nsMY4cyf7/6KM5J6v6olhb08cqqoKVGW0zDL1kdUDChuFRvuQdLDgUsvxadSnHC1Uy8pSyODnyCKeNLl4++GE2zdzJz4M6VhpZMVZmNGgAoQpJNgEZgyeEjFKPtEpbPDl6J6cse+Y9krI4NnKHTVPxxJYljhs7i87xJbQetZZPCDYX5wHoaVze6vn+TbCjeQD6AHwLwN5Sx6yVbHKiSO5poAo9yn7BLr8+FI/rKUgy1TS9UyjELLZGxjhp7mDTHovnrJchYOTKvV93NXTT1J8zulXa0l84Hdo0dYpXoXmz7FLqVlrVb0JzriAqKPpMqCkmm5quEJW7BBVSVir7wvXrZJbF+nds8iRmeJPu4pFDdzJz2K2uBjfayu2j1fQ3R7gmEtJfhfAgBp7QqgR14FkWc2JVG3POeyedZl5ayuqIgOIINnkU85xeldSpdqEe8gml5+L8OoA1eKZDADAGYNk2CpcBfDTI8Wohm9x9oeDcciW28/Yhy9LGorO/rVuz6ZpZW0rxsvEQp0C8FS8zoDLrZQjo/fbqa858xk5/zdhF7vRqQK9YrDKLfbyy9MEKPfaplLY1S/qImj1fnRiXdafzDDzLKjo/CrPTCbPzF41sWeZIRPHIiM7FNk2dix2kk5bTR6vpb83uq4JQL8TAE1qVwAqbLaAts0tnhmxaOYqru+ImoHhkRIkMbxPCIJ9qIZvcfYGIMw7xguPnfLbz9iG3LuaOzOXOY6c4QikewTwDVmbdeNxzoAAGhVdf82u77sr6mJOYZWt4NKtwlYiOlW2zlWhzqUqktYwQ1tQeE4W1IRSTTeEcg7exgb6zf45BnIWJFMaGUnlpyH19wNAQIRJhDB1iLLy6D+k0YXEROH0asCwtQoKkMZcz1K2awpY1KIopCIIglEGhIS15Y5BsAW1YKQws/hmuvngNc3PevekxegBhYYFEhgtthbsvxGJa5zl+3H/8XKHtvPpUtE9h7O4LICgACmNjnDNe7/x54PZtQpojWDBHoPuQ/j1P1ypSEMDpr0Cuvuaeu9gho2txBPMYwwa5Guw+hs/xmIEnntBzZ87O6s9Fh916q1C7Vs4O0WNMjW9CWVyeHljGeLyaF98UhbXphNLAU31RHN1yFos4jMNbXsTxb3TlTY1w9Chw9iwwOEj4xpm7MD5OmT7gFmBOJy1VQCRooZFsf2OMDW0i2p/tyKXG3svYWUEQhMbi58DzVYY8Arrvgb7MJM4AsGWLfk90dxOIdO2GjAwvt0qXIDQBd184flx/DqLHF3OC0/oaZm4NYRU7kDB248Tn13Omtnr724Hubv25u5sQi1FGPxsY8GmkT1/y9lfA1tdYr6ssxtqaduwnEnr90cGUnpYBc4gu/FkgA8U5zp49wGOP6f1NTekaLUeOlDCafISKvraMdJowf5qwMfE+bRAH1QPLiD6srekpImpmj4nC2nwKhfZadQmSZpCTflkwPTM3at3IMalOcRdl6tC1lbICR7IlpVkIIwhBChTXKA1KaH287xC7voIuzGAL6NwxRMxXrmSLOIyOusYPSSpTyxMG+VRL2eR9ZN0FV8rSTZwdOeNhYrG8HehZEpzpR1Smr6XTzMm4xWrVVZSgQF/yzVR0pVRP9i6xaepxfU76aSymOD78Yxk9LciJeY9z4QLnpGivrBTR3+Lx7HUgYl5ZYZVI8uTIa5laEcrUjS85tUSBS11oXctinowpJigGVO5tqLZoRKEpJoSaUEw2NV3olLsEEVLpNGc6anc38+Zm7u9Nr0TpkQLJ5XWZtkToaMKgQHGNlSihdfG+Q/wqCiqlvzcM5sOHtbLn61hcXteKm7wAWpYwyKdayqaCDo5ydSnvjhKJvFXUZoont5zTRk7vEqu0lT9GDqQ7WzyenTPP2M4qUXg+OqeISgLRTL0G9xKJaAPSfWLlVNOdnMy12Zw6LQX9OIlEbgPsla2JyZyAgLst5QQGiq2bjFscoZRdkyLFiVUr0IZl2WviyKoLHWfgrazk9pMtW3TVIeYAk2o2Ao+0UZZqrsHZCMRz01mUeb/DoEBxjZUoobUpMcdxXnVAgLm311XcYdOlDG51TZ8gJZJbjjDIp1rKJq8hE/RxzXstlPK2WxbzyEh2knOji5PL63afyxbJSyKaCZNbPVt5EjNsYpOHDyteXc3PzMo1EE9wbOsSG4bKTJmQCSa6IvJe+ySV8pyLp8iSSls5p+cYdwWNYscj5My8XsKCLpWJ5qZUrRWVSPIkTtiRwhMZw7jYhmXbazIBe13oOAPv4kXO88Y4JWWzD6RiK95Eg8PTE0Np/7itafHcdA4VeOrCoEBxjZUooT0opKO69RnvVFoqnVvpmUixaSqe3PoMW4Zdgj1PgxSaRRjkU7WyKaOjpPMNmaDb+74WLEtH0xIq/1FPJtkyItq4A+lIXkTx8DDz1q06nbAXL3MapMufj4xwElE2MxE5ZVeszZ1SIcfWMBWvrqicKe4iET3libvBybiVk3Kdl27tzcleXs4YhvG4XkpF/TMX2XuxfGSAXzXQQq/ddFrbjIbhmwWr2xk7oqeFiLmOV8QAL9tea3rqXANogiLfcQaeZTH39GRfqlu25M+rEqEUJ80ddTU4anmv284AdAsor+uqEzw39bph7fAgVOCpC4MCxTVQooT2xK9bOvqMO0Uro1wlk6zMCE9ixjbystGIi9in5946eJAtMjl58N2sUumCxxbqTxjkUzWyKcfeGL3DltlV9vu80GvBa8vE49l+lNpUPLJlmU1s8kj3hZzpFDJOE0Nx4sIaW6sJTpo72AJ4BKc4OyUJc2ZaEtup7+2bIyPan5JjfyRyG6wSSV+VJjNFXlq553XI7MhdYyEW06na8XiAV2SBd32hehGlrq97mKOvyltItyjwfUX2WjvoL5XSpBTUjjTwnNSY7u7cuVr09Vc6DF1Hg6OW97otU5e93izH3RVWz42bet2wdnkQKpD8YVCguEolSggflqWHDDgZV5lua/cRy+zixPAjHJvIFjgALJ7ELKdgZAzAyS3n2Eq1aH/vAMIgn6qRTbnGg+LkyCNlv899x8FZnBM5c8+tl0pptcEx1AxD8aFDnGfgAczj47pIiJNyubl/yJ4zT2W3R4oTRtapbw/XyzHScoLmPg12B9h027IqTjJpF9CbucRpo4vjGOCEsYMTF9d9z6+SwjTFVICgmQROW2tBS9hrLdEIbloKascZeH6pMTk5ygmlw9B1NDhK3etynsm2TF0OMptoWKnXDWunB0HG4AkCMxfptq4+Eo9rBTbzvsImL2NfJoUzgk1entnoCPHZioRBPlUjm9JpbQA5xol7bFoQ/GofOMaKO+Dl1tfchp9jgN25ozOyvAaeYeSmXF48b/GVofcxclI1LZ7EDFtmF1vxJCcSep+FDB/L0sVH0qv+6aObm9m29Pbqz/p8FPeaN5lgMWDx+LjKi/hVWpimEr3SG630TdFsV1rJ6d2kFNSOM/Cc6+wWHKOjnntfZ6u/2L0u95ls29TlVvGsNJp63bC2fRBKEwYFiqtUooRwEqTbOvUVAB3Jm9y6xBaZPGme5Ag2ude8yYaRP5ZIaAxhkE+Vyia3vpIzvUcF27v1HW+Sz8WL+ePS3MdNpbTR5466OcvEhN7WnQ7tjNFzR/Ei2OT48I/pAit2yuTwsMtwVblt9mRb5uhqevqGbPtzDdLcVFLT1MbkxER1Pu9KVQBnHGCx+k2trq75ts9j8VrxZHPPQcbgNUZIOdd5czPX89RIA7/Sikbl7EtoUTp5DF4FhEGB4iqUKCHcBOm2OUqYHSGxNtO8PLORE93Lc1YKdScM8qlS2RRUXylX33EXjXSiSoWmTXMXEHHPVTcywpkqmToKzr4pnE4ho8mRO5yIq5z25ETS7AMmE8plrPmfu9fYchuFThu927vn8CtakbMItVIB8quKtkYgzI+C7XPdBCt2JGO4V3UObaZjFZNNRoPmU284hgH09wMvvggsLACWBczPA2tremEuvQ+lfNb1/bJwGwYGAKLc76NRYGwMiET032g02Pn47UtoUep1w+RBEIS2I0i3NQxg+3Zg2zaATL2B0WVi35E+HD6c3fDs2ex7zLKAtYQCJwO+1AShBF4VJ4i+ohQwPQ3s2gVMTenPDs72pgkMDmq9TCkgmdS/E+nFsoCNDb2+00+cfnP1qtbf0mng1Vf1vpi1bmcY+n8iYHw8u88tW7L71vsiPPmVu9AfJQwNMiIRxtgoI8r2ybpOIvroFMbGGKYJ9Pb6nzsRMDMDXL4MPPlk9vPqKvD887qNDqapt9+2Lf985ueBWCz/2hVSNXNkiWeloOqp9349+2xuezY2Sj8XjWRjo0D7nIu+soKNz89gfp6KnkNJij3I7Ughy69Vl6BeKGeAbiSiQ+yOlyiol8LXY1DDSR/bzEkgCHUFIfCQcxnySRDK4bXXspWht2zJVvHr7VVsQheWsGJHWs/1HhLCIJ+CFqjzU3FK6SvFKjgm4xZvXknyyIjKVKCcmNA6mZNS6a5M6RzXHdH2tmt8nDORseHhbOpjLMb8ne8wHzyYTYt0FzhKp3UxFhMpHsE8b/a8iScxq4uzjLzGKeMuTiLKysym+6XThdMbC12vbMp1NtLoTm31zo/npHq6a0WU1FU9K7mrdZbSb933i0hHPt2pqEHP0/17PfXZoGnuVY9gaac6BzbFZFPThU65S1Ah5R486wzYLTQhrR++97mWkz4KgpAhDAoUB5RPglAO3vdZ7uKa7Nnc0RYKSTsSBvlUboG6cvTbQhUyncnEsxUt85/hSIT50KFseqWTyugtDOKkMToFX/z2ZZrauMumZer9OgZWPM52tVpmwOLz7kJGET0lQwSbPNm7pKc94GxVdidI4B2HV+h6eadCWF7OjrtLp7P7nJjIv3Z++80zojwrJZfX845XyMhx3y/3tSu0TbHzdOu+sVjuNBe1JGiae1WGZhvWOeg4A889+NUZs6CUHtcwOXqHIxHFk5PFB5z63uciN78NDX9BaBnCoEBxQPkkCOXgfZ+5ly1bshG8zATFHi1HskWqJwzyqZwCdZXot97nTOtEWQcEYOU9v2Nj2QiWk2nl6GZe48MZv7a6Wqw/+H9vmnqfiQS7DE3Fh3GGYzjBEaR4ZESxaaqMsecYVRcu5O7rwoXsORa7Xu7ftm7VbdiyRVfZHD10x3Ws/IqafkXI8wIInpWUpTIf3deyULDBsvT1cKKpxaprFjtPb9X6SoIcLSWjqm1Mg0+mmGwK5Ri8aFTnYpsmMDICnDoFECvQ0WnMnO3B5UPvBzNj926dZptO5+cWu1J7MTtr5zv7fpk9Zrnj6gRBEAShGO732fCwHq/jvNuuXyesxg3MJt4GOjGrX2KuMSQqrTA9Dezcqd9LltXssxFamSIqTh6lxmRldCJKYwynMd5zEUDuyq+9Biwu6ufyxg2tlCrFUAoYHc2u190NPPoosGMH8MAD2aFR3d16TJrTzldeYfsYjAcfBIaGAIChLMZjjzH6+4GREbLXISwah5E6OAI2TUQihDe8gez9Eu65R3elQ4dyz+vQoezwLL/r5VyXdFqfXzoN3Lypz/GVVxiWRVg4Rxi6+zk9DtA1No9Zbwvk7tc7Zm9jI/9mkUGYmQGWloBbt7J1JwqNRTMMfY+c60dU+F4Wey76+vR9cCh3DFzLDXurps5Bq51MIcuvVZdyxuAtL7vGzrnq6ybNHRnPkncO7mq8Di3lhRCENgIh8JBzGfJJEMrBW/Gu4HsmmWTLiOhxRIbJyeX1nGjHoUMyzUIlhEE+1VI2eYekpFJFxu7Zc8ldOO+fonngALN7KgPAYtNUPDycTdE0Tb8KmYoPH1b8ta/pcXa5+3BPj6AjhxFK6bak9fqGkR2f522TYeRPwG4YuWMG3SmXTl9MpXRE0jAKRxMBxaOY47TRxcnldd+pGdxj9pxIW9DIajqdq9MWW7cWmWfeDINyo7+tkv1WE/29FidTwzmEQxnBUwo4ehTYvx+YmmKoqaPAgQNATw9gmugbfSsGB7UXdGhIV2KqhdehHgUOm1m5SBAEQWg+7ndLsfeMuqcP0zSDXVjBFM2i7wfusaMYmnPndDQwkch9pzjvGcuS941QGKX0s/Pcc7kRpRde8K9yaBhA/4CB6ccHcOAgwTCyETaHpSXnv2xkzbIITz+tdTjT1M+s+zl21jt7FnjnOxlPP+3dh/O/syiM8jyitAEi4HWvAwyD8TraxOgoIxLJPc/Dh3Wk0NETJyZ0OycmdJbW3XcDDz0E3HMPsGsXY2p8E+kUY3ISOHNGX6dXXvG7gozhnudwyjgCc2IUA/v6Mv14YwOYm9N98MwZHalPp7WeuXu37pOXL+dG0Lz6oaP7nj2r2378eHF9tBaZZ06GQSSi23zlim4jczBZ0grZbzULvFV7MrWOABay/Fp1CeKFisdzvSV/i928jH1sGRG2Lixn5spwJs+sJOe8EV4HKdwidAoIgYecA8onQagXyeX1bNEIbHJyeZ3TaR25845JcgpGOO8Zw9CVOv2KSXQ6YZBP1com5zlxolhbt/pPSu7Vo7z6WKH/CSk+gIW8sXpONMuJTBF5I3Xe/eVObg4wG0hx/PAjzErljA00sckXDv4Ur1y2MhOee/VC7wTl587ln08Em7x88MOZ/QK6LznXamJCjx10z3HpVTaVyi0eY5rMMzOeCpvx7LZ+E9AHKs7iISfbrdQDUGBHftls5eiuzc5+q6k+X83JVNCQYrKp6UKn3CWIkFpdze2AJtIMKO41b/LKFVV2B/AjyGDkah/aVgldC0K9CYMCxQHlkyDUC2UpnuxdylQCVJZ++TiVBx2F01nica8Cnvubl2YrYs0iDPKpWtnkLaZhGMwXL+ZWhvR7NhIJ93PnpExavKXbynkeDxxQfOWZdR4ezk3lNIzcaRcSccWxLUtM2GQDqSJGnvs7xSPDitNpW3cbvcMmNrkXL3MEm9y7xcqkRjqGmHOu7vTQSCS3Sqez7168zCnqsov4aYfKa69lU0F9HSY+nckxYp2iM7kFU5QupGQrncm4lXM/HEO4ZHEWF+7pxIoaYmVabJXqrs2SLy1TPLOChnScgacrJXk7of578WLtbmSxh7EW0beWeehanU7VOqqlha5bGBQoroESJQjVYqUsPbbHyu3XlqUVckepJnJXFsxfEoncja14MpP9kvdOc2RJIS2/zQmDfKpWNjn6iNsoi8X0UkzPceaEM03Fsa1LfAW7eLhnmYkUv+Md+c/d2Fh2zkcnUugdN2qlLI4vJXnhL9ZzjLmDB3VbdLVK/dnd3oMHmTc3meOris8f+KlMtNu9j0hER9y2bs1u5+zPGV/n1S1NbHJy5BFObapMVVBnXj9fI8ejIKbuWJkomlMyIm/Kg0Su1aQSSR4dzbbFNPMDFl5DyztVg3c6sYKGWJkWW0Hd1UfncIuOZmas1Vodqnh/NRyD13ShU+4StNTvxITufATFJja1l6VXZTpQvd9BtYq+tZAO3ppIHmtltNh1C4MCxQHlkyA0Cz/FK/u+zC55StnkpC5ORqn8d5q7OoR7tuQQyeIwyKdayCZn2gB3RMudQugt98+so0TZSJbi+IX1zBQBTiqjN2LnLtrhTHHgHD+ZZE5vWhzbumRHA3UUbWgodwL0/fu1MeedM6+nx566YEs2lXPr1twiKjpqlx9JdCZVzxifmWlKZllNxHKiam4jL89B71IQU8Zd3LtFp6X29urr5Z0I3W9aBFYqY6Q5UyJ49UR3wRUnEuj89Ub0R0Zypw7L0T3LjDa4J6n3yhG3fHCLDsc4d56lHAeTtz0NpJLjNlK96jgDj9n28Dx1iRPGDt6EwcvGQ2zFq8txLOdGS/StQUgea2W02HULgwLFNVKiBKGe+L3HHIUsM07IRxlVAE/iRGYeWd8Jl4uEA3KO22aeyzDIp1rJJke3cYwEZ3xaLJav1FpW/riyRCI/cuSOtE1M6H05vxPp/d25k01ffOjtd5gy0Tf2pGRyjtHyt3+b/713OXyY+cqVrEEWiyme3LrEZKeTuvd/4IDbqFW8bDzEyj6R9GqSe3s5Y6yNjxcY0+pSEJcPfjhn/04kz21IZowEn0h5oa7kNp70RPJ+6avZ8y9WFTWV0uP/VKJ0ny1o3PjoHN4KnFu25E5u72wbOJW0xlRqqDVSveo8A6+QRzGdZiue5GRClf1eqeRGt9k7rD0RS7oyWuy6hUGB4qDySRDaCZessGJH8t+fbo3f/b71pGFlxY1iK3Yks56Vssp7TzbhxRoG+VRL2eRVuJ1ojVepjcfzo0RKcU7kKRbTn1dWmM8/Y3H8wjq/9j3FDz6Y3S4SYU86p2K/Yip+i18aqHeidCJtZBqGPZZtNcmW2cUr2MbDmPc9BhHzZMwzLi6hMvt2H4PIZ0yr/RzfeU1xdzdnjELn8fZOPZAXMfdEwrxdItfIUDyyZVmPNzRv2oVqsvt/6qn8VE73Z3ckspTuW9C48dE5vIVl3NFbZ9tUKnfcYyP90ZUaao1UrzrPwHP3DmdCk3SardgR7YGklH7JuB7UUu+MFgt4CG7Ekq6MFrpuYVCgOKh8EoR2o5SsKDEGz6tsJs0dzABbZrYwRSDHqWU5A7rYmtCFJhohvuolnwC8C8CLAF4C8Ms+v78OwJP2708DuM/12yfs718E8MOljlVL2eSnD2XH2um/SuWP73zmGR0lXl3NHbuXSmljiewiLD3GrRyjypvGWShyNz6eO/+ct6iQE63yVpV9/etzP8dXtYYeN3ayiZTvsU1TRwdnjut5/hyDxZ1y6F4/kciPlG9uZsf69fToKCVz9lq6I5i+EfNIhK14Mue6x+PZ1OuMkTFyh1dpJycQ5bTRxRdnNvKuTaGqqE7U1G38+UULHSO/WEVVZyyv20nkTiP1RoGdOQu9UdlGqSzVGGqNUq+aZuBVI7wKLYGEVDqd7TXOCN1kUo8hcEpIR1ROZaZS0bkWC3gIQqhotIFXD9nENVaiBCEs5L4/s1GP5Mgj7JSWD+Q4tUt+WiCexEwmXbTslK0aFjKodAFgAvhrAN8H4C4AFwDs9azzfwD4Hfv/xwE8af+/117/dQDeYu/HLHa8WsomP33IT4/yG9/pNb4yESPTrxpm8OXwYW04eaNz3uXhh0vva3VVD/OJjdxhbwEWJ5o1MZEbR3jtNX1tnOimaWanSnCilJOTuefvNQLPn89eY99xbD4XP76aP1bQMZodYzIWUzqwgRNsxY7oaruT+ddqaSl/DJ77vmYreuZOZu81RlOp4mmjXl3b3R29RWK8E9z7VfetJy3kB/elKQZeNcKr2BJISLnzApzYuNIvFXcEz7lhQaNzrX6jBaFdaaSBVy/ZxEHlkyB0IH5j8BxFM7Dj1K65n0TU5awtM6OmgvEWdTLwRgH8levzJwB8wrPOXwEYtf+PALgKPXN3zrru9QottZZNXn2okB61spKroLuX3IhR4QhekCUS0caMO4JX6bK6mp8m6cQLNjf1bzMzub899JB/euWhQ/lz1BVahocDOitcFz93Ggq9OPP55UfgFCcTKrML7zlcvFj4cMvL+emTzn0vmE7qodxMOHckE5Dgih/FZJOB+nEYwEvM/G1m3gTwBIBHPOs8AuAP7P+/COAYEVHVRybSzwOg/xIBRKDZGczE34aVuInZWYJzpKCTzxsGMDAA1KCFgiA0j+bJJkHoUHLen/YHMggzM8DKCjA7G+DdOjAAxGKImtcx1vscIhEu+s72ZWMDmJ8H0mn9d2Oj8pOqjp0Arrg+r9jf+a7DzGkANwDcG3BbENHHiGiRiBY3anyeXn2okB5lmvnbEgGxGHDlir7vhgHMzBBWVxiJi9fw8mt3Y3i4tLg1TWB4OHtMwwBefbV020dG9N+77y58btEoMDSU+/2tW8D165nHEIZLg750ST9K3u0uXACuXs1en2KcOxfwcXRdfKctkQiwdau+JkNDwMKCfsQXFvRnfY0I0QHK7GJyEujt1bvs7QX27St8uH37gPHx/PvrPi+i4jp0UF3bgUg/H/E4kEgElBFChnoaeNUIrxzKFlLOE2+a+u/AgP7eMGBsH8DANsp5SIhQ3ktGEIR2pmayCaivEiUIYacsx6n9sqbVFcxcexgrK1T+O7tcLbONYebPMPMgMw/29/fX9ViF9Ci3OjY8rH+Px4ETJ4Bt27LrGQawfaeBbe/oR6SLMD8PrK5qY8x9fw8d0kskog2OubnsMQcGskbI8DCwtARs2ZLbzokJ4NQpIJkEbt7UhsP4ePb3ycns83jqVO7xx8dzDddXXwUeeij3UXK2Gx3N/35mRp/76qr+6xiJW7ZU/ji6r/v163rfc3O5j/ipU/76rWFo43N5WW9rFLEICt1ftxEWjxfXoSvRtQ0D2L4991kRghFpdgOCwMyfAfAZABgcHOSSGzhPnONOCfBUOC8ZQRCEcihbPgmCUDn2y9pAhe9sR8ssQz+oE6sAdrs+77K/81tnhYgiAHoBXAu4bcPx06MqUMcy+9qxQxsra2s6GcvZP3Pu/tzH9N7a69d1dO3ee7VR5hhvzjbbtgHf+IY+hvO900bTzB7f+xsAvO512oj0nptpaqPK+71jrDg416WvLxvlq+RxdF9356/3OhTqK5EI8OCD5R/H+737vCrZh1B76mngVSO8qkeeIkEQ/GmubBIEobm0hn6wAOCtRPQWaHnzOICf8KzzVQA/BeA0gA8COM7MTERfBfDHRPRvAewA8FYAZxvW8jKp5nL7GQ/FDBbvsSIRHWUr9xhBfvM7XqnvC61T68exNR5xoZnUM0UzI7yI6C5o4fVVzzqO8AJcwquObRIEQRDZJAhCU7FTv38OukDK8wA+z8zPEdGniOi99mq/D+BeInoJwD8C8Mv2ts8B+DyASwD+EsDPMrPV6HMQBKF1qVsEj5nTROQILxPAZx3hBV315avQwuu/2sLrOrSiJQiCUDdENgmC0Aow858D+HPPd7/m+v81AD9eYNt/AeBf1LWBgiC0LXUdg1eN8BIEQagXIpsEQRAEQQgr9UzRFARBEARBEARBEBqIGHiCIAiCIAiCIAghQQw8QRAEQRAEQRCEkCAGniAIgiAIgiAIQkigdqv8TUQbAP424Op9AK7WsTn1QNrcGNqtze3WXqC8Nr+Zmfvr2ZhGYMun22i/e1Uu7fg8VkInnGcnnCNQ3Xm2vXzqINkEdMYz3QnnCHTGedZFNrWdgVcORLTIzIPNbkc5SJsbQ7u1ud3aC7Rnm2tBJ5x3J5wj0Bnn2QnnCHTOeRajU65BJ5xnJ5wj0BnnWa9zlBRNQRAEQRAEQRCEkCAGniAIgiAIgiAIQkgIu4H3mWY3oAKkzY2h3drcbu0F2rPNtaATzrsTzhHojPPshHMEOuc8i9Ep16ATzrMTzhHojPOsyzmGegyeIAiCIAiCIAhCJxH2CJ4gCIIgCIIgCELHIAaeIAiCIAiCIAhCSAiFgUdE7yKiF4noJSL6ZZ/fX0dET9q/P01E9zWhmd42lWrzPyKiS0R0kYieIqI3N6OdnjYVbbNrvQ8QERNRU0vbBmkvET1qX+fniOiPG91Gn/aUei72ENEMES3Zz8Z7mtFOV3s+S0TrRPRsgd+JiH7TPp+LRHSw0W1sBkT0G0T0gn3OXyGiNza7TbUiqBxoV4hot93HHLnw881uUz0hItOWJ/+j2W2pB0T0RiL6ot0fnyei0Wa3qZmIbGpvOkk+hV02AfWVT21v4BGRCeC3ALwbwF4AHyKivZ7VPgrgZWa+H8C/A/BvGtvKXAK2eQnAIDM/BOCLAP7vxrYyl4BtBhFtAfDzAJ5ubAvz2lGyvUT0VgCfADDOzPsA/INGt9PTniDX+J8B+DwzHwDwOID/1NhW5vE5AO8q8vu7AbzVXj4G4Lcb0KZW4GsAHrT77zehn7O2J6gcaHPSAH6BmfcCGAHwsyE8Rzc/D+D5ZjeijvwHAH/JzA8AeBjhPtcgiGxqbzpJPoVdNgF1lE9tb+ABOAzgJWb+NjNvAngCwCOedR4B8Af2/18EcIyIqIFt9FKyzcw8w8yv2h/PANjV4DZ6CXKdAeDXoQ3o1xrZOB+CtPd/A/BbzPwyADDzeoPb6CVImxnAVvv/XgDxBrYvD2b+BoDrRVZ5BMAfsuYMgDcS0fbGtK55MPP/ZOa0/bEV+m+tCCoH2hZmTjDzM/b/r0C/cHc2t1X1gYh2AfgRAL/X7LbUAyLqBTAJ4PcBgJk3mfm7TW1UkxHZ1N50inwKu2wC6i+fwmDg7QRwxfV5BfkPe2YdW7DdAHBvQ1rnT5A2u/kogL+oa4tKU7LNdvrdbmb+s0Y2rABBrvEPAPgBIpojojNEVCwS1QiCtPn/AvB3iWgFwJ8D+PuNaVrFlPush5GfQfP7b63oqPtpp/MfQJMzEurIvwfwSwBUk9tRL94CYAPAf7FTvX6PiLqb3agWQmRTGxNy+fTvEW7ZBNRZPoXBwAs1RPR3AQwC+I1mt6UYRGQA+LcAfqHZbSmDCHTq4BSADwH4z20wHuFDAD7HzLsAvAfAf7WvvdBgiOjrRPSsz/KIa51fgU6p+aPmtVSoBCLqAfAlAP+AmW82uz21hoh+FMA6M59rdlvqSATAQQC/bae13wYQyrFZbkQ2hZ8wy6cOkU1AneVTpFY7aiKrAHa7Pu+yv/NbZ4WIItCpbdca0zxfgrQZRPSDAH4FwBFmvtOgthWiVJu3AHgQwKyd/boNwFeJ6L3MvNiwVmYJco1XADzNzCkAf0NE34Q2+BYa08Q8grT5o7DHvDHzaSJ6PYA+AM1OLy1EoGe9HWHmHyz2OxF9BMCPAjjG4ZlwNLT30w0RdUErT3/EzF9udnvqxDiA99qFml4PYCsR/X/M/Heb3K5asgJghZmdCMcX0QEGnsgmACGVTUBHyKdOkE1AneVTGDz/CwDeSkRvIaK7oAtPfNWzzlcB/JT9/wcBHG+yUCvZZiI6AOB3Aby3BcaGASXazMw3mLmPme9j5vugc/ubZdwBwZ6LP4WO3oGI+qBTNr/dwDZ6CdLmywCOAQARvR1a+G00tJXl8VUAHybNCIAbzJxodqPqjZ3u+0vQfeDVUuu3EUGe0bbGHp/9+wCeZ+Z/2+z21Atm/gQz77Ll9ePQ78VQKVDMnARwhYjeZn91DMClJjap6Yhsam86QT51gmwC6i+f2j6Cx8xpIvo5AH8FwATwWWZ+jog+BWCRmb8K3Rn+KxG9BF0Q4vHmtThwm38DQA+AL9gRscvM/N4Wb3PLELC9fwXgh4joEgALwC8yc9MiuwHb/AvQqaT/ELrgykea6awgoj+BNpL77HGBnwTQBQDM/DvQ4wTfA+AlAK8C+OnmtLThfBrA6wB8ze6/Z5j5481tUvUUekab3KxaMw7gJwEsE9F5+7t/ysx/3rwmCVXw9wH8ka30fxudI4MKIbKpvRH5FC7qJp8oPNF5QRAEQRAEQRCEziYMKZqCIAiCIAiCIAgCxMATBEEQBEEQBEEIDWLgCYIgCIIgCIIghAQx8ARBEARBEARBEEKCGHiCIAiCIAiCIAghQQw8QRAEoWMhols+3/US0R8S0UtE9Nf2/72u399KRP/D/u0cEc0Q0aT920eI6NNE9CtEdN5eLNf//2cjz08QhPZEZJNQDWLgCYEhol1E9N+I6Fu28PgP9twd1e73PiJ6thZtdO3zEBEt20LwN+3JQb3rPEJEF23BtkhEE7VsgyAIbcvvA/g2M9/PzN8P4G8A/B4AENHrAfwZgM8w8/cz8yHouYy+z70DZv4XzLyfmfcD+J7zPzP/ZkPPRBCEMCGySQiEGHhCIGwD6csA/pSZ3wrgB6AnYv8XVe43UoPm+fHbAP43AG+1l3f5rPMUgIdtIfczsIWkIAidCxHdD+AQgF93ff0pAINE9P0A/hcAp5n5q86PzPwsM3+uoQ0VBKGjENkklIMYeEJQjgJ4jZn/CwAwswXgHwL4GSI6S0T7nBWJaJaIBomom4g+a/++RESP2L9/hIi+SkTHoY0suLa9j4hOEtEz9jJmfz9FRN8goj8joheJ6HeIyPf5JaLtALYy8xlmZgB/CODHvOsx8y37dwDoBsDedQRB6Dj2AjhvyzgAGXl3HsA+e3mmOU0TBKGDEdkkBEYMPCEo+wCcc3/BzDcBXIZOCXgUyBhX25l5EcCvADjOzIcBTAP4DSLqtjc/COCDzHzEc5x1AO9k5oMAHgPgThk4DJ1usBfA9wN4f4G27gSw4vq8Yn+XBxG9j4hesM/hZwrsTxAEwRci+goRPUtEX252WwRBEBxENnU2YuAJtWAWwAft/x8F8EX7/x8C8MtEdN5e5/UA9ti/fY2Zr/vsqwvAfyaiZQBfgDbmHM4y87dtj9WfAKh6zBwzf4WZH4CO8P16idUFQQg/lwDsd2cI2P/vt397DtpBBQBg5vcB+AiAexraSkEQOg2RTUJgxMATgnIJOvc7AxFthTbYFgBcI6KHoKNuTzqrAPiAawDvHmZ+3v7tdoHj/EMAawAeBjAIwF3ExZtCWSilchXALtfnXfZ3BWHmbwD4PiLqK7aeIAjhhplfArAE4J+5vv5nAJ6xf/tjAONE9F7X73c3sImCIHQgIpuEchADTwjKUwDuJqIPAwARmQD+XwCfY+ZXoY26XwLQy8wX7W3+CsDfdypYEtGBAMfpBZBgZgXgJwGYrt8OE9FbbI/VYwBO+e2AmRMAbhLRiH3sDwP4b971iOh+V9sOAngdgGsB2igIQni4m4hWXMs/AvBRAD9gVwv+a+iiUh8FAGb+HoAfBfBxIvo2EZ2GVrL+ebNOQBCEUCKySagYytaYEITiENFuAP8JwAPQzoE/B/CPmfkOEQ1AR8l+nZn/f/b6bwDw7wGM2ev/DTP/KBF9BMAgM/+cvd59AP4HMz9IRG8F8CXo6NxfAvhZZu4hoinoalGvALgfwAyA/8M2BP3aOgjgcwDeAOAvAPx9ZmYi+jgAMPPvENE/gTb+UgC+B+AXmdnXaBQEQRAEQRCEdkAMPKEtsA28f8zMP9rkpgiCIAiCIAhCyyIpmoIgCIIgCIIgCCFBInhCW0NET0OPnXPzk8y83Iz2CIIgCIIgCEIzEQNPEARBEARBEAQhJEiKpiAIgiAIgiAIQkgQA08QBEEQBEEQBCEkiIEnCIIgCIIgCIIQEsTAEwRBEARBEARBCAn/fxYGzMzh7XnvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot partially labelled \n",
    "# scatter plot, same as above, except outlier 94.\n",
    "# P vs X_NORM, RAW vs X_NORM, LOGIT vs X_NORM, ENTROPY vs X_NORM\n",
    "fig, axs = plt.subplots(2,3,figsize=(15,10))\n",
    "axs = axs.ravel()\n",
    "col_to_plot = ['Delta','RAW','LOGIT','ENTROPY']\n",
    "df_to_plot = soma_df[(abs(soma_df.LOGIT) < 10)&(soma_df['PC1/PC2']<30)]\n",
    "#df_to_plot=soma_df.copy()\n",
    "print(f\"Removing {len(soma_df) - len(df_to_plot)} Cells\")\n",
    "\n",
    "df_to_plot = df_to_plot[(df_to_plot.ImageNumber==13)|(df_to_plot.ImageNumber==213)]\n",
    "\n",
    "col_to_plot2 = ['ENTROPY','RAW']\n",
    "for i in range(len(axs)):\n",
    "    if i < 4:\n",
    "        col = col_to_plot[i]\n",
    "        df1 = df_to_plot[df_to_plot['True_Label'] == 1]\n",
    "        axs[i].scatter(df1['Overlap_0.3'], df1[col], s=5, color='r')\n",
    "        df2 = df_to_plot[df_to_plot['True_Label'] == 0]\n",
    "        axs[i].scatter(df2['Overlap_0.3'], df2[col], s=5, color='b')\n",
    "        axs[i].set_xlabel('Overlap_0.3')\n",
    "        axs[i].set_ylabel(col)\n",
    "        axs[i].set_title(f'Overlap_0.3 vs {col}')\n",
    "    else:\n",
    "        \n",
    "        df1 = df_to_plot[df_to_plot['True_Label'] == 1]\n",
    "        axs[i].scatter(df1['LOGIT'], df1[col_to_plot2[i-4]], s=5, color='r')\n",
    "        df2 = df_to_plot[df_to_plot['True_Label'] == 0]\n",
    "        axs[i].scatter(df2['LOGIT'], df2[col_to_plot2[i-4]], s=5, color='b')\n",
    "        \n",
    "        #axs[i].scatter(df_to_plot['LOGIT'], df_to_plot[col_to_plot2[i-4]], s=1)\n",
    "        axs[i].set_xlabel('LOGIT')\n",
    "        axs[i].set_ylabel(col_to_plot2[i-4])\n",
    "        axs[i].set_title(f'LOGIT vs {col_to_plot2[i-4]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "af423833",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removing 10353 Cells\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot partially labelled \n",
    "# scatter plot, same as above, except outlier 94.\n",
    "# P vs X_NORM, RAW vs X_NORM, LOGIT vs X_NORM, ENTROPY vs X_NORM\n",
    "fig, axs = plt.subplots(2,3,figsize=(15,10))\n",
    "axs = axs.ravel()\n",
    "col_to_plot = ['Delta','RAW','Jaccard','PC1/PC2']\n",
    "df_to_plot = soma_df[(abs(soma_df.LOGIT) < 10)&(soma_df['PC1/PC2']<30)]\n",
    "#df_to_plot=soma_df.copy()\n",
    "print(f\"Removing {len(soma_df) - len(df_to_plot)} Cells\")\n",
    "\n",
    "df_to_plot = df_to_plot[(df_to_plot.ImageNumber==13)|(df_to_plot.ImageNumber==213)]\n",
    "\n",
    "col_to_plot2 = ['ENTROPY','RAW']\n",
    "for i in range(len(axs)):\n",
    "    if i < 4:\n",
    "        col = col_to_plot[i]\n",
    "        df1 = df_to_plot[df_to_plot['True_Label'] == 1]\n",
    "        axs[i].scatter(df1['Overlap_0.1'], df1[col], s=5, color='r')\n",
    "        df2 = df_to_plot[df_to_plot['True_Label'] == 0]\n",
    "        axs[i].scatter(df2['Overlap_0.1'], df2[col], s=.5, color='b')\n",
    "        axs[i].set_xlabel('Overlap_0.1')\n",
    "        axs[i].set_ylabel(col)\n",
    "        axs[i].set_title(f'Overlap_0.1 vs {col}')\n",
    "    else:\n",
    "        \n",
    "        df1 = df_to_plot[df_to_plot['True_Label'] == 1]\n",
    "        axs[i].scatter(df1['Jaccard'], df1[col_to_plot2[i-4]], s=5, color='r')\n",
    "        df2 = df_to_plot[df_to_plot['True_Label'] == 0]\n",
    "        axs[i].scatter(df2['Jaccard'], df2[col_to_plot2[i-4]], s=.5, color='b')\n",
    "        \n",
    "        #axs[i].scatter(df_to_plot['LOGIT'], df_to_plot[col_to_plot2[i-4]], s=1)\n",
    "        axs[i].set_xlabel('Jaccard')\n",
    "        axs[i].set_ylabel(col_to_plot2[i-4])\n",
    "        axs[i].set_title(f'Jaccard vs {col_to_plot2[i-4]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "c4c6b9e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removing 10353 Cells\n"
     ]
    },
    {
     "data": {
      "image/png": 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xrMCzWCxHnVAIhg07etWgLBaLJScEgxDasIW1qJ3JdlJgy5Y8viqLxXIycLC5kLvO54O6deGzz47O0GQFnsViOepkZMCbbx7/VTQtFsu/n2AQXnsNtniK41AWgMr5ItaDZ7FYcp0DVcYMhf6+LjUVmjXT35xiBZ7FYjnq+HyaO9kQTYvFktf4fHDzzTBzlqFEjYoAbGrbyxZZsVgsuY7PBx06xOZD8cIOYutCIRg6FGbNOjrGcTv9slgsucKuXYfexmKxWHKTUAhGjYIdO2D+fPAlya5tjU8Wi+VYES/uxoyRqIsXfSBRl5mp9pxHQ+BZD57FYjnqbNgAo0frr8ViseQlzZvDkCGaXHlMBAhz+ulOXl+WxWI5wTmSOgOusEsUd337qrBvOHx05k5W4FkslqNO2bJw4436a7FYLHmB670LBKBfP+h7V4Q1c+YBsLbfBxCJ5PEVWiyWE5UD5dYdavvsCATg5Zdh7Vp9tjl4FovluCUlJa+vwGKxnMyEQrKGB4OaQPl3bqYWCwEo6qyGzZvz+AotFsuJSmJu3cEIhQ5ve1fg/fFHzq/PCjyLxXLU2b5dbRJsFU2LxZIXhEIwYgRkZcEbb2hMev6D4qyiJgC7k0vbKpoWiyVHHK64GzMmrr9dJAIbNxLaGwsTz8iAPn0gPV2fMzNzfm1W4FkslqNKKAQzZshSFQjk9dVYLJaTkWAQZs/WOHT33RqLatRwqN22DADFb77RVtG0WCy5js8HbdpIvI0aGSHYrguhMuUZ07Avwd2RfYbwihWhWLGjd14r8CwWy1HF54NGjTSYWQ+exWI51oRCKvIUCsG4cRqTMv6K8GGfqVQd9wwABb963ubgWSyWIyK7vLv4ZYnr/X7o1AlaV91M+sQkCIdos/Bdhv1vBzfeCL/8oibnjRpp+6MRPW4FnsViOaqEQkoQPuMM68GzWCx5x913g9cbzcHL3MzHf53HYM4HYPqGZJuDZ7FY/jHZFVeJX5b4HmLhmROWFierRj3w+qBlC0hNxeNRG5evv4ZXXtF2tk2CxWI5rnAHtu3bYfhw68GzWCzHnlAo2vPOpxy8116D4bOK42/RgMJoplU51WNz8CwWyz8mu2Ip7jKXDh32nw+99po+t+9g4P/+j9CK1Yx/ZCQdzjA884winipWhCZNtH+ZMkfhOnN+CIvFYhHuILd9u01vsVgsxxbXWu7zqWCB369qvnfeCT6fYVjWMJZctQcyYUPnO+0gZbFYDolbATOe+M/x60eN0t/27WH8eGjRAiZPhltv1XiUkQFz53k4o2MJmjRVASjH0fYAg38MA7B8qSGnPjgr8CwWy1HF59NAVq2a/losFktu4/a8C4f1mjcPevVS3ovPJ6NTnboewpF8gG1ybrFYDo3rhUv02LmiLnF9+/axJuZt2uz/NxSCCROgVi3lCAPUrAnNm8NVV0FWVhiWTQRaUfG/F8H9Xx9emc4DYEM0LRbLUScUgo0bD78BqMVisfwT4vNbQPOgTp2ga1fo0kXi7q67ZDHfvh3OOw+uuy5CxT3TACjyy5u2yIrFYjkgB+pdF59j5/PJS+fzKc9u/PjYNunpSlVJS9NfgNat4ayz9D4chgUL9L5FCyiTfzulmAXACOrC4sU5un4r8CwWy1EnIwOWLLE5eBaL5ejjeutGjYqKvGhfKZ9Xnrnx42NNzseMUSRBnz5w48U7qI1M58Gd222RFYsljzlejcDxveuyC8908+0yMuCtt/Q3PT3mrXMNTh076hW/rTs2tWkDVarAgw/KIPWXtzBjuR6A/CD3Xg6wAs9isRx1AgENXLaKpsViOdq4k6dOnZC469CBUJnyhNp1xOeJ0KIFfPghvPCCcvDcCdWwyamspiUA5Uom2SIrFksekl01yrwmPo+3TZuYsSg7Ro1SyKWbX+fu50YXhEL7G5umTIEbbtB2n38Or78Oc+dCuXKxMHJI1nFufww8OZNoVuBZLJajTigk4/jxNHBbLJZ/H2N+2EFwwnRGhdsyakI+Qus3EwjAzTfDokXKbxk9GoYMgY6dDMsKtwFg7VkP2CIrFksekl34Y16SKDj9/v0rY7rbuKGZrpHJNWR36qS/o0YpJDM9XVUx3Xy7+vVhwADYskXRl1deCfnyqf/duLERdsxcAWwBYOvWnH8fK/AsFstRZ9UqWLZMfy0Wi+Vo4k7EADpckIq/dWM6ecfRqfUefKWLEwrJWt6gAUybBm3bQtWq0K4d5M/vAbz4fFbcWSx5TV6Ku0QDdKLgdNfH97QbNTLCqK+3Edrr7AvFjB+PIBaa2b49TJwIv/6qPncDBmgcmjsXzjkHZs6EOnW0j/evbVQNzaAIywDYsnpDjr+fFXgWi+WoED9Yli2r6KeyZfPueiwWy7+T+ImYL8nAmDH41q3GNzZtn1cuKwv69ZPICwSU4/Lww7G0OxudabGcvBwoPDRe3O0zIrmiLxKh07Od6HR1KXyd2hPcHSvS1EaBAfvy9tzQzHbtNCQNGaI+d2eeKYNTr14wcCDs2gU//QTbthsWUYOt1Acg//ifc1wEygo8i8WSY+IHy1BIycTbttkiKxaLJXfYr6pdxAMlSoAx+yZsXbqoRkH//ppAjR2r5sGFCml9DgvUWSyWE5jExuQHWu/zgc8TIbRuI2O+3w6TJkE4RHDCdF77v11s366QzLQ07demjfre1amjHnehENxyi9Lpfv4Zvv9eBZ/eew/q1dP6Tp1g+K+F2UEqbg5ehK2wfn2OvqMVeBaLJcfED5ZjxshiXq4cpKbm6WVZLJZ/OfHV7tzqmj/8oPeBgAoghMPw3Xfwxx9QvXoECFO6tO2FZ7Gc7BysyIvPB6GsCKH2nfBVKEubNy+Bli0Z4+mIr1Uzbr0/wLRpan3g4ver5cG770KFCvDyywrRfOUVKF8eFi6UY65nT4WPP/EETJ0Ke/YYSldOBf7QuYnkOBHPCjyLxXJUcOPRO3RQEvFvv8GGnIeRHzHGmPeNMZuMMfMPsL69MWaHMWZ29PVo3LozjTFLjDHLjTEPHLurtlgsh4tbwtytdgcqZPDuu/DLL5p4udXtKlSAs7tFyD/+SwD8X7xk++BZLCcJ2Ym4eMN0MJj9PqO+3cGoCfkIhjyMn+yDzz6jw5qP8Y1NI1DA0KGDxhivd/8CLJGIDEvuX78f1qxRVMF//qNtihVTqOasWZoz/bEjAJQGYDt+qFYtR9/ZCjyLxXJU8fmgZEk4/XT9zUM+BM48xDbjHcepH309CWCM8QJvA2cBNYHLjTE5a0hjsViOKhkZ8Npr+hs/UZsxAz75RO0RRo+GESNgzhxYvRpmjt/FJBoDsGpLyPbBs1hOArLLt3Pfh0KqePnKK38XeT4fdLo4VcWbvIY2LcP4ypbcFw7ubuPzyZiUlhbrzdm7t8aerCwdKyMDNm6IsHj6TurUdrjmGnnzPvxQ1X4LF4b8ZAK7AMgkGZYuzdH3tgLPYrEcNdxBMyMD1q3L2xw8x3HGAUcS49AUWO44zkrHcbKA/wHnHdWLs1gsh03ixCwUUp7LzTerD9WoUbF1M2cqNLxdOy3r0gX69oULLoB1OwJEyAdAkQKn2EorFstJQGJ1zIyM/YuhdOwIt98e8/a7YwxEiziNGsWoT9aTdv9IgnvMvn2JRGDjRkJ7HSZMkKeueXN48UWNT88+K8NSZiZ8/FGEDzIvpMEb1/B5p/eoVy9CsWJw2mkK6axUCTJNCq4s8+BA9eo5+t5W4FkslqNCvJVs+5YQmZlhMv467kOgWhhj5hhjhhljakWXlQF+j9tmbXTZ3zDG3GSMmW6Mmb7ZegMslqNOYgEn932HDhJybi8q15LesKH2GzlS3rxQSLku7dpBv36Gm+9Wad9Sd9923PbBO1SIuDGmnDFmjDFmljFmrjGmW15cp8VyouCKu2AQ3npLosrni/2dPFljRTAog5HriQuFwJfsof0FhfH6TCwkfFyEjLZnklGqCnTsSOuWmuuEQtJ9s2dLMPp8iia4otNmFvy6lxQyuWLNM5wS2UXdugrPXL4cdu+GAv4woON4icCff+boO1uBZ7FYcowbd96hgz6MbtIXiLC9Xrvjudv5TKC84zj1gDeBH/7pARzHGeA4TmPHcRoXK1bsaF+fxXLSs181Ox+0aRVh/OBt4Dj7bePStav+zpsnizpAjRrQowe8/FKE2b9uA2DdH8fqG/wzDjNE/L/AV47jNAAuA/od26u0WPKGnE4n/H4VXvL7FZr5+usxgxHIo9e+vV6wf2ina0jy+6FJ+c28MLEFvZw3+GF8KqO/38G0afDqq1C7Ntx2m7atXFnjz3UPFmdjlWbUYw43BL5i5rIAXq+2WbcOBg+GiDcJKAjAHkrnOMLACjyLxZIj4q3qPh/4li/mUr6gOGuozMLjth654zh/OY6TEX0/FEgyxhQF1gGnxW1aNrrMYrHkMgcqhgBAJIL/rA50uEJ9qDL+imTr3fP74e67oVEj5eENHAhvvBahxU8PUn/C4wAU/eH547XIyuGEiDu4M0EohFt6z2L5F3Og3nWJ2xxsnRveDTIG9e0b87SFQvLO+XyxPnZt2sir9+KLsWMHgzBlZXFqVQ9RkdUk16xK23NTadhQ20yfDm++qX1KlYInn4QaNQwvJ/2X4S8t4JQ2TZgxwxAMQpEiEoDFi8NvvzmAkvbCePYzYh0JVuBZLJYckRjfTs2ahPIVYTcBgvmKqWzUcYgxpqQxitEyxjRF4+GfwDSgijGmojEmGVnIB+fdlVosJweHnMBt3gyTJuEL7yE4YTpvvbBrX4gVaDKWnh6rrpmSAk2b6nhPPhwkc+FyxqPkvIWbPcdrkZXDCRF/HLjKGLMWGArclt2BbAi55d/E3+YaCRxs/Mi2cXkcwaBaGqSlxYRdero+jxwprRUMKn9vxAho3sJQ6JWnuHX2zbSd8CwTJxkaNFC45a+/wiWXwJIl6sPZtq1Cx7dv9zB7VWG2bXdoWGM3mzY6nHGGitGpGEtM0GWyO8fj0wFuk8VisRw+7mApL54H/9K5FGqQhX/uAnX4zAOMMV8A7YGi0YnQY0ASgOM4/YGLgd7GmBCQCVzmOI4DhIwxtwLDAS/wvuM4C/LgK1gsJxWHmsBRvDi0bAmTJuFv2Zhb7w8QCMT637Vvr7DMESMgOVktEwYMUK0CjyeFrIW12bRUodTlCvlO5CIrlwMfOo7zsjGmBfCJMaa24zj7uSQdxxkADABo3LixbfxnOeE54NjA/uOHG1GU3TqIVc+cPTvmxevbN1Z4pUMHhWSGQhJ6N92kxuV79qgFVDgMbdp4uOuuElSooO2++ELtWLp3h88+07kGDYzw8v8FadUphQIFDMZE2DttFitDPjIpzObmZQmFPDgOlC4Ff6z3AhChuA3RtFgsxwf7rGdZEXyXnY9/6wp8l3TPszAox3EudxynlOM4SY7jlHUcZ5DjOP2j4g7Hcd5yHKeW4zj1HMdp7jjOpLh9hzqOU9VxnEqO4zyTJ1/AYjkJiZ+U/c0Sb4wGmbVrCY1KZ/IUs6+0eTisfTt23F/cZWaq2nivXoYlFz9GSpVGAJR98K7jtcjK4YSIXw98BeA4zmTADxQ9JldnsRzHuOIuO09eojiMD9F06d8/VnjFfbVvr4JNlStDgwZw3nkaY/x+uPBCuOUWjTfPPQcbN2osWr0aOneK8PUFn/Dbj9P44v7ZFC4coWKRHewKeSnCduoxlR7dd/Dnn7B+fYSd6zcCGpOKteuY4/HJCjyLxXJU2Gch27aZVZPXsILqrJ382/EaBmWxWI5jDhhu5fFAiRL4ksy+BuehUKzRsN+vUuWDBsnq3qIFVK2qXng1a3lIKRAAvGzfcVyKOzi8EPE1QEcAY0wNJPDsQGvJM46nWmqHE8rptlbx+fZv53TrrRAI7L9dKKRthg2TeAsEVJV3wgQZkF5/Hd59V8fq2lURBMWLw8p5u1j6RxLF2UCR3UthTxbfjkqldL4dFOJP5tOUOm1S2b4djBNmJ6fgCryMsT/m2DhuBZ7FYskx+3rG+CBYsDgzK14GhPE1qnMih0FZLJY84kCTtPiJpLuN369Gw27+3cSJUK2acmGaNoVPP4VatdQuYdmyCBBm6dLjM2LRcZwQ4IaIL0LVMhcYY540xpwb3awvcKMxZg7wBdAzGl5usRxzDqf4ybEmO3EXf33hsD4PHaqKl2vXwgsvwNixGkPclgnhsHLwFi1ST82UFIk7v1/VMl3R16OHircsXAhFi8KmTXBG9wDJhYuQTJjyJUK0aJ+P+vUNf9Vtw7wi3SnRuBwzZhrKl4dSxSKUZznKCoG97JQ7MAdYgWexWHJEYp+q8WMj1P1tCGBg9tJYrXKLxWL5B2Qn7twmw/FFE4JB9jUadgmHVc0uEICPPoJJk+CHHyI03fm5jj144PFaRTPbEHHHcR51HGdw9P1Cx3FaRcPL6zuOMyJvr9hyMnPI3Nlc5lDC0p2bDB8ee++VjqJLpwgv3rORQe851Kwp8ZaWJuH38svQqpXCMW+9Vbl3mZmqzPvTT/DQQxJ9xkgAZmRI4A0ZosqY551vaHhvF0pd050xp15BVpZh7VrYutXD9l1JpKQYnn8eVq6Ev4LJrKEaoJjzJPYe31U0bbNOi+Xfz9/6VBVdxAyqAYZQeNdx2ybBYrEcW46Ghb9FC4VlgsadUCjWu6pjR1nW27XTsOM48Msvyp9JTobHbt9BUyYCUHnvDBs+brEcJfJS3MV7D10Bl7g+GNQ4kJEhAde6NaSPiZDW9AF8tasTHjiQZJ8sRF6vajm5KXBerwxFNWtEmDdlJ5mZDh06wCuvqNrmypWKGpg7V73J775bAu+RR2DGLA/TFhcgEDCsWqUxq0oVSEqC/PkVaVC4MGzdavAn5cOtfelgclygLtf+SeKadXZGZX6nGWMGO46zMG4zt1nnO9FGnkOBCrl1TRaLJXfYr1pV3Zo4biy5t9Bx2ybBYrEcO9yJ1j+x9MdXwnMt8F5vrAkxSNy1aBF736GDJmN33KF9BgxQLl6/fpC1pyBrKA/AH5RTLJXFYjlhSayc6ebWuU3JIdbbrn59GDdO+bjt2kHrqpsZvWARY2hFgz9+ou0zXzHWN4J27T1MmAB16ki4tWun4nHJjz/E3oUF+Ca1MXN7d8ZB2xUponP06iUv38SJULasIgiqVlUj81NPVQuFZcvglFMk8pYt03hWuLDE3qJFHmAPkER+8kOJEjm6N7npwbPNOi2WkxGPh9bTXgd8+Gb+mmdtEiwWy/FDdmFcB/PoBYOarAWD7KuUGS/uhg/X8jZtFJ6Znq737r79+sHUqdCnD6Sm6q/ZvZvlVAVgOz7YsuVof02LxZJH+HwSdm57A3cMSU/X+m7d9OrdW2GWYxcVJ6tKLeZQD4Ax009hxthdBIM6RufOCvUeMgRee2YXDRd+jJcQVbdPoVaZHfTooSiB005TNc5Zs6BSJTU3T05Wjt6iRXDWWVC+vKpwOk6EnVuzCIcdkpLkyVu/HjJ3h/HxJ64sCzb5z3FdRdM267RYTjJcK70/4CMpyYs/vxV3FotFJIq7+NzdeNzeU1lZqkj3yita5lrlhw1TTt1rr2n79u1j61wL/u23K2RzyhSJwfR0iPjzEyY/AKez1XrwLJYTnPgQTIh58l55OULaN9to387Zz5vnGn9mzYJWrQ2B157hniaTOcukkVyzGtUaBbj3ngjbl2xkbLpDVhb8+COUrRFg4GlP4WC4seksfp6QyqOPSsQNHQobNqioU1aWjl2qlHrsbdmiWik+H9SoHmFAynWcunoym+f+znnnRli4EPz5IgRWz8XBA+QD4JSknGudvG50bpt1WiwnMAdqJrpqFezdm2eXZbFYjiMSxwmIjRXw99BN1xLv7nfGGfuHas6Zo8+33qpl6enaPiNDFve0NHn7OnWS+AsGVcq8Vtm/GIFbiSVTs68chkFZLJZjizsuuH/ddin7xpBIhLt/6QKTJuMf2BjGjCEU8jBqlIqk1KmjbVNTof0ZXnydRhFcvZGOSYZQ4Qgd3+hOWu38JNeoSvv0p8nK8rBokaHGs9fy09dBJl2awjkYvv9e4eDffgvPPqvwzJYtoW5d+OYbKFkSrrpKYaF+P6xYEKTP3DspxUq8lGXur0E6dszPht+zWM5phHGAEOBjw6R5ECmeowio3DSv22adFsu/mIM1E12wQO/dvxaL5eTkUCXU44VeIm6PuwkT5IUbPlzLH3gAHnxQk6b0dIm6jAzl3bVqJc9d69baNj1d569eHbxFCjKHkgD8QRnrwbNYTiDcsEvXY+f2qPP7Y+IuIwNefnoXoYm/Mj7SguCE6bB5Mz6fxoQZMzReTJkSK7jyw4/Qp+VMfq7Yh1dqDWDY5AI8yf1MXHQKrz60kUULHerVgzM6euh+aX5++cXQtKkiKNesjvDbzG088l+HnTuhQgUVWmnRAs4/H8qUUW7el19CzYYp7KAov1OJSslrWfFHCl4vbN6ej8L8pcqZUVlWjMU5LgKVmwLPNuu0WP7FHGhiFgzGjOLWOG6xWA7Uz27M6AihdRvBcf4mAuN73HXqJNEGEmzusdzqmV27almlSlo+bJg8dqGQJnXTp8P8+VCr+BZKRKc91Vhqc/AslhMEt4BKWlqsaArEWqb4fLF2KTWbBPC3bkwbz2TSa/YmWLD4vlDwVasURtm8ufLwMjLAu3MHZ2/5gDP5hT7rHiG5UiWKsp2qrMI36B1qf/0oWXsi3HOPIgPOPlutV9q2iVBu3MeEr7+e+Rc+Qq+bIkyYEKFjw21c29Nh0SJYswb694cmTSBttKFW59KEy9dgSZG2bNhgWLYMdu82rKcCeykBqOt6mOI57iGcayGajuOEjDFus04v8L7brBOYHu3n0hcYaIy5CxVcsc06LZYTBHfAdMOj3AH2tdf0GRS+YLFYTk7iK2cm4vNE6PB0J3yTx0PLlnQYOQafb3+bc3zIps8nIQcaZyZPlpXc79d5Jk9W3t24cYocuOGGmEV/1SooXRr6PFEc2AHA+gL1cjyBslgsxwafT8Yct2AKQOPGmm80aKBKl5Mny6AzYYIh9MsofNs2kzW9OGmjDVlZEmddu8oA5PXCzJkaG847N5Xl5bqStcbHb+U6Uvnx67hy8WrGPtOCR3iCpUtrMXXqTkqVKkRysgTm//4HH0wJ03hjCg2AHxbX4I+P97B31nze+3IXz91bkoINq7JmjYf69WHePKhYEXw+D8kpyVSprjacjgO7dkHJkh5WrowQiajuZM3AJq3MQaGVXK2AYJt1Wiz/TuKtafHNhf1+uPPO/bezWCwnJwdtgLx5s8RdKKSKKQcIR4rvb+Xm3bz1liZ1Y8dKwLnnCQQ0cbvhBhg4EH74Ad5+Gy64QFXuSpY0eMtXBiBw0205rlJnsViOHfEib+hQFUupVUth2ZMnS3j5/dpm/EQPoVNL4PUZWrWSBz8rC1JS4LzzNG/57TdVsAxHDD3GXM+HbT6kzKM38ONPXtLmluBUNtOLAdRkHpGIYeVKHSMU0jFKlfWRr3BhzmAiVcpmceX5mSSFd1KATfjIJGNriL594cor4dJLJeS2b1ee3rZt6p+3ciXs3KkiLbVODwIqQbI1Y9dxHaJpsVj+pbhFELp2jYVHQWwSNmVK3l6fxWI5Pjhgz7vixTXT8fkItWjDmAWxMCoX15Dk5t24+Ta33qoWCFOmwKuvar0r/kB5NrVrq1R53bpw5pkSf927QyDgAbyceqoVdxbLiYI7Frhzj27d4L771IJg2jR5830+jRMQC+N0m5TfdZfeL1woL9/KlXDRRfD++zByJEya4uH6m/2kpBh8Pnji+fzkK1kaBy/vFP8/KFCApCQJySuu0PizcaOh47OdCE2fi6dnT1btKMwp+b38QWVer/023pQkfvgBfvpJzrju3eHiixVavm2bcvVOOUV2plq1YPWmlH3f9y/KHL8hmhaL5d9Ntjk1Y2K9qCwWi+WAGKMBY/NmfMWL0yEswZVdM3S3Up6L3x/LyWvVSn8zMjT56thxf7E3YoRybaZO1bI/ot12ly3L5e9nsViOmMTKu6GQvGfBoJ5/iP1t0ULPfocOep+WpuVdu8b6ZrrbVqsmA1DPnmpIvnCh+t0NHSrRuGgRPPEEfPCBYVXrK7m07g6+G5PKgOsNgYAiAbp0USXf++6DxUs8fPZ5CS6/XGPK7+Xa0KbRLt4PDeTyGg7/+2wvW/IlMXKk4Ykn4PvvJfYuu0xjU6lSGgpXroRIxOD63fznXHlc98GzWCwnEfHhWLVra1lGRt5ek8ViOX4JRTxQogShsNmXZ+eOIe4Ez52gpafLgp+RIRHo8ynvZuJEeOEFeOklFTJwc/LGj4/1xmraVLk2tWtDudMiQJj8+W26v8WSm/yTFI3sPPfusmBQom36dHjxRXnEhg+P1QEYO1bCDvQ+MzN2nLS0WBTA3LnqTbd1q8TZO+9oPAkENH6kpMDyZREGvrKTObMdNmzw8HtGYWrWlDgLheRUe/VV/V2+XL3vdu+Gzz9XqKWDh+TCBdj5l8N7T/xOw2WDmDduIxdeEKFoUahRQ7l3b78NO3YofHPHDkVjekwEN0Qzedj72jAHWIFnsViy5Z/mz7nbjxoVqz5uq5BbLCcvBxtD4sudx1fQdMWdOykbP17LW7eW2HNzbUAV89q1g7vvhoYNY6XP09Ml9l59Vdb60aNVvjzFH8EzVwfM/+VLOZ5AWSyW7DlUexR3G/eV3bZuW4Tx4+WZv+8+qF9foZZu7n8oJOE3erTeh8PyyrVrp/Ver8aOQACuuQZ+/11FmM46C66/Xutnz9ZY0rljhO6zniDyzuukTB5J0yYRateWMGzZUqKsXj2FaF57rZZffjnkzy8j06RJCh/fuBGSI0G6ON+QQiZZeFi/Ksjnn8tTWKQIlC0LhQtr/9Wr1cblr50ObhGoteGIzcGzWCxHn8MZnF3reHYD9O+rZSVfs9payS2Wk5GDjSFu9V23KEJ82fN4XI+eW1glFIpVzgRN5iZM0Ptu3TSRGz1ay93x6dprZe2/6y6oX3ozm8kHgH/L0hxPoCwWS/a4YdUHysF1jTijRulzdp77ESM0TrjPvN+vvNozztj/OA0axM7ZpYtEoM+nfZs3l1dvyxaFXj79NCQlweLFes2YoeqWX34Jwd83s2Cpl3WcRultM/CHd9GihUIqf/lFnsPBg+W5u+suuOQSibSWLXWsmjUlREuXhnV/pjDCcwlzacQVRceQEVJ+Xf36ysPbvFlhnu61r1kDkmQFAChKco5z8KzAs1gsfyMxVCoRtx2CGy4FcT2rzojA/Tdr4T03Wiu5xXIScqgJnruNK/YSe+C5rVd8vlhhlbFj4Y03YkVVOnZUqfS33tJYNHKkJmyNGqkRerVqMGuWihts3w69HytKPVQBagPlbIiBxZJLJIZJJ+I+4/HPuWsUcveZP18CbfLkWL87rze2bvt2Pftt22q5Kw7dbbKydLwJEzTGnHsupKYqTLJBA6hSRd6/adN0rVs8xfGWLc19PMecAh3ZkxTgww8V4u33qw3L2WfDb79F6NZqG+PGOvzwAxQrpmItJUrATTcpkmBnhuH6R8tS4dwm3D76UurWNaxYIe/eN99INFatCsZE8HnCeDwOd9xhgCQAdra9xubgWSyW3CF+wE0UeW47hEDg75O44O+bmbtAg5R3/nxrJbdYTkLc0KrsDETxAi474kO33P0DARVNuPvuWJ5dWpryaG64Qf3vZsyIbfvii8qpmT5dOS433ADVyu5mHBcA4Ocv2+jcYsklXAPPgcYAdxu3f64r6lq0kBjz+fSsu3OM9HQ972645e23q3BSz54xj35amo4TDsfEoFt8adYsHfvVV+G99yT+1q6FAQNUqCk9HZYuM+y98jr+7+wZNLy+CbVrGxo1ksA75xyFd+JEqLx2DD8/M53W0/6PrKwIL7zgni/C5Wduo3Qph0qV4JtvPRQrl5/efQwjR+pawmGJzPr1oWzZCFdufp5ykfl4Mv5k5MgIkmVe8uXLeZVfW0XTYrEckIP1sXInWenp0b4z4zWAvv5FcWpUN7AYwrVr22bCFstJhmu9P5gHL76JebzYC4UUUjljhiZBycn7W/njj9exowoqBAL6XLOmQjXHjtXnrCxYsULhUw0awDU9Agz6SDkupcoUsmOTxZJLuC1NDtgHM0owKGNMJKJnNBzWMx8KSRDNmqVjuOHXo0frGff5lPP22WcKlezQQQWXfvlFOXher4TbG28oZ65IEY0pvXtD5cpw4YU698svK/Tyvvt0PVWqehg/IT/F98LHH8szN3Ag7Nkjh9onH2bRY8eHQJiRK8+kbJtdJCcX4PFHIzxz8XQ27FrKBVVXkPzEI9Sr72HiRFXm9Ho13IRCGo+KFoXgzhDlV/moxFIieAjtDgBSqwUL5vzfwHrwLJaTkH9SQOVgg3P8Nu5AXqeuYeW1bwCQv99A20zYYjnJcMcD17J+IOILq8Tv27Gjwiy7dJG4S9zHLcySkaHJWUaGhN5778Us940ayYP33HMKi5oxA779ztCoZWkANl78sB2bLCcd/7R42pGew438yW7+kBiOfe+9cP/9yq2bP1/CbNgwCbM6dbTdL7/IYDNjRuy4tWrB3r1aPm6cnvnFi+Xda9dO40/FivDFF9omK0uFUgYNioV5338//PCDROW770YY9u1umjV1WL8eevSQl3DZMv2tXh163pCP5aU7sJJqdK+6kuqNA5x2Gjz9WBa1d6VzLt/jXTqfmeN38fDDqs75++8ai1atUkBT8+ZaVqJsEqM857KU6qynLOs259t3X1yjVU6wAs9iOck4nAIq/4R46/v48ZqUNW+pMANfkp1AWSwnI4n5u4nvDzYOuSLPFYjudm5hBrdK5vTpyq+bNEmirkQJbdO4Mdxzj3JrUlPhttvgllugVy9YvUZj06ZNdmyynFwc7d/++OPGc7DIn/hrcN+7ebZ+v6rh+v2qcvn55zBvnkIp331XAs0TVS0ZGbBkiarjhsMKw3z9dYk+n0+euWHDJM6aNlUF3Q41N9LrJoc2bWDDBujTB4YMkYeufdsILZZ9ROWhL3PJmKtZsUK1A+bP1xynaVNFFsyaZTDX9KTmW7ez9dbHeOUVw/PPQ4ly+Zib0p4s8vFD6g0sWRfAcSRWixXTmFS0qPreLVqk75eVZSjTtgr+06vR/eJUSpWKjUkqupIzrMCzWE4yDjb4/hPcwTn+uG5VvO3btcymuFgsJyfZTeTi30P241AwqImUW3jFHa8gFs7ZurXEW/v2cOaZEndnngnnny9vQHo6lC+vvnfjxmnfcFjFD8qUiR3LYjmZOFq//fEcSDQeLDTbDd1OvB7XsOPm2wUC8uA99ZRybS+9VB43gH79FGr5xx+qRrl4sQw9bgsVr1cNzPv0geZNwoTvf4gXq/fn3nrDKVc2si9sMzNTIi/4+2aarf+RTLzcNPsO3nt2M4UKSUh+8omurXBhVdE8rZyHWasK805/Q7VqEpJnn21oeWtjkt96m5JXd+HPPw0FCsjz98QTsHSpvIllyqgQy5o18hrOngPFioWYOBFOPz12nw4V/XA4WIFnsZyEHOkAnxhaET84u3k3oZDCEeDohBkcKcaY940xm4wx8w+w/kpjzFxjzDxjzCRjTL24dauiy2cbY6Yfu6u2WP4dxI8Pie/jJ3iwf0GV9HQta916/3EqfhLp9sLz+WJtEUBFWM47T2KvWTONQ5mZ8PXX8gB88w1k7lYLl9RU28LFcvJxtA0bhyMaE733iYVX4t/7/Xr2s7IknAYNktHmq6/2r8h5++0SaK+/Lm9+1aoqpOTzqWrmzdFC3vfdGyHzgcfIWr4UiHD6tqmU8O/gq6/glFM0R7nvPpizvjgtmodYz2kE8nn4X1pxtm5VDmDTpsrzGz8e+vZV37o9e3T+GjV0rfPmQcTxMGdNYWbPNpx1FgwZEqF46m6efMLB64WZM5UfuGiR8os3rI9QeNtyfpu6Hs/6lYwZE8n2nhwpVuBZLJbDIjtLXeKg7vasWrVKn0uWPGaXlx0fAmceZP1vQDvHceoATwEDEtZ3cBynvuM4jXPp+iyWfzWJuXXw9wleMLh/P6xOnWTFnzAhVhod/m719/u1fto0TbBeeEEV8ubM0fK2bfVKSVG7hEGD4OknIyydtw6AjEH9bAsXiyWXSZw3xHvk3Wd/+PDY+lBIRZJmzpQX7vrrlUdbqZLaoDz/vJ71jAyt9/tl4Fm9Wo3HX3lF6/r1UyTR2a12MG9FMkM4l7rM4a5GEznjwlTmzdM8JRxW0ZasvYZfHxjM6beeT7PeDZk82TB8uAq/GKM2LVu2KH9u+XKd6803FRK6cqXE6JIlEnxXXAFTJkeotHM2lSf0p8mSj0hNyqBMGYe//lLecKVKUCglixrM4iZexgDly2YBMkDVrJlzA5QVeBaLJVsOFVcfPyDHD+Ljx0O9enlfv8BxnHHA1oOsn+Q4zrboxylA2WNyYRbLSUjiBM/N0Rs/XqGW7dvvXykzHFaY1vDh+4dsuscIBjURTErSvo0bK9cO9PemmyT4mjbVsocfhrGDd1CMlQBs351hW7hYLDnkYHl98SHWicbg4cP1fDduHOth577atVOuXdu2yqE97zx5571euOMOhWY+8ID2HT0aFixQf7rff1chlObN5bm/8krI9KfiLVOGzozAW7kab7T7miFDweuJcHbLbSyY7zB8uHLtmjbz0KJzARo2NCQnw0MPQYECMhBlZMiT+O23usYrrpDYfOWVCMlZOwkEHP78U+Gen3wCM6eFqZz1KyM5ixs2PUaZke+wcd5a9mZFKFlSuYEk52NTchWGczGVktcSJgnYDcDS1z/OsQHKCjyLxfI3DhVX767PrtF5mzayvjnOsanYdZS4HhgW99kBRhhjZhhjbjrQTsaYm4wx040x0zfbyaLFki0HG0/cyV+8V8/n04Qu/uUKQPd4aWny9N1+u4qteL2aDN5/P1x0kZoYV6sGv/4Kdetqn1c+SGUPRQDwJ6faNgkWSw45kIA7VCVNt6rlu+9KkKWlybP14ovax+PR/iNGaDsVONG65ctVpCQQkCGoQQPl24FCJfv2ldArUQKWLTNUfupaRl00gCzjJ+uVN/jplmHUfPoikm+9jupfP8riRRGuvlpe/qZNFXp50UW6lrJl5enr2FHFXubMkacuORmeeiJCqVXj+eZnw7YZy2nePMKrr8Lll0XIlxxhtq893fgRP3uZSUPKs5KKZbIAjVmnlTNkVKpPoG1z9jZqy/md/gIUb57MxhwboKzAs1gsfyPRyp7d+jZt9s+FiU+U7tIpQrIvRGqh4z/PxRjTAQm8++MWt3YcpyFwFnCLMaZtdvs6jjPAcZzGjuM0Llas2DG4WovlxCKxUEqi2HPXx/fMc0uYQ8xbl5a2/z5uKKZbbKVrV63z+2MevSVLNAE8+2xV2+zWzdD5iloApF5/U96HGVgs/wKyE3DZRfzEvxYs0Po77oiFWYKifwIBVb1NTpa4++EHvcqXV9GSmjW1LhhUqKRbRbNiRYV0btqkY3bvrhDKNb976NZhL8nL5tGAmXT48wtunN6br7iQRUs8lC+0gw8+0P5vvaVCKAMHwo4d8sZlZUnkffedzlOypIqtDHx9F4Hd62jHL9wbeYpGp+9gyuQIn9w3h+2/b+KqMkNIuak3b5Z+nq0UoVmxdZSumI+dO+Gvv+QNTEry8Nv6/Kz53fDqwIJAMgAlCarsZg6wAs9i+ReTEw+aK+6ys7zHNzF1qz3tK2M+MsKqC64jK+Sw9tyrj+s8F2NMXeA94DzHcf50lzuOsy76dxPwPdA0b67QYjl+yW5cSPw8alRseWL1zGAQXntNuTKuBy8jQ8vc6ICMDIVa/vrr/se//XZVxWzRIrZ86FB5Ae65R+e47bbYRPDjj5WnM39BtIWLz4o7iyW3iPfcxefaueNB9eqqcDt2rPLXQM9q27ba/r33VCipUyd507p3l5GnWTNtN3269q9VS977zEzlws2eraJK7dopXHLJEoVrp5ROZU7Zc5hLA9ac1p5rKk/mIn6gV7NZjJqRSigkITd2rMaWM86QYahMGbVXufpqeOQRePRRVe7cuBH25gtQrlSYIKmsKN2RnyemUr/0ZnYHw9zLMwxe3YjJs/Ixr9aVFGlYg8oPX862bYaCBSO0rreTBg2cfeOXW3kcdgGwiMI5LkNuBZ7F8i/lcHveHGx9duEX2YVexC/L2rSDEXNLAR7+mJ3zMIPcwhhTDvgOuNpxnKVxy08xxhRw3wNdgGwrcVosJyvZeeIONt4kVtIEGYduvRUmTowJtQkTtCwQ0PaBANx1l6rOuaLwpZe0bTiswgu33abwqblztc+FF+r4/frB1mgWbp8+8OSTspy757ZYLEdG/HMeX93SXTdmTKxIkptn64ZaDxmiZxUk2GbPlrBr1UqFS375BXr2VGuEl1+WgGvZEsqV0xjQti389psqZ7rMm6fKlvPmaZs339R+116r5uhgaPh6TxoPuo3eU69jxWWPMO/OD5lyz49072646y61LmjdGv7zHxVL+d//FOb58MMaR9LSNM4YI8GZlGRY3+pKrn2xEb0n9KByZYO/XHEerDaYQgRZQXU6nZWP9es9BE718+23hnA4Qur88cz/Yhr55k0jHIqwcydE9hnC1ezcoViOQ8itwLNY/qUcbvniQ4nAxP2zO278suTiqVzYYDWQRdVGhfMsz8UY8wUwGahmjFlrjLneGHOzMSZaQJlHgVOBfgntEEoAE4wxc4BfgSGO4/xyzL+AxXIck10Pq+zGhU6d9t8mHtdINGuWiiWkpcmK7oqv+P0WLdq/8ubEicqL6d5dE7DHH1cfqUBAE8BRoxRi9cILEoAZGSrMsE5FNFm8OFdui8Xyryd+3uB64eNFnhty7bY8cfvjgp7DgQOVI9uypcTf8uWxSrqVKsGPP8pw07u3cvEyMyX4QiF44w05trKy4OKL1frErXRZrRrceKPOk5wMDz6oZumZmVrWuYuHrFMK8+s0QyjkoU7zAsyZY/aNLbffHsu9e//9mBFp3jz48EONHdWqSVj27w+7dkGxYh5GzSjMQw8bypeHq3sYtt7yOOPP78d340qQPtbh6bs3Uriww6WXgnfvHtYFC3A+n+MJ7WL0iL3s3Blh9+4wkAXoYguedUmOQ8iPckcMi8VyPHGonjdH2vg00aMXb5Vv3sIw/7VPoZ1Dxptf5lmei+M4lx9i/Q3ADdksXwnU+/seFoslnuyMP4faxh0v3PDNTp0UUukuc3Pt4vfz++WBc1sj+P0KwfL7NWF89lkd4/vv9fmNNyQIK1RQj6vOnRXmuWyZLPNz5kDBgkf9dlgsxwUHKmxytEjsZXnnnX/3iPt8EmHBYCxXPy1N6665RqLO7WH38svy3H//vUTbww/DZZdpblKnjsaIlBQ9x9u3K8/u8stVXdPrhQ8+gL17YcUKjQOVK8vYc+edCvWcNEnPfpMmKurSr5+E4R13aP8KFVRxs25djUm9e8vrV66cpi933aUcvOrV1bD8P//RtaSlaSwZPFgeyawsib7+73ooEPDz28ytJM2Yz2Vj61LLM4Ny5Rpy9cWZfDxzG2PpRE2ms3hHa+pXDzJ1jkEFVpIAKOLfgmzNR4714FksJzk5+SGIt+S5+TN33QUj05Tnsmu3zXOxWE4mDhYNcKCIAb9/fyPRiBExj8D27Xr17x9bFj+hDATgmWcUtlm1qo59992xFgmBgLbzeLR+0yZ9zs0JsMWSVxxuakZOz5FY9TaRYFB5ciNHStyBxFS7dlCsmESUMfK2T56sdeeeq1DtpUvlKRs3Tvl0Y8aoaqbfL0H17ruqdBkISPjVrAn58kmolS8vUbh8OTRqpKIsAwdK8E2Zomv+7DOFa48cqQbql14KF1wA/nwRurTYya+/Olx0kRqnz5ih0M3evRUtEInIqzd1Kjz2mHLypk9XEZhvv5XAu+rKCPkWzCDzzrtYm5FCa8bQPDKa5XODLN1cmAIpPibTmo0FG9GkhY9wUgql+YN87AR2ArAlmPPIJyvwLBbLERNfHc/9UXnzTZU9tlgs/y7iJ40H6nt1oKJM8Pc8vPjwTXd9u3aaxKWlKRTrootk4b8h6mt/5RUYnRZh1NfbCO11CIVUbO6cc7T+sstkUR8wQKFZM2bI6BSJQNvWEdav3ws47N591G6LxXLccKRROYeDa2BJrLId/8y7eXdjx0oIzZ6tz+npyr8LBJTnNmOG9t++XZ67zExt27cvNGwIl1wi0ec48oyBjjNjhtoZZGZqfTiscMwbbpDxxutVLl9SkipytmolQZmSojHi889l6Hn3XY0rTz4JTzwBgVMiJH/xATcOrM+M71dTrUqE559XcaY77pBXceiQCMvn7Wb+fIdmzeCqq1TF9+WXdY2nnaYxbfaUIFdnvE4KuzmH4dRnEesLNWDx6hRWrDCY1m0oVqk4HR7rgM9n2LHDsCW5Insojrx2HgKBnBvHrcCzWCz7E4moRJRz8BYH8ZM2n0/hD337avmiRVpXqFAuX6vFYjkmJHrr3SIK8RyoNHr8BDCx9UqiGAwE1FC4a1f1tevdWxO+qVO17+23Ruj2cifaX1WWYIeujBkdIRTSJK9jR7juOh2jZk1V1bv5ZoVs7QlG+ObhmfhYCETw5zt+q/taLDnhaIu7xFy7xBDQxKq4waAMNS1b6ll2ve3bt8cKKTVqpOqWjz8uL1hysrzsZcoo/w0k1jwe9dVNS4s+/7dr2dy5CqmcNUsVdt9/H55+WmPFgAHy2A0ZAm+/rSlNVlYs/++99+QhvPNOeQu9Xnj5hRDV1gwnwE6aOBP5+qNddO6sMcjvh65dIqT838Oc9tOrLHpnDKNGRsjKgvXrVXGzWzdVAw2FIMubwsISXRlEL2pXCdH4nd50+r9O1Kxl2LsXFizycPW1yXzxP8PMmbpmv99D4cJeVGTFHJV/QyvwLBZLjEhEo3XZsjK3xbU4cCdr7vtES31qqmLq/X4lJYOKHFgslhOfRO9bmzaxMK34cSCxsi78XfS54tAtnb5PKEaNS/58zr7znH++wrO8Xm03YcgOMsbPIi3ShjcmNqJJ+c2EQrKkv/66QrPatpW1/tZb5Q28/npYuTDI9j1JOMgyXjRl0zG5bxbLiUjib72ba+fz/d3Q4+Lz6ZmbMEGvjh1lbPH5oHZtPcdbtkhoXXWVCiuddRZ89JFEXKVK8u716aPnvdMZEc5ts40//pDHLBTSPOPaa/WcP/SQBBooB2/QIB3bcWTceeYZCcnzzpP4euUVFWFp2VL7L1okT2CXLlC3YRItm2bxMM/RudJvRFICzJsX4dmHd/LMMw4/fxekxcL3AYflOwsR3B7EGIWEbtigYzz2mN5v2mTg8itpeFNzFl7wOFOXFuGd/oZ27RSWmj8/TJ8WYdumLHbvdlixItYH0CV//pz/G1qBZ7FYYmzerIzkUEh/oy0O3MmY28PmQI3Q/f5Yg1GIhVZYLJYTn8TCJ/Hh2Qfz5iVao93l7dvrc3o6hLIihNp3yta45DYvnzABMpJSebvcczRjOje1mMO4xcUZO1ZW8CZNZOGfOFEW/6FD9f6dd2DushTervsqNfgDgOD//ndc9+i0WPKKxFZI7vPq5srGP9fx84BRo2LPdadOOlYwqNecOfJyBQIyvlx3nUSWK3qCQeXN1q2rZ/ebryOMavkoybdez1m/PsGkiRFeeEFtCh57TMfo108eub59lW+7dy98+qmKuNSsKeE4Z47E4lNPqQ/mlCnatmdPvf/uO13z668bFj/+HW1fvICxTR+mYkWHyIefEHrjdSIffcJZ5+aD5k04hSw6FltMRiiFYsXUI2/dOhVzat9ex2rUCMKOB04pwLJlBmPk5Rs9Wp7HalUjnLXgGYqunkyV4BzOPy9CkSL7G8SPhgfPphlbLJZYyEXx4jJvTZqkv8WL79fHJn6yFh9/7070Ro1S/t0fmkNRrFhefBuLxZKbJFbOdb152bVJSAzNHDNm/+07dozus3EzYyYm0SHiwMSp+DZvJnRqiX3jiyvy0tIM14y/kX79riCcL4BntuGOOzRxdM/l9cpj0KOH+mz17g1XX2144Ym32Dt3IgC7d/4lA1aJnFWqs1hONNxKtC6JIZfZtUCJ3ybeGx/fzDwcVoEkr1ci5513tK5RI3m4GjVSbl6rVnper7tOBVbuvltFStau1dRj2DA4o+EOwl8sZj61iSw13FV5M1u3leCDDxS+vWSJjj1xokRcnz6qbJmRoYJMjqP2KN99p/DNcFjetnBYhucPPtAUp359HSMzE1q39vDZ54UpWQo++3APxTf4qIDDivU+Vn69h5GVB/Pka5v5+Jfi3FDekJys7+HmCk6aJLG6fLnEZKVKWrdihcRtvnxQsSL4Q7vg52WUohJ792zkkw9qkpWVTLFiyo4B3YucYgWexXKSEy/SfD6jD5s3S+wZc9Ck7UQLXlaWYt5PO03r162Dxo2P7fexWCy5x/7jhZa53rz4iaArtoYP19+uXfcfL1yjUHp61NpfvDgdWu0lNDEf42v0okOR4tlONLOy4ONPPfS5pwB+P/vCM5s00UTN61WY1+jREnfVqknotWoFnbufwopvol8kpWie9ei0WHJCTtoguHlybiXa7J5n+HsrpMRt4peFQvDWW8p3dcXS3XfLy+b1xs57771QurSEUDisapYbN6p1yaefqpqlm0s7cmQq5Up3p+IfaaQXuZxvxhXnnf7qkZeaqtBMtxdms2byxmVmynhUr54ak2dkyMvXp0+sRcr8+QrNTE5Wj8yiRSWmevfW2PLmm267lnxkPbqM5KXzSS5dlur/SaFzZ8Ps2SXocXWED97OIOQPsHixoVcvfc/kZOUBDhqk775qlY61eLG8iT17wimnwF87Aryd735a7hmGp1hZNp6aROUaqgz69VcRgnscatb0ALYPnsViyQF/E3Aez9+s2gf7MYmf1HXrpvyXXr20zm0warFYTkwOZd1P3DbeQ+eWR48/TuLEEdxeWYY2v4xi/M87aNM9FYwhGIwVb3A9DsnJsvxPn65JYjgsS3y/frKYn322ts3MlMcgMxOcSISsrbtYujQ/nmaNYSr4e/TOsx6dFsuRciBBdrj4/fu3GTmcqpvZbZMYqukes2PHWLXL5GR51xo21D6lS8fmCw0b6v1ZZ8nL1r+/Cq64xuEzzzTMLnk1X0z9D5f3yMennxratFFRlZQU5fq5fTH79ZMR58EH1VahVSsd6/LLZVi68EJ5FpcvV7+7Z55RWOfAgfKoffyxqmm++65CKfv1g9NPN6Q1eZRz7tpBVX8qw4cbibikCA82GU6nrZ9RqFoFvP/3JG3aehgxQsVevvhCkd/Ll6uP3t69Epc7d6q4S6lSsGWLoeczNfj6y0r8uSMfFYsYdu6EESMi7N0bArwsf/l9eO5azceOEJuDZ7FYDjhZOxiJSdjBIPg8EQI711O15FbAISXlqF+qxWI5RhxuT634Ygtt2sQ8en6/JlgdO+7fN8vN10lPl7dtwoTofvk9dLiwML4kw9ChamD+00/qcbd9u/bNzFTIVfPmmshNm6YKm9ddp/YIo0er0ME778iCP39ehOILRhEe8BZVn7+BxVNXALBl88GrBFssxxuJOXFHSmJT8kOdMzuPYfyzHArpmFu26DkPBuUVa9061jdu3Dhtc+ONWtexo0RakSLKmy1bVi1RqlSR58vrhQ0bPFxzg58LLzSUKqXAosxMRQW9/rqE2gsvwIIFEmflyikU9I8/NDZs2iRvXnq6KmpWrCgPW9++an0wf76WZ2TAf/8LxYpFGP3tNhYscBg2DJ562kNyicKMHmN44AHl9NYvvZl1W5P4iJ5kLVlK+M8dvPyyxOKePQoDLV9ePfmSkxWqeeqpEp9ly8pLuHUrrFrjoXhpPzt2GJYskcGq+Kl7gb8AyB/5IxaveYRYgWexWP5GYkn0+OWJ6/dV1BsXIdiuC5QrR9c32gNhShSzRQwslhOV7CaTBxJ9bsilK+TiwyqzC7Xs1CmW1xsO/z28MxyWg61NG6heXQUYtm9XeNV11yk8a8wY9btq1065MJUrq4BC//6KvlywADyZu+i8/QtG05HMUBZ7yAdA8t7NuXrvLJajSeJv7j/d93COm7g8sbhaooHmp5/0Gj5c4u6qqxQGOWmSQhR9vlgbgrZtle82a5ZaGKSnS2jNmKHzjBihMOvPP5cX7dxz5Y1fs0bnu+wyefuGDtW2y5Ypt61ePVXJdAu6rVgBDRpoDLj4Yoksr1eNzLt3l/j6+msoWVIi8dxzNXb07xch5euPmXPrW6wbOotQKMKsWRp/nnhCDdFDIZixpig3+j6jKBsJ+04hHCiIz6fvnk9DC6tW6btfeaW+fygEBQuqBcRff8XE59NPS5QGAhqvwnE2p2WUOGSrqkNhBZ7F8i/iUJb2wyW7BuaJPwTxOTd+PzQpv5n0iUkMpyO7SQEMwQ12EmWxnMgkTiYTxVpGRsx7dzBB6L6PJz1dFfPcdaNGabKXlqZJ2Z13yjsHmtClp6t4yuTJsuQnJ8v67/OpeEPNmlrn9cL//Z88BVnJAX4v0457eYlCSYYktgPgKXj85t8ZY840xiwxxiw3xjxwgG0uNcYsNMYsMMZ8fqyv0XJsOVLP3aG88Ad6ZocP13MYXxHTfT59PnnnZs+WYMvK0hzgww/V7mDaNFWLHDFCAqdBA3nwXPr21XFPP10evX794JNPtG///vDNNzFjTsWKcP/9En+//aZrTUmRYLv0UnkCw2FdbzisJuagcMmsrFjLJvd7NW4sg1G1arrmQECisqR3M761K6nNXJKC2zi92C4yMpSbd//98iyefTYsm72bDqFfSCLCT6GOzJ64mxo1Yvl2kYjE25498OWXEnCbNknIzpmj882fL0E3fTps26Zwzp07YesWLxAAoAbrcxSeCVbgWSwnPAfrTZcTsrO8x4u++HNmZED/74rTvIUDGMah5JuMpON3EmWxWA5OfOuD7HrdBYMqrtCixd8r7MVv6xZiSGx43qmTJmjt2u1fvMHNrXNDyTp10iQwJQVaNIswe/JOFi50aNUiwsSftzFiuENGBixcKOFXubImn+XKwbp1BtOzBx2mv4n3048oWq4J4KFs2eMz/84Y4wXeBs4CagKXG2NqJmxTBXgQaOU4Ti3gzmN9nZZjz5GEZR5ujt3B1rnrs7Ik+lwvYtOmKqYC8PLLMq5kZcmLZ4xy8u+5RyJwxgw9mzNmSCj26ycxNG4cXHGFRE7TpgptfPddFU1ZuVJGnO7d9TkrSzluO3awL/2jXz8Zf26/XXl9w4frfFu3KvyyZk2Jw++/1zaZmRKePXpo36++Ulj3y58W57YW07nEM5Te1dJp0CbAwoWKkgwGJViXLYOetwaYU/NqTmcV3aqswBQM4PUqd3DtWt2XK67Q9V10kfZ98EEJ4h49YPdufe8ZM/QqWFAevdKlIVDIABog//KUzXGFXyvwLJYTmAP1qzmaxJc3dz8n9sEJBODOuwz+kUPpuPJ9zB3ZGp0tFssJglttLxjMPmTb9dzfeaee/0Sy89a1afP33plpaWpAHArFet1lZUmguaXX+/XT+szdEQa1GMhdL5ajz7BuBM7vRPjqHmQ9+DALFkSoWFFV9gYNkgW/YUNZ+m+91cN/3yzBmrVedkfyAybbaz5OaAosdxxnpeM4WcD/gPMStrkReNtxnG0AjuPYru2WA/JP5gTus9m1a6zyrYvXq+fRbZ3kGmaSk9W/LitLQmjBglhvu99+0/KaNWOtDbxehVlXqQIvviih8/33qo75ww/wwANaduaZEkqdOsl407evDEppaRKDrge/Tx+FhSYnK1+3bl2JuDPP1PkGDYLnn5cIO+MMhY9+9hncdJPE47x5MHasIf2un9kwaSVLLn2SBQsNderA1VcrrHPdOl3vx58Y6g17juS772T5JY9ijGHKFF1X27YxETp/vvL86tbVODl/vq7vqquk2+rUUXTCxo3KGVy0KIKzdSOgsMzQWT1zXATKCjyL5QQmu9yWY32e+PevveEhdGoJWo56CICUh6+3zYQtlhOQ+Gp7iSHbwWBM8LletsRc3URB6ObZuSGdrlGqY0dVu3Q/u83PQyHl2XXpoolYv34wb8oueq15mBAOfadcwvbxc4EILFpErdI7+O03Td5uuimWD7NwoQRos2Yq2hIOa/mqVbl7/3JAGeD3uM9ro8viqQpUNcZMNMZMMcacmd2BjDE3GWOmG2Omb95sw+XzmqMVXXO0jxV/TDfnLn6Z68lv104iBeS1HztWRU5atVIPuhUr9Ow1biwv3jvvyGs1f75E3V13yaMXDsNDD8lr9/XXOubHH8PPP8v7Bqo+uXhxTFDWqSPR9vTTyp8bO1ZGnDFj9PfppyUGJ0/WPhUr6vPChRJdwSD88ovy7tzPEyYoDLR+fY0Pf+30cMHNJbi6h5qTz5mjfnljxmicSk7W9/v0cw+X31iAe+413Habijq1a6cxZfp0jWennw6FC8tQ9f778ty9+65yCDdv1vKkJLj2Wvj9d2jRIEghNgM7AahadkuO/z2twLNYTnByS9QdznniQ7j8flXFGjt4B2kLSgKwYd5GjWYWi+WYc7iTwANtF19tLz5MO77vHcQEn+vtcwsvQUzMeb2xkM4mTfYvqtKunbx4w4fH+kY1aKCJo8+nSd/tt0P9VgHSa/TiLW6jbFnDpBo3kEU+5p12Dln5U6lcWZPGli11juRkTQyDQS2/9FKFbyZ+txMQH1AFaA9cDgw0xqQmbuQ4zgDHcRo7jtO4WLFix/YKLftxNFMojnY6hvs77hpY3Jy7oUP1eukl/Z0wQdVr09IksBo1UhilWyGzTx/l4bmevKVLVV1y8WJ5q4YOlXj0eiUM69SRAAPlxCUnSxy+9BLkz68UtIwMhYFmZKgB+n//q3PNm6caJFu36tjvvqtjzJiharp16+q73HijvtdHH0XHkPoSkq5n0RiNP3/8IYF6xx0SkHXqqIBLUpIapkciGrsAzj9f4afPPitBWbq0pjm1a2vf2rUl+hQ9oP6AvXurwMrZZ6vYSrNmUKyYvJ1t2kD5ailspQxwCgDzf8v582oFnsVyEpKTH4b43lUvv6yB18XnA++pqbSpIetTyTolbDNhiyUPOJxJYHbFk+LXJX6Ot+zH58yNHy+LflqatsnI0DKICcJOneRJu/VWFWEIBiXoRo3SsRo10kTL9RwmJ8trFwzq2H4/YAzzLn6KOxffyoMLe3LGlP8j5ZNBlHvsOt4bZPj5Z1nKr7su1ntrzhxNxD75RJZy194UP24dZ6wDTov7XDa6LJ61wGDHcfY6jvMbsBQJvpOK3PBi5RZHM4XiaBzL/f+fkRELxc7I2L/o0fz5Mpbcc48ETfv2sUbmWVkSfBddpG2CQT3ftWrJg5aUBE89Ja9d5crybvXvL4/dlCkSiIMHx461ZImOA3pm775bIZkrVsg7WKiQvGhffSXhdfPNOtcrr+i6Fy2CG26Q5w6Up3f77br2a6Pt5Natk7CqUkXVM5OToXGjCIFdGzm3u0ODBhJ+Q4Yo1PuMMyTukpN1j377TeLwyy/h0Ucldtu00XFLl9Z1ejz6rp07q0VCu3YScUuWSOBecIEKwTRtKoG6fr2E3tChhgxTmGQ0QIVGfJ7j6KdcFXi2EpTFcmz4Jz90ObH+xfe8A1m5JkyIhXGMHw/NWxj6lZWpa0mvQbaZsMWSBxxqEhifV3s4rRDc4ifZ4Yo4r1fFBCZPjhVeSQzrDgRiuXigSZLbIHnCBAk+v1+hmbffDiNHyto/bJgmgh6vB1+ZEvwy3DBhkodGnQqTnm644QY1NO7bV721pk3T8evV02TqscfUCN39DqVL//N7eoyYBlQxxlQ0xiQDlwGDE7b5AXnvMMYURSGbK4/hNeY5R9uLdSw4mtE2OWmVkJEhT9X27XpWb71Vy199VR4wN/T6uuti/STd/TMzJcCystSgvEEDiaDevbVdZqaE219/SYSlpclrdsUVEmfnnSehFA4rJy4clhevWjWFYM6bp2NPnKg5Rb168hLWrauxoFo17fPggzLieDx6nXOO8uWKFJG4+t//YjVKZs2S0LzpJjVAr1lTom3hgghNnzuXkZVvYv59H9C+bYS77lJoZ/nyCv1s3lwis1IleQ8LFdK1XHxhhPCWbeA47NmjCqBbtuj73XKLvnePHhrTpkzR95g5E954Q/epYUN5ODdv1r9ByZJQODVCFpovFeH3HEc/5ZrAs5WgLJZjwz/9ofun1r/E6nlt2qhgQnq6JmFuzsz48VoXCEDbdh7AS8NGVtxZLHnFoarjxRdLOtA60BiQnv73YyQWX3K9dG3axAw/8UVZQJOb9PRYAZU33tB7v3//8LD0dO0zc6YmfOGwrOC9emn7d99VufMZMyTsunfXhAkUttWihd6Hw8p3GTNGE61N0XIku3cf9m08pjiOEwJuBYYDi4CvHMdZYIx50hhzbnSz4cCfxpiFwBjgXsdx/sybK84bcquo2L+RxDlCIKBctNTUWJE0vx/uvVdGFb9fz1j/dyJkrN/JHXc4PPOMjCzz5uk4rVrpWJMmKd/sxRf1bzFkiEIQ162LhTQuXKjnsWhRCatNm/Rcer16pl3R9v77EmGdO2ucuPJKhVrXqqVcuAoVYm0RypWTh/H22xXO+eOP8Omn8tytWaN9GzaUp85tozBjhuYsgYBE3HXnbGbAlPpMoRFLf0/ized3MW6ciqUkJelY998vj1uNGvpOmZmQsTPC9i7n8/ZNvzKs9eP4/RHOPltjUP/+Cs90HHjvPZ376qtjorh4cX3Pl16SWCxYUK0SNm2CzKAPKALABirmOPopNz14thKUxXIMOJIfuoNte6BiCfH7duqkl88nYRcMRq/BEyG4eiNLlqgS1F9//cMvY7FYjhn/pHR6p06xqnrxrQ7ivXGJ+8Z7/N3QzVdf1SRpwgSFQLkFVuJFpBsuNm6copSSkiT+6tSBAQO0XY8emvSFw5oQ+v3KfQF5Ef1+WeBnzoRdu3T87dtjwu54zsFzHGeo4zhVHcep5DjOM9FljzqOMzj63nEc527HcWo6jlPHcZz/5e0V5w1W3B0e2RlsDlRF1g2LDmVFaPjNgzR55xpe/+1smjeL0KGDvHWrV8tYUqKEwg/37tWz5vNJUDVqJOfToEExb95dd+n5O+ccedcKFZJwu/ZaibzkZHnaAwGNDe7nmTNjBVoaN5Zn7L33pH1mz1ao5+WXS4y9+aZaGRQvLm/fe+9JWE2bFsvRW7xY406zZvDxL8WpWNVHPsK83vxL7vpvYN/9qltXwjQSibVsqVBBXstHHgnz+tRmNGcKKUvn0rXpDubOlVexXz+4/nqNi9WqyRM5fLjuyZw5ur7kZF134cK6h+FwhJqVdlOhIkBBXUP7S4/rKpq2EpTFcow4WBjWPyE7QRff0DyxLYPPJwvbW29Bxl8RRjW8j7GVr+fymb2ACPnzH/FXslgsxwGJXrrECpnjx2c/NnTqtH8xFreKZsOGcNZZsfWtWsmb4Io6N7f3hx80wWvQQPtMny4hV6eOJkrJyeD1RGhbbSMTxjv8/LMmnfffLy9AKKTPCxdqMvXccypfXqhQBAgTCjl5eVstlmNKotEl/pkNBmNpF7Vr6zl864VdtFw4iIVUxzd5Mu1qbObNN2PNvOvXlzevdGkVKMnKUoji4sXygH35pTxrzzyjPLPff5dxZ9gwicHMTBVguf9+7du+vcIh3UIvnTvr+W/YUOeJ78V32WUSc1ddpYInX3+t/LvFi9XT7oUX5Onr00fzk4YNtf7jjxUeumWLDEUXXmRYcdmj1B1wJ78++BOj0tTyYORIjVcej/J3R46UEE1PVwjmzz/7aFl9F714D6rX4rdtqTz7rDx3K1eqBcPYsQpJ/ewz5di59zd/fonE88/XfS53WoTC25axbOp6IgvnAlkAeNJznoOX1/aP+EpQZYFxxpg6juNsj9/IcZwBwACAxo0b21HZYjkM3MH7n3j34suhu/u3aRObyHXooM/ue5B17OabYdqIHbReOICxtGTogrKAw7JlsqZbLJa8xRVeR7JtYouUeKOPOw7ENzSPx93O69Xfjh1jIWDp6ZqwuZUu331Xwq9vX02qJk/WpO3OO3WsQEATP5C4u3vkmfifGkfzZu15sd1QVq/28Nxz8uzVrq3QqJEjNcn66CNYsSKCs2UeUJvlr3wAL/TULM5iOUb8k+cwt4h/nt3iSDNmKDfN65X46dM3QGBcXSqPX8Ebpz3PTfmLEwpJuBij7fv3V77Zu++qkuXVVyuU8cor9bwZo7DM//xHgi45ORaa2bWrzu/16u/UqVqXlqa/4bC8d999J4MQyMgzdCj89JO8cAsXShTOm6cCLamp2m/bNnnHOnXSHGbhQhl9unSRF/Gxx3QPevWCXr08dOpWmHHjNJdZvVpjx4IFyr0LhSTc3LzgRYtgyBBDyS5P80bhR6nTOB8Fwga/Xx47kAEqElExl/Llte8NN6iK77Bh+g4ffaRjr1wQpCCbMBRkPcWAbUAJ8vOHmuSVKnXE/865ObLZSlAWSy5zMA9dTnIUXIt9fL6dW/bctcrHN1dPTYUOF6QSaNOAjp7xpBQvBHgoUuSfn9tisRxdDlQ0Jf5vdtvGe+/iSfTkxW//8suxcujxbVRat1a+3YgRWj5smJYlJ2uyFghowhUIxIRccrKMR6AogS1bZKEH8O7cAZOmMCrclnGTkmHXLipV0oSyUiVts2qVmh1PmqRQqZ2bguxCYVhFndW2hYvlmHKsCsNkd3y3EFp8JdxgUEaUdu0k7ubN03N3++2QWtiQ8cMohl30AeWevJH+76rxd0qKcs1+/FHP2pdfqkCI16tCKvPnw6mnKs+sdm2NFWedFeuft3ChxJ47rrRvr3WNGukYrkjyemXoeeoptTgJhRSm+dNPMHNGhI/e3smnnzrce6/CM887D/7v/2Qw+vJL+PZbjUOgKp59+2p8uf9+icszz4zZdsaMjtCy0kZ+/92hfHl9x7p1Jc6++ALeflv3afhwXV/58rB+g4eaDfyMHm3YvVtRCEOGaL+lS3UfHQcKFND9vO46eQ9BYvSJJ2D69AjBoMNqahBgOz72AoUBCJB1XIdo2kpQFksukljRMjsSxd3h/LDEC0O38EH79vocP5F75RWde985jIExYwgtXcmESrcDhq1bj+y7WSyWo0d2OTjxDcvjx5D4vLqDGYnc7eLDMP1+VedzS6q7+bnupPKmmzSZy8iQ9T8UksibMEGW+5SU2PgyYYImgm+9JVHXs6feu9UxW52dyuRaN9DeM5G2LbNo0DpA3bo69qBBCu2sXl05QP/3fzr2Kaem0MKnxltFi51iW7hYjilHuzDMgYRcdsacUaNi3jFX7KWlxXpSugLMffYyMmDiZA9nXZif334zVK2q/LkzzlAIJkhA+Xwy2vzwgwwqAwboWb3uOh0zLU2GmZ9+kvjq00desClTJJxefVXn69tX192liwRbZqYE1cMP6/0zz2hsGTc2QtYDj1L7tWupNPFjup8ToV07tUPwevWcf/JJLBw0K0vLnnhC9pyNG3VdPp+MR+FQhO97DYXatSgz+iPq1VWuoderba6+Gp5/Xvete3eJzdq1JSgLFpRQLFBAnsxXXlEY6FlnqeDL11/ru777buz+bt6skNZtWyNcuf4Zqo3vx6lsJrleU67rexqwS/8OnuKxMqBHSK4JPFsJymLJXdwJlpsDcygOZT1MLKTiLnM9eLC/565evWzi+iMeKFGCjh1leSqTmHV7DDHGvG+M2WSMmX+A9cYY80a0jctcY0zDuHXXGGOWRV/XHLurtlhyh/hJpTvR9Pv/Poa4oZPxFTATcdeNGBEbD9xJ4+TJ+5/D59Mk68UXZeVu3lwe/88+0/nHjpXIa9dO4Ztu/zzXu1e7tiZXM2bofcOGmnyNG29oMfElMuau5NWOw5gx0/D99xKDhQtrUjV/vs758MPar3kLw/SUswH46+J7bQsXyzHnn4i7Q/WwzO73PD7NAmLGm06dYs9Yerr6uf36q0RJWppEW/36sT53Y8fqufV65QH75RdYu1b5bffcE6F9vW0MHOAweLBE32uv6TxPPaWqkz17SiTu2KHiIw8+qHBpn09izO0hd+ONKqJ09tm6hqef1vX//LOKqJx9toqWTJ8ue8zgr4LMWewjC4Pvj1Uk795B587aZ+XKWEGVQEAiDLTvkiXyKnbsqLHljjtUfGnEz0Ee3XIrEGLaxjL8+FWQ4cM1ji1apNDRV1+FM7tE6Fp/I+Gws88z6PWqxcRNN+nYa9eqJcM11+jcFSuqwmjVqhKCd9yhkNjateGtRzezegX8ThmqMI89ew2vvQGQBMD6SEG5AHNArgaf20pQFkvuEm89PxQHsh66E7P4H4v4KnnxFTNDIQ3koVAshj7+2KGQrOx1a6uQQU4HqBzyIZBt4aYoZ6GQ8CrATcA7AMaYIsBjQDNUDfgxY0zhXL1SiyWXyW4iCLExxN0mPT1m5XcbkcePC+5YkZEB338f65E1alQslDvea+fzRSeP9SJ4du9k1CiH4cM1cRoxQhOykSM1iYLYODJhgq4jOVmvdu1kie/QQRPKuXPhlxEe+r5QgmrVDfXra7jJylLuSySiinUTJ0r07dihsalCeZ3HFoCy5AWHG555KINsYi5s4vFHjZLn7KWX5GnLyFCINEjgLF0qcdWsmV7Dh2tds2ax5y0clmfOrTzZqxcULRJhw5BfSX6gD4Vmj+abryOMHKlxpGpVnd+tbhsOy8iyZ4/+Pvusnne/XyGMjRurWMry5dq2QwdV4fz4Y+XJeTwKG/3oIwnC5s3hPz1T6NVsJkM4l6rVwsxencqQIXDffTIoezwSc6GQvsfcuQqNrFULvvlGVUBBlT9//hmat03hsVP7MYIu3FRlIhdekUI4LO/fkiUqlLJ9a4QX63zAqOo3U+PbJyhXLrLvGNdco6bsBQvKc5eaKrvR6tUqzOLmNH7/vcT0779rfHpqQHEWFWjBZkriDRRjZ2Y+InsdXA/eHnbK3ZgDbHaxxXKc8k/62h3uPtmJu/iJWWJujbtP/GTwzjtjn93tXCuh3w833xThvatGALCk12M5rgR1pDiOMw44WJDoecDHUUPTFCDVGFMK6AqMdBxna7SFy0gOLhQtluOaw8n9cXNiOnWSldvn0+TEDc9OHCtSUyXKpkzRuvgedq+9JqEFGhv6vR2h8ytnctcr5VjSdwCtWkTo2FHhWPfco8mem68D8uI1by5B16VLrD1DVpbKkIOKFpx5pkK3AgFNSCtUkGisX1/H27RJVTrLlVOhhG5nRSg8fwgAWYNey7OxyXJy8k9y8A4nnDP+uUysZOs+K1WiVS3GjJEA8/n0vPTpI69To0bycj/zjIwqb7yhfYNBPf/lTovQuNI2zj/P4eqrYfB3WbyUcQOFyCBpxwY2/Z61r7n5xx8rFLJJExl1vF5F+nTvLqE2Y4bWgYwuDRpI+LRqJVH40ks6b4UKCrN071ONGhJSgQB06GCYeOcPrG95CTz0BMaYfU3TU1Lk7XPvWZcuEpFdu+q79uypfNx775UY3LABvvzKULLHmfx8/ofw+GOEw4YzzoCLLlIu36ZNcO/tQeav8TOZJvy0tDKrFwdZsEBRAddeK0FXrBg88ICu/7335BVdsgR27pTAnD5dRq0yZXRf1q83lLn6DJpc34Ty17Rn506Dx+PgtkmoyR/HdQ6exWI5Qo4kGftI9on30Ll9oQ71w+Lm4kHM2u4WVgiFwL9zMzVDkwAos3ry8VzI4ECtXA6nxQtgW7hYjm/ijTSHeqbjw7rcMEnXc+/SuvX+Y0UgIMv7iBGxMG63h9QZZ8SuoXbZHfinjCPATm5Zcz9TvlpN2iiHESMUCjZ6tCaAwaC2b9NGlv727WPnGjFCE6M+feTxu+wyTUofeEDhVK7wK1tWAjUlRZOuUEiW9kWL4NeJWcymHADbt2ccz2OT5V9IbjRnT/y9d/Niu3WTZ6lQIYmt2bNV4MR9xvx+Pbu//qrQwaFDtaxWLS2/6y419F77/i80uL4uw5s/RqHwn/R/L5mHCg6kCTM4s9JKPPnzEQ5LzFx9tTxeIO/g0KEKWVy+XJVyGzVS7u2WLSpekpoqQVi6NHz+uQwzr78u71m1arpPNWuqYEq3bnpcx4yBJUs99LrFT3I+s6/iZY0aOu9HH6nwpJtbnJGh4idLl+q7tmypcPHmzSUsW7aE3n08VKiRn1mzDM8+q9zAW25R8ZimTeH0Wil0r7yCdZTlouqLea1/Cs2a6XyBAFxyie7ZuHEyJi1bpjDxSy5RBMG550rMNmgQy8UrVAhmz/awYlMBsrIMBQpA8VIGN0RzFUU1gOWAo/jfzGKxHC2OtHn5kfx4JIZ4uMsO5zzuD8Udd2iiFgrBiDnF+TVfJ9gDWVVr/asLGdgWLpbjlcQ2KYczLrjbu/sEgxJ7bdpIYM2fr0ljfMuEjh1lrW7fPnZekGhzQ7Rq10ol2LwtEyZ6IcVP6z71oGVzxj7wCx07egiFtO2QISpNfvfdylWJz/GdNUsTJND1ffWVCjOEQrGJ28svKzQqXmzedZccdXv3wqrN+fBwCgAFCxX4V49NluOTf9KqJLs2R/FtFtz2Re6z54ZFu570QEBGmUBAOXChkJ5Lr1fPbZ06eu7691fBkdWr9Yy5+4e37ODMPz/mAZ5h/tJanH1DP9q0mEzTW35iTKkFpE9N5ZoOhkBA4szrVSGWN97Q89qtm87dsKGEznvvyfM+erSiDzMydJ5vv5XgCoe17S23aF4xb56OWbOmvIBuVe4+ffT3rrvkpVu6VONGly7ar39/3aNJk+Q1vPRS1Stp2lQ5c5UqaUzweFSI6amn9J29Xn3u2lXh3MuXqyBKixaGlKf+S8WJu+j4aICpvxoaNlRY6Omn6969/VaEzWuzOLNzEnMn7qZ58wCDB5t91TR/+UXhmY0aSWhv3Cj9tnmz7lVGBmTsdIC9gI/8ZMp9eJy2SbBYLDngSKx8R9Lw/EhLN8dX4vP7Y/1szjjD0Pim1gCEH3n1eC5kcKBWLofT4sViOa7JrnJmfC7dgYozJE4e3dDt5GRNrFyPmpt7F7+9W6nPJTlZOS/J+QxjH/iF9tNepP2eEUxwmjN6Yj7YvgNQaFhWliz3bpPyN96QB8Ct1NuggSZod92l9RddpL8DBkSbqHsiJGft5IXnHVJSNGF7+mnYulUhX8rLM2TlP13ft+cduTI2GWPejBZvyvZ11E9o+VeSncE2u/QJ12Pn80nMxYu9jAx5xOKrXc+eHQvBXLJEhpRatbTu6af1zE6cqG1+SE8luVpVzmYoXfgFH2EmTfbS8+wtjJ5ZmPsfMKyL/jJ26KB9XS/g8uWxdgP33KNQzNNPl7f955/lgX/3XYm+NWvUSqB/f13zgAGqujl1qkRer15w8cWKCnAF6aRJKmSSnCzBdtppChFt2VL3pHZtibNXXpExqEEDjTMej67P41Ho5G+/wSOPKFfvhx8ifNA/yNtvORijAi+33CLx6fV5+H1bAYb9Yvj1V4Wnh0ISn/3fiRD55BMa//oqw3p/zbB3l9DgxyfocVWEunXVz++//5WncvbsWF/AChUkeN2CNEULhwGpXj+h47vIisViyXv+SbJ2dvse6HgQq8AH+mFJS9MgWqeuB/Aez+IO1LalR7SaZnNgh+M461F13y7GmMLR4ipdossslhMSV3y5venc99mJvPjn26206fPp+Z42TeszMmQJdwUY6Llv317W727dYrlzqanKj/P6PISq1cLfujHtPRPx1qpBu/NS8fk0gSpUSDk4qamKBnCt9G5FT5CnrnNnjTkzZmjiuGePSp2Paf1fnFdeYkqXR9ixPcLzz2vyeO218iLUrQsXXAD582tsCgZzbWyaDswA/EBDYFn0VR9Izq2TWk5cDvQ7m8iBjDBuHvxrr+k3OF4A1q8fe/5Hj5YgAYUT9uoF77yjZ/eii6BHDwmPRo0UZlihooHHH2fpHQPIV7o0DZhDVrV6PNG/OH376nnt0SNa1Xac8t3GjpXoOv105ds99JC8ZW6OXIMG0i2//67In9tuU0XdWrXgqqv0rJYrp4qY77+vfe+/XwVahgzRuW6+WWGnrVtLmDVrJm9+ixa6H3Xr6lwXXCChWby4WqfMmiXBd+GFysO76iqFlQ4aBCOGR+gw4/8oNf1b6v/4KL1ujPD77xq72rSB88+H557TNVx+ue5z+fISnXv+3IX3j9U0Yypn8yN1WUDTZR9y0fkhBg9WX8BZsyRSb7xRorV9e137jz+qkE3NmlC9SggNG5CfvbFGfUeIFXgWywnMYSVreyJ0qLkRn/fA1qDsxF1iZc34bRObnrshYF6vJmtz5mh9ZuZhfpFcwBjzBTAZqGaMWWuMud4Yc7MxJto6maGo7+ZyYCDQB8BxnK3AU6iX5zTgyegyi+W4JLsqevGGHTfX1i2YEl8ZN5EDGXwS+91deKEmNxMmKNwrPl8OYoYftyJm8+bwxpuG4LAxmr099zxjxxmGDlX+XP36KkSQkaHxIxTSeFK/vs7pVuWbPl3ehaQkTTDvvRfCf+4gedFcbud1pi32893nQcaN06TTGE0qS5eO5QJB7lXRdBznI8dxPgLqAu0dx3nTcZw3gY5I5Fks+4iPhIlfdjAjjLtN/LPqerk7dtRz6IZZtmun569xYz1PXm8s9y4UkoBatw6uvFI5aVlZMHCgipW0aAEYD/VaFyT5+mtoP78/rcc8RVaW4csvFWL48cf6nZ86VWGNWVkSdmvXyhNYvrxaK3g8WvfII6qQ2bChxFxysnLoKlSIVef9+GPtB/Lude6s+/PVV1r/5psyAPXurXGmRg0ZfDIzda6ZMyWi3FYrr72mIi2rVkmAfvedxoaGDXUMjwe+fHMzxVbOxMNeshYv48O3d3HFFYoYuPBC5Q1OmaJc3kBAoqxZM+UDj5gcoHRFL14MaZzNkzxG0ZbV6XhmEhUrqrDKgAGKJpgxQ331VqzQv0mVKhLJe/fCqj/8uJlzyygpZZgDrMCzWE5QDiu0MhKBDh3wVSirGVhc1bhDhW3Gh2e5y9xXenrsx8e1+ruW+IkTZT0DWeXzCsdxLnccp5TjOEmO45R1HGeQ4zj9HcfpH13vOI5zS7SNSx3HcabH7fu+4ziVo68P8u5bWCx/J1HQueNAoqhLFGrucwvZV9R1ya6abvw+Pp+8dO5nN1cv3igUX7QlHI71zsTjIX1RCVq1NrRqJU+f20cqM1Pjx003KQxr61ZNPDdsiBVS+PxzTfKqV9ckdfhwGPBVKs1aRAAf0wp0pkKNFHr2hO3bFQ62YoWandesKW0JmuzlMoVxS+KJQHSZxbKP7PrZHsoIEy8K439/O3aMbRMMyls1erSel3ffjbVAAD0H/fopBLF8eeXfVaumXLFKlSRI6taVl759e0Xl9Pu2BO+843Bhh23c0schOTnmwUtKijYOD8OHH8rjlpmp86Sn67j16qkI0pNPKlQxGNT1VKumoiudOinXrkcPhWhefbW8eV6vxNX06C90795qSVC1qryNffroGBs3qrLuvffqWX/zTQnPN9+UYG3WTOd97jnd75kzJWZvuw2+GVccb43qnMNwlp/WmSoNAsyaJXHm5gc3b677NXCg7s/UqQpzveYaw3sFH6T6qP7sueByHjlvIc+2Hsa6dYoSmDNH3/X11+HXXyM0qLGTefMc9mZFmDgmyE+DHS6+GPI5mRg2AHAaW3TwnPzfytHeFoslx8TnsfwTDquoyubNClYPhfR382YoUSLbBO7464iP7Y/vqwOxHx53O7dfVrNm+sGoU0cDIcSEnsVi+eckGlDcZYnFU+KfY9e7DvuPDe6kMXF54jHd9fGe+gNd2/jxskK7RRr8fln909JiDZXd4gljx8qbEApp4vfqq/KoXXCBxgk3vy89XeeuUkUTu1NP1eSuenVZ+Z9/Xtv9+KMKOiQnQ7PmBu78melpO7h2byqFUg1t22pyWbKk8vk+/1w5OYMG6fq35r5P/jlgljFmDGCAtsDjuX5Wy3HD4f62Z9fT7lCFzlxR2KJFLDx6xAgJmNtvVzh1nz4SFhMmKBxz6lQ9l653PCNDz9LatfJwL1smQdW0qQwjAwdKcPn9es5uvzUC3bsxalJ+xr5ek4XVn+SuuzzUqaNzzZghz1m1ahpPXnlF50tO1tjz2WfyyDdtqu87ZYo+P/CAwiVXrNBcYtky2aJLl5b4mjlT3riqVSU6+/fXNmXLxtqpzJunMFOvV/dn/nw9/127apzIyND3GThQc5QlSzQOPfywjlO7tqHJL0/zwdu7qNkgAMaQlaVcvkmTVO1z0CAdu2dPGZxmzZIBauRIKFvWwxejS1C1GlSrlp+lS3XuggXVKP6jj6BgwQh1ln/FuMnVqBn4i+pDJzJ61Zk0K7Kc/osvpnz5FJw1qdF/ZaNBLwdYD57FkoccaYETl0P+eBQvTqhF1A3XsuW+qnHZxfInNjofP16DYnx/LDfEKzHx+9dfZSXbs0fLduzY/6/FYvlnxFvpEwsrJBp24p/j9HRNkg5USCX+GY9f7oq7eG/ggY7lrmvQQJOcnj01oQwGJeTcqnBZWbJaZ2ToNXp0rEfeXXcpJyU5WQJw4sRYFMBrrykEqnVrVcADTagyMlR+3OuVcJs3T/vWqwfvvOuhdpvCrF5jaNkyln/0668617vvygNQvlwECFO1au4VvTXGeIAlQDPge+A7oEU0dNNyEnCg3/bE5y8+5/VgoZmJ+HwKhXQLFLlkZel4LVroOJdeKgNHWpoEVUZGNE++jqrOZmTIyNKsmcRTKCQx07GjQhc7ddJzO2MGjPlhB2Mm+fmaC5iy+BSWzA8xfTpccYWEZatW8pwtWiQB9tVXcMopenaN0bOakiLvm3utM2bA448rx83jkQitVEmi8qyz5P1q21YewnnzdD0XX6xrT0mR0ej222M9O0Fex4YNFQr64Yf6jhs2aPvLLtM1PvigiqiccYYKv7z5Jtxzn4fLbyxAcj7DN9+oMMzy5QoZfeYZeQ5btNB327QxwoCnNlKokEOZMup3V7267uvy5ZoLffSRzj1yJGzbBiULBcm/eRURPMzPKMuIVadzOsvwbf2d6pWDTJ8BoEnTOgrDn38eyX+9fRyWwDPGVDHGfGOMWWiMWem+cnRmi8WSK31x4gmFDWP+O4rQqrWERqXvV/QkcYIYfx2uhXDy5GgcfhQ3DCv+x8vv12DpvrxeJQ9bLJYjx30m43Pf4tcdaJ/27fUMZme0cQsxuIabg1XSPNCx3JfbSLlBA02imjTRPl27ypv33nuamNWqpSbIP/0kG9PNN6taps+nCdwZZyhM0z3PlCnaxuPR+BMOSwTWqqVJ2I4dOvf06bLQz5un9VWr6jrKl5d38IUXtG/ZsgoVX70avvwywvIVfwBQ8LuXc63RueM4EeBtx3E2OI7zY/S1IVdOZjkuOVgVzHijTeIzl11oZnx+nnucn35Szlvz5rEK1l26KGRx9mw9416vPOxFi0rArVyp53/rVnmjOnfW85aZGTO0rF2r61m+XN4r19tXpw4kF0+lTYssDJBcqgz3PZzEf/6jNgUZGRJWCxfK0169ujxydero/dq1Ol6XLjrm7bfrOd6xA268IcKY77Zx+WUO332ncM7HHlMhlqysmLGmTh15zL7+WiK1alUVaunbVzl1b7yh+/rCC7qejz7SIz51qnrS3XabPJLXXqucul9+kfiqWlUevT//lNhs0EDXfPbZEoMffSRxmJysca5pkwgvrjiXha1vYMYdn+DzRmjWTPdv6VJdZ0qKxrtwWNMujwcq1UqhXuUsqrKYgt4sKFicdZTkU3pRpnwK1aqCinZDHTbpHy4HHK4H7wPgHSAEdAA+Bj7N0ZktFguQe+LOPXaHMzxQogSj0sxhewpd4damjQZSNzwzvmpmixaxCaJbac8thjBjhpZvyOGUxhhzvjHGNquynJTEG1xcDvUM+/2xgiduDq07sfT54M47VSQg0YOfndfA79cE0j0WaLv0dE1eQMe69VZNFN1jzJqlZYGArqVaNeW9zJqlKpmvvaZjjhgRKzfuRgeEw9rmP//ROYcN07nat4dduyTiwmGte/ttlR/v3FkTqquu0gRr8WKFeC5apM9ZWZrUFkoJUYo1AGT9uSG3G52nGWMuMub4LiVsyT0Sf9sPZLRxn1E3py5+v4wMPS+uYdV9zZ0ro4db2Mjt/XjWWRIYfftKuCUl6TV1qhpu16wpj9rkyXpGpk1T4++6dWUsKV9ewmzVKq27806JRq9XodCBUT9T4dbz8V17NeecY2jeXIacOXNk1ClTRtf744/yrC9apOexc2e9d7/vxRdLyAZOifBBxvmcdWNZFl30MI0aRejWDT79VEIsOVlCbMYMRQP07q2Qx4ULY4adRx7Ro3zppVr+3nu6Dy1aqGplcrKiAQoVksfv3nsVMTBzpu7lqadKOBYpomsaNEhexOXL9b0WL9a/w08/Rb2l23fw6xQPmfhY8YeH0PZd1Kypa77sMoWKPvCA8vU+/1zfvWpV8CUZVvznv1z6ehfu7leds55tz5YSLTnl1FMYO86wc3sY9cGD5RSUCs0BhyvwUhzHSQOM4zirHcd5HDg7R2e2WCzHhMMRkAcq1OBOFt3Jl/vDFAqpgt3zz8d+eIYP12QMZDUEhVHlkKtQHssyY8xHxpibjDG1c3xUi+UEJPE5PdA2rqfd/et65N1n+EC5QYkV/TIyNFHcsiVm2HFDtSdNkhXe75eQiw/hdvN3hw5VGfbbbtPkzhWJ48bpXOGwCqeMHq0JajCoZSBLfyCg/JlhwxSStWGDQi0DAXkkUlO17ciRmsA++KC8B0lJOs5FF8WKOWRmQtOWXpKjpQeyyBfrnJw79AK+BvYYY/4yxuw0xvyVmye0HHv+aXrFgYw2w4frNzXeWxcMSojdequ2j2910rhxTCy2aiVDjJu/2rq1PNd790rcnHNOLKpm0SKFLF9/vcKpa9SQGPL75RXv2VPP0auv6jkqU0bbffmlrmPLVg/eggUAw4gREnKgXLaPP1bFyXPO0WvmTO3j8ahIyaJFeobHj5fRZuJEmDFuFwOn1icY8eBdNI+a5XYBMiy//bZE4pIlGofuuEMh1x9+CNddpzGnRg343/8k8gYPVguDvn0lTOvWVTjlr7/C3XdrDHA9a0OHat8LL5QBql07tXbweiXohg9XkZd69VS8ZelSnW/RIggXSCWrel3Aw2mlHaYtDpCcrHP36aPvOHasBPIFF2ifTZvkTaxR08PC9YX55FPDyt881GucRKVKhtRU2LLdC+g/gB+OmQdvTzSmfJkx5lZjzAWoIpTFYjkBcEM/DhbaFV8xMz7hOy0t1ovKnRj6fIpxb9Agtl1WVuxHpGJ55blk7clZnovjOBc7jlMG6Ix60dUFPjLGbDbGDM3RwS2WE4z4XLkD5enEewlcj3swGHuG3fBMdyIZ30Yhfh/XwPPyyxJP7vjg82nfb76JCTX3lZamiVNaWixErGpVCTH3uoNB5bZkZKgQglu2vXnzWK7PiBGxV2amJr5Llih8askSeRqaNtWxfvxRE8nq1XWcxYuhVClNUJ99Vh6+iy6SVyN9VBYG9W7ZTv4cV6k7GI7jFHAcx+M4TrLjOAWjnwseek/LicKR5tDHh0277zt2lCBxf0/jK1nH96Js3lxFRho10t8NGyQqnn9ez8qGDRJwEyfGPFHucxEOq7F3KCShU66cvHlJSTrHs8/qWIMHS6CsXh2bE5x5JqxfLy9aOKznbf58icOJE/U8P/SQnrs6deRJmz5d+19zjR61vXvllStVSlUomzWD3vcEcMqWYxwdaNjMcMdDAZYsUTjnb7/JoNOrl571zz7Ts9+woc756qvK1d2xAz75RNd+3nkSlP/9r7xnp5+u8M6KFSV2GzVSjuEjjyhkdfp0VfXcsiUm7i64QAL5scf0XVu10rGXLYv23YwYZpz3JHNv+5DQxVfRqpXZFyZbqJCE6ebN+vd74QWNlatWaf+sLF1PixYaGz0eXeOqVRBIUSXg6P+OY+bBuwPID9wONEJW9R45OrPFYjmmHMyTl1jmPJ745OX4vIFu3fRyCy4kJyskweuJMPgBxXTueOKJo5Ln4jjOKmAmMAuYDWwCbI1Oy0nHwZ7j7EIs40PBgkHl7TRpEnve49uduPs0aKD3bljnrFmxY27fHst36dZNy4YPl6hziz24+XmVKsn75nr5X3wx9rdoUU1EO3fW8ceN0741ayrkC2L5d7ffLiv7J59oslWtmqz0Tz2lSW5Wlsap8uXV06t5c3kcLrtMTrqxY1XkIH+hfNEATcjglFjX51zCGFPYGNPUGNPWfeXqCS3HlAPl0B9M8MV7yUeN0rPjpkAkJ+9fdMU99pgxeu7cQik33CDP9M03yyBy2mkSQ1u3qil4t24SJP36SVS8954qyJ5xBjz9tETTrFn67z91qkI1n31WXrECBST6FixQa5HevWW4LVRIxp41a/Rczp6tc7q5e+PHSwS6P/erV+u4r7yiHLZu3RR2ff758sSddpoE2jv9DbVevI4Nz31E9z0/8vU3hkmTFBr53//quR4xQt+raVOJPa9XAvLaa3Uc97u4QvjGGxVmeumlCpvMyopFFU2erDFnyRLNV2rVUrXOr7/WeOHx6D4lJ2v5/Pnab+JECcKVK3W831Z5KF6pAD/9ZPjPf3RcV/wuWQIffKDr7NJFeX8NG0p8v/9+rPjce+/Ju/fdd1CiBCSn+AAVVtlJ2X1F8Y6UwxV4FRzHyXAcZ63jONc6jnMRUC5HZ7ZY/oUcaTXM3OJwrscVdy1a7J9rA/t7/hK9fK6lMT1dArBjR1nD2lbdSM3gVAAKrZmh+IYjxBjzkDHmJ2PMFOBBIBl4C6jrOE6Hg+9tsZx4HM4zm+iRjw+tzq7qZryH7kA5eC7bt6sq3vbtsW1cz0JGhsTWV1/tnyvkWrlTUmITmuuu00Tu6adjeUKhkCaJ77wTq6qZkaHJVNvWEdpW28gP3zsMH65JVDgsb8Ly5ZrAhcOaJIXDEnNNmug8DRtqQpqcLI+C16ucP7dAlNuHr2kzDxFaA1Chz+2azeUSxpgbgHEo8uCJ6N/Hc+2EljwhvjItHNqrF+9hb91av5vus+w+ox0SftlatJAYcj3d06frfSCg/Zs0UYjhmNERyhXeSbIvzJwJO7nlFmefRvjzT/j2W4VSFi8eK3LUpo08eMnJEoU1a0og1aqlUM2+fWUg2bhR4Ztr1+q7zZih57xTJ4U8zpyp8/z2m/6edZaur0EDeeMaN9Yz/cEH+tyhg7xW1apB1l4PX40szDvvSNy9/baMPF9+KTG7dKm8jTfcIK/k6tXKwxs0SML1+uv1/X/8UaJ3wQKJ3U8/lfd+6VKdLzNT17lokcadESPkFXz/feUNPv+8BN3YsbF77wpakAh2Qy3DYYnjO+9UD78xYySoCxZUeGqvXgoX/+EHndMN/yxYUIaq007TtZctq+v/4gvo0sUAFQGoceOl+xXFOxIOd3R78DCXWSwnLTlteXC0OVDhhMTwEFfcTZ4cC+Vy4/wTJ47xzVhdceda7d0CCeOm5mM3Gpgy8Od0kOoBlAZ+AT4DPnccZ5bjOOGcHNRiOR451BiS2Nog/vmE7As4xO/jbuf+dcO+4p/rQECNxqdPjz3jLoGAJjEvvRTL5QsGNdmbOFETVlfMBQISX0WLyksRDMpj17y5vAKgSc0990DPHhGmtbsHqlcnMmIEc+dEmDdP2559tnTYwoXywrVurQlg7dq69tWrNe44jjwGy5dr0tmliyZqM2dqYuc4muQmJ3sBLxEn17tE3QE0AVZHjVENgO25fVLLsSXxmU00hGaHG+b86qvyyiVGzri/3cOHx9qUJCVJ9EyZEhOGIKEyaxYMGBDh8WUXc//bp5F87ZVE3nmDIrPT+O7bCM89J1H31lvw11/yeleurGe0cWN5nKpW1bWsXClvWWamvG7nnaeQynXrZNy5/XY9ly1a6Bl77bWY58r1os+apZy7cFjic+5cPYfLluncW7bo+5Ytq++wfLny14YN03V+8YW8cx5PrKpuJAKPPqp70b27jEk1aqiybiCgseX77zUfKV9ehVlcz9zSpTrn++9LWNWooby82bO17zvvqLl6s2YaY8JhjSXJyRJ1Xq8MTaCcRL9fYZz/938SeT6fxPPYsTJojR0bnQuN030yRv9+bmGqzZu1vnJl5ektWaLz//zz/v8HcspBRzhjzFnGmDeBMsaYN+JeH6KKmhaLJUputzw4UrKzLsb/oHToELPo+/2xYgluD6z09Kh3LzlCh5ob8XljeXWtW2vwcyeBHTtCZkph6pZTQ7yUiqcr9uAIcRynOsq/mw60B743xvxqjBlojLn2iA9ssRyHHGwMya7tgfsMx+fHucdxxVdiOfZEARj/2d3u/PNjlTjbt9dEJ/46HnxQE5rhw1XgxHEitKq8kbHpDsGg8vBefFETl2BQBVluvVXXUquWQsRA1vXu3WHmmB00mf8BIRyqb5tI74s2U7Omqt0tWBDr95svnyaAzZppgjd1aqzpsOPovDVratL744+6xt27Ndl0HIVtudb4YzBOBx3HCQIYY/I5jrMYqJbrZ7Uc8eT4SPbLrqdsvMHkYPs1bCjP0+TJsWfYNaS6UTHhcEzkBAL6PHq0vE8//CBh07s3/PTeZhZOCzGR5mTtCfIHZaj81xROPWU3b78tYdWtm0TlPfco5BJ07dWq6djVqys/rVgxedqGDpWorFpVeXSTJslTDxIsoZCKkNSure/i9UqUrVmjcM3MTD3DixdL3C1dKvFYtKiezd9+kxgsU0Yir3Jl7Tt2rMIzy5XTdxw8WIacNm0Upunek/fe0z3r3Flj1nPPSQiOHq1z/P67DFLnnKPvf9FFEo8TJkj47d6tcWH6dJ27UCHdS69X33/FCo0xxuhaPvhA59m1S/dm0iQZoBo0kFitVk3nffll3c8KFRRGvmKFzlOrlsIz27bVdS1Zovu0bp2uu337CLATgJUffJHj9JZDmbD+AGagsi4z4l6Dga45OrPF8i/keBJ32Vnns5vkJVb1civtdeqknlbt28PkSRGC7brgq1AW2rcnlBVh+HANlB07xqrnZWTAc88Z5nZ+AoCUJ5/LcZiB4zhbHcf5GXgURQ58jdq1vJejA1ssxyHZed7c5Yml1d1QrsQJpZvfk57+d29CdhU041spuMdJS9u/AIu7zbRpmrQsWxad7DkR6n7zX4LV6zLnlgEEd0fwelVI4KabYhUvzz9fE77rr9dkKhSScExOhhkrUnmpzEvcyetUrOLj/Z+Lk5WlSdHpp2tiWL++vsvQobLkd+6s4/XrB6tWRfhPh41s2eyQlaUJbCQiz4Pfr4nXZZfFJsigUKtcZq0xJhX4ARhpjPkRWJ3rZz3JyUnhk8T9sjvGgQobxb8/kAc98Thdu+5vXI1Pl3CPlZwsL9u772p9u3b7NzYvXhwGDoQxC4szrcz5NGImAYI8w4Pcz0tUqxRh9255r4YPl8BatEjPzs8/y0M1cKA8Vm7LgcGD9bx066bnfflyhTI+/ri2/f57eckeeEDHmT9f15ScrAIrp5+uZUOH6hiXXhrrY/nZZzLOrFghA/H48fIAjh+v/d98UyKuYkWJunbttCwjQyLttNMk7FatktfL55OgGzxYHkDXyz9rlsI0n3xSYu6jj2J5eVu3qmVEoUISta1bS6j98osEa716qvz70kuxsNi+fXVdzZrpelq1khFpwQLlC27frvvy7LMSfjt2yEuYkaFzVK2q8zdvrjDNggU1Rk2erH/7v/6CyeNjgUl72ZHjNi4HFXiO48xxHOdDoJLjOB/Fvb5zHGdbjs5ssViOKtn9kCRa5+HQvbUSc3f8fmhRaTPjJ3kJhRxCE6fC5s37iq/El2X3+ZR3s3iJB/BSKDVn4s4Yc64x5jljzHhUWOUloCjQFyiZo4NbLMcp2XnrIHthlt2E0s2LdXN63OVumXXXuxd/7FAoJhhDIU2Q4j2A7qtFC4Vb1a2r9fMm72LWIj/38ww91jzBxKE7aNdOx7n/fk1wunXT9QQCClNq1UpFWsJhCbUHHzLcM/daur9zLqsuf4TKVQzLl0ugucVbvF79HTRIYWFub6xwKMKZ057m9a4/M2DXRXRoF6FaNVnGFyxQ6FedOvC/LyLs2JJFyZKKQDgKLVwOiuM4FziOsz3aVuoRYBBwfu6e1XKkkTTZPUfZCb7DEY8HOwbouXnttf2fa/e5bdFCHqxXXtEyN+euZk0tHzlSObAtW+rZmjlT/5cHDjQsbt6Tl3stZ13F1vTgE9Kr96ZOywJs2yY7a+fO8rDVqKGwyVmzJKIuvljPyQ8/yOt0/vl6VkeOlHeqcmXllLnX4TjytD3+uMRaVpbEzumny/BTsaK8UmXLan3HjvLwzZghz9udd0rcrlyp79Cmjb6P16t7MXOmCqDcdx8ULqzrrlJF4aGvvaZrqlhRHrAhQ+QRK1tWArB2bVX7TE2NjYMXXqj7ec89MkqfdpqOe/XVkD+/9lm5UgWZunZVWOkff0h4PvusxrpJk1SBc+pUeSWLFFE+8OWX696OHasxx+vV9iNGqMjUxRfrfn8Rdcg1biwB2ayZ7nXx4jI2FS4MLdv6ALWJKOPz5W6RFWPMPGPMXGCmMWZu4itHZ7ZYLEeNg/3wuGEfB1ofH7YZn7cXP/GbsLQ4TZob8PoYU6MPFC9Op05a98YbGhCHDdMAnJwsax3kvNE50BPYDNwHlHQcp43jOPc7jvMjh59DbLEctxzomU1sUp440UzMx8vuGInLO3Vi33P7yiuxlgnbt8eaKbtjQYMGsaJL6elq8vv88xJWc+fKmv3ww9DnvgD3tpzE8zzMzJo9mLYsFdBkuWtXTei2b9fkqm1bWcLff18l00ETo2AQJk72EDitMDfcaPaFSm3aJAu8W6nPrWh3ww2aSK1cCauWBOm+7FWe4kE+nlaHyy/J4oILFPbVv79C2NasjrBlyhI2r9nEhg2ZQCTXPXjGmObGmAIAjuOMBdJRHp4llznSSJqDeeISlyU+gwc6XmLBFHf5nXfG8lgh9tfvlyC6+24t69dPIq97dy2vU0ciassW9Z279FJ5s0qWhIf/62HYhIIMKvoQmyu1pf7g/6NbN0OpUhJ29epJhIFEySWXyBNXtKhE1Omny8P+5596xjt31rO6cqXE3ltv6RjVqytH7sMP5Q3zePQ8X3GFNMn//qfvnZwsL5ZbIKZMGR1j9Gidf80ajUc7dmjOMGeOxoLixWNFW2bN0rajR8sw1L27Kn2648Gbb2q7J55QUZYiRTR2nHeehNuypRE+fT9Im9YOfr++kyuubrtNIm/xYt3rggUlVHv0kBBctUr3Z8gQtVzo00c5di1bamzr1Yt9+Y2DB+t6qlSREapMGX2vn3/WtiVLRrjl4o14PQ5Dhui7Nm0q72Hz5no/eLDBtVsXvOaGXC+ycg7Q/SAvi8VyHHCo3B23n052Xjt3MplYQCV+Upm119Cv21BCK1bTYebL+JI08EyYIOuUG0py9tkK5wicoj54a9bE8vWOBMdxLnQc52XHcSY7jpNljEk1xlxvjElDLRMslhOWRONK/HL3eUx8toPBWGn1RC+cixsy7RKfc+sKv3r1YgJu2jTlyPn9Mtb076/QKNeD7+ba1q2r5/zWWzUpfP11CBQwvNl1KO/es5ysJ56ldh3DyJGa1A0dqtDK9HRNuAIBvRo1knevc2dZ/197TX/r1VMvrfgQtF9/1QQyOVnra9RQuFWDBsqLueTqFEItWvAhN3Jpo0UsXpmPq66KULnARsqWcbjwQnj55Qins4jL+JzCzAGcYxFO/w6QEfc5I7rMcoJwIMNJvDc8vjH5gYj3grvPYrzxxi1u1r69th8/Xn/dRuRTpmibYcMkML7+WuF+TZooj23RInnJ582Tt61oMQ9Vaibz0MOGUEjPydKlEip//aXncts2PZNuD7ukJHmpHn1Uz/769bHKmuefr+e/WDEJqYoVJWJ27pSBt1o1CdFu3RT2eOWV2r9KFYnDpUsVinjeeVofDks8/fmnxpKLLlIRpYULJdgqVZLHLxTSdnXqaKx46ildx/XXy0NojL7/2rW6P08+qXvx5psSv088FqHZ0P/y2vi63NdwONdfH+HllyW8Ro2SwP31VwnmSEQ97r77TkLvtNN0zo0b5als2VL3yBW9L7+sdgpFiih8tnVreUMXLtSxKlXS+6+/htYtI3hGjWBM4zvJeuRRzuwaoWVLeTfnzJHnb/du3WfJMi9bt+ZM3EGso162OI6zL17cGFMeqOI4zihjTMqh9rVYLMeWA01YshN/7g+Luzw+lNPNt4nfp1s36NLFg99fYj/LZevW+hFyc/GCQUhOivDSRWOBtkQGPA+P35ejcuTR8eY84ApkAS+AQp3GHfFBLZbjANe44hYpim99kNiSBPavaunmr7jGmQ4d9PxNmBA7loub1xN/vI4d/97+xO3HdeutEmLuOd1jduigCeHEiZpouee49z4PwWCBfb3svF55zkBidOBAlSx3BWvXrppUTpmibWvX1rbvv69QqyZNlGdz5pkyGN12m/YbM0bW9h495D2cNQuuucbQ/6yfCTbbRcmHArwyzCH89FM82bocZ1ZbRfI9j3F6JS8pewuw8a9y5Cc/2/CwOvez4YzjOPssXI7jRIwxdt70L8D9nXRz5g4WEur+loZCCjd2UxtcgRfflgj2D5UGCQrQ76zb/qPo/7N33uFRVVsbf8/MpEGA0HvvJUAIndA7CliuvaCiKKBguehVr/167QIqiiBg7w1FeocQek0gkNB7TyCQZDIz5/vjx/YMuaEoetXvznqePJk5c87e++xz1trrXW2XIgQxKwtQNn8+HqOUFAqKfPghIGjSJM5xuQCEFSoQZnn//fDeZZdRuGjmTLY12LoVT9z27QDJiRPx5JlaH5ddBoDavJmxtGlDyOaePegQjRvjAXO7AYqffMJ1NWrgKVu6lHBJt5v7Sktjy4C77gIImYifqlUBiXfeyX1t345MGj+enL8PPgAser14+W+4gciA7GyOu93cy0fvntLWTfVl63H5Mo6pavFT6vtQEUVFMc8zZhBuatSTvXsJyVy2DNDm9zM/bdoA+EaPRi5mZeFhnTXL2QKhWDHCMf1+DFHPPcexbdsk3/7Dqnp8hcZpoAKb3ar8fY56XVlI3bpRCEZCrjVtevZ7c6lkBcmfc59kWXdJGiSphG3bNS3Lqi1prG3bXS99CL+Mmjdvbq9cufK/3W2IQvSnoYKKJPxW7ZzrmOSEepqtEdxujhtFMSuL8uU3dTuofq1TtUEJGqmbdf+BURespGlZ1irbtpsXcPxTSe0lzZT0uaS5ktJt267+y+/496WQbArRL6FgXsufi2OAVjDgM2Q8Bia8KzhcbNQoxxMnOUYcAwyDQV9BvwWPKXhsPh9KoNvt8HtODv3Mm4cCNHeu83tODiAwPp4S5FWqoBht3owS06EDAPCVV2hr6VInn3fWLJRKozQZWbNhA56CtWu55qabKOJSqRLhpnPn4im495qDUqOGylCkntK/taP5tSpXJVKdOwX0ySSvUtIjdPKkpYEDAZEXQ+eSTxe45lsRlmm8dkMkdbZt+4pf0s5vRSH59NuSMZK2aeMYQ8537uzZvMs9ejgFVebM4Xe3G4PN3Lm83w8+yDlTpzpFgSSuX7sWPpg5E9BRp4709deEOPr9gJbOnfFCNWiAl8jtZn1OTQVoffIJS/LixQCovDzCHw8fJsyxZElAk98PaDSgr04dxuByAcqio+Gh226Dh1escPL7DhzgXgMBPFk7d/K5b1+uOX4cw3Hz5ngKExPxyLlcGHymTIG3TeGWtDQ8glOnAg6LFgXQrVzJdbVqAX7/9jfmLhCQhg6xldj9KWVu3qHZxW/WvgbddeedlqKiAGapqcyrKYKSmoqX7uGHCSt/913aadEC+fjGG8ivrCxC1tu14xyT6zxwoPTUU4C2Z59lrDt2SNWr22r2wz91OHW/Posaqhtebqavv0YGud3M2+LFgNVduwKSbPXu7dLUqRf24p1PNl2sWX2opHaSTkiSbdtpki4t+y9EIQrRL6ZfWyGsICoIyBWU6xMcirJoEfHi4eEINwPyjHJZvbr0zjdltE9VJEneSg0uNVG4gaTjkjZJ2nRm/7tLi/sMUYj+YMrPa/mr2ZpcufxedwPGgitcGoqMPNvzFuyFL6jog/ESzJ+PImfazl9YZcYMpw+zYXhWFtZsn49QyVmzUDxNcZVRo/AsjBiBTJgzhxyVW291xnrZZZwzejRKXmQkYzcFFaZPd0qiZ2ej1Bratg1Pwl134bHweFC4Tp+WXpxURlcV+klP63mNaDZVJctHqGdPaf4Cl7xWpE6etH4ew+9M90hqK2mvpD2SWglDeYj+H5DxfCclnTu33YRem4rW5rpgT3zHjs6WJJJTTCUnBzC1erWzl+Py5fDRe+8RLpmWxnt/8iQgZ8AAAJLbDbBo0YLz27dn3za3m342bZLGjYO/du4kLHHBAjxkpqz/0qXklDVuDKhxuQCHu3cDRvr3Jw+vbFmMOB9+SF/Ll3NOiRIAnEmTAIcHDwIm3W4MP3Xr0rYBn/v3893jAZT26AHwkZABjRsTEtm5M7IhK4uqlTt2cM6CBYC7li2dGgDvjLWU1PNpfZ4wTk8u6K7y5S1NmQIArVMHQOtyEU5pqmwmJjpRBzt3Eip6+eW0Z8Y8fTrj+PprQlibNUOWPf649OgjARX1nNZ119mqVYu98lassJT10HP6svXbspo107Ztllq0AKRnZvLf55NycwKSjkqSTk+79G0SLtYPkHsm/0WSdCbMIKRkhShE/2X6tRXCCqKCqmrmTwg3yqb5bMLBEhJY2BISWKgSEpw8mcgoS3c+XFUvvWwr+h//vKREYdu2m1qWVU/SDZJmW5Z1RFIRy7LK2rZ98Fc3HKIQ/YF0IT4uyGtnQsESElCwgr138+Y5ymabNvwP5uVgPm7TxgntNNuhmAq42dm0bZRRn48ckY4dHWOO2aC5QQM+//3vhH41a+YoqU2aANR8PhSmq67CMDR+PMqayT+Jj8cCP3as43mcOxdPX6VKfB4zBqt6y5aESzVtSjiWBDjcsYM2XS4UsOnTLTUf1EI39qimZ8ffqsQFlipX5pysrHPP8W9Ntm0fknT979tLiP5IKmgPSckxjKxeDV/07OnwkvHgGY+5MbDEx3OtKSg0axZe60aNAHopKVR27NWL8ywLUOTxkMO2cKH08svw0d69gLt//Qv76pIleM39frY2qFmTcX/zDdsh5ObiLe/ShX6rVqUASp068H14OB71zz5zNvL+4guqYmZkMHbb5s9s7r1uHYCwXz/GO326s73B8eOMb8UK5MCOHYSIGm+j34/Xr107QOvddwMm77+feytdmnP278fbGB7OPQ0ejLxJSUE22ba0Zq1LN90SqfUbnAIzpqCLy8U8pqVRpOaxxwBtrVrR/7//TRh4VhZh4nv3EoLauzd9lizJPDVtSjhp8oaAPur1sQrvd+ma0m7t3HGdVq1y6YYbpNJlXLr59kj5/c6G6BKgMCKCe7Hkk4SFLqBjINBL2Ef4Yj14CyzLekxSlGVZ3cU+VD/+6l5DFKIQ/Wq6FMXEgLqCCjUYyu/Fy+9dMBujt2lzthX84YexdNWtK338yZlE4eOXnihs23aqbdtPndn0fLikDyWtsCxrySU3HqIQ/UF0sXxsCq6YvbGCDSv5i7AYkNe+fT4vYSAgHTyoGdNtLV5MW8Zb5/NR2S4rC8t+8+ZOvpvH44SLde3qhGeasCsJb12zZig8xuuXksJ5MTGAw6golDmfD8Vs9Gj6Gz+e9u+5ByA3ZQoK3uDBKD0GeL71FgCvUSO8CjNn0kaTJihI27dT/GHXLpS6tHSX7nikrDIzLVWrFtDB7adVo4atO+905nXbtt/sURZIlmV9cGYfPPO9uGVZE3/fXkP0W9D5ImQKqmCb3+ttclxHjADcSRhOmjQ5+zqPB17OygIMZWcDsjp0cEITs7PxhDVsSH7YiBGU9m/SBF658UY8cJs30+f27YRJ9u6NkWXXLgDiwoV4n6pXB7jt20eYc1QUnqqrrybssXZtDCq2TeiihLxo2pT7fPRRp1jLww+zlcmqVYQX1qkj3XEH/O/3k183axYevvBwQNTx4wCtSpWoTHn6NH9padzPHXdwz999x1g/+ACQ6nZzzS23cH9uN8ePHAEENm5MP5MnM5+zZgEc4+LgdRNuaQrChYUBHGvW5NhllzG35cuzybrXi0z67DOK29SoQT+XX871Zt+/QACPYHi4pKxT2rHfpcv1vdof/kb2qVP6+muKvlSpwv+PPuKZDBgA2C5bFlnXqJGU4w2ThN26jDvy990mIYj+IUqVb5B0t6Spkv55ST2HKEQhuiD9FqGY+Stjmjw66WxFsaCKfQVd7/NhtRs9mrYSElDAzHlRUSh2vwfZtr3Ktu2HJNWUNP336SVEIfpjKb8CabxykuN5y08mb88Ubfi5cq4rAFNXrSr94xEltA3I40EBmzPHKdnu8RBuNG4cfG0Kr5jiK3PmODlDzZrhwRs1iu/r1zvtZWWhVJn7WLwYi7gEeIyNRSnyelHUIiOxkt99N+20asW91q6NESo+HkVwzRqnzR9/dNq45x5C1D74AOWvenVk0MCBkjc3oLCUteo2bai2vzdH33zthDxVqXLJj+lC1Ni27Qzz5czewaFtEv7kdL40iOCUheB10fBa8Bq7aJHDP8aD3qOHUx3XGFlN8ZTx4wEJs2aRU9qkCUDn/felnOyAVs0/qdsG2HrlFXitTRvO792bHLj69TGmTJ3KNgXPPw9IOnQI73l2tjPeDz+E12bO5FiXLoCbWrUAZ0eP4tWbMoW2HngAb6TXSz5e+fJ4nPbvB/i0aIE3zOfDW+hyMabWrclry87mHt96C4D0ySfc18mTgLasLMTTsWNct2kTIaVRUQDWatUYb1IS81OjBsWXKlakr8cfx4AUCDDHJievWjX6vfde5EurVsimTp2QK71745VzuwF4b76JZ/Lpp5Efy5cTnTB1Kn3Oncs8fP45c/Pcc9y7iW4ILxGtGhV8csut6IY1NOTv0brySsa0eTMAuG1bvJGWxXy53YDesDApOtqSzqS3hF878HffJkES1Z8kfS9piG3bf7Nte7x9MdVZQhSiEP1q+i3y7YLbMODNhIf07Hn2JsgF9WVCTcz1bdqgxI0ejQBdvJjj997L5/nzEZo33cT1xYr9+rFLkmVZRS3LetSyrLcsy+phQfdK2iIp/tJaD1GIfnu6VKNMsJJovpvQS8kxyOTfCsHrhTenTeOcn4Hg4cNoXX6fvCmbteCHTM2c6YRdGi/effehaNWsidJp+pGQF127cr5RSkwoVU4Oekj79ihyI0agnC5axG/Z2QDBtWs5Jy2NNsaPB7T5fChJkZHkwMycyUbOqakoaHFxjp7j9+PJe+EFPAl9+mBgatwY5e6JJ8j/qViRyn5Nap5SeO4xbVEtHTjhUkxkjlzyS/Lr9KlLy2+5CHJZllXcfLEsq4RC1cf/9HS+8OmC8mODzw8Ofc6/abr5HFxRUwJ0fPstHqbVq6WskwGlrz+t++61NXmy1KxZQN02vCCNf1OFVi/QooUB5eWR23bDDYCm6dPxZPl88O+WLazbLiugMkVPa8Z0W2+/7RQoevJJvFrt23Nu3boAj+7dAW19+uBlSkigvZ49AXZJSfBvoUKAqthYjht5sH07XrohQwijdLkARps24bn66COA2owZeOlr1kQ0XXEFoHPiRIzDjRrRdno64NPk1FkWYZFz51KsxWyJcOgQssMUhZk/Hy/cjh2EXI4ejTzxepE5L77IXPt8yJjbbuOc8eMRl++8Q7GU1q0xZPXty3yYjcy3bEGuffgh4xoyhLaH32+p6Wu3atrVk9Rk2kv65xOWNmwAvIWHc58GDBcvzv3UrUu+Yl4e3rywMKKfpEuPfrrQRueWZVlPn8l72Sxps2VZhy3LevKSew5RiEJ0Xvot8u3ytxH8P9iyaBYk6Wyg5/M5gtBY4zt2ROmKjkYAzp/vtGOUTrNJ6cFLz5L7SFJdET1wp6R5kq6RdKVt2/0vufUQheg3pF9qlCnovOBcVxOiaDx4JsRxzpyzPe8SCkSrVig9CQlOlU1fiTLytWkvnytCm6r2UaueMUpOdsLD3nqL67t0YW+6H34g9yUhgeNm/7zISPh71ixnn7pAAGUoEEBRCw9HMfrwQxSZBQuccxs0QHl75BEnFKpePZTT4cMBl9WqYbW/9lpna4SUFABcw4ZYzY8do7jKDz8wtnvuIcTsrrtQeJ9+GmVs+XLpcE60JrZ4W421UaWK2Dp2OkIB7ZMkZb3/2m8TInFuek1SkmVZz1mW9S9JSyS9/Ht2GKLfhs5VSdr8lv/34O1G8nvXjZE0KwtDR3D1zK5d4YnLLwcE1K8XUNpjE/Xk5Fi5Fs7W7t0B5R45pRe3XaM5aqdtWTHat8evqlUJkf7pJ/6qVJFeeomcserVAWQPjwjo2d3X68WfGmj2IzNUpkxAAwYATm6/HV5duJBxvfQSvJeTgx7w2Wd41r7+mvM3bAC8lSzJOt+4MR6psWPxxu3aRX5dx46c26MHHvglSzDWzJ2L4eaRRzC8vPUWfY8bBzCdNw9QV6IE41+7Fg9fuXJUrpwwgfmKjUUuxsWR+3vzzcxf69aApdhYZODYsXj5w8IIAW/enFDWzZudoiZZWYDD1FRCTZOTuUfzPF97jYiAl17i3Fq1+O2JJwgDLVqUNlNSAIx//zvyKaqQS/2vL6RSpS29+CIGKRNuO3MmWzCMHAl4DQS4JjoanSk9HaD3W9GFVMcHRPXMFrZtb5cky7JqSHrHsqwHbNse+dsNJUQhClF++i0KAVyojeDfgwGhsTo2a+ac4/ezgPXo4ZRCNwpnu3ZU3mrdWj/vMVWnziUPv4Zt27GSZFnWe5L2S6pi23bO+S8LUYj++3SuEOeCeNCEexW0FUJk5NnFUExeXWQkypsBfsHhYabw0ZAhDrijAqYl/0Oz1eezwxpWtIwioywNG+aUar//fvrcvRulJinJyXu7886z5UFODkpfjRp46Vq1Il/IjH/WLMBd3bpO0ZaUFJSbzZspnV6pEkpdrVooNA0a4DFYvpz2evRAoZozBwVo1y4UOr+fsvC7duHBmzwZpWzlSsbz1FOEpT3+ON+feUb67DNLC7p/rUmWVz4rQv7ME5KiJEmldAQtuXHj3/gtgGzb/tCyrFWSTLmbq2zb3vi7dBai343y73VntgeRzi50JDm8aD6bvSnXrQPM3X+/Y6Tp0oV2srLgg6uuktJXZMq/d68m6TZlHz+hfbv9ymkYrfKFTivstK1bq07X3EZNtG0bnrbSpfEEnT5NSOGgQRhVDh6Uco6c0oOrXlMHLVbdYyulQk30wQflVK2apbw8wjCvuAL+3bMHoPGvf1GJ8q67WMP9fvht0yZ49uRJ7te28YRVqADIKVUKMDd1Kjy6Zw8hlg0aALLKlWNOChVCBsTEYOAZPZo+Nm+GFUuVwrvl8cCWUVGEnvbqhaFo/nx4e/Jk7j0zk3u44w76XrOG+Z4+HSPSunUAzBIluLfbbwdsDhqElz87mzaLF+dZdOgAwGzUiHmcMoXopJkzuacDB5DLmzfzbJ9/HrkzaRLyplgx9KXERCIpNmygr6wstk3o3Zv7qFePNqpX5/j99+OBzMx03rtLLKAp6cIhmrdIusGAO0mybXubpJsl3Xrp3YcoRCE6H/2+BuaC8+uCK/P5fE6SuOSEaM2cifXLVMTz+/l+7BjCbM8ezl+//pKH+LM968wWCXtC4C5Ef2bKD+4u5NErKCzaeMtNISMTWm1+f+MNeHD+fP1cbnvxYoBQYuLZOULx8VJyiksZEWU1d56lqVNRXjIyyPUx9OCDKF7h4fR1220oHjk5/M2ezb1UqYIyGR6Okrp0KUqT2Qdv0CCA16xZKG316lFS/cYb+Z+aSshTvXp46VJSnDF06eKEi779NsDwqqtQiiZMQPm74QaAZ+XKKKmZmYzNFH966SXyap59VqpQLqAPxntVrFSESpWy1CJOksIkSTtUGi3xdyTbtlMkfSnpB0lZlmX9/pl/IfrNKNiAYsCd8cIFh1IbL3f79vCkCZE24dWDBjlGl5EjKUoydy5rpdmLrlcvacG6GFWt6VKqamtXWEN17eHRtOmW3LGN1ffRJloU94iOHbOUlUWRlPLlHXCVmQnPHTjAsX2Z0SobliOPbN0V/pnqfPh37Ro/XbIDqlKF+h0tWwKCXngBUNO3L2P8+msAR8OGgLOlS+Gpdu0ccHfLLYRYzp+PkeWnnwA3H3xAHlu9evx+9dWAsP794VHbxtvWsCFgrWtXANKVV0qtWgYU7j+tnBxby5YB2nr1AmC+/DKgrUEDJ9d27Fj0j717MQz5/Tyvu+9GFrjdACW/n76+/RYvnSla1aULXsmtW5mz11/HYzltGh61Tp2Y20DAqdI7cSJzP2uWVKZ0QBNGnVTqJlu7d2Okuv76gMaMzlFWlq3q1QGxM2ZwH82a8f5MmQJwLlECgLhqFbLX5PJJ6FKXShcCeGG2bR/Jf9C27cMyUjJEIfofo98bdAX381vteZe/3fztFxTKacLEgs8z3obwcBKvO3QgNKNtW6x+EycSsnXttbTzGxRbaWJZ1okzfyclNTafLcs6caGLLcvqZVnWZsuy0i3L+kcBv4+0LGvtmb8tlmVlBP3mD/rth0u+kxD9T1D+cK7z5fN06uTwmLnW8Jopp53fAxgZCRjr04frFy7EkNKoESFPXq9TCdPvhydvvBHAlJiIUun3A4rq1KH91193ii9lZzuKaSDgeDCaN6f/rVup/rZqFd8TEpzNmP1+FNb0dO57yBAUoYwM8m8eegiFsVYtrPORkSh7pkiL2f+rShWUtD59UIw2bgTIxsai7NWpg+wpXpxry5YlJGvQmV3meveWbDugL/6xUsWWT9axuau1fl1AVw0oqmraIklqXz7bcS38DmRZVj/LstIkbZe0QNIOSdN+tw5D9JtTcN66xP/773e+B+fbmd+Dc/RMOOF77+EBW7CA3LMRIxxead2aAh9PPy2tXWfpvRKPy+rRT8PerKtChSzl5BD299aHxbVhgyW/HwOKAT55eXiD2rVztixo0EC6+RZL362tqXLXdNaD3if0uh7UfUf/obQNOZo7Fz4bOBBP+RNPABYPHgS83HILvP/TT1R4/NvfAGDHj/PXsiXeqtOnATVbtzoFTXJyuK/t2+H9rCw8d23b4o07fdoxEns80jXXMCdlywQU+fUH6vrTUB2ds1ZpaQGVLevs53fFFRQ3SUxkzKY4i9tN/7aNYblSJenjjzm3UiWA2a5deNuGDKE+QI8eyMOXXybnrmRJ5m/6dEDYs88S0bBxI/PbuzdyNDsbID1qlHQqK6DAJ58qZ8ybqpH0iUY8FNDA2wOqkjpL1yYNUepj7+uHyQHt3BnQv+47qKgo+2cvYmQk7a5bB6D0ejn2zjvOu1eo0KW/vxcCeN5f+VuIQvT/kgoCXb8X4LvUHLxzVQE7H6gLpmDPgbFMmvM6deL4ihVY0997D4vZxx8TWjF3LueaTZd/Ldm27bZtu+iZvyK2bXuCPhc937WWZbkljZHUW2yYfoNlWQ3ytf+AbdtNbdtuKulNSd8G/ZxtfrNtu9+l3UmI/j9SQd634HLp0vn5N38xhmCFMrioSUHXBbd9zz3w3qBBKI4jRqBo9ejBsU8/xeK9dCnW42bNpO+/R8Hw+VC85s3D6v7DD06Blxo1UFJjY+HxFi2wyteogVK2YAFK0cqVAC63GyXIlGCLicE7kJSEEmr2ALvuOme+7ruPMK42bVDYXn+dPBW/H2/HgQOMOxCgjTZtuJfHH3c2TW7ZEqC4bp30yit4Ca/slKkPT1+txtqo23NGq1wJr2640dKOM7WZZtT55yVXqbsAPSeptaQttm1Xl9RV0tLfs8MQ/f5kQptNZVnDn8Fe8+CcvDVr8IaPHct7/M47nJeczNYDzz8PPxQtiuEiPd2lgycKKSfX0vr1eJaOH6ffOnXgRbcb40bbthhvHnuMdz4+noIjsbHwz/gJLn06u5xWqK3y5NELRV/Xll1RKlqU9oyXKyODcZUuzb21akV7mzZhrElMxCBTqxZjjY+HzxcsAGi6XPBkair3WqIE+XjFiuHNq1aNe50/H498bCzgq0YN6dVXMU69/qpfG/dGaZ46KzZ3jupW8yo+nlDwdu0APFu3Ymjq0QNvXFwc91G1KsC0Z0/Gec01RAaEheFBrF6dvMLwcOTN3Ln0v2oV+svHHxOSmpzM2FatIvx0xw7u6c030XVatUKmdeggHT/oVdKhSpqhPlp+oJwObDisQXd41enoJ/pa16v2nlmqU+GkXt52hda3vlMtXuqvPr0DWraM53XkiBPyvn8/83nffc57Fh5+6e/qhQBesPX8RD5Leuyldx+iEP21KD8o+r28bMH9/Ro617guBOrO1X/+sDNT4KF9exage+/FWrd8OdYos0fWbxFmcAnUUlK6bdvbbNv2Svpc0vkKs9wg6bP/yshC9JengnjM8Ne5DEHBnrrgawoiU9QkuJpl/r5zclBKoqPxLERH8/fKK5w3cybga9gwlJ5vvuF/TAwW8SZN8AD6/ShiDz9MuJiEonTPPSio771HQYNJk1DmGjXCSp+djTKUmOh4HX/6iTbmzQNEvv02wC0QYDxTpmAF/+47aehQjtWsyZ5TkycDNgcNQsHZvJkQphtuQMG77z5kjMtFe1Wrohx16YK37/RpqgPm5EgbdsdoTYObJbnlrlVb7btGqE8fqVAhqtRVqvS7gjtJyrNt+6iopumybXuepOYXc+GFIg+CzrvasizbsqyLajdE56YLGUSDjy1ahOfahNQV5IEPrjy9YQP8+cgjvMORkbzn339PGGJuLobRpCSAXr16eONjY5EBublS4cL0MXMmm4cPHoz3b8MGZMS99xJVY9uswd9/j5eoc2dL9/+7knrfXkOB9l3kcluKj9fP3rEKFfC2FS0K78ybR0XP0qWpqnnZZfQ3YADtvfwyYGnVKvh23z4MMgsWYADatAle3bgR+fHqq4Rt3n47c9KsGW2tXOl4zKZPl/zyqHpZvzpooWa5r1Fcqwht2sScPPQQ4z18mHBMvx+Po9m43OXCGb9hA/1+9hlg77bbAGeHDyOD1qzh/nJzGWezZoDG9evZ8H3DBmTaV1/hubziCqIVvv2WPgsVIlLpyBGpRbsItSy7Vw2VojHx4/TJ7DLyWRHq0Py0vAqXatdVwLaUuCxcH+k63bhsmDJ3ZmrPHp7tiRN4Slu2RGbv2mVy8AKS/AoELn2jAuuvtttB8+bN7ZUrV/7RwwhRiH6mcxVR+KOpoHHlPxacc1fQ3nf5j5nzKN7AAtetGxbA4cMJ40hIkK66wq+kZdLTT1p66pkL78ZiWdYq27Z/UyXFsqy/Sepl2/adZ77fIqmVbdv3FnBuVWFdr3Qm10+WZfkkrZXkk/SibdvfF3DdIEmDJKlKlSrxO011mRD9T9C5eGzevLP3kQvee9KEZRbknc/KcgqqGL6cP9+pTlsQ75qiD6ZoS+vWgLZVq+DPBx/kvDlzsKqbXDm/nz9TfXP0aDwEUVEoHM2bo4jUqQOPr1iBAjd1KorQzp1U/5No4/LLGf/QoShe8fGMIVjG7NqFkrhtGyFUkZF44mybHJ1WrQjlrFOHsTVrxlzu3Yuzbfh9Ac2fnKnsiBi9N8H6uUKdKcKyfDm5PWZrlzFvBvTTp5l6bWKMqlWztGwZno+tW1Ey33//4p7zr5FPlmXNlnSFpBcllZR0SBSsa3uB69xiG5jukvZIWiFqIWzMd14RST9JCpd0r23b51WM/pd1pwut0YZnz7UHbP7tDvIXV5kxAy9TTMzZHj3DYz16cHzuXEBG48YYSL75Bt4+cjig3m0z9a+3YpSdbalsWcKU3W7p1Ck+L1wIGMvNBWDUqEHY4MSJgEVjVE1NxfCxfDnXmz3d6tcHJHbogDE2LQ1eKFWKz2PGcO7mzQDFMmVoc8UKZxuDNm0Ahdu20XaNGoy9aESO/v1alJ77l6UdO+DzGjUAdZ9/Ds+3bu3k75Uty72YDccrVsRTGN8soORlp3TailahwpbS0pxK3JmZnFetGuDs5puRaeXKwc/p6YypfXvkTk4OwDArizlZtw4wtW8fOso775BTV6MGc/P++8g3jwfAOnYsc7t3L3188w0AvVgxvK7du0v79wVUueQpRZSIVr16ZwxGdkCP/cOvv13n0YEDUt81T+r7zfV0qnBFFb28o9olWHr3Xfp/912ewQ8/cD8TJwa0f/8+SeV1XdgEfZ5zJ+j1PHQ+2XRR++CFKEQhOjcVtCBcCv1W3sBzKZ75rY0m3CR43y2zmWtWlnO9STD3+Qgf69iRxSknByuZ2Th41Ot+nVyWKEkKf3bwfy9p8dLoeklfG3B3hqqeEZw3ShplWVbN/BfZtj3Otu3mtm03L/07F2wI0R9D57LsS+dWGo2iGLzpcadOZ29Jkv/anBwqV8bGorhMnYq1vnVr/ufnR3N9cFl2vx9F0O0mTNPkpE2bhkJpwF3Xrig7UVHw8bJleOVMmfDmzRnzoEEO4DOg86efAFv//jeK07RpKExz5nDNNdfQtqmmu3MnSmpcHCFef/sb3oWPPkJp7N6dkKXwcPahuuEGLOrPPQfoe+opwqnq1A7ozfhJ+nbQD3L/+0m1aBH4Ofc3JYXS6i4Xyl+nTuS2TJ/p0k9LiuvgQUtDhuD127qVezxxwQzeS6Z+kk5LGi5puqR0SZdfxHUXG3nwnKSXJIWKTp2HLibK5lzVb2fPLrgdY1gx/1etYg00e9GZbYPi43mXMzKoUJudzfYBBkj06iWlpAS0auo+vf/P9eqT96WioiiCYvZse+wxeHf4cGe/SJeLUMmJE+Gv9u0BVadPw5sHDsA7fj+grWFD3vesLMDdPnYK0ciR8F3RovDZVVdx/Omn8VqlpTHm776DpxcsAGCWLo0n8sorAvKvWqOkRac0JGGdUlMDiooiLDI8HHC3bRvALhAgR65wYfrfswfgl5pKvtvevdLqNS4tXF1Eu/dYqlYND+GAAQChnj2p4jl9ulMMyraZe48HkPbggwDYatXI4Z02DW/e2rWAyqpVkSW7duH1v+IKAOu33zrFX6pVY57HjwckP/EEwDY2lueQlcV81a0r1artUrP2RbR5s6Vp04im8AdciikZpkaNLF1xhaU+S55R39F9lBXfUd17WCpdGpA4YwbP6MknebbJyVK7+FOSYiRJMXk7cT1eAoUAXohC9BvRbxGu+UvaKChs60IUXPI8+LvZ6sAsWt26OXtkmYphSUmEgRhvwCuvEFK1YAHCr2VLrtmdnKEiOiBJilIGEvyPob2SKgd9r3TmWEF0vfKFZ9q2vffM/22S5kuK++2HGKI/M50rROtcPGp+k87OpzP/zxXy7PPx+z33UC3SFEJZu5b21q8HdBl+LIg8HoCbBGjz+QgBev55KsEtW4ZHrGtX2rj11rOLsURFUYRgwwYqUY4eDZ97vZw/cyZtmzLiK1dyfv/+KELZ2SikZv+8Dz8kn65XLyzUfj8gb/NmxrZzJ6Ff48cTBrZrF+c9+iggsXRpjs+cCfD86esc3bXnCV2lb6TNm7UnLUdxcQDB665jnGlpAOPNm51NiWvXBmx+8AHeiqpVCIEqVOj3iV4KKgB1QFKGpOOS3pL0b7GX8FLLsrqep4mKknYHfd9z5lhwH80kVbZt+6cLjGWQZVkrLctaefgSlcW/Kl0ol/1Cxprg34O3Shg1Cj5dsABgcfvteL5eftmpPDtiBB6iN9/ke1ISnqEHHuC6PXsk5Xp1QpE6riJqdHSerIBfgQC836kTgMxsD7J2LWBu5kzkhdsNUFm0iDbXrwc0NWiARy42Fp6//np4ZMMG+DY5GSDz5ZcAr0AAIBYWBt/deCN9HjwI+GrSxCkMsmUL/L93r7Rp5SmV96ernjaqqXe+Al6f/H4MQoEA4KVmTXLy1qyhmElKCrImNxe5sXkzHjLbxgu3axcGobffZo7eeAO5WL06937ZZYw9PR0j0803A7bKl6cfv5952buXY243RqP27ZEVmzdjBFqzBuDm8wHAr7uO+3vpJca2ejWg8ZNPkB+NGyNL581jrKZSaMuWzI0pzBIdTYjnrl3M84xZLkVXLq7WrS1t3YpsHjIEUJeQgA41ciSRGklrCqmhlkmSclQYgXUJ9LsCvFAceYj+l6ig/LxLbeNclD/W/0LA0HjpzLWzZ6MImT6NYmjyfTye/6wYZsqQS5wfG0sbrVohsP1+FrDu1xRXhspKkrJUgtXmj6EVkmpbllXdsqxwAeL+oxqmZVn1JBWXlBR0rLhlWRFnPpcS+4GG9rD6H6OC+PFc1n5D+feRNNcEnxf833jLc3Lgr6FDUaK6d0dx7NsXPixVytmzzlxbUF7fmjV46qKj8ZK1bEnuyO23E4Y0Zw48PXgw/6cF1XX0eODr3bu5TkKpnD6dYgBZWbDzunUoYGYfK6NU+XyEHvn9Tmn4okXxDPTti+EoJ4d9q2JiUIbLlUNpO3gQr4Bt4+246y5kS3o6Xrc9x6KUWO92SR5tKN9H+45HadkylOhnnsEiHhaGtf/IERSyn37CaxgWxveTJwLauQugE/n9mN9ms6l8ZApA5fsrYtt2EUnlJN0tafSvbd+yLJek1yU9dBFjCUUY6Pzg7VzrZkFVboO95veeCfRfu5Z3+oMPMHYMGeKcW7UqACw9HaC3ahXeoY4dCTk8ckQqXTFCJXRMMTqpneXaK6qwRzk5tDNvHmvqtdcCBo8dA1QsXYpnr3hx8mMfeACDzcMPw/8jRzKmPXuQLcuW0VafPhQ7iY9HTq1YQSimy8VfdDQ8GhFBrmyxYvBpjRoYWdxueOnwYUDjzqPR8hcuqwgFdKBIE1WoEqbt27lmyxZkQPfujOOqq/AiNm1Ku8ePAyarVCEUvHx5jDSWhZwZMAAvnZEVY8cC8tat456OHUM+/vvfgLlPP+W5zJpF3l/TptzP668TXjlnDvfRsyd9lCsHgDx2jOe2ZIlThfOxxzA8LVwIGBwzhvmJiUGOdeyI8Sw5mfF164Z8rlAB2ffZZ4y1Th3ka5s2gOK0NGTbN99gIL/uOt6bhx/mPene+rRS1EiS5JXNC3IJ9LsBvIupYHfmvCIihGHZ7zWWEIXo96KCipiY47938RVTmtl4DM4FDH0+hJspZe7xYDlKTj47r65nT6e8sxl//j3xsrJQpEx4yejRCLaOHbEOLl8ubUp16XRVzPhZD4+5YAz570W2bfsk3StphqRNkr60bTvFsqxnLcsKrop5vaTP7bMTkutLWmlZ1jpJ80QOXgjg/Q/SubY4MBQc6mx4saBw6PznGb7r1MkppjJ7NkpKs2b8lpTklB03bRjPwezZbA1gwOGMGRxv3Bhe9/lQPrt3BwRdeaX09787xpzevQFu337rgLm330Y21KqF8hQVhTLYqRMg7M03nf2uxo1jjGavy86dUV4bNEAWvPIKYHXpUsKgZsxgrC4XSuWRIwC8K65gPzvbRvGrVw8FtXdvrO3Z2SiW7dpZ0lPPyT1hgmJfHaAWLayfQzl79iQHsGtX6Z//5HubNhRg+eEH2nntNalIlE9VlSxJSj8VuOQQqF9Ktm37bdteJyr2nosuFHlQRFIjSfMty9ohKnX+EDKQn5uCDSHBFGysKeh3433Pz8c+H+BgwQLe8ZUrCQns1QseefFFp4S/BH9u2aKfi5ssXhRQ1TKnVbqULX/A0h7VktW4lWr840ZFRlkqVgyeHzGC9XXhQmfd3rIFu0SVKniJJABeiRKAnXr18MyXKAE/1K7N+DZuBBxFRXHeO+9wf8nJAJ74eK5bsQJZVKgQv+fmYjQ5epSiJSdO4Ol6+mkpN9fSjioddPmjTXWkYScdOGCpSBFAVps2FHeaORPQtHgxoKphQ8Zl23j33G76DwSkIkUAoSb31u+X5s0N6NiBHFWvbisyEi9gQgJA1mz2vmIFcm/AAL77fE7BlNdeQzZ4PACwGTPos2NHwPtrrwEOW7XC0/f11wDDmBhA6YsvAjQbNUJ+paQw9h07GLNtI1++/97ZFiIri/DZ1FTk0dy5yNoyZQD7+/bhhbzzTvq+7Tbk85Hc6J/fPVdESS64BPo9Na9QHHmI/pJ0saDsXOFb0q/f4uCXAENTdMH0c76+TN6NOSc62im+YDwIpk2jeJqcG4nP06YhDJcvR4C98QbV80zIWGwsyltenuTzuSW55XL/sVHgtm1PtW27jm3bNW3bfv7MsSdt2/4h6Jynbdv+R77rlti2HWvbdpMz/yf8t8ceor8GGV4Jzq3z+c7+bHg6OGQzuPiKCYvu1Infe/bkf5s2KEZ+vxPideedZxtpTEjksmUoel6vU9zE63U2IX/jDcfY8/LLhCVt2IDys3w5hSCGDaM/2+a/201Oy9ixfG7cGKAnUVq8Th2UM7/fAbfh4ShK+/ZhlQ4Lo3T7ihWAwZYtsbDXrcv5qalOGXUDKH0+xlulCqGcY8Zwbxs3uZQVVlzr11vavx8vZLVqKLP33MMYmjXjvv/5TxS+xo2xxl99tVSsVJhyRNhTy5I5l6xA/Vqybfvd8/x83sgD27YzbdsuZdt2Ndu2q4niUP0uVGTlr0yXmvZg1rhzgbwLhV0HG1VMAR/juY6MBEiMG+eAvzp14LNAAANDYiIgYNEi6bFHAwqb9a1OLUzSwVlrVLNGQHFxLt0yMFILF1nq1g3eyMkBlK1fD2gZNoyImYULMexERNDv8eOcM2oUHr09e+Bt2yYHrkYNDC8lSzLe3bsJ9a5Zk89Nm7JuZ2UB2vx+wjMvuww5ZAy7ZkuFMmUoKGPOa9PWpSkLi6tMWUsJCZz/t7/hody2DWB47BjjWr4cgPvZZ/RZpw78Xa0aeYMnTjBXSUncZ726AaVP36JSq35QxpzlWr0qoCNHnFDUkydp5+RJ7v2mm/CsDRhAPuLOnYx9/HhsOe3bA3L37CHE8t57kVODBlEhODsbuXfLLcz3TTcx1gYNuPc1a5AtEREUrmnenK0qYmOJMhg2TD8XmbnrLsD2k08CkFu2ZNx799L/G28QnvnWWxjNEhOl9estSXjafVfcfcnbuPye2lcojjxEfzn6pQDrfFsmXCimv6BjwZ65C40zeBz5j+X/3RR3CCbz3WzMaq4zHoH5888uwJKcjLXSWPsffBAr2JQpWOtNjk1EBArXue41RCH6/0Q+nxPGFZyfE+wdb9+ecwwZpTLYsx7cjuQAuk6dMM4kJcFfb78NQPL5yI0x4dQPP4znq2NHFIhg3uvShZCv6Gh+93rxNEhYro03PysLC3YgwLGOHRlHvXooOOvWEbK0cSP5Kdu2OftyrV7N9StXArYeeQQL+4ABWM7dbsbQvTvKVPXqnLN5M/1FRTFOvx8wl5KC4nPnndzfmDEAvmnTUO6qVnXyGlu2RImyLP7efx/wevo0Y7rhBuTU1q22MoSjfuPRMs6GfX8i+gWRB/8TdKnRMGb9M9Ep5zrnXAZZk6oQHe0YU3w+3me32yl8ZAoapaXx7n37LSAqJQWANngw72Pntjk6eTxP1+kjhXuPa3Nyzs+hhZs2AVi8Xnjnxx9p46efWG8lQMULL2C4MDLl0UeRCS4X8sKyOG/rVmd7gSuvJAw6I4PvJ07AdzVr6uecv1OnAFpJSZyXnk744rBhgJMXX4SXR40CqEyfzp/XCw+eOEE/pjrn/v3MWfHiAL7DhwkpLVKEkEzLQh6Eh3PO5s3k30ZGAiK7NMvUFf6v9Kwe16DskTq4K+fntLT27QFoHTsCTG+8EXk4fDgGpXr1kDG2Td/9+3N/X3yB4ezIEWRTYqKzJURUFM9z7VrG+NRTgNDg7TB27mTOHn8co5rLxe9eL+/I3XdzP+3bA/Z69OBZLF9OuGjnzlxbpw4yLDUVMPvxx2ZbKZcktw4cuDRwZ1r6QygURx6iPyNdKGSjoPN/ybUXW7ThfAva+dowBVHM53NdH3xNcJ/BlTTNf68XIdikCWELTZo45Yd/+onQhLp1UQhbt+aaA9RY0enTBY8hRCH6/0L5+d4ohMY7bkBeQQqk8axLKGvt2/N59mwMLG3aOACuTRtnT7t77yX00RRHysrCS/f223gSXnyRawx4nDULQGeU0aZNMcTcfjshjnfcwTkjRvD73Xez/1NGBmFnJneoUCGMPCNGYOEfPpywyvbtURJ/+gkwVa4cuSVVqzKWPn3wFowejWK1aRPKXPfuKHm7d+MpNIUjrriCv4MHua9Zs1CUZ87EYr5nD4qV8ZIsWECO0L/+xTyNH49iNXYsSm7DhuQCliji0xldTeE6+F8P0bxYupjIg6BzO/1/9t792miYgtr5tb+bbUgWLICnFi+Gb1u1ggc+/hjjytSptHPTTZxv3tlKleCxY8ekCjWidCSyllIUq8ZlDmndpijVrg04MkXMVq+Gvx57DO9Rw4YAqGbNCNEcMcLJKbvpJgwuBkDt3s05y5cTVvjhh3j55s+H5wsVYvyHDtHn55/DwzVq4JGzbbzokycDhpKSuIfcXAwsERGc16MHXsHixfHSvfgi/OpyoRNs2sQ1U6bQX7NmAJ8rrsBTdttthIrm5aEvLFkCIJs0ibmLipLcJWNUt3ZAV+lHeWs1UI9+UapZkzkKBChmYnJ0TZGl115jvrZuZSweD8Drhx/In3v0UcZpPHPNmqG3FCrkgLQff2QuN25kLjZudGRpxYp8LlMGuRgIcD+JiRStSUxkniZNAqzu2sV9ly2L3rR1K1ENUVHc75o1Tv71FVegY0nOHn+XQr/bPniWZbWR9LRt2z3PfH9UkmzbfuHM92KStko6s+SonKRjukCowf/yXi4h+u+RAT8XW/CkoPLK57IYBp9vPudvw+xtda7+pILLrBsl0edDMAfn6eX/bLwNph/jtUtIkJKWBNSi6mElppWRLEsdO3LegQNYtZ54AoveF19QCa9dO6diWFoaVq7lyxHgjz56/vmTfp998P7bFJJN/1tUEN8H85aRA6ZQgwnjDD7fAML87Znf5s9HmTD710mO56B1a5QJrxcFwu/HAyahiGZnY3S59176N4VQNmzA69a5M9e3asUYFi8G+Pl8hDe++O+Axo08pSoNohVdxFJyMp65iRMJqdq2DS9Cly4AsOxsvBYmrHP8eHi/SRMA3/ffc2358vRRqxb3kJmJkma8JM89h8W7RQtyBmvXxtJ+++0olMePc93gwVQcrV+fOZg0CSV41y5AY69eAE6zb9ahQ9KpU7Y2bsyWFKGhxd7UW8eHX1QYVEg+/XUpmC+lXwcUzZpr+NnQnDl4X5KSeF+N0j5tGuDunXfw1lSpAm/Ex7M21q8X0MoVAf3zCbe++tpSWBgeplq1aG/mTACHx8P7XKkSnjOzp1tUFN6oMmXwqnftioGjTh1Azd69jPnYMXjq5En43+Taf/UVwCYsDPBSsSJsUKYMn+vV4/icOU4YaLly/G7bjOnwYYCe1wtPd++OESkhgb62bMFDn5zM/Js9AjdvBmjNnAlYfOIJKlU2bIjxp0MH7u+HH5Adtw8I6J3XTikvIlrLllmqWNEp3HLsGHwtIXdyczlmCqzExgKWT592gJpl0W54OMVOevbkuXbujIy1LHS3OXOY19KlmQO/n+0iUlIAdbVr462MjKSP9HTmZNgwxmMKtIwbBxCUaOfHH+mjUiXuf/16nk9EBNdv2MC5Awfi7bwQ/VH74IXiyEP0l6VfU82yoN/OFfOf/9r8imJwqFZB/eU/JiFo2rdHKVywwLH+S2fn6QWDuwIrhIUH1P65Hlpa/zZ5H35cHdsHlJSE8B04EIGcloYgvu46hOD777OI3X471vp162jr+PHzz1+IQvRXpIL4Pr/MCA6NNiGaPh98Z/J5TNVac37w58hIR5mcNYuwoQ4dUEg6dSKnzVSwbdfOuc5se7BhA+Bn0SKnEIqE0hMezudVqxjDG29g7R83Ds9gpYoBLe76pLyvvq6ol/+pTh0CP1vC77gD4HTbbU6I2rff0l61avwdOkTIaKtWFEt5/nkUuzvuQJG94grG0KYNil6NGli3d+yg/6wsQi4vu4xwqHfeQbm84QY8g02aAO7S0vBqzp5NCKfHQ/+XXYZX4sorkVknT3LfO3dacrtB1CvrXRy4C9Ffm/IXJDtXZIyJfgkms4bPmMF3UwV2zhze265dqeD64Yfwa61aGD5KlwYE3H03ivv8+fBOSkpApYrlyOWy5A7zaNZsS6VKAeZefRVv2MGDeOVOncLD9OyzGDQqVMA7V7o0/NK4Mcd79YJHMGAAwNLS4LeYGICf8Z41agSAc7vxJLndhCKWKMFvq1cDQB59FNlQ+Eyl/vBwjCuxsYDCGjXIac3LQ/7s24e8adkSPWHLFsa3fTtG4U6duO7667nHtDS+lykDWCtZ0sld+/prvH7r13PtXXe7dNpVRDNnsvH5/v08xwoVkG0dO3L/ffrwjEqU4FrbBlBt3Yq8WrGCqsJ5eQCv5cv5PH06kQozZgDOO3em8EmPHrTl9dLeunUYs++7D7GxbZuz3cLy5UQrdOjAO+H1cg99+mDU+uEH+ne7kc22zX0Yj2Hdusy3KZojOVFQl0K/G8ALxZGH6K9OF2PpO1cZ9eCqeOdaUM4HIi+mDLs5HhxaGawULlhwdsW+4JwgAyKDC6lIZ7yOxw8rcukCJdgLlLzJkuf4YbVvjxC76y4see3bI7C6dCG04rbbEHh33cWCYzwJf1ABzRCF6DenYIPNucKx8/NsMNjr3Bl+fO45clgSEs728hfEqxIKjNuNsrd0qdNeixZYzI0nb+1alM758+G/pk0db9/w4c7n3r1pc8UKrM1uNyXWJ04ECIWHS/dcdVju1GTFaq2+S62nJdMyde+9yBRThMV4D998kzbdbpSW+vXxrl11FQpm8+Yc27EDpXP1aizpFGNCEU5LYz5eegmgFh6Op2LtWoDcV18BbHv25PsPPzhAbt8+PH1ffeVszLxxo7NB85NPEp4ZGYky63aT41KtWgjc/X+l/GtufkNnfsrJIT/KFFAxbcye7eRkGePMsGEcM0ZMCW/1+vWAo2PHnCqUGRl4fE6dkgoXDijyxAG5Ni3Tznlp6t0zoJ074YcdOwBYQ4bgxfnoI/qaPBnwMn264wWybUBLRATesZo1ARZXXkk77dphQAkLIyyweXPG2rAhoOjVVzHGNmwIULFtPJDz5wPO2rcHhEVHw0cSwLV8eXg+Lo7QwyVLuMdq1fht7Vq88fv2Adx69HBCRVevxuBy6BC6QlgYY9m2jTnbsoX+AwGMxmFhyLDZswGT69czrnLlyD+sUgVvW5UqhFqmpzPO9HT6vvNO/j76iLE/+SRzdNNNjjxavpwxlC+PV/XgQYxBBw4wRrMllNsVkO/4SSUl2dq4EdAXG0tOZFwc3s5Spbi3pk2ZF4nP33/PHD/xBHOwfj3vR9++GLr27wfcZmQwxoQEgKL029R/+l3Vr1AceYj+F+hcidkXSuw21wYvKOfy0BVUht1cH+wdMCFfXbuicJnf5893ypQHF34wuQVn9VumjHJad1SkO6BmDfPkqYCkWbuW+1m06Gyr6OefoyxefjkCODISASoh5EIUor86FVSJL3/1vXPl1QYXLpo7FyvtPfecHZrp8wHOWrTg2NSp/D3/PNf4/YAeY7wxY0lM5Hu7diiHXboA+AyZ3BGPBwu7qca5eLGT7/ftt+SqVamC0tOggfToqDJaVr6/WmmlrJKl1aFfjDweZ8uCHj1oOy6OtiTG98wzeNiGD6fd2bNRPHfuRDGLjcXjdvIkx9591wnZXLCAcLavv0YhdbuZo0mTCAs/cIDz6tdnHO+/D8AsXRplq3p1xjVnjrRuXUD+I8fl8diqUgUl+OhRlKoiRQKS/Nq9+89XYCVEl04FedeDjScFUWQk71DPnmevyV4vRpXhw521skoVzhs8mHOPHMGwcPw4AGvOHICPywUYiI4GZBQv5JVb2cpQUfn8OUrf5FXRoqyZCQkYH4oWxUvXrh3enxo1yG3buRNQcN99TsjgP/+JZ27+fEDh5s0AmJEj8SyVLg2P7tjh5NilpNCHxPGNGymQVrEiIYx5efRVsSJg6corASTHjwMWixcHaE6b5uTkJSYSEhoIkNOXlcU1H33EOMLD+X3LFiIRXn4ZIDhgAKDryBHkYaNGXL91K2NIS4NvTchiVBTfhw7F+LNvH8dPnHDAVadOgOKVKzFYNWmCjIqK4t4bNAAsnzgBsFq7lmdcsyaAcONGzomKAoxlnw6o82t91Oyd29Viyydq2SKgrl3xXkZHE+L5ySd49l54AYNTdjZ99OrF+3HXXdz7hx9Kj4wIaOiNx1WsqK0OHZjnn34i3DQ9HZm3cyf3lZFx6bwQsq+HKEQXSb+0iteFti6Qzq5Sea797AyYMuXVpbOVx4KOSw64NGDThHYFh2cW1G9OrqXXe82Ub+tOdV3xkjxhljweFgBT7MGMy+fD8hUXRzjGyJH8ZqyNxpoVohD9lSm4El/+4wXxnjGgGPL7ATA9esAj0dEYXIzRJSsLD9SoUSzsycmEO+3YgRJnQnuMcaZbN6z0X39N+0OH4ln417+o5vfCC5w/ZIgTjjlokCQ7oJHPnZQ315bPh/J6+eXOFgXGEl+unKUd7Qdo/LCNCnTpKZ/f0iuvMMbMTKfowsqVAEq3m8+rVmEh79EDL9tXX9G314vnTQJMpqYC2u6+GzDncqHArl1LzuCDD1I+PC4OwLhtG0ryyJHMTdWqALbvvkPBveoq5E9yslSpUkDrp+1R1vAH9MK2K9WkcUB79jCf69YFdPTomS3llnz9u2x0HqI/lgqqbr1oEetWcEh0QV6+YKNNTo5jVImOhi9feQWP14wZVHr97DPp5pvJRXvmGYBGdDSAaMsW3nNTzOS0P0LVPXuUq6LyK1ylK0Wofn3O+fFH+KlhQ0BUYiJeP5cLAFS1KsDl/fd5x/ftIz9r3z7CKD/4gIqO776LRykzEy9YdDSevu3bAUq7d8Pn2dmAq9q1CcesVo0xNmiAsbZePQBXXh7gcMcOQg5r1XKKzBw7xpgjIpAblSsz79WrM1e7d3NtWBgg1lSb7NMHI9f06dxvzZq0Z7x/5cox9w0aAAxN9MK+ffTfoYMTMpqVRR2AW2/F69a6NbIvMpKxz52L0erbb2l/wgT6fPVV7iM3l7lfs8YpoiLRbs2a0sR3vfpxSYyWqZnWHCytuIqHNXYs4M/tZp7eeAMwetttyCNkEKDt/fdp+9NPpdGjAxrZ7H1p8J1afO/HeuSRgGrVQr7bNuPo2lUKC8MAVajQpRugQgAvRCG6CDpX5cpfcn3+801RE2Nhz7+fXfBCFBzqlV+pzMlxjpsCK6ZMe/7rDagLDs/M36/HIzVp6pLKltX8BdbP13ftimL36qt4F0zlp08+wTqWl4cSO3iwtHw5QiojI2QlD9Ffm/LnqBaUe5cf0BneNh4/UynT5yPPbeZMvnftCliLjibs6f77AUpDhhDeePnlhCweOYKi8NprKB8mJDs6GmWwenUKGtWpg0KZmwvYMkUS/v1v6eERAWU9+ozyXh+pli/21eJFgZ+BnYTCct11ALTwcKlff5dGPFNEN92MDDAlxzdvRiGMi+MalwvlMD2dsXs8ALLKlQFepsS414tSunQpSvEzz1CI4MMP8Vi0agX4mzqVfDyzhcTJk8687thBu2FhGKyiopBfhw+Tnzd5snRwj1en5NYoPaCHll+jrz/OUUoKyq/vtFcSVRmitPdPW0UzRL8dBRtIDeVfz/Pnw7dvD+hISOC6I0fgocWLUd5NVUS3GxCTnAzgGzqU68uWxWBRsiTgrFo1KTLS0trw9qoeX177wuqqRAlL27ah4B87BtCbOZM8tfh4DCS5uRgyduzAsxUTg2coKgr+NpU1PR4ARv/+XBMezl5uPXviVerRA+Bm7jcQcKpter2OB71uXdpOTna2PcnNBXRKgLwiReDXm27Cq24AY/HiTrGR6Gi8e+XKOTaUtm0BQsuWEdKYns48TZ1K5EGdOsztoUN4TGNikC316mEoNvsCrlwJ+Kpfn3lKTQXAdurEnPfsCbgaOhSvnNsNUFy6lGs/+IBn1bcvz7ZRI/6npjKfixYRLrlxo/TeBxFKLtdbq9RCb8RP0PoDZXTbbdxzQgLje+EFBxCnpvK/SRMnP9jvBwAvXehV2t5wZcujGfsbqKh1Sp9/zvvhdnPvCgSUl7ddkhT+3duXbIAKAbwQ/WXol3rQfss2C7IKXuzePAWFdxmrYqdOTlhIQRX5gj18wb8FK5XB4Znz53P8/vudkJJgEJh/Q2aTPG7aNeMzYzIU3H+TJk559hkzsLrHxzs5PiNfC6hG9iJJUsyPo0JW8hD9ZSk/nxckB8x/A+iyshze7tTJ2avKhEwPG4YV22xmbqhPH757vQCfadM4npiI5+rWW7E+T5+O8vLjj/Tbrh38N24cluJRowj13LKFHBC/H0D07L2HlbrFrVTV1DtLm+nYtkyNGOHkE61bRwjlwIEoKsnJ9N+2LcDvvfdQRuLj8bAVK4ZXJD0dK3bbtljZTVn3PXsYW3IyeSgjRrCNw8cf0252NrlLS5c6VTAHDGAeRozASr50KVbxGjVQWrt0AfBOn44yWqMG46hYEWXy8celQsUi1DgiXSV0TFXK2PIUifp5j7KGcREy2/FmqcofttF5iH4/OlcBJOP5zl9szPwenI/u8zle9yNHWE9XrABAjRuH52vlSrxCJjSzQgXef9sGAKxeTVubN8OLW7ZI8fEunfYXkkRV2saNAQcdOhBS2aQJfDh5MiApKwsjkNliYcUK+KtVK8b10kuswceP40kz6RMvvIAh9vvvAaWzZwOKGjVyNkHfupW948LC8Ka3akUuXl4e4y5fHsPNiRO0XbYsoG3NGgDTzp0YcsqXhw9PngSMmmqcTZoAbF0uJ+c2MpKw1ZIlsa2MGQOonT6da6tWpU1TobduXTxwhQuTr7ZhA/pGr148H/MMtm7l8zffOFs4vfEGEQ07djB/pUtjlCpWDHC7YAFy8uqrGeOOHU4xqwoVAJzRRSxtbXeL4ga21mc9vtCnn1l65x3A9Nq1ANG//Q39aOpUJ4fZ7WaO/X7k3P79Uot2EepTa7uu0Ez1LLtRh7KjNWAAAH3AAMb36fven9/ZQ6ezL9kAFQJ4IfrTU3CO2K8Beee65pe2mT9sMn+BkvNdlz8fL9iqWBCQCl6ADJgqaB6CwVowGcUxGAQGh2Wa+1+37uw8IRMyZq7t1InfXnsNQex2019UFIUaunYlLOK991j0Nm2SsrYf1nbFSJKKZqSGrOQh+svSuQohBe83GRw22akTSlKbNg4PGqNKQoJT4Ta4HWP8Cfboe714xTt2JD/l0UdRkkw+yksvwYuTJ8ObbdqgrLz1FmFkEuFMffvCow0aSMt2lJG/fGWFKaC7Wq1VVIWYn70RderQ/0cf4elq0AAF5eWXub/y5R0vm9uNHElIwIvQrx/jXLKEcaSmotRIALRAAKVvzhxCSgcMoM9jxwB1vXpxvs8HqHv+eTyXKSlc17+/qUDoFIlp3565b9IE8PnEE+QkhYdLpUpZ2lGmvVrc3ly1nrhJR45YSk01hWEsSeylW+byK0JVNP8fUkEFkApa44N5OivLCd/MyICPWrWCF5ctIw9u0iS8S/fe6+xdNncuVS4LFcLQsXAha+r27byrjRvz7lavzv9Tp/irVAngtGYNHuxNm+g7OxtZ0KwZbVauTI7WhAmAosaN2Wtt0ybaj4tD7mRnO9sTWRbA4+RJQq9zcuCTzEyATFQUfR06hMElOhqP1ooVyJ1ChQBwhw9zbWYm8uvwYQDYrFnMjwlT9PkIuYyNBcS1aIEnzIDL9u0BTMuX4w3cuZP7u/56x9vZsaOzH+aePYw9KgqP/uTJyJ7cXIxj3brh/TeGofR0ZIhlOUVy6tUjCqJ9e+Sjy0W7113HXA0fjsw4eNDZXuGRR5iL22/nuk8+oe/Nm12yCxdRvfqWKlcGTL7zDu/MzJk8g4ULyY88etTJoz54kL5mzGB8YWGWPqz8uLJWpspz6806fNjShg3I2PHjmd+jpyJUSOhLMRHRl2yAugj1NEQh+uPIKFGdO58ttC8GWOW/Pv8156tieb72gqtQXuz1BZ1TkNI4apTjfTO/d+zoFDIIDs8sqJ38OUL5c/RMwRW/Hw/CsGGOIhp8rcnTmz8fpXX4cOfY0qUodwsWIGD9fix427YhHMe9W0bltV9bJe2KbBqykofoL035vXUzZmCdHzGiYJlkDCpt2sDPcXGArKQkjCFLl3L+nDnOfnYeDzxuSpVLhBeNGwdAMjLB73cUK7MvkylwkJuLkrZvHx60775DsUlJgTdt21L3Zwco7bscrRh4s7xe6+f98376ietuvhkQOXQoyuv48SiH/frxJ3Hu999zf34/wHP9ekK0xo4FWC5ZgkJcrpwDWM02CStWoPhYFvf9ww94G+65Bw/k4MEoaG43Xkxzff/+zPnVV2P579GDsXq9eArmzuUeqleXqld3qX79Iho7llCy0aMDKlnEq2x/mGpqsbYqQb4pH0mBW0Klfv+ilD90OpiCc+nMmme86cHrp1nTzDYhWVm8w3feiUdIwhDx6KO833Pnsm6mpfGulSpFbuv27bRVpAhGhgMHaLd5c8KUFy7EG5WSAthp144iIGvWSKNH8zkzE/A3dCjeuQ0bAC933QW/nTzJ90AAnihShPF6PHiwS5Z0jCQmpHLz5oDC3V4dOxah0qUt7d5N6OOOHXjlvvySc199FU9ecjJjcLsBO6dPc7xlS4CuqQKcnk7/Xi/zcPXVeAgLF+YeTfjijh3Mh8fDPdeogQf/0CFAT926jGHBAsd4vGcPsis8HFAYFcX9Vq3KM2nUiPvMygLo1qpFv4MH4wkdM4ZxffAB7S9ZwueEBJ5lr17MzX338Xz69weYvv5aQBWLnVJkqWg99BBh6atXUyX4mWcAvmFhzNWECQCyV14BrPbuDfCtWhWZGRvLcytWjFxpt5vzmjVzacWusgqPwIBQr54TLdGli7Rpk6XTainJVqX7775kA1RIsoXoT035c8SCQw5/6fX56ZcARXN+sMX+fO3+GoqMdBQ5Qzk5KIamOIo5lj9/IPiY+Z//PFNMJSHBsbwlJRUcymIsmtnZKKJz56JsjRyJ8JT4v3IlVvnUVMIWVq+WsnMsHS7aWJJUbcQ9ISt5iP70dCGeDS5G1LWrk/eSXyYZfjNeu8aNOd8UJ1q61KmE6XZjYX/zTfjr2DEAlgmZNBuP33svCqKEIvHQQ1zbqxchSG3bkn+2bh0AasAAjvt8KBqtWqGI1KghpW91KTKmkOKaWdq8Gct9//4A0VatsFpTEID+WraEz3/6ifbmzkWB3L2bcxo3xtt3zz1Yx3v3Rtm75x5kQkYGytr06QCvd99FZrjdAK/33kNJ694dZahYMfrctg1FsVs3wpvefZf7euUVvBqPPoqCtnMnoVFt2yKPfT68itu3I7/uvlsqFBnQsb2ZykpNV17adm1VQ0lSKe0KRRf8RamgtIf8v+ePsgk2jppoFXPegw+iYK9YwbtbqhTrpKm6GBfHe1urFmGKx4/j8enYkWNFiwJC6tXDU5yXxztu9qarVAlw0KIFnp0pU3if4+PxPB89Ct8fOsR6anLytm8HcKalMe5atQAtRYrAWxkZ8OPOnXiT6tYl1K9CBUkK6PTW7Urfkq1/5I1QZERAWVkYhOrVY0xPPYW3/MgRxlCkiBQVFVD1SjmKjLTVvz/j/vFHrktJAdC1bo1cqFsXA9Dq1dLTTwM+R4/GMBUZCeisUwc94vvviRBo357v//oXwLpIEe73llsAdZUqIQcqV0Ze9u6NXD16lDlu2BCA+e23GLW+/RaQuGED4ewmdLNyZebX7+fvwAHGEh7Ocx4/Hlk6f740d05AZeZ9pu3vTlbWxI81elRA776L7nPHHdK11xK6bjafHzoU8Dl1KnKsWDHavu8+3hm/H7nWrJkDKDdscPYrHTiQc2bNAsD27cvzv/xyCVjm1q5dl643Wbb91yqC0Lx5c3ulMa2E6H+STKjhL/W+BdP5PHsXuu585//adgtqP9gCafLpcnIIj7j3XixsPh8Lldt9Ngg0HkazwBnQZjyBnTrRpsnNMzlE3brx+dVXsWKuXet460yVqS5daCcri+/r1qFITZyIcMvOlh57DEvcffchxC9ElmWtsm27+S+fsT8PhWTTX5POxbPB3vpgPgz+TTp7D8qsLBScpk2dokR9+jj85fejFCYloTSOG0exgM6dnc26TeXJsWNRNhctIrekZ0+Ut+bNCa2qUoUqbbm5gLcHHkBxNOGRLhegsm1bJ1clKgqlY9IklNBhw7DMu91YxseMQUnbuRNA5vXSzs6d8PGqVSh0992HInvkCKFmw4ej5KxYgTV90CDm4403GM/dd6OILlvG2LxelMIPPmBuVq4Ea91/vxNutWUL9xwejnV+zhyUy8GDmcuNG1G8TBvbt6M89+9PaFWzZih1du5pNZr8Dx1Wee1QDU1Ra0mV1FgztS7Q66IMUCH59Ocjk1pg1sb862abNvCZAXXGW2fWvTlzHF4057Zp4xQbmTQJUFKtGmF5hw/jjVm6FD6KjcUYUrs2AGX/fvhw+3YKm6xfjwd7+XIA1Z498MmYMazdJmQyLAwwUrgw66rHo589682b89uyZQDF06cBGcePM4bbboPHWrdmHQ4LA1R5PJLHzlFE+jLtVCX5VEzx7QqpTsNCys6GV7/7jnN378abV6KEtGNHQPbpo8qVR7Vde5RuNVSRIi5lZBCIc8stbIvUrh3AqkoV5MCpU3gGd+6kzbJlAZ8nT8JeEyeyV2V0NLImJYV7zM7G+2hy6r77jvk0PHzkCDqFkQPJyQCpjz4CGFaujGy8/nrmIzycuV69Gt3l5psxFh0/zrWtWjHnfj/Pq3LlM0B532lV//E1nZJHa9VCre5upYati2jmTJ7B4cPItrfeQma+/TbvUblyyPylS5H577zDc69Rg+fudqOrScybZfGXl4f8GjoU4Oz1OpVEd+zg94EDMYBdiM4nm0IevBD95ehceWe/hM5VLCE/FVRK+WLb/aWevHMVdDCK4+zZCIHgPbQkB9xJjkUzGNwZ74LxBJpcQBMmNnUq/41nLivLqXhnqoj17Imi2qcP17Zu7VjYH3kEgXrqlBNCduQIbZ0+/cvmIEQh+m9TQfm0BXnrg3nOKItZWeRQzJnjVJUdOhRw5/OhZAQXXWnVyknuX7MGZaF3b2e7gq++op2VK1F0liyBvxMSUDgbNUKJKFcO62+LFiilHTsSvliuHApCkyZYr1u2hI+bNiVEskMHePWBB7BMv/suPNu0KWCxaVN+u/JKAN7KlXgPqlRBqcnMxEoeHu5scDxxohOuvXcvIWn/+AfzV7Eic/Xee4ylbVsAmqnyN3s21usVKzjWtCl9X301itr+/Shky5Yxx3XqMJerV9P288+jjEdEUIX0ppsI+WzShGOXXy4ldI7SBNcIJamtapX1KSqqkiQppsPFgbsQ/XkpeKuf4HWzfXvWu/wGzlGjnPxZifc2Ls4Bd4bve/TAkxQTg7Lesyf8sH8/BpU9e/CqezwAuv37aW/HDicscNcuZ7ugHTuckv4VKgAIT51Cia9cmXf/6FFn/GYD8o0bATAnTzpA1rKcz59/zueTJxlXTo7jZdp9IELb1FBeRam4MrRjb5QSE/EQli7thHQaT2DlylKfjqfUQBvVUCmKCByT32//zCLNmmGQMtsaeDzIH48HXtuyBeDYoAHn5uTAo0OHMh/Dh9NH69bwdokSzEXp0s68S8iDVauQOdWrA3bS0uD5pUuRU02acJ+FCxM50LYt4wgEAHIeD+Bu1iwMUBs3Ig/NOXv2EAVg8o7vfzRKw1sv02FVVLNyBxVWPFpbtwJUJ08GzN9zD+/DVVcho264gfxJE967di3vTGIiz27wYP4SEpD9bjfPrls3BxR/+aWTf5yWxnMwRW7M9jaXQiGAF6K/JJ0LaF0sqAq2zp+r2MqvLewSbPX/peMpaA88kyNgBEVi4tkblhvAZjwE+Qu4GM9DsAci/9hatUKgHDmCojhsGBa3yMiz+8rJkV5/nWN+P4uciYdftgxhlZ7OYiOF0ltC9Ocn413Lz6v5q+yZcyWHD2bNApS0akUbWVlYeadNQ3mMjYVf4+K49sEHUVJmz4Yfo6Np2yzyM2dyvFo1DCheL8rS558DXiIjUXy+/BIvwUMPYV3etg3lY84cfpew6L/zDtbt5ctRHI4cwfKemoqXbeBABwTWqIFy9OabALuPP+ba48dRqOrVAzwZcBUVxRimTuV/l04B9Wx5XP/6l61OnVD+xoxh3Nu2ASabxAb05r9PKjXVVkoKoZ+dO+PFnDmTufB6UYYaNgjo1REH1bQpUUa1a+M58HrxqMyYAaBNS+MZdOzI/RcvDtA0Jdm/+NJSdsmKCotrpcIDb1R2tnXWswzRX5OC17n866Y5FuxxT0oiKkXCyx4fD9+NHcu7OmsWPB0RAZ/27csat24dhoWDB+HJrCy8WbbNu+ZyAVI8HngwKwvA1aoV4YRly+IFqlGD8aSlYfjs1g3+DN7XMSICgFi2LOGYXi+ApF492q9WjXEULcoYDCDIzATweL3wdlqaFBNjyV28hIpXKKXiTWpq/35LKSnOPW/axHW2jTzbvVv6bka0dquWMlREVliMXC6XGjWi3/Xrub+MDOYjKgojVXQ010dEIFNycjheuzag77XXCO3etg1ZZSrili7NOU2bAo6qVQMAff89nshPP0VubdpEeOO0acihcuXw3l1+OXy+dq1zHw0aOHxvWTz3w4epdNmkCb83bgy4euEFIgYSE6XZcywtfvAHXfFOXxW580YNG25p6FD6iY/HsJ2QAKibN4+IhBYtAHhVqhBm26gRz23JEgrv9OvnhKJPmcJ9mz0A+/bFoHDzzbxffj/Pz2xZI4U2Og9RiCSdnYt2MaAqf86M9NsVYfkl1wZXswz2FhTUltmrxeM5uzBD8L2azcyDc4OMN894EYK3S/B4UIr8fixzzZtjjb/sMgS5WRy9XhQnE+J5xx0I9Qcf5NrXX0cIDxyIgPJ6EbYSQj1EIforUbA1OZiCPQWRkRhBwsPxir37Lu+9x4MCER6OF69zZ44/9BDX/e1v8MmUKfCTCbuePBmw43ajMKxYgeK2Zg0KSKtWKG7DhpET9N57KBClSjmKSv36jNPlgh/NRsWPP47HoE4dFJPmzQFul1/OeEeMQPlwu502AgGs6337ck2xYo4F2ufDY1G3LkCrVi3JmxvQiCbTtWr4u1r/9WZNnx6Qz4d3PyYGRW7g7QG90+4D/fjOZuXNnas8b0CW5eT29OiBwvzmm9Lk7wOq88W/tLT5YH03ZLpWrwrolluwuq9dy3irVwc42zZzvGAB48rLY25NRcIGDaRatVyq2zhSr4+0JR2XJBVZ8mFoC5c/Ef0SY6ih/AaY81GLFvCNz8e7+847KNcGPKWkwLs1anDejBkAjfh4ANUttwCuYmJQ0vPyeHfDwnjXWrWCl71e0hh27nSqQebmwpdFi3K+xwN4qV2btXzlSsaVm+vk9B07Bg9GRAC+Dh7EGxUdzTs9ZAhgp2VLx9BbuzZrcunS/D9+3KXMk+HKy8Oo0bIlXrQDBxjP3r20ER2NvIgpbslVrrz2uRoppm1jlShhaf9+wFfZsvBXSgo8mJHB2MLCuF/bRm516QIg27GD3woVot/rruMev/yS7/37M2979yJXKleG//1+7v/aa/l/6hSh2enpAGPb5vkVK4YHPyYGmZGWBt8fPQpw2rED4NylC8/6pZcAtqtWAQjLlSOVZMMG3oH1G1zq+rfi8nrZCmHcOHSdNWuQI6ZIVo8eAMjmzQG0V1xBSsvEidKypQGtnXdcc+fYWrwYg3yzZhivjJcuJ4exXXYZ93fnnU4xlpgY530tUeLi+OF8FAJ4IfpL08UWPgmmgix/57rmUnL8LgTuTLhIQeMOXsR8Psddb8Ijx451wjanTj17awNzjTlmwlXatMHLYKpjzpjhVK8aNgzhMmwYgsa04fOxYHm9WOHM3lWtWzvx9HFxLA6m2pXbjeCXEO4hCtGfmYK94AXJEcMH8+efHcppDCDR0YQfdenCsT59nBDNMWNo64oruK5tWyzTXbsS8vjjj1zz6quEEXXsSIW2bdscsLhmDQrd3r0oF1WqoBCOGkX+2a23ouAMGULoULNmWKv795cefhhv28cfw8ODBpG38/33ePKmTUMJkgCZKSlOvlCvXgDX5GQUsuhoKugVK8b/tDTaXLNGmvpdjh468pAKK1cN/avUsXGm7roLReqBB1BaV8zOVMM909RDsxSesV+HduXo2WdRNBMTsfbHxgJWRz9+WJvTbWXLo6rHVippoVeTJgFK4+MBnUYm5uQQdpaVxRjT07Gir16NQrp3L+GbJ05I5UrmSSK8oITOaM0h+sPpYoyz+aNtLnRu8HkzZuA5N+kNUVF4jsy65fHw3nXrRsjl559zvHhx1rxRo+CFSpXgjcxM6fDhgCLCfLJtW7bN+nr6NCBt5ky85RkZXF+/Pmvj6tW8r34/HrhChQid3r0bb1GDBij/aWkUXNm+HYPQkSOM+8AB+G/HDnh31y7W9n374OmdO7n/o0e577AwQMTBg4xr3Tr6t2343JxbrBhG3KNHpZMnXapQ0a0HHrDkdnOvW7YwtvLlndDM6693cu1WrwbgLl6MPlKvnhPeWqkSYHjAAEBrbi7Ad+RI5JnEfCQlMc/lyqG7LFxI34MHw9eNGzO+zZuRm0uXAsR37CBstEsXwGPz5px/661Ozm+fPsigG290NmofPBjZ98EH6DNuNzJk1So8dU2bMucdOvBeeL0c27UL+RcVhbx84w3Oa9okoLjv/6kNg0bK++g/ldA2oCVL6O/ll538wN27Ge+UKehx2dnMj8fDvBj6LYzjIYAXor805VfGLhaQFXT+bxWyczGLlVlQgverCw7/mjcPYWMKypgcO58PK9m993Jtp04Ijk6d/nPRM0VRpP+cI+Pt69rVOccAvqwsFosZM1hEvvvOAW7du6M8mlLv4eEsbCkpjGvtWpSqyMiAJL+qVPlrFXEK0f8mnSusuqC8WEPBHvi5c1FYjFfOVJ5t0MC59pVXAHyDBmGdrVsX67Hx4m3ahIHl2WcBMI88gtK0bBng7/LLUSp276a9226j6uUNN6B8jBnjhFCuXYtS6PEAzsaORWnNzITHU1NRzkz126goFLHGjflcpw787vWibL7xBvf21FN4zt5/H6VPQgnNtqP0QPT7aqgUvdn6M+3JitErrzB3W7dynyvTY7SqfD8VUa5ebPGtrropSlu3AkorVQKYNm3K+Hwlyii9RBv9pMs1uOUqtUqIUO3azMeqVcxNXh5yaOlS5M8PP6B8d+mCAlayJManbdswSjVtKrldklRYknRA0aEcvD8JXcg4G+xVN0VTLrTGtm/vfDZ5rCtW8B737Ek7W7bwTpnNtmfMYL0dPZrct82bMXysX493bOtWPEpSQLm5ubLyjiojI1sREQFt2EBOWG4u3j6PhxDNnByuz8kBZNxwA4aenTvxnkdHA7Bq12ZNDwQABUY3+OEH+jx6lN/27SNsetcufi9dGnniduPRl/hutkrIzYWP69fnsykcu2sXgKN5c9o8dYrxS8iYG28EfBw+DEicMweey81lvPPncw8tWiBrChXis23zl5bGeI8fRyZFRnKvBw4ABGvXBgympsKnpUuTFnL6NMCwdWu8eCdOcC+FC2PcqlNHP+/fee+9gCW3mzYHDEDmjh3LM9y1C+PWwNsDigyc1tdf2RowgOfw9NPkB379NfPWsCHnjh1Lhc9jxzCQ3XUX79m77/LuBALkQ2dnY5yKjT1jvKt1WJdvHq2mWq2UTS7l7D6s7GwinczWFEYnCw/n/kuUIMxz4ULGGuzBM6H2l0IhgBeivzxdSrEVQ/kTtc9FFwMCL3axKqiogwF0LVqgVM2Zc3axFMlpe/Zs/ickcPy117BsGaDndjuewuBCD/k9luvWkX9gPHWvvkpIQ3w8ff3tbyiXJvyzSxennebNUZ5q12ZRqFJF2rHDr0KZSZKk8M9eD4VBhegvQYZvpYILrAR7+XJyAHRmwTZVKOfOhWezs509Ih98kO8NGqBQRUZiQTZFRY4cQTGxbTx9zz4LyPnuOzbjbdoU5cxUiTNAz+PBuOJyOXt2XXYZx0+dwgP35JMoqDExjPfNN1Fk3G7khUSIUGYm4Mjt5m/gQBReydl3q2NHJ3+mdm36veUWxtaokaWPUloo+v13pW9/1JSfLD3/PHJs504UsqrVLM0qeaPKPjtcz9T9Rn6/pa5dsZDXrYuxqX9/Qk+XLrNU+fZuqjr4Si0Z9p2WJFl65RVCr7ZsYa6uv575lri+Rg3kUmoqnoH69XlOVargxRw/XoosGi4p7MwTLw3KDdGfgs5X8Cx4TTV/+feDNdfm5LA2zpnD/6wseDcyknfi/vt51xMTCT1ctAgjyjffwKf16vG5VCmu69IFgHHiRHBPtlzaI68iVUspclt+VavGtT17UrgjJ8fxvGVm8h6mpaHMp6YiH555BgPEkSMAm02bAFHdusH3gQDfgwtulCnD/W/din3i8GFAk+TkcB04AJ/k5dFvTg6h2vXq4dWLjAR4paXhOStcGPDl9eLViomBx2rUgM+PHgXYnTgBON21i98zMriXypXxvG3Zggc9J4fvts29FyvmALVu3ZCFR486ef9btnA/ffpgnOrWDXl15AgGpIgIDECHDiEf1q0D2Hk8jCEyEqOO202oe6lSnNu/v3TH7QGtu+op1Zj6uu7Y96zGjwto5Ei8ZaNGObnPxsNWqhTHtmxBPIwezTvSsCHv0913A+rS0xlT5854464fXkZHWnRVd2uhGta19cqHZfTCC46hrV8/7qV/f57TI4/Qf6NGvAMlS9KeofT0X85D+Sm0TUKI/uepoDLowb8FLzwXuwWCKcdcUDsFfQ8+FlzQJBiMBf8+ezaLUffuAMFhwzj+xhsolMEVxEwFzeCKYsGlpM1nkzeUk4MlMyoKy2PDhuTiZGRg3fL5EEReLwtS/fqOtT/7dECfD5mnRTlldFoNdLv+pYkH7rmgIhUqQx6iP4IK8syZ4/m96qZynTHALFzohGVKKJKJiRhGxoxBkRo0CMvymjVnbz8gOaHR770HWFmzxvGeb9mCMvT22+SZ7N8v3X47itiGDViVzT5cCxdicY6ORlHNykJJSkmBN+vXR3nKzERuDBsGMLrhBmTCyJF47rxe+Py11xwr/ODB5Nju2QP4NBubb98OaFq1Cqt3TAxzM3o04aqmdPiaNShyycmcX6YMCmn58szb8OHSjTcG1KTGKUWXj9bw4Zbmz6fdrVsBkg0bci+1azNnEh6BqCg8LN27Ozkudeui+M2dy7P56CPO9/tRypYtkzZsCEiyFR/v0sqVF+fBC8mn/w79kjU2/7nBaQmm+nNGBnzy2muc+/33GBQ++QSe++ILJ3f84EG8Urm5eI8WLuRdP3oUkJOZ6fTtctkKBHDhR+qUipYpqe7drZ8ra+7ZA/+4XBg3DhyAvyIinHy8UqWckM6oKORF8eKOYm82CK9RAzCWmcl7fOoUnh9TqdqEnGZlAeiKF+d8lws+K1MG75HJ3ytcGI+dhBxbsQKj0YED8GZ0NPwdE4MXq0gR5J9lAUK6dwdg1a6NEcjlAkyVL4+sCQ8HVM6ezT337Ins++wz2oiKwuj0wAPMh2XRT7lyyIuYGM4ZPJiqwldd5RSQi4gAyCUmAuB79EA2DB2KAWfRIsYyYgT9/v3vUtWSJ7Xz7e8l+fWqHtO4BzerTPUi2r7daT86GuD/5ptsATN6NG2OHMm4v/wSWV63Lp7N8HBnT7vERO5zyRLpwIGA+nfIVHZkjLZssdgmY1lAfdtnSsVj5HZb8vuR4bGxvH8HDjiRGZmZTkXW664j6uJCFNomIUR/ebrU8MkLhUvmL4MuFVx++WK2QAjOrzN95/cOnquYSvB/U1Ql//iN127TJr43auSULY6PdypmGi9fcK5h8MawbdowztmzUQjbteOcpUsReD16IPTCw1koH3iAHIa//52wr02bUDq7dXP27Zk41it/ToZOq4wk6ZAqs8KEKER/MjIKYUGbJQcrjD/+SH7bjz+iVBjvuslLnTED/njrLYwub7+NQnPrreTTJSQQgtSyJWCpYUPajouDz+65B0u6qSRZpw7eu2HDOP7cc05lTlPAaMYMlLnZswFOS5ZQFMAoHbGxjMmbG9DmlSd16pStefMAhB9/jIJn9rFs3BjANHEi17hcgNDGjZFbUfZyCAABAABJREFUq1Zhof/6a6zjEt7BBx4grGrGDBS9l15CYR05kvZMOfDChQFmnTsT6lWxIkrqkiWSAgHdsPt57ZrwozaPma1XXw1oxQr6qFQJD+G2bU4eTloaSuCsWQC5GjUAgraNYrxxI89ozx6OJyQQgXDqFErr8eMB/ZyD9xsUMQjRb0sXk0cfvB7n38i8Wze8uElJvMtr1mAgMSBo9Gi24IiPxwt1yy28V/XrE7VSujS88fXXvEc7dvBOR0XBDxLvcPHilqQISeHKUUkdP27pq69Q0nfvhv8jIuDRbdsAb2XLAr48HsezlZEhxcQE5LbzdOqUrcxM7q9CBdbf3FxnE3JTMTM62uFdY8DNy6MPCc+U2w1Qys1lPLm5jCk3l7ZKlwZgpqczTrN/5sGDeOQMyOrXDy+j2bYkIQFwsnMn67/bjSGlUCHaMyGe+/Yhq0zBqcWLuffhw+l78WLmtls3x+jVrx99PPggx7/8Ek/fl1/S3pEjjPGaawjtLF0amZWU5FRBvvlmxpGayjN3u6U6zaLVr26a/qYftKL+barRKFo//ACQ69sXWZGXxzymp/PcbZt5HjyY5/3dd4xr/HiuiY8H2L3+OvfZuzfh66+84pK3cHFNmGCpZk0pcXFAz2y+SuGDbtXa+yeqc8eAvF6uT0jgmTVowLzVrMk7Eh3NHIa2SQjR/wT92u0KDF1M+GVBFvzgveSCzzMhIAW1Zyrs3Xvv2QvPL6nGmb/d/Iqoz+dUsYyOJqzBLGodOzoFIXw+J4QsGJQG5zM0bowQDAScLQ+Cq3GaNozQ83hQ5kaMQOl76CEUO7PNgt8ToaIxRdVEaGlNHx0QynMJ0Z+SjELYrRvfCwJ5CQlYrPv1Qxa43WcS6pvy2e93yo7Xq0dbcXGApPffR3EZNw6lSIJHJ0/GiBIXB18lJmINv/56+Dg+nvMTEvA09O+PomHb9HnHHQDMDh24rn59FJBbb0Xxe/tt+LpF84CsLz9R3pg31GraP9WrZ0APPYQSUb8+RVYGD0YJdrkIt373XUK0Ro1irD/9hBFnxAgUwX79uKdJk5BHe/eSyzJvHvLD5cLK7fczn7aNXHjtNe6zRg36++ILPIILfsjUx+mtlSO3eh5/X1bWKe3ezX3NmMH4ypRBkVuzhpycyEjkXHg4SvrmzbR7//0o4089Rb+BAF6BH36g0ujevQFVO7hIporm4VlTQ+Hjf0I6lwE1f5EVUywsv/EzMhLeeuQR3vOJEwEwxqs3dSpKfa1azgbmTzwBf8fGslw1a8Y7FRHBu3zgANEsEu8YXniXoqLcKlXKUl6eE0IdGYk3KyzM8TgbD9vhw4y7dGnCQ+vXD6j8gTWqe3ymcrMydPBgQB4P565Zw3u8Zw/fIyKcqrHGeBwe7oRcGiBq1uwTJ3i9s7OZl8KFAWKBAGCpSBGutSyMHVlZzEl0NIBq3z7kyty5GLeKFEEeuFyAzdxc/h8+zDyXLEm/ycmc07s37a1aRZvlyxN6XqgQeWfN4gJauThLhcNzVbWqrW++YRw7d3JOyZJcV7Ys11aogPypVQvjT0QEz6tkSUemSk6ergQ4/egjSxuvfloJa9/Rhuue19ZtlsaNo+3kZNpNTwfg9+rFdXl5GJHWr8eLFh6OgSgykrm75RYMdZblyLoFC4jcWL2aOW7dWnLl5Wjlcpcaa6V27XXpwIbDeuUV5POqVYTQz5rFPdaoATgPBu+XSiGAF6I/Pf1SgBRM5wJqF9tnQSGbJlwgPxkgmZODhcrE/5v2Lna8wWDUhIoFK6LBpdpN28Zrt2ABSqMROsFjW7SIRc7k7iQlYSXs3duJ/Z4/37lm6lQUrKwsQsZefJF+Nm5EKLrd9LF4MZ6HTp2k9u0t9Xm5i04rRpK06/V3Q0pUiP40ZBQjQ4Yvgw0oOTnOJuYjR7IAb9gAcDLbk/To4eSrud0YdKKi4M3u3VEu778fhWXYMLxdffvCo3XqkPf24YdO3uuyZVxXrBjj+eYbFKuaNVFoBg0CJKWm8nt0NL/Xq0eIUkoKoOy+++DVQYOkZYu8StpfSUvVWvGpnyjSf0oTJqAszZjBPe3bh2IRH4/CsnQpylmfPtxn+fIAKJ8PpaplS6qAlirF/bZtS9ElEw53002cv3w5c5eezngeeoh737IFD8ITT5zZ56l4jK6pulIbFKfU8r3VNCFazz7L/MbG0sbixYwnLQ0QfOwYcm7jRkLKV67ES9GnD2FVZuPnlSuRWZUq4YGNdHu1J6+4TJEVW0edihMh+tPQ+YodSU7xFLMOG34NPnflSgyPyckYRCZPho+nTMEL07OnU7L+3XfxJicmEo6XmsoaummTs5+r2+1UhDb5WdWrwytmLY6JOXsbguho+Coigu+ZmU44ZkTEmdy5fX4dsT1KUX3lKUyBgC2fzwFOpuhGeDgAbdUq3m1TpdoYMoLDNYPnITsb73Xduk4IZ14e123fzvjdbqe/W29FlhUtyvgfeYR5N4VgihdH1vTu7az/y5YxnqVLnW0XypfH4LN5M3xapgyee7PvZ9vWATVa97Gi5nyvhK1jFPbVxypWLCC3G6PNwIG0effdGHtq1sTQtX0797ZmDfM5fz7tXX45ssF4/W6/HX1nwgTA0513ubR2f1ndOgBj84QJXNu6Nf3s28e9bNzIu7NsGQB54ULeo7vvxqhn3pfmzTnvrruYj7lzedYSoC8+nrE8/UKUVle4XI/rRXWps1Nfzi+jHj1435YvRzZNncp7GhbGfUoUqPN4Lj197jfAiCEK0e9Pv9aacT5wWFAe3MW0Z4BW/pw6AyRNQYacHEKrgqtlFkTB48gfBlpQPkJwbl3wRq4eD0K3UyeOGbBmcvDatEFZ8nqdypumXbcbpSq4nw4dsLSHhwP03nkHRXXYMJRhswFsmTLOHl62LX3/ebb2ivinrNwTxH2UL//LJvo3IsuyekkaLckt6T3btl/M9/ttkl6RtPfMobds237vzG8DJP3zzPF/2bb9wX9l0CH6XciETht+NPwTzNNmA3PJKaPeuzfgS3L4p0cPlMvOnQF+vXtjOf77352Ks243AGjePBSSQYPwfDVpQn8LF2JYWbgQL1PVqvBa27ZOrludOigb3tyAVszMVE52jCZMsHTtteTu5eU5XrM6dVBOS5RA2YmKjFDKnr2aeaCexld6Wo3iorVxE5b4Pn1QKho1gsdvu41QshtuQMG66y6sy9u2wdPJyVizAwH4vW9fCrp4vSgmAwaQy/PRR7S5ZQseCp+P/3PmoJy1aEHbq1aZsvGWAn97RFUWeFXnrtpas9bSs88BNqtWRUmKiUHhu+MOgNz+/ShxDz1EmJfPRzTDuHEA3n/8gxyg3bsZ57FjANIypSN0cmMh6YzeVExhofDxPyEVtGYHF1VZtIjP3boBWkaN4h194AEnvC07m89+P+9P797O9aZg0NatvGMHDmCYmDiR9zY7++x8O3PN6dPO9xMn8hddIe9tzx74oXJlPFF79zogqnJl3teoKPhvwgQpJ8ej4yovKULhypH3jPHBeN4MwPR6ec+9XgCEOS4BUE6fhq9NaKHL5Yyzbl3y0iIimK+SJQFNS5cyZgOkS5QAqCxfzv2a4i7Fi9Pnt98im6KjWf9r1XIqfx44AE/GxqJr7NjhhIRKtHHqFDqDyyXt2+NXv0NHVEk5+kZXq/zhA/Jt96pSzUh5vbSxZw9gKi0No1BsLGOtU4e+R45kLnv2xMBs20QTjBvHXJuqpxUrcqxRIwxLPXpwX+HhTi2BFSvQcSZM4Pm9/z59r1jBOUuXIo8lvKiNG7O5+apVrBNeL33cfTeVNwMB5PyHH1myb7lNlU6eUoke0fpbV0tHjjCOChWY78aN6bNqVen77wOSTkqKVsak96SxA52H+Sso5MEL0f97Ohe4Cw6zLCgssqCwTgOk8reZ3+Pn8TiJu8EgLH9f5/LYSSib+XP+DJA0C1tGxtkbtptqYQZkduzoFFiJjMSDFwzujMcxOdnp03gzIiMpYR4e7oRbzZ2LpdPtRvANGEDIpqnkmZMjpe0I02kVkSSVUeYfFqJpWZZb0hhJvSU1kHSDZVkNCjj1C9u2m575M+CuhKSnJLWS1FLSU5ZlFf8vDT1Ev4DOFXqdn89M6LQBd6+99p8evaQkwMLDDzuKgDGQGP5ZuRK++/572h0yhGuXLaMCbXg4CoHxYi1fDvB6+GEUD68XUHfsGIrlN99wzO1msR8zhs+1aqEoXXdtQJ89tFzZAwfJ9fEkeXPZX+mBB1A6jLd+xgzOv/12igd8+JGlpv++UT8srqyI2wdoQ7KlQABF6fhxFJWMDPqLikIhmjcPC/zEicxDVhYYyBREadmS0MrkZJS6997DSzdpEgBs+3bGVLMmx7ZsAaT16OEUqnjvPUDx6tUoYC63S+99FKlFiy1dfz33/uCDAGa/H89Jt27cm8S8GRB+551Yy80efnl5KLN16+KNKVuWuT18WMrItHTdI9WFAiVVDstAqIXoT03BOXf5wV9iImvs4MHwqPHmTZ7Mu+v3490aPx5PzPPPY6gYOZJ3ceFCvFWffso7ExbmADKT05afChXCAGHIePdOnYL3ixXje2Qk/z0eKTw8oCMHfZLYM2/MGMZZsqQlqZSkaHlVUuXLWz9XwgwLw1Nkcsl8PgwXJuxZAkj4fPRpWQC6ihW5BwNA09IAm5ZFW+HhAM8yZQBg0dEcP3GCMVWqBFi76y5AS16es7/ezp3MmykWs3kz4PLQIcaQmMi82zYANTsbADZlCu1nZQFs7hvmUUqhNhqqt3WlvlN0hLTzQIRycpyCNMYm7PFgGFq7lmfscmFw69bNqRpasSJeuHvvRXY0bIhe0qULhp5t25wichs3IiuaNWP8fj8GpHLl8Og9+CAGochIPJpmq8xBg3gOHTrwvnTujEwyxrxAgOtef51Q0rQ0DE6xjV2KLFVEHTqiB/3znxgcWrRgLh97DMPanDlSxtGAos/Ymiv4Ui85wiAE8EL0p6RLLaryS9o+F5graPPxgkJHzkfB4G72bBabYGBZkMcuJwfFa/hwZ8EqqGz7PfegKM2ciXfOVN0MHtvixU5Ii9nnrlMnx3Nh2ho2jHPeegsltnlzfo+KQkg/+igKXJ8+gEaTf5ScTBWqXbtQfNetk26/K1ySV5K0T3X+yFLkLSWl27a9zbZtr6TPJfW/yGt7Sppl2/Yx27aPS5olqdfvNM4Q/Uo6nyEmeKPj2bPhjwULHD5q2vQ/z+3c2QkjknjHR41iMTbHtm6lnW7dAHSjR6NQGhDYti0eu9tu4/zYWEBRhQp4ut99FwVr5kysvFWrOtUfn3jCUUwnT2bMuzad0scn+qufflLzvT+ofvlMWRb9TJ+OAmZZ9DFiBDpBiRJYrKdNJzRpyBBLe/eikLRsiQK2bx+W9U8+oajE+vUoOR9+iLX+hRcIeWzUCAVq0iRCi3JyAE+PP45M6NWLezdGI2NwPnaM0Kl33kHBCi4+dfvtzKnbDSjctAk59cMPKFuDB3P9rl0Az6ZNkS9xcShhpUrR18MPkw9Towb3HRbGuatWsYGx8Yp26kSI2+uv29IZ45M377hTsi5EfxrKz7vB/B1cuMzng898PoBIixYYPz0e3rNly/DATJyIIbJuXcI0IyNpc+5cju3axTu7a5dTqdJ4sIxH0IRnFi8OuElJ4XtUFGulydVzueCvrVudipdeb0Cx/hWqkp0kKVvHjwcUHu5sPyC55PG4JVk6eZL1VnI8S2Zj9NOnASuWBf8a8Oj1Oh7HQADZkpEBQCpUiH6KFeOayEhe+d27nYJKxYvDIydOBLQ11asdO2wdPIgukJvreLmqVOF+zGbcxmtYuDDGmHr1OL9JEwDiiBHIk40bnY3GIyI4tnyFpRI9Wmnei+u0q+d9ymrSQS1aWKpalXvZuBGerlcP/jeFoAIBQmMff5z7KF6cqAGXC+/Z558D4leudDxkEtEGrVsjc3fupM3x4xnLq686srZdO+fdGDqUeevRg/t/5x3ajYzEqLdzJ2Ps1g3Ads899DVuHGB02zZk6qZNzMfSpch2ywJ4SqwHZl/T48el/le5lafSPP+iVS45wiAE8EL0p6NLLapyMW1LZ4daBoO58ymN5zov/+eCKDubEK784R/5q4LNn48weO01hMmiRQC4YE+e2Udv2DCUpFmzsF6ZjZazsmgnOxvBNXOms3CZ8E7T1owZThXO++/n+A030EbHjlKxogE9d+9BvT/JVlaWs2fe6tXk8cTHI2A9Hkr7fvOtJamaJKnErX9okZWKknYHfd9z5lh+utqyrPWWZX1tWVblX3KtZVmDLMtaaVnWysOhfJ7/Op0rBDv/cePlWrfOAXgdO8JbEny3aJFjUJk9G57p0AEeSkx09lvq14+Fe/16gM1116GQpKUBUHw+QNLGjSgkmzfThwmDbt4cgFSiBJbvPXtQZObOBbQdOIAFeuNGFKqkDdHytWqlxVZHZdVqpqlJMTp5EhlRrBi8aLZYcLnoLy0NJcPvB+zExODBsG0UkO7d+TxtGrkt3buj4Pr9KE2dOjG2rVvh7wYNUBjnzHHKyD/6KIDK7EsXGckefoMHozCWKsUcPf008iU+Hs/igQN4/Xr2pAiCKRTz1lsAyaNHsZbv34/c+/hjxlW6NO3t2MGc3Xcfz2bVKpS80qVRrvftQ8lctAiQl5sLgE1Olvw+vySsbrtVAhQZoj8VBfNu8GcTPj11qrMdQlwc712TJo6nx4C8jh15T2+9FSU7Koo19dVXeZ9atUJp9/vx0ni9Z4/D5XJy6E145vHjUuHCAbVoclqS/XP+bKFCeAJPn0Zpz8lxnMOFInxa7m0mn8JVTIck2QoPPzs13VR0Nf0ZCj7HyLKyZZ0KncaY4nbTd+HCjhw4dgwwVKGC064BhRJ8XqgQfJ2RQXXZowcOy3XikPbuDahwYSIO1q5l7g4cgD/Dwpxn06sXc7JtG3z26KOA5mXLkBUVKyILvv8eb+mwYUQthIVJp0+7NPqjkipVubBOnGD7gEWL8LK53dzf++87z8Vshr51K7Kse3fG37Il7aWmIqfnz0cue70YvSpVQia+8w5A7OWXkZt5echYA35nzOC3L74AWM+ZQ3TFhAkUmWnQgDFIvHMNG/I9MRG5ZopcNWjAfL3yipOjHBXFuzhhAkWgjGx+8UVA5qZN3FubNpYq1yrJ8x4w7JJ1pxDAC9Gfji5UVOVSgV9w28GeseD+jcIXbDkM3vog+LzghSi4zfzUty+Ly9atDhAM3ojchJGZgirGk9C+vVP22bTdpg0KkceDotS3L98jIxFGo0ejSG7YQJ9r1qCsms3PX30VgWbGaYqvmL/WrTmetCSghOd7aV3zO+QdN0GzZwX01VcIp8hILPHR0Qi6QADFrVSpgCTiGtI//PrPXmTlR0nVbNtuLLx0vyjPzrbtcbZtN7dtu3np0qV/lwGG6Px0MXLCVL988EEnRDMpyfHMRUY62x/MnMn7v24dPDdjBp6g++/nu6lUZ/jks88IZ6pSBSWiWzfAmtfLuXfdRVtLlgA46tXDq3foEAt8nz703707QOiRR/Cq+f0AmZgYSxN6fa96Se9ravMnlJtraccOeLplS5TTqCiu+eILxjhxImPu3RtLs9mjr3Jl+pgzB6Daty+RAh98QG7OqlUoSwcPIj/27kVBevNN2qhZE+W6Y0cUmrffBmSVKYNy1L07lS5TUvi/bh2yyrI4b9MmxEGDBsipa69F+ZowgXtwu+kvKQngOWEC2xyYjeTfeovn+fjjKLDvvce5cXHk+r37LkpbVhYhYbm5KIyVKuFtiCnhkZQnSaquI2ijIfrTUf71WHL4t08fpyJm165OVdWRI3k/YmNZU+fORQn/+98xAHz6KV6byy/n/YiMxOtTqRK8euiQ06fLxXtavLgjO6CA9uw5rXXrTkvKlMsV+DkH7vRpJwfO5P9J0qmcMEkuHVA5+VVIUVGu/wBywX1LgMVgiox0PHcHDjgFU0xwjDFgnTrFtYGAU3bfeOiaNYMn4uLgBWMI3rdPclu2pFwV0xFlqKiaN8tRTAzGl+PH6cOEhg4ahOyoWRMdaccOvPAffYT82LmTPo8cwQhcpgyGn0qVkI8NGvBMNm1CRqWm0k94ODIoLo45d7loY9s2+Hz5csa6ZAmyY8wYDGZt2vDMTcXSG29ELk6dSl9z55KTnJ5OqOi4ccxFkyYArdat2QbChFpeeSXvRbFigLEHH2Tcl13GPM+cSTtTpvDetGrFGmEqc/r99LtunaNzud28x1WqMBfVqnH+/Pl4AZcvByy//76zD2KoimaI/t/S+ZS2X+vdC/bemT4KApIGaAX/ZvJ3kpLOBn3BINCcm7/N4By5mBgEhseDouTx4No3VTeDc+1MWFlkJEJs7FjHQhmcT2T6ii4UUM7Og0pJtnX33QibYcO4dvhwvhtBExfHomhCPBYvdjY4X7AAZWrhQqlNzcOKXLpQXTVbj+0broQ6h1W1KsLu1luxwGdns+Du2IFXYUuK83DCtf+PrFS3V1LloO+V5BRTkSTZtn3Utm2Tsv6epPiLvTZE/326EN8XZJDxeJzwQQPuFi2CB8aORcnJyQEADRniKHRNmqBM1KwJcOraFcu08RLdfTcW69hYFBmPh3BBY413u+GRpUtRgJo3x6s1bRryYM8ePo8cSWjRhg0AkqefZjwlSqBUhYVJ113v0rjJZVWjhqXXXsOos3o1v5tQpvh47uXKKwk5qlABpSk3F2XknXdo98QJFCxTaMCyGPeBA8xJr1540Zo1Q2bcfTcybdAgAOORI4y9a1dky65dzjYSX32FonvDDcxdTAyypVgxvJqxsdyP14vnr3t3gJzLxfFvvgFkN2/uzPnChSjmN96I5b5KFazeZn+wokW5h+3bHVA9e7ZUvnxAB/bmad48W/XrI2frVsmQhOa9XaUYRIj+FHSufHjJMaxGRvL7G29gyDDrb2QknuN77wVk1K7t5Dft28c7U7MmwGb5ct6dr76S/v3vgLZs8evnyjtnyGwIfuqUY8wsXlyKKeyTFCaPMhUhn9xWQLm5ZxcrOXECHggEDACzJFk6qYryxJRWdjZeGctyqmFKZ+sM+QGg2evObK9gaNcu2gnOFzS5d7m56CuHD+NJO3SI8MoVK7i/qCgAVWys5Lddau3ZoIrKUHEd1ZGjUSpcGDBTsSJevsKFGcNrryFPatQgdLJ6ddb/+vUxOu3fj1xp3pznsGgRsm3tWuZj6FCeU/nyGJhat0aubN9OKO22bczd3/9OmGf//pxbvDjzWqIEwMgUfMnKwqB0440A/Fat8O5ffz3zUKIEnn+fD3BfowZyu2tXwsHbtGEu/H5kvt9PoaZq1fA2xsXxbGbNYtsIE0Lbuzcg0udjvGvWMC9jxyKbs7KQ8ykpjGn6dOazSxfykps2xdB2yy08l759pS8+C6icNkmSMt59+5KN4yGAF6K/FF3slgkFLRIFhWIWBO7ye/VMW9HRZ1ewNFUzC7pWcsIgTYil5IRWmgUrKwuBm5CAEmqA3Zw5KCvz53Ou2RA5Jsbp04DNnBxp3tyAcjr20OLat6nhZ49pSWJAr76K0tqxo37ePLhbNyd8JSmJ/jdudDx4bjdx6Js2oczOWldGM+oO00z1UE7r9nrghTJKTEQZfO45LOmbNwMKe/dGycz2uZ05V4k/slLdCkm1LcuqbllWuKTrJf0QfIJlWcHlPftJZ6SrNENSD8uyip8prtLjzLEQ/UF0IeNO8O+RkWdXy5w/3+GV+fPhR5Mnt3gxQGvVKs4xBQ3atuX488+z8Kens7gvX84r/eSTTjXZBg1QTEqUwDIbFQXAGTCA86tXRwEKD0fp6dkTZW3JErxU9eqhGHXt6kTllCvH+l6+PLl5ixdjBR879uxwpbp1naIpN94ISJo3j35XrsQbl5Li7OHUvj0FWsaMwShTujTXbtrE3GzY4OyVN3kyCljr1oR9fv45oHXgQGTSTTcxjybkaN8+qluuX491/oorUKhcLuZs1SoUnDlzqIZnilE1akSbl10GGPzyS+Zm2DDm3+12yrBLTp5RkSLcW2pqQBvXe7V7p19hgRxVrOBX05SP1e/AGzo8fbl2bc3Wu+/aWp5cXBJhmWESDy5EfzgF55/nLzpWUPRM/fq8D2Yt/PFH3pWpU/Hsrl6NQv3ss6x1rVqhsE+ZAkg5fVrKPk05eilblKZHmTaRM36/Ex6YkQGPZZwKk0teBVRIkfIpx+usdcFk9qczxVAsyyVbHnm9DrgzRU4kzjX3bIq8REU5IZjGg3j0KB4yc9zrxciTl3d2/9HRyIbsbHgyPR1vpilcsn8/a3hGBvNYvLilnEbtlNeglao0qajIKOvnTddNBc5AgHkzeXQeD/KkZk36On0acNm8Of1s24ZsuP56fmvdmvFGRjobv0+bBkhNT0e36d4dlly2jOf3/vvIpYQE2vjgA0Lie/TA8JOYSDTSwoU882nTAJ2DB+PpveYaQF+fPk7BrFmzkKMSBu3sbJ618cht2gSo3bKFNWTSJCItvF4iICRkZI8eyPXISO6xd290uNatmbcNG2j3zjuRZZs2oTOVLs27+swzXJOWxvscGSn59h/WAYH6vaePh4qshOh/j/IDr/wUvFjkPyd//lz+/XPOl49nwJpREg3YC16Q4uI4JyODRcnnc0IsTQ6fz4fQad6cEKe4OGcvufnzERI9ezqhKPPn097bbztWS+nsstF42hYowV6gvltGqk/zw/r731EYo6PPzuGbPZv+WrQA+N19t3O/nTohaO+5h0XI7bHUZNqLWvP3T5Xz9TTVqWvplltQwJ5/HnBYqxbW9pkzpXZtA/qk1EBJJC2E977xD8vBs23bJ+leAcw2SfrStu0Uy7KetSyr35nThlmWlWJZ1jpJwyTddubaY5KeEyBxhaRnzxwL0R9IBRl3DJ/nL1hkrP3GWGI+G4t8mzYoEM2bs/g3aAB4at4cD4Exrjz1FNeYMKCWLVF6/v1v+GXLFiqj5eWhXA4ZguFk61a8S/Xq0fenn6IQ3HcfvBceTttjxtB3165OaOfevZyTlIRl22xiPHGis39eTAz3s24doGjNGpS/Jk1gubg4PG0lS6Lc7t7Nb0eOoIQ0b45CdeAAY6pQAev6mjUoX999B0DLzOS+H3iAa5KTCTvdtImw0KZNGe+RI86+W+PGMea0NNo9cQLP4Nq1zOvjjwYUfuq4li/HuzZuHArWp59SuOXOOwn/njCB+/3oI+6/XDks3r17M8b27aW4uIBqrf9cZbYu0mM/tlXE2kW65/Czanlwsizlye/L0aGNe1Rh50K1aC5JVSRJmbGDLqkEeYh+Owou+JW/6JjHc3b12zlzeB9NmKbk5Jd/8w3hfKdP47VLTOT/rFl4sA8ccORH1ilbqMB+Ae7sn9tyuTDWBMsaXhVLARVWjsooU2UlWSpRAh4zFB3teNxMAQ1TrMXk8nk8gDvj7c/LcwoUBQL6eeN048Ax+kVEBOAoEHBe3eBwTgMKs7KcvfPcbs5Zt87x7pUsSZvlynFOVJS0f79LqVsjtWWLpdRUvEotWgAgN21yAE5YGADx4EEMOaVL06eJUPD5ANxHj3KsSxfGe+218PXBgwCd+vVpq3dvBwi/+y7/ixfnf+nS9DthAkagV18ln9bv5x5atCC/b+BAwFpWFtEEdesSVt6rFzJIAuh/9hl6izG4d+sGkOzUydl2wezr2aULY6tb15lrs3l906ZO2Ozs2YA9U+ynbl3AZt26yFITxn7XXcg3rxdQbSKm4uIw+k2dKr37fRmV0ilJUiC8dKjISoj+N+l81vzgYiXBYY8FnWNCPPJbDk0f+RO+jcXQxNeb330+GPShh2DYFStYlExYmOlLcrwLMTEIrZUr/3P7Ao8HMLdgAQJh3jwEYmKiM4bgMSVtLaOMll31hobL1yZBngpl5PE4WyqMHOkUXklI4C8xkbG+/TYV877/3ikvPX48gqxBA+mRR13K8RTRuPGWbr8da9Stt9L3ihUIr7Q0xnFqb6aeXn+T6mkbc5h15FIf9SWRbdtTbduuY9t2Tdu2nz9z7Enbtn848/lR27Yb2rbdxLbtzrZtpwZdO9G27Vpn/ib9UfcQov8MrzZktjsw+aTBimFBRZIkgJTxHN17L9biVq1QBuLiOD5kCN9fecUp252dDUBZvBg+Xb0aXujdG8+T202/8fEoEIcOceynnygG8NZbhD2OG4eyZVkoRC+8gKI0YwbWdgll5NNPkRVxcYA8y0Lp+vJLvOyzZ6Og1aiBBfy777BM79/v5BYGAhiB585FOXn9dSzmV1+NojxtGmPYv98JR922Db4+fBgw17GjUyhmzx5+u+EG7nv3bsBjx454Gd9/n/upXZu5OnwYQ1fDhsg3r1fq2iWg5zrMkH/wPWqe+pFWLA+odGmUJJN/M306SqVRlKOjmaPcXEDnzJnIrX/8Q7q6S6b27w/oSn2uDwNXqowOqtf2d5Rdo6G+1I06ohKK1mntzyumvbt8YktMt6IKhdSfPxMZMBX8PzhaxaxfBswtWOBsExQVRSjfmDGsTfPmAQD++U+8Pi4XuV4ShhcMuy4B7AqfGQHvQ3Y2xgpTrMRQkSIKOs8tQi8x6gTvS2fCK4N34MgfaZeXx9iDa/yYUD8DBoN/M54j420KbtOAtmLFAIzmuGXRj9/POXv3AhALF+a3EiXg3VOnkDsuF3Pm9YIr8vLwxEVEAFZycpAdjRohzxIT4ev0dJ7VjBn0bUBS3boA7l270EMWL8YbZlnIXbMfb3Q0hqW2bRl3Zib6xLp1zGHr1hSvatUKsH7kCPLs2mt5ppMmIbOefx5Zds01hHfWqMG5S5fiyX3nHSIOoqPp+957kZk+H+/VkiVEA3TqhAz3+dDTpkxhPTB5jAaM+nx4C3/8kWe+eDFh7CVKSDffTMilxDimTOF9veMO5GZCAnPSpw/jr1uXsaxbZykzLFaSVOruQZdsHL9AoFuIQvTno4I8bfnJFCuRzn9O+/aOZ84sJvPmOYAsvzdv/nwnbGP+fKeP+fOd0rfR0WcXXGnTxjkWvHh5PDC4CcE0bU2dirC74Qa8awkJWO6HDEFYSSh33bo5Smv7DpZmPjxVjTIy5bkxRrKsn+coJ8eRE1lZCEe/H+uS8Uy0aMHiOG0ayuMDD3Dd/fcjVB95xClL/dxzjMfsweX1Ms4FC6Rdu2LkD4tRah6myWzrDwvPDNH/IwpW9vIff+ghh58MPxiPtcT7vHChUzI7mHw+eOrTTwn9adkS4LN8OQpPvXoojMnJnGfCbWbOhCfcbhboO+/k3P79MZKMH8/1VatiPR8wAB6Jj2fBN1blyZORQ1OmELazcSPjrFMHsDR4MF7FihVRniyLsOgKFeDbw4fhu8suQ0mIjnYqAmZkoGQsW4ai+/zzyJKwMEBxlSp87tQJRempp+i7XDms71On0uesWeT1SQ7oXb8eUFixorNZb9u2KGQPPohi1awZYLRDB8DuV19xXd0ymapydIX88mvbAbfSPs1R+p5Cys3ld1MK3u9HGTdFbWybeXn0Uebv3nuNJyJG+yMb6ZWc5spUKd2sj7Sg7j2KeuopPZKSqdHPS131tdIKNdfGbCdZKX9YW4j+XGTSIDp3drzZnTrx25w5AIEhQ3jvBw7kvTChjeXKsSa73QCYtDTHwOCQJYCarWDAZujECSeU8MSJs0GaKXgSTI0b8/4WRPkrdErOXnKGihXjnoLz76pUASCZ3TwKFTp7w3WJ+/V4nC0ezH2att1uxlqkCADo2DFk1YYNRB/k5HCN2VOuUiUA5apVjqF66lQAYuHC9F+rFvLtiis4Z/p0ohMGDWL8SUm01amTs33Jhg149H76CeDYogVGnUWLkHG7dyNPPvvMAbSHDunnSqUffghIi493cihvvRVwZ7Y9cLkAoOPHIx/vvpvPW7eyfoSHo9v07k27x48H9OErh5V6rIwS2lvq0sUxqufkID/Xr0dGb9iAbPP5KGK3bh36UbdurA0mpSYrC12qa1f6tSx0pn/8AxDXrx/jmDwZWZqSwve8PMKAPR6X8vKcrTkuhUImrBD9peh8OXL5ySh952tr/nwWC7MRuOSEipgQzOAiKp06wYxdujj5bMG/G4FlyFS7DN6jLjg8NCuLcDATKpqTgxDweBBO3bqh6A0ahMfP9GnOfeEFhEROjrRuvUsd+heXLOus/YKSkhDSPh+WMJMkHBfHfbjd9BEbi8CaOBGlLicHBbVpU/ocN45z1q1jYa1XD8G1fTv3nJAgVahgKaZzvKhJIhUrmm8VDFGIfgUZXs1fiMiEcBk+nDEDZeSNN+CtrCz4z1i9JSf0MicHz3ajRiy6LVsC2uLj+XzTTYQDzTiTeZmQAD/v3w/QMAqR3w8PVaiAVblsWRSNN95AmRo4EG9Wy5Yois8+i7W3eHFCjszWAOvWYQ2vXh2l5ZNPUIw++wzlYc8eLMO2jRL2zDModenpTs6I2SC4Zk08/m+8gYL41FMcN2W+jVW/alVA5aFDgMO8PBTikiUBc82ace5XXwE2JSccdP9+LPV33sn9m7zGhQvpe88eDFjz5zM311wDUNx8KEaqUEXJaiJX8dLqe02UHnuM36tUQSZdcQUKoAnh8nhQLJcuJdTJ62Vsl10mZWRYanxjY/3j6bJq1KK4Dva8W982fkaff+nW4bwSqt+tkra1u0eLc9rLH3CU+OACFyH6femXFkULNrr6fE6BschIp2pmWhrv+d13wzuVKmF8WbIE79L27byDJrywIJCV3xsXFsb7bSgvD4CSnX329fnBXVZWweAueBsD6ey2g8GdBC9XqHD2sSNnAmDMel6QQ8fszWdZzEH+ufb7AadmS5MTJ5iTvDzGNWQI/GXbTujntm3MbSDg5PPGxADCatZkbtevB/QkJaH3VKvGPP3rX8xZWhp5yvHx/Nm2U/AlI4NzTdXJ2Fjy3OLjAaG1azvbXmzciM41aRKpIJmZAKi4OPqIjcVA1aABsjY1lfucPJlw3YYNkXPr19NW//7IpeuuDahC8kyValNbCc/3VNKSwM/P4auvMLD7/Y6cMAB/6VLeuSZNeM8iI1kbWrakz+3b0bNMUbvYWO7Fsni+U6eeiWToyrXNmyPHbriBz6afXbv+81n/Ugp58EL0l6Jz5cidy5tXUCGV4LY6dQLgBYd2mZBLs8AEe/IMMyclnX2ecbm3b3+2965zZ6fYQ3C/7dvT75o1CCDJ8cqNHMn5UVFOGNnixY6HT3K8d2Fh5Mt5PAichQv5fe1arE2mFPzChQjJf/+b0DLJqRbYpQvW9zffREjWrUvY6BtvoERt3owwqlMHwRoI8P2ee/hevToC65tvEJxvvmlLSpXUSPunfCT5bvptav6G6H+WPJ6z3/9z5ctKvM/GEithWe/SiVhFT4UykmVpzhw8exJ8sGYN73CFCvBa8+Z4vfbvJ6ymeHFAy9NPMw4TTm32evL7ATJ9+sDHhw8DvPr1I38kLY2woVtucfJiDh5E4fvxR8KP1q5FAYmLQ1FbsQKl6MgRDDzp6U4OR1ISbZ46xXhef93ZfuC667CSHzmCshQIwM8pKSi/2dmAt5dfRnH75hvCoPbuRekrXpzrNm0i3OjAAYCqx8P9DB+OkvjBB4x9wQLHG9G0KYpe27Z4744d45rISMeTMHOmpbv/cavS152SMqM1Y4al11/nfgcOJKSzenW8jJMn4z31epnXoUORnV98wdgbN0YZ/Pxzlzw3F1eFitJVVxVSdjbAuFQpKTnFpYiIQgoEjKJMvtXJky7l99qE6Lcnk/cdbJy8GDIGVsmpYG2iYSIjMcYcOQLftGuHQvzkk9LDD+PNGz+e8xcvvvg+IyL+c59ao3DnB2QXQ+ZaA9BOnSr4PKN/mBBtiXvM760rWvTsNipUwDhTpgz8tX07fUZEEClgjDALFjj7bkZFYVAylUF//BEAZCrQ5uYil8LDkUNVqzLPTZogn1JT+b1SJQCZ2TS9cmU+X3cdff/7305ur9vNuDZsQEZGROD169gR+ePzYdiuUoW/lSvJvwsPR2bExvLcBwxAnzp0iH7GjEFmfPcdc7x5s6NPNWzoAOtChTAMRUUh+xYvljrWP6zw42Plk62FiRFqWfaw5s4tq2nTMCqZDdpNLnezZk7V8bFjMQz6/UQ9rV5NX6+84oTYjhrlbLr+z39ijE9JYc5GjUJGr1/vGDG8Xt7l6GiewW9R/ymkdYXoL0f598jJD+7y5+JcqOqmEQL52zKfgxca4xFr08Yp5GC8fwYMBn+XHAERPIbISAqpmHwgn8/ZyyYx0fEKmD78fmcvK7ebxTIyEpC1ZAmC4p57ECatWzvgrX17BFByMhb4ceNQVA0IzckByFWrRl/JyQiyRx9FqH/4IWEQY8eiSPl8LKQZGYRL+HwI/k2bnA1Oy0RmafeZHQbK6hCSsnHjX/28QxSiYJo9m8WwRw+HtyTec5Ob07Urxzp2lDyugGa2eFSrN4arWf08NZv+by1f7pLXCyBp3x4wsGgRQOvrr1EYnnmGpP+tW3n377kH8DVlCgpSaip99+jheNg6dSK0ye8HGLVvDy+mpACo6tRB0XnqKazMbdsCLhs1csq7T5kC+KteHUXo+HEAXJ8+TkXcYGt4kSIAUjMHZcvS9jXXYPWuUYPzDh+mauZVVyFzJkxgnLm5UlRkQLVKZmriwhi5XNbPFnSJ8WdmMraoKIxZb75JEZR27ZjrkydRFtevRyn1+wGkd9xB319/zTkZGczRF1+6dPx4Ed1zD4VhXn0V4HvXXQDQl15CQW/bFuC8ZQv9z56NTH/5ZQDuhg38lStHO8arOn8+CtXDD/O9eHHm/cSJgLKz8yR5VDNlkhS4LVRo5U9EhpeN19ysg127YqgcNQoFf8ECeHXAAPZJzMoibNrj4ZzatZ3tf85NprCKA/RbtDg719cUNAmOAHC7z/5+LgoO43S5+Bzs+atQwQF059saIphMqKYZw9GjtFmiBHxjwjtzc5EfmzahM0gAsj174EOPB+B24IBTB6BkScBj797oOMWLA1zS0uDbEyfwUq1a5XirevXiPvv04ZmZKplt2/JczNYIyckAt6ZNAWFmK5eHH+ZZ1a2L7HC7mfOSJYkQGjqUeXj8cbxitWqx1UHnzgCk3r2RceXLM9bx45EHcXGMJSWF+alfH9mwZg1jzsqSHnq5jHrVaqaR6U20pUQbffFyGY0e7eR47t+PYbxDB/J969dn3tq2dZ5/ly5EO0nI9qQk59lkZTG2pk0xDBpwFx2NvK5aFSNYw4YYs9LSuGfzzh44cOF37EIUkmwh+kvSxZRKv1CeXrD3raDtEgzwyr/XXfv25J28/jpMbH4PLtySlYV1f+pUZz8UExZmyHgEJRQSQ2ZRa9fOKe/u9zuhZma8OTkAL68XwWCKtSxdyrVmzBs2YEkrVUoack9A7hPHNXKkraFDuY9bb0WprF0bsGaA5YcfIlDN5szh4Qj2fv0QQj4fC1dUlHTdNQH99EOeYorZahwnmZLTLtlnlxgLUYh+BQXzdUICIVlz5sADs2ejgHz3HQqFUXS83jNevMOHpY0b1UjrdWTTXt12s1d16zp8JqFU3HYbXoG//Y2QmVWrACleL0rJbbdh/DDV4W64AYv5pEmEAhlvggmH6t8fhSMlhT4qV8aQsno14Z9jxgCUvF6UmxUraKN+fQxIZmzJyViz16zBy7Z/PwqMz4cStnIl4GjyZK6ZOhVe//JL5mrVKhTABg0Yd2Qkfaano+ytWRNQ6rSt8o54QNbqlWoeD+82bIhd5tpriQZo2JA5Gz0am82HHxK21a8f/daqRbnwKlUAphUrMi/x8TyTb76hnS5d8GQ+8AByZONG6eOPnW0rtm0DtFaoAMAuWlSKCA+oT5vjCgTQkI030eyfV68exw4cQEk1RReaNkX2RUUxRycybUlHJUnH7NN/5B6d/zNkvO/nM7KaSriGx91ujBlGWZ45k99On+bc5cv5HxFBaN/hwyj5xmCamPifm4efTQbcBWTpuMx6Zbz+BvObXNBgMkU2LkT5i6zY9tltBXvrDLlc524/MtIZl5nLkiVp89AhJ7wyIgIwN3euU0xKQi8IBOCD5s3xrh8/zv+YGMZTqJBT+OToUcerV6wY4HDqVGTtwYPSQw8G9P3XOVq50lZWFvN/5AjeqvBwzs/MhCeNTE1NdXLonnwSkFO/PvrHrFnIEL8f/q1VCyPyk086nr1atXgXFizgXrp0ARhFRgL2P/rIicaYMYPIh0aNaK9NG56neb8qVbKUfsM/dV/yEL2Z2lO1almKjibvrls3+u7SBbk8aBDzcOQIe/OZvfdMW02asEasXu08Y+M1lACoCxeyZjz/POvX/v3I1gULCHu98kqnuqd53pdKIYAXor8cmZAPs3VBcF5Ofo/bucI2g889az+5eY7SeC6Q6PEgwAYN+s+CKqbNxYsRMsuXw8Bt2qCQvvba2fl2hjp1chan8HAWt4ULnfLuffoQ4tWli7MpuYQCZjYsNmWjvV5CBV5+mWuN1cjnDWhFx7+r7W21NXhmX9WoHlDbtggwnw/B6fUioObNc4o2xMU54XHdu/N/3Tr6btxY8vsC+qDXp4rcn6IfJ+5T4opImW0SjqiIo6mGKES/kjweeGjRIj4/8AAecMlJwn/sMXhk7VqUehOOmFWojNwN6ytbUfooeojGTohQjx5OkaBp0whNnDEDHpWccMYtW/htyxas4dHReMHdbqzLZo+tIkVQOKZNQ1nq2xcA5/XiSTt6lLHUrw/oKFUKhWHFCjziu3Zx3caN3Mfs2c6WLLNm8dvJk9x34cIAvtRUFJfcXMYXE4OcyMvDqJOZifX81luxemdmogDOno1SdPIkc9W0bo5q561WumrJn3NKuUdP6auvsFqPH49iOH26M9fp6Shzfj/KnjH8bN6MonPddcx/7doonhnHAso8lqOrr7L11VcolWPGMB+VKzOnV1xBGNbAgdzj5ZcT7hURIU2ZElCr5S9p2yNv6vCMlSpbJqCHH0Zuezwoq0Zu2rYTnrpmDWN/6y1A4I4dUqHCjspjqeQfuUfn/xQF56rnJ7PPndlGKDKS9TA6Gv7q0YPzvF4MKjNnOoUu1q9H6f/mGwyR33/PO1elyoVGZEvyKkyHZMst9xmAZ0Iqzf+C8vaCi1/81jsAGSDocv1nkY2cHGdcBkQcOsS7nZMDL5Uvz/xFRCBjqlaFR5s0QWbUqAEoNPtiRkfDZ34/nqlSpTAIbd3Kn9ngvHhxDESPPcb/VasCmvHSGjVd/obK7V+lt94KKDERL/ozzzjRAoMHcz8jR9KuZXGsRQvk5jffYKCybfrwermf++8HVHo86DGmuu7WrU4kROPG/H7oEGOKikKGPPgg9/XSS+gpaWlEXyQlcZ7x0EZGSrGNXVq5u6wioyyNGMHxI0co3GWitVq0cCKj3n+fugM7d5J7bIreXX45cqtZM3Sk3r3RDz/8kHf6uuv4fehQZOVDDzlF8x580Nn6o1kzxx4eE3Pp71MI4IXoL0/BQji/xy0/5S+hbvbdkZzwyvygsCCQ2Lo1ip4BWsFA0FTw7N7dSQiPjkYhHT7c2c9n1CgsQWZjc+ORMNbO9etRgkaOdIq0mApPZs+8Hj34c7sd62OHDgj42FgnFFSSdPiw4pI/1HC9rHeWNlOjSplatoyf+vZFcBqPxdKlKIkdOgAqZ8/GQ5CRQcx+djZ/69dL/qOZ2r7f0kCN1QmFK6FFrspoO89GFdAyQxSiX0j5tywxi+38+Rg5TNl0rxcr9GWXsTg3berkcSxfLv19hKWGP7yo9AfHK+7O1lq12tKYMU712EmT8HBVrOhsThsIYIHu2hVDh2WhdGVmEm6zeTMKyBdfOFbx+vXpNzkZkHHZZXi97r//TFjiF86Gzf36Aa4qVaIoQZEiKKp16vC9RAl4ukwZ+tm8Ga/8G28AzCZORBlr3x6PlWURVr1vH1sgxMVhHW7WDK/fZ5+hOEVGwtMHDwIOfT6p2+VRioqJkU/hssIKKXlntAoXZm5iYwGhBw4wDytWcL+rVjGuW27B+yahYG7c6OSo7N4t1awR0Bf9P1GLpJFqOec5/fhjQJs2ofy8+y6K3aOPAtCPHweovvuuU9wlPFz6xz2ZWrmjgkprp+zsk9q7NefnuS9dmnuPiECBKlcO+WlZKHONG/NbairKrtttSSonSToVf/0ftkfn/xrlX3eDedvjOTtP3azhWVlnb3AeHs47tmIFxoOGDVnb9u/nt/h4pyDGtm3nHotlSTExFFfJUzlJbpUq677o/MCjRx2bZf5iK78FmeIn+fPvJLzahQo5Wzb4fJyfm8s1J04gD8LCkAWZmciU9ev57cgR5vnkSXi6WjUHSKakwNNm24cePbi+YUPuc/9+9IPkZGnEoEy1yZ6uMPkk/ynt22vr5EkiINq3R1+oVg3Q1rs3RUuOHkV2vPsuz/a555xtXWrXRm6NGYP3dcsWZ5umpUsJzzQUHc2z3rSJ+zchk99/D6Dz+ZBPy5cjv812VSbCw+S9jRgBMEtIIBpr1izyBv/+d7Z8iYxEBpqUmeho3tNSpZAry5ejB5oN1BMTMSrNmiVNmxrQO6+cVLWqvCAdOgAOO3Xi/MREru/Zk77LlWPeb77Z8bie7x2+WAoBvBBdEv3S6li/RZumOIoJU0xOPtsrV1DhheBrC6qOKTm5c+cLKTHevYULcf1LZ3sQg71yZr87w7ASSqoBa2ZR69QJa5bf71TUjIxEMIWH08/cuRx3uwFcpsiD6ccUjPD5WAAffNDZhyUrC+/Ej8vLaFH9QaqlHRrcerWiK8WoY0f6nTSJezKha0lJLBzz5mG9T01F8UxOJgRjxw6s++npUpOOMdocEa8ISVGytfNoEZ2IJAGxaOd+ISUqRBdFwTyUXyE0fJuYyHufkACIaNoUxaR7dxSEDz6AR554AqXisccAZBPfd6lW0yLaf8DS2rUs0J07wyt9+2K8MJXYJBQPiff87bfhoSpVAG4lS7IYV6+OouFyMb5ixfAwFClCuE1UFGMpUYKxNGtGu8uWAVAbNeJemzcHWCYm0t/OnSh3VasCYIsVI2+kWDHOr1QJMFm4MEDtxAnA1IYNXL90KXxZuzZGmC1bUNjS0hj77t0oVSZvcPx7lkpf3137LrtXLYe00F13WapbFwXRhDu5XACwzz8n984Yglwuwovq1kUZmj0bYF29OuNqWvuUth5waa8qqXXa+ypZBGtcZCSA1u1mbm6+mWfQvz/zHh9P323aSKVrx6hEqTC9qUfUuNQhVasfpcKFAbEff4xClZFB2GtyMgaovXvxIixaxByEh3MMOrtyYoh+fzLrsnQ2bwfvbxn82eSvm6gar5djeXl4RcaOBSgsWgTo27MH/nEK6ZybbJvKq04JisI6eND6GWAGV7osiAoVKjgH77coa28oLw/+DiaXC548fRpelM7e5Nwpsw+P163LfDRrBtiqXBlba2YmwMRUr6xaFS9Y9+5ODlyLFgDAyEiniEpODmNo21YqVTNGVvkq2q1qqlIsTz/84FJsLG0++yxAa+tWjDCPPkoYbdeuhGouXcpYq1VDltSpg9ydPJlz7ryTe6hf3zEY2TaVK5s14/rOnfFKjhmD/Jk5E/lcv74ThWGin95808nbTUjAizZuHHrNzJnI9qZNAaIPP8x68N57hFKOHo38MNFVEtd06IB8SkxkLseORYeLi5PcroD8jz0p7+sjNXnYDA0eHNC8eXgR16xBV2vXjrnu1w9QGRfHeHbv5l3+rSgE8EL0q+lcGwpf7LW/pk0TSjl/Pv+Tktg/Kn+8cvDikd8TYEKfgsM8Z8xw8t2CF4hgi2Nwzp7b7YSOmN9nzHDy8mbP5rvJVZMcJXXKFH6X+D9lClajDh24l8WLEaiJiSiv7dtjkZo7F+U2eI+/UaMQCCNHOkVaTJhLVhYx8/ffj1DfsMGS/vUv3Zc8VEkjfpQ3DwUnIQHhuWmTky9UujQKqNfr5MasXo1i+/TTKK0SIRzJKZZ63VdHewa9oqpNS2vjRks5OShRBw+GlKgQXZgMr2ZknB2uZQwxhodMBVhTYGjVKsej9PDDji3B4wFIvfIKVuVNmzjep4+jwEydCrhYuhT+O3UK3v/sM4qgeL3O/kqbNgEmsrPpq2RJ+G7bNkBd4cJYkLt2xUv/7be0v3Yti/hdd6GYZWXBz1WqoED5/Vzr8+GlK1zYCYmqVAk+X7XKASoZGXgar72W++3XD7Dq8yFHKlZE8W3cGEX4/fcxMJUvD+Bcv54x330315g9+g4ccskXVkgHD1pKSEDxq1zZqbx39dXIpRo1aL9GDafEd8WKgNLWrTE+7doFmHzqKWnJ+kKqpEPyy9Y4DVXrhLCf83v9fpSzTZuQMVdeiZU9NRWg3ro14/jpJ0uHOl+vXjeX/z/2rjs8qip9v3da2qSRQugQIPQeegtdUbHtqquuvTcsq6667v52Xdd1UVREBbGta8dCE+kECBAgEEhCIHRIAgmkZ5JMJjP3/v54+Tg3Y0JRUXbNeZ48SWZuOffcc77zvl9F6bhrceiQdhKk89zf/Y6grFcvytBduwi2WrQgiLTbKbdqaupnSDQr3i60pmnaRZqm5Wiatk/TtD828P2jmqZla5qWoWnaSk3T2v0S/TzXZiZwUt5k6FC1nwuxW7mS+51h8L19/TWBtcNBZUBMjLJEFRbSMqXrnBNn3yywWBTRlwQl/lk0/VtDlrXTfd5Qk2RAjTWnsz5hDAj4vrXQ6eQebbNRgZObS7kiSuphw5SSqLpaJXopLeWaCAoifpISE04nxy88XCV5KimhfKuu5loPC6Ncfv99Dbv63wDLJVeg1e/H4Z13NbRoQbf2Zs0oNzt3JnkbMIDv68MPlTfS9u1UEiUnk1w5ncQYMi779tG1nVZ3yqJ33+U7GjKEMlvi1Vavppzs359rf+FCEsc9e/h9QgKVX1u3Uj5HR9NtPTubXk833EBZL95X4urdvz/JoCTTcrlIQj//XCnmxDvrnntwyhtqVNcTsOZkYRC24KqSOYiPrYLVyuMGDqTL+PTpvOe333J/2rGDz9q1q7LO/hTe42dplG5qv+bWWKkBs7WssWMa+vx02S3Nmj6xZJkTnqxYobRnDVnrvF4KDZ+P5u+G7iHXNzerleTJ66Wmp29fErjkZBIgKVMg54p2aONGFfsmWTEBZWE0991mo5B4+22mOxdLZHIyQW1EBI+rqaEQuOcedT2An8vziYXxllsITlu1okCX89auJViaNo2CKiuL5HH9egvW5cTg8w9r0KFbEFJTNWzbRgteUBCPXbuW187K4qYpgktadDTB5KWXUkvu8QD9+lnwxRehpiBxBrEHBzelIm9qZ27mBEX33KPWvax5gOusf3/+LRnbLr6Y2lABixKrAfDzDRuotc7LIwGLjyf5eeABAp077uCxCxdSIywFkV94gZ+vXUvy8pe/EBBIHalrr6VF6OBBatUzMxlbsX07iUZUFLXSOTkqVm7IEP5vs/E8l4vPtGED/x42TLl79unDa8XFcV126EDQ4fXyuT76iM+6aRMJmM9HDfyBA7RaNmtGoNqxo1JGdeig+n/iBK/dty+JqLiIdu7MpAZjxvAevXtznPLyeM7vfkcrnt1OMr1tG5913z6+m08+IZDcs4dj6nTUoSfSkQANs/AQ7gytwLqsSPToQXC1eDE1/XfcQVnarRvH4PbbOU4zZ7LvXq8Fq1aF4o9/pHweMIDjO3s2wd727RwnAcE2G/tUV6cKv/fqpdy6gB+W9v7naJqmWQG8AWACgDwAWzRNW2AYRrbpsHQAiYZhVGuadi+AfwG49ufv7dk3894upYcA7q0SitC/v0quImtAakympVEp8tBD3Pd0nXtRXh7d/35IMxcv/zmb1KIFGs7KKTU8pbC5zNWoKK5fn4/46MgRkqdjxzjX27WjjNG0+i6boaFcx/n5JA6lpXz2Tp2A66/n2C5dymslJnJtJidTForSpE8f3mPWLP42DAvGXh2MjRuJXbZvpzJs3ToSdK+XxOyDD+hZ8MgjdEvcuJFk9JprKMsiIohTPB7KgiNHGMe3bRvvK+7yzZsz225NDfs5axZj2dxuKrsuuYRkyesFUjfquO2SE3BVxmLfPg333adqKBYV0cPjmWfoJvrvf3MeAUo2WCycn04nZeC331L+793LZ5ozh7GEEioQFER5m5MDwIhFVpvJeCTvD7ANHwQMdWLiROXG+cAD/D14MC2E4i0ibsabN/P/n6Ky1HkleJqmXQTgNdAf4h3DMP7p9/2jAO4A4AVwAsBthmEcPp99amrn1vzJmD9hMxMvf7dG87lyrPw+U+mCpUspMKZOpUAQ90lzLSz/ewn5Gz2a2j//Y9xuCh+ppSPX8npV38VcL5kkfT51Lbmey0XNv9VKUGbun9dL7UzPnqrouQDXoUMJyD75hEJtxQoCPPO4qQxPBDd//CO/69WLfuiSUEWIXk0NyZ1k5JLA37FjCTg3bKD289VX2b/BA3XYrpyM7RsGoVO2Dbt/+ye8/74F69dTwD7yCEHSxIkEtj4fAbeQXZ+Pgm7FCvaxrIwgq3VrAtnCQsAwdABHALSBN20xoF/clIq8qX1P2eHfAgNV3SpzrUlxRU5OPln2wMa5Oncu19bMmQRAQ4fyc6uVCgirleAmI0MFrKemcq6mpKh4kQ0bCBwNgyDB6yVZuuUWuhZ99x3XWP/+BE7Hj9OFZ/x4KnVqaggY4uN5/r/+pbLLlpaSbB09yuu2a0fAUljI5/3iCwKq48cJwlavpgVx8WKCqDVrVG3KBQtIDMvLea/sbBI6p5M/a9YQ0I0YQVDYpg2Br9fLjHCtWhFYtW7N73NyaBm4+mrKh2PHVOzMvn2UYU4nr5eQoBIvlJYSFJaXc4zHjKFcW7aM4KR/f5ZiOHEC+PrrAHxgfwCoq0KwTcdH30bAbifZnDCB76xVK77DqCi6gQ8axNg+p5NgT2p/ORx0/+rShe/S4aBldM8evru2bYG+fXR4XW4czg1CSIiGgQPphnvoEEl4aCitPoD6fQG2QQD2GYZxAAA0TfsMwOUAThE8wzBWm45PBXDjz9pDUzvTujYfA3xfMSzK0Isv5vfr1nEPfuIJrqVVqzjvRcHwyCNcT61bcx1J3bfTEfazLW3QULNYzi8RlH5JGQNzq66mzAkKUuVHIiJU8ldxO5bi42VlxAoHDyplcZ8+XAMtWpAoS3mFK6/kOp89m3LJbqdMEHLTuzfw8ceUKQMG0Oq/cSPXel2dqvEZFET5KVa2/HyuYwn7aNeO66+igsQvLo54Ytw4ErSXX+aatlpVNt4XXyTpu+oq4rayMl77ttsoy/7zH5XF0umk3M3JoYu3zaLD9eizeHh3Vxx3dsKHOwdj0yYL0tNJrt54gy7vOTl0w4yI4N5SXk6Z4vHwfa9fT/nevz/J4Msv836iiN+4kfP0qaf4/d/+xutOmKhhZNodsNVcDndYLN67TsPwYTrefq0KRpAT48ZppzCqpvHZd+/ms/ftq959ScmPn1vnDXWZtFAXA+gO4HeapnX3O0y0UL0BfAlqoZraBdT8hfG5uGSaNXb+5zVk1TO7Mk6axMXvdCqCJJuE/Mh5co64TgLczOWa4na5bh213uImAFDLPH26MsHPnElyNG4cF7D4SgMqNshm46IcNoxAb+BApbGy2ahhdDiUVULq0a1ZQ02ZuAZJtktzH1eupFD+8EOl5XO7aS178026Ik2apKx/Dgf7+MIL9DWfMQOoqdax6qtSvPeugbFjCd4Aaquu+40HqzcE4F68gd17gT2ZbrjdPP+LL1Qsz4oVBJHy7MnJtOgByjJRVMRn6tyZAvy++7jRtIj1AOBD6ihpSkXe1E7JDomp8Xeblh9JIrRmjXLXEtdpn0/VCBo5ku4sLhetPlu2EJAsWkQSN3cugUZWFtfo1VfzuObNSRq6dycAmTSJ7jG/+x0BUGYmydWyZSQDd95J8PL007xHt24kJCEhdPl56SX2p2tXEqQZM0hCRavepw/XqmEQ2OzZQ1JWXs7riHuixcKMchYLM1AuWsT+V1RQ0XLZZbxeYCA18Pn5aizCw3mcYShlz9q1VJCdOEHQ2KMHCY3TSWKYnU3XpKuvJgG99FICyL59STYrKykvu3cn4Fq+nGDkyBG+i2nTeP2iIgLDf/6T4Ovxx3m/lBTKo169NMRcnIjoSYMROykRFRUamjUj6MvPp7y5/nrOiyFD6JY1ezbl9M6dPC4wkP3dtYuA/oYbOFYvvkjrQFUV+7xihY65j6xD9dqN6FPwDfbk6Dh4kM8yYQLfgxnk/xQa8vPUWgHINf2fd/KzxtrtAL5r6AtN0+7SNC1N07S0E+dBDp9uXZ/uGLNieNky5a0DcD/dvFllwlywgMcOHkxiceIE96a8PL770tIzW2N/KLkDzp3cSamCs20Sa5eXRxnkrws1h7CHhHA+h4fzHuHhimABqhB5RQXHOiKCMrC2lmu/eXMeK26dsbFUMLVoQTkwbBj/XrOGYx4XR4XL3Ll05c7KoizKz1ceEeKmfdttHKu6OsrG3/+efYmLU+VaLr6YRPaTT3j9tm35/t95h1jjgQcoO6dNI5F0uShbnnmGcyQ3l5azadPoqvvoo5QDgIqf/u6zclh3Z6Ez9uJD19WIs57A8OHKlR3gXPJ4qECfNo3ye/Zsys2MDL6D4cN5z6eeoms/QG8pl0thusxM4qqPP6aMyclhLN3Dj1rwwnvMzvnuHB3bLn4addNfgfbvd+H16Kf2udtu4z60fz/7duiQmvfm+Mof2s6nWv2UFsowDA8A0UKdaoZhrDYMQzyXUwG0Po/9aWo/sJ3J8na6pCRCxs5Uj27FivoxceIPDahMl425eso5Gzeq9MpTp/J4iYVLTubGsWmTEtguFwXibbfx/40bKWCcTmVNSE3FqUQkr76qCrA++STN+g8/zHvv2KE2MEmcIlY2eZ5x4whcAB47ahQFicTvLVvG7y65hFnyQkLYx/vvV9+ZibYQ4YkTVXa73r10jHzpUky+vQXuW3wxbBYda9ey77oOfPBxAC4eUYWNGAm9ZRsUlgVh+3YWIu7dm0Ltiiso0IRsLljAcbvlFoLRtDQCvUmTqLVKTeWGsnbtSdB2NAAAg/kq0LYpFXlTOyUD/Ney/9qXJEJWqzpGChaPHk2g4PXSzfmqq0hQpDh5dDTXmN1OkDF7Nomb08n1dfvtBCe7dnHdt2hBwhAZyQ16/nyut7Awkrzjx9nXsDBuurrOdfjaayRhQmb+8x+CrUOH2OcNGwhCO3bk1J8zh/3weFRNpoICrqVLLqEWef9+XqNHD97LYuFYtWlDkDZ3LrXvnToRgLhcVDIlJrL/kZF8bimNMmQIz+vShddZsIAErnt3yrLevfn72WcJoCTD5rx51Orn5RGwDBzI5w0NpdVt/Hheo1s33vPyy3nPzEw+06JFJGCbNlEu5eYCNpsF/3o1GD16aAgL47i++SbB0OuvEwB27kwZ8t13vH9QEEn1FVewn3/9KwHdsGGUjcePUzHn8xE4VlcDrjIfNtR0Rzx2YFN5Tzx2bzlKSiirFi7kuHTurOak3f6zLoHz0jRNuxFAIoBpDX1vGMbbhmEkGoaRGBMT85Pfv7F1fbpjzJ8PGUJFwcsvU1nw/PMqnrxTJ1o5WrWixal5cyoii4r4XWXlucW9nU1rKB/Yuc4TXW+YcJqJm/nvqiqu/chIKnH8CWWLFsqN7+hRKnJGjFD1PidP5rrUdf4cPcrPjx3j3Pd4eO3YWMqO5s3Zv7vu4jsICFAu2CEhHG+vly6Qhw9TmVJVRTlaU0PX7bg4XqddO8qxggJeU2pufvONypJbVkY5fcUVvM6VV/LeERGULw4HZfiIEZTrEgu5aRMTkAgGE/krteri4igD+venMig7m+d+szoCAwcb6IMsbOt2ExZuisUrr7Dvb75JUnrnncR3r76qEnXdeCOfo0cPKuZSUzkOnTpxX9mwQSVtWbpUKfAlc2d4OK3LF13E6w4YwM8/fKsKNdl74UAd7sr7MwIrT8DjoaxduZKWutateb3rr6/vvvtj2/nUYTWkhRp8muMb1UI1tQunNUbSzqQNPZPrhjlmTfzzTxfTJ7/N1kXz3+LeNX68ylq5bh0X6ujRvNasWQR977xD4jVuXP1UzRIbYLMRcDz8MP+WGDghlk8+SQAi1kaXS6XWlWuJqyZAEDN/PrXmUssFUMVdV62iIO7Vi59fdhmBzb/+RbcVcWEzt/T0k1k9y8rxh41X4yVswNsb+iHh43I4mkfixhsZW7MlTcOY+cuw88UqTH3YCctM7mjbt3MMMjMp/Navp6C32wn0JPHFli3UmAYHc6wuv5zP43JxDPbuBfr2NbBkCSOg6wBK+aZMmr/6ZlYUmZvZ7VrkgFkeAASCy5eTnD30EAnGvHm0Gr/7LknFhx9y3t56qyJCDgc35NpaZn+tqVHZJJ99lnN9/nzlVjh3Lu/nclFZ0bMngc+ePQRI/frxu1mzSFAGDCCQys9XcblOJ0GogJsbb+Q5tbUknl26kIxmZjK2Y+tWksG6OgItn49rb/9+3m/HDj6rxOfFxBBMWCwEuGlp/OyOO6jYKiwkUOncmW5V//oXkwp88QXlYlwcXbiKilRR46gokqYuXfjMUlB6zhx+/uWXqshwx47U6us6733//XQDf/BBjsHixdT2r15Njb8kfunUiQCvXTsCu9WrKXObN1cW1aQkun61bMn+Bwby3RQVUTP+wAN8X8HBBFNt25IQFhcDDz1ow9t/c8MBC8aF78C2nN+gvJwAubyc1zTHal3AFrx8AG1M/7c++Vm9pmnaeADPABhtGMYvFlHY2Lpu6BiA7132wi1buJ7XruW+euAALTJeL/e9sDCusagozt+iIr77devOrY8hIZx7Z2oNlT04m+iCmJgzO6qYiZs/ifN4+BMerrJGipdRVRWvbbFwP46MVMmmwsL495EjXCuGwbE6cYKYwZyVNyaGa8DlIjm+8Uau/5tvpmz94x9Jzt54g4qZ666jrLz2Wq69gADKqNGj+R6Skkh0KitJMvPzuXb//W/KhFWrlEIpPJzvXMJWREk1diythX37UjZJQp3MTK7/999n5s38fL73xx/nPZYsITny+WgVFMWB00kXyXfi58E3tAp9hjth1ZiUScouXHQR8dzWrRxr8YJ6+WW6p0uilxEj2LepUzlPR43i52PHKpwoinufj5+vWkUs1q8fPwsMBB562ok1Czuiy87deDb2bbwSHIsxY/isOTm0QubkqMRUdXW89pmyuZ5NuyACY86khTrfbgZN7ZdpZtcNccFsqBadv4uH203BYiZ7ZkueP/GbOZPXkc82buT/Dz9MLVLv3vXJHaC0i5J9LylJZaeSPokW5/nnqdUqKyMAmjpVaaGkjRmjLBRjxtC1S9Koi5CYNEllcbrnHoKgRx+lwFi5khqksjIVF7h0KcFUURH5k8MBXHpjBF4dNhcRVjduG5KJzNwI1NQw82VdHc9dv9GCrgNDYbNrSEzkZwsXUqB1704f97lzKeS7dSOJe+IJbiavvkprR34+AeicOdxUevemIM7IALZu8gGoAABYUdHkovkrbGdKVQ583+Vb6kFKwXG3mz87dvC7rCxqfgMDualLfaNly7h2qqpUQqF//5sg4JtveI2LLuJPmzbcWKX2UXU1rVPJyQQ5JSWc5xUVBIXr1tHt8rLLCD4jIkikOnfmBnzsGO87fjytZBddxDVjGOznkSN0/+nenWs9Lo5AKSeH1xo/noRs5072xWbjOevXs59WKzXetbUq2cDo0Vzvubk8tkMHArMvvmDiAl3ndWpquCbtdgImwyDAO3iQMtDlIngODqaVLiGBMmTTJo7jokUcD6+XpO7OOwnGp0whqFywgMRpwwaSwNWrSZIHDOB3hsGxuPpqWgH+8x9V6697d4KpzZupZLv4Yo7rJZfQ2jlxIvttGBwvm42keP58AlyJQ27fnqS0eXMgOkZD1zGt8G30/fha+w02bzJQcsKLW28x0KMH3x9jlnQAPlRUNIDmL4y2BUBnTdM6aJrmAHAdgAXmAzRN6wdgNoAphmEc/wX6eE7N7Lo5ciTXW3KyioeX0j9TptDKMmcO58PAgSp+S+JYKyo43+Q9smj56VtjLpxnQ97OJhnPD9nizAZVKW5eXs51KyUJAgMpA8LCuGbq6qi8OXaM/0t2WLHEWa2qWPj//R+VKwDxRFYWXQy//JKEprSUa2rtWn6+bRtlSJs2xCgREbyeeAvl5xMXbN9ObLRuHe85fjzf4b59xBFjx3L9/uY3tMBVVlJe/uEPqm5p+/ZU0CxZwr6Ywz+sViq3Q0LoNbRpE2XPk09S5nq9lAMDBrDPbjetvjNmEAutWAHccacFA8eEwuHQcPHFxFEOB/EJQOX66NF8FikxBVBxNWaMSsa0bVt9b66JE1WuhYULuVdJ+Spp/fqpxDRuN+AM1TBu8z8R/p+3MS3rEqxdp2HaNO4BnTrxmex2Kr5atVKFzn+KGOHzSfDOVQs1pTEt1Pl2M2hqv0wzEzGzC4c5g575OLOLx/bt9QmdbBorViiLnTSpN2fOtCeEMjlZkSy5n1xXtIs2mwKekpDF66UGaNky9uWWW3j82LHUBIWH829ApYB+7TWCqpdfptAaMEDdLzlZPWtiIoXVn/5EAZmYSJL1wQfUaA0ezD4PHkyAdNNNKkjZXath/VOLMe+tY3hr4kK4axnQ+9JLPPeKK3jejh0cF5eLgu+ppwj6tm9XVoYJE2ide/VVktHYWG7Aa9ZQoA8ZQq3f6NF83rg4CvVxk8yq3LAmF81fWXO76xcobqyZM2cCKmmJWNhXriTpuvdezu1776XFaOFCzsuFCwkEW7Tg+ggMJBi027l+YmJUQpV336UV7NAhEqCMDMaH9OhB8HTLLfwuNJQgoayM5OTbb6l8cTio0HjpJYKRtWtJ7G64geBs7lxqrQMCSPI6duS6PnaMa237dlqboqOBujodhYU+lBQb2LSJ8qhfP5XYKDiYm/3EiQRXX3/N56iqIuiy29m/mhqCrqgoEr6SEiqaLBYSpbAwgpeCAi5Bh4OWSEmJfs01fNbUVP5//DjJaGUlAVT79tQq9+/Pe+TkUAZlZ/Ozbt0ISC+5hC5Nw4YRoAQEkKQdOkQSumoVj7XZ+K6WLKFbaGkp3/Xs2Xy3mZm0Hqak8B21a0fFWp8+fE+SXbRdOz5rp04qy+KddzI5zd69FkS3sCMwwEBMUSZCK3bio7/twYEDOjQNCAnRQWIA1Gav/eXSKJ6mGYbhBfAAgKUAdgH4wjCMnZqm/U3TtCknD5sGBjrP1TRtu6ZpCxq53C/ezApaccVOSuJPSgqVlKtWUdE4eDCJQmYm3fV27eJPcrK5jiEgWZrV3w2/R3GvbCxvwM/x+v1LOEkTUihp8c1xgmFhlCt1dapw+ZQpXEPMUs11Gh2tiGJFBZVEYWH8/ec/c+wiIzmmnTpxDT/wANf5mDH0gIiIYOr/TZsot1wuruvDh7mfl5wMo3c46Db92muUA088QWtfSAi/e+UVKnESEhj2Ignl+vWjVfb//o/P+M03lAFJScqt8sgRkqj9++lxMGmSSjwSF0e3bHPN4/h4hee8Xo5V9+7EZJMnU+aOGqUUQevXUz7ZbJxrgrcAzs3p01VymYgIVXqrZ08VGy7vZ+lSKr/mzlV5G8rLeYxkLHY6ObZS9xgWC6zRkQgM0uDzUcl2663Ej+Iyv3+/KvsDnJ2S9EztfBK8/zkt1K+5nW1ilR/SpDYOUD/42lxA3N8NRGLsZOOQDJsjRijy5nKpBCOSxMTloiaoXz8uviFDCOBkE3K51DUlTa7bzUXo86mYupQUapkmTuQ958wB+vXRkbKwFBvWG6dczHw+fu900l3K4aCw7tGDAjItTSWQAHj/2bP5/UsvUYBnZyttksfD5/P5eO7DD9M1avJkfjdzJlDjtiD7WCQ6dNCQmkrw9PjjvH5REd3L+vcngVy8mGD1mmv4e/9+Fur8/cRC5OcZuPxyVfx8wwYef/gwheHhwwRxCQnsW/PmtDYuWqQBaAYA8MSNanLP/JW1wEClVGmolZV9X3kj61pqWw4fTgXIN9+QzM2dy/nXty83wbQ0EraSElq4hgwhIRFXyv/8h9MuIYHrraiIFgCrlWQDIAgSorR6NTdWTeNcLylRcWvLlilrvWj8JRPmokUEOFdfTSA1YABdvjt2pJz50594bE0Nyd7ePToqsvYgFIVIqFgLw9BRXc1nk2QSl13G85csIag9dIgAwjAIBsWiAfC4ggICwbAw/g4KUjW0xo6lTNm/nxYsl4tuijExJL0HD1Kz3rs3AUdWFmWDrvO6YWF8j1lZPC8vj2O8YQPlUmkpZdWkSQSBR4+SMAYEEJDefjuB3qFDlMWlpUDPHjqeua8UV11lID+f1zpyhODz/fepLEpNJYH/y19YlmHvXpLG7dup1LJYKNPKyzln1qxhn2NjT2rBIzzIQwzy0Q5VsCG+rRuGAbhcBgD6aXbC1gvWu8AwjMWGYSQYhtHRMIznT372Z8MwFpz8e7xhGM0Nw+h78mfK6a/4yzZRzNbUcB5IUjJJjObz8b0+8YSa63Y7FTEtWzZ0RX+rXcNWPHF3+yXb2QB1swVIyhsYhiqHEBJC61pQEMlN9+5UyAQGct43b04CExqqMowGBhKjBARw3URHE4d88AHXT0EBr330KO9XVkZsERpKfKRpXLtvvkmSFR1NZdMLL3CNPfIIz7FYiD3efpvk78UXKWMDA0lyBgzgO9y0ie+zXTtitQMH6H0wZw6V3oKX5swhFrnjDirYDh2i58aGDSSO69bxuTZtomUyLY39FaucxAquX6/cLDdt4vmPPqowm82mLHaDB/N5tmzhmKSkKOX98uV8L1KOB1A1UidNYh+WLuU1Jk1S+DU1VSXgS07mHpWSQiIryaPateM7HT6c77NZM5VJ86ewZZ03gve/poX6NbdzzZ55puP8s2n6x9tJ0hYhSZK9EuDf06dz0QLKAiBCVEoWyN8PPMC/Fy/meTYbhVBEBBfam29yIS9axMWYkqKKUQohFJI4bJjqk5RSkGboOjaN/SP6Xt8VOx6aA69Hx3ffUSAuX87rvPUWz7nrLmq2pkyhALTZTlrfTgKV22+n5m3DBn43dSrv4fEQfDkc7IuQ2TFjKMx27KBQvOgiaqBiYgjaevakhcPtprUvPp7P+I/nddSWVsOiGejUiQKnRZwOz4cf476k7Wj7n79j8EAd779f3+V0yxa+n7vuonDPyCAQtdsJCHmsBYAVzZs3kbv/9dbQem+I3Hm9BATXXkvwbs6q5/VyjU6bxs371Ve5XiZMIKHo0IEk5O23CdoefJDkTergffUV3TJrawkW5s/nxrluHQlTaKgqmizlP8aNU+5eO3bQOuT18vvoaLoq2e0kDT4fN9/4eAKqgQNJovr2pSwpKuJ5M2YQuOzZw5hAcfsMCjqZwryzGy4EogpByEAPlJfop9yohg9nP9PTqTCJjlYWxdxclcVy2zalxa+oIPhyuwmioqLYZ10nmZo1i3LA4SAonDSJRFQKRXfoQJet9HT2Nz2diqjmzVWSlmbNeP1Nm/hdfLyyCg4aRGD17beM5Xn9dQLK777j54WFyuMgLQ2wWHRckfl3ZD/9Np57ogw7s3R06UIrxYQJfAc33UTrRFWVsgjGx3NcDYMEoEsXWhOCgkg+c3P5XFFRPO54SQAiUYHBSEVrRyVsgScDa2ABQB+oAnRt8i74Ae1clL1m6514yzzwAN/zq6/SmjJ6tCpaXVXFeVJVReVKQkJjsXP+0FX+P3u3TeCn1z2aMUFDzZywRWrGVlYqxdGQISoLZ5s26rh9+7jHahrljN3OPd1i4fyXsicAFSTh4ZRpPh+t+n/5C9dv8+a0pC1bxiyXn3/ONTpnjiqaXlLCPb2mhhb3jAy1BmNiKJccDsqmp5/mcbm5JF/9+qnyKgkJxGCLFql+er2UTUuWsL8ZGVTeeL1U2HTrpnIPCHlNSGC/Pv2U2E3ipH0+PlNiIuWA5F4YPpwu7xIDN3q0Sm4HkHCJkmH0aOIZCdEZOFDFgE+cSPkuMXqzZvEew4dz/O68k+dK0pe0NGKfGTOIzUaMUMaLESOU2/1bb1Gx9sYbJL/DhvG7vDyO8cGDPEdiAX9M04yGokov4JaYmGikpaX90t341TVxhTxTk3g6/0x55r8bK3Luf5zUiZsxgxuClAeQNLVi7RNiNG5cfbO9tBUrSI7GjlUWueRklYDF5WL2ojvuoGAcN46fp6byPICL8LHH6OLo8XCxDxnCBRoUBAxoXQhnv86YYdyLBOzFpYfeQvKu5khMpBAaPZp9HD2aJDIxkX186ikSsnHjOG5pafzO4+F1JQg5NRU4dEjHK388AVvLWDzyqIaRIxkPd8stJLXPPKNKGyQk0EXhww8peJ1Ojsv8+XyePTk6fv/tVZi9eQC8LdvDcsMN+P1NFlz/Ww8m7Z6GzsjGUlyChPsvgzcoFA4Hhdjtt6vYl+xsunoEBVET5XAQ+H3+ubJI9uxJd5szNU3TthqGkXjmIy/c9muUTadbz40d53KpUiGSkGj0aG76n39OAmgGSh4PN8aVK7m5fv45Acm77xIcVFZyU+zShYTA7SYICAvjxp6RwWOiopTlWdZIdTU39eXLSYhCQkiU8vI4xyMiuPZatiSwOXjwZDIjULs7ZQoJkmiNdZ3roVkzHhsURDDmcnFNREUZKC52IQHZCIcbWzAKFouGiAiu13nzeO3oaFrlc3II9CRxSkwM+1ZYyD61bs1+Hz3KcbroIo6zFHzft4/PJGUIvv6aQOrbb9m/Hj14Xteu1EInJLCvLVsSkI0YQTJ+5ZUEeWvW8LMFCwjwundXyp0hQ+iGOncu77d7N68zeTL/37kTGNS9Eo+/3Qmv414EoBQ7Ln4BJ1zBp9yqPB66kmVmAvv360ho48aUa4KwOlk7Vdvv0CEa3iorqcD65BO+x5AQjkPHjsCuXTqCSw7iGJphWGAGyjqNRGaWkAAKp8QBwJa0MyDyk61JPrGd7Xo3N5eLwHvoUM4fyWZ4661cHxIDHhur6jquXMm5IMl+Gm7ipmkBoKG+2yZOfnZBpJlosAUFcSw0jYqL5s25h+bmct0cPUp55nKp+FyHg4Q3KIjrUeTNuHEky5KYSDJbXnMNLaOPPUali2FQbgUHq9px27dT0QNwPe/Zw/18/ny+s9692Udd570XLaJ86dSJsqhdO8qagAC+y927uU5PnKCyTTCXzcb/a2spM559lp4WYn0rK2NfBXMlJZFITZ7MZ3r+eRJAh4MyKCKC32dmUvGUlkYZFBjIzydO5HVkLkmdPfHYEgwpBoRp0zj/rrqK502YwDEdNYrPIOeUlRFTyrhLtnaxFvp8vPeyZdwbZsxQrqETJ/I6S5awL/36EcPdfz8VnP37M7nNjh38TPJHnK6dTjZduLO/qV1QrSEy5t8kSYpoQeSzxhKg+J9rPg7gQrHZaFYfN04dJ0lShAQuX063LXH7kCQs0u+kJAqFwEAKSwn0FVfLrVu52ezbRwC5cqUy7wNclDYb3bAmTODCvu02aneysqhR/uP0WGDYENyHt+HsEQ9by1gkJVEI+XyK3Enyg5kz6cr1l79wc3v5ZQq3vXvZx507VXr44cOB/v10vLL/UqT3vgmBUybgL8/qWLeOGq05c6hJe/55krw77qCbxOOPU1i99RbdKhYt4vNs2wZ0a1GO9M2ABxr2H7Xi0C43Pv4Y6NrLjvsHbcF+dEOaNhw1Nie6dmV/HnuMfRo2jGMVH09LY04On7G8nEKM2khqUouL/7sUSE3t3Jqs57M9zmZT5G7xYsZtbNqkYnBOnOAcmzCBPwAJ2rJltFStWUPA8Ze/cP1GR5OAHTjAzXHNGhKexx4jyJg0iaAgOJhrsVMnKmnCwkgUPB5utrW1JAjNmhF8hIezL/n5qkxKjx7ckNPSuC69Xio5JEOfALWyMtUfu10VJg8PB6KjNQAh2INE7Awehbg4DRYL18yOHcq6Nno0ZYvbzXFKS+P4pabSildbS0VQZiaPCw7mWOTnEyxJcihzUWSLhc+dn08ZdOIENdDt2/O5r7iCSq9du2hFKyujHBRLnsS+OBy894QJfH933015uHEjyXPz5iSEbdoojfz27XyWgyecWJVwL3aiC963PYan/hqEa66hdn7AABLXNm2Aykod40q/gGXlAhx67n0kjdYREEDw26ULFVq33MJ7FhayvwEBJAm7dgE9urihoQoafDjojkZmlsghk3Vn60fnN/bgf7D5799n462TksL35XRyPV50Ea26AIlIejrnkmHQjXnXLv4dG3smF0t6iZDIAXyvuunvC2Pvaciqp2mUPRJrB3Ael5dTaVNWxnUnFsagIK7fujrl4VRTw7U+YQKPv+IKrr0nnuC57dtz/Lt2pQzZu5cYw+mkrBDlWIcOxDJRUZTF3bvznbRrR/m2Zw/vtWED5c2AAVxrgYGUp3a7Sgj34IP87OhR4q633iKeefxx9qVPH1r9X3yR5O7AAZKylStp/fd4KCu++IKhJXPmEOMBlKfZ2Tzm5ptViYjbb6d8LC8nSVu0SJHEZcvYd4eDRE0+F/nt9fLekvHz9ddJar/5hkqs1FTKOMm/UFZGV06A15J1MH68SogjeHHsWMpBgN5jW7bwbyHqLhf73qmTus5vfgNUVhA7HT/+4+dvE8H7H2vn4kb5Q1wu5byGXDZtNi6OWbOU1s0MAP2LnPpfW9w0zLF3EgybnKwWumTZFB9+h4PCZfRoLtZFi7iYXC51P7HWzZypzO7y+ejRBDhduig/cGmZmbzO1KkqSBegYLJa6X/tdAKXX6HBu2AJtnx+AEPWTYPXp51yHfX5KAy+/Zb9czjopnLPPRS0V1zBa4prwu7dBLEOB4Xf2rVA+voq2DZuxEB9A+anROD9N6owbRqFeOvWdKV491015o88wnH/xz+o/duzh2OSmkphNeSiCNQk9MVmDEWHOAOX/y4IffsC11yjIe0PX2Pg2/fjmbfawm7XsHgxn7V3bwrLrVsJMktK+FlYGK/55Zd8Vl3XAVQCAJzH1lyQiQya2k/bGipe3tDf5kQLAMHCo49yM5w0iZt6djY33FWreEznznRHeuYZbrZ2O7W/Viu11MHBnP8LF3KqZWZSeZKdTbfNAwcIfMSdePlyHt++Pa8VHk5w06cP7yUuiACBytChPH/RIn7esiULcvfpQ5IkhZbF8h4Xx3O3bSPokFqXoaEkQFFRFoSGWhEQoPzDqqroitmyJfv01Vc8v7iYhMdiYeyM1NYLCeG9J03i+g8I4Fp0OgkKBYx0787n69OHsmfcuPoJBv7+d67d7GwCq/feo7x78UWe07Il1/zf/sb75+YSJLZsqZITvPIK+19ezvXfvDmB2Y03Elx6PJQ/eXlAQYGGmsf/jB4PXor7/tEGL72s4YsvTBkyI3SsW1qNmLAaaCfyUIA4tMhLQfISNxIT+cxduiglWbNmlD/r1/M5HQ6C24CQIOShA7xw4gSaQ0EdA0IIPNAobJvaObWGlLfS/BOVmZtYcbxezuf58/nZgQMkFuvWcU85dIjr6cgR/wLlZ3K/tJz8kXd8YcBbf4IXGSkZbXVUVPB5JCW+uCYHBXEdSm26igqu85oaykhJ+3/4MMlQTg5l1/DhjH++/nqShWef5XX27eM6dLvpWnjjjVzLV1+tvGwcDuKhZs2IT6KiSCDtdsqili1JrN59l1amqVOpSBO30xUr6EIPkBwuX857bt5MUr9unUo4tXYtPQgmT6bM6dKFpEsSt112GT+77TY1fhMmkERNnkyrV3o6n+2ZZ/gMGRmUA5s2cW6VlfGY7GzuM6tX00NCMJlkahZ8tngx5+YHH1De7dvHe/fty3GdMYPXf+UVPo9kyZT9TPCkGB4k6cratZTDdjuPF4viwYNUUl1yCcfK4QDatNFx+CA1cq1XTv/R2KnJRfN/qJ2Lu9TSpZx8SUn1a781ZlkzkzT527+UgTlRgjkGxz/mTq4p8XMjR1LTkpFB4ZSSUr82ln9/1q2jJv3tt6khluLIKSkEGdnZXFCysAF+v3Ejj//oIwow6eOKFRQQTz9Nl6ugIBWs63Kxzy+8QAImZvmyMmqnLBZq9pOSCCpvuYW/e/TgOK1dS0LUti3vO3cu7/vyyxQQHTvSF/6552iNs9l4zqhR/C3CJyPDgOXDD6Dn5SIlZBLiJg/C7Le1U2PyyisUGE8/zWQEnTuzj2++qYSqrnND+PvfCUL/8Q8d61d5MGR0AHr21LB3L+PqnnyS45WdreplDRzI80Xode1KDdxFF/H5cnN5z9xc4OhRHwAXACc6YR72Fowg6jtNa3KB+u9pjcmJhtywAf4t7lmAqhkk9RyXLCHAsNlInL74grGpyckENC+/zPUwfjwJ1tNPU9nSowfvlZFBC3ObNozJ+/BDAoVNmzjPU1NVVsvQUGqQ9+3j5t+8uSJXPh832fBwWrleeYUKEomPadaMazYkhCRCsvpNnMiNX5KpREbyWpIIxeHg2qisZP+vukodL9ewWgkALBYSVinXIAWfbTbKpI8/5rPs3Mn1HRDA42Ni2NfrrlP1svr143H33kvNdGIij09Npda+UyeCjeJigr7bbiPwCwujTN68maQsIoL9ufNOjrXUjmrdmmOxezcBZ0EBXcosFvbt6FGS6J49+W7kuJISvs833lBeDenpwID+OhxffYg+ed+i/wAdd259EFnoiE44jGYThyIrywpdJ+AqL9MREehGp15BqKzUkJOjw6j1oFlcAOx2DW3bAosW6QiyeqAFBGDnTiHTQhIs6IdPsc13/Vnlym+STw03f4Wt7OuiRBDAu3IlScLvf8/9sUsXgu24OM7xBQu4Dnv04Lr9Pq49W/dLf7fNC6NZLHymgACpc8d+aiiHAQciI4MQ7PAhIMSOAwc0tG5NeWOzcU1/9BHHaedO4pJnnqGcLCigPLDbqURp04Z788KFVNBERlKZtWEDCVrbtpQXeXlUqqSl8bxhw0j2rFbeQ5RVO3dSdu3axWt//TVdot94g8R8yhRFwHbvpvx4913lxu5wKJznrzi/9Vb2Yflyzg3ZM+6+mzL+b3/jXMjKIv6YPJl4LDWV+0ZgoAqx2b6dsldc8iXEZfhwHvPee+yT2d1y5UrVH7PbpttN5eLYsWpui5spoLK8C85dulS5ZQKM7xPFv2RIT0pS1mwpZD50KDHknXcyGdcf/wj8+fFqpC7ai1z0xBu4DvcVzPxR2OnCUHE0tZ+kNeb+2FCzWlWSDvFBNmvjGspy5/+3NNFYyDlmwihkUqxvosUbObJ+2QOrlcJdtCDmWDq5tsTODRzITeKuuyhAfD6l/cnJIQAYN45AbtgwLvg1ayio/v1vLj7JwOn1khS+/z7JigTUrlqlYvsACqOMDN6rrIwCo29fmtizsymIbrmFZCc+noJTYv+6dKEQk/EIDKQmzDBoXbv3XoIlSaoitWfKywl8tm8HevTQ0P+1m/FI1t14efUgxHekZe3qqyngevcmuP373wnIpkwhAf7iC/avb18KuM6d2cdVq4CBAy3492eB6NlTw7ffUvCkpioN/JIl7P+MGSS3aWmqHs7XX1OAZmcTuI4eDWzapOPoUR8GDQSYOwmoRCSleFP7n2jmpAnm5p8kySwnJHmRx6NqDq1Zw/Xq9RLcdenC+XvDDSQr331HK5bDQY+A1au5rpYs4fopKiIQ6NePhOSSSwhm3nyTwGPjRmqgQ0L4+f79nN8HD3K+HjnCtbd7N+fzsGFcdyEhXHcAN9ySEn4uAf8yBsIJ2rYlCAoPp/tUu3a06Am5A1TiIcNgjFt2NjXzLpeqgRQZqWKOamrowhYQwHHOy+PxH35IN670dI7rlCmUmQEB7M8f/sBnlnHfsYPgLiuLGn2xEjRrRmBYVsbzY2Io2559lmSvVSu6VA0bxncSFERFWWmpqksHECjefz8J64kTfJ9XXEHwefQo/8/MpEzet48gbtUq3u/AAcpSIbgDBwJ92pXDmncE29AHH2zti1vxDq7Cl3DBiSDUIipK3Nd0tNi9Cm2T38TS13Mwb54Pnq0Z6LbtbVjXr0L79jp69gTsdgsCwwIRE0Ogz36LlQewJZ4duWtq329mfCAyAVDljJKSVEIygPtDQgL3PVFcHjtGV7jNmzk3KyqUIvL7zUzuzmTFM7ttXhhNnqm2lmsoOMAHoAAWWBGFfagrLUVxYREOHKiEw6GjvJxrvbSUa8YwKMNCQriXFxbSkh8cTPkjdTc1jfO8RQuOZXU17338OGVTz55cc5MnEx+1aUMM0aULXTa//ZZr0+PhMYmJJIBeL2OfBwzg3z17UvZlZ6vYtjvuII4ClLIMoOJ60ybKAsGd3buT1CxfTsvck09Sud+nD7HHRRcxm/iXX7L/Ph/vm5pK+Th9OveAe+8lzhkyhNf+8EMSs6Qk3nvtWmK1u+5S5QtWrOC8lNrD5hrIggm/+Ua9u5kzVZI5wcuLFytsOHo0Ze2yZfxfSmYBCs8GBvJHssZu3849StdJiP/v/xhiM+nyIISDN3O17v2jk0A1Sbf/sXY25E4mndNZvw6VuTi4mez5g7fTNSEx/kAwKUnVx5I4OCGW69ZxkTgc9bUqomWpqaF71Msv82+nU7k3jh5NAJKYyAXUty+f6e23KYzS05XguPNOnMoWuXIlQYXbTdB4550q6Hn1agqWsjIKpq+/pvCSLJZPPUVt98iRNOf36kVANns2waGAmvR0LnyHg899770kpvPm8RpxcXyW//yH4EoKmEsBz3ffJYg7fJgaNE+dBc+93RyXXKohK0uR4sceUwLpkksoZPfsobb/rrvorllTQ9B8880UTi4Xf6Q/rVqpmCRN47MPHcpN9557eExUlMo8eOIEEB6mI755JfLyDFSU6+iJDISgADu3FAGgmioYFZTETe1/oplr15mz2zbUvF7OsVmzuD4zMri2PB7+vPIK/7/kEq6VO+5gDaTPPqOGuFkzgr9XX6UlaPZsKiiuuoprR1yxq6t5/okTtNBJwp/nnydA6tKFoMfhIECaN4/rNDCQWu3QUMqDoCCVvCQkRD3v0aO8jpARAWkAiWJNjSrCfPgwPzdn55MMm7m5JJRSRNfl4jjU1fEYAYEeD+VGfDyBmmRTq6khEI6M5PXXrCEQat6c1z54kIS0tpbrNyaGhO3YMVXfyuGgLI6Lo4zJyOA1JZ62Tx9eu6iIY791KwFfZCQBic/HvUPTOO6vvcb+SnzQ55+z/lanTrzP5ZcTCB45wme56CKObWIiM/22bk35u2cPsGRTBNrHW7AJQ5ESMhlp0ZdiJ/ohKqgOD/8xCBUVlLOj+lXh2bIHsRP9YMCNxK5l0Dzl+AJXYEt5O2xe50F6OvuVnU0ya7Opos8kAFZYrE3w54c0f4WuWaFjVsSuXMl5NG0agfy4cbTMjxzJudGjh8p6uns355BYN1QTi6uZsFlxoULXoKD6GKmh+Lu4OKC61oYEFKEbdsCHlojEUQSjBm2Qi4QOHvTrR0JcVkYycOgQx8dm41pZvpzrPSqK+CMnh+u4e3diiKNHqXwJCyMJvPJKlblxzBiVfVbqzdntVGJPmULC9c03xAmdOyvPgpISEsIbbiDRGjCA2GLCBJLMjRspF+6+m1jD5+O9xJ30wQe5JiWGrV07vu+UFMriiy5SxGzUKM4Li4W4SWTg+PG8Zl0d5dNVV6lafhMnsk8S8zd8OMneQw8RxwnmBBTuFAuby8V5OnAg+/LKK8RmgYH09kpJ4XESRiTKfoDHPPIIn3HRImK7gQP5nSg+xJUzJYXPNmgQlWePPUZZGB19Mk47QkPv3/EmQY//6UeneT0LOtDU/hebCCGxopndLM7FEijHy8IUS11SUn1XDYCARKxn4gNttarMR6NHq7gbf+C4cyc3BICLZdYsLryNG0nqHn+cQNFqpSWsY0dqbqQMwQcfEEBefjmvsX49BcSgQVzYTictVLm5tFgNHaoEltNJLc6uXQSUF13EhbprFzVZUqbA62U/dZ33vPNOXlNInqSILisjqcvIoLYqK4vPIHXutm6lcC4roxXh5pspRNes4bWeeYagbcAAWiy8Xj7HokUU7BkZBJRXXEHXrHbt2P9OnfjMzz9PYLZlC90/vvmGgnPLFpUJENBRdtSNfzwfhLvu1hAZSQG/dy83Hq/HB9eO/dibfgxdEYLD+3vBgUq0AKDDhwPoDQDIReQvmopc07SLALwGooJ3DMP4p9/3jwK4A4AXwAkAtxmGcfjkdz4AmScPPXKh15r6OZq4pfi7YTVk0RdXPnGH7tGD82v/foIJ8RYbN45ro66OLisJCbTeibVbCmp/8AG1vs2bc20+8wyvHxlJklZZyXXfujXXwJNPcr7qOsmIuD7W1JD8BAfz3PJygq6CAl4vP5/Hh4fzeeW7iAieU11Nq1pgIMlKcbF6dnHxkYB7p5OuZw4HjxML4ZAhyloREMB7HT/Oa1itvMe2bQQ4tbXsQ4sWvG9YGD/z+SirDIMAoU8fPr9YRHNyCM5OnKC7U3U13bdycvhuioooG9q0oZdFcLAKSbv+elpH160jCJRadaWl/L5NG8rvigrKoR49CDr37+dyHzxYeSAAJOtXXcX77t/P63z4IfteWcnn6dRJQ/alT6N3WRUczZxYuqQfwlp5cN3tAZjzjobJkwmcdu9y4q/hr6NreTqKHVPQoWcEDqdWIhRu9MAeeJ0dTqUsd7s5BwD239ykVmBT+/FNshFOn855KHv5m2+SWKenc73fey/fyyOP0OIhBe1FfpjXUn23TLPF7vTul5Ls6Jdo/gS1fgwh13ZhIdCunYajxb0wfkQVCreGwHXCi1JEwo1KlOYH4FA+5c2TT3K9mjP5LlxIWdG3L4lPbi7X9tGjXO8Sl796NcnY4cMkanv38ppS9Py771RMb+fOxEdRUcQWUuP2yBGu8+hortuUFMq0pUup7JZwkocf5vNJMp1LL1V5Dvr2VS74O3ZQpo0ezePnzSNGKSsjRjEMEtZt20h4DIN/jxqlwnnWrlVJTADKwIce4n2HDyeJveQSVRJLykyNGMF9QyzLQtak1uCBA9y3MjIo+y67jN9LjWUhhWPG1K/zKq6l48ZxrARPmvNPrFun3DQ3blTZN1NT1d+PP36yfMQeKi+coT9iIp5sTQSvqdWz2o0Zo4CVNP/4Gn8wB6iJLwshOZl/Syyd14tTWlVzelmXi5uAFB/OzORi3rKFi1kKR158saolN3IkiWJgoCJi06apBCZyj7vuIvEaN44LVkzxZWUUXJMnUyCIkAoKogUBYJ8Mg8LB7SbJ+v3vKeS6daNm+MEHuZDXreO1R48myCkv5ybz/vvUqOk6heGNN7JvYum74w66jHbrxg0vKorCXzR369bxHkFBFFp9+rAPQUEUtO+8w+xx+/bxPgkJyp2jXTuSvrfeYl9uv53nzZpFwLl5M0G2eUPq21dcSXVMLPsUh7+z4I1NEeg4ZBL27receq4B/XUgORnHdBt80FADG9zVdTiAvuiPDUjDKIiGNfoXLHSuaZoVwBsAJgDIA7BF07QFhmFkmw5LB5BoGEa1pmn3AvgXgGtPfldjGEbfn7PPF0JrKMYOUBZ42dxko/TPqOd287vkZFVQ1u3m5r5vH+NZRbHRuTPndufOnCY5OSQ/AQH8v3NnbnpxcdR0zpzJtXr8ONevuEKWlPCemzdzbYaHcy3pOtdV+/YkEV4vyUlICNdyZSWJZXU1QVF5Oc+x21VSgspK3qOsjL8la2ZdnT8gJQFZt04phgoKeHxCAgFTSYmykIeEkGSUliqyYbEoN/aYGF5v/nyCN2liZW/dmtevruZnhw5xTMaP59gOGEByV1FBgqVpHJNLL1Upz6OjSRjHj1elD0JD6Z65dKmy2HfoQKJutfIdaBrw8EM6UldVobDKibw8Db16AW+9oePNV91o3S4Ib7+tncpEnJFBmb5wId/lN99w7K66inNp1y5eMyvLAoslFB98AGiahk2bApGbR2C6YwdJ6YB+BlaX90JzRKONrQDrVrVDGbqiOfJRiEhkraVMlfppERENE4f4+AsrTuu/oZmTSfi3wEAmTJK/x47lfP/4Y1pEHniAa1OyVXftqua91HCr3xpiaQ2YxPzP+oXInTRR5pr3VnNJg4oKsfRbMG9JKMLDgdrAOMQEelGHGHi9GqKiqCA+fJi45sYbic/WrKFFfN8+rs05c4gtIiOp+Jo8mWEaViv/FyVMcjLH+IknKG9WrKC83L+f/bNYiBfEGnjFFcBLL/G+c+cSX8TH814SF+dyUSH0ySdM6LJlC2XQ5ZfzGl26EI9YrbyuzUa5+uKLVG75fJRH8+fzOMl/MHGiKisjOQhSU9V+43DQ8iUYtWdPVSZryxZiNsG0UjRcauIJvl28WNU+BfhMV11FbJmZyf6J4t5qVW6WQtg2blR/m40hUpsPUGW95Bghd1KnWY53u9nvoUM5Z7Zt43fiMfJj2oVp525q9drPkclZtA1eL0Gc2ZRtLl+wYoVyzTIXKhZtiH98nvwfGEhgGBSkAmQBTuz77uMicjj4t0x2MZ2Laf3ttwnUHnqI7pqLF1MwyL3feouxaBJku26d0iBPmqS06+vX8/9duwhiPvuMz/HAAwSI06dLNkje+913KQjEGiBEVRayOaunx0Ni17EjBZzPR+EuLkL9+lHI3XEHz5eNTVLAX3IJteHPPUdyd/nlvObevQSfH31EAeDzqdiW7t0JziRpgc9HQTJ7Nvt72WUU0osWEfAtWcJ+JCZyzNq1Y5ry7du5KXeIqUKXwlUoQzM8V3IvbrysHJdP0TF3ViFatjCQtc0Dn6sYFgBVCEULRzkCQoMRgUrkoSPaQ6nKa6obDKj4udogAPsMwzhgGIYHwGcALjcfYBjGasMwqk/+mwqg9c/cxwuqNZYhF1BrWOa6uLjIeUuXMubzwQf5/5AhnNMvvsj52a0bScmmTSQcGzdy7bVvz001L4+W6+PHCTgiIwkIg4O5riVb2xdfKEBx/Lhycdy1i2tfXCElg6UEuyckkOTJsULg6up4DyFRuq7i4STpCaB+S+p2f+08wDEJDaW1TTJxVlXxWY8do4xr0YLALSaGn7lcBGHBwfXjj8rLuSa7dFGhYlJXD+AzVlSQjBUVkTA+8wzdyx0OehqUlRF0dehAGVZTQ5k2fz7vGxurXEKvvpqg2+zWun270txXVFDenDjBTLk1L76A+V+UYd+SvSgt0dG+nY5lt3yEtotfRf8lf8WD9+soKCAYjIriePTooVxMIyP5HAcOKHkZEsI+/u537Mtdd/G9rVlDMjpqFPDuu16EwIX22Ius6li0bOlFKLwANJQiDjabBRUVag9rzCrk+XhWU4bfc2j+skFCMMwywGZT7ttLlhAvvPIK57kkV7ntNrVuU1L403Azw1MD/y1wVdPqk7uuXSl3mjfnnI+MVC6HEREct+hoCzSHA9XVGgICKItef50KkY4duZd37Mj1XFnJPX3xYrpeFhbympWVtJx36EAcERLC//fsIdERq53XS5JotXL6S+FxWQp33MH7TpzI91RTQzmQlkZZExHBWL2NG6mAKywkyUpNpQzfuZP3WrKE50pyGK+Xf2/ZQgzYrBkV6cnJnAuTJpGger1UFEhJg8zM+phRiJ6ULpDkJoJhJQTn1Vf5/GvWcF8QDLtsmXIZnTiRP5Mm0YiQnk5L2iOP8Hl8PlUuS+4h9xH8a25WK5UXVmv9OHUJNxJyN306v5eakL16EctKDgig8USD59KaLHgXeDvbzJgNndeY1a2xJpPXDOL861yJa6X8PWoUF8z27dQYi9aiocnp9SrNvrh1CamUzHqi4bDZOPlnzCCBWb+eG8PWrSQ9DgeFzYwZSogYhtKe+3yqjIE5O5LE9JmzY8fH1wdr/fpRc752Lf+//XZavvr2JWh94glaAQYMoMCEocNXXI6vV0UgaYyGsDASseho3vfZZ1Wq4H/+kxve3LnUOB08yH7abNwILr2U12zZkqTu5ZcJqtq2paAeNIiARywiYq1s25Yan5UrVd2r+HiCp6+/VmPy0EO83ubNFN4PPEChuno1iePu3UDqDie+tT+JpLr5WNPxDljtofD++yM8Pa0lWjTT8caqcXh2VDNYK3QMDNuHVc6r0aWdBVXVcaiq8MEbaAFKPAAcsFSsAzzDVMT1z9taAcg1/Z8HYPBpjr8dwHem/wM1TUsD3Tf/aRjGPP8TNE27C8BdANC2bdsf299fvJ3OPVvkwYoVXL8SiyfyQWrK/fnPPPbNN6mwESt9nz7KildQQFCQl8d553BwDssm3rUrgXllJdfCt98S3EhGs48+4vyNiSHAyM3lXNc0RaLq6nhuejrX6rffkhyZwZemcS317k0ZJp9J03W1nqqr1XM21jp0UIlcpEk2XoCfiwv33r1K7pSU8LfVqpRLlZUqfkXAV7NmlD2ZmQTLVVVcs3V1ysWzQwcCyo8+4vhaLPRYGDqUxLJdO46ZaOVTUzmuu3ZxDAMCKGtKS0ky7XbKvunTqdEfNgzo2qoKGxa1xXCk4Fnf8/i890a0cHrx5dE2KEFvbNlTgiHbq9C5c+ipTHtbtrA/x46R1Asxb9+eYzZlCvvUogXHx+OhR0F8vPKwOHAAcATacV3rhQjOy0EWhqGyNhhtxiZgV0ZHlLpsaBFDl/K2bVXNVNUMkORZEOY+xJd7hix1TY3NXzaY3bSTkpQskD09M5OKyxdfZN3K//yHn82bxzV2ZguFdvLHwIWUOKVZM7VeAZKr0lKFMfyPO3KE8iw2lsqS8nKOYUgIFR+6zvFo3pxzPDycMqN7d65lTaP8CA2ltX3YMCrMIiNJYHbu5Lrq2JEKMpuN5w8YQFL2xBP8v6iI1q2lS9nfjAye36WLImTffcdrSz269u15z7vv5jNt2kSZ1Lo11+ull3Iv2LZNHTNxIl26//Uv4jWfj5jK66Uc2LGDZGrIEOK3F17g+VJ43GZThdelQLk54Z5Y9kTRPXYsjzF7no0bp6xp48Ypj7HFi/lcDz2kCBygDACyl61Ywf6tX6/KHQhmFaI4cybxk/9emZqqnk0s2itXUv5JPec+fXiN7Gwqsd56i/8LYQY4TkOG/Li52kTwLtAmYOpc4+EAChkz8PIniKcjfGYQByiiJqBOForbTaICcJH06lW/GLncV7JllpUB11zDuAvJiCTHLltGjfLQoarPbrey6G3apBKCPPAAF53PpzQ/8+YRBNx6K4HC73/P+1RXU8Dk5ZFYbd5McCcCo1cvlf3p3XeVyd7nowbn2DGVjECEypYtSgP/2mvA3r06uqR+gN75ixEbdyXeP/Q7uFwWfPopz/3uO5LMoUMpRD0eZkw6fpx9nT6dAuTAAQIvl4uCsXNnCmzJIGXRdBzY6cYlvwnCe+9pKCqite7oUVriduxg9qYVK/g8AAX2+PF0oejaleOckKDIcUKCKoDq83EzYHyPBkf3TtisP4jP8+2IuXMvOhoONMNxJJUsx7KvBuJ4j7Fw2tzYXBiEYF1DaSkQGmpBQYEFNdU1AEjoSqFR2iclnf0E/gWapmk3AkgEMNr0cTvDMPI1TYsHsErTtEzDMPabzzMM420AbwNMQ/6zdfg8tsbInaxnczOXTBkzhuvrL3/hXO/end+NG6dSes+eTbeiHTu4Pm67je564eEEIDt3KrehsjKVMVPXqTARzb9khDUMboiShvyyywjq4+OpEc3P53HHj6vyBd27sy/FxSRIISGc+7r+ffB2Li0ykmva3JxOEqb27WnJEvfOkJDvWwBtNlrxKirqZ90Ti6FksM3P5/NKaYfWrdlncSEtLiYh276d4xp6MpZj3jxeKyGBxPeGG2jdk35oGgFFcTHvHx9P4hwTw/FzuQhIBw0CJox3YvnqA6jbsw8vNpuOg2lOHDwIDAoFvJVlGNziKKxhTmgaAY/drrwtjh0j0C8pAYYN1bEzS8fdd1vx9dca6urYR7udBLV5cx2RjkqkbA1E124OdIjXcPXVGl544UGEhHjhdALlZUBxsQURMRZUujm2e/aw/61aKTJB1z0LpBh2TIvwXzQ++L+xmWWDOTZJwiXM33fvzv1140bGzo4dS6BbWVnf5fj07cKz2pnlg6apuNS6Oj5vbS1lQceOPLZdO5K8w4eVcslmoxyIj+d6GzGC1rhLL+X6j4qigiI3l7JKFNbt2qmySx07cszfeIP/T5hAPHHZZeyLxUIZu3cvz/V4qKB56CGSuHHjSPBcLsqtSy7hj8NBzCDW+z/8QcUDZ2URI61Ywe+Tkuix5PUq5ZbPRyUeoMoSlJVR9j/4oCJyq1fTC8k/xi4wkMe9/jqv89prqjTBkCG8HsD5tGaNKrdjPt9mI9FMTq6fkd1qJelyOuuHE5nLfYhySeLjzN4sZoOLkDsppWWzqZAim025f0oSQcG9KSkqNEiynwL0bpCyFHv3mpNC/fB24a2epnbaLJZnc65M9IYIon8GrMbua46f8782wAXy+ONcROJzDvDaixeToImGT7TuUkbA46GgX7qU348aRQL21FNcLAsXUutXXk7gNXw4tdQ9eqhkLGPHEiT06kXXoh49CGY6daIPenw873XLLbz222+TGEq2yUceoXDato01V7xeVVMPoFCV4ONu3QiEMjLYp2PHaI3r0gU4ke9Bj/zvAPhwtABoEeHGgw8S0G7YoDTynTqRdI4axQ0hNpbC3O2mFkfXSR6XLSPJatOGQvrQIaBLgo4e85/HiW/XY8gbv8XNN+no3Jn9aN1aFSfev/+kdcHQYa0oRV6egUOHaJ0IDaX7lljyOnakZWPDBgqc/fu5IR86xLlSV2dBVItA6F4Do41FCIYXx9ESL+BpPP58OCoqLThcEIzycg1WK89jXIEOL5QaMwre77OCn6/lA2hj+r/1yc/qNU3TxgN4BsAUwzBq5XPDMPJP/j4AIBlAv/PZ2Qu5SfZMSfVsduVesYLrZcYMzr+2bZlMYccObtTLlvEzgMqamhrOldpabvDNmtGQcvAg17nXqwiduPzoOue41CNav54/GzZQBkhx8Y0bSXwkWZPTybmZl8e1W1HBfhUW8j6aRqAlhKq6WsnKsDASobNtAvTMzeUigMvP5/NIXJj8FhJst7M/ElMYGEhtfl0dgZMUS5cWHEwA4HAoAtemDeVBRYVKO2+18r6TJjF+RhJQFBURxC1YoCwtVVU8r7ycCreePXlOdTXHwetlXM7MmcA/X9Qwp/mzWDhpNg53nIDYWA1Dh2r4zfTRmPDQANy75gY4HBpatqTybOFC9mPTJiZ2+u1vgd/fqOOqgy8ivnQ9PvxnHgoLdFRUcF786U/AwEQfZhi3YecnWzAqZwZi1nyODRt0rFihY7g9BR2rUqEX5qJl/lrk5+unsoDm5bGv4tYuiWzYNEgcl/PRp36x+OD/5iYWDGnr1nGez5ypXDZdLioYe/ak8vLIEe6fHTs2lCnzv6M1ZOgVV8uAAO6r4eFccy4X9/O4OI5J69b8fMAAujgKsJfERNnZXCOdOvE7XSc2ycnhPM7PpxyLjeU9p08nUX7uOZK+vDx+/5vfELN8/TXlbmoqcVZODpW8V15JufDVV0y8lJjIPAAdOvD+e/YQW4WHEy8sWUIFz6hRPLZ/f+KmHTvYr5UrKePvvZfypFUrXu/3v+e4zJxJcvf228RLGzaQlLndfI6XXyaGlLAcce9NTydGu/Za5WK/eDG9yyRGWUodiPumhPWI66R4kpkTCErJG0CFDiUlqYyvL73E86VGNMCxnj5duYSKUnPNGiasu/tuWiGXLuV7l9AFsQrKvunx8ByPh/2zWhlLKNZWh4PnHTjA6/8U66SJ4F2g7VytdkB9l0pzkhT/JAiNNXMKdGniYyxlCMxY3bzIJGNSYiIF06uvKtdLt5s/hw6RrEmSFVmcM2fy2lOm8BpCAhctUhk2+/Xjvb/7jkDS66UgzMnhPb/7TqUR//vfKeh+9zsKwDVrKDh1nef4fHyu116jNvvSSwkSd+5UPtSjRjGT3NSpygWya1cu4jZteOzevcAd9wVg1OBaAFZ0aKGjW/8ghIfzHlJbKymJpNRioaB++WWCspoaBvZeeSWvnZhIYNq8OY/bsoX9vv2yE3DuS0cfpOGutHuxbIEbbdsq0nvVVaaSFIaOd+/fhtSp70JL34aYKB0LF1JQ+3z8nZJCQbZsmarTJ+nU27WjsJ46lcL8yqutGJrgwv14BT2QhWJEIlI/hoQEHVFRKs118+Yc31HDqhCIGoiWvBq2XxJEbQHQWdO0DpqmOQBcB2CB+QBN0/oBmA2Su+OmzyM1TQs4+Xc0gOEAzMlZ/qebv6wQ5Y4AOUAlOpK01OXlBPNSBsAwuAbS02k9HzCAVnivlxat4GACjeBgVb8pN5fzUBKfALTIxMbys/Jykg0hRgDn8fHjXGtFRfVTk7tcOGUVaijcSgCaNAGoNhuBkL+l4Uxl02Sqh4XV/1yKqfs3cRW3Wkl0LRZeIy4OqKnRERhQh2PHDOg6ZYYYnBwOZRncsoXkKTxcuXCuXs1nS0jgGFRVqZjAmBgqwKqrCb46d1ZZiufOpTwKCCAYMwzKqepqkicppM6ixha0SQhGZaWG4mLKkMwsC1p0DsVf/6bB7aZs+uYbglHJnpqcTMC/dpUXHx0eiigUojc24tHbyk8lk7j5Zh3LV3hw/Z4/oxCx2IBB+Kp0JI7ne+FANSqrgXAUoQe2I756GzTdgM1Gi4ndzjlljq2sn67eAsCKZlFN5O5cm8TaipLYjDcktGPhQipf/vIXEvUNGzgXP/uM670hRciF3KSMq7jOAfTiCQzkuggK4pq127murFauMYBr7tgxnhsQwHUWFcUQkCuu4Jrq04eYR+rEXXEFz7dYVK1Pu51YoFcvEpn776c8iI+ngnvoUGKGXr2oXNY0KpTvvJM4q7yc+/qQIbQ8ffopscBTT1FWJSZSTtfV8R1OmcL35nQSI6xfr5590yb2uX17KojWruUYvfce5daNN/L6o0ZRubxhA62G48crT6g1a3jPd9/lNceM4b1kLo0cSSIslj2AoSSvvqpc3qdPJ+575RXOuVWreG3/sh1iuACU1U6a5JJIS+N1xFvC7PL5xBO0+o0erfoiuHfIEFpQpSzDrFkq0ZZcX/bNb789WeZlFN/16NEq2+awk1EsQ4bwGOnDj21NBO8Ca+bJ+EPOa8z90nzdpKTGyaO5bALAv10uuvMUFCjLm8vF34sXK02J10sAd889KtOez8eFs2aNqlcilkHRttTWqr55vZzoAwdSywtwEXs8NNlnZDBzpNPJhderF9Ont2hBstejB4XWggXcUJ56ioBzxw6CiokTlVVNCnvu2MEFt3cvBdIzzxD0JCWpcgJ5eexrfDwF5ZYtBC3bt2u43jEPH4//EJabbsS99xI07N1LgrpoEQXwl1/Sz9rtZumDJ5+kEL/nHlorRaM1ZQoF18UXq6LDdzwdi3UxV+MY2qB5hA8TLw+Cw8HNJTWVmq477ySYsda50atmLXagL3pWJ2PhPA/GjeMxTidBoCSsGD6c7laSPjknh+NQUEBhGR4OfPmlhufq/owx2IQN6IMwlCMC+/H11wasVm4kf/gDj23eHFi7wXnS75t+X15ABTH9zM0wDC+ABwAsBbALwBeGYezUNO1vmqZNOXnYNLAq+1xN07ZrmiYEsBuANE3TdgBYDcbg/SoIXkOJViSttMToAoyrk3k7bhxJV+/enEf/+hevMXMm12xhIddSTg7nVlmZKjWwcCEVKB4PFQxt2+JUoV+Ax+zbRzkBcDplZ6taddXVSpkE8Le48fi3tm3r6xuERDY0BoDZ8sN2upwcAQGqELhY4gYN4u/G4vbkem43yYnDQRnj8egAyuGurYXb7UFQEA+UWlilpZQ/mzdzHI4epfvU0aNqHKXQeF0d5eHIkXyH335LWZidrVJ36zrvHxND1yvxXOjRg25b1dWURUOH8tj161V9ro4dCUJ9Pu4J//wn5ffGjdTAf/ABZeCgQZwre/fqiAiohOawoRhxOIoW2IFhWLwxAkePksT63F78AS8gAD7UQsdO9MbIwG2o8dqRtcuB/eiMzRiCA+iEw47+6NzVgogI4PBhHRVlPlRVGQgNVXGJVVXf98b8KQDUr7GJ0tJsGRHCV1ZGQl9erjI3lpVxT6mpqa94/m9pDSlmTpxQ88ecrElKIrVrR9JzzTWqELjVyv/Dwwn6v/mG5K+mhnJz/34VTxwVpcYwNJQK66lTSfh27VJ1PXWd8vOzz3i/ESOoAPu//6M17j//oXy46SaSmNmz2deePYnDJk2igjYjg8Rw8GDirHHjaNUrK6MMGTyY9/P5qKgbP57y5Te/ocXP61WeRIcO8fpOJ7/bvl2F9GzeTIID8Nm6d6+fmE/mVHKyqqEqNeUA4i6Xi7LlvvvojvrIIyrEX8oviOvkSy/Vt7p5vcRXLpeyCr75JvMsOBwKJyUm0pAg1kJJBiQGDiF/YqVLSSEWu+ceYilz3gohq6+8QnIsrpmydkRB6vEQx372GT/3Vz7+kNZE8C6w9kNi7s50nlnT5p/9zv84899mwigJTswamJoaLjjRkgQGcoK/8w5BnNtNQvXII8rnWDTkK1dyYs+YwftJxri1a6ktufRSLoxXXiE5qqkhmbv5ZhKkhQspfIKCeM8BAygs5sxRBckHD2b/xNJw8cXciNLTKQgvuYSEqmNHFcyclsZxeucdCuB58wgsx45VLmheLxdsWhpdzXr2tGBffjAqXdpJV1Adxw67sXCBgT59KFzHj6cAlFjGv/2Nm8Thw8oloWNHavj++EduFIZBARQapmFbl99h0rRLMGXGBBw5wpTkvXoRpPXoAeTl6egSW4pOPQNRGN4T/bENReE9MemSAHz1FUFddjZBVnk5f69erZ67XTu6nXbvzpITxcUEhY89BkycZEFsrBVuNEcQXKhEJwQEWFBQwHFfsIDjy8QSGlxoDoBo3AsoFegv0AzDWGwYRoJhGB0Nw3j+5Gd/Ngxjwcm/xxuG0dwwjL4nf6ac/HyDYRi9DMPoc/L3u7/YQ/zMzV8DCnCtvfoqXVHkmJ49+XefPozDePllWu/DwlTNsZYtCVji4zlPQ0K47oODVb259u0pk8rKCMIPHuQ1bDaeU1xM8CTJR4ToCVhsyEAsSU3825EjKo362RiWa2vPPj+Q9Et+AwQ0Z9sk4cv+/cDRowaAGkTgOJzIQ02Ngbo6AmWxdor1r2tXysFBgwgiIyIIHC0WgkPR3OfnUwYEBvKddeigCiQPHkxZFBREYLh3L2VC//4EnaGhJJUuF48dOZL9SE8n4ExLU0m0Bg1iDFL//uxfXR37ExMDLF2io9WBFKR9vgcTij7D6MgdOICuqEAgsnca2LyZ7mytO9iRGjwFnZEFN2KhIwwZLS9FVJSG9vF2dLcdggcO1CEUyb6ROHpUg8+nQ6suQbBxCOG+Y6is1HHnncp9V/RMEttitgI3tbNroswVy71k1Xa5qKh8/XXulTt2UF5kZXHN5uRwrfuXFflvaf6W+4AA/gQHc72JIkjTKLsyMvis2dmUW8HB/N2/P+ejKKdEvu3ezbU7ZgxlRlwcSZbdzrXYpQvdHl9/nevroYf4naap3AgFBVRoWywkHjt3UiFz993KYjZwIEleWpqy8k+cyHV8++3EWfKsPXownr+mhvJ58GBa+K+9lsRJ+puSwrUvtTcffphYS5pkI5fM414vccPChfXH1JyXAeBcu+ceEqcVK7g/jBzJ+w0dSmwHkJAJsVuzRs1JseiZlRAA962UFM5Zh4PYdscOKqkkBODtt+n9IORTQpYkP4W4awIcmxEjKPu2bFGJduQ4MWQ4nRyziRP5TmbNUjVTp03jd2++SbfPn6qdI41oaj9HOxtyd6bMmGayJkGhAN2ZJk78/rlC6IYO5SKZNIl/S/afPn1UwKlZuB84UD8BitNJweDxcNL27ctFtn07gZtkfZQiyJ07s2+33korIaCSxOzcqTI8LV3KjTktjdrYEydIAh9+mJaxPXtIUHw+ZRnr04cuIgD9xNetI9CsqCAY3bKF1587l9anrl10lB4ohz0mArm5GrZvpwA9fJjPMWwYr3XHHRTiFgvJ2McfU/hkZAD/+LuOBybtwXXuGVgUcjvm7OwHw7DggQcIXNq2pY96ZSUB1y23KJe1nBySNnNZhIwMbgrR0RbsOREJRzk16+np3DQuvRRYtVJH3oersKfyBMZ3OAjfbU/CWjsUzTUn0rdrGDaMGnSJfYyL47NPnKgE49q1Kp4yKIja/MpKCrp+/YDu3TVUteuEYIsbuw4GoWWIBpeLG3hkJIG93U6ATq05dzwD4ActWpx5Uje1C6pJvO6IEdyMbrmFG6HIlvJyEj6LhW5F0dFcf1Ka5Phxrp+WLVXiDquVa05cfkpLCXbsdq5bseDU1hJYFBaqbLD+TcBi43WvdKj06t9nc2dbL+t0WTN/ymbuT6tWFuTnN0c5CmFFCAALrFauzdatKZMkbi89nfJDXGNLSigXs7IoI/bt1bE9zQdnhA2AdsrK2KsXQd+cOSRwUogdoAy3Wil7Zs3iO8nNpWKqY0fK4vx8Vd9v3z6OU/Pm7EtGBj+PiOB7z84muLNrPhg1LfEIXkRcsQtb0RsXowQ7MAy1tWGoqHBg8GBg82YNg6/uj+VL+8DjtqFbKw12m468Iz707GPFJusQREd6kTgiGiXL6Q5aVWXAASAQQeiCndhmi8aSJXYUFGhwODgPDx3ivAXU76Z27s3tpsIzIYFxZJLRuVs37l1VVTxm8GBagBqzlF+oTZItBQRQFomlPSaG+2JEBGVT377EJC1aUIkhoRwxMdwTpR5m69a0Oi1axGMdDu6rHTsSu4SEUH527kwSlpPD+fr888Qwdjtl5ebN7NuePZy/NTVUAj/2GNfrrl38nZpKRdGVV9JFslUrKuFsNr6znBzK6h07iOd69ODPxRcrT6w77lCJ82bNIh686kod33zmxsNTgxAYpJ0iSsuXE6/168d7rFzJ6zsclEWpqfRacrvZn8xMYrCIiPoZMpOSFGGScgbDh/P766+nkhyon9RHEpdYrQqj2my89/331689Z7PR1VOsg7fcwmt266aePTCQBHrNmvrJDuXvESP4nVxPwppGjqz/t3iqSVyeuU70Bx9wfCWzp8OhyP2GDTyvKQbvV9jMPsVmEmeuSSfkSzQWoo13u2mVMqfylWuKC9by5bTUud1cGKNHkyhJXY9XXyWZmTGDC1ji5h54gItMsl8CBBAAhUivXtTwBQXxmuKKNH06F9T771MguVx0Rdi+nb7cu3bxnJdeopCTzJhZWRSaQUG0EF52GX3K6+ooPBITafZOTKTgSU5WKXU//5x9mjWLZO/aa4EuXXQ8dugWPP/kcegv/A2PPaIjJIRE0TAYtPz119Rq33ILQWpMDAVrXh7H4ZprgD8/7cFw9wJ8iytxddXb6NbejdtvJ3AdNozao4svJuFq3pwCPzubpO/bb1Wq9latuFkkJvJeSUkE0q1aceFv3Kg0o54KN3Ir7UhHAmYevAhZ22pRbQnF6tUaPB4K7poaCrJhw/i+iospdKKjOT4PP0w3z+7dSVoDA6nZkrpUmZmAy2XB1p3BaNNGQ3w8N/GdO5VWrrLyZCyURQdgBwBEo64pkcF/WfNPxGSzcTP66CPOpddeoxvJyy8T1JWUsDSCy8V1tWIF12doKDes9HT+n52tLOBijQsJIVHIzeU6khi80lJ+X1zccL05cxMLTf0m5E4HUAWJCT0/TQfgg8N+lozR1CR2B6ifAKC4WIPDoSE8NAZehAHQUFXFtXjoEI81DL6b5s35md3OMR80iO/q4EGgcycdhz9eB3fRCQTv24KwMP1UrJrHw1gZWcu6TtAYGUm5+N13dPXq149yr6aG4NIwqAwSN8+YGHpDBARQ/n/xBeWYAFy3Gyez7AITL7LAAQNzcSM+DLwHn+JmfIXfISaoGi63/ZSHSWmpjpRkL7p0tyE+XoMzREfQ3u24qPwtFK7fC00Djp5w4Jt5GsaOlfqAFkywb0EdHMhEdzi9ebDm7kdIiH7KrVXkH6DIbFM7+2au/fXQQyo7Y7du3Kvnz+c4b9iAUwpAkjuukYaLl194TTJl+lvwza6ZEr/VogWfOTKSio7qapI/t5trqrSUf//hD5Rxixcz4+XevRzLnj2pQBk7lgqUsjLuyx06cA1ZrSSJ8+bRgtexI9fdc88pa5uukxABfC9JSVx7AwYQW3XvTkt+v34kh716kahoGmV6Zib7NXUqrUi33ELl7rp13N/j44HH/6Bj3IxLcf386+C8agI2btBPuYEDvPaECcSBkq18yBCSuKQkKpG3buXnU6fyGf0NDUKYAMoQKbETGEgL47//zWu63Sqpj1jYJIPlzJnKmyA9XSU5EQscoDy7MjPp6hkUpPCtWNUAElUzpi4qUuROmsQMrlv3/TwWbjf7L2RUfh54gH1zuVSdP0BlRwYaDzM4l9ZE8C7A1lgyFDOxM/v4mjNner0kXyNGqAktkzowkC6P4sdsvqaYtLdt4+KXSWuzKSG+YoUqlNu9O49NT6dAcbt5zKpVFCqZmQRmAwdS+GRlUStUU8N71tTwPMnIt369Inht2tCi9847/EwsAr16EUT0789+VVaqApZOJ60IAwbQEjhqFI+Jjyfp27CBWZ6uvZYCedcu9rl9ewrw47kevLzjYtTAwMa9YXj7LS/WruU5ubkkk4MGkURmZLD/XbvS/XLsWAqvlSuBfoMC0K29F2Eox9zAO7HrUBA2beKxX33Fsejdm0CjXTsK6oIC9qVtW/Z11y5eKzaW7+DAAeCvf6XA+9OfKHhbtOAzpqUB1XoQCrWOqEEEBoftQ15REI4coSC0WkkC33+fvz/6iPcICeH7uvZabkiuSh1XXebBqlUGHA4CvXXrKHAOH+Zcys0FevfS8ZuJpQgKMtC8Ocelro7PJBYBn66kdhHsTXWm/ouaKJAkWD0piXNx0yZayJ97jnUojxwh4HjySZ4XEcG1u20bXf7WrKFSQ4pZBwSQHBw7xnMDAnR0jXejZ08DcXH146HM8QmGIdlZG2+SPKR+M0BAaQEQhPMHLHVYUALAB0+dF+dKJA2jfuF0mw2IjtIR7KiDxaKhTrcjNlZDQgKVO8HBXJN2u6ol17Ur13JoKAFbXh6v989/AnC7UVwN6NBxFNG49ZpyDBnCBBhlZbxndTXlyR//SGVZQgIBSUICFT/HjpFARkdzb6mt5Zjv3EnF2syZfKeTJ1OutGlD2SbZ73bt0tG7WzVsNh8OfJaCI2iBneiB/c1HoNIeAwOh2FQ7BN26aWjdGigo0GE9fgiFuceRk5yP/p3KAU8tqtzVSMcQ/N43AyMHujFwIIFZairHoVs3DSWJkzB8qIZRWI9IVKAHtqJLvAdRURyb7t15jlhVmtrZN/GskWyHANf7N99QSVNSwnexcCH37Px8yRYryhb5+3wqW86tNax7JBlt3txAixbchwMDGRfXrh1lmctFxZLNxu91XcUYHjhA5UpiItfryJGUYQsWEC8MHqySRcXEUJ4OHEh5esMNJB5duhDn7NpFXCDZvi+/nHvt119TQX3wIInggQO0Xh05QgVccjKxz/bttNDv2cNkIIK/MjJUGQKA8rdzZyrhJaGeEJ/hw7le2sdUwblpDUZhNWwbUzCyy4lTGSDHjOGxgYHEHbt28TlnzVLxblu38lrjxinyYs7IKhavlSu5f9x1F/eShx7i8ZdfTmIotekk9AZQGHfjRpInKZVgTjhoJpMSFyfJXZKSeF3zuWK9dLk4puXl/N7joZHCHMJgzl1hrqeXkkLlhznRoWT4FDfTqVP5/+uvk2zffjuP/Sk8RzTjbP1ULpCWmJhopKWl/dLdOG9NJoB/PJ1Y2UTImosMm83HkuVKCo7LuUICx4xR2g9JhCJkT1KLjx1b38wuLpbyt83GSXj77RRYdjsXSU0NBQdAYSF+yp06cRIDdKesraVFYONGCp+WLblBjxhBItKpE4VWYCCJ3pw51Ha1bk0L0YQJJGW9elHAbdtGLZjPx3i9vXupmf7rXwlOpFjwb35DMLNuHY+JjaXWKS0NMAwDgw9/jA0FbWBzhCBqwgDU1Gg4fJiAqWdPLr558wis8vNpIdywgc+fm0uB0bEjsHChDt3rw6WX2bBmLV0kaXHTkTSwCruPOPHbazTs3Uv3UIeDYLhnTwrJ2loK3lat2P8nn6RJ//33aRFZu5Zg7NFHKQQsFiDAoaO8yINR4wPQooWG+fNVUpmVK5X7gdPJ4xcv5jizJpeOolU7cKAmDD448fwbMXBVW/D113wPc+eezPgUomPRfDccKEULSw0cXeKxZ48F/fqRPMbGciPzekVba0EYUlDuG3nG9IOapm01DCPxJ1hCv1j7b5BN/q7d5v/dbhUrAXDuANQMnzhBd6H33uPva66hm3JREWM6IiII8Hr14nwBVJxKaSktPHv2SNydDqASAahCLaJAa6/l1DnmGLYf3sR6pwOoBk5awRpq5o363JsPgAes++hBSJAVVTVnGbRnalLHT9N0GIYHjF51gFEUFmiail+0WOgxsGKFSnxQWUmFT0CASkefng74fAYSvBkI8eZjE0ajd99gAJQ9cXEEltXVlDO7d3Pse/cmcXv7bcqLDh0o/7t25Tzo3ZtzwukkCEtL4z2LinSEBnlRWGxHmzYaRowAAuw+rHp3LzoiEwfQG6EoQDZ6wIdaxKMIe5CAcBQhtHVLHC8AHIEWOAM9KCjyoS32IwolsAPIQxsUIA7dsQVuNEdRRBfoOt1NY2K4N0gsYl6ugXHbn8c/D1yD93r9E88634XPx2Nvvpl7wYcfUgZGRJzd+/m1yyex6ktWa4Dr5tNPuQd+/DETetx0E3GAKC3YGjLBWxv47EJolE1ACFSBdQsCArj+ystJCkNDldvmZZcRU1RVcU2VltKd8NNPqTgZMoQ6zoULaRlv04YK8JAQKlECA3ncoUO8nmSBff555c5osTAr+F/+QkXKkSPsS69eJHddu/J3fDwt75dfTrnw3XeU49nZ3O9XrSJ+kaRIc+cymd3o0SQjQUF8f4sW8RojR1KO9OoFbN5kYNA/L8NTqVfioq4HEPTPv2PiJA3LlqkMkE4n15XPx3Fxu1VpALlXUJAic0uXKldO82c1NezzQw/Vr5snmdQlaY3gXLGmCsEy42jg+2XCAHU/CU2SwuQyv91uYieA4+P1sj/Tp1PmjRhBXGc2tvhbJN1unBofuZ/koJBkRYGBPG7RIlXG65NP2E8hmadrp5NNTQTvAmwNgTAz6ZN6UI2BNbNrlcvFhQtwQsp54mvsdtP9sbaWGqMJE7gghcxJUKgkR0lOptZh1Chqsz75hAKvd29a6WprKWg6dCDJ27evfjHHtm2pDQFIlt5/n4v+7rvZ39/9jla7pUsVkdu9mwTCaqUGRSyNAK18c+bwuj4fhSS1uXzu/v1VUctbb6WgCQ+nO+Lq1So1e2UlcMvNOhZ+5UFM6wAMHarh+ecpDK1WCm+7na6VX33F2LeFC3nuH/9IC8f+/QQahkHhPW4ctTKaBoxJ0nHokzVwVero3qwYi4N+gz59Lacyj3XuTOHVuzddSJOTSV5jY3md5cvZj8hI/gQGkhQOHMjx3rdPbR4FBTru/X05Xp4dgYEDNUyaxDHLyCCJvOIKpifu2JGE+LbfViLv429hgYF+2Ixt45/Htl3BpyywPXpw84a3Dl0LP8VK/AZdsQOFoQPhhQ0RERwH1cwbegqMgq5ntOL92gHUz9H85Ygoi2SDkng7CU6XjVeSKUmh7hEjOG90nXP+jTfoyjdsGOfBoUMkDBUVtNrJXG3XTqxxQoo8YAJT4PwAvtPH4P00TSyFGoBSAFFnuNf3+yRyGAA0zQfDqEE4CmGBFQExLXG82IHYWL6/0lK+l7g4Vcw4OJgkrVkzApX0dLqIiTtZ61Y6LLoXzVvb0awZSxqMGkVXytxclj6orKSMO3CAYDMjg7Lo4EHOjy+/pGwT8NSjh3JfatcOqKjQUZxbgnCUow4BCGrTAsXFGkKq8+GBFTZUIww1CEcJTiACxWiHDtgFF4IQBRdqEIo9aIcwVEMPjkHb6g2IQgUK0Ab9sBGf41aEBlnQsYOO3MIATJyoISSEc61zZ8rLqirKtvR04L13dDz2gBvlniDU1mq47TYCqIwMJqI4eJBg+WyzOv7a5ZNkHExLo+yQdP3XX8/9dd06/p2VxX23frZZUbZI+6nX4w9b51ITsn7zIQL58KIcPkTDGRmFZrEOHD3K9RYTQ4yyfDmfUWTkzp3EFZddRqyyfbtSUlssxGEtW/L/0lKStI8+4tqRePe2bUlqrriC+OQf/+A927dnz/LyeJ2KCmKmOXPo9ulw8LwHH1REqKaGRGrgQCryLRZinRdeoFJDMOBVV9GqJoTpkUeU0n/DBj7H9dfTc2PyZCBnt46bLjqB9xbFwmbX8NBDtHwNHqwyqM+Ywflx2WWKzJhj4wD1e/FiFTtnJlbLlnHP6d9fhZCI7Jk+ndbF1FSVzXXxYlo1r7qK/TTjYH+DiRkfy/3kmSXJiozjwIG8j3jDSVFzIW1yfzNpNF9XvpNafUJSZUzkmST+sKyMxpfnn+exSUlnM48bl01NLpoXYGtoIoq7pL/vsTSzqyWg/IVfe01l+Nm4UfkSyzmS+XLgwPq+5kLuABVQ/fLL1Bpt3kxy8NBDJF+6ftIPv6uOXq1L4fMZWL9euQPm51NIeb3U4t95JzVOKSk877nnCBSrqwk46upoHTh0iBt3YSG/czpJZGpqGJvz+99T2J44wf5u3kwe0a0bkNBZR1RwNQoLDbRqRQtihw4UpqWl3OzXr6dQttsp/N6aZUFodCAMQ8OmTeyXpAu+5BLeLzOTJHb2bArGK66gX7gUJK6oUIWDV6ygMG3WDNCqqnCoMhLdsA21JYUoLjJQVcXxDAnhu3vxRQqnXbvoDmqzUbhKYgufj/3fv5/vpKyMfTl4kO/OYgF699aB/HwYLzyLyPIDyMnRkZzMcZP+S9mHzZu5Yf3mFif2oDcCUIa3tD+ioi7oVCKF48cpOF0uID7BhqO2IWiFgyhBa3TrZT2VEfG07RfMotnU6jezxd/s1i1N3Kd9ProUuVxU+GzYwHl35AjJflERN7OWLan1tFg4D5cu5XFSJkASbwBmV0sL7HADCAZJ3vnahiwgcdRw/mKANKj+nw25qwOf2QsBvW43EBzM/hmGBsAKK6wIgA9FJfZT7l/du3O8LRa6SEpSlE6dKC+tVgJPl4uKnN69WUqmXXsLyqocKCrSEBfHmOqPP1Yunzt3snc2m5KRjz6q4o/S03ltSaBUVUUvggceoFw/eBBwVfhQjWBMwwMIQzEqco+iutpAFI5hAhbAjXCEoATFaIZyxCACxdiLnghFBXxw4gSi0R074QAzCxSiI1IwAjWwIxg1aKsdhRd29B0UiEGDNCQnc29IT2eii8ce4zx96ikS0a3pFvQdGoxZszS0b89kE7ffzvEpKFBxj03t9M2MQSZOJJC/7z4SvSVLqDSdPp17XGoqCfT3S4loUOvifJE7+fvs3T/9yR0JhgVliIQLXVGDSBSV2U+uT67TggJ6MeTkqNjh2lqcSoC0fTvHoF8/5V0zcCDj2aur6UnTuzfn7bvvUlk7YgTXW9++VBYXFFD50KKFKnrucBAbSOz+0aNcf+Hhqv8rVzLeq18/Wt83b1ZZef/1L4bnZGVxDTz8MOXE6NFcO5KFfP165ZI/diw/+/vfee/Ro4G+/SzYWdQciQM13HUXj/f5uD9Ikr2+fVXOBrGAyfiuXKlKbXm9fC6p+2Z2dxw0iBbBCROoTBIjRWAgx0ri+sRVeOxY4mIzuZPwJInpE2zsj5uluPrGjSpbtNfL87ZsqV9WLDCQcsdcKgSoHxIl1kS5jlj6gPphT+Z+1NQocvfdd/zsTLHnZ9OaCN4F2sxJU8RyJoRMtBnmJhNZjvN46A4pdUYCAylohFQtW6bq2b3zDjW6o0dzk5Ri5KNGcZKtXUsBlZJC4TBoEInajBlcoG3bArNn6/jqgZXYPvVN1K1ajZYtdNx8MwXj3/7G8yoqmEjks8/4fLNmUWN82WXcgEtKCDqSkykEW7fmRjxsGBd1ixYEiUK2rrmGmiJJwS1xglUuHV8/vAYlKzYies1nWLZMR8eOSstdW0vBVF5OzVe3bgRLY8eS/OXlKeK6dy/Ht6qKBOzIEfYrOprfb9jA4/fuJTgaPZrPsXcvxygigq6huaVOlKMZkjERK3A5LppsQWYmkJCgozDPg0mTDFgs1OhlZVEA9O1LLXtlJQlks2YktBK87fXy2JYtKRy6dQOcRhVaGQfwFh5Guc8HT7XvZIyAjm++9OCeuw24XLz2+PHUAj77Zw0FYV0xz3YXrnsgFuvXa9iyheDR7Da6d6+GmGGdYMR0xnG0RkEBtejHj3+/Xli91lAhoab2szZRAEmT+ARABZYPGcLMt2++yfkxfTo//+gjlX2tpISKl9JSKnz271exIjt2qHsBnMMul4qhVU1DHcLB7ScQ58+6Ju18xwCZiWTDjTWNDNMx3lN9slh02KsLANTAgRLExTmgh7VCAdojulkdAAPl5RxjqZUXFUW5O3o0QWLLlpSlgwdTzsXGUi69+y5BSkkJweaiRZQlERFU+litKvvfmjV8t8OGUT7m5nLp5uQQVG7bxrV+/DiVap9/TrkZHAzExtkA+DAL9+MYWsEDK5o38+IQOmMrhqK/PRuVaIkTaIEaOFCAaHgRjP324bD06o12WiHaIB9hGgCrDYkTW2L0iAAUBXXF0tgHEdO/A5o101BeTrk/bBi9KIYN49h+8glJx9atdC2T5Fv79/P4iAiCv06duJc8/fR/Z022n7OZky2JlUOyLicnk4TMncs54nJxvysra+xqZ14jP6z5K2zOXoETFvb9v202DV27hCA63AAQgOhoxoUWF3MOWa2qDIEoh0+coMI6KYk4RNMI2Lt0IeHZto37+OHDtIQvWEBF6+uv01Ijro2axj0+JoZrvUcPWqdzc/l3Tg6VKVdfTQX7mjWUvbt3Ew/t20cX2YwMWlc1jcrnuXOJWdLTFWmaOJHx/H368N4+H7HE6NHKLdBmY9z1zJl83qee4nqbNIkY8J13eN6wYbynEJaxYzlOkoxECoS7XCorphCcIUNIMF94QZG+ggImeRk8mHPOaiVJMhM3aVL6a+bM78e5mbHz4sXc28QTzVwCzFxcfcsWFYcnn/nLCUnoIta5xYuJhc0JEG024nSbrX79PCGkZndNl4vz4rHHiGf//Gd+d1pMdZatyUXzAmwySWRyiaA1m38bO89sDhagJYHRM2fSWmezKTcs0eiLn/S2bSSCgwZxYu7YQUFx220UHn/5C4VCcjJdLGNjqcnK2OZBp33fwAoD29APDz8bi7iukZg/nxvxvHlKAyYZ83fsIJG77joSrcxMEj3JxOfxUKj278/vpI6RBEZrGjVZ7dtTAAcFcbPp26Ma+1OPojMyUYVmOBgxHPfcb8M333AcoqMJYnWdIGD7doKhtm2phRRtdUEB+9C7NwV2ejoDkNPTuSHI0gkM5GIMC+NzdOzIn9hYunGGhfFe48bqsNVWYVeuE0ePakhM1FG1KBnu8mJ0bF6H5WHXoX0HC0pLKegPHSKwOnCAFsgDBzgvpNzCvHkk8JKZsKAAGDLEwFMHb8DN6X/A1S1XIbXHYwgJNlCzfB3iq9OwI2AEXl41EOvWW/DPf/IZIiO5Wcl1bDb2t6CAzyVupG3a8PhDhwgwi4uBNm10lJfqqKqxwueTzdvskpMCQx91xkyav3YXqPPVzBtiQ3/Lu01L4xxPTeXattvp8uPx0MLz3nuUPyUlnCuiHS4oUAVZG0rrHBoq9RF/yXahxAAZANwIQTGqEAvGHmpg/3QAXkQiD6WIB6AhECWohQE7HHCEhGLceCqFKiroIaBpfHe33kplWVER5UZlJUlgbS0BVHw8FVO7d1Nedu/O33FxKuNfbS3Xt65THuTkUE536KDetcQAAlRE5ecDXbvqqCzx4ugJO6xWHXZ3EYJRhEJ0gKYFwTAMBNgNRMdaUHBMR4hejESk4BB6AR06wRmqoaQE6NdPR7fWVeg20IkdGRqGDKFVQdKfS3M4qOBbtYrA8s036UmyYgX3GUDFjk+cyLkscS5A4zHuZ2q/RvkkMblDhnCc77qLyqBevQhIBw/m/xaLv4v+z9lO7/4ZEkLlbGOxva1bE1c4HJR1tbV8HtkXrVbOdY+HayM3l1a4Zcu4B8fGUokQGsp9cdAgrslWrTg+L79Mt8GiIu6Z2dnENH/5C5UNq1dTKSGWuvh4ekhERHBdDx1Ka9x99xHHTZjA6990E9dHYqLCL889x5CXXr1UOv4HH6Ri7qmn+P8rr6j6pT6fIhrTp1Mp/dZbKmOnw8Fr33cf+7NwobK4Pfoo1+GkScqKJ+sNUFkhxbhQVqbKEmRm1idO4vYo7dVXeVxcHL+bPp3POm5c/eu/9hpJkbi4i1tlQ3F34ipqjtlraP2bLdYiK4YOVaFNZhwu1xXPOnPiGPM1Fi9WlkVRlMi1xEo3YACtfFLi4oUXfpoYvCYL3gXYGsr+YzYHmzMP+Z8nE1+Kj9psJGziirl2rYqxS0sjcZBN2+UiSCsvp/bhyy+Vq8+cORSSF18MPPMMj5k0ie4Ab7wBPPmMHSHNIjAZi3Fn6wV496sIbNpEwbN9O4FEebnqU+fOFFaSJCUujoL27bdJIAcMoKa4rIxa56NHCVwqKriQDIOaoH79OBYtWlDgRkUBm9OD4EUActAbZYhDqzZWzJ5Nq8SaNRQYPp9KYZudTe3XpEmqTp/dzmuGhnLhdenChZ6dzXFo04Zj5nazT61acZysVvZJNr2SEmq4rVbAZrcgNSsUGzdqKCoCli/xIbM8GjnohN2FQdA9XgweTFKXlcV+HDmiksls2ULLSUkJNWZ2O/t94gRB96RJQKdOGnbc9xHiRnTFq5WPYd06DWVFbhyuDocOL7Jq2+DyKT689hrQs4eOPt2rEdXMQLduBHoOB99PXR2fafhw3tNmo5Zw6BAdVosPVS5urLm5PtS5DsPiK4baaM0uOWcmd03t/DTZoCSLmXlTEnfupUtxam0AtAIbBsl7eTlBwxtvUIny6acE+19+ybUpLi81Nd8ndwLKf3lyB3x/m/ultj0NQACqEAdF7qQ/tdDgQini0aOHBVHhPsRjFzTY0B6ZCHZ4MX8+5aHHo7JYhoTQeiUxPK++Srng81HJVFnJdyYKG4DErHt3ypTSUsoZKdqcn0/ZYxiUK4WFlAV2O+eRJJgoKQGCgnS4s/ZCO5oDa105QkI0OKJiUOPsAiAIhqHhD3+woFOCFZWVGpyhVgydFI3rp43BgN92wpN/1DBxIhV4111nwZAJobj0Mg2Rkez//feTtF15Jft68cUE0zYbAVN0NF1QnU7+CJgcN07JMSnKLU321nMhd7/GJq5rI0YQwHfrRnC9cSPfzW9+w31q7NjGstj+XO307p/M4lmf3AUFce60aME5XlvLdeTzcX4HBRH3tG7NfVwyhAMku4cPU5ESFERcEB1NeRcWxu+GDKFFffFijtOoUVyL5eX8v6SE7sJFRcRGnToRM+g6XaenT+c6HziQ5O+++1SyFa+XVvlbbyXRysriWm7Xjthm3z7iBLG8Xn01Zbu4K0rJK5HP06fTknjfffSkSk3lOl+0iGS1uppKcLeb60/cMdu25TXWruV1JDHf+PHEqoKzVq7kuampTKz3wQfEUeY1KUlUpMzAww8TD3q9VPD07avi1aTgeGCgei7zNWRtm9e3yAUpoyCfNdZWrFA4VQwjYvkD1B66YoXymJOaedLEQuh28x2ZEx2WlamxGjeOY7p1K/Fw79609gL1FVs/tDWJuQusmX2H5X+gfjIEKYwo2hfzeYGBFDoLFtAkPXUqtZ1TpvC4nTtVRkurlfFwH3xAwXPbbRQ6Ysm5/HJqabxe+mT370/Ny4IF1Ob070+BNnAgYLVqaH7DBLy3bjT25QagtkxDRQUns8SlffQRwWLr1tyo9++nkEpNpRtBXR0F7MqVdOWsqaFA2raNRCc8nIuhUydqmCQ4uVUr/j54kIs+OFhDYEAr1NV64AgKQESEhrBwCr+SEo5TUBA3qLZtaUE8eJCAIi6OWmuPh5tDv35cvNnZJI/t2/P3qlU4VSfvq69IQCsq+N3q1azht2KFInxr11JDlp9PQW6xADabDW5YcRwtcBzxCKu147nnmCq5Vy8+086d/HvePIKs8nK+Y4eD2kO6WOnYuN7A9u0WTJqkYf58C9q1Cz5174SuQchdH4yOOAgLbKjx2GAP1JGXcgA+oxplCMOevW3h8Vhw6BCBngQUHz7M+xkG8PFHOo4fOIFEZGBz7SAATozGN0jFsJMud2ZvgCbd0S/ZRB4MHUrQ36+f0oAmJfFzgAR+wACVzrq6mmtE4i8qKjhXpdZRaSl/V1fX5+1dutDqI+2niB/46ZqAwHNPxPBTNcmSabFYkJBgQW6uAp9Op4bq6mD0627B1iwLdu7UANgQizBEoRTH0R6VFXYMGcLxb9mS43vkCGWmaP4rKynDampIeA4epDwbPJiAdP9+Kp0EDIn3hcReWyzU9ufnKwXY7t38LjiYP2VlijzFRHjQIScdPmiIRgm0VoMR1TIQ+/db0Mqmkk3k5/NeBH8WBAVF4pr2lMFTpjAb8cSJ3GPS0lQmu8mTVUKIAQP4tznLHaD+Nnu3+Ne88m9N5O7MzezCDRB8er0kL+vXUy689x7Ba/2Mmb9Eq7/XOJ1KaQ1wLuzZw/USFETLXHQ0LW61tXxWKRkyeDCV0ldeSaV2aCivN3UqrWbXXUcwPmCAKruxdatSlLRvz7U4fz5d7USZ3qcPx2/LFrpNrl3LTK41NbS6/fvfXFcJCcrVde1a/u3x8Bq9evEauq5iwDIzua5vuYV17l55hYq4zz+nTL/5ZuImIRcTJ6q9YeVKWgBHjSJmWbqUxoGICJIagIqfdu34t9kFc+zY+i6YKSns46RJSskC8LecFxGh3Balmb3V/Et/CQmS0CR/y5j52uZz/JtYzcrK6Fb6wAMKKzfUampURk2nk30GlMLDv5C510uvLumPee1s3KgygQLEyo89RsuuKKVkr1y+nK65s2cTj3ft2nD/zqU1uWj+DK2xidfQcWYTsLhImCcSoLIIScBnTQ2JQL9+XGzTppHolJRwY/d4VLB8164UDtnZFBgZGSR2777L41q14mKtraXw6tWLn0+bxmsfPcqNODycwj4ri0KgqIj9iYtjv/PzKUijoriwioqoBYyP57OtX897SN272Fg+e2mpius7doygoqZGuQcFBbH/oaEqQ+eQIVxITicFaVYW+1dczD4VF/OcTp0IejwegiSnkwRm2DCOVXa2SjuustrxmSorKVjr6vi9pDKuqSEwionh33Y7nx1Qbh+XXUZCGBvLDUTTqCGjK5WOIQPcsAYEoaKSMW1RUbzmkiWqIPSQIdSmS180jZrT7t11tN8xF82Pp+JL3ILDQb3QqpUF7dvzOKeTG9mBAzqunliF/cedOHFCQ36eD62RjSI0RywKUBXVDTVuO7p04fs5eJAb+DPPcBMJCgKiIjwoPFqBMJSjCFHQEAoDZaCeKAT1Yywk7slyMnHE6duv0QXqfDWzGxrAjXv0aK6FxYu5jjIyOIcOHOBa69iR7zsvTyX+sVq5LurqOCebQinNTc3vsyGMdrtSYJm3XLEYjB/Pd+N0qrT/R47oqCrzwqvZMWaMhmPH+J6WLqVMu+wyKr8KCigjJBOcw6ESX8l9JUFKRISKkxLQ5PXqiInyoqjEDk3TEBGh5JymcS9xu/mzaxfBXZs2wLKlBv4U9Hfk7/ehfUcr9l/zJ/TsqWHrVloNpZjyjh1UJGZnE2D6a9fLyhSRk8/829nuoeer/Vrlk7iYeTy03LdpQ4VsQQH3l8LC89TZn7h160a8IGRU0v+Lx05MjCpN1LEjscmRI1RcFRRwHURHk9gdPMi1sXcvMc4ddxDcz59PUqVprC/75psscbRoEc/LyyNRDgnhWF55JfuyYwfX7N13k0TefTfltdQW3bCBio8ePSi3O3emUn7qVJW0IzWV1siDB4nbpL7wnDlcv5ddxnsJyfEnWkuXEptIPjRzzUMhpgMHkvj07VufKAp5Wr2a92koDABQf/uHG7lcxG8jR9L1VZLUiMVN+iGJVpKSiHuFUDZ2XXmO6dPpXvzkk4x53LyZmEbcO/1DFyTMyexyaQ6ZMmeflnvLHutfnLwhmVZWxvfl8/GcGTOIwbdu5Xy75hrOzSYXzf+CZg7o9P9cPhMyASg27/VysotrgNdL1g+Q3K1fz+8TT77Whx9WAdADBqjFItkwBw4kudu5k9Ygr5duCLt2UQjs2KFqnonrgstFi9uXX7KP6ekEDkOHchIuWkSh1b49BeLOnZykubk8XwChYfBnzRoujKIiApS6OkVi587lMeHhqtRCbS2tbJKdsrqaICUykudFRvKcI0cIYHr3Jvm027mIqqp4H4uF47BvH68vsXt79qhi4wcOEMRKgprwcBI/p5MgV6wbgNSrYv8Ng4tXfLTlma1WfhYRQU1Zr168xmefURhUVVFD3bu3BT5LMDSLdopA2u3MXNmrF7U5DgefMTCQm2peng6j1o2EBAPlxz2YfzwRr+Fx7EZHRIV7MXIkr3/JJXxnoaFAQoIFXy4NRW6uBrcbCA2zIFLTcRW+QIjVgLvWhqAgbuCbNzMJwYcfctztdtYuM3x0y6xBEDrgMAzUAQhGLIpR31XGnNRibUNp1ZraeWyiQRT5Mnw4N1C3m+Rt1izKpOJiKiUWLuRne/cS7NTVca4aBteCuGw2NWkyv31Q8XPyuQ9mS7YEytfVUSEk5M7hoJIpJITretEiwDB0/POJQhQWGoiJ1lFb7UVBsR1FRRrS0qjd37mTMjEuju/w0CHlQlZTw6XWuTPljsOh4iOPH1dyEFDZfb1eHUEogru4CIZRB4dDR10dgVqPHtTu6zplQHg4gV3fvpSFg4doaP6vZzA1614U3vonPPGEhmuuYf3RRx9l5r5rrgGefVaVuRENt/wIePT3XPFvTVa3X6aJDPF4+M7r6ujZ43BcWOTOalVrzTxXunfnb7F2N2vGPS4iggD/1luJL4qKCOpvvZVyMD6eSpZjx0h8wsKIR9atI+ETS9rhw8Q8ycncs5OSqEheupT3OHGC1+zdm+RjxQrur5s2cW/duZPk8+BBXnPQII5tYCDx21tvcQ0mJnLtrlvH+44cSUw3axaxoCRq6d6dYzFnDkmEWPsAyvQnn6S1T5qMlYTsiFthcrIK7dmyhZhv/Xo1nt99R2vhypXEWq+9xucXV0YzuTMn95J7SZPsleJRYrUSy06axP+F3G3cyGcU98ykpLNzYRS31IgI9jE6muM7dWr9enlmIjZ+fH2i5h8yZS5kLt8PH65cOf2bP/6XDKBy7n33UblaU0Pi3rs3vztDZamzak0WvJ+h+WsfZQEBFCpSdFyy8ohZ+oUXuJlKkepVq7jY16/nZNixgwv40CFmeHzrLV6jVy9OoN/+lpa3adNIaFq1UrXlPB66BRQXs97KQw9xIZSUUIB/8IGKWWvWjATo0ksp5Hr1osVv6FC6EVitXDgHDrBfLVoo61x4uNIAiwbGYuGPx0OSJm5fgYEkTkKexJLncKgMfadr4jrkdFJgWywct5gYlaBFWufO7J/dzn6Ul9ePI2rTRrlEmsGtJBcBlHbc6VTB2T16qFIOElfocPCZWrbk5hEQwOtMnEghnJXF64WFUXN1+LAkeNERGGCgTVsLiou1k2Omo6asHC2QiwiLjrDhvbB2XS2CTyZ3DuzYGeHhTFwQFsZnHDOG86qwkM999Cg3leOFOqorvMgrtCMoiNZDyeBlt6sMXSOG61i9uBg6PLBAgw8hMGzB8NTTWjigEleY/fOa6uD9HM1fC+lyqXpEO3bQLWXdOrpJiztzWRnfcWws3UOKi3mu1Vp/E264XtSvuXlhtto57D546zQAtdBRCaAZpHh7VBRliLgZ1dbiVHHutm2piMnJIbkDytEWeYizVWCXtzN0uBEKL+qi2gOwoEULKqVE6dRYu+EGAtqNG5UsBXg/KTETEED5FOV048bMB+BALTLQF4nTboMeHnkqCdfVVyvXI9GmT5ig7iXunuIGdWqEGtCKJyU17Bb1S1vnzqb9GuWTy0W54PNxLkkB6+PHOacvxOYvuwBltbZYiEW2bSNRyc2lK1xGBteL4BWJOR86lHIyOpoKj+++I94yDG5nkZH0srHbCcoDAvhd58681w038Nw1a0jEYmJYVmnUKCo/Tpzg3O/WjeWQ/vEP4o4ePYilOncmdrj4YhUvnZbGffwf/yBG69GD63H5csr5/v1pYQeUe6hYzVev5r22bSMpHTRIESlZnwCxqctFHPD442ptLl3K34MHq0LfEjs3dCj7GBHx/ZpwkgDF39IGqCQm4uLp832/jp2EiwhONidMku/l/7Ox/kt/zK6fjcmf013T7CVjTsZyNtcQq5/VqvC+YP6qKhoCtm+njD5Tayp0fgE1Me9K2lRzBiCzu4rbTYFitXJTzsmhZignh8JJCmxKrNi+fSQLs2fTB/veu3X86dEqpGY5MWGCBpuNlrbPPqNFR9yCvviCGpqOHSl8UlJUAe2vvqKA6tGDmub580nejh1j3+12CrxmzSjwW7Wilq+sjNcvLWX/IyJ4vMRvVFbyumdy+RJg2a0bhY2Ml2S0stnYh5oaarjS0pQrkrlJ/MvpWocOFObSIiJ4r4ICFYMmdaHE9cnh4NibhRHAZ9Y0JdwAjlHLliRvLVtybJxOvpPCQr6T7GyO4cGDgEXTodWU4AbMxjz8HiW21ggJscBhq4OteA+8cKAWgWjWujnKXDZEhHhRUmlHTIyGwEASSY+HfU9M5Dt2OFT8o9NJrdOSJXwXdXXsk1g46+qoYeQ78AEoRTB8sMGFgUjBStyIEJSiCpGgxcLsnklLH1tTFs3z3fw3m8WLuTl4PHQf+vBDun64XPQCiI8nQejUiaBk3jy+7wsHtJ2b++PP38R6J330ALDBjuOoQ8zJ75TCIy6OsqOiQgFEp5MeFFu2CCD1ASgHEAqgFgGoQi2EDTkBWBEURPlWVUVZ6vVStgUFUV6VlhJE9uunPDCKihRovfJKyoOaGr73gQOBUSMNvDv8HWi5R/DQsC1wJn8HaNopuSUEDmjcDeps2n8DiTtd+7XJJ5eLJGTPHqbKX7yYMqOmhtanCyXG1n9vDw2lV05OjlJsWCzccysq2O+wMK7JoiLimYoKWocyM1X8Xt++vO6SJQprXHUVrS2zZnGPT0sj8XM4iL2eeIKWoX//mxjvvfd434su4n2dTpWY5MQJ4P/+j3t+//6U0W3akMzNmkW5LTXtrFaO+cGDVL5LwpFVq5Sr5IwZ7BtAA8Dw4cp9UjI8FhURH4qVb+LE+sXFbTaSjuHDVWZMIVtyjNersrGb3aq9XsZ6S4kBM3nyJ3z+yh9z8i9zSJI52618769EOtuMuOb7LF3KZxM3zcY8B/xdMRv63kw2zzYMyz+/htlV9eWXOYf//Gcq11u3PvM1mwjeBdLMvrxeL7Vi4kq1bh217Y8/TtN3WpoKzn3qKS7qnTs5GXSdC/Cmm0gC9+7lRHA6Kdx2ZulonfYlPitJQnvrMdw/oxf27LVgyxZu7Dt2EBRMnEjBLfEVbjeFVWgoBdeKFRSSojmW+kpt26q6J6FOHaUlBjxegjFZ1AMGUChZLKrkAKBIW3CwcnlsqIl17HStIW2dNCFi0hq3QugAdDRrZkVJiQKTZtJmvpZcp39/biJC4oKDVSKD8HDl0w/wvR49ys1R6lU5ndxYBEBZreqcQYOAQ/vqUFlyHDWIwiR8jLRmN6G4xA6bzYDurcJgrMdRdILeJh65uRoGDeI86N6dv30+3m/cOLp6DBxIkhwRoZLBHD5MsA8w1bkj2I79BzT07w+kp+vQdQMDuruRsScIdV73ybGygcDWYRrDhoC4vBgNhnFmT/BfG4D6qZp/8VbZJBMTKRuWL6e77Z/+pKxF48bxd2YmZYkUJfdPJf7LWO7M7r1A/ULiF1LTYbPq8Pl8MGCDOVW7HeVwhEShqoo1tPLyGh5L9ZkODT4YsCACe1CGjiBppNLEZgtAWJiGgADK5bw8yt/hw6mNl7iNoUOVMm3ZMiaqevZZrvt+/bh/CHiTmEybDXBX68CJEwhsG3tGRcx/O1H7oe3XJJ8EBEvZFAHXCxb8kuUQGm8XXcQ97+BBFT7Rpo3y0Bk6lEplyUBdVUWl8datTJoyaBDXS0SEqlPWsiXXy6WXUgErbp11dTz2/vspQ5ct436fmKhKMowZw3XZrRvDPJ5+mv2aOZOKmZEjKZuXLOH9P/uMSdn+9CcWFd+xg+vz1VdVWatXXlF19QDiw379aL3buFG5U/7rX3xvhw8zJOe++4gpp01j7N0ddygZYSZuYoWThFxm+SA4SMiUy8V5kZRUnwCZY+nMSQGlNWTpN7eGLHIib/yJnJmwnSnRir8CVKyVQhjN8XX+50iM4+mueaY+nekcaatX02p7+eWU75LU5UytieD9zO1MJl+vF3jpJeWWuXIlzd4zZpBIXXklTeduN8sGxMVReC1cCPzhD/x85kySucpKVbD6gQfogpWV5UNzHEYNAA9C0bFbKJxhgafiaUJCGLjbogUFjiQSOXBAuUiGhvK7sDCSl8JC/pbMb2w6CERqYUMtYIuG12upF8h/ts0MLhuuWfNjtfoNna8DqIIqtmwBYDktsJW+mTWHLVpQO1ZXh1OATmXNU7+bNVNWS7PrVp8+HC9xI+F3BiwnjmEglmADLkFQq1i43RqKi4FOHXXkHjFQW2dBmzYaTpzgOfn5qn+dO0sdPR15eQb697dg3z4N/foBmZk6XBUk5Z07G9i71wPAQBiq0LZnM+zKBnx6NYAABOMINISftNRJKwVd0EJOjVnDYw0AKTB8I/lgp2m/JgD1UzW3m3LEMJjKes0abu4XXUTwMHcurbOapgBbfj4BSXk5XYyqquorQn751pDG5iyCLc6q/XjLIJVKcp2Kk9dygP0OREiAD1W1DsTEaPXcwkUO+MsWq0WHT69Bb6QjzOaD3mcQAsICceyYgRsvL0d46wgEB2vYtYuyQhJJ9e3LelkeD7XQERH1Y15EiSixwWbg8WslaT+m/Zrkk1hd+vYlwUhLo6fJxo0XRji1WKWaNSNhE6tbXJxSnF5xBT15QkKAb77huuvYkb+lzIGmce++8kriKYeDmMpi4THt25NUpaRQIX7LLZSrsbHcx8eOJcnzeOjV5PNRwfv113Sf7NVLWfoko7XTSc+J1q253z/1FN3yoqPpebViBa04gqFmz2YfpLTBxIl81tdf5/sJCqL1MTWVONDlooXQ41HxaosWcTzcblr9Jk/mOJpdJocMUZ5jQH2CtnSpcuUEVP02+awhguVPCuW7V1+t72Ypn4uRQwiV//FmktWYZc2fQDVmLfPvm9y7oevJceaa1A25hJrlrv+1JK7RLIvlc7EmTp6s8m6sWMEsqDt2/HgXzSYx/xM1cwHDxiaZNAnQlVi8jAwuzM6daVGZN48aoEOHqElatYpALiWFgmX+fGpbMjMpRL77jqD+k08oYBI6WxC5rwDZRjv4oGHnrgDExRHU1dYqQXf0qOrT/v38bRj13ScrKpSG//vuiAYAA92Qhl0YDHiJXM6V3AH1CV3j5K4QQDQ4bc9Fq68DkOhXBxQpMQAEwI4y1CEIADMSnE7nIX2Tjc5qpWZQLIl5efWPb9dOaRbNLql2O8dR4gsDAij05Z1brRoqQ+JQ0ec6VG8LQNUJHdAssFg05OVb4DMo3CvKdWiGjoICK1q21E4mldFx5JCOujoNJXnFsMKObdvCAABFRRpKSuoQgnz44IS3OhQhKEUtgBpYkJUlD+8A4EY1WsKCUihgbEDcyFTqef/m91lhoapu39R+dBOZEhhIhQ/A/2tquPH37ElZ4XKp+jyVldz0LRbliimxrxdWs+D7hYvPtpkVC/4kzvyd//U1nC35Y6xcLZQLZgACcAy1aAPAgqpaCnshd1Lo3Wrl2Hftqv5PSgL6tS/Hij8vwdX4Gk5vHUb+ezYC2wXB5dKwfXvkqfqnbjffpVmz/tRTvMeZ6rzJ3/6/m1pTa6jZbLQcLFlC2TF8uKrNdSG02lrl4cO9kp+LC2nbtlRMBwZSad21K13epk1Tmb2PHqUng9VKJXphIb1fXC5a05YuJSnKyqKn0e2389yOHTkmLVsSb/Xpw/27WTOSxLVr6XapaXSTdzho3UtK4n3uuovK9Q8/5Odjx9KSOHo0vXcmT+b3APt2++1U3t97Lz/3enmdBx9ULpIrVxJ/eL0kemPHEjNKjbrMTFoivV6G3QwbxuRrQrw8HibTk8QjZhnicvFYc4ZIcaE8neVKrGPmGLUxYxomd0IYBw9WWeElhKih49eto8XSX46Z+20mXHK+v/Wssb/N1wMUaWuIvDV0b39yt3AhMfurryrrqTSfj/hf4iStVq63vLyG4/nOtV2Ivi//Fc3se+xy8eWJH7NoIVwuTl6zC+TSpVx8N93ErD5ffklS99ln9L3eto0TpLZWpeMdOJCZyZxOpnk9doxCISeHLlgS93b4MPuTf1TDJmMIaizNUYco2O0srB0cTKKxd2/DmjgxsggRkaxUDT33yTNgRRV2YRiAAJy/6SQxL81BUHWuVmc5XlypDCgwZzWRu8b739BYAHw/ISEcu+Bg9bmMr8QrOhz1zxOXVSlhUVtLku3zcfMpKAB8PgtS0wKhu12wew6hZ+066LoOu13KMugor3Cjphbw+Xw4elRHba0Op14Aa10xOGZuRKAUATgEoAY7dxoATiAABnTYcTDfgSqEwYvQk7XsPLDZgO7YiDgcBGOLLAhG7smxI8mLwBEw2cRZvPOmQuc/WROtn9k1U2SQZFq76SZad/bsIXApLlZWuh+ugf9+hsjz0zScrnBx483ftVP6K302/I6rM30vPzj5eeODZNVI8KKwE+2wHzHIQwQciIiw4NNPNXTpQrepSZMo3994g6T7kUcIlNato0Z/0yZ+d88fI/DR8PfxO+tCXDayAtHdY+F0UnMrGnhJHDVpkqqdBPC7htydmghcU/sxzeslRklO5o8QhZ+z+W8ZVitJndVKBWxdnbJq5eeruFK7nZa76Giut507iZOeegqICNNh1FRj924D48aR6PTrxxwDt93GenTi9pyTw325Tx8SxNxc4jop39SvH8NQnE7iM0nM0qwZk6jcfTf/v/deXsfppLukxC9qGl1DV6ygbJbi15MmqdATKYCdkEC5sXUrx6JbN8oQQK11c0ZJIQpWK59RFETr15NAbtqkckDYbCQXUi9Z8CrAeZCSwudPSanvMulvuXK56hf3Fjws9xCi1xAxSkriGMyezT1s1y5aFIH68k3w9dChjE801zqUvpiJphAy+b8hYmaOEWysCVE1P4c8txkPN2T9A/j+X3pJZQs2Hz95MpW0IufHjSOZLinxx9o/rDVtBT+gmbUDkg3znnvq18gYOFD5TX/3HbUofftSe56RAXz8MS02Ph8nak4OBdTXX1NTYxgUNHV1wHPPMWBXsu14PARuERG0+HTpQuAmmnlmoLTA47Gc+h84c6pzf/D3fUuaf9PgQzOc/4QIQiKEYJwrkbSAU12koDyogEkhdw3332I5/VjIuDYEnkXIeTw6Ahw6NIsVbnf9+7RsyfcorbRUzSUBox60wtaTxcQrK2kF4HcaABcAYZcaKk7FxxWjGi3QBhtQjKGgOy0AxKAM5YhFCQoRBiFtYSgC4IHhaIts7wiE4TAAoBbhYGZASaTiQBm6nbyWIn0kfHZ8zwrzU+T7bWoAuP63biWgSUqi/PnwQ86ZkBC6Xx4+rAqW/zTN3/rVWFzcT5UcxbzeZX6d6XqNEU8v6m9z0j+733E6gAIAYQgOciIomNZ3sbK/8grlckGBFd5PvsXAwkWwdk4Apk7FqCmRgMZ4OyFlwPc1zw3Hnmhwrl1Ck19s/Ri4xjTKTa2pne/m81GOREfz98/dxIsmIIBEpbqa67BZMxKs+Hhaxz0eWjsCA2lxy83lOv33v5nF8rvvSIimPqjj/RuWI6wkE9twLXr3aoWdOy04cEAV8n72WSpWPvtMxc+9/z4VNW+/TWz217+SEOzdyyX75z+rUkibN/Oc114jSfH5SJokJl5q7I4Zo5LPvfYa8d/tt1Ph43QS5Ccnq/irV18lyTIMlcDOHEMmtdICA1UiE/ls3Tpa+1JTVc0/8TYyZ7AcMYLWyvvuU5h2/Hh+npKiMmz6W8GESCUn45S3gc2m3Cr967FK1kh/D4OgIJJMp5MZ3SXjujkTp9m9s1s31S+z55zZymYmZnIf4Puul2dq/sfL9c9EGMXSKSWzGjvenKglOZnvqVu3xmMVz6U1xeA10k4XKFnfTVG5z4wbp+Lpli1jULKmMdV0YiIX8l13kSwMGEBt0+7ddIGsraUQs1q5iFu3JsEzv3yH4/SJSS6s9lNnwjvb6zV2nL+G37/92H4KGDVnkzR/V2e6v7iJnk0T4iRWTIkXBIKDDVRX1528Vg1I8syAVgig7+TvWjBuzoDDqsMRpMHlAkJQiCrYAAQgFFWoRHMo0taQy5uMpefkswgIb6ilwKgbfkZ0+muKcfkhzewxMHcuE2hIPO0XX6j01+Y4z5+uNRQXd6b1JSTwh8qBc022YqB+PTqZ9zpisQfH0ROAhjatvHCG2WDdlYG/4zGkt74El371MLb87h+4+MBseAcOhO3zrxAYpNXbYMUdyusFoOuwlSpC1kS8/vfbr00+5eURoObknL/++CdS80+aJonHqqtJ5saMoXKre3e6j7ZvTyVWRQWPGz9eZSHXNBKumTMpHz/92IsnSh7AAvwGRYjG06+1Q3j7SCQkUNmelkYLyzXXkCRefTVB+dy5JF7r1lG+ZmYyvg6gnL3rLirz+/ThWA0YwJIJc+ao7Jv338++iOskoMiWx0NLY3w8CcTVV9OqI/XhxoxRSuJvv2V5qk8+Yd+mTycZWrOmPnEyx6gByptMiFxKCo+V0lUST+ef5AT4fvyZ//eAIjX+rpvynTnWTwiZ//nmZsbX5muZw6CkGLkkdTHHx50Ou8szmcemsTAqs6vn2R5vvk9DMYPmZzSTViHGXi+J9qBBNAR17fr95/BvTUlWzrE1Fkdn/twcE7FgATU2U6fSz1uKgl92GTVCNTX8vKqKVrbqai5u0Uz9MoHLPwUB879GYyTqTODsx9yzoe8auq/5u1KgXtIQAYe2H9hPHYAXGqpgIBTftzLKLlYLEkBzzbizvb75udSzBwToqKutgw4rmDBGsu+FgmnXIyGxcoGBFvh82slNVXbSwpPH2KDeoRdW1MF36lnYmCCC36s+nX7MQpAC145IVb2zkfZrA1Cna/4bSFkZ3aTEbefzz7mpLVtGbfX5j6UzkydzM6+vhkhgQzGaZzvvz5xsxWajMq24mCDr6FEdsSGVCA3RcPVNTnx706f4/YkX0H5oWxS9+y0CgzTl4qjriKg7AW+zWNjsGrye+qStqTU1c/s1ySevlxkXX3yxvmfJ+Whmkte7N0Gt3U5CZ7HQgudwKItXbS09Xlq3pvvlb39LYrVgAa9z7bWsaVdezmQmBw7QnXLDBgNj8/6Nm48+h/finsWgGTcjcaCGO+4gCXz+ecqS3FzGMEsiqnffZfK6iy7iuLz4IkncggW0/FVV8ZxOneih5fPRUjV5MkmTyO2sLJUIy+MhUbzvPsrvtDSWWADqx4uJdWnZMo7BkCHsQ3Q0fy9eTPdKsZ6J14/5f3mf5oyQ0vyzRPpbyeRcf+zbELkxX7OxsgKNESN/b7izKcMi15Lnbegc/zIGZqImrbF++h97uv74j29jBBf4fskIl0tl8hw6lH8vXMj5uGLF92P2GmpNBO8cm3/WG7N2QrQpL7zA361b0x+4fXsSupdeovvc2LHM3jR8OAVATo5kfapPUE6X6r/h1lg2yHMhaw0RsR8T73I27YdmwjM/m9miBHyfODYGCs+lvz+kn2cCowKQ5ccB81ifTZ0+nld78l42mK2AVqsBn88LErqQk/euAeMi7bBYNOh6/flhhQc++ODEHnjQHBZbOKKjgbwCIWzlAMLx/Xnhg9T9qk9YG26t8R1y8/qxwN9p2q8JQJ2u+W8ubjfdv6XGZFycqpWj699P6vPDWuMKhDOvHZnn/iTQP1mKtLNT9lgsBhx6EQZiM5Ja78HYDx+Gp459attWacvFsuaf4SwwkOn/AyubSFtT+/Ht1ySfDh1i3NeZyhT90Ga3E1uJYioykgrvmhqu2+BgyrfqahKsV17hEl69WmUBnjSJVpGePVUx8bFjSdIWL2YilZQUytGBA4nF2rXV4dSq4LE7YbFq0HU+47hxlCUpKSR7w4cDd97JsJj4eN7P4eDnU6fSrVLkzPLltM5ZLCSoWVnKYic16szJkQToL1rEMRgxgla9sWPrJ9Uw16VLT+ceMHs2SaR/cXKz9cec4dJsQZK/z5QA8FzKGMhnp8tc2VgiloZqyJnrQZ9tM+Nz83mnK4wuYwc0TiYbs/Y11oRomq/ZECk2j6GZGHq9qr95eSTz27f/+Dp4v0rnktOZmgEy6LQ0BuZKzFtBAYNUMzKY7XLdOg7+p59SQG3YQJP9wYPKzFpTUz+AU5E7QNyIfL5ziWVpKBbG8Pv+bABUQ4DtbM/9IeTuh1rvzMSpIbDYEEltKAOfYfp9OqD3Q/sp93WBRYn9ryOxfhILWL8PZ2fBNaCIVH2iaxgaCLQjT35eCSAMArp1vRZ07VTv2Heynp0LPXkRL1BeZQGtd4XwIQanvoDV9EwWkOBJYgrHyfscBxD7vWezQK8fAd7UTtskkFv88fv2pXzJyKBbkrSfzmrX0HqWeYIGvjM38zyXOW6WWxYAxwDYYEEteuAArn6sK7btbY7JkxlL43RyM7PZCNSkgHenThrc1VGIqEtEYNvJsNnrz6uzcV0JDLYAwU3xn02tqZ1Lc7vPD7mLjCTwrqsjoSstpeUrMFAlT6muJsGrrCRJy8ykPEhMVCn/O3Sgx5TVymOWLmWilPBwKn5at2aOgv/8h0nsRIm+Jc2CW28Nxd69tASmpTG5c58+dP1MSqIsWr2atetatWL8XL9+PD8wkOROrCpud30rj8dDciweXIDa+oSsWa0kdbt31ycfr73GcidCAsWlb9w4/ths9Ytye73s77JlJJJyD7ObpLiUm61yZ7JKnSnuq6EYvMbi3MzkyBy/NnRoffIlfW2odt6ZmjyzP+ncuFEVXW+o/zJOp7su8P0soI25j0r8o/lz/7Exk1z/RDBjxnA8AKUw+CnaebXgaZp2EYDXQIT4jmEY//T7PgDAhwAGACgGcK1hGIdOd81z1ZKXlRFElJXVD8b0eKiRESudZAxKSmKw7bJlTJebnk7L27ffUovk83GR5uZKoouzaacjRdYzHHMmN6jTXbOx1pjL1ZnOPd1zNOSOBfxwF9CzedbTuWmavzvd857uWufSfuqYQ/8mz2Amd2biZT6u4uR3QabjdZD4RZr6KGNsJmsn3TmxG250Ovm9DfXnxdmT/FCkoMI74owk73xpyH+MDNI07SkAt4OD85BhGEtPd68fa8ETzwGbjUAmPZ3KpszMs73CD5mD5+Q+ADVvqKho354acoeDstPnIwCT7LD9+/jgnjwFtm1pcA7vA+e6pXDXaqeN6WhqTe1Caz+3Be+XxE7JyecOtM+2BQfT26m2lj/FxQrstmxJ+VFURAteXBwJocXCGOPHHmN2yo4dKWNsNsqYDh2oWL/6apaY8vmYeG7/fl67Vy9avyIjqZw3y5vu3XluTQ0tJ23b8jyphye15gCVmVLi1159lXIuMZGZM9euBfr3J3686CKV9Vaez2xx868t559sQ5qZGJldLKV23cMPszC2w1HfameOeQMU9jUTqTO5HTZmdTpbeX22FryGjm/sWmdzDNC4JdK/T6dzKTU3l6u+Rx/QMLE8Xd8ac0X1L7E2dCjDMG6++ezG+hdx0dQ0zQpgD4AJAPIAbAHwO8Mwsk3H3Aegt2EY92iadh2AKw3DuPZ01z3XQOGRI6ndSU2ltkcKzVZWUoPk9fJ/m40CJy5OpbD/fvshAOp0YNgfaDfWGnODOt01f0i/TnfuD32OhojB2SZKOV37oUkbzK6e5zPz5/loPtCiJqRfLHfmJs8psXI+1C9hcRRASyhC7kP97KQ2qMQuMH3e2DgZYI1BawN9AdpjEQ4WDDljJs3zAaB+jAzSNK07gE8BDAIHbAWABMMwGl2sP4TgFRRw48jLA/71L2Zt+2HtXJOSSDP+n73rDq+qSN/vJJeQQJCANKWKIr2EmkAggdAt6Nrdde2IiAioq67rT91ddVUUC4qKortr11VB6QQSWiihdwSRoiAgJnIhl3CT+f3x3o8zOdw0SGfe58mTe0+ZM2fOud8379cG9Mj+Cob1ehGFdFwU3Qf9+wO/v/U66hzdhPMaNUKfz/+GRo2doiJmCf/8u5YTtEqkhUVFQWkSvLKeO40ZQ49ScSIykl73PXtI3po0Yf7ayZM0pG/fznDLF1+kR6p9e06k166lB2v5cpKr3r25jFBICHDllfTktWvHwiOrVwOPP84wzvBwkrd16+iNO3yYJA9wSFv37my/Uyd6Tbp3pzwTMvX66wzd7Ncvt+fM42EYqJC6fv0YhrlqFXPpfD5u37CB4ZorVzpRGWa+m8dzOsHweoMXDpEiKYDjuRs61Mnhkjw0M1x9dsAcKQVUkpNJfhMTi1Y0xV0VsiQNcoXJ0wsWVhnMY+gOrcwrxDS/ginA6bmJJgE3PXr5hWOa393nur2DPh+L97zzTuHWwiurEM3uAHZorX8IdOJTAMMAbDaOGQbgqcDnLwFMVEopXQys0+sFGjfmZ0kUzm+ZAMmDK5jcyeeiTKCCwSxMkh/yC4OSvrj3Fwbu4iMFkZ3CeOfyCpE0ryOf8xu/gsLCzmSS6B7HigghYOZ3E+YYK+TOjZPw1Lpg0ZcqyE0QzcIwsr0w74UCCWTwd/JHRHFiXzY4YxkU2P6p1voEgF1KqR2B9lKLq3PFy3XM30wm+Ixr5TpCFvmtX5+TorZtgSZNFFq2qIJGVUOBOs0Q7j0Mz4UdnM79a9TZk7OQELtUhoVF4VGmc6fiIHchIfRmff89Qye9XoZEVq/O/L79+zmhPXyYc68lS5hnFhcH/P3vJDnPPstJ7o8/cm3JFi1I8taupVF+3ToSs6NHORF+5BG2ozUrTPp8zL07fJj5dDk5/IuIYB8mTmQY3zvv0Av45psUce3b8x66d2eU19y5zppyQv6+/JL7JTBl0CDm2fn9DOEcOJDHL1jAvgvBApzqj337OhN7WSf5jTfopZRCKbJPosvMSos9e3JM+/d3yN3LLwPjxjnLJyQnO147qaApbZjLFgDBiYgZ9gmcnvcnx5vk1+2lE++XIK/cP9PLaXrfJNQRyF2vwmzHDIf0+4MTvWBENT8yaS53Y5I7uTf34ufuEM5gnj7x3JrXdYfNer00DkgdkLNBcZU2DIaGAPYa3/cFtgU9RmvtBys7nO9uSCk1XCmVppRKO3ToUKEuXqPGmXQ5P7jlZmHlqHuIQ5C7lH5RyYx5vkno3JUbCwt3f/I7rqC+uftj7i/K+OV1HxXN41bccMZXqRDUrp17LCLC/OC4HsfpxDsUgB8Kx4FA5cuQQKB3repZMN+fJk2AatV4jirUpD6vdxKoX79PWXptzkYGFebcM5JNPK/QhxYKTZuGYGztV/A5hmJvj5txMisKWiPX35EjnHAtXgy89RbXRxo2DGjVJgSRF9dHZM1QeBrWz905IWfW82ZhUVoo07lTcSAnh+QjI4Mi5N57uT0sjASte3dGUNWoAUydylDHYcO4ztz27RQ5zz3HNfjWrWMY5VtvkQABDOf0eCjXpk+nJ/DIEeDyy7n99deBP/yBucuPPurkFK5ezcIpQ4bwuvPnM3Jr5kwavNq35/UefJAFWxYu5Pp3S5ZQXr72GpfCatyYHsHMTG7z+djW/fczHSgpiUT59deZOy2ELSHB8dCZ3rwXXmA7LVvSW5mVxTZmz+b/7Gzn+PBwVuB8+20eBzj7OnZ0yIPkhUnuXWRkbrIRGuqEcMqi3UIGhcSZbQPsR1ISyYscv2AB78H87/XS63X4MAvTyKLkcrxJIs2F0bOzHVJqwuxzXu2YHjH5LqRLiKqQO/e5ZhvmAukCM4TSJHfmWnvu68v1TCK8dq3TrnkP5vinpjItrDg8pSUZonktgMFa67sC328B0ENrPco4ZmPgmH2B7zsDxxzOq92ihEEVv4V8ofG9D4oWpnkIwYpQnN5ub5Qs7w4OCV0F+CMKC6Pwql6dL11kJACdg2pVDiEzux6qVVPw+ymgO3emRUle8MxMWskyMpigvGsXEBmp4Vn6KariADJxIaoPuh7p6Qrt2lEBHD9OwVi7NvtwXo0c5Bw9BJ+nHvx+5vpIsvSBA0yAPnmS54aFOcUZIiPpqdi/n3H0EREULl4vz9m1i4ndoaEUjhkZ/Fy7NsNHWraks+LoUe5r1IhKonZtXkfuq3Zt/q9b12nDtFQ1aMBr1a3L/jZqxMTqJk2cMT4/oI5376ZCkwqtmZm0Vnq9jhXn55/Z32bNuL1BA25ft84p6dyxg8bWAbeiwbqF2NchAY2+fB/7flI4coT3FHVeDiIzD+FADr0xdeqwrUaNKIh37KBnJzKS1922jeMh66qJkEtPd0o179zJcRXhtG8fx+nQIeDJJ/PziLvfvxIJ0TxjGQRax5dprT8MbH8PwEyt9Zd5Xa+oIZoFyadq1ajgZcHbAmFDIS0sSgSlHKJZbuZO995LXef1AuedR9les6azeHPNmtRdP//M48LDc3tt/H7qrKgo6r86dahbGjSgHjEnxqK/zQrmgNOW6Ec5x/R8yfInZvijkAoJJQe4LyqKesnjyb2+pZkDJ30x86Ld26UvZlik2Xfzuxwr7QfzYJnHBENeXq/C5oG5ESxHrqA23ITKPP5sPXiFycsuTD8LMwYFHVNQ24XNBXTDnRsYrD2/n2NWmCUSgLIL0fwJQGPje6PAtmDH7FNKecC67PkEUhYNxctdFZDT+wwnUCEA8gpROpt2Sxv53UdBUEDODUW4z7O5VvGi0BNsF7q6fnJ5tdOpU8FttWsXfHtiovlNIW71B8ChQ7gkMMaXtDD3c0ybBWmnTp3TKxNKid78SvXm1S+AoSZljLORQYU596xQ7LY1GwppYVEZUMnmToRbVzRoEPw4d1iafA8WrhaskIYcF2yfbDOv7Z6Mm9/zyjF2H+M+1vS25Xeuu19FQWHbzgvB7qGgNoLtM+/f/C/9c49ffvdfUP8L08/CjEFRrhNse1HHSOB+ZsHaE8NDcaAkXUUrAbRQSl2klAoDcCOAaa5jpgG4NfD5WgDziyOGvMRQUuFK50oY1Llyn2UJO8YmzkYGTQNwo1KqqlLqIgAtAKwopX5bWFicu6h8cycLC4tSR4l58LTWfqXUKACzwQSdKVrrTUqpvwNI01pPA/AegP8GChgcAQWZhYWFxVnjbGRQ4LjPwcIGfgD35VdB08LCwqI4YOdOFhYWxYESXQevJHC2a01ZWFiUP5T2OlMlASubLCwqJ6x8srCwKI/ITzaVfjUPCwsLCwsLCwsLCwsLixKBJXgWFhYWFhYWFhYWFhaVBJbgWVhYWFhYWFhYWFhYVBJYgmdhYWFhYWFhYWFhYVFJYAmehYWFhYWFhYWFhYVFJUGFq6KplDoEYHchD68D4HAJdqckYPtcOqhofa5o/QWK1uemWuu6JdmZkkYRZRNQ8Z5pResvYPtcWqhofS5qf881+VTRnidg+1waqGj9BSp/n/OUTRWO4BUFSqm0ilba2Pa5dFDR+lzR+gtUzD6XJira+FS0/gK2z6WFitbnitbf0kZFHB/b55JHResvcG732YZoWlhYWFhYWFhYWFhYVBJYgmdhYWFhYWFhYWFhYVFJUNkJ3jtl3YEzgO1z6aCi9bmi9ReomH0uTVS08alo/QVsn0sLFa3PFa2/pY2KOD62zyWPitZf4Bzuc6XOwbOwsLCwsLCwsLCwsDiXUNk9eBYWFhYWFhYWFhYWFucMLMGzsLCwsLCwsLCwsLCoJKgUBE8pNVgptU0ptUMp9WiQ/VWVUp8F9i9XSjUrg266+1RQn8cppTYrpdYrpZKUUk3Lop+uPuXbZ+O4a5RSWilVpqVpC9NfpdT1gXHepJT6uLT7GKQ/Bb0XTZRSC5RSawLvxtCy6KfRnylKqYNKqY157FdKqdcC97NeKdW5tPtYlrCyqXRQ0WRToC8VSj5VNNkU6JOVT/nAyqeSh5VNpYOKJp9KRTZprSv0H4BQADsBNAcQBmAdgDauY0YCeCvw+UYAn1WAPvcFUC3w+d6K0OfAcTUALASwDEDX8txfAC0ArAFQK/C9XnkfYzD59t7A5zYAfizjPvcB0BnAxjz2DwUwE4ACEANgeVn2txw+TyubSqHPgePKhWwqwjiXG/lUEWVToB9WPp3dM7XyqYT7GzjOyqaS73O5kk+lIZsqgwevO4AdWusftNZZAD4FMMx1zDAA/w58/hJAolJKlWIf3Siwz1rrBVrr44GvywA0KuU+ulGYcQaAfwB4HoCvNDsXBIXp790A3tBa/wYAWuuDpdxHNwrTZw3gvMDnmgB+LsX+nQat9UIAR/I5ZBiA/2hiGYAopdQFpdO7MoeVTaWDiiabgIonnyqcbAKsfCoAVj6VPKxsKh1UOPlUGrKpMhC8hgD2Gt/3BbYFPUZr7QeQAeD8UuldcBSmzybuBJl8WaLAPgdcyI211tNLs2N5oDBjfCmAS5VSS5RSy5RSg0utd8FRmD4/BeBPSql9AGYAuL90unbGKOq7XplgZVPpoKLJJqDiyafKKJsAK5+sfCpZWNlUOqiM8umsZZOnWLtjUexQSv0JQFcA8WXdl/yglAoB8DKA28q4K0WBBww1SACtfAuVUu211ull2akCcBOAD7TWLymlYgH8VynVTmudU9Ydszi3YGVTiaOiyScrmyzKDSqCfLKyqVRxzsmnyuDB+wlAY+N7o8C2oMcopTyge/bXUuldcBSmz1BK9QfwOIArtdYnSqlveaGgPtcA0A5AslLqRzBmeFoZJgwXZoz3AZimtT6ptd4FYDsotMoKhenznQA+BwCtdSqAcAB1SqV3Z4ZCveuVFFY2lQ4qmmwCKp58qoyyCbDyycqnkoWVTaWDyiifzl42lUTyYGn+gZaEHwBcBCe5sq3rmPuQO1H48wrQ52gwabRFWY9xYfvsOj4ZZVtkpTBjPBjAvwOf64Du8PPLeZ9nArgt8Lk1GEeuyvjdaIa8E4UvQ+5E4RVl2ddy+DytbCqFPruOL1PZVIRxLjfyqaLKpkBfrHw682dq5VMJ99d1vJVNJdfnciefSlo2ldmNFfMgDQUtCDsBPB7Y9nfQegOQqX8BYAeAFQCaV4A+zwPwC4C1gb9p5b3PrmPLg6AqaIwVGB6xGcAGADeW9zEGqz8tCQiwtQAGlnF/PwGwH8BJ0Kp3J4ARAEYYY/xG4H42lPU7UQ6fp5VNpdBn17FlLpsKOc7lSj5VNNkU6JOVT2f3TK18KuH+uo61sqnk+lyu5FNpyCYVaMjCwsLCwsLCwsLCwsKigqMy5OBZWFhYWFhYWFhYWFhYwBI8CwsLCwsLCwsLCwuLSgNL8CwsLCwsLCwsLCwsLCoJLMGzsLCwsLCwsLCwsLCoJLAEz8LCwsLCwsLCwsLCopLAEjwLCwsLCwsLCwsLC4tKAkvwLAoNpVQjpdRUpdT3SqmdSqlXlVJhxdBuM6XUxuLoo9FmF6XUBqXUDqXUa0opFeSYVkqpVKXUCaXUQ8V5fQsLi9KDlU0WFhblEVY2WZQVLMGzKBQCP/SvAHyjtW4B4FIAkQCeOct2PcXQvWCYBOBuAC0Cf4ODHHMEwGgA40uoDxYWFiUMK5ssLCzKI6xssihLWIJnUVj0A+DTWr8PAFrrbABjAdyhlFqhlGorByqlkpVSXZVS1ZVSUwL71yilhgX236aUmqaUmg8gybxIwCq1SCm1OvDXM7A9QSm1UCk1XSm1TSn1llIq6PurlLoAwHla62Vaaw3gPwCuch+ntT6otV4J4GQxjI+FhUXZwMomCwuL8ggrmyzKDJbgWRQWbQGsMjdorX8HsAfAdADXA6eExAVa6zQAjwOYr7XuDqAvgBeVUtUDp3cGcK3WOt51nYMABmitOwO4AcBrxr7uAO4H0AbAxQD+kEdfGwLYZ3zfF9hmYWFR+WBlk4WFRXmElU0WZQZL8CyKA8kArg18vh7Al4HPAwE8qpRaGzgmHECTwL65WusjQdqqAmCyUmoDgC9AoSRYobX+IWAF+wRAXDHeg4WFReVDMqxssrCwKH9IhpVNFiWIkorjtah82AxHGAEAlFLngYJnJYBflVIdQOvRCDkEwDVa622u83oAOJbHdcYC+AVAR9AA4TP2adex7u+CnwA0Mr43CmyzsLCofLCyycLCojzCyiaLMoP14FkUFkkAqiml/gwASqlQAC8B+EBrfRzAZwD+AqCm1np94JzZAO6XSkxKqehCXKcmgP1a6xwAtwAINfZ1V0pdFIghvwHA4mANaK33A/hdKRUTuPafAUwt2u1aWFhUEFjZZGFhUR5hZZNFmcESPItCIZB0ezWA65RS3wPYDlqJ/ho45EsANwL43DjtH2DowHql1KbA94LwJoBblVLrALRCbovVSgATAWwBsAvA1/m0MxLAuwB2ANgJYCYAKKVGKKVGBD43UErtAzAOwN+UUvsC1jULC4sKAiubLCwsyiOsbLIoSyi+fxYW5RtKqQQAD2mtLy/jrlhYWFicgpVNFhYW5RFWNp3bsB48CwsLCwsLCwsLCwuLSgLrwbOo0FBKLQdQ1bX5Fq31hrLoj4WFhQVgZZOFhUX5hJVN5wYswbOwsLCwsLCwsLCwsKgksCGa5yCUUlopdUlZ98PCwsKitKCUSggUB7CwsLCo8LAyzSI/WIJXDqCUuk0ptUEpdVwpdUApNUkpFVXW/TobKKXGBu7ld6XUFKWUOxxAjgtTSn2plPoxQDwTSqFvtymlspVS3kD/1iqlLjf2n6eUekUptSdwzM7A9zqB/aOUUmlKqRNKqQ/yuMZjSqlnAwI4J9DOUaXUNqXU7cZxYUqpp5RS3yuljgXGYYpSqllg//jAvtQOLdkAAQAASURBVKNKqa1SbtnCojxCKZWslLqrrPtRVlBKfaCUygr83uVvXWBfs4CMm+E658OADPijcU6mITe8Silv4NgfA/u8Afn6gVIq0tVeT6XU/IDMyFBKfauUamPsDyqTlFJtA8df6movSSn1r5IbNQuL8gsr03LJtCNKqblKqVZBjktWSv1mzvUCsuUG43uvgAx0bzuqlLLrchczLMErYyilHgTwPICHwbVMYgA0BTBXKRVWzNcqlR+QUmoQgEcBJIL30hzA0/mcshjAnwAcKPnenUKq1joSQBSA9wB8rpSqFRjzJABtAQwGcB6AWAC/AugeOPdnAP8EMCWf9i8DIBO5nwPXOg/AIwAmGxOuLwFcCeBm8Pl3BLAKHDuA5Y6vCOy7FcCrSqmeZ37bFhaVH2U8WXhBax1p/HV07e8R7Destf5IzgEwBAG5YWwTXBH43glANIDHZIdSKhbAHHD9qgsBXARgHYAlSqnmRhunySRwAeTxAN5T6tQaXHcCaAjgqTMdDAsLi7NHeZBpoCz4CZwznULAIN0blCFXGrsWAuhjfO8DYGuQbalaa3/xd/vchiV4ZQjFtUOeBnC/1nqW1vqk1vpHANcDaAbgT0qpCwMW29rGedFKqcNKqSqB73copbYErCezlVJNjWO1Uuo+xTVYvg/Sh8uUUmsUPVl7lVJPGfvE4jxcKfWzUmq/UuqhQtzarQDe01pv0lr/Bq7jcluwA7XWWVrrV7TWiwFkFzBeNyil0lzbxiqlpgU+D1VKbQ5Yg34qTF8DC4NOARAB4GJwcc8mAK7WWm/WWudorQ9qrf+htZ4ROOcrrfU3IOkL1s9aAC4FkOq6lg6c9xuANkqp/gAGABimtV6ptfZrrTO01m9ord8LnPOk1nproB/LASwCCaeFRblFwFjynVLqUEAufaeUamTsr62Uej8gV35TSn1j7Bum6FX/XdF7Pjiw/faAnDuqlPpBKXWPcU6C4npMjyilDgB4XykVEbA+/6aU2gygWz79naSUGu/aNlUpNS7w+ZGATBGPV2LwlgqFFwA8cxbnAwC01gfARZE7udr+j9b6Va31Ua31Ea313wAsQxCS5pZJAP4FoAaAkUqp+qDx8Q6tte9s+2thUZFhZRqgtc4E1+zr5Nr1Z1DGfADO/wRugtcblCnubQsLurZF0WEJXtmiJ4BwAF+ZG7XWXtD7M0Br/TNIFK4xDrkZwJda65NKqWHgopl/AFAXJACfuK5zFYAeoAJ34xj444wCvU73KqWuch3TF0ALAAMBPBIgJvmhLWg1FqwDUF8pdX4B5xWEbwG0VEq1MLbdDODjwOf3ANyjta4BoB2A+QU1qGgVuwuAFyTA/QHMCjyDM8UgAEla61yEVSkVopS6GhzrDYFrrdBa7y1Mo0qpCFCgbzqLvllYlAZCALwPevCbAMgEF9sV/BdANVBW1AMwAQCUUt0B/AeMaIgCJwI/Bs45COBy0Ot0O4AJSqnORpsNANQOXHM4gCdBo83F4G/SnHi48QmAG5Q65bmqBcq7T5VSLQGMAtAtIFsGGX06E7wJ4NJCyNF8EZhcDgEXJYZSqhqoU74IcvjnoDHJ3UYumaS1PgmO7T8AfAjgQ6310rPpp4VFJcE5L9OUUtUB3ISAzDHwZwAfBf4GBYxDAIlb2wD5DQHQFcBnAKKMbb1gCV6JwBK8skUdAIfzcE3vD+wHSGBuAoDAj/VGOKRmBIDntNZbAu08C6CTMrx4gf1HAtaXXNBaJ2utNwQ8ROtBoRDvOuxprfWxQAnd96Uv+SASQIbxXT7XKOC8fKG1Pg6GHslYtADQCsC0wCEnQc/YeVrr37TWq/NpLkYplQ6Ghd4EeuwyAJwPjv3ZwAzPBIALA9c6DAroW7TW287gWm+BZHn2WfbPwqJEobX+VWv9P631ca31UdBjFQ8ASqkLQGIyIvA7Pam1TgmceieAKVrruQGZ9JPWemugzela650Br1MKGIrY27hsDoAntdYnArLuegDPBGTfXgCv5dPlRWB4kbR3LRg29DMYWVAVlC1VtNY/aq135tPWQ0qpdOPv3679mYHx+Gc+beSHb5RSRwHsBSeITwa21wZ1ejCZYuoTIG+ZBK31GtBY1ho0HlpYnPOwMk2lAzgKIA7ALbJDKRUHEtDPtdarAOwEDe/QWu8GsCfQh44Avg/cxxJjWxiA5flc2+IMYQle2eIwgDoqeGz1BYH9APA/ALEBIdIH/NEvCuxrCuZlpQd+gEcAKDBWWpCnh0gp1UMptSAQdpABEsY6rsPM83eDuR35wQtapATy+WgB5xUGp8guKES+CRA/gF7OoQB2K6VSFPNR8sIyrXWU1rqO1jpGaz0vsP1XcOzPCAGL1AAAs4zNPweuVVtr3Ulr/WlRr6WUehH0Sl6vtV3bxKJ8QylVTSn1tlJqt1Lqd9BCG6WUCgXQGMARzfBtNxqDE4RgbQ5RSi1TTPRPB3/rpqw65AolvBCny66gCPymPkVu2fJRYN8OAGPAEMeDSqlPlVL5ycDxgd+7/AWzsr8LRjVckU87eeGqgNU9ATRwyRj8BuqGYDLF1CdA3jJJsAnAj4ZstbA4p2Flmo4CU4cyAbQ09t0KYI7WWuTLxwgeptkHzrx1sbFthdb6RD7XtjhDWIJXtkgFcAIMrzwFxapoQ8BiHwgIjTkAbgB/pJ8ak/y9YFiiOaGIcIXV5EcIPgY9YI211jVBL5FyHdPY+NwELDKSHzaBlhlBRwC/aK2D5qwVEXMB1FVKdQIFl3gyoZnHNgwMj/gGDEsqKuaBIQbVz7B/3QDs1lofKuS1uisjjj8YlFJPg+/DQK3172fYLwuL0sSD4CSgh9b6PDg5FwqUWbVV8ErBe8Hwo1xQrMz2P7AISP3AZGMGcssqt5zbj9NlV374BMC1geiHHoHrsWGtP9Zai6Vag3kkZwytdRaYf/0PnC5vC9tGCpjzMj7w/RioU64Lcvj1COgTCwuLM8I5L9O01nsAPAA6FSICaSPXA4hXrOp7AMBYAB2VUjIHFILXGw7BW2Rss+GZJQRL8MoQgZDApwG8rpQarJSqoliN6HMA+8CYbsHHYJzztTBIDUjIHlNKtQUApVRNpVQwBZ8XaoCWJ18gVvzmIMc8EbBetQXjxD8roM3/ALhTKdUmIPD+Bk5EgkIpVVUpFR74GqaUCpe4cTc0c0S+APAiGJI0N9BGmGKZ8ZqBY34HrdlFxX9Bgfw/pVSrQI7K+Uqpvyqlhgau5Qn0NxRAaKC/4oUdCmB6YS4U8BrOBfC1UqpLoN0aSqkRSqk7Atd6DHwm/YuJIFtYlAZqgJbedMUCURJGCK31fgAzAbypWLigilJKJkvvAbhdKZUY+O01VCzJHQaGFB0C4FdKDQHzSfLD56BsrBUwotyf38GB0MTDoHdtttY6HQCUUi2VUv0CEzJf4L7ORLa48V8wB3vwWbTxCoABxmTqUQC3KqVGB2RJLaXUP8HCTPlVMrawsMgfVqaxzbmgkX84WN8hG6zv0Cnw1xokcLKk00Kw2m8fMDQTYA2Ci8D6DpbglRAswStjaK1fAPMcxoOkZDlIMBJdbutpYKGTA1rrdcb5X4OWl08DYQMbQW9PYTESwN8Vczr+D8G9XilgUm0S6KqfU8A9zQKruS0A4693wxCGSqlNSqk/GqdsAwVMQzC/LBO0KuWFj8ECJV/o3PmLtwD4MTAOIwD8MdjJBfT9RKDtrSD5+h3ACjBsQuLE/xbo46Pg8g6ZgW3A6fl3BeHawPGfgbmKG8FEZAkZfRa00u1QzppYNi/GojxDg8QjApxcLEPukGWAv9WT4O/sIBguBK31CgSKDYC/hxQATQM5L6NB+fQbaPSYhvzxNCh7doEREP/N/3AAjmwxjWhVweqSh8Gc3XowliYIgr+o3OvgHQ52kGYRpv8DDVVnhECkwH8C7UCzGvEgMCpkP3j/0QDitNanVVG2sLAoFM51mebGiwD+ApK897XWe7TWB+QPLD7zR6WUR2u9HSSxB4RgalYvXwGm79giTiUEZdN5LPJCwJu4C0AVbdcoKRCKlaPWAGho8+QszkUopVYD+Ltm6X0LCwuLCg0r0ywqKqwHz8Ki+FATwIOW3FmciwiEcLcGjRwWFhYWFRpWpllUZFiCZ3FGUErNdIUhnfPhg1rr7Vpr9xqEFhaVHkqp58GQoUcCpbEtLCwsKiysTLOo6LAhmhYWFhYWFhYWFhYWFpUE1oNnYWFhYWFhYWFhYWFRSRBsge1yjTp16uhmzZqVdTcsLCyKEatWrTqsta5b1v04G1jZZGFROWHlk4WFRXlEfrKpwhG8Zs2aIS0tray7YWFhUYxQSpVajkNgDcOFYKloD4AvtdZPuo6pCpaf7wLgVwA3aK1/zK9dK5ssLConSlM+lRSsfLKwqHzITzbZEE0LC4tzDScA9NNadwQXZh2slIpxHXMngN+01peA6xc9X7pdtLCwsLCwsLA4M1iCZ2FhcU5BE97A1yqBP3e1qWEA/h34/CWARKWUKqUuWlhYWFhYWFicMSzBs7CwOOeglApVSq0FcBDAXK31ctchDQHsBQCttR9ABoDzS7WTFhYWFhYWFhZnAEvwLCwszjlorbO11p0ANALQXSnV7kzaUUoNV0qlKaXSDh06VKx9tLCwsLCwsLA4E1iCZ2Fhcc5Ca50OYAGAwa5dPwFoDABKKQ+AmmCxFff572itu2qtu9atW6GL7FlYWJQilFJTlFIHlVIb89j/sFJqbeBvo1IqWylVO7DvR6XUhsA+WznFwsLiNFiCZ2FhcU5BKVVXKRUV+BwBYACAra7DpgG4NfD5WgDztdbuPD0LCwuLM8UHON2wdApa6xe11p0CkQaPAUjRWh8xDukb2N+1ZLtpYWFREVEqBE8pFa6UWqGUWqeU2qSUejqw/SKl1HKl1A6l1GdKqbDS6I+FhcU5jQsALFBKrQewEszB+04p9Xel1JWBY94DcL5SageAcQAeLaO+WlhYVEJorRcCOFLggcRNAD4pwe5YWFhUMpTWOnhSltyrlKoCYLFSaiY4cZqgtf5UKfUWWJp8Uin1ycLC4hyE1no9gOgg2//P+OwDcF1p9svCwsLCDaVUNdDTN8rYrAHMUUppAG9rrd/J49zhAIYDQJMmTUq6qxYWFuUIpULwAqFNwcqS9wNwc2D7vwE8BUvwygR+P+CpcMveW1hYnClK6zdf3mVLee+fxTmPKwAscYVnxmmtf1JK1QMwVym1NeARzIUA8XsHALp27WpDzCsp8pNhJSnfzuS6st3nA8LDTz/O73c+y3EAjxV4vc5+OebUcWE5wKFDiGxeD+kZCl4vz/V4gMhIHuf3A56QHPj2HkJ4k3rwnVDw+XiM9Ck9HYiK4jbpo9/vtGV+L2gsygql1h2lVCiAVQAuAfAGgJ0A0gMlyAFgH1iaPNi51gpVgvD7gQULgL59y98LamFhUfword98XtcpL8rQyj6LCoAb4QrP1Fr/FPh/UCn1NYDuAE4jeBaVH34/MG8e0L//6TKssPLtTOSxu22zjfzk/oIFQGws8NprwOjRQGoqj5P9yclAVhYQFgbExQGvvAJkZwOPPEIylZ4OjBwJaA00bw5ERwNffAGsXw+Ehuagxd75iPx9H/7R/Rv8yfMVtm4LQZ06wPnnA3feCcycCezfn4Pa6xdg59EoNIzYih8b9cahQyGIjATq1gWOHQN27wYuuIDfMzJyUDsyC96sqmjTRqF5c6BNG2DXLmDcOPbLfb8mKSwr3VJqRVbcZckBtCrCubZSXQnC47ETHIvcljOLyo2S+s2736Fg1xElXx7eNyv7LMozlFI1AcQDmGpsq66UqiGfAQwEELQSp8W5jcLItzOVxx4P0Lu3Q2KkDSE05j6z7b59SYg6duT/3r25/9tvgaQkICaG5C4mhvtHjQK6dnXamj0buPhih9DGxQHNmgEhIUBij2No8/sSvIgHsX5FDmLaH8M11wAtWvDa334L7N0LnFfFB8/RX3AMYUjK7ISTPj+6dslB3Zo+bNyo0aQJ8MGUHPy0Lxu7f8zG8e9/wk2rR+OxE0/j8sty8P33wOTJJIIpKfQKCkkF6GFcsMD5H2xsS0P/lXoVTaMseSyAqEAJcoDE76fS7o8FYSc45zbK06TbonQQzNp7NsjrHTItmvK9PJGq8tIPi3MLSqlPAKQCaKmU2qeUulMpNUIpNcI47GoAc7TWx4xt9cE6BusArAAwXWs9q/R6blGe4PGc7r1zhznmBSFjwYxwAglplM/mMYsW4VRoY9++jg7w+Zx9s2fzz+vlf+nToEH8nJREr9pXXwEZGcCyZSR3EycCM2YAS5YA8fE8x+sF3n0XuPVWEquWLYG1a4HHHgMefRR44l+R6NnmGKJCfMhs2RFVakVi+3ZgwgTg7ruBxo15jaMnI/D3Lt/gSkxHJE6gTv1QZC5Zgy6b3kKCZyGqV8vGtPtn41n/KPxRfYy/6r9iOWIx8PtXUVv9hmHDgB49gG7dgLZt6Y30+ehJ9XrZ9+hoYPHi3ETXHLvSmG+VVhXNYGXJt4BE79rAYbfCsFKVBezk1uJcRXmbdFuUDPKSccWhcPJ7h9ztu0lfYftZ1GOKclxJwOoUi7ygtb5Ja32B1rqK1rqR1vo9rfVbWuu3jGM+0Frf6DrvB611x8BfW631M6Xfe4vyhDOJkDCPy+t8nw94+WWHoL3yikPyxEsnRA7g5969Hc+ctJuVBcyfD6xalTt/zu8HQkOBAQOAYcOAnTtJ7iIjSZD69WN4pnjJwsMZntmoEXD11UDNmo53b84cYNFihfjUf8G//Qd82+XveOABhU8+ATZv5v5ffgGGDgViYhU+TPwUoxffjk596mLyM4fRyTcfSxGPPZk1oDLSkXDkY6SjHkIP7kEEsuBHCACNXt1OYvt29sXrJXFs1iz3sxg1Cli+nH13ezjlmNKYb5WWBy9oWXIAjwAYFyhFfj5YmrxMYD0YFkVBZXxPLLmr3MhPxrkVzpm+38GswO6QHXP/vHm5rcIF9bMoxxTluJKA1SkWFhaljcKSh7yOM7eHhzPHbNAgkq4xY3IXOxEit2gRv5v5eLItMRGIiCBZa9MGePNNEiM5RgjdVVcBY8cCK1fyvEGD2H5oKInSnDn8GzLE6WOvXsBDD1GH/OEPPHbx0hB4q9dH02YKK1YAK5bnoF/bX/DC8xrXXQc88QTQqhXw7XchSNlaH3fdpfBFcj2si0jAxdiGAXW24ZKutbDw/JvQCHuwvFp/TK95B5phL165cDxmr60PgF7En38G3n+fIaXz5zNcdNEi9i801PE8Bhvr0phvqYq2dm/Xrl11WlpaibRdXhL/Lco3bGGG4odSalVFX7C3JGVTYVAY+eX2oOV1zNm+39KGKH/57yaRs2dTESYk5J44iLW2oGvkV6UtWJJ7acv4olzP6p/yCyufLMobCivXSkOumJUxRc4DznXFiJecTI/bmjU8xudjeKMUKjFz+KRCpZw7cyY9gA89BCxcCHz2GfDqq7xeRATJlVzroYeAhg2Be+/JwZS4KWizdzqm1rkbze4ZjHtGhODBB4GlS4EmTYD77qMOSkvLAY4ew65DkfjDNQo9Y3Lw+MM+nFcvAlE1NW4dcgifzK+HkycVsrNZYOW119jXsWPZ1hNP5H4mycmn67biRn6yqdRz8MozSrqanEXlgA1ntChvKE6Plvl+n43ckmR687/bgjloEBXgokWOchfLbkHXzi8U1Oc7PfyoLDxqRSF31ttnYWFRGEj0g8jL/Ap55CdXiivM3fTaSaSGm+gkJ5OErVnjHCPFVuT8efPopZsxAxg/np4+8QQCwA8/8H569gSUIknr25dewPnzGU6anMxzO3QAPnjjGNrv/RYJWIA/HH4b7RplYPJk4JlngJtvBkaMoPctOxuoVi0ETdvVwJatCqtXA2vWhaDf0GpYulTheGYIRj5dH3ffrdCtG3DppfQ0zpoFvPMO8OSTufXlggX8n5XF/rgLzZQWLMErBVjlXflgyZ19n0sSZzK2wYwOZpikWwEXdL0zkVvmhEOQX6loUfJi8Z03L+97ye+aZntuQlmaeQ8FhZXmBWs0srCwOBPkJzuKkhd9tvnZEoZveuJke//+/IuMPF3/DBrkEMKEBBKufv2YgwfklufXXcf9UVEsZNKvH0nd888DmZksuvLddzxvxw5gxEORGNArE29jJGJjshFWLwrHjgEffgjcfz89f2vXkqjdcgu9guedR49eVhZQuzYwdSoJ5ciRvC7A+7j+euYA3nwz8J//8H7nz+f+bt34X6JTgNyEvLRgCV4poKIr7zN5Ie3kPzgqy7hYo0XJoahj6yZU7u2mN0uUZX7XM9srrCfPTezcoZiF8Rrm9z2/67rblnNNq7BZGKCkcCaW9GD9dm+3sLCwMOGunJmfXMtLfrsjNc4mP9s0IEo1yRkzcpM8uU5ysiOT3XorPJyEKDKS+XWvvspzfD7gjTdYjCUxkccuWcJ92dn8vH49cNllwL/+BdSpAzzwALA0VQEzZqHlOw9j0sBvkfG7Qloai6JMngxs2kQv3ssv85qhofQGduwIpKU5f999x37NncvvW7cCDz/Maz/xBP9HR/N8r5e5it99R5JojpnpeS0NWIJXSqjI5O5MrPh28n86KtO4VHSjRXlGUce2oGR5SYRPTj79XK/3dEVvlrwOpvzd/01FLe24QzHNNYIE7t+Be9JSEKR/wTySJrKznTCZvK5dmGsVpi9FsaTnR/oqi5ywsLAoGeSXc+f+7iYVJvGS//npnPyMdW7DXng4QzHXr89bxsfFOXrJhOTweb1cLqF9e0efdOrE/7J/zRoSu7FjuWD62LFs45FHuD89Hfj0U2D6zBCE1qkFv1/h++/pcatdm96+O2/PwUOjjmPCBI133uESDJGRJGazZ7P/cXH0HAK8Zk4O8/bCw+lNnDCB3sDLLyf5jIpitdHQUI7BnDlcCiIhofTnS5bgWeSLM5nI28l/cFS2caks91EeUdSxzcu6ml87Xi+tnELyTOXt9wMvveSscRQbm/sYIXWikGW/CfeaSXK8fDcnHYUhX3m1Z5bpDjYugwblJo5n6iEtTAhTQZZ09/eCqthZWFhYFBZ5EbCCjgFO9/QVxpvn3mYSRgmxdF/b72d4ZXq6482T7cnJDG9cvJiEaOBAp73ERIcUrlxJL1l8PJcjAIC33qIeaNKEbd92G1C/PrBhA/VTdDTJ2eefM4duypQczEt8Bj9PTUXbL59G+3Y52LwZ0Jqhl9OmsS9TpjBEc+JELrC+cyf7+eKLLPoydSrwpz/xvxgSw8MZ/vnAA0CfPiR7MjZ5rVlYEgY9S/AsCsSZTDTs5CQ47LhYlBTcJMrcvmjR6RbE8HCGwEjOBJC7+plYTH0+HmcuaGsWT+ndG0hNzU36ZM0ksxx2bOzpnjQg93nz5gUPYUlPZxiNGWppXt8syuLOczDDg8xzC5OHmN/x5v6CvIh5oSCLeTBYz56FRcVBaf5eg4VTumV/XhEVIn/NcPu8SF5+kGvGx59ePGvBAp4/ahRJWlxc7r5lZbFCZnb26eGcc+Y41ZVF9qek8NiwMKB5c3rKAGDdOuCDD5hPl5kJvP02SdbjjzP/7scfgVuvzEDkjjW4ED+j3/ZJuKLHIfTsCXTpwn5s2sR2R45k/7Kzge+/B1q0YJ/bt+caeJ9+SoIXGcn7kfuPi+O9vPmmU+FTPJ3udAgzjaI4YQleEWGVq4WFRVnCbWU1v5tkxyQ0kvxunifKdvZs/okiFSVvJr9LqIy0Z/ZDSJb8nzOHFtgRI/hfcv/cVt/+/fk5Odk5X5LxhZCZ+RetW5/ehlxf2vf5nHsJFkbkDk3Ka78b+eUhikLPy/pdnDrDhm9aWJQNzuQ3V9y/18K0E8y7JrLfPN8ddp+cTBJjnlcYo1Uw2dq3r1NQBchdPAtw9qWkkJTJuaGhzvpx4sVLSOD+r76ibjFTB0JD6eXr2ROYPh34+mvqicRE5uHdey/DJK+5xunbihXMs/sxPQoJsT40xy683fifOBxSDw89BBw5wra6dGH7CxbQwNmxI4lpjx7s81df8T6++AK48UZuX7yY4ygRLWFhwPDhPM4k1mbEh4xzSURtWIJXBFjlWrFgn5NFZYGpiCU3QMIc3QnrolRFVvl8wUs1mwolMZGKdPFiWjzd+Xqhobm/+3z00Pl8udsHqFBl8VrJf5NQSrclWRAsPEiImscDPPggcMUV+Xu8ZJIC8Pp5hRHldX5++/MLKQ1mNS+MJfxMYMM3LSxKH2f6Oy7O3+vZ9CGYLDLz38TgJka9orYvOdvST7mmO3w+v3B2CcOUUEyBLBw+cSLJnBjyZI05j4eFT66+mn87dnD/Sy9RB02ezFDJzz5jJMgXX/D6HTspRM77Dt3eux+tXrgLK1YqJCay3QEDWF0zPp46rFUr6sDJk0kmN28GLrqI9xIeTuL50EPUO6JHk5P5/e23Ty/y5X4nghVJKw5YglcEWOVacVCeyXh57JNF+YX5Lvv9DD8RBelOWJf1hgBH6SYnM+RE2vF6Tw8VAdhGTAxzB0RxBgv3lGPHjOExYvmVfR06OBXOEhPZZmoqrZqycK1p7RVP3owZ/BMCGxrKe3OHWJp9cROu/v1ZfU3yMUzk54Vz319+MMNp8vIImtcsjM4oikyw+sfConRxNnO/woaCn00f8mtT5D6QezmapKTcRiszh849fyqM3JSQfJPA9e7teOIAZ5keud6gQdQRpoFOjIVyjlkgTAhjUpJToRLgsYMHs70uXbitTRsuW9Dowhy89+wvaN1Kw+djBc1FiwIVL4+HALVqYfMWLl4eEcGiKbVr5/Ycbt7MsescnYOoE7/g4Yc0xo4FNm5kf4YO5f2HhzuVQPv35+f27U9/FvkZCIsTluAVEVa5VgyUVzJenonn2SC/ybfF2cF8l8PDgXHjcofcTJzo5LqZIY4AFVlMDENF/H6SrJQU/gcccpacTOvmW28BXbs6ROz55/k/NNTxqsm1zKTxxEQq3eRkrk2Unc3vM2cCr7/uJM6np9Oy6i6h7fczYT07m38eD5VkaiqvN3u2U/QFyF2KW/bLZGXlSobSFLQchHtfsNBO8xm4Q0qBvIsVuEuYF2YCZn8vFhblF2XphcuvD+7ogmDnmEVQTG9dXpUdzXPcBka5pvv4MWNOX+fO/G+2aY6BEDrpV0KCozvGj+e+zEzqj+hoyv7sbBK8qVOpA777jvrhpZcYKvnaazxnzeocpCdcjo9jXsEN06/DPcNzcOml1Ic9enCpg6wsksLYWIZminHR72clz+uuY6hneFgOBr3QH77GLZAS8wiWLslB69YkhF4vjZrjxzvFY7xe3odZxVlIrzukv6TmqZbgWVRalDdyB5Rf4ikojjwDM3TQTlqLB+b7YhKX8HCSmfnzWapZCqHIOVJxrGtXKr25c1l1LCWFx/TvT+tjXByPu+su4J13qJx8Poa7+P1OkvjKlawe5va2CQlMSHDKR/fqxXM6dnSS1JctY4lpIZxyLgAoxd9GWJhzb1LAJT6eYZpCPBct4j2tXct7AhxvX7C1/mQ88luuQfqfnzfO/CzjG6wt89iCJnUFWeYL63U0K5Tmd5yFhUXeKMnfSXHr/8L2NVg4JHA60TDbNfWIRINIBIg7XF1C6UU2uyM0TCOXmzya9yFyNSuL8rhVK27fsgVo147LIdx/P8nbypWOrtq0CfjjH4GqVSn7W7ak7mrVIANLUz24GFvxaVprvPHkIdSuTd1Upw7JWWQkK11u2AA8+SSXSujalToyIYHE75FHgOeeOIbDizbhFX0fsjZvR/avGUhIYFXNhQvZpkSvZGbSsJmVRX1m6hUzZ7yoz7GosATPwqKUUZ7JXXHkGYhQL8qaZhbBYT4LmeybfwAV1NChVChmRUy/n9+7deP6PaNHM7cgOtpZLBagUk5NdQqdtG5N5bZkCTBkCEmcLDg7ZgytnVL6Wayoo0fn9vwlJFDpbdhApbd4MUla//7sb0ICrZozZtDq6vezn7LPVIZC8sy8jm7dmHfRpk3upHwhnG6DgzleeS3qXtD7alqZzZCivPLtgk1o8kJeIah5FYwxn51MuiREKphhpTS9hJZIWlRUnM3v5EzJVlENMoWVXXkRN7Mfwar/SrvuEHcpHiKVK832srPzXjJHZJtp9DXlZ1xcblLo8zEksmdPYNs2bh89mqkJzZvTwDhoEHXQRx8BV15JIvj006xm6fez2uXw4UBYvSigTWsAoTjZ8GJ8NLceMjNZ2dLrdQyBy5ZRL374IXDsGPu1fDm9cW++yUiWNt0jManRP9EWW9A7Ngsb90UhPBy46iqge3e2MXAg+zZkCL2DV1zh5DXm5bErSdlsCZ5FuURlmSRUpPsozjyDvLZZFB7u0BgJSXRXvRSFGR7uKFEJW5SwS1G6KSnOArTz5gHffkvPXmysUzgFYPhKfDwV7NSpwK238twFC5xS1vHxPHbLFuCyyxyFeP/9/B8WBlx/PXDPPUBGBgmYWQwmLo7hnA8+SGI3aBC3u63KYvEEeA/dupF8xsUxZ0KOSUhwisHIxEXGwSRKbmOE6dULNrGRZ2FOTsRTKiFFbo+eu+x1QZOvvJS826toTr6E7PbuzfEbMya459K8z5L+TdpwU4uKjDP9nZzpe5/XeXmVzTePd4demscEyxMOZoDKa3mZbt0YXhgsjzk0lAZCUyYClO+xsTTGBTNySV6aKcuysymzJGXA52M7993HBcPvuYf6y+PhNbt35/4336Shce1a6qXQUODCC4G//Y1hks2bs1rm+g0KnWc+hw1j3kf0K7finhEKl19Owrh4McNA4+PZt6VLGcESEmBFWgOvvpqDxfN98GVqbN6s0OnVO9F369tIe2I62rVXpxZcf/hh6jiBFIYJRm7dKEnZrLTWxd9qCaJr1646LS2tRK9huqctSh8iiM70pS8vz+9s76Oi4kzuWym1SmvdtWR7VrIoCdlkvsvuymF+P6t3icI1C5ikpJBwde1KEhUezmciFthBg3hcUhJDS8LDqRhbtGB7337LpQlkDbx27diH9eu5WGxEBJXkkiUkg5GR7Eu/fsxj+P57LjT7xBMMe2nQgNdbvJjEbO5ceveUIiGMjGT7JnmSBdgjIx1vVnw818PLzgYee4zHLlvmJOQDVNoREbzOq68yyb1fP7axcmXu8CAzx0RIsFQ/i4pyngFABd2/P/v11ltOkRl518UinZ2dO1w12DMN9vsIJreCbRNLuUl6zePd2/LbXhIoL/K3OGHlk0VBONP33n2eyAf30jaFuY7ISZE/gCMrTJknxwKOzBXZlZ1N0pKTQ0IlusVNTmbP5n8pkmK2JcfPnu3omuTk3NEP8+bxWr16kbC1bs1+i/yU9e3i4x394PXSIPnnPwP/+Q+9dOHhTFHIyqLMT0mhYXLkSN776tUM1xw6lNe84grg8svZzwce4PeEBOa2Dx5M/fXGG4AnJAcpsY+i7eZP8GGTJ5D157sw8r4QbNhAAjxvHo2fTZowR++DD4C2bem9A5wxkGfpJuKFeZ6FQX6yyRI8F87VSXl5w9kIy/L0/CrjZKcwKOp92wlU4Sbh5rh6vSQ77ds7lS+Tkpzqk7NmUUH26+eQJCFOch1JAu/Xj6QrNJSf5893tkkYZrNmJGQeDxVaairP3byZZO+119gXU0l7veybucxC9+7Mc7jrLn6fMsUhkHJtCaNp1IhlpgG2P3IkSaV4DydOZDtLl7Ki2R13AI8+Sk9kVFRuwrtmDfMVo6LYr9RUR06Ip/OuuzgpmDbNCXkVeSLj/8orXOPPJICmN82c7LifmUkqz0bZ50UG8yKOsg6V24JeXmRTeepLMFj5ZFGaKMrvwS1f5s1zjIDBiKLoGVOuyXFu+HyOUUxknFmJMy7OqZAsOkYMYX4/9dPo0ZTpYog0PVvSZnS0U9kSoP5YtYpeOlkex9RfQui2bGGOd3Y2iVznzgzdj46m3unShZ68zEx+796dSx107sy+HD5M/dOpE48JDeV1H38ciDz2C7wXtECq7o5YtQK+Dd/j3W/rY8QIEtfISLa/ahXb8vmoM9q147VMMpuX4a445qv5ySYboulCaYWyWOSPMx3/wjy/0gwfOlffo3P1vs8U7kqQwWAWUBFvWKdOzKsThS6WW7+fxCsri14sOffwYeC550iUZs6k9239ekcBh4ZyMjBwILd99RWV1403An/9Kz1y997La//vf+xLp05se/hwEsqBA9knANi6lQpQSlp7vUxYb96chOrdd3lev35UzHPn8niAORhXXsnPixezz5LMHh7OicWIETwvNJT7N2wgAROLL8DjzAR4r5fEUMKC0tO5/a67SCoHDKCiNhenlUmJFLWR9uVZCbkLD89tqTXDmMywqbzIXV5hWe5teYVEB5N9EkrqJnd5vWulHV5pwzotKipK6p0tCrlzh4JLeX6RBya5M2WQKdfks7nMDkAZ3a0bP5sGLPM63brR+OYO55SKz34/jW+9euXOWRZ52rs3DXQbNlAPDB1KGRwW5hA+U175/czH69+foZFyfLdulOl79jACIyuL+7t3d4yMUVHclpjIypuTJwOXXJyDPi1/QWR1Ort276Ye8p1XD3NbjULvkFSEx3XF8l31cNddvP4zz9Bbt3Yt72vRIuqEsWPZb1mHNZh3Nq8w25KAJXhBYCenFRsFkTs7mbAoT/D5qCCzs/Ou4ujzMYTSXDC1f38qRFlU1e93SvlPnEjL6eWXM8dNvFTjxjH05tlnqfCWLydJ6tePhEo8Yz4fvw8bRqU4cKATcvLggyRhQ4cypKVnT1pqH36Y5MnrZZ/69GEfunfntXr2BH78kcpx6FAuDDtmDPvg91Mx9u1LwijW14gIet8yM9mvL75gkr3Px2OjopxlGiIjnQmNLOcg1uFly5zCMLKGH8Ccxvvuo5XX9LyZy1DIf/lbvNjJfxTPmGklz4s0mWtABUMwZV9Q/kawNtzXle2F9Qzml4dYWBTlWGtUtaiIKM25RF4yJdhvJ6/PIoNiY4OX6Rf9kZDg5JCNGkUPHnA6WRTZtGQJwytN4mce89ZbNL6JrBZDk5k/DjD6Qz5HRgJ/+YuzsDlA/SjGuUsvpUyXfs+f73j9Bg6kV65bN6BpU8prgLpN7tvnA77+GvBl5mDmA7OwsNVdiP/XIAwemIM//IHHe48pTGr0T/g2/wDf9CSsWKnw5ps8v1YtGhazstgPCe8PD+c2WbrIlKVmJIWZ912Scs8SPItzCufaZMIS2fIPsXQOHRo810Hg3i5hhgkJtBguWuTskypnsjCsKOJnnyVpWr3aaWPiRJKu664D/vUveuauvprEZ80a4MAB5tdJzkXz5vy/aRPDQCdNogKfMIFewzFjeM5rr/Gvc2dacJOTGUI5ZQrbnjWL97R8Ofs2Zgz7GhFBMij/u3ShlxEg4QwJcZZ5EGUtluH0dPY1LY3HREezlHZmJpWtSaBfeYWk74YbSE4l5AZwFLMUaJE/wJmkmM9FJg5ugmRauoOtAeV+roXxvhUW+U1Apd1g1wu2TlNRJrNnMvE9V+SxReVBac0lgv2e8vuN5fW7k/7KWnWiG0SGvvLK6aTRXNfO9P5LTl1CAo1rsrSNQCr7pqY6EQ/StoRZvvQSZaossSNrp4rxzONx+giQOC1ZQs+ZVFv2++mJW7WKMr5jR+bb+XzUM+3b09uXnc1jxds4ezb12GN3HsKkIzdiIOZg8dJQzP86A6Gh9CZGRgL/+U8IwpvWx/wFCu3bU0/XqUMdefnl1NkJCbkLy4SFsY9SPCavpShKwzhgCV4Zw07ASx/nymTCeisrDsy8hLwWz+7WLXf+3YMPOuEzUilTjpVwxOhohh127cp969Y5ZLJfP67hM2IEic3ddzN/bdgw4P33aSU9fhy45RZnbR+AIZbduzt9a92aJC0ykuGbffsyxOWOO6hYJ08meZw6ldtHjyaB3LzZqajZowfvacgQ4JJLeD8ZGTx27FgntGjwYCd/AqAH7uWXSXBjYugVzMri2CQmkuiNHs0+ffNN7nBHrTlWQ4c6IZdSUEAsrJI3kpjohAvJM1i0yCGMZriTmyCZoTqyX9owlzkwYXrQ8vK+FfS7LmgCmtd29zpNRQ0lOteMaBbnLorzHS+ImLk9c2b+nBlpEGypFvlsRinExpKkzZ7N72bRKDneNF4Fa19kk5lj7fc7S9uIZ0vWTfX5nGV5HniAcnX+fMr/7dvpZeva1SF2JhHdtInhkFdcweO8XpK7adNY6OSddyj3hYiNG8cCL1pTd40Zw77fcQeLgHXsCEQ2r4fI3tEI9+Qgrmc2Qs+PQu/ebMPvJ0mcOpVezLVrHS/c8uVO1Iy7ynNiIo2Tfj+NjKa8F6NaaclIS/DKEHYCblGSsBOt8o1gStN8Zm65IMsI+HwkJC+95BT6MJWzWESzsqiILr2URKdXL26XtiMj6XlLTaXFcvNmbp8zB/jvf4E772TO3q238pxVq5i/B1BpKcXPoaFUoF98QTL273+zOIqE81xyCT2DPXqQjPl8tH7+5S/MnWjbll7A+fPZp1WrWM0zIoLHXXUVwzZXr+a5Gzc64xYaSmW8ZAmv586/A7g9PJwTA1HQHg/7unixY8GeN49KW8jVokUkjXK+6fGS52SG3ZjXM5W6+Xzd3j5Z08/MYRFruljX83pnzJyUvHCmv/1gfS9KW1bmWFgUHgXNBfPKuTWJnmwz9Ycpd9xL7khFYyFnZt4ecHqunls/mWTFDLt8+WWH5MlSBGlplOtz5lDGytIuPh/w2WfcNno0r/vww5TpkjsuoaGtWjk51M8/zyV41qxhVErDhlzDrm1b3tM771CfLFxIghcWBjz1FNtfsYIVniMjASgF38wF8O3Yh8WPz0amT+G11xgx4vFQP37/PXWoUmwvLo66OC7OIXkyXlJBVLyL69blfr7m8y4NGWkJXhnCTsAtShr23SqfMEP53HJAlKk5gf/2Wx4rC4N7vY6ye+kl7ktKyr0QrZSYliUDVq6kdfS775jPJzkCGzYAt9/O0MeFC9mH1q2p5CZMIMlq25a5e7t2MXfh1VedktqZmVTk77/PkMdevehlW7QI+OEHXv+f/wR++okkcMUK9jU9nWGa27bRSxgZyX4CVIxZWVSukmy/cyfPk4qbixbx/qSgS3Iy24iJIWGaM4ft+Xz8vH498/cyMx3Lq4QFJSVRKZtKOzaWnkQz3NJ8fn4/x8HtfZPwJXOy5i5oIO1JyFTHjs5zlxxBdzine5Im24pSLCU/MpjXvmB5gRYWFmcP0xN2NnPBYGtuuts0P0vYt4Q6umFGIkg/3WTRfbz8b9fOCaFPSKD8HzfOKYgiIes+H/VN06Y0Lor8GzCAhsuWLXEq583rZcGuzEye060bl0mIjqauiImhUXHcOBLJkSOZi9ezJ3XHokUsEpaWRr35+OM0GvqO5+C5J45hztp66BStEBHBNh98kH2MimKI6e7dTrVOMQqmpDjFwwBnvLxeyn8pDGYS5/yidEoCluCVMUpzAm4Vs4VF+YA7lC+YHJA8uvR0rsszfTqV1X338fh163hu+/ZUemvW8LsQr/j43O3GxpKwffEFPWTp6fSGDR8OfPQRLaQ9e5KkZWVRQSUl0bL6xRe0YCYmsh9Hj5LwpaVx3/z5tNwuXEjFGRlJknjVVSRos2cDn3zCNlevBg4dzMFN1/jw6acaF11E62rnzgwVDQ0lMQwL40K3sm7RsGGcCGRnO+OXksK/xERnorJsGe8pNJT9euklnjN8OCusZWeT5Pr9tMTGx/NYv99ZoiEpidcYMeL0Z2Mu1n7ffSSoeclWczJkEjPTK+bxON5Z2S7eT7d1Xoq0iHcxL/1xJnk75gTOPZHLK0TL4syhlJqilDqolNqYx/4EpVSGUmpt4O//jH2DlVLblFI7lFKPll6vLYoLwcIdi3q+nJcfOTSjDEzZJWRDwjQlYmD27Nyhh27DVF7FosQQNnAg/ycl8X9cHEmVkD7x9CUnU+7+9a+M5vD5mCbw7rskSZGRjlfvrbfodRNjX58+1H8bNlBev/66Y2hbs4b3OHMmt//hDzSQXnAB11wND2c16LffysG07k9jx8v/Q8YjT+KRv+SgY0cSUhORkQz9F7IaF8d7EP0sz1HCXiUvPiGBRtWCiHdJwhK8IuJMLKDlAeVNMZeXfpQUSvP+zvZalf1ZlFe4c53ckMTw5csZ3iiVJVeu5DnDh1NxDhxIy+hdd5FgZWbSyzZ3Lj1XmZn0XHm9JDjNm5PA/fGPwMUX00p5550kbvfeS7I1aRIVpNcL1K7N/IhGjUi8GjQgSUtPp4dNKSqzBg0YDtOrF9uVpQ86d6Yy/+tfqaBHDM/Bjifex/VL70GVBbMxZFAOxo9nvsPq1SQuVaqQuD72GLBvH8chLIzbNm5k6KgbEl4p1TN79OBEoG1bjuW6dQwxjYvj2nozZ7Ld1FRec+5c5llkZ3N8vvuOnsAZM5zfiLQtyfMeD629ApnkuHMtALYjhVqC5dSY+XmmvHaHdwKOd9Hvz7tYikkO89oWbJ9U7ZR+AmdW1tvKlQLxAYDBBRyzSGvdKfD3dwBQSoUCeAPAEABtANyklGpToj21KHYU9rcULEw7GDlMTz/9ONO77/Odvlan3+8YxkR+iuHQfX0hZsFy/2RN1hkznDw0MZRJRInZXnIy5bDkUku0wq+/MryyTh0nvzk8nLptyhQe27atsxZqly7Myxav4eLFDhHMzGTBlS+/ZG559+5sS2vgvfcA36/HsGWbwjN4DJHb1mFozwy8/z6jXCZOdAivFO8aN477pKCKLCsEOOH6qam5KyrnFf1Q1Gd+prAErwgorAW0POJsrQbFeV/lfazOFqV5f2d7rcr+LIJBKdVYKbVAKbVZKbVJKfVAkGPytJ4XJ/Kzum7YwM+DBtHi2KcPCU90NBXqpEkMR/F6qYwefpjkLzubRUzMyfz27VQ+L77I8JSwMFo0Q0OdKmQDBpD8rV3Ltv/yFxK4tWuBxo3pgfP5gOrVgWuucapg+nw8Zvp0YP9+Js9PnkxlLyE0AweSoL7wAvD1BxnY9lNVLEQihh75N9KSMhAZyZCc3btJUv/xD+DTT52Fbv/xD14jIYH5EH/8I9fo69HD8X5lZZGkff45PZIPPMDrrVrFPhw6xPFLSWGO4ZAhHP9u3ThhWL2aCfhS0W3zZvZJlLjkxkmxm9hYTgTS0+klPHzYmeSYvyeZGEk1t2DP3SRwQqSkQIE7pBOgVXnMGCf8Jy+4QzrNbcHeRcmnMQlqsBBid9/dOBflSlGhtV4I4MgZnNodwA6t9Q9a6ywAnwIYVqydsygVFGaiHyxMG8j9m0xP51qlbpIn10hIIPkxyYfIM4AyNCGBcmXcOKeas7lEi9dLmSpGLtMYlZrKcMSwMO73+6kPevUiERs4kNeQ47OzKYdnzqRXTQpsff450wC8XhonX3yRcnXpUqBNGxYP+8MfHENXWBjbjIggWYyJAZ58kiTw++8pl7t0oV6ZOpW6ZeRIYO9eoFWXSKBRE0zBcKxudBUG3RCF1q05BiNGOOOUlcVQ0caNnaqi4eG8Xny8E45vroVqViXN61kW9pmfDZTW+uxbKUV07dpVp6Wlldn18wqnKmhfRYa8cMXpVq6sYyVw319J3u/Ztl0enoVSapXWumspXesCABdorVcrpWoAWAXgKq31ZuOYBAAPaa0vL2y7RZVNeVnzTGUu+yRk5dtvqRy3bmVugN/PqpbPP+8U7HjsMSpLWdy1Z09aUrOzqfTuu48K65JLuP/rr4G//Y3K8Mkn6RmqWpWhll4vS0L//DMJYZs2/Nu+HbjoIobDzJ/vhD+2aUNi+vHHXH5gyxaSoGefpaV12TJg9WqNBpuScHn6FNRt2wi9Fj0PTxWFuXOBI0dIJLt1Y1vTp7Pd4cOd4inZ2aywmZJC7+HEiVTcoaEkfBMm0Eo7ejT7JkRtxgwSl/nzgWuvday8GzdSoY8bR89jSAgnBT170gNp5qg8+ywnGRs2cJx9Pkf5163rVICLiMhdeEC8fvK85U+es8hX2S/FCtq1I7FfuTK35dycqJnvjVkMwXyXTPkthQDc71hREaztYP2pDCgp+aSUagbgO611uyD7EgD8D8A+AD+D8miTUupaAIO11ncFjrsFQA+t9aggbQwHMBwAmjRp0mX37t3FfQsWeaC43v/CziXS052iW+5jhay5vf0iCyQ00yRh8h84XQ+Z55nHp6czouHVV51rjB9PeS7euqQkfpbzfD7qhawsRqL4fJTRAOXzpk2U51rTC5eRAdSsyQgRs2/h4by+z0cDo99PGQ2QLGZlMdQ/PJyk8e23gfvuzYFv7yFMnloP945UeOgh6s6tW6lXpY9z5rCdgQPZ//h4EmYx2Mm9mWNijpGJwkY/FPbdyU82WQ9eEZHfoFcWZeZGScQMF2db5dFK7BbIJWnNPtuxrKzvbV7QWu/XWq8OfD4KYAuAhqXZB9M6alZPFEUrORGzZ5Ng3XAD15br3ZvKR9YCiozkGnbLl1PZrF7NUMpNm/g5M5P5e2vX0uMluQ+XXEIS4vU6BOfxx2kp7dCBoZCxsSRHSjH88pJL+CfVNvv1o1dKiOe//82+PfQQK2+GhwM33UTCNniwQzi1Vuj5t37Y89Db6Djzebw8QeHJJ1l85YcfmIMheXxXXknitGUL+/r55/xbtIj3LRbotWtJ7pYupddQlo+YMYNEMTISaNaM/6++mkq8Vy8SNa25/cYbqdQfe4yKfM0ah9yNH8977tqV47xzp1PN9M9/5nPs2pXPYeNGp1iMTA7E2v3ii/Qqmjkvbvkq2yT/5PXXc5PDvNapk3cpWF6PkEO/nxOUYGFeRX1/g3kSTJxrcqUEsBpAU611RwCvA/imqA1ord/RWnfVWnetW7ducffPIg8U9NsKFgLp/iwo7O9I1poLdq545t1tmYYeCc2UvptLtbivIeGc8+ZRLku7UVEkd5GRbDslxQnbT0qix04WThevV2QkENczBxtTj8KXqZGSwqiLnj25r2tXksbYWOqcmjVp9HruOUeezplD/XjDDfQmjh3rkDuPh8eb1ZXr1M7BAzf+gvAIhQ9m1sf9oxXq1KGcDw1lfrnIUo+H1+3Zk31es4b3lZBAQpyYyPGYM4cE0wxnF7k8e3bexbCCobhkpyV4FoVCeQjtzKv98h4KVJpJtRZFQ8CCHg1geZDdsUqpdUqpmUqptnmcP1wplaaUSjt06FChrysKNyHBqbwlpfEBWhtff53/IyNpbXzkEXrZ7rqLk3yfj567N9/k+WvWMN+tUyeSnNat6aX64x/pbTt8mARt5UqGq3i9VLr33kuv1bx5JHmffca2xo9nAZGmTam4tm9n5bLt20nE0tJIBjt35nEXX0ziuXIlFe3KlSRmJ086RVdWr+baeq++FoKIejUw+V2FX3+lF/HIEbazfDn7l5XlLHj+ww/05uXkkKg9+SSrdk6YQE/XRRdxHFetckIsJ01ieGfNmhyvKlVIviTkctkykr+HH+bzGDjQmZi48yOlWppMMF55hed/+y1zQ4YO5eQmMdFpb8ECjtvXXzuToE6dSNxkUmAu6uueDMjEonNnfpYJQrBlGACn+IxZrc2UjzJpk4nc2cgl89ySjEw4l6G1/l1r7Q18ngGgilKqDoCfADQ2Dm0U2GZRTpDfb8v8Teb1OS+4jzejPUR2mNvcoeLubeY+Cc0EnDXyZszITUzc52Zm5l7iBXCIlMdDT5fkvmVlUfZ27uyQu9hYwBOSg/DL+2Pkv5og8vIE9IrNQU6O4+mTdUrj4wF/Vg7WLyER3LOHumvkSLa7ahVz6664wvGcvfQS1z997TUSxeRktuFP6I+US+4EBg7AqJE5iIriPSxf7kR8LFnC/vn9JJI33MAUgFGjnPVSJUcwLo5Gxtdec4yLYgDr398JgQ1mmCtJ2BBNixJDSYR25nUdS54qNkozRNO4ZiSAFADPaK2/cu07D0CO1tqrlBoK4FWtdYv82juTEE0zvEUm+rJPPESS//DMMyRIXbtSmdx7r7MGXXY2PV/16/P4yy6jgmzYkEo6J4ehmT/9RBIVFkbv2KZNJGWrVnFbixbAeeeRiNx6K713t95KQiF5bLVr87pt21KZXnklyU6PHszP27KFSxKMGgX88gsVenw8cPPN9OjNng389hvDRR95hEnwTz7JPMN69WgBvv9+ktGffqK3LTaWfVy+nMSyd29ef/NmkrRp0+gljIykQp03j/2VAigrV5L0Jifz++WXc0wfeohE9s03ec8DB+YOq5kxgyTr+eepuENDaY2WPItly5j3sWwZFbgUD5A1plJT6Q394AN6SSdOZD8kb1Cs4WJdNydSSUm8nhQbkBArWSjYHbLpbkdkr5BCd+hQXu8kcGbh5cFCRM8UpaU7CosyCtFsAOAXrbVWSnUH8CWApgBCAWwHkAgSu5UAbtZab8rvWnbuVH7gDqN2G2vye+flmHnzKPclrFKiM/r3d/YDlB9mTp25TdozQ8TlGJFjcqyEmpu53fPm8Tj32qPSruwHKGdHjeKxQu5SU4Hel/6COc3uxga0wX14Gws/+AHeKrUwZQp1WqNG1BkrVuQAU95Dm32zEdb6Uqy6+p/o0jUEa9ey+ubKlYzIGD2aIfthYZTNCxZQd3TqRGPhoE6/wNe4BSbo+9AOm5E16QMMu63WKdnt85GoRUdT/kooqMjNxYv53fSImoRbZLPI3byeuzmeZyPjbIimRZmgtDxX5WECYFGxoJSqAua3fOQmd0C+1vNigYRimgJfvD4+n1PiOT2dn++5h+TujTdIwG67jYSmb196kbZuZYjk0KH01M2dSyvjfffxmN9/p+ctK4uetH/+k8qrRQu2u3s3K45Vr86+vPUWQ2tq16a36o47OHn4+98ZBrl6NdsYO5ZkLCKCfy1bknh9/DEV7sKFPNbrZWW02FiSq1q1mNN33nn8PG0a/yvFe96xg56/hg3pdbv7bva9alW20bYtr9epE8Nv9u6lR2/lSo7hxo28zwULOI7R0VSma9aQUH73Hcmi5GR06sTJ0pw57KtUTvviC4d49+nDYyZMYHjQhAmcEERFOZOqrCzmj2RnO562qCh6XWVNJZmQyTOX90H+xCsm5E4mCf37c//EiU7IJuAsBCxE0PTOiaXc9BLmR+7MogrmtoIszaaHsDgiKiQUtTLLdqXUJwBSAbRUSu1TSt2plBqhlBoROORaABuVUusAvAbgRk34AYwCMBsML/+8IHJXGVDePbpF6Z/5Xrvf8WC/H7cnzuPh712WdwH4uzaLIyUknO416t+fMktkgrRneuPNNfJkzTqRI7I8gvRZ9gNOyLkpO7KzKQ+nT+fniRO5Xdb/7N0b8FxYD2HtLiW5azMca3+MwsCBNAYOH055nJgIdGqagbH7/oJIeBG6ZSPG3HwIsbE0FD76KA2dJ09y/NasIbmLjOQYtW9PY2B8PIB69ZDSZgTuxhRktojG8+9E4cABp69Ll/L+unUjMXz5ZWe7GO5E1sp9SyhnZGTu5SSCeVFlbNwVTUsCpULw8qpap5R6Sin1k1Gpbmhp9KeyojwKwMqsoCsLivO9Kc3wgzOFUkoBeA/AFq31y3kc0yBwHALW8xAAvxZXH9w5D4AzqU1JoYfu4oud9X127GD1sN27uaTB7t0kcV4vPV1KkYB9/z09af36sVLkmDG0ZF5/PQulLF3Kdh97jG1PnsxtTZsy9DE0FDh2jPlw9evzmvfey8T2Q4e4Ft2vvzLM0uslEZw8mYq7Y0cSNZ+PoTExMWxv+3Z6AffupWdx6lQSq/79HWWYmsoQzYYNWTHz/vtJNsPD2e6tt/I+QkLY/02bSKZWrWIoTWgoQ1FDQzmGo0ZxzLKzWagkOZnkr21bhnOGhrKQSlgYx7tPH/Zj+XJnLbzkZFYV9XjoNY2MpIevUyfHeyrPUjxxEoaUmOgsPv/cc/Qk7tvnTHDMHLaYGCcc6uWXnXBOmUSYpMlcBN1czsAkcmb+THKyQ26FuAb7XQopNCtoFnXCKkSzOIx6cj/lVYYUB7TWN2mtL9BaV9FaN9Jav6e1fktr/VZg/0StdVutdUetdYzWeqlx7gyt9aVa64u11s+U3V2UDsp7KkZB/Svs9mBG8bzaFhnhJmtyjizj4l7WYPFikheTgLgJp9sA5W7HlEkLFuReJkHy8hYtYrRDdja333cfozlSUni+eBx9JxQSlr+A8B+3As/+C6MfUFi6lBWZ//MfGsb8fmDTT1EIj+uCeMWSmq9/Vg9vvEFv28SJlOWdOzvVoJcsccZFqoN6PLzesqv+hUnjdiDsmSfx1VcKn37KvoaFUXcOH86oCwmpHziQ+8SrKTmIEmlhEjV33nNez07kZUmitDx4fgAPaq3bAIgBcJ+xbssEY52XGaXUn0qH8i4ALYoXxfWci/O9KWo+QRmiF4BbAPQzjUuFsZ4XVwfCw6lwgNyFViRvoX17Z8HVAQOYFzdtGknQu+/Sg7d6NXDLLfTujRjBEJjjx0mCfvyR+XG//sowx/vuI2HbuRM4eNAJnbzmGsfCuXs3SdycOcw3uPRSHr9zJ5XRsGFs/5FHqKQPHaLi3L6dhCclhR67X39lyOIXX5CYCVF57jkSu8OHaWlt1YqkKyyMRVyuu4592b+f9x4dzX5v2cI+rV3rhM9s28ZJxMqVJLQjRvCc3bsZ4rl0KRVzRAQnMytXMo8PYC5eZiZz5bKy2M6SJU4Om3jqtmzhfS1Z4hC29HT2Y/Fitr94MT2xSUkkatu3B6zEYD979SKZfv55ThjMXJXevfn7eOstThyGDnXa9HqdBYiTkpycGDl/0SKOq3u9PfEayuQtK4v5m926OVXfgnkHTG+yGeIJON7JguCe4Ejb7msVBNMDaQ2EFkD5fx8Km3Mn34NtN9sqqG05V4yCpjHE/fuRP/G+ZWdTPst3IWiAI1/cnifZZnqdpH35P3IkZVhCAnVBbCzl7pAhjDxp0ICyNSuLUSESLfHyy8DM2SGYubo+1qxVAKgTxozhOenpbK9DRwXf1DlY/NFu9Ex+Dm3aKvzwg0OSpk7lf2kzI4NG0NBQRyaLN65d+xC07FIDm7ewuMqYMSSS0vd336V+GjCAYyVkWryaJrFevTrv55rfe1Ea73KpELzyULWusqOkBWA5naifkyhO8lTQe3MmVnxzwlkeFbLWerHWWmmtO5jGpcJaz4sLHg8n7TKZl6pkc+eSXAwYANx+OwnVypX0IoWFkRytXk1iV78+CYisI1elCgle69YkN8OHk1S99x49hldcwaqW7duTSL37LhVbu3b08EmO2Jdf8jqy3tyCBdzerBlDXe69l8q3USO2f/75JJ/XXUeS06ABPWSbN5NwNWzIapOrVjFnbsIEEg8JF9WansCdO0lGtm5lHl9KCvs2bBjb69KFIY7Nm5OwTZxIK++aNSQpzzzjJLkDHNsHH6SHsHFjjsno0VT6TZuSWE2fTpIoXj2v17nH1FRnrcF77mHY0PHjTNwHqOgTE3luZCTblrzJdu1IeP1+ksJRo5yk/XnzSNw8HoZvSg5LeDgnYJL7Im17PNyelMR3JdgyCfJbEy8aQMvzgw86E5P4+NNDh8SbLBZpacv8/bonqIUlau7zCgr3dE+OLCwE5f19yKt/5m/J/ZtwV88tbNtmm2LUkTbEYOiOphHZER/vhFZ6PLnX83zlFco6KfQk7Yi3TcidePHmzaPB7sUXKdOFIK5dy/+xsbxOZCTbffVVyrNRo+gpi4x01s+TvO6FC528vn37nLX3+vQBJr4ZAm+VWnjjTXUqj9zjoQ78wx9IJuvUofGuXz9GenTpQj2SksJUg86dqVu2baMuSklxxsjjoX6RXEG3R9QMh5Wx6NyZ20y5ZZLusnxvSz0HL0jVulFKqfVKqSlKqVql3Z/KhJIkd+XYGwOgfPetuFHc5Ck/clfU555fboFFbng8VGTbt5O8STWuiAiSC7+fxUfi46lwGTBKApeZSaXWuDEJyuHDPL9VKxZUWb2alseMDJLFEydI5OrVo7erfn1O/GVNvT/9iQSueXPm9F1xBZUgQC/fAw9wrbyjRxnaOX06PXgXXcTrZWSwrx06OPcGkGyNGkXCcv75VOIHD5IonjxJpfzII9x20UVcn657d47Hn//MXLm0NPYzK4t9DQ+nYt65kwuaN2jAtfL69yfR69eP4/PqqxyHSy5hTmDPng5J+u47Z/mEJk3Y302buO2WW5wQonXruE3Kf999N+9TqmgKSUtI4P2mpnIyMnEir7d+PS3GWVnsd5cuHAsJX01P52REymsDnGDIBEzyasLD+R6Y+/L7fco6eklJ/C4TstRUZzJoVuUUz6FJwMx8PnNRY3eenhvBSGJhZUh5NgxZWJwp8gp1FPj9wIL5OfD/9AutXUVoU8LDhZBlZzNv+6WXckcMhIY6hZ/kPIAyUDxYY8Y4hqO4uNxRBLLmm5DCxYsp9x95hIZIgGTJ42Hkxfz5XMJASOKsWdQVffpQd6Wk8BrLlzPSYeBA9lHC5A8cYLvx8SRjHg91VP/+1A+9e1MHyH0MGcJjJKXhjTeYY/7mm9QHmZnMGb/lFh5///00TmZl8f7MiqFLljj5z/LfLEIjhDc52amo6V5nVHKg3SjNuWqpErxA1br/ARijtf4dwCQAFwPoBGA/gJfyOO+MSpFbFA/Ku9ItDBGpbASwNJ7F2Tz3yjbeJYXwcHrZ3n2Xk/z58zn5f/ttKpIrriAZGz+eXqaICCrPtWvpZfN4ODn/+9+pCGfNonITT9nLL7Ni5c6dPEdyE4YOZWJ6VBRQowaVafPm7E+HDszB+9vfaF3dtYtKc/58Fi/Zto3tZ2YyX++//wWuugr4v//jQuBXXsniLHPnMlTy55+ZR1etGglc8+a81wsvpLIeOpREsEYNWnI3bGDbTz9N5bp8OT1mU6fSAhsdTaI3ZAjDSD/6iPl969dzTA4cIAG94QYSP1kzMDubhG3zZk4mtm3jexoaSqXcqhULtdSvz2fyxBP0MALcv3Qpx1eWpIiLc85dtIjH9e7tTJjq1GGVzn79eNzUqSTSGRkkh7Ju3pVXOsn57jBH+R35fE7+nvl7DPb79Pk49qNHkxCKtXnx4tM9f+Ykz8y/k30SvmUWanEfZ8Iti4OFexbksSivesbCojjgztHy+wFPSA76/rM/PM0aUejl5BSpPSmosngx9UBEBI1y5m9dvGLuXDox8ohsEZkoxqXEROdP5EBqqlPE5KqraGR7+GHKG7/fWbj8xx8dD+C0aTRcSpSCEMZVq2gw8/u5bfduypwVK0jw6tRxlnDo1Yv6Kz6ehr/Ro532ZN3YN9+knqhShcd7PNQrl19O4+Qdd5DopaXxnI0bqVvDwhxD3bp1zrNxF6mS7SIHpcKokDvJo/b7TyfZpe0sKTWCF6xqndb6F611ttY6B8BkAN2DnWsX6ywcSvKlKc9KtzBhhuXdA1lecabkzo53wRCFlJbGML2FC7mIt8fDcMANG6hw4uNJzkQpzp9PgrN3LwnanXfy//ff0/j7xBPMfevZk56rX34h4dKa5KhOHXrvqlfn92PH6FGbO5dKedcuehXvv5+ThCNHqOTq1aNCb9qUSu3772nBrFaN+RR79lCxffklwyfF27N5M71o7duTyGZlsS8XXMAw0RkzeH//939UyKGhPGbIECreBg24LzubYZwZGSRbX35JpfzeewwVDQ3l///7P47bRx85i76/9BJJWevWVO4APaFeL0Mux4zhNT0eEqlGjRhyNGgQJyZz55IUPvssc+m6deMYdO3KSVVsrFE0wJe7eIFMuiIjgU8/pUe0a1fgnXd4T2J5dueuyvjNns39pnXdXa3S/P/dd7RemxM2CfE0CaTkkZh9dXvghRjGxuYOOcpPLri9du7QbelnURb+tbAoSxTne2r+xk+tXbf/EDypgR/Y0qUMj8jjXHeenGlgAhxiJmHfomdee80p4CReOPHQSQEm8dYJ0QGc0E7A+R337k0Sl5hI+eX3k/SJNzEhgYTqzTedSIQrrySZM72BEj0gYfWRkcC//kX9k53t5KF7PNQTr73G4REIqZI16Hr1YnsSefHvf9NIOXAgj1+0iNskNWLePOqE5cudXHHp06lx6pGDRdN+gy9TIymJsnvixNykTtaw9XgYmi9LKXTqlPv5lbazpLSqaCoEqVqnlLrAOOxqABtLoz+VEef6pDq/H0x590BWBpjvnR3vgiE5EYATptKnD6tdAlRE339Pi+P8+VQaa9bQs/X11yycsncv9zdqRIPvnj0kKzffTELz3HMkaCtWUJE1aMDvK1eyEuXvv5PMhYSQHC1dyrDJTZsYihkayjDJK66g8qtfn9fyeNhW69bs+4YNJKmSj1ClCu/l4Yd53vr1DBHduJFK+/hxhpSuXUtiOXQowzL/9jd6uM4/nx7Ljz7ivl276CVUiiRszhx6AdeupTV3xQqGqoaH00J80UUcO7FMd+hAT5/fz0nCvn1UyMOG0cOWmsptXi9JqMcD/O9/JNwACWNEBBX4+++zwM2SJQxlHTuWhHP+fI6510uCde+99Dp6vc5kBnAqwr37rjPxCA93Frr3+3OTIfESAs7vSSZ0Qrq83tykMCKCYbhmGKdpRRaYYUd5kUV3Xl9+v2nTUxfMk1cQzlXdZVF2OJNc0qK26f4uBEnC/0JDQetZz57c2bMnv7sgRO3llxkBYRIyMTiZRZfk2osWkYiNHu2QML+fJEQISEoKPXKJiaffs4R2ioHHHVoqoYqxsbl/51LFU9qJjHQ8gaGhToj6ypUkZhKyuXIlIz4GDKC8F4SFMaR/zx62l5BAXSAFr0aO5Lky3v37U9+lpTFkNTmZbX75JZcY2rCBct6EhF9KuLrveA5Sej6K3jc1AgYOwJrVOVi+nPJ18WJHRoq89XicQjPh4Y7X0z0/Ki2UlgcvaNU6AC8opTYopdYD6AtgbCn1p9LBTqrzx9mOS0FC+1xGMAVo38OCIeX0/X5aNhcupG5PSaEye+EFEoWsLBIRqYLYqBF/63/7G4lRs2ZUXn36kJjdfTeVnZR9vv9+kpm4OM4b2renR2rIEHrfJk3idVu3BurWpcdPa36uXZsTiBdeIPn74QdeY9gwPuOICOaV1a7NMNH9+xlqKV65AwdIvsLCGG7Tty+V7aFDJHKtWnGS0qwZSWeLFrz/5s35eexYktqLL6YXsEULkqSkJHoi09JYrXPlSrbx3nsMyQR4n/fcw2t/+y239e1LC/G//82QyaZNSWLF2rxoEYnxs89yTG+7jWGl7do5ZbdXrCCp6t2bZDIigmMfHe0UC7j8clqRX36Z/U9JyV3BUrxxZsjTqFG53w+xios1XYiaVLKLjHSqY0pFzqQkegsvv9yx6ku+jBumN85dBML0EEoVzYJ+0+IVMEPQ8jpHQj3d4aBWrlqUFgr7zhVlbuVuM9g1hHSJAap/fwBKwT93AS1NyclOwnUA8rtPTKRsX7bMKcIk68u5wwjNvosXzcylzcxk/lyPHmzj9dedc8VrnysHLT4H4Rm/oHecPrVdikElJFDGCfEyDUIiE2Q7QDnZpQtDL6OjKVuXLaNcFdLl8zFiIzOT53fqxPy5evWc4lUbN9KjtmQJIyKiozkWmZmU5wD13ubNPE7yDyMjqRdvuolkT4itjJmsF4hDh7B2kwf+HI3wZSkYc/OhU2HvIs9nzHDGXyAewuJaNuZMUVpVNPOqWneL1rp9YPuVWuv9pdGfygo7qS4ZFEZon8uwxoWiQ7wzAJVDy5Ykcm+8QcXRpQvwl7/QCwZw/biYGO5bsMAhFs2bk0Sddx5DLjt3Jsl5/XUqniFDSGbS0pjb0Lw5Fws/dozHLVtGJZeUxJCWH39k7tpll1FBhYUxRLNGDf5t3Mj3/ssv6YVbuZJeqCZNSPCaNSMBvPxyktZff6VXctUqniv5ENWqUYlu3kwP2PPP0zI7dSqJZWgoz9m7l1bXzZuptF95hROdBg3oKezalfcUG0vPo6x9t307PZ3jxpG4KsV+79jB/5s2kVwCrML58sskdhs20Hr82WdsOyODE4E6dUiYq1alR3DyZCrx0aOZY7dkCcehXz8+l0GDSL7atuX99urFa82c6RQeEC+cOfF68UVOcCSUSiZn4tlzJ/O//jrHBeAzXL7cKXQgSf5CEs0F1k0CZ3rqTO9C7968R3fYqAlTJkqeoHgUC/MbMD9bGWJRmijKO1fY99LdZrBrBNs2bx4wb34I/OfXP0Xu5LdlGnU8HspsCbf2eCh3oqMd4uYOfTa3JyU5ObUDBgDX/CEHUSd+wcABGh078nc/aBDlRWqqs5bnvLk58CcOhL9hUyzq/iD8WTm57tHvZ0Er8ZTJ9b/9ljLt229zX1uKvZw86eTW7drFSIiLLuL4REUxX06qEkdFMW9cohoklHLNGhK0ceN4TIcOJG3isfR4HE9edDSNhocPM4qiTx/Ky5kzndBUj8cplIV69fBArzQsVvHwxcQjZUu9U2NiGuw6dMgdZp+VxZDRYKS7NFHqVTQtLCoaCiO0KwvOVBAFGwtLgPOGz8eqjH4/iUZYGP86dCBJW7WK4SgTJlBZ7dpFpRYWxnXTPvqIivPkSXq2evRg0ZDYWCquli2d9d1ycqj4DhxgqGSPHiRdVarQGlqjBrctW8ZQxk2b6CWsU4d/0dFOuMyVV7K/v//ukL/vvqNSP36cHjG/H7j6anrzBgxwQllWrKDCrVKFk4h585jftmIFPYhz5pCk1qhBj12XLiR4x49zqYacHHrwpkxxkuIBhqvefjs9ZkOHcvvhwySBP//MnMSsLBrH27TheK5axXM7d+Z43XQT8OGHXBYiLIxLNwAMvREv5rJlnDRs3co8PHkWs2axPb/fCXVasoSTh+3bnYqn2dnOmoMLFzqeNTOH5uKLSR6FBMq7IuGbQO5JXMeO7IfHw0lOjx6ONdosbS7hQzIBEXIJ5C62IPtkYgI48k6OleOkH0IWzTzBwpZ/l/0mybSwKC0U9M4VRYflFY4c7BruHNWEBMejLb8Hs8CRkDvxLg0axHPld5eYWHCaSkKCswRL795AZLUcDBw/EJ5mjeAZlIiwKjm5+iu/4dBQIKb5ISQtrgp/tkbfLW/C89uhXN791FQawiIiHJLk95N8tWnDNrKznfXxxJM3dCiPlWItW7ZQpolM2rqVeiA5mTL4sstotJTIl/BwR44Bznp+KSnc160b9Yp4LCUXMCqKERPh4dQNGzY4ofSyjt6IEUB4hEL4/FnI/s/H8E2bi9VrFObPz50PHxrq5PjNm8e+9uvnFIYpy3xjS/Asyh3KIzEojNDOC+XxfoKhOD2T1suZPyTkxO+ncgFINrKznUqL2dlU7uHhDMtp1ozhlA895FS/3L+fFs+9e1kpc9kyVoDs35/eo4svZhjnr7/Sgzd1KhVY9eokcUeO0FO1ZQuvs2YN//7xDxLGVq1IRk6c4HHS58hIXlO8bTfeyPZSU2nJve025vZNnkyCFB3NMMeQEBK2NWsYCnrsGFCrFgljp04kZX/8I0mgx0Plu3kzSdmllzK3bvRoKvvoaN6/z0ci9fbbzBccNMhZW65HD3r4hg2jl3H9enrIZs0isfzsMxLA8HCS0d27SdAaN+aYdOnCZyNrR0VGsk/r1lGJ33UXJzWyaP2KFXzvV61ycgJ37mQ7ERE8Z8AAejPNxe1lAlezJonmX//KffPm5SZOYj3OzOREbeBAxzPn8TifZ89mHqOENwHORAPgpEjClcRTN3u2810IpxmeGYzoyT73mnmCguSA9MkWXLEobwgWuZPXcfnlugY7z+0Vl99bsN+oaYgxw6pNL71ZACmvCrdC1uLiAsRj/yEsWhoKv18DS5eifwcWdvF6KTuk3wkJwMKt9bCqydWYicFAz57wnVcPL7/sGILE4yZVJaUdidgYOJAGL+gcLP72N6T/ppGWxpzmmTN5/ODBPDYujoY/r5cpAZMmUfcIoZozh0bF8eOZ5/zyywyp/8c/qBsyMpzrT5jAQmPPP0+5P306r+H1Un4mJfG7RGIkJ/MYM6TdnxOC7PNqYeEihbFjSUoHDnTC8WWZBBl78X66n0Fe70FJwhI8i3KFykYMinI/ZX3Pxe2ZrKxezuKAWF2lkEdGBqthdu3KRcEBVlyUUMhnnmH45o030qt2/fVUhP37kxDs2UNi4vHQi7Z7N4/bupWVOU+epOWzc2eGVS5cSIIma+wdOMBrinctPJxhlAcO8O+LL1iEZdIkKsGaNRn6mJpKj9/y5Y5ibdiQ5zZvTg/h9On0wM2YwVDSDh2Yg9ehA4lUVBT7LeGf993HfmdnMyTyxhvpAfv4Y2dsatcmabrwQpLVNm2Yezh8OD2ba9bwOKWYT7hpE8NPt23jpOE//+H5v/5K4hcSQvKYmUmi2KYNiejVV3Nh+OnTHS9XZiZJ3qxZDKOVktiLF9M7N2AACbnHw7Cg4cPp8YuJYe6jEPa33+YzlAlaejr7vHYtt82Zk7vanN/PsRdPoOS0SLimSazi4/mspUALkDvky1wPzyz2IFbxvBZmdufsmUUd5D6ErMrxhcnFK8zyCRYWpQnz3XVXrRWYvztzcWtBXuHNplHHNJyY5wm5EyJi/q5MyD6z7fwgERWoVw99e50EQj1Y0Hok0sPqYd48nPJQmWQzrKrCyJV3YMujH8I/OwlQCq1bO3l3MkYCr5fpBcOHkwj5/cBbk3LQ67mh6HRzK7zT7R1Ed8rBPfdQXkqfx46l3nr9dRrTJk6knDYrZ3q91A8jRvCY0aMph3v2JNGbP9+JSGnZklEfnTqREO7YwX1vveUUlUlMdNbli4lhf0eOZNsSBSFL7AgklNUMe5fn5o5gEO+pWbHY/W6UFCzBKwKs4il5VKbwRxF8hbmf4vixF8f7WRzjLvdiUTDCwujl2bePuXINGnDi3707FUpcHInE1KkkMOHhJADt25OwyCLm27axiuPWrfTKbd1K8tKyJfPhDhygEgsPp9fq119J+o4cISE8fJhK7NgxkpkjR9j+77+TzGRmkrAdPkzll5nJawP0TJ04wQnJjh0Mi6xShV418YY1bMh+1KzJ38OJEySYLVvynNtuIxHr0IH92rOHJLVhQ/Z54ED2b8sW9ql7d2dtvsce45jUqsVrXXABiVTz5hzXEydI6i64gGsCXnyxE/IaE8MJRKdOnAjs20dit3EjFfwnn3BZg2nTSLI/+oiTAwkFevZZkscJE9jGmjU89uGHeUxmJtsZNcoJtZTk+zZtHFLUvj3P6dHDKViwZg2JmjlR6N/fWVfPnChIFTuAhD0lhcdJGKtcJyaGz0lydkTmCMkzwzPduT8Cua75G3fnDJrIz5JteiTke2Uy8FlUbORluABO355fvh1wujfQTQbNtSIlB9Y0vuSVBiEGIWlfDE5ub6J4+MUrvyBZAfPmwfPTbnRb+BIeeVQhJoYeqkcecX7HQlCiaodg3JM14KmikJTkGI/kmi+8QCOe/KavvtpZqiE8HBjxh0NYuLQKHsWzuGP3/+Hy7ocQEUEZnp7OEM+5c52w7wcecHIMQ0Mpz/r0oeHrT38iEZSqm3JvDRpQFtepQ1n34480GG7bRkPigw9ynyxLJPmCEnK5YAFl/+DB7HdyMq9xxRUkn6eqaxoh7BJ94F4uwXxG8jzN98D9vpSEzLMEr5Cwiqf0UFnInflDLghnS2zL0/tZmUh6SUEUfFwcFXSHDlQC//oXFcSXX5IoHDjAEL+//IXhkJs28f8HHzDEcds2es7OP5/FTeLjSVh27aK3bNEihrNUq0Zlm5RET95FF5FIaU2lGBnJcEchHzEx9CYePkwCmJVFi2hmJpPHq1Th36hRVKKdO5NgVKnC5RQOHmS7t99OK27VqmzjwAFnwdtNm0g6L72UilHCEH/8kaGUsiD4q68yvLNePRK+Cy+k123SJN7De+9RyQMkhZ98QqtsSAj7vmIFPXnVq7MKW/v27OvGjZw4rFvHY+bO5SRhzx6Sz+uu42QhPp65Hz/8wOUaOnQggf76a/avbVu2mZLCCcCmTfQiejz0XH73HUn6669zvCXMcvVqtt++PUM6L7vMWW4iMZEkzE2WAF4nM5PjIpO5rCwnn08ghFA+9+7NcYqOdgio6amTSYgsvWAWgTHfW5mIyrleLydjQvIKeu9FTgXziljZYVEWyE9v5me4MIsgSYhzfm0E82znNdlPTnZCtOV37EZ4OEmLLGDu85EoffPN6Ytsy3Vz5diFsbBLVC2FV191oikkMkAKgUkItzlOAwdSTiUnUyZJrp3Ijn79KJOystiPlbvroV+vExiGb7Gize1AvXrIynJk3uuvUzd17cptL73E+5L+Ll7sEL6nniIpFC+nELV58yiTfT7KupEjSegk1F7C3N94g9fo1MnJo+vTx6k6Lc9aQuElLSEhgX9JSae/M+Hhudcqledo5lKeGnfjPTCPK+75myV4hURlUDzlYfJ/ruBM3pezebfK2/tZXvpRXuHxUDFFRlJ5DxxIi+LOnVQEd91FQtGmDQnQggUMU1m2jCRqzx6es3s3948cSfLz1Vds+5JLSAR37KAH68ILaYWsVYvK+OefqZx/+43evKFDua9ZM5LBX34hSezUiUpOa/Y1JITFTtLTSSBnz+a5c+eS0NWpQ8vtlVfSs3fttVSQ+/eT2LVqxe3JyVye4LffmFTfpg1DTZcvZ39r1+a16tYlQezYkZ67jh1JyN5+mxOIQYNIjnw+KumHHuL9t2jBCcPVV/N6vXvzGikp9FKmppL0DR/O+1yxglbbwYN53b/8hSE9Tzzh5Kr94Q8k1nXrchLw6qu85pYtnMSEhfG5dejAsUpP5zmhoU7VzgsvZHs9epAUy2QjO5sezv79nTCe0FBngiZETmR4RASXgEhJwSlrunjn4uNzr79keun69mXfrrzSmQgJ0fL5HJInZdIlBMsM0TQ9FjLxefVVx1pf0HsvHo1gXhE5xsKiOJHf3Ce//DlBMA+2ud3vp1xyt5GfodcdAm1+B3IXXgHyJgBCMD0e/gZff50FSx580DnXXBzdDCs17938/fp8lDlSCCwhgTLu1Vcp18wqngkJlDeS8/zGG87SCDExHJf584HefRQiF85C361vY+PNz8F7TGHDBsp7qfz73Xfstxga5b4SE0k258+n7Bs2jNcTL6f0p39/XlPkmBj+hIhKGHyHDvybNMlZh2/ZMt7jwoUkil4vZXunTrmNXn4/9YV46sxnJOROZKbISwmJNwm2+10oifmbJXhFQEVWPOXJw3OuoLTfl4r8fp5r8PnovRGvh8dDC+KLL3J/UhKJy/r19PDJ+msNGtALdfIkk8vDw+n5Wb6cYY6hofQonTjB0tTZ2awktnQpCUtEBBVbz54kfwcP0lu1ezfDGzduJAHy+UjK/vc/hlXGx5P8DR5MT9jx4869/P4725w7lySxXj1akFevpqdt7lzm3CUksKDMwoUkqP/+N/u7cCEnAY0bU4lfdx09g6tXs2+vvUYiV706leT11/Oeo6P516MH2/vpJ1pQ//hHhlr27s1wymefpYdw7VrHYtuiBccpLY2kq2FDICI8Bynf/IacHI3nnyeBXr6cIa2vv84+pqUx92PyZCr7sDB+nz6d47B1K8NpP/iA9xsdTav0Aw9wHK++mmP29tt8Lj/9RNIqC577/Vyu4cUXOWavvUYvqljl58zhZCQxkX0D+Dk+nhOUbt1yF08Jtg7TokWOpfyVV5yFkt3VM81Jq4QhmaFlshyCTA7z0i3B8o/y8opYWBQ3Cpr7yCTcHTJZmLbkv5TsP5XfZrRd0MTdvd80gOR3nMDtCRevl4RgHz7shIbLUglCwMTQKB4wae/FFym327d37jMsjEalN9+kbvJ6aeBLTnaIXlqaE3r+0ku8frt2zlj4c0KwbFd9DL9HYelShuj/9JOzjMzmzQyFDwsLLM8QIFrJyZTbq1dTpw0Z4oyRGMOk7yZBNMcsM5NyeOZMZ63S++939ick8L8UolmyhAbAKVMox1NTKScBGl/nzw+MWU4O/D8FFo+F40kUj2dh18IrifmbJXiVDPkJseKyELiFm4VFMNj3I39kZVFJ+HwkY6NHk0isWMHvt9/ukJCmTUkgEhNJZqpWZVimx8NJ+IYNJExRUTxu1SqSrR496K3r14/K7NgxHjtzJsM09+3jOUuWULmeOMHqnJdcwhy2kyd5TEYGw0FXrSJRadKERO7qq6l8V69mjltEBHMx0tPZZ/EW/fWvtIYeP842f/+d5ytFj2Dnzuyn10si8/XX9AZ27MixuuIKei4PH6a19/ffeZ0pU0jWtOZ4/Pwz29+5kwTpqaeojDt25L1NnEgL7/DhLGKzZAn7Pv6FHCRMuBzr730NzZZ8iOYX5aBnTxZeiYsj2b35ZlqY/X4WmJEKalFRvJdvv6WX7oILOPH45ht67pKSeB2A4zNgACcaq1czv+/yy3MTnSpVSOI/+YTrQNWpwwlXaCitzRMmOBb5+Hj+T0nh+7RkCSchSUl8Lu58EMCpvPfOO+xvr15sNzbWIXkJCSSMI0fm7pvoEb+fYxkd7Vi3g3lBhBy6q2TmF7pu5YbFmeBs5j5FmYTnVYCosMYKc/5k/i5M712w6+TVVnJy3p7w2Fgak6QgSUwM95me+cWLnaIq8ruU5Vf69aNRafx4ypeoKOqpLl2cpV8SEhyyGBfH85YtY+6b5ACbOcNZWZQZq1dz/zXXON5DrR2vW3y8s15eXBwjRcaMoR4yvWOyWLzIupdeyp0/6Pc7qQkvvUT5K/1KS6MMlIXKN21y8p5XreL/du1oTOvWjQZDCScdOhRATg5md34UsxvdCX984qn1AYUsms+7LBwsluBVIgSzVAULJwh2XlGvkVdVKQsLwHqMC4PAerbw+x1i1KMHraZDhzJkUcL+Ro8m4UpOpiXy0Uf5e65Rg9sktDIkhMTgyitJNAYPJoHKymI4yg8/kPhFRlIJXnghPTmRkcyP692b7axcScI0YACtrDt3chK/eTM9aFu35l4P7qKLSL527mQBlCZN6NVbs4ZKVIiHz0clnpHBtk+eZH+uu47XPnCAyy388guvn5rKe9y8md7MF16gxXfWLF5v6FB6+SIiSHyuvZYewaZN+ZecTIszwPu6/nqOcXo6yeCuXRwn//5DeDW1O7ajOars34XmtTPwxBP0ih46RCLbqBEnCZMmUflLbolYxn/5hc9m/356GKUSaI8e9ED27MmJjlh+16/nGIjnDuDnUaPYr48+IrnzeumBDQ0lEW7Z0plcSYgmwPYBZ19cXG6vhJAtqaI5ciTHbdkyZ6LUrZsz2cnO5j4g94RFcnRGjeLzNcu5C0GU9zo5mefGxRUu38TUL1Z2WBQWhfHSFYTCGr/d3jYzn8q9REEwEmd+z852vGdm+LJ5HZPsmQaUwsztPB5GLURGUo68+SYNOmZ13f79nRBGqZop+XUeD49t145yXnLb+vXjfiFKPh89VosXc3tCAslgfDxJU69ejmwIDeXfiBGUP0OGOATxwQedPD7Jae7Wjd5EgPfxwAO5F2GXaIJ772W0hhiUJPzc66Wc8vkou+fPd4huQgLblBx4yXv2eKiH581jZE1oKI8bM4b7RC7i0CHeIPzwL1mOef/LOPUshdS6c5ZLE5bgVSIES9QtaJJd1Im4GVpTnnK+ygPshMSBmatjcTo8HhKrPn2oxOLiSBouvZTkJTSU5AJgXt3DD3N/ejq9W//4B0MWjxyhAvP5GGYZGkpiIaF/335L79L+/SRSN95IMnjiBInXwYMkRA0b0nO0cKFTnCQtjYVbUlNJxA4f5jHffMN+PfCAY3k9fJj/Bw3iZKBOHXoJf/uNxGf9ehKPxEQS1bZtmZN3wQU897LLeJ+LFtHLd/75tJr++iuJsEwKsrI4afj1VyrrWbMYknnFFfRMNmvGvjduTGL73XcklNnZVNiPP85JyRVXkOBUrcrJzKMv18OxCy5GK+zE8O6rMX91FI4e5T21b8/xmzGDfWjThkR25Up6Rf1+VvKcNIn9HzaMZEuK5Dz8MO/xkUf4TGSZCplUuEnNkiVOCNWMGbQ6r13Ld6V3b06kevQguY2P55j36sUF23v14rbQ0NOXO0hKcrzGvXvz2SUmOgUc5s3jtUeMYB9ln1nwwZRxUthB8l0kJ0YmrH6/4xkQ0mhOZKVf7t+FtGHXxrMoLIo7hykvI7m5LZhXrTDXl6VPPB6nqqV5njkfM9fKNKs0unP1gvVPwql79OC2xYspm6VYiJBNwPGCiSxITnaiAHr1IhGLj3fCFGWduIQE59jMTMeQI9EDyMlB9u9HEV5Vn8pFi4/nGEyeTHnk8TgRAICzVE2XLmx/3jzKjwMHnEI2EkWwbh114pIl1CsjRzrVm+fMAf75T3of27Z1vHFZWTQGjh/PcwcNckLkExNzP6dJk9gv8VKe5qWtVw+DemciMWQR0DOWrBYO2RcZLNuCPaeShCV4lQzmS1QYoXcmgtG0LFkQpe2xKu8THzNXxyI4QkP5zognKCWFBCw2loToscfo6dq+nQqwYUMSs48+Iuk5epQKKjyc2/v14+T9vvvo5fn5Z3qtpKR/zZqcuKelkQDu2UPFXaUKc/u6dePn7GxWwjz/fMdSWa0at116Kb+Hh3NftWp8xhkZ7MOePQyh8XhYKKVePbZ3ww1U3C1bkoBVqcJ2tmwhyYyOJvmpX5/3P2UKC5R4vezb++/Ty/fvf9O71aMH1+bLygKefJKkc9o0ejbT0hgSunEjvW533EEyPGcOJwnjx/M+Bg1im2FhAJTCvrg/4p60e7Hpia+R0FfhyBEWbenUidf55huSyB49SIIld+6ddzjReO45fo+PB/7v/+jJ/PxzkvEGDbjYrqyl1KcPj3XnwXi9JPKzZtFrm51NIj1uHMd76VISu+XL+ewnTuQ5K1eSmC1bxncAyL3cgbxv3bs76w1K/owUD+jUySnHLgRTlkqIiXEmf+6cunnzcnshhNQlJzvvyqhR3CekTciduzKfHO9eG8/CoiAUJ7kz164LptcLa8CUcD3zOPHsmCGRQO6lRtyEDsj9OzK9fcnJuX9HJvnr1o2GJZ/PKeYla7slJTlkU/ohf/37Uz4mJJA8ZWXx+rGxTjVlyb3r358kcNo0etrEozZzRg7mxjwOvDweGDgAyMk51eeICBrHIiJo/HrtNeoACRdt2NCpChwWxjViJ0+m/HvhBbafmkrZ/s47vP5jj9GwOG6cQ8S05nj37etUywwLo6y77Tb+HT7syMHkZBrVAIbO/+9/NAaaxVPknv1+LjXhnzUPyR/uQ/Jf5yKhrwo6Pw72DpXGnNESvEqO4gxNsMgbxW1BzA8VIfzRevDyh8dDC+Xq1VSeb75Jb9L995Mkde1KwhAZSWIm67b96U8s4nHiBJVOZiaJ2FVX0XN3zz30hEnBk61b6X3LySG5OXqUZOySS5wQz/r1SVx8PoaD/v47o04yMxlqWasWSYXfTzJ26BDbWrGCXkOPh/05eZKeph9/ZMhm3bokgDfcQEvob79RGS9YwLDKn34iAatbl+dlZzvr9N14IycSN99MorduHRXxvfeyv5GRnIDExNBbdv75JDmffMJxbduW3s3Vq9m/V14h0frwQ/ZtxgyntP/s2Vye4vrrQ7ByT31k+hSSkxnOCXAZhpYtSQqvu47fL76Yz2P7dvaxalXey7ZtHJcBA0jC/vQnkjuvl8SzSxeeJ6TI5yNRkxy6hQvpMRw8mOGmffs6OSkxMTwmIsKxct91F/d365Y7B6ZXL5K/9HQW4/H5eP4HH3BSJCFEMTHMKxHvZqdOHKsXXiChlYnXW285IZ6zZ+eeUJqWf8AhiTJJBE4vPgHwu6zhZW6T34eVHecmylqvmbo8L73uNmDm1Wf3cUL4hEjIbzM9/fSlRoTQmeRC1mEzCVlCQu6QS7PPUVHOcgMASd0rr1BOJCY6v093/qxJ9jIzqR9kWYK4OMog8RzKcVdf7YRxfvMNkJZ8DNiyBV2xEv4lK/DKs8fg9VKmxMWxb1270ijXrJljSPrhBxrINmygMS47mykHXbpQFmlN0hkdTWPYxRdTV5geyeRk6oWePRlB4fFQh/Trx7+ICMrlDz/kPUlkQ9euTmGpRYtyL9NgVhsGuL9bNyC8WggSrq6F/gPUaUskuJ9HXu9ZScESPIsyRVkL8+JEaU1I8hMM5WU8rQcvf3i9zDfYto2Kctw4p9Li8uWccE+eTMV92WUMEaxThyRq6lRncdeQEIb6ffMNJ+cffcT2GzemEtSax1WvTq/fPffwf3o6C678+iuPy8xkVc333+c5EREkf02bkhT++CNJVM2ajpexbl0SO5nMA2xXKVpzw8LY33ffJVEMDeU1qldnYntkJD18deqQOP3+O0ngsGFU/q1bkzi99x7wn/+QvFWvzny81as5TosWAU8/zVDMqlWBv/2NRKtnT35/9lmO4/PPc3x9PhK8Ro04Rps2kTAuXUolv2IF70XWpPvnPznBuu8+kqmbbuJx997L+8nJ4fXvuIPKvnt3XrtmTXoW9+zhmEgVuylT6GnbupXPa+ZMkvqsLE48AI6b308vn6xb98ILvIevv2b7kosSFcVn8corbHf+fE6eZHF1gMQ0JYXP+bbb2Ic5cziZkrySqCiWQG/QgN+7duU7MHy4s76TLOmRmOgs4WCGqYnRyfS8ucMwZZ/f76zhZU5gy7vhyqJkUZrvQH7XMN/bYHrWHWacVzhxMF0ti5cPHUq57/fzd24WBwGcXFmzH2JMMa8lHm939Ja0sXKlc7ysrylhlHKsRBCYJE9y2DZvphx45x1naR8JxVywwIkkGDCA0QuRkSyc0iMxEn17+jAwJAXhcV0x6pFIREYyTFJC09PS6CkLC6NeA6gb/vUvGtUkND88nN7HwYMZOdGlC/sjeeExMc76oH4/5enrr9PQ5fPR4Nmxo0OSY2KcMZk2jWH369eTAHfseHo+pBA+qTYMcCzeeiv3GneCwr7HJT1nVDpQ2rOioGvXrjotLa2su2FRDJAfgc3lKzzMkI5g+0pjPPPrw5kcBwBKqVVa665n17OyRVFkk9/P8L1p0+ipCQujN+OOO5gfEBHBCo5Dh7JsNMAwlW+/JdH65RcqpS1bGAq5Zg3POXiQIYR+Py2gR47wuMOHSUZatSI5uuwyniNJ8l4vFfO+ffTY1arFEM+aNUnwDhwgcbvwQpLEZ5+l0qtXj0Tq119Z9fONN+i1+/vfeUyNGuyr1iQ/e/ey7aNH2Z8LL6RiHTaMBM/r5TE//0wyd955JLG3384J0PHjvL/mzUlYxAp88cX83qULyWCfPmwjJISkrW5dWoavucZZHqJ/f46BFLupVYuEbM4cWoa7dOG5GRkMmRw8mERyyBDm4CnFtfxWrWLYY5UqfH6TJ3P8d+0ikezShc+mXz/n9zBzJu+7bVtOYKTIyfz5LKTy17/Sqj16NCcPaWm8XqdO9NbKO7RokbPA8dy5/CzrOWVnM4y1RQuSyKVL+bxjYugpXLOGk71+/ThBkwICMmn1+WhB37mTHljxApj5PdJveYdSU538GNkvC6aba0GJnBKYeeOFlRlFOfZsca7Jp7JEaTxXt650X1Mm9Hntd7c1b17hQ4rNa/v9NM6Ip27ePCekc8EC5/dlXl9CN81FtWUNS3fIZ0qKUyzFvCZAQifVJxcv5vEREfTq+Xz0tMkSBwMHkvzIPukzQHkTF0ei9+GHznafD3j7rRy0uTADYfWiAKUQH89rBVu43byHw4dp+BN4PIwayMykkatFC15HqnE++CBl2urVJGG9etFg1aoVIx3q16deffddnrtpkxMxctNNjO547DEa0V58kfJWPK0JCbx36XtcnFOIxaxSHMzLK8jvXTtb5CebLMGzKFOUppKuCP3ID4UhcCV9HyVFIs+1CZTfzxCdzExOnHv1onJfupTK5eWX6TVr1owKpnNnkijxtDz6KInK3XeT2OzYQaJ18CC9MHv2kLDUr892Tp6kdywnh+GZOTkkWceO8bzjx3ls9epU1OefT5JwwQVUnLKcQZUqnAScOMHv2dkkaaGhJGMbNvD6R47wHmNiSFSPHmX7zZvzekeOMNQzM5Mete+/5z3KmnqNGpFUZmfzeps2kTCFhbGPMTGcfEREUKE/8QSJ7vr1vN/GjenpCgmhpy86mkT6739nKOJTT5H07d8P3Horr796NcMyr77aWUtu5Ur2Yds2elXvuYdkB+BE4cMPGYa5Zw/J3RtvOIT0/fc5hvv383rbtnGSkJLC571wIdvp3p1hRM2akQiHhfEZhYU5xP+ii9gvmVjI77BbN74T8j6FhpIAL1/Oa7z0Er2Pq1bxWvHxziRqzhxnqQfg9EklEPA+TsjBo3ccQniTevBnq1OTWTlHJjkLFpDImRNVs0KoeCSE2MnEEih6vl1pGwfPNfl0LsDMBXVPwN3kqqAJutlWYUke4Px+5DqzZ/M3KV66YNcHHIMc4BAuKY4kBp8XX6QseuSR3H2aMYNyRbxwL79MD92kSfRe9evneLnci5+Hh/P8lSvZts9HGRQTQ8PThx/SICTyZuRIyqL4eBqv5LpuMivjNns27+3dd3nugAEOqUpJoSzOzuZ2wKmuGRHBY4DcpBegcU5k0dy5HF8JOx0zhv1bupR6afhwEsvZsykXhdi99hr7I8vdyDMCcpN78/mbz8181qbhoDiQn2wq51Nai5JEeSA1Z3v94riHkvjRlQQKE7Nd3J61M+mDReEh1clSUqjY5s4lQbrmGuYd/Pe/DgFr2ZJepKefpneqd2/u93rpwdqwgcSmShVO+iMinBCYDRt4vV9/Ze7biRNUUhdcQGL1zTdsUxYqP3SI3qXffuN5OTn05jVoQAI1YoSz8Pevv5Ls/PwzFbi0uXw5/y69lGRq61Z6COvX56RgyxYm0+/bx/uqVo0KdepUEs8LLuB933orf5+1atG7J2v97d5NZf/aa9xepQrHqnp1jolSJIvZ2VTCVapwLB56iMo6NpaezO3bea+3386wyo8/Zv969CDh8vtJSFu35rU9Hj6LsDCH3I0cyb5Pn84J0tNPc1Lx448MGx0+nNfzeunty86ml3HJEk5gLr+cfRHIZGbgQI7lnXcy2d+0CsfG0vMm1n+pMvrGG7Rcezy859RU7uvZM3e7Awc6xVBkgiIVLGVCFHVeDh5KGozwZxfAH9sb/lnzkJ3NzA6PB6dyasaNc7x1vXvj1H4gd8EVmVT6fLn7XlSDVWWQQ0qpKQAuB3BQa90uyP4/AngEgAJwFMC9Wut1gX0/BrZlA/BXdPJZFjA9L2a+uPvdcodjBjMs5LdP4A75FGJgrtkm3jYg95zEbFMMI0KOJFdPPOQAf+chITTuACQsgwbx97p2LeWVHC9y7f772ZZU212yhOf4/dy2bh3PCw0laUxPd2RSWBjXFf38c0YdyPqcAJDtz4Hn10MIq1IPMTHq1JIqWVlOoZeNG9m2GPT++1/2KTmZxy1eTIOVm9RmZTlFVZKTeb4s+ZCV5RRVSUqiga1TJ8pdMVpGRrKviYm8D+mbPC/JJx492qkAahqt3M/WNGy5n5tENUgBq9KAzcErAVSEHILKkO9QHPdghhJVhMlCcRDisx2zijBOFQHZ2U5yvNdLD9X995PUXHQRCcyRI7SKnjhBq6Ukg9erR6+d1iQnx445i4Lv20cL5W+/0TuUmuosExASwn0hIVSkXi/DRGvWJKm88EJ6AU+cYH8Akp1atTgZUYohke++y/YbNWIYpoTVdOnCPs+YwWs2aUKSozVJotZUqtOnsw9795LkJSdTCdeowePCwnjdhg0ZklOvHicULVvymF69SEJXrSJBq1GDSnjoUHrrPv6YhHPaNLYXGUlL7tdfkyzt3etUKgVIyB57jPdesya9eRkZ3Ofx0FMq49WyJScL69bR89e+vUOw7r+fClwmGTk57N899zBvT6poygRKqoFu3MjtqalsS5Y68Pm476OPuH38eJLxb7/lmI8axffB5+NkbMgQkrvp07l/9Gi2k53NfJHMTN7bnDn8k7wVgcfjVMjz+wHvrkNYvDQUPn8IZi+KwJwvOCgSwiml12WiKcsmiHVbjGcJCfyTYisyKTWLGLhRkKyqBHLoAwCD89m/C0C81ro9gH8AeMe1v6/WupMld0WDhBib3yVfPK+8u7zIn/sYM+zYhBQoCparZxZqkX4EI5nSPyEvc+Y4Hi9ZqkSIxaBBDFtcvpxh5WvW8Lg33mDI/vLlDhlct477Jk50+iJ6SX6/8fE0Ukn4N0BP19dfU2ZmZTEXrnVrXj8lhXLpxRdysPb+KfBf1AJxzwzCyhU5p6plApSTQ4dSTpltr1rl5PMOHUrDkBR6Atj2qFHURUuW8NhOnZwxTUigDklMJKGbMYNyW/KaZU1Sn48RMYAzhosXO6QyLs4xTskyD/IM5Fma642a6xqaz01gtlEasASvmFFRiFNlsIAWxz1IG6etb1JJUZrPvbz/BsoSfj8JRq9e9Np99RUJwNChJFGZmSRM1auzKEaTJpy0T59OMrNnD5XT+efT07VjB71pP/zAtqtWZcji/v0kQocPO2GVdesyHOf88+nh6diRCnzXLrZ77BjJ1L59bG/DBobgzJvHfb/95ni1vv/eCdk87zyGgoqSrlqVRKpuXYcsde/Ozy1acILQujXHoVEjehajokgS77uPk5Lu3enZ+ugjEqnrrycB3rSJ35s0IUmqXZtKdu9eksG1azmG69eT0MTGchJRp45TvfOtt3hPbdty0vPbbwzjjIoikfrsM7YhnkKp6jl1Kp/VPfeQpMbGkkh+8gktz8uXcywnTCBp7NePk4nJk7n91ludCZRM5i691ClD7vdzgpSRwUIBUqLc4yF5W7PGsZR7vfz80kvOBDAykt7fBQs4+QkNJfF76CF6CkeNYp+k2uaGDbknmevWcdLi9wOvflIPma06ASEeoG1bhJ4fhfh4kvE5c5xiEcEmot9+y8mOOfE0y7IHnejk5JxK2sxvwlwZoLVeCOBIPvuXaq0DPnQsA9CoVDpWiRGMaJnvmTl3K6xhwU0YzTUt5ZriFRdPucfjVKGUa4on0V2dU/6kQqQsdyChhhMmOL81OVYMLkK8Ro2iXHvgAUZLiGfK73dykHNy+D0xke336kU5JAuNv/MOCdXatSyCMn485emkSZQrUhAF4O+8Z0+g+6UZGL73caToXkhZUgXdmh7Cu++yHXMsIyO5bfNmkrCsLBIvU6bddRdlhtdLo5jHw35mZzN37uabnaUOfD4jCiHKqSS6bBll7NKlTrGXTp2chdr9foccSihpZqZDAmXu1L+/k6MnYxQenntdQ+n7ggXOOn9ub7AbxT1nsgSvmFGRiFNF6GNBKI57qAzjkBeCCYzSIncVwdBRlhAlm5lJK+jcuSxj/+c/kxx16MD/MoZr1pCsVK3qELH+/UkoatUiUZPCJwcPksg0bEjF5vXy3Nq1SRIeeIDH1KvH60+bxu/p6SRsAMlaRgb3z5rFz3v30iPVuDGJoyTCHz/OEMuwMJLJgQN5fI0abOfnn0nqsrLoxRMPoxR6kWUgVq3iBOKDD5xQmJwckpM9e6jYZZmA9eup1KtX57kHD/KYDz/kshMHDvA4GccffiCROnGC72Z0NPdv3EgvY40anED87W8kL7fcQgKcmEjlHxbGcMQWLajUH3nEmYhIYZVbb3UK4EjRlFmzOPb338/J1RNP8B47duSkKT6ez/7wYYYdzZkTWEdqJolknz7OQuGhoez366/zHgGS3gcf5OeJEzkxGzyY9xUfzz+ZUMycSWI4d64zAZJ168QrJxPQ8HDggTEKYS88A8+eH5C48nnEJ6hTE8f163Lg/+kX+E8yj9+ciM6cSU9jTExw41lQPZmTw42NGrFzOTkArBwJ4E4AM43vGsAcpdQqpdTwMupThYJ4yEyiJZB3XiJ53PrLrC7pbtMkjGaY87x5DrkTo5c58Rci5/bU9e7ttDtjxuleP/ltJiZStt97b+5lC6TfUjRl3DgeJ4YjCe8E+Dk6mvtHjqR8mzmTXr8JE1hkasECyuWRIx0DXJcuDuEKD88ddeD3U75NmgT0GBSFKU3+jiyEAW3bIrxJPYwaRZ21apVDuA8c4PEtW/L+IiKoowDKxAMHKCtlOZjmzZ1xGDiQBsH776cxsFMnnjtnjlMJdM0ajpeEta5cSTLp97PfEyaQ9E2YwOOzsjhWInMfeMCRY1I52OMhCczOpjw2Zae8P4DjtZN3K9j7Je9Sccs6S/BKAJWZMFhUHJQlyapIho6ygJSeB+h9+9OfmKN22230eF1zDZXkgQOc74aEcKL/22/0WnXuzO9vvkmiBdDj5veT+EVGkmSsWcNn0LAhlebRo/S8Va/Oc2QJBKX4v0oV5tRVreo8ux49HI9gt24kaAcOkLj98guJ5P79JEhbt1LhiYexQQN+j4risdu38/xjx5ylG9q1I3lq1oz9ueceFijJyKBl+uhRnq81PYbVqvGed+8mgfJ4ODGpVYvn+Hz07tWuzZDTQ4d4vbQ0Erz9+5lrd/Ik2zh5kqStWjX29aOPnDXwvvuOE4VBg9iv8eOd/LvLLuN4xMZyclK9OrddeSUnBbNmcRKTlsbJg+RYNm/OSdnkyezXwoUs/rJqFclonz58fsOG8T1ZuJA5jwDbWr3aae+yy/guSKjjqFH02nk83C4hSN98Qyt2djbzNYHcnoe5cznWb7zBa5kV+UI9IfCfXx8zZ6lTE6CI8ByMmjUUKZfcgZdbvQPv7zmnJjQJCczNefVVPjcTMgEEglixDx3ijfn9/H/woJUjAJRSfUGC94ixOU5r3RnAEAD3KaX65HHucKVUmlIq7ZAIinMUZrROsPdJwhHdIZLuBceB3O+wLF0gbUqIsuTXJSTwvRfPkjnxd3vr5s2jdzw52VnTUgwx7sqT4gVcs8YhLh4PZZIsw9O1K7fNm8dtQO4cMQmB9Pud5XkAevU7dWLxp759Kc+lfTMiQfLJ/H4n7DI8nKTy4YeBOnUVxm25GwN2vIPElc8jOYUGol69nHv2emksu/hi6g9Z6kWI4113MYrl0ks5dikp7Jtcd84cXis2lvveeYf31LMnxzIpySFX4uWMiXHyEAHeT9euJIlSYTgri3Jx9ercY967N5/jjBm85z59KGtNeWa+P2b4rPtddBsZir14na2iaWFReSHKqrzjXKtS5/c7VQ8zMqhQly6l4jl5klZUeXYXX0yF07gxv2/bxrXo6tZlCejMTJLAkyepIC+6iB6zCy8k6YuIIEGT0MqwMBKvqCiHFJ48yb+ICDpOjh3j5w4dqIBlvTiAEwQhcvv28bzjx0k84+OpELdtY8hmlSrsc0gIPVTh4Q65jIwkOQwNpefwmms44WnYkNc/eJD3cOIE8+Kys3lfjRrxe7VqJIV79pD8SRjqgQMMSfzqK67j9+c/k+DccAP7M2sW7/+zzxgCWjUsB4d+ysKIB6oiNlZh4UJ6EGvWZFs9erAv11/vrBfYsiXHJDqa4/H667znK6/kdoD3NW4cP7/4Io8F6F3zeLiMRIcOnMysWsUJxYABnHRER3PClZVFr+WwYTx32jSOSdeuTpU3KRQgOSMTJnDCI9Xo/vQnZ5LSrx+3r1jhLAWRmelU7OzShe+ieCHkefv9nOS2ahVYpuGXX+Bp1ghefxV4QhTCf/4Bvpr1T4VeyuTVXWRA8ldkspqrKIXWvNiiRfwsnQgpezt0ScknpVQzAN8FK7IS2N8BwNcAhmitt+dxzFMAvFrr8fldy86d8oYYQ90TcZHBUkHSPNasjliY9uVYUycHW44hmM6WPphFjOR3JH2TqroPPEBZ2r07f+NSeXLTJsobM6Ra+jVvnlOgBCBp6tfP8fx9+y2NNgCjGaQq5tChlN1CxgTm+Pj9rNQ5ciRDJBMS2H5WFmVOaCjD5Bs14r2kpPA8KQYlZNfvZ4TCiBFOZU4Zs+eeI6GT4i7z59NA2r49ryHFYqRysCkvIyNzk/c5c3jtvn1pLPN6qQuksmlSEmWcUiTRck8eT97vUElW/M1PNpW95LQIivIYklIe+2SRPyoCuTsX4fdzIt2xIwlDp05c0LtqVSec55JLaOkUL021apz3dulCMvX88yQvtWoxxHHXLoZc7tpFoiMLlh8/TiVXrx6/ezyOR+zYMSrXnj1J4E6c4B/Aa6xcyfaUIpE7fJjkbts2EkYp7uLx8PPPP5MMnTxJAurx0CN4+DDvTSnu37uXVTSFUHbrxuucdx77Vrcu+7NvH9vMyKBHrnp1luFesoS5h+vW0SN38CDHQSmO0969VO6bN5Os/fIL18f75z85HqtX855vuD4Hl6z8CBmrNuHIE//Cbbfm4L332IfDh3nenj1sLyOD93777Zwsbd1K4p2czGv9858cF1l8vmtXKnrJL9mwwVnEHCCJCwsj2RISLRb4d9/lc+7bl89h/XpOWB57jOPxv/8BY8dyQpadzUlRVhbPlwV933iD5O7993m9Pn048Xn44UB+THdukwIvXbpwrGSpA8kz8Xg4oRkxgs/L7wc8F9aDLyYer2IM0DMW3mr1kJRET6pY9d3kTnKTBGZIHABAKfg++NQhdKmpjnv6HIRSqgmArwDcYpI7pVR1pVQN+QxgIICNZdPLkkVpzTmC5eKbETDBPC3B3mnzXHcun9mG7JfCRKb3Z9EiJ3TRzOGSCozJyU6lS1mvzu93iMU11/D3vXatU8hryBBGjMhanBKOLdfo35+yxPRupqQ4x4nnXXJ05Z737aNR6s47c4coCrmT9lu3psyW0NA1a5y+tW1L2eL18tzERBKwjRtzP/8lSxhpISGvL79Mw5nXS93Sq5fjkevXjzp1wACSO8AxWMmzELkp10hOpmFV+rVsGdvcudNZgmHRIhr8QkOpa6QdM8TX9Mqa70tZzMXs9K8coqQZ/5mgPPapIqKieNQsShbh4fQoffABvUEbN/LvsssYrjJgAJcC+PRTTu5r1KDFsWdPKsdmzeiFWr2aIYzHj1PhNG1K5depE8lZy5YkOb/8wjaqVCGJkgpiJ04w7O+SS3hMaCjn1w0bknjl5PA4rdmfWrVoyU1P59xbwj6rVSMhyspyCOr555McSZXGsDC2cfIkiVpiIq/t8fAetaa3csECfgZI8iS/LyODfevcmaGel17KicNLL/HeIiJ4Dx4P261Thx7RAwe4v3ZtthcWxj43bQrUDsnA3/aPxl04D3fvnoLfwvxIHByGzp05FidPOqRt1iy2f/w4+3bppfRorVvHbd98Q+9W164ka7160Ro8cCCfZ3Y2id/YsfT0ZWVx8jFvHq8xfDjlw7JlDNV95BHH2n7JJXxX9u4lsV+5kpM9KRsua98BzsRvxw4e164dJzzz55PUrVnjeP38fuYmtmvHMKiRI3OvfSVegvbtOaZSJa5/fwVP0hx0+jwD/mFRmPiGwogRjgcg2ELm5rIOgJOHJBNTnw945dMGGBObgPBlKXzZ69Urrp9cuYNS6hMACQDqKKX2AXgSQBUA0Fq/BeD/AJwP4E3FShiyHEJ9AF8HtnkAfKy1nlXqN1DCKKk5R1462E3ATKLiXgdP3t3Y2OCeaimIIoU4gNzrpgmJkG3mvcbGUtYLcZDvZqimeKM8HspAn88pqNSnDwtISai1YP58yqq773bIn/yexXEu95ud7ciRF16gPM7OpuwfPpy/4+xseggfeIAermBjKmGO8llIYIcO1He3386cacm5Hj+e+/r1c2RRQoJDaCVf+vXXKVN9Pt7rHXfwGt26MdKgQwd+T0lxCJ451pLDmJREgrhsGeX2m29S1g0c6JA2UyZKUMHYsU74pRSWAXIT9rxQmnNAG6JZSijqQy2PRMAMU7AoOopbYZWXdySvfhSlf6UZoqmUagzgP+BESQN4R2v9qusYBeBVAEMBHAdwm9Z6tbstE0WRTYcPU8GMHcsKZElJVHTr19Pa2rkzPUIJCfT8SKXKzp25BEBsLIlbZCRJ0w03sLrjzz9TuclCs5JnkZREstKqFfPOpFCGz0frJ+AQF1k7KDOT+2rVIkmqUoUWVFmgvGZNXv/HH9m3qCiSqMaNOZFQim1Vq0ZCd+AAvXy1a/NzzZr0Tnm9JFyXXkoCdfgwidiRIzx33z6SkZ9+oqdu2DC2v3cvCWz79ry3b75heGrt2ux/48YkmIcPk5DWrk2PZ9++LKSyfTvw+WcaW679KzI278a/I8fgHzO6YeYshZ076SHs1YvjtG4dPWPNmzMnJCuLBEwKt7z3HidOffvSa3b77Ryv6dO5QHlWFp+PUpzECMEfPpyTlksvpQfz5EmSrcsucxblzcri8+/Sxcm1mzOH70rbtpys+f2coHTrxsnOvfc6ckYWVh81iuPz4IPcPmECJ4eAEzYq74zHw+eSksLr3ngj8OWXzkTHDDXzeDi+EqokE0YhbWa75qLosiCzTMJOhcKF5cC3lwurnyrxV8Y410LIywuC6ZCz0XuF0cHBjjHfY5O8mbmq7jaC9df8DQgWLHC85vLdLPYyc6YTZmj2RxYof/NNyo3Onak7wsP521282Am/BkjIjhzh7+3FF3P/ls0wVCn2tHs3Qx/fe496Y/Nm6qslSyiT+vZlvpqEMwYLVZwzxzEyrV/PNlu0oAzy+0nO7rrL6ct33/H/5s386Y8dy+OWLHGMR9JPIX0rVzI3+7ffSGrDwhxPpYR3pqY6XldZvFwWbRc5KggL434pTjZxIr2f8jySknicSdALInSmwau4jRY2RLMIKImQgDMpdlEeJu4mzKpPJX2d8ga3sC7K8SaK4qov6DplWUClMP3w+52yw+UQfgAPaq3bAIgBixS0cR0zBECLwN9wAJOKswPh4VSaW7ZQmf7+O5VvQgIn/JmZJDfh4SRQgwc7E4ALL+Txt9zCiXt4OHPxoqJ43KpVJDNt2jBvLDycE/8//pET/Isuciyy2dkkdsePU6EpxW1aU9FddBG9R2Fh9OYdPEhPWvXqJFw7djil748d4/Neu5YT/vR0evFatKC3Udbfu/56kruWLXlvx4+zj1u38n+VKiR4J0866+xlZNAbd8kl9FTdeCPv/aWXOG7p6c4aeBs28Lx160jA2rdnlbWcHIYtPvIIf4fDhwNPPqXw3kXP4IOYKbjiqW546mmFpk3Z36+/5jhv2cIwnTVreG6jRs5veNcujlXduhwjmfD8738knBdc4JC7nBzuW72a548a5Uxatm8nMdOaeXazZnGy1qcP2+3Rg+MqeTh9+rCy6JAhJHO7dnF833+fRQYGDODzTElx1sm64gon+nH+fJLKjRs5UZGKcS+8wEnPgQPOQsKzZnFiJiXIk5J4zIsvOkUSli3j9SV0SyaML7+cuxKghIBJoYKUFI6HyInwcMCXFYJXPqkP34nyQe4syg7BiNPZ6L3C6GB36LDkjZrLFLgXKA/WhujApKTc4ZyLFjnHSEj2xIlOiKLpKZwzh7KkV6/c1wkPp2dpzRpWxe3c2SFygpgYEiWppNunD39v//gH872lAImEYc6ZQ3L34IOMAPnHP2h0fOABGpyU4j1kZfG3HhlJIrVpE+WerJ/p9Trj17Mn/0v131decSpSylqYycmUO99954SxjxtHcjd3LslVRoYzXsuWOZ43ObZNG/b18svpfVu8mMcmJOReAH7OHOoPKZozdCjbHzCAcr53b471a69Rp0housfD55+czLGU5RCCPX/3u2k+09IO17QePAMlGYZYFKtTefHMuHG2lrOCzi2PYaBFtb4Uxz0Uto3y8p4E64fPxwngww8XzutblhZypdRUABO11nONbW8DSNZafxL4vg1AgtZ6f17tFLXIyiefkNTIEgYnTvBz9eokOYcP0/r49df0HGVnM5+tVSvmnW3aRCV78iS9Vb/8QhKRlUXPm9YkaBKuV7s2lfSvv7IPHo+zqLjPx7DM/fu5TZY8OHmSijtQsR5t29KbJLfZrBkJ1u+/M/RTa15f8vjCwtiX6tUZfrlrF68vIaShofQQrV9PwrFjB0MnMzK4Jt1XX9HLt28fiWDDhvT2VanCe69alQRu925OXi65hB7Gyy/nOQcPcl/79iSkvXpx0qUUSZwUuUlMdMY7NZX/Q0NJRDt2ZNXNIUP4DDZv5n1cey3v9fvv+XsdNYrP5Phx5tX16MGxiu6Ug+xfM9DrsigsXKQwcKATQpWWxrF44w1O9Pr0Yf82buRE66WXnHXwWrbkhEcmXqNGcSLj9fIYsThv3+4QyHnzaOH//HNObLp0Yd9WrnSuFx7uFBaQ0CwJNx04kBVCr7iCpC8uzvmtz5njhIPddZezbMPo0U71TCF8fn/uYgZJSZycSiVZMyxUzitqtEhJykPrwSs/KKnnLO0G07/B8ujy64+EaYoHLTExd1vugi3R0cy7FU/RjBk8LybGKZokvx/xGpqev3nz+NtcvJie/IceYqRD794875VX2Lb0dfx4/mYl0iMri7J22DDKvb59STr/9CfKeIC//1WrKF9ffZV6qVmz3L/pLtE5+Ms4H668NgLZ2Qrff085t2iREwWxaRP1yPr1NLw9/DBl2urV7MfgwU5xl9mzec5jj1EOybgdOAA8/jjlY1SUU/hFyFxSkpNTKPfs83F7jx78LmHu4t1LT6csE29eRATHf9kyJywWOL3QjXhyzedZmPlfcb3H1oNXSJQkuy4KuSsPnplgOFvCUtA9lWUyal4oqvXFbYE722sWdFxZwZ1E7N7u8Tilg8szAlXsogEsd+1qCGCv8X1fYFuxwOejZ6RpUyq6Tp34eeRI/n/hBRKFjRvpMcrIIDFq1sxZy61hQ56XlUVyd/Qo246OJgFSimRkxw6mMh096hwTGUnP2IUXMgQzLMwhdwCJmiTWh4Q4CmzvXpKS888nCTxwgMSjShX2o0ULZ9HzJk3Yv6NH2e6GDZwcXHQRv194obP0wcmT9FBdeCG9Xk2aMDSoZk326/hxksjsbJI4qaBWrRrbbdaM/Th8mP8XLybx7NqVyvnnn9lWjRokOWPHss0bbmD4ULt2jkepdm0u5Pvdd8z1GD6cnsapUxlyuWcPx2TtWpKcq6/mM+vXj9s7duR99OgBdGifA+9jz2Djva/ilZgP8fbbOafWZ1q0yAmlEu+ex8Nn8cMP3Ld1K8e8TRv2/8UXOVG66y4+j65dOdHatIm/tx9/5ITqrrs4genf38lxy8qidy8jg8+rTx8eI7/ZdevYhw0baM2WAjIXX0xPXEYGx0dyirKzabkfNYr5jn/5C63pS5bklgNz5jgW8fHjnRCp6GjuD7bguZvcVZSIBoui4UyeV0mRO7OgSjD9G0ynm+e5dWJCAn/LvXrlLpySnJz799G3L38/Y8Y4hGTDBqfCY5cujiFHPE/i+Vu82CmSImtaLl9OAxdAwuL3O22LIaVVK+bcijdqwACeExHBz7LA9x13UKYePgw8+ihl2w8/UCY+9RTl/6nfqs5B6oDHceVnf0LW409j2rQctGjBa0ZEUC4NGULyOWAA9VN4OAmsKXvMwicAz2/e3Blrr5ch9oMGURf5fLyPlBSHeGWdyEHSl7/Bl6lPkTspzvL665RRktso6w6mpZHchYayf0LuZJ1QeYbuQjfJyZRt5vPMyxBgfnavb1gSsAQPeU9WywLlieQU18tXmHvKz0JW1jD7VJj+mVbAMxnD8uKZywvB7k0UnKkkTatleYRSKhLA/wCM0Vr/foZtnNE6Ux4PScDu3SQZUVFUKnv3Uml+/DG9XpmZJBQ5OcwfW76cpO/QISq6pk1JpqSIiM9HD01GBkMyxeN2/vkMexQCB5B4ifLOymI7tWqxb82akWidOOGE8AD8LF7A48f5ffVqp8qmlOMfMIDXWr/eWYS8QQNOeI4c4TX37XO8h7IEgxCepUu57eBBbouPJ8H5/XeOWWgo+7tlCwljaChJbLVqzgLwbdowFHHqVE4kOnd21verWZPnJyfzOv/+N8f+5EkWIrnsMp7z7rsMhQ0LozcrIoL7/vAHEsnffmO+nRm2uWEDiV58PLAu9Rhe3jkUjbED+35WqB3hO+W1lHDNNWs4jllZvPfYWE5olizhROyFF1gZTxY2Xr6cFvThw9n/K6/kGE6eDNx8Yw4mvXgUDz+kTy1g/Nhj7FdYGM+ZO5f/IyNJslJTeW8PPcRjHniAZC0sjM8vK4vXiIxkH6Q0u1T2FEj4peQECuS64eG8nljWBw3KvyiBtFEYWVqe9KZF4VDSpDyvdoNtl/fH/G6+f7JouZl2YIZUAnnfy7JluQulBFvPTrx68v/BB51824EDeU5kpEPUfD56zDMzaUARDBrE39dVV1EGt2rlrBUq4dTJyZRjUjzE56NM+P57ktGUFB7/2GM0FC1dSgPNwIGUV7I8zlNP0Yt24ADJTq8Wh7B5s0IC5mPbdo1m/8/edYZFdW3t98wMw6CjYsPexd5FRUVFjWJNT+696VETjT0mpt8kNz0xxhpjEk27Se6XXuwoiIqC2MGCvVdQQQcZh5k534+X5T5MBgQFNYnreXiAmXN2O/usvd5VK2VdchsNC2N7ixapsTRsqPqSTJ/PP5831g4gH2zZksqkbt14Vs6cSR4cEcGxLV6sXL2jl3iR8uRcOB4chpgOz8J5wXupwH3//lQ6+somxu/NZq7ZrFlsX/iVlKCIjeW1UVF08QwP53OROoO+Vl+RjXwBnYQ2lCT97QFecTCZ4mZQN8IhVdzMtzBBqH8l7euVChx/hrXwnZuMGchrvbwWMZtXSpqmBYDg7htd13/2c8lRALUM/9fM/SwP6br+ia7rYbquh1WuXLnQ/TudFNTPnyeYK1+eGTPdboKM2FgFXFwuWroOH+baHj5MC1dAAN3x6tQhELtwgQdUuXIEEJ98QjBlsVDrmp3Nv202ArgqVdhG/fp08ZQ23G6CyrNnZa0IhjRNlVyQ3BeS1dLlokVRaPlyCgFcI47p4kUCrMxMIDPTi4rBLui6fsmltEwZgjOJZzt4UFm1Vq0ieKtQgXPfv5/jq1uXMSrz51OL2rs3D9pq1Si01KlDoUXSjFevzjXZs4cH85kznKvDQeBcvTrXf/t2rsOnn/JgrlKF1/3nPyo75Zw5BNMdOhC4xMTQ2rVhA4Wh4GBgwkt2DK6RjHJw4p2OP6JFhyAEBVHgk0x69evz+SQnq2yWTZvSetegAS2Z4l7kdhO0jxrF7wQsxsQAtWt58d/IOWj8wVDctuElrFrpvVTgeOpU9fxr1SKo+/VXukd16KASM/z4IwWboCAKZUOGEExGRKhsdOLKNG4cLXYJCXlj70aNUjzAYmE7n3yi3KYsFsUvhHz5hBSE9mdR8bWWCN0I5+ZNKjyVFCj3VTb6KiILOl+N9/he53TS0iRAadw4BfIsFv9xe6IQ8Y3XMwr70pdY+IzuycaYPUB9LsXEu3Uj38rIUG3KubtihVLCSGHu6dPJ93r3JvBMT2cileRkWuJtNlqyBKzs2aPi6KxWWv1btaKnQHAwM/rOmUN+aKkegqcikmA35aB9s4uY+KodVivf+/Xr6X0QFMS2R4/y4uMPncjM1PNkCAWAuOVeLPzmLKZ8oF+KHzSb1TyEJ6xaxXvGjiVQHTOG/1svZGLkoeeRiiZwbNsHy9m0SyUw3G7yduGn0q+4ulosCriNHcvPZD2lBEXfvupa+alb94/8SZ5pfvvNmH2zpOhmDB6uzmKSn8/tjW6FKYiMvujXag5/5vUqbrrWa3El/Rnvyc/66nD8MbYmP7rGWTQ1AF8COKPr+vh8rhkIYDSYRbMTgOm6rncsqN2i8CaHA3j4YYK4nTsJHsaO5cE7bRoPZymDcPIkQc2FC3TlyczkQanrtCKJENCgAYGXZL7cu5dgxeWidevMGVr+unSh1vXIEX5nMhEQHT1KwJeTo9xnpMRBYCABiZROqFhRxfLJPQCB0LlzqrSC3c4+pNwBs4F6EXT6AE56y6IiMnGxQj2cPmNC9ersp3p1ao1lfmlp7LtOHdWGtF+2LIHj6dMER3feSYudw0FX1oQEAo64OFqoPvyQ2vGqVbnGSUnKJbF3bwo/K1fyWaSmMn33gw9SyHjlFY4tO5uCXqVKXOd168j/N2+m9W/3bs7jX//imrRt7cW/n3GifrMgNGuuoUcPWsguXKCwEBtL0AUQCD/yiIpXeeEFPvvRowmSGjakQDVypNpLKSncR5UtZ9HpwVB8gqF4CP/FV8/txJBxZfDpp1yL5GS+ox4PNeKSBS44mPth4kRaIkeNUi5TvXrRXfW553jve+9R6PvgAz5bY8a+iAjOJTmZQqAUFJ46xYsRd6bBXj8EtiBqBowCj6SNlyyBcqb6FgyW+4zJLq4Vn7wZg/fnIKM8JuS7l0QA9xfj6Xuuyd8OB3mJsTSJJC/yt2dlnxr3qJyHvt8Z34UlS/JmZ8zvPZD2PviASpjPPye/GDAgrzVQfiwW8sCwMIKtyEha0zZupIXs22/JE51OAj6TiUorl4uum1Jvr2VL9i0xv2JtE0DZtbMXK+dlwlwxGFH9NL/PyGLyIj1iEOISAmFt1hh9170Fi9VEULzci8i3+sIZvwErmz2OXglvI36NCS4X57tvH3mLx6NcSWX9JB7OYtbh7tUH8+PLYl6lIfhw30DYgrRL4LdtW/5OTmbSLLdblW+ReDyAzygyUj2/goqYG7OgyrMF8srRvnHFxSXnXfcYPE3TammatlzTtO2apm3TNG1c7ucVNE1bqmna7tzf5a/FeHzpahbZnxbqz2CFyY983eyuFd0Ed4quNbgr6l711Y76auMBMrOZM/0Xgb0BqCuABwH00jRtc+7PAE3TRmiaNiL3moUA9gHYA+BTACPzaeuKyWolKGnVivEJ5crR5WXHDgK+6tV5oIaEUAjPyiIoCwlR9epOnCDgCAnhPSdOEJisWMEDWBKg7N5NS01AAAGBw8G/AQI2AWghIQQRFSoQPAEEKxcv5o3hOHeOFjdA3QtwTDk5BF/ly3P8djvBzOnTbCeqSxZ0bxZKIROBcGBw7yw0asTxeL28Xtf5Hhw4wPaqVuX3Z89SY+10cnznz7PNRqFeJKz24OOPdRw6xHvOnuW67drF/6dPpxXwwQcJYlavZn9Hj9Iqdu4c92xaGsHZpk18NosXA998QwCUkKCSIgwaRLA9YgTdab1ePs9mzbgOHTowccHcz02YNLMUnhipISWFoPLllwnIJeW2JM755z9pwcvO5jzfe4/tz51LUCeuVeHh3D8SJ2m3A73vCkal7s3xOObiqzqv4KGRdmzYQMFv4EAKdxMnEuhu387YPYDaf8nIKcXOu3bldxaLSuYCqLTsAMeWnq7Ssa9Ywc9HjCBwNZu5udyfzMWqFsMR12EinBfoI2zkIZGRebXZcqb6CqnynWQxvHlm3CRf8o2ZB/5YfNrtVrFsRvKVeYxAT5J3CP8TEGWUlYz9yD6VPo3nocTnidAv56eENRhdOI3F143vgQAqcfNu3ZrvHECgY7SoL11KPhMRQcVZdjbvDwoiHxw5ksoqGfeTTxL4dOnCsyImhp+LUgjgb4+HIFHGk5QETJlmwsb95dE1QrvUvxEAA4DzcBo2rfWgH5bAvD0Fjv1pWLaM/XhOZ8IZvwGz8Dg821Ox4vdMREZS0bRvH3lR9+7kq0uX0nvj9dfZvsfDNpwXNcS9EI1+ez7Ch/sGwhKgYckSrkvLllyDlBSVTCUhQVkvjbK8tCfzMFpoffebcS/47h9/Hk2+ioSSwgrXykXTDf9pyZ8DEKPreiiAmNz//3SU30O/FgdQSbiHFlcq/xuR/oxjLkm6kr3qe4j6u19SIN+IdRN1XY/XdV3Tdb2Vruttcn8W6ro+O7fAMHTSKF3XG+i63lLX9WJVfdts1Lbu2UOrybffKteZsmWZzWzTJgKccuUI9sqUISgICKD2slo1gsGKFQkOjFYtTeNeN5n4Wf36qlC1ZMXMylI18DwegrvsbFr+Tp+m5RAgOKpUiaDw3Dm6UEZGqni+0qUJNgICVGKT2rV57dq1/EzTCHxatgQ27bHjMELhBXAE9bHrqB27dhGoHTtGgAlw/JUrs+9z55SbZ+PGHG+FCjx0TSYvTq5OxaDsuUjbcgRh7b2oXZvrExJCAahhQ4552zZaAtu3J8BxOChQNW/OOLMRIwgKhw/n/I8eJeC76y7gzTcJ2ho3poCTmkrwuGoVrbGi3R83jtcEB1P48HopjE2bxuedmQm89hrn2Lcv6x0eOkQL1ldfcb2tVq7jjBm0njVsyPXYsUO5TjocfPc0LTeOz6PBvXQ54j7ZjQbvPI65n2mXrHCAcjsFWFrh7rtprVy+nGPs25egtVUrvs8bNtAaOnEi+/rtN+C++wgIn3ySAHTqVAU2e/SgZl8SO9xyC2DPTsP4I8/ACifCtn2BuN8yL1mcJVkBoOL6hEQoWraMVg2jS5tReL9Jfw8qyrntGzrge0b5O5sKUnSKcG90m/QNSZB2jZZDI5A09inCviT+kMQg8l6Iq6bsebc772cCEnv35rvZty/PEmnb42Fc3pIlfFd//lmBus2beQ4sXUo+MmcO392MDLpwLlzIpCdOJ7NWtmxJZU9srCqiDpD3tWpFHjp/PsFrs2Ycz/jxXB/j+I2ZJ2NSQtCtiwc2sxfZjdti4qQQhIVxPr3vCsa6Fo9irPYRenZhcVVZP4kpBoB77iEv/u03lpWROG2Ph7zWlWNC/O4qsARol3hMWBg9JzweemhYrbQmdmt0ErZA/ZJngFDv3ipOTmIX/SVGMcpBUog+P0zgu9eEx5VUwpVrAvB0XT8uRYJ1XT8PYAeYke420FUKub9vvxbjKUnKz12tpPoqCUthYcFdSVkpSwqEFeQP/XemK9mr/jSdRhKt58219k8SE5GZCfz3vwQhX3/Nn0qVKIwfOEDBu1MnAiaA30k2zDVruL4LFxLIVKxIIHD6NK1ne/bwe13n36dOEYS53bS8AAqkORz83u1Wlj3pz+tlf6K9PXOGB1KjRvw/O5uCfU4OD8T16wlYxKWzZUuOIT2doGnvXg2NGllRoXYt1Glox8mT2qVad6VLcw3Kl6dAUK0a+xHA6nar+R08SFfCsoEu3O/9BAfQCNkwI2apB23aUKioWpVj0HUC1/LlOYaPPiJYXLzIi52bL2DdOh2//06gffgwS1i8+iotZ19/TReopCSgWRMvbu12Fls262jVCpg3j5khX3qJ+z0hgQJGy5YUol56icJQUhL7l2Qlr71G8D11Kq22YoULDSVA7N6dAsXu3Uqj/8wzrCGYmkp3zd27+QCbVTuLAwd0LFgAZJwzYfZ35QFNQ9u2bGfpUlrZHn6YacVvv50Wu27dKOT07EnXVtEwW63cD1JC47bbOAajtXDKFIJeifAQQSglhdZIye7nLBuCxBZDkY0gzK79FlylgvMIzB6Pqp01ZoyK/QGUFUSsGjdB3d+TCiNr5AfMjIpIo/XEeDaJNaWgZD/ybhjBirFdo3LCWPNRQJ3TmbdPydwo7YsFq0MHXhcZyR+pOSnxYhERecGhMautxJWJZcrj4RkhpQ9Wr6YFPieHPHrVKr6rHTsynm/dOsZTBwRwXq1b876VK/mdw8G/zWYqwr79li6it9/Oee7dy35XrmR8b3h43vmKxXLzFg1YEg0cPAj7lNcx6X0Na9bkJmAJ0NB59fuw7N+Nmf0WwZGl4ckneV9UFIHoUxO86FjnJD7/TMd775EHJyVxLcxmAtIBA7hWArgkQdXddyuglZLshbNXP8TUHQJ3rz7wuL15vJLcbiqxHA56f0RE5FVE+QN6xt/+9qavYqCkPRKueQxeblrylQBaADik63pw7ucagLPyv889j4PFhlG7du32Bw8evFbDLRIZ/b//DrFrxdW3r7m6JOqI+PrI36SSp6LUsvq7xbg4HMye6XDQNe6HHwiUPvmE4KNiRa7fhg202kiSj+bN6e7YtCldVo4fpzvhxx8rIdtm40FnNvP9KFuWVqjsbBVL53LxWhFQypRR9eWMLpfiihMSooqiy/3VqhG0tWmjkg/ouhL6AQafHzjAvwMC2J7Tyf7cbo6pf3/lfnr0KMd79iznWqsW1yI+noBMyjIcO8YDOzgY2LdXR/kLR9AA63EYLRAa1RDnzmlo1ozxjcHBFGREu71hA8HJzlQv9n+9Cseyy+Lrdu/i047fYnOyCZ07c9wnTxJcms1c3xbNvSj7+5doc3Q+XA1bY9c/X4LJZELt2gQ8pUsTtNWoQQ1zVBTnOnIkPzebmZTg88+5xhMmcP127CC/czhYb65fP/Vc4uMJ0po0UYWE77xTkg540eaHl5Cyw4yHOqbg36E/Y8ZM06XnCtC99NdfGUf3+eecT48eShMusTliobvzTvLI8eMZ1zduHK2wHg9dtmbMoJY+Pp6A/PffWSZixw4CNIllsdmUe1PXzl6sXpiJHrcFwxKgwWLJGyclFonoaApmxrgV43lgnNe1VKQCfz/+dKORv7PeV3A2JvjyDZnJ75r89pcvGc8yY/tSIDwlhbG9FkveGCyROcRibbHkuiJ6eM/w4bS+S5KmcuVU4g9R3gF833v3zlt3zdd1eeFCgsPFi2n1j4oiqOvRg59Jwq6PP6Y1q2NHjqFZM7bRuzfPnsceo2LP6eR7Hh5Oa+CCBfQGqFyZ186fT17luy5OJ/lJcDABYKNGdAcFOO+OHfO6hDocVDwdOEA++skntLCtWMF+RKl04gTw6SdePLrwdtjXrsLkmh9Ae/RhaBp5ngDBL75g9s9PPiGPtVg4fo+HAPaLL/j3uH+ehKVpKKbrT2Cs9hFWfHUQKF8eUVEKoAtvWrJEjWXcOI5XYq8L4kG+8mxJyOsF8aZrCvBy05KvAPCmrus/a5qWYQR0mqad1XW9wDi8a8GkruYhXE/Ada2oOOfoD9AVpv0rAYI3+rO50cdXFJLDSQS2y9HfTYBKT+dB3aULtayBgQRsERFcs6VL1XUbNxJwHDtG8JOVxYN12zau88WLtOaULUsQkJND98XTp2n50nV1WAEEa6dPE3SIgCDHQMOGtOSd81M0wmymi+jRo6p8Qtmyea8NDKTlp1Ilgk8BgQIaGzcmkNN1BTYvXOD/AwdSaABUxky7nRa1evU45qAgamiXL2f7Zcrw/g5hXuze6YHLY0FkpIZt2yhgdOrEtgMCqCFu0oTrWLMmkLLRhQv7D6I1NqIXYvB2hVno2s2CN95gbEpwMLXSHTvmZuyseB6H5/6GetiPVtgO88xZ8JQtjwULKEQFBdHlKSSELrFBQRQGpk3jGIYNI8iqUYMWwiFD6JJ5//281mxWWueVK9nvrFl0ixw+nG2EhNBiuG0b0KnuSVhaNMFqdEInrMeMCfvRvkcZ9OpFQalFC1U8ODyceyM1lcJdly4EajNmKAHN4WBmvchIYNFCL3o2T8PK1BB06aph9my6ZElhdIeDVoDwcJVq3W6nhn3dOgq1Ep8jlt++fSnoGoVpp5NCYOvWeQsT+wpFQF5B/FonWvm78acbjfyBNuMeAPK69RoTmOQH4HwtMPnJE+I6aWxD7pUEQUalia/CWva5ADsBa6LACQujJT8xkZmUBfxI+8ZEH0bwCOQtiC5gTsDVJQVLVypdbr+dfKZ1ayrkBPj9/DP51ejRBHgmE9/1oCDyI4n1a9aMyqqvvuJ7OmoU7+vQQc0pJobzOHCAbbnd5GFSJuXdd/ndoEH0Khk+nICzWTOur91OMCUZe4UyMqjIbBd6HtaPJgPwojW2ot+ej+CuWAUrV3KMv/7KTMepqeR3gwdzDIsWUbF35AiVXRs2AF276FjX/Sl03jYH9m5tkfFrHBLXaoiMVM/bCPRiYpTSTpLlFEauLGm57ronWckdhL+05Cc1TauW+301AKeu1Xjyo6t1PfyrCOj5UXG7ZvpzjSjMGhY2dsw43hv52RS0rjeam2NhxuN0At99d8MmWbnuZLPRHc9q5QFetSoBT2qqcvGRGDa3WyVFOXGC///8M7WvZ87Q7bBMGbpxWq1AjRpemLw5CAzULwG3mjV5cAcGKksWwDZ1nck9AgMJxvJL31y5MkFWdjaFEKuV4EqoTBleI+AGIAgTcBcYSGucJGHJyeH/HTrw3dyzh/cCHGt6OgFWhQpcn4EDmQVz7VquX2QkQUyNGkC204QatQNQvbqGn3+mxTMri+Bm2TIKPAA1w9nZtFBVqRmA2yomoQaO4vPACbj9DjNuv50us5UqEURLfOSFC8CRDDvqVNWRiC74JOhJ/BQTjKQkAlqpo1SlCu85fJjWw7g4avaffpqa+i1bGM/Spg0tlzNmUDP+4YcU8txuCjjZ2YyXGTkS+P57ulc+9hj3zEsv0SI747sQTKv1HjphPT6p8ybGPMe05BYLNdV9+vC3ycR12LmTVlGXi0qFfv24JiKA2u25grHJi6CXnwGaNMGPwxYiZpkXI0Zwzyxfzix7Tz2lwB3AOQlAFPezlBR+l52t9pQxRknegwkTVAZNGY+4dAnf9nW3u5lo5e9D/s5G3z2Q3z7w3TdCxpg23+t8+zbGj0mhbXG1i4zk3vUH7qRd428BQjabclFes4bKHHHPl7EBeRO1GP+X8iebNxMgzpqlSsqkpPAa6ctup8dCv378X8Cd3U6gNWgQXcrnzAGeHOfFUw+cRJBNv6RskgyWKSn0OtmwgQqoBg3IByTGF2D77drRLdRmI999/HFa05xO8owpU9j3qFFUEo0aRSAmiiaxoMk8lywhz//f/4B/v2tHuyYu6DDD2iwUluohSEyk8qh/f7Yj8eziUh4Xx79HjyaPrVSJoHf2x8od1Lk4DrM/1hARofaC2503GY94qYhCy6hAKEgmKkipUNJ0TSx4+aUl1zRtEoDTuq6/o2nacwAq6Lr+TEFtlZQWyp/W5Sb5pz/b+vxZxutvD16N229JzLuw48nIoOvWL78o4bog+rtpyDMyeBgdPEiNKkDwBlCY//57alxzcvj38ePA0KF0gcvIoOvinXfy4MzO5uFYuzawa5cXAUiHGW64URoWWxmYzUxBHRys6s0dP57r3rgvr0a6sCTA0GIh+Dp2jJ9lZfH7559nlkVdJ9grXZq/Dx0iIC1ViqCpVCkKEF4vwarXq1xLq1cneHM6OU5Jza3rdFGtVYv7q25dAs+ePQkKd+8mgPrlF66tw0ELXLt2nHO3bhRQQkKA5k29ePtNDxo3t8Dl0tCpE/sCVDxaly5s/4kngOCyXiz4NhNn9GAsWaJh0yYCtO++Y19r1nBto6KYgMXrpQBmNisX3HvuobDSrZtys33vPVrUOnakVr1FC4K+uDhaxI4c4fsUFkZh5/ffCXiDbF70b5+G6M0h6NWbmetEuyz7bMIECmFSsmLXLrpNpaRwfmPG8Jo77qCQZMs8CXeNOnB7dKSjIp69aw/u+GcpbN9OQbBPH6XZXrWKz+KFF3i/JFtZsYJ9de7MvTBpEgGuWPasViXoAspKIsBOUo9L2vKiuOKXSCzL34w/3WhUGHf/olhUjKntixIKIrFvkZH8Xyw9/qyFwB+Fe3lvpM2MDGV5k0ydYukbMID9jRnD2F15Z+LiyEusVnWd0VVTFC0C0MSCJbUtZ8+moqd/f7Y/blwuSC3tRff3BiA4KQbO8B6IeTYaPSJNeOcd8rX27Rnr9s03BEkyp/h4vutWq7L4uVxsH+B8srPJd0aO5Jji4gjCli0jXwoPJ0ht3ZprMW0a0Ka1F71bpsFdIQTxq7VLrqnOC15E/5CJvvcGX8qQKbzEqFB+/30q8iwWzv/XX3mmejxci06d+N2sWeR/8kzi4pTiQPadPDt5zlOnqqLzheU5JRXCdSNY8PymJQfwDoA+mqbtBnBL7v/XnHxR+J8BDFxPuhKB8HrS1TzPazlP3zhEYRw3UsH0wo7HZlPuXDfpj2S3M13/woUU/hs2JNg5eZJWvPPngbfeYnyH3U6A9L//KU1xdjateBUr8v+cHB6gQTYPLHDDiTJwoxRsAR5UrkyXypMned3BgxQsDh2iC2etWuo51a5Nt0tT7snQti2/CwlRYzeb2Y7bTQB24ACFDMnICaiMnZpG0HfqFN0zAwMpCKSlEdBJcpfbbycYqlGDwM5ioTCydy8B6aBB1BgLgNy6lYJDt24ExJmZLNK9axev2bGDro9dutAqmJPDNRYgcfgwY84WLDLBjQDs3KkhLIz9S13ATZs4lqpVOZfPPgO/LF8e33+voXt3AjKLhYAsM5Puq6LRHzKEwGztWgpcViuvP3SIazh0KOf18MMUPjp2JIBq2pRjdrv5OUDXJoD3dO3KcQUFAdZAExylq8Dj1fDmmxRqHA7Oc8kSPofXXqNQlpZGQeeRR2jNO3iQ62KxECRnZ+cWU68QAmd4d7yLZ5DYZAgaNg9Cv36GzHMWzicujuP9978JNnv2pHAmZROkOPGkSQTWPXtyXN9/z7WKjuYcRWju3FkVdRZw5y81eX5UUjzvJl1fEitaQc9VrGqFefZiIS5MErD8FP9iGTLuT2kX8J8ZUeYh76bDQQu+x/PHFP1Cdjuv6dlTWcEla23v3nlDIOLieJ7MnMm2Y2P5/6JFdOlu0oTukMOGUbkTHc33+D//ofdEx7ppGL/mXjjcAXCvTsKW1VmwWBjP9/33tNZVq0YlVUyMAjphYSqpyYoVBE5bt7J9cdMeNIjgLjGR94aFMXHJzz/z78RE8ksBaqOe8KLHO/2A+vWxIvxZhHf0Ijo613JmNcFahYmkZH3j4pQbpVhrW7XieCwWKlPffZfrOH8+1+fDDwkkJVlYfLwC1/K8jC7j4l1hsShwJ8/dCCzz21MFWYlLiq4JlNF1PR6Als/Xva/FGAqiKxWi/65UUpqIa02F1fRd63n67scr6du3jWttxbTZqHm8CfD804kTzJ74zDO0OAUHUxj+/HMejMeOEZSlpNBVU2Ljzp8nAMvIoLWqaVO2V7o0taal7RboTg9sOA03bMg4XwUZ51WyFHHplJID6enA/v0EYoACfeLYsWkTLXPt2vEAdDjUAdioEQGY1ariCAEKJStXqsQtUkcvOlrNXSgri0LD//5HoT47m+tRpgzByYkT7PPQIY65QQPu5d27CRgHD6ZwUrYsFQo5OTxs69YlAFq0SB344vY4YQLXqlQprqvFQnA5aBBdmbp25Zw6dOC6pqSoousOB7+z2+nS1LUrLVqtWxPoBgfz+uPHaUH897/5c+IE3wex5AHM0BkfT7fW9HS6olosnGP79nSFkiLjopHfskUJMuPG8f166im+6yIkLV3KMTRsSCuvxQJ8+ikFu5YtWeBXSh8kJbGNkSPZzsCBrCO14rnFcEVnAd3smNhfg81G4cblUu5xkhlw0iR+NnMmhchbbqHwNHw4246P557p3Zv89Ngx7iubjZZFqZ8nPAtQCRaKwj9unuN/Tbrcc5Uac6NHKxB1uX1gsymFQkHZM42WPpEFIiMJKMQlUqw8xlg9iZszKsONrp7iJtmuHX8bSZQkAN9Ri4Vg6PHH+d5v2kQLnPFsFxdxt5vvVGws+UxQEPnVsGF852+7jbyrTRte53Zz7XJyAISEoG4tD9yHA5HYbCjGvkBtYmys4vl3360Sq4hy5sMPyTuWLuW8PB4mPHG76Y755JO8d+VK9b6vXUuF0cqVHE92NvncoEG8NiUhC1gdjpYIwrbtgTjzdSaWJJW/VHg9PJxrIW6oQlu2KL7Rq5daR6eT7c+cSSWYzca1EAskkDfWEcgb79itG8+v5OQ/8iWJI5aELgXJjP7AXUnKmNcsBu9Gp7/qoVAS2oG/wkFaWN9pX5B0raiwJv/CtFFcmm1xUyhMWw4H446Mac9vUl5yuxkv4PXSojJwIIX2776j++Ltt9O6FhJC0OV00qIm9wIEPQABlNMJnD6tIadsDZxFLZxHFRj1aiYTD98TJ2hpklIJQN7Ml/J3QABBUE4O3QYdjrzxeeXKqaQBp0+zPXERuniRYKBCBQoYNpsqnC4kGTsvXiTQ27iR4woJ4YFaoQIBk9lMoCh1+MRtcv9+ukemp/P79espjGVm8u/QUIK+9HRqc1NTCdhiY2nBS06mi2RkJEHiK6/QFUoSydx6K+NDcnL4/caNwAMPEFQHBBDMlCrFNkuVogVx+HCO7+WXKQxt2aLWU+J2du4kIGzWjM/1jjsowJ09q9xCw3IdboKDqR3fsoVJCxo1Yozgzz9zPKtW8fugoLwW3nr1aHHMyGC/lSrRNUsS8bjd/F6KFScl0Xo7bx6fdfswEyzly2BLMveP08lrN29WfCAhgSB4/Xr2Ub8+raKASp6wdKmKhwEIhr/5hvtBSl9IavO4ONX2li1593lhedef+Uy6SfnT5cCaWFSMIMqXfK1pRguxv/1lsZCfTJ/O/S+ygMT/SqzY1KmqTlqHDsq6ZzwvAVUnTRQqdjvBQnw8lSfyEx9PZY64Tzqd9BBYv57WsREjyCMkRk3aBjhWt5u8d8ECegWIR0FoKMctCpWlSwmwRoxgP599rmFY/FAkfrsPrjcnwe3REB1N3hAfz3dfeLmA1/btyVvdbpb2GTFCxb6tXEm+uWQJeczPuZk3evfmNXY7Y+dsNrpzTp3K9lNSgMbt7WjT1A0zgKGdtmDZ+mC8+ip5+KJFBGouF9dJ1ttiIWgU8JmQoPioxUJg6HSSD37wgYqni4wkWDQWNTfuE3l+ZnNey51xn7Rure4rimxc0rL0TYD3F6aSdFm53gfp1c6psC9WcYOk4qKijKewcy2oLd+DqjDrVqPG9d8nNypZLLRs7dxJC4jXS5e5kBBaXho1IhDTdR5kFy7w8Nu1i4k8zGbl7lihgqpdp+vAuXMmAGaUKqWhQgV+XqoU+xCNrgBDQLljms0qOQpAAdyYRMVu57hLl+b/mzcDgYFelApwwePR4fEogFKhAoFRYKAChm63qrFksdC9VOr7SQpvj4dgbeFCWhtF+AoMZCKYMmXoavjcc6xT5/XS5aZVK9737bfcd/v3Mx323r0Ej4cPcz6zZ6vkMPXqsb//+z8CxwYNCP4SEjiWJUuo7U1IYD//+hfdKf/7X46tTx+u68svM45jxw4KOStWUIBr2pT/33knXSujoykANmuW604bhEvJS955h5rw0qX57B97jH2INa5RI1rfUlPVs1i3zovN8eeRna3j44+5TpLYxG6ndWDDBt43aRJdsRo25HotW8Yx9unDe7p0AZ59lpbPLVto8XvsMe6NBQuoiW/XjvvrkktlJy8SF51FeCcdycl8hnXq8NmtXcs5b93KGKOICApQkl68QwdVeysigtZHl0sJakZt+M36pX8tKonnKKBDzibf/eIvDEcAl/E7+TG226KFAkVitRJXYiO4dLnoThkdrdydgT9m8HQ4yC/E8nfLLQRh8hMRQUBls/F9ESAUFkZr2LRpyqJmHJPFQp6zfDl5XL9+9DKQcgePP06Q6HLxfd+8me/f9OmcT61awBdfmRDevzxcORqmTmU/jRuTLz74IK1ixnhEUfAlJlIZtnmzml/37hz/3r0E0jNnquckyWJWreL1q1Zxnr16kV/t3Kmhw+I3sO2Zr7Hx+d8Q1U/Dnj1c6z59+NtsJn/84APOSeYp3kpt21LJPG8en4koyUaPJn8RF12LRZUNEpIxGveU2Zy3JISRJOunyEhFoZKUkW4CvGtE1+NwMrq8/JWouMBWUV6sG81qWdTxFAbcFbSmxv4Ka13ct++mUJYfBQcz2UaXLjyEjxzhIS6ZJkuX5mG1Zw/BSNWqvC8jgwdmcDAtXzt28HPJstm2rXK3dDoJEgB+pml0hwwK4kHasCG/k2LnHk9eS15QEIGgxULB/eJFaoQluUqpUl7kXLyAM+fOAXChSogXlSrxsCxfnu0eP67cQy9e5H1ly3JfnDrFQ11q6+m6EhhGj6aAUqoU1yQtjQBo61YesnfeSWB26hSzVu7dqyxMv/zCe5s1Y9sVKtC6VL483SW7d+e8HnyQrp9HjhAEnjihkr/UqUOL3saNtH4dOUKXnnXrCCbvu48gad8+Pje7nXEu9epxne+9F3j9dQWQRUj77DPOYcQItc6ffsrPvvySVsH+/fnZvn18rvHxtKpJhrcnngDWrPZC//xzPPhBSxx4eQ6GDfVi40aVbrxPH/bZujWFmpYtKXwOHkywt307NewOB4H4zJkcS6lS1OY/8QQFombNCADr1uXYc3JyU8O7vXD2GwTcfx9st/ZBq5Ze2O2qFERmJp/FE0+oTH5du3LtrFauhcTlSfbOAQPyprw3CuEFxeLd5DF/HirM2X01z9O4b4z7ReLjfD8zxrkDVOpI7Jx817evqt+5bNkfY+8EtPTtSzfIvn25x/3NWVwCxbVe+jCeq+LS7HQSOM2bR7C4di15UIcOyvIVGUmL1ujRSpGWkqJ4nBQ9d7n4/oaFkafFxdFN86mn+Fl2NnlMaCivT03lGHv25Dk1bx757eTJHFdGBv/+8UeeCZGRfI/DwnhmDRlCxc6GDfx7zhy2K1kxxepmjLV1OlVJhpwc4LMvTBj2ZBl076HhwAHF/2bOVJZ/s5m8rXt3FfsLsC1Zr/79uV7h4QTINhvHakyOs307+ZMvjxELKaB4k/F5GjOe3mgyInAT4F0Tut7Wnz+r9tNXmyZ0pS/StQSE14KKczyFWdOi9nejrdeNRE4nYynWrCGgK1WKmtmgIB7A69cT1LVpw89276aVqUYNCu2nT9OaFRLCzypUIKBKTqa1p1q1vGDtvvsIrE6f5u9ff+WhL9Y7sdxJSYNSpQjIzp/neyNZN1etImgJDgYyM3V4YYUFOQDO49RJ/ZJm+dQpjg/g/SYTY81cLv6YzZybaETbt1fjLFWKh/jWrQRggYE8kGvVYhtnzhB0tWihxnfuHEHI7bfz2kOHmBige3dek5HBPvfsoYXp+HEKYePGMRayaVOuQd++BJKbN1O4ateOrpoSC7N+PcGgBONXq0ZBBSC4Wb6ccy1bliBr716u9dq1FCIeeojfx8bSZUncjH78kVbHH36gsPb55xS2gmxeDL3zLN59R4fdznWKjwc8pzNx8egJvIpX0OLwQvRtk4YzZwhAp0zhmrRsSaC4a5fac+PGUYhq3JjC0LPPcr+Ii9GDD3JPfvSR0mp7PGxjyBAlQDYun4ZnE+5EuB4PZ/wGmM9nokcPCoaff07hMjxcJWTIyOC6ms2MMTJmnzMKueJ2JkK2fCbJKXzpep+tN6lodLmwh+J4nv7izY1uk8bPjPsM4P6UWDZjorOICAKRyEj/hcZlj4oLp1jjgLxuoxYL77da+V4I2DFSZKRy3QwLoxKvRw+2N2gQlXPG9ycoiO6N4s79zDP8u0cP9rNiBe8ZO5bX79/PM0bG06UL+aJ4UyQmkpft3Ml2Y2LIb8ePp4JG3Bzvv5+87MMP6RWwcCHf96FDCeyys5XSTRRaERHKXVvieaUkwbRpvGfHDvKjxo3p5r16Nccu2YFHjyaf691bgemVK7lW27Ypd1mA4E6eSe/e/FtAqoQT2Gx0KU9M5P8C3Iz7RPaC/BaFgMRiCl2Nl1RJ0DUtdF4c9GdN9euP6fwd+r5SMvqXF0e9I3k5b5RyA9eLinsu+R2mFgutHjVrFq6dv1sacrebMVO//ELXwG3bKBwPHUqhPDub2tcjRwhwzp9nLFnz5jw85VCxWvOWJ/BH1avToiLX1Kyp3INMJv5kZPC67dspHFy4wM+dTlqVUlPZl4wFADp10rF2rQuAEwEAclAWwcEaMjJ4r8RZnD1LsJqTQ4AZGkrACih3pMaNmeXT6SSIk1IQJ0/yOrud61SjBvmCrtNV9dw53hMayvkcPMh52mx0y2zdmkBr3jzgzTd5kLvdChxKMpX9+9meaM/tdq7H3r10Ufz9d5YyaNFCCR9z53Ic1avTohgUxOeWlUUL7KZNFHoiIwlak5IoTNEFieM+cIDA/O23gUcfpfWvUycCsU8/8cI55xskHq+GKhW8CB1+C8aMNWHiRODtt3Q802YRbjvzGXp3voB3uyzAt//TULcu1zQ8nMJNejoFF7OZFrl69SjMAZxHeDgBWd26fMbz57PkgQinkyfzuUmiHYBzXLJYx5u7boN97SqMqfQN6g7vj6ee1rB6NdfP41FFhgEKiT16KM16795K8DUKwMZSCW63/zIKxhgYo8BUkjz678afSprkufk7k/M7owpzdsk572+fGNPpGz8z1mAU0Gbch1LMXADCsmV8bxITeY+xqLq/3xkZ5CfGcQmAiIv7YyFzQMWFmc15600uWcL3WDJSiquj71wlAVKHDrj0TorFKyyMShyPh21ZLMysu3o1+f8TTxCUicVMLInh4VS8tWpFPvbxxwR5axO92LEhC+NfsMMSoF1KxpWaSjBms1GZFR1Ni5o876lT6XEipR/Cw3mtw8HxARyjlFtwu8kXJ0/m/bImGRnAjOletKiZCQQHwxqoXXpWsg6SJKVvX/6WBDPjx9MN/6OPqOTbsYO/d+1SbuLGZ2lMpCOfA4UHdvnJoFcjl90IZRL+9nQ9AcKfDZzIZi/uYrY3UrmB60HFPRejBtS3j/R0Mv+MjOLp669I5coRNDRpQhCUkUHQcPIkgdSBA7RW7dzJgzkri5pLKScQFEQtaFYWBXexxrVqxd916lAre+wYrwkL4zVHj7LvU6fY/oEDvG77dt6XkaFirQAe1AIGjc967VoNQAAAO0oHlwWgoUwZgjmvVwn6AN0fBRiKZQ8goDKbCbAyMlRdvEqVlKXu7Fnup02bCA4qVVKg+PRpjklq7EmWyi5d6HLj8TBmbvBgxohI1kyrleM5f56ukEeP8nD/5hu6c54/rwqyx8RQCz52LAFkaqpyU501i3//+CMBXFISNdE//MD51a1Lq2qrVny2QUH8GTOG7kJ16xLUffYZBauvviI4XLcOaFM3Ex2O/4JhmI16Z5Jgys6C2817LAEa7pzdDz02f4g1zy7AgYMEd489RiCWnMz1HDqUAlO3bhRcdu+mxa5FC16zbh2B5ZIl/C3gbvVqZY1s25auXFYrwXK5csDgWzVseelXLPtkH+qN6I/hIzSsW6cyAiYnK+24202B0enknJOSlBZc3C8lyYFkLHS7VfIKQF1nTJdvjM37q/DovwPJ8wL8n8n5gbuCnrHRpdK4T3yv97Xi+cayyTUCxMR6J7U53W7yp5kzCUiM8onRAu1w8HdGBt8dp5NtxsWpGFax5sXFqXNUZB6xOEVE0Dq1bBnv692bfEhAm1ir5D1xu6lUmjyZ4G7dOvJBsZzfcouy9A0eTIAzejSVisnJVFzNnq1cHQHyuDNnOI7Ro/mZzcZrP5vrRfJTX6LF+w9jedcXYTF5ERGh4ovFKvbll8BLL3EcM2aQn44fr8C1x8M5zZ+vSkd07852nE7yArebSrrlyxVvcDqBxAQvxiwZAOvQB9HngyhA915ai+nTyVOcTuCnn9SZNmUK17RmTc6lXTtaR8eMIXCUZC2+e9FYziI/cJefVRrIH9yVFP+6CfBuIPorH1CFnZuva0RxgDvjC1ZUKqw7aEnFFBQn5TcXf+O70jEbXS4OHPDvVnWTlJb4oYeYDGTwYII0i0VZo7KyeIjVrk0QUrYsXfRMJgWSNI1gz2ZTsXQHDvD3wYMEPUJSSBwg4MrJUYLBwYPqOrG++Y7X6VTPMyhIAKUJ5cqZkZGhoU0b3nfiBIGUlBMQcrloldu8OW/bEn8XEEBLY9WqdBG65RaOS6xHzz/P9vfupYvlrbcSrJQrx/vr1OHnderQSiXKhdBQVTPQ5eKhXqcO3St37mSfBw7QZTQ4mH3qOsffpAmB5Nq1FEBCQ/mM9uwhINqwgYD8xAllhWzYUGXZLF2abrTbt9NiO2QIxzR1Kl2bNI3xgprGuYnW2+UCug0OhrlpYwAWWGtUR9MOdjz3HJ/B6NHAho0mPDelCjp20nDrrRQG+/XjmjdvzrlMngx8/TUVCVtTvHCePo/Fi3WYzbxm3ToKbllZjDn0eFRsntVK0CkungDnHhXFflq3MWHbsfIYMlTDV19RoASoHW/enIJaRgY16KNGqdjJMWNUggPhM+LOJUkSbDbO0VirTIQrQCVmEEG8KPXybtL1paLGc/veA/h3tRQQZNwnRsE5KiovIHO76QYu+1Dul/4AZQGKiOD/cXHc361aKQueUeHgdnMfSyyZxUJQInzQCCiln1tuIViUxC3SVkwMXbmTk8mbxF0SUJlnZ84k/5o+PW/Gaq9XlYJYuZL8RN4tsRJKiQOnUynkzGYVHzhtGr97/HHG+a1dy/uTkzkuqxVoXiMTpmMHEY54/LqjIRb9XyZiY9nf2rXKG+Kbb8gze/WikkeAnQClqCgC0C1b1FikNMzq1TwPpk/nmNavJ2+NjaU1znM6E1izFh54YUlYDWRkXirJ06IFf69eTVBrt/P3k0+quqTiUmuxkB8aLarG/WWUZST2ztfF1h9Yu9x+L8nYvZsA7wahv7IWsihzK4nNfrVtFtYtJD+gVBwxBcVFhdEeFXbMcjj508DabAQmN+vg+SenkwfvmTM8UD/6iACuVCmlvQ0OJug7dIjf6TpdNjt1IqDIzuZnpUrl1biKECGZNYX27ePvwMA/PpfMTIIegAesZN8EOA7ZC5Ur83d2Nq8LCeG9ZjPBUkAAQc3Bg5yjyZS3LQEOEyYQCFWuTKDVsKGymO3bx6K0v/zCtux2Cj8rVtDKVqsWr1u2jN+XKUNBrVEjJjYZMwb4xz/yWgX79OF+7NKFYDEuju6d3bsz7u/++/m5lFd47jmWL7j7brpDlSvHfkNDVRHylBQKHffco1yfpkxhQoHBgwkao6KYfbNjRwLCL7/k2mkaXTvNZiY3cDopOIWHU2h5+21g8gcakm59E3O7foEWkx9F374aBg+mcHnmDK1zU6dyXebN43w/+oiul6mpTIowaRL7Dwry4v4l96L07Enot/413NLLC7OZY0xJ4Z7o1Yvre+YMsGihF53qnkRSkn4pM5/HQwHT6WTWz4ceoqXzs89oUV22jC6wixd5Yc06ixbNdSQmUshcvZrZ7jweCn4dOvAZSAr1efP4bIzClSRC8KUlS5RmXqx9lyuEfZNuLLqSs9gIzHwzYt5yC8GAWOhEmDYCPd82bDblhgcoYCZ7UNzxOndW4AjgfZIQSMbicHB/R0eTd/fuTcAyeTLjXDMyVLyW260Ao1j5Zs9mPwD3d0wMQVLfvrnKnA187zt1UoDTYuFnQUHkYQkJvL9XLwJCgPNJSiKfkXsk4+TixTxfPv1UlX5JTiYP6NaN7/Ls2XwPp0zhvWvX0jLXty/7sYYEQ69RC1vQEdO6fI+edwTDbFaF2MUV227n3GNjuZZSOy49nconsfa3a0eLp5QvAKgQtFioQNu7l5/dcotSQnYdGIzptSZhE9rD3bkretwWjC1bFOAVwLl7t9obNhvv79s3777o2VNZDI2AzuNR1tdly/h8jPGExr1YWKt0Ub6/UroJ8Aqga3lglCSKv95U1LkV9xoUZ9yZvz1R0Pyu9rmWNPD3N76ijDm/aywWWmJKaj9rmma+/FU3LjmdBGsuF4XjatVoKTpwgC6JjRrxkDt6lGDGZOKBlZFBAfzcOdXW7t0EIKIlllg7SZjir2+rVSVhqVCBlsFSpdQ1x47xd61ayhKmaXTrDA5W1124oFxszGYvdu/24MIF/RKAzM5WmTzlf6eT9dLCw2lVO3uWY5bSDcHB/NztpiWoWTN+npzMv6tU4f8eD90209M5jlOnmDZ7wQKCzUqVKDTpOkGPrvN+KRi/fz+Fo6VLCVbFPXbPHvb95Zd0tUxIoKBy5IiyiHq9/Bk7luP1eFQdw59/JmjJyqLVToS5mTNZZmHPHlpdJ01iUpPbb+dzPnCAa717NxOzPPssMHyECV2629C6tYaPPqKwExRE0Pbmm7xn/nyC87vu4vw+/5zA9NtvqamuWhWoXSYTo9cPRU3sw8Jd9bH810yYzbSojRrFsYvFPSbGizd23g5by1BsHDUH0L1ITiZIFYGmfXu6k4aH04UzKIjf1a/vhfXV59F3SE0MntobEV2YXTMqisJ0UFBe7XxUFGNsJk8muF60KK9FBPgj/5O2ZL//lc/Om+SffN3kAGU1M5IR6PkDiBaL+lvqs0m7Au5sNmWNE4WmtBUTQzAkbn2SPXPVKt731FO0ktntig988AEBk9NJ/v/xx4yBs9v5mVjntm3jOBIT2X/HjlQetW3LuU6fTgVVdjatbFIjdcUKKm0yMtiOzUblSWwsz5CNG1lrddYs9vHYY+S9o0ZRSTViBJOlrFrFcQHkce3bE2DOmMH3NDYWcOVo6Pjho+h9YC5syxZh2nQNnTqxX1l/sa737s31kVi7Fi0Ya5eTo6z9W7eqdXa7yXuHD6eCyGqlgi0sTClBzWbAFqSh/awhGJc6GgkvL4ElQEOLFlx3SRTVvz9LJhjjL6V0jwB0KckyezbXQK4VrwXJohkZqUomyGfGLKk3Eh+6mWQlH5IHf6McHDfaxvmzUHE+R2NbQN7gW/m+KH0U9vqSePYlvZ/S07lOUmT5clTUJAaapm0G8ISu6wlXPsripaLwphMnqGVNS6MVrGlTuoeULs2D9PRpfle6NEHWkiU85CtVUpY7cWkUCgritbt383u7Pa/bjpRaOHGCoC4zk21arX+sAwTQupaWxmvPnCGIMLpyAsqCEtnDixUxF6DjHNywoUaNYJw6ZboEMjWNViIR2qtWJcjKyiKwDA1l2xkZtFYOGsSx79jB79PT+VOlCgFaUJACsC4X23e5KIh07Eiw0rYtta2VKlGoWLCAwKh5cwoS1auzn1dfZVtSzsHtpkWvfn26Bknq8LQ0Ci4tWlAoy8igkHHqFMf1wANs94MPuPclcUi/frRGvvIKa+ZZrbSwVaumnonHw9i6adPoQtqmDQWxtWuppV+1ita5qVPZXlwcY1juuovC2oED/FyEuJ07abH8/nuCp0nv6Xh+xz34Zn0LeGvWxcTkh7E8ToPHw/s1jUBz507gsVtPYl3YcJiRA8CELlvnIG5HFWzaxDTwq1dTMGvblq6nI0dy3Z1OACdPwta4Diyei8hAMGa/eAjjXypzaZ8A6re4WIrbWfv2dMOdMYO8aepUztHoyrVsWfHGZReWSiLJiqZpnwEYBOCUrust/HyvAZgGYACACwAe0XV9Y+53DwN4KffSN3Rd//Jy/d1ISVaulHzPYOPfy5apDJeFaQfgPnI4CCR869UZE/vExf1x3zkcBFmS4VHalb3qdqskKpIwJTJSuZFKshWnk2BryBAqmcaNYxsxMeQv4u2xZQtdpjdtUtY+m43zbtJEJTRzOBiH9/nnfDclFu/nn5nkJDubyquZM+l+7fGQd+7cqYBkp07kG4cPA6ENvRj3rzSs3h0CR5aGefPooeDxkKcKgBV3znr1qDwLDeU77fGQX0dGck1mzlTJV9xudUatX88zqVw5guHp01XtPo+HFtEVK/g7Pl7FL4qlT9ZdErXExvJzs1klyBHLbFycyowqFlrZOxkZeZWY8lwlLtjIk3z3k6+seS3k9ptJVq6AihKvVNL0V3bfLGkqTu2ur8uHMci/qM+oqG6rxUklsZ9827LZCFpK0EVzOIBpmqZ9qmla+RLrpYRIYtrsdgr5DgfLElStSutEhQoEYlu38rD2eCiEp6cTFAnIK1uW+0MO98OHFfi7cIHfCxldTs6cUdpuI7gTl0lNo2UNYDuSoRJQrp+lS3MeJhMQv0pHMPbCDBsAK04e8yInRx2UwcEENrIfzp1ju6VL0xIWGMj/g4I45t9/54/bTatZ7dpcH5eL17Zpw/3VujXH26QJAWjDhhQsSpUiEJkzh1a4pCQCsz17KNysWUN3ziFD2K64KO7Zw2u//ZZumpGRBHpmM0HcmDHUBo8fT+tYWhq19AJ+zGa6g548SQEiMpKCRlAQXT6ZLdWLJlXOYsUKHUlJnJPDwdp5lSvTCiclIsLC6Lb0xBMcT04OAfwbb3Cut99Od8nffmMb2dlK8/+PfxAwvvsu0LOXhq3PfI/Wn4xDq0kPA5qGHTu4JidPcs907EiBbGVqCH6q+BhcCESXzh58/EsIzp3jPnQ4CHIlznPkSGr7Rdhdsb0S3GHhcJpKYWbtdzHiKfulvS6JJCTWSHipCJibN9OyYbfnLSDty//+QnG9XwDoV8D3/QGE5v48DuAjANA0rQKAVwB0AtARwCt/Rh54JWQ8g31jmyQ5iT8yKheMwriAu86d8yoSABXLJ5YaX4E+IYH73xgnaiQppQIoF0mAFiIBNmLpa9SIrpIShx0dTZAVE0MA1b07eZ3dTsAWH0+w43SSR7z6KpU58+bxHQoKAv73jRed65/Ev1/S4XIR3Elc9JAhfIel4HlyMudijI87dgx45y0vxi0dgHXNHkKntwZiZ6oXb7zBdrZtI+825jj45z/Zxj338HevXlQCSYyhxULrmGTajI7mez5rFnn6rl38bbGQv69Zo8pGSKyc0bImbrnR0VSCSsKZpUvZ94ABvDcmRsXRxcXlLdEgReoFbM+erXiMxFUuX87rjTzJN86uIEvx9ZLdbwK8Aqgw8UpXS9crLu3vRFfjHumvLSNjMB4wRXVDLeozzU+wKep+LO79lN97UZL7Vdf1taCAsxHAek3TZmqaNl1+Sq7n4qGaNemGJ4CqTx8K77VqUdDfuZNuc5rGfVahQt7iubpOoFelCsFTWhqF++xsrntODg/fc+dY4BsggDpxwv945FmlpXlx8oQbZcroeVzlnE6CIItFJWqxWqltdbkAp8uENDQCcA5AANy6GVYrtaFWK+c1YADj5cqWzQvuatQgqGreXGXRrF2b/ek673U6CX5PnOB3DRoQ+GzcqFwuxX3H7SZo6dqVcWK//87xlipFAOdy0WrapAk/z8jg+3D0KAUUXScAvuMOzi8wkJa++vUpbD35JPupUIHPbuZMgsaPPuJnzZtzDO+/T021gLiWLYGEBC8ej74dfUeHQlu6BO4cL7ZupUD2n/8wi2XqDi9G3X0SUz7Q0a4dXSGDgigk3Xsv2//sMwoxjRvT2vviixzHTz9xTTWNgs4773BMO3YA3/9gQratPH6fp2HZMq7NG29wD734IsfarBmwaLGGN5L6w/rFXKx9YQGGDtNw+DCB8tdfc2533MG1E+HUbgcmjPei93tRWJVkhbNdZ3geGQq3R8MTT9CNVIRdY5ZDY9KIiAjksfT5pn6X2KXp05Xg9WcmXddXAjhTwCW3AfhKJyUCCNY0rRqAKABLdV0/o+v6WQBLUTBQvKHpSp6jb/26y7UvCllJxgKoItvyW/aUb2ZowL9A37Mnea9Y6o1KDEBZjmw2ZS0C+J6Ji2XbtmwrKIiuktHR5EfiQtm1Ky164ia6aBE/Dwujtev99wmC3niD37lc5F9mkxe2O/vhszZT8XrqbShX1otbbiFQNJvJ18LC6CHQuDEtbtFLvPjwvfNIT2Ps7JtvAltWZmL5mkB08K7BqgQrnGey8NlnnEerVvRKEItifDzblxIHq1dz3JJgSaz1y5bxnBLgeu+9BJcbNpCXTJzIuQBsWyyhxmdudOEU4NyjB8Hg0qVUbC1dqq73eBR/ERdL4T1uN62EY8bwbwFxxuyd4mZqrH/or4ahv1AX4PoZaAoN8DRNq6Vp2v9pmrZK07QXNE0LMHz3a4mM7gajayUY59f3Tbp25PtsCgNeivqMigrufAN/ZVxXwjyMWqYrpYICiy0WpY0sQaoAoAOANAAbfH5uaMrIoEXm9GmCjiVLqLFcu1ZZz7Zvp7AeEEDAcfEi7w0L42FUtiytOenp/E7T+L3vMxVLnD83TEAJL5rmBeCEV/fi3Dkn6tTxXoq5kkLoYrELCODf58/z0AsJ0VCmTCAuohYAK8xm7VLGSpeLFqlFizjvMmUYYxcQQPCkaYyz2LpVlVI4d47XHTpEUHf4sAKn58/T3SgtjaCtVStasw4coIC0ZQv7vXiRa/W//1Gb268fhZ+MDALAWrUoVP3zn7ROxcYyMcltt1F7/vPPBHUtWlBbLfXjunbls3M4CAqfe46A5V//Ilj99lv+3rqVVsV//pMWto0bgcP7PVi+Nghx6IoBZz6HzZN1qeRDUBCQccaLxJnrMKXNp5jR4QvkXPRi2DB+JzWj9u2j65LLRcAVFsa+Ll6k4DJqFK1xCQmMO9m8WbmgWq1cmw0bKIANHkzlwvz5FBa3b6cwtH6jCa7S5ZHt1C6VNahZkzE5s2ZRQPv5Z7po/fRTbpKE82mwJMSjm74CiRsC0Lb+OVgsygLao4ey3olQbDbz2ZjNXP/33+e74FtoeMkSuowBKjOeke/9hax6RqoB4LDh/yO5n+X3+R9I07THNU1br2na+rS0tBIb6JXSlZxf/q7ND5iJgG+0xEnclNRHs9uV0sHpVIoIIzD0Z40RvilJgmJilLuhMV5PgEjnzsqKFRVFUCOu3mYzweLMmXQpv/tuun6uXKmyRUr24ZEjCXjmziUgknvvvJNKwZEjgb5t0oA1iWiBLdialIOw2mmYOpXu4wAt9TNmqKRXNWt6kfrcZ3j47cZ4rvUCpJ3yYvZswBEQjJ8qjcAy9MHmmoPRrpsdYWFcr99+IzgTTx2Xi+M1Zkl2OtnP++9zHVwuKtw6dyZwDQ7m+y+ew1FRtP717Em+FBam1jIyMm8ypbg4jsPpJG9dsYLX9ulDHi2utQ4Hx7RiBdfaZlPlXAD+P3asklcExIkXgcWSt+yG7IktW/wrmi5n2buWVBQL3mcA4gCMAVANwApN0yrmflenmMd1w9LVumz60wLdBG9Fp5K2ohqfjb9DSF72/A6V4iajy1J+4ywqFXS4Xm4Ovvf69u9wqGQVJUGapo0AkJT701nX9c91Xf9Sfkqm1+IjWR8RPPbuJYARS5zbTaFf02jtatRI1blbv56HZocOPJxl7Y3h1ALIfEmseUYSgYbx2BcAXIQF2Th4UL/0XXa2AhAmk8qSFhLCQ+7UKeDCBRMCA81o0EBD/fq8Lz2d19hsBKQ5OcqqeP68So7y/fcUTGw2gqsjRzgvq5Ua5rJlaQFr3ZrXV6hAUNa5M0FJaiqB7JkzvL5mTVqmypThYS+ugcHBzGZ56hTHtXAh13L3bt7brRvH17SpijHZsIGJDtLTabH75hse7tnZBHjPP0+A1KUL6+HNnMl716zxIuPIeYwcqV+KdQwLtyAntBl+wZ1IrdEfjdrZER1NILhhAzDsURc+PH8fTPBi+1EbJr/nxtmz7G/+fAqRoaG0CgIcz7hx7O/oUSZuSUzMHcsPXrzx4gV07KijSxe6pPbowXXdu5cWucxMWh4rVuT6d+xIAGe1UmhKTmZbHTpw/h99xGe2Zw+tml27cj8sWwY4y4ZgSZOxsJg13BJxEd1vDcbq1SoluWjxo6M5D4mfsdu5lwYMICAVIGjkLVFRypIhlmzRjuen/LpJgK7rn+i6HqbrelhlSYF7A5HvOXs5kvdY4q6Mn/uSMYRCeKQI3BaLSqAin3frRiuUxOHKZ0aXYjnzBcg5nXyP1q8naJo3L29NOmP9M9n/cXH8zm6nxfzjj2mdj4tT4xG+vmEDx9O3r3IBF4vfd9+xVIzEpAHMjPnAA8APK0MwrdYkdEcCIrp4sGpnCFq0IGDq3p3v+M6dBD4uFxCYk4VWRxaiEk5jUNpn2L4uCwcPAr17a7jr4yjckvoROn40BP36aejShSD0gw/oTbBxo0rc1asXwdLgwcp6OWYMZRe7nZ9/8AE9EMTFdMUKWtd69ybv6tiRbqYr4rzo2vAkVq3ULwEuiT00Knaefprno8TZAeRJq1ap+n1t2vD5jB9PkCfPWdpau5ZrY3xu0qevK3DPniqpTnx8XhnwengzFURFAXiVdV2frev6Zl3XxwCYBWClpmkNAPy5MrUY6GotGP4eZn5t+ru+OBJ/XMt7i0tgL2iNigournYc+bVlPBTy860uybH5Un7xBVe6hwqKMxUtVVHvBXjflCmqyGkJUQSA8Fye9KfjP3Y7hfrlywlQLl5k0o/GjSlAS2mBMmX4k5HBQ9VsptYU4KGUk+PfVUnKJpgMHL5pU2XNEypVyggMTTDBjMrYh/o4jIoVTX+ohyfxUllZdDFNT1ffSaKTfftomaxfn+2fP8/x1K5Nl9Ry5QhG6tThwdyqFdfjyy957YoVbO/ECSYe2bOH88jMJJArX54WuH792FdoKNemdWse5Dt3ci1jYnj9smUsdt6gAdtauZLASGpglS3LkgNt2lBwOn6cLkv33st+AGa7HDqUVqvGjQmIrFamJz9zhgJnWhr7mzsXOHLYiwisRNC3H6La1qV44zUvunYFJk/WsCL83/hPfD+0nfowrFYN1apxrM2aAdVqBcLeqTkAE5pUv4iRYwMwdy4tnG+/zTFv3aqeb6NGfM/WrVOxfG+9BcDrxZt770To75MQNu8VZF/wXrIK1K9PjXhqKtfvhx+43zIyKCDa7RQCAwL4bOLiuPaPPEItd5cujAncsYPC6cCBbMt5UcO6u96Be+9BZPwUg5kfsvi5w6Gyb2ZkEDR27ZrXwmGsAbZihYqJAtSZIEJsRIQCgL6xMX8xOgqgluH/mrmf5ff5n5LyU6L6ktEaZ7Hkta4J6DPuGUmQ4o+cTipifGOsjO57IuAbz7m4OL57Umjb7Sb/kbqTUVHq3JP+ZX7iGirlRgDG84riQsYFcAxSZsboeZGcTNdDj4f3TJ+uAJLVyvF37Ajs2aNhaMJjWPnFPiwetwQ//0L3jr59+X6HhtLNumVLvsctwu0IatYQbi0Q/bo6UDrEjkmTcmOnA01YvacKekRqWLqUHglyHk2Zwjbj4zmGFSsIHiXWTRRoK1ZwTSwW3jdhAgEZQHC8YgXXyOEgT3G7vWj/5q1IbPoIOvynP+KWe5GRwfkuXMj1E+XQO++QX7vdCjBqGnnW3Llcv169CJYHDVKxe8aQB4+H1xjBvHEv+XonAexbLMK+3ky++/Z6UVEAXoCmaZdYqK7rXwMYB2AJaNH709HVCuP+hNzLAYbr5eJZHPde7XpdTsshTNCfVcx4TXGu4+U0iL4WKl+XD+Oh4tuebxvG/6/WJaWgz4tC/uZt1Fwan5m/e/Nrc+JEupn4ZqMqRpoJYJmmaQ5N0xI0TWtWYj2VAFkstDK1bk2wUKmSKpFQsSKBhdVKa1JGBgHVqVM8iL7/nm1kZipwZrHkLXMgZAR4O3bkLWAeEKAyUdLip8GLckhDCxwOaoV69ZhlUdoQIcQIp41KAF1XP2fOEDxkZFAgCg0l6AsJUbGCe/cS3K5eTRdMTeO9587x78aNeYja7QRYVivBbrNmFOhTU/l/kyY84LdsYZC+10sQc/EiAZjEiP36KwUqKWpepQoP9cOH6W7Urx/XsEMH9vH992xr1y66bkZGEuSsXcvnNm8erVEVKqiad7pOgHnikAsB54/hKGqg8tkUvPaiE+vXM6Ykqp8JXy+tgg0bNKxfT0DZty/HNul9DR/f8iv2DZ6AlpMexgMPavj9d679J59wPI0aEeRt20bQmZ3NMZ08ycx5Tz4JIC0NLyXdjqZIgXnnVlgvZF7SrH/9NS2WUVEEcDt3qtID33zDZ5aYyDUKD+e6paZSID52jAAvIUHFYt56K58HAHi8JjhKV8HEZzQMG0ZhcNgwtpWRwedYvz6feUZGXgsHwPVMSlICn5wJkrBC3N2kwLnw2r8guAOA3wE8pJHCAWTqun4clLn6appWPje5St/cz/60dLkz3SgziCXF17pmjN+U7wUI+p5fRo8YtzvvPurRQ6XQl/5EUXnLLXQBbN6crpPTptHNe+lSKruWLOFeN+5Rt5v7V+Kjje1HR7OduDi+a9On8/OICCaIGjqUXgTz5xNEjR7NcUiSkLFjlaXM4+F6jBxJa9WKVSas31se0DT076+sgidOEJi5XDwTdB0ICtLQddU7mPnCUbjnLcH4JzWkpHAsvXurMhE9eyrly7x5wDPPqHIPAK+VEia33EKr/GOPcc4CqOX5xMXx/7Zt+f3SpTwPhg4FLM4sfJzYFtm6GavXmJF9PBOJiZxv375q/ZYtoxVy6FCuj2QeHTuWfH3oUPJ14Q9mM129Y2O5xvKMt27l97Jn5Jm73cpl3J8Hl8hPkyfnPQuvNPlecVNRAN4cMKnBJdJ1fRmAewBsLc5BXSsqDqDge+/l2ixOU+3VjP9K7i0ud8D82hFf6/zSYPu2UVyUnwbR6OLhr3/REhZkjvdtV5iSvwBdf1QQGPbnNlpUyq99o/BUGIuev/aSkkrUbWomgKcBVATwAYApJdZTCZDTycN11y5VQLtqVVq3Tp/mbwFsNpvKXJmfIHvxIi1/AMGR1JTzfVckTg8guLNa2a9YhCpWNAEwIzub4KNyZQUKnU51v69lD8hbg6pMGf4ODCSQSk8noN23j2Nq3JhASpKsdO3Ka8PDqRRo0oTtlS/P+Lr//pf/O50KyLhc1MjGxfG60qV5X7lytNIFBDD+Tmr6hYRQi3viBH/v3EkBq2xZlcSmfn2CsLZtKfg0bUog/dZb1Ab360ehRgSl48dpmdy/n7WnnE5a8s67AvFm+18xEIuQUioCnSKD0LatKkvxwANsf/9+CiQLF1J4mzYN8OomDLqnFJKTNSxYwOx6I0fyeYWG0grZtCnn+txzFAAff5zXHT3KZ7NmTwhq1NBgBrCu2q34YWkwli7lOy317CwWgsQzp71YPf8s+kXp+N//VFIIj4euVFFRXL9vvmH2vTVrKFS1aqXi4ZYupZAjz7dfPwpcY8ZQsNu8mSB0+HAKfS4XlUCSZEL2qNlMAVlcSd1unge9e6sMeiLMA3kVb3820jTtfwASADTWNO2IpmlDNU0bket+DgALAewDsAfApwBGAoCu62cAvA5gXe7Pa7mf/ampIHDnT2bwTXJmPHuMVuH8FK6+iTTEApSQQD4kCUHS0/n+/fqrsiSbzVR0eL2qEPjWrdyjGzaQtxkTckh2W6dTpeQH+F5kZytrVIsWHKvdzmyTlSrR0ma1qiQh69dzLIsW8b2T2GaXi26bTz9NBdX8+arES2oqf6en01Pi++/ZbocOvL5vX8BWyoRh48tg2nTtkgurMYYtOpqKmQ4dOEazmbzP7eac332X1xg9nzIyWPvT7VaAOiNDrcnMmRy3200FXbNmdPmc+Kodz0YkoL9pOXpHXIQ1JPjS/G02Pp+EBK5beDh5c3i4er6bNpH3lCunLP9Sc1PqBUrML0BAuGIFn7nTyTGOG8ffGzdyXpKF09cQYbGozJ8y74KS711LXnWzDt5NKjRdLbAqDmBW3OAuv7blsBAtXH79F2Y8vteI+0Jh6znl14e/8V4JAC/MHKQm0OW0rHLQpqdTkExKUrW+CqIrqIO3Sdf1tob/N+q63q6w95cEFYU3HTnCw7x8eR5M8fEEGnv3EqyJ+4jLRYBTtqyyiAF5rW+AquEmFBysrq1Qga6POTkUHIKD2X9+JM/Qt83Spflz6lTBc5PafampXtB734TSpTVkZREwVqmiLJJZWbSGnT9PwKNpytVTDtOoKAbm9+3L/VStmioCHxLC9WrRgsk+QkMJvPr3pzY4IIAHdFgYgeG4cQRGI0eqYsLBwQR2e/dy7IcPe9G9XRZWrLfjxEkNLVtyrE4nwUm9egRiEpTfpIkK9Jeaj7oOtGrlRXZaFs557Vi2TMMzz1CrvHUrn01YGLBhgxdD78xE6slgOBwaNm0iiAoKoqtTRAStXsOHM3YwOppzS02lFWHBAsYA7t5NIaxePVoTR4wAnnrKi4FdMvFrXDDq1NVgMlE427BBlWKA7kXirA14KGsa/ldmNIZ81BGLl5jQrx8ulVGwWinkLVhAq7xxX/zyCwXFlBS6e4WHE2hqGgFrdDRdXY1CjcTeOZ20FBr5IAutq6ya06Yxacy6dXkVTv6UaPl9XxxUEnXwrjX9mWQn41lSlHPN90z0/dtfe04n/xYXyvBw7ktx7YuNpXIiIIBgyGLhHnW5CLasVuDZZ5UlJzSUvHrECFXbbfdu/m+xECRJvNhPPxGIffQR5YwlS1QCkGnT+E716kUgZ7XSMjdnDttyu8kjdu1SdSxdLr5/PXpQCbRzJ9uwWglIx49ne0eP8nuzWSnrkpM59t9/Z4bL1atVTc1mzahMkxp+FgvPeIkFFguelIkRmWnhQgK5gQPJszMyWL7lscfI4zp14hxatuT1PXty3s8+C8DrRczPmeg6MBir12iX2g8L4xpILT2pEdu9OxV7xrp3QhaLes7GvRAdTZ4urp3Z2RyX2cx9IPX9BKyLDGRs1+HwX84lvz3pW0v5aqlY6uBpmjZJ07Thfj4frmnaO1czwJtU/FTcWoLiMDUXl0tlQVRc4zO6YRbUf2Hm5HuNv7o6Rbnf3+dXa829HPnGIviSPxeacuVKDowDKKdp2p3yA6YQN/5/w5PJpFwuu3XjIVm3rjpgTCaCn4MHCUAqVaKAXqqUAnc2GwGK0TIHEDiJ5e/MGXW9ZH4U8r0PUO+Q1N4ztukP3Ek2TNEWZ2UBO3d6URbH0RFLUR7H4XHTn+/0aY7n1CkeplYr75MsoadP0+XvxAmCrT59KGyZTBS0ypUjGDx8mJ/VqMFr9+3j3MqUobCwcycFntOnKdiUKUMwnZREjb3Xy3WVIu4Sx1a2jBeW5I1oMvsJdDnyDW671Yv69QkOZ8zg/L79ls/ozBm61Fatyu8rVybAatyYoOz++01Yu60Mtm/XMHo0LV916ijhYeJTXjxx4lVUGPcgxsQOxuHDXgQH06rXqROBcGgoXSrbtqWAVa8en+XevUqYGj+eY3G7uQ5SL+vuu03o+4/yMJk1NGmi9tuBAxTmDh4EHrstDdWyUmFHFoac/wADwtLQrx+Br8vF55OdrVxfPR6VjfOWW7gvIyIoeFqtXEMAl+a7ZAmFuuho5ZIl7mV2e95EB8I7oqJU0eGWLSloiuuUP68Kozve9XaJuklFJ98wgMt5+1wuNtxf+0Zri7+Mz+I9FBFBXiAxWnLd888T3MXHKzdLq5Vgo1Ur1U/z5uQ9oaF0qW7ThoqRYcOU+6TDod4Fu13FhbndfDfefZe8buhQ5YqYkkLPgGeeofXfYmFpk8cfJ3hq0IDjGTyYccJPPUU+2qaNsjbabOSxMTFUFu3Yod6/7t3JR8xm8iiLhWPZvl0pnBwOKsgWLaIF8eGHqcQVMpup5HrvPfXeJyezj8mTeX9wMC2It99OEGa306XTas273m43sGiJCYk7y2Pmhxrat+eai+uqgGXpt3t38mePR7lyi/u38I0PPiDglCQ3cq9Ycdu3J5+Tcgt2O/eNxIT7WuYE3I0bV3AJK99kdNeydEJRXDR7AfjEz+efAhhUPMO5vvRXORRK4pArDnfWkqbinndxzDW/sZTEOpb0s7lc+8b9YbfTTctuL7HhrAAw2PBj/P+G50c2GwGHpqki5UeOUDiQmDVNo9tKTo4SCHbuJPARcjoZs2bcZ2XK8J7AQAKCunXz9m102pAYSWP8nhHU2WzK3VK+832mpUvzoDx0yNiHDg0aHCgLNy4g0Oq51I/U8OvcmQLCpk0EapKM49tvCZaysihMHDhAYFerFsFclSoch8VCYcbl4toJWNu/n2v4++8ESoGB7LdxY7p6JierGlAVK7LPypUpJG1OcsHtzEIymmP9iWo4edAJm419fvghx/7SS+yzXTsKd9On0xrYqBHBs8lEwer113mNyUQA+Mwz5E+nT9MyFvtLJqw7k9EeCXAmbsHuVDdCQ5nA5PPPOZ4dO5RVy+Fgv198wXfLbOazuPdettuqFfvo2VO5JVksHPPixbQi9O5NQchqZVspp0Jwd5NUmKFhR41+WLghBMnJ3E+i5Z83j1a0e++loNerlxLGxIrQvTs/DwpiDGBwMDXjkycT6G3cyDWZMoWWRhGIxO12yRL1Y3Sv69tXzUO8CMRFzOg6Linu/wzn1E1SZHyGRlAngB34I7jLr1yQv3aNyioh2TtynTHmX7IrCuBzu1UyJlH2ut10D+zRg9Z0s5lWuPHjeW/79iy1IrFo9evzXrOZ77QoeFasICDcvZt8TqyC+/bR6jdnDt/5lSvJH6OjuS6vvMK5/fvfVKgsW0bAAxDExMTQ9TE2lp8lJXEsTietZxIbP2YM31eXS8XHbd+uzgeXizxz9mwvso+dxdpEHYMHc86DBtEdVDwWUlL43rVuTT7UqRMB7rBhnN/FiyrRymefsa/oaPIHAcxSYL1tW67x9u0EqsOG0Z1byqSIq6fEvUmB+9atqfAym/kcjXvIZuMaDBigiqMbC9F7PPRsGDuW10roTWSkAtkCGI1kt9PiaNwbxr1YkELhWvCpogC8QH/Z6nRd9wLwowf+c9FfSfNXUpvnRj80r8fhXtB+Kcqe+jPvO3/Mz+mk5r2kYvB0XX+0gJ8hJdNr8ZGUFjh0iHFcCxbQ3bBjR+7f5OS8QCw7m9eZTCoJSYsW6h6jpfn8eQIWl4sayQMH1HeSKd1kIsCUrJoSvyeFq8XlJCSE7UkMYPnyeTOjaho1tRcuqBIMFSoAFSuY0DpoN7ajIc6jLrzgS1m9OsFnUBBBWWIirVHHjhHQOZ0EQBcuEJRJwXOzmWOtWZP36TqBZb16dPcxmQhEjx3j9efO8fpFi/j/xIkcV61aFKqqVCFQO3CAIMzhoNCy62AgTqEG9qMeutY4gvemB11KZJKdTUHmwQeZvOXsWY6xeXOCmLp1OR63G3j5ZbbZti3Hd/AgAd/rr9Pd0uUC3vgwGLGV78E/8B2erTAXvfoEwGpVtbiGDiXIeuQRPus1a7hfypUjwNqyhSA1Pp6Fju12BbhatCCQcrspbEpxeAFOrVoRDHfurGHQhtdhnjsX7gcfxU8/aTh0iHvgnXeAV1+lgOt05nXNlOLCBw/yea5cyXGEhfGZLlnC+a9dy7k2aEBB0+ulRSA+ni5eGzYoQbRrV7WvjJpyQIG+Dh3A+lyOvF4WxqRQN/o5dZMUGZ+h0cVWALsv+SsXJCDNeL2vLGAEeG63/1qLAuqkTIfE4z3+ON8piQeV/SZKFLFov/8+38FevdTejY8n+Fq3jp+PG0fr+JIlfJ8lEVFQEPsNCiJwOXyY323fTrD39dfs+/hxFiD3eMhjPB5a/MRynpND9+0RI9iflNJ57z2+i1YrFWvTpjLutkd3HVYredj69ZzrgQNUpH38MV3I989eAjw+BF3f7g97ae+l5yNnRK9e6nxITiaPXLOGPKhSJYK08HDOz2ZTcYZWKxVHxoym0dFsJzFRZRfdvJk8bNQoPo+MDN7fsqUqtSBgTTwAIiJ8FAMXvHAfPQno+qXYvd69+exsNrqERkXx+VkseWsWyjP3zawpPzabUlL4SxJYGA+skqKiALxsTdNCfT/M/Sy7+IZ0feivpvn7q8yjqFTYeRcHoLocgCvsnroRlAuXA6oFXedvnunp1Nob0+gXN2ma1kLTtC+lkG/u3y1LrsfiI6fYC7lOAAEAAElEQVSTP9WrM6asbFkK3MnJSit68SIPnIAAAp5KlZQLoq6rwuC7dv1RGJJ6er4ktY5LlSJAkYNZMmWKlTAggODkyBGCg5wclXBFLGIAxxEYyL+lEHmDBkDWBQ3lendDUGAFAGYEB1MHuGsXBZbsbAI5sRY6HCoBSa1aBCBnzxL01a/PderWjfdlZREotGhBwCcpy5s0oVvSsWO0mO3YwbF16kSQJLFvtWpR2Nm7l9r2jh0ZYxIdDTz/vAZ3zfrw9roVjf7zEJ57XsMLL1AzXK8e4HB4cXe/80jeouPZZ9l206Z00/zwQ+V22q0b57dkCd+NXr2AlBQvpr91Hls265g/Hzh8WMOutv9Cm0c7ofaQPrDZNOzaRRes1FRa8apWpXD29tsqM92hQwRNtWpR4Hv5ZV7v8VCp0qgRY3p27aLFbMsWXCpWnpBAwal9e+672FjA7TXBWqU82renK+cLL3AuGRlsa/x4xvWsX8+fd97hmmdkcE06dqSlLaKLF3Omnkd4Jx3p6ew7O5t84MsvCaS9XgK0tDS6vF28yLXp3VvF/EgKeUDFggJc0+BgCq8JCXnBXGFcyEuCNE27XdO0kJJp/e9BvpY6seAZXemMZMyWKeTx/PF6ozAuVjj52bhRlUKIjMybLC0hgcBIipMHB9M6JKBN9p2ARnHXlEyWK1YowClp/zt0ULF8zZuTNz35JK3xP/6oFCZbt6oaa5Uq0epttVJJVLo0P9+4kdfeequyVv32G8caFEQF0tq1BIIPPKBcQQG+j3M+9WL4wkGIeqQaLAP6wGzyon9/BYpMJvKehx4CSiMLk08/AitcSEzQEFY77VIZk+hotRZS565JE85rwAAFdAE1TimsbrEQ9K1cSV7ywAO8bssW3ichLHFxyl12/Xry2oEDKVd0767W2+XivZIkZdo0lRTHecGL95rOxRMNF8IR2R/dunrzxMwJSJNnK3zSaAH0jaOTRHkStylKCinIfqPI30UBeC8DWKRp2iOaprXM/XkUwILc7/70dKM8lJtUslRcgKowAK6wMXq+tVOuJRW0HsbvCrrOyPzkt1FrWtykadptAH4BXTOH5P6sAPBz7nc3NNlsBB+nT1PYtVgozIsFqEIFgieHg+AqIICg5ehRHvDVqhHsWSzKBVPAl7hdilUrKIjXmc3KvVOscBKbJynvhcqX5yHq9RJEABznxYv8OzubVjCAn5Upw9gNgFkenU5g3nwTsi9aAGg4fJjflSmjMnzK3Gw29hMYyPtq1CDgDQriHtq/n20bs5gdOkTXwf37CULq1CHoLFuWLkj/+AetbC1aULDIySGY7NGDn586RXfNjRspOMyfz3GFhACNGplwJrsUtm9nYph16whyN23yIiQlBo991REHf9+Mhx/24uhRPpOWLTnWgwcJOmNj2d8ttxBsLV3qRffT3yHu8x1o+uvruOduL958Eyhb1oSTF8rAZNLQujWfZefOwJ13EuSGhzOD57ZtXK9jx5gxbts2ar07d6Z2PyyMglFUFPsECAqff56WsSee4DrrOpUF//0vY3jmzKH7prEu3b59tB6OHUtrW7ly/Kx5c/bTvj0B80svsb2ePQGLyQv74J4Y/VYNpEfdhVdf9eL8eQo7L7/MMhOVKnFP6joB6YABam9aLMqtVrLqiaXA6FLldPJ5+KtvVpj44BKgBwBs0jRtd66C6XFN01qUSE9/Yirq2hf0LH0tdsa4TXHlNZLNxtg3i0XVu5s4kUoFsdIYz/HOnbnHOnemoA+wfX9nYEyMslyLe6fEisXG0v1crt2wgd/160fAuGYNAd2IEcpaOGQIAUtsrAKI8+ax3U8/VSVMUlLYp8fDd+iNN/h+jR1LZU7jxpz3ffep96trVyoQm1TNxNoEDU6PCXGrA+A5nQm3mwqZWbOA//yH72m5ckCrznZYOrcHYEJYuIYPfwjBhg0c1y+/cDzvvcfxnjhBK6ZY72fOxKXadV27kpfNns3fTieVSC4Xz4/nnycfatBAAfjoaMULAK5p1aq0CCYlsV2XS7nKPvecssINH06e7nAAtvNpmHD4KdyJn7B6jRlxv2Xm2Y/CV4yeSALYxCpnzHouVj3htUZPA8B/ZvXrRYUGeLquLwJwO4CeAL7I/YkEcJeu6wuLf2g36VpTcW/KorRXnNde7vvitNYWp1LgelnxCloP38Bio1bLlxwO5aqwdSuF4hKsg/cagD66rn+m63py7s9nAPrkflcgaZr2maZppzRN25rP95GapmVqmrY596dYlVgWCwFJ6dIEbyYTcM89BHxOJ615AK1Xd9yhrGReL3/OnOE9YiEBVNKR8+fZrt2uioxbLIzry8pSY5A2/dGRIwR2djstRULnzimwVKOGisk7f57gBmBAv9wjfVgsvK5uXWphxeol7pelSvFwr1yZgk7jxuwnPZ1CzN69FAykZpzJxAP4xAmCutq1CTiXLKEgtXIlQcnu3by/bFlaDjt0INipX5/a8fh4gj2vl/19+SVBlNdLIBIayutiYwH9ogtRmV/jbbyASs6deOSOTMyZw747dVL1C//3P67LjBmcs8MBHD/gQrlTe/A7boN992ZsTcrCwYPUwr/xBuficvFZmkzUWCcnU0CcO5djX7SIQtzEiXSjGjqUVq/sbPY1ZQrXa/lyzuuFF8hPundnPxMnsu3Tp5mZ9ZtvuB4OB+9fvZrXdOmiav1t2sRx1axJQVIUURs30pIXHs77lv2UCceqTYjVu+Hf625Hq1AnAgMphGVksB2A1z/5JJ953750uxJ3u6Ag7vU+fZQ1pls3rqHEwgCKH+VXL1U+MwKAkvLO0XX9bl3Xa4B8ZwmAVgC+1DQtTdO0m3IRrgxgixBtfGbGdvxZ7Fat4l4W67JxHwjYkP0r6faNdc8kFlTit+x2VfPN7VblFKSN3r35Ex5OK12bNjz3YmPpcu9ycc+2a8f7pBh3bKxSfoaHM/HHP/9JnvjZZ3yfk5IIoKRMSf/+BH8ffcR3v3ZtKlm+/56KlxdfpMVq+XJaBUXJFRurPDyWLydvbRMZDE/TlojXeiC8sw5rSDAsFsbktWzJGOimTTnPjRs1jK49H2vHfouYcQvQtp2GkSMJMCdN4hq5XFz3LVuooLHbyX9Hj1alH5YvJzAdMYL8IjaWngT9+hG4TZnCNfniCz6nmBjyGJeLoHHtWgV6K1Qg7xg7ljwyNpbXG4FWpUp8XuvWARnWENgiwjDYvAxR3bJxy93Bl56pFFsXrwHjfjAqmHzLAsk1vjzlRvMELIoFDwBOApgBoLeu6+11XX9Y1/WUEhjXTbrGVBgmXBRgVZT2ihqrll9duKK0daO8gELXmzEUpl9heP7WVg7Rzp2Vn/3hwypVfwmQRdf1A74f5n4WUIj7vwDQ7zLXrNJ1vU3uz2VBY1FItMF16vDAstkYIH/qlIqHAwhSFiwgCJTsZgABQGYmgZS4Oebk0LJlNivNp7hSVq6saiEJXbzIewMCaLkyktfLZ6rrHIPdrko3CMjcuFFZGY2lMBYsICDo2FFZ/NxugrZjx7iHduxQhcsrViQglTIKkmr/zBkKO3Y7x7BwIdepSRMCRY+HgsjevXRz+vVXlTFU04B//YsATTKSulwUkAIDVTKWf/6TMR6BgQS199xDwcRk4k9gINvatw8oUzEQQaENkIVAbNE6Y9/p4EtF2J96im5GPXpQQBowgALMDz8wxbnbHAh31ToIhhMpNQfiosWO77/nmF94gZbb/fs5b9F2m0x0kx06lGv11FMU+qZMIUBNSKAlV0DrE08wlqRzZ2qw69blmo0Zwzgd2TdVq1KQSUxkv199xbWSgsmbNtFqePo099ivv/JaUeBs2sS+V6wgeOzRA4i8PRgrmo1Ad6xBw5oe3DckCE89xWfx7LN8TjZb3qLMU6ZwTiKMd+2qYmA8HgpdklzIZlOudP7eJePfy5YpQd8I8kqScvnORgCbAGwGcApAUMn2+uegKznb8oullLJFRuuJXNuzp6ofN3Omsrq43dwHEyZwr4kS0u1W+0v+BlQGVznTJA3+6NEKCE6eTIXLokUELM2aEZSNGUO+9+mn3MMTJlBhsXYtwYzUlNuyhX8vWUJ+2aEDlaFjx3JeZjP5tcRMT5umEoH07k1lSMWKfH/XrmX2TFHANG7MZEviESJu+P37M3vmG29qcP3nLYTv+AKJLy1CeGcNMTEEZd268f758+naaM4+D49HQ5OwMliwQEOXLnz/O3Rgv1IbdcECdQ507aoKhwPink6gJJlzxbIZH6+yaZYrR15UqRKf73PPcc2kdE52Nq831sOT8g++ewdgmx06AOOf1PDrmGXAwYOwrIiBJUCD00mAKiBPEqlI7KVxTxmznhdGRr2RZMuilEkYBmAbCPBSNU27tcRGdZOuOV2OCV8OOPl+X5T2inIA+Lv2Stsy3l8cdKOY5YuTCru2cgBKoge3m8JdCRY6d2uaVtv3Q03T6gC47JPQdX0lgDMlMbDCkN2u3FHOnqUlSvZPvXp5r61ShYDk4EF1jYDA/fvzJmNxuwlkcnJ4jVwniVl8Sdd5rcXshQlumEx582iJK6e0ayST4fTo1k0BwLZt6UJ44gSFi4oV+fmGDQQNAEFtqVIUuM6eJWjLzKQF6vBhWgQHDCCANZk4jnLlePh//DGBYbt2BGRmM/vJzuZ8Dh0isNy2TdUVlBpwNWtS892uHfsbNoxAyO3m57/8QqHLZCIoeeopgsA+fYBjxzQ4xr+EwP6D8PyHtWEyaZcEsQ8/5Lj37mVbe/dS8HjjDQorFStqWBf6AJybU9Fm+hA8+aSG+vVpMfN6gR3bvWgcchZffqHD4SAocrk4p/HjKeCNHUu3zR9+4Pjq1GGfDgfj2dxuxirGxvJZtGjBeMoXX1TPadw4gl63m3E9x4/TUvfSSxTq5FlWqkSQt28f92N4OC0BZjMTMezfT6vfm29SMeHI0pB05zuw7ElFh5mPwBqoXYrnef55ZvxLTydoW7qUezEsjMBz9Wr2NW2aikWKilJud6JU8rW4REbyWn/nkr9kHCVBmqa9oGnaPE3TEgE8D8AKYCaAVrquX0fH+xuLinoe+z5TAe5itTNaWYwlEADy1rZtVT25hAS+H3fcQXdsKeAtPE2AnbjdGeM6Jd5TCpQvWULFxvDhvGbzZlrve/ZUngMbNtDaZjZzb0v2yN9+ozLooYf4zv3+Oy1Qd9zB91JcM5cvp5V74kS6TKam0hMhKIgu1fv3EyA1aUIwNm8e3xtxIz18mCAzMZHu1B98wL/dbvLQ998HzBYTlm+vgmyndskLYO1a9ufxANC9iO/9MsZNrYf7tr2Ie+7yYvp08oXOnTnPxET+fuopvrtiKVy9mjxi6VIVB2d8ln378vmsXKnixOPiyGNSUtT18nzDw/ku792rXGHl+/HjOW7jPpEEOsuX8xm+/z6wI9UEZ7kqeQ5BXed6GxOpyN4wegcUJkTFd//eKFQUC954AM11Xe8MoAvIzG6SDxktSX82ulwsWUHAyd/3RWnPF7AVtH6XM4sX1holv4vjpb3aOI+rvb8o/Rh/X+5z4I9rm59blByEwlirV1cJQ0qAXgGwzE9McDSKLya4s6ZpWzRNW6RpWnN/F+TG3KzXNG19mmQwKQRlZFCLm5DAg/vsWbr4mEzIE68GKDdHAUoA77kcCeACCGYECBrLHpC8yDp+AmacgtebA4ABeTVq8NuePfMCdYm1k7i90FCCKJeLWmiTib9PnmQbp0+r8ZYqxTNW4usaN+b86tSh29Hp0xSMmjWjoHTunEoSILXsbDauhctFF8BDhwiC27al0DNiBO+Tg/7cObpopqUR5C1bpoL8p08nmLz1VmrLw8K4bqVLM9HAokUEOaNH8ycm1oSdh0oB0JigxE3L4xdfsJ+jRxU4a9gQeO0/XuzZegEHD+p48EETEvZVwZYtGmw24P77KcS5c7w48Gk0zKMfg/fLL2E2eVGzJtdnyxZaO++7j+/ThAlc32bNeG+9evze5aJAtX8/AXBSEoVAp5MxNS4XhbDoaF4vrkkSmwdwvaVm3RtvMM15q1YEw126cO26dKHlduBAWg9lLHFxwN59JjhKV0Gv3hq2blXubt98Q4C8Zo1yW+vYkWBt82YK4m3bsi9xhZOSB/6yK8r5INY838LDom0vaXCXSw8BqA5gMYBvAHyr6/omXddLMAL5r03G89yoMI6MLFz9WIuFe0ri53r2JD8aPFgJ8uIiabX+MSmGWPiM8Z5ixendm/euXav41KxZvEfT+H507cp2d+zg3z16kH/1708ek5xMQNa4Mfd9ZCSBW3Y234+ffqLlfcYMBWT65fqabNpEhVxSEq3uS5eSx37yCeA458VHk84joquOESPIC9eu5To4HAQzkll30yblNt2pk1o3KYVyW49MpKSasArhMO/YCvfxNKxbx3YkqYnXy9+JicoVE+DY4uIYdztjBuei6xyrWFWl/MnmzSrBVmIiAZbTScXPr78S6Hbtyuc3ciTnI3XujHGYkgTH7WabgMqsKx5FK1ao/SRlE+SMlHslw6YojyQsRXiNb4iKPznqeifMM1JRAJ5L1/U0ANB1fR+AAiI4/p4kD9e3GOuNQr4asaKSMZGGv/t9Ge+VuEnKi+sbX1GcLpdFtfgV5qW9WhfLa2F5zG9/Xu5z3+fgby2M4+/cmbFMgYEl566g6/qvAO4B63N+kfvTE8C9ud9dLW0EUEfX9dag14LfNnVd/0TX9TBd18Mqiz9NIcjpVAlOzp2jZnXHDhULVq0aD63gYFrCJJZL3G2MpQryI5crbyFaofPn/zALZCIAOagIK44A0NGwoSqILoHnUq9PgKII0IcPUxsNECCcOEEwFhBAl8yyZVVCGLebgtDp07SQHTpEy87ixVyDqlUZU1KpEgWXfv0I3sxmWpuOHlWuglu3ckzNm3NM2dkEV/PnU5jLyODa1qlDzbDTCbguerF+bQ6io3Xs3s3P2rblmOfOJZjp2ZMH/5kzFBbuvpvWroULae17/HEV7B8RQQ17djbHVK0aBTmTiYlVaq34GvcuG4KJOf9BpYpenDlDq+S0aeRxug40qJqFaRkPIwpLYDmyD44jmfB66TLaujXB5x13qMQJLpeqkXX0KNuoU4ca9KlTKQi1bEnhtEULFhUOD6fVYdcutle3Lt0rk5K4tu+8Q3A4fTqVAZrGdQ0Pp/WgZ0/upenTCQStVgJyq1VlubvlFmVJnDiRIHDkSFonypX1wpN+Fi6Xjr592fesWcoqbLVyHBKnI1kyjbzINx7YyC99vUeuFem63gSMv1sP5iT4RdO0JE3TPs1VON2kKyDhFUbria97ruwJf7F6vlkQpU3f/wXwSTINQGXwjItT4M54v9lMoNe3L3mWuGGOH88kIpMnkzc99hitWZLEaN8+KsNataJCaMwY7nvJrBkURGARFUWe0ijUi48nn0ft2joWLVLxsK1bE0j+3/9RiTRmDNC+nReWW/uhwXtDEP/vRbjjNi/i4thPejq9GTp2JA+ZOZNjePxxzmvyZPLH2rUZt5eYCPS+Kxhtmrhhhglo2gyxW0PQoQNBUnY216BVK/X+OxzK+gZwXf/5Typ3goLIVwGOX2IZu3Qh75o9myAzIoIKIbHwR0aqOL+FC2nxA7j2FgvH8dRT5N233cZ5Wiz8zBhjGR+ft8SL0YVTElIJCU+JiVEAUpRMQN7yHUbrsXFP+fMwu15UFIBXU9O06fLj5/+/LBX2ARl9dq9lPFVhxufLLK8UgOYHwArqrygkTNfItIsDMBf0EhZGI+irUczvuquh4nBjuVz7/vZnQZ8b60sZr/UH5mVv2GwU7vbvp5BaUqTr+hZd1x/KjQdun/v3Fn+um1fQ9jld1x25fy8EEKBpWrHZI2X9zGYCIF2n0F2nDrNCnj7NAzMjg4enw0ErmQA8fxQYyLaMlJSU93+jm6YkSKlWzYTyyAHgRA7KAzBdSvJiJAGkUtBcMnIarXtSF2/bNpU6XGr+NWzI+Xq9BECLFtH6VaoU57V5M4FgeDiFjF276H504QItdrpOQUmsfw0bEqyUL08By+NhjElAAEHCQw/x4N+yhWNLS/NixzcbcceJyVgYfBfuvNMLq5V7NCeHgK5KFQoVe/bQzWrDBlry7HZaJLOyKKCcOcPPLl7kfNq3p8b5vvv4eZs2wMAumWh3/HfsQih279JwcmcmnnuOsWcNGhCEhoUBh8/aEdv4caxEJOo1tGDql8HwemmdCwqilSAhgaAtJkYVSt60icBMUqm73SpW0m6ndc5uV8WMV6ygoHX77SxSvGULhSTZBw0aUIMfHc29uHkz3StDQ6l5f+op9jFmjLIu1q2baz1w0IWqVi1V/zI2li6iaxO96PFOP3iGDsHcxxPhOOdFYiLH3auXKho9YAD5vt1OvuN2U7BbuFDF1fl7h653/LKu62d0XZ8Peg48D+AHUNk05/qM6PrT1Qq1/pSv8rcx46HRNVNIzi0jSJR7ZFzGpBlud96i12Ktyy/uymxWFmKx/s2cye/GjycAGz1aWac++kgpkkJD6a48bJgCjj16cG5duyqLV5PGXgyc2Q9N3n8ES59ZCt3rRWgo29y5k+9i+/ZMUvTZZ0D7WmlYsdqKXWiEiNM/4pOPOOgHHiB/bNKESqXkZCpssrP5bmdm0rL+6KMEq5LAalW8BuukN9Brz6fovvptJKdoWL48V0G24jw8bh1btpCvLF5MkNusGefTvr2a27p1BLviwt+tG8FsbCyB3YQJ5NWzZvGz8HDyq5EjuW6zZnENBdz26KGAdp8+nFPVqow5tNv/qKCWZ9S9Oy7VGPWVm4wunXIfoLL3Gl2CfeWhy2UKv94WvaIAvIkANhh+fP//S1JRH1BR3AR9+7kSKuz4CtJ+FqV/fwDscv0VlYyaNtHc+dYkMVJ+Yy/IEpVfWwWN6Xq/rL7j8S28WZTkMv4ORX+f+1NW+K6dADs5JEXQ9NdecZKmaZ01Tbtb6lBpmtZK07RvAawuhrarahrhkKZpHUFeefpq2zWS7PP0dOXyUqECwZ3LxcPX6GZWt66KqROg5lvw15gl03gdoFLUC0mClBMnNJxFVZQOKgUdwQA0vwDP4VDWOIDjlCQuQqVKcQyaRnckq1XF6pnNytVSCqN7vdwzus77Dh1SWeaCgwlahw4lQKxShXvcaqXlbscOxlcEBFALnplJQebIEVrd9u0jUPvqK8a+fDUpDe9mDcUZVIF73RYEOM8j7agTdevqKFWK4OTJJ/nTuDHdZmfPphUsKorWwFKlmLnu2DHg8EEvvvjcg61bdfzyC4W87GwCn40bgfnxwXA1bIV2SEHLxm4czQrGSy/Rymi3U/A5ehSoVk3Db63/A8fMr1DuvX9jwAANbdoQAGVnE2AmJfHv+fM5nhYt2MfMmbSeNmnCdXY6VVbVqCilaf/lF7puNmtGgCk1qZKT+Qyil3ixa/MFjBqlIyhIAcm336ayJjmZ+09coQ4coBXx+HEC7Z07aZHt3Jna+GnTOIYJE4CotmmwJa7E7ZiH788NQCU9DRERBHQ2GwVfcadzuylwxcRQAbB+PT8PC6P10Jiu3Pdduh6kadqtmqa9o2naKjCxyvsAKgF4CkDV6zOq60u+yuSruR/IK3w7nXmzWfp77k6niqMClEXOWLdMLDcC/sQq55uUR5TZRiWW3OtwKGF/2LDcWFQHlVJr1hAgDBxIS1lCAnl6TAxdrS0WziMpiQBpzBj207AhFTupG7PgXr0OZrjR8EwCBnTJxM6d5I/NmvH+bduotNm2Dfjk1xB07eLGaHyMozUj0KtvAIKCyK8k6da6dQRyjRrxs5o1yUurVSP/3LKF72LHjry+U7gJ8burYPUaDaNHA9YALzq8dzvWz1yDNpPuwpHD9NE3m+lhEBnJWGR5Blu2kI8/8QTf4+xsAjaAip22bfn3+vVUXom7Z2QkPTiefJKfAwSFycnKzTMjgzxG3HHvuou8pEMH8sQTJ/hsJSPnrFkqa7KvLCluq1J6RYqt+5MRBeTlR75793ornzTdeOIXdKGmvQBgka7rm4rciaZ9BmAQgFO6rrfI/exVAI8BkMCVFwpTbiEsLExfL1z/GpE/LVFxt79s2R+zQl2r8cmmLMmNeDVjNN7rO9b8xu7vOn9Wp6LOu6T3wtXQtRybrJ24sBjB3qpVFNijoniN+LMXRJqmbdB1Payw/WuaNgnkKZsBNARTlA8D8DaAj3Vdd+Z/N6Bp2v9Al6pKYHbgV5CbfVPX9dmapo0G8AQAN4BsABN0XV9TUJtF4U0ZGdQqpqTQFdFupwUkMJBAp04dJlUJDub/tWoRwMjfR48SJJlMeWvYlSqVNwtnQRQYyOfl8fDQ37Xrj9eULUtg40vGfipWVMlThCpW5CFvtRLsCWAUcNeyJQHYP/5BAHbxoupLMmOGh3N8lStTsDlxgoDi2WcJDqdNY/bPIUOo4U1J4f2HD/PzNm0ovEjMzep4HYc3n0JbrMaxgGZoUOoYzmZ6cXutDej160S89oYJt97K/TpzJvs+dIjuUpUqEVSlpXHdnp7gxdju62HLOoS21bOQMfBB/PSzCV270qq4Ywct2NYALyZNTMPGIyFYFa9hwwbg0Ue8QEYmfl0RjGrVNJw8yb6aN6c2e+VKArl69bh29epxHffsYdzNjh0sxTB4MEGixcJ7FyygQPn779TEly5NoWjPHu6x0FDOZ88eZuGUvdOiuRd7XvocrsPHUKOeCR/geTz/vAn9+9PF65FHqO1//XVmB+zUiXvXZlNCrsvF/TNmDC14a9bwHqcTCC6nY3m7p9Bt+8dwdOoB25IFmDqNQuPKlSozqM1G4cpspjVj9Wq226ePKjrtW6DYl+cXxOuvhjfmx580TfsZVCitAbBB13WX4bsquq6fvPJei5eupewk50FRzlejUCx/CyCTDIyAis/0ffZyjyiEpS2x+vkK7NLPqlUEBm43XYxnzmRfco9RyRwXR/7w229UaowcSQt2gwZ8N0aNUu58K1eS77hc5CGtWhHUfP65ejdWriSgkji5pUuB114DNm3SgRdfwKYdVjzUIQXfDvwJbo+Ghx4iIDGb1Xg2bSKI2prihetUJhJSg2E2a2jcmNawW26hG+bQoRzHmTMqHKBTRy+6NEzDmj0hSFyrISCAPGbkSL6Do0axv/BwYOVvZ2F+5AE4YEUQ3OieOgeoUgWrV9Nql5KiykrExHA+qamcc/fuBG9SVqVrV143fTqTRwHk56NGqdJKTidr7Hk8PC/MZmV5FdBqNnN8Y8cqC15oKOOOX3mFGY3HjuUz/PBDguPBg/PuHYkrNJtVyYYxYzgOp5PPXp7psmUcj1wbH/9HuT0/XlNS8llBslNRAN4/APQH0BrAFgCLAETrun62EPd2B+AA8JUPwHPouv5+oQaQS8XBpG40Id0I8ICiM8XimEtJrklxA8j8DnZfEv/porR1pdcUlW60PVgQFbS+xsNU1jojg5aP55/nvpaEDgXRFQC87QDa6bru1DStPIDDAFr4K51wragovCk9nTEIJ06ocgX16/PgzY8CA3lt+fIE0FYrLVsWi7L+pafnf78RDNrtBE779xc8Tl8AKWS1qvgpTeN4qlUjeJMyBOIiIy6ZQUG0wh07xrkePcp4wIAA4Px5L4LLelClmgUnT2qX3AYPH6bVyOPh3DSN+65iRfZjtXK/HT/O/vv2ZR9ymO/bR2BcuTJdLDt29CI40ImLLh2Nl0/Hbfge/bEcNWqXwkWvFU8/TcDhcnHM5cuzn8hIVfC9Th0gdsEF7P19HSKxEkPxOUZ03oYGTYOwaxf713UFomw2xuN8/DFQq6YX+9/4CqlHrNhhCkPF8IZo25YusX37Ujtdvz4tcj17UgD87TfOu2pVAra33mIK8XvuobD0wQcUEEWY/P57gq3mzQk2t2+nlj8jg21Uq0aALoKu9eJ5PDGjMdahPVwIRPv4D/HFoirQNGVdbdOGbX/5JQG11UoNvcnEdjp1olbeZmM/48Yxw+Zdd3FONqsXi7/LxFsfBePppzWkpvIeyXQoQmFSEjX3iYm02q1frxRE4u4rCiVfBZP8LwJZfkrBK6HC8idN04IB3AXgPgBNdV2vfmU9Fj/dSMpxf2e40bpmfHZutwJW8qx9FeJLlhDoSPZMm021FxnJPSHg4/33ua/DwqiEFKugZB9+5RXeN2kSY0mN41y2jPf94x9UfPzrX2pPOhwqi/T771MZZzKRvz3yCN9dSc4ydizH5PFQIfPaa1RcDRpEBUzjxoDZ5EXymiwcOmPHpPfpijFhAsdoNjPWd98+8rnt2zlWgKBoyBBaFaVkyiOPUHl0yy2cV6tWbL/ftH6wr10OR6eeiJ24GI4sE5YsoRvp5s3kI48/Tn7apLGOgTOj4F69DrPqvIMhCY/j0zkaGjWiW+nkyVSEyfPweAjsVq5UFlKAlvlffyWgW76c85D1MwImccmOjc17v+/eWLyYQHL8eP4/fTotoXv2EBj26aPaX72az1yelfQTH6/cbRcuZH9RUeq5+7rqGkG/kf8UZFX25UvFRQXxpkK7aOq6/p2u64/out4WwDQA9QH8rGnaSk3TXs51Y8rv3uuWktyfO9+N4mYnJG6P+blP5kcFzeVy8/P9vigWrKKScU5FuT+/a/NzL/S9N7+abcZr8pt3Qe6dhaWCXEeLu82iXlPYewoaqwQxR0cr9xQpmtygAa+RNMglQE6x0uUqmXZfT3BXVLJYqCEsVYpxYhYLwV1ISN7sl0LlyqkYvLNnuc4C5gICVG06fyR1grxeVffO4aCbnT8ymxVYlzYtFqVBBygMlSnDz8xm/n38OMHUuXMq42fZsrxWrJS7d3PO58+rGnNly3ih4QIyzjlwYec+VKniRVYW761Vi2AnJ4djqVSJ48/IoDUqI4N7TLLMSY2p2rVp3WrYkP306kWtcdmyJlSpWwpaUCkssdyFHWiD1oEH0KtvANq0oeWpdWtqmjt25NxeeokCSfPmdHGKjQUqVA/CSa0eXDDjf7gPrfb9jAsXvJfi68xmXt+6Ncf90Ud8frELncg5chRWeDDQ+yO6tMrC6dPAyy/zHk2j69SCBRTmtmzhHPr1I0CW5AiNGlGJUqUKn01KCkHYrl3UYPfqRaBoNlPACwqigPfyyxQ069ThszhwAEg9YsfHtd5ANoLwsX0i7PVDADBeb8gQgq1OnSi0zZ6twJ2m0XJgtVKQczopHK1ZwzFXqkShy2IB4teYEDG4PBYu1HDvvRRwu3ZVacpjYmgFEJfi7GwC4vBwlfBCats5HH+Mh5EzJj+X8pL0TtE0LUjTtH9qmvY7gBQAkwG8DqBmyfT456H8zmZ/IRP5hX5YLHkLm/sK2gCF8fHjCQLE3TcyUgnt4roH0Er37LOqJILdzs+GDMnrcq7reZOvuN0qRjQsjF4GTqfqz2hdHj2a/CMsjO/df/5DIHbwIPuZNo3ZMsPCyONiYqis69yZ4G7BAiA5xYTHJpRB/wEaEhIIQmrV4rvfrx8tgY0bE9Du2aPeA6uV797//R9j9bZupTu1gM81a/h+bU/KQuzqQDjdJsSvMaNXizSUK0cL186dKhZO5Lbf52lYPG4xVv93Hx5f9zjmzNXQtCm9Bm6/ncoYqXvbo4eKwQaUMnDVKgKuu+5Sbqbz56t3Wsjh4Bq63VR8CdgyJtqJjqblDSA/W7qU+8rjoft3mzbkEVOmkC+JR0BGBgH6vHnsZ+ZMVVvPYiHvNJv/6Bosf1ssis8IuLNYlALB3373DTO6VlSUGLxLlJsG+O3cOi+DwPp4w66gqdGapiVrmvZZrhbeL11pKvL8GMmNVGleyF+MU2HuyS/hRUHA70oBxtUAE6M7ZWFBytUA8cs958ut0dXU1btc+yXRZlGuKe6xWq08CCQDltEpwBjMXsxUX9O03+UHQD2f/2948npp8Tp5kgdFgwaq3ICRypVTWQYl0YnbzfstFgHRXrgu5MBq1XPX3AvAA0DHGoNjqcmk2rBYaPUzkmQbk5iTZs0I1txuAi4pGg6o7GluN0GY3F+zJvdArVpsy2QioMvOZp/irpmWxvk6znugw4OyOIMzKI39e3XUrctYugsXmBAkJIRtV69OK1SdOhRimjYlyElK4jqeOsXvq1blmt55JwWtUqUoTAgIOXVKg6d+Qyzs/SE6DGuNnbs01KnD+w4cIIhs2pRCy3//q7T+zz5LIc0aqGHQ/eVQFi78C//F8ZM6qpXJwtNPc32jogiI1q2jMOHxAM2belGjihsdGmfjTvyG5zvH48W37Bg8mBr8OXMIpH76iVY2XefaPfggx2S1UuiZOJEWyXr1aOGMi+OaP/EEM9cJ4HO5CMg+/JBtdelCq9p993FsZ85QOPvXfRrqvvwIuiTOQNfxHWEJ0C4lRPj2W7VHdJ1JZ1JSCFwlsUGrVhSW/vlPgsHu3WmFAyiETfnAixM7zuKB+/VLrmgTJjBBzI8/8v8NGwiCW7SgQG020wooCS9atMhbl0ysw/6SZfnjVSUI7r4FsAvMpDkDQF0AZ3Vdj9N1PR+Vy9+b8jtT/LldilAthc1FWI6IUJYiiZGLj1fAHyBoionhNYmJfHcku6L0JWee08m99dhjagxt2hAUeDx5lQsA+YnUWezZU32fnk5esXy5cikMDuZ79tVXBE/r13M/9+xJi7jTyWyT994LvPoqlSfvvst+nnmGQC0pibwkMJDv1u23c55mM0FO3bocK8BxrVhBa+GyZVS6ffMN1yw4mPM8fBgY+Ywd5uZNAZMFaN4cluohiIqiFbFHD/KilSsJgMaNY5ydNdAEV+nysAVpl7Jo2mwqYdSTT1IZtmJF3vIT27bxtxSj79uXv4cNI7+bNInPSp6rxUKetnw5LX4SS9etmyphsXUrlUKbNjHRTFISn1WbNmoPrVnDte7bl3Pato39fvMNLYd2u3qOkognIYGKJYnXEwuyv6ysskcFKEob/vb7NSrbkocK7aJZYCOa1kfX9aWXuaYugPkGF80qANIB6KC2q5qu60Mu11dR3QxK2hXuWrnaGfu5UrdCoxkZuLJxX+18C3KvLKzrZXGNS8z0xdVeSbRxJW1eSb/5rX1BfuW+blIS3B4WRheP2bPVYXw5ugIXzR4Ffa/r+orCtlVcVNQYvI4daUWpXp1ui+JqWbo0D/L0dK6r2UwL2J49+VnpvADcICvNASBF8uSkCYDo8sTlMiBAAT1fqlSJQoGkkV64kO5GNWrQSuf1quctJC4x5cvTwmi10qqn6wQSNhs/O3dOuZrWqMF30OnUUeHifqTDjhyUR7lyFpQvr+H4cVqjzGYCvLNnqVXeu5fg6+hRxr0MH874Eqmf1Lgxwejhwxx/SgpdqcT1xunk/I8f57rffz8Bo5T2kGxtISEUuBo1UjGCJhOfHTOd6njw2JtYta8W3BWq4O31UZgxU8ORI/y+alW6e1WqBJQv70XlHcuxJ7M8nmn0O3ZHPQlL+bJo2VK7BKrT0ylcPP88E6Vs2sR+t2yhQAIADz/MuXbvTqBWpQqFx4ULydePHqVbksfDn61b2caRw14MisjEB58H49FHNaxZA9St40WXZplo2S0YXSMYsyPul2YzLcsOB4Uvk4mCU1CQSpMeHk7Bql49CpL/+Q+FSquVa/7MM4Db5YW7/wBMWdMR9UItuD3xJVisJizNlRZE4JN4J6eTAtdtt1HTP3MmhdmBAxkDWKGCctUDCueGXxxUQAzeZvDl+grA/+m6fkTTtH26rtcv+VEVja51DN7VnkG+n8l507kzhW/JYNi1KwGcx6Pq3xnj5XyBv7h4RkTkLacg8oDEWUk6freb4KJ1a4K0sWNpeZYMsJ07E9w98gjfN4dDWbVXrqQ1SZKCjBrF96JSJb7fn37KNuvXpyLv7Fnyoe++I0j673/Zx4sv8vpXXqE7JsDzY9o0KpzuvJNzXb+e7+nmzQQ2Tz/NtidMYJ8CUAWEJCZ4EdksDe4KIVixUkPv3gRUmzfzbBo0iHMZNEgVI582jWd8jx5ch759uU7Ll9NFdtUq8gepdSfk6+oaEcH7162jpdFi4f8bNlCJtXQpn8OPP6oQhjFjyB9vvVW5sMtaJCQoBeXWrZy/KJhljAsX8m9JIiVhUUuWkM9OmMC1kf3VuzevBfi3P14je9Th4Pclnc/Cl4rFRfMyNLeoN+i6flLXdU+ulutTAPm6eF4NlTS4Kyl3T183hKKWOMhPi2nMpFlQn0Vpt7DkD0QUNK+igLuiPgenU2lcjIJqUfsuiEpi7xWmzeIApsa6ePmVqfDVTIm2PThYgbpTp65uLAXQo7qur8jvp8R6LSZKSuIBajbnPXS9XgrQ4n4pGsFdu/4I7kyXuLcOWuu8IEvXwbwwTsP/JHlWOTkqmF1I9s2ZM/z+7FnGh2RlEcBlZSn30cDAvNa/PXsIaCwWapMlQ2dICA9niedq1Ii/q1cnALTZgPLlNWQE1cXgfkGoXt2Cixe1S4L+uXMqlXd2NgHExYuMPalbl5Yjmw1YtdKLXRvP4+JFWv9SU1XphVde4SGdlsYxHjlCcFmjBgWDoUMZx7FtG4Wy/fs59s8/p1B06hSfQUgI3SMXLiRgDA/XcPAfL+D1xH6YsjUKa5M0HD1KYWjcON57++2cc4emWdAy01ARZ2DdtR2WABMaN9awebPSOv/f/1Gw+eYbasH37CHQu3hR1YGqVIkCns1Gi9zbb9Od6vhxzmH3bmUBXryYVuHNm7wwxS5F0ITH8Ejme5gxw4tqVb1o/vvrMA99CFUfjcLqVV6MGsW23n6bcYi//Uah7uBBCk8SZ5eSwiQv//oXtewHDtAyaLPRtSwzU/EKy9k0uNeshwsWLN5dD5Nfz8KUKarWoM3GuYn1xW4nXxbt+ogRwA8/MCuqlFGQd8XXqnI9SNf1NgDuBVAGwDJN0+IBlMlVYP8tqTi9SPy52trtql5c794ECJGRyj3TWJg8P2udx0MA1bmzqnkm10ZEKGAg9yUlEYw1aqTkBbnfYiFvFAs0wPaXLSNYSU/nPt2zh3188AGVeEOG8F0ODeV7m5nJa8LD2Z/LRT5bqRL53rPPkpetXIlLLpt79vAdDA+nQvXECSqBmjRh8pdjx5R7a0YGMPxxL97993m8+46O8eOBsA4mxGytgtjlGtau5dx27KA3QK1aHF9KCucUGaks9p06cd6bNxMQAurZbNtGXh0dzXhAAUhS7sTtVusvdf0sFvX/vn0qs+ktt1CRJUqmDRuo6AnK1WEmJ5MnSXmYvn1pTZw4kTykVy9eJ+69UirBbM5bzLx3b3ohAarYubjwms0Es9KGcc8a94i46t5IHoKFBnhG9yefn3kAKha1Y03Tqhn+vQPA1qK2cb2ppNw9fZmdbz9XW1C7MH1eKRUllssXcF5NWYWi3muzUYAEyHz9gbzLUUkA++tBvgDOCNwKquuYn5Z22TKVkbFcuRIbdqsSa/kaUAjDnNCypcpSGRZG0FGxIl3w7HZao6RIrJEqV1aAz2w2AdAAZMGGCyhb1oRSOAcCvgswsnljhs0KFYwgkf+Hh1OQFrp4kb/T0ykgSGZMr5dAUO6vV48a1vR0Cvw5OTys9+8n2AsMpHbXZiO4M5kUGHG5gA4dTDhwqgxycjToOgWg775jnF1WFgFQw4ZcI5OJWTslsUxKihe7F6Rg6U8ncEfQT0hJ8aJsWYKekycp8Jw7R+vWiROsDbVtm0pKMncux9WjB7W4HTtSkCtXjrF8Tz5JgWnwYM5n/XqugdXqxfaNTjw3JQSTP9Dw668EkFu3Uuj57jsKNVFRQKnKdgSUr4j/4lHc2jUDY56zI8jmhX7+PDas1/HCC3yf5szhmGrXZp8PPEDB74036Mr07rtcCwFeX35JN9phw3h9nToEdhs2cJ337we+eC8NszLuhwsakg9Vxs+z0xDkycJXu8NxBqXw9uquSInPRFQUhchnniFP3LqVazRzJrNpvvIK94BktKtcmcLYgw8yscHTTwMvvEAtfKtW3CtvzgnB6IrfwA0zGtRwY9Qz9kvJIMxm7pfNm/lcJd7JmH4+OJjC2r//TUHe6aSAvHAhBUKjy+b1Il3XU3VdfyW36Pk40Jq3TtO0NZe59S9JV3IeF/YeEaYlFst4NvmLixLydaXr2lVZ8wGCOiliHR//x/+feILvncdDfiB8Kz6e78S+fdyPTz9NhUR2Nve1y8UU/brOsSUmsj+vVyn3Fi4kqPriC/LNDh34/ixaRF7csiXByg8/8J3es4f9tWlDfqZpjFcNDaXy6uOPyd/GjqXFsHVrKmucF7xA9BI8PLUlIuY/i3fe8sJiUclQrFau2YQJPHuOH+f71b49LfExP56FM1vHxo28xukk75EyA/IMRoygoqhXL/ISiZ/bupXvuc1GQNWjB8FjSooqjdOvH2P4165luytXcmyS0Kl9e1o8w8P5nfAZSZJjtNbGxPCZeDzKkhseTpd1UUCLAjsmhv1GR3P9pQ0B/L6Wu4Jk9BsF3AFFy6J5FsADYDbMPF8B+E7X9Xw1VvmkJI8E0AZULx8AMFzX9eOXG8eN5qJZUnQ5F8uScB8sDvfLgsZ3JS6X+d1zNWP1vfdK3HyK+ixuxH0ozMnoZun7vdGtBfiju0zPnuo6+U4CpL/8skTLJKQC+BfIf/5Auq5vLGxbxUVF4U0HDtDNMDCQwGXDBmpqxXJnLEMQHKyC18U1UkoTKFdJL8hKBex5ERToQfZFCwDtUqZLf2S1Eljml1HzH/+gIHP0KP+X91LcPMXFFFAufEYXUEm0cvEixxAQgEuChd3OffLUUwRhAgxHj1aCUng4A/Fvv52HfkaGKuzdogXgzrqAo2u2wYsARGEZljUYC91sRUSEcssZNIjCULVqBEcdOlA7vWcPLWFNmlBAmjaNlsHt2yk8vP023TRPniSYvPVWznXMKC8y+t+JB5PGoG3VEzjR7V944QUTXn+dglGVKhzf5s2MTateHfjtVy+e+Fcmth4Jhtero9XPryJ75x7Ym9ZDfJ/X0biJCbNmUWtttRLYm80UijIyGNszYACFJ6kP5XJxbWrV4j3jxvHaxx4D2rX1Ys0KF3pFWdH8p//gs13hmN3hU6Q8+wM2xGfhgfgh+HJ9K6wp1Q/th3dAuXIaQkMpWE6aRAWY18vYIJeLVoXTp6kl93joKtugAQW3pUs57ogI5eK6YQPjgvbs9qJB1Sy0ibBj504Njz9ON6sXX6TgfP/9wP79XmgXstCorR1ffKHh22/5PhhJ+LQI5XFxyqLnz528OHnuFfCnAADP67r+WvGM4OqppF00S+qMK6zrpm/pJOOZ5lsmwWYjP3j+eboC33uv+i4mhgBQ7v3tNwKmVrkqRSlRsGYNeceMGeTVVitdCJcuVRYmq5XXiDXr//6PAGX3bvLBmjUJWgTs2e3keYsWcSwyLkC5Xc+bR37SsSPB3GefUUk1fDjHHBenMopKSn8A6Nf2JH5pOB7R6IO38RI+mbAT+0+VuaTEslhUlkmA/VitLG6Ol17Cth0axnddh9iJi9G9h+lSWROPh8ovWefYWCrBnnlGWUXdbvKlOXPI2wVEr1jBtQZUts2oKOXVJUlU5BqJiezencD58cdpqaxblwo5cdHt0EGVY5g1i2BXwJ7HQ8DsmwVTxp6UxGfz7LP8fNo05UJa1DCWwuzlq6XictFMBHDBjztUHICdBd2o6/q/dF2vput6gK7rNXVdn6vr+oO6rrfUdb2Vruu3FgbcFZUuZ5Uqbq1fcbZ3ORfLq6HCuENcCV1ufEV1uczPNbAo1sbC3HslMRxFeRYl6cp7pWQcU37gTr4Xzbq4VwgJuPvgg7wFiC0WWnMAFSdRAlQDzFbn7+f9Euu1mEjWNTOTYMJkylvioE6dvNcKiXB7+rQqGUAyATBD4V0T3N6AS/8bwV1goHo+ISEUyCXbad26/N5I332nwF1YmMr2KIlPRBAPCqLlCcgb33fuHC2RJhMP4YAA9if18f77X95Xvz7ndf48D+20NIKpVasIYNasIRAdMIBg0eOhVedsViA6B++DBUBYg2yMHBOA8uW5rkeOEJSdOkVgcvw474uN5XgaNlQa+F27VKyHAM61a+nu+Y9/8LrFi5lJc/mvmZib1AZVcAKBJw7g+CEXXnuN7YflHrUNGtDy1KABE4pMnWZC1D/Lo3UbDfu2O5G4szSmYhwq7YjGr794oGl0W7VYqADweICff6bGecoUlkaQFOpBQdR2W62MVwNodVy1ikLkA/d78ejKfyJr4zZU+9+7KDfpZcyOb4ftL/yATeM+R87U6fj6WD9g1Dg8NK0DTpzQcNddBI/9+3PP1atHUJ2dTQvdxo10Z124kELsww9TED1+nHukc2dem5xMS4YkYnlvkgktO5fBwIEamjWjoDVwIAHgsGFAubJetP/xBTw9uQbu/KgPvvrCi0qV8rrPS5pxo6XAmHla4qqEV+V3dhQ3aZpWVtO05zVNm6lpWl+NNBpMvNK+ZHu/caikzriCZBVjUXI5i+R/Y4bVjAzlpWM808QqHR2tXOHj4riHx42j4mTRIrqpC7/Zvl1lZtywQQExSX5isxFwrVtH3tirF9scMICg5KuvqEjr1YvlTk6dIigJDydPys6mm/OgQQR3kZEEQlu38r0ym8mbTpygFeyrL7yo5DmJ1FQdDz5IxcyPPxJArl9PV2upm7c0OQS7a/RGTRxBYpMhaBthx6RJXLMnn1QupfKODR4M9OjmhfX4QXh27MBoTAfWJMJ6IRMWC+fl8ZAHiSVyypTchFLN+Z4vWcK1XbKE4xk2TNUljY/n3FeupAwhsoLE8ltMXvRochKuizqmTVPxdr16cX0ff5zKz5Ejuabt27PPDh0Iul0u9jVhguoTUIBXYi9lvnY72w4IUGA+Pp7KNH+WOdmDBcmqhdnLJUlFKZPQX9f15fl81734hlR8VJAQnt9iX+niX6uHV1yxYSXlJ1zUNv2tl6+LoL8XqzDjNz4T+SkqMCvqdwXN50ay4MkhGBf3R99y+V4OTUmUYqw7JNnNbDZqx8RfXQSxKpWZxfHokcJ5CFwB7dF1vZeu6z39/PQqqU6Li06c4G+TiYe6b3ydsR6exBjVrJn3GgFt5cv776NfP2p6jWS3E3wJiBMgdvYsDzbJbFnN4EAvtdxkLBJTd/48D8vkZB7y2dnKCiglEMSF88wZgjivl6CqfHmVEv/773kwr1nDPVStGn+fO8d2L17k/R07so/Dh/l74ECgfXsvdmw5h8SMJqhqyYRj7Av46WcN991HK2hiIq/dupUCG0DwcffdtKw9/DAFr2bNCG6tVr4Phw9TI261Uli5+25amtLTufc3HwxG00bAPfgFT4Un4IdfAtG4MVC1qhft6p2FBh3r11MgPHTIi38MPI/9+3V8+CGFtikfBaF8rfKYg6F4udSHaBdmgclEgWfUKIK1vn35c/IkLQ3vv0/QtWMHY2CcTgpwnTpx3efPpxDzwAPA11+48NqG29AVyzD3QG80KJeO1z6uAsfRcxh59EXY4ESzY9EATAgqpaFaNb7H58/THeq++zh2cTU7dozP+tFHuV59+lAYbNKEbqO7dvH/BQu8CK10FhFddTzzDONlHn6YltATJ2hZmDmTe2DuXM7XfD4TfVNnwKJfRHR8ED6bkYX0dFo83nmHe06y2AF/PGevM8/9L4DGYHmEYQCWA7gHwB26rt9W4r3fIFRS622x+E8/b4y/lLPdGGphzG45YwbdBqWtadN4zejRdKmeOTM3jjf3nBs0iDXd7Hbu81tvVTU3W7ZkX6tXsw7lF1/wHWjThsogt5tgzWRSiZPefJN7f+VKvtdSi27xYu7vWbOovJH0/MeP8+/sbFoEJ05kAhcA+Ppr9lu1KhCzzIt/hy1EesMwNEn8Ah996EWHDlyHe+8ln2vYkPPs0wcwmzWM3TgEHeeOQfI9rwPQYLeTf0yaxHm3b6/WE14v4rs+g+aju+MX7R4sQV9MrzUJ4f2CkZDAtQoK4v2zZhFQjRnDvnbtUm6wgErK9MknbNuYFRXgWdCtG0FrfDythjEdn8PShsOBF19Eq5ZedO+u5IzOndnWwoW8f9QoWgeTkvh9ixYq9tCoDOjShZ9HR/PHGBcolt2nniK4tdnIqwcMyB9DSH3F/GRV47XXQw4sSgxeQ03Tuvr5vKumaQ2Kd1jFRwVZk3wXu7AovLDt3ch0I4yzIFBs9KX2R4UZvxGkGC1Q+b2shR2bb3tFmU9BfRYX5TdmXxItuBG4+ZIwMHGVAPLudbdbBReLBrRzJy92vzIDAGB9c3z+Bdr+xiTulwEB/G02M+4MUIlMNC2vNU1i9SpXVp8FBhIsdTeo2KQ497x5tBAa+5HC42fOsE8Bhxcvst3wcIIdib0rX55AScDkzp3kk14vx+P10gUzIIDWuVKlVM0+KWtQtiyB5IMPUsC54w7+rlCBc2zYUMXmWSwqE9qkSQS1WVlcm5gYCj3btnFcZjNQo2wWgnABbRCPNe5WmDHVc8l16OhRWkJTUiisbdlCcHT+PN0L//1vatGHD6dL4+TJ1KjXrcu5du9ObXr16hTGli2jsEgrmoaUO1/G+uGf45mG8wBNQ5vWXpiXx8A1YRwQuwxh7b3YtMmLWt+9jXKfT0a3SQMx6gkvZswA4ldrGBI/DL+MTQDad8CECdolV6/Zs7nG0dF8n15+meN9/33Ow+ulpnzSJD6XVau4joMGcd1SU4GPPg1EnSo5CEEW/hf2HnZlhKBfP2BRYjCWho5CM6QCjZph7yk7UlLYzgMPUGBr0YIupqdOUYD1eOju1a4dBZ99+5iA5rffCPi2buU+M5m86JP0BhZNWICZ43cgO9uLo0fpgrtoES2L9etz7731Fi0dFguw5WAwnOHdEIM+8DRtgccn2LF+Pfdhu3a8RkpsGHm6r+Zckmr4um2WMNXPrQ/8Megy3gxAlK7rm0u85xuMilumEEubJEMTkri4ESP4e9ky9m1Mdy/3z5yp9sAHH/BvcW+OjeU7L99Lds5Fi2htkrgtu53Kjz59+C4kJJBXbttGK1JQEHnRmTPkDy1bEuBJlsx+/cjHFi/mtbfdRqvSpEm0LoaGEkTs26fcD91uvmM1a9KVOTWViqamTXn/k08Cdncmnk0bh+fwBlxHjuI/Lzjxf//HeSUnc06axuQvixfT6rV2nQk97yyPsDBq1xYsYJ9z5nDdevQgQFy4EHAeTkPm1v14Ea/jvA6YJ72PsVsfh72MdimhSkQE5zlkiIqZA1RZE3knIyP5u3UrLyynTwK6DoeDYKpjRwLk2bM578hIYEBYGrpu+xQpaAZPaio8pzMxaxYB+wcf8LmMHKliIgEC2okTlTJy+3aeZwL+162jJ4IkthL3VYnHW7ZMJXyS/RMX98c9KSRK8svFABvlwmstdxfFRXMqgHN+Pj+X+92fjnz9aS9XjPByVrobATQVhYp6+BX3YekPFJfEgbxqFZmGvyKqgH9gXxBgt1j8F2XN7578AFdJubT4As+CFBe+cQz+1gCgcOz7nZBcc2kMB49iRxpRyIndJ5R/X/HSMyXR6LUicZEUIAUQyAAERIGBFLp9XR1NJpWGG1BZweRgBficLl5UljeLhYJFaKhyAz15kgecsdh5ZiYBhqYpACpF1WWcYpUTa1eHDhxvVhavy8xU1wYFsT+3m8LPN98QDLz+OrXdx45xjidPMqbQZFIAplMnHtY1alAT3KMHr2nZkmty/DjBxw+L7aiALLTCTgwouwEPDw3A1q3st3Ztjs/hoCvPM89wr06fTsBQrRr7s9n4++672X6DBtQEL1tGy5hk0EtM5BwkycjevSYcOF0Gnbto+OgjwHkiE8cyLPgGD8J07hTG35eGR+/IxP/2dYILJkxJ6ISM3WnYuZNg56OPTXDbyqBdOw3Tp3Ne+/dT+52YSAHlpZd47fr1fI5WK92HPB4KhG+9xWQrt92m4nNatABee13D4R4P4dHEUfi67w/YsFFDr15A//4a5lR7CZtHf46UO19BzZoaatcmmNy2jc+jXDkKn1Wrcv2CgvhjMikX0uHDVUxRkyYEa67TWbDu2Yo78RMWu27Bp2+k4Y47KJxK9r1y5QhEa9dmOxMnAo0aa1j5zEK45v4Xm+9+C2uTNISFqVTzbjfw4w9eOA+evKRpMMbPiDuekQrrNlUMdOkN1XXdA+CIruvOAq6/RJqm9dM0baemaXs0TXvOz/dTNE3bnPuzS9O0DMN3HsN3vxfHRG4kkvPICNzkc3EN9D2HBdxNmqRi7p5+mtZvu53gyMgnrVbu45kz+d7Ex9O9ets2/rZYlFJp+nSCpB9/VO9/cjL50qZN5Huff04+KAoReU+feooKUqeTluy33+b7NHEi43vfe499NG9OV8ahQ3nePvQQxymWxh07uP1PnKDFLNMUjFfLz8RxVMcTHddj8L1BOHWKrtReLz0iGjWiosTl4pi7dqX1sUsX9jlnDufTpIkCw6Gh7HfxxhDsrN0PfbAEZSoFw1OzLiwB2iXvHbeba9a5M3nkvn3sJzaWYxCPCbc7tybhMi96vNMPqFMHy9pORGKCF2++Sct/t27sd8UKPl9HqRCsbv4YWmIb0KQZgqoFY+xYPkfJVLp6NUH3lCmqBmB8vMqS2awZn7fsg2efZRIoybAZFMT1EIAmQE9itwHOx6gwEL4iJJY74I/fCV1P409RAF4VXddTfD/M/axusY3oOpHRLdCXjL7dxeHedzXXFhcVFWCUFCAxAozi7MPXJVMEXX/X5QfsC3rO/nyy/WloCoohuJqXXtq73FoVND/jmgP5jychgRpM4yErv42CFEAgvW7xafTBbwCAILho8ih+ekHTtNh8fmJKosPiJIslr3XO46HwHBxMcCIgydf4WbYsD+aaNWkBu3BBJTgRd8jz55XF7vRpPqdz5ygYVK2atz1pX5KjJCRQwLn//j+OuXx5Aq9bb2W/uk6LXmYm4/G8XpUxs1YtXl+mDIWdoCAKEs2b8wAtU4Yg8d//5t+dO3PcAIWP3btZ2+n0aQo7P/7Iddm2jXMoV459NGqkoUGfhninzBS0GNsbi5doaNmS8V8bNxJQduxIASQ2lu099hjXrlcvCnk//siYm8qVlQuqxNFlZNBt6bHH6Gr14osqCUqDBrR0ffcd12PzgWBE1DiMDzEKtzXeiU9/C4G5YjCql8/Bo/gS8aX7Y+L7IahRg/c2b84+ypSh69Hgwey7Xj2uwbBhHNuwYRREExOV21OTJlwjyVpnNjOWsXJlftagAVC3nglrD1TB/gMEcZ98wjl36WLCqGfLwGTSkJREwbVPH+7Jpk15zdq1qhTDs8/Semiz0QorQHPdOva9fn1uLGegHb+WH4b1aI/ExkMQtyMEqakUyGbP5nOQRBTr13OvvfIKXW09XhP631ceE5/RLiVGWLuWQqfjnBempYvhbtgE7h69YTF5yc9MXuDkSdgC9TxKJl8X/xKm1pqmncv9OQ+glfytaZo/hTgAQNM0M4APAfQHrX7/0jStmfEaXdef1HW9TW4phhkAfjZ8nS3f6bp+a7HPqhjI39lU2GzVRuWiUSYTUNe1K0GXvzNX05SiU4AfoNwFJduilPSRgt0REdxzoaF8V6ScgvyEhZHXzp3Ld9TrpQUsPp7v4kMPsW/JhJudzXfW6STYmjWLfGTtWuC116jI6dOHbonLlpG31qjB9s+cIW8NDGQc7MMPK6+Lvn3J381mDQOnRmHYtDCkvvoL+vbVMGoUee348bR+f/kl37VFi/iu2mwc09Sp5KUzZlDhJnFzy5dzTu3aAbt2axi5bigq/3cWJif3x959tPrJc3E4FNDu1YtKpkGD+Hfr1oxdTE/n/B0OwHUqE/FrzIDHjcgdHyG8fhqSksibJ08mIGzfns9hxkwNnjffQc/UT5B63+voEanlscg7HFzH2NhcL4AtuX24lLeR2ZxXMS2eRgkJvCcykjysc2eCyqVLad1bulTFZG7YQAApCoPOnVVspxEX+MZ9+tL1kPGBogG84AK+C7rKcdwQ5M+S5CugFxbcFSUJyPVIwFFUgFGSWoiCQMaVrEthXDKNlB+wL2qfxng/IV93RiMVxYXInyuoUUNt3J9Gy6Kv4sJXGwUUnN7X935fdwNx3+zcmd/bbEDPsS1hBSOaPTDn9R8sPnoawESfn58ANABQtiQ6LE6y2/+fve8Oi+ra3n7PzACDgqIx9l7BhoooKiqKit2YcnPTE3tiixpTbm5u+k2isbdETTHJTY+xK4qKvSIWVFTsFVEERRmHmTnfH6/LfTjOICam/L5kP888M3PK7nvt9a619lr5wVapUgQ4WVn5x0GYEkniTfPoUW74bjeZ66Aggj8x3wwOViBP05jnlSsEeUWK3OqhMDdXgRqATIY5XbrEzXHhQprvicdLt5v/dZ2mNnl5LP/cOc6HU6fIDM2bR21dVhafyc0lA+PxUFt17RoZggULGB5h9WoyIaGh1LY1bCiB0Qnwatw4GNCrlwU1a1rx+ecarl6lRstuJ+PSpw/bGh7OUANlylAbV7IkzxuOH09mzmolgDt0iIzDiRMsKzeXwHn/fjJOYjLmdNLZyd69dMBSqxYw92cNV3o8jmGtduObam9i1WoNu3aRCdsxdTsa9YmEx0MmSc7onD5N5i0xkaD6yBH2h8VCjcCDD5IxHDmS9UlIoGlkYCDB3/r1vG+1kknas4d5DR+uxrNjRxWsfs0azp0NG2heWa4cxzU8nGV/8gm1uvPmEXB26MAyZ81i3gMGKOb1p59Yp5QU9t2OHRquteiATU1fwJYeb2PPHnrlnDOH7fj4Y5Zrt5NBj4wkeJWYe6dOsT2bN9P8qmVLai92rslGrwuzsQFRSFgfAMfJDCSu9iChyYtwVagCV9tYwOPxuU//lvurrutWXdeL3fgE67puM/wuiA41A88RH9F13QngWwAFndl7BMA3d7Puv2Xypj0Vd/aFBXmy12Rl3Tp+GzZQcGA+YgMojZ841ZB3/f0pZJg/n/NWzmZ17UoBi7wTGMj5J/9376aJ9MaNpB8NGlDgAlCjHxVFb7ASF1TX+V58PK/FxxOsLVlCujJ1KoVXuk7hip8f6zFjBtem00kae/gwzaZPn+YWunu30pD17891tyzegoMZJdA0klYErVqR3i9fTocus2cTOHbvTrDicCjnUseOka716MG+6dKFvydPZt7PPQfY/C1wFyuBoGANI0eq/l6yhDQmM5PgccWKG2adN8xnO3fmZ/Ro0uGPPgKcRUMQ3dINlyUAi2qPwKRvSgOgwMdmo2bz88+Zf3g40CnOAnuVMmjchDRz/HhlPjtpkupnMdecMUN58DXGwwPya/zNYTUkrqK/P/PZu5caR5eLgFPqJ8dRWrf2zrcb8xaA6Gve/178/p2ESfgGwCpd12eZrvcD0FHX9Yd/g/rdkn6Nq9/C2sDK4BkZ88K+Z9RI3Ymm79docn4P1e9vXU5B2i85c1EYECb5COgxvmMuwzzOv6auch3wnqe3suSaNy+W5ny9vWsEiL7uG5MQGtkAffW5tzIkf3OKj6cELiKCBFLA5ewZTvR/zopZHwH9BlpvfdGU7tQNuendtgBeA2AH8K6u60t/ST6/Nt1pmITatZUJ5r33EnBUrsxN0xi82Wrlx2LJv0mEhFB7FhhIcFS0KPPz8yMzbzyPYgy7YLXyv2jM7iQZwx/cfz8Zl5MnWZbFwk1XQOmZMwoUSR0BMiFJSQSBQUFsa4kSnENbtjD/SpWo+HW5WE8xSX3oIQKS06dVUN6+fXn/5EmCyoYNCRjDwwko/f1pmjNnDjfokBB1dq1iRa6/3bvZtvPnKZ2vUYPjERNDJuK771i/kiUpZU9LIxBzOFhGmTKUBrduzTOGo0aRMa1ZkwBVzIDKlOH6qFqV33Xrsn6bNrEv/vlPArdq1diG48cp4T9xgkzljh3sr/37md+pU9Qqzp7Ntr/8Mt/Jzqb0Oi2N5sBLlxLoRUUxjyNH2N4uXZRWYMIEnln85huOx8MPKw3nm2/SxFXXySweO8byBagOGUKAGhzM/MLCeG3XLvbbK6/we/Zs5pOaSquAIUNYTlgY358/n4A8MdEQXkXX4eoYB/vmtUDLlrCtWQnXmfOsrNuF1ZZYtDvxBVCmjE+6/Gv3rl9Dn3zk9yCAzrqu97vx/wkAzXVdH+Ll2SqgF/OKN8xAoWmaC8BOAC4A7+u6Ps9HOQMADACAypUrRxw/fvxuNSFfMu8bvva1woYkkjwaNyaT3ru3cnbhcnHfiY3NL7j88EN+N2vG+bx8Ob8TE1X8tVWrlPv8Nm24TsWhisvFdWK1ct43bUpQBzDPTZv432Lhuusc50FklQzYypfG9z9o6NOH5qEREaxrVhZNAzMzCU6+/pq0LTmZ60TM5Z95hsKcNm2oOYuLI8CbP5/rLSODIE3XVYy2Bx5gfcUBUVQUhTBuN2nOypUETDt2UCvVoAHBT2Ag63PyJMsVb7USSkAEvQsXKqBZpQr77tlnCa6iothfEmaiWjWuaRm3tWupxVu1inkAFAgVL07nKdYr2fgxIQQ9e2mIi2P5Q4ey/gDzTkigJk8AlZHXkSMJbdqo0AqxsRy73bs5ZyQ4uc2WH9x5m2eSEhLyh3Ravpzfsv/KmUyjVZS3PCVWZ3i4mqPGef9L+U5f6W6FSXgewDOapiVqmjbuxmcNgL5gcM8/dboTTZlZW/VLtHZ38o7x/52k30v750s7dTeTL6Ah4K4wkj9zfyQmqjp766uCtJLGtnrTnnnrByGOhfX8eTu1vrkfzO/6KtNXHSW4u68N1jzORk2dgDujC3KAm9Err3BDMxLAzGxWxum6ExJzZ0nTtDhN09aB4O5dXdej/yhwd6epbFkyypIyMrjZiubImMqVy38WACAAysnhph8UxGtXr1IjZjRFkiQmn6VLkwnwBu7E9FE0f5Lq1FG/BUCWL0+QkZFBsHjhAjd8h4PlpqfzeYuF+Qq4s1iU05PSpVmPvDy28fBhgpHr1yk0kHeKFWOdW7WiqanM9awsSvHPnCFgfOEFApvz55nvggWsT6dOBHdLlvAcX82aqr1t2pB5O3mS99q04bvJySz/4AEPxo3JQ3AwA/z27ElPdnv3EiSdOUNwvnOnMtF66SVeGzKEffPsszRtLF2adaldm/U/dIjl/PADJeB79tCjaL9+/C/a0a++IuB84QUySoGBBET+/gR3b75JTUB0NBnMGjWoLTtwgOPTty8FL+fOMb/wcJbfvTvzSk0lQ2ixsN5PPcW8rVYylJUrs38FzC1fzjYMHkyQe+IEwe2cORxXoQEffURm0m4ns5mUxDN7999PczC7nc906cKxf/55BWAB5jFmDLBosYaJccvgOnwcrviVcLk12MqXJtizaWjXKo//faQ/4uzLXU7/BPCjgLsbqcoNpu5RABN9ObrTdX2mrutNdV1veq/RO9NdTN54H19WMSJYLCgvYx6lStGMsFOnWy0bjP/tdq43Pz/OUZeLWigBQY8+yjXidBKciebnzTcJEpYsIUBITiYNaNqUzjvE6cnOncznyBHO/YoVPPhxWCJ6x5zBffUPoGSIB0uWcA1cusR5+8UXnOvTpxNE1qxJevHgg1x3JUtyTV2+zPlfoYISiO3bp4RlffoQtAQEsP6HDilLho0bKXx64gl+NI1awO7dVaiGYcPIZyxaRBrw5Zd8duZMtnn1apaxZg1BclYWgdK+fTQPHT2aAGzGDK7nESP4zK5dFAq9+KIai6FDKaRbsYJld+7M7yNHKHhbvMSCFl1LoPf9GnJzCYSGDCGvNm8e+8rlyg/uAGWNlZhIgC5mtkYLlx49qFVs04bjNmEC6ZrRC6tZoyyWXgD5F+NZ3pQUAtW4OHVPwF1kpG8+SkIzxMaqd8xmxr/XmbxCa/BuvqBp7QDUv/F3r67rq+56rQpIv4cG75emX5K/Ny3J3dAmFfQ8cKt26XZ5FKSdupv1k3fM5dyJ5E/6UyQy69YpgFLYdpoX/e20YwXVxdf/2z33ayQ9t6ujr7wL0kKL5FSkWWbtqEht58/zYOQj+3AGYRhdajzGpI9SB8R8pF8QSHgbgHsBjAWwyXz/zx7oPC2NTLqYmQBkzA8f5u+WLclsuN0KqHkz6a1WjUyD0VmLt+TnR+bb7VbeOCVJIHV/f5ZVvLjyvglQm+NwKHPFatVUPYsWVc5hhHkrX56AT4L/itavaFHWs2xZgii3m4zKc8/x3EdODjfFhQup4SpdmoxG6dJkyi5dYt4VKlDSnZtLZsflIgNRrx4BzXvvEXzUqcP2PPEEQcmVK2RYUlPJ0Ok6NWrvvktQ8913yrV4ejpw8qQHI5zvoPmhrzCn7L/hfOhxFCtuQaVKZDQ6dSKIEubMbme506ZRMydBi5OTaXaYnk4g+/HH7HMJJN65M+PdBQXxPFrv3jRbXLQov3bhH/+ghq9kSfZLaCjLr1mTzEiNGnz28GH2X2oqy7fb2T/dunHeiZOIdq1dWP3lSeTcUwUHD1kQHs66LFumzh3Nn886duvG+qSnU0Mhmk5dJ2C0WslULl1K5ufjjzn+y5ZxXD79lOM7ezZdvj/2GOd4dDQl7SLtT0qiBnL6dM6p8ePVGak2bdi3jRrxnTWJHsQ2yIC9cmm43JpPrdGfVIPXAsAbuq7H3fj/CgDouv6el2eTAQzWdX2jj7w+B7BI1/UfCyrztwx0Xti9TcLo+HJG5m1P8vaOkY8xlpGVxbUWFZX/3L0EERctnThb2reP5tVNmvC57dsJ7pxOrqmBAynICgsjsNm3j7+7triET19IRglcQDUcxY6mw9E4yo74eK79IkXoAGn2bK4Rh4Pz+swZWg20bUtw1rYtgWDt2jyT16cPaZXHQyFegwbKA3BWFgHWBx9wHRw7RoHOtGmkA48+ynXYogXfmTiRwqKtW1UfLFmizvsOHUoatngx2zRwIEFeUBDj3zlOUju5eYuG6GiuUbeb63vHDn7q12eZ+/eTjgvfFBjItgkfBrB9jz5KunTuHM0sf/6ZtKxhw/xHFhITlbOTlSuV9jEmhn05fDhpucTslODmkydzj+rXjzRW8nK7ubckJiovu0IvjMdTcnJUXjJ/jDyozL2pU291/vNbWQ/cLt0VDZ6maXZN054H8AAAJ4AZvze4uxvpt9R2FRY8eHvnl6L6wpbpcqmAk0Yvi7fTAhoJqWicClvHX+rFzFtf3E69bnxXvsVFb0FnzLzVF1DeuX7pQX1zvxakBfVmyulLc1fYdLt3jFo5X+9500JK/BpvkljRtu7ddhWRSAQAFL2QSq777qerAI4CeBD/BwOd22yUtookuUwZ1U0WC7UibjfB0T33qHcAgpyKFTkGubkETcaxMmrcJOXlURN2+bKKYyeYW871CdAMDua3bJC6znL8/MgIGZ2iyrkTkaZ6PJQ2lyjB+jmdBID+/mTMrVYCFauVm+m991Krc+oUmYV9+1jX4sUJUnJz+f4LL/D9wECCukWLCAQfeojgIS6OEuty5cgkud3Ms0ED0rtr18hQSawkQJmqDhlCpmz3bkq/f/6ZjFR0+FXEH6qBL/EE1p2rjB2bncjJIaPWqRPrOHgwGcQaNcgYxMezL/btU2UkJRFkdu5M5vHZZ8ks9ezJvihalO164w1KvhcvJhOkaWyT3c7P44+TmXM6Ce4EWAcGqqDqubk0verdmwxccDCZt2rVyPiJqVa71i5sqPQwNo38Csue/gYuJwff4SDYdLupUaxYUTnTuf9+mlG1bau0cFFRBHwLFyonB5s3cywbNVIB7i9cYL9nZ/N+mTLsm4gIFeDZ7WZZHo+y2mjYkO1r357vDRxIRm3VKiBphwUrU8rAcZ0OGIwOEADflht/krQNQC1N06ppmuYPaulu8YapaVoogBIwCLE0TSuhaVrAjd+lALQCsO93qbWP5Iu5Ne+BYtrmKw/Zk4yWNxKqx9s+eeFC/jhmMvdGjeJ8lPJDQtSe6u9PEPPcc5yfw4cT8HTsqOKSLlpE+vPyy1w/n35Kutu1K6/by4WgRflTCIIDjzbag6zcABw7RnrWoweBx/btnM9y3g8gcKhUiZr6115jfpcuEfi53QRKbjevnz6twhhUrco6iUnl+vW0Oli9muusXj2uJ6eTbXe5SCNmzKBGzenkmp04kW0PCiI4DAoi7Rg4kNr0uXNpRunq0hXD68djXMPPkHPFgzVrKGDZs4eaP4B0c98+1m/AAPa9CIZEaDN4MM/qxsSwv8Xr7tGjpBmlSrGtFSuqtbt8OffCVasI7iSYugC+kBCCQ/EgLcHIt22jtnLQIFo2iObMaHZpnmtGgOZwkIY3aEAwbwxi7nKxLh/e4CyM4M44z4zz09e6+D3TndhPzQHQFAzo2QX/B5goYxJJgtmFvPmZ37L82200d2MyeNvcRBW9ciXviSmdlFmQmaIZDBZkTmh+93ZhJwp6tzDAtbD9eTtNlrf6mgGhN9NFb2DNWLbZzNcYv+l275nb4K3e5rEubDI+X1CsP1/1M2oXhTDK71WrAC0oCCWC6V8gqFIVqmDuctJ1PcZHkPP/E4HOg4LI5AIETjk5BCfidCQjQ4Gjixf5nMNBQHLmjHJcIgHTXS5ueGXL3qqhEyB34gSfCQ4m4yHgzJyM0nCjyWdeHk2VRJJZu7bKv0wZ1qFoUWpt5IycAMl77iGDZLFQU1asGAEOoGLaRUWxHxo0ICNx9CjrcuwYJcvNm/PZBg3IhFWowPNh/furgLbdu1OD9803BALBwXwuNJTarzZtgAoVPKhf/Rqef56xmKpUYZ6HDrFOoaE3tIbZQahU3o3aOICrAeVQr0kAjh4lWFyyhIDl4488uJZ+BbNm6njwQTpdePhhjk/r1jQrfeYZtm/PHoK7Zs2o1UtLY5vdbmrGduygaVdgIBmMnj353tKlZMb8/Ahk27cn0/TaawS4/v4Em9WrU9L/yCP0Tvnxx9Tu7d3L/urdm+Zo//0vsGTWSdRzbMQ5VMBr+r9Qt9gJ7Nmj4gfm5lLrZ7GwT51O1u+552445GniwZK511A3TMeAAezXlSvJUH7/PedJaiqB5Rtv0Ez03DnOjagoAtvHHyfg3rzZgx+/uIY9u3U0bKhMWDVNeUy02VgHCZTs709mrnlzjr0wZwIEhOkC/jgX5QUlXdddAIYAiAewH8D3uq7v1TTtLU3TjF4x/wngWz2/qVUYgO2apu0CA6u/r+v6HwrwvCVfe6DNdqtLeeN+I/HIRPNjjDVmBH1yPi83V113Onme7d13ucYE9EtgdHGh73AQgEmAcvGSqGlcf2PHEkBVqULgUKEC51mvXgzRsm+fhuYTH8d7m+Pw73u+RNu2dCgUHc31tngx3ytThqbiNWoQpJ0+TYFQXh49aa5eTZBZqxbXd716pB+dO3P9V61K+lC8OIFbRgbjYj79NGnU4sVsz/Dh7Cc5q+xysR1165LWJycrk85580jbZ81S1iEhIQRsNhvQrl4GbJs2wg0g7NQy+F/LhtvNPWvkSAqp2rTh/wYN2N516wis588n3XG5CNwmTSJd2rKFzz/9NEO/HDjAcocOVY7FIiPJP2zaRCAlFh0tWxIwrlhBAZrLxbycTprvZmfTLD4sjH0eEkLLBzn7JsJHEfobtb7C4y5ZciOGbwvSrp4984dgcblYl4YNCSiNWr2pU281A/2zCJXuxMnKHl3XG9z4bQOwVdf1Jr9l5bylOzUzMJucAQVLm37pZlBYUPJbm4iuXs3JtmlTfrNE42QzmzUUlF9CQn6vjHfSBnOZhXnfW5kFPftL+1M2BFHZ365PvM0j2YB81dWXecqdOFUBvJuyGE1IExN910PaaZRUGevtK39fpjJms0wgv6lDQgKZv/5PXsK8+BCMHm3BmDGa78beSL/ARPNFXdfH3Pj9kK7rPxju/VfX9X8VNq+7le6ENp06xc3cCMYk2Pnly9ycAwIIJipVIrOhacoU02K5NYRCSAhBYmqqCkzuLZUty/sSu86c/PxYvni6bNqUzJDVqswOhcnWddZJnLhIvcTcU9NUXUqVUoxHx46kUWXLckMVExyXi+VarUpa7XLxd7Fi7DdN4zy0WskcBQQQNM2Zw2dKliQjY7MR1GRmMv+jR4FOHT14dM3jGJvcFc82TMBzeZ+iQQMLXnuNUuyyZdnfTicZoRZRHjzSJRvZlhDMm6chOZn98cADQKDdg4yX3sepoy6Uqe6HRze9hHETLLBYOLa5uWQqa9ZkHQIDKUU/epRtnzqVYKt8eZ4rqViR53N27iRjtnw5Gc1Nm8ggHTvGur36KvN98EH2h9PJ559+mozg4cNk/j79lFL5U6fIqDqdZILmzAFatvDg3IT/oalnJdagKy62eQBz5lixfTvnxOHDPHu3YgXB+fr17LtG1bKRerYYDs5IgDXrPHrWPow9vf+DPBfQqm42cvxC4O+vwe1m+ydMoPZ1wQJqNRISqIUcO5bt++E7D2xP3I9ntj6Hz5pNx97X5qJ5lAV2u2JeFyxgW9u355gK47Z0KbWtnTsTMArjHxnJdg8Zoszy/mwmmn9E+i1NNL2lgvrdaHYJ5N/zHQ5loifni417bnR0fu2J3a72wOhoCoPatlUmdlIP2b/EDK9FC96Lj6ewZfBgJRTo2FGBg4QEAqSLF7lOR43inNu0ic+JF1ibjRq/ESNoPrh2LYUaTZqwrL17qaHTNNK4hx7i/4MHea1zZwpVatXi2ihThnS3bFmCpFOnCFy3b2dfzZtHoVS9ehQenTrFdbVqFelARASFUDk51P7dfz/fczi4VyxfzntLlxIo2e3M/6UXddi6dsR761uiUZgL7Ta8i81bNMTEsL9WrKAgqUkTWjLk5PB9m43CNptN0XqXiwI3CUCekEBAlpKizOwPHCDQmzmT9Kl1a9ateHEKcB55hEKyefNId8XBzPLlpAkABUf797PvbTbOB6eTVhkvvMBnRPBjt+c3+c3J4ZwRhyhGIYII5eW3kQcygrzfwiy8sOluOVnJkx83pE9/+mRG0gWZ6f0ac7jCIvbfesDFhNLoecgoPTNqXwrSZBrz8xbM+06SUWv6SyQbBWlbfw24E42mmKXdLpn7QPqmIHDnyzylMOamt9P2GU1IfdVDNq/x45W01Fxvb3X3tha8ufqV+9Immw2IbunByjZvICN+LQDA3+/OzvjeQfqn4fcrpnudf6tC71ay2ZQzkxIl+C0H7gFufOIePDOTwOX6daV183hUcHBJWVnKUck99xB0AWQeAL5bvTqZjvR0Be5sNhVeoVgx1ssIEE+c4Lfbzc3Z6VTn6QACHvGsKWXm5ZHBCAtjntKmy5fZprQ0mjqePMn/999P7dD99ysHH9eusZ3Xr1OCLpLbevUImooV43wsX54b+cyZBEk1ahDwDBlCxic3l+VHRQFH9jnwRPILOIxKeGD3O4hpehXvvkvQc+IEmaqAAPZR8eJAh44WRPcogZIlNQQHU1Lu78/PhbRsvHH0cZxFUbx95DH8/Hk2GjUik3b5Ms/WhIZSm/bkk2Tw3n2XDErfvtQyVqpEyXfFimxzbi6ZyHnzKLxZv57XmzYlo5eUxH4YMoT9J+eFXn1VBSUPDye427r1RnDhhpfQpLGOo0fJ/EVEAA3DLXh9z8OYZRmNbWiEx8+8i+QdHvz4Ixmu3FzSx3btWJ8+T3tg//5ztBtWD6HfvglrVjr+jddx6KAbD7dLx+rp+xHWtykO/utzOK97MH8+BQ1163L+pafTuUP37gqcDx0KfPHxVXyytRGisBm7t3qQcyob06aRKfzxR45b796U4ovkXOihvz+1ICtWKDO41q1pKmYEd38WafpfKd2u341HJ4zvyL4eE6O0doAad6dTxSVLSODclOc7dLhxfqwthSciHDJrbATcrVnD82wLFvBMaEKC0lS/8w7BwsKFBDNiSVG7NungtGl0jiRCCH9/gpGxYymMmDePppcXL5KW7tlD8CNa+MuXCQ6bNKFW0GJhHqmpBFzlynF9ly/PtVC/PgUu27eTBixcyPUUGkq6cvw4aeSaNaxTRobSbgYGct3t2kXN2OjRLKNZM9Lh119nP8revmq1BtvK5RidNhjW997Fho08g7d8OTVjAGmMpI0bqQl95hkKp7ZsAbIyPUj44RKg64iIUPxBhw5s70svKTp66BDzGDCAfblxI2lvq1YEohIyZf9+0nkBpRLjbtMmgmNxHrVuHedDp07U8oog/6efODZLl+b3zLl+ff65KUIko4Bd+DGZV8a57Y2f+7NYDNxJNcINgTs1AIE3/msA9NvEfPlD0p2Ctl+ipfol5fyWyRvh/DXp12gzZUEb8ynIWYn8N2rUvGnzCqtt9TWOxnrJ/9vl6S2vgp4ryDzF2ztGjZ1oxLyZuHqrp6/+tNm4mRjdSd+u3r6e8eaB0+FQIFm8nK34MRsfHWiNPPBAmSU3A0CZWwv89Unz8dvb/z9dcrnU+btLl3gtL08F0BVv5rVq8bfDQQZfTDIBMg8lSvB90Wrk5RHkiQYNIFNRujQ3fSnLXJeAAH5yc8kkXbqkwOP58/nDI1gs3ICvXCH4Sk1l3e67j0xP/foEenv2EBDabARk16+rsAXbtxOQ1KpF6fLatQQ8NWool+BuN894OJ2sz8GDBKJHjhDcHTnCvLOyyAD8+98sOyOD3998Q5Mou52Ad8sWICIiEGEhmWif9T9MtbyCwFIV8Nln7ONGjfh8t27sy3vuoTnpypUUbmRmMk7TPffcCKi7KwSvVpuB9KMutCp+CIm72uPdf/DerFl03BIeTjA3f74CcKNHs6zjx9neQ4fIeIjXtfBwMnHLlxPkivtw8TK3ZQuZII+HYKZGDZqNde9O06kWLQjuDhzwYAqexuxXY/BCxDKkOb7FgAEW/PjjDfO19y8hzrMAHuhonTYHZesNxI/WMqhbl2MqpmFxccCWdQ64TwdgFD6A56gFlUq78Pb5N1CpnIZxs4vh49yO+B8ew8FTfri6/irOng1GtWp0erBpE+sl4TIcDoLp+vWBxq2C4F6ah5j9H8PWsimmnA9B375s17FjSoszfjz7zOFQgYzdbtZz7Fg+D6iwFkYp+59lb/6rpduFmTLei4nJf94qJuZWXmHdOs4jq1WdrZJ4ZUZtYFAQz2EJ427Mu2tX9Vzz5qQRZctyja1fTzrSqBE1TLGxFJC2akU6EBlJQDVnDtd27doENBYLzYUffZRr8NQpepbu3ZvPJCezznXrslyrlcI2h4MgJTycgjd/f3Xerl490shp0ygoSkwkIMvJUaEa+valKeGKFSre2+rVfP/IERUCRawjRJO4fr0yb/R4CPCqV2feHg/vuTyMQWdNVUJwp5OASTTp0vc7d5K+/+tftEjYvMmDCeGfoNGpxXC1ugb/V5cBsNwE8BK+IjZWgSQJeeB0KrA8cSI1oh07UjD173/znQ0bCGr79+e6lyNHYp4aGanmjdDU6Gi2+9tvladf0bzJGT2zIx853ydzyGZTPND/FZpSaA3erwjo+YemOx2Ewkj8vN37vQa7MJLI29XFm2bubqXbaU2NBNuXE5LC1L+gBXYn2kLjJnM7AGasq69yCwqVYZQ+e2uPJDn8683VtK96Gss2m8YWFBbhdu0z/jabZ4p3q6QkRew7PhSC6Ion0RzJAACn/90/f3cj6T5+e/v/p0shIWQkypfPf/3oURViACC4EslqZibBRdGi6r4AMYdDna0DOC4SzLxUKTIdVuutIRCMScwyL12iSZ9o/gAF7kJCyEBUqcL/Fgvrc/Ik3flfvEim+/p15dClWDFKis+dU+EeBNikpFA6fv066//kk3zv2DEyPwLOIiJojilayytXCFq7dyegCwsjGDlzhs/u3UvwO3QoweZjj3F+XrqkodvY9ggaPx5hPWuhWXMNoaF0jNCzp3LE8sMPKtbW/v08G5eQwDELC2Nfla+gIePRlzF050BMTG6Pbt00vPKKAtb79/PMyqBBbMeJE2Qmn3qK4yFxA6Wfxcvp7t2Uxo8YwTMlY8fyLEulStRqhYcT7MpaP3SIjFtgIAVCW7eyDjlnr2LwrmFoiQR8mdQAJYo4cfYsNYgeD/DS+NI4VrwpUlETMdoGXLCUxrvvkvkcMYIMalAQpe1TZwXiwdD96IIlsJW8Bw3fexxVn+uNJmMfR2y3Iviw5Dg44Y9O1Y8h8N4g9OlD7WJQEBmvhx+mZmTbNsWoHzoEtGipYVfvtzBl4B6sfXEJwupqmDOHDFnPnurMXaNGfGf9euXEQUKzlCqV3yrB6Dod+L/BiP1fS7fjj8TU0dc+bN6jZYysVo59YuKt+5hoZ1u1yp+X3DPmK4y/CHKdTqXxEcuW7dup0WrblvN94ECCqLg4ArOSJfl70yYVMFzM0uPjKXw6dIi0a9w4agKjoji/P/mE/+vU4e8mTfhuXBzfPXdOxY6LiiKNEpp4+DC/Q0JIh/r04fsBAdSuP/MM6ysxRPfsYZvW0mgGr7xCwOnvT3A5b55yfjJzJr+Tk7nXDB/OM3PDhzPvhg1Jf5YuZZ6tWnFMli1je5o1UwIWSXXrkvZUqECgnLThKuqeiocTwJoN/mhaOQMulwpF8cMP/F6+XIWHWb2afERSkortd/iwMp9MTuY4bd9OmjxkCIFlbq7iMaOjKSx8/nlqOMXhlXgVFQ1vYiLrIuf8Vq7Mz1e5XOpsXXS0mo8JCfmF6t7WwJ/NUuBOTDT/T6fCdnxhwENhTT5uBwjuNN2ubG9E1Nczd2PTKwis+AJKvp7z9p4ZiJrf91Wn2x2uF82gMaab0YSjoDYVNAa/du4YY7JIfoUVJhhBYmFiFpo3V2/tKwywbdeORF+cHdj8NEROfRqr6vwLgAWbNv1myrRwTdMua5p2BUDDG7/lf4PfqtC7lbKyuLEVL56fIfX3J/Mt2jOjxs3ppEbn6lW+I+DQ46GJnzG23b33kvGw2wl6AL7rdHoH/OfOKW+aAEGTMl/2ILhIHgD9puOVPXtU2QLkLl0iECxVikyC08l2ZGaSidE0AszsbF63WikRbtGCYKxmTQKAixc5F3v04PVy5Vi/y5fJRNSqxfutWrGdjRpx49Y0MgKTJrEe995Lr3BFirAtUt6BgxZ8MrcEMjK0m0zJwoWsxxtvEMA2bkxmbMUK1mvnTp692buXDE3TpqxrjZoWTPi6DJ5+RsPly9SkHThAEDdkCNu+ezfNyurWVY4Q8vIoYRfHA8uWsY0dOpDR7NmT45GQQMcsJ0/y+pAhZOTOn2cfNGiAm45Jdu5k27dvvxEe4d4gdCqXiuJw4nSpppg1JwB16/LMUIkSgKZpKPtoLE6H3ocXPiiDTz7V0LcvBQwrV5IJk/M1K1dp2PvgW9j/7Ey0+28cDh224sj5YOxJ0bB3r4bjtTviet/hWNroVbhcGg4dYv8sWMDxfuopgnObjXlOn07AummjB/hyDup+PARtxnRF964e9O1LZy3iDENM5zZsILNltRLoCSNoBAguV37rjL/T3U+328eM+4m3PdH4vvG5uDh+7HauVxlbo6mlaJ6WLFGBqEUrtGQJmXqj6bmY5wEU9ths6nxWdDSFIW43QZl4vo2Pp+lm9eqkz507k97t2sVnmjaltcLw4QRlNWtSqJWcTBrldtMSYNky5rdhAzX4Gzeynh06UDgVF8dzf7NmsR/69WOZ9epRIBUaSoA4YwbX4yOPUEi0fDnX5pYtpEnZ2aQvmzZRiOJw8Azqjh3Mo2pVgl05syyCvl69lEfh5GSaSMbFsfwdOwiWpk/n3h5o96Bj00vYukVHUpISOq5Ywba3bs3+HDoU8C8eBNSuiwW4H9l1IjDy/dKYP5/0qXVrtqdLFzUfIiL4/9lnCToXLWIfdumiQknUq8f3nE4F4Js2JT3OyeG1ZcuUGWrHjszD4SBNlDmxbh33IqP5pjgOM/I8Rm3dpk1KWOBLQQH8cq/xv2X6SwC8ggiSLwZarpuDaxfW5EPKvN2g3wlQNJdt1rT40oj5eubXpNsBncI8XxiTx4IWlLdk3lx81c8cBqEw4O12mj5fbfA1fsb7xjoZTQRuN2+99acZ6JnfKaguIg01bs7yrtnzGcDNq1UrSgEl+GjLVhbkOEoAoGex3yIZLAqM1gTyvwA91Z8jiabh0qX850DdbmrEgoPJBAQEqHviwlueAyhlbt5cnRmTlJFBwOdw5Nf4ASy3ePH85/eMYRMAnvegtsyDakjFtWvXAOSiVCkPrl7lWLdpQ9Di8agzGRKz7soVllGpEssLC1Nn9apVI2Dx86OZYmamqmN6Ot8tWpQgJj2dzNKxY2xjo0bsuyJFeNYkN5f5iyMVl4t94udHwHbtGsudMYOmRaVKsc5NmvD5xx5jH125QkYuIYGmRleuEGgOGsRzc+XLExjdfz+l6zNm8L4EHm7UiEzYRx+xL7/+mu1p354M5YEDHrgvXMKkSTp++IFS6YMHydR99x0Ztd69GXg5JYXMamgo8P77fKZkSZoriRS8Xz8CvgMHCOgmTWK/NmlCiX98PFCvnoaWUx9FbMo0xI3vjK/+pyE1VZ2NrFIFuJhpwYDB/riSo0HX2dYvviA4696dbbh+nXV68ikL1u0Mxlf/o9azd28CTGpULbjuF4z9qRpq1mTd166lxqBNG/bt9OnKdK1TJ2o4neezUf/kEuxGPYzd2BLzv8jG5MmU3kdFcRyTk8lgi0lzXByZyenTlbZUtDLjxilQaKZVf6e7kwrDA/my2pH/Ru+ExueMpnUdOvD++PGc9wC1tqNHK1NNMfNLTiYtcDo5L6KjFbgTICCC3+hoAjWXSwlD3G6u2xYtuJ95PDz7lZnJeZqbSyGKx0Mt0/btFF5s3EiQU6UKgcD773N99etHQU7nztRude3KuexwcM1evMh1tngx33/ySa7bp56ihu7xx9k/Ep7m+efZhnHjaE2wbBnX2LvvKo/KANf19u0sX9PU+byVKwnadu1iH0VGEjht20bzxwYN6Ixk7FiudYD1HzAA2L7NgzpvPYgVw+eh5Qfd8NwgD7Zt4/uBgaSRycnqzFyzZhrarn4DNUf2Rs9tb+C/72kICiLd/egjxUtZrfzI8Y6kJNIGOYcttA3gc6LFX7OGbdi4kYB48mSGy5g7l7TYZuMzO3bw3QYNlKll48asA8D6b95MkL1mjfJVIHNbzg0afQyIwMHIJwkwTEz8ZV7jf8v0lwB4hTFr83bd6FrXnF9ByQgECrLXLYyGxPyML8BzO41YYcDJ7VJhAIKvZNYSFbacOynvdn1kzs/YV77K8AaY76T/fI2ft/vGOrlc3mMGGaVMZgBo1F56G39fAFs0mpK3eeOVPI2bss1GAinnIUaN4ibmcpHwVqniAeCGxfKnt5b8w5LNRgAijkkAgqpLlyiVzcykhqdmTd7zeAi8JJ05w2e2bGG/Z2erGHaAAofXruUvV2LLCVMgXt00jXWx2ylJvXgRgEfHUZSDG05YcA0XLug3wy2Ie/+8PAIwcc5StSrrrevUaInjkD17WIbExTt/nkzRgQPMJzVVmR25XLweHMw6paURLE6bRo2Yvz8ZEpuN5b/9Nvvi5ZfJbAUF8bxc+fJATo4HHVtfQeJqHb16UUJ87Bi1ga+8Qqaudm2aT549SxOiBx9U52YGD+ZcdzppqvWPf/D8WM2arOOhQ9Qqnj9PpsLPT52dKV4ceHG0Bw/teQPdnquCGt++jTp1PHjmGTKqO3bg5pm3IUPYtiefZNlbtlAa3bcv+8HlYh26d2f7X3+doK5ePeZ1+jTnwCefsL7+/gA0ahjnz9eQl8dnGzVSDg7KleN6feYZagdefJHmlOfPk1FdulSZkCYlEcw1bsx3d+0iSB06lH194QLLTUxUAoaICI5RWhqZrGXLSLcaNSIg23k8BNbQUABWHC7RjP+tPG+TksJ2jR7NcerUifQH4PgOG0aaY9QAjRrF36IBEsn83+nXp8JY0BT0rlFAaHZBb9zDEhKU1s1mU+aGRj5s2jQl5Fq/nvP60CFqbkaO5PXERNKeLVsIeMSU0W6n9g0g7Vi8mOsqN5fvrl1LrbgIGhwOmjkuXHjj3GhjAqMpUwgeOnfmutuzh1YHxYur82BLlvDc16pVXB8XL3INPfss1/nBgwRGI0dyzVmtpCk//ki6lJBAOpOSQhr34YcEJeXLc/1s2ULaNXgw12laGgHt1q2k76GhCjDVr88+EwcrEiDd6SQdrlCBbRs+nOuoUyfShswj2Ri4bQDaIx6Jm+yYPvbqzUDgUVEEYQ0aEDg5HKQBM2db0Cg6GC63hn//m23s3l1pzaZO5TVjWAe3m4Cxc2dq70aMYFvE2sXpVGao4j0zJYVjuGkT85fwBrGxKlh5165KWDB7NueCzcZ5I2fO4+II6DdtunXeiiZZhEhi2rlu3a08mDf+7o9MfwmAB9zerM3b9aCgWx1L3C4Zgx6aAYSvcgrSHhWkaTHX3QxMvDH3v3QCegOj5vKMhNpbMrbTl3T1TjWDBb1n7LvbSXONC9QXqPulgeh9CRfkvjepj/E9ETYYAZw34YE3kG/O707ra9aIGufrpk35z0u4XJQU7kz24PJacmLnZn1+qz//vxPsdgK5zEwy2eIO/Px5pUlzucgkp6Xxv9WqPFoak9XKDRbIb6Z5/To1VuaQCRJI2s+P4EnKK1WKANLhUGfrPLCgGk6gGPJQDE4UK0YX9iEhBCPi6CUri20IC+MBfwGcTieZnqNHuYGXLs13RJotmj9xFFOuHJ9JT+e1mBhu5uXK0Qw1OJiMUq1alKDPnUtmR+LsNW+uzvYFBwPNm3lQdcvXSJ57EC8FfoBdOz1ISSGQEwZx5UoCPgHCZcrQbGrnTkqnz50jg9awIRmTd99l2TYbmbCff2ad+vVT5rY//kiGYdQooEfzDOw+aMU5vRgmpnVFrVLZaNpUaR4aN2Z+vXpRm3fyJL34LV5MyXJaGhnCqlXp4AEgY/TaawRM48czjwEDCLR27CDzuXkzr+fm3tCW3fAQuHAhGeK6ddnPVaoQ3I4ZQyaveHH+/t//WKdRo8j0tW1LJvL0aRXQ/J//JCiUc4XnzpGeTZ5MprNFC/bdwYMsa8oU9kunTgTjL72koXvSW3jt2CBM3BuHZs00hIUR7IpZ1fTpyiGCkf6sX+8ddKxbRwYvJoZj681j9B/NeP1fS7/W+kf2OTmrDSiQB6gxstk4bjExvB4fz+eiozmn4uM5FwSAGC1fxHMqwHVavz7XSW6uAjwuF0HXihXKI+S77xJYnD5N2pOTQ0dL772nnH507cr3r17lHH/0UXqx3L2bgo8nniDtiYjg2tq/n9cqVyaduXSJXhy7dSPta9aMgqb160mn163jd2Qk67tnD8tp04bCn507OafPnLlhfm2ntlBo7oYNXJt9+hD0ZWeTDn76KUESwHb5+1NLFxJCGl+lCvvO7fLgeOo1OK/rmDyZFgEXLpA+BZYPQd/am1EcDuyp1A0OvyCsXk06MnEiy5o5k1rHTZs4Pm63Wqv3368cm8TG8vv551mHqCjyD+IEx+kkzVu+nGAuN5dAuFo1ZULbti3zaN+e+9fgwXxv3z7STDHdjY29MfcsHrhOp8MeoGPIECUQjY7mOMl83LYtv0Ddm2InNpY01egjQb7NJpx/BpPNvwzAMycjGPEFrIA7A3fmoIcFmdYZy7kdgCgIVHg7b3U77Z5ZWubLjM+cfIFRySc+XgWi9JWE0Aso8Hb/bgEpUaU7HPlDBZiTEXSaz+T5Akx3Wjdf4yMaM291E1DVogXnlcuVH2zlq5PHA6Snw2YtWGPma66bvYpKPc11Nj4jwFRcVy9fTuI74L4MXAdtCYvhNMX5f6d86T//UeaWR46osAZVq+afZxIWwWJRHuYCA3nN35+butvtHfgB3KQB5muxqJAMpUvT+YnHQxpXpQoZcGGQnE5xyKLhfNEGCC5TCp5i5XD9unZzropm5+JFda5MgITNRg2ixULwFBLCel+6REYmI4PtjY2lmU1wMPP4+WcF7GrV9MB67QrWrNFx/TrzCQ7m2brt21lGeDiZpbAwArzhwwlQQkMJtgLdV1Hz3CrosCLthA2fz3SiRg32Q0YGGb0BA6ixe+45MhxbthCIlCrF/q9Zk+BS3KmXLk0Js83G55OScNMUef9+Mi+DBpHGjRsHfLuqNBb4P4gZGIzYsqnYfy4E06crEDNjBgHWli3Mo3Vrtq9jR7bB5SKzExhIz3HibODzz8kwtmhB5srhIJPZsCEB4aFDBFEnTqjYYSdP8tyQ203Q1b07hQMuFzVyb7+ttGweD8dh0iQyUevXs71WKxnR0qX5TvfuKtByjRqUwDdpQmAvEvqqVcn8TZrEfs/KovYgPh5YnmCB654ymDlLg9VKZnrwYJZtt3Nsp01ToRAkOZ0qlheQ3+pBTKqsVuUBT9KvBSt/xXQnFjQF3du5U/2WWHjmfWblSn7Ey6Ro3UaOpNZn1CjeW76cfEdWFtechE+w21VA7Ro1OM9atuRacLkIygRMHDvG4wWtW9/wFruFghPxKBkWRlri70+h0KRJPGc3eTLn5J49DKy+YAHp+CefEBSlpnKt7tlDelatGi0OkpO5Ll96ifW6fp1ArmJFgtDhwykcEm+VP/3oQcNKl/DlFzrefJOCl9RU1m3HDtK/xYtJNw8dohYtI4NaOo+HNGzqVNKPt99m/0iolWeeufHb6UHuv9+EtuBH+L/zb9Sr60GtWuyv0FCgdWsNJSf+B61TPsbovX0QFaXdNK8UEF2nDoVPYWEUyLhcBICTJpE+C7BevpzAGWAfT5tGABoby/z271dOdHJzeb1VK9KSunXZJxLeQoDipk3sg379VLxMmUPxyzy4EN0dE6tOgKNNR9gsHiQn8x5A7bDwZ2aBuljvCW8k8zAuzrcFmDwnWug/2mTzLwnwZADEoUVhz9R5+238L67kfcXfud01b3W43SYkWhmjiZ03QFcYs0VvgM/4jiSzJkcAgixSb+lOzDvu1ATS23vGuG3CCBaUr4BO86L0Zq5p/l2YOhr71awV83YewQj4jCEKvLbB42GGFSuSmzFozMwa14LmujdzTwG8QH7BhYRISEykJHXECD6zahUwc14pBCGLVYOfcuf4d7qZGjbM///4cWqQjh3LPw9EayXgTEyJxP21aOeys/ltPLNn/H3unIopB1BzaLczz+vXWf6995KhAsgAZWVxvCtXtiD9oj9CQzVcv06gkZvL/GS9O50EcWXLUgvkdBIcXb9OxiMsjIDA7aY2jk4+1Fk2iQ0nQG/NGg/0pCRs/N8uXDycgRo1PChRgoDVamUeSUkEKVu3kvmSmHolSnCDDggAKtUNwldFBsMFDdv82sPjF4AyZchIuN2s44wZdJc+Ywal+CVLksnTdZaXlcW+mjCBDITdTq1a5cpkUN5+m2ads2fz2saNBIjff09N4vQZGp75bxhOdB+Oum8/Bj8/7Wbcwbw8Pq9pBKaPPEJN2LRpZFqysxVz9Nhj3FumTVMaUYAMX7t2pCH+/mSETpwgw9m6NcH7hg18/6GH+L10KZkzgPX+6Scyt3PmkEF6+WVFg+R8UkwM8+vUiW0W06g9e0gLQkM5FpMnczwElO3cyTnz/vusV5MmPGt47RqdT2zdyjZqGhnxzZtZzsiR7PsePQj4JC6arA+3m3NgwgQF/oyeh0UbVFhPxH+ngtOdWtB4uycmtC6X2mvN2g+Z13Y7GXBA8WpBQQQNdjvnnQhkJk6kGa8EMt+6lXkPG0aT49mzFaAYNEidv3/pJT67dClpSZUqnJMlS1JbtmAB33W7CbqKF+ecLF6c59t691ZhBqpU4XP16lFLVrw46cxDD3EL/PZb0vPOnSnwaNWK74wZQzPkSZMoVLt6lf20bKkHH115EBgyEDNazsG4cR5Mm8Y9ol49lvXxxzRLfP990o66dSm06tSJdatSRQnDXnuN+bZqxTUzaxZBETIy8MXBKLyHlxCT+jEOJF1FYKAS0EybBjRqbMFL48vA5dbQtSvz79RJ0f/AQJ4J/uILgr169UhLHniA4zJ/PveWZs1o9pqVRV5BnMi4XOp8pd3O/WPmTNKVyEjSotmz+R0RoQRfsrY7dybQlnkqnnZzz2bj402NMAjTgI2bYL+Sgeef572JE9lXCxdyvIRHkjk5ZIiiOcZ4zr7WgZH/Nvp4+CPTXxLgGQfAiMR9JbPGxZv2ygjypAxvXiELc03ylby9nSMwMt4AJ7SxLr4AnRlc+PJSae6v251htNluJdTm58zBtn/tBmvO15jMoEikLr7yAApelAXNgV+SjFo9s6mlUXokY+pLIwyAIruNG/nAxo1ARka+eorTgYKIE+DdNFPq5nJxA8rKuiF1v2EGIQyUBC9u3x4Y9s8LyAVVTxbkKTXS3+lmEs0c4N2EUlJKCueDxZJ/Xrrd+TVyAAGWvGuxKDAHqDVy7RqBj9VKc9CzZ9W8yshQGhdN4xwoU4aaPZeLG7OfH8vOyGC9ypXj86LtunCBZ0Lq1uW3AJeqVQkiu3al6WejRtQg2u2sswhkMjMJQJwOD4rqZ5GBsghGBvbs8qB2bb6fm0sw0aYNtW9r1hAUNWtG6XpWFtv34IPAmjUaDvg3RtHQOrhaszEeeEDDpk1kQCScgsdDMFSnDpm1c+cIVFu0IIP0yCPM225nv1osSnL92GMUbD30EE2JVq7ks/37U6s3YQKZlvTzFjgtRbAsXkODBtSaffghGaOICNa5WzflsVM0dV9+yfJefpn0rEkTlv3f/7KcoCBK4uvXp0ZPYmCNGsXnN2+mMKFYMYKyTZvYnpMnyegFBrINy5ax/hYL86xWjWAsMZHCsZo1eb1lSxWnMz2dtKBmTZZbtCiZPD8/zpvr18mEHz3KOS6atuLF2S+vvsp+a9CAc23ECDLrogEWj4k5OQr0GWmvvz/7dsQIzgEzfRONni9Lkb9T4dPt9rrbWSC1aMH9w9eZJSMP4u/Pue9yUWss4XiWL1cmmzYb57h4OZQ9SOjIli2cvzNnUhjTrx+v1anDb9kXx42jmXFaGi0A3nlHOQwrXZrCqogI0rw9e7gW2rShVuzSJQKXTz7hGT05x7xwIesqZ1yPHCFYzMnhWlm+nPNy2TLWcdIkmjE++ST3T7ud5aafcmLZthDMRD/sP+WP2vdmw8+P2qugIPbpsWOkh6+/zvVRvDgB3+HDNFlMT6c209+fbRw1im3v35/v+vsD9sql8UydzdiNSEyr9B7qNgtCx47MS0wWQ0JY7rZt+TWv4k3ZamWd+vThd8+eBNK7dlFjV7w4z+qGhJCmTp3KvDp04D6xapWaE+vWEbA9+yzr1749BTnPPEN69OKLtFhYv5512b5dCTxlDqWk8HvfmRAMbrkDdosH4yuOxwVL6Zum3fXqKYAqRwVEg5yQoEyIha/1Fo9Zvs0g0Din/8j0lwR4kgo7AGZQdKfOPm5XZkHAye2+9RyBUfol7/rSnPkClebfshmazVl8tc94Fqww7fOmpfKWb2GuG/u3oHxvF9zbmEdi4u2fk7beqQTYG6D1Brzl+qZNlB6Z4875BJWlS5PzstmAli3hKln6tqEijMksLDDW2/i7QQNqAsTlvARQN0rVV6wAgqqWQjXsBfC3Bs9X8rZeg4KU+aX0vcPB604nwZPFokwjPR4VRkHMIQGCPtGu+fkpTV5EBIHL9etkRkJCbh2aunX5vgBH4xwIDmZdJCYewDzKl+cmnJvLeZCaSsBy7Jhi7nSd4HLDBkqoK1YkCLDZCOrk3F+dOmTiGoRbcc1eGXbkoJTlOnTNikWLyDB07cr8jh1TdDEvj6ZN1auTwXO5WNarrwKRkRY4NTuqV9fyeRrds4cmjS++yHcffJDnzs6fJ8MUGcl6fvwxAe0HH5Ap83gUw/fwwzSPys1lfSpXJvPy0ku8/8knHIfDh5XWr317moOuW0etwekbVswtWxKIJSezfj/8wLM+jRqRkfzwQzJF06d58MLga3C7dWRmUvuwciWZxJMnCX5dLoL3994jcKxUiefpIiIoUf/yS5p47dihYurNmsU13qED+23CBDJYTqcKMr5uHZnP/fupXRRBxbFjbG+bNpxzVitBd82aHJNr12jWWr062x4URNDWogUZ4vfeIwNs1N48/DDLlQDIErOqRQs1hkIjk5N9e7z+O/265E2o7S0VZIFkt5O2CFD3tifK/IqOJmBPTOTvuDilLZI4mhLjTMC/nFG32QgsXnlFCXCysyn3FBP35s3VHvbSSwRfJUvy9wcf8Ntq5ZpMSCBA272ba2P5cq71H34grapalUKl2bPJO4WHc95PmcJyLBbu57m5XH9BQaSXpUrRquH8ea4/gPO6a1eWbbMBDZsGYLp1FB7D/1CrvBt5RUPQsCHHIjmZffTqq6QtPXqQFsfGcr3Xr08B0PTpDOnQtCn/d+umwFjVqix31WoNBx5+HU22z0LjKf0QWESDzcZ31q1T9CQ5mXVck+hBi+rpCCqqY9gwlh0by7558UUFtKKjua7XrKGmdds25tO8OftStHALF/Le0qX8GEHV7t2Ay0kPxEFF9ZueSb/6StGEjRu5h8gxBZtNmV42aKghaPUS5Ow+gtofDsBHH2vIzKSVgdtNWhwYSNprPDYUE8NvuWZUBBkBnfjcABQIBP48JuCabhYf/8lT06ZN9e3iO/UXJiFYBZkxFgbkFGQC6c0b452aHpq1NsYyEhIUgTSChDstx1xWQVoub/3lq73ektjHCyH35uXR2zk647PG5wpq852Mp7f+9Pa8t3r/muRN4uyt/NtdB0CuMyODYE/TCn7WcA1QEnkzETP2t9mW3OVS4C4hgcQyPJwu5t8anI6aUefgRn20xgysPfcQufkCkqZpSbquNy1cz/05053QpokTlVmrpKJFCX7MSbRo3pLxHbudZpaVKilp9b59BHgC6MqWVW61Bw2iSc2VKzwP16wZx7JYMYLIwEBuwhIyoHt3apdOnybAKlKEG/WWLSy7WTM+63RSC5aTo7SI995L5v7oUTJr27aRuWnalG3LzWV5FSsSBK1eDZQr68HJoy7UDPODrmu4dIlnFS9eJFNWowZBTJEivL52LZmW6tW56UdEsM1JSTzPlpZGZyBJSWQcQkKoqTpzhksnIoLtqFZNueIuW5bPly7N+d+rF/OaMoWMX7t2ytRTHMrY7QQ8cXE0RfT3J/h56imaER0+rM7AhYRQmn7oENdSeDifHTWK5mHHj7OP69e/EZR4hgejmiQgI9uK8Y0/QV/XV2gfa0F4OMHpjBks48QJrtkzZ/ju7t0EVs8/Twbp8GGWlZtLQO5wcEzEWUpoKOdO5cpsa2ws2yFM9c6dvB4SQgDscpGBP3iQWrxp03jtzBkylcKQ+vlRq/HPf3Ju+fuTOVu4UPVvYCAZL6ExixZxDi1YwLndtKny4tq+vfKGFxh4q0nmnexPBaW/Gn0yp1+y35v36oQEJUCWPUXGyuEgMy334+OpYd6wQQkSxbIlNJTAbdQojn1mJjVEcq5Ozn7Z7aRDb7xB2hUTw/17xw7SKRGsPPoo31+yhMKa8HAy/DVqcD7n5fGdvn0JWmbN4rqMiVH08sgRCoimTiVtbd+eZZUty3JiY6l9X7aM6+vkSXrjFfPpI0e4Zt97TwUWL1UKSEz04POxGXhvdmkkJWkYPZpCo5kz2X5dJ+2TM8MrV3ItzJtHWhQXxzrVqkWt6PTpNDVt144CnEaN+HvsWJY/diz7LT6eZtseD/ePBx8k3cvK9ODT1p8i4sTPaN5Cx8xui9CnrwUhIRw3MeufPp3aPDmDB7CcbdtugMQ1HN+gII5Xu3bsy0WLOMabN3Mst231IPyn/yAwdSfatnLCtWAZVq+xoGNHzq/Nm0ljvvmGdE/6wO1Wmr2+ffnp359jNm0a59ChQxSkA+yjhg25NyUmKq+b8tvMp4rDIDnja7b6ulM88WtSQbTpL6nBM2tg7tT8zpf2xVve3p6/07oaGW+5ZgQj5vNxUqax/MK0pTDaK1/3CkPsxZmIHIQ3v2Muw9uzxroU5Bn0TsbTW3+a+0/q7w3o/FJJja85Ulhwl6+eFguRwA31jrlPfPURkP9wsbnPRAMj7snlXaNzg6goEtTPPyfzmHymNErcOIOXidrkjv9kSdO0TzVNO69pWoqP+5qmaZM1TUvTNG23pmlN7mb5Rk0SyyPzKskYk07AnVHrZ7MpiXaxYmRKHA4yDps3czzE69j16yrvtDSW5XTSw9rFi9zEc3PJzBQrRsB44QI39jNnuHkVL04zmpwcJQEuW5ZgpGZNMu7nz5PxOXyYeQYFEbBZrXxv3z4yY+fOqRhOPXuyfiEhLNPt5ufKFeDyFQuu5flj504NubkEURYLJeYBAcwnOJhtiokhMBOzw8hI1iMvjxt3ZiaBzpIlNN8R7dzq1WSSwsMJWnJzme+bbxKY7N5Nb3jS9+3aEfD5+dHk8ehRer785huepRGpbkoKgVlMDKXSeXlkbo4dU+PXuTM1Xg8+SOZD1uipU9QQfPUVAVZSEvvqkUeAoNwM1M7ejAhswYTkOLRufBVBQczryy/ZBoAM5bffEvBarQR3Nhvb8+OPHOvsbJ7zq1ePa1zi51WpooL/HjvG5/392Z8PPMBx6tmTZwxXr+ZYN2jANq9dq7QrDRvyHFDXrmQqAwL43Pnzyjth27YqFIiAO7ebjOawYSxr0SLS5//8h9J/eU8CnsfEkJmNjr7VYZVxf/o7Lt4vT+b9tzAMq1jHCP8ixx+Me4rsN4mJKs4hwN8JCWTSxfOmeDX39yf9sNs59iVLKscc4qQnIYFraMAAAjyrlYy908l5v3IlAVJwMNfZjh1ci//9L4GCxUKQVb8+gU9GBgUMAP+XKUN62KoV10XdutwTdZ208vHH2d5z51j3U6c4j/PyaIr52GNck9u2scy2bTm309LofKVCBdL9Z5+1YNT7ZVCzpobXXiOA+f57ZVItIU9eeontzczk//ffJ71du5YClblzCZzEsdfq1VyLTif7sXlzgpuPP+ba++knlt+rF81YrVbSiu5dXKh5Ih7NsQGTN0WifJFsPPooaWFODoFdfDzpzvTpFJq1bk3gnJCg5oPbzbo5HFzzdjvrPXGi8praqRMQXiUb+1N1RGATlm0IwoR3r2Lu3PwOWsqXJ+2rWFHNgfbt2abhw0mXPv6YmsyQEL7TqxfB3fTp7AsBdzKnhN+Ljr5ViSLzPy7O95Eeb9jij0h/SYAH+AZlhSFgxmcKY3J4p5uMaEVuB0rMeRvfvx3A8Va32yVznneyYUo5RrtmqYexjuZ4bMZnjXl5A53GM37m8SxM/czvSv3MoQkkSX3N5rO/NPkaR+O5BeO4GcHmnZZr7iNzuyVvh4MmJmKLHx9PhvXbb2nOMm8eiXKTJiSYViuwfZuOLJDa+uP6rYfM/hzpcwCdC7jfBUCtG58BAGbczcLlrBtACaeuU5MCEICZI0sUL04AY7FQGybjnZdHsCHOUQCOwYAB1O5VqECNlgQUz8tT4QsyM1mWHGoXl+Kyjo4f5/Vr1wiqKlXi706daMJ55Aj/u928d/Ysz7qUKqVCNJQpQwDh7w+EhHhQu7oDWVk6Ll8mw/b112xbly6s26FDZPbuuYd9UrcuJembN3Meli7NzbtePTJo/ftzE962TZ1vkdh7ZcoQIGVnk9HauZP92qQJzaM2bqTzjt69qQWcMIHg4/77CfD27yeDImapZ86QUYiIIAM3Zw77+tFHCXy6dyfDNWKExP3zYOPiS3j9PzqOHye4jI0lczFrFp0eREcTxPTpo8Z08GB6wxszhtJ/TWP/HD4MTPm+NPLKVEEQnOhY+yguOIJQowZNo1asoClnrVpkZp55hgzjhg304OdwsD+LFCE4j49n/oAyX9u3j6B9/37ONV1n+8XF/LBhPNMjbtQ//ZRzJC+P9OL++8lsPfAA63P5svIAOmAAJfwi0W/VSp3djYxkv7RvT03BK6/QjColhaapDge1fs2akbEKClLBsMWSYNOm/MBBktAxbzFt/053ngqrjRDgZhaEu1wE5kbwFxNz6zn51FQy6HJdBI5uN8GGOFCLiVFHByR9/z3XwhNPkKm3WrkucnIo5Bg0iFovm43rOS+Pnrb/9z+u8cuXVYw4TSNYSEmhhUq5cgRs167RNLN+fdKXw4e5no8dowDEYmF+jRqxTqdP87k1a0jTJG7c+vW85+fH9ZmXx494OC5fnufXAgNZt02bCHDFdHTcONKSTz4hsMvIIGC1WglcvvyS+ek6y2/cmO85naRDFy5w3Z08SeA5ejS1i3PmEDwnJbH+bdsCdRv6oVOLq0hEexwo0QKB5UJuhoex2Wg1Ig5VdF1p/du3J/0VM2unk/muWKHMISXA+tixHIusLCCwXAieab4HM/Ec5t3TD0NfDsKUKcyrTh0KAKKj+T8rK78W2Kj5ffNNNV+MXjAHDGC/SsgN0SQvX05aImEfzDx0YXwayJz/Ix06/X8P8ArLeJsBX2Ge96XtMTLlRtBhdtf/S8CAt3e8aWvMXhoL0v4UVhrnzfOjMe7f7eorfWaU3Pk6yyfXZPP2laRs8wZubpMvrVxhkjk0gbE9xsO3AvgKYiKkrsZkPANnlj4LwDVrLb31o9kZz+3a6K09LhcZv8REpUFt3Fg9a7WSAXvoITLKublk5p9+mqYW8+cDoWWzURSnAAB+OPenDJOg6/paAJkFPNILwBc602YAIZqmlbtb5Zctq35v3aoAX/HiCg8bQWB2Npl1scQFFENz8SK/AwIUWJo/n5v66dNkRiRpGhkehkDgGAL8n52tzvfVqKHO2hUtyjko4RA2biTAq1uXoFPXyQxduULG6tQpAssDB5QZ5r2lPMDJNKQdvILU7RexerUHdjufdbnIKFkslKgHBvLdlBQCvhMnCJqKFydDkJREk79duyh913UyOzVq8Jm+fQkwJJ7UP//JzVrOG548SQ1Wly58fudOzt133iFj9/bbBJgeD0Hdjh0869ikCds1YgQB1P79BHrffsu61K1LLVPZskDtWh5UvpCE3CHDMKf9Zzh31nPTacqsWaSba9eScXz9dTKZ9etTovzMMwRhP/xAKX1UFLUSkZFA//4azrZ/AgO3P4vi772Onr20myaNx4/zTM5XX3ENHzxIgCvObbZt43i99RbH+r//pSkaQO1Cp07UBDRqxPIHDuTvadM4DlWqsE/+8x/OiaJFPJj1TjpsNh2zZ3P8f/yR82vMGHrBlLAIQjd692Y/iabA4SCz53aT0ZwwQV0XwcPKlcDWLR4s+iQdpe7R8+0jDgf7MjExP6000zaj062/02+bfAmTzdeNgE+AoDDNXbtSKBESop5JSCDj3bEjtd6A2veczvwWDhYLAZ3brRz1/PwzTdIffZSgSeKpFSnCZ4WWpaQwX/HSmpJCunP6NLVu4Q080K9dQ0CAjjZtOKcqV6bWKra9B99OT0e7diTi333HdeVyUfCk66RFX31FkJqXR4Fp+/ass8SXXL5cOZbp0YMA9sABCj0EFI0YwXYANO18+GGurcREAruRIwmAypRhf73yCtfXF18QIHbqRPr16qvsw549ua8vXEj6+tVXpE9t27KMnTuByVM0BCUswp6Rc1Dz2TgEFtHQqRNpvM1Gwct//8u9aPhwakUnTPDgs1kOHDrIGHtiTt2/P4GmrPUBA/j+0KFsx8iRQOYlDZ92mYcBO4dg4v4uCCmh3dSyBQUpMJebqzSUOTmsg5xN9vdnu4UHdzgobHv2WdLb6Gi2b8kS9l1UFNtq5L/MPLTbrXgk4zw3zvU74a9/Kw3f/9cAz5sG5E7fKyh50w6ZNVVGaaLNptz1m7Vs5jwK8kbpbRJ5q5sZCBjTnUoVzGXZ7UrC4Svun7f6+tKWSj1FPS5ukW83duLhUTZwM5CVvjBr5cyaN291NgJQb/0heRuTLHxv9RXwZAb5wpy4XPlBqtFblZTjre+EECUm5teA+mpjQe0B8tuVN25Mid6SJSomTFAQN53WrcmwDh9OJrd4cTLZCAlBNkIAAMEI/FOaaBYiVQBw0vD/1I1r+ZKmaQM0Tduuadr2jDsAshVMOTmdStsUEcFrHg+lxcWKKUAGqLkgYMxupzSzbFlu+Bs20LxGTCmN4RKCgjhHixXj//PnOabXr5NBF4ZB11lm1apkQu65hxq/kiWVx15xjlKmDK81b04QInWX/E+dAsqVdCATJdEUW1EVqagX6sQbb5DRaNzIgyI2J6pU0W86XA0JoXe1gABqLJOTyWDVqO5B45pX0Chcx5kzZM727eNa6NuX9cvNJSPUujUBkgQLz8pie8qWJWiUOFUnTrAPqlThcyVKUNJttzOW1tmzBDo7dxLsvPQSNYCVK1NqP34822i1qnNA333pxKPXpmEt2mLgqX8jqv5VFC3KtpQowbNy337LsfzgA/Zd69Y0M+rUiZqqDRtYVnY215/bTZCmw4LNR8tgR7KGlBSC+RUryMQdPsx2xcayHv/7H7/btCHjs2wZ1+wLL7DtaWns7/vv55imppKh7dRJOYipUYMANzycWofBg4H5P3tg/24O9kQPQN2fX0dUcw+KFiWz9MYbHP+tWzmGCQlsw8iRBMsrVlBTYLGwTvPmUTDw9NPUuHg8BHlLl3IsP/7Yg83PfQ57o1DEN3oJLqcHLhdp0/jx1PCJY4eC9oq/wd1vl4x7vNG8PyZGAXHjdTN/Y3YmZ7Mp4a7swdHRymtjmzbUfDVuzPNTO3Zwzci5zqlTKaD4/HPOKX9/zrdnnqGAJzdXmTD37s31WLkyacTly1yPQ4cC69Z5gDwn4uJ0/POfwL//7cEnQ5NQdtl0lF3/Dfbs8WDdOp4J3LXLg3Ujv0Off1xCo23jsHUr7y1fTjoWFsY6iOv/rl25PnSdAhS3m+uxRQulmRYrhp9/5hyfO5fm2Fu2sI/Gj+d6jYujMGrsWAqoBg5kW3bupJAlKIgCJenTnTsJeg4cYFliEt2mDcGXxULwJ4DS7SaIS04GXB4LItoGY/RoDU2a8DwgoIQts2ax3hs3AhvWezB0RXfM31gOH5/ugpHPexAUxP795BPuO1Onkn6OHk1asHkz6yQxDK87LZjyfRlMmqxh3jwVoF40f3IWLjyc9ZR2SagE0SKOHUtT9zFjCKhnzCBNstmYx+7dCuxpmuLNJCSUka7Exam5bea3xGu5mOsXZu38Vmac/18DPCPYKgjQeANYAs4KY9JhHhwpx1yuUT0MKHBSUKB1b+0xaot8MfBm7ZiZAN8umSUTZmAipgUFqbGNANhM+OWaEdyJlETs8c3XvbVPDl5LHCRfpqlmDW1MjHcQLXU2g1FvZYvGzNguMRvyNX5xcSQq3sxOJfaKEGFj272Nm7EM8TolEi2jdtFcd7NE1aiFlffEGU5yMglj+/Yq2Lq8GxJCKVhICD/t25P5c3s0AA0AAFeaPaaQyP+HSdf1mbquN9V1vem9EtysEMkbsylewMQkxuUiULt6lRumAD+XS5nPAWQexCzx+HGl0cvNZddfucL/FgvNioKCVPnnz3PzEs+bxrrk5ZERKV+eWhiPh8AwLo73+/YlEJBzZadPE4BoGutQpw4l47oObNkVCD84sQuNcBj10PuBAFSpApw66cHqz48i88QJbN3qQsUKHvj5kdkSzY7dTrD5j4c8yNiUhrVfH4ZrdSKqVPbg6lUySX5+wLChHnhyHVi5Ukfp0mQaRAOo6zSDBCg5L1WKYCUlhe0LCCAjKOZNHTuSSXr7bQWIZ8+mFPzoUdbv5Em28fnn+UzLljQzPHkSKFUhAPeGlcc4vAxbVBNsTw3C7t1kREJDCcKEhs+YQW3dyy9zDQUFsT39+pEBnTuXEmmnk/T2/vu5zsPCSEsmTyZIHDKEQMrtJnNlsVAin5ZG2r1vH9tVvTq1GBcu8JOSwnxfeIG/N21SJpqNG1MT+MgjlOw//DDrs3VFNvRTJ5ALPyw8UAuWa1fRqBGZ0VGjOGenT6d28h8PeRDouIRyZXU0aUIgffw425+URLoyfjzw4/ceVPRLR/36OiwWtqNXL+DT9zIQdWYeViMayfv8cGF/Bj78kGC7QQP2f6dOHNvCeGv+O/26VJBQ1MyjCA/kjReSJPuinGkyCqeN+dnt3F/WrCETn5tLoNOvH9f3jBmc/zt2kIf4+GPSrC1bWE5oKM2oLRau1Sef5Jp64QXu2y1aUIverRvnuNPhQfG0bQg6thmbvziAjAwPmta5igeufoQ1aIfk8xVQo8JVXLpEwUitqg40Pr8Udjgw82QvNKh6Ff36UQBRtary1mmzcZ00a0ag07Urr124wLXRuDEFKj17Ujb65JOkYQMHUstVvDiFa889dyNGXSPSlQMHSEO2bCE4W7uWYMnh4FqSeJMjRxL0uFykZQBp95gxFORFR7PfZswg7cjNJSAUBy2rVilnNs88Q7ravj3HZP585jl8ONek+2I2Zm4Khw3XsXmThg1LshEdTc1i48Y0eR0yhNrCt98m7WvalPzEqFEUsI0ereaLvz/zBgicly4FnNc9aFMnHV066zeFbJqmzEZlTvXvTysMI3++fj3nStu2rIfNxn3h2Wfz86k7d+YHcsa5Lc8JTym8WHQ0aVthFUW/hRnnX9KLpiQhVEavUEZA4MtrohGYGL+N9wpKRuchYjPsK/ZaQXndTgVsZOA/+kiZqNwuXzF3NJq0yOQ2e06Mi1N18JavL0cp3rxjGvvQW16+8o6MVFIf83vGPI31NHrNNPaX1M3cfm99JPU3tikx0TfA89Ym42YpoM7bXPT1vjkvb95OzZ7MACV9iozk3BgyhMT1ZvuuebBybjba9grB+g3aTWmZ2LRPnsyNAlAx8bZsIWN44QKwbh3tB9u01rBm7e3lSH+ElzpN06oCWKTren0v9z4GkKjr+jc3/h8AEKPr+llf+d0Jbdq5kxucMVks1Eg5HAQQR47kvy+gGyDguHxZ3ROmGSATUawYgZfVSjCTk0Pt0YkTBA9BQQRG+/aRKapWTW32nTpxPZUvT6BWqRJ/r1hBRioggB9hErKzWUZ6OqWou3aRmdI0gqy6dfn/xHE39iQ5EVAsAE6nBcWKAc0aXsGqH86hG+bhDEKwvdTTgM0PXbtSuJCRQQnxwIFAkN2JpodmIh1lcA0l8ODrEVixvQRSUoBBAz04PfE7nDoPVL3XjfhSj6JkSQseeojn/M6cIUPyww/UAmgaQfHhwyqe1OjR1CSlpbEdDzxAhiU2llLt5cvJIAI0vdJ1ApCUFDJE586x7xYv5lmY5B0eICsbO46EoEZNmlJu366cRHz6KZ2z2GxkVBYs4Ni0akVTx0GDON7z55Pp7NWLZlkVK5LBkoDGAQHUNqxZQ5r82Wdkzl59lWO3eTM1wRUq8Nn69VnWgw+yj0NDaXK6ZAnX71NPsY9atGB5p0+zTg4HNYCdOgFBRXWE/vQWrAf3Ylf57vDv8wTCwhiGIjxcua4/cdyDPkvvw4Yt/ngv4E0sOhiGbUmWm27qO3Zkvo8+4sHXV7vDvnk9PqgwCfXHPoUDBy147jlg9As6xh7sho2brGjZwo2Z3RZjwEDtpsOOxEQyofv3K4n83WaW/opeNH3tV2Z+ydv+DRTO26a5DHlX9imjgDI+nvO+XTvuP8OG8Zo45nE6Oa/Llydw2r6dazQyknNNLI3ee4/3ihUjzatVi2ureHHSsgULOJ8aVr+CfdOX4DzKIgz7cKjJMyhVMQBYvRJlr+zEjsAOOBEcDodDQ7VqwPnzOv5T5B0cPmzBhZK1cKDOQyhbVrvphKpSJdY3PZ31io7m2lu5kmbTkyfz98iRbFOTJgS0YnIfFUXh0b//TU2UOBWx29n+AQMI+Ix8VtWqBGSdO5Mu1apFqwCXi+t93z4CxJwcmm4+8wz3ho0bue67dOF+EhFBwQ9AoYzbzbp07UqBk83G/CReqNTLZtXhat8R9s1rkNO8Hda8HI+2MfTynY/fcJB+SRiX994jfXzuOYI9EaZL21auJI2B7oH++Wewnj6BZ6OSkPTqAuRctcBq5dxZu5ZayfHjSePHjGEe27crYbiYy+/Zo8Dj+vX8Ft5czEhF2SH5G/lJ4YeN81ne+y3T3140vSSzhsubVsyI0M0aMLPJgfGemWn3xsSbAY/cMz5XGEcrvoCEpCVLKHnp169w4A7wfl5BJGjyOyYm/2FVYzvMbTUefJe+k+ve+kBU42Z1t7kvZXyEUMh78fFqg5DrRvW5t2QcO3P7vY2B8VyczA/RPJrzNefvrcyEhFtjGwJ3dlbSmyTIeM2o1RMt9bZtZCSNfei45sHKZi8j+YkPgS5xcLs8WLlSgdj166k9kOd371aOE2w24B8PuAEcAwBkrfvu/6o4fQGAJ29404wCkF0QuLvT5I3oly5NpsPlyg/uAgPJmBvnrtHMEiB4kzN7Hg83rYAAaubS05VHRI+HZjESakCcY2Rl8V2Jw1ayJAFoVhafTUjgexKHqmxZfkJDCRzatiWDlJSk5tLx42ScNmwAruV4oG9cj+BLB1Hq1E7UrOlB5crAlbwgVAi4issIwgb0gm61oX59CgkkkPq0aSyrZ28/rNZ6Yg8aoUiQDdO/CUGVKix73FgPlp8Pgw6gb8Zb6Nr2KiIiCJQAMkMffEDtVYMGBLQ2G8+TzZ5NRsbPj2ZB9euTcenWjYxXYiI39Pffz68lr1RJedpr3px5WK38bNwIbE+yIMevBDy6djM0wqlTdBBz9IgHn72fjqTtOqZPp5bswQeZ99y5BGht25J+nz9PTSRARzIDB3Lshg9nWevWKcYkKIgmmElJBLHh4TRFe/11jmta2g1tQy2Ctnr1lFnXvn1sy5w5fNZupyn24MEss3595aSlXn0Nixu/hh87fw489gSeekpDfDwZtJQUtjUoCLieeRWvbrkPrbAWz19/B6X0DPj7q/NSq1ax/uE1rmLlpiJYprfDsVNAq1oZaNSIefS+X4N92SKkvPw17PGLUb+Bhi1bFBMWHc018txzykujMf3fJD9/bDLzH8Y9yByyyBsPBPgGd7KPm61+ZK8H8lufyL6dm0tTRYAgyOWiOaDTSUHBAw8Q+Jw5wzlhtapz4lu38h3xXFm/PsHciBGc/8uXk07u3k3wFRwM1AwPwkX/KshCSSxDNwSVCoDDoeFQxfbYGjYEtvBwdO6s4Z57SPsuXNCwoParqPLWc1gV9BCKFNFw+jTXnDi6qlRJnb8+dIjCmGvX2I7Tp0mfFy2ioGngQLZn506CHKeTIGTQINLt2rVVuJhDh7iOtm5lHjYb176EngkM5HMuFwVDCQmkXU8/zXuBgeTHAgPZXydOKMFW8+asn6RGjbjff/ghtYxi6eZ2U2u6ZAlp2X//C5xL14D45XAdO4U1L8cj56qG994jTcnKUrzahQukOZs2ke5u2sT6Tp+uYhyuX89xEo1bVBQw/JEMPH/6JXjgwUub70dEpYybcQVXr6ZALyGBe9mYMWzHlClso5hfTp/O5wcPVtZoMv+MPK98GzXNkmJivMe29aW4+b3S76bB0zTtUwDdAZwXibmmaSUBfAegKsgR/kPX9UsF5XO3NXi+NgOzVMmb1MobOjcTOyPBkuvG94wSMGMZYvdrlggUpk1mjY0EmWzRgotECOcvkXIKcfalffP2vDdJnzcNntQXUPbVZjBsjoHnrRxJ5vvGeguQ81ZXYyoo9l1BIR+8jamv+Sb9YQbs3jR45nbdSfI218RLpvRpfDyAS5fQ6onqsOMa7DYPHGmnkLi/DGJi1CHktWvznzkKDFTmMdYLZzFhTgbyUA/VsABH9tTijlpA+r0l5JqmfQMgBkApAOkAXgfgBwC6rn+kaZoGYCroafMagGd0XS+Q8PwaDZ5ow3Q9v+mlx8PNPCCADHteHp+tVInaJyC/UEHObUj8OUnly3OsMzPVHChblkzAunVkMmQ87XYy1hUq0FQoOJjaEbudzwUEkMm3Wm94OgvkM6LVS0sjoy/OYxo2BC6edyL7RDoq4ihssCDFrwWKFLWiZ0/A38+DOV/oKBZsQcNw7abb87VrWcfEROadmgpERnpQPMCBslUCMfdnDbVqUZMZHKzjnv1rcCFHQ5GgImjSrynsdg1Xr5IJeucdzs0FC2iS9f33yivnuHHKO158PCX+165RW5eQwLVx9CgZjXPnyNRERBBIPfIINYS1arF+FgswsL8HMyZexYETQbBaNdx/P8fAaiXwevUVD1xdumLzJg2Nmlthm78AiWstaNqU3vBatyYQ1DQCuvnzCUhjYshsJSYSBI4ZQ/C8fTvNmgRctmhBr5r33sv6li/PsYiI4HNdu1KbuXw5y/riCw/Khjjw0OOBiOusYexYzk2rle7S27Uj6Lt0iQzT/v3soylTWH65cmRgmzThvKlXj+24/36gQX0d9id64eMtTbCxWFd8lxYJaBo2blSeW51O4JtvdPitWY6HL05H42Yavur+MxqGazcdVAQFsf/NFiUC8mSP8+WJ+NeaQP3VNHhGKxeg4Di4vvY0b9o5oyDUeNbbm1WQvB8fr2hcy5akV8JMO52s4+bNFDbNns1nBw+mpkocG7VoQY0fQLqyaxeFlIGBBHWbN3P9Fi1KM8XPPqNgJOuSB9czr2LTniBcvUp60qED21C7NmnPwYPK62+pUqRdTif7LC+PQhE/P7bnwgWeP01I4NquUYOAq3JlmgWuX8+6bdvGNXbxIjVzr73GtVujBgV19eoxj3ff5Rp8910KecQjJMC+e/ZZ0pHJk5UH0dxcFX8yMZHCnzlz+H5iIst3OglIdZ1m3qJtX7ZM9fuBAxSWdejAOohJvb8/x8nhIA3t0YPmlqtXKwB6/DhBoL8/6UbPnqRpe/eSTs2YQSDZujUtg3bu5JGApCTl9MXlAqZP0xH+zUtou+8joGUU7KsJIjdvJsjPymJ73G7l7GbNGoJX4beMvNeFC8pxVkhIfr7UzDebNdXGewWtq7tBj4ypINr0ewK8NgByQM90AvDGAMjUdf19TdNeBlBC1/WXCsrnbgK8wqbCgD5fyTh5bme6YAQnvlS+ha2vGeyI5sXp5EQ3mxcWNl8jADMSZl9miYWtp+Qh17yBYeMGX9Ciuh3gBG6tsy9TEXNAVuP91auVeSigJPvGuhnbU9DGZy7HGziMj+c945wwzk1f7ZY5aKyXvGsGqPHxgPO6jr0jZuH5ky8gqHVjuBIS4XJrACh0ADg327ThZgmQoI8bBxw84IEz6xoWrU4HUBU1sQiH3D3yB3bzkv5qDNSxY2QQxFOlJDETKl6cG6Gcn9M0MjCnT/MdiWdUsyYBlZ8frwcHqzNzmZnc8MPCFEDRdb6TmsrNf8YM5iMOdeT8n9XKZytWZL4nT3Lj3bOHWsH33ydYEqcEJUtyc8zKYtnXr3NOVanCup49q+N/VYfi29QoOIIrILVsDPz8NKSn08nB/Pl8p1YtgrrkZJZdrhw1WE88ocBMrVrsh/LlCSIWLuRz5856sD/FhQHP+mHzFg1dunA+d+xIjWjlynS6cPw4pf21a1Pi3K0bGSarlU5Jtm0jYGjcmCZLe/fyrN2DD3LOnz5NxqRMGYJHcUBTrhw9S76ytjMWbCiBn4r1Q/X+sRj1ggUff8zxW7QIeHtIOvZG9UEmimImBqLVkGY4ml4Ueq4DcfcF4tgxDbVrq3hvH3xAc9KJEwlK9+xRDl5ycoD0cx6MeSEDs+aXhuM6TUH79VOOJyZM4Fxq2pTjPnAgz+rExADxyzyI2/E2/A/tg39YTUQnvI3RL1kwZQrnwZIlnF8VKrCfH3lEObzp1Il9lptLJvqrr0hjlixh/R56iGf2unT2oHWDbDiLhCCwiIaFC9mPx46RqbTZSDuc1z0Y+nAGZi0ojQYNtJuMY3IyTcg3bFCgIDGR9ZM4Vb/mqENh0l+NPgH5+80slC6I9/ElBBaeRphrs5DVyK/Ex3PcBw3iM6tWcexbtaI5cq9eXB9t2nBeZGZS4FGzJj92O+no/v2c8/fdRyHByZPcs+fNI7AQi4bvv6f1gnjaffFFyiT/9S9qs86e5Rpo2JD0LzlZmcgfP878pc1ybm73bta7dWsAugfnTzgAeyBq1tJu1unIEQrrJk8mDapYUVlWjBnD92fM4Of77ymw6d5dxWwT88Eff1RA0WqlgOjDD9V+nZ1NS4g5c7jWunShyea1axyLjAyu308+IZ2Ki1P8hpjAisl2pUpc/xUqsJzu3dUz8nxsLGnT+PHUnG7Zwv8LFhDMAcohTps21KKOHk1+IimJfKqEqImJUZq2GjX43sKFBI4dOgB2fw8bULo0EtdocLuVgHDuXPZDYiLziopS/Waer3ItK0uBO+HLhL7IERx4PFg9LxvteocAmnbHSpi7Be6AP4mJpg+35L0AzLnxew6A+36v+hQ2GU0OzMmb5sb8rgSILsh0wZsZn9EEsrD1lG+jlkbMHtas4WYoHoUkrllBTlrM5oTAraprgIvxl6idzSDF2F/G/y4XF93w4QqseAsbIB9f3kl9gTjpI6MDGcnDHPvOmMTEMSZGnWkD8mtPxTOYODLJybm1z2U8zB5XzRJTSUYPnGLi4MvbqJwnlLZIn5m1j1KnuDigS1cN9Sb0w5ovjsOxLBHxy7WbphGxsfxYrSr2kHhAO37Mg7wVq5CxejuAQJZfofttwd1fMdnt+b1bBrK7kJ3Nb10nuAsPV/8PHyZTIdJvQGnx/P0pfbZYyDTIOunYUbn5z8vjGF+5wk36iy9U+SEhlBY3acJnKlcm41OvHt8rW5YmfMWLc+Pt25egJyhIae/Ea2fJkpxXLVoos9EaNTSMDZmMzh92w/2TY/D009pNz4xysL5qVeXJs2JFMkqVKxNA7dtHGlasGOu4ezfr2aYNn0lJATp2sqBuQ3+sWq3h7Fm2uWxZMnmHDhG81a/PdqWlkUn5+WcCuQMHlAOUd9+llL1/fzI9OTkEVfPmkTk8d46MwunTZO6+/579t2MH4My8CseGHdiHUFS7vB1H9jluevvr1o2CkL6vlEZ2nQjsQGNk2yvg6UFFUHX9/3B20WYc/M9XuH7dg8WLqS0T8zE5o5KSQubx8GEyZ/HLPIjZ9i62NR2A6t+9iWNHPahUiXWRwNHicS82lgzq1q3AO2958GDrdNQsl4PAQ3uwB3Xx7f4wTPngKsqXZ9+OHct+klhRFy+yL/z8OL5paewz8fy3YQMZxLJlAV33YPRwB55+Ssf8BRZcQgkcStNgtZJJ/Oorjsv775MeDRwI+AdYMO3HMjh4kEzahg3KjFRojVh2CM0VDZ4xSLY53U1m6q+UjHuPt73JmIx7uLfjBjabMmOTa97iFconNpZrceRIao2sVl4LCqJQJzqafIfMi8BAaqarV+f7orXet4/3zp0jCGzThl4l4+Io9PrxR97r1Yvn1OrXJ5g7epRCkqQkzuUifk507KhjyxbS6I0bCfyqVaNg6/Bhtb/XrUtg5udHOpawwoMfZl1Ahfhp8KxcidxrHmzeTNpz4IASmrVty/KaN2dd/v1vtr1KFdKk+HjSxORkWiHk5BDorFrFfunZUzlL+vBDnutNTaUAZupU1mfMGAKyrl3ZD//8JwWAs2d6kLLuEkJDdRw6RHqTksK+a9OGgrUpUwiImzXju//6F+u2dCn7ZOdOasuaN+e1kSPZnzabMtmWvrdaVSy+1avVGm/fnpo7m431FgFOUBDNaw8eZF4ffMB+W70aWBpvwZg5ZbBylXbTC+bmzcxLzgcuXsz5NGIEhWxGfnHJEhWaBVBHDGSeS1iGm+vB6YGtYzu0e7QcbB1iYLMYvJOZkje++PekR7+rkxWzUwNN07J0XQ+58VsDcEn+m94bAAYbRuXKlSOOizeB3yiZgYDZFFOcaQC+wxl40wzl5OQ/62QEYsLgixnlneRtzkOAQU6OCmNgNM0E8ksmvDkbMR9y9mZqKn0xZgyZvrZt8x+cLYykwqy9KkiTFhmp4uKY7xs1npKHOV/jf3PfiLmQADXxDurLHNSbBtFX3WWDlLJEimkGjsb5YW5jQoIKITF1qvK2KdeN8QK9mZIa7xuDl8uzS5aQ6Ip5zMKF7I/YWDKYbjelfkazBodDSU+PHgX6dEvH2qZDsQmNMBVPAyiDaliPI+7Wf2vwTOncOTLbElmhYsX88eokWSyUKl+8mN8Us3JlMhhFiihNmYAaea5mTQKy9HTm3bKl8q5YpgyFJn5+lMQWLcr3r13jZh0aSsbd7SaQE4+LxYtzrQQEsNwiRdgWcVF+/Dg9KFaowP8ZGay7nPXr3Zub9NmzBGt5eZTKnj/PciXmXokSLK9MGX6XL8+1Icza4cN8T9PIxLRvj5veNzdtYt4//MD3GzSgJBkgoxIfr2Kz/eMfZAIlTMWUKTx/e/o0QXW9emREjh0jQEtKooOVChUU2KtQgWspNxdYvFhH753/we4DNtSqpWFOhdcwaJCGuDiu7+7dyWS0be3Bay86ENM5EGXtWch9oi9yYUMgXMDMT5BrLwGrVZkXiSlTdDQl8QDbP+4DJzqkjoM/8nAU1VG+by/sOBiMPn04P8QRhUjL3W5gR5IHRz9ahg8vPoM1dfoDugW7D9qACpVQ5bWnsWathvbt6QDmo4/IuCUlkYaVLUvmcM4cgjKA2ldx+vDDD0DaIQ+eWPYgPtrWFIeCmyOtUjuEhlrwwAMs/4svqN1zOulAR84/SkBoaS+gzE7btiXtjI6mRjI8nGO+fj3BtjjZ+q2Yp78afTInX1YnRqGhUVtn1s4Z3/OWpy/Lk5wcCihkvxSnQwsWcH2+9RZ5gi1bOCfECVFSEmlIuXKkKT16cN6uXcs966efSOPS07muPvmEc/DaNc5x8UB88KAHdT27kQMrMlAR/3q7OEqVpkYe4Nx0uUijU1IIwCpVIvA7fpy0LvN8Hopm7sc0DMLLmITBH9TEjiMlcP68AlKlS3MfT0lh3evUoaCsenWaDD7wALV2kZEU9Lz2GrXmhw+TpoqzlBYtCGwzM9k3CxZQ4HfiBNfvpEnsx8mTlXOVllEefN1+Fp47/jI21++L+ovHYMbHFqSm4mY7R4wg7WrenP0tJpCrVnEfe/VVChMPH2Z/ytGN/fvZjw0bcn2uXk3hE0Dw53bTQuLQIYLaJ59kn/z8M2n2yy+T5gtvcuECtatjxuCmVYS/P8uW87ci3DTOsZwc1rlRI+W8RY4trVnDuvboweeNR6ME9BmP9qxbcIngzk0zFdexU7BVKON1rt9tc0xv6U+hwbtd0ok0vaLNX+qK3Fu6nabJqAGS/4mJtzLrMTGFi1VnNHEQl6nmZ2QCGKUF3vI2ammE6ZcJbHZF7HIpJl7cthpNH+U5s5mi1FkkpPKO0TmHMdntXHBt2+Z3CeutH731v7Huxv4y3pdnjODOWFejdNAIvo1aMaOWzBziITqaxFM0k+K4xawlNNfZ2J8F1V2eDwpiGbGxt0ovZX4UFDfFZlOhFMSjlVmbatYOykYr82XJErbdrLkzHhDOySFhFlt3gNIyh4PMYk4OieDUqWSA58zhhrbteGnsKtcdp1AdPLYGXAD+lIHO/+gkmjRJxkgSRucptWqR8ZCD8MWKqbN4ABmSChXUMwB/+/kRoB09Ss1Y6dLK1M5uJ6AqV44awbAwBfAeeICgDeCzbjfzOH+eDJUcpi9dmr8rVeIGu349PwLSHnmEWrmLF1nfevW4gS9aRLBZpQrBaGQk883K4m+7nW0rU4bMQqlSnKdhYWTK5s/n3AwMJCPmdpMupKZSK/TOO8wrOJiANjSU+U+bRjqVmUmQazyj+MgjLPPrr9XB//79qXVr25bt6NiRzJe/P02Jrlwhc/jhhywnKooMgtutAW+8CevIkSj54Wv47DNqrcaNI0PSpQvzfPNtCzr1KoKy5TQ0jwuBf1htpCEU7tr14S4WgmXLyHTY7ZTIP/cc1+CqVWTYBg+m9H3hEj+cvyccHtjgLl4GDVoEoUwZMkwVKpChFQYJIB0a/EA6qlzchmkYhLkHQrGz4yg0/WQo+q97Gt99r91k3CZOpNZz7lyC5D59OJZ2O9vcvj3rdPUqwd6pU5xjNUtl441tPaHBjYArZ/HT9Az84x+sR2oqzc1OnmS+c+fS5E6Ea1armscC7lq1yh/UWBjDNWvY7x99pCT9f6ffJnnb24x7oNFhGuA7FizAPSc+nnvIwoX59yzhj4TR3rhRledwUNi4bx81OBUq0Gz62WeVJ8QLFyjAiowkjdm7l+Bm6VJ1tmruXNKHZcu4jseO5bNWqweBfk6UvldHYCDndasIBwKRDSs88MNVzP3eiTlzSP8uXyZtES+Wbjfp3/btFAjVrUuQct1lg44ieArf4vFKq3HofAh27iQQrVmTAK1qVX6Cgz14pMcVfP6Zjho12Dc//EBzzv37KRyJjeWee+gQ9924OArYJeTDli2816sX383OplCqcmX269q1XM9NmrAfRw91oPzxNQBciEr5BDMnXcXevdy2ly8n3XrvPY5Dv34EV7m5XH8SqsLhYJnh4azH7t1c0+HhBHd79rAtO3cyH3luyRLS6NKlKSRKS2O+QUEEd926qbPhDgfBb8+ezGfgQI7vnj35Bc/LlyvN3PLlLNduZ3mlSuUHd+vXkx4HBvKd5ctvxPpzcS8YN47zUPglmw1o1zsEtlbNCe5atMbqvaVv4bmED/6twd3t0h8N8NI1TSsHADe+z/8WhXgzgfSVjITLl3rVG3Pv7b4xGT0zejMxNJpTCkAx1l+ApZhWAvkl+lJfo0RM6iGmoWZwaTQ9NZs2JibmJ9LegKyxbUFB+QONG9tobJ+vmH++zES9PePNDNNu5yYvGjIjqPPWJ/K/dWu+I7bq5j70NZ4F1de82KWfjXU1m7gYgZvZvFQANsC8jEDem8bQ+F/MQwUECyMl74lpk5heiq27eKRbuZKM3HPPKfv4VavIdDVsSELeuPENZwl5GtKiHoc7tgeEtJQG/q8GOv9NU06O2gSsVjK8pUtTYitnO4KDCaxOn+bzubkEbrpOcCHp7Fm+n53Ne3l5/FSvTiCzdSvNHDMyWKafHxn106epkZNAsW3aUHJaty6ZIgmbkJvL+0WKkHkSrZnDQQYpL08dwhcp+MyZrEuZMgQ1PXuSWatZk+adR46QoVm5knmdPUuGKzub/9esoZZw/35qqj7+mFrCJk2UN7jERDJJUVEEeW+8QY3c6dM0fcrO5n23m8zH6dNkDGrWJAPmdAKbN3nwUG8nDh/W8eijZPQ8Hkr5t2whmMvJoYmPmMaWLEnA2qkTmblJk6gdkGDnsR0sGPlaMNq00fDJJ8zH7SZTM3AggeTbb9MJSkQEMGWqhmYJ/0WDmSOw+8E34e+voWNHMsHVqjH/0aPZ3wcOECgnJbH9pe7VMG53ZzSd8SwQ2wFHjmg3nTeIt7j27RWTNHaMB9N6LEVTbMNAfISq5VywlSiGFl1LIPWAhqef5hmgihWpIejbl3UAyHzVrs28LBYVQF60m6++StBevEoIJrb4Ho2xG7Z7SmHX2dI4dIh0YssW0rjBgwkSbTY6w6hWje1JSeE4+/uz3s2bkx5duMD7Qqclyb4aFJSfbt5OkPtnSJqmddY07YCmaWk3fBCY7z+taVqGpmk7b3z6Ge49pWnaoRufp37fmjMZhdeTJ+cX7gpv4m2vFyFnq1Zk0AWcOxwEEkuWqPfENFMEt3L2WIJlf/st50p2NtfGZ5+RPlksXI9RUVwvJ06Q/h09SrD00EPc065f5zMulweVTqyD4+RJpK04BJvVg6FDgbOXAuG2haA6DmF4hW9xNS8A4eHK1LRkSQK0pCTW7do1lt2pEwHViy8C1aprqNCyOka/GowLj4zGwUM8g3foEOdz7dpcb7NmelA68XsEfTYBSV1fxYnjbgwfTrA6dCitGO65h/kfPUpaJVrvli3ZJz16kFY0bcr+bdBAacPdbgqFNmzgfxEeeeyB+LTISAzENKwOG4TQiCDY7QyXEhREmpWSwt+ff87+6tKFtGvRItJAgDQxOprtatiQ1/z92f8REQRRo0dzvXfoQHrfoQOvZ2QQhH7wgXICExenxhlQYWA6d2b7du7k97BhSku3fDlBrTiUkQDtANuQk6PAnYA+4R1tNpY7ZAifnz6dbY+LU9rjhAQAmgbXitWcYCtXonUbdQbP5SLd/uADdYbxj0x/NMBbAECI01MA5t/tAoxgwAw6fCWj2SPgXXNVEFAsCBwafxu1V61bK22SWatmPLtnLNuodTFq9LyV4XLlP4vlLfCoL3f65n70VpbkYQZexrr7Cjhv1kyayzP/F2Bm7AsxO4yJ4YaQmMgFJiDV2I8CduTMXEyM7zLNYEvqO3HircHgzSDWOOdk4Uu/CbEwA2V51lf/tmihfpuBpGiW5T9AAiabr9GW3DiHRFq6ciWJaP36yox1+3Ye3v7oI+Y1bBiJpti4N2tGcLFnD6/XrGWB01YUdIwLnLa3+f860PkvTcaxE2bo/Hn2JcDN/MoVBfYEYJUpo4KS+/nxXpEi3BQvX+Z1sYa9dIkMjAR9FW2Y06kY9exs1uX4cXo6K1aMG7TdrrQp/v58vnRpVT7A//XqURPYtStuhgIoWZJ73759bNN331ESeuECwdW337LMlBSWo2nUxoWFEehJHL+6ddmGffuU106LRXlhK1aMm7XLRVAiJpzSdoBMgJyFcbspyX/lFfZFgL8H/j98iaGp/RCwdjmCinpQpowyZbRaVeiD9HSui0GDCGrefpuMRNWq7PcWLcic6jrLmDKFa9jpJMAOC2Oer79OreeuXWTOpkyhwGTKNAvcxUogLU1DixYEwBUrcizS0lhOkyZ0NnPsGJm3qCgyEjM+5rsBARqGDuWaLFWKzGW3bhxz8a55eK8DrtOnEYmt+BT9cbTJQwgL0zBrFhkhkbrXqEETzaVL2d979rAfn32W/SqByB98kEzihg1s08KFQO06GoISFmH/85+iy4Q4WG0a+vVjW6ZOpfZhzRqaof3rXxQQ7NvH+fDEE8r1/apVpDs1alBgEBXFdvv7Kwc069YpWiiaIV+CxD9T0jTNCmAagC4A6gJ4RNO0ul4e/U7X9UY3PrNvvFsS9PrbHEAzAK9rmlbid6q6130pPFyNgwiojXuiOSUmcs7Uq6csTBwOznuhh3Fxyopk6lSuv7Q0zsHVq6nNT0mhueK0aaQtNhv3K5eL86t+fdLUsWOp0Tpzhmt38mTmGRREi4NgfweKXj+LYOQgEJk4ctiDM2eAtDQNFys0wNbg+/CVfRRatdIQHEzhWalSnH+HD5Mu9upFQVO3bqzf118r78W97rMgKa0EDhzQULUq21W7Nuf2119zPQ34RzZ2XyiNMOzB5bST2PrzSaTu9yApiW3s2JHt37+fNMRmo7lj374Eq3XqcB3J+AgNCw8nXylASMLojB5NPio0VEPLIU1w/4weaL3mvwgK0lClCstyOimUiYoiXyXmlVlZ1MR36cLxq1mT63/GDNLIDh04Nrm5vN+1qxpLEWJXrcq+E3B15gzNrzt35rMffUQT+BEjWA95f8UK1j0qijzI2rU8Bz15MoHuQw9xHASojRrF8oYNI98SGck65OSwzuIMxuUif7RtG9+rX1+BVxEwiPJg9RoLHMXLIGGlhuXxHrhOpwO6DptNWX0YnQn9Uel3A3g33JJvAlBH07RTmqb1BfA+gI6aph0C0OHG/7uavGlgfMVV8/VeQeYJ3pK3DcaX9lDyEJAn5++MmjNjWaLFM5pdSjLHYDOWIZJOY3u8tcv42/z/dposb5pJsyMab+DOqJk0g3FvAFTADKBc/Rv7RYBMXFx+M1Pj+9Lfko9I5Mxev5YsIXNq3KRE22YEv2awLm2R92TTi4nh85MmKWmk5CFzUgiJsY/kAPfkyb6ZGKl7fLySHnXtysPOsolKe6WvpH9EUtq2LYmm0QSzSxe2NzWVeezbR+K6Zo06eD1oEDfnGjXyB+A2u+v/OzH5ojtiennmDEFN+fIEUm43pcPHjvFbzq8BBE4CbACOeaVKvFanDp/NzaX0Nz2dkt977lExjkqVIhB7/HHmFRDAb7udzwEss0QJ1ikggCDs3DlqttLTuena7QpElitHgFqjBulUWBi1a0eOMJ9GjcgUdeqkGJ2QEEp+pa1+ftQw5uVRwrtnDxmCl1+mdPmdd8hEHDpEQLl5MwHisGFkGp58ksCxQwe2r39/0sCzZ8kk9mqbDduZYyiOy6iUuR3Iyoa/P0GT202mS8wEw8OVeeG+fWSyJk9mrKfgYPbHBx8Ab77JdfzEE+zfyEjlbCYoiIzaoUM0FZ02jWsnKkpJ3A8e5Nxo2JD5Pvssx3DgQDIjAweyrzZsUGu8bl3SBont53Yrif6yZVzLTif3Ft0eCFeZSvgc/fGPpqmwFSuCZs04PomJBFK5uaxHkSIEeEKL+vdn+xcs4PxavJjt6tCBfdWtGzXA778PnDpjwdGMYOzYoeF//6M0vEEDjt9TT7E+R47wXM/48eyjQYNogla7NpnZrl1Jd3r1IgObkMB8mjdXZ7KMe7QItwDfDqr+RKkZgDRd14/ouu4E8C3odK4wKQ7ACl3XM2+ElVoBhnP5zZMvQbIxJq5o6Ix7rDeLFKNpXHw81++UKdxv5CjF5Ml8p18/de4zKUkJX5KSyJC3asW8du0iIChShOthxQrSGRFAlCpF+tOkCddXejpp6sakQFQoCRRDFk6iKsqUs2LqVK6t2rUtCAi0IShIw/r1pE1bt9Iiwc+PNKZ2bZp0hoSoM39NmzLMQpC/Az/+qCM4mLTj6FGal54+TZCj69QwnrgcAhQJhhM2JKAjGmELmtXORs2afKZFC5b9xBPs27p1uW5TUwliX3yRGtGxYwlM5s7lHt25M9ed3U4waLNxfb/6Ks3aGzcGXn7FgsDyJTB5io5mVdIRGanj+HGu/dxc8kBGQPTCCyy3TRtq5CIj8/Nma9eyTxYtUuf1x48ngGvQgOs4NFSFVunZU3kGnTqVY/700xzfqlWVFtLfn2t7yhSWt2oV6drMmaT3ycnkXwDOp3btOBbCA4twukULfoeHs87ikdjIx3Xtqiyn1q3jniLzXPjaqGYepAybBUelWnC1jYXjmgfr16v574vv/73S7+lF8xFd18vpuu6n63pFXdc/0XX9oq7rsbqu19J1vYOu62Yvm3clmQGGMYjm7TRxhcnTDOQSE9UEMAK1grxuGk0DzZPCDHJ8nVczB1r0Zupn1CB5q0NByVgPo/MVb1pDceCSmHgrGDF+G4GZGdT5AsNAfiA2dapSuUvbZFOR8uXgrbFvhUHwdkbRZuOC7tqVEiCzW901a271XmkGnxLgHVCAUwCoxKwxt1P61hgLSOZTUBDBmuRjBqO+knG+CACXfhENp4SoGD+ebWvVisyV0elLvXrcOAYPxk1JleQbEkJpfmIikJfnASOiALq+llzs3ylfKluWDIYxiYtuSdeuEegZ531uLkGzrN9SpW5oTmuq91etIvNw5gw3fo+HDPWZMyw3J4fzUte5SRcpQhAoGkHZTHkmhGOblUX6sncvtX733styad5Epikri8DnwAHlQluAR4kS3ESPHiUYCg0leHQ6eS0piYDxyBFKx6tVY32eeYamTg0a0BSqTx8ChH37KImvV48goUEDbvTly7OcQ4fI3KWmksk4cwZI3uHB+LF5WLNGBwCkpofAU6EqnAjElqIdsC0tBOnpBC7jxrGvJEbW4sXUqDVsyP7MzaW50o4dPMMXEUGmZcgQrrGvvyYQ6dCBDNS+fXzm7bc5fsuWsd8aNWL/t27lgT07HQsX6NixQwXuFudRc+aQGXn6aTJDzZsTTIWFsY//8x+Oy9ixZH4mTOD/RYtYLsDx69VLQ9OJT6LfzmF4t84PKFlSQ9++ZNyio+lswt+fIP7llwkmGzTgGG3YQCDXvj1pROXKZOw+/phjZrGwnaVLE8i+8AL7bv16MtnTprGuSUks57HHOFdmzVJOr/LyCNrXrOG1KVPYhtWrqR38xz8IMpcuzU/zjDRUhGly/U+qzasA4KTh/6kb18zpAU3Tdmua9qOmaZXu8F1omjZA07TtmqZtz7hLZ6G9CZLNmgrZf31Z7bhcnBdGAaNZGAsoBnz7dgIa2cvatOE769dz///hBwqBwsMpFLpyhWuwSRMKHD7+mIAnO5v1P3aMe3tGBp8NDtbw8A//gK1Jc0TGloGfn4aiRWldcOWKCgEjnopr1yYNtFp5Dnf/ftIzq5XzeOtWhv04ue00nLu3IHnrFaxY7sG5c6Rza29si4GBpJWdOgFDh2koHdcEB+/tgHZYjZzgaoiIDcHXX5MW7d1LXuTQIYLJRYv4fvfuNE212ym8O3OGNKlmTfIgiYkEytOmkR4CLPvLL7luRfjUMsqDw9OXYWajqegwoQu6d/PA35+07sgRjumzz7J/33mH5b72GsfH35+AVgR0Ehu31w2Rxfr1HL9nn1VOZBYv5nPLlikgaLMRzC9bRkuL5s0JIFu2ZL82bUrNnoB/p5Nj1Lw52yBKkpwc5aFz3z5l2h0bSzonZ3rFzP6DD1SQdiPfLPNR/DXk5Kjzo/HxwOb4bDx34mWs0Vshfl0gVs7NzufR93bKoN86/dEmmr9L8gYUzISkoOdvl7evDcR4T0CHL2BlNgf1Vi9f5QMKGBjBkZis3M7E0GjCaLxm/Dan201YI5C229UhbKME0AhYzWA2J+dWM0hju4zPG89gmF00u920yxYtnBFoG0GZN82iOD3xFjPFGKTVm6ZRNjDR5hnDPwCsr7j0Fe2cmCpIXt7GQ4CwrzFwuQgAxZuogFDJx/yO261A3qpVKv7M9OnchKKjyUwNHcrnmzZlvZ59lnk2b06TiAsXuKm0bg14HNcBkJkoCuQ/MPZ3AsC+M+Peq1fJCEsSs8IzZ9Q1Y4BfTWO/i1TywgUCNbebGrSAAOUIQLRiWVlkckqVUlrrAwcICNu0oXnfvffSvfTVq2RcihUj0Nu2TZlKBgRwbpw/T4anWjWWef/9zCczk/Xx8+P82r2b7RFmKTSU4GbBAjIib7/NTbx0aTJAe/Z4sG6NCyl7dGRkcN6dO8dNtkwZmmj26sV6rFhBEDJsmDpXk5ZGsFejBp+dON6DYvM+R7fTU3Hi81XYs8cDj0cDnngCgTOno+mAZrh4UcO4cXTm0qcPXaiLafPcuWTamjdXAPj559leCTQeGcn3/vlP1mPGDAIuCelgt1Nz/9pr1PQNHsx1WjfMg7GNv0TH+ieQEPsWNm/23HQ1/vLLHOuBA9nvS5eSMVq5kmMDEDC/8QbHSRikhg3JxL7xBuu9YwcZtV27gPkLLHDdUwad4jRcvEiQPnAg1/ioUWyrxcLxSU7m3JE5VKMGwd6br3sQUf0SZszQceAAr2/ZosbzzBlqB2JiWOdz59S8Xb2aNEY8/1WrRnM7CVAdGUkB04YNZKRTUkhT332XplgSIzAqKv+ekphIBs64D/7RDNavTAsBVNV1vSGopZtzpxncTQd1ksyCZOBWPsjIv5gFti4X92SnU92PieFaGzKETLbsv61aKU+pbjcZ/9xc7k3+/qRXFSvS1Pe777j2TpzgfAgL49ovVYq065FHuA6OH+f6GT+ea0bW5qjRFhw7Y8elSxqKFFGgautWPnftGsHV2rWc0x07UkjRowcBS2oq10FeHufpxjVOFMFFuGFBCWTgnuJOtG3Ltfbtt8Cjj1IbV6kSBRnx8UD1GlYcjXkGq3tMwyNjI1G1Gs/xpqfT4dHHHxOUimYrIoKgMyWFtKhBA2rmhg1j/6akkP6sX0968Mwz7LOhQwm4UlNJq10uYPvKbHxwsR+GYSIcG5Ox9GcHcnNZr86dmd/GjeRVXn2VNPa99xR/1Lo1y0tOVnRDQHjTplzv69bxetu26lwwQFouMTQnTFACvI0bSVNGjlRneJ9+mjS3Tx/uQWfOcC8ULWNODueO08kyhg1TAvOVK0mDxVEcoBx1DR/O5wW8yTwUXq5xY46TeEGPiwM6PBiCoNaNYdWA2OjriHs45KaWz8zT/hHp/3uAZyQ8ZrAi/82M9O0kfkZC5c2UMCZGSbCE0ImbfLO5grxjzMOXQxJzW3xp+oz1EGIsde3QgeBC3OwbwaXUT8o21sEMNKQMY+gFYz1Fumqz5fcganzGCLCMJrMuFxk5qaO5XAElUj9zfQRUipbJqIUTDZsx9p238bbbaTIk0iJjuxIT8zsq8baAxczSm3kooPrE5SJDM3w4NxxjH8m5zKZNb+0/YzJukEZQnJhIImcEuMZ+sttvECkDQ2S1cvMYMCC/WcT997M+H33EdlWsSOLbvj3nzZw5/B0WBuxI8QdApHIVIHf4d8qXQkK4sRujR+TmKizs50emQrS81asrcyZ5R9M4N8LC1Dq/fJkA5/p1pX0tWRI3A4r37k2QUrEi3w8OJoPg70/GZetWnluLj+czzZsTxFWrxmerVuX5Nokjd+4cpeY7dvCZRYtoArl/P0HcuXMEWv7+ojX0oFalK/jySx0eD+dS2bJcHydPst1hYR60y50L++WjuPzNTzh5woPDhwkcqlfneqlVi5t9sWKcr8WLcw4++SQ36OrVOe2CgiiMmP/NVTjOnMcplMP5bKDmPdmoUwewWCyI6lICISF0bDJ/Pvu0UiVKxf39leZRwFr79gRZ4qX4rbcI2jIzKel2uVju0KFcV6GhHIdx49jH332nGIQPPgB2bboK25nj+AYP4sAhCzavdSIxkeP98MMEQRL8NzeXTGdsLOvQqRPzPn6c2orGjVl2UBCZua++4ntyju74cb7z4oscw7FjCcjFs2pGBuv02GNkoOScUvfurMOcOcDeFA9ejVoO54AB8HzxBfLyPDh+nBqT3r2puf3nP/ndoAGZ4SZNSIN79+b4vPsu69G6NZn2s2dZzxkzFAMv4RLCw0l3v/iCdKl4cTL+RtocE5Of7pv3qT9hOg2gkuF/xRvXbqYbFk5i5D4bQERh3/2tklmQDOTng4D8TtxkPzefTZfwH4mJFDqtXEkh1cSJ3K+NPIrbzfH/6ScKXbZt4xxp3FiFaylThmvl0iUP5sxywOXSsXUrhZadO6tztBYL6RpAGhwezrl35gzBm3jHbN6cgq9ixUhrrFbeK1dOOWjZto1A5Oefub7atmWsTF0niCpTKQAhdg1OBKO8LQf24AB06MB1+9xzKlh3Xh5p5LJlrFePHhY4rUXwv681HDtGsBQXx+dCQ1mXqCiCyf79SfMOHADq1/Xgzf/k4bPPdEREsA3h4SqEzeuvU7C0dSvX2Y4dQMUKHrz1eh7OndWRYwvBy6U+wViMxov3foYuvQPRsydpgNVK2ihevitXJl3duZP8hTh9slhoVWGxKIAVHU1ANmyY4im2biWdSUsjKGzaFNi/z4MmFdJx4riOPn04RzZt4p5S4YZ+euNGaiKzs0nDpk8nIHzhBeY1YQLp46BBNxy/OTmvZI62bUtey2Zj+4VOJCbymtDlVq1Y/sqVfEbOItavr0xJbTbA5qfBlpiADic/h33tCv633aow+aPS7xoH726kXxLLxQjkjIy2/DebCnrT6Jjzad06f7wMbxooiaPma8PxVY5o+1q0UJopeU60UFIHo8bndm2X9jkcrLsxtow5T6mnEOSJE5WXR7MZpTFvqWNiotoIZJKb7xvj1glokvekzeaxMveljJ23eHUCeMR7qfEdKVMAqlFDZh4L87uSvMXCMz9n1hQa22GMTydjYATdxrkpc0HyNm+wYsrVqhU3HnnX2F++4hIZnzEC7MhIFe8GoBQrN5d5T5hAqaVIVTt35sZVpQqwed1VrN2UDYK89dDP1iEXX0D6q8WZSksj4yCpSBFKiAEyK+np3Bw9HjIM99zD6xLEHOC1zEwCNDGFPHqUeTVsyM23bFkyMBUrEvSlpzMfi4X/K1RgfmfO8H9AADfoy5fJ8EgQ4RIllJOTK1cItjIyCD42buTaDQ/nphgaqs7SnDmjgEOrlh6s+CEDJXEOuSiBHgMqImGlBRcvci75+d2IoZR9BfPnnIMFudBRBMG1KiL7mh3/+Q+B4+bNbFeVKqyjlKHrBBCRkaz/hx+SCdi0CShXTsfRz1bgfKYVDe7NwLnoh5GRoaFMGfbbwIEELxL3TxzJbNjA9dG4MWnFmDFsb/PmBHDbtvGdr79mSIg5cygVdzrJCIgjiLAw1n1gfw9eHOZAuRqBGDpUg90OrF+vY9vzXyDq9FzsqNQTg7f0gc1PQ2IimbtjxwgOP/yQa+3HH6khuH6d2oqYGDpEEZNRp5Nrdvp0jmVcnKIPO3cSvH3zDZnD1FR1du3ChRuaET8Pfv7OgbKVA/HKvzQMH845k5REDcKcqVfgmTYZUdiMHBTB4p6foUpoEYweTSYoJ4d1rFmT5lWA8sibnEx6DChTKjHZ6t6d9FrTOA/ateNYR0WRqRs4kEy52fLCGANV6KoxHuzdSHebPmmaZgNwEEAsCM62AXhU1/W9hmfK6bp+9sbv3gBe0nU96oaTlSQATW48ugNAxO2OuPyaOHjm5GtPM94TMCB+BYwmmHLeyeHg2Napwz3E6A11+XK+1749tfSNG3NcFy+mdcHlyyoW4969gMftwdXPvkXC+bqoFJSFgJg2KFbcglWrSCcbNKCQ6tQp0o8DBwjYTp/m/MzMpABLaGzVqmzX7t1cw1Yr5+W2bXxezsWlpnKu79hBoFatGv+npQGXsz24luVERMsAnD2r3bR44Lrn3A4NJXht3JjCuHffpXnitWtsY5kyrOcnn1BgM2UK6e6pUxSgffYZAI8He3r9GxtSgzCk2VbM6TIXjz3OeH2nT3MfqVGDtKFxY4K+gQM8ePT4f7HoSHU8FpYC//feQXY20LtVBlwlS2PtOg0ABUADBxLUyZnktDTSkZYtuSYdDppVi7AvJ4casPvuI92UOJUA740bRzrdrBmvvfKyB6/vvx+p264jp04EAse+hVbRFkyaxH1SBJ1bt7L/Ro/m/4QEzhuAgFHoht3O+SPAMjmZ7f7oIxX+ZtUqzi2Zt+JNWhQ0kZGK50pIuPN4m78FHfKW/k/Ewfstk1GLYmTQjR1vjpkmycj4ilZKmPqdOxUxMjtuEemuMRUUlsCoYTR61TRLvqSO3g4vG5MRYIhZ4rp1SjW9fTu/ZRJKH0g9jfkaXVGbJ6sxb2MdhWmQw7XGJG5p5b48b9wgEhNvNeWUDcGo6fQmOTTX25sW0Fimy0XmacwYpZI35mGcA8Y+lfnkTdNqBKfiFMVseivgWkIfGAGYPCdzISiIz+bk3OogyGYj8bFa88fvkzERT3PG+gmolDAKIm2y2UioBdzl5PD/ihXK3bB4zVqwQEnRW7Qgw123LuBw2QEY7HjukmnQ/2/JYlGOPDRNxZ8TbZyYxkkSMzlJFy/yWrly7OKiRclgXLtGoOXxENxFRBBgnT6tAoifOkWJ8MWLZCLuu4+S4DZteL9iRY5lxYoECU4nmY/WrQlWxLHLpk00mVu/nsz8N9/wbITHw3kjUtSOHYG9u10YhbdxLy6iHrbC7r6KOnWUSd4TT7APipYJQvvgPaiIC3is7EKkZwagbl22fds2aqE8HgKc3bs5Rxs35nzMzCTz9913uKkh7NYNCA/XULNvB0yJr49TUQ/jwgUNTz5JGvDDD6x7Xh6Zui++IOPRsiUBVP/+ZPCWLCED8dVX/P7XvwhO3G4yIWXLkq42asTnz50jEyn1gO7BhrjX4J7/EyIWvYmNGzx44QUgO1vD1ppPoMH6mbA83Qcut4YRIziG/fvzbKvNxrEcMYIaxWHDgObNPHh9UDpOnNAxdSr7xukkuJo0iYynaGP37uW9tDQCqZQUmn/m5lKD8u67PAu3f58H+M9/0PGnfrD/8BlcTg969iTwL1uWzJyleBAs5cohEkk4Wrkj2ncNxKlTHHdxgJKZSaY9Pp6gDeD1WrVIS8aPpzR83z7eE2cwERFkEgGC6+holjl4MIH1kiXq/J3Q2Ojo/JYqRhP9P2vSdd0FYAiAeAD7AXyv6/peTdPe0jSt543HhmmatlfTtF0AhgF4+sa7mQDeBkHhNgBv/Vb+C7wl455n3tMkiXB1/HjuTcIUy36zapXS2vXtS+HHmjUq1ur8+VxbSUm8NncuhTWpqRQ0795NetesmTrzlZN+FcPO/wuh2IurOR6cO+HE0qXKnLxECeZVsqTiJc6c4fx3OglgTp/m86VKkRbm5nKtly3LvfXkSdK9+vX5zL33kn4eO0ahWpEipLW5uaz75i0WuKx27N1Lz5SZmXy3eHHSnrp1qVmvVIl533svtWu6Tu1e48b8PXgw6ePIkaQHdjuBTZMmNG+M7eDGytQQWODGZ1sbomJQNvr3Z//GxpK/qVePtHP+fAb7fqJHNo4dcSEUB2HdvxcNy2bgq//RO6Q9UIPbrejrgQMEogEBrEdAANs7ejTpnM3GPhw9mm0PDFRmkRMmkHcQvmPtWoLonByO6YoVQMfIbHy5tR6i9PXofnACrFeyAVDbVrIk9w+A5VgszMPlosZ07FgPVv10CZFNdbRuzTYnJnL8bDYKnSIj6YilXj0F7nJzOT8lbh7AeSJHijZvVmVGRZG+GvlUWQu+ktlPgqyL3zP9JTR4koToDBqkYmHYbErLYza5M0qnABInkUIYNTDyrPw3Dqg3jY0keUbcaVutSgop942SSmmDaHRsNu8SAl9SNZdLae6M7ZQkktC2bRVA9ZW/N42VsSxpu1HTaew3MR80xmUza7uMSaR+kyfn9wppbp9Z+2jMS8bP3KfSrw4HibhxzOU90S4C+fOIj1eaUG9973BQstS0qeoD4zwyaz+NGtGVK9VYtGihwlw0aULtgIyNHDh+7jnvEm5zP8k4A8pOvnlzYPZsro2pU0m8mzWjxP6pp+jEoVw5MpljxpAwDxtGUDBwIInoqVPclD792InUw9cBFAGwHvq50PyHy7ykv5oGLyeHzjI2b1ax6fLyuJk3asRrp0/zmsNBABgYyA1K3DgXLUowU7GiCloeGEhmXoKdS9y4n3+mts5q5YZcsiSZpaJFyTysXk3m4+BBvte6NYHbrl0sLzqaG+uBAwSQZctSG9igARn5lBTeFw3h7t0EYK+8QjB45Ahgs+oITfoCV85kYIdfW4S0b4o9ezQEBdEs69w5MlkXLgA//ODB/R2vYvW2INSoqd0MoF6vHhmh7t3JEA4cyDJsNjIeu3ezLVWqsF9WrGA/nz6tAPKOHZy/Z86QMRs5kqAoNJQAomtX9o/bTU3ZffeRVn3+OddFu3a8JyBi7VqOR5MmXC/+/jzvM2SIMnEcPRp4e0g6tkcNwi40QA2kYfl9s1GhZpGbZpxpaUBYqAf7tl9Fg6ggABoWL6aUuWRJFR8KADrGerAg8i0sPlgdWUUrwq9DDIoWtWDcOGpEatfm+Eo4jUcfJZP85JNc/wcOcJ7FxrL9lSqx/w5uuYScpwdiMbrhOmywdO+NBx6n5GH3bgK0Q4cA6B5Ycq+idpMgLF+u4aWXeP/YMdIh0cwcPEjQPHo0ac/LL7PM1FTO1UGD2IfjxvF78GDSEtkPAaXB2bNHnRsyCu6MNFToW2Gl7IVNfzX65Mu6yNt9XxYnsvesX6+scsSj6549nHeAshaR8Vy0iOaLb77J/cxuJwibOFGdN7ZamY+u816RIsC+fToeD/wJAZeOo1+Tbdg68BvMmqXB4+H6OXOG+6nDwTUbEUG65e/PPC5eZL4OhzKDv3aNdPPKFc7XIkVIk7OyFFgsVUq906wZ6UutWqz7pk2kWWFhBGJ9+nCti2fgy5cJ9sRBlsXC9VWjBk3hRThUvjxp3ief0Ex50SLeq1WLgLFBfR3Pt9mCpjmrEBmai92930Kt2hr27yeoq1KF/VihAv9fvw5ERupw/+dNuA+mwj+0Jnbf/zaeeEJDSgrbmpPDtRoSwr19926aRiYkUNAkDmM+/5wgvXVrfj/9NN9fsIBrd+lS5cylWTMV1zAoiPRi4EBgwAAd1jdeQ2DqTsRGO+BYsAKDh2jo3Zu8V1AQgVj79nxXYovmXvMgbkpX2Devg6tFK0zttgz9+lsQFOSdd5ZrY8fyWoMGHHMRREr4lTVreF94ZcENkq9x3hv5N2/rBOBzkZEUiJstyn5tKog2/aUAHqBMJxs3JkMr5iK+Ngdf5pFm0COaqvDwWxl+ASdm80bju2KOIPa9BaWsLE4UcdXvzUzTG1MfHc2J+sILqr3x8flNWxITlRRDkjfwaAYl3kCTGaTJe2IOa3zHW58ataYCsJxOMmBmECigRc5tiJrdWJ6YgXoD2S6XMkM1Mg4tWqgNytgfAijHj1eAU5LME+lTOZAtWjijGSpw63hL3h9+yHkq8VSM+RrbHx+vpGaxsWqDNQsbjPPY2A4xTRDvUllZ6jyWrvO5Z56h+dngwST4339PIHHgAO937EiAMH060LOHjslTnABsANZD97S5bSy8vxoDdeECpYISzFvX85tp3nMPGY7gYI5NVhbnwokTvA6QUShSRMUmksDR4u0wL4/5FClCUNSjBwG6zUYmqUgRAsOLF8mE7NypzqBkZ/NeVhbXU9WqvBYdzfl77BjLt9sJMtu0UfnMmKHOqem6cnYQGQlUreJB0hYnTqUHoFgxDeXK8ZxLjx4EcNnZ9Hp3zz0845GcTMbs4kWafC5ZAqSf86BuDQcq1AhEerqGs2d5RvTTT8ksZWWxP0aMIAMXEKDiA9aqRdARGUlaMn8+mZ+TJwkIT56kmaec84qPZx7h4bz/7LPU+H37rdJsSjDedevIuD73HOtdqRI1Y927c4zsdh347FM8cvK/ePeeKYj5oAsCAzUsWEAt49KlHnTa/jaWH6qG6S2/hm3pEixYZMGsWdTkBQYq5rhRlUuYP2A+WmMFPkdfXK4VjXlL/FG1qtof/vlPPpubSzOuWbOUF9SUFPZBly7KNHPcOOCnH3V83mY2Bp36N5bVGozlEa+hew8NgYFc31OmEBw3acI+qF2bbd26lcB+zBg6gxg+nKDscpYHi+c6YAsOhKZpeO01alcFKG/YoMz0xfKlcWNqQqOiqIls2JDM5eDByoTPbld7tfyXZBak3o30V6JP3gTEct3bPg+ovSkhgcJM4a1sNq6h5s3JlLdty/1UeAwxfVu9WgkvAK6tV1/lmhNtUXIy1+rEidTGiYCreHE+c+QIcD7dg0uXdFSpakHNmqQvixYRMFWrRjoi3hBFyFmjBuufnU3A1rw5GfzWrTk/JVanMWSL06lCwVy8yPdyckhjqlbl8+nppJGPP842PvAA73XporSVuk6h1pkzfFfT2L+9e6szYitWsB9feIE0rk8f0tb9+0lbGjYkbcjO8uDY3qsY/GIQZnykoU4d0uWpUyngEucr48eTz2naFDh+3IOMM040bhaA5cs11KpF+j95Mp9t0YJjcfgw+2n/ftZ3+nQKDZOT6Y3zwgXOl4ceoiDpuefYt1OmKGc6ERGsL0Ceo1Ur1q1WLfItWzZ74DyfjcByIahaTcPbb3P8T5+mkG3fPuYrguiEBGDnhiuImPgEAnENHaxrkbP3OKb/VAb16ys+EFC8ozh2cbvJs9hsqi4bNqhnrFYlXG/XjmMrGj0jD1mQGabxnlHwYfRMfjfS3wAPtwI1ATKyYUjyJv2TzUcAglnTZ3wGuHXAHdc8GPfOVQx/JQhBwVq+d4yaIJEgGLV45jbIRBETOtHA+JJYGrVXMTH5z97l5HADHT5cTTrjNXFj7O1cmvHcmwAh0TQZ+9QsXTVuEJKPN3Mas2ZO3pffMoZAfrBkBHEXLhCMSH7mxSjmh9J/OTnq/Jq0UzSZRhCdlXXrOTdvGrK2bUmcd+5UkishJna7MlPq2vXWNkv9JG8j0DUD1IULlbS7Uyel0YuIUNIot5vlOBzqTERUlNL4iURNBBGRkSS+jz9O5rBRI+bRvTuff/ddbkgPPUQmcv58gr3atYG1az24cMEFmmmuh+5und+biJf0V2KgAAKkevXY7+XKEdhJcG5JgYEqVqKc2xAACJCpuHyZ41K8ODfDixepPRJnFeXLk4nweMhwBARQmnrsGKW5aWnMq2hRPqNp+csQc1yZj9Wr85zf+fOqbhJKQdcJAooXJ2Ozbx8ZIjl0LzH8JLZSjx40sWncmGu1fXvljjwri0zclSsEe6VLUwjWto0HS766iOI4iQBbERRvWhulSlmg62RCRowgUzN7NteEmEgGBJC+ZmYqkzB/f3UGrEED9k/FimxLv34EQZmZZGI8Hn4/9hjNqNq0Ic2eNYtlff452yW0VNbgrl1s59GjBIerVnqw9GcHSlUiOO3enesnIgLIPXMJqcMnAwBe0cZh/f+Oo37rEtiwQQmu9uxhnw8dogO9umHLJgsyazbD4ojX0LOnlo+Of/45pf2dO9MEUqTyP/9MRrlJE+b500/KOVNQEDB+vAd9umXg35NLo117DadPU0u7axfBeuvWZFa/+45nDyXIsXjeBEjrNm7wYPuQT1Hv1DLEtriG+X0W4cxZC8LCFM2eOZMCMqF9OTk06RIHNWvXcl6sWqWYM2HchNbLniZ5eAMnvzb91eiTN2GtUUDr6xy+WVgo+9PcuaQdo0fn37+E75k7l+MuQsmVK7lPHTlC4Q3A9ehyUUDTti0FLb16kYZ8+CHno2jTixalMKNyZdKhZ5+ldj00lLQlJ4ffxYpxjRuTmEsCpIclSwJOpwelQly4dMUPVaoQPC1bxnzKl6dwo25dluXvTxp49ixpnoQPEKB55Qr3/ZQU0pqMDL7z6KNco4sWcX1MmsT6HzlCmjN+POlShw6KHvXqxedLlmQeVapwX8/NpZCnVSvStpYtqRksV47fGzeyTgcPUrh09iwBzsmTtIj4+WeOl92uvPl+8w01f40bE3y2acP8P/iAZbRpA/z73+qM90svMc/mzTm2zZvzmdWrSS/CwzlXWrYkHejTh7S4Rg3yGfPmUTAYEcG5tGoVx6R9eyVcz8vT0WL+K+h0YArs0U3hSkiE47pydLJqFfslOlp589y4UcX9dbkUD2i816qVOlsoCgjRSouA3psSyNt6MStC7nb6y5/BM9qMA+xktzu/1k7ONXmzsU1IUKYFiYnq7JLRrlbOg90S98XjAeI6odF7j8DeLfamb3Spk0yg2FhORJEqGQGj/JZ6tG7NySeBF82TzdxuQAED0fAIIz9wIL+N2qQGDfjb6ELfmLc5wKycFxRQkJWlyvUV6NQo0ZD7CQncDCQWiRzSlj4HlAdMOUMnZ/nknJm0MyuLzNi8earuUm8Zs0mT2MaYGN4Xs12AdZEwBwKwXC4yoKNGqcCesviN4xUVxe8VK0gIR4xQqv7t21kuQAmZ1Zp/fhrnxfr1+UGsMDXmebF/P4mkuPhdvZrtat6ceTidypZ+6lTWLyqK9RK3wpMns22TJqnDxYMGkYGT80+7dpHYf/cdhQtZWZQ0pqSQIDdpwk3v4gUdgFMN+N9hEm5JdjuZi9q1lRe1mjUJRAACGgFQskFcu5ZfERoQQIZA02gymZrKjXr1auZlt9M8r0YNbrjFinGD3r9faXxLleKcZ14eaB4XbDYdzZqRSbrnHkpQT58mI3LhAoHX1aucV9Wrc/5cvcrnpa7Jybwmbqxr1SKTEhtLaWz79vxtPJSflMTnjx+n6c+WLewfgGXfey+Qc8GB3vgCjbEPQ13vITr8KqpUYf1LlGAf/O9/7JOrVwnw0tJYnsXCfnY6udafeorzvHhxro+8PJb79NP0ihkaSnq0eTMZocceI2MpPLLVSgHI3r2qnsuXU/ixejXBVe3aXJP9+5NGHjhoQadeRVC0qIYxYzgGsh/tOxsCS4VKaIg9cERFI8MVgqee4ljNns38hw7lup3xkYYt/1qEVqmf4KHdr+HDD2nOKXOma1eeJbzvPrZRAksLE2ixsK8iI6nZWLeOZYwdC1y/bsGrk8ugTFmCu+eeY1uPHeP37t3cd0qV4l4RGMi1X7kyGcMff2Q9m1XNQL1TyxCIazi3KQ1TJ7nwyMMeWC9fwocf6hgxgkwdQEZ/3s8eJPxwCYmJOsaOpZbB6VRBh6X+QuPFqiIpif0uQrDbxZv9O90+eRO4yp7v7Ry+OLYQfkQEDTYbBRxTphDcmf0KxMZSo3X//Spem8PBsX7pJQoWWrUiKBk8mILSqlXJuJcqRUuS1FSu2ePHSSNCQjhHixQhTYiMpBCjVSvSM5eLgrDixUkjxEqialXW9/p11j00lECsWDEPAq+cQaWTSxGTtwBXr3qwZQtpa82a1Pzl5qo9UWhBTg4B1Zo1LO/qVdKx69e5l9atC1y44IHN7UBoqI6vv6YARzxfu1ykLSVKEOS8+CLbcOwYaWq9euwvCXjevr3SgjZpojxZLlpE4FWpEkHk6tUUOB08SIAo4RVeeIFr+P33+d7Bgxyfd94hTatTh3RWvHiKUOX4cZa7YweBdlgYgV18POtrs9HEs1kzAr3du8lbBAayztu307Jj5rgryLmi46uvyE8cOcJ2z57NPmzWjP2Tk0MNYq1agJ+fhvab/wv7mSNwJSQifrmGNWvYf8uWcQ+JilJ+H2bOZNtateIz48eTT4qOpgBAHNVJiCzhvyIjlWDc21rxRldkTfhaU79H+ksAPCFAxkEQjYfNlp95NjLQxvtxcdxkYmIU0wwooGL2PinJdTYD6zZaEYsVsG1axxUOBYykPLtdBY42gk4Bk0D+sAdGN/tmSZoRyJodbsgBUjGFSU6+dUOU2CXGmEJmEGJ8XoBTSAg35c2bVZ52OxeHt/fbtVPBKQEust27qfE6d45MggSsFHe1smikXZMmsS/kv/RnSAgZPZEiGvtFDuiPGqW0ZwI2N23if2O4BUkOB4nNBx/kDx4u9Y+PJ6CcPp1EAiAxlnoB3KSaNlV9I6EUjIBZ2ih1MJtYGoEgoEzCWrUiQbRaKUnfsoV92qWLMssdNIhSTptNeZ+aOpWb0pYt3LA2bGA7tm/nmR3R7InZybRpjFFTrBgB3jPPcCPatg2oU9uDLn6Lkc/Jyv8xK4HfI9nt3LjFU+WRI/wIwBMzTEAxHDYbAVVICMc4JITj4vFww3e5uKkfP84N3OGghm/nToKvy5fJlFStSgAZEqKcAWRmepCZeRlXHQ6Udh1FntOD2rWV19SiRTm3Tp0ic9K0Kcc/PJxzrXZtMkpt2xJsDhtGhmLlSuUBrWFDmhbt3KniKh07Ronv+fP8HRDATT8wkJLlAwcUILFageuWQKQEtkclnMSR8rHQg4KwejXpc2QkJdDVqhHU7dpFhqV9ezJzgYE817NqFfDkEx6kbbuEvn10PP44wVitWgTDsjZ27yZzOmEC85o3j8zQvn1c6z/8QCGOmDDVrk1AeOgQ18769eyb7t25NoSp8/fnWtq2jW0SpyLHj2sYuPFp7BzyGYZWWYylSzU8+ijff+45xaAAfCeiqQUTvi6Dpcu0m9YcAPt17FhK8F0uMp3PP0+GRzRgo0ZR2zhrFvPq0kWZ/DdpQhoXHKzO9fr785m0NErhR45UoQycTrZ3zBjuUbVqcY6tP1Qai+55BhkogUft89A6xop99/0L2X0HI+Lnf6F7Nw+SkjhHsrM8WPDcUnQYUB3fOXsgqrkH992nBGCAMu0z7rM2Gxnfrl0V4PPlfMy8D/yd7jwZHcUZk3lMjEmEp+IwbP167rVLl6pYZO3aUfD05JNcbxMmMJ/GjXnurEoVJUROSyPNatWK612ETY0bq7hvYrGwfTtBQalSKrRKbi5BSFgY6WHx4qzDsWNcw+fP879Y0GguF3KhYRNaYd7VjtDynDh1ivTU5WIep05xn7xyhWAyKUmd1atXj/WReSfxS7dv8+DCllTUSvoUG+YcgsvlgcNBmte6Ndf9yJHkwyIiSG/PnaOlR7t2BKuRkax///50/lS7NjWSgwcTqEVHc11Oncr+7diRQsBKlQheFy9mrMz167mvHz/ONf3YY+yPiRPJD1SvzrPMI0bcOvbVqrEccToYH8/27tz5/9j77rAoz+zt+50ZBkYGQUWsWBBRAXtFRBG7JqaYZnq1RhOTmMRkk5i+ibG3mJhmzMYUjV2xYgdEQJoFu6IiqJRBhmHmne+P2+MzEDfZbMlvv02e6+ICZt7y1HPu03n/zp3EaVYr52T8eK6JnOUe3XSceGUxwj96BJj6BjTo8PPj/C9ezHXr3p3uqY88ws8efph9bdcOMJlZ11O0n336KL62ezf7s2UL99+4caTfO3fyGruddNJkYr9E4SaQxeVSWNvhUPKAp1BXHdt6YrP/6/aHEPAAZdIVS1D1YqiycDer6+YZ9ySaANGUeALymzVTwyD0ja6EyajBGRVDZOXxXJdLbYodO3gopH/V3S59fJQgajL9PEOkCBvVBQFPAULiHLp2pcZCYrU8hQjRwlWP4RIhWQRP4OdZgaoT96Iigr1Nm36epVOevXUrC2aaTNTaTZxI96Jhw9T4RMir/q7w8KqaFU8LaECAGocIgJ7MXcadkKBcSz0FLU93UnmOZBMVlya53+nkuw8fJlgMCCAxXbhQCXKbNhGgiSBvtyuNlGffPN/rdJJAr1/P72T97XbO54YNJMBt25Ip3n8/nzdgAOds21YdCT9dhb3cjenTuXY//khN+4YNfI6kgBdNaatWdJlp3ZqZAiXuYehQgtoFC1hTJzeX/Rs1igJes2bA1UIntlX2BOCxEX7FPfOP2I4dI8P18qIwV15OplJSwu9dLgLtoCDlYuntTUHCaKQQZTLRpbBJE1pwZZoNhuvARCMYKCvj+4xGMthTp7hWGRlkaN7eQA0fHVwzF4rgg4vnXWjRguBhzx6Cnvr1gRoWHfXqOHDpkht+fhRogoOpYAgKImDIyqIwJACpqEhZ0u+/n8Bk/Xree/48BdsuXXjec3IIlsLC2GcBW8HBkm1Uw5Xg9rg28mmkhD2Eu+7SEB7O7ySLZHo65/S22xizKJkumzalljkzU4fx/XfQ+v52eKp9IlL26zCbKRQHB9Mql5bG/q9dS8AWHMw+PfMMgWGfPhT2hg8nrXrsMc7VPfcQWEVEUKDLyCCvMBqpXBEaERmp3JMkyP/OOwGfGgacKvTD0KEa3nuPtFKK686fz3Xbvp37ZfZszpG46d9xh0o+ceIE+wlQ4TZkSFUPESlYfPw4r3c6CYbFzSw1lfdv2kQa4XJxfpo3JziaOZP7Ii2NY+zYkTShYUOC0s6dgf79NczMGYK6X87DoKfboG/HEoQcWoF38Bo65iyF1VmMPn04l7npZRhW+AV8cA0HEt0wlhYjNpZ0ZcMGFTO1YwcBmyeQ8qTXN7MwSful7/5sv95uNn+/JizfLAxDQPPKlbR+rV3LvRQZqc7wyJHkm0OGkA9t2MAY8JkzSRPCwpRbeY0apHFLl1Jp5nbzrIt7pVidvL25jy5fZuzZ/mQXatd239hbDRrwOWazSih25QrgNnmhrfkYGiEP4V4nYXNQC9e4MZUdggXvuIPYwOXie8rL+U6DgV4vkZF0i69Xj3T+5AkdlXBhH3rAVVmKW/oUIy+P9HLRIvL5zz/nUf9OVAAA+qdJREFUWNLSVGbkW24hHx47lpY1s5nzefky3/vVV3zvU0/xnL/0Er9PSyNOMBj4d/fuPLdmMxVfzZpRodWkiSrV0KED6YG4TG/ZQmyQnMx127iR/GTfPiqH9+3jM8aN43ufflrNz8aNpEWFhXzv5s3EfTvXFOPZsy9gMDbi+JFKjL+7AM9P0nEqsxRPPuHGkSMcR1iY2h8HD9JqazYTF27ZwnmTuV+3jjhGMuyKMOnjwzGJy2qnTirRk5SEMplobTaZeK8oGgYOrIoLPZVF1TO5/z0683srl/4wyMtkUprS6tJ19YLZnu6X0jyl9X37fj0W74bLnqbBuXEL4r+8gC1TtsJWpqF6EwGhTx8yRnmmp+BZ/V03c8WUEggieKxfrwpcm0xKGBPLWYcOVeuu3cw6V92FNSFBuaoWFdEkL6n7bTYS8x49lCtlYiIJn2dxSE9h02TiuAWc+viwbx07qoBcQFkuPdfL6VT+1dU1KqK18Wzi4hobq66T8ezeXVWocjqrvgeo6oY7ZowSPsUKN3QoBUABh/PmqfXt149zIAlZxAV09GgKTGvW/By4VHeNld/l5VxXm43EtriYWqgJE+ijL8WWMw7qwCuvoMf9Idjf+3mMH0ut+IwZ1H69844i9AsWELi1bEmgVllJbWdwMJlJ374ElBERZLCLFqnkFBUVvFbXgcvFXohENgCXmvhfyaD5R2xdu/K3p4KkuqEzNfWGwR8Az2tgIM9CXh4Zb2EhhZiNGwl4mjZVmde8vHhfs2YqrmDkSGo9jUbSGh8fap1r1TYiACUAjDDCC14+JqxcyX0QGUmgtHePjqunr6DJ+a0oO3EOpSU6vLwoUNWuTSZcqxbBRXExxzNoEAUFPz8qM6Qob26uSvPdrBn30Pr1ZMxHjhDY1anDfmZnE0S4XNyPgAF7MvwQFqZhzBgKngcPkh5VVFBQadWKICQkhLRFzmh4OGBwOZGVa8CXeBgzSx/BxVN2tG9Pl6SNGzmGjh0p/IaEUKiTIr0OB/nIwoUUbKKjOUeffsp3dOxIpciaNRy32cy1FgHtk08YAygJU2bOZH/FsiZF2rOy+PmIEQRqAOcsO1sVozeZSAf691dCYmYmrxE3Obud9NhioYuTJIwpL6flq39/xs9u3qyu69xZ1VF89VW6wQF0p9yyRcXuHD9OeuByUVlhtSpF3PLltCIm7Teg92214O+vIWpIAD4InIUpeAcBPSPg8gvAjh0cR0R3K4ytW8OOGnCFRwIBAdi7l3Q2NZW0tHNn0lGx6nl6qHi2v+cy9Wf7be1mc1gdG/xSMWdP5angjK1bufdSUihEHD5MZWN0NNfS4eBnI0Yw5stmU/FRycncZxcv0gq/axdpV0UFlUsxMRTIrl3jPq5Zk3THYuF9c+ZIz3SUlV1DE0cKTGdyoes62rThucvPV4k2AgMpiOXladjvjoGzYRtcDQxH8+YamjQhnahdm3y/fn3ywBYtSHf9/CiYlpSQbjduTOVIWBjH6e0NjH/aCB3+aIKTiAk6hoVLA+Dvr5SuUq5m3To+W+Zw8WIKtRs2sGTClSt0KXc6eS6DgqhoevFF0q7QUM6Xy0Ua8u67zJi7dCkFndq1acUDSNPOnuXZfvpprpXDwbnesIF4YcgQJSQajaRBUVGknbt2kf7k5CjlVGSkwkLt23O+xo697vHTCjDWCYBPry7wMep4vEcm5i4LBG4diuc+rI+AewagQ3sdcXEKA06cyP3y6qvsX/fupF3PP895WriQCu4zZ4iJcnI4fs/s7SkpVOSZzaTJdvt1hfg24iPxzBN38JslTvQskyVnQ0KO/t55+L09CP4QAp4siCQvqR4g/Pc0TJ6Cmlitbna/CANSw0V+5J6EnQagVi30iNIwb17VLIaDBqnMdLt3U9i5Wcya59+xscp/WKw/MTHUbiYnK3dGs5kHXJKSTJ+uhAjP+ai+WUWwkfF6uiv2709BJTaWAoG4qop2NSpKxbLJ9UOGqDF4Cpue7+jYsaoQK/7dCQk82HFxKtta9fXavZvP8bTwidbGc/3FxdXzUMbGKvceWUtZP2FO0uSdYtYvKuL1nu6+csitVmVhlTmSTKkmE/vWvz+ZiIBHseYCKqAXIHOLi2N/ZIwSQ3rbbWRgYrWpXVu9t0toMXrnLEIiuqJj1hLs31IMu53WxXbtCPaiokgURz2po2eLfAwb6sbw4WR2RiMJ5IQJ1FQGB5MBXLyg44u5pUhOdsPtJpN44AEyBLtdQwpiAHjJrP1qBs0/YqOgcvPm6Q3gKfTJPrLbVeyduG/6+/O+Tp1IE9xupTRxOJQr3fbtBBcdOxJ8lJRQMIztq6Fd7/qoVdMHw+8PRI8eTNiRn8/3lZYCFy+4UQogHZEIRRa8DC5UVHCfhIayL2VlFMxGjuT/27bxLI8bx/58/jmf162biuUYPpznoXt30qG6dQmSzGZqk5OTqZCw2Xiunn+eZyYwkHszP1/F7x0/TqF4/XrqFcxmXnfoEMf//PPAT2u8MKXnbrTBESwPnoyBt1lw8CDPo6bx3X36ECR07ky61q8fBc8pU66nJW9LQXrhQv6WONWpU0nvbr2V4xo/nvEvDgeVOY0aVeUzJ09SSDIaOScbN9IamJhIANW1K9+Zmkow99RTHJOuEywlJxO4ulx879ixfPbXX1PYHzeOglZyMjB7to5jmdcQ28cNh4MWih49VMIcUYiFhNAqP348371tG9+/fz9pcEqKKpwcF8f9FBmp4nTT00lTRakgtNDkpcE1YDDw0TzMHrAB5XbthsY9M1PD923fwvRnz8Dx5l/Ru4+GjAzl4v/kk7QIOJ1VY5Gre5QA6rObKWn/dNH8x9o/MlfiMeRZA9fzfoB7QpSnEm5x+DB52qFDPMd33cVrZ84kL3K76Yq3Zg3389dfU6i5nr7ghnu6tzf70LkzPRWMRiYEq1WLe7WwkMLKmTOqBA1ZkRuAC7mIwDVUoKafE/XqUTki++jqVdKVq1clOYwBlS4zQkI0ZGfz7OTlkS7u3El6npvL/9u3Jz12OEh7NI0/K1bwTK9YQRqXmaVh2KjG8I4bgivR96BGDQ2lpVSwSvIts5lWeqeTdDUggMqu48cpcPn7c4yiDDOZiAkOHSJ/X7SIwlx8PJW3x49TAbVkCYVHEXgMBuCtt2hJfestKt26duX7hfaKS73RSP5hMACOCh2Lppdi8aduvPIKz35ICNdEsNPHH1PYHTyYoR3btnHNIyPZz+49NGDLFqxceAHvdV2Dr5fo+GBvNJy6Gwl7vOC4VAyAz05NJc0FSGOzslQm/OnTldXR31/xCEkQB5DeZWXx3aNGEbtMnKi8rEQJL95ckozQc4970pvqGeEl0eB/iwfB/7yAV13LVN16V93FUlztZs0iSBBG4XIpwUmeI/Fx69fTLWfDBn7Xrx8ZoUj//fureiICvqU/nhZB0bp6bpjqRFOEFbOZzHvHDsWY+/VT2RpFMJNgZx8fgps+fZSLX/WNJptV4vSEiVb3q09IUH936aI0HPIeiWWTvm/dSmIttWwkeFXeUd31UoQ4p1MlB5Dg7IQE5erp6U4KKEAhrqy7d/O9soby+bZtyoIiY5EyF6Ix7NeP14pLUHy8Cnw2mahZXrBAWQRF8Js2TblvCnEQN8zly7lHiopoDZP5sVo5RkmSsnw5Qdu5c1XnRmJBhwwhGDaZCPRjYlRMjOy5hAQgelgAEiOfQDH8sLDJe2gTFXBj/kVIN5mApCQdMzsvwZ2RmVjX7TU4KnScOkUmOWkS+xoTQ0LYuZOOv71wAE3nPI2Ox7+B2UuHrpNxjB5NQe+pJwAyUQDYpTjzn+1Ge/jhn38mcrCnAqhjR/V9VhZBSnAwhZBWrSjE1KnDe0tKeG5q1+Y5cDq5t/z8uCeOHeP9ly/zO88smCkpQHm5AUNvNSExScP33/MaLy/ul4gIoH5DA3zhRlOcxTG0g18tE9q3p+Bjt1MYKy0lONiyhQKSxUIN6Y8/8nxlZ5PerlvHuNBVqwgazGaOKz+fgOTYMWrq27alptXPjwqEli15FoXWSmy0pnGbXbyoykW0aqWeazJRGHz6aSCwroadL67HkRe+QINXn8ThwxxvjRpMStKiBTXPmzdzjj74gHMxZw7HUL++Wh+A7zUa6XHwzjuc8/Xreb/VyrN25AjByQcfkG4I4LjtNt6bmkoh7P33OX8TJnDtDx7kOT13jrT27bc5P2fOUKCNi8MNF1XJnPnUU9Tiv/suhex27YBnJ+owffMFmq2aDtw2DFu36HjkEQKsp57i/StXcv4feIDWR6uV8zlrFoUrsaaWl3NfGAyksW3bcp6zsmihkKQL7dqRT65Zw7mYOxcYONCAb9bVQuNgAmWbjYBt7FigZUsD2nT1Q2YmD0Lr1spNymrlvFTnhwJQhVf9UvvTRfMfb782VzLXnuEPgok8FaiCg2w28g+Xi7wzJYU8OCmJ18XH8979+/mzYgXPmcvFffbww1RYuN10Ay4upkDl50e643DQ0+Shh5SSyWJRCjIfH1XuwGw2gND3Ki6hOfIveeHiRVr8AL63rEx5QOTlka4WFHCP16pFa1x5OS1mJ07w3Pj4sD85OTybUlN23z5eHxhIwTA8nLQ6NRU4l2fAyYs10K2bhrp1+T7JrPnUU6Q5cXF8dvv29JJ47TUKgXPm0PXT25t0ondv0giXi1jg0CEK019/TVpQuza9qUaMIK/OzuacHTxILHbnnZyfgABe0707f4YMUfR2+HDSsv79gcgIHZa3X8EDH7XF0xuH4NRJHY89ps7k559TWeXlxT5t20Y8lZLCd2Zmcv5mzgTOnTfg+821sG2bhltvN6FtSxd8jDp6RLmxbk8AnE5ikZde4u89e0hvn3+eAuWWLeQzNptyCZWMqr17K1fv7dtVRuonniBd2bePmGzVqqoGCDHczJlTVWjzpDeeWTJFuS8Kjb93rv604P0bm6eWCbg50apuzRs4kIKYWHYkAYqnpUaEMLFoScppz3TNngKhZ5ITu125Nt6sZpm4N4pg4ak5EO2B9EssVSIIiFVOCGx1S+COHSRMnq6c8p0Iuk4nN6lnPBtQVUsqB6l3byUAinXMs7SA06nSIO/cWdW1UeIZJYOoNDk4MmeiFZHkNk4ntTVS7wrggevRg+BJDmmvXiRyni6cTifBpmTrknFt20ZQk5hYtRSB1EOJjiZxF3dUq5VClWdiHB8f+m4//bSaowULqAkPDGSfzWbOQ9u2Vfu0b596z9q1JLaPPaYydEmRc3ERFWumw8F3FhXR1cVmUzEze/Zq6LHjQxx98UuEffgUPl2s4do1jl0C3IuKgO5hxRh77hUY4YLj8DGsX2FHSAi1XM2aEbTNnHndL72iDP62E9iAATh4oR76tC9GYqKqVVZSAny9tJqvoaef4Z8NAOe1enO7VSZKLy+CgvPnFT3x9qb7Udeu1O7m5dE9x2rlfWYzhZSYGJVlzmRS2m2bjVrbK1doKTp2jHt1xAjeU1xMS1j37hSkxDsgJ0firzTUDamD6JFtYajfEFeuaCgvJ5g6fpz3R0eTCZ89S+HAZCIAi4hQMYcSL9O+Pd8vMYX79pH5Z2dTOPPyovb11Vf53Jwc7sOcHAKRkBBqbLOzed3Vq3xOp07A669TcGzYkLSwWTO6UbZtS4VFud2A+0f7YeHHrJXl7c3zduoUr5HYtuHD6aK4bx/fdeQIhbC0NIKLsWMpfGVm0mLwxhucy1mzVBmG48fp3uhyEbweP853CGg6coRnrbycoMjppLbbYuEcJSQwPjE7mwJiZiafNXMmBaeMDNKBRYsoDB8+TBetmTNpSbVaAdPVArQ/uxYmOGBN2oFZrxTA35/jENo7aBDXTdzpnU4Kr0JzevYkmD16lPPSti37YjRyzJrGd915p4o7TEnhj1iRb7mF83z6NAXpo0c5dpNJWW07dOCarV5NWiZ8t1UrColbtyrvjP79VfIzTw+Qm8XSy3d/tn+seYZ1eLbq1j2ZU0+c5cn/ExIIyIcN41ru3cv9IOU/bDaudWQkz+iOHVRwWCy0eJ89SyHF7SYrKSig8C+ulAcOELuUlNAaPWgQeZGUY6lXj3tTElg1aaLBbPZFnYD60OELg0G7kYG4Vy9aD0V5CpCmZmaSlrRvzzNx6BCf5+3Ne2w24gNdp5C3axfrRD74oKJ5Tz1FoQJgf4YOJY23WDi+Ll1Inw8dIi1Zt47Kjx07ridV6szzk5PDcb30kvJMunpVZUdfv540/uJF0r1WrUh3JFZXslcmJXF9mjWjhbxNG873jh18R1ISz3lKilrrfv24DoWFwLrlduTlXMAgbMT6vX7oFVaAW29lH/v0oYVs6VLOZ0YG74+O5t9uN+mHZMt89VUK8HS/1LAp6nXYj5yGNWEDZs7SsH07cYsUID9wgL8DAuiKajaT5k2Zwnf07Knc9vfvV7GARiP7NW8e6aTs1bQ0YvLERM7fe++RzjudnHvPPBUOh/K681QsVcfNN2u/txfB/7yAB/w8rb+0m02yECZx+wNUPTRhKmJJEsuZpHEeNIj/iyYrLU0BeM+4M6dTFZAW10pP69zfE0L792cfJIWr+AibTMo1U67t25d9kJID1ZvESogAKfd5vstzPGJ9czrJgF96ib8//riqK6doMTyJ+5AhqvhxYiL7Llo9yQQqDNlu53uAqhkzTSYKcAJGmjfnvaL5k35LnKVkTRLzuwjIPj4kFC++SDAiVrrUVFocRHAWQbtXL36/cydBmjAlabNmEXR5zuPcuSQOmzeTaUkWSsl0KS6nMrdOpxJgARK6Q4dIdM1mAsCICMBkYGowe7n7hkVhwAACuawsavfnzaMVUbJ4+tQwYOxkPwwapKFbN7pnjBunwOfIkUD7PgEI7BWOiViAW3sW4Z6HLXj+eRJ5gH3RdaYyfmpkGSy1/BGCs3ggJBlHCwJw7hzBfcuWFE71avKdZ2KhPxubZ0yptBYtlFXNbKbgIAqNoCDu75AQCghHjvBMennRSpOXx3uuXKFwUqMG/3Y4KLBJIP6BAwpMizUsOZn7u6KCzH/VKmrHLRa6ErVrx/uLi4HCQgOat/NDRKSGoCBaxxo35vMkLnDcOPY3JIRn9swZWocWLybNOXCAQf1t2vDzrCwmEiopIXCTMgQ9e/LMfPIJ37N9O7XsEycStJw4Qfp57BhBg5Q2mDmT8xgfT+VErVo6Du8vRcZBNx5/nABx+XKOe8IE5WL1xRc8Z3FxSgDfvJlWOZuNwpvJpAreOp0EMLNn8zlffEElSFKSSmKyYL6OZgFX8dliNw4eZH+bNmX5kY8+IuidOJFznZ3NuRP+43DwmbfdhhsxKOLy3awZ+xEVxfUdPJiWtyVLOOeSCdli4ffzfwiCo1U7mADYe8TA1DDohpUN4LnNzeX7n3mGz96wgWs0dy75yK5dXFMpOJ6drUrq7N2jo2OzqxjQ343YWO4XcdHSdQXGt27l3m3Zkgmgnr47H31j3Vi8mCDMYiG/GNBPx139rsLq60avXlRIxMdToO7XjzR5+nTFN2+WOfNPd8x/rQkv9vQ2uhlG8bTYeWbtLixUGCU6WoUhuFxUDPfpQ2XM+vWkM8uXU9m0cCGvP3aMZ/T8ee5pyawYEEBhrKSEQozbTZwhMbq7dl1XalzHIJLoBKBgxYRRBpTZvWAyaTfc3Lt1I82dPVsVHAdU/dDycgqbwv9r1eIYHQ7u9Zo1Of5mzZRyrH9/oLRUR36eA9Onu/Haa8zuffky6WVqKhVkVivp6LZtFEj9/dmHIUNI6w4dIl2T61euVJ5Af/sbPxs1ivdJ3HN+vsISycns/8GDqvRI9+50c3Q4OP+SRMls5jnr2FEpGfv0IY3dsIF84okngD6DLChoEoMX8FdYwkOxLSsI775LZf7996skbosWUXHWrRvfK14pR45w3HPnchxms6pb99cPDEC9eojfpMFuJ49o1UoZUHRdKbNNJgpxr75KOmgy8ZlhYdxLUvZJvMwOHCAue+YZpbyKiVF4cOBA1UeTSSnxJZGLxOxJ9v2b4ea/p0j6vb0I/hAC3s18w6tL0hJ7JRK5w0F3nDVryJS6duXmk2vEpbK6ydXTzbFjR0UkPa1wO3bQzOxZM8qztp4s/qBBakN7ukSMGaPi3ORaTVMbEVAuhz/8UFUAEgHDbFZCwM2aCMNiTRMtiNNJN6B336WFQPyfPedw+nQSbbEkyfN27+ahfOYZAor58ynEiDunJDERN1Z5v1g8584lod+8mdqtMWM4P/v28XOJYQRUPNvu3VXjMWw2ghfPOenXr2pdO2kiYEpQ+Cef8LmyJpJV86WXlDDs48O1FbeluDiVZrdrVwqIEswLqFIZO3bw8507qamTgvdWKwHgrcN0oH9/2BuGYFabRbBf0xEZqeapvJzEX2JhpP3wA/soQp/TqTJ59utHxvDpYg2bXtiEqPRPkPiXDTCaNNhs7GtoKIXmVmE6DN8swaKYz1DPdBlnhk1AfPtXkJqqwdeXxLRLF0DTdNRy5AEQ1W/PP2PwbtIEAHi2EyeUAsNqVVnYBNhrGhnlwYP8LCqKShaDgYy4qIj3XLtGV8mmTQnId+2iFS08nIJXeDiBkM1GkF5YyPcWFCirrZ8fGXK9etRm+vkRPDRvTi17Rgb3zsSJBFlGIwFK06bU2JrNFHTWrWO8Rfv2FPSaNmUfatXi2bx6lc8UTbS/P/dvdjYFli1baJ387jv2q25dzkOHDtzLmzapOMFvv6Vg+8ILfI6/P2D11VE/eytMn85Gs8S/weXSkZ7OmJx16zh3Xl4ENGfPcv7mzGHfHQ4CrKlTaVl77TUVkzp2LOn2mDEEnLt2UQAeM4a/r1wBjhzW8fD6O3Fm4jSMjr8VL7+o33C1kgRXM2bwzHfqxHHJeY6NJfiaPJl/v/oqz9fGjaSdo0YRJIt3ybp1tBCIYJqSQkFd3LoeeVTDqYfewCOJT2P2gA2YMVND9+7cZ4MHk7aOGkWavH+/igcESM/i4jgf7dqR3vbsqdxEzV468MoUuJ54Aqu7TMWkSToKCii0V1RwnTZvZv+yszlOi4+O6cdugbUdE0A9+biOgIDrtEvXsS/6BQx8vDFssUOwI0HH4cPkK4GByoPh+efZv+ox2b+kKP2z/ePNx4c8/2b1gj1bdS8pk4n87/nn+dtu5/5KTeWZkXpp772naq95eVFQs1q571atohDXvj0/P3OG50TXuT+vXVMlZi5fVta2s2fpiihJM9xuCm1FRTyXkl9BPKmcTtIi6fe5c6oOn9N5fa9auI8vX2afxGW4oIB9MJs59uBg8tbdu0kTz50D1q/TcfKkHfXPbkZF/iWEhOho2pR9E2tfVhaFsE8/JT365BMq8S5fpsu4rvP59euTFp4+TeE3MZHnIDiYNHb5cp6zbduIOdet431CXzMyOKb9+4lrk5JoZZw6ldbJDh04lvJyrtehQ3xu/fpU5OfksI+RkTz//gEaHt7+OPaMWAC8/R7CIzRs3UoB6ckn2c8nn+S7Bw/mM6SOc40apAeS8ETCEYKDuUYLFzLsafdu0sDHHuOcyNmOjCRdeu89rldYGPsu6+blxbmJjOS+kX0rYTzZ2Sq8ymYjH/noI/bPZCLdnzKF+0JqOwNc91GjlFVTPOGqn4dfar9mZPp3tv95AU8IkwgcnvF4N2MA4s43cCA3vNHIDb99O4FVnz5VXfiqEz7PeL5+/XgIhUgCP69lJxmTPJmTCHuSOET+F5dN0dxKjQ9AxZTJ+PbtI/iaO1fF4dntdBkEOA7RRN9szpxObnxPa5rJxOeOGcOAVxnzjBk8AFu38rmi/dixQ8UuilB8++3U1tx+OxlAYKB6tsTWCUORce/YQSFl8mSVhlgKOUsQvghOO3bwfZI1r0cPtV69enH8xcV83sWLqiaexCZ6CuLSh6NHqdl+8UXuC8958ow1lDVzOnlPdDT7s3s394D0HaBGTbRPsbEqy6a4O0VHc0w9epAg288WYP0uK6A78ejpN5AYXwyjUQny6el8tiSK2LmTGq+vvyYQPHmS/Zs5U6VPl+LwI0fSZe2FafXQoSMTH0yeTAa2ejXf/8WnDgScS8RGDMa+gsZoXN+NW4drWLhQBX6npgJul4581ANw3R8Ge/9Uo9+k5eWpvwVgAAQSkvIaoPAiyQFatiQAaNqUa332LLWg/v7Usj71FPf5kSO8/vRpXt+kCf+/fJnCnYCl/HwKaYGBZIii9ZVyAWfO8FkTJ9Lt7uhRvuvMGfbJz4/Kmrp12efatfm+pUuVgiMyknWsDh/mHrFa6V7asiX7FBsLjLhTR+OgcgQYSzCgvxvRPXXs32vHkiVu9OlDBhsRwTMqmR2Li7l3pf6V2UwmP2QIgUt6OoFJTKsCtChKRBtkomb+UXz2XgFiYig8Tp/OPk2fTsFoxgyOXQLwjxzh1s3O5hlp0UK5xb/8Mse4Zw/pZHo6xzNqlHKpbmApxjfJ4XgEi/FpYnsU5Rbgww8JvMS1PTyc17/8sqqb5RnUbzKRLk2fzrnLyuIYly7les2bR1CXmcmz7e+vYk8GDqT2fNkyWk/vf8CApZvroXUbDW3bqiLAorh88UUCx5gY/kyfzvc5nQTdixaRFvzwA59/222kbd2bFyDu0EJkog3WHA1FbXMZVq+mIjA3l3tixgyOpUuX63WwEsuwa58ZW/UYdM36AovnlKGoiO9DQQH6HloAm+6FSXvvRvfmBXjhBe5TTysSUFWhClRV3P4p3P1rrTp/Ew8i4OfKcU9PG5uNgkBgIIH6nDlUirz0EgUDEfwiI0kHJkxQ7nNRUfz5/nsKNp5JnKKiSPdatuT7pHRMQQFporTsbO456V/1EHC3m7SsRQv+L1ZBqZnr60s6ULcuaVqnTrwnNJR0rqKC2FBw5JEj7GNxMWmyvz+fUVEBRIbbURcXcAphiEAKCi66cOECz0H//rxO4l0bNqRiKCSE+Kh2bZ7zEyf4f40a5LG6TuGmWzcmRHnxRQrEvr4U8gICqKj58ksKpOvWkd55eXGekpJoxapZk3xj+XLS4T59SEOzs1VdT7OZQvlPP3FNY2JIfzp3ppCzcJEBzdvUQNduGpYt47o1akR+MWYMrZWRkZyTyEjimpQU8qqFC/nM0FDldcFYcO6Ptm05z0OGqGR04uV0yy28p107Yvv16+lhMX8+PxfMLWVjunRR7p0mE+9LS+P+FmX7s89WTeQnSvxHH2VfRTa4/XbOl9PJ9ZBQrJsZjX4J+vwe7pr/8wKexGKJu2RMjKqbAfx8cj2TlgwdSs1lTo7KKGgyqRg5EQKE8CUkqGd6JnPxLEUgQse0aXSpe/bZqolBEhKoTbDZuPHS0viM2FhuvkGDeL+Yk6WJJUuYXUwMwUdiYlUXzOee47is1qpumEDV8grr13MunnxSMc+EBJUh0tOC2aYNr09OVsKxuKzGxZFweFo7pU+efs1SwkAE8fh4/i/1ojzn8fBhroVYBCVpwcCBnAdJpV1URELomRxl4kT2a+BAgp7ycqVhttnUoRTBdPdugrYvv1QCt5QoEHfUOXP4LoeDbglz51IgtFqr1mARDVJMDBnIunUc79atikiUl3ONpk/nPO3cyXdsPhiE7+qMwQrchpfrfoYOsQE3iFHnzhyny6nDdDkfBgNr+7z5JrWB9euTmQlzFrdgh4OgcvRoFSeTnEyBOTycFpGSEhL6Dl29sdTnWQzHtziO1jhy3orFn+qY9XYp8s65UVFBpulwGgE4AFSqjZWV9Q+d1T9S87S2X71KoCKZL00mMjSAzLpBAzL+fftomcvM5HUWixL0OnfmPq+sJCOX5E5S+PzIEQpv+fm02FksqtxCXByFtooKgpSwMO71CxcoxFVWUkioXZuM+sEHCRQuX6aW+swZfic1huTvWrUIIIKCVFH0GjUIYB55hM9OS9OR9pcfoKXuRZOFE7Fw0hF0Xj8VH++NgPeyL3Dv3Trcbo5x8WJaFKU+32OP8RydOMF5WLeOAoXEAALArG+DsNNnGNZgOELCNHyzJeiGUkUK7+7bp4TP0FDOVVycikG6coVnITSUdKprV4Ks3bt575YtVKakpRF0bdnCvuWVBeCR7lnYj5641rAVFq4IQrt2pBdWK2mF1Mn76185Pw8+SPfurl1Jby9e5P+FhRxP+/aMi2nThmv45JMUcE+coLDdubOqzWU283oBW0uWEBybzQTjkowhOZk0dcYMgheTideYTLT+bdnCvTJ6NPt1223kgVL64dn3goCePTABizAo9BQulVkxbBjHEhrKtSgqIlDLy+P7XBYrHK0j4YIZPr06Y8zzVqSlXXdRbRgEe4/e+BjjMaDlWSSdDLpBPz3j0BMSqlrugD8td//O5ql0FcWxhFR4hpx4JlcRoXDkSApeEyaQnwUEKMur1JfMziZ/37OH+7KwkN9t3sy9Urs2FYx9+hCD7dtHWnbiBN9Xp07VhBrNm6s4O8k47NlEOVt5nTUVFlIoqqwk3duwQcXLFRTQlbOkhDSlvJx7v27d63Ubc0kDSkvJX8vLqSTy8SE9dzopsGXmWDDAmgUf2HECbVEnyARvb947ezbp4unTpGsBATzXRUXkxwkJPPv33EPFirjqh4aSPnXqxD7l5PDvgQPZP4OBuOLQIZ5Bt5veB1evko7Pm0e63LYt12bTJp7zceNIY8PCOJfHjpF+5Oaq82QwqLN/5AjX7oEH6OFQUUHeJLg0JYU0Q+p95uTw7BYXk3aaTLTqff31dezgYBiMYLHMTCrO1q7l2gguKypinxMSSHOXLyeN2b1bZRwV61p5OV1GJdNwdDSvi4qShDvsG1A1NMczY/rixeqZYvFLSOBzJk0i3qyeYV+82W5m8fY8X/9pWqW5qxdf+i9vXbp0cadIkMavNAH1djvdRTp35uHcvZtg18eHxMlzUeS+hAQllIimRvy8xUUvPl4JFp7feQou1QUZgJskJYWHC+DBlma3c0MfOsTNA1Ttp91Oojl3LvuwaRM3qfgCe1oCt2xRh81kqjpWgBtVnt21Kw/bo49yQ7duzYOZk6N8lSXNv9GoUpwnJvLAStH0KVMoPIjwMW0aD9Kzz6q4EkD1WeZZxj5nDgUqiWH54gtqeZYto1bGZOLB7ddP/ZY5ttvV2m7eTADUo4ea/+nTVX0nt5vCXkoKtTtPP03idffdvE/2hzxbgnVjYhQTk9ISGzaQWbVurRhOz55KI7htG9/ZoQO/i4uja4GXF/dkXJzSIhmNJKoBARyDJO7ZtIkpiS0VxWjfJwBffqXh0UeptXr3XQC6jvtbpeB721DsbfMkyl9/Dyt+MmDRIs7Zxo3K/ULcWYSIhofz/5wc+ta/+y77ZDaTESQmUsiosOvYtd2JCt0LERFunFmVhjNogOaG82h6RyesXWu4Hs9QCaU72g33AT9yoF9omqYdcLvdXX7xov/y9ltok1jhpXXoQJBdqxYFJ29v7oG8PIKQOnXIvPfvJ4MvLycAGTOGDDIggPc1aECBKyKCWlpfX9yorVRSwuu8vHj2Ll4kg2zRgs8/dYrf16rFM5uVxX60aEEmX1ZGa2C3bjx7JSU8JwcOEDy8+CL/7tmTTDIoiJpfTSMQCgtjnyWr5fnzwIXzLgRUnEQnpKEVjsILOp7CZ5iK11EEX5xsdQeahpjRpAkZebt2BEV33qncucRd1GxmX8+e5fnu3p2xKrpLR4NadoS1s8ClazfqXbVpQ+YfGEgX0LffVglZunUjTVy8mLS4vJwgNDWVzxUN/AcfkD4884xKwLR2LcHjQw8BZ07rcJSUweBrRbt2LD0hIHfOHHX2srNp2bDZOIf9+qkkLBcvsp+PPUYA8/jjtPg1asQ1u3SJNNJqJZ0BCJy//JL7wLM5HDzz3bqRXw0YQNojFsXRo1XtP6mjJ+718syWLbkfRPlnMgHQdUx7swxHz1tRv76GGjUInI1GAkmrlfty8WKuCTPp6ejUvBgICIDRpN2ggy4XALcOFBXDWCcAjkrthuDpyUeFv+3a9TsApT8YfQKU8CZxdYKFPMu4AErxmZZGPu90cv8MHcoY0927lSdKx47AvfcSL2RmUpBaupTn6do1XlunjrKkbd5MQUnydEkinvr1uQeDglS5j9TUmwt2AHm7ry/pkGdr2pQCljRNU5k3a9Rgn+rUIW2VzLGi9Cou5nVffcXxNm3K82k0UmDz8RH+qiPvrI7+A4xI2KFB03jOmzfnO86dI00TV2sfH9Ld5s159n18mP0yL4+f7drFM3///ezTunWkpTYb+YHLRUtaSQnP87Jl5B2XL5PnTJjArKRLlnBNd+xQCdqMRtKtHj0oNA8fThpz+DAt91I+weEgXqysJFZo1440a9EiKu8OHiTNdLn42ejRnKv0dCrlmjVTtUbLy5XXw+LFtB6uXEkXdfneYuG94eH8rlkzVaN140bSQh8f0i6LhVimbVuOJyND1R9OSFAZWFu2JC3euRM4cEDHpPsLkHgiCD2iNAAKl4un3c6dnIOQEGJz8Yxbv57z5+ktJ/i7Vy9FR6vX0ft3tV+iTf+zFjxP86ePj8p46Bmv5amJAn4egyVNXAHFvVKu6dNHGSekFIJnsPeWLaogePXyDF26qID96vE4R48yxsPHR5mkhcjKYRfXHUAVFvccu2ipnU4VA+g51sJCHnSbTcXYPfkkGfiTT/KQDBjAg7l7t3J7lKySXbrwgPfoQY3uihUU7kwmHm7Z0BMm0M/eauW1AAmCCJ7iEy/m8HHjVKyb1cq+HD5MwrVjBwUdT+vH1q383GajRqy4mEwhLU1ZOjds4Ge6zjno3Jn9DwjgNXv3clwffkjNYa9eyjVFgsUFkPv48JmeZRaGDCFjs1oJlHr2BO67j4BRUq136IAqxTp79CCRGDq0KsOMiKDQJtY+8Sk3GoHBQwyIvqUWUtMYGP7ll7TSzZ8PzP2gDIG2ozChAgOPzIX5WvGN9MHr1qnMWzk5nNtOnTgv4r6yahXneepUWkDS0+nq8N57gJ9Vx4F9dhQUahg70YxXX9Xwl9EFKERN1MIFhOppWL/OjcaNZRRa1Q1t+J8lM/90E0YgLT1dacFFw/zww9Rie3mpEgCdO5PRFxVxTyxZQqGuUSOCl8JCgpTMTO7D5s0JUvz8uOdCQkindu7kM265hWdnyhQyz7p1yRDLy/k8TeOzKyv5ntJS7p+lSzmGvXtpvbrrLtKJgwdpUd+7l4JWrVo8H489RqHixRfJxB9+mO8yextQA07YYUQquiKsOfBm7Xnoje04aW6P5qFeiIigkCpAJTSUTD0/n3tbEng0bUrha8AAjvHLL6lZXrvOgLmLa2DyiwRX8+axP/37U1Ds3Zua7rp1yfA7d+Z6LFpEutOiBc+ljw9ddby8lFLqxAkqRXbuJIOfOpXzdf/9XKe27Qxo280PHTtqN8qh2GxUNrVtq4qUu93K1bNfP8Dp0JGyvRQpKW5cvEgBzmLhen7xBelGp05ASHMdDw67imXfunHgAOlqhw5cnzFjOBdGI58bFaUykPbsqWJyjEbFK4cN4/rOnk2rot1O+tqhA+mS8IYHH6SrrPCTzVsNaBvlh1tv1VBYyH3mZdIBWynMXm64XLSEPv+8Kuyu6wbEDCc9O3CA7z9wgEA9/SC/69Zdw7p1KgZGeJskNPh7WvBfc4/6s/1685xbEa6rC3eAKpMUEUEB3ceHyVIGDVJeNrNnq1CUunVpxS0uJu8SF/+MDCqBUlJI6xo1ogBXWEiBadIkPqOoiNe7XKQBDRqQPzscpDeeLu/SLJafC3cAlQ7+/gT7ktwkIoI0QJKvlJbiRnyo1KD0fNaLL5LF+dfU4W2sxLVrbpw6xesaNwbq1DHArZlw6rSGO++k4FKzJpVip0+ruqYSA1xeToXziy+qcgLLllHBtWwZhduKCvJniY2rXZtnXbL8fvABsVdSEmntzJlUaI8cSavb6NGkWffey/cNHsz+eHkBZaU6nhl7De+968bbbzPJ1P79pFEi/Er5m/79OV85Oaou76OPkvbt30/act99qg6rwUAaYzCo8KejR+k58Mgj7F+rVvxeEjUdP06M2qEDld+apnjAkSN8XlQUn//118R+x46pDL+jRillfY8eKoHd0aPXaYRbh/OzJdjZ+imUP/8qpn+k4+67ldcEwP01cCBp/KRJHJuckaFDlXDn6S0XG0scKaFOniVEfi/a9D9vwZMWH6/c5cSSZDYrJuF5z/btVf36b/ZM0WzJ/3Jt9fpy4tYwZozaAFu3qhgrh4MbTjQMgLIWSZxd//4qrs4zTs2zuHefPlVNzF278gCbzT/XIkjcVrNmJAyeKaUluYZoX44fJ2EQy5n0+8ABCmPCrD0towD7KlZB0erNnKlcCkXQkTTio0aRGA0adHNth1jKMjOVZka0zpJ5KTKS4LJTJxLGrl15zTPPkKgajfztOQ9z5nAcTiffL0RcLH92O4ntrbdyDiUxzfPP09J14IDKmiRZQceMUW5NLhfdTGbM4PPENVIS3cjcx8eT4cXHkzgDHEvHjtTuR0So5BMREZzngACVtGbAADeOv/YFOp1bjb497dj+/AakpmlwOqkhnDaN75Y+5eYSpH3xBfuTk0Pi/s471EiuXi0xgzqaHNuOuOJvcKROfxxoeR+C6hlgMrrRPmU2ap7JxHTtDVz1DYauswwD4PI4MbvhPhPyy5W98cfTkCckKKVN/foENKKZLi+nValGDYIek4kMXer/lJQQGPj58T6xljidZLw+PnyOxULAcuwY72/USBXdraykVtxopPY3LIz71u3mHrZYVImRigoKiTVq8Ccpiftk2TIyQVECZWfTyhcWxndKnN3RowQbb79NYFOzJs9w48a8Pi1NR/55J14cVQLzV4tw6qQL4zolYUbv1XDDiPbtGcdXVEQg+cIL1Jrm5tJ1sl49Vf5h4UKChP79ue8XLuQZfuMNVc9u3jzSpIULSTP69uW8tm9PYe2rr3DDxXnmTK5Vr15k4gA10Hfeyb/nz+e8SVZaOVtnz1I5IvVHb7+d9PzAAQq/+/ZR+MzO5j3t2rG0wx13MIPktqgp+DYnAkNbnsStia9hzz4D9uzhmD78kADZVqoj+6UvYco7hUk9k2Hdvh62a4YqtAkgzRIFo8QADx5MmtC5M9/92mu0zrrdnAezmXRh9WoKzj16qPqCH32kkn1ZrVzXY8fo/TB4MNdpyss6Bia/g2O5ToS3cqPPtjdhrWmA3c4YvCZNuO9mz+ZP+/Zcs507SbO3bydv8fYmQF22jO8TfiI8trpXCqC058DP+fs/2/5o9Kl6kzn9e/Mp7msZGTyfgDo327bxfFkspA3799MCFRJCcL5qFYW0pUvJ3+vU4d5r1IhWp4sXSa8sFhWXB6js1NVbQEDVEkqeVjnPJnvc0zLcsSOFRykt4+WlPLgMBlXzUnCClHJJTtZhqiyGC060wEU0iI1AcakBR46QxjVowN9RUXz2+fOqLl7r1hxjcDDHa7NRWDt/ngqkoiKVzVhi9Hbt4pwUFpKWlpZSWNu7l0qu7dt5diZM4NmuU4d0YOpUvmftWtK/3bvJKwYNIi29524dp97+CicueqFxAzee2f0APvvCgLAwjvvwYdL1jAz2WXCF1MCcNo1YMC2N4xw7looyyWoaHc05zsjguE+f5rk2mVSNvpISnvnUVNKA1q259omJVPA/9xznZ/580pLFiylUfvIJ50aUp1268P4FC0jnzp+nRTIjgzkGtmzh3xPvzcecyAWYiNkIMNphP3Iadv96SEtTIV2A8hozGn/uMQdwn1T3JvD0IvzTgvcfaJ7Scmysij0bOJC/RdPnKXl7JkoBfl4vDqjq+y/Fo8W1c/16lTbfauUGTkxUcWUul2K4Q4YoV01Axah17aoKoNvtSgOWkFC1ALmkft22je9NSOC98+aRwYsVy1OLIOUDrFYlkAG8f/t2Asy0NDJ30X5IQoPt23mAO3Soev+2bXzHzJnKd3//fnV49+0jsZkyRcXnde9OYPDwwzyESUlKkJWWkEAilpDAgzVuHD/fv1/VgQI4zo8/5tz16sU5kOyD06fTUmGxKNcom42EKSyMzGfvXmWh7NKF93z4YVUlwZUrFKa2byfRFEujMACrlcLgnj0EOlITZvBgPn/OHBKarCxlxRQi0acPiefQoXznQw/x2VFRZJgDBlAIa9mSwuuLL3JeJCPm6dMaHoh/FKnPfo05A5kJc/x4Wk1uu43jXbyYYxAinZ2tMn4WFlIb+tliHaGBV9Ey1I2HHwZ6dSiDoTgf3+JBbLkcjsKLTpw+DRxI1TD17ERk3DsLT00NxrBhnlY7TwEPVbntnw1AVbfsq1e5B4KD6UZ09SqFq+JiMnG3m8Dik09UfTlA/a6sJACoWZM0oqyMgsDVqwRMNhu/t9n4IwlKonvquHjBBU1zIyKCNGPBArpEVVQQMOTnq5INbdpw3zVqRCFGCqx/+qlKhlC/Ps/DgAFK0Ckq4nW9eqnMngMGEDAVFVHD3b6TGUdOeeO1kw+jCN6YndoLx7IqsHkzxzdqFGl2Xh7P5nvvkfE//TT37sCBfMe+fZxbyeo5cyataZMnU0CbO5cCwq5dHI/U4+renedq0SKCnjFjOJatW+kStG0b16BjRyprJPvk11+TrjgcFAwHDyaNe+89CnBJSexvVBQtXuI98NhjtDCW21xwHD2JLz5zoUmdUsT0cmPbT8XonfMJbsdP2JTbBCgoQL9+pHHvvUdg6HIBG1ba0TovHpPxEUz79sJ+tgB7duv44Ll8BNZx36gDtnOncs0EOE6nUyV4eeMN/nTpQsBqsZAHDhpEi2GnTrx/1y4Kd0Yj5z0ggM8RlyspZpyaCjSrWwZLbjoew6dYdSQMI+92YONG5XViMnFeJDsswLUClIeEJKE5fFi9T5SFnmn5q4Ml0Z7/u4S7P1vVkBNp4rom+GnoUCXcSbwUQB50/jwtOWvXEpe8+y73lmQk/OQTgvu+fcnPhE9JrHtAAOlARQXpTq1aVE5LohTg7691deHO02PLbK6a5Fkymtesyf6LN4UId4Aal2TCLC0FGtd3ogP2IQRHcQr1Ub922Y24fMFfJhNp1cWLnI+WLUlLa9WitTw1lXQ6JoZzcu4c31tczL8zMkg7IyI49suXSW/mzOH/y5dT2WU2k6bl5lL5JnkW9u2jlWzlSs7n2bNU8l66xHM5bRqwa7MduHgODXEWFy4Ai+eWoUULhbMef5w4MSlJYc9Vq4ijLBZinmeeoQKnXz++o18/9qNRI+VCKjFqTz4JpOzX4czLR8+ebjRqxD2RlUVFj9t9ve6nU0ePVlfxw/du3Horec748RxH69bKHf3kSVXzddQo0kzJNj98OL87cUIpgI4fBxAUBC24CRZiPGzdY7H7aBACApQC1jMsS0KTgJ8nVKmezddzr93MWPR7tP9pAU8EDc8DDShBzG7npl6/vmowpOcCFRWRoUtwpSRXkaQYkkpYhJJevZSGRwpJJyZyU0iSFEncEh2ttEee9WZcLh5Qz5p84eEUEnr1UlYfSdrSp48iOiLEPvsswbuUBJDslz4+itikp/PgbtnCz1JSSGSMRgIZyVQpQdQHDtDXWIj3pk0Ukrp3V8KRFK318eHBkkydUVFVzdpOJ8GW+GuHh/Odcp9oPWw2Cjuhobw3KYm/27fnPWIJNJnISMaNIwAaOJDzu3UrQZvdzjXYs4dMQayUFgvnT4q29+nDg3rqFIlZQIAqWrxlCxmTy6UsXz17KoFRWnKyIoh2O//u3ZvPS07mHAHKnVfWXdP47M8/J3CcNImAWvZl27bs70svcU8uXUrw+v33BLNPTzSg0tsP48Zr6N2bRLC8nP0cNYoA89tvyUiF6QjhqV8fuGuEjr0L0rD2hY1w79iOv/1Nx+nLVkQ3O436uIinW6xDaBsveHmRAVtqGLA10Q9vvqVh5UpaeeiNaax6ECVt539R0zRtsKZpRzRNO6Zp2ss3+f5RTdMKNE1Lv/7z5L/z/efOqb8llfzx4xTsatfmvq5ViwzTbFYZBKW4ucVChlarlqrRVFZGph4SQuDRoAGf8/jjpEN9+tBCVacOcPWKjoM/ncDdzoVo507Hrl06zp3j9VlZ3DN2O8/ipUvceytXUsvqcPAciiuezcbz2bs3XaqKi2kdbNeO75P6bF5eFGxsNqbplnT8eXncf+baVtxVdw8uoAkSLf2gWSxo0oSCk8tFpZKmcY/dcYc6k6++SmH0wQcpYDz6KM9YZiatSiYT6VpoqHKZWbWK/Vy3TrmQ7dxJSyFAZUj37vx70CDSqOPH2Q+JKdq2jcC0vJyWiGXLVFa2zz8nOOzenbzmk09IO5KT+Ux/f6B9pAvmR0bCPHk82r5zF8bMaoldsa9g2caa2Bk+GsO0rbit9TEkngiC08l5f+UVpeF/f4YFW+s+hELUwbg63+LdTwKxd8wSJEU+DmfcALicOjIz2T+pcRcbS4Czfj3BNkB+8v33XOvJk7mOs2dzbjt35j3Z2crrReJPhGeGhlIIzM7m8+LigGdesSIreAg+w2jcFpaLMRO8MXgw77/zTu6zfftIGzt2VOEPcXEc3xNP0FVr8OCqWasBVXIG+PvWJM+QhT/bv9ZkPnv1qsqzPvyQVvHly8mP5PNp0/i/uCLPn09+/MMPVFCIt1F8PJW7eXmkYXL2JC5e06ikKi8n/RI+ePkyPz979jpA9+gn8Ov6RLHw1KihEo0BKpKgvJxWJIB0VLBHv36kV15e/N/bWyV9O3PeC/sQi9Nojkr4IDGNL2nZks8wGDgfXl6kO40acfzipjl2LOl1YSHpx9mzytOnTh3iOS8vPkOSigwaRO+I1asp5CxcSItgTg7psrc3ecuSJaSvzZvp6Bp6Fd8sdWPlSirb/P35/H79uGYOowXNQjQUoBGGtTyJVp2sOHmSdGfNGq7RoEHEbUeOEMNVViph/LHHSEvee4/KyhMn2Kf+/UmzP/2UeEzXydsCauqwTXodMyMXofX3b8Pl1JGUpLBJWBhw+rQO11+mwvHEk3g7eh3Wr9Nht3NfSQ4KyYDZrRtx87ZtxEaBgaRp4kY8eDDnBmCfQkMBH4uGyVmPYdKxiUj8ywa4dO2G0mLDBhU+IQkSFyxQ5R6kiWHoHzlLfxY6/zc1EeQkzgBQ2bckjb5kH4qN/fmki8vd44/z/xkzuLAiwcv1Vqty19y3T6W8dzhUUfIFC5TVzWpVxWTj41UafXmeZIEElDZywACCDEn1CqiEHzt2qOB6z9ICUpTb6VQWtcJClRBg4kTljpiYSEYbGUmr4u23K0Yu7qpRUSTqJhPBk8OhLIOpqXy/JBmx2ZQrrMRxREWpDKSSCnzBAgUcRPMybx61XPPm8RA+/DDdpRwOJYwZjQQUvXvz7x07OOeDB/P9q1bx2dHR1B7NnMn1kzi0zp1JHPv0UZbIWbNIGIxGuh199ZVKiCMuAZKqPDSUzwAov0gcoLhgZmRwnImJZHhOJwWz/fu5JjIfRUX8fts2AprsbGo2JeuYME3pe3q6yrL49NMU7MaMIXGbN48CZ0IC175ePRJll4saySlTqDl8+mkS988+o6CxZg3HsXWdHc7yUugAGhQfgL3UgVOn3Jhx6kHYYUJOUQucOe1Gu3Z8dqdO7JvbrdxmfH1vchAlQv6/pGmaZgQwH8AQAOEARmqaFn6TS79zu90drv8s/nf2QYR8aQIu/Py45m43QYDURWzViusfEkIhasgQakn9/AiEvLx4fY8eZP4GA/ftlStMVe3jQ/rx+efX4yxKXDC5LqIIddHdtRVWbyd8fXn9wIEEOF27cv82asS1vv127rvAQIKyq1dpuZEECevWEaw8+iifc+ECAZ7BwH16+bIqP3PiBAXUNWv4vBUrgN27NZjvvwdet96G4VO7wlKDCVFOn6YwkpLC55w9SxpgtVLJUb8+3/Ptt/z76FGCj5Ejed/nn9NTwGymxjg7G/jLX3jm3nqLZ0d36Th1+BocFYxlW7SI2vzvvgNysnWcPHQNzZu7byQX2LJFubGvWKGSJK1aRZr03HOcs127KBiLskziUnr3BoY0O4SBFasQhwSkox1ewDQ4cnLRsuE1xO17D8jNhWXaO+gRpWHbNtKV4GDyjJkzgf0pGgbOHIqlL+YgbtpQ6LZrMOadRB9sB/buA4qKMWECQdzTT3P+9u7lvjGbuRYuF5VZ99xD+rBxI/fctWvcLwCF+YgI5UEhoQaiiHzuOXoJdOnCMW/bBqQc0PB0yhPweuF59Nv9JvwDaCYxmykMS4H0tDQ+R5KOWa2kfd98w35L2RvxiDGZOJbExL+foa66YvfP9q81T68mT1DbuTOtKBJiAfBsSOZMf38KAoJlwsK4/yIjuUeys6vmLJBY4VOnuOeaN+f/AJVegA6LVyUAmuTEMvdb11nuKyzkuRSB7Wah4gEBxCK6zn2YnKwEQnF1dzgAf38NgA9cpiCEhVvRqLEGg4H8UdznKyq4b4uKKFw2b66yaObnK0tdRQXpvNnMPhkMpHmFhaRnx4/zXquV/ZLEduvXkyZJco9PPyVu8fcHLl7U0eand5DxzAI0/OYdzJihQ9d5vVi9QkKAIUM0LGv8Cuo+dgfwl9ewcaOG8nJioWbNuIbr1pFOnDhBwbJlS+KJN94gVm7WDEhL1WG4VopmTd0YOpQKM6OR1zdooKxhRbkFSDpiwQ5EYfWxFkjfW4bOnUlTCwspYA3sWgzL0TRYYMcHBU/gaHrZDXok+M/p5N4TPDd4MMcl7pUtW1bF4AcP8nfbttdLWe01wNSoHvoP0NCvn8pmn5nJvb1ggaqhPG6csl6KsCY/06f/vC6eJ436vbP8/k8LeIAS5ESA27VLlRwQ65dkiJwxQ7kciFAilqu4OBI0iWdwuZQVEFCxdV27KpdGs5nM6JZbqsbYAcpVtF8/lXDk6acJBgAeUKmBJzU5Jk9WG0vcPRMSaM2S9Ngff8x7xVrYpw8tTIAy/b/5ZlWQkpjIPsTE8OA6nSRC8+Yp90ibjdrohQuVMCLafonbkOQGEtsmgvDmzZxDYbwJCSTgf/sb5ysqSiUgSUwkQPzsM1Uk02IhoJXU3YMG8ZDI/5J0ReL8CgsJ6h5+mGslAn3Hjpxzt5uEety4qtZdTeMculzs2/jxdDuRpCsyrhYtuF+kpIHDwZ/du7k/LBbeazLxWXv3ci6nT6eQJwHHmzfTpSUsjMDoyBFq2j/7TKUFvu8+9q+iggyxVSv+b7OpPpWXs58jRhD8r15NAWzePBJTi4X3BQUxHujRR6llGzaMlsJmzXQMjbqK3HM+cBu8ocMLCdqtsFV4w3bVCTMKkYswbL3cBkG17Lh6lXVsJGOY+NcDNw9k/y8sdN4NwDG3233C7XY7ACwDcNvv2QGTiUxemp8fGeO1a2T0ko7a4SAgP3lSuTTabNxTeXnc67VqUYlRsybXPjKSn4WE0N0yJIT7XrLBXbgAlJabcBTtsQfRsNdqBrfJC40acZ9J8qG8PO5Bm42fX7qkYtP8/XmdWIhWrKDGWOostWtH0NK+PUFLYaGqlVlQwP4EBtLq5nCwb7VrAyYvAxo0r4H5CzT06UN68frrvH7bNp7dFi3UlgoIoFJD1wkmfXwIgI4d409EBK9p0kR5J5SXc9+Xl/Osbd+mo/uqVzB7dXN0XfsXADrOnKH1Ly1NxwNb7kOrNdPQadUb8K+pY+lS9kdiC1u1Yh9SUkjrJSmXlCO4/XaVIvzUKc7NzJnAprxwbPIeDhNcCEcOLqEeundzYtJfrIDBgBnf1EO5XcPOnRQM165V5+6hhzgW/wADHhzrh+PHNZy9asWo7mkwGTRsaD0RP24JuGF5CaipI7ZNPnrHuG94mKxbRwH7oYeAH3+kUkvShp87x7UTlzmjkZaDuXN5T0ICaZ3Lpbw0ysv5LKmvZ61pQHh3PyQla+jQQVk+7XaC0ZgYCoUWi7LSCRgKCCAvGTRI8TyJA5MSIL8UD+YpOPzZfnu72dyJ51J8PNe/Tx+uk2SjFuHbauUadelCWjF4MAH+9u1MACLutRMnkvfVqsVzWr8+161OHX4vYSEAUFysw4BSlFfawSzN+i/2tXoTxaO4PAI/T4pRPYlMjRrc3xLLXD15i93O8YeGUvFUr54BTt2Ec+c07Nqlats5napsiWTzdLvpdXDtmko8U1KiSlBlZ7Of4hHldnMeGzakcBcSQryQk8M+DBpEOikZhJs3Z5z0mjXEAn07FCPzmBHf4S4cPmZE/x5leOwxJpkZM4a0VxLF1aljQPpxP6SmaRg+XCnkxGMqP5/KoWbNOA/t2/P+u+4i7hp+iw7z6y+jxZxxOPdFPDIydLRoodwj33mHlrb0dGDiO0FIsg7CfViGYS2PIbyrFUVF5D3bt1M4Pn4lAL17VsIBb/j07IhOvaw3rHZy/qXetNNJbHbkCPsdHU1au2qVwuoTJpBXSY3j7t2rWqcB0paePYmrfXw4RsFzO3cq7N21a9WYX8mU7rlHqiuifk/F0/+8gAco4SshoapmLz5e1TAzm0lwAJX9sk8fCh9S+iA6mov7008UBGJjuSkEuCQnV3XXEw1nQoJyOazer4QE9f0779DUvWEDwYy480RFqdgFsYABKhbuiSdICH18uOmFSMk427RRfzscPAwhIdRipKaqjJ4pKbQm2WwUPIqLOZ4rVyiwzJzJ/nXrRleyvn0pMHTvzsO7caPKaimESVqHDkp70qMH50Y02g88wHujo1Xa9VatCGI/+ICuTwcPsu9ikd2zhwLg7t3KqimWD6uVGicfHwKX1FRqagYM4Ps7duT7t2xRriQAgRdAgj5xogIcu3fz/o8/JtC1WKixjoujX7cQbymt0KED+y4ZP6WmFsC1W7SIYKhvX8a0ANTCjR1LArlsGfs6bx7neto0XtO6Nedp1Sre17EjhcRjx0jc/PyoberSScfLz9kRG+tG9+5kDJGRFBJyc/lz+TLXs3kzHR1WTEX3yd3hzs1F99HdEPL4ILQZ3gp9+2po09YLg+seQS/sQk2zjguXLbhwgc+oX59WnO7dq1rJf0ZW6tX7+aH8v22NAJz1+P/c9c+qtxGapmVomvajpmnBN3uQpmmjNE1L0TQtpeA3WioFvHh5cf4uXCCjr1FDuV263dxToaEqSYFkljtzhuteXq6yeFmtBAdnzvC5paU8Kzt2UECrU4cg3G7XUK+RLwIaBOFg8xHw9mYqel9f3Mh4ePIk+1dezjMpWermzWM/Hn2UQlVpKYUuSSPucrHfHTsScJw/Ty14VBQZ/BtvEDCNHs2x9ejB/e7rq85nhw4UhlJTKQxs26bo8OjRnB+Xi2dh2jQKK2Yz72vVigDHYCC9nzaNngerVxNUuFyqKHyzZgCKitE7ZxGS0QXmQxnoHFKMO+7guXprUjFOHLCjE1JgOZIG1+ViJCeTD7Rvz/lu3Vq5sJnNPPdCo8U938uL5/D9d3V8MbcUx4+5ER5hQPpLy2DfdQCWH5bhsdld8WqLnzBzloaNG5Vnw8CBKg17Rgbp/Rtv8PuICILoFi2Apk01fDJgJTZ9fg54+z0Mu4VS8O5dOux9BmJrs8exPfpVmL10DBpEl6nQUD7zySdJi1NTuQb33svxzJzJeYuJ4bUTJnAfdunCOfCMSZGyNO3acc+JBryggHziyhW6fs6dq2qxSu1SQNWNknITwvNE2+/ZbpY10/O7P2vh/fPtZm5koiQXgVuS1QmwdbmU1058PAWLpCSC5FGjuP937SIf/eYbWt43biQ9OXOGPK9mTbqZ+/pSaMnIUJ4hLpcbOoCayANDAKoF1v1KKyvjb8+i5zYbaUxQkBqruOIZDBS+JFxFcixInT3PuZKMjQUFpN0S15eWRgEsLIwCYGUlaU5ZGenwtm08A4GBFJp8fUlLiotpqQ8JATIO6giuew0HD7rRrBmFQKORAsyZM/RaCAsjPw8P5zy3aMGz85e/kNZHRwOLvg8AGjTBLViFVi1cOHulBsY8YYet1I1ly3g2t27lnC9YQOV4QgKfZbORfufk8O+oKM7byZPERKmpqsj7K68A6/5WjPbZS/EN7kfUleXYvN6JNWs4j61bM9FLTAyxakgLDR0f64Tvu83Hp3Vfx4IFGu6+m94XKuxHg+3b9VgQ8zdM77sBnTprWLOGe2zGDOIPMZI88wzpYtu2NApYrcRVRiPQprWOee+XIinRjTFjuD6jRhGPbdzI/m3dymdevEhlfOfOyjIoOS/MZoVVFywgnZMQKc9M+oLv5bv/i/Y/n0UzPh43TK6xsVwkyQYFKPdJAfTx8WoxPTWK8fEUMiZO5GeSJMEzc6Jcv2+fyoApG0+yt0ktN+lHbCyJimSmHDWKvzt3poAnNfYcDjJFsSzK5gd4rdWqsmzK9XFx1JiZTHS33LmTYG3VKgIgcZEKC1PfOxwES2+8wTiz8HDldhkVxViScePIpMVFMS6OgtTmzbRSydxs2qRASrt29AUXN0S3W5VG86xz0rmz8j0fMIDgYuFCWqLuuYcacrudAo6mETiIJk4sjS4X18poJFFt147v7NhRZeGUbHdPPsl5c7k47wYDQUxAAN/RurXKJvr442qPuFwkCiLgxcVxvaWY5pYtajxDhhCQv/oq9112tool6tiRBDI3l6Cqf3+VHWrGDLoWfPSRcpeVzFAHD1Joy8oi0OzShfF6r7ysY/0dnyLuzAL81OA5+Dz5ECZMNGDVKlXc2mbjWrRpAzw7Mh+vxW7BaTRGMfzR/+EWSD/mhy6ddaTud2DYbd5YvtyN47k6mrcwols3DXXqkChGRCjh+ucZzRRVc7urobObtN8zS52maXcBGOx2u5+8/v9DALq73e6nPa6pA8DmdrsrNE0bDeBet9sd90vP/S20KStLuak1bEjhKzaWe1BScV+7RgDUuzfBUlgYz1iNGmT0kZG8Nz2doOHaNV7jdtP6VFHBfbd9u1Ji3XorGd4bbxBUHDpE8PHII7RquVwUvgwGAo/CQmpnL12iMF9YKIVrdZRediCigzcyszQMGkRBTKz/V67Q42DtWuXSJG6VVitlfk1j30JCeH23bjyrhw5Rs9q8Ocdy5gzPQevWPCcGA/eu0Uj60LAhz/CXXxL4dO7MfXngAAFHfDwt1iEhFI7ef5+0VM5Bz55udFv9CjJyTBgfdQB7X1qHbt01TJkCvP+eGwf6v4R+h+fDGRWNHS/Hw1Gp3Sjeu2kTaWKDBqRT27fzvVFRKuPuxImkE7166vi856docXoT2nfR8G6r79GkqQEdO1Jx9uijPFfPPEMFkcOhrFSbNxME33cf6cmGDfx+40YCke+/V0oAqW2XmkraEBOWD2u7EGzSY2EE0OfY50C9eti9m3Rj7172My2N81y7NgW1mTNV3aqXX+Z8DxvGa0Xp9NRTnMP27ZUC1GhUnhzx8WqNYmKqZj72FNo8S/AIH7PblXdAdWFDhBBR2Hry9P+EYPdHy6JZvd6d53wLaBXPmd27ycMzMsiP7XZiKnFty8qiABIRQSXWmTPk/WJVCQ6mcDB8OD+z2XieiotVzBoFOgdovbOASsSqniGhodyrNwPUdev+cqSA1FsDbp6dUzCbKG7MZrVnBa9I3PP58xRWw8N5vcTEHT5MWlu/PufK4eB1XbqoAt233MK9fPUqEBmhI213MVoiA438nbjYsi9KSgxo1Ij0ccAA4hS7nXPcqRP5RMuWKjfCM8/QS2LSJJZx+PBdJ2rXMyL61N8w4fxLsPZsh3ei1uPkSQPcbgqw77/P/gUFkY8YDMQWbjcxyeuv0yuhsJBrNmiQqvN75gxgNLoxIuN1lB05hdONY9Fk6uM4elTD+fPEs2vXMg7XbAb27tHh7SrDkxOtSEqmO+j69fS4SE4m5rJYSPdvv52/LRau9dtv893HjnGuBw8mdpVwlWefVXU1HRU6LG+9gt6HFsEU3RU7Xt6IPrEG+PhwHM8/zzV6/nmu78cfExtKpnIfHwp/sufnzFHJEYUeSQZN+UzOjef//4n2h86iaTRycvv3VwTL4VATL+ZXEc5SU5UlSrQ54hYobpb79ytXTsmSKQlErFYy9337VIbJhAQSwSefJKMWK5nEx338MZljhw4EB8XFBEfiXiqJW+x29g1Qmk+Xiwdhxw5lLu7Rg4JMUZGqU7V9Oxm40UjrU8eOBDm33kqguWcPn2ux8OBYreyPWKdiYvjMceO4kcUlNCODB2TmTGql5s4l6JGCvQcO8O9HHiEIzcoiiGvfnu/r2pWAIiaGBKRdO1X7Rea3Rw8eKIuFn02bRvB69CiJoTAjmadVq2gpaNNGxY6cPMl1F33GkSNct337KJzu38/n5eaSmJ07R0CZlsY+P/44rWvx8RTA0tJoFRg8WO2x2Fi+Y+dOfu9wcD+tWEFhuX9/ArMHHyQzOHGC6xcZeV1bl8F7w8M5HikEOmkSf3/3HcHcgQME8hkZHEt5OffLhQvA8q/LcOeZD3EfvsOeC43R2FqM998n4QoL1ZGxpxRbNrsRGck98+hLQTjj1RrnEAzd4ItyoxVFV3U0Xf0ByhOTMe+VPJw+DdQMMOH8eQ27d3PcrVvjekkErp/N5hl/58lhd/83+krlAfC0yDW+/tmN5na7L7vd7uv6YywG0Pnf2QFPgn/+PKdo504KQ/n5nM+yMp7hZcuoYBC3XQEitWvz7F27xvV/6CF+3qAB99zly6RjI0fy2tJSKnQGD+b5adiQ69itG+mEry8Z+vnzPHcFBSo75pkzao1DQ3V8kH8fRiaNwYbPzsLLS8e8eaQlNhuZXM+e3Pv33ce/P/uMf6ek8JqVK/kTHU16VK8e37N2Lc+hrpNxC1266y7SPincnZPDM/HBB1SGjB5NunXlCmlQSgrnKz6e17duzfP0zjucq5wcaqEfewzo3FnDgH3vouOXz2HX5HXYn6IhOZlA50CqBsfbf0VR6gnsez0enbsQVJaXE+ysXk2hceFCzpmUP+ncmXQCUGUGpjxnx/2n30EmIvFmynDc0qsYkyfz+gcfpNdBp04qCURmJs/atGmkE61bqxioIUMoiL/9Ntf64Yc5b5I5WDIaOhzAs+8FYUOrZ+CCCX2iK2FqGIQ5c5SySJKbjB/PORe+FR5O2rRiBa9Zs0bxLoB8ZckSFZOyfz+fM2gQ9+mcOez3sGGqRuj+/craLy6YApr69AGsNXT0Dc+Hs9KNZ59VSgOgai1Zcff39Gj5vRMY/K82zyR0nnMpsfhisd2xgwC6Vy8KG3LvokXcG+npVG7MmUPe5eenME1enipDcP48z7JgBYC8zOlU7pSABl9fL/w94Q7g3v171hJP4a5mzarfadrPLcR16/K3uNE7ncr6J4pjt1u5XErZhPx89czCQiqpXC7Sck3jWA8f5jy2bEmByu3mPNSoQXqfn8+/jx1xoT9+RAEaoX5xOo4f1VFRQewi3mFLlvBMBQaSdhcVke4NHUoh6eWXObfHjwPQDHB7mVHXrwKR59dhPzoDe5Ogl5TB5aISr3dv4tQtW+ipVrMmXbMvXiROevllnuX4eK5XmzZUXH/9NbHc7bcDRqOGlFvexNohixD67uOwWjW0aUOaJYJwRgbLvKR8koJWs55E2rBXYPHR4e9Putqjhwovycjg3xMncj+0bs2+fPABBcXQUPKSdetUMsTWrblmaWnch+ZrxbAdOoHt6IVte7yxd3MZNm8mTdu3j/grMpJzKp5wMs558zinycnK40pcNiUcy8enas1IadX//73b/7SAJ8KR/C2xdenpivmLVC4LMWGCiisRIc9TGASUy+SWLTxQe/Yo864k2ujalcRv61b2ITaWICozk+B8507lpihulQMHqnT5zZqpxC2SrGTdOm62iIifb5riYlrPRo8mkxs/ngCrVy8V3zZ+PA+M2cx+bNlC0GQ0UtsjmSenTCGxKC9XYGXhQo5x1y7loiruSRkZHJ9klXI4CG5ataKg1LMntXQTJvDg9u+vYv8ef5zPSEoi0XzpJRI8ScfbrRvHYberYNpTpzhmXSeRmTmT67BjB+fsttuYMMVoJLB0OAiyLBaCkf792TeJk5w+neO+5x5q4198kX+Xl/MdkZHU5AiQ6dqVh33bNr6zvFwxxNRUgjSnk0Q7OJjrNnIkGaRkzDp1iuBGrI3iIty7t7KYynfHjyt3viZN2KesLDKX0aO5Xy5cYFB1WCcrrN0jcCd+wsMt9mLz/gCEhgI9euj47rZv4Jw7G72T34PVV0fr1iTGteM6wFI/CN6tW2DzZg1BAXb8cDoKRaiJeu7j8K/hhMXC95vN3L+HD3PupZaZrMdN2+HD/8zx/U+2/QBaaprWXNM0M4D7AKz2vEDTtAYe/w4HcOjf2YHAQCYvkVajhkqYEhDA7y0WnrFJk7h/mzShVnXYMCpCGjUiiJBMaRIzl5HB7xISeM/Chbw/NJRgpGZN5Zp05gyz2+3cScvy7NnUHufmqgxvVivf4+dHJYeP5sBfDtyBE2iBDs496BhWhjvv5PUAwVRoKAXIL76gdWnsWAog7dtTWFu2jGO+fJnbQ9fpDnT6tErgoWk8o6WlVBBJ8fbnnycN7tSJwtpt16MnX3uN5+LUKdKqgQNJ13JzKczExfHZ779P4SUsjNbC7GwABgN631YLgIb27ZXizGYDEpMMeOiFeghuouGFFygUZWUpQVtqDzqdfOfEiby3bVv2v317nuPBt1uQEf4AWuEItDpB6HNbADfjfmrFR48mUPbxYV8nTVIu34MH8/9Zs5hSHSBtz8oijfniC75n4EC1p0wmjq95iIauG99B6rNLsfm5eEAj4EpJId3u1490LCmJfXY6yZMOHuS6ZGYCuUd1/HVSPjq0d2PhQtxI3iLldkQhJXHru3eTxq5fz/l55hnu66goWnUEiHsmCdu2VYet92CYmjWGz7B+mDVDx9ChijdXz1InST+knJD8//+La+Y/kMn3OU3Tcq67iW/VNK2px3cujwy/q6vf+682yZItClSJv7PZqJDu3p1YJSOD6+d0kiY5nVR4iGJ22jSe3cJC8rG8PAoSly5RiDKZSL8k06un8CU8T1pZmQF0z6RwV7t21fCTf7RVt8653VU/s9lIwzRNKTHZHx2ACy6XG243LXGVlfwNcByyr41G8sVNm4hv6tShEqtePfY3OJjYyOmksqm8nPOxZg3vq6xknHQihsAHpQhvXomgBkYEBREj3X8/z9Qddyjl/6hRxDFSWql1a/4/ahQxYX4++UrMQAtyGg7Bt7gXy1s8h427rdB1Yq369Ykxd+wg7xD30tGj2b+4ONKJjz9WdSoBnulFi9iXadMA3W3A0TM1kHJAw3ffUTASxX2zZtwr5mvF6Fq6CWY4kHLIgm7NCpCYSAzWqxdDd6KjqQg/epTz9tln3F8PPkiaUVSkyksNGsRMxVOmkOfs3EleMXky0DYmAJ/XfA4H0AXtuhlx5ooVNpsqHfP888Q25eVcM0DFGT/7LJWXkyerHBtSekKaZ/1rkRf+GxRN/9MumpJ9MDKyqqujw6HS44u748CBuFEgfMYMbkDRxop2RvzOPd0Tpk/nQkoGprZtlRtgdjYPmb8/GdDWrRSWDhxQbomi7fzrXwlY4uLYJykoDqh+Si2iS5eoKbFY2JeePWldlAySEtuwYQMPu2iSu3bl53Y7N78AHqeTG3zECPW9FOoWt8zycgINo5ECkhROnz2broVPPUUAWKMGx5KRwX5t3cq569iRBLxrVz777bf5nMceo9b+ySd57fz5JBZffUXQumePSj3+9tskQMuX85lduxIc9e2rCuO2a0et+l13cczJyey3uBCsWkXimpjI/nXrpoTYRYsoBD/7LAWtO++klsjHh4zk2DGugdTHsVhI+D79lAB23z7unZ49CVT79CGQbd+egl/DhgRMQUGc15wcauMKC0mw6tbl3gkP515buJDzk5TEz48dI7jPylI1ucTlctYs9uWDD4CmTXXUr2lH/RALtm7VUKsWcPmiA3VP7kId5CMXbTBkVCjCu/khJ1uH7asf8f2VXqhEDTRpUxMFBRoaFWUi3+mDttbzsLXtg8REDfXqqZTPUtj52jUygGPHPE9eVQueuzL6V7nw7+0CpWnaUACzQMTwudvtflfTtLcApLjd7tWapr0PCnZOAFcAjHW73b8oqf4W2lRURDCaleV5PwFQzZr8vqKCe+H0aSpwOndWgteQIbSsSKtZk/snMJCa4dOnq6aKr1+f+7iigoJUUhL3flYW17BFC96fnU2B6csvea5tNu7br76iYsliAWrUcCMvIRdxlSuhBwbDded9yDmk4Z57+N7ycoKJgACeVYBC5Ouvky6Eh7M/+fmMxVm0iLQqOJjMdeBACmkDBtB1y2ik65LDQdrXrh3P2MKF7Hd5OQHAiRPcZuI++OCD1PwK3di8uWo5mWee4fM//5znUOIMPQPlMzO55++9l2OJiCDdstv5/Hnz+G6Xi9d26MBn5ORQ+Nm+nXM8diwVfOXXdGQllaHCywovL2bai4wkvbdalXu2FPQGlNt/27ZUPg0fzj7s30+6NGUKwdiUKRy/1FoVWm63k4bn5HC+Jk8mXZWMc3a7UnTu2MHfAk5mzQJeeE5H0cDbsT/JgK7ddUwJXYm581h4PTpa9eMvf+F89+qlXDTl2O/Ywc8kaU9AgEpxfqOW6vKryHj0IzyLmdit9YHji79h6AO1qrhoSmFzoCqg8nTb/E/E3/276dP1TL5HAQwAY4D3AxjpdrtzPK7pCyDJ7XZf0zRtLIBYt9t97/XvbG632/pb3vlbsVNCghKcJb6uf3/SpsWLGW4i7aOPeA6Tk0kv7riD63rvvVQWf/utciXUdSqgbDZa3D0hqGe9OWleXuS3kvb/l5q/v6fVD2AyFjeqW/1EkXb1qnq+Z6tVi27wyiJI4Q4AauIMTLWa48pVAwwG9t9zDKI4vnaN9KJuXdKtyEhlkQ4MpDVPsiU3a0YBSAwKksTObNbRqJYdBqsFAQEaioqIKR0O0oSCgut1+Brzt6eCMCmJ83/2rPKeGjOG1rahQ3RkpDgQ2dkbGRkaOncmduvZk9jn/fdJg0+dIm1p2lTFZF+6RHxVuzb5weDBpLllZUoh9be/sU/nz3NuTSZi5UWLKOjfeitw7Jgbzs++QMu8rVjiNwFDXuuOpUs1DBjAvjz+OPtqs5FuzZtHxdIdd7AfCQk0NjRurBLYdOjAfXjoEDGtjw/55FdfAdOn6bCWF2DZtiDcN1LD0qXEnQcOkI7Nnq2w/OTJxJwSLjV0aNX19aQ1Tidxn4R5SeiVZ128X6JHQr/+2fZf76KpadopTdMyr2uj/jEK9A80k0kVxva05pnNirlFR6uU9jExBP7t2inXREnL36MHD1SXLqoWksnE/196icxV4qTGjuU9jzxC7UKvXnx+QQHBROfOBBTdu6uFdbtV+nypEyPZFk0m9rNvXxKwoUN5+KOjVRZDiY3IyVEBzz/9xE1qMOCGVtpu50GRorTbtxN43HorBb6EBL47NpZCyfz5POAVFQQ4y5fzGStXsq8PPEBz+cyZPOwNG3IupAZOejqFnClTlCuoaKPz8khIHn2UpvR+/Tif77/PsbRowTk3GukG8PLLFIaOHuXzFy7k82029lWEjJAQruWAARSkAIK/6Gg+s00brqckYZBnCYiQufnxR1p5g4NJmEeN4tp16MD5dDjoDiY1Wdq25TPDwlSR5MpKvs9gIBF45x1aTYVxGAy07n33naonlpxMgT0jgzFEBw+SEN11F+f4xAnOy+7dyl00OJgEuGlT4JFHDAhuVQNpaRr8/CgwBzbwwp1Nk9EUZ1CCOrDBio0bgaMZdmRfqYNIHEAbHIS/jwMvPOdGeXArDBhRHyfr98GpUxoaNVKWupYtlZLD7VaEn+0mZrzLl//pM/yfam63e73b7Q5zu90t3G73u9c/e93tdq++/vcUt9sd4Xa727vd7r6/Jtz91iaaR2nR0VRQeHmRAUosW24uFQKaxn1w8SL3cFoagUP37twTNWuqBEPr1vEZrVrRYte+PYGZKKwOHeK+zMvj/61bEwSUl3MP/+1vZPSXL6sMbo8/TlrKQroaguNCsb/DRCwpvw8//KjBZKJQUq+esmYfPkyr2r33kuktWkSQExhIi7OXlwJDS5dSwXXLLTz/7dpRgSNZY6WG5aOPqmRLkpnt2DGeiYsXScP27CGd/fJLjnvUKNLCFStI1yQW8IMPeP6//pp7+bnnKPS6XKSjZjMBRfv2vLaykvfv308Q9cgjBK4CKNu25f2HDyvrXU4OlV9JSez3kaMGRPbwQ4cOGjSNnx08SPo4bRqFX6ljKsBg3z7OxZdfKrfwjz+mgmfJEtJNqdmXkMB1TEvjeGfPpnA3cSLpvqapWGuxtkk9PBG+3n+f10j4gu1kAR5NGo8ZGI+FSV1wS6/iG/xp/nzlpjV1KoFZdLRSGoqFzjML6IMP0npx4ADp/b591z1Y7glAx/BK+Bh19IhyI+NMwA2vG6nFKoBK/geqgqNfEu5Emflf0rrhVzL5ut3u7W63W2xIiaAr+e/SBAP4+CiLbGwsv0tLo6CwbRs/B3iuu3bleZg+nZ/t20eB3+VStCQ6mvRG0/iZ240b3iHAzb1ARPgS4c7z+uqNZ1GEMRcAO4ByUE+n38i+63Yrj4Pqwp2PD7+r6u7pBlAML9hQgmCUlLKjQh9F2RoZyT127Rrpm9QMHTZMuad7e/PzevWIE4qLKQiJcs7bWyn1SksN6DWwBpKTNfz4I+lmRcX12ntnVIbj/HxiAJV1lEKjzUZ6KAmotm0jRrD6GdCxhw927NDQsSNpp6aR7m/fTnz25pv8PDCQ57VZM4UlP/uMim2pqTx2LNdW8FFuLnFQcDDXOSSE4xcFcU4O8NhjGiI+eBTHn/0Yr67ojunTNcyfT5zncul4/eVrGHmfGz4+xBjPPadcd++7j+8ePpz77vnn+V7Jetq+Pce9YgUx3PTpwFvvGPDYy/Vwy60aMjK4D8UDa+dOKvxeekklBIyLU4K7Z6ZM6DpMl/PRN9Z9Yw+0acOzUN2lWTz9PK16ntf8p93K/ysEvOut7/V6U/8WLZksyPbtSggQV0eAf8vmb9mSzA+gMGaxqBg3cTXYuZMb6eOPuYkkY6KnFnT/fjKsTz4hcXv1VRKPoiIytrfeoutfZibBwccfq+Lp3bopP/Zt26jRttlITNeuVdrg0aNVQhKJSZBA3jVrFLFKSiIQOHaMh3XwYN6zcyfBk8RySFY5T7fADRsosEmM4Jo1ZMaNGtHiOHMm5+vKFSZruOsuanr79VOZKG02lSGzSRPlaiqFhv39qfULDeX8ifvn+fMEU2+/TaC5bh2LZs6dS23S5s2cy/XrOba6dXko69cnQRk0iEBx5kwe3rg4HmCjQceetVfRvJkb8fE8kFLbRoCHZP6cO5f3iWuZxUKiKMAhPZ1WwsOHOZ5r10jQvviC63rsmLLY1atH8KlpnIOjR0mMbr+dY9d1asIeeojWk8xMElNJIT1kCPfsa6+p/RkayvlLSCCR2raN7wBUIphTp/jOI0cku6iGz60vwfXQeDToEoyMDI1ZwqwWBNcsQjJ6obn/ZcDbC4aPP4Tt5BlkrTwBm80Ni4UMvUEDMp+LFwEvk45dOyoREOCuxpRv4hEQFPSbz+//epPyH9L27aPQkJfHvWYw0CIeHExaVFamlFTduxOAmM383bMn90ppKa/p0IGCjdRT9PcnrZCYugYNuOdatiTdeewxPrdOHZ7Ffv1obduxg6Dlq6+4nywWAujjxwEvLwPqNPRB42ANt9wCHDumo7bfNaxY4UZsLL0BevTgXt65k5bzvXsZK6Zp3Eft2lHpsWMHacFDD3Fvz5hBQQ+gQFdQQNoZHU0F3Hff8WfVKipBDAbSoFGjSB+WLyfNTUmhlahmTVVgG+D5XbWK45owgZ+Jtal3b57RiRM572lpnMtbbqHbU1qaop3Hj7O/ixermniffcazMmCAqneanq4SMYjCUTLf+vryXTNnch2FB5hMSojp2lXFemdm8rtnn+Xa3n8/569nT/4W/jV6NMf+yCMqCZe4UorXgstFhdmnnxIQTZ9OxV1uLsfjdFLrf+BsEJZFzcYPeBDd21yDpUEAbDbOzxNPAIn7dDSpXYr69dyYNEnFscyerVK4v/wy5/CeezhfU6eSfu7dq7TcPhYNg9L+Cufx09j54ga0a080npDAOZg3T5UNkpqqnsDJM0avehM88Pdq5/0ftEb4xzL5SnsCwAaP/32uZ+9N1DTt9r930z+b5VeSRlSvG+yZofTgQeIdcbHNzCSPX7iQZzAqirRo/nze7+1NHldeTtogcfji6vyPtl++XocS8MoBmEEhzwbAjX/EYU0UB/7+np8aANREQ5xAA+TB6TQCUHhS6jke8nDkr6xUZ+mTT4htAFqcLBaevbIynhGZXx8fUaKRdl65wvMUGUmMo2nKUgcQk0VH8xzWrMn+BAVRqVZSQvxkMikXez8/YoVmzaiE0jRiLxHe1q4llps4kes7YgT7JxbZadMo6K9eTeXSF19QgT95MoVKh4MKs/Hj6bVx+jQxWkQE58HXl0ruY8eovF65yoDMk3548y0NTZqQZuzaqaP7ylfw1netsKz/p3joAR15eaTDzz1HgfOhh5TAdfCgOtOZmSr5Tfv2FO7efJNjbtGCNHDCBPIRh4PK7wMHqFQXepuezmukNNjAgQr72UqYkRiNG8MZNwDbt+lwOquWehFvPcn9IYmybkaD/tMZf/8rXDQ1TTsFoIvb7S78tWt/q5uBp1tkdLTKrmMyKW2nCDi9e6uFj4tT2TA/+ogHdfZslXHMbCZxW7yYIOTll+maKBkZjUblSiJWMKORDE6yQLpc3IRr1vDZEuO3di37e+oUBYFhw5R7kacbkMXCPou7pc3Gd0kmoO3bVY00Hx8KbgMGcHPNnUvhJSmJ8yOWsrg4Wpik5pFoYCMiKGjVrk3C4+1NQSwpif24coWAKTCQwtk773C+d+8moRsxgu958UVlTpfYGYACW//+/D1iBAXSbdtINCQ5gqR+/+wzvmvECM59z5687+23SYgKC2nx+vJLPvuWW3SMK30fp084sdt3MAIHdkVoSwPOnAFuv00Hiorh8A3A559rCAoi4Fi+nJa1kye5Jzp14hy2a8c1aNqUBO7uu0mApU7fkSMkuvHxqqBwp05A5446Xn/ZjgbNLVi+XMNLL3F+S0o4tqwsvqt/fxK/H38koR89ms8aOpTznJVF4fTbbzkXTZtSSDx1imAzN5f/HzxI7VNMDPdhfDzX5tIlMqHgYK7hpUvA+fM6Si67YKlpQsPACthyjqIUvtCgIygsGDanD+rU4XtXrgSaNNFRknUUZ9AAgDfIRAEKdxqqCnm74XbF3LyKrEf7o2WpczoptLz9Nv+vXZtnNSCAf+fl8QwUF/P8Xbumsk82bcrPatakEFdSQi2ppN8uKuLekMyTaWncgwUFqh7d1at8jgT/V1ZS+IuMVBled+0ijTtzhvu+eXMywtxc/h8ezj1+7pyO7EW7kHKtBQKMFYgc2hzNmhtuKI0uXuS7T5/m+EpKKJgkJlJA69CBCoyPPiIIWrCAdOXNNwkEX3+dY1i8mOft++9pBTp7lkBCrAt2O2lf7drc44sXUwE1bBhp26BBfPb99/O7MWMUvTp1igBANPESY2y3kw6tWKGUgJK1Nz6eAGDiRAovZjPP7549VPBMmqQY++bNHGvLlkqBFhjItZg7l+tTPWTAZgPmztVxPMuOW++xwN9fuxEmEBvL56xaRb5jtTKZxcSJ6vtVq1StPslsLPVNpcSMuG1ZrbQOPv44AbrZTMUZcD2+0KEjYVUxuvQPQMIODYsWUViP36jDtXETRlz5BLf0KgHiN2HadAMmT/65a5IAnF69yGPE1d9kUpYAk4nzKvMnWYsllEFiHWVeqz//11yhgH8OTP0HXDTvwq9k8vW49kEATwPoI4mfNE1r5Ha78zRNCwGwDUA/t9t9/Jfe+VvoE1BVePZcS7tdZcj2DHWRzN/t2nEPNWxI4O92k47s38+/fX2VAkFarVoUXP514dvT7GYD4Hv9MxP+XnKWf7zd3N3Ts7VsSbpktyvPijp1uG9TU0mbfHxIbwsKeC4l0UrnzgpvlpQol/q+fUmfatfmfUePXi8YXl+HsdKOw6csOHNWw733cv6tVp4xiV8+eZLPmTqVRoeICNIBgDggPJyYo2lT4ttu3Yibb7uN63T+PAXIpCQVFtCmDbHXww+zz7rOd3fponCSw0Gad+YM1/XKFQp/+/eThr//ro69G4px6EIAWoRqN5JsLXorH2jdGnMwFmWwwOe5Z3H3Y343kqoMGKC8HaKjucdatFDZ0QEgO0uHubIULZu6YA2uBaOJsdVC9yWR1U8/0Xtt+3auj5cXx5SSQgWU1HU0mThnz4y+hqbff4DnMAP7DT0RdXQJrC3qVck6a7OpLJtyZm6WCfjf1X6JNv2H5Mbf3NwANmma5gawyO12f+L5paZpowCMAoAmTZr8ww81mbhBFyzgAerThxtPmIQwyexsAgVJ7ZuURIY8ZAif8fTTFMCSkpQ0b7eTST/1FDd2o0YkcqtXU8gwm1XB2/Hjf14ioWtXlUZbCnbPmEEGm5ND0CPuSyYTN/XmzQReUVHUurZoQYa5ahUPZXIyv9+yhQC/spIgauVKCoqHDilh1uFQh3nyZGpZrFZVukACj2ku58GOiSHxadSIQtbUqSTinTqR+Hz6KcFYVhatBBcuEFzFxfGZd9/Nw75unUpUIPFsb76ptOUS+JqUxDH16EGw26sXD+LUqQRHP/1EQSgwkEwjKkr5jo8cycPsdgPjRhbD/+U0jMVOhJQdhXXgPJjr1cKkZ3QkD3gFtpwT2Fj7UTz4wWCkHzTgo4+UQNWtG93EcnOpsW/Xjv9nZHAPbd1KopCTQ2AYGkp3s1tvpeBOxqYDb7wB95GWeKzzWhjvWQaz2YCQEALz0FDO3eXLXKMlS7iOubl8z/nznKvKSv49bBjX9YUXuH8jI3lvvXok1GFhdF1o2JB7audOfnbxohJQT53ivmIRYwMS8wxo0w7Iz/fGVdRDZyQiuI4dP10IhaYpdz6XCzh1QocvdJB8OK//Fq2pCHseraDgv7EW3v9pKyqiFUqariuLnMXC9U9L4xrWqKEyvbndSisqrtrJyRTUAwJ45uQMXb1KmlS7NhUgjRvzPaGhFNa8vcn8g4IozBmN3HNFRVQ81ahBbWaLFjx/Z89SM7xkCc9xSQnHENLQgQevfQFgGM64QhDoV4affvK7kbBj0ya+MziYdK1OHZ7N9u1p4bJayQw7dCAQeftt0m2pEfX++9ynlZXUFteqxbMYGUkh5aef+LmXF2n6t9/y2Tk5VHg5naQH3bvzHCxezDNx8iStVl268Edienv2pKB0xx1ci7g4/tjtvFdi4yTQ/pZbSG/Pn+c8SXZjEeokk+/48QS+t92mAIbJxD5JzJrDoZJ85R7VEf7dGwg/koOjKYMwKfNJ+PgYqiRyuOUWVSJn1ChVKNlk4t+SGW7QIJXuu7JS8UTJVLxjB/dYSgppQ7NmFGzN5usa6e0GlHvVwoKFXKvYWF770YsFSP7bAvTGdiTs6YXYggJERvKse8bLSdydaLcPH+ZciXA3diz3yEsvqVprwqerJ9v4e+Do10DTf1nylTz8SiZfANA0rT+AV+Eh3AGA2+3Ou/77hKZpCQA6AvhFAe+faWIlkbhTgOc5OVmVSQJUQenSUipfL16kxekvfyEmunyZ61mnDt0JmzcnrZEm7pL/ejMAqAANok1AQeyfF+4CApQw5OtruBGmYDRWdeGsU4djvHRJzVPjxqSTkllTMnJKGYWWLfl3RATPvJ8faZe/P2mr3Q7UCtDhLLMjL8+Chg01nDsn2Th1BB7ehYHlS9CsXj/sbHUfgoIMeOQRKrb37yfmfPddpUSWQuAFBTzfZjOx3IwZ/L16Nen5e+9xHAsXks7ec48S5Jo04Tl69FF6WI0Zo8KF5s8nTR0+nLRXEtm9+y73RIsWStF06y06koZORfYRI57qlobPtRV4/AkDNm0CnAGBSGg5Bo1yT+NEo/6wG6147z3yD0DNr9FIy6hYIw8evF4mw6mjZeJXGH1uMnYgBghvie7b/4qPPzHcEO7EePDVV8TWQ4aoslyvvUYB0mwm7ZS8EwEBwPR5FuzNsiPxUC/06OHG7qNB6FVPKdXEpX7iRFW+BVAxeb93fc7/FhfNXm63uxOAIQDGa5rW2/NLt9v9idvt7uJ2u7vUlfy1/0Cz23lwIiJ4KGw2LlpREc3QNpsqZCmJNFJSCA5WrKC2e8sWChoikHkWEZc4BoeDAMFs5uYePJjXSrkAq1UVfxUXk+hoMsDZs6ktBngAJF5ixAilQd20iWDBbCZISE+niTo+nu8eOJD3CaN0uQjkXniBB7F5c86DzEfPnhyrxBIGBdFiGBRE7XZJCd87axYJUHAwr61bl5rX3r0JvKZO5bwYjQQkkm62QwcCTXFHnT+f89SjB4XJw4epyWnWjH374QdqsXfu5OehoRzXyZMknIcO8RlTpvD73r0JfktKWDT14kWCkkceoSDUsCFBrYw9PjkAcy0vYzzmYJbPa3D5BQAAdqwuRnlOLo4gDD2vrIDVWYxLlzifBQUUutq0IZEeO5YarvJylcq9spJ7JDiYn584odK3r15NUNmmDbBhrRNlR06hPs5iwoEnEVqnGJ9/TsKv6zoeGFkJf3+6trVqRQA1YgQ1XklJqtbNTz9xPt9+mwxi3z4+v6CAc7FtG/sk2RH37+cc+/jQshgWRuFAWoMGJOTi1tuwIVBYqGHQ3XWR13wIjre6C2VlOuLiWGR1714+S4cR+QgFUAzADYu3G0AJKNzdJIjiTxfNmzbJ0FarFgW6ykru+wYNCHhuuYX76do10q/iYlVOoGFDCvMbrjttVVTwzNevT7qiaTxHAwZwf9avz72Tnc2zk5vL9zVpwn02cCAFN5OJzy4r49l+4QXu55wcfr5nDwFIaCiVRpWVQGQXbxypNwA14cBTTbagxGHF+PHcK15e3Hfz56t9+u23Ko33559TEVJQwGtDQtSefeQR9i0mhmOS7GlmM8/aunWci3ffZb/r1ycgqV2bYxWrkI8P3VBfe+162YBn+cwPPiDdlGRSL71Ey73TyX4MGECatXs3z99LL5GmSFC9JEQ4fJheDVJeRjIiA+Q9Bw9S0PPxIf3r31/V7JQ+FBbSYuVwUMl3/Djw5PACWI+kog8SMOnsC7CWF/zMwmE2q6zNixfzmQkJ7Fe/fgQURiN5niTNmTyZ7lHjx7Pfdjuvlb6FhXHe4uJUbTnxShGhMDCQoCUzPwi9ezqwHf1Q3roj7DWDbiQOionhtStXkhcUFqqwhmefJf0Szfedd6qkCNWtRatWKZ79W0si/Je4Y96s7cevZ/LtCGARgOFut/uSx+e1NE3zvv53IIBoADn/7g6Ki1lsrEoEJ5Y7t5t7q3t3rou4tyUnEzN07kwMEB9Pa1Pv3uRtzZqRVgnG+Pc3DeRDzQF4gTm0jKANwYXfUiDdz08Jd4BykwdIrz1tDaWlFOAkHrdZM46/egIZGbfU4S0rU1mEV60ixgoLIw27dZgOn6JzyE66CK+KAqSl6WjZ8npeBjiRXd4YH+BFFORfRUamGydOkIYHB/O9b73F/hw9SkE7JobnXNOIA8rKGCZkt3Ocwj/ee4+K7Xr12JcOHfi8yEiOITyctOb++3TEReRj9Wo3Pv2U9KRVK9INSTKYnU1jQL16VOxs3EiMnbKjDN8daYMd6IX5yV1wNMOOxYuBgQN0bI19AzNzB2CW12to+dbD6NRZw223kcaVl3O/hYdzX0qYQb9+xNRz5wIj+hWj3bm12IY+WITRsOWcxKYfilFezu8/+IB8bONG4qzFi1XG/MOHyecOHCANHzSoqotlWrqGuH3vI/bkl0j8ywaU27UqmdQBzrNY/WJjVYm231u4A/5LBDwPbdQlAD+BAcj/UnM6CXh1nQwlIkIFUIpb5dy5vFaKVBuNFNQWLSImnTKFDH7QIF4zfz4ZZHy8YhwrV1aNyzh8mBtn+nQVcCzxClJrRTJJJifzsG7ZQg2LZJiTrFVOJw9DcTEFl+7d+X9BAbUnU6YQuB85olL8f/gh+2TQdNhP52P9OvcN8Gg2E5ht2KACZA8e5PsefpjzdfIkBaq//IXfPfggBc78fL47NZXgauxYAjCbjWP48Ue+u359Wv18fXmNxDqmphK0/PAD5/T++wnsTp1SWZGSkihcm80UYo1GWgQbNybAjInhz8CBBEEXL1IAvPtuzpHbrVKrl5ezf59+Cpw/r6FWv07QBgxH6OBW6BnNgppzvw5AeqPhqI2LeAvvIu9aABo3JkGMiyOIef11BTL27KFwFxjIfWWzkei5XBxLTAzn5upVjuP4ca6bZvJCZv3BOI+m8LMaUWwIgK5TC3dfwTTcdW4aWqR+i8OHdcTGUuh6/nkSy+JiagY1jXt31Ch+73CQUSYkUNg/cYLMo0kTruOxY7S6BQYqv/3du1VMQKNGnO/wcCofgoNVEe2jRw04c8GEPXsroOmXEb+yAKdO6qhZEwhurOPaNR1kmvUAWOFlNoCuMMDPSUqMChj4s91oFy8SXAMU4A0GTpPbzfNcqxb3VEkJwZLJxM9SUnjGLBbuR03j9waDivls144aSU0jbREXmeBgPiMwkDTrhx+45lLe48svubck+YHBQFrRvz+vmz+f+33nThV/ajIB585pWGcdifJ+d2Bl0xdx7pyGr7/mPrtyhQqvzz8nA963j2CvUycVfyaJiZo25fvHjuU4FiygcLtpE4XenBxqmJs1oxW7ZUueuexsfnbuHH9GjOCZyMykZ8CMGaQZt9yiagnqOume1PM8cIBxfFOn8j7Rds+ZwzOYlERgIBl9W7Tgb6eTbud33EFlkwggAPsVG0t6N368un7uXJU8q0cPjqlxY2qNc3K4DqNGMfatIKwrHsQ32Nz6adhrBt2IhxJlpZTA8UzM4nIRsGzaxLH36sXvH3+cShpJjJGezjlZsIDzsm0bPQOeeUYJWVITDSBAeeYZ7gMRzKJ6atg+aR2+H/QZkoe/g5276Aol3g2SRXnJEpVkYvZsPjsmhn0UdzbPrJgivInwFxCgygkB6v5fc8n8b62L53a7naDbZTxYguV7t9udrWnaW5qmDb9+2TQAVgA/VCuH0AZAiqZpBwFsB/BXz+yb/44mySAkFk9Kl/TsqerKxsdz77RuTQVJ165MfmE2M9Y0Opp855tvqDA/fFhZ7QoL/529rd4M8CynQKWjbAIHbqqEvEkrLf15zTzZS5WVpKvSHI6qCWJOn1bYKCSENK5xY/LdDh1UwjKDgRhG+HliIgXD0lIgNcWFYBzGZdSCNypgv+ZGYeF1rye3F8J9z6ERLuKody/4+xsQH895NRpVvdCaNUmfSkqItSQeWNPIg8LCyA8CA9mPwkLitwMHiBWjo7meDRvSEJKYSF6xc6eOkeFpmN3hE9yR+hc89oh+I1vwZ5/x3Y0bk16mppK2PfMMecm1a0ByjhXugLqoj4swN2qIYSMsCA0FTmSWoeTwWRig49nKd2B1lSAjQ2Xtlhjh9HRi6w8+ULVPu3fn+qSfCkBivRE4hAg8icVwtYzApz8GoHVrzktkJOnI4MGqPnV0NIXdiAh6tvTpA5gMOpx5rMkpa9+mDbBjlwGoVw+xfbUbLsoWS9W615KwSmgn8H/jQfB/HoOnaZovAIPb7S69/vdmAG+53e6NN7v+H/UjdzrpliQgRAJ6b79dEa4xY6gNEGYm2Q1XrFCH8rXXFKOTsgOffcbNKs9ZsIBA2Wgk09m7VwXVx8RQ8Bo3jptq0yYenowMaqwHDeLm+PRTblCJAZk/n8JmUhIPyocfUlho3Zqa7OnT6RLRty83vNOpMuO9+YYOn8fuxCfJnbDLdxgW7u2I9z8wIC6OQA2gpkg2pGhnf/yRm7JNG4Kva9dIBGJjacoOCiKokjpokrpY5jYpiUREiucOGcKxXbhAAHb0qMpiuXYtQebGjRQod+3i/UFBZOgrVrBfV66Q8DidTNhSXExA1LEj1ycvj58dOEDXp5ISCnwdOlAjbjBwLcU1oVs3XuvvT833vffoyEm1wxpoQVKydiPLnrixnj2rLKrNm1MzJW5v7dsTxHXpQoYXF0flgK8vQaNktWzfHsjM0NGybjFW7QzA3r0a7roLuHjyGny3r0B/bMA3eBQt7+2Bmg39sH49+3z1KoHu4IE6vv7SgUp4o04dDdnZnIvLl7lmzZtz/+3cSSCXm0siLQkmCgrYl4gIjqlzZ67dypWq/o+kcm7Rgszn7FkXTLiKIOThPJqBOEODYo4S1yDNDeASKPR5tj9j8G7WbDYKBXl5191KXBTAfH15ZnSdQv7IkWRA6elUspw/r1J4S8mMv/2Ngtzu3VTKjBlDN+HAQO7dJ55gHFZhIYXGl1/m3po1i/s4PJzPWrpUh6/BjoYhFgTW1VBZyfNZVkaaN3s2z+bevXzWkCH83teX1uXHHqPAIrHCkqBh+XKeC0kdfvYsz3Xt2nzvjBk8J0eOsH9paaSHCxaQLnzzDcHjhQtkygIS27VTSWHsdoKIiAi+56efSBdOn1ZxeT17Ku3vBx9QuJk0ifRK4qGfeIJj/fJLnhVJ8S2ZSCdM4H2ZmSpZzAsvKLd9sUBJciqLhXMihYNnz+aYpN6dZ1wZQE26uG+uXQssX67j1l7FGP5wAKx+2g1l07p1FD49LV6FhZy7tm259jYb+ztzphKuLBaCmYAA3MhQKTzBbOZcAqQdEofetStpS0YGBV9x+QSALZt1lL/wKvbnWOBu1ASv5jwMGAw3yglJYpzYWO6FXr1UyQS7nXz09tvJxzxdST1dWG8WW/ePlkT4V1OQS/sj0SeJlRQMI2s1YQJ5a7du3HudOvEaOW9BQaRTUk6lZUvuPV9f8lzg5mUQ/vNNfCl1KAWk8ffuxM9avXqcD5OJvLdGDVXsnGE5biz/8RpaIxNOBCDqoVYovKxh//7riVGa6jh1xI7csxZUVGg3shKXlqocB76+XJ+MDD63spLC2tChpI0PPaQ8G5o2JV+oXx836uIlJ5NeV1bymcKjGvtexV9fLcDt+BGTMQ8P9TyBh57wQa1aPJNr1pA35eQoYdLXlwaDWbMo6LYOc+Hl2w7B1DYcM2eTZsye5cYzMUkILDuB6d2+R9pLP+GHHzW88gp52SOPkOaJMeall5THl8lEWlNRAZw/78JrD57GbfdaMPv7+sjK0nD77aRrd9yhrHLvv6+878rLVQx19646kuJexv5sb2jBTTA68QnMnW/AunXke1FRpI/iIeKZ/Gn3btK5OXMoTEotT+DfR4882397mYR6AHZf10YlA1j394S739LExcRs5maV9KnCUAEC402baP366SdqtNPTlbtIl+tTtn69qmQ/eTJBwLZtShM5cSI3ckqKktjNZm50Hx8KRHPmKO2uZGZs0ICaTYCxfE8/TfDSvz9BWkQELY1z5hC4FRdTE96pEwW+/v1pJWraVAU3N2wIjHrcgYeTJ+AKLHCXFeOL+WVo0IBWneHDOS9SNy49neMrLycTPnOGQlpYGIFQnz4qmYjBwPetWUOh7OxZburvvpNgXB1NapWiaxc3Zs2iQOftTe35U09xzI0akeg7ndz4hw6RWJ05QyGjeXM+69gxMozISFV0dMcOzoW3N90PzWYCxGHDCFoDArgmPXoQbKal8V4xn7dowfstFq4fALz/VwMuldZArdoaoqN5j66ruI+KCgrTd99NF6ddu/hMf3+ubUCAqoOzaJFKXNGggbLmRkYCP/xowN821ELTphrCwgjWzf4WXPEOxlFE4NamB7BqqxUOB+e9eXOOuUd3Hb6z3ob//tU4l3Iex47piIkhKN6+nW4kOTmcjzNnCPJatyYIz8vjPLrdJPp79/L3iRMUDK9do+AtgeASN+lykQPXRTEqYIUfKlGzpgGAG0aUAdCh3chMZgOzlWkA/o4r5m/I3vZHaSYT9+OVK2R8fn5kDt7eVICcO8czDtDjQBhk48Zktl5eBAUnT3JfzpnDPRkZyfNZsyYtwG+8QWYohWntdgoH339PWuFwcM+4dR11shJwYlculn9VhAb1dbRtS+bZti0t+ps2UVN66JBSoFVWEgyuXKnc+Y4cUd4BISFkmMuX81xcvEg6cu+97HtmJvvdqhXpriRTqVeP7/j4Y34v7u+33046p+sqDkaYpqapGLR336VQNnQoPztwgGe3bVt+tmqV8oiIjeXfWVm0cgUGUmgbNYpr0r69Kmlx550UQrp1I7jwtCrt3l3Vu0O0unv2cFyNG5MOpaeT/sTHq5ISW7Zw3SZPJhATPhIWZsDAe2th9x7tRsblQ4c4FxK3LS5WDzxAuvHooyre7s47qTjr0YPKqM6dVTZKgP2Q8hki3ImVcMcOjn//fvKMiAgCKKkBCwD92xXg1iMz8Rxm4Fwe4LxQcAPg9OnD60WRGBtLgbFPH1r2EhNJW41GCpBpadyL993H32J9uxkguln2uZtZ6v7L4u7+v2iSrdflUucrMZEKlLg4KqwlRn/DBu6tOnV4tu+9l/vzu+9UCnqJ4zWZ/hXhTmK8/xmDhMHjPjd+O+zlu83mm79bin3/1pafT35bUMB5uXqVCjyLhTQuLk5Du3YWtOjfDi0Gt8KVqxp0nWfc5QJWrzFgb2oNXLmiYe5cCnTR0apcTno6MUDz5sQEUnJGLPzXrlGZ1bChKh3z9tu4UTZB6vcOHkwMd/gw+U7HjsCQkQG4ve5+HEQX2Lr0RCW88eWX9Fo7doy5Go4fJ9a77z4KhiNHUtEWEwMUFenYsigX2/q/hb19pyCkuc4kLEs1dB3TDSFjbsXU1j+heYiGunUpiHXqRN4hceeffkr6NnYs561DB2b1fG6Sji1Bd+PMu19i732zEdaSpRZyc4kVXS7uZ8+yWJ07kxdt386/J0+0o3v253gB0xFxdgM+mVmGLl2I5156iXxzxgzyRKFRUVGkb7GxKq7cs37e/4VHwf85+XO73ScAtP9PPFtSom7aRKLz7LP8W7JlSkIRSZWq6wo4bN1KhigMTlLoDxmiGLrdrrLlPPccNaHp6co1cfp0bkjRMMTHE4zHxfEwpqYSKBiN1LAcPkytl9QYmj6doKV3bxLKjAwershIarWjoghKdu5kQOzBgwSGER29YTh2CTsu90MjnxIk51jRvj2/a9uWmht/f76rfn0CgzFj+K7mzVU2pfXrVV2XhASOAyBQKi3lgb12jeOAW8fFdSk4o1+Dn0XDQ1tisHq1AcePcxzr1lG4W7yY61JRwQPl68uxnD1LJnHyJIlS584EjXXqKO3K7t2cm2bNKFiNGEHrxSefEBA6HCQkFy5QWLz/fjKWt9/mPM+dy+fm59OVsayMQCkzk+PJymLfzp6lMG02k1j5+3OefvqJ940YQQvj1avs64ULXAuXS0dM+2L8cCkAq1drN4q5ZmVxTt97j4KopvG5TZpoOGCJwZXQ7mgW5o0u5RpCQlR21vx8YOpUN3o52uEMQqC7bfDzdiAuzgetWvEZmzbRonP5Mond5cvse3Y2hfTkZKBTJx3nz7pQq44JubksjyCZYg8c4JzZ7ZzX0yd1FJwtAGCGHV4ohR9gqglniYbgYAMKztrgghlu1Lh+ysrAWAfgZkHsXgAR85+tSisqopBx9aoqertuHdehsJD7/dw57neLhT+HD5NBVlTw7NasyWQ+GRkUtH74AbjzTh0ZaS7UrW9CTo6GwEDuifbtuc7nz/PMzZnD/VJSwmct+cKB7sX7YURjNEcuvl1yC7x8feB08qwFBVERdu0a+3j8OM9Y1648Y1evUlj99ls+f/9+0pI77lBxKEYjhZz8fJ7X774jQzYa+dmwYRxjp07s74svUmny0kuky5mZvDY9ne/v0IHna8YMgheXi/eOH0+6e+utShACFCDNyCAoE68Kp5OC1bRpFIYSEvhsycQr1rDERD5n5072w2Qin5CEIJLhUpSInqV0bDa+12jks+PiVIITQAXoR0VRiTduHN83YQJpcXExrY5eXpwbSQAmiWZ69CANb9aMCWd8fFRQ/549fEdlJQGIZy2mlBTOs8zRrl2qLBCgYkcAJbCKdc7hAIYMDoKpZ08E7N2LO8OOwtTwEbiOEdwDKtlMTg6VpvIso1HVVZNi5wMH8n1Ll3LMnunJb9Y8rab/qEXvz/brzekkkD14kOdJ8gxIcfNx4wjcN28mXRDlxCuvEAvs3k1+t2yZSsx1/Pi/0iMdSkDTQT7zW4Q0uV6Eu98SMqCDSVtM15W+P3939eLrfn7EE54tOJi4okED3Kjr5udH2mo2c37MZhX3l5REGlXD1wD41EDhRVVmoXVrYjgRqps0odGhbl3SmaIiYtCEBGKVGTMU/axRQ9XfS0ritQEBPPPNmwOLP9VRXlCGBg2sWLpUwyOPEDsfO0aMK1js4Uc0dH1wJB5uVIy/FQ1AtENjvoN40s2vvgK+W6bjs9llaNXZiosXGav27rvcOw0D7BjseA8WlKM85zhOHyrDs8/6Ye1a4LbbDDAamaTr9ttJIydNIs347DPgoQd1JO9ywL+uNyIiaNGUeMgvvgBaBRXDun8PNLRBu5xvsCTzVUyb5nfDg+6nnxhPun07x6XrVC5VVHD/FhcDA4ZbkJL1KHpkfwa0aY0Ovazo3Udlo8/IoBBuNKpEYpKQ6oYbuUEH8guwPTsIfeO0Kkqp/4Ql72btv8GC9x9rdjstOmvWEOwWFhIEffQRQXRUFBncG28oNx2DgcRs9Wpu5NmzaYkyGvmMzEweGKkl1KqVKnrety8FqPnzuVHsdm68+HgenqwsPtvp5PPOneP7JBtTSAgBxblzFCqmTKEgMXUqAdOJE9x899xDrc/jj3ODRkWRKWZkkIDs2KEhr9e96PtIS/gN64O779ZuaMpdLgpmdevynoYNOf5Ll/iekhKOcf16EvLSUoLK/Hxu6CeeILHq1o2AIydHRw2zA7XNNpzXa6MINTG8/BtEtnRg1iyClfPnOW4fH1qnwsI4pzNnkhFUVHBc4pffowe/79yZAPHECfZBtFDHjxOI3H47AWNQEOfzgQdUcfX8fALoW28lEXzoIc5NjRpc62PH2J+9e1UMkt1OItysGZlWTg77cvYsiaW/vxKmKysJXIXYZmfpyNiYh7++XwqvrBTcdaeOGjWUS0q3btw3lZXsg5+fJIYw4MxFH5SUaMjN5ZqfPUuiW6sWcPsdBpxGa7gBxNbKwukL3vj8c87d8uXsg66ToJ88KWnsddhKnLh6xY0mTXRsWV6Iy1dsMJ/KQPduOry9dLRsZseuXW6Ul7Mfhw5xbBcv6AjAJbTGcVxFLRhRcQMMnj2rwY4GYBC7ESQfNa7/vjkpqQS4uf5sVZrTyX1fu7Zi3GVlCjibTNxbDRsSxEthcKORe8nLi3tywwYCb4cD6NZNR+ZPRxGSH49TBy/BoOk4cIDfXb5M+iOlFdavVxZymw04c94boc103IE18K1ZG6cveOOOO1Rmxbw80rn69fnuWrXITLt0oUC3dCkVGFYrz3e7drz30CF6BCxZQsBy4QKD/41GJkwymVSiBlH8pKUR8D/xBGlhUBDn4uhR7m+nU9HpdeuoeEpJYf927uR7GzdWNSCdTtLGDRtofevShda5BQtUsq077uD7Bw3imU9PJ6DNzOT9e/ZQMJPSD2LtSk1VAfr79rHfu3aRlk2bRt6zbRv70rEjXTONRgLoSZP47i1bVGxgejrXRFKdi+v6F1/w+4kTVa088YLo0YNjGTBAZc8UdyEp+7NzJ/s+YABBuiQ4OXVKudJu3aruEdc8cf+UhAHSr+hoKuB++FHDlilb4Dx+GuYP38XuPRr69VOZMAcOVMnBxJ0UUNZeKcUhcYEAr/vpp6qabs+/xfpot6vEK//pelJ/pGa3c22dTs5pv37cNxERtP7Kmk2ZomK6XnqJlpmDB7lHLlwgX/q5dUsscb/FGlf9un/WimfEb8+k6QbtILZffXft2gCgo7TUhTq13VXqnJ49y99lZTxzALFVfr4Sfi0WYo8aNVRJpJAQFTPXqhXP5enTpJOVlSx70K4dFW1PP83fvXoRTzVpQqx44ADp2913UwnndvNMduvG52sa+c6M6TqKP/sWGz89jsolf0OlQ4fZTLpbWMj3ffIJ6YPLBZw4aUBuYS00DtYQGcl98fbbVNTVD9KBJV9i77xEHHjpW9Svp+ODD6gsi4kBmrax4HTjviiHD3KCh6BdDxoh9u6ld9EHH1ABOHUqXdVr1yYN7dxJh//013Fv4hhY9sYjO0u/kSzR4eDe/fi7ADxf50vUQx5eDPgUpaVuLFzgxrRpwOrVOs6etOOVV9yw2YhlQ0O518+do/D75pvAgRQ3Onz9PD6ceBqrO7+LTp01zJurY9WXV5Ge7kbHjnTVFz6Qnk46LMKb/ZqOLZ1eBJo2Rd93+lHYg/r+97Lk/Z/H4P3W9lvr4L35JoWKrCxK2V9/zY3jdPKgDBnCzZGayo07ejQJVFQULT4VFSRSY8cq02xICBcT4L2dOvH+1FS6V6WlXdeKLyFAe/VVmpjj4sjMR4zgAQsPJ+OWvnbrptwmFy7khu7enUJmWJjSnhw8yEPbqRPHcPw4GWrHjny3zUYBUEz5eXkUKFasoNZHgvIlBsPXl4SmaVOVbKNbNwpFDgfnJSGBhOPoUWVRKCzUsX9PGZ7He1jp9Ti6W9KRWNISfevl4ESHkcjK0hARQcGjZUtaLcPDeTAmTGC/fviBoFFi4xo2JHOw21WQct++nIvHHydQCQoiQG3ThmMU8LV5M4WwwEASvfPn+V3t2nwHQHeB/fs5F2KZKCmhwGsycfzr13NOJHmFrnO+vvuO1tSyMhLM9ev57MBAoPjCNXSJfxkrMQJBKATihuBKeQ20aUMtmZ8f57FePYIZqTd28CCfKUL/rl1KYC0poXUkP58+xjX8TCgt1VBcTKA+ejTBX0oKGcOpU4C3WUcr225cKTfgFFqiDHXQANnwAVACXzh8m6KizIau2Id0dEEZAmGxGFBezmeWlLjhi8vwxRX44wJOoD1c8MfNGSMZta+v6XrQ+M9piQm7UZnXkgv7C+2PFOMCUOnQsaNyrwMoOElGU4B7rmZN7tNz50iDVq6k+/E331D4aNWKtKGiAii+bMeAA69gAZ6DFVdRFtAGvjVNePRRKpmkxMpjj1HIOnaM9OSLL3jW69TR8cjwYvg3C8CpU7RAJyeTvpw9S6vfp5/SWnftGv+eOpXgb/FiVfIDYN+sVgogDgcFnYgIlVWzQweO5fXXCf6/+45CR3KyykR71128d8UKCmybNvF8S5IWEbaioqhdbtqUVqJdu5R7mdTs69mTQtTRo+re6GjO/dSp/MnNJUBasIDnzt9fWZnefZd0bNYsnrk+fTi+oiJVDke0sjYbrystJZ+Q2qN79hAQbN2qrI316/P6Z5/lPZ5JRKQenBTdTU3lOBYsUDExkyYpIc4z1kMsW0VF/F74lY8P3yMle0RBIJa5/v0V8JC+AHy/xOMtWMD5LiykEP7VVyr9udwnICYmhuOVVOMAeWBsLNepY0eCxjZt2A+5xm5X1k2Jb5FEZrNmKddYz/pT/8n2R6JPTidduA8dUpkLJdZVkteVl1MIr11bnbeaNbnfjhxRMew1a6oMwMoSJ79F4Po1O4MbTJKieVz/WwW1f7Z59tcFKjervlvGqpK5lIGKTy8YjYYbuA3gfEg5FM/WqpUqt1SvHjFQzZoqBlhqZopHxfnzxBS+vtz/V68SKzZpojBgdDQVSXXrErM2bEhaOnEiz67DQb4iSULefL0S484/gxW4F41xHqZbb0PvgTXw8cdUVn31FWmg2UxDxuXLxJPr11ORfvIkaWavXsDlU6UY82FzrMBAzMELCOgSjnGTfG644z/5JIU1s6MMrbpYYbFo+OQTKuiPHAFSD+hoVa8YNZsGwGwm5lm9GjA6rmHw6iexBA/jbnwL6+xZ2JhU6wZd7dWLoSoj7tAx6dkKONMPoWf5RqBeMM7F3I8Pjt+OvWk14GoZgZwRf8GOHQYYDJw7k4k0NShQx/734jHkylfYWOdRvJvYH9n7ilH+3gxkHjaiQ5tKDEl5Fyaz4QYt27RJraXLBRhLrsL46EMYhI0wmbQb0uPfq+H5r7T/9hi8/1iz2VQdi65dGdB94gSZCUAwLxl6JKDYaOQBsdsJYi5cIEA4eJC+yJJC2uUiw3E4aDmy2VSykTNn+B5xZfjhBwKUU6coiLlcBHhTp/LgN22q0g9LraQ336QANHMmwca6dTzU06fzPd268ZA88ADdFDt25Lv37qVLRYMGqnaW00kC0LGjii+7eJFCXc2aJCjDhpFgbN5M7bymEVDs20dLmM1Gi1FREcHb2bNAfp4TYcjAFgxCcaUbFzoMQY3I1thacyR8fDQMG6YyEh08SFD54Yecx2XLKGzXqsVDuXMn5zw9neO7coVMZcgQAq+GDXmPZB+VWLJvv1V1vCRLYFGRslAEBbEPvr4kPnPnco6kLqLRyDVu355AbMMGguWiIhUH5HJxvJJmODeXxC40lGu0ebOO9GwjPsc4FKEWzqMJjpyxwOFQoPLHH7kmqakkwg0bck/07k1iWbMm+xQURA3euHHcs3l5QO3aBrjghbIyascDAwmEfvxRWWUkoUurZnaklDfHCbSGETYAlbiKRghHEkywwL+mDj9cQSbCYIQT3qZKlJe7PRI9aLDWq40WnRvilKEnXPCHdj0LJjWU0iSYQkNZ2TX8Pc3m76Ck+v+yBQTQfdBi4R5s0YLMvHlz7mGLhfvu/Hnun5ISldDks89oFDWbCQoaNiSAOnvBG99gHNxwY1yT9bjzbiMqKkhb6tThdSEh3Fv+/irBTkkJlRZHjhjw7oJa2LpVw9tv87yuXMmz2KwZ6VJAAM+CxUINvqTEb9yYihizmc/r1InnW1yily7lvr5wgTTZZiOY+eIL7mfJttutG+N4pDhydjaVQ1arKuMQGclzaDTyncnJFDqef560WyxcLhcFGZeL9EHiYbt25fdbtjDBQJ06/C1Z4qRMgQhFAO8ZMUK5z8+YQZA0fz6BhZRkAHjNmDHk6S4XAfGePfzbZGLfHQ7O35o1vP6220gDN23i85xO0qcJEwAfs44l80tRXu7Gxx8r2tChgxLePIU7m43jlYK7XbrQ8jJ3Lp8rwlFCAudK0niLMLt1q8oEJ8+bM4fzs2cPeaCs+TffcP4/+oi0zvM+iUH3TDUu6yJZMNPSOJ5bb1XF6rdsUUKbZHGMiuJvk6lq3OPvIdz90ZrTSR7XqRN/pPwGUBWUOhwExOPGUSCURElK4CEtkNIBikeINc3zs19qGqoKg/9p4c4z3k/z+Pm5cAcQK/n64vr14gZKnqhpxHhBQZyr8nKVb8zLS1k4Cws5x7rO59Wrx7mTguVWK+lnRQXntmFDXjdgAM9Xs2akWTt2kF6PHEkBXfBq48ZUsN1xB72qDh+mottupxt8eTnQuKkJn+FZHEcIGtQBmray4PvvSZMffZS0XLywLl8mRr14ke8/fJgYr0sX0sTGra0w9eyCc2iJ/vUPIaqP943Y7K++Io33sRhwLN8PS5bQEFC37nVFkVtH8I/vY8YLJ7B/4tcoOFSIdT+WoVlTN4bdZQHCwtAeaVjmOx5ZeQEYPJj7rls3vvvKFeD5yQZEhDgRWJ6LCphwLh84u/oAEtIsSEMkcnI1tK5XfCNu22ymXFBWBqz9yYGYK9/hMMIQfHk/Moa9DNvDo9D/8FxMwQcYcHg+TFcLbtTolHwfgCrujoAA9Il2ULjr2RMICvpjxuD9p5qkaS4o4KaqXZtAqU0bbkaXi4D7q6+Ymeett6ghj4vjRr3vPgKTH34gKCgspPvkU0/RYpKZSbD+8MPcUFev0h1l714yaymQK7WSunblO3/8UcXTrVhBUGI0kjimp5NRxsRwo8+Zw81XXs6+79lDQaR1a2rPv/+eh2vWLBKNxx8nIYmI4OHu3FnVw5K4K7udCQQaN2Y8WNu2FOIKCwmq/PxIXFJTeX1ODje9281+lJTw96VLQKXDC07UgQX5GFg7HcV1WsJ2UUNgXc53VhaF2nbtSGhOniTAy82lUNK6tWISTzxBTW5lJb/38+OcHjjAOThxXIe9zIUOHUw4dEhDnToEq243+9KgAQnP/v1KcN+7l3/HxBDcSHyer68OTdexb58Rfn4aXC7uie+/5/x4e7NfSUmc94AAgkyx0lZU8LoNG4CyMh2Nr2UgH0EwwQ/1WtbGhVIf+Di1G2BYipCHhBDIBgdzXnx8aEWR4qhnzlCjn59PImy3c47EP9zl4t578EFVv8tq5fVSRPZAijccsMALDpSgDjSUwQ5frMPjaNjQiCtXnTABKEEw6uEoSpxmBKAc8GkETTPg2jXg0iUD8vOpPjd76XBWuuCGEVeueDI3NxibUAlm2BRGXZUBGoBfzaD5R2xOJ8/cww/THeXCBZ7Vs2cp7J04wT1TUMCzYLcra7KXl7LAHzrE/2vXBkJDNaQUhWBoTBn2+byIIGho0oT712jkNcuWUbg7coTKhYwMMtuAAFrWBTCHh5NG1K/PawIDCeQDAqigOHOGe/PLL9nnhg2pHAoPp9IpPp60rWNHCmkzZ3Lf5+VxHBYLFT1eXhz3lSsU0tau5fuzs0njjh8nTUxMBFau1NGpZRlycqw4ckS7Udt09WoC0aVLKdABVPzIGA8dIm0U99dHHiHdz8piv3bt4hkDeE1ysvIWkLhnqWu3a5fyNkhKohXdx0fFYptMZOIdO6psbSKESQwfQEAhhXsBCkPbt6v/N226nkjKS8fkzQMxcU8ydoSPQcRbf4XVargRvG+zEdQ884yynM2ZQ9ANcP5MJtJ8u11Z6WSsUqA3IUHVVJX4ONE0795Nvrl3L+/Jzub69eunLHdjxnBuREj0LHJevUm5IB8f0maxzm3dynnt0EFd6+l6Kb9/zxiWP2Izmbh/e/cm5vjiC2IisTzLObLbSU9q16byd98+3q/rpDFKsJNmgFIMOqDq1P1aEwva7yncSV9/3cJYUSFxeNI/K5h4zABfX86DRCnouvLSMBiUh5iUQwI4/zVq8POiImIbySjeowfPTYMGKnlTkyako02aEGNkZZH+NWigMFtuLtdvzhzizrp1+XwpUbt3LzBjhoZ77w3FlIeKcVW/D489zrkWI0RREQ0Mzz1HvrBnD2llx47kAcHB7FtpKZBzSIP12fVofX8xMs8FoEkTDTExxC+SNV3yMdjtjN9s0ID80FnmQNHJ/9fed8dHVeXtP2dmkkySIQVCEiAUaVJCr6ElgFIVG5bdta1tlbUrqK+77r66+u7PXSxYV93VVdfedQGREjqEDlKk95ZACimTycyc3x/PnJybIYEEAsmE83w++WTKnXvPPffe7/k+3zoO0ShA+0M/4e3HWyIJh9Gr42as8v0ZM2xPYeQtRega5YLPp717LhfPZ9Mm6pcrN0XhhOiFHbI9pnZ4DQ9v64zPcA32oCPaR2ejhSOu/PxSUujI2LEDeP21CPzj6uvQMmclerUvRv+t7+NG/Bub0An34R94s+Vz6Lo8ERC6r7RyjIeHc90DBJZGzsTwrtlwNE8EhM7BA85fvnCD1rw8Ht7c77/Ph2PHDoYVlJZScX/pJVpJ4+LoNp4zh65oxconTWIC+vvv04KRmMhkfJ+PRG/hQgq/666jN+6553gcFX45cya327WLi9bgwVTkcnJo8XG5eHMo8tOlC5WCb7/l+2bNKASaNaNC1b49b8brruND0bQplZCDB0nqCgtpUZk3j39OJ4ldq1YkJgkJPO4HH3AfO3fq6m67dnFecnL4sB85wjm4804qmomJXNTj46nUnDgBuEsFihM7IiclDcsSr0XPHhI2vwe/u0vihx/4wLdoQRLz888cc8eOJJUHD3JsYWGcw3/8g8dT+USqUEp+PkMU/Xt24kTOUaxalofiE2U4eJCNPT0ekmevlwqm00lhqqotZWZS2KmCJzabHx3yFyHBsx179pTh2DE/rriCv1F9fpSie/gwzzs7m3OieteonmItWwIdWnlwGIlwoAT5CMfWHeEICxM4coSk0WajQtWliw4N276dCnFkJMe0ZQvvm4QEzqtq0REZSUHfpAnH1qgR7+c33tCGA0DnMO7dC/h9EpEoghfhaIJ8NIJaZQWKCoHiEgdKEIu2WIdiOAHEoRl2Aj5/ubBXVbgiI/3wlJ2AH244cRQkc2qBtsGOPABO6IXQW34shWiAN5BBBahKiVlZvNbFxfwfHs4cyrIyH9q0kejfX/e3i47m87lpExdYl4uLdGkpn1/2u7Nh1tJGWL5c4D//oSFh2zYq5dOnk5Q5HHz+N27ksxMZyec9IoLPo+qN2bYt/+fk0JjRqxdlWNeuulrblCm8d/ftoxwZOZLHtNl0C4YxY3jOqtHxBx9QARw4kGRLFRkYOZLy7OabaVBp147bL1sG2G1+uP/1H6x6dyW873+Ibqn+8pzogwf5TEycqMP6LrpI97qbNInKhKoW2bcvz1H1h7v8ciotb71Fo9iaNaze6fPRWzR6tCa+aWncV8+eDOt55x1+rizlqleS8iYuXqzJnaomuXAhjX/KQ6j61a1ezTGOGMG/iAjgvuuz4Vw2Hy55Aumb3kR4cX6F3kqLFwNdOvsx/5tczP6JRpbUVH7udFIhW5HlB44cweJFsjzf22HzI73TEVwyUpb349uwgd9lZFQMFb3kEkZShIfz/2OP8VotXEg5tH49185vv9XES/Woc7tJ9mfP1t8p8gjoIi5OJ/c5ZUrVZcUr65NncG7g8+lomdWreW0GDOD9WVLCa338OJ+DFi343BYX87deb2XkDtCeMEDncZ+OsClyp177T7FtbUARSeXBq9zDaM2v0xCgz0SCK58onweXi+u4FaWlJCKlpZRXSv9R81pQECBLmyjbVf/Lw4ep64SFUYYXFFAW5+XRgPbaa5S7MTFM73n4YW47dCj1rO7dKY/uv59yUEUV7N8PTJxoQ/Mu8di+XeKx+4uxaaMP8370IDlZIiGBEVjqv8vFfY0bx3N7/XXK/IkTKXNXrLRh3d54rF4tMGUKdavnn+f9c9NNDM+/6y6tW6Wm0vPYd1AEWrsK4IAPu5CEDUjFESSh99YPsXebG85IG1ZsaYTvvxdYsIDnqKpkHjgQ6Oub4se+NfvQQm5BNDyY5HkbgxJ2YA86oGVEHlrecgn+8Q+BPn04J59/rvs3vvGmwGWvjMFdy36H7bc8C+eg3vgCv8bkQVlY/v5WdH3xTtgdAnY75/nNN3WRqPBwzufSpcDQYTY4WiRV6AOsDFRVGb9qGw2a4IWHU4Hp1Yuu64QEKuuqF9uTT2qX8lVX6YIomzbxdzk5vICZmQyn2rqVynhWFh+m3bu5KH74Iffj8/EGnjuXi+LcuRSOvXoxwfX55/kgDBjAB+EPf+DNrsLslizRve2EoAU6P58W1K5d+SD/8gsf5vx8Kv/qoZ81i3kiPh9DN3/9a1rX3nyTFvBt20g4VehmbIwf4WE+5OWy0AZ7n1HANGlCgdOoEcPB4uKovJWW0sr0739TgcvPB/LzbSjyhKOgQOLNpw8jb/tm/PbWEgwc4Mf06dxfQQH3mZ9PD+bSpVwolGVLWX7atOE1UM/D9u0klLffkI88RKA5DqAMTsCfj2jvQfh8fhw+zOuzZQsfnNxczqcK4YyMpKKUnc25aZHkwRpPZxTAjrbYgEOHJBYs4PV1u/3Y8YsHP/0kyy1q8fFU+gBasZRS8fPPVCwd0REoQxRaYD8iIOCHDYmJ3O7QIc7TqlW8D1S/s759afFZtoxjs9moYKkqoImJmqRu26bzJAsL/ZClHqjQj4ICbhcbS4V7xw6gsNiOQjSDF1GIQR78Ua2AQEuDooICABJlaIyd6AM3EgG4sRlpaNvBjgMHdGPXwkKgpETlHzjhRgLosVMWTsCHZOggAC9okbXBuihGw6urCBmUw+HgtS8r06XDfT6gtNQPp/84gBJkLSrAsmV+bN3Ke1G1/YiN5bU+doz35P79vNdKSngfJSfrcMlEGg/Rsyf3v2aN9j7t2EEDzjPPMEe4f3+O6+WXab13OHSBo1tuIUFMTtbVW9u3JzHZsYP3zbJllIUbN/Kchg2jcaV9e8qsjz5CeYhxSQk9VPfco4t5qJy63r2116+4mEpIWrtsRBzahRbYD8fB3UBePn76icaiG2/kszNxIudi0SJGOiQkaFLQqxfnxG7nszR4MMPe//Y3nceVmsooikceIbGzNt/Oy6NB0OulAnHppZTXd9zB73/4gdv83//RGv3ss1RoBgzgvJSU6MqSitAMG8ZzHjSI4+vdW/eeA+h1i+uYCG/aUHjtEVja9Q6MvCauQrij3ebH2BdHYeQtKch47lI4w/0YNozfeb3AmtV+pD09Bo52rWF/fDJGDvfDYfPDnT4Kr7Z/Ee7hozH7Jz8WL9YNzhVxC75fVSin6v2kzuPBB3lPqkI5KqzS2uhXhZqpiszW/avPFy6s+F71YwsmcqagyrmF18tnU/VUBHi9HnpIRweVlVH+5OVxO5UTWjGMvzKo8MzqkDugdgqs1ARq/dIhoWFhJ2+lcuoq/70DACMMnE7qaYWF2kMHUL8CtDdv507KTLeb/2fN4rO0ezefre7deb8XF1P3CA/nflNSqCOUlfG6LF7MsOlnnuH7W2+lbFJtrYYPZ6j5mDF8XqOiSBi//14bhSD9aLPofThmfIWwGV+hzfZv4J67AM8+60dxMdeclSspp1as4L5jYhiy+cUXHPMrr3B/J07wuPfcQ93z2mt5zNGjqV8uX07yumgR9fBXXgGWLRfY2Wwo0HsA/h+eRgROIAHZeLf5nxCTFInsbBbYa9GCerwq6NO4Mc8rPBxYvdyDYoRjEzqiH5biiRv3YuSLE3Hx2E5odHkGol023HefNqw/8ACPryKt0jNs+N83k3DNRBteGzsdzt1b4MycBXvTxrh0lMCoUSR0l11G2a3ysJWMVLLxVDgfRqoGS/BURbKCAlqn77lHN7j++GOUl5n//nu6d9Vi3K4dlQQp+dCtW8eF/q23qCjdcQcX7Cef5MVVlhfV46VdO+67Z08SA2XhfuMNPgzbt1NIXnedFqQzZlBxW7+eyn9RERWp9HRuv3MnlSi/nwqbx0PBunQp/yvrzbFj/O3UqTzngQP5nddLwZCSQnK6c4cfkQWHkFi2Cbm792PdOj+E0PtyufjwfPEFhXanTtrSU1TEh1YIEr/YWH6fe1zikD8ax9ECftiwc4sHPXqQBPn9JGClpVRinE5d2a+kRAvLhQv5Wgit9G7dCvz7izj0cu5CHhoDOIEyOFEGgdISPh0HD5KI79+vy5e3bs05LC4m6VMJxVt3RqAUDuQjBUfRGi6XDQ4H8NWXfsR6D+PgoQKU5uQgfZgfNhvHqJq0qjwEJfBjY4FVqwRKEIPIbv3gjWgCv19g1SqtuB88SINBdjaviyo44XKRPLZqxXFmZnJet22jQDxxgtf+4EF6Mvv28eNiuR55BSUoLMyD280JUkRZlUKPjRWw2RxwRYbjcGR3FBbbADgRhiJ4A4tXXJwNzZoJlMEO1eLg55/9KCz0oaBAL6B2uw1MFi8C4EcY3ABOgOE1ypKqFmsHKguhcSGXE2BQAarA0WWX6Zym6GjABokYHALggwelGD2sCI0b6zAZn4/3VVqaTrxXuaexsbzffT7d/zAigu+//573UdOm9KDMm0cj1qef0vDz6af8+/ZbWjFbtdLVZY8eZfjyhAmUH61b8/Xtt1O5X7eO+7v+ei6SkZG85MorP3Mmz8/no6z7+mvm9jkcVECUAgNwX6rX0vDhOnx50dZE9O1Sgql4DD0v9mDkNXF47jn+5pNPKFOnTOHvBw7k88V+SyRsPXroSpIPP8z5f+cdyuu5c/n8jRihF+gVK7ifWbNIbF97jWOdN0/nNX/yCcmw18uWE3FxjPCYOJHP9ODBXDcGDtQ9+9S1//FHKjP5+TTCAdzG6eS1feGFwLY+gXl/mA3s2YOhWVPhjBTlBAgAMrpkA0uWItM/GJmLw5C3LRuvv27Jc7s4G0uXCsDnxSVbXmXuyCF6BR/Ei3Atn4chHbMBaPKmwiatJGv2bB7P7a6omPz4o1Zs4uJ0RUtrT6iRI6nAqagD1ZfU2q4B0MdVeXjBJNMKQ+7OHbxeXT5ehdyqwiBvv015Y7fzens82lvn91PXqV0Eq6jnWmW1hmRyPbMWvqoJpKxYUEXVAQC4vjudJCXNm2u5LiVfFxdz+7Iyzu/q1bznVcXNli0510lJ9Ho5HHRSANTRVN5eWhp1Rr+fzoF332VY/cSJJHa7d/MYW7aQCDZvDmxeWYTUQzPwLB5HB2xBLpqgsAjom+pGhw7UhwoLKedvuIEka+9eyvz4eJ7Drl3UeXbu1NXsHQ7K0TVrGBU3cyZrThw+zHWpoEB7iePibVi5Phpt2znRpHULpD95KX7ueTNmzBDl3mUVtXTRRZTrubmcg0OHgIhGEShCHJoiFweRgj3Pvg/7M/+LaW86cdVVotwYceWVJKuXXqpz8fr14709ZgwNdhd30p64jAzKMpWn7HCQrAZHGZxOPp0vI1WDJXgAlYN586gw33YbF//4eF68khLeHE89xcV/+XJe3F//msy8Tx8WHklN5c00dCjDhubNo9J/8CBvdJUkHhamq5699hofqHfe4cP07bfsu5GXR6Xo66/pVcvOpjVFSloNdu3SDUQ/+USXRi8u5k2vrDkqTywqiuSqcWM+1JGRPNecHLrwVTneo0f5/cqVPPeCAok22AA/PMhDIkqL/eVFDps0Ianbs4fvjxxBOWEZMYLzFhbGhzEvj5+np7OcfxzykYr1AGzYvDMCGzZQiHXowH2pypDWvjE2m/ZKtG1LgZCTo635AFBcLLArcQjcUa3hhA3hcCMGJcg/QXKiSvna7aooCd+rkJG8PApUnw+QUiAsPBZAOBo1a4LiYomDB33Yv9eHPpgPH6IQg2zs2+VFx44km8nJFLZCUCEEeA2UsHY6bVi+IQoej6jygVXNqZV3Mj9fx8ar5Gqfj9tt367260fTOA/27pXY8YsXB5GIcBQiHKVQlsyiIgpHhR49gOhoCVeUO+CBI6nzwQ7Aj8bYjbw8Pw4d8gGBz4B8eDwSwBEAbriiFXkTCA8PA3MKSlGGeDD0xI4IHABOSpo/2SLrh63iAA0A6AU4M5MLHEBFwA8bTiAJQDia4QT+O9dFT7GDi3mTJryHZs7kc52QwP1cdRUtsnY7F3QVTpmeTgW7dWud77p6Na2nY8fSEKV6COXkUKZkZ9MKvGKFzkcdOZL3WkkJ5cHcuVT24uN573bsSIXj2WdJWkeN4vOxZg3l5ZgxJDpHj1IOjxvHAiIqTFzl9uzYwWfA56PsGTGCz/S69QIl//MsXvv9L3g98WncM0ngscdoPb75ZsrOUaP4O2Xs6N6d8n3yZCo2K1ZQgXA6ecwPPiAxs9tpWFOtA9xuLvKq+Xb//rTwqoJTah/KCr94sU6yV5VDVXNoVdAL0MQwM5Pzed99lNH33qvbJwTnljkcwPARNnibJGHhIlE+vtmzSTznbEhEZpd7MEQsw5BBPizbmYhJkzgGtxtAYiKGDy4rT/b/cU0iZq+nV9Dp8KNwwHDM35xYroRaC6RY2yNkZGgvm6pmqYyoasyqbUFhIZWgoUM1aVTNlWfNYtTLkCE61FKRRutxrfmBp1KCTJhm7cPr5XPaqpUOI3/lFb73eKjTKDmkqsieO1jDOs9HDp46TnU9jKeHukeLijQZjo2lLD5wgPOYlsZ7PS6O+pHXS0KmvOUuF/UFIVCeUuJ2U6eYOlXXLRg4kJECDzxAWfrf/9Ko1qoVIyn69aN8fegh6rYdO1K/+fWvKW8/+ADI97nwdvRkPIbnUYoYdMFaNHFJRDWOLC8GtmED14JduyiLmjXjuDMz6RyYMIHH27eP61BEBAmY0qVeeIG67n//y0iIq6/m+rNgAXW1tWupg8fG2VBQEoF3Pm+M7dsFmjWjzltYyLVPtbwaPZpz9Ic/cJ202QRuutGJmB5dcBF2ogM247utbfHa80XYsEHrt5ddhnLSpuTyuHGc82uuoS5/5ZXcdt48HclgLRx1piTtfBipGmybhMJCWpRnzybx6dePD8T27Xx44uP5N2wYSdd//sMbvmtXWhXi4nRRA6UArVvHm1spSidO0DV9xx26sqSUVLY8HrL/sjIqUt260YuXlkYloXt35n5s386wpvvv58OSmcmHbv9+3mxZWdqNDlAw7N7Nh71PHxJTQOfixMToBOjOnSkQVKJ9aSnfJyZKHDuQh2gcB2DDMbQBINCokS7moXoC9uqlPVhC8Pys5dwbNeJc8zaqmAytKvUpy7XTye1KSytatIPhdJITaKLpR5hDosxrQ2SkhMct4ZM2JCQI5OTo3CXVd3Dv3lPfG+o8Lmrjx67d+WDIYQyAUsQgHwWIBhAHm02U5/Eoa3Jk5MnhGU6nzqO6+GIK8qNHTy6F3K8fFUarUhIernvqNWnCa11cDBw75gd779hAL5sDMTgCF7JxEF1R1QI04TIffvzhCEoRD+Y5WOEFwzVdgfcqp8Ee+ByBuXABsJfPE6HMmDKwjQrHtNqI1PUXUOSvG2ZhvX9MhTj0ynAhlSEH+FxceSWf9YULGXajKmoWFflhk37ExNvh9YryHNl27bg4tm9PD1ifPny2U1N5T61cyfvnsstIOuLidKi2qgDpclEufPghF9Fbb9UhlV6vjhb46CMaqLKy+F3r1hx3+/ba47h1K+/nX36h5bdnT+7rxhupZEyezLzkp55iWHfXrvxeNfWdMIHnu369ljnLl1NZSEzks9+jB8c7YgS9YaWl3A+gc7XmzuU4u3TRec0rV1LZmTqVx7rpJp6PzcYQSlU8JCNDk47f/Y5FCHbtYrh479581mfNokFu5UodXp2RQcIyeDDntbJWAEOGcGyrVvFajRjB8Vqboyu5ogpYqP5zc+boVgzKi9a3L+fHbtfNyEeMYD5d5rf58DWKg6dMIDKS3z/wAO+Zhx/0w3kiG0hMxI+zWInXGe6He182XvgwER0vFti8mUrf/Pk0AEyerIu2zJ6tx6YIl2qBAFRU8JUHb/bsipUz1eeAJoRqH2oeVGN2q4fydOTufBUruJDkk9vNPMvMTBpmUlPpnRGC+sdf/8r/RUU06qhoG4PKYbdzvo4f18VnrEVo7Hbqb8ognZTEa6C+V71Hjx/ndhddRH1ywQJGZanqsmPHUudISOBvmjT246tPPPjVLRH48iuB5GQ+b//5DwmhCp0PC6NHsEkTkqzkZMDn9cN3ogiZWVE4sB/o2MkGj4c6IkD97OhR6rydO1Nm5uVx3KmpXHc+/5z7PXZM67EF+X7ENvKhZx8HliwR5cTVWlk0NZWGu5SUimqDy8U1obiYc9SuHf936ECnSXw81xQV8RIeDuTnS8x9bCbaHVuG2/qvw7ujv0bPXrrgi5JRVnlTVSuD+lrY6VSyqUETvJtvpttZVWts0oQKSaNGOkSuqIjfqwu8fTtvyrAw3phNm1J5UcnyAJWUHj14861ZQwF34AA9fvn5LH/92mvMX0tL482mescp17Uqz9qkCd+r4ixFRbxBw8P5AK5ezc+GDqVyt2EDt+/alTd7QQHKKzVFReleeU6nPs8tWzhu5UGKjQXCw/04fEjC57dBCIGYmKoSo0+FioROoUkToKDAj7Kyk78LhpUwRUVpsqNJkB8sOaxIhROKVFQkIKeH2l4Rd8AHJ3LhhhNABE7VZyc6mtdB7cNKJNX+lEd1wwZUGtYRFsbzCh6z6mFXcZw+kJCVAfDBbouCz69DR2JjBYqKAL/fD79fz3OkvRRe37GAt82BxtGl8DmikZ9fBCASwGEAdNcK5EDChSjkwgHgBMIh0QiVl4P2gaGZdvCanwDQCJroqXuhItq3t2HbttNbQi8kBQqgnOnenc9xkyZcrNVzMGgQrZrqnrv8cr4vKGBo95EjlCOdOtHwozweMTF8noYOpZV92DBGGmRlURY4HJQt+fkkEXv28N5bsYJeql27eI9efjnv0xdf5H3udFJ52LpVV4JVYT/ffkvD1axZJDLx8XwOxo3j/lq04PPw0Uc8ryuv5G/atqUR6+qred6//EKlZdQontNll/G3//kPvW/dupFcLFnC/Tz0EMenqrBNn87Ks7fcwmfpzjup6CiP15tv8pjr17N6sPLypadXXODnzycZW7WK382dy7XD5dLkZvp07aXLzNRkT5E2RYouuYTH/tvf6K1buVJXILT2fFMEasAATR5VNUxVmXP6dBoYU1Pp7Zs/XxNOlcOoqmSqsDp1TqqJ+JAh/J0KKVJhwtaCJ243z2X06Ir3q1JurIRNefN0i5XKi6NYiZh1f/Br4gkhykM2K9tfVThfSteFJJ8KCxnxpLwi69frNe/pp3XT60aNdEi4QeWwtowIhtIlXC7OudXgIwTle9OmwLp1fhQWSIwdb8MPPwiEh9OQvHUrjW25ubweL71Eg11ZGZCW5kfxsvXY42uKx1t9gK+aT8HVV9vw/vt8Zn/5hQ6K3ON+dGnrxqWXR6JxE4Hjx/z461988NkciIkRuOkmFhBU3rjOnXWxuHXrqJ+GhdHwFhnJZ3zfPq4xqjJ406a8T4qK/ADK0B8LsAqD0TTJCU+ZrTwUf+FC7iMpiST24EGSN7VG5ebyeS8s5DozeTJlfceO1HE7deKac//9lO8eD3X1u++inFm6IxFpg0QFg1Sw7FCyOyOjosGpPuNUsqmeD/3M4XRSeVJVM8PDtfekWzcqN0mBAjelpbxZ4+J0udqUFBKhLVtoTWjThu7k4mIqZJmZtCBMnKjLva5ZQyv25Zfz+B076lDBlBRaOIqLecM0asSHe+NGjrd9eypM27fzwWjdmjdYdDRv5i1beLyjR/kAq7Le7dpp757KxQN0ZUUrIiJ4TgcOAFbPiyraAlRNmqxeMkIp9IUgcXCU75PeJ+4kAodQimSgimhg1RfG7+fcWIVcYHSIxyHkogV0hSo9boXgJtEKLVqo86VXxAc7/H4RuDY2uMtLGjsR7BWzKn6qgqY65qFD+hgki34UF/uxenXVoR3B43M6eT3U9VPnzmOo+fIBiIbPL2HDPviRACAKXq+A1+uHHcfBsEl6+kp8jsB7/vZ4URScTgHm0kmQ3AXGJxoD0o1iJIBkMgJOpw1ud2Xjt8EBN7yIAYmeExUbwALae6dgR7NmlU7FBY+UFHqVoqJ0uE3btpRLW7bQqNSjBxfhhQu5vcdDBT0nh0RJzW2TJlyU4uN1O5XWrbn4njjB3xYW8n+7drx3VXuRnBzdbH3tWhKX9HQ2Q2/Thgt6aanOifrxR3ro3n+fpG/0aFr7O3YkQenQgXJCeXdmzuTvnnpK58BFRNDb5nDokC+Xi4vziBH0Tk6YQCILcJsvv9R9SgEqmy++qC3QffrQK5mVxXOcMoWRFGr7Tp0oa7KyeC7h4SS106aRNAH0ot52GwmptfDLqFGaeKh5GjWqIoFRub9KAbGG7wihm6M7HFw7Bg7Uv1dE8NVXdbuBtDRef7WPceO0Z1L1lhs5kr9XJNNK7p5/nnOg8uO8XoZqqdD3YM+c2kbtG9B5d1ZlRxEqlSuoSF5mpt7W2vRd/U9L0/ssKQF+3uBH788ex+gt0+AYPACYNw+FhTa8+SbnQLWUsHoBKyNz9V35CkV4vTQiAzqSp2VLkgJlkAYoWwxODUXu4uIoO1JSqI9IqXWJ5GSSl6Qkna7eogXXgDGj/NiyYB8aIQ+7pwNAKnr1tOHIUYFevSgj8/P5HNxzD/fVrBmwa5sPeb4WaIKD+HJvGo5ILz7/PByRkbqKfHycH8U79qPRjm/w/Jyr0G5oc0StmQ9bQQIciEIB2qKkhDUKDhzgcxsdTWOTlHodcThoo7HbGcbudGqnQ8uWlMdCAKuy/HAU7YNEGFKwFR7ZFZ062TBjBu8rp5PkLDOTY+zcmUQvIoLzoWpK7NnDY11xBQ2LP/7I1IB167iOuFxaho0dyxQaJCZheGstk6wGp6rkSnXIXX316ik02By8wkJaigEStqNHaUn41a8YdqBcwqoKoderXehlZVS6c3N18RLVxy4qir9zOLjQf/kllZtrrqHbe/VqWtLLykgK7HbelFu36p58QvDhUAVI/H5+r7w4fj/343Rqj1xODhc9K0mIieH+FPk4XbuxwkJFdqpGVR4xVeHJsmXgfyS0V8f6HZlRKSJQmWcneN8KFY/BL3JxEehh80BUccueTO78ALw4cEAGXpfBBwGgAAArE+rmpTGoLOTxVPkdFY/nRxQOB45jLUBCVFaFy27nuR45os/Z7db3F8ei8t/43o8UkFjJwDWX8CE6cA7HAEiEIxcJ2A+S4TAIYQuMVecVxMdzzFKWgPNaDFbIRBXkjsf3Ijbw2gFNiBWs19gPJVrqs/CrSxQW8nn2eDSBU9bw48f5TA8fTiIkJb13qohKr14kS7m5JHw//UTL5+7dNBSFh1P52ruXXrxhw0jWNm6kR+qXX0g4bDbKrp07GekwbJhuVt21q84NVoTm+ut1VERMDPvdFRYyFPDVV/mbl19mWObo0dzuj3/UeVxhYSRVLVpwzLt2cdwvvUSr74gRPNbdd1Oxf/ZZ/t5mo9FMNTCfNk2HadrttGYPG0aPnc+ncxKHDuX8Kg/D9u0kphkZ/Pyf/0R5vtqoUSzZ/d57lPWKWCkv26uvck148UX+1lryf9EietIAktOJEzkvqjrn44+T3C1axN8MGUKPorV5tMvFgjWqOMzSpZpkKs/W669rYqhIlvLeBnu81FqgZIvXy2JigwfrfapcEkUKFYm3Ek9VdEYRdlXV0uvlGNPSNIkcMkRXz1S5ePPm8f+rr/I3GRm85g/9Jhvpm94EfF5gyRK492XjzTeZ7rBiBbcLJnemNcL5gdNJI/h77/F+TkriMzdrFo0gJiSz5lCG9wMHUF7vACAR2r6dOua+fXxur7qK0Q0LFgCffOABUIYTiEeuH3DhCHZmHcLu3X4sW8btlKG4dWtem2PHgCEZDlyE/ShDI4RHALv3haGwkHrkvHl8vkoKvLAhD/MxHl7/cSxc4MfSgpbIRwKOIhI5ORJTp+qw+OhoOjsaNaLuKiU/S0riexVumZ1NI6XXy/f791MGRMfY0RrHkIZleLz9D2jTzlFeVC8ujvPyzTckqMOGMfIhPp4pU6mplDUxMZQlqamUDbt2sY/s5s2Usap9hVVGKlgLoFhDwa1yRcn86uTWhYJMatAhmmPG6FAc1Rw4Pl7nhXXsSIVC5W41a6at5wkJJGH5+Tp3ze/njbF8OR+iZs2oHOTkkEwmJ/NY+/Zpj1L79ny4c3L4XVQUH2K1uJ8OzZvz4akMCQl6v+cfEgwftIPkIBKaIClShcB/B6ryalUNP+gZKoUiK9VPsraGC6piIur/2YypKqhwyhMA4gOf2aveHAzPW7/+5M+tXkP9mYTXWwDOsR9ABIQQ6N1bYtUqxYh9ACIhkAtZTr7CTxqHytXUXjcvWCXTBc5x1XOiQknCw/zwlFnnV+1L5fzp66SUvtPhQgqBAniNP/yQC9LBg6w4eegQ5VJREYldZKSuiGmz8XWvXjqHTVWAHDqU3p65c0nA3nlH95K85BJaQefM4fs2bbjoDR5MUvn111yQp03jYuXxUGYp0jV4MJWHpCQe88svWb5/zx4u3K1aUS5260bimJtLYqcKgWzaxLCuxEQa12bP5javv86E/l69uE3TpiRQ99xDL5vHo8NuBg5kQZcnnuB+X3xRN8Pu1Ys5de3asVqmCmsEdHsY5clbu5bj/PprEtcHHqAMVVUtAR0mCVTMz8jJYShqaSnJnMulyd9nn1H5feklrjclJVx7Xn2VpE3lpPl8OjwyL48EX5Gr4Dw0qwds4UKe5+uv87xV7qEKsZw/X+fHKYVGEbupU3nO48bx2VWVLl96iUTa5dK5cLNm8RoPGVKxITqgw5ZU+KbDwf2pkE/g5Jw69d/qyVOhpN5SH17q+hZ6HfgBo4eWwDF/DtylukJoZYU76tJafiHJp7w8etC7d9dpIYcO6Vzdhh2SWXnayZmgqqiiypCSQjJUUKDbJDmdQP/+ErsXbMJedALgQSK24iiaAkhCVJQdqamUN3v28Fqp/sEFBYDH40dMtB8tWtmxaRPPxeHgNezUCdi8WULKEvTHEvyMPohJioXnyCFIFKO5KMaOiO7weNjvzeWi3G7aVBXi8qNnFzc694hEZqZAaSnPNToQPKQK5dlslM1vvUWD2ozpfqS2d+Pq30TC4xGYOZPEbckSzoHDwdBLh4Py7q67GK2mIgBefJHfAxXlTFV5c9XB2ciV+uDBuyBDNAsLK1ZnLyvjDRoRwQfixAlaINPTyf5PnKDCovLkDh7UrQ6+/JK5K3l5DDlSHpmDB6lUbd2q8tqonCl3PECrulqw3G7d0Li6OBWBqztyB1Rs6mkld+o7EfjuTImUErKRQfut7m+Df2MlOipnTOXbnS1sqHieJ+8zWNhXRu6Ayq1BXq8A890kYmPDkJ8vICVbNKCCh1QEyF0EggugKEJZWkoPSl6eDUVFhYExRwFwVCgq4/GcbKktLKTyn59vC5THlmB4ayToBVS5e/o6BTd3NSDcbsqS1q3pQevfn6EvBQW0lh46pL12KSk6yV4VINq/XyflZ2WR3Hg8wFdf6WT0Ll10n6p+/dgq4cgRhs20bUsv0vDhvM5/+QtJXEYGi7J0aO9H3jEfNm924F//Yi7GhAk0OO3dy2Pt3cuwwRdeIKnr3Jl/33zDQgyrVlGWXnwxz3PLFpLA996jzLXZSCZKSnjuKSms1mez0UJ7660831deIcktLCT56tZNhwW+/bYOg3I4KLOVZ+vBB0k6xo7lfKel8b8q8LRkiaXaJHSRF+U5Azh/qpqm18v1Y8wYTVZUD1KGAlXMXVPkDtD5e4rgqN54M2ZQcX7wwUBz3ICnLDNTh08OHMhrNWmSLqqlSOnatSSqVkJkJUgPPKBbFChC6XRybhUBVYTSbtfkTnkFVfinggp59XpJ7tas0cVg1H4ArXRZQzcVAcyc54fv0SfQ/cAWjBxQBMe8OYAQlRJhg/MP5VE+cIAK9osv0mujeto2XFgNw2evH1RG7ho3rthKQuXoHT+utvfj6FHqPm63wOrVAoXoDEAiGrkoQTyAJNhstvI2W8pHY60arqJ2rr1OIvPHIkRERMHrtZU/n7t3s6I44MQ6RwZ8sOPIUQGbvRkcNomYvjaUZVHPKCujfhwdTafIhg1+uIuLcHTlDvTesxElYb9CUZENN9xA+bh/P3UMgOvWQw9R3j/wALBqlQ2XXRuFbduok1x/PSMzLrqIXjtVHAyg0VLJFSULVPEnBfXa6p2rKc5GztR3GdVgPXi7d+sy1gqqclF4OBf7zZvpiVMVjpo25Q16+DBvrOJi7kP1CrHZdJ8SwFqsQ+euqbyZnTtr9bTPIWrPYlW7x5JgZceTvUKnh9W7qH4rofPFlNAWOJ2nrfo4X/PoR5jwwmYXKPUqgl0a+M5aNbPiOLTnruK+1LxERNhRWirK7+mWLenBASikVbuOk3M0fQByAUQjBgdRgDawzumTT5I8nA4XkoUc0FV+r72W1k0VnpOUxEUb4HtVWXPAAMqsOXN4HUeNYp/K3FyS6F27uNjYbCRvYWEkXz4fSZFaqJWRKz2dCvqYMfq3w4fTG/XrG/wY+M9f4+nVVyA5GUi443pMuMKGv/+dUQtSktT4/cyDOH6cpGDkSCqCNhvJ2jvv0Gr7zDM8n6NH+X1MDMfYowcJyq5dJI4RETSoeTwMJzx8mITg9ttJJCIj6T176aWKvTK7dychcDo5rnHj+LnyyvXpQwK8Z482yq1fz3l+5BFaiidN4u9VfpzynH39NYmt1TvmdOo+gnFxvJaqOmawN8tK1JRHTP1WFQKw23WFTXUMq6FHeepUtU5rMRdrURK13xUrtCcN4G8zM/Wx0tMZ/uTz0bAwapQe5+jRFcM0FcHzenVFT2vYpDIgKHL3wgu6MIwiwIpwl1vZDxyBt1VbOPylcDgENcKkpApjtYZnqmMFV+asCc7W0n4hyafdu2lEcTp5f+/a1dC9dgrWk5Q4W/2gsmgchcq9e0wl4XE9oOGVugrXZR808bRDCBKwqovNsTBaE+zHMbQNjKfi+ahCXo0b6xD6WbO4piQmcs1/+GHKC7+fUWp9Lz6B9u89jKUYghcxBV8/uwWvfxyPr79mMa1Jk3juKkd6yhSde1xYyAJaan6s8xTsgQuurGtQOU4lmxpsDl5lUIVEVLGCo0epVCiLSnZ2xRy18HBanBW83oqCzurhUA/YunVnQu5UOOL5JtvBFqtzGVxf02Mp7xRQc9KkvIvW3yqPlsq1U/urrXm37rsqnO11Zl5hmZQI8+4ACbAEFwOARAvl47Dm/lnJnc7z02MqLT0BwA+/n4v6gQN+iMBYy8q0h1pKLvxKeezY0YZwhCERO1GAlggWKfU5Pr0usX8/89BSU1n98eKLOVfR0drrGR9PMlRQQG+Yqmq5Zw9/U1BAhbddO+4nIYHXRZEfj0cX8MnLY2EWVf1u3Tpu17Qp832bN+cCXFYG/PsdD36z+jGUAcg+7EVJdhE2btSLr8p7Gz+exVg2b9bFntq35/v9+1kB86uvqDj87/+ScLpcJDP9+jH8c+9eeveUZ0jlaF11FUNYO3fmb4cPJ7lbsoTjTk3l+169eF/Om0dvmGrC63ZTVg8eTI/h1Kn0Vs2dyzDVQYN0znPnzvRuORzc5733slrp8eO6lcMrr/C38+dz/926kRwWFmris3q19gYOH65z09LTK+Z1DB1KEqZyRoYNY+7inDkVc0GUwvO3v+kcKI9HNwkHKlac7NePnr5+/U7e5pJLOO9qnu12nqfqvafG6XbzWD/8wH0MGcJxvfRSxXw+9f/VV3X4FEDSrsimNfdOnRMAeBsnYk7n3wN2By9EYmIFslpdEldd2RIKuTL1CS4XZUSjRrrYx9mhrvSbmsK6dlnTTc4MJ99veh4qD90MNkzrY/vLlU2tQ0nJz6werYr5/kwdOYamALwVxtOkCeVOx4402k2cyOqYqrrzE0/QW/bEE3z+nU6+HzECeOp5FyYN3YG3xX1IGNoVF/WKw4wZlKt9+lB/iIvjb6dMYWSCym278kqtP1jz3CrzwDmdhtydLRrs1LGIxumh3NoKqrqRCrEEKvN81CZqNyygZggWXudSANf0WGfrEatsDq2ho8HHOtfzbr3OHuicwpPRqFFVFcokKPw9KEQH6EUzGpWFh1qNESo0pFkzPw4d4rzGuHwoKCxFBArQE1uwN3EICkvC0amjH1lZRRAohRMelCAZPp8Nfj8XEJ+PHpj8fGDHDoGmyY1QWtwRKFBhqvraNW58rr3CoYmEBC6IH39MI1JWFpWpLVtI6qKjdRuODh248EpJpTs7m6SmbVuGeXboQE/aG2+QvB84oJvqDhtGq+zixfTOPfwwvYUHDzIU9IMPuK+uXbmgJiYCu3ZFQEb4cU3pDxjYtxRPHr0RS9/mwg+QvD3zDH/79tuMgnC5SPLWriV5SUkh0evUiWN59lku8GVltPLedBO9e+PHc4yTJ/O7OXO4D0UkN29mP1GA24wZQ0Lmcuk2BqrNQpcuJEBTp3J7m40KyYMP8n1kJMMhk5Pp1bzsMh5H9Y0DGAL50ksMf3/gAZJUp5N5SMOGkSjcey+J7LPP8jvV2Ds1leMJD9feOtVuQuWVAFpxUUVHnE4dZmm1ZCs89JD+7Zw5OmRSeexUsR5F2pxOHWJp9bYtXUoCq4poZGVx7C+/zEIGr79OgnbbbfS+qqIvAwbowjIq5FJV3dy9W58/QEKtQk1VmGxwn7w5cwXWXP9XjLz5EaB5Iryl4qQqnJUhuGeVtQfeqTx01a2IZ0Bs384Q8agokjtVzZao6bpcl/pNTVGZflBbY65qHqzzaYOOwnEGHVNCFy/zw44ctG7VGPsOOVFSQkdFWRnJ2sGDfP6bN3cg9sAG7ERLtMMmdLt+IOYvoNwsKaHx6t13uT6kp1M2pqZSNuzaRWPiiBHUHfr3121nli4VGD57Nly5bHGSUSqwcCHlqjW0Wj3HZ/PsmWf27FBfn7SzRmLimf/2/IYjnAnxsVrDzsY6Fnz5a+N2qGo8NTlWsDCsTeuf8rRZocI3awuVjVkJaABB7R6CUVX56egoNWdF0EVj7NBz6QQg0Kolj+/3y4BS7gf8HsTH+1By6HDgtyXo2JFz4UU4lmMQSjxhVJa3+DAen6IrNsGLcAj4cfQolaiyMhKTPXtIONgM2gZfeYEWehn550e3rufSKxy6cDrpBSsrI2l++GFed7tdtzQAdON7ZVXPyyMRVO0VhOD/99/nZ0lJVMqEIKH//HMSrKIifr57N5W3vn1JsDweEsYvviC5nDsXsNsFRM9eWPPbN/FEyufYt1+Ut0ooK+N+/vQnKvXbtpGMFhYy3DQ7W7cmyc+np3DXLh5/+nTddDwzk567DRvo+frTn6ikeDwkoB07ci6Ki0kYli0j0bPb6akbMEBXkNy2jWFBY8ZwLjt2pHetTx963Lxe3ZRcYeBAGgHnztU5birHb+FC5l3/85+0RDsc3JfLxXyRa64hcVm7luf08svcx6WXVmwGrrxSQujXAAC/H94DR/DCVIk5czRBcrtJTnNyGF46fTpzGefO1dZu1Y7B4SC5e+01TU6DkZmp+wCqQi2PPcbj2e1U8n78kQ3enU7g/nv9SO90BKtWUm4o8vrII5yX+fN1dUuA8/H3vwPvvevHwIuOICNdYsUK7dGbN0/nHAL6OgwYADw62QZHiyRMnyHKw0AzMjjOYM+H1QOnQjW93qor4VUGoyhWHy4X/xcX89nUfdzOJOLnfBqRawOV6SWVjbmmekll8xA8nxLa56KjgdgI3QbACyeOIgY5eLL5x3C6InDLLQzZfu45GtOuvpoy4cUXgX79bEi9ticWfnwUVz05ENddb0NWFo1yt93GZuRff83XV1/NisVPP81iWFOmMNTd5eL/5GSdmzl8OOAIt5X3GbN62ip7zsyzV3dosDl4n3zCG7X+w6r4A6e2jFkFQlWoacz4mXjKqvpN8PiCLV/VPVZVDLs2rX81mfeawDoHVsucOp4iZjU7XqNGVLYYlkFCGhZmR0KCKO/JR4XXjwR5GNlwAIhHp042lGzZiT1oDQeKEYNsJCMHEkBuYl8UFtvRo6sbD0+OxBP/w7DOjRslIpGPtliHX9ATTXACdzzRHN//14YjR6jQ22zMT925k2NLTqYSf+ygG7Ydq5CDxjiM9lj6/XEMvCzptOd3IeW4AAxn7NWLZFkI5jx26cJ8RdWqRTWPzc/XLQt8PlrU27UjEfL7aWGNiCBhuvpqemKOHNFVg5OTebzDh6m0X3opy52npJCAORz0yrVpw3ts0CBW6vR6eZ137aJnZ9Mmkps9e0gS+/fnb/fs4VhPnCB5W7+eVmQV/peYyLHm5rIMfmwsz/3tt1nA5K67qPg//jgJaGoqydX111O5ePJJHlcVJikp4byoAgVDhzIPbOhQVjN+5x16Rp1OkhKfj993764LrERGsiCLamjudpPMff+9bsewaJH2jKnCK7Nn8zPV6BzQBUcAfnf55brpOEACtmwZjztyuB/OscPhXbwc3rQhwI+zMGeeDT4fc+FmzOC1UQ3NSbg5xmBPmBrfiBF8rYiqKu6itvH5dM885emaM4fvBw/mfl+Y6kfqf/4H2LwR9q6dMXjBXxHXmLI2L48eYFVMxdrk3GHzwz1iDK36g8vgnTkbmQts5ecN6DEBVER372YYqNNJQvvAA5pUBHvi1PvCQu0ZnDNH5zRaPZTnUpG8kOTTsmUk6SfniVW2Lp9O3zgX6+yZRvec6nfW79R7hZrqOZVBzYMH9NLZEG73wuPzIgy5KEM07HBh0FA7li9niLoQ2tDjdAK/+pUf99+Yi0vTy7DpWBKWLBXl7VkiI3V4pDJ6zJrFqANVOddKwIK9+4aEhS4uyBw81Q+j/sOaD3Y6gVUdMl5Tj0l1cseC91+VFe901rrqHquq27K6518dy1pN5r0msB7XY3mvjmeH3V6z4wlBRU43QLcjKckBKQUGDgw0jbb74XSUoVGUDynYhr/iKQzAPBze70U2ouBCDgZgCSIRgZY4iCNoi1FjHGjVyoaVa6Pw1tssh3zrrUD79gL90hyIBHARdmMCPsX6FW6MGUMrX3IyQ+vGj/PjyGEfBgyQ5WGEuw9FwBkeiRjkAbDBHX0WrvQGjMJCetiuvprK1NixJEY9epA4DxzIUMTmzWmZtdtJpoUg0WvRgoS6Vy+SqcREhthkZ1OBDg8n6br6apK1hASSvSNHSNgKC0kmrruOBL1lS/4mOpok54orGKLz2GMMR7ziCuAPf2Aon7VUeng4v+vWjeMF6NkaP56hjq1b05O0bRuPr8JR336b59GqFYnM0KHsZ9S3L70GXi/DM6dPp3dm3jzOic9Hr9+6dVRoNm7UZAPgWN59V3uc0tNJnB55hN8PG8bf9OzJMRQW8rgPPUQCvGGD9phlZOicNaUcqfBIt5tEIzNTe9YGDCA5y8tj7tnAgdw+Lo65bD4fkPltPtyLVmK2bxgcSxcB2dnlJBIgOXzoIa5fDoduPD9tGudCVc+cNo37HDWKxGfgQB7nwQcDpGvPESxaKDFkCPfr9fJ6qDDLkSN1sRanE7j/hmyM2PI6wlGKARv/hdf+XlSeyxgXp0Mvrd7AzExg9pf5cC5bgOH+OXAsXQhHbjYuuYTjmTaN83DHHfo4Dz/M0DCV8/jII5rcqXlWcLt53RkWpgvHjB59cp6eUVBrD6pS5sl5YmcS8VPb62xl0T3VWe9PpbdU5kk71Zir75Vs25Z5Z61aCUy8RuB3v/Hi2mtt+L//Exg/wYHLY5ZhCUbh0RbfYOVqGx57jPLpvfcox9av57PUsyewcKENdz7WBO2HJGPceIE//5n7vvJKPhPJyTq3zemk7FAVKdXnCubZuTDQYC+tKqhSuzjbvLCqUF2erWK2FSrLJzvXHtlTHS94fGdqP6gqV+50+6tpvP+5sG9Y50Afn1UsKz+eqoLlcDDMTvXC8Xq1x6K0lCFZK1cyBK1RIyrX8+cDLVP8iPYdQpkvEmE4gaNoi5/RFZvQDyMvCcP8bxwYg6+wPiwDV/+uBW67PAK/+2MiYmMFGjfmApKbS2Xx6NHAsRGN5GgvhhXNgCehJTKXR+LpvzEv7O67gUinHyV//DNaFl2BwfO+h/2pp7B2rQ3PPy+wbWtPfP6+G0dW2tC+g8nBqwxOJxVbl4s5a127kpylpdHb5HDoZuXx8fTo2GwkgDNm0Btls7FIis2mQy9/+IHEZ/NmLvhZWczp83pJ9K68ku/37ydZ27SJCnRZGe+nw4dJRH75hSRj4kSSwA8/5Lh/+IG/KSnhvdi/P7f/9a/p+du3j+Pdvl2f5/jxJJBLl9IYsWED979tG/D88yw28+GHVGgcDhozUlNJFNPSSHg9HnrP1qzhnEydyv3ccQdDktLSWHimZUsS4d//ngVZVBP4vn1ZmXPoUM7d0qU8ht3OOe3cmWGuqhWBCukcPbqip87rJblUlTEVSRoyhM9imzb8/Y03ksQAJHnKwzfksjg4Bg+Ab2EYvGlDMGdDIn7+mf3/VPVIl0v3jxw+nO/vv1+Hm1rJmdfLc1f9/xw2P6b3+h+s3eRAt85+YNlfAgYlzpXyJCqvY1YW8wPnb06EvctdGLLpLSzqche69HOVVxu9+25WelXV8fr1o5f4/vsBZ1gMvC8PhHPVYhQOGI5F6xORMZz3tOpHaM3JW7aM95kiylXl26kqpmlpFXPojOfh3KNq3cm6LtdEBzrTddaqb50ujeJ06/2p9JbKvjuVZ9IGFkMpQFscQfKgLtiyhbJ10yYdXfCHP9A4t3y58q7ZwHZHysMu4PWkY8XshfjT2Di4Ggmkdqt4T6tQfYBGOoWqwiGtMM+GQYO9BWo/j662koWrQxKr2qYyARt8oufSKVuZd9B6vDNdACqDNVSiuvur23j/Xr2ANWs4B42ifDhRzJw4m43KYHw8iV5sLBXcjh1p7X/kESqrS5cyd6lzZy6y7dszN+nWW0n8+valEj9yJJXTTz5hoYh//cOLjtiOY4hFF2yFP2Mc1h76HQakhCE7W6DfqKZA5C146PJIhIcLPPt2EsLCuBDddRfHpohBbCwVs/nzBTZtGo47J6Rixd5E2OeL8v5nu3YB3VLy4fplPf6E9bhi+0/AkHswanQSVq4EYuNs+M2dUbjiVyQZBifD5eI1HDuWZMvpZC6Ey0XStGEDycrcucCdd7IaZUkJr//Ro1Qk7HbmkUVH836Ji+Pv9+5lHpcyHJw4QWW4Sxfd0mDYMHqwuncnwbvlFhKjvDwqJ198QeUkPZ25OElJ9LR9/DFzB48f5/dZWcxX++wz7r9tW90H6a23ePyuXWmRfucdnpeUJLPJySRhb7/NsS9cCPz3v5yfCRPoKXz9dT4n69dTYenVC7jvPm6flsbtFy7kPu+4gy0Vevfmfj0eztOkSSSGf/sb/w8bpnvAAbpR+F13+JHgy8bIEYn49juB++5jLsvy5Xo9sdtJxtRnivQtWkRv4Ntv83r98Y/8rdU7BQCLFgsM+WE2fF/nA9fFYXSYQHoGx6BynVTz8pHD/XAcy4Y7JhFOp4DPR0I8YABlgLU1gqpe6cjPBjZtQhcA2Cyw6L/5GDg2Hi+9xPGqYi4qF+6zzwIEVggMXPA8nCWTkdE4EY4wUU5sly/nNViwQHsaU1MBZ7gf3pGj8cLyNNzVJxxvjZ2B1DJxUlET1S7ikktI7FRBlVMVSVHVRoOLuqjfqN9b20EY1A5Ur8XKcbb6RXXX82B9qzo41Xp/KuOzDUAJWmMtjqI1eg9ujgkT+Exffz3D4VeupBH08ssBl0ugsMCBuDIfXG27YMlSgd69Kc9Uvimgn/2qejryexuGXx1/Vn3cDAyqQoO9nTZsqO091gZ5OBVJrCy/rjIiGSxg7Th3nsXgsVTmUQs+Xm0TzJrs7+w9iKqCZUyM9nyoXmOqslhpKRWd0lLmP2VkUAGOjGRIXevWNrRrZ8Mbb9AzcuwYlfZGjWiRczgYmlZSwgIHx45RuRszhopXx46sjPj737PwxTPPcN/jxnE/P/3EsLxPP+UikZcXhrafLsTsHa3w4sDP4PzqWhQWCaxYoSoM2nDPPVHIyuLiffQovS2TJ1MRttvZv+qtt0juXC7mPI0ebcObbybh7rup9Kenc9F7+GHAYY9D5ovtMXbTG3AMHoB5vyQibZBWxGbNIomtr1Z2IcQYAC+DD9A7Usq/Bn0fAeB9AH0AHANwvZRyd20d3+UiUVm5kiTr229JyCZM4LUOCyMJSkriPebz6fto1CjghhuYF7VoEb1GAwbQI+Zy0UBQUEAStG+f9gCPH898OdW4tlMnGhQiI0nA1GerV/N43bvzfj98mPdm48Ykgps3c5/ffw/84x80Qjz6qC5iIgSJ2ccfc0weD63Pq1bpIiG3386x9u1Lgmiz0WPn8zH/xOXis7FggW6K/tvfkui9+ir306UL3996K8eWnMzxfPQRSd66dTRgqEbvLhcJgddL0ql6Mw0eDLzwgh9rH3gXk/c/BAwaiF8unYlWrWxYskR76wDdIF1V1Jw2jZ6sjAwqd999R9KuzkHd/6o/ntsN/PSTDRv2xAM/8Zl2OjlHP/9M8rJgAbBurR8j/zoK7iVL8XzKy5iy+TYMGmTD2rUcr/LuqUIrv/sd5+XWWxJh79IJ9k1bgc5dkHFlHCBIZn/6SefSrVtHkvjaa7rq5rRXbejZM6m8CqjTyfw+r1eTO5XLBwDeQ9lwLF2EznBiySon7v80B87WSVV62FQbhIwMTeCsRVIUcbNW4VMFXdTn1t+kpZ26T1Z9lT1WnI0cEkI8AeB20Mp7v5Tyx9oY09kUqDs1amIkr45+FRzlU3FfKofQ6QTatRO47/cC+fuykXxxIlyNSMry8kjY4Hci2d4OhVGJiItn4ZBJkzRJU7lu+n6yAWBu+bhx+vNgg07F31SO+n6PGoQuGuytpUqM1x5qI/ywKpJ4quIp1RF05yOVsrJx1I/wuyFD6P3y+U72ICYlUXHt3JnK1cGDVKZat2YOU2Qkw9smTaKSrdrNTJxIBTsnRyvde/ZQmNtsFMqHDjGnafVqhmds305lMyODZOmnn7i4qDyW2bOpWNlsVELdboZxXHaZLqOel6fj5lVuzSuv0ELvcjH068orqaipkI0nnhBYNOR/8EbbbLja3oTZc3hdxo7VypCKyR81SjdNdjh0uJSqFKis4Q6HzulReS+qlw0XJIFLVj0PR+5kIDERaUWi3CvidPI4SpmvbxBC2AG8BuBSAPsBrBBCfCel3GTZ7HYAuVLK9kKIGwD8PwDX19YYHA5e2z/8gblR48fzOi1ezO+feEJXDASolG/eTI9UcjIJl8pry8ujZ2vjRu73L38hsdmzh4YEp5OE8IsvSPj69qV3cN06ltXu25det5QUegOvvZbkfN8+egZHjaI8zchgwROPR1fyfPRR3pc2Gz1Xzz3HvnXvvktv04QJHP+RIyRwPXvqxuqdO3N/NhvPf9YsksNRo2joePRR3kPjx5OAPPUU97l2Lb2I69frUNDJk/n7zz8n4XziCd7/KrRw2jTmCdrtJHxery6m4nQCnZLysWXfXszFUIQv8ePhf2fD0YIKnGourop9vPyy9uhNmqSLqWRkkNitXMk5dTh0fqAKp5w7l8bHu+6i9xPg55GRnL9lyzjGh36TDcfTi1DoD8O2vXYU7srGm18nlTegHjqUz/HixTyvuDjKnieeEPjbvL9iyYx8rN8bh6FFAm+8QWOCKjCjcuGUXAA45+q/emYLC3lvqvDw9HTtrfR4gFUrEzF54DDYF9uwpuXlGNU8sfx8AO1hW7hQh5wGN3/PyNDno34T7NVTBFDtKy2t4pxXRe6s+6qPOBs5JIToAuAGAF0BNAcwWwjRUUp51rFLL7xwtnuoCjUxktvAHHYBoBDAPkSgFPEoQav+w5CWJrBrF9C5kx+xIhsX9UhE4yai3NCTl0d5ptZTVoDUpKzy4yXBys+qyg0NRn29vwwMGmwVzdRUKjy1i7P1lKlKSgpVhVmikm3qGtaxqxj1Mx+X3c5FPymJlvjXX6fXKjaW+RulpSQfnTpREdy7l4qAqiiYkMBF/PHHea0/+oh9wAYMoOLaowcVnv37qVQmJ2vl7OKLSaqUsnL8OL1at96qS4OvX08lxufjOOx2Wu2dTiooAwdyzJMm8Xys1nqlCColxNq/ydp8GOB26vWPP1J5feSRij2xgitgASf3ggIq5qZY358KtZnToohjZfs+Hc5nlTohRBqAP0spRwfePwEAUsr/s2zzY2CbpUIIB4DDAJrKUwjMmlTRLCyk0j1mDL2lVV07VcxD3YvqflSVGmfO5GfjxzNc8eefeW/26UMP27RpJN3JybptQHo6PV2tW3NfmzfT0m23k2D16UNl+uWXOZ7wcD6jO3YAN9/MMMRu3fSYtm6lB+nnn/kszZzJfT/wAJUrr5chwOHh9AjNnctnafFi7kPlmanwJoeDRM7jYV+3rCwaUVJTadxQczRrlu6BN2WKbgresye3792b5zRqFLdVHiiVf2b1qt13r8Tfto6Ha/l8OAYPgGP+HECIcpKtPEmzZ/OYANeXRx+teI/Pns3j33ILc9cuvZSGGvWMzp7N67N4MYnmgw/ye9WkXPWxg5SY3XsyfD9vhqdLd4xd+Rxm/STKK2aWlPD43btrUqbGdvnl+rxUnl2fPvzMbud5u928/5TxSoVOKg+8Co1UY01P577WraPH0uvl9X3tFT8cudmYsyERIy8RFUIq1bW0EjX1X41P5ecpT5z1N5WRvQoVPB2nli+16cE7F/LpbOQQgMet21q3q+p41ZVP4pypGxLAAsv7YbDbBSIiuM63bMkQ7/btKW+SE/1w789GcrdEuKIlHLnZcDRPBISoNKzXwOBCxKlkU4MleM8/zwpw9Q+qVG4ENEGSAI4EvtsOYDAS4ouRd8KF8HCB8HBaooYOJdFRlvnmzUlEBgwgkXG7aTnOyWF4YXKyLjvevDl/e+AAlbMDBygcU1OpXG3cSGXs+HGGEhYV0TsQE0PSceedwIzpfgzrlo2I5ESsXiPQvz+PuW4d9xMdrRPpXS7dzFmRpg0b6GlQVZ5UfpbTyTAwZTGbOZOKhdpu8WJdztuqAHu9FctrK7KkiIaVJClUVi5Y/Q8mJ8EIJkLBhCYYNSFaavvgcVTnNw1hkTvPBG8igDFSyjsC728CMEBKea9lm58D2+wPvN8R2CYnaF93AbgLAFq1atVnz5491R6Hul9Pd/1Ody+q9+r+Ufe3201ipRp6ByvKwftQrxct0oq+9Xjq3rT+Vv3G+myq7yt7nipT9Cs7f0X21H6t5xW8T+szo45tfS4qO1bwc1NYyJyyed/kY/hVcXCEaS032AgSfN5WqG0Z+lV1YRDrs16VPPF6/AwjSKRSq4imImBDhlS8fyqbT+vcKVg9dFaPfbDnLfieCZZPhYUntzeorsyzeuUqk9PB816dz88lzhHBO2M5BODPAJZJKT8MfP5PADOklF8EHaPG8qk2CJ7TybWeOWv0bMfFAa6oive0gYHB2eGCJHgASd7WrbTkejxckIqL6SnKzyfBOH6c4UYXX0wCdPw4K9MVFtK7pIiCw0HCokKCUlK4IOfkkMQAeoFT5aVVc0i16OXkUNipfQLc1usF4PfDeSIbzlaJyMsX5ZZv636Vldvt1gurWmTV4qteq99V5VWpSuEJViat+6jK21NZroV138HHPh0q219DIDEGVSNUCZ4VNZFN5wtn8uxcyM9bfT732vS2V/c45xL1ea6DEaoEz4qayKdg7lVWFjrXysDgQsKpZFODfmSnTKnZ9p06nX6bvkHTqMidFdZytlao3CorNPnS8eHJp+jhZyWH1n1WZqGtuP+K3wUL66o+t+6jqmNUta9gVHeBqO7+DAzOEAcAtLS8Twl8Vtk2+wOhUbFgkYOQwpk8Oxfy81afz/1UMvdcHedcoj7P9XnC2cih6vz2jBFidn8DA4NKcD6qcxgYGBjUJ6wA0EEIcZEQIhwsVvBd0DbfAbgl8HoigLmnyr8zMDAwqCHORg59B+AGIUSEEOIiAB0AZJ2ncRsYGIQA6pzgCSHGCCF+EUJsF0I8XtfjMTAwaNiQUnoB3AvgRwCbAXwmpdwohHhaCBGo+4h/AmgihNgO4GEEihoYGBgY1AbORg5JKTcC+AzAJgAzAfy+NipoGhgYNBzUaZBENcsEGxgYGNQqpJTTAUwP+uwpy2s3gGvP97gMDAwuHJyNHJJSPgvg2XM6QAMDg5BFXXvw+gPYLqXcKaX0APgEwBV1PCYDAwMDAwMDAwMDA4OQRF0TvBYA9lne7w98VgFCiLuEECuFECuzs7PP2+AMDAwMDAwMDAwMDAxCCXVN8KoFKeVbUsq+Usq+TZs2revhGBgYGBgYGBgYGBgY1EvUNcE7p6V+DQwMDAwMDAwMDAwMLiTUNcGrTplgAwMDAwMDAwMDAwMDg2pA1HVrJyHEOAAvAbAD+FegMtSpts8GsKeau08AkHNWAzz/MGM+Pwi1MYfaeIGajbm1lDKk469rKJuA0LymCqE8diC0xx/KYwdCc/wXmnwKxWtkRSiPP5THDoT2+ENx7FXKpjoneOcSQoiVUsq+dT2OmsCM+fwg1MYcauMFQnPM5xOhPD+hPHYgtMcfymMHQn/8FwJC/RqF8vhDeexAaI8/lMdeGeo6RNPAwMDAwMDAwMDAwMCglmAInoGBgYGBgYGBgYGBQQNBQyd4b9X1AM4AZsznB6E25lAbLxCaYz6fCOX5CeWxA6E9/lAeOxD6478QEOrXKJTHH8pjB0J7/KE89pPQoHPwDAwMDAwMDAwMDAwMLiQ0dA+egYGBgYGBgYGBgYHBBQND8AwMDAwMDAwMDAwMDBoIGgTBE0KMEUL8IoTYLoR4vJLvI4QQnwa+Xy6EaFMHwwwe0+nG/LAQYpMQYr0QYo4QonVdjDNoTKccs2W7a4QQUghRp+VmqzNeIcR1gXneKIT46HyPsZLxnO6+aCWEmCeEWBO4N8bVxTgt4/mXEOKoEOLnKr4XQohpgfNZL4Tofb7HWJcIRdlkRSjKKStCTWZZEYryy4pQk2UXIkJZPhnZVHcwsilEIKUM6T+wQfoOAG0BhANYB6BL0DaTALwZeH0DgE9DYMzDAUQFXt8TCmMObNcIwAIAywD0rc/jBdABwBoA8YH3ifV9jsEk4HsCr7sA2F3HYx4GoDeAn6v4fhyAGQAEgIEAltfleOvh9axXsukMxl+v5FRNxx/Yrl7IrDOY+3olv85g/PVKll1of6Esn4xsqt9jN7Kpfvw1BA9efwDbpZQ7pZQeAJ8AuCJomysA/Dvw+gsAI4UQ4jyOMRinHbOUcp6UsjjwdhmAlPM8xmBUZ54B4BkA/w+A+3wOrhJUZ7x3AnhNSpkLAFLKo+d5jMGozpglgJjA61gAB8/j+E6ClHIBgOOn2OQKAO9LYhmAOCFEs/MzujpHKMomK0JRTlkRajLLilCUX1aEnCy7ABHK8snIprqDkU0hgoZA8FoA2Gd5vz/wWaXbSCm9APIBNDkvo6sc1RmzFbeDXpC6xGnHHAi/ayml/O/5HFgVqM4cdwTQUQixWAixTAgx5ryNrnJUZ8x/BnCjEGI/gOkA7js/Qztj1PReb0gIRdlkRSjKKStCTWZZEYryy4qGKMsaGkJZPhnZVHcwsilE4KjrARicGkKIGwH0BZBe12M5FYQQNgAvALi1jodSEzjAUIIM0Lq3QAjRTUqZV5eDOg1+BeA9KeVUIUQagA+EEKlSSn9dD8zgwkWoyCkrQlRmWRGK8ssKI8sMzjmMbKoTGNlUD9AQPHgHALS0vE8JfFbpNkIIB+hyPXZeRlc5qjNmCCEuAfAkgAlSytLzNLaqcLoxNwKQCiBTCLEbzLf6rg4Tg6szx/sBfCelLJNS7gKwFRRKdYXqjPl2AJ8BgJRyKQAngITzMrozQ7Xu9QaKUJRNVoSinLIi1GSWFaEov6xoiLKsoSGU5ZORTXUHI5tCBXWdBHi2f6ClYCeAi6ATJrsGbfN7VEwU/iwExtwLTATtUNdzXN0xB22fibotslKdOR4D4N+B1wmg275JPR/zDAC3Bl53BmPDRR3fG21QdZGV8ahYZCWrLsdaD69nvZJNZzD+eiWnajr+oO3rVGadwdzXK/l1BuOvd7LsQvoLZflkZFP9HruRTfXjr84HUEsXbBxoIdgB4MnAZ0+DVhuA7PtzANsBZAFoGwJjng3gCIC1gb/v6vuYg7atc4FUjTkWYBjEJgAbANxQ3+cYrOi0OCCU1gIYVcfj/RjAIQBloNXudgB3A7jbMsevBc5nQ13fE/XwetY72VTD8dc7OVWT8QdtW+cyq4ZzX+/kVw3HX69k2YX4F8ryycim+jt2I5vqx58InIyBgYGBgYGBgYGBgYFBiKMh5OAZGBgYGBgYGBgYGBgYwBA8AwMDAwMDAwMDAwODBgND8AwMDAwMDAwMDAwMDBoIDMEzMDAwMDAwMDAwMDBoIDAEz8DAwMDAwMDAwMDAoIHAEDwDAwMDgwsKQojCOj7+rUKIV+tyDAYGBvUPRjYZ1BYMwTOoNoQQKUKIb4UQ24QQO4QQLwshwmthv22EED/Xxhgt++wjhNgghNguhJgmhBCVbNNJCLFUCFEqhHi0No9vYGBgoCCEsNf1GAwMDAyCYWRTw4UheAbVQoAgfQXgGyllBwAdAbgAPHuW+3XUwvAqwxsA7gTQIfA3ppJtjgO4H8Dfz9EYDAwM6imEEC4hxBwhxOqAMegKy3c3CyHWCyHWCSE+CHyWJIT4OvDZOiHEoMDn3wghVgkhNgoh7rLso1AIMVUIsQ5AmhDit0KIrUKILACDz/f5GhgYhAaMbDKoDZwr5dqg4WEEALeU8l0AkFL6hBAPAdglhEgH8Fsp5UYAEEJkAngUwGYArwBIBRAG4M9Sym+FELcCuBokiHYAt6iDCCHaAPgAQHTgo3ullEuEEBkAngZwAkB7APMATJJS+oMHKoRoBiBGSrks8P59AFcCmGHdTkp5FMBRIcT4s5gXAwOD0IQbwFVSygIhRAKAZUKI7wB0AfAHAIOklDlCiMaB7acBmC+lvCpg9XYFPr9NSnlcCBEJYIUQ4ksp5TFQhi2XUj4SkEkfAegDIB+UX2vO25kaGBiEEoxsMjhrGIJnUF10BbDK+kFA+OwF8F8A1wH4U0BYNJNSrhRCPAdgrpTyNiFEHIAsIcTswM97A+geED5tLLs9CuBSKaVbCNEBwMcA+ga+6w8KuD0AZoIk8YtKxtoCwH7L+/2BzwwMDAwUBIDnhBDDAPhBGZEEGrM+l1LmAICU8nhg+xEAbg585gOVIQC4XwhxVeB1SzBi4BgAH4AvA58PAJAppcwGACHEp2AUhIGBgUEwjGwyOGuYEE2D2kAmgImB19dBk65RAB4XQqwNbOME0Crw3U8W4WRFGIC3hRAbAHwOEjqFLCnlzoAA+xjAkFo8BwMDgwsLvwHQFEAfKWVPAEdAGVVtBCILLgGQJqXsAVq+1T7cAVllYGBgUBMY2WRw1jAEz6C62AS68MshhIgBCdsKAMeEEN0BXA/gU7UJgGuklD0Df62klJsD3xVVcZyHQGHWA/TcWYu4yKBtg98rHACQYnmfEvjMwMDAQCEWwFEpZZkQYjiA1oHP5wK4VgjRBAAsYVBzANwT+MwuhIgN7CNXSlkshOgEYGAVx1oOIF0I0UQIEQbg2nNzSgYGBg0ARjYZnDUMwTOoLuYAiBJC3AyUV16aCuA9KWUxSOqmAIiVUq4P/OZHAPcFCrRACNGrGseJBXAokFt3E5ijp9BfCHGREMIGEslFle1ASnkIQIEQYmDg2DcD+LZmp2tgYNAQIVjYqRTAfwD0DUQL3AxgCwAEcomfBTA/UITghcBPHwAwPLD9KjC6YCYAhxBiM4C/AlhW2TEDMunPAJYCWAzmJxsYGBiUw8gmg9qEkLIqJ4iBQUUIIVoCeB1AJ9A4MB3Ao1LKUiFEEugle0ZK+b+B7SMBvARgUGD7XVLKywJFVvpKKe8NbNcGwA9SytRA3t2XoHduJoDfSyldNSmyEthnXwDvAYgEi6vcJ6WUQoi7AUBK+aYQIhnASgAxYJx7IYAuUsqCWpkwAwODegchRA8Ab0sp+9f1WAwMDAwUjGwyqE0YgmcQEggQvEellJfV8VAMDAxCFAEDz/0AHpRSzqrr8RgYGBgARjYZ1D4MwTMICRiCZ2BgYGBgYGBgYHB6GIJnENIQQiwHEBH08U1Syg11MR4DAwMDAwMDAwODuoQheAYGBgYGBgYGBgYGBg0EpoqmgYGBgYGBgYGBgYFBA4EheAYGBgYGBgYGBgYGBg0EhuAZGBgYGBgYGBgYGBg0EBiCZ2BgYGBgYGBgYGBg0EDw/wGzwSLJ+tCSzgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot all points\n",
    "fig, axs = plt.subplots(2,3,figsize=(15,10))\n",
    "axs = axs.ravel()\n",
    "col_to_plot = ['Delta','RAW','Jaccard','PC1/PC2']\n",
    "df_to_plot = soma_df[(abs(soma_df.LOGIT) < 10)&(soma_df['PC1/PC2']<30)]\n",
    "#df_to_plot=soma_df.copy()\n",
    "print(f\"Removing {len(soma_df) - len(df_to_plot)} Cells\")\n",
    "\n",
    "#df_to_plot = df_to_plot[(df_to_plot.ImageNumber==13)|(df_to_plot.ImageNumber==213)]\n",
    "\n",
    "col_to_plot2 = ['ENTROPY','RAW']\n",
    "for i in range(len(axs)):\n",
    "    if i < 4:\n",
    "        col = col_to_plot[i]\n",
    "        df1 = df_to_plot[df_to_plot['True_Label'] == 1]\n",
    "        axs[i].scatter(df1['Overlap_0.1'], df1[col], s=5, color='r')\n",
    "        df2 = df_to_plot[df_to_plot['True_Label'] == 0]\n",
    "        axs[i].scatter(df2['Overlap_0.1'], df2[col], s=0.01, color='b')\n",
    "        axs[i].set_xlabel('Overlap_0.1')\n",
    "        axs[i].set_ylabel(col)\n",
    "        axs[i].set_title(f'Overlap_0.1 vs {col}')\n",
    "    else:\n",
    "        \n",
    "        df1 = df_to_plot[df_to_plot['True_Label'] == 1]\n",
    "        axs[i].scatter(df1['Jaccard'], df1[col_to_plot2[i-4]], s=5, color='r')\n",
    "        df2 = df_to_plot[df_to_plot['True_Label'] == 0]\n",
    "        axs[i].scatter(df2['Jaccard'], df2[col_to_plot2[i-4]], s=0.01, color='b')\n",
    "        \n",
    "        #axs[i].scatter(df_to_plot['LOGIT'], df_to_plot[col_to_plot2[i-4]], s=1)\n",
    "        axs[i].set_xlabel('Jaccard')\n",
    "        axs[i].set_ylabel(col_to_plot2[i-4])\n",
    "        axs[i].set_title(f'Jaccard vs {col_to_plot2[i-4]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d924172d",
   "metadata": {},
   "source": [
    "## Classify Procode+ cells with Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "38259729",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removing 83 Cells\n",
      "635 635\n",
      "Best: 0.887411 using {'C': 2.782559402207126, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.729724 (0.001226) with: {'C': 0.0001, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.792799 (0.018166) with: {'C': 0.0001, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "nan (nan) with: {'C': 0.0001, 'max_iter': 5000, 'penalty': 'elasticnet', 'solver': 'liblinear'}\n",
      "0.729724 (0.001226) with: {'C': 0.000774263682681127, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.792799 (0.018166) with: {'C': 0.000774263682681127, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "nan (nan) with: {'C': 0.000774263682681127, 'max_iter': 5000, 'penalty': 'elasticnet', 'solver': 'liblinear'}\n",
      "0.729724 (0.001226) with: {'C': 0.005994842503189409, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.810802 (0.014997) with: {'C': 0.005994842503189409, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "nan (nan) with: {'C': 0.005994842503189409, 'max_iter': 5000, 'penalty': 'elasticnet', 'solver': 'liblinear'}\n",
      "0.826583 (0.011333) with: {'C': 0.046415888336127774, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.858095 (0.020922) with: {'C': 0.046415888336127774, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "nan (nan) with: {'C': 0.046415888336127774, 'max_iter': 5000, 'penalty': 'elasticnet', 'solver': 'liblinear'}\n",
      "0.882916 (0.027023) with: {'C': 0.3593813663804626, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.885163 (0.029541) with: {'C': 0.3593813663804626, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "nan (nan) with: {'C': 0.3593813663804626, 'max_iter': 5000, 'penalty': 'elasticnet', 'solver': 'liblinear'}\n",
      "0.885163 (0.023868) with: {'C': 2.782559402207126, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.887411 (0.030067) with: {'C': 2.782559402207126, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "nan (nan) with: {'C': 2.782559402207126, 'max_iter': 5000, 'penalty': 'elasticnet', 'solver': 'liblinear'}\n",
      "0.880644 (0.021946) with: {'C': 21.54434690031882, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.882916 (0.022984) with: {'C': 21.54434690031882, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "nan (nan) with: {'C': 21.54434690031882, 'max_iter': 5000, 'penalty': 'elasticnet', 'solver': 'liblinear'}\n",
      "0.880644 (0.021946) with: {'C': 166.81005372000558, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.880644 (0.021946) with: {'C': 166.81005372000558, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "nan (nan) with: {'C': 166.81005372000558, 'max_iter': 5000, 'penalty': 'elasticnet', 'solver': 'liblinear'}\n",
      "0.880644 (0.021946) with: {'C': 1291.5496650148827, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.880644 (0.021946) with: {'C': 1291.5496650148827, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "nan (nan) with: {'C': 1291.5496650148827, 'max_iter': 5000, 'penalty': 'elasticnet', 'solver': 'liblinear'}\n",
      "0.880644 (0.021946) with: {'C': 10000.0, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.880644 (0.021946) with: {'C': 10000.0, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "nan (nan) with: {'C': 10000.0, 'max_iter': 5000, 'penalty': 'elasticnet', 'solver': 'liblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:378: FitFailedWarning: \n",
      "50 fits failed out of a total of 150.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "50 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1091, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 71, in _check_solver\n",
      "    raise ValueError(\n",
      "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/model_selection/_search.py:953: UserWarning: One or more of the test scores are non-finite: [0.72972421 0.79279877        nan 0.72972421 0.79279877        nan\n",
      " 0.72972421 0.81080184        nan 0.82658325 0.85809499        nan\n",
      " 0.88291624 0.88516343        nan 0.88516343 0.88741062        nan\n",
      " 0.88064351 0.88291624        nan 0.88064351 0.88064351        nan\n",
      " 0.88064351 0.88064351        nan 0.88064351 0.88064351        nan]\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "# Define inputs\n",
    "soma_df_train = soma_df[(soma_df.ImageNumber == 13) | (soma_df.ImageNumber == 213)]\n",
    "\n",
    "# remove outliers\n",
    "df_to_plot = soma_df_train[(abs(soma_df_train.LOGIT) < 10)]\n",
    "df_to_plot = df_to_plot[df_to_plot['PC1/PC2'] < 30]\n",
    "print(f\"Removing {len(soma_df_train) - len(df_to_plot)} Cells\")\n",
    "\n",
    "feature_cols = [\"X_NORM\",\"RAW\",\"Overlap_0.1\",\"Overlap_0.2\",\"Overlap_0.3\",\n",
    "                \"Delta\",\"Jaccard\",\"PC1/PC2\"]\n",
    "X=df_to_plot[feature_cols]\n",
    "y = df_to_plot[\"True_Label\"]\n",
    "print(len(X), len(y))\n",
    "\n",
    "# scale and train/test split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "df_to_plot[feature_cols] = sc.fit_transform(X)\n",
    "X=df_to_plot[feature_cols]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y , test_size=0.3, random_state=2)\n",
    "\n",
    "## define models and params\n",
    "logreg = LogisticRegression(class_weight='balanced')\n",
    "penalty = ['l1', 'l2','elasticnet']\n",
    "c_values = np.logspace(-4, 4, 10)\n",
    "solver = ['liblinear']\n",
    "max_iter = [5000]\n",
    "\n",
    "# define grid search # use 5-fold cross-validation\n",
    "grid = dict(penalty=penalty, C=c_values, solver=solver, max_iter = max_iter)\n",
    "cv = RepeatedStratifiedKFold(n_splits = 5)\n",
    "clf = GridSearchCV(logreg, param_grid = grid, n_jobs = -1, verbose=False, scoring='accuracy')\n",
    "best_clf = clf.fit(X_train, y_train)\n",
    "\n",
    "# summarize results\n",
    "print(\"Best: %f using %s\" % (best_clf.best_score_, best_clf.best_params_))\n",
    "means = best_clf.cv_results_['mean_test_score']\n",
    "stds = best_clf.cv_results_['std_test_score']\n",
    "params = best_clf.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "0f12a341",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9057591623036649"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print param weights?\n",
    "params = best_clf.best_params_\n",
    "# build another classifier instance with selected model params\n",
    "# define models and params\n",
    "best_logreg = LogisticRegression(penalty = params['penalty'], C=params['C'], max_iter = params['max_iter'],\n",
    "                                 solver = params['solver'], class_weight='balanced')\n",
    "best_logreg.fit(X_train, y_train)\n",
    "best_logreg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "69e12434",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "logit_roc_auc = roc_auc_score(y_test, best_logreg.predict(X_test))\n",
    "fpr, tpr, thresholds = roc_curve(y_test, best_logreg.predict_proba(X_test)[:,1])\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('Log_ROC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "279099f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_NORM [-1.40793148]\n",
      "RAW [0.1184341]\n",
      "Overlap_0.1 [2.1975943]\n",
      "Overlap_0.2 [-1.32992319]\n",
      "Overlap_0.3 [0.36292001]\n",
      "Delta [0.93505178]\n",
      "Jaccard [1.3167649]\n",
      "PC1/PC2 [0.47719979]\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(best_logreg.feature_names_in_)):\n",
    "    feature = best_logreg.feature_names_in_[i]\n",
    "    coef = best_logreg.coef_[:,i]\n",
    "    print(feature, coef)\n",
    "               "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "367bd8ee",
   "metadata": {},
   "source": [
    "## remove outliers before prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c109a6dc",
   "metadata": {},
   "source": [
    "determine LOGIT cutoffs from histogram! --> abs(LOGIT) < 10 and PC1/PC2 < 30\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5f2d495d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>label</th>\n",
       "      <th>Barcode_Idx</th>\n",
       "      <th>SUM</th>\n",
       "      <th>Barcode</th>\n",
       "      <th>RAW</th>\n",
       "      <th>P</th>\n",
       "      <th>LOGIT</th>\n",
       "      <th>ENTROPY</th>\n",
       "      <th>X_NORM</th>\n",
       "      <th>Gene</th>\n",
       "      <th>...</th>\n",
       "      <th>intensity_mean-0</th>\n",
       "      <th>intensity_mean-1</th>\n",
       "      <th>intensity_mean-2</th>\n",
       "      <th>intensity_mean-3</th>\n",
       "      <th>intensity_mean-4</th>\n",
       "      <th>intensity_mean-5</th>\n",
       "      <th>intensity_mean-6</th>\n",
       "      <th>Delta</th>\n",
       "      <th>embedding1</th>\n",
       "      <th>embedding2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.057395</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.038489</td>\n",
       "      <td>0.670602</td>\n",
       "      <td>0.710910</td>\n",
       "      <td>0.864662</td>\n",
       "      <td>0.035280</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015248</td>\n",
       "      <td>0.025134</td>\n",
       "      <td>0.012385</td>\n",
       "      <td>0.000820</td>\n",
       "      <td>0.005333</td>\n",
       "      <td>0.000442</td>\n",
       "      <td>0.002411</td>\n",
       "      <td>0.007052</td>\n",
       "      <td>8.929145</td>\n",
       "      <td>8.983325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.067428</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.054958</td>\n",
       "      <td>0.815054</td>\n",
       "      <td>1.483194</td>\n",
       "      <td>0.793739</td>\n",
       "      <td>0.043299</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015638</td>\n",
       "      <td>0.025196</td>\n",
       "      <td>0.024860</td>\n",
       "      <td>0.002435</td>\n",
       "      <td>0.000203</td>\n",
       "      <td>0.006315</td>\n",
       "      <td>0.000928</td>\n",
       "      <td>0.009323</td>\n",
       "      <td>11.182019</td>\n",
       "      <td>8.415074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>intensity_mean-8</td>\n",
       "      <td>0.035084</td>\n",
       "      <td>D3</td>\n",
       "      <td>0.024482</td>\n",
       "      <td>0.697820</td>\n",
       "      <td>0.836939</td>\n",
       "      <td>1.319092</td>\n",
       "      <td>0.026690</td>\n",
       "      <td>PPP2R2B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000581</td>\n",
       "      <td>0.002030</td>\n",
       "      <td>0.011947</td>\n",
       "      <td>0.011055</td>\n",
       "      <td>0.001660</td>\n",
       "      <td>0.000317</td>\n",
       "      <td>0.014562</td>\n",
       "      <td>0.009024</td>\n",
       "      <td>11.592212</td>\n",
       "      <td>10.634103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>intensity_mean-3</td>\n",
       "      <td>0.261197</td>\n",
       "      <td>C12</td>\n",
       "      <td>0.179114</td>\n",
       "      <td>0.685744</td>\n",
       "      <td>0.780298</td>\n",
       "      <td>1.402972</td>\n",
       "      <td>0.108538</td>\n",
       "      <td>HSF1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013229</td>\n",
       "      <td>0.013549</td>\n",
       "      <td>0.009692</td>\n",
       "      <td>0.039075</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.062781</td>\n",
       "      <td>0.061092</td>\n",
       "      <td>0.025526</td>\n",
       "      <td>11.081718</td>\n",
       "      <td>3.503708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.272067</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.267507</td>\n",
       "      <td>0.983239</td>\n",
       "      <td>4.071811</td>\n",
       "      <td>0.117349</td>\n",
       "      <td>0.142667</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.074397</td>\n",
       "      <td>0.080426</td>\n",
       "      <td>0.088755</td>\n",
       "      <td>0.000502</td>\n",
       "      <td>0.002689</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.001225</td>\n",
       "      <td>0.071709</td>\n",
       "      <td>10.925993</td>\n",
       "      <td>1.490282</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>133695</th>\n",
       "      <td>258</td>\n",
       "      <td>intensity_mean-13</td>\n",
       "      <td>0.022828</td>\n",
       "      <td>E4</td>\n",
       "      <td>0.019829</td>\n",
       "      <td>0.868626</td>\n",
       "      <td>1.888866</td>\n",
       "      <td>0.667654</td>\n",
       "      <td>0.014764</td>\n",
       "      <td>HRAS</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.000898</td>\n",
       "      <td>0.008159</td>\n",
       "      <td>0.009501</td>\n",
       "      <td>0.003234</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000670</td>\n",
       "      <td>0.002336</td>\n",
       "      <td>10.888363</td>\n",
       "      <td>12.755734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133696</th>\n",
       "      <td>259</td>\n",
       "      <td>intensity_mean-13</td>\n",
       "      <td>0.326837</td>\n",
       "      <td>E4</td>\n",
       "      <td>0.114697</td>\n",
       "      <td>0.350931</td>\n",
       "      <td>-0.614949</td>\n",
       "      <td>1.860739</td>\n",
       "      <td>0.119025</td>\n",
       "      <td>HRAS</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>0.004210</td>\n",
       "      <td>0.066644</td>\n",
       "      <td>0.071050</td>\n",
       "      <td>0.032583</td>\n",
       "      <td>0.009312</td>\n",
       "      <td>0.031562</td>\n",
       "      <td>0.001021</td>\n",
       "      <td>12.442838</td>\n",
       "      <td>2.811264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133697</th>\n",
       "      <td>260</td>\n",
       "      <td>intensity_mean-9</td>\n",
       "      <td>0.188926</td>\n",
       "      <td>D4</td>\n",
       "      <td>0.161259</td>\n",
       "      <td>0.853556</td>\n",
       "      <td>1.762765</td>\n",
       "      <td>0.616539</td>\n",
       "      <td>0.088308</td>\n",
       "      <td>FAN1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031186</td>\n",
       "      <td>0.000526</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>0.048998</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.003363</td>\n",
       "      <td>0.064340</td>\n",
       "      <td>0.027823</td>\n",
       "      <td>10.910836</td>\n",
       "      <td>4.071362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133698</th>\n",
       "      <td>261</td>\n",
       "      <td>intensity_mean-19</td>\n",
       "      <td>0.003397</td>\n",
       "      <td>G8</td>\n",
       "      <td>0.002275</td>\n",
       "      <td>0.669709</td>\n",
       "      <td>0.706868</td>\n",
       "      <td>1.034844</td>\n",
       "      <td>0.021284</td>\n",
       "      <td>RAPGEF2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>0.007132</td>\n",
       "      <td>0.012290</td>\n",
       "      <td>0.000637</td>\n",
       "      <td>0.005305</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>0.010320</td>\n",
       "      <td>0.001827</td>\n",
       "      <td>10.747713</td>\n",
       "      <td>11.903031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133699</th>\n",
       "      <td>262</td>\n",
       "      <td>intensity_mean-15</td>\n",
       "      <td>1.048392</td>\n",
       "      <td>E8</td>\n",
       "      <td>0.684923</td>\n",
       "      <td>0.653308</td>\n",
       "      <td>0.633613</td>\n",
       "      <td>1.344290</td>\n",
       "      <td>0.397249</td>\n",
       "      <td>WASL</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>0.157899</td>\n",
       "      <td>0.021261</td>\n",
       "      <td>0.326146</td>\n",
       "      <td>0.141081</td>\n",
       "      <td>0.012467</td>\n",
       "      <td>0.004988</td>\n",
       "      <td>0.119821</td>\n",
       "      <td>10.127234</td>\n",
       "      <td>-5.068211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133700 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        label        Barcode_Idx       SUM Barcode       RAW         P  \\\n",
       "0           1   intensity_mean-0  0.057395      A2  0.038489  0.670602   \n",
       "1           2   intensity_mean-0  0.067428      A2  0.054958  0.815054   \n",
       "2           3   intensity_mean-8  0.035084      D3  0.024482  0.697820   \n",
       "3           4   intensity_mean-3  0.261197     C12  0.179114  0.685744   \n",
       "4           5   intensity_mean-0  0.272067      A2  0.267507  0.983239   \n",
       "...       ...                ...       ...     ...       ...       ...   \n",
       "133695    258  intensity_mean-13  0.022828      E4  0.019829  0.868626   \n",
       "133696    259  intensity_mean-13  0.326837      E4  0.114697  0.350931   \n",
       "133697    260   intensity_mean-9  0.188926      D4  0.161259  0.853556   \n",
       "133698    261  intensity_mean-19  0.003397      G8  0.002275  0.669709   \n",
       "133699    262  intensity_mean-15  1.048392      E8  0.684923  0.653308   \n",
       "\n",
       "           LOGIT   ENTROPY    X_NORM     Gene  ...  intensity_mean-0  \\\n",
       "0       0.710910  0.864662  0.035280   PGGT1B  ...          0.015248   \n",
       "1       1.483194  0.793739  0.043299   PGGT1B  ...          0.015638   \n",
       "2       0.836939  1.319092  0.026690  PPP2R2B  ...          0.000581   \n",
       "3       0.780298  1.402972  0.108538     HSF1  ...          0.013229   \n",
       "4       4.071811  0.117349  0.142667   PGGT1B  ...          0.074397   \n",
       "...          ...       ...       ...      ...  ...               ...   \n",
       "133695  1.888866  0.667654  0.014764     HRAS  ...          0.000058   \n",
       "133696 -0.614949  1.860739  0.119025     HRAS  ...          0.000329   \n",
       "133697  1.762765  0.616539  0.088308     FAN1  ...          0.031186   \n",
       "133698  0.706868  1.034844  0.021284  RAPGEF2  ...          0.000314   \n",
       "133699  0.633613  1.344290  0.397249     WASL  ...          0.000239   \n",
       "\n",
       "        intensity_mean-1  intensity_mean-2  intensity_mean-3  \\\n",
       "0               0.025134          0.012385          0.000820   \n",
       "1               0.025196          0.024860          0.002435   \n",
       "2               0.002030          0.011947          0.011055   \n",
       "3               0.013549          0.009692          0.039075   \n",
       "4               0.080426          0.088755          0.000502   \n",
       "...                  ...               ...               ...   \n",
       "133695          0.000898          0.008159          0.009501   \n",
       "133696          0.004210          0.066644          0.071050   \n",
       "133697          0.000526          0.000544          0.048998   \n",
       "133698          0.007132          0.012290          0.000637   \n",
       "133699          0.157899          0.021261          0.326146   \n",
       "\n",
       "        intensity_mean-4  intensity_mean-5  intensity_mean-6     Delta  \\\n",
       "0               0.005333          0.000442          0.002411  0.007052   \n",
       "1               0.000203          0.006315          0.000928  0.009323   \n",
       "2               0.001660          0.000317          0.014562  0.009024   \n",
       "3               0.004378          0.062781          0.061092  0.025526   \n",
       "4               0.002689          0.000070          0.001225  0.071709   \n",
       "...                  ...               ...               ...       ...   \n",
       "133695          0.003234          0.000016          0.000670  0.002336   \n",
       "133696          0.032583          0.009312          0.031562  0.001021   \n",
       "133697          0.000056          0.003363          0.064340  0.027823   \n",
       "133698          0.005305          0.000244          0.010320  0.001827   \n",
       "133699          0.141081          0.012467          0.004988  0.119821   \n",
       "\n",
       "        embedding1  embedding2  \n",
       "0         8.929145    8.983325  \n",
       "1        11.182019    8.415074  \n",
       "2        11.592212   10.634103  \n",
       "3        11.081718    3.503708  \n",
       "4        10.925993    1.490282  \n",
       "...            ...         ...  \n",
       "133695   10.888363   12.755734  \n",
       "133696   12.442838    2.811264  \n",
       "133697   10.910836    4.071362  \n",
       "133698   10.747713   11.903031  \n",
       "133699   10.127234   -5.068211  \n",
       "\n",
       "[133700 rows x 29 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load soma_df\n",
    "soma_df = pd.read_csv('Coverslip1_soma_df.csv',sep=',',index_col=0)\n",
    "soma_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d49c28ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5469\n",
      "4884\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'feature_cols' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [6]\u001b[0m, in \u001b[0;36m<cell line: 9>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m soma_final \u001b[38;5;241m=\u001b[39m soma_final[(\u001b[38;5;28mabs\u001b[39m(soma_final\u001b[38;5;241m.\u001b[39mLOGIT)\u001b[38;5;241m<\u001b[39m\u001b[38;5;241m10\u001b[39m) \u001b[38;5;241m&\u001b[39m (soma_final[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPC1/PC2\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m30\u001b[39m)]\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28mprint\u001b[39m(N\u001b[38;5;241m-\u001b[39m\u001b[38;5;28mlen\u001b[39m(soma_final))\n\u001b[0;32m----> 9\u001b[0m X \u001b[38;5;241m=\u001b[39m soma_final[\u001b[43mfeature_cols\u001b[49m]\n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m# Scale and predict infected cells \u001b[39;00m\n\u001b[1;32m     11\u001b[0m sc \u001b[38;5;241m=\u001b[39m StandardScaler()\n",
      "\u001b[0;31mNameError\u001b[0m: name 'feature_cols' is not defined"
     ]
    }
   ],
   "source": [
    "# load model saved under coverslip1\n",
    "filename = '../101222_D10_Coverslip1_Processed/best_logreg.sav'\n",
    "best_logreg = pickle.load(open(filename, 'rb'))\n",
    "\n",
    "# filter out outliers\n",
    "soma_final = soma_df.copy()\n",
    "soma_final=soma_final[~soma_final.isna().any(axis=1)]\n",
    "print(len(soma_df)-len(soma_final))\n",
    "N=len(soma_final)\n",
    "soma_final = soma_final[(abs(soma_final.LOGIT)<10) & (soma_final['PC1/PC2'] < 30)]\n",
    "print(N-len(soma_final))\n",
    "\n",
    "feature_cols = [\"X_NORM\",\"RAW\",\"Overlap_0.1\",\"Overlap_0.2\",\"Overlap_0.3\",\n",
    "                \"Delta\",\"Jaccard\",\"PC1/PC2\"]\n",
    "X = soma_final[feature_cols]\n",
    "# Scale and predict infected cells \n",
    "sc = StandardScaler()\n",
    "X = sc.fit_transform(X)\n",
    "y_pred = best_logreg.predict(X)\n",
    "# append the result to InfectedCells\n",
    "soma_final[\"InfectedCells\"] = y_pred\n",
    "soma_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "b08a3918",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6755"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "133700-126945"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "98737681",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removing 43 Cells\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot predicted QC-passing cells\n",
    "# plot partially labelled \n",
    "# scatter plot, same as above, except outlier 94.\n",
    "# P vs X_NORM, RAW vs X_NORM, LOGIT vs X_NORM, ENTROPY vs X_NORM\n",
    "fig, axs = plt.subplots(2,3,figsize=(15,10))\n",
    "axs = axs.ravel()\n",
    "col_to_plot = ['Delta','RAW','Jaccard','PC1/PC2']\n",
    "# cutting off outliers\n",
    "df_to_plot = soma_final[abs(soma_final.LOGIT) < 10]\n",
    "df_to_plot = soma_final[soma_final['PC1/PC2']<30]\n",
    "print(f\"Removing {len(soma_final) - len(df_to_plot)} Cells\")\n",
    "\n",
    "col_to_plot2 = ['ENTROPY','RAW']\n",
    "for i in range(len(axs)):\n",
    "    if i < 4:\n",
    "        col = col_to_plot[i]\n",
    "        df1 = df_to_plot[df_to_plot['InfectedCells'] == 1]\n",
    "        axs[i].scatter(df1['Overlap_0.1'], df1[col], s=0.1, color='r')\n",
    "        df2 = df_to_plot[df_to_plot['InfectedCells'] == 0]\n",
    "        axs[i].scatter(df2['Overlap_0.1'], df2[col], s=0.1, color='b')\n",
    "        axs[i].set_xlabel('Overlap_0.1')\n",
    "        axs[i].set_ylabel(col)\n",
    "        axs[i].set_title(f'Overlap_0.1 vs {col}')\n",
    "    else:\n",
    "        \n",
    "        df1 = df_to_plot[df_to_plot['InfectedCells'] == 1]\n",
    "        axs[i].scatter(df1['Jaccard'], df1[col_to_plot2[i-4]], s=0.1, color='r')\n",
    "        df2 = df_to_plot[df_to_plot['InfectedCells'] == 0]\n",
    "        axs[i].scatter(df2['Jaccard'], df2[col_to_plot2[i-4]], s=0.1, color='b')\n",
    "        \n",
    "        #axs[i].scatter(df_to_plot['LOGIT'], df_to_plot[col_to_plot2[i-4]], s=1)\n",
    "        axs[i].set_xlabel('Jaccard')\n",
    "        axs[i].set_ylabel(col_to_plot2[i-4])\n",
    "        axs[i].set_title(f'Jaccard vs {col_to_plot2[i-4]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "92c7d2ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='embedding1', ylabel='embedding2'>"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot UMAP of 5 images - embedding based on epitope signals\n",
    "fig, ax = plt.subplots(1,3,figsize=(15,5))\n",
    "sns.scatterplot(data = soma_final, x='embedding1', y='embedding2', hue = 'InfectedCells', ax=ax[0], s=1)\n",
    "#ax[0].legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0)\n",
    "\n",
    "sns.scatterplot(data = soma_final[soma_final.InfectedCells==1]\n",
    "                , x='embedding1', y='embedding2', hue = 'InfectedCells', ax=ax[1], s=1)\n",
    "\n",
    "sns.scatterplot(data = soma_final[soma_final.InfectedCells==0]\n",
    "                , x='embedding1', y='embedding2', hue = 'InfectedCells', ax=ax[2], s=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "f734d54f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.31812989877506004\n"
     ]
    }
   ],
   "source": [
    "print(sum(soma_final.InfectedCells)/len(soma_final))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "3c1cc9d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40385\n"
     ]
    }
   ],
   "source": [
    "print(sum(soma_final.InfectedCells))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "5be8a2d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MINK1',\n",
       " 'HRAS',\n",
       " 'NCKAP1',\n",
       " 'HTT',\n",
       " 'non-target',\n",
       " 'ARPC3',\n",
       " 'SNX8',\n",
       " 'IER5',\n",
       " 'HSF1',\n",
       " 'PPP2R2B',\n",
       " 'MLH1',\n",
       " 'GAS7',\n",
       " 'DGKE',\n",
       " 'MSH3',\n",
       " 'FAN1',\n",
       " 'MAP4K4',\n",
       " 'WASL',\n",
       " 'RAPGEF2',\n",
       " 'PGGT1B',\n",
       " 'NCK1',\n",
       " 'SLC25A11',\n",
       " 'PPP2R1A']"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(set(soma_final.Gene.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "f3627c9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['non-target',\n",
       " 'MINK1',\n",
       " 'HRAS',\n",
       " 'NCKAP1',\n",
       " 'HTT',\n",
       " 'ARPC3',\n",
       " 'SNX8',\n",
       " 'IER5',\n",
       " 'HSF1',\n",
       " 'PPP2R2B',\n",
       " 'MLH1',\n",
       " 'GAS7',\n",
       " 'DGKE',\n",
       " 'MSH3',\n",
       " 'FAN1',\n",
       " 'MAP4K4',\n",
       " 'WASL',\n",
       " 'RAPGEF2',\n",
       " 'PGGT1B',\n",
       " 'NCK1',\n",
       " 'SLC25A11',\n",
       " 'PPP2R1A']"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genes = list(set(df.Gene.values))\n",
    "genes.pop(genes.index('non-target'))\n",
    "genes = ['non-target'] + genes\n",
    "genes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "478f120c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40385\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_list = list(set(soma_final.Gene.values))\n",
    "#fig, axs = plt.subplots(1,1, figsize = (20,10))\n",
    "df =soma_final[soma_final.InfectedCells == 1].copy()\n",
    "print(len(df))\n",
    "\n",
    "genes = list(set(df.Gene.values))\n",
    "genes.pop(genes.index('non-target'))\n",
    "genes = ['non-target'] + genes\n",
    "\n",
    "fig, axs = plt.subplots(1,1,figsize=(20,10))\n",
    "for i in range(len(genes)):\n",
    "    x_pos = np.arange(len(genes))\n",
    "    feature_name = \"Number of Cells\"\n",
    "    means = []\n",
    "    stds = []\n",
    "    for gene in genes:\n",
    "        mean_val = len(df[df.Gene == gene])\n",
    "        #std_val = np.std(df[df.Gene == gene][feature_name].values)\n",
    "        means.append(mean_val)\n",
    "        #stds.append(std_val)\n",
    "    axs.bar(x_pos, means, align='center', alpha=0.5, ecolor='black', capsize=2)\n",
    "    axs.set_ylabel(feature_name)\n",
    "    axs.set_xticks(x_pos)\n",
    "    axs.set_xticklabels(genes)\n",
    "    axs.set_title(feature_name)\n",
    "    axs.yaxis.grid(True)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "bad9104c",
   "metadata": {},
   "outputs": [],
   "source": [
    "soma_final.to_csv('Coverslip1_soma_final.csv',sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "10c6b06a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pickle model weights\n",
    "import pickle\n",
    "fname = 'best_logreg.sav'\n",
    "pickle.dump(best_logreg, open(fname, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "f982f137",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.40793148  0.1184341   2.1975943  -1.32992319  0.36292001  0.93505178\n",
      "   1.3167649   0.47719979]]\n"
     ]
    }
   ],
   "source": [
    "print(best_logreg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "9431c92f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.40793148  0.1184341   2.1975943  -1.32992319  0.36292001  0.93505178\n",
      "   1.3167649   0.47719979]]\n"
     ]
    }
   ],
   "source": [
    "loaded_model = pickle.load(open(fname, 'rb'))\n",
    "print(loaded_model.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9421affe",
   "metadata": {},
   "source": [
    "## Establish Baseline of Area shape feature distributions?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3677426d",
   "metadata": {},
   "outputs": [],
   "source": [
    "soma_df = pd.read_csv('Coverslip1_soma_final.csv',sep=',', index_col = 0)\n",
    "mpcp_df = pd.read_csv(f'mp_cp_output/MPFeat_SYTO_.csv', sep=',')\n",
    "seg_df = pd.read_csv('seg/SegFeatCyto_.csv', sep=',')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "16a22f8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ImageNumber', 'ObjectNumber', 'FileName_Cyto', 'FileName_Nuclei',\n",
       "       'FileName_Soma', 'PathName_Cyto', 'PathName_Nuclei', 'PathName_Soma',\n",
       "       'AreaShape_Area', 'AreaShape_BoundingBoxArea',\n",
       "       'AreaShape_BoundingBoxMaximum_X', 'AreaShape_BoundingBoxMaximum_Y',\n",
       "       'AreaShape_BoundingBoxMinimum_X', 'AreaShape_BoundingBoxMinimum_Y',\n",
       "       'AreaShape_Center_X', 'AreaShape_Center_Y', 'AreaShape_Compactness',\n",
       "       'AreaShape_ConvexArea', 'AreaShape_Eccentricity',\n",
       "       'AreaShape_EquivalentDiameter', 'AreaShape_EulerNumber',\n",
       "       'AreaShape_Extent', 'AreaShape_FormFactor', 'AreaShape_MajorAxisLength',\n",
       "       'AreaShape_MaxFeretDiameter', 'AreaShape_MaximumRadius',\n",
       "       'AreaShape_MeanRadius', 'AreaShape_MedianRadius',\n",
       "       'AreaShape_MinFeretDiameter', 'AreaShape_MinorAxisLength',\n",
       "       'AreaShape_Orientation', 'AreaShape_Perimeter', 'AreaShape_Solidity',\n",
       "       'Location_Center_X', 'Location_Center_Y', 'Location_Center_Z',\n",
       "       'Number_Object_Number'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fbcc3575",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2yFveJEmSJEmSNCMmlCRJkiRJkjQjJpQkSZIkSZI0IyaUJEmSJEmSNCMmlCRJkiRJkjQjJpQkSZIkSZI0IyaUJI20JI9Pck2SLyW5MclbWvkFSb6R5Pr2OrSVJ8l7kmxOckOSw3q2tTrJLe21ekCHJEmSJElDb8mgA5CkXfQA8JKq+l6S3YHPJflUW/Zfq+oj4+ofByxvryOAc4EjkuwNnAmsAAq4Lsn6qrp7Xo5CkiRJkkaIPZQkjbTqfK/N7t5eNcUqq4CL2nobgT2T7AccC2yoqh0tibQBWDmXsUuSJEnSqDKhJGnkJdktyfXAXXRJoavborPabW3nJHlcK9sf2NKz+tZWNlm5JEmSJGkcE0qSRl5V/bCqDgUOAA5P8lzgDODZwM8AewNvmq39JVmTZFOSTdu3b5+tzUqSJEnSyFgUCaVlay9l2dpLBx2GpDlWVfcAVwIrq+r2dlvbA8BfAoe3atuAA3tWO6CVTVY+0X7Oq6oVVbVi6dKls3wU0twb/3dxbN6/lRomfialxcHfdWl0LYqEkqSFK8nSJHu26T2AlwJfbeMikSTACcBX2irrgVPa096OBO6tqtuBy4FjkuyVZC/gmFYmLSp+sZckSVI/fMqbpFG3H3Bhkt3okuSXVNUnk3wmyVIgwPXAb7b6lwHHA5uB+4FXA1TVjiRvA65t9d5aVTvm7zAkSZIkaXSYUJI00qrqBuD5E5S/ZJL6BZw+ybJ1wLpZDVCStEvGeszddvbLBhyJJEnqZUJJkqRFylvbJEmStLNMKEmStABNlCyyh4ckSZJmS1+DcifZM8lHknw1yc1JXphk7yQbktzSfu7V6ibJe5JsTnJDksN6trO61b8lyeqe8hck+XJb5z1tEF1JkiRJkiQNoX57KL0b+HRVvSLJY4EfA/4AuKKqzk6yFlgLvAk4DljeXkcA5wJHJNkbOBNYARRwXZL1VXV3q/Ma4Gq6AXNXAp+apWOUJEl4i5tGg59TSZJGw7Q9lJI8Bfg54HyAqvpBVd0DrAIubNUupHssN638oupsBPZsj+8+FthQVTtaEmkDsLIte3JVbWyD5V7Usy1JkiRJkiQNmX5ueTsY2A78ZZIvJnl/kicA+1bV7a3OHcC+bXp/YEvP+ltb2VTlWycolyRJkiRJ0hDqJ6G0BDgMOLeqng/8K93tbQ9pPYtq9sN7pCRrkmxKsmn79u1zvTtJkhatZWsv9dYjSZIkTaqfhNJWYGtVXd3mP0KXYLqz3a5G+3lXW74NOLBn/QNa2VTlB0xQ/ihVdV5VraiqFUuXLu0jdEmSJC0EJjmlhW3sd9zfc2l0TJtQqqo7gC1JfrIVHQ3cBKwHxp7Uthr4RJteD5zSnvZ2JHBvuzXucuCYJHu1J8IdA1zeln03yZHt6W6n9GxLkiQNkF/uJUmSNJF+n/L2W8AH2xPebgVeTZeMuiTJacA3gRNb3cuA44HNwP2tLlW1I8nbgGtbvbdW1Y42/VrgAmAPuqe7+YQ3SZIkSZKkIdVXQqmqrgdWTLDo6AnqFnD6JNtZB6yboHwT8Nx+YpEkSZOzN5EkSZLmQz9jKEmSJEmSJEkP6feWN0mSNMTsmSRJkqT5ZA8lSZI0LQfnliRJUi8TSpIkSbMgye8kuTHJV5J8KMnjkxyc5Ookm5N8uD3ghCSPa/Ob2/JlPds5o5V/LcmxAzsgSZKkKZhQkiRJ2kVJ9gd+G1hRVc8FdgNOAt4BnFNVzwLuBk5rq5wG3N3Kz2n1SHJIW+85wErgfUl2m89jkSRJ6ocJJUmSpNmxBNgjyRLgx4DbgZcAH2nLLwROaNOr2jxt+dFJ0sovrqoHquobwGbg8PkJX5IkqX8mlCRJknZRVW0D/hj4Fl0i6V7gOuCeqnqwVdsK7N+m9we2tHUfbPWf2ls+wTqPkGRNkk1JNm3fvn12D0iSJGkaJpQkSZJ2UZK96HoXHQw8DXgC3S1rc6aqzquqFVW1YunSpXO5q6EyNkC8g8RLkGS3JF9M8sk2P2vjtiVZ2co2J1k77wcnaeiZUJIkaQR5QT10fgH4RlVtr6p/Az4GvAjYs90CB3AAsK1NbwMOBGjLnwJ8p7d8gnUkabzXAzf3zM/KuG1t7Lb3AscBhwAnt7qS9BATSpIkSbvuW8CRSX6sjYV0NHATcCXwilZnNfCJNr2+zdOWf6aqqpWf1HoTHAwsB66Zp2OQNEKSHAC8DHh/mw+zN27b4cDmqrq1qn4AXNzqStJDlkxfRZIkSVOpqquTfAT4AvAg8EXgPOBS4OIkb29l57dVzgc+kGQzsIOuhwBVdWOSS+iSUQ8Cp1fVD+f1YCSNincBvw88qc0/lT7HbUvSO27bxp5t9q4zfjy3I2Y5fkkjzoSSJEnSLKiqM4EzxxXfygRPaauq7wOvnGQ7ZwFnzXqAkhaMJC8H7qqq65IcNeBY1gBrAA466KBBhiJpnnnLmyRJkiSNlhcBv5TkNrrb0V4CvJvZG7et7/HcFusDAiSZUJIkSZKkkVJVZ1TVAVW1jO6W2c9U1a8ze+O2XQssb0+Ne2zbx/p5ODRJI8Rb3iRJGmGDfNLb2L5vO/tlA4tBkvQIb2KWxm1L8jrgcmA3YF1V3TivRyJp6JlQkiRJ0kANMjEqjbqqugq4qk3P2rhtVXUZcNkshippgfGWN0mSJEmSJM2ICSVJIy3J45Nck+RLSW5M8pZWfnCSq5NsTvLhdv8/bYyAD7fyq5Ms69nWGa38a0mOHdAhSZIkSdLQM6EkadQ9ALykqp4HHAqsTHIk8A7gnKp6FnA3cFqrfxpwdys/p9UjySF04wk8B1gJvC/JbvN5IJIkSZI0KhxDSdJIa08o+V6b3b29iu7xub/Wyi8E3gycC6xq0wAfAf40SVr5xVX1APCNNmjl4cDn5/4opNHhWDeSJEkCE0qSFoDWk+g64FnAe4GvA/dU1YOtylZg/za9P7AFoKoeTHIv8NRWvrFns73rSEPBZI4kSZKGhbe8SRp5VfXDqjoUOICuV9Gz53J/SdYk2ZRk0/bt2+dyV5IkSZI0lOyhJGnBqKp7klwJvBDYM8mS1kvpAGBbq7YNOBDYmmQJ8BTgOz3lY3rXGb+f84DzAFasWFFzcSxSL3smSZIkadjYQ0nSSEuyNMmebXoP4KXAzcCVwCtatdXAJ9r0+jZPW/6ZNg7TeuCk9hS4g4HlwDXzchCSJEmSNGLsoSRp1O0HXNjGUXoMcElVfTLJTcDFSd4OfBE4v9U/H/hAG3R7B92T3aiqG5NcAtwEPAicXlU/nOdjkSRJkqSRYEJJ0kirqhuA509QfivdeErjy78PvHKSbZ0FnDXbMUqSJEnSQuMtb5IkaZcsW3up4zxJkiQtMiaUJEmSJEmSNCMmlCRJkiRJkjQjJpQkSZIkSZI0IyaUJEmSJEmSNCMmlCRJkiRJkjQjJpQkSZIkSZI0I30llJLcluTLSa5PsqmV7Z1kQ5Jb2s+9WnmSvCfJ5iQ3JDmsZzurW/1bkqzuKX9B2/7mtm5m+0AlSZIkSZI0O2bSQ+nnq+rQqlrR5tcCV1TVcuCKNg9wHLC8vdYA50KXgALOBI4ADgfOHEtCtTqv6Vlv5U4fkSRJkiRJkubUrtzytgq4sE1fCJzQU35RdTYCeybZDzgW2FBVO6rqbmADsLIte3JVbayqAi7q2ZYkSZI0oWVrL2XZ2ksHHYYkSYtSvwmlAv4+yXVJ1rSyfavq9jZ9B7Bvm94f2NKz7tZWNlX51gnKJUmSJEmSNISW9FnvxVW1LcmPAxuSfLV3YVVVkpr98B6pJbPWABx00EFzvTtJkiRJkiRNoK8eSlW1rf28C/g43RhId7bb1Wg/72rVtwEH9qx+QCubqvyACconiuO8qlpRVSuWLl3aT+iSJEmSJEmaZdMmlJI8IcmTxqaBY4CvAOuBsSe1rQY+0abXA6e0p70dCdzbbo27HDgmyV5tMO5jgMvbsu8mObI93e2Unm1JkqQR4Xg2kiRJi0c/t7ztC3y8y/WwBPjrqvp0kmuBS5KcBnwTOLHVvww4HtgM3A+8GqCqdiR5G3Btq/fWqtrRpl8LXADsAXyqvSRJkiRJkjSEpk0oVdWtwPMmKP8OcPQE5QWcPsm21gHrJijfBDy3j3glSVrwxnr53Hb2ywYciSRJkjSxfp/yJkmSJEmSJAH9P+VtQfA/vpKkUeJ4RJIkSRpW9lCSJEmSJEnSjJhQkiRJkiRJ0oyYUJIkSZIkSdKMLKoxlCRJGlaOlyRJkqRRYg8lSZIkSZIkzYgJJUmSJEmSJM2ICSVJkiRJkiTNiAklSZI0q5atvdQxoSRJkhY4E0qSJEkaaSYxJUmafyaUJEmSJEmSNCMmlCSNtCQHJrkyyU1Jbkzy+lb+5iTbklzfXsf3rHNGks1Jvpbk2J7yla1sc5K1gzgeLT72rJAkSdIoWjLoACRpFz0IvLGqvpDkScB1STa0ZedU1R/3Vk5yCHAS8BzgacA/JPmJtvi9wEuBrcC1SdZX1U3zchSSJEmSNEJMKEkaaVV1O3B7m74vyc3A/lOssgq4uKoeAL6RZDNweFu2uapuBUhycatrQklzwl5JkiRJGmUmlCQtGEmWAc8HrgZeBLwuySnAJrpeTHfTJZs29qy2lYcTUFvGlR8x1zFL0mJmYlWSpNHlGEqSFoQkTwQ+Cryhqr4LnAs8EziUrgfTn8zivtYk2ZRk0/bt22drs5IkSZI0MkwoSRp5SXanSyZ9sKo+BlBVd1bVD6vqR8Bf8PBtbduAA3tWP6CVTVb+KFV1XlWtqKoVS5cund2DkSRJkqQRYEJJ0khLEuB84OaqemdP+X491X4Z+EqbXg+clORxSQ4GlgPXANcCy5McnOSxdAN3r5+PY5AkSZKkUeMYSpJG3YuAVwFfTnJ9K/sD4OQkhwIF3Ab8Z4CqujHJJXSDbT8InF5VPwRI8jrgcmA3YF1V3Th/hyFJkiRJo8OEkqSRVlWfAzLBosumWOcs4KwJyi+baj1JmkqSPYH3A8+lS2b/R+BrwIeBZXTJ7ROr6u7Wu/LdwPHA/cCpVfWFtp3VwB+1zb69qi6cv6OQJEnqj7e8SZIkzY53A5+uqmcDzwNuBtYCV1TVcuCKNg9wHN0tt8uBNXQPEiDJ3sCZdE+ZPBw4M8le83kQkiRJ/TChJEmStIuSPAX4Obox3aiqH1TVPcAqYKyH0YXACW16FXBRdTYCe7ax344FNlTVjqq6G9gArJy3A5EkSeqTCSVJkqRddzCwHfjLJF9M8v4kTwD2rarbW507gH3b9P7Alp71t7ayycol6SFJHp/kmiRfSnJjkre08oOTXJ1kc5IPtweN0B5G8uFWfnWSZT3bOqOVfy3JsT3lK1vZ5iRrHxWEpEXPhJIkSfNo2dpLWbb20kGHodm3BDgMOLeqng/8Kw/f3gZAVRXd2EqzIsmaJJuSbNq+fftsbVbSaHgAeElVPQ84FFiZ5EjgHcA5VfUs4G7gtFb/NODuVn5Oq0eSQ+iebPscut6Q70uyW5LdgPfS3Z57CN3DTg6Zr4OTNBpMKEmSJO26rcDWqrq6zX+ELsF0Z7uVjfbzrrZ8G3Bgz/oHtLLJyh+lqs6rqhVVtWLp0qWzdiCShl+7XfZ7bXb39irgJXTtDzz6Ntux228/AhzdHg6wCri4qh6oqm8Am+nGbzsc2FxVt1bVD4CLW11JeogJJUmSpF1UVXcAW5L8ZCs6GrgJWA+sbmWrgU+06fXAKekcCdzbbo27HDgmyV5tMO5jWpkkPULrSXQ9XaJ6A/B14J6qerBV6b1l9qHbadvye4Gn4u23knbBkkEHIEmSFqbeW/tuO/tlA4xk3vwW8ME2ZsmtwKvp/nl3SZLTgG8CJ7a6lwHH0/UGuL/Vpap2JHkbcG2r99aq2jF/hyBpVFTVD4FDk+wJfBx49iDiSLKG7mmVHHTQQYMIQdKAmFCSJEmaBVV1PbBigkVHT1C3gNMn2c46YN2sBidpwaqqe5JcCbyQ7omRS1ovpN5bZsdup92aZAnwFOA7TH2bbd+33wLnAaxYsWLWxomTNPy85U2SJEmSRkiSpa1nEkn2AF4K3AxcCbyiVRt/m+3Y7bevAD7TEtvrgZPaU+AOBpYD19D1klzenhr3WLqBu9fP+YFJGin2UJIkSZKk0bIfcGF7GttjgEuq6pNJbgIuTvJ24IvA+a3++cAHkmwGdtAliKiqG5NcQjfm24PA6e1WOpK8jm4Mt92AdVV14/wdnqRRYEJJkiRJkkZIVd0APH+C8lvpntA2vvz7wCsn2dZZwFkTlF9GN96bJE2o71ve2lMEvpjkk23+4CRXJ9mc5MOtKyStu+SHW/nVSZb1bOOMVv61JMf2lK9sZZuTrJ3F45MkSdIisWztpY8YDF6SJM2dmYyh9Hq6+3LHvAM4p6qeBdwNnNbKTwPubuXntHokOYSua+VzgJXA+1qSajfgvcBxwCHAya2uJEmSJEmShlBfCaUkBwAvA97f5gO8BPhIq3IhcEKbXtXmacuPbvVXARdX1QNV9Q26x+Qe3l6bq+rWqvoBcHGrK0mSJEmSpCHU7xhK7wJ+H3hSm38qcE97HCXAVmD/Nr0/sAWgqh5Mcm+rvz+wsWebvetsGVd+RP+HIEnS8PM2HEmSJC0k0/ZQSvJy4K6qum4e4pkuljVJNiXZtH379kGHI0mSJEmStCj1c8vbi4BfSnIb3e1oLwHeDeyZZKyH0wHAtja9DTgQoC1/CvCd3vJx60xW/ihVdV5VraiqFUuXLu0jdEmSJEmSJM22aW95q6ozgDMAkhwF/F5V/XqSvwFeQZdkWg18oq2yvs1/vi3/TFVVkvXAXyd5J/A0YDlwDRBgeZKD6RJJJwG/NlsHKEnSoHibmyRJkhaqfsdQmsibgIuTvB34InB+Kz8f+ECSzcAOugQRVXVjkkuAm4AHgdOr6ocASV4HXA7sBqyrqht3IS5JkiRJkiTNoRkllKrqKuCqNn0r3RPaxtf5PvDKSdY/CzhrgvLLgMtmEoskSZIkSZIGo58xlCRJkiRJkqSHmFCSJElzbtnaSx1TSpIkaQExoSRJkiRJkqQZMaEkSZIkSZKkGdmVp7xJkqQJeGuXJEmSFjp7KEmSJEmSJGlGTChJkiRJkiRpRkwoSZIkaUHxqYKSJM09E0qSRlqSA5NcmeSmJDcmeX0r3zvJhiS3tJ97tfIkeU+SzUluSHJYz7ZWt/q3JFk9qGPS6PIiVpIkSYuFCSVJo+5B4I1VdQhwJHB6kkOAtcAVVbUcuKLNAxwHLG+vNcC50CWggDOBI4DDgTPHklCSJEmSpEcyoSRppFXV7VX1hTZ9H3AzsD+wCriwVbsQOKFNrwIuqs5GYM8k+wHHAhuqakdV3Q1sAFbO35FIkiRJ0ugwoSRpwUiyDHg+cDWwb1Xd3hbdAezbpvcHtvSstrWVTVYuSZIkSRrHhJKkBSHJE4GPAm+oqu/2LquqAmoW97UmyaYkm7Zv3z5bm5UkSZKkkWFCSdLIS7I7XTLpg1X1sVZ8Z7uVjfbzrla+DTiwZ/UDWtlk5Y9SVedV1YqqWrF06dLZOxBJkiRJGhEmlCSNtCQBzgdurqp39ixaD4w9qW018Ime8lPa096OBO5tt8ZdDhyTZK82GPcxrUySJEmSNM6SQQcgSbvoRcCrgC8nub6V/QFwNnBJktOAbwIntmWXAccDm4H7gVcDVNWOJG8Drm313lpVO+blCCRJkiRpxJhQkjTSqupzQCZZfPQE9Qs4fZJtrQPWzV50kiRJkrQwecubJEmSJEmSZsQeSpIkSZpXy9ZeOugQJEnSLrKHkiRJkiRJkmbEhJIkSZIkSZJmxFveJEnaRd6+I0mSpMXGHkqSJEmSJEmaERNKkiRp3ixbe6k9uiRJkhYAE0qSJEmSJEmaERNKkiRJkiRJmhEH5ZYkaSd565YkSZIWKxNKkiRJWpB6k763nf2yAUYiSdLC4y1vkiRJkiRJmhF7KEmSNAPe5iZJkiTZQ0mSJEmSJEkzZEJJkiRJkiRJM2JCSZIkSZIkSTMybUIpyeOTXJPkS0luTPKWVn5wkquTbE7y4SSPbeWPa/Ob2/JlPds6o5V/LcmxPeUrW9nmJGvn4DglSZIkSZI0S/oZlPsB4CVV9b0kuwOfS/Ip4HeBc6rq4iR/BpwGnNt+3l1Vz0pyEvAO4FeTHAKcBDwHeBrwD0l+ou3jvcBLga3AtUnWV9VNs3ickiTtEgfjliRJkh42bQ+l6nyvze7eXgW8BPhIK78QOKFNr2rztOVHJ0krv7iqHqiqbwCbgcPba3NV3VpVPwAubnUlSZIkSZI0hPoaQynJbkmuB+4CNgBfB+6pqgdbla3A/m16f2ALQFt+L/DU3vJx60xWPlEca5JsSrJp+/bt/YQuSZIkSZKkWdZXQqmqflhVhwIH0PUoevZcBjVFHOdV1YqqWrF06dJBhCBJkiRJkrTozegpb1V1D3Al8EJgzyRjYzAdAGxr09uAAwHa8qcA3+ktH7fOZOWSJEkjpfXq/mKST7b5WXuIiSRJ0jDp5ylvS5Ps2ab3oBs8+2a6xNIrWrXVwCfa9Po2T1v+maqqVn5S+wJ1MLAcuAa4FljevnA9lm7g7vWzcGySJEnz7fV035PGvIPuISbPAu6me3gJ9DzEBDin1WPcQ0xWAu9Lsts8xS5JktS3fnoo7QdcmeQGuuTPhqr6JPAm4HeTbKYbI+n8Vv984Kmt/HeBtQBVdSNwCXAT8Gng9HYr3YPA64DL6b6AXdLqSpIkjYwkBwAvA97f5sPsPcREkiRpqCyZrkJV3QA8f4LyW5ngC05VfR945STbOgs4a4Lyy4DL+ohXkiQtAMvWXgrAbWe/bMCRzKp3Ab8PPKnNP5U+H2KSpPchJht7tjnpw0okLV5JDgQuAvalewL3eVX17iR7Ax8GlgG3ASdW1d0tYf1u4HjgfuDUqvpC29Zq4I/apt9eVRe28hcAFwB70F2rvb7deSJJwAzHUJIkSdKjJXk5cFdVXTeP+/Tpt9Li9SDwxqo6BDgSOL3dMrsWuKKqlgNXtHmA4+iGHFkOrAHOBWgJqDOBI+g6C5yZZK+2zrnAa3rWWzkPxyVphEzbQ0mSpMVsrCeNNI0XAb+U5Hjg8cCT6XoD7JlkSeulNNFDTLb2+RCTR6mq84DzAFasWGGvAWkRqarbgdvb9H1JbqbrzbgKOKpVuxC4im6oklXARa2H0cYkeybZr9XdUFU7AJJsAFYmuQp4clVtbOUX0d2y+6l5ODxJI8IeSpIkTWDZ2ktNJqlvVXVGVR1QVcvoBtX+TFX9OrP3EBNJmlB7SuTzgauBfVuyCeAOulvioOc222bsdtqpyrdOUD7R/u0tKS1SJpQkSZLmzqw8xGTeo16ATBJrIUryROCjwBuq6ru9y1qSes57L1bVeVW1oqpWLF26dK53J2mImFCSNPKSrEtyV5Kv9JS9Ocm2JNe31/E9y85IsjnJ15Ic21O+spVtTrJ2/H4kqR9VdVVVvbxN31pVh1fVs6rqlVX1QCv/fpt/Vlt+a8/6Z1XVM6vqJ6vK20skTSjJ7nTJpA9W1cda8Z3tVjbaz7ta+WS3005VfsAE5ZL0EBNKkhaCC5h4oMhzqurQ9roMoA1YeRLwnLbO+5LslmQ34L10g1YeApzc6kqSJA2V9tS284Gbq+qdPYt6b6cdf5vtKekcCdzbbo27HDgmyV5tMO5jgMvbsu8mObLt65SebUkS4KDckhaAqvpsGz+gH6uAi1svgW+0200Ob8s2j/USSHJxq3vTbMcrSZK0i14EvAr4cpLrW9kfAGcDlyQ5DfgmcGJbdhlwPLAZuB94NUBV7UjyNuDaVu+tYwN0A6+l+6fdHnSDcc9qj0lvQZVGnwklSQvZ65KcAmyie7Tu3XQDSm7sqdM7yOT4QSmPmGijSdbQPXKXgw46aLZjliRJmlJVfQ7IJIuPnqB+AadPsq11wLoJyjcBz92FMCUtcN7yJmmhOhd4JnAo3WN1/2S2Nuzgk5IkSZIWO3soSVqQqurOsekkfwF8ss1ONvgkU5RLkiRJknrYQ0nSgjT2hJPml4GxJ8CtB05K8rgkBwPLgWvoxg5YnuTgJI+lG7h7/XzGLEmSJEmjwh5KkkZekg8BRwH7JNkKnAkcleRQoIDbgP8MUFU3JrmEbrDtB4HTq+qHbTuvo3vayW7Auqq6cX6PRMPAQUIlSZKk6ZlQkjTyqurkCYrPn6L+WcBZE5RfRvcUFEmSJEnSFEwoSZIWPXslSZIkSTPjGEqSJEmSJEmaERNKkiRJkiRJmhETSpIkSZIkSZoRE0qSJEmSJEmaEQflliQtWg7GLUmSJO0ceyhJkiRp0Vi29lKTyZIkzQITSpIkSZIkSZoRE0qSJEmSJEmaEcdQkiRJ0rzwVjNJkhYOeyhJkiRJkiRpRkwoSZIkSZIkaUZMKEmSJEmSJGlGTChJkqSB8RHukiRJo8mEkiRJkiRJkmbEhJIkSZIkSZJmZMmgA5Akab55i5UkSZK0a+yhJEmSJEmSpBkxoSRJkiRJkqQZmTahlOTAJFcmuSnJjUle38r3TrIhyS3t516tPEnek2RzkhuSHNazrdWt/i1JVveUvyDJl9s670mSuThYSZIkSZIk7bp+eig9CLyxqg4BjgROT3IIsBa4oqqWA1e0eYDjgOXttQY4F7oEFHAmcARwOHDmWBKq1XlNz3ord/3QJEmSpIktW3up46lJQ8rfT2k0TJtQqqrbq+oLbfo+4GZgf2AVcGGrdiFwQpteBVxUnY3Ankn2A44FNlTVjqq6G9gArGzLnlxVG6uqgIt6tiVJkiRJkqQhM6OnvCVZBjwfuBrYt6pub4vuAPZt0/sDW3pW29rKpirfOkG5JEmzyv92SpIkSbOj70G5kzwR+Cjwhqr6bu+y1rOoZjm2iWJYk2RTkk3bt2+f691JkiRJkiRpAn0llJLsTpdM+mBVfawV39luV6P9vKuVbwMO7Fn9gFY2VfkBE5Q/SlWdV1UrqmrF0qVL+wldkiRJkiRJs6yfp7wFOB+4uare2bNoPTD2pLbVwCd6yk9pT3s7Eri33Rp3OXBMkr3aYNzHAJe3Zd9NcmTb1yk925IkaZc5uKckSZI0u/oZQ+lFwKuALye5vpX9AXA2cEmS04BvAie2ZZcBxwObgfuBVwNU1Y4kbwOubfXeWlU72vRrgQuAPYBPtZckSZIkSZKG0LQJpar6HJBJFh89Qf0CTp9kW+uAdROUbwKeO10skiRJkiRJGry+B+WWpGGVZF2Su5J8pads7yQbktzSfu7VypPkPUk2J7khyWE966xu9W9JsnqifUmSJEmSTChJWhguAFaOK1sLXFFVy4Er2jzAccDy9loDnAtdAgo4EzgCOBw4cywJJWnujY1z5VhXkiRJo8GEkqSRV1WfBXaMK14FXNimLwRO6Cm/qDobgT3bkyqPBTZU1Y6quhvYwKOTVJKkBcZEpiRJO6efQbklaRTt254iCXAHsG+b3h/Y0lNvayubrFwjygtESZIkae7YQ0nSgtceFlCztb0ka5JsSrJp+/bts7VZSZIkSRoZJpQkLVR3tlvZaD/vauXbgAN76h3QyiYrf5SqOq+qVlTViqVLl8564JIkSZI07EwoSVqo1gNjT2pbDXyip/yU9rS3I4F7261xlwPHJNmrDcZ9TCuTJEmSJI3jGEqSRl6SDwFHAfsk2Ur3tLazgUuSnAZ8EzixVb8MOB7YDNwPvBqgqnYkeRtwbav31qoaP9C3JEmSJAkTSpIWgKo6eZJFR09Qt4DTJ9nOOmDdLIYmSZIkSQuSt7xJkiRJkiRpRkwoSZIkSZIkaUa85U2StKAsW3vpoEOQJEmSFjwTSpKkBcFEkjS8/P2UJGnh8ZY3SZIkSZIkzYgJJUmSJEmSJM2ICSVJkqRdlOTAJFcmuSnJjUle38r3TrIhyS3t516tPEnek2RzkhuSHNazrdWt/i1JVg/qmBabZWsvfeglSZKmZ0JJkiRp1z0IvLGqDgGOBE5PcgiwFriiqpYDV7R5gOOA5e21BjgXugQUcCZwBHA4cOZYEkqSeiVZl+SuJF/pKZu1JHaSFyT5clvnPUkyv0coadgtyoSS/32SJEmzqapur6ovtOn7gJuB/YFVwIWt2oXACW16FXBRdTYCeybZDzgW2FBVO6rqbmADsHL+jkTSCLmAR7cPs5nEPhd4Tc96tkWSHsGnvEmSRpr/INCwSbIMeD5wNbBvVd3eFt0B7Num9we29Ky2tZVNVj7RftbQXRhy0EEHzVL0kkZFVX22tTe9VgFHtekLgauAN9GTxAY2JhlLYh9FS2IDJNkArExyFfDklvAmyUV0CfFPzd0RSRo1i7KHkiRJ0lxI8kTgo8Abquq7vcvahVzN1r6q6ryqWlFVK5YuXTpbmxX2ZtdIm60k9v5teny5JD3EhJIkSdIsSLI7XTLpg1X1sVZ8Z+sFQPt5VyvfBhzYs/oBrWyyckmakdlOYk8myZokm5Js2r59+1zvTtIQMaEkSRpJ9iBYuEbxvW2D1Z4P3FxV7+xZtB4YG+R2NfCJnvJT2kC5RwL3tl4FlwPHJNmrjWNyTCuTpH7MVhJ7W5seX/4o9paUFi8TSpIkSbvuRcCrgJckub69jgfOBl6a5BbgF9o8wGXArcBm4C+A1wK0cUzeBlzbXm8dG9tEkvowK0nstuy7SY5sCfNTerYlSYCDckuSJO2yqvocMNkjtY+eoH4Bp0+yrXXAutmLTtJClORDdINq75NkK93T2s4GLklyGvBN4MRW/TLgeLok9v3Aq6FLYicZS2LDI5PYr6V7ktwedINxOyC3pEcwoSRJkiRJI6aqTp5k0awksatqE/DcXYlR0sJmQkmSNDJGbVwdSZIkaaFyDCVJkiRJkiTNiAklSZIkSZIkzYi3vEmShp63ukmSJEnDxR5KkiRJ0gSWrb3UhLYkSZMwoSRJkiRJkqQZMaEkSZIkSZKkGTGhJEmSJEmSpBmZNqGUZF2Su5J8pads7yQbktzSfu7VypPkPUk2J7khyWE966xu9W9Jsrqn/AVJvtzWeU+SzPZBSpIkaX6NjT/kGESSJC1M/fRQugBYOa5sLXBFVS0HrmjzAMcBy9trDXAudAko4EzgCOBw4MyxJFSr85qe9cbvS5IkLUImIyRJkobXkukqVNVnkywbV7wKOKpNXwhcBbyplV9UVQVsTLJnkv1a3Q1VtQMgyQZgZZKrgCdX1cZWfhFwAvCpXTkoSdLCYDJB0jAYa4tuO/tlA45EkqThsbNjKO1bVbe36TuAfdv0/sCWnnpbW9lU5VsnKJ9QkjVJNiXZtH379p0MXZIkSZIkSbtilwflbr2RahZi6Wdf51XViqpasXTp0vnYpaQRl+S2Nk7b9Uk2tbIZjwMnSZIkSXrYtLe8TeLOJPtV1e3tlra7Wvk24MCeege0sm08fIvcWPlVrfyACepL0mz6+ar6l575sXHgzk6yts2/iUeOA3cE3RhvR8x3sIuZt7hJkiRJo2FneyitB8ae1LYa+ERP+Sntv/xHAve2W+MuB45JslfrCXAMcHlb9t0kR7anu53Ssy1Jmiur6MZ/o/08oaf8oupsBMbGgZMkSZIk9Zi2h1KSD9H1LtonyVa6p7WdDVyS5DTgm8CJrfplwPHAZuB+4NUAVbUjyduAa1u9t44N0A28lu5JcnvQDcbtgNySZlMBf5+kgD+vqvOY+ThwtyNJWvQcnFuSpIf185S3kydZdPQEdQs4fZLtrAPWTVC+CXjudHFI0k56cVVtS/LjwIYkX+1dWFXVkk19S7IGWANw0EEHzV6ki5S3uUmSJEmjZ5cH5ZakYVZV29rPu4CPA4fTxoED6HMcuPHb9AEBkiRJkhY1E0qSFqwkT0jypLFpuvHbvsLMx4GTJOkhy9Ze+tBLkqTFamef8iZJo2Bf4OPdmP8sAf66qj6d5FpmMA6cJEmSJOmRTChJWrCq6lbgeROUf4cZjgMnSZIkSXqYCSVJ0kB4q4j65ZO1Rou/25IkLQ4mlCRJ88qLTUmS1A//oSANNwflliRJknaSg3NLkhYrE0qSJEmSJEmaERNKkiRJkiRJmhHHUJIkzQtvCZG0kDnWiyRpsbGHkiRJkjRLHFNJkrRY2ENJkiRJu8wkiiRJi4sJJUnSnPECU5IkSVqYTChJkmadiSRJkiRpYXMMJUmSJEmSJM2IPZQkSZK00+yRODGf+iZJWujsoSRJkiRJkqQZsYeSJGnW2FNBkh6pt120t5IkaSGxh5IkSRoJy9ZeatJSI83PsCRpIVnUPZS8t12Sdo0XRpIkSdLitKgTSpIkSdJ8G5+M95+bkqRRZEJJkiRJM2YPRUmSFjcTSpKkGfEiUpIkzSeHKpGGkwklSVJfTCRJ0tyYqn31AlqSNKx8ypskSZIkSZJmxB5KkqQJ2b1c0nj2VJQkSWNMKEmSpuQFpIZN72fShKcWOp8IJ0kaViaUJEkmjSRpRJhQ1WJm72lpuJhQkiRJ0pRMOg8nL64lSYNkQgn/0yNJkqTRZWJJkjQIJpQkaRGyt4GkfthWjJap3i+TTVpI7BAgDQcTSpK0CHhRqIXKnhlSf/xdkSTNtqFJKCVZCbwb2A14f1WdPYg4/GMrLW7D0hZJWtwG3RaZhF64Jntv/e6riQy6LeqH12/S4AxFQinJbsB7gZcCW4Frk6yvqpsGG5mkxWShtEVeCEqjbaG0RRotE91C5IX64jZqbZGfV2n+DUVCCTgc2FxVtwIkuRhYBQyssRp/QWbDJC0KQ9cW9cMEkrTgLiTmtS2yDdF44z8TU30vXmC/e3qkkf5e5GdSmnvDklDaH9jSM78VOGJAsUyony9bNlrSyBuatsgLPGnnLJALiXlpi2xntLMm+uzszOdp/O+pvaSGztB8L9oZs9XG+dmTJjcsCaW+JFkDrGmz30vytT5W2wf4l7mL6mF5x6xubt7inmXGPf9GNfZ9gKcPOoidMUFb9B1G9z0w7vkzqnHDCMbe/ib3G/dCaYu+xmi8V8Meo/Htmr7jm+q78/hls/g9e5jP30Jqi6Yz6PdhVj6nc73vOTLI/Xvso7PvvtqjYUkobQMO7Jk/oJU9QlWdB5w3kw0n2VRVK3YtvPln3PNrVOOG0Y29xb1s0HGMs1Nt0Yi/B8Y9T0Y1bhjd2Ec1bnbhe9EoHPOwx2h8u8b4FpQFe402yP177B77Qtr3Y+ZiozvhWmB5koOTPBY4CVg/4JgkLT62RZKGgW2RpGFgWyRpSkPRQ6mqHkzyOuByukdSrquqGwcclqRFxrZI0jCwLZI0DGyLJE1nKBJKAFV1GXDZHGx6Rt0vh4hxz69RjRtGN/ahjHsn26KhPJY+GPf8GtW4YXRjH9W4d+V70Sgc87DHaHy7xvgWkAV8jTbI/Xvsi3P/C3Lfqaq52rYkSZIkSZIWoGEZQ0mSJEmSJEkjYkEnlJKsTPK1JJuTrB10PABJbkvy5STXJ9nUyvZOsiHJLe3nXq08Sd7T4r8hyWE921nd6t+SZPUcxLkuyV1JvtJTNmtxJnlBOw+b27qZw7jfnGRbO+fXJzm+Z9kZLYavJTm2p3zCz04blPDqVv7hNkDhbMR9YJIrk9yU5MYkr2/lQ33Op4h76M/5zpiuTUnyuBbj5hbzsgGE+Sh9xP277T28IckVSYbiscXTxd1T71eSVJKheGpPP3EnObHn9+av5zvGifTxOTmo/b5/sX1Wjp9oO/NtonZ/3PJJ28tRNuztUR/x/VySLyR5MMkr5jO2PuMbeLvYR4y/mYe/U34uySHDFF9PvYG00X2cv1OTbO/5rvKf5jO+xazfz85ObHder7Mm+vszm/vLFN/dJ9n3vHz/zoCvW6bY/5wff5LHJ7kmyZfavt8yTf1J/xbvREyT7fuCJN/oOe5D5+K8T6qqFuSLbuC4rwPPAB4LfAk4ZAjiug3YZ1zZ/wTWtum1wDva9PHAp4AARwJXt/K9gVvbz73a9F6zHOfPAYcBX5mLOIFrWt20dY+bw7jfDPzeBHUPaZ+LxwEHt8/LblN9doBLgJPa9J8B/2WW4t4POKxNPwn45xbfUJ/zKeIe+nO+E8c6bZsCvBb4szZ9EvDhQcS6E3H/PPBjbfq/jErcPZ+7zwIbgRWjEDewHPhiz+/mj49I3OeN/f613+XbBh13i+VR7f645RO2l6P8Gvb2qM/4lgE/DVwEvGIIz99A28U+Y3xyz/QvAZ8epvhavYG00X2ev1OBP53P99XX3F6jMc/XWRP9/ZnN/THFd/dJ9v1m5uH7NwO+bpli/3N+/C2eJ7bp3YGrW5yT1Z/wb/FOxjTZvi9ggr+js33eJ3st5B5KhwObq+rWqvoBcDGwasAxTWYVcGGbvhA4oaf8oupsBPZMsh9wLLChqnZU1d3ABmDlbAZUVZ8FdsxFnG3Zk6tqY3Wf3It6tjUXcU9mFXBxVT1QVd8ANtN9bib87LQM7UuAj7T1e8/BrsZ9e1V9oU3fB9wM7M+Qn/Mp4p7M0JzzndBPm9L7fn0EOLqvzP7cmjbuqrqyqu5vsxuBA+Y5xon024a/DXgH8P35DG4K/cT9GuC97XeUqrprnmOcSD9xF/DkNv0U4NvzGN+k+mj3J2svR9mwt0f9tDu3VdUNwI/mKaaZxjfodrGfGL/bM/sEut/RoYmvGVQbPUrXAYvNfL83c3adNcjrpUFe8wz6umWQ1x/tGL7Xyndvr5rifE32t3hGMU2z76mOe86vFxdyQml/YEvP/Fam/qDNlwL+Psl1Sda0sn2r6vY2fQewb5ue7BgGdWyzFef+bXp8+Vx6Xevqt26s++U08U1U/lTgnqp6cFz5rGpdIZ9Pl3UemXM+Lm4YoXPep35+7x6q02K+l+4YBmmm7cVpdP+RGLRp425ddw+sqkvnM7Bp9HO+fwL4iST/lGRjkln9h8BO6ifuNwO/kWQr3RN/fmt+Qttlw/p9YFcMe3s07Od8FNrFvmJMcnqSr9P1DPjteYoNhr+N7vc9/pX2XeUjSQ6cn9AWvblsH4bhOmvQ393n9fv3oK9bBnH9kWS3JNcDd9ElY74+Rf3J/hbv1Odv/L6rauy4z2rHfU6Sx+3kce/UZ24hJ5SG1Yur6jDgOOD0JD/Xu7BlA+fzP0w7ZVTibM4FngkcCtwO/MlAo5lCkicCHwXeMO4/j0N9zieIe2TOuR6W5DeAFcD/GnQs00nyGOCdwBsHHctOWEJ329tRwMnAXyTZc5AB9elk4IKqOoCuG/UH2vsgLVjD3i5W1Xur6pnAm4A/GnQ8Y0akjf47YFlV/TTdReGF09TX8Buq66wBfHef1+/fg75uGdT1R1X9sKoOpeu5ejjw7LnYTz/7TvJc4IwWw8/Q3cb2pvmKBxZ2Qmkb0PufhgNa2UBV1bb28y7g43QfwjvHut+3n2O3P0x2DIM6ttmKcxuP7Do+p/FX1Z3tl+9HwF/QnfOdifs7dF0Fl8xF3El2p2sUP1hVH2vFQ3/OJ4p7VM75DPXze/dQnRbzU+iOYZD6ai+S/ALwh8AvVdUD8xTbVKaL+0nAc4GrktxGd7/3+gx+YO5+zvdWYH1V/Vvr5vzPdAmmQeon7tPoxgigqj4PPB7YZ16i2zVD+X1gFw17ezTs53wU2sWZnsOLmd9bwoe9jZ72/FXVd3re1/cDL5in2Ba7OWsfhuQ6a2Df3efz+/egr1uG4fqjqu4BrgReOEX9yf4W79Lnr2ffK9stgNXas7/chePeuevFGoLB2ebiRfcf4FvpBrkaG9DqOQOO6QnAk3qm/w/dPbn/i0cOYPY/2/TLeORAWtfUwwNpfYNuEK292vTecxDvMh450NusxcmjB/w6fg7j3q9n+nfo7lcFeA6PHAztVrqB0Cb97AB/wyMHXHvtLMUcuvtU3zWufKjP+RRxD/0534ljnbZNAU7nkQPvXTKIWHci7ufTddddPuh4ZxL3uPpXMRyDcvdzvlcCF7bpfei6HT91BOL+FHBqm/4pujGUMuhz3uJZxuSDck/YXo7ya9jbo5n8/jLJYKKDjm/Q7WKfMS7vmf5FYNMwxTeu/ry20X2ev97vKr8MbBzEe73YXjP97MxguwO5zhr/92c298c0390n2Pe8fP9mwNctU+x/zo8fWArs2ab3AP4RePkU9Sf8W7yTMU227/16zsu7gLPn6jM34e/AoBuVuXzRdcn/Z7ovBH84BPE8o30ovgTcOBYT3X2UVwC3AP/Q84YGeG+L/8v0/CEG/iPd4F2bgVfPQawfousq+G90/00/bTbjpOs+/pW2zp8ySxclk8T9gRbXDcB6HtnY/GGL4Ws88ukBE3522nt4TTuevwEeN0txv5iuW+gNwPXtdfywn/Mp4h76c76Tx/uoGIG30v33GroeG3/TYr0GeMagYp1h3P8A3NnzHq4fdMz9xD2u7lUMQUKpz/MdultBbmq/JycNOuY+4z4E+Ce6v2HXA8cMOuYW10Tt/m8Cv9lzvidsL0f5NeztUR/x/Ux7v/6V7r+1Nw5ZfANvF/uI8d103yevp/tP9bz+43TY2+g+zt//aOfvS+38PXu+3+PF+provZmFbc77ddYkf3/m5bv7JPuel+/fDPi6ZYr9z/nx0z2d9IttH18B/ts09Sf9W7wTMU2278+04/4K8Fc8/CS4ebleTFtRkiRJkiRJ6stCHkNJkiRJkiRJc8CEkiRJkiRJkmbEhJIkSZIkSZJmxISSJEmSJEmSZsSEkiRJkiRJkmbEhJIkSZIkSZJmxISSJEmSJEmSZsSEkiRJkiRJkmbk/wfBUVDSBMHG2AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot distributions of 1) shape 2) eccentricity 3) ConvexArea 4) neighbors\n",
    "\n",
    "fig,axs = plt.subplots(1,4,figsize=(20,5))\n",
    "axs=axs.ravel()\n",
    "cols_to_plot = ['Area','Eccentricity','FormFactor','ConvexArea']\n",
    "for i in range(len(axs)):\n",
    "    col = 'AreaShape_'+cols_to_plot[i]\n",
    "    if col == 'AreaShape_Area':\n",
    "        \n",
    "        axs[i].hist(seg_df[col], bins=100)\n",
    "        #axs[i].hist(barcoded[col],bins=25)\n",
    "        axs[i].set_title(col)\n",
    "    else:\n",
    "        axs[i].hist(seg_df[col], bins = 100)\n",
    "        axs[i].set_title(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "106e31b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot distributions of 1) shape 2) eccentricity 3) ConvexArea 4) neighbors\n",
    "\n",
    "fig,axs = plt.subplots(1,4,figsize=(20,5))\n",
    "axs=axs.ravel()\n",
    "cols_to_plot = ['Area','Eccentricity','FormFactor','ConvexArea']\n",
    "for i in range(len(axs)):\n",
    "    col = 'AreaShape_'+cols_to_plot[i]\n",
    "    if col == 'AreaShape_Area':\n",
    "        \n",
    "        axs[i].hist(seg_df[abs(seg_df[col])<5000][col], bins=100)\n",
    "        #axs[i].hist(barcoded[col],bins=25)\n",
    "        axs[i].set_title(col)\n",
    "    elif col == 'AreaShape_ConvexArea':\n",
    "        axs[i].hist(seg_df[abs(seg_df[col])<5000][col], bins=100)\n",
    "        #axs[i].hist(barcoded[col],bins=25)\n",
    "        axs[i].set_title(col)\n",
    "    else:\n",
    "        axs[i].hist(seg_df[col], bins = 100)\n",
    "        axs[i].set_title(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "130ffe33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot partially labelled \n",
    "# scatter plot, same as above, except outlier 94.\n",
    "# P vs X_NORM, RAW vs X_NORM, LOGIT vs X_NORM, ENTROPY vs X_NORM\n",
    "fig, axs = plt.subplots(1,3,figsize=(20,5))\n",
    "axs = axs.ravel()\n",
    "cols_to_plot = ['Eccentricity','FormFactor','ConvexArea']\n",
    "col_to_plot2 = ['ENTROPY','RAW']\n",
    "df_to_plot=seg_df[seg_df['AreaShape_Area']<5000]\n",
    "\n",
    "for i in range(len(cols_to_plot)):\n",
    "    col = 'AreaShape_'+cols_to_plot[i]\n",
    "    axs[i].scatter(df_to_plot['AreaShape_Area'], df_to_plot[col], s=0.01)\n",
    "    axs[i].set_xlabel('AreaShape_Area')\n",
    "    axs[i].set_ylabel(col)\n",
    "    axs[i].set_title(f'Area vs {cols_to_plot[i]}')\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5e128c27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot partially labelled \n",
    "# scatter plot, same as above, except outlier 94.\n",
    "# P vs X_NORM, RAW vs X_NORM, LOGIT vs X_NORM, ENTROPY vs X_NORM\n",
    "fig, axs = plt.subplots(1,3,figsize=(20,5))\n",
    "axs = axs.ravel()\n",
    "cols_to_plot = ['Eccentricity','FormFactor','ConvexArea']\n",
    "col_to_plot2 = ['ENTROPY','RAW']\n",
    "df_to_plot=seg_df[seg_df['AreaShape_Area']<1000]\n",
    "\n",
    "for i in range(len(cols_to_plot)):\n",
    "    col = 'AreaShape_'+cols_to_plot[i]\n",
    "    axs[i].scatter(df_to_plot['AreaShape_Area'], df_to_plot[col], s=0.01)\n",
    "    axs[i].set_xlabel('AreaShape_Area')\n",
    "    axs[i].set_ylabel(col)\n",
    "    axs[i].set_title(f'Area vs {cols_to_plot[i]}')\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45d6dae7",
   "metadata": {},
   "source": [
    "## Assign labels from Segmentation Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ce7fcbda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n"
     ]
    },
    {
     "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>label</th>\n",
       "      <th>Barcode_Idx</th>\n",
       "      <th>SUM</th>\n",
       "      <th>Barcode</th>\n",
       "      <th>RAW</th>\n",
       "      <th>P</th>\n",
       "      <th>LOGIT</th>\n",
       "      <th>ENTROPY</th>\n",
       "      <th>X_NORM</th>\n",
       "      <th>Gene</th>\n",
       "      <th>...</th>\n",
       "      <th>intensity_mean-5</th>\n",
       "      <th>intensity_mean-6</th>\n",
       "      <th>Delta</th>\n",
       "      <th>embedding1</th>\n",
       "      <th>embedding2</th>\n",
       "      <th>MP_UMAP1</th>\n",
       "      <th>MP_UMAP2</th>\n",
       "      <th>True_Label</th>\n",
       "      <th>InfectedCells</th>\n",
       "      <th>Seg_Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.057395</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.038489</td>\n",
       "      <td>0.670602</td>\n",
       "      <td>0.710910</td>\n",
       "      <td>0.864662</td>\n",
       "      <td>0.035280</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000442</td>\n",
       "      <td>0.002411</td>\n",
       "      <td>0.007052</td>\n",
       "      <td>8.919654</td>\n",
       "      <td>8.924275</td>\n",
       "      <td>8.274383</td>\n",
       "      <td>-0.655967</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.067428</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.054958</td>\n",
       "      <td>0.815054</td>\n",
       "      <td>1.483194</td>\n",
       "      <td>0.793739</td>\n",
       "      <td>0.043299</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006315</td>\n",
       "      <td>0.000928</td>\n",
       "      <td>0.009323</td>\n",
       "      <td>10.795541</td>\n",
       "      <td>8.218206</td>\n",
       "      <td>13.875708</td>\n",
       "      <td>11.552190</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>intensity_mean-8</td>\n",
       "      <td>0.035084</td>\n",
       "      <td>D3</td>\n",
       "      <td>0.024482</td>\n",
       "      <td>0.697820</td>\n",
       "      <td>0.836939</td>\n",
       "      <td>1.319092</td>\n",
       "      <td>0.026690</td>\n",
       "      <td>PPP2R2B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000317</td>\n",
       "      <td>0.014562</td>\n",
       "      <td>0.009024</td>\n",
       "      <td>11.490246</td>\n",
       "      <td>10.536827</td>\n",
       "      <td>6.743976</td>\n",
       "      <td>19.614027</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>intensity_mean-3</td>\n",
       "      <td>0.261197</td>\n",
       "      <td>C12</td>\n",
       "      <td>0.179114</td>\n",
       "      <td>0.685744</td>\n",
       "      <td>0.780298</td>\n",
       "      <td>1.402972</td>\n",
       "      <td>0.108538</td>\n",
       "      <td>HSF1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.062781</td>\n",
       "      <td>0.061092</td>\n",
       "      <td>0.025526</td>\n",
       "      <td>12.307695</td>\n",
       "      <td>3.216018</td>\n",
       "      <td>10.193657</td>\n",
       "      <td>8.133395</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.272067</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.267507</td>\n",
       "      <td>0.983239</td>\n",
       "      <td>4.071811</td>\n",
       "      <td>0.117349</td>\n",
       "      <td>0.142667</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.001225</td>\n",
       "      <td>0.071709</td>\n",
       "      <td>9.866774</td>\n",
       "      <td>1.810647</td>\n",
       "      <td>7.693904</td>\n",
       "      <td>17.100174</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>133695</th>\n",
       "      <td>258</td>\n",
       "      <td>intensity_mean-13</td>\n",
       "      <td>0.022828</td>\n",
       "      <td>E4</td>\n",
       "      <td>0.019829</td>\n",
       "      <td>0.868626</td>\n",
       "      <td>1.888866</td>\n",
       "      <td>0.667654</td>\n",
       "      <td>0.014764</td>\n",
       "      <td>HRAS</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000670</td>\n",
       "      <td>0.002336</td>\n",
       "      <td>11.176550</td>\n",
       "      <td>12.540327</td>\n",
       "      <td>-4.482765</td>\n",
       "      <td>7.153024</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133696</th>\n",
       "      <td>259</td>\n",
       "      <td>intensity_mean-13</td>\n",
       "      <td>0.326837</td>\n",
       "      <td>E4</td>\n",
       "      <td>0.114697</td>\n",
       "      <td>0.350931</td>\n",
       "      <td>-0.614949</td>\n",
       "      <td>1.860739</td>\n",
       "      <td>0.119025</td>\n",
       "      <td>HRAS</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009312</td>\n",
       "      <td>0.031562</td>\n",
       "      <td>0.001021</td>\n",
       "      <td>11.251929</td>\n",
       "      <td>2.987429</td>\n",
       "      <td>10.204902</td>\n",
       "      <td>5.165628</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133697</th>\n",
       "      <td>260</td>\n",
       "      <td>intensity_mean-9</td>\n",
       "      <td>0.188926</td>\n",
       "      <td>D4</td>\n",
       "      <td>0.161259</td>\n",
       "      <td>0.853556</td>\n",
       "      <td>1.762765</td>\n",
       "      <td>0.616539</td>\n",
       "      <td>0.088308</td>\n",
       "      <td>FAN1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003363</td>\n",
       "      <td>0.064340</td>\n",
       "      <td>0.027823</td>\n",
       "      <td>9.917559</td>\n",
       "      <td>4.129629</td>\n",
       "      <td>10.297348</td>\n",
       "      <td>1.767311</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133698</th>\n",
       "      <td>261</td>\n",
       "      <td>intensity_mean-19</td>\n",
       "      <td>0.003397</td>\n",
       "      <td>G8</td>\n",
       "      <td>0.002275</td>\n",
       "      <td>0.669709</td>\n",
       "      <td>0.706868</td>\n",
       "      <td>1.034844</td>\n",
       "      <td>0.021284</td>\n",
       "      <td>RAPGEF2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>0.010320</td>\n",
       "      <td>0.001827</td>\n",
       "      <td>10.905128</td>\n",
       "      <td>11.772167</td>\n",
       "      <td>8.326226</td>\n",
       "      <td>4.388920</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133699</th>\n",
       "      <td>262</td>\n",
       "      <td>intensity_mean-15</td>\n",
       "      <td>1.048392</td>\n",
       "      <td>E8</td>\n",
       "      <td>0.684923</td>\n",
       "      <td>0.653308</td>\n",
       "      <td>0.633613</td>\n",
       "      <td>1.344290</td>\n",
       "      <td>0.397249</td>\n",
       "      <td>WASL</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012467</td>\n",
       "      <td>0.004988</td>\n",
       "      <td>0.119821</td>\n",
       "      <td>6.712374</td>\n",
       "      <td>-2.731318</td>\n",
       "      <td>7.098932</td>\n",
       "      <td>3.543230</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133700 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        label        Barcode_Idx       SUM Barcode       RAW         P  \\\n",
       "0           1   intensity_mean-0  0.057395      A2  0.038489  0.670602   \n",
       "1           2   intensity_mean-0  0.067428      A2  0.054958  0.815054   \n",
       "2           3   intensity_mean-8  0.035084      D3  0.024482  0.697820   \n",
       "3           4   intensity_mean-3  0.261197     C12  0.179114  0.685744   \n",
       "4           5   intensity_mean-0  0.272067      A2  0.267507  0.983239   \n",
       "...       ...                ...       ...     ...       ...       ...   \n",
       "133695    258  intensity_mean-13  0.022828      E4  0.019829  0.868626   \n",
       "133696    259  intensity_mean-13  0.326837      E4  0.114697  0.350931   \n",
       "133697    260   intensity_mean-9  0.188926      D4  0.161259  0.853556   \n",
       "133698    261  intensity_mean-19  0.003397      G8  0.002275  0.669709   \n",
       "133699    262  intensity_mean-15  1.048392      E8  0.684923  0.653308   \n",
       "\n",
       "           LOGIT   ENTROPY    X_NORM     Gene  ...  intensity_mean-5  \\\n",
       "0       0.710910  0.864662  0.035280   PGGT1B  ...          0.000442   \n",
       "1       1.483194  0.793739  0.043299   PGGT1B  ...          0.006315   \n",
       "2       0.836939  1.319092  0.026690  PPP2R2B  ...          0.000317   \n",
       "3       0.780298  1.402972  0.108538     HSF1  ...          0.062781   \n",
       "4       4.071811  0.117349  0.142667   PGGT1B  ...          0.000070   \n",
       "...          ...       ...       ...      ...  ...               ...   \n",
       "133695  1.888866  0.667654  0.014764     HRAS  ...          0.000016   \n",
       "133696 -0.614949  1.860739  0.119025     HRAS  ...          0.009312   \n",
       "133697  1.762765  0.616539  0.088308     FAN1  ...          0.003363   \n",
       "133698  0.706868  1.034844  0.021284  RAPGEF2  ...          0.000244   \n",
       "133699  0.633613  1.344290  0.397249     WASL  ...          0.012467   \n",
       "\n",
       "        intensity_mean-6     Delta  embedding1  embedding2   MP_UMAP1  \\\n",
       "0               0.002411  0.007052    8.919654    8.924275   8.274383   \n",
       "1               0.000928  0.009323   10.795541    8.218206  13.875708   \n",
       "2               0.014562  0.009024   11.490246   10.536827   6.743976   \n",
       "3               0.061092  0.025526   12.307695    3.216018  10.193657   \n",
       "4               0.001225  0.071709    9.866774    1.810647   7.693904   \n",
       "...                  ...       ...         ...         ...        ...   \n",
       "133695          0.000670  0.002336   11.176550   12.540327  -4.482765   \n",
       "133696          0.031562  0.001021   11.251929    2.987429  10.204902   \n",
       "133697          0.064340  0.027823    9.917559    4.129629  10.297348   \n",
       "133698          0.010320  0.001827   10.905128   11.772167   8.326226   \n",
       "133699          0.004988  0.119821    6.712374   -2.731318   7.098932   \n",
       "\n",
       "         MP_UMAP2  True_Label  InfectedCells  Seg_Label  \n",
       "0       -0.655967           0              0          0  \n",
       "1       11.552190           0              0          0  \n",
       "2       19.614027           0              0          0  \n",
       "3        8.133395           0              1          0  \n",
       "4       17.100174           0              1          0  \n",
       "...           ...         ...            ...        ...  \n",
       "133695   7.153024           0              0          0  \n",
       "133696   5.165628           0              0          0  \n",
       "133697   1.767311           0              0          0  \n",
       "133698   4.388920           0              0          0  \n",
       "133699   3.543230           0              1          0  \n",
       "\n",
       "[133700 rows x 29 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display segmentation labels\n",
    "seg_labels = {}\n",
    "seg_labels[13] = pd.read_csv('Coverslip1_GroundTruth_13.csv', sep=',', header=None)\n",
    "seg_labels[213] = pd.read_csv('Coverslip1_GroundTruth_213.csv', sep=',', header=None)\n",
    "image_num_labelled = [13,213]\n",
    "soma_df['Seg_Label'] = 0\n",
    "for image_num in image_num_labelled:\n",
    "    N_cells = seg_labels[image_num][1].values.shape[0]\n",
    "    labels = seg_labels[image_num][1].values.reshape((N_cells,))\n",
    "    idx = soma_df[soma_df.ImageNumber==image_num].index\n",
    "    print(len(idx) == len(labels))\n",
    "    soma_df.loc[idx,'Seg_Label'] = labels\n",
    "soma_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1c69e187",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "        \n",
    "# plot partially labelled \n",
    "# scatter plot, same as above, except outlier 94.\n",
    "# P vs X_NORM, RAW vs X_NORM, LOGIT vs X_NORM, ENTROPY vs X_NORM\n",
    "fig, axs = plt.subplots(2,3,figsize=(20,10))\n",
    "cols_to_plot = ['Eccentricity','FormFactor','ConvexArea']\n",
    "col_to_plot2 = ['ENTROPY','RAW']\n",
    "df_to_plot1=seg_df[seg_df['AreaShape_Area']<5000]\n",
    "df_to_plot2=seg_df[seg_df['AreaShape_Area']<1000]\n",
    "\n",
    "for i in range(len(cols_to_plot)):\n",
    "    col = 'AreaShape_'+cols_to_plot[i]\n",
    "    df1 = soma_df.loc[df_to_plot1.index]\n",
    "    idx1 = df1[df1['Seg_Label'] == 1].index # Seg Label is only in soma_df\n",
    "    axs[0][i].scatter(df_to_plot1.loc[idx1,'AreaShape_Area'], df_to_plot1.loc[idx1, col], s=5, color = 'r')\n",
    "    idx2 = df1[df1['Seg_Label'] == 0].index\n",
    "    axs[0][i].scatter(df_to_plot1.loc[idx2,'AreaShape_Area'], df_to_plot1.loc[idx2, col], s=0.01, color = 'b')\n",
    "    axs[0][i].set_xlabel('AreaShape_Area')\n",
    "    axs[0][i].set_ylabel(col)\n",
    "    axs[0][i].set_title(f'Area vs {cols_to_plot[i]}')\n",
    "    \n",
    "    \n",
    "for i in range(len(cols_to_plot)):\n",
    "    col = 'AreaShape_'+cols_to_plot[i]\n",
    "    df2 = soma_df.loc[df_to_plot2.index]\n",
    "    idx1 = df2[df2['Seg_Label'] == 1].index\n",
    "    axs[1][i].scatter(df_to_plot2.loc[idx1,'AreaShape_Area'], df_to_plot2.loc[idx1, col], s=5, color = 'r')\n",
    "    idx2 = df2[df2['Seg_Label'] == 0].index\n",
    "    axs[1][i].scatter(df_to_plot2.loc[idx2,'AreaShape_Area'], df_to_plot2.loc[idx2, col], s=0.01, color = 'b')\n",
    "    axs[1][i].set_xlabel('AreaShape_Area')\n",
    "    axs[1][i].set_ylabel(col)\n",
    "    axs[1][i].set_title(f'Area vs {cols_to_plot[i]}')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e731f261",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "        \n",
    "# plot partially labelled \n",
    "# scatter plot, same as above, except outlier 94.\n",
    "# P vs X_NORM, RAW vs X_NORM, LOGIT vs X_NORM, ENTROPY vs X_NORM\n",
    "fig, axs = plt.subplots(2,3,figsize=(20,10))\n",
    "cols_to_plot = ['Eccentricity','FormFactor','ConvexArea']\n",
    "col_to_plot2 = ['ENTROPY','RAW']\n",
    "df_to_plot1=seg_df[seg_df['AreaShape_Area']<5000]\n",
    "df_to_plot2=seg_df[seg_df['AreaShape_Area']<1000]\n",
    "\n",
    "for i in range(len(cols_to_plot)):\n",
    "    col = 'AreaShape_'+cols_to_plot[i]\n",
    "    df1 = soma_df.loc[df_to_plot1.index]\n",
    "    \n",
    "    # only display images that are labelled?\n",
    "    df1 = df1[(df1.ImageNumber == 13) | (df1.ImageNumber == 213)]\n",
    "    \n",
    "    idx1 = df1[df1['Seg_Label'] == 1].index # Seg Label is only in soma_df\n",
    "    axs[0][i].scatter(df_to_plot1.loc[idx1,'AreaShape_Area'], df_to_plot1.loc[idx1, col], s=3, color = 'r')\n",
    "    idx2 = df1[df1['Seg_Label'] == 0].index\n",
    "    axs[0][i].scatter(df_to_plot1.loc[idx2,'AreaShape_Area'], df_to_plot1.loc[idx2, col], s=3, color = 'b')\n",
    "    axs[0][i].set_xlabel('AreaShape_Area')\n",
    "    axs[0][i].set_ylabel(col)\n",
    "    axs[0][i].set_title(f'Area vs {cols_to_plot[i]}')\n",
    "    \n",
    "    \n",
    "for i in range(len(cols_to_plot)):\n",
    "    col = 'AreaShape_'+cols_to_plot[i]\n",
    "    df2 = soma_df.loc[df_to_plot2.index]\n",
    "    \n",
    "    # only display images that are labelled?\n",
    "    df2 = df2[(df2.ImageNumber == 13) | (df2.ImageNumber == 213)]\n",
    "\n",
    "    idx1 = df2[df2['Seg_Label'] == 1].index\n",
    "    axs[1][i].scatter(df_to_plot2.loc[idx1,'AreaShape_Area'], df_to_plot2.loc[idx1, col], s=3, color = 'r')\n",
    "    idx2 = df2[df2['Seg_Label'] == 0].index\n",
    "    axs[1][i].scatter(df_to_plot2.loc[idx2,'AreaShape_Area'], df_to_plot2.loc[idx2, col], s=3, color = 'b')\n",
    "    axs[1][i].set_xlabel('AreaShape_Area')\n",
    "    axs[1][i].set_ylabel(col)\n",
    "    axs[1][i].set_title(f'Area vs {cols_to_plot[i]}')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e7caf050",
   "metadata": {},
   "outputs": [],
   "source": [
    "# MAD normalization after filtering columns\n",
    "def MAD(df_in, veto_list, eps): # seg_df,  ['ImageNumber', 'ObjectNumber',...,'Number', 'Parent'], 1e-15\n",
    "    # filter columns for veto (nonsense columns like object number, location, coordinates, etc)\n",
    "    L = df_in.columns.tolist()\n",
    "    removeL = []\n",
    "    for v in veto:\n",
    "        for l in L:\n",
    "            if l.startswith(v):\n",
    "                removeL.append(l)       \n",
    "    \n",
    "    for l in L:\n",
    "        if 'BoundingBoxMaximum' in l.split('_'):\n",
    "            print(l)\n",
    "            removeL.append(l)\n",
    "        if 'BoundingBoxMinimum' in l.split('_'):\n",
    "            print(l)\n",
    "            removeL.append(l)\n",
    "        \n",
    "    removeL.append('AreaShape_Center_X')\n",
    "    removeL.append('AreaShape_Center_Y')\n",
    "    print('removing ', removeL)\n",
    "    for x in removeL: L.remove(x)\n",
    "        \n",
    "        \n",
    "    # filter nan cols\n",
    "    df_in = df_in[L]\n",
    "    nan_cols = [i for i in df_in.columns if df_in[i].isnull().any()]\n",
    "    print(nan_cols)\n",
    "    print(\"NAN columns\", len(nan_cols))\n",
    "    df_in = df_in[df_in.columns[~df_in.columns.isin(nan_cols)]]\n",
    "    \n",
    "    # calculate MAD\n",
    "    MAD = stats.median_abs_deviation(df_in, nan_policy='omit')\n",
    "    df_in_scaled = df_in - np.median(df_in, axis=0)\n",
    "    df_in_scaled = np.divide(df_in_scaled, MAD+eps)\n",
    "    \n",
    "    return df_in_scaled\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "50398feb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ImageNumber', 'ObjectNumber', 'FileName_Cyto', 'FileName_Nuclei',\n",
       "       'FileName_Soma', 'PathName_Cyto', 'PathName_Nuclei', 'PathName_Soma',\n",
       "       'AreaShape_Area', 'AreaShape_BoundingBoxArea',\n",
       "       'AreaShape_BoundingBoxMaximum_X', 'AreaShape_BoundingBoxMaximum_Y',\n",
       "       'AreaShape_BoundingBoxMinimum_X', 'AreaShape_BoundingBoxMinimum_Y',\n",
       "       'AreaShape_Center_X', 'AreaShape_Center_Y', 'AreaShape_Compactness',\n",
       "       'AreaShape_ConvexArea', 'AreaShape_Eccentricity',\n",
       "       'AreaShape_EquivalentDiameter', 'AreaShape_EulerNumber',\n",
       "       'AreaShape_Extent', 'AreaShape_FormFactor', 'AreaShape_MajorAxisLength',\n",
       "       'AreaShape_MaxFeretDiameter', 'AreaShape_MaximumRadius',\n",
       "       'AreaShape_MeanRadius', 'AreaShape_MedianRadius',\n",
       "       'AreaShape_MinFeretDiameter', 'AreaShape_MinorAxisLength',\n",
       "       'AreaShape_Orientation', 'AreaShape_Perimeter', 'AreaShape_Solidity',\n",
       "       'Location_Center_X', 'Location_Center_Y', 'Location_Center_Z',\n",
       "       'Number_Object_Number'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "00dad444",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21\n",
      "30\n",
      "113\n",
      "166\n",
      "180\n",
      "264\n"
     ]
    }
   ],
   "source": [
    "# inspect a few images that look much larger\n",
    "idx = soma_df[soma_df.ImageNumber == 13].index\n",
    "df = seg_df.loc[idx] # contains labels\n",
    "for cell in df[(df.AreaShape_Area > 2000)&(df.AreaShape_Area < 3000)]['ObjectNumber']:\n",
    "    print(cell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "91560755",
   "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>ImageNumber</th>\n",
       "      <th>ObjectNumber</th>\n",
       "      <th>FileName_Cyto</th>\n",
       "      <th>FileName_Nuclei</th>\n",
       "      <th>FileName_Soma</th>\n",
       "      <th>PathName_Cyto</th>\n",
       "      <th>PathName_Nuclei</th>\n",
       "      <th>PathName_Soma</th>\n",
       "      <th>AreaShape_Area</th>\n",
       "      <th>AreaShape_BoundingBoxArea</th>\n",
       "      <th>...</th>\n",
       "      <th>AreaShape_MedianRadius</th>\n",
       "      <th>AreaShape_MinFeretDiameter</th>\n",
       "      <th>AreaShape_MinorAxisLength</th>\n",
       "      <th>AreaShape_Orientation</th>\n",
       "      <th>AreaShape_Perimeter</th>\n",
       "      <th>AreaShape_Solidity</th>\n",
       "      <th>Location_Center_X</th>\n",
       "      <th>Location_Center_Y</th>\n",
       "      <th>Location_Center_Z</th>\n",
       "      <th>Number_Object_Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>F000_max_clean_Cyto.tiff</td>\n",
       "      <td>F000_max_clean_Nuclei.tiff</td>\n",
       "      <td>F000_max_clean_Soma.tiff</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>4666</td>\n",
       "      <td>9120</td>\n",
       "      <td>...</td>\n",
       "      <td>7.280110</td>\n",
       "      <td>73.173414</td>\n",
       "      <td>63.858926</td>\n",
       "      <td>-10.383178</td>\n",
       "      <td>511.209199</td>\n",
       "      <td>0.715425</td>\n",
       "      <td>1279.176811</td>\n",
       "      <td>82.330690</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>F000_max_clean_Cyto.tiff</td>\n",
       "      <td>F000_max_clean_Nuclei.tiff</td>\n",
       "      <td>F000_max_clean_Soma.tiff</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>4553</td>\n",
       "      <td>7979</td>\n",
       "      <td>...</td>\n",
       "      <td>7.615773</td>\n",
       "      <td>69.349619</td>\n",
       "      <td>70.233086</td>\n",
       "      <td>-14.116441</td>\n",
       "      <td>441.203102</td>\n",
       "      <td>0.809854</td>\n",
       "      <td>1327.216561</td>\n",
       "      <td>105.901384</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>F000_max_clean_Cyto.tiff</td>\n",
       "      <td>F000_max_clean_Nuclei.tiff</td>\n",
       "      <td>F000_max_clean_Soma.tiff</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>4907</td>\n",
       "      <td>8170</td>\n",
       "      <td>...</td>\n",
       "      <td>5.385165</td>\n",
       "      <td>84.844011</td>\n",
       "      <td>86.617992</td>\n",
       "      <td>21.256936</td>\n",
       "      <td>598.943218</td>\n",
       "      <td>0.718869</td>\n",
       "      <td>1343.771143</td>\n",
       "      <td>797.784593</td>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>2</td>\n",
       "      <td>51</td>\n",
       "      <td>F001_max_clean_Cyto.tiff</td>\n",
       "      <td>F001_max_clean_Nuclei.tiff</td>\n",
       "      <td>F001_max_clean_Soma.tiff</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>8193</td>\n",
       "      <td>13334</td>\n",
       "      <td>...</td>\n",
       "      <td>9.055385</td>\n",
       "      <td>91.479059</td>\n",
       "      <td>96.967061</td>\n",
       "      <td>48.388692</td>\n",
       "      <td>552.842712</td>\n",
       "      <td>0.884582</td>\n",
       "      <td>608.309166</td>\n",
       "      <td>341.712560</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>2</td>\n",
       "      <td>214</td>\n",
       "      <td>F001_max_clean_Cyto.tiff</td>\n",
       "      <td>F001_max_clean_Nuclei.tiff</td>\n",
       "      <td>F001_max_clean_Soma.tiff</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>4936</td>\n",
       "      <td>9324</td>\n",
       "      <td>...</td>\n",
       "      <td>6.324555</td>\n",
       "      <td>80.622577</td>\n",
       "      <td>81.294641</td>\n",
       "      <td>-11.188996</td>\n",
       "      <td>485.629509</td>\n",
       "      <td>0.794463</td>\n",
       "      <td>1727.927674</td>\n",
       "      <td>1356.315235</td>\n",
       "      <td>0</td>\n",
       "      <td>214</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>133208</th>\n",
       "      <td>224</td>\n",
       "      <td>71</td>\n",
       "      <td>F223_max_clean_Cyto.tiff</td>\n",
       "      <td>F223_max_clean_Nuclei.tiff</td>\n",
       "      <td>F223_max_clean_Soma.tiff</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>6998</td>\n",
       "      <td>14098</td>\n",
       "      <td>...</td>\n",
       "      <td>7.615773</td>\n",
       "      <td>85.466028</td>\n",
       "      <td>75.170318</td>\n",
       "      <td>29.196223</td>\n",
       "      <td>761.837662</td>\n",
       "      <td>0.724731</td>\n",
       "      <td>1556.829380</td>\n",
       "      <td>387.129608</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133393</th>\n",
       "      <td>224</td>\n",
       "      <td>256</td>\n",
       "      <td>F223_max_clean_Cyto.tiff</td>\n",
       "      <td>F223_max_clean_Nuclei.tiff</td>\n",
       "      <td>F223_max_clean_Soma.tiff</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>5881</td>\n",
       "      <td>11875</td>\n",
       "      <td>...</td>\n",
       "      <td>5.656854</td>\n",
       "      <td>93.093983</td>\n",
       "      <td>78.324663</td>\n",
       "      <td>20.254425</td>\n",
       "      <td>720.938167</td>\n",
       "      <td>0.672883</td>\n",
       "      <td>632.083319</td>\n",
       "      <td>1515.613501</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133396</th>\n",
       "      <td>224</td>\n",
       "      <td>259</td>\n",
       "      <td>F223_max_clean_Cyto.tiff</td>\n",
       "      <td>F223_max_clean_Nuclei.tiff</td>\n",
       "      <td>F223_max_clean_Soma.tiff</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>3162</td>\n",
       "      <td>6450</td>\n",
       "      <td>...</td>\n",
       "      <td>5.830952</td>\n",
       "      <td>64.633700</td>\n",
       "      <td>55.555118</td>\n",
       "      <td>62.408406</td>\n",
       "      <td>393.268073</td>\n",
       "      <td>0.737751</td>\n",
       "      <td>602.055345</td>\n",
       "      <td>1574.285895</td>\n",
       "      <td>0</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133447</th>\n",
       "      <td>225</td>\n",
       "      <td>10</td>\n",
       "      <td>F224_max_clean_Cyto.tiff</td>\n",
       "      <td>F224_max_clean_Nuclei.tiff</td>\n",
       "      <td>F224_max_clean_Soma.tiff</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>10125</td>\n",
       "      <td>28044</td>\n",
       "      <td>...</td>\n",
       "      <td>7.280110</td>\n",
       "      <td>83.644973</td>\n",
       "      <td>70.935472</td>\n",
       "      <td>-71.555325</td>\n",
       "      <td>1113.975793</td>\n",
       "      <td>0.644330</td>\n",
       "      <td>609.942815</td>\n",
       "      <td>289.513086</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133500</th>\n",
       "      <td>225</td>\n",
       "      <td>63</td>\n",
       "      <td>F224_max_clean_Cyto.tiff</td>\n",
       "      <td>F224_max_clean_Nuclei.tiff</td>\n",
       "      <td>F224_max_clean_Soma.tiff</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/101222_D...</td>\n",
       "      <td>4019</td>\n",
       "      <td>11556</td>\n",
       "      <td>...</td>\n",
       "      <td>6.324555</td>\n",
       "      <td>61.165692</td>\n",
       "      <td>54.384152</td>\n",
       "      <td>-28.610655</td>\n",
       "      <td>494.451840</td>\n",
       "      <td>0.629345</td>\n",
       "      <td>522.123663</td>\n",
       "      <td>833.597412</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ImageNumber  ObjectNumber             FileName_Cyto  \\\n",
       "5                 1             6  F000_max_clean_Cyto.tiff   \n",
       "13                1            14  F000_max_clean_Cyto.tiff   \n",
       "131               1           132  F000_max_clean_Cyto.tiff   \n",
       "330               2            51  F001_max_clean_Cyto.tiff   \n",
       "493               2           214  F001_max_clean_Cyto.tiff   \n",
       "...             ...           ...                       ...   \n",
       "133208          224            71  F223_max_clean_Cyto.tiff   \n",
       "133393          224           256  F223_max_clean_Cyto.tiff   \n",
       "133396          224           259  F223_max_clean_Cyto.tiff   \n",
       "133447          225            10  F224_max_clean_Cyto.tiff   \n",
       "133500          225            63  F224_max_clean_Cyto.tiff   \n",
       "\n",
       "                   FileName_Nuclei             FileName_Soma  \\\n",
       "5       F000_max_clean_Nuclei.tiff  F000_max_clean_Soma.tiff   \n",
       "13      F000_max_clean_Nuclei.tiff  F000_max_clean_Soma.tiff   \n",
       "131     F000_max_clean_Nuclei.tiff  F000_max_clean_Soma.tiff   \n",
       "330     F001_max_clean_Nuclei.tiff  F001_max_clean_Soma.tiff   \n",
       "493     F001_max_clean_Nuclei.tiff  F001_max_clean_Soma.tiff   \n",
       "...                            ...                       ...   \n",
       "133208  F223_max_clean_Nuclei.tiff  F223_max_clean_Soma.tiff   \n",
       "133393  F223_max_clean_Nuclei.tiff  F223_max_clean_Soma.tiff   \n",
       "133396  F223_max_clean_Nuclei.tiff  F223_max_clean_Soma.tiff   \n",
       "133447  F224_max_clean_Nuclei.tiff  F224_max_clean_Soma.tiff   \n",
       "133500  F224_max_clean_Nuclei.tiff  F224_max_clean_Soma.tiff   \n",
       "\n",
       "                                            PathName_Cyto  \\\n",
       "5       /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "13      /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "131     /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "330     /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "493     /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "...                                                   ...   \n",
       "133208  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "133393  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "133396  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "133447  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "133500  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "\n",
       "                                          PathName_Nuclei  \\\n",
       "5       /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "13      /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "131     /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "330     /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "493     /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "...                                                   ...   \n",
       "133208  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "133393  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "133396  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "133447  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "133500  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...   \n",
       "\n",
       "                                            PathName_Soma  AreaShape_Area  \\\n",
       "5       /home/imaging/Desktop/Brian/NAS/Brian/101222_D...            4666   \n",
       "13      /home/imaging/Desktop/Brian/NAS/Brian/101222_D...            4553   \n",
       "131     /home/imaging/Desktop/Brian/NAS/Brian/101222_D...            4907   \n",
       "330     /home/imaging/Desktop/Brian/NAS/Brian/101222_D...            8193   \n",
       "493     /home/imaging/Desktop/Brian/NAS/Brian/101222_D...            4936   \n",
       "...                                                   ...             ...   \n",
       "133208  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...            6998   \n",
       "133393  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...            5881   \n",
       "133396  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...            3162   \n",
       "133447  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...           10125   \n",
       "133500  /home/imaging/Desktop/Brian/NAS/Brian/101222_D...            4019   \n",
       "\n",
       "        AreaShape_BoundingBoxArea  ...  AreaShape_MedianRadius  \\\n",
       "5                            9120  ...                7.280110   \n",
       "13                           7979  ...                7.615773   \n",
       "131                          8170  ...                5.385165   \n",
       "330                         13334  ...                9.055385   \n",
       "493                          9324  ...                6.324555   \n",
       "...                           ...  ...                     ...   \n",
       "133208                      14098  ...                7.615773   \n",
       "133393                      11875  ...                5.656854   \n",
       "133396                       6450  ...                5.830952   \n",
       "133447                      28044  ...                7.280110   \n",
       "133500                      11556  ...                6.324555   \n",
       "\n",
       "        AreaShape_MinFeretDiameter  AreaShape_MinorAxisLength  \\\n",
       "5                        73.173414                  63.858926   \n",
       "13                       69.349619                  70.233086   \n",
       "131                      84.844011                  86.617992   \n",
       "330                      91.479059                  96.967061   \n",
       "493                      80.622577                  81.294641   \n",
       "...                            ...                        ...   \n",
       "133208                   85.466028                  75.170318   \n",
       "133393                   93.093983                  78.324663   \n",
       "133396                   64.633700                  55.555118   \n",
       "133447                   83.644973                  70.935472   \n",
       "133500                   61.165692                  54.384152   \n",
       "\n",
       "        AreaShape_Orientation  AreaShape_Perimeter  AreaShape_Solidity  \\\n",
       "5                  -10.383178           511.209199            0.715425   \n",
       "13                 -14.116441           441.203102            0.809854   \n",
       "131                 21.256936           598.943218            0.718869   \n",
       "330                 48.388692           552.842712            0.884582   \n",
       "493                -11.188996           485.629509            0.794463   \n",
       "...                       ...                  ...                 ...   \n",
       "133208              29.196223           761.837662            0.724731   \n",
       "133393              20.254425           720.938167            0.672883   \n",
       "133396              62.408406           393.268073            0.737751   \n",
       "133447             -71.555325          1113.975793            0.644330   \n",
       "133500             -28.610655           494.451840            0.629345   \n",
       "\n",
       "        Location_Center_X  Location_Center_Y  Location_Center_Z  \\\n",
       "5             1279.176811          82.330690                  0   \n",
       "13            1327.216561         105.901384                  0   \n",
       "131           1343.771143         797.784593                  0   \n",
       "330            608.309166         341.712560                  0   \n",
       "493           1727.927674        1356.315235                  0   \n",
       "...                   ...                ...                ...   \n",
       "133208        1556.829380         387.129608                  0   \n",
       "133393         632.083319        1515.613501                  0   \n",
       "133396         602.055345        1574.285895                  0   \n",
       "133447         609.942815         289.513086                  0   \n",
       "133500         522.123663         833.597412                  0   \n",
       "\n",
       "        Number_Object_Number  \n",
       "5                          6  \n",
       "13                        14  \n",
       "131                      132  \n",
       "330                       51  \n",
       "493                      214  \n",
       "...                      ...  \n",
       "133208                    71  \n",
       "133393                   256  \n",
       "133396                   259  \n",
       "133447                    10  \n",
       "133500                    63  \n",
       "\n",
       "[300 rows x 37 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg_df[seg_df.AreaShape_Area > 3000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f7c7e7ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>Barcode_Idx</th>\n",
       "      <th>SUM</th>\n",
       "      <th>Barcode</th>\n",
       "      <th>RAW</th>\n",
       "      <th>P</th>\n",
       "      <th>LOGIT</th>\n",
       "      <th>ENTROPY</th>\n",
       "      <th>X_NORM</th>\n",
       "      <th>Gene</th>\n",
       "      <th>...</th>\n",
       "      <th>intensity_mean-5</th>\n",
       "      <th>intensity_mean-6</th>\n",
       "      <th>Delta</th>\n",
       "      <th>embedding1</th>\n",
       "      <th>embedding2</th>\n",
       "      <th>MP_UMAP1</th>\n",
       "      <th>MP_UMAP2</th>\n",
       "      <th>True_Label</th>\n",
       "      <th>InfectedCells</th>\n",
       "      <th>Seg_Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.057395</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.038489</td>\n",
       "      <td>0.670602</td>\n",
       "      <td>0.710910</td>\n",
       "      <td>0.864662</td>\n",
       "      <td>0.035280</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000442</td>\n",
       "      <td>0.002411</td>\n",
       "      <td>0.007052</td>\n",
       "      <td>8.919654</td>\n",
       "      <td>8.924275</td>\n",
       "      <td>8.274383</td>\n",
       "      <td>-0.655967</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.067428</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.054958</td>\n",
       "      <td>0.815054</td>\n",
       "      <td>1.483194</td>\n",
       "      <td>0.793739</td>\n",
       "      <td>0.043299</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006315</td>\n",
       "      <td>0.000928</td>\n",
       "      <td>0.009323</td>\n",
       "      <td>10.795541</td>\n",
       "      <td>8.218206</td>\n",
       "      <td>13.875708</td>\n",
       "      <td>11.552190</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>intensity_mean-8</td>\n",
       "      <td>0.035084</td>\n",
       "      <td>D3</td>\n",
       "      <td>0.024482</td>\n",
       "      <td>0.697820</td>\n",
       "      <td>0.836939</td>\n",
       "      <td>1.319092</td>\n",
       "      <td>0.026690</td>\n",
       "      <td>PPP2R2B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000317</td>\n",
       "      <td>0.014562</td>\n",
       "      <td>0.009024</td>\n",
       "      <td>11.490246</td>\n",
       "      <td>10.536827</td>\n",
       "      <td>6.743976</td>\n",
       "      <td>19.614027</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>intensity_mean-3</td>\n",
       "      <td>0.261197</td>\n",
       "      <td>C12</td>\n",
       "      <td>0.179114</td>\n",
       "      <td>0.685744</td>\n",
       "      <td>0.780298</td>\n",
       "      <td>1.402972</td>\n",
       "      <td>0.108538</td>\n",
       "      <td>HSF1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.062781</td>\n",
       "      <td>0.061092</td>\n",
       "      <td>0.025526</td>\n",
       "      <td>12.307695</td>\n",
       "      <td>3.216018</td>\n",
       "      <td>10.193657</td>\n",
       "      <td>8.133395</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.272067</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.267507</td>\n",
       "      <td>0.983239</td>\n",
       "      <td>4.071811</td>\n",
       "      <td>0.117349</td>\n",
       "      <td>0.142667</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.001225</td>\n",
       "      <td>0.071709</td>\n",
       "      <td>9.866774</td>\n",
       "      <td>1.810647</td>\n",
       "      <td>7.693904</td>\n",
       "      <td>17.100174</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>133695</th>\n",
       "      <td>258</td>\n",
       "      <td>intensity_mean-13</td>\n",
       "      <td>0.022828</td>\n",
       "      <td>E4</td>\n",
       "      <td>0.019829</td>\n",
       "      <td>0.868626</td>\n",
       "      <td>1.888866</td>\n",
       "      <td>0.667654</td>\n",
       "      <td>0.014764</td>\n",
       "      <td>HRAS</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000670</td>\n",
       "      <td>0.002336</td>\n",
       "      <td>11.176550</td>\n",
       "      <td>12.540327</td>\n",
       "      <td>-4.482765</td>\n",
       "      <td>7.153024</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133696</th>\n",
       "      <td>259</td>\n",
       "      <td>intensity_mean-13</td>\n",
       "      <td>0.326837</td>\n",
       "      <td>E4</td>\n",
       "      <td>0.114697</td>\n",
       "      <td>0.350931</td>\n",
       "      <td>-0.614949</td>\n",
       "      <td>1.860739</td>\n",
       "      <td>0.119025</td>\n",
       "      <td>HRAS</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009312</td>\n",
       "      <td>0.031562</td>\n",
       "      <td>0.001021</td>\n",
       "      <td>11.251929</td>\n",
       "      <td>2.987429</td>\n",
       "      <td>10.204902</td>\n",
       "      <td>5.165628</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133697</th>\n",
       "      <td>260</td>\n",
       "      <td>intensity_mean-9</td>\n",
       "      <td>0.188926</td>\n",
       "      <td>D4</td>\n",
       "      <td>0.161259</td>\n",
       "      <td>0.853556</td>\n",
       "      <td>1.762765</td>\n",
       "      <td>0.616539</td>\n",
       "      <td>0.088308</td>\n",
       "      <td>FAN1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003363</td>\n",
       "      <td>0.064340</td>\n",
       "      <td>0.027823</td>\n",
       "      <td>9.917559</td>\n",
       "      <td>4.129629</td>\n",
       "      <td>10.297348</td>\n",
       "      <td>1.767311</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133698</th>\n",
       "      <td>261</td>\n",
       "      <td>intensity_mean-19</td>\n",
       "      <td>0.003397</td>\n",
       "      <td>G8</td>\n",
       "      <td>0.002275</td>\n",
       "      <td>0.669709</td>\n",
       "      <td>0.706868</td>\n",
       "      <td>1.034844</td>\n",
       "      <td>0.021284</td>\n",
       "      <td>RAPGEF2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>0.010320</td>\n",
       "      <td>0.001827</td>\n",
       "      <td>10.905128</td>\n",
       "      <td>11.772167</td>\n",
       "      <td>8.326226</td>\n",
       "      <td>4.388920</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133699</th>\n",
       "      <td>262</td>\n",
       "      <td>intensity_mean-15</td>\n",
       "      <td>1.048392</td>\n",
       "      <td>E8</td>\n",
       "      <td>0.684923</td>\n",
       "      <td>0.653308</td>\n",
       "      <td>0.633613</td>\n",
       "      <td>1.344290</td>\n",
       "      <td>0.397249</td>\n",
       "      <td>WASL</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012467</td>\n",
       "      <td>0.004988</td>\n",
       "      <td>0.119821</td>\n",
       "      <td>6.712374</td>\n",
       "      <td>-2.731318</td>\n",
       "      <td>7.098932</td>\n",
       "      <td>3.543230</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133700 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        label        Barcode_Idx       SUM Barcode       RAW         P  \\\n",
       "0           1   intensity_mean-0  0.057395      A2  0.038489  0.670602   \n",
       "1           2   intensity_mean-0  0.067428      A2  0.054958  0.815054   \n",
       "2           3   intensity_mean-8  0.035084      D3  0.024482  0.697820   \n",
       "3           4   intensity_mean-3  0.261197     C12  0.179114  0.685744   \n",
       "4           5   intensity_mean-0  0.272067      A2  0.267507  0.983239   \n",
       "...       ...                ...       ...     ...       ...       ...   \n",
       "133695    258  intensity_mean-13  0.022828      E4  0.019829  0.868626   \n",
       "133696    259  intensity_mean-13  0.326837      E4  0.114697  0.350931   \n",
       "133697    260   intensity_mean-9  0.188926      D4  0.161259  0.853556   \n",
       "133698    261  intensity_mean-19  0.003397      G8  0.002275  0.669709   \n",
       "133699    262  intensity_mean-15  1.048392      E8  0.684923  0.653308   \n",
       "\n",
       "           LOGIT   ENTROPY    X_NORM     Gene  ...  intensity_mean-5  \\\n",
       "0       0.710910  0.864662  0.035280   PGGT1B  ...          0.000442   \n",
       "1       1.483194  0.793739  0.043299   PGGT1B  ...          0.006315   \n",
       "2       0.836939  1.319092  0.026690  PPP2R2B  ...          0.000317   \n",
       "3       0.780298  1.402972  0.108538     HSF1  ...          0.062781   \n",
       "4       4.071811  0.117349  0.142667   PGGT1B  ...          0.000070   \n",
       "...          ...       ...       ...      ...  ...               ...   \n",
       "133695  1.888866  0.667654  0.014764     HRAS  ...          0.000016   \n",
       "133696 -0.614949  1.860739  0.119025     HRAS  ...          0.009312   \n",
       "133697  1.762765  0.616539  0.088308     FAN1  ...          0.003363   \n",
       "133698  0.706868  1.034844  0.021284  RAPGEF2  ...          0.000244   \n",
       "133699  0.633613  1.344290  0.397249     WASL  ...          0.012467   \n",
       "\n",
       "        intensity_mean-6     Delta  embedding1  embedding2   MP_UMAP1  \\\n",
       "0               0.002411  0.007052    8.919654    8.924275   8.274383   \n",
       "1               0.000928  0.009323   10.795541    8.218206  13.875708   \n",
       "2               0.014562  0.009024   11.490246   10.536827   6.743976   \n",
       "3               0.061092  0.025526   12.307695    3.216018  10.193657   \n",
       "4               0.001225  0.071709    9.866774    1.810647   7.693904   \n",
       "...                  ...       ...         ...         ...        ...   \n",
       "133695          0.000670  0.002336   11.176550   12.540327  -4.482765   \n",
       "133696          0.031562  0.001021   11.251929    2.987429  10.204902   \n",
       "133697          0.064340  0.027823    9.917559    4.129629  10.297348   \n",
       "133698          0.010320  0.001827   10.905128   11.772167   8.326226   \n",
       "133699          0.004988  0.119821    6.712374   -2.731318   7.098932   \n",
       "\n",
       "         MP_UMAP2  True_Label  InfectedCells  Seg_Label  \n",
       "0       -0.655967           0              0          0  \n",
       "1       11.552190           0              0          0  \n",
       "2       19.614027           0              0          0  \n",
       "3        8.133395           0              1          0  \n",
       "4       17.100174           0              1          0  \n",
       "...           ...         ...            ...        ...  \n",
       "133695   7.153024           0              0          0  \n",
       "133696   5.165628           0              0          0  \n",
       "133697   1.767311           0              0          0  \n",
       "133698   4.388920           0              0          0  \n",
       "133699   3.543230           0              1          0  \n",
       "\n",
       "[133700 rows x 29 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soma_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3d172ec5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.233983286908078 0.7479108635097493\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "150"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = soma_df[(soma_df.ImageNumber == 13) | (soma_df.ImageNumber == 213)]\n",
    "procode_positives = df.True_Label.sum()/len(df)\n",
    "seg_positives = df.Seg_Label.sum()/len(df)\n",
    "print(procode_positives, seg_positives)\n",
    "\n",
    "AND = len(df[(df.True_Label ==1)& (df.Seg_Label == 1)])\n",
    "AND"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f79fe038",
   "metadata": {},
   "source": [
    "~90% of labelled cells (2 images) with Procode assignment are correctly segmented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b373fcee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "106dc35b",
   "metadata": {},
   "source": [
    "## Classifier for all features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "6dec6a8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df85630a",
   "metadata": {},
   "source": [
    "## LR with Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "ef90f858",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gridCV_fit(params):\n",
    "    best_logreg = LogisticRegression(penalty = params['penalty'], C=params['C'], max_iter = params['max_iter'],\n",
    "                                 solver = params['solver'], class_weight='balanced')\n",
    "    return best_logreg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "0d6a5542",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AreaShape_BoundingBoxMaximum_X\n",
      "AreaShape_BoundingBoxMaximum_Y\n",
      "AreaShape_BoundingBoxMinimum_X\n",
      "AreaShape_BoundingBoxMinimum_Y\n",
      "removing  ['ImageNumber', 'ObjectNumber', 'FileName_Cyto', 'FileName_Nuclei', 'FileName_Soma', 'PathName_Cyto', 'PathName_Nuclei', 'PathName_Soma', 'Location_Center_X', 'Location_Center_Y', 'Location_Center_Z', 'Number_Object_Number', 'AreaShape_BoundingBoxMaximum_X', 'AreaShape_BoundingBoxMaximum_Y', 'AreaShape_BoundingBoxMinimum_X', 'AreaShape_BoundingBoxMinimum_Y', 'AreaShape_Center_X', 'AreaShape_Center_Y']\n",
      "[]\n",
      "NAN columns 0\n",
      "(133700, 19)\n",
      "(718, 19)\n",
      "Removing 0 Cells\n",
      "718 718\n",
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:378: FitFailedWarning: \n",
      "50 fits failed out of a total of 150.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "50 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1091, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 71, in _check_solver\n",
      "    raise ValueError(\n",
      "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/model_selection/_search.py:953: UserWarning: One or more of the test scores are non-finite: [0.2549703  0.36243564        nan 0.2549703  0.36243564        nan\n",
      " 0.37835644 0.36243564        nan 0.74291089 0.36243564        nan\n",
      " 0.81083168 0.36243564        nan 0.79687129 0.36243564        nan\n",
      " 0.79683168 0.36243564        nan 0.79487129 0.36243564        nan\n",
      " 0.79685149 0.36243564        nan 0.79685149 0.36243564        nan]\n",
      "  warnings.warn(\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=0.3593813663804626, class_weight=&#x27;balanced&#x27;, max_iter=5000,\n",
       "                   penalty=&#x27;l1&#x27;, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.3593813663804626, class_weight=&#x27;balanced&#x27;, max_iter=5000,\n",
       "                   penalty=&#x27;l1&#x27;, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(C=0.3593813663804626, class_weight='balanced', max_iter=5000,\n",
       "                   penalty='l1', solver='liblinear')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "# 1) Logistic Regression on barcoded dataframe, then do linear kernel SVM\n",
    "# remove outliers\n",
    "veto = ['ImageNumber', 'ObjectNumber','FileName','PathName','Children','Location', 'Number', 'Parent']\n",
    "seg_df_scaled = MAD(seg_df, veto, 1e-10)\n",
    "print(seg_df_scaled.shape)\n",
    "\n",
    "train_idx = seg_df[(seg_df.ImageNumber == 13) | (seg_df.ImageNumber == 213)].index\n",
    "seg_df_scaled = seg_df_scaled.loc[train_idx,:]\n",
    "print(seg_df_scaled.shape)\n",
    "\n",
    "df_to_plot = seg_df_scaled[(seg_df_scaled.AreaShape_Area < 3000)] # removing outliers?\n",
    "print(f\"Removing {len(seg_df_scaled) - len(df_to_plot)} Cells\")\n",
    "\n",
    "# all columns?\n",
    "feature_cols = seg_df_scaled.columns\n",
    "\n",
    "# comment this out when using all features\n",
    "#cc = ['Eccentricity','Area','FormFactor','ConvexArea']\n",
    "#feature_cols = ['AreaShape_' + x for x in cc]\n",
    "#print(feature_cols)\n",
    "\n",
    "X=df_to_plot.loc[train_idx,feature_cols]\n",
    "y = soma_df.loc[train_idx,\"Seg_Label\"]\n",
    "print(len(X), len(y))\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y , test_size=0.3, random_state=1)\n",
    "\n",
    "# define models and params\n",
    "logreg = LogisticRegression(class_weight='balanced')\n",
    "penalty = ['l1', 'l2','elasticnet']\n",
    "c_values = np.logspace(-4, 4, 10)\n",
    "solver = ['liblinear']\n",
    "max_iter = [5000]\n",
    "# define grid search # use 5-fold cross-validation\n",
    "grid = dict(penalty=penalty, C=c_values, solver=solver, max_iter = max_iter)\n",
    "\n",
    "cv = RepeatedStratifiedKFold(n_splits = 5)\n",
    "clf = GridSearchCV(logreg, param_grid = grid, n_jobs = -1, verbose=True, scoring='accuracy')\n",
    "best_clf = clf.fit(X_train, y_train)\n",
    "\n",
    "best_clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6fb1db14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.810832 using {'C': 0.3593813663804626, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.254970 (0.004166) with: {'C': 0.0001, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.362436 (0.040131) with: {'C': 0.0001, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "nan (nan) with: {'C': 0.0001, 'max_iter': 5000, 'penalty': 'elasticnet', 'solver': 'liblinear'}\n",
      "0.254970 (0.004166) with: {'C': 0.000774263682681127, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.362436 (0.040131) with: {'C': 0.000774263682681127, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "nan (nan) with: {'C': 0.000774263682681127, 'max_iter': 5000, 'penalty': 'elasticnet', 'solver': 'liblinear'}\n",
      "0.378356 (0.040110) with: {'C': 0.005994842503189409, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.362436 (0.040131) with: {'C': 0.005994842503189409, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "nan (nan) with: {'C': 0.005994842503189409, 'max_iter': 5000, 'penalty': 'elasticnet', 'solver': 'liblinear'}\n",
      "0.742911 (0.061464) with: {'C': 0.046415888336127774, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.362436 (0.040131) with: {'C': 0.046415888336127774, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "nan (nan) with: {'C': 0.046415888336127774, 'max_iter': 5000, 'penalty': 'elasticnet', 'solver': 'liblinear'}\n",
      "0.810832 (0.030252) with: {'C': 0.3593813663804626, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.362436 (0.040131) with: {'C': 0.3593813663804626, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "nan (nan) with: {'C': 0.3593813663804626, 'max_iter': 5000, 'penalty': 'elasticnet', 'solver': 'liblinear'}\n",
      "0.796871 (0.026723) with: {'C': 2.782559402207126, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.362436 (0.040131) with: {'C': 2.782559402207126, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "nan (nan) with: {'C': 2.782559402207126, 'max_iter': 5000, 'penalty': 'elasticnet', 'solver': 'liblinear'}\n",
      "0.796832 (0.029120) with: {'C': 21.54434690031882, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.362436 (0.040131) with: {'C': 21.54434690031882, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "nan (nan) with: {'C': 21.54434690031882, 'max_iter': 5000, 'penalty': 'elasticnet', 'solver': 'liblinear'}\n",
      "0.794871 (0.028172) with: {'C': 166.81005372000558, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.362436 (0.040131) with: {'C': 166.81005372000558, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "nan (nan) with: {'C': 166.81005372000558, 'max_iter': 5000, 'penalty': 'elasticnet', 'solver': 'liblinear'}\n",
      "0.796851 (0.029609) with: {'C': 1291.5496650148827, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.362436 (0.040131) with: {'C': 1291.5496650148827, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "nan (nan) with: {'C': 1291.5496650148827, 'max_iter': 5000, 'penalty': 'elasticnet', 'solver': 'liblinear'}\n",
      "0.796851 (0.029609) with: {'C': 10000.0, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "0.362436 (0.040131) with: {'C': 10000.0, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "nan (nan) with: {'C': 10000.0, 'max_iter': 5000, 'penalty': 'elasticnet', 'solver': 'liblinear'}\n"
     ]
    }
   ],
   "source": [
    "# summarize results\n",
    "print(\"Best: %f using %s\" % (best_clf.best_score_, best_clf.best_params_))\n",
    "means = best_clf.cv_results_['mean_test_score']\n",
    "stds = best_clf.cv_results_['std_test_score']\n",
    "params = best_clf.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d5aabb8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "logit_roc_auc = roc_auc_score(y_test, clf.predict(X_test))\n",
    "fpr, tpr, thresholds = roc_curve(y_test, clf.predict_proba(X_test)[:,1])\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('Log_ROC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7b579734",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7824074074074074"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print param weights?\n",
    "params = best_clf.best_params_\n",
    "# build another classifier instance with selected model params\n",
    "# define models and params\n",
    "best_logreg = LogisticRegression(penalty = params['penalty'], C=params['C'], max_iter = params['max_iter'],\n",
    "                                 solver = params['solver'], class_weight='balanced')\n",
    "best_logreg.fit(X_train, y_train)\n",
    "best_logreg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "00a04378",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21,)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_logreg.feature_names_in_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f76b5585",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21,)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(best_logreg.coef_).reshape((21,)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "be989af0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15     AreaShape_MinorAxisLength\n",
      "14    AreaShape_MinFeretDiameter\n",
      "2          AreaShape_Compactness\n",
      "10    AreaShape_MaxFeretDiameter\n",
      "1      AreaShape_BoundingBoxArea\n",
      "Name: Feature, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Top 10 model\n",
    "coef_df = pd.DataFrame(columns = ['Feature','Coef'])\n",
    "coef_df['Coef'] = abs(best_logreg.coef_).reshape((19,))\n",
    "coef_df['Feature'] = best_logreg.feature_names_in_\n",
    "df = coef_df.sort_values(by=['Coef'], ascending = False)\n",
    "print(df['Feature'][:5])\n",
    "# top 5 model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "97a40f83",
   "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>Feature</th>\n",
       "      <th>Coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AreaShape_Area</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AreaShape_BoundingBoxArea</td>\n",
       "      <td>4.775674e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AreaShape_Compactness</td>\n",
       "      <td>6.775125e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AreaShape_ConvexArea</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AreaShape_Eccentricity</td>\n",
       "      <td>1.745903e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AreaShape_EquivalentDiameter</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AreaShape_EulerNumber</td>\n",
       "      <td>2.211186e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AreaShape_Extent</td>\n",
       "      <td>2.309745e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AreaShape_FormFactor</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AreaShape_MajorAxisLength</td>\n",
       "      <td>3.816062e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>AreaShape_MaxFeretDiameter</td>\n",
       "      <td>4.870636e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>AreaShape_MaximumRadius</td>\n",
       "      <td>1.172165e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>AreaShape_MeanRadius</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>AreaShape_MedianRadius</td>\n",
       "      <td>9.037111e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>AreaShape_MinFeretDiameter</td>\n",
       "      <td>7.488333e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>AreaShape_MinorAxisLength</td>\n",
       "      <td>1.470984e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>AreaShape_Orientation</td>\n",
       "      <td>1.234599e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>AreaShape_Perimeter</td>\n",
       "      <td>3.756098e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>AreaShape_Solidity</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Feature          Coef\n",
       "0                 AreaShape_Area  0.000000e+00\n",
       "1      AreaShape_BoundingBoxArea  4.775674e-01\n",
       "2          AreaShape_Compactness  6.775125e-01\n",
       "3           AreaShape_ConvexArea  0.000000e+00\n",
       "4         AreaShape_Eccentricity  1.745903e-01\n",
       "5   AreaShape_EquivalentDiameter  0.000000e+00\n",
       "6          AreaShape_EulerNumber  2.211186e-09\n",
       "7               AreaShape_Extent  2.309745e-01\n",
       "8           AreaShape_FormFactor  0.000000e+00\n",
       "9      AreaShape_MajorAxisLength  3.816062e-01\n",
       "10    AreaShape_MaxFeretDiameter  4.870636e-01\n",
       "11       AreaShape_MaximumRadius  1.172165e-01\n",
       "12          AreaShape_MeanRadius  0.000000e+00\n",
       "13        AreaShape_MedianRadius  9.037111e-11\n",
       "14    AreaShape_MinFeretDiameter  7.488333e-01\n",
       "15     AreaShape_MinorAxisLength  1.470984e+00\n",
       "16         AreaShape_Orientation  1.234599e-02\n",
       "17           AreaShape_Perimeter  3.756098e-01\n",
       "18            AreaShape_Solidity  0.000000e+00"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "59c5d391",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AreaShape_MinorAxisLength',\n",
       " 'AreaShape_MinFeretDiameter',\n",
       " 'AreaShape_Compactness',\n",
       " 'AreaShape_BoundingBoxArea',\n",
       " 'AreaShape_MaxFeretDiameter',\n",
       " 'AreaShape_MajorAxisLength',\n",
       " 'AreaShape_Perimeter',\n",
       " 'AreaShape_Extent',\n",
       " 'AreaShape_Eccentricity',\n",
       " 'AreaShape_Center_X',\n",
       " 'AreaShape_MaximumRadius',\n",
       " 'AreaShape_Center_Y',\n",
       " 'AreaShape_Orientation',\n",
       " 'AreaShape_EulerNumber',\n",
       " 'AreaShape_MedianRadius',\n",
       " 'AreaShape_Area',\n",
       " 'AreaShape_FormFactor',\n",
       " 'AreaShape_MeanRadius',\n",
       " 'AreaShape_EquivalentDiameter',\n",
       " 'AreaShape_ConvexArea',\n",
       " 'AreaShape_Solidity']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Feature'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ce5b15f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17     AreaShape_MinorAxisLength\n",
      "16    AreaShape_MinFeretDiameter\n",
      "4          AreaShape_Compactness\n",
      "1      AreaShape_BoundingBoxArea\n",
      "12    AreaShape_MaxFeretDiameter\n",
      "Name: Feature, dtype: object\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot top 5 features\n",
    "        \n",
    "# plot partially labelled \n",
    "# scatter plot, same as above, except outlier 94.\n",
    "# P vs X_NORM, RAW vs X_NORM, LOGIT vs X_NORM, ENTROPY vs X_NORM\n",
    "fig, axs = plt.subplots(2,3,figsize=(20,10))\n",
    "\n",
    "df = coef_df.sort_values(by=['Coef'], ascending = False)\n",
    "print(df['Feature'][:5])\n",
    "\n",
    "cols_to_plot = df['Feature'].tolist()[:3]\n",
    "col_to_plot2 = ['ENTROPY','RAW']\n",
    "df_to_plot1=seg_df[seg_df['AreaShape_Area']<5000]\n",
    "df_to_plot2=seg_df[seg_df['AreaShape_Area']<1000]\n",
    "\n",
    "for i in range(len(cols_to_plot)):\n",
    "    #col = 'AreaShape_'+cols_to_plot[i]\n",
    "    col = cols_to_plot[i]\n",
    "    df1 = soma_df.loc[df_to_plot1.index]\n",
    "    \n",
    "    # only display images that are labelled?\n",
    "    df1 = df1[(df1.ImageNumber == 13) | (df1.ImageNumber == 213)]\n",
    "    \n",
    "    idx1 = df1[df1['Seg_Label'] == 1].index # Seg Label is only in soma_df\n",
    "    axs[0][i].scatter(df_to_plot1.loc[idx1,'AreaShape_Area'], df_to_plot1.loc[idx1, col], s=3, color = 'r')\n",
    "    idx2 = df1[df1['Seg_Label'] == 0].index\n",
    "    axs[0][i].scatter(df_to_plot1.loc[idx2,'AreaShape_Area'], df_to_plot1.loc[idx2, col], s=3, color = 'b')\n",
    "    axs[0][i].set_xlabel('AreaShape_Area')\n",
    "    axs[0][i].set_ylabel(col)\n",
    "    axs[0][i].set_title(f'Area vs {cols_to_plot[i]}')\n",
    "    \n",
    "    \n",
    "for i in range(len(cols_to_plot)):\n",
    "    #col = 'AreaShape_'+cols_to_plot[i]\n",
    "    col = cols_to_plot[i]\n",
    "    df2 = soma_df.loc[df_to_plot2.index]\n",
    "    \n",
    "    # only display images that are labelled?\n",
    "    df2 = df2[(df2.ImageNumber == 13) | (df2.ImageNumber == 213)]\n",
    "\n",
    "    idx1 = df2[df2['Seg_Label'] == 1].index\n",
    "    axs[1][i].scatter(df_to_plot2.loc[idx1,'AreaShape_Area'], df_to_plot2.loc[idx1, col], s=3, color = 'r')\n",
    "    idx2 = df2[df2['Seg_Label'] == 0].index\n",
    "    axs[1][i].scatter(df_to_plot2.loc[idx2,'AreaShape_Area'], df_to_plot2.loc[idx2, col], s=3, color = 'b')\n",
    "    axs[1][i].set_xlabel('AreaShape_Area')\n",
    "    axs[1][i].set_ylabel(col)\n",
    "    axs[1][i].set_title(f'Area vs {cols_to_plot[i]}')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3100f07c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "4e67d761",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8085106382978723\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "best_logreg = gridCV_fit(best_clf.best_params_)\n",
    "best_logreg.fit(X_train, y_train)\n",
    "print(best_logreg.score(X_test,y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3d365a7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
